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Ajouter la documentation pour le projet "Dropout Reduces Underfitting" avec une implémentation TensorFlow/Keras et des objectifs scientifiques
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content/projects/artmcp.md
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---
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slug: artmcp
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title: 🤖 ArtMcp
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description: A comprehensive Model Context Protocol (MCP) server exposing professional profile information about Arthur Danjou.
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publishedAt: 2025/10/27
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readingTime: 3
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favorite: true
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tags:
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- web
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- nuxt
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- mcp
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---
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🤖 [ArtMcp](https://github.com/arthurdanjou/artmcp) - Arthur Danjou's MCP Server
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A comprehensive [Model Context Protocol (MCP)](https://modelcontextprotocol.io) server exposing professional profile information about Arthur Danjou. Built with [Nuxt](https://nuxt.com) and deployed on [NuxtHub](https://hub.nuxt.com) at the Edge.
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🔗 **Live Server**: https://mcp.arthurdanjou.fr
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## 🎯 Features
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### MCP Resources
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The server exposes the following resources through the Model Context Protocol:
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- **📊 Skills** (`resource://artmcp/skills`) - Complete list of technical skills (programming languages, frameworks, tools)
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- **💼 Experiences** (`resource://artmcp/experiences`) - Professional work experience and projects
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- **🚀 Projects** (`resource://artmcp/projects`) - Portfolio of personal and professional projects
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- **🎓 Education** (`resource://artmcp/education`) - Academic background and degrees
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- **🌐 Languages** (`resource://artmcp/languages`) - Spoken languages with proficiency levels
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- **👤 Profile** (`resource://artmcp/profile`) - Comprehensive profile with bio, location, availability, career goals, and work preferences
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- **🎨 Hobbies** (`resource://artmcp/hobbies`) - Personal interests and activities
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- **📞 Contact** (`resource://artmcp/contact`) - Professional contact information and social links
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- **🛠️ Uses** (`resource://artmcp/uses`) - Tools, hardware, and software setup
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### MCP Tools
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- **`activity`** - Real-time current activity and status of Arthur Danjou
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- **`resume-link`** - Get download link for resume in English or French
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- **`stats`** - Detailed coding statistics and analytics from WakaTime
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- **`status-page`** - Real-time status and uptime monitoring for homelab infrastructure
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- **`uses-by-category`** - Filter uses by category (homelab, ide, hardware, software)
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- **`weather`** - Get current weather for a city
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### MCP Prompts
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Pre-configured prompts for common queries about:
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- Resume generation
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- Skills and expertise
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- Projects showcase
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- Real-time activity
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- Contact information
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- And more...
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## 🏗️ Architecture
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This project uses:
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- **Nuxt 4** with Nitro for server-side rendering
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- **@nuxt/content** for content management
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- **@nuxtjs/mcp-toolkit** for MCP server implementation
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- **NuxtHub** for edge deployment on Cloudflare Workers
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- **nuxt-studio** for content management studio
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- **Zod** for schema validation
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## 🚀 Getting Started
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### Prerequisites
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- Node.js 18+ or Bun
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- pnpm 10.12.1+
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### Installation
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```bash
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# Install dependencies
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pnpm install
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```
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### Environment Variables
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Create a `.env` file (optional):
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```bash
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# Discord integration (optional)
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NUXT_DISCORD_USER_ID=""
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NUXT_DISCORD_ID=""
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NUXT_DISCORD_TOKEN=""
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# Wakatime integration (optional)
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NUXT_WAKATIME_USER_ID=""
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NUXT_WAKATIME_CODING=""
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NUXT_WAKATIME_EDITORS=""
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NUXT_WAKATIME_LANGUAGES=""
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NUXT_WAKATIME_OS=""
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# Status page (optional)
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NUXT_STATUS_PAGE=""
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```
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### Development
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Start the development server on `http://localhost:3000`:
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```bash
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pnpm dev
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```
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### Production
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Build the application for production:
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```bash
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pnpm build
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```
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### Deployment
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Deploy to NuxtHub/Cloudflare:
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```bash
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pnpm deploy
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```
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## 📚 API Endpoints
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All resources are also available as REST API endpoints:
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- `GET /api/skills`
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- `GET /api/experiences`
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- `GET /api/projects`
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- `GET /api/education`
