{"metadata":{"kernelspec":{"display_name":"Python 3","name":"python3","language":"python"},"language_info":{"nbconvert_exporter":"python","version":"3.6.4","pygments_lexer":"ipython3","mimetype":"text/x-python","name":"python","file_extension":".py","codemirror_mode":{"name":"ipython","version":3}}},"nbformat_minor":1,"nbformat":4,"cells":[{"metadata":{"_cell_guid":"c9968084-fa69-4ba0-819b-1becbd0d0887","_uuid":"d45934e9e3aec51c9a9f7450458282d1d4b1d968"},"outputs":[],"execution_count":null,"cell_type":"code","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.\nimport tensorflow as tf\nprint(tf)"},{"metadata":{},"outputs":[],"execution_count":null,"cell_type":"code","source":"print(tf.__version__)"},{"metadata":{},"outputs":[],"execution_count":null,"cell_type":"code","source":"from tensorflow.examples.tutorials.mnist import mnist\nprint(mnist)"}]}