🔧 Neural Network Surgery

Understanding Neural Network Architecture

Network architecture is crucial for model performance. The right architecture can mean the difference between state-of-the-art results and failure. Architecture engineering is both art and science.

Layer Types

• Dense (Fully Connected):
  • Every neuron connected to all previous layer neurons
  • Parameters: input_size × output_size + bias
  • Use for: Final classification, small networks
• Convolutional:
  • Local connectivity, weight sharing
  • Parameters: kernel_size × kernel_size × channels
  • Use for: Images, spatial data
• Pooling:
  • Downsampling, no learned parameters
  • Max pooling, average pooling
  • Reduces dimensions, adds translation invariance
• Dropout:
  • Randomly drops neurons during training
  • Prevents overfitting
  • Typical rates: 0.2-0.5
• Batch Normalization:
  • Normalizes layer inputs
  • Stabilizes training
  • Allows higher learning rates

Architecture Design Principles

• Depth vs Width:
  • Deep: More layers, hierarchical features
  • Wide: More neurons per layer
  • Generally: Deeper is better (with skip connections)
• Regularization:
  • Dropout after dense layers
  • Batch norm after conv/dense
  • Weight decay (L2 regularization)
• Skip Connections:
  • Enable very deep networks (100+ layers)
  • Residual connections (ResNet)
  • Dense connections (DenseNet)

Famous Architectures

• LeNet (1998): First successful CNN
• AlexNet (2012): Started deep learning revolution
• VGGNet (2014): Simple, deep with 3×3 filters
• ResNet (2015): Skip connections, 100+ layers
• Inception (2015): Multi-scale features
• EfficientNet (2019): Compound scaling
• Vision Transformer (2020): Transformers for images

Model Compression

• Pruning: Remove unimportant weights/neurons
• Quantization: Use lower precision (INT8 vs FP32)
• Knowledge Distillation: Train small model from large
• Low-Rank Factorization: Decompose weight matrices

Neural Architecture Search (NAS)

• Automatically discover architectures
• Evolution, reinforcement learning, gradient-based
• Computationally expensive
• Can find non-obvious designs

Tips for Architecture Design

• Start with proven architecture for your domain
• Gradually modify, don't redesign from scratch
• Add regularization before making network larger
• Use skip connections for deep networks
• Batch normalization helps training stability
• Monitor parameter count vs performance
• Consider inference time requirements

Experiment with Surgery Tool

Use the interactive tool above to:

• Build custom architectures layer by layer
• See parameter counts update in real-time
• Load and modify template architectures
• Understand memory and compute requirements
• Experiment with different layer combinations

Architecture design is crucial for deep learning success. This tool helps you understand how different architectural choices affect model capacity and efficiency!