32c7386aef93737926069ee284d827f8e954e086,vae/main.py,,train,#Any#,104

Before Change

    print("====> Epoch: {} Loss: {:.4f}".format(
          epoch,
          train_loss.data[0] / training_data.size(0)))


def test(epoch):

After Change

def train(epoch):
    model.train()
    train_loss = 0
    for batch_idx, (data, _) in enumerate(train_loader):
        data = Variable(data)
        optimizer.zero_grad()
        recon_batch, mu, logvar = model(data)
        loss = loss_function(recon_batch, data, mu, logvar)
        loss.backward()
        train_loss += loss.data[0]
        optimizer.step()
        if batch_idx % args.log_interval == 0:
            print("Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader),
                loss.data[0] / len(data)))

    print("====> Epoch: {} Average loss: {:.4f}".format(
          epoch, train_loss / len(train_loader.dataset)))

Instances