Data Science, Machine Learning, Artificial Intelligence, Technology, Stock Market.
range(epochs): model.train() optimizer.zero_grad() output = model(X_train_tensor) loss = criterion(output, y_train_tensor) loss.backward() optimizer.step() if epoch % 10 == 0: model.eval() test_pred = model(X_test_tensor) test_loss = criterion(test_pred, y_test_tensor) print(f'Epoch {epoch}, Loss: