lvae's Introduction

Probabilistic Ladder

Code to run experiments in

Sønderby, C.K., Raiko, T., Maaløe, L., Sønderby, S.K. and Winther, O., 2016.
How to Train Deep Variational Autoencoders and Probabilistic Ladder Networks.
arXiv preprint arXiv:1602.02282.

The code is not well documented at the moment, please feel free to ask questions by writing me an email or creating a github-issue.

Example

To run a VAE model with 3 layers of stochastic units, each connected by a two-layer MLP:

VAE: X->MLP->Z1->MLP->Z2->MLP->Z3->MLP->Z2->MLP->Z1->MLP->Xrecon

python run_models.py \
	-lr 0.00020 \
	-modeltype VAE \
	-batch_size 256 \
	-dataset mnistresample \
	-mlp_layers 2 \
	-latent_sizes 64,32,16 \
	-hidden_sizes 512,256,128 \
	-nonlin_dec leaky_rectify \
	-nonlin_enc leaky_rectify \
	-only_mu_up True \
	-eq_samples 1 \
	-iw_samples 1 \
	-ramp_n_samples True \
	-batch_norm True \
	-batch_norm_output False \
	-temp_start 0.00 -temp_epochs 200 \
	-num_epochs 2000 -eval_epochs 100,200,300,400,500,600,700,800,900,1000,1100,1200,1300,1400,1500,1600,1700,1800,1900,2000 \
	-ladder_share_params False \
	-only_mu_up True \
	-lv_eps_z 1e-5 \
	-lv_eps_out 1e-5 \
	-outfolder results/mnistresample_VAE

Corresponding ladderVAE model

python run_models.py \
	-lr 0.00020 \
	-modeltype ladderVAE \
	-batch_size 256 \
	-dataset mnistresample \
	-mlp_layers 2 \
	-latent_sizes 64,32,16 \
	-hidden_sizes 512,256,128 \
	-nonlin_dec leaky_rectify \
	-nonlin_enc leaky_rectify \
	-only_mu_up True \
	-eq_samples 1 \
	-iw_samples 1 \
	-ramp_n_samples True \
	-batch_norm True \
	-batch_norm_output False \
	-temp_start 0.00 -temp_epochs 200 \
	-num_epochs 2000 -eval_epochs 100,200,300,400,500,600,700,800,900,1000,1100,1200,1300,1400,1500,1600,1700,1800,1900,2000 \
	-ladder_share_params False \
	-only_mu_up True \
	-lv_eps_z 1e-5 \
	-lv_eps_out 1e-5 \
	-outfolder results/mnistresample_ladderVAE

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