tf.tile
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Constructs a tensor by tiling a given tensor.
tf.tile(
input: Annotated[Any, TV_Tile_T],
multiples: Annotated[Any, TV_Tile_Tmultiples],
name=None
) -> Annotated[Any, TV_Tile_T]
Used in the notebooks
| Used in the guide |
Used in the tutorials |
|
|
|
This operation creates a new tensor by replicating input multiples times.
The output tensor's i'th dimension has input.dims(i) * multiples[i] elements,
and the values of input are replicated multiples[i] times along the 'i'th
dimension. For example, tiling [a b c d] by [2] produces
[a b c d a b c d].
a = tf.constant([[1,2,3],[4,5,6]], tf.int32)
b = tf.constant([1,2], tf.int32)
tf.tile(a, b)
<tf.Tensor: shape=(2, 6), dtype=int32, numpy=
array([[1, 2, 3, 1, 2, 3],
[4, 5, 6, 4, 5, 6]], dtype=int32)>
c = tf.constant([2,1], tf.int32)
tf.tile(a, c)
<tf.Tensor: shape=(4, 3), dtype=int32, numpy=
array([[1, 2, 3],
[4, 5, 6],
[1, 2, 3],
[4, 5, 6]], dtype=int32)>
d = tf.constant([2,2], tf.int32)
tf.tile(a, d)
<tf.Tensor: shape=(4, 6), dtype=int32, numpy=
array([[1, 2, 3, 1, 2, 3],
[4, 5, 6, 4, 5, 6],
[1, 2, 3, 1, 2, 3],
[4, 5, 6, 4, 5, 6]], dtype=int32)>
Args |
|---|
input
|
A Tensor. Can be of any rank.
|
multiples
|
A Tensor. Must be one of the following types: int32, int64.
1-D. Length must be the same as the number of dimensions in input
|
name
|
A name for the operation (optional).
|
Returns |
|---|
A Tensor. Has the same type as input.
|
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Last updated 2024-04-26 UTC.
[null,null,["Last updated 2024-04-26 UTC."],[],[],null,["# tf.tile\n\n\u003cbr /\u003e\n\nConstructs a tensor by tiling a given 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.manip.tile`](https://www.tensorflow.org/api_docs/python/tf/tile), [`tf.compat.v1.tile`](https://www.tensorflow.org/api_docs/python/tf/tile)\n\n\u003cbr /\u003e\n\n tf.tile(\n input: Annotated[Any, TV_Tile_T],\n multiples: Annotated[Any, TV_Tile_Tmultiples],\n name=None\n ) -\u003e Annotated[Any, TV_Tile_T]\n\n### Used in the notebooks\n\n| Used in the guide | Used in the tutorials |\n|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| - [Better performance with the tf.data API](https://www.tensorflow.org/guide/data_performance) - [Ragged tensors](https://www.tensorflow.org/guide/ragged_tensor) - [Understanding masking \\& padding](https://www.tensorflow.org/guide/keras/understanding_masking_and_padding) | - [Time series forecasting](https://www.tensorflow.org/tutorials/structured_data/time_series) - [Calculate gradients](https://www.tensorflow.org/quantum/tutorials/gradients) - [Quantum data](https://www.tensorflow.org/quantum/tutorials/quantum_data) - [Parametrized Quantum Circuits for Reinforcement Learning](https://www.tensorflow.org/quantum/tutorials/quantum_reinforcement_learning) - [A Tutorial on Multi-Armed Bandits with Per-Arm Features](https://www.tensorflow.org/agents/tutorials/per_arm_bandits_tutorial) |\n\nThis operation creates a new tensor by replicating `input` `multiples` times.\nThe output tensor's i'th dimension has `input.dims(i) * multiples[i]` elements,\nand the values of `input` are replicated `multiples[i]` times along the 'i'th\ndimension. For example, tiling `[a b c d]` by `[2]` produces\n`[a b c d a b c d]`. \n\n a = tf.constant([[1,2,3],[4,5,6]], tf.int32)\n b = tf.constant([1,2], tf.int32)\n tf.tile(a, b)\n \u003ctf.Tensor: shape=(2, 6), dtype=int32, numpy=\n array([[1, 2, 3, 1, 2, 3],\n [4, 5, 6, 4, 5, 6]], dtype=int32)\u003e\n c = tf.constant([2,1], tf.int32)\n tf.tile(a, c)\n \u003ctf.Tensor: shape=(4, 3), dtype=int32, numpy=\n array([[1, 2, 3],\n [4, 5, 6],\n [1, 2, 3],\n [4, 5, 6]], dtype=int32)\u003e\n d = tf.constant([2,2], tf.int32)\n tf.tile(a, d)\n \u003ctf.Tensor: shape=(4, 6), dtype=int32, numpy=\n array([[1, 2, 3, 1, 2, 3],\n [4, 5, 6, 4, 5, 6],\n [1, 2, 3, 1, 2, 3],\n [4, 5, 6, 4, 5, 6]], dtype=int32)\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|-------------|---------------------------------------------------------------------------------------------------------------------------------------|\n| `input` | A `Tensor`. Can be of any rank. |\n| `multiples` | A `Tensor`. Must be one of the following types: `int32`, `int64`. 1-D. Length must be the same as the number of dimensions in `input` |\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 `input`. ||\n\n\u003cbr /\u003e"]]
