深度学习框架caffe源码学习（一） — caffe.proto

引言

要看caffe源码，我认为首先应该看的就是caffe.proto。
它位于…\src\caffe\proto目录下，在这个文件夹下还有一个.pb.cc和一个.pb.h文件，这两个文件都是由caffe.proto编译而来的。
在caffe.proto中定义了很多结构化数据，包括：

• BlobProto
• Datum
• FillerParameter
• NetParameter
• SolverParameter
• SolverState
• LayerParameter
• ConcatParameter
• ConvolutionParameter
• DataParameter
• DropoutParameter
• HDF5DataParameter
• HDF5OutputParameter
• ImageDataParameter
• InfogainLossParameter
• InnerProductParameter
• LRNParameter
• MemoryDataParameter
• PoolingParameter
• PowerParameter
• WindowDataParameter
• V0LayerParameter

正文

一、什么是protocol buffer

推荐阅读资料：
Google Protocol Buffer 的使用和原理
Protocol Buffer技术详解(C++实例)

message的没一个字段，都要用如下的三个修饰符(modifier)来声明：

1. required：必须赋值，不能为空，否则该条message会被认为是“uninitialized”。build一个“uninitialized”message会抛出一个RuntimeException异常，解析一条“uninitialized”message会抛出一条IOException异常。除此之外，“required”字段跟“optional”字段并无差别。proto提供requireed字段，但是Google程序员都懒得用，经常会出现奇怪bug，所以一律用optional替代requireed。
2. optional： 字段可以赋值，也可以不赋值。假如没有赋值的话，会被赋上默认值。对于简单类型，默认值可以自己设定。如果没有自行设定，会被赋上一个系统默认值，数字类型会被赋为0，String类型会被赋为空字符串，bool类型会被赋为false。对于内置的message，默认值为该message的默认实例或者原型，即其内所有字段均为设置。当获取没有显式设置值的optional字段的值时，就会返回该字段的默认值。
3. repeated： 该字段可以重复任意次数，包括0次。重复数据的顺序将会保存在protocol buffer中，将这个字段想象成一个可以自动设置size的数组就可以了。

二、 caffe.proto源码学习

从caffe.proto编译而来的，无非就是一些关于这些数据结构（类）的标准化操作，比如：

void CopyFrom();//在ByteString中定义实现ByteString和字节数组/字符串互相转换函数
void MergeFrom();//用于合并
void Clear();
bool IsInitialized() const;
int ByteSize() const;
//解码时可以调用C++接口ParseFromArray，
// 编码时可以先调用C++接口ByteSize预先获得编码后的数据大小，
// 让后动态分配内存后调用SerializeToArray进行编码即可。
bool MergePartialFromCodedStream();
void SerializeWithCachedSizes() const;//序列化打包
SerializeWithCachedSizesToArray() const;
int GetCachedSize()//打包后出来的大小
void SharedCtor();
void SharedDtor();
void SetCachedSize() const;

现在正式介绍caffe.proto源码：

//
caffe.proto文件注释，
//
syntax = "proto2";
package caffe;

// 该结构描述了Blob数据块形状信息{指定Blob的形状或维度-4D （Num×Channel×Height×Wight）}
message BlobShape {
  //数据块形状定义为Num×Channel×Height×Wight原因在于caffe基于容器的多维嵌套来实现高维数据的封装。即vector(N)>。
  repeated int64 dim = 1 [packed = true];//packed表示这些值在内存中紧密排布，没有空洞
}

//blob的属性以及blob中的数据(data\diff),数据块{形状，数据，微分}
//blob包含三类数据：
//1、data： 前向传播所用到的数据
//2、diff： 反向传播所用到的梯度数据
//3、shape： 解释data和diff的shape数据
message BlobProto {
  optional BlobShape shape = 7;
  repeated float data = 5 [packed = true];//存储数据或权值
  repeated float diff = 6 [packed = true];//存储增量信息
  repeated double double_data = 8 [packed = true];//double类型的data
  repeated double double_diff = 9 [packed = true];//double类型的diff

  //数据4D形状 -- 旧版本，已使用"BlobShape shape"代替：
  optional int32 num = 1 [default = 0]; //样本
  optional int32 channels = 2 [default = 0];
  optional int32 height = 3 [default = 0];
  optional int32 width = 4 [default = 0];
}

// 存放多个BlobProto实例的对应Index，易于引用
message BlobProtoVector {
  repeated BlobProto blobs = 1;
}

// 数据:{C,H,W,data(uchar&float),label} 图像样本
message Datum {
  optional int32 channels = 1;
  optional int32 height = 2;
  optional int32 width = 3;
  // the actual image data, in bytes
  optional bytes data = 4;
  optional int32 label = 5;
  // Optionally, the datum could also hold float data.
  repeated float float_data = 6;
  // If true data contains an encoded image that need to be decoded
  optional bool encoded = 7 [default = false];
}

//滤波器参数{Type(const|uniform|gauss)}
// Filler层的作用实际上就是根据proto中给出的参数对权重进行初始化，初始化的方式有很多种，文件 
// filler.hpp提供了7种权值初始化的方法，分别为常量初始化（constant）、高斯分布初始化（gaussian）、 
// positive_unitball初始化、均匀分布初始化（uniform）、xavier初始化、msra初始化、双线性初始化（bilinear）这么几种。 
// 1、 常量初始化（constant）： 它就是把权值或着偏置初始化为一个常数，具体是什么常数，自己可以定义啦。
//     它的值等于上面的.prototxt文件中的 value 的值，默认为0
// 2、 高斯分布初始化（gaussian）： 给定高斯函数的均值与标准差,生成高斯分布
// 3、 positive_unitball初始化： 通俗一点，它干了点什么呢？即让每一个单元的输入的权值的和为 1. 例如吧，一个神经元有100个输入，
// 这样的话，让这100个输入的权值的和为1. 源码中怎么实现的呢？ 首先给这100个权值赋值为在（0，1）之间的均匀分布，然后，
// 每一个权值再除以它们的和就可以啦。感觉这么做，可以有助于防止权值初始化过大，使激活函数（sigmoid函数）
// 进入饱和区。所以呢，它应该适合simgmoid形的激活函数，不需要参数去控制。
// 4、 均匀分布初始化（uniform）： 把权值与偏置进行均匀分布的初始化。用min与max来控制它们的的上下限，默认为（0，1）.
// 5、 xavier初始化： 它的思想就是让一个神经元的输入权重的（当反向传播时，就变为输出了）的方差等于：1/输
// 入的个数；这样做的目的就是可以让信息可以在网络中均匀的分布一下。对于权值的分布：是一个让均值为0，方差
// 为1/输入的个数的均匀分布。如果我们更注重前向传播的话，
// 我们可以选择fan_in，即正向传播的输入个数；如果更注重后向传播的话，我们选择fan_out, 
// 因为吧，等着反向传播的时候，fan_out就是神经元的输入个数；如果两者都考虑的话，
// 那就选average=(fan_in+fan_out)/2。 
// ReLU激活函数和 Xavier Filler初始化搭配使用。对于这个初始化的方法，是有理论的。
// 它来自这篇论文《Understanding the difficulty of training deep feedforward neural networks》。
// 如果不想看论文的话，可以看看 https://zhuanlan.zhihu.com/p/22028079，我觉得写的很棒，
// 另外，http://blog.csdn.net/shuzfan/article/details/51338178可以作为补充。
// 6、 msra初始化： 与上面基本类似，
// 基于《Delving Deep into Rectifiers:Surpassing Human-Level Performance on ImageNet Classification》
// 来推导的，并且呢，它是基于激活函数为ReLU函数哦，对于权值的分布，是基于均值为0，方差为2/输入的个数的高斯分布，
// 这也是和上面的Xavier Filler不同的地方；它特别适合激活函数为ReLU函数的啦。
message FillerParameter {
  // The filler type. // 初始化类型
  optional string type = 1 [default = 'constant'];
  // 如果是常数初始化的话需要该值
  optional float value = 2 [default = 0]; // the value in constant filler
  // 如果是均匀分布初始化则需要min和max
  optional float min = 3 [default = 0]; // the min value in uniform filler
  optional float max = 4 [default = 1]; // the max value in uniform filler
  // 如果是高斯分布初始化则需要mean和std
  optional float mean = 5 [default = 0]; // the mean value in Gaussian filler
  optional float std = 6 [default = 1]; // the std value in Gaussian filler
  // 给定输入与权值相乘后应该得到非零输出，默认值-1意为不稀疏化高斯模板。//  是否需要稀疏特性
  optional int32 sparse = 7 [default = -1];//Gaussian filler
  // Normalize the filler variance by fan_in, fan_out, or their average.
  // Applies to 'xavier' and 'msra' fillers.（扇入，扇出）
  // 通过fanIn,fanOut,及其均值来归一化填充值的方差，有“xavier法”或“msra法”
  // 对于xavier和msra两种权重初始化需要设置归一化的类型是
  // 使用扇入还是扇出还是扇入+扇出进行归一化
  enum VarianceNorm {
    FAN_IN = 0;
    FAN_OUT = 1;
    AVERAGE = 2;
  }
  optional VarianceNorm variance_norm = 8 [default = FAN_IN];
}

