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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"
#ifdef HAVE_TBB
#include <tbb/tbb.h>
#endif
CvANN_MLP_TrainParams::CvANN_MLP_TrainParams()
{
term_crit = cvTermCriteria( CV_TERMCRIT_ITER + CV_TERMCRIT_EPS, 1000, 0.01 );
train_method = RPROP;
bp_dw_scale = bp_moment_scale = 0.1;
rp_dw0 = 0.1; rp_dw_plus = 1.2; rp_dw_minus = 0.5;
rp_dw_min = FLT_EPSILON; rp_dw_max = 50.;
}
CvANN_MLP_TrainParams::CvANN_MLP_TrainParams( CvTermCriteria _term_crit,
int _train_method,
double _param1, double _param2 )
{
term_crit = _term_crit;
train_method = _train_method;
bp_dw_scale = bp_moment_scale = 0.1;
rp_dw0 = 1.; rp_dw_plus = 1.2; rp_dw_minus = 0.5;
rp_dw_min = FLT_EPSILON; rp_dw_max = 50.;
if( train_method == RPROP )
{
rp_dw0 = _param1;
if( rp_dw0 < FLT_EPSILON )
rp_dw0 = 1.;
rp_dw_min = _param2;
rp_dw_min = MAX( rp_dw_min, 0 );
}
else if( train_method == BACKPROP )
{
bp_dw_scale = _param1;
if( bp_dw_scale <= 0 )
bp_dw_scale = 0.1;
bp_dw_scale = MAX( bp_dw_scale, 1e-3 );
bp_dw_scale = MIN( bp_dw_scale, 1 );
bp_moment_scale = _param2;
if( bp_moment_scale < 0 )
bp_moment_scale = 0.1;
bp_moment_scale = MIN( bp_moment_scale, 1 );
}
else
train_method = RPROP;
}
CvANN_MLP_TrainParams::~CvANN_MLP_TrainParams()
{
}
CvANN_MLP::CvANN_MLP()
{
layer_sizes = wbuf = 0;
min_val = max_val = min_val1 = max_val1 = 0.;
weights = 0;
rng = &cv::theRNG();
default_model_name = "my_nn";
clear();
}
CvANN_MLP::CvANN_MLP( const CvMat* _layer_sizes,
int _activ_func,
double _f_param1, double _f_param2 )
{
layer_sizes = wbuf = 0;
min_val = max_val = min_val1 = max_val1 = 0.;
weights = 0;
rng = &cv::theRNG();
default_model_name = "my_nn";
create( _layer_sizes, _activ_func, _f_param1, _f_param2 );
}
CvANN_MLP::~CvANN_MLP()
{
clear();
}
void CvANN_MLP::clear()
{
cvReleaseMat( &layer_sizes );
cvReleaseMat( &wbuf );
cvFree( &weights );
activ_func = SIGMOID_SYM;
f_param1 = f_param2 = 1;
max_buf_sz = 1 << 12;
}
void CvANN_MLP::set_activ_func( int _activ_func, double _f_param1, double _f_param2 )
{
CV_FUNCNAME( "CvANN_MLP::set_activ_func" );
__BEGIN__;
if( _activ_func < 0 || _activ_func > GAUSSIAN )
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;
default:
min_val = max_val = min_val1 = max_val1 = 0.;
_f_param1 = 1.;
_f_param2 = 0.;
}
f_param1 = _f_param1;
f_param2 = _f_param2;
__END__;
}
void CvANN_MLP::init_weights()
{
int i, j, k;
for( i = 1; i < layer_sizes->cols; i++ )
{
int n1 = layer_sizes->data.i[i-1];
int n2 = layer_sizes->data.i[i];
double val = 0, G = n2 > 2 ? 0.7*pow((double)n1,1./(n2-1)) : 1.;
double* w = weights[i];
// 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 < layer_sizes->cols - 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);
}
}
}
}
void CvANN_MLP::create( const CvMat* _layer_sizes, int _activ_func,
double _f_param1, double _f_param2 )
{
CV_FUNCNAME( "CvANN_MLP::create" );
__BEGIN__;
int i, l_step, l_count, buf_sz = 0;
int *l_src, *l_dst;
clear();
if( !CV_IS_MAT(_layer_sizes) ||
(_layer_sizes->cols != 1 && _layer_sizes->rows != 1) ||
CV_MAT_TYPE(_layer_sizes->type) != CV_32SC1 )
CV_ERROR( CV_StsBadArg,
"The array of layer neuron counters must be an integer vector" );
CV_CALL( set_activ_func( _activ_func, _f_param1, _f_param2 ));
l_count = _layer_sizes->rows + _layer_sizes->cols - 1;
l_src = _layer_sizes->data.i;
l_step = CV_IS_MAT_CONT(_layer_sizes->type) ? 1 :
_layer_sizes->step / sizeof(l_src[0]);
CV_CALL( layer_sizes = cvCreateMat( 1, l_count, CV_32SC1 ));
l_dst = layer_sizes->data.i;
max_count = 0;
for( i = 0; i < l_count; i++ )
{
int n = l_src[i*l_step];
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" );
l_dst[i] = n;
max_count = MAX( max_count, n );
if( i > 0 )
buf_sz += (l_dst[i-1]+1)*n;
}
buf_sz += (l_dst[0] + l_dst[l_count-1]*2)*2;
CV_CALL( wbuf = cvCreateMat( 1, buf_sz, CV_64F ));
CV_CALL( weights = (double**)cvAlloc( (l_count+1)*sizeof(weights[0]) ));
weights[0] = wbuf->data.db;
weights[1] = weights[0] + l_dst[0]*2;
for( i = 1; i < l_count; i++ )
weights[i+1] = weights[i] + (l_dst[i-1] + 1)*l_dst[i];
weights[l_count+1] = weights[l_count] + l_dst[l_count-1]*2;
__END__;
}
float CvANN_MLP::predict( const CvMat* _inputs, CvMat* _outputs ) const
{
int i, j, n, dn = 0, l_count, dn0, buf_sz, min_buf_sz;
if( !layer_sizes )
CV_Error( CV_StsError, "The network has not been initialized" );
if( !CV_IS_MAT(_inputs) || !CV_IS_MAT(_outputs) ||
!CV_ARE_TYPES_EQ(_inputs,_outputs) ||
