Caffe源代码(caffe version commit: 09868ac , date: 2015.08.15)中有一些重要的头文件，这里介绍下include/caffe/solver.hpp文件的内容：

1.      include文件：

<caffe/solver.hpp>：此文件的介绍能够參考： 

2.      模板类Solver：虚基类

3.      模板类WorkerSolver：继承父类Solver，用于多GPU训练时仅计算梯度

4.      模板类SGDSolver：继承父类Solver

5.      模板类NesterovSolver：继承SGDSolver

6.      模板类AdaGradSolver：继承SGDSolver

7.      模板类RMSPropSolver：继承SGDSolver

8.      模板类AdaDeltaSolver：继承SGDSolver

9.      模板类AdamSolver：继承SGDSolver

10.  函数GetSolver：new solver对象

Solver通过协调Net的前向判断计算和反向梯度计算(forward inference and backward gradients)，来对參数进行更新。从而达到降低loss的目的。Caffe模型的学习被分为两个部分：由Solver进行优化、更新參数。由Net计算出loss和gradient。

solver.prototxt是一个配置文件用来告知Caffe如何对网络进行训练。

有了Net就能够进行神经网络的前后向传播计算了。可是还缺少神经网络的训练和预測功能，Solver类进一步封装了训练和预測相关的一些功能。Solver定义了针对Net网络模型的求解方法，记录神经网络的训练过程，保存神经网络模型參数，中断并恢复网络的训练过程。自己定义Solver能够实现不同的神经网络求解方式。

Caffe支持的solvers包含：

(1)、Stochastic Gradient Descent(type: “SGD”)即随机梯度下降：利用负梯度和上一次权重的更新值的线性组合来更新权重。学习率(learning rate)是负梯度的权重。

动量是上一次更新值的权重。一般将学习速率初始化为0.01。然后在训练(training)中当loss达到稳定时，将学习速率除以一个常数(比如10)，将这个过程重复多次。

对于动量一般设置为0.9，动量使weight得更新更为平缓，使学习过程更为稳定、高速。

(2)、AdaDelta(type:“AdaDelta”)：是一种”鲁棒的学习率方法”，同SGD一样是一种基于梯度的优化方法。

(3)、Adaptive Gradient(type: “AdaGrad”)即自适应梯度下降，与随机梯度下降一样是基于梯度的优化方法。

(4)、Adam(type:“Adam”)：也是一种基于梯度的优化方法。

它包含一对自适应时刻预计变量，能够看做是AdaGrad的一种泛化形式。

(5)、Nesterov’s Accelerated Gradient(type: “Nesterov”)：Nesterov提出的加速梯度下降(Nesterov’s accelerated gradient)是凸优化的一种最优算法，其收敛速度能够达到O(1/t^2)，而不是O(1/t)。

虽然在使用Caffe训练深度神经网络时非常难满足O(1/t^2)收敛条件。但实际中NAG对于某些特定结构的深度学习模型仍是一个非常有效的方法。

(6)、RMSprop(type:“RMSProp”)：是一种基于梯度的优化方法(同SGD相似)。

Solver：

(1)、用于优化过程的记录、创建训练网络(用于学习)和測试网络(用于评估)；

(2)、通过forward和backward过程来迭代地优化和更新參数；

(3)、周期性地用測试网络评估模型性能；

(4)、在优化过程中记录模型和solver状态的快照(snapshot)。

每一次迭代过程中：

(1)、调用Net的前向过程计算出输出和loss。

(2)、调用Net的反向过程计算出梯度(loss对每层的权重w和偏置b求导)。

(3)、依据以下所讲的Solver方法。利用梯度更新參数；

(4)、依据学习率(learning rate)。历史数据和求解方法更新solver的状态。使权重从初始化状态逐步更新到终于的学习到的状态。

Solvers的运行模式有CPU/GPU两种模式。

Solver方法：用于最小化损失(loss)值。

给定一个数据集D，优化的目标是D中全部数据损失的均值，即平均损失。取得最小值。

注：以上关于Solver内容的介绍主要摘自由CaffeCN社区翻译的《Caffe官方教程中译本》。

<caffe/solver.hpp>文件的具体介绍例如以下：

#ifndef CAFFE_OPTIMIZATION_SOLVER_HPP_#define CAFFE_OPTIMIZATION_SOLVER_HPP_#include 
     
