https://github.com/davityak03/fashion-mnist-cnn-model-optimized-using-keras-tuner

https://github.com/davityak03/fashion-mnist-cnn-model-optimized-using-keras-tuner

cnn hyperparameters keras keras-tuner python randomsearch scikit-learn tensorflow

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• Host: GitHub
• URL: https://github.com/davityak03/fashion-mnist-cnn-model-optimized-using-keras-tuner
• Owner: Davityak03
• Created: 2024-07-20T10:43:59.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2024-07-21T11:59:06.000Z (7 months ago)
• Last Synced: 2024-11-17T18:12:09.547Z (3 months ago)
• Topics: cnn, hyperparameters, keras, keras-tuner, python, randomsearch, scikit-learn, tensorflow
• Language: Jupyter Notebook
• Homepage:
• Size: 131 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# 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](#introduction)

- [Dataset](#dataset)

- [Model Architecture](#model-architecture)

- [Hyperparameter Optimization](#hyperparameter-optimization)

- [Results](#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:

```python

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.