(CV_MAT_TYPE(_inputs->type) != CV_32FC1 &&
CV_MAT_TYPE(_inputs->type) != CV_64FC1) ||
_inputs->rows != _outputs->rows )
CV_Error( CV_StsBadArg, "Both input and output must be floating-point matrices "
"of the same type and have the same number of rows" );
if( _inputs->cols != layer_sizes->data.i[0] )
CV_Error( CV_StsBadSize, "input matrix must have the same number of columns as "
"the number of neurons in the input layer" );
if( _outputs->cols != layer_sizes->data.i[layer_sizes->cols - 1] )
CV_Error( CV_StsBadSize, "output matrix must have the same number of columns as "
"the number of neurons in the output layer" );
n = dn0 = _inputs->rows;
min_buf_sz = 2*max_count;
buf_sz = n*min_buf_sz;
if( buf_sz > max_buf_sz )
{
dn0 = max_buf_sz/min_buf_sz;
dn0 = MAX( dn0, 1 );
buf_sz = dn0*min_buf_sz;
}
cv::AutoBuffer<double> buf(buf_sz);
l_count = layer_sizes->cols;
for( i = 0; i < n; i += dn )
{
CvMat hdr[2], _w, *layer_in = &hdr[0], *layer_out = &hdr[1], *temp;
dn = MIN( dn0, n - i );
cvGetRows( _inputs, layer_in, i, i + dn );
cvInitMatHeader( layer_out, dn, layer_in->cols, CV_64F, &buf[0] );
scale_input( layer_in, layer_out );
CV_SWAP( layer_in, layer_out, temp );
for( j = 1; j < l_count; j++ )
{
double* data = buf + (j&1 ? max_count*dn0 : 0);
int cols = layer_sizes->data.i[j];
cvInitMatHeader( layer_out, dn, cols, CV_64F, data );
cvInitMatHeader( &_w, layer_in->cols, layer_out->cols, CV_64F, weights[j] );
cvGEMM( layer_in, &_w, 1, 0, 0, layer_out );
calc_activ_func( layer_out, _w.data.db + _w.rows*_w.cols );
CV_SWAP( layer_in, layer_out, temp );
}
cvGetRows( _outputs, layer_out, i, i + dn );
scale_output( layer_in, layer_out );
}
return 0.f;
}
void CvANN_MLP::scale_input( const CvMat* _src, CvMat* _dst ) const
{
int i, j, cols = _src->cols;
double* dst = _dst->data.db;
const double* w = weights[0];
int step = _src->step;
if( CV_MAT_TYPE( _src->type ) == CV_32F )
{
const float* src = _src->data.fl;
step /= sizeof(src[0]);
for( i = 0; i < _src->rows; i++, src += step, dst += cols )
for( j = 0; j < cols; j++ )
dst[j] = src[j]*w[j*2] + w[j*2+1];
}
else
{
const double* src = _src->data.db;
step /= sizeof(src[0]);
for( i = 0; i < _src->rows; i++, src += step, dst += cols )
for( j = 0; j < cols; j++ )
dst[j] = src[j]*w[j*2] + w[j*2+1];
}
}
void CvANN_MLP::scale_output( const CvMat* _src, CvMat* _dst ) const
{
int i, j, cols = _src->cols;
const double* src = _src->data.db;
const double* w = weights[layer_sizes->cols];
int step = _dst->step;
if( CV_MAT_TYPE( _dst->type ) == CV_32F )
{
float* dst = _dst->data.fl;
step /= sizeof(dst[0]);
for( i = 0; i < _src->rows; i++, src += cols, dst += step )
for( j = 0; j < cols; j++ )
dst[j] = (float)(src[j]*w[j*2] + w[j*2+1]);
}
else
{
double* dst = _dst->data.db;
step /= sizeof(dst[0]);
for( i = 0; i < _src->rows; i++, src += cols, dst += step )
for( j = 0; j < cols; j++ )
dst[j] = src[j]*w[j*2] + w[j*2+1];
}
}
void CvANN_MLP::calc_activ_func( CvMat* sums, const double* bias ) const
{
int i, j, n = sums->rows, cols = sums->cols;
double* data = sums->data.db;
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;
default:
;
}
assert( CV_IS_MAT_CONT(sums->type) );
if( activ_func != GAUSSIAN )
{
for( i = 0; i < n; i++, data += cols )
for( j = 0; j < cols; j++ )
data[j] = (data[j] + bias[j])*scale;
if( activ_func == IDENTITY )
return;
}
else
{
for( i = 0; i < n; i++, data += cols )
for( j = 0; j < cols; j++ )
{
double t = data[j] + bias[j];
data[j] = t*t*scale;
}
}
cvExp( sums, sums );
n *= cols;
data -= n;
switch( activ_func )
{
case SIGMOID_SYM:
for( i = 0; i <= n - 4; i += 4 )
{
double x0 = 1.+data[i], x1 = 1.+data[i+1], x2 = 1.+data[i+2], x3 = 1.+data[i+3];
double a = x0*x1, b = x2*x3, d = scale2/(a*b), t0, t1;
a *= d; b *= d;
t0 = (2 - x0)*b*x1; t1 = (2 - x1)*b*x0;
data[i] = t0; data[i+1] = t1;
t0 = (2 - x2)*a*x3; t1 = (2 - x3)*a*x2;
data[i+2] = t0; data[i+3] = t1;
}
for( ; i < n; i++ )
{
double t = scale2*(1. - data[i])/(1. + data[i]);
data[i] = t;
}
break;
case GAUSSIAN:
for( i = 0; i < n; i++ )
data[i] = scale2*data[i];
break;
default:
;
}
}
void CvANN_MLP::calc_activ_func_deriv( CvMat* _xf, CvMat* _df,
const double* bias ) const
{
int i, j, n = _xf->rows, cols = _xf->cols;
double* xf = _xf->data.db;
double* df = _df->data.db;
double scale, scale2 = f_param2;
assert( CV_IS_MAT_CONT( _xf->type & _df->type ) );
if( activ_func == IDENTITY )
{
for( i = 0; i < n; i++, xf += cols, df += cols )
for( j = 0; j < cols; j++ )
{
xf[j] += bias[j];
