tf.math.add_n
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Adds all input tensors element-wise.
tf.math.add_n(
inputs, name=None
)
tf.math.add_n performs the same operation as tf.math.accumulate_n.
This op does not broadcast
its inputs. If you need broadcasting, use tf.math.add (or the + operator)
instead.
For example:
a = tf.constant([[3, 5], [4, 8]])
b = tf.constant([[1, 6], [2, 9]])
tf.math.add_n([a, b, a])
<tf.Tensor: shape=(2, 2), dtype=int32, numpy=
array([[ 7, 16],
[10, 25]], dtype=int32)>
Args |
|---|
inputs
|
A list of tf.Tensor or tf.IndexedSlices objects, each with the
same shape and type. tf.IndexedSlices objects will be converted into
dense tensors prior to adding.
|
name
|
A name for the operation (optional).
|
Returns |
|---|
A tf.Tensor of the same shape and type as the elements of inputs.
|
Raises |
|---|
ValueError
|
If inputs don't all have same shape and dtype or the shape
cannot be inferred.
|
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Last updated 2023-03-17 UTC.
[null,null,["Last updated 2023-03-17 UTC."],[],[],null,["# tf.math.add_n\n\n\u003cbr /\u003e\n\n|------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.10.1/tensorflow/python/ops/math_ops.py#L4007-L4059) |\n\nAdds all input tensors element-wise.\n\n#### View aliases\n\n\n**Main aliases**\n\n[`tf.add_n`](https://www.tensorflow.org/api_docs/python/tf/math/add_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.add_n`](https://www.tensorflow.org/api_docs/python/tf/math/add_n), [`tf.compat.v1.math.add_n`](https://www.tensorflow.org/api_docs/python/tf/math/add_n)\n\n\u003cbr /\u003e\n\n tf.math.add_n(\n inputs, name=None\n )\n\n[`tf.math.add_n`](../../tf/math/add_n) performs the same operation as [`tf.math.accumulate_n`](../../tf/math/accumulate_n).\n\nThis op does not [broadcast](https://docs.scipy.org/doc/numpy-1.13.0/user/basics.broadcasting.html)\nits inputs. If you need broadcasting, use [`tf.math.add`](../../tf/math/add) (or the `+` operator)\ninstead.\n\n#### For example:\n\n a = tf.constant([[3, 5], [4, 8]])\n b = tf.constant([[1, 6], [2, 9]])\n tf.math.add_n([a, b, a])\n \u003ctf.Tensor: shape=(2, 2), dtype=int32, numpy=\n array([[ 7, 16],\n [10, 25]], dtype=int32)\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|----------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `inputs` | A list of [`tf.Tensor`](../../tf/Tensor) or [`tf.IndexedSlices`](../../tf/IndexedSlices) objects, each with the same shape and type. [`tf.IndexedSlices`](../../tf/IndexedSlices) objects will be converted into dense tensors prior to adding. |\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 [`tf.Tensor`](../../tf/Tensor) of the same shape and type as the elements of `inputs`. ||\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Raises ------ ||\n|--------------|----------------------------------------------------------------------------------|\n| `ValueError` | If `inputs` don't all have same shape and dtype or the shape cannot be inferred. |\n\n\u003cbr /\u003e"]]
View source on GitHub