【caffe Layer】代码中文注释

src/caffe/proto/caffe.proto 中LayerParameter部分

 // NOTE
 // Update the next available ID when you add a new LayerParameter field.
 // 如果增加一个新的LayerParameter域，需要更新下一个可用的ID
 // LayerParameter next available layer-specific ID: 147 (last added: recurrent_param)
 message LayerParameter {
   optional string name = ; // the layer name 名称
   optional string type = ; // the layer type 类型
   repeated string bottom = ; // the name of each bottom blob 输入的Bottom Blob的名称
   repeated string top = ; // the name of each top blob 输出的Top Blob名称

   // The train / test phase for computation.当前阶段TRAIN或TEST
   optional Phase phase = ;

   // The amount of weight to assign each top blob in the objective.
   // Each layer assigns a default value, usually of either 0 or 1,
   // to each top blob.
   // 为每个输出Top Blob分配对损失函数的权重，每个Layer都有默认值，0表示不参与计算，1表示参与损失函数计算
   repeated float loss_weight = ;

   // Specifies training parameters (multipliers on global learning constants,
   // and the name and other settings used for weight sharing).
   // 指定训练参数（例如相对全局学习常熟的缩放因子，以及用于权值共享的名称或其他设置）
   repeated ParamSpec param = ;

   // The blobs containing the numeric parameters of the layer.
   // 承载该曾数值参数的Blob
   repeated BlobProto blobs = ;

   // 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.
   // 是否对Bottom Blob进行反向传播过程。该字段维度应与Bottom Blob个数一致。
   // The size must be either 0 or equal to the number of bottoms.
   repeated bool propagate_down = ;

   // Rules controlling whether and when a layer is included in the network,
   // based on the current NetState.  You may specify a non-zero number of rules
   // to include OR exclude, but not both.  If no include or exclude rules are
   // specified, the layer is always included.  If the current NetState meets
   // ANY (i.e., one or more) of the specified rules, the layer is
   // included/excluded.
   // 控制某个层在某个时刻是否包含在网络中（基于当前的NetState）
   // 可以为include或exclude指定非零值（不能同时）
   // 如果没有规则，该层一直包含在网络中
   // 如果当前的NetState满足一定条件，那么该层被包含或被排斥
   repeated NetStateRule include = ;
   repeated NetStateRule exclude = ;

   // Parameters for data pre-processing. 数据预处理参数
   optional TransformationParameter transform_param = ;

   // Parameters shared by loss layers. 所有损失层共享的参数
   optional LossParameter loss_param = ;

   // Layer type-specific parameters.特定类型层参数
   // 注意：一些层实现时可能有多于一种计算引擎，这些层通过选择引擎类型和引擎参数来实现。
   // 默认引擎是在编译阶段由引擎开关设置的
   // 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.
   optional AccuracyParameter accuracy_param = ;
   optional ArgMaxParameter argmax_param = ;
   optional BatchNormParameter batch_norm_param = ;
   optional BiasParameter bias_param = ;
   optional ConcatParameter concat_param = ;
   optional ContrastiveLossParameter contrastive_loss_param = ;
   optional ConvolutionParameter convolution_param = ;
   optional CropParameter crop_param = ;
   optional DataParameter data_param = ;
   optional DropoutParameter dropout_param = ;
   optional DummyDataParameter dummy_data_param = ;
   optional EltwiseParameter eltwise_param = ;
   optional ELUParameter elu_param = ;
   optional EmbedParameter embed_param = ;
   optional ExpParameter exp_param = ;
   optional FlattenParameter flatten_param = ;
   optional HDF5DataParameter hdf5_data_param = ;
   optional HDF5OutputParameter hdf5_output_param = ;
   optional HingeLossParameter hinge_loss_param = ;
   optional ImageDataParameter image_data_param = ;
   optional InfogainLossParameter infogain_loss_param = ;
   optional InnerProductParameter inner_product_param = ;
   optional InputParameter input_param = ;
   optional LogParameter log_param = ;
   optional LRNParameter lrn_param = ;
   optional MemoryDataParameter memory_data_param = ;
   optional MVNParameter mvn_param = ;
   optional ParameterParameter parameter_param = ;
   optional PoolingParameter pooling_param = ;
   optional PowerParameter power_param = ;
   optional PReLUParameter prelu_param = ;
   optional PythonParameter python_param = ;
   optional RecurrentParameter recurrent_param = ;
   optional ReductionParameter reduction_param = ;
   optional ReLUParameter relu_param = ;
   optional ReshapeParameter reshape_param = ;
   optional ScaleParameter scale_param = ;
   optional SigmoidParameter sigmoid_param = ;
   optional SoftmaxParameter softmax_param = ;
   optional SPPParameter spp_param = ;
   optional SliceParameter slice_param = ;
   optional TanHParameter tanh_param = ;
   optional ThresholdParameter threshold_param = ;
   optional TileParameter tile_param = ;
   optional WindowDataParameter window_data_param = ;
 }

