{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "id": "DZzFYmPLg7Dk" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "zEM2-ex1soQp" }, "outputs": [], "source": [ "import keras\n", "#from keras.layers.core import Layer\n", "import keras.backend as K\n", "import tensorflow as tf\n", "from keras.models import Model\n", "from keras.layers import Conv2D, MaxPool2D, \\\n", " Dropout, Dense, Input, concatenate, \\\n", " GlobalAveragePooling2D, AveragePooling2D,\\\n", " Flatten, BatchNormalization, ReLU\n", "\n", "import cv2 \n", "import numpy as np \n", "from keras import backend as K " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "fXXkOaUZr_jM" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "eaKOZ9DFwmgM" }, "outputs": [], "source": [ "from tensorflow.keras.layers import BatchNormalization" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Vmfjy-KPszqw" }, "outputs": [], "source": [ "from tensorflow.keras.optimizers import Adam\n", "from tensorflow.keras.layers import BatchNormalization\n", "from keras.layers.convolutional import *\n", "from keras.metrics import categorical_crossentropy\n", "from keras.preprocessing.image import ImageDataGenerator\n", "from keras.preprocessing import image\n", "from keras.models import Model\n", "from keras.applications import imagenet_utils\n", "from sklearn.metrics import confusion_matrix\n", "import itertools\n", "import matplotlib.pyplot as plt\n", "from skimage import exposure\n", "from keras.utils.vis_utils import plot_model\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Use6VvZZs3yI", "outputId": "9ac1e0af-71ab-4247-a45c-f8497a17babd" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Mounted at /content/drive\n" ] } ], "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "wapeAhQBs-7L", "outputId": "1936fc39-363b-4f23-e8ea-44cd531e6cc8" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n", " inflating: er/Training/Sad/00017807.jpg \n", " inflating: er/Training/Sad/00017830.jpg \n", " inflating: er/Training/Sad/00017839.jpg \n", " inflating: er/Training/Sad/00017841.jpg \n", " inflating: er/Training/Sad/00017849.jpg \n", " inflating: er/Training/Sad/00017851.jpg \n", " inflating: er/Training/Sad/00017857.jpg \n", " inflating: er/Training/Sad/00017862.jpg \n", " inflating: er/Training/Sad/00017869.jpg \n", " inflating: er/Training/Sad/00017870.jpg \n", " inflating: er/Training/Sad/00017884.jpg \n", " inflating: er/Training/Sad/00017887.jpg \n", " 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er/Training/Surprise/00028643.jpg \n", " inflating: er/Training/Surprise/00028651.jpg \n", " inflating: er/Training/Surprise/00028653.jpg \n", " inflating: er/Training/Surprise/00028662.jpg \n", " inflating: er/Training/Surprise/00028666.jpg \n", " inflating: er/Training/Surprise/00028667.jpg \n", " inflating: er/Training/Surprise/00028671.jpg \n", " inflating: er/Training/Surprise/00028703.jpg \n" ] } ], "source": [ "!unzip \"/content/drive/My Drive/er.zip\" " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "E_VmuzUE6fSq", "outputId": "3ffad84d-fb06-4506-a999-f6a3872161f0" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Model: \"inception_v1\"\n", "__________________________________________________________________________________________________\n", " Layer (type) Output Shape Param # Connected to \n", "==================================================================================================\n", " input_2 (InputLayer) [(None, 48, 48, 1)] 0 [] \n", " \n", " conv_1_3x3/1 (Conv2D) (None, 48, 48, 32) 320 ['input_2[0][0]'] \n", " \n", " batch_normalization_22 (BatchN (None, 48, 48, 32) 128 ['conv_1_3x3/1[0][0]'] \n", " ormalization) \n", " \n", " conv_1a_3x3/1 (Conv2D) (None, 48, 48, 32) 9248 ['batch_normalization_22[0][0]'] \n", " \n", " batch_normalization_23 (BatchN (None, 48, 48, 32) 128 ['conv_1a_3x3/1[0][0]'] \n", " ormalization) \n", " \n", " max_pool_1_3x3/2 (MaxPooling2D (None, 24, 24, 32) 0 ['batch_normalization_23[0][0]'] \n", " ) \n", " \n", " batch_normalization_24 (BatchN (None, 24, 24, 32) 128 ['max_pool_1_3x3/2[0][0]'] \n", " ormalization) \n", " \n", " dropout_6 (Dropout) (None, 24, 24, 32) 0 ['batch_normalization_24[0][0]'] \n", " \n", " conv_2a_3x3/1 (Conv2D) (None, 24, 24, 64) 18496 ['dropout_6[0][0]'] \n", " \n", " conv_2b_3x3/1 (Conv2D) (None, 24, 24, 64) 18496 ['dropout_6[0][0]'] \n", " \n", " batch_normalization_25 (BatchN (None, 24, 24, 64) 256 ['conv_2a_3x3/1[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_27 (BatchN (None, 24, 24, 64) 256 ['conv_2b_3x3/1[0][0]'] \n", " ormalization) \n", " \n", " conv_2a1_3x3/1 (Conv2D) (None, 24, 24, 64) 36928 ['batch_normalization_25[0][0]'] \n", " \n", " conv_2b1_3x3/1 (Conv2D) (None, 24, 24, 64) 36928 ['batch_normalization_27[0][0]'] \n", " \n", " batch_normalization_26 (BatchN (None, 24, 24, 64) 256 ['conv_2a1_3x3/1[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_28 (BatchN (None, 24, 24, 64) 256 ['conv_2b1_3x3/1[0][0]'] \n", " ormalization) \n", " \n", " inception_dense_1 (Concatenate (None, 24, 24, 160) 0 ['dropout_6[0][0]', \n", " ) 'batch_normalization_26[0][0]', \n", " 'batch_normalization_28[0][0]'] \n", " \n", " conv_t1_3x3/1 (Conv2D) (None, 24, 24, 64) 10304 ['inception_dense_1[0][0]'] \n", " \n", " batch_normalization_29 (BatchN (None, 24, 24, 64) 256 ['conv_t1_3x3/1[0][0]'] \n", " ormalization) \n", " \n", " max_pool_t2_3x3/2 (MaxPooling2 (None, 12, 12, 64) 0 ['batch_normalization_29[0][0]'] \n", " D) \n", " \n", " batch_normalization_30 (BatchN (None, 12, 12, 64) 256 ['max_pool_t2_3x3/2[0][0]'] \n", " ormalization) \n", " \n", " dropout_7 (Dropout) (None, 12, 12, 64) 0 ['batch_normalization_30[0][0]'] \n", " \n", " conv_3a_3x3/1 (Conv2D) (None, 12, 12, 128) 73856 ['dropout_7[0][0]'] \n", " \n", " conv_3b_3x3/1 (Conv2D) (None, 12, 12, 128) 73856 ['dropout_7[0][0]'] \n", " \n", " batch_normalization_31 (BatchN (None, 12, 12, 128) 512 ['conv_3a_3x3/1[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_33 (BatchN (None, 12, 12, 128) 512 ['conv_3b_3x3/1[0][0]'] \n", " ormalization) \n", " \n", " conv_3a1_3x3/1 (Conv2D) (None, 12, 12, 128) 147584 ['batch_normalization_31[0][0]'] \n", " \n", " conv_3b1_3x3/1 (Conv2D) (None, 12, 12, 128) 147584 ['batch_normalization_33[0][0]'] \n", " \n", " batch_normalization_32 (BatchN (None, 12, 12, 128) 512 ['conv_3a1_3x3/1[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_34 (BatchN (None, 12, 12, 128) 512 ['conv_3b1_3x3/1[0][0]'] \n", " ormalization) \n", " \n", " inception_dense_2 (Concatenate (None, 12, 12, 320) 0 ['dropout_7[0][0]', \n", " ) 'batch_normalization_32[0][0]', \n", " 'batch_normalization_34[0][0]'] \n", " \n", " conv_t3_3x3/1 (Conv2D) (None, 12, 12, 128) 41088 ['inception_dense_2[0][0]'] \n", " \n", " batch_normalization_35 (BatchN (None, 12, 12, 128) 512 ['conv_t3_3x3/1[0][0]'] \n", " ormalization) \n", " \n", " max_pool_t4_3x3/2 (MaxPooling2 (None, 6, 6, 128) 0 ['batch_normalization_35[0][0]'] \n", " D) \n", " \n", " batch_normalization_36 (BatchN (None, 6, 6, 128) 512 ['max_pool_t4_3x3/2[0][0]'] \n", " ormalization) \n", " \n", " dropout_8 (Dropout) (None, 6, 6, 128) 0 ['batch_normalization_36[0][0]'] \n", " \n", " conv_4a_3x3/1 (Conv2D) (None, 6, 6, 256) 295168 ['dropout_8[0][0]'] \n", " \n", " conv_4b_3x3/1 (Conv2D) (None, 6, 6, 256) 295168 ['dropout_8[0][0]'] \n", " \n", " batch_normalization_37 (BatchN (None, 6, 6, 256) 1024 ['conv_4a_3x3/1[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_39 (BatchN (None, 6, 6, 256) 1024 ['conv_4b_3x3/1[0][0]'] \n", " ormalization) \n", " \n", " conv_4a1_3x3/1 (Conv2D) (None, 6, 6, 256) 590080 ['batch_normalization_37[0][0]'] \n", " \n", " conv_4b1_3x3/1 (Conv2D) (None, 6, 6, 256) 590080 ['batch_normalization_39[0][0]'] \n", " \n", " batch_normalization_38 (BatchN (None, 6, 6, 256) 1024 ['conv_4a1_3x3/1[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_40 (BatchN (None, 6, 6, 256) 1024 ['conv_4b1_3x3/1[0][0]'] \n", " ormalization) \n", " \n", " inception_dense_4 (Concatenate (None, 6, 6, 640) 0 ['dropout_8[0][0]', \n", " ) 'batch_normalization_38[0][0]', \n", " 'batch_normalization_40[0][0]'] \n", " \n", " conv_t5_3x3/1 (Conv2D) (None, 6, 6, 256) 164096 ['inception_dense_4[0][0]'] \n", " \n", " batch_normalization_41 (BatchN (None, 6, 6, 256) 1024 ['conv_t5_3x3/1[0][0]'] \n", " ormalization) \n", " \n", " dropout_9 (Dropout) (None, 6, 6, 256) 0 ['batch_normalization_41[0][0]'] \n", " \n", " avg_pool_5_3x3/1 (GlobalAverag (None, 256) 0 ['dropout_9[0][0]'] \n", " ePooling2D) \n", " \n", " dense_3 (Dense) (None, 512) 131584 ['avg_pool_5_3x3/1[0][0]'] \n", " \n", " batch_normalization_42 (BatchN (None, 512) 2048 ['dense_3[0][0]'] \n", " ormalization) \n", " \n", " dropout_10 (Dropout) (None, 512) 0 ['batch_normalization_42[0][0]'] \n", " \n", " dense_4 (Dense) (None, 256) 131328 ['dropout_10[0][0]'] \n", " \n", " batch_normalization_43 (BatchN (None, 256) 1024 ['dense_4[0][0]'] \n", " ormalization) \n", " \n", " dropout_11 (Dropout) (None, 256) 0 ['batch_normalization_43[0][0]'] \n", " \n", " dense_5 (Dense) (None, 7) 1799 ['dropout_11[0][0]'] \n", " \n", "==================================================================================================\n", "Total params: 2,827,175\n", "Trainable params: 2,820,583\n", "Non-trainable params: 6,592\n", "__________________________________________________________________________________________________\n" ] } ], "source": [ "# model 2 -2.8\n", "img_rows,img_cols=48,48\n", "\n", "\n", "input_layer = Input(shape=(48, 48, 1))\n", "\n", "x = Conv2D(32, (3, 3), padding='same',strides=(1, 1), activation='relu', name='conv_1_3x3/1')(input_layer)\n", "x=BatchNormalization()(x)\n", "x = Conv2D(32, (3, 3), padding='same',strides=(1, 1), activation='relu', name='conv_1a_3x3/1')(x)\n", "x=BatchNormalization()(x)\n", "x = MaxPool2D((2, 2), name='max_pool_1_3x3/2')(x)\n", "x=BatchNormalization()(x)\n", "x=Dropout(0.2)(x)\n", "\n", "#Block 2A\n", "x1 = Conv2D(64, (3, 3), padding='same',strides=(1, 1), activation='relu', name='conv_2a_3x3/1')(x)\n", "x1=BatchNormalization()(x1)\n", "x12 = Conv2D(64, (3, 3), padding='same',strides=(1, 1), activation='relu', name='conv_2a1_3x3/1')(x1)\n", "x12=BatchNormalization()(x12)\n", "#Block 2B\n", "y1 = Conv2D(64, (3, 3), padding='same',strides=(1, 1), activation='relu', name='conv_2b_3x3/1')(x)\n", "y1=BatchNormalization()(y1)\n", "y12 = Conv2D(64, (3, 3), padding='same',strides=(1, 1), activation='relu', name='conv_2b1_3x3/1')(y1)\n", "y12=BatchNormalization()(y12)\n", "\n", "concat_b2 = concatenate([x, x12, y12], axis=3,name='inception_dense_1')\n", "#trans\n", "t1 = Conv2D(64, (1, 1), padding='same',strides=(1, 1), activation='relu', name='conv_t1_3x3/1')(concat_b2)\n", "t1=BatchNormalization()(t1)\n", "t2 = MaxPool2D((2, 2), name='max_pool_t2_3x3/2')(t1)\n", "t2=BatchNormalization()(t2)\n", "t2=Dropout(0.2)(t2)\n", "\n", "#Block 3A\n", "x2 = Conv2D(128, (3, 3), padding='same',strides=(1, 1), activation='relu', name='conv_3a_3x3/1')(t2)\n", "x2=BatchNormalization()(x2)\n", "x22 = Conv2D(128, (3, 3), padding='same',strides=(1, 1), activation='relu', name='conv_3a1_3x3/1')(x2)\n", "x22=BatchNormalization()(x22)\n", "#Block 3B\n", "y2 = Conv2D(128, (3, 3), padding='same',strides=(1, 1), activation='relu', name='conv_3b_3x3/1')(t2)\n", "y2=BatchNormalization()(y2)\n", "y22 = Conv2D(128, (3, 3), padding='same',strides=(1, 1), activation='relu', name='conv_3b1_3x3/1')(y2)\n", "y22=BatchNormalization()(y22)\n", "\n", "concat_b3 = concatenate([t2, x22, y22], axis=3,name='inception_dense_2')\n", "#trans\n", "t3 = Conv2D(128, (1, 1), padding='same',strides=(1, 1), activation='relu', name='conv_t3_3x3/1')(concat_b3)\n", "t3=BatchNormalization()(t3)\n", "t4 = MaxPool2D((2, 2), name='max_pool_t4_3x3/2')(t3)\n", "t4=BatchNormalization()(t4)\n", "t4=Dropout(0.2)(t4)\n", "\n", "\n", "\n", "#Block 4A\n", "x3 = Conv2D(256, (3, 3), padding='same',strides=(1, 1), activation='relu', name='conv_4a_3x3/1')(t4)\n", "x3=BatchNormalization()(x3)\n", "x32 = Conv2D(256, (3, 3), padding='same',strides=(1, 1), activation='relu', name='conv_4a1_3x3/1')(x3)\n", "x32=BatchNormalization()(x32)\n", "#Block 4B\n", "y3 = Conv2D(256, (3, 3), padding='same',strides=(1, 1), activation='relu', name='conv_4b_3x3/1')(t4)\n", "y3=BatchNormalization()(y3)\n", "y32 = Conv2D(256, (3, 3), padding='same',strides=(1, 1), activation='relu', name='conv_4b1_3x3/1')(y3)\n", "y32=BatchNormalization()(y32)\n", "concat_b4 = concatenate([t4, x32, y32], axis=3,name='inception_dense_4')\n", "#trans\n", "t5 = Conv2D(256, (1, 1), padding='same',strides=(1, 1), activation='relu', name='conv_t5_3x3/1')(concat_b4)\n", "t5=BatchNormalization()(t5)\n", "#t6 = MaxPool2D((2, 2), name='max_pool_t6_3x3/2')(t5)\n", "#t6=BatchNormalization()(t6)\n", "t5=Dropout(0.2)(t5)\n", "\n", "t8 = GlobalAveragePooling2D(name='avg_pool_5_3x3/1')(t5)\n", "\n", "\n", "# Block-6\n", "\n", "xf=Dense(512,activation='relu')(t8)\n", "xf=BatchNormalization()(xf)\n", "xf=Dropout(0.5)(xf)\n", "\n", "# Block-6\n", "\n", "xf=Dense(256,activation='relu')(xf)\n", "xf=BatchNormalization()(xf)\n", "xf=Dropout(0.5)(xf)\n", "\n", "# Block-7\n", "\n", "xf=Dense(7,activation='softmax')(xf)\n", "#xf=Dense(2,activation='sigmoid')(xf)\n", "model = Model(input_layer, [xf], name='inception_v1')\n", "model.summary()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ldKMS86-mEMW", "outputId": "bc11acfb-4966-4382-a0fd-36e3f9abd298" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Found 28709 images belonging to 7 classes.\n", "Found 3589 images belonging to 7 classes.\n", "Found 3589 images belonging to 7 classes.\n" ] } ], "source": [ "#data generation with seed value\n", "seed=1\n", "batch_size=256\n", "train_sample=28709\n", "valid_sample=3589\n", "test_sample=3589\n", "img_rows,img_cols=48,48\n", "#train_path = 'sorted_set_3_frame_split_8_2_new/train'\n", "#valid_path = 'sorted_set_3_frame_split_8_2_new/val'\n", "#test_path = 'sorted_set_3_frame_split_8_2_new/val'\n", "train_path = 'er/Training'\n", "valid_path = 'er/PrivateTest'\n", "test_path = 'er/PrivateTest'\n", "train_datagen = ImageDataGenerator(\n", "\t\t\t\t\trescale=1./255,\n", "\t\t\t\t\trotation_range=30,\n", "\t\t\t\t\tshear_range=0.3,\n", "\t\t\t\t\tzoom_range=0.3,\n", "\t\t\t\t\twidth_shift_range=0.2,\n", "\t\t\t\t\theight_shift_range=0.2,\n", "\t\t\t\t\thorizontal_flip=True, \n", "\t\t\t\t\tfill_mode='nearest')\n", "\n", "validation_datagen = ImageDataGenerator(rescale=1./255)\n", "\n", "train_generator = train_datagen.flow_from_directory(\n", "\t\t\t\t\ttrain_path,\n", " color_mode='grayscale',\n", "\t\t\t\t\ttarget_size=(img_rows,img_cols),\n", "\t\t\t\t\tbatch_size=batch_size,\n", "\t\t\t\t\tclass_mode='categorical',seed=seed,\n", "\t\t\t\t\tshuffle=True)\n", "\n", "validation_generator = validation_datagen.flow_from_directory(\n", "\t\t\t\t\t\t\tvalid_path,\n", "\t\t\t\t\t\t\tcolor_mode='grayscale',\n", "\t\t\t\t\t\t\ttarget_size=(img_rows,img_cols),\n", "\t\t\t\t\t\t\tbatch_size=batch_size,\n", "\t\t\t\t\t\t\tclass_mode='categorical',seed=seed,\n", "\t\t\t\t\t\t\tshuffle=True)\n", "test_generator= ImageDataGenerator(rescale=1./255).flow_from_directory(test_path, \n", "\t\t\t\t\t\t\tcolor_mode='grayscale',\n", "\t\t\t\t\t\t\ttarget_size=(img_rows,img_cols),\n", "\t\t\t\t\t\t\tbatch_size=batch_size,\n", "\t\t\t\t\t\t\tclass_mode='categorical', seed=seed,\n", "\t\t\t\t\t\t\tshuffle=False)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "kxjr2HCmtcUW", "outputId": "99e0550f-35e5-471a-bbf7-15e87d48a24d" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Epoch 1/150\n", "113/113 [==============================] - ETA: 0s - loss: 2.4099 - accuracy: 0.1888\n", "Epoch 1: val_accuracy improved from -inf to 0.24492, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_w_aug.hdf5\n", "113/113 [==============================] - 31s 254ms/step - loss: 2.4099 - accuracy: 0.1888 - val_loss: 1.9063 - val_accuracy: 0.2449 - lr: 0.0010\n", "Epoch 2/150\n", "113/113 [==============================] - ETA: 0s - loss: 2.0035 - accuracy: 0.2264\n", "Epoch 2: val_accuracy did not improve from 0.24492\n", "113/113 [==============================] - 25s 225ms/step - loss: 2.0035 - accuracy: 0.2264 - val_loss: 1.9381 - val_accuracy: 0.2343 - lr: 0.0010\n", "Epoch 3/150\n", "113/113 [==============================] - ETA: 0s - loss: 1.8761 - accuracy: 0.2502\n", "Epoch 3: val_accuracy did not improve from 0.24492\n", "113/113 [==============================] - 26s 228ms/step - loss: 1.8761 - accuracy: 0.2502 - val_loss: 1.8604 - val_accuracy: 0.2430 - lr: 0.0010\n", "Epoch 4/150\n", "113/113 [==============================] - ETA: 0s - loss: 1.8028 - accuracy: 0.2845\n", "Epoch 4: val_accuracy did not improve from 0.24492\n", "113/113 [==============================] - 27s 240ms/step - loss: 1.8028 - accuracy: 0.2845 - val_loss: 1.9201 - val_accuracy: 0.1850 - lr: 0.0010\n", "Epoch 5/150\n", "113/113 [==============================] - ETA: 0s - loss: 1.7462 - accuracy: 0.3121\n", "Epoch 5: val_accuracy improved from 0.24492 to 0.30621, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_w_aug.hdf5\n", "113/113 [==============================] - 27s 234ms/step - loss: 1.7462 - accuracy: 0.3121 - val_loss: 1.7047 - val_accuracy: 0.3062 - lr: 0.0010\n", "Epoch 6/150\n", "113/113 [==============================] - ETA: 0s - loss: 1.6706 - accuracy: 0.3538\n", "Epoch 6: val_accuracy improved from 0.30621 to 0.34327, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_w_aug.hdf5\n", "113/113 [==============================] - 27s 238ms/step - loss: 1.6706 - accuracy: 0.3538 - val_loss: 1.7351 - val_accuracy: 0.3433 - lr: 0.0010\n", "Epoch 7/150\n", "113/113 [==============================] - ETA: 0s - loss: 1.5660 - accuracy: 0.4030\n", "Epoch 7: val_accuracy improved from 0.34327 to 0.43884, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_w_aug.hdf5\n", "113/113 [==============================] - 27s 237ms/step - loss: 1.5660 - accuracy: 0.4030 - val_loss: 1.4606 - val_accuracy: 0.4388 - lr: 0.0010\n", "Epoch 8/150\n", "113/113 [==============================] - ETA: 0s - loss: 1.4895 - accuracy: 0.4342\n", "Epoch 8: val_accuracy improved from 0.43884 to 0.47144, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_w_aug.hdf5\n", "113/113 [==============================] - 27s 239ms/step - loss: 1.4895 - accuracy: 0.4342 - val_loss: 1.3747 - val_accuracy: 0.4714 - lr: 0.0010\n", "Epoch 9/150\n", "113/113 [==============================] - ETA: 0s - loss: 1.4160 - accuracy: 0.4567\n", "Epoch 9: val_accuracy did not improve from 0.47144\n", "113/113 [==============================] - 26s 226ms/step - loss: 1.4160 - accuracy: 0.4567 - val_loss: 1.4077 - val_accuracy: 0.4503 - lr: 0.0010\n", "Epoch 10/150\n", "113/113 [==============================] - ETA: 0s - loss: 1.3508 - accuracy: 0.4823\n", "Epoch 10: val_accuracy improved from 0.47144 to 0.51658, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_w_aug.hdf5\n", "113/113 [==============================] - 27s 236ms/step - loss: 1.3508 - accuracy: 0.4823 - val_loss: 1.2775 - val_accuracy: 0.5166 - lr: 0.0010\n", "Epoch 11/150\n", "113/113 [==============================] - ETA: 0s - loss: 1.3068 - accuracy: 0.5022\n", "Epoch 11: val_accuracy improved from 0.51658 to 0.53636, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_w_aug.hdf5\n", "113/113 [==============================] - 28s 251ms/step - loss: 1.3068 - accuracy: 0.5022 - val_loss: 1.2278 - val_accuracy: 0.5364 - lr: 0.0010\n", "Epoch 12/150\n", "113/113 [==============================] - ETA: 0s - loss: 1.2749 - accuracy: 0.5133\n", "Epoch 12: val_accuracy improved from 0.53636 to 0.54946, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_w_aug.hdf5\n", "113/113 [==============================] - 28s 248ms/step - loss: 1.2749 - accuracy: 0.5133 - val_loss: 1.2018 - val_accuracy: 0.5495 - lr: 0.0010\n", "Epoch 13/150\n", "113/113 [==============================] - ETA: 0s - loss: 1.2479 - accuracy: 0.5285\n", "Epoch 13: val_accuracy did not improve from 0.54946\n", "113/113 [==============================] - 26s 228ms/step - loss: 1.2479 - accuracy: 0.5285 - val_loss: 1.2091 - val_accuracy: 0.5469 - lr: 0.0010\n", "Epoch 14/150\n", "113/113 [==============================] - ETA: 0s - loss: 1.2182 - accuracy: 0.5368\n", "Epoch 14: val_accuracy improved from 0.54946 to 0.59292, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_w_aug.hdf5\n", "113/113 [==============================] - 27s 236ms/step - loss: 1.2182 - accuracy: 0.5368 - val_loss: 1.0695 - val_accuracy: 0.5929 - lr: 0.0010\n", "Epoch 15/150\n", "113/113 [==============================] - ETA: 0s - loss: 1.1995 - accuracy: 0.5466\n", "Epoch 15: val_accuracy did not improve from 0.59292\n", "113/113 [==============================] - 26s 227ms/step - loss: 1.1995 - accuracy: 0.5466 - val_loss: 1.0870 - val_accuracy: 0.5812 - lr: 0.0010\n", "Epoch 16/150\n", "113/113 [==============================] - ETA: 0s - loss: 1.1845 - accuracy: 0.5529\n", "Epoch 16: val_accuracy did not improve from 0.59292\n", "113/113 [==============================] - 26s 226ms/step - loss: 1.1845 - accuracy: 0.5529 - val_loss: 1.1067 - val_accuracy: 0.5793 - lr: 0.0010\n", "Epoch 17/150\n", "113/113 [==============================] - ETA: 0s - loss: 1.1645 - accuracy: 0.5631\n", "Epoch 17: val_accuracy improved from 0.59292 to 0.60379, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_w_aug.hdf5\n", "113/113 [==============================] - 27s 237ms/step - loss: 1.1645 - accuracy: 0.5631 - val_loss: 1.0532 - val_accuracy: 0.6038 - lr: 0.0010\n", "Epoch 18/150\n", "113/113 [==============================] - ETA: 0s - loss: 1.1476 - accuracy: 0.5660\n", "Epoch 18: val_accuracy improved from 0.60379 to 0.60936, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_w_aug.hdf5\n", "113/113 [==============================] - 27s 236ms/step - loss: 1.1476 - accuracy: 0.5660 - val_loss: 1.0229 - val_accuracy: 0.6094 - lr: 0.0010\n", "Epoch 19/150\n", "113/113 [==============================] - ETA: 0s - loss: 1.1498 - accuracy: 0.5686\n", "Epoch 19: val_accuracy did not improve from 0.60936\n", "113/113 [==============================] - 26s 227ms/step - loss: 1.1498 - accuracy: 0.5686 - val_loss: 1.0242 - val_accuracy: 0.6088 - lr: 0.0010\n", "Epoch 20/150\n", "113/113 [==============================] - ETA: 0s - loss: 1.1002 - accuracy: 0.5855\n", "Epoch 20: val_accuracy improved from 0.60936 to 0.62134, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_w_aug.hdf5\n", "113/113 [==============================] - 29s 259ms/step - loss: 1.1002 - accuracy: 0.5855 - val_loss: 0.9835 - val_accuracy: 0.6213 - lr: 5.0000e-04\n", "Epoch 21/150\n", "113/113 [==============================] - ETA: 0s - loss: 1.0868 - accuracy: 0.5924\n", "Epoch 21: val_accuracy improved from 0.62134 to 0.63444, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_w_aug.hdf5\n", "113/113 [==============================] - 27s 242ms/step - loss: 1.0868 - accuracy: 0.5924 - val_loss: 0.9604 - val_accuracy: 0.6344 - lr: 5.0000e-04\n", "Epoch 22/150\n", "113/113 [==============================] - ETA: 0s - loss: 1.0777 - accuracy: 0.5948\n", "Epoch 22: val_accuracy did not improve from 0.63444\n", "113/113 [==============================] - 26s 228ms/step - loss: 1.0777 - accuracy: 0.5948 - val_loss: 1.0143 - val_accuracy: 0.6094 - lr: 5.0000e-04\n", "Epoch 23/150\n", "113/113 [==============================] - ETA: 0s - loss: 1.0729 - accuracy: 0.5950\n", "Epoch 23: val_accuracy did not improve from 0.63444\n", "113/113 [==============================] - 26s 227ms/step - loss: 1.0729 - accuracy: 0.5950 - val_loss: 0.9871 - val_accuracy: 0.6197 - lr: 5.0000e-04\n", "Epoch 24/150\n", "113/113 [==============================] - ETA: 0s - loss: 1.0664 - accuracy: 0.5962\n", "Epoch 24: val_accuracy improved from 0.63444 to 0.63527, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_w_aug.hdf5\n", "113/113 [==============================] - 27s 235ms/step - loss: 1.0664 - accuracy: 0.5962 - val_loss: 0.9617 - val_accuracy: 0.6353 - lr: 5.0000e-04\n", "Epoch 25/150\n", "113/113 [==============================] - ETA: 0s - loss: 1.0520 - accuracy: 0.6078\n", "Epoch 25: val_accuracy did not improve from 0.63527\n", "113/113 [==============================] - 26s 229ms/step - loss: 1.0520 - accuracy: 0.6078 - val_loss: 0.9609 - val_accuracy: 0.6328 - lr: 5.0000e-04\n", "Epoch 26/150\n", "113/113 [==============================] - ETA: 0s - loss: 1.0503 - accuracy: 0.6075\n", "Epoch 26: val_accuracy did not improve from 0.63527\n", "113/113 [==============================] - 26s 227ms/step - loss: 1.0503 - accuracy: 0.6075 - val_loss: 0.9464 - val_accuracy: 0.6347 - lr: 5.0000e-04\n", "Epoch 27/150\n", "113/113 [==============================] - ETA: 0s - loss: 1.0457 - accuracy: 0.6091\n", "Epoch 27: val_accuracy improved from 0.63527 to 0.64698, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_w_aug.hdf5\n", "113/113 [==============================] - 27s 239ms/step - loss: 1.0457 - accuracy: 0.6091 - val_loss: 0.9287 - val_accuracy: 0.6470 - lr: 5.0000e-04\n", "Epoch 28/150\n", "113/113 [==============================] - ETA: 0s - loss: 1.0367 - accuracy: 0.6110\n", "Epoch 28: val_accuracy did not improve from 0.64698\n", "113/113 [==============================] - 27s 235ms/step - loss: 1.0367 - accuracy: 0.6110 - val_loss: 0.9655 - val_accuracy: 0.6339 - lr: 5.0000e-04\n", "Epoch 29/150\n", "113/113 [==============================] - ETA: 0s - loss: 1.0316 - accuracy: 0.6143\n", "Epoch 29: val_accuracy did not improve from 0.64698\n", "113/113 [==============================] - 27s 234ms/step - loss: 1.0316 - accuracy: 0.6143 - val_loss: 0.9421 - val_accuracy: 0.6464 - lr: 5.0000e-04\n", "Epoch 30/150\n", "113/113 [==============================] - ETA: 0s - loss: 1.0268 - accuracy: 0.6124\n", "Epoch 30: val_accuracy did not improve from 0.64698\n", "113/113 [==============================] - 26s 225ms/step - loss: 1.0268 - accuracy: 0.6124 - val_loss: 0.9514 - val_accuracy: 0.6378 - lr: 5.0000e-04\n", "Epoch 31/150\n", "113/113 [==============================] - ETA: 0s - loss: 1.0131 - accuracy: 0.6201\n", "Epoch 31: val_accuracy did not improve from 0.64698\n", "113/113 [==============================] - 25s 225ms/step - loss: 1.0131 - accuracy: 0.6201 - val_loss: 0.9332 - val_accuracy: 0.6470 - lr: 5.0000e-04\n", "Epoch 32/150\n", "113/113 [==============================] - ETA: 0s - loss: 1.0163 - accuracy: 0.6205\n", "Epoch 32: val_accuracy improved from 0.64698 to 0.64921, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_w_aug.hdf5\n", "113/113 [==============================] - 27s 235ms/step - loss: 1.0163 - accuracy: 0.6205 - val_loss: 0.9374 - val_accuracy: 0.6492 - lr: 5.0000e-04\n", "Epoch 33/150\n", "113/113 [==============================] - ETA: 0s - loss: 1.0071 - accuracy: 0.6228\n", "Epoch 33: val_accuracy improved from 0.64921 to 0.65143, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_w_aug.hdf5\n", "113/113 [==============================] - 27s 236ms/step - loss: 1.0071 - accuracy: 0.6228 - val_loss: 0.9291 - val_accuracy: 0.6514 - lr: 5.0000e-04\n", "Epoch 34/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.9986 - accuracy: 0.6233\n", "Epoch 34: val_accuracy did not improve from 0.65143\n", "113/113 [==============================] - 26s 226ms/step - loss: 0.9986 - accuracy: 0.6233 - val_loss: 0.9227 - val_accuracy: 0.6512 - lr: 5.0000e-04\n", "Epoch 35/150\n", "113/113 [==============================] - ETA: 0s - loss: 1.0022 - accuracy: 0.6252\n", "Epoch 35: val_accuracy did not improve from 0.65143\n", "113/113 [==============================] - 25s 225ms/step - loss: 1.0022 - accuracy: 0.6252 - val_loss: 0.9476 - val_accuracy: 0.6333 - lr: 5.0000e-04\n", "Epoch 36/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.9881 - accuracy: 0.6299\n", "Epoch 36: val_accuracy improved from 0.65143 to 0.65171, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_w_aug.hdf5\n", "113/113 [==============================] - 27s 238ms/step - loss: 0.9881 - accuracy: 0.6299 - val_loss: 0.9305 - val_accuracy: 0.6517 - lr: 5.0000e-04\n", "Epoch 37/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.9863 - accuracy: 0.6304\n", "Epoch 37: val_accuracy improved from 0.65171 to 0.66063, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_w_aug.hdf5\n", "113/113 [==============================] - 27s 239ms/step - loss: 0.9863 - accuracy: 0.6304 - val_loss: 0.9241 - val_accuracy: 0.6606 - lr: 5.0000e-04\n", "Epoch 38/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.9842 - accuracy: 0.6319\n", "Epoch 38: val_accuracy did not improve from 0.66063\n", "113/113 [==============================] - 26s 227ms/step - loss: 0.9842 - accuracy: 0.6319 - val_loss: 0.9361 - val_accuracy: 0.6509 - lr: 5.0000e-04\n", "Epoch 39/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.9751 - accuracy: 0.6356\n", "Epoch 39: val_accuracy did not improve from 0.66063\n", "113/113 [==============================] - 26s 226ms/step - loss: 0.9751 - accuracy: 0.6356 - val_loss: 0.9149 - val_accuracy: 0.6565 - lr: 5.0000e-04\n", "Epoch 40/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.9458 - accuracy: 0.6476\n", "Epoch 40: val_accuracy improved from 0.66063 to 0.66314, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_w_aug.hdf5\n", "113/113 [==============================] - 26s 233ms/step - loss: 0.9458 - accuracy: 0.6476 - val_loss: 0.9008 - val_accuracy: 0.6631 - lr: 2.5000e-04\n", "Epoch 41/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.9424 - accuracy: 0.6468\n", "Epoch 41: val_accuracy did not improve from 0.66314\n", "113/113 [==============================] - 25s 224ms/step - loss: 0.9424 - accuracy: 0.6468 - val_loss: 0.8963 - val_accuracy: 0.6590 - lr: 2.5000e-04\n", "Epoch 42/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.9320 - accuracy: 0.6504\n", "Epoch 42: val_accuracy improved from 0.66314 to 0.66704, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_w_aug.hdf5\n", "113/113 [==============================] - 27s 235ms/step - loss: 0.9320 - accuracy: 0.6504 - val_loss: 0.8871 - val_accuracy: 0.6670 - lr: 2.5000e-04\n", "Epoch 43/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.9185 - accuracy: 0.6593\n", "Epoch 43: val_accuracy improved from 0.66704 to 0.66871, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_w_aug.hdf5\n", "113/113 [==============================] - 27s 237ms/step - loss: 0.9185 - accuracy: 0.6593 - val_loss: 0.8888 - val_accuracy: 0.6687 - lr: 2.5000e-04\n", "Epoch 44/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.9176 - accuracy: 0.6554\n", "Epoch 44: val_accuracy improved from 0.66871 to 0.67205, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_w_aug.hdf5\n", "113/113 [==============================] - 27s 236ms/step - loss: 0.9176 - accuracy: 0.6554 - val_loss: 0.8794 - val_accuracy: 0.6721 - lr: 2.5000e-04\n", "Epoch 45/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.9237 - accuracy: 0.6556\n", "Epoch 45: val_accuracy did not improve from 0.67205\n", "113/113 [==============================] - 26s 226ms/step - loss: 0.9237 - accuracy: 0.6556 - val_loss: 0.9001 - val_accuracy: 0.6620 - lr: 2.5000e-04\n", "Epoch 46/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.9186 - accuracy: 0.6576\n", "Epoch 46: val_accuracy did not improve from 0.67205\n", "113/113 [==============================] - 25s 224ms/step - loss: 0.9186 - accuracy: 0.6576 - val_loss: 0.8812 - val_accuracy: 0.6670 - lr: 2.5000e-04\n", "Epoch 47/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.9128 - accuracy: 0.6600\n", "Epoch 47: val_accuracy did not improve from 0.67205\n", "113/113 [==============================] - 26s 226ms/step - loss: 0.9128 - accuracy: 0.6600 - val_loss: 0.8916 - val_accuracy: 0.6668 - lr: 2.5000e-04\n", "Epoch 48/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.9051 - accuracy: 0.6646\n", "Epoch 48: val_accuracy did not improve from 0.67205\n", "113/113 [==============================] - 28s 244ms/step - loss: 0.9051 - accuracy: 0.6646 - val_loss: 0.9021 - val_accuracy: 0.6623 - lr: 2.5000e-04\n", "Epoch 49/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.9124 - accuracy: 0.6608\n", "Epoch 49: val_accuracy did not improve from 0.67205\n", "113/113 [==============================] - 25s 225ms/step - loss: 0.9124 - accuracy: 0.6608 - val_loss: 0.9271 - val_accuracy: 0.6537 - lr: 2.5000e-04\n", "Epoch 50/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.8924 - accuracy: 0.6678\n", "Epoch 50: val_accuracy did not improve from 0.67205\n", "113/113 [==============================] - 26s 225ms/step - loss: 0.8924 - accuracy: 0.6678 - val_loss: 0.9414 - val_accuracy: 0.6478 - lr: 2.5000e-04\n", "Epoch 51/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.8965 - accuracy: 0.6666\n", "Epoch 51: val_accuracy did not improve from 0.67205\n", "113/113 [==============================] - 26s 226ms/step - loss: 0.8965 - accuracy: 0.6666 - val_loss: 0.9107 - val_accuracy: 0.6556 - lr: 2.5000e-04\n", "Epoch 52/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.8889 - accuracy: 0.6691\n", "Epoch 52: val_accuracy improved from 0.67205 to 0.67679, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_w_aug.hdf5\n", "113/113 [==============================] - 27s 237ms/step - loss: 0.8889 - accuracy: 0.6691 - val_loss: 0.8801 - val_accuracy: 0.6768 - lr: 2.5000e-04\n", "Epoch 53/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.8903 - accuracy: 0.6708\n", "Epoch 53: val_accuracy did not improve from 0.67679\n", "113/113 [==============================] - 26s 227ms/step - loss: 0.8903 - accuracy: 0.6708 - val_loss: 0.8981 - val_accuracy: 0.6662 - lr: 2.5000e-04\n", "Epoch 54/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.8822 - accuracy: 0.6701\n", "Epoch 54: val_accuracy did not improve from 0.67679\n", "113/113 [==============================] - 26s 226ms/step - loss: 0.8822 - accuracy: 0.6701 - val_loss: 0.8970 - val_accuracy: 0.6609 - lr: 2.5000e-04\n", "Epoch 55/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.8840 - accuracy: 0.6690\n", "Epoch 55: val_accuracy did not improve from 0.67679\n", "113/113 [==============================] - 25s 223ms/step - loss: 0.8840 - accuracy: 0.6690 - val_loss: 0.9005 - val_accuracy: 0.6634 - lr: 2.5000e-04\n", "Epoch 56/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.8772 - accuracy: 0.6716\n", "Epoch 56: val_accuracy did not improve from 0.67679\n", "113/113 [==============================] - 26s 225ms/step - loss: 0.8772 - accuracy: 0.6716 - val_loss: 0.8953 - val_accuracy: 0.6695 - lr: 2.5000e-04\n", "Epoch 57/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.8788 - accuracy: 0.6737\n", "Epoch 57: val_accuracy did not improve from 0.67679\n", "113/113 [==============================] - 26s 225ms/step - loss: 0.8788 - accuracy: 0.6737 - val_loss: 0.9536 - val_accuracy: 0.6417 - lr: 2.5000e-04\n", "Epoch 58/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.8677 - accuracy: 0.6798\n", "Epoch 58: val_accuracy did not improve from 0.67679\n", "113/113 [==============================] - 27s 241ms/step - loss: 0.8677 - accuracy: 0.6798 - val_loss: 0.8924 - val_accuracy: 0.6679 - lr: 2.5000e-04\n", "Epoch 59/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.8659 - accuracy: 0.6795\n", "Epoch 59: val_accuracy did not improve from 0.67679\n", "113/113 [==============================] - 25s 225ms/step - loss: 0.8659 - accuracy: 0.6795 - val_loss: 0.8772 - val_accuracy: 0.6748 - lr: 2.5000e-04\n", "Epoch 60/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.8491 - accuracy: 0.6852\n", "Epoch 60: val_accuracy did not improve from 0.67679\n", "113/113 [==============================] - 25s 223ms/step - loss: 0.8491 - accuracy: 0.6852 - val_loss: 0.9057 - val_accuracy: 0.6590 - lr: 1.2500e-04\n", "Epoch 61/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.8460 - accuracy: 0.6858\n", "Epoch 61: val_accuracy did not improve from 0.67679\n", "113/113 [==============================] - 25s 224ms/step - loss: 0.8460 - accuracy: 0.6858 - val_loss: 0.8846 - val_accuracy: 0.6743 - lr: 1.2500e-04\n", "Epoch 62/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.8382 - accuracy: 0.6873\n", "Epoch 62: val_accuracy did not improve from 0.67679\n", "113/113 [==============================] - 25s 225ms/step - loss: 0.8382 - accuracy: 0.6873 - val_loss: 0.8765 - val_accuracy: 0.6734 - lr: 1.2500e-04\n", "Epoch 63/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.8402 - accuracy: 0.6871\n", "Epoch 63: val_accuracy did not improve from 0.67679\n", "113/113 [==============================] - 25s 224ms/step - loss: 0.8402 - accuracy: 0.6871 - val_loss: 0.8786 - val_accuracy: 0.6732 - lr: 1.2500e-04\n", "Epoch 64/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.8346 - accuracy: 0.6888\n", "Epoch 64: val_accuracy did not improve from 0.67679\n", "113/113 [==============================] - 26s 225ms/step - loss: 0.8346 - accuracy: 0.6888 - val_loss: 0.8787 - val_accuracy: 0.6732 - lr: 1.2500e-04\n", "Epoch 65/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.8330 - accuracy: 0.6894\n", "Epoch 65: val_accuracy did not improve from 0.67679\n", "113/113 [==============================] - 25s 224ms/step - loss: 0.8330 - accuracy: 0.6894 - val_loss: 0.8788 - val_accuracy: 0.6718 - lr: 1.2500e-04\n", "Epoch 66/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.8302 - accuracy: 0.6898\n", "Epoch 66: val_accuracy improved from 0.67679 to 0.68097, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_w_aug.hdf5\n", "113/113 [==============================] - 27s 235ms/step - loss: 0.8302 - accuracy: 0.6898 - val_loss: 0.8640 - val_accuracy: 0.6810 - lr: 1.2500e-04\n", "Epoch 67/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.8252 - accuracy: 0.6944\n", "Epoch 67: val_accuracy did not improve from 0.68097\n", "113/113 [==============================] - 26s 225ms/step - loss: 0.8252 - accuracy: 0.6944 - val_loss: 0.8912 - val_accuracy: 0.6701 - lr: 1.2500e-04\n", "Epoch 68/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.8272 - accuracy: 0.6925\n", "Epoch 68: val_accuracy did not improve from 0.68097\n", "113/113 [==============================] - 27s 242ms/step - loss: 0.8272 - accuracy: 0.6925 - val_loss: 0.8831 - val_accuracy: 0.6723 - lr: 1.2500e-04\n", "Epoch 69/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.8274 - accuracy: 0.6927\n", "Epoch 69: val_accuracy did not improve from 0.68097\n", "113/113 [==============================] - 26s 227ms/step - loss: 0.8274 - accuracy: 0.6927 - val_loss: 0.8859 - val_accuracy: 0.6670 - lr: 1.2500e-04\n", "Epoch 70/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.8164 - accuracy: 0.6956\n", "Epoch 70: val_accuracy did not improve from 0.68097\n", "113/113 [==============================] - 26s 225ms/step - loss: 0.8164 - accuracy: 0.6956 - val_loss: 0.8789 - val_accuracy: 0.6776 - lr: 1.2500e-04\n", "Epoch 71/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.8181 - accuracy: 0.6985\n", "Epoch 71: val_accuracy did not improve from 0.68097\n", "113/113 [==============================] - 25s 223ms/step - loss: 0.8181 - accuracy: 0.6985 - val_loss: 0.8827 - val_accuracy: 0.6757 - lr: 1.2500e-04\n", "Epoch 72/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.8116 - accuracy: 0.6968\n", "Epoch 72: val_accuracy did not improve from 0.68097\n", "113/113 [==============================] - 25s 225ms/step - loss: 0.8116 - accuracy: 0.6968 - val_loss: 0.9135 - val_accuracy: 0.6743 - lr: 1.2500e-04\n", "Epoch 73/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.8053 - accuracy: 0.7024\n", "Epoch 73: val_accuracy did not improve from 0.68097\n", "113/113 [==============================] - 25s 225ms/step - loss: 0.8053 - accuracy: 0.7024 - val_loss: 0.9174 - val_accuracy: 0.6690 - lr: 1.2500e-04\n", "Epoch 74/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.8074 - accuracy: 0.6994\n", "Epoch 74: val_accuracy did not improve from 0.68097\n", "113/113 [==============================] - 25s 223ms/step - loss: 0.8074 - accuracy: 0.6994 - val_loss: 0.8840 - val_accuracy: 0.6773 - lr: 1.2500e-04\n", "Epoch 75/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.8072 - accuracy: 0.7011\n", "Epoch 75: val_accuracy improved from 0.68097 to 0.68376, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_w_aug.hdf5\n", "113/113 [==============================] - 27s 237ms/step - loss: 0.8072 - accuracy: 0.7011 - val_loss: 0.8767 - val_accuracy: 0.6838 - lr: 1.2500e-04\n", "Epoch 76/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.8066 - accuracy: 0.7008\n", "Epoch 76: val_accuracy did not improve from 0.68376\n", "113/113 [==============================] - 25s 224ms/step - loss: 0.8066 - accuracy: 0.7008 - val_loss: 0.9087 - val_accuracy: 0.6698 - lr: 1.2500e-04\n", "Epoch 77/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.8057 - accuracy: 0.7006\n", "Epoch 77: val_accuracy did not improve from 0.68376\n", "113/113 [==============================] - 27s 239ms/step - loss: 0.8057 - accuracy: 0.7006 - val_loss: 0.8922 - val_accuracy: 0.6773 - lr: 1.2500e-04\n", "Epoch 78/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.8019 - accuracy: 0.7010\n", "Epoch 78: val_accuracy did not improve from 0.68376\n", "113/113 [==============================] - 25s 224ms/step - loss: 0.8019 - accuracy: 0.7010 - val_loss: 0.8926 - val_accuracy: 0.6762 - lr: 1.2500e-04\n", "Epoch 79/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.8031 - accuracy: 0.7027\n", "Epoch 79: val_accuracy did not improve from 0.68376\n", "113/113 [==============================] - 25s 223ms/step - loss: 0.8031 - accuracy: 0.7027 - val_loss: 0.8979 - val_accuracy: 0.6729 - lr: 1.2500e-04\n", "Epoch 80/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7846 - accuracy: 0.7092\n", "Epoch 80: val_accuracy improved from 0.68376 to 0.68487, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_w_aug.hdf5\n", "113/113 [==============================] - 27s 236ms/step - loss: 0.7846 - accuracy: 0.7092 - val_loss: 0.8788 - val_accuracy: 0.6849 - lr: 6.2500e-05\n", "Epoch 81/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7901 - accuracy: 0.7044\n", "Epoch 81: val_accuracy did not improve from 0.68487\n", "113/113 [==============================] - 26s 225ms/step - loss: 0.7901 - accuracy: 0.7044 - val_loss: 0.8804 - val_accuracy: 0.6832 - lr: 6.2500e-05\n", "Epoch 82/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7790 - accuracy: 0.7120\n", "Epoch 82: val_accuracy did not improve from 0.68487\n", "113/113 [==============================] - 25s 224ms/step - loss: 0.7790 - accuracy: 0.7120 - val_loss: 0.8888 - val_accuracy: 0.6815 - lr: 6.2500e-05\n", "Epoch 83/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7800 - accuracy: 0.7104\n", "Epoch 83: val_accuracy did not improve from 0.68487\n", "113/113 [==============================] - 26s 226ms/step - loss: 0.7800 - accuracy: 0.7104 - val_loss: 0.8871 - val_accuracy: 0.6793 - lr: 6.2500e-05\n", "Epoch 84/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7779 - accuracy: 0.7099\n", "Epoch 84: val_accuracy improved from 0.68487 to 0.68654, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_w_aug.hdf5\n", "113/113 [==============================] - 26s 232ms/step - loss: 0.7779 - accuracy: 0.7099 - val_loss: 0.8760 - val_accuracy: 0.6865 - lr: 6.2500e-05\n", "Epoch 85/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7747 - accuracy: 0.7133\n", "Epoch 85: val_accuracy did not improve from 0.68654\n", "113/113 [==============================] - 25s 222ms/step - loss: 0.7747 - accuracy: 0.7133 - val_loss: 0.8857 - val_accuracy: 0.6807 - lr: 6.2500e-05\n", "Epoch 86/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7744 - accuracy: 0.7119\n", "Epoch 86: val_accuracy did not improve from 0.68654\n", "113/113 [==============================] - 28s 243ms/step - loss: 0.7744 - accuracy: 0.7119 - val_loss: 0.8823 - val_accuracy: 0.6818 - lr: 6.2500e-05\n", "Epoch 87/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7745 - accuracy: 0.7131\n", "Epoch 87: val_accuracy did not improve from 0.68654\n", "113/113 [==============================] - 25s 225ms/step - loss: 0.7745 - accuracy: 0.7131 - val_loss: 0.8778 - val_accuracy: 0.6851 - lr: 6.2500e-05\n", "Epoch 88/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7703 - accuracy: 0.7154\n", "Epoch 88: val_accuracy did not improve from 0.68654\n", "113/113 [==============================] - 26s 226ms/step - loss: 0.7703 - accuracy: 0.7154 - val_loss: 0.8808 - val_accuracy: 0.6838 - lr: 6.2500e-05\n", "Epoch 89/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7738 - accuracy: 0.7139\n", "Epoch 89: val_accuracy did not improve from 0.68654\n", "113/113 [==============================] - 25s 221ms/step - loss: 0.7738 - accuracy: 0.7139 - val_loss: 0.8789 - val_accuracy: 0.6835 - lr: 6.2500e-05\n", "Epoch 90/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7671 - accuracy: 0.7128\n", "Epoch 90: val_accuracy did not improve from 0.68654\n", "113/113 [==============================] - 25s 225ms/step - loss: 0.7671 - accuracy: 0.7128 - val_loss: 0.9042 - val_accuracy: 0.6751 - lr: 6.2500e-05\n", "Epoch 91/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7689 - accuracy: 0.7130\n", "Epoch 91: val_accuracy did not improve from 0.68654\n", "113/113 [==============================] - 25s 224ms/step - loss: 0.7689 - accuracy: 0.7130 - val_loss: 0.8803 - val_accuracy: 0.6846 - lr: 6.2500e-05\n", "Epoch 92/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7702 - accuracy: 0.7140\n", "Epoch 92: val_accuracy did not improve from 0.68654\n", "113/113 [==============================] - 25s 223ms/step - loss: 0.7702 - accuracy: 0.7140 - val_loss: 0.8840 - val_accuracy: 0.6832 - lr: 6.2500e-05\n", "Epoch 93/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7689 - accuracy: 0.7149\n", "Epoch 93: val_accuracy did not improve from 0.68654\n", "113/113 [==============================] - 25s 221ms/step - loss: 0.7689 - accuracy: 0.7149 - val_loss: 0.8963 - val_accuracy: 0.6818 - lr: 6.2500e-05\n", "Epoch 94/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7641 - accuracy: 0.7154\n", "Epoch 94: val_accuracy did not improve from 0.68654\n", "113/113 [==============================] - 25s 224ms/step - loss: 0.7641 - accuracy: 0.7154 - val_loss: 0.8863 - val_accuracy: 0.6815 - lr: 6.2500e-05\n", "Epoch 95/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7594 - accuracy: 0.7172\n", "Epoch 95: val_accuracy did not improve from 0.68654\n", "113/113 [==============================] - 25s 225ms/step - loss: 0.7594 - accuracy: 0.7172 - val_loss: 0.8894 - val_accuracy: 0.6810 - lr: 6.2500e-05\n", "Epoch 96/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7598 - accuracy: 0.7206\n", "Epoch 96: val_accuracy did not improve from 0.68654\n", "113/113 [==============================] - 26s 225ms/step - loss: 0.7598 - accuracy: 0.7206 - val_loss: 0.9015 - val_accuracy: 0.6821 - lr: 6.2500e-05\n", "Epoch 97/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7563 - accuracy: 0.7167\n", "Epoch 97: val_accuracy did not improve from 0.68654\n", "113/113 [==============================] - 27s 241ms/step - loss: 0.7563 - accuracy: 0.7167 - val_loss: 0.8877 - val_accuracy: 0.6849 - lr: 6.2500e-05\n", "Epoch 98/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7565 - accuracy: 0.7206\n", "Epoch 98: val_accuracy did not improve from 0.68654\n", "113/113 [==============================] - 26s 226ms/step - loss: 0.7565 - accuracy: 0.7206 - val_loss: 0.8836 - val_accuracy: 0.6821 - lr: 6.2500e-05\n", "Epoch 99/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7582 - accuracy: 0.7198\n", "Epoch 99: val_accuracy improved from 0.68654 to 0.68877, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_w_aug.hdf5\n", "113/113 [==============================] - 26s 232ms/step - loss: 0.7582 - accuracy: 0.7198 - val_loss: 0.8909 - val_accuracy: 0.6888 - lr: 6.2500e-05\n", "Epoch 100/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7563 - accuracy: 0.7167\n", "Epoch 100: val_accuracy did not improve from 0.68877\n", "113/113 [==============================] - 25s 224ms/step - loss: 0.7563 - accuracy: 0.7167 - val_loss: 0.8830 - val_accuracy: 0.6851 - lr: 3.1250e-05\n", "Epoch 101/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7442 - accuracy: 0.7233\n", "Epoch 101: val_accuracy did not improve from 0.68877\n", "113/113 [==============================] - 25s 224ms/step - loss: 0.7442 - accuracy: 0.7233 - val_loss: 0.8910 - val_accuracy: 0.6854 - lr: 3.1250e-05\n", "Epoch 102/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7498 - accuracy: 0.7191\n", "Epoch 102: val_accuracy did not improve from 0.68877\n", "113/113 [==============================] - 25s 220ms/step - loss: 0.7498 - accuracy: 0.7191 - val_loss: 0.8968 - val_accuracy: 0.6868 - lr: 3.1250e-05\n", "Epoch 103/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7481 - accuracy: 0.7235\n", "Epoch 103: val_accuracy improved from 0.68877 to 0.69128, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_w_aug.hdf5\n", "113/113 [==============================] - 26s 232ms/step - loss: 0.7481 - accuracy: 0.7235 - val_loss: 0.8831 - val_accuracy: 0.6913 - lr: 3.1250e-05\n", "Epoch 104/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7456 - accuracy: 0.7244\n", "Epoch 104: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 25s 223ms/step - loss: 0.7456 - accuracy: 0.7244 - val_loss: 0.8773 - val_accuracy: 0.6888 - lr: 3.1250e-05\n", "Epoch 105/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7497 - accuracy: 0.7204\n", "Epoch 105: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 27s 241ms/step - loss: 0.7497 - accuracy: 0.7204 - val_loss: 0.8869 - val_accuracy: 0.6863 - lr: 3.1250e-05\n", "Epoch 106/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7414 - accuracy: 0.7238\n", "Epoch 106: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 26s 225ms/step - loss: 0.7414 - accuracy: 0.7238 - val_loss: 0.8939 - val_accuracy: 0.6846 - lr: 3.1250e-05\n", "Epoch 107/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7505 - accuracy: 0.7196\n", "Epoch 