Defined in tensorflow/contrib/training/python/training/

See the guide: Training (contrib) > Bucketing

Lazy bucketing of inputs according to their length.

This method calls 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.


  • 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.


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.


  • 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.