diff --git a/maca_common/include/util.hpp b/maca_common/include/util.hpp
index ff7dbf5a5572e6cbd325b1fb5785213d89fc621c..17574130facaf0471cff1f7741060cf38c6cdb2a 100644
--- a/maca_common/include/util.hpp
+++ b/maca_common/include/util.hpp
@@ -175,6 +175,20 @@ std::string getTime();
/// @return True if the file exists
bool fileExists(const std::string & s);
+/// @brief Return true with a certain probability.
+///
+/// @param probability The probability (between 0.0 and 1.0) to return true.
+///
+/// @return True with a certain probability, false otherwise.
+bool choiceWithProbability(float probability);
+
+/// @brief Return a random number between -range and +range
+///
+/// @param range The random value will be between -range and +range
+///
+/// @return Random number between -range and +range
+float getRandomValueInRange(int range);
+
/// @brief Macro giving informations about an error.
#define ERRINFO (getFilenameFromPath(std::string(__FILE__))+ ":l." + std::to_string(__LINE__)).c_str()
diff --git a/maca_common/src/Dict.cpp b/maca_common/src/Dict.cpp
index 3c1b3367bab8662c98f38488b2ac0f52122aba9c..d974ca927cb5579cce10ecb3714db8ad60967113 100644
--- a/maca_common/src/Dict.cpp
+++ b/maca_common/src/Dict.cpp
@@ -323,13 +323,7 @@ void Dict::initEmbeddingRandom(unsigned int index)
int range = 1;
for (auto & val : vec)
- {
- float sign = (rand() % 100000) >= 50000 ? 1.0 : -1.0;
- float result = ((rand() % range) + 1) * sign;
- float decimal = (rand() % 100000) / 100000.0;
- result += decimal;
- val = result;
- }
+ val = getRandomValueInRange(range);
lookupParameter.initialize(index, vec);
}
diff --git a/maca_common/src/File.cpp b/maca_common/src/File.cpp
index 3f8eaf2b60206b86aa241d8629016761aa803dd0..42d7014c1e12cf0fbd61fee93c77189a8e2beeae 100644
--- a/maca_common/src/File.cpp
+++ b/maca_common/src/File.cpp
@@ -30,7 +30,7 @@ File::File(const std::string & filename, const std::string & mode)
this->filename = filename;
endHasBeenReached = false;
- if (mode != "r" && mode != "w")
+ if (mode != "r" && mode != "w" && mode != "a")
{
fprintf(stderr, "ERROR (%s) : \"%s\" is an invalid mode when opening a file\n", ERRINFO, mode.c_str());
diff --git a/maca_common/src/util.cpp b/maca_common/src/util.cpp
index a65d8a177b0466dc62e198a26d132d81f43530af..e5fbd86d872ee42bc4f0afd4b4fa149dfd40ad03 100644
--- a/maca_common/src/util.cpp
+++ b/maca_common/src/util.cpp
@@ -399,3 +399,27 @@ bool fileExists(const std::string & s)
return true;
}
+float getRandomValueInRange(int range)
+{
+ int maxValue = 1000000;
+
+ float sign = choiceWithProbability(0.5) ? 1.0 : -1.0;
+ float result = sign*range*(rand() % (maxValue+1)) / maxValue;
+
+ return result;
+}
+
+bool choiceWithProbability(float probability)
+{
+ if (probability < 0 || probability > 1.0)
+ {
+ fprintf(stderr, "ERROR (%s) : invalid probability \'%f\'. Aborting.\n", ERRINFO, probability);
+ exit(1);
+ }
+
+ int maxVal = 100000;
+ int threshold = maxVal * probability;
+
+ return (rand() % maxVal) < threshold;
+}
+
diff --git a/neural_network/include/GeneticAlgorithm.hpp b/neural_network/include/GeneticAlgorithm.hpp
index 8b1df471c83ac2d096b554fd7e7ce212534e0fa9..73ecb8887d7477ac979a3b5f2b1f198ee482b6a0 100644
--- a/neural_network/include/GeneticAlgorithm.hpp
+++ b/neural_network/include/GeneticAlgorithm.hpp
@@ -22,6 +22,8 @@ class GeneticAlgorithm : public NeuralNetwork
/// It can be evaluated against a particular metric, can mutate and reproduce with another of its kind.
struct Individual
{
+ /// @brief A counter for giving unique id to objects of this struct.
+ static int idCount;
/// @brief The neural network corresponding to this Individual.
MLPBase mlp;
/// @brief The value of this Individual.
