How to get classes from generator in case of Shuffle=True

I’ve 10100 images split into 101 folders(classes) that I want to store as single dataset using keras datagen.flow_from_directory.

Here is the code

datagen = ImageDataGenerator(rescale=1./255)

generator = datagen.flow_from_directory(
        train_data_dir,
        target_size=(150, 150),
        batch_size=12,
        class_mode='sparse',
        shuffle=True)

However I want to shuffle the data while doing so,hence ideally generator.classes output should have been something like this [0, 4, 5, 3, 2 . . …<some random number between 0-100>] …and 101 such column each with 100 elements vertically one under other.However the actual class out put isn’t showing any random fashion and rather showing the value like this 0, 0, . 100 such 0’s followed by 100 such 1’s …upto 100 such 100.hence no random fashion is seen. Any idea whether this is a bug or there is a work around for this.

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[jwc-rad]

This works for both shuffle=False and shuffle=True:

for batch_x, batch_y in generator:
        # batch_x contains a batch of images
        # batch_y contains a batch of classes in form of one-hots

Hope this helps.

[varun-bankiti]

generator.classes gives the class assigned to each sample based on the sorted order of folder names, you can check it here, It is just a list of length nb_samples (in your case 10100) with each field having sample’s class index, they are not shuffled at this point.

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.

Hope this helps.