Caffe源码中Solver文件分析

Caffe源码(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>：此文件的介绍可以参考： http://blog.csdn.net/fengbingchun/article/details/62423060

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的前向推断计算和反向梯度计算(forward inference and backward gradients)，来对参数进行更新，从而达到减少loss的目的。Caffe模型的学习被分为两个部分：由Solver进行优化、更新参数，由Net计算出loss和gradient。

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

有了Net就可以进行神经网络的前后向传播计算了，但是还缺少神经网络的训练和预测功能，Solver类进一步封装了训练和预测相关的一些功能。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，保存神经网络模型参数，中断并恢复网络的训练过程。自定义Solver能够实现不同的神经网络求解方式。

Caffe支持的solvers包括：

(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(例如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、快速。

(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估计变量，可以看做是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，其收敛速度可以达到O(1/t^2)，而不是O(1/t)。尽管在使用Caffe训练深度神经网络时很难满足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类似)。

Solver：

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

(2)、通过forward和backward过程来迭代地优化和更新参数；

(3)、周期性地用测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)、根据下面所讲的Solver方法，利用梯度更新参数；

(4)、根据学习率(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最终的学习到的状态。

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中所有数据损失的均值，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>文件的详细介绍如下：

#ifndef CAFFE_OPTIMIZATION_SOLVER_HPP_#define CAFFE_OPTIMIZATION_SOLVER_HPP_#include <string>#include <vector>#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 <typename Dtype>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，包括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，然后执行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()); }// 反复执行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.// 加载已有的模型  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<Dtype> > net() { return net_; }// 获得test Net  inline const vector<shared_ptr<Net<Dtype> > >& 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 <typename T>    friend class Solver;  };// 获得Callback  const vector<Callback*>& callbacks() const { return callbacks_; }// 添加一个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  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();// 执行测试网络，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，只有声明没有实现  void DisplayOutputBlobs(const int net_id);// Caffe中类的成员变量名都带有后缀"_"，这样就容易区分临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<Dtype> > net_; // train net  vector<shared_ptr<Net<Dtype> > > test_nets_; // test net  vector<Callback*> 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 <typename Dtype>class WorkerSolver : public Solver<Dtype> { // 模板类WorkerSolver，继承父类Solver public:// 显示构造函数  explicit WorkerSolver(const SolverParameter& param, const Solver<Dtype>* root_solver = NULL)      : Solver<Dtype>(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 <typename Dtype>class SGDSolver : public Solver<Dtype> { // 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<Dtype>(param) { PreSolve(); }  explicit SGDSolver(const string& param_file) : Solver<Dtype>(param_file) { PreSolve(); }// 获取history数据  const vector<shared_ptr<Blob<Dtype> > >& 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);// 计算并更新相应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，如果梯度值过大，就会对梯度做一个修剪，// 对所有的参数乘以一个缩放因子，使得所有参数的平方和不超过参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"_"，这样就容易区分临时变量和类成员变量  vector<shared_ptr<Blob<Dtype> > > history_, update_, temp_;// 禁止使用SGDSolver类的拷贝和赋值操作  DISABLE_COPY_AND_ASSIGN(SGDSolver);};template <typename Dtype>class NesterovSolver : public SGDSolver<Dtype> { // 模板类NesterovSolver，继承SGDSolver public:// 显示构造函数  explicit NesterovSolver(const SolverParameter& param) : SGDSolver<Dtype>(param) {}  explicit NesterovSolver(const string& param_file) : SGDSolver<Dtype>(param_file) {} protected:// 计算并更新相应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 <typename Dtype>class AdaGradSolver : public SGDSolver<Dtype> { // 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<Dtype>(param) { constructor_sanity_check(); }  explicit AdaGradSolver(const string& param_file) : SGDSolver<Dtype>(param_file) { constructor_sanity_check(); } protected:// 计算并更新相应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 <typename Dtype>class RMSPropSolver : public SGDSolver<Dtype> { // 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<Dtype>(param) { constructor_sanity_check(); }  explicit RMSPropSolver(const string& param_file) : SGDSolver<Dtype>(param_file) { constructor_sanity_check(); } protected:// 计算并更新相应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 <typename Dtype>class AdaDeltaSolver : public SGDSolver<Dtype> { // 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<Dtype>(param) { AdaDeltaPreSolve(); }  explicit AdaDeltaSolver(const string& param_file) : SGDSolver<Dtype>(param_file) { AdaDeltaPreSolve(); } protected:  void AdaDeltaPreSolve();// 计算并更新相应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 <typename Dtype>class AdamSolver : public SGDSolver<Dtype> { // 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<Dtype>(param) { AdamPreSolve();}  explicit AdamSolver(const string& param_file) : SGDSolver<Dtype>(param_file) { AdamPreSolve(); } protected:  void AdamPreSolve();// 计算并更新相应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 <typename Dtype>Solver<Dtype>* GetSolver(const SolverParameter& param) {  SolverParameter_SolverType type = param.solver_type();  switch (type) {  case SolverParameter_SolverType_SGD:      return new SGDSolver<Dtype>(param);  case SolverParameter_SolverType_NESTEROV:      return new NesterovSolver<Dtype>(param);  case SolverParameter_SolverType_ADAGRAD:      return new AdaGradSolver<Dtype>(param);  case SolverParameter_SolverType_RMSPROP:      return new RMSPropSolver<Dtype>(param);  case SolverParameter_SolverType_ADADELTA:      return new AdaDeltaSolver<Dtype>(param);  case SolverParameter_SolverType_ADAM:      return new AdamSolver<Dtype>(param);  default:      LOG(FATAL) << "Unknown SolverType: " << type;  }  return (Solver<Dtype>*) NULL;}}  // namespace caffe#endif  // CAFFE_OPTIMIZATION_SOLVER_HPP_

