Davityak03/Fashion-Mnist-Cnn-model-optimized-using-keras-tuner

Fashion-Mnist-Cnn-model-optimized-using-keras-tuner

This repository contains a Convolutional Neural Network (CNN) model for classifying images from the Fashion-MNIST dataset. The model is optimized using Keras Tuner to find the best hyperparameters for improved accuracy.

Table of Contents

• Introduction
• Dataset
• Model Architecture
• Hyperparameter Optimization
• Results

Introduction

Fashion-MNIST is a dataset of Zalando's article images, consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image associated with a label from 10 classes. This project aims to build a CNN model for classifying these images and optimize the model using Keras Tuner.

Dataset

The Fashion-MNIST dataset includes the following classes:

1. T-shirt/top
2. Trouser
3. Pullover
4. Dress
5. Coat
6. Sandal
7. Shirt
8. Sneaker
9. Bag
10. Ankle boot

The dataset can be loaded directly using TensorFlow:

from tensorflow.keras.datasets import fashion_mnist

(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()

Model Architecture

The CNN model consists of several convolutional layers, followed by flatten and then dense layers. The architecture is defined using Keras and optimized using Keras Tuner.

Hyperparameter Optimization

Keras Tuner is used to search for the best hyperparameters for the CNN model. The following hyperparameters are tuned:

• Number of filters in each convolutional layer
• Kernel size
• Learning rate

Results

The best model found by Keras Tuner achieves a high accuracy on the test set. Detailed results and the best hyperparameters are documented in the notebook.