MATLAB: How to change the softmax layer with a custom one in classification problems using trainNetwork

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I am using Convolutional Neural Networks for deep learning classification in MATLAB R2018b, and I would like to use a custom softmax layer instead of the default one.
I tried to build a custom softmax layer using the Intermediate Layer Template present in Define Custom Deep Learning Layers, but when I train the net with trainNetwork I get the following error:
Error using trainNetwork (line xxx)
Invalid network.
Caused by:
Layer 'WeightedClass': Missing softmax layer. A classification layer must be preceded by a softmax layer.
This is the code which defines the custom softmax layer:
classdef mySoftmaxLayer < nnet.layer.Layer
% Custom softmax layer.
properties (Learnable)
% Layer learnable parameters.
function layer = mySoftmaxLayer(name)
% layer = mySoftmaxLayer(name) creates a layer
% and specifies the layer name.
% Set layer name.
layer.Name = name;
% Set layer description.
layer.Description = "My softmax layer";
function Z = predict(layer, X)
% Z = predict(layer, X) forwards the input data X through the
% layer and outputs the result Z.
Z = myFunctionSoftmax(X);
function dLdX = backward(layer, X, Z, dLdZ, memory)
% dLdX = backward(layer, X, Z, dLdZ, memory)
% backward propagates the derivative of the loss function
% through the layer.
% Inputs:
% layer - Layer to backward propagate through
% X - Input data
% Z - Output of layer forward function
% dLdZ - Gradient propagated from the deeper layer
% memory - Memory value which can be used in backward
% propagation
% Outputs:
% dLdX - Derivative of the loss with respect to the
% input data
dLdX = myDerivativeSoftmax(X) .* dLdZ;
This is the code in which the network is defined and trained:
%% Define the CNN architecture and training options
layers = [
imageInputLayer([height width channels],'Name','Input');
convolution2DLayer([height winSize],numFilters,'Name','Conv');
options = trainingOptions(...
%% Train the CNN
net = trainNetwork(XTrain,YTrain,layers,options);

Best Answer

  • Hi Davide,
    In order to use a softmax layer before classification layer, you could define the ClassificationLayer as a custom regression output layer. Feel free to refer to the following documentation to create a custom regression output layer:
    To clarify further, you would need to have both the custom softmax layer, defined with
    And the classification layer defined as a custom regression output layer that behaves the same as the classification output layer but does not require a softmaxLayer.
    Hope this helps.
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