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/*
* backprop.c
* Backpropagation neural network library.
*
* 2016, December 13 - fixed bkp_loadfromfile. Changed the file format
* to include a file type, 'A', and the network type. Updated
* bkp_savetofile to match.
* 2016, April 7 - made bkp_query return BiasVals, BHWeights and BIWeights
* 2016, April 3 - cleaned up version for website publication
* 1992 - originally written around this time
* A note of credit:
* This code had its origins as code obtained back around 1992 by sending
* a floppy disk to The Amateur Scientist, Scientific American magazine.
* I've since modified and added to it a great deal, and it's even on
* its 3rd OS (MS-DOS -> QNX -> Windows). As I no longer have the
* original I can't know how much is left to give credit for.
*/
#define CMDIFFSTEPSIZE 1 /* set to 1 for Chen & Mars differential step size */
#define DYNAMIC_LEARNING 0 /* set to 1 for Dynamic Learning */
#include <errno.h>
#include <fcntl.h>
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <unistd.h>
#include <sys/stat.h>
#include <sys/types.h>
#include "backprop.h"
static void bkp_setup_all(bkp_network_t *n);
static void bkp_forward(bkp_network_t *n);
static void bkp_backward(bkp_network_t *n);
/* The following sigmoid returns values from 0.0 to 1.0 */
#define sigmoid(x) (1.0 / (1.0 + (float)exp(-(double)(x))))
#define sigmoidDerivative(x) ((float)(x)*(1.0-(x)))
/* random() for -1 to +1 */
#define random() ((((float)rand()/(RAND_MAX)) * 2.0) - 1.0)
/* random() for -0.5 to +0.5
#define random() (((float)rand()/(RAND_MAX)) - 0.5)
*/
/*
* bkp_create_network - Create a new network with the given configuration.
* Returns a pointer to the new network in 'n'.
*
* Return Value:
* int 0: Success
* -1: Error, errno is set to:
* ENOMEM - out of memory
* EINVAL - config.Type is one which this server does not handle
*/
int bkp_create_network(bkp_network_t **n, bkp_config_t *config)
{
if (config->Type != BACKPROP_TYPE_NORMAL) {
errno = EINVAL;
return -1;
}
if ((*n = (bkp_network_t *) malloc(sizeof(bkp_network_t))) == NULL) {
errno = ENOMEM;
return -1;
}
(*n)->NumInputs = config->NumInputs;
(*n)->NumHidden = config->NumHidden;
(*n)->NumOutputs = config->NumOutputs;
(*n)->NumConsecConverged = 0;
(*n)->Epoch = (*n)->LastRMSError = (*n)->RMSSquareOfOutputBetas = 0.0;
(*n)->NumBias = 1;
if (config->StepSize == 0)
(*n)->StepSize = 0.5;
else
(*n)->StepSize = config->StepSize;
#if CMDIFFSTEPSIZE
(*n)->HStepSize = 0.1 * (*n)->StepSize;
#endif
if (config->Momentum == -1)
(*n)->Momentum = 0.5;
else
(*n)->Momentum = config->Momentum;
(*n)->Cost = config->Cost;
if (((*n)->GivenInputVals = (float *) malloc((*n)->NumInputs * sizeof(float))) == NULL)
goto memerrorout;
if (((*n)->GivenDesiredOutputVals = (float *) malloc((*n)->NumOutputs * sizeof(float))) == NULL)
goto memerrorout;
if (((*n)->IHWeights = (float *) malloc((*n)->NumInputs * (*n)->NumHidden * sizeof(float))) == NULL)
goto memerrorout;
if (((*n)->PrevDeltaIH = (float *) malloc((*n)->NumInputs * (*n)->NumHidden * sizeof(float))) == NULL)
goto memerrorout;
if (((*n)->PrevDeltaHO = (float *) malloc((*n)->NumHidden * (*n)->NumOutputs * sizeof(float))) == NULL)
goto memerrorout;
if (((*n)->PrevDeltaBH = (float *) malloc((*n)->NumBias * (*n)->NumHidden * sizeof(float))) == NULL)
goto memerrorout;
if (((*n)->PrevDeltaBO = (float *) malloc((*n)->NumBias * (*n)->NumOutputs * sizeof(float))) == NULL)
goto memerrorout;
if (((*n)->HiddenVals = (float *) malloc((*n)->NumHidden * sizeof(float))) == NULL)
goto memerrorout;
if (((*n)->HiddenBetas = (float *) malloc((*n)->NumHidden * sizeof(float))) == NULL)
goto memerrorout;
if (((*n)->HOWeights = (float *) malloc((*n)->NumHidden * (*n)->NumOutputs * sizeof(float))) == NULL)
goto memerrorout;
if (((*n)->BiasVals = (float *) malloc((*n)->NumBias * sizeof(float))) == NULL)
goto memerrorout;
if (((*n)->BHWeights = (float *) malloc((*n)->NumBias * (*n)->NumHidden * sizeof(float))) == NULL)
goto memerrorout;
if (((*n)->BOWeights = (float *) malloc((*n)->NumBias * (*n)->NumOutputs * sizeof(float))) == NULL)
goto memerrorout;
if (((*n)->OutputVals = (float *) malloc((*n)->NumOutputs * sizeof(float))) == NULL)
goto memerrorout;
if (((*n)->OutputBetas = (float *) malloc((*n)->NumOutputs * sizeof(float))) == NULL)
goto memerrorout;
bkp_setup_all(*n);
return 0;
memerrorout:
bkp_destroy_network(*n);
errno = ENOMEM;
return -1;
}
/*
* bkp_destroy_network - Frees up any resources allocated for the
* given neural network.