include/caffe/layer.hpp

 #ifndef CAFFE_LAYER_H_
 #define CAFFE_LAYER_H_

 #include <algorithm>
 #include <string>
 #include <vector>

 #include "caffe/blob.hpp"
 #include "caffe/common.hpp"
 #include "caffe/layer_factory.hpp"
 #include "caffe/proto/caffe.pb.h"
 #include "caffe/util/math_functions.hpp"

 /**
  Forward declare boost::thread instead of including boost/thread.hpp
  to avoid a boost/NVCC issues (#1009, #1010) on OSX.
  */
 namespace boost { class mutex; }

 namespace caffe {

 /**
  * @brief An interface for the units of computation which can be composed into a
  *        Net.
  *
  * Layer%s must implement a Forward function, in which they take their input
  * (bottom) Blob%s (if any) and compute their output Blob%s (if any).
  * They may also implement a Backward function, in which they compute the error
  * gradients with respect to their input Blob%s, given the error gradients with
  * their output Blob%s.
  */
 template <typename Dtype>
 class Layer {
  public:
   /**
    * You should not implement your own constructor. Any set up code should go
    * to SetUp(), where the dimensions of the bottom blobs are provided to the
    * layer.
    */
    // 显式构造函数，从LayerParameter中加载配置
   explicit Layer(const LayerParameter& param)
     : layer_param_(param) {
       // Set phase and copy blobs (if there are any).
       phase_ = param.phase();//设置当前阶段（训练或预测）
       if (layer_param_.blobs_size() > ) {
         blobs_.resize(layer_param_.blobs_size());
         //按照layer_param_设置本身Blob对象个数，并依次把每个Blob对象尺寸调整为与layer_param_中Blob尺寸一致
         for (int i = ; i < layer_param_.blobs_size(); ++i) {
           blobs_[i].reset(new Blob<Dtype>());
           blobs_[i]->FromProto(layer_param_.blobs(i));
         }
       }
     }
   virtual ~Layer() {}

   /**
    * @brief Implements common layer setup functionality.
    *
    * @param bottom the preshaped input blobs
    * @param top
    *     the allocated but unshaped output blobs, to be shaped by Reshape
    *
    * Checks that the number of bottom and top blobs is correct.
    * Calls LayerSetUp to do special layer setup for individual layer types,
    * followed by Reshape to set up sizes of top blobs and internal buffers.
    * Sets up the loss weight multiplier blobs for any non-zero loss weights.
    * This method may not be overridden.
    */
    //配置函数，实现常用层配置借口，不可被覆盖
   void SetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
     CheckBlobCounts(bottom, top);//检查blob
     LayerSetUp(bottom, top);     //与层类型相关的配置过程
     Reshape(bottom, top);        //对TopBlob进行变形
     SetLossWeights(top);         //设置损失权值因子Blob
   }

   /**
    * @brief Does layer-specific setup: your layer should implement this function
    *        as well as Reshape.
    *
    * @param bottom
    *     the preshaped input blobs, whose data fields store the input data for
    *     this layer
    * @param top
    *     the allocated but unshaped output blobs
    *
    * This method should do one-time layer specific setup. This includes reading
    * and processing relevent parameters from the <code>layer_param_</code>.
    * Setting up the shapes of top blobs and internal buffers should be done in
    * <code>Reshape</code>, which will be called before the forward pass to
    * adjust the top blob sizes.
    */
    //层配置虚函数，做特定类型层相关配置，由该类型层自己实现
   virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {}

