{"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 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.","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"from fastai import * \nfrom fastai.vision import *","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"path=Path('/kaggle/input/vehicle/train/train/')\nImageList.from_folder(path)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data = (ImageList.from_folder(path)\n        #Where to find the data? \n        .split_by_rand_pct(0.20,seed=44)\n        .label_from_folder()\n        #Data augmentation? -> use tfms with a size of 128\n        .transform(get_transforms(do_flip=True,flip_vert= False,max_zoom=1.1, max_lighting=0.2, max_warp=0.2),size=512)\n        .databunch(bs=8)\n        .normalize(imagenet_stats))  \nprint(data.classes)\ndata.show_batch(rows=3, figsize=(5,5))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"Path('/tmp/.cache/torch/checkpoints/').mkdir(exist_ok=True, parents=True)\n!cp ../input/resnet34/resnet34.pth /tmp/.cache/torch/checkpoints/resnet34-333f7ec4.pth\nlearn = cnn_learner(data, models.resnet34, metrics=[accuracy],model_dir='/kaggle',pretrained=True)\nlearn.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"learn.lr_find()\nlearn.recorder.plot()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"lr = 1e-2\nlearn.fit_one_cycle(1, slice(lr))","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":1}