//网络参数{网名，输入参数，数据块形状，forceBack，NetState,debugInfo,}
message NetParameter {
  optional string name = 1; // consider giving the network a name
  // 旧版--输入网络的数据块Blobs; 改为新版--InputParameter
  repeated string input = 3;
  // DEPRECATED. See InputParameter. The shape of the input blobs.
  // 旧版--输入的Blobs的形状； 改为新版--InputerParameter
  repeated BlobShape input_shape = 8;

  // 指定Blobs的4D输入形状 -- 已改为新版：input_shape代替
  // 如要使用旧版，对每个输入的blob都需要指定4个参数，Num×Cha×H×W
  // 因此 input_dim需要重复4次
  repeated int32 input_dim = 4;

  //确定网络是否要让每个层都强制反向传播。
  //如果设置为false,将根据网络结构和学习率来自动确定是否需要反向传播。
  // Whether the network will force every layer to carry out backward operation.
  // If set False, then whether to carry out backward is determined
  // automatically according to the net structure and learning rates.
  optional bool force_backward = 5 [default = false];

  //网络的当前状态"state"包括"phase","level","stage"。(???)
  //某些层需要设置phase属性，使其跳过网络运行时的某些状态.
  optional NetState state = 6;

  // 当运行Net::Forward/Backward/Update时，打印调试信息，默认false.
  optional bool debug_info = 7 [default = false];

  // 构成net的layers。每个layer的链接和行为通过LayerParameter配置。
  repeated LayerParameter layer = 100;  // ID 100 so layers are printed last.

  // DEPRECATED: use 'layer' instead.
  repeated V1LayerParameter layers = 2;
}

// NOTE：注意
// Update the next available ID when you add a new SolverParameter field.
// 当你添加一个新的SolverParameter属性时，需要更新下一个可获得的ID
// SolverParameter next available ID: 41 (last added: type)

//求解器参数{网络，}
message SolverParameter {
  //
  // Specifying the train and test networks
  //
  // Exactly one train net must be specified using one of the following fields:
  //     train_net_param, train_net, net_param, net
  // One or more test nets may be specified using any of the following fields:
  //     test_net_param, test_net, net_param, net
  // If more than one test net field is specified (e.g., both net and
  // test_net are specified), they will be evaluated in the field order given
  // above: (1) test_net_param, (2) test_net, (3) net_param/net.
  // A test_iter must be specified for each test_net.
  // A test_level and/or a test_stage may also be specified for each test_net.
  //
  //指定网络，可有以下的多种形式
  // Proto filename for the train net, possibly combined with one or more
  // test nets.
  optional string net = 24;
  // Inline train net param, possibly combined with one or more test nets.
  optional NetParameter net_param = 25;

  optional string train_net = 1; // Proto filename for the train net.
  repeated string test_net = 2; // Proto filenames for the test nets.
  optional NetParameter train_net_param = 21; // Inline train net params.
  repeated NetParameter test_net_param = 22; // Inline test net params.

 // 指定网络状态
 // The states for the train/test nets. Must be unspecified or
  // specified once per net.
  //
  // By default, all states will have solver = true;
  // train_state will have phase = TRAIN,
  // and all test_state's will have phase = TEST.
  // Other defaults are set according to the NetState defaults.
  optional NetState train_state = 26;
  repeated NetState test_state = 27;

  //测试迭代批次数：
  //合理设置可使得测试遍历完全部测试样本
  //合理值 = 测试样本总数/每批次测试数 = totalTestSamples/batchSize
  repeated int32 test_iter = 3;

  //训练迭代批次数：
  //两次测试之间所经历的训练迭代次数:合理设置可使得训练遍历完全部训练样本
  //合理值 = 训练样本总数/每批次训练数 = totalTrainSamples/batchSize
  optional int32 test_interval = 4 [default = 0];
  //训练test_interval个批次，再测试test_iter个批次，为一个回合(epoch)
  //合理设置应使得每个回合内，遍历覆盖到全部训练样本和测试样本

  //默认不计算测试时损失
  optional bool test_compute_loss = 19 [default = false];

  // 如设置为真，则在训练前运行一次测试，以确保内存足够，并打印初始损失值
  optional bool test_initialization = 32 [default = true];
  // 基本学习速率
  optional float base_lr = 5; // The base learning rate
  // 打印信息的遍历间隔，遍历多少个批次打印一次信息。设置为0则不打印。
  optional int32 display = 6;
  // Display the loss averaged over the last average_loss iterations
  // 打印最后一个迭代批次下的平均损失（？）
  optional int32 average_loss = 33 [default = 1];
  // 训练最大迭代次数
  optional int32 max_iter = 7;
  // accumulate gradients over `iter_size` x `batch_size` instances
  // 累积梯度误差基于“iter_size×batchSize”个样本实例
  // “批次数×批量数”=“遍历的批次数×每批的样本数”个样本实例
  optional int32 iter_size = 36 [default = 1];

  //学习率衰减策略(7种)
  // The learning rate decay policy. The currently implemented learning rate
  // policies are as follows:
  //    - fixed: always return base_lr.
  //    - step: return base_lr * gamma ^ (floor(iter / step))
  //    - exp: return base_lr * gamma ^ iter
  //    - inv: return base_lr * (1 + gamma * iter) ^ (- power)
  //    - multistep: similar to step butallows non uniform steps defined by
  //      stepvalue
  //    - poly: the effective learning rate follows a polynomial decay, to be
  //      zero by the max_iter. return base_lr (1 - iter/max_iter) ^ (power)
  //    - sigmoid: the effective learning rate follows a sigmod decay
  //      return base_lr ( 1/(1 + exp(-gamma * (iter - stepsize))))
  //
  // 在上述参数中，base_lr, max_iter, gamma, step, stepvalue and power 被定义
  // 在solver.prototxt文件中，iter是当前迭代次数。
  optional string lr_policy = 8; //学习率调节策略
  optional float gamma = 9; // The parameter to compute the learning rate.
  optional float power = 10; // The parameter to compute the learning rate.
  optional float momentum = 11; // The momentum value.动量值，通常取0.9
  optional float weight_decay = 12; // The weight decay.权值衰减系数，通常取0.0005
  //由权值衰减系数所控制的正则化类型：L1或L2范数，默认L2
  optional string regularization_type = 29 [default = "L2"];
  //"step"策略下，学习率的步长值
  optional int32 stepsize = 13;
  //"multistep"策略下的步长值
  repeated int32 stepvalue = 34;

  //设置梯度裁剪阈值为>=0，当其实际L2范数超出此值时(?)
  optional float clip_gradients = 35 [default = -1];

  //快照间隔，遍历多少次对模型和求解器状态保存一次
  optional int32 snapshot = 14 [default = 0]; // The snapshot interval
  optional string snapshot_prefix = 15; // The prefix for the snapshot.
  //是否对diff快照，有助调试，但最终的protocol buffer尺寸会很大
  optional bool snapshot_diff = 16 [default = false];
  //快照数据保存格式{hdf5,binaryproto(默认)}
  enum SnapshotFormat {
    HDF5 = 0;
    BINARYPROTO = 1;
  }
  optional SnapshotFormat snapshot_format = 37 [default = BINARYPROTO];
  // the mode solver will use: 0 for CPU and 1 for GPU. Use GPU in default.
  enum SolverMode {
    CPU = 0;
    GPU = 1;
  }
  求解模式{GPU(device_id),CPU}
  optional SolverMode solver_mode = 17 [default = GPU];
  optional int32 device_id = 18 [default = 0];
  //随机数种子
  //设为非负数时，则表示Solver会以随机数生成器生成的种子作为随机数初始化caffe，易于重复试验；
  // 否则（或是设为默认值-1）使用从系统时钟导出的种子进行初始化。
  optional int64 random_seed = 20 [default = -1];

  //求解器类型=SGD(默认)//优化器类型，{"SGD","Nesterov","AdaGrad","RMSProp","AdaDelta","ADAM"}
  optional string type = 40 [default = "SGD"];

  // numerical stability for RMSProp, AdaGrad and AdaDelta and Adam
  // RMSProp，AdaGrad和AdaDelta和Adam的数值稳定性
  optional float delta = 31 [default = 1e-8];
  // parameters for the Adam solver
  optional float momentum2 = 39 [default = 0.999];

