{"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 cv2\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\nfrom tensorflow.keras.models import Sequential\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Flatten\nfrom keras.layers import Conv2D, MaxPooling2D\nfrom keras.utils import to_categorical\nfrom keras.preprocessing import image\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import train_test_split\nfrom tqdm import tqdm\n%matplotlib inline","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_data = []\nfor i in os.listdir('../input/histopathologic-cancer-detection/train'):\n    img = cv2.imread(os.path.join('../input/histopathologic-cancer-detection/train',i))\n    img = np.array(img).reshape(96,96,3)\n    train_data.append(img)\nx = np.array(train_data)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data = pd.read_csv('../input/histopathologic-cancer-detection/train_labels.csv')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y = np.array(data.drop(['id'], axis=1))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(y)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train, X_test, y_train, y_test = train_test_split(x,y,random_state=4, test_size = 0.20)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import tensorflow as tf\nmodel = tf.keras.Sequential()\nmodel.add(layers.Conv2D(16, activation=\"relu\", kernel_size=(3, 3),input_shape=(96,96,3)))\nmodel.add(layers.MaxPooling2D(pool_size=(2,2)))\nmodel.add(layers.Dropout(0.25))\nmodel.add(layers.Conv2D(32, activation=\"relu\", kernel_size=(3, 3)))\nmodel.add(layers.MaxPooling2D(pool_size=(2,2)))\nmodel.add(layers.Dropout(0.25))\nmodel.add(layers.Conv2D(64, activation=\"relu\", kernel_size=(3, 3)))\nmodel.add(layers.MaxPooling2D(pool_size=(2,2)))\nmodel.add(layers.Dropout(0.25))\nmodel.add(layers.Conv2D(128, activation=\"relu\", kernel_size=(3, 3)))\nmodel.add(layers.MaxPooling2D(pool_size=(2,2)))\nmodel.add(layers.Dropout(0.25))\nmodel.add(layers.Flatten())\nmodel.add(layers.Dense(1, activation='sigmoid'))\nmodel.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.compile(optimizer='adam',loss='binary_crossentropy', metrics=['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.fit(X_train,y_train, epochs=1, validation_data=(X_test,y_test), batch_size=32)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","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":1}