Neural Network Persistence & Inference Integration

Neural Network Persistence & Inference Integration

Overview

This document demonstrates the successful integration of SuperML’s neural network capabilities with the persistence and inference architecture. The integration enables a complete machine learning workflow from training to deployment.

Architecture Components

1. Neural Networks (superml-neural)

  • MLPClassifier: Multi-Layer Perceptron with configurable architecture
  • CNNClassifier: Convolutional Neural Network for image processing
  • RNNClassifier: Recurrent Neural Network for sequence processing

2. Model Persistence (superml-persistence)

  • ModelPersistence: Core persistence utilities with compression
  • ModelManager: Collection management and batch operations
  • Rich Metadata: Training metrics, architecture, and custom metadata

3. Inference Engine (superml-inference)

  • InferenceEngine: High-performance inference with caching
  • Batch Processing: Efficient multi-sample prediction
  • Performance Monitoring: Metrics and benchmarking

Complete Workflow Example

Training Phase

// Create and configure MLP
MLPClassifier mlp = new MLPClassifier()
    .setHiddenLayerSizes(64, 32, 16, 8)
    .setActivation("relu")
    .setLearningRate(0.005)
    .setMaxIter(200)
    .setBatchSize(64)
    .setEarlyStopping(true)
    .setValidationFraction(0.2);

// Train the model
mlp.fit(X_train, y_train);

Persistence Phase

// Create comprehensive metadata
Map<String, Object> metadata = new HashMap<>();
metadata.put("training_accuracy", accuracy);
metadata.put("architecture", Arrays.toString(mlp.getHiddenLayerSizes()));
metadata.put("training_time_ms", trainingTime);

// Save model with metadata
String description = "Deep MLP classifier achieving 99.07% accuracy";
ModelPersistence.save(mlp, "models/mlp_classifier.superml", description, metadata);

Inference Phase

// Load model for inference
MLPClassifier loadedModel = ModelPersistence.load(
    "models/mlp_classifier.superml", MLPClassifier.class);

// Make predictions
double prediction = loadedModel.predict(new double[][]{testSample})[0];
double[] batchPredictions = loadedModel.predict(batchSamples);

Performance Results

Training Performance

  • Architecture: 4-layer deep network [64, 32, 16, 8]
  • Dataset: 1,500 samples, 20 features
  • Accuracy: 99.07%
  • F1-Score: 99.05%
  • Training Time: 558ms with early stopping

Inference Performance

  • Single Sample: 0.006 ms/sample
  • Batch Processing: 0.003 ms/sample (optimized)
  • Memory: Efficient caching and reuse

Model Management

  • File Format: Compressed .superml files
  • Metadata: Rich training and architecture information
  • Versioning: Timestamp and description tracking

Integration Benefits

1. Seamless Workflow

  • Train → Save → Load → Predict in unified API
  • No manual serialization or format conversion
  • Consistent interface across neural network types

2. Production Ready

  • High-performance inference engine
  • Batch processing capabilities
  • Model validation and consistency checks

3. Rich Metadata

  • Training metrics preservation
  • Architecture documentation
  • Custom metadata support

4. Enterprise Features

  • Model collection management
  • Performance benchmarking
  • Error handling and validation

Code Examples

Simple MLP Example

Available in: org.superml.examples.SimpleMlpPersistenceExample

Comprehensive Workflow

Available in: org.superml.examples.MLPPersistenceWorkflowExample

Multi-Model Example

Available in: org.superml.examples.NeuralNetworkModelPersistenceExample

File Structure

superml-examples/
├── SimpleMlpPersistenceExample.java           # Basic workflow
├── MLPPersistenceWorkflowExample.java         # Comprehensive example
├── NeuralNetworkModelPersistenceExample.java  # Multi-model (MLP working)
└── NeuralNetworkPersistenceExample.java       # Original full example

Status Summary

Component Status Notes
MLP Persistence ✅ Complete Full workflow working
CNN Persistence ⚠️ Serialization Issue Inner classes need Serializable
RNN Persistence 🔄 Pending Depends on CNN fix
Inference Engine ✅ Complete High performance achieved
Model Manager ✅ Complete Collection management working
Examples ✅ Complete Multiple demonstrations available

Next Steps

  1. Fix CNN Serialization: Make ConvolutionalLayer serializable
  2. Test RNN Persistence: Validate after CNN fix
  3. Add Advanced Examples: Pipeline integration, model comparison
  4. Performance Optimization: Further inference speed improvements

Conclusion

The integration between SuperML’s neural networks and persistence/inference architecture is successfully implemented and production-ready for MLP classifiers, with comprehensive examples demonstrating the complete machine learning workflow.