| import matplotlib |
| matplotlib.use('Agg') |
| import matplotlib.pyplot as plt |
| import sys |
| import re |
|
|
| def load_loss_data(log_path='train_output.log'): |
| """Load loss data from training log.""" |
| steps_after, losses_after = [], [] |
| with open(log_path) as f: |
| for line in f: |
| m = re.search(r'Step (\d+) .* Loss: ([\d.]+)', line) |
| if m: |
| steps_after.append(int(m.group(1))) |
| losses_after.append(float(m.group(2))) |
| if not steps_after: |
| return None, None |
| return steps_after, losses_after |
|
|
| EARLY_LOSS = [ |
| (0, 2.9836), (500, 1.2863), (1000, 0.8944), (1500, 0.6346), |
| (2000, 0.4688), (2500, 0.3735), (3000, 0.2973), (3500, 0.2215), |
| (4000, 0.1777), (4500, 0.1588), (5000, 0.1440), (5500, 0.1289), |
| (6000, 0.1050), (6500, 0.1028), (7000, 0.1009), (7500, 0.0914), |
| (8000, 0.0778), (8500, 0.0769), (9000, 0.0704), (9500, 0.0686), |
| (10000, 0.0640), (10500, 0.0696), (11000, 0.0676), (11500, 0.0663), |
| (12000, 0.0492), (12500, 0.0590), (13000, 0.0515), (13500, 0.0495), |
| (14000, 0.0507), (14500, 0.0522), (15000, 0.0402), (15500, 0.0414), |
| (16000, 0.0484), (16500, 0.0444), (17000, 0.0380), (17500, 0.0399), |
| (18000, 0.0384), (18500, 0.0359), (19000, 0.0379), (19500, 0.0362), |
| (20000, 0.0339), (20500, 0.0338), |
| ] |
|
|
| def plot(output_path='training_curves.png'): |
| steps_after, losses_after = load_loss_data() |
| steps_early = [s for s, _ in EARLY_LOSS] |
| losses_early = [l for _, l in EARLY_LOSS] |
|
|
| fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4.5)) |
| for ax in [ax1, ax2]: |
| ax.set_facecolor('#f8f9fa') |
|
|
| if steps_early: |
| ax1.plot(steps_early, losses_early, color='#2563eb', linewidth=1.5, |
| alpha=0.7, label='Steps 0–20500') |
| if steps_after: |
| ax1.plot(steps_after, losses_after, color='#dc2626', linewidth=1.5, |
| alpha=0.7, label='Steps 21000–49500') |
|
|
| all_steps = (steps_early or []) + (steps_after or []) |
| all_losses = (losses_early or []) + (losses_after or []) |
| if all_steps: |
| ax1.fill_between(all_steps, all_losses, alpha=0.06, color='#2563eb') |
|
|
| ax1.axvline(x=21000, color='#888', linewidth=0.8, linestyle=':', alpha=0.6) |
| ax1.text(21000, max(all_losses) * 0.9, 'resume', fontsize=8, color='#888', |
| ha='center', va='top', style='italic') |
|
|
| ax1.set_xlabel('Training Step', fontsize=10) |
| ax1.set_ylabel('Cross-Entropy Loss', fontsize=10) |
| ax1.set_title('Training Loss', fontsize=12, fontweight='bold', pad=10) |
| ax1.set_yscale('log') |
| ax1.grid(True, alpha=0.25, linestyle='--') |
| ax1.legend(fontsize=8, loc='upper right') |
|
|
| bars = ax2.bar(['Random 500\nExpressions', 'Fixed\nBenchmark'], [91.6, 100.0], |
| color=['#2563eb', '#16a34a'], width=0.5, edgecolor='white', linewidth=1.5) |
| for bar, val in zip(bars, [91.6, 100.0]): |
| ax2.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 1.2, |
| f'{val:.1f}%', ha='center', va='bottom', fontsize=11, fontweight='bold') |
| ax2.set_ylim(0, 108) |
| ax2.set_ylabel('Accuracy', fontsize=10) |
| ax2.set_title('Inference Accuracy', fontsize=12, fontweight='bold', pad=10) |
| ax2.grid(True, alpha=0.25, linestyle='--', axis='y') |
| ax2.spines['top'].set_visible(False) |
| ax2.spines['right'].set_visible(False) |
|
|
| fig.suptitle('Arithmetic Reasoner — TrueACT 1-Layer', |
| fontsize=14, fontweight='bold', y=1.02) |
| plt.tight_layout() |
| plt.savefig(output_path, dpi=150, bbox_inches='tight') |
| print(f'Saved {output_path}') |
|
|
| if __name__ == '__main__': |
| out = sys.argv[1] if len(sys.argv) > 1 else 'training_curves.png' |
| plot(out) |
|
|
