tensorflowtf.data.dataset

Shuffle the Batches in tensorflow dataset


I was reading the tutorial English-to-Spanish translation with a sequence-to-sequence Transformer.

def make_dataset(pairs, batch_size=64):
    eng_texts, fra_texts = zip(*pairs)
    eng_texts = list(eng_texts)
    fra_texts = list(fra_texts)
    dataset = tf.data.Dataset.from_tensor_slices((eng_texts, fra_texts))
    dataset = dataset.batch(batch_size)
    dataset = dataset.map(format_dataset, num_parallel_calls=4)
    return dataset.shuffle(2048).prefetch(AUTOTUNE).cache()

specifically in this line dataset.shuffle(2048).prefetch(16).cache()

My questions:

  1. According to my knowledge 2048 here will be the number of data points that are stored in the buffer, not batches, but shuffling will be applied to batches, right?
  2. prefetch(16). The number of batches to be prefetched, right?

Edit: 3. Is map applied to batches each time it is fetched from the dataset or is it only applied the first time during training.


Solution

  • Question 1

    The order of applying the Dataset.shuffle() and Dataset.batch() transformations can have an impact on the resulting dataset:

    1. Applying Dataset.shuffle() before Dataset.batch():

      • When you apply Dataset.shuffle() before Dataset.batch(), the shuffling operation is applied to the individual elements of the dataset. This means that the order of the elements within each batch is randomized, but the batches themselves remain intact.
      • This can be useful when you want to randomize the order of individual elements while still maintaining the batch structure. It ensures that each batch contains randomly shuffled elements, but the relative order of elements within each batch remains consistent.
    2. Applying Dataset.shuffle() after Dataset.batch():

      • When you apply Dataset.shuffle() after Dataset.batch(), the shuffling operation is applied to the entire batches, rather than individual elements.
      • This means that the order of the batches themselves will be randomized, potentially leading to different batch compositions between epochs.
      • This can be useful when you want to shuffle the batches themselves, introducing a different distribution of data in each epoch. It can help in scenarios where you want to reduce the impact of the order of batches during training, which can be particularly relevant when dealing with sequential data.

    Question 2

    The order of applying the Dataset.prefetch() and Dataset.batch() transformations can affect the behavior and performance of the dataset:

    1. Applying Dataset.prefetch() before Dataset.batch():

      • When you apply Dataset.prefetch() before Dataset.batch(), the prefetching operation is performed on the individual elements of the dataset. This means that the next batch of elements is fetched and prepared in the background while the current batch is being processed by the model.
      • Prefetching before batching allows for overlapped execution, where the data preparation for the next batch is happening concurrently with the model's execution on the current batch. This can help to reduce idle time and improve the overall efficiency of data processing and model training.
      • This order is often recommended since prefetching before batching can lead to smoother pipeline execution and better GPU or CPU utilization.
    2. Applying Dataset.prefetch() after Dataset.batch():

      • When you apply Dataset.prefetch() after Dataset.batch(), the prefetching operation is performed on entire batches of data, rather than individual elements.
      • This means that multiple batches will be fetched and prepared in the background while the model is processing the current batch.
      • Prefetching after batching can still provide some performance benefits by overlapping the data preparation of multiple batches with the model's execution. However, it may not be as efficient as prefetching before batching because it works at the batch level rather than at the individual element level.

    Question 3

    If you want to apply a transformation once and reuse it across multiple epochs, you can explicitly cache the transformed dataset using the cache() method. This allows the transformed dataset to be stored in memory or on disk and reused in subsequent epochs without recomputing the transformation.