{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"9985d9c8-d812-cd98-fbd3-0e377e2bd2ae"},"outputs":[],"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 the files in the input directory\n\nfrom subprocess import check_output\nprint(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n\n# Any results you write to the current directory are saved as output."},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"e4248b1b-2fde-e3e5-31cb-2daed1d07d9c"},"outputs":[],"source":"import cv2                 # working with, mainly resizing, images\nimport numpy as np         # dealing with arrays\nimport os                  # dealing with directories\nfrom random import shuffle # mixing up or currently ordered data that might lead our network astray in training.\nfrom tqdm import tqdm      # a nice pretty percentage bar for tasks. Thanks to viewer Daniel Bühler for this suggestion\n\nTRAIN_DIR = '../input/train'\nTEST_DIR = '../input/test'\nIMG_SIZE = 50\nLR = 1e-3\nMODEL_NAME = 'dogsvscats-{}-{}.model'.format(LR, '2conv-basic') # just so we remember which saved model is which, sizes must match\n\n\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":"3.6.0"}},"nbformat":4,"nbformat_minor":0}