Neural Network Classification with PyTorch

Overview

• Classification Problems:
  • Predicting a thing (e.g., whether an email is spam or not) or multiple categories (e.g., types of food).
  • Binary Classification: Predict between two categories.
  • Multi-class Classification: Predict between more than two categories.
  • Multi-label Classification: Each instance can belong to multiple categories.

Examples of Classification

• Binary Classification:

  • Email classification (spam or not spam).
  • Photo categorization (dog or cat).

• Multi-class Classification:

  • Photo categorization (sushi, steak, or pizza).

• Multi-label Classification:

  • Tagging (e.g., Wikipedia articles with multiple relevant tags).

Classification Inputs and Outputs

• Inputs: Numerical representation of data (e.g., images converted into tensors with shape [width, height, color channels]).
• Outputs: Numerical probabilities for each class.

Architecture of a Classification Model

• Input Layer: Shape is the number of features.
• Hidden Layers: Composed of various operations (e.g., nn.Linear).
• Output Layer Shape: Number of output classes.
• Activation Functions:
  • Hidden Layer Activation: Typically ReLU.
  • Output Activation: Sigmoid for binary, Softmax for multi-class.

PyTorch Workflow for Classification

1. Data Preparation and Loading: Convert data into tensors.
2. Model Building: Define neural network architecture.
3. Training Loop: Loop through data to optimize model parameters.
4. Evaluation Loop: Evaluate model performance on test data.
5. Saving and Loading Models: Persist model states for later use.

Getting Help

• Follow along with code and try it yourself.
• Use docstrings for understanding functions.
• Search online for additional resources or answers.
• Ask questions on the course GitHub discussions page.

Tools and Resources

• PyTorch Documentation: Essential for understanding PyTorch functions.
• Scikit-learn: Useful for generating datasets (e.g., make_circles).
• Matplotlib: Useful for visualization in data exploration.

Practical Coding Steps

• Creating Data: Use make_circles from sklearn for synthetic data.
• Visualize Data: Use plotting to better understand data distribution.
• Model Implementation: Use nn.Module and nn.Linear to build models.
• Training and Evaluating: Implement loops to iteratively improve model predictions.
• Device Agnostic Code: Utilize GPU if available, otherwise default to CPU.