+++ /dev/null
-/*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 "_ml.h"
-
-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 = cvRNG(-1);
- 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 = cvRNG(-1);
- 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 = cvRandReal(&rng)*2-1.;
- w[k*n2 + j] = val;
- s += val;
- }
-
- if( i < layer_sizes->cols - 1 )
- {
- s = 1./(s - 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
-{
- CV_FUNCNAME( "CvANN_MLP::predict" );
-
- __BEGIN__;
-
- double* buf;
- 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;
- }
-
- buf = (double*)cvStackAlloc( buf_sz*sizeof(buf[0]) );
- 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 );
-
- 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 );
- }
-
- __END__;
-
- 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;
- }
- }
- 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;
- }
-
- cvExp( _xf, _xf );
-
- 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
- switch( activ_func )
- {
- case SIGMOID_SYM:
- scale *= -2*f_param2;
- for( i = 0; i <= n - 4; i += 4 )
- {
- double x0 = 1.+xf[i], x1 = 1.+xf[i+1], x2 = 1.+xf[i+2], x3 = 1.+xf[i+3];
- double a = x0*x1, b = x2*x3, d = 1./(a*b), t0, t1;
- a *= d; b *= d;
-
- t0 = b*x1; t1 = b*x0;
- df[i] = scale*xf[i]*t0*t0;
- df[i+1] = scale*xf[i+1]*t1*t1;
- t0 *= scale2*(2 - x0); t1 *= scale2*(2 - x1);
- xf[i] = t0; xf[i+1] = t1;
-
- t0 = a*x3; t1 = a*x2;
- df[i+2] = scale*xf[i+2]*t0*t0;
- df[i+3] = scale*xf[i+3]*t1*t1;
- t0 *= scale2*(2 - x2); t1 *= scale2*(2 - x3);
- xf[i+2] = t0; xf[i+3] = t1;
- }
-
- for( ; i < n; i++ )
- {
- double t0 = 1./(1. + xf[i]);
- double t1 = scale*xf[i]*t0*t0;
- t0 *= scale2*(1. - xf[i]);
- df[i] = t1;
- xf[i] = t0;
- }
- break;
-
- case GAUSSIAN:
- for( i = 0; i < n; i++ )
- df[i] *= xf[i];
- break;
- default:
- ;
- }
-}
-
-
-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*scale[j*2] + 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 = MIN( 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 )
- {
- if( fabs(prev_E - E) < epsilon )
- break;
- prev_E = E;
- E = 0;
-
- // shuffle indices
- for( i = 0; i < count; i++ )
- {
- int tt;
- j = (unsigned)cvRandInt(&rng) % count;
- k = (unsigned)cvRandInt(&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;
-}
-
-
-int CvANN_MLP::train_rprop( CvVectors x0, CvVectors u, const double* sw )
-{
- const int max_buf_sz = 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, dcount=0;
- 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_sz/(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, si, j, k;
- double* w;
- CvMat _w, _dEdw, hdr1, hdr2, ghdr1, ghdr2, _df;
- CvMat *x1, *x2, *grad1, *grad2, *temp;
- double E = 0;
-
- // first, iterate through all the samples and compute dEdw
- for( si = 0; si < count; si += dcount )
- {
- 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( i = 0; i < dcount; i++ )
- {
- const float* x0data = x0.data.fl[si+i];
- double* xdata = x[0]+i*ivcount;
- for( j = 0; j < ivcount; j++ )
- xdata[j] = x0data[j]*w[j*2] + w[j*2+1];
- }
- else
- for( i = 0; i < dcount; i++ )
- {
- const double* x0data = x0.data.db[si+i];
- double* xdata = x[0]+i*ivcount;
- for( 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( 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];
- 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( 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( 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( 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( 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
- for( 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 );
- 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( j = 0; j < n2; j++ )
- dst[j] += src[j];
- }
- cvInitMatHeader( &_w, n1, n2, CV_64F, weights[i] );
- 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 );
- }
- }
-
- // 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.;
- }
- }
- }
-
- 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 )
-{
- //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 )
-{
- 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__;
-}
-
-/* End of file. */
projects / opencv / blobdiff