Hi,
I want to add a constraint (max_norm) to my 2D convolutional layer’s weights. For example we can do that easily in Keras using:
keras.layers.Conv2D(8, (3, 2), activation='relu', kernel_constraint=max_norm(1.))
which makes a convolutional layer with 8 kernels each one has a size of (3, 2).
Is there a way to do the same in Pytorch? i searched in the forum but can’t find something relevant.
Thank you