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I am currently working with a quite large image-dataset and I loaded it using ImageDataGenerator from tensorflow.keras in python. As the classification of my data is very imbalanced I wanted to do a stratified train-test-split to possibly achieve a higher accuracy.

I know how to do a simple random train-test-split using ImageDataGenerator but I couldn't find any equivalent of the stratified train_test_split you can do in sklearn.

Is there any way to stratified train-test-split a tensorflow.data.Dataset? And if not, how do you deal with large imbalanced datasets? I would very appreciate your help!

Here is the relevant code:

from tensorflow.keras.preprocessing.image import ImageDataGenerator

datagen = ImageDataGenerator()
dataset = datagen.flow_from_directory(
    path_images, 
    target_size=(ImageHeight, ImageWidth), 
    color_mode='rgb', 
    class_mode='sparse', 
    batch_size=BatchSize, 
    shuffle=True, 
    seed=Seed,
)
user18398060
  • 31
  • 1

1 Answers1

0

flow( x, y=None, batch_size=32, shuffle=True, sample_weight=None, seed=None, save_to_dir=None, save_prefix='', save_format='png', ignore_class_split=False, subset=None )

Pramod Sharma
  • 26
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