unable to incorporate own design Loss function in r2024a
Switching from r2023b to r2024 I made some changes in my Net (CNN). e.g. modified input/output and replace RegressionLayer with SoftmaxLayer, using trainnet function, etc.
I expected better performance, perspective compatibility (RegressionLayre is not more recommended) and have a vision of my Net optimization with use of Prune approach etc.
To the contrary to the previous version I am not able to involve my own Loss function (as it was done in previous version).
The (siplified) code is below, the used synthax was inspired by example:
https://www.mathworks.com/matlabcentral/answers/2100631-how-can-i-define-a-custom-loss-function-using-trainnet
The error message is:
Error using trainnet (line 46)
Error calling function during training.
Error in callMyLoss (line 55)
myTrainedNet = trainnet(Y,target,net, @(Y,target) myOwnLoss(name,Y,target),options);
Caused by:
Error using myOwnLoss
The specified superclass ‘nnet.layer.softmaxLayer’ contains a parse error, cannot be found on MATLAB’s
search
path, or is shadowed by another file with the same name.
Error in callMyLoss>@(Y,target)myOwnLoss(name,Y,target) (line 55)
myTrainedNet = trainnet(Y,target,net, @(Y,target) myOwnLoss(name,Y,target),options);
Error in nnet.internal.cnn.util.UserCodeException.fevalUserCode (line 11)
[varargout{1:nargout}] = feval(F, varargin{:});
classdef myOwnLoss < nnet.layer.softmaxLayer
% own Loss
methods
%function layer = sseClassificationLayer(name)
function layer = myOwnLoss(name)
% layer = sseClassificationLayer(name) creates a sum of squares
% error classification layer and specifies the layer name.
% Set layer name.
layer.Name = name;
% Set layer description.
layer.Description = ‘my own Loss v.2024a’;
end
function loss = forwardLoss(layer, Y, T)
%%% function loss = forwardLoss(Yo, To)
% loss = forwardLoss(layer, Y, T) returns the Tdiff loss between
% the predictions Y and the training targets T.
disp("myLoss");
aa=1;
% just something very simple
loss = sum(Y-T,’all’);
end
% original backwardLoss
function dX = backwardLoss(layer, Y, T)
numObservations = size( Y, 3);
dX = (Y – T)./numObservations;
end
end
end
%=======================eof=========================
classdef myOwnLoss < nnet.layer.softmaxLayer
% own Loss
methods
%function layer = sseClassificationLayer(name)
function layer = myOwnLoss(name)
% layer = sseClassificationLayer(name) creates a sum of squares
% error classification layer and specifies the layer name.
% Set layer name.
layer.Name = name;
% Set layer description.
layer.Description = ‘my own Loss v.2024a’;
end
function loss = forwardLoss(layer, Y, T)
%%% function loss = forwardLoss(Yo, To)
% loss = forwardLoss(layer, Y, T) returns the Tdiff loss between
% the predictions Y and the training targets T.
disp("myLoss");
aa=1;
% just something very simple
loss = sum(Y-T,’all’);
end
% original backwardLoss
function dX = backwardLoss(layer, Y, T)
numObservations = size( Y, 3);
dX = (Y – T)./numObservations;
end
end
end
%=======================eof=========================Switching from r2023b to r2024 I made some changes in my Net (CNN). e.g. modified input/output and replace RegressionLayer with SoftmaxLayer, using trainnet function, etc.
I expected better performance, perspective compatibility (RegressionLayre is not more recommended) and have a vision of my Net optimization with use of Prune approach etc.
To the contrary to the previous version I am not able to involve my own Loss function (as it was done in previous version).
The (siplified) code is below, the used synthax was inspired by example:
https://www.mathworks.com/matlabcentral/answers/2100631-how-can-i-define-a-custom-loss-function-using-trainnet
The error message is:
Error using trainnet (line 46)
Error calling function during training.
Error in callMyLoss (line 55)
myTrainedNet = trainnet(Y,target,net, @(Y,target) myOwnLoss(name,Y,target),options);
Caused by:
Error using myOwnLoss
The specified superclass ‘nnet.layer.softmaxLayer’ contains a parse error, cannot be found on MATLAB’s
search
path, or is shadowed by another file with the same name.
Error in callMyLoss>@(Y,target)myOwnLoss(name,Y,target) (line 55)
myTrainedNet = trainnet(Y,target,net, @(Y,target) myOwnLoss(name,Y,target),options);
Error in nnet.internal.cnn.util.UserCodeException.fevalUserCode (line 11)
[varargout{1:nargout}] = feval(F, varargin{:});
classdef myOwnLoss < nnet.layer.softmaxLayer
% own Loss
methods
%function layer = sseClassificationLayer(name)
function layer = myOwnLoss(name)
% layer = sseClassificationLayer(name) creates a sum of squares
% error classification layer and specifies the layer name.
