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Simulated Annealing for ANN_MLP training method (#10213)

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* Simulated Annealing for ANN_MLP training method

* EXPECT_LT

* just to test new data

* manage RNG

* Try again

* Just run buildbot with new data

* try to understand

* Test layer

* New data- new test

* Force RNG in backprop

* Use Impl to avoid virtual method

* reset all weights

* try to solve ABI

* retry

* ABI solved?

* till problem with dynamic_cast

* Something is wrong

* Solved?

* disable backprop test

* remove ANN_MLP_ANNEALImpl

* Disable weight in varmap

* Add example for SimulatedAnnealing
This commit is contained in:
LaurentBerger
2017-12-15 11:57:39 +01:00
committed by Vadim Pisarevsky
parent 6df8ac0342
commit 7ad308ea47
5 changed files with 685 additions and 26 deletions
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modules/ml/test/test_mltests2.cpp
+86 -5
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@@ -79,8 +79,10 @@ int str_to_ann_train_method( String& str )
{
if( !str.compare("BACKPROP") )
return ANN_MLP::BACKPROP;
if( !str.compare("RPROP") )
if (!str.compare("RPROP"))
return ANN_MLP::RPROP;
if (!str.compare("ANNEAL"))
return ANN_MLP::ANNEAL;
CV_Error( CV_StsBadArg, "incorrect ann train method string" );
return -1;
}
@@ -241,13 +243,92 @@ TEST(ML_ANN, ActivationFunction)
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];
double n = cvtest::norm(rx, ry, NORM_INF);
EXPECT_LT(n,FLT_EPSILON) << "Predict are not equal for " << dataname + activationName[i] + ".yml and " << activationName[i];
#endif
}
}
//#define GENERATE_TESTDATA
TEST(ML_ANN, Method)
{
String folder = string(cvtest::TS::ptr()->get_data_path());
String original_path = folder + "waveform.data";
String dataname = folder + "waveform";
Ptr<TrainData> tdata2 = TrainData::loadFromCSV(original_path, 0);
Mat responses(tdata2->getResponses().rows, 3, CV_32FC1, Scalar(0));
for (int i = 0; i<tdata2->getResponses().rows; i++)
responses.at<float>(i, static_cast<int>(tdata2->getResponses().at<float>(i, 0))) = 1;
Ptr<TrainData> tdata = TrainData::create(tdata2->getSamples(), ml::ROW_SAMPLE, responses);
ASSERT_FALSE(tdata.empty()) << "Could not find test data file : " << original_path;
RNG& rng = theRNG();
rng.state = 0;
tdata->setTrainTestSplitRatio(0.8);
vector<int> methodType;
methodType.push_back(ml::ANN_MLP::RPROP);
methodType.push_back(ml::ANN_MLP::ANNEAL);
// methodType.push_back(ml::ANN_MLP::BACKPROP); -----> NO BACKPROP TEST
vector<String> methodName;
methodName.push_back("_rprop");
methodName.push_back("_anneal");
// methodName.push_back("_backprop"); -----> NO BACKPROP TEST
#ifdef GENERATE_TESTDATA
Ptr<ml::ANN_MLP> xx = ml::ANN_MLP_ANNEAL::create();
Mat_<int> layerSizesXX(1, 4);
layerSizesXX(0, 0) = tdata->getNVars();
layerSizesXX(0, 1) = 30;
layerSizesXX(0, 2) = 30;
layerSizesXX(0, 3) = tdata->getResponses().cols;
xx->setLayerSizes(layerSizesXX);
xx->setActivationFunction(ml::ANN_MLP::SIGMOID_SYM);
xx->setTrainMethod(ml::ANN_MLP::RPROP);
xx->setTermCriteria(TermCriteria(TermCriteria::COUNT, 1, 0.01));
xx->train(tdata, ml::ANN_MLP::NO_OUTPUT_SCALE + ml::ANN_MLP::NO_INPUT_SCALE);
FileStorage fs;
fs.open(dataname + "_init_weight.yml.gz", FileStorage::WRITE + FileStorage::BASE64);
xx->write(fs);
fs.release();
#endif
for (size_t i = 0; i < methodType.size(); i++)
{
FileStorage fs;
fs.open(dataname + "_init_weight.yml.gz", FileStorage::READ + FileStorage::BASE64);
Ptr<ml::ANN_MLP> x = ml::ANN_MLP_ANNEAL::create();
x->read(fs.root());
x->setTrainMethod(methodType[i]);
if (methodType[i] == ml::ANN_MLP::ANNEAL)
{
x->setAnnealInitialT(12);
x->setAnnealFinalT(0.15);
x->setAnnealCoolingRatio(0.96);
x->setAnnealItePerStep(11);
}
x->setTermCriteria(TermCriteria(TermCriteria::COUNT, 100, 0.01));
x->train(tdata, ml::ANN_MLP::NO_OUTPUT_SCALE + ml::ANN_MLP::NO_INPUT_SCALE + ml::ANN_MLP::UPDATE_WEIGHTS);
ASSERT_TRUE(x->isTrained()) << "Could not train networks with " << methodName[i];
#ifdef GENERATE_TESTDATA
x->save(dataname + methodName[i] + ".yml.gz");
#endif
Ptr<ml::ANN_MLP> y = Algorithm::load<ANN_MLP>(dataname + methodName[i] + ".yml.gz");
ASSERT_TRUE(y != NULL) << "Could not load " << dataname + methodName[i] + ".yml";
Mat testSamples = tdata->getTestSamples();
Mat rx, ry, dst;
for (int j = 0; j < 4; j++)
{
rx = x->getWeights(j);
ry = y->getWeights(j);
double n = cvtest::norm(rx, ry, NORM_INF);
EXPECT_LT(n, FLT_EPSILON) << "Weights are not equal for " << dataname + methodName[i] + ".yml and " << methodName[i] << " layer : " << j;
}
x->predict(testSamples, rx);
y->predict(testSamples, ry);
double n = cvtest::norm(rx, ry, NORM_INF);
EXPECT_LT(n, FLT_EPSILON) << "Predict are not equal for " << dataname + methodName[i] + ".yml and " << methodName[i];
}
}
// 6. dtree
// 7. boost