That error actually comes from the function compilation that happens inside
op2.
One option is to add "inline=True" to the call to OpFromGraph returning
op2, in that case the gradient gets overriden, but you will have the
inlined subgraph in the global one, and OpFromGraph will not build an inner
function.
The other one would be to add "on_unused_input='ignore'" to the call to
OpFromGraph:
op2 = theano.OpFromGraph(
inputs=[z_var, m_var, r_var],
outputs=[z_var],
grad_overrides=grad_op2,
on_unused_input='ignore')
With it, the program worked, but I still got an error in g.eval(...):
TypeError: Cannot convert Type DisconnectedType (of Variable
<DisconnectedType>) into Type TensorType(float64, scalar). You can try to
manually convert <DisconnectedType> into a TensorType(float64, scalar).
I'm not sure where that comes from, please let us know if you do not figure
it out.
On Tuesday, July 11, 2017 at 5:32:50 AM UTC-4, [email protected]
wrote:
>
> Hi,
>
> I am trying to split an computation over two ops in order to avoid
> spurious computations when computing the gradient.
> My current attempt uses a first op which returns the desired result for
> the forward part and extra intermediate results. The second op just
> forwards the desired result, but its grad is overriden to compute the
> gradient based on the intermediate results.
>
> In this configuration, Theano complains about unused inputs in the forward
> computation because the intermediate results are not used for the forward
> method of the second op.
>
> Is this an expected behaviour or a bug?
>
> ----
>
> import numpy as np
> import theano.tensor as T
> import theano
>
>
> def make_ops():
> x_var = T.vector()
> m_var = T.bvector()
>
> r = m_var.sum().astype('floatX')
> z = x_var * m_var / r
>
>
> def grad_op1(inputs, output_gradients):
> return [
> output_gradients[0], # computation delegated to op2
> theano.gradient.DisconnectedType()()
> ]
>
>
> op1 = theano.OpFromGraph(
> inputs=[x_var, m_var],
> outputs=[z, r],
> grad_overrides=grad_op1)
>
>
> z_var = T.vector()
> r_var = T.scalar()
>
> def grad_op2(inputs, output_gradients):
> _, m_, r_ = inputs
> return [
> m_ * r_,
> theano.gradient.DisconnectedType()(),
> theano.gradient.DisconnectedType()()
> ]
>
> op2 = theano.OpFromGraph(
> inputs=[z_var, m_var, r_var],
> outputs=[z_var],
> grad_overrides=grad_op2)
>
> return op1, op2
>
>
> op1, op2 = make_ops()
> x_var = T.vector()
> m_var = T.bvector()
> z_, r = op1(x_var, m_var)
> z = op2(z_, m_var, r)
>
> print(z_.eval({x_var: np.array([1., .3, .0, .2], dtype=np.float32),
> m_var: np.array([1, 0, 1, 1], dtype=np.int8)}))
>
> f = theano.function([x_var, m_var], [z], on_unused_input='ignore') #
> raises anyway
>
> print(f(np.array([1., .3, .0, .2], dtype=np.float32),
> np.array([1, 0, 1, 1], dtype=np.int8)))
>
> # g = theano.grad(T.sum(z), wrt=x_var)
> # print(g.eval({x_var: np.array([1., .3, .0, .2], dtype=np.float32),
> # m_var: np.array([1, 0, 1, 1], dtype=np.int8)}))
>
>
>
--
---
You received this message because you are subscribed to the Google Groups
"theano-users" group.
To unsubscribe from this group and stop receiving emails from it, send an email
to [email protected].
For more options, visit https://groups.google.com/d/optout.
