tf.linalg.matvec  |  TensorFlow v2.16.1 (original) (raw)

tf.linalg.matvec

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Multiplies matrix a by vector b, producing a * b.

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Compat aliases for migration

SeeMigration guide for more details.

tf.compat.v1.linalg.matvec

tf.linalg.matvec(
    a,
    b,
    transpose_a=False,
    adjoint_a=False,
    a_is_sparse=False,
    b_is_sparse=False,
    name=None
)

Used in the notebooks

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 Falseby default.

If one or both of the inputs contain a lot of zeros, a more efficient multiplication algorithm can be used by setting the correspondinga_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,  139]
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)