Covid19_GradientCrescent_Multi.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Covid19_GradientCrescent_Multi.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"markdown","metadata":{"id":"gyG4OC_n_Bgb","colab_type":"text"},"source":["## COVID19 CT Pneumonia Detection - Multiclass Case\n","By Adrian Yijie Xu\n","(https://medium.com/@adrianitsaxu)\n","\n","As discussed in \"Detecting COVID-19 induced Pneumonia from Chest X-rays with Transfer Learning: An implementation in Tensorflow and Keras.\"\n","(https://github.com/EXJUSTICE/GradientCrescent)\n","\n","50% test accuracy with 4 classes\n","100% test accuracy with 3 classes\n","\n","Conclusion: Viral Pneumonia indistinguishable from COVID19: acceptable, given that COVID19 is itself caused by viral means.\n","\n"]},{"cell_type":"code","metadata":{"id":"JHtRS9Wf9mZc","colab_type":"code","outputId":"e21794bf-fa33-4fb2-ff46-e9dc1491e87f","executionInfo":{"status":"ok","timestamp":1584625353133,"user_tz":-60,"elapsed":13306,"user":{"displayName":"Yijie Xu","photoUrl":"","userId":"14695674479284134427"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["# This Python 3 environment comes with many helpful analytics libraries installed\n","# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n","# For example, here's several helpful packages to load in \n","#set randomness for reproducibility\n","from numpy.random import seed\n","seed(8) #1\n","from tensorflow import set_random_seed\n","set_random_seed(7) #2\n","\n","#\n","\n","import numpy as np # linear algebra\n","import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n","\n","# Input data files are available in the \"../input/\" directory.\n","# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n","\n","import os\n","\n","\n","\n","!gdown https://drive.google.com/uc?id=1coM7x3378f-Ou2l6Pg2wldaOI7Dntu1a\n","!unzip Covid_Data_GradientCrescent.zip\n","\n"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"text/html":["<p style=\"color: red;\">\n","The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n","We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n","or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n","<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["Downloading...\n","From: 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 inflating: two/train/normal/NORMAL2-IM-1365-0001.jpeg  \n","  inflating: two/train/normal/NORMAL2-IM-1371-0001.jpeg  \n","  inflating: two/train/normal/NORMAL2-IM-1376-0001.jpeg  \n","  inflating: two/train/normal/NORMAL2-IM-1379-0001.jpeg  \n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"cvTuKxbA_6Qx","colab_type":"code","outputId":"281f8a61-9103-4d93-fe61-7352d8d347ec","executionInfo":{"status":"ok","timestamp":1584625368917,"user_tz":-60,"elapsed":3091,"user":{"displayName":"Yijie Xu","photoUrl":"","userId":"14695674479284134427"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["print(os.listdir(\"/content/\"))\n","\n","#Print and remove zips once done\n","\n","!rm Covid_Data_GradientCrescent.zip\n","\n"],"execution_count":0,"outputs":[{"output_type":"stream","text":["['.config', 'Covid_Data_GradientCrescent.zip', 'all', 'two', 'sample_data']\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"wf3LKpv0BGGk","colab_type":"code","colab":{}},"source":["#Enable this cell to remove viral pneumonia class. Remember to Change model's final layer to 3 neurons.\n","!rm -rf /content/all/test/pneumonia_vir\n","\n","!rm -rf /content/all/train/pneumonia_vir"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"923QFSnj-E_1","colab_type":"code","outputId":"c1ab9965-b420-43ba-f692-2794213164a3","executionInfo":{"status":"ok","timestamp":1584625446281,"user_tz":-60,"elapsed":1076,"user":{"displayName":"Yijie Xu","photoUrl":"","userId":"14695674479284134427"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["from os import listdir\n","data_list = listdir('/content/all/train')\n","\n","#Delete some classes that may interfere\n","\n","\n","data_list"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['pneumonia_bac', 'covid', 'normal']"]},"metadata":{"tags":[]},"execution_count":6}]},{"cell_type":"markdown","metadata":{"id":"SW7NXqkh-Gk8","colab_type":"text"},"source":["Case 2: COVID vs Normal vs Tertiary Pneumonia (Bacterial and Viral)\n","VGG Initialization"]},{"cell_type":"code","metadata":{"id":"0BfmpmKd-JOb","colab_type":"code","outputId":"9efe72c3-0f3c-498a-8ad7-4c135fbb0bef","executionInfo":{"status":"ok","timestamp":1584625446680,"user_tz":-60,"elapsed":1458,"user":{"displayName":"Yijie Xu","photoUrl":"","userId":"14695674479284134427"}},"colab":{"base_uri":"https://localhost:8080/","height":126}},"source":["from tensorflow.keras import backend as K\n","from tensorflow.keras.models import Model ,load_model\n","from tensorflow.keras.layers import Flatten, Dense, Dropout\n","from tensorflow.keras.applications.inception_resnet_v2 import InceptionResNetV2, preprocess_input\n","from keras.applications.vgg16 import preprocess_input\n","from keras.applications.vgg16 import decode_predictions\n","from keras.applications.vgg16 import VGG16\n","from tensorflow.keras.optimizers import Adam, RMSprop\n","from tensorflow.keras.preprocessing.image import ImageDataGenerator\n","from