{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "JAFFE Peerj.ipynb", "provenance": [], "collapsed_sections": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" }, "accelerator": "GPU", "gpuClass": "standard" }, "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "id": "DZzFYmPLg7Dk" }, "outputs": [], "source": [ "" ] }, { "cell_type": "code", "metadata": { "id": "zEM2-ex1soQp" }, "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 " ], "execution_count": 1, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "Vmfjy-KPszqw" }, "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" ], "execution_count": 2, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Use6VvZZs3yI", "outputId": "f6eab296-248f-4adb-8449-8fffed95e60d" }, "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')" ], "execution_count": 3, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Mounted at /content/drive\n" ] } ] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "wapeAhQBs-7L", "outputId": "6616950e-a788-4cc9-a043-ad20186219a6" }, "source": [ "!unzip \"/content/drive/My Drive/xfold1.zip\" " ], "execution_count": 4, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Archive: /content/drive/My Drive/xfold1.zip\n", " creating: fold3/\n", " creating: fold3/test/\n", " creating: fold3/test/AN/\n", " inflating: fold3/test/AN/NA.AN1.211.tiff \n", " inflating: fold3/test/AN/NM.AN2.105.tiff \n", " inflating: fold3/test/AN/TM.AN3.192.tiff \n", " creating: fold3/test/DI/\n", " inflating: fold3/test/DI/MK.DI3.130.tiff \n", " inflating: fold3/test/DI/NA.DI1.214.tiff \n", " inflating: fold3/test/DI/NM.DI1.107.tiff \n", " creating: fold3/test/FE/\n", " inflating: fold3/test/FE/MK.FE3.133.tiff \n", " inflating: fold3/test/FE/NM.FE3.112.tiff \n", " inflating: 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\n", " inflating: fold5/train/HA/KL.HA1.158.tiff \n", " inflating: fold5/train/HA/KL.HA2.159.tiff \n", " inflating: fold5/train/HA/KL.HA3.160.tiff \n", " inflating: fold5/train/HA/KM.HA1.4.tiff \n", " inflating: fold5/train/HA/KM.HA2.5.tiff \n", " inflating: fold5/train/HA/KM.HA4.7.tiff \n", " inflating: fold5/train/HA/KR.HA1.74.tiff \n", " inflating: fold5/train/HA/KR.HA2.75.tiff \n", " inflating: fold5/train/HA/MK.HA1.116.tiff \n", " inflating: fold5/train/HA/MK.HA2.117.tiff \n", " inflating: fold5/train/HA/MK.HA3.118.tiff \n", " inflating: fold5/train/HA/NA.HA1.202.tiff \n", " inflating: fold5/train/HA/NA.HA3.204.tiff \n", " inflating: fold5/train/HA/NM.HA1.95.tiff \n", " inflating: fold5/train/HA/NM.HA2.96.tiff \n", " inflating: fold5/train/HA/NM.HA3.97.tiff \n", " inflating: fold5/train/HA/TM.HA1.180.tiff \n", " inflating: fold5/train/HA/TM.HA2.181.tiff \n", " inflating: fold5/train/HA/TM.HA3.182.tiff \n", " inflating: fold5/train/HA/UY.HA1.137.tiff \n", " inflating: 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\n", " inflating: fold6/train/AN/KR.AN2.84.tiff \n", " inflating: fold6/train/AN/KR.AN3.85.tiff \n", " inflating: fold6/train/AN/MK.AN1.125.tiff \n", " inflating: fold6/train/AN/MK.AN2.126.tiff \n", " inflating: fold6/train/AN/MK.AN3.127.tiff \n", " inflating: fold6/train/AN/NA.AN1.211.tiff \n", " inflating: fold6/train/AN/NA.AN2.212.tiff \n", " inflating: fold6/train/AN/NA.AN3.213.tiff \n", " inflating: fold6/train/AN/NM.AN1.104.tiff \n", " inflating: fold6/train/AN/NM.AN2.105.tiff \n", " inflating: fold6/train/AN/NM.AN3.106.tiff \n", " inflating: fold6/train/AN/TM.AN2.191.tiff \n", " inflating: fold6/train/AN/TM.AN3.192.tiff \n", " inflating: fold6/train/AN/UY.AN1.146.tiff \n", " inflating: fold6/train/AN/UY.AN2.147.tiff \n", " inflating: fold6/train/AN/UY.AN3.148.tiff \n", " inflating: fold6/train/AN/YM.AN1.61.tiff \n", " inflating: fold6/train/AN/YM.AN2.62.tiff \n", " inflating: fold6/train/AN/YM.AN3.63.tiff \n", " creating: fold6/train/DI/\n", " inflating: 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\n", " inflating: fold7/train/AN/KR.AN3.85.tiff \n", " inflating: fold7/train/AN/MK.AN1.125.tiff \n", " inflating: fold7/train/AN/MK.AN2.126.tiff \n", " inflating: fold7/train/AN/MK.AN3.127.tiff \n", " inflating: fold7/train/AN/NA.AN1.211.tiff \n", " inflating: fold7/train/AN/NA.AN2.212.tiff \n", " inflating: fold7/train/AN/NA.AN3.213.tiff \n", " inflating: fold7/train/AN/NM.AN2.105.tiff \n", " inflating: fold7/train/AN/NM.AN3.106.tiff \n", " inflating: fold7/train/AN/TM.AN1.190.tiff \n", " inflating: fold7/train/AN/TM.AN2.191.tiff \n", " inflating: fold7/train/AN/TM.AN3.192.tiff \n", " inflating: fold7/train/AN/UY.AN1.146.tiff \n", " inflating: fold7/train/AN/UY.AN2.147.tiff \n", " inflating: fold7/train/AN/UY.AN3.148.tiff \n", " inflating: fold7/train/AN/YM.AN1.61.tiff \n", " inflating: fold7/train/AN/YM.AN2.62.tiff \n", " inflating: fold7/train/AN/YM.AN3.63.tiff \n", " creating: fold7/train/DI/\n", " inflating: fold7/train/DI/KA.DI1.42.tiff \n", " inflating: 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\n", " inflating: fold8/test/NE/NM.NE1.92.tiff \n", " inflating: fold8/test/NE/YM.NE3.51.tiff \n", " creating: fold8/test/SA/\n", " inflating: fold8/test/SA/KL.SA1.161.tiff \n", " inflating: fold8/test/SA/KM.SA5.13.tiff \n", " inflating: fold8/test/SA/NA.SA3.207.tiff \n", " creating: fold8/test/SU/\n", " inflating: fold8/test/SU/MK.SU1.122.tiff \n", " inflating: fold8/test/SU/NM.SU3.103.tiff \n", " inflating: fold8/test/SU/UY.SU1.143.tiff \n", " creating: fold8/train/\n", " creating: fold8/train/AN/\n", " inflating: fold8/train/AN/KA.AN1.39.tiff \n", " inflating: fold8/train/AN/KA.AN2.40.tiff \n", " inflating: fold8/train/AN/KL.AN1.167.tiff \n", " inflating: fold8/train/AN/KL.AN2.168.tiff \n", " inflating: fold8/train/AN/KL.AN3.169.tiff \n", " inflating: fold8/train/AN/KM.AN1.17.tiff \n", " inflating: fold8/train/AN/KM.AN2.18.tiff \n", " inflating: fold8/train/AN/KM.AN3.19.tiff \n", " inflating: fold8/train/AN/KR.AN1.83.tiff \n", " inflating: fold8/train/AN/KR.AN2.84.tiff \n", " 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\n", " inflating: fold8/train/DI/KL.DI1.170.tiff \n", " inflating: fold8/train/DI/KL.DI2.171.tiff \n", " inflating: fold8/train/DI/KL.DI3.172.tiff \n", " inflating: fold8/train/DI/KL.DI4.173.tiff \n", " inflating: fold8/train/DI/KM.DI1.20.tiff \n", " inflating: fold8/train/DI/KR.DI2.87.tiff \n", " inflating: fold8/train/DI/KR.DI3.88.tiff \n", " inflating: fold8/train/DI/MK.DI2.129.tiff \n", " inflating: fold8/train/DI/MK.DI3.130.tiff \n", " inflating: fold8/train/DI/NA.DI1.214.tiff \n", " inflating: fold8/train/DI/NA.DI2.215.tiff \n", " inflating: fold8/train/DI/NA.DI3.216.tiff \n", " inflating: fold8/train/DI/NM.DI1.107.tiff \n", " inflating: fold8/train/DI/NM.DI3.109.tiff \n", " inflating: fold8/train/DI/TM.DI1.193.tiff \n", " inflating: fold8/train/DI/TM.DI2.194.tiff \n", " inflating: fold8/train/DI/TM.DI3.195.tiff \n", " inflating: fold8/train/DI/UY.DI1.149.tiff \n", " inflating: fold8/train/DI/UY.DI2.150.tiff \n", " inflating: fold8/train/DI/UY.DI3.151.tiff \n", " inflating: 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\n", " inflating: fold9/train/NE/UY.NE1.134.tiff \n", " inflating: fold9/train/NE/UY.NE3.136.tiff \n", " inflating: fold9/train/NE/YM.NE1.49.tiff \n", " inflating: fold9/train/NE/YM.NE2.50.tiff \n", " inflating: fold9/train/NE/YM.NE3.51.tiff \n", " creating: fold9/train/SA/\n", " inflating: fold9/train/SA/KA.SA1.33.tiff \n", " inflating: fold9/train/SA/KA.SA2.34.tiff \n", " inflating: fold9/train/SA/KA.SA3.35.tiff \n", " inflating: fold9/train/SA/KL.SA1.161.tiff \n", " inflating: fold9/train/SA/KL.SA2.162.tiff \n", " inflating: fold9/train/SA/KL.SA3.163.tiff \n", " inflating: fold9/train/SA/KM.SA2.10.tiff \n", " inflating: fold9/train/SA/KM.SA3.11.tiff \n", " inflating: fold9/train/SA/KM.SA5.13.tiff \n", " inflating: fold9/train/SA/KR.SA1.77.tiff \n", " inflating: