numpy 3darray矩阵乘法功能 [英] Numpy 3darray matrix multiplication function

问题描述

假设我有一个ndarray，W的形状为(m，n，n)，向量C的尺寸为(m，n).我需要按以下方式将这两个数相乘

Suppose I have an ndarray, W of shape (m,n,n) and a vector C of dimension (m,n). I need to multiply these two in the following way

result = np.empty(m,n)
for i in range(m):
    result[i] = W[i] @ C[i]

我该如何以向量化的方式来执行此操作，而不会出现循环和所有事件?

How do I do this in a vectorized way without loops and all?

推荐答案

自此，您需要使W和C的第一个轴保持对齐，同时使用矩阵乘法从它们的最后一个轴上松开，我建议使用 np.einsum 这样非常有效的方法-

Since, you need to keep the first axis from both W and C aligned, while loosing the last axis from them with the matrix-multiplication, I would suggest using np.einsum for a very efficient approach, like so -

np.einsum('ijk,ik->ij',W,C)

np.tensordot或np.dot没有保持轴对齐的功能，这是np.einsum得以改进的地方.

np.tensordot or np.dot doesn't have the feature to keep axes aligned and that's where np.einsum improves upon.

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