{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","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\nimport numpy as np # linear algebra\nimport 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 all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# Any results you write to the current directory are saved as output.","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import numpy as np \nimport os\nimport pandas as pd\nimport matplotlib\nimport cv2","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"DATA_PATH=\"/kaggle/input/covidlarge/train\"\nCATEGORIES=['Covid', 'NORMAL', 'PNEUMONIA']\nimg_size=150\ntraining_data = []\ndef create_training_data():\n\t#iterating through different categories\n    for category in CATEGORIES:\n        path=os.path.join(DATA_PATH, category)\n        print(path)\n        class_num = CATEGORIES.index(category)\n        print(class_num)\n        for img in os.listdir(path):\n            try:\n                img_array = cv2.imread(os.path.join(path,img), cv2.IMREAD_GRAYSCALE)\n                new_array = cv2.resize(img_array,(img_size,img_size)) #resizeing the all the images to same size(70,70)\n                training_data.append([new_array,class_num])\n            except Exception as e:\n                pass","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"create_training_data()\nprint(len(training_data))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import random\nrandom.shuffle(training_data)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X=[]\ny=[]\n\nfor features,label in training_data: #new_array is features and class_num is label\n\tX.append(features)\n\ty.append(label)\n\n\nX=np.array(X).reshape(-1, img_size, img_size, 1) #transforming X into numpy array\nprint(X.shape)\nprint(len(y))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import tensorflow as tf\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Dropout, Activation, Flatten\nfrom tensorflow.keras.layers import Conv2D, MaxPooling2D, LeakyReLU\nfrom tensorflow.keras.utils import to_categorical\nfrom tensorflow.keras import optimizers\nfrom tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau, EarlyStopping","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X=X/255\nmodel=Sequential()\nmodel.add(Conv2D(32, (3,3), input_shape=X.shape[1:]))\nmodel.add(Activation('relu'))\n#model.add(Conv2D(32,(3,3)))\n#model.add(Activation('relu'))\nmodel.add(MaxPooling2D(2,2))\n\n\n\n#model.add(Conv2D(64, (3,3)))\n#model.add(Activation('relu'))\nmodel.add(Conv2D(64, (3,3)))\nmodel.add(Activation('relu'))\nmodel.add(MaxPooling2D(2,2))\n\n#model.add(Conv2D(256, (3,3)))\n#model.add(Activation('relu'))\nmodel.add(Conv2D(256, (3,3)))\nmodel.add(Activation('relu'))\nmodel.add(MaxPooling2D(2,2))\n\n#model.add(Conv2D(512, (3,3)))\n#model.add(Activation('relu'))\nmodel.add(Conv2D(512, (3,3)))\nmodel.add(Activation('relu'))\nmodel.add(MaxPooling2D(2,2))\n\nmodel.add(Conv2D(1024, (3,3)))\nmodel.add(Activation('relu'))\nmodel.add(MaxPooling2D(2,2))\n\n#model.add(Conv2D(2048, (3,3)))\n#model.add(Activation('relu'))\n#model.add(MaxPooling2D(2,2))\n\n\n\n\nmodel.add(Flatten())\nmodel.add(Dense(500))\nmodel.add(Activation('relu'))\nmodel.add(Dropout(0.25))\n\nmodel.add(Dense(300))\nmodel.add(Activation('relu'))\nmodel.add(Dropout(0.25))\n\nmodel.add(Dense(100))\nmodel.add(Activation('relu'))\nmodel.add(Dropout(0.25))\n\nmodel.add(Dense(3))\nmodel.add(Activation('softmax'))\nmodel.summary()\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"target=to_categorical(y, num_classes=3)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"batch_size=64\nepochs=15\n\ncheckpoint = ModelCheckpoint(filepath='covid19.h5', save_best_only=True)\n#lr_reduce = ReduceLROnPlateau(monitor='val_loss', factor=0.3, patience=4, verbose=2, mode='max')\nearly_stop = EarlyStopping(monitor='val_loss', min_delta=0.1, patience=1, mode='min')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.compile(optimizer='Adamax', loss='categorical_crossentropy', metrics=['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"history=model.fit(X, target, epochs=epochs, batch_size=batch_size, validation_split=0.2, callbacks=[checkpoint])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.save('covidLarge.h5')","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":4}