torch、(二) Generators

参考 torch、(二) Generators - 云+社区 - 腾讯云

目录

class torch._C.Generator(device='cpu') → Generator

device

get_state() → Tensor

initial_seed() → int

manual_seed(seed) → Generator

seed() → int

set_state(new_state) → void

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class torch._C.Generator(device='cpu') → Generator

Creates and returns a generator object which manages the state of the algorithm that produces pseudo random numbers. Used as a keyword argument in many In-place random sampling functions.

Parameters

device (torch.device, optional) – the desired device for the generator.

Returns

An torch.Generator object.

Return type

Generator

Example:

>>> g_cpu = torch.Generator()
>>> g_cuda = torch.Generator(device='cuda')

device

Generator.device -> device

Gets the current device of the generator.

Example:

>>> g_cpu = torch.Generator()
>>> g_cpu.device
device(type='cpu')

get_state() → Tensor

Returns the Generator state as a torch.ByteTensor.

Returns

A torch.ByteTensor which contains all the necessary bits to restore a Generator to a specific point in time.

Return type

Tensor

Example:

>>> g_cpu = torch.Generator()
>>> g_cpu.get_state()

initial_seed() → int

Returns the initial seed for generating random numbers.

Example:

>>> g_cpu = torch.Generator()
>>> g_cpu.initial_seed()
2147483647

manual_seed(seed) → Generator

Sets the seed for generating random numbers. Returns a torch.Generator object. It is recommended to set a large seed, i.e. a number that has a good balance of 0 and 1 bits. Avoid having many 0 bits in the seed.

Parameters

seed (int) – The desired seed.

Returns

An torch.Generator object.

Return type

Generator

Example:

>>> g_cpu = torch.Generator()
>>> g_cpu.manual_seed(2147483647)

seed() → int

Gets a non-deterministic random number from std::random_device or the current time and uses it to seed a Generator.

Example:

>>> g_cpu = torch.Generator()
>>> g_cpu.seed()
1516516984916

set_state(new_state) → void

Sets the Generator state.

Parameters

new_state (torch.ByteTensor) – The desired state.

Example:

>>> g_cpu = torch.Generator()
>>> g_cpu_other = torch.Generator()
>>> g_cpu.set_state(g_cpu_other.get_state())