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Code for reproducing results of NIPS 2014 paper "Semi-Supervised Learning with Deep Generative Models"
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Code for reproducing some key results of our NIPS 2014 paper on semi-supervised learning (SSL) with deep generative models.

D.P. Kingma, D.J. Rezende, S. Mohamed, M. Welling
Semi-Supervised Learning with Deep Generative Models
Advances in Neural Information Processing Systems 27 (NIPS 2014), Montreal

Please cite this paper when using this code for your research.

Warning: This code is far from fully commented.

For questions and bug reports, please send me an e-mail at dpkingma[at]


  1. Make sure that recent versions installed of:

    • Python (version 2.7 or higher)
    • Numpy (e.g. pip install numpy)
    • Theano (e.g. pip install Theano)
  2. Set floatX = float32 in the [global] section of Theano config (usually ~/.theanorc). Alternatively you could prepend THEANO_FLAGS=floatX=float32 to the python commands below.

  3. Clone this repository, e.g.:

git clone
  1. Set an environment variable ML_DATA_PATH that points to subdirectory data/. For example, if you checked out this repo to your home directory:
export ML_DATA_PATH="$HOME/nips14-ssl/data"

Qualitative results

Flying through latent space of M2 model

To generate movies of flying through latent-space of the M2 model, run:

python [dataset] 1 output.mkv

where dataset is 'mnist' or 'svhn', and target_filename is the filename to save the movie file to. NOTE: This script requires ffmpeg to be installed.



python [dataset] 1

Quantitative results

Learning M1 model

To train model M1 (a standard Variational Auto-Encoder / DLGM with sperical Gaussian latent space):

python [dataset]

The M1 model does not incorporate class label, but is used in the paper's experiments for feature extration.

Learning M1+M2 model, partially observed labels

To run the semi-supervised learning experiments with model M1+M2:

python [n_labels] [seed]

where n_labels is the number of labels, and seed is the random seed for Numpy. To reproduce the experimental results in the paper, the number of labels should be in (100,600,1000,3000). The random seed can be any integer. Each experiment will run for 3000 epochs; since this code is not GPU-optimized, running many epochs might take a few days to complete. However, it is often not necessary to run the the algorithm for so many epochs to produce good results.

Learning M2 model with fully observed labels

For training a generative model with all labels:

python [dataset]

where dataset is 'mnist', 'svhn', 'norb' or 'norb_reshuffled'.

Evaluate test-set error of models trained with all labels

For evaluating the test-set classification error using already trained generative models of MNIST and SVHN:

python [dataset]

This iteratively builds, for each test-set image, an importance-sampled estimate of the posterior probability distribution over the class labels. This is an expensive procedure, but may be speed up by using fitting an inference model to the posterior distribution of class labels (which wasn't done in this case).

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