fold9/train/SA/KR.SA2.78.tiff \n", " inflating: fold9/train/SA/KR.SA3.79.tiff \n", " inflating: fold9/train/SA/MK.SA1.119.tiff \n", " inflating: fold9/train/SA/MK.SA2.120.tiff \n", " inflating: fold9/train/SA/MK.SA3.121.tiff \n", " inflating: fold9/train/SA/NA.SA1.205.tiff \n", " inflating: fold9/train/SA/NA.SA3.207.tiff \n", " inflating: fold9/train/SA/NM.SA1.98.tiff \n", " inflating: fold9/train/SA/NM.SA2.99.tiff \n", " inflating: fold9/train/SA/NM.SA3.100.tiff \n", " inflating: fold9/train/SA/TM.SA1.184.tiff \n", " inflating: fold9/train/SA/TM.SA2.185.tiff \n", " inflating: fold9/train/SA/TM.SA3.186.tiff \n", " inflating: fold9/train/SA/UY.SA1.140.tiff \n", " inflating: fold9/train/SA/UY.SA2.141.tiff \n", " inflating: fold9/train/SA/UY.SA3.142.tiff \n", " inflating: fold9/train/SA/YM.SA1.55.tiff \n", " inflating: fold9/train/SA/YM.SA2.56.tiff \n", " creating: fold9/train/SU/\n", " inflating: fold9/train/SU/KA.SU1.36.tiff \n", " inflating: fold9/train/SU/KA.SU2.37.tiff \n", " inflating: fold9/train/SU/KA.SU3.38.tiff \n", " inflating: fold9/train/SU/KL.SU1.164.tiff \n", " inflating: fold9/train/SU/KL.SU2.165.tiff \n", " inflating: fold9/train/SU/KL.SU3.166.tiff \n", " inflating: 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fold9/train/SU/YM.SU3.60.tiff \n", " creating: fold10/\n", " creating: fold10/test/\n", " creating: fold10/test/AN/\n", " inflating: fold10/test/AN/KA.AN1.39.tiff \n", " inflating: fold10/test/AN/KL.AN1.167.tiff \n", " inflating: fold10/test/AN/KM.AN1.17.tiff \n", " creating: fold10/test/DI/\n", " inflating: fold10/test/DI/KA.DI1.42.tiff \n", " inflating: fold10/test/DI/KR.DI3.88.tiff \n", " inflating: fold10/test/DI/TM.DI3.195.tiff \n", " creating: fold10/test/FE/\n", " inflating: fold10/test/FE/KA.FE1.45.tiff \n", " inflating: fold10/test/FE/KR.FE2.90.tiff \n", " inflating: fold10/test/FE/TM.FE1.196.tiff \n", " creating: fold10/test/HA/\n", " inflating: fold10/test/HA/KA.HA1.29.tiff \n", " inflating: fold10/test/HA/KR.HA1.74.tiff \n", " inflating: fold10/test/HA/TM.HA1.180.tiff \n", " creating: fold10/test/NE/\n", " inflating: fold10/test/NE/KA.NE1.26.tiff \n", " inflating: fold10/test/NE/KR.NE3.73.tiff \n", " inflating: fold10/test/NE/TM.NE2.178.tiff \n", " creating: 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\n", " inflating: fold10/train/NE/TM.NE1.177.tiff \n", " inflating: fold10/train/NE/TM.NE3.179.tiff \n", " inflating: fold10/train/NE/UY.NE1.134.tiff \n", " inflating: fold10/train/NE/UY.NE2.135.tiff \n", " inflating: fold10/train/NE/UY.NE3.136.tiff \n", " inflating: fold10/train/NE/YM.NE1.49.tiff \n", " inflating: fold10/train/NE/YM.NE2.50.tiff \n", " inflating: fold10/train/NE/YM.NE3.51.tiff \n", " creating: fold10/train/SA/\n", " inflating: fold10/train/SA/KA.SA2.34.tiff \n", " inflating: fold10/train/SA/KA.SA3.35.tiff \n", " inflating: fold10/train/SA/KL.SA1.161.tiff \n", " inflating: fold10/train/SA/KL.SA2.162.tiff \n", " inflating: fold10/train/SA/KL.SA3.163.tiff \n", " inflating: fold10/train/SA/KM.SA1.9.tiff \n", " inflating: fold10/train/SA/KM.SA2.10.tiff \n", " inflating: fold10/train/SA/KM.SA3.11.tiff \n", " inflating: fold10/train/SA/KM.SA5.13.tiff \n", " inflating: fold10/train/SA/KR.SA1.77.tiff \n", " inflating: fold10/train/SA/KR.SA3.79.tiff \n", " inflating: fold10/train/SA/MK.SA1.119.tiff \n", " inflating: fold10/train/SA/MK.SA2.120.tiff \n", " inflating: fold10/train/SA/MK.SA3.121.tiff \n", " inflating: fold10/train/SA/NA.SA1.205.tiff \n", " inflating: fold10/train/SA/NA.SA2.206.tiff \n", " inflating: fold10/train/SA/NA.SA3.207.tiff \n", " inflating: fold10/train/SA/NM.SA1.98.tiff \n", " inflating: fold10/train/SA/NM.SA2.99.tiff \n", " inflating: fold10/train/SA/NM.SA3.100.tiff \n", " inflating: fold10/train/SA/TM.SA2.185.tiff \n", " inflating: fold10/train/SA/TM.SA3.186.tiff \n", " inflating: fold10/train/SA/UY.SA1.140.tiff \n", " inflating: fold10/train/SA/UY.SA2.141.tiff \n", " inflating: fold10/train/SA/UY.SA3.142.tiff \n", " inflating: fold10/train/SA/YM.SA1.55.tiff \n", " inflating: fold10/train/SA/YM.SA2.56.tiff \n", " inflating: fold10/train/SA/YM.SA3.57.tiff \n", " creating: fold10/train/SU/\n", " inflating: fold10/train/SU/KA.SU2.37.tiff \n", " inflating: fold10/train/SU/KA.SU3.38.tiff \n", " inflating: fold10/train/SU/KL.SU1.164.tiff \n", " inflating: fold10/train/SU/KL.SU2.165.tiff \n", " inflating: fold10/train/SU/KL.SU3.166.tiff \n", " inflating: fold10/train/SU/KM.SU1.14.tiff \n", " inflating: fold10/train/SU/KM.SU2.15.tiff \n", " inflating: fold10/train/SU/KM.SU3.16.tiff \n", " inflating: fold10/train/SU/KR.SU1.80.tiff \n", " inflating: fold10/train/SU/KR.SU2.81.tiff \n", " inflating: fold10/train/SU/MK.SU1.122.tiff \n", " inflating: fold10/train/SU/MK.SU2.123.tiff \n", " inflating: fold10/train/SU/MK.SU3.124.tiff \n", " inflating: fold10/train/SU/NA.SU1.208.tiff \n", " inflating: fold10/train/SU/NA.SU2.209.tiff \n", " inflating: fold10/train/SU/NA.SU3.210.tiff \n", " inflating: fold10/train/SU/NM.SU1.101.tiff \n", " inflating: fold10/train/SU/NM.SU2.102.tiff \n", " inflating: fold10/train/SU/NM.SU3.103.tiff \n", " inflating: fold10/train/SU/TM.SU1.187.tiff \n", " inflating: fold10/train/SU/TM.SU3.189.tiff \n", " inflating: fold10/train/SU/UY.SU1.143.tiff \n", " inflating: fold10/train/SU/UY.SU2.144.tiff \n", " inflating: fold10/train/SU/UY.SU3.145.tiff \n", " inflating: fold10/train/SU/YM.SU1.58.tiff \n", " inflating: fold10/train/SU/YM.SU2.59.tiff \n", " inflating: fold10/train/SU/YM.SU3.60.tiff \n" ] } ] }, { "cell_type": "code", "source": [ "batch_size=21\n", "train_sample=192\n", "valid_sample=21\n", "test_sample=21\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 = 'fold7/train'\n", "valid_path = 'fold7/test'\n", "test_path = 'fold7/test'\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" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "FKhQLX7GQWnt", "outputId": "d5b23bf4-ac57-4cbf-a2d7-8edd760a5773" }, "execution_count": 23, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.7/dist-packages/keras_preprocessing/image/utils.py:179: UserWarning: Using \".tiff\" files with multiple bands will cause distortion. Please verify your output.\n", " warnings.warn('Using \".tiff\" files with multiple bands '\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Found 192 images belonging to 7 classes.\n", "Found 21 images belonging to 7 classes.\n", "Found 21 images belonging to 7 classes.\n" ] } ] }, { "cell_type": "code", "metadata": { "id": "E_VmuzUE6fSq", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "19c87a5f-e61a-4869-cb4c-2663bebbe73d" }, "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", "\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", "#x=Dropout(0.2)\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", "#x=Dropout(0.2)\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", "#x=Dropout(0.2)\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" ], "execution_count": 10, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Model: \"inception_v1\"\n", "__________________________________________________________________________________________________\n", " Layer (type) Output Shape Param # Connected to \n", "==================================================================================================\n", " input_1 (InputLayer) [(None, 48, 48, 1)] 0 [] \n", " \n", " conv_1_3x3/1 (Conv2D) (None, 48, 48, 32) 320 ['input_1[0][0]'] \n", " \n", " batch_normalization (BatchNorm (None, 48, 48, 32) 128 ['conv_1_3x3/1[0][0]'] \n", " alization) \n", " \n", " conv_1a_3x3/1 (Conv2D) (None, 48, 48, 32) 9248 ['batch_normalization[0][0]'] \n", " \n", " batch_normalization_1 (BatchNo (None, 48, 48, 32) 128 ['conv_1a_3x3/1[0][0]'] \n", " rmalization) \n", " \n", " max_pool_1_3x3/2 (MaxPooling2D (None, 24, 24, 32) 0 ['batch_normalization_1[0][0]'] \n", " ) \n", " \n", " batch_normalization_2 (BatchNo (None, 24, 24, 32) 128 ['max_pool_1_3x3/2[0][0]'] \n", " rmalization) \n", " \n", " conv_2a_3x3/1 (Conv2D) (None, 24, 24, 64) 18496 ['batch_normalization_2[0][0]'] \n", " \n", " conv_2b_3x3/1 (Conv2D) (None, 24, 24, 64) 18496 ['batch_normalization_2[0][0]'] \n", " \n", " batch_normalization_3 (BatchNo (None, 24, 24, 64) 256 ['conv_2a_3x3/1[0][0]'] \n", " rmalization) \n", " \n", " batch_normalization_5 (BatchNo (None, 24, 24, 64) 256 ['conv_2b_3x3/1[0][0]'] \n", " rmalization) \n", " \n", " conv_2a1_3x3/1 (Conv2D) (None, 24, 24, 64) 36928 ['batch_normalization_3[0][0]'] \n", " \n", " conv_2b1_3x3/1 (Conv2D) (None, 24, 24, 64) 36928 ['batch_normalization_5[0][0]'] \n", " \n", " batch_normalization_4 (BatchNo (None, 24, 24, 64) 256 ['conv_2a1_3x3/1[0][0]'] \n", " rmalization) \n", " \n", " batch_normalization_6 (BatchNo (None, 24, 24, 64) 256 ['conv_2b1_3x3/1[0][0]'] \n", " rmalization) \n", " \n", " inception_dense_1 (Concatenate (None, 24, 24, 160) 0 ['batch_normalization_2[0][0]', \n", " ) 'batch_normalization_4[0][0]', \n", " 'batch_normalization_6[0][0]'] \n", " \n", " conv_t1_3x3/1 (Conv2D) (None, 24, 24, 64) 10304 ['inception_dense_1[0][0]'] \n", " \n", " batch_normalization_7 (BatchNo (None, 24, 24, 64) 256 ['conv_t1_3x3/1[0][0]'] \n", " rmalization) \n", " \n", " max_pool_t2_3x3/2 (MaxPooling2 (None, 12, 12, 64) 0 ['batch_normalization_7[0][0]'] \n", " D) \n", " \n", " batch_normalization_8 (BatchNo (None, 12, 12, 64) 256 ['max_pool_t2_3x3/2[0][0]'] \n", " rmalization) \n", " \n", " conv_3a_3x3/1 (Conv2D) (None, 12, 12, 128) 73856 ['batch_normalization_8[0][0]'] \n", " \n", " conv_3b_3x3/1 (Conv2D) (None, 12, 12, 128) 73856 ['batch_normalization_8[0][0]'] \n", " \n", " batch_normalization_9 (BatchNo (None, 12, 12, 128) 512 ['conv_3a_3x3/1[0][0]'] \n", " rmalization) \n", " \n", " batch_normalization_11 (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_9[0][0]'] \n", " \n", " conv_3b1_3x3/1 (Conv2D) (None, 12, 12, 128) 147584 ['batch_normalization_11[0][0]'] \n", " \n", " batch_normalization_10 (BatchN (None, 12, 12, 128) 512 ['conv_3a1_3x3/1[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_12 (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 ['batch_normalization_8[0][0]', \n", " ) 'batch_normalization_10[0][0]', \n", " 'batch_normalization_12[0][0]'] \n", " \n", " conv_t3_3x3/1 (Conv2D) (None, 12, 12, 128) 41088 ['inception_dense_2[0][0]'] \n", " \n", " batch_normalization_13 (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_13[0][0]'] \n", " D) \n", " \n", " batch_normalization_14 (BatchN (None, 6, 6, 128) 512 ['max_pool_t4_3x3/2[0][0]'] \n", " ormalization) \n", " \n", " conv_4a_3x3/1 (Conv2D) (None, 6, 6, 256) 295168 ['batch_normalization_14[0][0]'] \n", " \n", " conv_4b_3x3/1 (Conv2D) (None, 6, 6, 256) 295168 ['batch_normalization_14[0][0]'] \n", " \n", " batch_normalization_15 (BatchN (None, 6, 6, 256) 1024 ['conv_4a_3x3/1[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_17 (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_15[0][0]'] \n", " \n", " conv_4b1_3x3/1 (Conv2D) (None, 6, 6, 256) 590080 ['batch_normalization_17[0][0]'] \n", " \n", " batch_normalization_16 (BatchN (None, 6, 6, 256) 1024 ['conv_4a1_3x3/1[0][0]'] \n", " ormalization) \n", " \n", " batch_normalization_18 (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 ['batch_normalization_14[0][0]', \n", " ) 'batch_normalization_16[0][0]', \n", " 'batch_normalization_18[0][0]'] \n", " \n", " conv_t5_3x3/1 (Conv2D) (None, 6, 6, 256) 164096 ['inception_dense_4[0][0]'] \n", " \n", " batch_normalization_19 (BatchN (None, 6, 6, 256) 1024 ['conv_t5_3x3/1[0][0]'] \n", " ormalization) \n", " \n", " avg_pool_5_3x3/1 (GlobalAverag (None, 256) 0 ['batch_normalization_19[0][0]'] \n", " ePooling2D) \n", " \n", " dense (Dense) (None, 512) 131584 ['avg_pool_5_3x3/1[0][0]'] \n", " \n", " batch_normalization_20 (BatchN (None, 512) 2048 ['dense[0][0]'] \n", " ormalization) \n", " \n", " dropout (Dropout) (None, 512) 0 ['batch_normalization_20[0][0]'] \n", " \n", " dense_1 (Dense) (None, 256) 131328 ['dropout[0][0]'] \n", " \n", " batch_normalization_21 (BatchN (None, 256) 1024 ['dense_1[0][0]'] \n", " ormalization) \n", " \n", " dropout_1 (Dropout) (None, 256) 0 ['batch_normalization_21[0][0]'] \n", " \n", " dense_2 (Dense) (None, 7) 1799 ['dropout_1[0][0]'] \n", " \n", "==================================================================================================\n", "Total params: 2,827,175\n", "Trainable params: 2,820,583\n", "Non-trainable params: 6,592\n", "__________________________________________________________________________________________________\n" ] } ] }, { "cell_type": "code", "metadata": { "id": "v1zaifBX6m_i" }, "source": [ "model.load_weights('/content/drive/My Drive/fer_new_vgg/PeerJ_emo_seed_1.hdf5') " ], "execution_count": 24, "outputs": [] }, { "cell_type": "code", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "kxjr2HCmtcUW", "outputId": "45cb42e1-8d22-4aaa-8fec-64d84edcf9fd" }, "source": [ "#from keras.optimizers import RMSprop,SGD,Adam\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/PeerJ_JAFFE_seed_5.hdf5\"\n", "checkpoint_dir = os.path.dirname(filepath)\n", "\n", "\n", "checkpoint = ModelCheckpoint(filepath,\n", " monitor='val_accuracy',\n", " mode='max',\n", " save_best_only=True,\n", " verbose=2)\n", "\n", "callbacks = [checkpoint]\n", "\n", "model.compile(loss='categorical_crossentropy',\n", " optimizer = Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0),\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)" ], "execution_count": 25, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Epoch 1/150\n", "10/10 [==============================] - ETA: 0s - loss: 2.1066 - accuracy: 0.5417\n", "Epoch 1: val_accuracy improved from -inf to 0.61905, saving model to /content/drive/My Drive/fer_new_vgg/PeerJ_JAFFE_seed_5.hdf5\n", "10/10 [==============================] - 4s 208ms/step - loss: 2.1066 - accuracy: 0.5417 - val_loss: 2.5571 - val_accuracy: 0.6190\n", "Epoch 2/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.3717 - accuracy: 0.9064\n", "Epoch 2: val_accuracy improved from 0.61905 to 0.66667, saving model to /content/drive/My Drive/fer_new_vgg/PeerJ_JAFFE_seed_5.hdf5\n", "10/10 [==============================] - 1s 99ms/step - loss: 0.4527 - accuracy: 0.8906 - val_loss: 1.5177 - val_accuracy: 0.6667\n", "Epoch 3/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.2658 - accuracy: 0.9153\n", "Epoch 3: val_accuracy improved from 0.66667 to 0.76190, saving model to /content/drive/My Drive/fer_new_vgg/PeerJ_JAFFE_seed_5.hdf5\n", "10/10 [==============================] - 1s 97ms/step - loss: 0.2979 - accuracy: 0.9115 - val_loss: 0.9836 - val_accuracy: 0.7619\n", "Epoch 4/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.1802 - accuracy: 0.9583\n", "Epoch 4: val_accuracy did not improve from 0.76190\n", "10/10 [==============================] - 1s 56ms/step - loss: 0.1802 - accuracy: 0.9583 - val_loss: 1.5581 - val_accuracy: 0.6667\n", "Epoch 5/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.1118 - accuracy: 0.9708\n", "Epoch 5: val_accuracy did not improve from 0.76190\n", "10/10 [==============================] - 1s 52ms/step - loss: 0.1116 - accuracy: 0.9740 - val_loss: 1.6913 - val_accuracy: 0.5714\n", "Epoch 6/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.1304 - accuracy: 0.9688\n", "Epoch 6: val_accuracy did not improve from 0.76190\n", "10/10 [==============================] - 1s 57ms/step - loss: 0.1304 - accuracy: 0.9688 - val_loss: 1.5495 - val_accuracy: 0.5714\n", "Epoch 7/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.1779 - accuracy: 0.9649\n", "Epoch 7: val_accuracy did not improve from 0.76190\n", "10/10 [==============================] - 1s 56ms/step - loss: 0.1752 - accuracy: 0.9635 - val_loss: 0.8820 - val_accuracy: 0.7143\n", "Epoch 8/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.1174 - accuracy: 0.9788\n", "Epoch 8: val_accuracy did not improve from 0.76190\n", "10/10 [==============================] - 1s 52ms/step - loss: 0.1293 - accuracy: 0.9688 - val_loss: 0.5746 - val_accuracy: 0.7143\n", "Epoch 9/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.0483 - accuracy: 1.0000\n", "Epoch 9: val_accuracy did not improve from 0.76190\n", "10/10 [==============================] - 1s 52ms/step - loss: 0.0512 - accuracy: 1.0000 - val_loss: 0.6475 - val_accuracy: 0.7619\n", "Epoch 10/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.1201 - accuracy: 0.9740\n", "Epoch 10: val_accuracy did not improve from 0.76190\n", "10/10 [==============================] - 1s 51ms/step - loss: 0.1201 - accuracy: 0.9740 - val_loss: 0.5625 - val_accuracy: 0.7143\n", "Epoch 11/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.0447 - accuracy: 0.9942\n", "Epoch 11: val_accuracy improved from 0.76190 to 0.80952, saving model to /content/drive/My Drive/fer_new_vgg/PeerJ_JAFFE_seed_5.hdf5\n", "10/10 [==============================] - 1s 98ms/step - loss: 0.0545 - accuracy: 0.9896 - val_loss: 0.4585 - val_accuracy: 0.8095\n", "Epoch 12/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.1036 - accuracy: 0.9649\n", "Epoch 12: val_accuracy improved from 0.80952 to 0.90476, saving model to /content/drive/My Drive/fer_new_vgg/PeerJ_JAFFE_seed_5.hdf5\n", "10/10 [==============================] - 1s 100ms/step - loss: 0.0994 - accuracy: 0.9688 - val_loss: 0.4830 - val_accuracy: 0.9048\n", "Epoch 13/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.1841 - accuracy: 0.9271\n", "Epoch 13: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 54ms/step - loss: 0.1841 - accuracy: 0.9271 - val_loss: 0.6226 - val_accuracy: 0.8571\n", "Epoch 14/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.0649 - accuracy: 0.9792\n", "Epoch 14: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 52ms/step - loss: 0.0649 - accuracy: 0.9792 - val_loss: 0.5290 - val_accuracy: 0.9048\n", "Epoch 15/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.0628 - accuracy: 0.9788\n", "Epoch 15: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 51ms/step - loss: 0.0684 - accuracy: 0.9740 - val_loss: 0.5132 - val_accuracy: 0.8095\n", "Epoch 16/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.1246 - accuracy: 0.9583\n", "Epoch 16: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 55ms/step - loss: 0.1246 - accuracy: 0.9583 - val_loss: 1.2028 - val_accuracy: 0.6190\n", "Epoch 17/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.2581 - accuracy: 0.9115\n", "Epoch 17: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 54ms/step - loss: 0.2581 - accuracy: 0.9115 - val_loss: 2.2892 - val_accuracy: 0.6190\n", "Epoch 18/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.2672 - accuracy: 0.9123\n", "Epoch 18: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 52ms/step - loss: 0.2601 - accuracy: 0.9115 - val_loss: 2.3880 - val_accuracy: 0.5238\n", "Epoch 19/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.3016 - accuracy: 0.9010\n", "Epoch 19: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 54ms/step - loss: 0.3016 - accuracy: 0.9010 - val_loss: 2.3206 - val_accuracy: 0.5238\n", "Epoch 20/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.4148 - accuracy: 0.8772\n", "Epoch 20: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 53ms/step - loss: 0.3767 - accuracy: 0.8906 - val_loss: 3.3030 - val_accuracy: 0.3333\n", "Epoch 21/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.1462 - accuracy: 0.9649\n", "Epoch 21: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 53ms/step - loss: 0.1506 - accuracy: 0.9531 - val_loss: 2.8657 - val_accuracy: 0.3333\n", "Epoch 22/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.2111 - accuracy: 0.9357\n", "Epoch 22: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 53ms/step - loss: 0.1960 - accuracy: 0.9375 - val_loss: 2.3725 - val_accuracy: 0.5238\n", "Epoch 23/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.0988 - accuracy: 0.9896\n", "Epoch 23: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 55ms/step - loss: 0.0988 - accuracy: 0.9896 - val_loss: 1.7596 - val_accuracy: 0.5238\n", "Epoch 24/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.1551 - accuracy: 0.9532\n", "Epoch 24: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 54ms/step - loss: 0.1437 - accuracy: 0.9583 - val_loss: 1.0585 - val_accuracy: 0.6190\n", "Epoch 25/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.0760 - accuracy: 0.9708\n", "Epoch 25: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 61ms/step - loss: 0.0851 - accuracy: 0.9688 - val_loss: 0.6374 - val_accuracy: 0.6667\n", "Epoch 26/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.0983 - accuracy: 0.9792\n", "Epoch 26: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 54ms/step - loss: 0.0983 - accuracy: 0.9792 - val_loss: 0.5595 - val_accuracy: 0.7619\n", "Epoch 27/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.0389 - accuracy: 0.9942\n", "Epoch 27: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 51ms/step - loss: 0.0417 - accuracy: 0.9948 - val_loss: 0.6532 - val_accuracy: 0.7143\n", "Epoch 28/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.1308 - accuracy: 0.9766\n", "Epoch 28: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 51ms/step - loss: 0.1329 - accuracy: 0.9688 - val_loss: 0.6520 - val_accuracy: 0.6667\n", "Epoch 29/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.0938 - accuracy: 0.9792\n", "Epoch 29: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 57ms/step - loss: 0.0938 - accuracy: 0.9792 - val_loss: 0.5971 - val_accuracy: 0.7619\n", "Epoch 30/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.0509 - accuracy: 0.9896\n", "Epoch 30: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 56ms/step - loss: 0.0509 - accuracy: 0.9896 - val_loss: 0.3932 - val_accuracy: 0.8095\n", "Epoch 31/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.0144 - accuracy: 1.0000\n", "Epoch 31: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 58ms/step - loss: 0.0207 - accuracy: 0.9948 - val_loss: 0.2523 - val_accuracy: 0.9048\n", "Epoch 32/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.0360 - accuracy: 0.9883\n", "Epoch 32: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 52ms/step - loss: 0.0386 - accuracy: 0.9896 - val_loss: 0.3019 - val_accuracy: 0.8571\n", "Epoch 33/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.0756 - accuracy: 0.9883\n", "Epoch 33: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 51ms/step - loss: 0.0689 - accuracy: 0.9896 - val_loss: 0.4176 - val_accuracy: 0.8571\n", "Epoch 34/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.0296 - accuracy: 1.0000\n", "Epoch 34: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 52ms/step - loss: 0.0312 - accuracy: 1.0000 - val_loss: 0.3291 - val_accuracy: 0.8571\n", "Epoch 35/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.0220 - accuracy: 1.0000\n", "Epoch 35: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 52ms/step - loss: 0.0224 - accuracy: 1.0000 - val_loss: 0.3326 - val_accuracy: 0.8095\n", "Epoch 36/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.0658 - accuracy: 0.9735\n", "Epoch 36: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 52ms/step - loss: 0.0791 - accuracy: 0.9688 - val_loss: 0.3370 - val_accuracy: 0.8571\n", "Epoch 37/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.0517 - accuracy: 0.9844\n", "Epoch 37: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 54ms/step - loss: 0.0517 - accuracy: 0.9844 - val_loss: 0.5014 - val_accuracy: 0.8095\n", "Epoch 38/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.1668 - accuracy: 0.9474\n", "Epoch 38: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 52ms/step - loss: 0.1555 - accuracy: 0.9479 - val_loss: 0.7028 - val_accuracy: 0.8095\n", "Epoch 39/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.1993 - accuracy: 0.9415\n", "Epoch 39: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 53ms/step - loss: 0.2012 - accuracy: 0.9375 - val_loss: 0.5969 - val_accuracy: 0.7619\n", "Epoch 40/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.1299 - accuracy: 0.9531\n", "Epoch 40: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 52ms/step - loss: 0.1299 - accuracy: 0.9531 - val_loss: 0.6690 - val_accuracy: 0.6667\n", "Epoch 41/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.2089 - accuracy: 0.9298\n", "Epoch 41: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 53ms/step - loss: 0.1951 - accuracy: 0.9323 - val_loss: 1.1585 - val_accuracy: 0.7143\n", "Epoch 42/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.2625 - accuracy: 0.9271\n", "Epoch 42: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 56ms/step - loss: 0.2625 - accuracy: 0.9271 - val_loss: 1.0154 - val_accuracy: 0.7619\n", "Epoch 43/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.2070 - accuracy: 0.9357\n", "Epoch 43: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 56ms/step - loss: 0.1995 - accuracy: 0.9375 - val_loss: 0.6049 - val_accuracy: 0.9048\n", "Epoch 44/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.1552 - accuracy: 0.9479\n", "Epoch 44: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 53ms/step - loss: 0.1552 - accuracy: 0.9479 - val_loss: 0.6593 - val_accuracy: 0.9048\n", "Epoch 45/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.1120 - accuracy: 0.9577\n", "Epoch 45: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 51ms/step - loss: 0.1243 - accuracy: 0.9531 - val_loss: 0.7417 - val_accuracy: 0.8571\n", "Epoch 46/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.1944 - accuracy: 0.9298\n", "Epoch 46: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 53ms/step - loss: 0.1777 - accuracy: 0.9375 - val_loss: 1.2891 - val_accuracy: 0.7619\n", "Epoch 47/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.1164 - accuracy: 0.9583\n", "Epoch 47: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 54ms/step - loss: 0.1164 - accuracy: 0.9583 - val_loss: 1.4985 - val_accuracy: 0.7619\n", "Epoch 48/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.1336 - accuracy: 0.9531\n", "Epoch 48: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 55ms/step - loss: 0.1336 - accuracy: 0.9531 - val_loss: 1.2200 - val_accuracy: 0.7143\n", "Epoch 49/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.0639 - accuracy: 0.9841\n", "Epoch 49: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 51ms/step - loss: 0.0654 - accuracy: 0.9844 - val_loss: 0.9979 - val_accuracy: 0.7619\n", "Epoch 50/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.0918 - accuracy: 0.9688\n", "Epoch 50: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 57ms/step - loss: 0.0918 - accuracy: 0.9688 - val_loss: 1.1637 - val_accuracy: 0.8095\n", "Epoch 51/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.1844 - accuracy: 0.9427\n", "Epoch 51: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 53ms/step - loss: 0.1844 - accuracy: 0.9427 - val_loss: 1.8117 - val_accuracy: 0.6190\n", "Epoch 52/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.2783 - accuracy: 0.8906\n", "Epoch 52: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 52ms/step - loss: 0.2783 - accuracy: 0.8906 - val_loss: 2.3003 - val_accuracy: 0.6190\n", "Epoch 53/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.2974 - accuracy: 0.9298\n", "Epoch 53: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 53ms/step - loss: 0.2853 - accuracy: 0.9271 - val_loss: 1.8863 - val_accuracy: 0.3810\n", "Epoch 54/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.2028 - accuracy: 0.9532\n", "Epoch 54: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 52ms/step - loss: 0.1947 - accuracy: 0.9531 - val_loss: 0.8102 - val_accuracy: 0.6667\n", "Epoch 55/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.1463 - accuracy: 0.9708\n", "Epoch 55: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 52ms/step - loss: 0.1330 - accuracy: 0.9740 - val_loss: 0.7041 - val_accuracy: 0.7619\n", "Epoch 56/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.1662 - accuracy: 0.9415\n", "Epoch 56: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 54ms/step - loss: 0.1722 - accuracy: 0.9427 - val_loss: 1.7684 - val_accuracy: 0.4286\n", "Epoch 57/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.2657 - accuracy: 0.9115\n", "Epoch 57: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 58ms/step - loss: 0.2657 - accuracy: 0.9115 - val_loss: 1.5878 - val_accuracy: 0.3333\n", "Epoch 58/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.1319 - accuracy: 0.9766\n", "Epoch 58: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 54ms/step - loss: 0.1304 - accuracy: 0.9740 - val_loss: 1.1260 - val_accuracy: 0.5238\n", "Epoch 59/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.1264 - accuracy: 0.9688\n", "Epoch 59: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 54ms/step - loss: 0.1264 - accuracy: 0.9688 - val_loss: 0.8344 - val_accuracy: 0.6667\n", "Epoch 60/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.0612 - accuracy: 0.9825\n", "Epoch 60: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 55ms/step - loss: 0.0612 - accuracy: 0.9844 - val_loss: 0.7751 - val_accuracy: 0.7143\n", "Epoch 61/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.0934 - accuracy: 0.9825\n", "Epoch 61: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 52ms/step - loss: 0.0880 - accuracy: 0.9844 - val_loss: 1.3831 - val_accuracy: 0.5714\n", "Epoch 62/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.1347 - accuracy: 0.9479\n", "Epoch 62: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 53ms/step - loss: 0.1347 - accuracy: 0.9479 - val_loss: 2.7359 - val_accuracy: 0.3810\n", "Epoch 63/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.1273 - accuracy: 0.9531\n", "Epoch 63: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 52ms/step - loss: 0.1273 - accuracy: 0.9531 - val_loss: 3.3070 - val_accuracy: 0.2857\n", "Epoch 64/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.0866 - accuracy: 0.9740\n", "Epoch 64: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 51ms/step - loss: 0.0866 - accuracy: 0.9740 - val_loss: 2.5524 - val_accuracy: 0.4286\n", "Epoch 65/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.0831 - accuracy: 0.9825\n", "Epoch 65: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 53ms/step - loss: 0.0758 - accuracy: 0.9844 - val_loss: 1.6055 - val_accuracy: 0.6190\n", "Epoch 66/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.0655 - accuracy: 0.9844\n", "Epoch 66: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 52ms/step - loss: 0.0655 - accuracy: 0.9844 - val_loss: 1.3192 - val_accuracy: 0.6190\n", "Epoch 67/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.0352 - accuracy: 0.9896\n", "Epoch 67: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 54ms/step - loss: 0.0352 - accuracy: 0.9896 - val_loss: 1.1934 - val_accuracy: 0.6667\n", "Epoch 68/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.0379 - accuracy: 0.9947\n", "Epoch 68: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 53ms/step - loss: 0.0505 - accuracy: 0.9896 - val_loss: 0.8178 - val_accuracy: 0.7143\n", "Epoch 69/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.0478 - accuracy: 0.9883\n", "Epoch 