  // RMSProp decay value
  // MeanSquare(t) = rms_decay*MeanSquare(t-1) + (1-rms_decay)*SquareGradient(t)
  optional float rms_decay = 38;

  //若真，则打印网络状态信息，有助于调试问题
  optional bool debug_info = 23 [default = false];

  //若假，则不会在训练后保存快照
  optional bool snapshot_after_train = 28 [default = true];

  // DEPRECATED: old solver enum types, use string instead
  enum SolverType {
    SGD = 0;
    NESTEROV = 1;
    ADAGRAD = 2;
    RMSPROP = 3;
    ADADELTA = 4;
    ADAM = 5;
  }
  // DEPRECATED: use type instead of solver_type
  optional SolverType solver_type = 30 [default = SGD];
}

//对求解器状态进行快照的消息
message SolverState {
  optional int32 iter = 1; // The current iteration
  optional string learned_net = 2; // The file that stores the learned net.
  repeated BlobProto history = 3; // The history for sgd solvers
  optional int32 current_step = 4 [default = 0]; // The current step for learning rate
}

enum Phase {
   TRAIN = 0;
   TEST = 1;
}
//NetState{phase,level,stage}
message NetState {
  optional Phase phase = 1 [default = TEST];
  optional int32 level = 2 [default = 0];
  repeated string stage = 3;
}

//网络状态规则{phases,levels,stages}
message NetStateRule {
  //在NetState中设置phase值(TRAIN|TEST)，使其符合此规则
  optional Phase phase = 1;

  //设置layer中所使用的最小最大levels。使其不定义以满足忽视level的规则。
  optional int32 min_level = 2;
  optional int32 max_level = 3;

  // Customizable sets of stages to include or exclude.
  // The net must have ALL of the specified stages and NONE of the specified
  // "not_stage"s to meet the rule.
  // (Use multiple NetStateRules to specify conjunctions of stages.)
  //可定制的stages集合，用于include或exclude在网络中。网络必须包含全
  //部制定的"stages"或不包含全部制定的"not_stage"
  repeated string stage = 4;
  repeated string not_stage = 5;
}

// Specifies training parameters (multipliers on global learning constants,
// and the name and other settings used for weight sharing).
//指定训练参数(乘数及全局学习率常数)和其名称，以及其他用于权值共享的设置。
message ParamSpec {
  // 设定参数blobs的名称--用于在层间共享参数，若无此需求则不用设计。
  optional string name = 1;

  //共享权重时是否需要其形状相同或仅仅数量相同，默认为形状相同
  optional DimCheckMode share_mode = 2;
  enum DimCheckMode {
    // STRICT (default) 形状相同(num, channels, height, width)都匹配.
    STRICT = 0;
    // PERMISSIVE 数量相同
    PERMISSIVE = 1;
  }

  // The multiplier on the global learning rate for this parameter.
  // 全局学习率的乘数
  optional float lr_mult = 3 [default = 1.0];

  // The multiplier on the global weight decay for this parameter.
  // 全局权值衰减系数的乘数
  optional float decay_mult = 4 [default = 1.0];
}

//注意：
//当在LayerParameter中新增字段时，需要为其更新下一个可用ID。
//比如，最近新增了smooth_l1_loss_param层，则为其指定层专属ID:149。
//层参数{名称，类型，输入底，输出顶，阶段，损失加权系数，全局乘数，}
message LayerParameter {
  optional string name = 1; // 类名称
  optional string type = 2; // 类类型
  repeated string bottom = 3; // the name of each bottom blob 输入blob名称
  repeated string top = 4; // the name of each top blob 输出blob名称

  // The train / test phase for computation. //阶段，运行时状态
  optional Phase phase = 10;

  //每层输出blob在目标损失函数中的加权系数，每层默认为0或1
  repeated float loss_weight = 5;

  //指定训练参数(全局学习率上的乘数lr_mrlt)
  repeated ParamSpec param = 6;


  // The blobs containing the numeric parameters of the layer.
  //包含每层数值参数的blobs
  repeated BlobProto blobs = 7;

  // Specifies whether to backpropagate to each bottom. If unspecified,
  // Caffe will automatically infer whether each input needs backpropagation
  // to compute parameter gradients. If set to true for some inputs,
  // backpropagation to those inputs is forced; if set false for some inputs,
  // backpropagation to those inputs is skipped.
  //
  // The size must be either 0 or equal to the number of bottoms.
  repeated bool propagate_down = 11;//设定是否进行反向传播

  // Rules控制每层是否被包含在网络中，基于当前的NetState. 可使用非0数规则来
  // include或exclude，但不能兼有。如果未指定include或exclude规则，则该层总是
  // 被包含在内。
  repeated NetStateRule include = 8;
  repeated NetStateRule exclude = 9;

  // 用于数据预处理的参数
  optional TransformationParameter transform_param = 100;

  // 由loss层共享的参数.
  optional LossParameter loss_param = 101;

  // Layer type-specific parameters.
  //
  // Note: certain layers may have more than one computational engine
  // for their implementation. These layers include an Engine type and
  // engine parameter for selecting the implementation.
  // The default for the engine is set by the ENGINE switch at compile-time.
  // 层类型指定参数
  // 注意：
  optional AccuracyParameter accuracy_param = 102;
  optional ArgMaxParameter argmax_param = 103;
  optional BatchNormParameter batch_norm_param = 139;
  optional BiasParameter bias_param = 141;
  optional ConcatParameter concat_param = 104;
  optional ContrastiveLossParameter contrastive_loss_param = 105;
  optional ConvolutionParameter convolution_param = 106;
  optional CropParameter crop_param = 144;
  optional DataParameter data_param = 107;
  optional DropoutParameter dropout_param = 108;
  optional DummyDataParameter dummy_data_param = 109;
  optional EltwiseParameter eltwise_param = 110;
  optional ELUParameter elu_param = 140;
  optional EmbedParameter embed_param = 137;
  optional ExpParameter exp_param = 111;
  optional FlattenParameter flatten_param = 135;
  optional HDF5DataParameter hdf5_data_param = 112;
  optional HDF5OutputParameter hdf5_output_param = 113;
  optional HingeLossParameter hinge_loss_param = 114;
  optional ImageDataParameter image_data_param = 115;
  optional InfogainLossParameter infogain_loss_param = 116;
  optional InnerProductParameter inner_product_param = 117;
  optional InputParameter input_param = 143;
  optional LogParameter log_param = 134;
  optional LRNParameter lrn_param = 118;
  optional MemoryDataParameter memory_data_param = 119;
  optional MVNParameter mvn_param = 120;
  optional ParameterParameter parameter_param = 145;
  optional PoolingParameter pooling_param = 121;
  optional PowerParameter power_param = 122;
  optional PReLUParameter prelu_param = 131;
  optional PythonParameter python_param = 130;
  optional RecurrentParameter recurrent_param = 146;
  optional ReductionParameter reduction_param = 136;
  optional ReLUParameter relu_param = 123;
  optional ReshapeParameter reshape_param = 133;
  optional ROIPoolingParameter roi_pooling_param = 147;
  optional ScaleParameter scale_param = 142;
  optional SigmoidParameter sigmoid_param = 124;
  optional SmoothL1LossParameter smooth_l1_loss_param = 148;
  optional SoftmaxParameter softmax_param = 125;
  optional SPPParameter spp_param = 132;
  optional SliceParameter slice_param = 126;
  optional TanHParameter tanh_param = 127;
  optional ThresholdParameter threshold_param = 128;
  optional TileParameter tile_param = 138;
  optional WindowDataParameter window_data_param = 129;
  optional MILDataParameter mil_data_param = 0x004d4944; //"MID"
  optional MILParameter mil_param = 0x004d494c; //"MIL"
}

// 对数据层进行转换的参数
message TransformationParameter {
  // 对data执行预处理，比如简单缩放,去均值。
  optional float scale = 1 [default = 1];
  // Specify if we want to randomly mirror data.//镜像
  optional bool mirror = 2 [default = false];
  // Specify if we would like to randomly crop an image.//随机裁剪
  optional uint32 crop_size = 3 [default = 0];
  // 指定均值文件或均值，二者不可兼有；在对应通道上减去此均值；
  optional string mean_file = 4;
  repeated float mean_value = 5;
  // 强制转换图像为3通道彩色
  optional bool force_color = 6 [default = false];
  // 强制转换为灰度图
  optional bool force_gray = 7 [default = false];
}

