Re: [Kaldi-developers] obtaining 0'os when computing posteriorgrams

[Daniel P. <dp...@gm...>]

Hm, thanks, but I don't think this is the right way to fix the problem.
 Update code should always take into account the possibility that
occupancies will be zero.  It's expected that exp() on very negative values
will produce zero.
Dan


On Mon, Apr 8, 2013 at 3:15 PM, Xavier Anguera <xan...@gm...> wrote:

> Hi Dan,
> the segmentation fault comes from a division by 0 when using the occupancy
> of the Gaussians, that has been computed by adding together all the
> posterior probabilities for each Gaussian and a set of features. When all
> posteriors for a given Gaussian and all features is 0, there is a division
> by 0.
> I am pasting the "hack" I wrote to prevent this. I believe though that
> maybe the exp() function should be revisited. Tell me what you think.
>
> Real VectorBase<Real>::ApplySoftMax() {
>   Real max = this->Max(), sum = 0.0;
>   for (MatrixIndexT i = 0; i < dim_; i++) {
>       data_[i] = exp(data_[i] - max);
>       if(data_[i] < FLT_MIN )
>           data_[i] = FLT_MIN; //very small value
>       sum += data_[i];
>   }
>
>   this->Scale(1.0 / sum);
>   return max + log(sum);
> }
>
>
> On Mon, Apr 8, 2013 at 8:58 PM, Daniel Povey <dp...@gm...> wrote:
>
>> Firstly, this should give you numerical problems but not a segmentation
>> fault.
>> You'll have to look in the code and see if it's behaving as expected.
>>  E.g. is it due to a number so small that it cannot be represented in
>> floating point, or is it larger than that and unexpectedly becoming zero?
>> It might be an issue with your algorithm design.
>> Let me know if that function needs to be fixed.
>>
>> Dan
>>
>>
>> On Mon, Apr 8, 2013 at 2:54 PM, Xavier Anguera <xan...@gm...>wrote:
>>
>>>  Hi,
>>> when using the function template<typename Real> Real
>>> VectorBase<Real>::ApplySoftMax() in kaldi-vector.cc file, I noticed that
>>> very small likelihoods are rounded to a posterior probability of 0.0
>>> Is this an expected behavior? I am trying to perform an EM training of a
>>> simple GMM and I keep bumping into segmentation fault due to this.
>>>
>>> Thanks
>>>
>>> Xavi Anguera
>>>
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>>
>