Your suggestion put me on the right track and I'm halfway there.
I actually have two Ops - one that's doing a perform() using Python
functions (I got this to work) and another that I can make purely from
theano functions:
def make_node(self, y0, theta, dt, n):
t_y0 = tt.as_tensor_variable(y0)
t_theta = tt.as_tensor_variable(theta)
t_dt = tt.as_tensor_variable(dt)
t_n = tt.as_tensor_variable(n)
Y_hat, updates = theano.scan(fn=self.dydt,
outputs_info =[{'initial':t_y0}],
sequences=[theano.tensor.arange(t_dt,
dt*(t_n+1), t_dt)],
non_sequences=[t_dt, t_theta],
n_steps=n-1)
Y_hat = tt.concatenate((t_y0[None,:], Y_hat)) # scan does not
return y0...
Y_hat = tt.transpose(Y_hat) # return as
(len(y0),n)
apply_node = theano.Apply(self,
[t_y0, t_theta], # symbolic
inputs: y0 and theta
[Y_hat]) # symbolic
outputs: Y_hat
return apply_node
The computation graph leading to Y_hat is fine, but Apply() raises an
exception: "All output variables passed to Apply must belong to it."
because Y_hat.owner is already defined.
>From my understanding I should not use perform() here because the inputs
and outputs to the actual computation should not happend via numpy.
Where is the right place to put this sub-graph in the Op implementation?
Am Dienstag, 1. August 2017 19:25:34 UTC+2 schrieb nouiz:
> Just don't use otypes, itypes. Implement the make_node() method. It isn't
> hard. We won't extend shortly as_op or otypes/itypes.
>
> If you have question about make_node(), you can ask them.
>
> Fred
>
> On Tue, Aug 1, 2017 at 12:33 PM 'Michael Osthege' via theano-users <
> [email protected] <javascript:>> wrote:
>
>> sorry to dig up this old thread, but I am also working with pymc3 and
>>>> have a related problem:
>>>>
>>>
>> I am trying to create custom Ops for integrating an ODE model. I can
>> already do it with as_op, but that can't be pickled leading to problems
>> with parallelization in pymc3.
>>
>> I followed the theano documentation to implement a custom Op, but I
>> noticed a problem with the otypes. The *Ops otypes is a list of dvector,
>> but the length of that list can change with the Op parameters*. But the
>> itypes/otypes are not instance-attributes but class attributes. So
>> theoretically I *can't have multiply custom Ops of the same type that
>> have different itypes/otypes*, right?
>>
>> Also, do you have any idea how I could circumvent this?
>>
>> cheers
>>
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