// Loss层参数
message LossParameter {
  // 如果被指定，则忽略给定label的实例
  optional int32 ignore_label = 1;
  // 如何对loss层损失归一化，使其跨越"batches,spatial(H*W)"或其他维度。
  // 目前仅仅在SoftmaxWithLoss层中实现。
  // 归一化模式
  enum NormalizationMode {
    // 基于batchSize×spatialDim归一化.所设定的忽略标签将不被忽略。
    FULL = 0;
    // 基于输出位置的总数量(batchSize×H×W)归一化，不包括被忽视的标签。
    // 若未设置被忽视标签，则其行为与FULL相同。
    VALID = 1;
    // Divide by the batch size.基于batchSize进行归一化。
    BATCH_SIZE = 2;
    // Do not normalize the loss.不归一化损失
    NONE = 3;
  }
  optional NormalizationMode normalization = 3 [default = VALID];
  // 旧版--新版如上所述。
  // 若"normalization"被指定则忽略此参数；若未被指定，可设置下值为false
  // 则基于batchSize归一化。
  optional bool normalize = 2;
}

// Messages that store parameters used by individual layer types follow, in
// alphabetical order.
// Accuracy完成的任务是统计预测正确样本的个数信息。如总样本N个，正确分类n个，正确率为n/N。
// 主要变量：
//     label_axis_为标签对应的轴（对应的blob中的那个维度）
//     outer_num_总的来说是样本数量，详细解释见后面
//     inner_num_同上，总的来说是样本数量，详细解释见后面
//     top_k为取前k个最高评分（的预测标签）
message AccuracyParameter {
  // When computing accuracy, count as correct by comparing the true label to
  // the top k scoring classes.  By default, only compare to the top scoring
  // class (i.e. argmax). //Topk正确率计算
  optional uint32 top_k = 1 [default = 1];

  // The "label" axis of the prediction blob, whose argmax corresponds to the
  // predicted label -- may be negative to index from the end (e.g.,-1 for the
  // last axis).  For example, if axis == 1 and the predictions are
  // (N x C x H x W), the label blob is expected to contain N*H*W ground truth
  // labels with integer values in {0, 1, ..., C-1}.
  // 预测blob的"label"轴--其最大值才对应于预测标签--的索引有可能从负值开始。
  // 即: predicted_labels=argmax(predictions blob,label_axis),
  // 比如axis==1,其预测blob为(N x C x H x W), 而标签blob被期望包含(N×H×W)个真实标签，且标签值为{0,1,2...C-1}。
  // 定义中关于axis的说明：
  // 1. axis指出在预测blob中，哪一维是label轴，如(N x C x H x W)的blob，axis=0，则N为label对应的维度。
  // axis=1,则C为label对应的维度，而剩下的N为outer样本数量， H x W为inner样本数量。
  // 2. 由代码可知, 当axis=k时，outer_num_=blob.shape[0,..,k)，
  //                         inner_num_=blob.shape[k+1,..,shape.size)
  // 一般的，label blob的维度为(N x C)，N为样本数量，C为标签数量（即类别个数）。
  // axis=1,outer_num_=N,inner_num_=shape[2,2)=1(即没有inner)
  //   outer_num_ = bottom[0]->count(0, label_axis_);
  //   inner_num_ = bottom[0]->count(label_axis_ + 1);
  optional int32 axis = 2 [default = 1];

  // If specified, ignore instances with the given label.
  // 如果指定，则忽略给定标签的对应实例
  optional int32 ignore_label = 3;
}
//输出最大化参数，对预测标签进行最大化
message ArgMaxParameter {
  // If true produce pairs (argmax, maxval)
  // 如果真，则产生(argmax,maxval)对
  optional bool out_max_val = 1 [default = false];
  optional uint32 top_k = 2 [default = 1];
  // The axis along which to maximise -- may be negative to index from the
  // end (e.g., -1 for the last axis).
  // By default ArgMaxLayer maximizes over the flattened trailing dimensions
  // for each index of the first / num dimension. ??
  //
  optional int32 axis = 3;
}
//拼接参数
message ConcatParameter {
  // The axis along which to concatenate -- may be negative to index from the
  // end (e.g., -1 for the last axis).  Other axes must have the
  // same dimension for all the bottom blobs.
  // By default, ConcatLayer concatenates blobs along the "channels" axis (1).
  optional int32 axis = 2 [default = 1];

  // DEPRECATED: alias for "axis" -- does not support negative indexing.
  optional uint32 concat_dim = 1 [default = 1];
}

// BatchNormParameter参数，源于论文batchNorm，网络训练时，用来加速收敛速度
// 注意：
// 1、已经将BN集成为一个layer了，使用时需要和scale层一起使用
// 2、训练的时候，将BN层的use_global_stats设置为false； 
// 测试的时候将use_global_stats设置为true，不然训练的时候会报“NAN”或者模型不收敛，
// 测试如果不设置为true，会导致准确率极低
message BatchNormParameter {
  // If false, accumulate global mean/variance values via a moving average. If
  // true, use those accumulated values instead of computing mean/variance
  // across the batch.
  // 如果为假，则采用滑动平均计算新的均值和方差。使用了每个Batch里的数据的均值和方差； （训练）
  // 如果为真，则使用保存的均值和方差。使用了caffe内部的均值和方差； （测试）
  // 该参数缺省的时候，如果是测试阶段则等价为真，如果是训练阶段则等价为假。  （怀疑，未证实）
  optional bool use_global_stats = 1;
  // How much does the moving average decay each iteration?
  // 滑动平均的衰减系数，默认为0.999
  optional float moving_average_fraction = 2 [default = .999];
  // Small value to add to the variance estimate so that we don't divide by
  // zero.
  // 分母附加值，防止除以方差时出现除0操作，默认为1e-5
  optional float eps = 3 [default = 1e-5];
}
//偏置参数
message BiasParameter {
  // The first axis of bottom[0] (the first input Blob) along which to apply
  // bottom[1] (the second input Blob).  May be negative to index from the end
  // (e.g., -1 for the last axis).
  //
  // For example, if bottom[0] is 4D with shape 100x3x40x60, the output
  // top[0] will have the same shape, and bottom[1] may have any of the
  // following shapes (for the given value of axis):
  //    (axis == 0 == -4) 100; 100x3; 100x3x40; 100x3x40x60
  //    (axis == 1 == -3)          3;     3x40;     3x40x60
  //    (axis == 2 == -2)                   40;       40x60
  //    (axis == 3 == -1)                                60
  // Furthermore, bottom[1] may have the empty shape (regardless of the value of
  // "axis") -- a scalar bias.
  optional int32 axis = 1 [default = 1];

  // (num_axes is ignored unless just one bottom is given and the bias is
  // a learned parameter of the layer.  Otherwise, num_axes is determined by the
  // number of axes by the second bottom.)
  // The number of axes of the input (bottom[0]) covered by the bias
  // parameter, or -1 to cover all axes of bottom[0] starting from `axis`.
  // Set num_axes := 0, to add a zero-axis Blob: a scalar.
  optional int32 num_axes = 2 [default = 1];

  // (filler is ignored unless just one bottom is given and the bias is
  // a learned parameter of the layer.)
  // The initialization for the learned bias parameter.
  // Default is the zero (0) initialization, resulting in the BiasLayer
  // initially performing the identity operation.
  optional FillerParameter filler = 3;
}
//对比度损失参数
message ContrastiveLossParameter {
  // margin for dissimilar pair
  optional float margin = 1 [default = 1.0];
  // The first implementation of this cost did not exactly match the cost of
  // Hadsell et al 2006 -- using (margin - d^2) instead of (margin - d)^2.
  // legacy_version = false (the default) uses (margin - d)^2 as proposed in the
  // Hadsell paper. New models should probably use this version.
  // legacy_version = true uses (margin - d^2). This is kept to support /
  // reproduce existing models and results
  optional bool legacy_version = 2 [default = false];
}
//卷积参数
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 脿 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; //CPU
    CAFFE = 1;   //GPU-CUDA
    CUDNN = 2;   //GPU-CUDA-CUDNN
  }
  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];
}
//裁剪参数
message CropParameter {
  // To crop, elements of the first bottom are selected to fit the dimensions
  // of the second, reference bottom. The crop is configured by
  // - the crop `axis` to pick the dimensions for cropping
  // - the crop `offset` to set the shift for all/each dimension
  // to align the cropped bottom with the reference bottom.
  // All dimensions up to but excluding `axis` are preserved, while
  // the dimensions including and trailing `axis` are cropped.
  // If only one `offset` is set, then all dimensions are offset by this amount.
  // Otherwise, the number of offsets must equal the number of cropped axes to
  // shift the crop in each dimension accordingly.
  // Note: standard dimensions are N,C,H,W so the default is a spatial crop,
  // and `axis` may be negative to index from the end (e.g., -1 for the last
  // axis).
  optional int32 axis = 1 [default = 2];
  repeated uint32 offset = 2;
}
//数据参数
message DataParameter {
  // 输入数据使用的DB类型
  enum DB {
    LEVELDB = 0;  // 使用LEVELDB
    LMDB = 1;  // 使用LMDB
  }
  // Specify the data source. 源数据的路径
  optional string source = 1;
  // Specify the batch size. 一个批量数据包含的图片数目
  optional uint32 batch_size = 4;
  // The rand_skip variable is for the data layer to skip a few data points
  // to avoid all asynchronous sgd clients to start at the same point. The skip
  // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
  // be larger than the number of keys in the database.
  // DEPRECATED. Each solver accesses a different subset of the database.
  // 随机跳过若干图片，跳跃数目为rand_skip × rand(0,1)
  optional uint32 rand_skip = 7 [default = 0];
  optional DB backend = 8 [default = LEVELDB];
  // DEPRECATED. See TransformationParameter. For data pre-processing, we can do
  // simple scaling and subtracting the data mean, if provided. Note that the
  // mean subtraction is always carried out before scaling.
  optional float scale = 2 [default = 1];
  optional string mean_file = 3;
  // DEPRECATED. See TransformationParameter. Specify if we would like to randomly
  // crop an image.
  optional uint32 crop_size = 5 [default = 0];
  // DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror
  // data.
  optional bool mirror = 6 [default = false];
  // Force the encoded image to have 3 color channels
  optional bool force_encoded_color = 9 [default = false];
  // Prefetch queue (Number of batches to prefetch to host memory, increase if
  // data access bandwidth varies).
  optional uint32 prefetch = 10 [default = 4];
}
//DropoutParameter参数
message DropoutParameter {
  optional float dropout_ratio = 1 [default = 0.5]; // dropout ratio 丢弃的数据的概率
  optional bool scale_train = 2 [default = true];  // scale train or test phase
}

