torch随机

原文: https://pytorch.org/docs/stable/random.html


torch.random.fork_rng(devices=None, enabled=True, _caller='fork_rng', _devices_kw='devices')¶

分叉 RNG,以便在您返回时将 RNG 重置为之前的状态。

参数

  • 设备(可迭代的 CUDA ID 的)–派生 RNG 的 CUDA 设备。 CPU RNG 状态始终为分叉。 默认情况下, fork_rng() 可在所有设备上运行,但是如果您的计算机上有很多设备,则将发出警告,因为在这种情况下此功能运行非常缓慢。 如果您明确指定设备,该警告将被取消

  • 启用 (bool )–如果False,则不分叉 RNG。 这是一个方便的参数,用于轻松禁用上下文管理器,而不必删除它并取消其下的 Python 代码的缩进。


torch.random.get_rng_state()¶

以 <cite>torch.ByteTensor</cite> 的形式返回随机数生成器状态。


torch.random.initial_seed()¶

返回长为 Python <cite>long</cite> 的用于生成随机数的初始种子。


torch.random.manual_seed(seed)¶

设置用于生成随机数的种子。 返回一个<cite>torch.生成器</cite>对象。

Parameters

种子 (python:int )–所需的种子。


torch.random.seed()¶

将用于生成随机数的种子设置为不确定的随机数。 返回用于播种 RNG 的 64 位数字。


torch.random.set_rng_state(new_state)¶

设置随机数生成器状态。

Parameters

new_state (torch.ByteTensor )–所需状态

随机数发生器


torch.random.get_rng_state()

Returns the random number generator state as a <cite>torch.ByteTensor</cite>.


torch.random.set_rng_state(new_state)

Sets the random number generator state.

Parameters

new_state (torch.ByteTensor) – The desired state


torch.random.manual_seed(seed)

Sets the seed for generating random numbers. Returns a <cite>torch.Generator</cite> object.

Parameters

seed (python:int) – The desired seed.


torch.random.seed()

Sets the seed for generating random numbers to a non-deterministic random number. Returns a 64 bit number used to seed the RNG.


torch.random.initial_seed()

Returns the initial seed for generating random numbers as a Python <cite>long</cite>.


torch.random.fork_rng(devices=None, enabled=True, _caller='fork_rng', _devices_kw='devices')

Forks the RNG, so that when you return, the RNG is reset to the state that it was previously in.

Parameters

  • devices (iterable of CUDA IDs) – CUDA devices for which to fork the RNG. CPU RNG state is always forked. By default, fork_rng() operates on all devices, but will emit a warning if your machine has a lot of devices, since this function will run very slowly in that case. If you explicitly specify devices, this warning will be suppressed

  • enabled (bool) – if False, the RNG is not forked. This is a convenience argument for easily disabling the context manager without having to delete it and unindent your Python code under it.


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