tensorflow.keras.callbacks import ModelCheckpoint\n","import numpy as np\n","import tensorflow as tf\n","\n","\n","DATASET_PATH  = '/content/all/train'\n","test_dir =  '/content/all/test'\n","IMAGE_SIZE    = (150, 150)\n","NUM_CLASSES   = len(data_list)\n","BATCH_SIZE    = 10  # try reducing batch size or freeze more layers if your GPU runs out of memory\n","NUM_EPOCHS    = 100\n","LEARNING_RATE =0.0001\n","\n","\n","\n","#Train datagen here is a preprocessor\n","train_datagen = ImageDataGenerator(rescale=1./255,\n","                                   rotation_range=50,\n","                                   featurewise_center = True,\n","                                   featurewise_std_normalization = True,\n","                                   width_shift_range=0.2,\n","                                   height_shift_range=0.2,\n","                                   shear_range=0.25,\n","                                   zoom_range=0.1,\n","                                   zca_whitening = True,\n","                                   channel_shift_range = 20,\n","                                   horizontal_flip = True ,\n","                                   vertical_flip = True ,\n","                                   validation_split = 0.2,\n","                                   fill_mode='constant')\n","\n","# test_datagen = ImageDataGenerator(preprocessing_function=preprocess_input,\n","#                                    fill_mode='constant')\n","\n","train_batches = train_datagen.flow_from_directory(DATASET_PATH,\n","                                                  target_size=IMAGE_SIZE,\n","                                                  shuffle=True,\n","                                                  batch_size=BATCH_SIZE,\n","                                                  subset = \"training\",\n","                                                  seed=42,\n","                                                  class_mode=\"categorical\"\n","                                                  )\n","\n","valid_batches = train_datagen.flow_from_directory(DATASET_PATH,\n","                                                  target_size=IMAGE_SIZE,\n","                                                  shuffle=True,\n","                                                  batch_size=BATCH_SIZE,\n","                                                  subset = \"validation\",\n","                                                  seed=42,\n","                                                  class_mode=\"categorical\"\n","                                                 \n","                                                  )\n","\n"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Using TensorFlow backend.\n","/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py:341: UserWarning: This ImageDataGenerator specifies `zca_whitening` which overrides setting of`featurewise_std_normalization`.\n","  warnings.warn('This ImageDataGenerator specifies '\n"],"name":"stderr"},{"output_type":"stream","text":["Found 160 images belonging to 3 classes.\n","Found 40 images belonging to 3 classes.\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"Arc_63PU-O5K","colab_type":"text"},"source":["Layer Modification at upper layer of VGG16"]},{"cell_type":"code","metadata":{"id":"lyGm7tYQBBr1","colab_type":"code","colab":{}},"source":["#Simple CNN model based on VGG16. Set dense layer neuron count to 3 if viral pneumonia class is removed\n","\n","\n","from keras import models\n","from keras import layers\n","from keras.applications import VGG16\n","from keras import optimizers\n","\n","conv_base = VGG16(weights='imagenet',\n","                  include_top=False,\n","                  input_shape=(150, 150, 3))\n","\n","\n","conv_base.trainable = False\n","\n","\n","model = models.Sequential()\n","model.add(conv_base)\n","model.add(layers.Flatten())\n","model.add(layers.Dense(256, activation='relu'))\n","model.add(layers.Dense(3, activation='softmax'))\n","\n","\n","model.compile(loss='categorical_crossentropy',\n","              \n","              optimizer=optimizers.Adam(lr=LEARNING_RATE),\n","              metrics=['acc'])"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"GKDLJZjL-UlA","colab_type":"code","outputId":"c04d5d58-f967-4f7b-8f82-ab997e67132c","executionInfo":{"status":"ok","timestamp":1584625481306,"user_tz":-60,"elapsed":1405,"user":{"displayName":"Yijie Xu","photoUrl":"","userId":"14695674479284134427"}},"colab":{"base_uri":"https://localhost:8080/","height":319}},"source":["print(model.summary())"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Model: \"sequential_2\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","vgg16 (Model)                (None, 4, 4, 512)         14714688  \n","_________________________________________________________________\n","flatten_2 (Flatten)          (None, 8192)              0         \n","_________________________________________________________________\n","dense_3 (Dense)              (None, 256)               2097408   \n","_________________________________________________________________\n","dense_4 (Dense)              (None, 3)                 771       \n","=================================================================\n","Total params: 16,812,867\n","Trainable params: 2,098,179\n","Non-trainable params: 14,714,688\n","_________________________________________________________________\n","None\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"qh3QQZLz-yLW","colab_type":"text"},"source":["Training"]},{"cell_type":"code","metadata":{"id":"F7IwDhx5-iKe","colab_type":"code","outputId":"e7618c23-a4fc-4802-bfb1-4150267e6559","executionInfo":{"status":"ok","timestamp":1584625921438,"user_tz":-60,"elapsed":441482,"user":{"displayName":"Yijie Xu","photoUrl":"","userId":"14695674479284134427"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["#FIT MODEL\n","print(len(train_batches))\n","print(len(valid_batches))\n","\n","STEP_SIZE_TRAIN=train_batches.n//train_batches.batch_size\n","STEP_SIZE_VALID=valid_batches.n//valid_batches.batch_size\n","\n","result=model.fit_generator(train_batches,\n","                        steps_per_epoch =STEP_SIZE_TRAIN,\n","                        validation_data = valid_batches,\n","                        validation_steps = STEP_SIZE_VALID,\n","                        epochs= NUM_EPOCHS,                        \n","                       )\n","\n"],"execution_count":0,"outputs":[{"output_type":"stream","text":["16\n","4\n","Epoch 1/100\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py:716: UserWarning: This ImageDataGenerator specifies `featurewise_center`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n","  warnings.warn('This ImageDataGenerator specifies '\n","/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py:735: UserWarning: This ImageDataGenerator specifies `zca_whitening`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n","  warnings.warn('This ImageDataGenerator specifies '\n"],"name":"stderr"},{"output_type":"stream","text":["16/16 [==============================] - 10s 637ms/step - loss: 1.0498 - acc: 0.4313 - val_loss: 0.9339 - val_acc: 0.5500\n","Epoch 2/100\n","16/16 [==============================] - 4s 270ms/step - loss: 0.8062 - acc: 0.6250 - val_loss: 0.8428 - val_acc: 0.5500\n","Epoch 3/100\n","16/16 [==============================] - 4s 264ms/step - loss: 0.7823 - acc: 0.6500 - val_loss: 0.6858 - val_acc: 0.6750\n","Epoch 4/100\n","16/16 [==============================] - 4s 263ms/step - loss: 0.7373 - acc: 0.6750 - val_loss: 0.6383 - val_acc: 0.6750\n","Epoch 5/100\n","16/16 [==============================] - 4s 266ms/step - loss: 0.6715 - acc: 0.7063 - val_loss: 0.6969 - val_acc: 0.6500\n","Epoch 6/100\n","16/16 [==============================] - 4s 269ms/step - loss: 0.6397 - acc: 0.7188 - val_loss: 0.7267 - val_acc: 0.6000\n","Epoch 7/100\n","16/16 [==============================] - 4s 271ms/step - loss: 0.6145 - acc: 0.7687 - val_loss: 0.5519 - val_acc: 0.8000\n","Epoch 8/100\n","16/16 [==============================] - 4s 268ms/step - loss: 0.5296 - acc: 0.8312 - val_loss: 0.5873 - val_acc: 0.7000\n","Epoch 9/100\n","16/16 [==============================] - 4s 266ms/step - loss: 0.5200 - acc: 0.7938 - val_loss: 0.5974 - val_acc: 0.6750\n","Epoch 10/100\n","16/16 [==============================] - 4s 263ms/step - loss: 0.4508 - acc: 0.8625 - val_loss: 0.5438 - val_acc: 0.7500\n","Epoch 11/100\n","16/16 [==============================] - 4s 265ms/step - loss: 0.4888 - acc: 0.8437 - val_loss: 0.6560 - val_acc: 0.7000\n","Epoch 12/100\n","16/16 [==============================] - 4s 263ms/step - loss: 0.4873 - acc: 0.8250 - val_loss: 0.3684 - val_acc: 0.8750\n","Epoch 13/100\n","16/16 [==============================] - 4s 262ms/step - loss: 0.4412 - acc: 0.8375 - val_loss: 0.4848 - val_acc: 0.8000\n","Epoch 14/100\n","16/16 [==============================] - 4s 261ms/step - loss: 0.4017 - acc: 0.8812 - val_loss: 0.6040 - val_acc: 0.7000\n","Epoch 15/100\n","16/16 [==============================] - 4s 266ms/step - loss: 0.4023 - acc: 0.8437 - val_loss: 0.4515 - val_acc: 0.8500\n","Epoch 16/100\n","16/16 [==============================] - 4s 260ms/step - loss: 0.4230 - acc: 0.8562 - val_loss: 0.3597 - val_acc: 0.8750\n","Epoch 17/100\n","16/16 [==============================] - 4s 266ms/step - loss: 0.3787 - acc: 0.8687 - val_loss: 0.4388 - val_acc: 0.8000\n","Epoch 18/100\n","16/16 [==============================] - 4s 265ms/step - loss: 0.3748 - acc: 0.9000 - val_loss: 0.5102 - val_acc: 0.8250\n","Epoch 19/100\n","16/16 [==============================] - 4s 263ms/step - loss: 0.3672 - acc: 0.8812 - val_loss: 0.6221 - val_acc: 0.7250\n","Epoch 20/100\n","16/16 [==============================] - 4s 263ms/step - loss: 0.4125 - acc: 0.8438 - val_loss: 0.4431 - val_acc: 0.8250\n","Epoch 21/100\n","16/16 [==============================] - 4s 265ms/step - loss: 0.3897 - acc: 0.8437 - val_loss: 0.4752 - val_acc: 0.8500\n","Epoch 22/100\n","16/16 [==============================] - 4s 266ms/step - loss: 0.3391 - acc: 0.8812 - val_loss: 0.3713 - val_acc: 0.8250\n","Epoch 23/100\n","16/16 [==============================] - 4s 264ms/step - loss: 0.3423 - acc: 0.8500 - val_loss: 0.6167 - val_acc: 0.6500\n","Epoch 24/100\n","16/16 [==============================] - 4s 270ms/step - loss: 0.3050 - acc: 0.9000 - val_loss: 0.2512 - val_acc: 0.9500\n","Epoch 25/100\n","16/16 [==============================] - 4s 270ms/step - loss: 0.3294 - acc: 0.8875 - val_loss: 0.4502 - val_acc: 0.7750\n","Epoch 26/100\n","16/16 [==============================] - 4s 274ms/step - loss: 0.3327 - acc: 0.9000 - val_loss: 0.3551 - val_acc: 0.9000\n","Epoch 27/100\n","16/16 [==============================] - 4s 271ms/step - loss: 0.2844 - acc: 0.9000 - val_loss: 0.4075 - val_acc: 0.8250\n","Epoch 28/100\n","16/16 [==============================] - 4s 271ms/step - loss: 0.3648 - acc: 0.8750 - val_loss: 0.4135 - val_acc: 0.8250\n","Epoch 29/100\n","16/16 [==============================] - 4s 273ms/step - loss: 0.3066 - acc: 0.9125 - val_loss: 0.5162 - val_acc: 0.8000\n","Epoch 30/100\n","16/16 [==============================] - 4s 274ms/step - loss: 0.3062 - acc: 0.8750 - val_loss: 0.3713 - val_acc: 0.8500\n","Epoch 31/100\n","16/16 [==============================] - 4s 270ms/step - loss: 0.2922 - acc: 0.8875 - val_loss: 0.4748 - val_acc: 0.7250\n","Epoch 32/100\n","16/16 [==============================] - 4s 275ms/step - loss: 0.2781 - acc: 0.8750 - val_loss: 0.5921 - val_acc: 0.7500\n","Epoch 33/100\n","16/16 [==============================] - 4s 267ms/step - loss: 0.3685 - acc: 0.8312 - val_loss: 0.4834 - val_acc: 0.7000\n","Epoch 34/100\n","16/16 [==============================] - 4s 273ms/step - loss: 0.3199 - acc: 0.8812 - val_loss: 0.6061 - val_acc: 0.7500\n","Epoch 35/100\n","16/16 [==============================] - 4s 277ms/step - loss: 0.3069 - acc: 0.8812 - val_loss: 0.4015 - val_acc: 0.8750\n","Epoch 36/100\n","16/16 [==============================] - 5s 285ms/step - loss: 0.2747 - acc: 0.9125 - val_loss: 0.6850 - val_acc: 0.6750\n","Epoch 37/100\n","16/16 [==============================] - 4s 271ms/step - loss: 0.3369 - acc: 0.8687 - val_loss: 0.3932 - val_acc: 0.7750\n","Epoch 38/100\n","16/16 [==============================] - 4s 278ms/step - loss: 0.3191 - acc: 0.8625 - val_loss: 0.4894 - val_acc: 0.7750\n","Epoch 39/100\n","16/16 [==============================] - 4s 280ms/step - loss: 0.2960 - acc: 0.8812 - val_loss: 0.3257 - val_acc: 0.9000\n","Epoch 40/100\n","16/16 [==============================] - 4s 279ms/step - loss: 0.2690 - acc: 0.9187 - val_loss: 0.4838 - val_acc: 0.7500\n","Epoch 41/100\n","16/16 [==============================] - 4s 274ms/step - loss: 0.2918 - acc: 0.9062 - val_loss: 0.2917 - val_acc: 0.9500\n","Epoch 42/100\n","16/16 [==============================] - 4s 275ms/step - loss: 0.2654 - acc: 0.9125 - val_loss: 0.3139 - val_acc: 0.9000\n","Epoch 43/100\n","16/16 [==============================] - 4s 276ms/step - loss: 0.2937 - acc: 0.8875 - val_loss: 0.4062 - val_acc: 0.8250\n","Epoch 44/100\n","16/16 [==============================] - 4s 281ms/step - loss: 0.2640 - acc: 0.8875 - val_loss: 0.2849 - val_acc: 0.8750\n","Epoch 45/100\n","16/16 [==============================] - 4s 278ms/step - loss: 0.2371 - acc: 0.9187 - val_loss: 0.4868 - val_acc: 0.8250\n","Epoch 46/100\n","16/16 [==============================] - 4s 275ms/step - loss: 0.2096 - acc: 0.9312 - val_loss: 0.3798 - val_acc: 0.8000\n","Epoch 47/100\n","16/16 [==============================] - 4s 275ms/step - loss: 0.2144 - acc: 0.9125 - val_loss: 0.4773 - val_acc: 0.8000\n","Epoch 48/100\n","16/16 [==============================] - 4s 266ms/step - loss: 0.2406 - acc: 0.9312 - val_loss: 0.3882 - val_acc: 0.8500\n","Epoch 49/100\n","16/16 [==============================] - 4s 272ms/step - loss: 0.2710 - acc: 0.9062 - val_loss: 0.3972 - val_acc: 0.9000\n","Epoch 50/100\n","16/16 [==============================] - 4s 273ms/step - loss: 0.2491 - acc: 0.9062 - val_loss: 0.3848 - val_acc: 0.9000\n","Epoch 51/100\n","16/16 [==============================] - 4s 272ms/step - loss: 0.2630 - acc: 0.8750 - val_loss: 0.3471 - val_acc: 0.8750\n","Epoch 52/100\n","16/16 [==============================] - 4s 274ms/step - loss: 0.2012 - acc: 0.9312 - val_loss: 0.4871 - val_acc: 0.8250\n","Epoch 53/100\n","16/16 [==============================] - 4s 272ms/step - loss: 0.2329 - acc: 0.9187 - val_loss: 0.3532 - val_acc: 0.8000\n","Epoch 54/100\n","16/16 [==============================] - 4s 272ms/step - loss: 0.2951 - acc: 0.8812 - val_loss: 0.4450 - val_acc: 0.7750\n","Epoch 55/100\n","16/16 [==============================] - 4s 271ms/step - loss: 0.2317 - acc: 0.9312 - val_loss: 0.2686 - val_acc: 0.9000\n","Epoch 56/100\n","16/16 [==============================] - 4s 266ms/step - loss: 0.3243 - acc: 0.8687 - val_loss: 0.4179 - val_acc: 0.7750\n","Epoch 57/100\n","16/16 [==============================] - 4s 276ms/step - loss: 0.2610 - acc: 0.9125 - val_loss: 0.3619 - val_acc: 0.8250\n","Epoch 58/100\n","16/16 [==============================] - 4s 273ms/step - loss: 0.2264 - acc: 0.9187 - val_loss: 0.3839 - val_acc: 0.8500\n","Epoch 59/100\n","16/16 [==============================] - 4s 268ms/step - loss: 0.2402 - acc: 0.9250 - val_loss: 0.6557 - val_acc: 0.7000\n","Epoch 60/100\n","16/16 [==============================] - 4s 276ms/step - loss: 0.2480 - acc: 0.9125 - val_loss: 0.5197 - val_acc: 0.8000\n","Epoch 61/100\n","16/16 [==============================] - 4s 274ms/step - loss: 0.2044 - acc: 0.9250 - val_loss: 0.4299 - val_acc: 0.8000\n","Epoch 62/100\n","16/16 [==============================] - 4s 278ms/step - loss: 0.2301 - acc: 0.9250 - val_loss: 0.2088 - val_acc: 0.9250\n","Epoch 63/100\n","16/16 [==============================] - 4s 277ms/step - loss: 0.2156 - acc: 0.9312 - val_loss: 0.4026 - val_acc: 0.8000\n","Epoch 64/100\n","16/16 [==============================] - 4s 276ms/step - loss: 0.1804 - acc: 0.9312 - val_loss: 0.3116 - val_acc: 0.8500\n","Epoch 65/100\n","16/16 [==============================] - 4s 274ms/step - loss: 0.2786 - acc: 0.9062 - val_loss: 0.5450 - val_acc: 0.8500\n","Epoch 66/100\n","16/16 [==============================] - 4s 278ms/step - loss: 0.2619 - acc: 0.9000 - val_loss: 0.4454 - val_acc: 0.8500\n","Epoch 67/100\n","16/16 [==============================] - 4s 268ms/step - loss: 0.2681 - acc: 0.9187 - val_loss: 0.5667 - val_acc: 0.8000\n","Epoch 68/100\n","16/16 [==============================] - 4s 276ms/step - loss: 0.2720 - acc: 0.8875 - val_loss: 0.7804 - val_acc: 0.7250\n","Epoch 69/100\n","16/16 [==============================] - 4s 276ms/step - loss: 0.1899 - acc: 0.9312 - val_loss: 0.3514 - val_acc: 0.9000\n","Epoch 70/100\n","16/16 [==============================] - 4s 270ms/step - loss: 0.2097 - acc: 0.9437 - val_loss: 0.3281 - val_acc: 0.8500\n","Epoch 71/100\n","16/16 [==============================] - 4s 272ms/step - loss: 0.2307 - acc: 0.9312 - val_loss: 0.3554 - val_acc: 0.8250\n","Epoch 72/100\n","16/16 [==============================] - 4s 281ms/step - loss: 0.1642 - acc: 0.9312 - val_loss: 0.2341 - val_acc: 0.9500\n","Epoch 73/100\n","16/16 [==============================] - 5s 281ms/step - loss: 0.1738 - acc: 0.9500 - val_loss: 0.4238 - val_acc: 0.8000\n","Epoch 74/100\n","16/16 [==============================] - 4s 275ms/step - loss: 0.1538 - acc: 0.9375 - val_loss: 0.3037 - val_acc: 0.8750\n","Epoch 75/100\n","16/16 [==============================] - 4s 275ms/step - loss: 0.2580 - acc: 0.9062 - val_loss: 0.2859 - val_acc: 0.8500\n","Epoch 76/100\n","16/16 [==============================] - 4s 265ms/step - loss: 0.2285 - acc: 0.9125 - val_loss: 0.3642 - val_acc: 0.8000\n","Epoch 77/100\n","16/16 [==============================] - 4s 272ms/step - loss: 0.2299 - acc: 0.9000 - val_loss: 0.4593 - val_acc: 0.7500\n","Epoch 78/100\n","16/16 [==============================] - 4s 274ms/step - loss: 0.2053 - acc: 0.9250 - val_loss: 0.2634 - val_acc: 0.9000\n","Epoch 79/100\n","16/16 [==============================] - 4s 277ms/step - loss: 0.2037 - acc: 0.9187 - val_loss: 0.6324 - val_acc: 0.7750\n","Epoch 80/100\n","16/16 [==============================] - 4s 280ms/step - loss: 0.2397 - acc: 0.8937 - val_loss: 0.3234 - val_acc: 0.8500\n","Epoch 81/100\n","16/16 [==============================] - 4s 277ms/step - loss: 0.2453 - acc: 0.9062 - val_loss: 0.5734 - val_acc: 0.8500\n","Epoch 82/100\n","16/16 [==============================] - 4s 280ms/step - loss: 0.1784 - acc: 0.9687 - val_loss: 0.3941 - val_acc: 0.8000\n","Epoch 83/100\n","16/16 [==============================] - 4s 272ms/step - loss: 0.2302 - acc: 0.8937 - val_loss: 0.5198 - val_acc: 0.8250\n","Epoch 84/100\n","16/16 [==============================] - 4s 271ms/step - loss: 0.2203 - acc: 0.9250 - val_loss: 0.6268 - val_acc: 0.7750\n","Epoch 85/100\n","16/16 [==============================] - 4s 266ms/step - loss: 0.1873 - acc: 0.9312 - val_loss: 0.1590 - val_acc: 0.9500\n","Epoch 86/100\n","16/16 [==============================] - 4s 273ms/step - loss: 0.2030 - acc: 0.9375 - val_loss: 0.3140 - val_acc: 0.9000\n","Epoch 87/100\n","16/16 [==============================] - 4s 265ms/step - loss: 0.1702 - acc: 0.9312 - val_loss: 0.3678 - val_acc: 0.8750\n","Epoch 88/100\n","16/16 [==============================] - 4s 264ms/step - loss: 0.1711 - acc: 0.9437 - val_loss: 0.2630 - val_acc: 0.9250\n","Epoch 89/100\n","16/16 [==============================] - 4s 266ms/step - loss: 0.2215 - acc: 0.9375 - val_loss: 0.5020 - val_acc: 0.8000\n","Epoch 90/100\n","16/16 [==============================] - 4s 270ms/step - loss: 0.2037 - acc: 0.9250 - val_loss: 0.3523 - val_acc: 0.8750\n","Epoch 91/100\n","16/16 [==============================] - 4s 266ms/step - loss: 0.1913 - acc: 0.9187 - val_loss: 0.5466 - val_acc: 0.8000\n","Epoch 92/100\n","16/16 [==============================] - 4s 272ms/step - loss: 0.1809 - acc: 0.9187 - val_loss: 0.3347 - val_acc: 0.8500\n","Epoch 93/100\n","16/16 [==============================] - 4s 269ms/step - loss: 0.2262 - acc: 0.9187 - val_loss: 0.3390 - val_acc: 0.8500\n","Epoch 94/100\n","16/16 [==============================] - 4s 275ms/step - loss: 0.1838 - acc: 0.9125 - val_loss: 0.2570 - val_acc: 0.9000\n","Epoch 95/100\n","16/16 [==============================] - 4s 266ms/step - loss: 0.1306 - acc: 0.9625 - val_loss: 0.5554 - val_acc: 0.8000\n","Epoch 96/100\n","16/16 [==============================] - 4s 265ms/step - loss: 0.1620 - acc: 0.9500 - val_loss: 0.1875 - val_acc: 0.9750\n","Epoch 97/100\n","16/16 [==============================] - 4s 268ms/step - loss: 0.1760 - acc: 0.9250 - val_loss: 0.4353 - val_acc: 0.8250\n","Epoch 98/100\n","16/16 [==============================] - 4s 268ms/step - loss: 0.2218 - acc: 0.9250 - val_loss: 0.3306 - val_acc: 0.9000\n","Epoch 99/100\n","16/16 [==============================] - 4s 267ms/step - loss: 0.1863 - acc: 0.9437 - val_loss: 0.4057 - val_acc: 0.8250\n","Epoch 100/100\n","16/16 [==============================] - 4s 271ms/step - loss: 0.2445 - acc: 0.9187 - val_loss: 0.6943 - val_acc: 0.7500\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"89ZEQXk50A9-","colab_type":"text"},"source":["Evaluation"]},{"cell_type":"code","metadata":{"id":"tNIFRzOvp1yA","colab_type":"code","outputId":"9e9deed9-8a8d-4d51-d56e-d422cdade423","executionInfo":{"status":"ok","timestamp":1584625923061,"user_tz":-60,"elapsed":443058,"user":{"displayName":"Yijie Xu","photoUrl":"","userId":"14695674479284134427"}},"colab":{"base_uri":"https://localhost:8080/","height":429}},"source":["import matplotlib.pyplot as plt\n","\n","\n","def plot_acc_loss(result, epochs):\n","    acc = result.history['acc']\n","    loss = result.history['loss']\n","    val_acc = result.history['val_acc']\n","    val_loss = result.history['val_loss']\n","    plt.figure(figsize=(15, 5))\n","    plt.subplot(121)\n","    plt.plot(range(1,epochs), acc[1:], label='Train_acc')\n","    plt.plot(range(1,epochs), val_acc[1:], label='Test_acc')\n","    plt.title('Accuracy over ' + str(epochs) + ' Epochs', size=15)\n","    plt.legend()\n","    plt.grid(True)\n","    plt.subplot(122)\n","    plt.plot(range(1,epochs), loss[1:], label='Train_loss')\n","    plt.plot(range(1,epochs), val_loss[1:], label='Test_loss')\n","    plt.title('Loss over ' + str(epochs) + ' Epochs', size=15)\n","    plt.legend()\n","    plt.grid(True)\n","    plt.show()\n","    \n","plot_acc_loss(result, 100)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py:716: UserWarning: This ImageDataGenerator specifies `featurewise_center`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n","  warnings.warn('This ImageDataGenerator specifies '\n","/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py:735: UserWarning: This ImageDataGenerator specifies `zca_whitening`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n","  warnings.warn('This ImageDataGenerator specifies '\n"],"name":"stderr"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAA2oAAAFBCAYAAAAG31zXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nvGOkpZRbhBAH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4tJKTlYxqi+EOn0pAkZ4rQ83tnnzbeASACnlOiGEFUgFyvxPJqV8DngO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UTrY/lgjH+FCfOS4yo1VLU5g1lvVOtj1MngVN/5v3zfB++FsOJonYYwEVVVS+rqloE8BUA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\n","text/plain":["<Figure size 1080x360 with 2 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"g1W_MvpGJ39q","colab_type":"code","outputId":"05ba006a-2d2c-4527-c295-ff9d7979cdc6","executionInfo":{"status":"ok","timestamp":1584625923063,"user_tz":-60,"elapsed":443049,"user":{"displayName":"Yijie Xu","photoUrl":"","userId":"14695674479284134427"}},"colab":{"base_uri":"https://localhost:8080/","height":108}},"source":["model.save('Covid_Multi.h5')"],"execution_count":0,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py:716: UserWarning: This ImageDataGenerator specifies `featurewise_center`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n","  warnings.warn('This ImageDataGenerator specifies '\n","/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py:735: UserWarning: This ImageDataGenerator specifies `zca_whitening`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n","  warnings.warn('This ImageDataGenerator specifies '\n"],"name":"stderr"}]},{"cell_type":"markdown","metadata":{"id":"5X6TGa0N-5R0","colab_type":"text"},"source":["Predict"]},{"cell_type":"code","metadata":{"id":"B1Qqa6YG-6kB","colab_type":"code","outputId":"13f7944e-906c-40a5-f1aa-4f84b71ecc63","executionInfo":{"status":"ok","timestamp":1584625923064,"user_tz":-60,"elapsed":443041,"user":{"displayName":"Yijie Xu","photoUrl":"","userId":"14695674479284134427"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["\n","#Dont forget shuffle false\n","#See https://github.com/keras-team/keras/issues/6499\n","test_datagen = ImageDataGenerator(rescale=1. / 255)\n","\n","eval_generator = test_datagen.flow_from_directory(\n","        test_dir,target_size=IMAGE_SIZE,\n","        batch_size=1,\n","        shuffle=True,\n","        seed=42,\n","        \n","        class_mode=\"categorical\")\n","eval_generator.reset()    \n"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Found 27 images belonging to 3 classes.