void LayerNet::gen_init (
TrainingSet *tptr , // Training set to use
struct LearnParams *lptr // User's general learning parameters
)
{
int i, istart, individual, best_individual, generation, n_cross ;
int first_child, parent1, parent2, improved, crosspt, nchoices, *choices ;
int initpop, popsize, gens, climb, nvars, chromsize, split, ind ;
float pcross, pmutate, error, besterror, *errors, *fitness, worst ;
float fquit, favor_best, fitfac, maxerr, minerr, avgerr, overinit ;
SingularValueDecomp *sptr ;
struct GenInitParams *gptr ; // User's genetic initialization parameters
char *pool1, *pool2, *oldpop, *newpop, *popptr, *temppop, *best ;
char msg[80] ;
gptr = lptr->gp ;
popsize = gptr->pool ;
gens = gptr->gens ;
climb = gptr->climb ;
overinit = gptr->overinit ;
pcross = gptr->pcross ;
pmutate = gptr->pmutate ;
fquit = lptr->quit_err ;
favor_best = 3.1 ;
fitfac = -20.0 ;
/*
--------------------------------------------------------------------------------
Do all scratch memory allocation.
--------------------------------------------------------------------------------
*/
/*
Allocate the singular value decomposition object for REGRESS.
*/
if (nhid2 == 0) // One hidden layer
nvars = nhid1 + 1 ;
else // Two hidden layers
nvars = nhid2 + 1 ;
MEMTEXT ( "GEN_INIT: new SingularValueDecomp" ) ;
sptr = new SingularValueDecomp ( tptr->ntrain , nvars , 1 ) ;
if ((sptr == NULL) || ! sptr->ok) {
memory_message("for genetic initialization. Try ANNEAL NOREGRESS.");
neterr = 1.0 ; // Flag failure to LayerNet::learn which called us
if (sptr != NULL)
delete sptr ;
return ;
}
chromsize = nhid1 * (nin+1) ; // Length of an individual's chromosome
if (nhid2) // is the number of hidden weights
chromsize += nhid2 * (nhid1+1) ;
errors = fitness = NULL ;
choices = NULL ;
pool1 = pool2 = NULL ;
MEMTEXT ( "GEN_INIT: errors, fitness, choices, best, pool1,pool2");
if (((errors = (float*) MALLOC ( popsize * sizeof(float))) == NULL)
|| ((fitness = (float*) MALLOC ( popsize * sizeof(float))) == NULL)
|| ((best = (char*) MALLOC( chromsize )) == NULL)
|| ((choices = (int*) MALLOC ( popsize * sizeof(int))) == NULL)
|| ((pool1 = (char*) MALLOC( popsize * chromsize )) == NULL)
|| ((pool2 = (char*) MALLOC( popsize * chromsize )) == NULL)) {
if (errors != NULL)
FREE ( errors ) ;
if (fitness != NULL)
FREE ( fitness ) ;
if (choices != NULL)
FREE ( choices ) ;
if (pool1 != NULL)
FREE ( pool1 ) ;
if (pool2 != NULL)
FREE ( pool2 ) ;
delete sptr ;
memory_message("for genetic initialization. Try ANNEAL NOREGRESS." ) ;
neterr = 1.0 ; // Flag failure to LayerNet::learn which called us
return ;
}
/*
Generate initial population pool.
We also preserve the best weights across all generations,
as this is what we will ultimately return to the user.
Its mean square error is besterror.
