backpropagation.py 文件源码

def train_(self, epoch):
        self.train()
        for batch_idx, (data, target) in enumerate(train_loader):
            correct = 0
            if args.cuda:
                data, target = data.cuda(), target.cuda()
            data, target = Variable(data), Variable(target)
            self.optimizer.zero_grad()
            output = self(data)
            loss = F.nll_loss(output, target)
            loss.backward()
            pred = output.data.max(1)[1] # get the index of the max log-probability
            correct += pred.eq(target.data).cpu().sum()
            accuracy = 100. * correct / len(data)
            self.optimizer.step()
            if batch_idx % args.log_interval == 0:
                print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}, Accuracy: {}/{} ({:.4f}%)'.format(
                    epoch, batch_idx * len(data), len(train_loader.dataset),
                    100. * batch_idx / len(train_loader), loss.data[0],
                    correct, len(data),
                    accuracy))

            self.train_acc.append(accuracy)