How to Compute the Error Function of a Tensor in PyTorch
Last Updated :
03 May, 2022
In this article, we are going to cover how to compute the error function of a tensor in Python using PyTorch.
torch.special.erf() method
We can compute the error function of a tensor by using torch.special.erf() method. This method accepts the input tensor of any dimension and it returns a tensor with a computed error function with the same dimension as the input tensor. The below syntax is used to compute the error function of a tensor.
Syntax: torch.special.erf(input)
Parameters:
- input: This is our input tensor.
Return: This method returns a tensor with computed error function of input tensor.
Example 1:
The following program is to understand how to compute the error function of the 1D tensor.
Python3
import torch
tens = torch.tensor([ - 0.7336 , - 0.9200 , - 0.4742 ,
- 0.4470 , - 0.3472 ])
print ( "\n Input Tensor:" , tens)
er = torch.special.erf(tens)
print ( "\n After Computed Error function :" , er)
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Output:
Example 2:
The following program is to know how to compute the error function of a batch of tensors.
Python3
import torch
tens = torch.tensor([[[ 0.8636 , - 0.4195 , - 0.4681 ],
[ 0.1265 , 1.2233 , 0.1978 ],
[ 1.1389 , 0.3686 , 1.2339 ]],
[[ 1.6362 , 0.6235 , 1.2631 ],
[ 0.3336 , 1.5336 , 1.3677 ],
[ 0.5637 , 1.3236 , 0.2696 ]]])
print ( "\n\n Input Tensor: \n" , tens)
er = torch.special.erf(tens)
print ( "\n\n After Computed Error function: \n" , er)
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Output:
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