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FCLayer.m
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FCLayer.m
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classdef FCLayer < nnet.layer.Layer
properties (Learnable)
% Layer learnable parameters
Weights;
Biases;
end
methods
function layer = FCLayer(inputDim,outputDim,name,initialWeights)
% layer = weightedAdditionLayer(numInputs,name) creates a
% Set number of inputs.
%layer.NumInputs = inputDim;
%layer.NumOutputs = numOutputs;
% Set layer name.
layer.Name = name;
% Set layer description.
layer.Description = "FC layer without bias neurons with " + inputDim + ...
" inputs";
% Initialize layer weights.
% layer.Weights = dlarray(randn(outputDim,inputDim).*0.0001);
% layer.Biases = dlarray(randn(outputDim,1).*0.0001);
stdGlorot = sqrt(2/(inputDim + outputDim));
layer.Weights = dlarray(rand(outputDim,inputDim).*stdGlorot);
layer.Biases = dlarray(rand(outputDim,1).*stdGlorot);
%layer.Biases = rand(outputDim,1);
if numel(initialWeights) ~= 0
layer.Weights = initialWeights;
end
end
function Z = predict(layer, X)
if ndims(X) >= 3
batchSize = size(X,4);
else
batchSize = size(X,ndims(X));
end
Z = (layer.Weights*squeeze(X)+(layer.Biases ));
end
end
end