// DummyDataLayer fills any number of arbitrarily shaped blobs with random
// (or constant) data generated by "Fillers" (see "message FillerParameter").
message DummyDataParameter {
  // This layer produces N >= 1 top blobs.  DummyDataParameter must specify 1 or N
  // shape fields, and 0, 1 or N data_fillers.
  //
  // If 0 data_fillers are specified, ConstantFiller with a value of 0 is used.
  // If 1 data_filler is specified, it is applied to all top blobs.  If N are
  // specified, the ith is applied to the ith top blob.
  repeated FillerParameter data_filler = 1;
  repeated BlobShape shape = 6;

  // 4D dimensions -- deprecated.  Use "shape" instead.
  repeated uint32 num = 2;
  repeated uint32 channels = 3;
  repeated uint32 height = 4;
  repeated uint32 width = 5;
}

message EltwiseParameter {
  enum EltwiseOp {
    PROD = 0;
    SUM = 1;
    MAX = 2;
  }
  optional EltwiseOp operation = 1 [default = SUM]; // element-wise operation
  repeated float coeff = 2; // blob-wise coefficient for SUM operation

  // Whether to use an asymptotically slower (for >2 inputs) but stabler method
  // of computing the gradient for the PROD operation. (No effect for SUM op.)
  optional bool stable_prod_grad = 3 [default = true];
}

// Message that stores parameters used by ELULayer
message ELUParameter {
  // Described in:
  // Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015). Fast and Accurate
  // Deep Network Learning by Exponential Linear Units (ELUs). arXiv
  optional float alpha = 1 [default = 1];
}

// Message that stores parameters used by EmbedLayer
message EmbedParameter {
  optional uint32 num_output = 1; // The number of outputs for the layer
  // The input is given as integers to be interpreted as one-hot
  // vector indices with dimension num_input.  Hence num_input should be
  // 1 greater than the maximum possible input value.
  optional uint32 input_dim = 2;

  optional bool bias_term = 3 [default = true]; // Whether to use a bias term
  optional FillerParameter weight_filler = 4; // The filler for the weight
  optional FillerParameter bias_filler = 5; // The filler for the bias

}

// Message that stores parameters used by ExpLayer
message ExpParameter {
  // ExpLayer computes outputs y = base ^ (shift + scale * x), for base > 0.
  // Or if base is set to the default (-1), base is set to e,
  // so y = exp(shift + scale * x).
  optional float base = 1 [default = -1.0];
  optional float scale = 2 [default = 1.0];
  optional float shift = 3 [default = 0.0];
}

/// Message that stores parameters used by FlattenLayer
message FlattenParameter {
  // The first axis to flatten: all preceding axes are retained in the output.
  // May be negative to index from the end (e.g., -1 for the last axis).
  optional int32 axis = 1 [default = 1];

  // The last axis to flatten: all following axes are retained in the output.
  // May be negative to index from the end (e.g., the default -1 for the last
  // axis).
  optional int32 end_axis = 2 [default = -1];
}

// Message that stores parameters used by HDF5DataLayer
message HDF5DataParameter {
  // Specify the data source.
  optional string source = 1;
  // Specify the batch size.
  optional uint32 batch_size = 2;

  // Specify whether to shuffle the data.
  // If shuffle == true, the ordering of the HDF5 files is shuffled,
  // and the ordering of data within any given HDF5 file is shuffled,
  // but data between different files are not interleaved; all of a file's
  // data are output (in a random order) before moving onto another file.
  optional bool shuffle = 3 [default = false];
}

message HDF5OutputParameter {
  optional string file_name = 1;
}

message HingeLossParameter {
  enum Norm {
    L1 = 1;
    L2 = 2;
  }
  // Specify the Norm to use L1 or L2
  optional Norm norm = 1 [default = L1];
}
//数据集参数
message ImageDataParameter {
  // 指定数据源文件
  optional string source = 1;
  // 指定批量大小batchSize
  optional uint32 batch_size = 4 [default = 1];
  // The rand_skip variable is for the data layer to skip a few data points
  // to avoid all asynchronous sgd clients to start at the same point. The skip
  // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
  // be larger than the number of keys in the database.
  // 随机跳过rand_skip * rand(0,1)个样本，以使得SGD从不同状态点启动
  optional uint32 rand_skip = 7 [default = 0];
  // Whether or not ImageLayer should shuffle the list of files at every epoch.
  // 是否在每个回合都混排图片，默认否
  optional bool shuffle = 8 [default = false];
  // It will also resize images if new_height or new_width are not zero.
  // 若以下2个值不为0，则将图片缩放为下面的形状
  optional uint32 new_height = 9 [default = 0];
  optional uint32 new_width = 10 [default = 0];
  // Specify if the images are color or gray指明是彩色还是灰度图
  optional bool is_color = 11 [default = true];
  // DEPRECATED. See TransformationParameter. For data pre-processing, we can do
  // simple scaling and subtracting the data mean, if provided. Note that the
  // mean subtraction is always carried out before scaling.
  // 旧版--图片预处理参数，新版用TransformationParameter
  optional float scale = 2 [default = 1];
  optional string mean_file = 3;
  // DEPRECATED. See TransformationParameter. Specify if we would like to randomly
  // crop an image.
  optional uint32 crop_size = 5 [default = 0];
  // DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror
  // data.
  optional bool mirror = 6 [default = false];
  optional string root_folder = 12 [default = ""];
}
//信息增益损失参数
message InfogainLossParameter {
  // Specify the infogain matrix source.
  optional string source = 1;
}
//内积参数
message InnerProductParameter {
  optional uint32 num_output = 1; // The number of outputs for the layer
  optional bool bias_term = 2 [default = true]; // 是否带偏置项
  optional FillerParameter weight_filler = 3; // The filler for the weight
  optional FillerParameter bias_filler = 4; // The filler for the bias

  // The first axis to be lumped into a single inner product computation;
  // all preceding axes are retained in the output.
  // May be negative to index from the end (e.g., -1 for the last axis).
  optional int32 axis = 5 [default = 1];
  // Specify whether to transpose the weight matrix or not.
  // If transpose == true, any operations will be performed on the transpose
  // of the weight matrix. The weight matrix itself is not going to be transposed
  // but rather the transfer flag of operations will be toggled accordingly.
  optional bool transpose = 6 [default = false];
}
//输入参数
message InputParameter {
  // This layer produces N >= 1 top blob(s) to be assigned manually.
  // Define N shapes to set a shape for each top.
  // Define 1 shape to set the same shape for every top.
  // Define no shape to defer to reshaping manually.
  // 此层管理输入(top)blobs，当输入blob个数N≥1，可使其自动分配。
  // 设定N个shapes为N个输入blob；设定1个shape使得全部输入blob形状相同；
  // 不设定，可手动调整。
  // 可查看.\models\bvlc_reference_caffenet\deploy.prototxt中指定1个shape
  repeated BlobShape shape = 1;
}

