experiments/appendix_learning_curves.py

# Appendix A – Learning Curves

import matplotlib.pyplot as plt
import pickle
import os
import numpy as np
from src import config

# --- Configuration ---
BLUE_DEEP = "#5E81AC"
ORANGE = "#D08770"

def plot_curves(history, title, filename):
    epochs = range(1, len(history['train_loss']) + 1)
    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))
    
    # Loss
    ax1.plot(epochs, history['train_loss'], label='Train', color=BLUE_DEEP, marker='o', markersize=4)
    ax1.plot(epochs, history['val_loss'], label='Val', color=ORANGE, marker='s', markersize=4)
    ax1.set_title('Loss'); ax1.legend(); ax1.grid(True, alpha=0.3)
    
    # Acc
    ax2.plot(epochs, history['train_acc'], label='Train', color=BLUE_DEEP, marker='o', markersize=4)
    ax2.plot(epochs, history['val_acc'], label='Val', color=ORANGE, marker='s', markersize=4)
    ax2.set_title('Accuracy'); ax2.legend(); ax2.grid(True, alpha=0.3)
    
    plt.suptitle(title, fontweight='bold')
    plt.tight_layout()
    plt.savefig(os.path.join(config.RESULTS_DIR, filename), dpi=300)
    plt.close()
    print(f"Saved {filename}")

def main():
    for f_name, label, out in [
        ('cnn_10class_history.pkl', 'MNIST 10-class', 'fig_14_learning_curves.png'),
        ('cnn_fashion_history.pkl', 'Fashion-MNIST', 'fig_15_learning_curves_fashion.png')
    ]:
        path = os.path.join(config.MODELS_DIR, f_name)
        if os.path.exists(path):
            with open(path, 'rb') as f:
                history = pickle.load(f)
            plot_curves(history, label, out)
        else:
            print(f"Skipping {f_name}: Not found.")

if __name__ == "__main__":
    main()