*
* Return Values:
* (none)
*/
void bkp_destroy_network(bkp_network_t *n)
{
if (n == NULL)
return;
if (n->GivenInputVals == NULL) return;
bkp_clear_training_set(n);
free(n->GivenInputVals);
if (n->GivenDesiredOutputVals != NULL) { free(n->GivenDesiredOutputVals); n->GivenDesiredOutputVals = NULL; }
if (n->IHWeights != NULL) { free(n->IHWeights); n->IHWeights = NULL; }
if (n->PrevDeltaIH != NULL) { free(n->PrevDeltaIH); n->PrevDeltaIH = NULL; }
if (n->PrevDeltaHO != NULL) { free(n->PrevDeltaHO); n->PrevDeltaHO = NULL; }
if (n->PrevDeltaBH != NULL) { free(n->PrevDeltaBH); n->PrevDeltaBH = NULL; }
if (n->PrevDeltaBO != NULL) { free(n->PrevDeltaBO); n->PrevDeltaBO = NULL; }
if (n->HiddenVals != NULL) { free(n->HiddenVals); n->HiddenVals = NULL; }
if (n->HiddenBetas != NULL) { free(n->HiddenBetas); n->HiddenBetas = NULL; }
if (n->HOWeights != NULL) { free(n->HOWeights); n->HOWeights = NULL; }
if (n->BiasVals != NULL) { free(n->BiasVals); n->BiasVals = NULL; }
if (n->BHWeights != NULL) { free(n->BHWeights); n->BHWeights = NULL; }
if (n->BOWeights != NULL) { free(n->BOWeights); n->BOWeights = NULL; }
if (n->OutputVals != NULL) { free(n->OutputVals); n->OutputVals = NULL; }
if (n->OutputBetas != NULL) { free(n->OutputBetas); n->OutputBetas = NULL; }
n->GivenInputVals = NULL;
free(n);
}
/*
* bkp_set_training_set - Gives addresses of the input and target data
* in the form of a input values and output values. No data is copied
* so do not destroy the originals until you call
* bkp_clear_training_set(), or bkp_destroy_network().
*
* Return Values:
* int 0: Success
* -1: Error, errno is:
* ENOENT if no bkp_create_network() has been done yet.
*/
int bkp_set_training_set(bkp_network_t *n, int ntrainset, float *tinputvals, float *targetvals)
{
if (!n) {
errno = ENOENT;
return -1;
}
bkp_clear_training_set(n);
n->NumInTrainSet = ntrainset;
n->TrainSetInputVals = tinputvals;
n->TrainSetDesiredOutputVals = targetvals;
return 0;
}
/*
* bkp_clear_training_set - Invalidates the training set such that
* you can no longer use it for training. After this you can free
* up any memory associated with the training data you'd passed to
* bkp_set_training_set(). It has not been modified in any way.