*/
besterror = 1.e30 ; // For saving best (across all individuals and gens)
maxerr = avgerr = 0.0 ; // For progress display only
best_individual = 0 ; // Safety only
initpop = popsize * overinit ; // Overinitialization of initial population
progress_message ( "\nGenerating initial population" ) ;
for (ind=0 ; ind<initpop ; ind++) { // Try overinitialization times
if (ind<popsize) // If still in pop size limit
individual = ind ; // just use next avail space
else { // Else we search entire pop
worst = -1. ; // for the worst member
for (i=0 ; i<popsize ; i++) { // which we will then replace
if (errors[i] > worst) {
worst = errors[i] ;
individual = i ;
}
}
avgerr -= worst ; // Exclude discards from average
}
popptr = pool1 + individual * chromsize ; // Build init pop in pool1
rand_ind ( popptr , chromsize ) ; // Randomly generate individual
decode ( popptr , nin , nhid1 , nhid2 , // Convert genotype (chromosome)
hid1_coefs , hid2_coefs ); // to phenotype (weights)
error = regress ( tptr , sptr ) ; // Evaluate network error
errors[individual] = error ; // and keep all errors
if (error < besterror) { // Keep track of best
besterror = error ; // as it is returned to user
best_individual = individual ; // This is its index in pool1
}
if (error > maxerr) // Max and average error are
maxerr = error ; // for progress display only
avgerr += error ;
if (error <= fquit)
break ;
progress_message ( "." ) ;
}
sprintf (msg , "\nInitial pop: Min err=%7.4lf Max=%7.4lf Avg=%7.4lf",
100. * besterror, 100. * maxerr, 100.0 * avgerr / (float) popsize);
progress_message ( msg ) ;
/*
The initial population has been built in pool1.
Copy its best member to 'best' in case it never gets beat (unlikely
but possible!).
Also, we will need best if the climb option is true.
*/
popptr = pool1 + best_individual * chromsize ; // Point to best
memcpy ( best , popptr , chromsize ) ; // and save it
/*
This is the main generation loop. There are two areas for population pool
storage: pool1 and pool2. At any given time, oldpop will be set to one of
them, and newpop to the other. This avoids a lot of copying.
*/
oldpop = pool1 ; // This is the initial population
newpop = pool2 ; // The next generation is created here
for (generation=0 ; generation<gens ; generation++) {
if (error <= fquit) // We may have satisfied this in init pop
break ; // So we test at start of generation loop
error_to_fitness ( popsize , favor_best , fitfac , errors , fitness ) ;
fitness_to_choices ( popsize , fitness , choices ) ;
nchoices = popsize ; // Will count down as choices array emptied
n_cross = pcross * popsize ; // Number crossing over
first_child = 1 ; // Generating first of parent's 2 children?
improved = 0 ; // Flags if we beat best
if (climb) { // If we are to hill climb
memcpy ( newpop , best , chromsize ) ; // start with best
errors[0] = besterror ; // Record its error
istart = 1 ; // and start children past it
}
else
istart = 0 ;
/*
Generate the children
*/
maxerr = avgerr = 0.0 ; // For progress display only
minerr = 1.0 ; // Ditto
for (individual=istart ; individual<popsize ; individual++) {
popptr = newpop + individual * chromsize ; // Will put this child here
if (first_child) // If this is the first of 2 children, pick parents
pick_parents ( &nchoices , choices , &parent1 , &parent2 ) ;
if (n_cross-- > 0) // Do crossovers first
reproduce ( oldpop + parent1 * chromsize , oldpop + parent2 * chromsize ,
first_child , chromsize , popptr , &crosspt , &split ) ;
else if (first_child) // No more crossovers, so just copy parent
memcpy ( popptr , oldpop + parent1 * chromsize , chromsize ) ;
else
memcpy ( popptr , oldpop + parent2 * chromsize , chromsize );
if (pmutate > 0.0)
mutate ( popptr , chromsize , pmutate ) ;
decode ( popptr , nin , nhid1 , nhid2 , hid1_coefs , hid2_coefs ) ;
error = regress ( tptr , sptr ) ; // Evaluate child's error
errors[individual] = error ; // and keep each
if (error < besterror) { // Keep track of best
besterror = error ; // It will be returned to user
best_individual = individual ; // This is its index in newpop
improved = 1 ; // Flag so we copy it later
}
if (error > maxerr) // Min, max and average error
maxerr = error ; // for progress display only
if (error < minerr)
minerr = error ;
avgerr += error ;
if (error <= fquit)
break ;
first_child = ! first_child ;
} // For all genes in population
/*
We finished generating all children. If we improved (one of these
children beat the best so far) then copy that child to the best.
