tf.keras.ops.tensordot
Stay organized with collections
Save and categorize content based on your preferences.
Compute the tensor dot product along specified axes.
tf.keras.ops.tensordot(
x1, x2, axes=2
)
Args |
x1
|
First tensor.
|
x2
|
Second tensor.
|
axes
|
- If an integer, N, sum over the last N axes of
x1 and the
first N axes of x2 in order. The sizes of the corresponding
axes must match.
- Or, a list of axes to be summed over, first sequence applying
to
x1 , second to x2 . Both sequences must be of the
same length.
|
Returns |
The tensor dot product of the inputs.
|
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates. Some content is licensed under the numpy license.
Last updated 2024-06-07 UTC.
[null,null,["Last updated 2024-06-07 UTC."],[],[],null,["# tf.keras.ops.tensordot\n\n\u003cbr /\u003e\n\n|-------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/keras-team/keras/tree/v3.3.3/keras/src/ops/numpy.py#L5013-L5032) |\n\nCompute the tensor dot product along specified axes.\n\n#### View aliases\n\n\n**Main aliases**\n\n[`tf.keras.ops.numpy.tensordot`](https://www.tensorflow.org/api_docs/python/tf/keras/ops/tensordot)\n\n\u003cbr /\u003e\n\n tf.keras.ops.tensordot(\n x1, x2, axes=2\n )\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|--------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `x1` | First tensor. |\n| `x2` | Second tensor. |\n| `axes` | \u003cbr /\u003e - If an integer, N, sum over the last N axes of `x1` and the first N axes of `x2` in order. The sizes of the corresponding axes must match. - Or, a list of axes to be summed over, first sequence applying to `x1`, second to `x2`. Both sequences must be of the same length. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| The tensor dot product of the inputs. ||\n\n\u003cbr /\u003e"]]