      #include 
      
       #include "caffe/net.hpp"namespace caffe {/** * @brief An interface for classes that perform optimization on Net%s. * * Requires implementation of ApplyUpdate to compute a parameter update * given the current state of the Net parameters. */template 
       
        class Solver { // Solver模板类，虚基类 public:// 显示构造函数, 内部会调用Init函数  explicit Solver(const SolverParameter& param, const Solver* root_solver = NULL);  explicit Solver(const string& param_file, const Solver* root_solver = NULL);// 成员变量赋值，包含param_、iter_、current_step_,并调用InitTrainNet和InitTestNets函数  void Init(const SolverParameter& param);// 为成员变量net_赋值  void InitTrainNet();// 为成员变量test_nets_赋值  void InitTestNets();  // The main entry of the solver function. In default, iter will be zero. Pass  // in a non-zero iter number to resume training for a pre-trained net.// 依次调用函数Restore、Step、Snapshot，然后运行net_的前向传播函数ForwardPrefilled，最后调用TestAll函数  virtual void Solve(const char* resume_file = NULL);  inline void Solve(const string resume_file) { Solve(resume_file.c_str()); }// 重复运行net前向传播反向传播计算,期间会调用函数TestAll、ApplyUpdate、Snapshot及类Callback两个成员函数  void Step(int iters);  // The Restore method simply dispatches to one of the  // RestoreSolverStateFrom___ protected methods. You should implement these  // methods to restore the state from the appropriate snapshot type.// 载入已有的模型  void Restore(const char* resume_file);// 虚析构函数  virtual ~Solver() {} // 获得slover parameter  inline const SolverParameter& param() const { return param_; }// 获得train Net  inline shared_ptr
        
         
           > net() { return net_; }// 获得test Net  inline const vector
          
           
            
              > >& test_nets() { return test_nets_; }// 获得当前的迭代数 int iter() { return iter_; } // Invoked at specific points during an iteration// 内部Callback类，仅在多卡GPU模式下使用 class Callback { protected: virtual void on_start() = 0; virtual void on_gradients_ready() = 0; template 
             
               friend class Solver; };// 获得Callback const vector
              
               & callbacks() const { return callbacks_; }// 加入一个Callback void add_callback(Callback* value) { callbacks_.push_back(value); } protected: // Make and apply the update value for the current iteration.// 更新net的权值和偏置 virtual void ApplyUpdate() = 0; // The Solver::Snapshot function implements the basic snapshotting utility // that stores the learned net. You should implement the SnapshotSolverState() // function that produces a SolverState protocol buffer that needs to be // written to disk together with the learned net.// 快照，内部会调用SnapshotToBinaryProto或SnapshotToHDF5、SnapshotSolverState函数 void Snapshot();// 获取快照文件名称 string SnapshotFilename(const string extension);// 写proto到.caffemodel string SnapshotToBinaryProto();// 写proto到HDF5文件 string SnapshotToHDF5(); // The test routine// 内部会循环调用Test函数 void TestAll();// 运行測试网络。net前向传播 void Test(const int test_net_id = 0);// 存储snapshot solver state virtual void SnapshotSolverState(const string& model_filename) = 0;// 读HDF5文件到solver state virtual void RestoreSolverStateFromHDF5(const string& state_file) = 0;// 读二进制文件.solverstate到solver state virtual void RestoreSolverStateFromBinaryProto(const string& state_file) = 0;// dummy function，仅仅有声明没有实现 void DisplayOutputBlobs(const int net_id);// Caffe中类的成员变量名都带有后缀"_"。这样就easy区分暂时变量和类成员变量 SolverParameter param_; // solver parameter int iter_; // 当前的迭代数 int current_step_; // shared_ptr
               