Swap oldpop and newpop for the next generation.
*/
if (improved) {
popptr = newpop + best_individual * chromsize ; // Point to best
memcpy ( best , popptr , chromsize ) ; // and save it
}
temppop = oldpop ; // Switch old and new pops for next generation
oldpop = newpop ;
newpop = temppop ;
sprintf(msg, "\nGeneration %3d: Min err=%7.4lf Max=%7.4lf Avg=%7.4lf",
generation+1, 100. * minerr, 100. * maxerr,
100.0 * avgerr / (float) popsize ) ;
progress_message ( msg ) ;
}
/*
We are all done.
*/
decode ( best , nin , nhid1 , nhid2 , hid1_coefs , hid2_coefs ) ;
besterror = regress ( tptr , sptr ) ; // Evaluate network error
MEMTEXT ( "GEN_INIT: errors, fitness, choices, best, pool1,pool2");
FREE ( errors ) ;
FREE ( fitness ) ;
FREE ( choices ) ;
FREE ( best ) ;
FREE ( pool1 ) ;
FREE ( pool2 ) ;
MEMTEXT ( "GEN_INIT: delete sptr" ) ;
delete sptr ;
}
void LayerNet::anx_dd ( TrainingSet *tptr , struct LearnParams *lptr )
{
int itry, n_escape, n_retry, bad_count, new_record, refined ;
long seed ;
double err, prev_err, best_err, start_of_loop_error, best_inner_error ;
double initial_accuracy, final_accuracy ;
char msg[80] ;
LayerNet *worknet, *worknet2, *bestnet ;
n_escape = n_retry = 0 ;
/*
Allocate scratch memory
*/
MEMTEXT ( "ANX_DD::learn new worknet, bestnet" ) ;
worknet = new LayerNet ( model , outmod , outlin , nin , nhid1 , nhid2 ,
nout , 0 , 0 ) ;
bestnet = new LayerNet ( model , outmod , outlin , nin , nhid1 , nhid2 ,
nout , 0 , 1 ) ;
if ((worknet == NULL) || (! worknet->ok)
|| (bestnet == NULL) || (! bestnet->ok)) {
memory_message ( "to learn" ) ;
if (worknet != NULL)
delete worknet ;
if (bestnet != NULL)
delete bestnet ;
errtype = 0 ;
return ;
}
if ((lptr->method == METHOD_AN2_CJ) || (lptr->method == METHOD_AN2_LM)) {
worknet2 = new LayerNet ( model , outmod , outlin , nin , nhid1 , nhid2 ,
nout , 0 , 0 ) ;
if ((worknet2 == NULL) || (! worknet2->ok)) {
if (worknet2 != NULL)
delete worknet2 ;
delete worknet ;
delete bestnet ;
memory_message ( "to learn" ) ;
errtype = 0 ;
return ;
}
}
else
worknet2 = NULL ;
/*
Start by annealing around the starting weights. These will be zero if the
net was just created. If it was restored or partially trained already,
they will be meaningful. Anneal1 guarantees that it will not return all
zero weights if there is at least one hidden layer, even if that means
that the error exceeds the amount that could be attained by all zeros.
*/
best_err = best_inner_error = 1.e30 ;
if ((lptr->method == METHOD_AN1_CJ) || (lptr->method == METHOD_AN1_LM))
anneal1 ( tptr , lptr , worknet , 1 , 0 ) ;
else if ((lptr->method == METHOD_AN2_CJ) || (lptr->method == METHOD_AN2_LM))
anneal2 ( tptr , lptr , worknet , worknet2 , 1 ) ;
/*
Do direct descent optimization, finding local minimum.
Then anneal to break out of it. If successful, loop back up to
do direct descent again. Otherwise restart totally random.
