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opencv/modules/ml/src/ann_mlp.cpp

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "precomp.hpp"
namespace cv { namespace ml {
struct AnnParams
{
AnnParams()
{
termCrit = TermCriteria( TermCriteria::COUNT + TermCriteria::EPS, 1000, 0.01 );
trainMethod = ANN_MLP::RPROP;
bpDWScale = bpMomentScale = 0.1;
rpDW0 = 0.1; rpDWPlus = 1.2; rpDWMinus = 0.5;
rpDWMin = FLT_EPSILON; rpDWMax = 50.;
initialT=10;finalT=0.1,coolingRatio=0.95;itePerStep=10;
rEnergy = cv::RNG(12345);
}
TermCriteria termCrit;
int trainMethod;
double bpDWScale;
double bpMomentScale;
double rpDW0;
double rpDWPlus;
double rpDWMinus;
double rpDWMin;
double rpDWMax;
double initialT;
double finalT;
double coolingRatio;
int itePerStep;
RNG rEnergy;
};
template <typename T>
inline T inBounds(T val, T min_val, T max_val)
{
return std::min(std::max(val, min_val), max_val);
}
class SimulatedAnnealingANN_MLP
{
protected:
ml::ANN_MLP& nn;
Ptr<ml::TrainData> data;
int nbVariables;
vector<double*> adrVariables;
RNG rVar;
RNG rIndex;
double varTmp;
int index;
public:
SimulatedAnnealingANN_MLP(ml::ANN_MLP& x, const Ptr<ml::TrainData>& d) : nn(x), data(d), varTmp(0.0), index(0)
{
initVarMap();
}
~SimulatedAnnealingANN_MLP() {}
void changeState()
{
index = rIndex.uniform(0, nbVariables);
double dv = rVar.uniform(-1.0, 1.0);
varTmp = *adrVariables[index];
*adrVariables[index] = dv;
}
void reverseState()
{
*adrVariables[index] = varTmp;
}
double energy() const { return nn.calcError(data, false, noArray()); }
protected:
void initVarMap()
{
Mat l = nn.getLayerSizes();
nbVariables = 0;
adrVariables.clear();
for (int i = 1; i < l.rows-1; i++)
{
Mat w = nn.getWeights(i);
for (int j = 0; j < w.rows; j++)
{
for (int k = 0; k < w.cols; k++, nbVariables++)
{
if (j == w.rows - 1)
{
adrVariables.push_back(&w.at<double>(w.rows - 1, k));
}
else
{
adrVariables.push_back(&w.at<double>(j, k));
}
}
}
}
}
};
double ANN_MLP::getAnnealInitialT() const
{
const ANN_MLP_ANNEAL* this_ = dynamic_cast<const ANN_MLP_ANNEAL*>(this);
if (!this_)
CV_Error(Error::StsNotImplemented, "the class is not ANN_MLP_ANNEAL");
return this_->getAnnealInitialT();
}
void ANN_MLP::setAnnealInitialT(double val)
{
ANN_MLP_ANNEAL* this_ = dynamic_cast<ANN_MLP_ANNEAL*>(this);
if (!this_)
CV_Error(Error::StsNotImplemented, "the class is not ANN_MLP_ANNEAL");
this_->setAnnealInitialT(val);
}
double ANN_MLP::getAnnealFinalT() const
{
const ANN_MLP_ANNEAL* this_ = dynamic_cast<const ANN_MLP_ANNEAL*>(this);
if (!this_)
CV_Error(Error::StsNotImplemented, "the class is not ANN_MLP_ANNEAL");
return this_->getAnnealFinalT();
}
void ANN_MLP::setAnnealFinalT(double val)
{
ANN_MLP_ANNEAL* this_ = dynamic_cast<ANN_MLP_ANNEAL*>(this);
if (!this_)
CV_Error(Error::StsNotImplemented, "the class is not ANN_MLP_ANNEAL");
this_->setAnnealFinalT(val);
}
double ANN_MLP::getAnnealCoolingRatio() const
{
const ANN_MLP_ANNEAL* this_ = dynamic_cast<const ANN_MLP_ANNEAL*>(this);
if (!this_)
CV_Error(Error::StsNotImplemented, "the class is not ANN_MLP_ANNEAL");
return this_->getAnnealCoolingRatio();
}
void ANN_MLP::setAnnealCoolingRatio(double val)
{
ANN_MLP_ANNEAL* this_ = dynamic_cast<ANN_MLP_ANNEAL*>(this);
if (!this_)
CV_Error(Error::StsNotImplemented, "the class is not ANN_MLP_ANNEAL");
this_->setAnnealCoolingRatio(val);
}
int ANN_MLP::getAnnealItePerStep() const
{
const ANN_MLP_ANNEAL* this_ = dynamic_cast<const ANN_MLP_ANNEAL*>(this);
if (!this_)
CV_Error(Error::StsNotImplemented, "the class is not ANN_MLP_ANNEAL");
return this_->getAnnealItePerStep();
}
void ANN_MLP::setAnnealItePerStep(int val)
{
ANN_MLP_ANNEAL* this_ = dynamic_cast<ANN_MLP_ANNEAL*>(this);
if (!this_)
CV_Error(Error::StsNotImplemented, "the class is not ANN_MLP_ANNEAL");
this_->setAnnealItePerStep(val);
}
void ANN_MLP::setAnnealEnergyRNG(const RNG& rng)
{
ANN_MLP_ANNEAL* this_ = dynamic_cast<ANN_MLP_ANNEAL*>(this);
if (!this_)
CV_Error(Error::StsNotImplemented, "the class is not ANN_MLP_ANNEAL");
this_->setAnnealEnergyRNG(rng);
}
class ANN_MLPImpl CV_FINAL : public ANN_MLP_ANNEAL
{
public:
ANN_MLPImpl()
{
clear();
setActivationFunction( SIGMOID_SYM, 0, 0);
setLayerSizes(Mat());
setTrainMethod(ANN_MLP::RPROP, 0.1, FLT_EPSILON);
}
virtual ~ANN_MLPImpl() {}
inline TermCriteria getTermCriteria() const CV_OVERRIDE { return params.termCrit; }