// LogLayer的参数
message LogParameter {
  // LogLayer computes outputs y = log_base(shift + scale * x), for base > 0.
  // Or if base is set to the default (-1), base is set to e,
  // so y = ln(shift + scale * x) = log_e(shift + scale * x)
  optional float base = 1 [default = -1.0];
  optional float scale = 2 [default = 1.0];
  optional float shift = 3 [default = 0.0];
}

// LRNLayer层参数
message LRNParameter {
  optional uint32 local_size = 1 [default = 5];
  optional float alpha = 2 [default = 1.];
  optional float beta = 3 [default = 0.75];
  enum NormRegion {
    ACROSS_CHANNELS = 0;
    WITHIN_CHANNEL = 1;
  }
  optional NormRegion norm_region = 4 [default = ACROSS_CHANNELS];
  optional float k = 5 [default = 1.];
  enum Engine {
    DEFAULT = 0;
    CAFFE = 1;
    CUDNN = 2;
  }
  optional Engine engine = 6 [default = DEFAULT];
}

//数据内存占用参数
message MemoryDataParameter {
  optional uint32 batch_size = 1;
  optional uint32 channels = 2;
  optional uint32 height = 3;
  optional uint32 width = 4;
}
//MVN参数{均值，方差，跨通道}(mean-varance-normalization)
message MVNParameter {
  // This parameter can be set to false to normalize mean only
  // 设定为false时仅归一化均值，否则包括方差
  optional bool normalize_variance = 1 [default = true];

  // This parameter can be set to true to perform DNN-like MVN
  // 执行跨通道归一化,类似于DNN的MVN；默认否，只执行Spatial内归一化。
  optional bool across_channels = 2 [default = false];

  // Epsilon for not dividing by zero while normalizing variance
  // 防止除0的极小数
  optional float eps = 3 [default = 1e-9];
}

//？？
message ParameterParameter {
  optional BlobShape shape = 1;
}
//池化层参数
message PoolingParameter {
  enum PoolMethod {
    MAX = 0;
    AVE = 1;
    STOCHASTIC = 2;
  }
  optional PoolMethod pool = 1 [default = MAX]; // The pooling method
  // Pad, kernel size, and stride are all given as a single value for equal
  // dimensions in height and width or as Y, X pairs.
  optional uint32 pad = 4 [default = 0]; // The padding size (equal in Y, X)
  optional uint32 pad_h = 9 [default = 0]; // The padding height
  optional uint32 pad_w = 10 [default = 0]; // The padding width
  optional uint32 kernel_size = 2; // The kernel size (square)
  optional uint32 kernel_h = 5; // The kernel height
  optional uint32 kernel_w = 6; // The kernel width
  optional uint32 stride = 3 [default = 1]; // The stride (equal in Y, X)
  optional uint32 stride_h = 7; // The stride height
  optional uint32 stride_w = 8; // The stride width
  enum Engine {
    DEFAULT = 0;
    CAFFE = 1;
    CUDNN = 2;
  }
  optional Engine engine = 11 [default = DEFAULT];
  // If global_pooling then it will pool over the size of the bottom by doing
  // kernel_h = bottom->height and kernel_w = bottom->width
  optional bool global_pooling = 12 [default = false];
}

message PowerParameter {
  // PowerLayer computes outputs y = (shift + scale * x) ^ power.
  optional float power = 1 [default = 1.0];
  optional float scale = 2 [default = 1.0];
  optional float shift = 3 [default = 0.0];
}
//Python参数
message PythonParameter {
  optional string module = 1;
  optional string layer = 2;
  // This value is set to the attribute `param_str` of the `PythonLayer` object
  // in Python before calling the `setup()` method. This could be a number,
  // string, dictionary in Python dict format, JSON, etc. You may parse this
  // string in `setup` method and use it in `forward` and `backward`.
  optional string param_str = 3 [default = ''];
  // Whether this PythonLayer is shared among worker solvers during data parallelism.
  // If true, each worker solver sequentially run forward from this layer.
  // This value should be set true if you are using it as a data layer.
  optional bool share_in_parallel = 4 [default = false];
}

// RecurrentLayer参数
message RecurrentParameter {
  // 输出表示的维度必须是非0的
  optional uint32 num_output = 1 [default = 0];

  optional FillerParameter weight_filler = 2; //weight权值参数
  optional FillerParameter bias_filler = 3;   //bias偏置参数

  // Whether to enable displaying debug_info in the unrolled recurrent net.
  // 在展开RCNN时是否打印deuginfo
  optional bool debug_info = 4 [default = false];

  // Whether to add as additional inputs (bottoms) the initial hidden state
  // blobs, and add as additional outputs (tops) the final timestep hidden state
  // blobs.  The number of additional bottom/top blobs required depends on the
  // recurrent architecture -- e.g., 1 for RNNs, 2 for LSTMs.
  // 是否添加初始化的隐藏blobs作为额外输入(bottoms)，以及添加最终的timestep隐
  // 藏blobs作为额外输出（tops）。
  optional bool expose_hidden = 5 [default = false];
}

// ReductionLayer参数
message ReductionParameter {
  enum ReductionOp {
    SUM = 1;
    ASUM = 2;
    SUMSQ = 3;
    MEAN = 4;
  }

  optional ReductionOp operation = 1 [default = SUM]; // reduction operation

  // The first axis to reduce to a scalar -- may be negative to index from the
  // end (e.g., -1 for the last axis).
  // (Currently, only reduction along ALL "tail" axes is supported; reduction
  // of axis M through N, where N < num_axes - 1, is unsupported.)
  // Suppose we have an n-axis bottom Blob with shape:
  //     (d0, d1, d2, ..., d(m-1), dm, d(m+1), ..., d(n-1)).
  // If axis == m, the output Blob will have shape
  //     (d0, d1, d2, ..., d(m-1)),
  // and the ReductionOp operation is performed (d0 * d1 * d2 * ... * d(m-1))
  // times, each including (dm * d(m+1) * ... * d(n-1)) individual data.
  // If axis == 0 (the default), the output Blob always has the empty shape
  // (count 1), performing reduction across the entire input --
  // often useful for creating new loss functions.
  optional int32 axis = 2 [default = 0];

  optional float coeff = 3 [default = 1.0]; // coefficient for output
}

// ReLULayer参数
message ReLUParameter {
  // 允许非0斜率可以加速优化:
  // Maas, A. L., Hannun, A. Y., & Ng, A. Y. (2013). Rectifier nonlinearities
  // improve neural network acoustic models. In ICML Workshop on Deep Learning
  // for Audio, Speech, and Language Processing.
  optional float negative_slope = 1 [default = 0];
  enum Engine {
    DEFAULT = 0;
    CAFFE = 1;
    CUDNN = 2;
  }
  optional Engine engine = 2 [default = DEFAULT];
}

message ReshapeParameter {
  // Specify the output dimensions. If some of the dimensions are set to 0,
  // the corresponding dimension from the bottom layer is used (unchanged).
  // Exactly one dimension may be set to -1, in which case its value is
  // inferred from the count of the bottom blob and the remaining dimensions.
  // For example, suppose we want to reshape a 2D blob "input" with shape 2 x 8:
  //
  //   layer {
  //     type: "Reshape" bottom: "input" top: "output"
  //     reshape_param { ... }
  //   }
  //
  // If "input" is 2D with shape 2 x 8, then the following reshape_param
  // specifications are all equivalent, producing a 3D blob "output" with shape
  // 2 x 2 x 4:
  //
  //   reshape_param { shape { dim:  2  dim: 2  dim:  4 } }
  //   reshape_param { shape { dim:  0  dim: 2  dim:  4 } }
  //   reshape_param { shape { dim:  0  dim: 2  dim: -1 } }
  //   reshape_param { shape { dim:  0  dim:-1  dim:  4 } }
  //
  optional BlobShape shape = 1;

