{"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":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"#imports, setting the seed\nimport random\nrandom.seed(42)\nimport pandas as pd\nfrom fastai.vision.all import *","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# reading csv from input data\n# Training with 10k input files for the trial submission\nbase_path=\"/kaggle/input/cassava-leaf-disease-classification\"\ndf_train = pd.read_csv(base_path+\"/train.csv\")[:10000]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#creating the data loader\ndls = ImageDataLoaders.from_df(df_train, path=base_path+\"/train_images\", valid_pct=0.2,\n                               seed=42, fn_col=\"image_id\", label_col=\"label\",bs=64,item_tfms=Resize(256))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"dls.show_batch()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"learnRes50_Pre = cnn_learner(dls, resnet50, metrics=[error_rate,accuracy], pretrained=True)\nlearnRes50_Pre.fine_tune(15)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#saving the model\nlearnRes50_Pre.export(Path(\"/kaggle/working/res50-cassava.pkl\"))\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"learnVGG16_Pre = cnn_learner(dls, vgg16_bn, metrics=[error_rate,accuracy], pretrained=True)\nlearnVGG16_Pre.fine_tune(15)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#saving the model\nlearnVGG16_Pre.export(Path(\"/kaggle/working/vgg16-cassava.pkl\"))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"learnDensenet121_Pre = cnn_learner(dls, densenet121, metrics=[error_rate,accuracy], pretrained=True)\nlearnDensenet121_Pre.fine_tune(15)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#saving the model\nlearnDensenet121_Pre.export(Path(\"/kaggle/working/densenet121-cassava.pkl\"))","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}