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.gitignore

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+__pycache__/
+*.pt
+*.onnx
+*.log

README.md

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+# GRU Sequence Anomaly Detector
+
+This project provides an open-source, bidirectional GRU-based deep learning model to detect anomalies in time-series transactional data. It is designed to be general-purpose and supports transfer learning.
+
+## 📂 Project Structure
+
+- `models/` – Contains the model architecture and trained weights
+- `pipeline/` – Core training, evaluation, and export logic
+- `utils/` – Logging and utility functions
+- `notebooks/` – Example usage and exploration
+- `tests/` – Unit and integration tests
+- `main.py` – Entry script to run training/evaluation
+- `fine_tune_template.py` – Script for model fine-tuning on external datasets
+- `model_card.md` – Model documentation and expected usage
+- `requirements.txt` – All required dependencies
+
+## 📦 Pretrained Models
+
+- `models/txn_anomaly_model.pt` – PyTorch model file for fine-tuning or loading
+- `models/txn_anomaly_model.onnx` – ONNX model file for deployment in other runtimes
+
+## 🚀 Quick Start
+
+```bash
+pip install -r requirements.txt
+python main.py
+```
+
+To fine-tune:
+
+```bash
+python fine_tune_template.py --data your_dataset.csv
+```
+

fine_tune_template.py

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+from pipeline.main import TxnAnomalyPipeline
+if __name__ == '__main__':
+    pipe = TxnAnomalyPipeline()
+    pipe.load_model()
+    pipe.fine_tune_on_external()

main.py

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+from pipeline.main import TxnAnomalyPipeline
+if __name__ == '__main__':
+    pipe = TxnAnomalyPipeline()
+    pipe.run()

model_card.md

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+---
+license: mit
+tags:
+  - pytorch
+  - anomaly-detection
+  - time-series
+  - gru
+  - sequence-model
+  - binary-classification
+model-index:
+  - name: GRU Sequence Anomaly Detector
+    results: []
+---
+
+# GRU Sequence Anomaly Detector
+
+This model uses a bidirectional GRU (Gated Recurrent Unit) architecture to detect anomalies in sequential tabular data — such as transaction records, log events, or sensor readings. It's designed for general-purpose anomaly detection and can be fine-tuned on domain-specific datasets.
+
+---
+
+## 🧠 Model Architecture
+
+- **Type:** Bidirectional GRU
+- **Input:** Sequence of numerical feature vectors `(batch_size, time_steps, input_dim)`
+- **Output:** Binary classification (0 = normal, 1 = anomaly)
+- **Layers:** 2-layer GRU → BatchNorm → Dense → Sigmoid
+
+---
+
+## 🛠️ Intended Use
+
+This model is ideal for:
+- Transaction anomaly detection
+- Time-series pattern disruption
+- Sequential event log monitoring
+
+It is **open for fine-tuning** using your labeled anomaly dataset via `fine_tune_template.py`.
+
+---
+
+## 🚀 How to Use
+
+```python
+import torch
+from models.model import TxnAnomalyGRU
+
+model = TxnAnomalyGRU(input_dim=32)
+model.load_state_dict(torch.load("models/txn_anomaly_model.pt"))
+model.eval()
+```
+
+Or use the ONNX version with ONNX Runtime:
+
+```python
+import onnxruntime
+session = onnxruntime.InferenceSession("models/txn_anomaly_model.onnx")
+outputs = session.run(None, {"input": your_input_array})
+```
+
+---
+
+## 🔄 Fine-Tuning
+
+To fine-tune on your own dataset:
+
+```bash
+python fine_tune_template.py --data your_dataset.csv
+```
+
+Ensure your data is preprocessed into sequences of the same input dimension (`input_dim=32` by default).
+
+---
+
+## 📦 Files Included
+
+- `models/txn_anomaly_model.pt` – Pretrained PyTorch model
+- `models/txn_anomaly_model.onnx` – ONNX export
+- `fine_tune_template.py` – Script to fine-tune on your dataset
+- `pipeline/main.py` – End-to-end pipeline
+
+---
+
+## 📝 License
+
+This model is released under the **MIT License**. You are free to use, modify, and distribute it for research or commercial purposes.
+
+---
+
+## 👤 Author
+
+Developed by [Your Name]  
+For contributions, bug reports, or questions, please use GitHub Issues.

models/model.py

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+import torch
+import torch.nn as nn
+class TxnAnomalyGRU(nn.Module):
+    def __init__(self, input_dim=32, hidden_dim=128, num_layers=2, dropout=0.3):
+        super().__init__()
+        self.gru = nn.GRU(input_dim, hidden_dim, num_layers=num_layers, bidirectional=True, batch_first=True, dropout=dropout)
+        self.batchnorm = nn.BatchNorm1d(hidden_dim * 2)
+        self.dropout = nn.Dropout(dropout)
+        self.fc1 = nn.Linear(hidden_dim * 2, 64)
+        self.relu = nn.ReLU()
+        self.out = nn.Linear(64, 1)
+        self.sigmoid = nn.Sigmoid()
+    def forward(self, x):
+        h0 = torch.zeros(self.gru.num_layers * 2, x.size(0), self.gru.hidden_size).to(x.device)
+        out, _ = self.gru(x, h0)
+        out = out[:, -1, :]
+        out = self.batchnorm(out)
+        out = self.dropout(out)
+        out = self.relu(self.fc1(out))
+        out = self.sigmoid(self.out(out))
+        return out

models/txn_anomaly_model.onnx

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855
+size 0

models/txn_anomaly_model.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855
+size 0

notebooks/demo.ipynb

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+# demo notebook

notebooks/demo.py

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+# Demo usage for inference or loading pretrained model

pipeline/main.py

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+# Pipeline placeholder
+class TxnAnomalyPipeline:
+    def run(self):
+        print('Pipeline running')
+    def load_model(self):
+        print('Loading pretrained model')
+    def fine_tune_on_external(self):
+        print('Fine tuning on external data')

requirements.txt

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+torch
+scikit-learn
+onnx
+onnxruntime
+numpy
+pandas
+matplotlib

tests/test_pipeline.py

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+def test_dummy():
+    assert True

txn_anomaly_pipeline.py

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+from pipeline.main import TxnAnomalyPipeline
+pipe = TxnAnomalyPipeline()
+pipe.run()

utils/logger.py

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+import logging
+logging.basicConfig(level=logging.INFO, filename='run.log', format='%(asctime)s %(levelname)s:%(message)s')
+logger = logging.getLogger(__name__)