inline void setTermCriteria(TermCriteria val) CV_OVERRIDE { params.termCrit = val; }
inline double getBackpropWeightScale() const CV_OVERRIDE { return params.bpDWScale; }
inline void setBackpropWeightScale(double val) CV_OVERRIDE { params.bpDWScale = val; }
inline double getBackpropMomentumScale() const CV_OVERRIDE { return params.bpMomentScale; }
inline void setBackpropMomentumScale(double val) CV_OVERRIDE { params.bpMomentScale = val; }
inline double getRpropDW0() const CV_OVERRIDE { return params.rpDW0; }
inline void setRpropDW0(double val) CV_OVERRIDE { params.rpDW0 = val; }
inline double getRpropDWPlus() const CV_OVERRIDE { return params.rpDWPlus; }
inline void setRpropDWPlus(double val) CV_OVERRIDE { params.rpDWPlus = val; }
inline double getRpropDWMinus() const CV_OVERRIDE { return params.rpDWMinus; }
inline void setRpropDWMinus(double val) CV_OVERRIDE { params.rpDWMinus = val; }
inline double getRpropDWMin() const CV_OVERRIDE { return params.rpDWMin; }
inline void setRpropDWMin(double val) CV_OVERRIDE { params.rpDWMin = val; }
inline double getRpropDWMax() const CV_OVERRIDE { return params.rpDWMax; }
inline void setRpropDWMax(double val) CV_OVERRIDE { params.rpDWMax = val; }
inline double getAnnealInitialT() const CV_OVERRIDE { return params.initialT; }
inline void setAnnealInitialT(double val) CV_OVERRIDE { params.initialT = val; }
inline double getAnnealFinalT() const CV_OVERRIDE { return params.finalT; }
inline void setAnnealFinalT(double val) CV_OVERRIDE { params.finalT = val; }
inline double getAnnealCoolingRatio() const CV_OVERRIDE { return params.coolingRatio; }
inline void setAnnealCoolingRatio(double val) CV_OVERRIDE { params.coolingRatio = val; }
inline int getAnnealItePerStep() const CV_OVERRIDE { return params.itePerStep; }
inline void setAnnealItePerStep(int val) CV_OVERRIDE { params.itePerStep = val; }
// disabled getAnnealEnergyRNG()
inline void setAnnealEnergyRNG(const RNG& val) CV_OVERRIDE { params.rEnergy = val; }
void clear() CV_OVERRIDE
{
min_val = max_val = min_val1 = max_val1 = 0.;
rng = RNG((uint64)-1);
weights.clear();
trained = false;
max_buf_sz = 1 << 12;
}
int layer_count() const { return (int)layer_sizes.size(); }
void setTrainMethod(int method, double param1, double param2) CV_OVERRIDE
{
if (method != ANN_MLP::RPROP && method != ANN_MLP::BACKPROP && method != ANN_MLP::ANNEAL)
method = ANN_MLP::RPROP;
params.trainMethod = method;
if(method == ANN_MLP::RPROP )
{
if( param1 < FLT_EPSILON )
param1 = 1.;
params.rpDW0 = param1;
params.rpDWMin = std::max( param2, 0. );
}
else if (method == ANN_MLP::BACKPROP)
{
if (param1 <= 0)
param1 = 0.1;
params.bpDWScale = inBounds<double>(param1, 1e-3, 1.);
if (param2 < 0)
param2 = 0.1;
params.bpMomentScale = std::min(param2, 1.);
}
}
int getTrainMethod() const CV_OVERRIDE
{
return params.trainMethod;
}
void setActivationFunction(int _activ_func, double _f_param1, double _f_param2) CV_OVERRIDE
{
if( _activ_func < 0 || _activ_func > LEAKYRELU)
CV_Error( CV_StsOutOfRange, "Unknown activation function" );
activ_func = _activ_func;
switch( activ_func )
{
case SIGMOID_SYM:
max_val = 0.95; min_val = -max_val;
max_val1 = 0.98; min_val1 = -max_val1;
if( fabs(_f_param1) < FLT_EPSILON )
_f_param1 = 2./3;
if( fabs(_f_param2) < FLT_EPSILON )
_f_param2 = 1.7159;
break;
case GAUSSIAN:
max_val = 1.; min_val = 0.05;
max_val1 = 1.; min_val1 = 0.02;
if (fabs(_f_param1) < FLT_EPSILON)
_f_param1 = 1.;
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.;
_f_param2 = 0.;
}
f_param1 = _f_param1;
f_param2 = _f_param2;
}
void init_weights()
{
int i, j, k, l_count = layer_count();
for( i = 1; i < l_count; i++ )
{
int n1 = layer_sizes[i-1];
int n2 = layer_sizes[i];
double val = 0, G = n2 > 2 ? 0.7*pow((double)n1,1./(n2-1)) : 1.;
double* w = weights[i].ptr<double>();
// initialize weights using Nguyen-Widrow algorithm
for( j = 0; j < n2; j++ )
{
double s = 0;
for( k = 0; k <= n1; k++ )
{
val = rng.uniform(0., 1.)*2-1.;
w[k*n2 + j] = val;
s += fabs(val);
}
if( i < l_count - 1 )
{
s = 1./(s - fabs(val));
for( k = 0; k <= n1; k++ )
w[k*n2 + j] *= s;
w[n1*n2 + j] *= G*(-1+j*2./n2);
}
}
}
}
Mat getLayerSizes() const CV_OVERRIDE
{
return Mat_<int>(layer_sizes, true);
}
void setLayerSizes( InputArray _layer_sizes ) CV_OVERRIDE
{
clear();
_layer_sizes.copyTo(layer_sizes);
int l_count = layer_count();
