[python] Difference between numpy dot() and Python 3.5+ matrix multiplication @

In mathematics, I think the dot in numpy makes more sense

dot(a,b)_{i,j,k,a,b,c} = formula

since it gives the dot product when a and b are vectors, or the matrix multiplication when a and b are matrices


As for matmul operation in numpy, it consists of parts of dot result, and it can be defined as

>matmul(a,b)_{i,j,k,c} = formula

So, you can see that matmul(a,b) returns an array with a small shape, which has smaller memory consumption and make more sense in applications. In particular, combining with broadcasting, you can get

matmul(a,b)_{i,j,k,l} = formula

for example.


From the above two definitions, you can see the requirements to use those two operations. Assume a.shape=(s1,s2,s3,s4) and b.shape=(t1,t2,t3,t4)

  • To use dot(a,b) you need

    1. t3=s4;
  • To use matmul(a,b) you need

    1. t3=s4
    2. t2=s2, or one of t2 and s2 is 1
    3. t1=s1, or one of t1 and s1 is 1

Use the following piece of code to convince yourself.

Code sample

import numpy as np
for it in xrange(10000):
    a = np.random.rand(5,6,2,4)
    b = np.random.rand(6,4,3)
    c = np.matmul(a,b)
    d = np.dot(a,b)
    #print 'c shape: ', c.shape,'d shape:', d.shape

    for i in range(5):
        for j in range(6):
            for k in range(2):
                for l in range(3):
                    if not c[i,j,k,l] == d[i,j,k,j,l]:
                        print it,i,j,k,l,c[i,j,k,l]==d[i,j,k,j,l] #you will not see them

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