🧬 Augmented Population-Based Training

An implementation of Augmented Population-Based Training (APBT) - a hyperparameter optimization and neural architecture search algorithm that evolves neural networks through competitive training.

🚀 Quick Start - Interactive Dashboard

Your dashboard is running at: http://localhost:8501

Try it now (2-minute demo):

1. Open http://localhost:8501 in your browser
2. Configure: Iris dataset, 40 networks, 100 epochs
3. Click "▶️ Start Training"
4. Watch neural networks compete and evolve in real-time!

Available Datasets:

• Iris (150 samples, 4 features, 3 classes) - Classic flower classification
• Tennis (14 samples, 4 features, 2 classes) - Weather prediction
• Identity (100 samples, 5 features, 5 classes) - Synthetic dataset
• MNIST (10,000 samples, 784 features, 10 classes) - Handwritten digits (requires setup)
• Fashion MNIST (10,000 samples, 784 features, 10 classes) - Clothing items (requires setup)

To use MNIST datasets:

python download_mnist.py  # Download real MNIST & Fashion MNIST data
streamlit run app.py       # Start dashboard

To start/restart the dashboard:

conda activate torch
streamlit run app.py

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📚 Table of Contents

• What is APBT?
• Dashboard Features
• Installation
• Usage
  • Web Dashboard
  • Command Line
• How It Works
• Understanding the Algorithm
• Visualization Guide
• Advanced Topics
• Examples & Results

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What is APBT?

Augmented Population-Based Training combines three powerful ideas:

1. 🧬 Population-Based Training

• Multiple neural networks (population of 20-100) train in parallel
• Networks compete for survival based on performance
• Bottom 20% copy weights from top 20% (exploitation)
• Automatic hyperparameter optimization

2. 🔍 Neural Architecture Search

• Networks can dynamically add/remove units
• Finds optimal topology automatically
• Balances accuracy vs complexity

3. ⚖️ Multi-Objective Optimization

Fitness Function: f(accuracy, size) = 1.09^(accuracy×100) / 1.02^size

• Rewards accuracy exponentially
• Penalizes large models
• Finds the optimal tradeoff automatically

The Evolution Loop

Initialize Population (random hyperparameters & architectures)
    ↓
┌─────────────────────────────────────┐
│ For each epoch:                     │
│  1. Train all networks (parallel)   │
│  2. Evaluate fitness                │
│  3. Update leaderboard              │
│  4. Exploit (copy winners)          │
│  5. Explore (mutate & try new)      │
└─────────────────────────────────────┘
    ↓
Return Best Network

Result: Automatically discovers optimal hyperparameters AND architecture without manual tuning!

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Dashboard Features

The Streamlit dashboard provides 5 comprehensive visualization tabs:

📊 Tab 1: Training Progress

• Real-time performance metrics
• Accuracy evolution (best vs most accurate)
• Model size changes over time
• Average population statistics
• Live progress bar

🏆 Tab 2: Leaderboard

• Top 20 networks ranked by performance
• Gold/Silver/Bronze highlighting
• Full hyperparameter details
• Population distribution histograms
• Watch exploitation events happen!

🧠 Tab 3: Architecture

• Interactive network topology visualization
• Layer-by-layer breakdown
• Parameter counts
• Color-coded nodes (input/hidden/output)
• See optimal structure emerge

📈 Tab 4: Hyperparameters

• Learning rate evolution
• Momentum tracking
• Weight decay changes
• Statistics with deltas
• Understand adaptation patterns

🎯 Tab 5: Fitness Landscape

• 3D surface plot (interactive, rotatable!)
• Accuracy vs Size tradeoff visualization
• Current best network position marked
• Population scatter plot (Pareto front)
• Color-coded by rank

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Installation

Prerequisites

• Python 3.8+
• Conda (recommended)

Install Dependencies

# Activate your environment
conda activate torch

# Install required packages
pip install streamlit pandas numpy plotly

Or use the requirements file:

pip install -r requirements.txt

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Usage

Web Dashboard (Recommended)

Start the interactive dashboard:

conda activate torch
streamlit run app.py

Then open http://localhost:8501 in your browser.

