Introduction to TensorFlow

import tensorflow as tf

def main():
    print("This program demonstrates how to use TensorFlow to create a neural network to classify handwritten digits from the MNIST dataset.")

    # Load the MNIST dataset
    mnist = tf.keras.datasets.mnist
    (X_train, y_train), (X_test, y_test) = mnist.load_data()

    # Normalize the data
    X_train, X_test = X_train / 255.0, X_test / 255.0

    # Create a simple neural network model
    model = tf.keras.models.Sequential([
        tf.keras.layers.Flatten(input_shape=(28, 28)),
        tf.keras.layers.Dense(128, activation='relu'),
        tf.keras.layers.Dropout(0.2),
        tf.keras.layers.Dense(10, activation='softmax')
    ])

    # Compile the model
    model.compile(optimizer='adam',
                  loss='sparse_categorical_crossentropy',
                  metrics=['accuracy'])

    # Train the model
    model.fit(X_train, y_train, epochs=5)

    # Evaluate the model on the test dataset
    loss, accuracy = model.evaluate(X_test, y_test)
    print(f"Accuracy of the neural network on the test set: {accuracy * 100:.2f}%")

if __name__ == "__main__":
    main()