tf.stack
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Stacks a list of rank-R tensors into one rank-(R+1) tensor.
tf.stack(
values, axis=0, name='stack'
)
Used in the notebooks
| Used in the guide |
Used in the tutorials |
|
|
|
See also tf.concat, tf.tile, tf.repeat.
Packs the list of tensors in values into a tensor with rank one higher than
each tensor in values, by packing them along the axis dimension.
Given a list of length N of tensors of shape (A, B, C);
if axis == 0 then the output tensor will have the shape (N, A, B, C).
if axis == 1 then the output tensor will have the shape (A, N, B, C).
Etc.
For example:
x = tf.constant([1, 4])
y = tf.constant([2, 5])
z = tf.constant([3, 6])
tf.stack([x, y, z])
<tf.Tensor: shape=(3, 2), dtype=int32, numpy=
array([[1, 4],
[2, 5],
[3, 6]], dtype=int32)>
tf.stack([x, y, z], axis=1)
<tf.Tensor: shape=(2, 3), dtype=int32, numpy=
array([[1, 2, 3],
[4, 5, 6]], dtype=int32)>
This is the opposite of unstack. The numpy equivalent is np.stack
np.array_equal(np.stack([x, y, z]), tf.stack([x, y, z]))
True
Args |
|---|
values
|
A list of Tensor objects with the same shape and type.
|
axis
|
An int. The axis to stack along. Defaults to the first dimension.
Negative values wrap around, so the valid range is [-(R+1), R+1).
|
name
|
A name for this operation (optional).
|
Returns |
|---|
output
|
A stacked Tensor with the same type as values.
|
Raises |
|---|
ValueError
|
If axis is out of the range [-(R+1), R+1).
|
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Last updated 2024-04-26 UTC.
[[["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complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-04-26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.stack\n\n\u003cbr /\u003e\n\n|---------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.16.1/tensorflow/python/ops/array_ops_stack.py#L24-L85) |\n\nStacks a list of rank-`R` tensors into one rank-`(R+1)` tensor.\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.stack`](https://www.tensorflow.org/api_docs/python/tf/stack)\n\n\u003cbr /\u003e\n\n tf.stack(\n values, axis=0, name='stack'\n )\n\n### Used in the notebooks\n\n| Used in the guide | Used in the tutorials |\n|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| - [Extension types](https://www.tensorflow.org/guide/extension_type) - [tf.data: Build TensorFlow input pipelines](https://www.tensorflow.org/guide/data) - [TensorFlow basics](https://www.tensorflow.org/guide/basics) - [Random number generation](https://www.tensorflow.org/guide/random_numbers) | - [Time series forecasting](https://www.tensorflow.org/tutorials/structured_data/time_series) - [Custom training: walkthrough](https://www.tensorflow.org/tutorials/customization/custom_training_walkthrough) - [pix2pix: Image-to-image translation with a conditional GAN](https://www.tensorflow.org/tutorials/generative/pix2pix) - [Overfit and underfit](https://www.tensorflow.org/tutorials/keras/overfit_and_underfit) - [Load CSV data](https://www.tensorflow.org/tutorials/load_data/csv) |\n\nSee also [`tf.concat`](../tf/concat), [`tf.tile`](../tf/tile), [`tf.repeat`](../tf/repeat).\n\nPacks the list of tensors in `values` into a tensor with rank one higher than\neach tensor in `values`, by packing them along the `axis` dimension.\nGiven a list of length `N` of tensors of shape `(A, B, C)`;\n\nif `axis == 0` then the `output` tensor will have the shape `(N, A, B, C)`.\nif `axis == 1` then the `output` tensor will have the shape `(A, N, B, C)`.\nEtc.\n\n#### For example:\n\n x = tf.constant([1, 4])\n y = tf.constant([2, 5])\n z = tf.constant([3, 6])\n tf.stack([x, y, z])\n \u003ctf.Tensor: shape=(3, 2), dtype=int32, numpy=\n array([[1, 4],\n [2, 5],\n [3, 6]], dtype=int32)\u003e\n tf.stack([x, y, z], axis=1)\n \u003ctf.Tensor: shape=(2, 3), dtype=int32, numpy=\n array([[1, 2, 3],\n [4, 5, 6]], dtype=int32)\u003e\n\nThis is the opposite of unstack. The numpy equivalent is `np.stack` \n\n np.array_equal(np.stack([x, y, z]), tf.stack([x, y, z]))\n True\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|----------|-----------------------------------------------------------------------------------------------------------------------------------------|\n| `values` | A list of `Tensor` objects with the same shape and type. |\n| `axis` | An `int`. The axis to stack along. Defaults to the first dimension. Negative values wrap around, so the valid range is `[-(R+1), R+1)`. |\n| `name` | A name for this operation (optional). |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|----------|----------------------------------------------------|\n| `output` | A stacked `Tensor` with the same type as `values`. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Raises ------ ||\n|--------------|-----------------------------------------------|\n| `ValueError` | If `axis` is out of the range \\[-(R+1), R+1). |\n\n\u003cbr /\u003e"]]
View source on GitHub