                
                  > net_; // train net vector
                 
                  
                   
                     > > test_nets_; // test net vector
                    
                      callbacks_; // Callback // The root solver that holds root nets (actually containing shared layers) // in data parallelism const Solver* const root_solver_;// 禁止使用Solver类的拷贝和赋值操作 DISABLE_COPY_AND_ASSIGN(Solver);};/** * @brief Solver that only computes gradients, used as worker * for multi-GPU training. */template 
                     
                      class WorkerSolver : public Solver
                      
                        { // 模板类WorkerSolver。继承父类Solver public:// 显示构造函数 explicit WorkerSolver(const SolverParameter& param, const Solver
                       
                        * root_solver = NULL) : Solver
                        
                         (param, root_solver) {} protected: void ApplyUpdate() {} void SnapshotSolverState(const string& model_filename) { LOG(FATAL) << "Should not be called on worker solver."; } void RestoreSolverStateFromBinaryProto(const string& state_file) { LOG(FATAL) << "Should not be called on worker solver."; } void RestoreSolverStateFromHDF5(const string& state_file) { LOG(FATAL) << "Should not be called on worker solver."; }};/** * @brief Optimizes the parameters of a Net using * stochastic gradient descent (SGD) with momentum. */template 
                         
                          class SGDSolver : public Solver
                          
                            { // 模板类SGDSolver,继承父类Solver public:// 显示构造函数，调用PreSolve函数 explicit SGDSolver(const SolverParameter& param) : Solver
                           
                            (param) { PreSolve(); } explicit SGDSolver(const string& param_file) : Solver
                            
                             (param_file) { PreSolve(); }// 获取history数据 const vector
                             
                              
                               
                                 > >& history() { return history_; } protected:// 成员变量history_, update_, temp_初始化 void PreSolve();// 获取学习率 Dtype GetLearningRate();// 内部会调用ClipGradients、Normalize、Regularize、ComputeUpdateValue，更新net权值和偏置 virtual void ApplyUpdate();// 调用caffe_scal函数 virtual void Normalize(int param_id);// 调用caffe_axpy函数 virtual void Regularize(int param_id);// 计算并更新对应Blob值，调用caffe_cpu_axpby和caffe_copy函数 virtual void ComputeUpdateValue(int param_id, Dtype rate);// clip parameter gradients to that L2 norm，假设梯度值过大，就会对梯度做一个修剪。// 对全部的參数乘以一个缩放因子，使得全部參数的平方和不超过參数中设定的梯度总值 virtual void ClipGradients();// 存储snapshot solver state，内部会掉用SnapshotSolverStateToBinaryProto或SnapshotSolverStateToHDF5函数 virtual void SnapshotSolverState(const string& model_filename);// 写solver state到二进制文件.solverstate virtual void SnapshotSolverStateToBinaryProto(const string& model_filename);// 写solver state到HDF5 virtual void SnapshotSolverStateToHDF5(const string& model_filename); // 读HDF5文件到solver state virtual void RestoreSolverStateFromHDF5(const string& state_file); // 读二进制文件.solverstate到solver state virtual void RestoreSolverStateFromBinaryProto(const string& state_file); // history maintains the historical momentum data. // update maintains update related data and is not needed in snapshots. // temp maintains other information that might be needed in computation // of gradients/updates and is not needed in snapshots// Caffe中类的成员变量名都带有后缀"_"，这样就easy区分暂时变量和类成员变量 vector
                                
                                 
                                  
                                    > > history_, update_, temp_;// 禁止使用SGDSolver类的拷贝和赋值操作 DISABLE_COPY_AND_ASSIGN(SGDSolver);};template 
                                   
                                    class NesterovSolver : public SGDSolver
                                    
                                      { // 模板类NesterovSolver，继承SGDSolver public:// 显示构造函数 explicit NesterovSolver(const SolverParameter& param) : SGDSolver
                                     
