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Question about marginal_likelihood in VAE #13
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Hi. There are two tricks on implementation of marignal likelihood. With these tricks, marginal likelihood is approximated to log(p(x_i|z)). |
@hwalsuklee Hi, thanks for the explanation. I understand it. |
Dear @hwalsuklee: |
Thank you for your working and sharing. I learned a lot from them.
![image](https://user-images.githubusercontent.com/18458114/32472443-912f89b0-c317-11e7-909e-17051156a0dd.png)
However, I have a question about the VAE implementation.
In VAE.py, you calculate the marginal_likelihood as an cross entropy:
marginal_likelihood = tf.reduce_sum(self.inputs * tf.log(self.out) + (1 - self.inputs) * tf.log(1 - self.out),[1, 2])
However, I am confusing as the formular is :
where the first item on the right side should correspond to the marginal_likelihood. I think the latent variable z should be included to calculate the likelihood but you use the initial input, self.inputs.
So I am a little confusing, can you explain it?
Thank you very much!
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