{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"33541a2d-59d5-d48f-ee91-36ebfb15f946"},"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/train\"]).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":"4f25bb97-148d-0236-5b4d-440234755223"},"outputs":[],"source":"import os\nfrom scipy import misc\nlocation = \"../input/train\"\nX, y = np.array([]), np.array([])\ncount = 0\nfor curr_file in os.listdir(location):\n    category = curr_file.split(\".\")[0]\n    image = misc.imread(location + \"/\" + curr_file)\n    X = np.append(X, image)\n    print(image.shape)\n    if category == \"dog\":\n        y = np.append(y, 1)\n    else:\n        y = np.append(y, 0)\n    count+=1\n    if count == 20:\n        break\n\nprint(X.shape, y.shape)\n"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"bd149b16-d9d9-e101-2b37-7c17434bb20d"},"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}