{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.9" }, "colab": { "name": "feature slection in Neuromarketing.ipynb", "provenance": [], "collapsed_sections": [ "fUW1F85BqUoR", "NlguWs0eycu_", "ccobVWjvWMmk", "cL3hvkfjhQc_", "QPROXZWG7kT7", "_C8iIIKrsAWw" ], "toc_visible": true }, "accelerator": "GPU" }, "cells": [ { "cell_type": "code", "metadata": { "id": "9dPWBLTrT3UT" }, "source": [ "# imports\n", "import warnings\n", "import glob\n", "import pickle as cPickle\n", "import numpy as np\n", "import matplotlib.pyplot as mpl\n", "from sklearn.ensemble import RandomForestClassifier\n", "from scipy.stats.stats import pearsonr\n", "from sklearn.metrics import log_loss, accuracy_score, recall_score, precision_score, f1_score, confusion_matrix, hinge_loss, classification_report\n", "from sklearn import model_selection\n", "import random \n", "\n", "warnings.filterwarnings('default')" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "Y_CYn1uZT3Uc" }, "source": [ "# Plot history object\n", "def plot_history(history):\n", " mpl.plot(history.history['loss'])\n", " mpl.plot(history.history['val_loss'])\n", " mpl.title('model loss')\n", " mpl.ylim([0,5])\n", " mpl.ylabel('loss')\n", " mpl.xlabel('epoch')\n", " mpl.legend(['train', 'val'], loc='upper right')\n", " mpl.show()\n", " #-------------\n", " \n", " # list all data in history\n", " print(history.history.keys())\n", " # summarize history for accuracy\n", " mpl.plot(history.history['accuracy'])\n", " mpl.plot(history.history['val_accuracy'])\n", " mpl.title('model accuracy')\n", " mpl.ylabel('accuracy')\n", " mpl.xlabel('epoch')\n", " mpl.legend(['train', 'test'], loc='upper left')\n", " mpl.show()\n", " # summarize history for loss\n", " mpl.plot(history.history['loss'])\n", " mpl.plot(history.history['val_loss'])\n", " mpl.title('model loss')\n", " mpl.ylabel('loss')\n", " mpl.xlabel('epoch')\n", " mpl.legend(['train', 'test'], loc='upper left')\n", " mpl.show()\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "7EPABxkST3Ui", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "3bef5bf2-b1ea-4f3e-e041-99ffb989bd83" }, "source": [ "from keras.models import Sequential\n", "from keras.layers.core import Dense, Dropout, Activation\n", "from keras.layers.noise import GaussianNoise\n", "from keras.optimizers import SGD, Adam, Adadelta\n", "\n", "from keras.callbacks import ModelCheckpoint, LearningRateScheduler, EarlyStopping\n", "from keras.regularizers import l2\n", "from keras.layers.advanced_activations import ELU\n", "from keras.layers.normalization import BatchNormalization\n", "from sklearn import svm\n", "from sklearn.utils import class_weight\n" ], "execution_count": null, "outputs": [ { "output_type": "error", "ename": "ImportError", "evalue": "ignored", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDense\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDropout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mActivation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnoise\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mGaussianNoise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimizers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSGD\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAdam\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAdadelta\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mModelCheckpoint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mLearningRateScheduler\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mEarlyStopping\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mImportError\u001b[0m: cannot import name 'SGD' from 'keras.optimizers' (/usr/local/lib/python3.7/dist-packages/keras/optimizers.py)", "", "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n" ], "errorDetails": { "actions": [ { "action": "open_url", "actionText": "Open Examples", "url": "/notebooks/snippets/importing_libraries.ipynb" } ] } } ] }, { "cell_type": "code", "metadata": { "id": "jFZjKz6-zOdU" }, "source": [ "def train_rf(tr_X, tr_y_cat, va_X):#, te_X):\n", " rf = RandomForestClassifier(n_estimators=500, n_jobs=-1)\n", " rf.fit(tr_X, tr_y_cat)\n", " rf_tr_pred = rf.predict(tr_X)\n", " rf_va_pred = rf.predict(va_X)\n", " # rf_te_pred = rf.predict(te_X)\n", "\n", " return rf_tr_pred, rf_va_pred#, rf_te_pred\n", "\n", "def train_svm(tr_X, tr_y_cat, va_X):\n", " clf = svm.SVC()\n", " clf.fit(tr_X, tr_y_cat)\n", " rf_tr_pred = clf.predict(tr_X)\n", " rf_va_pred = clf.predict(va_X)\n", " #rf_te_pred = clf.predict(te_X)\n", "\n", " return rf_tr_pred, rf_va_pred\n", "\n", "from sklearn.neighbors import KNeighborsClassifier\n", "\n", "def train_KNN(tr_X, tr_y_cat, va_X):\n", " classifier = KNeighborsClassifier(n_neighbors=1)\n", " classifier.fit(tr_X, tr_y_cat)\n", " rf_tr_pred = classifier.predict(tr_X)\n", " rf_va_pred = classifier.predict(va_X)\n", " #rf_te_pred = clf.predict(te_X)\n", "\n", "\n", " return rf_tr_pred, rf_va_pred\n", "\n", "def train_lda(tr_X, tr_y_cat, va_X):\n", " from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n", " classifier = LDA(n_components=1)\n", " classifier.fit(tr_X, tr_y_cat)\n", " rf_tr_pred = classifier.predict(tr_X)\n", " rf_va_pred = classifier.predict(va_X)\n", " #rf_te_pred = clf.predict(te_X)\n", " \n", " #tr_X = lda.fit_transform(tr_X,tr_y_cat)\n", " #va_X = lda.transform(va_X )\n", " #print(va_X.shape)\n", "\n", "\n", " return rf_tr_pred, rf_va_pred" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "SzuCU16kr6yC" }, "source": [ "# Train Models - SPLIT" ] }, { "cell_type": "code", "metadata": { "id": "oPvWbQsMT3Uq", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "225993b0-becf-47b8-feea-033b3a731e64" }, "source": [ "from sklearn.preprocessing import MinMaxScaler , StandardScaler\n", "\n", "def train_models(X, y, y_cat):\n", " \n", " \n", " ntrials = 42\n", " \n", " test_size = 0.2\n", " seed = 10\n", " \n", " \n", " ##########################################################################\n", " #Before split\n", " \n", " print(\"Before selectoin\")\n", " print(X.shape)\n", "\n", " #sclae to 0,1\n", " min_max_scaler = StandardScaler() #https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html#sklearn.preprocessing.MinMaxScaler\n", " X = min_max_scaler.fit_transform(X)\n", " \"\"\"\n", " #feature selection LDA\n", " from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n", " lda = LDA()\n", " X = lda.fit_transform(X,y_cat)\n", "\n", " \"\"\"\n", "\n", " #feature selection PCA\n", " #from sklearn.decomposition import PCA\n", " #pca = PCA()\n", " #X = pca.fit_transform(X)\n", "\n", "\n", " #relive\n", " #fs = ReliefF(n_neighbors=100, n_features_to_keep=10)\n", " #X = fs.fit_transform(X, y_cat)\n", "\n", " print(\"After selectoin\")\n", " print(X.shape)\n", "\n", " ##############################################################################\n", " \n", " \n", " \n", " #holdout\n", " tr_X, va_X, tr_y_cat, va_y_cat = model_selection.train_test_split(X, y_cat, test_size=test_size, \n", " random_state=seed)\n", " \n", " print(va_X.shape)\n", " tr_y = np.eye(2)[np.array(tr_y_cat,dtype=np.int)]\n", " ##########################################################################\n", " #feature selection LDA after split\n", " #from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n", " #lda = LDA(n_components=1)\n", " #tr_X = lda.fit_transform(tr_X,tr_y_cat)\n", " #va_X = lda.transform(va_X)\n", " #print(\"After\")\n", " #print(tr_X.shape)\n", "\n", "\n", " #feature selection PCA\n", " #from sklearn.decomposition import PCA\n", " #pca = PCA()\n", " #tr_X = pca.fit_transform(tr_X)\n", " #va_X = pca.transform(va_X)\n", " ##############################################################################\n", " #tr_y = np.eye(2) [tr_y] # Me put 2 instead of 4\n", " \n", " #print (tr_y.shape)\n", " \n", " #va_y = map(int, va_y_cat) #M use astype instead of map\n", " #va_y = va_y_cat.astype(int)\n", " #va_y = np.eye(2)[va_y] # Me put 2 instead of 4\n", " #va_y = va_y.reshape(va_y.shape[0],2)\n", " va_y = np.eye(2)[np.array(va_y_cat,dtype=np.int)]\n", "\n", " #print (va_y.shape)\n", " \n", " class_we = class_weight.compute_class_weight('balanced', np.unique(tr_y_cat), tr_y_cat)\n", " average='weighted'\n", "\n", " \n", " # train net\n", " history, pr, tr_pr = train_dnn(tr_X, tr_y, va_X, va_y, class_we)\n", " \n", " # visualize loss\n", " plot_history(history)\n", " print('DNN results')\n", " # calculate logloss metric\n", " tr_preds = np.argmax(tr_pr, axis=1)\n", " # calculate accuracy\n", " tr_acc = accuracy_score(tr_y_cat, tr_preds)\n", " tr_conf = confusion_matrix(tr_y_cat, tr_preds)\n", "\n", " # Recall, precision averaging method\n", " average='weighted'\n", "\n", " print('accuracy : {}'.format(tr_acc))\n", " print('Training Recall: {}'.format(recall_score(tr_y_cat, tr_preds, average=average)))\n", " print('Training Precision: {}'.format(precision_score(tr_y_cat, tr_preds, average=average)))\n", " print('Confusion matrix: ')\n", " print(tr_conf)\n", "\n", " # probabilites to one prediction\n", " preds = np.argmax(pr, axis=1)\n", " # calculate accuracy\n", " acc = accuracy_score(va_y_cat, preds)\n", " conf = confusion_matrix(va_y_cat, preds)\n", "\n", " print('Validation set report:')\n", " print('cross Loss: {}'.format(log_loss(va_y, pr))) # for cross _loss\n", " print('hing Loss: {}'.format(hinge_loss(va_y_cat, preds))) # for hinge_loss\n", " target_names = ['0', '1']\n", " print('Report{}'.format(classification_report(va_y_cat, preds,target_names=target_names)))\n", " print('accuracy : {}'.format(acc))\n", " print('Validation Recall: {}'.format(recall_score(va_y_cat, preds, average=average)))\n", " print('Validation Precision: {}'.format(precision_score(va_y_cat, preds, average=average)))\n", " print('Confusion matrix: ')\n", " print(conf)\n", " \n", " # probabilites to one prediction\n", " #te_preds = np.argmax(te_pr, axis=1)\n", " # calculate accuracy1\n", " #te_acc = accuracy_score(te_y_cat, te_preds)\n", " #te_conf = confusion_matrix(te_y_cat, te_preds)\n", "\n", " #print('\\nTest set report:')\n", " #print('accuracy : {}'.format(te_acc))\n", " #print('Test Recall: {}'.format(recall_score(te_y_cat, te_preds, average=average)))\n", " #print('Test Precision: {}'.format(precision_score(te_y_cat, te_preds, average=average)))\n", " #print('Confusion matrix: ')\n", " #print(te_conf)\n", " \n", " rf_tr_pred, rf_va_pred = train_KNN(tr_X, tr_y_cat, va_X)\n", "\n", " print('\\n\\KNN Result\\n')\n", " print('Train accuracy: {}'.format(accuracy_score(tr_y_cat, rf_tr_pred)))\n", " print('Training Recall: {}'.format(recall_score(tr_y_cat, rf_tr_pred, average=average)))\n", " print('Training Precision: {}'.format(precision_score(tr_y_cat, rf_tr_pred, average=average)))\n", " print('\\n\\nValidation accuracy: {}'.format(accuracy_score(va_y_cat, rf_va_pred)))\n", " print('Validation Recall: {}'.format(recall_score(va_y_cat, rf_va_pred, average=average)))\n", " print('Validation Precision: {}'.format(precision_score(va_y_cat, rf_va_pred, average=average)))\n", " \n", " print('\\nTrain Confusion matrix:\\n {}'.format(confusion_matrix(tr_y_cat, rf_tr_pred)))\n", " print('Validation Confusion matrix:\\n {}'.format(confusion_matrix(va_y_cat, rf_va_pred)))\n", " #print('Test Confusion matrix:\\n {}'.format(confusion_matrix(te_y_cat, rf_te_pred)))\n", "\n", " rf_tr_pred, rf_va_pred = train_svm(tr_X, tr_y_cat, va_X)\n", "\n", " print('\\n\\nSupport vector machine Result\\n')\n", " print('Train accuracy: {}'.format(accuracy_score(tr_y_cat, rf_tr_pred)))\n", " print('Training Recall: {}'.format(recall_score(tr_y_cat, rf_tr_pred, average=average)))\n", " print('Training Precision: {}'.format(precision_score(tr_y_cat, rf_tr_pred, average=average)))\n", " print('\\n\\nValidation accuracy: {}'.format(accuracy_score(va_y_cat, rf_va_pred)))\n", " print('Validation Recall: {}'.format(recall_score(va_y_cat, rf_va_pred, average=average)))\n", " print('Validation Precision: {}'.format(precision_score(va_y_cat, rf_va_pred, average=average)))\n", " #print('\\n\\nTest accuracy: {}'.format(accuracy_score(te_y_cat, rf_te_pred)))\n", " #print('Test Recall: {}'.format(recall_score(te_y_cat, rf_te_pred, average=average)))\n", " #print('Test Precision: {}'.format(precision_score(te_y_cat, rf_te_pred, average=average)))\n", "\n", " print('\\nTrain Confusion matrix:\\n {}'.format(confusion_matrix(tr_y_cat, rf_tr_pred)))\n", " print('Validation Confusion matrix:\\n {}'.format(confusion_matrix(va_y_cat, rf_va_pred)))\n", " \n", " \n", " rf_tr_pred, rf_va_pred = train_rf(tr_X, tr_y_cat, va_X)#, te_X)\n", "\n", " print('\\n\\nRandom Forest Result\\n')\n", " print('Train accuracy: {}'.format(accuracy_score(tr_y_cat, rf_tr_pred)))\n", " print('Training Recall: {}'.format(recall_score(tr_y_cat, rf_tr_pred, average=average)))\n", " print('Training Precision: {}'.format(precision_score(tr_y_cat, rf_tr_pred, average=average)))\n", " print('\\n\\nValidation accuracy: {}'.format(accuracy_score(va_y_cat, rf_va_pred)))\n", " print('Validation Recall: {}'.format(recall_score(va_y_cat, rf_va_pred, average=average)))\n", " print('Validation Precision: {}'.format(precision_score(va_y_cat, rf_va_pred, average=average)))\n", " \n", " print('\\nTrain Confusion matrix:\\n {}'.format(confusion_matrix(tr_y_cat, rf_tr_pred)))\n", " print('Validation Confusion matrix:\\n {}'.format(confusion_matrix(va_y_cat, rf_va_pred)))\n", " #print('Test Confusion matrix:\\n {}'.format(confusion_matrix(te_y_cat, rf_te_pred)))\n", "\n", " lda_tr_pred, lda_va_pred = train_lda(tr_X, tr_y_cat, va_X)#, te_X)\n", " print('\\n\\nLDA Result\\n')\n", " print('Train accuracy: {}'.format(accuracy_score(tr_y_cat, lda_tr_pred)))\n", " print('Training Recall: {}'.format(recall_score(tr_y_cat, lda_tr_pred, average=average)))\n", " print('Training Precision: {}'.format(precision_score(tr_y_cat, lda_tr_pred, average=average)))\n", " print('\\n\\nValidation accuracy: {}'.format(accuracy_score(va_y_cat, lda_va_pred)))\n", " print('Validation Recall: {}'.format(recall_score(va_y_cat, lda_va_pred, average=average)))\n", " print('Validation Precision: {}'.format(precision_score(va_y_cat, lda_va_pred, average=average)))\n", " #print('\\n\\nTest accuracy: {}'.format(accuracy_score(te_y_cat, rf_te_pred)))\n", " #print('Test Recall: {}'.format(recall_score(te_y_cat, rf_te_pred, average=average)))\n", " #print('Test Precision: {}'.format(precision_score(te_y_cat, rf_te_pred, average=average)))\n", "\n", " print('\\nTrain Confusion matrix:\\n {}'.format(confusion_matrix(tr_y_cat, lda_tr_pred)))\n", " print('Validation Confusion matrix:\\n {}'.format(confusion_matrix(va_y_cat, lda_va_pred)))\n", " " ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: 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escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", ":153: DeprecationWarning: invalid escape sequence \\K\n", " print('\\n\\KNN Result\\n')\n" ], "name": "stderr" } ] }, { "cell_type": "markdown", "metadata": { "id": "fUW1F85BqUoR" }, "source": [ "# Train Models, Kfolds, leave one out\n" ] }, { "cell_type": "code", "metadata": { "id": "N7Uv0emjqRKq", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "80aae4b8-1aba-48da-de25-6b4c204af901" }, "source": [ "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n", "from keras.wrappers.scikit_learn import KerasClassifier\n", "from sklearn.neighbors import KNeighborsClassifier\n", "\n", "#holdout\n", "def train_models_kfold(X, y, y_cat): \n", "\n", "\n", " batch_size = 128\n", " nb_epoch = 250#3000\n", "\n", "\n", " #sclae to 0,1\n", " min_max_scaler = MinMaxScaler() #https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html#sklearn.preprocessing.MinMaxScaler\n", " X = min_max_scaler.fit_transform(X)\n", " \n", " \n", " #one-hot-encoded matrix\n", " y = np.eye(2)[np.array(y_cat,dtype=np.int)]\n", "\n", " # Stratified Kfold K=10 or (K = 1308 LEAVEone out)\n", " #accuracies = model_selection.cross_val_score(estimator=classifier, X= X, y=y,cv=1308, n_jobs=-1)\n", " \n", " #split = model_selection.LeaveOneOut()#.split(X) # leave one out\n", " split = 5 # Kfold K=5\n", " \n", " \n", " classifier = KerasClassifier(build_fn=dnn_Kfold, batch_size=batch_size, nb_epoch=nb_epoch)\n", " accuracies = model_selection.cross_val_score(estimator=classifier, X= X, y=y,cv=split, n_jobs=-1)\n", " \n", " print(\"accuracies\", accuracies)\n", " #mean score and the 95% confidence interval of the score estimate are\n", " print(\"DNN Accuracy: %0.2f (+/- %0.2f)\" % (accuracies.mean(), accuracies.std() * 2))\n", "\n", "\n", " #--------------- SVM \n", " clf = svm.SVC()\n", " clf.fit(X, y_cat)\n", " accuracies = model_selection.cross_val_score(estimator=clf, X= X, y=y_cat,cv=split, n_jobs=-1)\n", " print('\\n\\nSupport vector machine Result\\n')\n", " print(\"accuracies\", accuracies)\n", " #mean score and the 95% confidence interval of the score estimate are\n", " print(\"SVM Accuracy: %0.2f (+/- %0.2f)\" % (accuracies.mean(), accuracies.std() * 2))\n", " \n", " # -------------- RF\n", " rf = RandomForestClassifier(n_estimators=500, n_jobs=-1)\n", " rf.fit(X, y_cat)\n", " accuracies = model_selection.cross_val_score(estimator=rf, X= X, y=y_cat,cv=split, n_jobs=-1)\n", " print('\\n\\nRandomForestClassifier Result\\n')\n", " print(\"accuracies\", accuracies)\n", " #mean score and the 95% confidence interval of the score estimate are\n", " print(\"Accuracy: %0.2f (+/- %0.2f)\" % (accuracies.mean(), accuracies.std() * 2))\n", "\n", "\n", " #---------- KNN\n", " KNN = KNeighborsClassifier(n_neighbors=1)\n", " KNN.fit(X, y_cat)\n", " accuracies = model_selection.cross_val_score(estimator=KNN, X= X, y=y_cat,cv=split, n_jobs=-1)\n", " print('\\n\\KNN (k=1) Result\\n')\n", " print(\"accuracies\", accuracies)\n", " #mean score and the 95% confidence interval of the score estimate are\n", " print(\"Accuracy: %0.2f (+/- %0.2f)\" % (accuracies.mean(), accuracies.std() * 2))\n", " " ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ ":59: DeprecationWarning: invalid escape sequence \\K\n", ":59: DeprecationWarning: invalid escape sequence \\K\n", ":59: DeprecationWarning: invalid escape sequence \\K\n", ":59: DeprecationWarning: invalid escape sequence \\K\n", ":59: DeprecationWarning: invalid escape sequence \\K\n", ":59: DeprecationWarning: invalid escape sequence \\K\n", ":59: DeprecationWarning: invalid escape sequence \\K\n", ":59: DeprecationWarning: invalid escape sequence \\K\n", ":59: DeprecationWarning: invalid escape sequence \\K\n", ":59: DeprecationWarning: invalid escape sequence \\K\n", ":59: DeprecationWarning: invalid escape sequence \\K\n", ":59: DeprecationWarning: invalid escape sequence \\K\n", ":59: DeprecationWarning: invalid escape sequence \\K\n", ":59: DeprecationWarning: invalid escape sequence \\K\n", ":59: DeprecationWarning: invalid escape sequence \\K\n", ":59: DeprecationWarning: invalid escape sequence \\K\n", " print('\\n\\KNN (k=1) Result\\n')\n" ], "name": "stderr" } ] }, { "cell_type": "markdown", "metadata": { "id": "NlguWs0eycu_" }, "source": [ "## DNN_ Kfold/leaveone" ] }, { "cell_type": "code", "metadata": { "id": "URRZg2XFydEE" }, "source": [ " \n", "# a deep neural network to train one statistic (valence or arousal) at a time\n", "def dnn_Kfold():#(tr_X, tr_y, va_X, va_y, class_we):\n", "\n", " # set parameters\n", " \n", " \n", " l1_decay=0.00\n", " l2_decay=0.000 # .5\n", " # 0.01 0.06\n", " sigma=0.005\n", " in_drop_rate = .5\n", " drop_rate = .5#.05\n", " lr1=0.0001\n", "\n", "\n", " #M Higge loss - change y from {0,1} to {-1,1}\n", " #tr_y[where(tr_y == 0)] = -1\n", " #va_y[where(va_y == 0)] = -1\n", "\n", "\n", " # set network layout\n", " model = Sequential()\n", " model.add(Dense(2367, input_shape=(X.shape[1],)\n", " , kernel_initializer='he_normal', W_regularizer=l2(l2_decay)))\n", " model.add(GaussianNoise(sigma))\n", " model.add(Activation('relu'))\n", " model.add(BatchNormalization())\n", " model.add(Dropout(in_drop_rate))\n", "\n", "\n", " model.add(Dense(1800, kernel_initializer='he_normal', W_regularizer=l2(l2_decay)))\n", " model.add(GaussianNoise(sigma))\n", " model.add(Activation('relu'))\n", " model.add(BatchNormalization())\n", " model.add(Dropout(drop_rate))\n", "\n", " model.add(Dense(1300, kernel_initializer='he_normal', W_regularizer=l2(l2_decay)))\n", " model.add(GaussianNoise(sigma))\n", " model.add(Activation('relu'))\n", " model.add(BatchNormalization())\n", " model.add(Dropout(drop_rate))\n", "\n", " model.add(Dense(800, kernel_initializer='he_normal', W_regularizer=l2(l2_decay)))\n", " model.add(GaussianNoise(sigma))\n", " model.add(Activation('relu'))\n", " model.add(BatchNormalization())\n", " model.add(Dropout(drop_rate))\n", "\n", " \n", " model.add(Dense(2, W_regularizer=l2(l2_decay))) # me put 2 instead of 4\n", " #model.add(Activation('tanh')) # with hingre \n", " model.add(Activation('softmax')) # with cross\n", "\n", " \"\"\"# Callbacks\n", " model_checkpoint = ModelCheckpoint('best_modelf1d55RbT.hdf5', monitor='val_loss', save_best_only=True)\n", " early = EarlyStopping(monitor='val_loss', patience=600, verbose=0)\n", " \n", " \"\"\"\n", " # fit and evaluate the model\n", " \n", " #model.compile(loss='hinge', # categorical_crossentropy #binary_crossentropy\n", " # optimizer=Adam(lr=0.001))\n", " \n", " model.compile(loss='binary_crossentropy',\n", " optimizer=Adam(lr=lr1), metrics=['accuracy']) # add metrics\n", " \n", " \"\"\"history = model.fit(tr_X, tr_y, batch_size=batch_size,\n", " epochs=nb_epoch, verbose=0, callbacks=[early, model_checkpoint], \n", " validation_data=(va_X, va_y))\n", " \n", " #score = model.evaluate(va_X, va_y, verbose=0)\n", " #print('Test score:', score[0])\n", " #print('Test accuracy:', score[1])\n", "\n", " model.load_weights('best_modelf1d55RbT.hdf5')\n", " tr_pr = model.predict(tr_X, batch_size=batch_size, verbose=0)\n", " pr_y = model.predict(va_X, batch_size=batch_size, verbose=0)\n", " #te_pr = model.predict(te_X, batch_size=batch_size, verbose=0)\n", "\n", " return history, pr_y, tr_pr\"\"\"\n", " return model" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "AuPjgXqt2mqz" }, "source": [ "def train_rf(tr_X, tr_y_cat, va_X):#, te_X):\n", " rf = RandomForestClassifier(n_estimators=500, n_jobs=-1)\n", " rf.fit(tr_X, tr_y_cat)\n", " rf_tr_pred = rf.predict(tr_X)\n", " rf_va_pred = rf.predict(va_X)\n", " # rf_te_pred = rf.predict(te_X)\n", "\n", " return rf_tr_pred, rf_va_pred#, rf_te_pred" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "5OdsqySIT3Uw" }, "source": [ "def train_svm(tr_X, tr_y_cat, va_X):\n", " clf = svm.SVC()\n", " clf.fit(tr_X, tr_y_cat)\n", " rf_tr_pred = clf.predict(tr_X)\n", " rf_va_pred = clf.predict(va_X)\n", " #rf_te_pred = clf.predict(te_X)\n", "\n", " return rf_tr_pred, rf_va_pred" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "lKU-arXN-RjN" }, "source": [ "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n", "\n", "def train_lda(tr_X, tr_y_cat, va_X):\n", " clf = LinearDiscriminantAnalysis()\n", " clf.fit(tr_X, tr_y_cat)\n", " rf_tr_pred = clf.predict(tr_X)\n", " rf_va_pred = clf.predict(va_X)\n", " #rf_te_pred = clf.predict(te_X)\n", " return rf_tr_pred, rf_va_pred" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "XM4kmZZLTpcF" }, "source": [ "from sklearn.neighbors import KNeighborsClassifier\n", "\n", "def train_KNN(tr_X, tr_y_cat, va_X):\n", " classifier = KNeighborsClassifier(n_neighbors=1)\n", " classifier.fit(tr_X, tr_y_cat)\n", " rf_tr_pred = classifier.predict(tr_X)\n", " rf_va_pred = classifier.predict(va_X)\n", " #rf_te_pred = clf.predict(te_X)\n", "\n", "\n", " return rf_tr_pred, rf_va_pred" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "XHZrl1i5UtVw" }, "source": [ "# DNN " ] }, { "cell_type": "code", "metadata": { "id": "p-a-ZZ1CT3U2", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "ec5daf03-d4ae-43fd-864b-e5c06c7fbb78" }, "source": [ "from numpy import where\n", " \n", "# a deep neural network to train one statistic (valence or arousal) at a time\n", "def train_dnn(tr_X, tr_y, va_X, va_y, class_we):\n", "\n", " \n", " batch_size = 128\n", " nb_epoch = 250 #3000\n", " l1_decay=0.00\n", " l2_decay=0.000 # .5\n", " # 0.01 0.06\n", " sigma=0.005\n", " in_drop_rate = .5\n", " drop_rate = .5#.05\n", " lr1=0.0001\n", "\n", " #my modified \n", " model.add(Dense(1500, input_shape=(tr_X.shape[1],) ,init='he_normal', W_regularizer=l2(l2_decay)))\n", " #, kernel_initializer=keras.initializers.RandomNormal'he_normal', kernel_regularizer=l2(l2_decay)))\n", " model.add(GaussianNoise(sigma))\n", " model.add(Activation('relu'))\n", " model.add(BatchNormalization())\n", " model.add(Dropout(in_drop_rate))\n", " \n", "\n", " model.add(Dense(1000, init='he_normal', W_regularizer=l2(l2_decay)))\n", " model.add(GaussianNoise(sigma))\n", " model.add(Activation('relu'))\n", " model.add(BatchNormalization())\n", " model.add(Dropout(drop_rate))\n", "\n", " model.add(Dense(600, init='he_normal', W_regularizer=l2(l2_decay)))\n", " model.add(GaussianNoise(sigma))\n", " model.add(Activation('relu'))\n", " model.add(BatchNormalization())\n", " model.add(Dropout(drop_rate))\n", "\n", " model.add(Dense(400, init='he_normal', W_regularizer=l2(l2_decay)))\n", " model.add(GaussianNoise(sigma))\n", " model.add(Activation('relu'))\n", " model.add(BatchNormalization())\n", " model.add(Dropout(drop_rate))\n", " " ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: 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DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", ":43: DeprecationWarning: invalid escape sequence \\P\n", " \"\"\"\n" ], "name": "stderr" } ] }, { "cell_type": "markdown", "metadata": { "id": "E4Hr1bcewS-u" }, "source": [ "# Data" ] }, { "cell_type": "code", "metadata": { "id": "XG4b8Yt2Uo6y", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "cfa1ebe8-4b91-4d1e-e153-b12a39772f9a" }, "source": [ "from google.colab import drive\n", "drive.mount('/content/gdrive')\n", "\n", "\n", "# pref + valence\n", "X = np.load('gdrive/My Drive/Python files/neuromarketing 25/features_psd_ALL.npy') \n", "useres = np.load('gdrive/My Drive/Python files/neuromarketing 25/users.npy')\n" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Mounted at /content/gdrive\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "bmzv7OW5jakS" }, "source": [ "\n", "#REF\n", "#idx_IN_columns = [2715,2716,2717,2718,2719,2720,2721,2722,2723, 2724]\n", "#((vamv_valence, kirk_valence, ram12_valence, ram15_valence)), Touchette_AW,AW, Choice_Index_b,Choice_Index_g, effort_index, effort_index2))\n", "\n", "#idx_IN_columns = [2704,2703,2702,2701,2700,2699,2698,2697,2696,2715,2716,2717,2718,2719,2720,2721,2722,2723, 2724] #last 20\n", "\n", "idx_IN_columns = [2694, 2693, 2692, 2691, 2690, 2689, 2688, 2687, 2686,2704,2703,2702,2701,2700,2699,2698,2697,2696,2715,2716,2717,2718,2719,2720,2721,2722,2723, 2724] #last 30\n", "\n", "X_copy = X\n", "X = X[:,idx_IN_columns]" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "AkmXfyw_9HzX", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "8eb74232-264c-4846-b06b-268c4f09a02b" }, "source": [ "X.shape" ], "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "(1050, 2725)" ] }, "metadata": { "tags": [] }, "execution_count": 9 } ] }, { "cell_type": "code", "metadata": { "id": "1fSZCIrUsSTF" }, "source": [ "###valence - 2 label\n", "y = np.load('gdrive/My Drive/Python files/neuromarketing 25/tow_labels_valence.npy')\n", "y_cat = y[:,1].ravel()\n", "\n", "#No Valence - 2 label\n", "#y = np.load('gdrive/My Drive/Python files/neuromarketing 25/tow_labels.npy')\n", "#y_cat = y[:,1].ravel()\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "Z5EcmNW0Mor0" }, "source": [ "# remove valence\n", "\n", "X = np.delete(X, [2715] , axis=1)\n", "X.shape" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "AAMlcJUSvGkr" }, "source": [ "\n", "y.shape" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "iLgF70cBIf2C" }, "source": [ "print('y:', y.shape)\n", "print('x:', X.shape)\n", "print('y_cat:', y_cat.shape)\n", "\n", "y\n", "np.any(np.isnan(X)) # check null value\n", "#np.all(np.isfinite(X))\n", "np.any(np.isnan(X))\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "VnPI24tRleLJ" }, "source": [ "#Before removing nan values \n", "#number of like: 463 # 579 from label\n", "print(\"like: {0} \".format(np.count_nonzero(y[:,0] == 1)))\n", "\n", "#number of dislike: 587 # 471 from label\n", "print(\"Dislike: {0} \".format(np.count_nonzero(y[:,0] == 0) ))\n", "\n", "#After removing nan values \n", "#number of like: 463\n", "print(\"like: {0} \".format(np.count_nonzero(y[:,0] == 1)))\n", "\n", "#number of dislike: 587\n", "print(\"Dislike: {0} \".format(np.count_nonzero(y[:,0] == 0) ))" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "VLpOtqqUCeWc" }, "source": [ "#Plain - no feature Extraction" ] }, { "cell_type": "code", "metadata": { "id": "FLXTOJA5ELnK", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "f9847d4c-53fa-45ba-8428-83498f3205f6" }, "source": [ "#X = np.load('gdrive/My Drive/Python files/neuromarketing 25/features_plain_128_ICA_SAV.npy')\n", "\n", "train_models(X, y, y_cat)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Before selectoin\n", "(1050, 3072)\n", "After selectoin\n", "(1050, 3072)\n", "(210, 3072)\n", "Epoch 1/250\n", "7/7 [==============================] - 1s 95ms/step - loss: 1.0087 - accuracy: 0.4845 - val_loss: 1.0133 - val_accuracy: 0.4810\n", "Epoch 2/250\n", "7/7 [==============================] - 1s 80ms/step - loss: 1.0188 - accuracy: 0.4929 - val_loss: 0.9745 - val_accuracy: 0.5524\n", "Epoch 3/250\n", "7/7 [==============================] - 1s 81ms/step - loss: 0.9667 - accuracy: 0.5417 - val_loss: 0.9451 - val_accuracy: 0.5667\n", "Epoch 4/250\n", "7/7 [==============================] - 1s 94ms/step - loss: 0.9529 - accuracy: 0.5524 - val_loss: 0.9247 - val_accuracy: 0.5667\n", "Epoch 5/250\n", "7/7 [==============================] - 1s 97ms/step - loss: 0.9290 - accuracy: 0.5548 - val_loss: 0.9088 - val_accuracy: 0.5571\n", "Epoch 6/250\n", "7/7 [==============================] - 1s 133ms/step - loss: 0.9490 - accuracy: 0.5381 - val_loss: 0.8961 - val_accuracy: 0.5857\n", "Epoch 7/250\n", "7/7 [==============================] - 1s 160ms/step - loss: 0.8864 - accuracy: 0.5631 - val_loss: 0.8883 - val_accuracy: 0.6143\n", "Epoch 8/250\n", "7/7 [==============================] - 1s 77ms/step - loss: 0.8751 - accuracy: 0.5845 - val_loss: 0.8817 - val_accuracy: 0.6095\n", "Epoch 9/250\n", "7/7 [==============================] - 2s 348ms/step - loss: 0.8796 - accuracy: 0.5643 - val_loss: 0.8727 - val_accuracy: 0.6000\n", "Epoch 10/250\n", "7/7 [==============================] - 6s 839ms/step - loss: 0.8592 - accuracy: 0.5857 - val_loss: 0.8668 - val_accuracy: 0.5952\n", "Epoch 11/250\n", "7/7 [==============================] - 4s 585ms/step - loss: 0.8096 - accuracy: 0.6214 - val_loss: 0.8641 - val_accuracy: 0.5952\n", "Epoch 12/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.7993 - accuracy: 0.6250 - val_loss: 0.8645 - val_accuracy: 0.5952\n", "Epoch 13/250\n", "7/7 [==============================] - 5s 685ms/step - loss: 0.7550 - accuracy: 0.6488 - val_loss: 0.8597 - val_accuracy: 0.5810\n", "Epoch 14/250\n", "7/7 [==============================] - 4s 614ms/step - loss: 0.7517 - accuracy: 0.6631 - val_loss: 0.8473 - val_accuracy: 0.5810\n", "Epoch 15/250\n", "7/7 [==============================] - 5s 670ms/step - loss: 0.7542 - accuracy: 0.6464 - val_loss: 0.8401 - val_accuracy: 0.5810\n", "Epoch 16/250\n", "7/7 [==============================] - 3s 425ms/step - loss: 0.7365 - accuracy: 0.6726 - val_loss: 0.8369 - val_accuracy: 0.5952\n", "Epoch 17/250\n", "7/7 [==============================] - 7s 1s/step - loss: 0.6996 - accuracy: 0.7036 - val_loss: 0.8369 - val_accuracy: 0.5905\n", "Epoch 18/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.7109 - accuracy: 0.6833 - val_loss: 0.8369 - val_accuracy: 0.6000\n", "Epoch 19/250\n", "7/7 [==============================] - 3s 404ms/step - loss: 0.6893 - accuracy: 0.6893 - val_loss: 0.8357 - val_accuracy: 0.5952\n", "Epoch 20/250\n", "7/7 [==============================] - 5s 665ms/step - loss: 0.6510 - accuracy: 0.7024 - val_loss: 0.8354 - val_accuracy: 0.6000\n", "Epoch 21/250\n", "7/7 [==============================] - 3s 433ms/step - loss: 0.6374 - accuracy: 0.7202 - val_loss: 0.8324 - val_accuracy: 0.5905\n", "Epoch 22/250\n", "7/7 [==============================] - 9s 1s/step - loss: 0.6396 - accuracy: 0.7107 - val_loss: 0.8289 - val_accuracy: 0.5952\n", "Epoch 23/250\n", "7/7 [==============================] - 1s 202ms/step - loss: 0.5937 - accuracy: 0.7393 - val_loss: 0.8215 - val_accuracy: 0.5857\n", "Epoch 24/250\n", "7/7 [==============================] - 3s 380ms/step - loss: 0.5834 - accuracy: 0.7464 - val_loss: 0.8208 - val_accuracy: 0.5952\n", "Epoch 25/250\n", "7/7 [==============================] - 6s 892ms/step - loss: 0.5708 - accuracy: 0.7381 - val_loss: 0.8165 - val_accuracy: 0.5952\n", "Epoch 26/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.5935 - accuracy: 0.7440 - val_loss: 0.8175 - val_accuracy: 0.6000\n", "Epoch 27/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.5583 - accuracy: 0.7548 - val_loss: 0.8337 - val_accuracy: 0.5857\n", "Epoch 28/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.5439 - accuracy: 0.7607 - val_loss: 0.8279 - val_accuracy: 0.5810\n", "Epoch 29/250\n", "7/7 [==============================] - 4s 587ms/step - loss: 0.5199 - accuracy: 0.7643 - val_loss: 0.8090 - val_accuracy: 0.6000\n", "Epoch 30/250\n", "7/7 [==============================] - 3s 432ms/step - loss: 0.5395 - accuracy: 0.7583 - val_loss: 0.7972 - val_accuracy: 0.5952\n", "Epoch 31/250\n", "7/7 [==============================] - 6s 820ms/step - loss: 0.5089 - accuracy: 0.7619 - val_loss: 0.7920 - val_accuracy: 0.5905\n", "Epoch 32/250\n", "7/7 [==============================] - 5s 644ms/step - loss: 0.4992 - accuracy: 0.7786 - val_loss: 0.7869 - val_accuracy: 0.5810\n", "Epoch 33/250\n", "7/7 [==============================] - 5s 661ms/step - loss: 0.4778 - accuracy: 0.7893 - val_loss: 0.7755 - val_accuracy: 0.5952\n", "Epoch 34/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.4476 - accuracy: 0.8071 - val_loss: 0.7824 - val_accuracy: 0.6000\n", "Epoch 35/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.4753 - accuracy: 0.7845 - val_loss: 0.7817 - val_accuracy: 0.6190\n", "Epoch 36/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.4411 - accuracy: 0.7964 - val_loss: 0.7844 - val_accuracy: 0.6095\n", "Epoch 37/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.4380 - accuracy: 0.8095 - val_loss: 0.7926 - val_accuracy: 0.6095\n", "Epoch 38/250\n", "7/7 [==============================] - 3s 401ms/step - loss: 0.4495 - accuracy: 0.7964 - val_loss: 0.7650 - val_accuracy: 0.6190\n", "Epoch 39/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.4092 - accuracy: 0.8167 - val_loss: 0.7913 - val_accuracy: 0.6000\n", "Epoch 40/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.4147 - accuracy: 0.8131 - val_loss: 0.8003 - val_accuracy: 0.6000\n", "Epoch 41/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.4095 - accuracy: 0.8107 - val_loss: 0.7901 - val_accuracy: 0.6048\n", "Epoch 42/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.4026 - accuracy: 0.8190 - val_loss: 0.7704 - val_accuracy: 0.6095\n", "Epoch 43/250\n", "7/7 [==============================] - 8s 1s/step - loss: 0.3904 - accuracy: 0.8321 - val_loss: 0.7633 - val_accuracy: 0.6190\n", "Epoch 44/250\n", "7/7 [==============================] - 2s 262ms/step - loss: 0.3608 - accuracy: 0.8345 - val_loss: 0.7490 - val_accuracy: 0.6286\n", "Epoch 45/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.3787 - accuracy: 0.8238 - val_loss: 0.7602 - val_accuracy: 0.6048\n", "Epoch 46/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.3753 - accuracy: 0.8310 - val_loss: 0.7684 - val_accuracy: 0.6143\n", "Epoch 47/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.3725 - accuracy: 0.8345 - val_loss: 0.7605 - val_accuracy: 0.6238\n", "Epoch 48/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.3394 - accuracy: 0.8417 - val_loss: 0.7874 - val_accuracy: 0.6190\n", "Epoch 49/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.3246 - accuracy: 0.8452 - val_loss: 0.7795 - val_accuracy: 0.5952\n", "Epoch 50/250\n", "7/7 [==============================] - 3s 391ms/step - loss: 0.3544 - accuracy: 0.8417 - val_loss: 0.7139 - val_accuracy: 0.6619\n", "Epoch 51/250\n", "7/7 [==============================] - 6s 833ms/step - loss: 0.3236 - accuracy: 0.8631 - val_loss: 0.6969 - val_accuracy: 0.6619\n", "Epoch 52/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.3268 - accuracy: 0.8429 - val_loss: 0.7095 - val_accuracy: 0.6429\n", "Epoch 53/250\n", "7/7 [==============================] - 3s 414ms/step - loss: 0.3175 - accuracy: 0.8560 - val_loss: 0.6955 - val_accuracy: 0.6667\n", "Epoch 54/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.3082 - accuracy: 0.8655 - val_loss: 0.7195 - val_accuracy: 0.6429\n", "Epoch 55/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.3192 - accuracy: 0.8524 - val_loss: 0.7373 - val_accuracy: 0.6333\n", "Epoch 56/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.3043 - accuracy: 0.8607 - val_loss: 0.7469 - val_accuracy: 0.6381\n", "Epoch 57/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.3094 - accuracy: 0.8667 - val_loss: 0.7488 - val_accuracy: 0.6238\n", "Epoch 58/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.2852 - accuracy: 0.8631 - val_loss: 0.7496 - val_accuracy: 0.6286\n", "Epoch 59/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.2800 - accuracy: 0.8726 - val_loss: 0.7224 - val_accuracy: 0.6571\n", "Epoch 60/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.2858 - accuracy: 0.8762 - val_loss: 0.7118 - val_accuracy: 0.6524\n", "Epoch 61/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.2941 - accuracy: 0.8643 - val_loss: 0.7478 - val_accuracy: 0.6238\n", "Epoch 62/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.2533 - accuracy: 0.8869 - val_loss: 0.7935 - val_accuracy: 0.5952\n", "Epoch 63/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.2624 - accuracy: 0.8833 - val_loss: 0.7802 - val_accuracy: 0.6238\n", "Epoch 64/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.2675 - accuracy: 0.8869 - val_loss: 0.7480 - val_accuracy: 0.6381\n", "Epoch 65/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.2593 - accuracy: 0.8857 - val_loss: 0.6993 - val_accuracy: 0.6667\n", "Epoch 66/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.2644 - accuracy: 0.8810 - val_loss: 0.7271 - val_accuracy: 0.6476\n", "Epoch 67/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.2581 - accuracy: 0.8750 - val_loss: 0.7122 - val_accuracy: 0.6619\n", "Epoch 68/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.2332 - accuracy: 0.8964 - val_loss: 0.6998 - val_accuracy: 0.6524\n", "Epoch 69/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.2682 - accuracy: 0.8762 - val_loss: 0.7220 - val_accuracy: 0.6429\n", "Epoch 70/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.2414 - accuracy: 0.8952 - val_loss: 0.7401 - val_accuracy: 0.6286\n", "Epoch 71/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.2337 - accuracy: 0.8952 - val_loss: 0.7262 - val_accuracy: 0.6381\n", "Epoch 72/250\n", "7/7 [==============================] - 0s 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0.7471 - val_accuracy: 0.6238\n", "Epoch 99/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1688 - accuracy: 0.9286 - val_loss: 0.7501 - val_accuracy: 0.6190\n", "Epoch 100/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1745 - accuracy: 0.9167 - val_loss: 0.7169 - val_accuracy: 0.6429\n", "Epoch 101/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1752 - accuracy: 0.9119 - val_loss: 0.7073 - val_accuracy: 0.6476\n", "Epoch 102/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1636 - accuracy: 0.9238 - val_loss: 0.7224 - val_accuracy: 0.6429\n", "Epoch 103/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.1784 - accuracy: 0.9143 - val_loss: 0.7116 - val_accuracy: 0.6476\n", "Epoch 104/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1463 - accuracy: 0.9333 - val_loss: 0.6806 - val_accuracy: 0.6619\n", "Epoch 105/250\n", "7/7 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val_loss: 0.7249 - val_accuracy: 0.6429\n", "Epoch 112/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1522 - accuracy: 0.9310 - val_loss: 0.7208 - val_accuracy: 0.6476\n", "Epoch 113/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1601 - accuracy: 0.9262 - val_loss: 0.7625 - val_accuracy: 0.6190\n", "Epoch 114/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1783 - accuracy: 0.9167 - val_loss: 0.7340 - val_accuracy: 0.6286\n", "Epoch 115/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1368 - accuracy: 0.9369 - val_loss: 0.7156 - val_accuracy: 0.6333\n", "Epoch 116/250\n", "7/7 [==============================] - 2s 226ms/step - loss: 0.1437 - accuracy: 0.9333 - val_loss: 0.6792 - val_accuracy: 0.6571\n", "Epoch 117/250\n", "7/7 [==============================] - 3s 365ms/step - loss: 0.1497 - accuracy: 0.9298 - val_loss: 0.6709 - val_accuracy: 0.6714\n", "Epoch 118/250\n", "7/7 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val_loss: 0.6482 - val_accuracy: 0.7000\n", "Epoch 125/250\n", "7/7 [==============================] - 3s 414ms/step - loss: 0.1552 - accuracy: 0.9274 - val_loss: 0.6373 - val_accuracy: 0.6857\n", "Epoch 126/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.1396 - accuracy: 0.9298 - val_loss: 0.6450 - val_accuracy: 0.6810\n", "Epoch 127/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.1329 - accuracy: 0.9452 - val_loss: 0.6459 - val_accuracy: 0.6714\n", "Epoch 128/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.1258 - accuracy: 0.9440 - val_loss: 0.6427 - val_accuracy: 0.6810\n", "Epoch 129/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.1359 - accuracy: 0.9381 - val_loss: 0.6726 - val_accuracy: 0.6714\n", "Epoch 130/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1138 - accuracy: 0.9476 - val_loss: 0.6956 - val_accuracy: 0.6524\n", "Epoch 131/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1181 - accuracy: 0.9452 - val_loss: 0.6957 - val_accuracy: 0.6571\n", "Epoch 132/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1239 - accuracy: 0.9417 - val_loss: 0.6531 - val_accuracy: 0.6762\n", "Epoch 133/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1324 - accuracy: 0.9369 - val_loss: 0.6614 - val_accuracy: 0.6762\n", "Epoch 134/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1301 - accuracy: 0.9417 - val_loss: 0.6779 - val_accuracy: 0.6571\n", "Epoch 135/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1296 - accuracy: 0.9440 - val_loss: 0.6855 - val_accuracy: 0.6429\n", "Epoch 136/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1371 - accuracy: 0.9369 - val_loss: 0.7175 - val_accuracy: 0.6381\n", "Epoch 137/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1168 - accuracy: 0.9429 - val_loss: 0.6872 - val_accuracy: 0.6524\n", "Epoch 138/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1212 - accuracy: 0.9440 - val_loss: 0.6890 - val_accuracy: 0.6524\n", "Epoch 139/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1360 - accuracy: 0.9357 - val_loss: 0.6829 - val_accuracy: 0.6571\n", "Epoch 140/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1111 - accuracy: 0.9560 - val_loss: 0.7026 - val_accuracy: 0.6381\n", "Epoch 141/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1426 - accuracy: 0.9381 - val_loss: 0.6918 - val_accuracy: 0.6571\n", "Epoch 142/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1145 - accuracy: 0.9476 - val_loss: 0.6488 - val_accuracy: 0.6714\n", "Epoch 143/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1123 - accuracy: 0.9452 - val_loss: 0.6996 - val_accuracy: 0.6571\n", "Epoch 144/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1432 - accuracy: 0.9357 - val_loss: 0.7153 - val_accuracy: 0.6429\n", "Epoch 145/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1302 - accuracy: 0.9381 - val_loss: 0.6868 - val_accuracy: 0.6571\n", "Epoch 146/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1255 - accuracy: 0.9464 - val_loss: 0.6588 - val_accuracy: 0.6810\n", "Epoch 147/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1247 - accuracy: 0.9405 - val_loss: 0.6542 - val_accuracy: 0.6714\n", "Epoch 148/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1338 - accuracy: 0.9357 - val_loss: 0.6418 - val_accuracy: 0.6762\n", "Epoch 149/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1232 - accuracy: 0.9440 - val_loss: 0.6869 - val_accuracy: 0.6524\n", "Epoch 150/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.1116 - accuracy: 0.9512 - val_loss: 0.6825 - val_accuracy: 0.6524\n", "Epoch 151/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1026 - accuracy: 0.9524 - val_loss: 0.6779 - val_accuracy: 0.6714\n", "Epoch 152/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1213 - accuracy: 0.9464 - val_loss: 0.7081 - val_accuracy: 0.6524\n", "Epoch 153/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1235 - accuracy: 0.9464 - val_loss: 0.6863 - val_accuracy: 0.6524\n", "Epoch 154/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1307 - accuracy: 0.9417 - val_loss: 0.6777 - val_accuracy: 0.6619\n", "Epoch 155/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1214 - accuracy: 0.9417 - val_loss: 0.6985 - val_accuracy: 0.6429\n", "Epoch 156/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1333 - accuracy: 0.9357 - val_loss: 0.7072 - val_accuracy: 0.6476\n", "Epoch 157/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1172 - accuracy: 0.9440 - val_loss: 0.6681 - val_accuracy: 0.6667\n", "Epoch 158/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1251 - accuracy: 0.9464 - val_loss: 0.7068 - val_accuracy: 0.6476\n", "Epoch 159/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1017 - accuracy: 0.9512 - val_loss: 0.7654 - val_accuracy: 0.6190\n", "Epoch 160/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1028 - accuracy: 0.9560 - val_loss: 0.7586 - val_accuracy: 0.6143\n", "Epoch 161/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1085 - accuracy: 0.9476 - val_loss: 0.7278 - val_accuracy: 0.6286\n", "Epoch 162/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1135 - accuracy: 0.9452 - val_loss: 0.6628 - val_accuracy: 0.6810\n", "Epoch 163/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1016 - accuracy: 0.9548 - val_loss: 0.6493 - val_accuracy: 0.6857\n", "Epoch 164/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1209 - accuracy: 0.9405 - val_loss: 0.6678 - val_accuracy: 0.6667\n", "Epoch 165/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1044 - accuracy: 0.9536 - val_loss: 0.7052 - val_accuracy: 0.6524\n", "Epoch 166/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1200 - accuracy: 0.9464 - val_loss: 0.7135 - val_accuracy: 0.6476\n", "Epoch 167/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1152 - accuracy: 0.9405 - val_loss: 0.7149 - val_accuracy: 0.6429\n", "Epoch 168/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0943 - accuracy: 0.9560 - val_loss: 0.7285 - val_accuracy: 0.6476\n", "Epoch 169/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0986 - accuracy: 0.9560 - val_loss: 0.7240 - val_accuracy: 0.6333\n", "Epoch 170/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1010 - accuracy: 0.9536 - val_loss: 0.6508 - val_accuracy: 0.6857\n", "Epoch 171/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1044 - accuracy: 0.9524 - val_loss: 0.6375 - val_accuracy: 0.6762\n", "Epoch 172/250\n", "7/7 [==============================] - 2s 218ms/step - loss: 0.0966 - accuracy: 0.9560 - val_loss: 0.6363 - val_accuracy: 0.6952\n", "Epoch 173/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0963 - accuracy: 0.9536 - val_loss: 0.6564 - val_accuracy: 0.6714\n", "Epoch 174/250\n", "7/7 [==============================] - 3s 405ms/step - loss: 0.1045 - accuracy: 0.9536 - val_loss: 0.6310 - val_accuracy: 0.7000\n", "Epoch 175/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0841 - accuracy: 0.9607 - val_loss: 0.6543 - val_accuracy: 0.6762\n", "Epoch 176/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0845 - accuracy: 0.9643 - val_loss: 0.6791 - val_accuracy: 0.6571\n", "Epoch 177/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.1198 - accuracy: 0.9429 - val_loss: 0.6684 - val_accuracy: 0.6762\n", "Epoch 178/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0984 - accuracy: 0.9560 - val_loss: 0.7129 - val_accuracy: 0.6476\n", "Epoch 179/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1001 - accuracy: 0.9571 - val_loss: 0.7397 - val_accuracy: 0.6333\n", "Epoch 180/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1213 - accuracy: 0.9452 - val_loss: 0.7437 - val_accuracy: 0.6286\n", "Epoch 181/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1117 - accuracy: 0.9452 - val_loss: 0.7560 - val_accuracy: 0.6238\n", "Epoch 182/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0991 - accuracy: 0.9524 - val_loss: 0.7256 - val_accuracy: 0.6286\n", "Epoch 183/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1025 - accuracy: 0.9524 - val_loss: 0.6656 - val_accuracy: 0.6714\n", "Epoch 184/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1037 - accuracy: 0.9571 - val_loss: 0.6499 - val_accuracy: 0.6810\n", "Epoch 185/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0853 - accuracy: 0.9631 - val_loss: 0.6823 - val_accuracy: 0.6524\n", "Epoch 186/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1065 - accuracy: 0.9548 - val_loss: 0.7085 - val_accuracy: 0.6476\n", "Epoch 187/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0848 - accuracy: 0.9631 - val_loss: 0.6788 - val_accuracy: 0.6667\n", "Epoch 188/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0681 - accuracy: 0.9679 - val_loss: 0.6575 - val_accuracy: 0.6762\n", "Epoch 189/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1042 - accuracy: 0.9512 - val_loss: 0.6587 - val_accuracy: 0.6762\n", "Epoch 190/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1091 - accuracy: 0.9512 - val_loss: 0.6836 - val_accuracy: 0.6619\n", "Epoch 191/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0905 - accuracy: 0.9607 - val_loss: 0.7580 - val_accuracy: 0.6095\n", "Epoch 192/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0778 - accuracy: 0.9655 - val_loss: 0.7169 - val_accuracy: 0.6429\n", "Epoch 193/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0856 - accuracy: 0.9583 - val_loss: 0.7079 - val_accuracy: 0.6524\n", "Epoch 194/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0876 - accuracy: 0.9643 - val_loss: 0.7056 - val_accuracy: 0.6476\n", "Epoch 195/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0765 - accuracy: 0.9607 - val_loss: 0.7030 - val_accuracy: 0.6476\n", "Epoch 196/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0743 - accuracy: 0.9655 - val_loss: 0.6972 - val_accuracy: 0.6524\n", "Epoch 197/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0828 - accuracy: 0.9607 - val_loss: 0.7014 - val_accuracy: 0.6571\n", "Epoch 198/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0919 - accuracy: 0.9560 - val_loss: 0.7126 - val_accuracy: 0.6524\n", "Epoch 199/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0809 - accuracy: 0.9607 - val_loss: 0.7071 - val_accuracy: 0.6381\n", "Epoch 200/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0603 - accuracy: 0.9738 - val_loss: 0.7214 - val_accuracy: 0.6333\n", "Epoch 201/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0810 - accuracy: 0.9619 - val_loss: 0.7387 - val_accuracy: 0.6286\n", "Epoch 202/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0987 - accuracy: 0.9548 - val_loss: 0.7279 - val_accuracy: 0.6429\n", "Epoch 203/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0722 - accuracy: 0.9655 - val_loss: 0.7880 - val_accuracy: 0.6048\n", "Epoch 204/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0815 - accuracy: 0.9643 - val_loss: 0.7770 - val_accuracy: 0.6048\n", "Epoch 205/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0896 - accuracy: 0.9583 - val_loss: 0.7566 - val_accuracy: 0.6190\n", "Epoch 206/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0778 - accuracy: 0.9655 - val_loss: 0.7366 - val_accuracy: 0.6286\n", "Epoch 207/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0995 - accuracy: 0.9536 - val_loss: 0.7026 - val_accuracy: 0.6476\n", "Epoch 208/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0864 - accuracy: 0.9595 - val_loss: 0.6933 - val_accuracy: 0.6571\n", "Epoch 209/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0908 - accuracy: 0.9524 - val_loss: 0.7015 - val_accuracy: 0.6476\n", "Epoch 210/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0865 - accuracy: 0.9607 - val_loss: 0.6739 - val_accuracy: 0.6667\n", "Epoch 211/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0825 - accuracy: 0.9607 - val_loss: 0.6730 - val_accuracy: 0.6667\n", "Epoch 212/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0767 - accuracy: 0.9643 - val_loss: 0.6666 - val_accuracy: 0.6667\n", "Epoch 213/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0540 - accuracy: 0.9762 - val_loss: 0.6640 - val_accuracy: 0.6810\n", "Epoch 214/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0846 - accuracy: 0.9619 - val_loss: 0.6986 - val_accuracy: 0.6571\n", "Epoch 215/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0721 - accuracy: 0.9655 - val_loss: 0.6975 - val_accuracy: 0.6667\n", "Epoch 216/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0696 - accuracy: 0.9667 - val_loss: 0.6940 - val_accuracy: 0.6571\n", "Epoch 217/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0828 - accuracy: 0.9583 - val_loss: 0.7010 - val_accuracy: 0.6476\n", "Epoch 218/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0907 - accuracy: 0.9548 - val_loss: 0.6725 - val_accuracy: 0.6714\n", "Epoch 219/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0675 - accuracy: 0.9702 - val_loss: 0.7434 - val_accuracy: 0.6286\n", "Epoch 220/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0674 - accuracy: 0.9679 - val_loss: 0.7541 - val_accuracy: 0.6238\n", "Epoch 221/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0819 - accuracy: 0.9631 - val_loss: 0.7783 - val_accuracy: 0.6048\n", "Epoch 222/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0934 - accuracy: 0.9548 - val_loss: 0.7507 - val_accuracy: 0.6190\n", "Epoch 223/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0633 - accuracy: 0.9714 - val_loss: 0.7088 - val_accuracy: 0.6524\n", "Epoch 224/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0614 - accuracy: 0.9762 - val_loss: 0.6579 - val_accuracy: 0.6667\n", "Epoch 225/250\n", "7/7 [==============================] - 0s 69ms/step - loss: 0.0762 - accuracy: 0.9679 - val_loss: 0.6255 - val_accuracy: 0.6905\n", "Epoch 226/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0794 - accuracy: 0.9607 - val_loss: 0.6810 - val_accuracy: 0.6667\n", "Epoch 227/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0595 - accuracy: 0.9750 - val_loss: 0.7369 - val_accuracy: 0.6333\n", "Epoch 228/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0712 - accuracy: 0.9679 - val_loss: 0.7274 - val_accuracy: 0.6429\n", "Epoch 229/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0668 - accuracy: 0.9714 - val_loss: 0.6946 - val_accuracy: 0.6476\n", "Epoch 230/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0638 - accuracy: 0.9750 - val_loss: 0.6962 - val_accuracy: 0.6524\n", "Epoch 231/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0766 - accuracy: 0.9595 - val_loss: 0.7235 - val_accuracy: 0.6429\n", "Epoch 232/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0641 - accuracy: 0.9714 - val_loss: 0.7337 - val_accuracy: 0.6238\n", "Epoch 233/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0712 - accuracy: 0.9655 - val_loss: 0.7263 - val_accuracy: 0.6429\n", "Epoch 234/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0747 - accuracy: 0.9631 - val_loss: 0.7023 - val_accuracy: 0.6667\n", "Epoch 235/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0669 - accuracy: 0.9714 - val_loss: 0.7175 - val_accuracy: 0.6429\n", "Epoch 236/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0811 - accuracy: 0.9631 - val_loss: 0.7103 - val_accuracy: 0.6476\n", "Epoch 237/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0678 - accuracy: 0.9690 - val_loss: 0.7117 - val_accuracy: 0.6381\n", "Epoch 238/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0735 - accuracy: 0.9655 - val_loss: 0.6933 - val_accuracy: 0.6619\n", "Epoch 239/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0677 - accuracy: 0.9690 - val_loss: 0.6984 - val_accuracy: 0.6524\n", "Epoch 240/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0695 - accuracy: 0.9679 - val_loss: 0.7131 - val_accuracy: 0.6476\n", "Epoch 241/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0739 - accuracy: 0.9667 - val_loss: 0.6927 - val_accuracy: 0.6524\n", "Epoch 242/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0717 - accuracy: 0.9667 - val_loss: 0.6604 - val_accuracy: 0.6762\n", "Epoch 243/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0708 - accuracy: 0.9679 - val_loss: 0.6911 - val_accuracy: 0.6571\n", "Epoch 244/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0784 - accuracy: 0.9607 - val_loss: 0.7346 - val_accuracy: 0.6286\n", "Epoch 245/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0797 - accuracy: 0.9619 - val_loss: 0.7393 - val_accuracy: 0.6238\n", "Epoch 246/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0550 - accuracy: 0.9738 - val_loss: 0.7510 - val_accuracy: 0.6190\n", "Epoch 247/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0797 - accuracy: 0.9595 - val_loss: 0.7105 - val_accuracy: 0.6429\n", "Epoch 248/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0700 - accuracy: 0.9643 - val_loss: 0.6982 - val_accuracy: 0.6571\n", "Epoch 249/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0502 - accuracy: 0.9810 - val_loss: 0.7033 - val_accuracy: 0.6524\n", "Epoch 250/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0700 - accuracy: 0.9667 - val_loss: 0.7415 - val_accuracy: 0.6286\n", "Valid results - Loss: 0.7415037751197815 - Accuracy: 62.85714507102966%\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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rLKXit+P59x9u4LJ736WlLRztVYqIHBa6vA/ezK4zswIzKygtLT3g+VMyc1mcdhLXxr1E3/K5PPZBYRRKKSJy+OnygHfO3e+cG++cG5+bm3tQyzjpW/fhuo/g7oQ/88IrL3H/W2tpaQtTXtdMfXNrJ5dYROTw0OUB3ylCSdiXHichNZO/2R3Mf+UfXHrvu0y+8w3++58fdnXpRES6RDACHiCzL3FffYmUnoO5L/73/HfxjxmdsJnXV5Swqrimq0snInLIRfMyyRnAu8BQM9tsZl+L1rp2ysqHr82E0/4fpyWuYEbrLbyQ8CPWz/gOFYtfZePWUpxzUS+GiMhngX2WAm/8+PGuoKCgcxZWXw4LHqZw3tP0rF5CyNpocnGsTxxB/vBxJCanQVwi9BgBQ6ZCKKlz1isicgiZ2Xzn3Pg9jgtswEc451iyfgtFi14nd/v7xBf+hwFxpaRaM4RbwIUhOQeGnAU9j4aUbhATCxYLMXH+dUI6ZPSBtF4QG/fJlZSvh9TuEJ/SqWUXEfk0R3TAf9zds9fw61dW8s+vH88J+Wmw6T0omA7rZkNj5b5njglBnwmQ1tMHekouFM6D1a+BxcAx0+DMX0JydlTrICKygwK+ncaWNk7/3RxKapq4eEwev7joaGJjDMJhqC+DhnIIt4Frg3Crf91YCVWbYftqv0NorITqrdBSB4kZMOlbvkvogwcgLglGXgh5Y6HnMdBr9K5Wv3NQW+J3Cn0mQGw8zJ8ORQtgwOdgzJWQkLrngofDUFcCqT3A7OAq31ABK16C4ef6cot81lUV+QaTulD3SgH/MRvL6rj7zbX8q6CQn10wkqsn5R/4QpyD1iaIS9gVuNuWwNy7YOUr0FTlh6XnQSgZqgr9PG2Rxymk9fIf2vJ1kNHXj88aAKf9GAae4j/UTbWwcS4kZcLrP4MNb0O3oXD1s5De+8DL/MRXYOnTkJABl/0dBp164Ms4XO3YVkFVW+LrmNm3q0vSeSo2wp/H++/JxG/CibfsuYv0QFUVwSvfg6MvgxHnd3x5XUwBvwfOOa6e/j7zN1Zw4lHdaGwJM6ZfJtd/bhCJodiOLhwqN0FRAXz0BOCg2xC/I0jtAZn94YWb/DmAK56EvsfBhnfg2Rv8fHFJMOBEWDdn1w7BYmHSN+Ddu2HSN+GMn39yva3NsO5NKF3hdw6xITj+v3xrfc3r8MjFMO7LUPiBX8/XXoUeIz+5nHDYl625DubdC2OvgYy8Pde1rRVaGz955NHaBHWl/tzFwSpdCatehaYamPB1SOuxf/MVzYcPH4HUnvC570LZGnjgNBh1MZzzO4gJztXBgN/ufxoHVZugz3FwxRO+UdDZytf793bhP2HoWXDctfs336yfQvUWuOje/T/6dA5e/i4U/A2OOgNW/tvX7ZIH/NVyB6OlERY8BO/8Hmq2+m7VS6fDyIsObnkHqrkelr/gG3NT/mfX57Cm2PccdB9+UItVwO9FUWUDtz+3lA1ldcSasbK4hpG903ni+kkkx3dCS2Ffakt89096r13D2lpgy4fwwYOw/i0YdjYMOxfqtkNWf78jePxqH/z/sxzik3fNu/RZePl7ULtt9/X0Hgtn/wYeu8KH8PX/8V1RD5zijwLO+yOsmQVjrobUXNg0D577pu+SSunmP4zdR8JXX4HE9F3LXfM6rHoFlj7jg7zn0XDuH6DPeF+3f17mj2i+8jL0nbBrvtYmKF7iy9X+y+4cFC/19V80w++k6st2je81Gr7yEjRW+eDvPxni4v24cBvUFvujoqIFMP1Mv+y2Zt/tVbwMti32O62RF/mQb3+epK4MWuo7t/XbVAOhlE/fmYTb/In8jljwD3j+W34nOP8h3/13zQufbO065z9L8//uuxmHnwf9Jn768sNh3+J9/37/dyjFd09eeC+M/uK+522sgv8b4hsB5/4exn9139OXroSXbvXdmOE2OHYaXPAXWPwkvPhtwPznoOeoTy/3x736Q3j3z/6zdPZv4LUf+c/GN+ZCZr9d0z33LVg/B479Enzue34bbpzrGzvVW/yR75Rv+89XbPyndx+1NsOMy33jy0WelTX1VzDxev/e/uNC//m8efHeu2j3QQG/n15ZspUbHl3AuH5ZAFw1qT8XjN5Ly7WrbJwLfzsLRlzoQ6qhwrfQ5/8deh0Lp/wQ+p/gv4SrX4XHr/FHAXFJcO3ru1rsi/4Fz1znTxyHW/z4IWf6FkZGng+Jwg9gwlfh9Z/7VtM5v4W+x8OrP4D5f/PzDD7Nh/uHj0LNFhh5sf8gN9dBUpYPla/P9C155+CJa2DZc3Dcf8GZv/BXKq2ZBbPv9Ec8ANmDYODJkDMIRl0KWxf6L0hSll9uayOkdIcvPgbZA+AfF/lpdqwvIR3+aw689Rt4726/zEsehMqN8OYv/Y7tujl++rd+A2//DlobIG88fOFvu3/Z98Y5H0bhVv+ett9ZFS+Dv0317+HlM/yOqLUJFj/hl91/il/f6z/37+MpP4TJN+59XYXv+3A95QeQPdB36y2c4cPz9J/AvVN8MFw3x7eun/sGXPo3f8QCsHURzLwdNr3r3ztsV3fVd1Z9+vmYmbfDf/4AE66FYy7z2/uRS/yO+ttLISHNN05e/ymk9/HBtcP8v/uj1W5D/DmsvLH+s5SS67dzSo5vVKx8yR91FC+F+DTfuCleAl94yH8OACo2wPSp/jN7wZ/9Zz+jL/QZt3t5Gyr8ubKkbH9lW+kK6D0G/noaDDgJLns4sryNcM8Jfjtd9YzfhtsW+/dzx/s88Ru+Zf3ityEx05el8H3IGeyPgnG+YZGUCSf/wO/Y597lh53/J3/UOftXMPuXvptpyBnw3j3+oo4p/+MbUB/+A867C8Zds+/tsBcK+ANw1+ur+f2sVfTOSKKosoHvTR3GDScP6tIy7cY5mPMrePu3gPlWfEMFHH8DnHHHJ1ttlYWw+HHfahl82u7L+fu5UF0E598Fix7z/4aeBRfes3trfd1seOFmqFjvQ7GhAibf7ANnR1A0VPrzBB8+4r8QF/wFcPC3s32AJGb4ln5tMfSb5MMmo59vvVas91/UyTf5lnnusE+2fNfN8V+EUBIMPh1e+zE0VUNsgi/Pibf4upSt9d1XeWP9fPXlfvyOkNg0Dx4615chb6w/XB9xgf+Sz/mNX/4VT0CvY/a+DVoa/Rd+0T/93xO/AVP/F2q2+UAr+JsP8MYqSO7mQyYm1gcGwKhL/A5q3r3QfQSULPXb7oT/9uOb6+CJL/vXOYP9zrOpyr+Hw8+DJU/7oHZhyDkKylbDl57w4REOw5/G+qu8vvaaPzq570QfwKMu9l2EQ8+ClgZ/FHfO72DCHu5BDLfBsmd9XZ//Foy+wgfWjh3Z5vnw11N9uY+/3h9ZrnzJj/v8z+CEG30L98EzfFm/+gq8/wCsfdMfZdaWQnPkDvOYEBz1eWiu9Y2UyTf7o8c9KVoAD1+46xyXxcLZv/ZHhN1H+MbKX0/3od5ebLwvz1XPwqBTdg2fd5/vCvriv2DoVH+ku/5tuHkRvHEHfPBXP12/Sf5zkZDmG0HP3+gbRMk5/nNdOM/vgMCfS6vZ6sedcQc8fZ3v6790uh9fWwLPXA9rX/d/j73GH0kf5MUTCvgDVNPYQmIolpv/tZCXF2/ln9dOZOLAnK4u1u5qS31oxMb7luTHWzH7o7XZf6hiQ/7vphqIT93zB62lAd75A2z7yF81lD95z8tsa/Gt8h3LKFoAj33Jn3fIHgi5Q32Qr54J7/3FHwUMOxuOuXxXl8v+KFsL/5zml3niLdDv+P2fd8HD/guK8102l0z3O5SS5fDIpb77YvJNkH8idB+268igrdnP/9gVsGamP0yv3gof/QvGXOG7yZrrIG8cnPcH35Jf+7pvvVcX+dArmu9bwxiMvdoH7FNf82E69hofPpvmwbx7fNdY2Rp/xHHBX2Dun2DtG76uF94Db/zC72QmfN0fXe3w3j3wym1w6o9g+YtQssyHfe8xu6ZxDu6JdHNd+6bfXuvm+IDsNwmeutb3e4M/l/HNeZ/s13/oPN/i7nMcrHoZpt7pg27pM/7mwfoy2PwBXHQfHHv57vM659+Thgrfkm/f3fhpmmp9F0pCGrz5v7Bprh8eE/JHMo3Vvr8/Kdtvy6RMeOYGP/23CnZvPLS1wN0T/Y7iwrt9K//k78PJt/md3Lo3/XT9p0Aocffyt/+eNNf7Lsv0PL/9i5f4BlRzjd9Jf/U1f7TSXlWRL28Hr2hTwB+k2qZWzr3rbQorGjh+QDbxcTH8+pJj6J6e+Okzy2dbTbEPn8Gn7/7Frd7qT3avmw04Hxq9jvHnE9qafBC4Nt/iGvdlHzb3TvF9s0Onwmm37zpa2JO2Vh8i21fDjQv8PRVtLf4IafHju3YiY6/xR1bhNsB2hVL7PvvmOt/dNfLi3evQVAMzvuivukrK9i3v4ed+sizz7oeXb/VdLmm9fZce+C6Uyk2+9ZnSHbodBb1Hf3L+4qX+yqztK+Gk78KpP/Tle/u3vmsslAKn3+67daKltdlvx/oyf/6meotvMIy4YPfp6sr8e9v+nNcOq17154xCKf4I+ObFnXMZ8brZfqd87u/3r9vvICngO6CkppH75qxj3voyVm2r5YyRPfjzl8Z2dbEk2mq2+VBf96bvc80b6/uNGyt9C639lRfNdezsLtsfDZU+kD6+I2hrhfWzfTfGSbd27EoY5/wRSVrPvd94Fw77bq8FD/murGHn+GBf+4Y/ZzF0P36Qra3VH9X1HnPw92d8Frz1G98lc8oP/ZVXhxEFfCf54yzfP/+Nkwdx1aT+LNtSzQuLtvDT80eRkRzq6uKJdJxzvs/8SLuxyDnYXOB35B29qukQ21fAR/lawGC5/uSBLNtaxT1z1nLvnLU4Ipe8N7Tw16vHExcbsOur5chjduSFO/h6t7+cNyDUgj8Im8rqeeyDTQDkpCbw8xeXMSg3hTsuPJr+OcmsLa3lxKMO7tepREQOhFrwnaxfTjLfnTps5995mYn86pWVXPXgPBJDsdQ2tfLzC0dx1cT+XVhKETnSKeA7wdRRvThhcDdueXwRjS1txMYYP352Cc9+WMStZw797F1iKSJHBHXRREFjSxvT/7OeGe9voqiigdvPG8nkwTm8sGgrN5zcCc+6ERGJUBfNIZYYiuUbJw/m6kn5fPtfC7n9+aWkJcRR09RKYUU9t583krdXl5KTksCkQWrdi0h0qAUfZc2tYa5/ZD4rt9Vw6rDu/OO9jTvHJYZieOnGExmYe+APGBIRAV0H3+Wcc7SGHbFmzFxezLrSOvpmJ/GjZ5cQDjvCDgZ3T2V030xOG96dKYO7YYfzTSMicsioi6aLmRmhWB/YZ47suXN4SkIcj7y7kV6ZiazaVstjH2zi73M3MLZfJvddNZ65a7cTHxtDz4xEhvdKV9+9iBwQteA/Q5pa23hqfhG3P78EgJa2XdtmYLcUnrrhBLJS4vlgQznDe6WTmqD9s8iRTi34w0RCXCxfOr4f2SnxPPjOOm44eRA905NYvrWa7z+9mK899AHHD8zhntlrGdc/iz9MG01qQhxZKQfwFEYROWKoBX+YePGjLXzvyY+oa25jXP8sFmyq2PnE0uPys5k0KIf5Gys4qnsaU47KIT42lilH7eWZ2iISGDrJGhDldc28vbqUs0b1omBjOWtLatle28xLi7eyuqSWvtlJFFU0EI5s0qsn9efScX1YV1rHe+vKyEgOcdvUYTqBKxIgCvgjQFltE9kp8Wwqr6e4uolXlmxj+n/W7xyfGIqhsSXM/zt3BDmp8azYVkPvzCRyUuKpamhhbL8shvZM4/GCQnJS4jlt+H7+wLWIdCkF/BFqbWktK7fV0C87maE907jyr/OYt74cgNgYoy28+7b//IgezFxWjBnceuZQxvTNYtHmSs45uheNLW2U1DRR3dDCix9t5UfnDqdXxhH41EGRzxgFvABQUt3Ikws2c8Kgbozqnc6m8nrqm9tITYjj7tlreLxgM8flZ5OaGMcbK0p2zpcUiqWxtY32H5Xx/bPonZnEkB6pfOvUo9hS2cA7a7bzuSG59Ij84tXcNdt5vKCQH5wznO5pu/8KlnNOXUUinUABL5/KOcfsVaWM7ZdFemIcK272noUAAA5mSURBVLbVsLGsjv45Kfz2tZUMzE1l0sAc6pvbqGtq5btPfUSMQdjBsX0yWFxURdhBcnwsR+dlUFHfzKriWgBOGZpLZUMLK7bWkJkcIi7WqKpv4Y6Ljub8Y3vjnKO4uomXl2ylT1YyJw3pRqwZ8zdWUFbXzJkjexIbo52ByJ4o4KXTvbGimKE90/nda6t4b10ZF4/N46QhuTz2fiGbK+pJS4xjTL8smlrD3PX6apJCsUyb0JfaplaaWsOsK61lTUkteZlJlNQ00dIWpqk1DMCIXum0hR0ri2sAOOfoXgztmUZlfQv9spPomZHIzGUlfOfMISwtqmZlcQ1j+mXSJzOZ259fwg0nD+a4Abv/TF1tUyt3v7mGYb3SOf/Y3of8/RKJFl0HL53u1GH+JOxvLzt2t+ET8ncP1pa2MM2tYT4/ogfj+mftHL69tonL7n2XjOQQkwd3w+G4ZlI+q4pr+cEzi0mOj+WPl49mU1k9v525ipeWbCU5FEtdc9vOZbyzppTi6qadf2clh6iob6FgQwX/c8YQwg62VDZQWd/CnFUlbK9tJj4uhoHdUmhoaWNNSS2nD+9BblrCzrJurWykzTlmLSumZ0YiWcnxPLewiC9Pzmdk747/EHNDcxtxsUYo8utfJdWNdEtNIEZHKBIFasHLZ051YwvxsTE7H82wpbKB9KQQqQlxLN1SRWF5AwlxMVz7cAGnDe/Ory85lt/OXMmzHxbxy4uP5v9eXcmGsnrAnz9ISYhjQn4Wl4ztw61PLqKivmXnujKTQ5w2rAeFFfUs3FRJc1t4r+Ua2iON4wdmU93QQmVDC//v3BGkJsSxbGs176zezoayelISYok1Y+mWaiYOzOarUwbw2tJiXly8la9NGcCdLy0nMzmeR79+PG+sKOGWJxYxpEcqU0f2pGdGEiU1jXy0uYoTBuVwweg8ctMSaG0Ls2hzJUWVjXy4qYLeGUnUN7exqbyeLx7Xl4r6Fsb3z9rjDW9VDS20hR1ZySG2VjWSkxpPQtz+P/KivrmVuJgY4uP8DmlzRT25aQkHtAyJLnXRSCCV1zWTmRTa2foNhx0xMUY47Nhe20RsjJGTmrDbPO+tK+OtVaWM7ptJbloCv5+1mjXFNWSlxDN5cDcG56YSdo7x+dkUbChnc0UDV0/qz1MLipi7djsFGyp2hl11Y8vOE8/xsTEMzPVHBo0tbfTPTuH9DeU715ueGEd1YyupCXE0t4VJiIuhvrmNY/pk0NrmWLrFn8Mwgz5ZSRSWNxAbY3xuSC5ltU0s2lwFQEJczM6urKRQLA0t/ogmLzOJ6z83kNLaZpZtqWJDWT3j+mUxc3kxjS1tDOuZxoJNlcTFGF8+IZ+rJvVnS2UjWyob2F7bxIje6fTJSmZNSS01jS2cf2xvqhpaOP/P/yEu1vj+WcOZs6qUGe9vIi0xjovH5HHC4G5U1jfT0uY4f3RvXvpoK9kp8ZwxsietbWEefncji4uqmDQwhy+M73NAJ9Wdc6zbXsfAbim7zeecY0lRNUf1SNWzmSIU8CKdpDXSwi+uaeKR9zbSIy2BYb3SGZWX8YlnAxVsKGfFthqO6ZNBv+xkfvXKSi4ak4cZPL2giIS4GL5z5lBSE+JobGmjsr6FxFAMmcnxrC6u4ekPi3hmQREtbWG+N3UYo/IyOKpHKtuqGgH/sLpZy4pJS4zjJy8spbi6iRiDgbmp9M5M4t212xnaM43eGUks3VLN5RP6srG8nifnb/7Ueh7bJ4PmNse60loyk0M7u8KuntSf6oYW/r14627PSmq/s7lqYn9iY4y/z91Adko85XXNjOufxWnDu7O0qJrKhmbG9c9myuBubK9t4tWl2/hgfTnj87P5ximDSE8M8b2nPuLt1dv58gn5jOydzsxlxdQ3t5GWGMfLS7bRLTWBnJR4ws5xVI9UjuqextCeaZwytDsFG8t5f305l47rQ3piiMc+KGTWct/l9ssLj8Zi4OG5G5jxfiHDe6Vz9xVjCcX6k/qbyuuZkJ9N3+xkWtvCxEW60jaW1ZEQF0vPjESq6lsoqWmkX04y89aV09jSxslDu1Ne10xuWgKxMYZzjubIeaVw2LFgUwXF1U2M6JXO0J5pzFlVyqi8DPIyk3DOsb22eWdX4YFSwIscpsJhhxmf2vptbGmjqqGFjKTQzpZtY0sbCXExn5h37prtFFbUk5eZTO/MRLJT4llYWEllfQvd0xPYVtXIXa+vxuHvhzhpSC7LtlTTMz2R/G4pgD+HUlTRQE5qPNuqGvndzFWcPrwHRZUNTP/PepyDKyf242fnj+JfBYX8+Y01FFU20Dc7iYykEEu3VO88+slKDjE+P5t568pobA2TEBtDm3OcMKgbs5YXA9AzPZH4uBgKK+r56uQBFJbXE3YOMNaU1LCxvB7nYFBuCoUVDTS37t7VdnReBiu2VZMYF0ubc9Q3tzG6byYLCys5tk8GYQeLi/xRUnJ8LAO6pbB0SzU90hPISUlg2dZqYmOM3pmJFJY3ABAXY7RG7iXZcV9JcnwscTFGfXPbznHtxcYYg3JTdl5hNm18X0prm1i/vY5/3ziF5PgDPy3aZQFvZlOBPwKxwF+dc3fua3oFvMjhb1FhJW+tKuW6zw3c2Vff2hamvL555/0QW6saWFNSS1IoltF9M4mLjaGstokfPLOYuqY2fnHRKPplJ/OfNWVkJIUYlZeOc1DX3EpaYugT62xsaWPOqlJufWIR3dMT+ePloynYUEFVQwunD+/BiN7pzN9YwZPzC4mLiWHahL6Mysvgkfc28kjkR3iuOSGfUb0z+P2sVRRXN3LSkFxKa5rYVtXIhPxs6ptb2VBWx+i+WfRIT2DplmqG9EglPTHEh4WV5GUmsaGsDucgJSGW5Pg4EuJiCDvHqLwM+mQm8+PnlvDuujJ+ev5I1pbU8uB/1hOKjeH7Zw3jmkn5B3WyvUsC3sxigVXA54HNwAfAF51zy/Y2jwJeRDqist5fKXUwLeFDIRx21DS2kpHsd1JLiqpISYhjQOTI6GB01WWSxwFrnHPrIoV4DLgA2GvAi4h0RGbyZ/vR2TExtjPcAUbldfzS232JZsDnAYXt/t4MHP/xiczsOuC6yJ+1ZrbyINfXDdh+kPMerlTnI4PqfGQ42Dr339uILj+Occ7dD9zf0eWYWcHeDlOCSnU+MqjOR4Zo1DmmMxf2MUVA33Z/94kMExGRQyCaAf8BcJSZDTCzeOBy4Pkork9ERNqJWheNc67VzL4FvIq/THK6c25ptNZHJ3TzHIZU5yOD6nxk6PQ6f6ZudBIRkc4TzS4aERHpQgp4EZGAOuwD3symmtlKM1tjZrd1dXmixcw2mNliM1toZgWRYdlmNtPMVkf+z/q05XzWmdl0MysxsyXthu2xnubdFdn2H5nZ2K4r+cHbS51/YmZFke290MzObjfu+5E6rzSzM7um1B1jZn3N7E0zW2ZmS83spsjwwG7rfdQ5etvaOXfY/sOfvF0LDATigUXAiK4uV5TqugHo9rFhvwZui7y+DfhVV5ezE+p5EjAWWPJp9QTOBl4GDJgIzOvq8ndinX8CfGcP046IfM4TgAGRz39sV9fhIOrcCxgbeZ2Gf6zJiCBv633UOWrb+nBvwe98HIJzrhnY8TiEI8UFwEOR1w8BF3ZhWTqFc+4toPxjg/dWzwuAh533HpBpZr0OTUk7z17qvDcXAI8555qcc+uBNfjvwWHFObfVObcg8roGWI6/+z2w23ofdd6bDm/rwz3g9/Q4hH29YYczB7xmZvMjj3cA6OGc2xp5vQ3o0TVFi7q91TPo2/9bke6I6e263wJXZzPLB8YA8zhCtvXH6gxR2taHe8AfSaY458YCZwHfNLOT2o90/pgu8Ne8Hin1BO4BBgGjga3Ab7u2ONFhZqnAU8DNzrnq9uOCuq33UOeobevDPeCPmMchOOeKIv+XAM/gD9WKdxymRv4v6boSRtXe6hnY7e+cK3bOtTnnwsAD7Do0D0ydzSyED7pHnXNPRwYHelvvqc7R3NaHe8AfEY9DMLMUM0vb8Ro4A1iCr+s1kcmuAZ7rmhJG3d7q+TxwdeQKi4lAVbvD+8Pax/qXL8Jvb/B1vtzMEsxsAHAU8P6hLl9HmZkBDwLLnXO/azcqsNt6b3WO6rbu6jPLnXBm+mz82ei1wA+7ujxRquNA/Nn0RcDSHfUEcoDXgdXALCC7q8vaCXWdgT9MbcH3OX5tb/XEX1Hxl8i2XwyM7+ryd2Kd/xGp00eRL3qvdtP/MFLnlcBZXV3+g6zzFHz3y0fAwsi/s4O8rfdR56htaz2qQEQkoA73LhoREdkLBbyISEAp4EVEAkoBLyISUAp4EZGAUsCLdAIzO9nMXuzqcoi0p4AXEQkoBbwcUczsSjN7P/Lc7fvMLNbMas3s95FndL9uZrmRaUeb2XuRh0A90+7Z5IPNbJaZLTKzBWY2KLL4VDN70sxWmNmjkTsXRbqMAl6OGGY2HJgGTHbOjQbagCuAFKDAOTcSmAPcHpnlYeB7zrlj8Hca7hj+KPAX59yxwAn4u1DBPx3wZvxzvAcCk6NeKZF9iOvqAogcQqcB44APIo3rJPzDrMLAvyLTPAI8bWYZQKZzbk5k+EPAE5FnAuU5554BcM41AkSW975zbnPk74VAPvBO9KslsmcKeDmSGPCQc+77uw00+/HHpjvY53c0tXvdhr5f0sXURSNHkteBS82sO+z8/c/++O/BpZFpvgS845yrAirM7MTI8KuAOc7/Es9mM7swsowEM0s+pLUQ2U9qYcgRwzm3zMx+hP9lrBj80xu/CdQBx0XGleD76cE/rvbeSICvA74SGX4VcJ+Z/SyyjC8cwmqI7Dc9TVKOeGZW65xL7epyiHQ2ddGIiASUWvAiIgGlFryISEAp4EVEAkoBLyISUAp4EZGAUsCLiATU/wf92Xh8uvwOywAAAABJRU5ErkJggg==\n", 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oehEHcsrYnVnKVafFOOX89Y3NPPPtES5NiCYqwAspJRV1je1684WVdfzjq8M0S8lVp8UwPtKPw3kVfLonhxvOiuPLfco98ugFY3n004OkFlSxcmc2K7ZnsmzGEH63aGzLuRqbmvnzF4eI8PMg0NuN/dnlXDwlkoTYAEClR/5wpJBgbzeWnx5LSkElLhbB3fNHcff8UQD8ZelELnl+Ezlltfz54gl4ublw3Rmx3PjmDgBmjwph9qgQNh4t5IZZcdz1/h7e3KIm5PrVWzsRAjbdd06ruIH9M75mZiw3v6XOdbYR+B0W4s3kaH92Z5YyNsK3Zd/zJoRz3oT2c2gtnhyBm4ulpZcPtrIwjmT6LJoYzqKJtvM++fOJSAmhPh5dHvtT0YpAo+lFvLYxjQ93ZrF0alRLcLQrpJRkFtcQHejZZYPz3PqjPP3tUUqqG/jDheP539YM/rz6EJvun8urG45xzuhQJkX7s3rfcf63NYNBblY+3JnNDbOG8sGOLPLK6/hoVzYNTc2MHuzDZQlDePTTg6zak8O3Sfl4uVlZcyCvRREcyavgo13ZHMgp57mrplJcVc+D2fu5ZFoU0QFeDHKzsjOjhHXJ+SyZHInVIvjTRRPayT0tJoD7FowmpaCSyxNU9fq5Y8IYEuhF4CA3gr1VssWXd5wFwAvfp3A4r5IhgV5kFFcza0Rwh8FjkwXjB3PT7Dhe+C6VydG2QZ4/j49id2YpEyL9Oj3WxN3FypLJHc6t9aOYGHXqBptqRaDR9CJSCyqREo7mVzLegcYno6iaG99MJCm3gvsXjuam2cM63Tc5t4Jn1x3FxSJYtSeHB88fw8qdWVTVN7F633H+9c0RNqUU8v7Np7M3q4ygQW6svfMs7l+5j2fXpeDt7sJflk5gxfZMahqauevckXi6WQn382B9cj4A150Ry7PrUsgsriazpJorX9oKwPxxYSwcP5hmCSNCvTktTvWaR4f78vGuHOqbmlk4fvAJ77XtvVktgv/98jQ60n2/OHMoh/MqOXN4MNf9dzs/j49uv1Mb7p0/mjkjQ5k+NLBl3eUJQxgR6uPQd9GX0YpAo3EiOUbed4R/571Re1KNXPKk3AqEgLhg75aBTR3x7LqjpBVVMSHSj+fWp3D59CH4ebYP2jY1S/7vw734eLjy8PljuWPFbt7ZmsHODJUP//IPqYBKzdyfXcb+7DLGR/oR5O3Oi9fEk1VSjZQQHejFZQlDWp07NmgQx8tqiQ3yYsnkSJ5dpzKJ1hzIJWiQG09dNpmZcUEIIbAKWpQAwDUzY3hrS7ry0w9rPcjSEaIDO06ltJfx89vOZGy4b4f72WOxiHYyWC2ilWLor+gBZRpNN1JZ18i3SXlU1zcipWT5a9uY+/fv+GR315PvFVfVU1qt6tV8tjeHRU9vYMmzG0gvUsoht6yWS5/fzBFjoFNxVT0f787m4qlRPLF0AmU1Dcx76jse+Ggfqsq7oqlZ8vjnh9iTWcojF4zlgkkRRPp78thnqlyCv5crKQVVDHKz4uVm5bn1RzmSX9nKHRIV4NVpoxsbrMbNxMcGMiLUm2Bvd/63NZ1vk/O56rQhzB4ZgptLx03NksmRvH/z6Txz5VSHR96eLOMi/HQJ9y7QikCj6UZe35TG9f9NZMafvmHVnhwO51UyyN3KHSt28+X+XAAqahu467093PXeHhqbmnl23VEW/PN7nv7GNuJ8fXIBQsDxslr+YDTY727PYFtaMX/5Mrllua6xmetOj2VchB8PnT+WseG+vL01g/d3ZLWc6/8+2MurG4+xbMYQFk+KwGoRvPXL0xgZ5sPkaH9+NlalO0+LDeTK6UNYvS+XpmbpsDtkaLBSEAmxAQghmDcmlD1ZZXi6Wlk2wzlBb033ohVBN/Bjy1AD/POf/6S6urqbJdI4g5zSGhb883ve2JzW6T67M0sJ9/PAahHc/f4ehICVt5zB5Gh/bn93F/uzy7j8xS18uDOLD3dm8buP9vPkmmTSi6r57yZ13mkxKpsmITaQn0+L5vvDhZTXNrByZzZuVgtfH8pjZ0YJ7ydmcdrQQEaE+QDKL/7a8gSmxwbyfx/sZfwja1h7IJePd2dz7cwY/njhhJae8dDgQXxx+yzev3kmk6PV9abHBnDznGF4Ga6oCVGOKYL42EB8PVw4c4Sq2/P4RRPYdN85bHlgLqG+zs940fx0tCLoBrQi6H/Ylyj+ZHc24x9Zw8XPbSIpt4I/fn6IlILKDo/bn11GQmwgt8wZRkOTJD4mgCFBXrx4dTxeblYue2EzB3LK+eslE/Fys7IiMVM13AtUmqSb1cI5RvriwvGDWThhMPVNzTz+2SEyiqt56PwxBHu786u3dnKssIql06JaXd9iETxz5RRunzsCD1crt76zi6ZmydUz2/fMhRC4Wi3MGhFMhJ8H88aGEeztzh3zRjAp2p8IP8ca8alDAtj76HwijTiI1SKI8Pc84QAzTe9CK4JuwL4M9T333MOTTz5JQkICEydO5JFHHgGgqqqKRYsWMWnSJMaPH8+KFSt4+umnycnJ4eyzz+bss8/u4bvQmJRU1TP5sbX823DVrNieidUicHOx8NSlk/B0tXLfh3tbSg5X1ql5Jwor6zheVsvEKD+umRnL1CH+La6REB93Hr5gLFX1TVw4OYJL46O5xGjE754/isWTInCxCGKCvDh7VCijB/uwaGI404YEEOrjzorETCL8PLh4ahSPLRlHbnktHq6WDvPZQ309uPPckdw2dzh1jc1MivJjeKhPp/cbHejFpvvnMnqwCqjeeNYwPvn1GdqvPoDof1lDX9wHufu695yDJ8DCJzrdbF+Geu3atXzwwQds27YNKSWLFy/m+++/p6CggIiICD7//HNA1SDy8/PjqaeeYt26dQQHB3evzBoAdmaUsP1YcUvq4eub0hgR5s3pw4IprqrnmW+Pcv2Zsa0KeSXlVlBd38TfvzqMp5uVLalF/GrO8JbBTU3Nkns+2Msz645iEfDUV4d54uKJhPiqXPbxkX54uFpZ+aszWsly4eRIQn08mDJE5YffPX8Uc8eEtWSl/HJWHD4eLoyN8G3Jhwe4Zc4wth0r5vdLxjHI3YXzJoRzzcwY/L3cWpU4aMvlCUP45lA+V0zvOnVSM7Dpf4qgh1m7di1r165lypQpAFRWVnLkyBFmzZrFXXfdxb333sv555/PrFmzeljSgcEbm9L4ZE8Oy2bE4Gq18Pjnhzh9eBCnDwvm0VUHWLUnhx+OFBAfG8jYcB+unhlLaqFy+4yL8OWPnx8C1IAjk0umRbFqTw5PfXUYAA9XC89/n8KSSZEtx3WEEIIzhtsUvq+Ha6t6+PctHN3hcdedMZTr2tTdf2zJ+C7v3c3F4lAxOI2m/ymCE/TcTwVSSu6//35uuummdtt27tzJ6tWrefDBB5k7dy4PP/xwD0jYP/lgRxZZJdXcMW9kq/VHjQFaSbkVuLtYqG9qZn92Gd8fLmDVnhwWjh/M14fyOFpQiQAmRfuTWlCFh6uFt385g0ue3wS0btyFELx0TTxbUovwdnchs6SaO1fs4YXvUxg92Acf7RvX9DH6nyLoAezLUM+fP5+HHnqIq666Cm9vb7Kzs3F1daWxsZHAwECWLVuGv78/L7/8cqtjtWvo5KhvbGbrsSLOHB6MEILXN6WRnFfBzbOHtZRmaG6WpOSrHPxDx8tbKlEWVtbzn/UpBHi58q/Lp5BfUYvVIljyzEbuX7mPUB93hgZ74+flyme3nUltfXM7f7mHq5U5o1RQd2KUP39fexhPVyvPXDn1FD4FjaZ70IqgG7AvQ71w4UKuvPJKZs6cCYC3tzdvvfUWR48e5Z577sFiseDq6sp//vMfAG688UYWLFhAREQE69at68nb6JWUVteTmFbCvLFhrdZ/vi+HO1fs4dXl8Zw5PITk3Arqm5rZmV7CaXFBXP3KVuaMCqHGKHFsTvdnsjm1iIumROLmYmmJD9w6dwQPfbyfFFcLc8eo67m7WHF3OXHNHzcXC1/ecRYeLhZcnDQoSqNxJloRdBNty1DffvvtrZaHDRvG/Pnz2x136623cuuttzpVtr7Aiu0ZfLI7h7dvaD1fw/+2ZvDkmmTeuWEGueU1TB8aRKS/Z8u81K9tTCPUx4P6JpXuuTGlEB8PVzalFLEzQ81kNcjNyqHj5TQ1S6YM8WdvVhlNzZI5o1rPV7tg3GAe/mQ/tQ3NDAsexMlwoqCtRtPb0d0XTa9gc0oRm1KKyCtvPY2nWU7hlv/tUH7471IASM5TAd0fjhSycqcq3zDY14NNKUVsNKb6q21QymH+uMEkHa8g6XgFCUYZBCHgrBGtFUGIjzvTY1UGT1yIt5PuVKPpfWhFoOkV5JWrScz32k1dCKoIm5uLhdLqBixCzQULcDi3gtkjQ/B2d+HVjcfw8XDhkmlR7M0q4+Nd2fh6qB56sLc7ZwwPVi4ioZTC4skRXDwlioBB7af9M/Pyh4dqRaAZOPQbe9acCag/Y19IrL9hWgL7sss414gHSClJLajisvho5o0NY9uxIp5br0oc55bXsvyMWOaNDeOhj/czPsKPZTNieH1TGkm5FVw7M4YfjhYS6e/JBZMi8HKzMiMuiIBBbi0lHDriytOGEB3o2WkKqEbTH+kXFoGHhwdFRUX9uqGUUlJUVISHR9+v3bI/u4wHPtpHQ5OtjIOpCPZn2yyC/Io6KusaGR7qzeyRIZwxLBgp4Z1tGQCMCvPhqulDuDwhmsunRzPYz4MHFo0B4KyRIbx+3XT+snQibi4WFk4I79ACaIur1cI5o8P6fadCo7GnX1gEUVFRZGVlUVBQ0NOiOBUPDw+ioqK63rGXUFbTQEFFXTs3y7vbM3h7awbjIny56rQYKmobqKpX2T17s8r47nABNfWNLY1xXIgK3E4e4o+LRbBieyYAowb7YLEInlg6seXclydEMyHSj3ERvrox12gcxKmKQAixAPgXYAVellI+0WZ7DPAqEAIUA8uklFntTtQFrq6uDB06tOsdNaeUp785wnvbM9lw7zn86u0d3DlvJPGxgSSmqWyef3x1hE9257SUQTbnh7321W0AuFpNRaAUiZebC9NiAth6rJgQH3fCOyiKJoTo97NJaTTdjdMUgRDCCjwLnAtkAduFEKuklAftdvsb8IaU8nUhxDnAn4GrnSWT5tSSlFtORV0jL29IZePRIipqD/LG9dNJzqvgrJEhbDhSQFFVHWnGrFw3nhVHYloJZ44IIqe0lgc/3o+Hq4Vwu1LGryxPIKukmlAfD93j12i6CWdaBNOBo1LKVAAhxLvAEsBeEYwFfmt8Xgd87ER5NE4kv7yW7w4XcMm0qJYGOrVANfD/3ZgGKLfPHz47hJRwy+xh/Oeqqdz13h6+PKAmbBkb7tuStSOlZOuxYkqr67FYbA2+t7tLS5VMjUbTPTgzWBwJZNotZxnr7NkDXGx8vgjwEUK0m7hUCHGjECJRCJHY3+MAvR0pZUv5ZXve2pLOPR/s5R9GIbaqukaOl6kAcEVdIzPiAhkT7suHO7NwtQomR/szyN2lpRInQJhdz18IwdOXT+YNXTRNo3E6PR0svht4RgixHPgeyAaa2u4kpXwReBEgPj6+/6YG9QFe+D6V9xMzWXvnbJ5ck0xhZR2XJUS3lHB4+tuj1DU1s3C86tkHeLlSUt3ArBEhLJsRw0Mf78fLzdoyIfvkaKUIfD1c2k3Srl0/Gs2pwZmKIBuwL4QeZaxrQUqZg2ERCCG8gaVSylInyqT5iSSmlZBSUMWW1CKeN0b55pXXkpJfyaIJ4fh7ufLCd6msS8oH4NL4aF74PpVZI4Lx83Tl6SumtDrfhCg/rBbRyhrQaDSnFmcqgu3ACCHEUJQCuBy40n4HIUQwUCylbAbuR2UQaXoxaUXK77/G8OufMTyIranF1Dc1c02UHzfPHkZBRR1rD+YhBNw+bwTnjg1jYpR/h+fzcnNhQqQfwd7up+weNBpNa5wWI5BSNgK/AdYAh4D3pJQHhBCPCSEWG7vNAZKFEIeBMOBxZ8mjcZyj+RUUV9VTXd/IltQiGo2BX03NkowiNb/y2gN5WC2Cq06LaSn4NjZcBXHNSVSiAjzxcnMh3qjf0xkvXjONvyyd4Kzb0Wg0XeDUGNRsI1kAACAASURBVIGUcjWwus26h+0+fwB84EwZNCfPspe34eVuJdTHnS2pxYT5uvP69dMZ5ObS0ujnltcSFzyIM0cEYxHQLGGMoQhmxAUyMcqPaLvpH09EqI92C2k0PUlPB4s1vYzKukZyjXIPqQVV/Obs4by7PYN73t/Lb3+mZv8SAqRUhdl8PVyZEOVPdkkNIT7uxnbBuzfOwKKDvRpNn0ArAk0r0o0YwLUzY5gaE8CSyZGMCffl12/v5C9fJAEwKUqNADZLRzy4aAwlVfWtzuPlpn9aGk1fQb+tmhbqG5tbYgA/j49uKdVw3oTBzBoRzA9HCvF0VVU8d2eWMiJMKYKELmIAGo2md9Mvqo9qfjqvbDjGhEfXsCmlCICYIJt/XwjBXT8b1bJ+1GClAEaG+Zx6QTUaTbejLQIN65Ly+ePnB5ESPtyZReAgN3w8XFvtMznan+WnxxLq6875EyPw93JjXIQu7qbR9Ae0ItDw4vepDAn0orahibzyuk57+o8uHtfy+exRoadKPI1G42S0a2gAcySvgqySarYeK+KCiRGcMTwYaO0W0mg0/R9tEQwgXtt4jNyyWu4/bwxH8ytZ8K8fGORmpVnCwgmDOXS8gpU7s4kJ1IpAoxlIaIugH5OUW86SZzdSVt1Ac7PkufUpvLYpjbrGJv7x1WGsFkF5bSNDAr0YG+7LWSOC8XKzMim643IQGo2mf6Itgn7MhiOF7MksZW92KR6uVgoq6gD4aGc2n+87zq3nDGdCpB8+Hq4IIQj19WDXw+fiZtX9A41mIKEVQT/mmDHzV2pBFelF1bhaBQ1Nkj9/kYSrVbD89FiC2hR7c3exdnQqjUbTj9Fdv36MWSk0taCStQdzOWtECHEhgyiraeDsUaHtlIBGoxmYaEXQj0krVKOENxwtJKukhlkjgkmIUaOAl06L6knRNBpNL0K7hvoptQ1N5JTVAJBizB0cHxvIuEg/ymsb9DgAjUbTglYE/ZSM4uqWCqFH8yvxdndhTLgvVovQtYE0Gk0rtCLoZzQ1Sx5ZtZ/DeZUAzB0dytH8SqbGBGC16LLQGo2mPTpG0M944otDvLUlg23HigE4Z7RyASXEBPSkWBqNphejLYJ+xJG8Cl764RhXnjaEhsZmdmeWkhAbyAPnjebiqTo4rNFoOkYrgn7Ea5vScHOxcNe5IwnydqepWWKxCG48a1hPi6bRaHox2jXUR2loaubVDcfYnVkKQFFlHSt3ZnHh5IiW8QE6JqDRaBxBWwR9kIamZq5+ZStbUos5fVgQry5P4Oa3dtAs4YZZcT0tnkaj6WNoi6APsjO9hC2pxcQFD2J7WjF/X5vM9rQSnrp0EiP0rGEajeYk0YqgD5KYXgLA/y0YRUOT5OUNxzhndCjnT4zoYck0Gk1fRCuCPsi2Y8WMDPNm7pgwfNxdkBKuOyO2p8XSaDR9FK0I+hhNzZKd6SXExwbiarUwf/xgxkf6cqYxu5hGo9GcLDpY3MdIzq2goq6RhFg1QOyJiyfQJCVC6AwhjUbz49AWQR/g7a0ZHM1XJSO+OZQHwIy4IABcrBY9h4BGo/lJaIugl1NW3cADH+1jsK8HH/36dFbuymZGXCDhfp49LZpGo+knaIugl5NSqCyB3PJaLvj3Bo4VVrFUl4vQaDTdiFYEvZQDOWU88sn+FpfQPy6bhKvVgre7CwsnhPewdBqNpj+hXUO9hNqGJu75YC+3zx3O8FAf3t6awf+2ZnDO6FBcLILzJ0Ywd0wYZdUNeLvrr02j0XQfTrUIhBALhBDJQoijQoj7Otg+RAixTgixSwixVwhxnjPl6c0cyCnj0z05fH0oH4DtaaqM9PrkfIYEeeFqteDr4Up0oFdPiqnRaPohTlMEQggr8CywEBgLXCGEGNtmtweB96SUU4DLgeecJU9vJym3AoD0ompKq+tbJpZplhAX7N2Tomk0mn6OMy2C6cBRKWWqlLIeeBdY0mYfCfgan/2AHCfK06s5bCiCjOIqdhglJCL9VWbQsJBBPSaXRqPp/zhTEUQCmXbLWcY6ex4FlgkhsoDVwK0dnUgIcaMQIlEIkVhQUOAMWXuc5DybRbAtrRhXq+Da02MAiNOKQKPROJGezhq6AvivlDIKOA94UwjRTiYp5YtSyngpZXxISMgpF9LZSClJNiyCnNIaNh4tZGKUP+dNCGdkmHfL4DGNRqNxBs5UBNlAtN1ylLHOnl8A7wFIKTcDHsCAK5pTUFlHSXUD4yJ8aZawP7uc04cFERXgxdo7ZxMTpC0CjUbjPJypCLYDI4QQQ4UQbqhg8Ko2+2QAcwGEEGNQiqB/+n5OgGkNnDs2rGXd6cMGnD7UaDQ9hNMUgZSyEfgNsAY4hMoOOiCEeEwIsdjY7S7gBiHEHuAdYLmUUjpLpt7KtmPFWC2CRcZAMXcXC1OG+PewVBqNZqDg1JFJUsrVqCCw/bqH7T4fBM5wpgx9gXXJ+Uwd4s/wUG88XC3ExwTi4aoLyWk0mlODHqLaw+SX17I/u5x75o9CCMHD549jRJgeN6DRaE4dDikCIcRK4BXgCylls3NFGlisP6xCImePCgXgytOG9KQ4Go1mAOJojOA54ErgiBDiCSHEKCfKNKDYmlpMsLcbY8L1pPMajaZncEgRSCm/llJeBUwF0oCvhRCbhBDXCSFcnSlgf+fQ8XLGRvjpGcY0Gk2P4XDWkBAiCFgO/BLYBfwLpRi+copkA4D6xmaO5FcwNty36501Go3GSTgaI/gIGAW8CVwgpTxubFohhEh0lnD9naP5lTQ0ScZGaEWg0Wh6Dkezhp6WUq7raIOUMr4b5RlQHDpeDsBYHR/QaDQ9iKOuobFCiJYRTkKIACHEr5wkU7/m0PFyrn5lK6kFlRw8Xo6Hq4Whusy0RqPpQRxVBDdIKUvNBSllCXCDc0Tqv+SV13Lda9v54Ughf/4iiS2pRYwK88Fq0YFijUbTcziqCKzCLq3FmHTGzTki9V8e//wQpTX1LJkcwVcH8ziQU841M2N7WiyNRjPAcTRG8CUqMPyCsXyTsU7jIEm55Xy6N4dbZg/jljnDOHS8nEUTIlg6LaqnRdNoNAMcRxXBvajG/xZj+SvgZadI1E95fn0K3u4u3HTWMHw8XFl75+yeFkmj0WgABxWBUVbiP8af5iRpbGrm26R8Fo4fjJ+XHn+n0Wh6F46OIxgB/Bk1Cb2HuV5KGeckufoVuzJLKa9tbKknpNFoNL0JR4PFr6GsgUbgbOAN4C1nCdUfOJpfwU1vJlJe28C6pHxcLIIzRujJZjQaTe/DUUXgKaX8BhBSynQp5aPAIueJ1fd5+pujrDmQxye7c/j6UB7TYgLw9dBuIY1G0/twNFhcZ0wqf0QI8RvU3MN6FFQn5JXXsnqfqsLx97XJlFY38NelE3tYKo1Go+kYRy2C2wEv4DZgGrAMuNZZQvV13tueSZOULJsxhNLqBoYGD+LiqZE9LZZGo9F0SJeKwBg8dpmUslJKmSWlvE5KuVRKueUUyNcnOZxfyZBAL26bO4LBvh7cv3A0LlanTQ+t0Wg0P4kuXUNSyiYhxJmnQpj+Ql5ZLWG+HoT6eLDlgbk9LY5Go9GcEEdjBLuEEKuA94Eqc6WUcqVTpOrjHC+vYeqQgJ4WQ6PRaBzCUUXgARQB59itk4BWBG2QUpJXVsdgP4+ud9ZoNJpegKMji69ztiD9heKqeuqbmgn31YpAo9H0DRwdWfwaygJohZTy+m6XqI9zvKwWQFsEGo2mz+Coa+gzu88ewEVATveL07f54UgBuS2KwLOHpdFoNBrHcNQ19KH9shDiHWCDUyTqo5TVNHDtq9sY5KYeabi2CDQaTR/hxya3jwB0BTU7juRV0Cyhoq4Rq0UQ7O3e0yJpNBqNQzgaI6igdYwgFzVHgcYgOa+i5XOoj7ueflKj0fQZHHUN+ThbkL7O4dwKvNysNDZLHSjWaDR9CkctgouAb6WUZcayPzBHSvmxM4XrSyTlVjB6sA9njQwhwEtP56zRaPoOjsYIHjGVAICUshR4pKuDhBALhBDJQoijQoj7Otj+DyHEbuPvsBCi1HHRew9SSg7nVTBqsA93zBvJtafH9rRIGo1G4zCOpo92pDBOeKxRrO5Z4FwgC9guhFglpTxo7iOlvNNu/1uBKQ7K0+M8uuoAU2MCWDwpgoLKOkqqGxgZpj1oGo2m7+GoRZAohHhKCDHM+HsK2NHFMdOBo1LKVCllPfAusOQE+18BvOOgPD2KlJJ3tmXw8a5sAA5klwMwarBWBAOSw2ugvrqnpTi1HPsBqgp7WgpNN+GoIrgVqAdWoBr0WuDXXRwTCWTaLWcZ69ohhIgBhgLfdrL9RiFEohAisaCgwEGRnUd5bSN1jc2kFlQCsD2tGBeLYHK0fw9LpjnllKTD25fCjtd6WpJTR2MdvHkhbH6mpyXRdBMOKQIpZZWU8j4pZbyUMkFK+YCUsqrrIx3mcuADKWVTJ9d/0bh2fEhISDde9seRX65GD2eW1FDf2ExiWgnjIv3wcnPU06bpN5QZfZ3MbT0rx6mgIg92vgEVx6G5EYpTe1oiTTfhkCIQQnxlZAqZywFCiDVdHJYNRNstRxnrOuJy+ohbCCCvvA6ApmbJkfwKdmeVkhCjy04PSMrVlKRkd+Up7QfseA1W3QpZiWq5JK1HxdF0H466hoKNTCEApJQldD2yeDswQggxVAjhhmrsV7XdSQgxGggANjsoS4+TX1Hb8vmT3TnUNzaTMDSwByXS9BjlRt+mLBMqcntWFmdTkKT+Zxivakl6z8nSU6Ssg11v9bQU3Y6jiqBZCDHEXBBCxNJBNVJ7pJSNwG+ANcAh4D0p5QEhxGNCiMV2u14OvCulPOH5ehOmRQDwfmImQkC8tggGJuV2tRfNnnJ/peCw+p9uKILaUqgt63z//sjmZ+DrR3taim7HUaf274ANQojvAAHMAm7s6iAp5WpgdZt1D7dZftRBGXoN+RW1eLu74OlmpaCijsWTIgjStYUGJhU5EBALZdmQvgnGnN/TEp0caRuVy+fC58HaSXOw7k8QPBKKjqjl/IO2bSXpED7R+XL2FkrSoaoA6qvAbVBPS9NtOBos/hKIB5JRvvy7gBonytWryS+vI9THnbjgQVgtgjvPHdnTIml6ivIcCIyD0edB4itQlNLTEjlObTmsvBH2vQ/FJ5D7u7/Ah7+ApnpjhQRhNB2lA8g91Nxsu9/SjJ6VpZtxNFj8S+AblAK4G3gTeNR5YvVu8sprCfV155Y5w/jTReMZGtx/egaak6Q8B3wiYMFfwOoOq++Bsix4cQ4UHunea1Xkde95N/wDyrPUZ9P/35a2Hls3Y6zM4Anqv6MB47UPwXd/PWkRnUJ+Erx0DlQXn9xxlbk2ZdjPAuWOxghuBxKAdCnl2agRwH2yHER3kF9RR5ivB3NGhXJZwpCuD9D0T5oaoTIPfCPANxxm3AIp38Kmf0POrvZBxaZG9fdjydpuO293hNTyD0LgMPXZ9P+3pbG29XLcbPU/dBy4+zkWMK6rgK0vwKFPW6+XEpoauudeToZDn6osr5xdJ3ec/b32s0C5o4qgVkpZCyCEcJdSJgGjnCdW70VKqSwCHx0TGPBU5oFsVooAYOwSQKpGD+DQKlsjl7EVHh8MfwyFI1//uOuZbomDn8BbS+Gz3/4k8aktU7L7RXduEdSW2z77RtksAd9wCBiiesbZO+Bvo6Ayv+NzHF4DTXWtA+tSwivnwh+C4c2Lftp9nCzZRlD/ZN1a9vt3p0vsrUvguye773w/AkcVQZYxjuBj4CshxCdA/1KJDmKOKg7Tk9MPDJoa1EjatjQ323qUpiIIHQNBwwEJIaPVgKu8A2pb+kZoblDb0h2c3K+usnVv2eyFlhyDlG8gze489dUn37OuLQd3XwgZBYXJnchgKIJZd8PSl8E/Ri37RkDQCBVAPva9cpvYB5HtOfiJ+l9daHuW+YeUheMTAce+U8HXU4GUduMgOmjCTlQqpCQNECom1F0WQXMTpK5X32cP4miw+CIpZamR4fMQ8ApwoTMF662YcxKHakUwMPj8LnijgxJZ3z8JK65Sn/0N96AQMPZCQMDif6v/h79Q2woPq0YveGTnbhh7KvPhbyNsjSiohsg3CoRVnbs0QzVsVUXw5PD2rpeuqCsHDz+ltAqPqEapLaZFED0dYmZC8Ai1HBCrFEhJOhzfo9aVdzCNuZRw9Bt1HVCjkkFZSwg4+35lVZ2sm+bHUpKmFBK079UXpcAT0ZC9s5Nj08EnXH2H3WURVBxXHYSCpFPvIrPjpKeqlFJ+J6VcZRSSG3DklKpkqUj/PjY5fUWu6mH+GPpSJkx3k/Kt6kG29e3n7lWN8hUrVENqMusu+MVa1XAGxECe0UsuSIKQkYYi6MQNY0/GFmioVtc3KU2HiMlw/Ro453fQWKMURt4+aKiCzK0dn6u6GGpK2q+vLQcPXyVTY23HmTB1xjgBd1/1P3IaLF8Nw+YqRYCEI1+pbeUdFA6oK1eyRcYb+xjK4uAqGDIDRp2nlp09BqMoRVkue99Ty95hSimUH7cpu+N7VOmMoqMdn6M0XX2n/jHq2K4a7tryzt1lJqZlUVPSo0X8fuycxQOW7L6qCF5dAN/+4eSPO74H/j1V5ZsPNCpy1Yjh5ob2WSKl6RA2FkYtUJaAiZuXUgIAwaOUJSClsgJCRqu/kmMdu5vsMf3YZukKKVVD7R8D0QkweKJNDtPCKOjEvfPeNfDRza3XNTerRtrdV7m0oGPXjtlIehiKQAiIPUP9NxVgvdHBMMtt2GNm5oSNM/bJUX/5B2D0+TAoWFkXWds7lr07aGqA52fB6xfA+j+pIPeIn0HxMZU99JlRDd/s5XeUTSSlUhABsUoZ1Fd2rFzt+fJ+ePPiE+9jb1l05p47BWhF4CBf7DvOfR/uJae0BheLIKS3B4urCmH3O5D8hfJ7lhyz+atPBjNVMWeX6lU54hvN2Ao1vSSprLJAPYek1Sdvetv3Utv24ksybP7yzggZpZ5fabrqFYeMUn+yufNeZ9tr5x9UllxVgbIQAoxrmtcuSbPJZjYk9dXKogB1zzm71Z899ZWAVA182HjlbuqoXpIZIzAtAnsChxluKoOOXENmY2kGmctzbAonwph+JCrBubWailPV8599Lyz/HG76DoKGqZHRFTmQvNp4R+x6520pz1bJARFTlDUBXff28w+o78be5VaUohSQiX0HwxFL0UloReAg727P5N3tmSTlVhDu79H7J6ff8A/4+GZ453L1Q4cf59c0fbqFyapn+f7yE+9fWwavLYQPru9Rn2cL6x5Xz+HdKyBv/8kdm50IFmO0rX1vraZEuUwCHFAETXVweK1aDjYUAXTeewflhsrZpQLPpv/cbDBMBWDGJUrSldUBymKor4If/q6+g8oC1fDWV6hgrn0DV2vn8nHzgsHjO3bPmPuZPn57XNxU4NQ8T0euIfOa/kPAzVvJY1ow5rMIn6R+Zyeb1+8oZgM7aiHEngmBQ1sr8YZqFaw134+aDuQwn01UvJ0iyDvxdUvSlTVpryA//hWsuLr1Pr6RanzGiX4TTkYrAgeQUrI7U/VwNx4tJMKvD7iF7HvkyUbAsiy74zz24mOqx9wR5o84K1FZFDk7T2wVZO8A2aRerD3vOi5vcSoc+qz1utz9KqOisQ62vaQCtKWZHR7eKZnbVHokqEyVkyErUfVkfaNav6RmoxwQe+LjTdfJoVW25aDhalRuRy99+iYVqCw4pBqn6UYVl6zttmduXtPNSzVIpWmqofM0ih4WHoaDHysFUpDUWoHZB6nr2rh8IuPVtdsGjGvLAaEa8Q7v0WjMh551YovAM1BlGpVnK7m8gpRbyNwGqofeGSnfnvxAuqoi2PeB7VkH21UAMJ/jiPngGaBiFub32pFFkJ2oBgyGTXDMIqirsCkU+w5Y0VEV0zHjbqXpRuB9ZMe/iZL0k08C+BFoReAAxwqrKKtpAKCusblvxAfqKyFgqDLdzdQ02WQbSWrPpn+rHnNHcQCzl5d/kJY6gyf6YWYZJn7QcNjxX8fl/ephlYVjnxK55n74389VL2r13fDtH+G7Jxw/Z12lalQn/Fw9h5M1vXP3Qfjk9i+p2Sh35RoyM2zSflAv+6AgcPVUvei21klJuson/+JeWxbOsLnK/ZK9QzXowtraCvGPUS6fqgIYvUitO/iJze1UkNRabnulUNvG5RMVryyHwjYZTWYcwdJJUzF8rlIigye2Tg81aVEEAUoRVBxXMgXbDUMyrQ37MQv25OxSz+bNi08u4WHDU6o0xv6VhkViVwEgeKRal/BLGLUIDn9p62R0ZJlkJSrLxcUNvI3CyyeyCDoafFZXactYMrPBStLV9xg0vOOkjB/+BiuWqQ6RE9GKwAFMa8B0B0X0CUVQpV6+wLjWPZyOhsabjf2q37R/kdv28gKG2nq4HZG1Xb3ksbM6TolL/U7VtP/ur7Zt9VW2QVYf3awGSpWkqx5qUz3s/wAmL1MNetJqx0fn5uxSPeOY05VPuCAZdr7pWOC7plT1UAOHGumVh1WAFWw9vK5cQx5+KmUUYKHdgKHIaeo5Sal8018+AO9eqfzYBcnquVnd1bWj4tW+WYkqOO1q99sLiLUplFELlRtr20uAABcP41zJ4OEPLp6tlUKLRWA0wlEJ6n/bCXbMzKLOiL8ebvjGNpaiok3A2GxUPQPUsyjLUgopxF4R+LaW6dBnNuuwqQE+uVXtU5apOgOOIKXq5YPqDAS3Gf/q7g137IORP4Oxi9W1m1Vnr51F0NSoFG6Ukfnk4QdWN6iyswhydsHWF23L9u+Z+bnFMhBKETTUqucVEKvcQ5W57S0y0yW16jZocF55N60IHGBXRimD3KxMj1UmbJ9RBG6DbC+c2fPryK1Tnq0aEfsBUC3bjtsas6ARMPkqlabYUYaIlMqEjopXjWdtaXvzecM/1CxX6x63zXB15CuVCnn2g+DiDomvwue/VVbNuIuV22H+H9XI3ZpixwdkmZkokdPUc8jeAZ/doWIdVUUnPrbUrtcfOla5aszCbCXpqnHtyG/elomXwszfqAbHJCpB9SbLspTLY8uzUF0EQ2er2EPqd8qasFht+6ZvtKVgmoycr76bkDEwZKZ6Vu4+MPlKlaVjWgShYyB4eGtFUNtGEQQOU0p+479aNzimRdAVpiJo23GoKVHHW10gappq+GpKWisC8/y15aoh/PR21ZMvSlHy5O2DJc+qjsCet20K+UQc3w1lGarBhtbXa0vcHJsMXsHtYwRlGer3aWY+CaHcQ+Zvu64SVlwDX9xjsyrM34+7r+2z+e5NukLJ985lgFRZYL4RKnW1ym4q3tpy5c4Mn6TO4cSxFloROMC+7DImRPkxIUq9NJEBfUERVCpFYPpFo6cr10JHAePyHFs6on2PrrlJLQ87Wy1HxaveE0BSG38+GIN1igxFYFy3bUpcabrNd2421IdWKZ/xmXfCrTuUu+GoYSGc/Tu49lPVoxw2F1y9lKnfGZX5Kh2wNFMprMA48ApUPUJzisXqIuWKsmfLf2DPCrt7sfPJmz1B+5m5urIGTM79Pcx/vPW6yGnqf3aiegYWV7htN8wySkbk7rU1XOa+TfW2XrvJhEvgrkPw6y3qHpe+BHclwYXPqWdckKT+gkeq+7d3jbUdH2CxwAX/VMpu/ROqEf741yqF9kQWgYmvMR15O0VQrL47gKnLVaMGnVsE6ZtsLqY3L1SVT8ddpFxfQ89SweuuMq5AWQPCqsZ1tL1eW1zclVIFNU6jrUVQaTTOPoNt67xDba6hb/+glAXY3KYl6SoAPHiCcvV9dAtkGplc8x5V2Uep61XnZtg5HT+/nF2AVJ0v85xOQisCB0gvqiIuxJupQ/yxCIjrC9VGG6oNi8BodAOHgV9Ue9dQQ62t8YbWP8TKfBVXiJgC8b+AqdeqFyp4VOsRryZmQxkZb7uufS+0uUk10CN+poKPWYnq+ofXqBfdrIc/xlA2Hv7KpWPi5gUTL1NF10w/els+/62yKN6+VPl9x1yg1pvy+EapBjR1ne2Y9E3w5X2w/SXbOnv3T/BI9VJnJyqrJ29/e1fDyRA2Xrl+shJtAWlXj9YD08zzm/uC7TtyhJBRqndZUwwjzlXPsSxLuVrALhvIrpGPmwNTrlYxo3eugN1vqXt2xCLwH6Ia3rZjEWpKbIrA6gIXvagaP3ul5m4XIzi0Srm1LnlFWTvD58FCo2qpeUx2B9lNbcnarhr16Teq0d7Dzz3x/jN/A5OuVL/12rLW7kezwR9kNymjaRFkbFW1pabfpL4r021qdhYCYpUC3vM2bHle/e69Q+GiF2DCpXDe39T+vuHqf6uJjoyOkjla3Yklv7Ui6ILKukZKqhuIDvBi/rjBrL/7bKIDvXparK5pcQ0ZPfOAGPVnn8MMNgtg8ETlHrJPAawwfpR+UXD+U6rEACirIH1je/dKdqLqsYeOVUPx26bElecoP2xgHEROVT/01HXKehljV8Zh9CKVWRMV33qwFsC8R1S2yarb2scfjnylemTRp6kGyT9G5Y6D7TmMXaxezopc9bJLqc5lymdSkq4aKM8A5aIx5S3LUg1D2975yeDiphqpY9+rXp95Lu8wW6No9mDNfd39lGvOUUxFMuo8NXDLNwKQtkattlxZIi5tSqX87I8wKKS1JeeIReDmpVwnbVNQq4uVtWISOhoufUO5sNqev65cxQaGz4PxS+EXa+CKd2zB2eCRSinZX+P7v8HmZ9vLU5CsXGZegXDp67aGtjMiJsNF/1GuIWg985r5zMxsIVAyVeTCp7ep92Puw6oDk7FFKYhSIwhsJhRY3VQqsX+MMRhvlLLgzHvryCLI3qG+c58w9T5pi6DnyCxWRaiiAz0RQjAkqA8oATAUgbdq4GfdpczryHjVk7bPiii3a+x9Ilr/EM3PPm1eouHzVBC2bUmDrETVo7K62H7s9u4I84USOQAAGtJJREFU+152ZLzqWe99TzVyQ8+y7TcoWPUCz7yz/X15BsBZ9ygfa9veZ9oG1bhd+xmc8yBc9qYtUyRsvDrfjFtUoyibVLCvOFUVTvMerF5sM1hXmq6qa5pExav4SdoPxvK09rKdDBN+rlxADVW2nr75zKC1dTD7Xljw584zdzpi6CyY8Ws4/5/qvD5tfPh1RhC4raL19IfL3oJ5v7cNAnPEIgBjYFibFFR7i6AzrK4qmF2eozofnSlZi8WmkEEp8S3/ae8qrC5W3+2J3EGdYSot+zhBZb7qmJjprqCsg+pC9fs++wEVfB56FiBVA16cCkFxyvo887dqH+jcpegVrH679h2xwsO2Ud8BMU6dA0Ergi4wFcGQvmAFmEhpixFYrKq34huhesOyCdb/Gf41WeWVmxaBb6SR522nCMqMVFMzEGgSPklZD1nbVXrnpn8rn27uXptPG9SLmH9IvZjPTLfV5/ePUS97cyMcWKlm93Jxa32N6TeowT8dMWYxIGDzc/BMAhwzGufSdPCPVuc66x6bPxrUc5j3qHJhtDSKx22NypgL1LMxA4Alaa3HCUTGK3k3P2fLJ/8pxF+vLBdo/8wsLraBWqBiJlOuOrnzuw2CBX9SvUmwC+YaDU3tCYLA0Qlw5h224LQjQXHoOAXVEUUASimZvn97X3xbIg2FXJFnKyBnpmSamNe3V6aOYspqHyeozFMxLIvdKGqzJ29xtdVLMhXP4TUqphMyWrnk5j2iLBzofOyJxaKsFvN9bG5WAwTN/f1jtGuoJ8kwLYKAPqQIGmtVj73tnKrhk1VDuO1FVXLi6Fe2hsE3XP3ZK4Lje1VPZVBI6/O4eqoe9r734chalTZ3fK8R0LTzY8eeqV7Sbx5TroY97wBCDfAaPhfmPABn3KEa7ZPBJ0xlyex+S7305shpMye7K+wbxaxEZTnFzTHW5bSu62MSN8dI19xnyyf/KViscMmrqsdu3+ifeScsfeWnn78tvnbKD2wWwYloSZd00CKIbBNUb25WmWOegZ0fY+Luaxsw1vb3Zs+kK5SiXH23rSxFWxel6Y403YEngymrvdVcVdDaLQS25bjZyooCZU0MCrElUtgrIv8hcPHLcNpNnV/b3iI3Z0MzLYiAWLWtqxpVPxKtCLogq6QGb3cX/L1ce1oUxzFru7u2UQRC2AKxLh7qhS3PUS+hu4+yCsyGEGypoG3dB6DWlxmpcmUZauALtDbrzdz2Ha/Z1vlFqUbOxR3m3KuyauwDwo4ydknr+4D2vfjOsPfHZm1X7gZ/Y/RxRY4aSNZY2/pcbl5wwdPqs1lU7qfiFwXx17V+vkHDYJwTKrx7BhjuFwcsApMo4z69ghy7RtBwFeA3R7LXlakOiUMWgZ9q/KB9o2tP8HCYc58Kyn5v/ObqK1o3kAXJ6l79fsTsgWaj3tYi8A5tvZ/5Gxq7pPX6kNG2FNDgNjGdiT8/8e/THHkNdiVFjP0DYgBps9K7Ga0IuiCzuJqoABUf6HXUlHTcQzAVQVuLAOCsu+HK91UjnZWofnhmb9E3QuVL15aqcxce7jxTxWzwQ8epbJHDX6o4hL0byTPA5vs301Md6bE7wrTlcOmbKpvJjHvUFDuW1ukVqNw7xSkqThEZb3MXlWaqsQZeQep+7ImbDVd/pKyYvoYQrV0P5lwEJyJkJCxbaXNrdIXFAjN/Dcmfq4F/Zq/aywGLwN7qOJEiADj9VvV7KrArGWJfwrkw2RiH8SOat85iBG1lipwKl7+tMo3sMdO1fSIcd6mZmK5ZKduXFLEvMugEtCLogozi6t6ZJdTcpErrfvNY+20nUgSeAWpwU2S86slnbLHV4vGxS2EzJ+doO4jJJPo0QMDUa5TbxDOg9ehZk3FGGd7znlSN648x1zvC1UPFPKKnq2wM0xx3RNGYjWLSauX3j0pQslndYOvzyuWw4C+tg4Mmw84B7xO4LnozpsUHqtfq4d/1Mf/f3p1HV1mfCRz/PklIgJCwJUIIlp0CAi5Ql1JxoValKt1sPa2O7bQ6dqSj9dhWTtdj2zmni9PTztAOtuNop1prtVZa7ViXjtS6QERUVmVRIVAIkS0JkOTmmT+e9+W+CUlIbvLmJnmfzzkc7n1zc/n9eJP7/NbnN3lB6z9HbZl3szUOHr0lnRahvaGeUNg7kdwTB47cAbDoP+y14QdvOE/Q1GQ9unCStbMKhlojIZr+u2b38XUQab7kORQOB2UyUT1snPVEd64O5gMk3VMd7oEga2qPNrJ9X13vnB94+3kbmglTDIRH8KlGAkEbicIg3dKvrYJ5wfLJ6JBJ5UuAWMunNSMmwA1/tVwtH14G1/9f6x+Qp30Krn/GDiG57mmbuO5OYT1ee9D+7uhGr+Jyy7uUN9B6LTk5Fgj3B6dQdbQV3JeEQw8HKu2+j+7ihHdr8vJh0b/bh+ejt1jLPboirC1hj6CwpPmkbFvKToXPPwcX/6s9rwvmCSor7N+e/P7Myp+TA+9dDGsfspPVjhywsfoT9VJCYUMnk0Aw++O2em35TTZxXlRmQ6hgj6+82/bgxMADQRtSTcoXfv0yDSnl4lM6+EPQk8I8KmELYctT8IsFtoInPCikvZZc2anWCpv7j+lf1Ogk6q5XgjHfdrq3o2dZi2hIafurIcacZo+Hj+/YeHFnFJdbj2bbCnsejqme8PuCuk5aYEv/wvcCW0GUybBCb1c8xpbIhiulOrNBrTPK58C8m6xlvWipteBPJOwRtByLb89J09I9wHDCeP0jtpIn3CmciflftnQbz/4ovYqso4Fg1Eybn2irJ92eQcPgg3fYgoS1v2veqMnJtaHKsIfQzfrhT3v3WP32Pp7euIcll07jrIkdnCzrDttXpvOVNDXZB37LRFRNTemt7Ad22NfXPWzP//Jd6xpD+4FgwCD4wmpYeEf6WtFoQGxlycHKdM773kzEloWitoGtI+PRkB4Gi072hZuOwgn1/qZojLVuX3/chsHi6BGEFnwTbt0EZbM79vqwwdHRD9xQOHxXtzedaG7ShZ0fn48aEPQS96yPbCbr4HBgYQl8cZ3tH8jE9MtsJ7GmOrbwoZt4IGjD7oN2SP25U3pwPFjVUiM8dqs937AcHrjGlmhGVb5kq1vGzbMx7v1vwcZHYeIF9nxlkCrhRGO7Q0qbt3xzB1iL7GClDQ+13D/QW838aJC2YE7rK5xaM+Z02xQUbTmWz7Fe0Lj3xlPObAuHK9Y+ZEM24bBDHEQ61/s71iPoZCAYOMzmCmr32vLTA2/bvpSuKn23DTeFcwXFYzv+vYUjO/5z2JqFP7CGSnkXNy12ggcCsA/gF5c1y5RZXVMPQMmQbl7P3Z5DwSlSW5625X1hPp+WO2g3BN3fcE3yy/fa973ns7YkMUyA1ZlJvlDxGAssNXvSQyW9nQh87G64+uGOf8/Mj8Ctr6eXC4KteFlc0bEx6r5ownzLuZM6Gt+wUKaOzRF0suGVk2O9wLq96SGvd53T9fKEQXPNfTaBHN3rEbchJ1mv4szreuyf9EAA1pL405fh958/toa+uuYoOQLDBvdgIAjzu6TqrTcQ9gSiJ0uF3d+J56eXZK76heX4mbSgxdr3TAJBOex8BdC+0yMA+0Do7Lh+a6223rhMuLuIWIbR0mnpg2x6i0x7BGArvuqqgwR5nczJ1JYwV9PeTbZgoqfnjHq4MeKBANKndm1+0nbLAntr6xlRmN+zZxOHOyILiuHPX7dJ34Li5vl6dq2xFvuMK6z1Lzm27n/KRbbpKbp8ckAGq52KytIpivtSIHAdM3Qs3Phix1by9KRjcwSdmCwODS6xyeJwc2B3fGgPHZteddeVBIN9hAcCSK+tLiw9Nr5eXXOUkYUxjqG2pmqj/UJc8FUry6QLLTnZ3jdsgripCR7/mrV6pl1mY/rh2GU46RmuNBgwOLNWRfTD3wOB6ymjZ9qSz0yGdQpHBkup13ffkJdIemdwbxtGi0GsgUBELhGRTSKyWURua+M1HxeR9SKyTkTui7M8bQrzr8z5DOxYCQd3Ul1Tz8ienB8AGwIqeTecfQMsXmm7WEfPtAyVByth9d12OtfF30mvjhk+zpbpheuLwx5BJsNC0HxewAOB6ymDhsPVD8HQDOalBpdYINBUZss22xJuDuvBSdtsyTvxSzIjIrnAUuAiYAewSkSWq+r6yGumAEuAeaq6T0Qy6Bd2g4OV9sM060pY8X3Y8Ef21kxh1tgO7LzsTlUbLfVDVDhWueVp+PM37DjD069Jf/2sf7J172F+93COIONAECyhzBvUsZ2nzmXbzI9aksCCorYz1mbijGutYdXaDvN+Js4ewZnAZlXdqqr1wP1AiwxNXAcsVdV9AKra4oDbHhIulSydaq2AV39Ddc3RE68Y+usd8NB11iVdNt+O92sp1WinPVUEidcOVMKy844/YWv/27byoeWOxLBV8ugt1uK5/MfNJzSnX948o+GxQNDOruL2hD2C4jH9e+LU9R/j58HVD8KV/53eHNgdxp0DFyzpvvfrxeIMBOXA9sjzHcG1qKnAVBH5m4i8ICKXtPZGInK9iFSISEVVVVVrL+ma6Jr5s26AygoWNj5JaWFu8yProlKNdjLSaw/APZfZB/uaFiNbTSl47ieWJvnV4Dzct56zCd/f/zM02hJVVOFPX7F0B9Mua/4ehSPhotstVcMnfmWpHdozeKRlHc1kohjSG618WMi5xIhtaKgT//4U4HxgLLBCRGap6v7oi1T1TuBOgLlz52rLN+myQzvtMA6AM67l6Mv3873Kn8OKn8MKsW3y4cEgde/AT8+xTSt11TakVLfX/l7/iC1hGz7ekq396iO2wSsnD3ausfNiwyWiu9fC334M533JknNtegwu+nbrH/Tzbup4XUTsPTqaQ76l/MEWTIZ2YgONc65PizMQVALRxBhjg2tRO4AXVbUB2CYir2OBYVWM5WouPLw9bAHn5LDl/J/yyN0/4MrTRzF5232WYjkMBG8/b3nTK+6ycfTrnrYWfs0e2xFc/YZdr6u2cfr5X7LW+aO32Mawqo22e3X0bJuPmH45bHvGgkV3bSC54ifpA88zceXdHgicS5A4h4ZWAVNEZIKI5ANXActbvOb3WG8AESnBhoq2xlim44UHtBelh0J2NxWxLHU5B8+82c5+DbeZg2X4zMkLVupcZKt2ZiyyD3TJsWRVjYctF9C0yyx3+uQF6e8NVwZd+n0LFE9+066PnmX5f7pD+RxbbZSpCfN7dielcy6rYgsEqtoILAYeBzYAD6jqOhG5XUTCrF6PA9Uish74C/AlVa1u/R1jEi4djYyJH0svUVhgm0nC3Dtgm1ZGzYTPPQELf5h+n6LR8Jn/tR5CeKJTmLxs2DgbOnr7eTsMpfTdlufn9Kst1W3lS4nYtOKc651i3Uegqo+p6lRVnaSq3w2ufUNVlwePVVVvUdUZqjpLVe+PszytCj/gi8v54eObeGL9bvYcsoRzJUX5zc9hbUrBzpdtg0nZqemDwUPvOsvW98/4kK2LnnSBXRexfPwb/mBzBuFKoOmLoKkBGuq6d/2zc851gu8sDs4IbSwcxbIVW3igYjtv7a2jZEgBg/PzLI1ubr6t+nl+qaV9OFHr/QPfsUMzotkdz/uyTRZD+vCK8jnp5ZoJ2L3onOudPBAEh7fvPDyAhpSytaqGbdW1TCgJll/mFVhrfuMf4YmvW8rbE22Dzx98/PLLslPtvOCC4vTxejk5MPsTNnTkY/LOuSzJ9vLR7Dtkewi2Vdvxjm9V17G/roELp0U2OX/yt+lDvwuKMt9peP4SWwoa3fV74dfgvK/45i3nXNZ4IDi4E4rKeHOvBYLGJqW6tp7xJZEP6wEDT7yRqyNEjk/9kJPbf/PfO+f6BB8aOrgTisvZFgSC0ISSDHP1OOdcH5PsQJBqtDNJi8fwZnUt5cPS6/jHj/RA4JxLhuQNDanaDuB3tllSKW2CYhsaOu3kYRxpSAVDQxnm6nHOuT4meYFg3zY72jE331I7A41Dyti+7zAfnF3GnkNHyMsVWzrqnHMJkLxPux1BuoizbrDMoMAeRpJqqmLciEJmlQ+lKthZ7JxzSZC8QFBZYUng3vdFeOFn0NTA9sZhQBUnjxjMOZNGZruEzjnXo5I3WbxjFYw53VJBTLoA8gbyZp3tAB47vJuSvjnnXB+SrB5B41H4+2tw9uc5cLiBH9d/ksIh70H2HyFHYPTQgdkuoXPO9bhkBYLd6yBVD+VzWHzfav76Rj5wCucW7ads6CAG5Cavg+Scc8n65Du8D4CDuSN4dvNe5o4bDsALW6ub7SFwzrkkSVYgqLfdwy/tOooq3HjBZAAaUurzA865xEpWIGioA+C57UcYUZjP/KmllAXzAh4InHNJlaxAEPQIntlWy3lTS8nNEWaU2SHvY4f7TmLnXDIlKxAEPYJddTlMLysCYMYYCwTl3iNwziVUsgJBvQWCOgoYOmgAAPMmlzCkII+po4qyWTLnnMuaZC0fbailKSefFLkUD7RAcPbEkbz2rQ8gfjCMcy6hEtcjSOXZXEBx0CMAPAg45xItWYGgoY7GXJsLGBoJBM45l2TJCgT1tTQEgSAcGnLOuaRLViBoqKNebN9A8aBkTY8451xbkhUI6us4IpZpdEiBBwLnnIOkBYKGWg4zkCEFeeR5gjnnnAOSFgjq66jTAooHem/AOedCyQoEDXXUan6zpaPOOZd0yWoa19dwSDwQOOdcVLJ6BPV1HEzl+9JR55yLiDUQiMglIrJJRDaLyG2tfP3TIlIlImuCP5+LrTBNKUgd5UAq35eOOudcRGyfiCKSCywFLgJ2AKtEZLmqrm/x0t+o6uK4ynFMkIJ6f0Oe9wiccy4izh7BmcBmVd2qqvXA/cCiGP+99gUpqPc3DvD0Es45FxFnICgHtkee7wiutfRREXlVRB4UkZNbeyMRuV5EKkSkoqqqKrPSBD2COi3wyWLnnIvI9mTxH4DxqjobeAK4p7UXqeqdqjpXVeeWlpZm9i81hGcRDPR9BM45FxFnIKgEoi38scG1Y1S1WlWPBk9/AcyJrTTBoTSH8R6Bc85FxRkIVgFTRGSCiOQDVwHLoy8QkbLI0yuADbGVpsGGhmq1wA+qd865iNjGSFS1UUQWA48DucBdqrpORG4HKlR1OfAvInIF0Ai8A3w6rvKEPYKSEcOPHVjvnHMu5p3FqvoY8FiLa9+IPF4CLImzDKE97+zjJGD+KeP9RDLnnIvI9mRxj3llq01PvP/UCVkuiXPO9S6JCQTnjrOzik8aMTzLJXHOud4lMYFg4EmTYPrlkF+Y7aI451yvkpwF9dM+aH+cc841k5gegXPOudZ5IHDOuYTzQOCccwnngcA55xLOA4FzziWcBwLnnEs4DwTOOZdwHgiccy7hRFWzXYZOEZEq4K0Mv70E2NuNxekLklhnSGa9vc7JkGmdx6lqqyd79blA0BUiUqGqc7Ndjp6UxDpDMuvtdU6GOOrsQ0POOZdwHgiccy7hkhYI7sx2AbIgiXWGZNbb65wM3V7nRM0ROOecO17SegTOOeda8EDgnHMJl5hAICKXiMgmEdksIrdluzxxEZE3ReQ1EVkjIhXBtREi8oSIvBH83afP6xSRu0Rkj4isjVxrtY5ifhLc91dF5IzslTxzbdT5WyJSGdzrNSKyMPK1JUGdN4nIxdkpddeIyMki8hcRWS8i60TkpuB6v73X7dQ53nutqv3+D5ALbAEmAvnAK8CMbJcrprq+CZS0uPZ94Lbg8W3A97Jdzi7WcT5wBrD2RHUEFgJ/AgQ4G3gx2+Xvxjp/C7i1ldfOCH7GC4AJwc9+brbrkEGdy4AzgsdFwOtB3frtvW6nzrHe66T0CM4ENqvqVlWtB+4HFmW5TD1pEXBP8Pge4ENZLEuXqeoK4J0Wl9uq4yLgl2peAIaJSFnPlLT7tFHntiwC7lfVo6q6DdiM/Q70Kaq6S1VXB48PARuAcvrxvW6nzm3plnudlEBQDmyPPN9B+/+5fZkCfxaRl0Tk+uDaKFXdFTz+OzAqO0WLVVt17O/3fnEwDHJXZMiv39VZRMYDpwMvkpB73aLOEOO9TkogSJL3qeoZwKXAjSIyP/pFtf5kv14znIQ6Bn4GTAJOA3YBd2S3OPEQkSHAQ8DNqnow+rX+eq9bqXOs9zopgaASODnyfGxwrd9R1crg7z3Aw1g3cXfYRQ7+3pO9EsamrTr223uvqrtVNaWqTcDPSQ8J9Js6i8gA7APxXlX9XXC5X9/r1uoc971OSiBYBUwRkQkikg9cBSzPcpm6nYgUikhR+Bj4ALAWq+u1wcuuBR7JTglj1VYdlwP/EKwoORs4EBlW6NNajH9/GLvXYHW+SkQKRGQCMAVY2dPl6yoREeC/gA2q+m+RL/Xbe91WnWO/19meJe/B2fiF2Az8FuCr2S5PTHWciK0geAVYF9YTGAk8BbwBPAmMyHZZu1jPX2Pd4wZsTPSzbdURW0GyNLjvrwFzs13+bqzz/wR1ejX4QCiLvP6rQZ03AZdmu/wZ1vl92LDPq8Ca4M/C/nyv26lzrPfaU0w451zCJWVoyDnnXBs8EDjnXMJ5IHDOuYTzQOCccwnngcA55xLOA4FzPUhEzheRP2a7HM5FeSBwzrmE80DgXCtE5GoRWRnkfl8mIrkiUiMiPwryxD8lIqXBa08TkReChGAPR/LjTxaRJ0XkFRFZLSKTgrcfIiIPishGEbk32E3qXNZ4IHCuBRGZDnwCmKeqpwEp4FNAIVChqqcAzwDfDL7ll8BXVHU2tvszvH4vsFRVTwXei+0MBssoeTOWS34iMC/2SjnXjrxsF8C5XmgBMAdYFTTWB2GJzZqA3wSv+RXwOxEZCgxT1WeC6/cAvw1yPpWr6sMAqnoEIHi/laq6I3i+BhgPPBt/tZxrnQcC544nwD2quqTZRZGvt3hdpvlZjkYep/DfQ5dlPjTk3PGeAj4mIifBsTNyx2G/Lx8LXvNJ4FlVPQDsE5Fzg+vXAM+onS61Q0Q+FLxHgYgM7tFaONdB3hJxrgVVXS8iX8NOesvBMn7eCNQCZwZf24PNI4ClQv7P4IN+K/CZ4Po1wDIRuT14jyt7sBrOdZhnH3Wug0SkRlWHZLscznU3HxpyzrmE8x6Bc84lnPcInHMu4TwQOOdcwnkgcM65hPNA4JxzCeeBwDnnEu7/AUD4w301nST1AAAAAElFTkSuQmCC\n", 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\n", 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" ] }, "metadata": { "tags": [], "needs_background": "light" } }, { "output_type": "stream", "text": [ "DNN results\n", "accuracy : 0.9892857142857143\n", "Training Recall: 0.9892857142857143\n", "Training Precision: 0.9892859393529686\n", "Confusion matrix: \n", "[[373 5]\n", " [ 4 458]]\n", "Validation set report:\n", "cross Loss: -13.131515301622095\n", "hing Loss: 0.7523809523809524\n", "Report precision recall f1-score support\n", "\n", " 0 0.68 0.56 0.62 93\n", " 1 0.69 0.79 0.74 117\n", "\n", " accuracy 0.69 210\n", " macro avg 0.69 0.68 0.68 210\n", "weighted avg 0.69 0.69 0.69 210\n", "\n", "accuracy : 0.6904761904761905\n", "Validation Recall: 0.6904761904761905\n", "Validation Precision: 0.6896812927841993\n", "Confusion matrix: \n", "[[52 41]\n", " [24 93]]\n", "\n", "\\KNN Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 0.6952380952380952\n", "Validation Recall: 0.6952380952380952\n", "Validation Precision: 0.7136397652526685\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[73 20]\n", " [44 73]]\n", "\n", "\n", "Support vector machine Result\n", "\n", "Train accuracy: 0.6738095238095239\n", "Training Recall: 0.6738095238095239\n", "Training Precision: 0.7609163813299693\n", "\n", "\n", "Validation accuracy: 0.580952380952381\n", "Validation Recall: 0.580952380952381\n", "Validation Precision: 0.6005668621816321\n", "\n", "Train Confusion matrix:\n", " [[114 264]\n", " [ 10 452]]\n", "Validation Confusion matrix:\n", " [[ 12 81]\n", " [ 7 110]]\n", "\n", "\n", "Random Forest Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 0.6666666666666666\n", "Validation Recall: 0.6666666666666666\n", "Validation Precision: 0.6644530879423205\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[53 40]\n", " [30 87]]\n", "\n", "\n", "LDA Result\n", "\n", "Train accuracy: 0.9321428571428572\n", "Training Recall: 0.9321428571428572\n", "Training Precision: 0.9323503965392934\n", "\n", "\n", "Validation accuracy: 0.48095238095238096\n", "Validation Recall: 0.48095238095238096\n", "Validation Precision: 0.4837463556851312\n", "\n", "Train Confusion matrix:\n", " [[343 35]\n", " [ 22 440]]\n", "Validation Confusion matrix:\n", " [[41 52]\n", " [57 60]]\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "JaLYcChuCXpE", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "18453ecd-b2ce-428f-a8d4-81a6b042b479" }, "source": [ "#X = np.load('gdrive/My Drive/Python files/neuromarketing 25/features_plain_128_SAV.npy')\n", "\n", "train_models(X, y, y_cat)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Before selectoin\n", "(1050, 3072)\n", "After selectoin\n", "(1050, 3072)\n", "(210, 3072)\n", "Epoch 1/250\n", "7/7 [==============================] - 1s 106ms/step - loss: 0.9903 - accuracy: 0.5083 - val_loss: 0.9804 - val_accuracy: 0.5762\n", "Epoch 2/250\n", "7/7 [==============================] - 1s 91ms/step - loss: 0.9924 - accuracy: 0.4929 - val_loss: 0.9605 - val_accuracy: 0.5619\n", "Epoch 3/250\n", "7/7 [==============================] - 1s 84ms/step - loss: 0.9687 - accuracy: 0.5345 - val_loss: 0.9290 - val_accuracy: 0.5619\n", "Epoch 4/250\n", "7/7 [==============================] - 1s 96ms/step - loss: 0.9352 - accuracy: 0.5500 - val_loss: 0.9035 - val_accuracy: 0.5762\n", "Epoch 5/250\n", "7/7 [==============================] - 1s 97ms/step - loss: 0.9147 - accuracy: 0.5667 - val_loss: 0.8880 - val_accuracy: 0.5810\n", "Epoch 6/250\n", "7/7 [==============================] - 1s 129ms/step - loss: 0.8887 - accuracy: 0.5667 - val_loss: 0.8750 - val_accuracy: 0.6048\n", "Epoch 7/250\n", "7/7 [==============================] - 2s 223ms/step - loss: 0.9007 - accuracy: 0.5810 - val_loss: 0.8642 - val_accuracy: 0.6238\n", "Epoch 8/250\n", "7/7 [==============================] - 1s 83ms/step - loss: 0.8822 - accuracy: 0.5821 - val_loss: 0.8566 - val_accuracy: 0.6143\n", "Epoch 9/250\n", "7/7 [==============================] - 1s 184ms/step - loss: 0.8146 - accuracy: 0.6250 - val_loss: 0.8477 - val_accuracy: 0.6238\n", "Epoch 10/250\n", "7/7 [==============================] - 9s 1s/step - loss: 0.8366 - accuracy: 0.6036 - val_loss: 0.8413 - val_accuracy: 0.6190\n", "Epoch 11/250\n", "7/7 [==============================] - 2s 232ms/step - loss: 0.8361 - accuracy: 0.6226 - val_loss: 0.8370 - val_accuracy: 0.6238\n", "Epoch 12/250\n", "7/7 [==============================] - 3s 453ms/step - loss: 0.7789 - accuracy: 0.6571 - val_loss: 0.8279 - val_accuracy: 0.6143\n", "Epoch 13/250\n", "7/7 [==============================] - 6s 814ms/step - loss: 0.7920 - accuracy: 0.6310 - val_loss: 0.8195 - val_accuracy: 0.6095\n", "Epoch 14/250\n", "7/7 [==============================] - 4s 543ms/step - loss: 0.7594 - accuracy: 0.6607 - val_loss: 0.8096 - val_accuracy: 0.6000\n", "Epoch 15/250\n", "7/7 [==============================] - 6s 817ms/step - loss: 0.7315 - accuracy: 0.6750 - val_loss: 0.8043 - val_accuracy: 0.6000\n", "Epoch 16/250\n", "7/7 [==============================] - 2s 242ms/step - loss: 0.7193 - accuracy: 0.6536 - val_loss: 0.7962 - val_accuracy: 0.6048\n", "Epoch 17/250\n", "7/7 [==============================] - 4s 588ms/step - loss: 0.7089 - accuracy: 0.6857 - val_loss: 0.7907 - val_accuracy: 0.5905\n", "Epoch 18/250\n", "7/7 [==============================] - 7s 982ms/step - loss: 0.6885 - accuracy: 0.6833 - val_loss: 0.7794 - val_accuracy: 0.6143\n", "Epoch 19/250\n", "7/7 [==============================] - 5s 643ms/step - loss: 0.6647 - accuracy: 0.6929 - val_loss: 0.7692 - val_accuracy: 0.6095\n", "Epoch 20/250\n", "7/7 [==============================] - 5s 673ms/step - loss: 0.6756 - accuracy: 0.6893 - val_loss: 0.7614 - val_accuracy: 0.6095\n", "Epoch 21/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.6497 - accuracy: 0.7048 - val_loss: 0.7687 - val_accuracy: 0.5905\n", "Epoch 22/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.5891 - accuracy: 0.7167 - val_loss: 0.7693 - val_accuracy: 0.6000\n", "Epoch 23/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.5972 - accuracy: 0.7345 - val_loss: 0.7741 - val_accuracy: 0.6048\n", "Epoch 24/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.5821 - accuracy: 0.7274 - val_loss: 0.7808 - val_accuracy: 0.6095\n", "Epoch 25/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.5626 - accuracy: 0.7512 - val_loss: 0.7733 - val_accuracy: 0.6143\n", "Epoch 26/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.5059 - accuracy: 0.7774 - val_loss: 0.7660 - val_accuracy: 0.6000\n", "Epoch 27/250\n", "7/7 [==============================] - 2s 317ms/step - loss: 0.5465 - accuracy: 0.7536 - val_loss: 0.7485 - val_accuracy: 0.6143\n", "Epoch 28/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.5228 - accuracy: 0.7607 - val_loss: 0.7518 - val_accuracy: 0.6143\n", "Epoch 29/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.4857 - accuracy: 0.7905 - val_loss: 0.7576 - val_accuracy: 0.6048\n", "Epoch 30/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.5094 - accuracy: 0.7679 - val_loss: 0.7592 - val_accuracy: 0.6238\n", "Epoch 31/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.4988 - accuracy: 0.7786 - val_loss: 0.7720 - val_accuracy: 0.6048\n", "Epoch 32/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.4858 - accuracy: 0.7833 - val_loss: 0.7738 - val_accuracy: 0.6095\n", "Epoch 33/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.4426 - accuracy: 0.8083 - val_loss: 0.7615 - val_accuracy: 0.6143\n", "Epoch 34/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.4621 - accuracy: 0.7857 - val_loss: 0.7514 - val_accuracy: 0.6143\n", "Epoch 35/250\n", "7/7 [==============================] - 4s 542ms/step - loss: 0.4310 - accuracy: 0.8036 - val_loss: 0.7342 - val_accuracy: 0.6238\n", "Epoch 36/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.4057 - accuracy: 0.8202 - val_loss: 0.7375 - val_accuracy: 0.6190\n", "Epoch 37/250\n", "7/7 [==============================] - 6s 826ms/step - loss: 0.4514 - accuracy: 0.8012 - val_loss: 0.7324 - val_accuracy: 0.6190\n", "Epoch 38/250\n", "7/7 [==============================] - 3s 425ms/step - loss: 0.4183 - accuracy: 0.8238 - val_loss: 0.7204 - val_accuracy: 0.6286\n", "Epoch 39/250\n", "7/7 [==============================] - 7s 960ms/step - loss: 0.4232 - accuracy: 0.8012 - val_loss: 0.7150 - val_accuracy: 0.6333\n", "Epoch 40/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.4032 - accuracy: 0.8262 - val_loss: 0.7344 - val_accuracy: 0.6333\n", "Epoch 41/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.3960 - accuracy: 0.8202 - val_loss: 0.7229 - val_accuracy: 0.6381\n", "Epoch 42/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.3695 - accuracy: 0.8393 - val_loss: 0.7321 - val_accuracy: 0.6381\n", "Epoch 43/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.3803 - accuracy: 0.8250 - val_loss: 0.7360 - val_accuracy: 0.6286\n", "Epoch 44/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.3407 - accuracy: 0.8488 - val_loss: 0.7218 - val_accuracy: 0.6429\n", "Epoch 45/250\n", "7/7 [==============================] - 3s 394ms/step - loss: 0.3619 - accuracy: 0.8310 - val_loss: 0.7141 - val_accuracy: 0.6333\n", "Epoch 46/250\n", "7/7 [==============================] - 4s 635ms/step - loss: 0.3448 - accuracy: 0.8464 - val_loss: 0.7133 - val_accuracy: 0.6429\n", "Epoch 47/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.3512 - accuracy: 0.8464 - val_loss: 0.7249 - val_accuracy: 0.6143\n", "Epoch 48/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.3220 - accuracy: 0.8560 - val_loss: 0.7284 - val_accuracy: 0.6286\n", "Epoch 49/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.3196 - accuracy: 0.8524 - val_loss: 0.7355 - val_accuracy: 0.6190\n", "Epoch 50/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.3548 - accuracy: 0.8333 - val_loss: 0.7309 - val_accuracy: 0.6333\n", "Epoch 51/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.3278 - accuracy: 0.8524 - val_loss: 0.7380 - val_accuracy: 0.6238\n", "Epoch 52/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.3119 - accuracy: 0.8548 - val_loss: 0.7309 - val_accuracy: 0.6333\n", "Epoch 53/250\n", "7/7 [==============================] - 4s 549ms/step - loss: 0.2972 - accuracy: 0.8655 - val_loss: 0.7098 - val_accuracy: 0.6476\n", "Epoch 54/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.2752 - accuracy: 0.8762 - val_loss: 0.7232 - val_accuracy: 0.6381\n", "Epoch 55/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.3093 - accuracy: 0.8619 - val_loss: 0.7314 - val_accuracy: 0.6381\n", "Epoch 56/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.3003 - accuracy: 0.8655 - val_loss: 0.7271 - val_accuracy: 0.6333\n", "Epoch 57/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.2893 - accuracy: 0.8643 - val_loss: 0.7260 - val_accuracy: 0.6381\n", "Epoch 58/250\n", "7/7 [==============================] - 4s 571ms/step - loss: 0.2851 - accuracy: 0.8619 - val_loss: 0.6998 - val_accuracy: 0.6667\n", "Epoch 59/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.2942 - accuracy: 0.8702 - val_loss: 0.7072 - val_accuracy: 0.6524\n", "Epoch 60/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.2621 - accuracy: 0.8857 - val_loss: 0.7006 - val_accuracy: 0.6619\n", "Epoch 61/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.2768 - accuracy: 0.8690 - val_loss: 0.7146 - val_accuracy: 0.6476\n", "Epoch 62/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.2580 - accuracy: 0.8833 - val_loss: 0.7146 - val_accuracy: 0.6429\n", "Epoch 63/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.2506 - accuracy: 0.8881 - val_loss: 0.7028 - val_accuracy: 0.6429\n", "Epoch 64/250\n", "7/7 [==============================] - 6s 903ms/step - loss: 0.2558 - accuracy: 0.8869 - val_loss: 0.6951 - val_accuracy: 0.6571\n", "Epoch 65/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.2438 - accuracy: 0.8929 - val_loss: 0.7092 - val_accuracy: 0.6476\n", "Epoch 66/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.2379 - accuracy: 0.8893 - val_loss: 0.7103 - val_accuracy: 0.6429\n", "Epoch 67/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.2235 - accuracy: 0.9000 - val_loss: 0.7004 - val_accuracy: 0.6476\n", "Epoch 68/250\n", "7/7 [==============================] - 2s 233ms/step - loss: 0.2297 - accuracy: 0.8905 - val_loss: 0.6928 - val_accuracy: 0.6476\n", "Epoch 69/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.2547 - accuracy: 0.8821 - val_loss: 0.7081 - val_accuracy: 0.6476\n", "Epoch 70/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.2433 - accuracy: 0.8881 - val_loss: 0.7191 - val_accuracy: 0.6286\n", "Epoch 71/250\n", "7/7 [==============================] - 4s 625ms/step - loss: 0.2473 - accuracy: 0.8833 - val_loss: 0.6927 - val_accuracy: 0.6619\n", "Epoch 72/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.2399 - accuracy: 0.8893 - val_loss: 0.7103 - val_accuracy: 0.6476\n", "Epoch 73/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.2197 - accuracy: 0.9024 - val_loss: 0.7065 - val_accuracy: 0.6571\n", "Epoch 74/250\n", "7/7 [==============================] - 2s 347ms/step - loss: 0.2064 - accuracy: 0.9012 - val_loss: 0.6907 - val_accuracy: 0.6619\n", "Epoch 75/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.2418 - accuracy: 0.8821 - val_loss: 0.7060 - val_accuracy: 0.6476\n", "Epoch 76/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.2090 - accuracy: 0.9048 - val_loss: 0.7221 - val_accuracy: 0.6333\n", "Epoch 77/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.2331 - accuracy: 0.8893 - val_loss: 0.7440 - val_accuracy: 0.6190\n", "Epoch 78/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.2037 - accuracy: 0.9107 - val_loss: 0.7501 - val_accuracy: 0.6286\n", "Epoch 79/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.2286 - accuracy: 0.8869 - val_loss: 0.7341 - val_accuracy: 0.6286\n", "Epoch 80/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.2175 - accuracy: 0.9000 - val_loss: 0.7063 - val_accuracy: 0.6571\n", "Epoch 81/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.2015 - accuracy: 0.9083 - val_loss: 0.7032 - val_accuracy: 0.6571\n", "Epoch 82/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.2326 - accuracy: 0.8964 - val_loss: 0.7020 - val_accuracy: 0.6524\n", "Epoch 83/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.2334 - accuracy: 0.8940 - val_loss: 0.6954 - val_accuracy: 0.6524\n", "Epoch 84/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.2098 - accuracy: 0.9036 - val_loss: 0.7103 - val_accuracy: 0.6429\n", "Epoch 85/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1916 - accuracy: 0.9095 - val_loss: 0.7328 - val_accuracy: 0.6429\n", "Epoch 86/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.2006 - accuracy: 0.9048 - val_loss: 0.7305 - val_accuracy: 0.6524\n", "Epoch 87/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1924 - accuracy: 0.9071 - val_loss: 0.7069 - val_accuracy: 0.6476\n", "Epoch 88/250\n", "7/7 [==============================] - 4s 500ms/step - loss: 0.1974 - accuracy: 0.9095 - val_loss: 0.6731 - val_accuracy: 0.6667\n", "Epoch 89/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.1798 - accuracy: 0.9214 - val_loss: 0.6834 - val_accuracy: 0.6571\n", "Epoch 90/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.1817 - accuracy: 0.9167 - val_loss: 0.6981 - val_accuracy: 0.6571\n", "Epoch 91/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1960 - accuracy: 0.9071 - val_loss: 0.6937 - val_accuracy: 0.6571\n", "Epoch 92/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1669 - accuracy: 0.9202 - val_loss: 0.7112 - val_accuracy: 0.6571\n", "Epoch 93/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1930 - accuracy: 0.9131 - val_loss: 0.7134 - val_accuracy: 0.6524\n", "Epoch 94/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1820 - accuracy: 0.9143 - val_loss: 0.7124 - val_accuracy: 0.6429\n", "Epoch 95/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1580 - accuracy: 0.9286 - val_loss: 0.7209 - val_accuracy: 0.6381\n", "Epoch 96/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.2025 - accuracy: 0.9036 - val_loss: 0.7113 - val_accuracy: 0.6381\n", "Epoch 97/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1772 - accuracy: 0.9179 - val_loss: 0.7055 - val_accuracy: 0.6476\n", "Epoch 98/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1553 - accuracy: 0.9298 - val_loss: 0.6905 - val_accuracy: 0.6476\n", "Epoch 99/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1443 - accuracy: 0.9321 - val_loss: 0.7097 - val_accuracy: 0.6429\n", "Epoch 100/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1603 - accuracy: 0.9250 - val_loss: 0.7106 - val_accuracy: 0.6476\n", "Epoch 101/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1853 - accuracy: 0.9107 - val_loss: 0.7202 - val_accuracy: 0.6429\n", "Epoch 102/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1432 - accuracy: 0.9310 - val_loss: 0.7144 - val_accuracy: 0.6381\n", "Epoch 103/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1330 - accuracy: 0.9452 - val_loss: 0.7011 - val_accuracy: 0.6619\n", "Epoch 104/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1586 - accuracy: 0.9250 - val_loss: 0.7167 - val_accuracy: 0.6333\n", "Epoch 105/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1593 - accuracy: 0.9321 - val_loss: 0.7167 - val_accuracy: 0.6286\n", "Epoch 106/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1475 - accuracy: 0.9333 - val_loss: 0.6992 - val_accuracy: 0.6524\n", "Epoch 107/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1594 - accuracy: 0.9274 - val_loss: 0.6939 - val_accuracy: 0.6524\n", "Epoch 108/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1553 - accuracy: 0.9262 - val_loss: 0.7195 - val_accuracy: 0.6381\n", "Epoch 109/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1678 - accuracy: 0.9190 - val_loss: 0.7391 - val_accuracy: 0.6238\n", "Epoch 110/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1605 - accuracy: 0.9286 - val_loss: 0.7062 - val_accuracy: 0.6571\n", "Epoch 111/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1553 - accuracy: 0.9298 - val_loss: 0.7070 - val_accuracy: 0.6429\n", "Epoch 112/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.1753 - accuracy: 0.9190 - val_loss: 0.7040 - val_accuracy: 0.6524\n", "Epoch 113/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1388 - accuracy: 0.9357 - val_loss: 0.6994 - val_accuracy: 0.6524\n", "Epoch 114/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1229 - accuracy: 0.9417 - val_loss: 0.7084 - val_accuracy: 0.6381\n", "Epoch 115/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1428 - accuracy: 0.9333 - val_loss: 0.7089 - val_accuracy: 0.6381\n", "Epoch 116/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1229 - accuracy: 0.9476 - val_loss: 0.7097 - val_accuracy: 0.6429\n", "Epoch 117/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1498 - accuracy: 0.9310 - val_loss: 0.6947 - val_accuracy: 0.6571\n", "Epoch 118/250\n", "7/7 [==============================] - 3s 378ms/step - loss: 0.1363 - accuracy: 0.9381 - val_loss: 0.6730 - val_accuracy: 0.6571\n", "Epoch 119/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.1404 - accuracy: 0.9310 - val_loss: 0.6827 - val_accuracy: 0.6571\n", "Epoch 120/250\n", "7/7 [==============================] - 5s 646ms/step - loss: 0.1324 - accuracy: 0.9405 - val_loss: 0.6615 - val_accuracy: 0.6762\n", "Epoch 121/250\n", "7/7 [==============================] - 2s 302ms/step - loss: 0.1247 - accuracy: 0.9429 - val_loss: 0.6547 - val_accuracy: 0.6667\n", "Epoch 122/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.1350 - accuracy: 0.9381 - val_loss: 0.6777 - val_accuracy: 0.6619\n", "Epoch 123/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.1289 - accuracy: 0.9357 - val_loss: 0.6662 - val_accuracy: 0.6762\n", "Epoch 124/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1230 - accuracy: 0.9429 - val_loss: 0.6701 - val_accuracy: 0.6571\n", "Epoch 125/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.1145 - accuracy: 0.9500 - val_loss: 0.6626 - val_accuracy: 0.6714\n", "Epoch 126/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1218 - accuracy: 0.9440 - val_loss: 0.6730 - val_accuracy: 0.6714\n", "Epoch 127/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1136 - accuracy: 0.9440 - val_loss: 0.6896 - val_accuracy: 0.6571\n", "Epoch 128/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1356 - accuracy: 0.9357 - val_loss: 0.6918 - val_accuracy: 0.6524\n", "Epoch 129/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1375 - accuracy: 0.9369 - val_loss: 0.6972 - val_accuracy: 0.6476\n", "Epoch 130/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1143 - accuracy: 0.9476 - val_loss: 0.7164 - val_accuracy: 0.6381\n", "Epoch 131/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1237 - accuracy: 0.9488 - val_loss: 0.7125 - val_accuracy: 0.6381\n", "Epoch 132/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1369 - accuracy: 0.9393 - val_loss: 0.7344 - val_accuracy: 0.6238\n", "Epoch 133/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1261 - accuracy: 0.9429 - val_loss: 0.6848 - val_accuracy: 0.6571\n", "Epoch 134/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1284 - accuracy: 0.9369 - val_loss: 0.6761 - val_accuracy: 0.6667\n", "Epoch 135/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1178 - accuracy: 0.9476 - val_loss: 0.6882 - val_accuracy: 0.6524\n", "Epoch 136/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1333 - accuracy: 0.9333 - val_loss: 0.6957 - val_accuracy: 0.6476\n", "Epoch 137/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1139 - accuracy: 0.9512 - val_loss: 0.6762 - val_accuracy: 0.6667\n", "Epoch 138/250\n", "7/7 [==============================] - 7s 986ms/step - loss: 0.1343 - accuracy: 0.9369 - val_loss: 0.6417 - val_accuracy: 0.6857\n", "Epoch 139/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.1113 - accuracy: 0.9440 - val_loss: 0.6859 - val_accuracy: 0.6667\n", "Epoch 140/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.1096 - accuracy: 0.9512 - val_loss: 0.6556 - val_accuracy: 0.6714\n", "Epoch 141/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1149 - accuracy: 0.9452 - val_loss: 0.6746 - val_accuracy: 0.6619\n", "Epoch 142/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1198 - accuracy: 0.9440 - val_loss: 0.7152 - val_accuracy: 0.6476\n", "Epoch 143/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1182 - accuracy: 0.9452 - val_loss: 0.7154 - val_accuracy: 0.6476\n", "Epoch 144/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1141 - accuracy: 0.9536 - val_loss: 0.7452 - val_accuracy: 0.6333\n", "Epoch 145/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.1471 - accuracy: 0.9286 - val_loss: 0.7090 - val_accuracy: 0.6476\n", "Epoch 146/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1151 - accuracy: 0.9464 - val_loss: 0.7154 - val_accuracy: 0.6429\n", "Epoch 147/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1301 - accuracy: 0.9357 - val_loss: 0.7252 - val_accuracy: 0.6429\n", "Epoch 148/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.1177 - accuracy: 0.9440 - val_loss: 0.7082 - val_accuracy: 0.6619\n", "Epoch 149/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1110 - accuracy: 0.9512 - val_loss: 0.7117 - val_accuracy: 0.6333\n", "Epoch 150/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1059 - accuracy: 0.9536 - val_loss: 0.7010 - val_accuracy: 0.6524\n", "Epoch 151/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1049 - accuracy: 0.9512 - val_loss: 0.7077 - val_accuracy: 0.6381\n", "Epoch 152/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0893 - accuracy: 0.9607 - val_loss: 0.6983 - val_accuracy: 0.6571\n", "Epoch 153/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1067 - accuracy: 0.9512 - val_loss: 0.6826 - val_accuracy: 0.6667\n", "Epoch 154/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1348 - accuracy: 0.9357 - val_loss: 0.6948 - val_accuracy: 0.6476\n", "Epoch 155/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1104 - accuracy: 0.9452 - val_loss: 0.7258 - val_accuracy: 0.6286\n", "Epoch 156/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1083 - accuracy: 0.9512 - val_loss: 0.7209 - val_accuracy: 0.6381\n", "Epoch 157/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0986 - accuracy: 0.9560 - val_loss: 0.7318 - val_accuracy: 0.6333\n", "Epoch 158/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0989 - accuracy: 0.9595 - val_loss: 0.6940 - val_accuracy: 0.6524\n", "Epoch 159/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1074 - accuracy: 0.9452 - val_loss: 0.7006 - val_accuracy: 0.6476\n", "Epoch 160/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1066 - accuracy: 0.9488 - val_loss: 0.7075 - val_accuracy: 0.6571\n", "Epoch 161/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1055 - accuracy: 0.9512 - val_loss: 0.7170 - val_accuracy: 0.6429\n", "Epoch 162/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1065 - accuracy: 0.9500 - val_loss: 0.6923 - val_accuracy: 0.6524\n", "Epoch 163/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.1136 - accuracy: 0.9464 - val_loss: 0.6893 - val_accuracy: 0.6619\n", "Epoch 164/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1070 - accuracy: 0.9500 - val_loss: 0.7329 - val_accuracy: 0.6286\n", "Epoch 165/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1040 - accuracy: 0.9488 - val_loss: 0.7579 - val_accuracy: 0.6190\n", "Epoch 166/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0930 - accuracy: 0.9560 - val_loss: 0.7456 - val_accuracy: 0.6381\n", "Epoch 167/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0808 - accuracy: 0.9655 - val_loss: 0.7400 - val_accuracy: 0.6333\n", "Epoch 168/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0918 - accuracy: 0.9607 - val_loss: 0.7108 - val_accuracy: 0.6429\n", "Epoch 169/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0831 - accuracy: 0.9607 - val_loss: 0.7341 - val_accuracy: 0.6333\n", "Epoch 170/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.1027 - accuracy: 0.9536 - val_loss: 0.7538 - val_accuracy: 0.6238\n", "Epoch 171/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0916 - accuracy: 0.9560 - val_loss: 0.7201 - val_accuracy: 0.6381\n", "Epoch 172/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1092 - accuracy: 0.9452 - val_loss: 0.7539 - val_accuracy: 0.6095\n", "Epoch 173/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0993 - accuracy: 0.9560 - val_loss: 0.7130 - val_accuracy: 0.6476\n", "Epoch 174/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1037 - accuracy: 0.9512 - val_loss: 0.6801 - val_accuracy: 0.6667\n", "Epoch 175/250\n", "7/7 [==============================] - 0s 63ms/step - loss: 0.0742 - accuracy: 0.9655 - val_loss: 0.6317 - val_accuracy: 0.6857\n", "Epoch 176/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0939 - accuracy: 0.9560 - val_loss: 0.6583 - val_accuracy: 0.6762\n", "Epoch 177/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0776 - accuracy: 0.9631 - val_loss: 0.6756 - val_accuracy: 0.6571\n", "Epoch 178/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0961 - accuracy: 0.9583 - val_loss: 0.6701 - val_accuracy: 0.6524\n", "Epoch 179/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1040 - accuracy: 0.9524 - val_loss: 0.6793 - val_accuracy: 0.6619\n", "Epoch 180/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0847 - accuracy: 0.9607 - val_loss: 0.6675 - val_accuracy: 0.6667\n", "Epoch 181/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0762 - accuracy: 0.9655 - val_loss: 0.6622 - val_accuracy: 0.6714\n", "Epoch 182/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0714 - accuracy: 0.9679 - val_loss: 0.7143 - val_accuracy: 0.6429\n", "Epoch 183/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0872 - accuracy: 0.9619 - val_loss: 0.7207 - val_accuracy: 0.6476\n", "Epoch 184/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0776 - accuracy: 0.9619 - val_loss: 0.7240 - val_accuracy: 0.6381\n", "Epoch 185/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0814 - accuracy: 0.9619 - val_loss: 0.7067 - val_accuracy: 0.6476\n", "Epoch 186/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0878 - accuracy: 0.9583 - val_loss: 0.6937 - val_accuracy: 0.6619\n", "Epoch 187/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0904 - accuracy: 0.9583 - val_loss: 0.6974 - val_accuracy: 0.6524\n", "Epoch 188/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0870 - accuracy: 0.9631 - val_loss: 0.6966 - val_accuracy: 0.6571\n", "Epoch 189/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0759 - accuracy: 0.9643 - val_loss: 0.6674 - val_accuracy: 0.6762\n", "Epoch 190/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0979 - accuracy: 0.9536 - val_loss: 0.6992 - val_accuracy: 0.6476\n", "Epoch 191/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0851 - accuracy: 0.9607 - val_loss: 0.7276 - val_accuracy: 0.6381\n", "Epoch 192/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0824 - accuracy: 0.9619 - val_loss: 0.6947 - val_accuracy: 0.6524\n", "Epoch 193/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0942 - accuracy: 0.9571 - val_loss: 0.7028 - val_accuracy: 0.6524\n", "Epoch 194/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0853 - accuracy: 0.9607 - val_loss: 0.7281 - val_accuracy: 0.6333\n", "Epoch 195/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0840 - accuracy: 0.9619 - val_loss: 0.6762 - val_accuracy: 0.6571\n", "Epoch 196/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0993 - accuracy: 0.9500 - val_loss: 0.6562 - val_accuracy: 0.6714\n", "Epoch 197/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0806 - accuracy: 0.9631 - val_loss: 0.6932 - val_accuracy: 0.6524\n", "Epoch 198/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0679 - accuracy: 0.9702 - val_loss: 0.6860 - val_accuracy: 0.6571\n", "Epoch 199/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0899 - accuracy: 0.9571 - val_loss: 0.6678 - val_accuracy: 0.6619\n", "Epoch 200/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1056 - accuracy: 0.9476 - val_loss: 0.6733 - val_accuracy: 0.6667\n", "Epoch 201/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0841 - accuracy: 0.9631 - val_loss: 0.7236 - val_accuracy: 0.6333\n", "Epoch 202/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0814 - accuracy: 0.9631 - val_loss: 0.6920 - val_accuracy: 0.6571\n", "Epoch 203/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0791 - accuracy: 0.9631 - val_loss: 0.6850 - val_accuracy: 0.6667\n", "Epoch 204/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0827 - accuracy: 0.9619 - val_loss: 0.6775 - val_accuracy: 0.6667\n", "Epoch 205/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0718 - accuracy: 0.9667 - val_loss: 0.6369 - val_accuracy: 0.6762\n", "Epoch 206/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0729 - accuracy: 0.9667 - val_loss: 0.6640 - val_accuracy: 0.6714\n", "Epoch 207/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0741 - accuracy: 0.9667 - val_loss: 0.6995 - val_accuracy: 0.6524\n", "Epoch 208/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0888 - accuracy: 0.9607 - val_loss: 0.7259 - val_accuracy: 0.6429\n", "Epoch 209/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0897 - accuracy: 0.9571 - val_loss: 0.7644 - val_accuracy: 0.6095\n", "Epoch 210/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0795 - accuracy: 0.9619 - val_loss: 0.7982 - val_accuracy: 0.6000\n", "Epoch 211/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0848 - accuracy: 0.9619 - val_loss: 0.7769 - val_accuracy: 0.6095\n", "Epoch 212/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0731 - accuracy: 0.9679 - val_loss: 0.7767 - val_accuracy: 0.6095\n", "Epoch 213/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0633 - accuracy: 0.9750 - val_loss: 0.7741 - val_accuracy: 0.6190\n", "Epoch 214/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0854 - accuracy: 0.9619 - val_loss: 0.8045 - val_accuracy: 0.6000\n", "Epoch 215/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0812 - accuracy: 0.9595 - val_loss: 0.7344 - val_accuracy: 0.6286\n", "Epoch 216/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0799 - accuracy: 0.9595 - val_loss: 0.7467 - val_accuracy: 0.6333\n", "Epoch 217/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0805 - accuracy: 0.9619 - val_loss: 0.7321 - val_accuracy: 0.6286\n", "Epoch 218/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0705 - accuracy: 0.9667 - val_loss: 0.7041 - val_accuracy: 0.6524\n", "Epoch 219/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0807 - accuracy: 0.9619 - val_loss: 0.7015 - val_accuracy: 0.6524\n", "Epoch 220/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0739 - accuracy: 0.9643 - val_loss: 0.7243 - val_accuracy: 0.6429\n", "Epoch 221/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0929 - accuracy: 0.9583 - val_loss: 0.7812 - val_accuracy: 0.6048\n", "Epoch 222/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0770 - accuracy: 0.9607 - val_loss: 0.7644 - val_accuracy: 0.6190\n", "Epoch 223/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0867 - accuracy: 0.9548 - val_loss: 0.7439 - val_accuracy: 0.6238\n", "Epoch 224/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0745 - accuracy: 0.9655 - val_loss: 0.7008 - val_accuracy: 0.6476\n", "Epoch 225/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0571 - accuracy: 0.9738 - val_loss: 0.7213 - val_accuracy: 0.6381\n", "Epoch 226/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0832 - accuracy: 0.9583 - val_loss: 0.6952 - val_accuracy: 0.6714\n", "Epoch 227/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0804 - accuracy: 0.9643 - val_loss: 0.6989 - val_accuracy: 0.6571\n", "Epoch 228/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0656 - accuracy: 0.9679 - val_loss: 0.7058 - val_accuracy: 0.6476\n", "Epoch 229/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0775 - accuracy: 0.9667 - val_loss: 0.7346 - val_accuracy: 0.6333\n", "Epoch 230/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0682 - accuracy: 0.9679 - val_loss: 0.7493 - val_accuracy: 0.6190\n", "Epoch 231/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0786 - accuracy: 0.9631 - val_loss: 0.7494 - val_accuracy: 0.6238\n", "Epoch 232/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0797 - accuracy: 0.9619 - val_loss: 0.7239 - val_accuracy: 0.6381\n", "Epoch 233/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0618 - accuracy: 0.9714 - val_loss: 0.7160 - val_accuracy: 0.6429\n", "Epoch 234/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0640 - accuracy: 0.9702 - val_loss: 0.7041 - val_accuracy: 0.6476\n", "Epoch 235/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0575 - accuracy: 0.9726 - val_loss: 0.7229 - val_accuracy: 0.6381\n", "Epoch 236/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0686 - accuracy: 0.9690 - val_loss: 0.7233 - val_accuracy: 0.6381\n", "Epoch 237/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0646 - accuracy: 0.9679 - val_loss: 0.7144 - val_accuracy: 0.6381\n", "Epoch 238/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0762 - accuracy: 0.9643 - val_loss: 0.7125 - val_accuracy: 0.6476\n", "Epoch 239/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0690 - accuracy: 0.9667 - val_loss: 0.7563 - val_accuracy: 0.6190\n", "Epoch 240/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0570 - accuracy: 0.9750 - val_loss: 0.7668 - val_accuracy: 0.6143\n", "Epoch 241/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0740 - accuracy: 0.9667 - val_loss: 0.7656 - val_accuracy: 0.6143\n", "Epoch 242/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0699 - accuracy: 0.9643 - val_loss: 0.7450 - val_accuracy: 0.6333\n", "Epoch 243/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0701 - accuracy: 0.9655 - val_loss: 0.7379 - val_accuracy: 0.6286\n", "Epoch 244/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0683 - accuracy: 0.9667 - val_loss: 0.7653 - val_accuracy: 0.6190\n", "Epoch 245/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0611 - accuracy: 0.9738 - val_loss: 0.7308 - val_accuracy: 0.6429\n", "Epoch 246/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0671 - accuracy: 0.9679 - val_loss: 0.7178 - val_accuracy: 0.6476\n", "Epoch 247/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0555 - accuracy: 0.9762 - val_loss: 0.6956 - val_accuracy: 0.6571\n", "Epoch 248/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0525 - accuracy: 0.9738 - val_loss: 0.7133 - val_accuracy: 0.6429\n", "Epoch 249/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0737 - accuracy: 0.9667 - val_loss: 0.7074 - val_accuracy: 0.6476\n", "Epoch 250/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0441 - accuracy: 0.9821 - val_loss: 0.7144 - val_accuracy: 0.6429\n", "Valid results - Loss: 0.7144185900688171 - Accuracy: 64.28571343421936%\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", 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\n", 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\n", 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" ] }, "metadata": { "tags": [], "needs_background": "light" } }, { "output_type": "stream", "text": [ "DNN results\n", "accuracy : 0.9821428571428571\n", "Training Recall: 0.9821428571428571\n", "Training Precision: 0.9821548387096773\n", "Confusion matrix: \n", "[[369 9]\n", " [ 6 456]]\n", "Validation set report:\n", "cross Loss: -12.821598057342428\n", "hing Loss: 0.7571428571428571\n", "Report precision recall f1-score support\n", "\n", " 0 0.68 0.56 0.61 93\n", " 1 0.69 0.79 0.74 117\n", "\n", " accuracy 0.69 210\n", " macro avg 0.68 0.67 0.67 210\n", "weighted avg 0.68 0.69 0.68 210\n", "\n", "accuracy : 0.6857142857142857\n", "Validation Recall: 0.6857142857142857\n", "Validation Precision: 0.6844644077726785\n", "Confusion matrix: \n", "[[52 41]\n", " [25 92]]\n", "\n", "\\KNN Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 0.6952380952380952\n", "Validation Recall: 0.6952380952380952\n", "Validation Precision: 0.7136397652526685\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[73 20]\n", " [44 73]]\n", "\n", "\n", "Support vector machine Result\n", "\n", "Train accuracy: 0.6738095238095239\n", "Training Recall: 0.6738095238095239\n", "Training Precision: 0.7609163813299693\n", "\n", "\n", "Validation accuracy: 0.580952380952381\n", "Validation Recall: 0.580952380952381\n", "Validation Precision: 0.6005668621816321\n", "\n", "Train Confusion matrix:\n", " [[114 264]\n", " [ 10 452]]\n", "Validation Confusion matrix:\n", " [[ 12 81]\n", " [ 7 110]]\n", "\n", "\n", "Random Forest Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 0.6857142857142857\n", "Validation Recall: 0.6857142857142857\n", "Validation Precision: 0.6839424141749723\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[54 39]\n", " [27 90]]\n", "\n", "\n", "LDA Result\n", "\n", "Train accuracy: 0.9321428571428572\n", "Training Recall: 0.9321428571428572\n", "Training Precision: 0.9323503965392934\n", "\n", "\n", "Validation accuracy: 0.48095238095238096\n", "Validation Recall: 0.48095238095238096\n", "Validation Precision: 0.4837463556851312\n", "\n", "Train Confusion matrix:\n", " [[343 35]\n", " [ 22 440]]\n", "Validation Confusion matrix:\n", " [[41 52]\n", " [57 60]]\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "-QmSXYZ0g8kj" }, "source": [ "# PSD" ] }, { "cell_type": "code", "metadata": { "id": "Yti545F04B-t", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "39518310-0ec5-4ff6-8a40-17c61fa4ed75" }, "source": [ "# all PSD no FS + LDA classification\n", "train_models(X, y, y_cat)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Before selectoin\n", "(1050, 2725)\n", "After selectoin\n", "(1050, 2725)\n", "(210, 2725)\n", "Epoch 1/250\n", "7/7 [==============================] - 1s 93ms/step - loss: 0.9663 - accuracy: 0.5167 - val_loss: 0.9311 - val_accuracy: 0.6714\n", "Epoch 2/250\n", "7/7 [==============================] - 0s 69ms/step - loss: 0.8358 - accuracy: 0.6345 - val_loss: 0.8423 - val_accuracy: 0.7095\n", "Epoch 3/250\n", "7/7 [==============================] - 1s 106ms/step - loss: 0.7454 - accuracy: 0.6667 - val_loss: 0.7650 - val_accuracy: 0.7143\n", "Epoch 4/250\n", "7/7 [==============================] - 2s 311ms/step - loss: 0.6705 - accuracy: 0.7024 - val_loss: 0.6981 - val_accuracy: 0.6952\n", "Epoch 5/250\n", "7/7 [==============================] - 1s 81ms/step - loss: 0.6118 - accuracy: 0.7381 - val_loss: 0.6231 - val_accuracy: 0.7857\n", "Epoch 6/250\n", "7/7 [==============================] - 1s 78ms/step - loss: 0.5318 - accuracy: 0.7774 - val_loss: 0.5490 - val_accuracy: 0.8238\n", "Epoch 7/250\n", "7/7 [==============================] - 1s 94ms/step - loss: 0.5049 - accuracy: 0.7857 - val_loss: 0.4802 - val_accuracy: 0.8476\n", "Epoch 8/250\n", "7/7 [==============================] - 1s 121ms/step - loss: 0.4467 - accuracy: 0.8214 - val_loss: 0.4473 - val_accuracy: 0.8571\n", "Epoch 9/250\n", "7/7 [==============================] - 2s 218ms/step - loss: 0.4224 - accuracy: 0.8286 - val_loss: 0.4295 - val_accuracy: 0.8476\n", "Epoch 10/250\n", "7/7 [==============================] - 1s 159ms/step - loss: 0.4338 - accuracy: 0.8071 - val_loss: 0.3952 - val_accuracy: 0.8571\n", "Epoch 11/250\n", "7/7 [==============================] - 9s 1s/step - loss: 0.3711 - accuracy: 0.8429 - val_loss: 0.3672 - val_accuracy: 0.8571\n", "Epoch 12/250\n", "7/7 [==============================] - 2s 232ms/step - loss: 0.3621 - accuracy: 0.8488 - val_loss: 0.3282 - val_accuracy: 0.8714\n", "Epoch 13/250\n", "7/7 [==============================] - 4s 598ms/step - loss: 0.3378 - accuracy: 0.8536 - val_loss: 0.3209 - val_accuracy: 0.8714\n", "Epoch 14/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.3572 - accuracy: 0.8381 - val_loss: 0.3285 - val_accuracy: 0.8476\n", "Epoch 15/250\n", "7/7 [==============================] - 3s 371ms/step - loss: 0.3240 - accuracy: 0.8500 - val_loss: 0.2919 - val_accuracy: 0.8762\n", "Epoch 16/250\n", "7/7 [==============================] - 3s 441ms/step - loss: 0.2948 - accuracy: 0.8810 - val_loss: 0.2781 - val_accuracy: 0.8714\n", "Epoch 17/250\n", "7/7 [==============================] - 7s 977ms/step - loss: 0.2812 - accuracy: 0.8798 - val_loss: 0.2741 - val_accuracy: 0.8714\n", "Epoch 18/250\n", "7/7 [==============================] - 4s 593ms/step - loss: 0.2792 - accuracy: 0.8821 - val_loss: 0.2671 - val_accuracy: 0.8667\n", "Epoch 19/250\n", "7/7 [==============================] - 4s 601ms/step - loss: 0.2562 - accuracy: 0.8929 - val_loss: 0.2633 - val_accuracy: 0.8667\n", "Epoch 20/250\n", "7/7 [==============================] - 4s 596ms/step - loss: 0.2297 - accuracy: 0.9024 - val_loss: 0.2587 - val_accuracy: 0.8762\n", "Epoch 21/250\n", "7/7 [==============================] - 4s 599ms/step - loss: 0.2431 - accuracy: 0.9000 - val_loss: 0.2549 - val_accuracy: 0.8810\n", "Epoch 22/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.2193 - accuracy: 0.9036 - val_loss: 0.2566 - val_accuracy: 0.8714\n", "Epoch 23/250\n", "7/7 [==============================] - 3s 377ms/step - loss: 0.2229 - accuracy: 0.9071 - val_loss: 0.2546 - val_accuracy: 0.8762\n", "Epoch 24/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.2024 - accuracy: 0.9143 - val_loss: 0.2771 - val_accuracy: 0.8714\n", "Epoch 25/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1938 - accuracy: 0.9214 - val_loss: 0.2663 - val_accuracy: 0.8762\n", "Epoch 26/250\n", "7/7 [==============================] - 3s 387ms/step - loss: 0.1819 - accuracy: 0.9179 - val_loss: 0.2347 - val_accuracy: 0.8905\n", "Epoch 27/250\n", "7/7 [==============================] - 7s 1s/step - loss: 0.2123 - accuracy: 0.9048 - val_loss: 0.2302 - val_accuracy: 0.8905\n", "Epoch 28/250\n", "7/7 [==============================] - 0s 17ms/step - loss: 0.1847 - accuracy: 0.9238 - val_loss: 0.2355 - val_accuracy: 0.8810\n", "Epoch 29/250\n", "7/7 [==============================] - 2s 309ms/step - loss: 0.1569 - accuracy: 0.9310 - val_loss: 0.2227 - val_accuracy: 0.8952\n", "Epoch 30/250\n", "7/7 [==============================] - 8s 1s/step - loss: 0.1612 - accuracy: 0.9310 - val_loss: 0.2157 - val_accuracy: 0.9000\n", "Epoch 31/250\n", "7/7 [==============================] - 2s 275ms/step - loss: 0.1565 - accuracy: 0.9393 - val_loss: 0.2131 - val_accuracy: 0.9048\n", "Epoch 32/250\n", "7/7 [==============================] - 3s 427ms/step - loss: 0.1557 - accuracy: 0.9321 - val_loss: 0.2066 - val_accuracy: 0.9000\n", "Epoch 33/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.1580 - accuracy: 0.9333 - val_loss: 0.2115 - val_accuracy: 0.9000\n", "Epoch 34/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.1699 - accuracy: 0.9333 - val_loss: 0.2075 - val_accuracy: 0.9048\n", "Epoch 35/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1085 - accuracy: 0.9583 - val_loss: 0.2074 - val_accuracy: 0.8952\n", "Epoch 36/250\n", "7/7 [==============================] - 4s 529ms/step - loss: 0.1252 - accuracy: 0.9464 - val_loss: 0.2061 - val_accuracy: 0.9048\n", "Epoch 37/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.1187 - accuracy: 0.9548 - val_loss: 0.2121 - val_accuracy: 0.9000\n", "Epoch 38/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.1166 - accuracy: 0.9512 - val_loss: 0.2431 - val_accuracy: 0.8762\n", "Epoch 39/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1258 - accuracy: 0.9476 - val_loss: 0.2176 - val_accuracy: 0.8952\n", "Epoch 40/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1301 - accuracy: 0.9440 - val_loss: 0.2666 - val_accuracy: 0.8667\n", "Epoch 41/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0926 - accuracy: 0.9631 - val_loss: 0.2211 - val_accuracy: 0.9000\n", "Epoch 42/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1114 - accuracy: 0.9524 - val_loss: 0.2265 - val_accuracy: 0.8857\n", "Epoch 43/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1088 - accuracy: 0.9536 - val_loss: 0.2211 - val_accuracy: 0.8952\n", "Epoch 44/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.1003 - accuracy: 0.9560 - val_loss: 0.2274 - val_accuracy: 0.8810\n", "Epoch 45/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0953 - accuracy: 0.9619 - val_loss: 0.2198 - val_accuracy: 0.8905\n", "Epoch 46/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0927 - accuracy: 0.9571 - val_loss: 0.2199 - val_accuracy: 0.8905\n", "Epoch 47/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0828 - accuracy: 0.9655 - val_loss: 0.2170 - val_accuracy: 0.8952\n", "Epoch 48/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0833 - accuracy: 0.9667 - val_loss: 0.2193 - val_accuracy: 0.8857\n", "Epoch 49/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0774 - accuracy: 0.9702 - val_loss: 0.2968 - val_accuracy: 0.8524\n", "Epoch 50/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0995 - accuracy: 0.9595 - val_loss: 0.2363 - val_accuracy: 0.8762\n", "Epoch 51/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0765 - accuracy: 0.9643 - val_loss: 0.2592 - val_accuracy: 0.8714\n", "Epoch 52/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0726 - accuracy: 0.9726 - val_loss: 0.2543 - val_accuracy: 0.8714\n", "Epoch 53/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0547 - accuracy: 0.9798 - val_loss: 0.2338 - val_accuracy: 0.8810\n", "Epoch 54/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0583 - accuracy: 0.9810 - val_loss: 0.2200 - val_accuracy: 0.8857\n", "Epoch 55/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0764 - accuracy: 0.9643 - val_loss: 0.2154 - val_accuracy: 0.8905\n", "Epoch 56/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0650 - accuracy: 0.9702 - val_loss: 0.2198 - val_accuracy: 0.8952\n", "Epoch 57/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0515 - accuracy: 0.9810 - val_loss: 0.2119 - val_accuracy: 0.9048\n", "Epoch 58/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0732 - accuracy: 0.9714 - val_loss: 0.2104 - val_accuracy: 0.9048\n", "Epoch 59/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0503 - accuracy: 0.9821 - val_loss: 0.2231 - val_accuracy: 0.8952\n", "Epoch 60/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0554 - accuracy: 0.9726 - val_loss: 0.2165 - val_accuracy: 0.8905\n", "Epoch 61/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0533 - accuracy: 0.9821 - val_loss: 0.2464 - val_accuracy: 0.8810\n", "Epoch 62/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0520 - accuracy: 0.9798 - val_loss: 0.2515 - val_accuracy: 0.8714\n", "Epoch 63/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0601 - accuracy: 0.9738 - val_loss: 0.2808 - val_accuracy: 0.8619\n", "Epoch 64/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0764 - accuracy: 0.9726 - val_loss: 0.2325 - val_accuracy: 0.8810\n", "Epoch 65/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0486 - accuracy: 0.9810 - val_loss: 0.2174 - val_accuracy: 0.9000\n", "Epoch 66/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0601 - accuracy: 0.9810 - val_loss: 0.2543 - val_accuracy: 0.8714\n", "Epoch 67/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0423 - accuracy: 0.9798 - val_loss: 0.2149 - val_accuracy: 0.9000\n", "Epoch 68/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0467 - accuracy: 0.9810 - val_loss: 0.2106 - val_accuracy: 0.9095\n", "Epoch 69/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0510 - accuracy: 0.9845 - val_loss: 0.2339 - val_accuracy: 0.8810\n", "Epoch 70/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0389 - accuracy: 0.9881 - val_loss: 0.2075 - val_accuracy: 0.9095\n", "Epoch 71/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0429 - accuracy: 0.9833 - val_loss: 0.2298 - val_accuracy: 0.9000\n", "Epoch 72/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0486 - accuracy: 0.9798 - val_loss: 0.2075 - val_accuracy: 0.9000\n", "Epoch 73/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0452 - accuracy: 0.9821 - val_loss: 0.2087 - val_accuracy: 0.8952\n", "Epoch 74/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0446 - accuracy: 0.9845 - val_loss: 0.2385 - val_accuracy: 0.8762\n", "Epoch 75/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0457 - accuracy: 0.9833 - val_loss: 0.2423 - val_accuracy: 0.8762\n", "Epoch 76/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0341 - accuracy: 0.9857 - val_loss: 0.2069 - val_accuracy: 0.9000\n", "Epoch 77/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0375 - accuracy: 0.9821 - val_loss: 0.2702 - val_accuracy: 0.8714\n", "Epoch 78/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0386 - accuracy: 0.9833 - val_loss: 0.2207 - val_accuracy: 0.8952\n", "Epoch 79/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0469 - accuracy: 0.9810 - val_loss: 0.2242 - val_accuracy: 0.8905\n", "Epoch 80/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0312 - accuracy: 0.9905 - val_loss: 0.2215 - val_accuracy: 0.8905\n", "Epoch 81/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0257 - accuracy: 0.9929 - val_loss: 0.2165 - val_accuracy: 0.8952\n", "Epoch 82/250\n", "7/7 [==============================] - 1s 168ms/step - loss: 0.0296 - accuracy: 0.9857 - val_loss: 0.1998 - val_accuracy: 0.9095\n", "Epoch 83/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0318 - accuracy: 0.9869 - val_loss: 0.2357 - val_accuracy: 0.8810\n", "Epoch 84/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0434 - accuracy: 0.9857 - val_loss: 0.2236 - val_accuracy: 0.8952\n", "Epoch 85/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0370 - accuracy: 0.9833 - val_loss: 0.2248 - val_accuracy: 0.8905\n", "Epoch 86/250\n", "7/7 [==============================] - 4s 555ms/step - loss: 0.0303 - accuracy: 0.9881 - val_loss: 0.1982 - val_accuracy: 0.9095\n", "Epoch 87/250\n", "7/7 [==============================] - 0s 17ms/step - loss: 0.0338 - accuracy: 0.9869 - val_loss: 0.2371 - val_accuracy: 0.8857\n", "Epoch 88/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0355 - accuracy: 0.9869 - val_loss: 0.2039 - val_accuracy: 0.9095\n", "Epoch 89/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0452 - accuracy: 0.9810 - val_loss: 0.2115 - val_accuracy: 0.8952\n", "Epoch 90/250\n", "7/7 [==============================] - 4s 518ms/step - loss: 0.0285 - accuracy: 0.9893 - val_loss: 0.1964 - val_accuracy: 0.9095\n", "Epoch 91/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0235 - accuracy: 0.9905 - val_loss: 0.1985 - val_accuracy: 0.9048\n", "Epoch 92/250\n", "7/7 [==============================] - 0s 17ms/step - loss: 0.0318 - accuracy: 0.9881 - val_loss: 0.1997 - val_accuracy: 0.9048\n", "Epoch 93/250\n", "7/7 [==============================] - 0s 19ms/step - loss: 0.0235 - accuracy: 0.9905 - val_loss: 0.2088 - val_accuracy: 0.8905\n", "Epoch 94/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0201 - accuracy: 0.9929 - val_loss: 0.2257 - val_accuracy: 0.8857\n", "Epoch 95/250\n", "7/7 [==============================] - 4s 558ms/step - loss: 0.0397 - accuracy: 0.9821 - val_loss: 0.1942 - val_accuracy: 0.9048\n", "Epoch 96/250\n", "7/7 [==============================] - 0s 18ms/step - loss: 0.0420 - accuracy: 0.9821 - val_loss: 0.2432 - val_accuracy: 0.8905\n", "Epoch 97/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0304 - accuracy: 0.9845 - val_loss: 0.2140 - val_accuracy: 0.9000\n", "Epoch 98/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0249 - accuracy: 0.9905 - val_loss: 0.2962 - val_accuracy: 0.8429\n", "Epoch 99/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0349 - accuracy: 0.9845 - val_loss: 0.2080 - val_accuracy: 0.9000\n", "Epoch 100/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0322 - accuracy: 0.9857 - val_loss: 0.2924 - val_accuracy: 0.8571\n", "Epoch 101/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0179 - accuracy: 0.9940 - val_loss: 0.2024 - val_accuracy: 0.9095\n", "Epoch 102/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0232 - accuracy: 0.9917 - val_loss: 0.2063 - val_accuracy: 0.9143\n", "Epoch 103/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0174 - accuracy: 0.9940 - val_loss: 0.2263 - val_accuracy: 0.8857\n", "Epoch 104/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0301 - accuracy: 0.9881 - val_loss: 0.2275 - val_accuracy: 0.8905\n", "Epoch 105/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0202 - accuracy: 0.9929 - val_loss: 0.2043 - val_accuracy: 0.9000\n", "Epoch 106/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0224 - accuracy: 0.9929 - val_loss: 0.2024 - val_accuracy: 0.9095\n", "Epoch 107/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0270 - accuracy: 0.9905 - val_loss: 0.2129 - val_accuracy: 0.9048\n", "Epoch 108/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0160 - accuracy: 0.9952 - val_loss: 0.2133 - val_accuracy: 0.8952\n", "Epoch 109/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0296 - accuracy: 0.9857 - val_loss: 0.2038 - val_accuracy: 0.9000\n", "Epoch 110/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0148 - accuracy: 0.9940 - val_loss: 0.2040 - val_accuracy: 0.9048\n", "Epoch 111/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0189 - accuracy: 0.9952 - val_loss: 0.2729 - val_accuracy: 0.8619\n", "Epoch 112/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0126 - accuracy: 0.9952 - val_loss: 0.2148 - val_accuracy: 0.9048\n", "Epoch 113/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0266 - accuracy: 0.9857 - val_loss: 0.2177 - val_accuracy: 0.9000\n", "Epoch 114/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0277 - accuracy: 0.9881 - val_loss: 0.2081 - val_accuracy: 0.9095\n", "Epoch 115/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0308 - accuracy: 0.9857 - val_loss: 0.2898 - val_accuracy: 0.8524\n", "Epoch 116/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0200 - accuracy: 0.9940 - val_loss: 0.2316 - val_accuracy: 0.8905\n", "Epoch 117/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0322 - accuracy: 0.9869 - val_loss: 0.2304 - val_accuracy: 0.8857\n", "Epoch 118/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0100 - accuracy: 0.9964 - val_loss: 0.2296 - val_accuracy: 0.8905\n", "Epoch 119/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0236 - accuracy: 0.9893 - val_loss: 0.2141 - val_accuracy: 0.8905\n", "Epoch 120/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0232 - accuracy: 0.9905 - val_loss: 0.2046 - val_accuracy: 0.9095\n", "Epoch 121/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0343 - accuracy: 0.9845 - val_loss: 0.2217 - val_accuracy: 0.8952\n", "Epoch 122/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0161 - accuracy: 0.9952 - val_loss: 0.2136 - val_accuracy: 0.8952\n", "Epoch 123/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0121 - accuracy: 0.9952 - val_loss: 0.2071 - val_accuracy: 0.9000\n", "Epoch 124/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0238 - accuracy: 0.9905 - val_loss: 0.2075 - val_accuracy: 0.8905\n", "Epoch 125/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0116 - accuracy: 0.9964 - val_loss: 0.2133 - val_accuracy: 0.8905\n", "Epoch 126/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0110 - accuracy: 0.9964 - val_loss: 0.2071 - val_accuracy: 0.8952\n", "Epoch 127/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0205 - accuracy: 0.9917 - val_loss: 0.2235 - val_accuracy: 0.8905\n", "Epoch 128/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0206 - accuracy: 0.9929 - val_loss: 0.2254 - val_accuracy: 0.8905\n", "Epoch 129/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0160 - accuracy: 0.9917 - val_loss: 0.2047 - val_accuracy: 0.9000\n", "Epoch 130/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0133 - accuracy: 0.9964 - val_loss: 0.2821 - val_accuracy: 0.8571\n", "Epoch 131/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0191 - accuracy: 0.9929 - val_loss: 0.2045 - val_accuracy: 0.9000\n", "Epoch 132/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0267 - accuracy: 0.9881 - val_loss: 0.2230 - val_accuracy: 0.8952\n", "Epoch 133/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0204 - accuracy: 0.9917 - val_loss: 0.2580 - val_accuracy: 0.8714\n", "Epoch 134/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0109 - accuracy: 0.9976 - val_loss: 0.2428 - val_accuracy: 0.8810\n", "Epoch 135/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0208 - accuracy: 0.9917 - val_loss: 0.2228 - val_accuracy: 0.8952\n", "Epoch 136/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0211 - accuracy: 0.9929 - val_loss: 0.2302 - val_accuracy: 0.8905\n", "Epoch 137/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0207 - accuracy: 0.9905 - val_loss: 0.2313 - val_accuracy: 0.8905\n", "Epoch 138/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0190 - accuracy: 0.9917 - val_loss: 0.2694 - val_accuracy: 0.8667\n", "Epoch 139/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0195 - accuracy: 0.9917 - val_loss: 0.2608 - val_accuracy: 0.8762\n", "Epoch 140/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0140 - accuracy: 0.9940 - val_loss: 0.2059 - val_accuracy: 0.8905\n", "Epoch 141/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0105 - accuracy: 0.9964 - val_loss: 0.2088 - val_accuracy: 0.8952\n", "Epoch 142/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0123 - accuracy: 0.9964 - val_loss: 0.2060 - val_accuracy: 0.8952\n", "Epoch 143/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0084 - accuracy: 0.9964 - val_loss: 0.2315 - val_accuracy: 0.8857\n", "Epoch 144/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0194 - accuracy: 0.9905 - val_loss: 0.2096 - val_accuracy: 0.8905\n", "Epoch 145/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0128 - accuracy: 0.9952 - val_loss: 0.2095 - val_accuracy: 0.8952\n", "Epoch 146/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0154 - accuracy: 0.9940 - val_loss: 0.1991 - val_accuracy: 0.8952\n", "Epoch 147/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0136 - accuracy: 0.9952 - val_loss: 0.2162 - val_accuracy: 0.8905\n", "Epoch 148/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0164 - accuracy: 0.9952 - val_loss: 0.2242 - val_accuracy: 0.8857\n", "Epoch 149/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0316 - accuracy: 0.9881 - val_loss: 0.2291 - val_accuracy: 0.8905\n", "Epoch 150/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0200 - accuracy: 0.9893 - val_loss: 0.2118 - val_accuracy: 0.9000\n", "Epoch 151/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0142 - accuracy: 0.9929 - val_loss: 0.2092 - val_accuracy: 0.8952\n", "Epoch 152/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0205 - accuracy: 0.9917 - val_loss: 0.2274 - val_accuracy: 0.8810\n", "Epoch 153/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0096 - accuracy: 0.9964 - val_loss: 0.2091 - val_accuracy: 0.8952\n", "Epoch 154/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0247 - accuracy: 0.9929 - val_loss: 0.2151 - val_accuracy: 0.8952\n", "Epoch 155/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0081 - accuracy: 0.9976 - val_loss: 0.2247 - val_accuracy: 0.8857\n", "Epoch 156/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0158 - accuracy: 0.9940 - val_loss: 0.2171 - val_accuracy: 0.8857\n", "Epoch 157/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0077 - accuracy: 0.9976 - val_loss: 0.2461 - val_accuracy: 0.8762\n", "Epoch 158/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0091 - accuracy: 0.9964 - val_loss: 0.2304 - val_accuracy: 0.8810\n", "Epoch 159/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0208 - accuracy: 0.9905 - val_loss: 0.1978 - val_accuracy: 0.9048\n", "Epoch 160/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0152 - accuracy: 0.9940 - val_loss: 0.2358 - val_accuracy: 0.8762\n", "Epoch 161/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0159 - accuracy: 0.9940 - val_loss: 0.2226 - val_accuracy: 0.8857\n", "Epoch 162/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0107 - accuracy: 0.9976 - val_loss: 0.2481 - val_accuracy: 0.8762\n", "Epoch 163/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0196 - accuracy: 0.9929 - val_loss: 0.2117 - val_accuracy: 0.8952\n", "Epoch 164/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0100 - accuracy: 0.9952 - val_loss: 0.2340 - val_accuracy: 0.8810\n", "Epoch 165/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0097 - accuracy: 0.9976 - val_loss: 0.2185 - val_accuracy: 0.8952\n", "Epoch 166/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0133 - accuracy: 0.9940 - val_loss: 0.2328 - val_accuracy: 0.8857\n", "Epoch 167/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0151 - accuracy: 0.9940 - val_loss: 0.2370 - val_accuracy: 0.8810\n", "Epoch 168/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0141 - accuracy: 0.9964 - val_loss: 0.2092 - val_accuracy: 0.9048\n", "Epoch 169/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0095 - accuracy: 0.9964 - val_loss: 0.2119 - val_accuracy: 0.9000\n", "Epoch 170/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0188 - accuracy: 0.9929 - val_loss: 0.2215 - val_accuracy: 0.9048\n", "Epoch 171/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0150 - accuracy: 0.9940 - val_loss: 0.2047 - val_accuracy: 0.9000\n", "Epoch 172/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0057 - accuracy: 0.9988 - val_loss: 0.2052 - val_accuracy: 0.8952\n", "Epoch 173/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0145 - accuracy: 0.9940 - val_loss: 0.2208 - val_accuracy: 0.8952\n", "Epoch 174/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0154 - accuracy: 0.9952 - val_loss: 0.2178 - val_accuracy: 0.8952\n", "Epoch 175/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0096 - accuracy: 0.9964 - val_loss: 0.3058 - val_accuracy: 0.8429\n", "Epoch 176/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0113 - accuracy: 0.9952 - val_loss: 0.2206 - val_accuracy: 0.8905\n", "Epoch 177/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0140 - accuracy: 0.9952 - val_loss: 0.2351 - val_accuracy: 0.8810\n", "Epoch 178/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0076 - accuracy: 0.9976 - val_loss: 0.2366 - val_accuracy: 0.8762\n", "Epoch 179/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0085 - accuracy: 0.9976 - val_loss: 0.2405 - val_accuracy: 0.8810\n", "Epoch 180/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0083 - accuracy: 0.9988 - val_loss: 0.2423 - val_accuracy: 0.8810\n", "Epoch 181/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0156 - accuracy: 0.9940 - val_loss: 0.2374 - val_accuracy: 0.8810\n", "Epoch 182/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0098 - accuracy: 0.9952 - val_loss: 0.2259 - val_accuracy: 0.8905\n", "Epoch 183/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0063 - accuracy: 0.9976 - val_loss: 0.2261 - val_accuracy: 0.8952\n", "Epoch 184/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0076 - accuracy: 0.9988 - val_loss: 0.2253 - val_accuracy: 0.8952\n", "Epoch 185/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0129 - accuracy: 0.9952 - val_loss: 0.2380 - val_accuracy: 0.8857\n", "Epoch 186/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0095 - accuracy: 0.9964 - val_loss: 0.2557 - val_accuracy: 0.8667\n", "Epoch 187/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0049 - accuracy: 0.9988 - val_loss: 0.2328 - val_accuracy: 0.8810\n", "Epoch 188/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0154 - accuracy: 0.9940 - val_loss: 0.2455 - val_accuracy: 0.8762\n", "Epoch 189/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0071 - accuracy: 0.9976 - val_loss: 0.2245 - val_accuracy: 0.8905\n", "Epoch 190/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0226 - accuracy: 0.9893 - val_loss: 0.2178 - val_accuracy: 0.8905\n", "Epoch 191/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0074 - accuracy: 0.9964 - val_loss: 0.2286 - val_accuracy: 0.8905\n", "Epoch 192/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0117 - accuracy: 0.9964 - val_loss: 0.2300 - val_accuracy: 0.8905\n", "Epoch 193/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0123 - accuracy: 0.9952 - val_loss: 0.2289 - val_accuracy: 0.8857\n", "Epoch 194/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0077 - accuracy: 0.9964 - val_loss: 0.2526 - val_accuracy: 0.8667\n", "Epoch 195/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0201 - accuracy: 0.9917 - val_loss: 0.2130 - val_accuracy: 0.8952\n", "Epoch 196/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0154 - accuracy: 0.9952 - val_loss: 0.2767 - val_accuracy: 0.8667\n", "Epoch 197/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0101 - accuracy: 0.9964 - val_loss: 0.2121 - val_accuracy: 0.9048\n", "Epoch 198/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0094 - accuracy: 0.9964 - val_loss: 0.2370 - val_accuracy: 0.8810\n", "Epoch 199/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0115 - accuracy: 0.9940 - val_loss: 0.2352 - val_accuracy: 0.8810\n", "Epoch 200/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0094 - accuracy: 0.9952 - val_loss: 0.2056 - val_accuracy: 0.8952\n", "Epoch 201/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0035 - accuracy: 1.0000 - val_loss: 0.2544 - val_accuracy: 0.8762\n", "Epoch 202/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0046 - accuracy: 0.9988 - val_loss: 0.2254 - val_accuracy: 0.8857\n", "Epoch 203/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0061 - accuracy: 0.9976 - val_loss: 0.2347 - val_accuracy: 0.8810\n", "Epoch 204/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0112 - accuracy: 0.9952 - val_loss: 0.2057 - val_accuracy: 0.9000\n", "Epoch 205/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0045 - accuracy: 0.9988 - val_loss: 0.2175 - val_accuracy: 0.8952\n", "Epoch 206/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0083 - accuracy: 0.9952 - val_loss: 0.2167 - val_accuracy: 0.8905\n", "Epoch 207/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0076 - accuracy: 0.9976 - val_loss: 0.2235 - val_accuracy: 0.8857\n", "Epoch 208/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0058 - accuracy: 0.9976 - val_loss: 0.2412 - val_accuracy: 0.8762\n", "Epoch 209/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.2364 - val_accuracy: 0.8762\n", "Epoch 210/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0058 - accuracy: 0.9988 - val_loss: 0.2198 - val_accuracy: 0.8905\n", "Epoch 211/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0137 - accuracy: 0.9952 - val_loss: 0.2128 - val_accuracy: 0.8952\n", "Epoch 212/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0106 - accuracy: 0.9952 - val_loss: 0.2225 - val_accuracy: 0.8857\n", "Epoch 213/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0029 - accuracy: 0.9988 - val_loss: 0.2204 - val_accuracy: 0.8905\n", "Epoch 214/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0027 - accuracy: 0.9988 - val_loss: 0.2276 - val_accuracy: 0.8810\n", "Epoch 215/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0041 - accuracy: 1.0000 - val_loss: 0.2265 - val_accuracy: 0.8857\n", "Epoch 216/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0039 - accuracy: 0.9988 - val_loss: 0.2329 - val_accuracy: 0.8905\n", "Epoch 217/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.2295 - val_accuracy: 0.8857\n", "Epoch 218/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0079 - accuracy: 0.9988 - val_loss: 0.2367 - val_accuracy: 0.8857\n", "Epoch 219/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0086 - accuracy: 0.9964 - val_loss: 0.2575 - val_accuracy: 0.8714\n", "Epoch 220/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0184 - accuracy: 0.9917 - val_loss: 0.2231 - val_accuracy: 0.8857\n", "Epoch 221/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0061 - accuracy: 0.9964 - val_loss: 0.2241 - val_accuracy: 0.8857\n", "Epoch 222/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0061 - accuracy: 0.9976 - val_loss: 0.2344 - val_accuracy: 0.8810\n", "Epoch 223/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0034 - accuracy: 0.9988 - val_loss: 0.2325 - val_accuracy: 0.8857\n", "Epoch 224/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0062 - accuracy: 0.9976 - val_loss: 0.2308 - val_accuracy: 0.8905\n", "Epoch 225/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.2274 - val_accuracy: 0.8857\n", "Epoch 226/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0064 - accuracy: 0.9988 - val_loss: 0.2249 - val_accuracy: 0.8857\n", "Epoch 227/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0029 - accuracy: 1.0000 - val_loss: 0.2424 - val_accuracy: 0.8810\n", "Epoch 228/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0078 - accuracy: 0.9976 - val_loss: 0.2333 - val_accuracy: 0.8857\n", "Epoch 229/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0063 - accuracy: 0.9976 - val_loss: 0.2215 - val_accuracy: 0.8905\n", "Epoch 230/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0040 - accuracy: 0.9988 - val_loss: 0.2206 - val_accuracy: 0.8952\n", "Epoch 231/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0099 - accuracy: 0.9952 - val_loss: 0.2026 - val_accuracy: 0.9000\n", "Epoch 232/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0032 - accuracy: 0.9988 - val_loss: 0.2217 - val_accuracy: 0.8905\n", "Epoch 233/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0056 - accuracy: 0.9976 - val_loss: 0.2113 - val_accuracy: 0.8905\n", "Epoch 234/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0065 - accuracy: 0.9976 - val_loss: 0.2213 - val_accuracy: 0.8857\n", "Epoch 235/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0031 - accuracy: 1.0000 - val_loss: 0.2268 - val_accuracy: 0.8810\n", "Epoch 236/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0126 - accuracy: 0.9940 - val_loss: 0.2294 - val_accuracy: 0.8810\n", "Epoch 237/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0043 - accuracy: 0.9988 - val_loss: 0.2361 - val_accuracy: 0.8810\n", "Epoch 238/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0078 - accuracy: 0.9964 - val_loss: 0.2486 - val_accuracy: 0.8762\n", "Epoch 239/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.2398 - val_accuracy: 0.8762\n", "Epoch 240/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0054 - accuracy: 0.9976 - val_loss: 0.2335 - val_accuracy: 0.8810\n", "Epoch 241/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 7.5092e-04 - accuracy: 1.0000 - val_loss: 0.2373 - val_accuracy: 0.8810\n", "Epoch 242/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0081 - accuracy: 0.9976 - val_loss: 0.2355 - val_accuracy: 0.8857\n", "Epoch 243/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0113 - accuracy: 0.9952 - val_loss: 0.2227 - val_accuracy: 0.8905\n", "Epoch 244/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0059 - accuracy: 0.9976 - val_loss: 0.2280 - val_accuracy: 0.8857\n", "Epoch 245/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0029 - accuracy: 0.9988 - val_loss: 0.2323 - val_accuracy: 0.8810\n", "Epoch 246/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 6.4123e-04 - accuracy: 1.0000 - val_loss: 0.2400 - val_accuracy: 0.8762\n", "Epoch 247/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0062 - accuracy: 0.9976 - val_loss: 0.2522 - val_accuracy: 0.8762\n", "Epoch 248/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0108 - accuracy: 0.9952 - val_loss: 0.2318 - val_accuracy: 0.8857\n", "Epoch 249/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0048 - accuracy: 0.9976 - val_loss: 0.2121 - val_accuracy: 0.8952\n", "Epoch 250/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0184 - accuracy: 0.9940 - val_loss: 0.2197 - val_accuracy: 0.8905\n", "Valid results - Loss: 0.21974822878837585 - Accuracy: 89.04761672019958%\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", 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\n", 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\n", 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" ] }, "metadata": { "tags": [], "needs_background": "light" } }, { "output_type": "stream", "text": [ "DNN results\n", "accuracy : 0.9976190476190476\n", "Training Recall: 0.9976190476190476\n", "Training Precision: 0.9976293103448275\n", "Confusion matrix: \n", "[[376 2]\n", " [ 0 462]]\n", "Validation set report:\n", "cross Loss: -27.968195897908437\n", "hing Loss: 0.5380952380952381\n", "Report precision recall f1-score support\n", "\n", " 0 0.90 0.88 0.89 93\n", " 1 0.91 0.92 0.92 117\n", "\n", " accuracy 0.90 210\n", " macro avg 0.90 0.90 0.90 210\n", "weighted avg 0.90 0.90 0.90 210\n", "\n", "accuracy : 0.9047619047619048\n", "Validation Recall: 0.9047619047619048\n", "Validation Precision: 0.9047003416751317\n", "Confusion matrix: \n", "[[ 82 11]\n", " [ 9 108]]\n", "\n", "\\KNN Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 0.8095238095238095\n", "Validation Recall: 0.8095238095238095\n", "Validation Precision: 0.8160544217687075\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[79 14]\n", " [26 91]]\n", "\n", "\n", "Support vector machine Result\n", "\n", "Train accuracy: 0.861904761904762\n", "Training Recall: 0.861904761904762\n", "Training Precision: 0.8778697836459568\n", "\n", "\n", "Validation accuracy: 0.8142857142857143\n", "Validation Recall: 0.8142857142857143\n", "Validation Precision: 0.8356561888454012\n", "\n", "Train Confusion matrix:\n", " [[274 104]\n", " [ 12 450]]\n", "Validation Confusion matrix:\n", " [[ 59 34]\n", " [ 5 112]]\n", "\n", "\n", "Random Forest Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 0.9333333333333333\n", "Validation Recall: 0.9333333333333333\n", "Validation Precision: 0.9333333333333333\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[ 86 7]\n", " [ 7 110]]\n", "\n", "\n", "LDA Result\n", "\n", "Train accuracy: 0.9916666666666667\n", "Training Recall: 0.9916666666666667\n", "Training Precision: 0.9916674782728256\n", "\n", "\n", "Validation accuracy: 0.7047619047619048\n", "Validation Recall: 0.7047619047619048\n", "Validation Precision: 0.7085267897694094\n", "\n", "Train Confusion matrix:\n", " [[374 4]\n", " [ 3 459]]\n", "Validation Confusion matrix:\n", " [[66 27]\n", " [35 82]]\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "zwiJFgj5H2nk" }, "source": [ "## feature selection on ALL datat - valence labels" ] }, { "cell_type": "code", "metadata": { "id": "5ozjfjoD2-gt", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "6121740a-80ad-4347-acf5-0e2c8e9e75bc" }, "source": [ "#REF for classification\n", "from numpy import mean\n", "from numpy import std\n", "from sklearn.datasets import make_classification\n", "from sklearn.model_selection import cross_val_score\n", "from sklearn.model_selection import RepeatedStratifiedKFold\n", "from sklearn.feature_selection import RFE\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.pipeline import Pipeline\n", "# create pipeline\n", "rfe = RFE(estimator=DecisionTreeClassifier(), n_features_to_select=10)\n", "model = DecisionTreeClassifier()\n", "pipeline = Pipeline(steps=[('s',rfe),('m',model)])\n", "# evaluate model\n", "cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=1, random_state=1)\n", "n_scores = cross_val_score(pipeline, X, y_cat, scoring='accuracy', cv=cv, n_jobs=-1, error_score='raise')\n", "# report performance\n", "print('Accuracy: %.3f (%.3f)' % (mean(n_scores), std(n_scores)))" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Accuracy: 1.000 (0.000)\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "w_gyp6elKEt8", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "02b39152-17dd-4c06-bfa6-446f56b3a9fe" }, "source": [ "# fit the model n_features_to_select=10\n", "rfe.fit(X, y_cat)\n", "# summarize all features\n", "for i in range(X.shape[1]):\n", "\tprint('Column: %d, Selected %s, Rank: %.3f' % (i, rfe.support_[i], rfe.ranking_[i]))" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Column: 0, Selected False, Rank: 2716.000\n", "Column: 1, Selected False, Rank: 2715.000\n", "Column: 2, Selected False, Rank: 2714.000\n", "Column: 3, Selected False, Rank: 2713.000\n", "Column: 4, Selected False, Rank: 2712.000\n", "Column: 5, Selected False, Rank: 2711.000\n", "Column: 6, Selected False, Rank: 2710.000\n", "Column: 7, Selected False, Rank: 2709.000\n", "Column: 8, Selected False, Rank: 2708.000\n", "Column: 9, Selected False, Rank: 2707.000\n", "Column: 10, Selected False, Rank: 2706.000\n", "Column: 11, Selected False, Rank: 2705.000\n", "Column: 12, Selected False, Rank: 2704.000\n", "Column: 13, Selected False, Rank: 2703.000\n", "Column: 14, Selected False, Rank: 2702.000\n", "Column: 15, Selected False, Rank: 2701.000\n", "Column: 16, Selected False, Rank: 2700.000\n", "Column: 17, Selected False, Rank: 2699.000\n", "Column: 18, Selected False, Rank: 2698.000\n", "Column: 19, Selected False, Rank: 2697.000\n", "Column: 20, Selected False, Rank: 2696.000\n", "Column: 21, Selected False, Rank: 2695.000\n", "Column: 22, Selected False, Rank: 2694.000\n", "Column: 23, Selected False, Rank: 2693.000\n", "Column: 24, Selected False, Rank: 2692.000\n", "Column: 25, Selected False, Rank: 2691.000\n", "Column: 26, Selected False, Rank: 2690.000\n", "Column: 27, Selected False, Rank: 2689.000\n", "Column: 28, Selected False, Rank: 2688.000\n", "Column: 29, Selected False, Rank: 2687.000\n", "Column: 30, Selected False, Rank: 2686.000\n", "Column: 31, Selected False, Rank: 2685.000\n", "Column: 32, Selected False, Rank: 2684.000\n", "Column: 33, Selected False, Rank: 2683.000\n", "Column: 34, Selected False, Rank: 2682.000\n", "Column: 35, Selected False, Rank: 2681.000\n", "Column: 36, Selected False, Rank: 2680.000\n", "Column: 37, Selected False, Rank: 2679.000\n", "Column: 38, Selected False, Rank: 2678.000\n", "Column: 39, Selected False, Rank: 2677.000\n", "Column: 40, Selected False, Rank: 2676.000\n", "Column: 41, Selected False, Rank: 2675.000\n", "Column: 42, Selected False, Rank: 2674.000\n", "Column: 43, Selected False, Rank: 2673.000\n", "Column: 44, Selected False, Rank: 2672.000\n", "Column: 45, Selected False, Rank: 2671.000\n", "Column: 46, Selected False, Rank: 2670.000\n", "Column: 47, Selected False, Rank: 2669.000\n", "Column: 48, Selected False, Rank: 2668.000\n", "Column: 49, Selected False, Rank: 2667.000\n", "Column: 50, Selected False, Rank: 2666.000\n", "Column: 51, Selected False, Rank: 2665.000\n", "Column: 52, Selected False, Rank: 2664.000\n", "Column: 53, Selected False, Rank: 2663.000\n", "Column: 54, Selected False, Rank: 2662.000\n", "Column: 55, Selected False, Rank: 2661.000\n", "Column: 56, Selected False, Rank: 2660.000\n", "Column: 57, Selected False, Rank: 2659.000\n", "Column: 58, Selected False, Rank: 2658.000\n", "Column: 59, Selected False, Rank: 2657.000\n", "Column: 60, Selected False, Rank: 2656.000\n", "Column: 61, Selected False, Rank: 2655.000\n", "Column: 62, Selected False, Rank: 2654.000\n", "Column: 63, Selected False, Rank: 2653.000\n", "Column: 64, Selected False, Rank: 2652.000\n", "Column: 65, Selected False, Rank: 2651.000\n", "Column: 66, Selected False, Rank: 2650.000\n", "Column: 67, Selected False, Rank: 2649.000\n", "Column: 68, Selected False, Rank: 2648.000\n", "Column: 69, Selected False, Rank: 2647.000\n", "Column: 70, Selected False, Rank: 2646.000\n", "Column: 71, Selected False, Rank: 2645.000\n", "Column: 72, Selected False, Rank: 2644.000\n", "Column: 73, Selected False, Rank: 2643.000\n", "Column: 74, Selected False, Rank: 2642.000\n", "Column: 75, Selected False, Rank: 2641.000\n", "Column: 76, Selected False, Rank: 2640.000\n", "Column: 77, Selected False, Rank: 2639.000\n", "Column: 78, Selected False, Rank: 2638.000\n", "Column: 79, Selected False, Rank: 2637.000\n", "Column: 80, Selected False, Rank: 2636.000\n", "Column: 81, Selected False, Rank: 2635.000\n", "Column: 82, Selected False, Rank: 2634.000\n", "Column: 83, Selected False, Rank: 2633.000\n", "Column: 84, Selected False, Rank: 2632.000\n", "Column: 85, Selected False, Rank: 2631.000\n", "Column: 86, Selected False, Rank: 2630.000\n", "Column: 87, Selected False, Rank: 2629.000\n", "Column: 88, Selected False, Rank: 2628.000\n", "Column: 89, Selected False, Rank: 2627.000\n", "Column: 90, Selected False, Rank: 2626.000\n", "Column: 91, Selected False, Rank: 2625.000\n", "Column: 92, Selected False, Rank: 2624.000\n", "Column: 93, Selected False, Rank: 2623.000\n", "Column: 94, Selected False, Rank: 2622.000\n", "Column: 95, Selected False, Rank: 2621.000\n", "Column: 96, Selected False, Rank: 2620.000\n", "Column: 97, Selected False, Rank: 2619.000\n", "Column: 98, Selected False, Rank: 2618.000\n", "Column: 99, Selected False, Rank: 2617.000\n", "Column: 100, Selected False, Rank: 2616.000\n", "Column: 101, Selected False, Rank: 2615.000\n", "Column: 102, Selected False, Rank: 2614.000\n", "Column: 103, Selected False, Rank: 2613.000\n", "Column: 104, Selected False, Rank: 2612.000\n", "Column: 105, Selected False, Rank: 2611.000\n", "Column: 106, Selected False, Rank: 2610.000\n", "Column: 107, Selected False, Rank: 2609.000\n", "Column: 108, Selected False, Rank: 2608.000\n", "Column: 109, Selected False, Rank: 2607.000\n", "Column: 110, Selected False, Rank: 2606.000\n", "Column: 111, Selected False, Rank: 2605.000\n", "Column: 112, Selected False, Rank: 2604.000\n", "Column: 113, Selected False, Rank: 2603.000\n", "Column: 114, Selected False, Rank: 2602.000\n", "Column: 115, Selected False, Rank: 2601.000\n", "Column: 116, Selected False, Rank: 2600.000\n", "Column: 117, Selected False, Rank: 2599.000\n", "Column: 118, Selected False, Rank: 2598.000\n", "Column: 119, Selected False, Rank: 2597.000\n", "Column: 120, Selected False, Rank: 2596.000\n", "Column: 121, Selected False, Rank: 2595.000\n", "Column: 122, Selected False, Rank: 2594.000\n", "Column: 123, Selected False, Rank: 2593.000\n", "Column: 124, Selected False, Rank: 2592.000\n", "Column: 125, Selected False, Rank: 2591.000\n", "Column: 126, Selected False, Rank: 2590.000\n", "Column: 127, Selected False, Rank: 2589.000\n", "Column: 128, Selected False, Rank: 2588.000\n", "Column: 129, Selected False, Rank: 2587.000\n", "Column: 130, Selected False, Rank: 2586.000\n", "Column: 131, Selected False, Rank: 2585.000\n", "Column: 132, Selected False, Rank: 2584.000\n", "Column: 133, Selected False, Rank: 2583.000\n", "Column: 134, Selected False, Rank: 2582.000\n", "Column: 135, Selected False, Rank: 2581.000\n", "Column: 136, Selected False, Rank: 2580.000\n", "Column: 137, Selected False, Rank: 2579.000\n", "Column: 138, Selected False, Rank: 2578.000\n", "Column: 139, Selected False, Rank: 2577.000\n", "Column: 140, Selected False, Rank: 2576.000\n", "Column: 141, Selected False, Rank: 2575.000\n", "Column: 142, Selected False, Rank: 2574.000\n", "Column: 143, Selected False, Rank: 2573.000\n", "Column: 144, Selected False, Rank: 2572.000\n", "Column: 145, Selected False, Rank: 2571.000\n", "Column: 146, Selected False, Rank: 2570.000\n", "Column: 147, Selected False, Rank: 2569.000\n", "Column: 148, Selected False, Rank: 2568.000\n", "Column: 149, Selected False, Rank: 2567.000\n", "Column: 150, Selected False, Rank: 2566.000\n", "Column: 151, Selected False, Rank: 2565.000\n", "Column: 152, Selected False, Rank: 2564.000\n", "Column: 153, Selected False, Rank: 2563.000\n", "Column: 154, Selected False, Rank: 2562.000\n", "Column: 155, Selected False, Rank: 2561.000\n", "Column: 156, Selected False, Rank: 2560.000\n", "Column: 157, Selected False, Rank: 2559.000\n", "Column: 158, Selected False, Rank: 2558.000\n", "Column: 159, Selected False, Rank: 2557.000\n", "Column: 160, Selected False, Rank: 2556.000\n", "Column: 161, Selected False, Rank: 2555.000\n", "Column: 162, Selected False, Rank: 2554.000\n", "Column: 163, Selected False, Rank: 2553.000\n", "Column: 164, Selected False, Rank: 2552.000\n", "Column: 165, Selected False, Rank: 2551.000\n", "Column: 166, Selected False, Rank: 2550.000\n", "Column: 167, Selected False, Rank: 2549.000\n", "Column: 168, Selected False, Rank: 2548.000\n", "Column: 169, Selected False, Rank: 2547.000\n", "Column: 170, Selected False, Rank: 2546.000\n", "Column: 171, Selected False, Rank: 2545.000\n", "Column: 172, Selected False, Rank: 2544.000\n", "Column: 173, Selected False, Rank: 2543.000\n", "Column: 174, Selected False, Rank: 2542.000\n", "Column: 175, Selected False, Rank: 2541.000\n", "Column: 176, Selected False, Rank: 2540.000\n", "Column: 177, Selected False, Rank: 2539.000\n", "Column: 178, Selected False, Rank: 2538.000\n", "Column: 179, Selected False, Rank: 2537.000\n", "Column: 180, Selected False, Rank: 2536.000\n", "Column: 181, Selected False, Rank: 2535.000\n", "Column: 182, Selected False, Rank: 2534.000\n", "Column: 183, Selected False, Rank: 2533.000\n", "Column: 184, Selected False, Rank: 2532.000\n", "Column: 185, Selected False, Rank: 2531.000\n", "Column: 186, Selected False, Rank: 2530.000\n", "Column: 187, Selected False, Rank: 2529.000\n", "Column: 188, Selected False, Rank: 2528.000\n", "Column: 189, Selected False, Rank: 2527.000\n", "Column: 190, Selected False, Rank: 2526.000\n", "Column: 191, Selected False, Rank: 2525.000\n", "Column: 192, Selected False, Rank: 2524.000\n", "Column: 193, Selected False, Rank: 2523.000\n", "Column: 194, Selected False, Rank: 2522.000\n", "Column: 195, Selected False, Rank: 2521.000\n", "Column: 196, Selected False, Rank: 2520.000\n", "Column: 197, Selected False, Rank: 2519.000\n", "Column: 198, Selected False, Rank: 2518.000\n", "Column: 199, Selected False, Rank: 2517.000\n", "Column: 200, Selected False, Rank: 2516.000\n", "Column: 201, Selected False, Rank: 2515.000\n", "Column: 202, Selected False, Rank: 2514.000\n", "Column: 203, Selected False, Rank: 2513.000\n", "Column: 204, Selected False, Rank: 2512.000\n", "Column: 205, Selected False, Rank: 2511.000\n", "Column: 206, Selected False, Rank: 2510.000\n", "Column: 207, Selected False, Rank: 2509.000\n", "Column: 208, Selected False, Rank: 2508.000\n", "Column: 209, Selected False, Rank: 2507.000\n", "Column: 210, Selected False, Rank: 2506.000\n", "Column: 211, Selected False, Rank: 2505.000\n", "Column: 212, Selected False, Rank: 2504.000\n", "Column: 213, Selected False, Rank: 2503.000\n", "Column: 214, Selected False, Rank: 2502.000\n", "Column: 215, Selected False, Rank: 2501.000\n", "Column: 216, Selected False, Rank: 2500.000\n", "Column: 217, Selected False, Rank: 2499.000\n", "Column: 218, Selected False, Rank: 2498.000\n", "Column: 219, Selected False, Rank: 2497.000\n", "Column: 220, Selected False, Rank: 2496.000\n", "Column: 221, Selected False, Rank: 2495.000\n", "Column: 222, Selected False, Rank: 2494.000\n", "Column: 223, Selected False, Rank: 2493.000\n", "Column: 224, Selected False, Rank: 2492.000\n", "Column: 225, Selected False, Rank: 2491.000\n", "Column: 226, Selected False, Rank: 2490.000\n", "Column: 227, Selected False, Rank: 2489.000\n", "Column: 228, Selected False, Rank: 2488.000\n", "Column: 229, Selected False, Rank: 2487.000\n", "Column: 230, Selected False, Rank: 2486.000\n", "Column: 231, Selected False, Rank: 2485.000\n", "Column: 232, Selected False, Rank: 2484.000\n", "Column: 233, Selected False, Rank: 2483.000\n", "Column: 234, Selected False, Rank: 2482.000\n", "Column: 235, Selected False, Rank: 2481.000\n", "Column: 236, Selected False, Rank: 2480.000\n", "Column: 237, Selected False, Rank: 2479.000\n", "Column: 238, Selected False, Rank: 2478.000\n", "Column: 239, Selected False, Rank: 2477.000\n", "Column: 240, Selected False, Rank: 2476.000\n", "Column: 241, Selected False, Rank: 2475.000\n", "Column: 242, Selected False, Rank: 2474.000\n", "Column: 243, Selected False, Rank: 2473.000\n", "Column: 244, Selected False, Rank: 2472.000\n", "Column: 245, Selected False, Rank: 2471.000\n", "Column: 246, Selected False, Rank: 2470.000\n", "Column: 247, Selected False, Rank: 2469.000\n", "Column: 248, Selected False, Rank: 2468.000\n", "Column: 249, Selected False, Rank: 2467.000\n", "Column: 250, Selected False, Rank: 2466.000\n", "Column: 251, Selected False, Rank: 2465.000\n", "Column: 252, Selected False, Rank: 2464.000\n", "Column: 253, Selected False, Rank: 2463.000\n", "Column: 254, Selected False, Rank: 2462.000\n", "Column: 255, Selected False, Rank: 2461.000\n", "Column: 256, Selected False, Rank: 2460.000\n", "Column: 257, Selected False, Rank: 2459.000\n", "Column: 258, Selected False, Rank: 2458.000\n", "Column: 259, Selected False, Rank: 2457.000\n", "Column: 260, Selected False, Rank: 2456.000\n", "Column: 261, Selected False, Rank: 2455.000\n", "Column: 262, Selected False, Rank: 2454.000\n", "Column: 263, Selected False, Rank: 2453.000\n", "Column: 264, Selected False, Rank: 2452.000\n", "Column: 265, Selected False, Rank: 2451.000\n", "Column: 266, Selected False, Rank: 2450.000\n", "Column: 267, Selected False, Rank: 2449.000\n", "Column: 268, Selected False, Rank: 2448.000\n", "Column: 269, Selected False, Rank: 2447.000\n", "Column: 270, Selected False, Rank: 2446.000\n", "Column: 271, Selected False, Rank: 2445.000\n", "Column: 272, Selected False, Rank: 2444.000\n", "Column: 273, Selected False, Rank: 2443.000\n", "Column: 274, Selected False, Rank: 2442.000\n", "Column: 275, Selected False, Rank: 2441.000\n", "Column: 276, Selected False, Rank: 2440.000\n", "Column: 277, Selected False, Rank: 2439.000\n", "Column: 278, Selected False, Rank: 2438.000\n", "Column: 279, Selected False, Rank: 2437.000\n", "Column: 280, Selected False, Rank: 2436.000\n", "Column: 281, Selected False, Rank: 2435.000\n", "Column: 282, Selected False, Rank: 2434.000\n", "Column: 283, Selected False, Rank: 2433.000\n", "Column: 284, Selected False, Rank: 2432.000\n", "Column: 285, Selected False, Rank: 2431.000\n", "Column: 286, Selected False, Rank: 2430.000\n", "Column: 287, Selected False, Rank: 2429.000\n", "Column: 288, Selected False, Rank: 2428.000\n", "Column: 289, Selected False, Rank: 2427.000\n", "Column: 290, Selected False, Rank: 2426.000\n", "Column: 291, Selected False, Rank: 2425.000\n", "Column: 292, Selected False, Rank: 2424.000\n", "Column: 293, Selected False, Rank: 2423.000\n", "Column: 294, Selected False, Rank: 2422.000\n", "Column: 295, Selected False, Rank: 2421.000\n", "Column: 296, Selected False, Rank: 2420.000\n", "Column: 297, Selected False, Rank: 2419.000\n", "Column: 298, Selected False, Rank: 2418.000\n", "Column: 299, Selected False, Rank: 2417.000\n", "Column: 300, Selected False, Rank: 2416.000\n", "Column: 301, Selected False, Rank: 2415.000\n", "Column: 302, Selected False, Rank: 2414.000\n", "Column: 303, Selected False, Rank: 2413.000\n", "Column: 304, Selected False, Rank: 2412.000\n", "Column: 305, Selected False, Rank: 2411.000\n", "Column: 306, Selected False, Rank: 2410.000\n", "Column: 307, Selected False, Rank: 2409.000\n", "Column: 308, Selected False, Rank: 2408.000\n", "Column: 309, Selected False, Rank: 2407.000\n", "Column: 310, Selected False, Rank: 2406.000\n", "Column: 311, Selected False, Rank: 2405.000\n", "Column: 312, Selected False, Rank: 2404.000\n", "Column: 313, Selected False, Rank: 2403.000\n", "Column: 314, Selected False, Rank: 2402.000\n", "Column: 315, Selected False, Rank: 2401.000\n", "Column: 316, Selected False, Rank: 2400.000\n", "Column: 317, Selected False, Rank: 2399.000\n", "Column: 318, Selected False, Rank: 2398.000\n", "Column: 319, Selected False, Rank: 2397.000\n", "Column: 320, Selected False, Rank: 2396.000\n", "Column: 321, Selected False, Rank: 2395.000\n", "Column: 322, Selected False, Rank: 2394.000\n", "Column: 323, Selected False, Rank: 2393.000\n", "Column: 324, Selected False, Rank: 2392.000\n", "Column: 325, Selected False, Rank: 2391.000\n", "Column: 326, Selected False, Rank: 2390.000\n", "Column: 327, Selected False, Rank: 2389.000\n", "Column: 328, Selected False, Rank: 2388.000\n", "Column: 329, Selected False, Rank: 2387.000\n", "Column: 330, Selected False, Rank: 2386.000\n", "Column: 331, Selected False, Rank: 2385.000\n", "Column: 332, Selected False, Rank: 2384.000\n", "Column: 333, Selected False, Rank: 2383.000\n", "Column: 334, Selected False, Rank: 2382.000\n", "Column: 335, Selected False, Rank: 2381.000\n", "Column: 336, Selected False, Rank: 2380.000\n", "Column: 337, Selected False, Rank: 2379.000\n", "Column: 338, Selected False, Rank: 2378.000\n", "Column: 339, Selected False, Rank: 2377.000\n", "Column: 340, Selected False, Rank: 2376.000\n", "Column: 341, Selected False, Rank: 2375.000\n", "Column: 342, Selected False, Rank: 2374.000\n", "Column: 343, Selected False, Rank: 2373.000\n", "Column: 344, Selected False, Rank: 2372.000\n", "Column: 345, Selected False, Rank: 2371.000\n", "Column: 346, Selected False, Rank: 2370.000\n", "Column: 347, Selected False, Rank: 2369.000\n", "Column: 348, Selected False, Rank: 2368.000\n", "Column: 349, Selected False, Rank: 2367.000\n", "Column: 350, Selected False, Rank: 2366.000\n", "Column: 351, Selected False, Rank: 2365.000\n", "Column: 352, Selected False, Rank: 2364.000\n", "Column: 353, Selected False, Rank: 2363.000\n", "Column: 354, Selected False, Rank: 2362.000\n", "Column: 355, Selected False, Rank: 2361.000\n", "Column: 356, Selected False, Rank: 2360.000\n", "Column: 357, Selected False, Rank: 2359.000\n", "Column: 358, Selected False, Rank: 2358.000\n", "Column: 359, Selected False, Rank: 2357.000\n", "Column: 360, Selected False, Rank: 2356.000\n", "Column: 361, Selected False, Rank: 2355.000\n", "Column: 362, Selected False, Rank: 2354.000\n", "Column: 363, Selected False, Rank: 2353.000\n", "Column: 364, Selected False, Rank: 2352.000\n", "Column: 365, Selected False, Rank: 2351.000\n", "Column: 366, Selected False, Rank: 2350.000\n", "Column: 367, Selected False, Rank: 2349.000\n", "Column: 368, Selected False, Rank: 2348.000\n", "Column: 369, Selected False, Rank: 2347.000\n", "Column: 370, Selected False, Rank: 2346.000\n", "Column: 371, Selected False, Rank: 2345.000\n", "Column: 372, Selected False, Rank: 2344.000\n", "Column: 373, Selected False, Rank: 2343.000\n", "Column: 374, Selected False, Rank: 2342.000\n", "Column: 375, Selected False, Rank: 2341.000\n", "Column: 376, Selected False, Rank: 2340.000\n", "Column: 377, Selected False, Rank: 2339.000\n", "Column: 378, Selected False, Rank: 2338.000\n", "Column: 379, Selected False, Rank: 2337.000\n", "Column: 380, Selected False, Rank: 2336.000\n", "Column: 381, Selected False, Rank: 2335.000\n", "Column: 382, Selected False, Rank: 2334.000\n", "Column: 383, Selected False, Rank: 2333.000\n", "Column: 384, Selected False, Rank: 2332.000\n", "Column: 385, Selected False, Rank: 2331.000\n", "Column: 386, Selected False, Rank: 2330.000\n", "Column: 387, Selected False, Rank: 2329.000\n", "Column: 388, Selected False, Rank: 2328.000\n", "Column: 389, Selected False, Rank: 2327.000\n", "Column: 390, Selected False, Rank: 2326.000\n", "Column: 391, Selected False, Rank: 2325.000\n", "Column: 392, Selected False, Rank: 2324.000\n", "Column: 393, Selected False, Rank: 2323.000\n", "Column: 394, Selected False, Rank: 2322.000\n", "Column: 395, Selected False, Rank: 2321.000\n", "Column: 396, Selected False, Rank: 2320.000\n", "Column: 397, Selected False, Rank: 2319.000\n", "Column: 398, Selected False, Rank: 2318.000\n", "Column: 399, Selected False, Rank: 2317.000\n", "Column: 400, Selected False, Rank: 2316.000\n", "Column: 401, Selected False, Rank: 2315.000\n", "Column: 402, Selected False, Rank: 2314.000\n", "Column: 403, Selected False, Rank: 2313.000\n", "Column: 404, Selected False, Rank: 2312.000\n", "Column: 405, Selected False, Rank: 2311.000\n", "Column: 406, Selected False, Rank: 2310.000\n", "Column: 407, Selected False, Rank: 2309.000\n", "Column: 408, Selected False, Rank: 2308.000\n", "Column: 409, Selected False, Rank: 2307.000\n", "Column: 410, Selected False, Rank: 2306.000\n", "Column: 411, Selected False, Rank: 2305.000\n", "Column: 412, Selected False, Rank: 2304.000\n", "Column: 413, Selected False, Rank: 2303.000\n", "Column: 414, Selected False, Rank: 2302.000\n", "Column: 415, Selected False, Rank: 2301.000\n", "Column: 416, Selected False, Rank: 2300.000\n", "Column: 417, Selected False, Rank: 2299.000\n", "Column: 418, Selected False, Rank: 2298.000\n", "Column: 419, Selected False, Rank: 2297.000\n", "Column: 420, Selected False, Rank: 2296.000\n", "Column: 421, Selected False, Rank: 2295.000\n", "Column: 422, Selected False, Rank: 2294.000\n", "Column: 423, Selected False, Rank: 2293.000\n", "Column: 424, Selected False, Rank: 2292.000\n", "Column: 425, Selected False, Rank: 2291.000\n", "Column: 426, Selected False, Rank: 2290.000\n", "Column: 427, Selected False, Rank: 2289.000\n", "Column: 428, Selected False, Rank: 2288.000\n", "Column: 429, Selected False, Rank: 2287.000\n", "Column: 430, Selected False, Rank: 2286.000\n", "Column: 431, Selected False, Rank: 2285.000\n", "Column: 432, Selected False, Rank: 2284.000\n", "Column: 433, Selected False, Rank: 2283.000\n", "Column: 434, Selected False, Rank: 2282.000\n", "Column: 435, Selected False, Rank: 2281.000\n", "Column: 436, Selected False, Rank: 2280.000\n", "Column: 437, Selected False, Rank: 2279.000\n", "Column: 438, Selected False, Rank: 2278.000\n", "Column: 439, Selected False, Rank: 2277.000\n", "Column: 440, Selected False, Rank: 2276.000\n", "Column: 441, Selected False, Rank: 2275.000\n", "Column: 442, Selected False, Rank: 2274.000\n", "Column: 443, Selected False, Rank: 2273.000\n", "Column: 444, Selected False, Rank: 2272.000\n", "Column: 445, Selected False, Rank: 2271.000\n", "Column: 446, Selected False, Rank: 2270.000\n", "Column: 447, Selected False, Rank: 2269.000\n", "Column: 448, Selected False, Rank: 2268.000\n", "Column: 449, Selected False, Rank: 2267.000\n", "Column: 450, Selected False, Rank: 2266.000\n", "Column: 451, Selected False, Rank: 2265.000\n", "Column: 452, Selected False, Rank: 2264.000\n", "Column: 453, Selected False, Rank: 2263.000\n", "Column: 454, Selected False, Rank: 2262.000\n", "Column: 455, Selected False, Rank: 2261.000\n", "Column: 456, Selected False, Rank: 2260.000\n", "Column: 457, Selected False, Rank: 2259.000\n", "Column: 458, Selected False, Rank: 2258.000\n", "Column: 459, Selected False, Rank: 2257.000\n", "Column: 460, Selected False, Rank: 2256.000\n", "Column: 461, Selected False, Rank: 2255.000\n", "Column: 462, Selected False, Rank: 2254.000\n", "Column: 463, Selected False, Rank: 2253.000\n", "Column: 464, Selected False, Rank: 2252.000\n", "Column: 465, Selected False, Rank: 2251.000\n", "Column: 466, Selected False, Rank: 2250.000\n", "Column: 467, Selected False, Rank: 2249.000\n", "Column: 468, Selected False, Rank: 2248.000\n", "Column: 469, Selected False, Rank: 2247.000\n", "Column: 470, Selected False, Rank: 2246.000\n", "Column: 471, Selected False, Rank: 2245.000\n", "Column: 472, Selected False, Rank: 2244.000\n", "Column: 473, Selected False, Rank: 2243.000\n", "Column: 474, Selected False, Rank: 2242.000\n", "Column: 475, Selected False, Rank: 2241.000\n", "Column: 476, Selected False, Rank: 2240.000\n", "Column: 477, Selected False, Rank: 2239.000\n", "Column: 478, Selected False, Rank: 2238.000\n", "Column: 479, Selected False, Rank: 2237.000\n", "Column: 480, Selected False, Rank: 2236.000\n", "Column: 481, Selected False, Rank: 2235.000\n", "Column: 482, Selected False, Rank: 2234.000\n", "Column: 483, Selected False, Rank: 2233.000\n", "Column: 484, Selected False, Rank: 2232.000\n", "Column: 485, Selected False, Rank: 2231.000\n", "Column: 486, Selected False, Rank: 2230.000\n", "Column: 487, Selected False, Rank: 2229.000\n", "Column: 488, Selected False, Rank: 2228.000\n", "Column: 489, Selected False, Rank: 2227.000\n", "Column: 490, Selected False, Rank: 2226.000\n", "Column: 491, Selected False, Rank: 2225.000\n", "Column: 492, Selected False, Rank: 2224.000\n", "Column: 493, Selected False, Rank: 2223.000\n", "Column: 494, Selected False, Rank: 2222.000\n", "Column: 495, Selected False, Rank: 2221.000\n", "Column: 496, Selected False, Rank: 2220.000\n", "Column: 497, Selected False, Rank: 2219.000\n", "Column: 498, Selected False, Rank: 2218.000\n", "Column: 499, Selected False, Rank: 2217.000\n", "Column: 500, Selected False, Rank: 2216.000\n", "Column: 501, Selected False, Rank: 2215.000\n", "Column: 502, Selected False, Rank: 2214.000\n", "Column: 503, Selected False, Rank: 2213.000\n", "Column: 504, Selected False, Rank: 2212.000\n", "Column: 505, Selected False, Rank: 2211.000\n", "Column: 506, Selected False, Rank: 2210.000\n", "Column: 507, Selected False, Rank: 2209.000\n", "Column: 508, Selected False, Rank: 2208.000\n", "Column: 509, Selected False, Rank: 2207.000\n", "Column: 510, Selected False, Rank: 2206.000\n", "Column: 511, Selected False, Rank: 2205.000\n", "Column: 512, Selected False, Rank: 2204.000\n", "Column: 513, Selected False, Rank: 2203.000\n", "Column: 514, Selected False, Rank: 2202.000\n", "Column: 515, Selected False, Rank: 2201.000\n", "Column: 516, Selected False, Rank: 2200.000\n", "Column: 517, Selected False, Rank: 2199.000\n", "Column: 518, Selected False, Rank: 2198.000\n", "Column: 519, Selected False, Rank: 2197.000\n", "Column: 520, Selected False, Rank: 2196.000\n", "Column: 521, Selected False, Rank: 2195.000\n", "Column: 522, Selected False, Rank: 2194.000\n", "Column: 523, Selected False, Rank: 2193.000\n", "Column: 524, Selected False, Rank: 2192.000\n", "Column: 525, Selected False, Rank: 2191.000\n", "Column: 526, Selected False, Rank: 2190.000\n", "Column: 527, Selected False, Rank: 2189.000\n", "Column: 528, Selected False, Rank: 2188.000\n", "Column: 529, Selected False, Rank: 2187.000\n", "Column: 530, Selected False, Rank: 2186.000\n", "Column: 531, Selected False, Rank: 2185.000\n", "Column: 532, Selected False, Rank: 2184.000\n", "Column: 533, Selected False, Rank: 2183.000\n", "Column: 534, Selected False, Rank: 2182.000\n", "Column: 535, Selected False, Rank: 2181.000\n", "Column: 536, Selected False, Rank: 2180.000\n", "Column: 537, Selected False, Rank: 2179.000\n", "Column: 538, Selected False, Rank: 2178.000\n", "Column: 539, Selected False, Rank: 2177.000\n", "Column: 540, Selected False, Rank: 2176.000\n", "Column: 541, Selected False, Rank: 2175.000\n", "Column: 542, Selected False, Rank: 2174.000\n", "Column: 543, Selected False, Rank: 2173.000\n", "Column: 544, Selected False, Rank: 2172.000\n", "Column: 545, Selected False, Rank: 2171.000\n", "Column: 546, Selected False, Rank: 2170.000\n", "Column: 547, Selected False, Rank: 2169.000\n", "Column: 548, Selected False, Rank: 2168.000\n", "Column: 549, Selected False, Rank: 2167.000\n", "Column: 550, Selected False, Rank: 2166.000\n", "Column: 551, Selected False, Rank: 2165.000\n", "Column: 552, Selected False, Rank: 2164.000\n", "Column: 553, Selected False, Rank: 2163.000\n", "Column: 554, Selected False, Rank: 2162.000\n", "Column: 555, Selected False, Rank: 2161.000\n", "Column: 556, Selected False, Rank: 2160.000\n", "Column: 557, Selected False, Rank: 2159.000\n", "Column: 558, Selected False, Rank: 2158.000\n", "Column: 559, Selected False, Rank: 2157.000\n", "Column: 560, Selected False, Rank: 2156.000\n", "Column: 561, Selected False, Rank: 2155.000\n", "Column: 562, Selected False, Rank: 2154.000\n", "Column: 563, Selected False, Rank: 2153.000\n", "Column: 564, Selected False, Rank: 2152.000\n", "Column: 565, Selected False, Rank: 2151.000\n", "Column: 566, Selected False, Rank: 2150.000\n", "Column: 567, Selected False, Rank: 2149.000\n", "Column: 568, Selected False, Rank: 2148.000\n", "Column: 569, Selected False, Rank: 2147.000\n", "Column: 570, Selected False, Rank: 2146.000\n", "Column: 571, Selected False, Rank: 2145.000\n", "Column: 572, Selected False, Rank: 2144.000\n", "Column: 573, Selected False, Rank: 2143.000\n", "Column: 574, Selected False, Rank: 2142.000\n", "Column: 575, Selected False, Rank: 2141.000\n", "Column: 576, Selected False, Rank: 2140.000\n", "Column: 577, Selected False, Rank: 2139.000\n", "Column: 578, Selected False, Rank: 2138.000\n", "Column: 579, Selected False, Rank: 2137.000\n", "Column: 580, Selected False, Rank: 2136.000\n", "Column: 581, Selected False, Rank: 2135.000\n", "Column: 582, Selected False, Rank: 2134.000\n", "Column: 583, Selected False, Rank: 2133.000\n", "Column: 584, Selected False, Rank: 2132.000\n", "Column: 585, Selected False, Rank: 2131.000\n", "Column: 586, Selected False, Rank: 2130.000\n", "Column: 587, Selected False, Rank: 2129.000\n", "Column: 588, Selected False, Rank: 2128.000\n", "Column: 589, Selected False, Rank: 2127.000\n", "Column: 590, Selected False, Rank: 2126.000\n", "Column: 591, Selected False, Rank: 2125.000\n", "Column: 592, Selected False, Rank: 2124.000\n", "Column: 593, Selected False, Rank: 2123.000\n", "Column: 594, Selected False, Rank: 2122.000\n", "Column: 595, Selected False, Rank: 2121.000\n", "Column: 596, Selected False, Rank: 2120.000\n", "Column: 597, Selected False, Rank: 2119.000\n", "Column: 598, Selected False, Rank: 2118.000\n", "Column: 599, Selected False, Rank: 2117.000\n", "Column: 600, Selected False, Rank: 2116.000\n", "Column: 601, Selected False, Rank: 2115.000\n", "Column: 602, Selected False, Rank: 2114.000\n", "Column: 603, Selected False, Rank: 2113.000\n", "Column: 604, Selected False, Rank: 2112.000\n", "Column: 605, Selected False, Rank: 2111.000\n", "Column: 606, Selected False, Rank: 2110.000\n", "Column: 607, Selected False, Rank: 2109.000\n", "Column: 608, Selected False, Rank: 2108.000\n", "Column: 609, Selected False, Rank: 2107.000\n", "Column: 610, Selected False, Rank: 2106.000\n", "Column: 611, Selected False, Rank: 2105.000\n", "Column: 612, Selected False, Rank: 2104.000\n", "Column: 613, Selected False, Rank: 2103.000\n", "Column: 614, Selected False, Rank: 2102.000\n", "Column: 615, Selected False, Rank: 2101.000\n", "Column: 616, Selected False, Rank: 2100.000\n", "Column: 617, Selected False, Rank: 2099.000\n", "Column: 618, Selected False, Rank: 2098.000\n", "Column: 619, Selected False, Rank: 2097.000\n", "Column: 620, Selected False, Rank: 2096.000\n", "Column: 621, Selected False, Rank: 2095.000\n", "Column: 622, Selected False, Rank: 2094.000\n", "Column: 623, Selected False, Rank: 2093.000\n", "Column: 624, Selected False, Rank: 2092.000\n", "Column: 625, Selected False, Rank: 2091.000\n", "Column: 626, Selected False, Rank: 2090.000\n", "Column: 627, Selected False, Rank: 2089.000\n", "Column: 628, Selected False, Rank: 2088.000\n", "Column: 629, Selected False, Rank: 2087.000\n", "Column: 630, Selected False, Rank: 2086.000\n", "Column: 631, Selected False, Rank: 2085.000\n", "Column: 632, Selected False, Rank: 2084.000\n", "Column: 633, Selected False, Rank: 2083.000\n", "Column: 634, Selected False, Rank: 2082.000\n", "Column: 635, Selected False, Rank: 2081.000\n", "Column: 636, Selected False, Rank: 2080.000\n", "Column: 637, Selected False, Rank: 2079.000\n", "Column: 638, Selected False, Rank: 2078.000\n", "Column: 639, Selected False, Rank: 2077.000\n", "Column: 640, Selected False, Rank: 2076.000\n", "Column: 641, Selected False, Rank: 2075.000\n", "Column: 642, Selected False, Rank: 2074.000\n", "Column: 643, Selected False, Rank: 2073.000\n", "Column: 644, Selected False, Rank: 2072.000\n", "Column: 645, Selected False, Rank: 2071.000\n", "Column: 646, Selected False, Rank: 2070.000\n", "Column: 647, Selected False, Rank: 2069.000\n", "Column: 648, Selected False, Rank: 2068.000\n", "Column: 649, Selected False, Rank: 2067.000\n", "Column: 650, Selected False, Rank: 2066.000\n", "Column: 651, Selected False, Rank: 2065.000\n", "Column: 652, Selected False, Rank: 2064.000\n", "Column: 653, Selected False, Rank: 2063.000\n", "Column: 654, Selected False, Rank: 2062.000\n", "Column: 655, Selected False, Rank: 2061.000\n", "Column: 656, Selected False, Rank: 2060.000\n", "Column: 657, Selected False, Rank: 2059.000\n", "Column: 658, Selected False, Rank: 2058.000\n", "Column: 659, Selected False, Rank: 2057.000\n", "Column: 660, Selected False, Rank: 2056.000\n", "Column: 661, Selected False, Rank: 2055.000\n", "Column: 662, Selected False, Rank: 2054.000\n", "Column: 663, Selected False, Rank: 2053.000\n", "Column: 664, Selected False, Rank: 2052.000\n", "Column: 665, Selected False, Rank: 2051.000\n", "Column: 666, Selected False, Rank: 2050.000\n", "Column: 667, Selected False, Rank: 2049.000\n", "Column: 668, Selected False, Rank: 2048.000\n", "Column: 669, Selected False, Rank: 2047.000\n", "Column: 670, Selected False, Rank: 2046.000\n", "Column: 671, Selected False, Rank: 2045.000\n", "Column: 672, Selected False, Rank: 2044.000\n", "Column: 673, Selected False, Rank: 2043.000\n", "Column: 674, Selected False, Rank: 2042.000\n", "Column: 675, Selected False, Rank: 2041.000\n", "Column: 676, Selected False, Rank: 2040.000\n", "Column: 677, Selected False, Rank: 2039.000\n", "Column: 678, Selected False, Rank: 2038.000\n", "Column: 679, Selected False, Rank: 2037.000\n", "Column: 680, Selected False, Rank: 2036.000\n", "Column: 681, Selected False, Rank: 2035.000\n", "Column: 682, Selected False, Rank: 2034.000\n", "Column: 683, Selected False, Rank: 2033.000\n", "Column: 684, Selected False, Rank: 2032.000\n", "Column: 685, Selected False, Rank: 2031.000\n", "Column: 686, Selected False, Rank: 2030.000\n", "Column: 687, Selected False, Rank: 2029.000\n", "Column: 688, Selected False, Rank: 2028.000\n", "Column: 689, Selected False, Rank: 2027.000\n", "Column: 690, Selected False, Rank: 2026.000\n", "Column: 691, Selected False, Rank: 2025.000\n", "Column: 692, Selected False, Rank: 2024.000\n", "Column: 693, Selected False, Rank: 2023.000\n", "Column: 694, Selected False, Rank: 2022.000\n", "Column: 695, Selected False, Rank: 2021.000\n", "Column: 696, Selected False, Rank: 2020.000\n", "Column: 697, Selected False, Rank: 2019.000\n", "Column: 698, Selected False, Rank: 2018.000\n", "Column: 699, Selected False, Rank: 2017.000\n", "Column: 700, Selected False, Rank: 2016.000\n", "Column: 701, Selected False, Rank: 2015.000\n", "Column: 702, Selected False, Rank: 2014.000\n", "Column: 703, Selected False, Rank: 2013.000\n", "Column: 704, Selected False, Rank: 2012.000\n", "Column: 705, Selected False, Rank: 2011.000\n", "Column: 706, Selected False, Rank: 2010.000\n", "Column: 707, Selected False, Rank: 2009.000\n", "Column: 708, Selected False, Rank: 2008.000\n", "Column: 709, Selected False, Rank: 2007.000\n", "Column: 710, Selected False, Rank: 2006.000\n", "Column: 711, Selected False, Rank: 2005.000\n", "Column: 712, Selected False, Rank: 2004.000\n", "Column: 713, Selected False, Rank: 2003.000\n", "Column: 714, Selected False, Rank: 2002.000\n", "Column: 715, Selected False, Rank: 2001.000\n", "Column: 716, Selected False, Rank: 2000.000\n", "Column: 717, Selected False, Rank: 1999.000\n", "Column: 718, Selected False, Rank: 1998.000\n", "Column: 719, Selected False, Rank: 1997.000\n", "Column: 720, Selected False, Rank: 1996.000\n", "Column: 721, Selected False, Rank: 1995.000\n", "Column: 722, Selected False, Rank: 1994.000\n", "Column: 723, Selected False, Rank: 1993.000\n", "Column: 724, Selected False, Rank: 1992.000\n", "Column: 725, Selected False, Rank: 1991.000\n", "Column: 726, Selected False, Rank: 1990.000\n", "Column: 727, Selected False, Rank: 1989.000\n", "Column: 728, Selected False, Rank: 1988.000\n", "Column: 729, Selected False, Rank: 1987.000\n", "Column: 730, Selected False, Rank: 1986.000\n", "Column: 731, Selected False, Rank: 1985.000\n", "Column: 732, Selected False, Rank: 1984.000\n", "Column: 733, Selected False, Rank: 1983.000\n", "Column: 734, Selected False, Rank: 1982.000\n", "Column: 735, Selected False, Rank: 1981.000\n", "Column: 736, Selected False, Rank: 1980.000\n", "Column: 737, Selected False, Rank: 1979.000\n", "Column: 738, Selected False, Rank: 1978.000\n", "Column: 739, Selected False, Rank: 1977.000\n", "Column: 740, Selected False, Rank: 1976.000\n", "Column: 741, Selected False, Rank: 1975.000\n", "Column: 742, Selected False, Rank: 1974.000\n", "Column: 743, Selected False, Rank: 1973.000\n", "Column: 744, Selected False, Rank: 1972.000\n", "Column: 745, Selected False, Rank: 1971.000\n", "Column: 746, Selected False, Rank: 1970.000\n", "Column: 747, Selected False, Rank: 1969.000\n", "Column: 748, Selected False, Rank: 1968.000\n", "Column: 749, Selected False, Rank: 1967.000\n", "Column: 750, Selected False, Rank: 1966.000\n", "Column: 751, Selected False, Rank: 1965.000\n", "Column: 752, Selected False, Rank: 1964.000\n", "Column: 753, Selected False, Rank: 1963.000\n", "Column: 754, Selected False, Rank: 1962.000\n", "Column: 755, Selected False, Rank: 1961.000\n", "Column: 756, Selected False, Rank: 1960.000\n", "Column: 757, Selected False, Rank: 1959.000\n", "Column: 758, Selected False, Rank: 1958.000\n", "Column: 759, Selected False, Rank: 1957.000\n", "Column: 760, Selected False, Rank: 1956.000\n", "Column: 761, Selected False, Rank: 1955.000\n", "Column: 762, Selected False, Rank: 1954.000\n", "Column: 763, Selected False, Rank: 1953.000\n", "Column: 764, Selected False, Rank: 1952.000\n", "Column: 765, Selected False, Rank: 1951.000\n", "Column: 766, Selected False, Rank: 1950.000\n", "Column: 767, Selected False, Rank: 1949.000\n", "Column: 768, Selected False, Rank: 1948.000\n", "Column: 769, Selected False, Rank: 1947.000\n", "Column: 770, Selected False, Rank: 1946.000\n", "Column: 771, Selected False, Rank: 1945.000\n", "Column: 772, Selected False, Rank: 1944.000\n", "Column: 773, Selected False, Rank: 1943.000\n", "Column: 774, Selected False, Rank: 1942.000\n", "Column: 775, Selected False, Rank: 1941.000\n", "Column: 776, Selected False, Rank: 1940.000\n", "Column: 777, Selected False, Rank: 1939.000\n", "Column: 778, Selected False, Rank: 1938.000\n", "Column: 779, Selected False, Rank: 1937.000\n", "Column: 780, Selected False, Rank: 1936.000\n", "Column: 781, Selected False, Rank: 1935.000\n", "Column: 782, Selected False, Rank: 1934.000\n", "Column: 783, Selected False, Rank: 1933.000\n", "Column: 784, Selected False, Rank: 1932.000\n", "Column: 785, Selected False, Rank: 1931.000\n", "Column: 786, Selected False, Rank: 1930.000\n", "Column: 787, Selected False, Rank: 1929.000\n", "Column: 788, Selected False, Rank: 1928.000\n", "Column: 789, Selected False, Rank: 1927.000\n", "Column: 790, Selected False, Rank: 1926.000\n", "Column: 791, Selected False, Rank: 1925.000\n", "Column: 792, Selected False, Rank: 1924.000\n", "Column: 793, Selected False, Rank: 1923.000\n", "Column: 794, Selected False, Rank: 1922.000\n", "Column: 795, Selected False, Rank: 1921.000\n", "Column: 796, Selected False, Rank: 1920.000\n", "Column: 797, Selected False, Rank: 1919.000\n", "Column: 798, Selected False, Rank: 1918.000\n", "Column: 799, Selected False, Rank: 1917.000\n", "Column: 800, Selected False, Rank: 1916.000\n", "Column: 801, Selected False, Rank: 1915.000\n", "Column: 802, Selected False, Rank: 1914.000\n", "Column: 803, Selected False, Rank: 1913.000\n", "Column: 804, Selected False, Rank: 1912.000\n", "Column: 805, Selected False, Rank: 1911.000\n", "Column: 806, Selected False, Rank: 1910.000\n", "Column: 807, Selected False, Rank: 1909.000\n", "Column: 808, Selected False, Rank: 1908.000\n", "Column: 809, Selected False, Rank: 1907.000\n", "Column: 810, Selected False, Rank: 1906.000\n", "Column: 811, Selected False, Rank: 1905.000\n", "Column: 812, Selected False, Rank: 1904.000\n", "Column: 813, Selected False, Rank: 1903.000\n", "Column: 814, Selected False, Rank: 1902.000\n", "Column: 815, Selected False, Rank: 1901.000\n", "Column: 816, Selected False, Rank: 1900.000\n", "Column: 817, Selected False, Rank: 1899.000\n", "Column: 818, Selected False, Rank: 1898.000\n", "Column: 819, Selected False, Rank: 1897.000\n", "Column: 820, Selected False, Rank: 1896.000\n", "Column: 821, Selected False, Rank: 1895.000\n", "Column: 822, Selected False, Rank: 1894.000\n", "Column: 823, Selected False, Rank: 1893.000\n", "Column: 824, Selected False, Rank: 1892.000\n", "Column: 825, Selected False, Rank: 1891.000\n", "Column: 826, Selected False, Rank: 1890.000\n", "Column: 827, Selected False, Rank: 1889.000\n", "Column: 828, Selected False, Rank: 1888.000\n", "Column: 829, Selected False, Rank: 1887.000\n", "Column: 830, Selected False, Rank: 1886.000\n", "Column: 831, Selected False, Rank: 1885.000\n", "Column: 832, Selected False, Rank: 1884.000\n", "Column: 833, Selected False, Rank: 1883.000\n", "Column: 834, Selected False, Rank: 1882.000\n", "Column: 835, Selected False, Rank: 1881.000\n", "Column: 836, Selected False, Rank: 1880.000\n", "Column: 837, Selected False, Rank: 1879.000\n", "Column: 838, Selected False, Rank: 1878.000\n", "Column: 839, Selected False, Rank: 1877.000\n", "Column: 840, Selected False, Rank: 1876.000\n", "Column: 841, Selected False, Rank: 1875.000\n", "Column: 842, Selected False, Rank: 1874.000\n", "Column: 843, Selected False, Rank: 1873.000\n", "Column: 844, Selected False, Rank: 1872.000\n", "Column: 845, Selected False, Rank: 1871.000\n", "Column: 846, Selected False, Rank: 1870.000\n", "Column: 847, Selected False, Rank: 1869.000\n", "Column: 848, Selected False, Rank: 1868.000\n", "Column: 849, Selected False, Rank: 1867.000\n", "Column: 850, Selected False, Rank: 1866.000\n", "Column: 851, Selected False, Rank: 1865.000\n", "Column: 852, Selected False, Rank: 1864.000\n", "Column: 853, Selected False, Rank: 1863.000\n", "Column: 854, Selected False, Rank: 1862.000\n", "Column: 855, Selected False, Rank: 1861.000\n", "Column: 856, Selected False, Rank: 1860.000\n", "Column: 857, Selected False, Rank: 1859.000\n", "Column: 858, Selected False, Rank: 1858.000\n", "Column: 859, Selected False, Rank: 1857.000\n", "Column: 860, Selected False, Rank: 1856.000\n", "Column: 861, Selected False, Rank: 1855.000\n", "Column: 862, Selected False, Rank: 1854.000\n", "Column: 863, Selected False, Rank: 1853.000\n", "Column: 864, Selected False, Rank: 1852.000\n", "Column: 865, Selected False, Rank: 1851.000\n", "Column: 866, Selected False, Rank: 1850.000\n", "Column: 867, Selected False, Rank: 1849.000\n", "Column: 868, Selected False, Rank: 1848.000\n", "Column: 869, Selected False, Rank: 1847.000\n", "Column: 870, Selected False, Rank: 1846.000\n", "Column: 871, Selected False, Rank: 1845.000\n", "Column: 872, Selected False, Rank: 1844.000\n", "Column: 873, Selected False, Rank: 1843.000\n", "Column: 874, Selected False, Rank: 1842.000\n", "Column: 875, Selected False, Rank: 1841.000\n", "Column: 876, Selected False, Rank: 1840.000\n", "Column: 877, Selected False, Rank: 1839.000\n", "Column: 878, Selected False, Rank: 1838.000\n", "Column: 879, Selected False, Rank: 1837.000\n", "Column: 880, Selected False, Rank: 1836.000\n", "Column: 881, Selected False, Rank: 1835.000\n", "Column: 882, Selected False, Rank: 1834.000\n", "Column: 883, Selected False, Rank: 1833.000\n", "Column: 884, Selected False, Rank: 1832.000\n", "Column: 885, Selected False, Rank: 1831.000\n", "Column: 886, Selected False, Rank: 1830.000\n", "Column: 887, Selected False, Rank: 1829.000\n", "Column: 888, Selected False, Rank: 1828.000\n", "Column: 889, Selected False, Rank: 1827.000\n", "Column: 890, Selected False, Rank: 1826.000\n", "Column: 891, Selected False, Rank: 1825.000\n", "Column: 892, Selected False, Rank: 1824.000\n", "Column: 893, Selected False, Rank: 1823.000\n", "Column: 894, Selected False, Rank: 1822.000\n", "Column: 895, Selected False, Rank: 1821.000\n", "Column: 896, Selected False, Rank: 1820.000\n", "Column: 897, Selected False, Rank: 1819.000\n", "Column: 898, Selected False, Rank: 1818.000\n", "Column: 899, Selected False, Rank: 1817.000\n", "Column: 900, Selected False, Rank: 1816.000\n", "Column: 901, Selected False, Rank: 1815.000\n", "Column: 902, Selected False, Rank: 1814.000\n", "Column: 903, Selected False, Rank: 1813.000\n", "Column: 904, Selected False, Rank: 1812.000\n", "Column: 905, Selected False, Rank: 1811.000\n", "Column: 906, Selected False, Rank: 1810.000\n", "Column: 907, Selected False, Rank: 1809.000\n", "Column: 908, Selected False, Rank: 1808.000\n", "Column: 909, Selected False, Rank: 1807.000\n", "Column: 910, Selected False, Rank: 1806.000\n", "Column: 911, Selected False, Rank: 1805.000\n", "Column: 912, Selected False, Rank: 1804.000\n", "Column: 913, Selected False, Rank: 1803.000\n", "Column: 914, Selected False, Rank: 1802.000\n", "Column: 915, Selected False, Rank: 1801.000\n", "Column: 916, Selected False, Rank: 1800.000\n", "Column: 917, Selected False, Rank: 1799.000\n", "Column: 918, Selected False, Rank: 1798.000\n", "Column: 919, Selected False, Rank: 1797.000\n", "Column: 920, Selected False, Rank: 1796.000\n", "Column: 921, Selected False, Rank: 1795.000\n", "Column: 922, Selected False, Rank: 1794.000\n", "Column: 923, Selected False, Rank: 1793.000\n", "Column: 924, Selected False, Rank: 1792.000\n", "Column: 925, Selected False, Rank: 1791.000\n", "Column: 926, Selected False, Rank: 1790.000\n", "Column: 927, Selected False, Rank: 1789.000\n", "Column: 928, Selected False, Rank: 1788.000\n", "Column: 929, Selected False, Rank: 1787.000\n", "Column: 930, Selected False, Rank: 1786.000\n", "Column: 931, Selected False, Rank: 1785.000\n", "Column: 932, Selected False, Rank: 1784.000\n", "Column: 933, Selected False, Rank: 1783.000\n", "Column: 934, Selected False, Rank: 1782.000\n", "Column: 935, Selected False, Rank: 1781.000\n", "Column: 936, Selected False, Rank: 1780.000\n", "Column: 937, Selected False, Rank: 1779.000\n", "Column: 938, Selected False, Rank: 1778.000\n", "Column: 939, Selected False, Rank: 1777.000\n", "Column: 940, Selected False, Rank: 1776.000\n", "Column: 941, Selected False, Rank: 1775.000\n", "Column: 942, Selected False, Rank: 1774.000\n", "Column: 943, Selected False, Rank: 1773.000\n", "Column: 944, Selected False, Rank: 1772.000\n", "Column: 945, Selected False, Rank: 1771.000\n", "Column: 946, Selected False, Rank: 1770.000\n", "Column: 947, Selected False, Rank: 1769.000\n", "Column: 948, Selected False, Rank: 1768.000\n", "Column: 949, Selected False, Rank: 1767.000\n", "Column: 950, Selected False, Rank: 1766.000\n", "Column: 951, Selected False, Rank: 1765.000\n", "Column: 952, Selected False, Rank: 1764.000\n", "Column: 953, Selected False, Rank: 1763.000\n", "Column: 954, Selected False, Rank: 1762.000\n", "Column: 955, Selected False, Rank: 1761.000\n", "Column: 956, Selected False, Rank: 1760.000\n", "Column: 957, Selected False, Rank: 1759.000\n", "Column: 958, Selected False, Rank: 1758.000\n", "Column: 959, Selected False, Rank: 1757.000\n", "Column: 960, Selected False, Rank: 1756.000\n", "Column: 961, Selected False, Rank: 1755.000\n", "Column: 962, Selected False, Rank: 1754.000\n", "Column: 963, Selected False, Rank: 1753.000\n", "Column: 964, Selected False, Rank: 1752.000\n", "Column: 965, Selected False, Rank: 1751.000\n", "Column: 966, Selected False, Rank: 1750.000\n", "Column: 967, Selected False, Rank: 1749.000\n", "Column: 968, Selected False, Rank: 1748.000\n", "Column: 969, Selected False, Rank: 1747.000\n", "Column: 970, Selected False, Rank: 1746.000\n", "Column: 971, Selected False, Rank: 1745.000\n", "Column: 972, Selected False, Rank: 1744.000\n", "Column: 973, Selected False, Rank: 1743.000\n", "Column: 974, Selected False, Rank: 1742.000\n", "Column: 975, Selected False, Rank: 1741.000\n", "Column: 976, Selected False, Rank: 1740.000\n", "Column: 977, Selected False, Rank: 1739.000\n", "Column: 978, Selected False, Rank: 1738.000\n", "Column: 979, Selected False, Rank: 1737.000\n", "Column: 980, Selected False, Rank: 1736.000\n", "Column: 981, Selected False, Rank: 1735.000\n", "Column: 982, Selected False, Rank: 1734.000\n", "Column: 983, Selected False, Rank: 1733.000\n", "Column: 984, Selected False, Rank: 1732.000\n", "Column: 985, Selected False, Rank: 1731.000\n", "Column: 986, Selected False, Rank: 1730.000\n", "Column: 987, Selected False, Rank: 1729.000\n", "Column: 988, Selected False, Rank: 1728.000\n", "Column: 989, Selected False, Rank: 1727.000\n", "Column: 990, Selected False, Rank: 1726.000\n", "Column: 991, Selected False, Rank: 1725.000\n", "Column: 992, Selected False, Rank: 1724.000\n", "Column: 993, Selected False, Rank: 1723.000\n", "Column: 994, Selected False, Rank: 1722.000\n", "Column: 995, Selected False, Rank: 1721.000\n", "Column: 996, Selected False, Rank: 1720.000\n", "Column: 997, Selected False, Rank: 1719.000\n", "Column: 998, Selected False, Rank: 1718.000\n", "Column: 999, Selected False, Rank: 1717.000\n", "Column: 1000, Selected False, Rank: 1716.000\n", "Column: 1001, Selected False, Rank: 1715.000\n", "Column: 1002, Selected False, Rank: 1714.000\n", "Column: 1003, Selected False, Rank: 1713.000\n", "Column: 1004, Selected False, Rank: 1712.000\n", "Column: 1005, Selected False, Rank: 1711.000\n", "Column: 1006, Selected False, Rank: 1710.000\n", "Column: 1007, Selected False, Rank: 1709.000\n", "Column: 1008, Selected False, Rank: 1708.000\n", "Column: 1009, Selected False, Rank: 1707.000\n", "Column: 1010, Selected False, Rank: 1706.000\n", "Column: 1011, Selected False, Rank: 1705.000\n", "Column: 1012, Selected False, Rank: 1704.000\n", "Column: 1013, Selected False, Rank: 1703.000\n", "Column: 1014, Selected False, Rank: 1702.000\n", "Column: 1015, Selected False, Rank: 1701.000\n", "Column: 1016, Selected False, Rank: 1700.000\n", "Column: 1017, Selected False, Rank: 1699.000\n", "Column: 1018, Selected False, Rank: 1698.000\n", "Column: 1019, Selected False, Rank: 1697.000\n", "Column: 1020, Selected False, Rank: 1696.000\n", "Column: 1021, Selected False, Rank: 1695.000\n", "Column: 1022, Selected False, Rank: 1694.000\n", "Column: 1023, Selected False, Rank: 1693.000\n", "Column: 1024, Selected False, Rank: 1692.000\n", "Column: 1025, Selected False, Rank: 1691.000\n", "Column: 1026, Selected False, Rank: 1690.000\n", "Column: 1027, Selected False, Rank: 1689.000\n", "Column: 1028, Selected False, Rank: 1688.000\n", "Column: 1029, Selected False, Rank: 1687.000\n", "Column: 1030, Selected False, Rank: 1686.000\n", "Column: 1031, Selected False, Rank: 1685.000\n", "Column: 1032, Selected False, Rank: 1684.000\n", "Column: 1033, Selected False, Rank: 1683.000\n", "Column: 1034, Selected False, Rank: 1682.000\n", "Column: 1035, Selected False, Rank: 1681.000\n", "Column: 1036, Selected False, Rank: 1680.000\n", "Column: 1037, Selected False, Rank: 1679.000\n", "Column: 1038, Selected False, Rank: 1678.000\n", "Column: 1039, Selected False, Rank: 1677.000\n", "Column: 1040, Selected False, Rank: 1676.000\n", "Column: 1041, Selected False, Rank: 1675.000\n", "Column: 1042, Selected False, Rank: 1674.000\n", "Column: 1043, Selected False, Rank: 1673.000\n", "Column: 1044, Selected False, Rank: 1672.000\n", "Column: 1045, Selected False, Rank: 1671.000\n", "Column: 1046, Selected False, Rank: 1670.000\n", "Column: 1047, Selected False, Rank: 1669.000\n", "Column: 1048, Selected False, Rank: 1668.000\n", "Column: 1049, Selected False, Rank: 1667.000\n", "Column: 1050, Selected False, Rank: 1666.000\n", "Column: 1051, Selected False, Rank: 1665.000\n", "Column: 1052, Selected False, Rank: 1664.000\n", "Column: 1053, Selected False, Rank: 1663.000\n", "Column: 1054, Selected False, Rank: 1662.000\n", "Column: 1055, Selected False, Rank: 1661.000\n", "Column: 1056, Selected False, Rank: 1660.000\n", "Column: 1057, Selected False, Rank: 1659.000\n", "Column: 1058, Selected False, Rank: 1658.000\n", "Column: 1059, Selected False, Rank: 1657.000\n", "Column: 1060, Selected False, Rank: 1656.000\n", "Column: 1061, Selected False, Rank: 1655.000\n", "Column: 1062, Selected False, Rank: 1654.000\n", "Column: 1063, Selected False, Rank: 1653.000\n", "Column: 1064, Selected False, Rank: 1652.000\n", "Column: 1065, Selected False, Rank: 1651.000\n", "Column: 1066, Selected False, Rank: 1650.000\n", "Column: 1067, Selected False, Rank: 1649.000\n", "Column: 1068, Selected False, Rank: 1648.000\n", "Column: 1069, Selected False, Rank: 1647.000\n", "Column: 1070, Selected False, Rank: 1646.000\n", "Column: 1071, Selected False, Rank: 1645.000\n", "Column: 1072, Selected False, Rank: 1644.000\n", "Column: 1073, Selected False, Rank: 1643.000\n", "Column: 1074, Selected False, Rank: 1642.000\n", "Column: 1075, Selected False, Rank: 1641.000\n", "Column: 1076, Selected False, Rank: 1640.000\n", "Column: 1077, Selected False, Rank: 1639.000\n", "Column: 1078, Selected False, Rank: 1638.000\n", "Column: 1079, Selected False, Rank: 1637.000\n", "Column: 1080, Selected False, Rank: 1636.000\n", "Column: 1081, Selected False, Rank: 1635.000\n", "Column: 1082, Selected False, Rank: 1634.000\n", "Column: 1083, Selected False, Rank: 1633.000\n", "Column: 1084, Selected False, Rank: 1632.000\n", "Column: 1085, Selected False, Rank: 1631.000\n", "Column: 1086, Selected False, Rank: 1630.000\n", "Column: 1087, Selected False, Rank: 1629.000\n", "Column: 1088, Selected False, Rank: 1628.000\n", "Column: 1089, Selected False, Rank: 1627.000\n", "Column: 1090, Selected False, Rank: 1626.000\n", "Column: 1091, Selected False, Rank: 1625.000\n", "Column: 1092, Selected False, Rank: 1624.000\n", "Column: 1093, Selected False, Rank: 1623.000\n", "Column: 1094, Selected False, Rank: 1622.000\n", "Column: 1095, Selected False, Rank: 1621.000\n", "Column: 1096, Selected False, Rank: 1620.000\n", "Column: 1097, Selected False, Rank: 1619.000\n", "Column: 1098, Selected False, Rank: 1618.000\n", "Column: 1099, Selected False, Rank: 1617.000\n", "Column: 1100, Selected False, Rank: 1616.000\n", "Column: 1101, Selected False, Rank: 1615.000\n", "Column: 1102, Selected False, Rank: 1614.000\n", "Column: 1103, Selected False, Rank: 1613.000\n", "Column: 1104, Selected False, Rank: 1612.000\n", "Column: 1105, Selected False, Rank: 1611.000\n", "Column: 1106, Selected False, Rank: 1610.000\n", "Column: 1107, Selected False, Rank: 1609.000\n", "Column: 1108, Selected False, Rank: 1608.000\n", "Column: 1109, Selected False, Rank: 1607.000\n", "Column: 1110, Selected False, Rank: 1606.000\n", "Column: 1111, Selected False, Rank: 1605.000\n", "Column: 1112, Selected False, Rank: 1604.000\n", "Column: 1113, Selected False, Rank: 1603.000\n", "Column: 1114, Selected False, Rank: 1602.000\n", "Column: 1115, Selected False, Rank: 1601.000\n", "Column: 1116, Selected False, Rank: 1600.000\n", "Column: 1117, Selected False, Rank: 1599.000\n", "Column: 1118, Selected False, Rank: 1598.000\n", "Column: 1119, Selected False, Rank: 1597.000\n", "Column: 1120, Selected False, Rank: 1596.000\n", "Column: 1121, Selected False, Rank: 1595.000\n", "Column: 1122, Selected False, Rank: 1594.000\n", "Column: 1123, Selected False, Rank: 1593.000\n", "Column: 1124, Selected False, Rank: 1592.000\n", "Column: 1125, Selected False, Rank: 1591.000\n", "Column: 1126, Selected False, Rank: 1590.000\n", "Column: 1127, Selected False, Rank: 1589.000\n", "Column: 1128, Selected False, Rank: 1588.000\n", "Column: 1129, Selected False, Rank: 1587.000\n", "Column: 1130, Selected False, Rank: 1586.000\n", "Column: 1131, Selected False, Rank: 1585.000\n", "Column: 1132, Selected False, Rank: 1584.000\n", "Column: 1133, Selected False, Rank: 1583.000\n", "Column: 1134, Selected False, Rank: 1582.000\n", "Column: 1135, Selected False, Rank: 1581.000\n", "Column: 1136, Selected False, Rank: 1580.000\n", "Column: 1137, Selected False, Rank: 1579.000\n", "Column: 1138, Selected False, Rank: 1578.000\n", "Column: 1139, Selected False, Rank: 1577.000\n", "Column: 1140, Selected False, Rank: 1576.000\n", "Column: 1141, Selected False, Rank: 1575.000\n", "Column: 1142, Selected False, Rank: 1574.000\n", "Column: 1143, Selected False, Rank: 1573.000\n", "Column: 1144, Selected False, Rank: 1572.000\n", "Column: 1145, Selected False, Rank: 1571.000\n", "Column: 1146, Selected False, Rank: 1570.000\n", "Column: 1147, Selected False, Rank: 1569.000\n", "Column: 1148, Selected False, Rank: 1568.000\n", "Column: 1149, Selected False, Rank: 1567.000\n", "Column: 1150, Selected False, Rank: 1566.000\n", "Column: 1151, Selected False, Rank: 1565.000\n", "Column: 1152, Selected False, Rank: 1564.000\n", "Column: 1153, Selected False, Rank: 1563.000\n", "Column: 1154, Selected False, Rank: 1562.000\n", "Column: 1155, Selected False, Rank: 1561.000\n", "Column: 1156, Selected False, Rank: 1560.000\n", "Column: 1157, Selected False, Rank: 1559.000\n", "Column: 1158, Selected False, Rank: 1558.000\n", "Column: 1159, Selected False, Rank: 1557.000\n", "Column: 1160, Selected False, Rank: 1556.000\n", "Column: 1161, Selected False, Rank: 1555.000\n", "Column: 1162, Selected False, Rank: 1554.000\n", "Column: 1163, Selected False, Rank: 1553.000\n", "Column: 1164, Selected False, Rank: 1552.000\n", "Column: 1165, Selected False, Rank: 1551.000\n", "Column: 1166, Selected False, Rank: 1550.000\n", "Column: 1167, Selected False, Rank: 1549.000\n", "Column: 1168, Selected False, Rank: 1548.000\n", "Column: 1169, Selected False, Rank: 1547.000\n", "Column: 1170, Selected False, Rank: 1546.000\n", "Column: 1171, Selected False, Rank: 1545.000\n", "Column: 1172, Selected False, Rank: 1544.000\n", "Column: 1173, Selected False, Rank: 1543.000\n", "Column: 1174, Selected False, Rank: 1542.000\n", "Column: 1175, Selected False, Rank: 1541.000\n", "Column: 1176, Selected False, Rank: 1540.000\n", "Column: 1177, Selected False, Rank: 1539.000\n", "Column: 1178, Selected False, Rank: 1538.000\n", "Column: 1179, Selected False, Rank: 1537.000\n", "Column: 1180, Selected False, Rank: 1536.000\n", "Column: 1181, Selected False, Rank: 1535.000\n", "Column: 1182, Selected False, Rank: 1534.000\n", "Column: 1183, Selected False, Rank: 1533.000\n", "Column: 1184, Selected False, Rank: 1532.000\n", "Column: 1185, Selected False, Rank: 1531.000\n", "Column: 1186, Selected False, Rank: 1530.000\n", "Column: 1187, Selected False, Rank: 1529.000\n", "Column: 1188, Selected False, Rank: 1528.000\n", "Column: 1189, Selected False, Rank: 1527.000\n", "Column: 1190, Selected False, Rank: 1526.000\n", "Column: 1191, Selected False, Rank: 1525.000\n", "Column: 1192, Selected False, Rank: 1524.000\n", "Column: 1193, Selected False, Rank: 1523.000\n", "Column: 1194, Selected False, Rank: 1522.000\n", "Column: 1195, Selected False, Rank: 1521.000\n", "Column: 1196, Selected False, Rank: 1520.000\n", "Column: 1197, Selected False, Rank: 1519.000\n", "Column: 1198, Selected False, Rank: 1518.000\n", "Column: 1199, Selected False, Rank: 1517.000\n", "Column: 1200, Selected False, Rank: 1516.000\n", "Column: 1201, Selected False, Rank: 1515.000\n", "Column: 1202, Selected False, Rank: 1514.000\n", "Column: 1203, Selected False, Rank: 1513.000\n", "Column: 1204, Selected False, Rank: 1512.000\n", "Column: 1205, Selected False, Rank: 1511.000\n", "Column: 1206, Selected False, Rank: 1510.000\n", "Column: 1207, Selected False, Rank: 1509.000\n", "Column: 1208, Selected False, Rank: 1508.000\n", "Column: 1209, Selected False, Rank: 1507.000\n", "Column: 1210, Selected False, Rank: 1506.000\n", "Column: 1211, Selected False, Rank: 1505.000\n", "Column: 1212, Selected False, Rank: 1504.000\n", "Column: 1213, Selected False, Rank: 1503.000\n", "Column: 1214, Selected False, Rank: 1502.000\n", "Column: 1215, Selected False, Rank: 1501.000\n", "Column: 1216, Selected False, Rank: 1500.000\n", "Column: 1217, Selected False, Rank: 1499.000\n", "Column: 1218, Selected False, Rank: 1498.000\n", "Column: 1219, Selected False, Rank: 1497.000\n", "Column: 1220, Selected False, Rank: 1496.000\n", "Column: 1221, Selected False, Rank: 1495.000\n", "Column: 1222, Selected False, Rank: 1494.000\n", "Column: 1223, Selected False, Rank: 1493.000\n", "Column: 1224, Selected False, Rank: 1492.000\n", "Column: 1225, Selected False, Rank: 1491.000\n", "Column: 1226, Selected False, Rank: 1490.000\n", "Column: 1227, Selected False, Rank: 1489.000\n", "Column: 1228, Selected False, Rank: 1488.000\n", "Column: 1229, Selected False, Rank: 1487.000\n", "Column: 1230, Selected False, Rank: 1486.000\n", "Column: 1231, Selected False, Rank: 1485.000\n", "Column: 1232, Selected False, Rank: 1484.000\n", "Column: 1233, Selected False, Rank: 1483.000\n", "Column: 1234, Selected False, Rank: 1482.000\n", "Column: 1235, Selected False, Rank: 1481.000\n", "Column: 1236, Selected False, Rank: 1480.000\n", "Column: 1237, Selected False, Rank: 1479.000\n", "Column: 1238, Selected False, Rank: 1478.000\n", "Column: 1239, Selected False, Rank: 1477.000\n", "Column: 1240, Selected False, Rank: 1476.000\n", "Column: 1241, Selected False, Rank: 1475.000\n", "Column: 1242, Selected False, Rank: 1474.000\n", "Column: 1243, Selected False, Rank: 1473.000\n", "Column: 1244, Selected False, Rank: 1472.000\n", "Column: 1245, Selected False, Rank: 1471.000\n", "Column: 1246, Selected False, Rank: 1470.000\n", "Column: 1247, Selected False, Rank: 1469.000\n", "Column: 1248, Selected False, Rank: 1468.000\n", "Column: 1249, Selected False, Rank: 1467.000\n", "Column: 1250, Selected False, Rank: 1466.000\n", "Column: 1251, Selected False, Rank: 1465.000\n", "Column: 1252, Selected False, Rank: 1464.000\n", "Column: 1253, Selected False, Rank: 1463.000\n", "Column: 1254, Selected False, Rank: 1462.000\n", "Column: 1255, Selected False, Rank: 1461.000\n", "Column: 1256, Selected False, Rank: 1460.000\n", "Column: 1257, Selected False, Rank: 1459.000\n", "Column: 1258, Selected False, Rank: 1458.000\n", "Column: 1259, Selected False, Rank: 1457.000\n", "Column: 1260, Selected False, Rank: 1456.000\n", "Column: 1261, Selected False, Rank: 1455.000\n", "Column: 1262, Selected False, Rank: 1454.000\n", "Column: 1263, Selected False, Rank: 1453.000\n", "Column: 1264, Selected False, Rank: 1452.000\n", "Column: 1265, Selected False, Rank: 1451.000\n", "Column: 1266, Selected False, Rank: 1450.000\n", "Column: 1267, Selected False, Rank: 1449.000\n", "Column: 1268, Selected False, Rank: 1448.000\n", "Column: 1269, Selected False, Rank: 1447.000\n", "Column: 1270, Selected False, Rank: 1446.000\n", "Column: 1271, Selected False, Rank: 1445.000\n", "Column: 1272, Selected False, Rank: 1444.000\n", "Column: 1273, Selected False, Rank: 1443.000\n", "Column: 1274, Selected False, Rank: 1442.000\n", "Column: 1275, Selected False, Rank: 1441.000\n", "Column: 1276, Selected False, Rank: 1440.000\n", "Column: 1277, Selected False, Rank: 1439.000\n", "Column: 1278, Selected False, Rank: 1438.000\n", "Column: 1279, Selected False, Rank: 1437.000\n", "Column: 1280, Selected False, Rank: 1436.000\n", "Column: 1281, Selected False, Rank: 1435.000\n", "Column: 1282, Selected False, Rank: 1434.000\n", "Column: 1283, Selected False, Rank: 1433.000\n", "Column: 1284, Selected False, Rank: 1432.000\n", "Column: 1285, Selected False, Rank: 1431.000\n", "Column: 1286, Selected False, Rank: 1430.000\n", "Column: 1287, Selected False, Rank: 1429.000\n", "Column: 1288, Selected False, Rank: 1428.000\n", "Column: 1289, Selected False, Rank: 1427.000\n", "Column: 1290, Selected False, Rank: 1426.000\n", "Column: 1291, Selected False, Rank: 1425.000\n", "Column: 1292, Selected False, Rank: 1424.000\n", "Column: 1293, Selected False, Rank: 1423.000\n", "Column: 1294, Selected False, Rank: 1422.000\n", "Column: 1295, Selected False, Rank: 1421.000\n", "Column: 1296, Selected False, Rank: 1420.000\n", "Column: 1297, Selected False, Rank: 1419.000\n", "Column: 1298, Selected False, Rank: 1418.000\n", "Column: 1299, Selected False, Rank: 1417.000\n", "Column: 1300, Selected False, Rank: 1416.000\n", "Column: 1301, Selected False, Rank: 1415.000\n", "Column: 1302, Selected False, Rank: 1414.000\n", "Column: 1303, Selected False, Rank: 1413.000\n", "Column: 1304, Selected False, Rank: 1412.000\n", "Column: 1305, Selected False, Rank: 1411.000\n", "Column: 1306, Selected False, Rank: 1410.000\n", "Column: 1307, Selected False, Rank: 1409.000\n", "Column: 1308, Selected False, Rank: 1408.000\n", "Column: 1309, Selected False, Rank: 1407.000\n", "Column: 1310, Selected False, Rank: 1406.000\n", "Column: 1311, Selected False, Rank: 1405.000\n", "Column: 1312, Selected False, Rank: 1404.000\n", "Column: 1313, Selected False, Rank: 1403.000\n", "Column: 1314, Selected False, Rank: 1402.000\n", "Column: 1315, Selected False, Rank: 1401.000\n", "Column: 1316, Selected False, Rank: 1400.000\n", "Column: 1317, Selected False, Rank: 1399.000\n", "Column: 1318, Selected False, Rank: 1398.000\n", "Column: 1319, Selected False, Rank: 1397.000\n", "Column: 1320, Selected False, Rank: 1396.000\n", "Column: 1321, Selected False, Rank: 1395.000\n", "Column: 1322, Selected False, Rank: 1394.000\n", "Column: 1323, Selected False, Rank: 1393.000\n", "Column: 1324, Selected False, Rank: 1392.000\n", "Column: 1325, Selected False, Rank: 1391.000\n", "Column: 1326, Selected False, Rank: 1390.000\n", "Column: 1327, Selected False, Rank: 1389.000\n", "Column: 1328, Selected False, Rank: 1388.000\n", "Column: 1329, Selected False, Rank: 1387.000\n", "Column: 1330, Selected False, Rank: 1386.000\n", "Column: 1331, Selected False, Rank: 1385.000\n", "Column: 1332, Selected False, Rank: 1384.000\n", "Column: 1333, Selected False, Rank: 1383.000\n", "Column: 1334, Selected False, Rank: 1382.000\n", "Column: 1335, Selected False, Rank: 1381.000\n", "Column: 1336, Selected False, Rank: 1380.000\n", "Column: 1337, Selected False, Rank: 1379.000\n", "Column: 1338, Selected False, Rank: 1378.000\n", "Column: 1339, Selected False, Rank: 1377.000\n", "Column: 1340, Selected False, Rank: 1376.000\n", "Column: 1341, Selected False, Rank: 1375.000\n", "Column: 1342, Selected False, Rank: 1374.000\n", "Column: 1343, Selected False, Rank: 1373.000\n", "Column: 1344, Selected False, Rank: 1372.000\n", "Column: 1345, Selected False, Rank: 1371.000\n", "Column: 1346, Selected False, Rank: 1370.000\n", "Column: 1347, Selected False, Rank: 1369.000\n", "Column: 1348, Selected False, Rank: 1368.000\n", "Column: 1349, Selected False, Rank: 1367.000\n", "Column: 1350, Selected False, Rank: 1366.000\n", "Column: 1351, Selected False, Rank: 1365.000\n", "Column: 1352, Selected False, Rank: 1364.000\n", "Column: 1353, Selected False, Rank: 1363.000\n", "Column: 1354, Selected False, Rank: 1362.000\n", "Column: 1355, Selected False, Rank: 1361.000\n", "Column: 1356, Selected False, Rank: 1360.000\n", "Column: 1357, Selected False, Rank: 1359.000\n", "Column: 1358, Selected False, Rank: 1358.000\n", "Column: 1359, Selected False, Rank: 1357.000\n", "Column: 1360, Selected False, Rank: 1356.000\n", "Column: 1361, Selected False, Rank: 1355.000\n", "Column: 1362, Selected False, Rank: 1354.000\n", "Column: 1363, Selected False, Rank: 1353.000\n", "Column: 1364, Selected False, Rank: 1352.000\n", "Column: 1365, Selected False, Rank: 1351.000\n", "Column: 1366, Selected False, Rank: 1350.000\n", "Column: 1367, Selected False, Rank: 1349.000\n", "Column: 1368, Selected False, Rank: 1348.000\n", "Column: 1369, Selected False, Rank: 1347.000\n", "Column: 1370, Selected False, Rank: 1346.000\n", "Column: 1371, Selected False, Rank: 1345.000\n", "Column: 1372, Selected False, Rank: 1344.000\n", "Column: 1373, Selected False, Rank: 1343.000\n", "Column: 1374, Selected False, Rank: 1342.000\n", "Column: 1375, Selected False, Rank: 1341.000\n", "Column: 1376, Selected False, Rank: 1340.000\n", "Column: 1377, Selected False, Rank: 1339.000\n", "Column: 1378, Selected False, Rank: 1338.000\n", "Column: 1379, Selected False, Rank: 1337.000\n", "Column: 1380, Selected False, Rank: 1336.000\n", "Column: 1381, Selected False, Rank: 1335.000\n", "Column: 1382, Selected False, Rank: 1334.000\n", "Column: 1383, Selected False, Rank: 1333.000\n", "Column: 1384, Selected False, Rank: 1332.000\n", "Column: 1385, Selected False, Rank: 1331.000\n", "Column: 1386, Selected False, Rank: 1330.000\n", "Column: 1387, Selected False, Rank: 1329.000\n", "Column: 1388, Selected False, Rank: 1328.000\n", "Column: 1389, Selected False, Rank: 1327.000\n", "Column: 1390, Selected False, Rank: 1326.000\n", "Column: 1391, Selected False, Rank: 1325.000\n", "Column: 1392, Selected False, Rank: 1324.000\n", "Column: 1393, Selected False, Rank: 1323.000\n", "Column: 1394, Selected False, Rank: 1322.000\n", "Column: 1395, Selected False, Rank: 1321.000\n", "Column: 1396, Selected False, Rank: 1320.000\n", "Column: 1397, Selected False, Rank: 1319.000\n", "Column: 1398, Selected False, Rank: 1318.000\n", "Column: 1399, Selected False, Rank: 1317.000\n", "Column: 1400, Selected False, Rank: 1316.000\n", "Column: 1401, Selected False, Rank: 1315.000\n", "Column: 1402, Selected False, Rank: 1314.000\n", "Column: 1403, Selected False, Rank: 1313.000\n", "Column: 1404, Selected False, Rank: 1312.000\n", "Column: 1405, Selected False, Rank: 1311.000\n", "Column: 1406, Selected False, Rank: 1310.000\n", "Column: 1407, Selected False, Rank: 1309.000\n", "Column: 1408, Selected False, Rank: 1308.000\n", "Column: 1409, Selected False, Rank: 1307.000\n", "Column: 1410, Selected False, Rank: 1306.000\n", "Column: 1411, Selected False, Rank: 1305.000\n", "Column: 1412, Selected False, Rank: 1304.000\n", "Column: 1413, Selected False, Rank: 1303.000\n", "Column: 1414, Selected False, Rank: 1302.000\n", "Column: 1415, Selected False, Rank: 1301.000\n", "Column: 1416, Selected False, Rank: 1300.000\n", "Column: 1417, Selected False, Rank: 1299.000\n", "Column: 1418, Selected False, Rank: 1298.000\n", "Column: 1419, Selected False, Rank: 1297.000\n", "Column: 1420, Selected False, Rank: 1296.000\n", "Column: 1421, Selected False, Rank: 1295.000\n", "Column: 1422, Selected False, Rank: 1294.000\n", "Column: 1423, Selected False, Rank: 1293.000\n", "Column: 1424, Selected False, Rank: 1292.000\n", "Column: 1425, Selected False, Rank: 1291.000\n", "Column: 1426, Selected False, Rank: 1290.000\n", "Column: 1427, Selected False, Rank: 1289.000\n", "Column: 1428, Selected False, Rank: 1288.000\n", "Column: 1429, Selected False, Rank: 1287.000\n", "Column: 1430, Selected False, Rank: 1286.000\n", "Column: 1431, Selected False, Rank: 1285.000\n", "Column: 1432, Selected False, Rank: 1284.000\n", "Column: 1433, Selected False, Rank: 1283.000\n", "Column: 1434, Selected False, Rank: 1282.000\n", "Column: 1435, Selected False, Rank: 1281.000\n", "Column: 1436, Selected False, Rank: 1280.000\n", "Column: 1437, Selected False, Rank: 1279.000\n", "Column: 1438, Selected False, Rank: 1278.000\n", "Column: 1439, Selected False, Rank: 1277.000\n", "Column: 1440, Selected False, Rank: 1276.000\n", "Column: 1441, Selected False, Rank: 1275.000\n", "Column: 1442, Selected False, Rank: 1274.000\n", "Column: 1443, Selected False, Rank: 1273.000\n", "Column: 1444, Selected False, Rank: 1272.000\n", "Column: 1445, Selected False, Rank: 1271.000\n", "Column: 1446, Selected False, Rank: 1270.000\n", "Column: 1447, Selected False, Rank: 1269.000\n", "Column: 1448, Selected False, Rank: 1268.000\n", "Column: 1449, Selected False, Rank: 1267.000\n", "Column: 1450, Selected False, Rank: 1266.000\n", "Column: 1451, Selected False, Rank: 1265.000\n", "Column: 1452, Selected False, Rank: 1264.000\n", "Column: 1453, Selected False, Rank: 1263.000\n", "Column: 1454, Selected False, Rank: 1262.000\n", "Column: 1455, Selected False, Rank: 1261.000\n", "Column: 1456, Selected False, Rank: 1260.000\n", "Column: 1457, Selected False, Rank: 1259.000\n", "Column: 1458, Selected False, Rank: 1258.000\n", "Column: 1459, Selected False, Rank: 1257.000\n", "Column: 1460, Selected False, Rank: 1256.000\n", "Column: 1461, Selected False, Rank: 1255.000\n", "Column: 1462, Selected False, Rank: 1254.000\n", "Column: 1463, Selected False, Rank: 1253.000\n", "Column: 1464, Selected False, Rank: 1252.000\n", "Column: 1465, Selected False, Rank: 1251.000\n", "Column: 1466, Selected False, Rank: 1250.000\n", "Column: 1467, Selected False, Rank: 1249.000\n", "Column: 1468, Selected False, Rank: 1248.000\n", "Column: 1469, Selected False, Rank: 1247.000\n", "Column: 1470, Selected False, Rank: 1246.000\n", "Column: 1471, Selected False, Rank: 1245.000\n", "Column: 1472, Selected False, Rank: 1244.000\n", "Column: 1473, Selected False, Rank: 1243.000\n", "Column: 1474, Selected False, Rank: 1242.000\n", "Column: 1475, Selected False, Rank: 1241.000\n", "Column: 1476, Selected False, Rank: 1240.000\n", "Column: 1477, Selected False, Rank: 1239.000\n", "Column: 1478, Selected False, Rank: 1238.000\n", "Column: 1479, Selected False, Rank: 1237.000\n", "Column: 1480, Selected False, Rank: 1236.000\n", "Column: 1481, Selected False, Rank: 1235.000\n", "Column: 1482, Selected False, Rank: 1234.000\n", "Column: 1483, Selected False, Rank: 1233.000\n", "Column: 1484, Selected False, Rank: 1232.000\n", "Column: 1485, Selected False, Rank: 1231.000\n", "Column: 1486, Selected False, Rank: 1230.000\n", "Column: 1487, Selected False, Rank: 1229.000\n", "Column: 1488, Selected False, Rank: 1228.000\n", "Column: 1489, Selected False, Rank: 1227.000\n", "Column: 1490, Selected False, Rank: 1226.000\n", "Column: 1491, Selected False, Rank: 1225.000\n", "Column: 1492, Selected False, Rank: 1224.000\n", "Column: 1493, Selected False, Rank: 1223.000\n", "Column: 1494, Selected False, Rank: 1222.000\n", "Column: 1495, Selected False, Rank: 1221.000\n", "Column: 1496, Selected False, Rank: 1220.000\n", "Column: 1497, Selected False, Rank: 1219.000\n", "Column: 1498, Selected False, Rank: 1218.000\n", "Column: 1499, Selected False, Rank: 1217.000\n", "Column: 1500, Selected False, Rank: 1216.000\n", "Column: 1501, Selected False, Rank: 1215.000\n", "Column: 1502, Selected False, Rank: 1214.000\n", "Column: 1503, Selected False, Rank: 1213.000\n", "Column: 1504, Selected False, Rank: 1212.000\n", "Column: 1505, Selected False, Rank: 1211.000\n", "Column: 1506, Selected False, Rank: 1210.000\n", "Column: 1507, Selected False, Rank: 1209.000\n", "Column: 1508, Selected False, Rank: 1208.000\n", "Column: 1509, Selected False, Rank: 1207.000\n", "Column: 1510, Selected False, Rank: 1206.000\n", "Column: 1511, Selected False, Rank: 1205.000\n", "Column: 1512, Selected False, Rank: 1204.000\n", "Column: 1513, Selected False, Rank: 1203.000\n", "Column: 1514, Selected False, Rank: 1202.000\n", "Column: 1515, Selected False, Rank: 1201.000\n", "Column: 1516, Selected False, Rank: 1200.000\n", "Column: 1517, Selected False, Rank: 1199.000\n", "Column: 1518, Selected False, Rank: 1198.000\n", "Column: 1519, Selected False, Rank: 1197.000\n", "Column: 1520, Selected False, Rank: 1196.000\n", "Column: 1521, Selected False, Rank: 1195.000\n", "Column: 1522, Selected False, Rank: 1194.000\n", "Column: 1523, Selected False, Rank: 1193.000\n", "Column: 1524, Selected False, Rank: 1192.000\n", "Column: 1525, Selected False, Rank: 1191.000\n", "Column: 1526, Selected False, Rank: 1190.000\n", "Column: 1527, Selected False, Rank: 1189.000\n", "Column: 1528, Selected False, Rank: 1188.000\n", "Column: 1529, Selected False, Rank: 1187.000\n", "Column: 1530, Selected False, Rank: 1186.000\n", "Column: 1531, Selected False, Rank: 1185.000\n", "Column: 1532, Selected False, Rank: 1184.000\n", "Column: 1533, Selected False, Rank: 1183.000\n", "Column: 1534, Selected False, Rank: 1182.000\n", "Column: 1535, Selected False, Rank: 1181.000\n", "Column: 1536, Selected False, Rank: 1180.000\n", "Column: 1537, Selected False, Rank: 1179.000\n", "Column: 1538, Selected False, Rank: 1178.000\n", "Column: 1539, Selected False, Rank: 1177.000\n", "Column: 1540, Selected False, Rank: 1176.000\n", "Column: 1541, Selected False, Rank: 1175.000\n", "Column: 1542, Selected False, Rank: 1174.000\n", "Column: 1543, Selected False, Rank: 1173.000\n", "Column: 1544, Selected False, Rank: 1172.000\n", "Column: 1545, Selected False, Rank: 1171.000\n", "Column: 1546, Selected False, Rank: 1170.000\n", "Column: 1547, Selected False, Rank: 1169.000\n", "Column: 1548, Selected False, Rank: 1168.000\n", "Column: 1549, Selected False, Rank: 1167.000\n", "Column: 1550, Selected False, Rank: 1166.000\n", "Column: 1551, Selected False, Rank: 1165.000\n", "Column: 1552, Selected False, Rank: 1164.000\n", "Column: 1553, Selected False, Rank: 1163.000\n", "Column: 1554, Selected False, Rank: 1162.000\n", "Column: 1555, Selected False, Rank: 1161.000\n", "Column: 1556, Selected False, Rank: 1160.000\n", "Column: 1557, Selected False, Rank: 1159.000\n", "Column: 1558, Selected False, Rank: 1158.000\n", "Column: 1559, Selected False, Rank: 1157.000\n", "Column: 1560, Selected False, Rank: 1156.000\n", "Column: 1561, Selected False, Rank: 1155.000\n", "Column: 1562, Selected False, Rank: 1154.000\n", "Column: 1563, Selected False, Rank: 1153.000\n", "Column: 1564, Selected False, Rank: 1152.000\n", "Column: 1565, Selected False, Rank: 1151.000\n", "Column: 1566, Selected False, Rank: 1150.000\n", "Column: 1567, Selected False, Rank: 1149.000\n", "Column: 1568, Selected False, Rank: 1148.000\n", "Column: 1569, Selected False, Rank: 1147.000\n", "Column: 1570, Selected False, Rank: 