{"cells":[{"cell_type":"markdown","metadata":{"_cell_guid":"9fcb889d-8343-7351-f6cc-895a5327fd87"},"source":""},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"57e818cd-c5f7-1613-99a2-cf8e88cf0777"},"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\nimport os\nimport tensorflow as tf\n\nprint(len(os.listdir('../input/')))\n\n# Any results you write to the current directory are saved as output."},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"1519d933-18b8-84db-8997-99d9e24452c0"},"outputs":[],"source":"# Creates a graph.\nwith tf.device('/cpu:0'):\n  a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')\n  b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')\n  c = tf.matmul(a, b)\n# Creates a session with log_device_placement set to True.\nsess = tf.Session(config=tf.ConfigProto(log_device_placement=True))\n# Runs the op.\nprint(sess.run(c))\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}