How to make use of class_weights to calculated custom loss fuction while using custom training loop (i.e. not using .fit )

Question

I have written my custom training loop using tf.GradientTape(). My data has 2 classes. The classes are not balanced; class1 data contributes almost 80% and class2 contributes remaining 20%. Therefore in order to remove this imbalance I was trying to write custom loss function which will take into account this imbalance and apply the corresponding class weights and calculate the loss. i.e. I want to use the class_weights = [0.2, 0.8]. I am not able to find similar examples.

However all the examples I am seeing are using model.fit approach where its easier to pass the class_weights. I am not able to find out the example which uses class_weights with custom training loop using tf.GradientTape.

I did go through the suggestions of using sample_weight, however I don't have the data where in I can specify the weights for samples, therefore my preference is to use class weight.

I am using BinaryCrossentropy loss as loss function but I want to change the loss based on the class_weights. That's where I am stuck, how to tell BinaryCrossentropy to consider the class_weights.

Is my approach of using custom loss function correct or there is better way to make use of class_weights while training with custom training loop (not using model.fit)?

Answer 1

you can write your own loss function. in that loss function call BinaryCrossentropy and then multiply the result in the weight you want and return that