void NeuralNetwork::train(BlockSparseMatrix& input, BlockSparseMatrix& reference)
{
//create a backpropagate-data class
//given the neural network, inputs & reference outputs
util::log("NeuralNetwork") << "Running back propagation on input matrix (" << input.rows() << ") rows, ("
<< input.columns() << ") columns. Using reference output of (" << reference.rows() << ") rows, ("
<< reference.columns() << ") columns. \n";
auto backPropagation = createBackPropagation();
backPropagation->setNeuralNetwork(this);
backPropagation->setInput(&input);
backPropagation->setReferenceOutput(&reference);
auto solver = NeuralNetworkSolver::create(backPropagation);
try
{
solver->solve();
}
catch(...)
{
delete solver;
delete backPropagation;
throw;
}
delete solver;
delete backPropagation;
}
BackPropagation* NeuralNetwork::createBackPropagation() const
{
std::string backPropagationType;
if(_useSparseCostFunction)
{
backPropagationType = "SparseBackPropagation";
}
else
{
backPropagationType = "DenseBackPropagation";
}
backPropagationType = util::KnobDatabase::getKnobValue(
"BackPropagation::ForceType", backPropagationType);
auto backPropagation = BackPropagationFactory::create(backPropagationType);
if(backPropagation == nullptr)
{
throw std::runtime_error("Failed to create back propagation "
"structure with type: " + backPropagationType);
}
backPropagation->setNeuralNetwork(const_cast<NeuralNetwork*>(this));
return backPropagation;
}
Creature::Creature(int body_parts, int constraints, const NeuralNetwork & neural_network)
:
_name(""),
_number_of_body_parts(body_parts),
_number_of_constraints(constraints),
_body_parts(new BodyPart*[body_parts]),
_constraints(new Constraint*[constraints]),
_neural_network(NULL),
_initial_position(btVector3(0.0, 0.0, 0.0)),
_final_position(btVector3(0.0, 0.0, 0.0)),
_fitness(0.0) {
for (int i = 0; i < getNumberOfBodyParts(); ++i) {
setBodyPart(i, new BodyPart());
getBodyPart(i).setName("Body Part #" + TO_STRING(i));
}
for (int i = 0; i < getNumberOfConstraints(); ++i) {
setConstraint(i, new Constraint());
getConstraint(i).setName("Constraint #" + TO_STRING(i));
}
setNeuralNetwork(neural_network.clone());
}
