How to transform output of neural network and still train?
I have a neural network which outputs output. I want to transform output before the loss and backpropogation happen.
Here is my general code:
with torch.set_grad_enabled(training):
                  outputs = net(x_batch[:, 0], x_batch[:, 1]) # the prediction of the NN
                  # My issue is here:
                  outputs = transform_torch(outputs)
                  loss = my_loss(outputs, y_batch)

                  if training:
                      scheduler.step()
                      loss.backward()
                      optimizer.step()

I have a transformation function which I put my output through:
def transform_torch(predictions):
    torch_dimensions = predictions.size()
    torch_grad = predictions.grad_fn
    cuda0 = torch.device('cuda:0')
    new_tensor = torch.ones(torch_dimensions, dtype=torch.float64, device=cuda0, requires_grad=True)
    for i in range(int(len(predictions))):
      a = predictions[i]
      # with torch.no_grad(): # Note: no training happens if this line is kept in
      new_tensor[i] = torch.flip(torch.cumsum(torch.flip(a, dims = [0]), dim = 0), dims = [0])
    return new_tensor

My problem is that I get an error on the next to last line:
RuntimeError: a view of a leaf Variable that requires grad is being used in an in-place operation.

Any suggestions? I have already tried using &quot;with torch.no_grad():&quot; (commented), but this results in very poor training and I believe that the gradients don't backpropogate properly after the transformation function.
Thanks!

The error is quite correct about what the issue is - when you create a new tensor with requires_grad = True, you create a leaf node in the graph (just like parameters of a model) and not allowed to do in-place operation on it.
The solution is simple, you do not need to create the new_tensor in advance. It is not supposed to be a leaf node; just create it on the fly
new_tensor = [ ]
for i in range(int(len(predictions))):
    a = predictions[i]
    new_tensor.append(torch.flip(torch.cumsum(torch.flip(a, ...), ...), ...))

new_tensor = torch.stack(new_tensor, 0)    

This new_tensor will inherit all properties like dtype, device from predictions and will have require_grad = True already.
