python - How to weight classes using fit_generator() in Keras?

Question

I am trying to use keras to fit a CNN model to classify images. The data set has much more images form certain classes, so its unbalanced.

I read different thing on how to weight the loss to account for this in Keras, e.g.: https://datascience.stackexchange.com/questions/13490/how-to-set-class-weights-for-imbalanced-classes-in-keras, which is nicely explained. But, its always explaining for the fit() function, not the fit_generator() one.

Indeed, in the fit_generator() function we dont have the 'class_weights' parameter, but instead we have 'weighted_metrics', which I dont understand its description: "weighted_metrics: List of metrics to be evaluated and weighted by sample_weight or class_weight during training and testing."

How can I pass from 'class_weights' to 'weighted_metrics'? Would any one have a simple example?

Answer 1

We have class_weight in fit_generator (Keras v.2.2.2) According to docs:

Class_weight: Optional dictionary mapping class indices (integers) to a weight (float) value, used for weighting the loss function (during training only). This can be useful to tell the model to "pay more attention" to samples from an under-represented class.

Assume you have two classes [positive and negative], you can pass class_weight to fit_generator with:

model.fit_generator(gen,class_weight=[0.7,1.3])