Hello,
I am working on an implementation of the Gram-cam paper ( check it here 79 )
The problem is that it is coded in pure torch and as you problably know with torch we can backward directly a model/Sequential like model.backward(input,target).
I am stuck at this stage as my input is the output of  a conv2D  ( [torch.FloatTensor of size 1x256x13x13] )  and the target is a one dimension tensor with the classe targeted at 1 and the rest at 0 .
How could I do this backward  ? I tried to use an optimizer and a loss function but it seems not possible with such tensors.
Any ideas ?
Justin