Hi everybody, well I’m a little dubious about the result I got.

- Is it reasonable to have these figures for the first epoch?
- which Loss formula should I take to draw the LOSS_EPOCH graph? (Loss_sum, loss, or epoch_loss??)

```
for i, (xA, xB, score) in enumerate(train_loader, 1):
....
loss = criterion(..)
total_loss += loss.item()
total_loss2 += loss.item() * len(xA)
if i%5==0:
Loss_sum = ( total_loss / i ) * len(xA) ## Loss_sum(total) in code below
loss= total_loss / i ## loss(mean) in code below
#PRINT
.
.
epoch_loss = total_loss2 / len(train_loader.dataset)
....
#for evaluation
total_loss_eval+= loss.item()
cum_loss += (loss.item()*len(XA))
if i%3 ==0:
print(f'total_loss_eval{total_loss_eval / i * len(XA)}')
LOSS_eval= cum_loss / len(eval_loader2.dataset) ### EVAl Epoch in result below
```

Herein lies the first epoch output.

```
[0m 15s] Train Epoch: 1 [250/4500 (6%)] Loss_sum(total): 5.72 loss(mean) 0.11438
[0m 30s] Train Epoch: 1 [500/4500 (11%)] Loss_sum(total): 6.32 loss(mean) 0.12634
[0m 46s] Train Epoch: 1 [750/4500 (17%)] Loss_sum(total ): 6.49 loss(mean) 0.12977
begin validation ...
total_loss_eval 6.013869825336668
EVAl Epoch: 1 [500] Loss: 0.12
save currently the best model to [/content/model.pt]
[1m 3s] Train Epoch: 1 [1000/4500 (22%)] Loss_sum(total): 6.67 loss(mean) 0.13343
[1m 19s] Train Epoch: 1 [1250/4500 (28%)] Loss_sum(total): 6.29 loss(mean) 0.12571
.
.
total_loss_eval 2.934242847065131
EVAl Epoch: 1 [500] Loss: 0.06
.
.
[4m 43s] Train Epoch: 1 [4250/4500 (94%)] Loss_sum(total): 3.65 loss(mean) 0.07292
[4m 58s] Train Epoch: 1 [4500/4500 (100%)] Loss_sum(total): 3.56 loss(mean) 0.07124
begin validation ...
total_loss_eval 2.032051028476821
EVAl Epoch: 1 [500] Loss: 0.04
epoch_loss 0.0712374652425448
..........
```