Weighting Classes in a Binary Classification Neural Network

I am building a binary classification neural network. The last 3 layers of my CNN architecture are the following:

fullyConnectedLayer(2, 'Name', 'fc1');
softmaxLayer
classificationLayer

Currently, the classificationLayer uses a crossentropyex loss function, but this loss function weights the binary classes (0, 1) the same. Unfortunately, in my total data is have substantially less information about the 0 class than about the 1 class.

As a result, I want to weight the loss function to penalize misclassifying the 0 class more, with classWeights proportional to 1/(class frequency).

I noted that there is a way to weight classes in the pixelClassificationLayer but not the general classificationLayer, which I would be using as I am working on a classification problem.

How can I add class weights to my loss function for training?

[Samreen Aziz]

Hi Arjun,

Did you end up ever figuring this out?

Facing a similar problem.

Regards,

Samreen

[Arjun Desai]

Hi Samreen,

Unfortunately i could not find a good work around. might be something to pitch for future development.

Arjun

[Eugene Alexander]

Please take a look at Define Custom Weighted Classification Layer and the example on Speech Command Recognition using Deep Learning. I am trying it right now on a binary classification problem.