@@ -29,6 +31,8 @@ class GeneticAlgorithm : public NeuralNetwork
/// This metric is used to dertermine wich are the best Individuals of the generation.
/// For example it can be the inverse of the loss value of its MLP against the dev dataset.
float value;
+ /// @brief The loss of the MLP of this Individual.
+ float loss;
/// @brief Unique identifier for this individual.
int id;
/// @brief Create a new Individual from a certain topology.
@@ -39,10 +43,18 @@ class GeneticAlgorithm : public NeuralNetwork
/// @param nbOutputs The size of the mlp output layer..
Individual(dynet::ParameterCollection & model, int nbInputs, const std::string & topology, int nbOutputs);
void becomeChildOf(Individual * other);
+ void becomeChildOf(Individual * mom, Individual * dad);
+ void mutate(float probability);
};
/// @brief The current generation.
std::vector< std::unique_ptr<Individual> > generation;
+ /// @brief The topology of the GeneticAlgorithm
+ std::string topology;
+ /// @brief The input layer size of the Individual's MLP.
+ int nbInputs;
+ /// @brief The output layer size of the Individual's MLP.
+ int nbOutputs;
private :
diff --git a/neural_network/include/MLPBase.hpp b/neural_network/include/MLPBase.hpp
index 8021f6d0efcfae77523f1681f44323603b808ae5..fb32913f94c19e78baa3591e7dd75500bbb6270c 100644
--- a/neural_network/include/MLPBase.hpp
+++ b/neural_network/include/MLPBase.hpp
@@ -20,6 +20,8 @@ class MLPBase
public :
using Layer = NeuralNetwork::Layer;
+ /// @brief The name of this MLP.
+ std::string name;
/// @brief The Layers of the MLP.
std::vector<Layer> layers;
/// @brief The parameters corresponding to the layers of the MLP.
@@ -99,7 +101,9 @@ class MLPBase
/// @param nbOutputs The size of the output layer of the MLP.
void init(dynet::ParameterCollection & model, int nbInputs, const std::string & topology, int nbOutputs);
/// @brief Construct a new MLP for training.
- MLPBase();
+ ///
+ /// @param name The name of this MLP.
+ MLPBase(std::string name);
/// @brief Give a score to each possible class, given an input.
///
/// @param fd The input to use.
diff --git a/neural_network/src/GeneticAlgorithm.cpp b/neural_network/src/GeneticAlgorithm.cpp
index 347027e053b35139cea581dc74b5bb1d1a00c8d0..50c1abbf5f38fc060d60ef35663af73cdba24de0 100644
--- a/neural_network/src/GeneticAlgorithm.cpp
+++ b/neural_network/src/GeneticAlgorithm.cpp
@@ -2,6 +2,8 @@
#include "ProgramParameters.hpp"
#include "util.hpp"
+int GeneticAlgorithm::Individual::idCount = 0;
+
GeneticAlgorithm::GeneticAlgorithm()
{
randomSeed = ProgramParameters::seed;
@@ -18,6 +20,10 @@ GeneticAlgorithm::GeneticAlgorithm(const std::string & filename)
void GeneticAlgorithm::init(int nbInputs, const std::string & topology, int nbOutputs)
{
+ this->nbInputs = nbInputs;
+ this->nbOutputs = nbOutputs;
+ this->topology = topology;
+
auto splited = split(topology, ' ');
if (splited.size() != 2 || !isNum(splited[0]))
{
@@ -29,8 +35,6 @@ void GeneticAlgorithm::init(int nbInputs, const std::string & topology, int nbOu
for (int i = 0; i < nbElems; i++)
generation.emplace_back(new Individual(model, nbInputs, splited[1], nbOutputs));
-
- fprintf(stderr, "Init is done !\n");
}
std::vector<float> GeneticAlgorithm::predict(FeatureModel::FeatureDescription & fd)
@@ -47,7 +51,8 @@ float GeneticAlgorithm::update(FeatureModel::FeatureDescription & fd, int gold)
float loss = individual->mlp.update(fd, gold);
if (loss != 0.0)
{
- individual->value = loss2value(loss);
+ individual->loss = loss / ProgramParameters::batchSize;
+ individual->value = loss2value(individual->loss);
haveBeenUpdated = true;
}
}
@@ -61,20 +66,64 @@ float GeneticAlgorithm::update(FeatureModel::FeatureDescription & fd, int gold)
