Hyperparameter Tuning and Model Selection Deep Learning

What You’ll Learn 📚

• Understand what hyperparameters are and why they matter
• Learn how to select and tune hyperparameters effectively
• Explore model selection techniques
• Gain practical experience with examples and exercises

Introduction to Hyperparameters

Hyperparameters are the settings that you can adjust before training a machine learning model. They are different from model parameters, which are learned during training. Think of hyperparameters as the settings on your oven when baking a cake. 🍰 You choose the temperature and time, but the cake (model) bakes itself.

Key Terminology

• Hyperparameter: A configuration that is set before the learning process begins.
• Model Parameter: A variable that is learned from the data during training.
• Grid Search: A method to systematically work through multiple combinations of hyperparameter values.
• Random Search: A method that samples random combinations of hyperparameters.

Starting with the Simplest Example

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.svm import SVC

# Load dataset
iris = load_iris()
X, y = iris.data, iris.target

# Split the dataset
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Define the model
model = SVC()

# Define hyperparameters to tune
param_grid = {'C': [0.1, 1, 10], 'kernel': ['linear', 'rbf']}

# Setup the grid search
grid_search = GridSearchCV(model, param_grid, cv=5)

# Fit the model
grid_search.fit(X_train, y_train)

# Best parameters
print('Best Hyperparameters:', grid_search.best_params_)

Progressively Complex Examples

from sklearn.model_selection import RandomizedSearchCV

# Define hyperparameters to tune
param_dist = {'C': [0.1, 1, 10], 'kernel': ['linear', 'rbf'], 'gamma': ['scale', 'auto']}

# Setup the random search
random_search = RandomizedSearchCV(model, param_dist, n_iter=5, cv=5, random_state=42)

# Fit the model
random_search.fit(X_train, y_train)

# Best parameters
print('Best Hyperparameters:', random_search.best_params_)

import tensorflow as tf
from tensorflow import keras
from kerastuner.tuners import RandomSearch

# Define the model
def build_model(hp):
    model = keras.Sequential()
    model.add(keras.layers.Dense(units=hp.Int('units', min_value=32, max_value=512, step=32), activation='relu'))
    model.add(keras.layers.Dense(3, activation='softmax'))
    model.compile(optimizer=keras.optimizers.Adam(hp.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4])),
                  loss='sparse_categorical_crossentropy',
                  metrics=['accuracy'])
    return model

# Setup the tuner
tuner = RandomSearch(build_model,
                     objective='val_accuracy',
                     max_trials=5,
                     executions_per_trial=3,
                     directory='my_dir',
                     project_name='intro_to_kt')

# Perform the search
tuner.search(X_train, y_train, epochs=10, validation_data=(X_test, y_test))

# Get the best model
best_model = tuner.get_best_models(num_models=1)[0]

# Summary of the best model
best_model.summary()

Common Questions and Answers

1. What are hyperparameters?

   Hyperparameters are settings that you configure before training a model. They can include the learning rate, number of layers, and more.

2. Why is hyperparameter tuning important?

   Proper tuning can significantly improve model performance by finding the optimal settings for your specific dataset.

3. How do I choose which hyperparameters to tune?

   Start with the most impactful ones, such as learning rate, number of layers, and batch size. Experiment to see which ones affect your model the most.

4. What’s the difference between grid search and random search?

   Grid search tests all combinations of specified hyperparameters, while random search samples a fixed number of random combinations.

5. How can I avoid overfitting during hyperparameter tuning?

   Use cross-validation and keep an eye on validation performance to ensure your model generalizes well to unseen data.

Troubleshooting Common Issues

Warning: Be careful not to overfit your model by using too many hyperparameters or by tuning on the test set.

Tip: Start with a smaller grid or fewer random samples to save time and computational resources.

Remember, practice makes perfect! Try experimenting with different datasets and models to see how hyperparameter tuning can improve your results. Happy coding! 😊