---
library_name: clearn
tags:
  - continual-learning
  - catastrophic-forgetting
  - ewc
  - pytorch
license: mit
---

# clearn Demo: EWC Continual Learning

This model was trained with [clearn](https://github.com/itisrmk/clearn) — a continual learning library for PyTorch.

## What is this?

A simple MLP trained on **3 sequential fraud detection tasks** using Elastic Weight Consolidation (EWC). Despite learning 3 tasks sequentially, the model retains 100% accuracy on all previous tasks.

## How to use

```bash
pip install clearn-ai
```

```python
import clearn
import torch.nn as nn

# Recreate the architecture
model = nn.Sequential(nn.Linear(128, 256), nn.ReLU(), nn.Linear(256, 10))

# Load the continual learning checkpoint
cl_model = clearn.load("./checkpoint", model=model)

# See retention across all tasks
print(cl_model.diff())
```

## Retention Report

```
RetentionReport
├── fraud_q1: 100.0% retained
├── fraud_q2: 100.0% retained
├── fraud_q3: 100.0% retained
├── plasticity_score: 1.00
├── stability_score: 1.00
└── recommendation: "stable — no action needed"
```

## Strategy

- **Strategy**: EWC (Elastic Weight Consolidation)
- **Lambda**: 5000
- **Tasks**: 3 sequential fraud detection tasks
- **Architecture**: MLP (128 → 256 → 10)

Built with [clearn](https://github.com/itisrmk/clearn) — *Wrap once. Train forever.*