---
license: mit
language:
- en
pipeline_tag: tabular-classification
library_name: keras
tags:
- tabular-classification
- traffic
- keras
---
# TPnet-small

TPnet-small is a lightweight deep neural network (DNN) designed to predict traffic congestion using tabular smart mobility features. It serves as a compact yet powerful alternative to tree-based models.

## Model Details

- **Model type**: Feedforward Deep Neural Network (3 hidden layers)
- **Architecture**: [128, 64, 32] with ReLU activations and Dropout
- **Output**: 3-class Softmax output (`High`, `Medium`, `Low`)
- **Trained on**: Smart Mobility Traffic Dataset from Kaggle

## Training Details

- Optimizer: Adam
- Loss: Sparse Categorical Crossentropy
- Epochs: 50
- Batch size: 32
- Training time: ~6 seconds on CPU
- Train Accuracy: 99.1%
- Val Accuracy: 94.6%

## Evaluation

| Metric   | Value |
|----------|-------|
| Accuracy | 94.5% |
| F1 Score | 0.944 |
| Parameters | 13,123 |
| Model Size | ~156 KB (.h5 format) |

Includes training trajectory and confusion matrix plots.

## How to Use

```python
from tensorflow.keras.models import load_model

model = load_model("traffic_predictor_dnn.h5")
y_pred = model.predict(X_test)  # X_test must be scaled [n_samples, 20]
```

## Limitations

- Performance limited by small dataset size and feature coverage
    
- Currently optimized for CPU inference, not edge deployment
    

## Authors

- Created by [@Yukin3](https://github.com/Yukin3)