TinyTorch/modules/layers

🧱 Module 2: Layers - Neural Network Building Blocks

Build the fundamental transformations that compose into neural networks

🎯 Learning Objectives

After completing this module, you will:

• Understand layers as functions that transform tensors: y = f(x)
• Implement Dense layers with linear transformations: y = Wx + b
• Add activation functions for nonlinearity (ReLU, Sigmoid, Tanh)
• See how neural networks are just function composition
• Build intuition for neural network architecture before diving into training

🧱 Build → Use → Understand

This module follows the TinyTorch pedagogical framework:

1. Build: Dense layers and activation functions from scratch
2. Use: Transform tensors and see immediate results
3. Understand: How neural networks transform information

📚 What You'll Build

Dense Layer

layer = Dense(input_size=3, output_size=2)
x = Tensor([[1.0, 2.0, 3.0]])
y = layer(x)  # Shape: (1, 2)

Activation Functions

relu = ReLU()
sigmoid = Sigmoid()
tanh = Tanh()

x = Tensor([[-1.0, 0.0, 1.0]])
y_relu = relu(x)      # [0.0, 0.0, 1.0]
y_sigmoid = sigmoid(x)  # [0.27, 0.5, 0.73]
y_tanh = tanh(x)      # [-0.76, 0.0, 0.76]

Neural Networks

# 3 → 4 → 2 network
layer1 = Dense(input_size=3, output_size=4)
activation1 = ReLU()
layer2 = Dense(input_size=4, output_size=2)
activation2 = Sigmoid()

# Forward pass
x = Tensor([[1.0, 2.0, 3.0]])
h1 = layer1(x)
h1_activated = activation1(h1)
h2 = layer2(h1_activated)
output = activation2(h2)

🚀 Getting Started

Prerequisites

• Complete Module 1: Tensor ✅
• Understand basic linear algebra (matrix multiplication)
• Familiar with Python classes and methods

Quick Start

# Navigate to the layers module
cd modules/layers

# Work in the development notebook
jupyter notebook layers_dev.ipynb

# Or work in the Python file
code layers_dev.py

📖 Module Structure

modules/layers/
├── layers_dev.py           # Main development file (work here!)
├── layers_dev.ipynb        # Jupyter notebook version
├── tests/
│   └── test_layers.py      # Comprehensive tests
├── README.md              # This file
└── solutions/             # Reference implementations (if stuck)

🎓 Learning Path

Step 1: Dense Layer (Linear Transformation)

• Understand y = Wx + b
• Implement weight initialization
• Handle matrix multiplication and bias addition
• Test with single examples and batches

Step 2: Activation Functions

• Implement ReLU: max(0, x)
• Implement Sigmoid: 1 / (1 + e^(-x))
• Implement Tanh: tanh(x)
• Understand why nonlinearity is crucial

Step 3: Layer Composition

• Chain layers together
• Build complete neural networks
• See how simple layers create complex functions

Step 4: Real-World Application

• Build an image classification network
• Understand how architecture affects capability

🧪 Testing Your Implementation

Module-Level Tests

# Run comprehensive tests
python -m pytest tests/test_layers.py -v

# Quick test
python -c "from layers_dev import Dense, ReLU; print('✅ Layers working!')"

Package-Level Tests

# Export to package
python ../../bin/tito.py sync

# Test integration
python ../../bin/tito.py test --module layers

🎯 Key Concepts

Layers as Functions

• Input: Tensor with some shape
• Transformation: Mathematical operation
• Output: Tensor with possibly different shape

Linear vs Nonlinear

• Dense layers: Linear transformations
• Activation functions: Nonlinear transformations
• Composition: Linear + Nonlinear = Complex functions

Neural Networks = Function Composition

Input → Dense → ReLU → Dense → Sigmoid → Output

Why This Matters

• Modularity: Build complex networks from simple parts
• Reusability: Same layers work for different problems
• Understanding: Know how each part contributes to the whole

🔍 Common Issues

Import Errors

# Make sure you're in the right directory
import sys
sys.path.append('../../')
from modules.tensor.tensor_dev import Tensor

Shape Mismatches

# Check input/output sizes match
layer1 = Dense(input_size=3, output_size=4)
layer2 = Dense(input_size=4, output_size=2)  # 4 matches output of layer1

Gradient Issues (Later)

# Use proper weight initialization
limit = math.sqrt(6.0 / (input_size + output_size))
weights = np.random.uniform(-limit, limit, (input_size, output_size))

🎉 Success Criteria

You've successfully completed this module when:

• ✅ All tests pass (pytest tests/test_layers.py)
• ✅ You can build a 2-layer neural network
• ✅ You understand how layers transform tensors
• ✅ You see the connection between layers and neural networks
• ✅ Package export works (tito test --module layers)

🚀 What's Next

After completing this module, you're ready for:

• Module 3: Networks - Compose layers into common architectures
• Module 4: Training - Learn how networks improve through experience
• Module 5: Applications - Use networks for real problems

🤝 Getting Help

• Check the tests for examples of expected behavior
• Look at the solutions/ directory if you're stuck
• Review the pedagogical principles in docs/pedagogy/
• Remember: Build → Use → Understand!

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Great job building the foundation of neural networks! 🎉

This module implements the core insight: neural networks are just function composition of simple building blocks.