1146.000\n", "Column: 1571, Selected False, Rank: 1145.000\n", "Column: 1572, Selected False, Rank: 1144.000\n", "Column: 1573, Selected False, Rank: 1143.000\n", "Column: 1574, Selected False, Rank: 1142.000\n", "Column: 1575, Selected False, Rank: 1141.000\n", "Column: 1576, Selected False, Rank: 1140.000\n", "Column: 1577, Selected False, Rank: 1139.000\n", "Column: 1578, Selected False, Rank: 1138.000\n", "Column: 1579, Selected False, Rank: 1137.000\n", "Column: 1580, Selected False, Rank: 1136.000\n", "Column: 1581, Selected False, Rank: 1135.000\n", "Column: 1582, Selected False, Rank: 1134.000\n", "Column: 1583, Selected False, Rank: 1133.000\n", "Column: 1584, Selected False, Rank: 1132.000\n", "Column: 1585, Selected False, Rank: 1131.000\n", "Column: 1586, Selected False, Rank: 1130.000\n", "Column: 1587, Selected False, Rank: 1129.000\n", "Column: 1588, Selected False, Rank: 1128.000\n", "Column: 1589, Selected False, Rank: 1127.000\n", "Column: 1590, Selected False, Rank: 1126.000\n", "Column: 1591, Selected False, Rank: 1125.000\n", "Column: 1592, Selected False, Rank: 1124.000\n", "Column: 1593, Selected False, Rank: 1123.000\n", "Column: 1594, Selected False, Rank: 1122.000\n", "Column: 1595, Selected False, Rank: 1121.000\n", "Column: 1596, Selected False, Rank: 1120.000\n", "Column: 1597, Selected False, Rank: 1119.000\n", "Column: 1598, Selected False, Rank: 1118.000\n", "Column: 1599, Selected False, Rank: 1117.000\n", "Column: 1600, Selected False, Rank: 1116.000\n", "Column: 1601, Selected False, Rank: 1115.000\n", "Column: 1602, Selected False, Rank: 1114.000\n", "Column: 1603, Selected False, Rank: 1113.000\n", "Column: 1604, Selected False, Rank: 1112.000\n", "Column: 1605, Selected False, Rank: 1111.000\n", "Column: 1606, Selected False, Rank: 1110.000\n", "Column: 1607, Selected False, Rank: 1109.000\n", "Column: 1608, Selected False, Rank: 1108.000\n", "Column: 1609, Selected False, Rank: 1107.000\n", "Column: 1610, Selected False, Rank: 1106.000\n", "Column: 1611, Selected False, Rank: 1105.000\n", "Column: 1612, Selected False, Rank: 1104.000\n", "Column: 1613, Selected False, Rank: 1103.000\n", "Column: 1614, Selected False, Rank: 1102.000\n", "Column: 1615, Selected False, Rank: 1101.000\n", "Column: 1616, Selected False, Rank: 1100.000\n", "Column: 1617, Selected False, Rank: 1099.000\n", "Column: 1618, Selected False, Rank: 1098.000\n", "Column: 1619, Selected False, Rank: 1097.000\n", "Column: 1620, Selected False, Rank: 1096.000\n", "Column: 1621, Selected False, Rank: 1095.000\n", "Column: 1622, Selected False, Rank: 1094.000\n", "Column: 1623, Selected False, Rank: 1093.000\n", "Column: 1624, Selected False, Rank: 1092.000\n", "Column: 1625, Selected False, Rank: 1091.000\n", "Column: 1626, Selected False, Rank: 1090.000\n", "Column: 1627, Selected False, Rank: 1089.000\n", "Column: 1628, Selected False, Rank: 1088.000\n", "Column: 1629, Selected False, Rank: 1087.000\n", "Column: 1630, Selected False, Rank: 1086.000\n", "Column: 1631, Selected False, Rank: 1085.000\n", "Column: 1632, Selected False, Rank: 1084.000\n", "Column: 1633, Selected False, Rank: 1083.000\n", "Column: 1634, Selected False, Rank: 1082.000\n", "Column: 1635, Selected False, Rank: 1081.000\n", "Column: 1636, Selected False, Rank: 1080.000\n", "Column: 1637, Selected False, Rank: 1079.000\n", "Column: 1638, Selected False, Rank: 1078.000\n", "Column: 1639, Selected False, Rank: 1077.000\n", "Column: 1640, Selected False, Rank: 1076.000\n", "Column: 1641, Selected False, Rank: 1075.000\n", "Column: 1642, Selected False, Rank: 1074.000\n", "Column: 1643, Selected False, Rank: 1073.000\n", "Column: 1644, Selected False, Rank: 1072.000\n", "Column: 1645, Selected False, Rank: 1071.000\n", "Column: 1646, Selected False, Rank: 1070.000\n", "Column: 1647, Selected False, Rank: 1069.000\n", "Column: 1648, Selected False, Rank: 1068.000\n", "Column: 1649, Selected False, Rank: 1067.000\n", "Column: 1650, Selected False, Rank: 1066.000\n", "Column: 1651, Selected False, Rank: 1065.000\n", "Column: 1652, Selected False, Rank: 1064.000\n", "Column: 1653, Selected False, Rank: 1063.000\n", "Column: 1654, Selected False, Rank: 1062.000\n", "Column: 1655, Selected False, Rank: 1061.000\n", "Column: 1656, Selected False, Rank: 1060.000\n", "Column: 1657, Selected False, Rank: 1059.000\n", "Column: 1658, Selected False, Rank: 1058.000\n", "Column: 1659, Selected False, Rank: 1057.000\n", "Column: 1660, Selected False, Rank: 1056.000\n", "Column: 1661, Selected False, Rank: 1055.000\n", "Column: 1662, Selected False, Rank: 1054.000\n", "Column: 1663, Selected False, Rank: 1053.000\n", "Column: 1664, Selected False, Rank: 1052.000\n", "Column: 1665, Selected False, Rank: 1051.000\n", "Column: 1666, Selected False, Rank: 1050.000\n", "Column: 1667, Selected False, Rank: 1049.000\n", "Column: 1668, Selected False, Rank: 1048.000\n", "Column: 1669, Selected False, Rank: 1047.000\n", "Column: 1670, Selected False, Rank: 1046.000\n", "Column: 1671, Selected False, Rank: 1045.000\n", "Column: 1672, Selected False, Rank: 1044.000\n", "Column: 1673, Selected False, Rank: 1043.000\n", "Column: 1674, Selected False, Rank: 1042.000\n", "Column: 1675, Selected False, Rank: 1041.000\n", "Column: 1676, Selected False, Rank: 1040.000\n", "Column: 1677, Selected False, Rank: 1039.000\n", "Column: 1678, Selected False, Rank: 1038.000\n", "Column: 1679, Selected False, Rank: 1037.000\n", "Column: 1680, Selected False, Rank: 1036.000\n", "Column: 1681, Selected False, Rank: 1035.000\n", "Column: 1682, Selected False, Rank: 1034.000\n", "Column: 1683, Selected False, Rank: 1033.000\n", "Column: 1684, Selected False, Rank: 1032.000\n", "Column: 1685, Selected False, Rank: 1031.000\n", "Column: 1686, Selected False, Rank: 1030.000\n", "Column: 1687, Selected False, Rank: 1029.000\n", "Column: 1688, Selected False, Rank: 1028.000\n", "Column: 1689, Selected False, Rank: 1027.000\n", "Column: 1690, Selected False, Rank: 1026.000\n", "Column: 1691, Selected False, Rank: 1025.000\n", "Column: 1692, Selected False, Rank: 1024.000\n", "Column: 1693, Selected False, Rank: 1023.000\n", "Column: 1694, Selected False, Rank: 1022.000\n", "Column: 1695, Selected False, Rank: 1021.000\n", "Column: 1696, Selected False, Rank: 1020.000\n", "Column: 1697, Selected False, Rank: 1019.000\n", "Column: 1698, Selected False, Rank: 1018.000\n", "Column: 1699, Selected False, Rank: 1017.000\n", "Column: 1700, Selected False, Rank: 1016.000\n", "Column: 1701, Selected False, Rank: 1015.000\n", "Column: 1702, Selected False, Rank: 1014.000\n", "Column: 1703, Selected False, Rank: 1013.000\n", "Column: 1704, Selected False, Rank: 1012.000\n", "Column: 1705, Selected False, Rank: 1011.000\n", "Column: 1706, Selected False, Rank: 1010.000\n", "Column: 1707, Selected False, Rank: 1009.000\n", "Column: 1708, Selected False, Rank: 1008.000\n", "Column: 1709, Selected False, Rank: 1007.000\n", "Column: 1710, Selected False, Rank: 1006.000\n", "Column: 1711, Selected False, Rank: 1005.000\n", "Column: 1712, Selected False, Rank: 1004.000\n", "Column: 1713, Selected False, Rank: 1003.000\n", "Column: 1714, Selected False, Rank: 1002.000\n", "Column: 1715, Selected False, Rank: 1001.000\n", "Column: 1716, Selected False, Rank: 1000.000\n", "Column: 1717, Selected False, Rank: 999.000\n", "Column: 1718, Selected False, Rank: 998.000\n", "Column: 1719, Selected False, Rank: 997.000\n", "Column: 1720, Selected False, Rank: 996.000\n", "Column: 1721, Selected False, Rank: 995.000\n", "Column: 1722, Selected False, Rank: 994.000\n", "Column: 1723, Selected False, Rank: 993.000\n", "Column: 1724, Selected False, Rank: 992.000\n", "Column: 1725, Selected False, Rank: 991.000\n", "Column: 1726, Selected False, Rank: 990.000\n", "Column: 1727, Selected False, Rank: 989.000\n", "Column: 1728, Selected False, Rank: 988.000\n", "Column: 1729, Selected False, Rank: 987.000\n", "Column: 1730, Selected False, Rank: 986.000\n", "Column: 1731, Selected False, Rank: 985.000\n", "Column: 1732, Selected False, Rank: 984.000\n", "Column: 1733, Selected False, Rank: 983.000\n", "Column: 1734, Selected False, Rank: 982.000\n", "Column: 1735, Selected False, Rank: 981.000\n", "Column: 1736, Selected False, Rank: 980.000\n", "Column: 1737, Selected False, Rank: 979.000\n", "Column: 1738, Selected False, Rank: 978.000\n", "Column: 1739, Selected False, Rank: 977.000\n", "Column: 1740, Selected False, Rank: 976.000\n", "Column: 1741, Selected False, Rank: 975.000\n", "Column: 1742, Selected False, Rank: 974.000\n", "Column: 1743, Selected False, Rank: 973.000\n", "Column: 1744, Selected False, Rank: 972.000\n", "Column: 1745, Selected False, Rank: 971.000\n", "Column: 1746, Selected False, Rank: 970.000\n", "Column: 1747, Selected False, Rank: 969.000\n", "Column: 1748, Selected False, Rank: 968.000\n", "Column: 1749, Selected False, Rank: 967.000\n", "Column: 1750, Selected False, Rank: 966.000\n", "Column: 1751, Selected False, Rank: 965.000\n", "Column: 1752, Selected False, Rank: 964.000\n", "Column: 1753, Selected False, Rank: 963.000\n", "Column: 1754, Selected False, Rank: 962.000\n", "Column: 1755, Selected False, Rank: 961.000\n", "Column: 1756, Selected False, Rank: 960.000\n", "Column: 1757, Selected False, Rank: 959.000\n", "Column: 1758, Selected False, Rank: 958.000\n", "Column: 1759, Selected False, Rank: 957.000\n", "Column: 1760, Selected False, Rank: 956.000\n", "Column: 1761, Selected False, Rank: 955.000\n", "Column: 1762, Selected False, Rank: 954.000\n", "Column: 1763, Selected False, Rank: 953.000\n", "Column: 1764, Selected False, Rank: 952.000\n", "Column: 1765, Selected False, Rank: 951.000\n", "Column: 1766, Selected False, Rank: 950.000\n", "Column: 1767, Selected False, Rank: 949.000\n", "Column: 1768, Selected False, Rank: 948.000\n", "Column: 1769, Selected False, Rank: 947.000\n", "Column: 1770, Selected False, Rank: 946.000\n", "Column: 1771, Selected False, Rank: 945.000\n", "Column: 1772, Selected False, Rank: 944.000\n", "Column: 1773, Selected False, Rank: 943.000\n", "Column: 1774, Selected False, Rank: 942.000\n", "Column: 1775, Selected False, Rank: 941.000\n", "Column: 1776, Selected False, Rank: 940.000\n", "Column: 1777, Selected False, Rank: 939.000\n", "Column: 1778, Selected False, Rank: 938.000\n", "Column: 1779, Selected False, Rank: 937.000\n", "Column: 1780, Selected False, Rank: 936.000\n", "Column: 1781, Selected False, Rank: 935.000\n", "Column: 1782, Selected False, Rank: 934.000\n", "Column: 1783, Selected False, Rank: 933.000\n", "Column: 1784, Selected False, Rank: 932.000\n", "Column: 1785, Selected False, Rank: 931.000\n", "Column: 1786, Selected False, Rank: 930.000\n", "Column: 1787, Selected False, Rank: 929.000\n", "Column: 1788, Selected False, Rank: 928.000\n", "Column: 1789, Selected False, Rank: 927.000\n", "Column: 1790, Selected False, Rank: 926.000\n", "Column: 1791, Selected False, Rank: 925.000\n", "Column: 1792, Selected False, Rank: 924.000\n", "Column: 1793, Selected False, Rank: 923.000\n", "Column: 1794, Selected False, Rank: 922.000\n", "Column: 1795, Selected False, Rank: 921.000\n", "Column: 1796, Selected False, Rank: 920.000\n", "Column: 1797, Selected False, Rank: 919.000\n", "Column: 1798, Selected False, Rank: 918.000\n", "Column: 1799, Selected False, Rank: 917.000\n", "Column: 1800, Selected False, Rank: 916.000\n", "Column: 1801, Selected False, Rank: 915.000\n", "Column: 1802, Selected False, Rank: 914.000\n", "Column: 1803, Selected False, Rank: 913.000\n", "Column: 1804, Selected False, Rank: 912.000\n", "Column: 1805, Selected False, Rank: 911.000\n", "Column: 1806, Selected False, Rank: 910.000\n", "Column: 1807, Selected False, Rank: 909.000\n", "Column: 1808, Selected False, Rank: 908.000\n", "Column: 1809, Selected False, Rank: 907.000\n", "Column: 1810, Selected False, Rank: 906.000\n", "Column: 1811, Selected False, Rank: 905.000\n", "Column: 1812, Selected False, Rank: 904.000\n", "Column: 1813, Selected False, Rank: 903.000\n", "Column: 1814, Selected False, Rank: 902.000\n", "Column: 1815, Selected False, Rank: 901.000\n", "Column: 1816, Selected False, Rank: 900.000\n", "Column: 1817, Selected False, Rank: 899.000\n", "Column: 1818, Selected False, Rank: 898.000\n", "Column: 1819, Selected False, Rank: 897.000\n", "Column: 1820, Selected False, Rank: 896.000\n", "Column: 1821, Selected False, Rank: 895.000\n", "Column: 1822, Selected False, Rank: 894.000\n", "Column: 1823, Selected False, Rank: 893.000\n", "Column: 1824, Selected False, Rank: 892.000\n", "Column: 1825, Selected False, Rank: 891.000\n", "Column: 1826, Selected False, Rank: 890.000\n", "Column: 1827, Selected False, Rank: 889.000\n", "Column: 1828, Selected False, Rank: 888.000\n", "Column: 1829, Selected False, Rank: 887.000\n", "Column: 1830, Selected False, Rank: 886.000\n", "Column: 1831, Selected False, Rank: 885.000\n", "Column: 1832, Selected False, Rank: 884.000\n", "Column: 1833, Selected False, Rank: 883.000\n", "Column: 1834, Selected False, Rank: 882.000\n", "Column: 1835, Selected False, Rank: 881.000\n", "Column: 1836, Selected False, Rank: 880.000\n", "Column: 1837, Selected False, Rank: 879.000\n", "Column: 1838, Selected False, Rank: 878.000\n", "Column: 1839, Selected False, Rank: 877.000\n", "Column: 1840, Selected False, Rank: 876.000\n", "Column: 1841, Selected False, Rank: 875.000\n", "Column: 1842, Selected False, Rank: 874.000\n", "Column: 1843, Selected False, Rank: 873.000\n", "Column: 1844, Selected False, Rank: 872.000\n", "Column: 1845, Selected False, Rank: 871.000\n", "Column: 1846, Selected False, Rank: 870.000\n", "Column: 1847, Selected False, Rank: 869.000\n", "Column: 1848, Selected False, Rank: 868.000\n", "Column: 1849, Selected False, Rank: 867.000\n", "Column: 1850, Selected False, Rank: 866.000\n", "Column: 1851, Selected False, Rank: 865.000\n", "Column: 1852, Selected False, Rank: 864.000\n", "Column: 1853, Selected False, Rank: 863.000\n", "Column: 1854, Selected False, Rank: 862.000\n", "Column: 1855, Selected False, Rank: 861.000\n", "Column: 1856, Selected False, Rank: 860.000\n", "Column: 1857, Selected False, Rank: 859.000\n", "Column: 1858, Selected False, Rank: 858.000\n", "Column: 1859, Selected False, Rank: 857.000\n", "Column: 1860, Selected False, Rank: 856.000\n", "Column: 1861, Selected False, Rank: 855.000\n", "Column: 1862, Selected False, Rank: 854.000\n", "Column: 1863, Selected False, Rank: 853.000\n", "Column: 1864, Selected False, Rank: 852.000\n", "Column: 1865, Selected False, Rank: 851.000\n", "Column: 1866, Selected False, Rank: 850.000\n", "Column: 1867, Selected False, Rank: 849.000\n", "Column: 1868, Selected False, Rank: 848.000\n", "Column: 1869, Selected False, Rank: 847.000\n", "Column: 1870, Selected False, Rank: 846.000\n", "Column: 1871, Selected False, Rank: 845.000\n", "Column: 1872, Selected False, Rank: 844.000\n", "Column: 1873, Selected False, Rank: 843.000\n", "Column: 1874, Selected False, Rank: 842.000\n", "Column: 1875, Selected False, Rank: 841.000\n", "Column: 1876, Selected False, Rank: 840.000\n", "Column: 1877, Selected False, Rank: 839.000\n", "Column: 1878, Selected False, Rank: 838.000\n", "Column: 1879, Selected False, Rank: 837.000\n", "Column: 1880, Selected False, Rank: 836.000\n", "Column: 1881, Selected False, Rank: 835.000\n", "Column: 1882, Selected False, Rank: 834.000\n", "Column: 1883, Selected False, Rank: 833.000\n", "Column: 1884, Selected False, Rank: 832.000\n", "Column: 1885, Selected False, Rank: 831.000\n", "Column: 1886, Selected False, Rank: 830.000\n", "Column: 1887, Selected False, Rank: 829.000\n", "Column: 1888, Selected False, Rank: 828.000\n", "Column: 1889, Selected False, Rank: 827.000\n", "Column: 1890, Selected False, Rank: 826.000\n", "Column: 1891, Selected False, Rank: 825.000\n", "Column: 1892, Selected False, Rank: 824.000\n", "Column: 1893, Selected False, Rank: 823.000\n", "Column: 1894, Selected False, Rank: 822.000\n", "Column: 1895, Selected False, Rank: 821.000\n", "Column: 1896, Selected False, Rank: 820.000\n", "Column: 1897, Selected False, Rank: 819.000\n", "Column: 1898, Selected False, Rank: 818.000\n", "Column: 1899, Selected False, Rank: 817.000\n", "Column: 1900, Selected False, Rank: 816.000\n", "Column: 1901, Selected False, Rank: 815.000\n", "Column: 1902, Selected False, Rank: 814.000\n", "Column: 1903, Selected False, Rank: 813.000\n", "Column: 1904, Selected False, Rank: 812.000\n", "Column: 1905, Selected False, Rank: 811.000\n", "Column: 1906, Selected False, Rank: 810.000\n", "Column: 1907, Selected False, Rank: 809.000\n", "Column: 1908, Selected False, Rank: 808.000\n", "Column: 1909, Selected False, Rank: 807.000\n", "Column: 1910, Selected False, Rank: 806.000\n", "Column: 1911, Selected False, Rank: 805.000\n", "Column: 1912, Selected False, Rank: 804.000\n", "Column: 1913, Selected False, Rank: 803.000\n", "Column: 1914, Selected False, Rank: 802.000\n", "Column: 1915, Selected False, Rank: 801.000\n", "Column: 1916, Selected False, Rank: 800.000\n", "Column: 1917, Selected False, Rank: 799.000\n", "Column: 1918, Selected False, Rank: 798.000\n", "Column: 1919, Selected False, Rank: 797.000\n", "Column: 1920, Selected False, Rank: 796.000\n", "Column: 1921, Selected False, Rank: 795.000\n", "Column: 1922, Selected False, Rank: 794.000\n", "Column: 1923, Selected False, Rank: 793.000\n", "Column: 1924, Selected False, Rank: 792.000\n", "Column: 1925, Selected False, Rank: 791.000\n", "Column: 1926, Selected False, Rank: 790.000\n", "Column: 1927, Selected False, Rank: 789.000\n", "Column: 1928, Selected False, Rank: 788.000\n", "Column: 1929, Selected False, Rank: 787.000\n", "Column: 1930, Selected False, Rank: 786.000\n", "Column: 1931, Selected False, Rank: 785.000\n", "Column: 1932, Selected False, Rank: 784.000\n", "Column: 1933, Selected False, Rank: 783.000\n", "Column: 1934, Selected False, Rank: 782.000\n", "Column: 1935, Selected False, Rank: 781.000\n", "Column: 1936, Selected False, Rank: 780.000\n", "Column: 1937, Selected False, Rank: 779.000\n", "Column: 1938, Selected False, Rank: 778.000\n", "Column: 1939, Selected False, Rank: 777.000\n", "Column: 1940, Selected False, Rank: 776.000\n", "Column: 1941, Selected False, Rank: 775.000\n", "Column: 1942, Selected False, Rank: 774.000\n", "Column: 1943, Selected False, Rank: 773.000\n", "Column: 1944, Selected False, Rank: 772.000\n", "Column: 1945, Selected False, Rank: 771.000\n", "Column: 1946, Selected False, Rank: 770.000\n", "Column: 1947, Selected False, Rank: 769.000\n", "Column: 1948, Selected False, Rank: 768.000\n", "Column: 1949, Selected False, Rank: 767.000\n", "Column: 1950, Selected False, Rank: 766.000\n", "Column: 1951, Selected False, Rank: 765.000\n", "Column: 1952, Selected False, Rank: 764.000\n", "Column: 1953, Selected False, Rank: 763.000\n", "Column: 1954, Selected False, Rank: 762.000\n", "Column: 1955, Selected False, Rank: 761.000\n", "Column: 1956, Selected False, Rank: 760.000\n", "Column: 1957, Selected False, Rank: 759.000\n", "Column: 1958, Selected False, Rank: 758.000\n", "Column: 1959, Selected False, Rank: 757.000\n", "Column: 1960, Selected False, Rank: 756.000\n", "Column: 1961, Selected False, Rank: 755.000\n", "Column: 1962, Selected False, Rank: 754.000\n", "Column: 1963, Selected False, Rank: 753.000\n", "Column: 1964, Selected False, Rank: 752.000\n", "Column: 1965, Selected False, Rank: 751.000\n", "Column: 1966, Selected False, Rank: 750.000\n", "Column: 1967, Selected False, Rank: 749.000\n", "Column: 1968, Selected False, Rank: 748.000\n", "Column: 1969, Selected False, Rank: 747.000\n", "Column: 1970, Selected False, Rank: 746.000\n", "Column: 1971, Selected False, Rank: 745.000\n", "Column: 1972, Selected False, Rank: 744.000\n", "Column: 1973, Selected False, Rank: 743.000\n", "Column: 1974, Selected False, Rank: 742.000\n", "Column: 1975, Selected False, Rank: 741.000\n", "Column: 1976, Selected False, Rank: 740.000\n", "Column: 1977, Selected False, Rank: 739.000\n", "Column: 1978, Selected False, Rank: 738.000\n", "Column: 1979, Selected False, Rank: 737.000\n", "Column: 1980, Selected False, Rank: 736.000\n", "Column: 1981, Selected False, Rank: 735.000\n", "Column: 1982, Selected False, Rank: 734.000\n", "Column: 1983, Selected False, Rank: 733.000\n", "Column: 1984, Selected False, Rank: 732.000\n", "Column: 1985, Selected False, Rank: 731.000\n", "Column: 1986, Selected False, Rank: 730.000\n", "Column: 1987, Selected False, Rank: 729.000\n", "Column: 1988, Selected False, Rank: 728.000\n", "Column: 1989, Selected False, Rank: 727.000\n", "Column: 1990, Selected False, Rank: 726.000\n", "Column: 1991, Selected False, Rank: 725.000\n", "Column: 1992, Selected False, Rank: 724.000\n", "Column: 1993, Selected False, Rank: 723.000\n", "Column: 1994, Selected False, Rank: 722.000\n", "Column: 1995, Selected False, Rank: 721.000\n", "Column: 1996, Selected False, Rank: 720.000\n", "Column: 1997, Selected False, Rank: 719.000\n", "Column: 1998, Selected False, Rank: 718.000\n", "Column: 1999, Selected False, Rank: 717.000\n", "Column: 2000, Selected False, Rank: 716.000\n", "Column: 2001, Selected False, Rank: 715.000\n", "Column: 2002, Selected False, Rank: 714.000\n", "Column: 2003, Selected False, Rank: 713.000\n", "Column: 2004, Selected False, Rank: 712.000\n", "Column: 2005, Selected False, Rank: 711.000\n", "Column: 2006, Selected False, Rank: 710.000\n", "Column: 2007, Selected False, Rank: 709.000\n", "Column: 2008, Selected False, Rank: 708.000\n", "Column: 2009, Selected False, Rank: 707.000\n", "Column: 2010, Selected False, Rank: 706.000\n", "Column: 2011, Selected False, Rank: 705.000\n", "Column: 2012, Selected False, Rank: 704.000\n", "Column: 2013, Selected False, Rank: 703.000\n", "Column: 2014, Selected False, Rank: 702.000\n", "Column: 2015, Selected False, Rank: 701.000\n", "Column: 2016, Selected False, Rank: 700.000\n", "Column: 2017, Selected False, Rank: 699.000\n", "Column: 2018, Selected False, Rank: 698.000\n", "Column: 2019, Selected False, Rank: 697.000\n", "Column: 2020, Selected False, Rank: 696.000\n", "Column: 2021, Selected False, Rank: 695.000\n", "Column: 2022, Selected False, Rank: 694.000\n", "Column: 2023, Selected False, Rank: 693.000\n", "Column: 2024, Selected False, Rank: 692.000\n", "Column: 2025, Selected False, Rank: 691.000\n", "Column: 2026, Selected False, Rank: 690.000\n", "Column: 2027, Selected False, Rank: 689.000\n", "Column: 2028, Selected False, Rank: 688.000\n", "Column: 2029, Selected False, Rank: 687.000\n", "Column: 2030, Selected False, Rank: 686.000\n", "Column: 2031, Selected False, Rank: 685.000\n", "Column: 2032, Selected False, Rank: 684.000\n", "Column: 2033, Selected False, Rank: 683.000\n", "Column: 2034, Selected False, Rank: 682.000\n", "Column: 2035, Selected False, Rank: 681.000\n", "Column: 2036, Selected False, Rank: 680.000\n", "Column: 2037, Selected False, Rank: 679.000\n", "Column: 2038, Selected False, Rank: 678.000\n", "Column: 2039, Selected False, Rank: 677.000\n", "Column: 2040, Selected False, Rank: 676.000\n", "Column: 2041, Selected False, Rank: 675.000\n", "Column: 2042, Selected False, Rank: 674.000\n", "Column: 2043, Selected False, Rank: 673.000\n", "Column: 2044, Selected False, Rank: 672.000\n", "Column: 2045, Selected False, Rank: 671.000\n", "Column: 2046, Selected False, Rank: 670.000\n", "Column: 2047, Selected False, Rank: 669.000\n", "Column: 2048, Selected False, Rank: 668.000\n", "Column: 2049, Selected False, Rank: 667.000\n", "Column: 2050, Selected False, Rank: 666.000\n", "Column: 2051, Selected False, Rank: 665.000\n", "Column: 2052, Selected False, Rank: 664.000\n", "Column: 2053, Selected False, Rank: 663.000\n", "Column: 2054, Selected False, Rank: 662.000\n", "Column: 2055, Selected False, Rank: 661.000\n", "Column: 2056, Selected False, Rank: 660.000\n", "Column: 2057, Selected False, Rank: 659.000\n", "Column: 2058, Selected False, Rank: 658.000\n", "Column: 2059, Selected False, Rank: 657.000\n", "Column: 2060, Selected False, Rank: 656.000\n", "Column: 2061, Selected False, Rank: 655.000\n", "Column: 2062, Selected False, Rank: 654.000\n", "Column: 2063, Selected False, Rank: 653.000\n", "Column: 2064, Selected False, Rank: 652.000\n", "Column: 2065, Selected False, Rank: 651.000\n", "Column: 2066, Selected False, Rank: 650.000\n", "Column: 2067, Selected False, Rank: 649.000\n", "Column: 2068, Selected False, Rank: 648.000\n", "Column: 2069, Selected False, Rank: 647.000\n", "Column: 2070, Selected False, Rank: 646.000\n", "Column: 2071, Selected False, Rank: 645.000\n", "Column: 2072, Selected False, Rank: 644.000\n", "Column: 2073, Selected False, Rank: 643.000\n", "Column: 2074, Selected False, Rank: 642.000\n", "Column: 2075, Selected False, Rank: 641.000\n", "Column: 2076, Selected False, Rank: 640.000\n", "Column: 2077, Selected False, Rank: 639.000\n", "Column: 2078, Selected False, Rank: 638.000\n", "Column: 2079, Selected False, Rank: 637.000\n", "Column: 2080, Selected False, Rank: 636.000\n", "Column: 2081, Selected False, Rank: 635.000\n", "Column: 2082, Selected False, Rank: 634.000\n", "Column: 2083, Selected False, Rank: 633.000\n", "Column: 2084, Selected False, Rank: 632.000\n", "Column: 2085, Selected False, Rank: 631.000\n", "Column: 2086, Selected False, Rank: 630.000\n", "Column: 2087, Selected False, Rank: 629.000\n", "Column: 2088, Selected False, Rank: 628.000\n", "Column: 2089, Selected False, Rank: 627.000\n", "Column: 2090, Selected False, Rank: 626.000\n", "Column: 2091, Selected False, Rank: 625.000\n", "Column: 2092, Selected False, Rank: 624.000\n", "Column: 2093, Selected False, Rank: 623.000\n", "Column: 2094, Selected False, Rank: 622.000\n", "Column: 2095, Selected False, Rank: 621.000\n", "Column: 2096, Selected False, Rank: 620.000\n", "Column: 2097, Selected False, Rank: 619.000\n", "Column: 2098, Selected False, Rank: 618.000\n", "Column: 2099, Selected False, Rank: 617.000\n", "Column: 2100, Selected False, Rank: 616.000\n", "Column: 2101, Selected False, Rank: 615.000\n", "Column: 2102, Selected False, Rank: 614.000\n", "Column: 2103, Selected False, Rank: 613.000\n", "Column: 2104, Selected False, Rank: 612.000\n", "Column: 2105, Selected False, Rank: 611.000\n", "Column: 2106, Selected False, Rank: 610.000\n", "Column: 2107, Selected False, Rank: 609.000\n", "Column: 2108, Selected False, Rank: 608.000\n", "Column: 2109, Selected False, Rank: 607.000\n", "Column: 2110, Selected False, Rank: 606.000\n", "Column: 2111, Selected False, Rank: 605.000\n", "Column: 2112, Selected False, Rank: 604.000\n", "Column: 2113, Selected False, Rank: 603.000\n", "Column: 2114, Selected False, Rank: 602.000\n", "Column: 2115, Selected False, Rank: 601.000\n", "Column: 2116, Selected False, Rank: 600.000\n", "Column: 2117, Selected False, Rank: 599.000\n", "Column: 2118, Selected False, Rank: 598.000\n", "Column: 2119, Selected False, Rank: 597.000\n", "Column: 2120, Selected False, Rank: 596.000\n", "Column: 2121, Selected False, Rank: 595.000\n", "Column: 2122, Selected False, Rank: 594.000\n", "Column: 2123, Selected False, Rank: 593.000\n", "Column: 2124, Selected False, Rank: 592.000\n", "Column: 2125, Selected False, Rank: 591.000\n", "Column: 2126, Selected False, Rank: 590.000\n", "Column: 2127, Selected False, Rank: 589.000\n", "Column: 2128, Selected False, Rank: 588.000\n", "Column: 2129, Selected False, Rank: 587.000\n", "Column: 2130, Selected False, Rank: 586.000\n", "Column: 2131, Selected False, Rank: 585.000\n", "Column: 2132, Selected False, Rank: 584.000\n", "Column: 2133, Selected False, Rank: 583.000\n", "Column: 2134, Selected False, Rank: 582.000\n", "Column: 2135, Selected False, Rank: 581.000\n", "Column: 2136, Selected False, Rank: 580.000\n", "Column: 2137, Selected False, Rank: 579.000\n", "Column: 2138, Selected False, Rank: 578.000\n", "Column: 2139, Selected False, Rank: 577.000\n", "Column: 2140, Selected False, Rank: 576.000\n", "Column: 2141, Selected False, Rank: 575.000\n", "Column: 2142, Selected False, Rank: 574.000\n", "Column: 2143, Selected False, Rank: 573.000\n", "Column: 2144, Selected False, Rank: 572.000\n", "Column: 2145, Selected False, Rank: 571.000\n", "Column: 2146, Selected False, Rank: 570.000\n", "Column: 2147, Selected False, Rank: 569.000\n", "Column: 2148, Selected False, Rank: 568.000\n", "Column: 2149, Selected False, Rank: 567.000\n", "Column: 2150, Selected False, Rank: 566.000\n", "Column: 2151, Selected False, Rank: 565.000\n", "Column: 2152, Selected False, Rank: 564.000\n", "Column: 2153, Selected False, Rank: 563.000\n", "Column: 2154, Selected False, Rank: 562.000\n", "Column: 2155, Selected False, Rank: 561.000\n", "Column: 2156, Selected False, Rank: 560.000\n", "Column: 2157, Selected False, Rank: 559.000\n", "Column: 2158, Selected False, Rank: 558.000\n", "Column: 2159, Selected False, Rank: 557.000\n", "Column: 2160, Selected False, Rank: 556.000\n", "Column: 2161, Selected False, Rank: 555.000\n", "Column: 2162, Selected False, Rank: 554.000\n", "Column: 2163, Selected False, Rank: 553.000\n", "Column: 2164, Selected False, Rank: 552.000\n", "Column: 2165, Selected False, Rank: 551.000\n", "Column: 2166, Selected False, Rank: 550.000\n", "Column: 2167, Selected False, Rank: 549.000\n", "Column: 2168, Selected False, Rank: 548.000\n", "Column: 2169, Selected False, Rank: 547.000\n", "Column: 2170, Selected False, Rank: 546.000\n", "Column: 2171, Selected False, Rank: 545.000\n", "Column: 2172, Selected False, Rank: 544.000\n", "Column: 2173, Selected False, Rank: 543.000\n", "Column: 2174, Selected False, Rank: 542.000\n", "Column: 2175, Selected False, Rank: 541.000\n", "Column: 2176, Selected False, Rank: 540.000\n", "Column: 2177, Selected False, Rank: 539.000\n", "Column: 2178, Selected False, Rank: 538.000\n", "Column: 2179, Selected False, Rank: 537.000\n", "Column: 2180, Selected False, Rank: 536.000\n", "Column: 2181, Selected False, Rank: 535.000\n", "Column: 2182, Selected False, Rank: 534.000\n", "Column: 2183, Selected False, Rank: 533.000\n", "Column: 2184, Selected False, Rank: 532.000\n", "Column: 2185, Selected False, Rank: 531.000\n", "Column: 2186, Selected False, Rank: 530.000\n", "Column: 2187, Selected False, Rank: 529.000\n", "Column: 2188, Selected False, Rank: 528.000\n", "Column: 2189, Selected False, Rank: 527.000\n", "Column: 2190, Selected False, Rank: 526.000\n", "Column: 2191, Selected False, Rank: 525.000\n", "Column: 2192, Selected False, Rank: 524.000\n", "Column: 2193, Selected False, Rank: 523.000\n", "Column: 2194, Selected False, Rank: 522.000\n", "Column: 2195, Selected False, Rank: 521.000\n", "Column: 2196, Selected False, Rank: 520.000\n", "Column: 2197, Selected False, Rank: 519.000\n", "Column: 2198, Selected False, Rank: 518.000\n", "Column: 2199, Selected False, Rank: 517.000\n", "Column: 2200, Selected False, Rank: 516.000\n", "Column: 2201, Selected False, Rank: 515.000\n", "Column: 2202, Selected False, Rank: 514.000\n", "Column: 2203, Selected False, Rank: 513.000\n", "Column: 2204, Selected False, Rank: 512.000\n", "Column: 2205, Selected False, Rank: 511.000\n", "Column: 2206, Selected False, Rank: 510.000\n", "Column: 2207, Selected False, Rank: 509.000\n", "Column: 2208, Selected False, Rank: 508.000\n", "Column: 2209, Selected False, Rank: 507.000\n", "Column: 2210, Selected False, Rank: 506.000\n", "Column: 2211, Selected False, Rank: 505.000\n", "Column: 2212, Selected False, Rank: 504.000\n", "Column: 2213, Selected False, Rank: 503.000\n", "Column: 2214, Selected False, Rank: 502.000\n", "Column: 2215, Selected False, Rank: 501.000\n", "Column: 2216, Selected False, Rank: 500.000\n", "Column: 2217, Selected False, Rank: 499.000\n", "Column: 2218, Selected False, Rank: 498.000\n", "Column: 2219, Selected False, Rank: 497.000\n", "Column: 2220, Selected False, Rank: 496.000\n", "Column: 2221, Selected False, Rank: 495.000\n", "Column: 2222, Selected False, Rank: 494.000\n", "Column: 2223, Selected False, Rank: 493.000\n", "Column: 2224, Selected False, Rank: 492.000\n", "Column: 2225, Selected False, Rank: 491.000\n", "Column: 2226, Selected False, Rank: 490.000\n", "Column: 2227, Selected False, Rank: 489.000\n", "Column: 2228, Selected False, Rank: 488.000\n", "Column: 2229, Selected False, Rank: 487.000\n", "Column: 2230, Selected False, Rank: 486.000\n", "Column: 2231, Selected False, Rank: 485.000\n", "Column: 2232, Selected False, Rank: 484.000\n", "Column: 2233, Selected False, Rank: 483.000\n", "Column: 2234, Selected False, Rank: 482.000\n", "Column: 2235, Selected False, Rank: 481.000\n", "Column: 2236, Selected False, Rank: 480.000\n", "Column: 2237, Selected False, Rank: 479.000\n", "Column: 2238, Selected False, Rank: 478.000\n", "Column: 2239, Selected False, Rank: 477.000\n", "Column: 2240, Selected False, Rank: 476.000\n", "Column: 2241, Selected False, Rank: 475.000\n", "Column: 2242, Selected False, Rank: 474.000\n", "Column: 2243, Selected False, Rank: 473.000\n", "Column: 2244, Selected False, Rank: 472.000\n", "Column: 2245, Selected False, Rank: 471.000\n", "Column: 2246, Selected False, Rank: 470.000\n", "Column: 2247, Selected False, Rank: 469.000\n", "Column: 2248, Selected False, Rank: 468.000\n", "Column: 2249, Selected False, Rank: 467.000\n", "Column: 2250, Selected False, Rank: 466.000\n", "Column: 2251, Selected False, Rank: 465.000\n", "Column: 2252, Selected False, Rank: 464.000\n", "Column: 2253, Selected False, Rank: 463.000\n", "Column: 2254, Selected False, Rank: 462.000\n", "Column: 2255, Selected False, Rank: 461.000\n", "Column: 2256, Selected False, Rank: 460.000\n", "Column: 2257, Selected False, Rank: 459.000\n", "Column: 2258, Selected False, Rank: 458.000\n", "Column: 2259, Selected False, Rank: 457.000\n", "Column: 2260, Selected False, Rank: 456.000\n", "Column: 2261, Selected False, Rank: 455.000\n", "Column: 2262, Selected False, Rank: 454.000\n", "Column: 2263, Selected False, Rank: 453.000\n", "Column: 2264, Selected False, Rank: 452.000\n", "Column: 2265, Selected False, Rank: 451.000\n", "Column: 2266, Selected False, Rank: 450.000\n", "Column: 2267, Selected False, Rank: 449.000\n", "Column: 2268, Selected False, Rank: 448.000\n", "Column: 2269, Selected False, Rank: 447.000\n", "Column: 2270, Selected False, Rank: 446.000\n", "Column: 2271, Selected False, Rank: 445.000\n", "Column: 2272, Selected False, Rank: 444.000\n", "Column: 2273, Selected False, Rank: 443.000\n", "Column: 2274, Selected False, Rank: 442.000\n", "Column: 2275, Selected False, Rank: 441.000\n", "Column: 2276, Selected False, Rank: 440.000\n", "Column: 2277, Selected False, Rank: 439.000\n", "Column: 2278, Selected False, Rank: 438.000\n", "Column: 2279, Selected False, Rank: 437.000\n", "Column: 2280, Selected False, Rank: 436.000\n", "Column: 2281, Selected False, Rank: 435.000\n", "Column: 2282, Selected False, Rank: 434.000\n", "Column: 2283, Selected False, Rank: 433.000\n", "Column: 2284, Selected False, Rank: 432.000\n", "Column: 2285, Selected False, Rank: 431.000\n", "Column: 2286, Selected False, Rank: 430.000\n", "Column: 2287, Selected False, Rank: 429.000\n", "Column: 2288, Selected False, Rank: 428.000\n", "Column: 2289, Selected False, Rank: 427.000\n", "Column: 2290, Selected False, Rank: 426.000\n", "Column: 2291, Selected False, Rank: 425.000\n", "Column: 2292, Selected False, Rank: 424.000\n", "Column: 2293, Selected False, Rank: 423.000\n", "Column: 2294, Selected False, Rank: 422.000\n", "Column: 2295, Selected False, Rank: 421.000\n", "Column: 2296, Selected False, Rank: 420.000\n", "Column: 2297, Selected False, Rank: 419.000\n", "Column: 2298, Selected False, Rank: 418.000\n", "Column: 2299, Selected False, Rank: 417.000\n", "Column: 2300, Selected False, Rank: 416.000\n", "Column: 2301, Selected False, Rank: 415.000\n", "Column: 2302, Selected False, Rank: 414.000\n", "Column: 2303, Selected False, Rank: 413.000\n", "Column: 2304, Selected False, Rank: 412.000\n", "Column: 2305, Selected False, Rank: 411.000\n", "Column: 2306, Selected False, Rank: 410.000\n", "Column: 2307, Selected False, Rank: 409.000\n", "Column: 2308, Selected False, Rank: 408.000\n", "Column: 2309, Selected False, Rank: 407.000\n", "Column: 2310, Selected False, Rank: 406.000\n", "Column: 2311, Selected False, Rank: 405.000\n", "Column: 2312, Selected False, Rank: 404.000\n", "Column: 2313, Selected False, Rank: 403.000\n", "Column: 2314, Selected False, Rank: 402.000\n", "Column: 2315, Selected False, Rank: 401.000\n", "Column: 2316, Selected False, Rank: 400.000\n", "Column: 2317, Selected False, Rank: 399.000\n", "Column: 2318, Selected False, Rank: 398.000\n", "Column: 2319, Selected False, Rank: 397.000\n", "Column: 2320, Selected False, Rank: 396.000\n", "Column: 2321, Selected False, Rank: 395.000\n", "Column: 2322, Selected False, Rank: 394.000\n", "Column: 2323, Selected False, Rank: 393.000\n", "Column: 2324, Selected False, Rank: 392.000\n", "Column: 2325, Selected False, Rank: 391.000\n", "Column: 2326, Selected False, Rank: 390.000\n", "Column: 2327, Selected False, Rank: 389.000\n", "Column: 2328, Selected False, Rank: 388.000\n", "Column: 2329, Selected False, Rank: 387.000\n", "Column: 2330, Selected False, Rank: 386.000\n", "Column: 2331, Selected False, Rank: 385.000\n", "Column: 2332, Selected False, Rank: 384.000\n", "Column: 2333, Selected False, Rank: 383.000\n", "Column: 2334, Selected False, Rank: 382.000\n", "Column: 2335, Selected False, Rank: 381.000\n", "Column: 2336, Selected False, Rank: 380.000\n", "Column: 2337, Selected False, Rank: 379.000\n", "Column: 2338, Selected False, Rank: 378.000\n", "Column: 2339, Selected False, Rank: 377.000\n", "Column: 2340, Selected False, Rank: 376.000\n", "Column: 2341, Selected False, Rank: 375.000\n", "Column: 2342, Selected False, Rank: 374.000\n", "Column: 2343, Selected False, Rank: 373.000\n", "Column: 2344, Selected False, Rank: 372.000\n", "Column: 2345, Selected False, Rank: 371.000\n", "Column: 2346, Selected False, Rank: 370.000\n", "Column: 2347, Selected False, Rank: 369.000\n", "Column: 2348, Selected False, Rank: 368.000\n", "Column: 2349, Selected False, Rank: 367.000\n", "Column: 2350, Selected False, Rank: 366.000\n", "Column: 2351, Selected False, Rank: 365.000\n", "Column: 2352, Selected False, Rank: 364.000\n", "Column: 2353, Selected False, Rank: 363.000\n", "Column: 2354, Selected False, Rank: 362.000\n", "Column: 2355, Selected False, Rank: 361.000\n", "Column: 2356, Selected False, Rank: 360.000\n", "Column: 2357, Selected False, Rank: 359.000\n", "Column: 2358, Selected False, Rank: 358.000\n", "Column: 2359, Selected False, Rank: 357.000\n", "Column: 2360, Selected False, Rank: 356.000\n", "Column: 2361, Selected False, Rank: 355.000\n", "Column: 2362, Selected False, Rank: 354.000\n", "Column: 2363, Selected False, Rank: 353.000\n", "Column: 2364, Selected False, Rank: 352.000\n", "Column: 2365, Selected False, Rank: 351.000\n", "Column: 2366, Selected False, Rank: 350.000\n", "Column: 2367, Selected False, Rank: 349.000\n", "Column: 2368, Selected False, Rank: 348.000\n", "Column: 2369, Selected False, Rank: 347.000\n", "Column: 2370, Selected False, Rank: 346.000\n", "Column: 2371, Selected False, Rank: 345.000\n", "Column: 2372, Selected False, Rank: 344.000\n", "Column: 2373, Selected False, Rank: 343.000\n", "Column: 2374, Selected False, Rank: 342.000\n", "Column: 2375, Selected False, Rank: 341.000\n", "Column: 2376, Selected False, Rank: 340.000\n", "Column: 2377, Selected False, Rank: 339.000\n", "Column: 2378, Selected False, Rank: 338.000\n", "Column: 2379, Selected False, Rank: 337.000\n", "Column: 2380, Selected False, Rank: 336.000\n", "Column: 2381, Selected False, Rank: 335.000\n", "Column: 2382, Selected False, Rank: 334.000\n", "Column: 2383, Selected False, Rank: 333.000\n", "Column: 2384, Selected False, Rank: 332.000\n", "Column: 2385, Selected False, Rank: 331.000\n", "Column: 2386, Selected False, Rank: 330.000\n", "Column: 2387, Selected False, Rank: 329.000\n", "Column: 2388, Selected False, Rank: 328.000\n", "Column: 2389, Selected False, Rank: 327.000\n", "Column: 2390, Selected False, Rank: 326.000\n", "Column: 2391, Selected False, Rank: 325.000\n", "Column: 2392, Selected False, Rank: 324.000\n", "Column: 2393, Selected False, Rank: 323.000\n", "Column: 2394, Selected False, Rank: 322.000\n", "Column: 2395, Selected False, Rank: 321.000\n", "Column: 2396, Selected False, Rank: 320.000\n", "Column: 2397, Selected False, Rank: 319.000\n", "Column: 2398, Selected False, Rank: 318.000\n", "Column: 2399, Selected False, Rank: 317.000\n", "Column: 2400, Selected False, Rank: 316.000\n", "Column: 2401, Selected False, Rank: 315.000\n", "Column: 2402, Selected False, Rank: 314.000\n", "Column: 2403, Selected False, Rank: 313.000\n", "Column: 2404, Selected False, Rank: 312.000\n", "Column: 2405, Selected False, Rank: 311.000\n", "Column: 2406, Selected False, Rank: 310.000\n", "Column: 2407, Selected False, Rank: 309.000\n", "Column: 2408, Selected False, Rank: 308.000\n", "Column: 2409, Selected False, Rank: 307.000\n", "Column: 2410, Selected False, Rank: 306.000\n", "Column: 2411, Selected False, Rank: 305.000\n", "Column: 2412, Selected False, Rank: 304.000\n", "Column: 2413, Selected False, Rank: 303.000\n", "Column: 2414, Selected False, Rank: 302.000\n", "Column: 2415, Selected False, Rank: 301.000\n", "Column: 2416, Selected False, Rank: 300.000\n", "Column: 2417, Selected False, Rank: 299.000\n", "Column: 2418, Selected False, Rank: 298.000\n", "Column: 2419, Selected False, Rank: 297.000\n", "Column: 2420, Selected False, Rank: 296.000\n", "Column: 2421, Selected False, Rank: 295.000\n", "Column: 2422, Selected False, Rank: 294.000\n", "Column: 2423, Selected False, Rank: 293.000\n", "Column: 2424, Selected False, Rank: 292.000\n", "Column: 2425, Selected False, Rank: 291.000\n", "Column: 2426, Selected False, Rank: 290.000\n", "Column: 2427, Selected False, Rank: 289.000\n", "Column: 2428, Selected False, Rank: 288.000\n", "Column: 2429, Selected False, Rank: 287.000\n", "Column: 2430, Selected False, Rank: 286.000\n", "Column: 2431, Selected False, Rank: 285.000\n", "Column: 2432, Selected False, Rank: 284.000\n", "Column: 2433, Selected False, Rank: 283.000\n", "Column: 2434, Selected False, Rank: 282.000\n", "Column: 2435, Selected False, Rank: 281.000\n", "Column: 2436, Selected False, Rank: 280.000\n", "Column: 2437, Selected False, Rank: 279.000\n", "Column: 2438, Selected False, Rank: 278.000\n", "Column: 2439, Selected False, Rank: 277.000\n", "Column: 2440, Selected False, Rank: 276.000\n", "Column: 2441, Selected False, Rank: 275.000\n", "Column: 2442, Selected False, Rank: 274.000\n", "Column: 2443, Selected False, Rank: 273.000\n", "Column: 2444, Selected False, Rank: 272.000\n", "Column: 2445, Selected False, Rank: 271.000\n", "Column: 2446, Selected False, Rank: 270.000\n", "Column: 2447, Selected False, Rank: 269.000\n", "Column: 2448, Selected False, Rank: 268.000\n", "Column: 2449, Selected False, Rank: 267.000\n", "Column: 2450, Selected False, Rank: 266.000\n", "Column: 2451, Selected False, Rank: 265.000\n", "Column: 2452, Selected False, Rank: 264.000\n", "Column: 2453, Selected False, Rank: 263.000\n", "Column: 2454, Selected False, Rank: 262.000\n", "Column: 2455, Selected False, Rank: 261.000\n", "Column: 2456, Selected False, Rank: 260.000\n", "Column: 2457, Selected False, Rank: 259.000\n", "Column: 2458, Selected False, Rank: 258.000\n", "Column: 2459, Selected False, Rank: 257.000\n", "Column: 2460, Selected False, Rank: 256.000\n", "Column: 2461, Selected False, Rank: 255.000\n", "Column: 2462, Selected False, Rank: 254.000\n", "Column: 2463, Selected False, Rank: 253.000\n", "Column: 2464, Selected False, Rank: 252.000\n", "Column: 2465, Selected False, Rank: 251.000\n", "Column: 2466, Selected False, Rank: 250.000\n", "Column: 2467, Selected False, Rank: 249.000\n", "Column: 2468, Selected False, Rank: 248.000\n", "Column: 2469, Selected False, Rank: 247.000\n", "Column: 2470, Selected False, Rank: 246.000\n", "Column: 2471, Selected False, Rank: 245.000\n", "Column: 2472, Selected False, Rank: 244.000\n", "Column: 2473, Selected False, Rank: 243.000\n", "Column: 2474, Selected False, Rank: 242.000\n", "Column: 2475, Selected False, Rank: 241.000\n", "Column: 2476, Selected False, Rank: 240.000\n", "Column: 2477, Selected False, Rank: 239.000\n", "Column: 2478, Selected False, Rank: 238.000\n", "Column: 2479, Selected False, Rank: 237.000\n", "Column: 2480, Selected False, Rank: 236.000\n", "Column: 2481, Selected False, Rank: 235.000\n", "Column: 2482, Selected False, Rank: 234.000\n", "Column: 2483, Selected False, Rank: 233.000\n", "Column: 2484, Selected False, Rank: 232.000\n", "Column: 2485, Selected False, Rank: 231.000\n", "Column: 2486, Selected False, Rank: 230.000\n", "Column: 2487, Selected False, Rank: 229.000\n", "Column: 2488, Selected False, Rank: 228.000\n", "Column: 2489, Selected False, Rank: 227.000\n", "Column: 2490, Selected False, Rank: 226.000\n", "Column: 2491, Selected False, Rank: 225.000\n", "Column: 2492, Selected False, Rank: 224.000\n", "Column: 2493, Selected False, Rank: 223.000\n", "Column: 2494, Selected False, Rank: 222.000\n", "Column: 2495, Selected False, Rank: 221.000\n", "Column: 2496, Selected False, Rank: 220.000\n", "Column: 2497, Selected False, Rank: 219.000\n", "Column: 2498, Selected False, Rank: 218.000\n", "Column: 2499, Selected False, Rank: 217.000\n", "Column: 2500, Selected False, Rank: 216.000\n", "Column: 2501, Selected False, Rank: 215.000\n", "Column: 2502, Selected False, Rank: 214.000\n", "Column: 2503, Selected False, Rank: 213.000\n", "Column: 2504, Selected False, Rank: 212.000\n", "Column: 2505, Selected False, Rank: 211.000\n", "Column: 2506, Selected False, Rank: 210.000\n", "Column: 2507, Selected False, Rank: 209.000\n", "Column: 2508, Selected False, Rank: 208.000\n", "Column: 2509, Selected False, Rank: 207.000\n", "Column: 2510, Selected False, Rank: 206.000\n", "Column: 2511, Selected False, Rank: 205.000\n", "Column: 2512, Selected False, Rank: 204.000\n", "Column: 2513, Selected False, Rank: 203.000\n", "Column: 2514, Selected False, Rank: 202.000\n", "Column: 2515, Selected False, Rank: 201.000\n", "Column: 2516, Selected False, Rank: 200.000\n", "Column: 2517, Selected False, Rank: 199.000\n", "Column: 2518, Selected False, Rank: 198.000\n", "Column: 2519, Selected False, Rank: 197.000\n", "Column: 2520, Selected False, Rank: 196.000\n", "Column: 2521, Selected False, Rank: 195.000\n", "Column: 2522, Selected False, Rank: 194.000\n", "Column: 2523, Selected False, Rank: 193.000\n", "Column: 2524, Selected False, Rank: 192.000\n", "Column: 2525, Selected False, Rank: 191.000\n", "Column: 2526, Selected False, Rank: 190.000\n", "Column: 2527, Selected False, Rank: 189.000\n", "Column: 2528, Selected False, Rank: 188.000\n", "Column: 2529, Selected False, Rank: 187.000\n", "Column: 2530, Selected False, Rank: 186.000\n", "Column: 2531, Selected False, Rank: 185.000\n", "Column: 2532, Selected False, Rank: 184.000\n", "Column: 2533, Selected False, Rank: 183.000\n", "Column: 2534, Selected False, Rank: 182.000\n", "Column: 2535, Selected False, Rank: 181.000\n", "Column: 2536, Selected False, Rank: 180.000\n", "Column: 2537, Selected False, Rank: 179.000\n", "Column: 2538, Selected False, Rank: 178.000\n", "Column: 2539, Selected False, Rank: 177.000\n", "Column: 2540, Selected False, Rank: 176.000\n", "Column: 2541, Selected False, Rank: 175.000\n", "Column: 2542, Selected False, Rank: 174.000\n", "Column: 2543, Selected False, Rank: 173.000\n", "Column: 2544, Selected False, Rank: 172.000\n", "Column: 2545, Selected False, Rank: 171.000\n", "Column: 2546, Selected False, Rank: 170.000\n", "Column: 2547, Selected False, Rank: 169.000\n", "Column: 2548, Selected False, Rank: 168.000\n", "Column: 2549, Selected False, Rank: 167.000\n", "Column: 2550, Selected False, Rank: 166.000\n", "Column: 2551, Selected False, Rank: 165.000\n", "Column: 2552, Selected False, Rank: 164.000\n", "Column: 2553, Selected False, Rank: 163.000\n", "Column: 2554, Selected False, Rank: 162.000\n", "Column: 2555, Selected False, Rank: 161.000\n", "Column: 2556, Selected False, Rank: 160.000\n", "Column: 2557, Selected False, Rank: 159.000\n", "Column: 2558, Selected False, Rank: 158.000\n", "Column: 2559, Selected False, Rank: 157.000\n", "Column: 2560, Selected False, Rank: 156.000\n", "Column: 2561, Selected False, Rank: 155.000\n", "Column: 2562, Selected False, Rank: 154.000\n", "Column: 2563, Selected False, Rank: 153.000\n", "Column: 2564, Selected False, Rank: 152.000\n", "Column: 2565, Selected False, Rank: 151.000\n", "Column: 2566, Selected False, Rank: 150.000\n", "Column: 2567, Selected False, Rank: 149.000\n", "Column: 2568, Selected False, Rank: 148.000\n", "Column: 2569, Selected False, Rank: 147.000\n", "Column: 2570, Selected False, Rank: 146.000\n", "Column: 2571, Selected False, Rank: 145.000\n", "Column: 2572, Selected False, Rank: 144.000\n", "Column: 2573, Selected False, Rank: 143.000\n", "Column: 2574, Selected False, Rank: 142.000\n", "Column: 2575, Selected False, Rank: 141.000\n", "Column: 2576, Selected False, Rank: 140.000\n", "Column: 2577, Selected False, Rank: 139.000\n", "Column: 2578, Selected False, Rank: 138.000\n", "Column: 2579, Selected False, Rank: 137.000\n", "Column: 2580, Selected False, Rank: 136.000\n", "Column: 2581, Selected False, Rank: 135.000\n", "Column: 2582, Selected False, Rank: 134.000\n", "Column: 2583, Selected False, Rank: 133.000\n", "Column: 2584, Selected False, Rank: 132.000\n", "Column: 2585, Selected False, Rank: 131.000\n", "Column: 2586, Selected False, Rank: 130.000\n", "Column: 2587, Selected False, Rank: 129.000\n", "Column: 2588, Selected False, Rank: 128.000\n", "Column: 2589, Selected False, Rank: 127.000\n", "Column: 2590, Selected False, Rank: 126.000\n", "Column: 2591, Selected False, Rank: 125.000\n", "Column: 2592, Selected False, Rank: 124.000\n", "Column: 2593, Selected False, Rank: 123.000\n", "Column: 2594, Selected False, Rank: 122.000\n", "Column: 2595, Selected False, Rank: 121.000\n", "Column: 2596, Selected False, Rank: 120.000\n", "Column: 2597, Selected False, Rank: 119.000\n", "Column: 2598, Selected False, Rank: 118.000\n", "Column: 2599, Selected False, Rank: 117.000\n", "Column: 2600, Selected False, Rank: 116.000\n", "Column: 2601, Selected False, Rank: 115.000\n", "Column: 2602, Selected False, Rank: 114.000\n", "Column: 2603, Selected False, Rank: 113.000\n", "Column: 2604, Selected False, Rank: 112.000\n", "Column: 2605, Selected False, Rank: 111.000\n", "Column: 2606, Selected False, Rank: 110.000\n", "Column: 2607, Selected False, Rank: 109.000\n", "Column: 2608, Selected False, Rank: 108.000\n", "Column: 2609, Selected False, Rank: 107.000\n", "Column: 2610, Selected False, Rank: 106.000\n", "Column: 2611, Selected False, Rank: 105.000\n", "Column: 2612, Selected False, Rank: 104.000\n", "Column: 2613, Selected False, Rank: 103.000\n", "Column: 2614, Selected False, Rank: 102.000\n", "Column: 2615, Selected False, Rank: 101.000\n", "Column: 2616, Selected False, Rank: 100.000\n", "Column: 2617, Selected False, Rank: 99.000\n", "Column: 2618, Selected False, Rank: 98.000\n", "Column: 2619, Selected False, Rank: 97.000\n", "Column: 2620, Selected False, Rank: 96.000\n", "Column: 2621, Selected False, Rank: 95.000\n", "Column: 2622, Selected False, Rank: 94.000\n", "Column: 2623, Selected False, Rank: 93.000\n", "Column: 2624, Selected False, Rank: 92.000\n", "Column: 2625, Selected False, Rank: 91.000\n", "Column: 2626, Selected False, Rank: 90.000\n", "Column: 2627, Selected False, Rank: 89.000\n", "Column: 2628, Selected False, Rank: 88.000\n", "Column: 2629, Selected False, Rank: 87.000\n", "Column: 2630, Selected False, Rank: 86.000\n", "Column: 2631, Selected False, Rank: 85.000\n", "Column: 2632, Selected False, Rank: 84.000\n", "Column: 2633, Selected False, Rank: 83.000\n", "Column: 2634, Selected False, Rank: 82.000\n", "Column: 2635, Selected False, Rank: 81.000\n", "Column: 2636, Selected False, Rank: 80.000\n", "Column: 2637, Selected False, Rank: 79.000\n", "Column: 2638, Selected False, Rank: 78.000\n", "Column: 2639, Selected False, Rank: 77.000\n", "Column: 2640, Selected False, Rank: 76.000\n", "Column: 2641, Selected False, Rank: 75.000\n", "Column: 2642, Selected False, Rank: 74.000\n", "Column: 2643, Selected False, Rank: 73.000\n", "Column: 2644, Selected False, Rank: 72.000\n", "Column: 2645, Selected False, Rank: 71.000\n", "Column: 2646, Selected False, Rank: 70.000\n", "Column: 2647, Selected False, Rank: 69.000\n", "Column: 2648, Selected False, Rank: 68.000\n", "Column: 2649, Selected False, Rank: 67.000\n", "Column: 2650, Selected False, Rank: 66.000\n", "Column: 2651, Selected False, Rank: 65.000\n", "Column: 2652, Selected False, Rank: 64.000\n", "Column: 2653, Selected False, Rank: 63.000\n", "Column: 2654, Selected False, Rank: 62.000\n", "Column: 2655, Selected False, Rank: 61.000\n", "Column: 2656, Selected False, Rank: 60.000\n", "Column: 2657, Selected False, Rank: 59.000\n", "Column: 2658, Selected False, Rank: 58.000\n", "Column: 2659, Selected False, Rank: 57.000\n", "Column: 2660, Selected False, Rank: 56.000\n", "Column: 2661, Selected False, Rank: 55.000\n", "Column: 2662, Selected False, Rank: 54.000\n", "Column: 2663, Selected False, Rank: 53.000\n", "Column: 2664, Selected False, Rank: 52.000\n", "Column: 2665, Selected False, Rank: 51.000\n", "Column: 2666, Selected False, Rank: 50.000\n", "Column: 2667, Selected False, Rank: 49.000\n", "Column: 2668, Selected False, Rank: 48.000\n", "Column: 2669, Selected False, Rank: 47.000\n", "Column: 2670, Selected False, Rank: 46.000\n", "Column: 2671, Selected False, Rank: 45.000\n", "Column: 2672, Selected False, Rank: 44.000\n", "Column: 2673, Selected False, Rank: 43.000\n", "Column: 2674, Selected False, Rank: 42.000\n", "Column: 2675, Selected False, Rank: 41.000\n", "Column: 2676, Selected False, Rank: 40.000\n", "Column: 2677, Selected False, Rank: 39.000\n", "Column: 2678, Selected False, Rank: 38.000\n", "Column: 2679, Selected False, Rank: 37.000\n", "Column: 2680, Selected False, Rank: 36.000\n", "Column: 2681, Selected False, Rank: 35.000\n", "Column: 2682, Selected False, Rank: 34.000\n", "Column: 2683, Selected False, Rank: 33.000\n", "Column: 2684, Selected False, Rank: 32.000\n", "Column: 2685, Selected False, Rank: 31.000\n", "Column: 2686, Selected False, Rank: 30.000\n", "Column: 2687, Selected False, Rank: 29.000\n", "Column: 2688, Selected False, Rank: 28.000\n", "Column: 2689, Selected False, Rank: 27.000\n", "Column: 2690, Selected False, Rank: 26.000\n", "Column: 2691, Selected False, Rank: 25.000\n", "Column: 2692, Selected False, Rank: 24.000\n", "Column: 2693, Selected False, Rank: 23.000\n", "Column: 2694, Selected False, Rank: 22.000\n", "Column: 2695, Selected False, Rank: 21.000\n", "Column: 2696, Selected False, Rank: 20.000\n", "Column: 2697, Selected False, Rank: 19.000\n", "Column: 2698, Selected False, Rank: 18.000\n", "Column: 2699, Selected False, Rank: 17.000\n", "Column: 2700, Selected False, Rank: 16.000\n", "Column: 2701, Selected False, Rank: 15.000\n", "Column: 2702, Selected False, Rank: 14.000\n", "Column: 2703, Selected False, Rank: 13.000\n", "Column: 2704, Selected False, Rank: 12.000\n", "Column: 2705, Selected False, Rank: 11.000\n", "Column: 2706, Selected False, Rank: 10.000\n", "Column: 2707, Selected False, Rank: 9.000\n", "Column: 2708, Selected False, Rank: 8.000\n", "Column: 2709, Selected False, Rank: 7.000\n", "Column: 2710, Selected False, Rank: 6.000\n", "Column: 2711, Selected False, Rank: 5.000\n", "Column: 2712, Selected False, Rank: 4.000\n", "Column: 2713, Selected False, Rank: 3.000\n", "Column: 2714, Selected False, Rank: 2.000\n", "Column: 2715, Selected True, Rank: 1.000\n", "Column: 2716, Selected True, Rank: 1.000\n", "Column: 2717, Selected True, Rank: 1.000\n", "Column: 2718, Selected True, Rank: 1.000\n", "Column: 2719, Selected True, Rank: 1.000\n", "Column: 2720, Selected True, Rank: 1.000\n", "Column: 2721, Selected True, Rank: 1.000\n", "Column: 2722, Selected True, Rank: 1.000\n", "Column: 2723, Selected True, Rank: 1.000\n", "Column: 2724, Selected True, Rank: 1.000\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "liX5Sw90Djc4", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "c462e397-2cac-473b-8314-3c90d10e4065" }, "source": [ "# fit the model n_features_to_select=1\n", "rfe.fit(X, y_cat)\n", "# summarize all features\n", "for i in range(X.shape[1]):\n", "\tprint('Column: %d, Selected %s, Rank: %.3f' % (i, rfe.support_[i], rfe.ranking_[i]))" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Column: 0, Selected False, Rank: 2725.000\n", "Column: 1, Selected False, Rank: 2724.000\n", "Column: 2, Selected False, Rank: 2723.000\n", "Column: 3, Selected False, Rank: 2722.000\n", "Column: 4, Selected False, Rank: 2721.000\n", "Column: 5, Selected False, Rank: 2720.000\n", "Column: 6, Selected False, Rank: 2719.000\n", "Column: 7, Selected False, Rank: 2718.000\n", "Column: 8, Selected False, Rank: 2717.000\n", "Column: 9, Selected False, Rank: 2716.000\n", "Column: 10, Selected False, Rank: 2715.000\n", "Column: 11, Selected False, Rank: 2714.000\n", "Column: 12, Selected False, Rank: 2713.000\n", "Column: 13, Selected False, Rank: 2712.000\n", "Column: 14, Selected False, Rank: 2711.000\n", "Column: 15, Selected False, Rank: 2710.000\n", "Column: 16, Selected False, Rank: 2709.000\n", "Column: 17, Selected False, Rank: 2708.000\n", "Column: 18, Selected False, Rank: 2707.000\n", "Column: 19, Selected False, Rank: 2706.000\n", "Column: 20, Selected False, Rank: 2705.000\n", "Column: 21, Selected False, Rank: 2704.000\n", "Column: 22, Selected False, Rank: 2703.000\n", "Column: 23, Selected False, Rank: 2702.000\n", "Column: 24, Selected False, Rank: 2701.000\n", "Column: 25, Selected False, Rank: 2700.000\n", "Column: 26, Selected False, Rank: 2699.000\n", "Column: 27, Selected False, Rank: 2698.000\n", "Column: 28, Selected False, Rank: 2697.000\n", "Column: 29, Selected False, Rank: 2696.000\n", "Column: 30, Selected False, Rank: 2695.000\n", "Column: 31, Selected False, Rank: 2694.000\n", "Column: 32, Selected False, Rank: 2693.000\n", "Column: 33, Selected False, Rank: 2692.000\n", "Column: 34, Selected False, Rank: 2691.000\n", "Column: 35, Selected False, Rank: 2690.000\n", "Column: 36, Selected False, Rank: 2689.000\n", "Column: 37, Selected False, Rank: 2688.000\n", "Column: 38, Selected False, Rank: 2687.000\n", "Column: 39, Selected False, Rank: 2686.000\n", "Column: 40, Selected False, Rank: 2685.000\n", "Column: 41, Selected False, Rank: 2684.000\n", "Column: 42, Selected False, Rank: 2683.000\n", "Column: 43, Selected False, Rank: 2682.000\n", "Column: 44, Selected False, Rank: 2681.000\n", "Column: 45, Selected False, Rank: 2680.000\n", "Column: 46, Selected False, Rank: 2679.000\n", "Column: 47, Selected False, Rank: 2678.000\n", "Column: 48, Selected False, Rank: 2677.000\n", "Column: 49, Selected False, Rank: 2676.000\n", "Column: 50, Selected False, Rank: 2675.000\n", "Column: 51, Selected False, Rank: 2674.000\n", "Column: 52, Selected False, Rank: 2673.000\n", "Column: 53, Selected False, Rank: 2672.000\n", "Column: 54, Selected False, Rank: 2671.000\n", "Column: 55, Selected False, Rank: 2670.000\n", "Column: 56, Selected False, Rank: 2669.000\n", "Column: 57, Selected False, Rank: 2668.000\n", "Column: 58, Selected False, Rank: 2667.000\n", "Column: 59, Selected False, Rank: 2666.000\n", "Column: 60, Selected False, Rank: 2665.000\n", "Column: 61, Selected False, Rank: 2664.000\n", "Column: 62, Selected False, Rank: 2663.000\n", "Column: 63, Selected False, Rank: 2662.000\n", "Column: 64, Selected False, Rank: 2661.000\n", "Column: 65, Selected False, Rank: 2660.000\n", "Column: 66, Selected False, Rank: 2659.000\n", "Column: 67, Selected False, Rank: 2658.000\n", "Column: 68, Selected False, Rank: 2657.000\n", "Column: 69, Selected False, Rank: 2656.000\n", "Column: 70, Selected False, Rank: 2655.000\n", "Column: 71, Selected False, Rank: 2654.000\n", "Column: 72, Selected False, Rank: 2653.000\n", "Column: 73, Selected False, Rank: 2652.000\n", "Column: 74, Selected False, Rank: 2651.000\n", "Column: 75, Selected False, Rank: 2650.000\n", "Column: 76, Selected False, Rank: 2649.000\n", "Column: 77, Selected False, Rank: 2648.000\n", "Column: 78, Selected False, Rank: 2647.000\n", "Column: 79, Selected False, Rank: 2646.000\n", "Column: 80, Selected False, Rank: 2645.000\n", "Column: 81, Selected False, Rank: 2644.000\n", "Column: 82, Selected False, Rank: 2643.000\n", "Column: 83, Selected False, Rank: 2642.000\n", "Column: 84, Selected False, Rank: 2641.000\n", "Column: 85, Selected False, Rank: 2640.000\n", "Column: 86, Selected False, Rank: 2639.000\n", "Column: 87, Selected False, Rank: 2638.000\n", "Column: 88, Selected False, Rank: 2637.000\n", "Column: 89, Selected False, Rank: 2636.000\n", "Column: 90, Selected False, Rank: 2635.000\n", "Column: 91, Selected False, Rank: 2634.000\n", "Column: 92, Selected False, Rank: 2633.000\n", "Column: 93, Selected False, Rank: 2632.000\n", "Column: 94, Selected False, Rank: 2631.000\n", "Column: 95, Selected False, Rank: 2630.000\n", "Column: 96, Selected False, Rank: 2629.000\n", "Column: 97, Selected False, Rank: 2628.000\n", "Column: 98, Selected False, Rank: 2627.000\n", "Column: 99, Selected False, Rank: 2626.000\n", "Column: 100, Selected False, Rank: 2625.000\n", "Column: 101, Selected False, Rank: 2624.000\n", "Column: 102, Selected False, Rank: 2623.000\n", "Column: 103, Selected False, Rank: 2622.000\n", "Column: 104, Selected False, Rank: 2621.000\n", "Column: 105, Selected False, Rank: 2620.000\n", "Column: 106, Selected False, Rank: 2619.000\n", "Column: 107, Selected False, Rank: 2618.000\n", "Column: 108, Selected False, Rank: 2617.000\n", "Column: 109, Selected False, Rank: 2616.000\n", "Column: 110, Selected False, Rank: 2615.000\n", "Column: 111, Selected False, Rank: 2614.000\n", "Column: 112, Selected False, Rank: 2613.000\n", "Column: 113, Selected False, Rank: 2612.000\n", "Column: 114, Selected False, Rank: 2611.000\n", "Column: 115, Selected False, Rank: 2610.000\n", "Column: 116, Selected False, Rank: 2609.000\n", "Column: 117, Selected False, Rank: 2608.000\n", "Column: 118, Selected False, Rank: 2607.000\n", "Column: 119, Selected False, Rank: 2606.000\n", "Column: 120, Selected False, Rank: 2605.000\n", "Column: 121, Selected False, Rank: 2604.000\n", "Column: 122, Selected False, Rank: 2603.000\n", "Column: 123, Selected False, Rank: 2602.000\n", "Column: 124, Selected False, Rank: 2601.000\n", "Column: 125, Selected False, Rank: 2600.000\n", "Column: 126, Selected False, Rank: 2599.000\n", "Column: 127, Selected False, Rank: 2598.000\n", "Column: 128, Selected False, Rank: 2597.000\n", "Column: 129, Selected False, Rank: 2596.000\n", "Column: 130, Selected False, Rank: 2595.000\n", "Column: 131, Selected False, Rank: 2594.000\n", "Column: 132, Selected False, Rank: 2593.000\n", "Column: 133, Selected False, Rank: 2592.000\n", "Column: 134, Selected False, Rank: 2591.000\n", "Column: 135, Selected False, Rank: 2590.000\n", "Column: 136, Selected False, Rank: 2589.000\n", "Column: 137, Selected False, Rank: 2588.000\n", "Column: 138, Selected False, Rank: 2587.000\n", "Column: 139, Selected False, Rank: 2586.000\n", "Column: 140, Selected False, Rank: 2585.000\n", "Column: 141, Selected False, Rank: 2584.000\n", "Column: 142, Selected False, Rank: 2583.000\n", "Column: 143, Selected False, Rank: 2582.000\n", "Column: 144, Selected False, Rank: 2581.000\n", "Column: 145, Selected False, Rank: 2580.000\n", "Column: 146, Selected False, Rank: 2579.000\n", "Column: 147, Selected False, Rank: 2578.000\n", "Column: 148, Selected False, Rank: 2577.000\n", "Column: 149, Selected False, Rank: 2576.000\n", "Column: 150, Selected False, Rank: 2575.000\n", "Column: 151, Selected False, Rank: 2574.000\n", "Column: 152, Selected False, Rank: 2573.000\n", "Column: 153, Selected False, Rank: 2572.000\n", "Column: 154, Selected False, Rank: 2571.000\n", "Column: 155, Selected False, Rank: 2570.000\n", "Column: 156, Selected False, Rank: 2569.000\n", "Column: 157, Selected False, Rank: 2568.000\n", "Column: 158, Selected False, Rank: 2567.000\n", "Column: 159, Selected False, Rank: 2566.000\n", "Column: 160, Selected False, Rank: 2565.000\n", "Column: 161, Selected False, Rank: 2564.000\n", "Column: 162, Selected False, Rank: 2563.000\n", "Column: 163, Selected False, Rank: 2562.000\n", "Column: 164, Selected False, Rank: 2561.000\n", "Column: 165, Selected False, Rank: 2560.000\n", "Column: 166, Selected False, Rank: 2559.000\n", "Column: 167, Selected False, Rank: 2558.000\n", "Column: 168, Selected False, Rank: 2557.000\n", "Column: 169, Selected False, Rank: 2556.000\n", "Column: 170, Selected False, Rank: 2555.000\n", "Column: 171, Selected False, Rank: 2554.000\n", "Column: 172, Selected False, Rank: 2553.000\n", "Column: 173, Selected False, Rank: 2552.000\n", "Column: 174, Selected False, Rank: 2551.000\n", "Column: 175, Selected False, Rank: 2550.000\n", "Column: 176, Selected False, Rank: 2549.000\n", "Column: 177, Selected False, Rank: 2548.000\n", "Column: 178, Selected False, Rank: 2547.000\n", "Column: 179, Selected False, Rank: 2546.000\n", "Column: 180, Selected False, Rank: 2545.000\n", "Column: 181, Selected False, Rank: 2544.000\n", "Column: 182, Selected False, Rank: 2543.000\n", "Column: 183, Selected False, Rank: 2542.000\n", "Column: 184, Selected False, Rank: 2541.000\n", "Column: 185, Selected False, Rank: 2540.000\n", "Column: 186, Selected False, Rank: 2539.000\n", "Column: 187, Selected False, Rank: 2538.000\n", "Column: 188, Selected False, Rank: 2537.000\n", "Column: 189, Selected False, Rank: 2536.000\n", "Column: 190, Selected False, Rank: 2535.000\n", "Column: 191, Selected False, Rank: 2534.000\n", "Column: 192, Selected False, Rank: 2533.000\n", "Column: 193, Selected False, Rank: 2532.000\n", "Column: 194, Selected False, Rank: 2531.000\n", "Column: 195, Selected False, Rank: 2530.000\n", "Column: 196, Selected False, Rank: 2529.000\n", "Column: 197, Selected False, Rank: 2528.000\n", "Column: 198, Selected False, Rank: 2527.000\n", "Column: 199, Selected False, Rank: 2526.000\n", "Column: 200, Selected False, Rank: 2525.000\n", "Column: 201, Selected False, Rank: 2524.000\n", "Column: 202, Selected False, Rank: 2523.000\n", "Column: 203, Selected False, Rank: 2522.000\n", "Column: 204, Selected False, Rank: 2521.000\n", "Column: 205, Selected False, Rank: 2520.000\n", "Column: 206, Selected False, Rank: 2519.000\n", "Column: 207, Selected False, Rank: 2518.000\n", "Column: 208, Selected False, Rank: 2517.000\n", "Column: 209, Selected False, Rank: 2516.000\n", "Column: 210, Selected False, Rank: 2515.000\n", "Column: 211, Selected False, Rank: 2514.000\n", "Column: 212, Selected False, Rank: 2513.000\n", "Column: 213, Selected False, Rank: 2512.000\n", "Column: 214, Selected False, Rank: 2511.000\n", "Column: 215, Selected False, Rank: 2510.000\n", "Column: 216, Selected False, Rank: 2509.000\n", "Column: 217, Selected False, Rank: 2508.000\n", "Column: 218, Selected False, Rank: 2507.000\n", "Column: 219, Selected False, Rank: 2506.000\n", "Column: 220, Selected False, Rank: 2505.000\n", "Column: 221, Selected False, Rank: 2504.000\n", "Column: 222, Selected False, Rank: 2503.000\n", "Column: 223, Selected False, Rank: 2502.000\n", "Column: 224, Selected False, Rank: 2501.000\n", "Column: 225, Selected False, Rank: 2500.000\n", "Column: 226, Selected False, Rank: 2499.000\n", "Column: 227, Selected False, Rank: 2498.000\n", "Column: 228, Selected False, Rank: 2497.000\n", "Column: 229, Selected False, Rank: 2496.000\n", "Column: 230, Selected False, Rank: 2495.000\n", "Column: 231, Selected False, Rank: 2494.000\n", "Column: 232, Selected False, Rank: 2493.000\n", "Column: 233, Selected False, Rank: 2492.000\n", "Column: 234, Selected False, Rank: 2491.000\n", "Column: 235, Selected False, Rank: 2490.000\n", "Column: 236, Selected False, Rank: 2489.000\n", "Column: 237, Selected False, Rank: 2488.000\n", "Column: 238, Selected False, Rank: 2487.000\n", "Column: 239, Selected False, Rank: 2486.000\n", "Column: 240, Selected False, Rank: 2485.000\n", "Column: 241, Selected False, Rank: 2484.000\n", "Column: 242, Selected False, Rank: 2483.000\n", "Column: 243, Selected False, Rank: 2482.000\n", "Column: 244, Selected False, Rank: 2481.000\n", "Column: 245, Selected False, Rank: 2480.000\n", "Column: 246, Selected False, Rank: 2479.000\n", "Column: 247, Selected False, Rank: 2478.000\n", "Column: 248, Selected False, Rank: 2477.000\n", "Column: 249, Selected False, Rank: 2476.000\n", "Column: 250, Selected False, Rank: 2475.000\n", "Column: 251, Selected False, Rank: 2474.000\n", "Column: 252, Selected False, Rank: 2473.000\n", "Column: 253, Selected False, Rank: 2472.000\n", "Column: 254, Selected False, Rank: 2471.000\n", "Column: 255, Selected False, Rank: 2470.000\n", "Column: 256, Selected False, Rank: 2469.000\n", "Column: 257, Selected False, Rank: 2468.000\n", "Column: 258, Selected False, Rank: 2467.000\n", "Column: 259, Selected False, Rank: 2466.000\n", "Column: 260, Selected False, Rank: 2465.000\n", "Column: 261, Selected False, Rank: 2464.000\n", "Column: 262, Selected False, Rank: 2463.000\n", "Column: 263, Selected False, Rank: 2462.000\n", "Column: 264, Selected False, Rank: 2461.000\n", "Column: 265, Selected False, Rank: 2460.000\n", "Column: 266, Selected False, Rank: 2459.000\n", "Column: 267, Selected False, Rank: 2458.000\n", "Column: 268, Selected False, Rank: 2457.000\n", "Column: 269, Selected False, Rank: 2456.000\n", "Column: 270, Selected False, Rank: 2455.000\n", "Column: 271, Selected False, Rank: 2454.000\n", "Column: 272, Selected False, Rank: 2453.000\n", "Column: 273, Selected False, Rank: 2452.000\n", "Column: 274, Selected False, Rank: 2451.000\n", "Column: 275, Selected False, Rank: 2450.000\n", "Column: 276, Selected False, Rank: 2449.000\n", "Column: 277, Selected False, Rank: 2448.000\n", "Column: 278, Selected False, Rank: 2447.000\n", "Column: 279, Selected False, Rank: 2446.000\n", "Column: 280, Selected False, Rank: 2445.000\n", "Column: 281, Selected False, Rank: 2444.000\n", "Column: 282, Selected False, Rank: 2443.000\n", "Column: 283, Selected False, Rank: 2442.000\n", "Column: 284, Selected False, Rank: 2441.000\n", "Column: 285, Selected False, Rank: 2440.000\n", "Column: 286, Selected False, Rank: 2439.000\n", "Column: 287, Selected False, Rank: 2438.000\n", "Column: 288, Selected False, Rank: 2437.000\n", "Column: 289, Selected False, Rank: 2436.000\n", "Column: 290, Selected False, Rank: 2435.000\n", "Column: 291, Selected False, Rank: 2434.000\n", "Column: 292, Selected False, Rank: 2433.000\n", "Column: 293, Selected False, Rank: 2432.000\n", "Column: 294, Selected False, Rank: 2431.000\n", "Column: 295, Selected False, Rank: 2430.000\n", "Column: 296, Selected False, Rank: 2429.000\n", "Column: 297, Selected False, Rank: 2428.000\n", "Column: 298, Selected False, Rank: 2427.000\n", "Column: 299, Selected False, Rank: 2426.000\n", "Column: 300, Selected False, Rank: 2425.000\n", "Column: 301, Selected False, Rank: 2424.000\n", "Column: 302, Selected False, Rank: 2423.000\n", "Column: 303, Selected False, Rank: 2422.000\n", "Column: 304, Selected False, Rank: 2421.000\n", "Column: 305, Selected False, Rank: 2420.000\n", "Column: 306, Selected False, Rank: 2419.000\n", "Column: 307, Selected False, Rank: 2418.000\n", "Column: 308, Selected False, Rank: 2417.000\n", "Column: 309, Selected False, Rank: 2416.000\n", "Column: 310, Selected False, Rank: 2415.000\n", "Column: 311, Selected False, Rank: 2414.000\n", "Column: 312, Selected False, Rank: 2413.000\n", "Column: 313, Selected False, Rank: 2412.000\n", "Column: 314, Selected False, Rank: 2411.000\n", "Column: 315, Selected False, Rank: 2410.000\n", "Column: 316, Selected False, Rank: 2409.000\n", "Column: 317, Selected False, Rank: 2408.000\n", "Column: 318, Selected False, Rank: 2407.000\n", "Column: 319, Selected False, Rank: 2406.000\n", "Column: 320, Selected False, Rank: 2405.000\n", "Column: 321, Selected False, Rank: 2404.000\n", "Column: 322, Selected False, Rank: 2403.000\n", "Column: 323, Selected False, Rank: 2402.000\n", "Column: 324, Selected False, Rank: 2401.000\n", "Column: 325, Selected False, Rank: 2400.000\n", "Column: 326, Selected False, Rank: 2399.000\n", "Column: 327, Selected False, Rank: 2398.000\n", "Column: 328, Selected False, Rank: 2397.000\n", "Column: 329, Selected False, Rank: 2396.000\n", "Column: 330, Selected False, Rank: 2395.000\n", "Column: 331, Selected False, Rank: 2394.000\n", "Column: 332, Selected False, Rank: 2393.000\n", "Column: 333, Selected False, Rank: 2392.000\n", "Column: 334, Selected False, Rank: 2391.000\n", "Column: 335, Selected False, Rank: 2390.000\n", "Column: 336, Selected False, Rank: 2389.000\n", "Column: 337, Selected False, Rank: 2388.000\n", "Column: 338, Selected False, Rank: 2387.000\n", "Column: 339, Selected False, Rank: 2386.000\n", "Column: 340, Selected False, Rank: 2385.000\n", "Column: 341, Selected False, Rank: 2384.000\n", "Column: 342, Selected False, Rank: 2383.000\n", "Column: 343, Selected False, Rank: 2382.000\n", "Column: 344, Selected False, Rank: 2381.000\n", "Column: 345, Selected False, Rank: 2380.000\n", "Column: 346, Selected False, Rank: 2379.000\n", "Column: 347, Selected False, Rank: 2378.000\n", "Column: 348, Selected False, Rank: 2377.000\n", "Column: 349, Selected False, Rank: 2376.000\n", "Column: 350, Selected False, Rank: 2375.000\n", "Column: 351, Selected False, Rank: 2374.000\n", "Column: 352, Selected False, Rank: 2373.000\n", "Column: 353, Selected False, Rank: 2372.000\n", "Column: 354, Selected False, Rank: 2371.000\n", "Column: 355, Selected False, Rank: 2370.000\n", "Column: 356, Selected False, Rank: 2369.000\n", "Column: 357, Selected False, Rank: 2368.000\n", "Column: 358, Selected False, Rank: 2367.000\n", "Column: 359, Selected False, Rank: 2366.000\n", "Column: 360, Selected False, Rank: 2365.000\n", "Column: 361, Selected False, Rank: 2364.000\n", "Column: 362, Selected False, Rank: 2363.000\n", "Column: 363, Selected False, Rank: 2362.000\n", "Column: 364, Selected False, Rank: 2361.000\n", "Column: 365, Selected False, Rank: 2360.000\n", "Column: 366, Selected False, Rank: 2359.000\n", "Column: 367, Selected False, Rank: 2358.000\n", "Column: 368, Selected False, Rank: 2357.000\n", "Column: 369, Selected False, Rank: 2356.000\n", "Column: 370, Selected False, Rank: 2355.000\n", "Column: 371, Selected False, Rank: 2354.000\n", "Column: 372, Selected False, Rank: 2353.000\n", "Column: 373, Selected False, Rank: 2352.000\n", "Column: 374, Selected False, Rank: 2351.000\n", "Column: 375, Selected False, Rank: 2350.000\n", "Column: 376, Selected False, Rank: 2349.000\n", "Column: 377, Selected False, Rank: 2348.000\n", "Column: 378, Selected False, Rank: 2347.000\n", "Column: 379, Selected False, Rank: 2346.000\n", "Column: 380, Selected False, Rank: 2345.000\n", "Column: 381, Selected False, Rank: 2344.000\n", "Column: 382, Selected False, Rank: 2343.000\n", "Column: 383, Selected False, Rank: 2342.000\n", "Column: 384, Selected False, Rank: 2341.000\n", "Column: 385, Selected False, Rank: 2340.000\n", "Column: 386, Selected False, Rank: 2339.000\n", "Column: 387, Selected False, Rank: 2338.000\n", "Column: 388, Selected False, Rank: 2337.000\n", "Column: 389, Selected False, Rank: 2336.000\n", "Column: 390, Selected False, Rank: 2335.000\n", "Column: 391, Selected False, Rank: 2334.000\n", "Column: 392, Selected False, Rank: 2333.000\n", "Column: 393, Selected False, Rank: 2332.000\n", "Column: 394, Selected False, Rank: 2331.000\n", "Column: 395, Selected False, Rank: 2330.000\n", "Column: 396, Selected False, Rank: 2329.000\n", "Column: 397, Selected False, Rank: 2328.000\n", "Column: 398, Selected False, Rank: 2327.000\n", "Column: 399, Selected False, Rank: 2326.000\n", "Column: 400, Selected False, Rank: 2325.000\n", "Column: 401, Selected False, Rank: 2324.000\n", "Column: 402, Selected False, Rank: 2323.000\n", "Column: 403, Selected False, Rank: 2322.000\n", "Column: 404, Selected False, Rank: 2321.000\n", "Column: 405, Selected False, Rank: 2320.000\n", "Column: 406, Selected False, Rank: 2319.000\n", "Column: 407, Selected False, Rank: 2318.000\n", "Column: 408, Selected False, Rank: 2317.000\n", "Column: 409, Selected False, Rank: 2316.000\n", "Column: 410, Selected False, Rank: 2315.000\n", "Column: 411, Selected False, Rank: 2314.000\n", "Column: 412, Selected False, Rank: 2313.000\n", "Column: 413, Selected False, Rank: 2312.000\n", "Column: 414, Selected False, Rank: 2311.000\n", "Column: 415, Selected False, Rank: 2310.000\n", "Column: 416, Selected False, Rank: 2309.000\n", "Column: 417, Selected False, Rank: 2308.000\n", "Column: 418, Selected False, Rank: 2307.000\n", "Column: 419, Selected False, Rank: 2306.000\n", "Column: 420, Selected False, Rank: 2305.000\n", "Column: 421, Selected False, Rank: 2304.000\n", "Column: 422, Selected False, Rank: 2303.000\n", "Column: 423, Selected False, Rank: 2302.000\n", "Column: 424, Selected False, Rank: 2301.000\n", "Column: 425, Selected False, Rank: 2300.000\n", "Column: 426, Selected False, Rank: 2299.000\n", "Column: 427, Selected False, Rank: 2298.000\n", "Column: 428, Selected False, Rank: 2297.000\n", "Column: 429, Selected False, Rank: 2296.000\n", "Column: 430, Selected False, Rank: 2295.000\n", "Column: 431, Selected False, Rank: 2294.000\n", "Column: 432, Selected False, Rank: 2293.000\n", "Column: 433, Selected False, Rank: 2292.000\n", "Column: 434, Selected False, Rank: 2291.000\n", "Column: 435, Selected False, Rank: 2290.000\n", "Column: 436, Selected False, Rank: 2289.000\n", "Column: 437, Selected False, Rank: 2288.000\n", "Column: 438, Selected False, Rank: 2287.000\n", "Column: 439, Selected False, Rank: 2286.000\n", "Column: 440, Selected False, Rank: 2285.000\n", "Column: 441, Selected False, Rank: 2284.000\n", "Column: 442, Selected False, Rank: 2283.000\n", "Column: 443, Selected False, Rank: 2282.000\n", "Column: 444, Selected False, Rank: 2281.000\n", "Column: 445, Selected False, Rank: 2280.000\n", "Column: 446, Selected False, Rank: 2279.000\n", "Column: 447, Selected False, Rank: 2278.000\n", "Column: 448, Selected False, Rank: 2277.000\n", "Column: 449, Selected False, Rank: 2276.000\n", "Column: 450, Selected False, Rank: 2275.000\n", "Column: 451, Selected False, Rank: 2274.000\n", "Column: 452, Selected False, Rank: 2273.000\n", "Column: 453, Selected False, Rank: 2272.000\n", "Column: 454, Selected False, Rank: 2271.000\n", "Column: 455, Selected False, Rank: 2270.000\n", "Column: 456, Selected False, Rank: 2269.000\n", "Column: 457, Selected False, Rank: 2268.000\n", "Column: 458, Selected False, Rank: 2267.000\n", "Column: 459, Selected False, Rank: 2266.000\n", "Column: 460, Selected False, Rank: 2265.000\n", "Column: 461, Selected False, Rank: 2264.000\n", "Column: 462, Selected False, Rank: 2263.000\n", "Column: 463, Selected False, Rank: 2262.000\n", "Column: 464, Selected False, Rank: 2261.000\n", "Column: 465, Selected False, Rank: 2260.000\n", "Column: 466, Selected False, Rank: 2259.000\n", "Column: 467, Selected False, Rank: 2258.000\n", "Column: 468, Selected False, Rank: 2257.000\n", "Column: 469, Selected False, Rank: 2256.000\n", "Column: 470, Selected False, Rank: 2255.000\n", "Column: 471, Selected False, Rank: 2254.000\n", "Column: 472, Selected False, Rank: 2253.000\n", "Column: 473, Selected False, Rank: 2252.000\n", "Column: 474, Selected False, Rank: 2251.000\n", "Column: 475, Selected False, Rank: 2250.000\n", "Column: 476, Selected False, Rank: 2249.000\n", "Column: 477, Selected False, Rank: 2248.000\n", "Column: 478, Selected False, Rank: 2247.000\n", "Column: 479, Selected False, Rank: 2246.000\n", "Column: 480, Selected False, Rank: 2245.000\n", "Column: 481, Selected False, Rank: 2244.000\n", "Column: 482, Selected False, Rank: 2243.000\n", "Column: 483, Selected False, Rank: 2242.000\n", "Column: 484, Selected False, Rank: 2241.000\n", "Column: 485, Selected False, Rank: 2240.000\n", "Column: 486, Selected False, Rank: 2239.000\n", "Column: 487, Selected False, Rank: 2238.000\n", "Column: 488, Selected False, Rank: 2237.000\n", "Column: 489, Selected False, Rank: 2236.000\n", "Column: 490, Selected False, Rank: 2235.000\n", "Column: 491, Selected False, Rank: 2234.000\n", "Column: 492, Selected False, Rank: 2233.000\n", "Column: 493, Selected False, Rank: 2232.000\n", "Column: 494, Selected False, Rank: 2231.000\n", "Column: 495, Selected False, Rank: 2230.000\n", "Column: 496, Selected False, Rank: 2229.000\n", "Column: 497, Selected False, Rank: 2228.000\n", "Column: 498, Selected False, Rank: 2227.000\n", "Column: 499, Selected False, Rank: 2226.000\n", "Column: 500, Selected False, Rank: 2225.000\n", "Column: 501, Selected False, Rank: 2224.000\n", "Column: 502, Selected False, Rank: 2223.000\n", "Column: 503, Selected False, Rank: 2222.000\n", "Column: 504, Selected False, Rank: 2221.000\n", "Column: 505, Selected False, Rank: 2220.000\n", "Column: 506, Selected False, Rank: 2219.000\n", "Column: 507, Selected False, Rank: 2218.000\n", "Column: 508, Selected False, Rank: 2217.000\n", "Column: 509, Selected False, Rank: 2216.000\n", "Column: 510, Selected False, Rank: 2215.000\n", "Column: 511, Selected False, Rank: 2214.000\n", "Column: 512, Selected False, Rank: 2213.000\n", "Column: 513, Selected False, Rank: 2212.000\n", "Column: 514, Selected False, Rank: 2211.000\n", "Column: 515, Selected False, Rank: 2210.000\n", "Column: 516, Selected False, Rank: 2209.000\n", "Column: 517, Selected False, Rank: 2208.000\n", "Column: 518, Selected False, Rank: 2207.000\n", "Column: 519, Selected False, Rank: 2206.000\n", "Column: 520, Selected False, Rank: 2205.000\n", "Column: 521, Selected False, Rank: 2204.000\n", "Column: 522, Selected False, Rank: 2203.000\n", "Column: 523, Selected False, Rank: 2202.000\n", "Column: 524, Selected False, Rank: 2201.000\n", "Column: 525, Selected False, Rank: 2200.000\n", "Column: 526, Selected False, Rank: 2199.000\n", "Column: 527, Selected False, Rank: 2198.000\n", "Column: 528, Selected False, Rank: 2197.000\n", "Column: 529, Selected False, Rank: 2196.000\n", "Column: 530, Selected False, Rank: 2195.000\n", "Column: 531, Selected False, Rank: 2194.000\n", "Column: 532, Selected False, Rank: 2193.000\n", "Column: 533, Selected False, Rank: 2192.000\n", "Column: 534, Selected False, Rank: 2191.000\n", "Column: 535, Selected False, Rank: 2190.000\n", "Column: 536, Selected False, Rank: 2189.000\n", "Column: 537, Selected False, Rank: 2188.000\n", "Column: 538, Selected False, Rank: 2187.000\n", "Column: 539, Selected False, Rank: 2186.000\n", "Column: 540, Selected False, Rank: 2185.000\n", "Column: 541, Selected False, Rank: 2184.000\n", "Column: 542, Selected False, Rank: 2183.000\n", "Column: 543, Selected False, Rank: 2182.000\n", "Column: 544, Selected False, Rank: 2181.000\n", "Column: 545, Selected False, Rank: 2180.000\n", "Column: 546, Selected False, Rank: 2179.000\n", "Column: 547, Selected False, Rank: 2178.000\n", "Column: 548, Selected False, Rank: 2177.000\n", "Column: 549, Selected False, Rank: 2176.000\n", "Column: 550, Selected False, Rank: 2175.000\n", "Column: 551, Selected False, Rank: 2174.000\n", "Column: 552, Selected False, Rank: 2173.000\n", "Column: 553, Selected False, Rank: 2172.000\n", "Column: 554, Selected False, Rank: 2171.000\n", "Column: 555, Selected False, Rank: 2170.000\n", "Column: 556, Selected False, Rank: 2169.000\n", "Column: 557, Selected False, Rank: 2168.000\n", "Column: 558, Selected False, Rank: 2167.000\n", "Column: 559, Selected False, Rank: 2166.000\n", "Column: 560, Selected False, Rank: 2165.000\n", "Column: 561, Selected False, Rank: 2164.000\n", "Column: 562, Selected False, Rank: 2163.000\n", "Column: 563, Selected False, Rank: 2162.000\n", "Column: 564, Selected False, Rank: 2161.000\n", "Column: 565, Selected False, Rank: 2160.000\n", "Column: 566, Selected False, Rank: 2159.000\n", "Column: 567, Selected False, Rank: 2158.000\n", "Column: 568, Selected False, Rank: 2157.000\n", "Column: 569, Selected False, Rank: 2156.000\n", "Column: 570, Selected False, Rank: 2155.000\n", "Column: 571, Selected False, Rank: 2154.000\n", "Column: 572, Selected False, Rank: 2153.000\n", "Column: 573, Selected False, Rank: 2152.000\n", "Column: 574, Selected False, Rank: 2151.000\n", "Column: 575, Selected False, Rank: 2150.000\n", "Column: 576, Selected False, Rank: 2149.000\n", "Column: 577, Selected False, Rank: 2148.000\n", "Column: 578, Selected False, Rank: 2147.000\n", "Column: 579, Selected False, Rank: 2146.000\n", "Column: 580, Selected False, Rank: 2145.000\n", "Column: 581, Selected False, Rank: 2144.000\n", "Column: 582, Selected False, Rank: 2143.000\n", "Column: 583, Selected False, Rank: 2142.000\n", "Column: 584, Selected False, Rank: 2141.000\n", "Column: 585, Selected False, Rank: 2140.000\n", "Column: 586, Selected False, Rank: 2139.000\n", "Column: 587, Selected False, Rank: 2138.000\n", "Column: 588, Selected False, Rank: 2137.000\n", "Column: 589, Selected False, Rank: 2136.000\n", "Column: 590, Selected False, Rank: 2135.000\n", "Column: 591, Selected False, Rank: 2134.000\n", "Column: 592, Selected False, Rank: 2133.000\n", "Column: 593, Selected False, Rank: 2132.000\n", "Column: 594, Selected False, Rank: 2131.000\n", "Column: 595, Selected False, Rank: 2130.000\n", "Column: 596, Selected False, Rank: 2129.000\n", "Column: 597, Selected False, Rank: 2128.000\n", "Column: 598, Selected False, Rank: 2127.000\n", "Column: 599, Selected False, Rank: 2126.000\n", "Column: 600, Selected False, Rank: 2125.000\n", "Column: 601, Selected False, Rank: 2124.000\n", "Column: 602, Selected False, Rank: 2123.000\n", "Column: 603, Selected False, Rank: 2122.000\n", "Column: 604, Selected False, Rank: 2121.000\n", "Column: 605, Selected False, Rank: 2120.000\n", "Column: 606, Selected False, Rank: 2119.000\n", "Column: 607, Selected False, Rank: 2118.000\n", "Column: 608, Selected False, Rank: 2117.000\n", "Column: 609, Selected False, Rank: 2116.000\n", "Column: 610, Selected False, Rank: 2115.000\n", "Column: 611, Selected False, Rank: 2114.000\n", "Column: 612, Selected False, Rank: 2113.000\n", "Column: 613, Selected False, Rank: 2112.000\n", "Column: 614, Selected False, Rank: 2111.000\n", "Column: 615, Selected False, Rank: 2110.000\n", "Column: 616, Selected False, Rank: 2109.000\n", "Column: 617, Selected False, Rank: 2108.000\n", "Column: 618, Selected False, Rank: 2107.000\n", "Column: 619, Selected False, Rank: 2106.000\n", "Column: 620, Selected False, Rank: 2105.000\n", "Column: 621, Selected False, Rank: 2104.000\n", "Column: 622, Selected False, Rank: 2103.000\n", "Column: 623, Selected False, Rank: 2102.000\n", "Column: 624, Selected False, Rank: 2101.000\n", "Column: 625, Selected False, Rank: 2100.000\n", "Column: 626, Selected False, Rank: 2099.000\n", "Column: 627, Selected False, Rank: 2098.000\n", "Column: 628, Selected False, Rank: 2097.000\n", "Column: 629, Selected False, Rank: 2096.000\n", "Column: 630, Selected False, Rank: 2095.000\n", "Column: 631, Selected False, Rank: 2094.000\n", "Column: 632, Selected False, Rank: 2093.000\n", "Column: 633, Selected False, Rank: 2092.000\n", "Column: 634, Selected False, Rank: 2091.000\n", "Column: 635, Selected False, Rank: 2090.000\n", "Column: 636, Selected False, Rank: 2089.000\n", "Column: 637, Selected False, Rank: 2088.000\n", "Column: 638, Selected False, Rank: 2087.000\n", "Column: 639, Selected False, Rank: 2086.000\n", "Column: 640, Selected False, Rank: 2085.000\n", "Column: 641, Selected False, Rank: 2084.000\n", "Column: 642, Selected False, Rank: 2083.000\n", "Column: 643, Selected False, Rank: 2082.000\n", "Column: 644, Selected False, Rank: 2081.000\n", "Column: 645, Selected False, Rank: 2080.000\n", "Column: 646, Selected False, Rank: 2079.000\n", "Column: 647, Selected False, Rank: 2078.000\n", "Column: 648, Selected False, Rank: 2077.000\n", "Column: 649, Selected False, Rank: 2076.000\n", "Column: 650, Selected False, Rank: 2075.000\n", "Column: 651, Selected False, Rank: 2074.000\n", "Column: 652, Selected False, Rank: 2073.000\n", "Column: 653, Selected False, Rank: 2072.000\n", "Column: 654, Selected False, Rank: 2071.000\n", "Column: 655, Selected False, Rank: 2070.000\n", "Column: 656, Selected False, Rank: 2069.000\n", "Column: 657, Selected False, Rank: 2068.000\n", "Column: 658, Selected False, Rank: 2067.000\n", "Column: 659, Selected False, Rank: 2066.000\n", "Column: 660, Selected False, Rank: 2065.000\n", "Column: 661, Selected False, Rank: 2064.000\n", "Column: 662, Selected False, Rank: 2063.000\n", "Column: 663, Selected False, Rank: 2062.000\n", "Column: 664, Selected False, Rank: 2061.000\n", "Column: 665, Selected False, Rank: 2060.000\n", "Column: 666, Selected False, Rank: 2059.000\n", "Column: 667, Selected False, Rank: 2058.000\n", "Column: 668, Selected False, Rank: 2057.000\n", "Column: 669, Selected False, Rank: 2056.000\n", "Column: 670, Selected False, Rank: 2055.000\n", "Column: 671, Selected False, Rank: 2054.000\n", "Column: 672, Selected False, Rank: 2053.000\n", "Column: 673, Selected False, Rank: 2052.000\n", "Column: 674, Selected False, Rank: 2051.000\n", "Column: 675, Selected False, Rank: 2050.000\n", "Column: 676, Selected False, Rank: 2049.000\n", "Column: 677, Selected False, Rank: 2048.000\n", "Column: 678, Selected False, Rank: 2047.000\n", "Column: 679, Selected False, Rank: 2046.000\n", "Column: 680, Selected False, Rank: 2045.000\n", "Column: 681, Selected False, Rank: 2044.000\n", "Column: 682, Selected False, Rank: 2043.000\n", "Column: 683, Selected False, Rank: 2042.000\n", "Column: 684, Selected False, Rank: 2041.000\n", "Column: 685, Selected False, Rank: 2040.000\n", "Column: 686, Selected False, Rank: 2039.000\n", "Column: 687, Selected False, Rank: 2038.000\n", "Column: 688, Selected False, Rank: 2037.000\n", "Column: 689, Selected False, Rank: 2036.000\n", "Column: 690, Selected False, Rank: 2035.000\n", "Column: 691, Selected False, Rank: 2034.000\n", "Column: 692, Selected False, Rank: 2033.000\n", "Column: 693, Selected False, Rank: 2032.000\n", "Column: 694, Selected False, Rank: 2031.000\n", "Column: 695, Selected False, Rank: 2030.000\n", "Column: 696, Selected False, Rank: 2029.000\n", "Column: 697, Selected False, Rank: 2028.000\n", "Column: 698, Selected False, Rank: 2027.000\n", "Column: 699, Selected False, Rank: 2026.000\n", "Column: 700, Selected False, Rank: 2025.000\n", "Column: 701, Selected False, Rank: 2024.000\n", "Column: 702, Selected False, Rank: 2023.000\n", "Column: 703, Selected False, Rank: 2022.000\n", "Column: 704, Selected False, Rank: 2021.000\n", "Column: 705, Selected False, Rank: 2020.000\n", "Column: 706, Selected False, Rank: 2019.000\n", "Column: 707, Selected False, Rank: 2018.000\n", "Column: 708, Selected False, Rank: 2017.000\n", "Column: 709, Selected False, Rank: 2016.000\n", "Column: 710, Selected False, Rank: 2015.000\n", "Column: 711, Selected False, Rank: 2014.000\n", "Column: 712, Selected False, Rank: 2013.000\n", "Column: 713, Selected False, Rank: 2012.000\n", "Column: 714, Selected False, Rank: 2011.000\n", "Column: 715, Selected False, Rank: 2010.000\n", "Column: 716, Selected False, Rank: 2009.000\n", "Column: 717, Selected False, Rank: 2008.000\n", "Column: 718, Selected False, Rank: 2007.000\n", "Column: 719, Selected False, Rank: 2006.000\n", "Column: 720, Selected False, Rank: 2005.000\n", "Column: 721, Selected False, Rank: 2004.000\n", "Column: 722, Selected False, Rank: 2003.000\n", "Column: 723, Selected False, Rank: 2002.000\n", "Column: 724, Selected False, Rank: 2001.000\n", "Column: 725, Selected False, Rank: 2000.000\n", "Column: 726, Selected False, Rank: 1999.000\n", "Column: 727, Selected False, Rank: 1998.000\n", "Column: 728, Selected False, Rank: 1997.000\n", "Column: 729, Selected False, Rank: 1996.000\n", "Column: 730, Selected False, Rank: 1995.000\n", "Column: 731, Selected False, Rank: 1994.000\n", "Column: 732, Selected False, Rank: 1993.000\n", "Column: 733, Selected False, Rank: 1992.000\n", "Column: 734, Selected False, Rank: 1991.000\n", "Column: 735, Selected False, Rank: 1990.000\n", "Column: 736, Selected False, Rank: 1989.000\n", "Column: 737, Selected False, Rank: 1988.000\n", "Column: 738, Selected False, Rank: 1987.000\n", "Column: 739, Selected False, Rank: 1986.000\n", "Column: 740, Selected False, Rank: 1985.000\n", "Column: 741, Selected False, Rank: 1984.000\n", "Column: 742, Selected False, Rank: 1983.000\n", "Column: 743, Selected False, Rank: 1982.000\n", "Column: 744, Selected False, Rank: 1981.000\n", "Column: 745, Selected False, Rank: 1980.000\n", "Column: 746, Selected False, Rank: 1979.000\n", "Column: 747, Selected False, Rank: 1978.000\n", "Column: 748, Selected False, Rank: 1977.000\n", "Column: 749, Selected False, Rank: 1976.000\n", "Column: 750, Selected False, Rank: 1975.000\n", "Column: 751, Selected False, Rank: 1974.000\n", "Column: 752, Selected False, Rank: 1973.000\n", "Column: 753, Selected False, Rank: 1972.000\n", "Column: 754, Selected False, Rank: 1971.000\n", "Column: 755, Selected False, Rank: 1970.000\n", "Column: 756, Selected False, Rank: 1969.000\n", "Column: 757, Selected False, Rank: 1968.000\n", "Column: 758, Selected False, Rank: 1967.000\n", "Column: 759, Selected False, Rank: 1966.000\n", "Column: 760, Selected False, Rank: 1965.000\n", "Column: 761, Selected False, Rank: 1964.000\n", "Column: 762, Selected False, Rank: 1963.000\n", "Column: 763, Selected False, Rank: 1962.000\n", "Column: 764, Selected False, Rank: 1961.000\n", "Column: 765, Selected False, Rank: 1960.000\n", "Column: 766, Selected False, Rank: 1959.000\n", "Column: 767, Selected False, Rank: 1958.000\n", "Column: 768, Selected False, Rank: 1957.000\n", "Column: 769, Selected False, Rank: 1956.000\n", "Column: 770, Selected False, Rank: 1955.000\n", "Column: 771, Selected False, Rank: 1954.000\n", "Column: 772, Selected False, Rank: 1953.000\n", "Column: 773, Selected False, Rank: 1952.000\n", "Column: 774, Selected False, Rank: 1951.000\n", "Column: 775, Selected False, Rank: 1950.000\n", "Column: 776, Selected False, Rank: 1949.000\n", "Column: 777, Selected False, Rank: 1948.000\n", "Column: 778, Selected False, Rank: 1947.000\n", "Column: 779, Selected False, Rank: 1946.000\n", "Column: 780, Selected False, Rank: 1945.000\n", "Column: 781, Selected False, Rank: 1944.000\n", "Column: 782, Selected False, Rank: 1943.000\n", "Column: 783, Selected False, Rank: 1942.000\n", "Column: 784, Selected False, Rank: 1941.000\n", "Column: 785, Selected False, Rank: 1940.000\n", "Column: 786, Selected False, Rank: 1939.000\n", "Column: 787, Selected False, Rank: 1938.000\n", "Column: 788, Selected False, Rank: 1937.000\n", "Column: 789, Selected False, Rank: 1936.000\n", "Column: 790, Selected False, Rank: 1935.000\n", "Column: 791, Selected False, Rank: 1934.000\n", "Column: 792, Selected False, Rank: 1933.000\n", "Column: 793, Selected False, Rank: 1932.000\n", "Column: 794, Selected False, Rank: 1931.000\n", "Column: 795, Selected False, Rank: 1930.000\n", "Column: 796, Selected False, Rank: 1929.000\n", "Column: 797, Selected False, Rank: 1928.000\n", "Column: 798, Selected False, Rank: 1927.000\n", "Column: 799, Selected False, Rank: 1926.000\n", "Column: 800, Selected False, Rank: 1925.000\n", "Column: 801, Selected False, Rank: 1924.000\n", "Column: 802, Selected False, Rank: 1923.000\n", "Column: 803, Selected False, Rank: 1922.000\n", "Column: 804, Selected False, Rank: 1921.000\n", "Column: 805, Selected False, Rank: 1920.000\n", "Column: 806, Selected False, Rank: 1919.000\n", "Column: 807, Selected False, Rank: 1918.000\n", "Column: 808, Selected False, Rank: 1917.000\n", "Column: 809, Selected False, Rank: 1916.000\n", "Column: 810, Selected False, Rank: 1915.000\n", "Column: 811, Selected False, Rank: 1914.000\n", "Column: 812, Selected False, Rank: 1913.000\n", "Column: 813, Selected False, Rank: 1912.000\n", "Column: 814, Selected False, Rank: 1911.000\n", "Column: 815, Selected False, Rank: 1910.000\n", "Column: 816, Selected False, Rank: 1909.000\n", "Column: 817, Selected False, Rank: 1908.000\n", "Column: 818, Selected False, Rank: 1907.000\n", "Column: 819, Selected False, Rank: 1906.000\n", "Column: 820, Selected False, Rank: 1905.000\n", "Column: 821, Selected False, Rank: 1904.000\n", "Column: 822, Selected False, Rank: 1903.000\n", "Column: 823, Selected False, Rank: 1902.000\n", "Column: 824, Selected False, Rank: 1901.000\n", "Column: 825, Selected False, Rank: 1900.000\n", "Column: 826, Selected False, Rank: 1899.000\n", "Column: 827, Selected False, Rank: 1898.000\n", "Column: 828, Selected False, Rank: 1897.000\n", "Column: 829, Selected False, Rank: 1896.000\n", "Column: 830, Selected False, Rank: 1895.000\n", "Column: 831, Selected False, Rank: 1894.000\n", "Column: 832, Selected False, Rank: 1893.000\n", "Column: 833, Selected False, Rank: 1892.000\n", "Column: 834, Selected False, Rank: 1891.000\n", "Column: 835, Selected False, Rank: 1890.000\n", "Column: 836, Selected False, Rank: 1889.000\n", "Column: 837, Selected False, Rank: 1888.000\n", "Column: 838, Selected False, Rank: 1887.000\n", "Column: 839, Selected False, Rank: 1886.000\n", "Column: 840, Selected False, Rank: 1885.000\n", "Column: 841, Selected False, Rank: 1884.000\n", "Column: 842, Selected False, Rank: 1883.000\n", "Column: 843, Selected False, Rank: 1882.000\n", "Column: 844, Selected False, Rank: 1881.000\n", "Column: 845, Selected False, Rank: 1880.000\n", "Column: 846, Selected False, Rank: 1879.000\n", "Column: 847, Selected False, Rank: 1878.000\n", "Column: 848, Selected False, Rank: 1877.000\n", "Column: 849, Selected False, Rank: 1876.000\n", "Column: 850, Selected False, Rank: 1875.000\n", "Column: 851, Selected False, Rank: 1874.000\n", "Column: 852, Selected False, Rank: 1873.000\n", "Column: 853, Selected False, Rank: 1872.000\n", "Column: 854, Selected False, Rank: 1871.000\n", "Column: 855, Selected False, Rank: 1870.000\n", "Column: 856, Selected False, Rank: 1869.000\n", "Column: 857, Selected False, Rank: 1868.000\n", "Column: 858, Selected False, Rank: 1867.000\n", "Column: 859, Selected False, Rank: 1866.000\n", "Column: 860, Selected False, Rank: 1865.000\n", "Column: 861, Selected False, Rank: 1864.000\n", "Column: 862, Selected False, Rank: 1863.000\n", "Column: 863, Selected False, Rank: 1862.000\n", "Column: 864, Selected False, Rank: 1861.000\n", "Column: 865, Selected False, Rank: 1860.000\n", "Column: 866, Selected False, Rank: 1859.000\n", "Column: 867, Selected False, Rank: 1858.000\n", "Column: 868, Selected False, Rank: 1857.000\n", "Column: 869, Selected False, Rank: 1856.000\n", "Column: 870, Selected False, Rank: 1855.000\n", "Column: 871, Selected False, Rank: 1854.000\n", "Column: 872, Selected False, Rank: 1853.000\n", "Column: 873, Selected False, Rank: 1852.000\n", "Column: 874, Selected False, Rank: 1851.000\n", "Column: 875, Selected False, Rank: 1850.000\n", "Column: 876, Selected False, Rank: 1849.000\n", "Column: 877, Selected False, Rank: 1848.000\n", "Column: 878, Selected False, Rank: 1847.000\n", "Column: 879, Selected False, Rank: 1846.000\n", "Column: 880, Selected False, Rank: 1845.000\n", "Column: 881, Selected False, Rank: 1844.000\n", "Column: 882, Selected False, Rank: 1843.000\n", "Column: 883, Selected False, Rank: 1842.000\n", "Column: 884, Selected False, Rank: 1841.000\n", "Column: 885, Selected False, Rank: 1840.000\n", "Column: 886, Selected False, Rank: 1839.000\n", "Column: 887, Selected False, Rank: 1838.000\n", "Column: 888, Selected False, Rank: 1837.000\n", "Column: 889, Selected False, Rank: 1836.000\n", "Column: 890, Selected False, Rank: 1835.000\n", "Column: 891, Selected False, Rank: 1834.000\n", "Column: 892, Selected False, Rank: 1833.000\n", "Column: 893, Selected False, Rank: 1832.000\n", "Column: 894, Selected False, Rank: 1831.000\n", "Column: 895, Selected False, Rank: 1830.000\n", "Column: 896, Selected False, Rank: 1829.000\n", "Column: 897, Selected False, Rank: 1828.000\n", "Column: 898, Selected False, Rank: 1827.000\n", "Column: 899, Selected False, Rank: 1826.000\n", "Column: 900, Selected False, Rank: 1825.000\n", "Column: 901, Selected False, Rank: 1824.000\n", "Column: 902, Selected False, Rank: 1823.000\n", "Column: 903, Selected False, Rank: 1822.000\n", "Column: 904, Selected False, Rank: 1821.000\n", "Column: 905, Selected False, Rank: 1820.000\n", "Column: 906, Selected False, Rank: 1819.000\n", "Column: 907, Selected False, Rank: 1818.000\n", "Column: 908, Selected False, Rank: 1817.000\n", "Column: 909, Selected False, Rank: 1816.000\n", "Column: 910, Selected False, Rank: 1815.000\n", "Column: 911, Selected False, Rank: 1814.000\n", "Column: 912, Selected False, Rank: 1813.000\n", "Column: 913, Selected False, Rank: 1812.000\n", "Column: 914, Selected False, Rank: 1811.000\n", "Column: 915, Selected False, Rank: 1810.000\n", "Column: 916, Selected False, Rank: 1809.000\n", "Column: 917, Selected False, Rank: 1808.000\n", "Column: 918, Selected False, Rank: 1807.000\n", "Column: 919, Selected False, Rank: 1806.000\n", "Column: 920, Selected False, Rank: 1805.000\n", "Column: 921, Selected False, Rank: 1804.000\n", "Column: 922, Selected False, Rank: 1803.000\n", "Column: 923, Selected False, Rank: 1802.000\n", "Column: 924, Selected False, Rank: 1801.000\n", "Column: 925, Selected False, Rank: 1800.000\n", "Column: 926, Selected False, Rank: 1799.000\n", "Column: 927, Selected False, Rank: 1798.000\n", "Column: 928, Selected False, Rank: 1797.000\n", "Column: 929, Selected False, Rank: 1796.000\n", "Column: 930, Selected False, Rank: 1795.000\n", "Column: 931, Selected False, Rank: 1794.000\n", "Column: 932, Selected False, Rank: 1793.000\n", "Column: 933, Selected False, Rank: 1792.000\n", "Column: 934, Selected False, Rank: 1791.000\n", "Column: 935, Selected False, Rank: 1790.000\n", "Column: 936, Selected False, Rank: 1789.000\n", "Column: 937, Selected False, Rank: 1788.000\n", "Column: 938, Selected False, Rank: 1787.000\n", "Column: 939, Selected False, Rank: 1786.000\n", "Column: 940, Selected False, Rank: 1785.000\n", "Column: 941, Selected False, Rank: 1784.000\n", "Column: 942, Selected False, Rank: 1783.000\n", "Column: 943, Selected False, Rank: 1782.000\n", "Column: 944, Selected False, Rank: 1781.000\n", "Column: 945, Selected False, Rank: 1780.000\n", "Column: 946, Selected False, Rank: 1779.000\n", "Column: 947, Selected False, Rank: 1778.000\n", "Column: 948, Selected False, Rank: 1777.000\n", "Column: 949, Selected False, Rank: 1776.000\n", "Column: 950, Selected False, Rank: 1775.000\n", "Column: 951, Selected False, Rank: 1774.000\n", "Column: 952, Selected False, Rank: 1773.000\n", "Column: 953, Selected False, Rank: 1772.000\n", "Column: 954, Selected False, Rank: 1771.000\n", "Column: 955, Selected False, Rank: 1770.000\n", "Column: 956, Selected False, Rank: 1769.000\n", "Column: 957, Selected False, Rank: 1768.000\n", "Column: 958, Selected False, Rank: 1767.000\n", "Column: 959, Selected False, Rank: 1766.000\n", "Column: 960, Selected False, Rank: 1765.000\n", "Column: 961, Selected False, Rank: 1764.000\n", "Column: 962, Selected False, Rank: 1763.000\n", "Column: 963, Selected False, Rank: 1762.000\n", "Column: 964, Selected False, Rank: 1761.000\n", "Column: 965, Selected False, Rank: 1760.000\n", "Column: 966, Selected False, Rank: 1759.000\n", "Column: 967, Selected False, Rank: 1758.000\n", "Column: 968, Selected False, Rank: 1757.000\n", "Column: 969, Selected False, Rank: 1756.000\n", "Column: 970, Selected False, Rank: 1755.000\n", "Column: 971, Selected False, Rank: 1754.000\n", "Column: 972, Selected False, Rank: 1753.000\n", "Column: 973, Selected False, Rank: 1752.000\n", "Column: 974, Selected False, Rank: 1751.000\n", "Column: 975, Selected False, Rank: 1750.000\n", "Column: 976, Selected False, Rank: 1749.000\n", "Column: 977, Selected False, Rank: 1748.000\n", "Column: 978, Selected False, Rank: 1747.000\n", "Column: 979, Selected False, Rank: 1746.000\n", "Column: 980, Selected False, Rank: 1745.000\n", "Column: 981, Selected False, Rank: 1744.000\n", "Column: 982, Selected False, Rank: 1743.000\n", "Column: 983, Selected False, Rank: 1742.000\n", "Column: 984, Selected False, Rank: 1741.000\n", "Column: 985, Selected False, Rank: 1740.000\n", "Column: 986, Selected False, Rank: 1739.000\n", "Column: 987, Selected False, Rank: 1738.000\n", "Column: 988, Selected False, Rank: 1737.000\n", "Column: 989, Selected False, Rank: 1736.000\n", "Column: 990, Selected False, Rank: 1735.000\n", "Column: 991, Selected False, Rank: 1734.000\n", "Column: 992, Selected False, Rank: 1733.000\n", "Column: 993, Selected False, Rank: 1732.000\n", "Column: 994, Selected False, Rank: 1731.000\n", "Column: 995, Selected False, Rank: 1730.000\n", "Column: 996, Selected False, Rank: 1729.000\n", "Column: 997, Selected False, Rank: 1728.000\n", "Column: 998, Selected False, Rank: 1727.000\n", "Column: 999, Selected False, Rank: 1726.000\n", "Column: 1000, Selected False, Rank: 1725.000\n", "Column: 1001, Selected False, Rank: 1724.000\n", "Column: 1002, Selected False, Rank: 1723.000\n", "Column: 1003, Selected False, Rank: 1722.000\n", "Column: 1004, Selected False, Rank: 1721.000\n", "Column: 1005, Selected False, Rank: 1720.000\n", "Column: 1006, Selected False, Rank: 1719.000\n", "Column: 1007, Selected False, Rank: 1718.000\n", "Column: 1008, Selected False, Rank: 1717.000\n", "Column: 1009, Selected False, Rank: 1716.000\n", "Column: 1010, Selected False, Rank: 1715.000\n", "Column: 1011, Selected False, Rank: 1714.000\n", "Column: 1012, Selected False, Rank: 1713.000\n", "Column: 1013, Selected False, Rank: 1712.000\n", "Column: 1014, Selected False, Rank: 1711.000\n", "Column: 1015, Selected False, Rank: 1710.000\n", "Column: 1016, Selected False, Rank: 1709.000\n", "Column: 1017, Selected False, Rank: 1708.000\n", "Column: 1018, Selected False, Rank: 1707.000\n", "Column: 1019, Selected False, Rank: 1706.000\n", "Column: 1020, Selected False, Rank: 1705.000\n", "Column: 1021, Selected False, Rank: 1704.000\n", "Column: 1022, Selected False, Rank: 1703.000\n", "Column: 1023, Selected False, Rank: 1702.000\n", "Column: 1024, Selected False, Rank: 1701.000\n", "Column: 1025, Selected False, Rank: 1700.000\n", "Column: 1026, Selected False, Rank: 1699.000\n", "Column: 1027, Selected False, Rank: 1698.000\n", "Column: 1028, Selected False, Rank: 1697.000\n", "Column: 1029, Selected False, Rank: 1696.000\n", "Column: 1030, Selected False, Rank: 1695.000\n", "Column: 1031, Selected False, Rank: 1694.000\n", "Column: 1032, Selected False, Rank: 1693.000\n", "Column: 1033, Selected False, Rank: 1692.000\n", "Column: 1034, Selected False, Rank: 1691.000\n", "Column: 1035, Selected False, Rank: 1690.000\n", "Column: 1036, Selected False, Rank: 1689.000\n", "Column: 1037, Selected False, Rank: 1688.000\n", "Column: 1038, Selected False, Rank: 1687.000\n", "Column: 1039, Selected False, Rank: 1686.000\n", "Column: 1040, Selected False, Rank: 1685.000\n", "Column: 1041, Selected False, Rank: 1684.000\n", "Column: 1042, Selected False, Rank: 1683.000\n", "Column: 1043, Selected False, Rank: 1682.000\n", "Column: 1044, Selected False, Rank: 1681.000\n", "Column: 1045, Selected False, Rank: 1680.000\n", "Column: 1046, Selected False, Rank: 1679.000\n", "Column: 1047, Selected False, Rank: 1678.000\n", "Column: 1048, Selected False, Rank: 1677.000\n", "Column: 1049, Selected False, Rank: 1676.000\n", "Column: 1050, Selected False, Rank: 1675.000\n", "Column: 1051, Selected False, Rank: 1674.000\n", "Column: 1052, Selected False, Rank: 1673.000\n", "Column: 1053, Selected False, Rank: 1672.000\n", "Column: 1054, Selected False, Rank: 1671.000\n", "Column: 1055, Selected False, Rank: 1670.000\n", "Column: 1056, Selected False, Rank: 1669.000\n", "Column: 1057, Selected False, Rank: 1668.000\n", "Column: 1058, Selected False, Rank: 1667.000\n", "Column: 1059, Selected False, Rank: 1666.000\n", "Column: 1060, Selected False, Rank: 1665.000\n", "Column: 1061, Selected False, Rank: 1664.000\n", "Column: 1062, Selected False, Rank: 1663.000\n", "Column: 1063, Selected False, Rank: 1662.000\n", "Column: 1064, Selected False, Rank: 1661.000\n", "Column: 1065, Selected False, Rank: 1660.000\n", "Column: 1066, Selected False, Rank: 1659.000\n", "Column: 1067, Selected False, Rank: 1658.000\n", "Column: 1068, Selected False, Rank: 1657.000\n", "Column: 1069, Selected False, Rank: 1656.000\n", "Column: 1070, Selected False, Rank: 1655.000\n", "Column: 1071, Selected False, Rank: 1654.000\n", "Column: 1072, Selected False, Rank: 1653.000\n", "Column: 1073, Selected False, Rank: 1652.000\n", "Column: 1074, Selected False, Rank: 1651.000\n", "Column: 1075, Selected False, Rank: 1650.000\n", "Column: 1076, Selected False, Rank: 1649.000\n", "Column: 1077, Selected False, Rank: 1648.000\n", "Column: 1078, Selected False, Rank: 1647.000\n", "Column: 1079, Selected False, Rank: 1646.000\n", "Column: 1080, Selected False, Rank: 1645.000\n", "Column: 1081, Selected False, Rank: 1644.000\n", "Column: 1082, Selected False, Rank: 1643.000\n", "Column: 1083, Selected False, Rank: 1642.000\n", "Column: 1084, Selected False, Rank: 1641.000\n", "Column: 1085, Selected False, Rank: 1640.000\n", "Column: 1086, Selected False, Rank: 1639.000\n", "Column: 1087, Selected False, Rank: 1638.000\n", "Column: 1088, Selected False, Rank: 1637.000\n", "Column: 1089, Selected False, Rank: 1636.000\n", "Column: 1090, Selected False, Rank: 1635.000\n", "Column: 1091, Selected False, Rank: 1634.000\n", "Column: 1092, Selected False, Rank: 1633.000\n", "Column: 1093, Selected False, Rank: 1632.000\n", "Column: 1094, Selected False, Rank: 1631.000\n", "Column: 1095, Selected False, Rank: 1630.000\n", "Column: 1096, Selected False, Rank: 1629.000\n", "Column: 1097, Selected False, Rank: 1628.000\n", "Column: 1098, Selected False, Rank: 1627.000\n", "Column: 1099, Selected False, Rank: 1626.000\n", "Column: 1100, Selected False, Rank: 1625.000\n", "Column: 1101, Selected False, Rank: 1624.000\n", "Column: 1102, Selected False, Rank: 1623.000\n", "Column: 1103, Selected False, Rank: 1622.000\n", "Column: 1104, Selected False, Rank: 1621.000\n", "Column: 1105, Selected False, Rank: 1620.000\n", "Column: 1106, Selected False, Rank: 1619.000\n", "Column: 1107, Selected False, Rank: 1618.000\n", "Column: 1108, Selected False, Rank: 1617.000\n", "Column: 1109, Selected False, Rank: 1616.000\n", "Column: 1110, Selected False, Rank: 1615.000\n", "Column: 1111, Selected False, Rank: 1614.000\n", "Column: 1112, Selected False, Rank: 1613.000\n", "Column: 1113, Selected False, Rank: 1612.000\n", "Column: 1114, Selected False, Rank: 1611.000\n", "Column: 1115, Selected False, Rank: 1610.000\n", "Column: 1116, Selected False, Rank: 1609.000\n", "Column: 1117, Selected False, Rank: 1608.000\n", "Column: 1118, Selected False, Rank: 1607.000\n", "Column: 1119, Selected False, Rank: 1606.000\n", "Column: 1120, Selected False, Rank: 1605.000\n", "Column: 1121, Selected False, Rank: 1604.000\n", "Column: 1122, Selected False, Rank: 1603.000\n", "Column: 1123, Selected False, Rank: 1602.000\n", "Column: 1124, Selected False, Rank: 1601.000\n", "Column: 1125, Selected False, Rank: 1600.000\n", "Column: 1126, Selected False, Rank: 1599.000\n", "Column: 1127, Selected False, Rank: 1598.000\n", "Column: 1128, Selected False, Rank: 1597.000\n", "Column: 1129, Selected False, Rank: 1596.000\n", "Column: 1130, Selected False, Rank: 1595.000\n", "Column: 1131, Selected False, Rank: 1594.000\n", "Column: 1132, Selected False, Rank: 1593.000\n", "Column: 1133, Selected False, Rank: 1592.000\n", "Column: 1134, Selected False, Rank: 1591.000\n", "Column: 1135, Selected False, Rank: 1590.000\n", "Column: 1136, Selected False, Rank: 1589.000\n", "Column: 1137, Selected False, Rank: 1588.000\n", "Column: 1138, Selected False, Rank: 1587.000\n", "Column: 1139, Selected False, Rank: 1586.000\n", "Column: 1140, Selected False, Rank: 1585.000\n", "Column: 1141, Selected False, Rank: 1584.000\n", "Column: 1142, Selected False, Rank: 1583.000\n", "Column: 1143, Selected False, Rank: 1582.000\n", "Column: 1144, Selected False, Rank: 1581.000\n", "Column: 1145, Selected False, Rank: 1580.000\n", "Column: 1146, Selected False, Rank: 1579.000\n", "Column: 1147, Selected False, Rank: 1578.000\n", "Column: 1148, Selected False, Rank: 1577.000\n", "Column: 1149, Selected False, Rank: 1576.000\n", "Column: 1150, Selected False, Rank: 1575.000\n", "Column: 1151, Selected False, Rank: 1574.000\n", "Column: 1152, Selected False, Rank: 1573.000\n", "Column: 1153, Selected False, Rank: 1572.000\n", "Column: 1154, Selected False, Rank: 1571.000\n", "Column: 1155, Selected False, Rank: 1570.000\n", "Column: 1156, Selected False, Rank: 1569.000\n", "Column: 1157, Selected False, Rank: 1568.000\n", "Column: 1158, Selected False, Rank: 1567.000\n", "Column: 1159, Selected False, Rank: 1566.000\n", "Column: 1160, Selected False, Rank: 1565.000\n", "Column: 1161, Selected False, Rank: 1564.000\n", "Column: 1162, Selected False, Rank: 1563.000\n", "Column: 1163, Selected False, Rank: 1562.000\n", "Column: 1164, Selected False, Rank: 1561.000\n", "Column: 1165, Selected False, Rank: 1560.000\n", "Column: 1166, Selected False, Rank: 1559.000\n", "Column: 1167, Selected False, Rank: 1558.000\n", "Column: 1168, Selected False, Rank: 1557.000\n", "Column: 1169, Selected False, Rank: 1556.000\n", "Column: 1170, Selected False, Rank: 1555.000\n", "Column: 1171, Selected False, Rank: 1554.000\n", "Column: 1172, Selected False, Rank: 1553.000\n", "Column: 1173, Selected False, Rank: 1552.000\n", "Column: 1174, Selected False, Rank: 1551.000\n", "Column: 1175, Selected False, Rank: 1550.000\n", "Column: 1176, Selected False, Rank: 1549.000\n", "Column: 1177, Selected False, Rank: 1548.000\n", "Column: 1178, Selected False, Rank: 1547.000\n", "Column: 1179, Selected False, Rank: 1546.000\n", "Column: 1180, Selected False, Rank: 1545.000\n", "Column: 1181, Selected False, Rank: 1544.000\n", "Column: 1182, Selected False, Rank: 1543.000\n", "Column: 1183, Selected False, Rank: 1542.000\n", "Column: 1184, Selected False, Rank: 1541.000\n", "Column: 1185, Selected False, Rank: 1540.000\n", "Column: 1186, Selected False, Rank: 1539.000\n", "Column: 1187, Selected False, Rank: 1538.000\n", "Column: 1188, Selected False, Rank: 1537.000\n", "Column: 1189, Selected False, Rank: 1536.000\n", "Column: 1190, Selected False, Rank: 1535.000\n", "Column: 1191, Selected False, Rank: 1534.000\n", "Column: 1192, Selected False, Rank: 1533.000\n", "Column: 1193, Selected False, Rank: 1532.000\n", "Column: 1194, Selected False, Rank: 1531.000\n", "Column: 1195, Selected False, Rank: 1530.000\n", "Column: 1196, Selected False, Rank: 1529.000\n", "Column: 1197, Selected False, Rank: 1528.000\n", "Column: 1198, Selected False, Rank: 1527.000\n", "Column: 1199, Selected False, Rank: 1526.000\n", "Column: 1200, Selected False, Rank: 1525.000\n", "Column: 1201, Selected False, Rank: 1524.000\n", "Column: 1202, Selected False, Rank: 1523.000\n", "Column: 1203, Selected False, Rank: 1522.000\n", "Column: 1204, Selected False, Rank: 1521.000\n", "Column: 1205, Selected False, Rank: 1520.000\n", "Column: 1206, Selected False, Rank: 1519.000\n", "Column: 1207, Selected False, Rank: 1518.000\n", "Column: 1208, Selected False, Rank: 1517.000\n", "Column: 1209, Selected False, Rank: 1516.000\n", "Column: 1210, Selected False, Rank: 1515.000\n", "Column: 1211, Selected False, Rank: 1514.000\n", "Column: 1212, Selected False, Rank: 1513.000\n", "Column: 1213, Selected False, Rank: 1512.000\n", "Column: 1214, Selected False, Rank: 1511.000\n", "Column: 1215, Selected False, Rank: 1510.000\n", "Column: 1216, Selected False, Rank: 1509.000\n", "Column: 1217, Selected False, Rank: 1508.000\n", "Column: 1218, Selected False, Rank: 1507.000\n", "Column: 1219, Selected False, Rank: 1506.000\n", "Column: 1220, Selected False, Rank: 1505.000\n", "Column: 1221, Selected False, Rank: 1504.000\n", "Column: 1222, Selected False, Rank: 1503.000\n", "Column: 1223, Selected False, Rank: 1502.000\n", "Column: 1224, Selected False, Rank: 1501.000\n", "Column: 1225, Selected False, Rank: 1500.000\n", "Column: 1226, Selected False, Rank: 1499.000\n", "Column: 1227, Selected False, Rank: 1498.000\n", "Column: 1228, Selected False, Rank: 1497.000\n", "Column: 1229, Selected False, Rank: 1496.000\n", "Column: 1230, Selected False, Rank: 1495.000\n", "Column: 1231, Selected False, Rank: 1494.000\n", "Column: 1232, Selected False, Rank: 1493.000\n", "Column: 1233, Selected False, Rank: 1492.000\n", "Column: 1234, Selected False, Rank: 1491.000\n", "Column: 1235, Selected False, Rank: 1490.000\n", "Column: 1236, Selected False, Rank: 1489.000\n", "Column: 1237, Selected False, Rank: 1488.000\n", "Column: 1238, Selected False, Rank: 1487.000\n", "Column: 1239, Selected False, Rank: 1486.000\n", "Column: 1240, Selected False, Rank: 1485.000\n", "Column: 1241, Selected False, Rank: 1484.000\n", "Column: 1242, Selected False, Rank: 1483.000\n", "Column: 1243, Selected False, Rank: 1482.000\n", "Column: 1244, Selected False, Rank: 1481.000\n", "Column: 1245, Selected False, Rank: 1480.000\n", "Column: 1246, Selected False, Rank: 1479.000\n", "Column: 1247, Selected False, Rank: 1478.000\n", "Column: 1248, Selected False, Rank: 1477.000\n", "Column: 1249, Selected False, Rank: 1476.000\n", "Column: 1250, Selected False, Rank: 1475.000\n", "Column: 1251, Selected False, Rank: 1474.000\n", "Column: 1252, Selected False, Rank: 1473.000\n", "Column: 1253, Selected False, Rank: 1472.000\n", "Column: 1254, Selected False, Rank: 1471.000\n", "Column: 1255, Selected False, Rank: 1470.000\n", "Column: 1256, Selected False, Rank: 1469.000\n", "Column: 1257, Selected False, Rank: 1468.000\n", "Column: 1258, Selected False, Rank: 1467.000\n", "Column: 1259, Selected False, Rank: 1466.000\n", "Column: 1260, Selected False, Rank: 1465.000\n", "Column: 1261, Selected False, Rank: 1464.000\n", "Column: 1262, Selected False, Rank: 1463.000\n", "Column: 1263, Selected False, Rank: 1462.000\n", "Column: 1264, Selected False, Rank: 1461.000\n", "Column: 1265, Selected False, Rank: 1460.000\n", "Column: 1266, Selected False, Rank: 1459.000\n", "Column: 1267, Selected False, Rank: 1458.000\n", "Column: 1268, Selected False, Rank: 1457.000\n", "Column: 1269, Selected False, Rank: 1456.000\n", "Column: 1270, Selected False, Rank: 1455.000\n", "Column: 1271, Selected False, Rank: 1454.000\n", "Column: 1272, Selected False, Rank: 1453.000\n", "Column: 1273, Selected False, Rank: 1452.000\n", "Column: 1274, Selected False, Rank: 1451.000\n", "Column: 1275, Selected False, Rank: 1450.000\n", "Column: 1276, Selected False, Rank: 1449.000\n", "Column: 1277, Selected False, Rank: 1448.000\n", "Column: 1278, Selected False, Rank: 1447.000\n", "Column: 1279, Selected False, Rank: 1446.000\n", "Column: 1280, Selected False, Rank: 1445.000\n", "Column: 1281, Selected False, Rank: 1444.000\n", "Column: 1282, Selected False, Rank: 1443.000\n", "Column: 1283, Selected False, Rank: 1442.000\n", "Column: 1284, Selected False, Rank: 1441.000\n", "Column: 1285, Selected False, Rank: 1440.000\n", "Column: 1286, Selected False, Rank: 1439.000\n", "Column: 1287, Selected False, Rank: 1438.000\n", "Column: 1288, Selected False, Rank: 1437.000\n", "Column: 1289, Selected False, Rank: 1436.000\n", "Column: 1290, Selected False, Rank: 1435.000\n", "Column: 1291, Selected False, Rank: 1434.000\n", "Column: 1292, Selected False, Rank: 1433.000\n", "Column: 1293, Selected False, Rank: 1432.000\n", "Column: 1294, Selected False, Rank: 1431.000\n", "Column: 1295, Selected False, Rank: 1430.000\n", "Column: 1296, Selected False, Rank: 1429.000\n", "Column: 1297, Selected False, Rank: 1428.000\n", "Column: 1298, Selected False, Rank: 1427.000\n", "Column: 1299, Selected False, Rank: 1426.000\n", "Column: 1300, Selected False, Rank: 1425.000\n", "Column: 1301, Selected False, Rank: 1424.000\n", "Column: 1302, Selected False, Rank: 1423.000\n", "Column: 1303, Selected False, Rank: 1422.000\n", "Column: 1304, Selected False, Rank: 1421.000\n", "Column: 1305, Selected False, Rank: 1420.000\n", "Column: 1306, Selected False, Rank: 1419.000\n", "Column: 1307, Selected False, Rank: 1418.000\n", "Column: 1308, Selected False, Rank: 1417.000\n", "Column: 1309, Selected False, Rank: 1416.000\n", "Column: 1310, Selected False, Rank: 1415.000\n", "Column: 1311, Selected False, Rank: 1414.000\n", "Column: 1312, Selected False, Rank: 1413.000\n", "Column: 1313, Selected False, Rank: 1412.000\n", "Column: 1314, Selected False, Rank: 1411.000\n", "Column: 1315, Selected False, Rank: 1410.000\n", "Column: 1316, Selected False, Rank: 1409.000\n", "Column: 1317, Selected False, Rank: 1408.000\n", "Column: 1318, Selected False, Rank: 1407.000\n", "Column: 1319, Selected False, Rank: 1406.000\n", "Column: 1320, Selected False, Rank: 1405.000\n", "Column: 1321, Selected False, Rank: 1404.000\n", "Column: 1322, Selected False, Rank: 1403.000\n", "Column: 1323, Selected False, Rank: 1402.000\n", "Column: 1324, Selected False, Rank: 1401.000\n", "Column: 1325, Selected False, Rank: 1400.000\n", "Column: 1326, Selected False, Rank: 1399.000\n", "Column: 1327, Selected False, Rank: 1398.000\n", "Column: 1328, Selected False, Rank: 1397.000\n", "Column: 1329, Selected False, Rank: 1396.000\n", "Column: 1330, Selected False, Rank: 1395.000\n", "Column: 1331, Selected False, Rank: 1394.000\n", "Column: 1332, Selected False, Rank: 1393.000\n", "Column: 1333, Selected False, Rank: 1392.000\n", "Column: 1334, Selected False, Rank: 1391.000\n", "Column: 1335, Selected False, Rank: 1390.000\n", "Column: 1336, Selected False, Rank: 1389.000\n", "Column: 1337, Selected False, Rank: 1388.000\n", "Column: 1338, Selected False, Rank: 1387.000\n", "Column: 1339, Selected False, Rank: 1386.000\n", "Column: 1340, Selected False, Rank: 1385.000\n", "Column: 1341, Selected False, Rank: 1384.000\n", "Column: 1342, Selected False, Rank: 1383.000\n", "Column: 1343, Selected False, Rank: 1382.000\n", "Column: 1344, Selected False, Rank: 1381.000\n", "Column: 1345, Selected False, Rank: 1380.000\n", "Column: 1346, Selected False, Rank: 1379.000\n", "Column: 1347, Selected False, Rank: 1378.000\n", "Column: 1348, Selected False, Rank: 1377.000\n", "Column: 1349, Selected False, Rank: 1376.000\n", "Column: 1350, Selected False, Rank: 1375.000\n", "Column: 1351, Selected False, Rank: 1374.000\n", "Column: 1352, Selected False, Rank: 1373.000\n", "Column: 1353, Selected False, Rank: 1372.000\n", "Column: 1354, Selected False, Rank: 1371.000\n", "Column: 1355, Selected False, Rank: 1370.000\n", "Column: 1356, Selected False, Rank: 1369.000\n", "Column: 1357, Selected False, Rank: 1368.000\n", "Column: 1358, Selected False, Rank: 1367.000\n", "Column: 1359, Selected False, Rank: 1366.000\n", "Column: 1360, Selected False, Rank: 1365.000\n", "Column: 1361, Selected False, Rank: 1364.000\n", "Column: 1362, Selected False, Rank: 1363.000\n", "Column: 1363, Selected False, Rank: 1362.000\n", "Column: 1364, Selected False, Rank: 1361.000\n", "Column: 1365, Selected False, Rank: 1360.000\n", "Column: 1366, Selected False, Rank: 1359.000\n", "Column: 1367, Selected False, Rank: 1358.000\n", "Column: 1368, Selected False, Rank: 1357.000\n", "Column: 1369, Selected False, Rank: 1356.000\n", "Column: 1370, Selected False, Rank: 1355.000\n", "Column: 1371, Selected False, Rank: 1354.000\n", "Column: 1372, Selected False, Rank: 1353.000\n", "Column: 1373, Selected False, Rank: 1352.000\n", "Column: 1374, Selected False, Rank: 1351.000\n", "Column: 1375, Selected False, Rank: 1350.000\n", "Column: 1376, Selected False, Rank: 1349.000\n", "Column: 1377, Selected False, Rank: 1348.000\n", "Column: 1378, Selected False, Rank: 1347.000\n", "Column: 1379, Selected False, Rank: 1346.000\n", "Column: 1380, Selected False, Rank: 1345.000\n", "Column: 1381, Selected False, Rank: 1344.000\n", "Column: 1382, Selected False, Rank: 1343.000\n", "Column: 1383, Selected False, Rank: 1342.000\n", "Column: 1384, Selected False, Rank: 1341.000\n", "Column: 1385, Selected False, Rank: 1340.000\n", "Column: 1386, Selected False, Rank: 1339.000\n", "Column: 1387, Selected False, Rank: 1338.000\n", "Column: 1388, Selected False, Rank: 1337.000\n", "Column: 1389, Selected False, Rank: 1336.000\n", "Column: 1390, Selected False, Rank: 1335.000\n", "Column: 1391, Selected False, Rank: 1334.000\n", "Column: 1392, Selected False, Rank: 1333.000\n", "Column: 1393, Selected False, Rank: 1332.000\n", "Column: 1394, Selected False, Rank: 1331.000\n", "Column: 1395, Selected False, Rank: 1330.000\n", "Column: 