For Gradient Descent, we calculated the gradients for each sample individually. To improve the convergence behavior and to speed up the training process, we now consider batches of gradients. This stabilizes the convergence and prevents the parameters from being push in one direction and directly afterwards being pulled in the opposite direction. Therefore, we can use the gradient descent implementation and just swap the data loader to use the batched variant. The bad thing is that we need to iterate now multiple times over the dataset.帮我补全下面的代码。 net = ... epochs = 10 optimizer = GD(net.parameters(), 0.02) losses, states = ... fig = plot_losses(losses) conf_pltly() init_notebook_mode(connected=False) iplot(fig)

Sure! Here's the completed code:

net = ... # define your neural network architecture
epochs = 10
optimizer = GD(net.parameters(), 0.02) # define the optimizer

# define your batch size and create a data loader that batches the data
batch_size = 32
data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)

losses, states = [], []
for epoch in range(epochs):
    running_loss = 0.0
    for i, batch in enumerate(data_loader):
        optimizer.zero_grad() # zero the parameter gradients
        
        # forward + backward + optimize
        outputs = net(batch['input'])
        loss = loss_fn(outputs, batch['target'])
        loss.backward()
        optimizer.step()

        # track the loss and state of the network
        running_loss += loss.item()
        losses.append(running_loss / ((i+1) * batch_size))
        states.append(copy.deepcopy(net.state_dict()))

# plot the losses over training
fig = plot_losses(losses)
conf_pltly()
init_notebook_mode(connected=False)
iplot(fig)

Note that in this code, we've added a data loader that batches the data into sets of `batch_size` samples. We then iterate over this data loader, computing the gradients and updating the parameters for each batch. We also track the loss and network state over time, and use these to plot the loss curve at the end of training.