df[j] = 1;
}
return;
}
else if( activ_func == GAUSSIAN )
{
scale = -f_param1*f_param1;
scale2 *= scale;
for( i = 0; i < n; i++, xf += cols, df += cols )
for( j = 0; j < cols; j++ )
{
double t = xf[j] + bias[j];
df[j] = t*2*scale2;
xf[j] = t*t*scale;
}
cvExp( _xf, _xf );
n *= cols;
xf -= n; df -= n;
for( i = 0; i < n; i++ )
df[i] *= xf[i];
}
else
{
scale = f_param1;
for( i = 0; i < n; i++, xf += cols, df += cols )
for( j = 0; j < cols; j++ )
{
xf[j] = (xf[j] + bias[j])*scale;
df[j] = -fabs(xf[j]);
}
cvExp( _df, _df );
n *= cols;
xf -= n; df -= n;
// ((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++ )
{
int s0 = xf[i] > 0 ? 1 : -1;
double t0 = 1./(1. + df[i]);
double t1 = scale*df[i]*t0*t0;
t0 *= scale2*(1. - df[i])*s0;
df[i] = t1;
xf[i] = t0;
}
}
}
void CvANN_MLP::calc_input_scale( const CvVectors* vecs, int flags )
{
bool reset_weights = (flags & UPDATE_WEIGHTS) == 0;
bool no_scale = (flags & NO_INPUT_SCALE) != 0;
double* scale = weights[0];
int count = vecs->count;
if( reset_weights )
{
int i, j, vcount = layer_sizes->data.i[0];
int type = vecs->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 float* f = vecs->data.fl[i];
const double* d = vecs->data.db[i];
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 CvANN_MLP::calc_output_scale( const CvVectors* vecs, int flags )
{
int i, j, vcount = layer_sizes->data.i[layer_sizes->cols-1];
int type = vecs->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_sizes->cols;
double* scale = weights[l_count];
double* inv_scale = weights[l_count+1];
int count = vecs->count;
CV_FUNCNAME( "CvANN_MLP::calc_output_scale" );
__BEGIN__;
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 )
EXIT;
}
for( i = 0; i < count; i++ )
{
const float* f = vecs->data.fl[i];
const double* d = vecs->data.db[i];
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
{
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;
}
__END__;
}
bool CvANN_MLP::prepare_to_train( const CvMat* _inputs, const CvMat* _outputs,
const CvMat* _sample_weights, const CvMat* _sample_idx,
CvVectors* _ivecs, CvVectors* _ovecs, double** _sw, int _flags )
{
bool ok = false;
CvMat* sample_idx = 0;
CvVectors ivecs, ovecs;
double* sw = 0;
int count = 0;
CV_FUNCNAME( "CvANN_MLP::prepare_to_train" );
ivecs.data.ptr = ovecs.data.ptr = 0;
assert( _ivecs && _ovecs );
__BEGIN__;
const int* sidx = 0;
int i, sw_type = 0, sw_count = 0;
int sw_step = 0;
double sw_sum = 0;
if( !layer_sizes )
CV_ERROR( CV_StsError,
"The network has not been created. Use method create or the appropriate constructor" );
if( !CV_IS_MAT(_inputs) || (CV_MAT_TYPE(_inputs->type) != CV_32FC1 &&
CV_MAT_TYPE(_inputs->type) != CV_64FC1) || _inputs->cols != layer_sizes->data.i[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( !CV_IS_MAT(_outputs) || (CV_MAT_TYPE(_outputs->type) != CV_32FC1 &&
CV_MAT_TYPE(_outputs->type) != CV_64FC1) ||
_outputs->cols != layer_sizes->data.i[layer_sizes->cols - 1] )
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" );
if( _sample_idx )
{
CV_CALL( sample_idx = cvPreprocessIndexArray( _sample_idx, _inputs->rows ));
sidx = sample_idx->data.i;
count = sample_idx->cols + sample_idx->rows - 1;
}
else
count = _inputs->rows;
if( _sample_weights )
{
if( !CV_IS_MAT(_sample_weights) )
CV_ERROR( CV_StsBadArg, "sample_weights (if passed) must be a valid matrix" );
sw_type = CV_MAT_TYPE(_sample_weights->type);
sw_count = _sample_weights->cols + _sample_weights->rows - 1;
if( (sw_type != CV_32FC1 && sw_type != CV_64FC1) ||
(_sample_weights->cols != 1 && _sample_weights->rows != 1) ||
(sw_count != count && sw_count != _inputs->rows) )
CV_ERROR( CV_StsBadArg,
"sample_weights must be 1d floating-point vector containing weights "
"of all or selected training samples" );
sw_step = CV_IS_MAT_CONT(_sample_weights->type) ? 1 :
_sample_weights->step/CV_ELEM_SIZE(sw_type);
CV_CALL( sw = (double*)cvAlloc( count*sizeof(sw[0]) ));
}
CV_CALL( ivecs.data.ptr = (uchar**)cvAlloc( count*sizeof(ivecs.data.ptr[0]) ));
CV_CALL( ovecs.data.ptr = (uchar**)cvAlloc( count*sizeof(ovecs.data.ptr[0]) ));
ivecs.type = CV_MAT_TYPE(_inputs->type);
ovecs.type = CV_MAT_TYPE(_outputs->type);
ivecs.count = ovecs.count = count;
for( i = 0; i < count; i++ )
{
int idx = sidx ? sidx[i] : i;
ivecs.data.ptr[i] = _inputs->data.ptr + idx*_inputs->step;
ovecs.data.ptr[i] = _outputs->data.ptr + idx*_outputs->step;
if( sw )
{
int si = sw_count == count ? i : idx;
double w = sw_type == CV_32FC1 ?
