diff --git a/torch_modules/include/ConcatWordsNetwork.hpp b/torch_modules/include/ConcatWordsNetwork.hpp
index 4dd7aa3a4913fddd66f05936a3bf715abc2c1b00..064a00eda70334f9e104db793b6476e94f43e36c 100644
--- a/torch_modules/include/ConcatWordsNetwork.hpp
+++ b/torch_modules/include/ConcatWordsNetwork.hpp
@@ -11,15 +11,10 @@ class ConcatWordsNetworkImpl : public NeuralNetworkImpl
torch::nn::Linear linear1{nullptr};
torch::nn::Linear linear2{nullptr};
- std::vector<torch::Tensor> _denseParameters;
- std::vector<torch::Tensor> _sparseParameters;
-
public :
ConcatWordsNetworkImpl(int nbOutputs, int leftBorder, int rightBorder, int nbStackElements);
torch::Tensor forward(torch::Tensor input) override;
- std::vector<torch::Tensor> & denseParameters() override;
- std::vector<torch::Tensor> & sparseParameters() override;
};
#endif
diff --git a/torch_modules/include/NeuralNetwork.hpp b/torch_modules/include/NeuralNetwork.hpp
index 268312211f419f8c066e264243e96ee3455ba6d9..5299d2d6b1b295c7000d60dfb4596a636aaabef3 100644
--- a/torch_modules/include/NeuralNetwork.hpp
+++ b/torch_modules/include/NeuralNetwork.hpp
@@ -12,6 +12,7 @@ class NeuralNetworkImpl : public torch::nn::Module
int leftBorder{5};
int rightBorder{5};
int nbStackElements{2};
+ std::vector<std::string> columns{"FORM", "UPOS"};
protected :
@@ -21,8 +22,6 @@ class NeuralNetworkImpl : public torch::nn::Module
public :
- virtual std::vector<torch::Tensor> & denseParameters() = 0;
- virtual std::vector<torch::Tensor> & sparseParameters() = 0;
virtual torch::Tensor forward(torch::Tensor input) = 0;
std::vector<long> extractContext(Config & config, Dict & dict) const;
int getContextSize() const;
diff --git a/torch_modules/include/OneWordNetwork.hpp b/torch_modules/include/OneWordNetwork.hpp
index 29edb7d58931627b72b4bc157acbeaeb2ff82ee0..b4ad4753b8bb57a35afa15851742551f7227e0c3 100644
--- a/torch_modules/include/OneWordNetwork.hpp
+++ b/torch_modules/include/OneWordNetwork.hpp
@@ -11,15 +11,10 @@ class OneWordNetworkImpl : public NeuralNetworkImpl
torch::nn::Linear linear{nullptr};
int focusedIndex;
- std::vector<torch::Tensor> _denseParameters;
- std::vector<torch::Tensor> _sparseParameters;
-
public :
OneWordNetworkImpl(int nbOutputs, int focusedIndex);
torch::Tensor forward(torch::Tensor input) override;
- std::vector<torch::Tensor> & denseParameters() override;
- std::vector<torch::Tensor> & sparseParameters() override;
};
#endif
diff --git a/torch_modules/src/ConcatWordsNetwork.cpp b/torch_modules/src/ConcatWordsNetwork.cpp
index 4c1e3661d36451c82fe7cbb6f77d3ad397bafdc3..9d7d0c44db06c3cea838821e0323a5d9ec4307fb 100644
--- a/torch_modules/src/ConcatWordsNetwork.cpp
+++ b/torch_modules/src/ConcatWordsNetwork.cpp
@@ -7,25 +7,9 @@ ConcatWordsNetworkImpl::ConcatWordsNetworkImpl(int nbOutputs, int leftBorder, in
setRightBorder(rightBorder);
setNbStackElements(nbStackElements);
- wordEmbeddings = register_module("word_embeddings", torch::nn::Embedding(torch::nn::EmbeddingOptions(50000, embeddingsSize).sparse(false)));
- auto params = wordEmbeddings->parameters();
- _denseParameters.insert(_denseParameters.end(), params.begin(), params.end());
+ wordEmbeddings = register_module("word_embeddings", torch::nn::Embedding(torch::nn::EmbeddingOptions(50000, embeddingsSize).sparse(true)));
