【DeepLearning_Visualization】Note1:caffe保存训练日志和accuracy曲线,Loss曲线的可视化

(一)保存caffe的训练日志:

       我们打开./caffe/examples/myself/文件夹下面的训练脚本文件:train_quick.sh,然后,在这个脚本文件的后面添加下面的语句:

2>&1   | tee examples/myself/cifar_train_log.log     //这个是你的log日志文件的保存目录

       修改之后的完整的训练脚本文件如下所示:

      

#!/usr/bin/env sh
set -e

TOOLS=./build/tools

$TOOLS/caffe train \
  --solver=examples/myself/cifar10_quick_solver.prototxt 2>&1   | tee examples/myself/cifar_train_log.log

训练完成之后,就会在文件夹下面生成一个日志文件,如下图所示:

日志文件的具体内容如下所示:

I1103 21:24:56.893302  5598 caffe.cpp:210] Use CPU.
I1103 21:24:56.893492  5598 solver.cpp:48] Initializing solver from parameters: 
test_iter: 10
test_interval: 50
base_lr: 0.001
display: 20
max_iter: 500
lr_policy: "fixed"
momentum: 0.9
weight_decay: 0.004
snapshot: 400
snapshot_prefix: "examples/myself/cifar10_quick"
solver_mode: CPU
net: "examples/myself/cifar10_quick_train_test.prototxt"
train_state {
  level: 0
  stage: ""
}
中间省略.....................
I1103 21:25:25.338564  5598 solver.cpp:464] Snapshotting to HDF5 file examples/myself/cifar10_quick_iter_500.caffemodel.h5
I1103 21:25:25.339599  5598 sgd_solver.cpp:283] Snapshotting solver state to HDF5 file examples/myself/cifar10_quick_iter_500.solverstate.h5
I1103 21:25:25.363062  5598 solver.cpp:317] Iteration 500, loss = 0.0766577
I1103 21:25:25.363101  5598 solver.cpp:337] Iteration 500, Testing net (#0)
I1103 21:25:25.585652  5598 solver.cpp:404]     Test net output #0: accuracy = 0.91
I1103 21:25:25.585676  5598 solver.cpp:404]     Test net output #1: loss = 0.292251 (* 1 = 0.292251 loss)
I1103 21:25:25.585681  5598 solver.cpp:322] Optimization Done.
I1103 21:25:25.585683  5598 caffe.cpp:254] Optimization Done.

(二)Accuracy和Loss曲线的可视化:

        第一步:首先,将./caffe/tools/extra/文件夹下面的:

             1---extract_seconds.py

             2---parse_log.sh

             3---plot_training_log.py.example

将这三个文件复制到[日志文件]所在的目录下面,如下图所示:

         第二步:首先,使用命令:cd ./caffe/examples/myself/切换到日志文件所在的目录下,然后运行:

./parse_log.sh cifar_train_log.log

      如下图所示:

   

      对日志文件进行解析,日志文件解析成功之后,会在本目录下生成两个文件:

            1---cifar_train_log.log.test

            2---cifar_train_log.log.train

      如下图所示:

            第三步:执行命令,生成Accuracy和Loss曲线:

                     1---下面的命令是:以迭代次数为X轴,以准确率为Y轴的Accuracy曲线:

命令如下图所示:

Accuracy曲线如下图所示:

以运行时间为X轴,以准确率为Y轴,的命令和曲线图如下所示:

      第四步:Loss曲线的生成

以迭代次数为X轴,以损失率为Y轴,命令和Loss曲线如下所示:

各种曲线的具体参数,如下所示:

Supported chart types:    
    0: Test accuracy  vs. Iters    
    1: Test accuracy  vs. Seconds    
    2: Test loss  vs. Iters    
    3: Test loss  vs. Seconds    
    4: Train learning rate  vs. Iters    
    5: Train learning rate  vs. Seconds    
    6: Train loss  vs. Iters    
    7: Train loss  vs. Seconds