{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":15062,"databundleVersionId":545987,"sourceType":"competition"}],"dockerImageVersionId":28772,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"kaggle-GPU-compatible version of DCGAN using TPUGANEstimator\ngoal:\n- create a pipeline that allows for reading from zip file,\n- performing the processing,\n- and saving to zip file in kaggles environment\n\n- figure if I need to remove the tfhub part\n- how to change it from using tfds to reading the files from disk?\ndoes anybody use tfrecords?","metadata":{}},{"cell_type":"code","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 all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# Any results you write to the current directory are saved as output.","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import matplotlib\nimport tensorflow as tf\nimport pprint\nimport time\nimport warnings\n\nmodel_dir = 'tpuestimator-tfgan'\nprint('Using model dir: {}'.format(model_dir))\n\ntf.logging.set_verbosity(tf.logging.ERROR)\nwarnings.filterwarnings(\"ignore\")\n\ndataset_dir = '/kaggle/input/all-dogs/all-dogs'","metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"!ls /kaggle/input/all-dogs/all-dogs","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}