I have a similar problem:
out = lasagne.layers.get_output(nets[index], x)
theano.function([index], out, givens= {x: inputs})
I basically have a list of neural networks nets and want to dynamically
select which to evaluate, depending on index. How could I do this?
Cris
On Tuesday, March 10, 2015 at 9:10:58 AM UTC-4, nouiz wrote:
>
> Do all element of the list have the same size? If so, I would recommand to
> make an ndarray with 1 extra dimensions instead of putting this in a list.
> Then you can put this ndarray as a shared variable
>
> example:
>
> nd_list = numpy.ndarray(list)
> shared_var = theano.shared(nd_list)
>
> then you can do:
>
> shared_var[index]
>
> And use that in a Theano function.
>
> Otherwise, you can use typed list:
>
> http://deeplearning.net/software/theano/library/typed_list.html
>
> Fred
>
> On Tue, Mar 10, 2015 at 9:03 AM, Rob <[email protected] <javascript:>>
> wrote:
>
>> I have circumvented the problem. Rather than creating a list of tensor on
>> which I apply a single theano function, I just created a list of theano
>> functions (one for each tensor).
>>
>> If there are better solutions I would still love to here them.
>>
>>
>> On Tuesday, 10 March 2015 12:46:05 UTC, Rob wrote:
>>>
>>> I want a theano function that, given a scalar input, will return a
>>> tensor stored in a list at the index of that scalar. i.e
>>>
>>> theano.function([index],list[index],givens= {x: <some function of index
>>> >})
>>>
>>> where the list contains different x-dependent tensors that I want to
>>> evaluate depending on the index given.
>>>
>>> This generates error:
>>>
>>> TypeError: list indices must be integers, not TensorVariable
>>>
>>>
>>> I'm very new to Theano so I'm probably thinking about this problem in
>>> the wrong way. I would really appreciate any advice.
>>>
>>> Rob
>>>
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