{"metadata":{"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,"cells":[{"metadata":{"_cell_guid":"8f9fc251-eb23-bb06-dd5f-e0626e517182","_active":false,"collapsed":false},"source":null,"execution_count":null,"cell_type":"markdown","outputs":[],"execution_state":"idle"},{"metadata":{"_cell_guid":"263e5d69-93b7-8a55-7386-8d73e646731e","_active":false,"collapsed":false},"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.","execution_count":2,"cell_type":"code","outputs":[],"execution_state":"idle"},{"metadata":{"_cell_guid":"00eed5db-c30b-055f-4765-082db6b5fc25","_active":false,"collapsed":false},"source":"from keras.applications.vgg16 import VGG16\nfrom keras.preprocessing import image\nfrom keras.applications.vgg16 import preprocess_input\nimport numpy as np\n\nmodel = VGG16(weights='imagenet', include_top=False)\n","execution_count":3,"cell_type":"code","outputs":[],"execution_state":"idle"},{"metadata":{"_cell_guid":"bfa92eb7-4fc9-97ab-af69-5d2e360e6f8a","_active":false,"collapsed":false},"source":null,"execution_count":null,"cell_type":"code","outputs":[]}]}