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)
