{"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('../'):\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.vision import *","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def folder_name(number):\n    if(len(str(number))==6):\n        return number\n    gap = 6 - len(str(number))\n    return gap *'0' + str(number)\ndef from_preds_to_list(preds):\n    p=to_np(preds)\n    lista =[]\n    for i in range(len(p)):\n        lista.append(np.where(np.amax(p[i])==p[i])[0][0])\n    last=[]\n    for element in lista:\n        last.append(data.classes[element])\n    return last","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"path=\"/kaggle/input/vehicle/train/train/\"\nnp.random.seed(42)\ndata = ImageDataBunch.from_folder(path+'.', train=path+'.', valid_pct=0.2,\n                                  ds_tfms=get_transforms(), size=224, num_workers=4).normalize(imagenet_stats)\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"len(data.classes)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"data.show_batch(rows=3, figsize=(7, 8))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from fastai.metrics import error_rate # 1 - accuracy\nlearn = cnn_learner(data, models.resnet50, metrics=error_rate,model_dir=\"/tmp/model/\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"defaults.device = torch.device('cuda') # makes sure the gpu is used\nlearn.fit_one_cycle(1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"learn.model_dir='/kaggle/working/'\nlearn.export(\"/kaggle/working/export50.pkl\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#learn = load_learner(\"../input/kerneldfa3fd74eb/\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#load_test_data\nsubmission = pd.read_csv(\"/kaggle/input/vehicle/sample_submission.csv\")\ntestpath=\"/kaggle/input/vehicle/test/\"\nsubmission1 = submission\nsubmission1['Id'] = submission['Id'].apply(lambda x: folder_name(x))\nsubmission1\nlearn.data.add_test(ImageList.from_df(\n    submission1, testpath,\n    folder='testset',\n    suffix='.jpg'\n))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"preds, _ = learn.get_preds(DatasetType.Test)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission[\"Category\"]=from_preds_to_list(preds)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission.to_csv(\"submit2.csv\",index=False)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"<a href=\"./submit2.csv\"> Download File </a>"},{"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":1}