metadata
library_name: keras
tags:
- generative
Model description
The aim of this work is to map a simple distribution - which is easy to sample and whose density is simple to estimate - to a more complex one learned from the data. This kind of generative model is also known as "normalizing flow". The latent distribution we wish to map to in this example is Gaussian.
Training and evaluation data
This model is trained on a toy dataset, the make_moons from sklearn.datasets.
Training hyperparameters
The following hyperparameters were used during training:
| name | learning_rate | decay | beta_1 | beta_2 | epsilon | amsgrad | training_precision |
|---|---|---|---|---|---|---|---|
| Adam | 9.999999747378752e-05 | 0.0 | 0.8999999761581421 | 0.9990000128746033 | 1e-07 | False | float32 |