{"cells":[{"cell_type":"markdown","metadata":{"_cell_guid":"66efeaf3-7edc-cf90-b004-a645af1e9334"},"source":"# train"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"13bef231-4aeb-173b-82f7-118df7794082"},"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":"a7c90065-e05e-c2f5-3811-eba8e76ac9b5"},"outputs":[],"source":"import os,sys\nos.listdir('../input/train')"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"07524194-ca84-eff3-cdc9-22be6befea52"},"outputs":[],"source":"import keras\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}