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caffe框架下的二分类（猫狗大战 cat VS dog）ubuntu环境

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我是靠谱客的博主 洁净世界，最近开发中收集的这篇文章主要介绍caffe框架下的二分类（猫狗大战 cat VS dog）ubuntu环境，觉得挺不错的，现在分享给大家，希望可以做个参考。

概述

训练环境 NVIDIA GTX1060 6G 显卡
1. 去kaggel官方网址下载cat dog数据
稍后我把数据传到github上，项目也可以去github上面下载demo

2. 划分训练集、测试集

下在好的数据里面有trainset 25000张，其中猫狗对半，，每一张下面带有标签“cat.x.jpg”或者“dog.x.jpg”，testset 15000张,testset不带有标签。testset是在trianset中随机选取20%，对应脚本如下：
（对应文件名为：create_valset.py）

# -*- coding:utf-8 -*-
__author__ = 'Zubin'

import os
import shutil
import random

root_dir = '/home/hzb/PycharmProjects/cat_dog/data_cat_dog/train/' #注意自己的文件存放路径
output_dir = '/home/hzb/PycharmProjects/cat_dog/data_cat_dog/valset/'
ref = 1

for root, dirs, files in os.walk(root_dir):
    number_of_files = len(os.listdir(root))
    if number_of_files > ref:
        ref_copy = int(round(0.2 * number_of_files))  # 随机筛选20%的图片到新建的文件夹当中
        for i in xrange(ref_copy):
            chosen_one = random.choice(os.listdir(root))
            file_in_track = root
            file_to_copy = file_in_track + '/' + chosen_one
            if os.path.isfile(file_to_copy) == True:
                shutil.copy(file_to_copy, output_dir)
                print file_to_copy
    else:
        for i in xrange(len(files)):
            track_list = root
            file_in_track = files[i]
            file_to_copy = track_list + '/' + file_in_track
            if os.path.isfile(file_to_copy) == True:
                shutil.copy(file_to_copy, output_dir)
                print file_to_copy
print 'Finished !'

3. 生成trainset 、testset txt标签文件
使用的是shell脚本，在终端对应文件路径下执行命令

1>生成trainset标签文件(文件名为：create_flielist.sh）

# /home/hzb/PycharmProjects/cat_dog/caffes-and-dogs-master sh
DATA=input/train/
WORK=input
echo "create train.txt..."
rm -rf $DATA/train.txt
find $DATA -name cat.*.jpg | cut -d '/' -f3 | sed "s/$/ 0/">>$DATA/train.txt
find $DATA -name dog.*.jpg | cut -d '/' -f3 | sed "s/$/ 1/">>$DATA/tmp.txt
cat $DATA/tmp.txt>>$DATA/train.txt
rm -rf $DATA/tmp.txt
mv $DATA/train.txt $WORK/
echo "Done..."

2>生成testset标签文件(文件名为：create_flielist_test.sh）

# /home/hzb/PycharmProjects/cat_dog/caffes-and-dogs-master sh
DATA=input/test
WORK=input
echo "create test.txt......."
rm -rf $DATA/test.txt
find $DATA -name cat.*.jpg | cut -d '/' -f3 | sed "s/$/ 0/">>$DATA/test.txt
find $DATA -name dog.*.jpg | cut -d '/' -f3 | sed "s/$/ 1/">>$DATA/tmp.txt
cat $DATA/tmp.txt>>$DATA/test.txt
rm -rf $DATA/tmp.txt
mv $DATA/test.txt $WORK/
echo "Done........"

4. 把trainset、testset转换成LMDB格式

1>trainset脚本(文件名为：create_lmdb_train.sh)

# /home/hzb/PycharmProjects/cat_dog sh
DATA=data_cat_dog
WORK=data_cat_dog
TOOLS=/home/hzb/caffe/build/tools

RESIZE=true
if $RESIZE; then
  RESIZE_HEIGHT=32
  RESIZE_WIDTH=32
else
  RESIZE_HEIGHT=0
  RESIZE_WIDTH=0
fi

echo "Creating train lmdb..."
rm -rf $DATA/catdog_train_lmdb
GLOG_logtostderr=1 $TOOLS/convert_imageset 
    --resize_height=$RESIZE_HEIGHT 
    --resize_width=$RESIZE_WIDTH 
    --shuffle 
    /home/hzb/PycharmProjects/cat_dog/data_cat_dog/train/ 
    $WORK/train.txt 
    $DATA/img_train_lmdb

echo "Creating val lmdb..."

