关键词

caffe之(一)卷积层

在caffe中,网络的结构由prototxt文件中给出,由一些列的Layer(层)组成,常用的层如:数据加载层、卷积操作层、pooling层、非线性变换层、内积运算层、归一化层、损失计算层等;本篇主要介绍卷积层

参考

1. 卷积层总述

下面首先给出卷积层的结构设置的一个小例子(定义在.prototxt文件中) 

layer {

  name: "conv1" // 该层的名字
  type: "Convolution" // 该层的类型,具体地,可选的类型有:Convolution、
  bottom: "data" // 该层的输入数据Blob的名字
  top: "conv1" // 该层的输出数据Blob的名字

  // 该层的权值和偏置相关参数
  param { 
    lr_mult: 1  //weight的学习率
  }
  param {
    lr_mult: 2  // bias的学习率
  }

  // 该层(卷积层)的卷积运算相关的参数
  convolution_param {
    num_output: 20
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "xavier"  // weights初始化方法
    }
    bias_filler {
      type: "constant" // bias初始化方法
    }
  }

}

 

注:在caffe的原始proto文件中,关于卷积层的参数ConvolutionPraram定义如下:

message ConvolutionParameter {
  optional uint32 num_output = 1; // The number of outputs for the layer
  optional bool bias_term = 2 [default = true]; // whether to have bias terms

  // Pad, kernel size, and stride are all given as a single value for equal dimensions in all spatial dimensions, or once per spatial dimension.
  repeated uint32 pad = 3; // The padding size; defaults to 0
  repeated uint32 kernel_size = 4; // The kernel size
  repeated uint32 stride = 6; // The stride; defaults to 1
  // Factor used to dilate the kernel, (implicitly) zero-filling the resulting holes. (Kernel dilation is sometimes referred to by its use in the algorithme à trous from Holschneider et al. 1987.)
  repeated uint32 dilation = 18; // The dilation; defaults to 1

  // For 2D convolution only, the *_h and *_w versions may also be used to specify both spatial dimensions.
  optional uint32 pad_h = 9 [default = 0]; // The padding height (2D only)
  optional uint32 pad_w = 10 [default = 0]; // The padding width (2D only)
  optional uint32 kernel_h = 11; // The kernel height (2D only)
  optional uint32 kernel_w = 12; // The kernel width (2D only)
  optional uint32 stride_h = 13; // The stride height (2D only)
  optional uint32 stride_w = 14; // The stride width (2D only)

  optional uint32 group = 5 [default = 1]; // The group size for group conv

  optional FillerParameter weight_filler = 7; // The filler for the weight
  optional FillerParameter bias_filler = 8; // The filler for the bias
  enum Engine {
    DEFAULT = 0;
    CAFFE = 1;
    CUDNN = 2;
  }
  optional Engine engine = 15 [default = DEFAULT];

  // The axis to interpret as "channels" when performing convolution.
  // Preceding dimensions are treated as independent inputs;
  // succeeding dimensions are treated as "spatial".
  // With (N, C, H, W) inputs, and axis == 1 (the default), we perform
  // N independent 2D convolutions, sliding C-channel (or (C/g)-channels, for
  // groups g>1) filters across the spatial axes (H, W) of the input.
  // With (N, C, D, H, W) inputs, and axis == 1, we perform
  // N independent 3D convolutions, sliding (C/g)-channels
  // filters across the spatial axes (D, H, W) of the input.
  optional int32 axis = 16 [default = 1];

  // Whether to force use of the general ND convolution, even if a specific
  // implementation for blobs of the appropriate number of spatial dimensions
  // is available. (Currently, there is only a 2D-specific convolution
  // implementation; for input blobs with num_axes != 2, this option is
  // ignored and the ND implementation will be used.)
  optional bool force_nd_im2col = 17 [default = false];
}

2. 卷积层相关参数 

接下来,分别对卷积层的相关参数进行说明

(根据卷积层的定义,它的学习参数应该为filter的取值和bias的取值,其他的相关参数都为hyper-paramers,在定义模型时是要给出的)

lr_mult:学习率系数

放置在param{}中

该系数用来控制学习率,在进行训练过程中,该层参数以该系数乘solver.prototxt配置文件中的base_lr的值为学习率

即学习率=lr_mult*base_lr

如果该层在结构配置文件中有两个lr_mult,则第一个表示fitler的权值学习率系数,第二个表示偏执项的学习率系数(一般情况下,偏执项的学习率系数是权值学习率系数的两倍)

convolution_praram:卷积层的其他参数

放置在convoluytion_param{}中

该部分对卷积层的其他参数进行设置,有些参数为必须设置,有些参数为可选(因为可以直接使用默认值)

  • 必须设置的参数

  1. num_output:该卷积层的filter个数

  2. kernel_size:卷积层的filter的大小(直接用该参数时,是filter的长宽相等,2D情况时,也可以设置为不能,此时,利用kernel_h和kernel_w两个参数设定)
  • 其他可选的设置参数

  1. stride:filter的步长,默认值为1

  2. pad:是否对输入的image进行padding,默认值为0,即不填充(注意,进行padding可能会带来一些无用信息,输入image较小时,似乎不太合适)
  3. weight_filter:权值初始化方法,使用方法如下
    weight_filter{
          type:"xavier"  //这里的xavier是一冲初始化算法,也可以是“gaussian”;默认值为“constant”,即全部为0
    }
  4. bias_filter:偏执项初始化方法
    bias_filter{
          type:"xavier"  //这里的xavier是一冲初始化算法,也可以是“gaussian”;默认值为“constant”,即全部为0
    }
  5. bias_term:是否使用偏执项,默认值为Ture

 

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