*
* Return Values:
* (none)
*/
void bkp_clear_training_set(bkp_network_t *n)
{
if (n->NumInTrainSet > 0) {
n->TrainSetInputVals = NULL;
n->TrainSetDesiredOutputVals = NULL;
n->NumInTrainSet = 0;
}
}
static void bkp_setup_all(bkp_network_t *n)
{
int i, h, o, b;
n->InputReady = n->DesiredOutputReady = n->Learned = 0;
n->LearningError = 0.0;
for (i = 0; i < n->NumInputs; i++)
n->GivenInputVals[i] = 0.0;
for(h = 0; h < n->NumHidden; h++) {
n->HiddenVals[h] = 0.0;
for (i = 0; i < n->NumInputs; i++) {
n->IHWeights[i+(h*n->NumInputs)] = random();
n->PrevDeltaIH[i+(h*n->NumInputs)] = 0.0;
}
for (b = 0; b < n->NumBias; b++) {
n->BHWeights[b+(h*n->NumBias)] = random();
n->PrevDeltaBH[b+(h*n->NumBias)] = 0.0;
}
}
for(o = 0; o < n->NumOutputs; o++) {
n->OutputVals[o] = 0.0;
for (h = 0; h < n->NumHidden; h++) {
n->HOWeights[h+(o*n->NumHidden)] = random();
n->PrevDeltaHO[h+(o*n->NumHidden)] = 0.0;
}
for (b = 0; b < n->NumBias; b++) {
n->BOWeights[b+(o*n->NumBias)] = random();
n->PrevDeltaBO[b+(o*n->NumBias)] = 0.0;
}
}
for (b = 0; b < n->NumBias; b++)
n->BiasVals[b] = 1.0;
}
/*
* bkp_learn - Tells backprop to learn the current training set ntimes.
* The training set must already have been set by calling
* bkp_set_training_set(). This does not return until the training
* has been completed. You can call bkp_query() after this to find out
* the results of the learning.
*
* Return Values:
* int 0: Success
* -1: Error, errno is:
* ENOENT if no bkp_create_network() has been done yet.
* ESRCH if no bkp_set_training_set() has been done yet.
*/
int bkp_learn(bkp_network_t *n, int ntimes)
{
int item, run;
if (!n) {
errno = ENOENT;
return -1;
}
if (n->NumInTrainSet == 0) {
errno = ESRCH;
return -1;
}
for (run = 0; run < ntimes; run++) {
for (item = 0; item < n->NumInTrainSet; item++) {
/* set up for the given set item */
n->InputVals = &(n->TrainSetInputVals[item*n->NumInputs]);
n->DesiredOutputVals = &(n->TrainSetDesiredOutputVals[item*n->NumOutputs]);
/* now do the learning */
bkp_forward(n);
bkp_backward(n);
}
/* now that we have gone through the entire training set, calculate the
RMS to see how well we have learned */
n->Epoch++;
/* calculate the RMS error for the epoch just completed */
n->LastRMSError = sqrt(n->RMSSquareOfOutputBetas / (n->NumInTrainSet * n->NumOutputs));
n->RMSSquareOfOutputBetas = 0.0;
#if DYNAMIC_LEARNING
if (n->Epoch > 1) {
if (n->PrevRMSError < n->LastRMSError) {
/* diverging */
n->NumConsecConverged = 0;
n->StepSize *= 0.95; /* make step size smaller */
#if CMDIFFSTEPSIZE
n->HStepSize = 0.1 * n->StepSize;
#endif
#ifdef DISPLAYMSGS
printf("Epoch: %d Diverging: Prev %f, New %f, Step size %f\n",
n->Epoch, n->PrevRMSError, n->LastRMSError, n->StepSize);
#endif
} else if (n->PrevRMSError > n->LastRMSError) {
/* converging */
n->NumConsecConverged++;
if (n->NumConsecConverged == 5) {
n->StepSize += 0.04; /* make step size bigger */
#if CMDIFFSTEPSIZE
n->HStepSize = 0.1 * n->StepSize;
#endif
#ifdef DISPLAYMSGS
printf("Epoch: %d Converging: Prev %f, New %f, Step size %f\n",
n->Epoch, n->PrevRMSError, n->LastRMSError, n->StepSize);
#endif
n->NumConsecConverged = 0;
}
} else {
n->NumConsecConverged = 0;
}
}
n->PrevRMSError = n->LastRMSError;
#endif
}
n->Learned = 1;
return 0;
}
/*
* bkp_evaluate - Evaluate but don't learn the current input set.
* This is usually preceded by a call to bkp_set_input() and is
* typically called after the training set (epoch) has been learned.
*
* If you give eoutputvals as NULL then you can do a bkp_query() to
* get the results.
*
* If you give the address of a buffer to return the results of the
* evaluation (eoutputvals != NULL) then the results will copied to the
* eoutputvals buffer.
*
* Return Values:
* int 0: Success
* -1: Error, errno is:
* ENOENT if no bkp_create_network() has been done yet.
* ESRCH if no bkp_set_input() has been done yet.
* ENODEV if both bkp_create_network() and bkp_set_input()
* have been done but bkp_earn() has not been done
* yet (ie; neural net has not had any training).
* EINVAL if sizeofoutputvals is not the same as the
* size understood according to n. This is to help
* prevent buffer overflow during copying.
