Multiplies all slices of Tensorx 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 2-D or higher with shape [..., r_x, c_x]
and [..., r_y, c_y].
The output tensor is 2-D or higher with shape [..., r_o, c_o], where:
A Tensor. Must be one of the following types: bfloat16, half, float32, float64, int16, int32, int64, uint8, uint16, uint32, uint64, complex64, complex128.
2-D or higher with shape [..., r_x, c_x].
y
A Tensor. Must have the same type as x.
2-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.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-04-26 UTC."],[],[],null,["# tf.raw_ops.BatchMatMulV2\n\n\u003cbr /\u003e\n\nMultiplies slices of two tensors in batches.\n\n#### View aliases\n\n\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.raw_ops.BatchMatMulV2`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/BatchMatMulV2)\n\n\u003cbr /\u003e\n\n tf.raw_ops.BatchMatMulV2(\n x, y, adj_x=False, adj_y=False, grad_x=False, grad_y=False, name=None\n )\n\nMultiplies all slices of `Tensor` `x` and `y` (each slice can be\nviewed as an element of a batch), and arranges the individual results\nin a single output tensor of the same batch size. Each of the\nindividual slices can optionally be adjointed (to adjoint a matrix\nmeans to transpose and conjugate it) before multiplication by setting\nthe `adj_x` or `adj_y` flag to `True`, which are by default `False`.\n\nThe input tensors `x` and `y` are 2-D or higher with shape `[..., r_x, c_x]`\nand `[..., r_y, c_y]`.\n\nThe output tensor is 2-D or higher with shape `[..., r_o, c_o]`, where: \n\n r_o = c_x if adj_x else r_x\n c_o = r_y if adj_y else c_y\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| It is computed as ----------------- ||\n|---|---|\n| output\\[..., :, :\\] = matrix(x\\[..., :, :\\]) \\* matrix(y\\[..., :, :\\]) ||\n\n\u003cbr /\u003e\n\n| **Note:** `BatchMatMulV2` supports broadcasting in the batch dimensions. More about broadcasting [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html).\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|----------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `x` | A `Tensor`. Must be one of the following types: `bfloat16`, `half`, `float32`, `float64`, `int16`, `int32`, `int64`, `uint8`, `uint16`, `uint32`, `uint64`, `complex64`, `complex128`. 2-D or higher with shape `[..., r_x, c_x]`. |\n| `y` | A `Tensor`. Must have the same type as `x`. 2-D or higher with shape `[..., r_y, c_y]`. |\n| `adj_x` | An optional `bool`. Defaults to `False`. If `True`, adjoint the slices of `x`. Defaults to `False`. |\n| `adj_y` | An optional `bool`. Defaults to `False`. If `True`, adjoint the slices of `y`. Defaults to `False`. |\n| `grad_x` | An optional `bool`. Defaults to `False`. |\n| `grad_y` | An optional `bool`. Defaults to `False`. |\n| `name` | A name for the operation (optional). |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A `Tensor`. Has the same type as `x`. ||\n\n\u003cbr /\u003e"]]