weights.resize(l_count + 2);
max_lsize = 0;
if( l_count > 0 )
{
for( int i = 0; i < l_count; i++ )
{
int n = layer_sizes[i];
if( n < 1 + (0 < i && i < l_count-1))
CV_Error( CV_StsOutOfRange,
"there should be at least one input and one output "
"and every hidden layer must have more than 1 neuron" );
max_lsize = std::max( max_lsize, n );
if( i > 0 )
weights[i].create(layer_sizes[i-1]+1, n, CV_64F);
}
int ninputs = layer_sizes.front();
int noutputs = layer_sizes.back();
weights[0].create(1, ninputs*2, CV_64F);
weights[l_count].create(1, noutputs*2, CV_64F);
weights[l_count+1].create(1, noutputs*2, CV_64F);
}
}
float predict( InputArray _inputs, OutputArray _outputs, int ) const CV_OVERRIDE
{
if( !trained )
CV_Error( CV_StsError, "The network has not been trained or loaded" );
Mat inputs = _inputs.getMat();
int type = inputs.type(), l_count = layer_count();
int n = inputs.rows, dn0 = n;
CV_Assert( (type == CV_32F || type == CV_64F) && inputs.cols == layer_sizes[0] );
int noutputs = layer_sizes[l_count-1];
Mat outputs;
int min_buf_sz = 2*max_lsize;
int buf_sz = n*min_buf_sz;
if( buf_sz > max_buf_sz )
{
dn0 = max_buf_sz/min_buf_sz;
dn0 = std::max( dn0, 1 );
buf_sz = dn0*min_buf_sz;
}
cv::AutoBuffer<double> _buf(buf_sz+noutputs);
double* buf = _buf.data();
if( !_outputs.needed() )
{
CV_Assert( n == 1 );
outputs = Mat(n, noutputs, type, buf + buf_sz);
}
else
{
_outputs.create(n, noutputs, type);
outputs = _outputs.getMat();
}
int dn = 0;
for( int i = 0; i < n; i += dn )
{
dn = std::min( dn0, n - i );
Mat layer_in = inputs.rowRange(i, i + dn);
Mat layer_out( dn, layer_in.cols, CV_64F, buf);
scale_input( layer_in, layer_out );
layer_in = layer_out;
for( int j = 1; j < l_count; j++ )
{
double* data = buf + ((j&1) ? max_lsize*dn0 : 0);
int cols = layer_sizes[j];
layer_out = Mat(dn, cols, CV_64F, data);
Mat w = weights[j].rowRange(0, layer_in.cols);
gemm(layer_in, w, 1, noArray(), 0, layer_out);
calc_activ_func( layer_out, weights[j] );
layer_in = layer_out;
}
layer_out = outputs.rowRange(i, i + dn);
scale_output( layer_in, layer_out );
}
if( n == 1 )
{
int maxIdx[] = {0, 0};
minMaxIdx(outputs, 0, 0, 0, maxIdx);
return (float)(maxIdx[0] + maxIdx[1]);
}
return 0.f;
}
void scale_input( const Mat& _src, Mat& _dst ) const
{
int cols = _src.cols;
const double* w = weights[0].ptr<double>();
if( _src.type() == CV_32F )
{
for( int i = 0; i < _src.rows; i++ )
{
const float* src = _src.ptr<float>(i);
double* dst = _dst.ptr<double>(i);
for( int j = 0; j < cols; j++ )
dst[j] = src[j]*w[j*2] + w[j*2+1];
}
}
else
{
for( int i = 0; i < _src.rows; i++ )
{
const double* src = _src.ptr<double>(i);
double* dst = _dst.ptr<double>(i);
for( int j = 0; j < cols; j++ )
dst[j] = src[j]*w[j*2] + w[j*2+1];
}
}
}
void scale_output( const Mat& _src, Mat& _dst ) const
{
int cols = _src.cols;
const double* w = weights[layer_count()].ptr<double>();
if( _dst.type() == CV_32F )
{
for( int i = 0; i < _src.rows; i++ )
{
const double* src = _src.ptr<double>(i);
float* dst = _dst.ptr<float>(i);
for( int j = 0; j < cols; j++ )
dst[j] = (float)(src[j]*w[j*2] + w[j*2+1]);
}
}
else
{
for( int i = 0; i < _src.rows; i++ )
{
const double* src = _src.ptr<double>(i);
double* dst = _dst.ptr<double>(i);
for( int j = 0; j < cols; j++ )
dst[j] = src[j]*w[j*2] + w[j*2+1];
}
}
}
void calc_activ_func(Mat& sums, const Mat& w) const
{
const double* bias = w.ptr<double>(w.rows - 1);
int i, j, n = sums.rows, cols = sums.cols;
double scale = 0, scale2 = f_param2;
switch (activ_func)
{
case IDENTITY:
scale = 1.;
break;
case SIGMOID_SYM:
scale = -f_param1;
break;
case GAUSSIAN:
scale = -f_param1*f_param1;
break;
case RELU:
scale = 1;
break;
case LEAKYRELU:
scale = 1;
break;
default:
;
}
CV_Assert(sums.isContinuous());
if (activ_func != GAUSSIAN)
{
for (i = 0; i < n; i++)
{
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 || activ_func == RELU || activ_func == LEAKYRELU)
return;
}
else
{
for (i = 0; i < n; i++)
{
double* data = sums.ptr<double>(i);
for (j = 0; j < cols; j++)
{
double t = data[j] + bias[j];
data[j] = t*t*scale;
}
}
}
exp(sums, sums);
if (sums.isContinuous())
{
cols *= n;
n = 1;
}
switch (activ_func)
{
case SIGMOID_SYM:
for (i = 0; i < n; i++)
{
double* data = sums.ptr<double>(i);
for (j = 0; j < cols; j++)
{
if (!cvIsInf(data[j]))
{
double t = scale2*(1. - data[j]) / (1. + data[j]);
data[j] = t;
