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Add ReLU and LeakyReLU activation function in ml module

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LaurentBerger
2017-11-22 22:07:23 +01:00
parent f8ad289311
commit a44573c43b
3 changed files with 220 additions and 74 deletions
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modules/ml/test/test_mltests2.cpp
+72
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@@ -85,6 +85,22 @@ int str_to_ann_train_method( String& str )
return -1;
}
int str_to_ann_activation_function(String& str)
{
if (!str.compare("IDENTITY"))
return ANN_MLP::IDENTITY;
if (!str.compare("SIGMOID_SYM"))
return ANN_MLP::SIGMOID_SYM;
if (!str.compare("GAUSSIAN"))
return ANN_MLP::GAUSSIAN;
if (!str.compare("RELU"))
return ANN_MLP::RELU;
if (!str.compare("LEAKYRELU"))
return ANN_MLP::LEAKYRELU;
CV_Error(CV_StsBadArg, "incorrect ann activation function string");
return -1;
}
void ann_check_data( Ptr<TrainData> _data )
{
CV_TRACE_FUNCTION();
@@ -177,6 +193,62 @@ float ann_calc_error( Ptr<StatModel> ann, Ptr<TrainData> _data, map<int, int>& c
return err;
}
TEST(ML_ANN, ActivationFunction)
{
String folder = string(cvtest::TS::ptr()->get_data_path());
String original_path = folder + "waveform.data";
String dataname = folder + "waveform";
Ptr<TrainData> tdata = TrainData::loadFromCSV(original_path, 0);
ASSERT_FALSE(tdata.empty()) << "Could not find test data file : " << original_path;
RNG& rng = theRNG();
rng.state = 1027401484159173092;
tdata->setTrainTestSplit(500);
vector<int> activationType;
activationType.push_back(ml::ANN_MLP::IDENTITY);
activationType.push_back(ml::ANN_MLP::SIGMOID_SYM);
activationType.push_back(ml::ANN_MLP::GAUSSIAN);
activationType.push_back(ml::ANN_MLP::RELU);
activationType.push_back(ml::ANN_MLP::LEAKYRELU);
vector<String> activationName;
activationName.push_back("_identity");
activationName.push_back("_sigmoid_sym");
activationName.push_back("_gaussian");
activationName.push_back("_relu");
activationName.push_back("_leakyrelu");
for (size_t i = 0; i < activationType.size(); i++)
{
Ptr<ml::ANN_MLP> x = ml::ANN_MLP::create();
Mat_<int> layerSizes(1, 4);
layerSizes(0, 0) = tdata->getNVars();
layerSizes(0, 1) = 100;
layerSizes(0, 2) = 100;
layerSizes(0, 3) = tdata->getResponses().cols;
x->setLayerSizes(layerSizes);
x->setActivationFunction(activationType[i]);
x->setTrainMethod(ml::ANN_MLP::RPROP, 0.01, 0.1);
x->setTermCriteria(TermCriteria(TermCriteria::COUNT, 300, 0.01));
x->train(tdata, ml::ANN_MLP::NO_OUTPUT_SCALE);
ASSERT_TRUE(x->isTrained()) << "Could not train networks with " << activationName[i];
#ifdef GENERATE_TESTDATA
x->save(dataname + activationName[i] + ".yml");
#else
Ptr<ml::ANN_MLP> y = Algorithm::load<ANN_MLP>(dataname + activationName[i] + ".yml");
ASSERT_TRUE(y != NULL) << "Could not load " << dataname + activationName[i] + ".yml";
Mat testSamples = tdata->getTestSamples();
Mat rx, ry, dst;
x->predict(testSamples, rx);
y->predict(testSamples, ry);
absdiff(rx, ry, dst);
double minVal, maxVal;
minMaxLoc(dst, &minVal, &maxVal);
ASSERT_TRUE(maxVal<FLT_EPSILON) << "Predict are not equal for " << dataname + activationName[i] + ".yml and " << activationName[i];
#endif
}
}
// 6. dtree
// 7. boost
int str_to_boost_type( String& str )