# tensorplay.functional.lerp

Source: https://www.tensorplay.cn/docs/generated/tensorplay.functional.lerp.html

```python
tensorplay.functional.lerp(input, end, weight, *, out=None)
```

Does a linear interpolation of two tensors start (given by input) and end based
on a scalar or tensor weight and returns the resulting out tensor.

$$\text{out}_i = \text{start}_i + \text{weight}_i \times (\text{end}_i - \text{start}_i)$$

The shapes of start and end must be
[broadcastable](/docs/notes/broadcasting.html#broadcasting-semantics). If weight is a tensor, then
the shapes of weight, start, and end must be [broadcastable](/docs/notes/broadcasting.html#broadcasting-semantics).

Parameters:

- input ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)) – the tensor with the starting points

- end ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor)) – the tensor with the ending points

- weight ([float](https://docs.python.org/3/builtins/functions.html#float) or tensor) – the weight for the interpolation formula

Keyword Arguments:

out ([Tensor](/docs/generated/tensorplay.Tensor.html#tensorplay.Tensor), optional) – the output tensor.

Example:

```
>>> start = tensorplay.arange(1., 5.)
>>> end = tensorplay.empty(4).fill_(10)
>>> start
tensor([ 1.,  2.,  3.,  4.])
>>> end
tensor([ 10.,  10.,  10.,  10.])
>>> tensorplay.lerp(start, end, 0.5)
tensor([ 5.5000,  6.0000,  6.5000,  7.0000])
>>> tensorplay.lerp(start, end, tensorplay.full_like(start, 0.5))
tensor([ 5.5000,  6.0000,  6.5000,  7.0000])
```