| | --- |
| | license: mit |
| | tags: |
| | - pytorch |
| | - regression |
| | --- |
| | |
| | ## Model Description |
| |
|
| | `NumAdd-v1.0` is a lightweight feed-forward neural network (FNN) implemented in PyTorch for numerical sum prediction. |
| | **Architecture:** 2-input, 1-output, with two hidden layers (32, 64 neurons) and ReLU activations. |
| | **Parameters:** 2,273 trainable. |
| |
|
| | ## Evaluation |
| |
|
| | Benchmarked on 120,000 samples across five input magnitude ranges. Metrics: MAE, MSE, RMSE, R2. |
| |
|
| | | Range (Input Max) | MAE | MSE | RMSE | R2 | |
| | |-------------------|---------|----------|---------|---------| |
| | | 0-50 | 0.003 | 0.000 | 0.004 | 1.000 | |
| | | 51-500 | 0.003 | 0.000 | 0.004 | 1.000 | |
| | | 501-5000 | 0.004 | 0.000 | 0.006 | 1.000 | |
| | | 5001-50000 | 0.016 | 0.003 | 0.050 | 1.000 | |
| | | 50001-500000 | 0.1525 | 0.2377 | 0.4876 | 1.000 | |
| | | 500001-50000000 | 12.947 | 2143.782 | 46.301 | 1.000 | |
| |
|
| | ## Limitations |
| |
|
| | Performance degrades significantly for large magnitude inputs (>50,000), evidenced by increased MAE/MSE, despite maintaining high R2. |
