欢迎您访问程序员文章站本站旨在为大家提供分享程序员计算机编程知识!
您现在的位置是: 首页

Caffe - 训练日志 log 可视化分析

程序员文章站 2024-03-14 22:00:17
...

Caffe - 训练日志 log 可视化分析

在采用 shell 脚本进行 caffe 训练时,可以输出训练过程到log 文件,如

$CAFFE_ROOT/build/tools/caffe train \
    --solver=solver.prototxt \
    --gpu 0 \
    2>&1 | tee train.log

Caffe 提供了对输出 log 文件的解析工具 - parse_log.py:

$CAFFE_ROOT/tools/extra/parse_log.py train.log ./

输出两个解析文件:

train.log.train

train.log.test

其内容格式如:

NumIters,Seconds,LearningRate,loss
0.0,0.366678,0.05,4.30619
10.0,3.210073,0.05,2.73271
20.0,6.03005,0.05,8.48341
......
NumIters,Seconds,LearningRate,acc/top-1,acc/top-5,loss
7000.0,2266.206901,0.05,0.240812,0.591906,2.67359
14000.0,4538.298707,0.05,0.42175,0.780375,2.01819
21000.0,6798.336418,0.05,0.491844,0.832719,1.7494
......

根据解析的结果,即可绘制 train loss,test loss 和 accuracy 的变化曲线,如:

#
import pandas as pd
import matplotlib.pyplot as plt

train_log = pd.read_csv("train.log.train")
test_log = pd.read_csv("train.log.test")

_, ax1 = plt.subplots()
ax1.set_title("train loss and test loss")
ax1.plot(train_log["NumIters"], train_log["loss"], alpha=0.5)
ax1.plot(test_log["NumIters"], test_log["loss"], 'g')
ax1.set_xlabel('iteration')
ax1.set_ylabel('train loss')
plt.legend(loc='upper left')

ax2 = ax1.twinx()
ax2.plot(test_log["NumIters"], test_log["acc/top-1"], 'r')
ax2.plot(test_log["NumIters"], test_log["acc/top-5"], 'm')
ax2.set_ylabel('test accuracy')
plt.legend(loc='upper right')

plt.show()

print 'Done.'

Caffe - 训练日志 log 可视化分析

相关标签: Caffe Log Loss