(double)_sample_weights->data.fl[si*sw_step] :
_sample_weights->data.db[si*sw_step];
sw[i] = w;
if( w < 0 )
CV_ERROR( CV_StsOutOfRange, "some of sample weights are negative" );
sw_sum += w;
}
}
// normalize weights
if( sw )
{
sw_sum = sw_sum > DBL_EPSILON ? 1./sw_sum : 0;
for( i = 0; i < count; i++ )
sw[i] *= sw_sum;
}
calc_input_scale( &ivecs, _flags );
CV_CALL( calc_output_scale( &ovecs, _flags ));
ok = true;
__END__;
if( !ok )
{
cvFree( &ivecs.data.ptr );
cvFree( &ovecs.data.ptr );
cvFree( &sw );
}
cvReleaseMat( &sample_idx );
*_ivecs = ivecs;
*_ovecs = ovecs;
*_sw = sw;
return ok;
}
int CvANN_MLP::train( const CvMat* _inputs, const CvMat* _outputs,
const CvMat* _sample_weights, const CvMat* _sample_idx,
CvANN_MLP_TrainParams _params, int flags )
{
const int MAX_ITER = 1000;
const double DEFAULT_EPSILON = FLT_EPSILON;
double* sw = 0;
CvVectors x0, u;
int iter = -1;
x0.data.ptr = u.data.ptr = 0;
CV_FUNCNAME( "CvANN_MLP::train" );
__BEGIN__;
int max_iter;
double epsilon;
params = _params;
// initialize training data
CV_CALL( prepare_to_train( _inputs, _outputs, _sample_weights,
_sample_idx, &x0, &u, &sw, flags ));
// ... and link weights
if( !(flags & UPDATE_WEIGHTS) )
init_weights();
max_iter = params.term_crit.type & CV_TERMCRIT_ITER ? params.term_crit.max_iter : MAX_ITER;
max_iter = MAX( max_iter, 1 );
epsilon = params.term_crit.type & CV_TERMCRIT_EPS ? params.term_crit.epsilon : DEFAULT_EPSILON;
epsilon = MAX(epsilon, DBL_EPSILON);
params.term_crit.type = CV_TERMCRIT_ITER + CV_TERMCRIT_EPS;
params.term_crit.max_iter = max_iter;
params.term_crit.epsilon = epsilon;
if( params.train_method == CvANN_MLP_TrainParams::BACKPROP )
{
CV_CALL( iter = train_backprop( x0, u, sw ));
}
else
{
CV_CALL( iter = train_rprop( x0, u, sw ));
}
__END__;
cvFree( &x0.data.ptr );
cvFree( &u.data.ptr );
cvFree( &sw );
return iter;
}
int CvANN_MLP::train_backprop( CvVectors x0, CvVectors u, const double* sw )
{
CvMat* dw = 0;
CvMat* buf = 0;
double **x = 0, **df = 0;
CvMat* _idx = 0;
int iter = -1, count = x0.count;
CV_FUNCNAME( "CvANN_MLP::train_backprop" );
__BEGIN__;
int i, j, k, ivcount, ovcount, l_count, total = 0, max_iter;
double *buf_ptr;
double prev_E = DBL_MAX*0.5, E = 0, epsilon;
max_iter = params.term_crit.max_iter*count;
epsilon = params.term_crit.epsilon*count;
l_count = layer_sizes->cols;
ivcount = layer_sizes->data.i[0];
ovcount = layer_sizes->data.i[l_count-1];
// allocate buffers
for( i = 0; i < l_count; i++ )
total += layer_sizes->data.i[i] + 1;
CV_CALL( dw = cvCreateMat( wbuf->rows, wbuf->cols, wbuf->type ));
cvZero( dw );
CV_CALL( buf = cvCreateMat( 1, (total + max_count)*2, CV_64F ));
CV_CALL( _idx = cvCreateMat( 1, count, CV_32SC1 ));
for( i = 0; i < count; i++ )
_idx->data.i[i] = i;
CV_CALL( x = (double**)cvAlloc( total*2*sizeof(x[0]) ));
df = x + total;
buf_ptr = buf->data.db;
for( j = 0; j < l_count; j++ )
{
x[j] = buf_ptr;
df[j] = x[j] + layer_sizes->data.i[j];
buf_ptr += (df[j] - x[j])*2;
}
// 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* w = weights[0];
double sweight = sw ? count*sw[idx] : 1.;
CvMat _w, _dw, hdr1, hdr2, ghdr1, ghdr2, _df;
CvMat *x1 = &hdr1, *x2 = &hdr2, *grad1 = &ghdr1, *grad2 = &ghdr2, *temp;
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++ )
{
int tt;
j = (*rng)(count);
k = (*rng)(count);
CV_SWAP( _idx->data.i[j], _idx->data.i[k], tt );
}
}
idx = _idx->data.i[idx];
if( x0.type == CV_32F )
{
const float* x0data = x0.data.fl[idx];
for( j = 0; j < ivcount; j++ )
x[0][j] = x0data[j]*w[j*2] + w[j*2 + 1];
}
else
{
const double* x0data = x0.data.db[idx];
for( j = 0; j < ivcount; j++ )
x[0][j] = x0data[j]*w[j*2] + w[j*2 + 1];
}
cvInitMatHeader( x1, 1, ivcount, CV_64F, x[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++ )
{
cvInitMatHeader( x2, 1, layer_sizes->data.i[i], CV_64F, x[i] );
cvInitMatHeader( &_w, x1->cols, x2->cols, CV_64F, weights[i] );
cvGEMM( x1, &_w, 1, 0, 0, x2 );
_df = *x2;
_df.data.db = df[i];
calc_activ_func_deriv( x2, &_df, _w.data.db + _w.rows*_w.cols );
CV_SWAP( x1, x2, temp );
}
cvInitMatHeader( grad1, 1, ovcount, CV_64F, buf_ptr );
*grad2 = *grad1;
