Hi, 

I presume the same steps are to make the normal RBM take in real/gaussian 
numbers ?

On Monday, 11 March 2013 21:32:19 UTC, Nicolas Boulanger-Lewandowski wrote:
>
> Hi,
>
> The shared_normal and shared_zeros are simply convenience functions to 
> initialize weight and bias *parameters* respectively; they both return 
> float shared variables.
>
> For sequences of real-valued visible units, you can use the Gaussian RBM. 
> To adapt the code for Gaussian RBM, you need to alter two things:
>
> - Add 0.5*(v**2).sum() to the free energy
> - Modify the sampling for v_i from Bernoulli to normal with variance 1 :
> mean_v = T.dot(h, W.T) + vbias
> v = mean_v + rng.normal(size=mean_v.shape, avg=0.0, std=1.0, 
> dtype=theano.config.floatX)
>
> You can also replace the monitoring cost to the L2 error:  (v-mean_v)**2
>
>
> On Sunday, March 10, 2013 8:51:19 AM UTC-4, Alexander Bridi wrote:
>>
>> Hey all,
>>
>> I'm trying to modify the rnnrbm.py tutorial code to allow for non-binary 
>> inputs/outputs.
>>
>> So far, I found the modification must be made between the output of the 
>> RNN and the input of the RBM, possibly around the binary bh, bv, bu 
>> variables.
>>
>> I tried creating these variables via the shared_normal function (as 
>> opposed to the shared_zeros function), but I end up running into 
>> a ValueError around the IncSubtensor's dtype consistency.
>>
>> But more importantly, is this the right thing to do!? I have the feeling 
>> this isn't, because the shared_normal function just initializes the 
>> variable in a non-binary manor and doesn't play a role in the updates.. but 
>> I'm not sure how to modify the recurrence function to yield non-binary 
>> outputs/
>>
>> Has anyone tried doing this already?
>>
>> Any advice would be much appreciated!
>>
>> Thanks,
>>
>> Alexander
>>
>

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