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import matplotlib.pyplot as plt
import re
import numpy as np
# 设置中文字体
plt.rcParams['font.sans-serif'] = ['SimHei'] # 简体中文（根据系统调整）
plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题
# 从txt文件读取日志数据
def parse_log_file(file_path):
epochs = []
losses = []
with open(file_path, 'r', encoding='utf-8') as f:
for line in f:
# 使用正则匹配有效行
match = re.search(
r'Epoch:\s+(\d+)/*.*Loss:\s+(\d+\.\d+)',
line.strip()
)
if match:
epoch = int(match.group(1))
loss = float(match.group(2))
epochs.append(epoch)
losses.append(loss)
return epochs, losses
# 文件路径
log_file = "E:\\智能语音处理系统\\Noise-suppression-and-speech-recognition-systems-master\\WaveRNNModel\\checkpoints\\ljspeech_lsa_smooth_attention.tacotron\\log_test.txt"
# 提取数据
try:
epochs_read, losses = parse_log_file(log_file)
print(epochs_read)
epochs=np.arange(len(epochs_read))
print(epochs)
except FileNotFoundError:
print(f"错误：文件 {log_file} 不存在，请检查路径！")
exit()
except Exception as e:
print(f"解析文件时出错: {str(e)}")
exit()
# 绘制曲线
plt.figure(figsize=(10, 6))
plt.plot(epochs, losses, 'b-', linewidth=2, label='训练损失')
# 图表美化
plt.title('训练损失随轮次变化曲线', fontsize=14)
plt.xlabel('训练轮次 (Epoch)', fontsize=12)
plt.ylabel('损失值 (Loss)', fontsize=12)
#plt.xticks(range(1, len(epochs))) # 强制显示所有epoch刻度
plt.grid(True, linestyle='--', alpha=0.7)
plt.legend()
# 标注最低损失
min_loss = min(losses)
min_idx = losses.index(min_loss)
plt.annotate(
f'最低损失: {min_loss:.3f}',
xy=(epochs[min_idx], min_loss),
xytext=(epochs[min_idx]-3, min_loss+0.1),
arrowprops=dict(arrowstyle='->', color='red'),
fontsize=10,
color='red'
)
plt.tight_layout()
plt.show() |