return a->value > b->value;
});
- fprintf(stderr, "-----------------\n");
- for (auto & individual : generation)
- fprintf(stderr, "%d\t%f\n", individual->id, individual->value);
- fprintf(stderr, "-----------------\n");
+ fprintf(stderr, "Best : %3d with value %.2f Worst : %3d with value %.2f\n", generation[0]->id, generation[0]->value, generation.back()->id, generation.back()->value);
+
+ unsigned int quarter = generation.size() / 4;
+ if (quarter%2)
+ quarter++;
+ std::vector<unsigned int> reproductors;
+ std::vector<unsigned int> candidates;
+ for (unsigned int i = 0; i < generation.size(); i++)
+ candidates.push_back(i);
+
+ while (reproductors.size() < quarter)
+ for (unsigned int i = 0; i < candidates.size(); i++)
+ {
+ int parts = candidates.size() * ((candidates.size()+1) / 2.0);
+ float probability = (candidates.size()-i) / (1.0*parts);
+
+ if (choiceWithProbability(probability))
+ {
+ reproductors.push_back(candidates[i]);
+ candidates.erase(candidates.begin() + i);
+ i--;
+ }
+ }
+
+ while (reproductors.size() != quarter)
+ reproductors.pop_back();
- for (unsigned int i = 1; i < generation.size(); i++)
+ // Reproduction
+ for (unsigned int i = 2*quarter; i < 3*quarter; i++)
{
- generation[i]->becomeChildOf(generation[0].get());
+ int momIndex = reproductors.size()-1-(i-2*quarter);
+ int dadIndex = i-2*quarter;
+
+ generation[i]->becomeChildOf(generation[reproductors[momIndex]].get(), generation[reproductors[dadIndex]].get());
}
+
+ // Mutation
+ for (unsigned int i = 1*quarter; i < 2*quarter; i++)
+ generation[i]->mutate(0.01);
+
+ // Death and replace
+ for (unsigned int i = 3*quarter; i < generation.size(); i++)
+ generation[i]->mutate(1.0);
+
+ return generation[0]->loss;
}
void GeneticAlgorithm::save(const std::string & filename)
{
+ File * file = new File(filename, "w");
+ fprintf(file->getDescriptor(), "%d %d %s\n", nbInputs, nbOutputs, topology.c_str());
+ delete file;
+ for (auto & individual : generation)
+ {
+ individual->mlp.saveStruct(filename);
+ individual->mlp.saveParameters(filename);
+ }
}
void GeneticAlgorithm::printTopology(FILE * output)
@@ -92,14 +141,37 @@ void GeneticAlgorithm::printTopology(FILE * output)
void GeneticAlgorithm::load(const std::string & filename)
{
+ File * file = new File(filename, "r");
+ int i, o;
+ char buffer[1024];
+ if (fscanf(file->getDescriptor(), "%d %d %[^\n]\n", &i, &o, buffer) != 3)
+ {
+ fprintf(stderr, "ERROR (%s) : file \'%s\' bad format. Aborting.\n", ERRINFO, filename.c_str());
+ exit(1);
+ }
+ delete file;
+
+ this->nbInputs = i;
+ this->nbOutputs = o;
+ this->topology = buffer;
+
+ init(nbInputs, topology, nbOutputs);
+ for (auto & individual : generation)
+ {
+ individual->mlp.loadStruct(model, filename);
+ individual->mlp.loadParameters(model, filename);
+ }
}
-GeneticAlgorithm::Individual::Individual(dynet::ParameterCollection & model, int nbInputs, const std::string & topology, int nbOutputs)
+GeneticAlgorithm::Individual::Individual(dynet::ParameterCollection & model, int nbInputs, const std::string & topology, int nbOutputs) : mlp("MLP_" + std::to_string(idCount))
{
- static int id = 0;
- this->id = id++;
+ this->id = idCount++;
+ this->loss = 0.0;
+ this->value = 0.0;
mlp.init(model, nbInputs, topology, nbOutputs);
+ // mutate with probability 1.0 = random init
+ mutate(1.0);
}
float GeneticAlgorithm::loss2value(float loss)
@@ -128,8 +200,56 @@ void GeneticAlgorithm::Individual::becomeChildOf(Individual * other)
unsigned int nbValues = thisParameter.values()->d.size();
for (unsigned int k = 0; k < nbValues; k++)
- if (rand() % 1000 >= 500)
+ if (choiceWithProbability(0.5))
thisValues[k] = otherValues[k];
}
}
+void GeneticAlgorithm::Individual::becomeChildOf(Individual * mom, Individual * dad)