                                      (param) {} explicit NesterovSolver(const string& param_file) : SGDSolver
                                      
                                       (param_file) {} protected:// 计算并更新对应Blob值,调用caffe_cpu_axpby和caffe_copy函数 virtual void ComputeUpdateValue(int param_id, Dtype rate);// 禁止使用NesterovSolver类的拷贝和赋值操作 DISABLE_COPY_AND_ASSIGN(NesterovSolver);};template 
                                       
                                        class AdaGradSolver : public SGDSolver
                                        
                                          { // 模板类AdaGradSolver,继承SGDSolver public:// 显示构造函数，调用constuctor_sanity_check函数 explicit AdaGradSolver(const SolverParameter& param) : SGDSolver
                                         
                                          (param) { constructor_sanity_check(); } explicit AdaGradSolver(const string& param_file) : SGDSolver
                                          
                                           (param_file) { constructor_sanity_check(); } protected:// 计算并更新对应Blob值 virtual void ComputeUpdateValue(int param_id, Dtype rate); void constructor_sanity_check() { CHECK_EQ(0, this->param_.momentum()) << "Momentum cannot be used with AdaGrad."; }// 禁止使用AdaGradSolver类的拷贝和赋值操作 DISABLE_COPY_AND_ASSIGN(AdaGradSolver);};template 
                                           
                                            class RMSPropSolver : public SGDSolver
                                            
                                              { // 模板类RMSPropSolver,继承SGDSolver public:// 显示构造函数。调用constructor_sanity_check函数 explicit RMSPropSolver(const SolverParameter& param) : SGDSolver
                                             
                                              (param) { constructor_sanity_check(); } explicit RMSPropSolver(const string& param_file) : SGDSolver
                                              
                                               (param_file) { constructor_sanity_check(); } protected:// 计算并更新对应Blob值 virtual void ComputeUpdateValue(int param_id, Dtype rate); void constructor_sanity_check() { CHECK_EQ(0, this->param_.momentum()) << "Momentum cannot be used with RMSProp."; CHECK_GE(this->param_.rms_decay(), 0) << "rms_decay should lie between 0 and 1."; CHECK_LT(this->param_.rms_decay(), 1) << "rms_decay should lie between 0 and 1."; }// 禁止使用RMSPropSolver类的拷贝和赋值操作 DISABLE_COPY_AND_ASSIGN(RMSPropSolver);};template 
                                               
                                                class AdaDeltaSolver : public SGDSolver
                                                
                                                  { // 模板类AdaDeltaSolver。继承SGDSolver public:// 显示构造函数，调用AdaDeltaPreSolve函数 explicit AdaDeltaSolver(const SolverParameter& param) : SGDSolver
                                                 
                                                  (param) { AdaDeltaPreSolve(); } explicit AdaDeltaSolver(const string& param_file) : SGDSolver
                                                  
                                                   (param_file) { AdaDeltaPreSolve(); } protected: void AdaDeltaPreSolve();// 计算并更新对应Blob值 virtual void ComputeUpdateValue(int param_id, Dtype rate);// 禁止使用AdaDeltaSolver类的拷贝和赋值操作 DISABLE_COPY_AND_ASSIGN(AdaDeltaSolver);};/** * @brief AdamSolver, an algorithm for first-order gradient-based optimization * of stochastic objective functions, based on adaptive estimates of * lower-order moments. Described in [1]. * * [1] D. P. Kingma and J. L. Ba, "ADAM: A Method for Stochastic Optimization." * arXiv preprint arXiv:1412.6980v8 (2014). */template 
                                                   
                                                    class AdamSolver : public SGDSolver
                                                    
                                                      { // 模板类AdamSolver。继承SGDSolver public:// 显示构造函数，调用AdamPreSolve函数 explicit AdamSolver(const SolverParameter& param) : SGDSolver
                                                     
                                                      (param) { AdamPreSolve();} explicit AdamSolver(const string& param_file) : SGDSolver
                                                      
                                                       (param_file) { AdamPreSolve(); } protected: void AdamPreSolve();// 计算并更新对应Blob值 virtual void ComputeUpdateValue(int param_id, Dtype rate);// 禁止使用AdamSolver类的拷贝和赋值操作 DISABLE_COPY_AND_ASSIGN(AdamSolver);};// new一个指定的solver方法对象template 
                                                       
