{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"b564b521-ecf5-55d1-0a80-be08a0ae693d"},"outputs":[],"source":"%matplotlib inline\n\n# 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\npath_test = \"input/\"\n\nfrom __future__ import division,print_function\nimport os, json\nfrom glob import glob\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nnp.set_printoptions(precision=4, linewidth=100)\nfrom matplotlib import pyplot as plt"},{"cell_type":"code","execution_count":null,"metadata":{"_cell_guid":"bd8a1321-9797-4c80-c7bd-c6b1aa4b54a2"},"outputs":[],"source":"batch_size = 64\n\nimport vgg16; reload(vgg16)\nfrom vgg16 import Vgg16\n\nvgg = Vgg16()\n\nbatches = vgg.get_batches(path+'train', batch_size=batch_size)\nvgg.finetune(batches)\nvgg.fit(batches, nb_epoch=1)"}],"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}