{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "DNN on Uci.ipynb", "provenance": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" } }, "cells": [ { "cell_type": "code", "metadata": { "colab": { "resources": { "http://localhost:8080/nbextensions/google.colab/files.js": { "data": 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"ok": true, "headers": [ [ "content-type", "application/javascript" ] ], "status": 200, "status_text": "OK" } }, "base_uri": "https://localhost:8080/", "height": 504 }, "id": "0ClNO26W6LAM", "outputId": "1bde3026-19e9-451d-fa4f-d76f4c11e718" }, "source": [ "from google.colab import files\n", "import pandas as pd\n", "uploaded = files.upload()\n", "import io \n", "df = pd.read_csv(io.BytesIO(uploaded['diabetes_data_upload.csv']))\n", "df" ], "execution_count": 2, "outputs": [ { "output_type": "display_data", "data": { "text/html": [ "\n", " \n", " \n", " Upload widget is only available when the cell has been executed in the\n", " current browser session. Please rerun this cell to enable.\n", " \n", " " ], "text/plain": [ "" ] }, "metadata": { "tags": [] } }, { "output_type": "stream", "text": [ "Saving diabetes_data_upload.csv to diabetes_data_upload.csv\n" ], "name": "stdout" }, { "output_type": "execute_result", "data": { "text/html": [ "
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AgeGenderPolyuriaPolydipsiasuddenweightlossweaknessPolyphagiaGenitalthrushvisualblurringItchingIrritabilitydelayedhealingpartialparesismusclestiffnessAlopeciaObesityTarget
040MaleNoYesNoYesNoNoNoYesNoYesNoYesYesYesPositive
158MaleNoNoNoYesNoNoYesNoNoNoYesNoYesNoPositive
241MaleYesNoNoYesYesNoNoYesNoYesNoYesYesNoPositive
345MaleNoNoYesYesYesYesNoYesNoYesNoNoNoNoPositive
460MaleYesYesYesYesYesNoYesYesYesYesYesYesYesYesPositive
......................................................
51539FemaleYesYesYesNoYesNoNoYesNoYesYesNoNoNoPositive
51648FemaleYesYesYesYesYesNoNoYesYesYesYesNoNoNoPositive
51758FemaleYesYesYesYesYesNoYesNoNoNoYesYesNoYesPositive
51832FemaleNoNoNoYesNoNoYesYesNoYesNoNoYesNoNegative
51942MaleNoNoNoNoNoNoNoNoNoNoNoNoNoNoNegative
\n", "

520 rows × 17 columns

\n", "
" ], "text/plain": [ " Age Gender Polyuria Polydipsia ... musclestiffness Alopecia Obesity Target\n", "0 40 Male No Yes ... Yes Yes Yes Positive\n", "1 58 Male No No ... No Yes No Positive\n", "2 41 Male Yes No ... Yes Yes No Positive\n", "3 45 Male No No ... No No No Positive\n", "4 60 Male Yes Yes ... Yes Yes Yes Positive\n", ".. ... ... ... ... ... ... ... ... ...\n", "515 39 Female Yes Yes ... No No No Positive\n", "516 48 Female Yes Yes ... No No No Positive\n", "517 58 Female Yes Yes ... Yes No Yes Positive\n", "518 32 Female No No ... No Yes No Negative\n", "519 42 Male No No ... No No No Negative\n", "\n", "[520 rows x 17 columns]" ] }, "metadata": { "tags": [] }, "execution_count": 2 } ] }, { "cell_type": "code", "metadata": { "id": "R94s-Vhq6hv0" }, "source": [ "from sklearn import preprocessing\n", "label_enco = preprocessing.LabelEncoder() #Label encoding\n", "df['Gender']=label_enco.fit_transform(df['Gender'])\n", "df['Polyuria']=label_enco.fit_transform(df['Polyuria'])\n", "df['Polydipsia']=label_enco.fit_transform(df['Polydipsia'])\n", "df['suddenweightloss']=label_enco.fit_transform(df['suddenweightloss'])\n", "df['weakness']=label_enco.fit_transform(df['weakness'])\n", "df['Polyphagia']=label_enco.fit_transform(df['Polyphagia'])\n", "df['Genitalthrush']=label_enco.fit_transform(df['Genitalthrush'])\n", "df['visualblurring']=label_enco.fit_transform(df['visualblurring'])\n", "df['Itching']=label_enco.fit_transform(df['Itching'])\n", "df['Irritability']=label_enco.fit_transform(df['Irritability'])\n", "df['delayedhealing']=label_enco.fit_transform(df['delayedhealing'])\n", "df['partialparesis']=label_enco.fit_transform(df['partialparesis'])\n", "df['musclestiffness']=label_enco.fit_transform(df['musclestiffness'])\n", "df['Alopecia']=label_enco.fit_transform(df['Alopecia'])\n", "df['Obesity']=label_enco.fit_transform(df['Obesity'])\n", "df['Target']=label_enco.fit_transform(df['Target'])\n" ], "execution_count": 3, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Gj1AniFT6U8r", "outputId": "251e8dfc-c642-4ef9-ed4d-db36a8b5fff6" }, "source": [ "import tensorflow as tf\n", "from numpy import loadtxt\n", "from keras.models import Sequential\n", "from keras.layers import Dense\n", "from sklearn.model_selection import train_test_split\n", "from sklearn import preprocessing\n", "\n", "X = df.iloc[:,0:16].values\n", "Y = df.iloc[:,16].values\n", "x_train,x_test,y_train,y_test=train_test_split(X,Y,test_size=0.2, random_state=2)\n", "\n", "#define the keras models\n", "model = Sequential()\n", "model.add(Dense(16, input_dim=16, activation='relu'))\n", "model.add(Dense(14, activation='relu'))\n", "model.add(Dense(12,activation ='relu'))\n", "\n", "\n", "model.add(Dense(6, input_dim=4, activation='relu'))\n", "model.add(Dense(4, activation='relu'))\n", "model.add(Dense(1,activation ='sigmoid'))\n", "model.summary()" ], "execution_count": 25, "outputs": [ { "output_type": "stream", "text": [ "Model: \"sequential_5\"\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "dense_36 (Dense) (None, 16) 272 \n", "_________________________________________________________________\n", "dense_37 (Dense) (None, 14) 238 \n", "_________________________________________________________________\n", "dense_38 (Dense) (None, 12) 180 \n", "_________________________________________________________________\n", "dense_39 (Dense) (None, 6) 78 \n", "_________________________________________________________________\n", "dense_40 (Dense) (None, 4) 28 \n", "_________________________________________________________________\n", "dense_41 (Dense) (None, 1) 5 \n", "=================================================================\n", "Total params: 801\n", "Trainable params: 801\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ihyRRJLS68NC", "outputId": "a671936b-2a10-46b3-8e68-058616cd51e9" }, "source": [ "model.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=['accuracy'])\n", "\n", "#fit the keras model on the dataset\n", "history=model.fit(X,Y,validation_split=0.20,epochs=500, batch_size=50 )\n", "\n", "#evaluate the keras model\n", "_,accuracy = model.evaluate(x_train,y_train)\n", "print('Train Accuracy: %.2f' % (accuracy*100))\n", "predictions = model.predict_classes(x_train)\n", "\n", "_,accuracy = model.evaluate(x_test,y_test)\n", "print('Test Accuracy: %.2f' % (accuracy*100))\n", "predictions = model.predict_classes(x_test)\n", "\n", "\n", "# summarize the first 15 cases\n", "for i in range(15):\n", "\tprint('%s => %d (expected %d)' % (x_test[i].tolist(), predictions[i], y_test[i]))\n" ], "execution_count": 26, "outputs": [ { "output_type": "stream", "text": [ "Epoch 1/500\n", "9/9 [==============================] - 1s 29ms/step - loss: 0.7664 - accuracy: 0.3396 - val_loss: 0.6973 - val_accuracy: 0.4615\n", "Epoch 2/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.6982 - accuracy: 0.4249 - val_loss: 0.6893 - val_accuracy: 0.5385\n", "Epoch 3/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.6713 - accuracy: 0.6347 - val_loss: 0.7092 - val_accuracy: 0.5385\n", "Epoch 4/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.6665 - accuracy: 0.6150 - val_loss: 0.7077 - val_accuracy: 0.5385\n", "Epoch 5/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.6479 - accuracy: 0.6462 - val_loss: 0.7228 - val_accuracy: 0.5385\n", "Epoch 6/500\n", "9/9 [==============================] - 0s 10ms/step - loss: 0.6625 - accuracy: 0.6112 - val_loss: 0.7004 - val_accuracy: 0.5385\n", "Epoch 7/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.6406 - accuracy: 0.6444 - val_loss: 0.7033 - val_accuracy: 0.5385\n", "Epoch 8/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.6440 - accuracy: 0.6237 - val_loss: 0.7018 - val_accuracy: 0.5385\n", "Epoch 9/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.6327 - accuracy: 0.6570 - val_loss: 0.7038 - val_accuracy: 0.5385\n", "Epoch 10/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.6248 - accuracy: 0.6441 - val_loss: 0.6948 - val_accuracy: 0.5385\n", "Epoch 11/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.6104 - accuracy: 0.6437 - val_loss: 0.6796 - val_accuracy: 0.5385\n", "Epoch 12/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.6371 - accuracy: 0.6149 - val_loss: 0.6811 - val_accuracy: 0.5385\n", "Epoch 13/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.6045 - accuracy: 0.6429 - val_loss: 0.6469 - val_accuracy: 0.5385\n", "Epoch 14/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.5776 - accuracy: 0.6434 - val_loss: 0.6395 - val_accuracy: 