Function matvec

tensorflow/python/ops/math_ops.py:2758–2852

Multiplies matrix `a` by vector `b`, producing `a` * `b`. The matrix `a` must, following any transpositions, be a tensor of rank >= 2, and we must have `shape(b) = shape(a)[:-2] + [shape(a)[-1]]`. Both `a` and `b` must be of the same type. The supported types are: `float16`, `float32`, `fl

(a,
           b,
           transpose_a=False,
           adjoint_a=False,
           a_is_sparse=False,
           b_is_sparse=False,
           name=None)

@tf_export("linalg.matvec")
def matvec(a,
           b,
           transpose_a=False,
           adjoint_a=False,
           a_is_sparse=False,
           b_is_sparse=False,
           name=None):
  """Multiplies matrix `a` by vector `b`, producing `a` * `b`.

  The matrix `a` must, following any transpositions, be a tensor of rank >= 2,
  and we must have `shape(b) = shape(a)[:-2] + [shape(a)[-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:

  ```python
  # 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.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],