\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"OruQrAjTAbly","colab_type":"code","outputId":"a3603d56-6c95-4067-b315-71a9c18dc2f4","executionInfo":{"status":"ok","timestamp":1584625923541,"user_tz":-60,"elapsed":443508,"user":{"displayName":"Yijie Xu","photoUrl":"","userId":"14695674479284134427"}},"colab":{"base_uri":"https://localhost:8080/","height":70}},"source":["\n","eval_generator.reset()  \n","x = model.evaluate_generator(eval_generator,\n","                           steps = np.ceil(len(eval_generator) / BATCH_SIZE),\n","                           use_multiprocessing = False,\n","                           verbose = 1,\n","                           workers=1\n","                           )\n","\n","\n","print('Test loss:' , x[0])\n","print('Test accuracy:',x[1])\n","\n","#Poor test accuracy due to the small dataset size"],"execution_count":0,"outputs":[{"output_type":"stream","text":["3/3 [==============================] - 1s 199ms/step\n","Test loss: 0.006713376846164465\n","Test accuracy: 1.0\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"j-Xt7hwF0KK8","colab_type":"text"},"source":["Optional: Finetuning with a learning rate of 5E-5"]},{"cell_type":"code","metadata":{"id":"B_KzQruxRfbA","colab_type":"code","outputId":"b9fe2053-a12c-4c36-c20a-0461e688d5d2","executionInfo":{"status":"ok","timestamp":1584626141873,"user_tz":-60,"elapsed":661831,"user":{"displayName":"Yijie Xu","photoUrl":"","userId":"14695674479284134427"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["\n","model.compile(loss='categorical_crossentropy',\n","              \n","              optimizer=optimizers.Adam(lr=5e-5),\n","              metrics=['acc'])\n","\n","result=model.fit_generator(train_batches,\n","                        steps_per_epoch =STEP_SIZE_TRAIN,\n","                        validation_data = valid_batches,\n","                        validation_steps = STEP_SIZE_VALID,\n","                        epochs= 20,                        \n","                       )\n"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Epoch 1/50\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py:716: UserWarning: This ImageDataGenerator specifies `featurewise_center`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n","  warnings.warn('This ImageDataGenerator specifies '\n","/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py:735: UserWarning: This ImageDataGenerator specifies `zca_whitening`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n","  warnings.warn('This ImageDataGenerator specifies '\n"],"name":"stderr"},{"output_type":"stream","text":["16/16 [==============================] - 5s 300ms/step - loss: 0.1551 - acc: 0.9562 - val_loss: 0.4131 - val_acc: 0.7750\n","Epoch 2/50\n","16/16 [==============================] - 4s 255ms/step - loss: 0.1716 - acc: 0.9312 - val_loss: 0.5726 - val_acc: 0.8500\n","Epoch 3/50\n","16/16 [==============================] - 4s 261ms/step - loss: 0.2124 - acc: 0.9125 - val_loss: 0.4882 - val_acc: 0.8000\n","Epoch 4/50\n","16/16 [==============================] - 4s 261ms/step - loss: 0.1440 - acc: 0.9437 - val_loss: 0.3906 - val_acc: 0.8000\n","Epoch 5/50\n","16/16 [==============================] - 4s 267ms/step - loss: 0.1794 - acc: 0.9125 - val_loss: 0.4222 - val_acc: 0.9000\n","Epoch 6/50\n","16/16 [==============================] - 4s 273ms/step - loss: 0.2027 - acc: 0.9187 - val_loss: 0.4976 - val_acc: 0.8250\n","Epoch 7/50\n","16/16 [==============================] - 4s 269ms/step - loss: 0.2004 - acc: 0.9500 - val_loss: 0.2417 - val_acc: 0.9000\n","Epoch 8/50\n","16/16 [==============================] - 4s 269ms/step - loss: 0.2233 - acc: 0.9312 - val_loss: 0.3159 - val_acc: 0.8750\n","Epoch 9/50\n","16/16 [==============================] - 4s 267ms/step - loss: 0.1797 - acc: 0.9375 - val_loss: 0.5429 - val_acc: 0.7750\n","Epoch 10/50\n","16/16 [==============================] - 4s 268ms/step - loss: 0.1344 - acc: 0.9750 - val_loss: 0.3393 - val_acc: 0.8000\n","Epoch 11/50\n","16/16 [==============================] - 4s 264ms/step - loss: 0.1897 - acc: 0.9125 - val_loss: 0.4713 - val_acc: 0.8750\n","Epoch 12/50\n","16/16 [==============================] - 4s 266ms/step - loss: 0.1977 - acc: 0.9437 - val_loss: 0.3230 - val_acc: 0.8500\n","Epoch 13/50\n","16/16 [==============================] - 4s 274ms/step - loss: 0.1581 - acc: 0.9500 - val_loss: 0.3546 - val_acc: 0.9000\n","Epoch 14/50\n","16/16 [==============================] - 5s 288ms/step - loss: 0.1419 - acc: 0.9687 - val_loss: 0.2552 - val_acc: 0.8750\n","Epoch 15/50\n","16/16 [==============================] - 4s 275ms/step - loss: 0.1260 - acc: 0.9437 - val_loss: 0.5564 - val_acc: 0.8250\n","Epoch 16/50\n","16/16 [==============================] - 4s 276ms/step - loss: 0.1622 - acc: 0.9500 - val_loss: 0.4559 - val_acc: 0.9000\n","Epoch 17/50\n","16/16 [==============================] - 4s 279ms/step - loss: 0.1765 - acc: 0.9437 - val_loss: 0.3341 - val_acc: 0.8250\n","Epoch 18/50\n","16/16 [==============================] - 4s 268ms/step - loss: 0.1942 - acc: 0.9312 - val_loss: 0.3183 - val_acc: 0.8750\n","Epoch 19/50\n","16/16 [==============================] - 4s 276ms/step - loss: 0.2028 - acc: 0.9250 - val_loss: 0.5201 - val_acc: 0.8000\n","Epoch 20/50\n","16/16 [==============================] - 4s 276ms/step - loss: 0.1503 - acc: 0.9437 - val_loss: 0.4794 - val_acc: 0.7750\n","Epoch 21/50\n","16/16 [==============================] - 5s 285ms/step - loss: 0.1345 - acc: 0.9688 - val_loss: 0.4558 - val_acc: 0.8500\n","Epoch 22/50\n","16/16 [==============================] - 4s 278ms/step - loss: 0.1679 - acc: 0.9437 - val_loss: 0.3326 - val_acc: 0.8000\n","Epoch 23/50\n","16/16 [==============================] - 4s 280ms/step - loss: 0.1946 - acc: 0.9125 - val_loss: 0.2727 - val_acc: 0.9000\n","Epoch 24/50\n","16/16 [==============================] - 4s 280ms/step - loss: 0.1584 - acc: 