def random_shuffle(value, seed=None, name=None): """Randomly shuffles a tensor along its first dimension. The tensor is shuffled along dimension 0, such that each `value[j]` is mapped to one and only one `output[i]`. For example, a mapping that might occur for a 3x2 tensor is: ```python [[1, 2], [[5, 6], [3, 4], ==> [1, 2], [5, 6]] [3, 4]] ``` Args: value: A Tensor to be shuffled. seed: A Python integer. Used to create a random seed for the distribution. See `tf.random.set_seed` for behavior. name: A name for the operation (optional). Returns: A tensor of same shape and type as `value`, shuffled along its first dimension. """ seed1, seed2 = random_seed.get_seed(seed) return gen_random_ops.random_shuffle(value, seed=seed1, seed2=seed2, name=name)
def random_shuffle(value, seed=None, name=None): """Randomly shuffles a tensor along its first dimension. The tensor is shuffled along dimension 0, such that each `value[j]` is mapped to one and only one `output[i]`. For example, a mapping that might occur for a 3x2 tensor is: ```python [[1, 2], [[5, 6], [3, 4], ==> [1, 2], [5, 6]] [3, 4]] ``` Args: value: A Tensor to be shuffled. seed: A Python integer. Used to create a random seed for the distribution. See `tf.set_random_seed` for behavior. name: A name for the operation (optional). Returns: A tensor of same shape and type as `value`, shuffled along its first dimension. """ seed1, seed2 = random_seed.get_seed(seed) return gen_random_ops.random_shuffle( value, seed=seed1, seed2=seed2, name=name)