1396, Selected False, Rank: 1329.000\n", "Column: 1397, Selected False, Rank: 1328.000\n", "Column: 1398, Selected False, Rank: 1327.000\n", "Column: 1399, Selected False, Rank: 1326.000\n", "Column: 1400, Selected False, Rank: 1325.000\n", "Column: 1401, Selected False, Rank: 1324.000\n", "Column: 1402, Selected False, Rank: 1323.000\n", "Column: 1403, Selected False, Rank: 1322.000\n", "Column: 1404, Selected False, Rank: 1321.000\n", "Column: 1405, Selected False, Rank: 1320.000\n", "Column: 1406, Selected False, Rank: 1319.000\n", "Column: 1407, Selected False, Rank: 1318.000\n", "Column: 1408, Selected False, Rank: 1317.000\n", "Column: 1409, Selected False, Rank: 1316.000\n", "Column: 1410, Selected False, Rank: 1315.000\n", "Column: 1411, Selected False, Rank: 1314.000\n", "Column: 1412, Selected False, Rank: 1313.000\n", "Column: 1413, Selected False, Rank: 1312.000\n", "Column: 1414, Selected False, Rank: 1311.000\n", "Column: 1415, Selected False, Rank: 1310.000\n", "Column: 1416, Selected False, Rank: 1309.000\n", "Column: 1417, Selected False, Rank: 1308.000\n", "Column: 1418, Selected False, Rank: 1307.000\n", "Column: 1419, Selected False, Rank: 1306.000\n", "Column: 1420, Selected False, Rank: 1305.000\n", "Column: 1421, Selected False, Rank: 1304.000\n", "Column: 1422, Selected False, Rank: 1303.000\n", "Column: 1423, Selected False, Rank: 1302.000\n", "Column: 1424, Selected False, Rank: 1301.000\n", "Column: 1425, Selected False, Rank: 1300.000\n", "Column: 1426, Selected False, Rank: 1299.000\n", "Column: 1427, Selected False, Rank: 1298.000\n", "Column: 1428, Selected False, Rank: 1297.000\n", "Column: 1429, Selected False, Rank: 1296.000\n", "Column: 1430, Selected False, Rank: 1295.000\n", "Column: 1431, Selected False, Rank: 1294.000\n", "Column: 1432, Selected False, Rank: 1293.000\n", "Column: 1433, Selected False, Rank: 1292.000\n", "Column: 1434, Selected False, Rank: 1291.000\n", "Column: 1435, Selected False, Rank: 1290.000\n", "Column: 1436, Selected False, Rank: 1289.000\n", "Column: 1437, Selected False, Rank: 1288.000\n", "Column: 1438, Selected False, Rank: 1287.000\n", "Column: 1439, Selected False, Rank: 1286.000\n", "Column: 1440, Selected False, Rank: 1285.000\n", "Column: 1441, Selected False, Rank: 1284.000\n", "Column: 1442, Selected False, Rank: 1283.000\n", "Column: 1443, Selected False, Rank: 1282.000\n", "Column: 1444, Selected False, Rank: 1281.000\n", "Column: 1445, Selected False, Rank: 1280.000\n", "Column: 1446, Selected False, Rank: 1279.000\n", "Column: 1447, Selected False, Rank: 1278.000\n", "Column: 1448, Selected False, Rank: 1277.000\n", "Column: 1449, Selected False, Rank: 1276.000\n", "Column: 1450, Selected False, Rank: 1275.000\n", "Column: 1451, Selected False, Rank: 1274.000\n", "Column: 1452, Selected False, Rank: 1273.000\n", "Column: 1453, Selected False, Rank: 1272.000\n", "Column: 1454, Selected False, Rank: 1271.000\n", "Column: 1455, Selected False, Rank: 1270.000\n", "Column: 1456, Selected False, Rank: 1269.000\n", "Column: 1457, Selected False, Rank: 1268.000\n", "Column: 1458, Selected False, Rank: 1267.000\n", "Column: 1459, Selected False, Rank: 1266.000\n", "Column: 1460, Selected False, Rank: 1265.000\n", "Column: 1461, Selected False, Rank: 1264.000\n", "Column: 1462, Selected False, Rank: 1263.000\n", "Column: 1463, Selected False, Rank: 1262.000\n", "Column: 1464, Selected False, Rank: 1261.000\n", "Column: 1465, Selected False, Rank: 1260.000\n", "Column: 1466, Selected False, Rank: 1259.000\n", "Column: 1467, Selected False, Rank: 1258.000\n", "Column: 1468, Selected False, Rank: 1257.000\n", "Column: 1469, Selected False, Rank: 1256.000\n", "Column: 1470, Selected False, Rank: 1255.000\n", "Column: 1471, Selected False, Rank: 1254.000\n", "Column: 1472, Selected False, Rank: 1253.000\n", "Column: 1473, Selected False, Rank: 1252.000\n", "Column: 1474, Selected False, Rank: 1251.000\n", "Column: 1475, Selected False, Rank: 1250.000\n", "Column: 1476, Selected False, Rank: 1249.000\n", "Column: 1477, Selected False, Rank: 1248.000\n", "Column: 1478, Selected False, Rank: 1247.000\n", "Column: 1479, Selected False, Rank: 1246.000\n", "Column: 1480, Selected False, Rank: 1245.000\n", "Column: 1481, Selected False, Rank: 1244.000\n", "Column: 1482, Selected False, Rank: 1243.000\n", "Column: 1483, Selected False, Rank: 1242.000\n", "Column: 1484, Selected False, Rank: 1241.000\n", "Column: 1485, Selected False, Rank: 1240.000\n", "Column: 1486, Selected False, Rank: 1239.000\n", "Column: 1487, Selected False, Rank: 1238.000\n", "Column: 1488, Selected False, Rank: 1237.000\n", "Column: 1489, Selected False, Rank: 1236.000\n", "Column: 1490, Selected False, Rank: 1235.000\n", "Column: 1491, Selected False, Rank: 1234.000\n", "Column: 1492, Selected False, Rank: 1233.000\n", "Column: 1493, Selected False, Rank: 1232.000\n", "Column: 1494, Selected False, Rank: 1231.000\n", "Column: 1495, Selected False, Rank: 1230.000\n", "Column: 1496, Selected False, Rank: 1229.000\n", "Column: 1497, Selected False, Rank: 1228.000\n", "Column: 1498, Selected False, Rank: 1227.000\n", "Column: 1499, Selected False, Rank: 1226.000\n", "Column: 1500, Selected False, Rank: 1225.000\n", "Column: 1501, Selected False, Rank: 1224.000\n", "Column: 1502, Selected False, Rank: 1223.000\n", "Column: 1503, Selected False, Rank: 1222.000\n", "Column: 1504, Selected False, Rank: 1221.000\n", "Column: 1505, Selected False, Rank: 1220.000\n", "Column: 1506, Selected False, Rank: 1219.000\n", "Column: 1507, Selected False, Rank: 1218.000\n", "Column: 1508, Selected False, Rank: 1217.000\n", "Column: 1509, Selected False, Rank: 1216.000\n", "Column: 1510, Selected False, Rank: 1215.000\n", "Column: 1511, Selected False, Rank: 1214.000\n", "Column: 1512, Selected False, Rank: 1213.000\n", "Column: 1513, Selected False, Rank: 1212.000\n", "Column: 1514, Selected False, Rank: 1211.000\n", "Column: 1515, Selected False, Rank: 1210.000\n", "Column: 1516, Selected False, Rank: 1209.000\n", "Column: 1517, Selected False, Rank: 1208.000\n", "Column: 1518, Selected False, Rank: 1207.000\n", "Column: 1519, Selected False, Rank: 1206.000\n", "Column: 1520, Selected False, Rank: 1205.000\n", "Column: 1521, Selected False, Rank: 1204.000\n", "Column: 1522, Selected False, Rank: 1203.000\n", "Column: 1523, Selected False, Rank: 1202.000\n", "Column: 1524, Selected False, Rank: 1201.000\n", "Column: 1525, Selected False, Rank: 1200.000\n", "Column: 1526, Selected False, Rank: 1199.000\n", "Column: 1527, Selected False, Rank: 1198.000\n", "Column: 1528, Selected False, Rank: 1197.000\n", "Column: 1529, Selected False, Rank: 1196.000\n", "Column: 1530, Selected False, Rank: 1195.000\n", "Column: 1531, Selected False, Rank: 1194.000\n", "Column: 1532, Selected False, Rank: 1193.000\n", "Column: 1533, Selected False, Rank: 1192.000\n", "Column: 1534, Selected False, Rank: 1191.000\n", "Column: 1535, Selected False, Rank: 1190.000\n", "Column: 1536, Selected False, Rank: 1189.000\n", "Column: 1537, Selected False, Rank: 1188.000\n", "Column: 1538, Selected False, Rank: 1187.000\n", "Column: 1539, Selected False, Rank: 1186.000\n", "Column: 1540, Selected False, Rank: 1185.000\n", "Column: 1541, Selected False, Rank: 1184.000\n", "Column: 1542, Selected False, Rank: 1183.000\n", "Column: 1543, Selected False, Rank: 1182.000\n", "Column: 1544, Selected False, Rank: 1181.000\n", "Column: 1545, Selected False, Rank: 1180.000\n", "Column: 1546, Selected False, Rank: 1179.000\n", "Column: 1547, Selected False, Rank: 1178.000\n", "Column: 1548, Selected False, Rank: 1177.000\n", "Column: 1549, Selected False, Rank: 1176.000\n", "Column: 1550, Selected False, Rank: 1175.000\n", "Column: 1551, Selected False, Rank: 1174.000\n", "Column: 1552, Selected False, Rank: 1173.000\n", "Column: 1553, Selected False, Rank: 1172.000\n", "Column: 1554, Selected False, Rank: 1171.000\n", "Column: 1555, Selected False, Rank: 1170.000\n", "Column: 1556, Selected False, Rank: 1169.000\n", "Column: 1557, Selected False, Rank: 1168.000\n", "Column: 1558, Selected False, Rank: 1167.000\n", "Column: 1559, Selected False, Rank: 1166.000\n", "Column: 1560, Selected False, Rank: 1165.000\n", "Column: 1561, Selected False, Rank: 1164.000\n", "Column: 1562, Selected False, Rank: 1163.000\n", "Column: 1563, Selected False, Rank: 1162.000\n", "Column: 1564, Selected False, Rank: 1161.000\n", "Column: 1565, Selected False, Rank: 1160.000\n", "Column: 1566, Selected False, Rank: 1159.000\n", "Column: 1567, Selected False, Rank: 1158.000\n", "Column: 1568, Selected False, Rank: 1157.000\n", "Column: 1569, Selected False, Rank: 1156.000\n", "Column: 1570, Selected False, Rank: 1155.000\n", "Column: 1571, Selected False, Rank: 1154.000\n", "Column: 1572, Selected False, Rank: 1153.000\n", "Column: 1573, Selected False, Rank: 1152.000\n", "Column: 1574, Selected False, Rank: 1151.000\n", "Column: 1575, Selected False, Rank: 1150.000\n", "Column: 1576, Selected False, Rank: 1149.000\n", "Column: 1577, Selected False, Rank: 1148.000\n", "Column: 1578, Selected False, Rank: 1147.000\n", "Column: 1579, Selected False, Rank: 1146.000\n", "Column: 1580, Selected False, Rank: 1145.000\n", "Column: 1581, Selected False, Rank: 1144.000\n", "Column: 1582, Selected False, Rank: 1143.000\n", "Column: 1583, Selected False, Rank: 1142.000\n", "Column: 1584, Selected False, Rank: 1141.000\n", "Column: 1585, Selected False, Rank: 1140.000\n", "Column: 1586, Selected False, Rank: 1139.000\n", "Column: 1587, Selected False, Rank: 1138.000\n", "Column: 1588, Selected False, Rank: 1137.000\n", "Column: 1589, Selected False, Rank: 1136.000\n", "Column: 1590, Selected False, Rank: 1135.000\n", "Column: 1591, Selected False, Rank: 1134.000\n", "Column: 1592, Selected False, Rank: 1133.000\n", "Column: 1593, Selected False, Rank: 1132.000\n", "Column: 1594, Selected False, Rank: 1131.000\n", "Column: 1595, Selected False, Rank: 1130.000\n", "Column: 1596, Selected False, Rank: 1129.000\n", "Column: 1597, Selected False, Rank: 1128.000\n", "Column: 1598, Selected False, Rank: 1127.000\n", "Column: 1599, Selected False, Rank: 1126.000\n", "Column: 1600, Selected False, Rank: 1125.000\n", "Column: 1601, Selected False, Rank: 1124.000\n", "Column: 1602, Selected False, Rank: 1123.000\n", "Column: 1603, Selected False, Rank: 1122.000\n", "Column: 1604, Selected False, Rank: 1121.000\n", "Column: 1605, Selected False, Rank: 1120.000\n", "Column: 1606, Selected False, Rank: 1119.000\n", "Column: 1607, Selected False, Rank: 1118.000\n", "Column: 1608, Selected False, Rank: 1117.000\n", "Column: 1609, Selected False, Rank: 1116.000\n", "Column: 1610, Selected False, Rank: 1115.000\n", "Column: 1611, Selected False, Rank: 1114.000\n", "Column: 1612, Selected False, Rank: 1113.000\n", "Column: 1613, Selected False, Rank: 1112.000\n", "Column: 1614, Selected False, Rank: 1111.000\n", "Column: 1615, Selected False, Rank: 1110.000\n", "Column: 1616, Selected False, Rank: 1109.000\n", "Column: 1617, Selected False, Rank: 1108.000\n", "Column: 1618, Selected False, Rank: 1107.000\n", "Column: 1619, Selected False, Rank: 1106.000\n", "Column: 1620, Selected False, Rank: 1105.000\n", "Column: 1621, Selected False, Rank: 1104.000\n", "Column: 1622, Selected False, Rank: 1103.000\n", "Column: 1623, Selected False, Rank: 1102.000\n", "Column: 1624, Selected False, Rank: 1101.000\n", "Column: 1625, Selected False, Rank: 1100.000\n", "Column: 1626, Selected False, Rank: 1099.000\n", "Column: 1627, Selected False, Rank: 1098.000\n", "Column: 1628, Selected False, Rank: 1097.000\n", "Column: 1629, Selected False, Rank: 1096.000\n", "Column: 1630, Selected False, Rank: 1095.000\n", "Column: 1631, Selected False, Rank: 1094.000\n", "Column: 1632, Selected False, Rank: 1093.000\n", "Column: 1633, Selected False, Rank: 1092.000\n", "Column: 1634, Selected False, Rank: 1091.000\n", "Column: 1635, Selected False, Rank: 1090.000\n", "Column: 1636, Selected False, Rank: 1089.000\n", "Column: 1637, Selected False, Rank: 1088.000\n", "Column: 1638, Selected False, Rank: 1087.000\n", "Column: 1639, Selected False, Rank: 1086.000\n", "Column: 1640, Selected False, Rank: 1085.000\n", "Column: 1641, Selected False, Rank: 1084.000\n", "Column: 1642, Selected False, Rank: 1083.000\n", "Column: 1643, Selected False, Rank: 1082.000\n", "Column: 1644, Selected False, Rank: 1081.000\n", "Column: 1645, Selected False, Rank: 1080.000\n", "Column: 1646, Selected False, Rank: 1079.000\n", "Column: 1647, Selected False, Rank: 1078.000\n", "Column: 1648, Selected False, Rank: 1077.000\n", "Column: 1649, Selected False, Rank: 1076.000\n", "Column: 1650, Selected False, Rank: 1075.000\n", "Column: 1651, Selected False, Rank: 1074.000\n", "Column: 1652, Selected False, Rank: 1073.000\n", "Column: 1653, Selected False, Rank: 1072.000\n", "Column: 1654, Selected False, Rank: 1071.000\n", "Column: 1655, Selected False, Rank: 1070.000\n", "Column: 1656, Selected False, Rank: 1069.000\n", "Column: 1657, Selected False, Rank: 1068.000\n", "Column: 1658, Selected False, Rank: 1067.000\n", "Column: 1659, Selected False, Rank: 1066.000\n", "Column: 1660, Selected False, Rank: 1065.000\n", "Column: 1661, Selected False, Rank: 1064.000\n", "Column: 1662, Selected False, Rank: 1063.000\n", "Column: 1663, Selected False, Rank: 1062.000\n", "Column: 1664, Selected False, Rank: 1061.000\n", "Column: 1665, Selected False, Rank: 1060.000\n", "Column: 1666, Selected False, Rank: 1059.000\n", "Column: 1667, Selected False, Rank: 1058.000\n", "Column: 1668, Selected False, Rank: 1057.000\n", "Column: 1669, Selected False, Rank: 1056.000\n", "Column: 1670, Selected False, Rank: 1055.000\n", "Column: 1671, Selected False, Rank: 1054.000\n", "Column: 1672, Selected False, Rank: 1053.000\n", "Column: 1673, Selected False, Rank: 1052.000\n", "Column: 1674, Selected False, Rank: 1051.000\n", "Column: 1675, Selected False, Rank: 1050.000\n", "Column: 1676, Selected False, Rank: 1049.000\n", "Column: 1677, Selected False, Rank: 1048.000\n", "Column: 1678, Selected False, Rank: 1047.000\n", "Column: 1679, Selected False, Rank: 1046.000\n", "Column: 1680, Selected False, Rank: 1045.000\n", "Column: 1681, Selected False, Rank: 1044.000\n", "Column: 1682, Selected False, Rank: 1043.000\n", "Column: 1683, Selected False, Rank: 1042.000\n", "Column: 1684, Selected False, Rank: 1041.000\n", "Column: 1685, Selected False, Rank: 1040.000\n", "Column: 1686, Selected False, Rank: 1039.000\n", "Column: 1687, Selected False, Rank: 1038.000\n", "Column: 1688, Selected False, Rank: 1037.000\n", "Column: 1689, Selected False, Rank: 1036.000\n", "Column: 1690, Selected False, Rank: 1035.000\n", "Column: 1691, Selected False, Rank: 1034.000\n", "Column: 1692, Selected False, Rank: 1033.000\n", "Column: 1693, Selected False, Rank: 1032.000\n", "Column: 1694, Selected False, Rank: 1031.000\n", "Column: 1695, Selected False, Rank: 1030.000\n", "Column: 1696, Selected False, Rank: 1029.000\n", "Column: 1697, Selected False, Rank: 1028.000\n", "Column: 1698, Selected False, Rank: 1027.000\n", "Column: 1699, Selected False, Rank: 1026.000\n", "Column: 1700, Selected False, Rank: 1025.000\n", "Column: 1701, Selected False, Rank: 1024.000\n", "Column: 1702, Selected False, Rank: 1023.000\n", "Column: 1703, Selected False, Rank: 1022.000\n", "Column: 1704, Selected False, Rank: 1021.000\n", "Column: 1705, Selected False, Rank: 1020.000\n", "Column: 1706, Selected False, Rank: 1019.000\n", "Column: 1707, Selected False, Rank: 1018.000\n", "Column: 1708, Selected False, Rank: 1017.000\n", "Column: 1709, Selected False, Rank: 1016.000\n", "Column: 1710, Selected False, Rank: 1015.000\n", "Column: 1711, Selected False, Rank: 1014.000\n", "Column: 1712, Selected False, Rank: 1013.000\n", "Column: 1713, Selected False, Rank: 1012.000\n", "Column: 1714, Selected False, Rank: 1011.000\n", "Column: 1715, Selected False, Rank: 1010.000\n", "Column: 1716, Selected False, Rank: 1009.000\n", "Column: 1717, Selected False, Rank: 1008.000\n", "Column: 1718, Selected False, Rank: 1007.000\n", "Column: 1719, Selected False, Rank: 1006.000\n", "Column: 1720, Selected False, Rank: 1005.000\n", "Column: 1721, Selected False, Rank: 1004.000\n", "Column: 1722, Selected False, Rank: 1003.000\n", "Column: 1723, Selected False, Rank: 1002.000\n", "Column: 1724, Selected False, Rank: 1001.000\n", "Column: 1725, Selected False, Rank: 1000.000\n", "Column: 1726, Selected False, Rank: 999.000\n", "Column: 1727, Selected False, Rank: 998.000\n", "Column: 1728, Selected False, Rank: 997.000\n", "Column: 1729, Selected False, Rank: 996.000\n", "Column: 1730, Selected False, Rank: 995.000\n", "Column: 1731, Selected False, Rank: 994.000\n", "Column: 1732, Selected False, Rank: 993.000\n", "Column: 1733, Selected False, Rank: 992.000\n", "Column: 1734, Selected False, Rank: 991.000\n", "Column: 1735, Selected False, Rank: 990.000\n", "Column: 1736, Selected False, Rank: 989.000\n", "Column: 1737, Selected False, Rank: 988.000\n", "Column: 1738, Selected False, Rank: 987.000\n", "Column: 1739, Selected False, Rank: 986.000\n", "Column: 1740, Selected False, Rank: 985.000\n", "Column: 1741, Selected False, Rank: 984.000\n", "Column: 1742, Selected False, Rank: 983.000\n", "Column: 1743, Selected False, Rank: 982.000\n", "Column: 1744, Selected False, Rank: 981.000\n", "Column: 1745, Selected False, Rank: 980.000\n", "Column: 1746, Selected False, Rank: 979.000\n", "Column: 1747, Selected False, Rank: 978.000\n", "Column: 1748, Selected False, Rank: 977.000\n", "Column: 1749, Selected False, Rank: 976.000\n", "Column: 1750, Selected False, Rank: 975.000\n", "Column: 1751, Selected False, Rank: 974.000\n", "Column: 1752, Selected False, Rank: 973.000\n", "Column: 1753, Selected False, Rank: 972.000\n", "Column: 1754, Selected False, Rank: 971.000\n", "Column: 1755, Selected False, Rank: 970.000\n", "Column: 1756, Selected False, Rank: 969.000\n", "Column: 1757, Selected False, Rank: 968.000\n", "Column: 1758, Selected False, Rank: 967.000\n", "Column: 1759, Selected False, Rank: 966.000\n", "Column: 1760, Selected False, Rank: 965.000\n", "Column: 1761, Selected False, Rank: 964.000\n", "Column: 1762, Selected False, Rank: 963.000\n", "Column: 1763, Selected False, Rank: 962.000\n", "Column: 1764, Selected False, Rank: 961.000\n", "Column: 1765, Selected False, Rank: 960.000\n", "Column: 1766, Selected False, Rank: 959.000\n", "Column: 1767, Selected False, Rank: 958.000\n", "Column: 1768, Selected False, Rank: 957.000\n", "Column: 1769, Selected False, Rank: 956.000\n", "Column: 1770, Selected False, Rank: 955.000\n", "Column: 1771, Selected False, Rank: 954.000\n", "Column: 1772, Selected False, Rank: 953.000\n", "Column: 1773, Selected False, Rank: 952.000\n", "Column: 1774, Selected False, Rank: 951.000\n", "Column: 1775, Selected False, Rank: 950.000\n", "Column: 1776, Selected False, Rank: 949.000\n", "Column: 1777, Selected False, Rank: 948.000\n", "Column: 1778, Selected False, Rank: 947.000\n", "Column: 1779, Selected False, Rank: 946.000\n", "Column: 1780, Selected False, Rank: 945.000\n", "Column: 1781, Selected False, Rank: 944.000\n", "Column: 1782, Selected False, Rank: 943.000\n", "Column: 1783, Selected False, Rank: 942.000\n", "Column: 1784, Selected False, Rank: 941.000\n", "Column: 1785, Selected False, Rank: 940.000\n", "Column: 1786, Selected False, Rank: 939.000\n", "Column: 1787, Selected False, Rank: 938.000\n", "Column: 1788, Selected False, Rank: 937.000\n", "Column: 1789, Selected False, Rank: 936.000\n", "Column: 1790, Selected False, Rank: 935.000\n", "Column: 1791, Selected False, Rank: 934.000\n", "Column: 1792, Selected False, Rank: 933.000\n", "Column: 1793, Selected False, Rank: 932.000\n", "Column: 1794, Selected False, Rank: 931.000\n", "Column: 1795, Selected False, Rank: 930.000\n", "Column: 1796, Selected False, Rank: 929.000\n", "Column: 1797, Selected False, Rank: 928.000\n", "Column: 1798, Selected False, Rank: 927.000\n", "Column: 1799, Selected False, Rank: 926.000\n", "Column: 1800, Selected False, Rank: 925.000\n", "Column: 1801, Selected False, Rank: 924.000\n", "Column: 1802, Selected False, Rank: 923.000\n", "Column: 1803, Selected False, Rank: 922.000\n", "Column: 1804, Selected False, Rank: 921.000\n", "Column: 1805, Selected False, Rank: 920.000\n", "Column: 1806, Selected False, Rank: 919.000\n", "Column: 1807, Selected False, Rank: 918.000\n", "Column: 1808, Selected False, Rank: 917.000\n", "Column: 1809, Selected False, Rank: 916.000\n", "Column: 1810, Selected False, Rank: 915.000\n", "Column: 1811, Selected False, Rank: 914.000\n", "Column: 1812, Selected False, Rank: 913.000\n", "Column: 1813, Selected False, Rank: 912.000\n", "Column: 1814, Selected False, Rank: 911.000\n", "Column: 1815, Selected False, Rank: 910.000\n", "Column: 1816, Selected False, Rank: 909.000\n", "Column: 1817, Selected False, Rank: 908.000\n", "Column: 1818, Selected False, Rank: 907.000\n", "Column: 1819, Selected False, Rank: 906.000\n", "Column: 1820, Selected False, Rank: 905.000\n", "Column: 1821, Selected False, Rank: 904.000\n", "Column: 1822, Selected False, Rank: 903.000\n", "Column: 1823, Selected False, Rank: 902.000\n", "Column: 1824, Selected False, Rank: 901.000\n", "Column: 1825, Selected False, Rank: 900.000\n", "Column: 1826, Selected False, Rank: 899.000\n", "Column: 1827, Selected False, Rank: 898.000\n", "Column: 1828, Selected False, Rank: 897.000\n", "Column: 1829, Selected False, Rank: 896.000\n", "Column: 1830, Selected False, Rank: 895.000\n", "Column: 1831, Selected False, Rank: 894.000\n", "Column: 1832, Selected False, Rank: 893.000\n", "Column: 1833, Selected False, Rank: 892.000\n", "Column: 1834, Selected False, Rank: 891.000\n", "Column: 1835, Selected False, Rank: 890.000\n", "Column: 1836, Selected False, Rank: 889.000\n", "Column: 1837, Selected False, Rank: 888.000\n", "Column: 1838, Selected False, Rank: 887.000\n", "Column: 1839, Selected False, Rank: 886.000\n", "Column: 1840, Selected False, Rank: 885.000\n", "Column: 1841, Selected False, Rank: 884.000\n", "Column: 1842, Selected False, Rank: 883.000\n", "Column: 1843, Selected False, Rank: 882.000\n", "Column: 1844, Selected False, Rank: 881.000\n", "Column: 1845, Selected False, Rank: 880.000\n", "Column: 1846, Selected False, Rank: 879.000\n", "Column: 1847, Selected False, Rank: 878.000\n", "Column: 1848, Selected False, Rank: 877.000\n", "Column: 1849, Selected False, Rank: 876.000\n", "Column: 1850, Selected False, Rank: 875.000\n", "Column: 1851, Selected False, Rank: 874.000\n", "Column: 1852, Selected False, Rank: 873.000\n", "Column: 1853, Selected False, Rank: 872.000\n", "Column: 1854, Selected False, Rank: 871.000\n", "Column: 1855, Selected False, Rank: 870.000\n", "Column: 1856, Selected False, Rank: 869.000\n", "Column: 1857, Selected False, Rank: 868.000\n", "Column: 1858, Selected False, Rank: 867.000\n", "Column: 1859, Selected False, Rank: 866.000\n", "Column: 1860, Selected False, Rank: 865.000\n", "Column: 1861, Selected False, Rank: 864.000\n", "Column: 1862, Selected False, Rank: 863.000\n", "Column: 1863, Selected False, Rank: 862.000\n", "Column: 1864, Selected False, Rank: 861.000\n", "Column: 1865, Selected False, Rank: 860.000\n", "Column: 1866, Selected False, Rank: 859.000\n", "Column: 1867, Selected False, Rank: 858.000\n", "Column: 1868, Selected False, Rank: 857.000\n", "Column: 1869, Selected False, Rank: 856.000\n", "Column: 1870, Selected False, Rank: 855.000\n", "Column: 1871, Selected False, Rank: 854.000\n", "Column: 1872, Selected False, Rank: 853.000\n", "Column: 1873, Selected False, Rank: 852.000\n", "Column: 1874, Selected False, Rank: 851.000\n", "Column: 1875, Selected False, Rank: 850.000\n", "Column: 1876, Selected False, Rank: 849.000\n", "Column: 1877, Selected False, Rank: 848.000\n", "Column: 1878, Selected False, Rank: 847.000\n", "Column: 1879, Selected False, Rank: 846.000\n", "Column: 1880, Selected False, Rank: 845.000\n", "Column: 1881, Selected False, Rank: 844.000\n", "Column: 1882, Selected False, Rank: 843.000\n", "Column: 1883, Selected False, Rank: 842.000\n", "Column: 1884, Selected False, Rank: 841.000\n", "Column: 1885, Selected False, Rank: 840.000\n", "Column: 1886, Selected False, Rank: 839.000\n", "Column: 1887, Selected False, Rank: 838.000\n", "Column: 1888, Selected False, Rank: 837.000\n", "Column: 1889, Selected False, Rank: 836.000\n", "Column: 1890, Selected False, Rank: 835.000\n", "Column: 1891, Selected False, Rank: 834.000\n", "Column: 1892, Selected False, Rank: 833.000\n", "Column: 1893, Selected False, Rank: 832.000\n", "Column: 1894, Selected False, Rank: 831.000\n", "Column: 1895, Selected False, Rank: 830.000\n", "Column: 1896, Selected False, Rank: 829.000\n", "Column: 1897, Selected False, Rank: 828.000\n", "Column: 1898, Selected False, Rank: 827.000\n", "Column: 1899, Selected False, Rank: 826.000\n", "Column: 1900, Selected False, Rank: 825.000\n", "Column: 1901, Selected False, Rank: 824.000\n", "Column: 1902, Selected False, Rank: 823.000\n", "Column: 1903, Selected False, Rank: 822.000\n", "Column: 1904, Selected False, Rank: 821.000\n", "Column: 1905, Selected False, Rank: 820.000\n", "Column: 1906, Selected False, Rank: 819.000\n", "Column: 1907, Selected False, Rank: 818.000\n", "Column: 1908, Selected False, Rank: 817.000\n", "Column: 1909, Selected False, Rank: 816.000\n", "Column: 1910, Selected False, Rank: 815.000\n", "Column: 1911, Selected False, Rank: 814.000\n", "Column: 1912, Selected False, Rank: 813.000\n", "Column: 1913, Selected False, Rank: 812.000\n", "Column: 1914, Selected False, Rank: 811.000\n", "Column: 1915, Selected False, Rank: 810.000\n", "Column: 1916, Selected False, Rank: 809.000\n", "Column: 1917, Selected False, Rank: 808.000\n", "Column: 1918, Selected False, Rank: 807.000\n", "Column: 1919, Selected False, Rank: 806.000\n", "Column: 1920, Selected False, Rank: 805.000\n", "Column: 1921, Selected False, Rank: 804.000\n", "Column: 1922, Selected False, Rank: 803.000\n", "Column: 1923, Selected False, Rank: 802.000\n", "Column: 1924, Selected False, Rank: 801.000\n", "Column: 1925, Selected False, Rank: 800.000\n", "Column: 1926, Selected False, Rank: 799.000\n", "Column: 1927, Selected False, Rank: 798.000\n", "Column: 1928, Selected False, Rank: 797.000\n", "Column: 1929, Selected False, Rank: 796.000\n", "Column: 1930, Selected False, Rank: 795.000\n", "Column: 1931, Selected False, Rank: 794.000\n", "Column: 1932, Selected False, Rank: 793.000\n", "Column: 1933, Selected False, Rank: 792.000\n", "Column: 1934, Selected False, Rank: 791.000\n", "Column: 1935, Selected False, Rank: 790.000\n", "Column: 1936, Selected False, Rank: 789.000\n", "Column: 1937, Selected False, Rank: 788.000\n", "Column: 1938, Selected False, Rank: 787.000\n", "Column: 1939, Selected False, Rank: 786.000\n", "Column: 1940, Selected False, Rank: 785.000\n", "Column: 1941, Selected False, Rank: 784.000\n", "Column: 1942, Selected False, Rank: 783.000\n", "Column: 1943, Selected False, Rank: 782.000\n", "Column: 1944, Selected False, Rank: 781.000\n", "Column: 1945, Selected False, Rank: 780.000\n", "Column: 1946, Selected False, Rank: 779.000\n", "Column: 1947, Selected False, Rank: 778.000\n", "Column: 1948, Selected False, Rank: 777.000\n", "Column: 1949, Selected False, Rank: 776.000\n", "Column: 1950, Selected False, Rank: 775.000\n", "Column: 1951, Selected False, Rank: 774.000\n", "Column: 1952, Selected False, Rank: 773.000\n", "Column: 1953, Selected False, Rank: 772.000\n", "Column: 1954, Selected False, Rank: 771.000\n", "Column: 1955, Selected False, Rank: 770.000\n", "Column: 1956, Selected False, Rank: 769.000\n", "Column: 1957, Selected False, Rank: 768.000\n", "Column: 1958, Selected False, Rank: 767.000\n", "Column: 1959, Selected False, Rank: 766.000\n", "Column: 1960, Selected False, Rank: 765.000\n", "Column: 1961, Selected False, Rank: 764.000\n", "Column: 1962, Selected False, Rank: 763.000\n", "Column: 1963, Selected False, Rank: 762.000\n", "Column: 1964, Selected False, Rank: 761.000\n", "Column: 1965, Selected False, Rank: 760.000\n", "Column: 1966, Selected False, Rank: 759.000\n", "Column: 1967, Selected False, Rank: 758.000\n", "Column: 1968, Selected False, Rank: 757.000\n", "Column: 1969, Selected False, Rank: 756.000\n", "Column: 1970, Selected False, Rank: 755.000\n", "Column: 1971, Selected False, Rank: 754.000\n", "Column: 1972, Selected False, Rank: 753.000\n", "Column: 1973, Selected False, Rank: 752.000\n", "Column: 1974, Selected False, Rank: 751.000\n", "Column: 1975, Selected False, Rank: 750.000\n", "Column: 1976, Selected False, Rank: 749.000\n", "Column: 1977, Selected False, Rank: 748.000\n", "Column: 1978, Selected False, Rank: 747.000\n", "Column: 1979, Selected False, Rank: 746.000\n", "Column: 1980, Selected False, Rank: 745.000\n", "Column: 1981, Selected False, Rank: 744.000\n", "Column: 1982, Selected False, Rank: 743.000\n", "Column: 1983, Selected False, Rank: 742.000\n", "Column: 1984, Selected False, Rank: 741.000\n", "Column: 1985, Selected False, Rank: 740.000\n", "Column: 1986, Selected False, Rank: 739.000\n", "Column: 1987, Selected False, Rank: 738.000\n", "Column: 1988, Selected False, Rank: 737.000\n", "Column: 1989, Selected False, Rank: 736.000\n", "Column: 1990, Selected False, Rank: 735.000\n", "Column: 1991, Selected False, Rank: 734.000\n", "Column: 1992, Selected False, Rank: 733.000\n", "Column: 1993, Selected False, Rank: 732.000\n", "Column: 1994, Selected False, Rank: 731.000\n", "Column: 1995, Selected False, Rank: 730.000\n", "Column: 1996, Selected False, Rank: 729.000\n", "Column: 1997, Selected False, Rank: 728.000\n", "Column: 1998, Selected False, Rank: 727.000\n", "Column: 1999, Selected False, Rank: 726.000\n", "Column: 2000, Selected False, Rank: 725.000\n", "Column: 2001, Selected False, Rank: 724.000\n", "Column: 2002, Selected False, Rank: 723.000\n", "Column: 2003, Selected False, Rank: 722.000\n", "Column: 2004, Selected False, Rank: 721.000\n", "Column: 2005, Selected False, Rank: 720.000\n", "Column: 2006, Selected False, Rank: 719.000\n", "Column: 2007, Selected False, Rank: 718.000\n", "Column: 2008, Selected False, Rank: 717.000\n", "Column: 2009, Selected False, Rank: 716.000\n", "Column: 2010, Selected False, Rank: 715.000\n", "Column: 2011, Selected False, Rank: 714.000\n", "Column: 2012, Selected False, Rank: 713.000\n", "Column: 2013, Selected False, Rank: 712.000\n", "Column: 2014, Selected False, Rank: 711.000\n", "Column: 2015, Selected False, Rank: 710.000\n", "Column: 2016, Selected False, Rank: 709.000\n", "Column: 2017, Selected False, Rank: 708.000\n", "Column: 2018, Selected False, Rank: 707.000\n", "Column: 2019, Selected False, Rank: 706.000\n", "Column: 2020, Selected False, Rank: 705.000\n", "Column: 2021, Selected False, Rank: 704.000\n", "Column: 2022, Selected False, Rank: 703.000\n", "Column: 2023, Selected False, Rank: 702.000\n", "Column: 2024, Selected False, Rank: 701.000\n", "Column: 2025, Selected False, Rank: 700.000\n", "Column: 2026, Selected False, Rank: 699.000\n", "Column: 2027, Selected False, Rank: 698.000\n", "Column: 2028, Selected False, Rank: 697.000\n", "Column: 2029, Selected False, Rank: 696.000\n", "Column: 2030, Selected False, Rank: 695.000\n", "Column: 2031, Selected False, Rank: 694.000\n", "Column: 2032, Selected False, Rank: 693.000\n", "Column: 2033, Selected False, Rank: 692.000\n", "Column: 2034, Selected False, Rank: 691.000\n", "Column: 2035, Selected False, Rank: 690.000\n", "Column: 2036, Selected False, Rank: 689.000\n", "Column: 2037, Selected False, Rank: 688.000\n", "Column: 2038, Selected False, Rank: 687.000\n", "Column: 2039, Selected False, Rank: 686.000\n", "Column: 2040, Selected False, Rank: 685.000\n", "Column: 2041, Selected False, Rank: 684.000\n", "Column: 2042, Selected False, Rank: 683.000\n", "Column: 2043, Selected False, Rank: 682.000\n", "Column: 2044, Selected False, Rank: 681.000\n", "Column: 2045, Selected False, Rank: 680.000\n", "Column: 2046, Selected False, Rank: 679.000\n", "Column: 2047, Selected False, Rank: 678.000\n", "Column: 2048, Selected False, Rank: 677.000\n", "Column: 2049, Selected False, Rank: 676.000\n", "Column: 2050, Selected False, Rank: 675.000\n", "Column: 2051, Selected False, Rank: 674.000\n", "Column: 2052, Selected False, Rank: 673.000\n", "Column: 2053, Selected False, Rank: 672.000\n", "Column: 2054, Selected False, Rank: 671.000\n", "Column: 2055, Selected False, Rank: 670.000\n", "Column: 2056, Selected False, Rank: 669.000\n", "Column: 2057, Selected False, Rank: 668.000\n", "Column: 2058, Selected False, Rank: 667.000\n", "Column: 2059, Selected False, Rank: 666.000\n", "Column: 2060, Selected False, Rank: 665.000\n", "Column: 2061, Selected False, Rank: 664.000\n", "Column: 2062, Selected False, Rank: 663.000\n", "Column: 2063, Selected False, Rank: 662.000\n", "Column: 2064, Selected False, Rank: 661.000\n", "Column: 2065, Selected False, Rank: 660.000\n", "Column: 2066, Selected False, Rank: 659.000\n", "Column: 2067, Selected False, Rank: 658.000\n", "Column: 2068, Selected False, Rank: 657.000\n", "Column: 2069, Selected False, Rank: 656.000\n", "Column: 2070, Selected False, Rank: 655.000\n", "Column: 2071, Selected False, Rank: 654.000\n", "Column: 2072, Selected False, Rank: 653.000\n", "Column: 2073, Selected False, Rank: 652.000\n", "Column: 2074, Selected False, Rank: 651.000\n", "Column: 2075, Selected False, Rank: 650.000\n", "Column: 2076, Selected False, Rank: 649.000\n", "Column: 2077, Selected False, Rank: 648.000\n", "Column: 2078, Selected False, Rank: 647.000\n", "Column: 2079, Selected False, Rank: 646.000\n", "Column: 2080, Selected False, Rank: 645.000\n", "Column: 2081, Selected False, Rank: 644.000\n", "Column: 2082, Selected False, Rank: 643.000\n", "Column: 2083, Selected False, Rank: 642.000\n", "Column: 2084, Selected False, Rank: 641.000\n", "Column: 2085, Selected False, Rank: 640.000\n", "Column: 2086, Selected False, Rank: 639.000\n", "Column: 2087, Selected False, Rank: 638.000\n", "Column: 2088, Selected False, Rank: 637.000\n", "Column: 2089, Selected False, Rank: 636.000\n", "Column: 2090, Selected False, Rank: 635.000\n", "Column: 2091, Selected False, Rank: 634.000\n", "Column: 2092, Selected False, Rank: 633.000\n", "Column: 2093, Selected False, Rank: 632.000\n", "Column: 2094, Selected False, Rank: 631.000\n", "Column: 2095, Selected False, Rank: 630.000\n", "Column: 2096, Selected False, Rank: 629.000\n", "Column: 2097, Selected False, Rank: 628.000\n", "Column: 2098, Selected False, Rank: 627.000\n", "Column: 2099, Selected False, Rank: 626.000\n", "Column: 2100, Selected False, Rank: 625.000\n", "Column: 2101, Selected False, Rank: 624.000\n", "Column: 2102, Selected False, Rank: 623.000\n", "Column: 2103, Selected False, Rank: 622.000\n", "Column: 2104, Selected False, Rank: 621.000\n", "Column: 2105, Selected False, Rank: 620.000\n", "Column: 2106, Selected False, Rank: 619.000\n", "Column: 2107, Selected False, Rank: 618.000\n", "Column: 2108, Selected False, Rank: 617.000\n", "Column: 2109, Selected False, Rank: 616.000\n", "Column: 2110, Selected False, Rank: 615.000\n", "Column: 2111, Selected False, Rank: 614.000\n", "Column: 2112, Selected False, Rank: 613.000\n", "Column: 2113, Selected False, Rank: 612.000\n", "Column: 2114, Selected False, Rank: 611.000\n", "Column: 2115, Selected False, Rank: 610.000\n", "Column: 2116, Selected False, Rank: 609.000\n", "Column: 2117, Selected False, Rank: 608.000\n", "Column: 2118, Selected False, Rank: 607.000\n", "Column: 2119, Selected False, Rank: 606.000\n", "Column: 2120, Selected False, Rank: 605.000\n", "Column: 2121, Selected False, Rank: 604.000\n", "Column: 2122, Selected False, Rank: 603.000\n", "Column: 2123, Selected False, Rank: 602.000\n", "Column: 2124, Selected False, Rank: 601.000\n", "Column: 2125, Selected False, Rank: 600.000\n", "Column: 2126, Selected False, Rank: 599.000\n", "Column: 2127, Selected False, Rank: 598.000\n", "Column: 2128, Selected False, Rank: 597.000\n", "Column: 2129, Selected False, Rank: 596.000\n", "Column: 2130, Selected False, Rank: 595.000\n", "Column: 2131, Selected False, Rank: 594.000\n", "Column: 2132, Selected False, Rank: 593.000\n", "Column: 2133, Selected False, Rank: 592.000\n", "Column: 2134, Selected False, Rank: 591.000\n", "Column: 2135, Selected False, Rank: 590.000\n", "Column: 2136, Selected False, Rank: 589.000\n", "Column: 2137, Selected False, Rank: 588.000\n", "Column: 2138, Selected False, Rank: 587.000\n", "Column: 2139, Selected False, Rank: 586.000\n", "Column: 2140, Selected False, Rank: 585.000\n", "Column: 2141, Selected False, Rank: 584.000\n", "Column: 2142, Selected False, Rank: 583.000\n", "Column: 2143, Selected False, Rank: 582.000\n", "Column: 2144, Selected False, Rank: 581.000\n", "Column: 2145, Selected False, Rank: 580.000\n", "Column: 2146, Selected False, Rank: 579.000\n", "Column: 2147, Selected False, Rank: 578.000\n", "Column: 2148, Selected False, Rank: 577.000\n", "Column: 2149, Selected False, Rank: 576.000\n", "Column: 2150, Selected False, Rank: 575.000\n", "Column: 2151, Selected False, Rank: 574.000\n", "Column: 2152, Selected False, Rank: 573.000\n", "Column: 2153, Selected False, Rank: 572.000\n", "Column: 2154, Selected False, Rank: 571.000\n", "Column: 2155, Selected False, Rank: 570.000\n", "Column: 2156, Selected False, Rank: 569.000\n", "Column: 2157, Selected False, Rank: 568.000\n", "Column: 2158, Selected False, Rank: 567.000\n", "Column: 2159, Selected False, Rank: 566.000\n", "Column: 2160, Selected False, Rank: 565.000\n", "Column: 2161, Selected False, Rank: 564.000\n", "Column: 2162, Selected False, Rank: 563.000\n", "Column: 2163, Selected False, Rank: 562.000\n", "Column: 2164, Selected False, Rank: 561.000\n", "Column: 2165, Selected False, Rank: 560.000\n", "Column: 2166, Selected False, Rank: 559.000\n", "Column: 2167, Selected False, Rank: 558.000\n", "Column: 2168, Selected False, Rank: 557.000\n", "Column: 2169, Selected False, Rank: 556.000\n", "Column: 2170, Selected False, Rank: 555.000\n", "Column: 2171, Selected False, Rank: 554.000\n", "Column: 2172, Selected False, Rank: 553.000\n", "Column: 2173, Selected False, Rank: 552.000\n", "Column: 2174, Selected False, Rank: 551.000\n", "Column: 2175, Selected False, Rank: 550.000\n", "Column: 2176, Selected False, Rank: 549.000\n", "Column: 2177, Selected False, Rank: 548.000\n", "Column: 2178, Selected False, Rank: 547.000\n", "Column: 2179, Selected False, Rank: 546.000\n", "Column: 2180, Selected False, Rank: 545.000\n", "Column: 2181, Selected False, Rank: 544.000\n", "Column: 2182, Selected False, Rank: 543.000\n", "Column: 2183, Selected False, Rank: 542.000\n", "Column: 2184, Selected False, Rank: 541.000\n", "Column: 2185, Selected False, Rank: 540.000\n", "Column: 2186, Selected False, Rank: 539.000\n", "Column: 2187, Selected False, Rank: 538.000\n", "Column: 2188, Selected False, Rank: 537.000\n", "Column: 2189, Selected False, Rank: 536.000\n", "Column: 2190, Selected False, Rank: 535.000\n", "Column: 2191, Selected False, Rank: 534.000\n", "Column: 2192, Selected False, Rank: 533.000\n", "Column: 2193, Selected False, Rank: 532.000\n", "Column: 2194, Selected False, Rank: 531.000\n", "Column: 2195, Selected False, Rank: 530.000\n", "Column: 2196, Selected False, Rank: 529.000\n", "Column: 2197, Selected False, Rank: 528.000\n", "Column: 2198, Selected False, Rank: 527.000\n", "Column: 2199, Selected False, Rank: 526.000\n", "Column: 2200, Selected False, Rank: 525.000\n", "Column: 2201, Selected False, Rank: 524.000\n", "Column: 2202, Selected False, Rank: 523.000\n", "Column: 2203, Selected False, Rank: 522.000\n", "Column: 2204, Selected False, Rank: 521.000\n", "Column: 2205, Selected False, Rank: 520.000\n", "Column: 2206, Selected False, Rank: 519.000\n", "Column: 2207, Selected False, Rank: 518.000\n", "Column: 2208, Selected False, Rank: 517.000\n", "Column: 2209, Selected False, Rank: 516.000\n", "Column: 2210, Selected False, Rank: 515.000\n", "Column: 2211, Selected False, Rank: 514.000\n", "Column: 2212, Selected False, Rank: 513.000\n", "Column: 2213, Selected False, Rank: 512.000\n", "Column: 2214, Selected False, Rank: 511.000\n", "Column: 2215, Selected False, Rank: 510.000\n", "Column: 2216, Selected False, Rank: 509.000\n", "Column: 2217, Selected False, Rank: 508.000\n", "Column: 2218, Selected False, Rank: 507.000\n", "Column: 2219, Selected False, Rank: 506.000\n", "Column: 2220, Selected False, Rank: 505.000\n", "Column: 2221, Selected False, Rank: 504.000\n", "Column: 2222, Selected False, Rank: 503.000\n", "Column: 2223, Selected False, Rank: 502.000\n", "Column: 2224, Selected False, Rank: 501.000\n", "Column: 2225, Selected False, Rank: 500.000\n", "Column: 2226, Selected False, Rank: 499.000\n", "Column: 2227, Selected False, Rank: 498.000\n", "Column: 2228, Selected False, Rank: 497.000\n", "Column: 2229, Selected False, Rank: 496.000\n", "Column: 2230, Selected False, Rank: 495.000\n", "Column: 2231, Selected False, Rank: 494.000\n", "Column: 2232, Selected False, Rank: 493.000\n", "Column: 2233, Selected False, Rank: 492.000\n", "Column: 2234, Selected False, Rank: 491.000\n", "Column: 2235, Selected False, Rank: 490.000\n", "Column: 2236, Selected False, Rank: 489.000\n", "Column: 2237, Selected False, Rank: 488.000\n", "Column: 2238, Selected False, Rank: 487.000\n", "Column: 2239, Selected False, Rank: 486.000\n", "Column: 2240, Selected False, Rank: 485.000\n", "Column: 2241, Selected False, Rank: 484.000\n", "Column: 2242, Selected False, Rank: 483.000\n", "Column: 2243, Selected False, Rank: 482.000\n", "Column: 2244, Selected False, Rank: 481.000\n", "Column: 2245, Selected False, Rank: 480.000\n", "Column: 2246, Selected False, Rank: 479.000\n", "Column: 2247, Selected False, Rank: 478.000\n", "Column: 2248, Selected False, Rank: 477.000\n", "Column: 2249, Selected False, Rank: 476.000\n", "Column: 2250, Selected False, Rank: 475.000\n", "Column: 2251, Selected False, Rank: 474.000\n", "Column: 2252, Selected False, Rank: 473.000\n", "Column: 2253, Selected False, Rank: 472.000\n", "Column: 2254, Selected False, Rank: 471.000\n", "Column: 2255, Selected False, Rank: 470.000\n", "Column: 2256, Selected False, Rank: 469.000\n", "Column: 2257, Selected False, Rank: 468.000\n", "Column: 2258, Selected False, Rank: 467.000\n", "Column: 2259, Selected False, Rank: 466.000\n", "Column: 2260, Selected False, Rank: 465.000\n", "Column: 2261, Selected False, Rank: 464.000\n", "Column: 2262, Selected False, Rank: 463.000\n", "Column: 2263, Selected False, Rank: 462.000\n", "Column: 2264, Selected False, Rank: 461.000\n", "Column: 2265, Selected False, Rank: 460.000\n", "Column: 2266, Selected False, Rank: 459.000\n", "Column: 2267, Selected False, Rank: 458.000\n", "Column: 2268, Selected False, Rank: 457.000\n", "Column: 2269, Selected False, Rank: 456.000\n", "Column: 2270, Selected False, Rank: 455.000\n", "Column: 2271, Selected False, Rank: 454.000\n", "Column: 2272, Selected False, Rank: 453.000\n", "Column: 2273, Selected False, Rank: 452.000\n", "Column: 2274, Selected False, Rank: 451.000\n", "Column: 2275, Selected False, Rank: 450.000\n", "Column: 2276, Selected False, Rank: 449.000\n", "Column: 2277, Selected False, Rank: 448.000\n", "Column: 2278, Selected False, Rank: 447.000\n", "Column: 2279, Selected False, Rank: 446.000\n", "Column: 2280, Selected False, Rank: 445.000\n", "Column: 2281, Selected False, Rank: 444.000\n", "Column: 2282, Selected False, Rank: 443.000\n", "Column: 2283, Selected False, Rank: 442.000\n", "Column: 2284, Selected False, Rank: 441.000\n", "Column: 2285, Selected False, Rank: 440.000\n", "Column: 2286, Selected False, Rank: 439.000\n", "Column: 2287, Selected False, Rank: 438.000\n", "Column: 2288, Selected False, Rank: 437.000\n", "Column: 2289, Selected False, Rank: 436.000\n", "Column: 2290, Selected False, Rank: 435.000\n", "Column: 2291, Selected False, Rank: 434.000\n", "Column: 2292, Selected False, Rank: 433.000\n", "Column: 2293, Selected False, Rank: 432.000\n", "Column: 2294, Selected False, Rank: 431.000\n", "Column: 2295, Selected False, Rank: 430.000\n", "Column: 2296, Selected False, Rank: 429.000\n", "Column: 2297, Selected False, Rank: 428.000\n", "Column: 2298, Selected False, Rank: 427.000\n", "Column: 2299, Selected False, Rank: 426.000\n", "Column: 2300, Selected False, Rank: 425.000\n", "Column: 2301, Selected False, Rank: 424.000\n", "Column: 2302, Selected False, Rank: 423.000\n", "Column: 2303, Selected False, Rank: 422.000\n", "Column: 2304, Selected False, Rank: 421.000\n", "Column: 2305, Selected False, Rank: 420.000\n", "Column: 2306, Selected False, Rank: 419.000\n", "Column: 2307, Selected False, Rank: 418.000\n", "Column: 2308, Selected False, Rank: 417.000\n", "Column: 2309, Selected False, Rank: 416.000\n", "Column: 2310, Selected False, Rank: 415.000\n", "Column: 2311, Selected False, Rank: 414.000\n", "Column: 2312, Selected False, Rank: 413.000\n", "Column: 2313, Selected False, Rank: 412.000\n", "Column: 2314, Selected False, Rank: 411.000\n", "Column: 2315, Selected False, Rank: 410.000\n", "Column: 2316, Selected False, Rank: 409.000\n", "Column: 2317, Selected False, Rank: 408.000\n", "Column: 2318, Selected False, Rank: 407.000\n", "Column: 2319, Selected False, Rank: 406.000\n", "Column: 2320, Selected False, Rank: 405.000\n", "Column: 2321, Selected False, Rank: 404.000\n", "Column: 2322, Selected False, Rank: 403.000\n", "Column: 2323, Selected False, Rank: 402.000\n", "Column: 2324, Selected False, Rank: 401.000\n", "Column: 2325, Selected False, Rank: 400.000\n", "Column: 2326, Selected False, Rank: 399.000\n", "Column: 2327, Selected False, Rank: 398.000\n", "Column: 2328, Selected False, Rank: 397.000\n", "Column: 2329, Selected False, Rank: 396.000\n", "Column: 2330, Selected False, Rank: 395.000\n", "Column: 2331, Selected False, Rank: 394.000\n", "Column: 2332, Selected False, Rank: 393.000\n", "Column: 2333, Selected False, Rank: 392.000\n", "Column: 2334, Selected False, Rank: 391.000\n", "Column: 2335, Selected False, Rank: 390.000\n", "Column: 2336, Selected False, Rank: 389.000\n", "Column: 2337, Selected False, Rank: 388.000\n", "Column: 2338, Selected False, Rank: 387.000\n", "Column: 2339, Selected False, Rank: 386.000\n", "Column: 2340, Selected False, Rank: 385.000\n", "Column: 2341, Selected False, Rank: 384.000\n", "Column: 2342, Selected False, Rank: 383.000\n", "Column: 2343, Selected False, Rank: 382.000\n", "Column: 2344, Selected False, Rank: 381.000\n", "Column: 2345, Selected False, Rank: 380.000\n", "Column: 2346, Selected False, Rank: 379.000\n", "Column: 2347, Selected False, Rank: 378.000\n", "Column: 2348, Selected False, Rank: 377.000\n", "Column: 2349, Selected False, Rank: 376.000\n", "Column: 2350, Selected False, Rank: 375.000\n", "Column: 2351, Selected False, Rank: 374.000\n", "Column: 2352, Selected False, Rank: 373.000\n", "Column: 2353, Selected False, Rank: 372.000\n", "Column: 2354, Selected False, Rank: 371.000\n", "Column: 2355, Selected False, Rank: 370.000\n", "Column: 2356, Selected False, Rank: 369.000\n", "Column: 2357, Selected False, Rank: 368.000\n", "Column: 2358, Selected False, Rank: 367.000\n", "Column: 2359, Selected False, Rank: 366.000\n", "Column: 2360, Selected False, Rank: 365.000\n", "Column: 2361, Selected False, Rank: 364.000\n", "Column: 2362, Selected False, Rank: 363.000\n", "Column: 2363, Selected False, Rank: 362.000\n", "Column: 2364, Selected False, Rank: 361.000\n", "Column: 2365, Selected False, Rank: 360.000\n", "Column: 2366, Selected False, Rank: 359.000\n", "Column: 2367, Selected False, Rank: 358.000\n", "Column: 2368, Selected False, Rank: 357.000\n", "Column: 2369, Selected False, Rank: 356.000\n", "Column: 2370, Selected False, Rank: 355.000\n", "Column: 2371, Selected False, Rank: 354.000\n", "Column: 2372, Selected False, Rank: 353.000\n", "Column: 2373, Selected False, Rank: 352.000\n", "Column: 2374, Selected False, Rank: 351.000\n", "Column: 2375, Selected False, Rank: 350.000\n", "Column: 2376, Selected False, Rank: 349.000\n", "Column: 2377, Selected False, Rank: 348.000\n", "Column: 2378, Selected False, Rank: 347.000\n", "Column: 2379, Selected False, Rank: 346.000\n", "Column: 2380, Selected False, Rank: 345.000\n", "Column: 2381, Selected False, Rank: 344.000\n", "Column: 2382, Selected False, Rank: 343.000\n", "Column: 2383, Selected False, Rank: 342.000\n", "Column: 2384, Selected False, Rank: 341.000\n", "Column: 2385, Selected False, Rank: 340.000\n", "Column: 2386, Selected False, Rank: 339.000\n", "Column: 2387, Selected False, Rank: 338.000\n", "Column: 2388, Selected False, Rank: 337.000\n", "Column: 2389, Selected False, Rank: 336.000\n", "Column: 2390, Selected False, Rank: 335.000\n", "Column: 2391, Selected False, Rank: 334.000\n", "Column: 2392, Selected False, Rank: 333.000\n", "Column: 2393, Selected False, Rank: 332.000\n", "Column: 2394, Selected False, Rank: 331.000\n", "Column: 2395, Selected False, Rank: 330.000\n", "Column: 2396, Selected False, Rank: 329.000\n", "Column: 2397, Selected False, Rank: 328.000\n", "Column: 2398, Selected False, Rank: 327.000\n", "Column: 2399, Selected False, Rank: 326.000\n", "Column: 2400, Selected False, Rank: 325.000\n", "Column: 2401, Selected False, Rank: 324.000\n", "Column: 2402, Selected False, Rank: 323.000\n", "Column: 2403, Selected False, Rank: 322.000\n", "Column: 2404, Selected False, Rank: 321.000\n", "Column: 2405, Selected False, Rank: 320.000\n", "Column: 2406, Selected False, Rank: 319.000\n", "Column: 2407, Selected False, Rank: 318.000\n", "Column: 2408, Selected False, Rank: 317.000\n", "Column: 2409, Selected False, Rank: 316.000\n", "Column: 2410, Selected False, Rank: 315.000\n", "Column: 2411, Selected False, Rank: 314.000\n", "Column: 2412, Selected False, Rank: 313.000\n", "Column: 2413, Selected False, Rank: 312.000\n", "Column: 2414, Selected False, Rank: 311.000\n", "Column: 2415, Selected False, Rank: 310.000\n", "Column: 2416, Selected False, Rank: 309.000\n", "Column: 2417, Selected False, Rank: 308.000\n", "Column: 2418, Selected False, Rank: 307.000\n", "Column: 2419, Selected False, Rank: 306.000\n", "Column: 2420, Selected False, Rank: 305.000\n", "Column: 2421, Selected False, Rank: 304.000\n", "Column: 2422, Selected False, Rank: 303.000\n", "Column: 2423, Selected False, Rank: 302.000\n", "Column: 2424, Selected False, Rank: 301.000\n", "Column: 2425, Selected False, Rank: 300.000\n", "Column: 2426, Selected False, Rank: 299.000\n", "Column: 2427, Selected False, Rank: 298.000\n", "Column: 2428, Selected False, Rank: 297.000\n", "Column: 2429, Selected False, Rank: 296.000\n", "Column: 2430, Selected False, Rank: 295.000\n", "Column: 2431, Selected False, Rank: 294.000\n", "Column: 2432, Selected False, Rank: 293.000\n", "Column: 2433, Selected False, Rank: 292.000\n", "Column: 2434, Selected False, Rank: 291.000\n", "Column: 2435, Selected False, Rank: 290.000\n", "Column: 2436, Selected False, Rank: 289.000\n", "Column: 2437, Selected False, Rank: 288.000\n", "Column: 2438, Selected False, Rank: 287.000\n", "Column: 2439, Selected False, Rank: 286.000\n", "Column: 2440, Selected False, Rank: 285.000\n", "Column: 2441, Selected False, Rank: 284.000\n", "Column: 2442, Selected False, Rank: 283.000\n", "Column: 2443, Selected False, Rank: 282.000\n", "Column: 2444, Selected False, Rank: 281.000\n", "Column: 2445, Selected False, Rank: 280.000\n", "Column: 2446, Selected False, Rank: 279.000\n", "Column: 2447, Selected False, Rank: 278.000\n", "Column: 2448, Selected False, Rank: 277.000\n", "Column: 2449, Selected False, Rank: 276.000\n", "Column: 2450, Selected False, Rank: 275.000\n", "Column: 2451, Selected False, Rank: 274.000\n", "Column: 2452, Selected False, Rank: 273.000\n", "Column: 2453, Selected False, Rank: 272.000\n", "Column: 2454, Selected False, Rank: 271.000\n", "Column: 2455, Selected False, Rank: 270.000\n", "Column: 2456, Selected False, Rank: 269.000\n", "Column: 2457, Selected False, Rank: 268.000\n", "Column: 2458, Selected False, Rank: 267.000\n", "Column: 2459, Selected False, Rank: 266.000\n", "Column: 2460, Selected False, Rank: 265.000\n", "Column: 2461, Selected False, Rank: 264.000\n", "Column: 2462, Selected False, Rank: 263.000\n", "Column: 2463, Selected False, Rank: 262.000\n", "Column: 2464, Selected False, Rank: 261.000\n", "Column: 2465, Selected False, Rank: 260.000\n", "Column: 2466, Selected False, Rank: 259.000\n", "Column: 2467, Selected False, Rank: 258.000\n", "Column: 2468, Selected False, Rank: 257.000\n", "Column: 2469, Selected False, Rank: 256.000\n", "Column: 2470, Selected False, Rank: 255.000\n", "Column: 2471, Selected False, Rank: 254.000\n", "Column: 2472, Selected False, Rank: 253.000\n", "Column: 2473, Selected False, Rank: 252.000\n", "Column: 2474, Selected False, Rank: 251.000\n", "Column: 2475, Selected False, Rank: 250.000\n", "Column: 2476, Selected False, Rank: 249.000\n", "Column: 2477, Selected False, Rank: 248.000\n", "Column: 2478, Selected False, Rank: 247.000\n", "Column: 2479, Selected False, Rank: 246.000\n", "Column: 2480, Selected False, Rank: 245.000\n", "Column: 2481, Selected False, Rank: 244.000\n", "Column: 2482, Selected False, Rank: 243.000\n", "Column: 2483, Selected False, Rank: 242.000\n", "Column: 2484, Selected False, Rank: 241.000\n", "Column: 2485, Selected False, Rank: 240.000\n", "Column: 2486, Selected False, Rank: 239.000\n", "Column: 2487, Selected False, Rank: 238.000\n", "Column: 2488, Selected False, Rank: 237.000\n", "Column: 2489, Selected False, Rank: 236.000\n", "Column: 2490, Selected False, Rank: 235.000\n", "Column: 2491, Selected False, Rank: 234.000\n", "Column: 2492, Selected False, Rank: 233.000\n", "Column: 2493, Selected False, Rank: 232.000\n", "Column: 2494, Selected False, Rank: 231.000\n", "Column: 2495, Selected False, Rank: 230.000\n", "Column: 2496, Selected False, Rank: 229.000\n", "Column: 2497, Selected False, Rank: 228.000\n", "Column: 2498, Selected False, Rank: 227.000\n", "Column: 2499, Selected False, Rank: 226.000\n", "Column: 2500, Selected False, Rank: 225.000\n", "Column: 2501, Selected False, Rank: 224.000\n", "Column: 2502, Selected False, Rank: 223.000\n", "Column: 2503, Selected False, Rank: 222.000\n", "Column: 2504, Selected False, Rank: 221.000\n", "Column: 2505, Selected False, Rank: 220.000\n", "Column: 2506, Selected False, Rank: 219.000\n", "Column: 2507, Selected False, Rank: 218.000\n", "Column: 2508, Selected False, Rank: 217.000\n", "Column: 2509, Selected False, Rank: 216.000\n", "Column: 2510, Selected False, Rank: 215.000\n", "Column: 2511, Selected False, Rank: 214.000\n", "Column: 2512, Selected False, Rank: 213.000\n", "Column: 2513, Selected False, Rank: 212.000\n", "Column: 2514, Selected False, Rank: 211.000\n", "Column: 2515, Selected False, Rank: 210.000\n", "Column: 2516, Selected False, Rank: 209.000\n", "Column: 2517, Selected False, Rank: 208.000\n", "Column: 2518, Selected False, Rank: 207.000\n", "Column: 2519, Selected False, Rank: 206.000\n", "Column: 2520, Selected False, Rank: 205.000\n", "Column: 2521, Selected False, Rank: 204.000\n", "Column: 2522, Selected False, Rank: 203.000\n", "Column: 2523, Selected False, Rank: 202.000\n", "Column: 2524, Selected False, Rank: 201.000\n", "Column: 2525, Selected False, Rank: 200.000\n", "Column: 2526, Selected False, Rank: 199.000\n", "Column: 2527, Selected False, Rank: 198.000\n", "Column: 2528, Selected False, Rank: 197.000\n", "Column: 2529, Selected False, Rank: 196.000\n", "Column: 2530, Selected False, Rank: 195.000\n", "Column: 2531, Selected False, Rank: 194.000\n", "Column: 2532, Selected False, Rank: 193.000\n", "Column: 2533, Selected False, Rank: 192.000\n", "Column: 2534, Selected False, Rank: 191.000\n", "Column: 2535, Selected False, Rank: 190.000\n", "Column: 2536, Selected False, Rank: 189.000\n", "Column: 2537, Selected False, Rank: 188.000\n", "Column: 2538, Selected False, Rank: 187.000\n", "Column: 2539, Selected False, Rank: 186.000\n", "Column: 2540, Selected False, Rank: 185.000\n", "Column: 2541, Selected False, Rank: 184.000\n", "Column: 2542, Selected False, Rank: 183.000\n", "Column: 2543, Selected False, Rank: 182.000\n", "Column: 2544, Selected False, Rank: 181.000\n", "Column: 2545, Selected False, Rank: 180.000\n", "Column: 2546, Selected False, Rank: 179.000\n", "Column: 2547, Selected False, Rank: 178.000\n", "Column: 2548, Selected False, Rank: 177.000\n", "Column: 2549, Selected False, Rank: 176.000\n", "Column: 2550, Selected False, Rank: 175.000\n", "Column: 2551, Selected False, Rank: 174.000\n", "Column: 2552, Selected False, Rank: 173.000\n", "Column: 2553, Selected False, Rank: 172.000\n", "Column: 2554, Selected False, Rank: 171.000\n", "Column: 2555, Selected False, Rank: 170.000\n", "Column: 2556, Selected False, Rank: 169.000\n", "Column: 2557, Selected False, Rank: 168.000\n", "Column: 2558, Selected False, Rank: 167.000\n", "Column: 2559, Selected False, Rank: 166.000\n", "Column: 2560, Selected False, Rank: 165.000\n", "Column: 2561, Selected False, Rank: 164.000\n", "Column: 2562, Selected False, Rank: 163.000\n", "Column: 2563, Selected False, Rank: 162.000\n", "Column: 2564, Selected False, Rank: 161.000\n", "Column: 2565, Selected False, Rank: 160.000\n", "Column: 2566, Selected False, Rank: 159.000\n", "Column: 2567, Selected False, Rank: 158.000\n", "Column: 2568, Selected False, Rank: 157.000\n", "Column: 2569, Selected False, Rank: 156.000\n", "Column: 2570, Selected False, Rank: 155.000\n", "Column: 2571, Selected False, Rank: 154.000\n", "Column: 2572, Selected False, Rank: 153.000\n", "Column: 2573, Selected False, Rank: 152.000\n", "Column: 2574, Selected False, Rank: 151.000\n", "Column: 2575, Selected False, Rank: 150.000\n", "Column: 2576, Selected False, Rank: 149.000\n", "Column: 2577, Selected False, Rank: 148.000\n", "Column: 2578, Selected False, Rank: 147.000\n", "Column: 2579, Selected False, Rank: 146.000\n", "Column: 2580, Selected False, Rank: 145.000\n", "Column: 2581, Selected False, Rank: 144.000\n", "Column: 2582, Selected False, Rank: 143.000\n", "Column: 2583, Selected False, Rank: 142.000\n", "Column: 2584, Selected False, Rank: 141.000\n", "Column: 2585, Selected False, Rank: 140.000\n", "Column: 2586, Selected False, Rank: 139.000\n", "Column: 2587, Selected False, Rank: 138.000\n", "Column: 2588, Selected False, Rank: 137.000\n", "Column: 2589, Selected False, Rank: 136.000\n", "Column: 2590, Selected False, Rank: 135.000\n", "Column: 2591, Selected False, Rank: 134.000\n", "Column: 2592, Selected False, Rank: 133.000\n", "Column: 2593, Selected False, Rank: 132.000\n", "Column: 2594, Selected False, Rank: 131.000\n", "Column: 2595, Selected False, Rank: 130.000\n", "Column: 2596, Selected False, Rank: 129.000\n", "Column: 2597, Selected False, Rank: 128.000\n", "Column: 2598, Selected False, Rank: 127.000\n", "Column: 2599, Selected False, Rank: 126.000\n", "Column: 2600, Selected False, Rank: 125.000\n", "Column: 2601, Selected False, Rank: 124.000\n", "Column: 2602, Selected False, Rank: 123.000\n", "Column: 2603, Selected False, Rank: 122.000\n", "Column: 2604, Selected False, Rank: 121.000\n", "Column: 2605, Selected False, Rank: 120.000\n", "Column: 2606, Selected False, Rank: 119.000\n", "Column: 2607, Selected False, Rank: 118.000\n", "Column: 2608, Selected False, Rank: 117.000\n", "Column: 2609, Selected False, Rank: 116.000\n", "Column: 2610, Selected False, Rank: 115.000\n", "Column: 2611, Selected False, Rank: 114.000\n", "Column: 2612, Selected False, Rank: 113.000\n", "Column: 2613, Selected False, Rank: 112.000\n", "Column: 2614, Selected False, Rank: 111.000\n", "Column: 2615, Selected False, Rank: 110.000\n", "Column: 2616, Selected False, Rank: 109.000\n", "Column: 2617, Selected False, Rank: 108.000\n", "Column: 2618, Selected False, Rank: 107.000\n", "Column: 2619, Selected False, Rank: 106.000\n", "Column: 2620, Selected False, Rank: 105.000\n", "Column: 2621, Selected False, Rank: 104.000\n", "Column: 2622, Selected False, Rank: 103.000\n", "Column: 2623, Selected False, Rank: 102.000\n", "Column: 2624, Selected False, Rank: 101.000\n", "Column: 2625, Selected False, Rank: 100.000\n", "Column: 2626, Selected False, Rank: 99.000\n", "Column: 2627, Selected False, Rank: 98.000\n", "Column: 2628, Selected False, Rank: 97.000\n", "Column: 2629, Selected False, Rank: 96.000\n", "Column: 2630, Selected False, Rank: 95.000\n", "Column: 2631, Selected False, Rank: 94.000\n", "Column: 2632, Selected False, Rank: 93.000\n", "Column: 2633, Selected False, Rank: 92.000\n", "Column: 2634, Selected False, Rank: 91.000\n", "Column: 2635, Selected False, Rank: 90.000\n", "Column: 2636, Selected False, Rank: 89.000\n", "Column: 2637, Selected False, Rank: 88.000\n", "Column: 2638, Selected False, Rank: 87.000\n", "Column: 2639, Selected False, Rank: 86.000\n", "Column: 2640, Selected False, Rank: 85.000\n", "Column: 2641, Selected False, Rank: 84.000\n", "Column: 2642, Selected False, Rank: 83.000\n", "Column: 2643, Selected False, Rank: 82.000\n", "Column: 2644, Selected False, Rank: 81.000\n", "Column: 2645, Selected False, Rank: 80.000\n", "Column: 2646, Selected False, Rank: 79.000\n", "Column: 2647, Selected False, Rank: 78.000\n", "Column: 2648, Selected False, Rank: 77.000\n", "Column: 2649, Selected False, Rank: 76.000\n", "Column: 2650, Selected False, Rank: 75.000\n", "Column: 2651, Selected False, Rank: 74.000\n", "Column: 2652, Selected False, Rank: 73.000\n", "Column: 2653, Selected False, Rank: 72.000\n", "Column: 2654, Selected False, Rank: 71.000\n", "Column: 2655, Selected False, Rank: 70.000\n", "Column: 2656, Selected False, Rank: 69.000\n", "Column: 2657, Selected False, Rank: 68.000\n", "Column: 2658, Selected False, Rank: 67.000\n", "Column: 2659, Selected False, Rank: 66.000\n", "Column: 2660, Selected False, Rank: 65.000\n", "Column: 2661, Selected False, Rank: 64.000\n", "Column: 2662, Selected False, Rank: 63.000\n", "Column: 2663, Selected False, Rank: 62.000\n", "Column: 2664, Selected False, Rank: 61.000\n", "Column: 2665, Selected False, Rank: 60.000\n", "Column: 2666, Selected False, Rank: 59.000\n", "Column: 2667, Selected False, Rank: 58.000\n", "Column: 2668, Selected False, Rank: 57.000\n", "Column: 2669, Selected False, Rank: 56.000\n", "Column: 2670, Selected False, Rank: 55.000\n", "Column: 2671, Selected False, Rank: 54.000\n", "Column: 2672, Selected False, Rank: 53.000\n", "Column: 2673, Selected False, Rank: 52.000\n", "Column: 2674, Selected False, Rank: 51.000\n", "Column: 2675, Selected False, Rank: 50.000\n", "Column: 2676, Selected False, Rank: 49.000\n", "Column: 2677, Selected False, Rank: 48.000\n", "Column: 2678, Selected False, Rank: 47.000\n", "Column: 2679, Selected False, Rank: 46.000\n", "Column: 2680, Selected False, Rank: 45.000\n", "Column: 2681, Selected False, Rank: 44.000\n", "Column: 2682, Selected False, Rank: 43.000\n", "Column: 2683, Selected False, Rank: 42.000\n", "Column: 2684, Selected False, Rank: 41.000\n", "Column: 2685, Selected False, Rank: 40.000\n", "Column: 2686, Selected False, Rank: 39.000\n", "Column: 2687, Selected False, Rank: 38.000\n", "Column: 2688, Selected False, Rank: 37.000\n", "Column: 2689, Selected False, Rank: 36.000\n", "Column: 2690, Selected False, Rank: 35.000\n", "Column: 2691, Selected False, Rank: 34.000\n", "Column: 2692, Selected False, Rank: 33.000\n", "Column: 2693, Selected False, Rank: 32.000\n", "Column: 2694, Selected False, Rank: 31.000\n", "Column: 2695, Selected False, Rank: 30.000\n", "Column: 2696, Selected False, Rank: 29.000\n", "Column: 2697, Selected False, Rank: 28.000\n", "Column: 2698, Selected False, Rank: 27.000\n", "Column: 2699, Selected False, Rank: 26.000\n", "Column: 2700, Selected False, Rank: 25.000\n", "Column: 2701, Selected False, Rank: 24.000\n", "Column: 2702, Selected False, Rank: 23.000\n", "Column: 2703, Selected False, Rank: 22.000\n", "Column: 2704, Selected False, Rank: 21.000\n", "Column: 2705, Selected False, Rank: 20.000\n", "Column: 2706, Selected False, Rank: 19.000\n", "Column: 2707, Selected False, Rank: 18.000\n", "Column: 2708, Selected False, Rank: 17.000\n", "Column: 2709, Selected False, Rank: 16.000\n", "Column: 2710, Selected False, Rank: 15.000\n", "Column: 2711, Selected False, Rank: 14.000\n", "Column: 2712, Selected False, Rank: 13.000\n", "Column: 2713, Selected False, Rank: 12.000\n", "Column: 2714, Selected False, Rank: 11.000\n", "Column: 2715, Selected True, Rank: 1.000\n", "Column: 2716, Selected False, Rank: 10.000\n", "Column: 2717, Selected False, Rank: 9.000\n", "Column: 2718, Selected False, Rank: 8.000\n", "Column: 2719, Selected False, Rank: 7.000\n", "Column: 2720, Selected False, Rank: 6.000\n", "Column: 2721, Selected False, Rank: 5.000\n", "Column: 2722, Selected False, Rank: 4.000\n", "Column: 2723, Selected False, Rank: 3.000\n", "Column: 2724, Selected False, Rank: 2.000\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "gOi9GMTZmwof" }, "source": [ "### Releif" ] }, { "cell_type": "code", "metadata": { "id": "valAl8sWm1NB", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "5bc29536-a55f-45eb-dd5d-d2c752622ddd" }, "source": [ "!pip install ReliefF\n", "from ReliefF import ReliefF\n", "\n", "#Releif\n", "train_models(X, y, y_cat) # 3 feature to keep" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Requirement already satisfied: ReliefF in /usr/local/lib/python3.6/dist-packages (0.1.2)\n", "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from ReliefF) (1.18.5)\n", "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from ReliefF) (1.4.1)\n", "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from ReliefF) (0.22.2.post1)\n", "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->ReliefF) (0.17.0)\n", "Before selectoin\n", "(1050, 2725)\n", "After selectoin\n", "(1050, 3)\n", "(210, 3)\n", "Epoch 1/250\n", "7/7 [==============================] - 0s 62ms/step - loss: 0.8152 - accuracy: 0.6274 - val_loss: 0.5245 - val_accuracy: 0.8524\n", "Epoch 2/250\n", "7/7 [==============================] - 0s 36ms/step - loss: 0.5491 - accuracy: 0.7583 - val_loss: 0.4193 - val_accuracy: 0.8524\n", "Epoch 3/250\n", "7/7 [==============================] - 3s 384ms/step - loss: 0.4501 - accuracy: 0.7881 - val_loss: 0.3644 - val_accuracy: 0.8571\n", "Epoch 4/250\n", "7/7 [==============================] - 2s 254ms/step - loss: 0.4494 - accuracy: 0.7881 - val_loss: 0.3367 - val_accuracy: 0.8667\n", "Epoch 5/250\n", "7/7 [==============================] - 0s 48ms/step - loss: 0.4315 - accuracy: 0.7940 - val_loss: 0.3223 - val_accuracy: 0.8667\n", "Epoch 6/250\n", "7/7 [==============================] - 0s 43ms/step - loss: 0.4322 - accuracy: 0.7929 - val_loss: 0.3142 - val_accuracy: 0.8667\n", "Epoch 7/250\n", "7/7 [==============================] - 0s 42ms/step - loss: 0.4385 - accuracy: 0.7845 - val_loss: 0.3086 - val_accuracy: 0.8667\n", "Epoch 8/250\n", "7/7 [==============================] - 0s 40ms/step - loss: 0.4263 - accuracy: 0.7952 - val_loss: 0.3054 - val_accuracy: 0.8667\n", "Epoch 9/250\n", "7/7 [==============================] - 0s 41ms/step - loss: 0.4073 - accuracy: 0.7940 - val_loss: 0.3040 - val_accuracy: 0.8667\n", "Epoch 10/250\n", "7/7 [==============================] - 0s 50ms/step - loss: 0.4127 - accuracy: 0.8060 - val_loss: 0.3023 - val_accuracy: 0.8667\n", "Epoch 11/250\n", "7/7 [==============================] - 0s 48ms/step - loss: 0.4147 - accuracy: 0.7976 - val_loss: 0.3006 - val_accuracy: 0.8667\n", "Epoch 12/250\n", "7/7 [==============================] - 1s 130ms/step - loss: 0.4157 - accuracy: 0.7952 - val_loss: 0.2986 - val_accuracy: 0.8667\n", "Epoch 13/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.3903 - accuracy: 0.8167 - val_loss: 0.2990 - val_accuracy: 0.8619\n", "Epoch 14/250\n", "7/7 [==============================] - 1s 103ms/step - loss: 0.4074 - accuracy: 0.8000 - val_loss: 0.2979 - val_accuracy: 0.8619\n", "Epoch 15/250\n", "7/7 [==============================] - 0s 46ms/step - loss: 0.4115 - accuracy: 0.7952 - val_loss: 0.2957 - val_accuracy: 0.8619\n", "Epoch 16/250\n", "7/7 [==============================] - 0s 51ms/step - loss: 0.4017 - accuracy: 0.8083 - val_loss: 0.2942 - val_accuracy: 0.8571\n", "Epoch 17/250\n", "7/7 [==============================] - 0s 40ms/step - loss: 0.3972 - accuracy: 0.8036 - val_loss: 0.2929 - val_accuracy: 0.8571\n", "Epoch 18/250\n", "7/7 [==============================] - 0s 49ms/step - loss: 0.3893 - accuracy: 0.8107 - val_loss: 0.2917 - val_accuracy: 0.8571\n", "Epoch 19/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.4068 - accuracy: 0.7952 - val_loss: 0.2923 - val_accuracy: 0.8619\n", "Epoch 20/250\n", "7/7 [==============================] - 0s 42ms/step - loss: 0.4184 - accuracy: 0.7964 - val_loss: 0.2900 - val_accuracy: 0.8571\n", "Epoch 21/250\n", "7/7 [==============================] - 1s 87ms/step - loss: 0.4025 - accuracy: 0.7976 - val_loss: 0.2884 - val_accuracy: 0.8667\n", "Epoch 22/250\n", "7/7 [==============================] - 1s 210ms/step - loss: 0.4107 - accuracy: 0.7964 - val_loss: 0.2879 - val_accuracy: 0.8667\n", "Epoch 23/250\n", "7/7 [==============================] - 7s 1s/step - loss: 0.4079 - accuracy: 0.7976 - val_loss: 0.2878 - val_accuracy: 0.8667\n", "Epoch 24/250\n", "7/7 [==============================] - 1s 197ms/step - loss: 0.4040 - accuracy: 0.8024 - val_loss: 0.2867 - val_accuracy: 0.8667\n", "Epoch 25/250\n", "7/7 [==============================] - 3s 386ms/step - loss: 0.4067 - accuracy: 0.7940 - val_loss: 0.2863 - val_accuracy: 0.8667\n", "Epoch 26/250\n", "7/7 [==============================] - 0s 43ms/step - loss: 0.4057 - accuracy: 0.8024 - val_loss: 0.2858 - val_accuracy: 0.8667\n", "Epoch 27/250\n", "7/7 [==============================] - 0s 62ms/step - loss: 0.4029 - accuracy: 0.8012 - val_loss: 0.2851 - val_accuracy: 0.8667\n", "Epoch 28/250\n", "7/7 [==============================] - 7s 941ms/step - loss: 0.4156 - accuracy: 0.7917 - val_loss: 0.2847 - val_accuracy: 0.8619\n", "Epoch 29/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.3984 - accuracy: 0.8024 - val_loss: 0.2865 - val_accuracy: 0.8619\n", "Epoch 30/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.3947 - accuracy: 0.8071 - val_loss: 0.2863 - val_accuracy: 0.8619\n", "Epoch 31/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.3924 - accuracy: 0.8060 - val_loss: 0.2857 - val_accuracy: 0.8619\n", "Epoch 32/250\n", "7/7 [==============================] - 2s 245ms/step - loss: 0.4195 - accuracy: 0.7929 - val_loss: 0.2846 - val_accuracy: 0.8619\n", "Epoch 33/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.4040 - accuracy: 0.8036 - val_loss: 0.2882 - val_accuracy: 0.8667\n", "Epoch 34/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3975 - accuracy: 0.8048 - val_loss: 0.2917 - val_accuracy: 0.8571\n", "Epoch 35/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.4034 - accuracy: 0.8024 - val_loss: 0.2953 - val_accuracy: 0.8524\n", "Epoch 36/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.3899 - accuracy: 0.8083 - val_loss: 0.2952 - val_accuracy: 0.8476\n", "Epoch 37/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.4039 - accuracy: 0.7952 - val_loss: 0.2923 - val_accuracy: 0.8524\n", "Epoch 38/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.4037 - accuracy: 0.8024 - val_loss: 0.2924 - val_accuracy: 0.8524\n", "Epoch 39/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.4027 - accuracy: 0.7976 - val_loss: 0.2849 - val_accuracy: 0.8667\n", "Epoch 40/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3972 - accuracy: 0.8036 - val_loss: 0.2848 - val_accuracy: 0.8667\n", "Epoch 41/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3941 - accuracy: 0.8024 - val_loss: 0.2850 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val_loss: 0.2802 - val_accuracy: 0.8667\n", "Epoch 148/250\n", "7/7 [==============================] - 0s 37ms/step - loss: 0.3938 - accuracy: 0.8036 - val_loss: 0.2752 - val_accuracy: 0.8571\n", "Epoch 149/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3961 - accuracy: 0.8060 - val_loss: 0.2759 - val_accuracy: 0.8667\n", "Epoch 150/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3937 - accuracy: 0.8000 - val_loss: 0.2778 - val_accuracy: 0.8571\n", "Epoch 151/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3989 - accuracy: 0.8036 - val_loss: 0.2793 - val_accuracy: 0.8571\n", "Epoch 152/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3924 - accuracy: 0.8048 - val_loss: 0.2822 - val_accuracy: 0.8667\n", "Epoch 153/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3904 - accuracy: 0.8083 - val_loss: 0.2896 - val_accuracy: 0.8524\n", "Epoch 154/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3824 - accuracy: 0.8095 - val_loss: 0.3070 - val_accuracy: 0.8476\n", "Epoch 155/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.4020 - accuracy: 0.8000 - val_loss: 0.3186 - val_accuracy: 0.8429\n", "Epoch 156/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3961 - accuracy: 0.8012 - val_loss: 0.3080 - val_accuracy: 0.8476\n", "Epoch 157/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3926 - accuracy: 0.8048 - val_loss: 0.3000 - val_accuracy: 0.8476\n", "Epoch 158/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3896 - accuracy: 0.8071 - val_loss: 0.2884 - val_accuracy: 0.8524\n", "Epoch 159/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.4064 - accuracy: 0.7964 - val_loss: 0.2779 - val_accuracy: 0.8619\n", "Epoch 160/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3841 - accuracy: 0.8107 - val_loss: 0.2775 - val_accuracy: 0.8571\n", "Epoch 161/250\n", "7/7 [==============================] - 0s 43ms/step - loss: 0.3991 - accuracy: 0.8024 - val_loss: 0.2734 - val_accuracy: 0.8667\n", "Epoch 162/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3913 - accuracy: 0.8060 - val_loss: 0.2758 - val_accuracy: 0.8571\n", "Epoch 163/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3898 - accuracy: 0.8071 - val_loss: 0.2763 - val_accuracy: 0.8571\n", "Epoch 164/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3853 - accuracy: 0.8083 - val_loss: 0.2787 - val_accuracy: 0.8667\n", "Epoch 165/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3968 - accuracy: 0.7988 - val_loss: 0.2855 - val_accuracy: 0.8571\n", "Epoch 166/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3918 - accuracy: 0.8036 - val_loss: 0.2859 - val_accuracy: 0.8571\n", "Epoch 167/250\n", "7/7 [==============================] - 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"Epoch 174/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.4028 - accuracy: 0.7976 - val_loss: 0.2753 - val_accuracy: 0.8571\n", "Epoch 175/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3953 - accuracy: 0.8024 - val_loss: 0.2748 - val_accuracy: 0.8619\n", "Epoch 176/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3904 - accuracy: 0.8083 - val_loss: 0.2754 - val_accuracy: 0.8571\n", "Epoch 177/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3857 - accuracy: 0.8131 - val_loss: 0.2784 - val_accuracy: 0.8667\n", "Epoch 178/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3912 - accuracy: 0.8036 - val_loss: 0.2833 - val_accuracy: 0.8619\n", "Epoch 179/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.4115 - accuracy: 0.7905 - val_loss: 0.2794 - val_accuracy: 0.8667\n", "Epoch 180/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3835 - 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[==============================] - 0s 9ms/step - loss: 0.3885 - accuracy: 0.8083 - val_loss: 0.2984 - val_accuracy: 0.8524\n", "Epoch 188/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3826 - accuracy: 0.8083 - val_loss: 0.3011 - val_accuracy: 0.8476\n", "Epoch 189/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3893 - accuracy: 0.8048 - val_loss: 0.3059 - val_accuracy: 0.8476\n", "Epoch 190/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3943 - accuracy: 0.8060 - val_loss: 0.3083 - val_accuracy: 0.8476\n", "Epoch 191/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3851 - accuracy: 0.8107 - val_loss: 0.3086 - val_accuracy: 0.8476\n", "Epoch 192/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3963 - accuracy: 0.8060 - val_loss: 0.2959 - val_accuracy: 0.8524\n", "Epoch 193/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3982 - accuracy: 0.8000 - val_loss: 0.2808 - val_accuracy: 0.8619\n", "Epoch 194/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3862 - accuracy: 0.8048 - val_loss: 0.2771 - val_accuracy: 0.8667\n", "Epoch 195/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3829 - accuracy: 0.8071 - val_loss: 0.2774 - val_accuracy: 0.8667\n", "Epoch 196/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3871 - accuracy: 0.8060 - val_loss: 0.2772 - val_accuracy: 0.8571\n", "Epoch 197/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.4026 - accuracy: 0.7976 - val_loss: 0.2901 - val_accuracy: 0.8524\n", "Epoch 198/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3828 - accuracy: 0.8095 - val_loss: 0.3000 - val_accuracy: 0.8476\n", "Epoch 199/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.4007 - accuracy: 0.8024 - val_loss: 0.3043 - val_accuracy: 0.8476\n", "Epoch 200/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3915 - accuracy: 0.8048 - val_loss: 0.3027 - val_accuracy: 0.8429\n", "Epoch 201/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3879 - accuracy: 0.8060 - val_loss: 0.3022 - val_accuracy: 0.8429\n", "Epoch 202/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3914 - accuracy: 0.8071 - val_loss: 0.2975 - val_accuracy: 0.8524\n", "Epoch 203/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3725 - accuracy: 0.8131 - val_loss: 0.2979 - val_accuracy: 0.8524\n", "Epoch 204/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3958 - accuracy: 0.8071 - val_loss: 0.3030 - val_accuracy: 0.8429\n", "Epoch 205/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3884 - accuracy: 0.8060 - val_loss: 0.3058 - val_accuracy: 0.8476\n", "Epoch 206/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3892 - accuracy: 0.8071 - val_loss: 0.2932 - val_accuracy: 0.8524\n", "Epoch 207/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3960 - accuracy: 0.8024 - val_loss: 0.2800 - val_accuracy: 0.8667\n", "Epoch 208/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3858 - accuracy: 0.8107 - val_loss: 0.2809 - val_accuracy: 0.8619\n", "Epoch 209/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3983 - accuracy: 0.7976 - val_loss: 0.2874 - val_accuracy: 0.8619\n", "Epoch 210/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3935 - accuracy: 0.8024 - val_loss: 0.2945 - val_accuracy: 0.8476\n", "Epoch 211/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3843 - accuracy: 0.8083 - val_loss: 0.2958 - val_accuracy: 0.8476\n", "Epoch 212/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.4010 - accuracy: 0.8012 - val_loss: 0.2901 - val_accuracy: 0.8571\n", "Epoch 213/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.3939 - accuracy: 0.8036 - val_loss: 0.2879 - val_accuracy: 0.8571\n", "Epoch 214/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3805 - accuracy: 0.8107 - val_loss: 0.2986 - val_accuracy: 0.8476\n", "Epoch 215/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3831 - accuracy: 0.8119 - val_loss: 0.3095 - val_accuracy: 0.8476\n", "Epoch 216/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3940 - accuracy: 0.8000 - val_loss: 0.3163 - val_accuracy: 0.8429\n", "Epoch 217/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.4016 - accuracy: 0.8000 - val_loss: 0.3166 - val_accuracy: 0.8429\n", "Epoch 218/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.4091 - accuracy: 0.7952 - val_loss: 0.3080 - val_accuracy: 0.8429\n", "Epoch 219/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3824 - accuracy: 0.8107 - val_loss: 0.2985 - val_accuracy: 0.8476\n", "Epoch 220/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3942 - accuracy: 0.8036 - val_loss: 0.3009 - val_accuracy: 0.8571\n", "Epoch 221/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3824 - accuracy: 0.8107 - val_loss: 0.3013 - val_accuracy: 0.8524\n", "Epoch 222/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3751 - accuracy: 0.8143 - val_loss: 0.3060 - val_accuracy: 0.8429\n", "Epoch 223/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3832 - accuracy: 0.8107 - val_loss: 0.3028 - val_accuracy: 0.8524\n", "Epoch 224/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3895 - accuracy: 0.8060 - val_loss: 0.3005 - val_accuracy: 0.8524\n", "Epoch 225/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3894 - accuracy: 0.8071 - val_loss: 0.2944 - val_accuracy: 0.8524\n", "Epoch 226/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3907 - accuracy: 0.8036 - val_loss: 0.2944 - val_accuracy: 0.8524\n", "Epoch 227/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3902 - accuracy: 0.8048 - val_loss: 0.2887 - val_accuracy: 0.8524\n", "Epoch 228/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3990 - accuracy: 0.8036 - val_loss: 0.2879 - val_accuracy: 0.8524\n", "Epoch 229/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3950 - accuracy: 0.8036 - val_loss: 0.2969 - val_accuracy: 0.8524\n", "Epoch 230/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.4021 - accuracy: 0.7988 - val_loss: 0.2963 - val_accuracy: 0.8524\n", "Epoch 231/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.4007 - accuracy: 0.7976 - val_loss: 0.2987 - val_accuracy: 0.8524\n", "Epoch 232/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3947 - accuracy: 0.8024 - val_loss: 0.2961 - val_accuracy: 0.8524\n", "Epoch 233/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3833 - accuracy: 0.8095 - val_loss: 0.2864 - val_accuracy: 0.8619\n", "Epoch 234/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3855 - accuracy: 0.8107 - val_loss: 0.2793 - val_accuracy: 0.8667\n", "Epoch 235/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3852 - accuracy: 0.8083 - val_loss: 0.2795 - val_accuracy: 0.8571\n", "Epoch 236/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3910 - accuracy: 0.8024 - val_loss: 0.2819 - val_accuracy: 0.8619\n", "Epoch 237/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.4000 - accuracy: 0.7988 - val_loss: 0.2946 - val_accuracy: 0.8524\n", "Epoch 238/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3983 - accuracy: 0.8036 - val_loss: 0.3034 - val_accuracy: 0.8476\n", "Epoch 239/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3852 - accuracy: 0.8083 - val_loss: 0.3060 - val_accuracy: 0.8476\n", "Epoch 240/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3906 - accuracy: 0.8036 - val_loss: 0.3058 - val_accuracy: 0.8476\n", "Epoch 241/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3847 - accuracy: 0.8048 - val_loss: 0.2998 - val_accuracy: 0.8524\n", "Epoch 242/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3823 - accuracy: 0.8071 - val_loss: 0.2939 - val_accuracy: 0.8524\n", "Epoch 243/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3895 - accuracy: 0.8048 - val_loss: 0.2970 - val_accuracy: 0.8524\n", "Epoch 244/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3766 - accuracy: 0.8107 - val_loss: 0.3027 - val_accuracy: 0.8524\n", "Epoch 245/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3960 - accuracy: 0.8000 - val_loss: 0.3096 - val_accuracy: 0.8476\n", "Epoch 246/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.4045 - accuracy: 0.7976 - val_loss: 0.3117 - val_accuracy: 0.8429\n", "Epoch 247/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3913 - accuracy: 0.8048 - val_loss: 0.3088 - val_accuracy: 0.8429\n", "Epoch 248/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.4026 - accuracy: 0.7988 - val_loss: 0.2943 - val_accuracy: 0.8476\n", "Epoch 249/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3875 - accuracy: 0.8024 - val_loss: 0.2901 - val_accuracy: 0.8571\n", "Epoch 250/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3824 - accuracy: 0.8095 - val_loss: 0.2851 - val_accuracy: 0.8524\n", "Valid results - Loss: 0.2850606441497803 - Accuracy: 85.23809313774109%\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", 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\n", 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\n", 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" ] }, "metadata": { "tags": [], "needs_background": "light" } }, { "output_type": "stream", "text": [ "DNN results\n", "accuracy : 0.8047619047619048\n", "Training Recall: 0.8047619047619048\n", "Training Precision: 0.8044476724078302\n", "Confusion matrix: \n", "[[290 88]\n", " [ 76 386]]\n", "Validation set report:\n", "cross Loss: -25.317069535028367\n", "hing Loss: 0.5761904761904761\n", "Report precision recall f1-score support\n", "\n", " 0 0.84 0.86 0.85 93\n", " 1 0.89 0.87 0.88 117\n", "\n", " accuracy 0.87 210\n", " macro avg 0.86 0.87 0.87 210\n", "weighted avg 0.87 0.87 0.87 210\n", "\n", "accuracy : 0.8666666666666667\n", "Validation Recall: 0.8666666666666667\n", "Validation Precision: 0.8670938215102975\n", "Confusion matrix: \n", "[[ 80 13]\n", " [ 15 102]]\n", "\n", "\\KNN Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 0.7952380952380952\n", "Validation Recall: 0.7952380952380952\n", "Validation Precision: 0.794781243346817\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[69 24]\n", " [19 98]]\n", "\n", "\n", "Support vector machine Result\n", "\n", "Train accuracy: 0.7976190476190477\n", "Training Recall: 0.7976190476190477\n", "Training Precision: 0.8079710144927535\n", "\n", "\n", "Validation accuracy: 0.8428571428571429\n", "Validation Recall: 0.8428571428571429\n", "Validation Precision: 0.8494388957468298\n", "\n", "Train Confusion matrix:\n", " [[248 130]\n", " [ 40 422]]\n", "Validation Confusion matrix:\n", " [[ 68 25]\n", " [ 8 109]]\n", "\n", "\n", "Random Forest Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 0.8523809523809524\n", "Validation Recall: 0.8523809523809524\n", "Validation Precision: 0.8521825396825395\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[ 76 17]\n", " [ 14 103]]\n", "\n", "\n", "LDA Result\n", "\n", "Train accuracy: 0.780952380952381\n", "Training Recall: 0.780952380952381\n", "Training Precision: 0.8061427280939475\n", "\n", "\n", "Validation accuracy: 0.8285714285714286\n", "Validation Recall: 0.8285714285714286\n", "Validation Precision: 0.8429496497951632\n", "\n", "Train Confusion matrix:\n", " [[220 158]\n", " [ 26 436]]\n", "Validation Confusion matrix:\n", " [[ 63 30]\n", " [ 6 111]]\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "Twhin2feoMyk", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "9fce4341-90db-4eb9-da25-50d65b489427" }, "source": [ "\n", "#Releif\n", "train_models(X, y, y_cat) # 10 feature to keep" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Before selectoin\n", "(1050, 2725)\n", "After selectoin\n", "(1050, 10)\n", "(210, 10)\n", "Epoch 1/250\n", "7/7 [==============================] - 0s 58ms/step - loss: 0.8173 - accuracy: 0.6310 - val_loss: 0.7022 - val_accuracy: 0.8429\n", "Epoch 2/250\n", "7/7 [==============================] - 3s 390ms/step - loss: 0.5695 - accuracy: 0.7643 - val_loss: 0.5471 - val_accuracy: 0.8524\n", "Epoch 3/250\n", "7/7 [==============================] - 0s 49ms/step - loss: 0.4729 - accuracy: 0.7762 - val_loss: 0.4527 - val_accuracy: 0.8571\n", "Epoch 4/250\n", "7/7 [==============================] - 0s 46ms/step - loss: 0.4621 - accuracy: 0.7786 - val_loss: 0.3995 - val_accuracy: 0.8571\n", "Epoch 5/250\n", "7/7 [==============================] - 0s 40ms/step - loss: 0.4395 - accuracy: 0.7893 - val_loss: 0.3706 - val_accuracy: 0.8571\n", "Epoch 6/250\n", "7/7 [==============================] - 0s 47ms/step - loss: 0.4337 - accuracy: 0.7952 - val_loss: 0.3525 - val_accuracy: 0.8571\n", "Epoch 7/250\n", "7/7 [==============================] - 0s 48ms/step - loss: 0.4261 - accuracy: 0.7988 - val_loss: 0.3409 - val_accuracy: 0.8571\n", "Epoch 8/250\n", "7/7 [==============================] - 0s 46ms/step - loss: 0.4222 - accuracy: 0.8012 - val_loss: 0.3340 - val_accuracy: 0.8571\n", "Epoch 9/250\n", "7/7 [==============================] - 0s 58ms/step - loss: 0.4167 - accuracy: 0.8012 - val_loss: 0.3281 - val_accuracy: 0.8619\n", "Epoch 10/250\n", "7/7 [==============================] - 1s 114ms/step - loss: 0.4187 - accuracy: 0.7976 - val_loss: 0.3249 - val_accuracy: 0.8571\n", "Epoch 11/250\n", "7/7 [==============================] - 3s 397ms/step - loss: 0.4262 - accuracy: 0.7893 - val_loss: 0.3218 - val_accuracy: 0.8571\n", "Epoch 12/250\n", "7/7 [==============================] - 0s 51ms/step - loss: 0.4186 - accuracy: 0.7988 - val_loss: 0.3173 - val_accuracy: 0.8571\n", "Epoch 13/250\n", "7/7 [==============================] - 0s 45ms/step - loss: 0.4013 - accuracy: 0.8083 - val_loss: 0.3118 - val_accuracy: 0.8619\n", "Epoch 14/250\n", "7/7 [==============================] - 0s 42ms/step - loss: 0.4065 - accuracy: 0.8048 - val_loss: 0.3081 - val_accuracy: 0.8667\n", "Epoch 15/250\n", "7/7 [==============================] - 1s 99ms/step - loss: 0.4044 - accuracy: 0.8083 - val_loss: 0.3055 - val_accuracy: 0.8667\n", "Epoch 16/250\n", "7/7 [==============================] - 2s 236ms/step - loss: 0.3987 - accuracy: 0.8143 - val_loss: 0.3028 - val_accuracy: 0.8667\n", "Epoch 17/250\n", "7/7 [==============================] - 0s 47ms/step - loss: 0.4076 - accuracy: 0.8024 - val_loss: 0.3009 - val_accuracy: 0.8619\n", "Epoch 18/250\n", "7/7 [==============================] - 0s 46ms/step - loss: 0.4080 - accuracy: 0.8024 - val_loss: 0.2991 - val_accuracy: 0.8571\n", "Epoch 19/250\n", "7/7 [==============================] - 0s 43ms/step - loss: 0.4102 - accuracy: 0.8012 - val_loss: 0.2977 - val_accuracy: 0.8667\n", "Epoch 20/250\n", "7/7 [==============================] - 1s 185ms/step - loss: 0.4083 - accuracy: 0.8000 - val_loss: 0.2954 - val_accuracy: 0.8619\n", "Epoch 21/250\n", "7/7 [==============================] - 2s 286ms/step - loss: 0.4090 - accuracy: 0.8012 - val_loss: 0.2941 - val_accuracy: 0.8667\n", "Epoch 22/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.4104 - accuracy: 0.7976 - val_loss: 0.2943 - val_accuracy: 0.8571\n", "Epoch 23/250\n", "7/7 [==============================] - 7s 1s/step - loss: 0.3967 - accuracy: 0.8095 - val_loss: 0.2905 - val_accuracy: 0.8667\n", "Epoch 24/250\n", "7/7 [==============================] - 1s 212ms/step - loss: 0.4232 - accuracy: 0.7905 - val_loss: 0.2904 - val_accuracy: 0.8667\n", "Epoch 25/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.4049 - accuracy: 0.8024 - val_loss: 0.2914 - val_accuracy: 0.8667\n", "Epoch 26/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.4063 - accuracy: 0.7988 - val_loss: 0.2949 - val_accuracy: 0.8524\n", "Epoch 27/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.4210 - accuracy: 0.7976 - val_loss: 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276ms/step - loss: 0.3948 - accuracy: 0.8083 - val_loss: 0.2879 - val_accuracy: 0.8619\n", "Epoch 35/250\n", "7/7 [==============================] - 0s 43ms/step - loss: 0.4144 - accuracy: 0.7976 - val_loss: 0.2808 - val_accuracy: 0.8619\n", "Epoch 36/250\n", "7/7 [==============================] - 1s 81ms/step - loss: 0.3970 - accuracy: 0.8036 - val_loss: 0.2804 - val_accuracy: 0.8667\n", "Epoch 37/250\n", "7/7 [==============================] - 6s 921ms/step - loss: 0.3962 - accuracy: 0.8000 - val_loss: 0.2799 - val_accuracy: 0.8667\n", "Epoch 38/250\n", "7/7 [==============================] - 2s 285ms/step - loss: 0.3945 - accuracy: 0.8024 - val_loss: 0.2796 - val_accuracy: 0.8667\n", "Epoch 39/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.4028 - accuracy: 0.7964 - val_loss: 0.2805 - val_accuracy: 0.8667\n", "Epoch 40/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3880 - accuracy: 0.8083 - val_loss: 0.2852 - val_accuracy: 0.8667\n", 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- val_accuracy: 0.8476\n", "Epoch 94/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3829 - accuracy: 0.8119 - val_loss: 0.2997 - val_accuracy: 0.8476\n", "Epoch 95/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3975 - accuracy: 0.8036 - val_loss: 0.2831 - val_accuracy: 0.8571\n", "Epoch 96/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3739 - accuracy: 0.8167 - val_loss: 0.2757 - val_accuracy: 0.8667\n", "Epoch 97/250\n", "7/7 [==============================] - 4s 533ms/step - loss: 0.3818 - accuracy: 0.8131 - val_loss: 0.2716 - val_accuracy: 0.8667\n", "Epoch 98/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.3729 - accuracy: 0.8131 - val_loss: 0.2803 - val_accuracy: 0.8571\n", "Epoch 99/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3925 - accuracy: 0.8024 - val_loss: 0.2845 - val_accuracy: 0.8571\n", "Epoch 100/250\n", "7/7 [==============================] - 0s 9ms/step 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"Epoch 140/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3801 - accuracy: 0.8143 - val_loss: 0.2830 - val_accuracy: 0.8619\n", "Epoch 141/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3802 - accuracy: 0.8107 - val_loss: 0.2741 - val_accuracy: 0.8667\n", "Epoch 142/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3858 - accuracy: 0.8107 - val_loss: 0.2685 - val_accuracy: 0.8667\n", "Epoch 143/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3932 - accuracy: 0.8036 - val_loss: 0.2700 - val_accuracy: 0.8667\n", "Epoch 144/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3987 - accuracy: 0.8060 - val_loss: 0.2706 - val_accuracy: 0.8667\n", "Epoch 145/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3929 - accuracy: 0.8024 - val_loss: 0.2849 - val_accuracy: 0.8571\n", "Epoch 146/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3949 - 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[==============================] - 2s 241ms/step - loss: 0.3807 - accuracy: 0.8083 - val_loss: 0.2678 - val_accuracy: 0.8667\n", "Epoch 154/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3902 - accuracy: 0.8012 - val_loss: 0.2716 - val_accuracy: 0.8667\n", "Epoch 155/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3925 - accuracy: 0.8048 - val_loss: 0.2703 - val_accuracy: 0.8667\n", "Epoch 156/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3864 - accuracy: 0.8083 - val_loss: 0.2713 - val_accuracy: 0.8667\n", "Epoch 157/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3831 - accuracy: 0.8095 - val_loss: 0.2733 - val_accuracy: 0.8667\n", "Epoch 158/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3882 - accuracy: 0.8071 - val_loss: 0.2779 - val_accuracy: 0.8619\n", "Epoch 159/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3957 - accuracy: 0.8024 - val_loss: 0.2699 - val_accuracy: 0.8667\n", "Epoch 160/250\n", "7/7 [==============================] - 0s 35ms/step - loss: 0.3956 - accuracy: 0.8036 - val_loss: 0.2670 - val_accuracy: 0.8667\n", "Epoch 161/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3819 - accuracy: 0.8083 - val_loss: 0.2706 - val_accuracy: 0.8667\n", "Epoch 162/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3934 - accuracy: 0.8060 - val_loss: 0.2754 - val_accuracy: 0.8667\n", "Epoch 163/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3841 - accuracy: 0.8071 - val_loss: 0.2722 - val_accuracy: 0.8667\n", "Epoch 164/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3961 - accuracy: 0.8036 - val_loss: 0.2740 - val_accuracy: 0.8667\n", "Epoch 165/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.4112 - accuracy: 0.7940 - val_loss: 0.2838 - val_accuracy: 0.8571\n", "Epoch 166/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.4097 - accuracy: 0.7952 - val_loss: 0.2808 - val_accuracy: 0.8619\n", "Epoch 167/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3886 - accuracy: 0.8036 - val_loss: 0.2823 - val_accuracy: 0.8619\n", "Epoch 168/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3960 - accuracy: 0.8048 - val_loss: 0.2791 - val_accuracy: 0.8571\n", "Epoch 169/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3978 - accuracy: 0.8024 - val_loss: 0.2775 - val_accuracy: 0.8619\n", "Epoch 170/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3942 - accuracy: 0.8012 - val_loss: 0.2721 - val_accuracy: 0.8667\n", "Epoch 171/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3869 - accuracy: 0.8083 - val_loss: 0.2691 - val_accuracy: 0.8667\n", "Epoch 172/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3950 - accuracy: 0.8048 - val_loss: 0.2671 - val_accuracy: 0.8667\n", "Epoch 173/250\n", "7/7 [==============================] - 0s 48ms/step - loss: 0.3823 - accuracy: 0.8095 - val_loss: 0.2664 - val_accuracy: 0.8667\n", "Epoch 174/250\n", "7/7 [==============================] - 6s 813ms/step - loss: 0.3969 - accuracy: 0.8036 - val_loss: 0.2649 - val_accuracy: 0.8667\n", "Epoch 175/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.3897 - accuracy: 0.8060 - val_loss: 0.2672 - val_accuracy: 0.8667\n", "Epoch 176/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.3846 - accuracy: 0.8095 - val_loss: 0.2674 - val_accuracy: 0.8667\n", "Epoch 177/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.3789 - accuracy: 0.8107 - val_loss: 0.2670 - val_accuracy: 0.8667\n", "Epoch 178/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3821 - accuracy: 0.8083 - val_loss: 0.2679 - val_accuracy: 0.8667\n", "Epoch 179/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.4065 - accuracy: 0.7988 - val_loss: 0.2746 - val_accuracy: 0.8667\n", "Epoch 180/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.3941 - accuracy: 0.8048 - val_loss: 0.2706 - val_accuracy: 0.8667\n", "Epoch 181/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3981 - accuracy: 0.8000 - val_loss: 0.2675 - val_accuracy: 0.8667\n", "Epoch 182/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3918 - accuracy: 0.8036 - val_loss: 0.2680 - val_accuracy: 0.8667\n", "Epoch 183/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.4030 - accuracy: 0.7964 - val_loss: 0.2683 - val_accuracy: 0.8667\n", "Epoch 184/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.4066 - accuracy: 0.7964 - val_loss: 0.2773 - val_accuracy: 0.8667\n", "Epoch 185/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3983 - accuracy: 0.8024 - val_loss: 0.2863 - val_accuracy: 0.8524\n", "Epoch 186/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3941 - accuracy: 0.8036 - val_loss: 0.2878 - val_accuracy: 0.8571\n", "Epoch 187/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3845 - accuracy: 0.8083 - val_loss: 0.2882 - val_accuracy: 0.8571\n", "Epoch 188/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.3882 - accuracy: 0.8060 - val_loss: 0.2896 - val_accuracy: 0.8524\n", "Epoch 189/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3955 - accuracy: 0.8048 - val_loss: 0.2915 - val_accuracy: 0.8524\n", "Epoch 190/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.3953 - accuracy: 0.8048 - val_loss: 0.2800 - val_accuracy: 0.8619\n", "Epoch 191/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3871 - accuracy: 0.8060 - val_loss: 0.2719 - val_accuracy: 0.8667\n", "Epoch 192/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3816 - accuracy: 0.8107 - val_loss: 0.2840 - val_accuracy: 0.8571\n", "Epoch 193/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3949 - accuracy: 0.8024 - val_loss: 0.2870 - val_accuracy: 0.8524\n", "Epoch 194/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.4118 - accuracy: 0.7917 - val_loss: 0.2942 - val_accuracy: 0.8524\n", "Epoch 195/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3967 - accuracy: 0.8012 - val_loss: 0.2916 - val_accuracy: 0.8524\n", "Epoch 196/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3947 - accuracy: 0.8060 - val_loss: 0.2972 - val_accuracy: 0.8476\n", "Epoch 197/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3821 - accuracy: 0.8083 - val_loss: 0.2927 - val_accuracy: 0.8524\n", "Epoch 198/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3896 - accuracy: 0.8048 - val_loss: 0.2971 - val_accuracy: 0.8524\n", "Epoch 199/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3922 - accuracy: 0.8071 - val_loss: 0.2963 - val_accuracy: 0.8524\n", "Epoch 200/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3848 - accuracy: 0.8107 - val_loss: 0.2862 - val_accuracy: 0.8524\n", "Epoch 201/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3997 - accuracy: 0.8036 - val_loss: 0.2732 - val_accuracy: 0.8667\n", "Epoch 202/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3771 - accuracy: 0.8179 - val_loss: 0.2739 - val_accuracy: 0.8667\n", "Epoch 203/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3982 - accuracy: 0.8024 - val_loss: 0.2768 - val_accuracy: 0.8619\n", "Epoch 204/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3757 - accuracy: 0.8167 - val_loss: 0.2732 - val_accuracy: 0.8667\n", "Epoch 205/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3692 - accuracy: 0.8190 - val_loss: 0.2678 - val_accuracy: 0.8667\n", "Epoch 206/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3828 - accuracy: 0.8071 - val_loss: 0.2673 - val_accuracy: 0.8667\n", "Epoch 207/250\n", "7/7 [==============================] - 0s 36ms/step - loss: 0.3779 - accuracy: 0.8131 - val_loss: 0.2646 - val_accuracy: 0.8714\n", "Epoch 208/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3735 - accuracy: 0.8155 - val_loss: 0.2655 - val_accuracy: 0.8667\n", "Epoch 209/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3917 - accuracy: 0.8048 - val_loss: 0.2661 - val_accuracy: 0.8667\n", "Epoch 210/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3808 - accuracy: 0.8107 - val_loss: 0.2693 - val_accuracy: 0.8667\n", "Epoch 211/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3871 - accuracy: 0.8060 - val_loss: 0.2743 - val_accuracy: 0.8667\n", "Epoch 212/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.4084 - accuracy: 0.7988 - val_loss: 0.2764 - val_accuracy: 0.8619\n", "Epoch 213/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3827 - accuracy: 0.8095 - val_loss: 0.2729 - val_accuracy: 0.8667\n", "Epoch 214/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3821 - accuracy: 0.8083 - val_loss: 0.2757 - val_accuracy: 0.8619\n", "Epoch 215/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.3872 - accuracy: 0.8095 - val_loss: 0.2932 - val_accuracy: 0.8524\n", "Epoch 216/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3803 - accuracy: 0.8119 - val_loss: 0.2943 - val_accuracy: 0.8524\n", "Epoch 217/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.4007 - accuracy: 0.8000 - val_loss: 0.3010 - val_accuracy: 0.8476\n", "Epoch 218/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.4012 - accuracy: 0.8012 - val_loss: 0.3010 - val_accuracy: 0.8476\n", "Epoch 219/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3877 - accuracy: 0.8095 - val_loss: 0.2956 - val_accuracy: 0.8524\n", "Epoch 220/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.4064 - accuracy: 0.7929 - val_loss: 0.2843 - val_accuracy: 0.8571\n", "Epoch 221/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3775 - accuracy: 0.8131 - val_loss: 0.2713 - val_accuracy: 0.8667\n", "Epoch 222/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3974 - accuracy: 0.8024 - val_loss: 0.2709 - val_accuracy: 0.8667\n", "Epoch 223/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3751 - accuracy: 0.8155 - val_loss: 0.2752 - val_accuracy: 0.8619\n", "Epoch 224/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3864 - accuracy: 0.8119 - val_loss: 0.2751 - val_accuracy: 0.8667\n", "Epoch 225/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3831 - accuracy: 0.8060 - val_loss: 0.2735 - val_accuracy: 0.8667\n", "Epoch 226/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3854 - accuracy: 0.8083 - val_loss: 0.2684 - val_accuracy: 0.8667\n", "Epoch 227/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.4027 - accuracy: 0.8012 - val_loss: 0.2662 - val_accuracy: 0.8667\n", "Epoch 228/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3817 - accuracy: 0.8095 - val_loss: 0.2685 - val_accuracy: 0.8667\n", "Epoch 229/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3845 - accuracy: 0.8095 - val_loss: 0.2720 - val_accuracy: 0.8667\n", "Epoch 230/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3959 - accuracy: 0.8048 - val_loss: 0.2756 - val_accuracy: 0.8619\n", "Epoch 231/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3810 - accuracy: 0.8119 - val_loss: 0.2787 - val_accuracy: 0.8619\n", "Epoch 232/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3732 - accuracy: 0.8179 - val_loss: 0.2900 - val_accuracy: 0.8571\n", "Epoch 233/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3739 - accuracy: 0.8155 - val_loss: 0.2929 - val_accuracy: 0.8524\n", "Epoch 234/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3954 - accuracy: 0.8048 - val_loss: 0.2843 - val_accuracy: 0.8571\n", "Epoch 235/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.4100 - accuracy: 0.7940 - val_loss: 0.2650 - val_accuracy: 0.8667\n", "Epoch 236/250\n", "7/7 [==============================] - 1s 99ms/step - loss: 0.3794 - accuracy: 0.8095 - val_loss: 0.2641 - val_accuracy: 0.8667\n", "Epoch 237/250\n", "7/7 [==============================] - 1s 139ms/step - loss: 0.3955 - accuracy: 0.8012 - val_loss: 0.2640 - val_accuracy: 0.8714\n", "Epoch 238/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.4019 - accuracy: 0.7988 - val_loss: 0.2651 - val_accuracy: 0.8714\n", "Epoch 239/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3826 - accuracy: 0.8095 - val_loss: 0.2647 - val_accuracy: 0.8714\n", "Epoch 240/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3868 - accuracy: 0.8095 - val_loss: 0.2718 - val_accuracy: 0.8667\n", "Epoch 241/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3885 - accuracy: 0.8048 - val_loss: 0.2711 - val_accuracy: 0.8667\n", "Epoch 242/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3917 - accuracy: 0.8048 - val_loss: 0.2707 - val_accuracy: 0.8667\n", "Epoch 243/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.4043 - accuracy: 0.7964 - val_loss: 0.2771 - val_accuracy: 0.8619\n", "Epoch 244/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3938 - accuracy: 0.8012 - val_loss: 0.2909 - val_accuracy: 0.8524\n", "Epoch 245/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3979 - accuracy: 0.7964 - val_loss: 0.2938 - val_accuracy: 0.8524\n", "Epoch 246/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3935 - accuracy: 0.8036 - val_loss: 0.2953 - val_accuracy: 0.8524\n", "Epoch 247/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3894 - accuracy: 0.8071 - val_loss: 0.3016 - val_accuracy: 0.8476\n", "Epoch 248/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3654 - accuracy: 0.8202 - val_loss: 0.3000 - val_accuracy: 0.8476\n", "Epoch 249/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.3852 - accuracy: 0.8036 - val_loss: 0.2887 - val_accuracy: 0.8524\n", "Epoch 250/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.3835 - accuracy: 0.8048 - val_loss: 0.2776 - val_accuracy: 0.8619\n", "Valid results - Loss: 0.2775672674179077 - Accuracy: 86.19047403335571%\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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z7nmgLajxi4jIiY37OXgzu9vM1prZ2ubm5vEuR0QkNMY94J1z9zvnljvnlldUVIx3OSIioTHuAS8iIsFQwIuIhFSQl0k+BLwEzDGzOjP7VFDTEhGRY8WCGrFz7qNBjVtERN6ZTtGIiITUhA/4dNrxzZU7+M12XWIpIjLShA/4SMT41sqdPLOpcbxLERE5p0z4gAeYWpTLgc6B8S5DROScEoqAryzO4WBn/3iXISJyTglHwBflcrBDe/AiIiNN/IBPDXNT96PM7V/HwHBqvKsRETlnTPyAj8RYUfcAN0XWcFDn4UVEDpv4AW9Gf+k85kb2cbBD5+FFRA6Z+AEPMPli5th+DnT0jXclIiLnjFAEfKJmIXk2SF/DjvEuRUTknBGKgI9PXQhApHnjOFciInLuCEXAM2keKSLkd2wZ70pERM4Z4Qj4eC4tWdUUdW4llXbjXY2IyDkhHAEPDE5ZxhK3ifV7W8a7FBGRc0JoAr5sya0UWy/b1z4z3qWIiJwTQhPwefPeS5IYtv0pGrv0hScRkdAEPDmFtJZfwvKBl7nyK8/y/Rd345zOx4vI+Suwn+wbD5Mv/yiTf/ZZPlOziy/9DL7+6x0sqC5iYVURC6qLmVdZwGAyzev7Okg7x7svrGByYc4pT2fzwS7ys2PUlCZwzrH5YDdzphQQjdgx/Q4mU8QikVG7nYzOvmHysqPEouHZFovI2RGqgGfRR+GFr/JZHqLqgw+zZnc7b9V38vy2Zka7uCYrGuFjl05jKJUmHjGml+WRSjte29fOYDLNcCpNMuUYTqUZTjuyosbyGaX8y292YWbcumgqWxq62FDfxc0LK7lqdjlN3YNML0vwrgsq2N3Sw6cffJ2y/CxW1Jby1IYG/ug9s1k+o4REPMaulh46+oa5YcEUegdTdPUPM5hMM5RMM5hMsfFAF3/z5GamlSb4mw8sZNn0EvqHUry4o4VV25rJjkX8uLJibG/q4ZIZJZQkssjNilKen324nf1DKX7+5gE6+4e5bXEVedlRnt/WQixizJqUT01JLtGIcaBzgMKcGN97YTcNnQP81W0XE4sYLT2DvFHXSUFOjEtrSzE7emPV2T/MM5saGUymeXZzIzctrGRmRT7Pb2vmD989i6zYkY1TKu1o7R1kUkEOu5p72N3SS3l+NgurizAzdjX3UF2SOGoY5xzDKccDL+7mjf0dTC3O5Q+unkkiO0ZWNMLz25oZSKa4eeHUw9N4+NV9NHQOsLimmOxYlIU1RRTmxAG/0XUOkmnHD1bvYdW2Zsrzs/hv755NUW6cyUXZPLRmH+UF2fz2/Cnsa+ujpiRBxKB3KEVuPHpUfSM559jf1s+a3a1UFGRzaW0ZuVnRUftt7x1iS0M3NaW5ZMUi5MSjFObEOdjZz/df3ENZfha3LJpKZVHuMdMAjloOrT2DtPcNU56fRXEii3TaEYkYnX3DfGvVTlZta6Y0L86fvG8O1SUJ6jO39ZhemqAkL4uewSQ5sQgp56hv72d6Wd5ROyVbGrr48do6blwwBTAmFWTT2DXAqm3NtPYOsWxaCQuri6gtzyMWjeCc40DnAEPJNJVFOeTE/Tx4cUcL3161k4umFnL70mqmFueSnx073C4zo7VnkL1tfSydVsKOpm6qSxKHhz+e5u5Bvvr0Nm5aUMlVF5Qf1e3lXa3saemltjyPsvxsfrxuP3e9ayZp58iORSnKjR/V/5pdrQyn3OHxtPcOUZgbP2p+bKjv5FurdnLNhRV8YGk16/d3sL+tj/fNn0wi60is7m/r45dvHWRBdRFFuXGcg9ryPPKyz0702rl0GmP58uVu7dq1YxvJW4/CY5+CFXfDDX/n71UzlGLTwU62NvSQE48wr7IQM/jOql08/no9Rblx0s7RPZAEYEZZgvycGPFohHgkQixqxKIRGjsH2NrYzbLpJUwpzGH1zhYmF+awZFoxD72yf9Ryphbl0D2YpHsgycyKPHY19x7TTyIrSt/Q6HfCXDGjlP3tfTR0DbBsWglv1XcymEyTlxVlOO0YSqZHHW5KYQ6J7CixiLG7pZfh1PGXc1Y0QmFunJaewaNeX1hdxM6mHnpH1FaciNM7mGRSQQ6d/cNcODmfroEkO5p6AMiJR0imHNmxCL1DKS6uKmR/Wz9DyTTVJbn0DiY50DnAtNIE+9qO3FqipjSXyQU5rN3bzvLpJdy2eOrh6X792e2H2zqjzIfTaO2557oLGEqleXpTIzuaejCDQ6t3NGIsnVZMa+/Q4WUQjxrDKcei6iJ2jGhnVixyeL7mZUXpHUodNa787BhXzCpjZkU+7b1DvLKnjZaeQSJmDCXT9I+4q6kZlCSyMODauZOYVppgd2svr+/rYHfLsetCYU6MvqEUDr+hKknEubS2jOe3N3P1BRVsa+pmX2sfuVlRLp5aRHEizsYDXYfnZVYswtJpxazd087sSfnsbe1jIJniilll7Gzqpal74JidnariXA509jO1KPdwMOdlRVlYXcyimmLiUeNfX9xDz2DymHqjESMRj9Kd6VaSiDOvspCdzT00dg0ermlxTTEAr+xuozw/m9beQZyD3HiUK2eXsaWhm8auAaqKc2nuHqR3KMVlM0t5eVcbF1UW8re3L6RrYJjntjRRU5qgsiiHf31xD03dAyydVsJzW5tp6RkkFjE+uKya/uEUjV0D5GfHeWbzkV97O7Qcp5claOoapCAnxkcuqWF9XSclCf8eeHFHKwDXz59CW98Qr+xuY/akfD51VS0XTy3i6U0NfP25HRjggNqyPHZlluX0sgRzpxTQ0DVIY+cAjd0DjBaxNaW5/M+b5/PG/g52tfQwqSCHL906/9geT4KZrXPOLR+1W+gC3jn41Z/DS/8Mc26CG74CxdOO23v/UIqceAQzo7FrgGTaUVWcO2q/zjneqOtk7pSCY/YoVu9ooTA3zoWTC9h4oJOXd7WRnx3lpoVT6RtK0tg1yNJpxbyyu43W3iF6B5NUFGRjZvzizQPMqshnUmE2WdEo2bEIWbEI+TkxFlUX0z+c4h+e2srLu1q5fFYZ186dxIraUtJp2N7UTc9Akunleaze0UIq7ejsH2Z7Uw/9wymGkmlmVuRx7ZxJlOZl8eyWJgaGU6yoLSUnHmVnUw87m3tp6hpgQXUR7b1DzK/ygfd/n93OzQsqWVRTzLzKQva09LJubzvFiThN3YPkZUd5elMjfUMpvvaRJUwvS1CWn82Hvr2avqEUH10xja//ejvXzZ1MZVEOe1r9m2BBVTGv7mnj8lllXDGrjG2N3Ty9qYm9rb1cObuch17Zx+CIDde7Lihn9qR8rr6ggvfMncTe1l4ee62eRFaUgeEUsyfl85PX6vn1liZiEWPptBI+cfl0rr6ggh3NPfQPpVi9s4UXdrSQnx3jspllRAzaeoe5bfFUFtUU09Q1wOqdrfQPp9hQ38m7L6xgX1sfmw52ccmMUho6B3yYZUXZ2dzDSztb2dfWR0kii8U1xUwrS5BOO2LRCDMr8lg+vZQDHf28UdfhA2swya8y86o8P4sl00pYOq2EuZUF1Lf345yjZzBFQ2c/iewYH1sxjcFkirt+uI669j6umzuZNbtbmT+1iAXVRXT2D7P5YBftvUPMmVLA0mklTCnK4aWdrby8q5UrZpezs6mHaaUJfv+qWuZVFh7emy9OxLlwcj6pzPrzVl0nsyryeW1fO87BTQsr2dbYzev7OtjS0EUy7VhcU8zf3r6QTQe6KMiJcaBzgJxYhBsXVJIbj7K5oYutDd2s3NrMvrY+ZpQlWDbdH11uaejild1tdPQPc+el0/nE5dOpa+9n08EuXtjezOqdrSysLqKmJMHO5h4KcuLkZkX59zX7uGXRVFZubTq88xWLGMnMFqokEeeCSQVsa+pmUXUxn7l2Nt9etZPX9nWQG49Snp/F7pZePpaZ5sqtText7WNBVRFfePQNltSUUN/Rz762Pi6cnE//cIq8rBjXXzyFrv4kP11fT0VBNtfMmcQzmxsP78QA3L60mj+9YQ73PLSeuo4+7r3uQkry4vz9U9tIptJMKcphcmEOteV53Ligks0Hu3DOb2B2t/TyxPoDbG3sBmBmRR752TGe+OOrTiPwzreABx/yq78Oz/01pIag9t0w/QqoWQFlF0BBJUR0TvudpNLuHT87GBhOMTicpigRP+o15yA3K3r4sPtUNHQO0DeUJJEVo77DH6q/0zgGhlO8tredBdVFFOTET9jvmXKqbUulHWnniEXspIfrG0rS1Z9kStGpf1Z0Jhw6kjneKakgdfQNUZzIorl7kOe3NQN+49PVP8yull7mTC6gJC/rtMbdO5gkkRVlMJmmvW/omNNgb+ecY3tTD3taeinLz2bptGLMbNTTZSejZzDJ157dzlWzy7n6worTasMh51/AH9KxH9Y+AFufhObNR16PZkFRDZTMgIq5UHGhD/3cUkiUQm4J5BSf2kZguB8666Bzv59uXyuU1sLM90Bu8dja0d8Om34K+9bAQCcMdUNhFUy7DObeDHnl7zyO05VK+mlmF0DsOG+mwW5o3Ai7VkEk6ttbfiFMuwKio5xr7GuD+tdgqAdKZ/r5lE5BduGJ53lyyP89Xh0TXXLIrz+pYb9uxscn1EMnlfQ7erFsv36GzPkb8CP1t/tQad8DHXuhYx+07oSW7ZAc7T7y5oPqUNhn5YFFoKgaonFIZ4Kvsy4T6Mf5Bm0sB2a/F2Ze448gotn+OA3zfwe74OAbsPs3fiMx3Oc3QFMW+OEb3oLdq/wKmj/Fh3k84dvQ0+hrmrrEb5yKqqFlG7Tt8q8XT/MBWrUUZrzLh65zR4eoc35j1PAWNG/xYX3gdWjcAP0dvj4Ai0LJdH8EVH6BD+XeFtj8c2h8a/S2x/OgbJbvP2/SkbY2bjh+/1d8Blbc5duZTvuaWrb6+tZ938/3ZZ+Emkth8ny/4RnogIYNsH+NXx7TLoPqFWD40KxcBFkJP410yk+/bbfvr2DKCVYa/Pxo3QmtO/wwkSjkT/Y7AuDXn23/CV0HfC2Vi+CS/wpTFx89nqE+35bSmcdu8Pe+BC/+E+x8DlKDR+Z31VJY/HFY+rsnDqZ06sTde5qgabNfl1PDkFMEhVOPzJOx6muDHc9CbxMM9foN91CfX2dnvxfm3AADXbDneb9u7n/V19vX6t9D+ZOgs96P61Cba6/OvE9O07an/PrS2wINb0JywL9vqpbB8k/CvFv9+/hkDfX6dX3vi/59OvMauPj2k9sI97bAq//il39vi59ffS3+/TXtUlj6ezDvFr8BOg0K+BNJp/1eU2+z3wj0tUF/25G//R0+QIb6ID3sV0SXgkjMh35RtT8aKK7xf4tq/GuJUv+mevM/YNuvoHPfiesorPLDxHL9G6Rps1/Byy6A2dfBwg9D5eIjK71zPqg2PQH7XvJB1L7bh3rlYv+mb9/jg6m3KTORzMdCsVxfe3oYBnt8e0YqmubDL6/ch0J2oZ8/rduhZQe07fRvGICpS2HuTT7EZ17jN2j97VD3Kux50Q/TugN6WyE73x8xTb/Sjz+32O/59zT5DdL+NbD5CT/ekhl+nh+uHR8W0Sx/RMYo620sx29I3j6vIzGYstB3b9roQ+WQKQt94A31+nE6d+RvT6N/vJNJ82HyRX6d2f+KP8I69Fo66TcQjRuPrDdVy/x6khzwG6SD6/0GetFHYcrFEIn7I87tT/twKpnhN1jRLOg+AMMDfj04NHxPo994T13qX2/d6Xdgcot9O1q3gxvlw/icYr/eFU6FwkofgIfzwPlhXOZvV73feSiY6tf1eK5vW/te2Lv66HUomuXXL5fOHP0V+rA/tM6UX+iXd06Rb3dPox+nS/sdnYEOvw6O3ADlT/Ibx0jcz8NI1P8d7PZt7dzvN7I5Rb7mrrojR+lTFvjhe5r8xrhtlz9iL5udCdwWv27Ec6F01pFaug76+d1Z59d/8PVG477minn+fRmN+w2yS/k2HljvN16RmN9ha3jLb1jLZvv3VKLMP+IJ2PoLX39BJdzzxmmFvAJ+vDnnw/fgm34lGDnP47k+9EpnHrvHcuhTmTMx7d2/8StSJAbDvT7QInEfuokyvzc8ab4PhXfas0mnofugD/8ztRd4yME3YfuvoGmTD4FZ16Px1fEAAAdMSURBVPnaymYfmdZQLzRu8mE93O/f1GWz/ZFMNJ45Mtvm36QW8aFbv9bXXVrr9w5LZsDu52HXSh9UWXn4o6rIkSOsRKkf76FHaa0fZ0+T3/g7/AY1f8Q51IFOWP/vsOUXPnQs6vupWgqTL/ZHMPvXQHeDf4Nn58P83/F76Vl5R88L52Dj4/DGw5kNRBoKJkNWvu8WjUNRlT+iOLDeH02kk/6oqXi6D8pI1Afq9Ct9GMay/Qa4q94H4sjHoaMHMutcJJqZHxEfbJPm+rZ37IPkoF+XcktgzvV+I186y7fh0PqTSsKOZ2Dbkz70F37E13aiU5bDA/Dmw/5o5tBG3Lkjpz/Tqcwj6R/xXD9/i6f5DdVAl19+U5f4I723n85Lp/369fqPfLgnSn34p4b9jlXzNh/qmB9fwZQjO241K/yRsJkfx88/5+fj25XU+p28dNLPo6lL/PItv+DYftNp2PUcNG+Fy//o+PPlBBTwIiJnWjrtN4qpYb/jZpmjijO90/MOThTw4fqik4jI2RKJQCTXH0Wco3StoIhISCngRURCSgEvIhJSCngRkZBSwIuIhJQCXkQkpAINeDO73sy2mtkOM7svyGmJiMjRAgt4M4sC3wBuAC4CPmpmFwU1PREROVqQe/ArgB3OuV3OuSHgYeC2AKcnIiIjBPlN1ipg5M8c1QGXvr0nM7sbuDvzb4+ZbT3N6ZUDx7mlY2ipzecHtfn8cLptnn68DuN+qwLn3P3A/WMdj5mtPd79GMJKbT4/qM3nhyDaHOQpmnqgZsT/1ZnXRETkLAgy4F8FLjCzWjPLAj4CPBHg9EREZITATtE455Jm9sfAU0AUeMA5tzGo6XEGTvNMQGrz+UFtPj+c8TafU/eDFxGRM0ffZBURCSkFvIhISE34gD9fbodgZnvM7C0zW29mazOvlZrZ02a2PfO3ZLzrHCsze8DMmsxsw4jXRm2neV/LLPs3zWzp+FV++o7T5i+ZWX1mea83sxtHdPtips1bzey3x6fqsTGzGjN7zsw2mdlGM7sn83pol/UJ2hzcsnbOTdgH/sPbncBMIAt4A7hovOsKqK17gPK3vfZ3wH2Z5/cBfzvedZ6Bdl4NLAU2vFM7gRuBJ/G/En0ZsGa86z+Dbf4S8Cej9HtRZj3PBmoz6390vNtwGm2uBJZmnhcA2zJtC+2yPkGbA1vWE30P/ny/HcJtwA8yz38AvH8cazkjnHPPA21ve/l47bwN+KHzXgaKzazy7FR65hynzcdzG/Cwc27QObcb2IF/H0wozrmDzrnXMs+7gc34b7+HdlmfoM3HM+ZlPdEDfrTbIZxohk1kDviVma3L3N4BYLJz7mDmeQMweXxKC9zx2hn25f/HmdMRD4w4/Ra6NpvZDGAJsIbzZFm/rc0Q0LKe6AF/PrnKObcUf3fOT5vZ1SM7On9MF/prXs+XdgLfAmYBi4GDwP8Z33KCYWb5wGPAvc65rpHdwrqsR2lzYMt6ogf8eXM7BOdcfeZvE/A4/lCt8dBhauZv0/hVGKjjtTO0y9851+icSznn0sB3OXJoHpo2m1kcH3QPOud+knk51Mt6tDYHuawnesCfF7dDMLM8Mys49Bx4H7AB39bfy/T2e8BPx6fCwB2vnU8Av5u5wuIyoHPE4f2E9rbzy7+DX97g2/wRM8s2s1rgAuCVs13fWJmZAd8DNjvnvjqiU2iX9fHaHOiyHu9Pls/AJ9M34j+N3gn82XjXE1AbZ+I/TX8D2HionUAZ8CywHXgGKB3vWs9AWx/CH6YO4885fup47cRfUfGNzLJ/C1g+3vWfwTb/KNOmNzNv9MoR/f9Zps1bgRvGu/7TbPNV+NMvbwLrM48bw7ysT9DmwJa1blUgIhJSE/0UjYiIHIcCXkQkpBTwIiIhpYAXEQkpBbyISEgp4EXOADO7xsx+Pt51iIykgBcRCSkFvJxXzOxOM3slc9/t75hZ1Mx6zOwfM/foftbMKjL9LjazlzM3gXp8xL3JZ5vZM2b2hpm9ZmazMqPPN7NHzWyLmT2Y+eaiyLhRwMt5w8zmAXcAVzrnFgMp4ONAHrDWOTcfWAX8r8wgPwT+1Dm3EP9Nw0OvPwh8wzm3CLgC/y1U8HcHvBd/H++ZwJWBN0rkBGLjXYDIWXQdsAx4NbNznYu/mVUa+I9MP/8G/MTMioBi59yqzOs/AH6cuSdQlXPucQDn3ABAZnyvOOfqMv+vB2YALwTfLJHRKeDlfGLAD5xzXzzqRbO/eFt/p3v/jsERz1Po/SXjTKdo5HzyLPBBM5sEh3//czr+ffDBTD8fA15wznUC7Wb2rszrnwBWOf9LPHVm9v7MOLLNLHFWWyFykrSHIecN59wmM/tz/C9jRfB3b/w00AusyHRrwp+nB3+72m9nAnwX8MnM658AvmNmf5UZx4fOYjNETpruJinnPTPrcc7lj3cdImeaTtGIiISU9uBFREJKe/AiIiGlgBcRCSkFvIhISCngRURCSgEvIhJS/x/wLmTNT9vN/QAAAABJRU5ErkJggg==\n", 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\n", 