grad2->data.db = buf_ptr + max_count;
w = weights[l_count+1];
// calculate error
if( u.type == CV_32F )
{
const float* udata = u.data.fl[idx];
for( k = 0; k < ovcount; k++ )
{
double t = udata[k]*w[k*2] + w[k*2+1] - x[l_count-1][k];
grad1->data.db[k] = t*sweight;
E += t*t;
}
}
else
{
const double* udata = u.data.db[idx];
for( k = 0; k < ovcount; k++ )
{
double t = udata[k]*w[k*2] + w[k*2+1] - x[l_count-1][k];
grad1->data.db[k] = t*sweight;
E += t*t;
}
}
E *= sweight;
// backward pass, update weights
for( i = l_count-1; i > 0; i-- )
{
int n1 = layer_sizes->data.i[i-1], n2 = layer_sizes->data.i[i];
cvInitMatHeader( &_df, 1, n2, CV_64F, df[i] );
cvMul( grad1, &_df, grad1 );
cvInitMatHeader( &_w, n1+1, n2, CV_64F, weights[i] );
cvInitMatHeader( &_dw, n1+1, n2, CV_64F, dw->data.db + (weights[i] - weights[0]) );
cvInitMatHeader( x1, n1+1, 1, CV_64F, x[i-1] );
x[i-1][n1] = 1.;
cvGEMM( x1, grad1, params.bp_dw_scale, &_dw, params.bp_moment_scale, &_dw );
cvAdd( &_w, &_dw, &_w );
if( i > 1 )
{
grad2->cols = n1;
_w.rows = n1;
cvGEMM( grad1, &_w, 1, 0, 0, grad2, CV_GEMM_B_T );
}
CV_SWAP( grad1, grad2, temp );
}
}
iter /= count;
__END__;
cvReleaseMat( &dw );
cvReleaseMat( &buf );
cvReleaseMat( &_idx );
cvFree( &x );
return iter;
}
struct rprop_loop {
rprop_loop(const CvANN_MLP* _point, double**& _weights, int& _count, int& _ivcount, CvVectors* _x0,
int& _l_count, CvMat*& _layer_sizes, int& _ovcount, int& _max_count,
CvVectors* _u, const double*& _sw, double& _inv_count, CvMat*& _dEdw, int& _dcount0, double* _E, int _buf_sz)
{
point = _point;
weights = _weights;
count = _count;
ivcount = _ivcount;
x0 = _x0;
l_count = _l_count;
layer_sizes = _layer_sizes;
ovcount = _ovcount;
max_count = _max_count;
u = _u;
sw = _sw;
inv_count = _inv_count;
dEdw = _dEdw;
dcount0 = _dcount0;
E = _E;
buf_sz = _buf_sz;
}
const CvANN_MLP* point;
double** weights;
int count;
int ivcount;
CvVectors* x0;
int l_count;
CvMat* layer_sizes;
int ovcount;
int max_count;
CvVectors* u;
const double* sw;
double inv_count;
CvMat* dEdw;
int dcount0;
double* E;
int buf_sz;
void operator()( const cv::BlockedRange& range ) const
{
double* buf_ptr;
double** x = 0;
double **df = 0;
int total = 0;
for(int i = 0; i < l_count; i++ )
total += layer_sizes->data.i[i];
CvMat* buf;
buf = cvCreateMat( 1, buf_sz, CV_64F );
x = (double**)cvAlloc( total*2*sizeof(x[0]) );
df = x + total;
buf_ptr = buf->data.db;
for(int i = 0; i < l_count; i++ )
{
x[i] = buf_ptr;
df[i] = x[i] + layer_sizes->data.i[i]*dcount0;
buf_ptr += (df[i] - x[i])*2;
}
for(int si = range.begin(); si < range.end(); si++ )
{
if (si % dcount0 != 0) continue;
int n1, n2, k;
double* w;
CvMat _w, _dEdw, hdr1, hdr2, ghdr1, ghdr2, _df;
CvMat *x1, *x2, *grad1, *grad2, *temp;
int dcount = 0;
dcount = MIN(count - si , dcount0 );
w = weights[0];
grad1 = &ghdr1; grad2 = &ghdr2;
x1 = &hdr1; x2 = &hdr2;
// grab and preprocess input data
if( x0->type == CV_32F )
{
for(int i = 0; i < dcount; i++ )
{
const float* x0data = x0->data.fl[si+i];
double* xdata = x[0]+i*ivcount;
for(int j = 0; j < ivcount; j++ )
xdata[j] = x0data[j]*w[j*2] + w[j*2+1];
}
}
else
for(int i = 0; i < dcount; i++ )
{
const double* x0data = x0->data.db[si+i];
double* xdata = x[0]+i*ivcount;
for(int j = 0; j < ivcount; j++ )
xdata[j] = x0data[j]*w[j*2] + w[j*2+1];
}
cvInitMatHeader( x1, dcount, ivcount, CV_64F, x[0] );
// forward pass, compute y[i]=w*x[i-1], x[i]=f(y[i]), df[i]=f'(y[i])
for(int i = 1; i < l_count; i++ )
{
cvInitMatHeader( x2, dcount, layer_sizes->data.i[i], CV_64F, x[i] );
cvInitMatHeader( &_w, x1->cols, x2->cols, CV_64F, weights[i] );
cvGEMM( x1, &_w, 1, 0, 0, x2 );
_df = *x2;
_df.data.db = df[i];
point->calc_activ_func_deriv( x2, &_df, _w.data.db + _w.rows*_w.cols );
CV_SWAP( x1, x2, temp );
}
cvInitMatHeader( grad1, dcount, ovcount, CV_64F, buf_ptr );
w = weights[l_count+1];
grad2->data.db = buf_ptr + max_count*dcount;
// calculate error
if( u->type == CV_32F )
for(int i = 0; i < dcount; i++ )
{
const float* udata = u->data.fl[si+i];
const double* xdata = x[l_count-1] + i*ovcount;
double* gdata = grad1->data.db + i*ovcount;
double sweight = sw ? sw[si+i] : inv_count, E1 = 0;