*/
int bkp_evaluate(bkp_network_t *n, float *eoutputvals, int sizeofoutputvals)
{
if (!n) {
errno = ENOENT;
return -1;
}
if (!n->InputReady) {
errno = ESRCH;
return -1;
}
if (!n->Learned) {
errno = ENODEV;
return -1;
}
n->InputVals = n->GivenInputVals;
n->DesiredOutputVals = n->GivenDesiredOutputVals;
bkp_forward(n);
if (eoutputvals) {
if (sizeofoutputvals != n->NumOutputs*sizeof(float)) {
errno = EINVAL;
return -1;
}
memcpy(eoutputvals, n->OutputVals, n->NumOutputs*sizeof(float));
}
return 0;
}
/*
* bkp_forward - This makes a pass from the input units to the hidden
* units to the output units, updating the hidden units, output units and
* other components. This is how the neural network is run in order to
* evaluate a set of input values to get output values.
* When training the neural network, this is the first step in the
* backpropagation algorithm.
*/
static void bkp_forward(bkp_network_t *n)
{
int i, h, o, b;
n->LearningError = 0.0;
/*
* Apply input unit values to weights between input units and hidden units
* Apply bias unit values to weights between bias units and hidden units
*/
for (h = 0; h < n->NumHidden; h++) {
n->HiddenVals[h] = 0.0;
n->HiddenBetas[h] = 0.0; /* needed if doing a backward pass later */
for (i = 0; i < n->NumInputs; i++)
n->HiddenVals[h] = n->HiddenVals[h] + (n->InputVals[i] * n->IHWeights[i+(h*n->NumInputs)]);
for (b = 0; b < n->NumBias; b++)
n->HiddenVals[h] = n->HiddenVals[h] + (n->BiasVals[b] * n->BHWeights[b+(h*n->NumBias)]);
n->HiddenVals[h] = sigmoid(n->HiddenVals[h]);
}
/*
* Apply hidden unit values to weights between hidden units and outputs
* Apply bias unit values to weights between bias units and outputs
*/
for (o = 0; o < n->NumOutputs; o++) {
n->OutputVals[o] = 0.0;
for (h = 0; h < n->NumHidden; h++)
n->OutputVals[o] = n->OutputVals[o] + (n->HiddenVals[h] * n->HOWeights[h+(o*n->NumHidden)]);
for (b = 0; b < n->NumBias; b++)
n->OutputVals[o] = n->OutputVals[o] + (n->BiasVals[b] * n->BOWeights[b+(o*n->NumBias)]);
n->OutputVals[o] = sigmoid(n->OutputVals[o]);
n->LearningError = n->LearningError +
((n->OutputVals[o] - n->DesiredOutputVals[o]) * (n->OutputVals[o] - n->DesiredOutputVals[o]));
}
n->LearningError = n->LearningError / 2.0;
}
/*
* bkp_backward - This is the 2nd half of the backpropagation algorithm
* which is carried out immediately after bkp_forward() has done its
* step of calculating the outputs. This does the reverse, comparing
* those output values to those given as targets in the training set
* and updating the weights and other components appropriately, which
* is essentially the training of the neural network.
*/
static void bkp_backward(bkp_network_t *n)
{
float deltaweight;
int i, h, o, b;
for (o = 0; o < n->NumOutputs; o++) {
/* calculate beta for output units */
n->OutputBetas[o] = n->DesiredOutputVals[o] - n->OutputVals[o];
/* update for RMS error */
n->RMSSquareOfOutputBetas += (n->OutputBetas[o] * n->OutputBetas[o]);
/* update hidden to output weights */
for (h = 0; h < n->NumHidden; h++) {
/* calculate beta for hidden units for later */
n->HiddenBetas[h] = n->HiddenBetas[h] +
(n->HOWeights[h+(o*n->NumHidden)] * sigmoidDerivative(n->OutputVals[o]) * n->OutputBetas[o]);
#if CMDIFFSTEPSIZE
deltaweight = n->HiddenVals[h] * n->OutputBetas[o];
#else
deltaweight = n->HiddenVals[h] * n->OutputBetas[o] *
sigmoidDerivative(n->OutputVals[o]);
#endif
n->HOWeights[h+(o*n->NumHidden)] = n->HOWeights[h+(o*n->NumHidden)] +
(n->StepSize * deltaweight) +
(n->Momentum * n->PrevDeltaHO[h+(o*n->NumHidden)]);
n->PrevDeltaHO[h+(o*n->NumHidden)] = deltaweight;
}
/* update bias to output weights */
for (b = 0; b < n->NumBias; b++) {
