---
tags:
- regression
- pytorch
license: mit
---

## Model Description

`NumAdd-v2.0` is an optimized feed-forward neural network (FNN) 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.
**Precision:** Requires `torch.float64` (double precision).
**Training Config:** Optimal batch size: 2048, Final tuning learning rate: 1.0e-12.

## Evaluation

Benchmarked on 120,000 samples across six input magnitude ranges. Metrics: MAE, MSE, RMSE, R2.

| Range (Input Max) | MAE     | MSE      | RMSE    | R2      |
|-------------------|---------|----------|---------|---------|
| 0-50              | 0.004   | 0.000    | 0.004   | 1.000   |
| 51-500            | 0.003   | 0.000    | 0.004   | 1.000   |
| 501-5000          | 0.004   | 0.000    | 0.004   | 1.000   |
| 5001-50000        | 0.004   | 0.000    | 0.005   | 1.000   |
| 50001-500000      | 0.010   | 0.001    | 0.028   | 1.000   |
| 500001-50000000   | 0.706   | 6.333    | 2.517   | 1.000   |

## Limitations

Precision degrades for extremely large magnitude inputs (e.g., >500,000), indicated by increased MAE/MSE, although R2 remains high.