diff --git a/maca_common/src/programOptionsTemplates.cpp b/maca_common/src/programOptionsTemplates.cpp
index 0e973710408e2f0e7c842c8408fbd2498fd77157..8a67aaa0c84b988344439b05c4389a3dd7bb6003 100644
--- a/maca_common/src/programOptionsTemplates.cpp
+++ b/maca_common/src/programOptionsTemplates.cpp
@@ -54,7 +54,7 @@ po::options_description getTrainOptionsDescription()
("optimizer", po::value<std::string>()->default_value("amsgrad"),
"The learning algorithm to use : amsgrad | adam | sgd")
("loss", po::value<std::string>()->default_value("neglogsoftmax"),
- "The loss function to use : neglogsoftmax | weighted")
+ "The loss function to use : neglogsoftmax")
("dev", po::value<std::string>()->default_value(""),
"Development corpus formated according to the MCD")
("lang", po::value<std::string>()->default_value("fr"),
diff --git a/neural_network/include/GeneticAlgorithm.hpp b/neural_network/include/GeneticAlgorithm.hpp
index 83ce2d1997f3c54414e9ea706a6b086253897c58..b5ec4c3b016ff9856bdb7ad8f82692411862f653 100644
--- a/neural_network/include/GeneticAlgorithm.hpp
+++ b/neural_network/include/GeneticAlgorithm.hpp
@@ -105,6 +105,14 @@ class GeneticAlgorithm : public NeuralNetwork
/// @return The loss.
float update(FeatureModel::FeatureDescription & fd, int gold) override;
+ /// @brief Update the parameters according to the given gold vector.
+ ///
+ /// @param fd The input to use.
+ /// @param gold The gold vector for this input.
+ ///
+ /// @return The loss.
+ float update(FeatureModel::FeatureDescription & fd, const std::vector<float> & gold) override;
+
/// @brief Get the loss according to the given gold class.
///
/// @param fd The input to use.
@@ -113,6 +121,14 @@ class GeneticAlgorithm : public NeuralNetwork
/// @return The loss.
float getLoss(FeatureModel::FeatureDescription & fd, int gold) override;
+ /// @brief Get the loss according to the given gold vector.
+ ///
+ /// @param fd The input to use.
+ /// @param gold The gold vector for this input.
+ ///
+ /// @return The loss.
+ float getLoss(FeatureModel::FeatureDescription & fd, const std::vector<float> & gold) override;
+
/// @brief Save the GeneticAlgorithm to a file.
///
/// @param filename The file to write the GeneticAlgorithm to.
@@ -122,6 +138,7 @@ class GeneticAlgorithm : public NeuralNetwork
///
/// @param output Where the topology will be printed.
void printTopology(FILE * output) override;
+ void endOfIteration();
};
#endif
diff --git a/neural_network/include/MLP.hpp b/neural_network/include/MLP.hpp
index 47bc80e05c7a3e14e571315e180bbc98d83e95a6..37d707d0f517b2e40eb8cd57b2f8db2a61527bb1 100644
--- a/neural_network/include/MLP.hpp
+++ b/neural_network/include/MLP.hpp
@@ -60,7 +60,21 @@ class MLP : public NeuralNetwork
/// @param gold The gold class of this input.
///
/// @return The loss.
+ float update(FeatureModel::FeatureDescription & fd, const std::vector<float> & gold) override;
+ /// @brief Get the loss according to the given gold class.
+ ///
+ /// @param fd The input to use.
+ /// @param gold The gold class of this input.
+ ///
+ /// @return The loss.
float getLoss(FeatureModel::FeatureDescription & fd, int gold) override;
+ /// @brief Get the loss according to the given gold vector.
+ ///
+ /// @param fd The input to use.
+ /// @param gold The gold vector for this input.
+ ///
+ /// @return The loss.
+ float getLoss(FeatureModel::FeatureDescription & fd, const std::vector<float> & gold) override;
/// @brief Save the MLP to a file.
///
/// @param filename The file to write the MLP to.
