user2255757 - 2 months ago 14x

Python Question

code:

`a = T.vector()`

b = T.vector()

loss = T.sum(a-b)

dy = T.grad(loss, a)

d2y = T.grad(loss, dy)

f = theano.function([a,b], y)

print f([.5,.5,.5], [1,0,1])

output:

`theano.gradient.DisconnectedInputError: grad method was asked to compute`

the gradientwith respect to a variable that is not part of the

computational graph of the cost, or is used only by a non-differentiable

operator: Elemwise{second}.0

how is a derivative of the graph not part of the graph? Is this why scan is used to compute the hessian?

Answer

Here:

```
d2y = T.grad(loss, dy)
```

you are attempting to compute the gradient of the loss with respect to `dy`

. However the loss depends only on the values of `a`

and `b`

and not `dy`

, hence the error. It only makes sense to compute partial derivatives of the loss with respect to parameters that actually affect its value.

The easiest way to compute the Hessian in Theano is to use the `theano.gradient.hessian`

convenience function:

```
d2y = theano.gradient.hessian(loss, a)
```

See the documentation here for an alternative manual method that uses a combination of `theano.grad`

and `theano.scan`

.

In your example the Hessian will be a 3x3 matrix of zeros, since the partial derivative of the loss w.r.t. `a`

is independent of `a`

(it's just a vector of ones).

Source (Stackoverflow)

Comments