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4edc9aa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | import matplotlib.pyplot as plt
import os
from pathlib import Path
def plot_loss_curve(train_losses, val_accs=None, out_path=None, filename="loss_curve.png", prefix=""):
"""
Plots the training loss and optionally validation accuracy over epochs.
Saves the figure to out_path / filename.
"""
if out_path is None:
return
out_path = Path(out_path)
out_path.mkdir(parents=True, exist_ok=True)
fig, ax1 = plt.subplots(figsize=(10, 5))
epochs = range(len(train_losses))
color = 'tab:red'
ax1.set_xlabel('Epochs')
ax1.set_ylabel('Training Loss', color=color)
ax1.plot(epochs, train_losses, color=color, marker='o', label=f'{prefix} Train Loss')
ax1.tick_params(axis='y', labelcolor=color)
if val_accs and len(val_accs) == len(train_losses):
ax2 = ax1.twinx()
color = 'tab:blue'
ax2.set_ylabel('Validation Acc', color=color)
ax2.plot(epochs, val_accs, color=color, marker='x', label=f'{prefix} Val Acc')
ax2.tick_params(axis='y', labelcolor=color)
fig.tight_layout()
plt.title(f'{prefix} Training Metrics')
plt.grid(True)
save_path = out_path / filename
plt.savefig(save_path)
plt.close()
print(f"Saved {prefix} loss curve to {save_path}")
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