{"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_minor":4,"nbformat":4,"cells":[{"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","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-12-22T15:07:59.855411Z","iopub.execute_input":"2022-12-22T15:07:59.855864Z","iopub.status.idle":"2022-12-22T15:07:59.884804Z","shell.execute_reply.started":"2022-12-22T15:07:59.855814Z","shell.execute_reply":"2022-12-22T15:07:59.88377Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import os\nimport sys\nimport random\nimport warnings\n\nimport numpy as np\nimport pandas as pd\n\nimport matplotlib.pyplot as plt\n\nfrom tqdm import tqdm\nfrom itertools import chain\nfrom skimage.io import imread, imshow, imread_collection, concatenate_images\nfrom skimage.transform import resize\nfrom skimage.morphology import label\n\nfrom keras.models import Model, load_model\nfrom keras.layers import Input\nfrom keras.layers.core import Dropout, Lambda\nfrom keras.layers.convolutional import Conv2D, Conv2DTranspose\nfrom keras.layers.pooling import MaxPooling2D\nfrom keras.layers.merge import concatenate\nfrom keras.callbacks import EarlyStopping, ModelCheckpoint\nfrom keras import backend as K\n\nimport tensorflow as tf\n","metadata":{"execution":{"iopub.status.busy":"2022-12-23T06:34:03.170587Z","iopub.execute_input":"2022-12-23T06:34:03.171044Z","iopub.status.idle":"2022-12-23T06:34:10.847997Z","shell.execute_reply.started":"2022-12-23T06:34:03.170959Z","shell.execute_reply":"2022-12-23T06:34:10.847025Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Set some parameters\nBATCH_SIZE = 10 # the higher the better\nIMG_WIDTH = 128 # for faster computing on kaggle\nIMG_HEIGHT = 128 # for faster computing on kaggle\nIMG_CHANNELS = 3\n#TRAIN_PATH = train_dir\n#TEST_PATH = test_dir\nTRAIN_PATH = '/kaggle/input/data-science-bowl-2018/stage1_train.zip'\nTEST_PATH = '/kaggle/input/data-science-bowl-2018/stage1_test.zip'\n\nwarnings.filterwarnings('ignore', category=UserWarning, module='skimage')\nseed = 42","metadata":{"execution":{"iopub.status.busy":"2022-12-23T06:34:18.420597Z","iopub.execute_input":"2022-12-23T06:34:18.420973Z","iopub.status.idle":"2022-12-23T06:34:18.427179Z","shell.execute_reply.started":"2022-12-23T06:34:18.420932Z","shell.execute_reply":"2022-12-23T06:34:18.425978Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Get train and test IDs\ntrain_ids = next(os.walk(TRAIN_PATH))[1]#train_ids = os.listdir(TRAIN_PATH)\ntest_ids = next(os.walk(TEST_PATH))[1]#test_ids = os.listdir(TEST_PATH)\nnp.random.seed(10)","metadata":{"execution":{"iopub.status.busy":"2022-12-23T06:34:20.910646Z","iopub.execute_input":"2022-12-23T06:34:20.911008Z","iopub.status.idle":"2022-12-23T06:34:21.196004Z","shell.execute_reply.started":"2022-12-23T06:34:20.910977Z","shell.execute_reply":"2022-12-23T06:34:21.194234Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}