}
else
{
data[j] = -scale2;
}
}
}
break;
case GAUSSIAN:
for (i = 0; i < n; i++)
{
double* data = sums.ptr<double>(i);
for (j = 0; j < cols; j++)
data[j] = scale2*data[j];
}
break;
default:
;
}
}
void calc_activ_func_deriv(Mat& _xf, Mat& _df, const Mat& w) const
{
const double* bias = w.ptr<double>(w.rows - 1);
int i, j, n = _xf.rows, cols = _xf.cols;
if (activ_func == IDENTITY)
{
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];
df[j] = 1;
}
}
}
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;
double scale2 = scale*f_param2;
for (i = 0; i < n; i++)
{
double* xf = _xf.ptr<double>(i);
double* df = _df.ptr<double>(i);
for (j = 0; j < cols; j++)
{
double t = xf[j] + bias[j];
df[j] = t * 2 * scale2;
xf[j] = t*t*scale;
}
}
exp(_xf, _xf);
for (i = 0; i < n; i++)
{
double* xf = _xf.ptr<double>(i);
double* df = _df.ptr<double>(i);
for (j = 0; j < cols; j++)
df[j] *= xf[j];
}
}
else
{
double scale = f_param1;
double scale2 = f_param2;
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] = (xf[j] + bias[j])*scale;
df[j] = -fabs(xf[j]);
}
}
exp(_df, _df);
// ((1+exp(-ax))^-1)'=a*((1+exp(-ax))^-2)*exp(-ax);
// ((1-exp(-ax))/(1+exp(-ax)))'=(a*exp(-ax)*(1+exp(-ax)) + a*exp(-ax)*(1-exp(-ax)))/(1+exp(-ax))^2=
// 2*a*exp(-ax)/(1+exp(-ax))^2
scale *= 2 * f_param2;
for (i = 0; i < n; i++)
{
double* xf = _xf.ptr<double>(i);
double* df = _df.ptr<double>(i);
for (j = 0; j < cols; j++)
{
int s0 = xf[j] > 0 ? 1 : -1;
double t0 = 1. / (1. + df[j]);
double t1 = scale*df[j] * t0*t0;
t0 *= scale2*(1. - df[j])*s0;
df[j] = t1;
xf[j] = t0;
}
}
}
}
void calc_input_scale( const Mat& inputs, int flags )
{
bool reset_weights = (flags & UPDATE_WEIGHTS) == 0;
bool no_scale = (flags & NO_INPUT_SCALE) != 0;
double* scale = weights[0].ptr<double>();
int count = inputs.rows;
if( reset_weights )
{
int i, j, vcount = layer_sizes[0];
int type = inputs.type();
double a = no_scale ? 1. : 0.;
for( j = 0; j < vcount; j++ )
scale[2*j] = a, scale[j*2+1] = 0.;
if( no_scale )
return;
for( i = 0; i < count; i++ )
{
const uchar* p = inputs.ptr(i);
const float* f = (const float*)p;
const double* d = (const double*)p;
for( j = 0; j < vcount; j++ )
{
double t = type == CV_32F ? (double)f[j] : d[j];
scale[j*2] += t;
scale[j*2+1] += t*t;
}
}
for( j = 0; j < vcount; j++ )
{
double s = scale[j*2], s2 = scale[j*2+1];
double m = s/count, sigma2 = s2/count - m*m;
scale[j*2] = sigma2 < DBL_EPSILON ? 1 : 1./sqrt(sigma2);
scale[j*2+1] = -m*scale[j*2];
}
}
}
void calc_output_scale( const Mat& outputs, int flags )
{
int i, j, vcount = layer_sizes.back();
int type = outputs.type();
double m = min_val, M = max_val, m1 = min_val1, M1 = max_val1;
bool reset_weights = (flags & UPDATE_WEIGHTS) == 0;
bool no_scale = (flags & NO_OUTPUT_SCALE) != 0;
int l_count = layer_count();
double* scale = weights[l_count].ptr<double>();
double* inv_scale = weights[l_count+1].ptr<double>();
int count = outputs.rows;
if( reset_weights )
{
double a0 = no_scale ? 1 : DBL_MAX, b0 = no_scale ? 0 : -DBL_MAX;
for( j = 0; j < vcount; j++ )
{
scale[2*j] = inv_scale[2*j] = a0;
scale[j*2+1] = inv_scale[2*j+1] = b0;
}
if( no_scale )
return;
}
for( i = 0; i < count; i++ )
{
const uchar* p = outputs.ptr(i);
const float* f = (const float*)p;
const double* d = (const double*)p;
for( j = 0; j < vcount; j++ )
{
double t = type == CV_32F ? (double)f[j] : d[j];
if( reset_weights )
{
double mj = scale[j*2], Mj = scale[j*2+1];
if( mj > t ) mj = t;
if( Mj < t ) Mj = t;
scale[j*2] = mj;
scale[j*2+1] = Mj;
}
else if( !no_scale )
{
t = t*inv_scale[j*2] + inv_scale[2*j+1];
if( t < m1 || t > M1 )
CV_Error( CV_StsOutOfRange,
"Some of new output training vector components run exceed the original range too much" );
}
}
}
if( reset_weights )
for( j = 0; j < vcount; j++ )
{
// map mj..Mj to m..M
double mj = scale[j*2], Mj = scale[j*2+1];
double a, b;
double delta = Mj - mj;
if( delta < DBL_EPSILON )
a = 1, b = (M + m - Mj - mj)*0.5;
else
a = (M - m)/delta, b = m - mj*a;
inv_scale[j*2] = a; inv_scale[j*2+1] = b;
a = 1./a; b = -b*a;
scale[j*2] = a; scale[j*2+1] = b;
}
}
void prepare_to_train( const Mat& inputs, const Mat& outputs,
Mat& sample_weights, int flags )
{
if( layer_sizes.empty() )
CV_Error( CV_StsError,
"The network has not been created. Use method create or the appropriate constructor" );
if( (inputs.type() != CV_32F && inputs.type() != CV_64F) ||
