{"cells":[{"cell_type":"markdown","metadata":{"_cell_guid":"6f046fcb-8462-aca5-8746-9f9fffe0bdd2"},"source":"I've been inspired by the popular [CatdogNet Notebook][1] and tried to write my own version\n\n\n  [1]: https://www.kaggle.com/jeffd23/dogs-vs-cats-redux-kernels-edition/catdognet-keras-convnet-starter"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"ef98b8ab-119e-80ef-f4cb-10c0e0be3734"},"outputs":[],"source":"import os, cv2, random\nimport numpy as np\nimport pandas as pd\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n%matplotlib inline \n\nfrom keras.models import Sequential\nfrom keras.layers import Input, Dropout, Flatten, Convolution2D, MaxPooling2D, Dense, Activation\nfrom keras.optimizers import RMSprop\nfrom keras.callbacks import ModelCheckpoint, Callback, EarlyStopping\nfrom keras.utils import np_utils"},{"cell_type":"markdown","metadata":{"_cell_guid":"a7d9155e-68dc-6495-6df7-eede3372e004"},"source":"Data preparation and conversion\n-------------------------------"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"6a9f91b6-786b-159d-8e99-cacdb7535b30"},"outputs":[],"source":"TRAIN_DIR = '../input/train/'\nTEST_DIR = '../input/test/'\n\nROWS = 64\nCOLS = 64\nCHANNELS = 3\n\ntrain_images = [TRAIN_DIR+i for i in os.listdir(TRAIN_DIR)]\ntest_images =  [TEST_DIR+i for i in os.listdir(TEST_DIR)]\n\n"}],"metadata":{"_change_revision":0,"_is_fork":false,"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.5.2"}},"nbformat":4,"nbformat_minor":0}