mean squared logarithmic error loss function

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

Answer 1

[Sahithi Kanumarlapudi]

It is already answered here