{"cells":[{"metadata":{},"cell_type":"markdown","source":"# fastai training baseline"},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import numpy as np \nimport pandas as pd \nimport os\nfrom fastai import *\nfrom fastai.vision import *\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport torch\nfrom fastai.metrics import KappaScore","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"SIZE = 256\nBS = 64","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_df = pd.read_csv('../input/prostate-cancer-grade-assessment/train.csv')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_df.head(1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train = ImageList.from_df(train_df, path='../input/panda-challenge-resized-dataset/', cols='image_id',suffix='.jpeg')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"src = (train\n       .split_by_rand_pct(0.2) #split the dataset such that we have 20% as validation set\n#        .split_from_df()\n       .label_from_df(cols='isup_grade',label_cls=FloatList))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"tfms = get_transforms(do_flip=True,flip_vert=True,max_rotate=360,max_warp=0,max_zoom=1.1,max_lighting=0.1,p_lighting=0.5)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data= (src.transform(tfms,size=SIZE) #Data augmentation\n       .databunch(bs=BS,num_workers=1) \n       .normalize(imagenet_stats))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data.show_batch(2)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.metrics import cohen_kappa_score\ndef quadratic_kappa(y_hat, y):\n    return torch.tensor(cohen_kappa_score(torch.round(y_hat).cpu(), y.cpu(), weights='quadratic'),device='cuda:0')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"learn = cnn_learner(data,models.resnet18,metrics=[accuracy, quadratic_kappa], model_dir='../working/models/')\nlearn.path = Path('')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"learn.fit_one_cycle(2,2e-3)\nlearn.save('resnet18')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"learn.recorder.plot_losses()\nlearn.recorder.plot_metrics()","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}