static util::StringVector convertActivationsToGraphemeLabels(matrix::Matrix&& activations,
const model::Model& model)
{
assert(activations.size().size() >= 3);
if(activations.size().size() > 3)
{
activations = reshape(activations,
{activations.size().front(),
activations.size()[1],
activations.size().product() / (activations.size().front() * activations.size()[1])});
}
size_t timesteps = activations.size()[2];
size_t miniBatchSize = activations.size()[1];
size_t graphemes = activations.size()[0];
util::StringVector labels;
for(size_t miniBatch = 0; miniBatch < miniBatchSize; ++miniBatch)
{
std::string currentLabel;
size_t currentGrapheme = graphemes;
for(size_t timestep = 0; timestep < timesteps; ++timestep)
{
size_t maxGrapheme = 0;
double maxValue = std::numeric_limits<double>::min();
for(size_t grapheme = 0; grapheme < graphemes; ++grapheme)
{
if(activations(grapheme, miniBatch, timestep) >= maxValue)
{
maxValue = activations(grapheme, miniBatch, timestep);
maxGrapheme = grapheme;
}
}
if(maxGrapheme != currentGrapheme)
{
currentGrapheme = maxGrapheme;
auto newGrapheme = model.getOutputLabel(maxGrapheme);
currentLabel.insert(currentLabel.end(), newGrapheme.begin(), newGrapheme.end());
}
}
labels.push_back(currentLabel);
}
return labels;
}
static util::StringVector convertActivationsToLabels(matrix::Matrix&& activations,
const model::Model& model)
{
if(model.hasAttribute("UsesGraphemes") && model.getAttribute<bool>("UsesGraphemes"))
{
return convertActivationsToGraphemeLabels(std::move(activations), model);
}
if(activations.size().size() > 2)
{
activations = reshape(activations,
{activations.size().front(),
activations.size().product() / activations.size().front()});
}
size_t samples = activations.size()[1];
size_t columns = activations.size()[0];
util::StringVector labels;
for(size_t sample = 0; sample < samples; ++sample)
{
size_t maxColumn = 0;
double maxValue = 0.0f;
for(size_t column = 0; column < columns; ++column)
{
if(activations(column, sample) >= maxValue)
{
maxValue = activations(column, sample);
maxColumn = column;
}
}
labels.push_back(model.getOutputLabel(maxColumn));
}
return labels;
}
SimTK::State StateInitializer::getOptimizedInitState()
{
if(_n_Z == 0)
return _initState;
StateInitializerQP qp(this);
qp.setNumParameters(_n_controls);
PrintVector(_lower_bd_actuatorforces,"_lower_bd_actuatorforces",std::cout);
PrintVector(_upper_bd_actuatorforces,"_upper_bd_actuatorforces",std::cout);
qp.setParameterLimits(_lower_bd_actuatorforces,_upper_bd_actuatorforces);
qp.setNumEqualityConstraints(_UDotRef.size());
qp.setNumInequalityConstraints(0);
//SimTK::Optimizer opt(qp,SimTK::CFSQP);
SimTK::Optimizer opt(qp, SimTK::InteriorPoint);
opt.setConvergenceTolerance(1e-4); //tol
opt.setMaxIterations(200); //200
Vector result(_n_controls);
result.setToZero();
SimTK::Real f = 0.0;
// qp.testQP();
try{
f = opt.optimize(result);
}
catch(const std::exception& ex)
{
std::cout<<ex.what()<<std::endl;
}
std::cout<<"Initial State Optimization Error: "<<f<<std::endl;
SimTK::State stateOpt = _initState;
Vector muscleForces(_n_muscles);
Vector result_forces = result.elementwiseMultiply(_opt_actuator_forces);