@@ -73,6 +87,7 @@ class MLP : public NeuralNetwork
///
/// @return A pointer to the newly allocated trainer.
dynet::Trainer * createTrainer();
+ void endOfIteration();
};
#endif
diff --git a/neural_network/include/MLPBase.hpp b/neural_network/include/MLPBase.hpp
index 06c02ad3c7c0dafcef7cbda00e4fd66816f2a6fc..826a7d8a1a8f8fe1f1f95d90cc983b4c168d5a35 100644
--- a/neural_network/include/MLPBase.hpp
+++ b/neural_network/include/MLPBase.hpp
@@ -30,10 +30,14 @@ class MLPBase
/// @brief Must the Layer dropout rate be taken into account during the computations ? Usually it is only during the training step.
bool dropoutActive;
- /// @brief The current minibatch.
- std::vector<FeatureModel::FeatureDescription> fds;
+ /// @brief The current minibatch for one hot golds.
+ std::vector<FeatureModel::FeatureDescription> fdsOneHot;
/// @brief gold classes of the current minibatch.
- std::vector<unsigned int> golds;
+ std::vector<unsigned int> goldsOneHot;
+ /// @brief The current minibatch for continuous golds.
+ std::vector<FeatureModel::FeatureDescription> fdsContinuous;
+ /// @brief gold outputs of the current minibatch.
+ std::vector< std::vector<float> > goldsContinuous;
private :
@@ -86,16 +90,6 @@ class MLPBase
/// @param model The dynet model that will contain the loaded parameters.
/// @param filename The file from which the parameters will be read.
void loadParameters(dynet::ParameterCollection & model, const std::string & filename);
- /// @brief Get the loss expression
- ///
- /// @param output Output from the neural network
- /// @param oneHotGolds Indexes of gold classes (batched form)
- ///
- /// @return The loss expression
- dynet::Expression weightedLoss(dynet::Expression & output, std::vector<unsigned int> & oneHotGolds);
-
- dynet::Expression errorCorrectionLoss(dynet::ComputationGraph & cg, dynet::Expression & output, std::vector<unsigned int> & oneHotGolds);
-
/// @brief initialize a new untrained MLP from a desired topology.
///
/// topology example for 2 hidden layers : (150,RELU,0.3)(50,ELU,0.2)\n
@@ -123,6 +117,13 @@ class MLPBase
///
/// @return The loss.
float update(FeatureModel::FeatureDescription & fd, int gold);
+ /// @brief Update the parameters according to the given gold output.
+ ///
+ /// @param fd The input to use.
+ /// @param gold The gold output for this input.
+ ///
+ /// @return The loss.
+ float update(FeatureModel::FeatureDescription & fd, const std::vector<float> & gold);
/// @brief Get the loss according to the given gold class.
///
/// @param fd The input to use.
@@ -130,10 +131,19 @@ class MLPBase
///
/// @return The loss.
float getLoss(FeatureModel::FeatureDescription & fd, int gold);
+ /// @brief Get the loss according to the given output vector.
+ ///
+ /// @param fd The input to use.
+ /// @param gold The gold output for this input.
+ ///
+ /// @return The loss.
+ float getLoss(FeatureModel::FeatureDescription & fd, const std::vector<float> & gold);
/// @brief Print the topology (Layers) of the MLP.
///
/// @param output Where the topology will be printed.
void printTopology(FILE * output);
+ /// @brief Clear the current batch.
+ void endOfIteration();
};
#endif
diff --git a/neural_network/include/NeuralNetwork.hpp b/neural_network/include/NeuralNetwork.hpp
index 4b88540886d481d539518bc09f14243d93ee762a..7473548dfde8a42898c8c02ee205275c341b85ca 100644
--- a/neural_network/include/NeuralNetwork.hpp
+++ b/neural_network/include/NeuralNetwork.hpp
@@ -145,6 +145,14 @@ class NeuralNetwork
/// @return The loss.
virtual float update(FeatureModel::FeatureDescription & fd, int gold) = 0;
+ /// @brief Update the parameters according to the given gold vector.
+ ///
+ /// @param fd The input to use.
+ /// @param gold The gold vector for this input.
+ ///
+ /// @return The loss.
+ virtual float update(FeatureModel::FeatureDescription & fd, const std::vector<float> & gold) = 0;
+
/// @brief Get the loss according to the given gold class.
///
/// @param fd The input to use.
@@ -153,6 +161,14 @@ class NeuralNetwork
/// @return The loss.
virtual float getLoss(FeatureModel::FeatureDescription & fd, int gold) = 0;
+ /// @brief Get the loss according to the given gold vector.
