run.py

# Run the Neural Network Visualizer

import Neural as nn
from visualizer import NeuralNetworkVisualizer
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

def main():
    # Load dataset
    print("Loading Iris dataset...")
    iris = load_iris()
    X, y = iris.data, iris.target
#    digits = load_digits()
#    X, y = digits.data, digits.target

    # Split and normalize
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.2, random_state=42)
    scaler = StandardScaler()
    X_train = scaler.fit_transform(X_train)
    X_test = scaler.transform(X_test)

    # Create network
    print("Creating neural network...")
    network = nn.NeuralNetwork(
        layer_sizes=[4, 8, 6, 3],
        activation="relu",
        learning_rate=0.01
    )
    # Create visualizer
    print("Launching Visualizer...")
    print("\nControls:")
    print("  SPACE - Pause/Resume training")
    print("  S - Save Model")
    print("  ESC - Quit")
    print("\nStarting training visualization...\n")

    visualizer = NeuralNetworkVisualizer(network)

    # Train wih visualization
    visualizer.train_with_visualization(
        X_train, y_train, X_test, y_test,
        epochs=500,
        batch_size=32
    )

    print("Visualization ended.")

if __name__ == "__main__":
    main()