void fill()
{
order.clear();
MapPtr nodes = ::root_map->get( "Nodes" );
Map::iterator i = nodes->begin();
Map::iterator iend = nodes->end();
while( i != iend )
{
NodeLayerPtr node = nodes->get( i );
BooleanPtr enabled( node->get( "enabled" ) );
if( node->asyncRecallOrder() && enabled->get() )
{
size_t neurons = node->numNeurons();
for( size_t i = 0; i < neurons; ++i )
{
order.push_back( NodeNeuron( node, i ) );
}
}
i++;
}
}
void nnet::learn()
{
if( ::exec_map->empty() )
{
// train synchronous layers first
{
MapPtr nodes = ::root_map->get( "Nodes" );
Map::iterator i = nodes->begin();
Map::iterator iend = nodes->end();
while( i != iend )
{
NodeLayerPtr node = nodes->get( i );
BooleanPtr enabled( node->get( "enabled" ) );
if( !node->asyncRecallOrder() && enabled->get() )
{
learn( node );
}
i++;
}
}
// then train async layers
{
RecallOrder::iterator i = recall_order.order.begin();
RecallOrder::iterator iend = recall_order.order.end();
while( i != iend )
{
i->node->learn( i->neuron );
i++;
}
}
}
else
{
try
{
ExecEnginePtr exec( ::exec_map->first() );
exec->learn();
}
catch( std::exception& e )
{
LOG_EXCEPTION_E( e );
error::std_exception( "nnet::Learn() running ExecEngine", e.what() );
return;
}
catch( ... )
{
LOG_EXCEPTION;
error::alert( "Critical exception in ExecEngine!" );
}
}
nnet::global::learn_signal();
}