linear1 = register_module("linear1", torch::nn::Linear(getContextSize()*embeddingsSize, 500));
- params = linear1->parameters();
- _denseParameters.insert(_denseParameters.end(), params.begin(), params.end());
linear2 = register_module("linear2", torch::nn::Linear(500, nbOutputs));
- params = linear2->parameters();
- _denseParameters.insert(_denseParameters.end(), params.begin(), params.end());
-}
-
-std::vector<torch::Tensor> & ConcatWordsNetworkImpl::denseParameters()
-{
- return _denseParameters;
-}
-
-std::vector<torch::Tensor> & ConcatWordsNetworkImpl::sparseParameters()
-{
- return _sparseParameters;
}
torch::Tensor ConcatWordsNetworkImpl::forward(torch::Tensor input)
diff --git a/torch_modules/src/NeuralNetwork.cpp b/torch_modules/src/NeuralNetwork.cpp
index 215fda5b5d032b6f95bf612ae088b83629f67d4f..e39729bb5481b8029c9f3b98751f9f574dcf8e0d 100644
--- a/torch_modules/src/NeuralNetwork.cpp
+++ b/torch_modules/src/NeuralNetwork.cpp
@@ -3,13 +3,14 @@
std::vector<long> NeuralNetworkImpl::extractContext(Config & config, Dict & dict) const
{
std::stack<int> leftContext;
- for (int index = config.getWordIndex()-1; config.has(0,index,0) && (int)leftContext.size() < leftBorder; --index)
+ for (int index = config.getWordIndex()-1; config.has(0,index,0) && leftContext.size() < columns.size()*leftBorder; --index)
if (config.isToken(index))
- leftContext.push(dict.getIndexOrInsert(config.getLastNotEmptyConst("FORM", index)));
+ for (auto & column : columns)
+ leftContext.push(dict.getIndexOrInsert(config.getAsFeature(column, index)));
std::vector<long> context;
- while ((int)context.size() < leftBorder-(int)leftContext.size())
+ while ((int)context.size() < (int)columns.size()*(leftBorder-(int)leftContext.size()))
context.emplace_back(dict.getIndexOrInsert(Dict::nullValueStr));
while (!leftContext.empty())
{
@@ -17,25 +18,27 @@ std::vector<long> NeuralNetworkImpl::extractContext(Config & config, Dict & dict
leftContext.pop();
}
- for (int index = config.getWordIndex(); config.has(0,index,0) && (int)context.size() < leftBorder+rightBorder+1; ++index)
+ for (int index = config.getWordIndex(); config.has(0,index,0) && context.size() < columns.size()*(leftBorder+rightBorder+1); ++index)
if (config.isToken(index))
- context.emplace_back(dict.getIndexOrInsert(config.getLastNotEmptyConst("FORM", index)));
+ for (auto & column : columns)
+ context.emplace_back(dict.getIndexOrInsert(config.getAsFeature(column, index)));
- while ((int)context.size() < leftBorder+rightBorder+1)
+ while (context.size() < columns.size()*(leftBorder+rightBorder+1))
context.emplace_back(dict.getIndexOrInsert(Dict::nullValueStr));
for (int i = 0; i < nbStackElements; i++)
- if (config.hasStack(i))
- context.emplace_back(dict.getIndexOrInsert(config.getLastNotEmptyConst("FORM", config.getStack(i))));
- else
- context.emplace_back(dict.getIndexOrInsert(Dict::nullValueStr));
+ for (auto & column : columns)
+ if (config.hasStack(i))
+ context.emplace_back(dict.getIndexOrInsert(config.getAsFeature(column, config.getStack(i))));
+ else
+ context.emplace_back(dict.getIndexOrInsert(Dict::nullValueStr));
return context;
}
int NeuralNetworkImpl::getContextSize() const
{
- return 1 + leftBorder + rightBorder + nbStackElements;
+ return columns.size()*(1 + leftBorder + rightBorder + nbStackElements);
}
void NeuralNetworkImpl::setRightBorder(int rightBorder)
