#### Previous topic

numpy.outer

#### Next topic

numpy.einsum

# numpy.tensordot

`numpy.``tensordot`(a, b, axes=2)

  Compute tensor dot product along specified axes for arrays >= 1-D.

  Given two tensors (arrays of dimension greater than or equal to one), a and b, and an array_like object containing two array_like objects, `(a_axes, b_axes)`, sum the products of a‘s and b‘s elements (components) over the axes specified by `a_axes` and `b_axes`. The third argument can be a single non-negative integer_like scalar, `N`; if it is such, then the last `N` dimensions of a and the first `N` dimensions of b are summed over.

  Parameters : | a, b : array_like, len(shape) >= 1 Tensors to “dot”. axes : variable type integer_like scalar Number of axes to sum over (applies to both arrays); or (2,) array_like, both elements array_like of the same length List of axes to be summed over, first sequence applying to a, second to b.

  See also

  `dot`, `einsum`

  Notes

  When there is more than one axis to sum over - and they are not the last (first) axes of a (b) - the argument axes should consist of two sequences of the same length, with the first axis to sum over given first in both sequences, the second axis second, and so forth.

  Examples

  A “traditional” example:

      >>> a = np.arange(60.).reshape(3,4,5)
      >>> b = np.arange(24.).reshape(4,3,2)
      >>> c = np.tensordot(a,b, axes=([1,0],[0,1]))
      >>> c.shape
      (5, 2)
      >>> c
      array([[ 4400.,  4730.],
             [ 4532.,  4874.],
             [ 4664.,  5018.],
             [ 4796.,  5162.],
             [ 4928.,  5306.]])
      >>> # A slower but equivalent way of computing the same...
      >>> d = np.zeros((5,2))
      >>> for i in range(5):
      ...   for j in range(2):
      ...     for k in range(3):
      ...       for n in range(4):
      ...         d[i,j] += a[k,n,i] * b[n,k,j]
      >>> c == d
      array([[ True,  True],
             [ True,  True],
             [ True,  True],
             [ True,  True],
             [ True,  True]], dtype=bool)

  An extended example taking advantage of the overloading of + and *:

      >>> a = np.array(range(1, 9))
      >>> a.shape = (2, 2, 2)
      >>> A = np.array(('a', 'b', 'c', 'd'), dtype=object)
      >>> A.shape = (2, 2)
      >>> a; A
      array([[[1, 2],
              [3, 4]],
             [[5, 6],
              [7, 8]]])
      array([[a, b],
             [c, d]], dtype=object)

      >>> np.tensordot(a, A) # third argument default is 2
      array([abbcccdddd, aaaaabbbbbbcccccccdddddddd], dtype=object)

      >>> np.tensordot(a, A, 1)
      array([[[acc, bdd],
              [aaacccc, bbbdddd]],
             [[aaaaacccccc, bbbbbdddddd],
              [aaaaaaacccccccc, bbbbbbbdddddddd]]], dtype=object)

      >>> np.tensordot(a, A, 0) # "Left for reader" (result too long to incl.)
      array([[[[[a, b],
                [c, d]],
                ...

      >>> np.tensordot(a, A, (0, 1))
      array([[[abbbbb, cddddd],
              [aabbbbbb, ccdddddd]],
             [[aaabbbbbbb, cccddddddd],
              [aaaabbbbbbbb, ccccdddddddd]]], dtype=object)

      >>> np.tensordot(a, A, (2, 1))
      array([[[abb, cdd],
              [aaabbbb, cccdddd]],
             [[aaaaabbbbbb, cccccdddddd],
              [aaaaaaabbbbbbbb, cccccccdddddddd]]], dtype=object)

      >>> np.tensordot(a, A, ((0, 1), (0, 1)))
      array([abbbcccccddddddd, aabbbbccccccdddddddd], dtype=object)

      >>> np.tensordot(a, A, ((2, 1), (1, 0)))
      array([acccbbdddd, aaaaacccccccbbbbbbdddddddd], dtype=object)