Difference between numpy dot and Python 3 5 matrix multiplication

Question

I recently moved to Python 3 5 and noticed the new matrix multiplication operator     sometimes behaves differently from the numpy dot operator  In example  for 3d arrays   import numpy as np  a   np random rand 8 13 13  b   np random rand 8 13 13  c   a   b    Python 3 5  d   np dot a  b    The   operator returns an array of shape   c shape  8  13  13    while the np dot   function returns   d shape  8  13  8  13    How can I reproduce the same result with numpy dot  Are there any other significant differences

User · Answer

My experience with MATMUL and DOT

I was constantly getting "ValueError: Shape of passed values is (200, 1), indices imply (200, 3)" when trying to use MATMUL. I wanted a quick workaround and found DOT to deliver the same functionality. I don't get any error using DOT. I get the correct answer

with MATMUL

X.shape
>>>(200, 3)

type(X)

>>>pandas.core.frame.DataFrame

w

>>>array([0.37454012, 0.95071431, 0.73199394])

YY = np.matmul(X,w)

>>>  ValueError: Shape of passed values is (200, 1), indices imply (200, 3)"

with DOT

YY = np.dot(X,w)
# no error message
YY
>>>array([ 2.59206877,  1.06842193,  2.18533396,  2.11366346,  0.28505879, …

YY.shape

>>> (200, )

User · Answer

The answer by  ajcr explains how the dot and matmul  invoked by the   symbol  differ  By looking at a simple example  one clearly sees how the two behave differently when operating on  stacks of matricies  or tensors  To clarify the differences take a 4x4 array and return the dot product and matmul product with a 3x4x2  stack of matricies  or tensor  import numpy as np fourbyfour   np array                           1 2 3 4                           3 2 1 4                           5 4 6 7                           11 12 13 14                             threebyfourbytwo   np array                                  2 3   11 9   32 21   28 17                                   2 3   1 9   3 21   28 7                                   2 3   1 9   3 21   28 7                                    print  4x4 3x4x2 dot  n    n  format np dot fourbyfour threebyfourbytwo    print  4x4 3x4x2 matmul  n    n  format np matmul fourbyfour threebyfourbytwo     The products of each operation appear below  Notice how the dot product is      a sum product over the last axis of a and the second-to-last of b  and how the matrix product is formed by broadcasting the matrix together  4x4 3x4x2 dot      232 152     125 112     125 112       172 116     123  76     123  76       442 296     228 226     228 226       962 652     465 512     465 512     4x4 3x4x2 matmul      232 152     172 116     442 296     962 652       125 112     123  76     228 226     465 512       125 112     123  76     228 226     465 512

User · Answer

Here is a comparison with np einsum to show how the indices are projected np allclose np einsum  ijk ijk- gt ijk   a b   a b           True  np allclose np einsum  ijk ikl- gt ijl   a b   a b           True np allclose np einsum  ijk lkm- gt ijlm  a b   a dot b       True

User · Answer

Just FYI    and its numpy equivalents dot and matmul are all equally fast   Plot created with perfplot  a project of mine    Code to reproduce the plot  import perfplot import numpy   def setup n       A   numpy random rand n  n      x   numpy random rand n      return A  x   def at data       A  x   data     return A   x   def numpy dot data       A  x   data     return numpy dot A  x    def numpy matmul data       A  x   data     return numpy matmul A  x    perfplot show      setup setup      kernels  at  numpy dot  numpy matmul       n range  2    k for k in range 15

User · Answer

The   operator calls the array s   matmul   method  not dot  This method is also present in the API as the function np matmul   gt  gt  gt  a   np random rand 8 13 13   gt  gt  gt  b   np random rand 8 13 13   gt  gt  gt  np matmul a  b  shape  8  13  13   From the documentation   matmul differs from dot in two important ways   Multiplication by scalars is not allowed  Stacks of matrices are broadcast together as if the matrices were elements    The last point makes it clear that dot and matmul methods behave differently when passed 3D  or higher dimensional  arrays  Quoting from the documentation some more  For matmul   If either argument is N-D  N  gt  2  it is treated as a stack of matrices residing in the last two indexes and broadcast accordingly   For np dot   For 2-D arrays it is equivalent to matrix multiplication  and for 1-D arrays to inner product of vectors  without complex conjugation   For N dimensions it is a sum product over the last axis of a and the second-to-last of b

User · Answer

In mathematics  I think the dot in numpy makes more sense      dot a b   i j k a b c       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      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       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   t3 s4   To use matmul a b  you need    t3 s4  t2 s2  or one of t2 and s2 is 1 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

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

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