This model demonstrates real-valued non-volume preserving (real NVP) transformations, a
tractable yet expressive approach to modeling high-dimensional data.
This model is used 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.
Requirements
This model requires
Tensorflow 2.9.1
Tensorflow probability 0.17.0
Loading this Model
Only the model weights are stored. To load this model, use load_model function from load_model.py.
Original Data Distribution and Latent Distribution
Data Distribution
Latent Distribution
Training hyperparameters
The following hyperparameters were used during training:
