{"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_minor":4,"nbformat":4,"cells":[{"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\n\nimport torch","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-03-15T08:36:34.542006Z","iopub.execute_input":"2022-03-15T08:36:34.542353Z","iopub.status.idle":"2022-03-15T08:36:35.587786Z","shell.execute_reply.started":"2022-03-15T08:36:34.542252Z","shell.execute_reply":"2022-03-15T08:36:35.586906Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pd_train = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')\npd_test  = pd.read_csv('/kaggle/input/digit-recognizer/test.csv')\n\nX_train=pd_train.drop(columns=['label'])\ny_train=pd_train['label']\n\nX_train=torch.tensor(X_train.values).view(-1,28,28)\ny_train=torch.tensor(y_train.values)\n\nX_test = torch.tensor(pd_test.values)\n\nfrom torch.utils.data.dataloader import DataLoader\nX_train_loader=DataLoader(X_train,batch_size=8)\ny_train_loader=DataLoader(y_train,batch_size=8)","metadata":{"execution":{"iopub.status.busy":"2022-03-15T09:44:04.615196Z","iopub.execute_input":"2022-03-15T09:44:04.616606Z","iopub.status.idle":"2022-03-15T09:44:10.045067Z","shell.execute_reply.started":"2022-03-15T09:44:04.616528Z","shell.execute_reply":"2022-03-15T09:44:10.044095Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"execution":{"iopub.status.busy":"2022-03-15T09:24:38.31724Z","iopub.execute_input":"2022-03-15T09:24:38.317491Z","iopub.status.idle":"2022-03-15T09:24:38.322038Z","shell.execute_reply.started":"2022-03-15T09:24:38.317464Z","shell.execute_reply":"2022-03-15T09:24:38.321536Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from torch.utils.data.dataloader import DataLoader\ntrain_loader=DataLoader(train,batch_size = 8)\ntest_loader = DataLoader(test,batch_size = 8)\nfor item in train_loader:\n    ","metadata":{"execution":{"iopub.status.busy":"2022-03-15T09:03:12.784162Z","iopub.execute_input":"2022-03-15T09:03:12.78443Z","iopub.status.idle":"2022-03-15T09:03:14.423908Z","shell.execute_reply.started":"2022-03-15T09:03:12.784401Z","shell.execute_reply":"2022-03-15T09:03:14.423191Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}