#if CMDIFFSTEPSIZE
deltaweight = n->BiasVals[b] * n->OutputBetas[o];
#else
deltaweight = n->BiasVals[b] * n->OutputBetas[o] +
sigmoidDerivative(n->OutputVals[o]);
#endif
n->BOWeights[b+(o*n->NumBias)] = n->BOWeights[b+(o*n->NumBias)] +
(n->StepSize * deltaweight) +
(n->Momentum * n->PrevDeltaBO[b+(o*n->NumBias)]);
n->PrevDeltaBO[b+(o*n->NumBias)] = deltaweight;
}
}
for (h = 0; h < n->NumHidden; h++) {
/* update input to hidden weights */
for (i = 0; i < n->NumInputs; i++) {
deltaweight = n->InputVals[i] * sigmoidDerivative(n->HiddenVals[h]) *
n->HiddenBetas[h];
n->IHWeights[i+(h*n->NumInputs)] = n->IHWeights[i+(h*n->NumInputs)] +
#if CMDIFFSTEPSIZE
(n->HStepSize * deltaweight) +
#else
(n->StepSize * deltaweight) +
#endif
(n->Momentum * n->PrevDeltaIH[i+(h*n->NumInputs)]);
n->PrevDeltaIH[i+(h*n->NumInputs)] = deltaweight;
if (n->Cost)
n->IHWeights[i+(h*n->NumInputs)] = n->IHWeights[i+(h*n->NumInputs)] -
(n->Cost * n->IHWeights[i+(h*n->NumInputs)]);
}
/* update bias to hidden weights */
for (b = 0; b < n->NumBias; b++) {
deltaweight = n->BiasVals[b] * n->HiddenBetas[h] *
sigmoidDerivative(n->HiddenVals[h]);
n->BHWeights[b+(h*n->NumBias)] = n->BHWeights[b+(h*n->NumBias)] +
#if CMDIFFSTEPSIZE
(n->HStepSize * deltaweight) +
#else
(n->StepSize * deltaweight) +
#endif
(n->Momentum * n->PrevDeltaBH[b+(h*n->NumBias)]);
n->PrevDeltaBH[b+(h*n->NumBias)] = deltaweight;
if (n->Cost)
n->BHWeights[b+(h*n->NumBias)] = n->BHWeights[b+(h*n->NumBias)] -
(n->Cost * n->BHWeights[b+(h*n->NumBias)]);
}
}
}
/*
* bkp_query - Get the current state of the neural network.
*
* Parameters (all parameters return information unless given as NULL):
* float *qlastlearningerror: The error for the last set of inputs
* and outputs learned by bkp_learn()
* or evaluated by bkp_evaluate().
* It is the sum of the squares
* of the difference between the actual
* outputs and the target or desired outputs,
* all divided by 2
* float *qlastrmserror: The RMS error for the last epoch learned
* i.e. the learning of the training set.
* float *qinputvals: An array to fill with the current input
* values (must be at least
* bkp_config_t.NumInputs * sizeof(float))
* float *qihweights: An array to fill with the current input
* units to hidden units weights (must be at
* least bkp_config_t.NumInputs *
* bkp_config_t.NumHidden * sizeof(float)
* float *qhiddenvals: An array to fill with the current hidden
* unit values (must be at least
* bkp_config_t.NumHidden * sizeof(float))
* float *qhoweights: An array to fill with the current hidden
* units to output units weights (must be at
* least bkp_config_t.NumHidden *
* bkp_config_t.NumOutputs * sizeof(float))
* float *qoutputvals: An array to fill with the current output
* values (must be at least
* bkp_config_t.NumOutputs * sizeof(float))
* Note that for the following three, the size required is 1 * ...
* The reason for the 1 is because there is only one bias unit for
* everything. Theoretically there could be more though.
* float *qbhweights: An array to fill with the current bias
* units to hidden units weights (must be at
* least 1 * bkp_config_t->NumHidden *
* sizeof(float))
* float *qbiasvals: An array to fill with the current bias
* values (must be at least 1 * sizeof(float))
* float *qboweights: An array to fill with the current bias
* units to output units weights (must be at
* least 1 * (*n)->NumOutputs * sizeof(float))
*
* Return Values:
* int 0: Success
* -1: Error, errno is:
* ENOENT if no bkp_create_network() has been done yet.
* ENODEV if bkp_create_network() has been done
* but bkp_learn() has not been done yet (ie; neural
* net has not had any training).
