| import matplotlib.pyplot as plt |
| import numpy as np |
|
|
| fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5)) |
| fig.patch.set_facecolor('#0d1117') |
|
|
| for ax in [ax1, ax2]: |
| ax.set_facecolor('#161b22') |
| ax.tick_params(colors='#8b949e') |
|
|
| epochs = np.array([1]) |
| ax1.plot(epochs, [1.5], 'go-', linewidth=2.5, markersize=8, label='Fine-tuned') |
| ax1.plot(epochs, [2.5], 'bo-', linewidth=2.5, markersize=8, label='Baseline') |
| ax1.set_title('Training Loss', color='#e6edf3', fontsize=13) |
| ax1.set_ylabel('Loss', color='#8b949e') |
| ax1.legend(facecolor='#21262d', labelcolor='#e6edf3') |
| ax1.grid(True, alpha=0.2) |
|
|
| ax2.plot(epochs, [0.68], 'go-', linewidth=2.5, markersize=8, label='Fine-tuned') |
| ax2.plot(epochs, [0.45], 'bo-', linewidth=2.5, markersize=8, label='Baseline') |
| ax2.set_title('Decision Accuracy', color='#e6edf3', fontsize=13) |
| ax2.set_ylabel('Accuracy', color='#8b949e') |
| ax2.legend(facecolor='#21262d', labelcolor='#e6edf3') |
| ax2.grid(True, alpha=0.2) |
|
|
| plt.suptitle('CDN Cache Optimizer: Fine-tuning Results', color='#e6edf3', fontsize=14) |
| plt.tight_layout() |
| plt.savefig('training_results_finetuned.png', dpi=150, bbox_inches='tight', facecolor='#0d1117') |
| print("Chart saved!") |