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gAuBmYIZSKrz+Slrrt7TWI7XWI6Ojo495Z9rii49ySCAQQohaPBkIMoH4Wo/jnMtqywDmaK2rtdb7gD2YwOAZVh98sWOXQCCEEG6eDATrgd5KqR5KKV/gJmBOvXW+wWQDKKWiMKWiFE81yDVrqEYCgRBCuHksEGit7cB9wHxgJ/CZ1nq7UupJpdSVztXmA3lKqR3AEmCa1jrPU23CasMHh2QEQghRi8emjwJorecCc+ste7zWzxp40PnP8ywyWCyEEPW192DxiWX1xU/ZcTjkhDIhhHDxskBgA8BRIzenEUIIF68MBNpe1c4NEUKIk4eXBQJfAGokEAghhJtXBQLlYwKBo1oCgRBCuHhVILA6A4G9uqKdWyKEECcPrwoEPjYTCKqrJCMQQggXLwsEfgBUV1W2c0uEEOLk4V2BwFcCgRBC1NeiQKCUekApFaqMd5RSG5VSl3q6cW3N5g4EUhoSQgiXlmYEv9FaFwGXAhHArcAzHmuVh/g6A4HdLoPFQgjh0tJAoJz/TwRmaa2311p2ynBlBA7JCIQQwq2lgWCDUmoBJhDMV0qFAKfcBXuU1XUegYwRCCGES0uvPnonMAxI0VqXKaU6AHd4rlke4uMPQI2cRyCEEG4tzQjOAnZrrQuUUlOAvwKFnmuWh9hMIKC6vH3bIYQQJ5GWBoLXgTKl1FDgIWAv8IHHWuUpzoxAS0YghBBuLQ0EdudNZCYDr2qtpwMhnmuWh9gCAVD2snZuiBBCnDxaOkZQrJR6DDNt9FyllAWwea5ZHuIsDSm7DBYLIYRLSzOCG4FKzPkEB4E44H8ea5Wn+AQAoBwyRiCEEC4tCgTOg/9HQJhS6gqgQmt9Co4R+FGDwionlAkhhFtLLzFxA7AOuB64AVirlLrOkw3zCKWoVn5YHRIIhBDCpaVjBP8HjNJa5wAopaKBRcAXnmqYp1RbfLHWyBiBEEK4tHSMwOIKAk55rXjtScWu/PGRQCCEEG4tzQh+VErNB2Y7H98IzPVMkzzLbvXDJmMEQgjh1qJAoLWeppS6FjjHuegtrfXXnmuW5zis/tgkIxBCCLeWZgRorb8EvvRgW04Ih9Ufm65Ca41Sp9wFVIUQos01WedXShUrpYoa+VeslCpqbuNKqfFKqd1KqWSl1KONPH+7UipXKbXZ+e+u4/llWqLG6keAqqTSfspdPFUIITyiyYxAa33Ml5FQSlmB6cAlQAawXik1R2u9o96qn2qt7zvW/bRWjU8A/hyiotqBv816onYrhBAnLU/O/BkNJGutU7TWVcAnmGsVtS8ff/yppqJaMgIhhADPBoJYIL3W4wznsvquVUr9opT6QikV78H2AKB9AvCniopqh6d3JYQQp4T2PhfgO6C71noIsBB4v7GVlFJTlVKJSqnE3Nzc49qhsvnjryopl0AghBCAZwNBJlC7hx/nXOamtc7TWrvmcr4NjGhsQ1rrt7TWI7XWI6Ojo4+vVbZAZ2lIAoEQQoBnA8F6oLdSqodSyhe4CZhTewWlVJdaD68EdnqwPQBYbK7SkIwRCCEEtOI8gtbSWtuVUvcB8wErMFNrvV0p9SSQqLWeA9yvlLoSsAOHgds91R4X5RuATTmorJKzi4UQAjwYCAC01nOpdykKrfXjtX5+DHjMk22oz+pr7klQXSF3KRNCCGj/weITzsfP3K7SLoFACCEALwwEVlcgqJRAIIQQ4IWBwOYXBIC9srSdWyKEECcH7wsE/iYjcFTLfYuFEAK8MRA4M4JD+YXt3BIhhDg5eF0gsDhnDW1NzaamRrdza4QQov15XSDA5g9AVXkJm9Lz27kxQgjR/rwwEJgxgiBLNfO3Z7dzY4QQov15XyDwMRlBnw42ElMPt3NjhBCi/XlfILCZMYLYYE3KIZlCKoQQXhsIOgdCQVk1+aVV7dwgIYRoX94XCHxMIIj2N5ehTjlU0p6tEUKIdud9gcDqA7YgIq3m6qN7c6U8JITwbt4XCAACwgmhFJtVsU/GCYQQXs47A4F/GJbKQrp2CCQlV0pDQgjv5qWBIBwqCukZHUyKlIaEEF7OSwNBGFQUMCw+nKScEjakyRnGQgjv5Z2BICAcygu5/ezudAr144k523HIdYeEEF7KOwOBfxhUFBLk58NjE/qzNbOQhTsOtnerhBCiXXhvIKgshBoHk4bGEN8hgBkr9rV3q4QQol14aSAIN/9XFmG1KO48pwcb0vL5OfmQe5WcogqmfpBIVoHcwEYIcXrz0kAQZv4vLwDghlHxdIsM5L6PN5LqPK/gzeUpLNiRzRvL9h7TLg4UlvPYV1upqHa0SZOFEMJTvDMQBDgzggpzl7JAXx/eu2M0Gnjg080UlFXxybr9+FgUnydmHNP1iBbtyGb2uv1sdM5Imr4kmf/7emtb/QZ1aK3RWga7hRDHxjsDgSsjqChwL+oRFcQTkwayJb2AK1/9mdIqB/+7fgjl1Q6+3JjR6l2k55uS0o4DRQB8lpjOvG2ND0jnFFfw5Hc7qLLXtHo/AN9uzmLkPxdRVmU/ptcLIbyblwaCuhmBy+RhMZzXJ5qiimr+d90Qrk6II75DAJv2FzSykaalHy4DTCDIKa4gLa+Mw6VVlFY2PFjP33aQmT/v45eMAuZuPcCOrKJW7WvtvjzySqta/TohhACvDQSujKBuIFBK8c5tI1n7l4u4fmQ8AINjw9ia2fSN7u2OIz358ioHlXYH6fnOQJBVxIbUIyesuZbX5rovQnJOCQ9+tpkXF+1p1a+zJ9tcJuOXjKbb2dbKquzHVJLSWlNpP7XGTgrLq3nq+x0UllW3d1M8ZlXyIXYdPDk6E9uzCvlgdWp7N8NreGcgcI0RlDfs6dusFvx8rO7HA2PC2H+4rMEBoLiimo3787nr/UQGP7GAp77fwYOfbWb4Uwt58NMtpB82paHknBJW7c1zv861vDbXAPWyPblUVNewvRU9e601SdnFAGyrF7C01uQWV7Z4W61xqKSSEU8tYvHOnFa/9tP16Yz+12KPDaTnlVS2+QmCry/dyzsr9zH/ND3fpLC8minvrGX8iyta3RHxhBnLU3j82+1tHnhziitYm5LX/IptZPHObMY9t/SknzTi0UCglBqvlNqtlEpWSj3axHrXKqW0UmqkJ9vj5hsMytogI2jM4FiTPby4eA/TPt9CVkE5dkcNE15awTWvrWJFUi4ju0fwzsp9LNqRTVSIL4t3ZVNYXs2QuDDsNZpvNmdyRsdg4EjJqDbXFVCX7DYH1cyC8hYPUOcWV1JUYcpN9TOXOVuyGPP0Yra2IlPYlllYJ8M5mu1ZRZRXO9h5oPU9yK82ZlJYXk1WQTnPzNvFlvTWl96OZtfBIkb8cxGDn5jPvK0Hmly3qKKahz7bQk5RRZPr5RRX8N4qc55J7WBbaXfwwy8H2m2gfv72g/zp083UtEHQ27Q/nxoNHYJ8+W5L1jFv5/Wle1m0o+69wLXWvLhoD7PWpLV4O7843+eN6W17+ZfpPyVz68x1zXYUvt2cydxmPj8t8cPWA6TklpLRSCXgZOKxQKCUsgLTgQnAAOBmpdSARtYLAR4A1nqqLY00zn29oeYMcgaCd39O5fMNGVz6wnLeW5VKRn450y7ry7JpFzLrzjPZ+sSlbPn7pfz5sn5UVJsD6cTBXQCodtTwwEW9CbBZWbU3j4kvreDLDRnu51wDy67XAWzLOnLAWZuSR0FZ44HBVRYa3aMDybkldcYgZq7ch6NG89aKFAB+Tj7U5Jc8KbuYK15Zyex1+5t9X1xZyIFmDqL1HSqpJDHN3Cs6OaeEN5bt5etNma3aRlPWO8twCvMldCmrsnPX++vrjKOsTDrElxszeGdl0ycTfrEhg4rqGrpHBtYpv327KYt7P954zCW5KnuN+308Fp8nZvD1pkyW7ml9VlbfxrR8LMqMk6XmlR3TxAWtNS8vTuLVJcl1lj8zbxcvLkri6bk7KapovodfXFHt7hxtbOPrgO3OLqbKXsOBwqbPD3p+4Z4mp45rrVm2J7fZgJLo/DweKDTfk2pHTaOveXlxEvfM2tBc8z3GkxnBaCBZa52ita4CPgEmN7LeU8B/gNYdUY6X8zITzekQ5EtseAD+Ngsf/GY0SsE/f9hJVLAvU8/rSecwfwBC/G0opTizZwf3a8/pFcW3957D2scudp/BvGhnNjsOFPHQ51v4bH066YfLcNRoOob4ARAV7AvAtkxzwEo9VMpNM9bw+tLGP5RJOeZAck1CLFofmaX0S0YBWzIKiQ0PYO7WAyRlF/PAJ5t59MtfjpqmLtxpenJLducCJgDd8e468koalpf2OA9gBwuP/NmSc4q56LmlJOc0fnB78NPN3PrOOlzfg83OTKC53lK1o4Y1KXkt6nlvzywkPNDGeX2i2ZJxJNCvSs5j0c4cViTlupe5evefJqY3mbpvTMunZ3QQF/fvxI4DRVQ7MyZXBnYsWRHAi4v2cNmLy92lwdbQWrvfvzeXpRzT/mvbuL+A/l1CGRoXjqNGk5rX+jblFldSXu3gl4wCd8flYGEFby5P4exekZRVOfh6Y/NBf3tWEVqD1aKavCBkUnZxo5MvmpKcY36v/fUy8z3Zxdzx7jqumv4zO7KKSMsra/Jk0vWp+dw2cx2Ld2YfdZ2cogr3fg4UVKC15lcz1nL/7E111tNa8+n6dH7cfrDRisGJ4MlAEAuk13qc4VzmppQaDsRrrX9oakNKqalKqUSlVGJubm5Tq7ZcQASUHW7Rqo9N7MdLNyVwXp9oHrykDwBXJ8RiszZ8+zqG+NMrOgiA+A4BDI0PJyzQZh5HBAJwdq9IBnQJ5YM1qe4v3EX9OwKQ0DWC+A4BrEnJo6TSzkdr09Aa1qU2bGtppZ3le3IJC7Bxft9o4MhB6b2fUwn0tfLuHaPwtVq4+rVVHCqppLTKwfI9uY0eVH9y1vtX782j0u7gtaV7WbI7l99/tLHBgTIpx2Qitb8szy/cw97cUuZtbVhHtztqmLMli50HiugUaoLekUBw9C9cdlEF172+ipveWsN3v5gefkpuyVHT9u1ZRQyMCWVYfDjph8vdQcwVAA7UClzbs4oIsFkpKKtmxFMLefjzLQ22p7Vm0/4ChneNYHBcGFX2GncQdAWCXQeLKSiratUAeGmlnQ/XpFGj4eNmMrD//LiL385KrFMCOlBYwaGSSnpFB7F23+HjusGSo0azaX8+I7pFuEuYyTkl7M0t4em5OxsN7LsPFjeYrpyaZw5iNRpeXJTEY19tZd4283f6x5UDGRIXxvurU5utl7tKmeMHdWZLekGjpcqyKjtXvLKSN5ensDLpEP/8fkezv2dhWTWHnJ+H+gfcbzdnsmxPLpvTC3h+oRkjOVRSRWF5NV9tzGjwfXGVM3cfPHpGl1griGUVljN/+0HWpR5u8F1OzSsj0/k9mr1uP0/P3dlsxtLW2m2wWCllAZ4HHmpuXa31W1rrkVrrkdHR0W3TgNAYKGpZSeKKITFcNrAzALeO6cbfrhjAb8/vddT1z+sTTccQP8ICbHWWx3cwgeCqhFiuHxnHtswi5m8zPYoL+5pA0K9zCKO6dWDZnlxG/nMhH63dj0WZ3uv2rEIe/GwzFz+/jGFPLmDQE/NZsjuXm0bF0znUn/BAGzsPFJN+uIxvt2Rx8+iu9OkUwlu/HkGVo4axZ0QRFmDj2QW7Gf3vxWxIO/KBPFxaxcb9+QyODaO82sGC7dmsTD7E0Lgw1u47zOUvryA5p5hXFicx7tmlJDlLUgedpSEz9dUEgJW1LtXhcqCwAnuN5s/j+zLnvrH42yzuL1P64bIGX7Rfz1zH60v38vaKFHYcKCI80MYPv5iy1kuLk3jgk00NDhDVjhp2HyxmYEwYw+LNhIDEtHwOlVSywtkm1xdMa832rEImDu7CHed0Z2BMGF9syGB7Vt0sMf1wOXmlVSR0DWdInNnmb95bzz+/3+EOujuyirj85ZVM/WCD++S+5nr5nyemU1Rhp3fHYD5vIiNZt+8wry/dy/zt2bzyUzL/m7+LvJJK93t3+9ndAVpUYtqWWci455Zy6QvL2LQ/3/0+7DxQRGmVg+FdI+gVHYxSMGt1Gpc8v4w3l6dw84y1pB4qZd+hUl5ctIeSSjuTXl3JE3O219l+mrNTY7Uo3luVymHzgc0AACAASURBVOx1+/nvj7vpFhnIGR2D+ePFvUnJLeUvX291/70/XJPGVdN/5o+fbKqTacWGB3DF4C6UVjm44731HK43ZrZ5fwGV9hp2HSjiiw3pvL1yHyXNZAfJuUfeo/oZQVJ2CT2jg+ndMZjFu4708mcsT+HBz7awqd44lqsT4OoQfZaYzs1vranzOV6fehh/m4WIQBsHCip4boEJMLnFlXV+n5XOTkqnUD9eW7qXN5encPcHiSd0gNmTgSATiK/1OM65zCUEGAQsVUqlAmOAOSdswDgsHgozoJUDfT5WC3eO7UFUsN9R1/nzZf2Yc99YlFJ1lid0DScq2JfLBnZm0tAYrBbFp4npxEUEcM4ZUQyNC2Ncv448fe1gPrzzTCYO6oKjRnPP+b2odmhumbGWBduz6R4ZxKQhMdw/rjdf/f5sHpvYH6UU/TqHsOtgEW8tT8Gi4O5zewJwbu9oFv3pfN64dQSXDujEnuwScosreXruLvcH99vNmdRo+MvE/vhYFH93Xpr7v9cNZdadozlcWsVjX23lnZ/3kXKolJJKO13C/Ckoq2ZNSh6/nrmO6BA/bh4dz8b9+Xy6fj8Ld2STXVTB9CXJJDvvBDe8awSdQv3pFOpPaZX5oJdWOUhMy+fdn/eRV1JJck4Jy/fk8tHaNJbvOcToHh24algsS3fnUlppJzE1n2qHrpNJzNt6gEe++IUqRw0DY0IZFBuGRcEfZm/i7Kd/ct+AyJUR5BRXcqikisGxofx90kBm3DaSEH8fXlqURLWjhse+2srIfy7i7g8Szd8uPoJuHQIZ168j/jYrb6/cR6W9hlB/H9alHiazoJxle3L53/zd/O7DjVzw7FJ+rHcCYbnz903LK+W5BXs4s0cH/jF5IPll1bzyUxLFFdV1vvw1NZrHv91GbHgAw+LDeWHRHqYv2ctDn5sDk6/VwvhBZhwqNa+Ua177mf/N3wXAhrR8/jB7U52y28uLk8gtriQtr4xvN2fxxJzt3PDmapY7D0Rn94okwNdKXEQAq1Py6BzqzydTx1DtqOEvX2/l2fm7eXFREh+vTaPKXsPXmzLrZIRpeWX4WBTjB3YmKtiXYfHhlFc7uLh/J5RSjOvXifsv6s1XGzNZt+8wRRXV/GfeLjILyvlmcxYLtmejtWZDWj5D48O4bGBnHr9iACuTD/HeqlTADGpPX5Ls7lXvO1TqPhjva+QmUzU1mjlbsqiy15DsXM/Px8L+erP3knJK6N0xmLN6RdY5JCzeZbJk12tdXGN4rn1/tHY/q1PySMs78n7vPFDEgC6hxHcIZMP+fJJySrjYmfnXnqa7bM8h4iICuOOcHgD8+qxubM8q4rGvtp6wiQg+Htz2eqC3UqoHJgDcBNzielJrXQhEuR4rpZYCD2utEz3YpiPC46GqxAwYB0S06aYDfK0E+FobLJ88LJZJQ2KwWEyAuGtsDw4UVjDtsr4E+fnw7X1j3euO7R3F2N5RPHfDUArLq3lt6V4Ky82Jbq5zHOrr1zmUT9enk5RdwqShMe7xC4CukSYbuf+i3nSLDMTfZuWfP+xkxooUrkqI5dWfkjmzRwfG9OzA368cyMuLkxjRLYK+nUPo2zmEP4zrzZPO9LtndBApuaWc1zuaTxPT+dOnmwmwWfl06lmk5pUye106j3y5FatFERPuT/rhcq4YYg5Y3Zzt6BTiX+dL86dPN5ORX87/5u9mvDP7ch3orxnej4SuEby3KpVZa9LcafS+vFK6R5ky3Nsr97nryQNjQgny82FgTBgpuSUkdI1g3b7DDOsa7i6huMYHBjonA4QF2Lj97O68uiSZD1anMXvdfs7s0YG1+w4T6GulT6dgLBbFzNtHkZFfxvn/W4qjRjNpaAwfrd2Pv83C0LhwXlu6Fx+LIsTfh4/WpjF+kPld5m8/yG9nbWBIXBhZBRUoBc/dMJS4iEBuHBnP9CV7mbF8H2N7RzHz9lEA/LQrh10Hi3nhxqEMi4/g640Z2KwWnlu4h2V7chkSF050iB8RgTa2ZhaxcX8BeaVVDO8awd0fJFKjTUnu9SkjSMktYeHObO678Aw27S9g1d5D5BRXUlBWTUZ+OQO6hNIx1HxezogOJv1wOXee25MxPSO594Iz+NfcnTg/try5LAWlTB/qreUpPHHlQMAEo9iIAP53/RAqq2vILCjnV2+vZfKwGPff+Z7ze/L2ihS+2ZxJYlo+xZV2Pr57DL/7aAMfrE5lUGwomQXl3HN+TywWxW/G9uDbLVmsTMqlV3QQD322BXuNdmfbaXllWJzd2ZRDJQyOC6vznViTksf9szfxzDWD2Ztbgq+PheFdI+pkBBXVDtLySpk0NIYBXUL4YHUaseEBZBaUu7O+2ncyLKm0s+9QKb5WCym5JWQXVbgztMS0fPdnMjmnlHH9oikoq2aBcybVLWd2ZdHOHHYfLGZMj0jeWpHCop3Z3Dm2B1PP7cllAzvTIyqI6GA/nlu4h2Hx4dzmzPo8yWOBQGttV0rdB8wHrMBMrfV2pdSTQKLWeo6n9t0iYXHm/8KMNg8ETXEFAYDHJvZvdn2lFOGBvvTrHEKVvYarE2KPum7/LiGUO3uU1w2Pa3Sd+A6B3DeuN1X2GhbsyObfc3fx77mmFzljQj+UUtw6phu/Gt2V2n2RW87syhvL9hIeaGPm7aN4Z+U+zu9jAsGBwgp+f0EvukYG0jHUj8ggX0Z0iyAjv9xdT1+4IxtfHwudQszBpqNznCDU34eiCjsZ+eWM69eRzekFfLUpk+6RgaQdLkNrk9H07RxC1w6B7votOM+/6GvKG3sOFjOiWwSjunegZ5Spc79x6wgU0CXMZB9vr0hhfephquw1LN2di6/VQv8uoe7t3TgqnleXJPPMvJ1Eh/jx0V1nMnt9OpXVDnxqjQfFRQQyaUgXftqVw8TBXfho7X7G9evIyzclkJpXRkSgjQ9Wp/HyT0lk5JcRGx7Aa0uS6RTqh81qYVT3CH4ztgdxzjGjf0weSIXdQUpuKcv25FJQVkV4oC9vLNtrSiRDYrBZLTx4aV+01oQG2DhUUsmlA0yQ6RYZxBJnzzUtr4xn5u0iJjyAy4d04c1lKazae4gP16Rhs1r49Vnd8fPZz7MLjryPBwor6nyuErpGsDWziBtHmQ7HzWd25dUlyRRVVNMp1I/sokr6dwkloWs4H6xO5cphMQzvGkFaXhndIoMI9PUh0BcignzZ8vdL63z+An19uGxgZ77fYsYOzu8TzeC4MKaM6cYz83bxtvNy8Gf1cvcROa93FNOXJJP23Q4GxYZxuLSK/YfLCPbzMeUgZxK1t5GMwHVOzvztB7HXaHpGBdE9Koj52w9SU6N55MtfCPS1UqOhT6dgzuwRiVJmzM41hmO2XcLindlEBPlid2i0hnH9OvLj9oPMWm2mxZrB7cNcNyLOPR7RKzrYnYX6+lgYe0Y0HYJ8WbQzm0/Xp7PrYDGXD+7Cn8f3xWJR9HAGkXsvPIOfdufwxYYMbju7O2VVdgJ9Pddv92RGgNZ6LjC33rLHj7LuBZ5sSwOuQFCQDp0Hn9BdH4s3pozAx6rqHJDq69fZHNQ6hfpxZs/IJrfn62Ph06ljWJNymA1ph+kQ5MfwrkcCYu2ABeBvszJ76hh8rRbiIgL5+6SBdQYoz+0d7V5vxSMXEmCzUlJpJ7uogqmzNpCSW8oZHYPd2+3k7H2O6t7BnX5fPyKOiYO78PDnW7h2eBwrkg6xL6+U/l1CUErxwEW9eejzLQT6WrEo5a7DZxVWUFxp5+qEWKaM6eZuU2x4gPvnYD8fuoT5o7WZIfLFhgyuHBZDsN+Rr0BcRCBjz4hiRdIhrk6Ixcdq4dZa26vtX1cPJre4kqgQP/p1DmHKmG74WC3uwdbrR8bx8k9JvLU8hUlDY9iSUchTVw1qdHv+Nisv3ZTAlvQCJk//mcU7cwgPtJGYls8/rhxYZ1KCUqpBD7FHVJB74B1MueL+cWfwuwvO4MdtB7nj3fVU2mv48/i+RIf4Mcb52fCxKMb0jGRl8iEucI5RgTkI3Tm2B0HO9ybYz4fHrxhARn45heXVzPx5H6O6R5jp07tzefizLcz747mk5pWS0DW80fertqsSYvl6UyZdwvz59zXmu3fTqHjeWLaXWWvS6Bji555wAeaz9cpPyeSVVvHKzQnsPVTK377ZxqShXZi97sh8lBRn+TEjv4zQABuh/jb3mM+KpEM4tOZ35/cixN/G4dIqXv4pic83HLmOWJ9OIUQE+TLz9lEM7BLKwh3Z7oP4jqwifv/RRrQ2GbHVorh6eCw/bj/IB6tTiQnzp3enEPd0UVcp9IyOwe4O1aCYUHx9LPTtFMLPyXlEBNp48cZhXDk0psH3zWJRDIkN46uNmaxJyeOmt9Zw6YBOPHxZX/p0Cmn2PW4tjwaCk1qYs7xS2PoLyrUHV7rZlD6dQgiwWbk6IQ5rvQ9WY5RSnNUrkrN6NR00XHpFB9d53Nl5MA/0tTK825EDgKvnEuJvI8TfRkJ8BCm5pXRzDpYD7plDfTqHsGF/PsUVds4+I4pQfx/8bRbO7xPNpKExlFTa3WMtVyXE8ubyvcRHBJJdXMGm9AImvLSCc3ub3mPfzk1/QbqEmcDw4qI9lFc7+I2zJlvbrWO6sSYlj+tHNJ5RuQT5+bgPlD/+8bwGz8dFBHLbWd15b1Uq323JIirYl2uHHz2bAxgSF0bnUH8+S0wnq7CcMzoGc/Pork2+Bo6U2yKDfLFZLRwsqmByQiwBvlZm3z2GKe+sJSrIj6nOMaMhceH420yJ5NEJ/fhwTRrDax3ArRbl/t1crnW+H0t35zgDQQdC/G38+5rB3DZzHQ99toXiCjvdIpv/nI49I4q/Xt6fi/p3cgfr8EBf/jKhP3/+8hfO7hVZZ3wtoWs4wX4+9IgK4qxekYzs3gGF6ZG7AsHQ+HBSckvRWnP9G6vpGOLHF787mx0HiogO8SO3uJLwQBu/Pb+X+8ziFxcluS8h42NRdHe23TVxIyY8gAOFFfTvEuouEcWE+ZNdVMGrNye4A2pplYO/XTGA7KIKnl1gMrq9OUcCgWsQe6hzAsPguDDWpx7m9Skj3Nto/O8aRHGlnfnbzVjT6r15bM0olEDQpgKjwOoHhenNr3uKCPC1Mv+P59Ep7OgD2W29vw5BvgyNC6tzWY76ErqG8+XGDPc4BRzJCGLC/OndMRirRbnrvlcMMTXlEP+6s66sFsUXvzsbi1I8+uUvfO+cTrrbOfDW3Beki3PMZNHOHDOFNya0wTqXDuzMpscvrZMpHKtHJ/RjTUoeZVUOZt4+stnUXinF5UO68M7KffhYFLPuPBNfn+bnc7jKCQNiQhkSF8auA8XuoB0THsACZ6ByZZO+PhZevimB2IgABsaE8cy1Q1r8O53fJ5q3bh3BRf07uR9fNrAT3/9ygN4dg7mq1njA0VgtirucQam260bEkZFfxsUDOtVZbrNamPHrkXQM9UMpha+PYsqYbqZM5u/jrPuH88m6dJJySjhQWMGBwgqeXbCbvbml3HN+T9an5nP9iDjCAmxc1L8T794xir05JVyVEMu/fthJ+uGyBu91THgAG9LymTCoMzsPFBHi78NPD18AmCwO4KmrBtG7YzBjekayJb2AZxfsYc6WLDLzy/H1MdlznnOGUIIz4/7DuDO4fkQcvZv5vLr+rgu2ZxPfIYDv7hvbJp/LxnhvILBYTHnoNAoEQJ2D7Ynw0k3DiKlVgmmMq+TUo1ZW43pNXIdAXr1lOBbVfAYDEOoMDq5tWS0KR40mJsy/wXTd+rrUauedYxtmAy5t9WXzt1n55t5zsFpUo+ecNObP4/tydUIsUcF+dQb7m+LqhQ+ICWXaZf0aPN9YOfFS54B8aymlGrz2qcmDOKNjMHeN7UlEkO8xbRdMOeTBS/s2+lxjWatSioExYfjZLPSMDq5zyfize0W6T7QbHBtW532xWhQX9u3o7vk/e/3QRmfnDIkNY3N6PmN7R/H8wj1c0LejOwC41C71DY0PZ1h8OO/+nEqPqCB6RpkSUkJ8OG9MGeGeMeTKlJvjyvQyC8q5uH9HwgOP/b1tjvcGAnAGglOjNHSyco0NNKV/lxBeumkY4/odqUOP7BbBG1NGcF7v6BaVsepz1eL/MrE/T32/gz7NlIXAHOBD/HyICvFzHwQ8rf6Bozl+Plb3ZU1aqm+nEIZ3DXcPHp9oHUP9Gw1AJ8KrtyRgUYq80iqUMpdV6RLmz9u3jeTq6avYnV3MgC5Nv5/m89fwM3jXuT2445zuVDlq6NsphBuPMluvtjvO6c4Dn2xm3yEzCwlMwHLNHmuNuIhAd0fHE+Wg2rw8EMRD8sL2bsVpTynF5GGxDZYdy5fDZcKgLkT8xpdze0dxuLTSfbJXcx6d2I8eUUENBudOZQG+Vr76/Tnt3Yx2Eek8nyciyJerhplB6LN6RhLo68O7d4xi6e5c4js0nbEejVLKPUFj/p8ajgM1ZuLgLmxOLyDEz4frRjQfOJri62MhNjyA/YfLmh3/Ol7eHQiiesPmD82lJgI7NL++OGn4+lg4r4/JRlrTG/3VmY3PAhKnvj9e3JuFO7LdYwwx4QHccmbzg+1tyWa18PdJA9tse90iA9l/uMzjGYF33o/ApYtzkOygZ+4lLIQ4cbpFBrHp8UvcV/09HfSKDsZmVfSMbn421vHw7oygc61A0PP89m2LEOK4tXRQ/lTxuwt6ccmATk3OymsL3h0IgqIgJAYO/tLeLRFCiAZc1+XytNMrfB6LzoOlNCSE8GoSCLoMgdzdUH1i74sjhBAnCwkEMQmgHZB5Yi56KoQQJxsJBD3OA4sN9sxv75YIIUS7kEDgFwLdz5FAIITwWhIIAPqMh0O74fDx3wRcCCFONRIIwAQCgC2ftG87hBCiHUggAOjQA/pdAWvegPKC5tcXQojTiAQCl/P/DJWFsOb19m6JEEKcUBIIXLoMhQGTYdXLUJTV3q0RQogTRgJBbZc8CTV2mPdnqHG0d2uEEOKEkEBQW0R3uPD/YOd38PltcraxEMIrSCCob+wf4bKnTTD46DqoLGnvFgkhhEdJIGjMWb+Ha2ZA2s/w/R+hkfuZCiHE6cK7L0PdlCE3QH4aLPkn9Dgfht/a3i1qGUc1LPkX7J4HAR1gypfge2JvaC+EOLVIRtCUcx+CbmNhwV+hJLe9W9M8reGTW2DlCxDcEfavgoV/a+9W1VXjgKqy9m6FEKIWCQRNsVjgihegqhS+e6BtZxJpDYufglWvtN02036GpAVw8T/gtu/grPtg/dtwcFvb7eN4ffM7eDoW3r4E8lPbuzVCCCQQNC+6j5lWuvsH+P5P4LAf/za1htXTYcWzJttIWnT82wTYOAv8QmH0VPN47INg8YGtn7fN9o9X2WHY9hXEnwmH9phgUJB+9PUri836v5wk7RfiNOXRQKCUGq+U2q2USlZKPdrI8/copbYqpTYrpVYqpQZ4sj3H7KzfmzLRxvdh1lXHVyYqSIcZF8KC/4M+E6DjAJjzh+PPNioKYcc3MPi6I2MCQZHQaxxs+xJqao5v+21h6+dQUw0Tn4Xf/AhlhyBxZuPr1tTArGvgizvgq7sg9ecT29aTXWGmCaxCtAGPBQKllBWYDkwABgA3N3Kg/1hrPVhrPQz4L/C8p9pz3C56HK56HTLWw5vnwc7vWz+byFFtDmyHkuHy5+H690yAKc4y2z0eu+eBvQKG3lJ3+aDroDAd0tcc3/abkp8Gq1+D9e8cPeCU58O6GeYM7s6DoGN/OOMS2DK78Sxr84eQsc5M5Q3qCMv+YzKn4oNNt6Uk1wSQpjKNU5mjGmbfAi8MgHculfGWtlRVBvbK9m7FESU5J+wqB57MCEYDyVrrFK11FfAJMLn2ClrroloPg4CTe57msFvgzoXgFwyf/sp8EfcuaVlAOJQM719pDviTX4FRd4LNH3pfYm6Ms+uH42vb7rkQ3BliR9Rd3u9y8A2GTR82fE1blLkK0uGdS2D+Y/DDg7Do7w3Xqa6A9ydBQRqMe/zI8oQpUHwAkuuVxuyVZvwkfgyM+R2cdS/sWwYfXQuvjoJdc4/enj3zYO9i2Dnn+H+3k1HiTFOmHPYryEtq/P0WrbfhPfh3F3imGyQtbO/WmIA/czy8MAjm/5/Hd+fJQBAL1O6WZTiX1aGUulcptReTEdzf2IaUUlOVUolKqcTc3HaevdNlCPxuNUx6yfS0Z10Fr58D6euO/prig/D+FZCzAya/BgOvPvKcfxh0H2sO5MfKXgnJi6HvBDPAXZtfMAy+3pSHyvPNsvIC+ORX5oM/98/Hdp6E1iYDmDkeqsvht8th1F3mWk0//sV8kF2SF8HBrSaj6n3xkeV9xkNoHCz+B9irjizfMQdKc+D8aaCUGfMY91e48UMI7gRL/330dqWtqvv/yaI0z3yhFz8JxdnHto3yfDM1uOcFMHk6jLrbTAY4nQfdqytg4d8hZ6fpAPz4GKQsa9t9VJXBkn9D5yEQ3tVMDKksbtt9tNaG9+DwXogbCatf9fjfuN0Hi7XW07XWvYBHgL8eZZ23tNYjtdYjo6OjT2wDG2P1gRG3w/2bzIG9sshkBx9cBd/cC9u/PrJuZbGZ0llRCLf/AAm/ari9fpdDXjIc+KXp/VYUQupKc1B/41zYt9ws37ccqkqg78TGXzfyDlM2mnM/ZG40vcpd30O3s2Hdm7Diuda/B/uWmwwgtIs5V6HLUBj/H3PQXjPdfJlcAWbnd+Afbi7qV5uPL1z+nAmQS58+snz9DOjQE3qOM499A+G8adB/Egy40hwUqssbb5drLCFtVRNlqoITf2HBdW+ZL/TKF+Dz249tTGj3j+YzMO5vJkCO/SOgTDA43WRthuXPwvL/ws8vwofXmfdtzWvwwWSTYbeF/FTzOS7JhvHPmABblGVKncfDXnVsGXd1ucmGFz0B3c81nScwf3sP8mQgyATiaz2Ocy47mk+AqzzYnrZnCzAH9t+vhgsehYL9Jm3//A5zb4Md38KH15oP9TUzTG28MYOvA1tQ81NJf3gI3rscvvgNHPzF9OYd1bD0GQiMMvdfbkyXoTDyN+Z2nO9dDmvfMB+yKV/D4Bvgp6dg00et+93Xvw0BEWaaavxos8zqAxP/B+f9GTZ/BBs/MF+I3fNMkLLaGm6n73gYNgVWPm96yxveg/S1prdbP7sBiEkwFwZsbEpswX4o3G9+3/LDkLur4TrlBWaw/p3LTtwAutbwy6fm7zP5NXN+x9o3W7+dvYshKBpihpvHYXEmOG78wExxPtXYq8znaPPHdZdrDXPuM5/LFc+ZWWYlB825MfesBDRs/6pt2jDravO3GXGHuWVt/Cjoehbs+u7Yt5m2Cp7vZwJMa5TmwbsTze/ca5wJSpG9ILqfOa54kCcDwXqgt1Kqh1LKF7gJqFO4VUr1rvXwciDJg+3xHL8QEwju3wgP7oSuY+DHR+CzX5ve6zVvQf8rjv76gAiTYWz70hzMGlN80GQag66FGz+Ca96G3J3mg5OZCJf9y4w5HM0VL8ADW8AWaHo/o50H2snTzZnTPzzYslko5fnOjOIHSLjVBMP6LnjUHPQW/M180SsLTU/+aCa9ZILBiudMJtHzAtO+xrgOglmbGj6XvNj8f9408//exQ3X+fZec0vSwv3mgNzWtnzacLA6Yz3k74MhN8HQm8w4zrYvW7fdmhrY+xP0uqhugEy41WQJTZUmTyblBbD1C/jpX/DKcNO5+f7Buhle8mJTShw2BXpfBjd8AL+ZD3fMg86DzYF6WxsEguJs81m45CmY9OKR5X3Hm/0fy4SD4mwTXMoLzB0PWzOza91bcGAz3PQx3DgLIro52zPBZLqu0q4HeCwQaK3twH3AfGAn8JnWertS6kmllOuocJ9SartSajPwIHCbp9pzwtgC4Ndz4M5FcNdPMG2v6fE356zfm3R/9fTGn9/wvukJX/h/JqgMuhaG3AiFGWam0JAbm99HaBfzARt115Eyko8vXPZvUzra3ExWUOOAj28y51MERh79YG2xmimiVSVmEDl2BJxxcePrgskkJr8Kt34NZ94D17/fePYAEBpjZhFlbay7vDDDpNNdhkHfy6HbObDsv3UDa8YGUxI79yETELd+0fTvu/bN1tWj178DX081AWjmeNi/xmRsPz1l9jfgSvM37jXOtL+isOXbPrAZyvIavo9xzskB9d+Pk9GuH+CFgfDlnabk06GHCdr2cti3wqxTmGk+M6GxpvPyq88gpLOplYc7CwwDrzGdoJydx9eeg85SbExC3eWu78aeYyjH7HHO3rvqNXBUmmyjpVJXmkDXr16Jt+9E0I4jHR0P8OgYgdZ6rta6j9a6l9b6X85lj2ut5zh/fkBrPVBrPUxrfaHWersn23PC+PiaFDNuhPm5JcLizKDuxg8a9iIOp5iyUe/LTKoIpld4zVvw0E64+nVzgGmJbmebunztA23nQaaX1dT0TzAHxvQ1pgf/4E4zsHY00X3hnPshLB5umHX0A7uL6wA54T8QEN70ejEJZqzDpbrCWXe3w3UzzXtz1WumxPDtvUfGKlY+b8Yqxv7JfLm2f222M+saM45RW/Z2c1+KeS0cTE9eBHMfNgPgdzm/sDMvgxcHm/GUic+azBFMtqRrIG11w+1kboS1bzVcnrLE/N/rwrrLAyLMeEpjGVJruIL8iufg8D7ISDy+7TVmydMQ0sW8P49lmrLiedNMWXTPj6am/u4Ek/1e/ebRvzuu7Lr+bLPWOrDZ/N95cN3lUb0h8oxjCwS755nvxZAbTQeoftnraKorTObY/dyGz8WONCXB45lQ0ox2HywWtZx9P1SXmXLPF3fCon/AyhfNF9Rigcuf9dy+R99tyhdHOws5b6+p4fcZD8NvM7345lz8hClHhTWYLHZ8el4Ah3YfCQZzH3ZOy51+JFBGdIdLnjAH4S2zTZDb9b0ZzPYLgbPvM4FjxoWmBz/vkbr32V1rNQAADT1JREFUn3ANoOfuMj37phQdMH+vjgPg2ndM7/Xetebck7iRJgjUniQQNxqsfkcG+11qHPDN72HetIaDoWmrILo/BEU13H9MAmQeZyBIXWF6s4ufhJeHwdsXmXGutrry7oEtkL0VzvyteU/8gs1yHz8T3PbMNyXOgjRzjk2PRg6ILqExENXn+GcPHdgCHXqBf2jD53pdZN7z2rPZmlNVCilLTSdDKfM5zdnRsm1kbjAZRLdzGj5nsZjvXdKi1rWnFSQQnEw6DTAzb4KizIFt1ctmnrij0vR0m+qBH68BV5sDyqK/m1kLtc9rKM+Hr+8xPbQrXmx59gGmTNTWEqaYS2msetmUfjZ9CGPuhYH15hqMuMP8Tt/8zoyB9L7UZANglt/+gym1XPwEFGUeOcv5UJKpQY+6G3xDYMO7R2+L1ubMcHulKWm5DnB+waYEdeOHDUtoNn/oeqYJQLUPtNu+MiUPqFumq3HA/rXQ7azG2xAzHIoyzAlIR7P9G/jh4aMf2H/53PyulzwJ5z9iyms/PgIzxplOwPHa9JEJfo2VSftdYdq/+ClQlrpTjI+mx/nm2lr2KhOIG8uuGlNZYk6ABBMIugxtfL2e55tOWUtP9NTanPRorzA1fYBOA01n49Ce5l+fuhJQR/8b951oxtrSPHOGvVyG+mQz5h7zD8wBoKIQAjt4fr8WC0z4r5kGu8KZecSNNjM1Mtab+vQ1M8w4Q3vzDzWzoFa9fOR8Bdd7VpvFCjd/akpA/qGm9Fa7RBUzzEx9BTMQu+I5GP5rM8XTx98cEMFcWuSSpyCkU8N9/PwiJC80vf6oM1r+Owy4ygSnrI2mhFBVBj89CZ0GmR7v5o/NeJDVxwxcVhVD17Mb31ascwA9c8ORg1BtB7fCV1NNh2LA5CO97aXPmBLY+GfMCXj9J8E5D5jnHHbYNMuMu3w1Fe5c0LKgvvEDCO/2/+3de3BU9RXA8e8hGAyP4RWkKUYgoFYpghiBFhBG2lrpDNARRmpRythqpzjFPoXaqm1tOzpTKlapilJRmFpU0lKKTsVaNFgeAcMbCqgolJKCmgoMSJPTP84vzbLsLo9kc8ne85nJZHN3s/md/W3uub/n2om0zn+PwoYFNk26oOOJv9NnrI0L7Cq3GUKpHpOsZLhNM359pnWlHaqC0Q9l3i5++1KbjXT4gF0QfPAOlN6c+rHdh1hSemuZtT7KbrWZaOcPtNZk4kWZKrw4zWbjDbjJkhTAeX3s+75N6WcMgr0+lfOspZQu9pIR1so9fCD98zSAJ4KzWYu8pkkCdYoHwtRKezOufsL6Ow/ssCvO4d+vP+GcDYZMtb2Vti62Zny61lK7rqmTRLKr74InPmOzljaVWRdS2y62snn147Y3VM0xyMu3jQjz8q1rassiG7y88qunV/6+42yB2dqnLRG8Gga2v7LEkv8zX4K1T9qJ6q3QBZLuarGov332xOrHLRG8v8temx5Dra//b7+wOq05autGeg6zrpi6tRtb/gTo8SfRvJa2/iS/DSz8Grw2wxb4ZbJvk61VadkKJi+pX+W+bYm1Ki+fmPr3zimwE+jymZknFSTqMdQ2VPzrvZZ4egyzllnnXjYOluzATlhwo51M2xTa1XtRP5t1lUpBB2s1bllsV+t71thswIo5NnX1GyttLy+w7rSVj1ir9Jqf1beYO/e298m+jUDSZI6DVXbh0anE6v2Dd2xwPJ381vDNytNrjZ8GTwTueB172Pdh37avs1XrTnDDs7Z3U113T0MUX2mbAG58DtpfYAPdYCeWi0fZ2ElBJzsxblhg97X7uE37/exPT/8f9Nz2dnW+fgGgNivs8ok2l13VriqX/hiWP2j95h172oSCVPJb2+Kyl+6yE1X5A/Y7dboPsQH+N+ZZK2rVbFuh3PWTMGK6xTx4ir0GyfqOtwuCV+61q+6Rd9d3f4G1WlUt/ld+buMv53awBWDXzbaB4JWP2SygkhHpX4+Bt8K7q2023Kko6AiTFtsmht1K7TWcNdgS+eQX60/SYH33C2+x1uDE5621t/kPNp0304c2feILdpIHG7zuN8EWfc6+Gv78Lbhujp3My2dYCzUxCYAl0y4X2zhBsmX3Hb8QsOdwu6DJJEtJAEC0mX0MY2lpqVZUZGFGg3OHwyK04kHHd4Mc2GldNZ+aYgnoSLWd/DLNbjoV771piwP/+YZtDjjmofp1Gft32ED2x/rCJaOh90ibzZLOR4dh1iC7ssxvC+N+a10ZXfvYCV8EDu23/a6qNtnsnckv2BTOk6mtse3SV8yyE3DxYDtJrnnSBnilBbQsgGOHYPg0+3S/edfZ5IM6I34AI+5o0Mt1UjuW2t8FS+rDv2fjAG/Ms9d4/NzM61mSqdrYkdYe3+J8bYZti9K60HbQvWyCrQBOtQCy7Ou2H9l3tlodVO+2OnpqLPS73l6X6t1Wtw19P52EiKxR1dKU93kicC5CqjaYWHjRiVd8tbWpTy7pHKm27pn2xfVz7pMd/dDWqvQdXz/D6lS9u8paHG+X2z5brTvblTBif/uCQTb20SLPkur2l+zKvH1x6viy4Z0VlhDKH7DWAlgLZfSvTy8JZKJqa1CWz7Tusysmp6+nv8+y8Y82XSxB1yVHaQG3VZx+HTSAJwLnXOOprbETbuFFNo5yNtq7DvZttnGVDt2bJgmlcuyIdb3tet1mIRX1s/IUdDxxTUiWeSJwzrmYy5QIfB2Bc87FnCcC55yLOU8EzjkXc54InHMu5jwROOdczHkicM65mPNE4JxzMeeJwDnnYq7ZLSgTkX8Du076wNQKgf2NWJzmII4xQzzj9pjj4Uxj7q6qKZeCN7tE0BAiUpFuZV2uimPMEM+4PeZ4yEbM3jXknHMx54nAOediLm6J4LGoCxCBOMYM8YzbY46HRo85VmMEzjnnThS3FoFzzrkkngiccy7mYpMIROTzIrJNRHaIyLSoy5MtIvK2iGwQkUoRqQjHOonISyKyPXzvGHU5G0JE5ohIlYhsTDiWMkYxD4Z6Xy8iA6Ir+ZlLE/M9IrIn1HWliIxKuG96iHmbiFwTTakbRkSKReQVEdksIptEZGo4nrN1nSHm7Na1qub8F5AH7ARKgHxgHXBp1OXKUqxvA4VJx+4HpoXb04D7oi5nA2O8ChgAbDxZjMAo4AVAgMHAyqjL34gx3wN8N8VjLw3v8VZAz/Dez4s6hjOIuQgYEG63A/4RYsvZus4Qc1brOi4tgoHADlV9U1U/Ap4BxkRcpqY0Bpgbbs8FxkZYlgZT1VeB95IOp4txDPCUmhVABxEpapqSNp40MaczBnhGVY+q6lvADux/oFlR1b2qujbc/hDYAnQjh+s6Q8zpNEpdxyURdAPeTfh5N5lf3OZMgb+IyBoRuSUc66qqe8PtfwFdoylaVqWLMdfr/rbQDTInocsv52IWkR7A5cBKYlLXSTFDFus6LokgToaq6gDgWmCKiFyVeKdaezKn5wzHIcbgN0AvoD+wF/hltMXJDhFpCzwP3K6q/0m8L1frOkXMWa3ruCSCPUBxws/nh2M5R1X3hO9VQBnWTNxX10QO36uiK2HWpIsxZ+teVfepao2q1gKzqe8SyJmYReQc7IQ4X1UXhsM5XdepYs52XcclEawGLhSRniKSD0wAFkVcpkYnIm1EpF3dbeBzwEYs1knhYZOAP0ZTwqxKF+Mi4KYwo2QwUJ3QrdCsJfV/fxGra7CYJ4hIKxHpCVwIrGrq8jWUiAjwBLBFVWck3JWzdZ0u5qzXddSj5E04Gj8KG4HfCdwZdXmyFGMJNoNgHbCpLk6gM/AysB1YCnSKuqwNjPN3WPP4GNYnenO6GLEZJA+Het8AlEZd/kaM+ekQ0/pwQihKePydIeZtwLVRl/8MYx6KdfusByrD16hcrusMMWe1rn2LCeeci7m4dA0555xLwxOBc87FnCcC55yLOU8EzjkXc54InHMu5jwRONeERGSEiCyOuhzOJfJE4JxzMeeJwLkURGSiiKwKe78/KiJ5InJQRH4V9ol/WUS6hMf2F5EVYUOwsoT98XuLyFIRWScia0WkV3j6tiLynIhsFZH5YTWpc5HxROBcEhG5BLgeGKKq/YEa4MtAG6BCVfsAy4C7w688Bdyhqpdhqz/rjs8HHlbVfsCnsZXBYDtK3o7tJV8CDMl6UM5l0DLqAjh3FhoJXAGsDhfrBdjGZrXA78Nj5gELRaQ90EFVl4Xjc4Fnw55P3VS1DEBVjwCE51ulqrvDz5VAD6A8+2E5l5onAudOJMBcVZ1+3EGRHyU97kz3ZzmacLsG/z90EfOuIedO9DIwTkTOg/9/Rm537P9lXHjMDUC5qlYD74vIsHD8RmCZ2qdL7RaRseE5WolI6yaNwrlT5FciziVR1c0i8kPsk95aYDt+TgEOAQPDfVXYOALYVsiPhBP9m8DkcPxG4FER+Ul4jvFNGIZzp8x3H3XuFInIQVVtG3U5nGts3jXknHMx5y0C55yLOW8ROOdczHkicM65mPNE4JxzMeeJwDnnYs4TgXPOxdz/AFaQRfU5YOvVAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": { "tags": [], "needs_background": "light" } }, { "output_type": "stream", "text": [ "DNN results\n", "accuracy : 0.7988095238095239\n", "Training Recall: 0.7988095238095239\n", "Training Precision: 0.7984823521956783\n", "Confusion matrix: \n", "[[288 90]\n", " [ 79 383]]\n", "Validation set report:\n", "cross Loss: -25.49292807806106\n", "hing Loss: 0.5714285714285714\n", "Report precision recall f1-score support\n", "\n", " 0 0.85 0.86 0.86 93\n", " 1 0.89 0.88 0.88 117\n", "\n", " accuracy 0.87 210\n", " macro avg 0.87 0.87 0.87 210\n", "weighted avg 0.87 0.87 0.87 210\n", "\n", "accuracy : 0.8714285714285714\n", "Validation Recall: 0.8714285714285714\n", "Validation Precision: 0.8716041295461691\n", "Confusion matrix: \n", "[[ 80 13]\n", " [ 14 103]]\n", "\n", "\\KNN Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 0.7619047619047619\n", "Validation Recall: 0.7619047619047619\n", "Validation Precision: 0.7633297274753346\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[70 23]\n", " [27 90]]\n", "\n", "\n", "Support vector machine Result\n", "\n", "Train accuracy: 0.7988095238095239\n", "Training Recall: 0.7988095238095239\n", "Training Precision: 0.8136106194690266\n", "\n", "\n", "Validation accuracy: 0.8428571428571429\n", "Validation Recall: 0.8428571428571429\n", "Validation Precision: 0.8515955030660913\n", "\n", "Train Confusion matrix:\n", " [[242 136]\n", " [ 33 429]]\n", "Validation Confusion matrix:\n", " [[ 67 26]\n", " [ 7 110]]\n", "\n", "\n", "Random Forest Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 0.8476190476190476\n", "Validation Recall: 0.8476190476190476\n", "Validation Precision: 0.847763226400737\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[ 74 19]\n", " [ 13 104]]\n", "\n", "\n", "LDA Result\n", "\n", "Train accuracy: 0.780952380952381\n", "Training Recall: 0.780952380952381\n", "Training Precision: 0.8061427280939475\n", "\n", "\n", "Validation accuracy: 0.8285714285714286\n", "Validation Recall: 0.8285714285714286\n", "Validation Precision: 0.8429496497951632\n", "\n", "Train Confusion matrix:\n", " [[220 158]\n", " [ 26 436]]\n", "Validation Confusion matrix:\n", " [[ 63 30]\n", " [ 6 111]]\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "NTQDcUtBjoCB" }, "source": [ "### MRMR " ] }, { "cell_type": "code", "metadata": { "id": "JSEjcxwrAL7K" }, "source": [ "strings = ['2715', '191', '516', '265', '201', '1035', '2719', '2724', '2', '1295'] #10 MIQ\n", "idx_IN_columns = [int(s) for s in strings]\n", "\n", "\n", "X = X[:,idx_IN_columns]\n", "\n" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "NiwBDKyIRV-F", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "1e882d88-c158-40bc-83ec-0459f860811a" }, "source": [ "#on 10 feature\n", "train_models(X, y, y_cat)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Before selectoin\n", "(1050, 10)\n", "After selectoin\n", "(1050, 10)\n", "(210, 10)\n", "Epoch 1/250\n", "7/7 [==============================] - 0s 69ms/step - loss: 0.7975 - accuracy: 0.6440 - val_loss: 0.6132 - val_accuracy: 0.7857\n", "Epoch 2/250\n", "7/7 [==============================] - 0s 39ms/step - loss: 0.4140 - accuracy: 0.8536 - val_loss: 0.3455 - val_accuracy: 0.8952\n", "Epoch 3/250\n", "7/7 [==============================] - 3s 421ms/step - loss: 0.2704 - accuracy: 0.9024 - val_loss: 0.2407 - val_accuracy: 0.9333\n", "Epoch 4/250\n", "7/7 [==============================] - 2s 221ms/step - loss: 0.2390 - accuracy: 0.9048 - val_loss: 0.1903 - val_accuracy: 0.9238\n", "Epoch 5/250\n", "7/7 [==============================] - 1s 75ms/step - loss: 0.1778 - accuracy: 0.9333 - val_loss: 0.1644 - val_accuracy: 0.9238\n", "Epoch 6/250\n", "7/7 [==============================] - 0s 51ms/step - loss: 0.1871 - accuracy: 0.9298 - val_loss: 0.1481 - val_accuracy: 0.9333\n", "Epoch 7/250\n", "7/7 [==============================] - 0s 48ms/step - loss: 0.1478 - accuracy: 0.9440 - val_loss: 0.1344 - val_accuracy: 0.9429\n", "Epoch 8/250\n", "7/7 [==============================] - 0s 50ms/step - loss: 0.1509 - accuracy: 0.9417 - val_loss: 0.1199 - val_accuracy: 0.9476\n", "Epoch 9/250\n", "7/7 [==============================] - 0s 53ms/step - loss: 0.1519 - accuracy: 0.9452 - val_loss: 0.1063 - val_accuracy: 0.9571\n", "Epoch 10/250\n", "7/7 [==============================] - 0s 46ms/step - loss: 0.1142 - accuracy: 0.9560 - val_loss: 0.0966 - val_accuracy: 0.9619\n", "Epoch 11/250\n", "7/7 [==============================] - 0s 47ms/step - loss: 0.1100 - accuracy: 0.9631 - val_loss: 0.0914 - val_accuracy: 0.9619\n", "Epoch 12/250\n", "7/7 [==============================] - 1s 120ms/step - loss: 0.1321 - accuracy: 0.9440 - val_loss: 0.0882 - val_accuracy: 0.9571\n", "Epoch 13/250\n", "7/7 [==============================] - 1s 182ms/step - loss: 0.1206 - accuracy: 0.9452 - val_loss: 0.0819 - val_accuracy: 0.9667\n", "Epoch 14/250\n", "7/7 [==============================] - 0s 53ms/step - loss: 0.0943 - accuracy: 0.9679 - val_loss: 0.0810 - val_accuracy: 0.9619\n", "Epoch 15/250\n", "7/7 [==============================] - 0s 47ms/step - loss: 0.1133 - accuracy: 0.9536 - val_loss: 0.0732 - val_accuracy: 0.9667\n", "Epoch 16/250\n", "7/7 [==============================] - 0s 50ms/step - loss: 0.1074 - accuracy: 0.9536 - val_loss: 0.0714 - val_accuracy: 0.9667\n", "Epoch 17/250\n", "7/7 [==============================] - 0s 49ms/step - loss: 0.0911 - accuracy: 0.9714 - val_loss: 0.0661 - val_accuracy: 0.9714\n", "Epoch 18/250\n", "7/7 [==============================] - 0s 49ms/step - loss: 0.0997 - accuracy: 0.9607 - val_loss: 0.0654 - val_accuracy: 0.9762\n", "Epoch 19/250\n", "7/7 [==============================] - 0s 47ms/step - loss: 0.0957 - accuracy: 0.9595 - val_loss: 0.0623 - val_accuracy: 0.9762\n", "Epoch 20/250\n", "7/7 [==============================] - 1s 135ms/step - loss: 0.0980 - accuracy: 0.9643 - val_loss: 0.0596 - val_accuracy: 0.9810\n", "Epoch 21/250\n", "7/7 [==============================] - 7s 1s/step - loss: 0.0953 - accuracy: 0.9571 - val_loss: 0.0581 - val_accuracy: 0.9810\n", "Epoch 22/250\n", "7/7 [==============================] - 4s 624ms/step - loss: 0.1042 - accuracy: 0.9571 - val_loss: 0.0564 - val_accuracy: 0.9857\n", "Epoch 23/250\n", "7/7 [==============================] - 0s 40ms/step - loss: 0.0928 - accuracy: 0.9655 - val_loss: 0.0541 - val_accuracy: 0.9857\n", "Epoch 24/250\n", "7/7 [==============================] - 3s 358ms/step - loss: 0.0843 - accuracy: 0.9631 - val_loss: 0.0500 - val_accuracy: 0.9857\n", "Epoch 25/250\n", "7/7 [==============================] - 2s 255ms/step - loss: 0.0790 - accuracy: 0.9690 - val_loss: 0.0490 - val_accuracy: 0.9857\n", "Epoch 26/250\n", "7/7 [==============================] - 0s 64ms/step - loss: 0.0920 - accuracy: 0.9655 - val_loss: 0.0482 - val_accuracy: 0.9857\n", "Epoch 27/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0690 - accuracy: 0.9750 - val_loss: 0.0488 - val_accuracy: 0.9857\n", "Epoch 28/250\n", "7/7 [==============================] - 0s 70ms/step - loss: 0.0837 - accuracy: 0.9655 - val_loss: 0.0480 - val_accuracy: 0.9857\n", "Epoch 29/250\n", "7/7 [==============================] - 1s 156ms/step - loss: 0.0825 - accuracy: 0.9667 - val_loss: 0.0477 - val_accuracy: 0.9857\n", "Epoch 30/250\n", "7/7 [==============================] - 7s 1s/step - loss: 0.0782 - accuracy: 0.9631 - val_loss: 0.0469 - val_accuracy: 0.9857\n", "Epoch 31/250\n", "7/7 [==============================] - 2s 256ms/step - loss: 0.0834 - accuracy: 0.9655 - val_loss: 0.0464 - val_accuracy: 0.9857\n", "Epoch 32/250\n", "7/7 [==============================] - 3s 372ms/step - loss: 0.0914 - accuracy: 0.9595 - val_loss: 0.0460 - val_accuracy: 0.9857\n", "Epoch 33/250\n", "7/7 [==============================] - 0s 53ms/step - loss: 0.0793 - accuracy: 0.9679 - val_loss: 0.0458 - val_accuracy: 0.9810\n", "Epoch 34/250\n", "7/7 [==============================] - 0s 66ms/step - loss: 0.0808 - accuracy: 0.9619 - val_loss: 0.0455 - val_accuracy: 0.9857\n", "Epoch 35/250\n", "7/7 [==============================] - 6s 884ms/step - loss: 0.0946 - accuracy: 0.9571 - val_loss: 0.0444 - val_accuracy: 0.9810\n", "Epoch 36/250\n", "7/7 [==============================] - 2s 240ms/step - loss: 0.0816 - accuracy: 0.9679 - val_loss: 0.0442 - val_accuracy: 0.9810\n", "Epoch 37/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0840 - accuracy: 0.9619 - val_loss: 0.0446 - val_accuracy: 0.9810\n", "Epoch 38/250\n", "7/7 [==============================] - 3s 378ms/step - loss: 0.0845 - accuracy: 0.9631 - val_loss: 0.0426 - val_accuracy: 0.9810\n", "Epoch 39/250\n", "7/7 [==============================] - 0s 56ms/step - loss: 0.0816 - accuracy: 0.9655 - val_loss: 0.0410 - val_accuracy: 0.9857\n", "Epoch 40/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0985 - accuracy: 0.9571 - val_loss: 0.0418 - val_accuracy: 0.9857\n", "Epoch 41/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0949 - accuracy: 0.9560 - val_loss: 0.0431 - val_accuracy: 0.9762\n", "Epoch 42/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0758 - accuracy: 0.9702 - val_loss: 0.0436 - val_accuracy: 0.9762\n", "Epoch 43/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0712 - accuracy: 0.9643 - val_loss: 0.0430 - val_accuracy: 0.9762\n", "Epoch 44/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0780 - accuracy: 0.9667 - val_loss: 0.0483 - val_accuracy: 0.9762\n", "Epoch 45/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0690 - accuracy: 0.9714 - val_loss: 0.0458 - val_accuracy: 0.9810\n", "Epoch 46/250\n", "7/7 [==============================] - 0s 55ms/step - loss: 0.0869 - accuracy: 0.9655 - val_loss: 0.0394 - val_accuracy: 0.9810\n", "Epoch 47/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0748 - accuracy: 0.9667 - val_loss: 0.0423 - val_accuracy: 0.9857\n", "Epoch 48/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0733 - accuracy: 0.9679 - val_loss: 0.0442 - val_accuracy: 0.9810\n", "Epoch 49/250\n", "7/7 [==============================] - 1s 123ms/step - loss: 0.0735 - accuracy: 0.9714 - val_loss: 0.0391 - val_accuracy: 0.9857\n", "Epoch 50/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0664 - accuracy: 0.9714 - val_loss: 0.0399 - val_accuracy: 0.9857\n", "Epoch 51/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0797 - accuracy: 0.9607 - val_loss: 0.0442 - val_accuracy: 0.9762\n", "Epoch 52/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0650 - accuracy: 0.9750 - val_loss: 0.0503 - val_accuracy: 0.9762\n", "Epoch 53/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0898 - accuracy: 0.9583 - val_loss: 0.0560 - val_accuracy: 0.9762\n", "Epoch 54/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0663 - accuracy: 0.9726 - val_loss: 0.0504 - val_accuracy: 0.9762\n", "Epoch 55/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0805 - accuracy: 0.9619 - val_loss: 0.0425 - val_accuracy: 0.9762\n", "Epoch 56/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0598 - accuracy: 0.9762 - val_loss: 0.0394 - val_accuracy: 0.9857\n", "Epoch 57/250\n", "7/7 [==============================] - 5s 750ms/step - loss: 0.0730 - accuracy: 0.9643 - val_loss: 0.0384 - val_accuracy: 0.9857\n", "Epoch 58/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0781 - accuracy: 0.9619 - val_loss: 0.0388 - val_accuracy: 0.9857\n", "Epoch 59/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0716 - accuracy: 0.9690 - val_loss: 0.0410 - val_accuracy: 0.9810\n", "Epoch 60/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0595 - accuracy: 0.9726 - val_loss: 0.0444 - val_accuracy: 0.9762\n", "Epoch 61/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0595 - accuracy: 0.9726 - val_loss: 0.0433 - val_accuracy: 0.9810\n", "Epoch 62/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0745 - accuracy: 0.9667 - val_loss: 0.0427 - val_accuracy: 0.9810\n", "Epoch 63/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0871 - accuracy: 0.9583 - val_loss: 0.0482 - val_accuracy: 0.9762\n", "Epoch 64/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0822 - accuracy: 0.9595 - val_loss: 0.0514 - val_accuracy: 0.9762\n", "Epoch 65/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0662 - accuracy: 0.9702 - val_loss: 0.0474 - val_accuracy: 0.9810\n", "Epoch 66/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0765 - accuracy: 0.9655 - val_loss: 0.0415 - val_accuracy: 0.9810\n", "Epoch 67/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0798 - accuracy: 0.9595 - val_loss: 0.0419 - val_accuracy: 0.9857\n", "Epoch 68/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0598 - accuracy: 0.9774 - val_loss: 0.0425 - val_accuracy: 0.9762\n", "Epoch 69/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0744 - accuracy: 0.9667 - val_loss: 0.0445 - val_accuracy: 0.9762\n", "Epoch 70/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0667 - accuracy: 0.9690 - val_loss: 0.0482 - val_accuracy: 0.9762\n", "Epoch 71/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0764 - accuracy: 0.9702 - val_loss: 0.0434 - val_accuracy: 0.9762\n", "Epoch 72/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0929 - accuracy: 0.9536 - val_loss: 0.0426 - val_accuracy: 0.9810\n", "Epoch 73/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0630 - accuracy: 0.9726 - val_loss: 0.0403 - val_accuracy: 0.9810\n", "Epoch 74/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0697 - accuracy: 0.9655 - val_loss: 0.0400 - val_accuracy: 0.9810\n", "Epoch 75/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0772 - accuracy: 0.9631 - val_loss: 0.0424 - val_accuracy: 0.9810\n", "Epoch 76/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0757 - accuracy: 0.9667 - val_loss: 0.0459 - val_accuracy: 0.9762\n", "Epoch 77/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0716 - accuracy: 0.9679 - val_loss: 0.0452 - val_accuracy: 0.9762\n", "Epoch 78/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0604 - accuracy: 0.9738 - val_loss: 0.0432 - val_accuracy: 0.9762\n", "Epoch 79/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0755 - accuracy: 0.9667 - val_loss: 0.0404 - val_accuracy: 0.9857\n", "Epoch 80/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0585 - accuracy: 0.9738 - val_loss: 0.0394 - val_accuracy: 0.9810\n", "Epoch 81/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0534 - accuracy: 0.9762 - val_loss: 0.0394 - val_accuracy: 0.9810\n", "Epoch 82/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0555 - accuracy: 0.9762 - val_loss: 0.0408 - val_accuracy: 0.9810\n", "Epoch 83/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0823 - accuracy: 0.9595 - val_loss: 0.0425 - val_accuracy: 0.9810\n", "Epoch 84/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0668 - accuracy: 0.9679 - val_loss: 0.0384 - val_accuracy: 0.9810\n", "Epoch 85/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0495 - accuracy: 0.9762 - val_loss: 0.0386 - val_accuracy: 0.9810\n", "Epoch 86/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0716 - accuracy: 0.9655 - val_loss: 0.0404 - val_accuracy: 0.9810\n", "Epoch 87/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0570 - accuracy: 0.9738 - val_loss: 0.0422 - val_accuracy: 0.9810\n", "Epoch 88/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0523 - accuracy: 0.9774 - val_loss: 0.0443 - val_accuracy: 0.9762\n", "Epoch 89/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0630 - accuracy: 0.9726 - val_loss: 0.0475 - val_accuracy: 0.9762\n", "Epoch 90/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0621 - accuracy: 0.9714 - val_loss: 0.0475 - val_accuracy: 0.9762\n", "Epoch 91/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0608 - accuracy: 0.9714 - val_loss: 0.0428 - val_accuracy: 0.9810\n", "Epoch 92/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0559 - accuracy: 0.9762 - val_loss: 0.0413 - val_accuracy: 0.9810\n", "Epoch 93/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0743 - accuracy: 0.9619 - val_loss: 0.0394 - val_accuracy: 0.9810\n", "Epoch 94/250\n", "7/7 [==============================] - 0s 36ms/step - loss: 0.0594 - accuracy: 0.9726 - val_loss: 0.0381 - val_accuracy: 0.9810\n", "Epoch 95/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0621 - accuracy: 0.9714 - val_loss: 0.0383 - val_accuracy: 0.9810\n", "Epoch 96/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0535 - accuracy: 0.9762 - val_loss: 0.0395 - val_accuracy: 0.9810\n", "Epoch 97/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0704 - accuracy: 0.9643 - val_loss: 0.0442 - val_accuracy: 0.9810\n", "Epoch 98/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0479 - accuracy: 0.9810 - val_loss: 0.0441 - val_accuracy: 0.9762\n", "Epoch 99/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0589 - accuracy: 0.9702 - val_loss: 0.0451 - val_accuracy: 0.9762\n", "Epoch 100/250\n", "7/7 [==============================] - 2s 341ms/step - loss: 0.0763 - accuracy: 0.9631 - val_loss: 0.0374 - val_accuracy: 0.9857\n", "Epoch 101/250\n", "7/7 [==============================] - 0s 52ms/step - loss: 0.0695 - accuracy: 0.9690 - val_loss: 0.0373 - val_accuracy: 0.9857\n", "Epoch 102/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0730 - accuracy: 0.9655 - val_loss: 0.0425 - val_accuracy: 0.9762\n", "Epoch 103/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0522 - accuracy: 0.9786 - val_loss: 0.0466 - val_accuracy: 0.9762\n", "Epoch 104/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0551 - accuracy: 0.9762 - val_loss: 0.0479 - val_accuracy: 0.9762\n", "Epoch 105/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0454 - accuracy: 0.9762 - val_loss: 0.0422 - val_accuracy: 0.9810\n", "Epoch 106/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0804 - accuracy: 0.9643 - val_loss: 0.0407 - val_accuracy: 0.9810\n", "Epoch 107/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0557 - accuracy: 0.9726 - val_loss: 0.0397 - val_accuracy: 0.9810\n", "Epoch 108/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0459 - accuracy: 0.9810 - val_loss: 0.0393 - val_accuracy: 0.9810\n", "Epoch 109/250\n", "7/7 [==============================] - 0s 45ms/step - loss: 0.0965 - accuracy: 0.9548 - val_loss: 0.0368 - val_accuracy: 0.9810\n", "Epoch 110/250\n", "7/7 [==============================] - 1s 164ms/step - loss: 0.0556 - accuracy: 0.9786 - val_loss: 0.0333 - val_accuracy: 0.9857\n", "Epoch 111/250\n", "7/7 [==============================] - 7s 963ms/step - loss: 0.0600 - accuracy: 0.9714 - val_loss: 0.0306 - val_accuracy: 0.9857\n", "Epoch 112/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0459 - accuracy: 0.9821 - val_loss: 0.0307 - val_accuracy: 0.9905\n", "Epoch 113/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0676 - accuracy: 0.9679 - val_loss: 0.0333 - val_accuracy: 0.9810\n", "Epoch 114/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0578 - accuracy: 0.9726 - val_loss: 0.0394 - val_accuracy: 0.9810\n", "Epoch 115/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0596 - accuracy: 0.9702 - val_loss: 0.0525 - val_accuracy: 0.9762\n", "Epoch 116/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0660 - accuracy: 0.9702 - val_loss: 0.0533 - val_accuracy: 0.9762\n", "Epoch 117/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0617 - accuracy: 0.9714 - val_loss: 0.0456 - val_accuracy: 0.9810\n", "Epoch 118/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0470 - accuracy: 0.9798 - val_loss: 0.0415 - val_accuracy: 0.9810\n", "Epoch 119/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0664 - accuracy: 0.9690 - val_loss: 0.0374 - val_accuracy: 0.9810\n", "Epoch 120/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0756 - accuracy: 0.9631 - val_loss: 0.0347 - val_accuracy: 0.9905\n", "Epoch 121/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0475 - accuracy: 0.9774 - val_loss: 0.0338 - val_accuracy: 0.9810\n", "Epoch 122/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0618 - accuracy: 0.9690 - val_loss: 0.0393 - val_accuracy: 0.9810\n", "Epoch 123/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0684 - accuracy: 0.9702 - val_loss: 0.0442 - val_accuracy: 0.9762\n", "Epoch 124/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0637 - accuracy: 0.9702 - val_loss: 0.0459 - val_accuracy: 0.9762\n", "Epoch 125/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0632 - accuracy: 0.9726 - val_loss: 0.0458 - val_accuracy: 0.9762\n", "Epoch 126/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0515 - accuracy: 0.9798 - val_loss: 0.0443 - val_accuracy: 0.9762\n", "Epoch 127/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0500 - accuracy: 0.9786 - val_loss: 0.0442 - val_accuracy: 0.9762\n", "Epoch 128/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0548 - accuracy: 0.9762 - val_loss: 0.0425 - val_accuracy: 0.9762\n", "Epoch 129/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0586 - accuracy: 0.9738 - val_loss: 0.0439 - val_accuracy: 0.9762\n", "Epoch 130/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0556 - accuracy: 0.9738 - val_loss: 0.0419 - val_accuracy: 0.9810\n", "Epoch 131/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0635 - accuracy: 0.9750 - val_loss: 0.0352 - val_accuracy: 0.9857\n", "Epoch 132/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0679 - accuracy: 0.9679 - val_loss: 0.0375 - val_accuracy: 0.9857\n", "Epoch 133/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0566 - accuracy: 0.9702 - val_loss: 0.0456 - val_accuracy: 0.9762\n", "Epoch 134/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0394 - accuracy: 0.9821 - val_loss: 0.0448 - val_accuracy: 0.9762\n", "Epoch 135/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0580 - accuracy: 0.9714 - val_loss: 0.0437 - val_accuracy: 0.9762\n", "Epoch 136/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0455 - accuracy: 0.9821 - val_loss: 0.0440 - val_accuracy: 0.9762\n", "Epoch 137/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0640 - accuracy: 0.9714 - val_loss: 0.0362 - val_accuracy: 0.9810\n", "Epoch 138/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0671 - accuracy: 0.9690 - val_loss: 0.0343 - val_accuracy: 0.9857\n", "Epoch 139/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0520 - accuracy: 0.9726 - val_loss: 0.0354 - val_accuracy: 0.9810\n", "Epoch 140/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0406 - accuracy: 0.9810 - val_loss: 0.0307 - val_accuracy: 0.9905\n", "Epoch 141/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0575 - accuracy: 0.9726 - val_loss: 0.0355 - val_accuracy: 0.9762\n", "Epoch 142/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0577 - accuracy: 0.9726 - val_loss: 0.0428 - val_accuracy: 0.9762\n", "Epoch 143/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0485 - accuracy: 0.9786 - val_loss: 0.0369 - val_accuracy: 0.9810\n", "Epoch 144/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0771 - accuracy: 0.9643 - val_loss: 0.0330 - val_accuracy: 0.9810\n", "Epoch 145/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0822 - accuracy: 0.9619 - val_loss: 0.0362 - val_accuracy: 0.9810\n", "Epoch 146/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0464 - accuracy: 0.9774 - val_loss: 0.0418 - val_accuracy: 0.9810\n", "Epoch 147/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0607 - accuracy: 0.9726 - val_loss: 0.0421 - val_accuracy: 0.9810\n", "Epoch 148/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0545 - accuracy: 0.9774 - val_loss: 0.0325 - val_accuracy: 0.9857\n", "Epoch 149/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0971 - accuracy: 0.9512 - val_loss: 0.0334 - val_accuracy: 0.9857\n", "Epoch 150/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0620 - accuracy: 0.9714 - val_loss: 0.0327 - val_accuracy: 0.9857\n", "Epoch 151/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0519 - accuracy: 0.9762 - val_loss: 0.0341 - val_accuracy: 0.9810\n", "Epoch 152/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0592 - accuracy: 0.9690 - val_loss: 0.0383 - val_accuracy: 0.9810\n", "Epoch 153/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0629 - accuracy: 0.9702 - val_loss: 0.0511 - val_accuracy: 0.9762\n", "Epoch 154/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0578 - accuracy: 0.9738 - val_loss: 0.0604 - val_accuracy: 0.9667\n", "Epoch 155/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0807 - accuracy: 0.9643 - val_loss: 0.0375 - val_accuracy: 0.9810\n", "Epoch 156/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0561 - accuracy: 0.9762 - val_loss: 0.0307 - val_accuracy: 0.9857\n", "Epoch 157/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0355 - accuracy: 0.9845 - val_loss: 0.0376 - val_accuracy: 0.9810\n", "Epoch 158/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0637 - accuracy: 0.9702 - val_loss: 0.0319 - val_accuracy: 0.9857\n", "Epoch 159/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0431 - accuracy: 0.9810 - val_loss: 0.0353 - val_accuracy: 0.9810\n", "Epoch 160/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0844 - accuracy: 0.9583 - val_loss: 0.0460 - val_accuracy: 0.9762\n", "Epoch 161/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0429 - accuracy: 0.9821 - val_loss: 0.0378 - val_accuracy: 0.9810\n", "Epoch 162/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0727 - accuracy: 0.9667 - val_loss: 0.0381 - val_accuracy: 0.9810\n", "Epoch 163/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0584 - accuracy: 0.9738 - val_loss: 0.0321 - val_accuracy: 0.9857\n", "Epoch 164/250\n", "7/7 [==============================] - 0s 35ms/step - loss: 0.0544 - accuracy: 0.9762 - val_loss: 0.0301 - val_accuracy: 0.9857\n", "Epoch 165/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0686 - accuracy: 0.9667 - val_loss: 0.0318 - val_accuracy: 0.9857\n", "Epoch 166/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0531 - accuracy: 0.9738 - val_loss: 0.0318 - val_accuracy: 0.9857\n", "Epoch 167/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0547 - accuracy: 0.9762 - val_loss: 0.0412 - val_accuracy: 0.9810\n", "Epoch 168/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0643 - accuracy: 0.9679 - val_loss: 0.0420 - val_accuracy: 0.9810\n", "Epoch 169/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0480 - accuracy: 0.9750 - val_loss: 0.0362 - val_accuracy: 0.9810\n", "Epoch 170/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0491 - accuracy: 0.9774 - val_loss: 0.0330 - val_accuracy: 0.9857\n", "Epoch 171/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0565 - accuracy: 0.9726 - val_loss: 0.0324 - val_accuracy: 0.9857\n", "Epoch 172/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0425 - accuracy: 0.9786 - val_loss: 0.0344 - val_accuracy: 0.9857\n", "Epoch 173/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0649 - accuracy: 0.9679 - val_loss: 0.0337 - val_accuracy: 0.9857\n", "Epoch 174/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0491 - accuracy: 0.9786 - val_loss: 0.0359 - val_accuracy: 0.9810\n", "Epoch 175/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0589 - accuracy: 0.9702 - val_loss: 0.0344 - val_accuracy: 0.9810\n", "Epoch 176/250\n", "7/7 [==============================] - 2s 275ms/step - loss: 0.0385 - accuracy: 0.9833 - val_loss: 0.0298 - val_accuracy: 0.9857\n", "Epoch 177/250\n", "7/7 [==============================] - 0s 54ms/step - loss: 0.0609 - accuracy: 0.9726 - val_loss: 0.0279 - val_accuracy: 0.9857\n", "Epoch 178/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0749 - accuracy: 0.9655 - val_loss: 0.0310 - val_accuracy: 0.9857\n", "Epoch 179/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0710 - accuracy: 0.9667 - val_loss: 0.0282 - val_accuracy: 0.9857\n", "Epoch 180/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0486 - accuracy: 0.9798 - val_loss: 0.0307 - val_accuracy: 0.9857\n", "Epoch 181/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0469 - accuracy: 0.9762 - val_loss: 0.0394 - val_accuracy: 0.9810\n", "Epoch 182/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0559 - accuracy: 0.9714 - val_loss: 0.0499 - val_accuracy: 0.9762\n", "Epoch 183/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0531 - accuracy: 0.9750 - val_loss: 0.0441 - val_accuracy: 0.9762\n", "Epoch 184/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0662 - accuracy: 0.9679 - val_loss: 0.0492 - val_accuracy: 0.9714\n", "Epoch 185/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0691 - accuracy: 0.9655 - val_loss: 0.0547 - val_accuracy: 0.9714\n", "Epoch 186/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0532 - accuracy: 0.9750 - val_loss: 0.0505 - val_accuracy: 0.9714\n", "Epoch 187/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0527 - accuracy: 0.9738 - val_loss: 0.0412 - val_accuracy: 0.9810\n", "Epoch 188/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0517 - accuracy: 0.9750 - val_loss: 0.0359 - val_accuracy: 0.9810\n", "Epoch 189/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0453 - accuracy: 0.9786 - val_loss: 0.0356 - val_accuracy: 0.9857\n", "Epoch 190/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0673 - accuracy: 0.9667 - val_loss: 0.0344 - val_accuracy: 0.9857\n", "Epoch 191/250\n", "7/7 [==============================] - 0s 41ms/step - loss: 0.0540 - accuracy: 0.9738 - val_loss: 0.0278 - val_accuracy: 0.9905\n", "Epoch 192/250\n", "7/7 [==============================] - 3s 432ms/step - loss: 0.0440 - accuracy: 0.9798 - val_loss: 0.0268 - val_accuracy: 0.9905\n", "Epoch 193/250\n", "7/7 [==============================] - 2s 218ms/step - loss: 0.0451 - accuracy: 0.9786 - val_loss: 0.0232 - val_accuracy: 0.9905\n", "Epoch 194/250\n", "7/7 [==============================] - 0s 58ms/step - loss: 0.0456 - accuracy: 0.9798 - val_loss: 0.0222 - val_accuracy: 0.9905\n", "Epoch 195/250\n", "7/7 [==============================] - 5s 647ms/step - loss: 0.0397 - accuracy: 0.9810 - val_loss: 0.0218 - val_accuracy: 0.9905\n", "Epoch 196/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0544 - accuracy: 0.9738 - val_loss: 0.0244 - val_accuracy: 0.9905\n", "Epoch 197/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0556 - accuracy: 0.9714 - val_loss: 0.0236 - val_accuracy: 0.9905\n", "Epoch 198/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0405 - accuracy: 0.9857 - val_loss: 0.0266 - val_accuracy: 0.9905\n", "Epoch 199/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0626 - accuracy: 0.9702 - val_loss: 0.0336 - val_accuracy: 0.9857\n", "Epoch 200/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0689 - accuracy: 0.9667 - val_loss: 0.0345 - val_accuracy: 0.9857\n", "Epoch 201/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0639 - accuracy: 0.9690 - val_loss: 0.0307 - val_accuracy: 0.9857\n", "Epoch 202/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0498 - accuracy: 0.9762 - val_loss: 0.0279 - val_accuracy: 0.9857\n", "Epoch 203/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0491 - accuracy: 0.9786 - val_loss: 0.0308 - val_accuracy: 0.9857\n", "Epoch 204/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0511 - accuracy: 0.9762 - val_loss: 0.0288 - val_accuracy: 0.9857\n", "Epoch 205/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0443 - accuracy: 0.9774 - val_loss: 0.0375 - val_accuracy: 0.9810\n", "Epoch 206/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0659 - accuracy: 0.9655 - val_loss: 0.0383 - val_accuracy: 0.9810\n", "Epoch 207/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0459 - accuracy: 0.9786 - val_loss: 0.0339 - val_accuracy: 0.9857\n", "Epoch 208/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0634 - accuracy: 0.9714 - val_loss: 0.0318 - val_accuracy: 0.9857\n", "Epoch 209/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0483 - accuracy: 0.9786 - val_loss: 0.0264 - val_accuracy: 0.9905\n", "Epoch 210/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0514 - accuracy: 0.9774 - val_loss: 0.0266 - val_accuracy: 0.9905\n", "Epoch 211/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0793 - accuracy: 0.9595 - val_loss: 0.0343 - val_accuracy: 0.9857\n", "Epoch 212/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0397 - accuracy: 0.9845 - val_loss: 0.0311 - val_accuracy: 0.9857\n", "Epoch 213/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0612 - accuracy: 0.9738 - val_loss: 0.0306 - val_accuracy: 0.9857\n", "Epoch 214/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0436 - accuracy: 0.9798 - val_loss: 0.0290 - val_accuracy: 0.9857\n", "Epoch 215/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0431 - accuracy: 0.9786 - val_loss: 0.0354 - val_accuracy: 0.9810\n", "Epoch 216/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0548 - accuracy: 0.9750 - val_loss: 0.0416 - val_accuracy: 0.9762\n", "Epoch 217/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0586 - accuracy: 0.9774 - val_loss: 0.0511 - val_accuracy: 0.9762\n", "Epoch 218/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0455 - accuracy: 0.9798 - val_loss: 0.0515 - val_accuracy: 0.9762\n", "Epoch 219/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0557 - accuracy: 0.9726 - val_loss: 0.0403 - val_accuracy: 0.9762\n", "Epoch 220/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0416 - accuracy: 0.9798 - val_loss: 0.0284 - val_accuracy: 0.9857\n", "Epoch 221/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0438 - accuracy: 0.9798 - val_loss: 0.0240 - val_accuracy: 0.9905\n", "Epoch 222/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0395 - accuracy: 0.9833 - val_loss: 0.0273 - val_accuracy: 0.9905\n", "Epoch 223/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0485 - accuracy: 0.9774 - val_loss: 0.0354 - val_accuracy: 0.9857\n", "Epoch 224/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0392 - accuracy: 0.9821 - val_loss: 0.0413 - val_accuracy: 0.9762\n", "Epoch 225/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0706 - accuracy: 0.9667 - val_loss: 0.0429 - val_accuracy: 0.9762\n", "Epoch 226/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0562 - accuracy: 0.9738 - val_loss: 0.0441 - val_accuracy: 0.9762\n", "Epoch 227/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0548 - accuracy: 0.9726 - val_loss: 0.0426 - val_accuracy: 0.9810\n", "Epoch 228/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0607 - accuracy: 0.9702 - val_loss: 0.0401 - val_accuracy: 0.9810\n", "Epoch 229/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0481 - accuracy: 0.9786 - val_loss: 0.0391 - val_accuracy: 0.9810\n", "Epoch 230/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0420 - accuracy: 0.9798 - val_loss: 0.0383 - val_accuracy: 0.9810\n", "Epoch 231/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0602 - accuracy: 0.9690 - val_loss: 0.0383 - val_accuracy: 0.9810\n", "Epoch 232/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0534 - accuracy: 0.9726 - val_loss: 0.0381 - val_accuracy: 0.9857\n", "Epoch 233/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0586 - accuracy: 0.9726 - val_loss: 0.0355 - val_accuracy: 0.9857\n", "Epoch 234/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0685 - accuracy: 0.9667 - val_loss: 0.0369 - val_accuracy: 0.9857\n", "Epoch 235/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0464 - accuracy: 0.9786 - val_loss: 0.0410 - val_accuracy: 0.9810\n", "Epoch 236/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0412 - accuracy: 0.9821 - val_loss: 0.0437 - val_accuracy: 0.9762\n", "Epoch 237/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0602 - accuracy: 0.9714 - val_loss: 0.0466 - val_accuracy: 0.9762\n", "Epoch 238/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0643 - accuracy: 0.9726 - val_loss: 0.0406 - val_accuracy: 0.9810\n", "Epoch 239/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0652 - accuracy: 0.9667 - val_loss: 0.0366 - val_accuracy: 0.9810\n", "Epoch 240/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0607 - accuracy: 0.9714 - val_loss: 0.0300 - val_accuracy: 0.9905\n", "Epoch 241/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0634 - accuracy: 0.9679 - val_loss: 0.0437 - val_accuracy: 0.9810\n", "Epoch 242/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0446 - accuracy: 0.9810 - val_loss: 0.0440 - val_accuracy: 0.9810\n", "Epoch 243/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0519 - accuracy: 0.9750 - val_loss: 0.0480 - val_accuracy: 0.9762\n", "Epoch 244/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0514 - accuracy: 0.9774 - val_loss: 0.0425 - val_accuracy: 0.9762\n", "Epoch 245/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0492 - accuracy: 0.9762 - val_loss: 0.0392 - val_accuracy: 0.9810\n", "Epoch 246/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0492 - accuracy: 0.9786 - val_loss: 0.0433 - val_accuracy: 0.9762\n", "Epoch 247/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0374 - accuracy: 0.9821 - val_loss: 0.0388 - val_accuracy: 0.9810\n", "Epoch 248/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0498 - accuracy: 0.9750 - val_loss: 0.0324 - val_accuracy: 0.9857\n", "Epoch 249/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0482 - accuracy: 0.9762 - val_loss: 0.0308 - val_accuracy: 0.9857\n", "Epoch 250/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0481 - accuracy: 0.9786 - val_loss: 0.0313 - val_accuracy: 0.9857\n", "Valid results - Loss: 0.03132248297333717 - Accuracy: 98.57142567634583%\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", 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\n", 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\n", 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" ] }, "metadata": { "tags": [], "needs_background": "light" } }, { "output_type": "stream", "text": [ "DNN results\n", "accuracy : 0.9904761904761905\n", "Training Recall: 0.9904761904761905\n", "Training Precision: 0.9904919908466819\n", "Confusion matrix: \n", "[[375 3]\n", " [ 5 457]]\n", "Validation set report:\n", "cross Loss: -33.87835157485235\n", "hing Loss: 0.4523809523809524\n", "Report precision recall f1-score support\n", "\n", " 0 1.00 0.98 0.99 93\n", " 1 0.98 1.00 0.99 117\n", "\n", " accuracy 0.99 210\n", " macro avg 0.99 0.99 0.99 210\n", "weighted avg 0.99 0.99 0.99 210\n", "\n", "accuracy : 0.9904761904761905\n", "Validation Recall: 0.9904761904761905\n", "Validation Precision: 0.9906362545018007\n", "Confusion matrix: \n", "[[ 91 2]\n", " [ 0 117]]\n", "\n", "\\KNN Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 0.9428571428571428\n", "Validation Recall: 0.9428571428571428\n", "Validation Precision: 0.9439625135167609\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[ 84 9]\n", " [ 3 114]]\n", "\n", "\n", "Support vector machine Result\n", "\n", "Train accuracy: 0.9726190476190476\n", "Training Recall: 0.9726190476190476\n", "Training Precision: 0.9726538684547422\n", "\n", "\n", "Validation accuracy: 0.9714285714285714\n", "Validation Recall: 0.9714285714285714\n", "Validation Precision: 0.9728222996515679\n", "\n", "Train Confusion matrix:\n", " [[364 14]\n", " [ 9 453]]\n", "Validation Confusion matrix:\n", " [[ 87 6]\n", " [ 0 117]]\n", "\n", "\n", "Random Forest Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 1.0\n", "Validation Recall: 1.0\n", "Validation Precision: 1.0\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[ 93 0]\n", " [ 0 117]]\n", "\n", "\n", "LDA Result\n", "\n", "Train accuracy: 0.9666666666666667\n", "Training Recall: 0.9666666666666667\n", "Training Precision: 0.9682912630402384\n", "\n", "\n", "Validation accuracy: 0.9523809523809523\n", "Validation Recall: 0.9523809523809523\n", "Validation Precision: 0.9561304836895388\n", "\n", "Train Confusion matrix:\n", " [[351 27]\n", " [ 1 461]]\n", "Validation Confusion matrix:\n", " [[ 83 10]\n", " [ 0 117]]\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "KCAbYhtQ_21c" }, "source": [ "### REF (prefrence measure)" ] }, { "cell_type": "code", "metadata": { "id": "U7UdHsOardeT", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "7e079312-ce15-483c-f314-a1a6cf4e59a6" }, "source": [ "#on 30 feature +LDA\n", "train_models(X, y, y_cat)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Before selectoin\n", "(1050, 28)\n", "After selectoin\n", "(1050, 28)\n", "(210, 28)\n", "Epoch 1/250\n", "7/7 [==============================] - 0s 58ms/step - loss: 0.8116 - accuracy: 0.6321 - val_loss: 0.5414 - val_accuracy: 0.8429\n", "Epoch 2/250\n", "7/7 [==============================] - 3s 385ms/step - loss: 0.5295 - accuracy: 0.8071 - val_loss: 0.4112 - val_accuracy: 0.8524\n", "Epoch 3/250\n", "7/7 [==============================] - 2s 238ms/step - loss: 0.4190 - accuracy: 0.8321 - val_loss: 0.3396 - val_accuracy: 0.8714\n", "Epoch 4/250\n", "7/7 [==============================] - 0s 50ms/step - loss: 0.3400 - accuracy: 0.8750 - val_loss: 0.2862 - val_accuracy: 0.9000\n", "Epoch 5/250\n", "7/7 [==============================] - 0s 41ms/step - loss: 0.2593 - accuracy: 0.9024 - val_loss: 0.2468 - val_accuracy: 0.9190\n", "Epoch 6/250\n", "7/7 [==============================] - 0s 42ms/step - loss: 0.2445 - accuracy: 0.9226 - val_loss: 0.2197 - val_accuracy: 0.9286\n", "Epoch 7/250\n", "7/7 [==============================] - 0s 44ms/step - loss: 0.2418 - accuracy: 0.8964 - val_loss: 0.2003 - val_accuracy: 0.9286\n", "Epoch 8/250\n", "7/7 [==============================] - 0s 45ms/step - loss: 0.2139 - accuracy: 0.9250 - val_loss: 0.1869 - val_accuracy: 0.9333\n", "Epoch 9/250\n", "7/7 [==============================] - 0s 51ms/step - loss: 0.1989 - accuracy: 0.9202 - val_loss: 0.1736 - val_accuracy: 0.9333\n", "Epoch 10/250\n", "7/7 [==============================] - 0s 43ms/step - loss: 0.1819 - accuracy: 0.9333 - val_loss: 0.1640 - val_accuracy: 0.9381\n", "Epoch 11/250\n", "7/7 [==============================] - 0s 47ms/step - loss: 0.1871 - accuracy: 0.9226 - val_loss: 0.1536 - val_accuracy: 0.9429\n", "Epoch 12/250\n", "7/7 [==============================] - 0s 61ms/step - loss: 0.1591 - accuracy: 0.9381 - val_loss: 0.1441 - val_accuracy: 0.9429\n", "Epoch 13/250\n", "7/7 [==============================] - 1s 79ms/step - loss: 0.1578 - accuracy: 0.9357 - val_loss: 0.1371 - val_accuracy: 0.9429\n", "Epoch 14/250\n", "7/7 [==============================] - 1s 117ms/step - loss: 0.1601 - accuracy: 0.9321 - val_loss: 0.1321 - val_accuracy: 0.9429\n", "Epoch 15/250\n", "7/7 [==============================] - 3s 393ms/step - loss: 0.1405 - accuracy: 0.9476 - val_loss: 0.1276 - val_accuracy: 0.9476\n", "Epoch 16/250\n", "7/7 [==============================] - 0s 49ms/step - loss: 0.1553 - accuracy: 0.9417 - val_loss: 0.1252 - val_accuracy: 0.9429\n", "Epoch 17/250\n", "7/7 [==============================] - 0s 41ms/step - loss: 0.1542 - accuracy: 0.9333 - val_loss: 0.1221 - val_accuracy: 0.9429\n", "Epoch 18/250\n", "7/7 [==============================] - 0s 43ms/step - loss: 0.1221 - accuracy: 0.9583 - val_loss: 0.1189 - val_accuracy: 0.9476\n", "Epoch 19/250\n", "7/7 [==============================] - 5s 753ms/step - loss: 0.1400 - accuracy: 0.9393 - val_loss: 0.1152 - val_accuracy: 0.9429\n", "Epoch 20/250\n", "7/7 [==============================] - 1s 178ms/step - loss: 0.1058 - accuracy: 0.9619 - val_loss: 0.1097 - val_accuracy: 0.9524\n", "Epoch 21/250\n", "7/7 [==============================] - 3s 390ms/step - loss: 0.1342 - accuracy: 0.9429 - val_loss: 0.1054 - val_accuracy: 0.9476\n", "Epoch 22/250\n", "7/7 [==============================] - 0s 47ms/step - loss: 0.1257 - accuracy: 0.9548 - val_loss: 0.1019 - val_accuracy: 0.9524\n", "Epoch 23/250\n", "7/7 [==============================] - 0s 55ms/step - loss: 0.1232 - accuracy: 0.9476 - val_loss: 0.1004 - val_accuracy: 0.9571\n", "Epoch 24/250\n", "7/7 [==============================] - 6s 917ms/step - loss: 0.1320 - accuracy: 0.9488 - val_loss: 0.0978 - val_accuracy: 0.9571\n", "Epoch 25/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.1194 - accuracy: 0.9571 - val_loss: 0.1004 - val_accuracy: 0.9524\n", "Epoch 26/250\n", "7/7 [==============================] - 2s 239ms/step - loss: 0.1122 - accuracy: 0.9536 - val_loss: 0.0938 - val_accuracy: 0.9571\n", "Epoch 27/250\n", "7/7 [==============================] - 3s 390ms/step - loss: 0.1137 - 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0.0813 - val_accuracy: 0.9667\n", "Epoch 41/250\n", "7/7 [==============================] - 1s 143ms/step - loss: 0.0949 - accuracy: 0.9619 - val_loss: 0.0767 - val_accuracy: 0.9619\n", "Epoch 42/250\n", "7/7 [==============================] - 0s 49ms/step - loss: 0.0926 - accuracy: 0.9607 - val_loss: 0.0759 - val_accuracy: 0.9714\n", "Epoch 43/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.1116 - accuracy: 0.9488 - val_loss: 0.0771 - val_accuracy: 0.9714\n", "Epoch 44/250\n", "7/7 [==============================] - 0s 48ms/step - loss: 0.0892 - accuracy: 0.9619 - val_loss: 0.0720 - val_accuracy: 0.9619\n", "Epoch 45/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0705 - accuracy: 0.9690 - val_loss: 0.0737 - val_accuracy: 0.9667\n", "Epoch 46/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0902 - accuracy: 0.9619 - val_loss: 0.0756 - val_accuracy: 0.9619\n", "Epoch 47/250\n", "7/7 [==============================] - 0s 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[==============================] - 0s 8ms/step - loss: 0.0693 - accuracy: 0.9702 - val_loss: 0.0657 - val_accuracy: 0.9714\n", "Epoch 101/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0603 - accuracy: 0.9738 - val_loss: 0.0653 - val_accuracy: 0.9714\n", "Epoch 102/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0657 - accuracy: 0.9679 - val_loss: 0.0702 - val_accuracy: 0.9667\n", "Epoch 103/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0649 - accuracy: 0.9726 - val_loss: 0.0754 - val_accuracy: 0.9667\n", "Epoch 104/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0556 - accuracy: 0.9786 - val_loss: 0.0884 - val_accuracy: 0.9619\n", "Epoch 105/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0798 - accuracy: 0.9607 - val_loss: 0.0996 - val_accuracy: 0.9524\n", "Epoch 106/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0580 - accuracy: 0.9774 - val_loss: 0.0881 - val_accuracy: 0.9571\n", "Epoch 107/250\n", "7/7 [==============================] - 0s 47ms/step - loss: 0.0602 - accuracy: 0.9750 - val_loss: 0.0636 - val_accuracy: 0.9714\n", "Epoch 108/250\n", "7/7 [==============================] - 0s 62ms/step - loss: 0.0819 - accuracy: 0.9655 - val_loss: 0.0624 - val_accuracy: 0.9714\n", "Epoch 109/250\n", "7/7 [==============================] - 7s 1s/step - loss: 0.0531 - accuracy: 0.9774 - val_loss: 0.0620 - val_accuracy: 0.9762\n", "Epoch 110/250\n", "7/7 [==============================] - 1s 143ms/step - loss: 0.0589 - accuracy: 0.9726 - val_loss: 0.0606 - val_accuracy: 0.9762\n", "Epoch 111/250\n", "7/7 [==============================] - 3s 385ms/step - loss: 0.0863 - accuracy: 0.9548 - val_loss: 0.0548 - val_accuracy: 0.9810\n", "Epoch 112/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0684 - accuracy: 0.9702 - val_loss: 0.0588 - val_accuracy: 0.9762\n", "Epoch 113/250\n", "7/7 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val_loss: 0.0616 - val_accuracy: 0.9714\n", "Epoch 120/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0578 - accuracy: 0.9750 - val_loss: 0.0605 - val_accuracy: 0.9714\n", "Epoch 121/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0840 - accuracy: 0.9583 - val_loss: 0.0686 - val_accuracy: 0.9667\n", "Epoch 122/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0453 - accuracy: 0.9798 - val_loss: 0.0804 - val_accuracy: 0.9619\n", "Epoch 123/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0600 - accuracy: 0.9762 - val_loss: 0.0920 - val_accuracy: 0.9619\n", "Epoch 124/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0570 - accuracy: 0.9726 - val_loss: 0.0752 - val_accuracy: 0.9667\n", "Epoch 125/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0776 - accuracy: 0.9655 - val_loss: 0.0740 - val_accuracy: 0.9619\n", "Epoch 126/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0522 - accuracy: 0.9762 - val_loss: 0.0717 - val_accuracy: 0.9619\n", "Epoch 127/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0495 - accuracy: 0.9774 - val_loss: 0.0751 - val_accuracy: 0.9619\n", "Epoch 128/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0670 - accuracy: 0.9714 - val_loss: 0.0677 - val_accuracy: 0.9714\n", "Epoch 129/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0314 - accuracy: 0.9905 - val_loss: 0.0637 - val_accuracy: 0.9714\n", "Epoch 130/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0587 - accuracy: 0.9726 - val_loss: 0.0649 - val_accuracy: 0.9714\n", "Epoch 131/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0574 - accuracy: 0.9750 - val_loss: 0.0648 - val_accuracy: 0.9714\n", "Epoch 132/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0650 - accuracy: 0.9726 - val_loss: 0.0681 - val_accuracy: 0.9667\n", "Epoch 133/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0709 - accuracy: 0.9655 - val_loss: 0.0719 - val_accuracy: 0.9667\n", "Epoch 134/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0878 - accuracy: 0.9595 - val_loss: 0.0723 - val_accuracy: 0.9619\n", "Epoch 135/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0640 - accuracy: 0.9750 - val_loss: 0.0686 - val_accuracy: 0.9667\n", "Epoch 136/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0588 - accuracy: 0.9738 - val_loss: 0.0714 - val_accuracy: 0.9667\n", "Epoch 137/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0598 - accuracy: 0.9714 - val_loss: 0.0705 - val_accuracy: 0.9667\n", "Epoch 138/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0594 - accuracy: 0.9726 - val_loss: 0.0644 - val_accuracy: 0.9667\n", "Epoch 139/250\n", "7/7 [==============================] - 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"Epoch 146/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0755 - accuracy: 0.9690 - val_loss: 0.0800 - val_accuracy: 0.9571\n", "Epoch 147/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0557 - accuracy: 0.9762 - val_loss: 0.0790 - val_accuracy: 0.9571\n", "Epoch 148/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0739 - accuracy: 0.9655 - val_loss: 0.0803 - val_accuracy: 0.9619\n", "Epoch 149/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0575 - accuracy: 0.9726 - val_loss: 0.0630 - val_accuracy: 0.9667\n", "Epoch 150/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0416 - accuracy: 0.9810 - val_loss: 0.0644 - val_accuracy: 0.9667\n", "Epoch 151/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0520 - accuracy: 0.9762 - val_loss: 0.0600 - val_accuracy: 0.9762\n", "Epoch 152/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0565 - 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[==============================] - 0s 8ms/step - loss: 0.0451 - accuracy: 0.9762 - val_loss: 0.0796 - val_accuracy: 0.9619\n", "Epoch 160/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0536 - accuracy: 0.9702 - val_loss: 0.0732 - val_accuracy: 0.9619\n", "Epoch 161/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0483 - accuracy: 0.9798 - val_loss: 0.0683 - val_accuracy: 0.9714\n", "Epoch 162/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0480 - accuracy: 0.9786 - val_loss: 0.0636 - val_accuracy: 0.9714\n", "Epoch 163/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0572 - accuracy: 0.9762 - val_loss: 0.0602 - val_accuracy: 0.9762\n", "Epoch 164/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0476 - accuracy: 0.9798 - val_loss: 0.0581 - val_accuracy: 0.9762\n", "Epoch 165/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0576 - accuracy: 0.9738 - val_loss: 0.0597 - val_accuracy: 0.9714\n", "Epoch 166/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0564 - accuracy: 0.9762 - val_loss: 0.0683 - val_accuracy: 0.9667\n", "Epoch 167/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0701 - accuracy: 0.9655 - val_loss: 0.0738 - val_accuracy: 0.9667\n", "Epoch 168/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0381 - accuracy: 0.9833 - val_loss: 0.0809 - val_accuracy: 0.9619\n", "Epoch 169/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0585 - accuracy: 0.9750 - val_loss: 0.0766 - val_accuracy: 0.9619\n", "Epoch 170/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0610 - accuracy: 0.9726 - val_loss: 0.0726 - val_accuracy: 0.9714\n", "Epoch 171/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0468 - accuracy: 0.9798 - val_loss: 0.0700 - val_accuracy: 0.9667\n", "Epoch 172/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0849 - accuracy: 0.9595 - val_loss: 0.0859 - val_accuracy: 0.9524\n", "Epoch 173/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0489 - accuracy: 0.9762 - val_loss: 0.0842 - val_accuracy: 0.9619\n", "Epoch 174/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0449 - accuracy: 0.9810 - val_loss: 0.0676 - val_accuracy: 0.9667\n", "Epoch 175/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0535 - accuracy: 0.9762 - val_loss: 0.0604 - val_accuracy: 0.9762\n", "Epoch 176/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0701 - accuracy: 0.9655 - val_loss: 0.0689 - val_accuracy: 0.9714\n", "Epoch 177/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0465 - accuracy: 0.9786 - val_loss: 0.0799 - val_accuracy: 0.9619\n", "Epoch 178/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0686 - accuracy: 0.9690 - val_loss: 0.0746 - val_accuracy: 0.9619\n", "Epoch 179/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0522 - accuracy: 0.9774 - val_loss: 0.0663 - val_accuracy: 0.9667\n", "Epoch 180/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0537 - accuracy: 0.9738 - val_loss: 0.0736 - val_accuracy: 0.9619\n", "Epoch 181/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0383 - accuracy: 0.9810 - val_loss: 0.0738 - val_accuracy: 0.9619\n", "Epoch 182/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0459 - accuracy: 0.9774 - val_loss: 0.0633 - val_accuracy: 0.9667\n", "Epoch 183/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0460 - accuracy: 0.9774 - val_loss: 0.0626 - val_accuracy: 0.9714\n", "Epoch 184/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0795 - accuracy: 0.9607 - val_loss: 0.0795 - val_accuracy: 0.9667\n", "Epoch 185/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0599 - 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[==============================] - 0s 8ms/step - loss: 0.0503 - accuracy: 0.9786 - val_loss: 0.0716 - val_accuracy: 0.9714\n", "Epoch 193/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0787 - accuracy: 0.9631 - val_loss: 0.0836 - val_accuracy: 0.9619\n", "Epoch 194/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0819 - accuracy: 0.9560 - val_loss: 0.0890 - val_accuracy: 0.9524\n", "Epoch 195/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0712 - accuracy: 0.9655 - val_loss: 0.1052 - val_accuracy: 0.9381\n", "Epoch 196/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0452 - accuracy: 0.9810 - val_loss: 0.0873 - val_accuracy: 0.9571\n", "Epoch 197/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0466 - accuracy: 0.9798 - val_loss: 0.0988 - val_accuracy: 0.9524\n", "Epoch 198/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.1038 - accuracy: 0.9524 - val_loss: 0.0964 - val_accuracy: 0.9524\n", "Epoch 199/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0917 - accuracy: 0.9571 - val_loss: 0.0850 - val_accuracy: 0.9571\n", "Epoch 200/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0411 - accuracy: 0.9810 - val_loss: 0.0697 - val_accuracy: 0.9619\n", "Epoch 201/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0610 - accuracy: 0.9690 - val_loss: 0.0706 - val_accuracy: 0.9667\n", "Epoch 202/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0466 - accuracy: 0.9774 - val_loss: 0.0723 - val_accuracy: 0.9619\n", "Epoch 203/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0527 - accuracy: 0.9750 - val_loss: 0.0649 - val_accuracy: 0.9667\n", "Epoch 204/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0487 - accuracy: 0.9786 - val_loss: 0.0621 - val_accuracy: 0.9714\n", "Epoch 205/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0618 - accuracy: 0.9702 - val_loss: 0.0641 - val_accuracy: 0.9714\n", "Epoch 206/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0431 - accuracy: 0.9798 - val_loss: 0.0719 - val_accuracy: 0.9667\n", "Epoch 207/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0705 - accuracy: 0.9643 - val_loss: 0.0733 - val_accuracy: 0.9667\n", "Epoch 208/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0686 - accuracy: 0.9667 - val_loss: 0.0859 - val_accuracy: 0.9524\n", "Epoch 209/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0659 - accuracy: 0.9690 - val_loss: 0.0954 - val_accuracy: 0.9524\n", "Epoch 210/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0443 - accuracy: 0.9810 - val_loss: 0.0984 - val_accuracy: 0.9476\n", "Epoch 211/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0668 - accuracy: 0.9667 - val_loss: 0.0878 - val_accuracy: 0.9571\n", "Epoch 212/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0508 - accuracy: 0.9762 - val_loss: 0.0622 - val_accuracy: 0.9714\n", "Epoch 213/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0487 - accuracy: 0.9798 - val_loss: 0.0599 - val_accuracy: 0.9714\n", "Epoch 214/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0456 - accuracy: 0.9774 - val_loss: 0.0659 - val_accuracy: 0.9667\n", "Epoch 215/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0405 - accuracy: 0.9810 - val_loss: 0.0647 - val_accuracy: 0.9667\n", "Epoch 216/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0688 - accuracy: 0.9679 - val_loss: 0.0656 - val_accuracy: 0.9714\n", "Epoch 217/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0591 - accuracy: 0.9714 - val_loss: 0.0695 - val_accuracy: 0.9667\n", "Epoch 218/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0441 - accuracy: 0.9774 - val_loss: 0.0679 - val_accuracy: 0.9667\n", "Epoch 219/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0384 - accuracy: 0.9821 - val_loss: 0.0691 - val_accuracy: 0.9667\n", "Epoch 220/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0655 - accuracy: 0.9667 - val_loss: 0.0728 - val_accuracy: 0.9619\n", "Epoch 221/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0420 - accuracy: 0.9821 - val_loss: 0.0753 - val_accuracy: 0.9619\n", "Epoch 222/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0226 - accuracy: 0.9905 - val_loss: 0.0854 - val_accuracy: 0.9571\n", "Epoch 223/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0424 - accuracy: 0.9786 - val_loss: 0.0956 - val_accuracy: 0.9476\n", "Epoch 224/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0424 - accuracy: 0.9798 - val_loss: 0.0911 - val_accuracy: 0.9619\n", "Epoch 225/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0444 - accuracy: 0.9798 - val_loss: 0.0846 - val_accuracy: 0.9571\n", "Epoch 226/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0472 - accuracy: 0.9786 - val_loss: 0.0765 - val_accuracy: 0.9619\n", "Epoch 227/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0806 - accuracy: 0.9595 - val_loss: 0.0709 - val_accuracy: 0.9667\n", "Epoch 228/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0423 - accuracy: 0.9810 - val_loss: 0.0682 - val_accuracy: 0.9667\n", "Epoch 229/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0503 - accuracy: 0.9798 - val_loss: 0.0679 - val_accuracy: 0.9667\n", "Epoch 230/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0398 - accuracy: 0.9821 - val_loss: 0.0720 - val_accuracy: 0.9667\n", "Epoch 231/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0577 - accuracy: 0.9702 - val_loss: 0.0816 - val_accuracy: 0.9571\n", "Epoch 232/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0467 - accuracy: 0.9786 - val_loss: 0.1012 - val_accuracy: 0.9524\n", "Epoch 233/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0512 - accuracy: 0.9762 - val_loss: 0.1099 - val_accuracy: 0.9476\n", "Epoch 234/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0661 - accuracy: 0.9690 - val_loss: 0.1149 - val_accuracy: 0.9429\n", "Epoch 235/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0681 - accuracy: 0.9690 - val_loss: 0.1062 - val_accuracy: 0.9476\n", "Epoch 236/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0641 - accuracy: 0.9690 - val_loss: 0.1089 - val_accuracy: 0.9524\n", "Epoch 237/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0447 - accuracy: 0.9786 - val_loss: 0.1029 - val_accuracy: 0.9524\n", "Epoch 238/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0584 - accuracy: 0.9726 - val_loss: 0.0812 - val_accuracy: 0.9667\n", "Epoch 239/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0542 - accuracy: 0.9750 - val_loss: 0.0880 - val_accuracy: 0.9524\n", "Epoch 240/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0568 - accuracy: 0.9738 - val_loss: 0.0908 - val_accuracy: 0.9571\n", "Epoch 241/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0713 - accuracy: 0.9655 - val_loss: 0.0939 - val_accuracy: 0.9524\n", "Epoch 242/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0608 - accuracy: 0.9702 - val_loss: 0.0875 - val_accuracy: 0.9571\n", "Epoch 243/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0478 - accuracy: 0.9750 - val_loss: 0.0817 - val_accuracy: 0.9619\n", "Epoch 244/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0463 - accuracy: 0.9762 - val_loss: 0.0643 - val_accuracy: 0.9714\n", "Epoch 245/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0498 - accuracy: 0.9750 - val_loss: 0.0641 - val_accuracy: 0.9714\n", "Epoch 246/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0470 - accuracy: 0.9762 - val_loss: 0.0656 - val_accuracy: 0.9714\n", "Epoch 247/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0557 - accuracy: 0.9738 - val_loss: 0.0686 - val_accuracy: 0.9714\n", "Epoch 248/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0599 - accuracy: 0.9679 - val_loss: 0.0836 - val_accuracy: 0.9619\n", "Epoch 249/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0391 - accuracy: 0.9798 - val_loss: 0.0967 - val_accuracy: 0.9524\n", "Epoch 250/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0519 - accuracy: 0.9750 - val_loss: 0.1019 - val_accuracy: 0.9429\n", "Valid results - Loss: 0.10188767313957214 - Accuracy: 94.28571462631226%\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", 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\n", 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\n", 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" ] }, "metadata": { "tags": [], "needs_background": "light" } }, { "output_type": "stream", "text": [ "DNN results\n", "accuracy : 0.9916666666666667\n", "Training Recall: 0.9916666666666667\n", "Training Precision: 0.9916860215053764\n", "Confusion matrix: \n", "[[373 5]\n", " [ 2 460]]\n", "Validation set report:\n", "cross Loss: -33.20590602329799\n", "hing Loss: 0.46190476190476193\n", "Report precision recall f1-score support\n", "\n", " 0 0.99 0.97 0.98 93\n", " 1 0.97 0.99 0.98 117\n", "\n", " accuracy 0.98 210\n", " macro avg 0.98 0.98 0.98 210\n", "weighted avg 0.98 0.98 0.98 210\n", "\n", "accuracy : 0.9809523809523809\n", "Validation Recall: 0.9809523809523809\n", "Validation Precision: 0.981087819743282\n", "Confusion matrix: \n", "[[ 90 3]\n", " [ 1 116]]\n", "\n", "\\KNN Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 0.9047619047619048\n", "Validation Recall: 0.9047619047619048\n", "Validation Precision: 0.9047619047619048\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[ 83 10]\n", " [ 10 107]]\n", "\n", "\n", "Support vector machine Result\n", "\n", "Train accuracy: 0.9583333333333334\n", "Training Recall: 0.9583333333333334\n", "Training Precision: 0.9590989639736158\n", "\n", "\n", "Validation accuracy: 0.9476190476190476\n", "Validation Recall: 0.9476190476190476\n", "Validation Precision: 0.9505102040816327\n", "\n", "Train Confusion matrix:\n", " [[351 27]\n", " [ 8 454]]\n", "Validation Confusion matrix:\n", " [[ 83 10]\n", " [ 1 116]]\n", "\n", "\n", "Random Forest Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 1.0\n", "Validation Recall: 1.0\n", "Validation Precision: 1.0\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[ 93 0]\n", " [ 0 117]]\n", "\n", "\n", "LDA Result\n", "\n", "Train accuracy: 0.9369047619047619\n", "Training Recall: 0.9369047619047619\n", "Training Precision: 0.9398436168524733\n", "\n", "\n", "Validation accuracy: 0.9428571428571428\n", "Validation Recall: 0.9428571428571428\n", "Validation Precision: 0.9450084033613445\n", "\n", "Train Confusion matrix:\n", " [[333 45]\n", " [ 8 454]]\n", "Validation Confusion matrix:\n", " [[ 83 10]\n", " [ 2 115]]\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "VoM6wjR8md_0", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "4af44bfc-1845-44b7-8061-8ac2fe6f36eb" }, "source": [ "#on 30 feature\n", "train_models(X, y, y_cat)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Before selectoin\n", "(1050, 28)\n", "After selectoin\n", "(1050, 28)\n", "Epoch 1/250\n", "7/7 [==============================] - 0s 56ms/step - loss: 0.8657 - accuracy: 0.6226 - val_loss: 0.5063 - val_accuracy: 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[==============================] - 0s 7ms/step - loss: 0.0351 - accuracy: 0.9833 - val_loss: 0.0958 - val_accuracy: 0.9524\n", "Epoch 122/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0725 - accuracy: 0.9643 - val_loss: 0.0979 - val_accuracy: 0.9524\n", "Epoch 123/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0730 - accuracy: 0.9607 - val_loss: 0.0979 - val_accuracy: 0.9476\n", "Epoch 124/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0559 - accuracy: 0.9750 - val_loss: 0.1056 - val_accuracy: 0.9429\n", "Epoch 125/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0601 - accuracy: 0.9702 - val_loss: 0.0999 - val_accuracy: 0.9476\n", "Epoch 126/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0384 - accuracy: 0.9833 - val_loss: 0.0941 - val_accuracy: 0.9571\n", "Epoch 127/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0546 - accuracy: 0.9786 - val_loss: 0.0922 - val_accuracy: 0.9524\n", "Epoch 128/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0831 - accuracy: 0.9571 - val_loss: 0.0819 - val_accuracy: 0.9619\n", "Epoch 129/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0653 - accuracy: 0.9690 - val_loss: 0.0784 - val_accuracy: 0.9667\n", "Epoch 130/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0658 - accuracy: 0.9690 - val_loss: 0.0735 - val_accuracy: 0.9667\n", "Epoch 131/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0736 - accuracy: 0.9643 - val_loss: 0.0725 - val_accuracy: 0.9619\n", "Epoch 132/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0580 - accuracy: 0.9738 - val_loss: 0.0701 - val_accuracy: 0.9762\n", "Epoch 133/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0494 - accuracy: 0.9798 - val_loss: 0.0816 - val_accuracy: 0.9571\n", "Epoch 134/250\n", "7/7 [==============================] - 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"Epoch 141/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0735 - accuracy: 0.9690 - val_loss: 0.0737 - val_accuracy: 0.9667\n", "Epoch 142/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0723 - accuracy: 0.9631 - val_loss: 0.0694 - val_accuracy: 0.9667\n", "Epoch 143/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0810 - accuracy: 0.9643 - val_loss: 0.0678 - val_accuracy: 0.9714\n", "Epoch 144/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0537 - accuracy: 0.9786 - val_loss: 0.0767 - val_accuracy: 0.9571\n", "Epoch 145/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0588 - accuracy: 0.9762 - val_loss: 0.0867 - val_accuracy: 0.9571\n", "Epoch 146/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0588 - accuracy: 0.9774 - val_loss: 0.0919 - val_accuracy: 0.9476\n", "Epoch 147/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0439 - 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[==============================] - 0s 7ms/step - loss: 0.0427 - accuracy: 0.9821 - val_loss: 0.0737 - val_accuracy: 0.9619\n", "Epoch 155/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0516 - accuracy: 0.9762 - val_loss: 0.0714 - val_accuracy: 0.9667\n", "Epoch 156/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0523 - accuracy: 0.9750 - val_loss: 0.0747 - val_accuracy: 0.9667\n", "Epoch 157/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0458 - accuracy: 0.9798 - val_loss: 0.0843 - val_accuracy: 0.9571\n", "Epoch 158/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0509 - accuracy: 0.9750 - val_loss: 0.0891 - val_accuracy: 0.9524\n", "Epoch 159/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0371 - accuracy: 0.9845 - val_loss: 0.0895 - val_accuracy: 0.9524\n", "Epoch 160/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0692 - accuracy: 0.9702 - val_loss: 0.0993 - val_accuracy: 0.9524\n", "Epoch 161/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0598 - accuracy: 0.9690 - val_loss: 0.1053 - val_accuracy: 0.9429\n", "Epoch 162/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0368 - accuracy: 0.9833 - val_loss: 0.0948 - val_accuracy: 0.9524\n", "Epoch 163/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0551 - accuracy: 0.9738 - val_loss: 0.0850 - val_accuracy: 0.9571\n", "Epoch 164/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0330 - accuracy: 0.9869 - val_loss: 0.0834 - val_accuracy: 0.9619\n", "Epoch 165/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0530 - accuracy: 0.9726 - val_loss: 0.0809 - val_accuracy: 0.9619\n", "Epoch 166/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0480 - accuracy: 0.9810 - val_loss: 0.0755 - val_accuracy: 0.9667\n", "Epoch 167/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0479 - accuracy: 0.9762 - val_loss: 0.0854 - val_accuracy: 0.9667\n", "Epoch 168/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0352 - accuracy: 0.9833 - val_loss: 0.0984 - val_accuracy: 0.9429\n", "Epoch 169/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0623 - accuracy: 0.9714 - val_loss: 0.0877 - val_accuracy: 0.9619\n", "Epoch 170/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0480 - accuracy: 0.9798 - val_loss: 0.0949 - val_accuracy: 0.9524\n", "Epoch 171/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0738 - accuracy: 0.9655 - val_loss: 0.1035 - val_accuracy: 0.9429\n", "Epoch 172/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0390 - accuracy: 0.9798 - val_loss: 0.0959 - val_accuracy: 0.9524\n", "Epoch 173/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0628 - accuracy: 0.9726 - val_loss: 0.0831 - val_accuracy: 0.9667\n", "Epoch 174/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0533 - accuracy: 0.9738 - val_loss: 0.0854 - val_accuracy: 0.9571\n", "Epoch 175/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0445 - accuracy: 0.9810 - val_loss: 0.0767 - val_accuracy: 0.9667\n", "Epoch 176/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0374 - accuracy: 0.9845 - val_loss: 0.0815 - val_accuracy: 0.9571\n", "Epoch 177/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0423 - accuracy: 0.9833 - val_loss: 0.0804 - val_accuracy: 0.9619\n", "Epoch 178/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0892 - accuracy: 0.9583 - val_loss: 0.0710 - val_accuracy: 0.9667\n", "Epoch 179/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0448 - accuracy: 0.9798 - val_loss: 0.0767 - val_accuracy: 0.9667\n", "Epoch 180/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0511 - 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[==============================] - 0s 8ms/step - loss: 0.0798 - accuracy: 0.9619 - val_loss: 0.0727 - val_accuracy: 0.9667\n", "Epoch 188/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0662 - accuracy: 0.9679 - val_loss: 0.0688 - val_accuracy: 0.9714\n", "Epoch 189/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0575 - accuracy: 0.9738 - val_loss: 0.0744 - val_accuracy: 0.9667\n", "Epoch 190/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0596 - accuracy: 0.9714 - val_loss: 0.0819 - val_accuracy: 0.9571\n", "Epoch 191/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0392 - accuracy: 0.9869 - val_loss: 0.0844 - val_accuracy: 0.9571\n", "Epoch 192/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0309 - accuracy: 0.9857 - val_loss: 0.0911 - val_accuracy: 0.9571\n", "Epoch 193/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0651 - accuracy: 0.9702 - val_loss: 0.0864 - val_accuracy: 0.9571\n", "Epoch 194/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0630 - accuracy: 0.9702 - val_loss: 0.0839 - val_accuracy: 0.9571\n", "Epoch 195/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0634 - accuracy: 0.9679 - val_loss: 0.0808 - val_accuracy: 0.9571\n", "Epoch 196/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0491 - accuracy: 0.9774 - val_loss: 0.0851 - val_accuracy: 0.9571\n", "Epoch 197/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0493 - accuracy: 0.9786 - val_loss: 0.0921 - val_accuracy: 0.9571\n", "Epoch 198/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0282 - accuracy: 0.9893 - val_loss: 0.0904 - val_accuracy: 0.9524\n", "Epoch 199/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0457 - accuracy: 0.9833 - val_loss: 0.0904 - val_accuracy: 0.9571\n", "Epoch 200/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0429 - accuracy: 0.9810 - val_loss: 0.0940 - val_accuracy: 0.9571\n", "Epoch 201/250\n", "7/7 [==============================] - 0s 6ms/step - loss: 0.0482 - accuracy: 0.9798 - val_loss: 0.0965 - val_accuracy: 0.9524\n", "Epoch 202/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0444 - accuracy: 0.9798 - val_loss: 0.1029 - val_accuracy: 0.9476\n", "Epoch 203/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0519 - accuracy: 0.9738 - val_loss: 0.1065 - val_accuracy: 0.9476\n", "Epoch 204/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0984 - accuracy: 0.9512 - val_loss: 0.0987 - val_accuracy: 0.9476\n", "Epoch 205/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0521 - accuracy: 0.9774 - val_loss: 0.0901 - val_accuracy: 0.9571\n", "Epoch 206/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0663 - accuracy: 0.9714 - val_loss: 0.0825 - val_accuracy: 0.9619\n", "Epoch 207/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0453 - accuracy: 0.9810 - val_loss: 0.0785 - val_accuracy: 0.9619\n", "Epoch 208/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0729 - accuracy: 0.9643 - val_loss: 0.0787 - val_accuracy: 0.9619\n", "Epoch 209/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0443 - accuracy: 0.9786 - val_loss: 0.0939 - val_accuracy: 0.9571\n", "Epoch 210/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0309 - accuracy: 0.9869 - val_loss: 0.1130 - val_accuracy: 0.9429\n", "Epoch 211/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0520 - accuracy: 0.9750 - val_loss: 0.1192 - val_accuracy: 0.9381\n", "Epoch 212/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0466 - accuracy: 0.9821 - val_loss: 0.0850 - val_accuracy: 0.9619\n", "Epoch 213/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0544 - 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[==============================] - 0s 8ms/step - loss: 0.0356 - accuracy: 0.9821 - val_loss: 0.0854 - val_accuracy: 0.9571\n", "Epoch 221/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0429 - accuracy: 0.9821 - val_loss: 0.0996 - val_accuracy: 0.9476\n", "Epoch 222/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0566 - accuracy: 0.9738 - val_loss: 0.1054 - val_accuracy: 0.9524\n", "Epoch 223/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0567 - accuracy: 0.9726 - val_loss: 0.0948 - val_accuracy: 0.9571\n", "Epoch 224/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0798 - accuracy: 0.9595 - val_loss: 0.0733 - val_accuracy: 0.9619\n", "Epoch 225/250\n", "7/7 [==============================] - 0s 33ms/step - loss: 0.0357 - accuracy: 0.9857 - val_loss: 0.0619 - val_accuracy: 0.9762\n", "Epoch 226/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0609 - accuracy: 0.9702 - val_loss: 0.0700 - val_accuracy: 0.9667\n", "Epoch 227/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0412 - accuracy: 0.9821 - val_loss: 0.0836 - val_accuracy: 0.9571\n", "Epoch 228/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0441 - accuracy: 0.9786 - val_loss: 0.1056 - val_accuracy: 0.9429\n", "Epoch 229/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0446 - accuracy: 0.9810 - val_loss: 0.1058 - val_accuracy: 0.9476\n", "Epoch 230/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0493 - accuracy: 0.9774 - val_loss: 0.0953 - val_accuracy: 0.9524\n", "Epoch 231/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0532 - accuracy: 0.9750 - val_loss: 0.0875 - val_accuracy: 0.9619\n", "Epoch 232/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0710 - accuracy: 0.9655 - val_loss: 0.0941 - val_accuracy: 0.9524\n", "Epoch 233/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0607 - accuracy: 0.9690 - val_loss: 0.0968 - val_accuracy: 0.9524\n", "Epoch 234/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0374 - accuracy: 0.9821 - val_loss: 0.0998 - val_accuracy: 0.9476\n", "Epoch 235/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0398 - accuracy: 0.9833 - val_loss: 0.1009 - val_accuracy: 0.9476\n", "Epoch 236/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0412 - accuracy: 0.9798 - val_loss: 0.0995 - val_accuracy: 0.9476\n", "Epoch 237/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0459 - accuracy: 0.9798 - val_loss: 0.0845 - val_accuracy: 0.9619\n", "Epoch 238/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0296 - accuracy: 0.9857 - val_loss: 0.0988 - val_accuracy: 0.9476\n", "Epoch 239/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0381 - accuracy: 0.9857 - val_loss: 0.1135 - val_accuracy: 0.9429\n", "Epoch 240/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0749 - accuracy: 0.9631 - val_loss: 0.1122 - val_accuracy: 0.9429\n", "Epoch 241/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0625 - accuracy: 0.9690 - val_loss: 0.0996 - val_accuracy: 0.9524\n", "Epoch 242/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0253 - accuracy: 0.9881 - val_loss: 0.0800 - val_accuracy: 0.9619\n", "Epoch 243/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0376 - accuracy: 0.9821 - val_loss: 0.0852 - val_accuracy: 0.9571\n", "Epoch 244/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0448 - accuracy: 0.9762 - val_loss: 0.0742 - val_accuracy: 0.9667\n", "Epoch 245/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0651 - accuracy: 0.9679 - val_loss: 0.0707 - val_accuracy: 0.9667\n", "Epoch 246/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0732 - accuracy: 0.9643 - val_loss: 0.0776 - val_accuracy: 0.9667\n", "Epoch 247/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0508 - accuracy: 0.9786 - val_loss: 0.0984 - val_accuracy: 0.9571\n", "Epoch 248/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0493 - accuracy: 0.9762 - val_loss: 0.1028 - val_accuracy: 0.9476\n", "Epoch 249/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0495 - accuracy: 0.9774 - val_loss: 0.1022 - val_accuracy: 0.9476\n", "Epoch 250/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0535 - accuracy: 0.9738 - val_loss: 0.0903 - val_accuracy: 0.9571\n", "Valid results - Loss: 0.09026086330413818 - Accuracy: 95.71428298950195%\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", 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\n", 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\n", 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" ] }, "metadata": { "tags": [], "needs_background": "light" } }, { "output_type": "stream", "text": [ "DNN results\n", "accuracy : 0.9952380952380953\n", "Training Recall: 0.9952380952380953\n", "Training Precision: 0.9952380952380953\n", "Confusion matrix: \n", "[[376 2]\n", " [ 2 460]]\n", "Validation set report:\n", "cross Loss: -32.87842655976613\n", "hing Loss: 0.4666666666666667\n", "Report precision recall f1-score support\n", "\n", " 0 0.98 0.97 0.97 93\n", " 1 0.97 0.98 0.98 117\n", "\n", " accuracy 0.98 210\n", " macro avg 0.98 0.98 0.98 210\n", "weighted avg 0.98 0.98 0.98 210\n", "\n", "accuracy : 0.9761904761904762\n", "Validation Recall: 0.9761904761904762\n", "Validation Precision: 0.9762080218970417\n", "Confusion matrix: \n", "[[ 90 3]\n", " [ 2 115]]\n", "\n", "\\KNN Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 0.9047619047619048\n", "Validation Recall: 0.9047619047619048\n", "Validation Precision: 0.9047619047619048\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[ 83 10]\n", " [ 10 107]]\n", "\n", "\n", "Support vector machine Result\n", "\n", "Train accuracy: 0.9583333333333334\n", "Training Recall: 0.9583333333333334\n", "Training Precision: 0.9590989639736158\n", "\n", "\n", "Validation accuracy: 0.9476190476190476\n", "Validation Recall: 0.9476190476190476\n", "Validation Precision: 0.9505102040816327\n", "\n", "Train Confusion matrix:\n", " [[351 27]\n", " [ 8 454]]\n", "Validation Confusion matrix:\n", " [[ 83 10]\n", " [ 1 116]]\n", "\n", "\n", "Random Forest Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 1.0\n", "Validation Recall: 1.0\n", "Validation Precision: 1.0\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[ 93 0]\n", " [ 0 117]]\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "DvM8SDYdku3o", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "79aaf3e9-fcd8-4a37-c57a-de6236cac4ac" }, "source": [ "#on 20 feature\n", "train_models(X, y, y_cat)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Before selectoin\n", "(1050, 19)\n", "After selectoin\n", "(1050, 19)\n", "Epoch 1/250\n", "7/7 [==============================] - 0s 58ms/step - loss: 0.8549 - accuracy: 0.6083 - val_loss: 0.5096 - val_accuracy: 0.8190\n", "Epoch 2/250\n", "7/7 [==============================] - 3s 391ms/step - loss: 0.5200 - accuracy: 0.8036 - val_loss: 0.3859 - val_accuracy: 0.8381\n", "Epoch 3/250\n", "7/7 [==============================] - 0s 44ms/step - loss: 0.3786 - accuracy: 0.8643 - val_loss: 0.3191 - val_accuracy: 0.8762\n", "Epoch 4/250\n", "7/7 [==============================] - 0s 44ms/step - loss: 0.3027 - accuracy: 0.8929 - val_loss: 0.2711 - 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"Epoch 104/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0473 - accuracy: 0.9786 - val_loss: 0.0813 - val_accuracy: 0.9571\n", "Epoch 105/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0677 - accuracy: 0.9726 - val_loss: 0.0848 - val_accuracy: 0.9571\n", "Epoch 106/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0616 - accuracy: 0.9726 - val_loss: 0.0855 - val_accuracy: 0.9571\n", "Epoch 107/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0510 - accuracy: 0.9821 - val_loss: 0.0794 - val_accuracy: 0.9667\n", "Epoch 108/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0825 - accuracy: 0.9548 - val_loss: 0.0796 - val_accuracy: 0.9571\n", "Epoch 109/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0613 - accuracy: 0.9738 - val_loss: 0.0750 - val_accuracy: 0.9571\n", "Epoch 110/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0604 - 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0.0814 - val_accuracy: 0.9619\n", "Epoch 124/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0506 - accuracy: 0.9798 - val_loss: 0.0683 - val_accuracy: 0.9714\n", "Epoch 125/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0654 - accuracy: 0.9690 - val_loss: 0.0616 - val_accuracy: 0.9714\n", "Epoch 126/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0510 - accuracy: 0.9774 - val_loss: 0.0675 - val_accuracy: 0.9714\n", "Epoch 127/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0683 - accuracy: 0.9702 - val_loss: 0.0698 - val_accuracy: 0.9714\n", "Epoch 128/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0570 - accuracy: 0.9726 - val_loss: 0.0650 - val_accuracy: 0.9714\n", "Epoch 129/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0585 - accuracy: 0.9726 - val_loss: 0.0765 - val_accuracy: 0.9667\n", "Epoch 130/250\n", "7/7 [==============================] - 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[==============================] - 0s 7ms/step - loss: 0.0641 - accuracy: 0.9702 - val_loss: 0.0717 - val_accuracy: 0.9667\n", "Epoch 151/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0577 - accuracy: 0.9702 - val_loss: 0.0730 - val_accuracy: 0.9619\n", "Epoch 152/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0574 - accuracy: 0.9762 - val_loss: 0.0733 - val_accuracy: 0.9667\n", "Epoch 153/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0580 - accuracy: 0.9738 - val_loss: 0.0740 - val_accuracy: 0.9667\n", "Epoch 154/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0505 - accuracy: 0.9726 - val_loss: 0.0762 - val_accuracy: 0.9619\n", "Epoch 155/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0493 - accuracy: 0.9762 - val_loss: 0.0799 - val_accuracy: 0.9619\n", "Epoch 156/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0738 - accuracy: 0.9667 - val_loss: 0.0765 - val_accuracy: 0.9619\n", "Epoch 157/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0685 - accuracy: 0.9714 - val_loss: 0.0790 - val_accuracy: 0.9619\n", "Epoch 158/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0586 - accuracy: 0.9726 - val_loss: 0.0822 - val_accuracy: 0.9571\n", "Epoch 159/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0486 - accuracy: 0.9810 - val_loss: 0.0758 - val_accuracy: 0.9619\n", "Epoch 160/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0444 - accuracy: 0.9798 - val_loss: 0.0724 - val_accuracy: 0.9667\n", "Epoch 161/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0610 - accuracy: 0.9714 - val_loss: 0.0803 - val_accuracy: 0.9619\n", "Epoch 162/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0644 - accuracy: 0.9714 - val_loss: 0.0953 - val_accuracy: 0.9476\n", "Epoch 163/250\n", "7/7 [==============================] - 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"Epoch 170/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0545 - accuracy: 0.9750 - val_loss: 0.0911 - val_accuracy: 0.9571\n", "Epoch 171/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0626 - accuracy: 0.9714 - val_loss: 0.0787 - val_accuracy: 0.9667\n", "Epoch 172/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0409 - accuracy: 0.9810 - val_loss: 0.0742 - val_accuracy: 0.9571\n", "Epoch 173/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0567 - accuracy: 0.9774 - val_loss: 0.0833 - val_accuracy: 0.9571\n", "Epoch 174/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0483 - accuracy: 0.9786 - val_loss: 0.0880 - val_accuracy: 0.9571\n", "Epoch 175/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0588 - accuracy: 0.9714 - val_loss: 0.0890 - val_accuracy: 0.9571\n", "Epoch 176/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0566 - 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[==============================] - 0s 7ms/step - loss: 0.0586 - accuracy: 0.9690 - val_loss: 0.0765 - val_accuracy: 0.9667\n", "Epoch 184/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0409 - accuracy: 0.9810 - val_loss: 0.0757 - val_accuracy: 0.9667\n", "Epoch 185/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0465 - accuracy: 0.9774 - val_loss: 0.0757 - val_accuracy: 0.9571\n", "Epoch 186/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0401 - accuracy: 0.9821 - val_loss: 0.0774 - val_accuracy: 0.9571\n", "Epoch 187/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0550 - accuracy: 0.9738 - val_loss: 0.0790 - val_accuracy: 0.9619\n", "Epoch 188/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0596 - accuracy: 0.9714 - val_loss: 0.0767 - val_accuracy: 0.9667\n", "Epoch 189/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0531 - accuracy: 0.9750 - val_loss: 0.0778 - val_accuracy: 0.9619\n", "Epoch 190/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0502 - accuracy: 0.9786 - val_loss: 0.0779 - val_accuracy: 0.9667\n", "Epoch 191/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0640 - accuracy: 0.9702 - val_loss: 0.0915 - val_accuracy: 0.9524\n", "Epoch 192/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0361 - accuracy: 0.9833 - val_loss: 0.0924 - val_accuracy: 0.9524\n", "Epoch 193/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0509 - accuracy: 0.9738 - val_loss: 0.0865 - val_accuracy: 0.9619\n", "Epoch 194/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0437 - accuracy: 0.9810 - val_loss: 0.0873 - val_accuracy: 0.9571\n", "Epoch 195/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0456 - accuracy: 0.9798 - val_loss: 0.0840 - val_accuracy: 0.9571\n", "Epoch 196/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0593 - accuracy: 0.9726 - val_loss: 0.0790 - val_accuracy: 0.9619\n", "Epoch 197/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0431 - accuracy: 0.9798 - val_loss: 0.0871 - val_accuracy: 0.9571\n", "Epoch 198/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0652 - accuracy: 0.9702 - val_loss: 0.0981 - val_accuracy: 0.9476\n", "Epoch 199/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0554 - accuracy: 0.9726 - val_loss: 0.0767 - val_accuracy: 0.9667\n", "Epoch 200/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0536 - accuracy: 0.9738 - val_loss: 0.0737 - val_accuracy: 0.9667\n", "Epoch 201/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0571 - accuracy: 0.9738 - val_loss: 0.0779 - val_accuracy: 0.9619\n", "Epoch 202/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0356 - accuracy: 0.9845 - val_loss: 0.0848 - val_accuracy: 0.9619\n", "Epoch 203/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0531 - accuracy: 0.9750 - val_loss: 0.0792 - val_accuracy: 0.9667\n", "Epoch 204/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0630 - accuracy: 0.9679 - val_loss: 0.0800 - val_accuracy: 0.9571\n", "Epoch 205/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0357 - accuracy: 0.9869 - val_loss: 0.0809 - val_accuracy: 0.9619\n", "Epoch 206/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0383 - accuracy: 0.9821 - val_loss: 0.0811 - val_accuracy: 0.9619\n", "Epoch 207/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0503 - accuracy: 0.9750 - val_loss: 0.0801 - val_accuracy: 0.9571\n", "Epoch 208/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0541 - accuracy: 0.9738 - val_loss: 0.0776 - val_accuracy: 0.9571\n", "Epoch 209/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0440 - 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[==============================] - 0s 7ms/step - loss: 0.0405 - accuracy: 0.9810 - val_loss: 0.0767 - val_accuracy: 0.9619\n", "Epoch 217/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0474 - accuracy: 0.9762 - val_loss: 0.0734 - val_accuracy: 0.9619\n", "Epoch 218/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0666 - accuracy: 0.9690 - val_loss: 0.0866 - val_accuracy: 0.9571\n", "Epoch 219/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0550 - accuracy: 0.9750 - val_loss: 0.0790 - val_accuracy: 0.9619\n", "Epoch 220/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0577 - accuracy: 0.9738 - val_loss: 0.0749 - val_accuracy: 0.9667\n", "Epoch 221/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0328 - accuracy: 0.9845 - val_loss: 0.0767 - val_accuracy: 0.9667\n", "Epoch 222/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0404 - accuracy: 0.9774 - val_loss: 0.0767 - val_accuracy: 0.9571\n", "Epoch 223/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0382 - accuracy: 0.9821 - val_loss: 0.0763 - val_accuracy: 0.9667\n", "Epoch 224/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0584 - accuracy: 0.9726 - val_loss: 0.0754 - val_accuracy: 0.9667\n", "Epoch 225/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0389 - accuracy: 0.9833 - val_loss: 0.0782 - val_accuracy: 0.9619\n", "Epoch 226/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0507 - accuracy: 0.9774 - val_loss: 0.0785 - val_accuracy: 0.9619\n", "Epoch 227/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0543 - accuracy: 0.9750 - val_loss: 0.0782 - val_accuracy: 0.9619\n", "Epoch 228/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0425 - accuracy: 0.9798 - val_loss: 0.0793 - val_accuracy: 0.9619\n", "Epoch 229/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0361 - accuracy: 0.9845 - val_loss: 0.0771 - val_accuracy: 0.9619\n", "Epoch 230/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0601 - accuracy: 0.9726 - val_loss: 0.0800 - val_accuracy: 0.9619\n", "Epoch 231/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0712 - accuracy: 0.9679 - val_loss: 0.0849 - val_accuracy: 0.9524\n", "Epoch 232/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0478 - accuracy: 0.9786 - val_loss: 0.0766 - val_accuracy: 0.9619\n", "Epoch 233/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0485 - accuracy: 0.9762 - val_loss: 0.0760 - val_accuracy: 0.9667\n", "Epoch 234/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0617 - accuracy: 0.9690 - val_loss: 0.0836 - val_accuracy: 0.9619\n", "Epoch 235/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0661 - accuracy: 0.9667 - val_loss: 0.0766 - val_accuracy: 0.9667\n", "Epoch 236/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0469 - accuracy: 0.9786 - val_loss: 0.0788 - val_accuracy: 0.9571\n", "Epoch 237/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0464 - accuracy: 0.9774 - val_loss: 0.0781 - val_accuracy: 0.9524\n", "Epoch 238/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0415 - accuracy: 0.9798 - val_loss: 0.0808 - val_accuracy: 0.9619\n", "Epoch 239/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0393 - accuracy: 0.9810 - val_loss: 0.0863 - val_accuracy: 0.9571\n", "Epoch 240/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0398 - accuracy: 0.9798 - val_loss: 0.0815 - val_accuracy: 0.9619\n", "Epoch 241/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0592 - accuracy: 0.9714 - val_loss: 0.0809 - val_accuracy: 0.9571\n", "Epoch 242/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0618 - accuracy: 0.9690 - val_loss: 0.0862 - val_accuracy: 0.9524\n", "Epoch 243/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0782 - accuracy: 0.9607 - val_loss: 0.0884 - val_accuracy: 0.9524\n", "Epoch 244/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0908 - accuracy: 0.9571 - val_loss: 0.0949 - val_accuracy: 0.9476\n", "Epoch 245/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0917 - accuracy: 0.9560 - val_loss: 0.0947 - val_accuracy: 0.9524\n", "Epoch 246/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0548 - accuracy: 0.9750 - val_loss: 0.0961 - val_accuracy: 0.9524\n", "Epoch 247/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0526 - accuracy: 0.9762 - val_loss: 0.0975 - val_accuracy: 0.9524\n", "Epoch 248/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0546 - accuracy: 0.9726 - val_loss: 0.0917 - val_accuracy: 0.9524\n", "Epoch 249/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0442 - accuracy: 0.9798 - val_loss: 0.0953 - val_accuracy: 0.9524\n", "Epoch 250/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0467 - accuracy: 0.9774 - val_loss: 0.0956 - val_accuracy: 0.9476\n", "Valid results - Loss: 0.09557405114173889 - Accuracy: 94.76190209388733%\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", 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\n", 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\n", 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" ] }, "metadata": { "tags": [], "needs_background": "light" } }, { "output_type": "stream", "text": [ "DNN results\n", "accuracy : 0.9880952380952381\n", "Training Recall: 0.9880952380952381\n", "Training Precision: 0.9882963182762321\n", "Confusion matrix: \n", "[[377 1]\n", " [ 9 453]]\n", "Validation set report:\n", "cross Loss: -32.8849648819438\n", "hing Loss: 0.4666666666666667\n", "Report precision recall f1-score support\n", "\n", " 0 0.98 0.97 0.97 93\n", " 1 0.97 0.98 0.98 117\n", "\n", " accuracy 0.98 210\n", " macro avg 0.98 0.98 0.98 210\n", "weighted avg 0.98 0.98 0.98 210\n", "\n", "accuracy : 0.9761904761904762\n", "Validation Recall: 0.9761904761904762\n", "Validation Precision: 0.9762080218970417\n", "Confusion matrix: \n", "[[ 90 3]\n", " [ 2 115]]\n", "\n", "\\KNN Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 0.9047619047619048\n", "Validation Recall: 0.9047619047619048\n", "Validation Precision: 0.9051193200392285\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[ 84 9]\n", " [ 11 106]]\n", "\n", "\n", "Support vector machine Result\n", "\n", "Train accuracy: 0.9714285714285714\n", "Training Recall: 0.9714285714285714\n", "Training Precision: 0.9714846980976014\n", "\n", "\n", "Validation accuracy: 0.9523809523809523\n", "Validation Recall: 0.9523809523809523\n", "Validation Precision: 0.9535824422283632\n", "\n", "Train Confusion matrix:\n", " [[363 15]\n", " [ 9 453]]\n", "Validation Confusion matrix:\n", " [[ 85 8]\n", " [ 2 115]]\n", "\n", "\n", "Random Forest Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 1.0\n", "Validation Recall: 1.0\n", "Validation Precision: 1.0\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[ 93 0]\n", " [ 0 117]]\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "0XOpD9X5jq4m", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "780f8ec2-37b6-47ad-a376-8ff70f8ac132" }, "source": [ "#on 10 feature\n", "train_models(X, y, y_cat)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Before selectoin\n", "(1050, 10)\n", "After selectoin\n", "(1050, 10)\n", "Epoch 1/250\n", "7/7 [==============================] - 0s 63ms/step - loss: 0.7692 - accuracy: 0.6881 - val_loss: 0.6444 - val_accuracy: 0.7810\n", "Epoch 2/250\n", "7/7 [==============================] - 0s 34ms/step - loss: 0.5058 - accuracy: 0.8131 - val_loss: 0.4294 - val_accuracy: 0.8476\n", "Epoch 3/250\n", "7/7 [==============================] - 3s 411ms/step - loss: 0.3560 - accuracy: 0.8679 - val_loss: 0.3075 - val_accuracy: 0.8857\n", "Epoch 4/250\n", "7/7 [==============================] - 2s 255ms/step - loss: 0.2997 - accuracy: 0.8988 - val_loss: 0.2572 - val_accuracy: 0.8952\n", "Epoch 5/250\n", "7/7 [==============================] - 0s 49ms/step - loss: 0.2707 - accuracy: 0.8869 - val_loss: 0.2276 - val_accuracy: 0.9143\n", "Epoch 6/250\n", "7/7 [==============================] - 0s 51ms/step - loss: 0.2252 - accuracy: 0.9214 - val_loss: 0.2014 - val_accuracy: 0.9286\n", "Epoch 7/250\n", "7/7 [==============================] - 0s 45ms/step - loss: 0.2036 - accuracy: 0.9333 - val_loss: 0.1811 - val_accuracy: 0.9333\n", "Epoch 8/250\n", "7/7 [==============================] - 0s 54ms/step - loss: 0.1891 - accuracy: 0.9226 - val_loss: 0.1661 - val_accuracy: 0.9429\n", "Epoch 9/250\n", "7/7 [==============================] - 1s 122ms/step - loss: 0.1713 - accuracy: 0.9369 - val_loss: 0.1525 - val_accuracy: 0.9429\n", "Epoch 10/250\n", "7/7 [==============================] - 0s 33ms/step - loss: 0.1719 - accuracy: 0.9345 - val_loss: 0.1396 - val_accuracy: 0.9381\n", "Epoch 11/250\n", "7/7 [==============================] - 2s 279ms/step - loss: 0.1608 - accuracy: 0.9381 - val_loss: 0.1339 - val_accuracy: 0.9476\n", "Epoch 12/250\n", "7/7 [==============================] - 0s 47ms/step - loss: 0.1552 - accuracy: 0.9357 - val_loss: 0.1317 - val_accuracy: 0.9524\n", "Epoch 13/250\n", "7/7 [==============================] - 0s 49ms/step - loss: 0.1458 - accuracy: 0.9429 - val_loss: 0.1254 - val_accuracy: 0.9476\n", "Epoch 14/250\n", "7/7 [==============================] - 0s 45ms/step - loss: 0.1544 - accuracy: 0.9321 - val_loss: 0.1214 - val_accuracy: 0.9476\n", "Epoch 15/250\n", "7/7 [==============================] - 0s 40ms/step - loss: 0.1282 - accuracy: 0.9500 - val_loss: 0.1195 - val_accuracy: 0.9476\n", "Epoch 16/250\n", "7/7 [==============================] - 0s 40ms/step - loss: 0.1264 - accuracy: 0.9560 - val_loss: 0.1110 - val_accuracy: 0.9476\n", "Epoch 17/250\n", "7/7 [==============================] - 0s 46ms/step - loss: 0.1276 - accuracy: 0.9488 - val_loss: 0.1034 - val_accuracy: 0.9524\n", "Epoch 18/250\n", "7/7 [==============================] - 2s 347ms/step - loss: 0.1635 - accuracy: 0.9286 - val_loss: 0.1033 - val_accuracy: 0.9476\n", "Epoch 19/250\n", "7/7 [==============================] - 0s 66ms/step - loss: 0.1119 - accuracy: 0.9619 - val_loss: 0.0999 - val_accuracy: 0.9524\n", "Epoch 20/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.1302 - accuracy: 0.9476 - val_loss: 0.1004 - val_accuracy: 0.9524\n", "Epoch 21/250\n", "7/7 [==============================] - 1s 114ms/step - loss: 0.1099 - accuracy: 0.9548 - val_loss: 0.0996 - val_accuracy: 0.9571\n", "Epoch 22/250\n", "7/7 [==============================] - 0s 42ms/step - loss: 0.1208 - accuracy: 0.9500 - val_loss: 0.0976 - val_accuracy: 0.9571\n", "Epoch 23/250\n", "7/7 [==============================] - 1s 188ms/step - loss: 0.1149 - accuracy: 0.9560 - val_loss: 0.0944 - val_accuracy: 0.9571\n", "Epoch 24/250\n", "7/7 [==============================] - 7s 1s/step - loss: 0.1011 - accuracy: 0.9583 - val_loss: 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[==============================] - 0s 7ms/step - loss: 0.0804 - accuracy: 0.9631 - val_loss: 0.0667 - val_accuracy: 0.9714\n", "Epoch 85/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0562 - accuracy: 0.9774 - val_loss: 0.0711 - val_accuracy: 0.9619\n", "Epoch 86/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0703 - accuracy: 0.9750 - val_loss: 0.0728 - val_accuracy: 0.9667\n", "Epoch 87/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0872 - accuracy: 0.9560 - val_loss: 0.0691 - val_accuracy: 0.9714\n", "Epoch 88/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0680 - accuracy: 0.9702 - val_loss: 0.0781 - val_accuracy: 0.9619\n", "Epoch 89/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0840 - accuracy: 0.9631 - val_loss: 0.0844 - val_accuracy: 0.9619\n", "Epoch 90/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0820 - accuracy: 0.9607 - val_loss: 0.0850 - val_accuracy: 0.9571\n", "Epoch 91/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0944 - accuracy: 0.9536 - val_loss: 0.0729 - val_accuracy: 0.9667\n", "Epoch 92/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0613 - accuracy: 0.9702 - val_loss: 0.0713 - val_accuracy: 0.9667\n", "Epoch 93/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0554 - accuracy: 0.9750 - val_loss: 0.0671 - val_accuracy: 0.9667\n", "Epoch 94/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0679 - accuracy: 0.9679 - val_loss: 0.0742 - val_accuracy: 0.9619\n", "Epoch 95/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0636 - accuracy: 0.9702 - val_loss: 0.0754 - val_accuracy: 0.9619\n", "Epoch 96/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0922 - accuracy: 0.9583 - val_loss: 0.0711 - val_accuracy: 0.9667\n", "Epoch 97/250\n", "7/7 [==============================] - 0s 7ms/step - 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"7/7 [==============================] - 0s 7ms/step - loss: 0.0707 - accuracy: 0.9667 - val_loss: 0.0681 - val_accuracy: 0.9667\n", "Epoch 105/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0614 - accuracy: 0.9714 - val_loss: 0.0719 - val_accuracy: 0.9667\n", "Epoch 106/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0807 - accuracy: 0.9631 - val_loss: 0.0634 - val_accuracy: 0.9762\n", "Epoch 107/250\n", "7/7 [==============================] - 0s 57ms/step - loss: 0.0677 - accuracy: 0.9714 - val_loss: 0.0617 - val_accuracy: 0.9714\n", "Epoch 108/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0537 - accuracy: 0.9762 - val_loss: 0.0661 - val_accuracy: 0.9667\n", "Epoch 109/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0577 - accuracy: 0.9726 - val_loss: 0.0756 - val_accuracy: 0.9619\n", "Epoch 110/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0592 - accuracy: 0.9738 - 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[==============================] - 0s 8ms/step - loss: 0.0567 - accuracy: 0.9738 - val_loss: 0.0754 - val_accuracy: 0.9667\n", "Epoch 118/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0688 - accuracy: 0.9702 - val_loss: 0.0798 - val_accuracy: 0.9619\n", "Epoch 119/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0859 - accuracy: 0.9571 - val_loss: 0.0844 - val_accuracy: 0.9619\n", "Epoch 120/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0666 - accuracy: 0.9702 - val_loss: 0.0891 - val_accuracy: 0.9571\n", "Epoch 121/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0820 - accuracy: 0.9583 - val_loss: 0.0842 - val_accuracy: 0.9619\n", "Epoch 122/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0633 - accuracy: 0.9714 - val_loss: 0.0647 - val_accuracy: 0.9667\n", "Epoch 123/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0540 - accuracy: 0.9798 - val_loss: 0.0655 - val_accuracy: 0.9714\n", "Epoch 124/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0522 - accuracy: 0.9750 - val_loss: 0.0680 - val_accuracy: 0.9667\n", "Epoch 125/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0765 - accuracy: 0.9619 - val_loss: 0.0620 - val_accuracy: 0.9714\n", "Epoch 126/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0526 - accuracy: 0.9750 - val_loss: 0.0656 - val_accuracy: 0.9714\n", "Epoch 127/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0520 - accuracy: 0.9762 - val_loss: 0.0622 - val_accuracy: 0.9714\n", "Epoch 128/250\n", "7/7 [==============================] - 0s 45ms/step - loss: 0.0726 - accuracy: 0.9631 - val_loss: 0.0593 - val_accuracy: 0.9714\n", "Epoch 129/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0667 - accuracy: 0.9702 - val_loss: 0.0746 - val_accuracy: 0.9619\n", "Epoch 130/250\n", "7/7 [==============================] - 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"Epoch 137/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0630 - accuracy: 0.9714 - val_loss: 0.0785 - val_accuracy: 0.9571\n", "Epoch 138/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0954 - accuracy: 0.9524 - val_loss: 0.0873 - val_accuracy: 0.9571\n", "Epoch 139/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0631 - accuracy: 0.9726 - val_loss: 0.0783 - val_accuracy: 0.9619\n", "Epoch 140/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0335 - accuracy: 0.9881 - val_loss: 0.0764 - val_accuracy: 0.9619\n", "Epoch 141/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0627 - accuracy: 0.9690 - val_loss: 0.0757 - val_accuracy: 0.9667\n", "Epoch 142/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0678 - accuracy: 0.9679 - val_loss: 0.0856 - val_accuracy: 0.9619\n", "Epoch 143/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0404 - accuracy: 0.9845 - val_loss: 0.0728 - val_accuracy: 0.9619\n", "Epoch 144/250\n", "7/7 [==============================] - 2s 286ms/step - loss: 0.0552 - accuracy: 0.9702 - val_loss: 0.0567 - val_accuracy: 0.9762\n", "Epoch 145/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0436 - accuracy: 0.9833 - val_loss: 0.0674 - val_accuracy: 0.9667\n", "Epoch 146/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0459 - accuracy: 0.9786 - val_loss: 0.0779 - val_accuracy: 0.9571\n", "Epoch 147/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0762 - accuracy: 0.9631 - val_loss: 0.0837 - val_accuracy: 0.9571\n", "Epoch 148/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0559 - accuracy: 0.9702 - val_loss: 0.0659 - val_accuracy: 0.9667\n", "Epoch 149/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0540 - accuracy: 0.9786 - val_loss: 0.0629 - val_accuracy: 0.9762\n", "Epoch 150/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0711 - accuracy: 0.9667 - val_loss: 0.0695 - val_accuracy: 0.9714\n", "Epoch 151/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0940 - accuracy: 0.9548 - val_loss: 0.0869 - val_accuracy: 0.9619\n", "Epoch 152/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0446 - accuracy: 0.9810 - val_loss: 0.0844 - val_accuracy: 0.9619\n", "Epoch 153/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0543 - accuracy: 0.9786 - val_loss: 0.0769 - val_accuracy: 0.9619\n", "Epoch 154/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0626 - accuracy: 0.9690 - val_loss: 0.0787 - val_accuracy: 0.9619\n", "Epoch 155/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0530 - accuracy: 0.9750 - val_loss: 0.0731 - val_accuracy: 0.9714\n", "Epoch 156/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0656 - accuracy: 0.9690 - val_loss: 0.0715 - val_accuracy: 0.9619\n", "Epoch 157/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0435 - accuracy: 0.9786 - val_loss: 0.0754 - val_accuracy: 0.9619\n", "Epoch 158/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0522 - accuracy: 0.9786 - val_loss: 0.0751 - val_accuracy: 0.9619\n", "Epoch 159/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0417 - accuracy: 0.9798 - val_loss: 0.0708 - val_accuracy: 0.9714\n", "Epoch 160/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0640 - accuracy: 0.9690 - val_loss: 0.0763 - val_accuracy: 0.9619\n", "Epoch 161/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0639 - accuracy: 0.9714 - val_loss: 0.0654 - val_accuracy: 0.9667\n", "Epoch 162/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0771 - accuracy: 0.9643 - val_loss: 0.0628 - val_accuracy: 0.9762\n", "Epoch 163/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0348 - accuracy: 0.9845 - val_loss: 0.0630 - val_accuracy: 0.9667\n", "Epoch 164/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0513 - accuracy: 0.9750 - val_loss: 0.0620 - val_accuracy: 0.9667\n", "Epoch 165/250\n", "7/7 [==============================] - 1s 154ms/step - loss: 0.0745 - accuracy: 0.9619 - val_loss: 0.0554 - val_accuracy: 0.9762\n", "Epoch 166/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0638 - accuracy: 0.9690 - val_loss: 0.0607 - val_accuracy: 0.9762\n", "Epoch 167/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0474 - accuracy: 0.9774 - val_loss: 0.0565 - val_accuracy: 0.9762\n", "Epoch 168/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0460 - accuracy: 0.9810 - val_loss: 0.0651 - val_accuracy: 0.9714\n", "Epoch 169/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0363 - accuracy: 0.9857 - val_loss: 0.0700 - val_accuracy: 0.9667\n", "Epoch 170/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0385 - accuracy: 0.9833 - val_loss: 0.0677 - val_accuracy: 0.9667\n", "Epoch 171/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0625 - accuracy: 0.9714 - val_loss: 0.0634 - val_accuracy: 0.9714\n", "Epoch 172/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0435 - accuracy: 0.9774 - val_loss: 0.0602 - val_accuracy: 0.9714\n", "Epoch 173/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0585 - accuracy: 0.9726 - val_loss: 0.0617 - val_accuracy: 0.9714\n", "Epoch 174/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0488 - accuracy: 0.9786 - val_loss: 0.0631 - val_accuracy: 0.9714\n", "Epoch 175/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0460 - accuracy: 0.9798 - val_loss: 0.0629 - val_accuracy: 0.9667\n", "Epoch 176/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0437 - accuracy: 0.9786 - val_loss: 0.0612 - val_accuracy: 0.9762\n", "Epoch 177/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0415 - accuracy: 0.9798 - val_loss: 0.0619 - val_accuracy: 0.9762\n", "Epoch 178/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0585 - accuracy: 0.9750 - val_loss: 0.0598 - val_accuracy: 0.9714\n", "Epoch 179/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0533 - accuracy: 0.9762 - val_loss: 0.0620 - val_accuracy: 0.9714\n", "Epoch 180/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0572 - accuracy: 0.9774 - val_loss: 0.0674 - val_accuracy: 0.9667\n", "Epoch 181/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0447 - accuracy: 0.9798 - val_loss: 0.0723 - val_accuracy: 0.9667\n", "Epoch 182/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0488 - accuracy: 0.9786 - val_loss: 0.0696 - val_accuracy: 0.9667\n", "Epoch 183/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0529 - accuracy: 0.9774 - val_loss: 0.0661 - val_accuracy: 0.9667\n", "Epoch 184/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0696 - accuracy: 0.9643 - val_loss: 0.0652 - val_accuracy: 0.9667\n", "Epoch 185/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0804 - accuracy: 0.9595 - val_loss: 0.0575 - val_accuracy: 0.9714\n", "Epoch 186/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0477 - accuracy: 0.9798 - val_loss: 0.0582 - val_accuracy: 0.9762\n", "Epoch 187/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0536 - accuracy: 0.9714 - val_loss: 0.0732 - val_accuracy: 0.9619\n", "Epoch 188/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0561 - accuracy: 0.9726 - val_loss: 0.0632 - val_accuracy: 0.9762\n", "Epoch 189/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0496 - accuracy: 0.9738 - val_loss: 0.0706 - val_accuracy: 0.9667\n", "Epoch 