+{
+ auto & thisParameters = mlp.parameters;
+ auto & momParameters = mom->mlp.parameters;
+ auto & dadParameters = dad->mlp.parameters;
+
+ if (thisParameters.size() != momParameters.size() || thisParameters.size() != dadParameters.size())
+ {
+ fprintf(stderr, "ERROR (%s) : The three individuals are not compatibles. Sizes %lu and %lu and %lu. Aborting.\n", ERRINFO, thisParameters.size(), momParameters.size(), dadParameters.size());
+ exit(1);
+ }
+
+ for (unsigned int i = 0; i < thisParameters.size(); i++)
+ for (unsigned int j = 0; j < thisParameters[i].size(); j++)
+ {
+ auto & thisParameter = thisParameters[i][j];
+ auto & momParameter = momParameters[i][j];
+ auto & dadParameter = dadParameters[i][j];
+ float * thisValues = thisParameter.values()->v;
+ float * momValues = momParameter.values()->v;
+ float * dadValues = dadParameter.values()->v;
+ unsigned int nbValues = thisParameter.values()->d.size();
+
+ for (unsigned int k = 0; k < nbValues; k++)
+ if (choiceWithProbability(0.5))
+ thisValues[k] = momValues[k];
+ else
+ thisValues[k] = dadValues[k];
+ }
+}
+
+void GeneticAlgorithm::Individual::mutate(float probability)
+{
+ auto & thisParameters = mlp.parameters;
+
+ for (unsigned int i = 0; i < thisParameters.size(); i++)
+ for (unsigned int j = 0; j < thisParameters[i].size(); j++)
+ {
+ auto & thisParameter = thisParameters[i][j];
+ float * thisValues = thisParameter.values()->v;
+ unsigned int nbValues = thisParameter.values()->d.size();
+
+ for (unsigned int k = 0; k < nbValues; k++)
+ if (choiceWithProbability(probability))
+ thisValues[k] = getRandomValueInRange(1);
+ }
+}
+
diff --git a/neural_network/src/MLP.cpp b/neural_network/src/MLP.cpp
index 74e1113e9e975b2b5faef8ee9c8c24457f450b04..90eac755b64d3a0cfaf87d698d1f3c973f53f6ad 100644
--- a/neural_network/src/MLP.cpp
+++ b/neural_network/src/MLP.cpp
@@ -1,13 +1,13 @@
#include "MLP.hpp"
-MLP::MLP()
+MLP::MLP() : mlp("MLP")
{
randomSeed = ProgramParameters::seed;
trainer.reset(createTrainer());
initDynet();
}
-MLP::MLP(const std::string & filename)
+MLP::MLP(const std::string & filename) : mlp("MLP")
{
randomSeed = ProgramParameters::seed;
trainer.reset(createTrainer());
@@ -58,6 +58,8 @@ float MLP::update(FeatureModel::FeatureDescription & fd, int gold)
void MLP::save(const std::string & filename)
{
+ File * file = new File(filename, "w");
+ delete file;
mlp.saveStruct(filename);
mlp.saveParameters(filename);
}
diff --git a/neural_network/src/MLPBase.cpp b/neural_network/src/MLPBase.cpp
index 5b9355b7e2f92c1edcb9cb4cec9cc9a24a9d9b85..41520295e343e1b719f5e739071beefbe17cbc9b 100644
--- a/neural_network/src/MLPBase.cpp
+++ b/neural_network/src/MLPBase.cpp
@@ -1,7 +1,8 @@
#include "MLPBase.hpp"
-MLPBase::MLPBase()
+MLPBase::MLPBase(std::string name)
{
+ this->name = name;
dropoutActive = true;
}
@@ -271,7 +272,7 @@ void MLPBase::printParameters(FILE * output)
void MLPBase::saveStruct(const std::string & filename)
{
- File file(filename, "w");
+ File file(filename, "a");
FILE * fd = file.getDescriptor();
for (auto & layer : layers)
@@ -283,7 +284,7 @@ void MLPBase::saveStruct(const std::string & filename)
void MLPBase::saveParameters(const std::string & filename)
{
dynet::TextFileSaver s(filename, true);
- std::string prefix("Layer_");
+ std::string prefix(name + "_Layer_");
for(unsigned int i = 0; i < parameters.size(); i++)
{
@@ -302,8 +303,17 @@ void MLPBase::loadStruct(dynet::ParameterCollection & model, const std::string &
int output;
float dropout;
- while (fscanf(fd, "Layer : %d %d %s %f\n", &input, &output, activation, &dropout) == 4)
- layers.emplace_back(input, output, dropout, NeuralNetwork::str2activation(activation));
