tf.batch_matmul()

tf.batch_matmul(x, y, adj_x=None, adj_y=None, name=None)

Multiplies slices of two tensors in batches.

Multiplies all slices of Tensor x and y (each slice can be viewed as an element of a batch), and arranges the individual results in a single output tensor of the same batch size. Each of the individual slices can optionally be adjointed (to adjoint a matrix means to transpose and conjugate it) before multiplication by setting the adj_x or adj_y flag to True, which are by default False.

The input tensors x and y are 3-D or higher with shape [..., r_x, c_x] and [..., r_y, c_y].

The output tensor is 3-D or higher with shape [..., r_o, c_o], where:

r_o = c_x if adj_x else r_x
c_o = r_y if adj_y else c_y

It is computed as:

output[..., :, :] = matrix(x[..., :, :]) * matrix(y[..., :, :])

Args:

• x: A Tensor. Must be one of the following types: half, float32, float64, int32, complex64, complex128. 3-D or higher with shape [..., r_x, c_x].
• y: A Tensor. Must have the same type as x. 3-D or higher with shape [..., r_y, c_y].
• adj_x: An optional bool. Defaults to False. If True, adjoint the slices of x. Defaults to False.
• adj_y: An optional bool. Defaults to False. If True, adjoint the slices of y. Defaults to False.
• name: A name for the operation (optional).

Returns:

A Tensor. Has the same type as x. 3-D or higher with shape [..., r_o, c_o]