如何绘制Caffe网络训练曲线

当我们设计好网络结构后，在神经网络训练的过程中，迭代输出的log信息中，一般包括，迭代次数，训练损失代价，测试损失代价，测试精度等。本文提供一段示例，简单讲述如何绘制训练曲线（training curve）。首先看一段训练的log输出，网络结构参数的那段忽略，直接跳到训练迭代阶段：

I0627 21:30:06.004370 15558 solver.cpp:242] Iteration 0, loss = 21.6953I0627 21:30:06.004420 15558 solver.cpp:258] Train net output #0: loss = 21.6953 （* 1 = 21.6953 loss）I0627 21:30:06.004426 15558 solver.cpp:571] Iteration 0, lr = 0.01I0627 21:30:28.592690 15558 solver.cpp:242] Iteration 100, loss = 13.6593I0627 21:30:28.592730 15558 solver.cpp:258] Train net output #0: loss = 13.6593 （* 1 = 13.6593 loss）I0627 21:30:28.592733 15558 solver.cpp:571] Iteration 100, lr = 0.01...I0627 21:37:47.926597 15558 solver.cpp:346] Iteration 2000, Testing net （#0）I0627 21:37:48.588079 15558 blocking_queue.cpp:50] Data layer prefetch queue emptyI0627 21:40:40.575474 15558 solver.cpp:414] Test net output #0: loss = 13.07728 （* 1 = 13.07728 loss）I0627 21:40:40.575477 15558 solver.cpp:414] Test net output #1: loss/top-1 = 0.00226I0627 21:40:40.575487 15558 solver.cpp:414] Test net output #2: loss/top-5 = 0.01204I0627 21:40:40.708261 15558 solver.cpp:242] Iteration 2000, loss = 13.1739I0627 21:40:40.708302 15558 solver.cpp:258] Train net output #0: loss = 13.1739 （* 1 = 13.1739 loss）I0627 21:40:40.708307 15558 solver.cpp:571] Iteration 2000, lr = 0.01...I0628 01:28:47.426129 15558 solver.cpp:242] Iteration 49900, loss = 0.960628I0628 01:28:47.426177 15558 solver.cpp:258] Train net output #0: loss = 0.960628 （* 1 = 0.960628 loss）I0628 01:28:47.426182 15558 solver.cpp:571] Iteration 49900, lr = 0.01I0628 01:29:10.084050 15558 solver.cpp:449] Snapshotting to binary proto file train_net/net_iter_50000.caffemodelI0628 01:29:10.563587 15558 solver.cpp:734] Snapshotting solver state to binary proto filetrain_net/net_iter_50000.solverstateI0628 01:29:10.692239 15558 solver.cpp:346] Iteration 50000, Testing net （#0）I0628 01:29:13.192075 15558 blocking_queue.cpp:50] Data layer prefetch queue emptyI0628 01:31:00.595120 15558 solver.cpp:414] Test net output #0: loss = 0.6404232 （* 1 = 0.6404232 loss）I0628 01:31:00.595124 15558 solver.cpp:414] Test net output #1: loss/top-1 = 0.953861I0628 01:31:00.595127 15558 solver.cpp:414] Test net output #2: loss/top-5 = 0.998659I0628 01:31:00.727577 15558 solver.cpp:242] Iteration 50000, loss = 0.680951I0628 01:31:00.727618 15558 solver.cpp:258] Train net output #0: loss = 0.680951 （* 1 = 0.680951 loss）I0628 01:31:00.727623 15558 solver.cpp:571] Iteration 50000, lr = 0.0096

这是一个普通的网络训练输出，含有1个loss，可以看出solver.prototxt的部分参数为：

test_interval: 2000base_lr: 0.01lr_policy: "step" # or "multistep"gamma: 0.96display: 100stepsize: 50000 # if is "multistep", the first stepvalue is set as 50000snapshot_prefix: "train_net/net"

当然，上面的分析，即便不理会，对下面的代码也没什么影响，绘制训练曲线本质就是文件操作，从上面的log文件中，我们可以看出：

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；
对于每个含有字段] Iteration和Testing net （#0）的文本行，含有测试的对应的训练迭代次数；
对于每个含有字段#2:和loss/top-5的文本行，含有测试top-5的精度。

根据这些分析，就可以对文本进行如下处理：

import osimport sysimport numpy as npimport matplotlib.pyplot as pltimport mathimport reimport pylabfrom pylab import figure, show, legendfrom mpl_toolkits.axes_grid1 import host_subplot# read the log filefp = open（"log.txt", "r"）train_iterations = []train_loss = []test_iterations = []test_accuracy = []for ln in fp:# get train_iterations and train_lossif "] Iteration " in ln and "loss = " in ln:arr = re.findall（r"ion d+,",ln）train_iterations.append（int（arr[0].strip（","）[4:]））train_loss.append（float（ln.strip（）.split（" = "）[-1]））# get test_iteraitionsif "] Iteration" in ln and "Testing net （#0）" in ln:arr = re.findall（r"ion d+,",ln）test_iterations.append（int（arr[0].strip（","）[4:]））# get test_accuracyif "#2:" in ln and "loss/top-5" in ln:test_accuracy.append（float（ln.strip（）.split（" = "）[-1]））fp.close（）host = host_subplot（111）plt.subplots_adjust（right=0.8） # ajust the right boundary of the plot windowpar1 = host.twinx（）# set labelshost.set_xlabel（"iterations"）host.set_ylabel（"log loss"）par1.set_ylabel（"validation accuracy"）# plot curvesp1, = host.plot（train_iterations, train_loss, label="training log loss"）p2, = par1.plot（test_iterations, test_accuracy, label="validation accuracy"）# set location of the legend, # 1->rightup corner, 2->leftup corner, 3->leftdown corner# 4->rightdown corner, 5->rightmid ...host.legend（loc=5）# set label colorhost.axis["left"].label.set_color（p1.get_color（））par1.axis["right"].label.set_color（p2.get_color（））# set the range of x axis of host and y axis of par1host.set_xlim（[-1500, 160000]）par1.set_ylim（[0., 1.05]）plt.draw（）plt.show（）

示例代码中，添加了简单的注释，如果网络训练的log输出与本中所列出的不同，只需要略微修改其中的一些参数设置，就能绘制出训练曲线图。最后附上绘制出的训练曲线图： 本文永久更新链接地址：http://www.linuxidc.com/Linux/2016-11/136773.htm