*/
bad_count = 0 ; // Handles flat local mins
refined = 0 ; // Did we ever refine to high resolution? Not yet.
new_record = 0 ; // Refine every time a new inner error record set
initial_accuracy = pow ( 10.0 , -lptr->cj_acc ) ;
final_accuracy = initial_accuracy * pow ( 10.0 , -lptr->cj_refine ) ;
for (itry=1 ; ; itry++) {
if (neterr < best_err) { // Keep track of best
copy_weights ( bestnet , this ) ;
best_err = neterr ;
}
sprintf ( msg , "Try %d (best=%lf):", itry, best_err ) ;
normal_message ( msg ) ;
if (neterr <= lptr->quit_err)
break ;
start_of_loop_error = neterr ;
if ((lptr->method == METHOD_AN1_CJ) || (lptr->method == METHOD_AN2_CJ))
err = conjgrad ( tptr , 32767 , initial_accuracy ,
lptr->quit_err , lptr->cj_progress ) ;
else if ((lptr->method==METHOD_AN1_LM) || (lptr->method==METHOD_AN2_LM))
err = lev_marq ( tptr , 32767 , initial_accuracy ,
lptr->quit_err , lptr->cj_progress ) ;
neterr = fabs ( err ) ; // err<0 if user pressed ESCape
sprintf ( msg , " Gradient err=%lf", neterr ) ;
progress_message ( msg ) ;
if (neterr < best_err) { // Keep track of best
copy_weights ( bestnet , this ) ;
best_err = neterr ;
}
if (err <= lptr->quit_err) { // err<0 if user pressed ESCape
if (err < -1.e29) // or insufficient memory
printf ( "\nInsufficient memory for gradient learning." ) ;
break ;
}
seed = flrand() - (long) (itry * 97) ; // Insure new seed for anneal
sflrand ( seed ) ;
prev_err = neterr ; // So we can see if anneal helped
if ((lptr->method == METHOD_AN1_CJ) || (lptr->method == METHOD_AN1_LM))
anneal1 ( tptr , lptr , worknet , 0 , itry ) ;
else if ((lptr->method==METHOD_AN2_CJ) || (lptr->method==METHOD_AN2_LM))
anneal2 ( tptr , lptr , worknet , worknet2 , 0 ) ;
sprintf ( msg , " Anneal err=%lf", neterr ) ;
progress_message ( msg ) ;
if (neterr < best_err) { // Keep track of best
copy_weights ( bestnet , this ) ;
best_err = neterr ;
}
if (best_err <= lptr->quit_err)
break ;
if (neterr < best_inner_error) { // Keep track of best inner for refine
best_inner_error = neterr ;
new_record = 1 ; // Tells us to refine
}
if ((prev_err - neterr) > 1.e-7) { // Did we break out of local min?
if ((start_of_loop_error - neterr) < 1.e-3)
++bad_count ; // Avoid many unprofitable iters
else
bad_count = 0 ;
if (bad_count < 4) {
++n_escape ; // Count escapes from local min
continue ; // Escaped, so gradient learn again
}
}
/*
After first few tries, and after each inprovement thereafter, refine
to high resolution
*/
if ((itry-n_escape >= lptr->cj_pretries) && (new_record || ! refined)) {
if (! refined) { // If refining the best of the pretries
copy_weights ( this , bestnet ) ; // Get that net
neterr = best_err ;
}
refined = 1 ; // Only force refine once
new_record = 0 ; // Reset new inner error record flag
progress_message ( " REFINING" ) ;
if ((lptr->method == METHOD_AN1_CJ) || (lptr->method == METHOD_AN2_CJ))
err = conjgrad ( tptr , 0 , final_accuracy ,
lptr->quit_err , lptr->cj_progress ) ;
else if ((lptr->method==METHOD_AN1_LM)|| (lptr->method==METHOD_AN2_LM))
err = lev_marq ( tptr , 0 , final_accuracy ,
lptr->quit_err , lptr->cj_progress ) ;
neterr = fabs ( err ) ; // err<0 if user pressed ESCape
sprintf ( msg , " Attained err=%lf", neterr ) ;
progress_message ( msg ) ;
if (neterr < best_err) { // Keep track of best
copy_weights ( bestnet , this ) ;
best_err = neterr ;
}
}
if (++n_retry > lptr->retries)
break ;
progress_message ( " RESTART" ) ;
zero_weights () ; // Failed to break out, so retry random
seed = flrand() - (long) (itry * 773) ; // Insure new seed for anneal
sflrand ( seed ) ;
if ((lptr->method == METHOD_AN1_CJ) || (lptr->method == METHOD_AN1_LM))
anneal1 ( tptr , lptr , worknet , 1 , itry ) ;
else if ((lptr->method==METHOD_AN2_CJ) || (lptr->method==METHOD_AN2_LM))
anneal2 ( tptr , lptr , worknet , worknet2 , 1 ) ;
}
FINISH:
copy_weights ( this , bestnet ) ;
neterr = best_err ;
MEMTEXT ( "AN1_DD::learn delete worknet, bestnet" ) ;
delete worknet ;
delete bestnet ;
sprintf ( msg , "%d successful escapes, %d retries", n_escape, n_retry ) ;
normal_message ( msg ) ;
return ;
}
void LayerNet::anneal (
TrainingSet *tptr , // Training set to use
struct LearnParams *lptr , // User's general learning parameters
LayerNet *bestnet , // Work area used to keep best network
int init // Use zero suffix (initialization) anneal parms?