69: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 52ms/step - loss: 0.0438 - accuracy: 0.9896 - val_loss: 0.5948 - val_accuracy: 0.8095\n", "Epoch 70/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.0710 - accuracy: 0.9740\n", "Epoch 70: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 56ms/step - loss: 0.0710 - accuracy: 0.9740 - val_loss: 0.4797 - val_accuracy: 0.8095\n", "Epoch 71/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.1135 - accuracy: 0.9688\n", "Epoch 71: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 52ms/step - loss: 0.1135 - accuracy: 0.9688 - val_loss: 0.6284 - val_accuracy: 0.8095\n", "Epoch 72/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.1187 - accuracy: 0.9708\n", "Epoch 72: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 53ms/step - loss: 0.1188 - accuracy: 0.9635 - val_loss: 0.7145 - val_accuracy: 0.8095\n", "Epoch 73/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.0976 - accuracy: 0.9766\n", "Epoch 73: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 54ms/step - loss: 0.0953 - accuracy: 0.9740 - val_loss: 0.5522 - val_accuracy: 0.8095\n", "Epoch 74/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.0633 - accuracy: 0.9844\n", "Epoch 74: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 53ms/step - loss: 0.0633 - accuracy: 0.9844 - val_loss: 0.4026 - val_accuracy: 0.8571\n", "Epoch 75/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.1216 - accuracy: 0.9688\n", "Epoch 75: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 53ms/step - loss: 0.1216 - accuracy: 0.9688 - val_loss: 0.5148 - val_accuracy: 0.8095\n", "Epoch 76/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.1289 - accuracy: 0.9591\n", "Epoch 76: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 54ms/step - loss: 0.1221 - accuracy: 0.9635 - val_loss: 0.5240 - val_accuracy: 0.8095\n", "Epoch 77/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.1247 - accuracy: 0.9740\n", "Epoch 77: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 54ms/step - loss: 0.1247 - accuracy: 0.9740 - val_loss: 0.4934 - val_accuracy: 0.7143\n", "Epoch 78/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.1237 - accuracy: 0.9688\n", "Epoch 78: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 52ms/step - loss: 0.1237 - accuracy: 0.9688 - val_loss: 0.5263 - val_accuracy: 0.7619\n", "Epoch 79/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.0697 - accuracy: 0.9844\n", "Epoch 79: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 52ms/step - loss: 0.0697 - accuracy: 0.9844 - val_loss: 0.5087 - val_accuracy: 0.7619\n", "Epoch 80/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.1081 - accuracy: 0.9740\n", "Epoch 80: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 53ms/step - loss: 0.1081 - accuracy: 0.9740 - val_loss: 0.5218 - val_accuracy: 0.8571\n", "Epoch 81/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.0816 - accuracy: 0.9883\n", "Epoch 81: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 52ms/step - loss: 0.0749 - accuracy: 0.9896 - val_loss: 0.5199 - val_accuracy: 0.8571\n", "Epoch 82/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.0743 - accuracy: 0.9766\n", "Epoch 82: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 52ms/step - loss: 0.0752 - accuracy: 0.9740 - val_loss: 0.4339 - val_accuracy: 0.8571\n", "Epoch 83/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.0733 - accuracy: 0.9792\n", "Epoch 83: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 55ms/step - loss: 0.0733 - accuracy: 0.9792 - val_loss: 0.4170 - val_accuracy: 0.8571\n", "Epoch 84/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.0567 - accuracy: 0.9896\n", "Epoch 84: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 53ms/step - loss: 0.0567 - accuracy: 0.9896 - val_loss: 0.5299 - val_accuracy: 0.8095\n", "Epoch 85/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.0289 - accuracy: 0.9942\n", "Epoch 85: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 53ms/step - loss: 0.0311 - accuracy: 0.9948 - val_loss: 1.2434 - val_accuracy: 0.7143\n", "Epoch 86/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.0925 - accuracy: 0.9740\n", "Epoch 86: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 54ms/step - loss: 0.0925 - accuracy: 0.9740 - val_loss: 1.8590 - val_accuracy: 0.6667\n", "Epoch 87/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.1086 - accuracy: 0.9635\n", "Epoch 87: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 56ms/step - loss: 0.1086 - accuracy: 0.9635 - val_loss: 1.5970 - val_accuracy: 0.7143\n", "Epoch 88/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.1179 - accuracy: 0.9766\n", "Epoch 88: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 53ms/step - loss: 0.1103 - accuracy: 0.9792 - val_loss: 1.1255 - val_accuracy: 0.7619\n", "Epoch 89/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.0909 - accuracy: 0.9735\n", "Epoch 89: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 50ms/step - loss: 0.0909 - accuracy: 0.9740 - val_loss: 0.8424 - val_accuracy: 0.7619\n", "Epoch 90/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.0348 - accuracy: 0.9883\n", "Epoch 90: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 53ms/step - loss: 0.0332 - accuracy: 0.9896 - val_loss: 0.6144 - val_accuracy: 0.8571\n", "Epoch 91/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.0840 - accuracy: 0.9844\n", "Epoch 91: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 54ms/step - loss: 0.0840 - accuracy: 0.9844 - val_loss: 0.4613 - val_accuracy: 0.8095\n", "Epoch 92/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.1506 - accuracy: 0.9635\n", "Epoch 92: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 55ms/step - loss: 0.1506 - accuracy: 0.9635 - val_loss: 0.4861 - val_accuracy: 0.8571\n", "Epoch 93/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.2522 - accuracy: 0.9375\n", "Epoch 93: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 55ms/step - loss: 0.2522 - accuracy: 0.9375 - val_loss: 0.5461 - val_accuracy: 0.8095\n", "Epoch 94/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.1442 - accuracy: 0.9635\n", "Epoch 94: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 55ms/step - loss: 0.1442 - accuracy: 0.9635 - val_loss: 0.5975 - val_accuracy: 0.8095\n", "Epoch 95/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.2240 - accuracy: 0.9479\n", "Epoch 95: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 58ms/step - loss: 0.2240 - accuracy: 0.9479 - val_loss: 0.8168 - val_accuracy: 0.8095\n", "Epoch 96/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.1599 - accuracy: 0.9479\n", "Epoch 96: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 53ms/step - loss: 0.1599 - accuracy: 0.9479 - val_loss: 0.7160 - val_accuracy: 0.8571\n", "Epoch 97/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.1444 - accuracy: 0.9825\n", "Epoch 97: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 53ms/step - loss: 0.1393 - accuracy: 0.9792 - val_loss: 0.7920 - val_accuracy: 0.8571\n", "Epoch 98/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.0673 - accuracy: 0.9883\n", "Epoch 98: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 54ms/step - loss: 0.0798 - accuracy: 0.9844 - val_loss: 0.5742 - val_accuracy: 0.8095\n", "Epoch 99/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.1170 - accuracy: 0.9792\n", "Epoch 99: val_accuracy did not improve from 0.90476\n", "10/10 [==============================] - 1s 53ms/step - loss: 0.1170 - accuracy: 0.9792 - val_loss: 0.2243 - val_accuracy: 0.9048\n", "Epoch 100/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.0877 - accuracy: 0.9844\n", "Epoch 100: val_accuracy improved from 0.90476 to 0.95238, saving model to /content/drive/My Drive/fer_new_vgg/PeerJ_JAFFE_seed_5.hdf5\n", "10/10 [==============================] - 2s 164ms/step - loss: 0.0877 - accuracy: 0.9844 - val_loss: 0.1826 - val_accuracy: 0.9524\n", "Epoch 101/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.0403 - accuracy: 0.9844\n", "Epoch 101: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 56ms/step - loss: 0.0403 - accuracy: 0.9844 - val_loss: 0.3079 - val_accuracy: 0.9524\n", "Epoch 102/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.0480 - accuracy: 0.9896\n", "Epoch 102: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 54ms/step - loss: 0.0480 - accuracy: 0.9896 - val_loss: 0.3209 - val_accuracy: 0.9524\n", "Epoch 103/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.0669 - accuracy: 0.9792\n", "Epoch 103: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 51ms/step - loss: 0.0669 - accuracy: 0.9792 - val_loss: 0.2806 - val_accuracy: 0.9524\n", "Epoch 104/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.0878 - accuracy: 0.9825\n", "Epoch 104: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 52ms/step - loss: 0.0897 - accuracy: 0.9792 - val_loss: 0.2281 - val_accuracy: 0.9524\n", "Epoch 105/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.1273 - accuracy: 0.9740\n", "Epoch 105: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 53ms/step - loss: 0.1273 - accuracy: 0.9740 - val_loss: 0.5505 - val_accuracy: 0.8095\n", "Epoch 106/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.1770 - accuracy: 0.9427\n", "Epoch 106: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 58ms/step - loss: 0.1770 - accuracy: 0.9427 - val_loss: 1.3617 - val_accuracy: 0.5714\n", "Epoch 107/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.1861 - accuracy: 0.9298\n", "Epoch 107: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 54ms/step - loss: 0.1700 - accuracy: 0.9375 - val_loss: 1.5634 - val_accuracy: 0.5714\n", "Epoch 108/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.1967 - accuracy: 0.9688\n", "Epoch 108: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 55ms/step - loss: 0.1967 - accuracy: 0.9688 - val_loss: 1.1802 - val_accuracy: 0.6190\n", "Epoch 109/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.1967 - accuracy: 0.9427\n", "Epoch 109: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 59ms/step - loss: 0.1967 - accuracy: 0.9427 - val_loss: 0.4965 - val_accuracy: 0.8095\n", "Epoch 110/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.0761 - accuracy: 0.9792\n", "Epoch 110: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 68ms/step - loss: 0.0761 - accuracy: 0.9792 - val_loss: 0.4335 - val_accuracy: 0.8095\n", "Epoch 111/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.1421 - accuracy: 0.9688\n", "Epoch 111: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 78ms/step - loss: 0.1421 - accuracy: 0.9688 - val_loss: 0.7144 - val_accuracy: 0.7619\n", "Epoch 112/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.1669 - accuracy: 0.9427\n", "Epoch 112: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 71ms/step - loss: 0.1669 - accuracy: 0.9427 - val_loss: 0.9652 - val_accuracy: 0.7619\n", "Epoch 113/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.0765 - accuracy: 0.9844\n", "Epoch 113: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 59ms/step - loss: 0.0765 - accuracy: 0.9844 - val_loss: 0.9441 - val_accuracy: 0.7143\n", "Epoch 114/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.1257 - accuracy: 0.9766\n", "Epoch 114: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 53ms/step - loss: 0.1231 - accuracy: 0.9740 - val_loss: 0.7385 - val_accuracy: 0.7619\n", "Epoch 115/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.0893 - accuracy: 0.9792\n", "Epoch 115: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 62ms/step - loss: 0.0893 - accuracy: 0.9792 - val_loss: 0.5197 - val_accuracy: 0.8095\n", "Epoch 116/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.0490 - accuracy: 0.9894\n", "Epoch 116: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 53ms/step - loss: 0.0495 - accuracy: 0.9896 - val_loss: 0.5423 - val_accuracy: 0.7619\n", "Epoch 117/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.0700 - accuracy: 0.9894\n", "Epoch 117: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 53ms/step - loss: 0.1020 - accuracy: 0.9792 - val_loss: 0.3451 - val_accuracy: 0.8095\n", "Epoch 118/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.0761 - accuracy: 0.9844\n", "Epoch 118: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 54ms/step - loss: 0.0761 - accuracy: 0.9844 - val_loss: 0.4802 - val_accuracy: 0.7619\n", "Epoch 119/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.0939 - accuracy: 0.9788\n", "Epoch 119: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 56ms/step - loss: 0.1236 - accuracy: 0.9740 - val_loss: 0.8504 - val_accuracy: 0.7619\n", "Epoch 120/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.1349 - accuracy: 0.9531\n", "Epoch 120: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 54ms/step - loss: 0.1349 - accuracy: 0.9531 - val_loss: 0.9089 - val_accuracy: 0.7143\n", "Epoch 121/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.1178 - accuracy: 0.9708\n", "Epoch 121: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 55ms/step - loss: 0.1117 - accuracy: 0.9740 - val_loss: 2.3610 - val_accuracy: 0.5714\n", "Epoch 122/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.2899 - accuracy: 0.9474\n", "Epoch 122: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 54ms/step - loss: 0.2888 - accuracy: 0.9427 - val_loss: 3.0823 - val_accuracy: 0.5238\n", "Epoch 123/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.1836 - accuracy: 0.9427\n", "Epoch 123: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 53ms/step - loss: 0.1836 - accuracy: 0.9427 - val_loss: 2.3872 - val_accuracy: 0.7143\n", "Epoch 124/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.0961 - accuracy: 0.9688\n", "Epoch 124: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 55ms/step - loss: 0.0961 - accuracy: 0.9688 - val_loss: 1.1479 - val_accuracy: 0.7619\n", "Epoch 125/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.0477 - accuracy: 0.9948\n", "Epoch 125: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 54ms/step - loss: 0.0477 - accuracy: 0.9948 - val_loss: 0.4506 - val_accuracy: 0.8571\n", "Epoch 126/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.0844 - accuracy: 0.9735\n", "Epoch 126: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 54ms/step - loss: 0.1028 - accuracy: 0.9635 - val_loss: 0.3143 - val_accuracy: 0.8095\n", "Epoch 127/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.0533 - accuracy: 0.9896\n", "Epoch 127: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 54ms/step - loss: 0.0533 - accuracy: 0.9896 - val_loss: 1.8690 - val_accuracy: 0.6190\n", "Epoch 128/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.1950 - accuracy: 0.9591\n", "Epoch 128: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 52ms/step - loss: 0.1922 - accuracy: 0.9583 - val_loss: 2.3605 - val_accuracy: 0.5714\n", "Epoch 129/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.1656 - accuracy: 0.9766\n", "Epoch 129: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 53ms/step - loss: 0.1561 - accuracy: 0.9740 - val_loss: 2.0391 - val_accuracy: 0.6667\n", "Epoch 130/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.0897 - accuracy: 0.9844\n", "Epoch 130: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 54ms/step - loss: 0.0897 - accuracy: 0.9844 - val_loss: 1.7010 - val_accuracy: 0.7143\n", "Epoch 131/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.0427 - accuracy: 0.9948\n", "Epoch 131: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 55ms/step - loss: 0.0427 - accuracy: 0.9948 - val_loss: 1.4087 - val_accuracy: 0.7619\n", "Epoch 132/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.0433 - accuracy: 0.9896\n", "Epoch 132: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 54ms/step - loss: 0.0433 - accuracy: 0.9896 - val_loss: 1.3660 - val_accuracy: 0.7619\n", "Epoch 133/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.0388 - accuracy: 0.9942\n", "Epoch 133: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 56ms/step - loss: 0.0370 - accuracy: 0.9948 - val_loss: 1.2422 - val_accuracy: 0.7619\n", "Epoch 134/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.0366 - accuracy: 0.9942\n", "Epoch 134: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 54ms/step - loss: 0.0342 - accuracy: 0.9948 - val_loss: 1.1375 - val_accuracy: 0.7619\n", "Epoch 135/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.0354 - accuracy: 0.9896\n", "Epoch 135: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 55ms/step - loss: 0.0354 - accuracy: 0.9896 - val_loss: 0.8114 - val_accuracy: 0.8095\n", "Epoch 136/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.0320 - accuracy: 0.9948\n", "Epoch 136: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 53ms/step - loss: 0.0320 - accuracy: 0.9948 - val_loss: 0.6353 - val_accuracy: 0.8095\n", "Epoch 137/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.1078 - accuracy: 0.9792\n", "Epoch 137: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 53ms/step - loss: 0.1078 - accuracy: 0.9792 - val_loss: 0.4856 - val_accuracy: 0.8095\n", "Epoch 138/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.1287 - accuracy: 0.9649\n", "Epoch 138: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 57ms/step - loss: 0.1159 - accuracy: 0.9688 - val_loss: 0.4921 - val_accuracy: 0.8571\n", "Epoch 139/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.0953 - accuracy: 0.9844\n", "Epoch 139: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 56ms/step - loss: 0.0953 - accuracy: 0.9844 - val_loss: 0.4582 - val_accuracy: 0.9048\n", "Epoch 140/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.1183 - accuracy: 0.9688\n", "Epoch 140: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 56ms/step - loss: 0.1183 - accuracy: 0.9688 - val_loss: 0.2071 - val_accuracy: 0.9524\n", "Epoch 141/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.0708 - accuracy: 0.9844\n", "Epoch 141: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 54ms/step - loss: 0.0708 - accuracy: 0.9844 - val_loss: 0.1426 - val_accuracy: 0.9524\n", "Epoch 142/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.0943 - accuracy: 0.9792\n", "Epoch 142: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 55ms/step - loss: 0.0943 - accuracy: 0.9792 - val_loss: 0.3005 - val_accuracy: 0.9048\n", "Epoch 143/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.1242 - accuracy: 0.9427\n", "Epoch 143: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 55ms/step - loss: 0.1242 - accuracy: 0.9427 - val_loss: 0.6226 - val_accuracy: 0.9048\n", "Epoch 144/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.1712 - accuracy: 0.9474\n", "Epoch 144: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 56ms/step - loss: 0.1762 - accuracy: 0.9479 - val_loss: 0.8024 - val_accuracy: 0.8095\n", "Epoch 145/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.1547 - accuracy: 0.9577\n", "Epoch 145: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 54ms/step - loss: 0.1529 - accuracy: 0.9583 - val_loss: 0.7844 - val_accuracy: 0.8095\n", "Epoch 146/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.1134 - accuracy: 0.9688\n", "Epoch 146: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 54ms/step - loss: 0.1134 - accuracy: 0.9688 - val_loss: 0.5150 - val_accuracy: 0.9048\n", "Epoch 147/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.0657 - accuracy: 0.9792\n", "Epoch 147: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 55ms/step - loss: 0.0657 - accuracy: 0.9792 - val_loss: 0.3447 - val_accuracy: 0.9048\n", "Epoch 148/150\n", " 9/10 [==========================>...] - ETA: 0s - loss: 0.0361 - accuracy: 0.9825\n", "Epoch 148: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 54ms/step - loss: 0.0327 - accuracy: 0.9844 - val_loss: 0.2938 - val_accuracy: 0.9048\n", "Epoch 149/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.1422 - accuracy: 0.9583\n", "Epoch 149: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 57ms/step - loss: 0.1422 - accuracy: 0.9583 - val_loss: 0.4034 - val_accuracy: 0.8095\n", "Epoch 150/150\n", "10/10 [==============================] - ETA: 0s - loss: 0.0477 - accuracy: 0.9896\n", "Epoch 150: val_accuracy did not improve from 0.95238\n", "10/10 [==============================] - 1s 57ms/step - loss: 0.0477 - accuracy: 0.9896 - val_loss: 0.4053 - val_accuracy: 0.8571\n" ] } ] }, { "cell_type": "code", "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_jaffe_2013.png')\n", "files.download(\"acc_jaffe_2013.png\")\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 404 }, "id": "3UC2U6DxutSe", "outputId": "84d041a5-2165-46d6-9891-fb1d808a5d1f" }, "execution_count": 26, "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_98456ffb-947f-4ce6-9912-815e0da0cc69\", \"acc_jaffe_2013.png\", 49483)" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "code", "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_jaffe_2013.png')\n", "files.download(\"loss_jaffe_2013.png\")\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 404 }, "id": "nbzD33JCu_yO", "outputId": "0135a2b1-5537-4147-95b4-812d5a7adb44" }, "execution_count": 27, "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 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\n" }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "code", "source": [ "" ], "metadata": { "id": "xUiUdCHElx5g" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "P8qrKxXu_4Jg" }, "source": [ "model.load_weights('/content/drive/My Drive/fer_new_vgg/PeerJ_JAFFE_seed_5.hdf5') " ], "execution_count": 29, "outputs": [] }, { "cell_type": "code", "metadata": { "id": "988SDaJt6I5s" }, "source": [ "predictions = model.predict(test_generator, steps=test_sample//batch_size, verbose=0)" ], "execution_count": 30, "outputs": [] }, { "cell_type": "code", "source": [ "" ], "metadata": { "id": "YxQKjklDzJaw" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "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", "p.set_xticklabels(p.get_xmajorticklabels(), fontsize = 18)\n", "p.set_yticklabels(p.get_ymajorticklabels(), fontsize = 18)\n", "plt.ylabel('Actual', fontsize=18)\n", "plt.xlabel('Predicted', fontsize=18)\n", "plt.savefig('conf_jaffe_2013.png')\n", "files.download(\"conf_jaffe_2013.png\")\n", "plt.show(block=False)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 411 }, "id": "wrfJDfhhliEB", "outputId": "b2409f34-0883-4b23-d3fd-369cf56a8eb8" }, "execution_count": 31, "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_29ac1e54-e613-46a3-9506-86f3c2688aae\", \"conf_jaffe_2013.png\", 31558)" ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": { "needs_background": "light" } } ] }, { "cell_type": "code", "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))" ], "metadata": { "id": "BilB_6uybMkj", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "626c4763-5594-4dc1-a706-0da8ee3b9879" }, "execution_count": 32, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " precision recall f1-score support\n", "\n", " AN 1.0000 1.0000 1.0000 3\n", " DI 1.0000 1.0000 1.0000 3\n", " FE 0.7500 1.0000 0.8571 3\n", " HA 1.0000 1.0000 1.0000 3\n", " NE 1.0000 1.0000 1.0000 3\n", " SA 1.0000 0.6667 0.8000 3\n", " SU 1.0000 1.0000 1.0000 3\n", "\n", " accuracy 0.9524 21\n", " macro avg 0.9643 0.9524 0.9510 21\n", "weighted avg 0.9643 0.9524 0.9510 21\n", "\n" ] } ] } ] }