Repeat elements of `input` 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:
(input, repeats, axis=None, name=None)
| 4985 | |
| 4986 | @tf_export("repeat") |
| 4987 | def repeat(input, repeats, axis=None, name=None): # pylint: disable=redefined-builtin |
| 4988 | """Repeat elements of `input` |
| 4989 | |
| 4990 | Args: |
| 4991 | input: An `N`-dimensional Tensor. |
| 4992 | repeats: An 1-D `int` Tensor. The number of repetitions for each element. |
| 4993 | repeats is broadcasted to fit the shape of the given axis. `len(repeats)` |
| 4994 | must equal `input.shape[axis]` if axis is not None. |
| 4995 | axis: An int. The axis along which to repeat values. By default (axis=None), |
| 4996 | use the flattened input array, and return a flat output array. |
| 4997 | name: A name for the operation. |
| 4998 | |
| 4999 | Returns: |
| 5000 | A Tensor which has the same shape as `input`, except along the given axis. |
| 5001 | If axis is None then the output array is flattened to match the flattened |
| 5002 | input array. |
| 5003 | #### Examples: |
| 5004 | ```python |
| 5005 | >>> repeat(['a', 'b', 'c'], repeats=[3, 0, 2], axis=0) |
| 5006 | ['a', 'a', 'a', 'c', 'c'] |
| 5007 | >>> repeat([[1, 2], [3, 4]], repeats=[2, 3], axis=0) |
| 5008 | [[1, 2], [1, 2], [3, 4], [3, 4], [3, 4]] |
| 5009 | >>> repeat([[1, 2], [3, 4]], repeats=[2, 3], axis=1) |
| 5010 | [[1, 1, 2, 2, 2], [3, 3, 4, 4, 4]] |
| 5011 | >>> repeat(3, repeats=4) |
| 5012 | [3, 3, 3, 3] |
| 5013 | >>> repeat([[1,2], [3,4]], repeats=2) |
| 5014 | [1, 1, 2, 2, 3, 3, 4, 4] |
| 5015 | ``` |
| 5016 | """ |
| 5017 | if axis is None: |
| 5018 | input = reshape(input, [-1]) |
| 5019 | axis = 0 |
| 5020 | return repeat_with_axis(input, repeats, axis, name) |
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