Image-Classification-CNN-Model/plot_learning_curves.py at main · NPKLTacocat/Image-Classification-CNN-Model · GitHub

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import matplotlib.pyplot as plt
import json
import os
import argparse

def plot_accuracy_curves(history_files):
    """
    Plots accuracy learning curves from history JSON files

    Args:
        history_files: List of history JSON file paths to plot
    """
    plt.figure(figsize=(12, 7))

    colors = ['b', 'r', 'g', 'm', 'c', 'y', 'k']

    for i, history_path in enumerate(history_files):
        model_name = os.path.basename(history_path).replace('_history.json', '')
        color = colors[i % len(colors)]

        try:
            with open(history_path, 'r') as f:
                history = json.load(f)


            plt.plot(history["epochs"], history["train_acc"], f'{color}-', label=f'{model_name} - Train Acc')
            plt.plot(history["epochs"], history["val_acc"], f'{color}--', label=f'{model_name} - Val Acc')


            best_epoch = history["val_acc"].index(max(history["val_acc"]))
            best_acc = max(history["val_acc"])
            plt.plot(history["epochs"][best_epoch], best_acc, f'{color}o', markersize=8)
            plt.annotate(f'{best_acc:.4f}',
                        (history["epochs"][best_epoch], best_acc),
                        textcoords="offset points",
                        xytext=(0,10),
                        ha='center')

        except Exception as e:
            print(f"Error loading or plotting history from {history_path}: {e}")


    plt.title('Model Accuracy During Training', fontsize=16)
    plt.xlabel('Epochs', fontsize=14)
    plt.ylabel('Accuracy', fontsize=14)
    plt.grid(True, linestyle='--', alpha=0.7)
    plt.legend(loc='lower right')


    plt.ylim(top=plt.ylim()[1] * 1.05)
    plt.tight_layout()
    plt.savefig('accuracy_curves.png', dpi=300)

    print(f"Accuracy curves saved to 'accuracy_curves.png'")

def plot_loss_curves(history_files):
    """
    Plots loss learning curves from history JSON files

    Args:
        history_files: List of history JSON file paths to plot
    """
    plt.figure(figsize=(12, 7))

    colors = ['b', 'r', 'g', 'm', 'c', 'y', 'k']

    for i, history_path in enumerate(history_files):
        model_name = os.path.basename(history_path).replace('_history.json', '')
        color = colors[i % len(colors)]

        try:
            with open(history_path, 'r') as f:
                history = json.load(f)


            plt.plot(history["epochs"], history["train_loss"], f'{color}-', label=f'{model_name} - Train Loss')
            plt.plot(history["epochs"], history["val_loss"], f'{color}--', label=f'{model_name} - Val Loss')


            best_epoch = history["val_loss"].index(min(history["val_loss"]))
            best_loss = min(history["val_loss"])
            plt.plot(history["epochs"][best_epoch], best_loss, f'{color}o', markersize=8)
            plt.annotate(f'{best_loss:.4f}',
                        (history["epochs"][best_epoch], best_loss),
                        textcoords="offset points",
                        xytext=(0,10),
                        ha='center')

        except Exception as e:
            print(f"Error loading or plotting history from {history_path}: {e}")


    plt.title('Model Loss During Training', fontsize=16)
    plt.xlabel('Epochs', fontsize=14)
    plt.ylabel('Loss', fontsize=14)
    plt.grid(True, linestyle='--', alpha=0.7)
    plt.legend(loc='upper right')

    plt.tight_layout()
    plt.savefig('loss_curves.png', dpi=300)

    print(f"Loss curves saved to 'loss_curves.png'")

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Plot learning curves from model history files")
    args = parser.parse_args()

    print("Plotting learning curves for models:")
    for history_file in args.history_files:
        print(f" - {history_file}")

    plot_accuracy_curves(args.history_files)
    plot_loss_curves(args.history_files)

    #show plots after saving both
    plt.show()