Dashboard Configuration:

• Dataset: Choose from Iris, Tennis, Identity, MNIST, or Fashion MNIST
• Population Size: 10-500 networks (40 recommended, 20 for MNIST)
• Epochs: 10-1000 (100+ for good results, 50+ for MNIST)
• Advanced Settings: Learning rate range, readiness threshold (defaults to 5% of epochs), truncation %

Note: MNIST and Fashion MNIST use 784 input features (28x28 images), so training takes longer. The app automatically adjusts default parameters for these datasets.

Command Line

Run training from the command line:

python source/main.py \
-a data/iris/iris-attr.txt \
-d data/iris/iris-train.txt \
-t data/iris/iris-test.txt \
-w models/weights.txt \
  -k 40 \
  -e 100 \
--debug

Arguments:

• -a, --attributes: Path to attributes file (required)
• -d, --training: Path to training data (required)
• -t, --testing: Path to test data (optional)
• -w, --weights: Path to save weights (optional)
• -k, --k-inds: Population size (required)
• -e, --epochs: Number of epochs (required)
• --debug: Enable debug output (optional)

Run Experiment Files

Pre-configured experiments:

python source/testIris.py
python source/testTennis.py
python source/testIdentity.py

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How It Works

Core Components

1. ANN (Artificial Neural Network) - source/ann.py

Feed-forward neural network with backpropagation:

• Flexible topology: [input, hidden1, hidden2, ..., output]
• Hyperparameters: learning rate, momentum, decay
• Activation: Sigmoid function
• Training: Stochastic Gradient Descent (SGD)

2. APBT Algorithm - source/apbt.py

Population-based training manager:

• Initialization: Create k networks with random hyperparameters & architectures
• Training: Each network trains for 1 epoch per iteration
• Evaluation: Fitness = f(accuracy, model_size)
• Exploitation: Bottom 20% copy from top 20% (every ~5% of total epochs by default)
• Exploration: Perturb hyperparameters (×0.8 or ×1.2) and architecture (±1 unit)

Key Parameters

# Hyperparameter Ranges
LR_RANGE = (1e-4, 1e-1)        # Learning rate
M_RANGE = (0.0, 0.9)           # Momentum
D_RANGE = (0.0, 0.1)           # Decay
HL_RANGE = (1, 4)              # Hidden layers
HUPL_RANGE = (2, 10)           # Units per layer

# Algorithm Parameters
READINESS = 5% of epochs       # Epochs before exploitation (dynamic)
TRUNC = 0.2                    # Top/bottom 20%
PERTS = (0.8, 1.2)            # Perturbation factors
X, Y = 1.09, 1.02             # Fitness function factors

The Fitness Function

def f(accuracy, size):
    return 1.09 ** (accuracy * 100) / 1.02 ** size

Examples:

• Network A: 95% acc, 100 params → fitness = 4,371 ✅
• Network B: 98% acc, 200 params → fitness = 3,812
• Network C: 90% acc, 50 params → fitness = 3,103

Network A wins! Best balance of accuracy and size.

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Understanding the Algorithm

Exploitation (Truncation Selection)

Every ~5% of total epochs (configurable), if a network is in the bottom 20%:

if my_rank > 80th_percentile:
    top_performer = random.choice(top_20%)
    copy(top_performer.weights)
    copy(top_performer.hyperparameters)

Why it works:

• Poor performers don't waste time training from scratch
• Copies proven successful configurations
• Maintains diversity by choosing randomly from top 20%

Exploration (Perturbation)

After exploitation, explore nearby solutions:

# Hyperparameter perturbation (±20%)
learning_rate *= random.choice([0.8, 1.2])
momentum *= random.choice([0.8, 1.2])
decay *= random.choice([0.8, 1.2])

# Architecture perturbation (±1 unit)
random_layer = pick_random_hidden_layer()
random_layer.units += random.choice([-1, 0, 1])

# Adjust weights accordingly
if added_unit:
    add_new_random_weights()
elif removed_unit:
    delete_associated_weights()

Why it works:

• Small changes prevent wild swings
• Explores nearby solutions
• Maintains good performance while searching

Evolution Example (Iris Dataset)

Generation 0 (Random initialization):

• Network #27: [4,8,4,3] - 38% accuracy, performance: 18.7 (Best)
• Network #12: [4,3,7,3] - 35% accuracy, performance: 15.3
• Network #35: [4,2,9,6,3] - 33% accuracy, performance: 12.1

Generation 50 (Learning & exploitation):

