{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"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\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 read-only \"../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# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!pip install tifffile\n!pip install kaggle","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import tensorflow as tf\nprint (\"tensorflow version used in this notebook:\", tf.__version__)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport os\nimport time\nimport random\nimport matplotlib.pyplot as plt\n%matplotlib inline\n!pip install scipy\nimport sklearn\n\nfrom keras.models import Model\nfrom keras.layers import Input, concatenate, Conv2D, MaxPooling2D, UpSampling2D, Dropout\nfrom keras.optimizers import Adam,Adamax,Nadam,Adadelta,SGD,RMSprop\nfrom keras.callbacks import ModelCheckpoint, LearningRateScheduler\nfrom keras import backend as K\n#from keras.utils.io_utils import HDF5Matrix\nimport h5py\n\n#from sklearn.metrics import jaccard_similarity_score\nfrom sklearn.model_selection import train_test_split\n\nimport cv2\nimport shapely.wkt\nimport shapely.affinity\nfrom shapely.geometry import MultiPolygon, Polygon\nfrom collections import defaultdict\nfrom shapely.wkt import loads as wkt_loads\nimport tifffile as tiff","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"base_dir = 'output1'\nos.mkdir(base_dir)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"! ls -ltrh","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!unzip  ../input/dstl-satellite-imagery-feature-detection -d inputs","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!unzip  ../input/dstl-satellite-imagery-feature-detection/sixteen_band.zip -d output","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"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"}},"nbformat":4,"nbformat_minor":4}