)
{
int ntemps, niters, setback, reg, nvars, key, user_quit ;
int i, iter, improved, ever_improved, itemp ;
long seed, bestseed ;
char msg[80] ;
double tempmult, temp, fval, bestfval, starttemp, stoptemp, fquit ;
SingularValueDecomp *sptr ;
struct AnnealParams *aptr ; // User's annealing parameters
aptr = lptr->ap ;
/*
The parameter 'init' is nonzero if we are initializing
weights for learning. If zero we are attempting to break
out of a local minimum. The main effect of this parameter
is whether or not we use the zero suffix variables in the
anneal parameters.
A second effect is that regression is used only for
initialization, not for escape.
*/
if (init) {
ntemps = aptr->temps0 ;
niters = aptr->iters0 ;
setback = aptr->setback0 ;
starttemp = aptr->start0 ;
stoptemp = aptr->stop0 ;
}
else {
ntemps = aptr->temps ;
niters = aptr->iters ;
setback = aptr->setback ;
starttemp = aptr->start ;
stoptemp = aptr->stop ;
}
/*
Initialize other local parameters. Note that there is no sense using
regression if there are no hidden layers. Also, regression is almost
always counterproductive for local minimum escape.
*/
fquit = lptr->quit_err ;
reg = init && nhid1 && (lptr->init != 1) ;
/*
Allocate the singular value decomposition object for REGRESS.
Also allocate a work area for REGRESS to preserve matrix.
*/
if (reg) {
if (nhid1 == 0) // No hidden layer
nvars = nin + 1 ;
else if (nhid2 == 0) // One hidden layer
nvars = nhid1 + 1 ;
else // Two hidden layers
nvars = nhid2 + 1 ;
MEMTEXT ( "ANNEAL: new SingularValueDecomp" ) ;
sptr = new SingularValueDecomp ( tptr->ntrain , nvars , 1 ) ;
if ((sptr == NULL) || ! sptr->ok) {
memory_message (
"for annealing with regression. Try ANNEAL NOREGRESS.");
if (sptr != NULL)
delete sptr ;
neterr = 1.0 ; // Flag failure to LayerNet::learn which called us
return ;
}
}
/*
For every temperature, the center around which we will perturb is the
best point so far. This is kept in 'bestnet', so initialize it to the
user's starting estimate. Also, initialize 'bestfval', the best
function value so far, to be the function value at that starting point.
*/
copy_weights ( bestnet , this ) ; // Current weights are best so far
if (init)
bestfval = 1.e30 ; // Force it to accept SOMETHING
else
bestfval = trial_error ( tptr ) ;
/*
This is the temperature reduction loop and the iteration within
temperature loop. We use a slick trick to keep track of the
best point at a given temperature. We certainly don't want to
replace the best every time an improvement is had, as then we
would be moving our center about, compromising the global nature
of the algorithm. We could, of course, have a second work area
in which we save the 'best so far for this temperature' point.