• Network #27: [4,8,4,3] - 89% accuracy, performance: 1,285 (Still best)
• Network #12: [4,8,4,3] - 87% accuracy, performance: 1,103 (Copied #27!)
• Network #8: [4,7,5,3] - 85% accuracy, performance: 978

Generation 200 (Converged):

• Network #27: [4,7,5,3] - 97% accuracy, performance: 5,820 (Evolved!)
• Network #12: [4,7,5,3] - 96% accuracy, performance: 5,231
• Network #8: [4,7,5,3] - 96% accuracy, performance: 5,231

Result: Population discovers optimal architecture: [4, 7, 5, 3]

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Visualization Guide

Key Patterns to Watch

1. Exploitation Events

Sudden performance jumps at readiness intervals (default: every 5% of total epochs)

• Bottom 20% of networks copy weights from top 20%
• Performance chart shows dramatic vertical jumps
• Leaderboard shows major reshuffling
• This is "survival of the fittest" in action!

2. Architecture Evolution

Model size fluctuations throughout training

• Networks add/remove units during exploration
• Size chart shows spikes and dips
• Eventually converges to optimal complexity
• Shows the architecture search process

3. Population Convergence

Distribution narrowing over time

• Early training: Wide spread in leaderboard, diverse architectures
• Mid training: Some clustering, common patterns emerging
• Late training: Tight cluster, similar architectures
• Population agrees on optimal solution

Interpreting the 3D Fitness Landscape

The fitness landscape shows the optimization objective:

• X-axis: Accuracy (%)
• Y-axis: Model Size (parameters)
• Z-axis: Fitness value
• Peak: Optimal accuracy/size balance
• Red Diamond: Your current best network

Ideal position: Bottom-right area = High accuracy, small size!

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Advanced Topics

Customizing the Fitness Function

Edit source/apbt.py line 309:

def f(self, acc, size):
    # Current: Exponential reward/penalty
    return self.X ** (acc * 100) / self.Y ** size
    
    # Alternative: Stronger size penalty
    # return acc ** 2 / (size ** 0.8)
    
    # Alternative: Linear tradeoff
    # return acc * 1000 - size * 0.1

Adjusting Exploitation Timing

In the dashboard's Advanced Settings, adjust the Readiness Threshold slider. Default: 5% of total epochs (e.g., 5 epochs for 100 total, 50 epochs for 1000 total)

Or edit source/apbt.py line 73:

self.READINESS = 220  # Custom value: lower for more frequent, higher for less frequent

Changing Selection Pressure

Edit source/apbt.py line 74:

self.TRUNC = 0.2  # Try: 0.1 (top 10%), 0.3 (top 30%)

Adding Custom Datasets

1. Create attribute file: data/mydata/mydata-attr.txt
2. Create training file: data/mydata/mydata-train.txt
3. Create test file: data/mydata/mydata-test.txt
4. Add to dashboard: Edit app.py dataset_map dictionary

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Examples & Results

Iris Dataset (150 examples, 4 features, 3 classes)

Configuration:

Population: 40 networks
Epochs: 100
Time: ~2 minutes

Expected Results:

Accuracy:        94-97%
Model Size:      60-100 parameters
Best Topology:   [4, 6-8, 4-6, 3]
Learning Rate:   ~0.02-0.05

Tennis Dataset (14 examples, weather features, 2 classes)

Configuration:

Population: 20 networks
Epochs: 100
Time: ~30 seconds

Expected Results:

Accuracy:        85-100%
Model Size:      40-60 parameters
Best Topology:   [10, 3-5, 2]
Learning Rate:   ~0.01-0.03

Identity Dataset (identity function learning)

Configuration:

Population: 40 networks
Epochs: 200
Time: ~3 minutes

Expected Results:

Accuracy:        90-98%
Model Size:      Variable
Best Topology:   Depends on problem size
Learning Rate:   ~0.03-0.07

MNIST Dataset (28x28 digit images, 784 features, 10 classes)

Configuration:

Population: 20 networks (smaller due to network size)
Epochs: 50-100
Time: ~10-20 minutes

Expected Results:

Accuracy:        70-85% (with limited training)
Model Size:      2000-5000 parameters
Best Topology:   [784, 32-64, 16-32, 10]
Learning Rate:   ~0.001-0.01

Setup:

python download_mnist.py  # Downloads real MNIST dataset

Fashion MNIST Dataset (28x28 fashion images, 784 features, 10 classes)

Configuration:

Population: 20 networks (smaller due to network size)
Epochs: 50-100
Time: ~10-20 minutes