#GLOG_logtostderr=1 $TOOLS/convert_imageset 
 #   --resize_height=$RESIZE_HEIGHT 
 #   --resize_width=$RESIZE_WIDTH 
#    --shuffle 
#    $VAL_DATA_ROOT 
#    $DATA/val.txt 
 #   $EXAMPLE/face_val_lmdb

echo "Done."

生成的文件img_train_lmdb下含有:data.mdb和lock.mdb文件

2>testset脚本(文件名为：create_lmdb_test.sh)

DATA=data_cat_dog
WORK=data_cat_dog
TOOLS=/home/hzb/caffe/build/tools

RESIZE=true
if $RESIZE; then
  RESIZE_HEIGHT=32#图片统一缩放成32×32大小
  RESIZE_WIDTH=32
else
  RESIZE_HEIGHT=0
  RESIZE_WIDTH=0
fi

#echo "Creating train lmdb..."
#rm -rf $DATA/catdog_train_lmdb
#GLOG_logtostderr=1 $TOOLS/convert_imageset 
#   --resize_height=$RESIZE_HEIGHT 
#    --resize_width=$RESIZE_WIDTH 
#    --shuffle 
#    /home/hzb/PycharmProjects/cat_dog/data_cat_dog/train/ 
#    $WORK/train.txt 
#    $DATA/img_train_lmdb

echo "Creating val lmdb..."

#rm -rf $DATA/catdog_test_lmdb
GLOG_logtostderr=1 $TOOLS/convert_imageset 
    --resize_height=$RESIZE_HEIGHT 
    --resize_width=$RESIZE_WIDTH 
    --shuffle 
    /home/hzb/PycharmProjects/cat_dog/data_cat_dog/valset/ 
    $WORK/test.txt 
    $DATA/img_test_lmdb

echo "Done."

生成的文件img_test_lmdb下含有:data.mdb和lock.mdb文件

5. 把trainset生成均值
图片做归一化处理 (image-meanvalue)/255,在对应文件路路径下现新建好mean_file文件夹

#!/usr/bin/env sh
# Compute the mean image from the imagenet training lmdb
TOOLS=/home/hzb/caffe/build/tools
DATA=/home/hzb/PycharmProjects/cat_dog
rm -rf $DATA/data_cat_dog/mean_file/mean.binaryproto
$TOOLS/compute_image_mean $DATA/data_cat_dog/img_train_lmdb 
  $DATA/data_cat_dog/mean_file/mean.binaryproto
echo "Done."

网络的结构

layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 1.0#局部学习率
                      #在这没有设置惩罚系数
  }
  param {
    lr_mult: 2.0
  }
  convolution_param {
    num_output: 32
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "conv1"
  top: "conv2"
  param {
    lr_mult: 1.0
  }
  param {
    lr_mult: 2.0
  }
  convolution_param {
    num_output: 32
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv2"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "pool1"
  top: "conv3"
  param {
    lr_mult: 1.0
  }
  param {
    lr_mult: 2.0
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "conv4"
  type: "Convolution"
  bottom: "conv3"
  top: "conv4"
  param {
    lr_mult: 1.0
  }
  param {
    lr_mult: 2.0
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu4"
  type: "ReLU"
  bottom: "conv4"
  top: "conv4"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv4"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv5"
  type: "Convolution"
  bottom: "pool2"
  top: "conv5"
  param {
    lr_mult: 1.0
  }
  param {
    lr_mult: 2.0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu5"
  type: "ReLU"
  bottom: "conv5"
  top: "conv5"
}
layer {
  name: "conv6"
  type: "Convolution"
  bottom: "conv5"
  top: "conv6"
  param {
    lr_mult: 1.0
  }
  param {
    lr_mult: 2.0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu6"
  type: "ReLU"
  bottom: "conv6"
  top: "conv6"
}
layer {
  name: "pool3"
  type: "Pooling"
  bottom: "conv6"
  top: "pool3"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv7"
  type: "Convolution"
  bottom: "pool3"
  top: "conv7"
  param {
    lr_mult: 1.0
  }
  param {
    lr_mult: 2.0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu7"
  type: "ReLU"
  bottom: "conv7"
  top: "conv7"
}
layer {
  name: "conv8"
  type: "Convolution"
  bottom: "conv7"
  top: "conv8"
  param {
    lr_mult: 1.0
  }
  param {
    lr_mult: 2.0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu8"
  type: "ReLU"
  bottom: "conv8"
  top: "conv8"
}
layer {
  name: "pool4"
  type: "Pooling"
  bottom: "conv8"
  top: "pool4"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "ip1"
  type: "InnerProduct"
  bottom: "pool4"
  top: "ip1"
  param {
    lr_mult: 1.0
  }
  param {
    lr_mult: 2.0
  }
  inner_product_param {
    num_output: 256
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "drop1"
  type: "Dropout"
  bottom: "ip1"
  top: "ip1"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "ip2"
  type: "InnerProduct"
  bottom: "ip1"
  top: "ip2"
  param {
    lr_mult: 1.0
  }
  param {
    lr_mult: 2.0
  }
  inner_product_param {
    num_output: 256
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu9"
  type: "ReLU"
  bottom: "ip2"
  top: "ip2"
}
layer {
  name: "drop2"
  type: "Dropout"
  bottom: "ip2"
  top: "ip2"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "ip3"
  type: "InnerProduct"
  bottom: "ip2"
  top: "ip3"
  param {
    lr_mult: 1.0
  }
  param {
    lr_mult: 2.0
  }
  inner_product_param {
    num_output: 2
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "accuracy"
  type: "Accuracy"
  bottom: "ip3"
  bottom: "label"
  top: "accuracy"
  include{
   phase:TEST
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "ip3"
  bottom: "label"
  top: "loss"
}

