{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"9261ce2f-acfb-0433-2f4b-9208fca24b9f"},"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\"))\ndata= pd.read_csv('../input/training.csv')\n\n\n\n# Any results you write to the current directory are saved as output."},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"5a65c426-2d20-3b87-7b8a-b9f63acacf0a"},"outputs":[],"source":"#shuffle data\ndata = data.sample(frac=1)\ntraining_data=data.head(38000)\ntest_data=data.tail(4000)\n\ntraining_labels=training_data['label'].values\ntraining_features= training_data.drop('label', axis=1).values\n\n"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"2c3bff24-2ae5-daf1-16ab-594f0d135ee1"},"outputs":[],"source":"from keras.models import Sequential\nfrom keras.optimizers import SGD\nfrom keras.layers.core import Dense, Activation, Dropout\nfrom keras.utils import "},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"694166cf-8007-fcee-7b1d-6216e6abf239"},"outputs":[],"source":""}],"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}