Machine Learning Overview

Hyperparameter Tuning with Keras Tuner _ Day 17

08 Aug

A Comprehensive Guide to Hyperparameter Tuning with Keras Tuner

Introduction

In the world of machine learning, the performance of your model can heavily depend on the choice of hyperparameters. Hyperparameter tuning, the process of finding the optimal settings for these parameters, can be time-consuming and complex. This guide will walk you through the essentials of hyperparameter tuning using Keras Tuner, helping you build more efficient and effective models.

Why Hyperparameter Tuning Matters

Hyperparameters are critical settings that can influence the performance of your machine learning models. These include the learning rate, the number of layers in a neural network, the number of neurons per layer, and many more. Finding the right combination of these settings can dramatically improve your model’s accuracy and efficiency.

Introducing Keras Tuner

Keras Tuner is an open-source library that provides a streamlined approach to hyperparameter tuning for Keras models. It supports various search algorithms, including random search, Hyperband, and Bayesian optimization. This tool not only saves time but also ensures a systematic exploration of the hyperparameter space.

Step-by-Step Guide to Using Keras Tuner

1. Define Your Model with Hyperparameters

Begin by defining a model-building function that includes hyperparameters: 


import keras_tuner as kt
import tensorflow as tf

def build_model(hp):
    n_hidden = hp.Int("n_hidden", min_value=0, max_value=8, default=2)
    n_neurons = hp.Int("n_neurons", min_value=16, max_value=256)
    learning_rate = hp.Float("learning_rate", min_value=1e-4, max_value=1e-2, sampling="log")
    optimizer_choice = hp.Choice("optimizer", values=["sgd", "adam"])

    if optimizer_choice == "sgd":
        optimizer = tf.keras.optimizers.SGD(learning_rate=learning_rate)
    else:
        optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)

    model = tf.keras.Sequential()
    model.add(tf.keras.layers.Flatten())
    for _ in range(n_hidden):
        model.add(tf.keras.layers.Dense(n_neurons, activation="relu"))
    model.add(tf.keras.layers.Dense(10, activation="softmax"))

    model.compile(loss="sparse_categorical_crossentropy",
                  optimizer=optimizer,
                  metrics=["accuracy"])
    return model

2. Choose a Tuning Strategy

Keras Tuner offers several strategies for hyperparameter search. One of the simplest is RandomSearch, but more advanced options like Hyperband are also available. Here’s how to set up a random search: 


tuner = kt.RandomSearch(
    build_model,
    objective="val_accuracy",
    max_trials=5,
    directory="my_dir",
    project_name="intro_to_kt"
)

3. Run the Tuner

Once the tuner is set up, you can start the hyperparameter search. This involves specifying the training and validation data: 


tuner.search(X_train, y_train, epochs=10, validation_data=(X_valid, y_valid))

4. Retrieve the Best Model

After the search is complete, you can retrieve the best model and its hyperparameters: 


best_hps = tuner.get_best_hyperparameters(num_trials=1)[0]
model = tuner.hypermodel.build(best_hps)
model.fit(X_train, y_train, epochs=10, validation_data=(X_valid, y_valid))

Advanced Tuning with Custom Classes

For more complex scenarios, such as including custom preprocessing steps or additional tuning logic, you can create a custom hypermodel class: 


class MyClassificationHyperModel(kt.HyperModel):
    def build(self, hp):
        return build_model(hp)

    def fit(self, hp, model, X, y, **kwargs):
        if hp.Boolean("normalize"):
            norm_layer = tf.keras.layers.Normalization()
            X = norm_layer(X)
        return model.fit(X, y, **kwargs)

hypermodel = MyClassificationHyperModel()
tuner = kt.Hyperband(
    hypermodel,
    objective="val_accuracy",
    max_epochs=10,
    directory="my_dir",
    project_name="hyperband"
)

Visualizing Results with TensorBoard

Keras Tuner integrates seamlessly with TensorBoard, providing visual insights into the tuning process. This includes tracking the performance of different hyperparameter combinations and visualizing learning curves. 