% Set layer name.
layer.Name = name;
% Set layer description.
layer.Description = ‘my own Loss v.2024a’;
end
function loss = forwardLoss(layer, Y, T)
%%% function loss = forwardLoss(Yo, To)
% loss = forwardLoss(layer, Y, T) returns the Tdiff loss between
% the predictions Y and the training targets T.
disp("myLoss");
aa=1;
% just something very simple
loss = sum(Y-T,’all’);
end
% original backwardLoss
function dX = backwardLoss(layer, Y, T)
numObservations = size( Y, 3);
dX = (Y – T)./numObservations;
end
end
end
%=======================eof=========================
classdef myOwnLoss < nnet.layer.softmaxLayer
% own Loss
methods
%function layer = sseClassificationLayer(name)
function layer = myOwnLoss(name)
% layer = sseClassificationLayer(name) creates a sum of squares
% error classification layer and specifies the layer name.
% Set layer name.
layer.Name = name;
% Set layer description.
layer.Description = ‘my own Loss v.2024a’;
end
function loss = forwardLoss(layer, Y, T)
%%% function loss = forwardLoss(Yo, To)
% loss = forwardLoss(layer, Y, T) returns the Tdiff loss between
% the predictions Y and the training targets T.
disp("myLoss");
aa=1;
% just something very simple
loss = sum(Y-T,’all’);
end
% original backwardLoss
function dX = backwardLoss(layer, Y, T)
numObservations = size( Y, 3);
dX = (Y – T)./numObservations;
end
end
end
%=======================eof========================= Switching from r2023b to r2024 I made some changes in my Net (CNN). e.g. modified input/output and replace RegressionLayer with SoftmaxLayer, using trainnet function, etc.
I expected better performance, perspective compatibility (RegressionLayre is not more recommended) and have a vision of my Net optimization with use of Prune approach etc.
To the contrary to the previous version I am not able to involve my own Loss function (as it was done in previous version).
The (siplified) code is below, the used synthax was inspired by example:
https://www.mathworks.com/matlabcentral/answers/2100631-how-can-i-define-a-custom-loss-function-using-trainnet
The error message is:
Error using trainnet (line 46)
Error calling function during training.
Error in callMyLoss (line 55)
myTrainedNet = trainnet(Y,target,net, @(Y,target) myOwnLoss(name,Y,target),options);
Caused by:
Error using myOwnLoss
The specified superclass ‘nnet.layer.softmaxLayer’ contains a parse error, cannot be found on MATLAB’s
search
path, or is shadowed by another file with the same name.
Error in callMyLoss>@(Y,target)myOwnLoss(name,Y,target) (line 55)
myTrainedNet = trainnet(Y,target,net, @(Y,target) myOwnLoss(name,Y,target),options);
Error in nnet.internal.cnn.util.UserCodeException.fevalUserCode (line 11)
[varargout{1:nargout}] = feval(F, varargin{:});
classdef myOwnLoss < nnet.layer.softmaxLayer
% own Loss
methods
%function layer = sseClassificationLayer(name)
function layer = myOwnLoss(name)
% layer = sseClassificationLayer(name) creates a sum of squares
% error classification layer and specifies the layer name.
% Set layer name.
layer.Name = name;
% Set layer description.
layer.Description = ‘my own Loss v.2024a’;
end
function loss = forwardLoss(layer, Y, T)
%%% function loss = forwardLoss(Yo, To)
% loss = forwardLoss(layer, Y, T) returns the Tdiff loss between
% the predictions Y and the training targets T.
disp("myLoss");
aa=1;
% just something very simple
loss = sum(Y-T,’all’);
end
% original backwardLoss
function dX = backwardLoss(layer, Y, T)
numObservations = size( Y, 3);
dX = (Y – T)./numObservations;
end
end
end
%=======================eof=========================
classdef myOwnLoss < nnet.layer.softmaxLayer
% own Loss
methods
%function layer = sseClassificationLayer(name)
function layer = myOwnLoss(name)
% layer = sseClassificationLayer(name) creates a sum of squares
% error classification layer and specifies the layer name.
% Set layer name.
layer.Name = name;
% Set layer description.
layer.Description = ‘my own Loss v.2024a’;
end
function loss = forwardLoss(layer, Y, T)
%%% function loss = forwardLoss(Yo, To)
% loss = forwardLoss(layer, Y, T) returns the Tdiff loss between
% the predictions Y and the training targets T.
disp("myLoss");
aa=1;
% just something very simple
loss = sum(Y-T,’all’);
end
% original backwardLoss
function dX = backwardLoss(layer, Y, T)
numObservations = size( Y, 3);
dX = (Y – T)./numObservations;
end
end
end
%=======================eof========================= loss function, trainnet MATLAB Answers — New Questions
