Function batch

tensorflow/python/training/input.py:929–1020

Creates batches of tensors in `tensors`. 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`. This function is implemented using a queue. A `QueueRunner` for the queue is added to the current `Graph`'s

(tensors, batch_size, num_threads=1, capacity=32,
          enqueue_many=False, shapes=None, dynamic_pad=False,
          allow_smaller_final_batch=False, shared_name=None, name=None)

    "`tf.data.Dataset.batch(batch_size)` (or `padded_batch(...)` if "
    "`dynamic_pad=True`).")
def batch(tensors, batch_size, num_threads=1, capacity=32,
          enqueue_many=False, shapes=None, dynamic_pad=False,
          allow_smaller_final_batch=False, shared_name=None, name=None):
  """Creates batches of tensors in `tensors`.

  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`.

  This function is implemented using a queue. A `QueueRunner` for the
  queue is added to the current `Graph`'s `QUEUE_RUNNER` collection.

  If `enqueue_many` is `False`, `tensors` is assumed to represent a single
  example.  An input tensor with shape `[x, y, z]` will be output as a tensor
  with shape `[batch_size, x, y, z]`.

  If `enqueue_many` is `True`, `tensors` is assumed to represent a batch of
  examples, where the first dimension is indexed by example, and all members of
  `tensors` should have the same size in the first dimension.  If an input
  tensor has shape `[*, x, y, z]`, the output will have shape `[batch_size, x,
  y, z]`.  The `capacity` argument controls the how long the prefetching is
  allowed to grow the queues.

  The returned operation is a dequeue operation and will throw
  `tf.errors.OutOfRangeError` if the input queue is exhausted. If this
  operation is feeding another input queue, its queue runner will catch
  this exception, however, if this operation is 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 queue is 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
  `shape` property will have a first `Dimension` value of `None`, and
  operations that depend on fixed batch_size would fail.

  Args:
    tensors: The list or dictionary of tensors to enqueue.
    batch_size: The new batch size pulled from the queue.
    num_threads: The number of threads enqueuing `tensors`.  The batching will
      be nondeterministic if `num_threads > 1`.
    capacity: An integer. The maximum number of elements in the queue.
    enqueue_many: Whether each tensor in `tensors` is a single example.
    shapes: (Optional) The shapes for each example.  Defaults to the
      inferred shapes for `tensors`.