@@ -16,6 +16,13 @@ REBAR applied to multilayer sigmoid belief networks is implemented in rebar.py a
...
@@ -16,6 +16,13 @@ REBAR applied to multilayer sigmoid belief networks is implemented in rebar.py a
The code is not optimized and some computation is repeated for ease of
The code is not optimized and some computation is repeated for ease of
implementation. We hope that this code will be a useful starting point for future research in this area.
implementation. We hope that this code will be a useful starting point for future research in this area.
## Errata
11/27/2019
The _generator_network function has separate paths for the unconditional and conditional generative models. In the conditional generative models code path, the generative model does not have multiple stochastic layers even when n_layers is > 1. My intention was to have multiple stochastic layers in the conditional generative model, however, due to a bug this is not how it was implemented. As the code is currently, with the conditional generative model and n_layers > 1, the recognition network has multiple stochastic layers, but the generative model has a single stochastic layer.
Hai-Tao Yu (yuhaitao@slis.tsukuba.ac.jp) discovered this issue.
