the comment in vae.py is totally wrong ! #2
Description
`# ELBO
marginal_likelihood = tf.reduce_sum(x * tf.log(x_) + (1 - x) * tf.log(1 - x_), 1)
KL_divergence = 0.5 * tf.reduce_sum(tf.square(mu) + tf.square(sigma) - tf.log(1e-8 + tf.square(sigma)) - 1, 1)
marginal_likelihood = tf.reduce_mean(marginal_likelihood)
KL_divergence = tf.reduce_mean(KL_divergence)
ELBO = marginal_likelihood - KL_divergence
# minimize loss instead of maximizing ELBO
loss = -ELBO`
The result going right is just lucky.