Table of Contents
Shortcuts

# Generator

class torch.Generator(device='cpu')

  Creates and returns a generator object that manages the state of the algorithm which 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. Any 32-bit integer is a valid seed.

    Parameters:

      seed (int) – The desired seed. Value must be within the inclusive range [-0x8000_0000_0000_0000, 0xffff_ffff_ffff_ffff]. Otherwise, a RuntimeError is raised. Negative inputs are remapped to positive values with the formula 0xffff_ffff_ffff_ffff + 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())

## Docs

Access comprehensive developer documentation for PyTorch

View Docs

## Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

## Resources

Find development resources and get your questions answered

View Resources

To analyze traffic and optimize your experience, we serve cookies on this site. By clicking or navigating, you agree to allow our usage of cookies. As the current maintainers of this site, Facebook’s Cookies Policy applies. Learn more, including about available controls: Cookies Policy.