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Use Nguyen-Widrow method for weight initialization to make the Distracted SequenceRecallTestNetwork more stable.

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marcus
2016-03-07 15:08:17 +01:00
parent fc5a4ac8a7
commit cff6a986f2
2 changed files with 10 additions and 9 deletions
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src/mlpack/tests/recurrent_network_test.cpp
+5 -4
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@@ -17,6 +17,7 @@
#include <mlpack/core/optimizers/sgd/sgd.hpp>
#include <mlpack/methods/ann/activation_functions/logistic_function.hpp>
#include <mlpack/methods/ann/init_rules/random_init.hpp>
#include <mlpack/methods/ann/init_rules/nguyen_widrow_init.hpp>
#include <boost/test/unit_test.hpp>
#include "old_boost_test_definitions.hpp"
@@ -105,7 +106,7 @@ BOOST_AUTO_TEST_CASE(SequenceClassificationTest)
RNN<decltype(modules), BinaryClassificationLayer, RandomInitialization,
MeanSquaredErrorFunction> net(modules, classOutputLayer);
SGD<decltype(net)> opt(net, 0.5, 400 * input.n_cols, -100);
SGD<decltype(net)> opt(net, 0.5, 500 * input.n_cols, -100);
net.Train(input, labels, opt);
@@ -533,13 +534,13 @@ void DistractedSequenceRecallTestNetwork(HiddenLayerType& hiddenLayer0)
auto modules = std::tie(linearLayer0, recurrentLayer0, hiddenLayer0,
hiddenLayer, hiddenBaseLayer);
RNN<decltype(modules), BinaryClassificationLayer, RandomInitialization,
RNN<decltype(modules), BinaryClassificationLayer, NguyenWidrowInitialization,
MeanSquaredErrorFunction> net(modules, classOutputLayer);
SGD<decltype(net)> opt(net, 0.05, 2, -200);
SGD<decltype(net)> opt(net, 0.04, 2, -200);
arma::mat inputTemp, labelsTemp;
for (size_t i = 0; i < 30; i++)
for (size_t i = 0; i < 40; i++)
{
for (size_t j = 0; j < trainDistractedSequenceCount; j++)
{
src/mlpack/tests/rmsprop_test.cpp
+5 -5
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@@ -45,9 +45,9 @@ BOOST_AUTO_TEST_CASE(SimpleRMSpropTestFunction)
arma::mat coordinates = f.GetInitialPoint();
optimizer.Optimize(coordinates);
BOOST_REQUIRE_SMALL(coordinates[0], 1e-3);
BOOST_REQUIRE_SMALL(coordinates[1], 1e-3);
BOOST_REQUIRE_SMALL(coordinates[2], 1e-3);
BOOST_REQUIRE_SMALL(coordinates[0], 0.1);
BOOST_REQUIRE_SMALL(coordinates[1], 0.1);
BOOST_REQUIRE_SMALL(coordinates[2], 0.1);
}
/**
@@ -141,8 +141,8 @@ BOOST_AUTO_TEST_CASE(FeedforwardTest)
FFN<decltype(modules), decltype(classOutputLayer), RandomInitialization,
MeanSquaredErrorFunction> net(modules, classOutputLayer);
RMSprop<decltype(net)> opt(net, 0.1, 0.88, 1e-15,
300 * input.n_cols, 1e-18);
RMSprop<decltype(net)> opt(net, 0.03, 0.88, 1e-15,
300 * input.n_cols, -10);
net.Train(input, labels, opt);