Method shuffle

tensorflow/python/data/ops/dataset_ops.py:947–974

Randomly shuffles the elements of this dataset. This dataset fills a buffer with `buffer_size` elements, then randomly samples elements from this buffer, replacing the selected elements with new elements. For perfect shuffling, a buffer size greater than or equal to the full size of

(self, buffer_size, seed=None, reshuffle_each_iteration=None)

    return Dataset.zip((Dataset.range(start, max_value), self))

  def shuffle(self, buffer_size, seed=None, reshuffle_each_iteration=None):
    """Randomly shuffles the elements of this dataset.

    This dataset fills a buffer with `buffer_size` elements, then randomly
    samples elements from this buffer, replacing the selected elements with new
    elements. For perfect shuffling, a buffer size greater than or equal to the
    full size of the dataset is required.

    For instance, if your dataset contains 10,000 elements but `buffer_size` is
    set to 1,000, then `shuffle` will initially select a random element from
    only the first 1,000 elements in the buffer. Once an element is selected,
    its space in the buffer is replaced by the next (i.e. 1,001-st) element,
    maintaining the 1,000 element buffer.

    Args:
      buffer_size: A `tf.int64` scalar `tf.Tensor`, representing the number of
        elements from this dataset from which the new dataset will sample.
      seed: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the random
        seed that will be used to create the distribution. See
        `tf.compat.v1.set_random_seed` for behavior.
      reshuffle_each_iteration: (Optional.) A boolean, which if true indicates
        that the dataset should be pseudorandomly reshuffled each time it is
        iterated over. (Defaults to `True`.)

    Returns:
      Dataset: A `Dataset`.
    """
    return ShuffleDataset(self, buffer_size, seed, reshuffle_each_iteration)

  def cache(self, filename=""):
    """Caches the elements in this dataset.