metadata

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

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:

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:

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