+ ///
+ /// @param fd The input to use.
+ /// @param gold The gold vector for this input.
+ ///
+ /// @return The loss.
+ virtual float getLoss(FeatureModel::FeatureDescription & fd, const std::vector<float> & gold) = 0;
+
/// @brief Save the NeuralNetwork to a file.
///
/// @param filename The file to write the NeuralNetwork to.
@@ -163,6 +179,8 @@ class NeuralNetwork
/// @param output Where the topology will be printed.
virtual void printTopology(FILE * output) = 0;
+ virtual void endOfIteration() = 0;
+
/// @brief Return the model.
///
/// @return The model of this NeuralNetwork.
diff --git a/neural_network/src/GeneticAlgorithm.cpp b/neural_network/src/GeneticAlgorithm.cpp
index 6d206e93b81e11345e211c5d72de83026fd835ad..c57577c71f675a6a18913695a7050063c1f26db5 100644
--- a/neural_network/src/GeneticAlgorithm.cpp
+++ b/neural_network/src/GeneticAlgorithm.cpp
@@ -71,6 +71,18 @@ float GeneticAlgorithm::getLoss(FeatureModel::FeatureDescription &, int)
return loss;
}
+float GeneticAlgorithm::getLoss(FeatureModel::FeatureDescription &, const std::vector<float> &)
+{
+ fprintf(stderr, "ERROR (%s) : not implemented. Aborting.\n", ERRINFO);
+ exit(1);
+}
+
+float GeneticAlgorithm::update(FeatureModel::FeatureDescription & , const std::vector<float> & )
+{
+ fprintf(stderr, "ERROR (%s) : not implemented. Aborting.\n", ERRINFO);
+ exit(1);
+}
+
float GeneticAlgorithm::update(FeatureModel::FeatureDescription & fd, int gold)
{
bool haveBeenUpdated = false;
@@ -300,3 +312,9 @@ void GeneticAlgorithm::Individual::mutate(float probability)
}
}
+void GeneticAlgorithm::endOfIteration()
+{
+ for (auto & it : generation)
+ it->mlp.endOfIteration();
+}
+
diff --git a/neural_network/src/MLP.cpp b/neural_network/src/MLP.cpp
index 15b5742a874ade9d297051b0dfbe6baf192532d3..1b65be207f970a930c361051c348c8294c518144 100644
--- a/neural_network/src/MLP.cpp
+++ b/neural_network/src/MLP.cpp
@@ -68,9 +68,41 @@ float MLP::update(FeatureModel::FeatureDescription & fd, int gold)
}
}
+float MLP::update(FeatureModel::FeatureDescription & fd, const std::vector<float> & gold)
+{
+ try
+ {
+ float loss = mlp.update(fd, gold);
+ trainer->update();
+ return loss;
+ } catch (BatchNotFull &)
+ {
+ return 0.0;
+ }
+}
+
float MLP::getLoss(FeatureModel::FeatureDescription & fd, int gold)
{
- return mlp.getLoss(fd, gold);
+ try
+ {
+ float loss = mlp.getLoss(fd, gold);
+ return loss;
+ } catch (BatchNotFull &)
+ {
+ return 0.0;
+ }
+}
+
+float MLP::getLoss(FeatureModel::FeatureDescription & fd, const std::vector<float> & gold)
+{
+ try
+ {
+ float loss = mlp.getLoss(fd, gold);
+ return loss;
+ } catch (BatchNotFull &)
+ {
+ return 0.0;
+ }
}
void MLP::save(const std::string & filename)
@@ -86,3 +118,8 @@ void MLP::printTopology(FILE * output)
mlp.printTopology(output);
}
+void MLP::endOfIteration()
+{
+ mlp.endOfIteration();
+}
+
diff --git a/neural_network/src/MLPBase.cpp b/neural_network/src/MLPBase.cpp
index b88147c23bb6b90ecb2d0f0d0e765341f705883e..00ce27cf4d3526f1f7c052999275b8dd1fa9e379 100644
--- a/neural_network/src/MLPBase.cpp
+++ b/neural_network/src/MLPBase.cpp
@@ -91,16 +91,16 @@ std::vector<float> MLPBase::predict(FeatureModel::FeatureDescription & fd)
float MLPBase::update(FeatureModel::FeatureDescription & fd, int gold)
{
- fds.emplace_back(fd);
- golds.emplace_back(gold);
+ fdsOneHot.emplace_back(fd);
+ goldsOneHot.emplace_back(gold);
