Method from_tensor_slices

tensorflow/python/data/ops/dataset_ops.py:470–488

Creates a `Dataset` whose elements are slices of the given tensors. Note that if `tensors` contains a NumPy array, and eager execution is not enabled, the values will be embedded in the graph as one or more `tf.constant` operations. For large datasets (> 1 GB), this can waste memory

(tensors)

  @staticmethod
  def from_tensor_slices(tensors):
    """Creates a `Dataset` whose elements are slices of the given tensors.

    Note that if `tensors` contains a NumPy array, and eager execution is not
    enabled, the values will be embedded in the graph as one or more
    `tf.constant` operations. For large datasets (> 1 GB), this can waste
    memory and run into byte limits of graph serialization. If `tensors`
    contains one or more large NumPy arrays, consider the alternative described
    in [this guide](
    https://tensorflow.org/guide/datasets#consuming_numpy_arrays).

    Args:
      tensors: A dataset element, with each component having the same size in
        the 0th dimension.

    Returns:
      Dataset: A `Dataset`.
    """
    return TensorSliceDataset(tensors)

  class _GeneratorState(object):
    """Stores outstanding iterators created from a Python generator.