tfp.experimental.distributions.marginal_fns.ps.repeat

Repeat elements of input.

tfp.experimental.distributions.marginal_fns.ps.repeat(
    input, repeats, axis=None, name=None
)

See also tf.concat, tf.stack, tf.tile.

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.

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)>