Hi,
In a convolution layer, the depth of a filter is equal to the depth of the input or the number of input channels. Hence, the dimension of weights in a convolution layer can be calculated as :-
(filter height) x (filter width) x (input depth or number of input channels) x (number of filters).
For example, if input to a network is an image with single channel and each convolution layer is defined as :-
convolution2dLayer([m,m], M, 'Padding', 'same');
Under the assumption that the network contains only convolution layers, the weights in the first convolution layer will have dimension = m x m x 1 x M (as the input depth = 1) and the output of this layer will have dimension = (input image height) x (input image width) x (number of filters = M). These output activations will be the input to second convolution layer, hence the weights of the second convolution layer will have the following dimension :-
(filter height = m) x (filter width = m) x (input depth = M) x (number of filters = M)
Similarly, the dimension of weights in subsequent convolution layers will be m x m x M x M.