- if ((int)fds.size() < ProgramParameters::batchSize)
+ if ((int)fdsOneHot.size() < ProgramParameters::batchSize)
throw BatchNotFull();
std::vector<dynet::Expression> inputs;
dynet::ComputationGraph cg;
- for (auto & example : fds)
+ for (auto & example : fdsOneHot)
{
std::vector<dynet::Expression> expressions;
@@ -117,15 +117,7 @@ float MLPBase::update(FeatureModel::FeatureDescription & fd, int gold)
if (ProgramParameters::loss == "neglogsoftmax")
{
- batchedLoss = dynet::sum_batches(pickneglogsoftmax(output, golds));
- }
- else if (ProgramParameters::loss == "weighted")
- {
- batchedLoss = weightedLoss(output, golds);
- }
- else if (ProgramParameters::loss == "errorCorrection")
- {
- batchedLoss = errorCorrectionLoss(cg, output, golds);
+ batchedLoss = dynet::sum_batches(pickneglogsoftmax(output, goldsOneHot));
}
else
{
@@ -137,42 +129,83 @@ float MLPBase::update(FeatureModel::FeatureDescription & fd, int gold)
checkGradients();
- fds.clear();
- golds.clear();
+ fdsOneHot.clear();
+ goldsOneHot.clear();
return as_scalar(batchedLoss.value());
}
-float MLPBase::getLoss(FeatureModel::FeatureDescription & fd, int gold)
+float MLPBase::update(FeatureModel::FeatureDescription & fd, const std::vector<float> & gold)
{
+ fdsContinuous.emplace_back(fd);
+ goldsContinuous.emplace_back(gold);
+
+ if ((int)fdsContinuous.size() < ProgramParameters::batchSize)
+ throw BatchNotFull();
+
std::vector<dynet::Expression> inputs;
- std::vector<unsigned int> goldss;
- goldss.emplace_back(gold);
dynet::ComputationGraph cg;
- std::vector<dynet::Expression> expressions;
+ for (auto & example : fdsContinuous)
+ {
+ std::vector<dynet::Expression> expressions;
- for (auto & featValue : fd.values)
- expressions.emplace_back(NeuralNetwork::featValue2Expression(cg, featValue));
+ for (auto & featValue : example.values)
+ expressions.emplace_back(NeuralNetwork::featValue2Expression(cg, featValue));
- dynet::Expression input = dynet::concatenate(expressions);
- inputs.emplace_back(input);
+ dynet::Expression input = dynet::concatenate(expressions);
+ inputs.emplace_back(input);
+ }
dynet::Expression batchedInput = dynet::concatenate_to_batch(inputs);
dynet::Expression output = run(cg, batchedInput);
dynet::Expression batchedLoss;
+ std::vector<dynet::Expression> goldExpressions;
+ for (auto & gold : goldsContinuous)
+ goldExpressions.emplace_back(dynet::input(cg, dynet::Dim({1,(unsigned int)gold.size()}), gold));
- if (ProgramParameters::loss == "neglogsoftmax")
- {
- batchedLoss = dynet::sum_batches(pickneglogsoftmax(output, goldss));
- }
- else if (ProgramParameters::loss == "weighted")
+ dynet::Expression batchedGold = dynet::concatenate_to_batch(goldExpressions);
+ batchedLoss = dynet::sum_batches(dynet::squared_distance(output, batchedGold));
+
+ cg.backward(batchedLoss);
+
+ checkGradients();
+
+ fdsContinuous.clear();
+ goldsContinuous.clear();
+
+ return as_scalar(batchedLoss.value());
+}
+
+float MLPBase::getLoss(FeatureModel::FeatureDescription & fd, int gold)
+{
+ fdsOneHot.emplace_back(fd);
+ goldsOneHot.emplace_back(gold);
+
+ if ((int)fdsOneHot.size() < ProgramParameters::batchSize)
+ throw BatchNotFull();
+
+ std::vector<dynet::Expression> inputs;
+ dynet::ComputationGraph cg;
+
+ for (auto & example : fdsOneHot)
{
- batchedLoss = weightedLoss(output, goldss);
+ std::vector<dynet::Expression> expressions;
