tf.contrib.data.bucket_by_sequence_length

tf.contrib.data.bucket_by_sequence_length(
    element_length_func,
    bucket_boundaries,
    bucket_batch_sizes,
    padded_shapes=None,
    padding_values=None,
    pad_to_bucket_boundary=False
)

Defined in tensorflow/contrib/data/python/ops/grouping.py.

A transformation that buckets elements in a Dataset by length.

Elements of the Dataset are grouped together by length and then are padded and batched.

This is useful for sequence tasks in which the elements have variable length. Grouping together elements that have similar lengths reduces the total fraction of padding in a batch which increases training step efficiency.

Args:

• element_length_func: function from element in Dataset to tf.int64, determines the length of the element, which will determine the bucket it goes into.
• bucket_boundaries: list<int>, upper length boundaries of the buckets.
• bucket_batch_sizes: list<int>, batch size per bucket. Length should be len(bucket_boundaries) + 1.
• padded_shapes: Nested structure of tf.TensorShape to pass to tf.data.Dataset.padded_batch. If not provided, will use dataset.output_shapes, which will result in variable length dimensions being padded out to the maximum length in each batch.
• padding_values: Values to pad with, passed to tf.data.Dataset.padded_batch. Defaults to padding with 0.
• pad_to_bucket_boundary: bool, if False, will pad dimensions with unknown size to maximum length in batch. If True, will pad dimensions with unknown size to bucket boundary, and caller must ensure that the source Dataset does not contain any elements with length longer than max(bucket_boundaries).

Returns:

A Dataset transformation function, which can be passed to tf.data.Dataset.apply.

Raises:

• ValueError: if len(bucket_batch_sizes) != len(bucket_boundaries) + 1.