5D Neural Network Interpolator Documentation

Introduction

This application allows you to upload 5D datasets, train neural networks, and make predictions.

The system consists of:

• Backend API (FastAPI)

• Frontend (Next.js)

• Neural Network Package (PyTorch)

Contents

• Installation
  • Prerequisites
  • Backend Setup
  • Frontend Setup
  • Running the Application
  • Running the Testing Suite
  • Running the Benchmarking Script
  • Build the Documentation
• User Guide
  • How to Use the Application
• Usage Examples
  • Example 1: Basic Training and Prediction
  • Example 2: Custom Hyperparameter Tuning
  • Example 3: Complete Workflow with Visualization
  • Tips for Using the Package
• Frontend Usage Examples
  • Example 1: Basic Neural Network Training and Prediction
  • Example 2: Hyperparameter Tuning
• API Reference
  • Backend API Endpoints
• Performance and Profiling
  • Overview
  • Running the Benchmark
  • Experiment 1: Training Time vs Dataset Size
  • Experiment 2: Memory Usage Analysis
  • Experiment 3: Accuracy Metrics vs Dataset Size
  • Summary and Recommendations
  • Summary of Benchmark Configuration
• Testing Suite
  • Overview
  • Running the Tests
  • Test Files
  • Test Structure
  • Best Practices
• Project Approach
  • Question 3: Data Handling
  • Question 4: Neural Network
  • Question 5: Backend
  • Question 6: Frontend
  • Question 7: Testing and Reproducibility
  • Question 8: Benchmarking
  • Best Practices
  • General Notes