tf.repeat
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Repeat elements of input.
tf.repeat(
input, repeats, axis=None, name=None
)
Used in the notebooks
See also tf.concat, tf.stack, tf.tile.
Args |
|---|
input
|
An N-dimensional Tensor.
|
repeats
|
An 1-D int Tensor. The number of repetitions for each element.
repeats is broadcasted to fit the shape of the given axis. len(repeats)
must equal input.shape[axis] if axis is not None.
|
axis
|
An int. The axis along which to repeat values. By default, (axis=None),
use the flattened input array, and return a flat output array.
|
name
|
A name for the operation.
|
Returns |
|---|
A Tensor which has the same shape as input, except along the given axis.
If axis is None then the output array is flattened to match the flattened
input array.
|
Example usage:
repeat(['a', 'b', 'c'], repeats=[3, 0, 2], axis=0)
<tf.Tensor: shape=(5,), dtype=string,
numpy=array([b'a', b'a', b'a', b'c', b'c'], dtype=object)>
repeat([[1, 2], [3, 4]], repeats=[2, 3], axis=0)
<tf.Tensor: shape=(5, 2), dtype=int32, numpy=
array([[1, 2],
[1, 2],
[3, 4],
[3, 4],
[3, 4]], dtype=int32)>
repeat([[1, 2], [3, 4]], repeats=[2, 3], axis=1)
<tf.Tensor: shape=(2, 5), dtype=int32, numpy=
array([[1, 1, 2, 2, 2],
[3, 3, 4, 4, 4]], dtype=int32)>
repeat(3, repeats=4)
<tf.Tensor: shape=(4,), dtype=int32, numpy=array([3, 3, 3, 3], dtype=int32)>
repeat([[1,2], [3,4]], repeats=2)
<tf.Tensor: shape=(8,), dtype=int32,
numpy=array([1, 1, 2, 2, 3, 3, 4, 4], dtype=int32)>
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Last updated 2024-04-26 UTC.
[null,null,["Last updated 2024-04-26 UTC."],[],[],null,["# tf.repeat\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.py#L6592-L6643) |\n\nRepeat elements of `input`.\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.repeat`](https://www.tensorflow.org/api_docs/python/tf/repeat)\n\n\u003cbr /\u003e\n\n tf.repeat(\n input, repeats, axis=None, name=None\n )\n\n### Used in the notebooks\n\n| Used in the tutorials |\n|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| - [Transfer learning with YAMNet for environmental sound classification](https://www.tensorflow.org/tutorials/audio/transfer_learning_audio) - [Parametrized Quantum Circuits for Reinforcement Learning](https://www.tensorflow.org/quantum/tutorials/quantum_reinforcement_learning) - [Recommend movies for users with TensorFlow Ranking](https://www.tensorflow.org/ranking/tutorials/quickstart) - [Listwise ranking](https://www.tensorflow.org/recommenders/examples/listwise_ranking) |\n\nSee also [`tf.concat`](../tf/concat), [`tf.stack`](../tf/stack), [`tf.tile`](../tf/tile).\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|-----------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `input` | An `N`-dimensional Tensor. |\n| `repeats` | An 1-D `int` Tensor. The number of repetitions for each element. repeats is broadcasted to fit the shape of the given axis. `len(repeats)` must equal `input.shape[axis]` if axis is not None. |\n| `axis` | An int. The axis along which to repeat values. By default, (axis=None), use the flattened input array, and return a flat output array. |\n| `name` | A name for the operation. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A Tensor which has the same shape as `input`, except along the given axis. If axis is None then the output array is flattened to match the flattened input array. ||\n\n\u003cbr /\u003e\n\n#### Example usage:\n\n repeat(['a', 'b', 'c'], repeats=[3, 0, 2], axis=0)\n \u003ctf.Tensor: shape=(5,), dtype=string,\n numpy=array([b'a', b'a', b'a', b'c', b'c'], dtype=object)\u003e\n\n repeat([[1, 2], [3, 4]], repeats=[2, 3], axis=0)\n \u003ctf.Tensor: shape=(5, 2), dtype=int32, numpy=\n array([[1, 2],\n [1, 2],\n [3, 4],\n [3, 4],\n [3, 4]], dtype=int32)\u003e\n\n repeat([[1, 2], [3, 4]], repeats=[2, 3], axis=1)\n \u003ctf.Tensor: shape=(2, 5), dtype=int32, numpy=\n array([[1, 1, 2, 2, 2],\n [3, 3, 4, 4, 4]], dtype=int32)\u003e\n\n repeat(3, repeats=4)\n \u003ctf.Tensor: shape=(4,), dtype=int32, numpy=array([3, 3, 3, 3], dtype=int32)\u003e\n\n repeat([[1,2], [3,4]], repeats=2)\n \u003ctf.Tensor: shape=(8,), dtype=int32,\n numpy=array([1, 1, 2, 2, 3, 3, 4, 4], dtype=int32)\u003e"]]
View source on GitHub