107: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 25s 222ms/step - loss: 0.7505 - accuracy: 0.7196 - val_loss: 0.8903 - val_accuracy: 0.6868 - lr: 3.1250e-05\n", "Epoch 108/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7417 - accuracy: 0.7230\n", "Epoch 108: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 25s 220ms/step - loss: 0.7417 - accuracy: 0.7230 - val_loss: 0.8993 - val_accuracy: 0.6838 - lr: 3.1250e-05\n", "Epoch 109/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7466 - accuracy: 0.7221\n", "Epoch 109: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 25s 221ms/step - loss: 0.7466 - accuracy: 0.7221 - val_loss: 0.8902 - val_accuracy: 0.6882 - lr: 3.1250e-05\n", "Epoch 110/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7402 - accuracy: 0.7260\n", "Epoch 110: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 25s 223ms/step - loss: 0.7402 - accuracy: 0.7260 - val_loss: 0.8912 - val_accuracy: 0.6860 - lr: 3.1250e-05\n", "Epoch 111/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7402 - accuracy: 0.7258\n", "Epoch 111: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 25s 221ms/step - loss: 0.7402 - accuracy: 0.7258 - val_loss: 0.8927 - val_accuracy: 0.6871 - lr: 3.1250e-05\n", "Epoch 112/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7391 - accuracy: 0.7248\n", "Epoch 112: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 25s 224ms/step - loss: 0.7391 - accuracy: 0.7248 - val_loss: 0.8944 - val_accuracy: 0.6879 - lr: 3.1250e-05\n", "Epoch 113/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7376 - accuracy: 0.7282\n", "Epoch 113: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 25s 223ms/step - loss: 0.7376 - accuracy: 0.7282 - val_loss: 0.8937 - val_accuracy: 0.6846 - lr: 3.1250e-05\n", "Epoch 114/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7316 - accuracy: 0.7300\n", "Epoch 114: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 25s 221ms/step - loss: 0.7316 - accuracy: 0.7300 - val_loss: 0.8974 - val_accuracy: 0.6832 - lr: 3.1250e-05\n", "Epoch 115/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7458 - accuracy: 0.7203\n", "Epoch 115: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 25s 223ms/step - loss: 0.7458 - accuracy: 0.7203 - val_loss: 0.9022 - val_accuracy: 0.6840 - lr: 3.1250e-05\n", "Epoch 116/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7387 - accuracy: 0.7253\n", "Epoch 116: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 27s 240ms/step - loss: 0.7387 - accuracy: 0.7253 - val_loss: 0.9068 - val_accuracy: 0.6818 - lr: 3.1250e-05\n", "Epoch 117/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7345 - accuracy: 0.7261\n", "Epoch 117: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 26s 225ms/step - loss: 0.7345 - accuracy: 0.7261 - val_loss: 0.9014 - val_accuracy: 0.6838 - lr: 3.1250e-05\n", "Epoch 118/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7379 - accuracy: 0.7251\n", "Epoch 118: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 26s 229ms/step - loss: 0.7379 - accuracy: 0.7251 - val_loss: 0.9005 - val_accuracy: 0.6871 - lr: 3.1250e-05\n", "Epoch 119/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7342 - accuracy: 0.7296\n", "Epoch 119: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 26s 229ms/step - loss: 0.7342 - accuracy: 0.7296 - val_loss: 0.9037 - val_accuracy: 0.6815 - lr: 3.1250e-05\n", "Epoch 120/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7330 - accuracy: 0.7286\n", "Epoch 120: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 26s 227ms/step - loss: 0.7330 - accuracy: 0.7286 - val_loss: 0.9027 - val_accuracy: 0.6835 - lr: 1.5625e-05\n", "Epoch 121/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7315 - accuracy: 0.7289\n", "Epoch 121: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 26s 226ms/step - loss: 0.7315 - accuracy: 0.7289 - val_loss: 0.9015 - val_accuracy: 0.6838 - lr: 1.5625e-05\n", "Epoch 122/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7326 - accuracy: 0.7285\n", "Epoch 122: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 25s 224ms/step - loss: 0.7326 - accuracy: 0.7285 - val_loss: 0.9010 - val_accuracy: 0.6879 - lr: 1.5625e-05\n", "Epoch 123/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7288 - accuracy: 0.7322\n", "Epoch 123: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 25s 224ms/step - loss: 0.7288 - accuracy: 0.7322 - val_loss: 0.9079 - val_accuracy: 0.6851 - lr: 1.5625e-05\n", "Epoch 124/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7314 - accuracy: 0.7289\n", "Epoch 124: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 26s 232ms/step - loss: 0.7314 - accuracy: 0.7289 - val_loss: 0.9060 - val_accuracy: 0.6877 - lr: 1.5625e-05\n", "Epoch 125/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7315 - accuracy: 0.7273\n", "Epoch 125: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 26s 234ms/step - loss: 0.7315 - accuracy: 0.7273 - val_loss: 0.9003 - val_accuracy: 0.6888 - lr: 1.5625e-05\n", "Epoch 126/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7281 - accuracy: 0.7313\n", "Epoch 126: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 26s 227ms/step - loss: 0.7281 - accuracy: 0.7313 - val_loss: 0.8993 - val_accuracy: 0.6846 - lr: 1.5625e-05\n", "Epoch 127/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7293 - accuracy: 0.7331\n", "Epoch 127: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 25s 225ms/step - loss: 0.7293 - accuracy: 0.7331 - val_loss: 0.9048 - val_accuracy: 0.6860 - lr: 1.5625e-05\n", "Epoch 128/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7279 - accuracy: 0.7297\n", "Epoch 128: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 26s 225ms/step - loss: 0.7279 - accuracy: 0.7297 - val_loss: 0.8945 - val_accuracy: 0.6885 - lr: 1.5625e-05\n", "Epoch 129/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7317 - accuracy: 0.7330\n", "Epoch 129: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 25s 225ms/step - loss: 0.7317 - accuracy: 0.7330 - val_loss: 0.8973 - val_accuracy: 0.6885 - lr: 1.5625e-05\n", "Epoch 130/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7325 - accuracy: 0.7251\n", "Epoch 130: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 26s 226ms/step - loss: 0.7325 - accuracy: 0.7251 - val_loss: 0.8968 - val_accuracy: 0.6868 - lr: 1.5625e-05\n", "Epoch 131/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7285 - accuracy: 0.7287\n", "Epoch 131: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 26s 228ms/step - loss: 0.7285 - accuracy: 0.7287 - val_loss: 0.9008 - val_accuracy: 0.6849 - lr: 1.5625e-05\n", "Epoch 132/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7216 - accuracy: 0.7320\n", "Epoch 132: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 27s 236ms/step - loss: 0.7216 - accuracy: 0.7320 - val_loss: 0.9055 - val_accuracy: 0.6863 - lr: 1.5625e-05\n", "Epoch 133/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7197 - accuracy: 0.7341\n", "Epoch 133: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 26s 233ms/step - loss: 0.7197 - accuracy: 0.7341 - val_loss: 0.8994 - val_accuracy: 0.6885 - lr: 1.5625e-05\n", "Epoch 134/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7310 - accuracy: 0.7287\n", "Epoch 134: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 25s 223ms/step - loss: 0.7310 - accuracy: 0.7287 - val_loss: 0.9025 - val_accuracy: 0.6874 - lr: 1.5625e-05\n", "Epoch 135/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7239 - accuracy: 0.7323\n", "Epoch 135: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 25s 224ms/step - loss: 0.7239 - accuracy: 0.7323 - val_loss: 0.9052 - val_accuracy: 0.6851 - lr: 1.5625e-05\n", "Epoch 136/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7252 - accuracy: 0.7310\n", "Epoch 136: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 25s 224ms/step - loss: 0.7252 - accuracy: 0.7310 - val_loss: 0.9051 - val_accuracy: 0.6879 - lr: 1.5625e-05\n", "Epoch 137/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7220 - accuracy: 0.7330\n", "Epoch 137: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 25s 224ms/step - loss: 0.7220 - accuracy: 0.7330 - val_loss: 0.9007 - val_accuracy: 0.6893 - lr: 1.5625e-05\n", "Epoch 138/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7219 - accuracy: 0.7292\n", "Epoch 138: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 25s 224ms/step - loss: 0.7219 - accuracy: 0.7292 - val_loss: 0.9004 - val_accuracy: 0.6868 - lr: 1.5625e-05\n", "Epoch 139/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7229 - accuracy: 0.7283\n", "Epoch 139: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 26s 225ms/step - loss: 0.7229 - accuracy: 0.7283 - val_loss: 0.9048 - val_accuracy: 0.6863 - lr: 1.5625e-05\n", "Epoch 140/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7284 - accuracy: 0.7301\n", "Epoch 140: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 25s 225ms/step - loss: 0.7284 - accuracy: 0.7301 - val_loss: 0.9030 - val_accuracy: 0.6871 - lr: 7.8125e-06\n", "Epoch 141/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7257 - accuracy: 0.7311\n", "Epoch 141: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 28s 243ms/step - loss: 0.7257 - accuracy: 0.7311 - val_loss: 0.9052 - val_accuracy: 0.6863 - lr: 7.8125e-06\n", "Epoch 142/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7196 - accuracy: 0.7319\n", "Epoch 142: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 26s 228ms/step - loss: 0.7196 - accuracy: 0.7319 - val_loss: 0.9045 - val_accuracy: 0.6849 - lr: 7.8125e-06\n", "Epoch 143/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7206 - accuracy: 0.7301\n", "Epoch 143: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 26s 226ms/step - loss: 0.7206 - accuracy: 0.7301 - val_loss: 0.9024 - val_accuracy: 0.6857 - lr: 7.8125e-06\n", "Epoch 144/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7242 - accuracy: 0.7299\n", "Epoch 144: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 26s 227ms/step - loss: 0.7242 - accuracy: 0.7299 - val_loss: 0.9041 - val_accuracy: 0.6874 - lr: 7.8125e-06\n", "Epoch 145/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7232 - accuracy: 0.7319\n", "Epoch 145: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 26s 225ms/step - loss: 0.7232 - accuracy: 0.7319 - val_loss: 0.9064 - val_accuracy: 0.6860 - lr: 7.8125e-06\n", "Epoch 146/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7166 - accuracy: 0.7344\n", "Epoch 146: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 26s 227ms/step - loss: 0.7166 - accuracy: 0.7344 - val_loss: 0.9032 - val_accuracy: 0.6865 - lr: 7.8125e-06\n", "Epoch 147/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7234 - accuracy: 0.7348\n", "Epoch 147: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 26s 229ms/step - loss: 0.7234 - accuracy: 0.7348 - val_loss: 0.9067 - val_accuracy: 0.6868 - lr: 7.8125e-06\n", "Epoch 148/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7203 - accuracy: 0.7353\n", "Epoch 148: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 27s 236ms/step - loss: 0.7203 - accuracy: 0.7353 - val_loss: 0.9104 - val_accuracy: 0.6860 - lr: 7.8125e-06\n", "Epoch 149/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7234 - accuracy: 0.7315\n", "Epoch 149: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 26s 231ms/step - loss: 0.7234 - accuracy: 0.7315 - val_loss: 0.9064 - val_accuracy: 0.6893 - lr: 7.8125e-06\n", "Epoch 150/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7209 - accuracy: 0.7312\n", "Epoch 150: val_accuracy did not improve from 0.69128\n", "113/113 [==============================] - 26s 231ms/step - loss: 0.7209 - accuracy: 0.7312 - val_loss: 0.9068 - val_accuracy: 0.6877 - lr: 7.8125e-06\n" ] } ], "source": [ "from keras.optimizers import RMSprop,SGD,Adam\n", "import math\n", "from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau, LearningRateScheduler\n", "import os\n", "#filepath=\"/content/drive/My Drive/fer_new_vgg/fold1_jaffe_epochs:{epoch:03d}-accuracy:{val_accuracy:.4f}.hdf5\"\n", "\n", "filepath=\"/content/drive/My Drive/fer_new_vgg/fer_peerj_w_aug.hdf5\"\n", "checkpoint_dir = os.path.dirname(filepath)\n", "initial_lrate = 0.001\n", "def step_decay(epoch): \n", " drop = 0.5\n", " epochs_drop = 20.0\n", " lrate = initial_lrate * math.pow(drop, math.floor((1+epoch)/epochs_drop))\n", " return lrate\n", "lrate = LearningRateScheduler(step_decay)\n", "checkpoint = ModelCheckpoint(filepath,\n", " monitor='val_accuracy',\n", " mode='max',\n", " save_best_only=True,\n", " verbose=2)\n", "\n", "callbacks = [checkpoint, lrate]\n", "\n", "model.compile(loss='categorical_crossentropy',\n", " optimizer = Adam(learning_rate=0.001),\n", " metrics=['accuracy'])\n", "\n", "epochs=150\n", "\n", "history=model.fit(\n", " train_generator,\n", " steps_per_epoch=train_sample//batch_size+1,\n", " epochs=epochs,\n", " callbacks=callbacks,\n", " validation_data=validation_generator,\n", " validation_steps=valid_sample//batch_size+1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 404 }, "id": "3UC2U6DxutSe", "outputId": "1748da01-f755-4f93-ab99-4f8576f17f17" }, "outputs": [ { "data": { "application/javascript": [ "\n", " async function download(id, filename, size) {\n", " if (!google.colab.kernel.accessAllowed) {\n", " return;\n", " }\n", " const div = document.createElement('div');\n", " const label = document.createElement('label');\n", " label.textContent = `Downloading \"${filename}\": `;\n", " div.appendChild(label);\n", " const progress = document.createElement('progress');\n", " progress.max = size;\n", " div.appendChild(progress);\n", " document.body.appendChild(div);\n", "\n", " const buffers = [];\n", " let downloaded = 0;\n", "\n", " const channel = await google.colab.kernel.comms.open(id);\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", "\n", " for await (const message of channel.messages) {\n", " // Send a message to notify the kernel that we're ready.\n", " 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from google.colab import files\n", "loss_train = history.history['accuracy']\n", "loss_val = history.history['val_accuracy']\n", "epochs = range(1,151)\n", "plt.figure(figsize=(8,6))\n", "plt.rcParams['font.size'] = 18\n", "plt.plot(epochs, loss_train, 'g--', label='Training accuracy')\n", "plt.plot(epochs, loss_val, 'b', label='validation accuracy')\n", "#plt.title('Training and Validation accuracy')\n", "plt.xlabel('Epochs',fontsize=18)\n", "plt.ylabel('Accuracy',fontsize=18)\n", "plt.legend()\n", "plt.savefig('acc_FER_2013_review.png')\n", "files.download(\"acc_FER_2013_review.png\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 410 }, "id": "nbzD33JCu_yO", "outputId": "7d4f932e-f9c4-4bda-b464-e36ad9a3a571" }, "outputs": [ { "data": { "application/javascript": [ "\n", " async function download(id, filename, size) {\n", " if (!google.colab.kernel.accessAllowed) {\n", " return;\n", " }\n", " const div = document.createElement('div');\n", " const label = document.createElement('label');\n", " label.textContent = `Downloading \"${filename}\": `;\n", " div.appendChild(label);\n", " const progress = document.createElement('progress');\n", " progress.max = size;\n", " div.appendChild(progress);\n", " document.body.appendChild(div);\n", "\n", " const buffers = [];\n", " let downloaded = 0;\n", "\n", " const channel = await google.colab.kernel.comms.open(id);\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", "\n", " for await (const message of channel.messages) {\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", " if (message.buffers) {\n", " for (const buffer of message.buffers) {\n", " buffers.push(buffer);\n", " downloaded += buffer.byteLength;\n", " progress.value = downloaded;\n", " }\n", " }\n", " }\n", " const blob = new Blob(buffers, {type: 'application/binary'});\n", " const a = document.createElement('a');\n", " a.href = window.URL.createObjectURL(blob);\n", " a.download = filename;\n", " div.appendChild(a);\n", " a.click();\n", " div.remove();\n", " }\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "download(\"download_b23066e3-db57-4a49-924e-2c819d95895a\", \"loss_FER_2013_review.png\", 33124)" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from google.colab import files\n", "loss_train = history.history['loss']\n", "loss_val = history.history['val_loss']\n", "epochs = range(1,151)\n", "plt.figure(figsize=(8,6))\n", "plt.plot(epochs, loss_train, 'g--', label='Training loss')\n", "plt.plot(epochs, loss_val, 'b', label='validation loss')\n", "#plt.title('Training and Validation accuracy')\n", "plt.xlabel('Epochs',fontsize=18)\n", "plt.ylabel('Loss',fontsize=18)\n", "plt.legend()\n", "plt.savefig('loss_FER_2013_review.png')\n", "files.download(\"loss_FER_2013_review.png\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "xUiUdCHElx5g" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "P8qrKxXu_4Jg" }, "outputs": [], "source": [ "model.load_weights(\"/content/drive/My Drive/fer_new_vgg/fer_peerj_review.hdf5\") " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "988SDaJt6I5s" }, "outputs": [], "source": [ "predictions = model.predict(test_generator, steps=test_sample//batch_size+1, verbose=0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "YxQKjklDzJaw" }, "outputs": [], "source": [ "from google.colab import files" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "wrfJDfhhliEB" }, "outputs": [], "source": [ "cm_plot_labels = ['AN',\n", " 'DI',\n", " 'FE',\n", " 'HA',\n", " 'NE',\n", " 'SA',\n", " 'SU']\n", "from sklearn.metrics import confusion_matrix\n", "import seaborn as sns\n", "\n", "cm = confusion_matrix(test_generator.classes, predictions.argmax(axis=1))\n", "# Normalise\n", "cmn = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n", "\n", "fig, ax = plt.subplots(figsize=(10,6))\n", "p=sns.heatmap(cmn, annot=True, fmt='.2f', xticklabels=cm_plot_labels, yticklabels=cm_plot_labels)\n", "sns.set(font_scale=5)\n", "p.set_xticklabels(p.get_xmajorticklabels(), fontsize = 15)\n", "p.set_yticklabels(p.get_ymajorticklabels(), fontsize = 15)\n", "plt.ylabel('Actual', fontsize=18)\n", "plt.xlabel('Predicted', fontsize=18)\n", "#plt.savefig('conf_FER_2013_review.png')\n", "#files.download(\"conf_FER_2013_review.png\")\n", "plt.show(block=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 277 }, "id": "bcVL1ahXdupk", "outputId": "44a2781e-ecd8-41bc-dd20-8750f2dc2243" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " AN 0.6198 0.6640 0.6411 491\n", " DI 0.8039 0.7455 0.7736 55\n", " FE 0.6361 0.3939 0.4865 528\n", " HA 0.9223 0.8908 0.9062 879\n", " NE 0.5935 0.7907 0.6781 626\n", " SA 0.5747 0.5505 0.5623 594\n", " SU 0.7852 0.8173 0.8009 416\n", "\n", " accuracy 0.7021 3589\n", " macro avg 0.7051 0.6932 0.6927 3589\n", "weighted avg 0.7062 0.7021 0.6973 3589\n", "\n" ] }, { "data": { "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from sklearn.metrics import classification_report\n", "print(classification_report(test_generator.classes, predictions.argmax(axis=1),target_names=cm_plot_labels,digits=4))\n", "plt.savefig('classification_report_FER_2013.png')\n", "#files.download(\"classification_report_FER_2013.png\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "d70c8-Q8pAnm", "outputId": "7c159f14-eb50-4306-bfb8-c28a4c10ab66" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/80\n", "113/113 [==============================] - ETA: 0s - loss: 2.3790 - accuracy: 0.1893\n", "Epoch 1: val_accuracy improved from -inf to 0.24492, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_001.hdf5\n", "113/113 [==============================] - 31s 229ms/step - loss: 2.3790 - accuracy: 0.1893 - val_loss: 1.8562 - val_accuracy: 0.2449 - lr: 0.0010\n", "Epoch 2/80\n", "113/113 [==============================] - ETA: 0s - loss: 1.9718 - accuracy: 0.2342\n", "Epoch 2: val_accuracy did not improve from 0.24492\n", "113/113 [==============================] - 24s 215ms/step - loss: 1.9718 - accuracy: 0.2342 - val_loss: 1.9035 - val_accuracy: 0.1683 - lr: 0.0010\n", "Epoch 3/80\n", "113/113 [==============================] - ETA: 0s - loss: 1.8650 - accuracy: 0.2667\n", "Epoch 3: val_accuracy did not improve from 0.24492\n", "113/113 [==============================] - 24s 215ms/step - loss: 1.8650 - accuracy: 0.2667 - val_loss: 2.2366 - val_accuracy: 0.2223 - lr: 0.0010\n", "Epoch 4/80\n", "113/113 [==============================] - ETA: 0s - loss: 1.7899 - accuracy: 0.2993\n", "Epoch 4: val_accuracy did not improve from 0.24492\n", "113/113 [==============================] - 24s 212ms/step - loss: 1.7899 - accuracy: 0.2993 - val_loss: 1.7908 - val_accuracy: 0.2329 - lr: 0.0010\n", "Epoch 5/80\n", "113/113 [==============================] - ETA: 0s - loss: 1.6922 - accuracy: 0.3468\n", "Epoch 5: val_accuracy improved from 0.24492 to 0.38256, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_001.hdf5\n", "113/113 [==============================] - 25s 224ms/step - loss: 1.6922 - accuracy: 0.3468 - val_loss: 1.5814 - val_accuracy: 0.3826 - lr: 0.0010\n", "Epoch 6/80\n", "113/113 [==============================] - ETA: 0s - loss: 1.6080 - accuracy: 0.3894\n", "Epoch 6: val_accuracy did not improve from 0.38256\n", "113/113 [==============================] - 24s 213ms/step - loss: 1.6080 - accuracy: 0.3894 - val_loss: 1.9077 - val_accuracy: 0.3775 - lr: 0.0010\n", "Epoch 7/80\n", "113/113 [==============================] - ETA: 0s - loss: 1.5143 - accuracy: 0.4275\n", "Epoch 7: val_accuracy improved from 0.38256 to 0.44887, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_001.hdf5\n", "113/113 [==============================] - 25s 223ms/step - loss: 1.5143 - accuracy: 0.4275 - val_loss: 1.5088 - val_accuracy: 0.4489 - lr: 0.0010\n", "Epoch 8/80\n", "113/113 [==============================] - ETA: 0s - loss: 1.4331 - accuracy: 0.4515\n", "Epoch 8: val_accuracy improved from 0.44887 to 0.48119, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_001.hdf5\n", "113/113 [==============================] - 28s 246ms/step - loss: 1.4331 - accuracy: 0.4515 - val_loss: 1.4081 - val_accuracy: 0.4812 - lr: 0.0010\n", "Epoch 9/80\n", "113/113 [==============================] - ETA: 0s - loss: 1.3765 - accuracy: 0.4719\n", "Epoch 9: val_accuracy improved from 0.48119 to 0.50766, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_001.hdf5\n", "113/113 [==============================] - 25s 223ms/step - loss: 1.3765 - accuracy: 0.4719 - val_loss: 1.2523 - val_accuracy: 0.5077 - lr: 0.0010\n", "Epoch 10/80\n", "113/113 [==============================] - ETA: 0s - loss: 1.3190 - accuracy: 0.4965\n", "Epoch 10: val_accuracy improved from 0.50766 to 0.54026, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_001.hdf5\n", "113/113 [==============================] - 26s 228ms/step - loss: 1.3190 - accuracy: 0.4965 - val_loss: 1.2426 - val_accuracy: 0.5403 - lr: 0.0010\n", "Epoch 11/80\n", "113/113 [==============================] - ETA: 0s - loss: 1.2884 - accuracy: 0.5081\n", "Epoch 11: val_accuracy improved from 0.54026 to 0.55614, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_001.hdf5\n", "113/113 [==============================] - 26s 227ms/step - loss: 1.2884 - accuracy: 0.5081 - val_loss: 1.1794 - val_accuracy: 0.5561 - lr: 0.0010\n", "Epoch 12/80\n", "113/113 [==============================] - ETA: 0s - loss: 1.2610 - accuracy: 0.5247\n", "Epoch 12: val_accuracy did not improve from 0.55614\n", "113/113 [==============================] - 24s 214ms/step - loss: 1.2610 - accuracy: 0.5247 - val_loss: 1.1293 - val_accuracy: 0.5500 - lr: 0.0010\n", "Epoch 13/80\n", "113/113 [==============================] - ETA: 0s - loss: 1.2295 - accuracy: 0.5337\n", "Epoch 13: val_accuracy improved from 0.55614 to 0.57983, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_001.hdf5\n", "113/113 [==============================] - 25s 221ms/step - loss: 1.2295 - accuracy: 0.5337 - val_loss: 1.0937 - val_accuracy: 0.5798 - lr: 0.0010\n", "Epoch 14/80\n", "113/113 [==============================] - ETA: 0s - loss: 1.2043 - accuracy: 0.5439\n", "Epoch 14: val_accuracy improved from 0.57983 to 0.58568, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_001.hdf5\n", "113/113 [==============================] - 25s 224ms/step - loss: 1.2043 - accuracy: 0.5439 - val_loss: 1.1094 - val_accuracy: 0.5857 - lr: 0.0010\n", "Epoch 15/80\n", "113/113 [==============================] - ETA: 0s - loss: 1.1905 - accuracy: 0.5534\n", "Epoch 15: val_accuracy did not improve from 0.58568\n", "113/113 [==============================] - 24s 213ms/step - loss: 1.1905 - accuracy: 0.5534 - val_loss: 1.1130 - val_accuracy: 0.5756 - lr: 0.0010\n", "Epoch 16/80\n", "113/113 [==============================] - ETA: 0s - loss: 1.1687 - accuracy: 0.5563\n", "Epoch 16: val_accuracy did not improve from 0.58568\n", "113/113 [==============================] - 25s 223ms/step - loss: 1.1687 - accuracy: 0.5563 - val_loss: 1.0651 - val_accuracy: 0.5848 - lr: 0.0010\n", "Epoch 17/80\n", "113/113 [==============================] - ETA: 0s - loss: 1.1569 - accuracy: 0.5637\n", "Epoch 17: val_accuracy improved from 0.58568 to 0.60435, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_001.hdf5\n", "113/113 [==============================] - 25s 223ms/step - loss: 1.1569 - accuracy: 0.5637 - val_loss: 1.0525 - val_accuracy: 0.6043 - lr: 0.0010\n", "Epoch 18/80\n", "113/113 [==============================] - ETA: 0s - loss: 1.1447 - accuracy: 0.5675\n", "Epoch 18: val_accuracy did not improve from 0.60435\n", "113/113 [==============================] - 24s 212ms/step - loss: 1.1447 - accuracy: 0.5675 - val_loss: 1.0847 - val_accuracy: 0.5932 - lr: 0.0010\n", "Epoch 19/80\n", "113/113 [==============================] - ETA: 0s - loss: 1.1409 - accuracy: 0.5681\n", "Epoch 19: val_accuracy did not improve from 0.60435\n", "113/113 [==============================] - 24s 214ms/step - loss: 1.1409 - accuracy: 0.5681 - val_loss: 1.1506 - val_accuracy: 0.5659 - lr: 0.0010\n", "Epoch 20/80\n", "113/113 [==============================] - ETA: 0s - loss: 1.0937 - accuracy: 0.5897\n", "Epoch 20: val_accuracy improved from 0.60435 to 0.61828, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_001.hdf5\n", "113/113 [==============================] - 26s 226ms/step - loss: 1.0937 - accuracy: 0.5897 - val_loss: 0.9921 - val_accuracy: 0.6183 - lr: 5.0000e-04\n", "Epoch 21/80\n", "113/113 [==============================] - ETA: 0s - loss: 1.0785 - accuracy: 0.5925\n", "Epoch 21: val_accuracy improved from 0.61828 to 0.64113, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_001.hdf5\n", "113/113 [==============================] - 25s 226ms/step - loss: 1.0785 - accuracy: 0.5925 - val_loss: 0.9650 - val_accuracy: 0.6411 - lr: 5.0000e-04\n", "Epoch 22/80\n", "113/113 [==============================] - ETA: 0s - loss: 1.0717 - accuracy: 0.5959\n", "Epoch 22: val_accuracy did not improve from 0.64113\n", "113/113 [==============================] - 25s 224ms/step - loss: 1.0717 - accuracy: 0.5959 - val_loss: 0.9845 - val_accuracy: 0.6225 - lr: 5.0000e-04\n", "Epoch 23/80\n", "113/113 [==============================] - ETA: 0s - loss: 1.0590 - accuracy: 0.6014\n", "Epoch 23: val_accuracy did not improve from 0.64113\n", "113/113 [==============================] - 24s 215ms/step - loss: 1.0590 - accuracy: 0.6014 - val_loss: 0.9666 - val_accuracy: 0.6375 - lr: 5.0000e-04\n", "Epoch 24/80\n", "113/113 [==============================] - ETA: 0s - loss: 1.0533 - accuracy: 0.6047\n", "Epoch 24: val_accuracy did not improve from 0.64113\n", "113/113 [==============================] - 24s 213ms/step - loss: 1.0533 - accuracy: 0.6047 - val_loss: 0.9925 - val_accuracy: 0.6303 - lr: 5.0000e-04\n", "Epoch 25/80\n", "113/113 [==============================] - ETA: 0s - loss: 1.0498 - accuracy: 0.6035\n", "Epoch 25: val_accuracy did not improve from 0.64113\n", "113/113 [==============================] - 24s 214ms/step - loss: 1.0498 - accuracy: 0.6035 - val_loss: 0.9689 - val_accuracy: 0.6328 - lr: 5.0000e-04\n", "Epoch 26/80\n", "113/113 [==============================] - ETA: 0s - loss: 1.0399 - accuracy: 0.6101\n", "Epoch 26: val_accuracy improved from 0.64113 to 0.65199, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_001.hdf5\n", "113/113 [==============================] - 25s 223ms/step - loss: 1.0399 - accuracy: 0.6101 - val_loss: 0.9415 - val_accuracy: 0.6520 - lr: 5.0000e-04\n", "Epoch 27/80\n", "113/113 [==============================] - ETA: 0s - loss: 1.0314 - accuracy: 0.6124\n", "Epoch 27: val_accuracy did not improve from 0.65199\n", "113/113 [==============================] - 24s 213ms/step - loss: 1.0314 - accuracy: 0.6124 - val_loss: 0.9478 - val_accuracy: 0.6492 - lr: 5.0000e-04\n", "Epoch 28/80\n", "113/113 [==============================] - ETA: 0s - loss: 1.0277 - accuracy: 0.6156\n", "Epoch 28: val_accuracy did not improve from 0.65199\n", "113/113 [==============================] - 24s 211ms/step - loss: 1.0277 - accuracy: 0.6156 - val_loss: 0.9420 - val_accuracy: 0.6486 - lr: 5.0000e-04\n", "Epoch 29/80\n", "113/113 [==============================] - ETA: 0s - loss: 1.0263 - accuracy: 0.6171\n", "Epoch 29: val_accuracy did not improve from 0.65199\n", "113/113 [==============================] - 24s 212ms/step - loss: 1.0263 - accuracy: 0.6171 - val_loss: 1.0134 - val_accuracy: 0.6255 - lr: 5.0000e-04\n", "Epoch 30/80\n", "113/113 [==============================] - ETA: 0s - loss: 1.0151 - accuracy: 0.6222\n", "Epoch 30: val_accuracy improved from 0.65199 to 0.65617, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_001.hdf5\n", "113/113 [==============================] - 27s 234ms/step - loss: 1.0151 - accuracy: 0.6222 - val_loss: 0.9308 - val_accuracy: 0.6562 - lr: 5.0000e-04\n", "Epoch 31/80\n", "113/113 [==============================] - ETA: 0s - loss: 1.0081 - accuracy: 0.6228\n", "Epoch 31: val_accuracy did not improve from 0.65617\n", "113/113 [==============================] - 24s 213ms/step - loss: 1.0081 - accuracy: 0.6228 - val_loss: 0.9557 - val_accuracy: 0.6431 - lr: 5.0000e-04\n", "Epoch 32/80\n", "113/113 [==============================] - ETA: 0s - loss: 1.0009 - accuracy: 0.6241\n", "Epoch 32: val_accuracy did not improve from 0.65617\n", "113/113 [==============================] - 24s 213ms/step - loss: 1.0009 - accuracy: 0.6241 - val_loss: 0.9562 - val_accuracy: 0.6467 - lr: 5.0000e-04\n", "Epoch 33/80\n", "113/113 [==============================] - ETA: 0s - loss: 1.0077 - accuracy: 0.6236\n", "Epoch 33: val_accuracy did not improve from 0.65617\n", "113/113 [==============================] - 24s 212ms/step - loss: 1.0077 - accuracy: 0.6236 - val_loss: 0.9528 - val_accuracy: 0.6436 - lr: 5.0000e-04\n", "Epoch 34/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.9944 - accuracy: 0.6276\n", "Epoch 34: val_accuracy did not improve from 0.65617\n", "113/113 [==============================] - 24s 212ms/step - loss: 0.9944 - accuracy: 0.6276 - val_loss: 0.9323 - val_accuracy: 0.6534 - lr: 5.0000e-04\n", "Epoch 35/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.9890 - accuracy: 0.6275\n", "Epoch 35: val_accuracy did not improve from 0.65617\n", "113/113 [==============================] - 24s 210ms/step - loss: 0.9890 - accuracy: 0.6275 - val_loss: 0.9511 - val_accuracy: 0.6470 - lr: 5.0000e-04\n", "Epoch 36/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.9816 - accuracy: 0.6322\n", "Epoch 36: val_accuracy improved from 0.65617 to 0.66732, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_001.hdf5\n", "113/113 [==============================] - 25s 224ms/step - loss: 0.9816 - accuracy: 0.6322 - val_loss: 0.9238 - val_accuracy: 0.6673 - lr: 5.0000e-04\n", "Epoch 37/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.9766 - accuracy: 0.6321\n", "Epoch 37: val_accuracy did not improve from 0.66732\n", "113/113 [==============================] - 24s 213ms/step - loss: 0.9766 - accuracy: 0.6321 - val_loss: 0.9610 - val_accuracy: 0.6442 - lr: 5.0000e-04\n", "Epoch 38/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.9726 - accuracy: 0.6347\n", "Epoch 38: val_accuracy did not improve from 0.66732\n", "113/113 [==============================] - 24s 213ms/step - loss: 0.9726 - accuracy: 0.6347 - val_loss: 0.9338 - val_accuracy: 0.6523 - lr: 5.0000e-04\n", "Epoch 39/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.9719 - accuracy: 0.6325\n", "Epoch 39: val_accuracy did not improve from 0.66732\n", "113/113 [==============================] - 25s 223ms/step - loss: 0.9719 - accuracy: 0.6325 - val_loss: 0.9379 - val_accuracy: 0.6531 - lr: 5.0000e-04\n", "Epoch 40/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.9481 - accuracy: 0.6440\n", "Epoch 40: val_accuracy did not improve from 0.66732\n", "113/113 [==============================] - 24s 212ms/step - loss: 0.9481 - accuracy: 0.6440 - val_loss: 0.9178 - val_accuracy: 0.6573 - lr: 2.5000e-04\n", "Epoch 41/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.9384 - accuracy: 0.6504\n", "Epoch 41: val_accuracy did not improve from 0.66732\n", "113/113 [==============================] - 24s 211ms/step - loss: 0.9384 - accuracy: 0.6504 - val_loss: 0.9706 - val_accuracy: 0.6411 - lr: 2.5000e-04\n", "Epoch 42/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.9302 - accuracy: 0.6515\n", "Epoch 42: val_accuracy did not improve from 0.66732\n", "113/113 [==============================] - 24s 213ms/step - loss: 0.9302 - accuracy: 0.6515 - val_loss: 0.9526 - val_accuracy: 0.6475 - lr: 2.5000e-04\n", "Epoch 43/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.9262 - accuracy: 0.6525\n", "Epoch 43: val_accuracy improved from 0.66732 to 0.67150, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_001.hdf5\n", "113/113 [==============================] - 25s 223ms/step - loss: 0.9262 - accuracy: 0.6525 - val_loss: 0.8984 - val_accuracy: 0.6715 - lr: 2.5000e-04\n", "Epoch 44/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.9161 - accuracy: 0.6590\n", "Epoch 44: val_accuracy did not improve from 0.67150\n", "113/113 [==============================] - 24s 214ms/step - loss: 0.9161 - accuracy: 0.6590 - val_loss: 0.9121 - val_accuracy: 0.6612 - lr: 2.5000e-04\n", "Epoch 45/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.9082 - accuracy: 0.6593\n", "Epoch 45: val_accuracy did not improve from 0.67150\n", "113/113 [==============================] - 24s 212ms/step - loss: 0.9082 - accuracy: 0.6593 - val_loss: 0.9082 - val_accuracy: 0.6659 - lr: 2.5000e-04\n", "Epoch 46/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.9056 - accuracy: 0.6583\n", "Epoch 46: val_accuracy improved from 0.67150 to 0.67373, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_001.hdf5\n", "113/113 [==============================] - 27s 235ms/step - loss: 0.9056 - accuracy: 0.6583 - val_loss: 0.9043 - val_accuracy: 0.6737 - lr: 2.5000e-04\n", "Epoch 47/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.9062 - accuracy: 0.6608\n", "Epoch 47: val_accuracy did not improve from 0.67373\n", "113/113 [==============================] - 24s 213ms/step - loss: 0.9062 - accuracy: 0.6608 - val_loss: 0.9184 - val_accuracy: 0.6698 - lr: 2.5000e-04\n", "Epoch 48/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.9007 - accuracy: 0.6647\n", "Epoch 48: val_accuracy did not improve from 0.67373\n", "113/113 [==============================] - 24s 213ms/step - loss: 0.9007 - accuracy: 0.6647 - val_loss: 0.9049 - val_accuracy: 0.6673 - lr: 2.5000e-04\n", "Epoch 49/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.8936 - accuracy: 0.6676\n", "Epoch 49: val_accuracy did not improve from 0.67373\n", "113/113 [==============================] - 24s 212ms/step - loss: 0.8936 - accuracy: 0.6676 - val_loss: 0.9427 - val_accuracy: 0.6576 - lr: 2.5000e-04\n", "Epoch 50/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.8969 - accuracy: 0.6630\n", "Epoch 50: val_accuracy did not improve from 0.67373\n", "113/113 [==============================] - 24s 212ms/step - loss: 0.8969 - accuracy: 0.6630 - val_loss: 0.9095 - val_accuracy: 0.6640 - lr: 2.5000e-04\n", "Epoch 51/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.8935 - accuracy: 0.6651\n", "Epoch 51: val_accuracy did not improve from 0.67373\n", "113/113 [==============================] - 24s 213ms/step - loss: 0.8935 - accuracy: 0.6651 - val_loss: 0.9177 - val_accuracy: 0.6701 - lr: 2.5000e-04\n", "Epoch 52/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.8922 - accuracy: 0.6657\n", "Epoch 52: val_accuracy did not improve from 0.67373\n", "113/113 [==============================] - 24s 213ms/step - loss: 0.8922 - accuracy: 0.6657 - val_loss: 0.8938 - val_accuracy: 0.6709 - lr: 2.5000e-04\n", "Epoch 53/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.8830 - accuracy: 0.6725\n", "Epoch 53: val_accuracy did not improve from 0.67373\n", "113/113 [==============================] - 24s 211ms/step - loss: 0.8830 - accuracy: 0.6725 - val_loss: 0.9078 - val_accuracy: 0.6634 - lr: 2.5000e-04\n", "Epoch 54/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.8805 - accuracy: 0.6692\n", "Epoch 54: val_accuracy improved from 0.67373 to 0.67512, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_001.hdf5\n", "113/113 [==============================] - 27s 235ms/step - loss: 0.8805 - accuracy: 0.6692 - val_loss: 0.8957 - val_accuracy: 0.6751 - lr: 2.5000e-04\n", "Epoch 55/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.8750 - accuracy: 0.6749\n", "Epoch 55: val_accuracy did not improve from 0.67512\n", "113/113 [==============================] - 24s 215ms/step - loss: 0.8750 - accuracy: 0.6749 - val_loss: 0.9194 - val_accuracy: 0.6645 - lr: 2.5000e-04\n", "Epoch 56/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.8747 - accuracy: 0.6738\n", "Epoch 56: val_accuracy did not improve from 0.67512\n", "113/113 [==============================] - 24s 213ms/step - loss: 0.8747 - accuracy: 0.6738 - val_loss: 0.9116 - val_accuracy: 0.6737 - lr: 2.5000e-04\n", "Epoch 57/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.8710 - accuracy: 0.6752\n", "Epoch 57: val_accuracy did not improve from 0.67512\n", "113/113 [==============================] - 24s 214ms/step - loss: 0.8710 - accuracy: 0.6752 - val_loss: 0.9180 - val_accuracy: 0.6687 - lr: 2.5000e-04\n", "Epoch 58/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.8619 - accuracy: 0.6791\n", "Epoch 58: val_accuracy did not improve from 0.67512\n", "113/113 [==============================] - 24s 213ms/step - loss: 0.8619 - accuracy: 0.6791 - val_loss: 0.9415 - val_accuracy: 0.6656 - lr: 2.5000e-04\n", "Epoch 59/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.8627 - accuracy: 0.6809\n", "Epoch 59: val_accuracy improved from 0.67512 to 0.68013, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_001.hdf5\n", "113/113 [==============================] - 25s 224ms/step - loss: 0.8627 - accuracy: 0.6809 - val_loss: 0.8900 - val_accuracy: 0.6801 - lr: 2.5000e-04\n", "Epoch 60/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.8495 - accuracy: 0.6806\n", "Epoch 60: val_accuracy did not improve from 0.68013\n", "113/113 [==============================] - 24s 216ms/step - loss: 0.8495 - accuracy: 0.6806 - val_loss: 0.9007 - val_accuracy: 0.6723 - lr: 1.2500e-04\n", "Epoch 61/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.8412 - accuracy: 0.6870\n", "Epoch 61: val_accuracy did not improve from 0.68013\n", "113/113 [==============================] - 24s 215ms/step - loss: 0.8412 - accuracy: 0.6870 - val_loss: 0.8942 - val_accuracy: 0.6782 - lr: 1.2500e-04\n", "Epoch 62/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.8352 - accuracy: 0.6883\n", "Epoch 62: val_accuracy did not improve from 0.68013\n", "113/113 [==============================] - 25s 222ms/step - loss: 0.8352 - accuracy: 0.6883 - val_loss: 0.8903 - val_accuracy: 0.6776 - lr: 1.2500e-04\n", "Epoch 63/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.8321 - accuracy: 0.6898\n", "Epoch 63: val_accuracy improved from 0.68013 to 0.68348, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_001.hdf5\n", "113/113 [==============================] - 25s 223ms/step - loss: 0.8321 - accuracy: 0.6898 - val_loss: 0.8959 - val_accuracy: 0.6835 - lr: 1.2500e-04\n", "Epoch 64/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.8233 - accuracy: 0.6922\n", "Epoch 64: val_accuracy did not improve from 0.68348\n", "113/113 [==============================] - 24s 215ms/step - loss: 0.8233 - accuracy: 0.6922 - val_loss: 0.8855 - val_accuracy: 0.6787 - lr: 1.2500e-04\n", "Epoch 65/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.8288 - accuracy: 0.6923\n", "Epoch 65: val_accuracy did not improve from 0.68348\n", "113/113 [==============================] - 24s 214ms/step - loss: 0.8288 - accuracy: 0.6923 - val_loss: 0.9072 - val_accuracy: 0.6796 - lr: 1.2500e-04\n", "Epoch 66/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.8276 - accuracy: 0.6920\n", "Epoch 66: val_accuracy did not improve from 0.68348\n", "113/113 [==============================] - 24s 214ms/step - loss: 0.8276 - accuracy: 0.6920 - val_loss: 0.9104 - val_accuracy: 0.6754 - lr: 1.2500e-04\n", "Epoch 67/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.8198 - accuracy: 0.6942\n", "Epoch 67: val_accuracy did not improve from 0.68348\n", "113/113 [==============================] - 24s 215ms/step - loss: 0.8198 - accuracy: 0.6942 - val_loss: 0.9104 - val_accuracy: 0.6707 - lr: 1.2500e-04\n", "Epoch 68/80\n", "112/113 [============================>.] - ETA: 0s - loss: 0.8148 - accuracy: 0.6965\n", "Epoch 68: val_accuracy did not improve from 0.68348\n", "113/113 [==============================] - 24s 216ms/step - loss: 0.8148 - accuracy: 0.6965 - val_loss: 0.9044 - val_accuracy: 0.6734 - lr: 1.2500e-04\n", "Epoch 69/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.8156 - accuracy: 0.6997\n", "Epoch 69: val_accuracy did not improve from 0.68348\n", "113/113 [==============================] - 24s 216ms/step - loss: 0.8156 - accuracy: 0.6997 - val_loss: 0.9059 - val_accuracy: 0.6807 - lr: 1.2500e-04\n", "Epoch 70/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.8202 - accuracy: 0.6939\n", "Epoch 70: val_accuracy did not improve from 0.68348\n", "113/113 [==============================] - 26s 226ms/step - loss: 0.8202 - accuracy: 0.6939 - val_loss: 0.9056 - val_accuracy: 0.6737 - lr: 1.2500e-04\n", "Epoch 71/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.8132 - accuracy: 0.6974\n", "Epoch 71: val_accuracy did not improve from 0.68348\n", "113/113 [==============================] - 24s 213ms/step - loss: 0.8132 - accuracy: 0.6974 - val_loss: 0.9252 - val_accuracy: 0.6718 - lr: 1.2500e-04\n", "Epoch 72/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.8118 - accuracy: 0.6974\n", "Epoch 72: val_accuracy did not improve from 0.68348\n", "113/113 [==============================] - 24s 211ms/step - loss: 0.8118 - accuracy: 0.6974 - val_loss: 0.8957 - val_accuracy: 0.6782 - lr: 1.2500e-04\n", "Epoch 73/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.8114 - accuracy: 0.7008\n", "Epoch 73: val_accuracy improved from 0.68348 to 0.68376, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_001.hdf5\n", "113/113 [==============================] - 25s 224ms/step - loss: 0.8114 - accuracy: 0.7008 - val_loss: 0.8997 - val_accuracy: 0.6838 - lr: 1.2500e-04\n", "Epoch 74/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.8020 - accuracy: 0.7021\n", "Epoch 74: val_accuracy did not improve from 0.68376\n", "113/113 [==============================] - 24s 213ms/step - loss: 0.8020 - accuracy: 0.7021 - val_loss: 0.8962 - val_accuracy: 0.6818 - lr: 1.2500e-04\n", "Epoch 75/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.8022 - accuracy: 0.7017\n", "Epoch 75: val_accuracy improved from 0.68376 to 0.68431, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_001.hdf5\n", "113/113 [==============================] - 25s 223ms/step - loss: 0.8022 - accuracy: 0.7017 - val_loss: 0.8853 - val_accuracy: 0.6843 - lr: 1.2500e-04\n", "Epoch 76/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.7946 - accuracy: 0.7055\n", "Epoch 76: val_accuracy did not improve from 0.68431\n", "113/113 [==============================] - 24s 212ms/step - loss: 0.7946 - accuracy: 0.7055 - val_loss: 0.8955 - val_accuracy: 0.6799 - lr: 1.2500e-04\n", "Epoch 77/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.7995 - accuracy: 0.7013\n", "Epoch 77: val_accuracy did not improve from 0.68431\n", "113/113 [==============================] - 24s 213ms/step - loss: 0.7995 - accuracy: 0.7013 - val_loss: 0.9003 - val_accuracy: 0.6832 - lr: 1.2500e-04\n", "Epoch 78/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.7923 - accuracy: 0.7069\n", "Epoch 78: val_accuracy did not improve from 0.68431\n", "113/113 [==============================] - 25s 223ms/step - loss: 0.7923 - accuracy: 0.7069 - val_loss: 0.9003 - val_accuracy: 0.6771 - lr: 1.2500e-04\n", "Epoch 79/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.7890 - accuracy: 0.7044\n", "Epoch 79: val_accuracy did not improve from 0.68431\n", "113/113 [==============================] - 24s 213ms/step - loss: 0.7890 - accuracy: 0.7044 - val_loss: 0.8852 - val_accuracy: 0.6843 - lr: 1.2500e-04\n", "Epoch 80/80\n", "113/113 [==============================] - ETA: 0s - loss: 0.7842 - accuracy: 0.7060\n", "Epoch 80: val_accuracy did not improve from 0.68431\n", "113/113 [==============================] - 24s 213ms/step - loss: 0.7842 - accuracy: 0.7060 - val_loss: 0.8932 - val_accuracy: 0.6838 - lr: 6.2500e-05\n" ] } ], "source": [ "from keras.optimizers import RMSprop,SGD,Adam\n", "import math\n", "from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau, LearningRateScheduler\n", "import os\n", "#filepath=\"/content/drive/My Drive/fer_new_vgg/fold1_jaffe_epochs:{epoch:03d}-accuracy:{val_accuracy:.4f}.hdf5\"\n", "\n", "filepath=\"/content/drive/My Drive/fer_new_vgg/fer_peerj_001.hdf5\"\n", "checkpoint_dir = os.path.dirname(filepath)\n", "initial_lrate = 0.001\n", "def step_decay(epoch): \n", " drop = 0.5\n", " epochs_drop = 20.0\n", " lrate = initial_lrate * math.pow(drop, math.floor((1+epoch)/epochs_drop))\n", " return lrate\n", "lrate = LearningRateScheduler(step_decay)\n", "checkpoint = ModelCheckpoint(filepath,\n", " monitor='val_accuracy',\n", " mode='max',\n", " save_best_only=True,\n", " verbose=2)\n", "\n", "callbacks = [checkpoint, lrate]\n", "\n", "model.compile(loss='categorical_crossentropy',\n", " optimizer = Adam(learning_rate=0.001),\n", " metrics=['accuracy'])\n", "\n", "epochs=80\n", "\n", "history=model.fit(\n", " train_generator,\n", " steps_per_epoch=train_sample//batch_size+1,\n", " epochs=epochs,\n", " callbacks=callbacks,\n", " validation_data=validation_generator,\n", " validation_steps=valid_sample//batch_size+1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "A0hXK_IIIiSn" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 404 }, "id": "-F4ABUfHIipA", "outputId": "91dd0b12-7586-4589-ebe8-0c99c4713cd1" }, "outputs": [ { "data": { "application/javascript": [ "\n", " async function download(id, filename, size) {\n", " if (!google.colab.kernel.accessAllowed) {\n", " return;\n", " }\n", " const div = document.createElement('div');\n", " const label = document.createElement('label');\n", " label.textContent = `Downloading \"${filename}\": `;\n", " div.appendChild(label);\n", " const progress = document.createElement('progress');\n", " progress.max = size;\n", " div.appendChild(progress);\n", " document.body.appendChild(div);\n", "\n", " const buffers = [];\n", " let downloaded = 0;\n", "\n", " const channel = await google.colab.kernel.comms.open(id);\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", "\n", " for await (const message of channel.messages) {\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", " if (message.buffers) {\n", " for (const buffer of message.buffers) {\n", " buffers.push(buffer);\n", " downloaded += buffer.byteLength;\n", " progress.value = downloaded;\n", " }\n", " }\n", " }\n", " const blob = new Blob(buffers, {type: 'application/binary'});\n", " const a = document.createElement('a');\n", " a.href = window.URL.createObjectURL(blob);\n", " a.download = filename;\n", " div.appendChild(a);\n", " a.click();\n", " div.remove();\n", " }\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from google.colab import files\n", "loss_train = history.history['accuracy']\n", "loss_val = history.history['val_accuracy']\n", "epochs = range(1,151)\n", "plt.figure(figsize=(8,6))\n", "plt.rcParams['font.size'] = 18\n", "plt.plot(epochs, loss_train, 'g--', label='Training accuracy')\n", "plt.plot(epochs, loss_val, 'b', label='validation accuracy')\n", "#plt.title('Training and Validation accuracy')\n", "plt.xlabel('Epochs',fontsize=18)\n", "plt.ylabel('Accuracy',fontsize=18)\n", "plt.legend()\n", "plt.savefig('acc_FER_2013_review.png')\n", "files.download(\"acc_FER_2013_review.png\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "yWUiKcwlsSIA" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "EOPpq8K6IipD" }, "outputs": [], "source": [ "model.load_weights('/content/drive/My Drive/fer_new_vgg/fer_peerj_review_adam03.hdf5') " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "background_save": true }, "id": "-cQYvSxEIipD" }, "outputs": [], "source": [ "predictions = model.predict(test_generator, steps=test_sample//batch_size+1, verbose=0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "JqY75nGkIipF" }, "outputs": [], "source": [ "from google.colab import files" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "background_save": true }, "id": "8F4mWoWjIipG" }, "outputs": [], "source": [ "cm_plot_labels = ['AN',\n", " 'DI',\n", " 'FE',\n", " 'HA',\n", " 'NE',\n", " 'SA',\n", " 'SU']\n", "from sklearn.metrics import confusion_matrix\n", "import seaborn as sns\n", "\n", "cm = confusion_matrix(test_generator.classes, predictions.argmax(axis=1))\n", "# Normalise\n", "cmn = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n", "\n", "fig, ax = plt.subplots(figsize=(10,6))\n", "p=sns.heatmap(cmn, annot=True, fmt='.2f', xticklabels=cm_plot_labels, yticklabels=cm_plot_labels)\n", "sns.set(font_scale=5)\n", "p.set_xticklabels(p.get_xmajorticklabels(), fontsize = 17)\n", "p.set_yticklabels(p.get_ymajorticklabels(), fontsize = 17)\n", "plt.ylabel('Actual', fontsize=18)\n", "plt.xlabel('Predicted', fontsize=18)\n", "plt.savefig('conf_FER_2013_review.png')\n", "files.download(\"conf_FER_2013_review.png\")\n", "plt.show(block=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Ghf3z1G0IipG" }, "outputs": [], "source": [ "from sklearn.metrics import classification_report\n", "print(classification_report(test_generator.classes, predictions.argmax(axis=1),target_names=cm_plot_labels,digits=4))\n", "plt.savefig('classification_report_FER_2013.png')\n", "files.download(\"classification_report_FER_2013.png\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "MOt-W53dzfrH" }, "outputs": [], "source": [ "adam_train_102 = history.history['accuracy']\n", "adam_val_102 = history.history['val_accuracy']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "tCbNlJxGz24q" }, "outputs": [], "source": [ "adam_train_102_loss = history.history['loss']\n", "adam_val_102_loss = history.history['val_loss']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "hEExXhhX_WHX", "outputId": "3f3ce8da-bcc3-42d9-b33d-3977058f7bfc" }, "outputs": [ { "data": { "text/plain": [ "[1.8562263250350952,\n", " 1.9034936428070068,\n", " 2.236605167388916,\n", " 1.7907860279083252,\n", " 1.5813572406768799,\n", " 1.9076611995697021,\n", " 1.5087940692901611,\n", " 1.408145546913147,\n", " 1.2522530555725098,\n", " 1.2426210641860962,\n", " 1.179383397102356,\n", " 1.1292723417282104,\n", " 1.0937449932098389,\n", " 1.1093641519546509,\n", " 1.1129637956619263,\n", " 1.0651054382324219,\n", " 1.0525412559509277,\n", " 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"code", "execution_count": null, "metadata": { "id": "p5F07iNBJG4k" }, "outputs": [], "source": [ "step_train_102 = history.history['accuracy']\n", "step_val_102 = history.history['val_accuracy']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "NcBllKL9JLc9" }, "outputs": [], "source": [ "step_train_102_loss=[2.4112496376037598,\n", " 1.99918794631958,\n", " 1.8865187168121338,\n", " 1.7922449111938477,\n", " 1.7133780717849731,\n", " 1.6188770532608032,\n", " 1.5517560243606567,\n", " 1.44344961643219,\n", " 1.3839056491851807,\n", " 1.3339000940322876,\n", " 1.285117745399475,\n", " 1.2696458101272583,\n", " 1.2423356771469116,\n", " 1.2296360731124878,\n", " 1.2028957605361938,\n", " 1.1863703727722168,\n", " 1.1703218221664429,\n", " 1.1643704175949097,\n", " 1.151103138923645,\n", " 1.1017792224884033,\n", " 1.0863420963287354,\n", " 1.0752266645431519,\n", " 1.0781805515289307,\n", " 1.062042236328125,\n", " 1.0568362474441528,\n", " 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#from google.colab import files\n", "#loss_train = history.history['loss']\n", "#loss_val = history.history['val_loss']\n", "epochs = range(1,151)\n", "plt.figure(figsize=(8,6))\n", "plt.plot(epochs, step_train_102_loss, 'g--', label='Training loss')\n", "plt.plot(epochs, step_val_102_loss, 'b', label='validation loss')\n", "#plt.title('Training and Validation accuracy')\n", "plt.xlabel('Epochs',fontsize=18)\n", "plt.ylabel('Loss',fontsize=18)\n", "plt.legend()\n", "plt.savefig('loss_FER_2013_review_step.png')\n", "#files.download(\"loss_FER_2013_review_step.png\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 404 }, "id": "Mq-Wc8fM8X6f", "outputId": "bcbfb7b2-32bf-49a3-a4df-c7ef02ef1604" }, "outputs": [ { "data": { "application/javascript": [ "\n", " async function download(id, filename, size) {\n", " if (!google.colab.kernel.accessAllowed) {\n", " return;\n", " }\n", " const div = document.createElement('div');\n", " const label = document.createElement('label');\n", " label.textContent = `Downloading \"${filename}\": `;\n", " div.appendChild(label);\n", " const progress = document.createElement('progress');\n", " progress.max = size;\n", " div.appendChild(progress);\n", " document.body.appendChild(div);\n", "\n", " const buffers = [];\n", " let downloaded = 0;\n", "\n", " const channel = await google.colab.kernel.comms.open(id);\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", "\n", " for await (const message of channel.messages) {\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", " if (message.buffers) {\n", " for (const buffer of message.buffers) {\n", " buffers.push(buffer);\n", " downloaded += buffer.byteLength;\n", " progress.value = downloaded;\n", " }\n", " }\n", " }\n", " const blob = new 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from google.colab import files\n", "epochs = range(1,81)\n", "plt.figure(figsize=(8,6))\n", "plt.rcParams['font.size'] = 18\n", "plt.plot(epochs, adam_val_101_loss, 'g--', label='LR_01')\n", "plt.plot(epochs, adam_val_102_loss, 'r--', label='LR_001')\n", "plt.plot(epochs, adam_val_103_loss, 'm--', label='LR_0001')\n", "plt.plot(epochs, adam_val_104_loss, 'b--', label='LR_00001')\n", "#plt.title('Training accuracy')\n", "plt.xlabel('Epochs')\n", "plt.ylabel('Validation Loss')\n", "plt.legend()\n", "plt.savefig('loss_LR_comparison.png')\n", "files.download(\"loss_LR_comparison.png\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 296 }, "id": "jts7qwnyMo-K", "outputId": "67b9fced-64dc-4616-a591-1ec0eefe3f83" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING:matplotlib.legend:No handles with labels found to put in legend.\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "epochs = range(1,81)\n", "plt.figure(figsize=(5,4))\n", "plt.plot(epochs, adam_val_102_loss, 'r--')\n", "plt.plot(epochs, adam_val_101_loss, 'g--')\n", "plt.plot(epochs, adam_val_103_loss, 'm--')\n", "plt.plot(epochs, adam_val_104_loss, 'b--')\n", "#plt.title('Training accuracy')\n", "plt.xlabel('Epochs')\n", "plt.ylabel('Validation Loss')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 404 }, "id": "QS4V_Z7TMbGV", "outputId": "e59182d9-39fb-4912-c376-529dbafb6cdf" }, "outputs": [ { "data": { "application/javascript": [ "\n", " async function download(id, filename, size) {\n", " if (!google.colab.kernel.accessAllowed) {\n", " return;\n", " }\n", " const div = document.createElement('div');\n", " const label = document.createElement('label');\n", " label.textContent = `Downloading \"${filename}\": `;\n", " div.appendChild(label);\n", " const progress = document.createElement('progress');\n", " progress.max = size;\n", " div.appendChild(progress);\n", " document.body.appendChild(div);\n", "\n", " const buffers = [];\n", " let downloaded = 0;\n", "\n", " const channel = await google.colab.kernel.comms.open(id);\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", "\n", " for await (const message of channel.messages) {\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", " if (message.buffers) {\n", " for (const buffer of message.buffers) {\n", " buffers.push(buffer);\n", " downloaded += buffer.byteLength;\n", " progress.value = downloaded;\n", " }\n", " }\n", " }\n", " const blob = new Blob(buffers, {type: 'application/binary'});\n", " const a = document.createElement('a');\n", " a.href = window.URL.createObjectURL(blob);\n", " a.download = filename;\n", " div.appendChild(a);\n", " a.click();\n", " 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "epochs = range(1,81)\n", "plt.figure(figsize=(8,6))\n", "plt.rcParams['font.size'] = 18\n", "plt.plot(epochs, adam_val_101, 'g--', label='LR_01')\n", "plt.plot(epochs, adam_val_102, 'r--', label='LR_001')\n", "plt.plot(epochs, adam_val_103, 'm--', label='LR_0001')\n", "plt.plot(epochs, adam_val_104, 'b--', label='LR_00001')\n", "#plt.title('Training accuracy')\n", "plt.xlabel('Epochs')\n", "plt.ylabel('Validation Accuracy')\n", "plt.legend()\n", "plt.savefig('Acc_LR_comparison.png')\n", "files.download(\"Acc_LR_comparison.png\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "trkudxjSsj4E", "outputId": "a1579a7f-d42f-4260-99a7-1c9671a67678" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found 28709 images belonging to 7 classes.\n", "Found 3589 images belonging to 7 classes.\n", "Found 3589 images belonging to 7 classes.\n" ] } ], "source": [ "seed=1\n", "batch_size=256\n", "train_sample=28709\n", "valid_sample=3589\n", "test_sample=3589\n", "img_rows,img_cols=48,48\n", "#train_path = 'sorted_set_3_frame_split_8_2_new/train'\n", "#valid_path = 'sorted_set_3_frame_split_8_2_new/val'\n", "#test_path = 'sorted_set_3_frame_split_8_2_new/val'\n", "train_path = 'er/Training'\n", "valid_path = 'er/PrivateTest'\n", "test_path = 'er/PrivateTest'\n", "\"\"\"\n", "train_datagen = ImageDataGenerator(\n", "\t\t\t\t\trescale=1./255,\n", "\t\t\t\t\trotation_range=30,\n", "\t\t\t\t\tshear_range=0.3,\n", "\t\t\t\t\tzoom_range=0.3,\n", "\t\t\t\t\twidth_shift_range=0.3,\n", "\t\t\t\t\theight_shift_range=0.3,\n", "\t\t\t\t\thorizontal_flip=True,\n", "\t\t\t\t\tfill_mode='nearest')\n", "\"\"\"\n", "validation_datagen = ImageDataGenerator(rescale=1./255)\n", "\n", "train_generator = ImageDataGenerator(rescale=1./255).flow_from_directory(\n", "\t\t\t\t\ttrain_path,\n", " color_mode='grayscale',\n", "\t\t\t\t\ttarget_size=(img_rows,img_cols),\n", "\t\t\t\t\tbatch_size=batch_size,\n", "\t\t\t\t\tclass_mode='categorical',\n", "\t\t\t\t\tshuffle=True)\n", "\n", "validation_generator = validation_datagen.flow_from_directory(\n", "\t\t\t\t\t\t\tvalid_path,\n", "\t\t\t\t\t\t\tcolor_mode='grayscale',\n", "\t\t\t\t\t\t\ttarget_size=(img_rows,img_cols),\n", "\t\t\t\t\t\t\tbatch_size=batch_size,\n", "\t\t\t\t\t\t\tclass_mode='categorical',\n", "\t\t\t\t\t\t\tshuffle=True)\n", "test_generator= ImageDataGenerator(rescale=1./255).flow_from_directory(valid_path, \n", "\t\t\t\t\t\t\tcolor_mode='grayscale',\n", "\t\t\t\t\t\t\ttarget_size=(img_rows,img_cols),\n", "\t\t\t\t\t\t\tbatch_size=batch_size,\n", "\t\t\t\t\t\t\tclass_mode='categorical',\n", "\t\t\t\t\t\t\tshuffle=False)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "g9Wb5UNKtTmb", "outputId": "9dba1390-bab1-48b0-866b-c74a9b7c8e72" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/150\n", "113/113 [==============================] - ETA: 0s - loss: 2.3530 - accuracy: 0.2002\n", "Epoch 1: val_accuracy improved from -inf to 0.24492, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_wo_aug.hdf5\n", "113/113 [==============================] - 31s 168ms/step - loss: 2.3530 - accuracy: 0.2002 - val_loss: 2.1678 - val_accuracy: 0.2449 - lr: 0.0010\n", "Epoch 2/150\n", "113/113 [==============================] - ETA: 0s - loss: 1.8179 - accuracy: 0.3142\n", "Epoch 2: val_accuracy did not improve from 0.24492\n", "113/113 [==============================] - 16s 138ms/step - loss: 1.8179 - accuracy: 0.3142 - val_loss: 1.9118 - val_accuracy: 0.1747 - lr: 0.0010\n", "Epoch 3/150\n", "113/113 [==============================] - ETA: 0s - loss: 1.5790 - accuracy: 0.4120\n", "Epoch 3: val_accuracy improved from 0.24492 to 0.26916, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_wo_aug.hdf5\n", "113/113 [==============================] - 17s 147ms/step - loss: 1.5790 - accuracy: 0.4120 - val_loss: 1.7962 - val_accuracy: 0.2692 - lr: 0.0010\n", "Epoch 4/150\n", "113/113 [==============================] - ETA: 0s - loss: 1.4125 - accuracy: 0.4794\n", "Epoch 4: val_accuracy did not improve from 0.26916\n", "113/113 [==============================] - 16s 139ms/step - loss: 1.4125 - accuracy: 0.4794 - val_loss: 2.1135 - val_accuracy: 0.1733 - lr: 0.0010\n", "Epoch 5/150\n", "113/113 [==============================] - ETA: 0s - loss: 1.2800 - accuracy: 0.5136\n", "Epoch 5: val_accuracy improved from 0.26916 to 0.48370, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_wo_aug.hdf5\n", "113/113 [==============================] - 17s 148ms/step - loss: 1.2800 - accuracy: 0.5136 - val_loss: 1.3788 - val_accuracy: 0.4837 - lr: 0.0010\n", "Epoch 6/150\n", "113/113 [==============================] - ETA: 0s - loss: 1.1901 - accuracy: 0.5523\n", "Epoch 6: val_accuracy improved from 0.48370 to 0.54834, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_wo_aug.hdf5\n", "113/113 [==============================] - 17s 149ms/step - loss: 1.1901 - accuracy: 0.5523 - val_loss: 1.2069 - val_accuracy: 0.5483 - lr: 0.0010\n", "Epoch 7/150\n", "113/113 [==============================] - ETA: 0s - loss: 1.1324 - accuracy: 0.5714\n", "Epoch 7: val_accuracy improved from 0.54834 to 0.55419, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_wo_aug.hdf5\n", "113/113 [==============================] - 17s 150ms/step - loss: 1.1324 - accuracy: 0.5714 - val_loss: 1.1937 - val_accuracy: 0.5542 - lr: 0.0010\n", "Epoch 8/150\n", "113/113 [==============================] - ETA: 0s - loss: 1.0768 - accuracy: 0.5948\n", "Epoch 8: val_accuracy improved from 0.55419 to 0.56701, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_wo_aug.hdf5\n", "113/113 [==============================] - 17s 150ms/step - loss: 1.0768 - accuracy: 0.5948 - val_loss: 1.1707 - val_accuracy: 0.5670 - lr: 0.0010\n", "Epoch 9/150\n", "113/113 [==============================] - ETA: 0s - loss: 1.0229 - accuracy: 0.6127\n", "Epoch 9: val_accuracy improved from 0.56701 to 0.59627, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_wo_aug.hdf5\n", "113/113 [==============================] - 17s 152ms/step - loss: 1.0229 - accuracy: 0.6127 - val_loss: 1.0907 - val_accuracy: 0.5963 - lr: 0.0010\n", "Epoch 10/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.9881 - accuracy: 0.6330\n", "Epoch 10: val_accuracy did not improve from 0.59627\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.9881 - accuracy: 0.6330 - val_loss: 1.1407 - val_accuracy: 0.5829 - lr: 0.0010\n", "Epoch 11/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.9401 - accuracy: 0.6493\n", "Epoch 11: val_accuracy did not improve from 0.59627\n", "113/113 [==============================] - 17s 147ms/step - loss: 0.9401 - accuracy: 0.6493 - val_loss: 1.1275 - val_accuracy: 0.5874 - lr: 0.0010\n", "Epoch 12/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.9093 - accuracy: 0.6615\n", "Epoch 12: val_accuracy improved from 0.59627 to 0.61215, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_wo_aug.hdf5\n", "113/113 [==============================] - 19s 168ms/step - loss: 0.9093 - accuracy: 0.6615 - val_loss: 1.0297 - val_accuracy: 0.6121 - lr: 0.0010\n", "Epoch 13/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.8683 - accuracy: 0.6799\n", "Epoch 13: val_accuracy improved from 0.61215 to 0.61716, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_wo_aug.hdf5\n", "113/113 [==============================] - 17s 153ms/step - loss: 0.8683 - accuracy: 0.6799 - val_loss: 1.0623 - val_accuracy: 0.6172 - lr: 0.0010\n", "Epoch 14/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.8340 - accuracy: 0.6911\n", "Epoch 14: val_accuracy improved from 0.61716 to 0.61995, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_wo_aug.hdf5\n", "113/113 [==============================] - 17s 154ms/step - loss: 0.8340 - accuracy: 0.6911 - val_loss: 1.0574 - val_accuracy: 0.6199 - lr: 0.0010\n", "Epoch 15/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7957 - accuracy: 0.7111\n", "Epoch 15: val_accuracy did not improve from 0.61995\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.7957 - accuracy: 0.7111 - val_loss: 1.1044 - val_accuracy: 0.6191 - lr: 0.0010\n", "Epoch 16/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7598 - accuracy: 0.7207\n", "Epoch 16: val_accuracy improved from 0.61995 to 0.62301, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_wo_aug.hdf5\n", "113/113 [==============================] - 17s 153ms/step - loss: 0.7598 - accuracy: 0.7207 - val_loss: 1.0357 - val_accuracy: 0.6230 - lr: 0.0010\n", "Epoch 17/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.7173 - accuracy: 0.7394\n", "Epoch 17: val_accuracy improved from 0.62301 to 0.63193, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_wo_aug.hdf5\n", "113/113 [==============================] - 17s 152ms/step - loss: 0.7173 - accuracy: 0.7394 - val_loss: 1.0530 - val_accuracy: 0.6319 - lr: 0.0010\n", "Epoch 18/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.6722 - accuracy: 0.7571\n", "Epoch 18: val_accuracy improved from 0.63193 to 0.63555, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_wo_aug.hdf5\n", "113/113 [==============================] - 17s 152ms/step - loss: 0.6722 - accuracy: 0.7571 - val_loss: 1.1130 - val_accuracy: 0.6356 - lr: 0.0010\n", "Epoch 19/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.6383 - accuracy: 0.7697\n", "Epoch 19: val_accuracy did not improve from 0.63555\n", "113/113 [==============================] - 17s 152ms/step - loss: 0.6383 - accuracy: 0.7697 - val_loss: 1.1619 - val_accuracy: 0.6197 - lr: 0.0010\n", "Epoch 20/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.5135 - accuracy: 0.8179\n", "Epoch 20: val_accuracy improved from 0.63555 to 0.65032, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_wo_aug.hdf5\n", "113/113 [==============================] - 17s 153ms/step - loss: 0.5135 - accuracy: 0.8179 - val_loss: 1.1067 - val_accuracy: 0.6503 - lr: 5.0000e-04\n", "Epoch 21/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.4203 - accuracy: 0.8506\n", "Epoch 21: val_accuracy improved from 0.65032 to 0.65088, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_wo_aug.hdf5\n", "113/113 [==============================] - 17s 153ms/step - loss: 0.4203 - accuracy: 0.8506 - val_loss: 1.2201 - val_accuracy: 0.6509 - lr: 5.0000e-04\n", "Epoch 22/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.3876 - accuracy: 0.8610\n", "Epoch 22: val_accuracy improved from 0.65088 to 0.65422, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_wo_aug.hdf5\n", "113/113 [==============================] - 17s 153ms/step - loss: 0.3876 - accuracy: 0.8610 - val_loss: 1.1791 - val_accuracy: 0.6542 - lr: 5.0000e-04\n", "Epoch 23/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.3567 - accuracy: 0.8739\n", "Epoch 23: val_accuracy did not improve from 0.65422\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.3567 - accuracy: 0.8739 - val_loss: 1.2021 - val_accuracy: 0.6439 - lr: 5.0000e-04\n", "Epoch 24/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.3242 - accuracy: 0.8847\n", "Epoch 24: val_accuracy did not improve from 0.65422\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.3242 - accuracy: 0.8847 - val_loss: 1.4157 - val_accuracy: 0.6336 - lr: 5.0000e-04\n", "Epoch 25/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.3171 - accuracy: 0.8893\n", "Epoch 25: val_accuracy did not improve from 0.65422\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.3171 - accuracy: 0.8893 - val_loss: 1.3253 - val_accuracy: 0.6531 - lr: 5.0000e-04\n", "Epoch 26/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.2767 - accuracy: 0.9029\n", "Epoch 26: val_accuracy did not improve from 0.65422\n", "113/113 [==============================] - 17s 153ms/step - loss: 0.2767 - accuracy: 0.9029 - val_loss: 1.3713 - val_accuracy: 0.6525 - lr: 5.0000e-04\n", "Epoch 27/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.2467 - accuracy: 0.9143\n", "Epoch 27: val_accuracy improved from 0.65422 to 0.65506, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_wo_aug.hdf5\n", "113/113 [==============================] - 17s 153ms/step - loss: 0.2467 - accuracy: 0.9143 - val_loss: 1.3853 - val_accuracy: 0.6551 - lr: 5.0000e-04\n", "Epoch 28/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.2159 - accuracy: 0.9256\n", "Epoch 28: val_accuracy improved from 0.65506 to 0.65673, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_wo_aug.hdf5\n", "113/113 [==============================] - 17s 154ms/step - loss: 0.2159 - accuracy: 0.9256 - val_loss: 1.4565 - val_accuracy: 0.6567 - lr: 5.0000e-04\n", "Epoch 29/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.2136 - accuracy: 0.9267\n", "Epoch 29: val_accuracy did not improve from 0.65673\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.2136 - accuracy: 0.9267 - val_loss: 1.4697 - val_accuracy: 0.6548 - lr: 5.0000e-04\n", "Epoch 30/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.1969 - accuracy: 0.9324\n", "Epoch 30: val_accuracy improved from 0.65673 to 0.66007, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_wo_aug.hdf5\n", "113/113 [==============================] - 17s 154ms/step - loss: 0.1969 - accuracy: 0.9324 - val_loss: 1.5135 - val_accuracy: 0.6601 - lr: 5.0000e-04\n", "Epoch 31/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.1766 - accuracy: 0.9371\n", "Epoch 31: val_accuracy did not improve from 0.66007\n", "113/113 [==============================] - 16s 145ms/step - loss: 0.1766 - accuracy: 0.9371 - val_loss: 1.5194 - val_accuracy: 0.6551 - lr: 5.0000e-04\n", "Epoch 32/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.1791 - accuracy: 0.9390\n", "Epoch 32: val_accuracy did not improve from 0.66007\n", "113/113 [==============================] - 16s 145ms/step - loss: 0.1791 - accuracy: 0.9390 - val_loss: 1.5591 - val_accuracy: 0.6506 - lr: 5.0000e-04\n", "Epoch 33/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.1642 - accuracy: 0.9438\n", "Epoch 33: val_accuracy improved from 0.66007 to 0.66230, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_wo_aug.hdf5\n", "113/113 [==============================] - 19s 168ms/step - loss: 0.1642 - accuracy: 0.9438 - val_loss: 1.5850 - val_accuracy: 0.6623 - lr: 5.0000e-04\n", "Epoch 34/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.1498 - accuracy: 0.9492\n", "Epoch 34: val_accuracy did not improve from 0.66230\n", "113/113 [==============================] - 17s 145ms/step - loss: 0.1498 - accuracy: 0.9492 - val_loss: 1.6589 - val_accuracy: 0.6495 - lr: 5.0000e-04\n", "Epoch 35/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.1553 - accuracy: 0.9481\n", "Epoch 35: val_accuracy did not improve from 0.66230\n", "113/113 [==============================] - 16s 145ms/step - loss: 0.1553 - accuracy: 0.9481 - val_loss: 1.6688 - val_accuracy: 0.6422 - lr: 5.0000e-04\n", "Epoch 36/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.1381 - accuracy: 0.9530\n", "Epoch 36: val_accuracy did not improve from 0.66230\n", "113/113 [==============================] - 16s 145ms/step - loss: 0.1381 - accuracy: 0.9530 - val_loss: 1.6380 - val_accuracy: 0.6567 - lr: 5.0000e-04\n", "Epoch 37/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.1346 - accuracy: 0.9540\n", "Epoch 37: val_accuracy did not improve from 0.66230\n", "113/113 [==============================] - 16s 145ms/step - loss: 0.1346 - accuracy: 0.9540 - val_loss: 1.7069 - val_accuracy: 0.6601 - lr: 5.0000e-04\n", "Epoch 38/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.1264 - accuracy: 0.9573\n", "Epoch 38: val_accuracy did not improve from 0.66230\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.1264 - accuracy: 0.9573 - val_loss: 1.6691 - val_accuracy: 0.6517 - lr: 5.0000e-04\n", "Epoch 39/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.1230 - accuracy: 0.9583\n", "Epoch 39: val_accuracy did not improve from 0.66230\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.1230 - accuracy: 0.9583 - val_loss: 1.6293 - val_accuracy: 0.6531 - lr: 5.0000e-04\n", "Epoch 40/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0893 - accuracy: 0.9682\n", "Epoch 40: val_accuracy improved from 0.66230 to 0.66342, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_wo_aug.hdf5\n", "113/113 [==============================] - 17s 152ms/step - loss: 0.0893 - accuracy: 0.9682 - val_loss: 1.6340 - val_accuracy: 0.6634 - lr: 2.5000e-04\n", "Epoch 41/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0700 - accuracy: 0.9781\n", "Epoch 41: val_accuracy did not improve from 0.66342\n", "113/113 [==============================] - 17s 151ms/step - loss: 0.0700 - accuracy: 0.9781 - val_loss: 1.6803 - val_accuracy: 0.6592 - lr: 2.5000e-04\n", "Epoch 42/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0636 - accuracy: 0.9791\n", "Epoch 42: val_accuracy improved from 0.66342 to 0.66509, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_wo_aug.hdf5\n", "113/113 [==============================] - 17s 153ms/step - loss: 0.0636 - accuracy: 0.9791 - val_loss: 1.7080 - val_accuracy: 0.6651 - lr: 2.5000e-04\n", "Epoch 43/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0581 - accuracy: 0.9812\n", "Epoch 43: val_accuracy improved from 0.66509 to 0.66871, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_wo_aug.hdf5\n", "113/113 [==============================] - 17s 152ms/step - loss: 0.0581 - accuracy: 0.9812 - val_loss: 1.7180 - val_accuracy: 0.6687 - lr: 2.5000e-04\n", "Epoch 44/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0520 - accuracy: 0.9829\n", "Epoch 44: val_accuracy did not improve from 0.66871\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0520 - accuracy: 0.9829 - val_loss: 1.7769 - val_accuracy: 0.6665 - lr: 2.5000e-04\n", "Epoch 45/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0565 - accuracy: 0.9816\n", "Epoch 45: val_accuracy did not improve from 0.66871\n", "113/113 [==============================] - 16s 145ms/step - loss: 0.0565 - accuracy: 0.9816 - val_loss: 1.8202 - val_accuracy: 0.6609 - lr: 2.5000e-04\n", "Epoch 46/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0611 - accuracy: 0.9801\n", "Epoch 46: