{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"6b8e1bb4-1c41-92a6-4081-9cb5a817ed45"},"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":"998f60b1-f527-2d2e-26c5-0c52b05b03cc"},"outputs":[],"source":"import numpy as np\nimport pandas as pd\nfrom keras.datasets import mnist\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Activation, Flatten\nfrom keras.layers import Convolution2D, MaxPooling2D\nfrom keras.utils import np_utils\nfrom keras import backend as K\nimport matplotlib.pyplot as plt\n\n\nbatch_size = 128\nnb_classes = 10\nnb_epoch = 5\n\n\n\n"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"943eda89-1a00-b640-6df8-4297066d971b"},"outputs":[],"source":"\nX_test_pd = pd.read_csv('../test.csv')"}],"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}