MATLAB: How to change input values for weight classfication layer.

deep learningDeep Learning Toolboxneural networks

I am using weigth classfication fucntion which given as example in MATALAB documentaion.
But whenI use it in my network it gives error "Error using 'backwardLoss' in Layer weightedClassificationLayer. The function threw an error and could not be executed". I think the error is due to input value but i am not sure where to change these valuse. The weighted classification function works well according to input valuse assigned in example.
the code I am using for weighted classification function
classdef weightedClassificationLayer < nnet.layer.ClassificationLayer
% Row vector of weights corresponding to the classes in the
% training data.
function layer = weightedClassificationLayer(classWeights, name)
% layer = weightedClassificationLayer(classWeights) creates a
% weighted cross entropy loss layer. classWeights is a row
% vector of weights corresponding to the classes in the order
% that they appear in the training data.
% layer = weightedClassificationLayer(classWeights, name)
% additionally specifies the layer name.
% Set class weights.
layer.ClassWeights = classWeights;
% Set layer name.
if nargin == 2
layer.Name = name;
% Set layer description
layer.Description = 'Weighted cross entropy';
function loss = forwardLoss(layer, Y, T)
% loss = forwardLoss(layer, Y, T) returns the weighted cross
% entropy loss between the predictions Y and the training
% targets T.
N = size(Y,4);
Y = squeeze(Y);
T = squeeze(T);
W = layer.ClassWeights;
loss = -sum(W*(T.*log(Y)))/N;
function dLdY = backwardLoss(layer, Y, T)
% dLdX = backwardLoss(layer, Y, T) returns the derivatives of
% the weighted cross entropy loss with respect to the
% predictions Y.
[~,~,K,N] = size(Y);
Y = squeeze(Y);
T = squeeze(T);
W = layer.ClassWeights;
dLdY = -(W'.*T./Y)/N;
dLdY = reshape(dLdY,[1 1 K N]);

Best Answer

  • This is a way to initialize 'classWeights'
    classWeights = 1./countcats(YTrain);
    classWeights = classWeights'/mean(classWeights);
    and you can use it here:
    Network = [
    imageInputLayer([256 256 3],"Name","imageinput")
    convolution2dLayer([3 3],2,"Name","conv","Padding","same")
    I think this should solve the problem.