Function shuffle_batch

tensorflow/python/training/input.py:1251–1347

Creates batches by randomly shuffling tensors. This function adds the following to the current `Graph`: * A shuffling queue into which tensors from `tensors` are enqueued. * A `dequeue_many` operation to create batches from the queue. * A `QueueRunner` to `QUEUE_RUNNER` collection, to enqu

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

    None, "Queue-based input pipelines have been replaced by `tf.data`. Use "
    "`tf.data.Dataset.shuffle(min_after_dequeue).batch(batch_size)`.")
def shuffle_batch(tensors, batch_size, capacity, min_after_dequeue,
                  num_threads=1, seed=None, enqueue_many=False, shapes=None,
                  allow_smaller_final_batch=False, shared_name=None, name=None):
  """Creates batches by randomly shuffling tensors.

  This function adds the following to the current `Graph`:

  * A shuffling queue into which tensors from `tensors` are enqueued.
  * A `dequeue_many` operation to create batches from the queue.
  * A `QueueRunner` to `QUEUE_RUNNER` collection, to enqueue the tensors
    from `tensors`.

  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.

  For example:

  ```python
  # Creates batches of 32 images and 32 labels.
  image_batch, label_batch = tf.compat.v1.train.shuffle_batch(
        [single_image, single_label],
        batch_size=32,
        num_threads=4,
        capacity=50000,
        min_after_dequeue=10000)
  ```

  *N.B.:* 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 `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.