Expected Results:

Accuracy:        65-80% (with limited training)
Model Size:      2000-5000 parameters
Best Topology:   [784, 32-64, 16-32, 10]
Learning Rate:   ~0.001-0.01

Setup: Same as MNIST - run download_mnist.py (downloads real Fashion MNIST images)

Fashion MNIST Classes:

• 0: T-shirt/top
• 1: Trouser
• 2: Pullover
• 3: Dress
• 4: Coat
• 5: Sandal
• 6: Shirt
• 7: Sneaker
• 8: Bag
• 9: Ankle boot

🖼️ Image Dataset Features

When using MNIST or Fashion MNIST, the dashboard includes:

Sample Image Display:

• 5 sample images displayed horizontally
• Ground truth labels shown under each image
• After training: Model predictions with confidence %
• ✓ Green checkmark for correct predictions
• ✗ Red X for incorrect predictions

Automatic Optimizations:

• Smaller default population (20 vs 40) for faster training
• Adjusted epoch recommendations (100-200 epochs)
• Warning about large input size (784 features)
• Real-time visualization updates during training

Data Source:

• Uses PyTorch's torchvision to download official datasets
• 10,000 training + 2,000 test samples (subsets for speed)
• Full datasets available: 60,000 training + 10,000 test images
• To use full datasets, modify subset_size_train and subset_size_test in download_mnist.py

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Why APBT is Powerful

Method	Hyperparameters	Architecture	Parallel	Manual Tuning
Grid Search	✓ Exhaustive	✗ Fixed	✓ Yes	✓ Required
Random Search	✓ Random	✗ Fixed	✓ Yes	✓ Required
Bayesian Opt	✓ Smart	✗ Fixed	✗ No	✓ Required
NAS	✗ Fixed	✓ Search	✓ Yes	✓ Required
APBT	✓ Evolving	✓ Evolving	✓ Yes	✗ None!

Advantages:

1. ✅ No manual hyperparameter tuning
2. ✅ Automatic architecture search
3. ✅ Parallel efficiency (all networks train simultaneously)
4. ✅ Multi-objective optimization (accuracy vs size)
5. ✅ Adaptive (hyperparameters evolve during training)

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File Structure

├── app.py                      # Streamlit dashboard (600+ lines)
├── requirements.txt            # Python dependencies
├── source/
│   ├── ann.py                 # Neural network implementation
│   ├── apbt.py                # APBT algorithm
│   ├── main.py                # Command-line interface
│   ├── utils.py               # Utility functions
│   ├── testIris.py            # Iris experiment
│   ├── testTennis.py          # Tennis experiment
│   └── testIdentity.py        # Identity experiment
├── data/
│   ├── iris/                  # Iris dataset
│   ├── tennis/                # Tennis dataset
│   └── identity/              # Identity dataset
├── models/                     # Saved weights
└── docs/                       # Original documentation

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Tips for Best Results

1. Start Small: Use Iris dataset with 40 networks for quick experiments
2. Be Patient: Real improvements often take 200+ epochs
3. Watch Leaderboard: See competitive dynamics and exploitation events
4. Check Architecture Tab: See how optimal network structure evolves
5. Monitor Fitness Landscape: Understand why certain networks win
6. Export Charts: Hover over charts and click camera icon to save

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Troubleshooting

Dashboard won't load?

• Ensure Streamlit is running: streamlit run app.py
• Check http://localhost:8501
• Try refreshing the page

Training too slow?

• Reduce population size (try 20)
• Reduce epochs (try 50)
• Use simpler dataset (Tennis)

Want to stop/restart?

# Stop
pkill -f "streamlit run app.py"

# Restart
conda activate torch
streamlit run app.py

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Research Context

This implementation is based on:

• Population Based Training (DeepMind, 2017)
• Neural Architecture Search (Google Brain, 2017)
• Regularized Evolution (Google, 2019)

Key Innovation: Combines hyperparameter optimization WITH architecture search in a single unified framework.

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License

See LICENSE file for details.

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Acknowledgments

Original implementation by Josias Moukpe (Machine Learning Course, April 2022)

Interactive dashboard and comprehensive documentation added to make the algorithm accessible and understandable through beautiful visualizations.

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Enjoy exploring evolutionary neural network training! 🧬🚀

For questions or issues, check the dashboard help tooltips or review the inline code comments.