diff --git a/torch_modules/src/OneWordNetwork.cpp b/torch_modules/src/OneWordNetwork.cpp
index c054e6dd8c5d9a5810a57a448ad318030164efa2..c2a11db6c3fbc8e14545a0c04ce4e17170b8b18e 100644
--- a/torch_modules/src/OneWordNetwork.cpp
+++ b/torch_modules/src/OneWordNetwork.cpp
@@ -5,12 +5,7 @@ OneWordNetworkImpl::OneWordNetworkImpl(int nbOutputs, int focusedIndex)
constexpr int embeddingsSize = 30;
wordEmbeddings = register_module("word_embeddings", torch::nn::Embedding(torch::nn::EmbeddingOptions(200000, embeddingsSize).sparse(true)));
- auto params = wordEmbeddings->parameters();
- _sparseParameters.insert(_sparseParameters.end(), params.begin(), params.end());
-
linear = register_module("linear", torch::nn::Linear(embeddingsSize, nbOutputs));
- params = linear->parameters();
- _denseParameters.insert(_denseParameters.end(), params.begin(), params.end());
int leftBorder = 0;
int rightBorder = 0;
@@ -26,16 +21,6 @@ OneWordNetworkImpl::OneWordNetworkImpl(int nbOutputs, int focusedIndex)
setNbStackElements(0);
}
-std::vector<torch::Tensor> & OneWordNetworkImpl::denseParameters()
-{
- return _denseParameters;
-}
-
-std::vector<torch::Tensor> & OneWordNetworkImpl::sparseParameters()
-{
- return _sparseParameters;
-}
-
torch::Tensor OneWordNetworkImpl::forward(torch::Tensor input)
{
// input dim = {batch, sequence, embeddings}
diff --git a/trainer/include/Trainer.hpp b/trainer/include/Trainer.hpp
index 0f9c3ec0425946b15243f318155ba980850923f8..a63c977799d1b2b334baca1153e1f4dd2bc9bf40 100644
--- a/trainer/include/Trainer.hpp
+++ b/trainer/include/Trainer.hpp
@@ -16,8 +16,7 @@ class Trainer
ReadingMachine & machine;
DataLoader dataLoader{nullptr};
- std::unique_ptr<torch::optim::Adam> denseOptimizer;
- std::unique_ptr<torch::optim::SparseAdam> sparseOptimizer;
+ std::unique_ptr<torch::optim::Adam> optimizer;
std::size_t epochNumber{0};
int batchSize{100};
int nbExamples{0};
diff --git a/trainer/src/Trainer.cpp b/trainer/src/Trainer.cpp
index a439c885351936359718d5b96ed0e67c9f6fb201..6c2222cf15417010f9c63732455a4b7947aefcbb 100644
--- a/trainer/src/Trainer.cpp
+++ b/trainer/src/Trainer.cpp
@@ -58,8 +58,7 @@ void Trainer::createDataset(SubConfig & config, bool debug)
dataLoader = torch::data::make_data_loader(Dataset(contexts, classes).map(torch::data::transforms::Stack<>()), torch::data::DataLoaderOptions(batchSize).workers(0).max_jobs(0));
- denseOptimizer.reset(new torch::optim::Adam(machine.getClassifier()->getNN()->denseParameters(), torch::optim::AdamOptions(2e-3).beta1(0.5)));
- sparseOptimizer.reset(new torch::optim::SparseAdam(machine.getClassifier()->getNN()->sparseParameters(), torch::optim::SparseAdamOptions(2e-3).beta1(0.5)));
+ optimizer.reset(new torch::optim::Adam(machine.getClassifier()->getNN()->parameters(), torch::optim::AdamOptions(1e-2)));
}
float Trainer::epoch(bool printAdvancement)
@@ -74,8 +73,7 @@ float Trainer::epoch(bool printAdvancement)
for (auto & batch : *dataLoader)
{
- denseOptimizer->zero_grad();
- sparseOptimizer->zero_grad();
+ optimizer->zero_grad();
auto data = batch.data;
auto labels = batch.target.squeeze();
@@ -90,8 +88,7 @@ float Trainer::epoch(bool printAdvancement)
} catch(std::exception & e) {util::myThrow(e.what());}
loss.backward();
- denseOptimizer->step();
- sparseOptimizer->step();
+ optimizer->step();
if (printAdvancement)
{