[mlpack-git] master: Widen tolerance slightly and disable non-deterministic behavior by setting the shuffle parameter to false. (67e0a13)
gitdub at big.cc.gt.atl.ga.us
gitdub at big.cc.gt.atl.ga.us
Tue Oct 20 05:41:43 EDT 2015
Repository : https://github.com/mlpack/mlpack
On branch : master
Link : https://github.com/mlpack/mlpack/compare/fecf1194c123ced12d56e7daad761c7b9aaac262...67e0a132c7f62820c734eb508fe1bc83128a3e13
>---------------------------------------------------------------
commit 67e0a132c7f62820c734eb508fe1bc83128a3e13
Author: marcus <marcus.edel at fu-berlin.de>
Date: Tue Oct 20 11:41:27 2015 +0200
Widen tolerance slightly and disable non-deterministic behavior by setting the shuffle parameter to false.
>---------------------------------------------------------------
67e0a132c7f62820c734eb508fe1bc83128a3e13
src/mlpack/tests/ada_delta_test.cpp | 6 +++---
src/mlpack/tests/adam_test.cpp | 2 +-
src/mlpack/tests/rmsprop_test.cpp | 10 +++++-----
3 files changed, 9 insertions(+), 9 deletions(-)
diff --git a/src/mlpack/tests/ada_delta_test.cpp b/src/mlpack/tests/ada_delta_test.cpp
index aa78119..961fede 100644
--- a/src/mlpack/tests/ada_delta_test.cpp
+++ b/src/mlpack/tests/ada_delta_test.cpp
@@ -36,7 +36,7 @@ BOOST_AUTO_TEST_SUITE(AdaDeltaTest);
BOOST_AUTO_TEST_CASE(SimpleAdaDeltaTestFunction)
{
const size_t hiddenLayerSize = 10;
- const size_t maxEpochs = 100;
+ const size_t maxEpochs = 300;
// Load the dataset.
arma::mat dataset, labels, labelsIdx;
@@ -49,7 +49,7 @@ BOOST_AUTO_TEST_CASE(SimpleAdaDeltaTestFunction)
labels(labelsIdx(0, i), i) = 1;
// Construct a feed forward network using the specified parameters.
- RandomInitialization randInit(0.5, 0.5);
+ RandomInitialization randInit(0.1, 0.1);
LinearLayer<AdaDelta, RandomInitialization> inputLayer(dataset.n_rows,
hiddenLayerSize, randInit);
@@ -90,7 +90,7 @@ BOOST_AUTO_TEST_CASE(SimpleAdaDeltaTestFunction)
BOOST_REQUIRE_GE(classificationError, 0.09);
// Train the feed forward network.
- Trainer<decltype(net)> trainer(net, maxEpochs, 1, 0.01);
+ Trainer<decltype(net)> trainer(net, maxEpochs, 1, 0.01, false);
trainer.Train(dataset, labels, dataset, labels);
// Evaluate the feed forward network.
diff --git a/src/mlpack/tests/adam_test.cpp b/src/mlpack/tests/adam_test.cpp
index 423268c..b212d3b 100644
--- a/src/mlpack/tests/adam_test.cpp
+++ b/src/mlpack/tests/adam_test.cpp
@@ -91,7 +91,7 @@ BOOST_AUTO_TEST_CASE(SimpleAdamTestFunction)
BOOST_REQUIRE_GE(classificationError, 0.09);
// Train the feed forward network.
- Trainer<decltype(net)> trainer(net, maxEpochs, 1, 0.01);
+ Trainer<decltype(net)> trainer(net, maxEpochs, 1, 0.01, false);
trainer.Train(dataset, labels, dataset, labels);
// Evaluate the feed forward network.
diff --git a/src/mlpack/tests/rmsprop_test.cpp b/src/mlpack/tests/rmsprop_test.cpp
index 0ae76ba..13c4bdd 100644
--- a/src/mlpack/tests/rmsprop_test.cpp
+++ b/src/mlpack/tests/rmsprop_test.cpp
@@ -36,7 +36,7 @@ BOOST_AUTO_TEST_SUITE(RMSPropTest);
BOOST_AUTO_TEST_CASE(SimpleRMSPropTestFunction)
{
const size_t hiddenLayerSize = 10;
- const size_t maxEpochs = 100;
+ const size_t maxEpochs = 300;
// Load the dataset.
arma::mat dataset, labels, labelsIdx;
@@ -49,7 +49,7 @@ BOOST_AUTO_TEST_CASE(SimpleRMSPropTestFunction)
labels(labelsIdx(0, i), i) = 1;
// Construct a feed forward network using the specified parameters.
- RandomInitialization randInit(0.5, 0.5);
+ RandomInitialization randInit(0.1, 0.1);
LinearLayer<RMSPROP, RandomInitialization> inputLayer(dataset.n_rows,
hiddenLayerSize, randInit);
@@ -87,10 +87,10 @@ BOOST_AUTO_TEST_CASE(SimpleRMSPropTestFunction)
// Check if the selected model isn't already optimized.
double classificationError = 1 - double(error) / dataset.n_cols;
- BOOST_REQUIRE_GE(classificationError, 0.05);
+ BOOST_REQUIRE_GE(classificationError, 0.09);
// Train the feed forward network.
- Trainer<decltype(net)> trainer(net, maxEpochs, 1, 0.01);
+ Trainer<decltype(net)> trainer(net, maxEpochs, 1, 0.01, false);
trainer.Train(dataset, labels, dataset, labels);
// Evaluate the feed forward network.
@@ -107,7 +107,7 @@ BOOST_AUTO_TEST_CASE(SimpleRMSPropTestFunction)
classificationError = 1 - double(error) / dataset.n_cols;
- BOOST_REQUIRE_LE(classificationError, 0.05);
+ BOOST_REQUIRE_LE(classificationError, 0.09);
}
BOOST_AUTO_TEST_SUITE_END();
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