Training SOEN Models

Understanding how to train superconducting optoelectronic neural networks

Training Overview

Training SOEN models involves optimizing the parameters of superconducting circuits to perform specific computational tasks. Unlike traditional neural networks, SOEN models operate with temporal dynamics and physical constraints that require specialized training approaches.

The training process encompasses several key components: data preparation, loss function selection, optimization strategies, and evaluation metrics. Each component must be carefully configured to account for the unique properties of superconducting optoelectronic hardware.

Loss Functions

The choice of loss function fundamentally shapes how your SOEN model learns. Different objectives require different approaches:

  • • Cross Entropy - Standard classification
  • • Gap Loss - Margin-based robust learning
  • • Custom Losses - Task-specific objectives
📚 Detailed Loss Functions Guide →

Optimization

SOEN models benefit from adaptive optimization algorithms that can handle the unique parameter landscapes of superconducting circuits.

  • • AdamW - Adaptive learning with weight decay
  • • Learning Rate Scheduling - Dynamic rate adjustment
  • • Gradient Clipping - Stability for physical parameters
📝 Detailed optimization guide - page yet to be populated

Data Handling

Preparing data for SOEN models requires consideration of temporal dynamics and input encoding schemes.

  • • Temporal Sequences - Time-series data handling
  • • Input Encoding - Raw vs. one-hot encoding
  • • Batch Processing - Efficient data loading
📝 Comprehensive data guide - page yet to be populated

Evaluation

Evaluating SOEN models requires metrics that capture both accuracy and the unique aspects of temporal neural dynamics.

  • • Classification Metrics - Accuracy, top-k accuracy
  • • Sequence Metrics - Perplexity, bits per character
  • • Temporal Analysis - Convergence dynamics
📝 Evaluation metrics guide - page yet to be populated

Current Method: YAML Configuration

Currently, SOEN experiments are defined using YAML configuration files. This approach provides a structured way to specify all training parameters, from basic settings like batch size and learning rate to complex multi-loss objectives and advanced callbacks.

Note: This is the current method for defining experiments. Future versions may include additional configuration approaches and programmatic APIs.

Key Configuration Sections

Experiment Metadata - Description, seed, reproducibility
Training Settings - Batch size, epochs, autoregressive mode
Data Configuration - Paths, preprocessing, encoding
Model Parameters - Architecture, simulation settings
Callbacks - Learning rate scheduling, early stopping
Logging - Metrics tracking, checkpoints, debugging
📚 See Configuration Examples

🚀 Getting Started

Quick Start

  1. 1. Prepare your dataset in the required format
  2. 2. Choose appropriate loss functions for your task
  3. 3. Configure training parameters via YAML
  4. 4. Run training using the provided scripts

SOEN Training Documentation