From 9811325d5b17f8e36f48a7daaf62481af0c1a8f7 Mon Sep 17 00:00:00 2001
From: Hartbook <franck.dary@etu.univ-amu.fr>
Date: Sun, 1 Jul 2018 22:36:28 +0200
Subject: [PATCH] First version of a working training
---
MLP/include/MLP.hpp | 2 ++
MLP/src/MLP.cpp | 50 ++++++++++++++++++----------
tape_machine/include/TapeMachine.hpp | 5 ++-
tape_machine/src/Classifier.cpp | 6 +++-
trainer/src/Trainer.cpp | 28 ++++++++++++++--
5 files changed, 69 insertions(+), 22 deletions(-)
diff --git a/MLP/include/MLP.hpp b/MLP/include/MLP.hpp
index bb42ccc..1c205fc 100644
--- a/MLP/include/MLP.hpp
+++ b/MLP/include/MLP.hpp
@@ -57,6 +57,8 @@ class MLP
dynet::Parameter & featValue2parameter(const FeatureModel::FeatureValue & fv);
dynet::Expression run(dynet::ComputationGraph & cg, dynet::Expression x);
inline dynet::Expression activate(dynet::Expression h, Activation f);
+ dynet::Expression getLoss(dynet::ComputationGraph & cg, dynet::Expression x, unsigned int label);
+ void printParameters(FILE * output);
public :
diff --git a/MLP/src/MLP.cpp b/MLP/src/MLP.cpp
index 7f2a84f..e6bef2a 100644
--- a/MLP/src/MLP.cpp
+++ b/MLP/src/MLP.cpp
@@ -120,25 +120,16 @@ std::vector<float> MLP::predict(FeatureModel::FeatureDescription & fd, int goldC
}
dynet::Expression input = dynet::concatenate(expressions);
- dynet::Expression output = run(cg, input);
-
- /*
- int nbInputs = layers[0].input_dim;
-
- dynet::Expression x = reshape(concatenate_cols(cur_batch),
- dynet::Dim({nb_inputs}, cur_batch_size));
- dynet::Expression loss_expr = get_loss(x_batch, cur_labels);
-
- loss += as_scalar(computation_graph.forward(loss_expr));
- nb_samples += cur_batch_size;
- computation_graph.backward(loss_expr);
- trainer.update();
- */
+ dynet::Expression output = run(cg, input);
- std::vector<float> res = as_vector(cg.forward(output));
+ if(trainMode)
+ {
+ cg.backward(pickneglogsoftmax(output, goldClass));
+ trainer.update();
+ }
- return res;
+ return as_vector(cg.forward(output));
}
dynet::DynetParams & MLP::getDefaultParams()
@@ -158,9 +149,11 @@ dynet::Parameter & MLP::featValue2parameter(const FeatureModel::FeatureValue & f
if(it != ptr2parameter.end())
return it->second;
- ptr2parameter[fv.vec] = model.add_parameters({1,fv.vec->size()}, dynet::ParameterInitFromVector(*fv.vec));
+ //ptr2parameter[fv.vec] = model.add_parameters({fv.vec->size(),1}, dynet::ParameterInitFromVector(*fv.vec));
+ ptr2parameter[fv.vec] = model.add_parameters({fv.vec->size(),1});
it = ptr2parameter.find(fv.vec);
- it->second.set_updated(fv.policy == FeatureModel::Policy::Final ? false : true);
+
+// it->second.values()->v = fv.vec->data();
return it->second;
}
@@ -230,3 +223,24 @@ inline dynet::Expression MLP::activate(dynet::Expression h, Activation f)
return h;
}
+dynet::Expression MLP::getLoss(dynet::ComputationGraph & cg, dynet::Expression x, unsigned int label)
+{
+ dynet::Expression y = run(cg, x);
+ return pickneglogsoftmax(y, label);
+}
+
+void MLP::printParameters(FILE * output)
+{
+ for(auto & it : ptr2parameter)
+ {
+ auto & param = it.second;
+ dynet::Tensor * tensor = param.values();
+ float * value = tensor->v;
+ int dim = tensor->d.size();
+ fprintf(output, "Param : ");
