Master Thesis
HTWG Konstanz, Institute for Optical Systems
Author: Stefan Hörtling
Description: The repository contains both sample experiments and the universal VIMLTS Keras layer.
| No. | Experiment | Motivation |
|---|---|---|
| 01 | Simple regression | - Demonstrate basic functionality - Comparison of the approaches |
| 02 | Single weight behavior | - Prove the ability of VIMLTS to fit a multimodal posterior according to MCMC - Independence assumption of MFVI should not play a role |
| 03 | Small and shallow networks | Check assumption: posterior approximations could be complex in small and shallow networks |
| 04 | Going deeper | Check behavior in deeper BNNs |
To use the VIMLTS Keras layer, you have to import the
src.vimlts_keras.py
file to your Notebook and create a layer instance for your architecture. Please see the Small and shallow networks or the Going deeper experiment for an example.
Example:
from src.vimlts_keras import DenseVIMLTS
x_in = Input(shape=(1,),name="VIMLTS_il")
x_arch = DenseVIMLTS(units=num_hidden_units, num_samples_per_epoch=num_samples_per_epoch, activation='relu', kl_weight=kl_weight, name="VIMLTS_hl_1", **prior_params)(x_in)
x_arch = DenseVIMLTS(units=1, num_samples_per_epoch=num_samples_per_epoch, kl_weight=kl_weight, name="VIMLTS_ol", **prior_params)(x_arch)
model_VIMLTS = Model(x_in, x_arch,name="model_VIMLTS")