Keras: class weights (class_weight) for one-hot encoding

Question

I'd like to use class_weight argument in keras model.fit to handle the imbalanced training data. By looking at some documents, I understood we can pass a dictionary like this:

class_weight = {0 : 1,     1: 1,     2: 5} 

(In this example, class-2 will get higher penalty in the loss function.)

The problem is that my network's output has one-hot encoding i.e. class-0 = (1, 0, 0), class-1 = (0, 1, 0), and class-3 = (0, 0, 1).

How can we use the class_weight for one-hot encoded output?

By looking at some codes in Keras, it looks like _feed_output_names contain a list of output classes, but in my case, model.output_names/model._feed_output_names returns ['dense_1']

Related: How to set class weights for imbalanced classes in Keras?

Answer 1

Here's a solution that's a bit shorter and faster. If your one-hot encoded y is a np.array:

import numpy as np from sklearn.utils.class_weight import compute_class_weight  y_integers = np.argmax(y, axis=1) class_weights = compute_class_weight('balanced', np.unique(y_integers), y_integers) d_class_weights = dict(enumerate(class_weights)) 

d_class_weights can then be passed to class_weight in .fit.