What is the behavior of data augmentation layer?

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I am trying to understand the tensor flow data augmentation tutorial

In the following defined model

model = tf.keras.Sequential([
  resize_and_rescale,
  data_augmentation,
  layers.Conv2D(16, 3, padding='same', activation='relu'),
  layers.MaxPooling2D(),
  # Rest of your model
])

My understanding is no matter how many image rotate/zoom/transform defined in data_augmentation. This data_augmentation layer output just 1 image from 1 input image, am I correct?

I saw another post Does ImageDataGenerator add more images to my dataset?. Someone answers each epoch ImageDataGenerator will create different images, is that the same behavior here?

Otherwise, it is just the same transformed image trained epoch after epoch, which makes no sense.

keras tensorflow
1 Answers

Yes! Data augmentation layer would just transform images and return same shape as input (batch_size, *image_dims). But, due to randomisation in data augmentation layer, you are likely to get a different output each time that layer is called. For instance, in linked tutorial, random rotate or zoom is applied with a 20% chance, in addition the zoom factor and rotation angle are randomly selected(within specified limits) each time that layer is called.

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