Chiebot-Mirror
/
opencv
mirror of https://github.com/opencv/opencv.git
8
0
Fork 0
Code Issues Projects Releases Wiki Activity

Merge pull request #10141 from LaurentBerger:MLP_ReLU

Browse Source
pull/10190/head
Vadim Pisarevsky 7 years ago
parent 2a8344f75b a44573c43b
commit cfd845ac07
3 changed files with 220 additions and 74 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 8
      modules/ml/include/opencv2/ml.hpp
  2. 76
      modules/ml/src/ann_mlp.cpp
  3. 72
      modules/ml/test/test_mltests2.cpp

8
modules/ml/include/opencv2/ml.hpp
Unescape View File

@ -1503,14 +1503,18 @@ public:
enum ActivationFunctions {
/** Identity function: \f$f(x)=x\f$ */
IDENTITY = 0,
/** Symmetrical sigmoid: \f$f(x)=\beta*(1-e^{-\alpha x})/(1+e^{-\alpha x}\f$
/** Symmetrical sigmoid: \f$f(x)=\beta*(1-e^{-\alpha x})/(1+e^{-\alpha x})\f$
@note
If you are using the default sigmoid activation function with the default parameter values
fparam1=0 and fparam2=0 then the function used is y = 1.7159\*tanh(2/3 \* x), so the output
will range from [-1.7159, 1.7159], instead of [0,1].*/
SIGMOID_SYM = 1,
/** Gaussian function: \f$f(x)=\beta e^{-\alpha x*x}\f$ */
GAUSSIAN = 2
GAUSSIAN = 2,
/** ReLU function: \f$f(x)=max(0,x)\f$ */
RELU = 3,
/** Leaky ReLU function: for x>0 \f$f(x)=x \f$ and x<=0 \f$f(x)=\alpha x \f$*/
LEAKYRELU= 4
};
/** Train options */

76
modules/ml/src/ann_mlp.cpp
Unescape View File

@ -135,7 +135,7 @@ public:
void setActivationFunction(int _activ_func, double _f_param1, double _f_param2 )
{
if( _activ_func < 0 || _activ_func > GAUSSIAN )
if( _activ_func < 0 || _activ_func > LEAKYRELU)
CV_Error( CV_StsOutOfRange, "Unknown activation function" );
activ_func = _activ_func;
@ -158,6 +158,18 @@ public:
if (fabs(_f_param2) < FLT_EPSILON)
_f_param2 = 1.;
break;
case RELU:
if (fabs(_f_param1) < FLT_EPSILON)
_f_param1 = 1;
min_val = max_val = min_val1 = max_val1 = 0.;
_f_param2 = 0.;
break;
case LEAKYRELU:
if (fabs(_f_param1) < FLT_EPSILON)
_f_param1 = 0.01;
min_val = max_val = min_val1 = max_val1 = 0.;
_f_param2 = 0.;
break;
default:
min_val = max_val = min_val1 = max_val1 = 0.;
_f_param1 = 1.;
@ -385,6 +397,12 @@ public:
case GAUSSIAN:
scale = -f_param1*f_param1;
break;
case RELU:
scale = 1;
break;
case LEAKYRELU:
scale = 1;
break;
default:
;
}
@ -397,10 +415,18 @@ public:
{
double* data = sums.ptr<double>(i);
for (j = 0; j < cols; j++)
{
data[j] = (data[j] + bias[j])*scale;
if (activ_func == RELU)
if (data[j] < 0)
data[j] = 0;
if (activ_func == LEAKYRELU)
if (data[j] < 0)
data[j] *= f_param1;
}
}
if( activ_func == IDENTITY )
if (activ_func == IDENTITY || activ_func == RELU || activ_func == LEAKYRELU)
return;
}
else
@ -478,6 +504,46 @@ public:
}
}
}
else if (activ_func == RELU)
{
for (i = 0; i < n; i++)
{
double* xf = _xf.ptr<double>(i);
double* df = _df.ptr<double>(i);
for (j = 0; j < cols; j++)
{
xf[j] += bias[j];
if (xf[j] < 0)
{
xf[j] = 0;
df[j] = 0;
}
else
df[j] = 1;
}
}
}
else if (activ_func == LEAKYRELU)
{
for (i = 0; i < n; i++)
{
double* xf = _xf.ptr<double>(i);
double* df = _df.ptr<double>(i);
for (j = 0; j < cols; j++)
{
xf[j] += bias[j];
if (xf[j] < 0)
{
xf[j] = f_param1*xf[j];
df[j] = f_param1;
}
else
df[j] = 1;
}
}
}
else if (activ_func == GAUSSIAN)
{
double scale = -f_param1*f_param1;
@ -1110,7 +1176,9 @@ public:
{
const char* activ_func_name = activ_func == IDENTITY ? "IDENTITY" :
activ_func == SIGMOID_SYM ? "SIGMOID_SYM" :
activ_func == GAUSSIAN ? "GAUSSIAN" : 0;
activ_func == GAUSSIAN ? "GAUSSIAN" :
activ_func == RELU ? "RELU" :
activ_func == LEAKYRELU ? "LEAKYRELU" : 0;
if( activ_func_name )
fs << "activation_function" << activ_func_name;
@ -1191,6 +1259,8 @@ public:
{
activ_func = activ_func_name == "SIGMOID_SYM" ? SIGMOID_SYM :
activ_func_name == "IDENTITY" ? IDENTITY :
activ_func_name == "RELU" ? RELU :
activ_func_name == "LEAKYRELU" ? LEAKYRELU :
activ_func_name == "GAUSSIAN" ? GAUSSIAN : -1;
CV_Assert( activ_func >= 0 );
}

72
modules/ml/test/test_mltests2.cpp
Unescape View File

@ -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 )

Write Preview
Loading…
Cancel
Save