val_accuracy did not improve from 0.66871\n", "113/113 [==============================] - 16s 145ms/step - loss: 0.0611 - accuracy: 0.9801 - val_loss: 1.8202 - val_accuracy: 0.6615 - lr: 2.5000e-04\n", "Epoch 47/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0554 - accuracy: 0.9818\n", "Epoch 47: val_accuracy did not improve from 0.66871\n", "113/113 [==============================] - 16s 145ms/step - loss: 0.0554 - accuracy: 0.9818 - val_loss: 1.8450 - val_accuracy: 0.6682 - lr: 2.5000e-04\n", "Epoch 48/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0519 - accuracy: 0.9839\n", "Epoch 48: val_accuracy did not improve from 0.66871\n", "113/113 [==============================] - 17s 151ms/step - loss: 0.0519 - accuracy: 0.9839 - val_loss: 1.8456 - val_accuracy: 0.6656 - lr: 2.5000e-04\n", "Epoch 49/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0488 - accuracy: 0.9837\n", "Epoch 49: val_accuracy did not improve from 0.66871\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0488 - accuracy: 0.9837 - val_loss: 1.8436 - val_accuracy: 0.6598 - lr: 2.5000e-04\n", "Epoch 50/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0528 - accuracy: 0.9825\n", "Epoch 50: val_accuracy did not improve from 0.66871\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0528 - accuracy: 0.9825 - val_loss: 1.8332 - val_accuracy: 0.6601 - lr: 2.5000e-04\n", "Epoch 51/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0507 - accuracy: 0.9836\n", "Epoch 51: val_accuracy improved from 0.66871 to 0.66927, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_wo_aug.hdf5\n", "113/113 [==============================] - 18s 159ms/step - loss: 0.0507 - accuracy: 0.9836 - val_loss: 1.8633 - val_accuracy: 0.6693 - lr: 2.5000e-04\n", "Epoch 52/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0456 - accuracy: 0.9850\n", "Epoch 52: val_accuracy did not improve from 0.66927\n", "113/113 [==============================] - 16s 145ms/step - loss: 0.0456 - accuracy: 0.9850 - val_loss: 1.8622 - val_accuracy: 0.6662 - lr: 2.5000e-04\n", "Epoch 53/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0412 - accuracy: 0.9863\n", "Epoch 53: val_accuracy improved from 0.66927 to 0.67484, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_wo_aug.hdf5\n", "113/113 [==============================] - 17s 153ms/step - loss: 0.0412 - accuracy: 0.9863 - val_loss: 1.9067 - val_accuracy: 0.6748 - lr: 2.5000e-04\n", "Epoch 54/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0437 - accuracy: 0.9858\n", "Epoch 54: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 16s 145ms/step - loss: 0.0437 - accuracy: 0.9858 - val_loss: 1.9440 - val_accuracy: 0.6637 - lr: 2.5000e-04\n", "Epoch 55/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0473 - accuracy: 0.9851\n", "Epoch 55: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 17s 150ms/step - loss: 0.0473 - accuracy: 0.9851 - val_loss: 1.8685 - val_accuracy: 0.6651 - lr: 2.5000e-04\n", "Epoch 56/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0446 - accuracy: 0.9862\n", "Epoch 56: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 16s 145ms/step - loss: 0.0446 - accuracy: 0.9862 - val_loss: 1.9837 - val_accuracy: 0.6617 - lr: 2.5000e-04\n", "Epoch 57/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0465 - accuracy: 0.9847\n", "Epoch 57: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 16s 145ms/step - loss: 0.0465 - accuracy: 0.9847 - val_loss: 1.9644 - val_accuracy: 0.6684 - lr: 2.5000e-04\n", "Epoch 58/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0497 - accuracy: 0.9836\n", "Epoch 58: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 16s 145ms/step - loss: 0.0497 - accuracy: 0.9836 - val_loss: 1.9623 - val_accuracy: 0.6634 - lr: 2.5000e-04\n", "Epoch 59/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0477 - accuracy: 0.9839\n", "Epoch 59: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0477 - accuracy: 0.9839 - val_loss: 1.9359 - val_accuracy: 0.6701 - lr: 2.5000e-04\n", "Epoch 60/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0369 - accuracy: 0.9882\n", "Epoch 60: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 16s 145ms/step - loss: 0.0369 - accuracy: 0.9882 - val_loss: 1.9295 - val_accuracy: 0.6726 - lr: 1.2500e-04\n", "Epoch 61/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0368 - accuracy: 0.9882\n", "Epoch 61: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0368 - accuracy: 0.9882 - val_loss: 1.9344 - val_accuracy: 0.6665 - lr: 1.2500e-04\n", "Epoch 62/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0336 - accuracy: 0.9891\n", "Epoch 62: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 18s 160ms/step - loss: 0.0336 - accuracy: 0.9891 - val_loss: 1.9380 - val_accuracy: 0.6676 - lr: 1.2500e-04\n", "Epoch 63/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0305 - accuracy: 0.9898\n", "Epoch 63: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 17s 145ms/step - loss: 0.0305 - accuracy: 0.9898 - val_loss: 1.9442 - val_accuracy: 0.6654 - lr: 1.2500e-04\n", "Epoch 64/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0324 - accuracy: 0.9897\n", "Epoch 64: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 16s 145ms/step - loss: 0.0324 - accuracy: 0.9897 - val_loss: 1.9176 - val_accuracy: 0.6737 - lr: 1.2500e-04\n", "Epoch 65/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0297 - accuracy: 0.9902\n", "Epoch 65: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 16s 145ms/step - loss: 0.0297 - accuracy: 0.9902 - val_loss: 1.9531 - val_accuracy: 0.6665 - lr: 1.2500e-04\n", "Epoch 66/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0278 - accuracy: 0.9908\n", "Epoch 66: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0278 - accuracy: 0.9908 - val_loss: 1.9481 - val_accuracy: 0.6684 - lr: 1.2500e-04\n", "Epoch 67/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0304 - accuracy: 0.9903\n", "Epoch 67: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0304 - accuracy: 0.9903 - val_loss: 1.9455 - val_accuracy: 0.6743 - lr: 1.2500e-04\n", "Epoch 68/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0281 - accuracy: 0.9912\n", "Epoch 68: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 16s 145ms/step - loss: 0.0281 - accuracy: 0.9912 - val_loss: 1.9175 - val_accuracy: 0.6746 - lr: 1.2500e-04\n", "Epoch 69/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0273 - accuracy: 0.9911\n", "Epoch 69: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 16s 145ms/step - loss: 0.0273 - accuracy: 0.9911 - val_loss: 1.9487 - val_accuracy: 0.6687 - lr: 1.2500e-04\n", "Epoch 70/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0274 - accuracy: 0.9907\n", "Epoch 70: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 17s 151ms/step - loss: 0.0274 - accuracy: 0.9907 - val_loss: 1.9722 - val_accuracy: 0.6668 - lr: 1.2500e-04\n", "Epoch 71/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0259 - accuracy: 0.9923\n", "Epoch 71: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 16s 147ms/step - loss: 0.0259 - accuracy: 0.9923 - val_loss: 2.0065 - val_accuracy: 0.6723 - lr: 1.2500e-04\n", "Epoch 72/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0258 - accuracy: 0.9915\n", "Epoch 72: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0258 - accuracy: 0.9915 - val_loss: 2.0311 - val_accuracy: 0.6746 - lr: 1.2500e-04\n", "Epoch 73/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0234 - accuracy: 0.9925\n", "Epoch 73: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0234 - accuracy: 0.9925 - val_loss: 1.9995 - val_accuracy: 0.6651 - lr: 1.2500e-04\n", "Epoch 74/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0258 - accuracy: 0.9912\n", "Epoch 74: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0258 - accuracy: 0.9912 - val_loss: 2.0124 - val_accuracy: 0.6679 - lr: 1.2500e-04\n", "Epoch 75/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0274 - accuracy: 0.9907\n", "Epoch 75: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 16s 145ms/step - loss: 0.0274 - accuracy: 0.9907 - val_loss: 2.0352 - val_accuracy: 0.6670 - lr: 1.2500e-04\n", "Epoch 76/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0254 - accuracy: 0.9912\n", "Epoch 76: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 16s 145ms/step - loss: 0.0254 - accuracy: 0.9912 - val_loss: 2.0659 - val_accuracy: 0.6645 - lr: 1.2500e-04\n", "Epoch 77/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0238 - accuracy: 0.9924\n", "Epoch 77: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 17s 151ms/step - loss: 0.0238 - accuracy: 0.9924 - val_loss: 2.0466 - val_accuracy: 0.6679 - lr: 1.2500e-04\n", "Epoch 78/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0230 - accuracy: 0.9925\n", "Epoch 78: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0230 - accuracy: 0.9925 - val_loss: 2.0830 - val_accuracy: 0.6637 - lr: 1.2500e-04\n", "Epoch 79/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0249 - accuracy: 0.9926\n", "Epoch 79: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0249 - accuracy: 0.9926 - val_loss: 2.0392 - val_accuracy: 0.6629 - lr: 1.2500e-04\n", "Epoch 80/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0196 - accuracy: 0.9939\n", "Epoch 80: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0196 - accuracy: 0.9939 - val_loss: 2.0416 - val_accuracy: 0.6648 - lr: 6.2500e-05\n", "Epoch 81/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0200 - accuracy: 0.9925\n", "Epoch 81: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0200 - accuracy: 0.9925 - val_loss: 2.0180 - val_accuracy: 0.6715 - lr: 6.2500e-05\n", "Epoch 82/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0179 - accuracy: 0.9941\n", "Epoch 82: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 16s 146ms/step - loss: 0.0179 - accuracy: 0.9941 - val_loss: 2.0372 - val_accuracy: 0.6704 - lr: 6.2500e-05\n", "Epoch 83/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0182 - accuracy: 0.9941\n", "Epoch 83: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0182 - accuracy: 0.9941 - val_loss: 2.0587 - val_accuracy: 0.6687 - lr: 6.2500e-05\n", "Epoch 84/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0168 - accuracy: 0.9943\n", "Epoch 84: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 17s 154ms/step - loss: 0.0168 - accuracy: 0.9943 - val_loss: 2.0700 - val_accuracy: 0.6698 - lr: 6.2500e-05\n", "Epoch 85/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0175 - accuracy: 0.9940\n", "Epoch 85: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0175 - accuracy: 0.9940 - val_loss: 2.0542 - val_accuracy: 0.6709 - lr: 6.2500e-05\n", "Epoch 86/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0171 - accuracy: 0.9943\n", "Epoch 86: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0171 - accuracy: 0.9943 - val_loss: 2.0534 - val_accuracy: 0.6704 - lr: 6.2500e-05\n", "Epoch 87/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0179 - accuracy: 0.9943\n", "Epoch 87: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0179 - accuracy: 0.9943 - val_loss: 2.0842 - val_accuracy: 0.6698 - lr: 6.2500e-05\n", "Epoch 88/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0173 - accuracy: 0.9944\n", "Epoch 88: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0173 - accuracy: 0.9944 - val_loss: 2.0819 - val_accuracy: 0.6670 - lr: 6.2500e-05\n", "Epoch 89/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0181 - accuracy: 0.9936\n", "Epoch 89: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0181 - accuracy: 0.9936 - val_loss: 2.0915 - val_accuracy: 0.6712 - lr: 6.2500e-05\n", "Epoch 90/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0170 - accuracy: 0.9944\n", "Epoch 90: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0170 - accuracy: 0.9944 - val_loss: 2.1252 - val_accuracy: 0.6701 - lr: 6.2500e-05\n", "Epoch 91/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0180 - accuracy: 0.9941\n", "Epoch 91: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0180 - accuracy: 0.9941 - val_loss: 2.0843 - val_accuracy: 0.6682 - lr: 6.2500e-05\n", "Epoch 92/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0164 - accuracy: 0.9943\n", "Epoch 92: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 17s 150ms/step - loss: 0.0164 - accuracy: 0.9943 - val_loss: 2.0944 - val_accuracy: 0.6712 - lr: 6.2500e-05\n", "Epoch 93/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0168 - accuracy: 0.9939\n", "Epoch 93: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0168 - accuracy: 0.9939 - val_loss: 2.1248 - val_accuracy: 0.6698 - lr: 6.2500e-05\n", "Epoch 94/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0152 - accuracy: 0.9949\n", "Epoch 94: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0152 - accuracy: 0.9949 - val_loss: 2.1340 - val_accuracy: 0.6673 - lr: 6.2500e-05\n", "Epoch 95/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0159 - accuracy: 0.9948\n", "Epoch 95: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0159 - accuracy: 0.9948 - val_loss: 2.1303 - val_accuracy: 0.6709 - lr: 6.2500e-05\n", "Epoch 96/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0147 - accuracy: 0.9951\n", "Epoch 96: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0147 - accuracy: 0.9951 - val_loss: 2.1255 - val_accuracy: 0.6718 - lr: 6.2500e-05\n", "Epoch 97/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0156 - accuracy: 0.9947\n", "Epoch 97: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0156 - accuracy: 0.9947 - val_loss: 2.1165 - val_accuracy: 0.6673 - lr: 6.2500e-05\n", "Epoch 98/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0157 - accuracy: 0.9947\n", "Epoch 98: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 16s 146ms/step - loss: 0.0157 - accuracy: 0.9947 - val_loss: 2.1445 - val_accuracy: 0.6709 - lr: 6.2500e-05\n", "Epoch 99/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0144 - accuracy: 0.9952\n", "Epoch 99: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0144 - accuracy: 0.9952 - val_loss: 2.1427 - val_accuracy: 0.6679 - lr: 6.2500e-05\n", "Epoch 100/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0140 - accuracy: 0.9952\n", "Epoch 100: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 17s 151ms/step - loss: 0.0140 - accuracy: 0.9952 - val_loss: 2.1244 - val_accuracy: 0.6693 - lr: 3.1250e-05\n", "Epoch 101/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0146 - accuracy: 0.9951\n", "Epoch 101: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0146 - accuracy: 0.9951 - val_loss: 2.1317 - val_accuracy: 0.6712 - lr: 3.1250e-05\n", "Epoch 102/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0136 - accuracy: 0.9952\n", "Epoch 102: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0136 - accuracy: 0.9952 - val_loss: 2.1335 - val_accuracy: 0.6712 - lr: 3.1250e-05\n", "Epoch 103/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0137 - accuracy: 0.9953\n", "Epoch 103: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 16s 145ms/step - loss: 0.0137 - accuracy: 0.9953 - val_loss: 2.1514 - val_accuracy: 0.6687 - lr: 3.1250e-05\n", "Epoch 104/150\n", "112/113 [============================>.] - ETA: 0s - loss: 0.0128 - accuracy: 0.9955\n", "Epoch 104: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 16s 145ms/step - loss: 0.0128 - accuracy: 0.9955 - val_loss: 2.1338 - val_accuracy: 0.6704 - lr: 3.1250e-05\n", "Epoch 105/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0129 - accuracy: 0.9957\n", "Epoch 105: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0129 - accuracy: 0.9957 - val_loss: 2.1485 - val_accuracy: 0.6718 - lr: 3.1250e-05\n", "Epoch 106/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0133 - accuracy: 0.9956\n", "Epoch 106: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0133 - accuracy: 0.9956 - val_loss: 2.1505 - val_accuracy: 0.6648 - lr: 3.1250e-05\n", "Epoch 107/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0125 - accuracy: 0.9953\n", "Epoch 107: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 17s 151ms/step - loss: 0.0125 - accuracy: 0.9953 - val_loss: 2.1372 - val_accuracy: 0.6726 - lr: 3.1250e-05\n", "Epoch 108/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0128 - accuracy: 0.9958\n", "Epoch 108: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0128 - accuracy: 0.9958 - val_loss: 2.1312 - val_accuracy: 0.6712 - lr: 3.1250e-05\n", "Epoch 109/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0119 - accuracy: 0.9956\n", "Epoch 109: val_accuracy did not improve from 0.67484\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0119 - accuracy: 0.9956 - val_loss: 2.1411 - val_accuracy: 0.6687 - lr: 3.1250e-05\n", "Epoch 110/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0142 - accuracy: 0.9950\n", "Epoch 110: val_accuracy improved from 0.67484 to 0.67651, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_wo_aug.hdf5\n", "113/113 [==============================] - 17s 153ms/step - loss: 0.0142 - accuracy: 0.9950 - val_loss: 2.1358 - val_accuracy: 0.6765 - lr: 3.1250e-05\n", "Epoch 111/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0139 - accuracy: 0.9950\n", "Epoch 111: val_accuracy improved from 0.67651 to 0.67763, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_wo_aug.hdf5\n", "113/113 [==============================] - 17s 153ms/step - loss: 0.0139 - accuracy: 0.9950 - val_loss: 2.1469 - val_accuracy: 0.6776 - lr: 3.1250e-05\n", "Epoch 112/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0130 - accuracy: 0.9955\n", "Epoch 112: val_accuracy did not improve from 0.67763\n", "113/113 [==============================] - 16s 145ms/step - loss: 0.0130 - accuracy: 0.9955 - val_loss: 2.1502 - val_accuracy: 0.6754 - lr: 3.1250e-05\n", "Epoch 113/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0110 - accuracy: 0.9962\n", "Epoch 113: val_accuracy did not improve from 0.67763\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0110 - accuracy: 0.9962 - val_loss: 2.1416 - val_accuracy: 0.6715 - lr: 3.1250e-05\n", "Epoch 114/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0136 - accuracy: 0.9953\n", "Epoch 114: val_accuracy did not improve from 0.67763\n", "113/113 [==============================] - 17s 149ms/step - loss: 0.0136 - accuracy: 0.9953 - val_loss: 2.1416 - val_accuracy: 0.6712 - lr: 3.1250e-05\n", "Epoch 115/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0124 - accuracy: 0.9957\n", "Epoch 115: val_accuracy did not improve from 0.67763\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0124 - accuracy: 0.9957 - val_loss: 2.1399 - val_accuracy: 0.6737 - lr: 3.1250e-05\n", "Epoch 116/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0127 - accuracy: 0.9952\n", "Epoch 116: val_accuracy did not improve from 0.67763\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0127 - accuracy: 0.9952 - val_loss: 2.1494 - val_accuracy: 0.6732 - lr: 3.1250e-05\n", "Epoch 117/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0115 - accuracy: 0.9960\n", "Epoch 117: val_accuracy did not improve from 0.67763\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0115 - accuracy: 0.9960 - val_loss: 2.1427 - val_accuracy: 0.6748 - lr: 3.1250e-05\n", "Epoch 118/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0126 - accuracy: 0.9957\n", "Epoch 118: val_accuracy did not improve from 0.67763\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0126 - accuracy: 0.9957 - val_loss: 2.1574 - val_accuracy: 0.6732 - lr: 3.1250e-05\n", "Epoch 119/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0125 - accuracy: 0.9953\n", "Epoch 119: val_accuracy did not improve from 0.67763\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0125 - accuracy: 0.9953 - val_loss: 2.1566 - val_accuracy: 0.6698 - lr: 3.1250e-05\n", "Epoch 120/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0118 - accuracy: 0.9959\n", "Epoch 120: val_accuracy did not improve from 0.67763\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0118 - accuracy: 0.9959 - val_loss: 2.1604 - val_accuracy: 0.6704 - lr: 1.5625e-05\n", "Epoch 121/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0123 - accuracy: 0.9953\n", "Epoch 121: val_accuracy did not improve from 0.67763\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0123 - accuracy: 0.9953 - val_loss: 2.1669 - val_accuracy: 0.6701 - lr: 1.5625e-05\n", "Epoch 122/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0116 - accuracy: 0.9963\n", "Epoch 122: val_accuracy did not improve from 0.67763\n", "113/113 [==============================] - 17s 153ms/step - loss: 0.0116 - accuracy: 0.9963 - val_loss: 2.1614 - val_accuracy: 0.6709 - lr: 1.5625e-05\n", "Epoch 123/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0104 - accuracy: 0.9959\n", "Epoch 123: val_accuracy did not improve from 0.67763\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0104 - accuracy: 0.9959 - val_loss: 2.1647 - val_accuracy: 0.6715 - lr: 1.5625e-05\n", "Epoch 124/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0107 - accuracy: 0.9964\n", "Epoch 124: val_accuracy did not improve from 0.67763\n", "113/113 [==============================] - 17s 147ms/step - loss: 0.0107 - accuracy: 0.9964 - val_loss: 2.1602 - val_accuracy: 0.6726 - lr: 1.5625e-05\n", "Epoch 125/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0114 - accuracy: 0.9959\n", "Epoch 125: val_accuracy did not improve from 0.67763\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0114 - accuracy: 0.9959 - val_loss: 2.1612 - val_accuracy: 0.6757 - lr: 1.5625e-05\n", "Epoch 126/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0108 - accuracy: 0.9963\n", "Epoch 126: val_accuracy did not improve from 0.67763\n", "113/113 [==============================] - 17s 147ms/step - loss: 0.0108 - accuracy: 0.9963 - val_loss: 2.1596 - val_accuracy: 0.6740 - lr: 1.5625e-05\n", "Epoch 127/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0115 - accuracy: 0.9960\n", "Epoch 127: val_accuracy did not improve from 0.67763\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0115 - accuracy: 0.9960 - val_loss: 2.1756 - val_accuracy: 0.6762 - lr: 1.5625e-05\n", "Epoch 128/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0117 - accuracy: 0.9961\n", "Epoch 128: val_accuracy did not improve from 0.67763\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0117 - accuracy: 0.9961 - val_loss: 2.1724 - val_accuracy: 0.6760 - lr: 1.5625e-05\n", "Epoch 129/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0114 - accuracy: 0.9959\n", "Epoch 129: val_accuracy did not improve from 0.67763\n", "113/113 [==============================] - 17s 152ms/step - loss: 0.0114 - accuracy: 0.9959 - val_loss: 2.1768 - val_accuracy: 0.6734 - lr: 1.5625e-05\n", "Epoch 130/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0103 - accuracy: 0.9964\n", "Epoch 130: val_accuracy did not improve from 0.67763\n", "113/113 [==============================] - 17s 147ms/step - loss: 0.0103 - accuracy: 0.9964 - val_loss: 2.1770 - val_accuracy: 0.6740 - lr: 1.5625e-05\n", "Epoch 131/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0113 - accuracy: 0.9960\n", "Epoch 131: val_accuracy did not improve from 0.67763\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0113 - accuracy: 0.9960 - val_loss: 2.1739 - val_accuracy: 0.6743 - lr: 1.5625e-05\n", "Epoch 132/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0113 - accuracy: 0.9959\n", "Epoch 132: val_accuracy did not improve from 0.67763\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0113 - accuracy: 0.9959 - val_loss: 2.1742 - val_accuracy: 0.6773 - lr: 1.5625e-05\n", "Epoch 133/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0108 - accuracy: 0.9963\n", "Epoch 133: val_accuracy did not improve from 0.67763\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0108 - accuracy: 0.9963 - val_loss: 2.1718 - val_accuracy: 0.6751 - lr: 1.5625e-05\n", "Epoch 134/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0113 - accuracy: 0.9958\n", "Epoch 134: val_accuracy did not improve from 0.67763\n", "113/113 [==============================] - 17s 147ms/step - loss: 0.0113 - accuracy: 0.9958 - val_loss: 2.1734 - val_accuracy: 0.6748 - lr: 1.5625e-05\n", "Epoch 135/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0128 - accuracy: 0.9954\n", "Epoch 135: val_accuracy improved from 0.67763 to 0.67790, saving model to /content/drive/My Drive/fer_new_vgg/fer_peerj_wo_aug.hdf5\n", "113/113 [==============================] - 17s 153ms/step - loss: 0.0128 - accuracy: 0.9954 - val_loss: 2.1822 - val_accuracy: 0.6779 - lr: 1.5625e-05\n", "Epoch 136/150\n", "112/113 [============================>.] - ETA: 0s - loss: 0.0122 - accuracy: 0.9957\n", "Epoch 136: val_accuracy did not improve from 0.67790\n", "113/113 [==============================] - 17s 152ms/step - loss: 0.0122 - accuracy: 0.9956 - val_loss: 2.1737 - val_accuracy: 0.6737 - lr: 1.5625e-05\n", "Epoch 137/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0100 - accuracy: 0.9964\n", "Epoch 137: val_accuracy did not improve from 0.67790\n", "113/113 [==============================] - 17s 147ms/step - loss: 0.0100 - accuracy: 0.9964 - val_loss: 2.1777 - val_accuracy: 0.6743 - lr: 1.5625e-05\n", "Epoch 138/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0113 - accuracy: 0.9959\n", "Epoch 138: val_accuracy did not improve from 0.67790\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0113 - accuracy: 0.9959 - val_loss: 2.1724 - val_accuracy: 0.6762 - lr: 1.5625e-05\n", "Epoch 139/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0086 - accuracy: 0.9970\n", "Epoch 139: val_accuracy did not improve from 0.67790\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0086 - accuracy: 0.9970 - val_loss: 2.1827 - val_accuracy: 0.6746 - lr: 1.5625e-05\n", "Epoch 140/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0113 - accuracy: 0.9959\n", "Epoch 140: val_accuracy did not improve from 0.67790\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0113 - accuracy: 0.9959 - val_loss: 2.1771 - val_accuracy: 0.6732 - lr: 7.8125e-06\n", "Epoch 141/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0106 - accuracy: 0.9967\n", "Epoch 141: val_accuracy did not improve from 0.67790\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0106 - accuracy: 0.9967 - val_loss: 2.1798 - val_accuracy: 0.6726 - lr: 7.8125e-06\n", "Epoch 142/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0097 - accuracy: 0.9966\n", "Epoch 142: val_accuracy did not improve from 0.67790\n", "113/113 [==============================] - 17s 147ms/step - loss: 0.0097 - accuracy: 0.9966 - val_loss: 2.1775 - val_accuracy: 0.6734 - lr: 7.8125e-06\n", "Epoch 143/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0110 - accuracy: 0.9962\n", "Epoch 143: val_accuracy did not improve from 0.67790\n", "113/113 [==============================] - 18s 161ms/step - loss: 0.0110 - accuracy: 0.9962 - val_loss: 2.1801 - val_accuracy: 0.6754 - lr: 7.8125e-06\n", "Epoch 144/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0089 - accuracy: 0.9970\n", "Epoch 144: val_accuracy did not improve from 0.67790\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0089 - accuracy: 0.9970 - val_loss: 2.1793 - val_accuracy: 0.6751 - lr: 7.8125e-06\n", "Epoch 145/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0116 - accuracy: 0.9960\n", "Epoch 145: val_accuracy did not improve from 0.67790\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0116 - accuracy: 0.9960 - val_loss: 2.1795 - val_accuracy: 0.6715 - lr: 7.8125e-06\n", "Epoch 146/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0103 - accuracy: 0.9961\n", "Epoch 146: val_accuracy did not improve from 0.67790\n", "113/113 [==============================] - 17s 146ms/step - loss: 0.0103 - accuracy: 0.9961 - val_loss: 2.1828 - val_accuracy: 0.6715 - lr: 7.8125e-06\n", "Epoch 147/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0095 - accuracy: 0.9966\n", "Epoch 147: val_accuracy did not improve from 0.67790\n", "113/113 [==============================] - 17s 147ms/step - loss: 0.0095 - accuracy: 0.9966 - val_loss: 2.1855 - val_accuracy: 0.6721 - lr: 7.8125e-06\n", "Epoch 148/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0098 - accuracy: 0.9966\n", "Epoch 148: val_accuracy did not improve from 0.67790\n", "113/113 [==============================] - 17s 147ms/step - loss: 0.0098 - accuracy: 0.9966 - val_loss: 2.1815 - val_accuracy: 0.6707 - lr: 7.8125e-06\n", "Epoch 149/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0113 - accuracy: 0.9957\n", "Epoch 149: val_accuracy did not improve from 0.67790\n", "113/113 [==============================] - 17s 147ms/step - loss: 0.0113 - accuracy: 0.9957 - val_loss: 2.1819 - val_accuracy: 0.6718 - lr: 7.8125e-06\n", "Epoch 150/150\n", "113/113 [==============================] - ETA: 0s - loss: 0.0123 - accuracy: 0.9958\n", "Epoch 150: val_accuracy did not improve from 0.67790\n", "113/113 [==============================] - 17s 151ms/step - loss: 0.0123 - accuracy: 0.9958 - val_loss: 2.1844 - val_accuracy: 0.6751 - lr: 7.8125e-06\n" ] } ], "source": [ "from keras.optimizers import RMSprop,SGD,Adam\n", "import math\n", "from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau, LearningRateScheduler\n", "import os\n", "#filepath=\"/content/drive/My Drive/fer_new_vgg/fold1_jaffe_epochs:{epoch:03d}-accuracy:{val_accuracy:.4f}.hdf5\"\n", "\n", "filepath=\"/content/drive/My Drive/fer_new_vgg/fer_peerj_wo_aug.hdf5\"\n", "checkpoint_dir = os.path.dirname(filepath)\n", "initial_lrate = 0.001\n", "def step_decay(epoch): \n", " drop = 0.5\n", " epochs_drop = 20.0\n", " lrate = initial_lrate * math.pow(drop, math.floor((1+epoch)/epochs_drop))\n", " return lrate\n", "lrate = LearningRateScheduler(step_decay)\n", "checkpoint = ModelCheckpoint(filepath,\n", " monitor='val_accuracy',\n", " mode='max',\n", " save_best_only=True,\n", " verbose=2)\n", "\n", "callbacks = [checkpoint, lrate]\n", "\n", "model.compile(loss='categorical_crossentropy',\n", " optimizer = Adam(learning_rate=0.001),\n", " metrics=['accuracy'])\n", "\n", "epochs=150\n", "\n", "history=model.fit(\n", " train_generator,\n", " steps_per_epoch=train_sample//batch_size+1,\n", " epochs=epochs,\n", " callbacks=callbacks,\n", " validation_data=validation_generator,\n", " validation_steps=valid_sample//batch_size+1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "aouO_uM-6oSG" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "7NHsFGkG62ro" }, "outputs": [], "source": [ "w_aug_loss = history.history['loss']\n", "w_aug_val_loss = history.history['val_loss']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Q_J2ZiIO62r1" }, "outputs": [], "source": [ "w_aug_acc = history.history['accuracy']\n", "w_aug_val_acc = history.history['val_accuracy']" ] }, { "cell_type": "code", "source": [ "from google.colab import files\n", "epochs = range(1,151)\n", "plt.figure(figsize=(8,6))\n", "plt.rcParams['font.size'] = 14\n", "plt.plot(epochs, w_aug_acc, 'g--', label='Training_WA')\n", "plt.plot(epochs, w_aug_val_acc, 'r--', label='Validation_WA')\n", "plt.plot(epochs, wo_aug_acc, 'm--', label='Training_WOA')\n", "plt.plot(epochs, wo_aug_val_acc, 'b--', label='Validation_WOA')\n", "#plt.title('Training accuracy')\n", "plt.xlabel('Epochs')\n", "plt.ylabel('Accuracy')\n", "plt.legend()\n", "plt.savefig('aug_acc.png')\n", "files.download(\"aug_acc.png\")\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 396 }, "id": "4rZ6DWTJJs-g", "outputId": "9683ddac-db00-4c4e-8e0c-3bada687f3af" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "\n", " async function download(id, filename, size) {\n", " if (!google.colab.kernel.accessAllowed) {\n", " return;\n", " }\n", " const div = document.createElement('div');\n", " const label = document.createElement('label');\n", " label.textContent = `Downloading \"${filename}\": `;\n", " div.appendChild(label);\n", " const progress = document.createElement('progress');\n", " progress.max = size;\n", " div.appendChild(progress);\n", " document.body.appendChild(div);\n", "\n", " const buffers = [];\n", " let downloaded = 0;\n", "\n", " const channel = await google.colab.kernel.comms.open(id);\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", "\n", " for await (const message of channel.messages) {\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", " if (message.buffers) {\n", " for (const buffer of message.buffers) {\n", " buffers.push(buffer);\n", " downloaded += buffer.byteLength;\n", " progress.value = downloaded;\n", " }\n", " }\n", " }\n", " const blob = new Blob(buffers, {type: 'application/binary'});\n", " const a = document.createElement('a');\n", " a.href = window.URL.createObjectURL(blob);\n", " a.download = filename;\n", " div.appendChild(a);\n", " a.click();\n", " div.remove();\n", " }\n", " " ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "download(\"download_679d6ddc-69c6-4555-a0ec-69bd6f9d38da\", \"aug_acc.png\", 36936)" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "
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e7RexhXX3rsPVykWb+9vUdThKKaVqQNvs1X7Z8cUOCuYV0GVsF33/vFJK1RNaslc15i/xs/6h9UT0jCB+WHxdh6OUUqqGtGSvaswusWl6RlPir4zHcuiLbZRSqr7QZK/2qWh5Eb58H9F9o+nyfpe6DkcppdR+0mp8tVfbx21n/rHzWXvXWkT0eXqllKqPNNmrarnXuFl902qi+kfR/cfuWJZW3SulVH2kyV5VyS61WXH1ChwuB10/7Yor1lXXISmllDpA2mavqrT9w+0UzC2g2/huhLYOretwlFJKHQRN9qpKCdclEBwbTIuLWtR1KEoppQ6SVuOrCkQEX4EPR5BDE71SSjUQmuxVBTs+38GcjnNwr3bXdShKKaUOEU32ahdfoY91D6wjpHUIYR3C6jocpZRSh4i22atdNj+7GW+al+QvkrGc+pidUko1FFqyVwAULChgywtbiLsqjugTo+s6HKWUUoeQJnsFQMbnGbjiXXR6rVNdh6KUUuoQ02SvAOjwQgd6z+1NcLPgug5FKaXUIabJvpHL/ikb92o3lmUR0jKkrsNRSil1GGgHvUZu09ObEJ9w3Ozj6joUpZRSh4mW7Bsx22NTML+A6JO0Q55SSjVkmuwbsYIFBYhHtPe9Uko1cJrsG7G8mXkARPfXZK+UUg2ZJvtGLH9WPmEdw3DF6+trlVKqIdMOeo1Yl0+64NniqeswlFJKHWaa7BuxoIgggrrqV0AppRo6rcZvpLJ/zmbDoxvwF/vrOhSllFKHmSb7RipzYiZp/0nDEaJfAaWUauj0St9I5c3MI7p/NJZD326nlFINnSb7Rsib5aV4VTFRJ0bVdShKKaVqgSb7Rij/r3wAHUxHKaUaCU32jVBpVilBzYKI7B1Z16EopZSqBfrcVSPU8tqWJIxIwLK0vV4ppRqDWi/ZW5Z1q2VZGyzLKrEsa4FlWQP2sfyVlmX9bVmW27Ks7ZZljbMsK6G24m2oNNErpVTjUavJ3rKsy4BXgWeAXsAs4CfLstpWs/yJwCfAR0AyMBToBnxaKwE3QCLCogGL2P7J9roORSmlVC2p7ZL9PcBYEXlPRFaIyO3ANuCWapbvB2wVkZdFZIOIzAZeB06opXgbHE+ah7wZefiLdDAdpZRqLGot2VuW5QKOAybvMWsy0L+a1WYCLS3LOt8yYoHLgUmHL9KGzb3CDUCTLk3qOBKllFK1pTZL9rGAE8jYY3oGUGUbvIj8hUnunwJeIBOwgGuqWt6yrBsty5pvWdb8zMzMQxV3g+JeqcleKaUamyP60TvLsrphqu3/jakVGIS5MXinquVF5F0RSRGRlBYtWtReoPWIe6WboJggfa2tUko1IrX56F0W4Afi95geD1TXW+whYK6IvFD2c6plWUXAn5ZljRKRrYcn1IbLleCi+XnNtTe+Uko1IrWW7EXEa1nWAuBMYOJus84EvqpmtSaYG4Tdlf98RNdKHKnaPdqurkNQSilVy2p7UJ0xwCeWZc3FdL67GUgE3gawLOtjABEZXrb8D8B7lmXdAvwCtAReARaKyOZajr3eExEt0SulVCNUq6VjEfkCuAt4BPgbOAkYLCKbyhZpW/YpX34s5nG924ClwJfAauCC2ou64SiYV8DMhJnkzsit61CUUkrVolofLldE/gP8p5p5A6uY9jqmk546SO6VbkozSnHFaec8pZRqTLTduxFxr3BjBVuEtg+t61CUUkrVIk32jYh7pZuwjmE4gvXXrpRSjYle9RsR90o3TbrqYDpKKdXY6CtuG5HmQ5oTnhxe12EopZSqZZrsG5EOozvUdQhKKaXqgFbjNwKluaWkv5OO+KWuQ1FKKVUHNNk3Ahv/byOrb1lN0bKiug5FKaVUHdBk38AVLi4k7c00Em9JJKJHRF2Ho5RSqg5osm/g1t61luBmwbR/qn1dh6KUUqqOaLJvwAoWFpA7LZe2D7UluGlwXYejlFKqjmiyb8Bsr03MaTEkXJdQ16EopZSqQ/roXQMW3Teanr/2rOswlFJK1TEt2TdQeX/l4c3y1nUYSimljgCa7Bsg22ez/NLlrBy2sq5DUUopdQTQZN8AZX2bhWerh8SbE+s6FKWUUkcATfYNUPpb6YQkhdD8vOZ1HYpSSjVqIkKRtwi/7a/TOLSDXgPjXuUm97dc2j/dHstp1XU4Sim1TyKCZQWuVwWeArLcWbRvWv34IH7bz/z0+UxeN5nQoFDuOOEOQoJC9roPoMJ+DqXyY5i9dTY3/XgT+Z588kryyPfk4xc/Hwz5gOt6XXdY9l0TmuwbmNxpuVguSx+3U0pVK7MokxbhLfZrHRFBEBxW9RXCpf5SxqWO46W/XiIqJIqHBzzMuZ3PrbCMx+fhvYXvsXTHUt4+720ALhh/AS0jWnLZMZfx3crvGLdkHG+c80aVyd4WG4flYOmOpfT9oC8WFoLw6ZJP+fSiT0mOS2bRtkWc/vHpxDaJJTkumQhXBNM2TuOTCz9hYLuBbMnbgi02STFJVR7HF0u/YGXWSjblbWJT3ia2FWzj7A5n8/KglwFYumMpLqeLLXlbmLp+KpPXT+bWlFu5vvf1RLgiaB/TnqiQKKJDookOjSbCFcFV3a8C4JPFnzBh+QTCg8MZ0XMEgzoO2q/fw4HSZN/AJN6USOxFsbhauOo6FKXUEWJbwTa+X/U9N6XcBMD131/PzSk3M7jT4Bpv48YfbuTj1I9pF9OO9jHtcTqceHwepg6fCsD7C9/nmT+fYUPuBnom9CTTncnSHUs5t/O5ZBZl8s3Kb7DF5pk/n2FL/hZSElPw234EISEigY8Wf8S7C98l2BHMFd2v4NT2pwKQV5JHdGg0MzfP5LmZz5EYkcg7579D9/juTLh4Aqe2P5W/tvzF9d9fzyljT2HTXZvoHt+dS5MvJcudxfLM5eQU5zAgaQBNgpsA8PBvDzN+6Xi6x3enTVQbokKicDqcfHjBhwA8P+t5Fm5bSMuIliTFJNG5eWdiQmN2nYuUd1Pw+D0ABDmC6Ne6H9Gh0QAcE3cM317+bbXncdaWWWzN34q71M05hefU+PwfLKu8aqOhSUlJkfnz59d1GLXK9tk4grQbhlL1iYiQ5c7C5XTtShj7u/63K7/lv3//lwf6P8CApAGs37meTxZ/QtvotkzbNI3Pl3yOX/zkPJBDWHAY5352LgvSF7DwpoW0i2kHmBL3qF9HkVaQRqY7k/jweJKik7g55WaSYpJYnrmcd+a/Q3phOht2bsCyLMKDw/l1+K84HU4emvoQf239i/v738/gToOxxcZn+wgJCuG7ld8x9IuhABzf6niePu1pTm9/eoUq9cyiTH7d8CsD2w0kIcLUTE5eN5nLvryMY+KOYcbmGTQPa859/e9j5EkjK52HjMIM/t7+N2d3PHuf52xr/lZenf0qyzKXsTV/KznFOfRr048JF0/AsiwyCjOICY2ptlngq+VfUeIroVlYMwYkDSDCdeS8d8SyrAUiklJpuib7hmPRwEVEHBtBp1c71XUoSqkyPtvHqF9HUeIr4eEBDxMfEb9r3pi/xjDq11F4/B7CgsK444Q7ePDEB2ka1pSF2xZyxVdXcF6n83jk5EdoGta0Qtv2h4s+xOv38v6i95mfPp+k6CQ+GvoRp7Q7hR9W/cCQ8UMACA8O57pe13HHCXfQsVlHANbmrCXl3RQ6NuvItBHTiHBFYItNs9HNaBHeghZNWpBRlMHmvM1c2f1KPhr60UGdAxFhQ+4GstxZ9EnsU+N284zCDE77+DTySvK4r/993ND7BsJd4QcVS0Onyb6By5+bz8ITFtLhxQ60ubdNXYejVIOTU5zDO/PfYV76PG7ofQODOg7albRemPkCv6z7hcTIRJKik7gk+RJ6xPcA4PmZz/Pg1AdxWk7CgsO4tNuljDxpJJ2ad+LX9b/y89qfaRPdhrlpc/lsyWd0aNaBVbetotRfyukfn86sLbOICY3hnE7nkF6Qzm/Df8OyLFq+1JLthdtJik7i8YGPc3WPqwlyBFpmPT4Pm/M2Ex8RT1RIVKXj+X7V91ww/gISIxNZesvSSjcTYDrBlZfO60qpvxSH5cDpcNZZDPWJJvsGrDS7lAUpCxCfkJKaoi+9UeoQySjMoHmT5gQ5grjm22v4ePHHNA9rTnZxNn0S+/DhBR+SHJfM39v/5pb/3UJ6QTpp+Wn4xU//Nv356aqfcDld/LTmJ5Ljkhk5dSTfrPyGG3rfwLvnv1tpf6kZqWzYuYELulxQYdp9k+/jj01/cFX3q3hj8BuEBYfhLnWTXpBO2+i2uJwH1kfnxVkvMjdtLq8OepWWkS0P+DypI4cm+wZK/ELq4FRyp+XS689eRB1f+Q5eqYZGRNheuJ34iPgKvcOLvEVMWT+Fb1d+y/RN00mKTuLOE+7kwq4X4rN9FHoLiQ6Jxuv3UuIrIb0gnZaRLYkJjWFe2jyen/U86QXp7CzeSU5xDhlFGcy7YR4piSmsyV5Dia+Eo2OP5sNFH/LvP/7N2R3O5oMLPqgQW05xDmP/HsvctLmMv3h8pdi35G0hLjxuv0vLe5a6lapKdclee+PXc8VriymYX0CnNztpolcNxpR1U/h86eckt0imT6s+9G/TnyBHEB6fh/FLxzNm9hhSM1IJDw7nsuTL+OCCD7DFJvaFWEp8JcSExnBa+9N29XoGWLZjGT3fqfxiqAkXT+CS5Eso8ZWwJGMJraJakRyXTHRINJ2bd6ZlhCnxdmoe6AtzU8pNXN/7eqoqLDULa8Y9/e6p9tjaRB9YM5smenUwNNnXc02ObsLxq47HFauP2qn6x13q5rHfH6NnQk+u7H4llmXx4+ofueiLiwh2Bu9K1LkP5hIdGs2jvz/KC7Ne4Ji4Yxh9xmjSC9JpE2WSp8Ny8OKZL3J07NGcknQKwc6KzVktwlvwwpkvkO/JJzQolBBnCC0jW9KvTT8ABiQNYOVtNX+fxO7t40od6bQavx5zr3ET2i4UR7A+bqfqn+2F2xny+RDmpc8D4IbeN/DqoFfp+HpHEiMTmTJsCh6fh/np8xncaTCWZfHl8i+JConizKPO1JKuUlXQavwGRkRIPSeViB4RHPP1MXUdjlL7Zf3O9Zz60alkubP4+tKvSS9I5+jYowkLDmPy1ZNJjEzcNYjJ7iOwXdzt4roKWal6TZN9PZX3Zx4l60po91i7ug5FNSI+2weYKuwJyybw+4bfuajrRazJWcOsLbPMsKUXfQrAE9OeYH3uelwOF26fmwJPAcktknn2jGdpFdmKvq378uCJD9K7Ze8K+0iOS67141KqodNkX09t/3A7zkgnLS7av/GtldofT05/kklrJtEnsQ99W/flnQXvcFLbk3jm9GdYv3M9YxeP5e0FZnzzhIgEzukYGP5zRdYK5qTNwePzEBYcRqQrksiQSABCgkL44uIv6uSYlGqMNNnXQ75CHzsm7iDu8jic4TrQhNo/+Z581u9cT8+Eyj3Ttxdu57Mln3Frn1sJDQolOiSaIEcQH/79IW/Me4Pw4HBuOs6Mrz7ypJHcdNxNzNoyi+S4ZJKikyq0o1f12JlSqm5osq+Hsn/Ixi6yaXmtDoKhak5EGL90PPdOvpc7TriDngk9ySnO4drvrsXj81DgLWDO1jn4xU+3Ft0Y1HEQd/a9kzv73onP9pGakUpCRAKJkYm7ttk0rGmlt5oppY48muzrobjL4ghtG0pUf32uXlVU6i9l4baFrM5ezbBjhwHw8eKPmbJ+CquyVjEvfR7HtTxu19vOVmevZmPuRsKCwggNCuXefvdyba9r6RLbpcJ2gxxBldrWVRVsG6ZPhxNOgCZNql9u+3ZIKHsN9eDBkJQEl14KJ58Mzv2orXO7weWCoGou5bYNjiPgaZ3iYggL2/dy27bBhg3m/5GR0K3bvs9HaSkE12DU0C1bYOJE83sZOjRw/mvT9u3w2WfQrBn07QudO9fa70cfvatnSnNKCW6mw+E2RjuLd5JRlEFmUSars1dzZfcrCQsO49PUT3l34bv4bT8Lty2k2FcMgPcRL8HOYB6c8iATl0+kWVgzru91PTced2P9HGfc54P33oMzz4SOHes6mspKSmDYMPjyS4iLg1degSuuMNMzM2HTJvj7b5g2DX7+GVasgPh4uPpq+N//TOKOi/27disAACAASURBVIOLLoIbb4Revcx2N2ww8zduhMsug5QUsCxYv94kwyZN4Oyz4dRTYedOGDIEunY1+xgyBI46Cu68E4YPh/D9eIlMbq453xs2wKOPQsu91CTOmQPJyRARAWvWmOMtKjLJ9YcfoFUrKL8e33uvievkkyE9HRYuNP+PjobXX4c77ghsNyoKTjwRPvzQnCswCXPxYrPPSZPMMpMnm3lnn21uEs4916w3ZQr06we9e8OMGTBggFnOssz/u3eHRx4xif+HH+DTT83voVUraN0aOnWC8883NyolJZCWBmvXwuzZ5iNizjOY9Tdtgvz8wCc+Hh57zGzz1ltNoi8tDRzfmDFw9901/53UQHWP3iEiDfJz3HHHSUOTNzdPpodPl6xJWXUdiqpFhZ5CGf7NcOFxKnxmb5ktIiKfpn4qA8cOlAH/HSB3TLpDJi6bKGuz14rf9tduoAsWiHz99f6tU1Qk0r+/yPHHi9xxh8jnn4ts2CBi2xWXKykRufBCERD58cfK29m+XeT//k9k40bz87x5InfdJfLEEyLPPy/yzTcVt5mfL3LppSLnnSeyaVMgFn8NztnOnSLvvisyYIDIBReYadnZIiedZOJ78EGRQYMCcX71lZle/mneXOShh0QKCgLbLCwUmTjRxNSkicj775vpX34ZWC8oyPzbq5c5XhGRF14QGTFCJC4usNzYsWbeunUi990nkpJipjdtauIt3+/EieacP/igyLRpIl5v4BxlZIhERAT226KFyE8/mXkej8iHH5p9P/GEyEUXmeWefdbMv/XWQCzh4SKXXSby5puB7bZpY+bFxFSOedMmkV9+MZ9PPhG56SaR444TKS018+++O7COZZnvTfl+RURuvFEkMbHi+X700UDcO3aILF0q8thjIj17mnNS/vt/802RTp1EevQwv6PyfZSfr2uuqbjv7t1NfOX69w/MDwkx5+zSS8082xY5+WSR224TWbVKZNkykQ8+MP8/xID5UkVOrPOkfLg+DS3Z+4p9MqfrHJnZaqZ4d3rrOhx1GHl9XtlZvFM8Po+IiCxMXyihT4XK3T/fLeMWj5PJayfL6qzV+5fMfT6TQMqTxKE2bpyIy2UugqmpNV+vtFTk8cfNRTYsLHCxvO22wDKbNpnkCSJPPx2Y/tFHIjffLHLaaYF9lyfJsWNN0tz9oj96tJln2yZBORwiyckiublm+qOPmnWOPVbklFNEzj9f5KqrAjcAzz5rEm1IiNne0UcHkthPP5n4x4+vfIwbNpibgx9/FNm6tfKNzJ6Ki82Nh4hJTmPGmKSQlyfyn/+Ym549t+H3i6xda25i9mTbIn/+KXL11SY5lq87YoRJ6MHB5niiokSGDg2sN3q0yMKFJjF1724SY26u2VfTpoHzGh1tbrTKk2JenknU33wj4nZXjsftNjcKN9wg8vrrIn/8UfVyVXnrLZGXXxb57Tdzg1UV2xZZtMj8bv7+u2bbrUpRkciSJYGff/vN3OT8/rs5xj1t2yaSmWluTKuLqxZosq/nNo3eJL/zu2T/XM0XXDUIMzbNkJYvthQeRyKeidg1PT0//eA2fO+95s994MB9l15t25T00tL2vV3bNhd6MCWXf/5TZPPmA4vR6zW1A2++KfLrr2ba6tWyqyT17ruBZVeuNCXOZs1E+vYVufNOs+yefD6TAC+/XOTMM83PTz9ttvnSSxWXnTLFlHTPP98k+169RLp2Dcx/6imRc84xpeV58ypfvA/XjdR+qCoHVctbVmjIzzc1MjfdJPLkk1Uv63abYy63datJ7j7fAcd6MEpLRVasqH73WVkiEyaY3NvYaLKvx2yfLbOSZsmiUxfVdShqX5YuFYmNFfnlFyksFFm+vGar2bYtb859U4KeDJKOr3WUl2a9JGNmjal6Ya9XJCen5iWFsWPNn3qfPqaqOTtb0tJMbAsWmJrVCl59NVBqu/9+M83nk/Tv50nOtj1KLb/9Zpa79lpTTbqn1183VdEipnTav7+Jx+02VejffLP32LdtMyXMn3+uPK+6ElRVPB6z/IIF5sbhyitrraRVG6ZMEUlIMDXj5Yd1882mEmDPFoyayMgQWb/+0J0ijydQ8C8pMd+91FRTWTJwoGlpWLnSzN+2zdxL7G73OJ59NlBT37y5qbCYOjWw7tVXBypfoqJEnnsuUHGwc6dZdtQoc2964YUir71Ws2Nwu02LzGOPmfvFMWNMhU5NWn6qkp5efeXEwdBkX4/lzc2T3x2/S8aEPa/K6ojz1FMiIEXB0XJCb684nSJz5lS9qN/2y+ZcUwremrdVIp6JkHM/PVd2Fu8sW8Av8vLL4nl3rEyaJPLf/5YVxs4+2/zpll8dP/hA5IQTRE48UWTgQJnY40kZELtM0pZkmzZsl8tUdXu9Upjnk6uukgq12/desduVNTXVXCnPPttUtU6ZIiIiQ04rEBDpELRBcv43q+KB/PVXxatxaqppL3/5ZbOD8nbLJUtMtTkE2oM/++wgT/h+sm1zvsqryfdTZqapLT8Qfr8pie6tMOz3m6b2Pe9jNm82yeXbbyt3aZg2zbQgJCebyory7Y8cKdKqlTnNvXuL/PCD6RpQfhyvv24SXuvWppn6lFNEtmwx859/3qwXH2+a3KdN27/Ev3SpyKRJpjLm7rtNl4LySoM//6z4/evVy1SYbNhg5o8aZaZ37Ghii4oyrSvlrr3WLP/mmyaxN29ubmxEzK81KUnkX/8yifi880RCQwPN8uXdPpxO06LRubNpISo3aJBZZ8QIkXvuMd0PPvjAzMvONveJu8e+e+tQQYE5fxs3mlaEqVNFJk+u+FUrKBCZOVNk2DDTelLeneBQ0mRfzxVvLha/p5Y7XDVA3p2FUrKhrEr8f/8zV4sDvTXfzZIlptbbU1oi8996UoZaX4uFX5zhOdKxs1fcbpGZm2fKu/PflR9X/Sj3/nKvtB7TWo5+/ehd20jdvsS0w3s8Ij//LKWlIje0/EFirJ27LiwDe+2UTJqbkml5cWXcOJOcTztN5OSTJTvlLJOYO9imv9pnn4lkZYltm4K1ZdnyQMuP5POQa2QCF0sJLpEbbpD1qQVS0q2XucJnZMiMGYFT8+roYnlo6HIJxiODmCS+Sy4XmT696pNRXpMApm3cu1sfE9s2V8DzzjNX7bIMYtumAqB8f08/LXLJJSIvvigyY4bI/PkmSZQ77zyTyLp1C/STK7/olm9PxFQmDB1a8YK/aJGpkCgqMhUGF14octZZgXXHjQv08ysoEHnllcAFe/TowP3L5s3m0MoTaG5uoKTn8Zjm4t3vZYYNC5yWPn1MQi2/cbBt08oQGWnmd+sWKN1u3izSrl3FBPP662ben3+aPnBdu1ZRQyOmuvujj0TatzfrvfKKmT5jhvk5OVlk+HCRIUNMpc+ssvu4NWtM94BhwwJ91Y4/PvCrvPlmc16vvNIsc8YZpiWlXIsWgVidTrP933838zIyzI3rBx9U3VK0Zo25Z774YvMVufNOkxTLvxt7/rn6fBWbL/a8mSq/iRAxCXjSpIpdG8r7/vn9prXn2GPNTUZYmOmacM89gWUXLAhUEuXkmK4J6WWXk9deq3wjAIHv0v33B6aFh4vcfrs51kNNk309ZTegqsY6s2GDyO23y5jWL0kLMiTMWSLnnivy1uXTJI/Iir15RUR++UW8z7xg/npTU2Xp0sAfdDnbtuXpP56Wsz45S577ZLYEBYk4nH4Ju/Au4f7m0jwiVV7hDrn+H/+SIUNLJSdH5O6f797Vkz74yWC5+q2zZNmIc6VwxkK5+GJTkpk6VcxVzuEQWblSXrxxlQxnrPzwr59k7FiREIdHxkXdIlJUJG+8YRZ96CFTWpg0yfTnEhGZPdtU6bZpYzpMl19Uv/tOZPKz80WOOcZcsX/4QeSBB8Td4wRJamtLZJhXzuu7Q4YPN1eH996reNxvv1osIDKKp0QGD5ZXXjE3EOefL3LLLeaUbdnoEzntNCm+8jp57+1SOecck5y81fQrLS01zcVgLp4iphtAeYIq/yQnB9a5915T+vrHP0w1cPfuprtA+faOOcbEFBRkKhFuuilwUR8xQnZ1AwCTmMqPs7TUVISAqVpu1sz8f8IEM3/tWpN4QkNNabNtW1OCFjF9x8q3V97nDQLfnW++MaXbUaMCHeRPOSVwTMOHm5LkCy+YpJ+UZL66Q4aY0u0ff5hKlLfeCvw+33/f9G3c8/u5J6/XdL5futT87PfXvImpqEjknXfM96zcVVeZc96xo4mzb19zXOV+/tl8J8tviBqDpUvN9+j99011/++/m0/5Jfznn81N7MSJpjnhcDmoZA8MBZw1WfZI+TSUZL/u4XWyePBi8Zdqqb4mdmwulqKC3c7V5s3mihwWJre2+kYGHbVKbrsobVciubj1X6bo8csvu4oM6658RLqyTL5liIjDIQMTl4hl+eWBB/1SWiri8/vk+u+uFx5H2j92mkTGlEiXLiK39/hR3hpwvvyw6gfZnpNrMkTZNn0+kdff8MsV1+RLytGb5L4z/5K0ZTtFHA7Zfs9o6dAh8ETSNXwo069828Rv26YYeNRRIn/8IZtoY7KBmKTVoYMJvzyxDBsWOPRFiwIlrH01jUtJiUyebPJ/584mEd5+e9XN4rffLvLxU5tE3G555BGRfv1Maai8BNi2rQk7M9MkxYQEM71TJ5GPPw5sc906k0ROOcXMHzmycqlt2zZTdf3VV6ZTeE1kZYlccYU59ltuqdxvLjfX3OM89JApee95jGvXmnbevn1N6fWvvyrvY+NGs4/TTw90J/B4RL74wiTCkSPNU4SrVlVf/b1uXfW/lwULzI2M221Kwrv3jdvd3LlHRL9AdQQ52GRfBGwHRgOda7JOXX8aQrL3e/wyI36GpJ6/H48y1TNer8h115lS6UGbPVueCf+3JAZtl3dvWyz5ebZcd9Iq+TP8bJEFC3aV7ETMBXj6dJG1i/JFOnWSrSTKB1f9KqNHi8TF2dK0qS2/f7lV5v6jnyy2usr5zcaZavRTben/6oXC48gDPz4pxxxjS9OmIqsXu01x7LrrKse1fLnYA0+VuCYF0tSxU45jnlj4JTjYlvePelqkb19xu82F/b5BS8SBT3p0cgcS3zffmD/Vf/7T1Fnv/ny2mFLC+PEmwezZ4aeo6MB6JFfV164qeyayVat2NfOLiOkgb9vmqbNu3cxhlFeJl9ceJCaKvP32/seolKrsYJN9JHATMBvwAzOAa4HwmqxfF5+GkOw3PVf2uN3k+vm43e6JIC2t6j5RZf3ZJC4u8LhzVTZsMB19YmNtefXOdZUX+O47kbAw2dKmn/QLXSAgEuryiWWJvPxk5WePd28esRenykXxM3aVjtt18MhV7/xbWo9pLTyO3PXC6bJt1hT58EORkBC/YPnkyUn/kdJS0543ZYqYbAuBR8Z299NPIjExkmXFin3a6SJvvy1rU4vkjjtEltz0uqmyL6/Xu/RSWdFigGRl7nby/H4zoMcff1R/guqB0lJTEi23cuXeS75Kqf13yNrsgWTgpbKSfj7wHtB3f7dzuD/1Pdm717tleth0WTJ0yb4XPkLcfbfpJ/bMM6YD1Pnnmxxo26bzT8eOFfNVTo7pqGI6jZkO3FW5/34Rh8OWoCBbYiOLZRonm15Uu3n+nF9l2tE3imRkiF3ikS/v/EPOPNPeNeiXiMhXy7+SrXlbxbZtOfPjM+XeX+6Vz1I/k7GLPpKHxo+VCTPmSl6eyOJtqRL6VKgM+XyITFq9W5WDxyMLhz0vw65Ik5ycPYI8/3zTY6y6rtb5+aZ+eU/lXZO/+sqcqO7dq64dUEqpGjikHfSA1sCTQElZwi8F5gA9DmR7h+NT35P9ihEr5I+IP6R4c/Fh39fuJauq8lG5P/4wPYjff9+0R954o3lspry6eeRI0zFq9xEjy6tnf/010KN4xIhAO+PCheb/I0dW7gwmYtouQeTKJl/Llu7nmPrlk04SadJE1n0+R+a8t1heeMEsc9M/SytvoMzUdVMl6Mkgueaba8Ttdcvwb4aL4wlHheFn7598f9n5sKXQU1h5I8uXm95exx5bsUfU11+bAHbvoVRTXq/p4fTOO1K280rV9EopVVMHneyBYOBS4Oey5D4DGAGEAYnAWGBFTbd3uD/1PdmX5pdKzu97Fh8Pjaws0yv4tttMJ6NevUzO+eUXU9KeMaPq9Z57LpDIyx8fufxyqVTK3bHDNDPvOexzQYEZhjs42LTfZk5fZurBjznGdEBLTjYP/u42osaWLSL/av2t5IXFmzsDEZH0dBkbfXuFWFJSqn90etqGaRLzXIwkv5ksucWBtoL0/HRZmblS1mavrfkIdT//bLphx8ebh49FTKn94Yf3b5CX3Wk9tlLqEDnYNvvXgSwgExgDdKtimQTArsn2auNTX5O9v9Qvtv/wXvyffdb85ps2NVXrQ4eaXs95eSbntm1b9chOtm3y2saN5hlit7tsYlXDlJaWVv0yk48+klXTt8nQoSLjb51unnM64wzThXnwYHmr51vy8G25gVVTU02wzz9fYVM7fpwjr3R9W74avUbWrQvULny+5HP5YKEZBSPHnSMn/fck4XGk1UutZMPODQd13nZZutR0WYfAs0yHwlln7eppr5RSB+Jgk/2vwOWAay/LBAGn1GR7tfGpr8k+/b/pMvvo2VKSfoClxBq48EJTsq7KvHmm5L3nuzYWLKimOfqxx8zXaPe3nbndppQ+ZEjgIVu/3zxLBGa0jvLl9qiyvvFGs8hdd5UNQf6PZ017wN7aF8rM2DRDeBw5deypImKq4y8cf6G8Nvs1KfIe2Ihp1crLMwPyPPHEwW8rOzswotweNzVKKbU/dFCdesD22zKn6xyZe+zcwzqYjs9Xeezp3b34ovlmlL/QKz3dPMtd5TsyPvrILHzUUeZtXSJm1JHyIVHL3zpVflMwevReq639fpHbL922q3r+zVMnmoel98Hj80i3N7tJ25fbSra7nj29YNuy64APZU2BUqrRqS7ZOyq94L4KlmU9bVnWzVVMv9myrH/XZBtq37J/zMa9wk3bB9piWdZh24/TCa1aVT//7rvh3HMhI8P8/NFH4PfD5ZdXsfDw4TB1KqxfD6+8AmvXwujRcOWVsHo1HHssfPklPPEEjBgB998Pezk2hyW8uvwsHk94izPOEP7588Xwn//s85henPUiyzOX8+bgN2kW1myfyx9RLAuuuAJcLujWra6jUUo1QJa5EdjHQpa1GbhERObsMb0P8KWIJB2m+A5YSkqKzJ8/v67D2C8LT1yIN93L8WuOxxFUo/uwfSoshKAgCA01P3/8McyeDa+9ZqZXx+83NwUi0LkzJCbC9Om7LfDdd7BlC9xyi1nwwgvh11/htNPgt99g1Spo2dIse/HFsHkz/PknhITsO+h334WbboI33oB//Wufi2e7s2n7SlsGdxrMxEsm7nv7RyK/H3y+mp0fpZSqhmVZC0QkZc/pe7ncVxCH6Zy3p2wg/mACU0beX3nkz8qn4+sdD0miFzEFxjVrTKH622/N9AkTTOF7V6L/4ANIT4dHH62wvtNp/n32WbP8/43ywSefQ1iYSebjx0NSEtxcVuEzZgxs2wbdu8PffwcSvd8Pp59uEn5NE9lVV5lkf9ttEBcHl1xS5WJ5JXkEO4Np3qQ5U4dNJSnmiLvnrDmnM3DSlVLqEKtpst8MDADW7zH9ZGDrIY2okYrsE0m38d1ofn7zg97WK6/AokWm+n3aNJg0yeTf7t1N4XpXdfz69abkbNtw550QFVVxQwUFON7+mG5J1/CP+Plw7nAzPSICTj4ZXnghcNfQvr35AAwYENiG02lK//sjPBzuustUPwweDJi+JX9u/pMsdxZLMpawYNsCpm+aziMDHuH+E++nX5t++7cPpZRqRGpahHwHeNmyrBssy+pQ9rkRM5Leu4cvvMbDEeQg7rI4nE0OrnT31VemzT0/35Tur73WFMaffdYk/Px8OOWUsoVzcqBDBygthcmTK2/srbcYueU2ln6xnCbnnALz5sFff5n1/ve/w9u+/NJL2Hm5bCwNVCid8fEZ/GPCP3hi+hOszVnLRV0v4swOZx6+GJRSqoGoUcleRF6yLCsWeA1wlU32Aq+KyPOHK7jGIuPzDIpXF9P24bYHVYU/bx4MGwb9+sHnn5tq/JgYU3h/7jlo2tQstyvZp6TA4sUQHw/ff2+q2su53fDSS3DWWVgnHB9Yvhbke/KZvnE6z896nnU561h12yoiQyKZOnwqEa4IOjfvTIQrolZiUUqphqDGmUVEHgJigb5lnxYiMvJwBdaYbB2zlaxvsw4q0S9aBEOGmLz97beBDnlgasRDQuCdd0yib9XCa3rMFxSYavinnoJ//KPiBt9/H3bsgEceOeCY9sUWm9lbZ1PoLQRg6vqpHP/e8TQb3Ywh44ewInMFT5/2NOGucABOTjqZ3i17a6JXSqn9VNM2ewBEpAiYd5hiaZSKlhVRML+ADi93qNHy2dkwcaLpU9e6NfToAX37mnkuF/z4o+nTtru4OBg1ypTyb78dePE1GDnSrHzOOZXb1H0+eP550y6/e/v7QVq2Yxkvz36Z3JJcCr2F/L39bzKKMph4yUQu7mZqFSJcEYw8aSSntz+dfm36ERoUuo+tKqWU2pcaPXoHYFnWqcAVQFsCVfkAiMhphz60g1NfHr1b98A6tr68lX5p/XDFuapdzueDq6+Gr782TeyWZdrkr7oKxo0LLLO3x+kA02O+c2cYOBB++CEwffVq2L7dJHiAL76AhITd6vwP3vqd6xk0bhAup4sIVwTtm7bn/M7nM7jTYGJCYw7ZfpRSqrE6qEfvLMsaAbwNfAMMBL4DOgPtgXGHLMpGxvbZZIzLoNngZtUm+vLn3YOCoHdv87z7NddAcrLJ27vfq+0z0QM8+CB4vfDyyxWn/+tfsHWraQ8IDYXLLjvg48r35DN+6XgcloNT251KWHAYCREJHNX0KFbdtuqwDhiklFKqsppW498H3CYi71uWVQA8JCLrLct6Ayg8fOE1bL6dPqJOiCLh2oQq5+fnm4L26NFw9tnwwAMV57dps587nDkTPvnE1Ol37Fhx3vnnm8fv2rWDV1/d72Rvi43DciAi9P+gP8syl+2a57Sc3Nf/Pp474zlN9EopVQdqmuyPAqaW/d8DlPeQegOYBtS4o55lWbcC9wMtgWXAXSLy516WdwGPAMMwr9LNAF4Ukddqus8jlauFi2O+Oaba+d99ZzrLFx7o7VRRkelgt3o1jB0LsbGm3n/UqMrLlif7ggIzxO3eNustosBbQGpGKjM2z2DG5hks3bGUtHvSCHYG89wZzxEXHkekK5JpG6exPHM51xx7zQEehFJKqYNV02SfDUSW/T8NOAZIBZpj3mdfI5ZlXQa8CtwKzCj79yfLsrqJyOZqVhsPtAZuBNZgRuyr8T6PVJ5tHmy3TViH6g/l++/NQHQXXngAO0hNhUsvDYxPHxICRx8daODfU/v25gH9k06CLl0qzS71l+J0OHFYDj5a/BH/mmSGsXVYDnol9OLK7lfiLnUT7YzmvM7n7Vqva4uuBxC8UkqpQ6mmyf5P4CxgCTABeM2yrDOB04Ep+7G/e4CxIvJe2c+3W5Y1CLgFeGjPhS3LOqtsHx1EJKts8sb92N8Ra8tLW0h7PY3+2/oT3Cy40nyPB37+2bxPxrG/T+RNmgQXXQTNmpnx6k89tWbrjRlT7awnpj/BtI3TmDJsCie1PYnXz3mdLrFdOKHVCUSGRFa7nlJKqbpX02R/G1D+DNSzgA84EZP4n6rJBsqq448DXtxj1mSgfzWrDcU86nePZVnDgWLgJ2CUiNTbvgJ+t5/tH2wndmhslYkezDC3hYXm2fn9NmGCeS5v1qzKz+EdgEXbFvHcjOe4usfVhAWH0SO+Bz3iexz0dpVSStWOfSZ7y7KCgMuBbwFExAZGH8C+YgEnps19dxnAGdWscxRwEqafwD+AGOB1TNv9xXsuXDaE740Abdu2PYAQa0fGZxn4cn20uq3VrhfW7Kl1a/NM/GkH8lDj2LGQlwfR0QcbKl6/l2u/u5YW4S0Yc3b1JX+llFJHrn1WEIuID3gBqLoIeng5AAGuFJE5IvILppbhH5ZlVXrbnoi8KyIpIpLSokWL2o61RkSEtDfSCO8RziJfNC1awIoVlZdLTjbvgQk70N4J+5no1+9czyuzX+HBKQ8yeZ0ZJ39dzjpaj2nN4ozFvHXuW/XvPfFKKaWAmlfjz8ZUwW86iH1lAX4qvxI3HthezTrbgDQRydttWnlqbEvlWoIjXsmGEtzL3HR8rSPPfW2RnQ333WfeK1Nu82YzQt7xx++lvX7jRvOY3J6uv968le7VV2sUzxdLv+C5mc/x9/a/AQhxhhDhiuCsDmeRGJnIeZ3P47T2pzG0y9D9Ok6llFJHjpom+/eAFy3LagssAIp2nykiC/e1ARHxWpa1ADgTmLjbrDOBr6pZbSZwiWVZEbu10Xcu+/dgbjzqTNhRYfTf0R8r2OK3E8z4NZMmmZfOnXWWWea//4Unn4SMDKiygmLKFDPM7fff73oFLAAlJWbku6uvrlEsHp+HJ/94kmBHMC+d9RIXdrmQ9k3bB2INDuO/F/z3II5WKaXUkaBGw+ValmXvZbaISI3ey1r26N0nmEfuZgI3A9cDySKyybKsj8s2OLxs+QhMSX428Dimzf4dYIWIXLK3fdWH4XLXrTNvi5061QxgFxUFy5aZR94TE2HGjCpWEjEj4B13nBl1Z9kyiCzrDT9pEpx7rvn3nHOq3a8tNj7bh8vpYnvhdpqFNcPlrH6oXqWUUvVDdcPl1vShrvZ7+RxV0yBE5AvgLswgOX9jOt8NFpHyUnrbsk/58oWYznvRmF75E4DpwHU13eeRpGhlEYsGLKJwsamk6NAB+vSBhx4yL7G55x7zSPzOnfDoo9VsZMUKaNvWA/DDTQAAIABJREFUjHC3datZudx335kq/H306ntn/jtc/fXV2GKTEJGgiV4ppRq4mr7P/pBVmYvIf4D/VDNvYBXTVmGe8a/3sr7KIm9GHsGxwbz/PoSHwxVXmHl//21eTfvPf5o3zsbGVrOR5cvNq2fPPddUC7zyihkI59JLTbX+oEFmAJ1q+G0/L/71Ii2atMBCh65VSqnGoKYvwrlob/NF5OtDE07DlvlVJlH9oghpFcIzz5hSfHmy79LFNMV32NebbpcvN8/qdeli7grmzgW3G4qLYdiwwFvrqvH9qu9Zv3M9o88YrePUK6VUI1HTDnpfVjO9vMG/Rm32jVnx+mIKFxXS4cUObNgAGzaY0WnLxcSYzz4tX26Gtm3SxPw8cya7HtZ//vl9rv7SXy/RPqY9F3Y5kDF4lVJK1Uc1arMXEcfuH8z77E/ADKO796KkAiDz60wAYi+K5fffzbQDGjBn2TLo1q3itBqW0OdsncPMLTO584Q7cTr0/kwppRqL/R11HTAD7YjIPGAU1bS/q4rC2ofR8oaWhLUP47ffID6+cs6ukfPOO8A340C7mHY8evKjXNerXvZvVEopdYBq9OhdtStbVjdgrohE7HPhWnYkP3p3xhlmyPrPPjv8+3KXuvlu5Xf4xc/VPWr2/L1SSqn6qbpH72raQa/3npMw76N/EFh08OE1bLnTcwnvEU5wUzPi8NSpUFp6ABsqKIDgYDMSz16ICH9u/pOPF3/MhGUTKPAWcHLSyZrslVKqkappB735mM54ezYOzwauPaQRNTB+t58lFywhdkgsbd/qSlYWJCWZnL3fXnsNHnvMDKZT3kGvCnf9fBevzX2NCFcEF3e7mOE9hnNKu1MO/CCUUkrVazVN9u33+NkGMkWk5BDH0+BkfpnJ/7d35/ExXvsDxz8n+yYRggixFEmpndBQklDt1VK1lJbaLkpVS2+pbrdXVbVqK90rKm1paYsufm3p1aStvXYquNSeCEGJLduc3x/PZGSSETORTCT5vl+v52XmnGf5nknkO8/znOec7PPZVB9WnalTjcfiDxyA6tULsbM9e4zp8ApI9ABj2oyhhn8Nnoh4Al8P38IFLoQQosxw+qA65U3SR0l4h3lzOiSAGTOMge8KlejBSPYF9Oo7d+UcFb0q0qByA55t/2whDyKEEKKssas3vlLqNaXUKBvlo5RSrxZ9WGXDpT8vcWHtBTz712DIEIWnJ0ybVsidZWfD3r3XTfaXMy8T/Uk0j//f44UPWAghRJlk76N3A7HdEW8LMKjowilbzvzfGS64uvPAvBps2QIffVSIs/qkJCPRHz5szGpnI9mfuHCCjgs6sitlFz3CexRJ7EIIIcoOe5N9VeC0jfIz5J+fXpjVerYW951py9BhirVr4eGHHdxBQoJxj75GDaMn/syZ+YbD3Zy0mYh5Eew7s4/vHvmOrg2uP9udEEKI8sneDnpHgQ7AX3nKOwLHizSiMsYtwI1XXinEhllZMHYshIYak940awbNm1utcinjEl0XdcXX3Zd1/1xHk2pNiiZoIYQQZYq9yf5DYLZSygP4xVzWGXgdKOxd6DItPSmd3ndewbuRL1/9VIjn7GJjYedO+Ppr6N3b5iq+Hr6sfHQlVX2rUtO/5k1GLIQQoqyytzf+TKVUEDAXY1x8gAxgjtb6xrOvlENpm9NIPOZFeO1CbJydDdOnQ3Q09Mo/4aDWmt+O/EZUnShaVs873pEQQghhze6x8bXWzwNBwJ3mpYrW+rniCqy0S9uaRgpe3HZHISaccXWFdetg/nybk9zMWj+L6E+i+fngz0UQqRBCiLLO3uFygwE3rfVx4I9c5TWBTK11SjHFV2qd3HiZS7hR+0bz0+dlMoGLizFTjg1f7PqC8T+Pp0+jPtx92903H6gQQogyz94z+4WArW7e9wKfFV04ZcfBrRkA1Krl4Ibvvw8REfD33/mqfjrwE4O+GURU7Sg+6/kZys6pbYUQQpRv9ib71sBvNsp/N9eJXLIvZ+Md7M79La7QsKGDG3/8sXHPvmJFq+J9qfvo/WVvGldtzLcPf4uXW8GT4QghhBA57O2N7wZ42ij3uk55uebq40rvHY2x3Ye+ANu3w9at8Pbb+arCKofx3cPf0bhqYwK8AookTiGEEOWDvWf2GwFb47A+Qa57+OIak6kQGy1YAB4e0L+/VbHWGqUUnW/rTDU/GcNICCGEY+w9s38R+EUp1ZRrz9l3AlpiPG8vckkclMiEX6uTHFSRLVvs3Cg9HRYtgp49oVIlq6oei3vQpkYbXur4UtEHK4QQosyz68xea70BiAQOA73My18Yj+AVPN9qOXR+zXlOXnbH19HZZd98E8aNsypac3QN3+//Hh93+ZiFEEIUjr1n9mitdwADwPLI3VBgOVAbKMTD5GVT5rlMrh66ysmKnkQ70hPf0xP++c98xa/8+gpVfasyqnW+SQeFEEIIu9g9qI5SylUp1Usp9X/AIeBB4AOgfnEFVxpd3HqRbCAlzdWxx+5Wr4Zdu6yKfvjfD/z3r/8yod0EObMXQghRaDdM9kqpcKXUdCAJmIEx1a0CBmqt39RaHyrmGEuV8+vPcxYPsrKVY8l+xAirye6zTFmM+WEMjas2ZkybMUUfqBBCiHKjwMv4SqnfgcbAUqCv1vpXc/lEJ8RWKvmE+xAyuBpPVzLGxrGLyQTHjxsz3Jm5ubixov8Ksk3Z8ky9EEKIm3Kje/aRwLvAR1rrP50QT6lX9aGqVH2oKu0d2SglBTIzLck+KS2JkAohNKrSqFhiFEIIUb7c6DJ+BMYXgjVKqW1KqafN4+QLG7LSssg8k8n583D1qgMbHjtm/BsaSmZ2Jq0+asXkXycXS4xCCCHKnwKTvdZ6m9b6CaA6MAt4ADhm3u5+pVRg8YdYeqQuS2Vt0FqeeyKT6tUd2DBXsv/xwI+cvHiSFsEtiiVGIYQQ5Y+9z9lf1Vp/prWOARoC04GngZNKqR+LM8DS5MKGC7j6u5J0wS337fcb69QJfv0VwsNZsH0B1Xyr0bWBrXmHhBBCCMfZ/ehdDq31AfM89qFAXyCjyKMqpc6vP49/W3+OHnOwJ35gIHTsyClTGiv2r2Bg04G4udg9BIIQQghRoEJnFK11NvCteSn3stKyuLTrEkEvBXH0HYiMdGDj774Dd3cWVdxLlimLoS2GFlucQgghyh85fSwiaZvTwAQuzfw5e9bBeeynToUKFRjy3ZfSC18IIUSRc/gyvrDNp6EPYR+F4d/Gn1mzoEsXBzY+ehRCQwn0DqRf437FFqMQQojySZJ9EfEM9iRkRAiBNd15+mlo1crODTMy4ORJNrkks2DbgmKNUQghRPkkyb6IpHyeQvqJdC5ehH37HHjOPikJtGbpxU18s++bYo1RCCFE+STJvghkpWWROCCRlEUpJCTA7bfnm9Pm+szP2G93P0u3Bt2KLUYhhBDllyT7IpB+PB0Az5qeZJgfRPT0tHPjO+/kgyXPsqYW3NfgvuIJUAghRLkmyb4IpB8zJ/vQa8new8POjd3dWXRpHeG1WlDDv0bxBCiEEKJck2RfBHIn+3Tj5fWT/c6d8MknsHcvANlfLqHnL8k8EP6AEyIVQghRHslz9kXg6rGroMAzxI7L+EuWGM/VA/zvf7gu+px/HfZFR/3HKbEKIYQofyTZF4GQUSEE3h2Ii4cLd90FH3xgjIBr09q1UL06nDwJn3+OPnoEFRqKUsqpMQshhCg/JNkXAc9gTzyDjVP5hg2NxaaMDNi4EUaNgh070IsWcfb4/zhWx5vmzgtXCCFEOSP37ItA8sfJnF93HoATJ2DLFjCZbKy4bZvxAH779jBgAGr/fipf1rjVruvcgIUQQpQrkuxvktaaA2MPcOrLUwDMnw+tW4PWNlbevt34t3176N3bUlyvaXTxByqEEKLckmR/k7LOZ5F9MRvPmsZl/PR0cHU1lnxGjoTkZKhenWz/Cjz8WCWGftAV7/6DnBu0EEKIckWS/U3KGVDHK9QLMG7LF/iMfXAwAGuPrWVJyFn+0X4weHkVd5hCCCHKMUn2Nyn3M/ZgJHubj9399Rf06WMZR7d+pfq81uk17g+731mhCiGEKKck2d8kW8ne5pn9b7/B0qWW6/shFUJ4ocML+Hn4OStUIYQQ5ZQ8eneTggcHE9g5EM8QI9kPGwb33GNjxTVrjIfvb7+dnSk72X9mPw+EP4CHq73j6gohhBCFI2f2N8nF0wXvet4oV2NQnNatoWdPGytu2wYREeDiwvt/vM/gbwaTZcpybrBCCCHKJUn2N+nEeydI+SLF8n7nTvjjDxsrnjoFISFkm7JZmriU7mHd8XH3cV6gQgghyi1J9jfp+JzjpC5Ptbx/+WUYMcLGiqGhEBbG9pPbOX35ND3CezgvSCGEEOWa3LO/CVpr0o+lU/n+ypay63bQW7cOgN/WzwagY+2OzghRCCGEkDP7m5F1NgvTFZOlJz4Yg+oU9Jz9rlO7qBdYT+auF0II4TSS7G/C1WNXAaySvc0z+927oW1b2LCB+Q/MZ9OITU6MUgghRHknyf4mZJw0Jq/PGT0PrpPsjx+HTZvAZEIpRSXvSk6MUgghRHkn9+xvQuV/VKbDlQ4ot2tz0c+ebWNc/FSjA9/3ZzfwxdJ3iH0gVnriCyGEcBpJ9jfJ1cs6s7drZ2Mlc7L/JnUNv53dhLebtxMiE0IIIQxyGf8mHH/7OIenHLYqW7XKGD/HSmoq2tWVn06vp2PtjiilEEIIIZzF6cleKTVaKXVIKXVVKbVFKdXBzu3uUkplKaV2F3eM9jrz/RnOrDhjVTZ8OMydm2fF4GAuR7cn6fJJeeROCCGE0zk12Sul+gFzgKlAC2Ad8KNSqtYNtgsEPgVWF3uQDshMzcSjinVvPJuz3o0Zw5fThwDyfL0QQgjnc/aZ/b+AOK31PK11otb6SSAZePwG280HPgHWF3eAjshMzcStsnW3h+sNquPm4kaHWh1oGNTQSdEJIYQQBqcle6WUB9AKWJWnahVgq1tbznajgWrAlOKLrnAyz2TiHuRuVWZzUJ1OnRj42Q5+G/qb3K8XQgjhdM48sw8CXIGUPOUpQLCtDZRSTYD/AI9qrbNvdACl1GNKqc1Kqc2nT5++2XgLZEo34erjikfVG1/G17t3oy9eLNZ4hBBCiOu5ZR+9U0p5AkuA8VrrQ/Zso7X+CPgIoHXr1roYw8PF04X2p9vnK4+Phxq5R8I1meDMGabvX0DDfffTPbx7cYYlhChmJpOJ48ePc+nSpZIORZQz7u7uVK1aFX9/f4e3dWayTwWyMS7J51YNOGlj/epAQ2CBUmqBucwFUEqpLOA+rXXeWwIl7q678hScO4cymTjhmUEHn6ASiUkIUXRSU1NRShEeHo6Lizy9LJxDa82VK1c4ceIEgMMJ32m/qVrrDGAL0CVPVReMXvl5nQCaAM1zLR8AB8yvbW3jNBc2X2B3r91cPnDZUpaRAZ9+Cvv25VrRPKBOqg+EB4U7OUohRFH7+++/qVatmiR64VRKKXx8fKhRowanTp1yeHtn/7bOAoYopYYrpRoqpeYAIRhJHKXUp0qpTwG01pla6925F+AUkG5+X6I3wa8cuELq8lR05rW7BRcuwODB8PPPuVZ0c2NLZB1SQyrKmPhClAHZ2dm4u7vfeEUhioG3tzeZmZkOb+fUe/Za6yVKqcrASxiX6XdjXI4/Yl6lwOftbyWZqcaHnbs3foYxL451b/x69Xh6RCiamk6MTghRnOSpGlFSCvu75/QOelrr94D3rlMXfYNtJwGTijyoQshMzQQFboHXPkKbyR64v8H9+Hs63qFCCCGEKApy06mQMlMzcavohovbtY/QZrKfPp2JPWfweJOhzg1QCCGK0ZAhQ+jWrZtD20RHRzNmzJhiikgURJJ9Ibn6uuLb2NeqzFayz0w6jr58Gby8EEIIZ1NKFbgMGTKkUPudM2cOCxcudGibZcuW8frrrxfqeIURGRnJ8OHDrcoWLlyIUooZM2ZYlb/00kvUqmV9J3nWrFm4urry4osvFnusxU2SfSHVm1aPFr+1sC6rZ8x417nztbIjBzZz1P0yB84ecHKEQggBycnJlmXevHn5yubMmWO1vr2dvwICAqhYsaJDsVSqVIkKFSo4tM3NiImJISEhwaosPj6e0NBQm+UxMTFWZfPnz+e5554jLi6O7Owbjut2S5NkX4S8vaF5cwgMvFaWdSqFVF9F7YDaJReYEKLcCg4Otiw5yTnn/dWrV6lYsSJffPEFnTp1wtvbmw8//JAzZ87wyCOPULNmTby9vbnjjjtYsGCB1X7zXsaPjo5m9OjRvPDCCwQFBVG1alXGjx+PyWSyWif3Zfw6deowZcoURo4cib+/PzVr1mT69OlWx9m/fz9RUVF4eXkRHh7ODz/8gJ+fH3FxcTdse0xMDAcPHuTYsWOWsvj4eJ5//nl+//13SwK/dOkSf/zxB506dbKst379elJTU5k0aRLe3t78+OOPdnzaty5J9oW064FdHHvrmFXZ8ePw3nuQlHStzOXMWS4H+ODuKo/qCCFuTc8//zyjR49mz549PPjgg1y9epWWLVuyYsUK/vzzT8aOHcvIkSNZvbrgiUcXLVqEm5sb69at45133uGtt95iyZIlBW4ze/ZsmjRpwtatW5k4cSLPPvss69cbc56ZTCZ69uyJm5sbGzZsIC4ujldeeYX09HS72tW+fXs8PDyIj48H4MiRI5w4cYLBgwfj5+fHli1bAFizZg2ZmZlWZ/axsbE8/PDDuLu78+ijjxIbG2vXMW9Vt+xwube6cz+fw+d2H6uyPXvgiSegWTMICTHKVt7uimeVenQogRiFEM4RHRedr6zvHX0ZHTGay5mXuW/RffnqhzQfwpDmQ0i9nEqfL/vkq3+89eP0a9yPY+ePMXD5QKu6hCEJRRU6AE8++SR9+ljHMGHCBMvrxx57jF9++YUvvviCzrnvU+bRqFEjJk+eDEBYWBjz5s1j9erVPPLII9fd5p577rGc7T/55JPMnTuX1atXExkZyc8//8y+fftYtWoVNczjkM+ePZv27fMPVW6Lj48Pbdq0IT4+nkGDBhEfH09ERAQ+Pj5ERUURHx9vqa9Xr57lnv3Fixf58ssvLV8SBg4cyNSpUzl58iTBwTancrnlyZl9IWRfzsZ01YR7Zeuz9bwd9LJMWTzT9jx/Dejq5AiFEMJ+rVu3tnqfnZ3Na6+9RtOmTalcuTJ+fn4sW7aMo0ePFrifpk2bWr0PCQm54WhvBW2zd+9eQkJCLIkeICIiwqHRCzt16mRJ2vHx8URHRwPGLYXc5bnP6hcvXkzNmjUtn0u9evWIiIjgk08+sfu4txo5sy8EWwPqwLVknzPrXVZ2JnM7z6BFrbbODE8I4WQFnWn7uPsUWB/kE1RgfWhAaJGfyefl62v9ZNGMGTOYOXMmc+bMoUmTJvj5+fHCCy/cMHHnHVlQKWV1z76otnFETEwMkydP5vDhwyQkJDB//nwAoqKieOaZZzh79ixbt25l3Lhxlm1iY2PZt28fbm7XUqTJZOL06dNMnDixyGJzJkn2hXCjZJ9zZu91+hyj2o+FefNguCR8IUTpsGbNGrp3787AgcbtA601+/fvd7j3/c26/fbbSUpKIikpiRDzvdHNmzc79GUgMjISLy8vYmNjOXnyJO3atQMgPDwcPz8/Zs2aRVZWluXM/s8//2Tjxo38/PPPVpfsr1y5Qvv27fntt9/o2LFjEbbSOSTZF1KFNhXwrGk9cX1On5GcZH/04FZqAbpiRWRwTSFEaREWFsaSJUtYs2YNQUFBvP322xw6dIgWLVrceOMi1KVLF8LDwxk8eDAzZszgypUr/Otf/8LNzc3uYWM9PT2JjIxk7ty5lvv1OaKiopg7dy4NGza0JPbY2FhatGjB3XffnW9fnTt3JjY2tlQme7lnXwgVWlag1cZWVGhl/bxo795w4ACEhhrvFye8A4CqUsXZIQohRKG99NJLtGnThq5du9KxY0d8fX0ZMGCA0+NwcXFh+fLlpKen06ZNGwYPHsyLL76IUgovBwYqi4mJIS0tzXK/Pkd0dDRpaWmWR+4yMjJYuHBhvs6KOR566CG+/vprzp8/X+g2lRSltb7xWqVQ69at9ebNm0s0hudG1OWN2MOwezfccUeJxiKEKBqJiYk0bNiwpMMot3bs2EHz5s3ZvHkzrVq1KulwSkRBv4NKqS1a69Z5y+XMvhCOzT7G5labyftFacMGmDbNuJyfZcriysnjRkVQUAlEKYQQpd/y5ctZtWoVhw4dIj4+niFDhtCsWTNatmxZ0qGVKpLsC+HKgStcPXI13z2jhAR47jkwmWBf6j62VMliz6CuULlyyQQqhBClXFpaGmPGjKFRo0YMGDCAhg0bsnLlSpRSTJ06FT8/P5tL167yyHNu0kGvEDJTM/M9Yw/WvfF3puxkbW3IGvUGuMnHLIQQhTFo0CAGDRpks27UqFH07dvXZp23t3dxhlXqSBYqhMzUzHyP3YGR7F1djeXu2+7ml1Zvc3vF+iUQoRBClH2VKlWiUqVKJR1GqSCX8Qvhesk+Pf3aY3dVLpqI6f4kHu9+4OTohBBCCGuS7AvBv40//pH++cozMq6NnvfDF8b40LTO1ylSCCGEcCpJ9oUQPi+c2s/ln7J26lTYtw/OXD7Dxm/fQysFTh6EQgghhMhLkn0R8vWFqlWNznmtk+BSvVpQocKNNxRCCCGKkSR7B109cpW11dZyeunpfHVLlsDs2bDj5HZaJ4FrRJsSiFAIIYSwJsneQRmnM8g8lYlyzz8u87Jlxpw3u07u5Jl+FfF+eoKNPQghROkyadIkGjdufN33towZMybf8LRFcWxROJLsHZR1JgvIP+MdGB30PDxgZ+puUqMiICLC2eEJIYSVBx54gM6dO9usS0xMRCnFqlWrHNrn+PHj+fXXX4siPIvDhw+jlCLvMOfFcayCVK9enSlTpliVTZkyBaUUX3/9tVX5o48+SocOHazKnnrqKVxdXZk3b16xx+oISfYOut70tnDt0bvfb5vCoorDnR2aEELkM2zYMOLj4zl8+HC+uvnz51O7dm2bM7wVxM/Pj8pOGhnUmccCY9KchIQEq7L4+HhCQ0NtludMogOQnp7OokWLeO6554iNjXVCtPaTZO8gS7K/zgh6Hh7gNX02VV5+3dmhCSFEPvfffz/VqlVjwYIFVuWZmZl89tlnDB06lBEjRlC3bl28vb1p0KABb775ZoFzxue9tJ6dnc348eMJDAwkMDCQcePGkZ2dbbXNTz/9RIcOHQgMDKRSpUrce++9JCYmWurr1q0LQEREBEopyy2AvMcymUy8+uqrhIaG4unpSZMmTfj2228t9TlXCJYuXUqXLl3w8fGhUaNG/Pzzz3Z9XjExMaxbt44M85Co6enprFu3jueff574+HjLevv37ycpKckq2S9btow6derw4osvsmfPHnbv3m3XMZ1Bkr2DvOt7U6VfFVwDXPPVZWTA+cxTXFr/G9kt5ZE7IUTJc3NzY/DgwcTFxVkl8O+//57U1FT++c9/UqNGDb788ksSExN57bXXmDp1ar4vBwWZOXMm8+bN48MPP2T9+vVkZ2ezaNEiq3UuXbrEuHHj2LRpEwkJCQQEBNC9e3dLUt20aRNgfClITk5m2bJlNo81Z84cpk+fzrRp09i1axc9e/akV69ebN++3Wq9F198kaeeeoodO3YQERHBww8/zMWLF2/YlpiYGK5cucKGDRsA2LBhA0FBQQwaNIgDBw6QkpICGGf13t7e3HnnnZZtY2NjefTRR/Hx8aF379631tm91rpMLq1atdLOlpGh9bCZ92sN2vT++04/vhCi+O3Zsyd/YVRU/uXdd426S5ds1y9YYNSfPm27fvFio/7o0fx1Dtq/f78G9MqVKy1l9913n/7HP/5hc/2JEyfqzp07W97/5z//0Xfcccd131evXl1PmTLF8j47O1s3aNBARxUQ68WLF7WLi4v+/ffftdZaHzp0SAP6jz/+sFov77FCQkL0K6+8YrVOVFSUHjBggNV+PvjgA0v98ePHNWA51o2EhobqSZMmWY6fs+/IyEi92Pxz6devn9Vn9Ndff2l3d3ednJystdZ69erVunLlyvrq1at2HdMRNn8HzYDN2kZOlDN7B+k809rmplyzOL3HuMyjGjVyVkhCCFGgBg0aEBUVxccffwxAUlISK1euZNiwYQB88MEHtG7dmipVquDn58fs2bM5evSoXfs+f/48ycnJREZGWspcXFxo27at1XoHDx6kf//+1KtXD39/f6pVq4bJZLL7OAAXLlwgKSmJ9u3bW5Xfdddd7Nmzx6qsadOmltchISEAnDp1yq7jxMTEWC7Zx8fHW24pREdHW8oTEhKIiYmxbDN//nw6d+5McHCwZV0fHx+++eYbu9tXnGQiHAft7rGbrHNZtPg9/2X6x589wYmdDwGfQGCg84MTQpSMPB23rPj4FFwfFFRwfWhowfV2GjZsGCNGjODs2bPExcVRqVIlevTowZIlSxg3bhwzZsygXbt2+Pv78+6777J8+fKbPmZu3bp1o2bNmnz44YfUqFEDNzc3GjVqZLmMf7PyTjnu7u6er66gfgi5xcTEMGrUKM6dO8fGjRuZP38+AFFRUYwdO5bExERSUlIs9+uzs7OJi4sjKSkJt1yznJpMJmJjY+nXr99Nta0oyJm9gzLP2n7GHmDpF37suNyFv7dvgLAwJ0cmhBDX16dPH7y8vFi4cCEff/wxgwYNwt3dnTVr1tC2bVvGjBlDy5YtqV+/PgcPHrR7vwEBAVSvXt1yjxuMK6A59+ABzpw5w969e3nhhRe4++67adiwIWlpaWRlZVnW8TDPIpa3Y19u/v7+hISEsHbtWqvyNWvW0KgIr6bGxMSQnp7OzJkzqVKlCvXrG7OXtm/fnoMHD7Jo0SL8/PyIMD9e/dM17CS9AAAgAElEQVRPP3HmzBk2b97M9u3bLcuKFStYvXq1zSchnE3O7B2UdS4Ln9t9bNZlZ7lStVIVKjZra7NeCCFKire3N/3792fSpEmcO3fOcgk/LCyMuLg4fvzxR+rXr8/ixYv59ddfCXTg6uTYsWN5/fXXCQsLo0mTJrz33nskJydTvXp1AAIDAwkKCmLevHmEhoZy4sQJJkyYYHUWXLVqVby9vVm5ciV16tTBy8uLgICAfMeaMGECL7/8Mg0aNKBVq1YsXLiQ33//na1bt97kJ3RN7dq1qVu3LnPnzuWBBx6wlPv5+dGqVSvmzp1Lhw4dLPHHxsbStWtXWrZsabWfxo0bEx4ezscff8zkyZOLLL7CkDN7B2Wdy8It0PZ3JG9Vke4Bd8Dbbzs5KiGEuLHhw4dz7tw52rVrR8OGDQEYOXIkffv2pX///kRERHD48GGeeeYZh/b7zDPPMHToUIYPH07btm0xmUwMGDDAUu/i4sKSJUvYuXMnjRs35oknnuDVV1/FM2eaUIynBubOnUtsbCwhISH06NHD5rGeeuopJkyYwLPPPkvjxo1Zvnw5S5cupVmzZoX4RK4vJiaGtLS0fKMARkdHk5aWZrmEn5KSwooVK+jTp4/N/Tz00EMsWLDA7lsIxUUV1OGsNGvdurXOOxJTUfjN+zdqjKlBven1rMozszOpVsWdR2smMPdEbzhzpsiPLYQoeYmJiZZEKURJKOh3UCm1RWudb251ObN3gDZpQh4PISAq/6WlMT+MIS3jPB5Zl6BixRKITgghhLBNkr0DlIui/qz6BHULyle36q9VdJs/hBm3vS/JXgghblGjRo3Cz8/P5jJq1KiSDq/YSAc9B5iyTOh0jYuPi9VjHscvHOfw34cZ13Yc/P2VJHshhLhFTZ48mfHjx9us8/f3d3I0ziPJ3gFpG9PYdtc2mvzYhMr/uDYxw4bjGyDLnVUzB9Dg2F7ua23fwA1CCCGcq2rVqlStWrWkw3A6uYzvgMxz5klwAq0nwdlwfAMeOoAfvg4iccQs+OijkghPCCGEsEnO7B2Qdc4YACLvo3fRdaLxuBrC64BHgDdU9i6B6IQQQgjbJNk7IG+yP3vWmOmuW1g3WvphJPsVy6BFVbjrrhKMVAghhLhGLuM7wJLsKxrJPjQUqleH/Wf2k55ujFfgueo72LatxGIUQggh8pJk74CADgHUfrk2Lu7Gx3b5slEe/k44J86fpHJgNj5clt74QgghbilyGd8BgZ0CCex0bbxoX1/wqZWIp39N7mpendRVmyHiK6g4sASjFEIIIazJmb0D0k+mk/l3puW9mxtcDdpAZE3zPM5//238K2f2QogybsiQIXTr1s2hbaKjoxkzZkwxRSQKIsneAXv67mH3g7st70c/fYG00xVp7BvD3r3Q+6WG7KCpJHshxC1DKVXgMmTIkELtd86cOSxcuNChbZYtW8brr79eqOMVRmRkJMOHD7cqW7hwIUopZsyYYVX+0ksvUatWLauyzz//nMjISPz8/PD19aVt27bXbXNGRgZVqlTBz8+P8+fPF21DioAkewdkncuyesZeVdsNe3tSI/suUlJg2cYanP3pDyjCeZWFEOJmJCcnW5Z58+blK5szZ47V+pmZmbZ2k09AQAAVHTyxqVSpEhUqVHBom5sRExNDQkKCVVl8fDyhoaE2y2NiYizvJ06cyNChQ+nRowdbtmxh27Zt9OrVi2HDhvHcc8/lO9Y333xD3bp1iYyM5PPPPy+O5twUSfYOyDyXaXns7tw5MJ1oDkBlwsnIMNbx8PMAV9eSClEIIawEBwdblpzknPP+6tWrVKxYkS+++IJOnTrh7e3Nhx9+yJkzZ3jkkUeoWbMm3t7e3HHHHSxYsMBqv3kv40dHRzN69GheeOEFgoKCqFq1KuPHj7ea2jXvZfw6deowZcoURo4cib+/PzVr1mT69OlWx9m/fz9RUVF4eXkRHh7ODz/8gJ+fH3FxcTdse0xMDAcPHuTYsWOWsvj4eJ5//nl+//13srOzAbh06RJ//PGHZdraTZs28eabbzJt2jSee+45wsPDCQsLY+LEiUybNo1p06axadMmq2PNnz+fgQMHMmjQIObPn3/D2JxNkr0Dcs9lv20bvDHFB4AL5zxITzfW8XhnVkmFJ4QoIduit+VbTrx3AoDsy9k265PjkgHISM2wWX9qiTHs9tVjV/PVFbXnn3+e0aNHs2fPHh588EGuXr1Ky5YtWbFiBX/++Sdjx45l5MiRrF69usD9LFq0CDc3N9atW8c777zDW2+9xZIlSwrcZvbs2TRp0oStW7cyceJEnn32WdavXw+AyWSiZ8+euLm5sWHDBuLi4njllVdIz/mDewPt27fHw8OD+Ph4AI4cOcKJEycYPHgwfn5+bNmyBYA1a9aQmZlpObNftGgRfn5+jB49Ot8+H3/8cXx9ffniiy8sZUeOHCEhIYGHH36YXr16sXfvXnbs2GFXjM4iyd5OpqtZmC6bLMl+39Fr89WnpmI5s/f8YXlJhCeEEIX25JNP0qdPH+rWrUvNmjWpUaMGEyZMoHnz5tx222089thj9OrVyyrB2dKoUSMmT55MWFgYffv2JSYm5oZfEO655x7GjBlD/fr1efLJJ6lfv75lm59//pl9+/bx6aef0rx5cyIjI5k9ezZZWVl2tcvHx4c2bdpYkn18fDwRERH4+PgQFRVlVV6vXj3LPfv9+/dz22234eHhkW+fnp6e1KtXj3379lnKFixYQJcuXahSpQq+vr706tXLcsvkViGP3tnh7Fm4t94RBlY7Scu7jUv3v/y5EzC+BZ4+DWFhUNv3ND4B7gXsSQhRFrVIaHHdOlcf1wLrPYI8Cqz3CvUqsL4otG7d2up9dnY2b7zxBkuWLOHEiROkp6eTkZFBdHR0gftp2rSp1fuQkBBOnSp4YrCCttm7dy8hISHUqFHDUh8REYGLi/3nqZ06deKTTz4BjKSe04bo6GiWLVvGxIkT892vd4TJZGLBggW8+eablrKBAwfSt29fZsyYgZeXV6H2W9TkzN4O/v6w9XxdTqf8D/+EDwDY/L/DACQTzKuvQrducDh6KPWr3Hq9MIUQoiC+vr5W72fMmMHMmTOZMGECq1evZvv27Tz44INk5FzCvA53d+uTHaWU1T37otrGETExMRw5coTDhw+TkJBgSfZRUVGsXbuWs2fPsnXrVsv9eoCwsDAOHjxo83ZBeno6Bw8eJCwsDIBVq1Zx9OhRBgwYgJubG25ubnTt2pW///6bpUuXFlk7bpYkezu4uUFwsOJwSCeyX3qVc6v/j8PJl/DzSCOYFNwyrxgr/v23PHYnhCj11qxZQ/fu3Rk4cCDNmzenXr167N+/3+lx3H777SQlJZGUlGQp27x5s0NfBiIjI/Hy8iI2NpaTJ0/Srl07AMLDw/Hz82PWrFlkZWVZndk/8sgjXLp0iffffz/f/t577z0uXbpE//79AaNjXq9evdi+fbvVMmLEiFuqo55cxrdTcIUs9u5vyKWKzQi8uxu3DajP5LYViXt7MKkvX6B+e2/e2z2Lr2LeJaCkgxVCiJsQFhbGkiVLWLNmDUFBQbz99tscOnSIFi2K93ZCXl26dCE8PJzBgwczY8YMrly5wr/+9S/c3NxQStm1D09PTyIjI5k7d67lfn2OqKgo5s6dS8OGDQkODraU33nnnTzzzDNMnDiR9PR0evbsiVKK5cuX8+9//5uJEyfSpk0bTp8+zXfffcdXX31F48aNrY47bNgwIiMjOXjwIPXq1SuaD+QmyJm9naoHZHMaT9zmzWL7Pc0IbGKif7+6/MB9xC725dAh+Pl8G4j7pKRDFUKIm/LSSy/Rpk0bunbtSseOHfH19WXAgAFOj8PFxYXly5eTnp5OmzZtGDx4MC+++CJKKYfuhcfExJCWlpavz0F0dDRpaWlWl/BzzJgxg9jYWJYvX07z5s1p1qwZS5cuJTY2ljfeeAOAzz77DE9PT+69995827dp04bQ0NBb5uxeaa1LOoZi0bp1a7158+Yi29/Lvf5mw/KrrEiphEdVD75elkXtCheJG7+LJUcjmfCcG889Z0yO4y3T2QtRZiUmJtKwYcOSDqPc2rFjB82bN2fz5s20atWqpMMpEQX9DiqltmitW+ctlzN7O/2zxd+8wF7Lo3djn3Tjg8UVCXqwA2fPu3HlojE4g8eqFSUZphBClCnLly9n1apVHDp0iPj4eIYMGUKzZs1o2bJlSYdWqkiyt1PWuSxcfF146beX6LggitOnNVWqQFDFTLSGk8cycCUL16OHSjpUIYQoM9LS0hgzZgyNGjViwIABNGzYkJUrV6KUYurUqfj5+dlcunbtWtKh31Kkg56dTrQIpq9bXaqtXIZnqDuZmcpI9p/Oxl09jVt2Bs3YI73xhRCiCA0aNIhBgwbZrBs1ahR9+/a1Wect91OtSLK3U812fpw6D+cPXaFveAc2A0FB0LfdcR4+GIQauxoWRkDF70o6VCGEKBcqVapEpUqVSjqMUkEu49vJL+kCAOlng6jtbvR9qFIFXOvVQaVdgEPmy/dyZi+EEOIWI8neTkef2Ie/ewZcqMkDHeuwdSu0bw8XgsMYRiwdn23Lg14/gnzLFEIIcYtxerJXSo1WSh1SSl1VSm1RSnUoYN1eSqlVSqnTSqk0pdRGpdQDzow3R9a5LIJ80gnREbSoFU6LFhAQAO71avExw/j9cC12hfwD7rijJMITQgghrsupyV4p1Q+YA0wFWgDrgB+VUrWus0kU8Atwv3n9H4DlBX1BKC6Z5zLp0TiDcf1a88dGNz78ELKzwbtxPXw9jPGibUyQJIQQQpQ4Z5/Z/wuI01rP01onaq2fBJKBx22trLUeq7V+Q2u9SWt9QGv9CrAFeNCJMWPKNGG6ZGJYp5NMmADLl8PYseDiAvj6ElTdyPIeSYegjA5SJIQQovRyWrJXSnkArYBVeapWAe0c2FUF4FxRxWWPrHPG3MkvbXmJRdu+4tQpoyd+ztDMVQKMM3vPS2evFQohRBkxadIkq7Hf8763ZcyYMTecErcwxxaF48wz+yDAFUjJU54CBOdfPT+l1BNATeCz69Q/ppTarJTafPr06ZuJ1YprBVdOzT1FfHYIj7Z8iC1bjJ74OWqd2QZAcx/nzwolhBAFeeCBB+jcubPNusTERJRSrFqV9xysYOPHj+fXX38tivAsDh8+jFKKvMOcF8exClK9enWmTJliVTZlyhSUUnz99ddW5Y8++igdOly7q2wymXj77bdp3rw53t7e+Pv706lTJ3788Uebx0pJScHLy4tatWoV6bS+tpSa3vhKqd7AdKC/1vqIrXW01h9prVtrrVtXyZ2Nb5Krtytr66/lavXjAOzebZzZ51g68Bs0io/qvlFkxxRCiKIwbNgw4uPjOXz4cL66+fPnU7t2be6++26H9unn50flypWLKMJb51hgTJqTkJBgVRYfH09oaKjN8tyT6PTv359///vfjBw5kj///JP169cTERFBt27dbE6X+8knn9C9e3e8vLxYuXJlcTTHwpnJPhXIBqrlKa8GnCxoQ6VUH4yz+UFa6++LJ7yCbU7eTKN6/pb3uZM9desa/1665NyghBDiBu6//36qVavGggULrMozMzP57LPPGDp0KCNGjKBu3bp4e3vToEED3nzzzQLPNPNeWs/Ozmb8+PEEBgYSGBjIuHHjyM7Ottrmp59+okOHDgQGBlKpUiXuvfdeEhMTLfV1zX9HIyIiUEpZbgHkPZbJZOLVV18lNDQUT09PmjRpwrfffmupz7lCsHTpUrp06YKPjw+NGjXi559/tuvziomJYd26dWRkGLdn09PTWbduHc8//zzx8fGW9fbv309SUpIl2X/55ZcsWbKEuLg4Hn/8cW677TbuuOMOpk2bxpNPPsm4ceM4fvy41bE+/vhjBg0axMCBA4t9djynJXutdQZG57oueaq6YPTKt0kp1Rcj0Q/RWn99vfWKU5Ypi23J22h7e00AJk6E2bOv1X+b3AaFZuTZqSURnhCihEVH51/ee8+ou3zZdn1cnFGfmmq7fskSo/7Ysfx1jnBzc2Pw4MHExcVZJfDvv/+e1NRU/vnPf1KjRg2+/PJLEhMTee2115g6dWq+LwcFmTlzJvPmzePDDz9k/fr1ZGdns2jRIqt1Ll26xLhx49i0aRMJCQkEBATQvXt3S1LdtGkTYHwpSE5OZtmyZTaPNWfOHKZPn860adPYtWsXPXv2pFevXmzfvt1qvRdffJGnnnqKHTt2EBERwcMPP8zFixdv2JaYmBiuXLnChg0bANiwYQNBQUEMGjSIAwcOkJJi3ImOj4/H29ubO++8E4BFixbRoEEDHnwwf//xCRMmkJGRwdKlSy1lv//+O2fOnOEf//gHjz76KCtWrKAobz/n5ezL+LOAIUqp4UqphkqpOUAI8AGAUupTpdSnOSsrpR4GFgHPAb8ppYLNi1NHrsk2ZfPufe8y5M6e+PpCejoE5+plcNK1BgC7KkU7MywhhLDLsGHDOHr0KP/9738tZfPnz+eee+4hNDSUyZMnExERQZ06dejbty+jRo3iiy++sHv/b731Fs8++yx9+/bl9ttvZ86cOQQHW3fF6t27N71796ZBgwY0bdqUBQsWcOjQIUuSz7n1WrlyZYKDg687DO6MGTMYP348/fv3JywsjMmTJ9OhQwdmzJhhtd7TTz9N9+7dadCgAVOnTuXs2bP5vhDYUr9+fUJDQy1n8fHx8URFReHr60urVq0sl/Lj4+Np164dnp6egHGmf71pZ2vUqIG/vz/79u2zlM2fP59+/frh7u5O3bp1adOmDZ988skN4yssp46Nr7VeopSqDLwEVAd2A/flugef93n7URgxvmVecvwKRBdvtNd4unkytMVQAEaOhP/+F3btgiZNjPoqYYEAXHANdFZIQohbSJ5buVZ8fAquDwoquD40tOB6ezRo0ICoqCg+/vhj7rnnHpKSkli5ciWLFy8G4IMPPiA2NpYjR45w5coVMjMzqV27tl37Pn/+PMnJyURGRlrKXFxcaNu2LceOHbOUHTx4kH//+99s3LiR06dPYzKZMJlMHD161O52XLhwgaSkJNq3b29Vftddd/HDDz9YlTVt2tTyOiQkBIBTp07ZdZyYmBji4+P5z3/+Q3x8PAMHDgQgOjqa+Ph4+vXrR0JCAk8++aTdsedtx1dffcUvv/xiKRs4cCCzZs1i/PjxhdrnjTi9g57W+j2tdR2ttafWupXW+rdcddFa6+g875WNJdrWvp1hyBDYuRP27r1WFhRsfGe6cMW9ZIISQogbGDZsGN988w1nz54lLi6OSpUq0aNHD5YsWcK4ceMYMmQIK1euZPv27YwePdpyeb2odOvWjdOnT/Phhx+yceNGtm3bhpubW5EdR+V57Nnd3T1fnb093mNiYtiwYQPnzp1j48aNlv4DUVFRJCQkkJiYSEpKilXnvLCwMKs+CLmdOHGCCxcuEBYWBsDnn3/O5cuXad++PW5ubri5ufH444+zd+9e1q5da3ebHVFqeuPfKk6cMP7N3dk/p7PehQvOj0cIIezRp08fvLy8WLhwoaVjmLu7O2vWrKFt27aMGTOGli1bUr9+fQ4ePGj3fgMCAqhevbrlHjeA1tpyeR7gzJkz7N27lxdeeIG7776bhg0bkpaWRlZWlmUdD/MQpHk79uXm7+9PSEhIvoS4Zs0aGjVqZHfMNxITE0N6ejozZ86kSpUq1K9fH4D27dtz8OBBFi1ahJ+fHxEREZZt+vfvz//+9z+++eabfPt788038fDwoE+fPoBxCX/MmDFs377darn//vuLraOeTHHroDFjjH9zPwmSk/hbtHB+PEIIYQ9vb2/69+/PpEmTOHfuHMOGDQOMM9K4uDh+/PFH6tevz+LFi/n1118JDLT/tuTYsWN5/fXXCQsLo0mTJrz33nskJydTvXp1AAIDAwkKCmLevHmEhoZy4sQJJkyYgJvbtRRUtWpVvL29WblyJXXq1MHLy4uAgIB8x5owYQIvv/wyDRo0oFWrVixcuJDff/+drVu33uQndE3t2rWpW7cuc+fO5YEHrk3H4ufnR6tWrZg7dy4dOnSwir9v3758/fXXDBkyhDfeeIN7772XK1eu8Omnn/LOO+/wzjvvULNmTXbu3MnmzZuZP39+vsGCBg4cyLBhw5gzZw4VKlQosvaAnNk7rF8/49+qVa+VValijJKb66kMIYS45QwfPpxz587Rrl07S2eykSNH0rdvX/r3709ERASHDx/mmWeecWi/zzzzDEOHDmX48OG0bdsWk8nEgAEDLPUuLi4sWbKEnTt30rhxY5544gleffVVS+c2MJ4amDt3LrGxsYSEhNCjRw+bx3rqqaeYMGECzz77LI0bN2b58uUsXbqUZs2aFeITub6YmBjS0tLyjQIYHR1NWlqa1SV8MG4VLF68mFdeeYX333+fRo0a0bZtWzZu3MiKFSt4/HFjVPjY2FhLJ8W8unXrhslkcqhzpL2ULqNjubdu3VrnHYmpKGRnQ0oKmPt7CCHKmcTExOv2uhbCGQr6HVRKbdFat85bLmf2DnJ1lUQvhBCidJFkL4QQotwYNWoUfn5+NpdRo0aVdHjFRjroCSGEKDcmT5583WfZ/f39bZaXBZLshRBClBtVq1alau4e1uWEXMYXQggHldWOzeLWV9jfPUn2QgjhAFdXVzIzM0s6DFFOXblyxWp0QHtJshdCCAdUrFiRlJQUu4deFaIoaK25fPkyJ06cKNRtCLlnL4QQDggKCuL48eNWM5gJ4Qzu7u5Uq1atUB0JJdkLIYQDXFxcqFUr7wSdQtza5DK+EEIIUcZJshdCCCHKOEn2QgghRBknyV4IIYQo4yTZCyGEEGVcmZ3iVil1GjhSBLsKAlKLYD+3mrLaLii7bSur7YKy27ay2i4ou20r7e2qrbWukrewzCb7oqKU2mxrbuDSrqy2C8pu28pqu6Dstq2stgvKbtvKarvkMr4QQghRxkmyF0IIIco4SfY39lFJB1BMymq7oOy2ray2C8pu28pqu6Dstq1Mtkvu2QshhBBlnJzZCyGEEGWcJHshhBCijJNkfx1KqdFKqUNKqatKqS1KqQ4lHZOjlFLPK6X+UEpdUEqdVkp9r5RqnGcdpZSapJRKUkpdUUolKKXuKKmYC8PcTq2UeidXWalsl1KqulLqE/PP66pSao9SKipXfWltl6tS6tVc/6cOKaWmKKXccq1TKtqmlOqolPpOKXXC/Hs3JE/9DduhlApUSn2mlDpvXj5TSlV0akPyKKhdSil3pdQ0pdROpdQlpVSyUupzpVStPPvwVEq9rZRKNa/3nVKqptMbk8eNfmZ51v3QvM74POW3ZNvsJcneBqVUP2AOMBVoAawDfsz7i10KRAPvAe2ATkAW8F+lVKVc6zwLPAM8CUQAp4CflVIVnBtq4Sil7gQeA3bmqSp17TL/sV8LKOB+oCFG/KdyrVbq2mU2EXgCeAq4HRhrfv98rnVKS9v8gN0Ybbhio96ednwOtAT+YV5aAp8VY8z2KKhdPhgxvmb+twcQCvyU+wsb8BbQG3gE6AD4AyuUUq7FG/oN3ehnBoBSqg/QBkiyUX2rts0+WmtZ8izARmBenrL/Aa+XdGw32S4/IBvobn6vgGTgxVzreANpwMiSjteO9gQAB4EYIAF4pzS3C+PL5doC6ktlu8xxrgA+yVP2CbCiNLcNuAgMceRnhPElTgPtc61zl7ksvKTbZKtd11mnkTnmJub3AUAGMCDXOqGACbi3pNt0o7YBtYET5p/PYWB8rrpS0baCFjmzz0Mp5QG0AlblqVqFcYZcmlXAuJpzzvy+LhBMrrZqra8Av1E62voR8LXWOj5PeWlt14PARqXUEqXUKaXUdqXUGKWUMteX1nYBrAFilFK3AyilGmFcbfrBXF+a25abPe2IxEg463Jttxa4ROlqq7/535y/J60Ad6zbfgxI5BZvl/nqxBfAFK11oo1VSm3bcrjdeJVyJwhwBVLylKcAdzs/nCI1B9gOrDe/Dzb/a6utNZwVVGEopUYA9YFHbVSX1nbdBowGZgNvAM2Bt81171B62wUwDePL5h6lVDbG357XtNbvmetLc9tys6cdwcBpbT49BNBaa6XUqVzb39LMJ0Uzge+11sfNxcEYVw7zjiufwq3frleAVK31+9epL81tAyTZlxtKqVkYlwrv0lpnl3Q8N0MpFY5xyfsurXVmScdThFyAzVrrnPvY25RSDTDubb9z/c1KhX7AIKA/8CfGF5k5SqlDWuv5JRqZcIj5LHghUBF4oITDuWlKqWhgCMbvZJkll/HzS8X4BlctT3k14KTzw7l5SqnZGJ1KOmmt/8pVldOe0tbWSIwrMH8qpbKUUllAFDDa/PqMeb3S1q5kYE+eskQgp2Noaf15AUwHZmitF2utd2mtPwNmca2DXmluW272tOMkUCXX7RnMr6tyi7c11+XupkBnrfWZXNUnMa6KBuXZ7Fb/GUYD1YHkXH9PagPTlFI5Vy1Ka9ssJNnnobXOALYAXfJUdcH6HlupoJSaw7VEvzdP9SGMX9Quudb3wuhpeiu39RugCcY38ZxlM7DY/Ho/pbNda4HwPGVhXJuqubT+vMDozZ33ilI21/4Glea25WZPO9ZjdJaNzLVdJODLLdxWpZQ7sAQj0cdorfMmuS1AJtZtr4nR4e2WbRfGE0tNsf57koRxO62zeZ3S2rZrSrqH4K24YFxyzACGY/ww52B0qKld0rE52I53gQsYHaGCcy1+udaZCJwHegGNMRJmElChpON3sK0JmHvjl9Z2YTymlQm8iNEf4SFzG54oze0yxx0HHMd4pLAO0BM4DcwsbW3DSNQ5SeEy8LL5dS172wH8COzCSPKR5tff36rtwrjl+w1Gb/WWef6eeOfax/vmn/PdGI8tx2P0E3K9Vdt2nfUPk3xRdd0AAAPUSURBVKs3/q3cNrs/g5IO4FZdMDpKHQbSMb7VdSzpmArRBn2dZVKudRQwCeMS8lXgV6BxScdeiLYmYJ3sS2W7zMlwhznm/RjPpasy0K4KGM8pH8F4zvkvjH4XXqWtbRiXfW39v4qztx1AIMZ97wvmZSFQ8VZtF8YXtOv9PRmSax+eGJ1Kz2Ak1e+B0Fv9Z2Zj/cPkT/a3ZNvsXWQiHCGEEKKMk3v2QgghRBknyV4IIYQo4yTZCyGEEGWcJHshhBCijJNkL4QQQpRxkuyFEEKIMk6SvRCiRCiltHn+cCFEMZNkL0Q5pJSKMyfbvMuGko5NCFH0ZNY7Icqv/wID85RllEQgQojiJWf2QpRf6Vrrk3mWs2C5xD5GKfV/SqnLSqkjSqlHc2+slGqilPqvUuqKUuqs+WpBQJ51Biuldiml0pVSKUqpT/LEUEkp9ZVS6pJS6i8bx3jZfOx0pdRJpdSnxfJJCFHGSbIXQlzPK8B3GBOGfAR8qpRqDaCU8gVWYkwQ1QZjYpt2wMc5GyulRgIfAgswZhW7D9id5xgvA98CzTBmVPtYKVXLvH1vYDzGPBUNgG7ApmJopxBlnoyNL0Q5pJSKAx7FmKglt3e11hOVUhqI1VqPyLXNf4GTWutHlVIjgBlATa11mrk+GmMmsAZa6wPmucAXaq2fu04MGnhDa/28+b0bxqQwj2mtFyql/gWMxJhEJrPIGi9EOST37IUov34DHstT9neu1+vz1K3HmJUPjKmfd+YkerN1gAlopJS6ANQAVt8ghp05L7TWWUqp00BVc9FXwFjgkFJqJfAT8J3WOv0G+xRC5CGX8YUovy5rrQ/kWVKLYL+OXC7Me8auMf9d0lofA8Ixzu4vADOBLeZbCEIIB0iyF0Jcz5023ieaXycCTZRSFXLVt8P4m5KotT4FnAA630wAWuurWuv/01o/DUQAdwDtb2afQpRHchlfiPLLUykVnKcsW2t92vy6l1LqDyAB6IORuNua6xZhdOD7VCn1MhCI0Rlvmdb6gHmd14DZSqkU4P8AH6Cz1nqmPcEppYZg/I3aiNERsB/GlYD/OdhOIco9SfZClF93A8l5yk4ANc2vJwG9gbnAaWCo1voPAK31ZaXUvcBbGD3kr2L0qh+bsyOt9ftKqQzgGWAacBb4wYH4/gYmYnQEdAf2AL201occ2IcQAumNL4SwwdxT/iGt9dclHYsQ4ubJPXshhBCijJNkL4QQQpRxchlfCCGEKOPkzF4IIYQo4yTZCyGEEGWcJHshhBCijJNkL4QQQpRxkuyFEEKIMk6SvRBCCFHG/T/uyYbFCTUKaAAAAABJRU5ErkJggg==\n" }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "code", "source": [ "from google.colab import files\n", "epochs = range(1,151)\n", "plt.figure(figsize=(8,6))\n", "plt.rcParams['font.size'] = 14\n", "plt.plot(epochs, w_aug_loss, 'g--', label='Training_WA')\n", "plt.plot(epochs, w_aug_val_loss, 'r--', label='Validation_WA')\n", "plt.plot(epochs, wo_aug_loss, 'm--', label='Training_WOA')\n", "plt.plot(epochs, wo_aug_val_loss, 'b--', label='Validation_WOA')\n", "#plt.title('Training accuracy')\n", "plt.xlabel('Epochs')\n", "plt.ylabel('Loss')\n", "plt.legend()\n", "plt.savefig('aug_loss.png')\n", "files.download(\"aug_loss.png\")\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 398 }, "id": "rtdRQmT-K16R", "outputId": "cfc74bc6-26cf-4697-d17f-4dc677497c34" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "\n", " async function download(id, filename, size) {\n", " if (!google.colab.kernel.accessAllowed) {\n", " return;\n", " }\n", " const div = document.createElement('div');\n", " const label = document.createElement('label');\n", " label.textContent = `Downloading \"${filename}\": `;\n", " div.appendChild(label);\n", " const progress = document.createElement('progress');\n", " progress.max = size;\n", " div.appendChild(progress);\n", " document.body.appendChild(div);\n", "\n", " const buffers = [];\n", " let downloaded = 0;\n", "\n", " const channel = await google.colab.kernel.comms.open(id);\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", "\n", " for await (const message of channel.messages) {\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", " if (message.buffers) {\n", " for (const buffer of message.buffers) {\n", " buffers.push(buffer);\n", " downloaded += buffer.byteLength;\n", " progress.value = downloaded;\n", " }\n", " }\n", " }\n", " const blob = new Blob(buffers, {type: 'application/binary'});\n", " const a = document.createElement('a');\n", " a.href = window.URL.createObjectURL(blob);\n", " a.download = filename;\n", " div.appendChild(a);\n", " a.click();\n", " div.remove();\n", " }\n", " " ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "download(\"download_c8a05962-6e0d-413d-9221-d2f41ba1e7a5\", \"aug_loss.png\", 37772)" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" 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