0.5385\n", "Epoch 15/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.5530 - accuracy: 0.6589 - val_loss: 0.6102 - val_accuracy: 0.5385\n", "Epoch 16/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.5456 - accuracy: 0.6541 - val_loss: 0.5916 - val_accuracy: 0.5385\n", "Epoch 17/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.5345 - accuracy: 0.6376 - val_loss: 0.5844 - val_accuracy: 0.5385\n", "Epoch 18/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.5334 - accuracy: 0.6302 - val_loss: 0.5462 - val_accuracy: 0.6827\n", "Epoch 19/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.4838 - accuracy: 0.7395 - val_loss: 0.5219 - val_accuracy: 0.8173\n", "Epoch 20/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.5022 - accuracy: 0.7957 - val_loss: 0.4981 - val_accuracy: 0.8173\n", "Epoch 21/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.4490 - accuracy: 0.8503 - val_loss: 0.4756 - val_accuracy: 0.8173\n", "Epoch 22/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.4306 - accuracy: 0.8237 - val_loss: 0.4509 - val_accuracy: 0.8558\n", "Epoch 23/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.4381 - accuracy: 0.8576 - val_loss: 0.4450 - val_accuracy: 0.8173\n", "Epoch 24/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.4238 - accuracy: 0.8440 - val_loss: 0.4375 - val_accuracy: 0.8173\n", "Epoch 25/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.4169 - accuracy: 0.8091 - val_loss: 0.4063 - val_accuracy: 0.8365\n", "Epoch 26/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.3974 - accuracy: 0.8210 - val_loss: 0.3873 - val_accuracy: 0.9231\n", "Epoch 27/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.3881 - accuracy: 0.8522 - val_loss: 0.3977 - val_accuracy: 0.8173\n", "Epoch 28/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.3701 - accuracy: 0.8329 - val_loss: 0.3521 - val_accuracy: 0.8654\n", "Epoch 29/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.3467 - accuracy: 0.8664 - val_loss: 0.3442 - val_accuracy: 0.8654\n", "Epoch 30/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.3355 - accuracy: 0.8625 - val_loss: 0.3169 - val_accuracy: 0.9231\n", "Epoch 31/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.3233 - accuracy: 0.8892 - val_loss: 0.3066 - val_accuracy: 0.8750\n", "Epoch 32/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.2874 - accuracy: 0.9052 - val_loss: 0.2837 - val_accuracy: 0.9231\n", "Epoch 33/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.2984 - accuracy: 0.8864 - val_loss: 0.2657 - val_accuracy: 0.9327\n", "Epoch 34/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.3026 - accuracy: 0.8840 - val_loss: 0.2610 - val_accuracy: 0.9135\n", "Epoch 35/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.2942 - accuracy: 0.8702 - val_loss: 0.3001 - val_accuracy: 0.8750\n", "Epoch 36/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.3186 - accuracy: 0.8535 - val_loss: 0.2481 - val_accuracy: 0.9135\n", "Epoch 37/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.2849 - accuracy: 0.8685 - val_loss: 0.2227 - val_accuracy: 0.9615\n", "Epoch 38/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.2537 - accuracy: 0.9278 - val_loss: 0.2105 - val_accuracy: 0.9615\n", "Epoch 39/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.2450 - accuracy: 0.9002 - val_loss: 0.3378 - val_accuracy: 0.8365\n", "Epoch 40/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.3056 - accuracy: 0.8628 - val_loss: 0.2028 - val_accuracy: 0.9615\n", "Epoch 41/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.2252 - accuracy: 0.9219 - val_loss: 0.2179 - val_accuracy: 0.9135\n", "Epoch 42/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.2665 - accuracy: 0.8802 - val_loss: 0.1937 - val_accuracy: 0.9135\n", "Epoch 43/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.2320 - accuracy: 0.9061 - val_loss: 0.1898 - val_accuracy: 0.9135\n", "Epoch 44/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.2393 - accuracy: 0.8844 - val_loss: 0.1772 - val_accuracy: 0.9615\n", "Epoch 45/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.2366 - accuracy: 0.9075 - val_loss: 0.1766 - val_accuracy: 0.9423\n", "Epoch 46/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.2472 - accuracy: 0.8908 - val_loss: 0.1851 - val_accuracy: 0.9135\n", "Epoch 47/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.2279 - accuracy: 0.8919 - val_loss: 0.1657 - val_accuracy: 0.9712\n", "Epoch 48/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.2151 - accuracy: 0.9117 - val_loss: 0.1669 - val_accuracy: 0.9423\n", "Epoch 49/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.2378 - accuracy: 0.8944 - val_loss: 0.1593 - val_accuracy: 0.9712\n", "Epoch 50/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.2108 - accuracy: 0.9116 - val_loss: 0.1607 - val_accuracy: 0.9519\n", "Epoch 51/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.2151 - accuracy: 0.9007 - val_loss: 0.1714 - val_accuracy: 0.9135\n", "Epoch 52/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1914 - accuracy: 0.9203 - val_loss: 0.1574 - val_accuracy: 0.9423\n", "Epoch 53/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.2512 - accuracy: 0.8931 - val_loss: 0.1563 - val_accuracy: 0.9519\n", "Epoch 54/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.2070 - accuracy: 0.9284 - val_loss: 0.1547 - val_accuracy: 0.9519\n", "Epoch 55/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.2071 - accuracy: 0.9174 - val_loss: 0.1643 - val_accuracy: 0.9135\n", "Epoch 56/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.2018 - accuracy: 0.8937 - val_loss: 0.1513 - val_accuracy: 0.9519\n", "Epoch 57/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.2087 - accuracy: 0.9068 - val_loss: 0.1455 - val_accuracy: 0.9615\n", "Epoch 58/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.2065 - accuracy: 0.9201 - val_loss: 0.1648 - val_accuracy: 0.9231\n", "Epoch 59/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.2024 - accuracy: 0.9239 - val_loss: 0.1504 - val_accuracy: 0.9231\n", "Epoch 60/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.2328 - accuracy: 0.8823 - val_loss: 0.1410 - val_accuracy: 0.9615\n", "Epoch 61/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.1868 - accuracy: 0.9356 - val_loss: 0.1386 - val_accuracy: 0.9615\n", "Epoch 62/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.2206 - accuracy: 0.9135 - val_loss: 0.1477 - val_accuracy: 0.9231\n", "Epoch 63/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.1959 - accuracy: 0.9222 - val_loss: 0.1571 - val_accuracy: 0.9231\n", "Epoch 64/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1845 - accuracy: 0.9035 - val_loss: 0.1343 - val_accuracy: 0.9615\n", "Epoch 65/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1938 - accuracy: 0.9300 - val_loss: 0.1494 - val_accuracy: 0.9231\n", "Epoch 66/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1882 - accuracy: 0.9157 - val_loss: 0.1335 - val_accuracy: 0.9615\n", "Epoch 67/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.2074 - accuracy: 0.9187 - val_loss: 0.1341 - val_accuracy: 0.9615\n", "Epoch 68/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.2035 - accuracy: 0.9099 - val_loss: 0.1386 - val_accuracy: 0.9519\n", "Epoch 69/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.2128 - accuracy: 0.9159 - val_loss: 0.1764 - val_accuracy: 0.9231\n", "Epoch 70/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.2074 - accuracy: 0.9100 - val_loss: 0.1323 - val_accuracy: 0.9615\n", "Epoch 71/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.2126 - accuracy: 0.9104 - val_loss: 0.1293 - val_accuracy: 0.9615\n", "Epoch 72/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1953 - accuracy: 0.9110 - val_loss: 0.1364 - val_accuracy: 0.9519\n", "Epoch 73/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1900 - accuracy: 0.9017 - val_loss: 0.1284 - val_accuracy: 0.9615\n", "Epoch 74/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.2110 - accuracy: 0.9270 - val_loss: 0.1295 - val_accuracy: 0.9712\n", "Epoch 75/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1950 - accuracy: 0.9320 - val_loss: 0.1360 - val_accuracy: 0.9615\n", "Epoch 76/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.1884 - accuracy: 0.9143 - val_loss: 0.1263 - val_accuracy: 0.9615\n", "Epoch 77/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.1980 - accuracy: 0.9291 - val_loss: 0.1260 - val_accuracy: 0.9615\n", "Epoch 78/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1729 - accuracy: 0.9424 - val_loss: 0.1333 - val_accuracy: 0.9615\n", "Epoch 79/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.2182 - accuracy: 0.9170 - val_loss: 