---
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.