+
+ for (auto & featValue : example.values)
+ expressions.emplace_back(NeuralNetwork::featValue2Expression(cg, featValue));
+
+ dynet::Expression input = dynet::concatenate(expressions);
+ inputs.emplace_back(input);
}
- else if (ProgramParameters::loss == "errorCorrection")
+
+ dynet::Expression batchedInput = dynet::concatenate_to_batch(inputs);
+ dynet::Expression output = run(cg, batchedInput);
+ dynet::Expression batchedLoss;
+
+ if (ProgramParameters::loss == "neglogsoftmax")
{
- batchedLoss = errorCorrectionLoss(cg, output, goldss);
+ batchedLoss = dynet::sum_batches(pickneglogsoftmax(output, goldsOneHot));
}
else
{
@@ -180,6 +213,51 @@ float MLPBase::getLoss(FeatureModel::FeatureDescription & fd, int gold)
exit(1);
}
+ checkGradients();
+
+ fdsOneHot.clear();
+ goldsOneHot.clear();
+
+ return as_scalar(batchedLoss.value());
+}
+
+float MLPBase::getLoss(FeatureModel::FeatureDescription & fd, const std::vector<float> & gold)
+{
+ fdsContinuous.emplace_back(fd);
+ goldsContinuous.emplace_back(gold);
+
+ if ((int)fdsContinuous.size() < ProgramParameters::batchSize)
+ throw BatchNotFull();
+
+ std::vector<dynet::Expression> inputs;
+ dynet::ComputationGraph cg;
+
+ for (auto & example : fdsContinuous)
+ {
+ std::vector<dynet::Expression> expressions;
+
+ for (auto & featValue : example.values)
+ expressions.emplace_back(NeuralNetwork::featValue2Expression(cg, featValue));
+
+ dynet::Expression input = dynet::concatenate(expressions);
+ inputs.emplace_back(input);
+ }
+
+ dynet::Expression batchedInput = dynet::concatenate_to_batch(inputs);
+ dynet::Expression output = run(cg, batchedInput);
+ dynet::Expression batchedLoss;
+ std::vector<dynet::Expression> goldExpressions;
+ for (auto & gold : goldsContinuous)
+ goldExpressions.emplace_back(dynet::input(cg, dynet::Dim({1,(unsigned int)gold.size()}), gold));
+
+ dynet::Expression batchedGold = dynet::concatenate_to_batch(goldExpressions);
+ batchedLoss = dynet::sum_batches(dynet::squared_distance(output, batchedGold));
+
+ checkGradients();
+
+ fdsContinuous.clear();
+ goldsContinuous.clear();
+
return as_scalar(batchedLoss.value());
}
@@ -206,46 +284,6 @@ void MLPBase::checkGradients()
}
}
-dynet::Expression MLPBase::weightedLoss(dynet::Expression & output, std::vector<unsigned int> & oneHotGolds)
-{
- std::vector<dynet::Expression> lossExpr;
- for (unsigned int i = 0; i < output.dim().batch_elems(); i++)
- {
- lossExpr.emplace_back(dynet::pickneglogsoftmax(dynet::pick_batch_elem(output, i), oneHotGolds[i]));
- auto outputVect = dynet::as_vector(dynet::pick_batch_elem(output,i).value());
- int prediction = 0;
- for (unsigned int j = 1; j < outputVect.size(); j++)
- if(outputVect[j] > outputVect[prediction])
- prediction = j;
- int gold = oneHotGolds[i];
- if (prediction == 1 && gold == 0)
- {
- lossExpr.back() = lossExpr.back() * 100.0;
- }
- }
-
- return dynet::sum(lossExpr);
-}
-
-dynet::Expression MLPBase::errorCorrectionLoss(dynet::ComputationGraph & cg, dynet::Expression & output, std::vector<unsigned int> & oneHotGolds)
-{
- std::vector<dynet::Expression> lossExpr;
- for (unsigned int i = 0; i < output.dim().batch_elems(); i++)
- {
- unsigned int u = 0;
- dynet::Expression c = dynet::pick(dynet::one_hot(cg, layers.back().output_dim, oneHotGolds[i]),u);
- dynet::Expression a = dynet::pick(dynet::softmax(dynet::pick_batch_elem(output,i)),u);
