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 required librariesimport torchÂ
# creating a 1D tensortens = torch.tensor([-0.7336, -0.9200, -0.4742,                     -0.4470, -0.3472])Â
# print above created tensorprint("\n Input Tensor:", tens)Â
# compute the error functioner = torch.special.erf(tens)Â
# Display resultprint("\n After Computed Error function :", er) |
Output:
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Example 2:
The following program is to know how to compute the error function of a batch of tensors.
Python3
# import required librariesimport torchÂ
# creating a batch of tensortens = 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]]])Â
# print above created tensorprint("\n\n Input Tensor: \n", tens)Â
# compute the error functioner = torch.special.erf(tens)Â
# Display resultprint("\n\n After Computed Error function: \n", er) |
Output:
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