mindspore.nn.Generator

class mindspore.nn.Generator

A generator that manages the state of random numbers and provides seed and offset for random functions. When the seed and offset are fixed, the random function generates the same random sequence.

Inputs:

• step (int) - Set the step size for offset update.

Outputs:

Tuple consisting of the seed and offset of generator.

Supported Platforms:

Ascend GPU CPU

Examples

>>> import mindspore as ms
>>> from mindspore.nn import Generator
>>> import numpy as np
>>> np.random.seed(10)
>>> ms.set_context(mode=1)
>>> generator = Generator()
>>> print(generator.get_state())
(Tensor(shape=[], dtype=Int32, value= 0), Tensor(shape=[], dtype=Int32, value= 0))
>>> print(generator(12))
(0, 0)
>>> print(generator.get_state())
(Tensor(shape=[], dtype=Int32, value= 0), Tensor(shape=[], dtype=Int32, value= 12))
>>> generator.manual_seed(20)
>>> print(generator.get_state())
(Tensor(shape=[], dtype=Int32, value= 20), Tensor(shape=[], dtype=Int32, value= 0))
>>> print(generator.seed())
1165313289
>>> print(generator.initial_seed())
1165313289

construct(step)

Update the value of offset, and return the seed and the previous offset.

Parameters

step (int) – Update offset by step.

Returns

Seed and offset before update.

get_state()

Get the generator state.

Returns

Tuple consisting of the seed and offset of generator.

initial_seed()

Return the initial seed of generator.

Returns

The initial seed of generator.

manual_seed(seed)

Sets the generator seed.

Parameters

seed (int) – Sets the generator seed.

Returns

The generator self.

seed()

Generate random seeds that can be used as seeds for generator.

Returns

Tensor, randomly generated seeds.

set_state(seed, offset=None)

Sets the generator state.

Parameters

• seed (int) – Seed of the generator.

• offset (int, optional) – Offset of the generator, default: None , means 0.