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torch.inner#

torch.inner(input,other,*,out=None)→Tensor#

Computes the dot product for 1D tensors. For higher dimensions, sums the productof elements frominput andother along their last dimension.

Note

If eitherinput orother is a scalar, the result is equivalenttotorch.mul(input, other).

If bothinput andother are non-scalars, the size of their lastdimension must match and the result is equivalent totorch.tensordot(input,other, dims=([-1], [-1]))

Parameters

  • input (Tensor) – First input tensor

  • other (Tensor) – Second input tensor

Keyword Arguments

out (Tensor,optional) – Optional output tensor to write result into. The outputshape isinput.shape[:-1] + other.shape[:-1].

Example:

# Dot product>>>torch.inner(torch.tensor([1,2,3]),torch.tensor([0,2,1]))tensor(7)# Multidimensional input tensors>>>a=torch.randn(2,3)>>>atensor([[0.8173,1.0874,1.1784],[0.3279,0.1234,2.7894]])>>>b=torch.randn(2,4,3)>>>btensor([[[-0.4682,-0.7159,0.1506],[0.4034,-0.3657,1.0387],[0.9892,-0.6684,0.1774],[0.9482,1.3261,0.3917]],[[0.4537,0.7493,1.1724],[0.2291,0.5749,-0.2267],[-0.7920,0.3607,-0.3701],[1.3666,-0.5850,-1.7242]]])>>>torch.inner(a,b)tensor([[[-0.9837,1.1560,0.2907,2.6785],[2.5671,0.5452,-0.6912,-1.5509]],[[0.1782,2.9843,0.7366,1.5672],[3.5115,-0.4864,-1.2476,-4.4337]]])# Scalar input>>>torch.inner(a,torch.tensor(2))tensor([[1.6347,2.1748,2.3567],[0.6558,0.2469,5.5787]])