pva-faster-rcnn（caffe）绘制训练loss和lr曲线
为了观察神经网络训练效果，绘制loss曲线，得到模型训练的变化趋势。同时，为了观察学习率的变化对loss的影响，同时在一个图中绘制出learningrate曲线。
1 保存训练日志
创建一个sh文件，输入以下内容
#!/usr/bin/env sh
LOG="/home/lthpc/pva-faster-rcnn2/experiments/logs-`date +%Y-%m-%d-%H-%M-%S`.log"
exec &> >(tee -a "$LOG")
echo Logging output to "$LOG"
python tools/train_net.py --solver models/pvanet/example_train_384/solver.prototxt --weights models/pvanet/example_train_384/original.model --cfg models/pvanet/cfgs/train.yml其中：
LOG输入保存日志的路径；最后一行为执行训练时的语句
2 绘制loss和lr曲线
输出的训练日志的格式如下
I0704 16:56:08.642755 48434 solver.cpp:238] Iteration 410, loss = 0.515833
I0704 16:56:08.642813 48434 solver.cpp:254] Train net output #0: cls_loss = 0.117412 (* 1 = 0.117412 loss)
I0704 16:56:08.642822 48434 solver.cpp:254] Train net output #1: loss_bbox = 0.000175524 (* 1 = 0.000175524 loss)
I0704 16:56:08.642828 48434 solver.cpp:254] Train net output #2: rpn_cls_loss = 0.0477779 (* 1 = 0.0477779 loss)
I0704 16:56:08.642833 48434 solver.cpp:254] Train net output #3: rpn_loss_bbox = 0.0296075 (* 1 = 0.0296075 loss)
I0704 16:56:08.642839 48434 sgd_solver.cpp:138] Iteration 410, lr = 0.001
I0704 16:56:13.248749 48434 solver.cpp:238] Iteration 420, loss = 0.509081
I0704 16:56:13.248798 48434 solver.cpp:254] Train net output #0: cls_loss = 0.294207 (* 1 = 0.294207 loss)
I0704 16:56:13.248808 48434 solver.cpp:254] Train net output #1: loss_bbox = 0.0603984 (* 1 = 0.0603984 loss)
I0704 16:56:13.248813 48434 solver.cpp:254] Train net output #2: rpn_cls_loss = 0.0187975 (* 1 = 0.0187975 loss)
I0704 16:56:13.248819 48434 solver.cpp:254] Train net output #3: rpn_loss_bbox = 0.0398177 (* 1 = 0.0398177 loss)
I0704 16:56:13.248826 48434 sgd_solver.cpp:138] Iteration 420, lr = 0.001
I0704 16:56:17.768085 48434 solver.cpp:238] Iteration 430, loss = 0.510896总结可发现：对于每个出现字段] Iteration和loss =的文本行，含有训练的迭代次数以及损失代价；对于每个出现字段] Iteration和lr =的文本行，含有训练的迭代次数以及学习率，因此设计如下程序绘制曲线图
#!/usr/bin/env python
import os
import sys
import numpy as np
import matplotlib.pyplot as plt
import math
import re
import pylab
from pylab import figure, show, legend
from mpl_toolkits.axes_grid1 import host_subplot fp = open('train-2018-07-09-18-10-18.log', 'r') train_iterations = []
learningrate = []
train_loss = []
test_iterations = []
#test_accuracy = [] for ln in fp: if '] Iteration ' in ln and 'loss = ' in ln: arr = re.findall(r'ion \b\d+\b,',ln) train_iterations.append(int(arr[0].strip(',')[4:])) train_loss.append(float(ln.strip().split(' = ')[-1]))# get train_iterations and train_loss if '] Iteration ' in ln and 'lr = ' in ln: learningrate.append(float(ln.strip().split(' = ')[-1])) fp.close() a1 = plt.subplot(211)
a1.set_title("loss")
a1.plot(train_iterations,train_loss)a2 = plt.subplot(212)
a2.set_title("learningrate")
a2.plot(train_iterations,learningrate)# a1.margins(x=0.2)
plt.tight_layout()
plt.show()
注意找到日志路径，运行后输出曲线如图所示
可以看到学习率下降的太快，导致后边loss降不下来，因此需重新调整学习策略，调整方法参考《caffe不同lr_policy参数设置方法》
本文来自互联网用户投稿，文章观点仅代表作者本人，不代表本站立场，不承担相关法律责任。如若转载，请注明出处。 如若内容造成侵权/违法违规/事实不符，请点击【内容举报】进行投诉反馈！