Added LossMultiplier

reading_machine/include/Classifier.hpp

@@ -11,6 +11,7 @@ class Classifier
 
   std::string name;
   std::map<std::string, std::unique_ptr<TransitionSet>> transitionSets;
+  std::map<std::string, float> lossMultipliers;
   std::shared_ptr<NeuralNetworkImpl> nn;
   std::unique_ptr<torch::optim::Adam> optimizer;
   std::string optimizerType, optimizerParameters;
@@ -33,6 +34,7 @@ class Classifier
   void saveOptimizer(std::filesystem::path path);
   torch::optim::Adam & getOptimizer();
   void setState(const std::string & state);
+  float getLossMultiplier();
 };
 
 #endif

reading_machine/src/Classifier.cpp

@@ -36,6 +36,28 @@ Classifier::Classifier(const std::string & name, std::filesystem::path path, std
         }))
     util::myThrow(fmt::format("Invalid line '{}', expected '{}'\n", definition[0], "(Transitions :) {tsFile1.ts tsFile2.ts...}"));
 
+  for (auto & it : this->transitionSets)
+    lossMultipliers[it.first] = 1.0;
+
+  if (!util::doIfNameMatch(std::regex("(?:(?:\\s|\\t)*)(?:LossMultiplier :|)(?:(?:\\s|\\t)*)\\{(.+)\\}"), definition[1], [this](auto sm)
+        {
+          auto pairs = util::split(sm.str(1), ' ');
+          for (auto & it : pairs)
+          {
+            auto splited = util::split(it, ',');
+            if (splited.size() != 2)
+              util::myThrow(fmt::format("invalid '{}' must have 2 elements", it));
+            try
+            {
+              lossMultipliers.at(splited[0]) = std::stof(splited[1]);
+            } catch (std::exception & e)
+            {
+              util::myThrow(fmt::format("caugh '{}' in '{}'", e.what(), it));
+            }
+          }
+        }))
+    util::myThrow(fmt::format("Invalid line '{}', expected '{}'\n", definition[0], "(LossMultiplier :) {state1,multiplier1 state2,multiplier2...}"));
+
   initNeuralNetwork(definition);
 }
 
@@ -73,7 +95,7 @@ void Classifier::initNeuralNetwork(const std::vector<std::string> & definition)
   for (auto & it : this->transitionSets)
     nbOutputsPerState[it.first] = it.second->size();
 
-  std::size_t curIndex = 1;
+  std::size_t curIndex = 2;
 
   std::string networkType;
   if (curIndex >= definition.size() || !util::doIfNameMatch(std::regex("(?:(?:\\s|\\t)*)(?:Network type :|)(?:(?:\\s|\\t)*)(\\S+)"), definition[curIndex], [&curIndex,&networkType](auto sm)
@@ -153,3 +175,8 @@ void Classifier::resetOptimizer()
     util::myThrow(expected);
 }
 
+float Classifier::getLossMultiplier()
+{
+  return lossMultipliers.at(state);
+}
+

trainer/src/Trainer.cpp

@@ -190,7 +190,7 @@ float Trainer::processDataset(DataLoader & loader, bool train, bool printAdvance
 
     labels = labels.reshape(labels.dim() == 0 ? 1 : labels.size(0));
 
-    auto loss = lossFct(prediction, labels);
+    auto loss = machine.getClassifier()->getLossMultiplier()*lossFct(prediction, labels);
     try
     {
       totalLoss += loss.item<float>();