0
$\begingroup$

I am training a model to generate images.

The model contains 5+5 layers:

Conv2D -> Upsample -> Conv2D -> Upsample -> Conv2D -> Upsample -> Conv2D -> Upsample -> Conv2D -> Upsample

I am modifying it as

Conv2D -> BatchNorm -> Upsample -> Conv2D -> BatchNorm -> Upsample -> Conv2D -> BatchNorm -> Upsample -> Conv2D -> BatchNorm -> Upsample -> Conv2D -> BatchNorm -> Upsample

I am applying the batch normalization layers just before upsampling as shown above and hence I am not getting the results that are at least comparable to the results by the model without any batch normalization layer.

Is my placement of the batch normalization layer wrong? If yes, then why?

$\endgroup$
2
  • $\begingroup$ I don't know if it makes any difference, but it may be a good idea to tell us which specific model you're using. $\endgroup$
    – nbro
    Nov 29 '21 at 12:14
  • $\begingroup$ @nbro Okay, I will update today. $\endgroup$
    – hanugm
    Nov 30 '21 at 22:30

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.