**error function**is a mathematical function that represents the probability of a random variable falling within a particular range. It is frequently used in statistics, machine learning, etc. PyTorch provides a “torch.special.erf()” method to calculate the error function of the specific tensor. Users need to provide a tensor as input to this method and it returns a tensor of the same type and shape/size as output.

This blog will illustrate the method to calculate/find the error function of tensors in PyTorch.

## How to Calculate/Find the Error Function of a Tensor in PyTorch?

To calculate the error function of a specific tensor in PyTorch, the **“torch.special.erf()”** method is used. Users need to pass the desired tensor to this method to find its error function. It applies the element-wise error function to the individual element of the input tensor.

Go through the next provided examples for a better understanding.

## Example 1: Calculate/Find Error Function of 1D Tensor

In the first section, we will create a 1D tensor and compute its error function. Let’s follow the provided steps:

**Step 1: Import PyTorch Library**

First, import the “**torch**” library to compute the error function:

**Step 2: Create 1D Tensor **

Then, create a 1D tensor using the **“torch.tensor()”** function and print its elements. Here, we are creating the following “**Tens1**” 1D tensor from a list:

print(Tens1)

The tensor has been created successfully:

**Step 3: Calculate Error Function**

Now, utilize the **“torch.special.erf()”** method to calculate the error function of the “**Tens1**” tensor:

**Step 4: Display Computed Error Function**

Finally, display the calculated error function for verification:

The below output shows the calculated error function of the “**Tens1**” tensor:

## Example 2: Calculate/Find Error Function of 2D Tensor

In the second example, we will create a 2D tensor and find its error function. Let’s follow the below step-by-step procedure:

**Step 1: Import PyTorch Library**

First, import the “**torch**” library to compute the error function:

**Step 2: Create 2D Tensor **

Then, create a 2D tensor and print its elements. Here, we are creating the following “**Tens2**” 2D tensor from the random numbers:

print(Tens2)

This has created the 2D random tensor:

**Step 3: Calculate Error Function**

Now, compute the error function of the “**Tens2**” tensor using the** “torch.special.erf()”** method:

**Step 4: Display Computed Error Function**

Finally, print the calculated error function:

In the below output, the calculated error function of the “**Tens2**” tensor can be seen:

We have efficiently explained the method of calculating the error function in PyTorch.

**Note**: You can access our Google Colab Notebook at this link.

## Conclusion

To calculate/find the error function of a tensor in PyTorch, first, import the “torch” library. Then, create the desired 1D or 2D tensor and view its elements. Next, use the **“torch.special.erf()”** method to find/compute the error function of the input tensor. Lastly, print the calculated error function. This blog has illustrated the method to calculate/find the error function of tensors in PyTorch.