*/
int bkp_query(bkp_network_t *n,
float *qlastlearningerror, float *qlastrmserror,
float *qinputvals, float *qihweights, float *qhiddenvals,
float *qhoweights, float *qoutputvals,
float *qbhweights, float *qbiasvals, float *qboweights)
{
if (!n) {
errno = ENOENT;
return -1;
}
if (!n->Learned) {
errno = ENODEV;
return -1;
}
if (qlastlearningerror)
*qlastlearningerror = n->LearningError;
if (qlastrmserror)
*qlastrmserror = n->LastRMSError;
if (qinputvals)
memcpy(qinputvals, n->InputVals, n->NumInputs*sizeof(float));
if (qihweights)
memcpy(qihweights, n->IHWeights, (n->NumInputs*n->NumHidden)*sizeof(float));
if (qhiddenvals)
memcpy(qhiddenvals, n->HiddenVals, n->NumHidden*sizeof(float));
if (qhoweights)
memcpy(qhoweights, n->HOWeights, (n->NumHidden*n->NumOutputs)*sizeof(float));
if (qoutputvals)
memcpy(qoutputvals, n->OutputVals, n->NumOutputs*sizeof(float));
if (qbhweights)
memcpy(qbhweights, n->BHWeights, n->NumBias*n->NumHidden*sizeof(float));
if (qbiasvals)
memcpy(qbiasvals, n->BiasVals, n->NumBias*sizeof(float));
if (qboweights)
memcpy(qboweights, n->BOWeights, n->NumBias*n->NumOutputs*sizeof(float));
return 0;
}
/*
* bkp_set_input - Use this to set the current input values of the neural
* network. Nothing is done with the values until bkp_learn() is called.
*
* Parameters:
* int setall: If 1: Set all inputs to Val. Any sinputvals are ignored so
* you may as well give sinputvals as NULL.
* float val: See SetAll.
* float sinputvals: An array of input values. The array should contain
* bkp_config_t.NumInputs elements.
*
* Return Values:
* int 0: Success
* -1: Error, errno is:
* ENOENT if no bkp_create_network() has been done yet.
*/
int bkp_set_input(bkp_network_t *n, int setall, float val, float *sinputvals)
{
int i;
if (!n) {
errno = ENOENT;
return -1;
}
if (setall) {
for (i = 0; i < n->NumInputs; i++)
n->GivenInputVals[i] = val;
} else {
memcpy(n->GivenInputVals, sinputvals, n->NumInputs*sizeof(float));
}
n->InputReady = 1;
return 0;
}
/*
* bkp_set_output - Use this so that bkp_evaluate() can calculate the
* error between the output values you passs to bkp_set_output() and
* the output it gets by evaulating the network using the input values
* you passed to the last call to bkp_set_input(). The purpose is so
* that you can find out what that error is using bkp_query()'s
* qlastlearningerror argument. Typically bkp_set_output() will have been
* accompanied by a call to bkp_set_input().
*
* Parameters:
* int setall: If 1: Set all outputs to val. Any soutputvals
* are ignored so you may as well give
* soutputvals as NULL.
* If 0: val is ignored. You must provide soutputvals.
* float val: See setall.
* float sonputvals: An array of input values. The array should contain
* bkp_config_t.NumInputs elements.
*
* Return Values:
* int 0: Success
* -1: Error, errno is:
* ENOENT if no bkp_create_network() has been done yet.
*/
int bkp_set_output(bkp_network_t *n, int setall, float val, float *soutputvals)
{
int i;
if (!n) {
errno = ENOENT;
return -1;
}
if (setall) {
for (i = 0; i < n->NumOutputs; i++)
n->GivenDesiredOutputVals[i] = val;
} else {
memcpy(n->GivenDesiredOutputVals, soutputvals, n->NumOutputs*sizeof(float));
}
n->DesiredOutputReady = 1;
return 0;
}
/*
* bkp_loadfromfile - Creates a neural network using the information
* loaded from the given file and returns a pointer to it in n.
* If successful, the end result of this will be a neural network
* for which bkp_create_network() will effectively have been done.
*
* Return Values:
* int 0: Success
* -1: Error, errno is:
* EOK or any applicable errors from the open() or read() functions.
* ENOMEM if no memory.
* EINVAL if the file is not in the correct format.
