{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"90462bb7-d245-1826-9cc8-f9f78862fd91"},"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)\nimport os\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"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"94db4de8-481e-507e-7fa8-ddeb2a9ac5df"},"outputs":[],"source":"TRAIN_DIR = \"../input/train\"\nTEST_DIR = \"../input/test\""},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"185e7852-3909-c83c-fe34-5e798e48c33a"},"outputs":[],"source":"# 2000 training images and 1000 test images\ntraining_files = [i for i in os.listdir(TRAIN_DIR)][:2000]\nt\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}