0.1282 - val_accuracy: 0.9615\n", "Epoch 80/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.1774 - accuracy: 0.9252 - val_loss: 0.1400 - val_accuracy: 0.9231\n", "Epoch 81/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.2135 - accuracy: 0.9383 - val_loss: 0.1620 - val_accuracy: 0.9231\n", "Epoch 82/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1867 - accuracy: 0.9122 - val_loss: 0.1410 - val_accuracy: 0.9519\n", "Epoch 83/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.2023 - accuracy: 0.9220 - val_loss: 0.1272 - val_accuracy: 0.9712\n", "Epoch 84/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1696 - accuracy: 0.9514 - val_loss: 0.1268 - val_accuracy: 0.9615\n", "Epoch 85/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1992 - accuracy: 0.9185 - val_loss: 0.1437 - val_accuracy: 0.9135\n", "Epoch 86/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.1938 - accuracy: 0.9230 - val_loss: 0.1236 - val_accuracy: 0.9712\n", "Epoch 87/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.1862 - accuracy: 0.9399 - val_loss: 0.1409 - val_accuracy: 0.9231\n", "Epoch 88/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1820 - accuracy: 0.9302 - val_loss: 0.1311 - val_accuracy: 0.9519\n", "Epoch 89/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.1975 - accuracy: 0.9132 - val_loss: 0.1486 - val_accuracy: 0.9519\n", "Epoch 90/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1994 - accuracy: 0.9158 - val_loss: 0.1271 - val_accuracy: 0.9519\n", "Epoch 91/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.1847 - accuracy: 0.9217 - val_loss: 0.1460 - val_accuracy: 0.9135\n", "Epoch 92/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.1916 - accuracy: 0.9127 - val_loss: 0.1247 - val_accuracy: 0.9712\n", "Epoch 93/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.1616 - accuracy: 0.9450 - val_loss: 0.1201 - val_accuracy: 0.9615\n", "Epoch 94/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1558 - accuracy: 0.9486 - val_loss: 0.1262 - val_accuracy: 0.9712\n", "Epoch 95/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1479 - accuracy: 0.9292 - val_loss: 0.1286 - val_accuracy: 0.9519\n", "Epoch 96/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1675 - accuracy: 0.9460 - val_loss: 0.1811 - val_accuracy: 0.9231\n", "Epoch 97/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.1890 - accuracy: 0.9184 - val_loss: 0.1631 - val_accuracy: 0.9423\n", "Epoch 98/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.2061 - accuracy: 0.9225 - val_loss: 0.1507 - val_accuracy: 0.9135\n", "Epoch 99/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1816 - accuracy: 0.9165 - val_loss: 0.1197 - val_accuracy: 0.9712\n", "Epoch 100/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.1863 - accuracy: 0.9365 - val_loss: 0.1317 - val_accuracy: 0.9231\n", "Epoch 101/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1645 - accuracy: 0.9349 - val_loss: 0.1211 - val_accuracy: 0.9712\n", "Epoch 102/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1616 - accuracy: 0.9417 - val_loss: 0.1198 - val_accuracy: 0.9712\n", "Epoch 103/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1816 - accuracy: 0.9392 - val_loss: 0.1445 - val_accuracy: 0.9135\n", "Epoch 104/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1984 - accuracy: 0.9148 - val_loss: 0.1177 - val_accuracy: 0.9712\n", "Epoch 105/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1790 - accuracy: 0.9356 - val_loss: 0.1205 - val_accuracy: 0.9615\n", "Epoch 106/500\n", "9/9 [==============================] - 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0s 9ms/step - loss: 0.1750 - accuracy: 0.9357 - val_loss: 0.1191 - val_accuracy: 0.9519\n", "Epoch 163/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1958 - accuracy: 0.9098 - val_loss: 0.1132 - val_accuracy: 0.9712\n", "Epoch 164/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1671 - accuracy: 0.9332 - val_loss: 0.1455 - val_accuracy: 0.9135\n", "Epoch 165/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.1882 - accuracy: 0.9184 - val_loss: 0.1084 - val_accuracy: 0.9808\n", "Epoch 166/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.1552 - accuracy: 0.9490 - val_loss: 0.1071 - val_accuracy: 0.9808\n", "Epoch 167/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1414 - accuracy: 0.9468 - val_loss: 0.1104 - val_accuracy: 0.9808\n", "Epoch 168/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.1578 - accuracy: 0.9517 - val_loss: 0.1062 - val_accuracy: 0.9808\n", "Epoch 169/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1462 - accuracy: 0.9473 - val_loss: 0.1055 - val_accuracy: 0.9808\n", "Epoch 170/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1633 - accuracy: 0.9355 - val_loss: 0.1110 - val_accuracy: 0.9712\n", "Epoch 171/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.1748 - accuracy: 0.9378 - val_loss: 0.1168 - val_accuracy: 0.9712\n", "Epoch 172/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.1684 - accuracy: 0.9402 - val_loss: 0.1036 - val_accuracy: 0.9808\n", "Epoch 173/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1641 - accuracy: 0.9445 - val_loss: 0.1023 - val_accuracy: 0.9808\n", "Epoch 174/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1290 - accuracy: 0.9509 - val_loss: 0.1254 - val_accuracy: 0.9231\n", "Epoch 175/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.1648 - accuracy: 0.9378 - val_loss: 0.1193 - val_accuracy: 0.9519\n", "Epoch 176/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1709 - accuracy: 0.9354 - val_loss: 0.1452 - val_accuracy: 0.9135\n", "Epoch 177/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.1767 - accuracy: 0.9260 - val_loss: 0.1028 - val_accuracy: 0.9808\n", "Epoch 178/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.1508 - accuracy: 0.9404 - val_loss: 0.1043 - val_accuracy: 0.9808\n", "Epoch 179/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.1630 - accuracy: 0.9412 - val_loss: 0.1101 - val_accuracy: 0.9712\n", "Epoch 180/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1383 - accuracy: 0.9537 - val_loss: 0.1009 - val_accuracy: 0.9808\n", "Epoch 181/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1838 - accuracy: 0.9289 - val_loss: 0.1067 - val_accuracy: 0.9712\n", "Epoch 182/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1500 - accuracy: 0.9397 - val_loss: 0.1005 - val_accuracy: 0.9808\n", "Epoch 183/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.1681 - accuracy: 0.9347 - val_loss: 0.1022 - val_accuracy: 0.9808\n", "Epoch 184/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1751 - accuracy: 0.9307 - val_loss: 0.0995 - val_accuracy: 0.9808\n", "Epoch 185/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1486 - accuracy: 0.9465 - val_loss: 0.0990 - val_accuracy: 0.9808\n", "Epoch 186/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.1861 - accuracy: 0.9248 - val_loss: 0.0990 - val_accuracy: 0.9808\n", "Epoch 187/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1610 - accuracy: 0.9408 - val_loss: 0.1112 - val_accuracy: 0.9808\n", "Epoch 188/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1276 - accuracy: 0.9521 - val_loss: 0.1027 - val_accuracy: 0.9808\n", "Epoch 189/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1830 - accuracy: 0.9248 - val_loss: 0.1019 - val_accuracy: 0.9808\n", "Epoch 190/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1540 - accuracy: 0.9379 - val_loss: 0.1258 - val_accuracy: 0.9231\n", "Epoch 191/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1503 - accuracy: 0.9246 - val_loss: 0.1061 - val_accuracy: 0.9808\n", "Epoch 192/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.1693 - accuracy: 0.9413 - val_loss: 0.1130 - val_accuracy: 0.9712\n", "Epoch 193/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.1609 - accuracy: 0.9351 - val_loss: 0.0990 - val_accuracy: 0.9808\n", "Epoch 194/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.1640 - accuracy: 0.9368 - val_loss: 0.1052 - val_accuracy: 0.9808\n", "Epoch 195/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1731 - accuracy: 0.9426 - val_loss: 0.1158 - val_accuracy: 0.9712\n", "Epoch 196/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1400 - accuracy: 0.9438 - val_loss: 0.1032 - val_accuracy: 0.9808\n", "Epoch 197/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1795 - accuracy: 0.9308 - val_loss: 0.0974 - val_accuracy: 0.9808\n", "Epoch 198/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.1391 - accuracy: 0.9499 - val_loss: 0.1018 - val_accuracy: 0.9808\n", "Epoch 199/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1762 - accuracy: 0.9154 - val_loss: 0.1094 - val_accuracy: 