   /**
    * @brief Adjust the shapes of top blobs and internal buffers to accommodate
    *        the shapes of the bottom blobs.
    *
    * @param bottom the input blobs, with the requested input shapes
    * @param top the top blobs, which should be reshaped as needed
    *
    * This method should reshape top blobs as needed according to the shapes
    * of the bottom (input) blobs, as well as reshaping any internal buffers
    * and making any other necessary adjustments so that the layer can
    * accommodate the bottom blobs.
    */
    //纯虚函数。变形函数，修改Top Blob以及内部Blob缓冲区形状
   virtual void Reshape(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) = ;

   /**
    * @brief Given the bottom blobs, compute the top blobs and the loss.
    *
    * @param bottom
    *     the input blobs, whose data fields store the input data for this layer
    * @param top
    *     the preshaped output blobs, whose data fields will store this layers'
    *     outputs
    * \return The total loss from the layer.
    *
    * The Forward wrapper calls the relevant device wrapper function
    * (Forward_cpu or Forward_gpu) to compute the top blob values given the
    * bottom blobs.  If the layer has any non-zero loss_weights, the wrapper
    * then computes and returns the loss.
    *
    * Your layer should implement Forward_cpu and (optionally) Forward_gpu.
    */
    // 前向传播函数
    // 给定Bottom Blob，计算TopBlob和loss，返回值为当前层的loss
    // 该函数会调用相应设备包装函数，如Forward_cpu or Forward_gpu来实现真正计算过程
    // 如果该层有非零loss权重参数，包装函数会计算并返回loss
    // 派生类应该实现Forward_cpu，Forward_gpu（可选）
   inline Dtype Forward(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top);

   /**
    * @brief Given the top blob error gradients, compute the bottom blob error
    *        gradients.
    *
    * @param top
    *     the output blobs, whose diff fields store the gradient of the error
    *     with respect to themselves
    * @param propagate_down
    *     a vector with equal length to bottom, with each index indicating
    *     whether to propagate the error gradients down to the bottom blob at
    *     the corresponding index
    * @param bottom
    *     the input blobs, whose diff fields will store the gradient of the error
    *     with respect to themselves after Backward is run
    *
    * The Backward wrapper calls the relevant device wrapper function
    * (Backward_cpu or Backward_gpu) to compute the bottom blob diffs given the
    * top blob diffs.
    *
    * Your layer should implement Backward_cpu and (optionally) Backward_gpu.
    */
    //反向传播函数
    //给定输出的Top Blob误差梯度，计算输入的Bottom Blob的误差梯度
    //propagate_down为多路开关，与Bottom Blob矢量维度相同，每个值表示是否将误差梯度传递到对应的Bottom Blob
    //该函数会调用相应设备包装函数，如Backward_cpu and (可选) Backward_gpu实现计算过程，由派生类负责实现
   inline void Backward(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down,
       const vector<Blob<Dtype>*>& bottom);

   /**
    * @brief Returns the vector of learnable parameter blobs.
    */
   vector<shared_ptr<Blob<Dtype> > >& blobs() {
     return blobs_;//返回Layer内部可训练的权值、偏置项Blob向量
   }

   /**
    * @brief Returns the layer parameter.
    */
    //返回Layer初始化参数（由ProtoBuffer提供）
   const LayerParameter& layer_param() const { return layer_param_; }

   /**
    * @brief Writes the layer parameter to a protocol buffer
    */
    //将Layer初始化参数写入ProtoBuffer缓冲区
   virtual void ToProto(LayerParameter* param, bool write_diff = false);

   /**
    * @brief Returns the scalar loss associated with a top blob at a given index.
    */
    //返回与某个Top Blob相关的标量loss值
   inline Dtype loss(const int top_index) const {
     return (loss_.size() > top_index) ? loss_[top_index] : Dtype();
   }

   /**
    * @brief Sets the loss associated with a top blob at a given index.
    */
    //设置与某个Top Blob相关的标量loss值
   inline void set_loss(const int top_index, const Dtype value) {
     if (loss_.size() <= top_index) {
       loss_.resize(top_index + , Dtype());
     }
     loss_[top_index] = value;
   }

   /**
    * @brief Returns the layer type.
    */
    //返回层类型字符串，便于识别，由派生类实现
   virtual inline const char* type() const { return ""; }

