Multiple softmax vectors in output layer of neural network using softmaxLayer
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Isuru Rathnayaka
le 12 Déc 2021
Commenté : Christian Holz
le 14 Juin 2023
I'm using deep learning toolbox in MATLAB 2021a. And the neural network that I'm trying to build has multiple softmax vectors in output layer. (e.g. 10 softmax vectors of length 8). That is, the calculation is similar to how in-built softmax() function applies to each column of a matrix.
e.g.
>> a = randn(2,2)
a =
-1.1803 0.2963
1.6926 -0.1352
>> softmax(a)
ans =
0.0535 0.6062
0.9465 0.3938
However, I couldn't find a way to do this with softmaxLayer.
My code looks like this.
layersDNN = [
featureInputLayer(numInputs, 'Name', 'in')
fullyConnectedLayer(numInputs*2, 'Name', 'fc1')
batchNormalizationLayer('Name', 'bn1')
reluLayer('Name', 'relu1')
fullyConnectedLayer(numInputs*8, 'Name', 'fc2')
softmaxLayer('Name', 'sm1')
];
I'm trying to get the softmaxLayer to divide numInputs*8 nodes in last layer to numInputs vectors of length 8 and apply softmax function separately.
Alternatively I'm trying to remove softmaxLayer and apply softmax to reshaped output of network. Something like this.
lgraphDNN = layerGraph(layersDNN);
dlnetDNN = dlnetwork(lgraphDNN);
out1 = forward(dlnetDNN, X);
out2 = reshape(out1, [numInputs, 8]);
pred = softmax(out2);
% calculate loss, gradients etc.
I'm not sure if this is a good solution. I'd like to know if there's a way to do this using softmaxLayer, since the requirement doesn't feel like an extreme case.
2 commentaires
Abolfazl Chaman Motlagh
le 12 Déc 2021
so what is the problem? did you face any error? did you test your last idea?
Réponse acceptée
Prachi Kulkarni
le 10 Jan 2022
Modifié(e) : Prachi Kulkarni
le 12 Jan 2022
Hi,
From the R2021b release onwards, you can create numInputs number of fully connected layers, each with output size 8. Every fully connected layer can then be connected to its own softmax layer.
The outputs from the softmax layers can be concatenated using a concatenation layer and then passed on to the output layer.
2 commentaires
Christian Holz
le 14 Juin 2023
Hello all,
a question regarding the proposed solution: were you able to implement it? Can you show the details?
Thank you
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