0.9615\n", "Epoch 200/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1834 - accuracy: 0.9300 - val_loss: 0.1057 - val_accuracy: 0.9712\n", "Epoch 201/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1792 - accuracy: 0.9241 - val_loss: 0.0946 - val_accuracy: 0.9808\n", "Epoch 202/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1439 - accuracy: 0.9505 - val_loss: 0.0955 - val_accuracy: 0.9808\n", "Epoch 203/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1644 - accuracy: 0.9473 - val_loss: 0.1000 - val_accuracy: 0.9712\n", "Epoch 204/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1503 - accuracy: 0.9365 - val_loss: 0.0943 - val_accuracy: 0.9808\n", "Epoch 205/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1387 - accuracy: 0.9430 - val_loss: 0.0939 - val_accuracy: 0.9808\n", "Epoch 206/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1409 - accuracy: 0.9551 - val_loss: 0.1050 - val_accuracy: 0.9808\n", "Epoch 207/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1538 - accuracy: 0.9371 - val_loss: 0.0922 - val_accuracy: 0.9808\n", "Epoch 208/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.1490 - accuracy: 0.9443 - val_loss: 0.0939 - val_accuracy: 0.9808\n", "Epoch 209/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.1369 - accuracy: 0.9554 - val_loss: 0.0966 - val_accuracy: 0.9808\n", "Epoch 210/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1683 - accuracy: 0.9363 - val_loss: 0.1039 - val_accuracy: 0.9808\n", "Epoch 211/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1528 - accuracy: 0.9447 - val_loss: 0.0918 - val_accuracy: 0.9808\n", "Epoch 212/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1502 - accuracy: 0.9422 - val_loss: 0.1083 - val_accuracy: 0.9615\n", "Epoch 213/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1442 - accuracy: 0.9430 - val_loss: 0.1019 - val_accuracy: 0.9808\n", "Epoch 214/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1275 - accuracy: 0.9568 - val_loss: 0.0906 - val_accuracy: 0.9808\n", "Epoch 215/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.1575 - accuracy: 0.9372 - val_loss: 0.0931 - val_accuracy: 0.9808\n", "Epoch 216/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1420 - accuracy: 0.9561 - val_loss: 0.0947 - val_accuracy: 0.9808\n", "Epoch 217/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1327 - accuracy: 0.9480 - val_loss: 0.0917 - val_accuracy: 0.9904\n", "Epoch 218/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.1394 - accuracy: 0.9534 - val_loss: 0.0889 - val_accuracy: 0.9808\n", "Epoch 219/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1198 - accuracy: 0.9527 - val_loss: 0.0877 - val_accuracy: 0.9808\n", "Epoch 220/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1581 - accuracy: 0.9419 - val_loss: 0.1005 - val_accuracy: 0.9808\n", "Epoch 221/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1626 - accuracy: 0.9455 - val_loss: 0.0973 - val_accuracy: 0.9808\n", "Epoch 222/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1438 - accuracy: 0.9472 - val_loss: 0.0992 - val_accuracy: 0.9712\n", "Epoch 223/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1517 - accuracy: 0.9311 - val_loss: 0.0951 - val_accuracy: 0.9904\n", "Epoch 224/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.1025 - accuracy: 0.9664 - val_loss: 0.0861 - val_accuracy: 0.9808\n", "Epoch 225/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1276 - accuracy: 0.9503 - val_loss: 0.0951 - val_accuracy: 0.9808\n", "Epoch 226/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.1508 - accuracy: 0.9497 - val_loss: 0.0909 - val_accuracy: 0.9904\n", "Epoch 227/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1527 - accuracy: 0.9430 - val_loss: 0.0974 - val_accuracy: 0.9904\n", "Epoch 228/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1388 - accuracy: 0.9594 - val_loss: 0.0945 - val_accuracy: 0.9808\n", "Epoch 229/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1979 - accuracy: 0.9256 - val_loss: 0.1110 - val_accuracy: 0.9519\n", "Epoch 230/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1251 - accuracy: 0.9595 - val_loss: 0.1181 - val_accuracy: 0.9519\n", "Epoch 231/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1486 - accuracy: 0.9491 - val_loss: 0.0856 - val_accuracy: 0.9808\n", "Epoch 232/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1489 - accuracy: 0.9502 - val_loss: 0.0847 - val_accuracy: 0.9808\n", "Epoch 233/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.1690 - accuracy: 0.9264 - val_loss: 0.0838 - val_accuracy: 0.9904\n", "Epoch 234/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.1401 - accuracy: 0.9545 - val_loss: 0.0927 - val_accuracy: 0.9904\n", "Epoch 235/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1408 - accuracy: 0.9533 - val_loss: 0.0871 - val_accuracy: 0.9904\n", "Epoch 236/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.1495 - accuracy: 0.9521 - val_loss: 0.0937 - val_accuracy: 0.9904\n", "Epoch 237/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1089 - accuracy: 0.9749 - val_loss: 0.0828 - val_accuracy: 0.9904\n", "Epoch 238/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1273 - accuracy: 0.9538 - val_loss: 0.0891 - val_accuracy: 0.9808\n", "Epoch 239/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.1816 - accuracy: 0.9294 - val_loss: 0.0874 - val_accuracy: 0.9904\n", "Epoch 240/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1313 - accuracy: 0.9581 - val_loss: 0.0821 - val_accuracy: 0.9904\n", "Epoch 241/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1303 - accuracy: 0.9558 - val_loss: 0.0800 - val_accuracy: 0.9904\n", "Epoch 242/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1403 - accuracy: 0.9581 - val_loss: 0.0831 - val_accuracy: 0.9904\n", "Epoch 243/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1379 - accuracy: 0.9589 - val_loss: 0.0844 - val_accuracy: 0.9808\n", "Epoch 244/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1158 - accuracy: 0.9608 - val_loss: 0.1021 - val_accuracy: 0.9712\n", "Epoch 245/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.1579 - accuracy: 0.9343 - val_loss: 0.0848 - val_accuracy: 0.9904\n", "Epoch 246/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1278 - accuracy: 0.9639 - val_loss: 0.0924 - val_accuracy: 0.9904\n", "Epoch 247/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1459 - accuracy: 0.9595 - val_loss: 0.0837 - val_accuracy: 0.9904\n", "Epoch 248/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1160 - accuracy: 0.9558 - val_loss: 0.0998 - val_accuracy: 0.9615\n", "Epoch 249/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.1159 - accuracy: 0.9456 - val_loss: 0.0775 - val_accuracy: 0.9904\n", "Epoch 250/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1211 - accuracy: 0.9565 - val_loss: 0.0793 - val_accuracy: 0.9904\n", "Epoch 251/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1276 - accuracy: 0.9527 - val_loss: 0.0771 - val_accuracy: 0.9904\n", "Epoch 252/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.1013 - accuracy: 0.9659 - val_loss: 0.0773 - val_accuracy: 0.9904\n", "Epoch 253/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1169 - accuracy: 0.9651 - val_loss: 0.0808 - val_accuracy: 0.9904\n", "Epoch 254/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.1298 - accuracy: 0.9718 - val_loss: 0.0785 - val_accuracy: 0.9904\n", "Epoch 255/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1039 - accuracy: 0.9751 - val_loss: 0.0829 - val_accuracy: 0.9904\n", "Epoch 256/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1307 - accuracy: 0.9634 - val_loss: 0.1210 - val_accuracy: 0.9519\n", "Epoch 257/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1449 - accuracy: 0.9518 - val_loss: 0.0913 - val_accuracy: 0.9808\n", "Epoch 258/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.1624 - accuracy: 0.9213 - val_loss: 0.0790 - val_accuracy: 0.9904\n", "Epoch 259/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1352 - accuracy: 0.9631 - val_loss: 0.0791 - val_accuracy: 0.9904\n", "Epoch 260/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1390 - accuracy: 0.9546 - val_loss: 0.1036 - val_accuracy: 0.9712\n", "Epoch 261/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1392 - accuracy: 0.9469 - val_loss: 0.0946 - val_accuracy: 0.9712\n", "Epoch 262/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1343 - accuracy: 0.9392 - val_loss: 0.0741 - val_accuracy: 0.9904\n", "Epoch 263/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0953 - accuracy: 0.9733 - val_loss: 0.0781 - val_accuracy: 0.9904\n", "Epoch 264/500\n", "9/9 [==============================] - 0s 10ms/step - loss: 0.1331 - accuracy: 0.9638 - val_loss: 0.0733 - val_accuracy: 0.9904\n", "Epoch 265/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1174 - accuracy: 0.9702 - val_loss: 0.0747 - val_accuracy: 0.9904\n", "Epoch 266/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1137 - accuracy: 0.9650 - val_loss: 0.0821 - val_accuracy: 0.9904\n", "Epoch 267/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1042 - accuracy: 0.9716 - val_loss: 0.0751 - val_accuracy: 0.9904\n", "Epoch 268/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1146 - accuracy: 0.9729 - val_loss: 0.0759 - val_accuracy: 0.9904\n", "Epoch 269/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.1435 - accuracy: 0.9523 - val_loss: 0.0738 - val_accuracy: 0.9904\n", "Epoch 270/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1205 - accuracy: 0.9736 - val_loss: 0.0732 - val_accuracy: 0.9904\n", "Epoch 271/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1001 - accuracy: 0.9648 - val_loss: 0.0762 - val_accuracy: 0.9904\n", "Epoch 272/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0828 - accuracy: 0.9823 - val_loss: 0.0719 - val_accuracy: 0.9904\n", "Epoch 273/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1188 - accuracy: 0.9602 - val_loss: 0.0887 - val_accuracy: 0.9904\n", "Epoch 274/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1127 - accuracy: 0.9631 - val_loss: 0.0743 - val_accuracy: 0.9904\n", "Epoch 275/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1006 - accuracy: 0.9630 - val_loss: 0.0873 - val_accuracy: 0.9712\n", "Epoch 276/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1504 - accuracy: 0.9436 - val_loss: 0.1108 - val_accuracy: 0.9615\n", "Epoch 277/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.1119 - accuracy: 0.9572 - val_loss: 0.0818 - val_accuracy: 0.9808\n", "Epoch 278/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0987 - accuracy: 0.9700 - val_loss: 0.0764 - val_accuracy: 0.9904\n", "Epoch 279/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0920 - accuracy: 0.9761 - val_loss: 0.0715 - val_accuracy: 0.9904\n", "Epoch 280/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1037 - accuracy: 0.9638 - val_loss: 0.0738 - val_accuracy: 0.9904\n", "Epoch 281/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0845 - accuracy: 0.9836 - val_loss: 0.0718 - val_accuracy: 0.9904\n", "Epoch 282/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1104 - accuracy: 0.9701 - val_loss: 0.0735 - val_accuracy: 0.9904\n", "Epoch 283/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.1075 - accuracy: 0.9728 - val_loss: 0.0760 - val_accuracy: 0.9904\n", "Epoch 284/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1098 - accuracy: 0.9697 - val_loss: 0.0729 - val_accuracy: 0.9904\n", "Epoch 285/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.1212 - accuracy: 0.9637 - val_loss: 0.0829 - val_accuracy: 0.9904\n", "Epoch 286/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1015 - accuracy: 0.9757 - val_loss: 0.0759 - val_accuracy: 0.9904\n", "Epoch 287/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0970 - accuracy: 0.9518 - val_loss: 0.0695 - val_accuracy: 0.9904\n", "Epoch 288/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1016 - accuracy: 0.9637 - val_loss: 0.0794 - val_accuracy: 0.9904\n", "Epoch 289/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0963 - accuracy: 0.9669 - val_loss: 0.0830 - val_accuracy: 0.9808\n", "Epoch 290/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1134 - accuracy: 0.9620 - val_loss: 0.0727 - val_accuracy: 0.9904\n", "Epoch 291/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1169 - accuracy: 0.9443 - val_loss: 0.0935 - val_accuracy: 0.9808\n", "Epoch 292/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0923 - accuracy: 0.9709 - val_loss: 0.0766 - val_accuracy: 0.9808\n", "Epoch 293/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1052 - accuracy: 0.9492 - val_loss: 0.0675 - val_accuracy: 0.9904\n", "Epoch 294/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1046 - accuracy: 0.9724 - val_loss: 0.0756 - val_accuracy: 0.9904\n", "Epoch 295/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0948 - accuracy: 0.9752 - val_loss: 0.0680 - val_accuracy: 0.9904\n", "Epoch 296/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0972 - accuracy: 0.9754 - val_loss: 0.0713 - val_accuracy: 0.9904\n", "Epoch 297/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1080 - accuracy: 0.9656 - val_loss: 0.0693 - val_accuracy: 0.9904\n", "Epoch 298/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0844 - accuracy: 0.9790 - val_loss: 0.0716 - val_accuracy: 0.9904\n", "Epoch 299/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.1087 - accuracy: 0.9681 - val_loss: 0.0681 - val_accuracy: 0.9904\n", "Epoch 300/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0932 - accuracy: 0.9725 - val_loss: 0.0677 - val_accuracy: 0.9904\n", "Epoch 301/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0983 - accuracy: 0.9759 - val_loss: 0.0968 - val_accuracy: 0.9712\n", "Epoch 302/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.1076 - accuracy: 0.9590 - val_loss: 0.0854 - val_accuracy: 0.9712\n", "Epoch 303/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1276 - accuracy: 0.9497 - val_loss: 0.0741 - val_accuracy: 0.9904\n", "Epoch 304/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1036 - accuracy: 0.9730 - val_loss: 0.0703 - val_accuracy: 0.9904\n", "Epoch 305/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0883 - accuracy: 0.9747 - val_loss: 0.0703 - val_accuracy: 0.9904\n", "Epoch 306/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1068 - accuracy: 0.9683 - val_loss: 0.0673 - val_accuracy: 0.9904\n", "Epoch 307/500\n", "9/9 [==============================] - 0s 10ms/step - loss: 0.1035 - accuracy: 0.9690 - val_loss: 0.0931 - val_accuracy: 0.9712\n", "Epoch 308/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1261 - accuracy: 0.9575 - val_loss: 0.0809 - val_accuracy: 0.9808\n", "Epoch 309/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0916 - accuracy: 0.9760 - val_loss: 0.0702 - val_accuracy: 0.9904\n", "Epoch 310/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1183 - accuracy: 0.9605 - val_loss: 0.0690 - val_accuracy: 0.9904\n", "Epoch 311/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0951 - accuracy: 0.9804 - val_loss: 0.0708 - val_accuracy: 0.9808\n", "Epoch 312/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0748 - accuracy: 0.9833 - val_loss: 0.0648 - val_accuracy: 0.9904\n", "Epoch 313/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0915 - accuracy: 0.9724 - val_loss: 0.0677 - val_accuracy: 0.9904\n", "Epoch 314/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0823 - accuracy: 0.9783 - val_loss: 0.0694 - val_accuracy: 0.9904\n", "Epoch 315/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1042 - accuracy: 0.9712 - val_loss: 0.0684 - val_accuracy: 0.9904\n", "Epoch 316/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1022 - accuracy: 0.9689 - val_loss: 0.0703 - val_accuracy: 0.9904\n", "Epoch 317/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0925 - accuracy: 0.9724 - val_loss: 0.0678 - val_accuracy: 0.9904\n", "Epoch 318/500\n", "9/9 [==============================] - 0s 10ms/step - loss: 0.0967 - accuracy: 0.9774 - val_loss: 0.0726 - val_accuracy: 0.9904\n", "Epoch 319/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.1093 - accuracy: 0.9729 - val_loss: 0.0640 - val_accuracy: 0.9904\n", "Epoch 320/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0749 - accuracy: 0.9794 - val_loss: 0.0677 - val_accuracy: 0.9904\n", "Epoch 321/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0976 - accuracy: 0.9673 - val_loss: 0.0717 - val_accuracy: 0.9904\n", "Epoch 322/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1211 - accuracy: 0.9644 - val_loss: 0.0883 - val_accuracy: 0.9904\n", "Epoch 323/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1076 - accuracy: 0.9708 - val_loss: 0.0653 - val_accuracy: 0.9904\n", "Epoch 324/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0901 - accuracy: 0.9771 - val_loss: 0.0673 - val_accuracy: 0.9904\n", "Epoch 325/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0839 - accuracy: 0.9654 - val_loss: 0.0648 - val_accuracy: 0.9904\n", "Epoch 326/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0971 - accuracy: 0.9694 - val_loss: 0.0932 - val_accuracy: 0.9712\n", "Epoch 327/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0889 - accuracy: 0.9762 - val_loss: 0.0878 - val_accuracy: 0.9615\n", "Epoch 328/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1279 - accuracy: 0.9493 - val_loss: 0.1148 - val_accuracy: 0.9519\n", "Epoch 329/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1092 - accuracy: 0.9439 - val_loss: 0.0675 - val_accuracy: 0.9904\n", "Epoch 330/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0726 - accuracy: 0.9817 - val_loss: 0.0668 - val_accuracy: 0.9904\n", "Epoch 331/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0789 - accuracy: 0.9759 - val_loss: 0.0637 - val_accuracy: 0.9904\n", "Epoch 332/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0772 - accuracy: 0.9796 - val_loss: 0.0658 - val_accuracy: 0.9904\n", "Epoch 333/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0737 - accuracy: 0.9786 - val_loss: 0.0664 - val_accuracy: 0.9904\n", "Epoch 334/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0853 - accuracy: 0.9815 - val_loss: 0.0716 - val_accuracy: 0.9808\n", "Epoch 335/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0862 - accuracy: 0.9748 - val_loss: 0.0652 - val_accuracy: 0.9904\n", "Epoch 336/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0636 - accuracy: 0.9820 - val_loss: 0.0635 - val_accuracy: 0.9904\n", "Epoch 337/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.0925 - accuracy: 0.9662 - val_loss: 0.0663 - val_accuracy: 0.9904\n", "Epoch 338/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0826 - accuracy: 0.9757 - val_loss: 0.0686 - val_accuracy: 0.9808\n", "Epoch 339/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0772 - accuracy: 0.9800 - val_loss: 0.0645 - val_accuracy: 0.9904\n", "Epoch 340/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0808 - accuracy: 0.9767 - val_loss: 0.0651 - val_accuracy: 0.9904\n", "Epoch 341/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.0768 - accuracy: 0.9780 - val_loss: 0.0665 - val_accuracy: 0.9904\n", "Epoch 342/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0725 - accuracy: 0.9806 - val_loss: 0.0682 - val_accuracy: 0.9904\n", "Epoch 343/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0917 - accuracy: 0.9647 - val_loss: 0.0851 - val_accuracy: 0.9904\n", "Epoch 344/500\n", "9/9 [==============================] - 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0s 8ms/step - loss: 0.0836 - accuracy: 0.9783 - val_loss: 0.0844 - val_accuracy: 0.9712\n", "Epoch 352/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.1044 - accuracy: 0.9538 - val_loss: 0.0696 - val_accuracy: 0.9904\n", "Epoch 353/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0928 - accuracy: 0.9724 - val_loss: 0.0639 - val_accuracy: 0.9904\n", "Epoch 354/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0612 - accuracy: 0.9875 - val_loss: 0.0696 - val_accuracy: 0.9808\n", "Epoch 355/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0785 - accuracy: 0.9713 - val_loss: 0.0864 - val_accuracy: 0.9904\n", "Epoch 356/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0774 - accuracy: 0.9787 - val_loss: 0.0639 - val_accuracy: 0.9904\n", "Epoch 357/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0741 - accuracy: 0.9766 - val_loss: 0.0950 - val_accuracy: 0.9615\n", "Epoch 358/500\n", "9/9 [==============================] - 0s 10ms/step - loss: 0.0918 - accuracy: 0.9537 - val_loss: 0.0825 - val_accuracy: 0.9904\n", "Epoch 359/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0934 - accuracy: 0.9760 - val_loss: 0.0631 - val_accuracy: 0.9904\n", "Epoch 360/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0750 - accuracy: 0.9839 - val_loss: 0.0791 - val_accuracy: 0.9808\n", "Epoch 361/500\n", "9/9 [==============================] - 0s 10ms/step - loss: 0.0703 - accuracy: 0.9756 - val_loss: 0.0617 - val_accuracy: 0.9904\n", "Epoch 362/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0626 - accuracy: 0.9810 - val_loss: 0.0627 - val_accuracy: 0.9904\n", "Epoch 363/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0891 - accuracy: 0.9768 - val_loss: 0.0619 - val_accuracy: 0.9904\n", "Epoch 364/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0747 - accuracy: 0.9812 - val_loss: 0.0652 - val_accuracy: 0.9808\n", "Epoch 365/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0832 - accuracy: 0.9728 - val_loss: 0.0625 - val_accuracy: 0.9904\n", "Epoch 366/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0596 - accuracy: 0.9855 - val_loss: 0.0661 - val_accuracy: 0.9904\n", "Epoch 367/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0648 - accuracy: 0.9857 - val_loss: 0.0625 - val_accuracy: 0.9904\n", "Epoch 368/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0612 - accuracy: 0.9852 - val_loss: 0.0650 - val_accuracy: 0.9904\n", "Epoch 369/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0802 - accuracy: 0.9820 - val_loss: 0.0663 - val_accuracy: 0.9808\n", "Epoch 370/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0738 - accuracy: 0.9789 - val_loss: 0.0691 - val_accuracy: 0.9904\n", "Epoch 371/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0771 - accuracy: 0.9755 - val_loss: 0.0894 - val_accuracy: 0.9712\n", "Epoch 372/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1094 - accuracy: 0.9570 - val_loss: 0.0668 - val_accuracy: 0.9904\n", "Epoch 373/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0858 - accuracy: 0.9776 - val_loss: 0.0683 - val_accuracy: 0.9808\n", "Epoch 374/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0665 - accuracy: 0.9790 - val_loss: 0.0681 - val_accuracy: 0.9904\n", "Epoch 375/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0902 - accuracy: 0.9772 - val_loss: 0.0644 - val_accuracy: 0.9808\n", "Epoch 376/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0579 - accuracy: 0.9850 - val_loss: 0.0641 - val_accuracy: 0.9904\n", "Epoch 377/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0780 - accuracy: 0.9771 - val_loss: 0.0604 - val_accuracy: 0.9904\n", "Epoch 378/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0585 - accuracy: 0.9846 - val_loss: 0.0803 - val_accuracy: 0.9808\n", "Epoch 379/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0882 - accuracy: 0.9800 - val_loss: 0.0683 - val_accuracy: 0.9904\n", "Epoch 380/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0774 - accuracy: 0.9776 - val_loss: 0.0692 - val_accuracy: 0.9808\n", "Epoch 381/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0823 - accuracy: 0.9711 - val_loss: 0.0691 - val_accuracy: 0.9904\n", "Epoch 382/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0776 - accuracy: 0.9712 - val_loss: 0.0615 - val_accuracy: 0.9904\n", "Epoch 383/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0759 - accuracy: 0.9758 - val_loss: 0.0618 - val_accuracy: 0.9904\n", "Epoch 384/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0630 - accuracy: 0.9820 - val_loss: 0.0641 - val_accuracy: 0.9904\n", "Epoch 385/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0570 - accuracy: 0.9850 - val_loss: 0.0615 - val_accuracy: 0.9904\n", "Epoch 386/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0664 - accuracy: 0.9857 - val_loss: 0.0624 - val_accuracy: 0.9904\n", "Epoch 387/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0768 - accuracy: 0.9759 - val_loss: 0.0629 - val_accuracy: 0.9808\n", "Epoch 388/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0641 - accuracy: 0.9727 - val_loss: 0.0648 - val_accuracy: 0.9904\n", "Epoch 389/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0755 - accuracy: 0.9688 - val_loss: 0.0652 - val_accuracy: 0.9904\n", "Epoch 390/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0655 - accuracy: 0.9809 - val_loss: 0.0746 - val_accuracy: 0.9808\n", "Epoch 391/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0842 - accuracy: 0.9711 - val_loss: 0.0617 - val_accuracy: 0.9904\n", "Epoch 392/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0714 - accuracy: 0.9789 - val_loss: 0.0608 - val_accuracy: 0.9904\n", "Epoch 393/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0610 - accuracy: 0.9853 - val_loss: 0.0788 - val_accuracy: 0.9808\n", "Epoch 394/500\n", "9/9 [==============================] - 0s 10ms/step - loss: 0.0880 - accuracy: 0.9699 - val_loss: 0.0606 - val_accuracy: 0.9904\n", "Epoch 395/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0725 - accuracy: 0.9808 - val_loss: 0.0602 - val_accuracy: 0.9904\n", "Epoch 396/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0609 - accuracy: 0.9823 - val_loss: 0.0713 - val_accuracy: 0.9808\n", "Epoch 397/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0807 - accuracy: 0.9699 - val_loss: 0.0602 - val_accuracy: 0.9904\n", "Epoch 398/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0704 - accuracy: 0.9720 - val_loss: 0.0639 - val_accuracy: 0.9904\n", "Epoch 399/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0687 - accuracy: 