excel-cnn-testing.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[],"dockerImageVersionId":30558,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"%load_ext autoreload\n%autoreload 2\n\n# !git clone https://github.com/Flagro/ExcelTableCNN.git\n\n!apt-get install -y libreoffice --fix-missing\n\n!pip install py7zr\n!pip install tqdm\n!pip install torchvision\n!pip install torch","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%cd /kaggle/working/ExcelTableCNN\n!git pull origin main","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from ExcelTableCNN.excel_table_cnn.train_test_helpers.train_test_composer import get_train_test\n\ntrain_df, test_df = get_train_test(train_size=12, testing_size=4,\n                                   data_folder_path=\"/kaggle/working/ExcelTableCNN/data\")","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from ExcelTableCNN.excel_table_cnn.dl_classification.tensors import SpreadsheetDataset\n\ntrain_dataset = SpreadsheetDataset(train_df)\ntest_dataset = SpreadsheetDataset(test_df)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from torch.utils.data import DataLoader\n\ndef collate_fn(batch):\n    return tuple(zip(*batch))\n\nbatch_size = 1  # For different-sized inputs\ntrain_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, collate_fn=collate_fn)\ntest_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, collate_fn=collate_fn)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import torch\nfrom ExcelTableCNN.excel_table_cnn.dl_classification.train_eval import get_model\n\ndevice = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')\nmodel = get_model()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import torch.optim as optim\nfrom ExcelTableCNN.excel_table_cnn.dl_classification.train_eval import train_model\n\noptimizer = optim.SGD(model.parameters(), lr=0.005, momentum=0.9, weight_decay=0.0005)\ntrain_model(model, train_loader, optimizer, 10, device)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from ExcelTableCNN.excel_table_cnn.dl_classification.train_eval import evaluate_model\n\nevaluate_model(model, test_loader, device)","metadata":{"trusted":true},"execution_count":null,"outputs":[]}]}