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

[Xavier A. <xan...@gm...>]

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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>