  // axis and num_axes control the portion of the bottom blob's shape that are
  // replaced by (included in) the reshape. By default (axis == 0 and
  // num_axes == -1), the entire bottom blob shape is included in the reshape,
  // and hence the shape field must specify the entire output shape.
  //
  // axis may be non-zero to retain some portion of the beginning of the input
  // shape (and may be negative to index from the end; e.g., -1 to begin the
  // reshape after the last axis, including nothing in the reshape,
  // -2 to include only the last axis, etc.).
  //
  // For example, suppose "input" is a 2D blob with shape 2 x 8.
  // Then the following ReshapeLayer specifications are all equivalent,
  // producing a blob "output" with shape 2 x 2 x 4:
  //
  //   reshape_param { shape { dim: 2  dim: 2  dim: 4 } }
  //   reshape_param { shape { dim: 2  dim: 4 } axis:  1 }
  //   reshape_param { shape { dim: 2  dim: 4 } axis: -3 }
  //
  // num_axes specifies the extent of the reshape.
  // If num_axes >= 0 (and axis >= 0), the reshape will be performed only on
  // input axes in the range [axis, axis+num_axes].
  // num_axes may also be -1, the default, to include all remaining axes
  // (starting from axis).
  //
  // For example, suppose "input" is a 2D blob with shape 2 x 8.
  // Then the following ReshapeLayer specifications are equivalent,
  // producing a blob "output" with shape 1 x 2 x 8.
  //
  //   reshape_param { shape { dim:  1  dim: 2  dim:  8 } }
  //   reshape_param { shape { dim:  1  dim: 2  }  num_axes: 1 }
  //   reshape_param { shape { dim:  1  }  num_axes: 0 }
  //
  // On the other hand, these would produce output blob shape 2 x 1 x 8:
  //
  //   reshape_param { shape { dim: 2  dim: 1  dim: 8  }  }
  //   reshape_param { shape { dim: 1 }  axis: 1  num_axes: 0 }
  //
  optional int32 axis = 2 [default = 0];
  optional int32 num_axes = 3 [default = -1];
}

// ROIPoolingLayer参数
message ROIPoolingParameter {
  // Pad, kernel size, and stride are all given as a single value for equal
  // dimensions in height and width or as Y, X pairs.
  optional uint32 pooled_h = 1 [default = 0]; // The pooled output height
  optional uint32 pooled_w = 2 [default = 0]; // The pooled output width
  // Multiplicative spatial scale factor to translate ROI coords from their
  // input scale to the scale used when pooling
  optional float spatial_scale = 3 [default = 1];
}
//ScaleParameter参数
message ScaleParameter {
  // The first axis of bottom[0] (the first input Blob) along which to apply
  // bottom[1] (the second input Blob).  May be negative to index from the end
  // (e.g., -1 for the last axis).
  // ？？？？？？？？？？？？？？？？？？？？？？？？？？？？？？？
  // 第一个输入Blob的首axis，被应用到第二个输入Blob。但第2个Blob的形状可能不同
  // For example, if bottom[0] is 4D with shape 100x3x40x60, the output
  // top[0] will have the same shape, and bottom[1] may have any of the
  // following shapes (for the given value of axis):
  //    (axis == 0 == -4) 100; 100x3; 100x3x40; 100x3x40x60
  //    (axis == 1 == -3)          3;     3x40;     3x40x60
  //    (axis == 2 == -2)                   40;       40x60
  //    (axis == 3 == -1)                                60
  // Furthermore,bottom[1]may have the empty shape (regardless of the value of
  // "axis") -- a scalar multiplier.
  optional int32 axis = 1 [default = 1];

  // (num_axes is ignored unless just one bottom is given and the scale is
  // a learned parameter of the layer.  Otherwise, num_axes is determined by the
  // number of axes by the second bottom.)
  // The number of axes of the input (bottom[0]) covered by the scale
  // parameter, or -1 to cover all axes of bottom[0] starting from `axis`.
  // Set num_axes := 0, to multiply with a zero-axis Blob: a scalar.
  optional int32 num_axes = 2 [default = 1];

  // (filler is ignored unless just one bottom is given and the scale is
  // a learned parameter of the layer.)
  // The initialization for the learned scale parameter.
  // Default is the unit (1) initialization, resulting in the ScaleLayer
  // initially performing the identity operation.
  optional FillerParameter filler = 3;

  // Whether to also learn a bias (equivalent to a ScaleLayer+BiasLayer, but
  // may be more efficient).  Initialized with bias_filler (defaults to 0).
  optional bool bias_term = 4 [default = false];
  optional FillerParameter bias_filler = 5;
}
//SigmoidParameter参数
message SigmoidParameter {
  enum Engine {
    DEFAULT = 0;
    CAFFE = 1;
    CUDNN = 2;
  }
  optional Engine engine = 1 [default = DEFAULT];
}
//SliceParameter参数
message SliceParameter {
  // The axis along which to slice -- may be negative to index from the end
  // (e.g., -1 for the last axis).
  // By default, SliceLayer concatenates blobs along the "channels" axis (1).
  optional int32 axis = 3 [default = 1];
  repeated uint32 slice_point = 2;

  // DEPRECATED: alias for "axis" -- does not support negative indexing.
  optional uint32 slice_dim = 1 [default = 1];
}
//SmoothL1LossParameter参数
message SmoothL1LossParameter {
  // SmoothL1Loss(x) =
  //   0.5 * (sigma * x) ** 2    -- if x < 1.0 / sigma / sigma
  //   |x| - 0.5 / sigma / sigma -- otherwise
  optional float sigma = 1 [default = 1];
}

//SoftmaxLayer, SoftmaxWithLossLayer的参数
message SoftmaxParameter {
  enum Engine {
    DEFAULT = 0;
    CAFFE = 1;
    CUDNN = 2;
  }
  optional Engine engine = 1 [default = DEFAULT];

  // The axis along which to perform the softmax -- may be negative to index
  // from the end (e.g., -1 for the last axis).
  // Any other axes will be evaluated as independent softmaxes.
  // 沿着哪一个轴运用softmax，该轴上必须是相互独立的分量。
  // eg.预测时对类标签运用，计算损失时对每个类的对数损失运用。
  optional int32 axis = 2 [default = 1];
}
//TanHParameter参数
message TanHParameter {
  enum Engine {
    DEFAULT = 0;
    CAFFE = 1;
    CUDNN = 2;
  }
  optional Engine engine = 1 [default = DEFAULT];
}

// TileLayer参数
message TileParameter {
  // The index of the axis to tile.
  optional int32 axis = 1 [default = 1];

  // The number of copies (tiles) of the blob to output.
  optional int32 tiles = 2;
}

// ThresholdLayer参数
message ThresholdParameter {
  optional float threshold = 1 [default = 0]; // Strictly positive values
}

// MILLayer参数
message MILParameter {
  enum MILType {
    MAX = 0;
    NOR = 1;
  }
  optional MILType type = 1 [default = MAX]; // The MIL method
}

//窗口数据参数：专用于目标检测或分割
message WindowDataParameter {
  // Specify the data source.指定数据源
  optional string source = 1;
  // 数据预处理：尺度缩放，去均值等。去均值应在缩放前执行。
  optional float scale = 2 [default = 1];
  optional string mean_file = 3;
  // 指定批处理的数据量
  optional uint32 batch_size = 4;
  // 是否随机裁剪
  optional uint32 crop_size = 5 [default = 0];
  // 是否镜像变换
  optional bool mirror = 6 [default = false];
  // Foreground (object) overlap threshold 前景目标重合阈值
  optional float fg_threshold = 7 [default = 0.5];
  // Background (non-object) overlap threshold背景重合阈值
  optional float bg_threshold = 8 [default = 0.5];
  // Fraction of batch that should be foreground objects
  // 前景目标在batch中的比例
  optional float fg_fraction = 9 [default = 0.25];
  // Amount of contextual padding to add around a window
  // (used only by the window_data_layer)
  // 窗口周边需要添加的上下文padding
  optional uint32 context_pad = 10 [default = 0];
  // Mode for cropping out a detection window
  // warp: cropped window is warped to a fixed size and aspect ratio
  // square: the tightest square around the window is cropped
  // mode:裁剪出一个检测窗口的模式
  // warp:裁剪窗口被扭曲为某个固定尺寸和形状
  // square:裁剪窗口周边最紧？的方框
  optional string crop_mode = 11 [default = "warp"];
  // cache_images: will load all images in memory for faster access
  //将全部图像(裁剪得到的小图像)放入内存以便快速存取
  optional bool cache_images = 12 [default = false];
  // append root_folder to locate images
  // 添加根文件夹以定位文件
  optional string root_folder = 13 [default = ""];
}
//MILDataParameter参数
message MILDataParameter {
  // Specify the data source.
  optional string source = 1;

  // Number of scales for each image
  optional uint32 num_scales = 2 [default = 1];

  // Side length ratio between neighbouring scales
  optional float scale_factor = 6 [default = 1];

  // Number of channels in the image
  optional uint32 channels = 4 [default = 3];

  // Specify the number of images per batch
  optional uint32 images_per_batch = 3;
  // Specify the number of classes
  optional uint32 n_classes = 5;
  // specify the box_dir and label_dir
  optional string label_file = 7;

  // Root directory which contains all the images
  optional string root_dir = 11;
  // Extention for the file
  optional string ext = 12;

