矩阵矩阵乘法的函数numpy.dot()，@和方法.dot()之间有什么区别? [英] What is difference between the function numpy.dot(), @, and method .dot() for matrix-matrix multiplication?

问题描述

有什么区别吗?如果不是，按惯例首选什么? 性能似乎几乎相同.

Is there any difference? If not, what is preferred by convention? The performance seems to be almost the same.

a=np.random.rand(1000,1000)
b=np.random.rand(1000,1000)
%timeit a.dot(b)     #14.3 ms ± 374 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
%timeit np.dot(a,b)  #14.7 ms ± 315 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
%timeit a @ b        #15.1 ms ± 779 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

推荐答案

它们基本上都在做相同的事情.在计时方面，根据Numpy的文档，此处 :

They are all basically doing the same thing. In terms of timing, based on Numpy's documentation here:

• 如果a和b都是一维数组，则它是向量的内积 (没有复杂的共轭).

• If both a and b are 1-D arrays, it is inner product of vectors (without complex conjugation).

如果a和b均为二维数组，则为矩阵乘法，但是 最好使用matmul或a @ b.

If both a and b are 2-D arrays, it is matrix multiplication, but using matmul or a @ b is preferred.

如果a或b为0-D(标量)，则等于乘和 最好使用numpy.multiply(a, b)或a * b.

If either a or b is 0-D (scalar), it is equivalent to multiply and using numpy.multiply(a, b) or a * b is preferred.

如果a是一个N-D数组而b是一个一维数组，则它是 a和b的最后一个轴.

If a is an N-D array and b is a 1-D array, it is a sum product over the last axis of a and b.

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