*/
int bkp_loadfromfile(bkp_network_t **n, char *fname)
{
char file_format;
int fd, returncode;
bkp_config_t config;
returncode = -1;
if ((fd = open(fname, O_RDONLY)) == -1)
return returncode;
if (read(fd, &file_format, sizeof(char)) == -1)
goto cleanupandret;
if (file_format != 'A') {
errno = EINVAL;
goto cleanupandret;
}
if (read(fd, &config.Type, sizeof(short)) == -1)
goto cleanupandret;
if (read(fd, &config.NumInputs, sizeof(int)) == -1)
goto cleanupandret;
if (read(fd, &config.NumHidden, sizeof(int)) == -1)
goto cleanupandret;
if (read(fd, &config.NumOutputs, sizeof(int)) == -1)
goto cleanupandret;
if (read(fd, &config.StepSize, sizeof(float)) == -1)
goto cleanupandret;
if (read(fd, &config.Momentum, sizeof(float)) == -1)
goto cleanupandret;
if (read(fd, &config.Cost, sizeof(float)) == -1)
goto cleanupandret;
if (bkp_create_network(n, &config) == -1) {
goto cleanupandret;
}
(*n)->InputVals = (*n)->GivenInputVals;
(*n)->DesiredOutputVals = (*n)->GivenDesiredOutputVals;
if (read(fd, (int *) &(*n)->NumBias, sizeof(int)) == -1)
goto errandret;
if (read(fd, (int *) &(*n)->InputReady, sizeof(int)) == -1)
goto errandret;
if (read(fd, (int *) &(*n)->DesiredOutputReady, sizeof(int)) == -1)
goto errandret;
if (read(fd, (int *) &(*n)->Learned, sizeof(int)) == -1)
goto errandret;
if (read(fd, (*n)->InputVals, (*n)->NumInputs * sizeof(float)) == -1)
goto errandret;
if (read(fd, (*n)->DesiredOutputVals, (*n)->NumOutputs * sizeof(float)) == -1)
goto errandret;
if (read(fd, (*n)->IHWeights, (*n)->NumInputs * (*n)->NumHidden * sizeof(float)) == -1)
goto errandret;
if (read(fd, (*n)->PrevDeltaIH, (*n)->NumInputs * (*n)->NumHidden * sizeof(float)) == -1)
goto errandret;
if (read(fd, (*n)->PrevDeltaHO, (*n)->NumHidden * (*n)->NumOutputs * sizeof(float)) == -1)
goto errandret;
if (read(fd, (*n)->PrevDeltaBH, (*n)->NumBias * (*n)->NumHidden * sizeof(float)) == -1)
goto errandret;
if (read(fd, (*n)->PrevDeltaBO, (*n)->NumBias * (*n)->NumOutputs * sizeof(float)) == -1)
goto errandret;
if (read(fd, (*n)->HiddenVals, (*n)->NumHidden * sizeof(float)) == -1)
goto errandret;
if (read(fd, (*n)->HiddenBetas, (*n)->NumHidden * sizeof(float)) == -1)
goto errandret;
if (read(fd, (*n)->HOWeights, (*n)->NumHidden * (*n)->NumOutputs * sizeof(float)) == -1)
goto errandret;
if (read(fd, (*n)->BiasVals, (*n)->NumBias * sizeof(float)) == -1)
goto errandret;
if (read(fd, (*n)->BHWeights, (*n)->NumBias * (*n)->NumHidden * sizeof(float)) == -1)
goto errandret;
if (read(fd, (*n)->BOWeights, (*n)->NumBias * (*n)->NumOutputs * sizeof(float)) == -1)
goto errandret;
if (read(fd, (*n)->OutputVals, (*n)->NumOutputs * sizeof(float)) == -1)
goto errandret;
if (read(fd, (*n)->OutputBetas, (*n)->NumOutputs * sizeof(float)) == -1)
goto errandret;
returncode = 0;
goto cleanupandret;
errandret:
bkp_destroy_network(*n);
cleanupandret:
close(fd);
return returncode;
}
/*
* bkp_savetofile
*
* The format of the file is:
*
* 1. File format version e.g. 'A' (sizeof(char))
* 2. Network type BACKPROP_TYPE_* (sizeof(short))
* 3. Number of inputs (sizeof(int))
* 4. Number of hidden units (sizeof(int))
* 5. Number of outputs (sizeof(int))
* 6. StepSize (sizeof(float))
* 7. Momentum (sizeof(float))
* 8. Cost (sizeof(float))
* 9. Number of bias units (sizeof(int))
* 10. Is input ready? 0 = no, 1 = yes (sizeof(int))
* 11. Is desired output ready? 0 = no, 1 = yes (sizeof(int))
* 12. Has some learning been done? 0 = no, 1 = yes (sizeof(int))
* 13. Current input values (InputVals) (NumInputs * sizeof(float))
* 14. Current desired output values (DesiredOutputVals) (NumOutputs * sizeof(float))
* 15. Current input-hidden weights (IHWeights) (NumInputs * NumHidden * sizeof(float))
* 16. Previous input-hidden weight deltas (PrevDeltaIH) (NumInputs * NumHidden * sizeof(float))
* 17. Previous output-hidden weight deltas (PrevDeltaHO) (NumHidden * NumOutputs * sizeof(float))
* 18. Previous bias-hidden weight deltas (PrevDeltaBH) (NumBias * NumHidden * sizeof(float))
* 19. Previous bias-output weight deltas (PrevDeltaBO) (NumBias * NumOutputs * sizeof(float))
* 20. Current hidden unit values (HiddenVals) (NumHidden * sizeof(float))
* 21. Current hidden unit beta values (HiddenBetas) (NumHidden * sizeof(float))
* 22. Current hidden-output weights (HOWeights) (NumHidden * NumOutputs * sizeof(float))
* 23. Current bias unit values (BiasVals) (NumBias * sizeof(float))
* 24. Current bias-hidden weights (BHWeights) (NumBias * NumHidden * sizeof(float))
* 25. Current bias-output weights (BOWeights) (NumBias * NumOutputs * sizeof(float))
* 26. Current output values (OutputVals) (NumOutputs * sizeof(float))
* 27. Current output unit betas (OutputBetas) (NumOutputs * sizeof(float))
*
* Return Values:
* int 0: Success
* -1: Error, errno is:
* ENOENT if no bkp_create_network() has been done yet.
