Keras - class_weight vs sample_weights in the fit_generator

Question

In Keras (using TensorFlow as a backend) I am building a model which is working with a huge dataset that is having highly imbalanced classes (labels). To be able to run the training process, I created a generator which feeds chunks of data to the fit_generator.

According to the documentation for the fit_generator, the output of the generator can either be the tuple (inputs, targets) or the tuple (inputs, targets, sample_weights). Having that in mind, here are a few questions:

1. My understanding is that the class_weight regards the weights of all classes for the entire dataset whereas the sample_weights regards the weights of all classes for each individual chunk created by the generator. Is that correct? If not, can someone elaborate on the matter?
2. Is it necessary to give both the class_weight to the fit_generator and then the sample_weights as an output for each chunk? If yes, then why? If not then which one is better to give?
3. If I should give the sample_weights for each chunk, how do I map the weights if some of the classes are missing from a specific chunk? Let me give an example. In my overall dataset, I have 7 possible classes (labels). Because these classes are highly imbalanced, when I create smaller chunks of data as an output from the fit_generator, some of the classes are missing from the specific chunk. How should I create the sample_weights for these chunks?

Answer 1

My understanding is that the class_weight regards the weights of all classes for the entire dataset whereas the sample_weights regards the weights of all classes for each individual chunk created by the generator. Is that correct? If not, can someone elaborate on the matter?

class_weight affects the relative weight of each class in the calculation of the objective function. sample_weights, as the name suggests, allows further control of the relative weight of samples that belong to the same class.

Is it necessary to give both the class_weight to the fit_generator and then the sample_weights as an output for each chunk? If yes, then why? If not then which one is better to give?

It depends on your application. Class weights are useful when training on highly skewed data sets; for example, a classifier to detect fraudulent transactions. Sample weights are useful when you don't have equal confidence in the samples in your batch. A common example is performing regression on measurements with variable uncertainty.

If I should give the sample_weights for each chunk, how do I map the weights if some of the classes are missing from a specific chunk? Let me give an example. In my overall dataset, I have 7 possible classes (labels). Because these classes are highly imbalanced, when I create smaller chunks of data as an output from the fit_generator, some of the classes are missing from the specific chunk. How should I create the sample_weights for these chunks?

This is not an issue. sample_weights is defined on a per-sample basis and is independent from the class. For this reason, the documentation states that (inputs, targets, sample_weights) should be the same length.

The function _weighted_masked_objective in engine/training.py has an example of sample_weights are being applied.