   /**
    * @brief Returns the exact number of bottom blobs required by the layer,
    *        or -1 if no exact number is required.
    *
    * This method should be overridden to return a non-negative value if your
    * layer expects some exact number of bottom blobs.
    */
    //返回Layer需要的输入Bottom Blob数目，-1表示不关心，需要派生类实现
   virtual inline int ExactNumBottomBlobs() const { return -; }
   /**
    * @brief Returns the minimum number of bottom blobs required by the layer,
    *        or -1 if no minimum number is required.
    *
    * This method should be overridden to return a non-negative value if your
    * layer expects some minimum number of bottom blobs.
    */
   virtual inline int MinBottomBlobs() const { return -; }
   /**
    * @brief Returns the maximum number of bottom blobs required by the layer,
    *        or -1 if no maximum number is required.
    *
    * This method should be overridden to return a non-negative value if your
    * layer expects some maximum number of bottom blobs.
    */
   virtual inline int MaxBottomBlobs() const { return -; }
   /**
    * @brief Returns the exact number of top blobs required by the layer,
    *        or -1 if no exact number is required.
    *
    * This method should be overridden to return a non-negative value if your
    * layer expects some exact number of top blobs.
    */
   //返回Layer需要的输出Top Blob数目，-1表示不关心，需要派生类实现
   virtual inline int ExactNumTopBlobs() const { return -; }
   /**
    * @brief Returns the minimum number of top blobs required by the layer,
    *        or -1 if no minimum number is required.
    *
    * This method should be overridden to return a non-negative value if your
    * layer expects some minimum number of top blobs.
    */
   virtual inline int MinTopBlobs() const { return -; }
   /**
    * @brief Returns the maximum number of top blobs required by the layer,
    *        or -1 if no maximum number is required.
    *
    * This method should be overridden to return a non-negative value if your
    * layer expects some maximum number of top blobs.
    */
   virtual inline int MaxTopBlobs() const { return -; }
   /**
    * @brief Returns true if the layer requires an equal number of bottom and
    *        top blobs.
    *
    * This method should be overridden to return true if your layer expects an
    * equal number of bottom and top blobs.
    */
    //返回Layer是否有相同的输入输出Blob，需要派生类实现
   virtual inline bool EqualNumBottomTopBlobs() const { return false; }

   /**
    * @brief Return whether "anonymous" top blobs are created automatically
    *        by the layer.
    *
    * If this method returns true, Net::Init will create enough "anonymous" top
    * blobs to fulfill the requirement specified by ExactNumTopBlobs() or
    * MinTopBlobs().
    */
    //返回是否允许匿名Top Blob，即由该层自动创建。
    //如果为真，Net::Init 会创建足够多的匿名Top Blob来满足 ExactNumTopBlobs() or MinTopBlobs()需求
   virtual inline bool AutoTopBlobs() const { return false; }

   /**
    * @brief Return whether to allow force_backward for a given bottom blob
    *        index.
    *
    * If AllowForceBackward(i) == false, we will ignore the force_backward
    * setting and backpropagate to blob i only if it needs gradient information
    * (as is done when force_backward == false).
    */
    //是否允许强制反向传播。如果AllowForceBackward(i) == false，忽略force_backward设定
   virtual inline bool AllowForceBackward(const int bottom_index) const {
     return true;
   }

   /**
    * @brief Specifies whether the layer should compute gradients w.r.t. a
    *        parameter at a particular index given by param_id.
    *
    * You can safely ignore false values and always compute gradients
    * for all parameters, but possibly with wasteful computation.
    */
    //该Layer是否计算相对权值或偏置项的梯度，具体相对谁由param_id指定
   inline bool param_propagate_down(const int param_id) {
     return (param_propagate_down_.size() > param_id) ?
         param_propagate_down_[param_id] : false;
   }
   /**
    * @brief Sets whether the layer should compute gradients w.r.t. a
    *        parameter at a particular index given by param_id.
    */
    //设置该Layer是否计算相对权值或偏置项的梯度，具体相对谁由param_id指定
   inline void set_param_propagate_down(const int param_id, const bool value) {
     if (param_propagate_down_.size() <= param_id) {
       param_propagate_down_.resize(param_id + , true);
     }
     param_propagate_down_[param_id] = value;
   }

  protected:
   /** The protobuf that stores the layer parameters */
   LayerParameter layer_param_;//保存Layer参数的ProtoBuffer对象
   /** The phase: TRAIN or TEST */
   Phase phase_;//Layer当前所属阶段，可选TRAIN或TEST
   /** The vector that stores the learnable parameters as a set of blobs. */
   vector<shared_ptr<Blob<Dtype> > > blobs_;//Layer内部权值偏置项，由Blob组织
   /** Vector indicating whether to compute the diff of each param blob. */
   vector<bool> param_propagate_down_;//标志位，是否计算对应的参数的误差梯度

