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--- |
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library_name: keras |
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tags: |
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- generative |
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--- |
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## Model description |
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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. |
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## Training and evaluation data |
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This model is trained on a toy dataset, the make_moons from sklearn.datasets. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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| name | learning_rate | decay | beta_1 | beta_2 | epsilon | amsgrad | training_precision | |
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|----|-------------|-----|------|------|-------|-------|------------------| |
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|Adam|9.999999747378752e-05|0.0|0.8999999761581421|0.9990000128746033|1e-07|False|float32| |
