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--- |
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license: mit |
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tags: |
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- pytorch |
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- regression |
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--- |
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## Model Description |
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`NumAdd-v1.0` is a lightweight feed-forward neural network (FNN) implemented 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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## Evaluation |
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Benchmarked on 120,000 samples across five 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 | 0.003 | 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 | 0.006 | 1.000 | |
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| 5001-50000 | 0.016 | 0.003 | 0.050 | 1.000 | |
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| 50001-500000 | 0.1525 | 0.2377 | 0.4876 | 1.000 | |
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| 500001-50000000 | 12.947 | 2143.782 | 46.301 | 1.000 | |
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## Limitations |
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Performance degrades significantly for large magnitude inputs (>50,000), evidenced by increased MAE/MSE, despite maintaining high R2. |
