Model optimization whit adam not working

[KFrank]

Hi Ione!

@Ione_Ianniruberto

    # Aggiorna i valori per l'iterazione successiva senza usare in-place
    Qda_new[i + 1] = Qda_next
    Pin_predicted[i + 1] = Pin_i
print('fine for')

As you note in your comment, writing into Pin_predicted with
an index is an inplace modification and could well be the
cause of the error you are seeing.

# Zeroing gradients
optimizer.zero_grad()
# Backward pass
# Aggiorna i parametri
optimizer.step()

The code you posted doesn’t show any call to .backward(),
so it’s not possible to track down where things are going wrong.

the error is this:
…
RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.FloatTensor ], which is output 0 of AsStridedBackward0, is at version 321; expected version 320 instead.

You can find some suggestions for locating and fixing
inplace-modification errors in this post:

"RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.FloatTensor [64, 1]], which is output 0 of AsStridedBackward0, is at version 3; expected version 2 instead. Hint: the backtrace further a

Hi Fahmyadan and Sangyoon! Here are some suggestions about how to track down (and maybe fix) inplace-modification errors. Note that an inplace modification in the forward pass is not necessarily* an error – it depends on whether and how the tensor that was modified is used in the backward pass. Note that inplace operations can be useful for saving memory – if you replace an innocent inplace operation with an out-of-place equivalent, your training will use more memory (and, to a minor e…

Good luck!

K. Frank