(0) preds = torch.argmax(outputs, dim=1) train_epoch_loss = train_running_loss / len(self.dataset_config.train_data) # Metrics on all valid data train_accuracy = self.accuracy.compute() train_precision = self.precision.compute() train_recall = self.recall.compute()...
acc = (torch.argmax(y_pred, 1) == torch.argmax(y_test, 1)).float().mean() print(f"Epoch {epoch} validation: Cross-entropy={float(ce)}, Accuracy={float(acc)}") That’s almost everything you need to finish a deep learning model in PyTorch. But you may also want to do a bi...