+ for(int i = 0; i < dim; i++)
+ fprintf(output, "%.2f ", value[i]);
+ fprintf(output, "\n");
+ }
+}
+
diff --git a/tape_machine/include/TapeMachine.hpp b/tape_machine/include/TapeMachine.hpp
index 8e00b07..ac562f7 100644
--- a/tape_machine/include/TapeMachine.hpp
+++ b/tape_machine/include/TapeMachine.hpp
@@ -29,13 +29,16 @@ class TapeMachine
private :
- std::string name;
std::map< std::string, std::unique_ptr<Classifier> > str2classifier;
std::map< std::string, std::unique_ptr<State> > str2state;
State * currentState;
public :
+ std::string name;
+
+ public :
+
TapeMachine(const std::string & filename);
State * getCurrentState();
Transition * getTransition(const std::string & action);
diff --git a/tape_machine/src/Classifier.cpp b/tape_machine/src/Classifier.cpp
index 18dd133..f728c14 100644
--- a/tape_machine/src/Classifier.cpp
+++ b/tape_machine/src/Classifier.cpp
@@ -72,7 +72,11 @@ Classifier::WeightedActions Classifier::weightActions(Config & config, const std
for (unsigned int i = 0; i < scores.size(); i++)
result.emplace_back(scores[i], as->actions[i].name);
- std::sort(result.begin(), result.end());
+ std::sort(result.begin(), result.end(),
+ [](const std::pair<float, std::string> & a, const std::pair<float, std::string> & b)
+ {
+ return a.first > b.first;
+ });
return result;
}
diff --git a/trainer/src/Trainer.cpp b/trainer/src/Trainer.cpp
index a85780d..3f181df 100644
--- a/trainer/src/Trainer.cpp
+++ b/trainer/src/Trainer.cpp
@@ -7,8 +7,14 @@ Trainer::Trainer(TapeMachine & tm, MCD & mcd, Config & config)
void Trainer::train()
{
- for (int i = 0; i < 2; i++)
+ int nbIter = 20;
+
+ fprintf(stderr, "Training of \'%s\' :\n", tm.name.c_str());
+
+ for (int i = 0; i < nbIter; i++)
{
+ std::map< std::string, std::pair<int, int> > nbExamples;
+
while (!config.isFinal())
{
TapeMachine::State * currentState = tm.getCurrentState();
@@ -20,7 +26,12 @@ void Trainer::train()
std::string neededActionName = classifier->oracle->getAction(config);
auto weightedActions = classifier->weightActions(config, neededActionName);
- printWeightedActions(stderr, weightedActions);
+ //printWeightedActions(stderr, weightedActions);
+ std::string & predictedAction = weightedActions[0].second;
+
+ nbExamples[classifier->name].first++;
+ if(predictedAction == neededActionName)
+ nbExamples[classifier->name].second++;
//fprintf(stderr, "Action : \'%s\'\n", neededActionName.c_str());
@@ -29,13 +40,26 @@ void Trainer::train()
config.moveHead(transition->headMvt);
}
+ fprintf(stderr, "Iteration %d/%d :\n", i+1, nbIter);
+ for(auto & it : nbExamples)
+ fprintf(stderr, "\t%s %.2f%% accuracy\n", it.first.c_str(), 100.0*it.second.second / it.second.first);
+
config.reset();
}
}
void Trainer::printWeightedActions(FILE * output, Classifier::WeightedActions & wa)
{
+ int nbCols = 80;
+ char symbol = '-';
+
+ for(int i = 0; i < nbCols; i++)
+ fprintf(output, "%c%s", symbol, i == nbCols-1 ? "\n" : "");
+
for (auto it : wa)
fprintf(output, "%.2f\t%s\n", it.first, it.second.c_str());
+
+ for(int i = 0; i < nbCols; i++)
+ fprintf(output, "%c%s", symbol, i == nbCols-1 ? "\n" : "");
}
--
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