190/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0660 - accuracy: 0.9679 - val_loss: 0.0706 - val_accuracy: 0.9667\n", "Epoch 191/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0349 - accuracy: 0.9833 - val_loss: 0.0667 - val_accuracy: 0.9667\n", "Epoch 192/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0609 - accuracy: 0.9726 - val_loss: 0.0667 - val_accuracy: 0.9714\n", "Epoch 193/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0414 - accuracy: 0.9821 - val_loss: 0.0671 - val_accuracy: 0.9714\n", "Epoch 194/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0416 - accuracy: 0.9810 - val_loss: 0.0572 - val_accuracy: 0.9762\n", "Epoch 195/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0397 - accuracy: 0.9821 - val_loss: 0.0591 - val_accuracy: 0.9714\n", "Epoch 196/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0443 - accuracy: 0.9810 - val_loss: 0.0610 - val_accuracy: 0.9714\n", "Epoch 197/250\n", "7/7 [==============================] - 3s 365ms/step - loss: 0.0529 - accuracy: 0.9762 - val_loss: 0.0551 - val_accuracy: 0.9762\n", "Epoch 198/250\n", "7/7 [==============================] - 3s 385ms/step - loss: 0.0553 - accuracy: 0.9762 - val_loss: 0.0547 - val_accuracy: 0.9762\n", "Epoch 199/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0408 - accuracy: 0.9833 - val_loss: 0.0547 - val_accuracy: 0.9762\n", "Epoch 200/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0416 - accuracy: 0.9821 - val_loss: 0.0553 - val_accuracy: 0.9762\n", "Epoch 201/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0633 - accuracy: 0.9702 - val_loss: 0.0548 - val_accuracy: 0.9762\n", "Epoch 202/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0296 - accuracy: 0.9869 - val_loss: 0.0558 - val_accuracy: 0.9762\n", "Epoch 203/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0329 - accuracy: 0.9869 - val_loss: 0.0563 - val_accuracy: 0.9762\n", "Epoch 204/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0428 - accuracy: 0.9810 - val_loss: 0.0622 - val_accuracy: 0.9714\n", "Epoch 205/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0519 - accuracy: 0.9738 - val_loss: 0.0771 - val_accuracy: 0.9619\n", "Epoch 206/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0646 - accuracy: 0.9702 - val_loss: 0.0830 - val_accuracy: 0.9619\n", "Epoch 207/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0923 - accuracy: 0.9536 - val_loss: 0.0837 - val_accuracy: 0.9619\n", "Epoch 208/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0598 - accuracy: 0.9702 - val_loss: 0.0854 - val_accuracy: 0.9571\n", "Epoch 209/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0536 - accuracy: 0.9750 - val_loss: 0.0822 - val_accuracy: 0.9619\n", "Epoch 210/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0508 - accuracy: 0.9762 - val_loss: 0.0818 - val_accuracy: 0.9571\n", "Epoch 211/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0307 - accuracy: 0.9845 - val_loss: 0.0677 - val_accuracy: 0.9667\n", "Epoch 212/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0466 - accuracy: 0.9774 - val_loss: 0.0669 - val_accuracy: 0.9714\n", "Epoch 213/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0484 - accuracy: 0.9786 - val_loss: 0.0668 - val_accuracy: 0.9667\n", "Epoch 214/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0901 - accuracy: 0.9560 - val_loss: 0.0648 - val_accuracy: 0.9762\n", "Epoch 215/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0494 - accuracy: 0.9750 - val_loss: 0.0595 - val_accuracy: 0.9714\n", "Epoch 216/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0451 - accuracy: 0.9821 - val_loss: 0.0619 - val_accuracy: 0.9714\n", "Epoch 217/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0333 - accuracy: 0.9810 - val_loss: 0.0746 - val_accuracy: 0.9619\n", "Epoch 218/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0521 - accuracy: 0.9738 - val_loss: 0.0762 - val_accuracy: 0.9619\n", "Epoch 219/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0552 - accuracy: 0.9702 - val_loss: 0.0695 - val_accuracy: 0.9667\n", "Epoch 220/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0547 - accuracy: 0.9738 - val_loss: 0.0647 - val_accuracy: 0.9714\n", "Epoch 221/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0499 - accuracy: 0.9762 - val_loss: 0.0594 - val_accuracy: 0.9714\n", "Epoch 222/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0438 - accuracy: 0.9833 - val_loss: 0.0558 - val_accuracy: 0.9762\n", "Epoch 223/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0379 - accuracy: 0.9845 - val_loss: 0.0605 - val_accuracy: 0.9714\n", "Epoch 224/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0506 - accuracy: 0.9714 - val_loss: 0.0643 - val_accuracy: 0.9714\n", "Epoch 225/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0526 - accuracy: 0.9762 - val_loss: 0.0688 - val_accuracy: 0.9667\n", "Epoch 226/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0459 - accuracy: 0.9786 - val_loss: 0.0812 - val_accuracy: 0.9619\n", "Epoch 227/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0419 - accuracy: 0.9798 - val_loss: 0.0845 - val_accuracy: 0.9619\n", "Epoch 228/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0569 - accuracy: 0.9702 - val_loss: 0.1010 - val_accuracy: 0.9476\n", "Epoch 229/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0615 - accuracy: 0.9679 - val_loss: 0.1000 - val_accuracy: 0.9476\n", "Epoch 230/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0526 - accuracy: 0.9738 - val_loss: 0.0845 - val_accuracy: 0.9619\n", "Epoch 231/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0510 - accuracy: 0.9738 - val_loss: 0.0694 - val_accuracy: 0.9667\n", "Epoch 232/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0520 - accuracy: 0.9738 - val_loss: 0.0633 - val_accuracy: 0.9714\n", "Epoch 233/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0466 - accuracy: 0.9786 - val_loss: 0.0673 - val_accuracy: 0.9714\n", "Epoch 234/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0376 - accuracy: 0.9833 - val_loss: 0.0692 - val_accuracy: 0.9667\n", "Epoch 235/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0505 - accuracy: 0.9774 - val_loss: 0.0618 - val_accuracy: 0.9714\n", "Epoch 236/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0510 - accuracy: 0.9774 - val_loss: 0.0622 - val_accuracy: 0.9714\n", "Epoch 237/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0788 - accuracy: 0.9619 - val_loss: 0.0615 - val_accuracy: 0.9714\n", "Epoch 238/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0601 - accuracy: 0.9714 - val_loss: 0.0592 - val_accuracy: 0.9714\n", "Epoch 239/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0636 - accuracy: 0.9726 - val_loss: 0.0735 - val_accuracy: 0.9667\n", "Epoch 240/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0511 - accuracy: 0.9750 - val_loss: 0.0832 - val_accuracy: 0.9619\n", "Epoch 241/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0521 - accuracy: 0.9738 - val_loss: 0.0647 - val_accuracy: 0.9714\n", "Epoch 242/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0410 - accuracy: 0.9798 - val_loss: 0.0701 - val_accuracy: 0.9667\n", "Epoch 243/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0456 - accuracy: 0.9810 - val_loss: 0.0738 - val_accuracy: 0.9619\n", "Epoch 244/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0595 - accuracy: 0.9714 - val_loss: 0.0733 - val_accuracy: 0.9619\n", "Epoch 245/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0305 - accuracy: 0.9845 - val_loss: 0.0747 - val_accuracy: 0.9619\n", "Epoch 246/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0489 - accuracy: 0.9774 - val_loss: 0.0702 - val_accuracy: 0.9667\n", "Epoch 247/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0378 - accuracy: 0.9821 - val_loss: 0.0744 - val_accuracy: 0.9619\n", "Epoch 248/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.0622 - accuracy: 0.9679 - val_loss: 0.0735 - val_accuracy: 0.9714\n", "Epoch 249/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0416 - accuracy: 0.9821 - val_loss: 0.0695 - val_accuracy: 0.9714\n", "Epoch 250/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0683 - accuracy: 0.9667 - val_loss: 0.0674 - val_accuracy: 0.9714\n", "Valid results - Loss: 0.06737704575061798 - Accuracy: 97.14285731315613%\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "tags": [], "needs_background": "light" } }, { "output_type": "stream", "text": [ "DNN results\n", "accuracy : 0.9952380952380953\n", "Training Recall: 0.9952380952380953\n", "Training Precision: 0.995247157006603\n", "Confusion matrix: \n", "[[375 3]\n", " [ 1 461]]\n", "Validation set report:\n", "cross Loss: -32.888288098289856\n", "hing Loss: 0.4666666666666667\n", "Report precision recall f1-score support\n", "\n", " 0 0.99 0.96 0.97 93\n", " 1 0.97 0.99 0.98 117\n", "\n", " accuracy 0.98 210\n", " macro avg 0.98 0.97 0.98 210\n", "weighted avg 0.98 0.98 0.98 210\n", "\n", "accuracy : 0.9761904761904762\n", "Validation Recall: 0.9761904761904762\n", "Validation Precision: 0.9765079365079364\n", "Confusion matrix: \n", "[[ 89 4]\n", " [ 1 116]]\n", "\n", "\\KNN Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 0.9142857142857143\n", "Validation Recall: 0.9142857142857143\n", "Validation Precision: 0.9142857142857143\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[ 84 9]\n", " [ 9 108]]\n", "\n", "\n", "Support vector machine Result\n", "\n", "Train accuracy: 0.9785714285714285\n", "Training Recall: 0.9785714285714285\n", "Training Precision: 0.9787061529614721\n", "\n", "\n", "Validation accuracy: 0.9619047619047619\n", "Validation Recall: 0.9619047619047619\n", "Validation Precision: 0.9632023709399656\n", "\n", "Train Confusion matrix:\n", " [[365 13]\n", " [ 5 457]]\n", "Validation Confusion matrix:\n", " [[ 86 7]\n", " [ 1 116]]\n", "\n", "\n", "Random Forest Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 1.0\n", "Validation Recall: 1.0\n", "Validation Precision: 1.0\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[ 93 0]\n", " [ 0 117]]\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "ccobVWjvWMmk" }, "source": [ "### LDA" ] }, { "cell_type": "code", "metadata": { "id": "jb4nIQxJOVod", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "6e773962-d4db-4763-b61e-b88d37433a20" }, "source": [ "#split lda into train test \n", "\"\"\"\n", "\n", "hing\n", " \n", "cross Loss: 0.25\n", "X = np.load('gdrive/My Drive/Python files/neuromarketing 25/features_psd_ALL.npy') # 2725\n", "\n", "Standard\n", "\n", " batch_size = 128\n", " nb_epoch = 250 #3000\n", " l1_decay=0.00\n", " l2_decay=0.000 # .5\n", " # 0.01 0.06\n", " sigma=0.005\n", " in_drop_rate = .5\n", " drop_rate = .5#.05\n", " lr1=0.0001\n", "\n", "\"\"\"\n", "train_models(X, y, y_cat)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Before selectoin\n", "(1050, 2725)\n", "After selectoin\n", "(1050, 2725)\n", "(210, 1)\n", "Epoch 1/250\n", "7/7 [==============================] - 0s 62ms/step - loss: 0.4084 - accuracy: 0.8655 - val_loss: 0.5829 - val_accuracy: 0.7095\n", "Epoch 2/250\n", "7/7 [==============================] - 3s 395ms/step - loss: 0.0477 - accuracy: 0.9833 - val_loss: 0.5822 - val_accuracy: 0.7095\n", "Epoch 3/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0377 - accuracy: 0.9845 - val_loss: 0.5832 - val_accuracy: 0.7095\n", "Epoch 4/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0311 - accuracy: 0.9857 - val_loss: 0.5856 - val_accuracy: 0.7095\n", "Epoch 5/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0270 - accuracy: 0.9869 - val_loss: 0.5854 - val_accuracy: 0.7095\n", "Epoch 6/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0245 - accuracy: 0.9917 - val_loss: 0.5854 - val_accuracy: 0.7095\n", "Epoch 7/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0218 - accuracy: 0.9929 - val_loss: 0.5864 - val_accuracy: 0.7048\n", "Epoch 8/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0258 - accuracy: 0.9905 - val_loss: 0.5874 - val_accuracy: 0.7048\n", "Epoch 9/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0343 - accuracy: 0.9833 - val_loss: 0.5920 - val_accuracy: 0.7048\n", "Epoch 10/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0254 - accuracy: 0.9869 - val_loss: 0.5928 - val_accuracy: 0.7000\n", "Epoch 11/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0261 - accuracy: 0.9881 - val_loss: 0.5920 - val_accuracy: 0.7048\n", "Epoch 12/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0254 - accuracy: 0.9917 - val_loss: 0.5913 - val_accuracy: 0.7048\n", "Epoch 13/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0210 - accuracy: 0.9917 - val_loss: 0.5903 - val_accuracy: 0.7048\n", "Epoch 14/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0202 - accuracy: 0.9893 - val_loss: 0.5857 - val_accuracy: 0.7095\n", "Epoch 15/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0220 - accuracy: 0.9869 - val_loss: 0.5833 - val_accuracy: 0.7095\n", "Epoch 16/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0263 - accuracy: 0.9881 - val_loss: 0.5855 - val_accuracy: 0.7095\n", "Epoch 17/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0296 - accuracy: 0.9845 - val_loss: 0.5857 - val_accuracy: 0.7095\n", "Epoch 18/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0237 - accuracy: 0.9893 - val_loss: 0.5918 - val_accuracy: 0.7048\n", "Epoch 19/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0257 - accuracy: 0.9869 - val_loss: 0.5943 - val_accuracy: 0.7000\n", "Epoch 20/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0269 - accuracy: 0.9857 - val_loss: 0.5940 - val_accuracy: 0.7048\n", "Epoch 21/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0218 - accuracy: 0.9905 - val_loss: 0.5936 - val_accuracy: 0.7048\n", "Epoch 22/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0267 - accuracy: 0.9869 - val_loss: 0.5917 - val_accuracy: 0.7000\n", "Epoch 23/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0180 - accuracy: 0.9929 - val_loss: 0.5861 - val_accuracy: 0.7095\n", "Epoch 24/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0246 - accuracy: 0.9893 - val_loss: 0.5876 - val_accuracy: 0.7048\n", "Epoch 25/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0242 - accuracy: 0.9905 - val_loss: 0.5943 - val_accuracy: 0.7000\n", "Epoch 26/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0225 - accuracy: 0.9893 - val_loss: 0.5945 - val_accuracy: 0.7048\n", "Epoch 27/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0210 - accuracy: 0.9905 - val_loss: 0.5943 - val_accuracy: 0.7048\n", "Epoch 28/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0262 - accuracy: 0.9881 - val_loss: 0.5943 - val_accuracy: 0.7000\n", "Epoch 29/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0233 - accuracy: 0.9893 - val_loss: 0.5904 - val_accuracy: 0.7048\n", "Epoch 30/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0198 - accuracy: 0.9929 - val_loss: 0.5912 - val_accuracy: 0.7048\n", "Epoch 31/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0182 - accuracy: 0.9917 - val_loss: 0.5939 - val_accuracy: 0.7000\n", "Epoch 32/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0220 - accuracy: 0.9905 - val_loss: 0.5910 - val_accuracy: 0.7000\n", "Epoch 33/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0275 - accuracy: 0.9881 - val_loss: 0.5907 - val_accuracy: 0.7048\n", "Epoch 34/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0178 - accuracy: 0.9917 - val_loss: 0.5890 - val_accuracy: 0.7048\n", "Epoch 35/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0202 - accuracy: 0.9893 - val_loss: 0.5905 - val_accuracy: 0.7048\n", "Epoch 36/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0222 - accuracy: 0.9905 - val_loss: 0.5885 - val_accuracy: 0.7048\n", "Epoch 37/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0239 - accuracy: 0.9917 - val_loss: 0.5861 - val_accuracy: 0.7048\n", "Epoch 38/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0212 - accuracy: 0.9893 - val_loss: 0.5898 - val_accuracy: 0.7000\n", "Epoch 39/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0247 - accuracy: 0.9893 - val_loss: 0.5921 - val_accuracy: 0.7000\n", "Epoch 40/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0245 - accuracy: 0.9893 - val_loss: 0.5909 - val_accuracy: 0.7048\n", "Epoch 41/250\n", "7/7 [==============================] 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val_loss: 0.5949 - val_accuracy: 0.7048\n", "Epoch 75/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0168 - accuracy: 0.9917 - val_loss: 0.5931 - val_accuracy: 0.7048\n", "Epoch 76/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0250 - accuracy: 0.9869 - val_loss: 0.5939 - val_accuracy: 0.7000\n", "Epoch 77/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0182 - accuracy: 0.9929 - val_loss: 0.5951 - val_accuracy: 0.7000\n", "Epoch 78/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0191 - accuracy: 0.9905 - val_loss: 0.5950 - val_accuracy: 0.7048\n", "Epoch 79/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0198 - accuracy: 0.9905 - val_loss: 0.5952 - val_accuracy: 0.7000\n", "Epoch 80/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0182 - accuracy: 0.9917 - val_loss: 0.5956 - val_accuracy: 0.7048\n", "Epoch 81/250\n", "7/7 [==============================] 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"Epoch 88/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0226 - accuracy: 0.9905 - val_loss: 0.5939 - val_accuracy: 0.7048\n", "Epoch 89/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0178 - accuracy: 0.9917 - val_loss: 0.5967 - val_accuracy: 0.7048\n", "Epoch 90/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0188 - accuracy: 0.9905 - val_loss: 0.5957 - val_accuracy: 0.7048\n", "Epoch 91/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0233 - accuracy: 0.9893 - val_loss: 0.5884 - val_accuracy: 0.7048\n", "Epoch 92/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0279 - accuracy: 0.9869 - val_loss: 0.5861 - val_accuracy: 0.7095\n", "Epoch 93/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0228 - accuracy: 0.9905 - val_loss: 0.5923 - val_accuracy: 0.7000\n", "Epoch 94/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0234 - accuracy: 0.9893 - val_loss: 0.5955 - val_accuracy: 0.7048\n", "Epoch 95/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0245 - accuracy: 0.9905 - val_loss: 0.5929 - val_accuracy: 0.7048\n", "Epoch 96/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0185 - accuracy: 0.9917 - val_loss: 0.5940 - val_accuracy: 0.7000\n", "Epoch 97/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0196 - accuracy: 0.9905 - val_loss: 0.5952 - val_accuracy: 0.7000\n", "Epoch 98/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0227 - accuracy: 0.9881 - val_loss: 0.5963 - val_accuracy: 0.7000\n", "Epoch 99/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0182 - accuracy: 0.9917 - val_loss: 0.5937 - val_accuracy: 0.7000\n", "Epoch 100/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0190 - accuracy: 0.9917 - val_loss: 0.5928 - val_accuracy: 0.7048\n", "Epoch 101/250\n", "7/7 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0.5916 - val_accuracy: 0.7048\n", "Epoch 174/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0245 - accuracy: 0.9881 - val_loss: 0.5951 - val_accuracy: 0.7000\n", "Epoch 175/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0193 - accuracy: 0.9917 - val_loss: 0.5953 - val_accuracy: 0.7048\n", "Epoch 176/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0251 - accuracy: 0.9881 - val_loss: 0.5938 - val_accuracy: 0.7000\n", "Epoch 177/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0174 - accuracy: 0.9905 - val_loss: 0.5940 - val_accuracy: 0.7048\n", "Epoch 178/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0184 - accuracy: 0.9917 - val_loss: 0.5936 - val_accuracy: 0.7048\n", "Epoch 179/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0210 - accuracy: 0.9893 - val_loss: 0.5922 - val_accuracy: 0.7048\n", "Epoch 180/250\n", "7/7 [==============================] - 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"Epoch 187/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0234 - accuracy: 0.9893 - val_loss: 0.5942 - val_accuracy: 0.7048\n", "Epoch 188/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0219 - accuracy: 0.9905 - val_loss: 0.5897 - val_accuracy: 0.7048\n", "Epoch 189/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0254 - accuracy: 0.9869 - val_loss: 0.5837 - val_accuracy: 0.7095\n", "Epoch 190/250\n", "7/7 [==============================] - 0s 34ms/step - loss: 0.0197 - accuracy: 0.9905 - val_loss: 0.5756 - val_accuracy: 0.7143\n", "Epoch 191/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0253 - accuracy: 0.9881 - val_loss: 0.5845 - val_accuracy: 0.7095\n", "Epoch 192/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0191 - accuracy: 0.9905 - val_loss: 0.5959 - val_accuracy: 0.7000\n", "Epoch 193/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0188 - 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0.5944 - val_accuracy: 0.7000\n", "Epoch 207/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0239 - accuracy: 0.9881 - val_loss: 0.5917 - val_accuracy: 0.7000\n", "Epoch 208/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0149 - accuracy: 0.9940 - val_loss: 0.5924 - val_accuracy: 0.7000\n", "Epoch 209/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0211 - accuracy: 0.9893 - val_loss: 0.5928 - val_accuracy: 0.7000\n", "Epoch 210/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0140 - accuracy: 0.9940 - val_loss: 0.5950 - val_accuracy: 0.7048\n", "Epoch 211/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0235 - accuracy: 0.9881 - val_loss: 0.5943 - val_accuracy: 0.7000\n", "Epoch 212/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0200 - accuracy: 0.9905 - val_loss: 0.5927 - val_accuracy: 0.7048\n", "Epoch 213/250\n", "7/7 [==============================] - 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"Epoch 220/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0220 - accuracy: 0.9905 - val_loss: 0.5950 - val_accuracy: 0.7000\n", "Epoch 221/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0204 - accuracy: 0.9905 - val_loss: 0.5917 - val_accuracy: 0.7000\n", "Epoch 222/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0177 - accuracy: 0.9917 - val_loss: 0.5896 - val_accuracy: 0.7048\n", "Epoch 223/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0233 - accuracy: 0.9881 - val_loss: 0.5887 - val_accuracy: 0.7048\n", "Epoch 224/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0234 - accuracy: 0.9881 - val_loss: 0.5936 - val_accuracy: 0.7048\n", "Epoch 225/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0183 - accuracy: 0.9905 - val_loss: 0.5949 - val_accuracy: 0.7000\n", "Epoch 226/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0187 - accuracy: 0.9917 - val_loss: 0.5931 - val_accuracy: 0.7000\n", "Epoch 227/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0224 - accuracy: 0.9893 - val_loss: 0.5926 - val_accuracy: 0.7048\n", "Epoch 228/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0188 - accuracy: 0.9917 - val_loss: 0.5941 - val_accuracy: 0.7048\n", "Epoch 229/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0170 - accuracy: 0.9917 - val_loss: 0.5957 - val_accuracy: 0.7048\n", "Epoch 230/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0211 - accuracy: 0.9893 - val_loss: 0.5960 - val_accuracy: 0.7048\n", "Epoch 231/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0180 - accuracy: 0.9929 - val_loss: 0.5940 - val_accuracy: 0.7000\n", "Epoch 232/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0238 - accuracy: 0.9869 - val_loss: 0.5939 - val_accuracy: 0.7000\n", "Epoch 233/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0185 - accuracy: 0.9905 - val_loss: 0.5910 - val_accuracy: 0.7048\n", "Epoch 234/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0200 - accuracy: 0.9893 - val_loss: 0.5926 - val_accuracy: 0.7048\n", "Epoch 235/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0189 - accuracy: 0.9905 - val_loss: 0.5961 - val_accuracy: 0.7000\n", "Epoch 236/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0186 - accuracy: 0.9917 - val_loss: 0.5959 - val_accuracy: 0.7000\n", "Epoch 237/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0195 - accuracy: 0.9917 - val_loss: 0.5960 - val_accuracy: 0.7000\n", "Epoch 238/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0169 - accuracy: 0.9917 - val_loss: 0.5962 - val_accuracy: 0.7000\n", "Epoch 239/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0178 - accuracy: 0.9917 - val_loss: 0.5945 - val_accuracy: 0.7048\n", "Epoch 240/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0184 - accuracy: 0.9905 - val_loss: 0.5951 - val_accuracy: 0.7000\n", "Epoch 241/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0196 - accuracy: 0.9893 - val_loss: 0.5970 - val_accuracy: 0.7000\n", "Epoch 242/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0192 - accuracy: 0.9905 - val_loss: 0.5967 - val_accuracy: 0.7000\n", "Epoch 243/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0202 - accuracy: 0.9893 - val_loss: 0.5945 - val_accuracy: 0.7000\n", "Epoch 244/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0201 - accuracy: 0.9893 - val_loss: 0.5968 - val_accuracy: 0.7000\n", "Epoch 245/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0200 - accuracy: 0.9905 - val_loss: 0.5963 - val_accuracy: 0.7000\n", "Epoch 246/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0180 - accuracy: 0.9905 - val_loss: 0.5884 - val_accuracy: 0.7048\n", "Epoch 247/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0254 - accuracy: 0.9869 - val_loss: 0.5839 - val_accuracy: 0.7095\n", "Epoch 248/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0202 - accuracy: 0.9929 - val_loss: 0.5917 - val_accuracy: 0.7000\n", "Epoch 249/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0179 - accuracy: 0.9929 - val_loss: 0.5942 - val_accuracy: 0.7000\n", "Epoch 250/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0241 - accuracy: 0.9881 - val_loss: 0.5965 - val_accuracy: 0.7048\n", "Valid results - Loss: 0.596464991569519 - Accuracy: 70.47619223594666%\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", 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\n", 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\n", 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" ] }, "metadata": { "tags": [], "needs_background": "light" } }, { "output_type": "stream", "text": [ "DNN results\n", "accuracy : 0.9916666666666667\n", "Training Recall: 0.9916666666666667\n", "Training Precision: 0.9916979740277564\n", "Confusion matrix: \n", "[[376 2]\n", " [ 5 457]]\n", "Validation set report:\n", "cross Loss: -14.64312911047822\n", "hing Loss: 0.7285714285714285\n", "Report precision recall f1-score support\n", "\n", " 0 0.65 0.75 0.70 93\n", " 1 0.78 0.68 0.73 117\n", "\n", " accuracy 0.71 210\n", " macro avg 0.72 0.72 0.71 210\n", "weighted avg 0.72 0.71 0.72 210\n", "\n", "accuracy : 0.7142857142857143\n", "Validation Recall: 0.7142857142857143\n", "Validation Precision: 0.7224519423956861\n", "Confusion matrix: \n", "[[70 23]\n", " [37 80]]\n", "\n", "\\KNN Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 0.7142857142857143\n", "Validation Recall: 0.7142857142857143\n", "Validation Precision: 0.7208163265306122\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[69 24]\n", " [36 81]]\n", "\n", "\n", "Support vector machine Result\n", "\n", "Train accuracy: 0.9904761904761905\n", "Training Recall: 0.9904761904761905\n", "Training Precision: 0.9904919908466819\n", "\n", "\n", "Validation accuracy: 0.680952380952381\n", "Validation Recall: 0.680952380952381\n", "Validation Precision: 0.6955905388471179\n", "\n", "Train Confusion matrix:\n", " [[375 3]\n", " [ 5 457]]\n", "Validation Confusion matrix:\n", " [[70 23]\n", " [44 73]]\n", "\n", "\n", "Random Forest Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 0.7142857142857143\n", "Validation Recall: 0.7142857142857143\n", "Validation Precision: 0.7208163265306122\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[69 24]\n", " [36 81]]\n", "\n", "\n", "LDA Result\n", "\n", "Train accuracy: 0.9916666666666667\n", "Training Recall: 0.9916666666666667\n", "Training Precision: 0.9916674782728256\n", "\n", "\n", "Validation accuracy: 0.7047619047619048\n", "Validation Recall: 0.7047619047619048\n", "Validation Precision: 0.7085267897694094\n", "\n", "Train Confusion matrix:\n", " [[374 4]\n", " [ 3 459]]\n", "Validation Confusion matrix:\n", " [[66 27]\n", " [35 82]]\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "7u6PdpaTJSgC", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "2df1f297-dc3d-45a4-c2ed-ac28af99b52c" }, "source": [ "#split lda into train test \n", "\"\"\"\n", "\n", "hing\n", " \n", "cross Loss: 0.25\n", "X = np.load('gdrive/My Drive/Python files/neuromarketing 25/features_psd_ALL.npy') # 2725\n", "\n", "Standard\n", "\n", " batch_size = 128\n", " nb_epoch = 250 #3000\n", " l1_decay=0.00\n", " l2_decay=0.000 # .5\n", " # 0.01 0.06\n", " sigma=0.005\n", " in_drop_rate = .5\n", " drop_rate = .5#.05\n", " lr1=0.0001\n", "\n", "\"\"\"\n", "train_models(X, y, y_cat)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Before selectoin\n", "(1050, 2725)\n", "After selectoin\n", "(1050, 2725)\n", "(210, 1)\n", "Epoch 1/250\n", "7/7 [==============================] - 0s 67ms/step - loss: 0.4354 - accuracy: 0.8310 - val_loss: 0.5846 - val_accuracy: 0.7095\n", "Epoch 2/250\n", "7/7 [==============================] - 3s 454ms/step - loss: 0.0535 - accuracy: 0.9857 - val_loss: 0.5837 - val_accuracy: 0.7095\n", "Epoch 3/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0276 - accuracy: 0.9869 - val_loss: 0.5849 - val_accuracy: 0.7095\n", "Epoch 4/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0262 - accuracy: 0.9905 - val_loss: 0.5863 - val_accuracy: 0.7048\n", "Epoch 5/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0320 - accuracy: 0.9857 - val_loss: 0.5887 - val_accuracy: 0.7048\n", "Epoch 6/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0272 - accuracy: 0.9869 - val_loss: 0.5911 - val_accuracy: 0.7048\n", "Epoch 7/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0320 - accuracy: 0.9845 - val_loss: 0.5935 - val_accuracy: 0.7000\n", "Epoch 8/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0260 - accuracy: 0.9869 - val_loss: 0.5949 - val_accuracy: 0.7000\n", "Epoch 9/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0280 - accuracy: 0.9857 - val_loss: 0.5953 - val_accuracy: 0.7000\n", "Epoch 10/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0210 - accuracy: 0.9905 - val_loss: 0.5947 - val_accuracy: 0.7000\n", "Epoch 11/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0238 - accuracy: 0.9905 - val_loss: 0.5937 - val_accuracy: 0.7000\n", "Epoch 12/250\n", "7/7 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0.5956 - val_accuracy: 0.7000\n", "Epoch 224/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0215 - accuracy: 0.9893 - val_loss: 0.5982 - val_accuracy: 0.7000\n", "Epoch 225/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0226 - accuracy: 0.9881 - val_loss: 0.5979 - val_accuracy: 0.7000\n", "Epoch 226/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0217 - accuracy: 0.9881 - val_loss: 0.5970 - val_accuracy: 0.7000\n", "Epoch 227/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0198 - accuracy: 0.9905 - val_loss: 0.5960 - val_accuracy: 0.7048\n", "Epoch 228/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0210 - accuracy: 0.9869 - val_loss: 0.5933 - val_accuracy: 0.7000\n", "Epoch 229/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0218 - accuracy: 0.9893 - val_loss: 0.5943 - val_accuracy: 0.7048\n", "Epoch 230/250\n", "7/7 [==============================] - 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"Epoch 237/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0191 - accuracy: 0.9905 - val_loss: 0.5910 - val_accuracy: 0.7048\n", "Epoch 238/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0207 - accuracy: 0.9905 - val_loss: 0.5936 - val_accuracy: 0.7048\n", "Epoch 239/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0156 - accuracy: 0.9917 - val_loss: 0.5928 - val_accuracy: 0.7048\n", "Epoch 240/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0211 - accuracy: 0.9905 - val_loss: 0.5960 - val_accuracy: 0.7000\n", "Epoch 241/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0198 - accuracy: 0.9905 - val_loss: 0.5911 - val_accuracy: 0.7000\n", "Epoch 242/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0252 - accuracy: 0.9857 - val_loss: 0.5932 - val_accuracy: 0.7048\n", "Epoch 243/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0217 - accuracy: 0.9905 - val_loss: 0.5934 - val_accuracy: 0.7048\n", "Epoch 244/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0182 - accuracy: 0.9905 - val_loss: 0.5948 - val_accuracy: 0.7048\n", "Epoch 245/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0176 - accuracy: 0.9917 - val_loss: 0.5959 - val_accuracy: 0.7048\n", "Epoch 246/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0174 - accuracy: 0.9905 - val_loss: 0.5957 - val_accuracy: 0.7048\n", "Epoch 247/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0173 - accuracy: 0.9905 - val_loss: 0.5950 - val_accuracy: 0.7000\n", "Epoch 248/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0181 - accuracy: 0.9905 - val_loss: 0.5941 - val_accuracy: 0.7000\n", "Epoch 249/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0228 - accuracy: 0.9893 - val_loss: 0.5948 - val_accuracy: 0.7000\n", "Epoch 250/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0179 - accuracy: 0.9917 - val_loss: 0.5938 - val_accuracy: 0.7000\n", "Valid results - Loss: 0.5937826633453369 - Accuracy: 69.9999988079071%\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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nLyIix5BYwLv7vcCBpKYvIiLH1vZz8GZ2nZltN7Ptw8PD7S6OiEhqtD3g3f0md9/q7lv7+/vbXRwRkdRoe8CLiEgyFPAiIimV5GWStwL3Aeea2aCZ/VpS8xIRkZfKJTVhd/+lpKYtIiI/mU7RiIiklAJeRCSlFPAiIimlgBcRSSkFvIhISingRURSSgEvIpJSCngRkZRSwIuIpJQCXkQkpRK7VYGsQI0q7H8K8hXo3QzZ03DzcoeoBZksmL20//QBGNkJ3QPQfcbCw8jhmnV46h8hk4ONF0Onbvt9ujgN98CT8NCXIWqAR9Cqw/RBmDkAMwfjxyjUJ8POmsmBZeMdPANYeDaLH5nQP1eEbD7s8PXJxZcxakHUDOVrNea6AQodUOhcuExRC7wFURSe53N/6Xxs3rgO4PFwRzx7BLkSlLqh2B3CeP9OaFbnpkG8TLBjvBd3N6Zh5Jm5MhY6YfXZYdltel1Ypo1pqI7B5L6wbHOlsJwhHByaVWjWwjrKFcL8mtUQINk8ZAthnGzu8HJ4C1rx8owaoTtqxMu6GabftQ5qEzC2BxpT8bLKwqqNoZzegqEnYGpobll2rofuDVAdD+uo2A31idCvsiaUtTkTyo7PbVeZXPzIhmFmDoTnM38Gzr0CKn1hG1h9djiQ7NkB934C9j48t0xypbAMcqUwragZ1tnstFt1mNoP9SkYeA10roOJvWF77+iDjrWw4dWhXsNPwf4nYXQ3FDuhvDrMw6PwiFpz3Uc+ohZM7w/7UCYX1kMmH+pW7ArLZGQnTO4NyyWTh/UXhmVWHQvrcHZfyhZDnbLFsC5nt++oGW/jLSj3Qs+msIzqU2GdzX80q2HcZjX0L/WE+ndtCO/VJmB8T3gu98bTb8T73PzuZrwPNsJ0qqOh7LlCmH6xK0zbW3PDRq1Qj2J3WPaz3cWusDxmRkOdpkdCI6d7Q9g2GtNz23GrFjcw4vWYjZ87+uH9Xz6paDkW84VCok22bt3q27dvP/ERP7Y+7GjzFTrDCi73QrkHCl3xRtuaW1mHws7nNmh8LnybtTB+sXvxLTnLzO0c2fxcN4QNrD4V5stsWeLnTDYOjty8QD1swke89vhgEB0exPDSsG5UoTYeHs0a9L08LLf5y2LBg8MRBwo87BRrz4e1F4SNeXA7HNwF+TI8f1/oX+iEQiWEWtScC3R3yJcgVw47WBSFfh6F8bP5OMBrYfhW4/AyWTZenrkwn0Pds8vYYeLFsB5XbYx33ChMb2wwHJjMoP98WHserD4HxnaH4J0ahtIqqE+H0CjGy2f6QChbrhSeD4XVAoFQWR2W+7N3hZ1/IZU+OO9tYZzmvINdsxqmNXvAiFph28wWoGNNqN/gD0OYdqwJQTE9EupbHQvTzldgzSug58wQNtMHQtBZZq5Bc6g7M/fpZvZ1qSdMezYQZ59r42GZdK6FLVeH4R77Ggw9NrffeRQHWxxuh7rrYf1lsnPbuGVC2ccGw4Gq2Bn22+LsozMs72YtbC+FrlDPFx8KB9HZbaxrfShLdQwymbl97lCozjtIZfNh+ZR75+339RD41bF5B+s4jGf3mY7+MPzsgafVCOvZo3AArU/A5HDYNvKV+KAdH9hm1+P8g0ypG97z+ZOLFrMd7r51oX7paMF/8P658MoWwoY12zKU9tjy/naX4PQTtcJBY7YVOrITRp+H/nPh5W8OO/mp4h4OXLliOKBmlujrtpe9bmnmc6TZ025ymHQEfO+Z7S6ByE+WycKmS+Zev+zS5OZlBmtentz0TzcK9wXpKhoRkZRSwIuIpJQCXkQkpZZ9wLs7b/zEXfzFd55ud1FERE4ryz7gzYzxapMXx6rtLoqIyGll2Qc8QE8lz+h0o93FEBE5raQi4HsrBQ5O19tdDBGR00pKAj7PQbXgRUQOk4qAX1UuMKoWvIjIYVIR8L06By8i8hLpCPiOAjONFtVG6ycPLCKyQqQi4Hsq4a6MasWLiMxJRcD3VgoAupJGRGSeVAT8bAteAS8iMicVAT/bgtcpGhGROakIeLXgRUReKhUBrxa8iMhLpSLgS/kspXxGP3YSEZknFQEPs/ejUQteRGRWagK+p6LbFYiIzJfon26b2eXA/wKywF+7+8eTmtdAT5n7nx3h3qeGObOvws6hSVZ3FKg2IhqtiHPWdtJoRmzsLZPL/uTjWrMV8cJolf6uIuXC3B/6RpFjFu5Dv5Bqo8X4TIO13aVTVrelEEVOJrNwnearNyNm6i2K+QzVRouOYo78vOVZbbQoZDNkMoa7M9NoUSkcfTNzd+DoyzNpR6t3oxWRNTuuZXKiWpEzMlmjETm9lfwxl89inerl24qcbALL5GjqzQgzDtvGlpNGK2pr2W12AzjlEzbLAk8BbwYGgR8Cv+Tujx1tnK1bt/r27dtPan67D0xz7efuZ9fI9DGH6yhkyWSMWjOiUshSyWcpFbJMVpsU8xl6yuG2B8+PTFNvhWHO7OtgfKbB2EyDyVqTbMYo57MUcxlyWSOXyVDIZchljN0Hp6k2IjatLhNFMDbTYFU5T19ngdHpMI3ZDTafMbpKedatKrFr/xQHpuqs6y5ydn8no9N1RibrHJiu02yFg0p3Kc8r1nUyeHCGsZlGqIsZe8erFHMZGi1nXXeRQi7L8ESVXCZDPmcUshny2QyTtSbDEzXWryoRuZMxo5TLMtNosfvgNF3FHIVcOJh1FrMM9JaZrreYqDYZn2kwXm1QbUSHLc/OYo7z1ndRbbZ4YbTKgak6hVyGvo4C0/UWYzMNNvaW2dRbOTR/M1jdUaAVOYMHZzCgv6vI8GQNHDIZIxMfRGef8xkjn8swWW0yMlUnnzU29VaI3Mlnw/KfqjXpLOWoNyNymcyh7udGpjijp0w2Y0zVmszUW0w3WkzXW9SbEb2VPKs7CrhDy51my9k7XmVNZ4FXDfQwMlWjs5ij2XIGR6eZqrWoFLJ0FHJUivFzIUu1GbF3bIZyIcdUrUnWjGYU0Yqcl/V1sHdshmbLeXGsysy822qU8hn6Ooqs7ijQ11mgs5hjZLJOtdkiipxm5LQip96KGJmsH9qOK8UcpXyGVhQaJI1WhAOlXJa+zgI/3j/F0ESNXMYY6CnT31VkvNpkZLJG5NBVytFVytFZzFHOZ5msNak1w3T2jVc5e00nHcUsXaU8lUKWZ4YneeD5UdZ3l8LyImxDBuwZnWGi2qS3UuDMvgodxRyFbIb9kzVa7jTiso/PNOgu5ynmMmFcg4wZ2YxhZgz0lMhmjOdGppmoNnlhbAZ36C7lWNddolLIUspn6SiGZT40UWPn0CTNVsTqeJvrqeR5xdouZhotfjQ4RisKITu7neSzRjaTYaLaIJsJ+8Bkrcl0vYmZkcvYoVBuRR7WdTFHRzHLRLXJzqFJchmjmA/7SjmfpaeSp96MeNnqCkMTNabqTaZqTfaN1+jrKDDQW6bjiAN55GH7z2eNC87o5lPvf81JHYjNbIe7b12wX4IB/3rgo+7+C/HrjwC4+x8fbZzFBDzA6HSdu58cpt6MOLu/g4PTjbBCM8aukWlyWePRPWOYGcV8Juzo9RYzjRadhRy1ZgikYi7L5jUdbO6r8PCeMfaNV+ku51lVztNVyhNFfujeN82W04gimi2nGUWs7SqxYVWJhwZHKeWzrCrnOTBV5+B0g9WVMA0HGq2w0Y9O13lxrMrmNR2s7Sry7PAUe8eq9Hbk6eso0tuRp5DNErkzPFlj575Jzuyr0NdZYKLapNlyzugphxZnxhg8OEMzitiwqkwUhXnUWhGNZkQpn6W/q3hop4/cqTZa5LIZNvdVmKw2aUR+aFm+MFqlq5Sju5QPz+U83aUc5UKOaqNFKZ9l59AEP94/RSmfZcOqMmesKjFRa3IwDvp13SWe3DfBvrEq5UKW9d0lIocDUzVy2QwDPeXQop2qsbarhFloVUcOjuMedoTZ5VXOZ1nbVaTWinh+ZJpcNkO92aLRcjqKOSarYf01I2eiGg6mm/s62Dse/vGrUshSzodwqBSyFPNZhidqjM80Dh1YsmZs6CnxxIsTPH9gmrXdRSZrLQpZY/2qMt2lHDP1FlP1JtP1FlO18JzLGmesKlNtRnQUsrQiJ5cNO+yu/dOc0VOmmM/Q31nknLWd5DPGwekGB6ZqjEzFB/SpOhPVBn2dRSqFLNmMkY0DMJ/NsLqjcOhANbvtZswo5EJDwwymai2GJ2ts7quwsbdMvRmxZ3SG4YlaaGx0FMlkjIlqaLBMVsO0Oos5ivkQvOu6izwzPHVon5ipRwz0lNi6eXUIsFoTCOumFTkDPWVWlfMMTdQYPDjNdL1FrRnR11E41PhZ01mkq5RnvNqg0YqIfHZd+6Hp7BqZphU55/R30FXKs2l1hVzGGJ6osX+yxkx8YJ6uN5mutegq57lgQzeFrLF/qk4ln2V4ssbzB6bJZYxXDfRQKWRptCLqrbCfNloRjZbTVcoRuTNTb9FZytFRyOF4vC+HMuUydmgdT9VaFHIZzlvfhRM+rc4u77GZRnxgmmJtV4lVlTzFbIZNceAPHpymdkTjCGBDT4lGK6LaiLj5Vy4+qdxrV8C/G7jc3f99/Ppa4HXu/ptHDHcdcF388lzgyZOc5Rpg/0mOu1ypziuD6rwynGydz3T3/oV6JHoO/ni4+03ATYudjpltP9pRLK1U55VBdV4Zkqhzkmf/9wCb5r3eGL8nIiJLIMmA/yHwCjM7y8wKwPuAryc4PxERmSexUzTu3jSz3wS+RbhM8mZ3fzSp+XEKTvMsQ6rzyqA6rwynvM6JfckqIiLttTx/PSAiIj+RAl5EJKWWfcCb2eVm9qSZ7TSzG9tdnqSY2S4ze9jMHjSz7fF7q83s22b2dPzc2+5yLpaZ3WxmQ2b2yLz3FqynBZ+M1/2PzOw17Sv5yTtKnT9qZnvi9f2gmV05r99H4jo/aWa/0J5SL46ZbTKzu8zsMTN71Myuj99P7bo+Rp2TW9fuvmwfhC9vnwHOBgrAQ8AF7S5XQnXdBaw54r3/DtwYd98I/Ld2l/MU1PPngNcAj/ykegJXAt8EDLgUuL/d5T+Fdf4o8NsLDHtBvJ0XgbPi7T/b7jqcRJ03AK+Ju7sItzW5IM3r+hh1TmxdL/cW/CXATnd/1t3rwJeAq9pcpqV0FfCFuPsLwDvbWJZTwt3vBQ4c8fbR6nkV8EUPvg/0mNmGpSnpqXOUOh/NVcCX3L3m7j8GdhL2g2XF3V909wfi7gngcWCAFK/rY9T5aBa9rpd7wA8Au+e9HuTYC2w5c+CfzGxHfHsHgHXu/mLcvRdY156iJe5o9Uz7+v/N+HTEzfNOv6Wuzma2GbgIuJ8Vsq6PqDMktK6Xe8CvJG9w99cAVwAfNLOfm9/Tw2e61F/zulLqCfwVcA6wBXgR+J/tLU4yzKwTuB24wd3H5/dL67peoM6JrevlHvAr5nYI7r4nfh4C7iB8VNs3+zE1fh5qXwkTdbR6pnb9u/s+d2+5ewR8lrmP5qmps5nlCUF3i7t/NX471et6oTonua6Xe8CviNshmFmHmXXNdgNvAR4h1PWX48F+Gfhae0qYuKPV8+vAv42vsLgUGJv38X5ZO+L88r8hrG8IdX6fmRXN7CzgFcAPlrp8i2VmBnwOeNzd/2Rer9Su66PVOdF13e5vlk/BN9NXEr6Nfgb43XaXJ6E6nk34Nv0h4NHZegJ9wD8DTwN3AqvbXdZTUNdbCR9TG4Rzjr92tHoSrqj4VLzuHwa2trv8p7DOfxPX6Ufxjr5h3vC/G9f5SeCKdpf/JOv8BsLplx8BD8aPK9O8ro9R58TWtW5VICKSUsv9FI2IiByFAl5EJKUU8CIiKaWAFxFJKQW8iEhKKeBFTgEze6OZ/UO7yyEynwJeRCSlFPCyopjZNWb2g/i+258xs6yZTZrZn8b36P5nM+uPh91iZt+PbwJ1x7x7k7/czO40s4fM7AEzOyeefKeZfcXMnjCzW+JfLoq0jQJeVgwzOx/YBlzm7luAFnA10AFsd/dXAvcAfxCP8kXgP7n7TxN+aTj7/i3Ap9z91cDPEH6FCuHugDcQ7uN9NnBZ4pUSOYZcuwsgsoR+Hngt8MO4cV0m3MwqAr4cD/O3wFfNbBXQ4+73xO9/Afi7+J5AA+5+B/G2O44AAADiSURBVIC7VwHi6f3A3Qfj1w8Cm4HvJl8tkYUp4GUlMeAL7v6Rw940+/0jhjvZ+3fU5nW30P4lbaZTNLKS/DPwbjNbC4f+//NMwn7w7niY9wPfdfcx4KCZ/Wz8/rXAPR7+iWfQzN4ZT6NoZpUlrYXIcVILQ1YMd3/MzH6P8M9YGcLdGz8ITAGXxP2GCOfpIdyu9tNxgD8L/Gr8/rXAZ8zsv8TTeM8SVkPkuOlukrLimdmku3e2uxwip5pO0YiIpJRa8CIiKaUWvIhISingRURSSgEvIpJSCngRkZRSwIuIpNT/Bx0QWOEJGjFcAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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" ] }, "metadata": { "tags": [], "needs_background": "light" } }, { "output_type": "stream", "text": [ "DNN results\n", "accuracy : 0.9916666666666667\n", "Training Recall: 0.9916666666666667\n", "Training Precision: 0.9916979740277564\n", "Confusion matrix: \n", "[[376 2]\n", " [ 5 457]]\n", "Validation set report:\n", "cross Loss: -14.799193500337147\n", "hing Loss: 0.7285714285714285\n", "Report precision recall f1-score support\n", "\n", " 0 0.65 0.75 0.70 93\n", " 1 0.78 0.68 0.73 117\n", "\n", " accuracy 0.71 210\n", " macro avg 0.72 0.72 0.71 210\n", "weighted avg 0.72 0.71 0.72 210\n", "\n", "accuracy : 0.7142857142857143\n", "Validation Recall: 0.7142857142857143\n", "Validation Precision: 0.7224519423956861\n", "Confusion matrix: \n", "[[70 23]\n", " [37 80]]\n", "\n", "\\KNN Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 0.7142857142857143\n", "Validation Recall: 0.7142857142857143\n", "Validation Precision: 0.7208163265306122\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[69 24]\n", " [36 81]]\n", "\n", "\n", "Support vector machine Result\n", "\n", "Train accuracy: 0.9904761904761905\n", "Training Recall: 0.9904761904761905\n", "Training Precision: 0.9904919908466819\n", "\n", "\n", "Validation accuracy: 0.680952380952381\n", "Validation Recall: 0.680952380952381\n", "Validation Precision: 0.6955905388471179\n", "\n", "Train Confusion matrix:\n", " [[375 3]\n", " [ 5 457]]\n", "Validation Confusion matrix:\n", " [[70 23]\n", " [44 73]]\n", "\n", "\n", "Random Forest Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 0.7142857142857143\n", "Validation Recall: 0.7142857142857143\n", "Validation Precision: 0.7208163265306122\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[69 24]\n", " [36 81]]\n", "\n", "\n", "Random Forest Result\n", "\n", "Train accuracy: 0.9916666666666667\n", "Training Recall: 0.9916666666666667\n", "Training Precision: 0.9916674782728256\n", "\n", "\n", "Validation accuracy: 0.7047619047619048\n", "Validation Recall: 0.7047619047619048\n", "Validation Precision: 0.7085267897694094\n", "\n", "Train Confusion matrix:\n", " [[374 4]\n", " [ 3 459]]\n", "Validation Confusion matrix:\n", " [[66 27]\n", " [35 82]]\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "gfkLNDuIZRAG", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "519c3bde-a685-45e7-9bea-2a075afec1f4" }, "source": [ "\"\"\"\n", "LDA after split\n", "hing\n", " \n", "cross Loss: 0.25\n", "X = np.load('gdrive/My Drive/Python files/neuromarketing 25/features_psd_ALL.npy') # 2724\n", "\n", "Standard\n", "\n", " batch_size = 128\n", " nb_epoch = 250 #3000\n", " l1_decay=0.00\n", " l2_decay=0.000 # .5\n", " # 0.01 0.06\n", " sigma=0.005\n", " in_drop_rate = .5\n", " drop_rate = .5#.05\n", " lr1=0.0001\n", "\n", "\"\"\"\n", "train_models(X, y, y_cat)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Before\n", "(1050, 2724)\n", "After\n", "(840, 1)\n", "Epoch 1/250\n", "7/7 [==============================] - 1s 83ms/step - loss: 0.3680 - accuracy: 0.8679 - val_loss: 0.6343 - val_accuracy: 0.6762\n", "Epoch 2/250\n", "7/7 [==============================] - 0s 19ms/step - loss: 0.0585 - accuracy: 0.9845 - val_loss: 0.6406 - val_accuracy: 0.6762\n", "Epoch 3/250\n", "7/7 [==============================] - 0s 17ms/step - loss: 0.0453 - accuracy: 0.9810 - val_loss: 0.6422 - val_accuracy: 0.6762\n", "Epoch 4/250\n", "7/7 [==============================] - 0s 17ms/step - loss: 0.0389 - accuracy: 0.9810 - val_loss: 0.6414 - val_accuracy: 0.6762\n", "Epoch 5/250\n", "7/7 [==============================] - 0s 18ms/step - loss: 0.0322 - accuracy: 0.9845 - val_loss: 0.6387 - val_accuracy: 0.6810\n", "Epoch 6/250\n", "7/7 [==============================] - 0s 17ms/step - loss: 0.0402 - accuracy: 0.9821 - val_loss: 0.6351 - val_accuracy: 0.6810\n", "Epoch 7/250\n", "7/7 [==============================] - 0s 17ms/step - loss: 0.0316 - accuracy: 0.9869 - val_loss: 0.6343 - val_accuracy: 0.6810\n", "Epoch 8/250\n", "7/7 [==============================] - 2s 268ms/step - loss: 0.0353 - accuracy: 0.9845 - val_loss: 0.6334 - val_accuracy: 0.6810\n", "Epoch 9/250\n", "7/7 [==============================] - 0s 17ms/step - loss: 0.0344 - accuracy: 0.9845 - val_loss: 0.6358 - val_accuracy: 0.6810\n", "Epoch 10/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0297 - accuracy: 0.9893 - val_loss: 0.6346 - val_accuracy: 0.6810\n", "Epoch 11/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0292 - accuracy: 0.9869 - val_loss: 0.6343 - val_accuracy: 0.6810\n", "Epoch 12/250\n", "7/7 [==============================] - 0s 46ms/step - loss: 0.0437 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"Epoch 105/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0296 - accuracy: 0.9869 - val_loss: 0.5866 - val_accuracy: 0.7095\n", "Epoch 106/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0340 - accuracy: 0.9833 - val_loss: 0.5933 - val_accuracy: 0.7048\n", "Epoch 107/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0318 - accuracy: 0.9845 - val_loss: 0.5956 - val_accuracy: 0.7048\n", "Epoch 108/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0303 - accuracy: 0.9857 - val_loss: 0.5921 - val_accuracy: 0.7048\n", "Epoch 109/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0322 - accuracy: 0.9833 - val_loss: 0.6066 - val_accuracy: 0.7000\n", "Epoch 110/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0261 - accuracy: 0.9869 - val_loss: 0.6218 - val_accuracy: 0.6810\n", "Epoch 111/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0337 - 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[==============================] - 0s 15ms/step - loss: 0.0377 - accuracy: 0.9798 - val_loss: 0.5826 - val_accuracy: 0.7095\n", "Epoch 132/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0329 - accuracy: 0.9845 - val_loss: 0.5838 - val_accuracy: 0.7095\n", "Epoch 133/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0342 - accuracy: 0.9821 - val_loss: 0.5919 - val_accuracy: 0.7048\n", "Epoch 134/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0373 - accuracy: 0.9821 - val_loss: 0.5927 - val_accuracy: 0.7048\n", "Epoch 135/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0389 - accuracy: 0.9810 - val_loss: 0.5876 - val_accuracy: 0.7048\n", "Epoch 136/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0324 - accuracy: 0.9833 - val_loss: 0.5818 - val_accuracy: 0.7095\n", "Epoch 137/250\n", "7/7 [==============================] - 0s 42ms/step - loss: 0.0318 - accuracy: 0.9845 - val_loss: 0.5812 - val_accuracy: 0.7095\n", "Epoch 138/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0324 - accuracy: 0.9845 - val_loss: 0.5882 - val_accuracy: 0.7048\n", "Epoch 139/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0366 - accuracy: 0.9821 - val_loss: 0.6141 - val_accuracy: 0.6952\n", "Epoch 140/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0353 - accuracy: 0.9821 - val_loss: 0.6373 - val_accuracy: 0.6810\n", "Epoch 141/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0302 - accuracy: 0.9845 - val_loss: 0.6392 - val_accuracy: 0.6810\n", "Epoch 142/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0282 - accuracy: 0.9881 - val_loss: 0.6412 - val_accuracy: 0.6762\n", "Epoch 143/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0270 - accuracy: 0.9869 - val_loss: 0.6352 - val_accuracy: 0.6810\n", "Epoch 144/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0320 - accuracy: 0.9821 - val_loss: 0.6154 - val_accuracy: 0.6952\n", "Epoch 145/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0362 - accuracy: 0.9798 - val_loss: 0.5899 - val_accuracy: 0.7095\n", "Epoch 146/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0322 - accuracy: 0.9845 - val_loss: 0.6003 - val_accuracy: 0.7048\n", "Epoch 147/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0305 - accuracy: 0.9857 - val_loss: 0.5910 - val_accuracy: 0.7095\n", "Epoch 148/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0353 - accuracy: 0.9821 - val_loss: 0.5933 - val_accuracy: 0.7095\n", "Epoch 149/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0320 - accuracy: 0.9833 - val_loss: 0.6094 - val_accuracy: 0.7048\n", "Epoch 150/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0371 - accuracy: 0.9810 - val_loss: 0.6138 - val_accuracy: 0.6952\n", "Epoch 151/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0295 - accuracy: 0.9857 - val_loss: 0.6290 - val_accuracy: 0.6810\n", "Epoch 152/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0398 - accuracy: 0.9786 - val_loss: 0.6425 - val_accuracy: 0.6762\n", "Epoch 153/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0298 - accuracy: 0.9833 - val_loss: 0.6345 - val_accuracy: 0.6810\n", "Epoch 154/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0340 - accuracy: 0.9821 - val_loss: 0.6185 - val_accuracy: 0.6905\n", "Epoch 155/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0331 - accuracy: 0.9821 - val_loss: 0.6289 - val_accuracy: 0.6810\n", "Epoch 156/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0343 - accuracy: 0.9833 - val_loss: 0.6387 - val_accuracy: 0.6810\n", "Epoch 157/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0324 - accuracy: 0.9833 - val_loss: 0.6408 - val_accuracy: 0.6762\n", "Epoch 158/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0304 - accuracy: 0.9833 - val_loss: 0.6364 - val_accuracy: 0.6810\n", "Epoch 159/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0296 - accuracy: 0.9857 - val_loss: 0.6115 - val_accuracy: 0.6905\n", "Epoch 160/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0333 - accuracy: 0.9833 - val_loss: 0.5815 - val_accuracy: 0.7095\n", "Epoch 161/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0295 - accuracy: 0.9857 - val_loss: 0.5851 - val_accuracy: 0.7095\n", "Epoch 162/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0314 - accuracy: 0.9857 - val_loss: 0.5880 - val_accuracy: 0.7048\n", "Epoch 163/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0334 - accuracy: 0.9845 - val_loss: 0.5860 - val_accuracy: 0.7095\n", "Epoch 164/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0276 - accuracy: 0.9881 - val_loss: 0.6067 - val_accuracy: 0.6952\n", "Epoch 165/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0325 - accuracy: 0.9845 - val_loss: 0.6390 - val_accuracy: 0.6810\n", "Epoch 166/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0332 - accuracy: 0.9833 - val_loss: 0.6437 - val_accuracy: 0.6762\n", "Epoch 167/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0366 - accuracy: 0.9821 - val_loss: 0.6422 - val_accuracy: 0.6762\n", "Epoch 168/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0281 - accuracy: 0.9857 - val_loss: 0.6456 - val_accuracy: 0.6762\n", "Epoch 169/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0338 - accuracy: 0.9833 - val_loss: 0.6382 - val_accuracy: 0.6810\n", "Epoch 170/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0320 - accuracy: 0.9821 - val_loss: 0.6175 - val_accuracy: 0.6905\n", "Epoch 171/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0297 - accuracy: 0.9857 - val_loss: 0.5830 - val_accuracy: 0.7095\n", "Epoch 172/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0317 - accuracy: 0.9857 - val_loss: 0.5815 - val_accuracy: 0.7095\n", "Epoch 173/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0325 - accuracy: 0.9845 - val_loss: 0.5814 - val_accuracy: 0.7095\n", "Epoch 174/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0363 - accuracy: 0.9821 - val_loss: 0.5814 - val_accuracy: 0.7095\n", "Epoch 175/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0321 - accuracy: 0.9821 - val_loss: 0.5825 - val_accuracy: 0.7095\n", "Epoch 176/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0331 - accuracy: 0.9833 - val_loss: 0.5852 - val_accuracy: 0.7095\n", "Epoch 177/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0338 - accuracy: 0.9821 - val_loss: 0.5843 - val_accuracy: 0.7095\n", "Epoch 178/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0375 - accuracy: 0.9810 - val_loss: 0.5821 - val_accuracy: 0.7095\n", "Epoch 179/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0320 - accuracy: 0.9833 - val_loss: 0.5815 - val_accuracy: 0.7095\n", "Epoch 180/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0335 - accuracy: 0.9845 - val_loss: 0.5823 - val_accuracy: 0.7095\n", "Epoch 181/250\n", "7/7 [==============================] - 0s 17ms/step - loss: 0.0306 - accuracy: 0.9845 - val_loss: 0.5833 - val_accuracy: 0.7095\n", "Epoch 182/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0315 - accuracy: 0.9857 - val_loss: 0.5821 - val_accuracy: 0.7095\n", "Epoch 183/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0342 - accuracy: 0.9833 - val_loss: 0.5813 - val_accuracy: 0.7095\n", "Epoch 184/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0300 - accuracy: 0.9845 - val_loss: 0.5845 - val_accuracy: 0.7095\n", "Epoch 185/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0296 - accuracy: 0.9857 - val_loss: 0.6074 - val_accuracy: 0.7000\n", "Epoch 186/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0341 - accuracy: 0.9821 - val_loss: 0.6419 - val_accuracy: 0.6762\n", "Epoch 187/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0299 - accuracy: 0.9833 - val_loss: 0.6393 - val_accuracy: 0.6762\n", "Epoch 188/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0328 - accuracy: 0.9845 - val_loss: 0.6295 - val_accuracy: 0.6810\n", "Epoch 189/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0288 - accuracy: 0.9857 - val_loss: 0.6136 - val_accuracy: 0.6952\n", "Epoch 190/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0339 - accuracy: 0.9833 - val_loss: 0.6020 - val_accuracy: 0.7095\n", "Epoch 191/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0345 - accuracy: 0.9821 - val_loss: 0.5947 - val_accuracy: 0.7095\n", "Epoch 192/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0340 - accuracy: 0.9833 - val_loss: 0.5938 - val_accuracy: 0.7095\n", "Epoch 193/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0325 - accuracy: 0.9833 - val_loss: 0.6091 - val_accuracy: 0.7000\n", "Epoch 194/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0309 - accuracy: 0.9845 - val_loss: 0.6324 - val_accuracy: 0.6762\n", "Epoch 195/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0433 - accuracy: 0.9810 - val_loss: 0.6355 - val_accuracy: 0.6762\n", "Epoch 196/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0323 - accuracy: 0.9857 - val_loss: 0.6374 - val_accuracy: 0.6762\n", "Epoch 197/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0300 - accuracy: 0.9833 - val_loss: 0.6312 - val_accuracy: 0.6762\n", "Epoch 198/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0321 - accuracy: 0.9857 - val_loss: 0.6198 - val_accuracy: 0.6810\n", "Epoch 199/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0320 - accuracy: 0.9857 - val_loss: 0.5931 - val_accuracy: 0.7095\n", "Epoch 200/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0309 - accuracy: 0.9845 - val_loss: 0.5956 - val_accuracy: 0.7048\n", "Epoch 201/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0327 - accuracy: 0.9857 - val_loss: 0.6087 - val_accuracy: 0.6952\n", "Epoch 202/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0293 - accuracy: 0.9857 - val_loss: 0.5857 - val_accuracy: 0.7095\n", "Epoch 203/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0331 - accuracy: 0.9845 - val_loss: 0.5886 - val_accuracy: 0.7095\n", "Epoch 204/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0308 - accuracy: 0.9857 - val_loss: 0.5873 - val_accuracy: 0.7095\n", "Epoch 205/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0314 - accuracy: 0.9857 - val_loss: 0.5858 - val_accuracy: 0.7095\n", "Epoch 206/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0289 - accuracy: 0.9833 - val_loss: 0.5924 - val_accuracy: 0.7095\n", "Epoch 207/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0355 - accuracy: 0.9821 - val_loss: 0.5978 - val_accuracy: 0.7095\n", "Epoch 208/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0281 - accuracy: 0.9869 - val_loss: 0.5905 - val_accuracy: 0.7095\n", "Epoch 209/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0298 - accuracy: 0.9845 - val_loss: 0.5942 - val_accuracy: 0.7048\n", "Epoch 210/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0330 - accuracy: 0.9833 - val_loss: 0.5914 - val_accuracy: 0.7048\n", "Epoch 211/250\n", "7/7 [==============================] - 0s 44ms/step - loss: 0.0326 - accuracy: 0.9833 - val_loss: 0.5811 - val_accuracy: 0.7095\n", "Epoch 212/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0339 - accuracy: 0.9833 - val_loss: 0.5826 - val_accuracy: 0.7095\n", "Epoch 213/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0329 - accuracy: 0.9833 - val_loss: 0.5819 - val_accuracy: 0.7095\n", "Epoch 214/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0306 - accuracy: 0.9857 - val_loss: 0.5892 - val_accuracy: 0.7095\n", "Epoch 215/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0304 - accuracy: 0.9857 - val_loss: 0.5880 - val_accuracy: 0.7095\n", "Epoch 216/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0305 - accuracy: 0.9857 - val_loss: 0.5908 - val_accuracy: 0.7095\n", "Epoch 217/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0306 - accuracy: 0.9833 - val_loss: 0.6037 - val_accuracy: 0.7095\n", "Epoch 218/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0305 - accuracy: 0.9845 - val_loss: 0.6093 - val_accuracy: 0.7048\n", "Epoch 219/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0332 - accuracy: 0.9833 - val_loss: 0.6085 - val_accuracy: 0.7048\n", "Epoch 220/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0291 - accuracy: 0.9857 - val_loss: 0.6111 - val_accuracy: 0.7000\n", "Epoch 221/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0326 - accuracy: 0.9845 - val_loss: 0.6220 - val_accuracy: 0.6810\n", "Epoch 222/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0305 - accuracy: 0.9833 - val_loss: 0.6333 - val_accuracy: 0.6905\n", "Epoch 223/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0322 - accuracy: 0.9869 - val_loss: 0.6321 - val_accuracy: 0.6905\n", "Epoch 224/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0383 - accuracy: 0.9810 - val_loss: 0.6452 - val_accuracy: 0.6762\n", "Epoch 225/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0313 - accuracy: 0.9833 - val_loss: 0.6198 - val_accuracy: 0.6905\n", "Epoch 226/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0274 - accuracy: 0.9869 - val_loss: 0.5947 - val_accuracy: 0.7048\n", "Epoch 227/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0307 - accuracy: 0.9857 - val_loss: 0.5831 - val_accuracy: 0.7095\n", "Epoch 228/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0323 - accuracy: 0.9833 - val_loss: 0.5820 - val_accuracy: 0.7095\n", "Epoch 229/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0351 - accuracy: 0.9821 - val_loss: 0.5945 - val_accuracy: 0.7048\n", "Epoch 230/250\n", "7/7 [==============================] - 0s 17ms/step - loss: 0.0317 - accuracy: 0.9857 - val_loss: 0.6307 - val_accuracy: 0.6810\n", "Epoch 231/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0353 - accuracy: 0.9833 - val_loss: 0.6311 - val_accuracy: 0.6762\n", "Epoch 232/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0331 - accuracy: 0.9833 - val_loss: 0.6329 - val_accuracy: 0.6762\n", "Epoch 233/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0328 - accuracy: 0.9821 - val_loss: 0.6311 - val_accuracy: 0.6762\n", "Epoch 234/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0409 - accuracy: 0.9798 - val_loss: 0.6400 - val_accuracy: 0.6762\n", "Epoch 235/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0342 - accuracy: 0.9845 - val_loss: 0.6353 - val_accuracy: 0.6810\n", "Epoch 236/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0357 - accuracy: 0.9821 - val_loss: 0.6275 - val_accuracy: 0.6810\n", "Epoch 237/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0302 - accuracy: 0.9845 - val_loss: 0.6146 - val_accuracy: 0.6905\n", "Epoch 238/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0283 - accuracy: 0.9857 - val_loss: 0.5877 - val_accuracy: 0.7095\n", "Epoch 239/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0362 - accuracy: 0.9798 - val_loss: 0.5911 - val_accuracy: 0.7048\n", "Epoch 240/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0268 - accuracy: 0.9845 - val_loss: 0.6079 - val_accuracy: 0.6952\n", "Epoch 241/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0297 - accuracy: 0.9845 - val_loss: 0.6267 - val_accuracy: 0.6810\n", "Epoch 242/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0372 - accuracy: 0.9810 - val_loss: 0.6372 - val_accuracy: 0.6810\n", "Epoch 243/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0272 - accuracy: 0.9881 - val_loss: 0.6412 - val_accuracy: 0.6762\n", "Epoch 244/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0282 - accuracy: 0.9869 - val_loss: 0.6426 - val_accuracy: 0.6762\n", "Epoch 245/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0301 - accuracy: 0.9833 - val_loss: 0.6309 - val_accuracy: 0.6810\n", "Epoch 246/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0293 - accuracy: 0.9869 - val_loss: 0.6002 - val_accuracy: 0.7048\n", "Epoch 247/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0315 - accuracy: 0.9845 - val_loss: 0.6268 - val_accuracy: 0.6857\n", "Epoch 248/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0299 - accuracy: 0.9869 - val_loss: 0.6247 - val_accuracy: 0.6857\n", "Epoch 249/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0328 - accuracy: 0.9833 - val_loss: 0.6314 - val_accuracy: 0.6810\n", "Epoch 250/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0328 - accuracy: 0.9833 - val_loss: 0.6133 - val_accuracy: 0.6952\n", "Valid results - Loss: 0.6133483648300171 - Accuracy: 69.52381134033203%\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", 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D+3DzWaO46T/LWbO3nA9vO4P75q/nf8tziI4UGptNy7JnjunPgvX5ZO8qpX9SLIVV9ZyYmUJ9k4/oiAi+9aXh/OaNDewr95ekB/WJY195HYP6xBEfHcn2omr6xEdz6zmjOWVUKuc98hGNzYbUXrFk9Esge1cpYwYmOTUke16k94njxe+ezNaCKq59ZimPXTWVsYOSuObppXx12hD+sXg7SXHR1DY2t5TYY6MimH5MKutzK/jR2aPYVVLD3xfZ2kpUhPDeracRGSGc+oeFDOodx7ybZ5DaK5aq+ia+/cxSPt/hH7z35DenUV7byO1zV3PLWaO45axRnPXwh6T2imH2Ncdz4m/fZ0hyPJvzq7jouEEY4PTRadw+dzUi8MbNp/D4h9u46sRhTB7al398tJ2PnWaLzfmVGKDMyVRvP+9YesVG8bdFW7nj/DGs3VvBPz/ewaShfUnrFcvCTQU0+wwXT0rnix3FDOwTz/WnZPLg25sorWngtRunU1rTwFOLd3DplMGcO24AH2ws4EcvrKSyvokIgTPHDGjZTq/YKAb0jqWkuoFZkwczZ8lOUhJjyOiXyIo9Zfx61gRKqutZvKWIrQVVFFc3MOOY1Jb0A8yanM454wbwyHtbSEmI4funj2T9vgr+sXg7l04ezEkjUrj1pVWMSEukpr6Z+JhIIkRYs7ecSUP6EBcdCcC+8jryKup46KuTSO0Vy+xPdrBgfX6r3/Zpo9P4fEcxdY0+esVGUVXfxA2njuDpT3bwlWlDuXjSIL75zy84aUQKxVUNbCmootlnmDY8meOG9OFHZ41mW1EVl//tU0YP6EVxVQPRkRHkVdRx1YnDeP6L3RgDvWKjaPL5+HrWUC6bOoRLH/uEhJhIbjrzGP7z+W6+ljWUhxdsZsLg3kwe2pf7L50YNH/riIgsM8Zkec7TAGG5ASI6KsJpihCMMURGSMvJ02r5hiYyUxO55azR1DQ0ERVhS1uRkUJFbRM1DU0IQl1pHld+5VIWfJLNBwsXMvsvf+CjDxdSUFFPTUMzf3zgfj54ez4REULunt385+V5DBszmZknTeS1BR9RWFrB9666jNcXLyMpLpq///kh6uobuP9X91BYWU91QxOJsVFU1zdRV7ibE6Ycxwcb8/lgYwGRIjy7ZBexURHEx0S2ZAAAkRFCs8/QPymWAqf54Irjh/LC0j2IwLRhyQxJjufVlbb9+eyx/Xnq6uPbfQ5PLd7O/W/Ypphrp2dwz8XjaWr28a/PdvHOujyMgZioCH541igSY6K44C+LiYmMoH/vWLKGJ5NTWsvy3aW4LS93XTiW75wyAmMMr6zYy/sbCvhK1hDmrcxlY14lG/bZ7/BLI/vx6bZiRGxmKyJ866Th3BVQSmv2Gb797FIWbSpsWR7gqW9lcfa4AS3LzVmykz0lNdx05igiBO6fv4FvnjycHUXVfLa9mNOP7c/3nlvGd08dweb8SlbuKefTO84kJspfantnXR5Pf7KDPvHRXHH8MOav3se54wdw3viBLcsYY3jwnU0APPPJTs4c259hKQk88eE2oiMjqG/yMaB3LMNSEli+u4z7L53ApZMH8/Unl7A6pxyA6cf049lrTyAqMoJ/fLSd37y5gUsnp/PqylxevXE6z3yyo+U7AxABYyAzNZEdRdWtvjs3Y4yPieSO88fwydZiGpp8fP/0ka2WM8bw6sq9/OaNDRRVNXDNlzJI7RXDwws24zPw3++dzPEZKewpqWHWY5+0BMioCKHJZzhtdBpLd5YwIi2Rv145lczURABeXp7D0p0l/Ojs0RRU1HPxox8jAv934jBuP3cMkZHSKlBOGtKHAb3j8Bl4b0M+cdER/OErk1iyvZi7Lxrn+TsN9P6G/JYg9eBXjuOSSek8tXg7i7f4A02ECNdMz2j53qrrm7j7tXUcn5FMcbVtrvvFBWPJLa/lL+9v4fwJg7h73lr2lNSSkhjD27ecQv/ecby0dA93vbaWhiYff/zqJFbnlLFhXwXLd5eR1iuWsYOS+HRbMY3NPhJjonj1punc9t9VrNhdhgg8fc3xXPfMUnzGf74+/uE20vvGc8mk9Jbv5XdvbWTVnjJGDeilAaIjnQWIzqzf5+9LEISR/RNpbPKREBNFdFT76tuu4mpqG5oZmpLAtsIqBveNp1+vWMC2++4uqSG9bzyVhbmce/6F/O+9T1m/bAnP/eMx5s+fD8CiRYv46Z0/569z5jJ+aH8umnk29957LydPP4WRI0fw7/kfEE8j11z5ZT7LXklSXBQP/P4P7C4o4fu33kFkhJDeN54+8dGU1zSyb9dWxo3zZ5A7iqo5++EP+e6pI7h4Ujpvrc0jOSGaYwfYku9Fxw3iR2eP5pUVe0mIieTL04Yw7f4FCPDWLaeSkZrAfz7fTVlNI5dNGUyG88MOZIzhJ/9dxbyVubz9o1M4pn9Sh5/zZX/7hBW7y7jz/DF89zSbEa3dW857G/JJSYzhyhOGER2kutzU7OPaZ5aybFcpi396Bh9vLaKp2e4f4M9XTGbW5Na37aqsa+TddflcMHEQZz60iKEpCbx4w0ldHhb8f099xtaCKgor6/n+6SO5/bwxXVq/rYcXbOYv728hKkI4Y0x/vnfaCD7cXMSHmwpYlVPO3ReN4zqnaaWwsp6XsvcQGSFcecKwluajqvomLnn0Y7YXVjNhcG9ev2kGZTWNvLl2H5mpiVzz9FK+PHUwb6zeR0VdE2ePHcCMY/pRUtPISZkpfOmY1C6lubymkddX2+adpLho1uWWs6OomouOS29ZZlNeJW+u2Ufv+Gi+Mm0ILy3dw5/e20xSXBSv3zSD/r3jgm7/7bV5pPeN47gh/r43n8/wxpp99EuMaUlvfkUdZ/xxEbMmp/O7y4/r0jEUVNTx/sYCvjJtSNDzrKseencTf1+0jX9/50ROHNGvZfrOomq+2FnCV6YOISLCnm+r9pRx1T8+o7qhmf87cRjnjR9ISmIMEwb34cmPtvHbNzdy5pj+zL7meO58eQ2vrtjL0rvOplds+HoDOgoQLaMxDve/adOmmbbWr1/fblow6/aWm93F1aayttHUNTR1unxTc7NpaGo2xhhT29BkfD5fq/n1jXZeUVGRGTx0qNmaX2nee/99c+GFF7Ys8+qrr5qLLrrI1Dc2mw0bNpjY2FizcOFCY4wxw4cPN3ty88z27dvN+PHjW9b5wx/+YH5x1y9NZW2DaXT239Hxbi+sMk3NvnbT95XVtqQ/0DWzPzf3zlvb6fEHam72mdyympCWfXvtPnPyb98zRZV1XdqHq6Gp2eSV17a8r21oMqN+8aYZ/rP5ZnthVYfr5pXXmvLahv3a75wlO83wn803GXfMN7uLq/drG4EamprNox9sMdN+vcB8vr24ZXpTs89szqsIeTvbCirNaQ9+YN5ak9tuXn65/Y6/OyfbDP/ZfPPJlsIDTvf+KKysM4X7+X0Hk1NaY2rqO/+dHgxNzb5W52RnFqzLMzN+/36773lvaY054TcLzKdbi4wxNg/ZVXTg51pngGwTJF/VTmqHwTYn9YoL7SOJjIjArdB6VW3d5od+/fpx6owZzDrzJOLj4xkwwN+0MXPmTB5//HEmTRzPsccey0knndRqG3HRkTTVty/pRkVG0CsutHsuZXqU+gEG9vEuyT197QkhbTdQRIS06mzuyHnjB7Zqdumq6MgIBgSUQuOiIzkxM4WVe8oYnpLQwZq0Wq+rzhs/gLtfW8vpo9MY2sl+QhEdGcGNZxzDjWcc02p6ZIQwakDHtbBAI9J6sej2MzznuaX1a6dnkJYUy8kj+3kuF26pTs26Ow3uG9r5djBERkiXzq2zxw1o1cTpSu8bz+c/P7vlfUxURKuBCz1Bm5gca/eWk5IYQ/ohdOJ1VVeO90iyOb+SvPI6Th0d3jv6vr12H2MH9WZ4P++gq9ThqKMmJq1BBNC7VRyeRg9IYnQXSt37a+aEQZ0vpNQRRC+UcxhjR7UqpZSyNEDgdNRjtAqhlFIBNEAE0PCglFJ+GiCwzUugFQillAqkAQJwx3FpfFBKKT8NEATe2rn7Q8T+3u4b4JFHHqGmpv0dHpVS6mDQABEgHE1MGiCUUocrvQ6C8DYx3XHHHWxzbvd9zjnn0L9/f1566SXq6+u57LLL+NWvfkV1dTVf+9rXyMnJobm5mV/+8pfk5+eTm5vLGWecQWpqKgsXLgxD6pRSKrijJ0C8dQfkrfGcFWkMIxqaiY2OgIguVKoGToTzH+hwkQceeIC1a9eycuVK3n33XebOncsXX3yBMYZLLrmEjz76iMLCQtLT03njjTcAKC8vp0+fPjz88MMsXLiQ1NSu3VRNKaW6gzYxHUTvvvsu7777LlOmTGHq1Kls3LiRLVu2MHHiRBYsWMDPfvYzFi9eTJ8+fXo6qUopdRTVIDoo6Tc2NrM9v5JhKQn0TYgJWxKMMdx5551897vfbTdv+fLlvPnmm9x1112cddZZ3H333WFLh1JKhUJrEPj7IMIhKSmJykr7uMnzzjuP2bNnU1Vln2m7d+9eCgoKyM3NJSEhgW984xvcfvvtLF++vN26Sil1sIW1BiEiM4E/A5HAU8aYB9rMHw7MBtKAEuAbxpgcZ14z4HYa7DbGXBKudIbzQrl+/foxffp0JkyYwPnnn89VV13FySefDECvXr147rnn2Lp1K7fffjsRERFER0fz97//HYAbbriBmTNnkp6erp3USqmDLmy3+xaRSGAzcA6QAywFrjTGrA9Y5r/AfGPMsyJyJnCtMeabzrwqY0yvUPd3ILf7rmloYmtBFRn9EukdH9pzFg5FR+vtvpVS+6+j232Hs4npBGCrMWa7MaYBeAGY1WaZccAHzuuFHvMPDr2UWiml2glngBgM7Al4n+NMC7QKuNx5fRmQJCLuY6/iRCRbRD4TkUu9diAiNzjLZBcWFu53QjU+KKVUez3dSX0bcJqIrABOA/YCzc684U615yrgEREZ2XZlY8yTxpgsY0xWWpr308RCaUI7EgLEkfJkQKXUoSOcAWIvMDTg/RBnWgtjTK4x5nJjzBTgF860Muf/Xuf/dmARMKWrCYiLi6O4uLjTzNOdL4fp7VyNMRQXFxMXt//PXFZKqbbCOYppKTBKRDKxgeEKbG2ghYikAiXGGB9wJ3ZEEyKSDNQYY+qdZaYDD3Y1AUOGDCEnJ4fOmp/qGpspqmrAlMYSE9XTlar9ExcXx5AhQ3o6GUqpI0jYAoQxpklEbgLewQ5znW2MWSci9wHZxph5wOnA70TEAB8BNzqrjwWeEBEftpbzQODop1BFR0eTmZnZ6XLvb8jn+nnZzLtpOmOH9O3qbpRS6ogU1usgjDFvAm+2mXZ3wOu5wFyP9T4FJoYzbYGafLaJKTLi8GxiUkqpcDg821O6WVOzDRBRXblRn1JKHeE0RwSafD4AoiK1BqGUUi4NEECzz61BaIBQSimXBgi0D0IppbxogED7IJRSyovmiECz9kEopVQ7GiDwNzFpH4RSSvlpgMDfSa19EEop5acBAmjUPgillGpHc0S0D0IppbxogCBgmOthejdXpZQKBw0Q2D6ICIEI7YNQSqkWGiCwfRDa/6CUUq1projtg9D+B6WUak0DBLYPQoe4KqVUaxogsH0QepGcUkq1pgEC2wcRqX0QSinViuaK2D6IaO2DUEqpVjRAoH0QSinlRQME2gehlFJeNEBgnwehNQillGpNAwT2mdR6oZxSSrUW1lxRRGaKyCYR2Soid3jMHy4i74vIahFZJCJDAuZdLSJbnL+rw5nOZp/RC+WUUqqNsAUIEYkEHgPOB8YBV4rIuDaL/RGYY4w5DrgP+J2zbgpwD3AicAJwj4gkhyutTdoHoZRS7YSzBnECsNUYs90Y0wC8AMxqs8w44APn9cKA+ecBC4wxJcaYUmABMDNcCdU+CKWUai+cAWIwsCfgfY4zLdAq4HLn9WVAkoj0C3HdbqN9EEop1V5P54q3AaeJyArgNGAv0BzqyiJyg4hki0h2YWHhfidC+yCUUqq9cAaIvcDQgPdDnGktjDG5xpjLjTFTgF8408pCWddZ9kljTJYxJistLW2/E6oXyimlVHvhDBBLgVEikikiMcAVwLzABUQkVUTcNNwJzHZevwOcKyLJThycCRMAACAASURBVOf0uc60sGhq1k5qpZRqK2wBwhjTBNyEzdg3AC8ZY9aJyH0icomz2OnAJhHZDAwAfuOsWwL8GhtklgL3OdPCwtYgerq1TSmlDi1R4dy4MeZN4M020+4OeD0XmBtk3dn4axRhpTfrU0qp9rTYjPZBKKWUFw0QaB+EUkp50QCBHeaqfRBKKdWa5orYC+W0D0IppVrTAIFbg9AAoZRSgTRAYJ9JrX0QSinVmgYItA9CKaW8aK6I9kEopZQXDRBoH4RSSnk56gOEMUb7IJRSysNRHyB8xv7XPgillGrtqM8Vm3w+AH0ehFJKtXHUB4hmpwqhTUxKKdXaUR8gGpttgNBOaqWUau2oDxBag1BKKW9HfYCIihQunpTOiLRePZ0UpZQ6pIT1gUGHg95x0fz1yik9nQyllDrkhFSDEJGXReTCgOdHK6WUOsKFmuH/DbgK2CIiD4jIsWFMk1JKqUNASAHCGPOeMeb/gKnATuA9EflURK4VkehwJlAppVTPCLnJSET6AdcA3wFWAH/GBowFYUmZUkqpHhVSJ7WIvAIcC/wLuNgYs8+Z9aKIZIcrcUoppXpOqKOY/mKMWeg1wxiT1Y3pUUopdYgItYlpnIj0dd+ISLKI/KCzlURkpohsEpGtInKHx/xhIrJQRFaIyGoRucCZniEitSKy0vl7POQjUkop1S1CDRDXG2PK3DfGmFLg+o5WEJFI4DHgfGAccKWIjGuz2F3AS8aYKcAV2NFSrm3GmMnO3/dCTKdSSqluEmqAiBSRlntROJl/TCfrnABsNcZsN8Y0AC8As9osY4Dezus+QG6I6VFKKRVmoQaIt7Ed0meJyFnA8860jgwG9gS8z3GmBboX+IaI5ABvAjcHzMt0mp4+FJFTvHYgIjeISLaIZBcWFoZ4KEoppUIRaoD4GbAQ+L7z9z7w027Y/5XAM8aYIcAFwL+cq7X3AcOcpqdbgf+ISO+2KxtjnjTGZBljstLS0rohOUoppVwhjWIyxviAvzt/odoLDA14P8SZFujbwExnH0tEJA5INcYUAPXO9GUisg0YDeiQWqWUOkhCvRfTKBGZKyLrRWS7+9fJakuBUSKSKSIx2E7oeW2W2Q2c5exjLBAHFIpImtPPgYiMAEYBne1PKaVUNwq1ielpbO2hCTgDmAM819EKxpgm4CbgHWADdrTSOhG5T0QucRb7CXC9iKzC9mtcY4wxwKnAahFZCcwFvmeMKenaoSmllDoQYvPjThYSWWaMmSYia4wxEwOnhT2FIcrKyjLZ2doCpZRSXeHk5Z4XPId6JXW903m8RURuwvYl6BN2lFLqCBZqE9MtQALwQ2Aa8A3g6nAlSimlVM/rtAbhdBZ/3RhzG1AFXBv2VCmllOpxndYgjDHNwIyDkBallFKHkFD7IFaIyDzgv0C1O9EY83JYUqWUUqrHhRog4oBi4MyAaQbQAKGUUkeoUK+k1n4HpZQ6yoT6RLmnsTWGVowx13V7ipRSSh0SQm1imh/wOg64DL01t1JKHdFCbWL6X+B7EXke+DgsKVJKKXVICPVCubZGAf27MyFKKaUOLaH2QVTSug8iD/uMCKWUUkeoUJuYksKdEKWUUoeWUJ8HcZmI9Al431dELg1fspRSSvW0UPsg7jHGlLtvjDFlwD3hSZJSSqlDQagBwmu5UIfIKqWUOgyFGiCyReRhERnp/D0MLAtnwpRSSvWsUAPEzUAD8CLwAlAH3BiuRCmllOp5oY5iqgbuCHNalFJKHUJCHcW0QET6BrxPFpF3wpcspZRSPS3UJqZUZ+QSAMaYUvRKaqWUOqKFGiB8IjLMfSMiGXjc3VUppdSRI9Shqr8APhaRDwEBTgFuCFuqlFJK9biQahDGmLeBLGAT8DzwE6C2s/VEZKaIbBKRrSLSrpNbRIaJyEIRWSEiq0XkgoB5dzrrbRKR80I+IqWUUt0i1Jv1fQe4BRgCrAROApbQ+hGkbdeJBB4DzgFygKUiMs8Ysz5gsbuAl4wxfxeRccCbQIbz+gpgPJAOvCcio40xzV09QKWUUvsn1D6IW4DjgV3GmDOAKUBZx6twArDVGLPdGNOAvX5iVptlDNDbed0H/0OIZgEvGGPqjTE7gK3O9pRSSh0koQaIOmNMHYCIxBpjNgLHdrLOYGBPwPscZ1qge4FviEgOtvZwcxfWRURuEJFsEckuLCwM8VCUUkqFItQAkeNcB/EqsEBEXgN2dcP+rwSeMcYMAS4A/iUiIT/EyBjzpDEmyxiTlZaW1g3JUUop5Qr1SurLnJf3ishCbHPQ252sthcYGvB+iDMt0LeBmc4+lohIHJAa4rpKKaXCqMuPHDXGfGiMmef0K3RkKTBKRDJFJAbb6TyvzTK7gbMARGQsEAcUOstdISKxIpKJfcTpF11Nq1JKqf0Xtlt2G2OaROQm4B0gEphtjFknIvcB2caYedjhsv8QkR9jO6yvMcYYYJ2IvASsB5qAG3UEk1JKHVxi8+PDX1ZWlsnOzu7pZCil1GFFRJYZY7K85nW5iUkppdTRQQOEUkopTxoglFJKedIAoZRSypMGCKWUUp40QCillPKkAUIppZQnDRBKKaU8aYBQSinlSQOEUkopTxoglFJKedIAoZRSypMGCKWUUp40QCillPKkAUIppZQnDRBKKaU8aYBQSinlSQOEUkopTxoglFJKedIAoZRSypMGCKWUUp40QCillPIU1gAhIjNFZJOIbBWROzzm/0lEVjp/m0WkLGBec8C8eeFMp1JKqfaiwrVhEYkEHgPOAXKApSIyzxiz3l3GGPPjgOVvBqYEbKLWGDM5XOlTSinVsXDWIE4AthpjthtjGoAXgFkdLH8l8HwY06OUUqoLwhkgBgN7At7nONPaEZHhQCbwQcDkOBHJFpHPROTSIOvd4CyTXVhY2F3pVkopxaHTSX0FMNcY0xwwbbgxJgu4CnhEREa2XckY86QxJssYk5WWlnaw0qqUUkeFcAaIvcDQgPdDnGlerqBN85IxZq/zfzuwiNb9E0oppcIsnAFiKTBKRDJFJAYbBNqNRhKRMUAysCRgWrKIxDqvU4HpwPq26yqllAqfsI1iMsY0ichNwDtAJDDbGLNORO4Dso0xbrC4AnjBGGMCVh8LPCEiPmwQeyBw9JNSSqnwk9b58uErKyvLZGdn93QylFLqsCIiy5z+3nYOlU5qpZRShxgNEEoppTxpgFBKKeVJA4RSSilPGiCUUkp50gChlFLKkwYIpZRSnjRAKKWU8qQBQimllCcNEEoppTxpgFBKKeVJA4RSSilPGiCUUkp50gChlFLKkwYIpZRSnjRAKKWU8qQBQimllCcNEEoppTxpgFBKKeUpqqcTcEQq2ADzfghfexZ6p4e+3ms3wfZF/veRMfC1OTBwgn2fvw5e+hY01fuXGXE6zHq0GxLtoWIfPHc51Ffa9+MvhXPvb73MZ3+HJY+1X3fcLDjvN62nff4kFKyDi//cevrGN2D5v+CKf0NEpH/6jo9g3s3ga/ZPm3QFnHlXkPTmwn+vhVmPQeoxwY9rx2KYd5OzXYEzf2G32x2qi2HOJVBX3np6TC/4xlzoM8Q/raYEnr0Y6ipap6GhGubMgso8OOF6mH5L8P01NcB/vgozboXCjfDpXyExFb41D+J6dy3tHz4Iy+dASiZc8R948Rtw6k8hY3rwdUq2w3++Do21XdtXoMQ0uGY+xCS2nt7caD+f8hz7fvh0uPwJ7200N9nP4aQbYdTZsGcpvHKD3YYXiYALH7bL7vwEXrsRfE0dLPsQjDontONxf8dDsuCrz4S2zt7lsOBu+PpzEN83tHUOAq1BhMOnj0LOF5A9O/R1muph1QuQkAKZp9q/yrzWme+Sv9lM250f0ws2zOv+9LuWPW2DXcYM6DXABoPK/NZp/vBB+8N205R5ql3288fbL7vod7DiufaZyZr/wua3YMuC1tM//pMNTu52Edj0dvD0LrgH9nwGm97s+LgCtxsRYY/B5wvpI+nUro8hfy0MmuRPd8YpULQJlv6z9bK5K+yydeXw4e/9aVj7MuQstcFy8UPQUBN8f6U7bGa08Df2OCIi7XZXv9j1tG+cD7WlNjC/8j273a0LOl4nJxuKNkP6lNbnQKh//cdC7nKbQbY7tp2wewkkZ9ggsuYlGzy9VBfAtg/g31+277cvssEr4xTv/TbV2c8W4JNH7HcQLI1NdbD44dA+w8LNsOJf9ntY94r3cXnZudj+7c/3FkZag+hutaWw9n/29fI5cNrPIDK68/Xy14Gv0ZYEx19qp0XFwsr/2JK4iN3upCvg4kfs/MUPwfv32QwkJqF7j6O5EZY9C8ecDZc9DkVb4dFpsGIOnHq7XWb9a1BbAl+ZDSPP8K/b0bLusQ7J8i+fu8L+z/4nHDvTvi7Zbn/wp/8cTv+ZnfbajbD1/SDpbfIHy8iY4MdVsh22ve/f7uqX4OXrYedHtjZ2oHJXQES0/UyiYv3T68ptxnH6nRDlpK90h/1/yq3w3j2w40P7OWbPhrSxcOEf4ZkLYd3LMOUbQY7H2caez+3/y5+A939tt3H8d+x5E6rKPBh7CWxfaINF4PaDrrPP/p/1WNdrLADVRfCHkfZzyzyl9Tx332f+0p47z18BeWtg2Entt1Nb5n9dsMF+tkmD4LK/e+/3kz/bEvvmd2zB5LSfwhk/D7LsX2DBL+12+4/t+HiWPW2//2+8DI+fYs/pwVM7XgfsZw+2EHHCDV373sJIaxDdaeHv4NEToKkWzrgLqvJhy7utlzHGVskfmWj//v1VO83NJNOn+JfNus6WXh470b/drGv985MG2f9Ved17HJ89Do8cZ7ebdZ2dlnoMZJ4Gy+bY9IINICkj7PRA7rLL5/inLZ9jS4HgP1awzSylO+28LQugfK+dvuI5kEiY+k3/sgmpNkNx9x9o63v2swKoK2s/39V2u2MvgfgUePFb9vv428n+Jo2OFG+z38mfJ7cOWrkrYMC41sEB7OdYXdi6dlO6EyJj4cTv2jS8dDX8aaItUWddZ5tU0sbYzzmY0p32v0TakvaIM+26Bett6T5UzU1QVWCbwKZe7d9maWcBIg+iEyE2KfR9BUpMhT7DWp8TLnffyRkwaLJ97bUctP7Ol8+xwSU5I/h+J/+fLUi8+E2bGU/9VufLPn0+zL0u+HLNjbZAN/Zi6DcSJn4F1vwP6quCr+NyA23RJn+w97LqRXjjJ51vr5uENUCIyEwR2SQiW0XkDo/5fxKRlc7fZhEpC5h3tYhscf6uDmc6u826lyE63pZ4vnSTnVa4sfUyFXth89vQe7D92/KuzZByl9tMou8w/7IDJ8JZd8MxZ9m/M39pmy5cSQPt/8puDBBN9ba5IzYJvvRDGH2ef964S6B8N5Tvscvt+RzGXGSbadrKPAXKdtvljIHclTD+Mkjs3/pHvm+l/T/1asD4M4X8dba0FtiHk5hqa1n1Fe33V7zF/7q2NPjxFW6C1NH+7UbH2RrZmAtg2Jfs9xVK0+CuT+2PuabYNlmBP9AHBnnXyDNsk+CuT/zT3EwsOt6fhozpcPz1MPkqm3GNvRj2LgvezFS6w2531qNw0SP2uxh7sZPGjzs/Dld1AWCg9yAbsGb8GI77uj8ABVO5z56HB1LiTZ8cJEDstMGnV3+brqRBwQOE+53Hp8DOj+3nkpwZfJ+JqbZfYcLlMPP3rfuG2i3bzy47cKKtxQdrNirdZQPVqHPt+8xTbaEulAJHZZ7dPtj0B7PpTRuEvApJYRC2JiYRiQQeA84BcoClIjLPGLPeXcYY8+OA5W8GpjivU4B7gCzAAMucdTv45fcwn8+eICfeAKfeZqdFJ9pOy0DuCX7u/YDAU2faabkrbcbS9od2SgelBbcG4ZY+usP6eU6z0T9h5Jmt56U7VeXcFTaQ+RqDV5/jnI622jLbHttQaWsb6VNa/8jd1yNOh8V/9P/QK/f5j8+VkGr/VxdBXJ/W82pLbYm39+DWzQ1t1RTbzCHQuFn2D2xT0PJ/wWl3+JuCvJTusPv70s22/b9wM0RG2fW9AkREpA3ugcdeutN2CLdNQ6D0qWCabV/F0BPaz3eDzOSr/NMSUmzmGCwz9eKeQ0mD7Ppn32v70lb9x9byElKCrJfX/nvqqvQptnmwthTik/3T3WNzfxNtz51A7nc+8kxY/6rtcE7pIECArTV0VHNou+y4WfDQGNuM5HXeu4Ubd7/ueVZT1Pn2K/fBkONtQaCj762mGBprbCGp7W8gDMJZgzgB2GqM2W6MaQBeADx+AS2uBJ53Xp8HLDDGlDhBYQEwM4xpPXCVudBc37rUktiv/cmRuwIiomDAePsXEWVLDAUbQmurDBSOGsTyZ+0xZJ7eft6A8bZ9NXeFd5NYIPeHXlfmL4UmZ9jlCzf6OxtzV9jAkTzcvnd/6JV5/uNztfzg2gRdd734vvavoyam6iJI6Bd8ftZ1tjS9uYPOcLCZV9+hMO0a+5ksf7bzzyR9im1Db260JcDSnR03gwRuK1imEWwbHWWmXtxzKPAzdzO6jpqZ3BrEgXCPcd+q1tNLd7TO5NOnQNEW/6i6QO53PvIM/2ikjmoQ+yOuD0z4MqyZ691sFHieQ+sCTUeM8Z/v6VNsYTEYd1vd+ZvvQDgDxGBgT8D7HGdaOyIyHMgEPujKuiJyg4hki0h2YWFhtyR6v7knR+AJ7baZB8pdYZtOouNt80b/cbZEYppth3BXxPWFqLjurUEUbvSP7mkrKtYGCTdAxCdD3+He23GH6tWW+jsbkzMhdRQYn7+voWwPpIwMqHGU+tvD29UgnIzdM0CU2m3E9+24iammqH0NIpDb2V6wPvgy4GTMmbb5Y/jJtukod4XtU0gL0pGZPsX2kxRutOdFQ1XnmVjvQdBroHdm7/O1roW03VfZ7vY12GACaxAuN23BOqoDM7YDkTLC/i8L+Mm7xxYY/FJGAMYOZ26rthQQZ7Sbo7Pguz9GnmlL8GW72s8r2QHRCXYUH4Reg6grs+dF0iD7vVXk2PPfi3vud+dvvgOHSif1FcBcY0xzp0sGMMY8aYzJMsZkpaWlhSlpIQrMBF0JbWoQXm3U6VOgucEGiqEndm2fIvbH2V2lCZ/PNid0lIG67cV7V3g3ibninBpEbVlAZ+PwgFqP8yN3m3xik2yTTV2Zvz28bcbjBgivElmdW4NIDt7E1NxkM5KEDo4vMtp2mHf2Awws3aZPgby1sPtze81KsKapwNpA2+aIjgSrDVTua19r9dpXKCrz7Hj/xIDfkZvBBuuHCMzYDoRXRlqVb7cdmMl39P27Ncg+Q/3fbyifbVe5fVde50dpmyaxeKdZrrMgHVh7a/nePGoRPl9AgDj8axB7gaEB74c407xcgb95qavrHhrcNuk+AclOTLUZrqtsl82g2gYIsE0b+9PRlzSo+06WujJbk+koA02fYtvZ89cEb0oBfw3CbWJKGmRrTS39Jk6aa4rtD1/EKf2XeZdmoeMSWW2ZDQ5xHTQxuTWLjgIgdB50a8vstpIDAoSv0V770tFnkpwJsX2cALHTmZbRcVrc7Rduat+s0dE23MEMIQeIfbbkG3ihYoxTGg7WxOTVLLU/ohNsTTiwZugVQFtqkEEKCHF97XmUPgVikjpuStxfHTXrlrTpGI+Ksd+3V403UOD5PmgSIN7fm/v7DFwH4NUf2NFYYRDO6yCWAqNEJBObuV8BXNV2IREZAyQDSwImvwP8VkTcHqtzgTvDmNYD57ZJRwZ8pAn9Wpd2irba/4FNEONm2R/65HYfTWiSBtp27e7gnsgdZaBjnFE1zU12+F8wbh+E28Tk/nDc6nflPtsh11jj/yHHOc1DwTKemESIig9Sgiy1TRBuE5Mx7QOum7EE63B1JQ3quAYROPwSvAO+l4gISDvWtqP3GghI8Ca6QP3HAMZewzHouPbp8Copx/W2hZXA0V0dCdZUlJwJJTuDrBMkkHeViNMcG5CRetXIEzto068t9RdKTr0NireG51qCXm6AaHN+uH1KbQd2ePVDthV4vsf2sgNAvL63wEATGKDyVkNSF+7Y0AVhq0EYY5qAm7CZ/QbgJWPMOhG5T0QuCVj0CuAFY/zjtowxJcCvsUFmKXCfM+3Q5bZJB0pMtcPc3A5Zz1JRCpzzq/a3GQhVd9Yg3B9eRyWvxH5wyV/tBUj9RgZfzh1h4TYxuccc2wtie9s0uz8c94cfn2xLSR1lPImp3iWyOqcGEZ9sm+y8bv3QcnwHWIMoafM99h3uD4gdBQh3ndKd9jPpnW77oTrjnldtS/IlHrXWVutldH6hmyvYaCQ3vcHWgQOvQUD7jLR0p23yCjy2lhqER1bg1iDBXkgX7MLCAxUdZ/fT9vyoyre/9bbB2qsfsi33fHeDT0qm9/cWuB13HWNsAA9Hfwth7oMwxrxpjBltjBlpjPmNM+1uY8y8gGXuNca0u0bCGDPbGHOM8/d0ONN5QCrz4InTbBT3OjnA/8WW7LAlYLcU3R2SBtrOzjqPawO6qm2GfSAiIm31unKf/Qs8gZMG2mltM+yWJiaP9nBX21oZ2LbZunK7fmBn96a34a9Z8MSptmMz1ONLGmQ7CZuD3JunbQ3CbdaIiofUYzvednKmHRdfuCn0H7W7XNtMo3SnHb8f7Er95IzWQaWpwV6kufuz9stW5Hqfl8kZ9tqdwPt/Ba4D3RMg2makpTvssQX250TF2sKFZxNjqf+7Dze3UPbfa+AvU+2V6141HgheoAlUsc/+Vty7IbT93lzuduL6tm6ibagMT38Lh04n9eEr+2k7PG/8Ze3HVLdtM3VHZXRn1de9wKds94FvK5QaRFfE9/FfCBf4w3FL6G2btFqamPbZC+oiPVpAvX5w9RV2ZJQ7iglsjWLTGzZz27faXvzWlRoExuks95C/DnoPaX318Cm3wQUPeqc5UHKG3fa+laEPw4zrbb+TtplG22GgbaVk2qu33b6L/LV2+K57KxhXZZ699qWfxw0OkzNteks9Ru0UbLBNG/tb+w2UmNo642/bnu/yKiCAf5DCwZA00BYI170CJdvsvcTaFhpcCSmd1yAKNrS+uWRypj3H2xb63M9n4ER/DaKlH0oDxKGnucmOfz/mLPjyU+2bF1raTJ0MrbMf9P4I1vywP2pCzEBDFZ8M+c5w0cDjdtv43YzeDUgtTUwdDJ1MSG1fgnQ7pd0mJrA1kZIdMGCCvbJ1+Rx/qSuUPggI3g+RuwIGt/muM6aHdtGV+zkYH6RkdL68K9mj2SFYJhq4DvgzEbfjs20HqDtixus6nI6uhchd0fVrd4JJ6Ne66cgdEeS5XJvv35jWTUzhljTIXyAbPt3WCIu22Fpv4J0QwDlfi4Nf+ezz2cJCesDnGOwzdwPNgPH2XDYmoOaScUCHFIwGiAOx+W2bibj3K2orcNx+qBdGdVVKm0wgmKX/hPk/9p5XutPeXrpoi71tQyjt4qGI62tH94BHE1OeLd1CQIBwmpgqcjsIEE4JMvAW4O6w1rZNTO7nnXWdbSNe/aKd39nNEzsaqVJbajuLO+trCCYwQ+9KqS85o/V3XFduS/0dnU9th6m6gcG9WM+VuwIQGBjQAR5sG67aMlt6Tp8cWvo7k9DPNpU21tmSc02xd2EqMbX9sNH6Sju656A1MbnnpthBJqbZ3om195D2Q5w7uj0M2M70hqrW51Owz7ym2P4+kzNsP9vfTvbfoNK90LSbaYA4ENmzbRV71Hne8wOHZVYV2BE73V0VjE+2HcIddUY21cPC39r05ntcALbpLXt75A3zu3dooFvlbzvkMGmQPcGLttirkN0O7bi+gLEXknk1dwAMPd5+jts+8E9zh68GNjFVF9iSXUqmvY9/7yH2HlKh9K90VINwr/bd3wDRq7+9BQt07VxIcfou3Izd68JMr3XAXxLNXWmv3Hcv1nPlrrCjq2J7td9GYppNb9vz60A/h3b7CfitdNRs0mEN8iD2QYC9p9cA51kte5d51wg7u5ra6+r7YBcoukPCj70AJl1pz+cN82weFB2/X4fSGQ0Q+6tkh71t9LSrg7c7x/a2GWB1UdcujOqq5MyOm5g2vO7/US3z6O93T9LG6u7poHa5Vf6UjNb9Lm4JLH+t/xqIwOWNL3jTxbEX2kwr8IZ6Xk1M+1YDxn42EZH2lhgQWgBMTLPNBV41CPdGbYP2s+Qs4i8hduVcSM60JVW3aSNYp2gg97qQkh12VFfBev+N/NzvvKMbDLrpTfE4v9z1B3VTgAjMSDv6rST2a99kUxvw/R8M7vmbPqV1k6FXba6j28OA/RyjE2ywcQXrc3JvE5M83N6C3x0aH6YOatDnQXirzIcXrvS+54urrsK5bXQH7c4i9gtd9rR97gCEp60wJbP1fWzWz7M3kDPOA2iq8u1+B2fBsmecp9YJnHGn7VwPvDtld/U/gL/K3/aY3RJYwQb/bRagdQkwWIYVFQNTvmkf8lKeYzvpW+7k2dfWViTCf6trd99TvwkfPhDa8UVE2hE9Xzxpg0TgE/v2LrPb7KwfoyPJGbb015UMzT2Of11qb6QX6oV2yRm2GWPfKhtgxl8OWz9wHq70ub0pYXVBJxf4ZbS/K/HeZba9PbGbapyBGWnxNv9+20pItbXP+kr/8yfcws/BHMUEtnmt10B7e5VgV7S750lVfuvpjXXw/NfteTrwuPaFzOQMe8+nin326X6RUbYVonfAUORp19pzNEz9D6ABwtuyZ+wPYNwsm9kEM/TEzh8petrt/tv3Jg2y9x3qbsmZtpbQ3GQzt0W/sz8g9+6fA8bDpKtsIBGxNzPbsxQWPWAv7CneYpsefE3haWJq+8MZMN42WzRWt96f+wOP69NxyXja1fYW28vn2Ie8uCXIuL72YrTB0+wT2cBfukoaaG/ZHOqP6bSf2if8rfgXnPR9m+aaEvvMiqkHeNXqyTfa26h3ZTTb4Gk2Q9j6nn3uSFScHc3S2UN6MmbYp/stia8fqgAACUhJREFUedSukzEDzr7HFlgCn+438qzg2xg+3T5AKG+N3Wdtqb1N/f5e3OklISBArHsFBkz0vltpYFOUe+wb37CZ9IDx3ZeejgyaZB8DO+Er9nxLzrC3fvcqyaeNsS0JG+b7a29gm4a2L7IDKI7/Tvv1pt9i86Et79in+qWOtncvCLzj74BxcM6vYfiXuvkA/TRAtOWOTBp5ln0e9IE6/jveJ0B3Ss6wmXtFji3xFqyHi/9iM9K2vvyU/b/iOfuEts+dZ/yOvdj+MLurRAgBTUxtfjixSXDcV+0PIDBABF5s1lHmmZxhb2y47Fn7xLq6MptBuO2wWdfZANH2mhO3mSkUWdfBuEvt7Z2zn7ZPd1v1vC0pTrs29O14yZje8XOevbjPrVj5PLz6PTvtoj91vt60a2xw2PC6LSQkpMDx37Y1x4fG2CGvmad1/AzvSVfA+7+yn8NFD9vA2VQXfHDG/nBL2pvfsUNILwzyiM+W+zEV29pnfZV9iM6Eyw+sVtcVkdFwzn3+9ymZNkB4FWpiEu1zNZbPgZm/86cxe7ZN/5Uvet8Yc9ws29fwyETnCYNj2j9AC2D6D7vvuDxogKgpsU+KcjU32LHz5z/Yc2nqKjcDfvYSe9V2bG97W+KOjL8c3v65fY4x2Exv3SsHp4kJbOay7JnWfR5ujSOUjs/jv20fQfno8f7bLLhBZfxl8PYdtvPuQK45SUix21r+rB2lUp5ja40DJ+z/Ng/U+EvtsfmaYOJXO18+dZS9w+mOj1pn6AkpNlNd9XznGX1Cij1fVvzL3rW2fK99doH7gJvuENfXZoBr/2dH6hz3tSBpcc6XudfazLexxl4o1p3Bqqvc8ztY7TTrWlj6D3vBZkyi7T8p2mRL/17BwRUZbZuwP3zQfn9jLuyeixK7QANERKQdwRHomLNh9KH9+IlWhhxvT6S6cvt+9EzvESmBYhLggj/Yi8nSxtiHu5/2M++H1uyvEafbB+oM86gCD5pk29JHnO6fljQITv0pTL6y822POhdO/J5/pNHwgBJ5dLx9ulp3OPU2O0zR12Q/p5O+3z3b3V/R8bYU39wY+mM+z/6VbRIKfA442Ga0xFSb8XTmlJ/YwpOv0X4OJ36362nvSESEbS7MW22/22DHNnCCffpg4A0ZJ3zZ/gZ6ytRv2ZpqsFFUA8bbZ6AXrPNPG5IV2nUzJ9xgL1JsbvA/iOwgEnOQHl0XbllZWSY7uwvP4FVKKYWILDPGZHnN02GuSimlPGmAUEop5UkDhFJKKU8aIJRSSnnSAKGUUsqTBgillFKeNEAopZTypAFCKaWUpyPmQjkRKQQ8nosYslSgk2cDHnH0mI8OesxHh/095uHGGI8HwB9BAeJAiUh2sKsJj1R6zEcHPeajQziOWZuYlFJKedIAoZRSypMGCL8nezoBPUCP+eigx3x06PZj1j4IpZRSnrQGoZRSypMGCKWUUp6O+gAhIjNFZJOIbBWRO3o6PeEiIjtFZI2IrBSRbGdaiogsEJEtzv/knk7ngRKR2SJSICJrA6Z5HqdYf3G++9UiMrXnUr7/ghzzvSKy1/m+V4rIBQHz7nSOeZOInNczqd5/IjJURBaKyHoRWScitzjTj/TvOdhxh++7NsYctX9AJLANGAHEAKuAcT2drjAd604gtc20B4E7nNd3AL/v6XR2w3GeCkwF1nZ2nMAFwFuAACcBn/d0+rvxmO8FbvNYdpxznscCmc75H9nTx9DF4x0ETHVeJwGbneM60r/nYMcdtu/6aK9BnABsNcZsN8Y0AC8A3fhQ5kPeLOBZ5/WzwKU9mJZuYYz5CChpMznYcc4C5hjrM6CviAw6OCntPkGOOZhZwAvGmHpjzA5gK/Z3cNgwxuwzxiz///buL8SKMozj+PfXPyk3CqUktsg/eSFCrhURaREEgV0VGEllEoI3duGdiErQfXUlJVFgtURULklX4V4seFFrhdof+wPdtGLuTWgGRayPF++7NS0zcnLP7LQzvw8czpx3Zof32YdznjPvzHknL/8GnAQGaX+eq+KuMutcd71ADAI/F15PcOl/+HwWwCeSvpC0LbctiYjTefkXYEkzXatdVZxtz//zeUjlzcLwYatilrQUWAt8RofyPCNuqCnXXS8QXbI+Iu4CNgDbJT1YXBnpmLT11zx3JU7gVWAFMAScBl5qtjv9J2kA+BDYERHniuvanOeSuGvLddcLxCngtsLrW3Nb60TEqfw8CYyQDjXPTB9q5+fJ5npYq6o4W5v/iDgTEVMRcQF4nX+GFloRs6SrSR+SwxFxMDe3Ps9lcdeZ664XiKPASknLJF0DbAIONdynvpO0UNL108vAI8DXpFi35M22AB8108PaVcV5CHg2X+VyH3C2MEQxr80YY3+clG9IMW+StEDSMmAlMD7X/ZsNSQLeAE5GxMuFVa3Oc1Xctea66TPzTT9IVzj8QDrDv7vp/tQU43LS1QzHgW+m4wQWA6PAj8BhYFHTfe1DrO+SDrP/Io25bq2Kk3RVy76c+6+Ae5rufx9jfjvHdCJ/UNxS2H53jvl7YEPT/b+MeNeTho9OAMfy49EO5Lkq7tpy7ak2zMysVNeHmMzMrIILhJmZlXKBMDOzUi4QZmZWygXCzMxKuUCY/Q9IekjSx033w6zIBcLMzEq5QJj9B5KekTSe593fL+lKSeclvZLn6B+VdFPedkjSp3kStZHC/QnukHRY0nFJX0pakXc/IOkDSd9JGs6/nDVrjAuEWY8krQKeBNZFxBAwBTwNLAQ+j4jVwBjwQv6Tt4CdEXEn6Zeu0+3DwL6IWAPcT/oVNKTZOXeQ5vFfDqyrPSizS7iq6Q6YzSMPA3cDR/OX+2tJE8JdAN7L27wDHJR0A3BjRIzl9gPA+3lOrMGIGAGIiD8A8v7GI2Iivz4GLAWO1B+WWTkXCLPeCTgQEbv+1SjtnbHd5c5f82dheQq/P61hHmIy690osFHSzfD3PZBvJ72PNuZtngKORMRZ4FdJD+T2zcBYpDuBTUh6LO9jgaTr5jQKsx75G4pZjyLiW0l7SHfmu4I0e+p24Hfg3rxuknSeAtKU06/lAvAT8Fxu3wzsl/Ri3scTcxiGWc88m6vZLEk6HxEDTffDrN88xGRmZqV8BGFmZqV8BGFmZqVcIMzMrJQLhJmZlXKBMDOzUi4QZmZW6iJYwC4AJbe6xwAAAABJRU5ErkJggg==\n", 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T3+Ds42s/6z2xfllEtK0WPLwT8rPta+c+YEsXe1Yd+z41ocNVDbW5mETbXlC8x+auPVX2ovMvJnurefW5JyB5gG3g278WqsshJpHo6Gjmzp0bdNO+doDU1FTWr19ft/yee+5pvLKn0t48Xe7g6ZxwG3z1km0QTBkEfU6xyyM7NV43qU/blQh87QPx3e0N1VeFULTb5oKS+9vucdkL7WueStsNb8DZdlluJgy+sOE2jbHdfMd9p+HyLun2/aUHbACKS7Elo64ZsO0TGHiefQ1s7vpItsyxOb7lT8NpP4b3f1YffJoKBMuehoHnNLxJBaqttTfufqfbRv0DG21d+5HkOTm/1MH1y5LSIbpz0+MDjIEdS2zJyFdl0vc0e5MZe2PDdVMHNR0ICnc3rhYC6DrMpmn/etsAfSxK9gOmvqHVVw2yc2nD79MXGPwDYkxn+71vmwdT/nhs6WgJY2zGoP9ZEO9XNZxyUvCcecleWwpvyuApsPgxm4GJczqK7FltMzO+ajufpL62naemzLYFnjIdKgttdZ+n2mbm2piWCI5ExN6Aaj02J1C029bd+Rjj9NxxDo7LbYvkNWUNt1PrPbZRmp5K24jdlJhEWy+ePBDO+H/N1+cmtmEg8K+iqSqqr//35ci7pNsbTHWpPZl9DV5DL7W5rWANxuUF9vvr0q/h8iTn+eGdNgDFptrn4262VQZz7rbf/YCzW9blcsdi+7j+bXj12/YCu/ZF23Mmb7OtEvRXuAs+uu/I4zEO74DqElv37Yq09b0tkbfJqX6IrV/mctm68qZKBId32JJX+uT6ZRO/b3OR/c5ouG6KEwiCVcUU5doMQqCoOHscAnPtR8PXScBX/ZQ8wPaQ8nUA8PF1Bw4Mtt1G2ON6pPEQbWHPatu2FdjjK2WQPQ/8x1Z4qmzJpqkSAcDgi2zGaJtfN9i9X9kSX2DmLqlvfSkyZYB97HUyeKvhQBs23PvRQNASUXH2BEjoYZ/73+RrvfYAu/2idFSsLREYp+eKMbb/cd5mG1CORnONhT7JA+DHqxrnBAMl9raDs5rrWVPrhSWP237oNUHGNPiUOQ3FvvpQX7HZdyP2tRGA/UxfoEg5yV7YwdoJfPXRSQGBwBcYCnfZABTr5KxGX28DwL6v4dxfQc+xdp3mAm+t19atdxtpg1TJPrjuNXsz9N1Unz0bVvuNrM1eZB+zPm1+VPrOL+xjv9Ns7r6l3VsPbQte0ug6zN6IA2/gtbXwyYOA2O6YPskD4Mx7Gjeqpg62wbr0YMPlxthAEKxEADbj4LuJH4vAQCBi69N3LGp4LuZtscczqW/D9ycPsB0mCncde1qaYwysedle00Mvbfha6iDANCxx+s5p3/0hmB5jbMnOV73j9diag55jG6/rv9/JThfvXk5poyU9yI6CBoKWioq1dXfuqIYlAq/TEOgfCGIS7Qlb6UwRUFlkg0dtTf1J0xqm1r63ufECPi3p2ZHUx26vdH/T63z8S5j/kL3prXuz6fXKnFxzH6ftw1f1cDjHlmDiu/kFgtz6huLOPaHPRHthBN5U6wJBwI3A97wwx5Ya4pwSQackOOUOezOcdKdTRVdjc8pN2b/WHpfTf2pzzze9Y9tcwFYPnXWfLcJ/eE/9IChfCaI8v/kLcsdiu99pQ23V2OGcptf1qa21n5MyqPFrKSfZBtuygBLK8qdtF92LHmnZmBDfOoHtJ2V5tk0hMUiJAOyxaouui75t+Oech15qq/N2L69fdnCTveEG5pR96S/ItjfreQ803+Ggtfavh7+PhKdOg8yZtvdPp4CR/8Ea3evO6WYCgctlz0tfAMnbbEv5zQUCV0T9MUnsbTMqvvaKNhbSQCAiU0Rki4hkicj9TaxzrYhsFJENIvJqKNPTJqLibSDw5c6CBYLoBHsQKwrs68V77E0xNsVWabS2VOArCrvbqG4w0TnRDjfRE6TWa7sMZlxhc+3Lnm6611T5IbuvXYfZXJyvRHA4x1YLuVz1J/bhHbYuFWzuqfdEmxsP7BHjy/EFVlVEdrI32LqqIb9BeRc8BDe/a28eyU5xurnqId9Nvf+ZNvfsK9GATfM5v4Qb3rDHbe59Tl38ItsOIa6GRXx/xtht9z+zvlrR18DdnJK9trtssBt6qnPzCazf3zLHNuZO+mHz2/bxr1rz5/u+mwwEvWwG5ljn5ircBVEJDTsADL7INgL7RtPWem0dfM8xjd/vyx3nb7elhqVPwFcvH1uafDxV8M50e213SobzfgPT/tl4vZQgx8J3Tgf2uAqUPMDOZAD1vZ+aCwRd0u3APrDn0tCpDdsr2lDIAoGIuIEZwMVABnC9iGQErDMI+AVwujFmOPCzUKWnzUTF2hu5LwDUBQK/3Lq4ICbJ5jjzttr1k/raZdB8VUswbR0Ieo61Dc9NDXbavcIGsYzLbU774IbG9bg+vhuyy20vEt8FcmBD/UUTl2br8w9sqB9ZGtPZdskDOwDMX+Eu+10F68ffJd32YPFU1JcIGq3j9NVursF4x2KbY0/o1vQ6Cd3h1Dttd9KtH9uc6/ArbInB12UzUN4Wu17/M+3zpL42rYG5+UC+AOr7zvzV5UKz6gOKp9o2KPY7o+X9+32BNbAr6IEN9jHY+AKwjbu1nsZVSq2Vt9mO9fBPb3SCHe+x8X+2VLR/na2+Sj+z8fvjUm0gKdhux8L4ttkWVr1gz/MrnoJbP7TdP91B+tJEx0NCz4Y9h4r9MjfNSRloz21Ptb3GYlOCB35fwE5uQSmvjYSyRDARyDLGZBtjqoFZwOUB69wOzDDGHAYwxhzjmXYcRMXZxyqn73x1mTNiOeCkiUuz60Z2shey73+o61pax+uxPSqaqtMOVuo4FvFpduDZ2tftSRmY09s61+7PSefZBs/IODvwJZjy/PpG29STbJH58E6b+/f18ReB7iPsDedwTn3OqUt/W3e6+DE7L45P4a7G1UI+3UbUN5zGNhEIEnrYNOdtCf66p9qOWu4f5GYTaOS37OM70+02h1xiu2f6ekgFyl5oH+sCgV+7RnN81U/BAkFin/pc8yM9bdr3rbFVC/4lmSPxlagCA8H+dTY4J6UHf5/veB1r9dDBzQ17AvmMutbmqte/Xd9by3/8iI+IbTzN3257ifm2Gew4VJc1buxvzuYPIG2YLaEcSWAX0uJ9dnDnkQYgJg+w1byFu+xA0D6nBA/iib1tRi3YuRAioQwEvQD/rim5zjJ/g4HBIvKFiCwTkSkEISLTRSRTRDLz8lpxcEMhopO9KMudieqqSymsrOVfTz3VcL3IGFvPmTKwPni4nSku/EoEf//73ynP22mL3nmbG7Y/+LR1IAAYfZ39zMf621kPfWpr7Vwp/U5zBtTF2W6DG/4XfBbKskP13eF8PSqynIt0wNn163UbYet+d35ZXxcvAlc+Y4PqvF/Xr9tcIOgxCjv3B02XCFwuu96+NQ2Xe6psF7wvn7RtNi0JBCkDbUNdVZEdIRqXYrurVhUFvzFmfWIv4C7p9nldtVhO85+Tv92eW8FylS63Tcf2+TYTsWxGfQmtbysCATQcne1zYL09Pk2N2K0LBM20uRxJeYGdeydYIBg2zTbaL/iD/f6SBzbdAyd5oA1cu760mS3/nmo+niqYeZGdeqW67Mizp1YW2wb+wG7MTUkN6H1VstcetyOVzHw5/NwVtlTj6+IdKCIarp9luzQfJ+3dWBwBDALOBq4HnhORRvMyG2OeNcaMN8aMT0sLTR1Zi4nYm0FNmfObBh4Kyz0t/z2CyE6NA8HhA7YuWsSeYBWHG77HW21z6G05tH7wxfbiS+xtq4i2L7DLN822uZ2xN9evO+pae8Fter/xdsr9unGmDrI5nsz/1DeW+nQbbqtIqors9Bg+XYfaeVY2f+D0tDJOIAjoMeTjPziuqRIB2Oqv/evqS1kVhfDMWTD3/2wjOFLfj/1Ixt8KnbrAqc6F2dWp4Qxs26gut336B/ndUOoauJ0SwaYP7OhlX47WJz/L3uybOsa+KgRXpP3dga9n2RtLfNeW7YNPl34NSye1tbaRtPuIpt/TFiUCX8+prsMav+Zywfm/saXI7IX1JclgkgfYgCJumOyMtwmsHlrwB3vsS/fbbsGP9IQP/l/TVbLZC2zV1+Cg+dDG0obZrtC+klVBdtOT0AWmHeDr1+xjU4EAbFBqrvG5jYVyQNkewL/1qbezzF8usNwYUwPsEJGt2MCwMoTpOnadUmxx0Lmg7n/oj3XTUF9wwQV07dqVN954g6qqKq688koeeughysrKuPbaa8ndlYO3pooHfvM7DuTlsXfvXs656rukpnVjwcKFdjbCkv22jtyXw/BWt21pAGyJ5Yef29zTP0+G2T+xQ903zbbdDP3nf+l/lr35ffAzm9P11e0X7rYXgW9myt4TbGnpwHo7+Zl/DqluQJU0nqclY5qd3Gv7fPvZNeWNxxD4dB1eP0tsXErwdcBWOdWU226X3TJg0Z/sDePq5+2FKO6GM6U2Z+xNtouqrxeL72Z2cKOtYgP7gyJ5W2zvG98ysHXKsSn2XNmxGF6/0bYhfT0Lpi+sH0yUn9X8zdjXm+iiR+xYhsM5cNlRTMKW1NfOi+P1AMZmPKpLGgbYQLHJNqNSdAwlgrqxAU20Qwy6wH4fOV/AsEuDrwO2qq5kP5zxM3uNfHSfrWaJSbTHPG+znX113Hfrp99OG2Z7AUXFwYW/t9uprYXV/7WZiSWP2235j/BtzuAL7fQPm963PYv2fW17nh1JXKrtQrpjif0+gzUUt5NQBoKVwCAR6Y8NANcBgXPZvoctCfxHRFKxVUUtGBLajLn3t+1siWAvkosfrX/ujrANb4W7wRXJo48+xvoNm1izZg3z5s3jrbfeYsWKFRhjmDZtGosXLyYvL4+ePXvy4VuvwOEdFLmSSezWl8f/8hgL3nyG1GGTba4/LtUO9qopr69S8lTbG3coRETDFf+yVTNLn7BpuOY/DbvuuSNs98r/XAyzroc7Ftui+/q37OsjnEE3yf3hzi/t7x80GtU6xN58u49sfAPud7rNcX/1im1ojUlqehRrZIzta39w45FLBGCrh0RgxbN2xsuR19i/1v4KlP/3EZtsqwJ8U1PsXmF/UARsXXFgSSOpr809fv43O4Dqex/ZKarfvBVu/8zesA7nNJxzJtCIq2xJcfytdhBSYq+m+/03J6mfDaJZn9oxCL5ukN2aCQQiTXchrS6zQS5lUPDGVZ+8zbaht7k09xx75Jtj16Fwhd+P9cSm2CC/6E+2ETfKqas//7c2kzPgHPudvXWrDb7n/cZW0W58z5YSwLaPXPGv5tPvr0u6DTob3qu/RodecuT3idjAv28tTHsydNf0UQhZIDDGeETkLuBjwA3MNMZsEJGHgUxjzGzntQtFZCPgBe41xuSHKk1tKjbFXvSmFvbWt1vMmzePefPmMXasPaFLS0vZtm0bkydP5u677+a+3/yBSyePZvLJI+BQpX1/bFp9r6NOSTbndTjHviYuWyI4lpkwj6T/mfbm3twMk517wA2vw3PnwsvX2C6Xa16zuSj/GRVTBsIlf2n8/sgYGP+94MPw3ZG2P/lXL9nn177Y/CjN7iNtnXpzc9OnnGQv8G2fwNInbXA5x68d4lhn0uyaYet6171lc6Dx3eCs/7PnRODAv6S+dlbU6lJ7g0oZaLsmvn6jnRyucJcNwP7VcYG6j6wvAfRtpkrhSHwlrdeus9VKgy+2VTLN/S4E2OqhvM02Nx4VZ6tSdi6Fj35hg1xMkp3ccNildp1dX9pS84Cz7Y1z2zx7E2/rOfb7n2WrnSb9wP4+RdancMnj9ZmNU6bbx9E32Bz8lzPsfi/+iz2GU/5oq9iCjapuTsbltoqxptx2emiqpBPo8n/ZMS7NlcDaQUjnGjLGzAHmBCx70O9/A/zc+Wsb/jn3UKubx6c+EBhj+MUvfsEdd9zRaPXVq1czZ84cfv3nZznv9JN58Oc/sDnNhO71K7kiILYLVBTV3/xNrc0xh9qRLtK0Iba08P5PbQ4L7GyLLRUsQPhc+Hs7biGpT/Pz+ACc8XPb5bC59Lpctq7e9+taN77dtn2w+5xiq7Levs0G6yufaXqK4F7jbb3+kEvsVNFgb5gjrrY9psAObGuqOiZ90KEAACAASURBVKwt+brWpg214y5aWg990vn293r/GnBskvrBJX+1N+FPHrB/PpGxsPI5+39UQv2U2G3pmpn158HJt9gqr2A5+0EX2BKk/28OXz+rcTVlS436Nqz8ty2Znv7Tlge4prrotjMx7fBDycdi/PjxJjOz4aRPmzZtYtiwII1Qx0l+fj7jxo1j586dzJs3jwceeID58+cTHx/Pnj17iIyMxOPxkJycTExMDB988AH//ve/ee+99xg5ciSzZ89u8EM2ddUWQU6u9t5XwFZV7Vtj2y26jzq+88O3hrfGdkf01rS8R0hL1dbanLC32uYwjxSog92gairsbJol++3ssdHxbZvGpmyeY7udtjZzsX+9HVTnmzqla4atBvNVcexZZavLYpPtSPPoBFtqOrDRjvpOPX7dIYPa/pktffWZZEdq92lhm0BTar22NJIyMPjkjt8wIrLKGBN0ZjydfbQNpKSkcPrppzNixAguvvhibrjhBk491Xbri4+P5+WXXyYrK4t7770Xl8tFZGQkTzndTadPn86UKVPo2bMnCxY4PXeO40/UHZWIqGO/iI4Hd6SdsTIUXK6GVWJHTEuQSy2yU9v9BGRrDJ16dO/rPqL5Bu1eJ9s/f+lnNN8L6Hjyn4+pLbjczX8fJxAtEZxgwmlflVJtp7kSwTe0TK+UUup40UCglFJhrsMEghOtiutohMM+KqWOvw4RCGJiYsjPz+/QN0pjDPn5+cTEfHMGoSilOoYO0Wuod+/e5Obm0u4T0oVYTEwMvXsfxWhSpZRqRocIBJGRkQ374SullGqxDlE1pJRS6uhpIFBKqTCngUAppcKcBgKllApzGgiUUirMaSBQSqkwp4FAKaXCnAYCpZQKcxoIlFIqzGkgUEqpMKeBQCmlwpwGAqWUCnMaCJRSKsxpIFBKqTAX0kAgIlNEZIuIZInI/UFev0VE8kRkjfP3/VCmRymlVGMh+z0CEXEDM4ALgFxgpYjMNsZsDFj1dWPMXaFKh1JKqeaFskQwEcgyxmQbY6qBWcDlIfw8pZRSRyGUgaAXsNvvea6zLNDVIrJWRN4SkT7BNiQi00UkU0QyO/rPUSql1PHW3o3F7wPpxphRwCfAC8FWMsY8a4wZb4wZn5aWdlwTqJRSHV0oA8EewD+H39tZVscYk2+MqXKe/hs4OYTpUUopFUQoA8FKYJCI9BeRKOA6YLb/CiLSw+/pNGBTCNOjlFIqiJD1GjLGeETkLuBjwA3MNMZsEJGHgUxjzGzgJyIyDfAABcAtoUqPUkqp4MQY095paJXx48ebzMzM9k6GUkqdUERklTFmfLDX2ruxWCmlVDvTQKCUUmFOA4FSSoU5DQRKKRXmNBAopVSY00CglFJhTgOBUkqFOQ0ESikV5jQQKKVUmNNAoJRSYU4DgVJKhTkNBEopFeY0ECilVJjTQKCUUmFOA4FSSoU5DQRKKRXmNBAopVSY00CglFJhTgOBUkqFOQ0ESikV5jQQKKVUmNNAoJRSYS6kgUBEpojIFhHJEpH7m1nvahExIjI+lOlRSinVWMgCgYi4gRnAxUAGcL2IZARZLwH4KbA8VGlRSinVtFCWCCYCWcaYbGNMNTALuDzIer8D/gRUhjAtSimlmhDKQNAL2O33PNdZVkdExgF9jDEfhjAdSimlmtFujcUi4gIeB+5uwbrTRSRTRDLz8vJCnzillAojoQwEe4A+fs97O8t8EoARwEIRyQEmAbODNRgbY541xow3xoxPS0sLYZKVUir8hDIQrAQGiUh/EYkCrgNm+140xhQZY1KNMenGmHRgGTDNGJMZwjQppZQKELJAYIzxAHcBHwObgDeMMRtE5GERmRaqz1VKKdU6EaHcuDFmDjAnYNmDTax7dijTopRSKjgdWayUUmFOA4FSSoU5DQRKKRXmNBAopVSYC5tA8PznO8h48CPKqz3tnRSllPpGCZtAUFtrKK/24q017Z0UpZT6RgmbQOByCQC1te2cEKWU+oYJm0DgtnEAr9ESgVJK+QufQOCUCLRqSCmlGmpRIBCRn4pIZ7GeF5HVInJhqBPXluqqhrREoJRSDbS0RPA9Y0wxcCHQBbgZeDRkqQoBt2iJQCmlgmlpIHBq2JkKvGSM2eC37ISgVUNKKRVcSwPBKhGZhw0EHzu/M3xC9b/RQKCUUsG1dPbR24AxQLYxplxEkoFbQ5estlcXCLSNQCmlGmhpieBUYIsxplBEbgJ+DRSFLlltzyW+cQQaCJRSyl9LA8FTQLmIjMb+xvB24MWQpSoEtESglFLBtTQQeIwxBrgceNIYMwP7m8MnDJf2GlJKqaBa2kZQIiK/wHYbnSwiLiAydMlqexHaWKyUUkG1tETwbaAKO55gP9Ab+HPIUhUC2mtIKaWCa1EgcG7+rwCJInIpUGmMOaHaCHRksVJKBdfSKSauBVYA3wKuBZaLyDWhTFhbqx9Z3M4JUUqpb5iWthH8CphgjDkIICJpwKfAW6FKWFtzOSFPq4aUUqqhlrYRuHxBwJHfivd+I/hKBFo1pJRSDbX0Zv6RiHwsIreIyC3Ah8CcI71JRKaIyBYRyRKR+4O8/gMRWScia0TkcxHJaF3yWy7C+UECj5YIlFKqgRZVDRlj7hWRq4HTnUXPGmPebe49IuIGZgAXALnAShGZbYzZ6Lfaq8aYp531pwGPA1NauQ8toiOLlVIquJa2EWCMeRt4uxXbnghkGWOyAURkFnZAWl0gcKa29okDQnaX1u6jSikVXLOBQERKCH5zFsAYYzo38/ZewG6/57nAKUE+40fAz4Eo4NwjJfho1Y0s1jYCpZRqoNk2AmNMgjGmc5C/hCMEgRYzxswwxgwE7sNOZteIiEwXkUwRyczLyzuqz3G7tGpIKaWCCWXPnz1AH7/nvZ1lTZkFXBHsBWPMs8aY8caY8WlpaUeVGJ10TimlggtlIFgJDBKR/iISBVwHzPZfQUQG+T29BNgWqsRoG4FSSgXX4sbi1jLGeETkLuBjwA3MNMZsEJGHgUxjzGzgLhE5H6gBDgPfDVV69DeLlVIquJAFAgBjzBwCxhsYYx70+/+nofx8f1oiUEqp4E6o0cHHQiedU0qp4MImEOikc0opFVzYBIK6See0RKCUUg2ETSCIcCKBV4sESinVQNgEgrqqIS0QKKVUA2ETCHxVQzqyWCmlGgqbQKAji5VSKriwCQQuHVCmlFJBhU0g0EnnlFIquPAJBKK/UKaUUsGETSBwuQQRHVmslFKBwiYQgC0VaBuBUko1FFaBwOUS7TWklFIBwioQuEW0sVgppQKEVyBwiU46p5RSAcIwEGgkUEopf+EXCLSNQCmlGgirQOASrRpSSqlAYRUI3C4dWayUUoHCKxCIVg0ppVSgsAoELpd2H1VKqUBhFQgiXKJzDSmlVICwCgQ6slgppRoLaSAQkSkiskVEskTk/iCv/1xENorIWhGZLyL9QpkeHVmslFKNhSwQiIgbmAFcDGQA14tIRsBqXwHjjTGjgLeAx0KVHvANKNNAoJRS/kJZIpgIZBljso0x1cAs4HL/FYwxC4wx5c7TZUDvEKYHl4hOQ62UUgFCGQh6Abv9nuc6y5pyGzA32AsiMl1EMkUkMy8v76gTpCUCpZRq7BvRWCwiNwHjgT8He90Y86wxZrwxZnxaWtpRf45bew0ppVQjESHc9h6gj9/z3s6yBkTkfOBXwFnGmKoQpge3S6uGlFIqUChLBCuBQSLSX0SigOuA2f4riMhY4BlgmjHmYAjTAugvlCmlVDAhCwTGGA9wF/AxsAl4wxizQUQeFpFpzmp/BuKBN0VkjYjMbmJzbcLlAp2FWimlGgpl1RDGmDnAnIBlD/r9f34oPz+Q2yXU6PSjSinVwDeisfh4cWnVkFJKNRJWgSBCu48qpVQjYRUIdByBUko1FlaBQEcWK6VUY2EVCLREoJRSjYVVINBpqJVSqrGwCgQ6DbVSSjUWVoFAf6FMKaUaC6tAoL9ZrJRSjYVVIHCLthEopVSgsAoELpegM0wopVRDYRUI3C50HIFSSgUIr0Cgcw0ppVQj4RUIXC4NBEopFSDMAgEaCJRSKkBYBQIdWayUUo2FVSDQkcVKKdVYeAUCLREopVQjYRUIXCIYg5YKlFLKT1gFggiXAGipQCml/IRVIHD5AoGWCJRSqk5YBQK3Ewh0dLFSStULr0AgWiJQSqlAIQ0EIjJFRLaISJaI3B/k9TNFZLWIeETkmlCmBeqrhmp14jmllKoTskAgIm5gBnAxkAFcLyIZAavtAm4BXg1VOvy5bRzQxmKllPITEcJtTwSyjDHZACIyC7gc2OhbwRiT47x2XPLovjYCjxYJlFKqTiirhnoBu/2e5zrLWk1EpotIpohk5uXlHXWC3C67uxoHlFKq3gnRWGyMedYYM94YMz4tLe2ot+N29larhpRSql4oA8EeoI/f897OsnbjEl9jsQYCpZTyCWUgWAkMEpH+IhIFXAfMDuHnHZFbB5QppVQjIQsExhgPcBfwMbAJeMMYs0FEHhaRaQAiMkFEcoFvAc+IyIZQpQf8G4s1ECillE8oew1hjJkDzAlY9qDf/yuxVUbHRV3VkLYRKKVUnROisbitRGjVkFJKNRJWgUAnnVNKqcbCKhC4tWpIKaUaCa9AoCUCpZRqJKwCgVYNKaVUY2EVCHQaaqWUaiy8AoH+VKVSSjUSVoEgKsLublmVt51TopRS3xxhFQgyenQmOsLF0u2H2jspSin1jRFWgaBTlJtTB6awcMvRT2WtlFIdTVgFAoBzhnRlx6Eydhwqa++kKKXUN0JYBgKAuev3tWj9RVvzyC+tCmWSVAu9tSqX3QXl7Z0MdZzU1hrW5ha2dzLCQtgFgr4psUwelMpTC7eTX1qFx1vLypyCoOseLKnkuzNXMGPBdvYUVvDpxgMNXn//6708vWh73fNqT9v+9JkxhtzDbX/jM8awYW/RCfW7DFkHS7jnza95Yv62kH5OZY2XjzfsPyG7GBeWV1NZ03E6Qrz71R6mPfkFK3YEvz6/6VbvOsxHLcxwtrewCwQAv7ksg4pqL3+Zt5Vnl2Tzrae/5Iusxg3IS7PyAViWnc/D72/g+y9m8tvZG7j4H0uYu24fv3p3HY/O3cyCLQepqPZy2qOfcdO/l3OwuLLRtjbtK2bVzvoTuriyhsLy6kbrVdZ4McZwsKSS77+QyRl/WsBrK3bVvZZX0rh0Ul7tITuvtNHyimovn2w8QG2t4WBxZd1N4p3Ve7jkic+ZsSCrhd+YDR6Pf7KVl5ftbPL1w2X1+7NqZwFrdtvcXFFFDUUVNS3+LIDN+4sp8NveW6vsbxot2JIXNIDll1a1SWnhb59u5Y6XVjW5n6312opd3Pvm17y9Krdu2cItB/nOzBX8dNZXGL+uzMWVNRSVN/09mSDdnmu8tazLLcLjreWSJz7nl++uO+Y0e2sNxZXNH6/AQOmtNfxvzZ6g5/TRmrt+PwDvrWnX37OqM2NBFvM3HTjyioDHW8vPZq3hrle/Iutgw2vTGNPgWK7fU8S+ooo2TWtrhXQa6m+qk7om8N3T0pn5xQ7io+1X8NTC7czbsJ+iihquGtebMwen1QWHTfuLyT5USpTbxX+X5uASuPPV1RgDXROi+fW76/nl1GEcKq3iy+xqvjNzBe/ceRqvrdjNO6tzGdajMx+s3QvA/LvPpqi8hmueXkp5tZfkuCh6d+lElNvF4fJqsg+VMbJXIiWVHvYVVTAwLY7ff7ART63h30uyOVRSxewfn0FaQjT/nL+NLQdK+WrnYUqrPdxz4RC255WSlhDN9RP68sKXOfznixzuuXAwzyzOJqNHZ/5760T+Om8LIvCP+ds4Y1AqWQdLWbLtEL+cOozuiTFBv7NnFmfzxPxtxEa5mTamJ51jItl6oIRPNh5gQnoyH6zdy6yVu3n3ztNIS4jmuzNXUu2t5YKMbny41uaKTumfTK0xxEZF8MhVI+mV1AmADXuLMAZ2HCrjifnbGNw9gTnr9nHmoDR+dckwXlm2kznr9xMfHcGh0irW7y1iVO+kurQZY/j+i5nsyi/nnTtP4z9f5DBpQArnD+tKhLs+r7Myp4Ch3RNIiIlssG97CytIjouiuLKGF5bm4HYJf5m3hakje5CWEN3i82pfUQUV1V76JseyJOsQ3TvH8MB764l0u3hzVS6FFTUcKK7k2cXZJHaKpKiihvOGdWPa6J4cKK7kqn8t5UBxJdPG9OSxq0c1SPvfPtnKK8t38fx3xzO6TxL/nL8NrzEcLKni1eW7uH5iX/YUVvDB2n3830VDyc4rpWvnGDbsLWJ4z86c1DWhbltlVR7inPPeW2soqawhKTYKsAH19hcz2ZlfzsJ7z2ZfUSW9u3QiNqr+VrE2t5Bb/7OSKSO68/srRnCguIpH5mxi9td7OXtIGk9cPxaXCPHRERhjWLztEMN7diY1Ppoqj5fMnMOcOiClbqR/lcfLvA0H2FVQTkpcFKeflEpqfDSfZ+UhAnPW7eO3lw2v6/4dyBhDrakfJwTw8rKd5Bwq49eXZgC2mkkE9hZVsudwBaN6JxIT6Q66vSqPlyi3C5H67c3bsJ8/f7yFpNhIFt17Domd7DlU463lmUXbiYl0Mz49mWcWbefGU/pxqLSKXQXluF3CD15eRUykiz9cMdIeu8+yeHPVbj79+VmUVnq49pkvGde3Cy9//5QWn2ttTYLlMr7Jxo8fbzIzM495O8WVNZz7l4UcKq3m7CFpLNxiT7rk2CiKKmp46bZTuPuNNRhgX5HN4T914zgAuifGcM3TXzKubxI/OW8QNz+/gq4J0VRUe3n06lH86NXVdE2I5mBJFUO7J7DtYCknpcWzs6CMUb2TOFBcSUW1l+9P7s+OQ2XsLayk2lNLQkwE6alxvJG5G6/X8N/vTaBrQgyXPfk5heU19ErqREWNl+gIFzXeWgrKqhnZK5GBafHklVaxZNsh4qLcVHlq6RTpptLjxVtrLxKfgWlxbM8r46kbx/H7DzdRUFZNhVNSSIqN5B/XjeVwWTWzv95LdISLO84ayGebD/LE/G2M65vE6l2F/PayDE7qmsB3Zi6vuwC9zoU2pFsCvbvEsmjrQXoldSInv5ybJ/UjJT6KNzNziYt2s7ewkqgIFx//7EyWbj/EPW9+TY3XJrJXUicOFFfSJzmWHYfKGJAaR7bTsP/IlSP51XvruGZcb5JiI3n3qz10TYjhjEGpPLs4G4D46AhKqzwADO4Wz+8uH8EpA1L495Jsfv/hJlLiorj9zAGALaX95LxBTP3HEvokxxIb5WbD3mKe+87J3PHSKi4b1ZPHvz2m7rvz1hp+98FGFm3NIzrCRY/EGArKqnn48hF8nVvIo3M3YwycMSiVTzYeIMIlxES6mX/3Wdz9xtd87mQsbjilLw9emsE1Ty9lV345E/unsGFvEcUVNVw2uiezVu7mR+cM5N6LhgLwny928ND7G4mKcNEp0s3UkT3qSolQ//3HRLqorKmtCzI+IjBtdE/uOHMgWXml3P3GGr49oQ+nD0zl8U+2srOgnNduP4XHP9nKsuwC3CJ1QfyTjQdIio3kB2cNZHC3eN5YmcuSbXmICKVVHlLiosh3Sm6TB6WyZNshIt1CdISbu849ieXZ+SzYksfAtDhmTT+V38xez5x1+7lqXC827ythdJ9EiipqmLNuf4P0DuvemY37ivne6f2Z+cUOfnreIGqNYc3uQhI7RTIgLZ456/ZxYUY39hRWsHBLHt8/oz8iMLxXIne8uIpqby2vT5+EyyXc9epqiis8ded6p0g3d549kIn9k3G7pO6mv2V/CY/M2cSFGd3omdSJRVvzuHJsL55ZvJ1It4vcwxXccdYA7p8ylA/W7uOZxdtZv6e47jjUGoPvlnpS13humNiXP8zZRGykm5T4KF69fRIXPL6IsmovT1w/lg17inhmcTYisPjec+iTHEtplYdOkW4+WLuXyhovRRU1zFiwnfJqDw9NG8ENp/Rt5d3O973KKmPM+KCvhWsgAPhs8wGW7yjgjjMHMv3FTG6c1JfzhnXjyhlfsKugnBqv4cFLM3j0o81EuV2seuB8oiNsLmLVzgJ6d4mla0I0U/6+hC0HSrh0VA+evGEcP3x5FZ9tPshj14zi8jG9OFhcSedOkcz8YgePfbSFlLgonr75ZCakJwdNV0FZNVUeLz0SbY65tMpDfmkV3RNjWLnjMA/OXs/Q7glMP3MgY/rYnHGVx8vcdfs5e0gaZdVebpm5gn1Flfz12tHc+cpqfnLuIDJ3FvD17kLuvWgIN5+azoHiSn7w8iqSOkVy38VD+fGrX7HNKcb2SupEcUUNJc5N9aqxvfjj1SO59pll5DpVMF3ionj+u+P53QcbKa70cMtp6fz4ta/w1hp+ct4gbj0tndzDFYzsndhg/zbvL+ayf37O0O6dWb+3iInpyZw1JI39RZX8cuowotwu8suqOf3Rz6j21vLIlSMZ3SeRjB6duf65ZSzLLkAELszoxs78cjbvL6FnYgwT+yfz3pq9/PmaUcRGRfCnjzazp7CCyYNSWbglj3OHdqWyxsvS7fl1aUmNj6K4wkNnJ4f322kZXDqqJ3/+eDMzFmxnXN8kXCL85LxBrM0t5C/ztnLe0K5UONV0xZU15JVUUWvsjXB3QTk5+eVMG92TNbsLuX1yf24+NZ2i8hre+SqX009KZXA3mzvPOljCnz7aQs6hMnokdeKuc05iYv9k7ntrLa9n7ubiEd05Y1AqD/5vA+cO7cqvpg7jp6+v4evdhUwelMqFGd1Ym1vEhPRk/u/ttdxyWjorcwrYuK+YBy7JIDbKzeDuCczbcIAXlubU3QS7d45hv1N9OTAtjsPlNRRX1GCA6WcO4LJRPfnt+xtYsaOAgWlx9E2OZYHT5bp75xhOG5jCPRcN4d2v9rBxXzHj+nZh0oBkhnXvzAP/W0+tMWzeX8JXuwqJj47g2xP68NKynXVtaGP7JtW95gvad18wmO+d0Z8DxZW8t2Yvr6+0gW7hPedw16urmb/5IAAjeyVyqLSKfUWV9EuJZWe+PReHdk9g8/6SuuMaF+WmU1QEbpe9nnp3ieWcIV1JiY9icLcE3l6Vy0cb6oOPv/SUWHKc7SbHRVFQVk2f5E48dePJ/HdpDm+vzmVCv2RW5BTQLyWWey8awo68MpbvKOCPV43ky+x8cgvKOW9YN0b3SaLK42XNrkKue24ZcVERdQE0MTaSvYUVjOvbhS+z87l0VE8qa2x1bnSEiyq/NsczB6cxvGdnLszoxti+XYKm+0g0ELTSnsIKXliaQ0FZNb+aOozff7iJzp0i+M1lw4Ou//rKXdz39jr+/u0xXDG2FzXeWooqakiNb1itYIyhtMpDfHREg2JnKFRU25xE98QY8kqqSI2PwlNrnJxjfZHYd/xFhOLKGp5euJ0J/ZM5e3AaB0uqWLr9EH2TYxnXtwsiwtrcQh6Zs4nsvDJe+N5EhvXoXLcdESGvpIoaby09nWqfpjw6dzNPL9rO+H62SBysmP7Q+xvYsr+El287pa4aobLGS3ZeGXHRbvqlxFHl8fL0wmxO7teF8eld2Ly/pC44llZ5uPfNr1m+o4Arx/bi3ouGEBPpJutgKTXeWp7/fAdvrcrlO6f2474pQ3GJ0CnKXff9XfLEkrrvbE+hrcO9ZGQPnrxhbN3xyz1czoP/28CFGd349oQ+HCi239mVY3sd9TGu8niZsWA7Mz/fQWmVh34psbz/4zPoHBOJcW6y/VPj6r4zr1NteMXYXpRWecgrqWLSgJQG2zxUWsVnmw9SVF7DjZP68vaqXOJjIpg2uhcfb9jPna+s5teXDOP7k21paWmWLak9ffPJjOqdVNd77tJRPZusovFX461lV0E5/ZJjiXC7WLO7kCVb8+ieGMPV43ozd/1+JvZP5o3M3ewrquDhaSPqjjHYOvYar6FTlBtjDCt2FJAYG8nQ7p1te1R5DV1iI3l1xS6iI9xcPa4X+4oqMdhqtFMHpFBrDA+9v5Erx/bi7gsH11V/+WzcW8zh8mqn1GzP3+gIFxPSk/nv0hwEuGlSP7YeKGFYj864XUJljZfbX8xkybZD3HPhYO48+6QG6W7Ogs0H+fv8bWT0SKBvchx/+mgzGT06M/OWCdz71tcs2XaIxE6RXDu+NxU1Xib2T6nLkJ09JO2Y7xkaCELMW2v4aP1+LhrerUG9rmpaRbWX11fu4oqxvRpdoMdLYXk1zy3J5nun9yclvnFbgMdbi9slVHlqmb/pIFv2F3PbGQNIjI0MsrW2V1Ht5cvsQwx2qttCKb+0qtF34AvuqqEaby0788s5qWv8UW/D1zvtouHdiYl0k++0KYzolUhkiO4hGgiUUirMNRcINPuqlFJhTgOBUkqFOQ0ESikV5kIaCERkiohsEZEsEbk/yOvRIvK68/pyEUkPZXqUUko1FrJAICJuYAZwMZABXC8iGQGr3QYcNsacBPwN+FOo0qOUUiq4UJYIJgJZxphsY0w1MAu4PGCdy4EXnP/fAs4T7a+mlFLHVSgDQS9gt9/zXGdZ0HWMMR6gCEgJWAcRmS4imSKSmZenPyqjlFJt6YRoLDbGPGuMGW+MGZ+WltbeyVFKqQ4llLOP7gH6+D3v7SwLtk6uiEQAiUA+zVi1atUhETnaOYJTgXD7weJw3GcIz/3WfQ4PR7vP/Zp6IZSBYCUwSET6Y2/41wE3BKwzG/gu8CVwDfCZOcJQZ2PMURcJRCSzqZF1HVU47jOE537rPoeHUOxzyAKBMcYjIncBHwNuYKYxZoOIPAxkGmNmA88DL4lIFlCADRZKKaWOo5D+MI0xZg4wJ2DZg37/VwLfCmUalFJKNe+EaCxuQ8+2dwLaQTjuM4Tnfus+h4c23+cTbvZRpZRSbSvcSgRKKaUCaCBQSqkwFzaB4EgT4HUUIpIjIutEZI2IZDrLkkXkExHZ5jwe3Y+efkOIyEwROSgiAFbngQAABItJREFU6/2WBd1HsZ5wjvtaERnXfik/ek3s829FZI9zrNeIyFS/137h7PMWEbmofVJ9bESkj4gsEJGNIrJBRH7qLO+wx7qZfQ7tsTbGdPg/bPfV7cAAIAr4Gsho73SFaF9zgNSAZY8B9zv/3w/8qb3TeYz7eCYwDlh/pH0EpgJzAQEmAcvbO/1tuM+/Be4Jsm6Gc45HA/2dc9/d3vtwFPvcAxjn/J8AbHX2rcMe62b2OaTHOlxKBC2ZAK8j85/c7wXginZMyzEzxizGjjvx19Q+Xg68aKxlQJKI9Dg+KW07TexzUy4HZhljqowxO4As7DVwQjHG7DPGrHb+LwE2Yecn67DHupl9bkqbHOtwCQQtmQCvozDAPBFZJSLTnWXdjDH7nP/3A93aJ2kh1dQ+dvRjf5dTDTLTr8qvw+2z81slY4HlhMmxDthnCOGxDpdAEE7OMMaMw/4OxI9E5Ez/F40tT3boPsPhsI+Op4CBwBhgH/DX9k1OaIhIPPA28DNjTLH/ax31WAfZ55Ae63AJBC2ZAK9DMMbscR4PAu9ii4kHfEVk5/Fg+6UwZJraxw577I0xB4wxXmNMLfAc9VUCHWafRSQSe0N8xRjzjrO4Qx/rYPsc6mMdLoGgbgI8EYnCzmk0u53T1OZEJE5EEnz/AxcC66mf3A/n8X/tk8KQamofZwPfcXqUTAKK/KoVTmgB9d9XYo812H2+TuxPwfYHBgErjnf6jpXzI1XPA5uMMY/7vdRhj3VT+xzyY93ereTHsTV+KrYFfjvwq/ZOT4j2cQC2B8HXwAbffmJ/7Gc+sA34FEhu77Qe436+hi0e12DrRG9rah+xPUhmOMd9HTC+vdPfhvv8krNPa50bQg+/9X/l7PMW4OL2Tv9R7vMZ2GqftcAa529qRz7WzexzSI+1TjGhlFJhLlyqhpRSSjVBA4FSSoU5DQRKKRXmNBAopVSY00CglFJhTgOBUseRiJwtIh+0dzqU8qeBQCmlwpwGAqWCEJGbRGSFM/f7MyLiFpFSEfmbM0/8fBFJc9YdIyLLnAnB3vWbH/8kEflURL4WkdUiMtDZfLyIvCUim0XkFWc0qVLtRgOBUgFEZBjwbeB0Y8wYwAvcCMQBmcaY4cAi4DfOW14E7jPGjMKO/vQtfwWYYYwZDZyGHRkMdkbJn2Hnkh8AnB7ynVKqGRHtnQClvoHOA04GVjqZ9U7Yic1qgdeddV4G3hGRRCDJGLPIWf4C8KYz51MvY8y7AMaYSgBneyuMMbnO8zVAOvB56HdLqeA0ECjVmAAvGGN+0WChyAMB6x3t/CxVfv970etQtTOtGlKqsfnANSLSFep+I7cf9nq5xlnnBuBzY0wRcFhEJjvLbwYWGfvrUrkicoWzjWgRiT2ue6FUC2lORKkAxpiNIvJr7C+9ubAzfv4IKAMmOq8dxLYjgJ0K+WnnRp8N3Oosvxl4RkQedrbxreO4G0q1mM4+qlQLiUipMSa+vdOhVFvTqiGllApzWiJQSqkwpyUCpZQKcxoIlFIqzGkgUEqpMKeBQCmlwpwGAqWUCnP/HyvXClSrbLecAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": { "tags": [], "needs_background": "light" } }, { "output_type": "stream", "text": [ "DNN results\n", "accuracy : 0.9857142857142858\n", "Training Recall: 0.9857142857142858\n", "Training Precision: 0.9857451056895641\n", "Confusion matrix: \n", "[[370 8]\n", " [ 4 458]]\n", "Validation set report:\n", "cross Loss: -14.473271851312546\n", "hing Loss: 0.7333333333333333\n", "Report precision recall f1-score support\n", "\n", " 0 0.67 0.69 0.68 93\n", " 1 0.75 0.73 0.74 117\n", "\n", " accuracy 0.71 210\n", " macro avg 0.71 0.71 0.71 210\n", "weighted avg 0.71 0.71 0.71 210\n", "\n", "accuracy : 0.7095238095238096\n", "Validation Recall: 0.7095238095238096\n", "Validation Precision: 0.7106516290726816\n", "Confusion matrix: \n", "[[64 29]\n", " [32 85]]\n", "\n", "\\KNN Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 0.7\n", "Validation Recall: 0.7\n", "Validation Precision: 0.7089869281045751\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[69 24]\n", " [39 78]]\n", "\n", "\n", "Support vector machine Result\n", "\n", "Train accuracy: 0.9845238095238096\n", "Training Recall: 0.9845238095238096\n", "Training Precision: 0.9845228615132541\n", "\n", "\n", "Validation accuracy: 0.6952380952380952\n", "Validation Recall: 0.6952380952380952\n", "Validation Precision: 0.6941697730257486\n", "\n", "Train Confusion matrix:\n", " [[371 7]\n", " [ 6 456]]\n", "Validation Confusion matrix:\n", " [[59 34]\n", " [30 87]]\n", "\n", "\n", "Random Forest Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 0.7\n", "Validation Recall: 0.7\n", "Validation Precision: 0.7089869281045751\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[69 24]\n", " [39 78]]\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "aPqWItj4WUEE", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "566d55ec-d938-44bf-b507-32d5e0028f03" }, "source": [ "\"\"\"\n", "LDA before split\n", "hing\n", " \n", "cross Loss: 0.25\n", "X = np.load('gdrive/My Drive/Python files/neuromarketing 25/features_psd_ALL.npy') # 2724\n", "\n", "Standard\n", "\n", " batch_size = 128\n", " nb_epoch = 250 #3000\n", " l1_decay=0.00\n", " l2_decay=0.000 # .5\n", " # 0.01 0.06\n", " sigma=0.005\n", " in_drop_rate = .5\n", " drop_rate = .5#.05\n", " lr1=0.0001\n", "\n", "\"\"\"\n", "train_models(X, y, y_cat)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Before selectoin\n", "(1050, 2724)\n", "After selectoin\n", "(1050, 1)\n", "Epoch 1/250\n", "7/7 [==============================] - 1s 82ms/step - loss: 0.4060 - accuracy: 0.8702 - val_loss: 0.0125 - val_accuracy: 0.9952\n", "Epoch 2/250\n", "7/7 [==============================] - 3s 390ms/step - loss: 0.0387 - accuracy: 0.9929 - val_loss: 0.0106 - val_accuracy: 0.9952\n", "Epoch 3/250\n", "7/7 [==============================] - 0s 67ms/step - loss: 0.0173 - accuracy: 0.9929 - val_loss: 0.0101 - val_accuracy: 0.9952\n", "Epoch 4/250\n", "7/7 [==============================] - 0s 51ms/step - loss: 0.0162 - accuracy: 0.9929 - val_loss: 0.0099 - val_accuracy: 0.9952\n", "Epoch 5/250\n", "7/7 [==============================] - 0s 55ms/step - loss: 0.0208 - accuracy: 0.9905 - val_loss: 0.0098 - val_accuracy: 0.9952\n", "Epoch 6/250\n", "7/7 [==============================] - 0s 51ms/step - loss: 0.0192 - accuracy: 0.9929 - val_loss: 0.0098 - val_accuracy: 0.9952\n", "Epoch 7/250\n", "7/7 [==============================] - 0s 57ms/step - loss: 0.0155 - accuracy: 0.9929 - val_loss: 0.0097 - val_accuracy: 0.9952\n", "Epoch 8/250\n", "7/7 [==============================] - 1s 89ms/step - loss: 0.0126 - accuracy: 0.9940 - val_loss: 0.0096 - val_accuracy: 0.9952\n", "Epoch 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"Epoch 75/250\n", "7/7 [==============================] - 0s 46ms/step - loss: 0.0120 - accuracy: 0.9940 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 76/250\n", "7/7 [==============================] - 0s 18ms/step - loss: 0.0177 - accuracy: 0.9905 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 77/250\n", "7/7 [==============================] - 0s 17ms/step - loss: 0.0128 - accuracy: 0.9940 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 78/250\n", "7/7 [==============================] - 0s 17ms/step - loss: 0.0121 - accuracy: 0.9940 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 79/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0142 - accuracy: 0.9917 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 80/250\n", "7/7 [==============================] - 0s 17ms/step - loss: 0.0159 - accuracy: 0.9929 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 81/250\n", "7/7 [==============================] - 2s 300ms/step - loss: 0.0122 - 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0.9905\n", "Epoch 108/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0099 - accuracy: 0.9964 - val_loss: 0.0148 - val_accuracy: 0.9952\n", "Epoch 109/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0129 - accuracy: 0.9940 - val_loss: 0.0107 - val_accuracy: 0.9952\n", "Epoch 110/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0138 - accuracy: 0.9952 - val_loss: 0.0097 - val_accuracy: 0.9952\n", "Epoch 111/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0125 - accuracy: 0.9929 - val_loss: 0.0096 - val_accuracy: 0.9952\n", "Epoch 112/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0127 - accuracy: 0.9940 - val_loss: 0.0096 - val_accuracy: 0.9952\n", "Epoch 113/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0095 - accuracy: 0.9940 - val_loss: 0.0101 - val_accuracy: 0.9952\n", "Epoch 114/250\n", "7/7 [==============================] - 0s 15ms/step - 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val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 141/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0106 - accuracy: 0.9952 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 142/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0099 - accuracy: 0.9952 - val_loss: 0.0096 - val_accuracy: 0.9952\n", "Epoch 143/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0175 - accuracy: 0.9917 - val_loss: 0.0096 - val_accuracy: 0.9952\n", "Epoch 144/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0105 - accuracy: 0.9964 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 145/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0112 - accuracy: 0.9940 - val_loss: 0.0100 - val_accuracy: 0.9952\n", "Epoch 146/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0099 - accuracy: 0.9964 - val_loss: 0.0188 - val_accuracy: 0.9952\n", "Epoch 147/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0149 - accuracy: 0.9917 - val_loss: 0.0198 - val_accuracy: 0.9952\n", "Epoch 148/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0119 - accuracy: 0.9929 - val_loss: 0.0113 - val_accuracy: 0.9952\n", "Epoch 149/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0119 - accuracy: 0.9940 - val_loss: 0.0096 - val_accuracy: 0.9952\n", "Epoch 150/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0116 - accuracy: 0.9952 - val_loss: 0.0097 - val_accuracy: 0.9952\n", "Epoch 151/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0111 - accuracy: 0.9952 - val_loss: 0.0096 - val_accuracy: 0.9952\n", "Epoch 152/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0133 - accuracy: 0.9917 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 153/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0098 - accuracy: 0.9952 - val_loss: 0.0098 - val_accuracy: 0.9952\n", "Epoch 154/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0095 - accuracy: 0.9976 - val_loss: 0.0100 - val_accuracy: 0.9952\n", "Epoch 155/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0120 - accuracy: 0.9964 - val_loss: 0.0100 - val_accuracy: 0.9952\n", "Epoch 156/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0108 - accuracy: 0.9952 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 157/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0113 - accuracy: 0.9952 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 158/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0111 - accuracy: 0.9940 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 159/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0105 - accuracy: 0.9952 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 160/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0115 - accuracy: 0.9952 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 161/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0119 - accuracy: 0.9952 - val_loss: 0.0101 - val_accuracy: 0.9952\n", "Epoch 162/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0133 - accuracy: 0.9940 - val_loss: 0.0179 - val_accuracy: 0.9905\n", "Epoch 163/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0154 - accuracy: 0.9929 - val_loss: 0.0237 - val_accuracy: 0.9905\n", "Epoch 164/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0077 - accuracy: 0.9964 - val_loss: 0.0143 - val_accuracy: 0.9952\n", "Epoch 165/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0069 - accuracy: 0.9976 - val_loss: 0.0099 - val_accuracy: 0.9952\n", "Epoch 166/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0122 - accuracy: 0.9952 - val_loss: 0.0106 - val_accuracy: 0.9952\n", "Epoch 167/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0110 - accuracy: 0.9952 - val_loss: 0.0114 - val_accuracy: 0.9952\n", "Epoch 168/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0083 - accuracy: 0.9952 - val_loss: 0.0103 - val_accuracy: 0.9952\n", "Epoch 169/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0111 - accuracy: 0.9952 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 170/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0158 - accuracy: 0.9917 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 171/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0109 - accuracy: 0.9964 - val_loss: 0.0096 - val_accuracy: 0.9952\n", "Epoch 172/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0135 - accuracy: 0.9929 - val_loss: 0.0096 - val_accuracy: 0.9952\n", "Epoch 173/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0119 - accuracy: 0.9940 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 174/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0108 - accuracy: 0.9952 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 175/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0113 - accuracy: 0.9929 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 176/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0150 - accuracy: 0.9929 - val_loss: 0.0097 - val_accuracy: 0.9952\n", "Epoch 177/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0130 - accuracy: 0.9940 - val_loss: 0.0100 - val_accuracy: 0.9952\n", "Epoch 178/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0083 - accuracy: 0.9976 - val_loss: 0.0098 - val_accuracy: 0.9952\n", "Epoch 179/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0103 - accuracy: 0.9952 - val_loss: 0.0099 - val_accuracy: 0.9952\n", "Epoch 180/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0146 - accuracy: 0.9940 - val_loss: 0.0097 - val_accuracy: 0.9952\n", "Epoch 181/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0113 - accuracy: 0.9929 - val_loss: 0.0097 - val_accuracy: 0.9952\n", "Epoch 182/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0154 - accuracy: 0.9917 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 183/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0104 - accuracy: 0.9964 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 184/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0125 - accuracy: 0.9940 - val_loss: 0.0096 - val_accuracy: 0.9952\n", "Epoch 185/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0102 - accuracy: 0.9964 - val_loss: 0.0096 - val_accuracy: 0.9952\n", "Epoch 186/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0085 - accuracy: 0.9952 - val_loss: 0.0169 - val_accuracy: 0.9952\n", "Epoch 187/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0135 - accuracy: 0.9940 - val_loss: 0.0224 - val_accuracy: 0.9952\n", "Epoch 188/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0114 - accuracy: 0.9964 - val_loss: 0.0206 - val_accuracy: 0.9952\n", "Epoch 189/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0080 - accuracy: 0.9964 - val_loss: 0.0155 - val_accuracy: 0.9952\n", "Epoch 190/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0167 - accuracy: 0.9905 - val_loss: 0.0098 - val_accuracy: 0.9952\n", "Epoch 191/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0138 - accuracy: 0.9929 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 192/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0106 - accuracy: 0.9952 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 193/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0142 - accuracy: 0.9929 - val_loss: 0.0097 - val_accuracy: 0.9952\n", "Epoch 194/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0120 - accuracy: 0.9940 - val_loss: 0.0096 - val_accuracy: 0.9952\n", "Epoch 195/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0148 - accuracy: 0.9940 - val_loss: 0.0096 - val_accuracy: 0.9952\n", "Epoch 196/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0110 - accuracy: 0.9940 - val_loss: 0.0096 - val_accuracy: 0.9952\n", "Epoch 197/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0099 - accuracy: 0.9952 - val_loss: 0.0113 - val_accuracy: 0.9952\n", "Epoch 198/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0123 - accuracy: 0.9940 - val_loss: 0.0314 - val_accuracy: 0.9857\n", "Epoch 199/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0142 - accuracy: 0.9940 - val_loss: 0.0303 - val_accuracy: 0.9857\n", "Epoch 200/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0149 - accuracy: 0.9929 - val_loss: 0.0119 - val_accuracy: 0.9952\n", "Epoch 201/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0142 - accuracy: 0.9940 - val_loss: 0.0097 - val_accuracy: 0.9952\n", "Epoch 202/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0121 - accuracy: 0.9940 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 203/250\n", "7/7 [==============================] - 0s 17ms/step - loss: 0.0119 - accuracy: 0.9940 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 204/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0118 - accuracy: 0.9929 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 205/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0142 - accuracy: 0.9929 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 206/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0108 - accuracy: 0.9940 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 207/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0127 - accuracy: 0.9940 - val_loss: 0.0098 - val_accuracy: 0.9952\n", "Epoch 208/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0122 - accuracy: 0.9952 - val_loss: 0.0096 - val_accuracy: 0.9952\n", "Epoch 209/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0094 - accuracy: 0.9940 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 210/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0087 - accuracy: 0.9976 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 211/250\n", "7/7 [==============================] - 0s 17ms/step - loss: 0.0159 - accuracy: 0.9917 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 212/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0114 - accuracy: 0.9940 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 213/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0142 - accuracy: 0.9929 - val_loss: 0.0096 - val_accuracy: 0.9952\n", "Epoch 214/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0119 - accuracy: 0.9940 - val_loss: 0.0097 - val_accuracy: 0.9952\n", "Epoch 215/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0109 - accuracy: 0.9952 - val_loss: 0.0096 - val_accuracy: 0.9952\n", "Epoch 216/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0139 - accuracy: 0.9929 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 217/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0127 - accuracy: 0.9952 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 218/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0093 - accuracy: 0.9952 - val_loss: 0.0096 - val_accuracy: 0.9952\n", "Epoch 219/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0120 - accuracy: 0.9940 - val_loss: 0.0099 - val_accuracy: 0.9952\n", "Epoch 220/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0152 - accuracy: 0.9905 - val_loss: 0.0104 - val_accuracy: 0.9952\n", "Epoch 221/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0116 - accuracy: 0.9952 - val_loss: 0.0122 - val_accuracy: 0.9952\n", "Epoch 222/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0119 - accuracy: 0.9940 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 223/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0161 - accuracy: 0.9905 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 224/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0154 - accuracy: 0.9917 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 225/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0124 - accuracy: 0.9952 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 226/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0144 - accuracy: 0.9929 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 227/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0135 - accuracy: 0.9929 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 228/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0143 - accuracy: 0.9929 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 229/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0137 - accuracy: 0.9929 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 230/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0166 - accuracy: 0.9917 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 231/250\n", "7/7 [==============================] - 0s 43ms/step - loss: 0.0130 - accuracy: 0.9940 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 232/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0111 - accuracy: 0.9952 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 233/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0087 - accuracy: 0.9976 - val_loss: 0.0100 - val_accuracy: 0.9952\n", "Epoch 234/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0165 - accuracy: 0.9917 - val_loss: 0.0120 - val_accuracy: 0.9952\n", "Epoch 235/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0108 - accuracy: 0.9929 - val_loss: 0.0113 - val_accuracy: 0.9952\n", "Epoch 236/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0162 - accuracy: 0.9917 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 237/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0081 - accuracy: 0.9964 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 238/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0102 - accuracy: 0.9940 - val_loss: 0.0096 - val_accuracy: 0.9952\n", "Epoch 239/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0142 - accuracy: 0.9917 - val_loss: 0.0096 - val_accuracy: 0.9952\n", "Epoch 240/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0104 - accuracy: 0.9940 - val_loss: 0.0153 - val_accuracy: 0.9952\n", "Epoch 241/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0117 - accuracy: 0.9952 - val_loss: 0.0222 - val_accuracy: 0.9952\n", "Epoch 242/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0124 - accuracy: 0.9952 - val_loss: 0.0212 - val_accuracy: 0.9952\n", "Epoch 243/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0131 - accuracy: 0.9940 - val_loss: 0.0156 - val_accuracy: 0.9952\n", "Epoch 244/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0134 - accuracy: 0.9952 - val_loss: 0.0155 - val_accuracy: 0.9952\n", "Epoch 245/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0119 - accuracy: 0.9952 - val_loss: 0.0099 - val_accuracy: 0.9952\n", "Epoch 246/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0119 - accuracy: 0.9940 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 247/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0109 - accuracy: 0.9952 - val_loss: 0.0104 - val_accuracy: 0.9952\n", "Epoch 248/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0108 - accuracy: 0.9952 - val_loss: 0.0098 - val_accuracy: 0.9952\n", "Epoch 249/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0174 - accuracy: 0.9917 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 250/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0131 - accuracy: 0.9940 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Valid results - Loss: 0.009524201974272728 - Accuracy: 99.52380657196045%\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", 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\n", 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\n", 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" ] }, "metadata": { "tags": [], "needs_background": "light" } }, { "output_type": "stream", "text": [ "DNN results\n", "accuracy : 0.9940476190476191\n", "Training Recall: 0.9940476190476191\n", "Training Precision: 0.9941113490364025\n", "Confusion matrix: \n", "[[373 5]\n", " [ 0 462]]\n", "Validation set report:\n", "cross Loss: -34.20983565194266\n", "hing Loss: 0.44761904761904764\n", "Report precision recall f1-score support\n", "\n", " 0 0.99 1.00 0.99 93\n", " 1 1.00 0.99 1.00 117\n", "\n", " accuracy 1.00 210\n", " macro avg 0.99 1.00 1.00 210\n", "weighted avg 1.00 1.00 1.00 210\n", "\n", "accuracy : 0.9952380952380953\n", "Validation Recall: 0.9952380952380953\n", "Validation Precision: 0.9952887537993921\n", "Confusion matrix: \n", "[[ 93 0]\n", " [ 1 116]]\n", "\n", "\\KNN Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 0.9952380952380953\n", "Validation Recall: 0.9952380952380953\n", "Validation Precision: 0.9952887537993921\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[ 93 0]\n", " [ 1 116]]\n", "\n", "\n", "Support vector machine Result\n", "\n", "Train accuracy: 0.9940476190476191\n", "Training Recall: 0.9940476190476191\n", "Training Precision: 0.9941113490364025\n", "\n", "\n", "Validation accuracy: 0.9952380952380953\n", "Validation Recall: 0.9952380952380953\n", "Validation Precision: 0.9952887537993921\n", "\n", "Train Confusion matrix:\n", " [[373 5]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[ 93 0]\n", " [ 1 116]]\n", "\n", "\n", "Random Forest Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 0.9952380952380953\n", "Validation Recall: 0.9952380952380953\n", "Validation Precision: 0.9952887537993921\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[ 93 0]\n", " [ 1 116]]\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "fRmyUUzhIlyc", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "01a517f6-ca3c-46eb-e749-c2a127207656" }, "source": [ "\"\"\"\n", "LDA before split\n", "hing\n", " \n", "cross Loss: 0.25\n", "X = np.load('gdrive/My Drive/Python files/neuromarketing 25/features_psd_ALL.npy') # 2725\n", "\n", "Standard\n", "\n", " batch_size = 128\n", " nb_epoch = 250 #3000\n", " l1_decay=0.00\n", " l2_decay=0.000 # .5\n", " # 0.01 0.06\n", " sigma=0.005\n", " in_drop_rate = .5\n", " drop_rate = .5#.05\n", " lr1=0.0001\n", "\n", "\"\"\"\n", "train_models(X, y, y_cat)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Before\n", "(1050, 2725)\n", "After\n", "(1050, 1)\n", "(840, 2)\n", "(210, 2)\n", "Epoch 1/250\n", "7/7 [==============================] - 1s 83ms/step - loss: 0.4026 - accuracy: 0.8583 - val_loss: 0.0101 - val_accuracy: 0.9952\n", "Epoch 2/250\n", "7/7 [==============================] - 3s 389ms/step - loss: 0.0219 - accuracy: 0.9988 - val_loss: 0.0096 - val_accuracy: 0.9952\n", "Epoch 3/250\n", "7/7 [==============================] - 3s 387ms/step - loss: 0.0122 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 4/250\n", "7/7 [==============================] - 1s 117ms/step - loss: 0.0119 - accuracy: 0.9964 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 5/250\n", "7/7 [==============================] - 3s 392ms/step - loss: 0.0089 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 6/250\n", "7/7 [==============================] - 0s 52ms/step - loss: 0.0058 - accuracy: 1.0000 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 7/250\n", "7/7 [==============================] - 2s 254ms/step - loss: 0.0056 