Deep Convolutional Neural Network (CNN) from Scratch for Image Classification

Overview

This project implements a deep convolutional neural network (CNN) from scratch using Python. The CNN is designed for image classification and consists of multiple convolutional, pooling, and fully connected layers. The model is trained on the CIFAR-10 dataset and optimized using hyperparameter tuning techniques.

Features

• 4 Convolutional Layers: Extracts hierarchical features from images using filters and ReLU activation.
• 2 Max Pooling Layers: Reduces the spatial dimensions while retaining important features.
• 3 Fully Connected Layers: Makes final predictions based on extracted features.
• Hyperparameter Optimization: Tweaks learning rate, batch size, and number of epochs to improve performance.
• Training and Evaluation: Trains the CNN on image data and evaluates its accuracy.

Installation

To run this notebook, install the required dependencies:

pip install tensorflow numpy matplotlib

Ensure that you have a dataset available for training the model. You may use standard datasets like MNIST or CIFAR-10.

Usage

1. Define CNN Architecture: The model is implemented using layers for feature extraction and classification.
2. Train the Model: Train the CNN using a dataset and monitor loss and accuracy.
3. Optimize Hyperparameters: Tune parameters such as learning rate and number of epochs.
4. Evaluate the Model: Test the trained model on validation/test data.

Run the Jupyter notebook to execute the training pipeline step by step.

Results

The trained model achieves high accuracy on the test dataset. Performance can be further improved by fine-tuning hyperparameters and experimenting with additional layers.

This notebook was developed as part of a deep learning project focusing on computer vision and CNN architectures.