- lossExpr.emplace_back(dynet::pickneglogsoftmax(dynet::pick_batch_elem(output, i),oneHotGolds[i])+2-c-a*c+(dynet::acos(a-1)*(c-1)));
- if (ProgramParameters::debug)
- {
- cg.forward(lossExpr.back());
- fprintf(stderr, "a=%.2f c=%.2f loss=%.2f\n", dynet::as_scalar(a.value()),dynet::as_scalar(c.value()),dynet::as_scalar(lossExpr.back().value()));
- }
- }
-
- return dynet::sum(lossExpr);
-}
-
dynet::Expression MLPBase::run(dynet::ComputationGraph & cg, dynet::Expression x)
{
static std::vector< std::pair<std::string,dynet::Expression> > exprForDebug;
@@ -436,3 +474,11 @@ void MLPBase::printTopology(FILE * output)
fprintf(output, ")\n");
}
+void MLPBase::endOfIteration()
+{
+ fdsOneHot.clear();
+ fdsContinuous.clear();
+ goldsOneHot.clear();
+ goldsContinuous.clear();
+}
+
diff --git a/trainer/include/Trainer.hpp b/trainer/include/Trainer.hpp
index 1f1171c18da7d8f9eb6696612e3e82e6de2c5ab8..9cacbe44670c5326fe0ae941bed5ec9292706b23 100644
--- a/trainer/include/Trainer.hpp
+++ b/trainer/include/Trainer.hpp
@@ -113,6 +113,9 @@ class Trainer
/// on the current epoch is its all time best.\n
/// When a Classifier is saved that way, all the Dict involved are also saved.
void train();
+
+ /// @brief Prepare Classifiers for next iteration.
+ void endOfIteration();
};
#endif
diff --git a/trainer/src/TrainInfos.cpp b/trainer/src/TrainInfos.cpp
index c1f8137dab041136e60eb9523833d3d9e3080941..daaa46bd583543cf9c4eebaa80a1a78ed8fda250 100644
--- a/trainer/src/TrainInfos.cpp
+++ b/trainer/src/TrainInfos.cpp
@@ -130,7 +130,7 @@ void TrainInfos::addTrainLoss(const std::string & classifier, float loss)
void TrainInfos::addDevLoss(const std::string & classifier, float loss)
{
- devLossesPerClassifierPerEpoch[classifier].emplace_back(loss);
+ devLossesPerClassifierPerEpoch[classifier].back() += loss;
}
void TrainInfos::addTrainScore(const std::string & classifier, float score)
@@ -222,7 +222,11 @@ void TrainInfos::nextEpoch()
lastEpoch++;
saveToFilename();
for (auto & it : topologyPrinted)
+ {
trainLossesPerClassifierPerEpoch[it.first].emplace_back(0.0);
+ if (ProgramParameters::devLoss)
+ devLossesPerClassifierPerEpoch[it.first].emplace_back(0.0);
+ }
}
void TrainInfos::computeMustSaves()
diff --git a/trainer/src/Trainer.cpp b/trainer/src/Trainer.cpp
index 76bf1bda3207f4d1c69d2d1ccdc9dd50fced1664..61032a35bedc17a0e8584eeb6e2ac360af7975e6 100644
--- a/trainer/src/Trainer.cpp
+++ b/trainer/src/Trainer.cpp
@@ -461,12 +461,24 @@ void Trainer::doStepTrain()
trainConfig.addToEntropyHistory(entropy);
}
+void Trainer::endOfIteration()
+{
+ auto classifiers = tm.getClassifiers();
+ for (auto * cla : classifiers)
+ if (cla->needsTrain())
+ cla->endOfIteration();
+}
+
void Trainer::prepareNextEpoch()
{
+ endOfIteration();
+
printScoresAndSave(stderr);
nbSteps = 0;
TI.nextEpoch();
+ endOfIteration();
+
if (TI.getEpoch() > ProgramParameters::nbIter)
throw EndOfTraining();
}
diff --git a/transition_machine/include/Classifier.hpp b/transition_machine/include/Classifier.hpp
index a432feff60d7521c01ae6f6db2cc3d2216a8371c..001869a6495589ad9819edafb882f73cad368ecf 100644
--- a/transition_machine/include/Classifier.hpp
+++ b/transition_machine/include/Classifier.hpp
@@ -158,11 +158,25 @@ class Classifier
float trainOnExample(Config & config, int gold);
/// @brief Train the classifier on a training example.
