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:
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?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.
The order of applying the Dataset.shuffle()
and Dataset.batch()
transformations can have an impact on the resulting dataset:
Applying Dataset.shuffle()
before Dataset.batch()
:
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.Applying Dataset.shuffle()
after Dataset.batch()
:
Dataset.shuffle()
after Dataset.batch()
, the shuffling operation is applied to the entire batches, rather than individual elements.The order of applying the Dataset.prefetch()
and Dataset.batch()
transformations can affect the behavior and performance of the dataset:
Applying Dataset.prefetch()
before Dataset.batch()
:
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.Applying Dataset.prefetch()
after Dataset.batch()
:
Dataset.prefetch()
after Dataset.batch()
, the prefetching operation is performed on entire batches of data, rather than individual elements.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.