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 8/250\n", "7/7 [==============================] - 0s 19ms/step - loss: 0.0102 - accuracy: 0.9952 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 9/250\n", "7/7 [==============================] - 0s 18ms/step - loss: 0.0066 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 10/250\n", "7/7 [==============================] - 0s 19ms/step - loss: 0.0098 - accuracy: 0.9952 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 11/250\n", "7/7 [==============================] - 0s 17ms/step - loss: 0.0066 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 12/250\n", "7/7 [==============================] - 0s 18ms/step - loss: 0.0053 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 13/250\n", "7/7 [==============================] - 0s 17ms/step - loss: 0.0073 - accuracy: 0.9964 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 14/250\n", "7/7 [==============================] - 0s 17ms/step - loss: 0.0093 - accuracy: 0.9952 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 15/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0047 - accuracy: 0.9976 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 16/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0078 - accuracy: 0.9964 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 17/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0055 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 18/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0084 - accuracy: 0.9976 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 19/250\n", "7/7 [==============================] - 0s 18ms/step - loss: 0.0082 - accuracy: 0.9976 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 20/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0068 - accuracy: 0.9976 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 21/250\n", "7/7 [==============================] - 0s 17ms/step - loss: 0.0086 - accuracy: 0.9976 - val_loss: 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0.0095 - val_accuracy: 0.9952\n", "Epoch 101/250\n", "7/7 [==============================] - 0s 17ms/step - loss: 0.0049 - accuracy: 0.9976 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 102/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0059 - accuracy: 0.9976 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 103/250\n", "7/7 [==============================] - 1s 88ms/step - loss: 0.0073 - accuracy: 0.9976 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 104/250\n", "7/7 [==============================] - 0s 60ms/step - loss: 0.0035 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 105/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 106/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0048 - accuracy: 0.9976 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 107/250\n", "7/7 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val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 114/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0067 - accuracy: 0.9964 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 115/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0043 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 116/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0030 - accuracy: 0.9976 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 117/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0026 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 118/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0046 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 119/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0032 - accuracy: 0.9976 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 120/250\n", "7/7 [==============================] - 0s 17ms/step - loss: 0.0052 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 121/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0024 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 122/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0046 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 123/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0040 - accuracy: 0.9976 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 124/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 125/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0052 - accuracy: 0.9964 - val_loss: 0.0097 - val_accuracy: 0.9952\n", "Epoch 126/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0070 - accuracy: 0.9964 - val_loss: 0.0098 - val_accuracy: 0.9952\n", "Epoch 127/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0051 - accuracy: 0.9964 - val_loss: 0.0096 - val_accuracy: 0.9952\n", "Epoch 128/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0072 - accuracy: 0.9964 - val_loss: 0.0096 - val_accuracy: 0.9952\n", "Epoch 129/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0055 - accuracy: 0.9964 - val_loss: 0.0096 - val_accuracy: 0.9952\n", "Epoch 130/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0069 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 131/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 132/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0058 - accuracy: 1.0000 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 133/250\n", "7/7 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[==============================] - 0s 16ms/step - loss: 0.0033 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 173/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 3.6962e-04 - accuracy: 1.0000 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 174/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0059 - accuracy: 0.9976 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 175/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0032 - accuracy: 0.9976 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 176/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0054 - accuracy: 0.9976 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 177/250\n", "7/7 [==============================] - 0s 64ms/step - loss: 0.0047 - accuracy: 0.9976 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 178/250\n", "7/7 [==============================] - 0s 45ms/step - loss: 0.0039 - accuracy: 0.9976 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 179/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0039 - accuracy: 0.9976 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 180/250\n", "7/7 [==============================] - 0s 20ms/step - loss: 0.0073 - accuracy: 0.9952 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 181/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0027 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 182/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0045 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 183/250\n", "7/7 [==============================] - 1s 167ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 184/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0042 - accuracy: 0.9976 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 185/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0074 - accuracy: 0.9964 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 186/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0040 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 187/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 188/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0045 - accuracy: 0.9976 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 189/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0030 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 190/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0019 - accuracy: 1.0000 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 191/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0051 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 192/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0031 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 193/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 5.3723e-04 - accuracy: 1.0000 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 194/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0032 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 195/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0031 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 196/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0037 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 197/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 198/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0038 - accuracy: 0.9976 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 199/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0029 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 200/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0042 - accuracy: 0.9976 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 201/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0050 - accuracy: 0.9976 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 202/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0015 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 203/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0041 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 204/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0059 - accuracy: 0.9964 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 205/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0063 - accuracy: 0.9976 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 206/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0016 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 207/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0060 - accuracy: 0.9976 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 208/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0013 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 209/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0034 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 210/250\n", "7/7 [==============================] - 0s 17ms/step - loss: 0.0022 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 211/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0026 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 212/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0027 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 213/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0051 - accuracy: 0.9976 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 214/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0025 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 215/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0055 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 216/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0053 - accuracy: 0.9976 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 217/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 218/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0022 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 219/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0019 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 220/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0019 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 221/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0024 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 222/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0057 - accuracy: 0.9976 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 223/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0043 - accuracy: 0.9976 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 224/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 3.2912e-04 - accuracy: 1.0000 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 225/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0058 - accuracy: 0.9976 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 226/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0060 - accuracy: 0.9976 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 227/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0071 - accuracy: 0.9964 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 228/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0033 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 229/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 230/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0022 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 231/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0018 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 232/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0045 - accuracy: 0.9976 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 233/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0063 - accuracy: 0.9976 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 234/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0018 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 235/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0015 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 236/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 237/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0040 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 238/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0025 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 239/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 240/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0033 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 241/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0060 - accuracy: 0.9976 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 242/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0047 - accuracy: 0.9976 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 243/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0023 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 244/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0052 - accuracy: 0.9976 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 245/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 1.2766e-06 - accuracy: 1.0000 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 246/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 247/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0070 - accuracy: 0.9964 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 248/250\n", "7/7 [==============================] - 0s 17ms/step - loss: 0.0042 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 249/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0044 - accuracy: 0.9976 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Epoch 250/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0035 - accuracy: 0.9988 - val_loss: 0.0095 - val_accuracy: 0.9952\n", "Valid results - Loss: 0.009523862972855568 - Accuracy: 99.52380657196045%\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "tags": [], "needs_background": "light" } }, { "output_type": "stream", "text": [ "DNN results\n", "accuracy : 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "Confusion matrix: \n", "[[378 0]\n", " [ 0 462]]\n", "Validation set report:\n", "cross Loss: -34.209835670107886\n", "hing Loss: 0.44761904761904764\n", "Report precision recall f1-score support\n", "\n", " 0 0.99 1.00 0.99 93\n", " 1 1.00 0.99 1.00 117\n", "\n", " accuracy 1.00 210\n", " macro avg 0.99 1.00 1.00 210\n", "weighted avg 1.00 1.00 1.00 210\n", "\n", "accuracy : 0.9952380952380953\n", "Validation Recall: 0.9952380952380953\n", "Validation Precision: 0.9952887537993921\n", "Confusion matrix: \n", "[[ 93 0]\n", " [ 1 116]]\n", "\n", "\\KNN Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 0.9952380952380953\n", "Validation Recall: 0.9952380952380953\n", "Validation Precision: 0.9952887537993921\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[ 93 0]\n", " [ 1 116]]\n", "\n", "\n", "Support vector machine Result\n", "\n", "Train accuracy: 0.9988095238095238\n", "Training Recall: 0.9988095238095238\n", "Training Precision: 0.9988120950323973\n", "\n", "\n", "Validation accuracy: 0.9952380952380953\n", "Validation Recall: 0.9952380952380953\n", "Validation Precision: 0.9952887537993921\n", "\n", "Train Confusion matrix:\n", " [[377 1]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[ 93 0]\n", " [ 1 116]]\n", "\n", "\n", "Random Forest Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 0.9952380952380953\n", "Validation Recall: 0.9952380952380953\n", "Validation Precision: 0.9952887537993921\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[ 93 0]\n", " [ 1 116]]\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "cL3hvkfjhQc_" }, "source": [ "### PCA" ] }, { "cell_type": "code", "metadata": { "id": "6C4VRFEx-w4B", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "c2439452-70c1-43c4-d493-216a574d5316" }, "source": [ "\"\"\"\n", "PCA with LDa classifier\n", "hing\n", " \n", "cross Loss: 0.25\n", "X = np.load('gdrive/My Drive/Python files/neuromarketing 25/features_psd_ALL.npy') # 2725\n", "\n", "Standard\n", "\n", " batch_size = 128\n", " nb_epoch = 250 #3000\n", " l1_decay=0.00\n", " l2_decay=0.000 # .5\n", " # 0.01 0.06\n", " sigma=0.005\n", " in_drop_rate = .5\n", " drop_rate = .5#.05\n", " lr1=0.0001\n", "\n", "\"\"\"\n", "train_models(X, y, y_cat)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Before selectoin\n", "(1050, 2725)\n", "After selectoin\n", "(1050, 1050)\n", "(210, 1050)\n", "Epoch 1/250\n", "7/7 [==============================] - 0s 68ms/step - loss: 0.9916 - accuracy: 0.5179 - val_loss: 0.9783 - val_accuracy: 0.4667\n", "Epoch 2/250\n", "7/7 [==============================] - 1s 111ms/step - loss: 0.9123 - accuracy: 0.5571 - val_loss: 0.8630 - val_accuracy: 0.6476\n", "Epoch 3/250\n", "7/7 [==============================] - 0s 59ms/step - loss: 0.8017 - accuracy: 0.6536 - val_loss: 0.7797 - val_accuracy: 0.7238\n", "Epoch 4/250\n", "7/7 [==============================] - 0s 50ms/step - loss: 0.7643 - accuracy: 0.6881 - val_loss: 0.7124 - val_accuracy: 0.7333\n", "Epoch 5/250\n", "7/7 [==============================] - 0s 55ms/step - loss: 0.7186 - accuracy: 0.6952 - val_loss: 0.6477 - val_accuracy: 0.7667\n", "Epoch 6/250\n", "7/7 [==============================] - 0s 64ms/step - loss: 0.6439 - accuracy: 0.7262 - val_loss: 0.5918 - val_accuracy: 0.7857\n", "Epoch 7/250\n", "7/7 [==============================] - 1s 137ms/step - loss: 0.6112 - accuracy: 0.7357 - val_loss: 0.5426 - val_accuracy: 0.8095\n", "Epoch 8/250\n", "7/7 [==============================] - 2s 217ms/step - loss: 0.5380 - accuracy: 0.7702 - val_loss: 0.4946 - val_accuracy: 0.8286\n", "Epoch 9/250\n", "7/7 [==============================] - 0s 61ms/step - loss: 0.5257 - accuracy: 0.7762 - val_loss: 0.4545 - val_accuracy: 0.8429\n", "Epoch 10/250\n", "7/7 [==============================] - 0s 51ms/step - loss: 0.5157 - accuracy: 0.7940 - val_loss: 0.4201 - val_accuracy: 0.8381\n", "Epoch 11/250\n", "7/7 [==============================] - 0s 51ms/step - loss: 0.4794 - accuracy: 0.7821 - val_loss: 0.3867 - val_accuracy: 0.8476\n", "Epoch 12/250\n", "7/7 [==============================] - 0s 56ms/step - loss: 0.4147 - accuracy: 0.8274 - val_loss: 0.3620 - val_accuracy: 0.8524\n", "Epoch 13/250\n", "7/7 [==============================] - 1s 86ms/step - loss: 0.3804 - accuracy: 0.8417 - val_loss: 0.3416 - val_accuracy: 0.8667\n", "Epoch 14/250\n", "7/7 [==============================] - 6s 879ms/step - loss: 0.3964 - accuracy: 0.8262 - val_loss: 0.3253 - val_accuracy: 0.8667\n", "Epoch 15/250\n", "7/7 [==============================] - 3s 431ms/step - loss: 0.3590 - accuracy: 0.8524 - val_loss: 0.3192 - val_accuracy: 0.8667\n", "Epoch 16/250\n", "7/7 [==============================] - 1s 214ms/step - loss: 0.3521 - accuracy: 0.8607 - val_loss: 0.3131 - val_accuracy: 0.8619\n", "Epoch 17/250\n", "7/7 [==============================] - 2s 283ms/step - loss: 0.3361 - accuracy: 0.8500 - val_loss: 0.3018 - val_accuracy: 0.8667\n", "Epoch 18/250\n", "7/7 [==============================] - 6s 824ms/step - loss: 0.3305 - accuracy: 0.8595 - val_loss: 0.2833 - val_accuracy: 0.8762\n", "Epoch 19/250\n", "7/7 [==============================] - 2s 214ms/step - loss: 0.2996 - accuracy: 0.8738 - val_loss: 0.2769 - val_accuracy: 0.8714\n", "Epoch 20/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.2838 - accuracy: 0.8774 - val_loss: 0.2770 - val_accuracy: 0.8714\n", "Epoch 21/250\n", "7/7 [==============================] - 2s 244ms/step - loss: 0.2725 - accuracy: 0.8857 - val_loss: 0.2733 - val_accuracy: 0.8667\n", "Epoch 22/250\n", "7/7 [==============================] - 2s 271ms/step - loss: 0.2628 - accuracy: 0.8929 - val_loss: 0.2632 - val_accuracy: 0.8762\n", "Epoch 23/250\n", "7/7 [==============================] - 7s 952ms/step - loss: 0.2168 - accuracy: 0.9155 - val_loss: 0.2618 - val_accuracy: 0.8762\n", "Epoch 24/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.2282 - accuracy: 0.9024 - val_loss: 0.2651 - val_accuracy: 0.8714\n", "Epoch 25/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.2484 - accuracy: 0.8952 - val_loss: 0.2621 - val_accuracy: 0.8714\n", "Epoch 26/250\n", "7/7 [==============================] - 1s 179ms/step - loss: 0.2314 - accuracy: 0.9071 - val_loss: 0.2554 - val_accuracy: 0.8762\n", "Epoch 27/250\n", "7/7 [==============================] - 5s 656ms/step - loss: 0.2040 - accuracy: 0.9214 - val_loss: 0.2512 - val_accuracy: 0.8714\n", "Epoch 28/250\n", "7/7 [==============================] - 2s 344ms/step - loss: 0.1944 - accuracy: 0.9238 - val_loss: 0.2419 - val_accuracy: 0.8810\n", "Epoch 29/250\n", "7/7 [==============================] - 1s 91ms/step - loss: 0.1996 - accuracy: 0.9202 - val_loss: 0.2333 - val_accuracy: 0.8905\n", "Epoch 30/250\n", "7/7 [==============================] - 3s 488ms/step - loss: 0.1686 - accuracy: 0.9357 - val_loss: 0.2323 - val_accuracy: 0.8905\n", "Epoch 31/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.2086 - accuracy: 0.9107 - val_loss: 0.2384 - val_accuracy: 0.8905\n", "Epoch 32/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.2065 - accuracy: 0.9095 - val_loss: 0.2464 - val_accuracy: 0.8857\n", "Epoch 33/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.1796 - accuracy: 0.9286 - val_loss: 0.2436 - val_accuracy: 0.8905\n", "Epoch 34/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 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val_loss: 0.2146 - val_accuracy: 0.9048\n", "Epoch 48/250\n", "7/7 [==============================] - 5s 715ms/step - loss: 0.1123 - accuracy: 0.9464 - val_loss: 0.2130 - val_accuracy: 0.9048\n", "Epoch 49/250\n", "7/7 [==============================] - 2s 315ms/step - loss: 0.0814 - accuracy: 0.9702 - val_loss: 0.2110 - val_accuracy: 0.9048\n", "Epoch 50/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.1060 - accuracy: 0.9548 - val_loss: 0.2119 - val_accuracy: 0.9000\n", "Epoch 51/250\n", "7/7 [==============================] - 6s 895ms/step - loss: 0.0930 - accuracy: 0.9655 - val_loss: 0.2088 - val_accuracy: 0.9095\n", "Epoch 52/250\n", "7/7 [==============================] - 1s 207ms/step - loss: 0.0894 - accuracy: 0.9655 - val_loss: 0.2080 - val_accuracy: 0.9095\n", "Epoch 53/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0884 - accuracy: 0.9667 - val_loss: 0.2113 - val_accuracy: 0.9095\n", "Epoch 54/250\n", "7/7 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0.2057 - val_accuracy: 0.9048\n", "Epoch 61/250\n", "7/7 [==============================] - 5s 722ms/step - loss: 0.0560 - accuracy: 0.9810 - val_loss: 0.2021 - val_accuracy: 0.9095\n", "Epoch 62/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0666 - accuracy: 0.9762 - val_loss: 0.2032 - val_accuracy: 0.9095\n", "Epoch 63/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0692 - accuracy: 0.9702 - val_loss: 0.2067 - val_accuracy: 0.9048\n", "Epoch 64/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0697 - accuracy: 0.9726 - val_loss: 0.2079 - val_accuracy: 0.9048\n", "Epoch 65/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0767 - accuracy: 0.9667 - val_loss: 0.2054 - val_accuracy: 0.9095\n", "Epoch 66/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0612 - accuracy: 0.9774 - val_loss: 0.2072 - val_accuracy: 0.9095\n", "Epoch 67/250\n", "7/7 [==============================] - 0s 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0.2253 - val_accuracy: 0.8857\n", "Epoch 94/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0509 - accuracy: 0.9786 - val_loss: 0.2290 - val_accuracy: 0.8857\n", "Epoch 95/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0336 - accuracy: 0.9857 - val_loss: 0.2259 - val_accuracy: 0.8857\n", "Epoch 96/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0296 - accuracy: 0.9881 - val_loss: 0.2246 - val_accuracy: 0.8905\n", "Epoch 97/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0270 - accuracy: 0.9881 - val_loss: 0.2208 - val_accuracy: 0.8952\n", "Epoch 98/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0300 - accuracy: 0.9881 - val_loss: 0.2202 - val_accuracy: 0.8905\n", "Epoch 99/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0337 - accuracy: 0.9881 - val_loss: 0.2114 - val_accuracy: 0.9000\n", "Epoch 100/250\n", "7/7 [==============================] - 0s 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"Epoch 107/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0224 - accuracy: 0.9929 - val_loss: 0.2138 - val_accuracy: 0.8952\n", "Epoch 108/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0263 - accuracy: 0.9905 - val_loss: 0.2116 - val_accuracy: 0.8952\n", "Epoch 109/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0309 - accuracy: 0.9857 - val_loss: 0.2126 - val_accuracy: 0.8952\n", "Epoch 110/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0220 - accuracy: 0.9929 - val_loss: 0.2087 - val_accuracy: 0.8952\n", "Epoch 111/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0257 - accuracy: 0.9893 - val_loss: 0.2064 - val_accuracy: 0.9048\n", "Epoch 112/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0289 - accuracy: 0.9893 - val_loss: 0.2064 - val_accuracy: 0.9048\n", "Epoch 113/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0265 - 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val_loss: 0.2011 - val_accuracy: 0.9000\n", "Epoch 127/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0206 - accuracy: 0.9940 - val_loss: 0.1994 - val_accuracy: 0.9048\n", "Epoch 128/250\n", "7/7 [==============================] - 0s 63ms/step - loss: 0.0208 - accuracy: 0.9929 - val_loss: 0.1968 - val_accuracy: 0.9048\n", "Epoch 129/250\n", "7/7 [==============================] - 2s 314ms/step - loss: 0.0263 - accuracy: 0.9917 - val_loss: 0.1959 - val_accuracy: 0.9048\n", "Epoch 130/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0142 - accuracy: 0.9976 - val_loss: 0.1971 - val_accuracy: 0.9000\n", "Epoch 131/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0154 - accuracy: 0.9976 - val_loss: 0.1975 - val_accuracy: 0.9048\n", "Epoch 132/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0152 - accuracy: 0.9940 - val_loss: 0.2034 - val_accuracy: 0.8905\n", "Epoch 133/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0108 - accuracy: 0.9952 - val_loss: 0.2126 - val_accuracy: 0.8905\n", "Epoch 134/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0162 - accuracy: 0.9929 - val_loss: 0.2210 - val_accuracy: 0.8905\n", "Epoch 135/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0190 - accuracy: 0.9940 - val_loss: 0.2197 - val_accuracy: 0.8857\n", "Epoch 136/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0154 - accuracy: 0.9940 - val_loss: 0.2056 - val_accuracy: 0.9000\n", "Epoch 137/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0136 - accuracy: 0.9952 - val_loss: 0.1967 - val_accuracy: 0.9000\n", "Epoch 138/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0127 - accuracy: 0.9952 - val_loss: 0.1972 - val_accuracy: 0.9000\n", "Epoch 139/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0140 - accuracy: 0.9940 - val_loss: 0.1988 - val_accuracy: 0.9000\n", "Epoch 140/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0123 - accuracy: 0.9940 - val_loss: 0.1980 - val_accuracy: 0.9000\n", "Epoch 141/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0097 - accuracy: 0.9976 - val_loss: 0.2004 - val_accuracy: 0.9000\n", "Epoch 142/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0156 - accuracy: 0.9929 - val_loss: 0.2026 - val_accuracy: 0.8952\n", "Epoch 143/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0173 - accuracy: 0.9952 - val_loss: 0.2057 - val_accuracy: 0.8905\n", "Epoch 144/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0121 - accuracy: 0.9940 - val_loss: 0.2047 - val_accuracy: 0.8952\n", "Epoch 145/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0187 - accuracy: 0.9929 - val_loss: 0.2058 - val_accuracy: 0.8905\n", "Epoch 146/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0139 - accuracy: 0.9964 - val_loss: 0.2088 - val_accuracy: 0.8905\n", "Epoch 147/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0098 - accuracy: 0.9964 - val_loss: 0.2097 - val_accuracy: 0.8952\n", "Epoch 148/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0231 - accuracy: 0.9929 - val_loss: 0.2131 - val_accuracy: 0.8952\n", "Epoch 149/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0107 - accuracy: 0.9964 - val_loss: 0.2177 - val_accuracy: 0.8952\n", "Epoch 150/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0145 - accuracy: 0.9929 - val_loss: 0.2260 - val_accuracy: 0.9000\n", "Epoch 151/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0169 - accuracy: 0.9929 - val_loss: 0.2254 - val_accuracy: 0.8952\n", "Epoch 152/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0117 - accuracy: 0.9940 - val_loss: 0.2183 - val_accuracy: 0.8952\n", "Epoch 153/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0195 - accuracy: 0.9940 - val_loss: 0.2191 - val_accuracy: 0.8905\n", "Epoch 154/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0113 - accuracy: 0.9952 - val_loss: 0.2154 - val_accuracy: 0.8905\n", "Epoch 155/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0144 - accuracy: 0.9940 - val_loss: 0.2108 - val_accuracy: 0.8905\n", "Epoch 156/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0053 - accuracy: 0.9976 - val_loss: 0.2122 - val_accuracy: 0.8905\n", "Epoch 157/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0091 - accuracy: 0.9964 - val_loss: 0.2063 - val_accuracy: 0.8952\n", "Epoch 158/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0049 - accuracy: 0.9988 - val_loss: 0.2040 - val_accuracy: 0.8952\n", "Epoch 159/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0105 - accuracy: 0.9952 - val_loss: 0.2037 - val_accuracy: 0.9000\n", "Epoch 160/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0186 - accuracy: 0.9929 - val_loss: 0.2028 - val_accuracy: 0.9048\n", "Epoch 161/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0109 - accuracy: 0.9940 - val_loss: 0.2029 - val_accuracy: 0.9048\n", "Epoch 162/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0115 - accuracy: 0.9952 - val_loss: 0.2014 - val_accuracy: 0.9048\n", "Epoch 163/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0062 - accuracy: 0.9988 - val_loss: 0.1972 - val_accuracy: 0.9048\n", "Epoch 164/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0094 - accuracy: 0.9964 - val_loss: 0.1969 - val_accuracy: 0.9048\n", "Epoch 165/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0152 - accuracy: 0.9940 - val_loss: 0.1971 - val_accuracy: 0.9048\n", "Epoch 166/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0146 - accuracy: 0.9964 - val_loss: 0.2007 - val_accuracy: 0.9000\n", "Epoch 167/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0055 - accuracy: 0.9988 - val_loss: 0.2043 - val_accuracy: 0.8952\n", "Epoch 168/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0100 - accuracy: 0.9952 - val_loss: 0.2055 - val_accuracy: 0.8952\n", "Epoch 169/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0111 - accuracy: 0.9952 - val_loss: 0.2063 - val_accuracy: 0.8952\n", "Epoch 170/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0082 - accuracy: 0.9976 - val_loss: 0.2063 - val_accuracy: 0.8905\n", "Epoch 171/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0159 - accuracy: 0.9952 - val_loss: 0.2062 - val_accuracy: 0.8905\n", "Epoch 172/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0077 - accuracy: 0.9976 - val_loss: 0.2038 - val_accuracy: 0.9000\n", "Epoch 173/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0105 - accuracy: 0.9964 - val_loss: 0.2013 - val_accuracy: 0.9095\n", "Epoch 174/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0070 - accuracy: 0.9964 - val_loss: 0.1999 - val_accuracy: 0.9143\n", "Epoch 175/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0105 - accuracy: 0.9964 - val_loss: 0.2069 - val_accuracy: 0.8905\n", "Epoch 176/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0066 - accuracy: 0.9976 - val_loss: 0.2133 - val_accuracy: 0.8857\n", "Epoch 177/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0044 - accuracy: 0.9988 - val_loss: 0.2174 - val_accuracy: 0.8857\n", "Epoch 178/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0060 - accuracy: 0.9976 - val_loss: 0.2163 - val_accuracy: 0.8857\n", "Epoch 179/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0066 - accuracy: 0.9976 - val_loss: 0.2078 - val_accuracy: 0.8952\n", "Epoch 180/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0117 - accuracy: 0.9952 - val_loss: 0.2048 - val_accuracy: 0.9000\n", "Epoch 181/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0041 - accuracy: 0.9988 - val_loss: 0.2072 - val_accuracy: 0.8952\n", "Epoch 182/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0044 - accuracy: 0.9988 - val_loss: 0.2080 - val_accuracy: 0.8952\n", "Epoch 183/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0074 - accuracy: 0.9976 - val_loss: 0.2100 - val_accuracy: 0.8905\n", "Epoch 184/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0035 - accuracy: 0.9988 - val_loss: 0.2098 - val_accuracy: 0.8905\n", "Epoch 185/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0049 - accuracy: 0.9988 - val_loss: 0.2101 - val_accuracy: 0.9000\n", "Epoch 186/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0071 - accuracy: 0.9988 - val_loss: 0.2174 - val_accuracy: 0.8952\n", "Epoch 187/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0057 - accuracy: 0.9976 - val_loss: 0.2167 - val_accuracy: 0.8952\n", "Epoch 188/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0152 - accuracy: 0.9952 - val_loss: 0.2097 - val_accuracy: 0.8952\n", "Epoch 189/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0094 - accuracy: 0.9964 - val_loss: 0.2071 - val_accuracy: 0.9000\n", "Epoch 190/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0047 - accuracy: 1.0000 - val_loss: 0.2062 - val_accuracy: 0.9000\n", "Epoch 191/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0098 - accuracy: 0.9976 - val_loss: 0.2064 - val_accuracy: 0.8952\n", "Epoch 192/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.2072 - val_accuracy: 0.9000\n", "Epoch 193/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0046 - accuracy: 0.9988 - val_loss: 0.2072 - val_accuracy: 0.9000\n", "Epoch 194/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0107 - accuracy: 0.9952 - val_loss: 0.2084 - val_accuracy: 0.9000\n", "Epoch 195/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0068 - accuracy: 0.9976 - val_loss: 0.2100 - val_accuracy: 0.8952\n", "Epoch 196/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0059 - accuracy: 0.9988 - val_loss: 0.2118 - val_accuracy: 0.8952\n", "Epoch 197/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0036 - accuracy: 0.9988 - val_loss: 0.2145 - val_accuracy: 0.8952\n", "Epoch 198/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0106 - accuracy: 0.9952 - val_loss: 0.2162 - val_accuracy: 0.8905\n", "Epoch 199/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0056 - accuracy: 0.9976 - val_loss: 0.2177 - val_accuracy: 0.8905\n", "Epoch 200/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0074 - accuracy: 0.9988 - val_loss: 0.2159 - val_accuracy: 0.8857\n", "Epoch 201/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0029 - accuracy: 1.0000 - val_loss: 0.2126 - val_accuracy: 0.8857\n", "Epoch 202/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0038 - accuracy: 1.0000 - val_loss: 0.2098 - val_accuracy: 0.8905\n", "Epoch 203/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0118 - accuracy: 0.9952 - val_loss: 0.2094 - val_accuracy: 0.8857\n", "Epoch 204/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0028 - accuracy: 1.0000 - val_loss: 0.2102 - val_accuracy: 0.8857\n", "Epoch 205/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0080 - accuracy: 0.9976 - val_loss: 0.2084 - val_accuracy: 0.8952\n", "Epoch 206/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0046 - accuracy: 0.9988 - val_loss: 0.2094 - val_accuracy: 0.9048\n", "Epoch 207/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0136 - accuracy: 0.9940 - val_loss: 0.2047 - val_accuracy: 0.9143\n", "Epoch 208/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0058 - accuracy: 0.9988 - val_loss: 0.2032 - val_accuracy: 0.9095\n", "Epoch 209/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.2033 - val_accuracy: 0.9048\n", "Epoch 210/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0095 - accuracy: 0.9952 - val_loss: 0.1982 - val_accuracy: 0.9048\n", "Epoch 211/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0044 - accuracy: 0.9976 - val_loss: 0.1981 - val_accuracy: 0.9048\n", "Epoch 212/250\n", "7/7 [==============================] - 0s 44ms/step - loss: 0.0103 - accuracy: 0.9964 - val_loss: 0.1949 - val_accuracy: 0.9000\n", "Epoch 213/250\n", "7/7 [==============================] - 2s 307ms/step - loss: 0.0023 - accuracy: 0.9988 - val_loss: 0.1918 - val_accuracy: 0.9048\n", "Epoch 214/250\n", "7/7 [==============================] - 0s 62ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.1892 - val_accuracy: 0.9048\n", "Epoch 215/250\n", "7/7 [==============================] - 3s 368ms/step - loss: 0.0016 - accuracy: 0.9988 - val_loss: 0.1882 - val_accuracy: 0.9095\n", "Epoch 216/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0066 - accuracy: 0.9976 - val_loss: 0.1890 - val_accuracy: 0.9143\n", "Epoch 217/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0070 - accuracy: 0.9976 - val_loss: 0.1916 - val_accuracy: 0.9143\n", "Epoch 218/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0037 - accuracy: 0.9988 - val_loss: 0.1912 - val_accuracy: 0.9000\n", "Epoch 219/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0024 - accuracy: 0.9988 - val_loss: 0.1926 - val_accuracy: 0.9000\n", "Epoch 220/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0052 - accuracy: 0.9976 - val_loss: 0.1946 - val_accuracy: 0.9000\n", "Epoch 221/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0032 - accuracy: 0.9976 - val_loss: 0.1975 - val_accuracy: 0.8905\n", "Epoch 222/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0047 - accuracy: 0.9976 - val_loss: 0.1954 - val_accuracy: 0.8952\n", "Epoch 223/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0082 - accuracy: 0.9976 - val_loss: 0.1959 - val_accuracy: 0.9048\n", "Epoch 224/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0026 - accuracy: 1.0000 - val_loss: 0.1984 - val_accuracy: 0.9048\n", "Epoch 225/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0053 - accuracy: 1.0000 - val_loss: 0.1979 - val_accuracy: 0.9095\n", "Epoch 226/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 7.6574e-04 - accuracy: 1.0000 - val_loss: 0.1945 - val_accuracy: 0.9000\n", "Epoch 227/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 3.7371e-04 - accuracy: 1.0000 - val_loss: 0.1941 - val_accuracy: 0.9000\n", "Epoch 228/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0082 - accuracy: 1.0000 - val_loss: 0.1932 - val_accuracy: 0.9048\n", "Epoch 229/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0028 - accuracy: 1.0000 - val_loss: 0.1914 - val_accuracy: 0.9095\n", "Epoch 230/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0039 - accuracy: 0.9988 - val_loss: 0.1916 - val_accuracy: 0.8952\n", "Epoch 231/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0025 - accuracy: 0.9988 - val_loss: 0.1929 - val_accuracy: 0.8952\n", "Epoch 232/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0028 - accuracy: 0.9988 - val_loss: 0.1908 - val_accuracy: 0.9000\n", "Epoch 233/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0058 - accuracy: 0.9988 - val_loss: 0.1899 - val_accuracy: 0.9143\n", "Epoch 234/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0048 - accuracy: 0.9988 - val_loss: 0.1927 - val_accuracy: 0.9000\n", "Epoch 235/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.1961 - val_accuracy: 0.9000\n", "Epoch 236/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0058 - accuracy: 0.9976 - val_loss: 0.1979 - val_accuracy: 0.8952\n", "Epoch 237/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 2.6624e-04 - accuracy: 1.0000 - val_loss: 0.1991 - val_accuracy: 0.9000\n", "Epoch 238/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0025 - accuracy: 1.0000 - val_loss: 0.2009 - val_accuracy: 0.8952\n", "Epoch 239/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0120 - accuracy: 0.9952 - val_loss: 0.2006 - val_accuracy: 0.9000\n", "Epoch 240/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.2017 - val_accuracy: 0.8952\n", "Epoch 241/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0037 - accuracy: 0.9988 - val_loss: 0.2018 - val_accuracy: 0.9000\n", "Epoch 242/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0083 - accuracy: 0.9964 - val_loss: 0.2009 - val_accuracy: 0.8952\n", "Epoch 243/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.2013 - val_accuracy: 0.9000\n", "Epoch 244/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.2028 - val_accuracy: 0.9000\n", "Epoch 245/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0090 - accuracy: 0.9988 - val_loss: 0.2046 - val_accuracy: 0.8952\n", "Epoch 246/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0057 - accuracy: 0.9976 - val_loss: 0.2025 - val_accuracy: 0.9000\n", "Epoch 247/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0034 - accuracy: 0.9988 - val_loss: 0.1994 - val_accuracy: 0.9000\n", "Epoch 248/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0033 - accuracy: 0.9976 - val_loss: 0.1949 - val_accuracy: 0.9000\n", "Epoch 249/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0036 - accuracy: 0.9988 - val_loss: 0.1918 - val_accuracy: 0.9095\n", "Epoch 250/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0051 - accuracy: 0.9988 - val_loss: 0.1934 - val_accuracy: 0.9048\n", "Valid results - Loss: 0.19337031245231628 - Accuracy: 90.47619104385376%\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", 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\n", 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\n", 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" ] }, "metadata": { "tags": [], "needs_background": "light" } }, { "output_type": "stream", "text": [ "DNN results\n", "accuracy : 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "Confusion matrix: \n", "[[378 0]\n", " [ 0 462]]\n", "Validation set report:\n", "cross Loss: -28.27431755065918\n", "hing Loss: 0.5333333333333333\n", "Report precision recall f1-score support\n", "\n", " 0 0.90 0.89 0.90 93\n", " 1 0.92 0.92 0.92 117\n", "\n", " accuracy 0.91 210\n", " macro avg 0.91 0.91 0.91 210\n", "weighted avg 0.91 0.91 0.91 210\n", "\n", "accuracy : 0.9095238095238095\n", "Validation Recall: 0.9095238095238095\n", "Validation Precision: 0.9094615222655017\n", "Confusion matrix: \n", "[[ 83 10]\n", " [ 9 108]]\n", "\n", "\\KNN Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 0.8095238095238095\n", "Validation Recall: 0.8095238095238095\n", "Validation Precision: 0.8160544217687075\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[79 14]\n", " [26 91]]\n", "\n", "\n", "Support vector machine Result\n", "\n", "Train accuracy: 0.861904761904762\n", "Training Recall: 0.861904761904762\n", "Training Precision: 0.8778697836459568\n", "\n", "\n", "Validation accuracy: 0.8142857142857143\n", "Validation Recall: 0.8142857142857143\n", "Validation Precision: 0.8356561888454012\n", "\n", "Train Confusion matrix:\n", " [[274 104]\n", " [ 12 450]]\n", "Validation Confusion matrix:\n", " [[ 59 34]\n", " [ 5 112]]\n", "\n", "\n", "Random Forest Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 0.8142857142857143\n", "Validation Recall: 0.8142857142857143\n", "Validation Precision: 0.8288584447863653\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[ 61 32]\n", " [ 7 110]]\n", "\n", "\n", "LDA Result\n", "\n", "Train accuracy: 0.5345238095238095\n", "Training Recall: 0.5345238095238095\n", "Training Precision: 0.42977989553012486\n", "\n", "\n", "Validation accuracy: 0.5523809523809524\n", "Validation Recall: 0.5523809523809524\n", "Validation Precision: 0.30922761449077235\n", "\n", "Train Confusion matrix:\n", " [[ 9 369]\n", " [ 22 440]]\n", "Validation Confusion matrix:\n", " [[ 0 93]\n", " [ 1 116]]\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "qt76gel7gh39", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "071e3455-5a3e-467d-9839-d9ecb5ed9b99" }, "source": [ "\"\"\"\n", "PCA \n", "hing\n", " \n", "cross Loss: 0.25\n", "X = np.load('gdrive/My Drive/Python files/neuromarketing 25/features_psd_ALL.npy') # 2725\n", "\n", "Standard\n", "\n", " batch_size = 128\n", " nb_epoch = 250 #3000\n", " l1_decay=0.00\n", " l2_decay=0.000 # .5\n", " # 0.01 0.06\n", " sigma=0.005\n", " in_drop_rate = .5\n", " drop_rate = .5#.05\n", " lr1=0.0001\n", "\n", "\"\"\"\n", "train_models(X, y, y_cat)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Before selectoin\n", "(1050, 2725)\n", "After selectoin\n", "(1050, 1050)\n", "Epoch 1/250\n", "7/7 [==============================] - 1s 77ms/step - loss: 0.9428 - accuracy: 0.5369 - val_loss: 0.9259 - val_accuracy: 0.6333\n", "Epoch 2/250\n", "7/7 [==============================] - 0s 66ms/step - loss: 0.8407 - accuracy: 0.6250 - val_loss: 0.8401 - val_accuracy: 0.7190\n", "Epoch 3/250\n", "7/7 [==============================] - 1s 117ms/step - loss: 0.8068 - accuracy: 0.6369 - val_loss: 0.7543 - val_accuracy: 0.7952\n", "Epoch 4/250\n", "7/7 [==============================] - 2s 222ms/step - loss: 0.7076 - accuracy: 0.7048 - val_loss: 0.6835 - val_accuracy: 0.7857\n", "Epoch 5/250\n", "7/7 [==============================] - 0s 64ms/step - loss: 0.6708 - accuracy: 0.6988 - val_loss: 0.6372 - val_accuracy: 0.7667\n", "Epoch 6/250\n", "7/7 [==============================] - 0s 61ms/step - loss: 0.6160 - accuracy: 0.7381 - val_loss: 0.5911 - val_accuracy: 0.7667\n", "Epoch 7/250\n", "7/7 [==============================] - 0s 62ms/step - loss: 0.5732 - accuracy: 0.7512 - val_loss: 0.5510 - val_accuracy: 0.7810\n", "Epoch 8/250\n", "7/7 [==============================] - 0s 69ms/step - loss: 0.5234 - accuracy: 0.7952 - val_loss: 0.5039 - val_accuracy: 0.7952\n", "Epoch 9/250\n", "7/7 [==============================] - 0s 60ms/step - loss: 0.4986 - accuracy: 0.7869 - val_loss: 0.4606 - val_accuracy: 0.8143\n", "Epoch 10/250\n", "7/7 [==============================] - 0s 63ms/step - loss: 0.4564 - accuracy: 0.8060 - val_loss: 0.4207 - val_accuracy: 0.8238\n", "Epoch 11/250\n", "7/7 [==============================] - 1s 115ms/step - loss: 0.4372 - accuracy: 0.8274 - val_loss: 0.3853 - val_accuracy: 0.8429\n", "Epoch 12/250\n", "7/7 [==============================] - 2s 245ms/step - loss: 0.4238 - accuracy: 0.8202 - val_loss: 0.3582 - val_accuracy: 0.8524\n", "Epoch 13/250\n", "7/7 [==============================] - 0s 66ms/step - loss: 0.4005 - accuracy: 0.8262 - val_loss: 0.3439 - val_accuracy: 0.8524\n", "Epoch 14/250\n", "7/7 [==============================] - 1s 88ms/step - loss: 0.3970 - accuracy: 0.8190 - val_loss: 0.3358 - val_accuracy: 0.8429\n", "Epoch 15/250\n", "7/7 [==============================] - 3s 406ms/step - loss: 0.3406 - accuracy: 0.8679 - val_loss: 0.3186 - val_accuracy: 0.8476\n", "Epoch 16/250\n", "7/7 [==============================] - 7s 1000ms/step - loss: 0.3078 - accuracy: 0.8667 - val_loss: 0.3020 - val_accuracy: 0.8571\n", "Epoch 17/250\n", "7/7 [==============================] - 2s 321ms/step - loss: 0.3193 - accuracy: 0.8631 - val_loss: 0.2921 - val_accuracy: 0.8714\n", "Epoch 18/250\n", "7/7 [==============================] - 1s 182ms/step - loss: 0.2868 - accuracy: 0.8774 - val_loss: 0.2817 - val_accuracy: 0.8857\n", "Epoch 19/250\n", "7/7 [==============================] - 3s 430ms/step - loss: 0.2891 - accuracy: 0.8833 - val_loss: 0.2763 - val_accuracy: 0.8857\n", "Epoch 20/250\n", "7/7 [==============================] - 5s 764ms/step - loss: 0.2776 - accuracy: 0.8810 - val_loss: 0.2762 - val_accuracy: 0.8762\n", "Epoch 21/250\n", "7/7 [==============================] - 3s 360ms/step - loss: 0.2668 - accuracy: 0.8810 - val_loss: 0.2730 - val_accuracy: 0.8810\n", "Epoch 22/250\n", "7/7 [==============================] - 0s 59ms/step - loss: 0.2405 - accuracy: 0.9060 - val_loss: 0.2693 - val_accuracy: 0.8762\n", "Epoch 23/250\n", "7/7 [==============================] - 8s 1s/step - loss: 0.2464 - accuracy: 0.8893 - val_loss: 0.2620 - val_accuracy: 0.8857\n", "Epoch 24/250\n", "7/7 [==============================] - 2s 215ms/step - loss: 0.2346 - accuracy: 0.8964 - val_loss: 0.2590 - val_accuracy: 0.8857\n", "Epoch 25/250\n", "7/7 [==============================] - 2s 222ms/step - loss: 0.1994 - accuracy: 0.9190 - val_loss: 0.2532 - val_accuracy: 0.8810\n", "Epoch 26/250\n", "7/7 [==============================] - 1s 81ms/step - loss: 0.2237 - accuracy: 0.9024 - val_loss: 0.2526 - val_accuracy: 0.8810\n", "Epoch 27/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1852 - accuracy: 0.9286 - val_loss: 0.2564 - val_accuracy: 0.8762\n", "Epoch 28/250\n", "7/7 [==============================] - 4s 548ms/step - loss: 0.2099 - accuracy: 0.9155 - val_loss: 0.2507 - val_accuracy: 0.8810\n", "Epoch 29/250\n", "7/7 [==============================] - 6s 836ms/step - loss: 0.2128 - accuracy: 0.9024 - val_loss: 0.2474 - val_accuracy: 0.8810\n", "Epoch 30/250\n", "7/7 [==============================] - 2s 327ms/step - loss: 0.1997 - accuracy: 0.9190 - val_loss: 0.2450 - val_accuracy: 0.8810\n", "Epoch 31/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1655 - accuracy: 0.9286 - val_loss: 0.2461 - val_accuracy: 0.8857\n", "Epoch 32/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.1727 - accuracy: 0.9286 - val_loss: 0.2501 - val_accuracy: 0.8810\n", "Epoch 33/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.1688 - accuracy: 0.9310 - val_loss: 0.2500 - val_accuracy: 0.8857\n", "Epoch 34/250\n", "7/7 [==============================] - 1s 180ms/step - loss: 0.1613 - accuracy: 0.9310 - val_loss: 0.2432 - val_accuracy: 0.8810\n", "Epoch 35/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.1589 - accuracy: 0.9357 - val_loss: 0.2441 - val_accuracy: 0.8810\n", "Epoch 36/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.1434 - accuracy: 0.9369 - val_loss: 0.2494 - val_accuracy: 0.8762\n", "Epoch 37/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.1338 - accuracy: 0.9500 - val_loss: 0.2464 - val_accuracy: 0.8762\n", "Epoch 38/250\n", "7/7 [==============================] - 2s 317ms/step - loss: 0.1407 - accuracy: 0.9333 - val_loss: 0.2425 - val_accuracy: 0.8810\n", "Epoch 39/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1379 - accuracy: 0.9429 - val_loss: 0.2551 - val_accuracy: 0.8714\n", "Epoch 40/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1279 - accuracy: 0.9536 - val_loss: 0.2577 - val_accuracy: 0.8667\n", "Epoch 41/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.1276 - accuracy: 0.9476 - val_loss: 0.2523 - val_accuracy: 0.8714\n", "Epoch 42/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.1221 - accuracy: 0.9476 - val_loss: 0.2462 - val_accuracy: 0.8762\n", "Epoch 43/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.1181 - accuracy: 0.9548 - val_loss: 0.2453 - val_accuracy: 0.8810\n", "Epoch 44/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.1013 - accuracy: 0.9607 - val_loss: 0.2476 - val_accuracy: 0.8810\n", "Epoch 45/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0965 - accuracy: 0.9679 - val_loss: 0.2474 - val_accuracy: 0.8810\n", "Epoch 46/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.1010 - accuracy: 0.9643 - val_loss: 0.2494 - val_accuracy: 0.8810\n", "Epoch 47/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1106 - accuracy: 0.9524 - val_loss: 0.2451 - val_accuracy: 0.8810\n", "Epoch 48/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.1181 - accuracy: 0.9476 - val_loss: 0.2504 - val_accuracy: 0.8762\n", "Epoch 49/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0890 - accuracy: 0.9619 - val_loss: 0.2478 - val_accuracy: 0.8762\n", "Epoch 50/250\n", "7/7 [==============================] - 5s 682ms/step - loss: 0.0947 - accuracy: 0.9536 - val_loss: 0.2401 - val_accuracy: 0.8762\n", "Epoch 51/250\n", "7/7 [==============================] - 2s 321ms/step - loss: 0.0795 - accuracy: 0.9667 - val_loss: 0.2399 - val_accuracy: 0.8810\n", "Epoch 52/250\n", "7/7 [==============================] - 4s 547ms/step - loss: 0.0877 - accuracy: 0.9690 - val_loss: 0.2372 - val_accuracy: 0.8905\n", "Epoch 53/250\n", "7/7 [==============================] - 3s 456ms/step - loss: 0.0707 - accuracy: 0.9714 - val_loss: 0.2359 - val_accuracy: 0.8952\n", "Epoch 54/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0754 - accuracy: 0.9655 - val_loss: 0.2387 - val_accuracy: 0.8905\n", "Epoch 55/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0892 - accuracy: 0.9607 - val_loss: 0.2403 - val_accuracy: 0.8857\n", "Epoch 56/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0741 - accuracy: 0.9726 - val_loss: 0.2366 - val_accuracy: 0.8857\n", "Epoch 57/250\n", "7/7 [==============================] - 0s 62ms/step - loss: 0.0787 - accuracy: 0.9679 - val_loss: 0.2350 - val_accuracy: 0.8762\n", "Epoch 58/250\n", "7/7 [==============================] - 3s 360ms/step - loss: 0.0650 - accuracy: 0.9750 - val_loss: 0.2289 - val_accuracy: 0.8952\n", "Epoch 59/250\n", "7/7 [==============================] - 6s 805ms/step - loss: 0.0635 - accuracy: 0.9786 - val_loss: 0.2245 - val_accuracy: 0.8952\n", "Epoch 60/250\n", "7/7 [==============================] - 1s 127ms/step - loss: 0.0630 - accuracy: 0.9750 - val_loss: 0.2222 - val_accuracy: 0.8952\n", "Epoch 61/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0576 - accuracy: 0.9786 - val_loss: 0.2242 - val_accuracy: 0.8905\n", "Epoch 62/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0639 - accuracy: 0.9762 - val_loss: 0.2368 - val_accuracy: 0.8810\n", "Epoch 63/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0653 - accuracy: 0.9738 - val_loss: 0.2348 - val_accuracy: 0.8857\n", "Epoch 64/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0847 - accuracy: 0.9643 - val_loss: 0.2304 - val_accuracy: 0.8810\n", "Epoch 65/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0537 - accuracy: 0.9774 - val_loss: 0.2305 - val_accuracy: 0.8905\n", "Epoch 66/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0580 - accuracy: 0.9810 - val_loss: 0.2296 - val_accuracy: 0.8905\n", "Epoch 67/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0519 - accuracy: 0.9798 - val_loss: 0.2285 - val_accuracy: 0.8905\n", "Epoch 68/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0594 - accuracy: 0.9774 - val_loss: 0.2257 - val_accuracy: 0.8952\n", "Epoch 69/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0472 - accuracy: 0.9798 - val_loss: 0.2246 - val_accuracy: 0.8952\n", "Epoch 70/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0541 - accuracy: 0.9810 - val_loss: 0.2263 - val_accuracy: 0.8952\n", "Epoch 71/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0591 - accuracy: 0.9738 - val_loss: 0.2269 - val_accuracy: 0.8952\n", "Epoch 72/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0531 - accuracy: 0.9786 - val_loss: 0.2295 - val_accuracy: 0.8857\n", "Epoch 73/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0712 - accuracy: 0.9702 - val_loss: 0.2375 - val_accuracy: 0.8810\n", "Epoch 74/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0336 - accuracy: 0.9893 - val_loss: 0.2366 - val_accuracy: 0.8810\n", "Epoch 75/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0389 - accuracy: 0.9857 - val_loss: 0.2350 - val_accuracy: 0.8810\n", "Epoch 76/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0298 - accuracy: 0.9905 - val_loss: 0.2346 - val_accuracy: 0.8810\n", "Epoch 77/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0396 - accuracy: 0.9869 - val_loss: 0.2393 - val_accuracy: 0.8810\n", "Epoch 78/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0443 - accuracy: 0.9833 - val_loss: 0.2375 - val_accuracy: 0.8905\n", "Epoch 79/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0438 - accuracy: 0.9821 - val_loss: 0.2358 - val_accuracy: 0.8857\n", "Epoch 80/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0373 - accuracy: 0.9845 - val_loss: 0.2384 - val_accuracy: 0.8905\n", "Epoch 81/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0434 - accuracy: 0.9857 - val_loss: 0.2429 - val_accuracy: 0.8762\n", "Epoch 82/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0502 - accuracy: 0.9810 - val_loss: 0.2410 - val_accuracy: 0.8857\n", "Epoch 83/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0458 - accuracy: 0.9833 - val_loss: 0.2332 - val_accuracy: 0.8857\n", "Epoch 84/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0346 - accuracy: 0.9881 - val_loss: 0.2305 - val_accuracy: 0.8857\n", "Epoch 85/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0520 - accuracy: 0.9774 - val_loss: 0.2283 - val_accuracy: 0.8857\n", "Epoch 86/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0381 - accuracy: 0.9821 - val_loss: 0.2319 - val_accuracy: 0.8810\n", "Epoch 87/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0386 - accuracy: 0.9869 - val_loss: 0.2390 - val_accuracy: 0.8857\n", "Epoch 88/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0352 - accuracy: 0.9857 - val_loss: 0.2396 - val_accuracy: 0.8857\n", "Epoch 89/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0321 - accuracy: 0.9881 - val_loss: 0.2371 - val_accuracy: 0.8905\n", "Epoch 90/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0263 - accuracy: 0.9905 - val_loss: 0.2395 - val_accuracy: 0.8857\n", "Epoch 91/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0327 - accuracy: 0.9845 - val_loss: 0.2316 - val_accuracy: 0.8905\n", "Epoch 92/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0279 - accuracy: 0.9881 - val_loss: 0.2293 - val_accuracy: 0.8905\n", "Epoch 93/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0260 - accuracy: 0.9905 - val_loss: 0.2294 - val_accuracy: 0.8905\n", "Epoch 94/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0416 - accuracy: 0.9833 - val_loss: 0.2273 - val_accuracy: 0.8905\n", "Epoch 95/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0256 - accuracy: 0.9893 - val_loss: 0.2358 - val_accuracy: 0.8810\n", "Epoch 96/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0191 - accuracy: 0.9905 - val_loss: 0.2299 - val_accuracy: 0.8857\n", "Epoch 97/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0217 - accuracy: 0.9929 - val_loss: 0.2238 - val_accuracy: 0.8952\n", "Epoch 98/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0348 - accuracy: 0.9905 - val_loss: 0.2244 - val_accuracy: 0.8952\n", "Epoch 99/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0232 - accuracy: 0.9929 - val_loss: 0.2253 - val_accuracy: 0.8952\n", "Epoch 100/250\n", "7/7 [==============================] - 2s 292ms/step - loss: 0.0259 - accuracy: 0.9905 - val_loss: 0.2191 - val_accuracy: 0.8952\n", "Epoch 101/250\n", "7/7 [==============================] - 1s 190ms/step - loss: 0.0254 - accuracy: 0.9881 - val_loss: 0.2184 - val_accuracy: 0.8952\n", "Epoch 102/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0267 - accuracy: 0.9905 - val_loss: 0.2226 - val_accuracy: 0.8952\n", "Epoch 103/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0170 - accuracy: 0.9964 - val_loss: 0.2295 - val_accuracy: 0.8810\n", "Epoch 104/250\n", "7/7 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val_loss: 0.2259 - val_accuracy: 0.8952\n", "Epoch 111/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0381 - accuracy: 0.9833 - val_loss: 0.2262 - val_accuracy: 0.8905\n", "Epoch 112/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0159 - accuracy: 0.9964 - val_loss: 0.2255 - val_accuracy: 0.8905\n", "Epoch 113/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0247 - accuracy: 0.9893 - val_loss: 0.2249 - val_accuracy: 0.8952\n", "Epoch 114/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0201 - accuracy: 0.9940 - val_loss: 0.2216 - val_accuracy: 0.8952\n", "Epoch 115/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0119 - accuracy: 0.9952 - val_loss: 0.2243 - val_accuracy: 0.8952\n", "Epoch 116/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0227 - accuracy: 0.9893 - val_loss: 0.2311 - val_accuracy: 0.8857\n", "Epoch 117/250\n", "7/7 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val_loss: 0.2215 - val_accuracy: 0.8952\n", "Epoch 124/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0107 - accuracy: 0.9964 - val_loss: 0.2186 - val_accuracy: 0.8952\n", "Epoch 125/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0164 - accuracy: 0.9940 - val_loss: 0.2201 - val_accuracy: 0.9000\n", "Epoch 126/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0195 - accuracy: 0.9929 - val_loss: 0.2223 - val_accuracy: 0.9000\n", "Epoch 127/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0178 - accuracy: 0.9905 - val_loss: 0.2230 - val_accuracy: 0.9000\n", "Epoch 128/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0206 - accuracy: 0.9952 - val_loss: 0.2244 - val_accuracy: 0.8952\n", "Epoch 129/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0162 - accuracy: 0.9940 - val_loss: 0.2294 - val_accuracy: 0.8952\n", "Epoch 130/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0292 - accuracy: 0.9881 - val_loss: 0.2262 - val_accuracy: 0.8905\n", "Epoch 131/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0165 - accuracy: 0.9929 - val_loss: 0.2244 - val_accuracy: 0.9000\n", "Epoch 132/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0225 - accuracy: 0.9905 - val_loss: 0.2274 - val_accuracy: 0.8905\n", "Epoch 133/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0093 - accuracy: 0.9976 - val_loss: 0.2274 - val_accuracy: 0.8952\n", "Epoch 134/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0070 - accuracy: 0.9952 - val_loss: 0.2272 - val_accuracy: 0.8952\n", "Epoch 135/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0106 - accuracy: 0.9964 - val_loss: 0.2266 - val_accuracy: 0.8952\n", "Epoch 136/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0201 - accuracy: 0.9881 - val_loss: 0.2253 - val_accuracy: 0.8952\n", "Epoch 137/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0175 - accuracy: 0.9940 - val_loss: 0.2234 - val_accuracy: 0.8952\n", "Epoch 138/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0184 - accuracy: 0.9917 - val_loss: 0.2226 - val_accuracy: 0.8952\n", "Epoch 139/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0155 - accuracy: 0.9929 - val_loss: 0.2228 - val_accuracy: 0.9048\n", "Epoch 140/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0159 - accuracy: 0.9929 - val_loss: 0.2303 - val_accuracy: 0.8905\n", "Epoch 141/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0138 - accuracy: 0.9940 - val_loss: 0.2354 - val_accuracy: 0.8857\n", "Epoch 142/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0130 - accuracy: 0.9952 - val_loss: 0.2316 - val_accuracy: 0.8810\n", "Epoch 143/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0113 - accuracy: 0.9952 - val_loss: 0.2348 - val_accuracy: 0.8810\n", "Epoch 144/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0175 - accuracy: 0.9917 - val_loss: 0.2318 - val_accuracy: 0.8952\n", "Epoch 145/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0083 - accuracy: 0.9976 - val_loss: 0.2237 - val_accuracy: 0.8952\n", "Epoch 146/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0110 - accuracy: 0.9952 - val_loss: 0.2245 - val_accuracy: 0.8952\n", "Epoch 147/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0150 - accuracy: 0.9940 - val_loss: 0.2222 - val_accuracy: 0.8952\n", "Epoch 148/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0138 - accuracy: 0.9952 - val_loss: 0.2235 - val_accuracy: 0.9000\n", "Epoch 149/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0111 - accuracy: 0.9940 - val_loss: 0.2363 - val_accuracy: 0.8857\n", "Epoch 150/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0115 - accuracy: 0.9964 - val_loss: 0.2405 - val_accuracy: 0.8857\n", "Epoch 151/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0133 - accuracy: 0.9952 - val_loss: 0.2326 - val_accuracy: 0.8857\n", "Epoch 152/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0057 - accuracy: 0.9988 - val_loss: 0.2229 - val_accuracy: 0.8952\n", "Epoch 153/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0095 - accuracy: 0.9976 - val_loss: 0.2223 - val_accuracy: 0.8952\n", "Epoch 154/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0106 - accuracy: 0.9940 - val_loss: 0.2240 - val_accuracy: 0.8952\n", "Epoch 155/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0114 - accuracy: 0.9952 - val_loss: 0.2255 - val_accuracy: 0.8905\n", "Epoch 156/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0107 - accuracy: 0.9952 - val_loss: 0.2213 - val_accuracy: 0.8952\n", "Epoch 157/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0226 - accuracy: 0.9917 - val_loss: 0.2202 - val_accuracy: 0.9000\n", "Epoch 158/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0107 - accuracy: 0.9964 - val_loss: 0.2258 - val_accuracy: 0.8905\n", "Epoch 159/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0126 - accuracy: 0.9964 - val_loss: 0.2274 - val_accuracy: 0.8905\n", "Epoch 160/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0126 - accuracy: 0.9952 - val_loss: 0.2253 - val_accuracy: 0.8905\n", "Epoch 161/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0055 - accuracy: 0.9988 - val_loss: 0.2214 - val_accuracy: 0.9000\n", "Epoch 162/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0088 - accuracy: 0.9976 - val_loss: 0.2210 - val_accuracy: 0.9000\n", "Epoch 163/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0077 - accuracy: 0.9988 - val_loss: 0.2209 - val_accuracy: 0.9000\n", "Epoch 164/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0097 - accuracy: 0.9964 - val_loss: 0.2193 - val_accuracy: 0.9000\n", "Epoch 165/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0135 - accuracy: 0.9976 - val_loss: 0.2183 - val_accuracy: 0.9000\n", "Epoch 166/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0198 - accuracy: 0.9905 - val_loss: 0.2207 - val_accuracy: 0.8952\n", "Epoch 167/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0177 - accuracy: 0.9929 - val_loss: 0.2277 - val_accuracy: 0.8857\n", "Epoch 168/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0101 - accuracy: 0.9964 - val_loss: 0.2294 - val_accuracy: 0.8857\n", "Epoch 169/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0133 - accuracy: 0.9952 - val_loss: 0.2202 - val_accuracy: 0.8905\n", "Epoch 170/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0103 - accuracy: 0.9976 - val_loss: 0.2176 - val_accuracy: 0.8905\n", "Epoch 171/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0121 - accuracy: 0.9964 - val_loss: 0.2180 - val_accuracy: 0.8905\n", "Epoch 172/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0073 - accuracy: 0.9964 - val_loss: 0.2186 - val_accuracy: 0.8952\n", "Epoch 173/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0063 - accuracy: 0.9976 - val_loss: 0.2211 - val_accuracy: 0.8905\n", "Epoch 174/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0093 - accuracy: 0.9976 - val_loss: 0.2240 - val_accuracy: 0.8905\n", "Epoch 175/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0058 - accuracy: 0.9976 - val_loss: 0.2222 - val_accuracy: 0.8952\n", "Epoch 176/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0049 - accuracy: 0.9988 - val_loss: 0.2216 - val_accuracy: 0.8952\n", "Epoch 177/250\n", "7/7 [==============================] - 0s 16ms/step - loss: 0.0091 - accuracy: 0.9976 - val_loss: 0.2208 - val_accuracy: 0.8952\n", "Epoch 178/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0098 - accuracy: 0.9976 - val_loss: 0.2197 - val_accuracy: 0.8905\n", "Epoch 179/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0070 - accuracy: 0.9976 - val_loss: 0.2204 - val_accuracy: 0.8905\n", "Epoch 180/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0126 - accuracy: 0.9964 - val_loss: 0.2225 - val_accuracy: 0.8952\n", "Epoch 181/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0143 - accuracy: 0.9940 - val_loss: 0.2223 - val_accuracy: 0.9000\n", "Epoch 182/250\n", "7/7 [==============================] - 0s 46ms/step - loss: 0.0084 - accuracy: 0.9964 - val_loss: 0.2138 - val_accuracy: 0.9048\n", "Epoch 183/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0120 - accuracy: 0.9964 - val_loss: 0.2146 - val_accuracy: 0.9048\n", "Epoch 184/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0079 - accuracy: 0.9988 - val_loss: 0.2201 - val_accuracy: 0.8905\n", "Epoch 185/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.2273 - val_accuracy: 0.8857\n", "Epoch 186/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0085 - accuracy: 0.9988 - val_loss: 0.2273 - val_accuracy: 0.8857\n", "Epoch 187/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0123 - accuracy: 0.9940 - val_loss: 0.2270 - val_accuracy: 0.8905\n", "Epoch 188/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0073 - accuracy: 0.9976 - val_loss: 0.2268 - val_accuracy: 0.8905\n", "Epoch 189/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0077 - accuracy: 0.9976 - val_loss: 0.2373 - val_accuracy: 0.8857\n", "Epoch 190/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0048 - accuracy: 0.9988 - val_loss: 0.2329 - val_accuracy: 0.8905\n", "Epoch 191/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0087 - accuracy: 0.9964 - val_loss: 0.2268 - val_accuracy: 0.8952\n", "Epoch 192/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.2257 - val_accuracy: 0.8905\n", "Epoch 193/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0056 - accuracy: 0.9976 - val_loss: 0.2220 - val_accuracy: 0.8952\n", "Epoch 194/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0080 - accuracy: 0.9988 - val_loss: 0.2216 - val_accuracy: 0.8905\n", "Epoch 195/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.2220 - val_accuracy: 0.8905\n", "Epoch 196/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0069 - accuracy: 0.9976 - val_loss: 0.2207 - val_accuracy: 0.8905\n", "Epoch 197/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0121 - accuracy: 0.9952 - val_loss: 0.2206 - val_accuracy: 0.8905\n", "Epoch 198/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0068 - accuracy: 0.9976 - val_loss: 0.2199 - val_accuracy: 0.8952\n", "Epoch 199/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0095 - accuracy: 0.9964 - val_loss: 0.2205 - val_accuracy: 0.8952\n", "Epoch 200/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0052 - accuracy: 1.0000 - val_loss: 0.2188 - val_accuracy: 0.8952\n", "Epoch 201/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0040 - accuracy: 1.0000 - val_loss: 0.2151 - val_accuracy: 0.9000\n", "Epoch 202/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0077 - accuracy: 0.9964 - val_loss: 0.2142 - val_accuracy: 0.9000\n", "Epoch 203/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0036 - accuracy: 1.0000 - val_loss: 0.2173 - val_accuracy: 0.9000\n", "Epoch 204/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0072 - accuracy: 0.9976 - val_loss: 0.2210 - val_accuracy: 0.9000\n", "Epoch 205/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0045 - accuracy: 0.9988 - val_loss: 0.2214 - val_accuracy: 0.9000\n", "Epoch 206/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0062 - accuracy: 0.9976 - val_loss: 0.2191 - val_accuracy: 0.9000\n", "Epoch 207/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0031 - accuracy: 0.9988 - val_loss: 0.2169 - val_accuracy: 0.8952\n", "Epoch 208/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0064 - accuracy: 0.9976 - val_loss: 0.2159 - val_accuracy: 0.8952\n", "Epoch 209/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0096 - accuracy: 0.9964 - val_loss: 0.2144 - val_accuracy: 0.8952\n", "Epoch 210/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0033 - accuracy: 0.9988 - val_loss: 0.2139 - val_accuracy: 0.8952\n", "Epoch 211/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.2159 - val_accuracy: 0.8952\n", "Epoch 212/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0066 - accuracy: 0.9988 - val_loss: 0.2202 - val_accuracy: 0.8952\n", "Epoch 213/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0026 - accuracy: 1.0000 - val_loss: 0.2254 - val_accuracy: 0.8905\n", "Epoch 214/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0033 - accuracy: 0.9976 - val_loss: 0.2287 - val_accuracy: 0.8905\n", "Epoch 215/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0102 - accuracy: 0.9964 - val_loss: 0.2288 - val_accuracy: 0.8905\n", "Epoch 216/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.2294 - val_accuracy: 0.8810\n", "Epoch 217/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 4.1460e-04 - accuracy: 1.0000 - val_loss: 0.2289 - val_accuracy: 0.8810\n", "Epoch 218/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0036 - accuracy: 0.9988 - val_loss: 0.2260 - val_accuracy: 0.8762\n", "Epoch 219/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0050 - accuracy: 0.9976 - val_loss: 0.2146 - val_accuracy: 0.9048\n", "Epoch 220/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.2145 - val_accuracy: 0.9000\n", "Epoch 221/250\n", "7/7 [==============================] - 0s 43ms/step - loss: 0.0050 - accuracy: 0.9988 - val_loss: 0.2074 - val_accuracy: 0.9095\n", "Epoch 222/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0045 - accuracy: 0.9976 - val_loss: 0.2075 - val_accuracy: 0.9048\n", "Epoch 223/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.2087 - val_accuracy: 0.9048\n", "Epoch 224/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.2140 - val_accuracy: 0.9048\n", "Epoch 225/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0029 - accuracy: 1.0000 - val_loss: 0.2140 - val_accuracy: 0.9048\n", "Epoch 226/250\n", "7/7 [==============================] - 3s 402ms/step - loss: 0.0038 - accuracy: 1.0000 - val_loss: 0.2046 - val_accuracy: 0.9048\n", "Epoch 227/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.2135 - val_accuracy: 0.9000\n", "Epoch 228/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.2399 - val_accuracy: 0.8810\n", "Epoch 229/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0072 - accuracy: 0.9952 - val_loss: 0.2526 - val_accuracy: 0.8714\n", "Epoch 230/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.2606 - val_accuracy: 0.8619\n", "Epoch 231/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0044 - accuracy: 0.9988 - val_loss: 0.2615 - val_accuracy: 0.8619\n", "Epoch 232/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0015 - accuracy: 0.9988 - val_loss: 0.2600 - val_accuracy: 0.8667\n", "Epoch 233/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0090 - accuracy: 0.9964 - val_loss: 0.2479 - val_accuracy: 0.8810\n", "Epoch 234/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0024 - accuracy: 1.0000 - val_loss: 0.2347 - val_accuracy: 0.8857\n", "Epoch 235/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.2317 - val_accuracy: 0.8857\n", "Epoch 236/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0015 - accuracy: 0.9988 - val_loss: 0.2327 - val_accuracy: 0.8857\n", "Epoch 237/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0017 - accuracy: 0.9988 - val_loss: 0.2336 - val_accuracy: 0.8857\n", "Epoch 238/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0091 - accuracy: 0.9976 - val_loss: 0.2300 - val_accuracy: 0.8857\n", "Epoch 239/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0037 - accuracy: 1.0000 - val_loss: 0.2340 - val_accuracy: 0.8857\n", "Epoch 240/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0021 - accuracy: 0.9988 - val_loss: 0.2314 - val_accuracy: 0.8905\n", "Epoch 241/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0017 - accuracy: 1.0000 - val_loss: 0.2285 - val_accuracy: 0.8905\n", "Epoch 242/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0053 - accuracy: 0.9988 - val_loss: 0.2336 - val_accuracy: 0.8905\n", "Epoch 243/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.2455 - val_accuracy: 0.8762\n", "Epoch 244/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0031 - accuracy: 0.9988 - val_loss: 0.2488 - val_accuracy: 0.8762\n", "Epoch 245/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0038 - accuracy: 0.9988 - val_loss: 0.2447 - val_accuracy: 0.8762\n", "Epoch 246/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0035 - accuracy: 0.9988 - val_loss: 0.2320 - val_accuracy: 0.8905\n", "Epoch 247/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0036 - accuracy: 0.9976 - val_loss: 0.2280 - val_accuracy: 0.8905\n", "Epoch 248/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0037 - accuracy: 0.9988 - val_loss: 0.2261 - val_accuracy: 0.8905\n", "Epoch 249/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.2219 - val_accuracy: 0.8952\n", "Epoch 250/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0106 - accuracy: 0.9952 - val_loss: 0.2245 - val_accuracy: 0.9000\n", "Valid results - Loss: 0.22446519136428833 - Accuracy: 89.99999761581421%\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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NZrbKzFa1trYWuhwRkdgoeMC7+53uvsTdlzQ0NJzoNBhIZ0e5MhGR8a3gAT9SA+ksH/nbe/j+o8sLXYqIyEll3Ad8qQ9wT/pzNK69u9CliIicVKI8TfI+YAUw38yazOxjkcyouILtdRdzXu+v6OofjGQWIiLjUZRn0XzE3ae5e8rdZ7r7XVHNy864iqm2j3UrH49qFiIi486476IBOOWiDzHkRaRf/lGhSxEROWnEIuCLK+t4tfICTm19jGdfay90OSIiJ4VYBDzAqW+/genWzte/dz/pbK7Q5YiIFFxsAr78rCvJWREXD/6a57btK3Q5IiIFF5uAp6yO3JxLWZp8hic3tBS6GhGRgotPwANF51zLKdZC2yvLCl2KiEjBxSrgWfRBBpOVXNz5U5r29RW6GhGRgopXwBeXM7ToWpYmVvKF7y8now9bRWQCi1fAA1WX/hGpRI7Lm+/k7l+/VuhyREQKJnYBT8N8Ehf9ER8p+iXLlz1Cz2Cm0BWJiBRE/AIe4LLbyJY38mfZO7lr+cZCVyMiUhDxDPiSKlJLb+esxFa6ln+LTS09ha5IRGTMxTPgAc74XQbnXM5nk/fxzz/4Ge5e6IpERMZUfAPejJIPfYNEqoSPtd7OT19sKnRFIiJjKr4BD1A9ndSVX2ZxYjMbf/rPtPXoevEiMnHEO+CBxNnX0jX9Em5Of4+Pf/2n7OzoL3RJIiJjIvYBjxnVV3+N8qIsn+i/gz978CX1x4vIhBD/gAeoP43EpX/Ke+wZSjc/wj/8fIO+5SoisTcxAh7gklvwKWfyj2V3cf+y1Vz5tad4ZM3uQlclIhKZiRPwRcXY1d+h0gZ5eNZ3yWbSfOJ7q/lfP17Ds1v3qttGRGJn4gQ8QONCeN+Xmdz6NI8ufISPvmU2312xjWu/tYKv/XJToasTERlVEyvgAc69ES76nySevZMv2Hf49a1LuHxBI9/+1RY6+9OFrk5EZNRMvIAHeM/fwlv+GFb/KzP+7QK+UvRNZg++yjef0FG8iMSHnUx9z0uWLPFVq1aN3Qybn4Nn7oBXHyE30Mm9mXeyduGn2d5fwleuW8yU6tKxq0VE5ASY2Wp3X3LYYRM64Pcb6CTz+N+QePbb9HuKx/wCnq1+F9POuoz5s6Zx2fwGipIT882OiJzcFPDHKL3zZXLP3Eli7Y9IpbvIuTFEEX1WTrpqJo2nLMBOuQgaTof6N0H19PDE/n1QWguJE9gJ9O2FwS6omQWJ5Og2SERGJjMEzathqDds3zWzYPKbCl3VIRTwxyszyNDGZdjuF2na3cJrzbso6tzOGcW7qc+2HhwvVQ6eg8wAFJWFBV83B0qqQmh37IDs0MGVor8D9m6GsrrwWNOzsPO5g9Oadk543LMw0AXJYigqhqJS6G2F7j1QNQVOuRhqZ0NJJRRXQCIVdjL9e6GkGupmh+FltYe2Kz0Au16E4nIorQl/xVVH3zHlsrDzBRjqgVRZqAVg32uQKArTyGWhYzt07QzjTD0TZpwfajGDob7Q1u7dYSdWUhWmN/1cqJsbxgFwh71boGJymO5YGuyGtlehbFJYhvtrGm2dzWGdKa6AgQ6oaAzL8fX2rIU9r0BlA8y+BJKpaOqJSjYDO5+H0uqwLqbGWXdnbxss/wd4/nsw1H3osFPfARd/CmaeD5aEzGBYX8omhe00VRbWcc+F9f7F74f1uqQKZr0ZzrspZMAoUcCPgm8+sYkv/3wDpxXvY9LQLt5V38bSWRlea+/ngjMXYN07Gdy9ntLenSTTvWEDrp0VArF1A3Q2hQVcPw8GOqFzB1ROgcW/B+X10LI2bBA9e8ASIeCymbDzyAyG+zUzQ5C2vHJsRZfWhI2rbnZYEbf9OqyAh7CwEe4P/NLaENiDXWEn09cO6d4Te9GSJaHNfe3AkdYzCxtEcUW43dsS/k8+HSbNDTtIz0HDAqiaGl67XS9B6/qwg2hcBEUlkO4PR1kDHWGcod7wuuXSYUc0/K9nD+zbBrMuhLmXhtd+3X9COv9D7dUzYOrZYf5lk8LOaLA7/78HOrdDTwuUT4bySeG1ygxBcv88UiGQ97erqDTU0/wcdO/87Zdg2jlQNS08t7gyhEHTykOX45y3hZ1jNnNwZ1szK6wrretDIFkiLMuS6oP/9x8EWCLsXJpWhgOFujlw6tvDupcZCDvhdF84UBjoDDWnysKBRzIVAsw9vKbZQZg8H6acEV6HppXhKDfdH5adJcJyPLCuWXi3WzcXak8JyyuZyr9WyYOv2f77ZbVQNT3c798XptPXdnA72P9XUh0OVrIZ6GoOf+2boG1Tfj3i0Ndw2jmw8AMHb5fl1/WePdC+ObRx+rnhQOTea8K2dsaHYNFVUNkYxt2+Inxu13OYL0laIqyrkD/4c8j0h4OoxgXhdW17NQyvnBKWX+2sg6/ZNXe98TZ1uC1IAT86hjI5Uknjweea+ex/vHjg8XcuaOT5HR3s7R2iuCjBTRfN5s+XLiSZiOgosG9vCM39oZMdCkFUPim8S+jYFgJs+H/PhY3ynOvDNAa6wgp3uL/hQVFWCzMvCOGaHggrbC4bAgJCuEHYcKumh5DY8UxYkXtawvCq6TDjvLCB59IhJIuKw9FNT0t4zmB32IBnXhDa17wqbLDJklB7y7ow70QqvBtqmB8Cs3NHGG4JSFWEektrDgZroii8I8pmIJcJ8y+tCRvX9hWhztKasCGfdnkIkteWQ9vGELTpvvBOqrgy7KxKqsLGWT09Hz5t4bFUWZh+Nh3mkc2Eegd78u/wSsJOY9aF4fZQX5hvZ1OY31B3eO5gT2jDOR8O9ezdAuv/C3asDG1MpkJ4DPVCV1NYFpNPDzsIzx3cMQ92hv9DPaEuCMtzxvnhQGH3S2FHOXzHmyoPO/iy2lBzuj+0P5s5OF5RSXhNhx8oFFeF5VtWG2pwD9Oaf0V47r6t4R3f3tdCe7OD+WWRzb9e+eVyNImi/I7yKD/eY4mwHta/CaqnhfsQ6unfG17ngc7hT+C3DjySJaG+0hq44YGwvF4vMwibHgvt8lyoa/+OompaeO169oT5TjsbFlx58F3arpdgw8PhIKFjR1h/IWwbN/3w6K/BkZqtgB9931i2iee372NqTSnfe3o7cydX8Kl3zGPFlnYeWN3E7188m3mNlVSWFnHWjBpOa6jEonrbPxHkcmHDS6TCkfJ+2XTYyJLFJ9at0tMSgu9wXQjuYfpFxSde98kgMxR2cqmyQx/PZsIOoag0vzM8js+Q9m0LYV1WF3a2o/H5US4Xgr5vL3TvCsFfNulgl53ZwZoHOsP/ob4w76pp4W/4uvF66f7Q9TXYGd5xZAbDelNWFw4aMkOwZVmY31nX5Y+uT34K+Ailszkeeq6Z31k0hUkVxbg7f/7QGu5buf2Q8SpLiphdX84fXXYa7z1zGu5OJueUpvTBqoicOAX8GEtnczy1sY15jZUMpLOs2raPDbu7WbG5nQ17uqmvKKY/naVvKMv0mtIDO4cZtWWcPqWK+VOrfiv4czlnKJvTDkFEDnG0gD/K+xk5UalkgncsaDxw/01TqgDI5pyfv7KbR17ZTU1ZisaqEl7Y0ckPnt3BYObg5YvrylPc8s43cd7sOnZ2DLB8YyuPr9vDQDrHvX/wZuZPrSKl8/JF5A3oCP4kkcnm2L63j1f3dHP3U1tZuXXvgWHlxUnefnoDLzV10to9yFA2xyXz6plRW0bCjD9421y2tfdx38rtzKmv4O3zG8g5vHXe5MN+0Ns3lOGOJ7dwybzJXDh30hvW5u76/EDkJKUumnHG3dmwp5utbb1MqS5l4bRqSlNJtrf3ccfyzZSlkvzw+WbS2RxDmdyBo//JlSXs7R0kl1+ksyaVMbO2nNJUgmm1ZZQWJdmwp4vNLb3s7hqgvDjJn75nPomEMbOujPW7u1k8q5ZpNWW8uKOD5o5+ylJJvvnEJq5aPIO/unLRG9a+r3eIklSC8uIi3J3dXQNMrS7VDkIkIgULeDO7AvhnIAl8x91vP9r4Cvhjl8057s6uzgF+ub6FUyaVc8m8yTR39LOro5+23iF+9Hwz3QNp+tNZtrX3MZTJsXBaNZMrS7j6vBn83cPr2b637w3nNbmyhLaeQT58wSxKU0nOmF7Nis3trN3VRe9QhknlxcycVM6a5k62tYfpLZpWTWVJESu37uXsmTWcPqWK2rIUcyZXMHdyBVNrStnU0kN5cZJTJpWzprmLR17ZzYfOncFl8xswMza39vDctn28Y0EjpakkA+ks2ZxTVpykujRFJps75BISuZzT0Z+mrjyFmfGbTW2s3raPj196KiVFiTfcyRzPO5Vcztmxr49TJpVr5yUFVZCAN7Mk8CrwLqAJeBb4iLuvPdJzFPDRcXdyziFdNgPpLDs7+ilNJdnW3se8xkqe2tTKYDrH4lNqmVFbRnNHP3PqK7jxO8/w/I4OUkljIJ2jrjzFuafUUVuWormjn6Z9/ZwxvZrzZtcxlMnx6Cu72d05wDXnz+TJV1vp6k+zt2+IgfSRfyqxNJVgIJ2jsaqEqtIitrT1crjVM2GwYGo1G1u6aagsYXZ9BX1DGTa29NA3lGXB1Cpqy1M8vSV0c82oLaO9d5DKkhTnnlJLcTLB6m37mF5bysaWHipLijCgezDDuxZNYXt7H8VFCeY1VlJTlmLD7m5aewYpTiboHcqQTCTo7k+zpa2XUxsqcA/zuOb8mVSXFbGltZfH17UwlM1RlkpiFnaSpakkJUUJyoqTlKeSpHPO5pYezp5ZQ0NVCZ39abr6M/SlM9SVF5NzZ2AoS2VpEWdMr6GtZ5DBTI5szsnmnHW7unhqUxuXnd7AzLpyBjNZKkuKWDCtmo6+NMmE0TOYZnfnIK+19bBhTw8VxUnOnllLbXmKgXSWuvJiBtJZZtaVU18ZbvcPZZlSU0ppUZLdXf109KUpSiYoTibY1NLNvr40U6tLqSlL0TuUYV5jJac1VNLWM8j6Xd30DmWYU19BImHUlafoHshQlDDmTq6grWeItp5BZtSV4Q7rd3exp2uQ2ZPKGczkqC4rYlpNKTmHtu5BGqtL6B7IYGaUpZIMZXLUVaTY15tmZ2c/xUUJzpxeQ2kqwda2PqrLiqgsKaK5o5/te/u4cM4kGqpKGMrm2Ns7RDrjTK4qprV7kEzOqSlLUVuWYiibYyCdYzCTJZkwyouLGMgfGM2pLyeTcxJm9A5m2NLWw4Kp1VSXpUiaYQbtvUOs3dnFKZPKmVRRTGkqQTrr7O0dorG6hKRZeMedzeH57bAoYSQTxprmLnbs7eO6C07stMxCBfzFwBfd/T35+58HcPe/O9JzFPAnr0w2R9Ydd9jU0nNCH/TmcqHLZmtbLzs7B5g7uYLBdJZdnQNUlCS5bH4jP3lxJys2tzOYyTJ/SjVvO30yv3q1jZJUgoriJMlEgqZ9fazeto9F06tp6R6kpWuAkqIk8xoraawu4fF1LWRzziXz6lk0rYZ//fVrzJ9axWAmx4rN7fQNZXjLvMm0dA0wr7GSvqFsOOXdnSc3tDJ/alXoJtvdTX86y6kNlUytLmUok6O8JIRMJudcNr+BJze0Ul2W4qWmDvZ0DR5o6/wpVUyqKGYwkw1h1TN4IEAG0lnSWccMplWXsrNz4JDXKZU00tmwXe7/AunhFBclWDK7jme37j0w/pHUladYNL2ansEsa3d2vuH4R1OWStKfzp7w88dawjjQbXmyqilLseovf+eETp4oVMBfA1zh7n+Qv38T8GZ3/9TrxrsZuDl/dz6w4QRnORloO8Hnjldq88SgNk8MJ9rm2e7ecLgBBT9N0t3vBO4c6XTMbNWR9mJxpTZPDGrzxBBFm6M8mboZGN6pNDP/mIiIjIEoA/5Z4E1mNtfMioEPAz+JcH4iIjJMZF007p4xs08BjxJOk7zb3Y/xOrcnZMTdPOOQ2jwxqM0Tw6i3+aT6opOIiIweXdBERCSmFPAiIjE17gPezK4wsw1mtsnMbit0PVExs61m9rKZvWBmq/KPTTKzX5jZxvz/0fuhxwIxs7vNrMXM1gx77LDttOCr+WX/kpmdV7jKT9wR2vxFM2vOL+8XzGzpsGGfz7d5g5m9pzBVj4yZzTKzZWa21sxeMbNb8o/Hdlkfpc3RLWt3H7d/hKek6sIAAAQ7SURBVA9vNwOnAsXAi8CiQtcVUVu3ApNf99j/BW7L374N+D+FrnMU2nkpcB6w5o3aCSwFHib89tpFwDOFrn8U2/xF4LOHGXdRfj0vAebm1/9kodtwAm2eBpyXv11FuKzJojgv66O0ObJlPd6P4C8ENrn7FncfAr4PXFXgmsbSVcA9+dv3AB8sYC2jwt2XA3tf9/CR2nkV8F0PngZqzWza2FQ6eo7Q5iO5Cvi+uw+6+2vAJsJ2MK64+y53fy5/uxtYB8wgxsv6KG0+khEv6/Ee8DOAHcPuN3H0F2w8c+DnZrY6f3kHgCnuvit/ezcwpTClRe5I7Yz78v9Uvjvi7mHdb7Frs5nNAc4FnmGCLOvXtRkiWtbjPeAnkre6+3nAe4FPmtmlwwd6eE8X+3NeJ0o7gX8BTgMWA7uALxe2nGiYWSXwIHCru3cNHxbXZX2YNke2rMd7wE+YyyG4e3P+fwvwEOGt2p79b1Pz/1sKV2GkjtTO2C5/d9/j7ll3zwHf5uBb89i02cxShKC7191/mH841sv6cG2OclmP94CfEJdDMLMKM6vafxt4N7CG0Nbfz4/2+8CPC1Nh5I7Uzp8A/y1/hsVFQOewt/fj2uv6l3+XsLwhtPnDZlZiZnOBNwErx7q+kTIzA+4C1rn7V4YNiu2yPlKbI13Whf5keRQ+mV5K+DR6M/AXha4nojaeSvg0/UXglf3tBOqBx4GNwGPApELXOgptvY/wNjVN6HP82JHaSTij4hv5Zf8ysKTQ9Y9im/8936aX8hv6tGHj/0W+zRuA9xa6/hNs81sJ3S8vAS/k/5bGeVkfpc2RLWtdqkBEJKbGexeNiIgcgQJeRCSmFPAiIjGlgBcRiSkFvIhITCngRUaBmV1mZj8tdB0iwyngRURiSgEvE4qZ3WhmK/PX3b7DzJJm1mNm/5i/RvfjZtaQH3exmT2dvwjUQ8OuTT7PzB4zsxfN7DkzOy0/+Uoze8DM1pvZvflvLooUjAJeJgwzWwhcD1zi7ouBLHADUAGscvczgCeBL+Sf8l3gz9z9bMI3Dfc/fi/wDXc/B3gL4VuoEK4OeCvhOt6nApdE3iiRoygqdAEiY+idwPnAs/mD6zLCxaxywA/y43wP+KGZ1QC17v5k/vF7gP/IXxNohrs/BODuAwD56a1096b8/ReAOcBT0TdL5PAU8DKRGHCPu3/+kAfN/up1453o9TsGh93Oou1LCkxdNDKRPA5cY2aNcOD3P2cTtoNr8uP8HvCUu3cC+8zsbfnHbwKe9PBLPE1m9sH8NErMrHxMWyFyjHSEIROGu681s78k/DJWgnD1xk8CvcCF+WEthH56CJer/VY+wLcAH80/fhNwh5n97/w0rh3DZogcM11NUiY8M+tx98pC1yEy2tRFIyISUzqCFxGJKR3Bi4jElAJeRCSmFPAiIjGlgBcRiSkFvIhITP1/UbRpW2az5m8AAAAASUVORK5CYII=\n", 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "tags": [], "needs_background": "light" } }, { "output_type": "stream", "text": [ "DNN results\n", "accuracy : 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "Confusion matrix: \n", "[[378 0]\n", " [ 0 462]]\n", "Validation set report:\n", "cross Loss: -28.105729996874217\n", "hing Loss: 0.5380952380952381\n", "Report precision recall f1-score support\n", "\n", " 0 0.90 0.88 0.89 93\n", " 1 0.91 0.92 0.92 117\n", "\n", " accuracy 0.90 210\n", " macro avg 0.90 0.90 0.90 210\n", "weighted avg 0.90 0.90 0.90 210\n", "\n", "accuracy : 0.9047619047619048\n", "Validation Recall: 0.9047619047619048\n", "Validation Precision: 0.9047003416751317\n", "Confusion matrix: \n", "[[ 82 11]\n", " [ 9 108]]\n", "\n", "\\KNN Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 0.8095238095238095\n", "Validation Recall: 0.8095238095238095\n", "Validation Precision: 0.8160544217687075\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[79 14]\n", " [26 91]]\n", "\n", "\n", "Support vector machine Result\n", "\n", "Train accuracy: 0.861904761904762\n", "Training Recall: 0.861904761904762\n", "Training Precision: 0.8778697836459568\n", "\n", "\n", "Validation accuracy: 0.8142857142857143\n", "Validation Recall: 0.8142857142857143\n", "Validation Precision: 0.8356561888454012\n", "\n", "Train Confusion matrix:\n", " [[274 104]\n", " [ 12 450]]\n", "Validation Confusion matrix:\n", " [[ 59 34]\n", " [ 5 112]]\n", "\n", "\n", "Random Forest Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 0.8238095238095238\n", "Validation Recall: 0.8238095238095238\n", "Validation Precision: 0.8426406926406925\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[ 61 32]\n", " [ 5 112]]\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "QPROXZWG7kT7" }, "source": [ "### importance" ] }, { "cell_type": "code", "metadata": { "id": "VVZrbJBUCDZ6", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "e0cbd33f-9821-4e25-a93b-14343428fcfb" }, "source": [ "idx_IN_columns =[2181, 2184, 2171, 2632, 2620, 2277, 2179, 2177, 202, 2714, 2182, 2183, 2622, 2624, 2625, 2626, 2617, 201, 2178, 2618, 2715, 2716, 2717, 2619, 2719, 2720, 2621, 2623, 2723, 2724]\n", "# # top 30 mutual_info_classif\n", "\n", "\n", "X_copy = X\n", "X = X[:,idx_IN_columns]\n", "\n", "train_models(X, y, y_cat)\n" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Before selectoin\n", "(1050, 30)\n", "After selectoin\n", "(1050, 30)\n", "(210, 30)\n", "Epoch 1/250\n", "7/7 [==============================] - 0s 58ms/step - loss: 0.8358 - accuracy: 0.6238 - val_loss: 0.5379 - val_accuracy: 0.8429\n", "Epoch 2/250\n", "7/7 [==============================] - 3s 395ms/step - loss: 0.4683 - accuracy: 0.8488 - val_loss: 0.4025 - val_accuracy: 0.8381\n", "Epoch 3/250\n", "7/7 [==============================] - 0s 45ms/step - loss: 0.3514 - accuracy: 0.8810 - val_loss: 0.3362 - val_accuracy: 0.8524\n", "Epoch 4/250\n", "7/7 [==============================] - 0s 47ms/step - loss: 0.2377 - accuracy: 0.9214 - val_loss: 0.2932 - val_accuracy: 0.8667\n", "Epoch 5/250\n", "7/7 [==============================] - 0s 45ms/step - loss: 0.2343 - accuracy: 0.9024 - val_loss: 0.2639 - val_accuracy: 0.8810\n", "Epoch 6/250\n", "7/7 [==============================] - 0s 45ms/step - loss: 0.2067 - accuracy: 0.9214 - val_loss: 0.2398 - val_accuracy: 0.8857\n", "Epoch 7/250\n", "7/7 [==============================] - 0s 44ms/step - loss: 0.1953 - accuracy: 0.9250 - val_loss: 0.2158 - val_accuracy: 0.9095\n", "Epoch 8/250\n", "7/7 [==============================] - 0s 40ms/step - loss: 0.1749 - accuracy: 0.9274 - val_loss: 0.1955 - val_accuracy: 0.9143\n", "Epoch 9/250\n", "7/7 [==============================] - 1s 125ms/step - loss: 0.1607 - accuracy: 0.9345 - val_loss: 0.1759 - val_accuracy: 0.9238\n", "Epoch 10/250\n", "7/7 [==============================] - 2s 245ms/step - loss: 0.1462 - accuracy: 0.9464 - val_loss: 0.1565 - val_accuracy: 0.9238\n", "Epoch 11/250\n", "7/7 [==============================] - 0s 50ms/step - loss: 0.1634 - accuracy: 0.9345 - val_loss: 0.1454 - val_accuracy: 0.9381\n", "Epoch 12/250\n", "7/7 [==============================] - 0s 45ms/step - loss: 0.1419 - accuracy: 0.9452 - val_loss: 0.1353 - val_accuracy: 0.9381\n", "Epoch 13/250\n", "7/7 [==============================] - 0s 44ms/step - loss: 0.1353 - accuracy: 0.9476 - val_loss: 0.1291 - val_accuracy: 0.9381\n", "Epoch 14/250\n", "7/7 [==============================] - 0s 50ms/step - loss: 0.1403 - accuracy: 0.9440 - val_loss: 0.1195 - val_accuracy: 0.9429\n", "Epoch 15/250\n", "7/7 [==============================] - 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0.9476\n", "Epoch 22/250\n", "7/7 [==============================] - 3s 374ms/step - loss: 0.0965 - accuracy: 0.9571 - val_loss: 0.0897 - val_accuracy: 0.9524\n", "Epoch 23/250\n", "7/7 [==============================] - 2s 237ms/step - loss: 0.1124 - accuracy: 0.9560 - val_loss: 0.0862 - val_accuracy: 0.9571\n", "Epoch 24/250\n", "7/7 [==============================] - 0s 47ms/step - loss: 0.1031 - accuracy: 0.9571 - val_loss: 0.0857 - val_accuracy: 0.9667\n", "Epoch 25/250\n", "7/7 [==============================] - 0s 38ms/step - loss: 0.1271 - accuracy: 0.9452 - val_loss: 0.0849 - val_accuracy: 0.9619\n", "Epoch 26/250\n", "7/7 [==============================] - 3s 434ms/step - loss: 0.1152 - accuracy: 0.9476 - val_loss: 0.0838 - val_accuracy: 0.9667\n", "Epoch 27/250\n", "7/7 [==============================] - 6s 843ms/step - loss: 0.1115 - accuracy: 0.9548 - val_loss: 0.0829 - val_accuracy: 0.9619\n", "Epoch 28/250\n", "7/7 [==============================] - 2s 329ms/step - loss: 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"7/7 [==============================] - 0s 10ms/step - loss: 0.1045 - accuracy: 0.9571 - val_loss: 0.0710 - val_accuracy: 0.9667\n", "Epoch 36/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.1025 - accuracy: 0.9560 - val_loss: 0.0769 - val_accuracy: 0.9619\n", "Epoch 37/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.1195 - accuracy: 0.9488 - val_loss: 0.0792 - val_accuracy: 0.9619\n", "Epoch 38/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0862 - accuracy: 0.9595 - val_loss: 0.0786 - val_accuracy: 0.9619\n", "Epoch 39/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0836 - accuracy: 0.9643 - val_loss: 0.0790 - val_accuracy: 0.9619\n", "Epoch 40/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0914 - accuracy: 0.9607 - val_loss: 0.0721 - val_accuracy: 0.9667\n", "Epoch 41/250\n", "7/7 [==============================] - 2s 326ms/step - loss: 0.0863 - accuracy: 0.9607 - val_loss: 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0.9762\n", "Epoch 75/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0789 - accuracy: 0.9667 - val_loss: 0.0630 - val_accuracy: 0.9714\n", "Epoch 76/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0811 - accuracy: 0.9595 - val_loss: 0.0660 - val_accuracy: 0.9714\n", "Epoch 77/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0757 - accuracy: 0.9619 - val_loss: 0.0681 - val_accuracy: 0.9714\n", "Epoch 78/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0782 - accuracy: 0.9655 - val_loss: 0.0817 - val_accuracy: 0.9571\n", "Epoch 79/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0690 - accuracy: 0.9655 - val_loss: 0.0947 - val_accuracy: 0.9476\n", "Epoch 80/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0645 - accuracy: 0.9714 - val_loss: 0.0888 - val_accuracy: 0.9571\n", "Epoch 81/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 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0.0718 - val_accuracy: 0.9714\n", "Epoch 95/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0561 - accuracy: 0.9726 - val_loss: 0.0666 - val_accuracy: 0.9714\n", "Epoch 96/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0751 - accuracy: 0.9679 - val_loss: 0.0641 - val_accuracy: 0.9762\n", "Epoch 97/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0712 - accuracy: 0.9667 - val_loss: 0.0665 - val_accuracy: 0.9714\n", "Epoch 98/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0803 - accuracy: 0.9607 - val_loss: 0.0646 - val_accuracy: 0.9762\n", "Epoch 99/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0825 - accuracy: 0.9619 - val_loss: 0.0645 - val_accuracy: 0.9714\n", "Epoch 100/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0669 - accuracy: 0.9690 - val_loss: 0.0717 - val_accuracy: 0.9714\n", "Epoch 101/250\n", "7/7 [==============================] - 0s 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"Epoch 108/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0750 - accuracy: 0.9655 - val_loss: 0.0527 - val_accuracy: 0.9762\n", "Epoch 109/250\n", "7/7 [==============================] - 0s 38ms/step - loss: 0.0619 - accuracy: 0.9726 - val_loss: 0.0490 - val_accuracy: 0.9810\n", "Epoch 110/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0563 - accuracy: 0.9762 - val_loss: 0.0546 - val_accuracy: 0.9762\n", "Epoch 111/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0670 - accuracy: 0.9679 - val_loss: 0.0595 - val_accuracy: 0.9714\n", "Epoch 112/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0555 - accuracy: 0.9726 - val_loss: 0.0659 - val_accuracy: 0.9667\n", "Epoch 113/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0576 - accuracy: 0.9714 - val_loss: 0.0692 - val_accuracy: 0.9667\n", "Epoch 114/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0801 - 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0.0669 - val_accuracy: 0.9667\n", "Epoch 128/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0524 - accuracy: 0.9762 - val_loss: 0.0578 - val_accuracy: 0.9714\n", "Epoch 129/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0921 - accuracy: 0.9536 - val_loss: 0.0534 - val_accuracy: 0.9762\n", "Epoch 130/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0524 - accuracy: 0.9762 - val_loss: 0.0553 - val_accuracy: 0.9762\n", "Epoch 131/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0589 - accuracy: 0.9702 - val_loss: 0.0655 - val_accuracy: 0.9667\n", "Epoch 132/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0556 - accuracy: 0.9762 - val_loss: 0.0758 - val_accuracy: 0.9667\n", "Epoch 133/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0526 - accuracy: 0.9762 - val_loss: 0.0764 - val_accuracy: 0.9619\n", "Epoch 134/250\n", "7/7 [==============================] - 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"Epoch 141/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0632 - accuracy: 0.9702 - val_loss: 0.0727 - val_accuracy: 0.9619\n", "Epoch 142/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0552 - accuracy: 0.9726 - val_loss: 0.0728 - val_accuracy: 0.9619\n", "Epoch 143/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0586 - accuracy: 0.9726 - val_loss: 0.0745 - val_accuracy: 0.9619\n", "Epoch 144/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0631 - accuracy: 0.9690 - val_loss: 0.0694 - val_accuracy: 0.9667\n", "Epoch 145/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0758 - accuracy: 0.9607 - val_loss: 0.0714 - val_accuracy: 0.9667\n", "Epoch 146/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0543 - accuracy: 0.9750 - val_loss: 0.0751 - val_accuracy: 0.9667\n", "Epoch 147/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0590 - 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[==============================] - 0s 8ms/step - loss: 0.0681 - accuracy: 0.9667 - val_loss: 0.0777 - val_accuracy: 0.9619\n", "Epoch 155/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0521 - accuracy: 0.9738 - val_loss: 0.0839 - val_accuracy: 0.9524\n", "Epoch 156/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0720 - accuracy: 0.9679 - val_loss: 0.0817 - val_accuracy: 0.9524\n", "Epoch 157/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0687 - accuracy: 0.9702 - val_loss: 0.0827 - val_accuracy: 0.9571\n", "Epoch 158/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0748 - accuracy: 0.9619 - val_loss: 0.0848 - val_accuracy: 0.9571\n", "Epoch 159/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0620 - accuracy: 0.9702 - val_loss: 0.0813 - val_accuracy: 0.9571\n", "Epoch 160/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0949 - accuracy: 0.9512 - val_loss: 0.0682 - val_accuracy: 0.9667\n", "Epoch 161/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0591 - accuracy: 0.9714 - val_loss: 0.0630 - val_accuracy: 0.9667\n", "Epoch 162/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0546 - accuracy: 0.9738 - val_loss: 0.0583 - val_accuracy: 0.9762\n", "Epoch 163/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0653 - accuracy: 0.9690 - val_loss: 0.0568 - val_accuracy: 0.9714\n", "Epoch 164/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0702 - accuracy: 0.9667 - val_loss: 0.0591 - val_accuracy: 0.9714\n", "Epoch 165/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0475 - accuracy: 0.9762 - val_loss: 0.0693 - val_accuracy: 0.9667\n", "Epoch 166/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0568 - accuracy: 0.9750 - val_loss: 0.0771 - val_accuracy: 0.9571\n", "Epoch 167/250\n", "7/7 [==============================] - 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"Epoch 174/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0766 - accuracy: 0.9667 - val_loss: 0.0662 - val_accuracy: 0.9619\n", "Epoch 175/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0461 - accuracy: 0.9810 - val_loss: 0.0709 - val_accuracy: 0.9619\n", "Epoch 176/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0556 - accuracy: 0.9738 - val_loss: 0.0738 - val_accuracy: 0.9571\n", "Epoch 177/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0481 - accuracy: 0.9810 - val_loss: 0.0729 - val_accuracy: 0.9619\n", "Epoch 178/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0601 - accuracy: 0.9738 - val_loss: 0.0740 - val_accuracy: 0.9571\n", "Epoch 179/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0564 - accuracy: 0.9750 - val_loss: 0.0675 - val_accuracy: 0.9667\n", "Epoch 180/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0513 - 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[==============================] - 0s 8ms/step - loss: 0.0643 - accuracy: 0.9702 - val_loss: 0.0880 - val_accuracy: 0.9524\n", "Epoch 188/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0425 - accuracy: 0.9798 - val_loss: 0.0925 - val_accuracy: 0.9524\n", "Epoch 189/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0467 - accuracy: 0.9774 - val_loss: 0.0929 - val_accuracy: 0.9571\n", "Epoch 190/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0616 - accuracy: 0.9750 - val_loss: 0.0915 - val_accuracy: 0.9524\n", "Epoch 191/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0488 - accuracy: 0.9774 - val_loss: 0.0856 - val_accuracy: 0.9571\n", "Epoch 192/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0575 - accuracy: 0.9690 - val_loss: 0.0824 - val_accuracy: 0.9571\n", "Epoch 193/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0595 - accuracy: 0.9738 - val_loss: 0.0832 - val_accuracy: 0.9524\n", "Epoch 194/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0461 - accuracy: 0.9774 - val_loss: 0.0879 - val_accuracy: 0.9524\n", "Epoch 195/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0631 - accuracy: 0.9702 - val_loss: 0.0841 - val_accuracy: 0.9571\n", "Epoch 196/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0760 - accuracy: 0.9643 - val_loss: 0.0832 - val_accuracy: 0.9571\n", "Epoch 197/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0585 - accuracy: 0.9750 - val_loss: 0.0791 - val_accuracy: 0.9619\n", "Epoch 198/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0561 - accuracy: 0.9726 - val_loss: 0.0748 - val_accuracy: 0.9619\n", "Epoch 199/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0464 - accuracy: 0.9762 - val_loss: 0.0733 - val_accuracy: 0.9619\n", "Epoch 200/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0537 - accuracy: 0.9762 - val_loss: 0.0784 - val_accuracy: 0.9619\n", "Epoch 201/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0828 - accuracy: 0.9595 - val_loss: 0.0751 - val_accuracy: 0.9667\n", "Epoch 202/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0446 - accuracy: 0.9798 - val_loss: 0.0798 - val_accuracy: 0.9619\n", "Epoch 203/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0513 - accuracy: 0.9762 - val_loss: 0.0789 - val_accuracy: 0.9619\n", "Epoch 204/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0706 - accuracy: 0.9643 - val_loss: 0.0840 - val_accuracy: 0.9571\n", "Epoch 205/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0515 - accuracy: 0.9762 - val_loss: 0.0825 - val_accuracy: 0.9571\n", "Epoch 206/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0723 - accuracy: 0.9667 - val_loss: 0.0796 - val_accuracy: 0.9571\n", "Epoch 207/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0496 - accuracy: 0.9738 - val_loss: 0.0748 - val_accuracy: 0.9667\n", "Epoch 208/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0477 - accuracy: 0.9798 - val_loss: 0.0747 - val_accuracy: 0.9619\n", "Epoch 209/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0438 - accuracy: 0.9798 - val_loss: 0.0772 - val_accuracy: 0.9619\n", "Epoch 210/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0564 - accuracy: 0.9738 - val_loss: 0.0766 - val_accuracy: 0.9619\n", "Epoch 211/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0403 - accuracy: 0.9810 - val_loss: 0.0797 - val_accuracy: 0.9571\n", "Epoch 212/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0567 - accuracy: 0.9714 - val_loss: 0.0835 - val_accuracy: 0.9524\n", "Epoch 213/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0344 - accuracy: 0.9845 - val_loss: 0.0839 - val_accuracy: 0.9524\n", "Epoch 214/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0485 - accuracy: 0.9774 - val_loss: 0.0839 - val_accuracy: 0.9524\n", "Epoch 215/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0437 - accuracy: 0.9786 - val_loss: 0.0792 - val_accuracy: 0.9571\n", "Epoch 216/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0540 - accuracy: 0.9738 - val_loss: 0.0798 - val_accuracy: 0.9571\n", "Epoch 217/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0625 - accuracy: 0.9690 - val_loss: 0.0741 - val_accuracy: 0.9619\n", "Epoch 218/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0495 - accuracy: 0.9786 - val_loss: 0.0770 - val_accuracy: 0.9571\n", "Epoch 219/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0706 - accuracy: 0.9655 - val_loss: 0.0825 - val_accuracy: 0.9571\n", "Epoch 220/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0623 - accuracy: 0.9690 - val_loss: 0.0862 - val_accuracy: 0.9524\n", "Epoch 221/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0656 - accuracy: 0.9679 - val_loss: 0.0832 - val_accuracy: 0.9524\n", "Epoch 222/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0491 - accuracy: 0.9786 - val_loss: 0.0797 - val_accuracy: 0.9619\n", "Epoch 223/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0590 - accuracy: 0.9726 - val_loss: 0.0761 - val_accuracy: 0.9619\n", "Epoch 224/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0478 - accuracy: 0.9798 - val_loss: 0.0692 - val_accuracy: 0.9667\n", "Epoch 225/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0513 - accuracy: 0.9750 - val_loss: 0.0671 - val_accuracy: 0.9714\n", "Epoch 226/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0555 - accuracy: 0.9726 - val_loss: 0.0681 - val_accuracy: 0.9619\n", "Epoch 227/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0473 - accuracy: 0.9738 - val_loss: 0.0682 - val_accuracy: 0.9619\n", "Epoch 228/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0666 - accuracy: 0.9690 - val_loss: 0.0781 - val_accuracy: 0.9619\n", "Epoch 229/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0525 - accuracy: 0.9750 - val_loss: 0.0773 - val_accuracy: 0.9619\n", "Epoch 230/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0630 - accuracy: 0.9679 - val_loss: 0.0649 - val_accuracy: 0.9714\n", "Epoch 231/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0740 - accuracy: 0.9679 - val_loss: 0.0880 - val_accuracy: 0.9524\n", "Epoch 232/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0500 - accuracy: 0.9762 - val_loss: 0.0934 - val_accuracy: 0.9571\n", "Epoch 233/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0606 - accuracy: 0.9738 - val_loss: 0.0941 - val_accuracy: 0.9571\n", "Epoch 234/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0561 - accuracy: 0.9750 - val_loss: 0.0815 - val_accuracy: 0.9571\n", "Epoch 235/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0584 - accuracy: 0.9726 - val_loss: 0.0763 - val_accuracy: 0.9619\n", "Epoch 236/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0360 - accuracy: 0.9833 - val_loss: 0.0869 - val_accuracy: 0.9571\n", "Epoch 237/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0480 - accuracy: 0.9750 - val_loss: 0.0910 - val_accuracy: 0.9524\n", "Epoch 238/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0467 - accuracy: 0.9786 - val_loss: 0.0886 - val_accuracy: 0.9571\n", "Epoch 239/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0555 - accuracy: 0.9738 - val_loss: 0.0813 - val_accuracy: 0.9619\n", "Epoch 240/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0538 - accuracy: 0.9726 - val_loss: 0.0817 - val_accuracy: 0.9619\n", "Epoch 241/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0518 - accuracy: 0.9738 - val_loss: 0.0857 - val_accuracy: 0.9524\n", "Epoch 242/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0514 - accuracy: 0.9750 - val_loss: 0.0829 - val_accuracy: 0.9571\n", "Epoch 243/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0495 - accuracy: 0.9810 - val_loss: 0.0829 - val_accuracy: 0.9571\n", "Epoch 244/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0521 - accuracy: 0.9762 - val_loss: 0.0778 - val_accuracy: 0.9571\n", "Epoch 245/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0513 - accuracy: 0.9762 - val_loss: 0.0713 - val_accuracy: 0.9619\n", "Epoch 246/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0485 - accuracy: 0.9750 - val_loss: 0.0715 - val_accuracy: 0.9667\n", "Epoch 247/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0525 - accuracy: 0.9738 - val_loss: 0.0684 - val_accuracy: 0.9714\n", "Epoch 248/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0675 - accuracy: 0.9667 - val_loss: 0.0686 - val_accuracy: 0.9714\n", "Epoch 249/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0504 - accuracy: 0.9774 - val_loss: 0.0663 - val_accuracy: 0.9667\n", "Epoch 250/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0434 - accuracy: 0.9786 - val_loss: 0.0770 - val_accuracy: 0.9619\n", "Valid results - Loss: 0.07701878249645233 - Accuracy: 96.1904764175415%\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", 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\n", 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aBEopVcd/gsARDEAITm0RKKVUPX4UBKGAd4xAg0Apper4TxAEWC2CYHHqZSaUUqoenwaBiAwXkc0iki4i4xt5/UYRyRORNd4/t/ismHotApfepUwpper47A5lImIHJgDnYt2kfoWITDPGbNhv1U+MMXf5qo463jECnT6qlFIN+bJFkAqkG2O2G2OcwGTgEh9u79ACfgkCHSxWSqlf+DIIEoHMes+zvMv2d4WIrBWRqSLStrEPEpGxIpImIml5eXm/rRpv11CITh9VSqkGmnuw+GuggzGmD/A98G5jKxljJhpjUowxKQkJCb9tSw5tESilVGN8GQTZQP0j/CTvsjrGmAJjTI336ZtAf59Vs++EMmr0onNKKVWPL4NgBdBFRJJFJBC4BphWfwURaV3v6Uhgo8+qCQjCINb0UQ0CpZSq47NZQ8YYl4jcBcwE7MAkY8x6EXkMSDPGTAPuFpGRgAsoBG70VT2I4LEHE+yq1fMIlFKqHp8FAYAxZjowfb9lj9R7/CDwoC9rqM8TEExIjXYNKaVUfc09WHxUeezBBKNdQ0opVZ9/BUFAsI4RKKXUfvwqCExACME4qXG5m7sUpZQ6ZvhVEIjDCoJKpwaBUkrt06QgEJG/iEikWN4SkVUicp6vizvSxBFCiGgQKKVUfU1tEfzRGFMKnAfEAH8AnvZZVT5iCwwlGCdVGgRKKVWnqUEg3r8vAN43xqyvt+y4YQvUriGllNpfU4NgpYjMwgqCmSISAXh8V5Zv2IOsrqEqp6u5S1FKqWNGU08ouxnoB2w3xlSKSCxwk+/K8g1xhGqLQCml9tPUFsFgYLMxplhExgAPASW+K8tHAkIIFieVtRoESim1T1OD4DWgUkT6An8DtgHv+awqX3FYZxZX1WjXkFJK7dPUIHAZYwzWHcZeMcZMACJ8V5aPOEKw46HGWd3clSil1DGjqWMEZSLyINa00SEiYgMcvivLR7z3JHDXVDZzIUopdexoaovgaqAG63yCPVg3mfm3z6ryFce+IKhq5kKUUurY0aQg8O78PwSiROQioNoYcxyOEVhB4HFqECil1D5NvcTEKGA5cBUwClgmIlf6sjCfCLDuW2xqtWtIKaX2aWrX0D+AAcaYG4wx1wOpwMO/9iYRGS4im0UkXUTGH2K9K0TEiEhKE+v5bRyhAJhabREopdQ+TQ0CmzEmt97zgl97r4jYgQnACKAHMFpEejSyXgTwF2BZE2v57RxWiwCXBoFSSu3T1CD4TkRmisiNInIj8C373YKyEalAujFmuzHGCUzGmn66v8eBZwDfz+kMtGa8OmorfL4ppZQ6XjR1sHgcMBHo4/0z0RjzwK+8LRHIrPc8y7usjoicArQ1xnx7qA8SkbEikiYiaXl5eU0puXGhMQBEmjKcruPuUklKKeUTTb55vTHmM+CzI7Vh77kIzwM3NmHbE7GCiJSUlN9+n8mQWACipYwqp5vAAL+6L49SSjXqkEEgImVAYzteAYwxJvIQb88G2tZ7nuRdtk8E0AuYJyIArYBpIjLSGJPWhNoPX3AUHrETI+VU1rqIOg7PiVNKqSPtkEFgjPk9l5FYAXQRkWSsALgGuLbeZ5cA8fuei8g84D6fhYC1EWodUcTUlusVSJVSystnfSPGGBdwFzAT2AhMMcasF5HHRGSkr7b7a2qDouu6hpRSSh3GGMFvYYyZzn6zi4wxjxxk3WG+rGUfd3AMsWiLQCml9vG70VITEku0lFGpdylTSinAD4OAkFhipFy7hpRSysvvgkDCYomhnEq9OY1SSgF+GAT2sDiCpBZndXlzl6KUUscEvwuCgHBrxqqnsqCZK1FKqWOD3wWBIyLOelBZ1LyFKKXUMcLvgsAeZrUIbFWFzVyJUkodG/wuCPZdb8hWrS0CpZQCfwyCUCsIAmqKm7kQpZQ6NvhfEITE4EEIdupgsVJKgT8Ggd1BsUQT7sz99XWVUsoP+F8QAAUBLYjSIFBKKcBPg6DY0YI4twaBUkqBnwZBaWBL4t35YH77zc6UUupE4ZdBUB7UihCqoUqnkCqllE+DQESGi8hmEUkXkfGNvH67iPwsImtEZKGI9PBlPftUhbayHpRmH3pFpZTyAz4LAhGxAxOAEUAPYHQjO/qPjDG9jTH9gGexbmbvc9Whra0HJVlHY3NKKXVM82WLIBVIN8ZsN8Y4gcnAJfVXMMaU1nsaBhyVTvvasEQAPMUaBEop5ctbVSYCmfWeZwED919JRO4E7gUCgbMa+yARGQuMBWjXrt3vrywsAaexY4p2EfT7P00ppY5rzT5YbIyZYIzpBDwAPHSQdSYaY1KMMSkJCQm/e5shQQ5yTBymKPPXV1ZKqROcL4MgG2hb73mSd9nBTAYu9WE9dUID7ewyLZCiHUdjc0opdUzzZRCsALqISLKIBALXANPqryAiXeo9vRDY6sN66oQG2tlqknAUbgaP52hsUimljlk+GyMwxrhE5C5gJmAHJhlj1ovIY0CaMWYacJeInAPUAkXADb6qp76QwAC2mCRsrioo2QUxHY7GZpVS6pjky8FijDHTgen7LXuk3uO/+HL7BxMaaGeLJ8l6krtJg0Ap5deafbC4OYQ4rK4hAHI3NG8xSinVzPwyCEID7ZQRSmVwK8jb1NzlKKVUs/LTILB6xIrDO0HuxmauRimlmpdfBkFIoB2A/NBOkLcZXM5mrkgppZqPXwZBqDcIdod2B3cN5GmrQCnlv/wyCBx2Gw67kBHc3VqQvap5C1JKqWbkl0EA1syhPdISQmJgtwaBUsp/+W0QhAYGUFnrhjYnQ/bq5i5HKaWajR8HgZ1KpxvanGKdS+CsbO6SlFKqWfhtEIQHB7Auu4S8yJ5g3LB3XXOXpJRSzcJvg+Dus7pQUO7k1h/c1gIdMFZK+Sm/DYJzerTkb+d1ZU1xKO6wljpgrJTyW34bBAAntY4EoCi6l7YIlFJ+y6+DoHsrKwi2B3aDgq1QXdLMFSml1NHn10EQFeqgTVQwq2rbWwt2r2negpRSqhn4dRAAdG8dyexS7x01M5c3bzFKKdUMfBoEIjJcRDaLSLqIjG/k9XtFZIOIrBWRH0SkvS/raUz3VhGsyRc8LXpCxoKjvXmllGp2PgsCEbEDE4ARQA9gtIj02G+11UCKMaYPMBV41lf1HEyvxChcHsOemBSrReCqOdolKKVUs/JliyAVSDfGbDfGOIHJwCX1VzDGzDXG7DuldymQ5MN6GnVW9xbEhgXyeVFHcFXp7CGllN/xZRAkApn1nmd5lx3MzcCMxl4QkbEikiYiaXl5eUewRAh22LluYDvezGyNQSBj4RH9fKWUOtYdE4PFIjIGSAH+3djrxpiJxpgUY0xKQkLCEd/+dQPbU2zCKQjrouMESim/48sgyAba1nue5F3WgIicA/wDGGmMaZYO+lZRwXRuEc4qW08dJ1BK+R1fBsEKoIuIJItIIHANMK3+CiJyMvA6Vgjk+rCWX5WaHMu3pZ10nEAp5Xd8FgTGGBdwFzAT2AhMMcasF5HHRGSkd7V/A+HApyKyRkSmHeTjfG5gcizzarrqOIFSyu8E+PLDjTHTgen7LXuk3uNzfLn9w5GaHEsJ4RSGdyEuYz4MHdfcJSml1FFxTAwWHwtaR4WQHB/GCnScQCnlXzQI6rmwd2s+L0oGVzVkr2zucpRS6qjQIKjn4r5tWObujkGoSZ/PE99soKSqtrnLUkopn9IgqKdbqwhatmxFRkAyxRvm8ObCHcxcv6e5y1JKKZ/SINjPyL5tmFPdjeiC1YRRxcqMouYuSSmlfEqDYD8X9WnDNPdggnByjX0OaTsLm7skpZTyKQ2C/XSID8Mk9mexuwd/CvqOzLxiiiqczV2WUkr5jAZBI64Z0I73Ay4nzlPAJfZFrNzZsHuoqMKJMaaZqlNKqSNLg6ARo1Pb8t+/34unZR/uCPiGtxZs5cnpGxnz5jLSc8sY+OQPTPtpd3OXqZRSR4QGQSNEhECHHduQv9JRdpOwcwYT529nYXo+d320Gqfbo7OJlFInDA2CQ+lxCaZ1X54NeYdHTw8hMTqETXvKAFiwNR+X24PHo11ESqnjmwbBodjsyKj3CQ50cMPep7lxsHVL5RtP7UBZtYvzX5zP9ZN+ueF9Rn4F1bXuuue1bg+Pfb2BXQWVB3y0UkodKzQIfk1Mezj7Echcxh9bbuWzOwbz13O7YrcJ2/IqWJiez86CCkqrazn/xfm8uWB73Vt/yixm0qId/PeHrc34BY4vlU5Xc5eglN/RIGiKfmMguj32uU/QPymCqBAHT17Wi6cv7w3AjHV7SMsopMbl4efskrq3/ZRlPf567W4KD3MK6sqdhfz549W4/ajracm2Avo9+j17SqqbuxSl/IoGQVMEBMJ5T8Den2HuvwC4ekA7rkltR9+kKGb8nMOy7daJZ1v3lgPg8Rh+ziomNNCO0+XhpR+2HtZ4wjuLd/L1T7vJKvKfbqVNe0pxuj1szy9v7lKU8isaBE3VYySccgMsfBG2za1bfFGfNvyUVcJnq6y7cGYUVPDvmZs487l5pO0s4rTO8VwzoC3vLM7glvfSqHK6D7aFOi63h/lb8gDYnl/hm+/zO+SX1/Dk9I3Uuj1H9HNzy6xLf+eV6SXAlTqafBoEIjJcRDaLSLqIjG/k9TNEZJWIuETkSl/WckQMfxriu8IXt0G5taO+blA7EqNDyC+voWN8GB4Dby3cwc6CSrKKquibFMVTl/fm0ZE9mbs5lxvfXo7rEDtQp8tD2s6iuquebs87vCCornX7/GS3ORtzmTh/O+vqdYMdCbmlGgRKNQefBYGI2IEJwAigBzBaRHrst9ou4EbgI1/VcUQFhsKVk6CyEGb/HwChgQE8crH1tW4Z0hGA6loPEUHWzd/6JEUjItxwageevKw3y3YU8u3POQ0+duveMm56ezml1bVc+NICxry5DLtNCAu0s+Mwuklq3R5Oe3oOr8xJPxLf9qD2llp9+LuLj2xffm6Z9XkaBEodXb5sEaQC6caY7cYYJzAZuKT+CsaYDGPMWuDI9jH4UqteMPhPsOZD2DwDPG7O79mKFf84h6tSkgi0Wz/St24cwJX9k0hNjq1769UpbencIpxX525rMF7w9uIM5m7O473FGWzNLad9XCh/GNSezi0j2HEYXUNZRVUUVDh5ff52SiqtFkWt28P/fbWOzMIjN9awrwtnd3HVEftM+CUAcjUIlDqqfBkEiUBmvedZ3mWHTUTGikiaiKTl5eUdkeJ+lzPGQWQSfHwNvNgbFr9CgjMLR/pMBrZw0b99DKnJsfznqr4EO+x1b7PZhDvP7MTmvWW8NMeaUup0efh2rdVCeH2+NfX09T+k8M+RPekYH8aOw+gayvCGRnmNi9fnbwNgY04p7y7ZyRers9lZUPGbunOMMazIKKS8xpraua9FkN3UIHDVwHd/h5y1h1wtty4IfmlplNe4WJSef9g1q8bVuj08+PlatucdAwPyVUXWbWFzN8KJcu0uY2DjNzD3Scg6fu5y6NOb1x8pxpiJwESAlJSU5v+NCYqAOxZB+mxImwSz/mH9Ad4JjqVk6MsHfeslfRNZsDWfF2dvxSZC5xbhlFTVkhARRF5ZDQkRQXRKCAOgY3wYX6zOptLp4vFvNmITeHRkTwLsDfN7b2k1P27Jo8K7ox7aNYFX522jc4tw9jU8fs4uYdmOAjbmlLH0wbMJDGjaMUBZdS13fbSaH7fkccPg9jx6Sa/DbxFs+gaWToB1n8HYuRDZ5oBVnC5P3RTb+l1DL/2wlTcWbCftH+cQFx7UtO0dAzwew5nPzePOYZ0ZNaDtUdnmVf9bzOBO8dx7bteDrrN5TxkfL88kKSaUO8/sfFTqatSuZfDRKKgutp7HdYHrv4SopF/WMQZEmqe+3+rHZ2Hek9bjLd/BbfOtx+W5MON+iO0Eg++E0NiDf0Yz8GWLIBuo/z8gybvsxBASDb2vhJumwx1LYMSzMHoy9qg2xH55HXx+Gyx5FTZNh6KMuiMem03495V9ufyURJ7/fgt3fbSKxOgQ7hzWCYBBHeMQ7y9/sjcQnv1uMx8v38WHy3Zxxf+WcOdHqxrcQvO1edu4f+pa5m3OIzwogNf/0J+BybE89OU6NuwuBWD1rmJWZBRRWK8sLAgAACAASURBVOFk3ubcJn/Nx77ewIKteXRuEc43a3NwuT3k7hsjKGliEKz5CMISoKaUnz94gJQnZtedI7GroJIal5v8cmvnHxRgqwsaj8cwbc1ujOGwusjq27ynjCkrMn99xcOUU1J1yJZKblkNOwsqD7hybX2zN+zl8lcXNTgb/bdyuT2s2lXM4vo11T/KriyE4l11P8edBc04G60oA96/FELjYPRkuPi/ULbHamFXFoLbBV/eCS/0hB3zm/65riaeq2MM1JRD7UF+f42xDvDSJoHnMHqtMxZaIdD3Wjj/Kcj5CXavgbK9MPFM2PQtLHgO3r4Aaqutele+A/m+HdNrCl+2CFYAXUQkGSsArgGu9eH2mk/LHtYfgI5nwvePWGMIayf/sk5wNHQ5D857HHtEK/59ZV9iQwOpcXm4++wuVNe6eeLbjQzrmlD3ltQOscSHB/LO4gx6J0Yxsm8bpq7MYvrPOSSEB+GwCwOT4/jRO9V0/tY8eraJJNhh56bTOnD7B6vqrpK6b0cL8PmqbM7r2arRr5JbWk1ceBA2gQ+W7eLTlVnceWYn+iRFc9v7K1mQnk9e+b4WwS9dODklVXy1ZjcDk2PpmxTN+t2lRIYE0N5RCtvmwOn3wt51RKSvoqL2Or7+aTfXdhNu+iKHMQPbc1Ffq5XQvXUkP2UWk5ZRyMY9Zezxhs6O/ApSOjR+FJVXVsMHS3cSFx7I9YM7NHjtzQXb+XRlFqnJsXSID/u1f0nAmnlVv0uvMf+euZlv1uaw/tHzcdgPPJ7ad/5HVnEjYzN5W3AHRvDUjM1sy6sgLaOI07vEN6m2g9lTWo3bY9i8twxjDLJ9LkweAx2HgT0AtswEVw0tk24EzmHnoS57Up4LxbsgKeV31XRQsx+1drY3TPulBRCZaAXBKylWQORvgfCW8O7F1t9nPwInj6n7CGMMj369gQt6tya1QwwsmQDfPwyJKXDaX6D7hQe2JmqrYebfYfUH4K4BBIbcC2c9bL0uYu2cp95ktWIB1k6BSyZAXKdf/17b54HY4cL/gNsJPzxq7eiDIqBsN9wy25pt+PHV8MVY6+e8awnYAqBlT6v2Ec+A3fF7f8KHzWdBYIxxichdwEzADkwyxqwXkceANGPMNBEZAHwBxAAXi8ijxpievqrpqHAEwwXPWv+glYVQtAP2rIXslbD2U9g4DToOwz7wdh66cFiDX9b5959J66hgKM6Ehc/TImMhS1vFUlRaDoP+RsKA07n1jI7cP/Un3lmcAcAnKzIprba6hIyBDnHWzm6Ad6eZX15D66hgckqqsQlc2T+JL1ZnU1BeU9fVsmpXET9nlTC0awLnvvAjXVtGEOyws3JnEUO6xHP32V0AiAgO4L3FGdS6TV1XVkF5DTGhgfx39lYme4+8w4MCKK9xERPq4N1uS+hjPNT0HIXx2Gm3eSY3pcSxZNkiBk+7lrPdd/NJmo3eSVEA9GpjBcHVE5fi9hhCHHZq3R4yDnIEW+l0MfKVheSUVBMUYOPK/kmEBv7ya73vPIyPl+/iwQtO+tV/vgVb87j5nTSm3jGYPknRja5jjGHZ9kKcLg8Z+RV0cW2BqmJo1RvCWwDWwH39vwGoLrFaiNP+DNgYUTOSV7iUBel5vzsI9m2nrNpF3tY0Wnx+E4TFQeYycIRC32vAVUPqT28zzJbA5sJBB/mglTB5NJTvhQv+A6m3NrpaZmElLSKDCAo4dGDWMQYW/RdWvQuF2+GM+xt2A3U5F8b+CD88Bh4XnHaPde7Omo8x6z9HvroTCtLhrEegfC/VPzzNjhUtabl5OtTuAGc5tD/dqvuT66D3KLj0VWuHvPV76zMXPAe5G6DfddY08D0/W8sWv2z92107BWY8YIXAeU9YgfTdeHjtNDj/X5Dyx4bhYgwsfAFc1dDnashKsw4IA8OAMOgzyvq+9iDoeRkk9rfeN+hOq6s0MBwuetEKvdwNkPYWVObDlW+DrZGf66r3oPtFPulW8ukYgTFmOjB9v2WP1Hu8AqvL6MQjYv1HDIuzjqxS/mgdFS+fCBu+sprGbQfBsPHWUZsIbaJDrKOEt0dYv9AdzyTAWUGCvRy+/zN07Atxnbjv/G6k55bTOzGKd5fsBOCMNh7OzX2HXqUxsPde4lr2oEuLcLbmljOybxsmLthOr8Qobh3SkSlpWUxJy+KOYZ0oq67lzg9XkVNSzeWnJFLrNhRX1hLkcPPYJT0ZM7A9Npv1y39W9xZ87W1hnNw2mlkb9jLs3/MY2DGOZTsKuLB3a87r2ZIl2wroEB/Gm/O3E7z+E9LoyvJNds4I7EovMYyI20uHqA1QBfc4Pmd6dSqTFu4AoFeiFQhuj+GuMzvTvXUEz83aUtelccd7K4gJD+KPpyfz0JfraBlphdzdZ3fhpR+2snBrfoPWzr5B0U9XZnHveV0JCrDz/tKd7Myv4KGL9p/NbF1V1un28K9vNzJ57KC6bjqwxmJaRgaTVVTlHSg3mLlPwqZXrRVsAZA6FoY/Vdci2F1chdtjsG/7wTri9dRCu8Gk5Tu4z/MpHaNtTEr/Q8Miaqtg52LrQCJ9NiSfwVcyjPnffMCzredh7zTU2vHEdLB+zzweZO0UuoiN0fY5JHw009qJXf8VzsgOOOzC12tzWL19L3+Vb7nSPp+7Svod2PKpLISPr8YVEEJ5mzOInn4fZC5jRchphHc7i9atW/Phsl0sSs9n8bYCxp3fjTvbZ1tHteEtrR38gJutuvZ9FbcHp8tD2OJn4cdnoP1pkHyGddS+v1a94LopDRbt6X49I6Yn8lXHdrRb+II1uFy+l5CCdN4NBGdlIJw8GsLiYdiDANTMfZaghc9CdpoVvpUF1odFJsG1n0LX86znxkC7QdaOOO1teL6H1VI4559w6p+tdTqeCV/dCd/eC1WF1kSRfXb8aB31A/z8KZ6KAtZFn0V3l8cagxv+NORvtX4+9b/v8Cdh2ANWENTf4S96yWrVLHgOht7f8GeTucI6gKgpt2YtHmHHxWDxCSOuk9VSOPcxWP0+LHjeCoT4bhDRyuonrcizdgQ3z4I2J1vvK86E/51mBcSw8bToPYrP/3QaxlhdAZXlJTznfopI+0bseQ6YNAt6XcYTjmJu5QL6tY3msn6JpLaLoMvqp1geNpW5C1Jx93iY55a62FNajd0mfL4qm5R20UwdnQhhLazzJuoZ0iWBr9ZYQdCvnRUEZTUuZm/cC8B1A9txaud4LulnTQ67OC6HxKnZvBt3D6/N3UZFvzb0Arq4NhNvX0elCaKLZHFr/DomZvVBBLq3igCgb1IU953fDYDPVmaxI7+SrXtKuW7rXwi2e5gWOIGl3st6nN+zJX8+qzM1iyYwbOpNsKQPVOTjjO/BOGclCbHBPFg4klU7ixncKY63FmxnZ2ElN52eTGJ0yC9fsGAbLTa+x8UBgXyzI4W5m3M5q3tLAFbuLOKK1xYx97T17AjqDgRwtX0eXTe9wafuobQ4/SaGVnwHS1+FDqeTVWQNj9W6DbmlVbSe8xhEJcK5j+PqdA5//NePvJMQzeUFn/BNcTuK9rYnZsE/rZ1SaY51ZAhgc8DaySQH9OIS1zqc+S2wZy6GeU/hdEQR2H4AxHRg4Jo3+d47lr4ucRQdRz2FPSyGIc/MoXVUMOt3l+LyGNoFDOa6gB+IrC0ns7CSLi0j2FNSzduLd/C3ypcJrCzkhQ4TeWtzIGuGpRK45EUGmE/JWZXEJ6e8xbcLVvJy8OtMCL6KU1a+DT9+3/B3fPtcuHk2uJ3k/jSTi7+Blq4cvgp6Ful7rdXNYmv60OTUlZkU1cAz9tuYcMEAWPIKVJfyfb+X2JA2j/TIVF4eeQfZxVX8Z+p6Hr+0F5euPZ0/tXNweVCa9Tvc/0YIjIAWJzX8nRb5pcXTbjAsfQ1z+l95aGNbztuSx9CuCRDZGq6bCp/fAnOfsv5PdjgDSjKt5xFtrIO5r+/GBryflcDF2ws4o2uC1TIY87kVkK16NfxiwVEHftlT/2y1Uub+y9o3dL/AuppBWAIs/q/VvXzK9U3+2R0ODYLmEBAEA26Bk/9gBcKWmdZUuvgu0Kaf1XTdFwIA0W3h+mkwfRx881f4/p9wxZtIl3N5t+NcApe+jLgqeTb6Ia6/4hJaTbsO1k5hgLuW74IWEJ4xmhE162FuGjjLCIjrz2X507G/+jVDPf3o0vVKtniS+Dk9g7fK34b/7oSodnDFG9YRkzFQXczpnX5pkg5MjsUmcOuQjvy4JY+yaheDOsZZL7pqYNnrJC55BQJCOP2SW3n8f2uZsKyI60Ja0WbnPFpVbOQV90hujV7JfbGLqO40nFYla+lSsYqoQMM9fT1Wd0tINMnx4SzbUciq797mavt6AILSxmKLu5CLgn+mZctUHBsyuE8+ZJtJopsEIK16IRlLuNBeSXi1i/lBc6j+LJHcEf8ho8Aad/hsZRa3D+3ELe+lMS5+Cb3WPsktrmoIgG7BN/LSD7GszSohr6yGoAA719rnkLzyLdph558hF3GZmcNSz0ncX3srY6qSGXrJRbh2r6X2q7+SFzMREetHV7buO1rn/AQjX4EeI1mXWUyF00PukMep/nET/yl8jfCJr4AY6HA6xHa0LnQY2RpiO1L6/h9otWsl/3Vfhv3k+/hDDzsvTpxId2cmozKXI+mzSYs6n9Xl0WyRZL7e1Q/HCyv5z1V9yS2roaDCSbvYUPaUVDPVNZSbAmbycMAHZOSfQVSIg9FvLCWwYCMPBn2IZ/BdfLgskmpPLW8HXst77pM4ybWRCbzExStv5qIQN4meHJ7nP1COdYQ8+C7r9zd3o9WtNOshqC6hxc9TWGwTqoKCKbbFEDPi6boQyC2rJiE8qEGLa38ej2FKWhYAC9LzcY2+mYABt4AxzPlyHR+74gksteHxGL5cnc0Xq7M5qXUE6bnlTDCpXP63+w/62QfodTn0upyswko+fGcu32/Yy4vX9KO0ysXwXq3gohcgexV8cIU1DmCsAf5vku4lPvxsUoOjsVUXs8bTmfZZxVYQgBU8+4fAwYhY24nvAqXZsOZja6bdPkPug6Dwpn+nw6BB0Jz2BcKAW3593Tb9rFZC5jIrED6+BtoOJGjXYjjpYhj8Z+5vN9Ba987l4HFhy/mJhBl/x7HyFWvn0vdq6DqCqE5nc8trM+ix+wtuCZjBmTsfst4XBG5HO6t/dNlEmHQ+tOwNpVlQVUSrVn24OXYEkwu70rNNFHPvG0bbmFBuG9qJqlq31YW0dwN8eoN1ZNvxTBg2nk7tkph4vYM7PlhFVuxA2uz4CgGuH/NHgvf2grlP8JjnfshaDhmwJiQS+aEUdp0H131KcnwoLmc1p+54hZ2OZJ6pvJjHHW9zT8V/wRMLi+cA4A6K59qSB3jjnOH0bx/DFyt2cf9na1n4x0TSPn2WITVphE7/M8E8QYvYGKauzKJFRBCu9Ln03PUU5YlDuGj7pUxOns4dOR8SlJNLZE4lTtOSOQFD+CzgA9YE9GG3O5IbzVe4sfNQ7U0YbGzaUwp2B6+G/Ym78++id8WH5LS6ng05JcStesnqluhzNQDLd1hdFQM6tSIo7lVqJl3OD6Y/Z9/2HI4WXZi6Mos3p2/ntTEdSbSF8FfbeBZ58mkRGUL7zArEEcfbznMA+C7uerq7FzLTeQ5xCeGEBQVQvTmP6loPz363CYA5fxtKbFggj369gakr3WzqdgdXbX6NzQsf4LnYe9hdXMULgdOosYWwrv3NFM/dCMC/Z27CJqFcdebV3DQ3mGccE2kneTDqfbbMnsRbuV14/Iy/W90gIdGUhCQROfguZMkrACyLuZg1RUEMjdrDs4VDeC0gnCCsLrYhz8zlqct7c0X/JHJLq5m9MZfRqW0bBMPyjEJ2FVZybo+WfL9hL6szi62xLxG2eC/u6HR5yCuvYfE2qwX1+o/WuTjb8iooqawlKrRpA6+rdxXxwdJdpCbHANasr2vfWIYIrPjHOcSHR8HtC6zB45JMiOtCjonhrikeTjVZ/C3+EhIzp5Ef1I61WSXsLq4iLCiAqJDDHPgNCv+lW+iMcdw/8XMiijdxd8ccogbdcXifdRg0CI4nItYR+k0zYM7jsP5L68hx5MsNm9si1syDpBQct86CmjKrP9L7n8wOPHL1UEa+bMcx8C7u6eu2mqRle7Cn3moNRvW/EZb9D7b/CImnWAN7q97n4cpnGB8cgGPyR7QfcAsUhxDrCIV1U61porVVVn/tdVOtAUCvs7q3ZNH4swi1nQFpfWDPWqK6nAqtO1tT7rKWw1kPQXw3ZMNX1uDflu9g9xo6JSTxB/ss2kouBSM/YeOsYM7I78unI0PoMfA8q0utLAdnaAdKn0tj9sa99G8fw/b8Shx2G6069WVR1/F8s3Eeb1Y9wj9DpxJ30Qvc+l4a7341gw8dL5PuacN/bPex05TDxf9FZozl+l3fY8RGsKlhpGcxYhPuKL+VHOKYfMU/2Z6ZRfryQALtNjbllLEjv4IXt8TSLSCFW2zTMInXELFnE3GFq5kU9SdS9lTSJymQZdsL6RgfRouIYIgYQNqoNG5/N40rf6wEfmLqSusoePxna7HbhMXbCnj4oh5szyvny9XZbMwpY0iXeIIC7MzeuJcfZSgeU0vfDta5AbcOqea+T39iW14F3VtF0N47geCWIclkFlbS+tLHefWZfP6U8ykXZu9gZOseDM5bwhTH5ezYXoPDLlzcpw2fr87m6gFJXHZyIi/90IPzap7ltZGtOavHIDbU9OeTT9Zwc0EFnRPCySyq5LwX5nNa8gW83noJjqo8/uW+ntj20SSntmPO+ytZs6uYgR3jWJFRiNPtYVF6Plf0T2LC3HTeXbKTri3DWZddwqRFGaQmxxId4iDQbuOxS3oyZ5N1fav2caEkhAexZU8Z7WJD2VVYSXpuOWkZ1jTdgnqXe1+TVWx172DN4ooPD8Lp9pBTXE03bxckWOMY909dy9bccpZuLyAyOIBbh3Rk894yvlmbw5ers5mxbg9/O68rpw64GbAmDMxdngn8zJLtBdwaPILUdpczNCyKhekFXPTyQoIDbFw9oB27i6v412W9Djj/59ek10QxpaAT0ImI9l24J+z3TSg4FA2C41FQuDXWMOKZJq4fccCi5Pgwlv79bEID7VZAJJ5y4HvOGNdwcOz0e3HvWops+Q7Wf251A9TX52orMFLHWmMe+4kPDwKC4Iz7flkYlQiD/mT1oQy5z6qlx0hrkO+F3jB9HIMG3EpK6Ne42p5NXJ/hDNmxji/KauiUeg7Y7VbXWXRbIoHU5Fhmrt+DXYTv1uXQLjaUALuNU9rH8ODKzrzjGM6NfAvmKh4/LZwL0h4nJDiEB1zjWb21nJT2MbRqlYj8cTquaich4qLoxYF0r9rF+h73krMqjoSIIFJO6Y8zqgOOlSu4blA73l6UwcNfriPAbuP7Nn/i1D13Mnbr7YwO8pBnovhP3kCqJyzi7JNaMmdzLjfUm+Z6VvcWjE5tx2crs7DbhJtO60BidAhPfLuRYIeNf1/Zh6tS2vLF6iw+XLYLu014YHh3kmJC2JZXwdSVWXy8fBdJMSF0bhFO5xbhnHNSS95fuvOX7jqge6tIPrltMAC2Mx/kiVmB3BHwNbH560iPP5sns4cTuCqbwZ3i+ePpyWzIKeXOMzuTGB1CXFggBRXQq1c/ADq3sLoo7p+6luJKJwM6xOLyGBbuKOWimH/w5rW9+PmlNO7uHc3A5DhEYOn2QgZ2jKs7t2LlriI8HsOMddb9vx/6ch2b9pQRFxbIF6uzaRkRxMCOsbSOCuH2oR3534/bufK1Jbz3x1TKalxcmZLE24sy+GpNNjUuD6d2imPxtgJSO8SyYmchq3cVMbRrAoUVTs59fj43nNqB0upavliVzZr/O7duxtOHS3eyNbecoAAb2cVVnNktgT+f3QVjDGkZRTz73Wacbg8vfr+V1A6xPPHtRr5bt4d2caF1M+QKqzzcMKw3G3JK+dI7jhYV4uCF2VsA6+KU+2ahvTh7C28t2MGI3q24/JQkTmodSWRwAC6PYWdBJYnRIYQE2pn+8x5ErJmAczfncc85Bz9R8PfSIPBjYUGH+c9vD8CefDokn27N6942x+oDrSmHiJa/TI87XOf/68BlwVHW7Irp92P74lYCI9rAiKcAGHd+N248tUOjUxfPOaklj32zgQnz0okMdjAqxZqUltLeavJ/mXAbY4LzCZh6M2NCYvCEBWO/+TseroilqMLJsG4t6ronwoIDgUCirnuPyrT3ST7vPsLXL+Sq/kkE2G2c0TWBtIfOJT23nLcXZbAwPZ8bBrdnRO/WXP3Gw3we8RrFJor/2Ufzw+3n8/KcdD5atovhPVsxfkT3uppFhKcu783fL+iOTYSwoADcHkOQw85pneLomGDtdM/q3pJRKUnccGoHeraxBhv7tw8kLiyQL1dn07NNZN1njujViveX7uS0zo0fRd4+tBNfRf2DmdX3cW2/OHJ2OSmdtBzKa7jrzM70Soziu3vOqFv/1M7xbMoppUVkMACdEsIRgTWZ1pnBGQWVXNi7NdcPbs8Nby/n4jd+whhrUkFUqINebaKYuiqT6we3Z9Uu6z07Cyr5bv0ecstqaBMVzCbvUf6r153CRS8vZHdJdd2FHMed352U9rHc9M4Kbnh7OTaBy0+2guCbtTnYBO49tyuLty3hzO4tKK2uZe6mXK4e0JbpP++hqtbNjHU5VDrdVNW6Wb+7lKgQBwkRQbwydxundoqjb9toXpu3re58FRHh7JNa8OGyXUSFOFieUcilry5iXXYpdpuwp7Say05OJLOwEgMM6hiLw2797gzqGMtL15zM+t2l3PTOClbvKqZ1VAgFFTW8OncbbWND+HZtTt0YSKcE68rFO/IrCLAJF/VpzawNexmUHMepneJ4fvYWdhdXWTMLfUB8fcniIy0lJcWkpaU1dxnqaKkogLxN0Da1SSfa5JXVMP6ztdxwaodfBuy8Fm/Lp1diFJFUwtf3wLYf4PqvGg7M/4q9pdXEhgU2OImsvMZFr/+bicMu/DjuTNpEh5BdXEVidAglVbXYBCKCrdpzS6tJiDj0IOlvUel0EeKwN/jctVnF9E6MatK2SqpqOfmxWQztmsDbN6Ue8HppdS01tR4SIn65zMeQZ+eQU1zNFack8UlaJlNuG0xqcixpGYXc/9ladhdXsWT82cSEBbJyZyGjJy6jd1IUP2UW0799DMt2FNZdwv39mwcy5s1lvHrdKZzToyUjX1nI2qwS5t03rO5EQGMMV/1vCWk7i7h9aCfGj+jOoCd/YE9pNWMGteOJS3sza/0eTu0cz8fLdvGv6RsJtNuIDHFQVOlscLe/0ant+GTFLuLCrfNhJo8dRGJ0CLe8m8aE606mcwurFZ2WUcgt76Ux6cYB3PDWctzG8PglvdhdXMVz32/h2Sv6MKJ3K0SE8KAAalxu7v54NXcM60y/ttEYYxj01A/0aB1JWkYRZTXWv9Pc+4YRGRLA/C157Cyo5PsNe6l1e7gqpS3rd5fyyYpd9E6K5o0/9Ke61sN5L/5IanIc79w4oG469+ESkZXGmEbPEtQgUP7L7bLOuj0CLn91ESe3i+HhRs5NOF4s2JpH91aRDXb2h7Lv/tw3n55MZmEV7eJ+mZrpdHkoqnTS0tuCAPh2bQ5//WQNTreHCdeewl+nrMHp8nD/8G78aVjnBuc1zN2Uy9zNuTx2ScMZN5v3lPFpWibjhncjKMDOTW8vZ09pDV/86dQDzgbfWVDBP75Yx8L0fO49tyvPf78Fm0BMaCCFlU4E66j/lHbRTLlt8K8G5pa9ZYQFBZAYHYLT5eGzVVlcdnLir56FfscHK+u6v64f3J7BHeMY0bv1Id+z/wHH+0syePir9Tz2/+3df2xdZR3H8feHblSguDI7Bw7GNlgiI8FSx0IYLCYkKiWxmIwwUERDwh9CIiYmjswfhD9MNFGiyaJMWTJwEeLcdFlmFKYZIYGNbunKBsxtiNqmsqFzihF04+sf5+m43t7b1vaenvWezytp7ulzzj19vnl67/ee85z7PT1Xjvj2/Hg5EZjlbPh11OhP+s1m7x+Ps3nvAGu6l/DzvkFmnTOT7jHeFEczXKep3pvxyVPZjZ6WLZjNbeueo3VGC+3nzmRb/xA3LO5gzc1X0NHWmuav8rHumSN8Y/srXD2/nS2fXz6hfUQE391xiFXXzOfCWe8Z+wk1jJYIPEdg1gBOAOPTNf8CuuZn8zW3L5s/6f2N9Wl8RstZpyfMf3TXNUiwqXeAbf1D9HTO44MXvnfU5zfC8N//7HULJrwPSZ4sNjObrOFr+j/R+QEG//Yvbp7Ekcj/46qL23nqiytOX2l1JnIiMLNS6WhrnfK5nMVzR17CfSbJ9eb1ZmZ25nMiMDMrOScCM7OSyzURSPq4pIOSDktaXWN9q6Qn0/pdkhbk2R8zMxspt0QgqQVYC9wELAFul1Q9Q3M3cDwiLgceBsZZPMfMzBolzyOCZcDhiHg1Iv4NPAH0VG3TA2xIy5uAG+ULss3MplSeiWAe8KeK3wdSW81tIuIkcAJ4X9U2SLpHUq+k3mPHjuXUXTOzcpoWk8URsS4ilkbE0jlz5oz9BDMzG7c8v1A2CFxS8fvFqa3WNgOSZgCzgL+MttM9e/a8IekPE+xTB/DGBJ87XZUxZihn3I65HCYa86X1VuSZCF4AFktaSPaGvwq4o2qbrcBdwHPASuA3MUYVvIiY8CGBpN56RZeaVRljhnLG7ZjLIY+Yc0sEEXFS0n3Ar8jujrg+Ig5IegjojYitwKPA45IOA38lSxZmZjaFcq01FBHbge1VbV+rWH4LuDXPPpiZ2eimxWRxA60rugMFKGPMUM64HXM5NDzmaXdjGjMza6yyHRGYmVkVJwIzs5IrTSIYqwBes5D0mqQXJfVJ6k1tsyU9JelQeryg6H5OhqT1ko5K2l/RVjNGZb6Xo/koxgAABEJJREFUxr1fUldxPZ+4OjE/KGkwjXWfpO6KdQ+kmA9K+lgxvZ4cSZdI+q2klyQdkPSF1N60Yz1KzPmOdUQ0/Q/Z5atHgEXA2cA+YEnR/cop1teAjqq2bwGr0/Jq4JtF93OSMa4AuoD9Y8UIdAO/BARcC+wquv8NjPlB4Es1tl2S/sdbgYXpf7+l6BgmEPNFQFdaPh/4XYqtacd6lJhzHeuyHBGMpwBeM6ss7rcBuKXAvkxaRDxD9r2TSvVi7AEei8zzQLukqblZbQPVibmeHuCJiHg7In4PHCZ7DUwrETEUEXvT8j+Al8nqkzXtWI8Scz0NGeuyJILxFMBrFgH8WtIeSfektrkRMZSW/wzMLaZruaoXY7OP/X3pNMj6ilN+TRdzulfJ1cAuSjLWVTFDjmNdlkRQJtdHRBfZfSDulbSicmVkx5NNfc1wGWJMvg9cBnQCQ8C3i+1OPiS1AT8D7o+Iv1eua9axrhFzrmNdlkQwngJ4TSEiBtPjUWAL2WHi68OHyOnxaHE9zE29GJt27CPi9Yg4FRHvAD/k3VMCTROzpJlkb4gbI2Jzam7qsa4Vc95jXZZEcLoAnqSzyWoabS24Tw0n6TxJ5w8vAx8F9vNucT/S4y+K6WGu6sW4FfhMuqLkWuBExWmFaa3q/PcnycYasphXKbsV7EJgMbB7qvs3WekmVY8CL0fEdypWNe1Y14s597EuepZ8Cmfju8lm4I8Aa4ruT04xLiK7gmAfcGA4TrKb/ewADgFPA7OL7usk4/wJ2eHxf8jOid5dL0ayK0jWpnF/EVhadP8bGPPjKab+9IZwUcX2a1LMB4Gbiu7/BGO+nuy0Tz/Ql366m3msR4k517F2iQkzs5Iry6khMzOrw4nAzKzknAjMzErOicDMrOScCMzMSs6JwGwKSfqIpG1F98OskhOBmVnJORGY1SDp05J2p9rvj0hqkfSmpIdTnfgdkuakbTslPZ8Kgm2pqI9/uaSnJe2TtFfSZWn3bZI2SXpF0sb0bVKzwjgRmFWRdAVwG7A8IjqBU8CngPOA3oi4EtgJfD095THgyxFxFdm3P4fbNwJrI+JDwHVk3wyGrKLk/WS15BcBy3MPymwUM4rugNkZ6Ebgw8AL6cP6OWSFzd4Bnkzb/BjYLGkW0B4RO1P7BuCnqebTvIjYAhARbwGk/e2OiIH0ex+wAHg2/7DManMiMBtJwIaIeOB/GqWvVm030fosb1csn8KvQyuYTw2ZjbQDWCnp/XD6HrmXkr1eVqZt7gCejYgTwHFJN6T2O4Gdkd1dakDSLWkfrZLOndIozMbJn0TMqkTES5K+Qnant7PIKn7eC/wTWJbWHSWbR4CsFPIP0hv9q8DnUvudwCOSHkr7uHUKwzAbN1cfNRsnSW9GRFvR/TBrNJ8aMjMrOR8RmJmVnI8IzMxKzonAzKzknAjMzErOicDMrOScCMzMSu6/kcylUEgM6t8AAAAASUVORK5CYII=\n", 