import tensorflow as tf
from pathlib import Path

root_logdir = Path(tuner.project_dir) / "tensorboard"
tensorboard_cb = tf.keras.callbacks.TensorBoard(root_logdir)
early_stopping_cb = tf.keras.callbacks.EarlyStopping(patience=2)

tuner.search(
    X_train, y_train, 
    epochs=10, 
    validation_data=(X_valid, y_valid), 
    callbacks=[early_stopping_cb, tensorboard_cb]
)

Key Notes:

Hyperparameter tuning is a crucial step in building high-performing machine learning models. Keras Tuner simplifies this process, allowing you to efficiently explore a wide range of hyperparameter settings and find the optimal configuration for your models. By following the steps outlined in this guide, you can leverage Keras Tuner to enhance the accuracy and efficiency of your machine learning projects.

Let’s explain even deeper:

Now that we have showed you a quick overview of  how to use Keras Tuner, let’s dive deeper into what happens behind the scenes during the hyperparameter tuning process. 

 

 

1. Hyperparameter Space Definition

When you define the hyperparameter space, Keras Tuner uses this information to create a search space where each dimension corresponds to a hyperparameter. For example, the `hp.Int` and `hp.Float` functions define integer and float ranges, respectively. This search space is explored using different strategies.

2. Search Algorithms

Keras Tuner supports various search algorithms, each with different strategies for exploring the hyperparameter space:

  • Random Search: Samples hyperparameter values randomly. It is simple and effective but can be inefficient for high-dimensional spaces.
  • Hyperband: An efficient search strategy that combines random search with adaptive resource allocation. It allocates more resources to promising hyperparameter configurations and prunes less promising ones early.
  • Bayesian Optimization: Uses past trial results to model the performance surface and makes informed guesses about the next set of hyperparameters. It balances exploration and exploitation to find the best configuration efficiently.

3. Model Building and Training

For each set of hyperparameters sampled by the tuner, Keras Tuner builds a model using the `build_model` function. This model is then trained on the training data. The training process involves:

  • Forward Pass: Data passes through the network, and activations are computed at each layer.
  • Loss Computation: The loss function measures the difference between the predicted and actual values.
  • Backward Pass: Gradients are computed using backpropagation, and the optimizer updates the model weights to minimize the loss.

4. Performance Evaluation

After training the model on the training data, Keras Tuner evaluates its performance on the validation data. This involves:

  • Validation Metrics: Common metrics include accuracy, loss, precision, recall, and F1-score. The choice of metric depends on the problem type (e.g., classification or regression).
  • Early Stopping: A technique used to prevent overfitting by halting training when the model’s performance on the validation data stops improving.

5. Logging and Analysis

Keras Tuner logs the hyperparameter values and corresponding performance metrics for each trial. This data is stored in the specified directory and can be analyzed to understand the tuning process. Logging includes:

  • Hyperparameter Values: The specific values of each hyperparameter used in the trial.
  • Performance Metrics: The results of the evaluation on the validation set, such as accuracy or loss.
  • Intermediate Results: Information about the model’s performance at different stages of training.

6. Selecting the Best Hyperparameters

Once all trials are completed, Keras Tuner identifies the best set of hyperparameters based on the specified objective metric. This selection process involves:

  • Comparison: Evaluating the performance metrics of all trials to find the hyperparameter combination that yields the best results.
  • Retraining: Rebuilding and retraining the model using the best hyperparameters to ensure robust performance.

7. Advanced Tuning Techniques

For more complex scenarios, advanced tuning techniques can be employed:

  • Custom Hypermodels: Creating custom classes to define more complex model architectures and training procedures.
  • Conditional Hyperparameters: Defining hyperparameters that depend on the values of other hyperparameters, allowing for more flexible and dynamic tuning.

8. Mathematical Foundations

The mathematical foundation of hyperparameter tuning involves optimization techniques and statistical methods:

  • Gradient Descent: An optimization algorithm used to minimize the loss function by iteratively updating model parameters in the direction of the negative gradient.
  • Bayesian Optimization: A probabilistic model that predicts the performance of hyperparameters and guides the search towards the most promising regions of the search space.
  • Successive Halving: A resource allocation method used in Hyperband that allocates more resources to promising hyperparameter configurations and eliminates less promising ones early.

