MNIST Image Classification using TensorFlow and Keras

This project demonstrates how to build a simple neural network model using TensorFlow and Keras to classify handwritten digits from the MNIST dataset.

Prerequisites

• Python 3.x
• TensorFlow
• Keras
• Matplotlib

Getting Started

Installation

Ensure you have Python installed on your system. Install the required libraries using pip:

pip install tensorflow keras matplotlib

Usage

1. Clone the repository:

git clone https://github.com/your-username/your-repo.git
cd your-repo

2. Run the script:

python mnist_classification.py

Description

The script performs the following steps:

1. Loading the Dataset: The MNIST dataset is loaded using TensorFlow's Keras API. The training and test data are then assigned to their respective variables.

2. Data Preprocessing: Pixel values are scaled down from the range 0-255 to 0-1 to ease computation.

3. Visualizing Data: The shape of the training and test data is printed, and the first image in the training set is displayed using Matplotlib.

4. Defining the Neural Network: A sequential model is created using TensorFlow's Sequential API. It consists of a Flatten layer, a Dense layer with ReLU activation, and an output Dense layer with softmax activation.

5. Compiling the Model: The model is compiled using the Adam optimizer and sparse categorical cross-entropy loss function.

6. Training the Model: The model is trained on the training data for 20 epochs.

7. Model Evaluation: The trained model is evaluated on the test data to determine its accuracy.

8. Saving the Model: The trained model is saved for future use.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• This project was inspired by the desire to learn and implement neural networks for image classification.
• Credits to the MNIST dataset provided by Yann LeCun and Corinna Cortes.
• Special thanks to the TensorFlow and Keras communities for their excellent documentation and resources.