torch.inner

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

计算 1D 张量的点积。对于更高维，沿其最后维度对来自 input 和 other 的元素的乘积进行求和。

注意

如果 input 或 other 是标量，则结果等效于 torch.mul(input, other).

如果 input 和 other 都是非标量，则其最后维度的尺寸必须匹配，结果等效于 torch.tensordot(input, other, dims=([-1], [-1]))

参数

• input (Tensor) – 第一个输入张量

• other (Tensor) – 第二个输入张量

关键字参数

out (Tensor, 可选) – 可选的输出张量，用于写入结果。输出形状为 input.shape[:-1] + other.shape[:-1].

示例

# Dot product
>>> torch.inner(torch.tensor([1, 2, 3]), torch.tensor([0, 2, 1]))
tensor(7)

# Multidimensional input tensors
>>> a = torch.randn(2, 3)
>>> a
tensor([[0.8173, 1.0874, 1.1784],
        [0.3279, 0.1234, 2.7894]])
>>> b = torch.randn(2, 4, 3)
>>> b
tensor([[[-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]])