But if there are a lot of variables, the usual case, this wastes
memory. What we do is to save the seed of the random number
generator which created the improvement. Then later, when we
need to retrieve the best, simply set the random seed and
regenerate it. This technique also saves a lot of copying time
if many improvements are made for a single temperature.
*/
temp = starttemp ;
tempmult = exp( log( stoptemp / starttemp ) / (ntemps-1)) ;
ever_improved = 0 ; // Flags if improved at all
user_quit = 0 ; // Flags user pressed ESCape
for (itemp=0 ; itemp<ntemps ; itemp++) { // Temp reduction loop
improved = 0 ; // Flags if this temp improved
if (init) {
sprintf ( msg , "\nANNEAL temp=%.2lf ", temp ) ;
progress_message ( msg ) ;
}
for (iter=0 ; iter<niters ; iter++) { // Iters per temp loop
seed = longrand () ; // Get a random seed
slongrand ( seed ) ; // Brute force set it
perturb (bestnet, this, temp, reg) ;// Randomly perturb about best
if (reg) // If using regression, estimate
fval = regress ( tptr , sptr ) ; // out weights now
else // Otherwise just evaluate
fval = trial_error ( tptr ) ;
if (fval < bestfval) { // If this iteration improved
bestfval = fval ; // then update the best so far
bestseed = seed ; // and save seed to recreate it
ever_improved = improved = 1 ; // Flag that we improved
if (bestfval <= fquit) // If we reached the user's
break ; // limit, we can quit
iter -= setback ; // It often pays to keep going
if (iter < 0) // at this temperature if we
iter = 0 ; // are still improving
}
} // Loop: for all iters at a temp
if (improved) { // If this temp saw improvement
slongrand ( bestseed ) ; // set seed to what caused it
perturb (bestnet, this, temp, reg) ;// and recreate that point
copy_weights ( bestnet , this ) ; // which will become next center
slongrand ( bestseed / 2 + 999 ) ; // Jog seed away from best
if (init) {
sprintf ( msg , " err=%.3lf%% ", 100.0 * bestfval ) ;
progress_message ( msg ) ;
}
}
if (bestfval <= fquit) // If we reached the user's
break ; // limit, we can quit
/***********************************************************************
if (kbhit()) { // Was a key pressed?
key = getch () ; // Read it if so
while (kbhit()) // Flush key buffer in case function key
getch () ; // or key was held down
if (key == 27) { // ESCape
user_quit = 1 ; // Flags user that ESCape was pressed
break ;
}
}
***********************************************************************/
if (user_quit)
break ;
temp *= tempmult ; // Reduce temp for next pass
} // through this temperature loop
/*
The trials left this weight set and neterr in random condition.
Make them equal to the best, which will be the original
if we never improved.
Also, if we improved and are using regression, recall that bestnet
only contains the best hidden weights, as we did not bother to run
regress when we updated bestnet. Do that now before returning.
*/
copy_weights ( this , bestnet ) ; // Return best weights in this net
neterr = bestfval ; // Trials destroyed weights, err
if (ever_improved && reg)
neterr = regress ( tptr , sptr ) ; // regressed output weights
if (reg) {
MEMTEXT ( "ANNEAL: delete SingularValueDecomp" ) ;
delete sptr ;
}
}
int LayerNet::ssg_core (
TrainingSet *tptr , // Training set to use
struct LearnParams *lptr , // User's general learning parameters
LayerNet *avgnet , // Work area used to keep average weights
LayerNet *bestnet , // And the best so far
double *work1 , // Gradient work vector
double *work2 , // Ditto
double *grad , // Ditto
double *avg_grad , // Ditto
int n_grad // Length of above vectors
)
{
int ntemps, niters, setback, reg, nvars, user_quit ;
int i, iter, itemp, n_good, n_bad, use_grad ;
char msg[80] ;
double tempmult, temp, fval, bestfval, starttemp, stoptemp, fquit ;
double avg_func, new_fac, gradlen, grad_weight, weight_used ;
enum RandomDensity density ;
SingularValueDecomp *sptr ;
struct AnnealParams *aptr ; // User's annealing parameters
aptr = lptr->ap ;
ntemps = aptr->temps0 ;
niters = aptr->iters0 ;
setback = aptr->setback0 ;
starttemp = aptr->start0 ;
stoptemp = aptr->stop0 ;
if (aptr->random0 == ANNEAL_GAUSSIAN)
density = NormalDensity ;
else if (aptr->random0 == ANNEAL_CAUCHY)
density = CauchyDensity ;
if (! (ntemps * niters))
return 0 ;
/*
Initialize other local parameters. Note that there is no sense using
regression if there are no hidden layers.
