# PyTorch â€“ How to compute the error function of a tensor?

To compute the error function of a tensor, we use the torch.special.erf() method. It returns a new tensor with computed error function. It accepts torch tensor of any dimension. It is also known as Gauss error function

## Steps

We could use the following steps to compute the error function of a tensor element-wise −

• Import the required library. In all the following examples, the required Python library is torch. Make sure you have already installed it.

import torch
• Define a torch tensor. Here we define a 2D tensor of random numbers.

tensor = torch.randn(2,3,3)
• Compute the error function of the above-defined tensor using torch.special.erf(tensor). Optionally assign this value to a new variable.

err = torch.special.erf(tensor)
• Print the computed error function.

print("Entropy:", err)

## Example 1

In this example, we compute the error function of a 1D tensor.

# import necessary libraries
import torch

# define a 1D tensor
tensor1 = torch.tensor([-1,2,3,4,5])

# print above created tensor
print("Tensor:", tensor1)

# compute the error function of the tensor
err = torch.special.erf(tensor1)

# Display the computed error function
print("Error :", err)

## Output

Tensor: tensor([-1.0000, 1.0000, 3.0000, 0.0000, 0.5000])
Error : tensor([-0.8427, 0.8427, 1.0000, 0.0000, 0.5205])

## Example 2

In this example, we compute the error function of a 2D tensor

# import necessary libraries
import torch

# define a tensor of random numbers
tensor1 = torch.randn(2,3,3)

# print above created tensor
print("Tensor:", tensor1)

# compute the error function of the tensor
err = torch.special.erf(tensor1)

# Display the computed error function
print("Error:", err)

## Output

Tensor:
tensor([[[-1.0724, 0.3955, -0.3472],
[-0.7336, -0.8110, 1.2624],
[ 0.2334, -0.9200, -0.9879]],

[[ 0.8636, 0.3452, -0.4742],
[-0.6868, 0.8436, -0.4195],
[ 1.0410, -0.4681, 1.6284]]])
Error:
tensor([[[-0.8706, 0.4241, -0.3766],
[-0.7005, -0.7486, 0.9258],
[ 0.2586, -0.8068, -0.8376]],

[[ 0.7780, 0.3746, -0.4975],
[-0.6686, 0.7671, -0.4470],
[ 0.8590, -0.4921, 0.9787]]])