Repeat elements of input
.
tf.repeat(
input, repeats, axis=None, name=None
)
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)>