hi, i am a beginner of pytorch.
The problem is how to add all the loss which is iterate on the whole dataset.
Some of the code shown below may explain my problem clearly.
Thanks.
# my network
class MyNet(nn.Module):
    ...
    def forward(self, input):
        ...
        return a, b
net = MyNet()

# my dataloader for my own dataset
train_loader = Dataloader(
                       dataset=train_data, 
                       shuffle=True,
                       batch_size=1)

# my training 
# the optimizer and the criterion has been defined
for epoch in range(num_epochs):
    running_loss = 0.0
    optimizer.zero_grad()
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data
        outputs_a, outputs_b = net(inputs)
        loss_a = criterion(outputs_a, labels)
        loss_b = criterion(outputs_b, labels)
        running_loss  = running_loss + loss_a.item() + loss_b.item()
    running_loss.backward()
    optimizer.step()

i know there must be something wrong with my running_loss. And what i want to do is ,for one epoch,  add all the loss of one iteration over the data set and do the backward.
And is the batch_size = 1 correct or not? And how to define the running_loss. In some post, i saw loss.data[0] should be add together?
Thanks.