CIFAR-10 Classification and Hyperparameter Tuning with Keras Tuner

This repository contains a comprehensive exploration of deep learning techniques, focusing on image classification using the CIFAR-10 dataset. The project is divided into several key components:

1. Libraries Import

Import essential libraries, including TensorFlow, Keras Tuner, and visualization tools.

2. Dataset Import and Preprocessing

Load the CIFAR-10 dataset and preprocess the images, scaling them to the range [0, 1].

3. Model Building and Hyperparameter Tuning

3.1 Hyperparameter Tuning

Utilize Keras Tuner to search for optimal hyperparameters for the convolutional neural network (CNN) model. The hyperparameters include filter sizes, kernel sizes, regularization strengths, and learning rates.

3.2 Model Training and Evaluation

Train the CNN model using the optimal hyperparameters and evaluate its performance on the test set. Visualize accuracy and loss curves for model evaluation.

4. Best Model Recreation and Evaluation

Recreate the best-performing CNN model, optimize data handling with TensorFlow datasets, and evaluate its performance. Visualize the confusion matrix and showcase both misclassified and correctly classified images.

5. Plotting Results and Confusion Matrix

Explore the project's results through detailed visualizations. Plot accuracy and loss curves to understand the model's training process. Utilize a confusion matrix to gain insights into the model's classification performance on the test data.

Installation

To run the project, make sure to install the necessary modules listed in the requirements.txt file. You can install them using pip:

pip install -r requirements.txt

For detailed implementation and usage, refer to the Jupyter Notebook files in the repository.

Enjoy exploring the world of deep learning and image classification with CIFAR-10!