PyTorch torch.sum() method “returns the sum of all elements in the input tensor.”
torch.sum(input, dim, keepdim=False, *, dtype=None)
- input (Tensor): It is an input tensor.
- dim (int or tuple of ints, optional): It is the dimension or dimensions to reduce. If None, all dimensions are reduced.
- keepdim (bool): Whether the output tensor has dim retained or not.
- dtype: The desired data type of the returned tensor. If specified, the input tensor is cast to dtype before operating.
Example 1: Sum all elements of a tensor
import torch a = torch.Tensor([1, 2, 3, 4, 5]) result = torch.sum(a) print(result)
Example 2: Sum along a specific dimension
import torch a = torch.Tensor([[1, 2], [3, 4], [5, 6]]) result = torch.sum(a, dim=0) print(result)
tensor([ 9., 12.])
Example 3: Sum along multiple dimensions
import torch a = torch.Tensor([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]) result = torch.sum(a, dim=(0, 1)) print(result)
Example 4: Keep dimensions
import torch a = torch.Tensor([[1, 2], [3, 4], [5, 6]]) result = torch.sum(a, dim=0, keepdim=True) print(result)
tensor([[ 9., 12.]])
Example 6: Torch sum a tensor along an axis
To sum a tensor along an axis in PyTorch, use the “torch.sum() function”. The torch.sum() function accepts two arguments: the tensor you want to sum and the axis you want to sum over. The axis can be a single integer or a list of integers. If you specify a list of integers, the tensor will be summed over all the axes specified in the list.
import torch tensor = torch.rand(3, 4) sum = torch.sum(tensor, dim=0) print(sum)
tensor([1.1096, 1.9846, 0.5725, 1.8936])
The torch.sum() function is used to sum along the last axis of a tensor. To do this, you can use the -1 argument for the dim parameter.
import torch tensor = torch.rand(3, 4) sum = torch.sum(tensor, dim=-1) print(sum)
tensor([3.1583, 2.5213, 2.4922])