loss

[hcwu1993]

i read the code. and the loss function is -logp. why training loss is negative?

[dhgrs]

I had a same question. There is a related discussion. It would help you.
openai/glow#43

[hcwu1993]

thank you@dhgrs

[Cheneng]

thank you@dhgrs

Can you explain how to solve the problem?

[rafaelvalle]

Consider the probability density function of a normal distribution without the normalizing factor \fract{1}{/\sqrt\pi\sigma^2)}, which gives us e^{-\frac{(x - \mu)^2}{2\sigma^2}}.
Now let's take the log of that and substitute \mu with 0 to obtain: -\frac{x^2}{2\sigma^2}.
Using linear algebra notation, that becomes -\frac{x^Tx}{2\sigma^2}.

HTH.

[dhgrs]

@Cheneng
Did you read this comment?
openai/glow#43 (comment)
There is how to convert probability density into probability mass. It would help you.

And I reimplemented WaveGlow with this modification.
https://github.com/dhgrs/chainer-WaveGlow
I got the loss around 6.x at 80k iterations. It is close to WaveNet's one.

[hdmjdp]

@hcwu1993 what is your loss?

[hcwu1993]

[deepconsc]

@dhgrs what's the generation speed on your implementation? And, i guess it works on tacotron2 generated samples, right?

[dhgrs]

@deepconsc I think that your question is not related to this repository or issue. I never mind you open many issues on my repository so please open new issue.

[deepconsc]

@dhgrs correct, I've opened issue on your repository, and forgot to clean up here. I'm deleting both comments.