MNIST Digit Classification using TensorFlow

This project trains a Convolutional Neural Network (CNN) to classify handwritten digits from the MNIST dataset using TensorFlow and Keras.

Dataset

The MNIST dataset consists of 60,000 training images and 10,000 test images of handwritten digits (0-9). Each image is grayscale and has a size of 28x28 pixels.

Project Overview

1. Load the Dataset: The dataset is loaded using TensorFlow's built-in MNIST dataset.
2. Preprocess the Data:
   • Normalize pixel values to the range [0,1] for better training.
   • Reshape images to add a channel dimension (28x28x1) since CNNs expect multi-dimensional inputs.
3. Define the CNN Model:
   • Three convolutional layers with ReLU activation.
   • MaxPooling layers to reduce spatial dimensions.
   • Flattening layer to convert the feature map into a 1D array.
   • Fully connected dense layers for classification.
   • Softmax activation for output probabilities.
4. Compile and Train:
   • Uses Adam optimizer and sparse categorical cross-entropy loss.
   • Trains for 5 epochs with validation.
5. Evaluate the Model:
   • Measures accuracy on the test dataset.
   • Displays accuracy progression using a plot.

Requirements

• Python
• TensorFlow
• Matplotlib

Running the Code

Simply execute the Python script to train the model:

python mnist_model.py

Output

• The trained model will classify handwritten digits with high accuracy.
• A plot showing training and validation accuracy per epoch.

License

This project is open-source and free to use for learning purposes.