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--- |
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tags: |
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- regression |
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- pytorch |
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license: mit |
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--- |
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## Model Description |
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`NumAdd-v2.0` is an optimized feed-forward neural network (FNN) in PyTorch for numerical sum prediction. |
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**Architecture:** 2-input, 1-output, with two hidden layers (32, 64 neurons) and ReLU activations. |
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**Parameters:** 2,273 trainable. |
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**Precision:** Requires `torch.float64` (double precision). |
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**Training Config:** Optimal batch size: 2048, Final tuning learning rate: 1.0e-12. |
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## Evaluation |
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Benchmarked on 120,000 samples across six input magnitude ranges. Metrics: MAE, MSE, RMSE, R2. |
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| Range (Input Max) | MAE | MSE | RMSE | R2 | |
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|-------------------|---------|----------|---------|---------| |
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| 0-50 | 0.004 | 0.000 | 0.004 | 1.000 | |
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| 51-500 | 0.003 | 0.000 | 0.004 | 1.000 | |
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| 501-5000 | 0.004 | 0.000 | 0.004 | 1.000 | |
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| 5001-50000 | 0.004 | 0.000 | 0.005 | 1.000 | |
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| 50001-500000 | 0.010 | 0.001 | 0.028 | 1.000 | |
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| 500001-50000000 | 0.706 | 6.333 | 2.517 | 1.000 | |
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## Limitations |
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Precision degrades for extremely large magnitude inputs (e.g., >500,000), indicated by increased MAE/MSE, although R2 remains high. |
