mygrad.repeat#

mygrad.repeat(a: ArrayLike, repeats: int | Sequence[int], axis: int | None = None, *, constant: bool | None = None) → Tensor[source]#

Repeat elements of a tensor.

This docstring was adapted from numpy.repeat

Parameters:

aArrayLike

Input tensor.

repeatsUnion[int, Sequence[int]]

The number of repetitions for each element. repeats is broadcasted to fit the shape of the given axis.

axisOptional[int]

The axis along which to repeat values. By default, use the flattened input array, and return a flat output tensor.

constantOptional[bool]

If True, this tensor is treated as a constant, and thus does not facilitate back propagation (i.e. constant.grad will always return None).

Defaults to False for float-type data. Defaults to True for integer-type data.

Integer-type tensors must be constant.

Returns

——-

repeated_tensorTensor

Output tensor which has the same shape as a, except along the given axis.

Examples

>>> import mygrad as mg
>>> mg.repeat(3, 4)
Tensor([3, 3, 3, 3])
>>> x = mg.Tensor([[1, 2], [3, 4]])
>>> mg.repeat(x, 2)
Tensor([1, 1, 2, 2, 3, 3, 4, 4])
>>> mg.repeat(x, 3, axis=1)
Tensor([[1, 1, 1, 2, 2, 2],
        [3, 3, 3, 4, 4, 4]])
>>> mg.repeat(x, [1, 2], axis=0)
Tensor([[1, 2],
        [3, 4],
        [3, 4]])