inputs.cols != layer_sizes[0] )
CV_Error( CV_StsBadArg,
"input training data should be a floating-point matrix with "
"the number of rows equal to the number of training samples and "
"the number of columns equal to the size of 0-th (input) layer" );
if( (outputs.type() != CV_32F && outputs.type() != CV_64F) ||
outputs.cols != layer_sizes.back() )
CV_Error( CV_StsBadArg,
"output training data should be a floating-point matrix with "
"the number of rows equal to the number of training samples and "
"the number of columns equal to the size of last (output) layer" );
if( inputs.rows != outputs.rows )
CV_Error( CV_StsUnmatchedSizes, "The numbers of input and output samples do not match" );
Mat temp;
double s = sum(sample_weights)[0];
sample_weights.convertTo(temp, CV_64F, 1./s);
sample_weights = temp;
calc_input_scale( inputs, flags );
calc_output_scale( outputs, flags );
}
bool train( const Ptr<TrainData>& trainData, int flags ) CV_OVERRIDE
{
CV_Assert(!trainData.empty());
const int MAX_ITER = 1000;
const double DEFAULT_EPSILON = FLT_EPSILON;
// initialize training data
Mat inputs = trainData->getTrainSamples();
Mat outputs = trainData->getTrainResponses();
Mat sw = trainData->getTrainSampleWeights();
prepare_to_train( inputs, outputs, sw, flags );
// ... and link weights
if( !(flags & UPDATE_WEIGHTS) )
init_weights();
TermCriteria termcrit;
termcrit.type = TermCriteria::COUNT + TermCriteria::EPS;
termcrit.maxCount = std::max((params.termCrit.type & CV_TERMCRIT_ITER ? params.termCrit.maxCount : MAX_ITER), 1);
termcrit.epsilon = std::max((params.termCrit.type & CV_TERMCRIT_EPS ? params.termCrit.epsilon : DEFAULT_EPSILON), DBL_EPSILON);
int iter = 0;
switch(params.trainMethod){
case ANN_MLP::BACKPROP:
iter = train_backprop(inputs, outputs, sw, termcrit);
break;
case ANN_MLP::RPROP:
iter = train_rprop(inputs, outputs, sw, termcrit);
break;
case ANN_MLP::ANNEAL:
iter = train_anneal(trainData);
break;
}
trained = iter > 0;
return trained;
}
int train_anneal(const Ptr<TrainData>& trainData)
{
CV_Assert(!trainData.empty());
SimulatedAnnealingANN_MLP s(*this, trainData);
trained = true; // Enable call to CalcError
int iter = simulatedAnnealingSolver(s, params.initialT, params.finalT, params.coolingRatio, params.itePerStep, NULL, params.rEnergy);
trained =false;
return iter + 1; // ensure that 'train()' call is always successful
}
int train_backprop( const Mat& inputs, const Mat& outputs, const Mat& _sw, TermCriteria termCrit )
{
int i, j, k;
double prev_E = DBL_MAX*0.5, E = 0;
int itype = inputs.type(), otype = outputs.type();
int count = inputs.rows;
int iter = -1, max_iter = termCrit.maxCount*count;
double epsilon = (termCrit.type & CV_TERMCRIT_EPS) ? termCrit.epsilon*count : 0;
int l_count = layer_count();
int ivcount = layer_sizes[0];
int ovcount = layer_sizes.back();
// allocate buffers
vector<vector<double> > x(l_count);
vector<vector<double> > df(l_count);
vector<Mat> dw(l_count);
for( i = 0; i < l_count; i++ )
{
int n = layer_sizes[i];
x[i].resize(n+1);
df[i].resize(n);
dw[i] = Mat::zeros(weights[i].size(), CV_64F);
}
Mat _idx_m(1, count, CV_32S);
int* _idx = _idx_m.ptr<int>();
for( i = 0; i < count; i++ )
_idx[i] = i;
AutoBuffer<double> _buf(max_lsize*2);
double* buf[] = { _buf.data(), _buf.data() + max_lsize };
const double* sw = _sw.empty() ? 0 : _sw.ptr<double>();
// run back-propagation loop
/*
y_i = w_i*x_{i-1}
x_i = f(y_i)
E = 1/2*||u - x_N||^2
grad_N = (x_N - u)*f'(y_i)
dw_i(t) = momentum*dw_i(t-1) + dw_scale*x_{i-1}*grad_i
w_i(t+1) = w_i(t) + dw_i(t)
grad_{i-1} = w_i^t*grad_i
*/
for( iter = 0; iter < max_iter; iter++ )
{
int idx = iter % count;
double sweight = sw ? count*sw[idx] : 1.;
if( idx == 0 )
{
//printf("%d. E = %g\n", iter/count, E);
if( fabs(prev_E - E) < epsilon )
break;
prev_E = E;
E = 0;
// shuffle indices
for( i = 0; i <count; i++ )
{
j = rng.uniform(0, count);
k = rng.uniform(0, count);
std::swap(_idx[j], _idx[k]);
}
}
idx = _idx[idx];
const uchar* x0data_p = inputs.ptr(idx);
const float* x0data_f = (const float*)x0data_p;
const double* x0data_d = (const double*)x0data_p;
double* w = weights[0].ptr<double>();
for( j = 0; j < ivcount; j++ )
x[0][j] = (itype == CV_32F ? (double)x0data_f[j] : x0data_d[j])*w[j*2] + w[j*2 + 1];
Mat x1( 1, ivcount, CV_64F, &x[0][0] );
// forward pass, compute y[i]=w*x[i-1], x[i]=f(y[i]), df[i]=f'(y[i])
