jax.numpy.dot#
- jax.numpy.dot(a, b, *, precision=None, preferred_element_type=None)[source]#
Dot product of two arrays. Specifically,
LAX-backend implementation of
numpy.dot()
.In addition to the original NumPy arguments listed below, also supports
precision
for extra control over matrix-multiplication precision on supported devices.precision
may be set toNone
, which means default precision for the backend, aPrecision
enum value (Precision.DEFAULT
,Precision.HIGH
orPrecision.HIGHEST
) or a tuple of twoPrecision
enums indicating separate precision for each argument.Original docstring below.
If both a and b are 1-D arrays, it is inner product of vectors (without complex conjugation).
If both a and b are 2-D arrays, it is matrix multiplication, but using
matmul()
ora @ b
is preferred.If either a or b is 0-D (scalar), it is equivalent to
multiply()
and usingnumpy.multiply(a, b)
ora * b
is preferred.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.
If a is an N-D array and b is an M-D array (where
M>=2
), it is a sum product over the last axis of a and the second-to-last axis of b:dot(a, b)[i,j,k,m] = sum(a[i,j,:] * b[k,:,m])
It uses an optimized BLAS library when possible (see numpy.linalg).
- Parameters:
a (array_like) – First argument.
b (array_like) – Second argument.
preferred_element_type (dtype, optional) – If specified, accumulate results and return a result of the given data type. If not specified, the accumulation dtype is determined from the type promotion rules of the input array dtypes.
precision (PrecisionLike) –
- Returns:
output – Returns the dot product of a and b. If a and b are both scalars or both 1-D arrays then a scalar is returned; otherwise an array is returned. If out is given, then it is returned.
- Return type:
ndarray