for(int j = 0; j < ovcount; j++ )
{
double t = udata[j]*w[j*2] + w[j*2+1] - xdata[j];
gdata[j] = t*sweight;
E1 += t*t;
}
*E += sweight*E1;
}
else
for(int i = 0; i < dcount; i++ )
{
const double* udata = u->data.db[si+i];
const double* xdata = x[l_count-1] + i*ovcount;
double* gdata = grad1->data.db + i*ovcount;
double sweight = sw ? sw[si+i] : inv_count, E1 = 0;
for(int j = 0; j < ovcount; j++ )
{
double t = udata[j]*w[j*2] + w[j*2+1] - xdata[j];
gdata[j] = t*sweight;
E1 += t*t;
}
*E += sweight*E1;
}
// backward pass, update dEdw
#ifdef HAVE_TBB
static tbb::spin_mutex mutex;
tbb::spin_mutex::scoped_lock lock;
#endif
for(int i = l_count-1; i > 0; i-- )
{
n1 = layer_sizes->data.i[i-1]; n2 = layer_sizes->data.i[i];
cvInitMatHeader( &_df, dcount, n2, CV_64F, df[i] );
cvMul( grad1, &_df, grad1 );
#ifdef HAVE_TBB
lock.acquire(mutex);
#endif
cvInitMatHeader( &_dEdw, n1, n2, CV_64F, dEdw->data.db+(weights[i]-weights[0]) );
cvInitMatHeader( x1, dcount, n1, CV_64F, x[i-1] );
cvGEMM( x1, grad1, 1, &_dEdw, 1, &_dEdw, CV_GEMM_A_T );
// update bias part of dEdw
for( k = 0; k < dcount; k++ )
{
double* dst = _dEdw.data.db + n1*n2;
const double* src = grad1->data.db + k*n2;
for(int j = 0; j < n2; j++ )
dst[j] += src[j];
}
if (i > 1)
cvInitMatHeader( &_w, n1, n2, CV_64F, weights[i] );
#ifdef HAVE_TBB
lock.release();
#endif
cvInitMatHeader( grad2, dcount, n1, CV_64F, grad2->data.db );
if( i > 1 )
cvGEMM( grad1, &_w, 1, 0, 0, grad2, CV_GEMM_B_T );
CV_SWAP( grad1, grad2, temp );
}
}
cvFree(&x);
cvReleaseMat( &buf );
}
};
int CvANN_MLP::train_rprop( CvVectors x0, CvVectors u, const double* sw )
{
const int max_buf_size = 1 << 16;
CvMat* dw = 0;
CvMat* dEdw = 0;
CvMat* prev_dEdw_sign = 0;
CvMat* buf = 0;
double **x = 0, **df = 0;
int iter = -1, count = x0.count;
CV_FUNCNAME( "CvANN_MLP::train" );
__BEGIN__;
int i, ivcount, ovcount, l_count, total = 0, max_iter, buf_sz, dcount0;
double *buf_ptr;
double prev_E = DBL_MAX*0.5, epsilon;
double dw_plus, dw_minus, dw_min, dw_max;
double inv_count;
max_iter = params.term_crit.max_iter;
epsilon = params.term_crit.epsilon;
dw_plus = params.rp_dw_plus;
dw_minus = params.rp_dw_minus;
dw_min = params.rp_dw_min;
dw_max = params.rp_dw_max;
l_count = layer_sizes->cols;
ivcount = layer_sizes->data.i[0];
ovcount = layer_sizes->data.i[l_count-1];
// allocate buffers
for( i = 0; i < l_count; i++ )
total += layer_sizes->data.i[i];
CV_CALL( dw = cvCreateMat( wbuf->rows, wbuf->cols, wbuf->type ));
cvSet( dw, cvScalarAll(params.rp_dw0) );
CV_CALL( dEdw = cvCreateMat( wbuf->rows, wbuf->cols, wbuf->type ));
cvZero( dEdw );
CV_CALL( prev_dEdw_sign = cvCreateMat( wbuf->rows, wbuf->cols, CV_8SC1 ));
cvZero( prev_dEdw_sign );
inv_count = 1./count;
dcount0 = max_buf_size/(2*total);
dcount0 = MAX( dcount0, 1 );
dcount0 = MIN( dcount0, count );
buf_sz = dcount0*(total + max_count)*2;
CV_CALL( buf = cvCreateMat( 1, buf_sz, CV_64F ));
CV_CALL( x = (double**)cvAlloc( total*2*sizeof(x[0]) ));
df = x + total;
buf_ptr = buf->data.db;
for( i = 0; i < l_count; i++ )
{
x[i] = buf_ptr;
df[i] = x[i] + layer_sizes->data.i[i]*dcount0;
buf_ptr += (df[i] - x[i])*2;
}
// 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)
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) = 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++ )
{
int n1, n2, j, k;
double E = 0;
// first, iterate through all the samples and compute dEdw
cv::parallel_for(cv::BlockedRange(0, count),
rprop_loop(this, weights, count, ivcount, &x0, l_count, layer_sizes,
ovcount, max_count, &u, sw, inv_count, dEdw, dcount0, &E, buf_sz)
);
// now update weights
for( i = 1; i < l_count; i++ )
{
n1 = layer_sizes->data.i[i-1]; n2 = layer_sizes->data.i[i];
for( k = 0; k <= n1; k++ )
{
double* wk = weights[i]+k*n2;
size_t delta = wk - weights[0];
double* dwk = dw->data.db + delta;
double* dEdwk = dEdw->data.db + delta;
char* prevEk = (char*)(prev_dEdw_sign->data.ptr + delta);
for( 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 = MIN( dval, dw_max );
dwk[j] = dval;
wk[j] = wval + dval*s;
}
else if( ss < 0 )
{
dval *= dw_minus;
dval = MAX( dval, dw_min );
