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I understand that predict_generator outputs probabilities. To get the class, I just then find the index for the greatest probability and that will be the most probable class. However I find that after doing this, I get a different output than if I were to call predict_classes. I do not understand why. Can someone explain this please?

TriniPhantom
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1 Answers1

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Generator in Keras uses glob to list folders which are alphabetically sorted, you can get classes being used during training using 

# save classes to JSON
class_json = json.dumps(train_generator.class_indices)
with open("class.json", "w") as class_file:
    class_file.write(class_json)

The samples are shuffled with in the batch generator(here) so that when a batch is requested by the fit_generator or evaluate_generator random samples are given.

Another possibility if this is being done on images is not to use rescale=1./255 in ImageDataGenerator as mentioned in https://github.com/fchollet/keras/issues/3477

Hope that help!

Shabaz Patel
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