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- `GET /api/languages`
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- `GET /api/profile`
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- `GET /api/hobbies`
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- `GET /api/contact`
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- `GET /api/uses`
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- `GET /api/activity`
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- `GET /api/wakatime`
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- `GET /api/status-page`
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- `GET /api/resumes/{en|fr}` - Download resume
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## 🧪 Development
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### Linting
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```bash
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pnpm lint
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```
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### Type Checking
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```bash
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npx tsc --noEmit --skipLibCheck
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```
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## 📂 Content Structure
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Content is managed in the `content/` directory:
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```
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content/
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├── skills.json # Technical skills
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├── languages.json # Spoken languages
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├── profile.md # Comprehensive profile info
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├── contact.json # Contact information
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├── hobbies.md # Personal interests
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├── documentation.md # MCP documentation
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├── experiences/*.md # Work experiences
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├── projects/*.md # Project portfolio
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├── education/*.md # Academic background
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└── uses/*.md # Tools and setup
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```
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## 🔧 Technologies
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- **Frontend/Backend**: Nuxt 4, Vue 3, Nitro
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- **MCP**: @nuxtjs/mcp-toolkit
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- **Content**: Nuxt Content with better-sqlite3
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- **Content Studio**: nuxt-studio
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- **Deployment**: Cloudflare Workers via NuxtHub
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- **Validation**: Zod schemas
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## 📖 MCP Integration
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To use this server with an MCP client:
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1. Configure your MCP client to connect to `https://mcp.arthurdanjou.fr/mcp`
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2. Or use the API directly via REST endpoints
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Example MCP client configuration:
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```json
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{
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"mcpServers": {
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"artmcp": {
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"url": "https://mcp.arthurdanjou.fr/mcp"
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}
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}
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}
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```
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## 🤝 Contributing
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This is a personal portfolio project. Feel free to use it as inspiration for your own MCP server!
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## 📝 License
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Private project - All rights reserved
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## 👤 About
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**Arthur Danjou**
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- Data Science & Applied AI student at Paris Dauphine-PSL University, passionate about machine learning and mathematical modelling
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- 📍 Paris, France
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- 🔗 [LinkedIn](https://go.arthurdanjou.fr/linkedin)
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- 🐙 [GitHub](https://go.arthurdanjou.fr/github)
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- 📧 [Email](https://go.arthurdanjou.fr/mail-pro)
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---
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Built with ❤️ using Nuxt and the Model Context Protocol
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154
content/projects/dropout-reduces-underfitting.md
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154
content/projects/dropout-reduces-underfitting.md
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---
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slug: dropout-reduces-underfitting
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title: 🔬 Dropout reduces underfitting
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description: TensorFlow/Keras implementation of "Dropout Reduces Underfitting" (Liu et al., 2023). A comparative study of Early and Late Dropout strategies to optimize model convergence.
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publishedAt: 2054/12/10
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readingTime: 4
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favorite: false
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tags:
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- python
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- reserch
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- machine-learning
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- tensorflow
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---
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📉 [Dropout Reduces Underfitting](https://github.com/arthurdanjou/dropoutreducesunderfitting): Reproduction & Analysis
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> **Study and reproduction of the paper:** Liu, Z., et al. (2023). *Dropout Reduces Underfitting*. arXiv:2303.01500.
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The paper is available at: [https://arxiv.org/abs/2303.01500](https://arxiv.org/abs/2303.01500)
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This repository contains a robust and modular implementation in **TensorFlow/Keras** of **Early Dropout** and **Late Dropout** strategies. The goal is to verify the hypothesis that dropout, traditionally used to reduce overfitting, can also combat underfitting when applied solely during the initial training phase.
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## 🎯 Scientific Objectives
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The study aims to validate the three operating regimes of Dropout described in the paper:
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1. **Early Dropout** (Targeting Underfitting): Active only during the initial phase to reduce gradient variance and align their direction, allowing for better final optimization.
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2. **Late Dropout** (Targeting Overfitting): Disabled at the start to allow rapid learning, then activated to regularize final convergence.
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3. **Standard Dropout**: Constant rate throughout training (Baseline).
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4. **No Dropout**: Control experiment without dropout.
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## 🛠️ Technical Architecture
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Unlike naive Keras callback implementations, this project uses a **dynamic approach via the TensorFlow graph** to ensure the dropout rate is properly updated on the GPU without model recompilation.
