Added function to get index of a transition

dev/src/dev.cpp

@@ -8,69 +8,6 @@
 #include "TestNetwork.hpp"
 #include "ConfigDataset.hpp"
 
-//constexpr int batchSize = 50;
-//constexpr int nbExamples = 350000;
-//constexpr int embeddingSize = 20;
-//constexpr int nbClasses = 15;
-//constexpr int nbWordsPerDatapoint = 5;
-//constexpr int maxNbEmbeddings = 1000000;
-//
-//struct NetworkImpl : torch::nn::Module
-//{
-//  torch::nn::Linear linear{nullptr};
-//  torch::nn::Embedding wordEmbeddings{nullptr};
-//
-//  std::vector<torch::Tensor> _sparseParameters;
-//  std::vector<torch::Tensor> _denseParameters;
-//  NetworkImpl()
-//  {
-//    linear = register_module("dense_linear", torch::nn::Linear(embeddingSize, nbClasses));
-//    auto params = linear->parameters();
-//    _denseParameters.insert(_denseParameters.end(), params.begin(), params.end());
-//
-//    wordEmbeddings = register_module("sparse_word_embeddings", torch::nn::Embedding(torch::nn::EmbeddingOptions(maxNbEmbeddings, embeddingSize).sparse(true)));
-//    params = wordEmbeddings->parameters();
-//    _sparseParameters.insert(_sparseParameters.end(), params.begin(), params.end());
-//  };
-//  const std::vector<torch::Tensor> & denseParameters()
-//  {
-//    return _denseParameters;
-//  }
-//  const std::vector<torch::Tensor> & sparseParameters()
-//  {
-//    return _sparseParameters;
-//  }
-//  torch::Tensor forward(const torch::Tensor & input)
-//  {
-//    // I have a batch of sentences (list of word embeddings), so as the sentence embedding I take the mean of the embedding of its words
-//    auto embeddingsOfInput = wordEmbeddings(input).mean(1);
-//    return torch::softmax(linear(embeddingsOfInput),1);
-//  }
-//};
-//TORCH_MODULE(Network);
-
-//int main(int argc, char * argv[])
-//{
-//  auto nn = Network();
-//  torch::optim::SparseAdam sparseOptimizer(nn->sparseParameters(), torch::optim::SparseAdamOptions(2e-4).beta1(0.5));
-//  torch::optim::Adam denseOptimizer(nn->denseParameters(), torch::optim::AdamOptions(2e-4).beta1(0.5));
-//  std::vector<std::pair<torch::Tensor,torch::Tensor>> batches;
-//  for (int nbBatch = 0; nbBatch < nbExamples / batchSize; ++nbBatch)
-//    batches.emplace_back(std::make_pair(torch::randint(maxNbEmbeddings,{batchSize,nbWordsPerDatapoint}, at::kLong), torch::randint(nbClasses, batchSize, at::kLong)));
-//
-//  for (auto & batch : batches)
-//  {
-//    sparseOptimizer.zero_grad();
-//    denseOptimizer.zero_grad();
-//    auto prediction = nn(batch.first);
-//    auto loss = torch::nll_loss(torch::log(prediction), batch.second);
-//    loss.backward();
-//    sparseOptimizer.step();
-//    denseOptimizer.step();
-//  }
-//  return 0;
-//}
-
 int main(int argc, char * argv[])
 {
   if (argc != 5)
@@ -110,7 +47,7 @@ int main(int argc, char * argv[])
     auto context = config.extractContext(5,5,dict);
     contexts.push_back(torch::from_blob(context.data(), {(long)context.size()}, at::kLong).clone());
 
-    int goldIndex = 3;
+    int goldIndex = machine.getTransitionSet().getTransitionIndex(transition);
     auto gold = torch::zeros(1, at::kLong);
     gold[0] = goldIndex;
 
@@ -140,10 +77,10 @@ int main(int argc, char * argv[])
   auto dataLoader = torch::data::make_data_loader(std::move(dataset), torch::data::DataLoaderOptions(batchSize).workers(0).max_jobs(0));
 
   TestNetwork nn(machine.getTransitionSet().size(), 5);
-  torch::optim::Adam denseOptimizer(nn->denseParameters(), torch::optim::AdamOptions(2e-1).beta1(0.5));
-  torch::optim::SparseAdam sparseOptimizer(nn->sparseParameters(), torch::optim::SparseAdamOptions(2e-1).beta1(0.5));
+  torch::optim::Adam denseOptimizer(nn->denseParameters(), torch::optim::AdamOptions(2e-3).beta1(0.5));
+  torch::optim::SparseAdam sparseOptimizer(nn->sparseParameters(), torch::optim::SparseAdamOptions(2e-3).beta1(0.5));
 
-  for (int epoch = 1; epoch <= 2; ++epoch)
+  for (int epoch = 1; epoch <= 30; ++epoch)
   {
     float totalLoss = 0.0;
     float lossSoFar = 0.0;

reading_machine/include/TransitionSet.hpp

@@ -18,6 +18,7 @@ class TransitionSet
   TransitionSet(const std::string & filename);
   std::vector<std::pair<Transition*, int>> getAppliableTransitionsCosts(const Config & c);
   Transition * getBestAppliableTransition(const Config & c);
+  std::size_t getTransitionIndex(const Transition * transition) const;
   std::size_t size() const;
 };
 

reading_machine/src/TransitionSet.cpp

@@ -72,3 +72,11 @@ std::size_t TransitionSet::size() const
   return transitions.size();
 }
 
+std::size_t TransitionSet::getTransitionIndex(const Transition * transition) const
+{
+  if (!transition)
+    util::myThrow("transition is null");
+
+  return transition - &transitions[0];
+}
+