Hi Mirko Prezioso,
To connect the input of the optional branch to the input of the newly defined layer, you can simply add a separate input layer to the definition of the nested layer, then connect that input layer to the first layer of the existing network. we can define a separate input layer using imageInputLayer() with the appropriate input size and name. Then define the internal network and optional skip-connection in a similar manner as the example in the MATLAB documentation. A sample implementation is given below,
% sample implementation
function layer = nestedConvBlock(numFilters, includeSkipConvolution, stride)
% Define the internal network
convBlock = [
convolution2dLayer(3, numFilters, 'Padding', 'same', 'Stride', stride)
batchNormalizationLayer
reluLayer
convolution2dLayer(3, numFilters, 'Padding', 'same')
batchNormalizationLayer];
% Define the optional skip-connection
if includeSkipConvolution
skipConvolution = [
convolution2dLayer(1, numFilters, 'Stride', stride, 'Name', 'skipConv')
batchNormalizationLayer('Name', 'bnSkip')];
end
% Define the nested layer with input connectivity
input_layer = imageInputLayer([32 32 3], 'Name', 'input');
output_layer = convolution2dLayer(3,16, 'Padding', 'same');
lgraph = layerGraph(input_layer);
lgraph = addLayers(lgraph, convBlock);
lgraph = connectLayers(lgraph, 'input', 'convBlock/in');
if includeSkipConnection
lgraph = addLayers(lgraph, skipConvolution);
lgraph = connectLayers(lgraph, 'input', 'skipConv/in');
lgraph = connectLayers(lgraph, 'convBlock/bn_2', 'skipConv/bnSkip');
lgraph = connectLayers(lgraph, 'convBlock/bn_2', 'sum/in2');
layer = dlnetwork(lgraph, 'OutputNames', 'sum');
else
lgraph = connectLayers(lgraph, 'convBlock/bn_2', 'addition/in2');
layer = dlnetwork(lgraph, 'OutputNames', 'addition');
end
Keep in mind that this is just one way to connect the input of an optional branch to the input of a nested deep learning layer. Depending on the specifics of your implementation, there may be other ways to do this.