prevEk[j] = 0;
dwk[j] = dval;
wk[j] = wval + dval*s;
}
else
{
prevEk[j] = (char)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;
E = 0;
}
__END__;
cvReleaseMat( &dw );
cvReleaseMat( &dEdw );
cvReleaseMat( &prev_dEdw_sign );
cvReleaseMat( &buf );
cvFree( &x );
return iter;
}
void CvANN_MLP::write_params( CvFileStorage* fs ) const
{
//CV_FUNCNAME( "CvANN_MLP::write_params" );
__BEGIN__;
const char* activ_func_name = activ_func == IDENTITY ? "IDENTITY" :
activ_func == SIGMOID_SYM ? "SIGMOID_SYM" :
activ_func == GAUSSIAN ? "GAUSSIAN" : 0;
if( activ_func_name )
cvWriteString( fs, "activation_function", activ_func_name );
else
cvWriteInt( fs, "activation_function", activ_func );
if( activ_func != IDENTITY )
{
cvWriteReal( fs, "f_param1", f_param1 );
cvWriteReal( fs, "f_param2", f_param2 );
}
cvWriteReal( fs, "min_val", min_val );
cvWriteReal( fs, "max_val", max_val );
cvWriteReal( fs, "min_val1", min_val1 );
cvWriteReal( fs, "max_val1", max_val1 );
cvStartWriteStruct( fs, "training_params", CV_NODE_MAP );
if( params.train_method == CvANN_MLP_TrainParams::BACKPROP )
{
cvWriteString( fs, "train_method", "BACKPROP" );
cvWriteReal( fs, "dw_scale", params.bp_dw_scale );
cvWriteReal( fs, "moment_scale", params.bp_moment_scale );
}
else if( params.train_method == CvANN_MLP_TrainParams::RPROP )
{
cvWriteString( fs, "train_method", "RPROP" );
cvWriteReal( fs, "dw0", params.rp_dw0 );
cvWriteReal( fs, "dw_plus", params.rp_dw_plus );
cvWriteReal( fs, "dw_minus", params.rp_dw_minus );
cvWriteReal( fs, "dw_min", params.rp_dw_min );
cvWriteReal( fs, "dw_max", params.rp_dw_max );
}
cvStartWriteStruct( fs, "term_criteria", CV_NODE_MAP + CV_NODE_FLOW );
if( params.term_crit.type & CV_TERMCRIT_EPS )
cvWriteReal( fs, "epsilon", params.term_crit.epsilon );
if( params.term_crit.type & CV_TERMCRIT_ITER )
cvWriteInt( fs, "iterations", params.term_crit.max_iter );
cvEndWriteStruct( fs );
cvEndWriteStruct( fs );
__END__;
}
void CvANN_MLP::write( CvFileStorage* fs, const char* name ) const
{
CV_FUNCNAME( "CvANN_MLP::write" );
__BEGIN__;
int i, l_count = layer_sizes->cols;
if( !layer_sizes )
CV_ERROR( CV_StsError, "The network has not been initialized" );
cvStartWriteStruct( fs, name, CV_NODE_MAP, CV_TYPE_NAME_ML_ANN_MLP );
cvWrite( fs, "layer_sizes", layer_sizes );
write_params( fs );
cvStartWriteStruct( fs, "input_scale", CV_NODE_SEQ + CV_NODE_FLOW );
cvWriteRawData( fs, weights[0], layer_sizes->data.i[0]*2, "d" );
cvEndWriteStruct( fs );
cvStartWriteStruct( fs, "output_scale", CV_NODE_SEQ + CV_NODE_FLOW );
cvWriteRawData( fs, weights[l_count], layer_sizes->data.i[l_count-1]*2, "d" );
cvEndWriteStruct( fs );
cvStartWriteStruct( fs, "inv_output_scale", CV_NODE_SEQ + CV_NODE_FLOW );
cvWriteRawData( fs, weights[l_count+1], layer_sizes->data.i[l_count-1]*2, "d" );
cvEndWriteStruct( fs );
cvStartWriteStruct( fs, "weights", CV_NODE_SEQ );
for( i = 1; i < l_count; i++ )
{
cvStartWriteStruct( fs, 0, CV_NODE_SEQ + CV_NODE_FLOW );
cvWriteRawData( fs, weights[i], (layer_sizes->data.i[i-1]+1)*layer_sizes->data.i[i], "d" );
cvEndWriteStruct( fs );
}
cvEndWriteStruct( fs );
__END__;
}
void CvANN_MLP::read_params( CvFileStorage* fs, CvFileNode* node )
{
//CV_FUNCNAME( "CvANN_MLP::read_params" );
__BEGIN__;
const char* activ_func_name = cvReadStringByName( fs, node, "activation_function", 0 );
CvFileNode* tparams_node;
if( activ_func_name )
activ_func = strcmp( activ_func_name, "SIGMOID_SYM" ) == 0 ? SIGMOID_SYM :
strcmp( activ_func_name, "IDENTITY" ) == 0 ? IDENTITY :
strcmp( activ_func_name, "GAUSSIAN" ) == 0 ? GAUSSIAN : 0;
else
activ_func = cvReadIntByName( fs, node, "activation_function" );
f_param1 = cvReadRealByName( fs, node, "f_param1", 0 );
f_param2 = cvReadRealByName( fs, node, "f_param2", 0 );
set_activ_func( activ_func, f_param1, f_param2 );
min_val = cvReadRealByName( fs, node, "min_val", 0. );
max_val = cvReadRealByName( fs, node, "max_val", 1. );
min_val1 = cvReadRealByName( fs, node, "min_val1", 0. );
max_val1 = cvReadRealByName( fs, node, "max_val1", 1. );
tparams_node = cvGetFileNodeByName( fs, node, "training_params" );
params = CvANN_MLP_TrainParams();
