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Table 3.
Architecture and hyperparameters of the NDE.
| Layer type | Activation function | |
|---|---|---|
| Dense, 64 neurons | tanh | |
| Dense, 64 neurons | tanh | |
| Dense, 64 neurons | tanh | |
| Dense, 4 × 64 neurons | tanh | |
| (weights) Dense, 3 neurons | softmax | |
| (means) Dense, 64 × 3 neurons | sigmoid | |
| (variances) Dense, 64 × 3 neurons | softplus | |
| (off-diagonal terms) Dense, 64 × 4 × 3 neurons | linear | |
| Optimizer | Loss function | Batch size |
| RMSprop (learning rate = 2 × 10−3) | Log prob | 32 |
Notes. The weights, means, variances, and off-diagonal terms are predicted by different output layers.
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