here is the code:
classdef gaussianNoiseLayer < nnet.layer.Layer
% gaussianNoiseLayer Gaussian noise layer
% A Gaussian noise layer adds random Gaussian noise to the input.
%
% To create a Gaussian noise layer, use
% layer = gaussianNoiseLayer(sigma, name)
properties
% Standard deviation.
Sigma
end
methods
function layer = gaussianNoiseLayer(sigma, name)
% layer = gaussianNoiseLayer(sigma,name) creates a Gaussian
% noise layer and specifies the standard deviation and layer
% name.
layer.Name = name;
layer.Description = ...
"Gaussian noise with standard deviation " + sigma;
layer.Type = "Gaussian Noise";
layer.Sigma = sigma;
end
function Z = predict(layer, X)
% Z = predict(layer, X) forwards the input data X through the
% layer for prediction and outputs the result Z.
% At prediction time, the output is equal to the input.
Z = X;
end
function [Z, memory] = forward(layer, X)
% Z = forward(layer, X) forwards the input data X through the
% layer and outputs the result Z.
% At training time, the layer adds Gaussian noise to the input.
sigma = layer.Sigma;
noise = randn(size(X)) * sigma;
Z = X + noise;
memory = [];
end
function dLdX = backward(layer, X, Z, dLdZ, memory)
% [dLdX, dLdAlpha] = backward(layer, X, Z, dLdZ, memory)
% backward propagates the derivative of the loss function
% through the layer.
% Since the layer adds a random constant, the derivative dLdX
% is equal to dLdZ.
dLdX = dLdZ;
end
end
end
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