tf计算矩阵维度_tensorflow tf.matmul() （多维）矩阵相乘（多维矩阵乘法）

@tf_export("matmul")

def 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):

"""Multiplies matrix `a` by matrix `b`, producing `a` * `b`.

将矩阵a与矩阵b相乘，得出a * b。

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.

在进行任何换位后，输入必须为(秩？)> = 2的张量，其中内部2维指定有效的矩阵乘法自变量，

并且任何其他外部维匹配。

Both matrices must be of the same type. The supported types are:

`float16`, `float32`, `float64`, `int32`, `complex64`, `complex128`.

两种矩阵必须属于同一类型。 支持的类型有：`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.

通过将相应标志之一设置为“ True”，可以即时对矩阵进行转置或连接(共轭和转置)。 这些默认为False。

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-