num_epochs = 5
count = 0
loss_list = [] ------ ①
iteration_list = []
accuracy_list = []

predictions_list = []
labels_list = []

for epoch in range(num_epochs):
    for images, labels in train_loader: ------ ②
        images, labels = images.to(device), labels.to(device) ------ ③

        train = Variable(images.view(100, 1, 28, 28)) ------ ④
        labels = Variable(labels)

        outputs = model(train) ------ 학습 데이터를 모델에 적용
        loss = criterion(outputs, labels)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        count += 1

        if not (count % 50): ------ count를 50으로 나누었을 때 나머지가 0이 아니라면 실행
            total = 0
            correct = 0
            for images, labels in test_loader:
                images, labels = images.to(device), labels.to(device)
                labels_list.append(labels)
                test = Variable(images.view(100, 1, 28, 28))
                outputs = model(test)
                predictions = torch.max(outputs, 1)[1].to(device)
                predictions_list.append(predictions)
                correct += (predictions == labels).sum()
                total += len(labels)

            accuracy = correct * 100 / total ------ ⑤
            loss_list.append(loss.data) ------ ①′
            iteration_list.append(count)
            accuracy_list.append(accuracy)

        if not (count % 500):
            print("Iteration: {}, Loss: {}, Accuracy: {}%".format(count, loss.data, accuracy))