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### Key Components
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* **`DynamicDropout`**: A custom layer inheriting from `keras.layers.Layer` that reads its rate from a shared `tf.Variable`.
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* **`DropoutScheduler`**: A Keras `Callback` that drives the rate variable based on the current epoch and the chosen strategy (`early`, `late`, `standard`).
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* **`ExperimentPipeline`**: An orchestrator class that handles data loading (MNIST, CIFAR-10, Fashion MNIST), model creation (Dense or CNN), and execution of comparative benchmarks.
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## File Structure
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```
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.
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├── README.md # This documentation file
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├── Dropout reduces underfitting.pdf # Original research paper
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├── pipeline.py # Main experiment pipeline
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├── pipeline.ipynb # Jupyter notebook for experiments
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├── pipeline_mnist.ipynb # Jupyter notebook for MNIST experiments
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├── pipeline_cifar10.ipynb # Jupyter notebook for CIFAR-10 experiments
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├── pipeline_cifar100.ipynb # Jupyter notebook for CIFAR-100 experiments
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├── pipeline_fashion_mnist.ipynb # Jupyter notebook for Fashion MNIST experiments
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├── requirements.txt # Python dependencies
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├── .python-version # Python version specification
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└── uv.lock # Dependency lock file
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```
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## 🚀 Installation
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```bash
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# Clone the repository
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git clone https://github.com/arthurdanjou/dropoutreducesunderfitting.git
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cd dropoutreducesunderfitting
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```
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## Install dependencies
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```bash
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pip install tensorflow numpy matplotlib seaborn scikit-learn
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```
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## 📊 Usage
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The main notebook pipeline.ipynb contains all necessary code. Here is how to run a typical experiment via the pipeline API.
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1. Initialization
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Choose your dataset (cifar10, fashion_mnist, mnist) and architecture (cnn, dense).
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```python
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from pipeline import ExperimentPipeline
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# Fashion MNIST is recommended to observe underfitting/overfitting nuances
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exp = ExperimentPipeline(dataset_name="fashion_mnist", model_type="cnn")
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```
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2. Learning Curves Comparison
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Compare training dynamics (Loss & Accuracy) of the three strategies.
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```python
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exp.compare_learning_curves(
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modes=["standard", "early", "late"],
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switch_epoch=10, # The epoch where dropout state changes
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rate=0.4, # Dropout rate
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epochs=30
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)
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```
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3. Ablation Studies
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Study the impact of the "Early" phase duration or Dropout intensity.
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```python
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# Impact of the switch epoch on final performance
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exp.compare_switch_epochs(
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switch_epochs=[5, 10, 15, 20],
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modes=["early"],
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rate=0.4,
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epochs=30
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)
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# Impact of the dropout rate
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exp.compare_drop_rates(
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rates=[0.2, 0.4, 0.6],
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modes=["standard", "early"],
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switch_epoch=10,
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epochs=25
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)
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```
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4. Data Regimes (Data Scarcity)
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Verify the paper's hypothesis that Early Dropout shines on large datasets (or limited models) while Standard Dropout protects small datasets.
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```python
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# Training on 10%, 50% and 100% of the dataset
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exp.run_dataset_size_comparison(
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fractions=[0.1, 0.5, 1.0],
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modes=["standard", "early"],
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rate=0.3,
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switch_epoch=10
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)
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```
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## 📈 Expected Results
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According to the paper, you should observe:
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- Early Dropout: Higher initial Loss, followed by a sharp drop after the switch_epoch, often reaching a lower minimum than Standard Dropout (reduction of underfitting).
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- Late Dropout: Rapid rise in accuracy at the start (potential overfitting), then stabilized by the activation of dropout.
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## 📝 Authors
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- [Arthur Danjou](https://github.com/ArthurDanjou)
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- [Alexis Mathieu](https://github.com/Alex6535)
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- [Axelle Meric](https://github.com/AxelleMeric)
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- [Philippine Quellec](https://github.com/Philippine35890)
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- [Moritz Von Siemens](https://github.com/MoritzSiem)
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M.Sc. Statistical and Financial Engineering (ISF) - Data Science Track at Université Paris-Dauphine PSL
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Based on the work of Liu, Z., et al. (2023). Dropout Reduces Underfitting.
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