///
+ /// @param config The Config to work with.
+ /// @param gold The gold vector for this Config.
+ ///
+ /// @return The loss.
+ float trainOnExample(Config & config, const std::vector<float> & gold);
+ /// @brief Train the classifier on a training example.
+ ///
/// @param fd The FeatureDescription to work with.
/// @param gold The gold class of the FeatureDescription.
///
/// @return The loss.
float trainOnExample(FeatureModel::FeatureDescription & fd, int gold);
+ /// @brief Train the classifier on a training example.
+ ///
+ /// @param fd The FeatureDescription to work with.
+ /// @param gold The gold vector for this FeatureDescription.
+ ///
+ /// @return The loss.
+ float trainOnExample(FeatureModel::FeatureDescription & fd, const std::vector<float> & gold);
/// @brief Get the loss of the classifier on a training example.
///
/// @param config The Config to work with.
@@ -170,6 +184,13 @@ class Classifier
///
/// @return The loss.
float getLoss(Config & config, int gold);
+ /// @brief Get the loss of the classifier on a training example.
+ ///
+ /// @param config The Config to work with.
+ /// @param gold The gold vector for this Config.
+ ///
+ /// @return The loss.
+ float getLoss(Config & config, const std::vector<float> & gold);
/// @brief Get the name of an Action from its index.
///
/// The index of an Action can be seen as the index of the corresponding output neuron in the underlying neural network.
@@ -213,6 +234,8 @@ class Classifier
unsigned int getNbActions();
/// @brief Get a pointer to the FeatureModel.
FeatureModel * getFeatureModel();
+ /// @brief Prepare Classifier for next iteration.
+ void endOfIteration();
};
#endif
diff --git a/transition_machine/src/Classifier.cpp b/transition_machine/src/Classifier.cpp
index 89f9a93281560aca151ae47365c7f7c72cc8c787..d28ed2cff2e7fe77c1f51f5f18a027fbfc0ece5d 100644
--- a/transition_machine/src/Classifier.cpp
+++ b/transition_machine/src/Classifier.cpp
@@ -286,6 +286,15 @@ float Classifier::trainOnExample(Config & config, int gold)
return nn->update(fd, gold);
}
+float Classifier::trainOnExample(Config & config, const std::vector<float> & gold)
+{
+ if (ProgramParameters::noNeuralNetwork)
+ return 0.0;
+
+ auto & fd = fm->getFeatureDescription(config);
+ return nn->update(fd, gold);
+}
+
float Classifier::trainOnExample(FeatureModel::FeatureDescription & fd, int gold)
{
if (ProgramParameters::noNeuralNetwork)
@@ -294,6 +303,14 @@ float Classifier::trainOnExample(FeatureModel::FeatureDescription & fd, int gold
return nn->update(fd, gold);
}
+float Classifier::trainOnExample(FeatureModel::FeatureDescription & fd, const std::vector<float> & gold)
+{
+ if (ProgramParameters::noNeuralNetwork)
+ return 0.0;
+
+ return nn->update(fd, gold);
+}
+
float Classifier::getLoss(Config & config, int gold)
{
if (ProgramParameters::noNeuralNetwork)
@@ -303,6 +320,15 @@ float Classifier::getLoss(Config & config, int gold)
return nn->getLoss(fd, gold);
}
+float Classifier::getLoss(Config & config, const std::vector<float> & gold)
+{
+ if (ProgramParameters::noNeuralNetwork)
+ return 0.0;
+
+ auto & fd = fm->getFeatureDescription(config);
+ return nn->getLoss(fd, gold);
+}
+
void Classifier::explainCostOfActions(FILE * output, Config & config)
{
for (Action & a : as->actions)
@@ -362,3 +388,8 @@ FeatureModel * Classifier::getFeatureModel()
return fm.get();
}
+void Classifier::endOfIteration()
+{
+ nn->endOfIteration();
+}
+