1.使用中生成的Caffe静态库；
2.使用中生成的LMDB数据库文件；
3.新建一个train_mnist控制台工程；
4.修改源文件中的caffe/examples/mnist/lenet_solver.prototxt文件：
(1)、net: "E:/GitCode/Caffe/src/caffe/caffe/examples/mnist/lenet_train_test.prototxt"
(2)、snapshot_prefix:"E:/GitCode/Caffe/src/caffe/caffe/examples/mnist/lenet"
(3)、solver_mode: CPU
5.修改源文件中的caffe/examples/mnist/lenet_train_test.prototxt文件，指定LMDB数据库文件存放位置：
(1)、source:"E:/GitCode/Caffe/src/caffe/caffe/data/mnist/lmdb/train"
(2)、source:"E:/GitCode/Caffe/src/caffe/caffe/data/mnist/lmdb/test"
6.train_mnist.cpp文件中内容为(是对caffe/tools/caffe.cpp的修改)：
#include "stdafx.h"#include <iostream>#include <glog/logging.h>#include <cstring>#include <map>#include <string>#include <vector>#include "caffe/common.hpp"#include "boost/algorithm/string.hpp"#include "caffe/caffe.hpp"#include "caffe/util/io.hpp"#include "caffe/blob.hpp"#include "caffe/layer_factory.hpp"#include "boost/smart_ptr/shared_ptr.hpp"using caffe::Blob;using caffe::Caffe;using caffe::Net;using caffe::Layer;using caffe::Solver;using caffe::shared_ptr;using caffe::string;using caffe::Timer;using caffe::vector;using std::ostringstream;DEFINE_string(solver, "E:/GitCode/Caffe/src/caffe/caffe/examples/mnist/lenet_solver.prototxt","The solver definition protocol buffer text file.");DEFINE_string(snapshot, "E:/GitCode/Caffe/src/caffe/caffe/examples/mnist/lenet_iter_10000.solverstate","Optional; the snapshot solver state to resume training.");DEFINE_string(weights, "E:/GitCode/Caffe/src/caffe/caffe/examples/mnist/xxxx.caffemodel","Optional; the pretrained weights to initialize finetuning, ""separated by ‘,’. Cannot be set simultaneously with snapshot.");// A simple registry for caffe commands.typedef int(*BrewFunction)();typedef std::map<caffe::string, BrewFunction> BrewMap;BrewMap g_brew_map;#define RegisterBrewFunction(func) \namespace { \class __Registerer_##func { \ public: /* NOLINT */ \ __Registerer_##func() { \g_brew_map[#func] = &func; \ } \}; \__Registerer_##func g_registerer_##func; \}static BrewFunction GetBrewFunction(const caffe::string& name) {if (g_brew_map.count(name)) {return g_brew_map[name];}else {LOG(ERROR) << "Available caffe actions:";for (BrewMap::iterator it = g_brew_map.begin(); it != g_brew_map.end(); ++it) {LOG(ERROR) << "\t" << it->first;}LOG(FATAL) << "Unknown action: " << name;return NULL; // not reachable, just to suppress old compiler warnings.}}// Load the weights from the specified caffemodel(s) into the train and test nets.void CopyLayers(caffe::Solver<float>* solver, const std::string& model_list) {std::vector<std::string> model_names;boost::split(model_names, model_list, boost::is_any_of(","));for (int i = 0; i < model_names.size(); ++i) {LOG(INFO) << "Finetuning from " << model_names[i];solver->net()->CopyTrainedLayersFrom(model_names[i]);for (int j = 0; j < solver->test_nets().size(); ++j) {solver->test_nets()[j]->CopyTrainedLayersFrom(model_names[i]);}}}// Train / Finetune a model.int train() {CHECK_GT(FLAGS_solver.size(), 0) << "Need a solver definition to train.";//CHECK(!FLAGS_snapshot.size() || !FLAGS_weights.size()) << "Give a snapshot to resume training or weights to finetune but not both.";caffe::SolverParameter solver_param;caffe::ReadProtoFromTextFileOrDie(FLAGS_solver, &solver_param);Caffe::set_mode(Caffe::CPU);shared_ptr<Solver<float> > solver(caffe::GetSolver<float>(solver_param));//if (FLAGS_snapshot.size()) { // resume training//LOG(INFO) << "Resuming from " << FLAGS_snapshot;//solver->Restore(FLAGS_snapshot.c_str());//}//else if (FLAGS_weights.size()) { // finetune//CopyLayers(solver.get(), FLAGS_weights);//}LOG(INFO) << "Starting Optimization";solver->Solve();LOG(INFO) << "Optimization Done.";return 0;}RegisterBrewFunction(train);int main(int argc, char* argv[]){argc = 2;#ifdef _DEBUG argv[0] = "E:/GitCode/Caffe/lib/dbg/x86_vc12/train_mnist[dbg_x86_vc12].exe";#else argv[0] = "E:/GitCode/Caffe/lib/rel/x86_vc12/train_mnist[rel_x86_vc12].exe";#endif argv[1] = "train";// 每个进程中至少要执行1次InitGoogleLogging(),否则不产生日志文件google::InitGoogleLogging(argv[0]);// 设置日志文件保存目录，此目录必须是已经存在的FLAGS_log_dir = "E:\\GitCode\\Caffe";FLAGS_max_log_size = 1024;//MB// Print output to stderr (while still logging).FLAGS_alsologtostderr = 1;// Usage message.gflags::SetUsageMessage("command line brew\n""usage: caffe <command> <args>\n\n""commands:\n"" traintrain or finetune a model\n");// Run tool or show usage.//caffe::GlobalInit(&argc, &argv);// 解析命令行参数 gflags::ParseCommandLineFlags(&argc, &argv, true);if (argc == 2) {return GetBrewFunction(caffe::string(argv[1]))();}else {gflags::ShowUsageWithFlagsRestrict(argv[0], "tools/caffe");}std::cout << "OK!!!" << std::endl;return 0;}
生命太过短暂，今天放弃了明天不一定能得到