0.9759 - val_loss: 0.0704 - val_accuracy: 0.9808\n", "Epoch 400/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.0590 - accuracy: 0.9779 - val_loss: 0.0634 - val_accuracy: 0.9904\n", "Epoch 401/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0918 - accuracy: 0.9652 - val_loss: 0.0612 - val_accuracy: 0.9904\n", "Epoch 402/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0463 - accuracy: 0.9909 - val_loss: 0.0718 - val_accuracy: 0.9808\n", "Epoch 403/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0628 - accuracy: 0.9782 - val_loss: 0.0597 - val_accuracy: 0.9808\n", "Epoch 404/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0614 - accuracy: 0.9825 - val_loss: 0.0763 - val_accuracy: 0.9904\n", "Epoch 405/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0659 - accuracy: 0.9855 - val_loss: 0.0657 - val_accuracy: 0.9808\n", "Epoch 406/500\n", "9/9 [==============================] - 0s 12ms/step - loss: 0.0546 - accuracy: 0.9797 - val_loss: 0.0600 - val_accuracy: 0.9808\n", "Epoch 407/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0536 - accuracy: 0.9844 - val_loss: 0.0601 - val_accuracy: 0.9904\n", "Epoch 408/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0635 - accuracy: 0.9823 - val_loss: 0.0654 - val_accuracy: 0.9808\n", "Epoch 409/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0642 - accuracy: 0.9803 - val_loss: 0.0597 - val_accuracy: 0.9904\n", "Epoch 410/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0618 - accuracy: 0.9758 - val_loss: 0.0610 - val_accuracy: 0.9904\n", "Epoch 411/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0538 - accuracy: 0.9844 - val_loss: 0.1318 - val_accuracy: 0.9423\n", "Epoch 412/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1155 - accuracy: 0.9486 - val_loss: 0.0754 - val_accuracy: 0.9904\n", "Epoch 413/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.0966 - accuracy: 0.9692 - val_loss: 0.0593 - val_accuracy: 0.9808\n", "Epoch 414/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0464 - accuracy: 0.9837 - val_loss: 0.0607 - val_accuracy: 0.9808\n", "Epoch 415/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0656 - accuracy: 0.9681 - val_loss: 0.0599 - val_accuracy: 0.9808\n", "Epoch 416/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0546 - accuracy: 0.9812 - val_loss: 0.0699 - val_accuracy: 0.9808\n", "Epoch 417/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0572 - accuracy: 0.9894 - val_loss: 0.1199 - val_accuracy: 0.9327\n", "Epoch 418/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.1280 - accuracy: 0.9508 - val_loss: 0.0660 - val_accuracy: 0.9808\n", "Epoch 419/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0536 - accuracy: 0.9853 - val_loss: 0.0612 - val_accuracy: 0.9904\n", "Epoch 420/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0673 - accuracy: 0.9779 - val_loss: 0.0657 - val_accuracy: 0.9808\n", "Epoch 421/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0488 - accuracy: 0.9860 - val_loss: 0.0586 - val_accuracy: 0.9808\n", "Epoch 422/500\n", "9/9 [==============================] - 0s 10ms/step - loss: 0.0491 - accuracy: 0.9747 - val_loss: 0.0583 - val_accuracy: 0.9808\n", "Epoch 423/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0671 - accuracy: 0.9745 - val_loss: 0.0580 - val_accuracy: 0.9808\n", "Epoch 424/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0614 - accuracy: 0.9737 - val_loss: 0.0581 - val_accuracy: 0.9808\n", "Epoch 425/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0687 - accuracy: 0.9703 - val_loss: 0.0588 - val_accuracy: 0.9904\n", "Epoch 426/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0571 - accuracy: 0.9804 - val_loss: 0.0578 - val_accuracy: 0.9904\n", "Epoch 427/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0554 - accuracy: 0.9846 - val_loss: 0.0632 - val_accuracy: 0.9808\n", "Epoch 428/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0414 - accuracy: 0.9836 - val_loss: 0.0612 - val_accuracy: 0.9808\n", "Epoch 429/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0636 - accuracy: 0.9817 - val_loss: 0.0648 - val_accuracy: 0.9904\n", "Epoch 430/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0565 - accuracy: 0.9850 - val_loss: 0.0989 - val_accuracy: 0.9615\n", "Epoch 431/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.1144 - accuracy: 0.9542 - val_loss: 0.0695 - val_accuracy: 0.9808\n", "Epoch 432/500\n", "9/9 [==============================] - 0s 10ms/step - loss: 0.0578 - accuracy: 0.9813 - val_loss: 0.0576 - val_accuracy: 0.9904\n", "Epoch 433/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0442 - accuracy: 0.9875 - val_loss: 0.0587 - val_accuracy: 0.9904\n", "Epoch 434/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0430 - accuracy: 0.9880 - val_loss: 0.0618 - val_accuracy: 0.9808\n", "Epoch 435/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0491 - accuracy: 0.9831 - val_loss: 0.0572 - val_accuracy: 0.9904\n", "Epoch 436/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0504 - accuracy: 0.9855 - val_loss: 0.0574 - val_accuracy: 0.9808\n", "Epoch 437/500\n", "9/9 [==============================] - 0s 7ms/step - loss: 0.0491 - accuracy: 0.9867 - val_loss: 0.0673 - val_accuracy: 0.9808\n", "Epoch 438/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0734 - accuracy: 0.9734 - val_loss: 0.0664 - val_accuracy: 0.9904\n", "Epoch 439/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0592 - accuracy: 0.9739 - val_loss: 0.0649 - val_accuracy: 0.9808\n", "Epoch 440/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0463 - accuracy: 0.9818 - val_loss: 0.0715 - val_accuracy: 0.9808\n", "Epoch 441/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0879 - accuracy: 0.9664 - val_loss: 0.0566 - val_accuracy: 0.9904\n", "Epoch 442/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0712 - accuracy: 0.9783 - val_loss: 0.0769 - val_accuracy: 0.9808\n", "Epoch 443/500\n", "9/9 [==============================] - 0s 10ms/step - loss: 0.0778 - accuracy: 0.9834 - val_loss: 0.0880 - val_accuracy: 0.9808\n", "Epoch 444/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0623 - accuracy: 0.9806 - val_loss: 0.0704 - val_accuracy: 0.9808\n", "Epoch 445/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0526 - accuracy: 0.9844 - val_loss: 0.0618 - val_accuracy: 0.9904\n", "Epoch 446/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0552 - accuracy: 0.9862 - val_loss: 0.0669 - val_accuracy: 0.9808\n", "Epoch 447/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0478 - accuracy: 0.9875 - val_loss: 0.0795 - val_accuracy: 0.9808\n", "Epoch 448/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0603 - accuracy: 0.9724 - val_loss: 0.0559 - val_accuracy: 0.9808\n", "Epoch 449/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0650 - accuracy: 0.9797 - val_loss: 0.0577 - val_accuracy: 0.9808\n", "Epoch 450/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0496 - accuracy: 0.9856 - val_loss: 0.0663 - val_accuracy: 0.9904\n", "Epoch 451/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0658 - accuracy: 0.9761 - val_loss: 0.0577 - val_accuracy: 0.9808\n", "Epoch 452/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0538 - accuracy: 0.9721 - val_loss: 0.0559 - val_accuracy: 0.9808\n", "Epoch 453/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0641 - accuracy: 0.9844 - val_loss: 0.0555 - val_accuracy: 0.9904\n", "Epoch 454/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0531 - accuracy: 0.9821 - val_loss: 0.0627 - val_accuracy: 0.9808\n", "Epoch 455/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0642 - accuracy: 0.9855 - val_loss: 0.0572 - val_accuracy: 0.9904\n", "Epoch 456/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0556 - accuracy: 0.9847 - val_loss: 0.0607 - val_accuracy: 0.9808\n", "Epoch 457/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0546 - accuracy: 0.9768 - val_loss: 0.0556 - val_accuracy: 0.9808\n", "Epoch 458/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0654 - accuracy: 0.9758 - val_loss: 0.0556 - val_accuracy: 0.9808\n", "Epoch 459/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0553 - accuracy: 0.9802 - val_loss: 0.0553 - val_accuracy: 0.9904\n", "Epoch 460/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0389 - accuracy: 0.9901 - val_loss: 0.0657 - val_accuracy: 0.9808\n", "Epoch 461/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0679 - accuracy: 0.9791 - val_loss: 0.0618 - val_accuracy: 0.9904\n", "Epoch 462/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0522 - accuracy: 0.9802 - val_loss: 0.0563 - val_accuracy: 0.9808\n", "Epoch 463/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0434 - accuracy: 0.9939 - val_loss: 0.0552 - val_accuracy: 0.9808\n", "Epoch 464/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0539 - accuracy: 0.9831 - val_loss: 0.0555 - val_accuracy: 0.9808\n", "Epoch 465/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0593 - accuracy: 0.9807 - val_loss: 0.0564 - val_accuracy: 0.9904\n", "Epoch 466/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0495 - accuracy: 0.9809 - val_loss: 0.0697 - val_accuracy: 0.9808\n", "Epoch 467/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0450 - accuracy: 0.9859 - val_loss: 0.0650 - val_accuracy: 0.9904\n", "Epoch 468/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0453 - accuracy: 0.9884 - val_loss: 0.0619 - val_accuracy: 0.9808\n", "Epoch 469/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0348 - accuracy: 0.9888 - val_loss: 0.0554 - val_accuracy: 0.9904\n", "Epoch 470/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0524 - accuracy: 0.9895 - val_loss: 0.0548 - val_accuracy: 0.9904\n", "Epoch 471/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0413 - accuracy: 0.9860 - val_loss: 0.0585 - val_accuracy: 0.9808\n", "Epoch 472/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0613 - accuracy: 0.9808 - val_loss: 0.0633 - val_accuracy: 0.9904\n", "Epoch 473/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0451 - accuracy: 0.9840 - val_loss: 0.0589 - val_accuracy: 0.9808\n", "Epoch 474/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0617 - accuracy: 0.9776 - val_loss: 0.0556 - val_accuracy: 0.9904\n", "Epoch 475/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0479 - accuracy: 0.9825 - val_loss: 0.0580 - val_accuracy: 0.9808\n", "Epoch 476/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0466 - accuracy: 0.9838 - val_loss: 0.0552 - val_accuracy: 0.9904\n", "Epoch 477/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0513 - accuracy: 0.9899 - val_loss: 0.0564 - val_accuracy: 0.9808\n", "Epoch 478/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0386 - accuracy: 0.9796 - val_loss: 0.0580 - val_accuracy: 0.9808\n", "Epoch 479/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0634 - accuracy: 0.9755 - val_loss: 0.0588 - val_accuracy: 0.9904\n", "Epoch 480/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0564 - accuracy: 0.9902 - val_loss: 0.0686 - val_accuracy: 0.9808\n", "Epoch 481/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0759 - accuracy: 0.9748 - val_loss: 0.0616 - val_accuracy: 0.9904\n", "Epoch 482/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0449 - accuracy: 0.9799 - val_loss: 0.0630 - val_accuracy: 0.9808\n", "Epoch 483/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0515 - accuracy: 0.9848 - val_loss: 0.0756 - val_accuracy: 0.9808\n", "Epoch 484/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0463 - accuracy: 0.9835 - val_loss: 0.0544 - val_accuracy: 0.9808\n", "Epoch 485/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0542 - accuracy: 0.9823 - val_loss: 0.0539 - val_accuracy: 0.9904\n", "Epoch 486/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0611 - accuracy: 0.9848 - val_loss: 0.0539 - val_accuracy: 0.9904\n", "Epoch 487/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0498 - accuracy: 0.9897 - val_loss: 0.0590 - val_accuracy: 0.9808\n", "Epoch 488/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0479 - accuracy: 0.9864 - val_loss: 0.0617 - val_accuracy: 0.9904\n", "Epoch 489/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0671 - accuracy: 0.9782 - val_loss: 0.0558 - val_accuracy: 0.9808\n", "Epoch 490/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0452 - accuracy: 0.9876 - val_loss: 0.0573 - val_accuracy: 0.9808\n", "Epoch 491/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0516 - accuracy: 0.9817 - val_loss: 0.0539 - val_accuracy: 0.9904\n", "Epoch 492/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0393 - accuracy: 0.9861 - val_loss: 0.0537 - val_accuracy: 0.9904\n", "Epoch 493/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0454 - accuracy: 0.9860 - val_loss: 0.0536 - val_accuracy: 0.9904\n", "Epoch 494/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0327 - accuracy: 0.9921 - val_loss: 0.0551 - val_accuracy: 0.9808\n", "Epoch 495/500\n", "9/9 [==============================] - 0s 10ms/step - loss: 0.0442 - accuracy: 0.9834 - val_loss: 0.0561 - val_accuracy: 0.9904\n", "Epoch 496/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0392 - accuracy: 0.9876 - val_loss: 0.0538 - val_accuracy: 0.9904\n", "Epoch 497/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0599 - accuracy: 0.9827 - val_loss: 0.0636 - val_accuracy: 0.9808\n", "Epoch 498/500\n", "9/9 [==============================] - 0s 9ms/step - loss: 0.0612 - accuracy: 0.9797 - val_loss: 0.0658 - val_accuracy: 0.9904\n", "Epoch 499/500\n", "9/9 [==============================] - 0s 8ms/step - loss: 0.0444 - accuracy: 0.9901 - val_loss: 0.0625 - val_accuracy: 0.9808\n", "Epoch 500/500\n", "9/9 [==============================] - 0s 10ms/step - loss: 0.0681 - accuracy: 0.9833 - val_loss: 0.0551 - val_accuracy: 0.9904\n", "13/13 [==============================] - 0s 2ms/step - loss: 0.0360 - accuracy: 0.9880\n", "Train Accuracy: 98.80\n", "4/4 [==============================] - 0s 3ms/step - loss: 0.0929 - accuracy: 0.9808\n", "Test Accuracy: 98.08\n" ], "name": "stdout" }, { "output_type": "stream", "text": [ "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`, if your model does multi-class classification (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`, if your model does binary classification (e.g. if it uses a `sigmoid` last-layer activation).\n", " warnings.warn('`model.predict_classes()` is deprecated and '\n" ], "name": "stderr" }, { "output_type": "stream", "text": [ "[50, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0] => 1 (expected 1)\n", "[69, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1] => 1 (expected 1)\n", "[54, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0] => 0 (expected 0)\n", "[37, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0] => 0 (expected 1)\n", "[72, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0] => 0 (expected 0)\n", "[62, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1] => 1 (expected 1)\n", "[33, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] => 1 (expected 1)\n", "[38, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1] => 1 (expected 1)\n", "[45, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0] => 1 (expected 1)\n", "[59, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1] => 0 (expected 0)\n", "[70, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0] => 1 (expected 1)\n", "[35, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0] => 1 (expected 1)\n", "[55, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1] => 1 (expected 1)\n", "[65, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0] => 0 (expected 0)\n", "[43, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0] => 1 (expected 1)\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "xkTP0lzQ68hb", "outputId": "5d3702e9-0120-44ae-94fa-c203cba60b1f" }, "source": [ "from sklearn.metrics import classification_report\n", "import matplotlib.pyplot as plt\n", "print(classification_report(y_test,predictions ))" ], "execution_count": 27, "outputs": [ { "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.95 1.00 0.97 39\n", " 1 1.00 0.97 0.98 65\n", "\n", " accuracy 0.98 104\n", " macro avg 0.98 0.98 0.98 104\n", "weighted avg 0.98 0.98 0.98 104\n", "\n" ], "name": "stdout" } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 573 }, "id": "LjELrKeB8zUO", "outputId": "a6c44290-4927-470a-feea-ce0ac4ca293e" }, "source": [ "plt.plot(history.history['accuracy'])\n", "plt.plot(history.history['val_accuracy'])\n", "plt.title('model accuracy')\n", "plt.ylabel('accuracy')\n", "plt.xlabel('epoch')\n", "plt.legend(['train', 'test'], loc='upper left')\n", "plt.show()" ], "execution_count": 29, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": { "tags": [], "needs_background": "light" } } ] }, { "cell_type": "code", "metadata": { "id": "N5-ubN5J-YRx", "outputId": "3469ac04-d567-4fa7-ac2b-44594341dcce", "colab": { "base_uri": "https://localhost:8080/", "height": 295 } }, "source": [ "plt.plot(history.history['loss'])\n", "plt.plot(history.history['val_loss'])\n", "plt.title('model loss')\n", "plt.ylabel('loss')\n", "plt.xlabel('epoch')\n", "plt.legend(['train', 'test'], loc='upper left')\n", "plt.show()" ], "execution_count": 30, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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\n", 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