tf.contrib.training.bucket()

tf.contrib.training.bucket(tensors, which_bucket, batch_size, num_buckets, num_threads=1, capacity=32, shapes=None, dynamic_pad=False, allow_smaller_final_batch=False, keep_input=None, shared_name=None, name=None)

Lazy bucketing of input tensors according to which_bucket.

The argument tensors can be a list or a dictionary of tensors. The value returned by the function will be of the same type as tensors.

The tensors entering this function are put into the bucket given by which_bucket. Each bucket has its own queue. When a bucket contains batch_size elements, this minibatch is pushed onto a top queue. The tensors returned from this function are a the result of dequeueing the next minibatch from this top queue.

This function is implemented using several queues. A QueueRunner for the queues is added to the current Graph's QUEUE_RUNNER collection.

As the returned tensors are the result of of a dequeue operation, evaluating them will throw a tf.errors.OutOfRangeError when the input queue is exhausted. If these tensors are feeding another input queue, its queue runner will catch this exception, however, if they are used in your main thread you are responsible for catching this yourself.

N.B.: If dynamic_pad is False, you must ensure that either (i) the shapes argument is passed, or (ii) all of the tensors in tensors must have fully-defined shapes. ValueError will be raised if neither of these conditions holds.

If dynamic_pad is True, it is sufficient that the rank of the tensors is known, but individual dimensions may have shape None. In this case, for each enqueue the dimensions with value None may have a variable length; upon dequeue, the output tensors will be padded on the right to the maximum shape of the tensors in the current minibatch. For numbers, this padding takes value 0. For strings, this padding is the empty string. See PaddingFIFOQueue for more info.

If allow_smaller_final_batch is True, a smaller batch value than batch_size is returned when the queues are closed and there are not enough elements to fill the batch, otherwise the pending elements are discarded. In addition, all output tensors' static shapes, as accessed via the get_shape() method will have a 0th Dimension value of None, and operations that depend on fixed batch_size would fail.

Args:

• tensors: The list or dictionary of tensors, representing a single element, to bucket. Nested lists are not supported.
• which_bucket: An int32 scalar Tensor taking a value in [0, num_buckets).
• batch_size: The new batch size pulled from the queue (python int or int32 scalar).
• num_buckets: A python integer, the number of buckets.
• 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.
• 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: (Optional). 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. The default behavior is to assume keep_input=True.
• 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 (bucket, outputs) where bucket is a int32 scalar tensor and outputs is a list or dictionary of batched outputs corresponding to elements of tensors. Every step will receive a new bucket of outputs.

Raises:

• ValueError: If the shapes are not specified, and cannot be inferred from the elements of tensors.