0.9437 - val_loss: 0.4005 - val_acc: 0.8250\n","Epoch 25/50\n","16/16 [==============================] - 4s 274ms/step - loss: 0.1584 - acc: 0.9437 - val_loss: 0.4175 - val_acc: 0.8250\n","Epoch 26/50\n","16/16 [==============================] - 4s 275ms/step - loss: 0.2221 - acc: 0.9250 - val_loss: 0.2564 - val_acc: 0.9000\n","Epoch 27/50\n","16/16 [==============================] - 5s 283ms/step - loss: 0.1575 - acc: 0.9437 - val_loss: 0.4363 - val_acc: 0.8500\n","Epoch 28/50\n","16/16 [==============================] - 4s 277ms/step - loss: 0.1381 - acc: 0.9500 - val_loss: 0.4067 - val_acc: 0.8000\n","Epoch 29/50\n","16/16 [==============================] - 4s 272ms/step - loss: 0.1407 - acc: 0.9437 - val_loss: 0.3436 - val_acc: 0.8250\n","Epoch 30/50\n","16/16 [==============================] - 4s 264ms/step - loss: 0.1706 - acc: 0.9500 - val_loss: 0.5116 - val_acc: 0.8250\n","Epoch 31/50\n","16/16 [==============================] - 4s 268ms/step - loss: 0.1540 - acc: 0.9562 - val_loss: 0.2620 - val_acc: 0.9000\n","Epoch 32/50\n","16/16 [==============================] - 4s 268ms/step - loss: 0.1431 - acc: 0.9437 - val_loss: 0.4244 - val_acc: 0.8750\n","Epoch 33/50\n","16/16 [==============================] - 4s 272ms/step - loss: 0.1510 - acc: 0.9562 - val_loss: 0.3204 - val_acc: 0.8000\n","Epoch 34/50\n","16/16 [==============================] - 4s 265ms/step - loss: 0.1086 - acc: 0.9750 - val_loss: 0.3930 - val_acc: 0.8500\n","Epoch 35/50\n","16/16 [==============================] - 4s 265ms/step - loss: 0.1814 - acc: 0.9312 - val_loss: 0.4661 - val_acc: 0.7750\n","Epoch 36/50\n","16/16 [==============================] - 4s 275ms/step - loss: 0.1943 - acc: 0.9375 - val_loss: 0.4861 - val_acc: 0.8250\n","Epoch 37/50\n","16/16 [==============================] - 4s 276ms/step - loss: 0.1453 - acc: 0.9562 - val_loss: 0.5312 - val_acc: 0.7750\n","Epoch 38/50\n","16/16 [==============================] - 4s 267ms/step - loss: 0.1858 - acc: 0.9312 - val_loss: 0.2732 - val_acc: 0.9000\n","Epoch 39/50\n","16/16 [==============================] - 4s 270ms/step - loss: 0.1251 - acc: 0.9687 - val_loss: 0.4948 - val_acc: 0.8000\n","Epoch 40/50\n","16/16 [==============================] - 4s 270ms/step - loss: 0.2143 - acc: 0.9187 - val_loss: 0.5600 - val_acc: 0.7750\n","Epoch 41/50\n","16/16 [==============================] - 4s 270ms/step - loss: 0.1791 - acc: 0.9500 - val_loss: 0.3557 - val_acc: 0.8750\n","Epoch 42/50\n","16/16 [==============================] - 4s 270ms/step - loss: 0.1803 - acc: 0.9187 - val_loss: 0.2693 - val_acc: 0.9000\n","Epoch 43/50\n","16/16 [==============================] - 4s 275ms/step - loss: 0.1964 - acc: 0.9250 - val_loss: 0.4657 - val_acc: 0.8250\n","Epoch 44/50\n","16/16 [==============================] - 4s 275ms/step - loss: 0.2040 - acc: 0.9312 - val_loss: 0.4766 - val_acc: 0.8500\n","Epoch 45/50\n","16/16 [==============================] - 4s 279ms/step - loss: 0.1416 - acc: 0.9437 - val_loss: 0.5015 - val_acc: 0.8250\n","Epoch 46/50\n","16/16 [==============================] - 4s 266ms/step - loss: 0.1854 - acc: 0.9375 - val_loss: 0.3880 - val_acc: 0.8750\n","Epoch 47/50\n","16/16 [==============================] - 4s 273ms/step - loss: 0.1722 - acc: 0.9187 - val_loss: 0.4548 - val_acc: 0.8500\n","Epoch 48/50\n","16/16 [==============================] - 4s 274ms/step - loss: 0.1170 - acc: 0.9750 - val_loss: 0.5253 - val_acc: 0.8250\n","Epoch 49/50\n","16/16 [==============================] - 4s 264ms/step - loss: 0.1491 - acc: 0.9500 - val_loss: 0.4620 - val_acc: 0.8000\n","Epoch 50/50\n","16/16 [==============================] - 4s 267ms/step - loss: 0.2068 - acc: 0.9125 - val_loss: 0.2923 - val_acc: 0.8500\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"hpLLum4G9n0Y","colab_type":"code","outputId":"64110e7c-c130-41fe-ba28-cb4e429fdba5","executionInfo":{"status":"ok","timestamp":1584626141880,"user_tz":-60,"elapsed":661832,"user":{"displayName":"Yijie Xu","photoUrl":"","userId":"14695674479284134427"}},"colab":{"base_uri":"https://localhost:8080/","height":161}},"source":["eval_generator.reset()  \n","x = model.evaluate_generator(eval_generator,\n","                           steps = np.ceil(len(eval_generator) / BATCH_SIZE),\n","                           use_multiprocessing = False,\n","                           verbose = 1,\n","                           workers=1\n","                           )\n","\n","\n","print('Test loss:' , x[0])\n","print('Test accuracy:',x[1])\n"],"execution_count":0,"outputs":[{"output_type":"stream","text":["3/3 [==============================] - 0s 36ms/step\n","Test loss: 0.021353596743817132\n","Test accuracy: 1.0\n"],"name":"stdout"},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py:716: UserWarning: This ImageDataGenerator specifies `featurewise_center`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n","  warnings.warn('This ImageDataGenerator specifies '\n","/usr/local/lib/python3.6/dist-packages/keras_preprocessing/image/image_data_generator.py:735: UserWarning: This ImageDataGenerator specifies `zca_whitening`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n","  warnings.warn('This ImageDataGenerator specifies '\n"],"name":"stderr"}]}]}