torch101_minibatch.py

losses = []
train_step = make_train_step(model, loss_fn, optimizer)

for epoch in range(n_epochs):
    for x_batch, y_batch in train_loader:
        # the dataset "lives" in the CPU, so do our mini-batches
        # therefore, we need to send those mini-batches to the
        # device where the model "lives"
        x_batch = x_batch.to(device)
        y_batch = y_batch.to(device)
        
        loss = train_step(x_batch, y_batch)
        losses.append(loss)
        
print(model.state_dict())