                                                        Solver
                                                        
                                                         * GetSolver(const SolverParameter& param) { SolverParameter_SolverType type = param.solver_type(); switch (type) { case SolverParameter_SolverType_SGD: return new SGDSolver
                                                         
                                                          (param); case SolverParameter_SolverType_NESTEROV: return new NesterovSolver
                                                          
                                                           (param); case SolverParameter_SolverType_ADAGRAD: return new AdaGradSolver
                                                           
                                                            (param); case SolverParameter_SolverType_RMSPROP: return new RMSPropSolver
                                                            
                                                             (param); case SolverParameter_SolverType_ADADELTA: return new AdaDeltaSolver
                                                             
                                                              (param); case SolverParameter_SolverType_ADAM: return new AdamSolver
                                                              
                                                               (param); default: LOG(FATAL) << "Unknown SolverType: " << type; } return (Solver
                                                               
                                                                *) NULL;}} // namespace caffe#endif // CAFFE_OPTIMIZATION_SOLVER_HPP_
                                                               
                                                              
                                                             
                                                            
                                                           
                                                          
                                                         
                                                        
                                                       
                                                      
                                                     
                                                    
                                                   
                                                  
                                                 
                                                
                                               
                                              
                                             
                                            
                                           
                                          
                                         
                                        
                                       
                                      
                                     
                                    
                                   
                                  
                                 
                                
                               
                              
                             
                            
                           
                          
                         
                        
                       
                      
                     
                    
                   
                  
                 
                
               
              
             
            
           
          
         
        
       
      
     

