bool SMDTable::open( FILE* file,int offset,unsigned short pageSize/*=4096*/,bool exists /*= true*/ )
{
this->file = file;
this->offset = offset;
if(exists && !checkFlag())
{
return false;
}
if(!exists)
{
saveFlag(pageSize);
}
if(pageMgr != NULL) delete pageMgr;
pageMgr = new SMDPageManagement(this,exists,pageSize);
if(pageMgr == NULL)
{
return false;
}
// 初始化各个段结构
segMeta = new SMDMetaSegment(pageMgr);
segTable = new SMDTableSegment(pageMgr);
segData = new SMDDataSegment(segTable,pageMgr);
segIndex = new SMDIndexSegment(pageMgr);
return true;
}
int main(int argc, char* argv[])
{
if (argc != 2 && argc != 3)
{
cout << "usage rnnlib [-s | --save] config_file" << endl;
exit(0);
}
bool autosave = false;
string configFilename;
if (argc == 3)
{
string saveFlag(argv[1]);
autosave = (saveFlag == "-s" || saveFlag == "--save");
configFilename = argv[2];
}
else
{
configFilename = argv[1];
}
ConfigFile conf(configFilename);
#ifdef FAST_LOGISTIC
Logistic::fill_lookup();
#endif
string task = conf.get<string>("task");
if (task == "transcription" && conf.has("dictionary"))
{
task = conf.set<string>("task", "dictionary_transcription");
}
bool display = conf.get<bool>("display", false);
vector<int> jacobianCoords = conf.get_list<int>("jacobianCoords");
bool gradCheck = conf.get<bool>("gradCheck", false);
verbose = conf.get<bool>("verbose", false);
int displaySequence = conf.get<int>("sequence", 0);
string dataset = conf.get<string>("dataset", "train");
check(in(validDatasets, dataset),
dataset + " given as 'dataset' parameter in config file '"
+ configFilename + "'\nmust be one of '" + str(validDatasets) + "'");
string dataFileString = dataset + "File";
string saveName = "";
ofstream* logout = 0;
TeeDev* tdev = 0;
TeeStream* tout = 0;
string displayPath = "";
string logname = "";
if (display || jacobianCoords.size())
{
displayPath = conf.get<string>("displayPath");
logname = displayPath + "log";
}
else if (autosave)
{
if (in (conf.filename, '@'))
{
saveName = conf.filename.substr(0, conf.filename.find('@'));
}
else
{
saveName = conf.filename.substr(0, conf.filename.rfind('.'));
}
saveName += "@" + time_stamp();
logname = saveName + ".log";
}
if (autosave || display || jacobianCoords.size())
{
logout = new ofstream(logname.c_str());
check(logout->is_open(), "can't open log file " + logname);
tdev = new TeeDev(cout, *logout);
tout = new TeeStream(*tdev);
cout << "writing to log file " << logname << endl;
}
ostream& out = tout ? *tout : cout;
vector<string> dataFiles = conf.get_list<string>(dataFileString);
int dataFileNum = conf.get<int>("dataFileNum", 0);
check(dataFiles.size() > dataFileNum, "no " + ordinal(dataFileNum) + " file in size " + str(dataFiles.size()) + " file list " + dataFileString + " in " + configFilename);
DataHeader header(dataFiles[dataFileNum], task, 1);
DataSequence* testSeq = 0;
if (display || gradCheck || jacobianCoords.size())
{
NetcdfDataset* data = new NetcdfDataset(dataFiles[dataFileNum], task, displaySequence);
testSeq = new DataSequence((*data)[0]);
delete data;
}
Mdrnn *net;
if (task == "code")
{
net = new CodeNet(out, conf, header);
}
else if (task == "memory")
{
net = new MemoryNet(out, conf, header);
}
else if (task == "pm")
{
net = new PmNet(out, conf, header);
}
else
{
net = new MultilayerNet(out, conf, header);
}
out << endl << "network:" << endl;
PRINT(task, out);
out << *net;
//build weight container after net is created
WeightContainer::instance().build();
int numWeights = WeightContainer::instance().weights.size();
out << numWeights << " weights" << endl << endl;
//build the network after the weight container
net->build();
//only construct optimiser after weight container is built
Optimiser* opt;
if (conf.get<string>("optimiser", "steepest") == "rprop")
{
opt = new Rprop(out);
}
else
{
opt = new SteepestDescent(out, conf.get<double>("learnRate", 1e-4), conf.get<double>("momentum", 0.9));
}
Trainer trainer(out, net, opt, conf);
out << "setting random seed to " << Random::set_seed(conf.get<unsigned long int>("randSeed", 0)) << endl << endl;
if (conf.get<bool>("loadWeights", false))
{
out << "loading dynamic data from " << conf.filename << endl;
DataExportHandler::instance().load(conf, out);
out << "epoch = " << trainer.epoch << endl << endl;
}
double initWeightRange = conf.get<double>("initWeightRange", 0.1);
out << "randomising uninitialised weights with mean 0 std. dev. " << initWeightRange << endl << endl;
WeightContainer::instance().randomise(initWeightRange);
out << "optimiser:" << endl << *opt << endl;
if (gradCheck)
{
out << "data header:" << endl << header << endl;
out << "running gradient check for sequence " << displaySequence << endl;
out << *testSeq;
prt_line(out);
GradientCheck(out, net, *testSeq, conf.get<int>("sigFigs", 6),
conf.get<double>("pert", 1e-5), conf.get<bool>("verbose", false));
}
else if (jacobianCoords.size())
{
out << "data header:" << endl << header << endl;
out << "calculating Jacobian for sequence " << displaySequence << " at coords " << jacobianCoords << endl;
out << *testSeq;
out << "output path: " << endl << displayPath << endl;
net->feed_forward(*testSeq);
net->print_output_shape(out);
net->outputLayer->outputErrors.get(jacobianCoords) = net->outputLayer->outputActivations.get(jacobianCoords);
net->feed_back();
DataExportHandler::instance().display(displayPath);
}
else if (display)
{
out << "data header:" << endl << header << endl;
out << "displaying sequence " << displaySequence << endl;
out << *testSeq;
out << "output path: " << endl << displayPath << endl;
net->train(*testSeq);
net->print_output_shape(out);
out << "errors:" << endl << net->outputLayer->errorMap;
DataExportHandler::instance().display(displayPath);
}
else if (conf.get<bool>("errorTest", false))
{
trainer.calculate_all_errors();
}
else
{
out << "trainer:" << endl;
trainer.train(saveName);
}
if (logout)
{
delete logout;
}
if (tdev)
{
delete tdev;
}
// if (tout)
// {
// delete tout;
// }
delete net;
delete opt;
}
void ofApp::onButtonEvent(ofxDatGuiButtonEvent e)
{
if (e.target->is("SAVE FLAG")) {
saveFlag();
}
}
