{"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\nfrom fastai.vision.all import *\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\n# for 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":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"path = Path('../input/cassava-leaf-disease-classification/')\npath","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df = pd.read_csv(path/'train.csv')\ndf.head()\n# df.iloc[:, 0]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"dblock = DataBlock()\ndsets = dblock.datasets(df)\nlen(dsets.train),len(dsets.valid)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# type(dsets), type(dblock)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x,y = dsets.train[0]\nx,y","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# x['image_id'], x['label']","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# dblock = DataBlock(get_x = lambda r: r['image_id'], get_y = lambda r: r['label'])\n# dsets = dblock.datasets(df)\n# dsets.train[0]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# def get_x(r): return r['image_id']\n# def get_y(r): return r['label']\n# dblock = DataBlock(get_x = get_x, get_y = get_y)\n# dsets = dblock.datasets(df)\n# dsets.train[0]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def get_x(r): return path/'train_images'/r['image_id']\ndef get_y(r): return r['label']\ndblock = DataBlock(get_x = get_x, get_y = get_y)\ndsets = dblock.datasets(df)\ndsets.train[0]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# dblock = DataBlock(blocks=(ImageBlock, CategoryBlock),\n#                   splitter= RandomSplitter(valid_pct=0.1),\n#                    get_x=get_x, get_y=get_y,\n#                   item_tfms=RandomResizedCrop(256, min_scale=0.35))\n# dls = dblock.dataloaders(df)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"dblock = DataBlock(blocks=(ImageBlock, CategoryBlock),\n                   splitter= RandomSplitter(valid_pct=0.1),\n                   get_x=get_x, get_y=get_y,\n                   item_tfms=Resize(460),\n                   batch_tfms=aug_transforms(size=224, min_scale=0.75))\ndls = dblock.dataloaders(df)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"dls.show_batch(nrows=2, ncols=3)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# learn = cnn_learner(dls, resnet18)\n# learn.model.cuda()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# x,y = dls.train.one_batch()\n# activs = learn.model(x)\n# activs.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# activs[0] # final layer activations from the un-trained model","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n# device","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# learn = cnn_learner(dls, resnet101, metrics=accuracy)\n# learn.model.cuda()\n# learn.fine_tune(3, base_lr=3e-3, freeze_epochs=4)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"learn = cnn_learner(dls, resnet101, metrics=(error_rate, accuracy))\nlearn.model.cuda()\n# learn.lr_find()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"learn.fit_one_cycle(3, 3e-3)\nlearn.unfreeze()\nlearn.fit_one_cycle(5, lr_max=slice(1e-6,1e-4))","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}