+ while (fscanf(fd, "Layer : %d %d %s %f\n", &input, &output, activation, &dropout) != 4)
+ if (fscanf(fd, "%[^\n]\n", activation) != 1)
+ {
+ fprintf(stderr, "ERROR (%s) : Unexpected end of file \'%s\'. Aborting.\n", ERRINFO, filename.c_str());
+ exit(1);
+ }
+
+ do
+ {
+ layers.emplace_back(input, output, dropout, NeuralNetwork::str2activation(activation));
+ } while (fscanf(fd, "Layer : %d %d %s %f\n", &input, &output, activation, &dropout) == 4);
checkLayersCompatibility();
@@ -314,12 +324,20 @@ void MLPBase::loadStruct(dynet::ParameterCollection & model, const std::string &
void MLPBase::loadParameters(dynet::ParameterCollection & model, const std::string & filename)
{
dynet::TextFileLoader loader(filename);
- std::string prefix("Layer_");
+ std::string prefix(name + "_Layer_");
for(unsigned int i = 0; i < parameters.size(); i++)
{
- parameters[i][0] = loader.load_param(model, prefix + std::to_string(i) + "_W");
- parameters[i][1] = loader.load_param(model, prefix + std::to_string(i) + "_b");
+ try
+ {
+ parameters[i][0] = loader.load_param(model, prefix + std::to_string(i) + "_W");
+ parameters[i][1] = loader.load_param(model, prefix + std::to_string(i) + "_b");
+ } catch(const std::runtime_error e)
+ {
+ prefix = "Layer_";
+ parameters[i][0] = loader.load_param(model, prefix + std::to_string(i) + "_W");
+ parameters[i][1] = loader.load_param(model, prefix + std::to_string(i) + "_b");
+ }
}
}
diff --git a/transition_machine/include/Classifier.hpp b/transition_machine/include/Classifier.hpp
index 2b523d875d739fac8b731b4a860cb01f42035ab8..958cf169d6cbbf1b496b5be8b4e12dca029ba89d 100644
--- a/transition_machine/include/Classifier.hpp
+++ b/transition_machine/include/Classifier.hpp
@@ -61,6 +61,20 @@ class Classifier
/// For Classifier of type Information, the Oracle is used in train mode and decode mode too, it is simply a deterministic function that gives the correct Action given a Configuration.
Oracle * oracle;
+ private :
+
+ /// @brief Create the correct type of NeuralNetwork.
+ ///
+ /// @return A pointer to the newly created NeuralNetwork.
+ NeuralNetwork * createNeuralNetwork();
+
+ /// @brief Create the correct type of NeuralNetwork, from a file.
+ ///
+ /// @param modelFilename The name of the file containing the NeuralNetwork description.
+ ///
+ /// @return A pointer to the newly created NeuralNetwork.
+ NeuralNetwork * createNeuralNetwork(const std::string & modelFilename);
+
public :
/// @brief Return how many errors will an action introduce.
diff --git a/transition_machine/src/Classifier.cpp b/transition_machine/src/Classifier.cpp
index ea4fdea3a720f31d680052cf34b3c7d60b87815e..deb4dbdc610559dcaed4b7696cd034c6ed98302b 100644
--- a/transition_machine/src/Classifier.cpp
+++ b/transition_machine/src/Classifier.cpp
@@ -130,12 +130,12 @@ void Classifier::initClassifier(Config & config)
std::string modelFilename = ProgramParameters::expPath + name + ".model";
if (fileExists(modelFilename))
{
- nn.reset(new MLP(modelFilename));
+ nn.reset(createNeuralNetwork(modelFilename));
Dict::initDicts(nn->getModel(), name);
return;
}
- nn.reset(new GeneticAlgorithm());
+ nn.reset(createNeuralNetwork());
Dict::initDicts(nn->getModel(), name);
@@ -293,3 +293,23 @@ float Classifier::computeEntropy(WeightedActions & wa)
return entropy;
}
+NeuralNetwork * Classifier::createNeuralNetwork()
+{
+ auto splited = split(topology, ' ');
+
+ if (splited.size() == 2)
+ return new GeneticAlgorithm();
+
+ return new MLP();
+}
+
+NeuralNetwork * Classifier::createNeuralNetwork(const std::string & modelFilename)
+{
+ auto splited = split(topology, ' ');
+
+ if (splited.size() == 2)
+ return new GeneticAlgorithm(modelFilename);
+
+ return new MLP(modelFilename);
+}
+