muscleForces = result_forces.block(0,0,_n_muscles,1).getAsVector();
Vector tol_lm(_n_muscles), tol_acts(_n_muscles);
Vector muscleFiberLens(_n_muscles), activations(_n_muscles);
tol_lm.setTo(1.0e-6);
tol_acts.setTo(1.0e-3);
PrintVector(result,"result",std::cout);
rootSolveMuscleFiberLength(stateOpt,muscleForces,_lm_min,_lm_max,tol_lm,muscleFiberLens);
ROCINForceController::setStateFiberLength(*_model,muscleFiberLens,stateOpt);
Vector lm_dot(_n_muscles);
lm_dot.setToZero();
_model->getMultibodySystem().realize(_initState,SimTK::Stage::Dynamics);
int idx_musc = 0;
for(int i=0;i<_n_controls;i++)
{
Actuator& act = _model->getActuators().get(i);
Muscle* m = dynamic_cast<Muscle*>(&act);
if(m!= NULL)
{
lm_dot[idx_musc] = m->getSpeed(_initState);//m->getSpeed(_initState)/m->getCosPennationAngle(_initState);//0;//m->getSpeed(_initState);
idx_musc++;
}
}
rootSolveActivations(stateOpt,muscleForces,muscleFiberLens,lm_dot,_acts_min,_acts_max,tol_acts,activations);
ROCINForceController::setStateActivation(*_model,activations,stateOpt);
return stateOpt;
}
//---------------------------------------------------------------------------
// 8. True action of the node (if activate returned true)
void
NaPNTeacher::action ()
{
unsigned iLayer;
unsigned iInpLayer = pnn->get_nn_unit()->InputLayer();
// Take neural network state at the time it's feed forward calculation
NaNNUnit pastNN;
pnn->pop_nn(pastNN);
if(is_verbose()){
int i;
NaPrintLog("NaPNTeacher (%p, %s[%s]):\n NN input: ",
this, name(), pastNN.GetInstance());
for(i = 0; i < pastNN.Xinp0.dim(); ++i)
NaPrintLog(" %g", pastNN.Xinp0[i]);
if(errout.links() == 0){
NaPrintLog("\n NN target: ");
for(i = 0; i < desout.data().dim(); ++i)
NaPrintLog(" %g", desout.data()[i]);
}else{
NaPrintLog("\n NN error: ");
for(i = 0; i < errout.data().dim(); ++i)
NaPrintLog(" %g", errout.data()[i]);
}
NaPrintLog("\n");
}
if(bLearn){
// Let's calculate delta weight from the past NN
bpe->AttachNN(&pastNN);
// One more activations since last update
++nLastUpdate;
for(iLayer = pastNN.OutputLayer();
(int)iLayer >= (int)iInpLayer; --iLayer){
if(pastNN.OutputLayer() == iLayer){
// Output layer
if(errout.links() == 0){
// Input pack #1
// Doesn't really need nnout due to it's stored inside NN
bpe->DeltaRule(&desout.data()[0]);
}else{
// Input pack #2
bpe->DeltaRule(&errout.data()[0], true);
}
}else{
// Hidden layer
bpe->DeltaRule(iLayer, iLayer + 1);
}
}// backward step
// Compute error on input
if(errinp.links() > 0){
unsigned iInput;
NaVector &einp = errinp.data();
einp.init_zero();
for(iInput = 0; iInput < einp.dim(); ++iInput){
// Or may be += ??? I didn't recognize the difference...
einp[iInput] -= bpe->PartOfDeltaRule(iInpLayer, iInput);
}
}
// Now let's forget the past NN state since changes should be
// applied to the current neural net anyway
bpe->DetachNN();
// Autoupdate facility
if(0 != nAutoUpdateFreq && nLastUpdate >= nAutoUpdateFreq){
NaPrintLog("%s: Automatic update #%d of NN '%s' (%d sample)\n",
name(), iUpdateCounter,
bpe->nn().GetInstance()? bpe->nn().GetInstance(): "",
activations());
update_nn();
// Call procedure
if(NULL != auProc)
(*auProc)(iUpdateCounter, pData);
}// if bLearn is on
}
}