tf.raw_ops.ShuffleAndRepeatDataset

Creates a dataset that shuffles and repeats elements from input_dataset

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Compat aliases for migration

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tf.compat.v1.raw_ops.ShuffleAndRepeatDataset

tf.raw_ops.ShuffleAndRepeatDataset(
    input_dataset,
    buffer_size,
    seed,
    seed2,
    count,
    output_types,
    output_shapes,
    reshuffle_each_iteration=True,
    metadata='',
    name=None
)

pseudorandomly.

Args
input_dataset	A Tensor of type variant.
buffer_size	A Tensor of type int64. The number of output elements to buffer in an iterator over this dataset. Compare with the min_after_dequeue attr when creating a RandomShuffleQueue.
seed	A Tensor of type int64. A scalar seed for the random number generator. If either seed or seed2 is set to be non-zero, the random number generator is seeded by the given seed. Otherwise, a random seed is used.
seed2	A Tensor of type int64. A second scalar seed to avoid seed collision.
count	A Tensor of type int64. A scalar representing the number of times the underlying dataset should be repeated. The default is -1, which results in infinite repetition.
output_types	A list of tf.DTypes that has length >= 1.
output_shapes	A list of shapes (each a tf.TensorShape or list of ints) that has length >= 1.
reshuffle_each_iteration	An optional bool. Defaults to True.
metadata	An optional string. Defaults to "".
name	A name for the operation (optional).

Returns
A Tensor of type variant.