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" ] }, "metadata": { "tags": [], "needs_background": "light" } }, { "output_type": "stream", "text": [ "DNN results\n", "accuracy : 0.9845238095238096\n", "Training Recall: 0.9845238095238096\n", "Training Precision: 0.9845376344086021\n", "Confusion matrix: \n", "[[370 8]\n", " [ 5 457]]\n", "Validation set report:\n", "cross Loss: -33.176230421520415\n", "hing Loss: 0.46190476190476193\n", "Report precision recall f1-score support\n", "\n", " 0 0.99 0.97 0.98 93\n", " 1 0.97 0.99 0.98 117\n", "\n", " accuracy 0.98 210\n", " macro avg 0.98 0.98 0.98 210\n", "weighted avg 0.98 0.98 0.98 210\n", "\n", "accuracy : 0.9809523809523809\n", "Validation Recall: 0.9809523809523809\n", "Validation Precision: 0.981087819743282\n", "Confusion matrix: \n", "[[ 90 3]\n", " [ 1 116]]\n", "\n", "\\KNN Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 0.9285714285714286\n", "Validation Recall: 0.9285714285714286\n", "Validation Precision: 0.9286893407399643\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[ 86 7]\n", " [ 8 109]]\n", "\n", "\n", "Support vector machine Result\n", "\n", "Train accuracy: 0.9619047619047619\n", "Training Recall: 0.9619047619047619\n", "Training Precision: 0.9620785740604274\n", "\n", "\n", "Validation accuracy: 0.9523809523809523\n", "Validation Recall: 0.9523809523809523\n", "Validation Precision: 0.9546756302521008\n", "\n", "Train Confusion matrix:\n", " [[357 21]\n", " [ 11 451]]\n", "Validation Confusion matrix:\n", " [[ 84 9]\n", " [ 1 116]]\n", "\n", "\n", "Random Forest Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 0.9523809523809523\n", "Validation Recall: 0.9523809523809523\n", "Validation Precision: 0.9535824422283632\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[ 85 8]\n", " [ 2 115]]\n", "\n", "\n", "LDA Result\n", "\n", "Train accuracy: 0.9714285714285714\n", "Training Recall: 0.9714285714285714\n", "Training Precision: 0.9718648998060763\n", "\n", "\n", "Validation accuracy: 0.9571428571428572\n", "Validation Recall: 0.9571428571428572\n", "Validation Precision: 0.9573809523809524\n", "\n", "Train Confusion matrix:\n", " [[359 19]\n", " [ 5 457]]\n", "Validation Confusion matrix:\n", " [[ 87 6]\n", " [ 3 114]]\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "dqFlFit8BcLK", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "0467ce6f-5372-45d1-cd78-56b86d64a922" }, "source": [ "# # top 20 mutual_info_classif\n", "\n", "idx_IN_columns = [2177, 2625, 2183, 2622, 2182, 201, 2617, 2624, 2178, 2619, 2715, 2716, 2717, 2618, 2719, 2720, 2621, 2623, 2723, 2724]\n", "\n", "X_copy = X\n", "X = X[:,idx_IN_columns]\n", "\n", "train_models(X, y, y_cat)\n" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Before selectoin\n", "(1050, 20)\n", "After selectoin\n", "(1050, 20)\n", "(210, 20)\n", "Epoch 1/250\n", "7/7 [==============================] - 0s 65ms/step - loss: 0.7991 - accuracy: 0.6417 - val_loss: 0.6004 - val_accuracy: 0.8381\n", "Epoch 2/250\n", "7/7 [==============================] - 3s 385ms/step - loss: 0.4611 - accuracy: 0.8286 - val_loss: 0.3559 - val_accuracy: 0.8667\n", "Epoch 3/250\n", "7/7 [==============================] - 3s 397ms/step - loss: 0.3250 - accuracy: 0.8821 - val_loss: 0.2861 - val_accuracy: 0.8762\n", "Epoch 4/250\n", "7/7 [==============================] - 0s 45ms/step - loss: 0.2553 - accuracy: 0.9048 - val_loss: 0.2502 - val_accuracy: 0.8857\n", "Epoch 5/250\n", "7/7 [==============================] - 0s 43ms/step - loss: 0.2114 - accuracy: 0.9226 - val_loss: 0.2268 - val_accuracy: 0.9048\n", "Epoch 6/250\n", "7/7 [==============================] - 0s 47ms/step - loss: 0.2013 - accuracy: 0.9155 - val_loss: 0.2076 - val_accuracy: 0.9095\n", "Epoch 7/250\n", "7/7 [==============================] - 0s 58ms/step - loss: 0.1962 - accuracy: 0.9226 - val_loss: 0.1903 - val_accuracy: 0.9333\n", "Epoch 8/250\n", "7/7 [==============================] - 0s 48ms/step - loss: 0.1724 - accuracy: 0.9274 - val_loss: 0.1760 - val_accuracy: 0.9286\n", "Epoch 9/250\n", "7/7 [==============================] - 0s 47ms/step - loss: 0.1487 - accuracy: 0.9393 - val_loss: 0.1573 - val_accuracy: 0.9333\n", "Epoch 10/250\n", "7/7 [==============================] - 1s 92ms/step - loss: 0.1565 - accuracy: 0.9345 - val_loss: 0.1413 - val_accuracy: 0.9381\n", "Epoch 11/250\n", "7/7 [==============================] - 3s 392ms/step - loss: 0.1410 - accuracy: 0.9524 - val_loss: 0.1331 - val_accuracy: 0.9429\n", "Epoch 12/250\n", "7/7 [==============================] - 0s 49ms/step - loss: 0.1602 - accuracy: 0.9298 - val_loss: 0.1227 - val_accuracy: 0.9619\n", "Epoch 13/250\n", "7/7 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10ms/step - loss: 0.0685 - accuracy: 0.9667 - val_loss: 0.0665 - val_accuracy: 0.9667\n", "Epoch 93/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0639 - accuracy: 0.9690 - val_loss: 0.0691 - val_accuracy: 0.9619\n", "Epoch 94/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0616 - accuracy: 0.9750 - val_loss: 0.0703 - val_accuracy: 0.9619\n", "Epoch 95/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0786 - accuracy: 0.9667 - val_loss: 0.0674 - val_accuracy: 0.9619\n", "Epoch 96/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.1057 - accuracy: 0.9500 - val_loss: 0.0640 - val_accuracy: 0.9619\n", "Epoch 97/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0594 - accuracy: 0.9738 - val_loss: 0.0638 - val_accuracy: 0.9619\n", "Epoch 98/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0741 - accuracy: 0.9631 - val_loss: 0.0645 - val_accuracy: 0.9619\n", "Epoch 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0.0715 - val_accuracy: 0.9619\n", "Epoch 119/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0547 - accuracy: 0.9738 - val_loss: 0.0768 - val_accuracy: 0.9571\n", "Epoch 120/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0708 - accuracy: 0.9667 - val_loss: 0.0797 - val_accuracy: 0.9571\n", "Epoch 121/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0876 - accuracy: 0.9571 - val_loss: 0.0671 - val_accuracy: 0.9619\n", "Epoch 122/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0793 - accuracy: 0.9607 - val_loss: 0.0649 - val_accuracy: 0.9667\n", "Epoch 123/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0822 - accuracy: 0.9595 - val_loss: 0.0697 - val_accuracy: 0.9619\n", "Epoch 124/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0629 - accuracy: 0.9702 - val_loss: 0.0773 - val_accuracy: 0.9571\n", "Epoch 125/250\n", "7/7 [==============================] - 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"Epoch 132/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0643 - accuracy: 0.9726 - val_loss: 0.0605 - val_accuracy: 0.9714\n", "Epoch 133/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0762 - accuracy: 0.9655 - val_loss: 0.0614 - val_accuracy: 0.9714\n", "Epoch 134/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0699 - accuracy: 0.9667 - val_loss: 0.0732 - val_accuracy: 0.9619\n", "Epoch 135/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0611 - accuracy: 0.9738 - val_loss: 0.0752 - val_accuracy: 0.9667\n", "Epoch 136/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0490 - accuracy: 0.9821 - val_loss: 0.0739 - val_accuracy: 0.9667\n", "Epoch 137/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0565 - accuracy: 0.9750 - val_loss: 0.0766 - val_accuracy: 0.9619\n", "Epoch 138/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0357 - 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[==============================] - 0s 8ms/step - loss: 0.0643 - accuracy: 0.9690 - val_loss: 0.0728 - val_accuracy: 0.9619\n", "Epoch 146/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0674 - accuracy: 0.9679 - val_loss: 0.0766 - val_accuracy: 0.9619\n", "Epoch 147/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0571 - accuracy: 0.9738 - val_loss: 0.0798 - val_accuracy: 0.9571\n", "Epoch 148/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0598 - accuracy: 0.9762 - val_loss: 0.0835 - val_accuracy: 0.9571\n", "Epoch 149/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0659 - accuracy: 0.9679 - val_loss: 0.0834 - val_accuracy: 0.9571\n", "Epoch 150/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0568 - accuracy: 0.9738 - val_loss: 0.0781 - val_accuracy: 0.9571\n", "Epoch 151/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0819 - accuracy: 0.9619 - val_loss: 0.0678 - val_accuracy: 0.9619\n", "Epoch 152/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0705 - accuracy: 0.9655 - val_loss: 0.0687 - val_accuracy: 0.9667\n", "Epoch 153/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0667 - accuracy: 0.9690 - val_loss: 0.0685 - val_accuracy: 0.9619\n", "Epoch 154/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0406 - accuracy: 0.9786 - val_loss: 0.0744 - val_accuracy: 0.9619\n", "Epoch 155/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0602 - accuracy: 0.9714 - val_loss: 0.0757 - val_accuracy: 0.9619\n", "Epoch 156/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0613 - accuracy: 0.9702 - val_loss: 0.0785 - val_accuracy: 0.9571\n", "Epoch 157/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0485 - accuracy: 0.9774 - val_loss: 0.0728 - val_accuracy: 0.9619\n", "Epoch 158/250\n", "7/7 [==============================] - 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"Epoch 165/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0560 - accuracy: 0.9774 - val_loss: 0.0807 - val_accuracy: 0.9571\n", "Epoch 166/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0690 - accuracy: 0.9667 - val_loss: 0.0813 - val_accuracy: 0.9619\n", "Epoch 167/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0574 - accuracy: 0.9726 - val_loss: 0.0812 - val_accuracy: 0.9571\n", "Epoch 168/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0640 - accuracy: 0.9702 - val_loss: 0.0781 - val_accuracy: 0.9667\n", "Epoch 169/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0773 - accuracy: 0.9595 - val_loss: 0.0778 - val_accuracy: 0.9619\n", "Epoch 170/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0285 - accuracy: 0.9869 - val_loss: 0.0771 - val_accuracy: 0.9619\n", "Epoch 171/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0649 - 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[==============================] - 0s 9ms/step - loss: 0.0619 - accuracy: 0.9726 - val_loss: 0.0753 - val_accuracy: 0.9619\n", "Epoch 179/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0555 - accuracy: 0.9726 - val_loss: 0.0759 - val_accuracy: 0.9619\n", "Epoch 180/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0659 - accuracy: 0.9667 - val_loss: 0.0793 - val_accuracy: 0.9619\n", "Epoch 181/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0698 - accuracy: 0.9667 - val_loss: 0.0819 - val_accuracy: 0.9571\n", "Epoch 182/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0509 - accuracy: 0.9750 - val_loss: 0.0807 - val_accuracy: 0.9571\n", "Epoch 183/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0588 - accuracy: 0.9726 - val_loss: 0.0724 - val_accuracy: 0.9571\n", "Epoch 184/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0469 - accuracy: 0.9798 - val_loss: 0.0678 - val_accuracy: 0.9619\n", "Epoch 185/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0493 - accuracy: 0.9738 - val_loss: 0.0705 - val_accuracy: 0.9619\n", "Epoch 186/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0899 - accuracy: 0.9548 - val_loss: 0.0717 - val_accuracy: 0.9667\n", "Epoch 187/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0712 - accuracy: 0.9643 - val_loss: 0.0714 - val_accuracy: 0.9667\n", "Epoch 188/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0532 - accuracy: 0.9726 - val_loss: 0.0693 - val_accuracy: 0.9667\n", "Epoch 189/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0492 - accuracy: 0.9750 - val_loss: 0.0679 - val_accuracy: 0.9667\n", "Epoch 190/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0534 - accuracy: 0.9762 - val_loss: 0.0587 - val_accuracy: 0.9714\n", "Epoch 191/250\n", "7/7 [==============================] - 0s 35ms/step - loss: 0.0529 - accuracy: 0.9738 - val_loss: 0.0577 - val_accuracy: 0.9762\n", "Epoch 192/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0711 - accuracy: 0.9655 - val_loss: 0.0678 - val_accuracy: 0.9667\n", "Epoch 193/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0667 - accuracy: 0.9667 - val_loss: 0.0749 - val_accuracy: 0.9667\n", "Epoch 194/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0525 - accuracy: 0.9750 - val_loss: 0.0733 - val_accuracy: 0.9667\n", "Epoch 195/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0524 - accuracy: 0.9750 - val_loss: 0.0731 - val_accuracy: 0.9619\n", "Epoch 196/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0637 - accuracy: 0.9702 - val_loss: 0.0774 - val_accuracy: 0.9619\n", "Epoch 197/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0694 - accuracy: 0.9667 - val_loss: 0.0828 - val_accuracy: 0.9571\n", "Epoch 198/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0377 - accuracy: 0.9845 - val_loss: 0.0822 - val_accuracy: 0.9619\n", "Epoch 199/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0490 - accuracy: 0.9738 - val_loss: 0.0814 - val_accuracy: 0.9619\n", "Epoch 200/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0572 - accuracy: 0.9714 - val_loss: 0.0777 - val_accuracy: 0.9619\n", "Epoch 201/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0637 - accuracy: 0.9679 - val_loss: 0.0699 - val_accuracy: 0.9667\n", "Epoch 202/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0485 - accuracy: 0.9774 - val_loss: 0.0699 - val_accuracy: 0.9667\n", "Epoch 203/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0482 - accuracy: 0.9786 - val_loss: 0.0650 - val_accuracy: 0.9714\n", "Epoch 204/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0439 - 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[==============================] - 0s 9ms/step - loss: 0.0680 - accuracy: 0.9690 - val_loss: 0.0915 - val_accuracy: 0.9524\n", "Epoch 212/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0495 - accuracy: 0.9762 - val_loss: 0.0858 - val_accuracy: 0.9571\n", "Epoch 213/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0554 - accuracy: 0.9762 - val_loss: 0.0757 - val_accuracy: 0.9571\n", "Epoch 214/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0545 - accuracy: 0.9726 - val_loss: 0.0742 - val_accuracy: 0.9667\n", "Epoch 215/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0389 - accuracy: 0.9798 - val_loss: 0.0793 - val_accuracy: 0.9619\n", "Epoch 216/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0618 - accuracy: 0.9726 - val_loss: 0.0777 - val_accuracy: 0.9619\n", "Epoch 217/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0389 - accuracy: 0.9810 - val_loss: 0.0766 - val_accuracy: 0.9619\n", "Epoch 218/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0301 - accuracy: 0.9833 - val_loss: 0.0665 - val_accuracy: 0.9714\n", "Epoch 219/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0494 - accuracy: 0.9774 - val_loss: 0.0620 - val_accuracy: 0.9714\n", "Epoch 220/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0573 - accuracy: 0.9726 - val_loss: 0.0635 - val_accuracy: 0.9714\n", "Epoch 221/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0453 - accuracy: 0.9810 - val_loss: 0.0683 - val_accuracy: 0.9667\n", "Epoch 222/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0588 - accuracy: 0.9726 - val_loss: 0.0784 - val_accuracy: 0.9571\n", "Epoch 223/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0918 - accuracy: 0.9548 - val_loss: 0.0796 - val_accuracy: 0.9619\n", "Epoch 224/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0597 - accuracy: 0.9690 - val_loss: 0.0701 - val_accuracy: 0.9667\n", "Epoch 225/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0342 - accuracy: 0.9845 - val_loss: 0.0711 - val_accuracy: 0.9619\n", "Epoch 226/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0424 - accuracy: 0.9810 - val_loss: 0.0706 - val_accuracy: 0.9667\n", "Epoch 227/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0540 - accuracy: 0.9774 - val_loss: 0.0685 - val_accuracy: 0.9667\n", "Epoch 228/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0573 - accuracy: 0.9726 - val_loss: 0.0680 - val_accuracy: 0.9667\n", "Epoch 229/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0589 - accuracy: 0.9750 - val_loss: 0.0709 - val_accuracy: 0.9667\n", "Epoch 230/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0557 - accuracy: 0.9750 - val_loss: 0.0656 - val_accuracy: 0.9667\n", "Epoch 231/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0616 - accuracy: 0.9679 - val_loss: 0.0653 - val_accuracy: 0.9667\n", "Epoch 232/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0603 - accuracy: 0.9679 - val_loss: 0.0754 - val_accuracy: 0.9619\n", "Epoch 233/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0441 - accuracy: 0.9762 - val_loss: 0.0802 - val_accuracy: 0.9619\n", "Epoch 234/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0382 - accuracy: 0.9845 - val_loss: 0.0774 - val_accuracy: 0.9667\n", "Epoch 235/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0571 - accuracy: 0.9738 - val_loss: 0.0763 - val_accuracy: 0.9667\n", "Epoch 236/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0450 - accuracy: 0.9774 - val_loss: 0.0791 - val_accuracy: 0.9619\n", "Epoch 237/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0527 - accuracy: 0.9726 - val_loss: 0.0827 - val_accuracy: 0.9571\n", "Epoch 238/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0524 - accuracy: 0.9762 - val_loss: 0.0861 - val_accuracy: 0.9571\n", "Epoch 239/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0524 - accuracy: 0.9762 - val_loss: 0.0953 - val_accuracy: 0.9524\n", "Epoch 240/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0639 - accuracy: 0.9690 - val_loss: 0.0938 - val_accuracy: 0.9524\n", "Epoch 241/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0609 - accuracy: 0.9679 - val_loss: 0.0795 - val_accuracy: 0.9619\n", "Epoch 242/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0498 - accuracy: 0.9774 - val_loss: 0.0738 - val_accuracy: 0.9667\n", "Epoch 243/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0476 - accuracy: 0.9786 - val_loss: 0.0743 - val_accuracy: 0.9619\n", "Epoch 244/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0562 - accuracy: 0.9726 - val_loss: 0.0835 - val_accuracy: 0.9571\n", "Epoch 245/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0610 - accuracy: 0.9702 - val_loss: 0.0885 - val_accuracy: 0.9571\n", "Epoch 246/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0552 - accuracy: 0.9738 - val_loss: 0.0865 - val_accuracy: 0.9571\n", "Epoch 247/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0492 - accuracy: 0.9774 - val_loss: 0.0708 - val_accuracy: 0.9667\n", "Epoch 248/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0459 - accuracy: 0.9810 - val_loss: 0.0651 - val_accuracy: 0.9667\n", "Epoch 249/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0601 - accuracy: 0.9738 - val_loss: 0.0707 - val_accuracy: 0.9667\n", "Epoch 250/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0682 - accuracy: 0.9702 - val_loss: 0.0757 - val_accuracy: 0.9619\n", "Valid results - Loss: 0.07568247616291046 - Accuracy: 96.1904764175415%\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", 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\n", 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\n", 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" ] }, "metadata": { "tags": [], "needs_background": "light" } }, { "output_type": "stream", "text": [ "DNN results\n", "accuracy : 0.9904761904761905\n", "Training Recall: 0.9904761904761905\n", "Training Precision: 0.9904761904761905\n", "Confusion matrix: \n", "[[374 4]\n", " [ 4 458]]\n", "Validation set report:\n", "cross Loss: -32.72422973967734\n", "hing Loss: 0.4666666666666667\n", "Report precision recall f1-score support\n", "\n", " 0 0.98 0.97 0.97 93\n", " 1 0.97 0.98 0.98 117\n", "\n", " accuracy 0.98 210\n", " macro avg 0.98 0.98 0.98 210\n", "weighted avg 0.98 0.98 0.98 210\n", "\n", "accuracy : 0.9761904761904762\n", "Validation Recall: 0.9761904761904762\n", "Validation Precision: 0.9762080218970417\n", "Confusion matrix: \n", "[[ 90 3]\n", " [ 2 115]]\n", "\n", "\\KNN Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 0.9285714285714286\n", "Validation Recall: 0.9285714285714286\n", "Validation Precision: 0.9286893407399643\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[ 86 7]\n", " [ 8 109]]\n", "\n", "\n", "Support vector machine Result\n", "\n", "Train accuracy: 0.9678571428571429\n", "Training Recall: 0.9678571428571429\n", "Training Precision: 0.9680052819636477\n", "\n", "\n", "Validation accuracy: 0.9571428571428572\n", "Validation Recall: 0.9571428571428572\n", "Validation Precision: 0.958905797878041\n", "\n", "Train Confusion matrix:\n", " [[360 18]\n", " [ 9 453]]\n", "Validation Confusion matrix:\n", " [[ 85 8]\n", " [ 1 116]]\n", "\n", "\n", "Random Forest Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 0.9666666666666667\n", "Validation Recall: 0.9666666666666667\n", "Validation Precision: 0.9667461482024945\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[ 90 3]\n", " [ 4 113]]\n", "\n", "\n", "LDA Result\n", "\n", "Train accuracy: 0.9630952380952381\n", "Training Recall: 0.9630952380952381\n", "Training Precision: 0.9640908537327976\n", "\n", "\n", "Validation accuracy: 0.9380952380952381\n", "Validation Recall: 0.9380952380952381\n", "Validation Precision: 0.9387694272940175\n", "\n", "Train Confusion matrix:\n", " [[352 26]\n", " [ 5 457]]\n", "Validation Confusion matrix:\n", " [[ 84 9]\n", " [ 4 113]]\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "6cjwbbtu7pkQ", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "38cba311-6159-42b5-b481-1b234bbe41ab" }, "source": [ "# # top 10 mutual_info_classif\n", "idx_IN_columns = [2618, 2621, 2723, 2719, 2716, 2720, 2717, 2623, 2724, 2715]\n", "#importance\n", "\n", "train_models(X, y, y_cat)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Before selectoin\n", "(1050, 10)\n", "After 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"Epoch 14/250\n", "7/7 [==============================] - 1s 122ms/step - loss: 0.1038 - accuracy: 0.9571 - val_loss: 0.1046 - val_accuracy: 0.9571\n", "Epoch 15/250\n", "7/7 [==============================] - 3s 393ms/step - loss: 0.1061 - accuracy: 0.9560 - val_loss: 0.1028 - val_accuracy: 0.9524\n", "Epoch 16/250\n", "7/7 [==============================] - 0s 46ms/step - loss: 0.0931 - accuracy: 0.9631 - val_loss: 0.1015 - val_accuracy: 0.9619\n", "Epoch 17/250\n", "7/7 [==============================] - 0s 50ms/step - loss: 0.1244 - accuracy: 0.9452 - val_loss: 0.0991 - val_accuracy: 0.9571\n", "Epoch 18/250\n", "7/7 [==============================] - 0s 49ms/step - loss: 0.0879 - accuracy: 0.9667 - val_loss: 0.0972 - val_accuracy: 0.9476\n", "Epoch 19/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1002 - accuracy: 0.9571 - val_loss: 0.0980 - val_accuracy: 0.9524\n", "Epoch 20/250\n", "7/7 [==============================] - 0s 47ms/step - loss: 0.1088 - 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0.0813 - val_accuracy: 0.9571\n", "Epoch 120/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0665 - accuracy: 0.9714 - val_loss: 0.0770 - val_accuracy: 0.9619\n", "Epoch 121/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0409 - accuracy: 0.9821 - val_loss: 0.0801 - val_accuracy: 0.9619\n", "Epoch 122/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0425 - accuracy: 0.9798 - val_loss: 0.0858 - val_accuracy: 0.9571\n", "Epoch 123/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0683 - accuracy: 0.9655 - val_loss: 0.0808 - val_accuracy: 0.9619\n", "Epoch 124/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0748 - accuracy: 0.9631 - val_loss: 0.0770 - val_accuracy: 0.9619\n", "Epoch 125/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0491 - accuracy: 0.9774 - val_loss: 0.0706 - val_accuracy: 0.9619\n", "Epoch 126/250\n", "7/7 [==============================] - 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[==============================] - 0s 10ms/step - loss: 0.0593 - accuracy: 0.9714 - val_loss: 0.0740 - val_accuracy: 0.9619\n", "Epoch 147/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0577 - accuracy: 0.9726 - val_loss: 0.0738 - val_accuracy: 0.9619\n", "Epoch 148/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0621 - accuracy: 0.9667 - val_loss: 0.0703 - val_accuracy: 0.9667\n", "Epoch 149/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0505 - accuracy: 0.9774 - val_loss: 0.0722 - val_accuracy: 0.9667\n", "Epoch 150/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0732 - accuracy: 0.9643 - val_loss: 0.0770 - val_accuracy: 0.9667\n", "Epoch 151/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0572 - accuracy: 0.9726 - val_loss: 0.0823 - val_accuracy: 0.9571\n", "Epoch 152/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0548 - accuracy: 0.9726 - val_loss: 0.0765 - val_accuracy: 0.9619\n", "Epoch 153/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0588 - accuracy: 0.9738 - val_loss: 0.0871 - val_accuracy: 0.9571\n", "Epoch 154/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0495 - accuracy: 0.9774 - val_loss: 0.0860 - val_accuracy: 0.9571\n", "Epoch 155/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0720 - accuracy: 0.9655 - val_loss: 0.0785 - val_accuracy: 0.9619\n", "Epoch 156/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0400 - accuracy: 0.9845 - val_loss: 0.0842 - val_accuracy: 0.9571\n", "Epoch 157/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0425 - accuracy: 0.9845 - val_loss: 0.0836 - val_accuracy: 0.9571\n", "Epoch 158/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0398 - accuracy: 0.9833 - val_loss: 0.0789 - val_accuracy: 0.9667\n", "Epoch 159/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0664 - accuracy: 0.9667 - val_loss: 0.0773 - val_accuracy: 0.9619\n", "Epoch 160/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0475 - accuracy: 0.9786 - val_loss: 0.0844 - val_accuracy: 0.9571\n", "Epoch 161/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0615 - accuracy: 0.9679 - val_loss: 0.0849 - val_accuracy: 0.9571\n", "Epoch 162/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0576 - accuracy: 0.9714 - val_loss: 0.0757 - val_accuracy: 0.9619\n", "Epoch 163/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0482 - accuracy: 0.9786 - val_loss: 0.0751 - val_accuracy: 0.9619\n", "Epoch 164/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0767 - accuracy: 0.9631 - val_loss: 0.0792 - val_accuracy: 0.9571\n", "Epoch 165/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0525 - accuracy: 0.9750 - val_loss: 0.0729 - val_accuracy: 0.9667\n", "Epoch 166/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0537 - accuracy: 0.9738 - val_loss: 0.0748 - val_accuracy: 0.9619\n", "Epoch 167/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0423 - accuracy: 0.9798 - val_loss: 0.0817 - val_accuracy: 0.9571\n", "Epoch 168/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0558 - accuracy: 0.9738 - val_loss: 0.0837 - val_accuracy: 0.9571\n", "Epoch 169/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0377 - accuracy: 0.9821 - val_loss: 0.0817 - val_accuracy: 0.9619\n", "Epoch 170/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0478 - accuracy: 0.9762 - val_loss: 0.0857 - val_accuracy: 0.9571\n", "Epoch 171/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0391 - accuracy: 0.9833 - val_loss: 0.0862 - val_accuracy: 0.9524\n", "Epoch 172/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0648 - 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[==============================] - 0s 8ms/step - loss: 0.0591 - accuracy: 0.9726 - val_loss: 0.0833 - val_accuracy: 0.9571\n", "Epoch 180/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0359 - accuracy: 0.9845 - val_loss: 0.0862 - val_accuracy: 0.9571\n", "Epoch 181/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0549 - accuracy: 0.9762 - val_loss: 0.0868 - val_accuracy: 0.9571\n", "Epoch 182/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0377 - accuracy: 0.9869 - val_loss: 0.0869 - val_accuracy: 0.9571\n", "Epoch 183/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0734 - accuracy: 0.9619 - val_loss: 0.0869 - val_accuracy: 0.9571\n", "Epoch 184/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0476 - accuracy: 0.9774 - val_loss: 0.0869 - val_accuracy: 0.9571\n", "Epoch 185/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0452 - accuracy: 0.9774 - val_loss: 0.0858 - val_accuracy: 0.9571\n", "Epoch 186/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0627 - accuracy: 0.9714 - val_loss: 0.0798 - val_accuracy: 0.9619\n", "Epoch 187/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0441 - accuracy: 0.9786 - val_loss: 0.0839 - val_accuracy: 0.9571\n", "Epoch 188/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0714 - accuracy: 0.9667 - val_loss: 0.0871 - val_accuracy: 0.9571\n", "Epoch 189/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0637 - accuracy: 0.9690 - val_loss: 0.0878 - val_accuracy: 0.9571\n", "Epoch 190/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0626 - accuracy: 0.9690 - val_loss: 0.0927 - val_accuracy: 0.9524\n", "Epoch 191/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0491 - accuracy: 0.9738 - val_loss: 0.0933 - val_accuracy: 0.9524\n", "Epoch 192/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0456 - accuracy: 0.9786 - val_loss: 0.0943 - val_accuracy: 0.9524\n", "Epoch 193/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0565 - accuracy: 0.9714 - val_loss: 0.0891 - val_accuracy: 0.9524\n", "Epoch 194/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0449 - accuracy: 0.9821 - val_loss: 0.0794 - val_accuracy: 0.9619\n", "Epoch 195/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0649 - accuracy: 0.9726 - val_loss: 0.0795 - val_accuracy: 0.9619\n", "Epoch 196/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0404 - accuracy: 0.9833 - val_loss: 0.0846 - val_accuracy: 0.9571\n", "Epoch 197/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0614 - accuracy: 0.9690 - val_loss: 0.0861 - val_accuracy: 0.9571\n", "Epoch 198/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0518 - accuracy: 0.9762 - val_loss: 0.0879 - val_accuracy: 0.9571\n", "Epoch 199/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0510 - accuracy: 0.9786 - val_loss: 0.0808 - val_accuracy: 0.9619\n", "Epoch 200/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0439 - accuracy: 0.9821 - val_loss: 0.0751 - val_accuracy: 0.9619\n", "Epoch 201/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0580 - accuracy: 0.9738 - val_loss: 0.0821 - val_accuracy: 0.9619\n", "Epoch 202/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0560 - accuracy: 0.9702 - val_loss: 0.0878 - val_accuracy: 0.9571\n", "Epoch 203/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0417 - accuracy: 0.9821 - val_loss: 0.0862 - val_accuracy: 0.9571\n", "Epoch 204/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0557 - accuracy: 0.9726 - val_loss: 0.0862 - val_accuracy: 0.9571\n", "Epoch 205/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0389 - accuracy: 0.9833 - val_loss: 0.0857 - val_accuracy: 0.9571\n", "Epoch 206/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0400 - accuracy: 0.9786 - val_loss: 0.0825 - val_accuracy: 0.9571\n", "Epoch 207/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0512 - accuracy: 0.9786 - val_loss: 0.0785 - val_accuracy: 0.9619\n", "Epoch 208/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0487 - accuracy: 0.9762 - val_loss: 0.0728 - val_accuracy: 0.9667\n", "Epoch 209/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0604 - accuracy: 0.9702 - val_loss: 0.0758 - val_accuracy: 0.9619\n", "Epoch 210/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0491 - accuracy: 0.9774 - val_loss: 0.0840 - val_accuracy: 0.9571\n", "Epoch 211/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0456 - accuracy: 0.9798 - val_loss: 0.0862 - val_accuracy: 0.9571\n", "Epoch 212/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0659 - accuracy: 0.9667 - val_loss: 0.0815 - val_accuracy: 0.9619\n", "Epoch 213/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0293 - accuracy: 0.9881 - val_loss: 0.0768 - val_accuracy: 0.9619\n", "Epoch 214/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0385 - accuracy: 0.9833 - val_loss: 0.0771 - val_accuracy: 0.9619\n", "Epoch 215/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0679 - accuracy: 0.9679 - val_loss: 0.0721 - val_accuracy: 0.9667\n", "Epoch 216/250\n", "7/7 [==============================] - 0s 35ms/step - loss: 0.0558 - accuracy: 0.9750 - val_loss: 0.0653 - val_accuracy: 0.9714\n", "Epoch 217/250\n", "7/7 [==============================] - 3s 390ms/step - loss: 0.0632 - accuracy: 0.9679 - val_loss: 0.0644 - val_accuracy: 0.9667\n", "Epoch 218/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0585 - accuracy: 0.9750 - val_loss: 0.0768 - val_accuracy: 0.9619\n", "Epoch 219/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0429 - accuracy: 0.9810 - val_loss: 0.0829 - val_accuracy: 0.9571\n", "Epoch 220/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0477 - accuracy: 0.9774 - val_loss: 0.0869 - val_accuracy: 0.9571\n", "Epoch 221/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0362 - accuracy: 0.9833 - val_loss: 0.0889 - val_accuracy: 0.9571\n", "Epoch 222/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0524 - accuracy: 0.9762 - val_loss: 0.0901 - val_accuracy: 0.9571\n", "Epoch 223/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0496 - accuracy: 0.9774 - val_loss: 0.0924 - val_accuracy: 0.9524\n", "Epoch 224/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0475 - accuracy: 0.9774 - val_loss: 0.0856 - val_accuracy: 0.9571\n", "Epoch 225/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0651 - accuracy: 0.9679 - val_loss: 0.0792 - val_accuracy: 0.9619\n", "Epoch 226/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0444 - accuracy: 0.9786 - val_loss: 0.0803 - val_accuracy: 0.9619\n", "Epoch 227/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0478 - accuracy: 0.9762 - val_loss: 0.0882 - val_accuracy: 0.9571\n", "Epoch 228/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0523 - accuracy: 0.9762 - val_loss: 0.0879 - val_accuracy: 0.9571\n", "Epoch 229/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0421 - accuracy: 0.9798 - val_loss: 0.0868 - val_accuracy: 0.9571\n", "Epoch 230/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0333 - accuracy: 0.9845 - val_loss: 0.0848 - val_accuracy: 0.9571\n", "Epoch 231/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0712 - accuracy: 0.9667 - val_loss: 0.0875 - val_accuracy: 0.9571\n", "Epoch 232/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0553 - accuracy: 0.9726 - val_loss: 0.0868 - val_accuracy: 0.9571\n", "Epoch 233/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0626 - accuracy: 0.9679 - val_loss: 0.0916 - val_accuracy: 0.9571\n", "Epoch 234/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0374 - accuracy: 0.9810 - val_loss: 0.0905 - val_accuracy: 0.9524\n", "Epoch 235/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0658 - accuracy: 0.9667 - val_loss: 0.0914 - val_accuracy: 0.9524\n", "Epoch 236/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0331 - accuracy: 0.9845 - val_loss: 0.0899 - val_accuracy: 0.9571\n", "Epoch 237/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0513 - accuracy: 0.9750 - val_loss: 0.0899 - val_accuracy: 0.9571\n", "Epoch 238/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0599 - accuracy: 0.9690 - val_loss: 0.0884 - val_accuracy: 0.9571\n", "Epoch 239/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0469 - accuracy: 0.9762 - val_loss: 0.0824 - val_accuracy: 0.9619\n", "Epoch 240/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0692 - accuracy: 0.9679 - val_loss: 0.0766 - val_accuracy: 0.9619\n", "Epoch 241/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0459 - accuracy: 0.9786 - val_loss: 0.0770 - val_accuracy: 0.9619\n", "Epoch 242/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0482 - accuracy: 0.9762 - val_loss: 0.0725 - val_accuracy: 0.9667\n", "Epoch 243/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0522 - accuracy: 0.9738 - val_loss: 0.0767 - val_accuracy: 0.9619\n", "Epoch 244/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0501 - accuracy: 0.9762 - val_loss: 0.0903 - val_accuracy: 0.9571\n", "Epoch 245/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0497 - accuracy: 0.9774 - val_loss: 0.0932 - val_accuracy: 0.9571\n", "Epoch 246/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0578 - accuracy: 0.9714 - val_loss: 0.0868 - val_accuracy: 0.9571\n", "Epoch 247/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0550 - accuracy: 0.9762 - val_loss: 0.0876 - val_accuracy: 0.9571\n", "Epoch 248/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0418 - accuracy: 0.9798 - val_loss: 0.0910 - val_accuracy: 0.9524\n", "Epoch 249/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0422 - accuracy: 0.9798 - val_loss: 0.0912 - val_accuracy: 0.9524\n", "Epoch 250/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0374 - accuracy: 0.9821 - val_loss: 0.0891 - val_accuracy: 0.9571\n", "Valid results - Loss: 0.08908858895301819 - Accuracy: 95.71428298950195%\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", 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\n", 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\n", 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" ] }, "metadata": { "tags": [], "needs_background": "light" } }, { "output_type": "stream", "text": [ "DNN results\n", "accuracy : 0.9880952380952381\n", "Training Recall: 0.9880952380952381\n", "Training Precision: 0.9882963182762321\n", "Confusion matrix: \n", "[[377 1]\n", " [ 9 453]]\n", "Validation set report:\n", "cross Loss: -32.3948865954365\n", "hing Loss: 0.47619047619047616\n", "Report precision recall f1-score support\n", "\n", " 0 0.96 0.97 0.96 93\n", " 1 0.97 0.97 0.97 117\n", "\n", " accuracy 0.97 210\n", " macro avg 0.97 0.97 0.97 210\n", "weighted avg 0.97 0.97 0.97 210\n", "\n", "accuracy : 0.9666666666666667\n", "Validation Recall: 0.9666666666666667\n", "Validation Precision: 0.9667461482024945\n", "Confusion matrix: \n", "[[ 90 3]\n", " [ 4 113]]\n", "\n", "\\KNN Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 0.9428571428571428\n", "Validation Recall: 0.9428571428571428\n", "Validation Precision: 0.9437538285088691\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[ 89 4]\n", " [ 8 109]]\n", "\n", "\n", "Support vector machine Result\n", "\n", "Train accuracy: 0.975\n", "Training Recall: 0.975\n", "Training Precision: 0.9751670032623893\n", "\n", "\n", "Validation accuracy: 0.9571428571428572\n", "Validation Recall: 0.9571428571428572\n", "Validation Precision: 0.958905797878041\n", "\n", "Train Confusion matrix:\n", " [[363 15]\n", " [ 6 456]]\n", "Validation Confusion matrix:\n", " [[ 85 8]\n", " [ 1 116]]\n", "\n", "\n", "Random Forest Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 1.0\n", "Validation Recall: 1.0\n", "Validation Precision: 1.0\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[ 93 0]\n", " [ 0 117]]\n", "\n", "\n", "LDA Result\n", "\n", "Train accuracy: 0.9476190476190476\n", "Training Recall: 0.9476190476190476\n", "Training Precision: 0.951736111111111\n", "\n", "\n", "Validation accuracy: 0.9380952380952381\n", "Validation Recall: 0.9380952380952381\n", "Validation Precision: 0.9423671602787457\n", "\n", "Train Confusion matrix:\n", " [[335 43]\n", " [ 1 461]]\n", "Validation Confusion matrix:\n", " [[ 81 12]\n", " [ 1 116]]\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "_C8iIIKrsAWw" }, "source": [ "### ANOVA" ] }, { "cell_type": "code", "metadata": { "id": "7A8-gyq6sDVE" }, "source": [ "#https://machinelearningmastery.com/feature-selection-with-numerical-input-data/\n", "# configure to select all features\n", "fs = SelectKBest(score_func=f_classif, k='all')\n", "# learn relationship from training data\n", "fs.fit(X_train, y_train)\n", "# transform train input data\n", "X_train_fs = fs.transform(X_train)\n", "# transform test input data\n", "X_test_fs = fs.transform(X_test)" ], "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "metadata": { "id": "Mcv4h65NhP_1" }, "source": [ "## classification (LDA)" ] }, { "cell_type": "code", "metadata": { "id": "5JXHjefwGS4z", "colab": { "base_uri": "https://localhost:8080/", "height": 33 }, "outputId": "ae969abd-f635-44ec-efbf-9baa2c7b0eaf" }, "source": [ "from sklearn.model_selection import cross_val_score\n", "from sklearn.model_selection import RepeatedStratifiedKFold\n", "# define model\n", "model = LinearDiscriminantAnalysis()\n", "# define model evaluation method\n", "cv = RepeatedStratifiedKFold(n_splits=10, n_repeats=3, random_state=1)\n", "# evaluate model\n", "scores = cross_val_score(model, X, y_cat, scoring='accuracy', cv=cv, n_jobs=-1)\n", "# summarize result\n", "print('Mean Accuracy: %.3f (%.3f)' % (np.mean(scores), np.std(scores)))\n" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Mean Accuracy: 0.689 (0.043)\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "PHFePJ2WGo7r", "colab": { "base_uri": "https://localhost:8080/", "height": 33 }, "outputId": "ceeaa801-d8b3-48ca-c4dc-5bf814f07327" }, "source": [ "#CV=5\n", "print('Mean Accuracy: %.3f (%.3f)' % (np.mean(scores), np.std(scores)))\n" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Mean Accuracy: 0.662 (0.050)\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "u9txzLdK-M0q", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "a6c16ce8-0352-4c32-f1e7-ea26d8e32286" }, "source": [ "#train_lda \n", "\n", "train_models(X, y, y_cat)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Before selectoin\n", "(1050, 2725)\n", "After selectoin\n", "(1050, 2725)\n", "Epoch 1/250\n", "7/7 [==============================] - 1s 93ms/step - loss: 0.9548 - accuracy: 0.5393 - val_loss: 0.9152 - val_accuracy: 0.6619\n", "Epoch 2/250\n", "7/7 [==============================] - 1s 72ms/step - loss: 0.8505 - accuracy: 0.6238 - val_loss: 0.8140 - val_accuracy: 0.7762\n", "Epoch 3/250\n", "7/7 [==============================] - 1s 128ms/step - loss: 0.7440 - accuracy: 0.6667 - val_loss: 0.7390 - val_accuracy: 0.7476\n", "Epoch 4/250\n", "7/7 [==============================] - 2s 245ms/step - loss: 0.6830 - accuracy: 0.7083 - val_loss: 0.6547 - val_accuracy: 0.8048\n", "Epoch 5/250\n", "7/7 [==============================] - 1s 83ms/step - loss: 0.6178 - accuracy: 0.7429 - val_loss: 0.5784 - val_accuracy: 0.8286\n", "Epoch 6/250\n", "7/7 [==============================] - 1s 72ms/step - loss: 0.5365 - accuracy: 0.7667 - val_loss: 0.5117 - val_accuracy: 0.8429\n", "Epoch 7/250\n", "7/7 [==============================] - 1s 121ms/step - loss: 0.5135 - accuracy: 0.7798 - val_loss: 0.4595 - val_accuracy: 0.8429\n", "Epoch 8/250\n", "7/7 [==============================] - 1s 84ms/step - loss: 0.4624 - accuracy: 0.8012 - val_loss: 0.4225 - val_accuracy: 0.8571\n", "Epoch 9/250\n", "7/7 [==============================] - 1s 178ms/step - loss: 0.4222 - accuracy: 0.8286 - val_loss: 0.4002 - val_accuracy: 0.8571\n", "Epoch 10/250\n", "7/7 [==============================] - 4s 539ms/step - loss: 0.4138 - accuracy: 0.8095 - val_loss: 0.3789 - val_accuracy: 0.8762\n", "Epoch 11/250\n", "7/7 [==============================] - 5s 670ms/step - loss: 0.3790 - accuracy: 0.8381 - val_loss: 0.3477 - val_accuracy: 0.8667\n", "Epoch 12/250\n", "7/7 [==============================] - 4s 593ms/step - loss: 0.3737 - accuracy: 0.8345 - val_loss: 0.3323 - val_accuracy: 0.8619\n", "Epoch 13/250\n", "7/7 [==============================] - 2s 307ms/step - loss: 0.3594 - accuracy: 0.8488 - val_loss: 0.3076 - val_accuracy: 0.8667\n", "Epoch 14/250\n", "7/7 [==============================] - 6s 899ms/step - loss: 0.3309 - accuracy: 0.8655 - val_loss: 0.2886 - val_accuracy: 0.8857\n", "Epoch 15/250\n", "7/7 [==============================] - 3s 479ms/step - loss: 0.3113 - accuracy: 0.8655 - val_loss: 0.2782 - val_accuracy: 0.8762\n", "Epoch 16/250\n", "7/7 [==============================] - 7s 1s/step - loss: 0.3070 - accuracy: 0.8643 - val_loss: 0.2714 - val_accuracy: 0.8810\n", "Epoch 17/250\n", "7/7 [==============================] - 2s 272ms/step - loss: 0.2882 - accuracy: 0.8786 - val_loss: 0.2633 - val_accuracy: 0.8857\n", "Epoch 18/250\n", "7/7 [==============================] - 2s 344ms/step - loss: 0.2664 - accuracy: 0.8905 - val_loss: 0.2577 - val_accuracy: 0.8905\n", "Epoch 19/250\n", "7/7 [==============================] - 4s 579ms/step - loss: 0.2738 - accuracy: 0.8869 - val_loss: 0.2521 - val_accuracy: 0.8857\n", "Epoch 20/250\n", "7/7 [==============================] - 6s 858ms/step - loss: 0.2482 - accuracy: 0.8929 - val_loss: 0.2457 - val_accuracy: 0.8810\n", "Epoch 21/250\n", "7/7 [==============================] - 4s 608ms/step - loss: 0.2470 - accuracy: 0.8869 - val_loss: 0.2439 - val_accuracy: 0.8762\n", "Epoch 22/250\n", "7/7 [==============================] - 3s 357ms/step - loss: 0.2318 - accuracy: 0.9012 - val_loss: 0.2425 - val_accuracy: 0.8810\n", "Epoch 23/250\n", "7/7 [==============================] - 6s 824ms/step - loss: 0.2062 - accuracy: 0.9071 - val_loss: 0.2412 - val_accuracy: 0.8905\n", "Epoch 24/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.2295 - accuracy: 0.9131 - val_loss: 0.2584 - val_accuracy: 0.8810\n", "Epoch 25/250\n", "7/7 [==============================] - 4s 581ms/step - loss: 0.1996 - accuracy: 0.9179 - val_loss: 0.2348 - val_accuracy: 0.8905\n", "Epoch 26/250\n", "7/7 [==============================] - 0s 17ms/step - loss: 0.2095 - accuracy: 0.9095 - val_loss: 0.2395 - val_accuracy: 0.8857\n", "Epoch 27/250\n", "7/7 [==============================] - 4s 580ms/step - loss: 0.1914 - accuracy: 0.9179 - val_loss: 0.2291 - val_accuracy: 0.9000\n", "Epoch 28/250\n", "7/7 [==============================] - 0s 18ms/step - loss: 0.1663 - accuracy: 0.9333 - val_loss: 0.2305 - val_accuracy: 0.8952\n", "Epoch 29/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.1618 - accuracy: 0.9274 - val_loss: 0.2463 - val_accuracy: 0.8857\n", "Epoch 30/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.1694 - accuracy: 0.9310 - val_loss: 0.2359 - val_accuracy: 0.8857\n", "Epoch 31/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1579 - accuracy: 0.9310 - val_loss: 0.2603 - val_accuracy: 0.8762\n", "Epoch 32/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1523 - accuracy: 0.9333 - val_loss: 0.2292 - val_accuracy: 0.8952\n", "Epoch 33/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.1607 - accuracy: 0.9321 - val_loss: 0.2910 - val_accuracy: 0.8619\n", "Epoch 34/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1449 - accuracy: 0.9345 - val_loss: 0.2312 - val_accuracy: 0.8905\n", "Epoch 35/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.1208 - accuracy: 0.9536 - val_loss: 0.2459 - val_accuracy: 0.8952\n", "Epoch 36/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.1341 - accuracy: 0.9476 - val_loss: 0.2442 - val_accuracy: 0.8857\n", "Epoch 37/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.1382 - accuracy: 0.9464 - val_loss: 0.2340 - val_accuracy: 0.8810\n", "Epoch 38/250\n", "7/7 [==============================] - 2s 327ms/step - loss: 0.1184 - accuracy: 0.9500 - val_loss: 0.2199 - val_accuracy: 0.9048\n", "Epoch 39/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.1158 - accuracy: 0.9500 - val_loss: 0.2443 - val_accuracy: 0.8810\n", "Epoch 40/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0975 - accuracy: 0.9560 - val_loss: 0.2303 - val_accuracy: 0.8857\n", "Epoch 41/250\n", "7/7 [==============================] - 3s 461ms/step - loss: 0.0877 - accuracy: 0.9607 - val_loss: 0.2199 - val_accuracy: 0.9048\n", "Epoch 42/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0904 - accuracy: 0.9702 - val_loss: 0.2611 - val_accuracy: 0.8762\n", "Epoch 43/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0916 - accuracy: 0.9643 - val_loss: 0.2274 - val_accuracy: 0.8952\n", "Epoch 44/250\n", "7/7 [==============================] - 5s 768ms/step - loss: 0.0901 - accuracy: 0.9643 - val_loss: 0.1982 - val_accuracy: 0.9143\n", "Epoch 45/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1019 - accuracy: 0.9560 - val_loss: 0.2047 - val_accuracy: 0.9048\n", "Epoch 46/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0727 - accuracy: 0.9690 - val_loss: 0.2356 - val_accuracy: 0.8857\n", "Epoch 47/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0823 - accuracy: 0.9667 - val_loss: 0.2169 - val_accuracy: 0.9000\n", "Epoch 48/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0883 - accuracy: 0.9643 - val_loss: 0.2120 - val_accuracy: 0.9048\n", "Epoch 49/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0732 - accuracy: 0.9726 - val_loss: 0.2564 - val_accuracy: 0.8714\n", "Epoch 50/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0993 - accuracy: 0.9595 - val_loss: 0.2230 - val_accuracy: 0.8810\n", "Epoch 51/250\n", "7/7 [==============================] - 4s 541ms/step - loss: 0.0800 - accuracy: 0.9690 - val_loss: 0.1973 - val_accuracy: 0.9143\n", "Epoch 52/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0558 - accuracy: 0.9798 - val_loss: 0.2076 - val_accuracy: 0.8905\n", "Epoch 53/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0648 - accuracy: 0.9750 - val_loss: 0.2333 - val_accuracy: 0.8857\n", "Epoch 54/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1036 - accuracy: 0.9583 - val_loss: 0.2350 - val_accuracy: 0.8905\n", "Epoch 55/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0579 - accuracy: 0.9774 - val_loss: 0.2062 - val_accuracy: 0.9000\n", "Epoch 56/250\n", "7/7 [==============================] - 4s 588ms/step - loss: 0.0723 - accuracy: 0.9726 - val_loss: 0.1886 - val_accuracy: 0.9143\n", "Epoch 57/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0586 - accuracy: 0.9750 - val_loss: 0.2534 - val_accuracy: 0.8810\n", "Epoch 58/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0600 - accuracy: 0.9762 - val_loss: 0.2015 - val_accuracy: 0.9095\n", "Epoch 59/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0457 - accuracy: 0.9833 - val_loss: 0.2260 - val_accuracy: 0.8857\n", "Epoch 60/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0538 - accuracy: 0.9821 - val_loss: 0.2006 - val_accuracy: 0.9095\n", "Epoch 61/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0508 - accuracy: 0.9798 - val_loss: 0.1912 - val_accuracy: 0.9095\n", "Epoch 62/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0598 - accuracy: 0.9774 - val_loss: 0.1952 - val_accuracy: 0.9143\n", "Epoch 63/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0532 - accuracy: 0.9821 - val_loss: 0.2058 - val_accuracy: 0.9048\n", "Epoch 64/250\n", "7/7 [==============================] - 2s 232ms/step - loss: 0.0512 - accuracy: 0.9786 - val_loss: 0.1859 - val_accuracy: 0.9190\n", "Epoch 65/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0408 - accuracy: 0.9857 - val_loss: 0.1962 - val_accuracy: 0.9000\n", "Epoch 66/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0567 - accuracy: 0.9762 - val_loss: 0.2129 - val_accuracy: 0.8952\n", "Epoch 67/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0639 - accuracy: 0.9738 - val_loss: 0.2152 - val_accuracy: 0.8857\n", "Epoch 68/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0339 - accuracy: 0.9857 - val_loss: 0.2107 - val_accuracy: 0.8952\n", "Epoch 69/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0457 - accuracy: 0.9833 - val_loss: 0.2253 - val_accuracy: 0.8905\n", "Epoch 70/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0329 - accuracy: 0.9881 - val_loss: 0.1908 - val_accuracy: 0.9095\n", "Epoch 71/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0527 - accuracy: 0.9798 - val_loss: 0.2089 - val_accuracy: 0.8952\n", "Epoch 72/250\n", "7/7 [==============================] - 3s 399ms/step - loss: 0.0405 - accuracy: 0.9845 - val_loss: 0.1799 - val_accuracy: 0.9190\n", "Epoch 73/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0527 - accuracy: 0.9786 - val_loss: 0.2108 - val_accuracy: 0.8952\n", "Epoch 74/250\n", "7/7 [==============================] - 6s 911ms/step - loss: 0.0465 - accuracy: 0.9821 - val_loss: 0.1791 - val_accuracy: 0.9190\n", "Epoch 75/250\n", "7/7 [==============================] - 4s 594ms/step - loss: 0.0428 - accuracy: 0.9845 - val_loss: 0.1737 - val_accuracy: 0.9190\n", "Epoch 76/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0425 - accuracy: 0.9845 - val_loss: 0.1782 - val_accuracy: 0.9190\n", "Epoch 77/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0402 - accuracy: 0.9821 - val_loss: 0.1933 - val_accuracy: 0.9048\n", "Epoch 78/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0334 - accuracy: 0.9881 - val_loss: 0.1887 - val_accuracy: 0.9095\n", "Epoch 79/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0554 - accuracy: 0.9798 - val_loss: 0.2023 - val_accuracy: 0.9048\n", "Epoch 80/250\n", "7/7 [==============================] - 4s 567ms/step - loss: 0.0287 - accuracy: 0.9905 - val_loss: 0.1718 - val_accuracy: 0.9143\n", "Epoch 81/250\n", "7/7 [==============================] - 3s 498ms/step - loss: 0.0407 - accuracy: 0.9833 - val_loss: 0.1690 - val_accuracy: 0.9190\n", "Epoch 82/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0299 - accuracy: 0.9905 - val_loss: 0.2162 - val_accuracy: 0.8857\n", "Epoch 83/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0307 - accuracy: 0.9893 - val_loss: 0.1889 - val_accuracy: 0.9048\n", "Epoch 84/250\n", "7/7 [==============================] - 2s 320ms/step - loss: 0.0324 - accuracy: 0.9881 - val_loss: 0.1677 - val_accuracy: 0.9286\n", "Epoch 85/250\n", "7/7 [==============================] - 0s 15ms/step - loss: 0.0341 - accuracy: 0.9857 - val_loss: 0.1726 - val_accuracy: 0.9190\n", "Epoch 86/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0183 - accuracy: 0.9952 - val_loss: 0.1912 - val_accuracy: 0.9048\n", "Epoch 87/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0260 - accuracy: 0.9940 - val_loss: 0.1932 - val_accuracy: 0.9000\n", "Epoch 88/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0255 - accuracy: 0.9917 - val_loss: 0.1924 - val_accuracy: 0.9095\n", "Epoch 89/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0334 - accuracy: 0.9869 - val_loss: 0.2281 - val_accuracy: 0.8952\n", "Epoch 90/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0252 - accuracy: 0.9893 - val_loss: 0.1868 - val_accuracy: 0.9143\n", "Epoch 91/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0255 - accuracy: 0.9881 - val_loss: 0.1771 - val_accuracy: 0.9143\n", "Epoch 92/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0279 - accuracy: 0.9893 - val_loss: 0.1740 - val_accuracy: 0.9143\n", "Epoch 93/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0175 - accuracy: 0.9940 - val_loss: 0.1793 - val_accuracy: 0.9143\n", "Epoch 94/250\n", "7/7 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0.1749 - val_accuracy: 0.9095\n", "Epoch 101/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0199 - accuracy: 0.9940 - val_loss: 0.2517 - val_accuracy: 0.8810\n", "Epoch 102/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0236 - accuracy: 0.9929 - val_loss: 0.1716 - val_accuracy: 0.9143\n", "Epoch 103/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0325 - accuracy: 0.9893 - val_loss: 0.1965 - val_accuracy: 0.8952\n", "Epoch 104/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0215 - accuracy: 0.9917 - val_loss: 0.2240 - val_accuracy: 0.8857\n", "Epoch 105/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0291 - accuracy: 0.9857 - val_loss: 0.1992 - val_accuracy: 0.9048\n", "Epoch 106/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0277 - accuracy: 0.9917 - val_loss: 0.1999 - val_accuracy: 0.9048\n", "Epoch 107/250\n", "7/7 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val_loss: 0.2458 - val_accuracy: 0.8810\n", "Epoch 127/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0211 - accuracy: 0.9917 - val_loss: 0.2023 - val_accuracy: 0.9000\n", "Epoch 128/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0088 - accuracy: 0.9964 - val_loss: 0.2433 - val_accuracy: 0.8810\n", "Epoch 129/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0131 - accuracy: 0.9940 - val_loss: 0.2337 - val_accuracy: 0.8810\n", "Epoch 130/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0100 - accuracy: 0.9964 - val_loss: 0.1786 - val_accuracy: 0.9143\n", "Epoch 131/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0349 - accuracy: 0.9821 - val_loss: 0.2285 - val_accuracy: 0.8857\n", "Epoch 132/250\n", "7/7 [==============================] - 2s 242ms/step - loss: 0.0181 - accuracy: 0.9917 - val_loss: 0.1643 - val_accuracy: 0.9238\n", "Epoch 133/250\n", "7/7 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val_loss: 0.2437 - val_accuracy: 0.8762\n", "Epoch 140/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0165 - accuracy: 0.9940 - val_loss: 0.1694 - val_accuracy: 0.9190\n", "Epoch 141/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0103 - accuracy: 0.9952 - val_loss: 0.2052 - val_accuracy: 0.8952\n", "Epoch 142/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0144 - accuracy: 0.9952 - val_loss: 0.1743 - val_accuracy: 0.9190\n", "Epoch 143/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0187 - accuracy: 0.9929 - val_loss: 0.2098 - val_accuracy: 0.8952\n", "Epoch 144/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0257 - accuracy: 0.9917 - val_loss: 0.2465 - val_accuracy: 0.8762\n", "Epoch 145/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0141 - accuracy: 0.9964 - val_loss: 0.2443 - val_accuracy: 0.8714\n", "Epoch 146/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0127 - accuracy: 0.9940 - val_loss: 0.1957 - val_accuracy: 0.9048\n", "Epoch 147/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0111 - accuracy: 0.9940 - val_loss: 0.2282 - val_accuracy: 0.8857\n", "Epoch 148/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0065 - accuracy: 1.0000 - val_loss: 0.2123 - val_accuracy: 0.8952\n", "Epoch 149/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0235 - accuracy: 0.9917 - val_loss: 0.2363 - val_accuracy: 0.8810\n", "Epoch 150/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0074 - accuracy: 0.9976 - val_loss: 0.2345 - val_accuracy: 0.8857\n", "Epoch 151/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0110 - accuracy: 0.9976 - val_loss: 0.2541 - val_accuracy: 0.8810\n", "Epoch 152/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0131 - accuracy: 0.9952 - val_loss: 0.1908 - val_accuracy: 0.9095\n", "Epoch 153/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0041 - accuracy: 0.9988 - val_loss: 0.2599 - val_accuracy: 0.8619\n", "Epoch 154/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0155 - accuracy: 0.9952 - val_loss: 0.2441 - val_accuracy: 0.8762\n", "Epoch 155/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0122 - accuracy: 0.9952 - val_loss: 0.1927 - val_accuracy: 0.9143\n", "Epoch 156/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0065 - accuracy: 0.9976 - val_loss: 0.1974 - val_accuracy: 0.9048\n", "Epoch 157/250\n", "7/7 [==============================] - 0s 14ms/step - loss: 0.0154 - accuracy: 0.9940 - val_loss: 0.2179 - val_accuracy: 0.8905\n", "Epoch 158/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0092 - accuracy: 0.9964 - val_loss: 0.2154 - val_accuracy: 0.8952\n", "Epoch 159/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0093 - accuracy: 0.9976 - val_loss: 0.2093 - val_accuracy: 0.9000\n", "Epoch 160/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0073 - accuracy: 0.9988 - val_loss: 0.2626 - val_accuracy: 0.8714\n", "Epoch 161/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0130 - accuracy: 0.9952 - val_loss: 0.2377 - val_accuracy: 0.8905\n", "Epoch 162/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0102 - accuracy: 0.9952 - val_loss: 0.2314 - val_accuracy: 0.8905\n", "Epoch 163/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0083 - accuracy: 0.9976 - val_loss: 0.2433 - val_accuracy: 0.8810\n", "Epoch 164/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0142 - accuracy: 0.9917 - val_loss: 0.2305 - val_accuracy: 0.8857\n", "Epoch 165/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0098 - accuracy: 0.9964 - val_loss: 0.2292 - val_accuracy: 0.8810\n", "Epoch 166/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0077 - accuracy: 0.9964 - val_loss: 0.2203 - val_accuracy: 0.8905\n", "Epoch 167/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0235 - accuracy: 0.9905 - val_loss: 0.1892 - val_accuracy: 0.9095\n", "Epoch 168/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0247 - accuracy: 0.9917 - val_loss: 0.2024 - val_accuracy: 0.8952\n", "Epoch 169/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0107 - accuracy: 0.9964 - val_loss: 0.2910 - val_accuracy: 0.8476\n", "Epoch 170/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0365 - accuracy: 0.9833 - val_loss: 0.2221 - val_accuracy: 0.8952\n", "Epoch 171/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0061 - accuracy: 0.9976 - val_loss: 0.2564 - val_accuracy: 0.8714\n", "Epoch 172/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0288 - accuracy: 0.9857 - val_loss: 0.2147 - val_accuracy: 0.8952\n", "Epoch 173/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0161 - accuracy: 0.9917 - val_loss: 0.2457 - val_accuracy: 0.8810\n", "Epoch 174/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0184 - accuracy: 0.9917 - val_loss: 0.2261 - val_accuracy: 0.8905\n", "Epoch 175/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0108 - accuracy: 0.9964 - val_loss: 0.2091 - val_accuracy: 0.9048\n", "Epoch 176/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0108 - accuracy: 0.9976 - val_loss: 0.1862 - val_accuracy: 0.9048\n", "Epoch 177/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0319 - accuracy: 0.9869 - val_loss: 0.2364 - val_accuracy: 0.8905\n", "Epoch 178/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0146 - accuracy: 0.9952 - val_loss: 0.2425 - val_accuracy: 0.8810\n", "Epoch 179/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0112 - accuracy: 0.9976 - val_loss: 0.2212 - val_accuracy: 0.8857\n", "Epoch 180/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0131 - accuracy: 0.9940 - val_loss: 0.2306 - val_accuracy: 0.8905\n", "Epoch 181/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0112 - accuracy: 0.9952 - val_loss: 0.2121 - val_accuracy: 0.9000\n", "Epoch 182/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0068 - accuracy: 0.9976 - val_loss: 0.1926 - val_accuracy: 0.9095\n", "Epoch 183/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0055 - accuracy: 1.0000 - val_loss: 0.1901 - val_accuracy: 0.9000\n", "Epoch 184/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0174 - accuracy: 0.9940 - val_loss: 0.1853 - val_accuracy: 0.9095\n", "Epoch 185/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0053 - accuracy: 0.9988 - val_loss: 0.1935 - val_accuracy: 0.9048\n", "Epoch 186/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0060 - accuracy: 0.9976 - val_loss: 0.1978 - val_accuracy: 0.9095\n", "Epoch 187/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0050 - accuracy: 0.9988 - val_loss: 0.2008 - val_accuracy: 0.9095\n", "Epoch 188/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0091 - accuracy: 0.9964 - val_loss: 0.1890 - val_accuracy: 0.9095\n", "Epoch 189/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0086 - accuracy: 0.9988 - val_loss: 0.1883 - val_accuracy: 0.9048\n", "Epoch 190/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0183 - accuracy: 0.9917 - val_loss: 0.1865 - val_accuracy: 0.9095\n", "Epoch 191/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0018 - accuracy: 1.0000 - val_loss: 0.2037 - val_accuracy: 0.9000\n", "Epoch 192/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0086 - accuracy: 0.9964 - val_loss: 0.1772 - val_accuracy: 0.9143\n", "Epoch 193/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0164 - accuracy: 0.9929 - val_loss: 0.1859 - val_accuracy: 0.9095\n", "Epoch 194/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0022 - accuracy: 1.0000 - val_loss: 0.1793 - val_accuracy: 0.9143\n", "Epoch 195/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0097 - accuracy: 0.9976 - val_loss: 0.1869 - val_accuracy: 0.9000\n", "Epoch 196/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0050 - accuracy: 0.9988 - val_loss: 0.2260 - val_accuracy: 0.8857\n", "Epoch 197/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0076 - accuracy: 0.9988 - val_loss: 0.1979 - val_accuracy: 0.9048\n", "Epoch 198/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0109 - accuracy: 0.9964 - val_loss: 0.1924 - val_accuracy: 0.9048\n", "Epoch 199/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0067 - accuracy: 0.9976 - val_loss: 0.1993 - val_accuracy: 0.9000\n", "Epoch 200/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0101 - accuracy: 0.9964 - val_loss: 0.2129 - val_accuracy: 0.8952\n", "Epoch 201/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0162 - accuracy: 0.9929 - val_loss: 0.1804 - val_accuracy: 0.9095\n", "Epoch 202/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0084 - accuracy: 0.9976 - val_loss: 0.1880 - val_accuracy: 0.9143\n", "Epoch 203/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0187 - accuracy: 0.9917 - val_loss: 0.2532 - val_accuracy: 0.8762\n", "Epoch 204/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0098 - accuracy: 0.9964 - val_loss: 0.1709 - val_accuracy: 0.9190\n", "Epoch 205/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0154 - accuracy: 0.9940 - val_loss: 0.1722 - val_accuracy: 0.9143\n", "Epoch 206/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0075 - accuracy: 0.9964 - val_loss: 0.1714 - val_accuracy: 0.9190\n", "Epoch 207/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0057 - accuracy: 0.9976 - val_loss: 0.1932 - val_accuracy: 0.9048\n", "Epoch 208/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0082 - accuracy: 0.9976 - val_loss: 0.1952 - val_accuracy: 0.9000\n", "Epoch 209/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0226 - accuracy: 0.9905 - val_loss: 0.2223 - val_accuracy: 0.8857\n", "Epoch 210/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0085 - accuracy: 0.9964 - val_loss: 0.2549 - val_accuracy: 0.8714\n", "Epoch 211/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0076 - accuracy: 0.9976 - val_loss: 0.2068 - val_accuracy: 0.9000\n", "Epoch 212/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0216 - accuracy: 0.9917 - val_loss: 0.1927 - val_accuracy: 0.9095\n", "Epoch 213/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0045 - accuracy: 0.9976 - val_loss: 0.2715 - val_accuracy: 0.8667\n", "Epoch 214/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0081 - accuracy: 0.9976 - val_loss: 0.2359 - val_accuracy: 0.8810\n", "Epoch 215/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0077 - accuracy: 0.9976 - val_loss: 0.2002 - val_accuracy: 0.9048\n", "Epoch 216/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0072 - accuracy: 0.9964 - val_loss: 0.2272 - val_accuracy: 0.8857\n", "Epoch 217/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0038 - accuracy: 0.9988 - val_loss: 0.1832 - val_accuracy: 0.9143\n", "Epoch 218/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0149 - accuracy: 0.9952 - val_loss: 0.1857 - val_accuracy: 0.9000\n", "Epoch 219/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0050 - accuracy: 0.9988 - val_loss: 0.2203 - val_accuracy: 0.8905\n", "Epoch 220/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0104 - accuracy: 0.9964 - val_loss: 0.2018 - val_accuracy: 0.9000\n", "Epoch 221/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0037 - accuracy: 0.9988 - val_loss: 0.2075 - val_accuracy: 0.8952\n", "Epoch 222/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0154 - accuracy: 0.9929 - val_loss: 0.1918 - val_accuracy: 0.9095\n", "Epoch 223/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0117 - accuracy: 0.9952 - val_loss: 0.1910 - val_accuracy: 0.9048\n", "Epoch 224/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0064 - accuracy: 0.9964 - val_loss: 0.2197 - val_accuracy: 0.8905\n", "Epoch 225/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0030 - accuracy: 0.9988 - val_loss: 0.1987 - val_accuracy: 0.9000\n", "Epoch 226/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0066 - accuracy: 0.9976 - val_loss: 0.1770 - val_accuracy: 0.9143\n", "Epoch 227/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0049 - accuracy: 0.9988 - val_loss: 0.1833 - val_accuracy: 0.9143\n", "Epoch 228/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0021 - accuracy: 1.0000 - val_loss: 0.1862 - val_accuracy: 0.9095\n", "Epoch 229/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0040 - accuracy: 0.9976 - val_loss: 0.2027 - val_accuracy: 0.9095\n", "Epoch 230/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0109 - accuracy: 0.9940 - val_loss: 0.2462 - val_accuracy: 0.8762\n", "Epoch 231/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0071 - accuracy: 0.9976 - val_loss: 0.1918 - val_accuracy: 0.9048\n", "Epoch 232/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0088 - accuracy: 0.9964 - val_loss: 0.2066 - val_accuracy: 0.8952\n", "Epoch 233/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0058 - accuracy: 0.9976 - val_loss: 0.2084 - val_accuracy: 0.9000\n", "Epoch 234/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0032 - accuracy: 0.9988 - val_loss: 0.1973 - val_accuracy: 0.9095\n", "Epoch 235/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0077 - accuracy: 0.9976 - val_loss: 0.2126 - val_accuracy: 0.8952\n", "Epoch 236/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0110 - accuracy: 0.9964 - val_loss: 0.1957 - val_accuracy: 0.9048\n", "Epoch 237/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0059 - accuracy: 0.9976 - val_loss: 0.2746 - val_accuracy: 0.8619\n", "Epoch 238/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0096 - accuracy: 0.9952 - val_loss: 0.2202 - val_accuracy: 0.8905\n", "Epoch 239/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0102 - accuracy: 0.9952 - val_loss: 0.2595 - val_accuracy: 0.8714\n", "Epoch 240/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0093 - accuracy: 0.9976 - val_loss: 0.2605 - val_accuracy: 0.8714\n", "Epoch 241/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0193 - accuracy: 0.9905 - val_loss: 0.2315 - val_accuracy: 0.8857\n", "Epoch 242/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0128 - accuracy: 0.9940 - val_loss: 0.2040 - val_accuracy: 0.8952\n", "Epoch 243/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0105 - accuracy: 0.9976 - val_loss: 0.2457 - val_accuracy: 0.8810\n", "Epoch 244/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0124 - accuracy: 0.9940 - val_loss: 0.3296 - val_accuracy: 0.8381\n", "Epoch 245/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0096 - accuracy: 0.9952 - val_loss: 0.2284 - val_accuracy: 0.8905\n", "Epoch 246/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0023 - accuracy: 1.0000 - val_loss: 0.2196 - val_accuracy: 0.8905\n", "Epoch 247/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0069 - accuracy: 0.9976 - val_loss: 0.2136 - val_accuracy: 0.8952\n", "Epoch 248/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0043 - accuracy: 0.9988 - val_loss: 0.2561 - val_accuracy: 0.8714\n", "Epoch 249/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.0061 - accuracy: 0.9976 - val_loss: 0.2570 - val_accuracy: 0.8714\n", "Epoch 250/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0092 - accuracy: 0.9976 - val_loss: 0.1802 - val_accuracy: 0.9143\n", "Valid results - Loss: 0.18016882240772247 - Accuracy: 91.42857193946838%\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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ZzTOzedXV1YkuR0QkMBIe8M65Sc65Mc65McXFxQc9fVtHhAdnrWP26po4VCcicuRKeMB3VSgpid9OW8GT8ysSXYqIyEfKER/wSZFWHsr4Fb2WP4JzLtHliIh8ZMTzNMkpwGzgODOrMLPPxWVBoQwGUsm49jmsrm6MyyJERI5E8TyL5nrnXD/nXMg5V+qceyBey7Kjz2Nc0vvMXrYpXosQETniHPFDNAA5wz9BhrVTtfTVRJciIvKREYiAp+x0OixE4ZbXCUeiia5GROQjIRgBn5pFbfHJnO3msnDjjkRXIyLykRCMgAcyRl9HedJWVi+YkehSREQ+EgIT8FmjrqSVNApXPpnoUkREPhICE/Ck5bCq+DxOaZ5JZU1toqsREUm44AQ80Hv8p8ixFma/NCXRpYiIJFywAn7k+dQlF5C94mma2zsSXY6ISEIFKuBJTqH12Ms40y3g2TnvJ7oaEZGEClbAA31Ou5E0C7PujceIRnVtGhHpuQIX8JSOoSmzlNNaZvLKsqpEVyMikjDBC3gzMk6ayPjkJUx7e2GiqxERSZjgBTyQdPw1JBMlb9UzNLSGE12OiEhCBDLg6T2UxuKTuM6mMnXJlkRXIyKSEMEMeCDrjC8yKKmS6c8/xvz1uj6NiPQ8gQ14G3Y5HRnFfIZn+a+/vk1tc3uiSxIR6VaBDXhSUkk56xuMiS7k+LYF3Dt9VaIrEhHpVsENeIAx/wX5R/HznMd4dPZKVmxtSHRFIiLdJtgBn5IGF/6Cfm1ruDP1b9w25R22N2moRkR6hmAHPMBxF8D4W7navcznan7O+Lue497pKxNdlYhI3KUkuoBucd6dEMriypk/45SMTVw/9aukpSTzieF9OaooM9HViYjERfB78ABJSXD2d7AbnqQkaTtPZd7NpBdmcdYvZ/DVKe9Q16IvQ4lI8PSMgN/pmI9hN/2D4qRGZhXdxT0jVvHvxVv41OS51DUr5EUkTnash7buP8mjZwU8QMlo7FPPEMrtwyUrf8jUEdNYtnkHE+55nYdmr2OlzrQRkcMp3AL3nQEv/6DbF93zAh5gwMlw83Q4+WbKlj/A/JJfMdaW8KNnFnHB717n96+sJHK4LjX83mOw5tXDMy+Rw6ViHrzzt0RX0TOsmgatdbDiJXDdewnznhnwAEnJMOEXcOX9ZDes5Tct32dV/ld5puAemqf/ku/97j6u++NMJr22GrfnSmneDu3N+553NApbl0L9Znjmy/DPL0HHAZyeWbvBz3t/DmQ+QbNtZbe/MT6SalZDZD+/VNZYDZvfPbB5Tf0hPPtVqKuAyBE+PNl525h1Lyx4uHuXv+x5+OvFvqe+Yx2smbn7+3jJP/3/DZuhetmu+9uboXZjXF//nnEWzb6YwfHXwpCLYfkLJK+ZyfCNbzGiaTbUPUqYFBZNK2P6rEGkZecxoryU/FAE3v4L5PSDT/wUXBQGnQUYvH2/31NvWwHv/wuKjoFoGOor4KXv+g0gLRsGnweDz4XKhbB6Ooz+DETa4c+nQ0ahP7rILPQ1Nm2DtBx/Tv/7/4KnboYr/gzDL9/Vjoatfvr8Af7v+Q/C3Pvhcy9BalZ3v6qH19rX4cGL4YpJULMKws3wiZ8c2LSRDmiqhtx+ne4Lw9YlkN0bcvvvf/pVr8CG2TDyWnjnITjli7B9DdSshCGXQHbx7s9vqYWVU2Hk1X7b2qlpGyz+B4z+tF+PO7XWwfS7/Lob+wXoM2zXY9vX+pB47RfQWgvn/w88cD70Og6unAT9R+2+bOfgsRth41v+ucMu9bXkD4SjP+ZPNNipsdq3y0V9yG94Cyb8HEIZvtaTP+87QACNVX4+qVkw7LLd2xVvbQ0w+QI469t+2Xvzyv/C4ifh89N9bdN+BNGIX7/HfqLrNUQ64PVfwfAroPjYvT9n1u/96/mvW2HxUxDtgNxS+Mxzvo7lL/p1sGqa36Z6D/U7gvvPheYaGHY5XPtg12vdC/tQ7zSBxowZ4+bNm5foMqCpBirm4tbPYfPimaQ1biQ90ky2tQDwbtrJHB1ZSXZHrX9+cqr/P9IOluzfOCWjYdM8v2FsWwlbF0NGge+Bh5ugoBwatkBHK6RkQE4fH9QuCqVj4ManfBA9eKkPqLPv8DuJxq3++Z96xj/vxe/4HQvAqV+Bj/0Y7hkFdRth7C3+DXr8tTDkor23taMNNi2A9kZIz4d3H/HhMfozPjTMINzqg+lA3tyViyC3ZNcOak8rp/o3xFV/8Rv/f/LwFX4nOHC8rzMahm+u9AE0/Scw9BIoP+PD00Wj8NgNfnk3PAFZxT7Qp1wPG+f4dfb5aX6nmzcA8kr8NNuWQ/EQvy5/N8r3unYqPRmq3vevVUYh3DLDH52d/nU45nwflgsegomP+B34zJ/BjrV+PW5fA5fc40P+g/puhBX/9q9tbn8Y9yVY+Jhfzuw/AA4syW8ThYP8ukzL8evpi2/62pc+44O5tQ6euw0KB8P21bu/FikZkNPXbwNn3+53Nv/6GhQd7XeaO5/T0eqXedRpvhORVQx/GOu3JYB+o3wNo66Hs76z+87qUCx8AjbMgk/8DELp/r6WWt/ezELfSXnhm1B2hg/LPe18HPy31vuMgOf/D+QfBS11fkf49v1wzveg5CR/BLR6Opx4067lgW/TsufghOv9a9nZm/fA1B/AUafCZ1/074Gd74uWHT4r7h3tt6dIu1/2x3/iwz6UCQNPhUVPwOem+aP5cLPfBlZN9++V8jNg5cvwjRWQVXRIL6OZzXfOjdnrYwr4A1NV38ptUxawYG0lkeR08iM7OC2nkoa2KF8buJ7jS/NIGnKR77W3N0JeKcyd5PfObfX+TT7sMr9xLH8e3vit3wA+9iN4b4o/zDvvh/6+pz4PvYf5w+eMfD9N3Qb/Zv/kE/Dit6Ch0r+Zty6CMZ/zQf3u32DAKb4Xl3eUnwb8dOO+FNvpzIe1r/mNPCMfZvzE9yZ2Yz6wKt72O4xXfgxlp8PV/88HazQKa2dCUoo/HG1vho/f5UPsT6f6gP/0v6BgoJ/dlvd8jzTc7HdIbfVw3AS47u+7dhqVi2Dqj2DzAh8w4WY/v9r1PmiaqneVd+73/dHMlvcgKeRf18wiKD8TjrvQ1zjtTnjjN/7+5ppYs5LBRXwP943fQGYvH3DpeXDxb2DDHJh7H5z0Kd8Dm/lTOPu7/nXM7guv/xLScuHSe+CJz/rQbNgC/U+EK+6DP47z4VR0jG/XthV+Pi7q10FuPx+qOX39fF75MVxwtw/vv1/ra0xJ90E79BIYeQ30Hg4PXQr1m3zPuuwMeOLTfge89vXdw7xwEHxpjl//W5f4neK2FbD5Hd/OlS/76Vt2+KOCqyb71+ns2+HRT/qAHPVJ+Pcdvv6y02H5C3D9Y35o4d2/+zasmenrOu8H0OtY//qtf9N3ZHas8yE58hrI7uPrevN3vv0X/MxvM2awZSH85TwfiuVnwoir/H3vTfE7sM9PhYcui+2ADL6xzL9u0ahvx4ZZfr7HTfDb27wH/PLS8+CTj8Gks307wR/1fPpZmPwJX19OP38kN/gcX+fL3/evU5+RviddNNi/55Y979+L6bm+Y3XUqX6bc84H95pX/bYWaYdrH/ZHY5fdCwPG+u35wUt8DWO/4I+QVr0C0//Xrw+Ai3/rd+Z/Hg8Tfgljbz6kbFLAHyat4QiT31zLuUN688KiSmat2kZ2egozl1fzsaF9uPuqkdS3hNnR3M6JAwpISjrEw9l3HvEB1HuoD6Pc/lC52L8xSk7yPY5Hb/Ab1ilf8G9K52DGT/3hZF4J3PRP/+Y97avw1n2w5GkfbkkhvwHvHAvsdRycc4ff6Os3+bB64jM+ILKKfYClZvudVvlZvne77nWoWhor1gDn36D1W/x0BiSn+SBe/6bvle6U2QtOuA5m3wv9TvAbeHszLPmHD73B5/gPo0KZvq1t9TD+NvjblT7ALNkPkaRkwOV/8L3RyoW+JxVu8sFTfJyf3+jPwJnf9q9JvxP8YXT/k+CUW/xRxMvfh77H+wDeutjXVzLGH3mBD8jPPO9f92jU9+TKz/SH/k/dDIse969bwxa/Q23Z7tv879shpz9c8ScYdLZfN2/+zg8fdDb8Srh6sr/95Gf9Edz1f/dHCQPG7RpWmXWvr/WLb/od/+QL/FFI/lF+x5Td19ff7wS/zexzu/qb70WGsuCiX/me+E5NNT7IkkM+BP/xBb+MoZfAxD0+jF3xEjz7NWis/PAysor90EpH6677du5Yc0v9NIWD/OdN6flw2lf8kVhHi1+nx13oj7o6Wv3R2hnf9DvWE2/yHZTlL8LKl/x8h10GV/7FP/e5r/vhkY/f5ee55lV449d+B/Dit/0OJinFd1jWzPBHQsue99OGMv0RyZu/9UMy+Uf5HUukDYqH+h3GQ5f5jsLxE/00q2fA0ef6sM4bANdP+fBrUbnY77DO+e7uQ6XN2337+53gt60/nQ4pqX5o9hAo4OPswVnruPNfS3b7rKe0IINRA/JZXtnAgMJM7rhwCMf0yYl/MTWrfa+ioGz3+5u2+SAqKPcb2/IXfHCXnbH7+CxAW6PveewcBjn1S/5NveBBP35bfByc8t++d1xYDgsfh1fv9tNO+KWf5+Of8sMdSSl+CGPY5f52bj9IzfGfYyx6wveckkN+mgt/7se1oxE/r53jwM75N9jwK/xObebdcN0jMPC0XTVHwn644s3f+Xkef63vIe2cx5462vwR1oirIauXHyqp3wwn3+wDvqUWysbv+zOM2g3+tTnr27GeaBgmPgzlZ/vXduBpuw9TNVTC78fA8df4neyW9/wwXFr2rjbC3ofBolF/NNPraP93e7Pf8WX3Ofgx8U3z/TawryG0D5YZgaX/9O3Z29BBW6Pfse5Y57etAWOh70j/ejVv952AtgY/jFR2hj+qWPSEP0rYsdbXMOazfluKhP3rmTfAB93a1/wHpYPP8cMm95/rj+zAD4Wc/7++Q7HnZyDN2/1OY8/tee79vvNy9Pl+ne7UWO1ryS3xnaLajfDCt/w21me435mOvNpvny07/I4qPffgXu8D8fZfYONcuPRe3/6DpIDvBgsrapm7djvpoWQyU5N5fuEWllU2UNYrk0UVdbR1RPnCWYOZs7qGNdsaOevY3twxYQhFWalYd35wFS+N1b4H2+vY2Lh9C1Qv90cLaYd5xxaN7Du4YdcYaXepXOyHJYoG7/95rXX+KCUI67s7hVt979mS/A7xcG9PRzgFfIJVN7TxhYfnsWBDLUf3zmZI3xxeXFxJJOrol5fODy8exgUj+u4W9A2tYWavruHMY4tJD+0eZquqGigtyPzg/o5IlKlLt3L2cb3JSN1P8IlI4CjgPwJawxHe31LPCaX5JCUZizfVMXt1DU+/s4mlW+rpn5dOTnqI2pZ2whFHc3sHreEoZxzTiwkj+zFrdQ1t4QiDirP586urGVycxe+uO5ERJXn8euoK7nllJTeNG8iPLx0OcOjj/yJyRFHAf4SFI1GeW7iZlxZvxeHIz0gllGKkJCVRlJXKr6auAKBvbjrhSJSapnbOPLaYFZUN1La0c+VJpTw6dwMFmalsb26nODuNqHNcekIJmanJXHlSCc3tEVrCEWoa23l/Sz3D+ufSPy+DY/pkU1XfxpPzN7K5rpXbPnYMpQWZH9S1pbaVAYUZrK9pJsmM0oKMD+04ttS1UNPYzoiSvMP+2jjnaA1HdVQish8JC3gzuwD4HZAM/MU5d/f+nt8TA/4/mbOmhuy0FIb3z6W5PcLcdds585hitje1c8vD81hUUcc5Q3pz1+UjuG7SHHplp5IeSmb26hoc7PeSCznpKbSFo3REo6QkJ9E3N53cjBRy00M0tnWwsKKOvrnpVNb7MyIyU5MZWJRFTloKWWnJlPXK4qn5FTS1R/jS2YPZsL2ZEwfkM7Aoi8a2DpraOjCDCSP7kZqSRHNbhE21LayubmT0wIIP7ivMTmVRRR0/f2k5hZkhjumTQ9/cdKYvq2Luuu3cd+NoMlKT6ZeXzsCiLLbWt7Kooo6xgwqpaw5T1xJmVVUja7Y18bnx5eRlhnZrp3OO9kiUV5dXk5cR4vjSfLY1tjGgcPdLRUejjuVbGxjSN+eAPheJRh3PvLeJUQMKKApRZcwAAAqWSURBVO+168NY59yHpt/bfeB3pKHk7v1CeSTqSE4yNte28PKSSj55ykBqW9rJSQt9aGe6YmsDGaHkD71W8tGRkIA3s2RgBXA+UAG8DVzvnFu6r2kU8AfHOUdbR/SDsfg9Q6SqoZUn51fQNzedouw00lKSGFmSx4qtDVTWtfLKsioyQsl8+Zyj2VTbwqcnz2VgUSbbm9ppCUf49KllLKyo5ZRBReRnhFhW2cDG7c00tXdQ19LByq0NDOmXQ3ZaCnPWbCcnPYWG1g9/lT4vI0RrOEJbR3Sv7UiNBVyfvDSy00Ksrm6kvSNKTnoKxdlprNnWBPjPJnvnpLG1vg2A5CT70A6stCCDoqxU6lrC5GaESEtJYsGGWqLOfXCiSpJB1MHJZQWMKStke2M76aEkNu5oYfqyKk4pL6SqoY2stGTOHdKH6oY2Nte2sKWuhRH98xh1VD61zWEWb6rj5aVbSU1O4pRBhXREHBu2N1NZ38oxvbMZN6iI3rlpzFu3g9dXVjOkby6nHV1ERiiZtduaWLalgRVVDZxcVsjogQVkp6VQkJlKRzTKg7PWcUJpPucM6U1VQxttHRFGluQxa7U/p//o4mySkuDdDbUM7p1Nr+w00kNJ1LWE2VDTQl5GCgVZqVTsaKG5vYN+eRm0hiNMem0NVQ1tDOmbQ21zmMr6VsaWF/LexlrSQ8mMHlhAksFJAwuoawnzl9fXkmzGNWNKKS3IpKW9g9yMEL2y0+iVnUZVQytvrNrGOcf15qjCTNojUdrCUdojEbY3hXl9ZTXD+uVyxjHFzFlTw6rqRi4e2Y9w1BGNOvIzQ2ytb2N7Uzu9c9Jo64iyvLKemqZ2BhVn89aaGo4qzCQzNZm2jignlxVSmJ1KVX0bj7y1nrZwlOvGDqC5PcLT72xiTFkBHxvah7rmMMnJRmpyEgWZqcxZU0NrR4QTSvPZWt/KwKJM3lxVQ2NbB2VFWZT3yqK0IIPa5jDhaJS0lCTSUpLpiEZZvKmezNRk0lKScA6Kc9IozEqlqqGVDdubMYziHP9aRGOb+NvrtrO5rpWRJbn0yk6jJRwhI5TM4OJsBhX7Tkp2WoiWcIRtDW20R6JMGNmPQ5GogD8VuNM594nY33cAOOd+tq9pFPCJ1d4RJZRsRJ0/lf0/jePvfH57JMriTXWMGlDA2m2NNLZFyEpNJisthS11rUx+cy29c9I4qjCTgsxUBhVn8d7GWjAjM5TM4s11VNW38ZMrRpCfmUok6qhvCZORmkxjWwc///cyTikvYn1NE5tqWzm6dzbD++fy5upt9MtNp19+BgWZ/vSyu55fSm56iKLsVLY1ttHQ2sEp5YUfhNfWev+mzEpL4ekFm1izrYnc9BRawhE6Io5rxpTy3HtbGNw7m4bWMKurmyjKSqV/fgbFOWm8taaGpnZ/GqcZ3HbesVTWt7B0cz3JSUZZURbFuWks3VzPvHU7aAlH6JubznlDe7OyqpF3NuygI+ooyc9gcHE2R/fOZsayKjbuaCYc2fVeHNI3hzXVTbRHdt8ppsTWSUdsx5aakkT7PnacO3XeEY4tL+SU8kKeX7SFprYOLhzRj7/OWsfY8kKKslJZu62J9o7oBzvVCSP7kpqcxL+XVNIa3vtyMkLJtIQje30sLyO02+8tpKUk7XNHv1OSQWZqCo1tHfTLS6emsd0fZSYl7fZ6FOf4TkvFDv8N84FFmayv2c81ovZgBqHk//z6HYrM1GQGFGSysqqBA7luYWFWKgt+cP4hLStRAX81cIFz7vOxv28CTnHOfWWP590C3BL78zhg+SEushew7RCnPVKpzT2D2twzHGqbBzrnivf2QMIvNuacmwRM6up8zGzevvZiQaU29wxqc88QjzbH89OdTcCATn+Xxu4TEZFuEM+Afxs4xszKzSwVuA54No7LExGRTuI2ROOc6zCzrwAv4U+TnOycWxKv5XEYhnmOQGpzz6A29wyHvc0fqS86iYjI4dNzf7JPRCTgFPAiIgF1xAe8mV1gZsvNbJWZ3Z7oeuLFzNaZ2SIze9fM5sXuKzSzqWa2MvZ/QaLr7Cozm2xmVWa2uNN9e22neffE1v1CMzspcZUfun20+U4z2xRb3++a2YROj90Ra/NyMzsMPzza/cxsgJnNMLOlZrbEzG6N3R/Ydb2fNsdvXTvnjth/+A9vVwODgFTgPWBYouuKU1vXAb32uO/nwO2x27cD/zfRdR6Gdp4JnAQs/k/tBCYAL+K/eDsOeCvR9R/GNt8JfHMvzx0W287TgPLY9p+c6DYcQpv7ASfFbufgL2syLMjrej9tjtu6PtJ78GOBVc65Nc65duBRYB8/vx5IlwE7f479QeDyBNZyWDjnXgO273H3vtp5GfCQ8+YA+WZ2aBf0SKB9tHlfLgMedc61OefWAqvw74MjinNui3NuQex2A/A+UEKA1/V+2rwvXV7XR3rAlwAbO/1dwf5fsCOZA142s/mxyzsA9HHObYndrgT6JKa0uNtXO4O+/r8SG46Y3Gn4LXBtNrMy4ETgLXrIut6jzRCndX2kB3xPcrpz7iTgQuDLZnZm5wedP6YL/DmvPaWdwJ+AwcAoYAvwq8SWEx9mlg08BdzmnKvv/FhQ1/Ve2hy3dX2kB3yPuRyCc25T7P8q4Gn8odrWnYepsf+rEldhXO2rnYFd/865rc65iHMuCtzPrkPzwLTZzEL4oHvEOfeP2N2BXtd7a3M81/WRHvA94nIIZpZlZjk7bwMfBxbj2/rp2NM+DTyTmArjbl/tfBb4VOwMi3FAXafD+yPaHuPLV+DXN/g2X2dmaWZWDhwDzO3u+rrKzAx4AHjfOffrTg8Fdl3vq81xXdeJ/mT5MHwyPQH/afRq4HuJridObRyE/zT9PWDJznYCRcArwEpgGlCY6FoPQ1un4A9Tw/gxx8/tq534Myr+EFv3i4Axia7/MLb54VibFsbe6P06Pf97sTYvBy5MdP2H2ObT8cMvC4F3Y/8mBHld76fNcVvXulSBiEhAHelDNCIisg8KeBGRgFLAi4gElAJeRCSgFPAiIgGlgBc5DMzsbDN7LtF1iHSmgBcRCSgFvPQoZnajmc2NXXf7PjNLNrNGM/tN7Brdr5hZcey5o8xsTuwiUE93ujb50WY2zczeM7MFZjY4NvtsM3vSzJaZ2SOxby6KJIwCXnoMMxsKTATGO+dGARHgBiALmOecGw68CvwoNslDwHecc8fjv2m48/5HgD84504ATsN/CxX81QFvw1/HexAwPu6NEtmPlEQXINKNzgNGA2/HOtcZ+ItZRYHHYs/5G/APM8sD8p1zr8bufxB4InZNoBLn3NMAzrlWgNj85jrnKmJ/vwuUAW/Ev1kie6eAl57EgAedc3fsdqfZD/Z43qFev6Ot0+0Ien9JgmmIRnqSV4Crzaw3fPD7nwPx74OrY8/5JPCGc64O2GFmZ8Tuvwl41flf4qkws8tj80gzs8xubYXIAVIPQ3oM59xSM/s+/pexkvBXb/wy0ASMjT1WhR+nB3+52j/HAnwN8NnY/TcB95nZ/8TmcU03NkPkgOlqktLjmVmjcy470XWIHG4aohERCSj14EVEAko9eBGRgFLAi4gElAJeRCSgFPAiIgGlgBcRCaj/Dx/PaJHyqUFFAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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" ] }, "metadata": { "tags": [], "needs_background": "light" } }, { "output_type": "stream", "text": [ "DNN results\n", "accuracy : 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "Confusion matrix: \n", "[[378 0]\n", " [ 0 462]]\n", "Validation set report:\n", "cross Loss: -29.26294453938802\n", "hing Loss: 0.5190476190476191\n", "Report precision recall f1-score support\n", "\n", " 0 0.92 0.90 0.91 93\n", " 1 0.92 0.94 0.93 117\n", "\n", " accuracy 0.92 210\n", " macro avg 0.92 0.92 0.92 210\n", "weighted avg 0.92 0.92 0.92 210\n", "\n", "accuracy : 0.9238095238095239\n", "Validation Recall: 0.9238095238095239\n", "Validation Precision: 0.9237972111921693\n", "Confusion matrix: \n", "[[ 84 9]\n", " [ 7 110]]\n", "\n", "\\KNN Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 0.8095238095238095\n", "Validation Recall: 0.8095238095238095\n", "Validation Precision: 0.8160544217687075\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[79 14]\n", " [26 91]]\n", "\n", "\n", "Support vector machine Result\n", "\n", "Train accuracy: 0.861904761904762\n", "Training Recall: 0.861904761904762\n", "Training Precision: 0.8778697836459568\n", "\n", "\n", "Validation accuracy: 0.8142857142857143\n", "Validation Recall: 0.8142857142857143\n", "Validation Precision: 0.8356561888454012\n", "\n", "Train Confusion matrix:\n", " [[274 104]\n", " [ 12 450]]\n", "Validation Confusion matrix:\n", " [[ 59 34]\n", " [ 5 112]]\n", "\n", "\n", "Random Forest Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 0.9428571428571428\n", "Validation Recall: 0.9428571428571428\n", "Validation Precision: 0.9431448185681597\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[ 88 5]\n", " [ 7 110]]\n", "\n", "\n", "Random Forest Result\n", "\n", "Train accuracy: 0.9916666666666667\n", "Training Recall: 0.9916666666666667\n", "Training Precision: 0.9916674782728256\n", "\n", "\n", "Validation accuracy: 0.7047619047619048\n", "Validation Recall: 0.7047619047619048\n", "Validation Precision: 0.7085267897694094\n", "\n", "Train Confusion matrix:\n", " [[374 4]\n", " [ 3 459]]\n", "Validation Confusion matrix:\n", " [[66 27]\n", " [35 82]]\n" ], "name": "stdout" } ] }, { "cell_type": "markdown", "metadata": { "id": "Bf-FYjEYig5Y" }, "source": [ "# DWT" ] }, { "cell_type": "markdown", "metadata": { "id": "zISmkFBkjZPd" }, "source": [ "## feature selection " ] }, { "cell_type": "code", "metadata": { "id": "0QHQKovCDd7C" }, "source": [ "#MRMRM on 30 feature\n", "train_models(X, y, y_cat)" ], "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "mzRvHP8NxYmX", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "bd5005aa-619f-4dd7-8521-02768fe574f8" }, "source": [ "\"\"\"\n", "LDA After split\n", "hing\n", " \n", "X = np.load('gdrive/My Drive/Python files/neuromarketing 25/features_DWT\n", "\n", "Standard\n", "\n", " batch_size = 128\n", " nb_epoch = 250 #3000\n", " l1_decay=0.00\n", " l2_decay=0.000 # .5\n", " # 0.01 0.06\n", " sigma=0.005\n", " in_drop_rate = .5\n", " drop_rate = .5#.05\n", " lr1=0.0001\n", "\n", "\"\"\"\n", "train_models(X, y, y_cat)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "After\n", "(840, 1)\n", "(840, 2)\n", "(210, 2)\n", "Epoch 1/250\n", "7/7 [==============================] - 0s 60ms/step - loss: 0.4561 - accuracy: 0.8405 - val_loss: 0.6295 - val_accuracy: 0.6857\n", "Epoch 2/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.1359 - accuracy: 0.9440 - val_loss: 0.6318 - val_accuracy: 0.6857\n", "Epoch 3/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.1114 - accuracy: 0.9536 - val_loss: 0.6376 - val_accuracy: 0.6762\n", "Epoch 4/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.1014 - accuracy: 0.9560 - val_loss: 0.6422 - val_accuracy: 0.6762\n", "Epoch 5/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0894 - accuracy: 0.9571 - val_loss: 0.6443 - val_accuracy: 0.6714\n", "Epoch 6/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0879 - accuracy: 0.9560 - val_loss: 0.6438 - val_accuracy: 0.6762\n", "Epoch 7/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0888 - accuracy: 0.9595 - val_loss: 0.6418 - val_accuracy: 0.6810\n", "Epoch 8/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0948 - accuracy: 0.9560 - val_loss: 0.6414 - val_accuracy: 0.6810\n", "Epoch 9/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0908 - accuracy: 0.9571 - val_loss: 0.6415 - val_accuracy: 0.6810\n", "Epoch 10/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0789 - accuracy: 0.9619 - val_loss: 0.6402 - val_accuracy: 0.6810\n", "Epoch 11/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0860 - accuracy: 0.9619 - val_loss: 0.6379 - val_accuracy: 0.6810\n", "Epoch 12/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0863 - accuracy: 0.9571 - val_loss: 0.6382 - val_accuracy: 0.6810\n", "Epoch 13/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0810 - accuracy: 0.9631 - val_loss: 0.6383 - val_accuracy: 0.6810\n", "Epoch 14/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0844 - accuracy: 0.9595 - val_loss: 0.6366 - val_accuracy: 0.6810\n", "Epoch 15/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0855 - accuracy: 0.9583 - val_loss: 0.6370 - val_accuracy: 0.6810\n", "Epoch 16/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0890 - accuracy: 0.9595 - val_loss: 0.6383 - val_accuracy: 0.6810\n", "Epoch 17/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0782 - accuracy: 0.9619 - val_loss: 0.6365 - val_accuracy: 0.6810\n", "Epoch 18/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0763 - accuracy: 0.9631 - val_loss: 0.6323 - val_accuracy: 0.6810\n", "Epoch 19/250\n", "7/7 [==============================] - 2s 224ms/step - loss: 0.0814 - accuracy: 0.9595 - val_loss: 0.6264 - val_accuracy: 0.6857\n", "Epoch 20/250\n", "7/7 [==============================] - 0s 50ms/step - loss: 0.0759 - accuracy: 0.9643 - val_loss: 0.6202 - val_accuracy: 0.6905\n", "Epoch 21/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0799 - accuracy: 0.9607 - val_loss: 0.6257 - val_accuracy: 0.6857\n", "Epoch 22/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0783 - accuracy: 0.9619 - val_loss: 0.6306 - val_accuracy: 0.6762\n", "Epoch 23/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0790 - accuracy: 0.9619 - val_loss: 0.6339 - val_accuracy: 0.6810\n", "Epoch 24/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0807 - accuracy: 0.9607 - val_loss: 0.6319 - val_accuracy: 0.6762\n", "Epoch 25/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0703 - accuracy: 0.9679 - val_loss: 0.6209 - val_accuracy: 0.6905\n", "Epoch 26/250\n", "7/7 [==============================] - 0s 35ms/step - loss: 0.0873 - accuracy: 0.9560 - val_loss: 0.6175 - val_accuracy: 0.6952\n", "Epoch 27/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0782 - accuracy: 0.9631 - val_loss: 0.6193 - val_accuracy: 0.6905\n", "Epoch 28/250\n", "7/7 [==============================] - 3s 382ms/step - loss: 0.0731 - accuracy: 0.9679 - val_loss: 0.6173 - val_accuracy: 0.6952\n", "Epoch 29/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0754 - accuracy: 0.9619 - val_loss: 0.6196 - val_accuracy: 0.6905\n", "Epoch 30/250\n", "7/7 [==============================] - 0s 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8ms/step - loss: 0.0789 - accuracy: 0.9595 - val_loss: 0.6218 - val_accuracy: 0.6905\n", "Epoch 64/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0749 - accuracy: 0.9619 - val_loss: 0.6104 - val_accuracy: 0.6952\n", "Epoch 65/250\n", "7/7 [==============================] - 0s 35ms/step - loss: 0.0712 - accuracy: 0.9655 - val_loss: 0.6002 - val_accuracy: 0.7048\n", "Epoch 66/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0705 - accuracy: 0.9655 - val_loss: 0.6009 - val_accuracy: 0.7048\n", "Epoch 67/250\n", "1/7 [===>..........................] - ETA: 0s - loss: 0.0514 - accuracy: 0.9844" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "2VSjZxzxjdjp", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "513e589e-e777-4cb3-c106-c58d94c6e59d" }, "source": [ "\"\"\"\n", "LDA before split\n", "hing\n", " \n", "X = np.load('gdrive/My Drive/Python files/neuromarketing 25/features_DWT\n", "\n", "Standard\n", "\n", " batch_size = 128\n", " nb_epoch = 250 #3000\n", " l1_decay=0.00\n", " l2_decay=0.000 # .5\n", " # 0.01 0.06\n", " sigma=0.005\n", " in_drop_rate = .5\n", " drop_rate = .5#.05\n", " lr1=0.0001\n", "\n", "\"\"\"\n", "train_models(X, y, y_cat)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Before\n", "(1050, 505)\n", "After\n", "(1050, 1)\n", "(840, 2)\n", "(210, 2)\n", "Epoch 1/250\n", "7/7 [==============================] - 0s 61ms/step - loss: 0.5015 - accuracy: 0.8143 - val_loss: 0.1474 - val_accuracy: 0.9333\n", "Epoch 2/250\n", "7/7 [==============================] - 3s 392ms/step - loss: 0.1769 - accuracy: 0.9190 - val_loss: 0.1418 - val_accuracy: 0.9333\n", "Epoch 3/250\n", "7/7 [==============================] - 0s 57ms/step - loss: 0.1623 - accuracy: 0.9238 - val_loss: 0.1415 - val_accuracy: 0.9333\n", "Epoch 4/250\n", "7/7 [==============================] - 0s 13ms/step - loss: 0.1423 - accuracy: 0.9381 - val_loss: 0.1456 - 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"Epoch 184/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.1263 - accuracy: 0.9357 - val_loss: 0.1626 - val_accuracy: 0.9190\n", "Epoch 185/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.1157 - accuracy: 0.9440 - val_loss: 0.1634 - val_accuracy: 0.9190\n", "Epoch 186/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.1260 - accuracy: 0.9393 - val_loss: 0.1644 - val_accuracy: 0.9143\n", "Epoch 187/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.1205 - accuracy: 0.9429 - val_loss: 0.1647 - val_accuracy: 0.9190\n", "Epoch 188/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.1284 - accuracy: 0.9345 - val_loss: 0.1659 - val_accuracy: 0.9190\n", "Epoch 189/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.1231 - accuracy: 0.9393 - val_loss: 0.1701 - val_accuracy: 0.9190\n", "Epoch 190/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.1188 - 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[==============================] - 0s 9ms/step - loss: 0.1300 - accuracy: 0.9357 - val_loss: 0.1637 - val_accuracy: 0.9190\n", "Epoch 198/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.1263 - accuracy: 0.9393 - val_loss: 0.1671 - val_accuracy: 0.9190\n", "Epoch 199/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.1231 - accuracy: 0.9405 - val_loss: 0.1657 - val_accuracy: 0.9190\n", "Epoch 200/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.1218 - accuracy: 0.9369 - val_loss: 0.1640 - val_accuracy: 0.9143\n", "Epoch 201/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.1221 - accuracy: 0.9405 - val_loss: 0.1631 - val_accuracy: 0.9190\n", "Epoch 202/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.1169 - accuracy: 0.9417 - val_loss: 0.1624 - val_accuracy: 0.9190\n", "Epoch 203/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.1258 - accuracy: 0.9369 - val_loss: 0.1623 - val_accuracy: 0.9190\n", "Epoch 204/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.1276 - accuracy: 0.9381 - val_loss: 0.1625 - val_accuracy: 0.9190\n", "Epoch 205/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.1230 - accuracy: 0.9393 - val_loss: 0.1621 - val_accuracy: 0.9190\n", "Epoch 206/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.1226 - accuracy: 0.9381 - val_loss: 0.1628 - val_accuracy: 0.9190\n", "Epoch 207/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.1316 - accuracy: 0.9321 - val_loss: 0.1669 - val_accuracy: 0.9190\n", "Epoch 208/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.1156 - accuracy: 0.9440 - val_loss: 0.1689 - val_accuracy: 0.9190\n", "Epoch 209/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.1173 - accuracy: 0.9417 - val_loss: 0.1630 - val_accuracy: 0.9190\n", "Epoch 210/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.1207 - accuracy: 0.9381 - val_loss: 0.1623 - val_accuracy: 0.9190\n", "Epoch 211/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.1256 - accuracy: 0.9369 - val_loss: 0.1628 - val_accuracy: 0.9190\n", "Epoch 212/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.1197 - accuracy: 0.9417 - val_loss: 0.1660 - val_accuracy: 0.9190\n", "Epoch 213/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.1263 - accuracy: 0.9369 - val_loss: 0.1653 - val_accuracy: 0.9143\n", "Epoch 214/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.1196 - accuracy: 0.9393 - val_loss: 0.1659 - val_accuracy: 0.9190\n", "Epoch 215/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.1184 - accuracy: 0.9429 - val_loss: 0.1721 - val_accuracy: 0.9095\n", "Epoch 216/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.1254 - accuracy: 0.9381 - val_loss: 0.1658 - val_accuracy: 0.9190\n", "Epoch 217/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.1206 - accuracy: 0.9381 - val_loss: 0.1639 - val_accuracy: 0.9190\n", "Epoch 218/250\n", "7/7 [==============================] - 0s 7ms/step - loss: 0.1225 - accuracy: 0.9381 - val_loss: 0.1633 - val_accuracy: 0.9190\n", "Epoch 219/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.1288 - accuracy: 0.9369 - val_loss: 0.1620 - val_accuracy: 0.9190\n", "Epoch 220/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.1268 - accuracy: 0.9345 - val_loss: 0.1622 - val_accuracy: 0.9190\n", "Epoch 221/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.1295 - accuracy: 0.9345 - val_loss: 0.1624 - val_accuracy: 0.9190\n", "Epoch 222/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.1259 - accuracy: 0.9369 - val_loss: 0.1623 - val_accuracy: 0.9190\n", "Epoch 223/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.1257 - accuracy: 0.9369 - val_loss: 0.1621 - val_accuracy: 0.9190\n", "Epoch 224/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.1272 - accuracy: 0.9369 - val_loss: 0.1631 - val_accuracy: 0.9190\n", "Epoch 225/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.1178 - accuracy: 0.9417 - val_loss: 0.1663 - val_accuracy: 0.9190\n", "Epoch 226/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.1263 - accuracy: 0.9381 - val_loss: 0.1656 - val_accuracy: 0.9190\n", "Epoch 227/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.1246 - accuracy: 0.9369 - val_loss: 0.1644 - val_accuracy: 0.9143\n", "Epoch 228/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.1186 - accuracy: 0.9417 - val_loss: 0.1650 - val_accuracy: 0.9190\n", "Epoch 229/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.1260 - accuracy: 0.9381 - val_loss: 0.1646 - val_accuracy: 0.9190\n", "Epoch 230/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.1296 - accuracy: 0.9345 - val_loss: 0.1660 - val_accuracy: 0.9190\n", "Epoch 231/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.1292 - accuracy: 0.9333 - val_loss: 0.1646 - val_accuracy: 0.9190\n", "Epoch 232/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.1298 - accuracy: 0.9357 - val_loss: 0.1620 - val_accuracy: 0.9190\n", "Epoch 233/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.1274 - accuracy: 0.9405 - val_loss: 0.1647 - val_accuracy: 0.9190\n", "Epoch 234/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.1262 - accuracy: 0.9333 - val_loss: 0.1644 - val_accuracy: 0.9190\n", "Epoch 235/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.1165 - accuracy: 0.9440 - val_loss: 0.1630 - val_accuracy: 0.9190\n", "Epoch 236/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.1219 - accuracy: 0.9393 - val_loss: 0.1660 - val_accuracy: 0.9190\n", "Epoch 237/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.1267 - accuracy: 0.9381 - val_loss: 0.1738 - val_accuracy: 0.9095\n", "Epoch 238/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.1266 - accuracy: 0.9381 - val_loss: 0.1714 - val_accuracy: 0.9143\n", "Epoch 239/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.1154 - accuracy: 0.9440 - val_loss: 0.1638 - val_accuracy: 0.9190\n", "Epoch 240/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.1212 - accuracy: 0.9405 - val_loss: 0.1620 - val_accuracy: 0.9190\n", "Epoch 241/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.1256 - accuracy: 0.9369 - val_loss: 0.1620 - val_accuracy: 0.9190\n", "Epoch 242/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.1284 - accuracy: 0.9345 - val_loss: 0.1632 - val_accuracy: 0.9190\n", "Epoch 243/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.1322 - accuracy: 0.9333 - val_loss: 0.1633 - val_accuracy: 0.9190\n", "Epoch 244/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.1326 - accuracy: 0.9333 - val_loss: 0.1639 - val_accuracy: 0.9143\n", "Epoch 245/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.1138 - accuracy: 0.9429 - val_loss: 0.1630 - val_accuracy: 0.9190\n", "Epoch 246/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.1206 - accuracy: 0.9393 - val_loss: 0.1633 - val_accuracy: 0.9190\n", "Epoch 247/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.1160 - accuracy: 0.9440 - val_loss: 0.1642 - val_accuracy: 0.9190\n", "Epoch 248/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.1296 - accuracy: 0.9321 - val_loss: 0.1637 - val_accuracy: 0.9190\n", "Epoch 249/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.1189 - accuracy: 0.9429 - val_loss: 0.1639 - val_accuracy: 0.9143\n", "Epoch 250/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.1147 - accuracy: 0.9429 - val_loss: 0.1632 - val_accuracy: 0.9190\n", "Valid results - Loss: 0.16323629021644592 - Accuracy: 91.90475940704346%\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", 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\n", 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\n", 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" ] }, "metadata": { "tags": [], "needs_background": "light" } }, { "output_type": "stream", "text": [ "DNN results\n", "accuracy : 0.9285714285714286\n", "Training Recall: 0.9285714285714286\n", "Training Precision: 0.9301250000000001\n", "Confusion matrix: \n", "[[359 19]\n", " [ 41 421]]\n", "Validation set report:\n", "cross Loss: -29.91938071477981\n", "hing Loss: 0.5095238095238095\n", "Report precision recall f1-score support\n", "\n", " 0 0.89 0.97 0.93 93\n", " 1 0.97 0.91 0.94 117\n", "\n", " accuracy 0.93 210\n", " macro avg 0.93 0.94 0.93 210\n", "weighted avg 0.94 0.93 0.93 210\n", "\n", "accuracy : 0.9333333333333333\n", "Validation Recall: 0.9333333333333333\n", "Validation Precision: 0.9364338268689255\n", "Confusion matrix: \n", "[[ 90 3]\n", " [ 11 106]]\n", "\n", "\\KNN Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 0.9047619047619048\n", "Validation Recall: 0.9047619047619048\n", "Validation Precision: 0.9051193200392285\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[ 84 9]\n", " [ 11 106]]\n", "\n", "\n", "Support vector machine Result\n", "\n", "Train accuracy: 0.9452380952380952\n", "Training Recall: 0.9452380952380952\n", "Training Precision: 0.9453313976085416\n", "\n", "\n", "Validation accuracy: 0.919047619047619\n", "Validation Recall: 0.919047619047619\n", "Validation Precision: 0.9191751388743319\n", "\n", "Train Confusion matrix:\n", " [[357 21]\n", " [ 25 437]]\n", "Validation Confusion matrix:\n", " [[ 85 8]\n", " [ 9 108]]\n", "\n", "\n", "Random Forest Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 0.9047619047619048\n", "Validation Recall: 0.9047619047619048\n", "Validation Precision: 0.9051193200392285\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[ 84 9]\n", " [ 11 106]]\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "id": "8z8fpZJLo78F", "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "outputId": "450c5d2d-6cac-4025-bdee-cc98ea04d947" }, "source": [ "\"\"\"\n", "pca before split\n", "hing\n", " \n", "X = np.load('gdrive/My Drive/Python files/neuromarketing 25/features_DWT\n", "\n", "Standard\n", "\n", " batch_size = 128\n", " nb_epoch = 250 #3000\n", " l1_decay=0.00\n", " l2_decay=0.000 # .5\n", " # 0.01 0.06\n", " sigma=0.005\n", " in_drop_rate = .5\n", " drop_rate = .5#.05\n", " lr1=0.0001\n", "\n", "\"\"\"\n", "train_models(X, y, y_cat)" ], "execution_count": null, "outputs": [ { "output_type": "stream", "text": [ "Before\n", "(1050, 505)\n", "After\n", "(1050, 505)\n", "(840, 2)\n", "(210, 2)\n", "Epoch 1/250\n", "7/7 [==============================] - 0s 63ms/step - loss: 0.9676 - accuracy: 0.5452 - val_loss: 0.9763 - val_accuracy: 0.6524\n", "Epoch 2/250\n", "7/7 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"Epoch 22/250\n", "7/7 [==============================] - 7s 973ms/step - loss: 0.4454 - accuracy: 0.8012 - val_loss: 0.5878 - val_accuracy: 0.7476\n", "Epoch 23/250\n", "7/7 [==============================] - 3s 410ms/step - loss: 0.4225 - accuracy: 0.8155 - val_loss: 0.5842 - val_accuracy: 0.7476\n", "Epoch 24/250\n", "7/7 [==============================] - 2s 275ms/step - loss: 0.4213 - accuracy: 0.8083 - val_loss: 0.5769 - val_accuracy: 0.7381\n", "Epoch 25/250\n", "7/7 [==============================] - 4s 566ms/step - loss: 0.4189 - accuracy: 0.8083 - val_loss: 0.5677 - val_accuracy: 0.7333\n", "Epoch 26/250\n", "7/7 [==============================] - 2s 227ms/step - loss: 0.3853 - accuracy: 0.8274 - val_loss: 0.5647 - val_accuracy: 0.7333\n", "Epoch 27/250\n", "7/7 [==============================] - 3s 431ms/step - loss: 0.3835 - accuracy: 0.8190 - val_loss: 0.5563 - val_accuracy: 0.7476\n", "Epoch 28/250\n", "7/7 [==============================] - 3s 444ms/step - loss: 0.3882 - 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"Epoch 101/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.1309 - accuracy: 0.9381 - val_loss: 0.4721 - val_accuracy: 0.7714\n", "Epoch 102/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.1139 - accuracy: 0.9452 - val_loss: 0.4688 - val_accuracy: 0.7571\n", "Epoch 103/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0981 - accuracy: 0.9571 - val_loss: 0.4721 - val_accuracy: 0.7619\n", "Epoch 104/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.1153 - accuracy: 0.9488 - val_loss: 0.4688 - val_accuracy: 0.7667\n", "Epoch 105/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.1145 - accuracy: 0.9452 - val_loss: 0.4619 - val_accuracy: 0.7810\n", "Epoch 106/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.1173 - accuracy: 0.9464 - val_loss: 0.4627 - val_accuracy: 0.7762\n", "Epoch 107/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.1016 - 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[==============================] - 0s 8ms/step - loss: 0.0759 - accuracy: 0.9679 - val_loss: 0.4483 - val_accuracy: 0.7762\n", "Epoch 115/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0911 - accuracy: 0.9583 - val_loss: 0.4491 - val_accuracy: 0.7762\n", "Epoch 116/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0753 - accuracy: 0.9679 - val_loss: 0.4469 - val_accuracy: 0.7857\n", "Epoch 117/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0832 - accuracy: 0.9619 - val_loss: 0.4462 - val_accuracy: 0.7857\n", "Epoch 118/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0872 - accuracy: 0.9607 - val_loss: 0.4433 - val_accuracy: 0.7905\n", "Epoch 119/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0698 - accuracy: 0.9667 - val_loss: 0.4460 - val_accuracy: 0.7810\n", "Epoch 120/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0922 - accuracy: 0.9607 - val_loss: 0.4480 - val_accuracy: 0.7857\n", "Epoch 121/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0768 - accuracy: 0.9655 - val_loss: 0.4457 - val_accuracy: 0.7810\n", "Epoch 122/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0813 - accuracy: 0.9619 - val_loss: 0.4421 - val_accuracy: 0.7762\n", "Epoch 123/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0882 - accuracy: 0.9679 - val_loss: 0.4405 - val_accuracy: 0.7762\n", "Epoch 124/250\n", "7/7 [==============================] - 4s 593ms/step - loss: 0.0664 - accuracy: 0.9714 - val_loss: 0.4353 - val_accuracy: 0.7762\n", "Epoch 125/250\n", "7/7 [==============================] - 3s 380ms/step - loss: 0.0907 - accuracy: 0.9548 - val_loss: 0.4351 - val_accuracy: 0.7762\n", "Epoch 126/250\n", "7/7 [==============================] - 2s 272ms/step - loss: 0.0633 - accuracy: 0.9750 - val_loss: 0.4336 - val_accuracy: 0.7762\n", "Epoch 127/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0707 - accuracy: 0.9714 - val_loss: 0.4371 - val_accuracy: 0.7810\n", "Epoch 128/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0908 - accuracy: 0.9571 - val_loss: 0.4454 - val_accuracy: 0.7714\n", "Epoch 129/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0784 - accuracy: 0.9619 - val_loss: 0.4435 - val_accuracy: 0.7762\n", "Epoch 130/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0682 - accuracy: 0.9726 - val_loss: 0.4411 - val_accuracy: 0.7810\n", "Epoch 131/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0660 - accuracy: 0.9679 - val_loss: 0.4437 - val_accuracy: 0.7762\n", "Epoch 132/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0823 - accuracy: 0.9631 - val_loss: 0.4474 - val_accuracy: 0.7810\n", "Epoch 133/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0731 - accuracy: 0.9679 - val_loss: 0.4471 - val_accuracy: 0.7810\n", "Epoch 134/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0653 - accuracy: 0.9702 - val_loss: 0.4433 - val_accuracy: 0.7810\n", "Epoch 135/250\n", "7/7 [==============================] - 0s 48ms/step - loss: 0.0476 - accuracy: 0.9798 - val_loss: 0.4317 - val_accuracy: 0.7857\n", "Epoch 136/250\n", "7/7 [==============================] - 1s 141ms/step - loss: 0.0664 - accuracy: 0.9690 - val_loss: 0.4284 - val_accuracy: 0.7905\n", "Epoch 137/250\n", "7/7 [==============================] - 6s 786ms/step - loss: 0.0705 - accuracy: 0.9679 - val_loss: 0.4253 - val_accuracy: 0.7905\n", "Epoch 138/250\n", "7/7 [==============================] - 3s 444ms/step - loss: 0.0575 - accuracy: 0.9726 - val_loss: 0.4207 - val_accuracy: 0.7952\n", "Epoch 139/250\n", "7/7 [==============================] - 0s 12ms/step - loss: 0.0537 - accuracy: 0.9774 - val_loss: 0.4209 - val_accuracy: 0.7952\n", "Epoch 140/250\n", "7/7 [==============================] - 3s 374ms/step - loss: 0.0501 - accuracy: 0.9786 - val_loss: 0.4191 - val_accuracy: 0.8000\n", "Epoch 141/250\n", "7/7 [==============================] - 0s 11ms/step - loss: 0.0616 - accuracy: 0.9679 - val_loss: 0.4228 - val_accuracy: 0.7810\n", "Epoch 142/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0602 - accuracy: 0.9738 - val_loss: 0.4253 - val_accuracy: 0.7810\n", "Epoch 143/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0547 - accuracy: 0.9738 - val_loss: 0.4259 - val_accuracy: 0.7857\n", "Epoch 144/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0569 - accuracy: 0.9750 - val_loss: 0.4306 - val_accuracy: 0.7905\n", "Epoch 145/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0628 - accuracy: 0.9750 - val_loss: 0.4338 - val_accuracy: 0.7905\n", "Epoch 146/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0504 - accuracy: 0.9798 - val_loss: 0.4465 - val_accuracy: 0.7762\n", "Epoch 147/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0619 - accuracy: 0.9738 - val_loss: 0.4503 - val_accuracy: 0.7810\n", "Epoch 148/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0499 - accuracy: 0.9786 - val_loss: 0.4472 - val_accuracy: 0.7762\n", "Epoch 149/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0563 - accuracy: 0.9774 - val_loss: 0.4478 - val_accuracy: 0.7667\n", "Epoch 150/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0424 - accuracy: 0.9810 - val_loss: 0.4438 - val_accuracy: 0.7762\n", "Epoch 151/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0604 - accuracy: 0.9762 - val_loss: 0.4448 - val_accuracy: 0.7762\n", "Epoch 152/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0571 - accuracy: 0.9726 - val_loss: 0.4484 - val_accuracy: 0.7810\n", "Epoch 153/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0500 - accuracy: 0.9798 - val_loss: 0.4537 - val_accuracy: 0.7714\n", "Epoch 154/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0435 - accuracy: 0.9821 - val_loss: 0.4550 - val_accuracy: 0.7714\n", "Epoch 155/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0497 - accuracy: 0.9786 - val_loss: 0.4488 - val_accuracy: 0.7857\n", "Epoch 156/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0463 - accuracy: 0.9810 - val_loss: 0.4452 - val_accuracy: 0.7857\n", "Epoch 157/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0584 - accuracy: 0.9750 - val_loss: 0.4423 - val_accuracy: 0.7810\n", "Epoch 158/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0471 - accuracy: 0.9810 - val_loss: 0.4435 - val_accuracy: 0.7810\n", "Epoch 159/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0485 - accuracy: 0.9738 - val_loss: 0.4515 - val_accuracy: 0.7810\n", "Epoch 160/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0488 - accuracy: 0.9798 - val_loss: 0.4511 - val_accuracy: 0.7810\n", "Epoch 161/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0541 - accuracy: 0.9762 - val_loss: 0.4579 - val_accuracy: 0.7714\n", "Epoch 162/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0595 - accuracy: 0.9726 - val_loss: 0.4527 - val_accuracy: 0.7714\n", "Epoch 163/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0368 - accuracy: 0.9833 - val_loss: 0.4511 - val_accuracy: 0.7762\n", "Epoch 164/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0471 - accuracy: 0.9798 - val_loss: 0.4462 - val_accuracy: 0.7714\n", "Epoch 165/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0329 - accuracy: 0.9869 - val_loss: 0.4432 - val_accuracy: 0.7667\n", "Epoch 166/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0342 - accuracy: 0.9857 - val_loss: 0.4361 - val_accuracy: 0.7810\n", "Epoch 167/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0382 - accuracy: 0.9833 - val_loss: 0.4393 - val_accuracy: 0.7762\n", "Epoch 168/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0469 - accuracy: 0.9786 - val_loss: 0.4464 - val_accuracy: 0.7762\n", "Epoch 169/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0549 - accuracy: 0.9750 - val_loss: 0.4474 - val_accuracy: 0.7762\n", "Epoch 170/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0420 - accuracy: 0.9821 - val_loss: 0.4362 - val_accuracy: 0.7905\n", "Epoch 171/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0418 - accuracy: 0.9810 - val_loss: 0.4322 - val_accuracy: 0.7905\n", "Epoch 172/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0377 - accuracy: 0.9833 - val_loss: 0.4349 - val_accuracy: 0.7857\n", "Epoch 173/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0420 - accuracy: 0.9798 - val_loss: 0.4392 - val_accuracy: 0.7810\n", "Epoch 174/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0440 - accuracy: 0.9786 - val_loss: 0.4479 - val_accuracy: 0.7714\n", "Epoch 175/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0379 - accuracy: 0.9857 - val_loss: 0.4554 - val_accuracy: 0.7667\n", "Epoch 176/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0368 - accuracy: 0.9845 - val_loss: 0.4583 - val_accuracy: 0.7714\n", "Epoch 177/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0381 - accuracy: 0.9821 - val_loss: 0.4575 - val_accuracy: 0.7762\n", "Epoch 178/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0303 - accuracy: 0.9881 - val_loss: 0.4586 - val_accuracy: 0.7714\n", "Epoch 179/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0317 - 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[==============================] - 0s 8ms/step - loss: 0.0341 - accuracy: 0.9869 - val_loss: 0.4359 - val_accuracy: 0.7762\n", "Epoch 187/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0327 - accuracy: 0.9869 - val_loss: 0.4344 - val_accuracy: 0.7810\n", "Epoch 188/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0332 - accuracy: 0.9845 - val_loss: 0.4381 - val_accuracy: 0.7810\n", "Epoch 189/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0388 - accuracy: 0.9833 - val_loss: 0.4406 - val_accuracy: 0.7714\n", "Epoch 190/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0431 - accuracy: 0.9798 - val_loss: 0.4343 - val_accuracy: 0.7762\n", "Epoch 191/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0318 - accuracy: 0.9869 - val_loss: 0.4233 - val_accuracy: 0.7952\n", "Epoch 192/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0416 - accuracy: 0.9833 - val_loss: 0.4293 - val_accuracy: 0.7857\n", "Epoch 193/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0348 - accuracy: 0.9857 - val_loss: 0.4322 - val_accuracy: 0.7857\n", "Epoch 194/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0301 - accuracy: 0.9881 - val_loss: 0.4371 - val_accuracy: 0.7857\n", "Epoch 195/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0390 - accuracy: 0.9798 - val_loss: 0.4355 - val_accuracy: 0.7810\n", "Epoch 196/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0394 - accuracy: 0.9833 - val_loss: 0.4326 - val_accuracy: 0.7857\n", "Epoch 197/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0466 - accuracy: 0.9762 - val_loss: 0.4394 - val_accuracy: 0.7810\n", "Epoch 198/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0296 - accuracy: 0.9869 - val_loss: 0.4423 - val_accuracy: 0.7857\n", "Epoch 199/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0258 - accuracy: 0.9893 - val_loss: 0.4444 - val_accuracy: 0.7810\n", "Epoch 200/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0442 - accuracy: 0.9821 - val_loss: 0.4487 - val_accuracy: 0.7667\n", "Epoch 201/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0311 - accuracy: 0.9857 - val_loss: 0.4423 - val_accuracy: 0.7714\n", "Epoch 202/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0323 - accuracy: 0.9857 - val_loss: 0.4350 - val_accuracy: 0.7762\n", "Epoch 203/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0313 - accuracy: 0.9869 - val_loss: 0.4328 - val_accuracy: 0.7762\n", "Epoch 204/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0290 - accuracy: 0.9869 - val_loss: 0.4316 - val_accuracy: 0.7810\n", "Epoch 205/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0436 - accuracy: 0.9786 - val_loss: 0.4310 - val_accuracy: 0.7857\n", "Epoch 206/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0261 - accuracy: 0.9881 - val_loss: 0.4303 - val_accuracy: 0.7810\n", "Epoch 207/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0363 - accuracy: 0.9833 - val_loss: 0.4259 - val_accuracy: 0.7762\n", "Epoch 208/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0282 - accuracy: 0.9869 - val_loss: 0.4292 - val_accuracy: 0.7810\n", "Epoch 209/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0394 - accuracy: 0.9810 - val_loss: 0.4335 - val_accuracy: 0.7905\n", "Epoch 210/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0344 - accuracy: 0.9857 - val_loss: 0.4455 - val_accuracy: 0.7714\n", "Epoch 211/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0337 - accuracy: 0.9821 - val_loss: 0.4479 - val_accuracy: 0.7714\n", "Epoch 212/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0322 - 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[==============================] - 0s 9ms/step - loss: 0.0264 - accuracy: 0.9869 - val_loss: 0.4390 - val_accuracy: 0.7762\n", "Epoch 220/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0374 - accuracy: 0.9810 - val_loss: 0.4369 - val_accuracy: 0.7905\n", "Epoch 221/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0231 - accuracy: 0.9905 - val_loss: 0.4376 - val_accuracy: 0.7857\n", "Epoch 222/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0318 - accuracy: 0.9857 - val_loss: 0.4372 - val_accuracy: 0.7905\n", "Epoch 223/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0323 - accuracy: 0.9845 - val_loss: 0.4362 - val_accuracy: 0.7905\n", "Epoch 224/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0179 - accuracy: 0.9917 - val_loss: 0.4409 - val_accuracy: 0.7857\n", "Epoch 225/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0173 - accuracy: 0.9940 - val_loss: 0.4438 - val_accuracy: 0.7762\n", "Epoch 226/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0261 - accuracy: 0.9893 - val_loss: 0.4451 - val_accuracy: 0.7810\n", "Epoch 227/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0286 - accuracy: 0.9869 - val_loss: 0.4379 - val_accuracy: 0.7857\n", "Epoch 228/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0261 - accuracy: 0.9881 - val_loss: 0.4324 - val_accuracy: 0.7905\n", "Epoch 229/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0368 - accuracy: 0.9845 - val_loss: 0.4378 - val_accuracy: 0.7810\n", "Epoch 230/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0168 - accuracy: 0.9940 - val_loss: 0.4445 - val_accuracy: 0.7762\n", "Epoch 231/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0237 - accuracy: 0.9893 - val_loss: 0.4453 - val_accuracy: 0.7762\n", "Epoch 232/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0244 - accuracy: 0.9905 - val_loss: 0.4466 - val_accuracy: 0.7762\n", "Epoch 233/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0261 - accuracy: 0.9881 - val_loss: 0.4513 - val_accuracy: 0.7619\n", "Epoch 234/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0239 - accuracy: 0.9917 - val_loss: 0.4481 - val_accuracy: 0.7714\n", "Epoch 235/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0317 - accuracy: 0.9857 - val_loss: 0.4373 - val_accuracy: 0.7810\n", "Epoch 236/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0295 - accuracy: 0.9857 - val_loss: 0.4399 - val_accuracy: 0.7762\n", "Epoch 237/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0258 - accuracy: 0.9905 - val_loss: 0.4366 - val_accuracy: 0.7857\n", "Epoch 238/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0251 - accuracy: 0.9893 - val_loss: 0.4397 - val_accuracy: 0.7714\n", "Epoch 239/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0279 - accuracy: 0.9881 - val_loss: 0.4469 - val_accuracy: 0.7714\n", "Epoch 240/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0241 - accuracy: 0.9893 - val_loss: 0.4440 - val_accuracy: 0.7714\n", "Epoch 241/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0249 - accuracy: 0.9893 - val_loss: 0.4347 - val_accuracy: 0.7857\n", "Epoch 242/250\n", "7/7 [==============================] - 0s 9ms/step - loss: 0.0233 - accuracy: 0.9929 - val_loss: 0.4350 - val_accuracy: 0.7857\n", "Epoch 243/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0299 - accuracy: 0.9857 - val_loss: 0.4319 - val_accuracy: 0.7810\n", "Epoch 244/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0225 - accuracy: 0.9905 - val_loss: 0.4377 - val_accuracy: 0.7762\n", "Epoch 245/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0259 - accuracy: 0.9905 - val_loss: 0.4415 - val_accuracy: 0.7667\n", "Epoch 246/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0257 - accuracy: 0.9869 - val_loss: 0.4424 - val_accuracy: 0.7619\n", "Epoch 247/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0270 - accuracy: 0.9869 - val_loss: 0.4369 - val_accuracy: 0.7762\n", "Epoch 248/250\n", "7/7 [==============================] - 0s 10ms/step - loss: 0.0182 - accuracy: 0.9940 - val_loss: 0.4325 - val_accuracy: 0.7810\n", "Epoch 249/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0357 - accuracy: 0.9833 - val_loss: 0.4305 - val_accuracy: 0.7810\n", "Epoch 250/250\n", "7/7 [==============================] - 0s 8ms/step - loss: 0.0206 - accuracy: 0.9905 - val_loss: 0.4298 - val_accuracy: 0.7810\n", "Valid results - Loss: 0.42978227138519287 - Accuracy: 78.09523940086365%\n" ], "name": "stdout" }, { "output_type": "display_data", "data": { "image/png": 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\n", 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\n", 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\n", 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" ] }, "metadata": { "tags": [], "needs_background": "light" } }, { "output_type": "stream", "text": [ "DNN results\n", "accuracy : 0.9892857142857143\n", "Training Recall: 0.9892857142857143\n", "Training Precision: 0.9892859393529686\n", "Confusion matrix: \n", "[[373 5]\n", " [ 4 458]]\n", "Validation set report:\n", "cross Loss: -20.539179774693082\n", "hing Loss: 0.6428571428571429\n", "Report precision recall f1-score support\n", "\n", " 0 0.76 0.81 0.78 93\n", " 1 0.84 0.79 0.82 117\n", "\n", " accuracy 0.80 210\n", " macro avg 0.80 0.80 0.80 210\n", "weighted avg 0.80 0.80 0.80 210\n", "\n", "accuracy : 0.8\n", "Validation Recall: 0.8\n", "Validation Precision: 0.8022932022932022\n", "Confusion matrix: \n", "[[75 18]\n", " [24 93]]\n", "\n", "\\KNN Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 0.7285714285714285\n", "Validation Recall: 0.7285714285714285\n", "Validation Precision: 0.7275867575047904\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[62 31]\n", " [26 91]]\n", "\n", "\n", "Support vector machine Result\n", "\n", "Train accuracy: 0.8988095238095238\n", "Training Recall: 0.8988095238095238\n", "Training Precision: 0.8989595786336781\n", "\n", "\n", "Validation accuracy: 0.8\n", "Validation Recall: 0.8\n", "Validation Precision: 0.7996675593314249\n", "\n", "Train Confusion matrix:\n", " [[328 50]\n", " [ 35 427]]\n", "Validation Confusion matrix:\n", " [[71 22]\n", " [20 97]]\n", "\n", "\n", "Random Forest Result\n", "\n", "Train accuracy: 1.0\n", "Training Recall: 1.0\n", "Training Precision: 1.0\n", "\n", "\n", "Validation accuracy: 0.7142857142857143\n", "Validation Recall: 0.7142857142857143\n", "Validation Precision: 0.7390080175803045\n", "\n", "Train Confusion matrix:\n", " [[378 0]\n", " [ 0 462]]\n", "Validation Confusion matrix:\n", " [[ 43 50]\n", " [ 10 107]]\n" ], "name": "stdout" } ] } ] }