also what is the constant that cancels out, does it equate to vbias**2

On Monday, 16 December 2013 18:35:26 UTC, Nicolas Boulanger-Lewandowski 
wrote:
>
> You can use that term provided you remove the -(v dot vbias) term in the 
> original free energy function.
>
> The difference between the two formulation is then a constant that cancels 
> out when you subtract the negative phase, so you can use either.
>
>
>
> On Monday, December 16, 2013 1:55:31 AM UTC-5, C. Ng wrote:
>>
>> Hi Nicolas and all,
>>
>> I have also seen the additional free energy term written as 
>> 0.5*(v-vbias)**2).sum() 
>>
>> Does it make a difference?
>>
>>
>>  
>>
>> On Tuesday, March 12, 2013 5:32:19 AM UTC+8, 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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