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TMVA, DNN, reduce memory allocation in minimizer #262

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TMVA, DNN, reduce memory allocation in minimizer #262

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71aa5c2
Ontology: steepest gradient descent. Allow for small variations in th…
re-cursion Jul 18, 2016
ced47cd
started to add reuse of memory for maximizing algorithm
re-cursion Sep 19, 2016
3f1dd9a
Ontology: steepest gradient descent. Allow for small variations in th…
re-cursion Jul 18, 2016
b782b0b
started to add reuse of memory for maximizing algorithm
re-cursion Sep 19, 2016
e720a88
resuse vector within the same thread. Avoid getting the memory at eac…
re-cursion Sep 22, 2016
ab8e3ee
resolved conflict. Two differing versions of reducing memory allocati…
re-cursion Sep 22, 2016
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Ontology: steepest gradient descent. Allow for small variations in th… 
…e learning rate
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@re-cursion
re-cursion committed Sep 21, 2016
commit 71aa5c217555f692bb9fbfce7c059ea9e1e48b80
14 changes: 7 additions & 7 deletions tmva/tmva/inc/TMVA/NeuralNet.icc
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Expand Up @@ -299,7 +299,7 @@ template <bool HasDropOut, typename ItSource, typename ItWeight, typename ItPrev
// plotWeights (localWeights);

double alpha = gaussDouble (m_alpha, m_alpha/2.0);
// double alpha = m_alpha;
// double alpha = m_alpha;

auto itG = begin (gradients);
auto itGEnd = end (gradients);
Expand Down Expand Up @@ -781,7 +781,7 @@ template <typename LAYERDATA>
size_t patternPerThread = testPattern.size () / numThreads;
std::vector<Batch> batches;
auto itPat = testPattern.begin ();
// auto itPatEnd = testPattern.end ();
// auto itPatEnd = testPattern.end ();
for (size_t idxThread = 0; idxThread < numThreads-1; ++idxThread)
{
batches.push_back (Batch (itPat, itPat + patternPerThread));
Expand All @@ -798,24 +798,24 @@ template <typename LAYERDATA>
std::async (std::launch::async, [&]()
{
std::vector<double> localOutput;
pass_through_type passThrough (settings, batch, dropContainerTest);
pass_through_type passThrough (settings, batch, dropContainerTest);
double testBatchError = (*this) (passThrough, weights, ModeOutput::FETCH, localOutput);
return std::make_tuple (testBatchError, localOutput);
})
);
}

auto itBatch = batches.begin ();
auto itBatch = batches.begin ();
for (auto& f : futures)
{
std::tuple<double,std::vector<double>> result = f.get ();
testError += std::get<0>(result) / batches.size ();
std::vector<double> output = std::get<1>(result);

//if (output.size () == testPattern.size ())
//if (output.size () == testPattern.size ())
{
//auto it = begin (testPattern);
auto it = (*itBatch).begin ();
//auto it = begin (testPattern);
auto it = (*itBatch).begin ();
for (double out : output)
{
settings.testSample (0, out, (*it).output ().at (0), (*it).weight ());
Expand Down