   /** The vector that indicates whether each top blob has a non-zero weight in
    *  the objective function. */
   vector<Dtype> loss_;//标志位，在目标函数中是否每个Top Blob都有非零权值

   //以下四个函数会在派生类中经常看到

   /** @brief Using the CPU device, compute the layer output. */
   virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) = ;
   /**
    * @brief Using the GPU device, compute the layer output.
    *        Fall back to Forward_cpu() if unavailable.
    */
   virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,
       const vector<Blob<Dtype>*>& top) {
     // LOG(WARNING) << "Using CPU code as backup.";
     return Forward_cpu(bottom, top);
   }

   /**
    * @brief Using the CPU device, compute the gradients for any parameters and
    *        for the bottom blobs if propagate_down is true.
    */
   virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down,
       const vector<Blob<Dtype>*>& bottom) = ;
   /**
    * @brief Using the GPU device, compute the gradients for any parameters and
    *        for the bottom blobs if propagate_down is true.
    *        Fall back to Backward_cpu() if unavailable.
    */
   virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,
       const vector<bool>& propagate_down,
       const vector<Blob<Dtype>*>& bottom) {
     // LOG(WARNING) << "Using CPU code as backup.";
     Backward_cpu(top, propagate_down, bottom);
   }

   /**
    * Called by the parent Layer's SetUp to check that the number of bottom
    * and top Blobs provided as input match the expected numbers specified by
    * the {ExactNum,Min,Max}{Bottom,Top}Blobs() functions.
    */
    //校验输入输出Blob数目是否满足Layer要求
   virtual void CheckBlobCounts(const vector<Blob<Dtype>*>& bottom,
                                const vector<Blob<Dtype>*>& top) {
     if (ExactNumBottomBlobs() >= ) {
       CHECK_EQ(ExactNumBottomBlobs(), bottom.size())
           << type() << " Layer takes " << ExactNumBottomBlobs()
           << " bottom blob(s) as input.";
     }
     if (MinBottomBlobs() >= ) {
       CHECK_LE(MinBottomBlobs(), bottom.size())
           << type() << " Layer takes at least " << MinBottomBlobs()
           << " bottom blob(s) as input.";
     }
     if (MaxBottomBlobs() >= ) {
       CHECK_GE(MaxBottomBlobs(), bottom.size())
           << type() << " Layer takes at most " << MaxBottomBlobs()
           << " bottom blob(s) as input.";
     }
     if (ExactNumTopBlobs() >= ) {
       CHECK_EQ(ExactNumTopBlobs(), top.size())
           << type() << " Layer produces " << ExactNumTopBlobs()
           << " top blob(s) as output.";
     }
     if (MinTopBlobs() >= ) {
       CHECK_LE(MinTopBlobs(), top.size())
           << type() << " Layer produces at least " << MinTopBlobs()
           << " top blob(s) as output.";
     }
     if (MaxTopBlobs() >= ) {
       CHECK_GE(MaxTopBlobs(), top.size())
           << type() << " Layer produces at most " << MaxTopBlobs()
           << " top blob(s) as output.";
     }
     if (EqualNumBottomTopBlobs()) {
       CHECK_EQ(bottom.size(), top.size())
           << type() << " Layer produces one top blob as output for each "
           << "bottom blob input.";
     }
   }