在caffe.proto文件中，主要有一个message是与solver相关的，如下：

// NOTE// Update the next available ID when you add a new SolverParameter field.//// SolverParameter next available ID: 40 (last added: momentum2)message SolverParameter { // Solver参数  //////////////////////////////////////////////////////////////////////////////  // 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， 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，train net  repeated string test_net = 2; // Proto filenames for the test nets, .prototxt文件名，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; // 对于测试网络(用于评估)执行一次需要迭代的次数, test_iter * batch_size = 测试图像总数量  // The number of iterations between two testing phases.  optional int32 test_interval = 4 [default = 0];　// 指定执行多少次训练网络执行一次测试网络  optional bool test_compute_loss = 19 [default = false]; // 执行测试网络时是否计算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]; // 在总的开始前，是否先执行一次测试网络  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,可取的值包括：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的测试代码如下：

#include "funset.hpp"#include <string>#include <vector>#include <map>#include "common.hpp"int test_caffe_solver(){caffe::Caffe::set_mode(caffe::Caffe::CPU); // set run caffe modeconst std::string solver_prototxt{ "E:/GitCode/Caffe_Test/test_data/model/mnist/lenet_solver.prototxt" };caffe::SolverParameter solver_param;if (!caffe::ReadProtoFromTextFile(solver_prototxt.c_str(), &solver_param)) {fprintf(stderr, "parse solver.prototxt fail\n");return -1;}boost::shared_ptr<caffe::Solver<float> > solver(caffe::GetSolver<float>(solver_param));caffe::SolverParameter param = solver->param();if (param.has_net())fprintf(stderr, "net: %s\n", param.net().c_str());if (param.has_net_param()) {fprintf(stderr, "has net param\n");caffe::NetParameter net_param = param.net_param();if (net_param.has_name())fprintf(stderr, "net param name: %s\n", net_param.name().c_str());}if (param.has_train_net())fprintf(stderr, "train_net: %s\n", param.train_net());if (param.test_net().size() > 0) {for (auto test_net : param.test_net())fprintf(stderr, "test_net: %s\n", test_net);}if (param.has_train_net_param()) {fprintf(stderr, "has train net param\n");caffe::NetParameter train_net_param = param.train_net_param();}if (param.test_net_param().size() > 0) {fprintf(stderr, "has test net param\n");std::vector<caffe::NetParameter> test_net_param;for (auto net_param : param.test_net_param())test_net_param.push_back(net_param);}if (param.has_train_state()) {fprintf(stderr, "has train state\n");caffe::NetState state = param.train_state();}if (param.test_state().size()) {fprintf(stderr, "has test state\n");}if (param.test_iter_size() > 0) {fprintf(stderr, "has test iter\n");for (auto iter : param.test_iter())fprintf(stderr, "  %d  ", iter);fprintf(stderr, "\n");}if (param.has_test_interval())fprintf(stderr, "test interval: %d\n", param.test_interval());bool test_compute_loss = param.test_compute_loss();fprintf(stderr, "test compute loss: %d\n", test_compute_loss);bool test_initialization = param.test_initialization();fprintf(stderr, "test initializtion: %d\n", test_initialization);if (param.has_base_lr()) {float base_lr = param.base_lr();fprintf(stderr, "base lr: %f\n", base_lr);}if (param.has_display()) {int display = param.display();fprintf(stderr, "display: %d\n", display);}int average_loss = param.average_loss();fprintf(stderr, "average loss: %d\n", average_loss);if (param.has_max_iter()) {int max_iter = param.max_iter();fprintf(stderr, "max iter: %d\n", max_iter);}int iter_size = param.iter_size();fprintf(stderr, "iter size: %d\n", iter_size);if (param.has_lr_policy())fprintf(stderr, "lr policy: %s\n", param.lr_policy().c_str());if (param.has_gamma())fprintf(stderr, "gamma: %f\n", param.gamma());if (param.has_power())fprintf(stderr, "power: %f\n", param.power());if (param.has_momentum())fprintf(stderr, "momentum: %f\n", param.momentum());if (param.has_weight_decay())fprintf(stderr, "weight decay: %f\n", param.weight_decay());std::string regularization_type = param.regularization_type();fprintf(stderr, "regularization type: %s\n", param.regularization_type().c_str());if (param.has_stepsize())fprintf(stderr, "stepsize: %d\n", param.stepsize());if (param.stepvalue_size() > 0) {fprintf(stderr, "has stepvalue\n");for (auto value : param.stepvalue())fprintf(stderr, "  %d  ", value);fprintf(stderr, "\n");}fprintf(stderr, "clip gradients: %f\n", param.clip_gradients());fprintf(stderr, "snapshot: %d\n", param.snapshot());if (param.has_snapshot_prefix())fprintf(stderr, "snapshot prefix: %s\n", param.snapshot_prefix().c_str());fprintf(stderr, "snapshot diff: %d\n", param.snapshot_diff());caffe::SolverParameter_SnapshotFormat snapshot_format = param.snapshot_format();fprintf(stderr, "snapshot format: %s\n", snapshot_format == 0 ? "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;}

部分输出结果如下：

GitHub：https://github.com/fengbingchun/Caffe_Test