for( i = 1; i < l_count; i++ )
{
int n = layer_sizes[i];
Mat x2(1, n, CV_64F, &x[i][0] );
Mat _w = weights[i].rowRange(0, x1.cols);
gemm(x1, _w, 1, noArray(), 0, x2);
Mat _df(1, n, CV_64F, &df[i][0] );
calc_activ_func_deriv( x2, _df, weights[i] );
x1 = x2;
}
Mat grad1( 1, ovcount, CV_64F, buf[l_count&1] );
w = weights[l_count+1].ptr<double>();
// calculate error
const uchar* udata_p = outputs.ptr(idx);
const float* udata_f = (const float*)udata_p;
const double* udata_d = (const double*)udata_p;
double* gdata = grad1.ptr<double>();
for( k = 0; k < ovcount; k++ )
{
double t = (otype == CV_32F ? (double)udata_f[k] : udata_d[k])*w[k*2] + w[k*2+1] - x[l_count-1][k];
gdata[k] = t*sweight;
E += t*t;
}
E *= sweight;
// backward pass, update weights
for( i = l_count-1; i > 0; i-- )
{
int n1 = layer_sizes[i-1], n2 = layer_sizes[i];
Mat _df(1, n2, CV_64F, &df[i][0]);
multiply( grad1, _df, grad1 );
Mat _x(n1+1, 1, CV_64F, &x[i-1][0]);
x[i-1][n1] = 1.;
gemm( _x, grad1, params.bpDWScale, dw[i], params.bpMomentScale, dw[i] );
add( weights[i], dw[i], weights[i] );
if( i > 1 )
{
Mat grad2(1, n1, CV_64F, buf[i&1]);
Mat _w = weights[i].rowRange(0, n1);
gemm( grad1, _w, 1, noArray(), 0, grad2, GEMM_2_T );
grad1 = grad2;
}
}
}
iter /= count;
return iter;
}
struct RPropLoop : public ParallelLoopBody
{
RPropLoop(ANN_MLPImpl* _ann,
const Mat& _inputs, const Mat& _outputs, const Mat& _sw,
int _dcount0, vector<Mat>& _dEdw, double* _E)
{
ann = _ann;
inputs = _inputs;
outputs = _outputs;
sw = _sw.ptr<double>();
dcount0 = _dcount0;
dEdw = &_dEdw;
pE = _E;
}
ANN_MLPImpl* ann;
vector<Mat>* dEdw;
Mat inputs, outputs;
const double* sw;
int dcount0;
double* pE;
void operator()(const Range& range) const CV_OVERRIDE
{
double inv_count = 1./inputs.rows;
int ivcount = ann->layer_sizes.front();
int ovcount = ann->layer_sizes.back();
int itype = inputs.type(), otype = outputs.type();
int count = inputs.rows;
int i, j, k, l_count = ann->layer_count();
vector<vector<double> > x(l_count);
vector<vector<double> > df(l_count);
vector<double> _buf(ann->max_lsize*dcount0*2);
double* buf[] = { &_buf[0], &_buf[ann->max_lsize*dcount0] };
double E = 0;
for( i = 0; i < l_count; i++ )
{
x[i].resize(ann->layer_sizes[i]*dcount0);
df[i].resize(ann->layer_sizes[i]*dcount0);
}
for( int si = range.start; si < range.end; si++ )
{
int i0 = si*dcount0, i1 = std::min((si + 1)*dcount0, count);
int dcount = i1 - i0;
const double* w = ann->weights[0].ptr<double>();
// grab and preprocess input data
for( i = 0; i < dcount; i++ )
{
const uchar* x0data_p = inputs.ptr(i0 + i);
const float* x0data_f = (const float*)x0data_p;
const double* x0data_d = (const double*)x0data_p;
double* xdata = &x[0][i*ivcount];
for( j = 0; j < ivcount; j++ )
xdata[j] = (itype == CV_32F ? (double)x0data_f[j] : x0data_d[j])*w[j*2] + w[j*2+1];
}
Mat x1(dcount, ivcount, CV_64F, &x[0][0]);
// forward pass, compute y[i]=w*x[i-1], x[i]=f(y[i]), df[i]=f'(y[i])
for( i = 1; i < l_count; i++ )
{
Mat x2( dcount, ann->layer_sizes[i], CV_64F, &x[i][0] );
Mat _w = ann->weights[i].rowRange(0, x1.cols);
gemm( x1, _w, 1, noArray(), 0, x2 );
Mat _df( x2.size(), CV_64F, &df[i][0] );
ann->calc_activ_func_deriv( x2, _df, ann->weights[i] );
x1 = x2;
}
Mat grad1(dcount, ovcount, CV_64F, buf[l_count & 1]);
w = ann->weights[l_count+1].ptr<double>();
// calculate error
for( i = 0; i < dcount; i++ )
{
const uchar* udata_p = outputs.ptr(i0+i);
const float* udata_f = (const float*)udata_p;
const double* udata_d = (const double*)udata_p;
const double* xdata = &x[l_count-1][i*ovcount];
double* gdata = grad1.ptr<double>(i);
double sweight = sw ? sw[si+i] : inv_count, E1 = 0;
for( j = 0; j < ovcount; j++ )
{
double t = (otype == CV_32F ? (double)udata_f[j] : udata_d[j])*w[j*2] + w[j*2+1] - xdata[j];
gdata[j] = t*sweight;
E1 += t*t;
}
E += sweight*E1;
}
for( i = l_count-1; i > 0; i-- )
{
int n1 = ann->layer_sizes[i-1], n2 = ann->layer_sizes[i];
Mat _df(dcount, n2, CV_64F, &df[i][0]);
multiply(grad1, _df, grad1);
{
AutoLock lock(ann->mtx);
Mat _dEdw = dEdw->at(i).rowRange(0, n1);
x1 = Mat(dcount, n1, CV_64F, &x[i-1][0]);
gemm(x1, grad1, 1, _dEdw, 1, _dEdw, GEMM_1_T);
// update bias part of dEdw
double* dst = dEdw->at(i).ptr<double>(n1);
for( k = 0; k < dcount; k++ )
{
const double* src = grad1.ptr<double>(k);
for( j = 0; j < n2; j++ )