   /**
    * Called by SetUp to initialize the weights associated with any top blobs in
    * the loss function. Store non-zero loss weights in the diff blob.
    */
    //该函数在Layer的Setup函数中调用，主要目的是初始化与TopBlob相关的loss权重，放到top blob的diff域
    //实际由Forward（）计算loss
    //loss_weight==0 表示当前层不参与loss计算，大部分layer属于这一类
    //loss_weight==1 表示当前层参与loss计算，损失层（LossLayer）属于这一类
   inline void SetLossWeights(const vector<Blob<Dtype>*>& top) {
     //从ProtoBuffer对象中获得Layer参数，这里需要loss_weight参数
     const int num_loss_weights = layer_param_.loss_weight_size();
     if (num_loss_weights) {//如果ProtoBuffer中至少有一个loss_weight 参数
       //loss_weight个数应该与TopBlob相同，或者不要Loss_weigth参数
       CHECK_EQ(top.size(), num_loss_weights) << "loss_weight must be "
           "unspecified or specified once per top blob.";
       //遍历每一个Top Blob
       for (int top_id = ; top_id < top.size(); ++top_id) {
         //从ProtoBuffer对象中获得loss_weight参数（0或者1）
         const Dtype loss_weight = layer_param_.loss_weight(top_id);
         if (loss_weight == Dtype()) { continue; }//为0，跳过
         this->set_loss(top_id, loss_weight);//不为0，进行网络的相关设置
         const int count = top[top_id]->count();//本地记录loss_weight的值
         Dtype* loss_multiplier = top[top_id]->mutable_cpu_diff();
         //将loss_weight写入TopBlob的diff中，传递到需要使用的地方，实现远程同步
         caffe_set(count, loss_weight, loss_multiplier);
       }
     }
   }

  private:
   DISABLE_COPY_AND_ASSIGN(Layer);//禁用拷贝构造函数和赋值运算函数
 };  // class Layer

 // Forward and backward wrappers. You should implement the cpu and
 // gpu specific implementations instead, and should not change these
 // functions.
 //前向传播函数、后向传播函数的包装，不需要修改这两个函数
 //使用时只需要在派生类中改写Forward_cpu等
 template <typename Dtype>
 inline Dtype Layer<Dtype>::Forward(const vector<Blob<Dtype>*>& bottom,
     const vector<Blob<Dtype>*>& top) {
   Dtype loss = ;
   Reshape(bottom, top);
   switch (Caffe::mode()) {//判断计算设备
   case Caffe::CPU://在CPU上执行Forward计算
     Forward_cpu(bottom, top);//调用CPU版本的Forward
     //如果需要计算loss还需要进一步操作
     for (int top_id = ; top_id < top.size(); ++top_id) {
       if (!this->loss(top_id)) { continue; }
       const int count = top[top_id]->count();
       //若为损失层，则已经通过Forward函数计算出全局损失函数，放在Top Blob data中
       const Dtype* data = top[top_id]->cpu_data();
       //若loss_weight不为0，则已经在SetLossWeight中将loss权重放在Top Blob diff 中
       const Dtype* loss_weights = top[top_id]->cpu_diff();
       loss += caffe_cpu_dot(count, data, loss_weights);//加权loss之和，得到标量loss
     }
     break;
   case Caffe::GPU:
     Forward_gpu(bottom, top);
 #ifndef CPU_ONLY
     for (int top_id = ; top_id < top.size(); ++top_id) {
       if (!this->loss(top_id)) { continue; }
       const int count = top[top_id]->count();
       const Dtype* data = top[top_id]->gpu_data();
       const Dtype* loss_weights = top[top_id]->gpu_diff();
       Dtype blob_loss = ;
       caffe_gpu_dot(count, data, loss_weights, &blob_loss);
       loss += blob_loss;
     }
 #endif
     break;
   default:
     LOG(FATAL) << "Unknown caffe mode.";
   }
   return loss;
 }
 //反向传播函数，直接调用对应设备函数
 template <typename Dtype>
 inline void Layer<Dtype>::Backward(const vector<Blob<Dtype>*>& top,
     const vector<bool>& propagate_down,
     const vector<Blob<Dtype>*>& bottom) {
   switch (Caffe::mode()) {
   case Caffe::CPU:
     Backward_cpu(top, propagate_down, bottom);
     break;
   case Caffe::GPU:
     Backward_gpu(top, propagate_down, bottom);
     break;
   default:
     LOG(FATAL) << "Unknown caffe mode.";
   }
 }
 //将层配置参数序列化为ProtoBuffer
 // Serialize LayerParameter to protocol buffer
 template <typename Dtype>
 void Layer<Dtype>::ToProto(LayerParameter* param, bool write_diff) {
   param->Clear();
   param->CopyFrom(layer_param_);
   param->clear_blobs();
   for (int i = ; i < blobs_.size(); ++i) {
     blobs_[i]->ToProto(param->add_blobs(), write_diff);
   }//权值偏置项也会保存
 }

 }  // namespace caffe

 #endif  // CAFFE_LAYER_H_

待更新

摘抄参考赵永科《深度学习 21天实战caffe》