  // To randomize or not
  optional bool randomize = 13 [default = true];
}

//SPP参数,源于论文SPPNet
message SPPParameter {
  enum PoolMethod {
    MAX = 0;
    AVE = 1;
    STOCHASTIC = 2;
  } //池化方法，获得金字塔的方法，最大/平均/随机
  optional uint32 pyramid_height = 1; //金字塔高度
  optional PoolMethod pool = 2 [default = MAX]; // The pooling method
  enum Engine {
    DEFAULT = 0;
    CAFFE = 1;
    CUDNN = 2;
  }
  optional Engine engine = 6 [default = DEFAULT];
}

// DEPRECATED: use LayerParameter.
// 旧版：使用层参数。 V1可能是第一版version1的意思
message V1LayerParameter {
  repeated string bottom = 2;  //输入
  repeated string top = 3;     //输出
  optional string name = 4;    //层名称
  repeated NetStateRule include = 32; //运行时状态：包含
  repeated NetStateRule exclude = 33; //运行时状态：不包含
  enum LayerType {             //层类型
    NONE = 0;
    ABSVAL = 35;
    ACCURACY = 1;
    ARGMAX = 30;
    BNLL = 2;
    CONCAT = 3;
    CONTRASTIVE_LOSS = 37;
    CONVOLUTION = 4;
    DATA = 5;
    DECONVOLUTION = 39;
    DROPOUT = 6;
    DUMMY_DATA = 32;
    EUCLIDEAN_LOSS = 7;
    ELTWISE = 25;
    EXP = 38;
    FLATTEN = 8;
    HDF5_DATA = 9;
    HDF5_OUTPUT = 10;
    HINGE_LOSS = 28;
    IM2COL = 11;
    IMAGE_DATA = 12;
    INFOGAIN_LOSS = 13;
    INNER_PRODUCT = 14;
    LRN = 15;
    MEMORY_DATA = 29;
    MULTINOMIAL_LOGISTIC_LOSS = 16;
    MVN = 34;
    POOLING = 17;
    POWER = 26;
    RELU = 18;
    SIGMOID = 19;
    SIGMOID_CROSS_ENTROPY_LOSS = 27;
    SILENCE = 36;
    SOFTMAX = 20;
    SOFTMAX_LOSS = 21;
    SPLIT = 22;
    SLICE = 33;
    TANH = 23;
    WINDOW_DATA = 24;
    THRESHOLD = 31;
  }
  optional LayerType type = 5;
  repeated BlobProto blobs = 6;
  repeated string param = 1001;
  repeated DimCheckMode blob_share_mode = 1002;
  enum DimCheckMode {
    STRICT = 0;
    PERMISSIVE = 1;
  }
  repeated float blobs_lr = 7;
  repeated float weight_decay = 8;
  repeated float loss_weight = 35;
  optional AccuracyParameter accuracy_param = 27;
  optional ArgMaxParameter argmax_param = 23;
  optional ConcatParameter concat_param = 9;
  optional ContrastiveLossParameter contrastive_loss_param = 40;
  optional ConvolutionParameter convolution_param = 10;
  optional DataParameter data_param = 11;
  optional DropoutParameter dropout_param = 12;
  optional DummyDataParameter dummy_data_param = 26;
  optional EltwiseParameter eltwise_param = 24;
  optional ExpParameter exp_param = 41;
  optional HDF5DataParameter hdf5_data_param = 13;
  optional HDF5OutputParameter hdf5_output_param = 14;
  optional HingeLossParameter hinge_loss_param = 29;
  optional ImageDataParameter image_data_param = 15;
  optional InfogainLossParameter infogain_loss_param = 16;
  optional InnerProductParameter inner_product_param = 17;
  optional LRNParameter lrn_param = 18;
  optional MemoryDataParameter memory_data_param = 22;
  optional MVNParameter mvn_param = 34;
  optional PoolingParameter pooling_param = 19;
  optional PowerParameter power_param = 21;
  optional ReLUParameter relu_param = 30;
  optional SigmoidParameter sigmoid_param = 38;
  optional SoftmaxParameter softmax_param = 39;
  optional SliceParameter slice_param = 31;
  optional TanHParameter tanh_param = 37;
  optional ThresholdParameter threshold_param = 25;
  optional WindowDataParameter window_data_param = 20;
  optional TransformationParameter transform_param = 36;
  optional LossParameter loss_param = 42;
  optional V0LayerParameter layer = 1;
}

// DEPRECATED: V0LayerParameter is the old way of specifying layer parameters
// in Caffe.  We keep this message type around for legacy support.
// 旧版本：V0LayerParameter version-0版
message V0LayerParameter {
  optional string name = 1; // the layer name
  optional string type = 2; // the string to specify the layer type

  // Parameters to specify layers with inner products.
  optional uint32 num_output = 3; // The number of outputs for the layer
  optional bool biasterm = 4 [default = true]; // whether to have bias terms
  optional FillerParameter weight_filler = 5; // The filler for the weight
  optional FillerParameter bias_filler = 6; // The filler for the bias

  optional uint32 pad = 7 [default = 0]; // The padding size
  optional uint32 kernelsize = 8; // The kernel size
  optional uint32 group = 9 [default = 1]; // The group size for group conv
  optional uint32 stride = 10 [default = 1]; // The stride
  enum PoolMethod {
    MAX = 0;
    AVE = 1;
    STOCHASTIC = 2;
  }
  optional PoolMethod pool = 11 [default = MAX]; // The pooling method
  optional float dropout_ratio = 12 [default = 0.5]; // dropout ratio

  optional uint32 local_size = 13 [default = 5]; // for local response norm
  optional float alpha = 14 [default = 1.]; // for local response norm
  optional float beta = 15 [default = 0.75]; // for local response norm
  optional float k = 22 [default = 1.];

  // For data layers, specify the data source
  optional string source = 16;
  // For data pre-processing, we can do simple scaling and subtracting the
  // data mean, if provided. Note that the mean subtraction is always carried
  // out before scaling.
  optional float scale = 17 [default = 1];
  optional string meanfile = 18;
  // For data layers, specify the batch size.
  optional uint32 batchsize = 19;
  // For data layers, specify if we would like to randomly crop an image.
  optional uint32 cropsize = 20 [default = 0];
  // For data layers, specify if we want to randomly mirror data.
  optional bool mirror = 21 [default = false];

  // The blobs containing the numeric parameters of the layer
  repeated BlobProto blobs = 50;
  // The ratio that is multiplied on the global learning rate. If you want to
  // set the learning ratio for one blob, you need to set it for all blobs.
  repeated float blobs_lr = 51;
  // The weight decay that is multiplied on the global weight decay.
  repeated float weight_decay = 52;

  // The rand_skip variable is for the data layer to skip a few data points
  // to avoid all asynchronous sgd clients to start at the same point. The skip
  // point would be set as rand_skip * rand(0,1). Note that rand_skip should not
  // be larger than the number of keys in the database.
  optional uint32 rand_skip = 53 [default = 0];

  // Fields related to detection (det_*)
  // foreground (object) overlap threshold
  optional float det_fg_threshold = 54 [default = 0.5];
  // background (non-object) overlap threshold
  optional float det_bg_threshold = 55 [default = 0.5];
  // Fraction of batch that should be foreground objects
  optional float det_fg_fraction = 56 [default = 0.25];

  // optional bool OBSOLETE_can_clobber = 57 [default = true];

  // Amount of contextual padding to add around a window
  // (used only by the window_data_layer)
  optional uint32 det_context_pad = 58 [default = 0];

  // Mode for cropping out a detection window
  // warp: cropped window is warped to a fixed size and aspect ratio
  // square: the tightest square around the window is cropped
  optional string det_crop_mode = 59 [default = "warp"];

  // For ReshapeLayer, one needs to specify the new dimensions.
  optional int32 new_num = 60 [default = 0];
  optional int32 new_channels = 61 [default = 0];
  optional int32 new_height = 62 [default = 0];
  optional int32 new_width = 63 [default = 0];

  // Whether or not ImageLayer should shuffle the list of files at every epoch.
  // It will also resize images if new_height or new_width are not zero.
  optional bool shuffle_images = 64 [default = false];

  // For ConcatLayer, one needs to specify the dimension for concatenation, and
  // the other dimensions must be the same for all the bottom blobs.
  // By default it will concatenate blobs along the channels dimension.
  optional uint32 concat_dim = 65 [default = 1];

  optional HDF5OutputParameter hdf5_output_param = 1001;
}
//PReLUParameter,源于论文
message PReLUParameter {
  // Parametric ReLU described in K. He et al, Delving Deep into Rectifiers:
  // Surpassing Human-Level Performance on ImageNet Classification, 2015.

  // Initial value of a_i. Default is a_i=0.25 for all i.
  optional FillerParameter filler = 1;
  // Whether or not slope paramters are shared across channels.
  optional bool channel_shared = 2 [default = false];
}