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Is it possible to use class_weights with a one-hot encoding?

I've tried sparse_categorical_crossentropy and, for some reason, it's significantly worse than my classic categorical_crossentropy with one hot encoding model.

This is how I'm calculating the class_weights with sparse:

unique_class_weights = np.unique(labels)
class_weights = class_weight.compute_class_weight('balanced', unique_class_weights, labels)
class_weights_dict = { unique_class_weights[i]: w for i,w in enumerate(class_weights) }

Training like:

full_model.compile(loss='sparse_categorical_crossentropy',
                   optimizer='rmsprop',
                   metrics=['accuracy'])


full_model.fit_generator(line_generator(data_train, labels_train),
                         validation_data=line_generator(data_test, labels_test),
                         validation_steps=1,
                         steps_per_epoch=len(data)/GENERATOR_BATCH_SIZE,
                         class_weight=class_weights_dict,
                         epochs=1)
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I'm not allowed to comment, but have you tried using the numpy array that you get from class_weight.compute_class_weight(), rather than converting it to a dict? I've always skipped that part, and in your case I would say class_weight=class_weights. Sorry if I'm suggesting something you already ruled out. Good luck.

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  • $\begingroup$ I have tried that. Unfortunately, I couldn't find an official reference that it's a correct approach. On Github the official examples are using a dict, and there are some issues where it's been said that one hot encoding and class weights don't work. So I'm confused about this to be honest. I don't know. $\endgroup$ – MB. Nov 14 '17 at 6:15
  • $\begingroup$ Good point. It looks like the code called by fit_generator actually checks for a dict, then converts it to a numpy array. Hope you find your answer soon. $\endgroup$ – Hunter McGushion Nov 14 '17 at 6:34

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