First of all, thanks a lot for sharing the wonderful VAE-IAF codes !
I am trying to implement the codes with other datasets (time series data), but I have difficulties in running made.py file.
I guess the error occurs because of keras version.... etc.
Can you tell me which version of keras did you use in making made.py file ??
Thanks again !!

I read your post on VAE. I am using pytorch resnet50 architecture as my encoder and have used a simple 5 layer transpose convolutions to get to the input dimension for decoder. The reconstructed image is noisy. There is not much change in the loss. I see that you have a VAE-resnet implementation, will that work for a huge data set with millions of images? I read another post of yours which talks about PCA trick. How is VAE resnet implemention results different from that of PCA trick implementation ? . I will also try with a pretrained resnet50 on imagenet to see whats the issue. Basically i am unable to rule out whether the problem is with the encoder, decoder or the dataset size. Any suggestion would help. Thanks.

Hello,

Sorry for sharing the file name but I thought this would be the most convenient way to track. Anyway, under the fit_model function in the notebook, there is a comment that says **_# Repeat the labeled data to match length of unlabeled data_**. I wonder wouldn't approach cause overfitting in the training session. I also saw this type of approach somewhere else but couldn't comprehend how the mechanism works here. I am aware that this is done due to handling the different sized labeled and unlabeled loaders though. I hope this clearly states my concern over here.

Thanks in advance!

I don't understand what the N is in cls_loss in vae-m2-fit-mnist.ipynb in vae-semi_supervised_learning. Neither the original paper https://arxiv.org/pdf/1406.5298.pdf nor in your blog post it is specified.
If I'm not mistaken in your implementation this variable correspond to the number of labeled samples , however, I looked at the implementation in the original paper https://github.com/dpkingma/nips14-ssl/blob/master/learn_yz_x_ss.py (line 248) and they are using nb_samples / nb_labeled samples as N instead. Could you clear my confusion?

Thank you and keep up your good work! I'm finding your blog really helpful =)

Very informative notebooks. Thanks!
In loss function logx_loss, isn't x_decoded_mean supposed to be _x_decoded_mean?

MaskingDense function in the notebook example takes ages. Is it expected?

keras 2.1.1
tensorflow 1.4.1

Hi, bjlkeng,
I read your blog and GitHub code. That's quite helpful for me as a new learner to VAE methods.

 When I was reading the code of semi-VAE m2 model, I found that the unlabeled_vae_loss function was different from Kingma's paper?
 I am not quite sure, but should it be an iteration over all possible y and a sum loss as in the first term of Eq(7) in paper "Semi-supervised Learning with Deep Generative Models"?
 
 I will appreciate it if you can reply to my confusion.