I am new in this forum but I use actively TensorFlow from few years. I am also willing to contribute.
Here I have this simple question about convolution padding:
I am developing one model using Conv2D layers. Conv2D has padding attribute which controls if a layer output has to keep the same size as the input, docs says that :
padding: one of
"valid"means no padding.
"same"results in padding with zeros evenly to the left/right or up/down of the input. When
strides=1, the output has the same size as the input.
But I want padding to behave differently in X and Y dimension, so if my input is image with size (W,H) to be able to keep the output size to W but H to be as if the
"valid" case, and so to be able to tuples as for stride, dilation and kernel size.
Has been there such feature request in the more recent tensorflows?