深度学习实战——caffe windows 下训练自己的网络模型

1、相关准备

1.1 手写数字数据集

这篇博客上有.jpg格式的图片下载，附带标签信息，有需要的自行下载，博客附带百度云盘下载地址(手写数字.jpg 格式)：http://blog.csdn.net/eddy_zheng/article/details/50496194

1.2深度学习框架

本实战基于caffe深度学习框架，需自行参考相关博客搭建环境，这里不再对如何搭建环境作介绍。

2、数据准备

2.1 准备训练与验证图像

准备好你想训练识别的图像数据之后，将其划分为训练集与验证集，并准备好对应的图像名称以及对应的标签信息。这里的验证集和测试集并是不同的，如下图所示，你可以这样简单的划分：

*这里要注意的是，图片名与对应的类别标签一定不能有错，不然你的训练就全乱套了。对了，图片名与标签之间对应一个 space 就可以了。

2.2 转换数据格式

以上工作准备完毕之后，还需将其转换为 caffe 训练的 lmdb 格式。找到你编译的图像转换 convert_imageset.exe 位置。如下我的 caffe bin目录：

转换训练数据：创建如下文件，写批处理命令：

内部代码如下所示，略作解释，1：是你转换图像 convert_imageset.exe 所在位置,2：转换图像数据所在的文件夹位置，3：接着是图像名称对应标签 .txt 文件,4：最后是生成的 lmdb 的位置及文件夹名字：

SET GLOG_logtostderr=1
C:\Users\Administrator\Desktop\caffe-windows-master\bin\convert_imageset.exe C:\Users\Administrator\Desktop\caffe-windows-master\data\Digits\train\ C:\Users\Administrator\Desktop\caffe-windows-master\data\Digits\train.txt C:\Users\Administrator\Desktop\caffe-windows-master\data\Digits\mtrainldb 
pause

转换验证数据：操作同上，写批处理命令：

文件名：convert_imageldb_valset.bat

SET GLOG_logtostderr=1
C:\Users\Administrator\Desktop\caffe-windows-master\bin\convert_imageset.exe C:\Users\Administrator\Desktop\caffe-windows-master\data\Digits\val\ C:\Users\Administrator\Desktop\caffe-windows-master\data\Digits\val.txt C:\Users\Administrator\Desktop\caffe-windows-master\data\Digits\mvalldb 
pause

3. 网络层参数

文件：train_val.prorotxt，参照 lenet-5 ; 注意将地址对应自己的转换数据的位置，代码如下：

name: "LeNet"
layer {
  name: "mnist"
  transform_param {
    scale: 0.00390625

  }
  type: "Data"
  top: "data"
  top: "label"
  data_param {
    source: "C:/Users/Administrator/Desktop/caffe-windows-master/data/Digits/mtrainldb"
    backend: LMDB
    batch_size: 80
  }

  include: { phase: TRAIN }
}
layer {
  name: "mnist"
  transform_param {
    scale: 0.00390625
  }
  type: "Data"
  top: "data"
  top: "label"
  data_param {
    source: "C:/Users/Administrator/Desktop/caffe-windows-master/data/Digits/mvalldb"
    backend: LMDB
    batch_size: 4
  }

  include: { phase: TEST }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 1
    #decay_mult: 1
  }
  param {
    lr_mult: 2
    #decay_mult: 0
  }
  convolution_param {
    num_output: 120
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv1"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 1
  }
}
layer {
  name: "norm2"
  type: "LRN"
  bottom: "pool2"
  top: "norm2"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "norm2"
  top: "conv3"
  param {
    lr_mult: 1
    #decay_mult: 1
  }
  param {
    lr_mult: 2
    #decay_mult: 0
  }
  convolution_param {
    num_output: 180
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "pool3"
  type: "Pooling"
  bottom: "conv3"
  top: "pool3"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "norm3"
  type: "LRN"
  bottom: "pool3"
  top: "norm3"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "conv4"
  type: "Convolution"
  bottom: "norm3"
  top: "conv4"
  param {
    lr_mult: 1
    #decay_mult: 1
  }
  param {
    lr_mult: 2
    #decay_mult: 0
  }
  convolution_param {
    num_output: 210
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu5"
  type: "ReLU"
  bottom: "conv4"
  top: "conv4"
}
layer {
  name: "pool5"
  type: "Pooling"
  bottom: "conv4"
  top: "pool5"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 1
  }
}
layer {
  name: "norm5"
  type: "LRN"
  bottom: "pool5"
  top: "norm5"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "ip1"
  type: "InnerProduct"
  bottom: "norm5"
  top: "ip1"
  param {
    lr_mult: 1
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 256
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu7"
  type: "Insanity"
  bottom: "ip1"
  top: "ip1"
}
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
  }
  param {
    lr_mult: 2
  }
  inner_product_param {
    num_output: 512
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu8"
  type: "Insanity"
  bottom: "ip2"
  top: "ip2"
}
layer {
  name: "drop2"
  type: "Dropout"
  bottom: "ip2"
  top: "ip2"
  dropout_param {
    dropout_ratio: 0.5
  }
}

layer {
  name: "accuracy"
  type: "Accuracy"
  bottom: "ip2"
  bottom: "label"
  top: "accuracy"
  include {
    phase: TEST
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "ip2"
  bottom: "label"
  top: "loss"
}

文件：solver.prototxt:

net: "C:/Users/Administrator/Desktop/caffe-windows-master/data/Digits/train_val.prototxt"
test_iter: 100
test_interval: 100
base_lr: 0.001
momentum: 0.9
weight_decay: 0.0005
lr_policy: "inv"
gamma: 0.0001
power: 0.75
display: 100
max_iter: 10000
snapshot: 2000
snapshot_prefix: "C:/Users/Administrator/Desktop/caffe-windows-master/data/Digits/Training_model/MyLenet"
# solver mode: CPU or GPU
solver_mode:  GPU

4. 开始训练

Digist 文件夹下创建, caffe.bat,内容如下：

LOG=log/train-`date +%Y-%m-%d-%H-%M-%S`.log
C:\Users\Administrator\Desktop\caffe-windows-master\bin\caffe.exe train --solver C:\Users\Administrator\Desktop\caffe-windows-master\data\Digits\solver.prototxt
pause

准备完成之后，双击 caffe.bat；