Added MLP topology to .cla files

MLP/include/MLP.hpp

@@ -51,7 +51,7 @@ class MLP
   std::map< Dict*, std::pair<dynet::LookupParameter, std::map<void*, unsigned int> > > lookupParameters;
 
   dynet::ParameterCollection model;
-  dynet::AmsgradTrainer trainer;
+  std::unique_ptr<dynet::AmsgradTrainer> trainer;
   bool trainMode;
   bool dropoutActive;
 
@@ -74,6 +74,7 @@ class MLP
   public :
 
   MLP(std::vector<Layer> layers);
+  MLP(int nbInputs, const std::string & topology, int nbOutputs);
   MLP(const std::string & filename);
   std::vector<float> predict(FeatureModel::FeatureDescription & fd);
 

MLP/src/MLP.cpp

@@ -78,9 +78,53 @@ void MLP::initDynet()
   dynet::initialize(getDefaultParams());
 }
 
+MLP::MLP(int nbInputs, const std::string & topology, int nbOutputs)
+{
+  std::string topo = topology;
+  std::replace(topo.begin(), topo.end(), '(', ' ');
+  std::replace(topo.begin(), topo.end(), ')', ' ');
+
+  auto groups = split(topo);
+  for (auto group : groups)
+  {
+    if(group.empty())
+      continue;
+
+    std::replace(group.begin(), group.end(), ',', ' ');
+    auto layer = split(group);
+
+    if (layer.size() != 3)
+    {
+      fprintf(stderr, "ERROR (%s) : invalid topology \'%s\'. Aborting.\n", ERRINFO, topology.c_str());
+      exit(1);
+    }
+
+    int input = layers.empty() ? nbInputs : layers.back().output_dim;
+    int output = std::stoi(layer[0]); 
+    float dropout = std::stof(layer[2]);
+    layers.emplace_back(input, output, dropout, str2activation(layer[1]));
+  }
+
+  layers.emplace_back(layers.back().output_dim, nbOutputs, 0.0, Activation::LINEAR);
+
+  trainer.reset(new dynet::AmsgradTrainer(model, 0.001, 0.9, 0.999, 1e-8));
+
+  initDynet();
+
+  trainMode = true;
+  dropoutActive = true;
+
+  checkLayersCompatibility();
+
+  for(Layer layer : layers)
+    addLayerToModel(layer);
+}
+
 MLP::MLP(std::vector<Layer> layers)
-: layers(layers), trainer(model, 0.001, 0.9, 0.999, 1e-8)
+: layers(layers)
 {
+  trainer.reset(new dynet::AmsgradTrainer(model, 0.001, 0.9, 0.999, 1e-8));
+
   initDynet();
 
   trainMode = true;
@@ -308,7 +352,7 @@ int MLP::trainOnBatch(Examples & examples, int start, int end)
     dynet::Expression batchedLoss = pickneglogsoftmax(output, goldClasses);
     dynet::Expression loss = sum_batches(batchedLoss);
     cg.backward(loss);
-    trainer.update();
+    trainer->update();
   }
 
   int nbCorrect = 0;
@@ -450,8 +494,8 @@ void MLP::loadParameters(const std::string & filename)
 }
 
 MLP::MLP(const std::string & filename)
-: trainer(model, 0.001, 0.9, 0.999, 1e-8)
 {
+  trainer.reset(new dynet::AmsgradTrainer(model, 0.001, 0.9, 0.999, 1e-8));
   initDynet();
 
   trainMode = false;

tape_machine/include/Classifier.hpp

@@ -30,6 +30,7 @@ class Classifier
   std::unique_ptr<FeatureModel> fm;
   std::unique_ptr<ActionSet> as;
   std::unique_ptr<MLP> mlp;
+  std::string topology;
   Oracle * oracle;
 
   public :

tape_machine/src/Classifier.cpp

@@ -61,6 +61,11 @@ Classifier::Classifier(const std::string & filename, bool trainMode, const std::
     badFormatAndAbort(ERRINFO);
 
   as.reset(new ActionSet(expPath + buffer, false));
+
+  if(fscanf(fd, "Topology : %s\n", buffer) != 1)
+    badFormatAndAbort(ERRINFO);
+
+  topology = buffer;
 }
 
 Classifier::Type Classifier::str2type(const std::string & s)
@@ -120,7 +125,6 @@ void Classifier::initClassifier(Config & config)
   }
 
   int nbInputs = 0;
-  int nbHidden = 300;
   int nbOutputs = as->actions.size();
 
   auto fd = fm->getFeatureDescription(config);
@@ -128,8 +132,7 @@ void Classifier::initClassifier(Config & config)
   for (auto feat : fd.values)
     nbInputs += feat.vec->size();
 
-  mlp.reset(new MLP({{nbInputs, nbHidden, 0.3, MLP::Activation::RELU},
-                     {nbHidden, nbOutputs, 0.0, MLP::Activation::LINEAR}}));
+  mlp.reset(new MLP(nbInputs, topology, nbOutputs));
 }
 
 FeatureModel::FeatureDescription Classifier::getFeatureDescription(Config & config)