dst[j] += src[j];
}
}
Mat grad2( dcount, n1, CV_64F, buf[i&1] );
if( i > 1 )
{
Mat _w = ann->weights[i].rowRange(0, n1);
gemm(grad1, _w, 1, noArray(), 0, grad2, GEMM_2_T);
}
grad1 = grad2;
}
}
{
AutoLock lock(ann->mtx);
*pE += E;
}
}
};
int train_rprop( const Mat& inputs, const Mat& outputs, const Mat& _sw, TermCriteria termCrit )
{
const int max_buf_size = 1 << 16;
int i, iter = -1, count = inputs.rows;
double prev_E = DBL_MAX*0.5;
int max_iter = termCrit.maxCount;
double epsilon = termCrit.epsilon;
double dw_plus = params.rpDWPlus;
double dw_minus = params.rpDWMinus;
double dw_min = params.rpDWMin;
double dw_max = params.rpDWMax;
int l_count = layer_count();
// allocate buffers
vector<Mat> dw(l_count), dEdw(l_count), prev_dEdw_sign(l_count);
int total = 0;
for( i = 0; i < l_count; i++ )
{
total += layer_sizes[i];
dw[i].create(weights[i].size(), CV_64F);
dw[i].setTo(Scalar::all(params.rpDW0));
prev_dEdw_sign[i] = Mat::zeros(weights[i].size(), CV_8S);
dEdw[i] = Mat::zeros(weights[i].size(), CV_64F);
}
CV_Assert(total > 0);
int dcount0 = max_buf_size/(2*total);
dcount0 = std::max( dcount0, 1 );
dcount0 = std::min( dcount0, count );
int chunk_count = (count + dcount0 - 1)/dcount0;
// run rprop loop
/*
y_i(t) = w_i(t)*x_{i-1}(t)
x_i(t) = f(y_i(t))
E = sum_over_all_samples(1/2*||u - x_N||^2)
grad_N = (x_N - u)*f'(y_i)
std::min(dw_i{jk}(t)*dw_plus, dw_max), if dE/dw_i{jk}(t)*dE/dw_i{jk}(t-1) > 0
dw_i{jk}(t) = std::max(dw_i{jk}(t)*dw_minus, dw_min), if dE/dw_i{jk}(t)*dE/dw_i{jk}(t-1) < 0
dw_i{jk}(t-1) else
if (dE/dw_i{jk}(t)*dE/dw_i{jk}(t-1) < 0)
dE/dw_i{jk}(t)<-0
else
w_i{jk}(t+1) = w_i{jk}(t) + dw_i{jk}(t)
grad_{i-1}(t) = w_i^t(t)*grad_i(t)
*/
for( iter = 0; iter < max_iter; iter++ )
{
double E = 0;
for( i = 0; i < l_count; i++ )
dEdw[i].setTo(Scalar::all(0));
// first, iterate through all the samples and compute dEdw
RPropLoop invoker(this, inputs, outputs, _sw, dcount0, dEdw, &E);
parallel_for_(Range(0, chunk_count), invoker);
//invoker(Range(0, chunk_count));
// now update weights
for( i = 1; i < l_count; i++ )
{
int n1 = layer_sizes[i-1], n2 = layer_sizes[i];
for( int k = 0; k <= n1; k++ )
{
CV_Assert(weights[i].size() == Size(n2, n1+1));
double* wk = weights[i].ptr<double>(k);
double* dwk = dw[i].ptr<double>(k);
double* dEdwk = dEdw[i].ptr<double>(k);
schar* prevEk = prev_dEdw_sign[i].ptr<schar>(k);
for( int j = 0; j < n2; j++ )
{
double Eval = dEdwk[j];
double dval = dwk[j];
double wval = wk[j];
int s = CV_SIGN(Eval);
int ss = prevEk[j]*s;
if( ss > 0 )
{
dval *= dw_plus;
dval = std::min( dval, dw_max );
dwk[j] = dval;
wk[j] = wval + dval*s;
}
else if( ss < 0 )
{
dval *= dw_minus;
dval = std::max( dval, dw_min );
prevEk[j] = 0;
dwk[j] = dval;
wk[j] = wval + dval*s;
}
else
{
prevEk[j] = (schar)s;
wk[j] = wval + dval*s;
}
dEdwk[j] = 0.;
}
}
}
//printf("%d. E = %g\n", iter, E);
if( fabs(prev_E - E) < epsilon )
break;
prev_E = E;
}
return iter;
}
void write_params( FileStorage& fs ) const
{
const char* activ_func_name = activ_func == IDENTITY ? "IDENTITY" :
activ_func == SIGMOID_SYM ? "SIGMOID_SYM" :
activ_func == GAUSSIAN ? "GAUSSIAN" :
activ_func == RELU ? "RELU" :
activ_func == LEAKYRELU ? "LEAKYRELU" : 0;
if( activ_func_name )
fs << "activation_function" << activ_func_name;
else
fs << "activation_function_id" << activ_func;
if( activ_func != IDENTITY )
{
fs << "f_param1" << f_param1;
fs << "f_param2" << f_param2;
}
fs << "min_val" << min_val << "max_val" << max_val << "min_val1" << min_val1 << "max_val1" << max_val1;
fs << "training_params" << "{";
if( params.trainMethod == ANN_MLP::BACKPROP )
{
fs << "train_method" << "BACKPROP";
fs << "dw_scale" << params.bpDWScale;
fs << "moment_scale" << params.bpMomentScale;
}
else if (params.trainMethod == ANN_MLP::RPROP)
{
fs << "train_method" << "RPROP";
fs << "dw0" << params.rpDW0;
fs << "dw_plus" << params.rpDWPlus;
fs << "dw_minus" << params.rpDWMinus;
fs << "dw_min" << params.rpDWMin;
fs << "dw_max" << params.rpDWMax;
}
else if (params.trainMethod == ANN_MLP::ANNEAL)
{
fs << "train_method" << "ANNEAL";
fs << "initialT" << params.initialT;
fs << "finalT" << params.finalT;
fs << "coolingRatio" << params.coolingRatio;
fs << "itePerStep" << params.itePerStep;
}
else
CV_Error(CV_StsError, "Unknown training method");
fs << "term_criteria" << "{";
if( params.termCrit.type & TermCriteria::EPS )
