torch.normal

torch.normal(mean, std, *, generator=None, out=None) → Tensor

Returns a tensor of random numbers drawn from separate normal distributions whose mean and standard deviation are given.

The mean is a tensor with the mean of each output element’s normal distribution

The std is a tensor with the standard deviation of each output element’s normal distribution

The shapes of mean and std don’t need to match, but the total number of elements in each tensor need to be the same.

Note

When the shapes do not match, the shape of mean is used as the shape for the returned output tensor

Note

When std is a CUDA tensor, this function synchronizes its device with the CPU.

Parameters:

• mean (float or Tensor) – per-element mean(s). Only floating point types are supported.

• std (float or Tensor) – per-element standard deviation(s). Only floating point types are supported.

Keyword Arguments:

• generator (torch.Generator, optional) – a pseudorandom number generator for sampling

• out (Tensor, optional) – the output tensor.

Example:

>>> torch.normal(mean=torch.arange(1., 11.), std=torch.arange(1, 0, -0.1))
tensor([  1.0425,   3.5672,   2.7969,   4.2925,   4.7229,   6.2134,
          8.0505,   8.1408,   9.0563,  10.0566])

torch.normal(mean=0.0, std, *, out=None) → Tensor

Similar to the function above, but the means are shared among all drawn elements.

Parameters:

• mean (float, optional) – the mean for all distributions

• std (Tensor) – the tensor of per-element standard deviations

Keyword Arguments:

out (Tensor, optional) – the output tensor.

Example:

>>> torch.normal(mean=0.5, std=torch.arange(1., 6.))
tensor([-1.2793, -1.0732, -2.0687,  5.1177, -1.2303])

torch.normal(mean, std=1.0, *, out=None) → Tensor

Similar to the function above, but the standard deviations are shared among all drawn elements.

Parameters:

• mean (Tensor) – the tensor of per-element means

• std (float, optional) – the standard deviation for all distributions

Keyword Arguments:

out (Tensor, optional) – the output tensor

Example:

>>> torch.normal(mean=torch.arange(1., 6.))
tensor([ 1.1552,  2.6148,  2.6535,  5.8318,  4.2361])

torch.normal(mean, std, size, *, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) → Tensor

Similar to the function above, but the means and standard deviations are shared among all drawn elements. The resulting tensor has size given by size.

Parameters:

• mean (float) – the mean for all distributions

• std (float) – the standard deviation for all distributions

• size (int...) – a sequence of integers defining the shape of the output tensor.

Keyword Arguments:

• generator (torch.Generator, optional) – a pseudorandom number generator for sampling

• out (Tensor, optional) – the output tensor.

• dtype (torch.dtype, optional) – the desired data type of returned tensor. Default: if None, uses a global default (see torch.set_default_dtype()).

• layout (torch.layout, optional) – the desired layout of returned Tensor. Default: torch.strided.

• device (torch.device, optional) – the desired device of returned tensor. Default: if None, uses the current device for the default tensor type (see torch.set_default_device()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.

• requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default: False.

• pin_memory (bool, optional) – If set, returned tensor would be allocated in the pinned memory. Works only for CPU tensors. Default: False.

Example:

>>> torch.normal(2, 3, size=(1, 4))
tensor([[-1.3987, -1.9544,  3.6048,  0.7909]])