{"cells":[{"metadata":{"trusted":true,"_uuid":"2c9710a807b66a5e347f0ec307acabc18bbceb80"},"cell_type":"code","source":"import cv2\nimport numpy as np\nimport pandas as pd\n\nimport matplotlib.pyplot as plt\n%matplotlib inline\n\nimport os\nimport random\nimport gc","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"train_dir = '../input/train'\ntest_dir = '../input/test'\n\ntrain_dogs = ['../input/train/{}'.format(i) for i in os.listdir(train_dir) if 'dog' in i]\ntrain_cats = ['../input/train/{}'.format(i) for i in os.listdir(train_dir) if 'cat' in i]\n\ntest_imgs = ['../input/test/{}'.format(i) for i in os.listdir(test_dir)]\n\ntrain_imgs = train_dogs[:2000] + train_cats[:2000]\nrandom.shuffle(train_imgs)\n\ndel train_dogs\ndel train_cats\ngc.collect()","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"import matplotlib.image as mpimg\nfor ima in train_imgs[0:3]:\n    img = mpimg.imread(ima)\n    imgplot = plt.imshow(img)\n    plt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"0ca298833181dca0649bbc48bf2b174467c53b5d"},"cell_type":"code","source":"nrows = 150\nncolumns = 150\nchannels = 3","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"53bf5e54770fa03440bae23d65225e64d7bc7349"},"cell_type":"code","source":"def read_and_process_image(list_of_images):\n    X = []\n    y = []\n    \n    for image in list_of_images:\n        X.append(cv2.resize(cv2.imread(image, cv2.IMREAD_COLOR), (nrows, ncolumns), interpolation=cv2.INTER_CUBIC))\n        if 'dog' in image:\n            y.append(1)\n        else:\n            y.append(0)\n            \n    return X, y","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"dfd4f01617e84d414a680cdab8ad8269cb56d23e"},"cell_type":"code","source":"X, y = read_and_process_image(train_imgs)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"0a0b1bd644db81c94660d43c6ae9b675d2f951eb"},"cell_type":"code","source":"plt.figure(figsize=(20,10))\ncolumns = 5\nfor i in range(columns):\n    plt.subplot(5/columns + 1, columns, i+1)\n    plt.imshow(X[i])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"cd7f2eda7639201e02fc39e54df486bf70ecd9c1"},"cell_type":"code","source":"import seaborn as sns\ndel train_imgs\ngc.collect()\n\nX = np.array(X)\ny = np.array(y)\n\nsns.countplot(y)\nplt.title('Labels for Cats and Dogs')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"630bc234d7319124098f13b51ef8a0847b29ef95"},"cell_type":"code","source":"print('shape of train images is:', X.shape)\nprint('shape of labels is:', y.shape)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"752726f36ae1e3894ff4b6c9f91a37ff417c8af0"},"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nX_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.20, random_state=2)\n\nprint('shape of train images is', X_train.shape)\nprint('shape of val images is:', X_val.shape)\nprint('shape of train labels is:', y_train.shape)\nprint('shape of val labels is:', y_val.shape)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"9cdb07479158857614ff044c4d88708f39bf5d15"},"cell_type":"code","source":"del X\ndel y\ngc.collect()\n\nntrain = len(X_train)\nnval = len(X_val)\n\nbatch_size=32","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"6723dffb2a671868366ad19ebc202ffee6a6c3de"},"cell_type":"code","source":"from keras import layers, models, optimizers\nfrom keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"33f49c7d50ee21c6d87a4c158e05df0c377b8a51"},"cell_type":"code","source":"model = models.Sequential()\nmodel.add(layers.Conv2D(32, (3,3), activation='relu', input_shape=(150,150,3)))\nmodel.add(layers.MaxPooling2D((2,2)))\nmodel.add(layers.Conv2D(64, (3, 3), activation='relu'))\nmodel.add(layers.MaxPooling2D((2,2)))\nmodel.add(layers.Conv2D(128, (3,3), activation='relu'))\nmodel.add(layers.MaxPooling2D(2,2))\nmodel.add(layers.Conv2D(128, (3,3), activation='relu'))\nmodel.add(layers.MaxPooling2D(2,2))\nmodel.add(layers.Flatten())\nmodel.add(layers.Dropout(0.5))\nmodel.add(layers.Dense(512, activation='relu'))\nmodel.add(layers.Dense(1, activation='sigmoid'))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"c580341685fe4edb5215d65da459d29ad2da04a1"},"cell_type":"code","source":"model.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"23ebb30b2f29f40106955cfb08226079220bbd97"},"cell_type":"code","source":"model.compile(loss='binary_crossentropy', optimizer=optimizers.RMSprop(lr=1e-4), metrics=['acc'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"596a5c4a448713c52b1f53a3595d06cb4096d241"},"cell_type":"code","source":"train_datagen = ImageDataGenerator(rescale=1./255, rotation_range=40, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, zoom_range=0.2, horizontal_flip=True)\nval_datagen = ImageDataGenerator(rescale=1./255)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"ec6ef9d1f3641bcab47011fd45c221deb6897e44"},"cell_type":"code","source":"train_generator = train_datagen.flow(X_train, y_train, batch_size=batch_size)\nval_generator = val_datagen.flow(X_val, y_val, batch_size=batch_size)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"a6da0555ca997d3b81f602c31e8cfa8aafba2a85"},"cell_type":"code","source":"history = model.fit_generator(train_generator, steps_per_epoch=ntrain, epochs=64, validation_data=val_generator, validation_steps=nval)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"c9b1622937eedcbe27f30763ae488fd89c2cb3f7"},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}