* EOK or any applicable errors from the open() or write()
* functions.
*/
int bkp_savetofile(bkp_network_t *n, char *fname)
{
int fd, returncode;
short type = BACKPROP_TYPE_NORMAL;
returncode = -1;
fd = open(fname, O_WRONLY | O_CREAT | O_TRUNC,
S_IRUSR | S_IWUSR);
// For Unix/Linux-like environments the following can also be used
// | S_IRGRP | S_IWGRP | S_IROTH | S_IWOTH);
if (fd == -1)
return returncode;
if (write(fd, (char *) "A", sizeof(char)) == -1) // file format version A
goto cleanupandret;
if (write(fd, (short *) &type, sizeof(short)) == -1) // BACKPROP_TYPE_*
goto cleanupandret;
if (write(fd, (int *) &n->NumInputs, sizeof(int)) == -1)
goto cleanupandret;
if (write(fd, (int *) &n->NumHidden, sizeof(int)) == -1)
goto cleanupandret;
if (write(fd, (int *) &n->NumOutputs, sizeof(int)) == -1)
goto cleanupandret;
if (write(fd, (float *) &n->StepSize, sizeof(float)) == -1)
goto cleanupandret;
if (write(fd, (float *) &n->Momentum, sizeof(float)) == -1)
goto cleanupandret;
if (write(fd, (float *) &n->Cost, sizeof(float)) == -1)
goto cleanupandret;
if (write(fd, (int *) &n->NumBias, sizeof(int)) == -1)
goto cleanupandret;
if (write(fd, (int *) &n->InputReady, sizeof(int)) == -1)
goto cleanupandret;
if (write(fd, (int *) &n->DesiredOutputReady, sizeof(int)) == -1)
goto cleanupandret;
if (write(fd, (int *) &n->Learned, sizeof(int)) == -1)
goto cleanupandret;
if (write(fd, n->InputVals, n->NumInputs * sizeof(float)) == -1)
goto cleanupandret;
if (write(fd, n->DesiredOutputVals, n->NumOutputs * sizeof(float)) == -1)
goto cleanupandret;
if (write(fd, n->IHWeights, n->NumInputs * n->NumHidden * sizeof(float)) == -1)
goto cleanupandret;
if (write(fd, n->PrevDeltaIH, n->NumInputs * n->NumHidden * sizeof(float)) == -1)
goto cleanupandret;
if (write(fd, n->PrevDeltaHO, n->NumHidden * n->NumOutputs * sizeof(float)) == -1)
goto cleanupandret;
if (write(fd, n->PrevDeltaBH, n->NumBias * n->NumHidden * sizeof(float)) == -1)
goto cleanupandret;
if (write(fd, n->PrevDeltaBO, n->NumBias * n->NumOutputs * sizeof(float)) == -1)
goto cleanupandret;
if (write(fd, n->HiddenVals, n->NumHidden * sizeof(float)) == -1)
goto cleanupandret;
if (write(fd, n->HiddenBetas, n->NumHidden * sizeof(float)) == -1)
goto cleanupandret;
if (write(fd, n->HOWeights, n->NumHidden * n->NumOutputs * sizeof(float)) == -1)
goto cleanupandret;
if (write(fd, n->BiasVals, n->NumBias * sizeof(float)) == -1)
goto cleanupandret;
if (write(fd, n->BHWeights, n->NumBias * n->NumHidden * sizeof(float)) == -1)
goto cleanupandret;
if (write(fd, n->BOWeights, n->NumBias * n->NumOutputs * sizeof(float)) == -1)
goto cleanupandret;
if (write(fd, n->OutputVals, n->NumOutputs * sizeof(float)) == -1)
goto cleanupandret;
if (write(fd, n->OutputBetas, n->NumOutputs * sizeof(float)) == -1)
goto cleanupandret;
returncode = 0;
cleanupandret:
close(fd);
return returncode;
}
