Difference between tf.data.Datasets.repeat(EPOCHS) vs model.fit epochs=EPOCHS

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While training, I set epochs to number of times to iterate over the data. I was wondering what is the use of tf.data.Datasets.repeat(EPOCHS) when I can already do the same thing with model.fit(train_dataset,epochs=EPOCHS)?

tf.data.dataset tensorflow-datasets tensorflow2.0 tensorflow
2 Answers

It does work slightly different.

Let's pick 2 different examples.

  1. dataset.repeat(20) and model.fit(epochs=10)
  2. dataset.repeat(10) and model.fit(epochs=20)

Let's also assume that you have a dataset with 100 records.

If you pick option 1, each epoch will have 2,000 records. You will be "checking" how your model is improving after passing 2,000 records thru your model and you will do that 10 times.

If you choose option 2, each epoch will have 1,000 records. You will be evaluating how your model is improving after pushing 1,000 records and you will do that 20 times.

In both options, the total number of records that you will use for training is the same but the "time" when you evaluate, log, etc the behavior of your model is different.

user_39261520

tf.data.Datasets.repeat() can be useful for data augmentation on tf.data.Datasets in the case of image data .

Suppose you want to increase the number of images in the training dataset, using random transformations then repeating training dataset count times and apply random transformations as shown below

train_dataset = (
    train_dataset
    .map(resize, num_parallel_calls=AUTOTUNE)
    .map(rescale, num_parallel_calls=AUTOTUNE)
    .map(onehot, num_parallel_calls=AUTOTUNE)
    .shuffle(BUFFER_SIZE, reshuffle_each_iteration=True)
    .batch(BATCH_SIZE)
    .repeat(count=5)
    .map(random_flip, num_parallel_calls=AUTOTUNE)
    .map(random_rotate, num_parallel_calls=AUTOTUNE)
    .prefetch(buffer_size=AUTOTUNE)
)

Without repeat() method you have to create copies of dataset, apply transformations seperately and then concatenate datasets. But using repeat() simplifies this, also takes advantage of method chaining and have a neat looking code.

More on data augmentation : https://www.tensorflow.org/tutorials/images/data_augmentation#apply_augmentation_to_a_dataset

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