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Multiplies matrix a
by vector b
, producing a
* b
.
tf.linalg.matvec(
a,
b,
transpose_a=False,
adjoint_a=False,
a_is_sparse=False,
b_is_sparse=False,
name=None
)
The matrix a
must, following any transpositions, be a tensor of rank >= 2,
with shape(a)[-1] == shape(b)[-1]
, and shape(a)[:-2]
able to broadcast
with shape(b)[:-1]
.
Both a
and b
must be of the same type. The supported types are:
float16
, float32
, float64
, int32
, complex64
, complex128
.
Matrix a
can be transposed or adjointed (conjugated and transposed) on
the fly by setting one of the corresponding flag to True
. These are False
by default.
If one or both of the inputs contain a lot of zeros, a more efficient
multiplication algorithm can be used by setting the corresponding
a_is_sparse
or b_is_sparse
flag to True
. These are False
by default.
This optimization is only available for plain matrices/vectors (rank-2/1
tensors) with datatypes bfloat16
or float32
.
For example:
# 2-D tensor `a`
# [[1, 2, 3],
# [4, 5, 6]]
a = tf.constant([1, 2, 3, 4, 5, 6], shape=[2, 3])
# 1-D tensor `b`
# [7, 9, 11]
b = tf.constant([7, 9, 11], shape=[3])
# `a` * `b`
# [ 58, 64]
c = tf.linalg.matvec(a, b)
# 3-D tensor `a`
# [[[ 1, 2, 3],
# [ 4, 5, 6]],
# [[ 7, 8, 9],
# [10, 11, 12]]]
a = tf.constant(np.arange(1, 13, dtype=np.int32),
shape=[2, 2, 3])
# 2-D tensor `b`
# [[13, 14, 15],
# [16, 17, 18]]
b = tf.constant(np.arange(13, 19, dtype=np.int32),
shape=[2, 3])
# `a` * `b`
# [[ 86, 212],
# [410, 563]]
c = tf.linalg.matvec(a, b)
Raises | |
---|---|
ValueError
|
If transpose_a and adjoint_a are both set to True. |