Hyperparameter Optimization 5 - Advanced Techniques, Hyperband and Population-Based Training for Hyperparameter Optimization

In the previous blog posts, we introduced the concept of hyperparameter optimization and explored various techniques, including Grid Search, Random Search, and automated optimization using popular Python libraries like Optuna, Hyperopt, and Scikit-Optimize, and genetic algorithms. In this post, we will dive into more advanced techniques for hyperparameter optimization: Hyperband and Population-Based Training (PBT). These methods are particularly useful for optimizing deep learning models, as they can efficiently search for the best hyperparameters in large search spaces while reducing the computational cost.

Hyperband

Hyperband is an advanced hyperparameter optimization technique that combines random search with adaptive resource allocation and early stopping. The main idea behind Hyperband is to allocate more resources to promising configurations and stop training for less promising ones early on. This allows Hyperband to explore a large search space more efficiently than traditional methods like Grid Search or Random Search.

Population-Based Training (PBT)

Population-Based Training (PBT) is another advanced technique for hyperparameter optimization that combines ideas from genetic algorithms and early stopping. PBT maintains a population of models with different hyperparameter configurations and trains them in parallel. Periodically, poorly performing models are replaced with better-performing ones, and their hyperparameters are perturbed to explore new configurations. This process allows PBT to efficiently search for the best hyperparameters while also adapting them during training.

Example: Hyperparameter Optimization with Hyperband and PBT in Python

In this example, we will demonstrate hyperparameter optimization using Hyperband and PBT on the famous CIFAR-10 dataset with a simple convolutional neural network (CNN) using the Keras library.

1. Import necessary libraries and load the dataset:

import numpy as np
import pandas as pd
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.utils import to_categorical
from kerastuner.tuners import Hyperband
from ray.tune.schedulers import PopulationBasedTraining
from ray import tune

(X_train, y_train), (X_test, y_test) = cifar10.load_data()
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)

2. Normalize the data:

X_train = X_train.astype('float32') / 255.0
X_test = X_test.astype('float32') / 255.0

3. Define the CNN model:

def create_cnn_model(hp):
    model = Sequential()
    model.add(Conv2D(filters=hp.Int('filters_1', 32, 128, step=32), kernel_size=3, activation='relu', input_shape=(32, 32, 3)))
    model.add(MaxPooling2D(pool_size=2))
    model.add(Conv2D(filters=hp.Int('filters_2', 32, 128, step=32), kernel_size=3, activation='relu'))
    model.add(MaxPooling2D(pool_size=2))
    model.add(Flatten())
    model.add(Dense(units=hp.Int('units', 128, 512, step=64), activation='relu'))
    model.add(Dropout(rate=hp.Float('dropout', 0.1, 0.5, step=0.1)))
    model.add(Dense(10, activation='softmax'))

    model.compile(optimizer=Adam(learning_rate=hp.Float('learning_rate', 1e-4, 1e-2, sampling='log')), loss='categorical_crossentropy', metrics=['accuracy'])
    return model

4. Perform hyperparameter optimization using Hyperband:

hyperband_tuner = Hyperband(create_cnn_model, objective='val_accuracy', max_epochs=50, hyperband_iterations=2, directory='hyperband', project_name='cifar10')
hyperband_tuner.search(X_train, y_train, validation_split=0.2, epochs=50)

5. Define the objective function for PBT:

def pbt_objective(config):
    model = create_cnn_model(config)
    history = model.fit(X_train, y_train, validation_split=0.2, epochs=50)
    tune.report(accuracy=history.history['val_accuracy'][-1])

6. Perform hyperparameter optimization using PBT:

pbt_scheduler = PopulationBasedTraining(time_attr='training_iteration', metric='accuracy', mode='max', perturbation_interval=5, hyperparam_mutations={'learning_rate': tune.loguniform(1e-4, 1e-2), 'filters_1': tune.randint(32, 128), 'filters_2': tune.randint(32, 128), 'units': tune.randint(128, 512), 'dropout': tune.uniform(0.1, 0.5)})

pbt_analysis = tune.run(pbt_objective, config={'learning_rate': tune.loguniform(1e-4, 1e-2), 'filters_1': tune.randint(32, 128), 'filters_2': tune.randint(32, 128), 'units': tune.randint(128, 512), 'dropout': tune.uniform(0.1, 0.5)}, num_samples=10, scheduler=pbt_scheduler, resources_per_trial={'cpu': 2, 'gpu': 1})

Conclusion

In this blog post, we explored advanced techniques for hyperparameter optimization, such as Hyperband and Population-Based Training (PBT). These methods are particularly useful for optimizing deep learning models, as they can efficiently search for the best hyperparameters in large search spaces while reducing the computational cost. By leveraging these advanced techniques, machine learning practitioners can further improve the performance of their models and make better predictions. In the future, we can expect even more advanced techniques and tools to emerge, making hyperparameter optimization an increasingly important aspect of machine learning.