Deep Learning Layers, incorrect output

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Hon Wah Yeung
Hon Wah Yeung 2019-11-4
I have created the following layer. Outside the layer, I have tried that if I have input1 size of [68,68,1], input2 size of [68,1,3] and input3 size of [68,17,3], I should be able to get back Z1 of size [17,17,1] and Z2 of size [17,1,3]. But instead when I try to analyze the layer, it gave me back scalars for Z1 and Z2 (out1 and out2 both equals to 17), I don't know what I have done wrong.
classdef SpectralPoolingLayer < nnet.layer.Layer
% Example custom weighted addition layer.
properties
end
properties (Learnable)
% Layer learnable parameters
% Scaling coefficient
end
methods
function layer = SpectralPoolingLayer(numInputs,numOutputs,name)
% layer = weightedAdditionLayer(numInputs,name) creates a
% weighted addition layer and specifies the number of inputs
% and the layer name.
% Set number of inputs.
layer.NumInputs = numInputs;
layer.NumOutputs = numOutputs;
% Set layer name.
layer.Name = name;
% Set layer description.
layer.Description = "SpectralPooling of " + numInputs + ...
" inputs";
% Initialize layer weights.
end
function [Z1,Z2] = predict(~, X1,X2,X3)
% Z = predict(layer, X1, ..., Xn) forwards the input data X1,
% ..., Xn through the layer and outputs the result Z.
% Initialize output
A = X1(:,:,1);
P = X3(:,:,1);
sz3 = size(X3);
Z1 = zeros([sz3(2),sz3(2),1],'like',X3);
sz = size(X2);
Z2 = zeros([sz3(2),1,sz(3)],'like',X2);
%start Implementing
Z1(:,:,1) = P'*A*P;
Z1 = max(0,Z1);
for i = 1:sz(3)
Z2(:,1,i) = P'*X2(:,1,i);
end
end
end
end

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