d85a3d2fe863c72d884c6c79d7c690065413c2f2,beginner_source/examples_nn/two_layer_net_module.py,,,#,39

Before Change

    // Compute and print loss
    loss = criterion(y_pred, y)
    print(t, loss.item())

    // Zero gradients, perform a backward pass, and update the weights.
    optimizer.zero_grad()
    loss.backward()

After Change

    // Compute and print loss
    loss = criterion(y_pred, y)
    if t % 100 == 99:
        print(t, loss.item())

    // Zero gradients, perform a backward pass, and update the weights.
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

Instances