mean squared logarithmic error loss function

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VICTOR CATALA
VICTOR CATALA 2019-7-5
回答： Sahithi Kanumarlapudi 2019-7-17
Hi.
I'm trying to write a MSLE regression layer with no success. Can you help me, please?
I have followed the template and suggested procedure but I can't make it work.
Thanks.
Here is my code:
classdef msleRegressionLayer < nnet.layer.RegressionLayer
% Custom regression layer with mean-squared-logarithmic-error loss.
methods
function layer = msleRegressionLayer(name)
% layer = msleRegressionLayer(name) creates a
% mean-squared-logarithmic-error regression layer and specifies the layer
% name.
% Set layer name.
layer.Name = name;
% Set layer description.
layer.Description = 'Mean squared logarithmic error';
end
function loss = forwardLoss(layer, Y, T)
% loss = forwardLoss(layer, Y, T) returns the MSLE loss between
% the predictions Y and the training targets T.
% Calculate MSLE.
R = size(Y,3);
%meanAbsoluteError = sum(abs(Y-T),3)/R;
msle=sum((log10((Y+1)./(T+1))).^2,3)/R;
% Take mean over mini-batch.
N = size(Y,4);
loss = sum(msle)/N;
end
function dLdY = backwardLoss(layer, Y, T)
% Returns the derivatives of the MSLE loss with respect to the predictions Y
R = size(Y,3);
N = size(Y,4);
dLdY = 2/(N*R)*(log10(Y+1)-log10(T+1))./(Y+1)*2.3;
end
end
end

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Sahithi Kanumarlapudi
It is already answered here

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