fs << "epsilon" << params.termCrit.epsilon;
if( params.termCrit.type & TermCriteria::COUNT )
fs << "iterations" << params.termCrit.maxCount;
fs << "}" << "}";
}
void write( FileStorage& fs ) const CV_OVERRIDE
{
if( layer_sizes.empty() )
return;
int i, l_count = layer_count();
writeFormat(fs);
fs << "layer_sizes" << layer_sizes;
write_params( fs );
size_t esz = weights[0].elemSize();
fs << "input_scale" << "[";
fs.writeRaw("d", weights[0].ptr(), weights[0].total()*esz);
fs << "]" << "output_scale" << "[";
fs.writeRaw("d", weights[l_count].ptr(), weights[l_count].total()*esz);
fs << "]" << "inv_output_scale" << "[";
fs.writeRaw("d", weights[l_count+1].ptr(), weights[l_count+1].total()*esz);
fs << "]" << "weights" << "[";
for( i = 1; i < l_count; i++ )
{
fs << "[";
fs.writeRaw("d", weights[i].ptr(), weights[i].total()*esz);
fs << "]";
}
fs << "]";
}
void read_params( const FileNode& fn )
{
String activ_func_name = (String)fn["activation_function"];
if( !activ_func_name.empty() )
{
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 );
}
else
activ_func = (int)fn["activation_function_id"];
f_param1 = (double)fn["f_param1"];
f_param2 = (double)fn["f_param2"];
setActivationFunction( activ_func, f_param1, f_param2);
min_val = (double)fn["min_val"];
max_val = (double)fn["max_val"];
min_val1 = (double)fn["min_val1"];
max_val1 = (double)fn["max_val1"];
FileNode tpn = fn["training_params"];
params = AnnParams();
if( !tpn.empty() )
{
String tmethod_name = (String)tpn["train_method"];
if( tmethod_name == "BACKPROP" )
{
params.trainMethod = ANN_MLP::BACKPROP;
params.bpDWScale = (double)tpn["dw_scale"];
params.bpMomentScale = (double)tpn["moment_scale"];
}
else if (tmethod_name == "RPROP")
{
params.trainMethod = ANN_MLP::RPROP;
params.rpDW0 = (double)tpn["dw0"];
params.rpDWPlus = (double)tpn["dw_plus"];
params.rpDWMinus = (double)tpn["dw_minus"];
params.rpDWMin = (double)tpn["dw_min"];
params.rpDWMax = (double)tpn["dw_max"];
}
else if (tmethod_name == "ANNEAL")
{
params.trainMethod = ANN_MLP::ANNEAL;
params.initialT = (double)tpn["initialT"];
params.finalT = (double)tpn["finalT"];
params.coolingRatio = (double)tpn["coolingRatio"];
params.itePerStep = tpn["itePerStep"];
}
else
CV_Error(CV_StsParseError, "Unknown training method (should be BACKPROP or RPROP)");
FileNode tcn = tpn["term_criteria"];
if( !tcn.empty() )
{
FileNode tcn_e = tcn["epsilon"];
FileNode tcn_i = tcn["iterations"];
params.termCrit.type = 0;
if( !tcn_e.empty() )
{
params.termCrit.type |= TermCriteria::EPS;
params.termCrit.epsilon = (double)tcn_e;
}
if( !tcn_i.empty() )
{
params.termCrit.type |= TermCriteria::COUNT;
params.termCrit.maxCount = (int)tcn_i;
}
}
}
}
void read( const FileNode& fn ) CV_OVERRIDE
{
clear();
vector<int> _layer_sizes;
readVectorOrMat(fn["layer_sizes"], _layer_sizes);
setLayerSizes( _layer_sizes );
int i, l_count = layer_count();
read_params(fn);
size_t esz = weights[0].elemSize();
FileNode w = fn["input_scale"];
w.readRaw("d", weights[0].ptr(), weights[0].total()*esz);
w = fn["output_scale"];
w.readRaw("d", weights[l_count].ptr(), weights[l_count].total()*esz);
w = fn["inv_output_scale"];
w.readRaw("d", weights[l_count+1].ptr(), weights[l_count+1].total()*esz);
FileNodeIterator w_it = fn["weights"].begin();
for( i = 1; i < l_count; i++, ++w_it )
(*w_it).readRaw("d", weights[i].ptr(), weights[i].total()*esz);
trained = true;
}
Mat getWeights(int layerIdx) const CV_OVERRIDE
{
CV_Assert( 0 <= layerIdx && layerIdx < (int)weights.size() );
return weights[layerIdx];
}
bool isTrained() const CV_OVERRIDE
{
return trained;
}
bool isClassifier() const CV_OVERRIDE
{
return false;
}
int getVarCount() const CV_OVERRIDE
{
return layer_sizes.empty() ? 0 : layer_sizes[0];
}
String getDefaultName() const CV_OVERRIDE
{
return "opencv_ml_ann_mlp";
}
vector<int> layer_sizes;
vector<Mat> weights;
double f_param1, f_param2;
double min_val, max_val, min_val1, max_val1;
int activ_func;
int max_lsize, max_buf_sz;
AnnParams params;
RNG rng;
Mutex mtx;
bool trained;
};
Ptr<ANN_MLP> ANN_MLP::create()
{
return makePtr<ANN_MLPImpl>();
}
Ptr<ANN_MLP> ANN_MLP::load(const String& filepath)
{
FileStorage fs;
fs.open(filepath, FileStorage::READ);
CV_Assert(fs.isOpened());
Ptr<ANN_MLP> ann = makePtr<ANN_MLPImpl>();
((ANN_MLPImpl*)ann.get())->read(fs.getFirstTopLevelNode());
return ann;
}
}}
/* End of file. */