Generator¶
-
class
torch.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
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.ByteTensorwhich contains all the necessary bits to restore a Generator to a specific point in time.- Return type
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.
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())