if( tparams_node )
{
const char* tmethod_name = cvReadStringByName( fs, tparams_node, "train_method", "" );
CvFileNode* tcrit_node;
if( strcmp( tmethod_name, "BACKPROP" ) == 0 )
{
params.train_method = CvANN_MLP_TrainParams::BACKPROP;
params.bp_dw_scale = cvReadRealByName( fs, tparams_node, "dw_scale", 0 );
params.bp_moment_scale = cvReadRealByName( fs, tparams_node, "moment_scale", 0 );
}
else if( strcmp( tmethod_name, "RPROP" ) == 0 )
{
params.train_method = CvANN_MLP_TrainParams::RPROP;
params.rp_dw0 = cvReadRealByName( fs, tparams_node, "dw0", 0 );
params.rp_dw_plus = cvReadRealByName( fs, tparams_node, "dw_plus", 0 );
params.rp_dw_minus = cvReadRealByName( fs, tparams_node, "dw_minus", 0 );
params.rp_dw_min = cvReadRealByName( fs, tparams_node, "dw_min", 0 );
params.rp_dw_max = cvReadRealByName( fs, tparams_node, "dw_max", 0 );
}
tcrit_node = cvGetFileNodeByName( fs, tparams_node, "term_criteria" );
if( tcrit_node )
{
params.term_crit.epsilon = cvReadRealByName( fs, tcrit_node, "epsilon", -1 );
params.term_crit.max_iter = cvReadIntByName( fs, tcrit_node, "iterations", -1 );
params.term_crit.type = (params.term_crit.epsilon >= 0 ? CV_TERMCRIT_EPS : 0) +
(params.term_crit.max_iter >= 0 ? CV_TERMCRIT_ITER : 0);
}
}
__END__;
}
void CvANN_MLP::read( CvFileStorage* fs, CvFileNode* node )
{
CvMat* _layer_sizes = 0;
CV_FUNCNAME( "CvANN_MLP::read" );
__BEGIN__;
CvFileNode* w;
CvSeqReader reader;
int i, l_count;
_layer_sizes = (CvMat*)cvReadByName( fs, node, "layer_sizes" );
CV_CALL( create( _layer_sizes, SIGMOID_SYM, 0, 0 ));
l_count = layer_sizes->cols;
CV_CALL( read_params( fs, node ));
w = cvGetFileNodeByName( fs, node, "input_scale" );
if( !w || CV_NODE_TYPE(w->tag) != CV_NODE_SEQ ||
w->data.seq->total != layer_sizes->data.i[0]*2 )
CV_ERROR( CV_StsParseError, "input_scale tag is not found or is invalid" );
CV_CALL( cvReadRawData( fs, w, weights[0], "d" ));
w = cvGetFileNodeByName( fs, node, "output_scale" );
if( !w || CV_NODE_TYPE(w->tag) != CV_NODE_SEQ ||
w->data.seq->total != layer_sizes->data.i[l_count-1]*2 )
CV_ERROR( CV_StsParseError, "output_scale tag is not found or is invalid" );
CV_CALL( cvReadRawData( fs, w, weights[l_count], "d" ));
w = cvGetFileNodeByName( fs, node, "inv_output_scale" );
if( !w || CV_NODE_TYPE(w->tag) != CV_NODE_SEQ ||
w->data.seq->total != layer_sizes->data.i[l_count-1]*2 )
CV_ERROR( CV_StsParseError, "inv_output_scale tag is not found or is invalid" );
CV_CALL( cvReadRawData( fs, w, weights[l_count+1], "d" ));
w = cvGetFileNodeByName( fs, node, "weights" );
if( !w || CV_NODE_TYPE(w->tag) != CV_NODE_SEQ ||
w->data.seq->total != l_count - 1 )
CV_ERROR( CV_StsParseError, "weights tag is not found or is invalid" );
cvStartReadSeq( w->data.seq, &reader );
for( i = 1; i < l_count; i++ )
{
w = (CvFileNode*)reader.ptr;
CV_CALL( cvReadRawData( fs, w, weights[i], "d" ));
CV_NEXT_SEQ_ELEM( reader.seq->elem_size, reader );
}
__END__;
}
using namespace cv;
CvANN_MLP::CvANN_MLP( const Mat& _layer_sizes, int _activ_func,
double _f_param1, double _f_param2 )
{
layer_sizes = wbuf = 0;
min_val = max_val = min_val1 = max_val1 = 0.;
weights = 0;
rng = &cv::theRNG();
default_model_name = "my_nn";
create( _layer_sizes, _activ_func, _f_param1, _f_param2 );
}
void CvANN_MLP::create( const Mat& _layer_sizes, int _activ_func,
double _f_param1, double _f_param2 )
{
CvMat cvlayer_sizes = _layer_sizes;
create( &cvlayer_sizes, _activ_func, _f_param1, _f_param2 );
}
int CvANN_MLP::train( const Mat& _inputs, const Mat& _outputs,
const Mat& _sample_weights, const Mat& _sample_idx,
CvANN_MLP_TrainParams _params, int flags )
{
CvMat inputs = _inputs, outputs = _outputs, sweights = _sample_weights, sidx = _sample_idx;
return train(&inputs, &outputs, sweights.data.ptr ? &sweights : 0,
sidx.data.ptr ? &sidx : 0, _params, flags);
}
float CvANN_MLP::predict( const Mat& _inputs, Mat& _outputs ) const
{
CV_Assert(layer_sizes != 0);
_outputs.create(_inputs.rows, layer_sizes->data.i[layer_sizes->cols-1], _inputs.type());
CvMat inputs = _inputs, outputs = _outputs;
return predict(&inputs, &outputs);
}
/* End of file. */