{"cells":[{"cell_type":"markdown","metadata":{"_cell_guid":"ca3426c6-a05b-4c0f-a4dc-83228145e9ea"},"source":"This is a test to load, view, and clean data "},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"69fdc1cb-b4fa-7fe5-6ad9-c35af1338dbe"},"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%matplotlib inline\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\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":"24c6ed82-0141-f957-acab-a740077068f4"},"outputs":[],"source":"#print(check_output([\"ls\", \"../input/train/Type_1\"]).decode(\"utf8\"))\nimage_location = \"../input/train/\"\n\nimage_mat = plt.imread(image_location + \"Type_1/0.jpg\")\n#image_mat.shape\nimg = plt.imshow(image_mat)\n#plt.show()"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"7bcd099a-6edd-57e1-8d4f-55b0bed921cd"},"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":"3.6.0"}},"nbformat":4,"nbformat_minor":0}