Awesome
chainer-Variational-AutoEncoder
Variational Auto Encoder implemented by Chainer
Requirement
- Chainer
M1 model
Train
Start training the model using train_VAE.py, for example
$python train_VAE.py
Generate data
You can generate data by giving a latent space vector. For example,
$python generated.py --model [model/created_model.pkl]
M2 model
Train
Start training the model using train_VAE_yz_x.py, for example
$python train_VAE_yz_x.py
Generate data set giving 1 sample input.
You can generate data set by giving a sample input. For example,
$python generated_yz_x.py --model [model/created_model.pkl]
Flying through latent space of M2 model
To generate movies of flying through latent-space of the M2 model, run:
$python run_flying.py --dataset [dataset] --model [model/created_model.pkl] --output_file [output file name]
where dataset is 'mnist' or 'svhn', and output_file is the filename to save the movie file to.
NOTE: This script requires ffmpeg to be installed.
NOTE: Unzip sample model saved in model folder
ToDo
- GPU implementation
Reference
- Justin Bayer's Chainer based Variational Auto Encoder http://nbviewer.ipython.org/gist/duschendestroyer/a41fcab5f7f9ffa45387
- http://papers.nips.cc/paper/5352-semi-supervised-learning-with-deep-generative-models.pdf
- https://github.com/dpkingma/nips14-ssl
- http://www.slideshare.net/beam2d/semisupervised-learning-with-deep-generative-models