draw_loss.py

def binary_crossentropy(t,o):
    return -(t*tf.log(o+eps) + (1.0-t)*tf.log(1.0-o+eps))

# reconstruction term appears to have been collapsed down to a single scalar value (rather than one per item in minibatch)
x_recons=tf.nn.sigmoid(cs[-1])

# after computing binary cross entropy, sum across features then take the mean of those sums across minibatches
Lx=tf.reduce_sum(binary_crossentropy(x,x_recons),1) # reconstruction term
Lx=tf.reduce_mean(Lx)

kl_terms=[0]*T
for t in range(T):
    mu2=tf.square(mus[t])
    sigma2=tf.square(sigmas[t])
    logsigma=logsigmas[t]
    kl_terms[t]=0.5*tf.reduce_sum(mu2+sigma2-2*logsigma,1)-T*.5 # each kl term is (1xminibatch)
KL=tf.add_n(kl_terms) # this is 1xminibatch, corresponding to summing kl_terms from 1:T
Lz=tf.reduce_mean(KL) # average over minibatches
cost=Lx+Lz