Ensure all original images are seen when using augmentation

General Discussion
,
#1

I am using tensorflow datasets for image data, created with image_dataset_from_directory, and then want to apply augmentation to train when fit is called:

from tensorflow.keras.layers import RandomFlip
#
train_root = \
  (data_path + train_dir + '/train/')
train = image_dataset_from_directory(
  train_root, 
  batch_size =  batch_size,
  shuffle = False,
  labels = 'inferred', 
  label_mode = 'categorical',
  color_mode = 'grayscale',
  image_size = (resize, resize),
  seed = random_seed)
#
val = image_dataset_from_directory(
  val_root, 
  batch_size =  batch_size,
  shuffle = False,
  labels = 'inferred', 
  label_mode = 'categorical',
  color_mode = 'grayscale',
  image_size = (resize, resize),
  seed = random_seed)
#
data_augmentation = tf.keras.Sequential([
  RandomFlip("horizontal_and_vertical")])
history = model.fit(train.map(lambda x, y: 
                              (data_augmentation(x), y)).
                    shuffle(buffer_size = train.cardinality() * batch_size,
                            seed = random_seed,
                            reshuffle_each_iteration = False),
                    validation_data = val,
                    callbacks = callbacks,
                    batch_size = batch_size,
                    epochs = epochs)

If I understand what happens when I map data_augmentation to the train dataset, some but not all the images are affected (flipped, in my case). As this is random, it occurred to me that it’s possible even after a large number of epochs that not all the original images will have been seen by the model, as they are being replaced by the flipped ones. I verified that when I used the above code, the number of batches remains the same, so I believe it is flipping images in place, not adding to the train data. Is there any way to ensure all the original images are seen:

#3

Hi @Blaine_Bateman, Instead of doing data augmentation during training i recommend you to do it before training and add the augmented images to the original images dataset and pass the concatenated dataset(original,augmented) to the training. Thank You.