Hyperparameter tuning using GridSearchCV and KerasClassifier

In the field of machine learning, hyperparameter tuning plays a crucial role in optimizing the performance of models and one of the popular techniques for hyperparameter tuning is using GridSearchCV in combination with KerasClassifier. This powerful combination allows data scientists and machine learning practitioners to efficiently explore and identify the best set of hyperparameters for their deep learning models. In this article, we will dive into the concept of hyperparameter tuning, understand the GridSearchCV algorithm, and explore how to use it with KerasClassifier.

Understanding Hyperparameters

Hyperparameters are parameters in machine learning models that are set by data scientists or machine learning practitioners rather than learned from the data itself. They define the behavior and characteristics of the model and can greatly influence its performance. Examples of hyperparameters include learning rate, batch size, the number of hidden layers in a neural network, and the choice of activation functions.

The process of hyperparameter tuning is a crucial step in developing machine learning models. It involves finding the optimal values for these hyperparameters, which directly impact how the model learns and generalizes from the data. By carefully selecting and fine-tuning these hyperparameters, we can improve the model's performance, making it more accurate and reliable in making predictions or classifications.

The Need for Hyperparameter Tuning

Hyperparameter tuning holds significant importance as it allows us to choose the most suitable hyperparameters for a machine learning model, leading to substantial improvements in its performance. By fine-tuning the hyperparameters, we can enhance the model's accuracy, mitigate overfitting issues, and enhance its ability to make accurate predictions on new and unseen data. Ultimately, this process enables us to create a well-optimized model that performs better and generalizes well beyond the training data.

Introducing GridSearchCV

GridSearchCV is a technique used for hyperparameter optimization. It systematically searches through a predefined set of hyperparameters and evaluates the model's performance for each combination. It exhaustively tries every possible combination to identify the best set of hyperparameters.

GridSearchCV Workflow

The workflow of GridSearchCV involves the following steps −

• Define the model − Specify the machine learning model to be tuned.

• Define the hyperparameter grid − Create a dictionary with the hyperparameters and their corresponding values to be explored.

• Define the scoring metric − Select a metric to evaluate the model's performance.

• Perform grid search − Fit the GridSearchCV object with the training data and hyperparameter grid.

• Retrieve the best hyperparameters − Access the best hyperparameters found by GridSearchCV.

• Evaluate the model − Use the best hyperparameters to train the model and evaluate its performance on the test data.

Hyperparameter Tuning with KerasClassifier and GridSearchCV

KerasClassifier is a wrapper class in the Keras library that allows us to use Keras models with Scikit-learn's GridSearchCV. By combining KerasClassifier with GridSearchCV, we can easily tune hyperparameters for deep learning models built using Keras.

To use KerasClassifier with GridSearchCV, we need to define a Keras model as a function and pass it to the KerasClassifier. We can then proceed with the regular GridSearchCV workflow by specifying the hyperparameter grid and scoring metric.

Below are the steps that we will follow for Hyperparameter Tuning with KerasClassifier and GridSearchCV −

Algorithm

• Import the required libraries − This step imports the necessary libraries and modules such as NumPy, scikit-learn, and Keras to perform hyperparameter tuning using GridSearchCV and KerasClassifier.

• Load the dataset −

• Split the data into training and test sets −

• Define a function to create the Keras model: A function named `create_model()` is defined to create a simple Keras model.

• Create the KerasClassifier object −

• Define the hyperparameter grid − The program below defines a dictionary named `param_grid` that specifies the hyperparameters to be tuned and their corresponding values

• Create the GridSearchCV object

• Fit the GridSearchCV object to the training data −

• Print the best parameters and score: Evaluate the best model on the test data −

Example

# Import the required libraries
import numpy as npp
from sklearn.model_selection import GridSearchCV
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasClassifier

# Load the Iris dataset
irisd = load_iris()
X = irisd.data
y = irisd.target

# Split the data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Define a function to create the Keras model
def create_model(units=10, activation='relu'):
   model = Sequential()
   model.add(Dense(units=units, activation=activation, input_dim=4))
   model.add(Dense(units=3, activation='softmax'))
   model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
   return model

# Create the KerasClassifier object
model = KerasClassifier(build_fn=create_model)

# Define the hyperparameter grid to search over
param_grid = {
   'units': [5, 10, 15],
   'activation': ['relu', 'sigmoid']
}

# Create the GridSearchCV object
grid = GridSearchCV(estimator=model, param_grid=param_grid, cv=3)

# Fit the GridSearchCV object to the training data
grid_result = grid.fit(X_train, y_train)

# Print the best parameters and score
print("Best Parameters: ", grid_result.best_params_)
print("Best Score: ", grid_result.best_score_)

# Evaluate the best model on the test data
best_model = grid_result.best_estimator_
test_accuracy = best_model.score(X_test, y_test)
print("Test Accuracy: ", test_accuracy)

Output

Best Parameters:  {'activation': 'sigmoid', 'units': 5}
Best Score:  0.42499999205271405
1/1 [==============================] - 0s 74ms/step - loss: 1.1070 - accuracy: 0.1667
Test Accuracy:  0.1666666716337204

Benefits of Using GridSearchCV and KerasClassifier

The combination of GridSearchCV and KerasClassifier offers several benefits −

• Automated hyperparameter tuning − GridSearchCV performs an exhaustive search, saving us from manually testing different combinations.

• Improved model performance − By identifying the best set of hyperparameters, we can enhance the model's performance and achieve better results.

• Time and resource efficiency − GridSearchCV optimizes the hyperparameter search process, reducing the time and computational resources required.

Best Practices for Hyperparameter Tuning

When performing hyperparameter tuning, it's essential to keep the following best practices in mind −

• Define a reasonable search space − Limit the range of hyperparameters to avoid inefficient search or overfitting.

• Utilize cross-validation − Cross-validation helps assess the model's performance and ensures the chosen hyperparameters generalize well.

• Consider computational constraints − Be mindful of the computational resources required for hyperparameter tuning, especially for large datasets and complex models.

• Track and document experiments − Keep a record of the different hyperparameter settings and their corresponding performance metrics to track progress and reproduce results.

Conclusion

In conclusion, Hyperparameter tuning is a crucial step in the machine learning model development process. GridSearchCV in combination with KerasClassifier provides an efficient and automated approach to identifying the best hyperparameters for deep learning models. By leveraging this technique, data scientists and machine learning practitioners can enhance model performance, achieve better results, and save time and computational resources.