Keras2 ImageDataGenerator or TensorFlow tf.data?

Question

With Keras2 being implemented into TensorFlow and TensorFlow 2.0 on the horizon, should you use Keras ImageDataGenerator with e.g, flow_from_directory or tf.data from TensorFlow which also can be used with fit_genearator of Keras now?

Will both methods will have their place by serving a different purpose or will tf.data be the new way to go and Keras generators deprecated in the future?

Thanks, I would like to take the path which keeps me up to date a bit longer in this fast moving field.

Answer 1

Alongside custom defined Python generators, you can wrap the ImageDataGenerator from Keras inside tf.data.

The following snippets are taken from the TensorFlow 2.0 documentation.

img_gen = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1./255, rotation_range=20)
ds = tf.data.Dataset.from_generator(
    img_gen.flow_from_directory, args=[flowers], 
    output_types=(tf.float32, tf.float32), 
    output_shapes = ([32,256,256,3],[32,5])
)

Therefore, one can still use the typical Keras ImageDataGenerator, you just need to wrap it into a tf.data.Dataset like above.