Train loss vs loss on progress bar

❓ Questions and Help

What is your question?

I don’t understand why train_loss is different than loss even though I assign the same value. Perhaps one loss is calculated over the whole dataset and the other one is only for a recent batch? But if that’s the case, which one is which?

Epoch 1:  79%|███████▉  | 691/870 [00:07<00:01, 100.85batch/s, accuracy=0.7, batch_idx=690, gpu=0, loss=0.442, train_loss=0.535, v_num=2]  

Code

    def training_step(self, batch, batch_idx):
        ...     
        loss = self.loss(...)
        tqdm_dict = {'train_loss': loss}
        outputs = {
            'loss': loss,
            'progress_bar': tqdm_dict,
            'log': tqdm_dict
        }

What’s your environment?

• OS: Ubuntu
• Packaging: pip
• Version: 0.6.0

[williamFalcon]

loss on progress bar is a running average. what you return (train_loss) is not

[williamFalcon]

@Borda can we note this in the docs?