9. Potential Challenges and Considerations

While Keras Tuner simplifies hyperparameter tuning, there are challenges and considerations to keep in mind:

  • Computational Resources: Hyperparameter tuning can be computationally expensive, requiring significant processing power and time.
  • Overfitting: There’s a risk of overfitting to the validation set if not managed properly with techniques like early stopping.
  • Choice of Metric: The choice of objective metric significantly impacts the tuning process and results. Selecting the right metric for the problem is crucial.

10. Practical Applications

Hyperparameter tuning with Keras Tuner can be applied to various machine learning tasks, including:

  • Image Classification: Tuning convolutional neural networks (CNNs) for better accuracy and efficiency.
  • Natural Language Processing: Optimizing models for text classification, sentiment analysis, and language generation.
  • Time Series Forecasting: Improving the performance of models used for predicting future values in time series data.

Conclusion

Understanding what happens behind the scenes during hyperparameter tuning with Keras Tuner provides valuable insights into optimizing machine learning models. By leveraging various search algorithms, mathematical foundations, and advanced tuning techniques, Keras Tuner helps automate and streamline the process of finding the best hyperparameters, leading to improved model performance and efficiency.

Experiment with different tuning strategies and custom hypermodels to suit your specific needs and achieve the best results for your machine learning projects.

So let’s see the complete code from our today lesson: 

# Install necessary packages
!pip install -q -U keras-tuner

import keras_tuner as kt
import tensorflow as tf
from tensorflow.keras.datasets import mnist
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten
import matplotlib.pyplot as plt
import numpy as np

# Load and preprocess the MNIST dataset
(X_train, y_train), (X_valid, y_valid) = mnist.load_data()
X_train, X_valid = X_train / 255.0, X_valid / 255.0

# Define the model-building function
def build_model(hp):
    n_hidden = hp.Int("n_hidden", min_value=0, max_value=8, default=2)
    n_neurons = hp.Int("n_neurons", min_value=16, max_value=256)
    learning_rate = hp.Float("learning_rate", min_value=1e-4, max_value=1e-2, sampling="log")
    optimizer = hp.Choice("optimizer", values=["sgd", "adam"])

    if optimizer == "sgd":
        optimizer = tf.keras.optimizers.SGD(learning_rate=learning_rate)
    else:
        optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)

    model = Sequential()
    model.add(Flatten(input_shape=(28, 28)))
    for _ in range(n_hidden):
        model.add(Dense(n_neurons, activation="relu"))
    model.add(Dense(10, activation="softmax"))

    model.compile(loss="sparse_categorical_crossentropy",
                  optimizer=optimizer,
                  metrics=["accuracy"])
    return model

# Initialize the tuner
tuner = kt.RandomSearch(
    build_model,
    objective="val_accuracy",
    max_trials=5,
    directory="my_dir",
    project_name="intro_to_kt"
)

# Run the hyperparameter search
tuner.search(X_train, y_train, epochs=10, validation_data=(X_valid, y_valid))

# Retrieve the best hyperparameters and build the best model
best_hps = tuner.get_best_hyperparameters(num_trials=1)[0]
model = tuner.hypermodel.build(best_hps)

# Train the best model
model.fit(X_train, y_train, epochs=10, validation_data=(X_valid, y_valid))

# Print the best hyperparameters
print(f"""
The hyperparameter search is complete. The optimal number of hidden layers is {best_hps.get('n_hidden')},
the optimal number of units in each layer is {best_hps.get('n_neurons')}, and the optimal learning rate
for the optimizer is {best_hps.get('learning_rate')}.
""")

# Make predictions on the validation set
predictions = model.predict(X_valid)

# Function to plot the results
def plot_images(predictions, true_labels, images, num_rows=3, num_cols=3):
    plt.figure(figsize=(10, 10))
    for i in range(num_rows * num_cols):
        plt.subplot(num_rows, num_cols, i + 1)
        plt.xticks([])
        plt.yticks([])
        plt.grid(False)
        plt.imshow(images[i], cmap=plt.cm.binary)
        predicted_label = np.argmax(predictions[i])
        true_label = true_labels[i]
        color = 'blue' if predicted_label == true_label else 'red'
        plt.xlabel(f"Pred: {predicted_label} (True: {true_label})", color=color)
    plt.show()

# Plot a sample of the predictions
plot_images(predictions, y_valid, X_valid)






MNIST Model Explanation


Keras Tuner MNIST Example

Explanation:

Installation and Imports:
keras-tuner is installed and necessary libraries are imported.
matplotlib is imported for visualization purposes. 

