Method batch

tensorflow/python/data/ops/dataset_ops.py:1070–1091

Combines consecutive elements of this dataset into batches. The components of the resulting element will have an additional outer dimension, which will be `batch_size` (or `N % batch_size` for the last element if `batch_size` does not divide the number of input elements `N` evenly a

(self, batch_size, drop_remainder=False)

    return ShardDataset(self, num_shards, index)

  def batch(self, batch_size, drop_remainder=False):
    """Combines consecutive elements of this dataset into batches.

    The components of the resulting element will have an additional outer
    dimension, which will be `batch_size` (or `N % batch_size` for the last
    element if `batch_size` does not divide the number of input elements `N`
    evenly and `drop_remainder` is `False`). If your program depends on the
    batches having the same outer dimension, you should set the `drop_remainder`
    argument to `True` to prevent the smaller batch from being produced.

    Args:
      batch_size: A `tf.int64` scalar `tf.Tensor`, representing the number of
        consecutive elements of this dataset to combine in a single batch.
      drop_remainder: (Optional.) A `tf.bool` scalar `tf.Tensor`, representing
        whether the last batch should be dropped in the case it has fewer than
        `batch_size` elements; the default behavior is not to drop the smaller
        batch.

    Returns:
      Dataset: A `Dataset`.
    """
    return BatchDataset(self, batch_size, drop_remainder)

  def padded_batch(self,
                   batch_size,