# tf.repeat

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.7.4/tensorflow/python/ops/array_ops.py#L6622-L6673) |

Repeat elements of `input`.

#### View aliases


**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

[`tf.compat.v1.repeat`](https://www.tensorflow.org/api_docs/python/tf/repeat)

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    tf.repeat(
        input, repeats, axis=None, name=None
    )

See also [`tf.concat`](https://www.tensorflow.org/versions/r2.7/api_docs/python/tf/concat), [`tf.stack`](https://www.tensorflow.org/versions/r2.7/api_docs/python/tf/stack), [`tf.tile`](https://www.tensorflow.org/versions/r2.7/api_docs/python/tf/tile).

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| ## 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. |

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| ## 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. ||

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#### 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)>