51 lines
1.8 KiB
Python
51 lines
1.8 KiB
Python
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"""
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plot_training_curves.py
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从 training_log.csv 读取日志，绘制 Loss / F1 / Accuracy / LR 曲线
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"""
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import pandas as pd
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import matplotlib.pyplot as plt
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import matplotlib
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matplotlib.rcParams['font.sans-serif'] = ['SimHei', 'DejaVu Sans']
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matplotlib.rcParams['axes.unicode_minus'] = False
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# ============ 读取数据 ============
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df = pd.read_csv('training_log.csv')
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best_rows = df[df['best'] == 'best']
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fig, axes = plt.subplots(2, 2, figsize=(14, 10))
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# ---- 1. Loss ----
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ax = axes[0, 0]
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ax.plot(df['epoch'], df['train_loss'], label='Train Loss', color='#1f77b4', lw=1.5)
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ax.plot(df['epoch'], df['val_loss'], label='Val Loss', color='#ff7f0e', lw=1.5)
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ax.set_xlabel('Epoch'); ax.set_ylabel('Loss'); ax.set_title('Loss vs Epoch')
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ax.legend(); ax.grid(True, alpha=0.3)
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# ---- 2. F1 Score ----
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ax = axes[0, 1]
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ax.plot(df['epoch'], df['train_f1'], label='Train F1', color='#1f77b4', lw=1.5)
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ax.plot(df['epoch'], df['val_f1'], label='Val F1', color='#ff7f0e', lw=1.5)
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ax.set_xlabel('Epoch'); ax.set_ylabel('F1 Score'); ax.set_title('F1 Score vs Epoch')
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ax.legend(); ax.grid(True, alpha=0.3)
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# ---- 3. Accuracy ----
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ax = axes[1, 0]
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ax.plot(df['epoch'], df['train_acc'], label='Train Acc', color='#1f77b4', lw=1.5)
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ax.plot(df['epoch'], df['val_acc'], label='Val Acc', color='#ff7f0e', lw=1.5)
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ax.set_xlabel('Epoch'); ax.set_ylabel('Accuracy (%)'); ax.set_title('Accuracy vs Epoch')
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ax.legend(); ax.grid(True, alpha=0.3)
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# ---- 4. Learning Rate ----
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ax = axes[1, 1]
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ax.plot(df['epoch'], df['lr'], color='#2ca02c', lw=1.5)
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ax.set_xlabel('Epoch'); ax.set_ylabel('Learning Rate'); ax.set_title('Learning Rate vs Epoch')
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ax.ticklabel_format(style='scientific', axis='y', scilimits=(0, 0))
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ax.grid(True, alpha=0.3)
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plt.tight_layout()
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plt.savefig('training_curves.png', dpi=150, bbox_inches='tight')
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plt.show()
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print("训练曲线已保存: training_curves.png")
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