How to perform element-wise subtraction on tensors in PyTorch?
Last updated Jun 24, 2022PyTorch is a deep learning framework that can also be used to compute numerically. In this article, we'll go through the fundamentals of PyTorch's element-wise subtraction.
The torch.sub() function of PyTorch is used to execute element-wise subtraction on tensors. Tensors are subtracted from their respective elements. A scalar or tensor may be subtracted from yet another scalar or tensor. A tensor may be subtracted from another tensor, whether the dimensions are similar or distinct. The resulting tensor's dimensions are just the same as the greater tensor's dimension.
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Performing element-wise subtraction on tensors in PyTorch
Simply follow the simple steps below to perform element-wise subtraction operation on tensors:
Step 1: Import the necessary torch library and make sure it is already installed.
Step 2: Create at least two tensors using PyTorch and print them out.
Step 3: Define the subtract a scalar quantity as well.
Step 4: Use a torch to subtract one scalar or one tensor from another, then set the result as a new variable.
Step 5: This is the last step in the process, and it requires printing the final tensor.
Example
In this section, we will look at a Python code that subtracts a scalar number from a tensor
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import torch T1 = torch.Tensor([[5,6],[3,4]]) T2 = torch.Tensor([7, 8]) print("T1:\n", T1) print("T2:\n", T2) v = torch.sub(T1, T2) print("Element-wise subtraction result:\n", v)
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Output
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T1: tensor([[5., 6.], [3., 4.]]) T2: tensor([7., 8.]) Element-wise subtraction result: tensor([[-2., -2.], [-4., 14.]]) |
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