How to use which supports generators (after fit_generator deprecation)

As mentioned in the documentation (emphasis mine):

x: Input data. It could be

  • A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs).
  • A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs).
  • A dict mapping input names to the corresponding array/tensors, if the model has named inputs.
  • A dataset. Should return a tuple of either (inputs, targets) or (inputs, targets, sample_weights)
  • A generator or keras.utils.Sequence returning (inputs, targets) or (inputs, targets, sample weights). A more detailed description of unpacking behavior for iterator types (Dataset, generator, Sequence) is given below.

you can simply pass the generator to as similar to Model.fit_generator

data_gen_train = ImageDataGenerator(rescale=1/255.)

data_gen_valid = ImageDataGenerator(rescale=1/255.)

train_generator = data_gen_train.flow_from_directory(train_dir, target_size=(128,128), batch_size=128, class_mode="binary")

valid_generator = data_gen_valid.flow_from_directory(validation_dir, target_size=(128,128), batch_size=128, class_mode="binary"), epochs=2, validation_data=valid_generator) 

Model.fit_generator is deprecated starting from tensorflow 2.1.0 which is currently is in rc1. You can find the documentation for tf-2.1.0-rc1 here:

As you can see the first argument of the can take a generator so just pass it your generator.