*/
use_grad = (grad != NULL) ;
fquit = lptr->quit_err ;
reg = nhid1 ;
/*
Allocate the singular value decomposition object for REGRESS.
Also allocate a work area for REGRESS to preserve matrix.
*/
if (reg) { // False if no hidden layers
if (nhid2 == 0) // One hidden layer
nvars = nhid1_n ;
else // Two hidden layers
nvars = nhid2_n ;
i = (model == NETMOD_COMPLEX) ? 2 * tptr->ntrain : tptr->ntrain ;
if (i < nvars) {
warning_message ( "Too few training sets for regression." ) ;
reg = 0 ;
}
else {
MEMTEXT ( "SSG: new SingularValueDecomp" ) ;
sptr = new SingularValueDecomp ( i , nvars , 1 ) ;
if ((sptr == NULL) || ! sptr->ok) {
memory_message (
"for SS(G) with regression. Using total randomization.");
if (sptr != NULL)
delete sptr ;
reg = 0 ;
}
}
}
/*
For the basic algorithm, we will keep the current 'average' network
weight set in avgnet. This will be the moving center about which the
perturbation is done.
Although not directly related to the algorithm itself, we keep track
of the best network ever found in bestnet. That is what the user
will get at the end.
*/
copy_weights ( bestnet , this ) ; // Current weights are best so far
copy_weights ( avgnet , this ) ; // Center of perturbation
bestfval = trial_error ( tptr ) ;
/*
If this is being used to initialize the weights, make sure that they are
not identically zero. Do this by setting bestfval huge so that
SOMETHING is accepted later.
*/
if (nhid1) {
i = nhid1 * nin_n ;
while (i--) {
if (fabs(hid1_coefs[i]) > 1.e-10)
break ;
}
if (i < 0)
bestfval = 1.e30 ;
}
/*
Initialize by cumulating a bunch of points
*/
normal_message ( "Initializing..." ) ;
avg_func = 0.0 ; // Mean function around center
if (use_grad) {
for (i=0 ; i<n_grad ; i++) // Zero the mean gradient
avg_grad[i] = 0.0 ;
}
for (iter=0 ; iter<niters ; iter++) { // Initializing iterations
perturb ( avgnet , this , starttemp , reg , density ) ; // Move point
if (reg) // If using regression, estimate
fval = regress ( tptr , sptr ) ; // out weights now, ignore fval
if (use_grad) // Also need gradient?
fval = gradient ( tptr , work1 , work2 , grad ) ; // fval redundant
else if (! reg) // If reg we got fval from regress
fval = trial_error ( tptr ) ;
avg_func += fval ; // Cumulate mean function
if (use_grad) { // Also need gradient?
for (i=0 ; i<n_grad ; i++) // Cumulate mean gradient
avg_grad[i] += grad[i] ;
}
if (fval < bestfval) { // If this iteration improved
bestfval = fval ; // then update the best so far
copy_weights ( bestnet , this ) ; // Keep the network
if (bestfval <= fquit) // If we reached the user's
goto FINISH ; // limit, we can quit
}
if ((user_quit = user_pressed_escape ()) != 0)
goto FINISH ;
} // Loop: for all initial iters
avg_func /= niters ; // Mean of all points around avgnet
new_fac = 1.0 / niters ; // Weight of each point
sprintf ( msg , " avg=%.6lf best=%.6lf", avg_func, bestfval ) ;
progress_message ( msg ) ;
if (use_grad) { // Also need gradient?
gradlen = 0.0 ; // Will cumulate grad length
for (i=0 ; i<n_grad ; i++) { // Find gradient mean and length
avg_grad[i] /= niters ;
gradlen += avg_grad[i] * avg_grad[i] ;
}
gradlen = sqrt ( gradlen ) ;
grad_weight = 0.5 ;
}
/*
This is the temperature reduction loop and the iteration within
temperature loop.
