fit_generator issue using Neural Structured learning

Question

I passed two days trying to use Neural Structured language to adapt into me CNN Model I use ImageDataGenerator and flow_from_directory when I use model.fit_generator I got an error message: ValueError:

When passing input data as arrays, do not specify steps_per_epoch/steps argument. Please use batch_size instead.

I use Keras 2.3.1 and TensorFlow 2.0 as backend

This is a snipped of my code:

num_classes = 4
img_rows, img_cols = 224, 224
batch_size = 16

train_datagen = ImageDataGenerator(
  rescale=1./255,
  rotation_range=30,
  width_shift_range=0.3,
  height_shift_range=0.3,
  horizontal_flip=True,
  fill_mode='nearest')

  validation_datagen = ImageDataGenerator(rescale=1./255)
  train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(img_rows, img_cols),
    batch_size=batch_size, shuffle=True,
    class_mode='categorical')

    validation_generator = validation_datagen.flow_from_directory(
    validation_data_dir,
    target_size=(img_rows, img_cols),
    batch_size=batch_size, shuffle=True,
    class_mode='categorical')

    def vgg():
      model1 = Sequential([ ])
      return model1

base_model = vgg()

I adapte Datagenerated from (x,y) format to a dictionary format

def convert_training_data_generator():
   for x ,y in train_generator:
    return {'feature': x, 'label':y}

def convert_testing_data_generator():
   for x ,y in validation_generator:
    return {'feature': x, 'label': y}

adv_config = nsl.configs.make_adv_reg_config(multiplier=0.2, adv_step_size=0.05)
model = nsl.keras.AdversarialRegularization(base_model, adv_config=adv_config)
train= convert_training_data_generator()
test= convert_testing_data_generator()

history = model.fit_generator(train,
   steps_per_epoch= nb_train_samples // batch_size,
    epochs = epochs,
    callbacks = callbacks,
    validation_data = test,
    validation_steps = nb_validation_samples // batch_size)

Answer 1

I think here there is the same error. Maybe you should consider using instead model.fit() function. You should define in that case your train input your train labels and the batch_size. In order to figure out the difference between fit and fit_generator, you can follow that link.