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--- |
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license: mit |
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language: |
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- en |
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pipeline_tag: tabular-classification |
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library_name: keras |
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tags: |
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- tabular-classification |
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- traffic |
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- keras |
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--- |
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# TPnet-small |
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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. |
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## Model Details |
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- **Model type**: Feedforward Deep Neural Network (3 hidden layers) |
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- **Architecture**: [128, 64, 32] with ReLU activations and Dropout |
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- **Output**: 3-class Softmax output (`High`, `Medium`, `Low`) |
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- **Trained on**: Smart Mobility Traffic Dataset from Kaggle |
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## Training Details |
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- Optimizer: Adam |
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- Loss: Sparse Categorical Crossentropy |
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- Epochs: 50 |
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- Batch size: 32 |
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- Training time: ~6 seconds on CPU |
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- Train Accuracy: 99.1% |
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- Val Accuracy: 94.6% |
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## Evaluation |
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| Metric | Value | |
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|----------|-------| |
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| Accuracy | 94.5% | |
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| F1 Score | 0.944 | |
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| Parameters | 13,123 | |
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| Model Size | ~156 KB (.h5 format) | |
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Includes training trajectory and confusion matrix plots. |
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## How to Use |
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```python |
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from tensorflow.keras.models import load_model |
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model = load_model("traffic_predictor_dnn.h5") |
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y_pred = model.predict(X_test) # X_test must be scaled [n_samples, 20] |
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``` |
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## Limitations |
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- Performance limited by small dataset size and feature coverage |
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- Currently optimized for CPU inference, not edge deployment |
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## Authors |
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- Created by [@Yukin3](https://github.com/Yukin3) |