*/
temp = starttemp ;
tempmult = exp( log( stoptemp / starttemp ) / (ntemps-1)) ;
user_quit = 0 ; // Flags user pressed ESCape
for (itemp=0 ; itemp<ntemps ; itemp++) { // Temp reduction loop
n_good = n_bad = 0 ; // Counts better and worse
sprintf ( msg , "Temp=%.3lf ", temp ) ;
normal_message ( msg ) ;
for (iter=0 ; iter<niters ; iter++) { // Iters per temp loop
if ((n_bad >= 10) &&
((double) n_good / (double) (n_good+n_bad) < 0.15))
break ;
perturb ( avgnet , this , temp ,
reg , density ) ; // Randomly perturb about center
if (use_grad) // Bias per gradient?
weight_used = shift ( grad , this , grad_weight , reg ) ;
if (reg) { // If using regression, estimate
fval = regress ( tptr , sptr ) ; // out weights now
if ((user_quit = user_pressed_escape ()) != 0)
break ;
if (fval >= avg_func) { // If this would raise mean
++n_bad ; // Count this bad point for user
continue ; // Skip it and try again
}
}
if (use_grad) // Need gradient, fval redundant
fval = gradient ( tptr , work1 , work2 , grad ) ;
else if (! reg) // If reg we got fval from regress
fval = trial_error ( tptr ) ;
if ((user_quit = user_pressed_escape ()) != 0)
break ;
if (fval >= avg_func) { // If this would raise mean
++n_bad ; // Count this bad point for user
continue ; // Skip it and try again
}
++n_good ;
if (fval < bestfval) { // If this iteration improved
bestfval = fval ; // then update the best so far
copy_weights ( bestnet , this ) ; // Keep the network
if (bestfval <= fquit) // If we reached the user's
break ; // limit, we can quit
iter -= setback ; // It often pays to keep going
if (iter < 0) // at this temperature if we
iter = 0 ; // are still improving
}
adjust ( avgnet , this , reg , new_fac ) ; // Move center slightly
avg_func = new_fac * fval + (1.0 - new_fac) * avg_func ;
if (use_grad) {
grad_weight = new_fac * weight_used + (1.0 - new_fac) * grad_weight ;
for (i=0 ; i<n_grad ; i++) // Adjust mean gradient
avg_grad[i] = new_fac * grad[i] + (1.0 - new_fac) * avg_grad[i] ;
}
} // Loop: for all iters at a temp
/*
Iters within temp loop now complete
*/
sprintf ( msg , " %.3lf%% improved avg=%.5lf best=%.5lf",
100.0 * n_good / (double) (n_good+n_bad), avg_func, bestfval ) ;
progress_message ( msg ) ;
if (use_grad) {
gradlen = 0.0 ; // Will cumulate grad length
for (i=0 ; i<n_grad ; i++) // Find gradient length
gradlen += avg_grad[i] * avg_grad[i] ;
gradlen = sqrt ( gradlen ) ;
sprintf ( msg , " grad=%.5lf", gradlen ) ;
progress_message ( msg ) ;
}
if (bestfval <= fquit) // If we reached the user's
break ; // limit, we can quit
if (user_quit)
break ;
temp *= tempmult ; // Reduce temp for next pass
} // through this temperature loop
/*
The trials left this weight set and neterr in random condition.
Make them equal to the best, which will be the original
if we never improved.
*/
FINISH:
copy_weights ( this , bestnet ) ; // Return best weights in this net
neterr = bestfval ; // Trials destroyed weights, err
if (reg) {
MEMTEXT ( "SSG: delete SingularValueDecomp" ) ;
delete sptr ;
}
if (user_quit)
return 1 ;
else
return 0 ;
}
