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tf.contrib.training.bucket_by_sequence_length

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Lazy bucketing of inputs according to their length.

tf.contrib.training.bucket_by_sequence_length(
    input_length,
    tensors,
    batch_size,
    bucket_boundaries,
    num_threads=1,
    capacity=32,
    bucket_capacities=None,
    shapes=None,
    dynamic_pad=False,
    allow_smaller_final_batch=False,
    keep_input=True,
    shared_name=None,
    name=None
)

This method calls tf.contrib.training.bucket under the hood, after first subdividing the bucket boundaries into separate buckets and identifying which bucket the given input_length belongs to. See the documentation for which_bucket for details of the other arguments.

Args:

  • input_length: int32 scalar Tensor, the sequence length of tensors.
  • tensors: The list or dictionary of tensors, representing a single element, to bucket. Nested lists are not supported.
  • batch_size: The new batch size pulled from the queue (all queues will have the same size). If a list is passed in then each bucket will have a different batch_size. (python int, int32 scalar or iterable of integers of length num_buckets).
  • bucket_boundaries: int list, increasing non-negative numbers. The edges of the buckets to use when bucketing tensors. Two extra buckets are created, one for input_length < bucket_boundaries[0] and one for input_length >= bucket_boundaries[-1].
  • num_threads: An integer. The number of threads enqueuing tensors.
  • capacity: An integer. The maximum number of minibatches in the top queue, and also the maximum number of elements within each bucket.
  • bucket_capacities: (Optional) None or a list of integers, the capacities of each bucket. If None, capacity is used (default). If specified, it must be a list of integers of length one larger than bucket_boundaries. Its i-th element is used as capacity for the i-th bucket queue.
  • shapes: (Optional) The shapes for each example. Defaults to the inferred shapes for tensors.
  • dynamic_pad: Boolean. Allow variable dimensions in input shapes. The given dimensions are padded upon dequeue so that tensors within a batch have the same shapes.
  • allow_smaller_final_batch: (Optional) Boolean. If True, allow the final batches to be smaller if there are insufficient items left in the queues.
  • keep_input: A bool scalar Tensor. If provided, this tensor controls whether the input is added to the queue or not. If it evaluates True, then tensors are added to the bucket; otherwise they are dropped. This tensor essentially acts as a filtering mechanism.
  • shared_name: (Optional). If set, the queues will be shared under the given name across multiple sessions.
  • name: (Optional) A name for the operations.

Returns:

A tuple (sequence_length, outputs) where sequence_length is a 1-D Tensor of size batch_size and outputs is a list or dictionary of batched, bucketed, outputs corresponding to elements of tensors.

Raises:

  • TypeError: if bucket_boundaries is not a list of python integers.
  • ValueError: if bucket_boundaries is empty or contains non-increasing values or if batch_size is a list and it's length doesn't equal the number of buckets.