在caffe.proto文件里。主要有一个message是与solver相关的，例如以下：

// NOTE// Update the next available ID when you add a new SolverParameter field.//// SolverParameter next available ID: 40 (last added: momentum2)message SolverParameter { // Solver參数  //  // 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.  //  // Proto filename for the train net, possibly combined with one or more test nets.  optional string net = 24; // .prototxt文件名称， train or test net  // Inline train net param, possibly combined with one or more test nets.  optional NetParameter net_param = 25; // net parameter类  optional string train_net = 1; // Proto filename for the train net, .prototxt文件名称，train net  repeated string test_net = 2; // Proto filenames for the test nets, .prototxt文件名称，test net  optional NetParameter train_net_param = 21; // Inline train net params, train net parameter类  repeated NetParameter test_net_param = 22; // Inline test net params, test net parameter类  // 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; // train net state  repeated NetState test_state = 27; // test net state  // The number of iterations for each test net.  repeated int32 test_iter = 3; // 对于測试网络(用于评估)运行一次须要迭代的次数, test_iter * batch_size = 測试图像总数量  // The number of iterations between two testing phases.  optional int32 test_interval = 4 [default = 0];　// 指定运行多少次训练网络运行一次測试网络  optional bool test_compute_loss = 19 [default = false]; // 运行測试网络时是否计算loss  // If true, run an initial test pass before the first iteration,  // ensuring memory availability and printing the starting value of the loss.  optional bool test_initialization = 32 [default = true]; // 在总的開始前，是否先运行一次測试网络  optional float base_lr = 5; // The base learning rate,基础学习率  // the number of iterations between displaying info. If display = 0, no info  // will be displayed.  optional int32 display = 6; // 指定迭代多少次显示一次结果信息  // Display the loss averaged over the last average_loss iterations  optional int32 average_loss = 33 [default = 1]; //   optional int32 max_iter = 7; // the maximum number of iterations  // accumulate gradients over `iter_size` x `batch_size` instances  optional int32 iter_size = 36 [default = 1]; //   // The learning rate decay policy. The currently implemented learning rate  // policies are as follows: // 学习率衰减策略  //    - 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 but it allows 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))))  //  // where base_lr, max_iter, gamma, step, stepvalue and power are defined  // in the solver parameter protocol buffer, and iter is the current iteration.  optional string lr_policy = 8; // 学习策略,可取的值包含：fixed、step、exp、inv、multistep、poly、sigmoid  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  optional float weight_decay = 12; // The weight decay. //   // regularization types supported: L1 and L2  // controlled by weight_decay  optional string regularization_type = 29 [default = "L2"]; // L1 or L2  // the stepsize for learning rate policy "step"  optional int32 stepsize = 13; //  // the stepsize for learning rate policy "multistep"  repeated int32 stepvalue = 34; //  // Set clip_gradients to >= 0 to clip parameter gradients to that L2 norm,  // whenever their actual L2 norm is larger.  optional float clip_gradients = 35 [default = -1]; //  optional int32 snapshot = 14 [default = 0]; // The snapshot interval, 迭代多少次保存下结果(如权值、偏置)  optional string snapshot_prefix = 15; // The prefix for the snapshot,指定保存文件名称的前缀  // whether to snapshot diff in the results or not. Snapshotting diff will help  // debugging but the final protocol buffer size will be much larger.  optional bool snapshot_diff = 16 [default = false]; //  enum SnapshotFormat {    HDF5 = 0;    BINARYPROTO = 1;  }  optional SnapshotFormat snapshot_format = 37 [default = BINARYPROTO]; // HDF5 or BINARYPROTO  // the mode solver will use: 0 for CPU and 1 for GPU. Use GPU in default.  enum SolverMode {    CPU = 0;    GPU = 1;  }  optional SolverMode solver_mode = 17 [default = GPU]; // 指定solve mode是CPU还是GPU  // the device_id will that be used in GPU mode. Use device_id = 0 in default.  optional int32 device_id = 18 [default = 0]; // GPU mode下使用  // If non-negative, the seed with which the Solver will initialize the Caffe  // random number generator -- useful for reproducible results. Otherwise,  // (and by default) initialize using a seed derived from the system clock.  optional int64 random_seed = 20 [default = -1]; //   // Solver type  enum SolverType { // solver优化方法    SGD = 0;    NESTEROV = 1;    ADAGRAD = 2;    RMSPROP = 3;    ADADELTA = 4;    ADAM = 5;  }  optional SolverType solver_type = 30 [default = SGD]; // 指定solver优化方法  // numerical stability for RMSProp, AdaGrad and AdaDelta and Adam  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; //   // If true, print information about the state of the net that may help with  // debugging learning problems.  optional bool debug_info = 23 [default = false]; //   // If false, don't save a snapshot after training finishes.  optional bool snapshot_after_train = 28 [default = true]; //}

solver的測试代码例如以下：

#include "funset.hpp"#include 
     
      #include 
      
       #include 
       
      
     

"HDF5" : "BINARYPROTO"); caffe::SolverParameter_SolverMode solver_mode = param.solver_mode(); fprintf(stderr, "solver mode: %s\n", solver_mode == 0 ? "CPU" : "GPU"); if (param.has_device_id()) fprintf(stderr, "device id: %d\n", param.device_id()); fprintf(stderr, "random seed: %d\n", param.random_seed()); caffe::SolverParameter_SolverType solver_type = param.solver_type(); std::string solver_method[] {"SGD", "NESTEROV", "ADAGRAD", "RMSPROP", "ADADELTA", "ADAM"}; fprintf(stderr, "solver type: %s\n", solver_method[solver_type].c_str()); fprintf(stderr, "delta: %f\n", param.delta()); fprintf(stderr, "momentum2: %f\n", param.momentum2()); if (param.has_rms_decay()) fprintf(stderr, "rms decy: %f\n", param.rms_decay()); fprintf(stderr, "debug info: %d\n", param.debug_info()); fprintf(stderr, "snapshot after train: %d\n", param.snapshot_after_train()); boost::shared_ptr<caffe::Net<float>> net = solver->net(); std::vector<boost::shared_ptr<caffe::Net<float>>> test_nets = solver->test_nets(); fprintf(stderr, "test nets size: %d\n", test_nets.size()); fprintf(stderr, "iter: %d\n", solver->iter()); return 0; }

部分输出结果例如以下：

GitHub：