{"cells":[{"cell_type":"markdown","metadata":{"_cell_guid":"fa1caa61-ccb6-788f-aecf-6b71ef5311d9"},"source":"Datobig's version of MNIST Digit Recognizer"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"d9122df2-6db8-50d4-2778-499e025d107a"},"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\nprint(check_output([\"head\", \"../input/test.csv\"]).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":"8892f29f-f652-05ff-e398-0adf5075fea3"},"outputs":[],"source":"train_pdf = pd.read_csv('../input/train.csv',encoding='utf-8')\ntest_pdf = pd.read_csv('../input/test.csv',encoding='utf-8')"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"7a2b0c45-72e5-1877-21b5-f54eb63b7c81"},"outputs":[],"source":"#Print all rows and columns. Dont hide any\npd.set_option('display.max_rows', None)\npd.set_option('display.max_columns', None)"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"f077c415-9bd1-ea0a-ed83-1bf42589a69a"},"outputs":[],"source":"#Display the first five rows to get a feel of the data\nprint(train_pdf.head(1))"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"f10f3667-097b-bcc4-4102-28ef6e652a6e"},"outputs":[],"source":"from keras.models import Sequential\nfrom keras.layers import Dense, Activation\n"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"4094030d-9768-894c-cb6a-38d174539e75"},"outputs":[],"source":"import matplotlib.pyplot as plt"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"ba792556-3fac-2d23-bbaf-7fd29d0d1ff3"},"outputs":[],"source":"import plotly.plotly as py\nimport plotly.graph_objs as go"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"ca82ff2a-a5f4-abfa-e751-365c61944ca3"},"outputs":[],"source":"print(plotly.__version__)"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"5014a1da-d6ba-7510-513c-ff5bbd401d33"},"outputs":[],"source":"train_pdf.index\n"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"b87abc84-6401-7538-9355-de41dd3235c0"},"outputs":[],"source":"train_pdf.columns"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"bd229281-5281-48b8-0d24-254b86c4c935"},"outputs":[],"source":"train_pdfvalues = train_pdf[['label']]"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"9820b89d-e3ab-91fa-7703-a18fd84cfce5"},"outputs":[],"source":"train_pdfvalues"}],"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}