TensorFlow 1 version | View source on GitHub |
Multiplies matrix a
by matrix b
, producing a
* b
.
tf.linalg.matmul(
a, b, transpose_a=False, transpose_b=False, adjoint_a=False, adjoint_b=False,
a_is_sparse=False, b_is_sparse=False, name=None
)
The inputs must, following any transpositions, be tensors of rank >= 2 where the inner 2 dimensions specify valid matrix multiplication arguments, and any further outer dimensions match.
Both matrices must be of the same type. The supported types are:
float16
, float32
, float64
, int32
, complex64
, complex128
.
Either matrix 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 matrices 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 (rank-2 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])
# 2-D tensor `b`
# [[ 7, 8],
# [ 9, 10],
# [11, 12]]
b = tf.constant([7, 8, 9, 10, 11, 12], shape=[3, 2])
# `a` * `b`
# [[ 58, 64],
# [139, 154]]
c = tf.matmul(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])
# 3-D tensor `b`
# [[[13, 14],
# [15, 16],
# [17, 18]],
# [[19, 20],
# [21, 22],
# [23, 24]]]
b = tf.constant(np.arange(13, 25, dtype=np.int32),
shape=[2, 3, 2])
# `a` * `b`
# [[[ 94, 100],
# [229, 244]],
# [[508, 532],
# [697, 730]]]
c = tf.matmul(a, b)
# Since python >= 3.5 the @ operator is supported (see PEP 465).
# In TensorFlow, it simply calls the `tf.matmul()` function, so the
# following lines are equivalent:
d = a @ b @ [[10.], [11.]]
d = tf.matmul(tf.matmul(a, b), [[10.], [11.]])
Args | |
---|---|
a
|
Tensor of type float16 , float32 , float64 , int32 , complex64 ,
complex128 and rank > 1.
|
b
|
Tensor with same type and rank as a .
|
transpose_a
|
If True , a is transposed before multiplication.
|
transpose_b
|
If True , b is transposed before multiplication.
|
adjoint_a
|
If True , a is conjugated and transposed before
multiplication.
|
adjoint_b
|
If True , b is conjugated and transposed before
multiplication.
|
a_is_sparse
|
If True , a is treated as a sparse matrix.
|
b_is_sparse
|
If True , b is treated as a sparse matrix.
|
name
|
Name for the operation (optional). |
Returns | |
---|---|
A Tensor of the same type as a and b where each inner-most matrix is
the product of the corresponding matrices in a and b , e.g. if all
transpose or adjoint attributes are False :
|
|
Note
|
This is matrix product, not element-wise product. |
Raises | |
---|---|
ValueError
|
If transpose_a and adjoint_a, or transpose_b and adjoint_b are both set to True. |