网络结构图参数设置

net: "/home/hzb/PycharmProjects/cat_dog/train_test.prototxt"
test_iter: 50
test_interval: 500#迭代500次测试一次
base_lr:0.001
lr_policy: "inv"
gamma:0.0001
max_iter:100000#最大迭代次数为100000次
momentum:0.9
weight_decay: 0.0005
power:0.75
display: 2000#迭代2000次打印一次输出信息
snapshot:5000#迭代5000次保存一下模型
snapshot_prefix: "/home/hzb/PycharmProjects/cat_dog/model"#模型保存的路径
solver_mode: GPU

6.最后一步进行训练

训练过程会把训练日志保存在对应文件路径中。tee 命令后为你所要保存log日志文件的路径

# /home/hzb/caffe sh
./build/tools/caffe train --solver=/home/hzb/PycharmProjects/cat_dog/solver.prototxt 2>&1| tee /home/hzb/PycharmProjects/cat_dog/train.log

训练结束后根据日志生成train-loss 、test-accuracy
train losstest losstest accuracy
###脚本生成准确率 损失函数图

import math
import os
import re
import sys
import matplotlib.pyplot as plt
import numpy as np
import pylab
from mpl_toolkits.axes_grid1 import host_subplot
from pylab import figure, show, legend

# read the log file
fp = open('/home/hzb/PycharmProjects/cat_dog/train.log', 'r')

train_iterations = []
train_loss = []
test_iterations = []
test_accuracy = []

for ln in fp:
    # get train_iterations and train_loss
    if '] Iteration ' in ln and 'loss = ' in ln:
        arr = re.findall(r'ion d+.*?', ln)
        train_iterations.append(int(arr[0][4:]))
        train_loss.append(float(ln.strip().split(' = ')[-1]))

    # get test_iteraitions
    if '] Iteration' in ln and 'Testing net (#0)' in ln:
        arr = re.findall(r'ion bd+b,', ln)
        test_iterations.append(int(arr[0].strip(',')[4:]))
    # get test_accuracy
    if '#0:' in ln and 'accuracy =' 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 window
par1 = host.twinx()
# set labels
host.set_xlabel("iterations")
host.set_ylabel("log loss")
par1.set_ylabel("test accuracy")

# plot curves
p1, = host.plot(train_iterations, train_loss, 'ob-', label="training loss")
p2, = par1.plot(test_iterations, test_accuracy, 'xg-', label="test accuracy")
#p3, = par1.plot(test_iterations, test_accuracy, 'xg-', 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 color
host.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 par1
host.set_xlim([0, 100000])
par1.set_ylim([0., 1.05])
plt.draw()
plt.show()

train loss & test accuracy

github code (https://github.com/ZubinHuang/cat-VS-dog)

参考链接](https://blog.csdn.net/mdjxy63/article/details/78946455)

最后

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