# Train model: model, loss_data = my_models.train_model(model, optimizer, train_loader, val_loader, device, dtype, epoches=2) # Save model to file: torch.save(model.state_dict(), MODEL_PATH + MODEL_NAME) # Save loss data to file: np.savetxt(LOSS_PATH + 'data_part_4_train.csv', loss_data, delimiter=',') fix_loss_data('data_part_4_train.csv') print('Training is Finished !') if test: print('Checking model Accuracy over Test Set...') if model is None: # load model: model = my_models.model_2() model.load_state_dict(torch.load(MODEL_PATH + MODEL_NAME)) model.eval() # Check accuracy on test set: test_acc = my_models.check_accuracy(test_loader, model, device, dtype) # Saving acc to file acc_train_data = np.loadtxt(LOSS_PATH + 'data_part_4_train.csv', delimiter=',')
# Train model: model, loss_data = my_models.train_model(model, optimizer, train_loader, val_loader, device, dtype, epoches=2, print_every=5) # Save model to file: torch.save(model.state_dict(), MODEL_PATH + MODEL_NAME) # Save loss data to file: np.savetxt(LOSS_PATH + 'data_part_4_train.csv', loss_data, delimiter=',') fix_loss_data('data_part_4_train.csv', SECTION_PATH) print('Training is Finished !') if test: print('Checking model Accuracy over Test Set...') if model is None: # load model: model = my_models.model_2() model.load_state_dict(torch.load(MODEL_PATH + MODEL_NAME)) model.eval() # Check accuracy on test set: test_acc = my_models.check_accuracy(test_loader, model, device, dtype) # Saving acc to file acc_train_data = np.loadtxt(LOSS_PATH + 'data_part_4_train.csv', delimiter=',')