!pip install -q -U keras-tuner

import keras_tuner as kt
import tensorflow as tf
from tensorflow.keras.datasets import mnist
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten
import matplotlib.pyplot as plt
import numpy as np

Data Preprocessing:
The MNIST dataset is loaded and normalized. 

# Load and preprocess the MNIST dataset
(X_train, y_train), (X_valid, y_valid) = mnist.load_data()
X_train, X_valid = X_train / 255.0, X_valid / 255.0

Model Building:
The build_model function defines the architecture with tunable hyperparameters. 

def build_model(hp):
    n_hidden = hp.Int("n_hidden", min_value=0, max_value=8, default=2)
    n_neurons = hp.Int("n_neurons", min_value=16, max_value=256)
    learning_rate = hp.Float("learning_rate", min_value=1e-4, max_value=1e-2, sampling="log")
    optimizer = hp.Choice("optimizer", values=["sgd", "adam"])

    if optimizer == "sgd":
        optimizer = tf.keras.optimizers.SGD(learning_rate=learning_rate)
    else:
        optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)

    model = Sequential()
    model.add(Flatten(input_shape=(28, 28)))
    for _ in range(n_hidden):
        model.add(Dense(n_neurons, activation="relu"))
    model.add(Dense(10, activation="softmax"))

    model.compile(loss="sparse_categorical_crossentropy",
                  optimizer=optimizer,
                  metrics=["accuracy"])
    return model

Hyperparameter Tuning:
A RandomSearch tuner is initialized and used to find the best hyperparameters. 

tuner = kt.RandomSearch(
    build_model,
    objective="val_accuracy",
    max_trials=5,
    directory="my_dir",
    project_name="intro_to_kt"
)

Running the Hyperparameter Search:
The tuner searches for the optimal hyperparameters by training multiple models. 

tuner.search(X_train, y_train, epochs=10, validation_data=(X_valid, y_valid))

Training the Best Model:
The best model is built using the optimal hyperparameters and trained. 

best_hps = tuner.get_best_hyperparameters(num_trials=1)[0]
model = tuner.hypermodel.build(best_hps)

# Train the best model
model.fit(X_train, y_train, epochs=10, validation_data=(X_valid, y_valid))

print(f"""
The hyperparameter search is complete. The optimal number of hidden layers is {best_hps.get('n_hidden')},
the optimal number of units in each layer is {best_hps.get('n_neurons')}, and the optimal learning rate
for the optimizer is {best_hps.get('learning_rate')}.
""")

Making Predictions:
The trained model is used to make predictions on the validation set. 

predictions = model.predict(X_valid)

Visualization:
The plot_images function plots a sample of images from the validation set along with their true and predicted labels. The colors indicate whether the prediction is correct (blue) or incorrect (red). 

def plot_images(predictions, true_labels, images, num_rows=3, num_cols=3):
    plt.figure(figsize=(10, 10))
    for i in range(num_rows * num_cols):
        plt.subplot(num_rows, num_cols, i + 1)
        plt.xticks([])
        plt.yticks([])
        plt.grid(False)
        plt.imshow(images[i], cmap=plt.cm.binary)
        predicted_label = np.argmax(predictions[i])
        true_label = true_labels[i]
        color = 'blue' if predicted_label == true_label else 'red'
        plt.xlabel(f"Pred: {predicted_label} (True: {true_label})", color=color)
    plt.show()

# Plot a sample of the predictions
plot_images(predictions, y_valid, X_valid)

Summary:
By running this code in Google Colab, you can visualize how the model classifies handwritten digits from the MNIST dataset. The hyperparameter tuning process uses Keras Tuner to find optimal model parameters, builds and trains the best model, and then visualizes the predictions with color-coded labels to indicate correct (blue) or incorrect (red) classifications.

By running this code in Google Colab, you can visualize how the model classifies the handwritten digits from the MNIST dataset.


Here is the results :

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