# tensorplay.autograd.functional.vjp

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

# tensorplay.autograd.functional.vjp

tensorplay.autograd.functional.vjp(func, inputs, v=None, create_graph=False, strict=False)[[source]](../_modules/tensorplay/autograd/functional.html#vjp)

Compute the dot product between a vector v and the Jacobian of the given function at the point given by the inputs.

Parameters:

- func ( function ) – a Python function that takes Tensor inputs and returns a tuple of Tensors or a Tensor.

- inputs ([tuple](https://docs.python.org/3/library/stdtypes.html#tuple) of Tensors or Tensor ) – inputs to the function func .

- v ([tuple](https://docs.python.org/3/library/stdtypes.html#tuple) of Tensors or Tensor ) – The vector for which the vector Jacobian product is computed. Must be the same size as the output of func . This argument is optional when the output of func contains a single element and (if it is not provided) will be set as a Tensor containing a single 1 .

- create_graph ([bool](https://docs.python.org/3/library/functions.html#bool) , optional ) – If True , both the output and result will be computed in a differentiable way. Note that when strict is False , the result can not require gradients or be disconnected from the inputs. Defaults to False .

- strict ([bool](https://docs.python.org/3/library/functions.html#bool) , optional ) – If True , an error will be raised when we detect that there exists an input such that all the outputs are independent of it. If False , we return a Tensor of zeros as the vjp for said inputs, which is the expected mathematical value. Defaults to False .

Returns:

tuple with:

func_output (tuple of Tensors or Tensor): output of func(inputs)

vjpval (tuple of Tensors or Tensor): result of the dot product with
the same shape as the inputs.

Return type:

output ([tuple](https://docs.python.org/3/library/stdtypes.html#tuple))

Example

```
>>> def exp_reducer(x):
...     return x.exp().sum(dim=1)
>>> inputs = tensorplay.rand(4, 4)
>>> v = tensorplay.ones(4)
>>> vjp(exp_reducer, inputs, v)
(tensor([5.7817, 7.2458, 5.7830, 6.7782]),
 tensor([[1.4458, 1.3962, 1.3042, 1.6354],
        [2.1288, 1.0652, 1.5483, 2.5035],
        [2.2046, 1.1292, 1.1432, 1.3059],
        [1.3225, 1.6652, 1.7753, 2.0152]]))
```