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torch.normal#

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

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

Themean is a tensor with the mean ofeach output element’s normal distribution

Thestd is a tensor with the standard deviation ofeach output element’s normal distribution

The shapes ofmean andstd don’t need to match, but thetotal number of elements in each tensor need to be the same.

Note

When the shapes do not match, the shape ofmeanis used as the shape for the returned output tensor

Note

Whenstd is a CUDA tensor, this function synchronizesits device with the CPU.

Parameters

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

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

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 drawnelements.

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 amongall 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,*,out=None)→Tensor

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

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

out (Tensor,optional) – the output tensor.

Example:

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