class_methods_end
int FFNet_init (FFNet me, long numberOfInputs, long nodesInLayer1, long nodesInLayer2,
long numberOfOutputs, int outputsAreLinear)
{
long numberOfLayers = 3;
if (numberOfInputs < 1 || numberOfOutputs < 1) return 0;
if (nodesInLayer1 < 1) numberOfLayers--;
if (nodesInLayer2 < 1) numberOfLayers--;
my nLayers = numberOfLayers;
my nUnitsInLayer = NUMlvector (0, numberOfLayers);
if (my nUnitsInLayer == NULL) return 0;
my nUnitsInLayer[numberOfLayers--] = numberOfOutputs;
if (nodesInLayer2 > 0) my nUnitsInLayer[numberOfLayers--] = nodesInLayer2;
if (nodesInLayer1 > 0) my nUnitsInLayer[numberOfLayers--] = nodesInLayer1;
my nUnitsInLayer[numberOfLayers] = numberOfInputs;
Melder_assert (numberOfLayers == 0);
my outputsAreLinear = outputsAreLinear;
if (! bookkeeping (me)) return 0;
FFNet_setCostFunction (me, FFNet_COST_MSE);
FFNet_setNonLinearity (me, FFNet_NONLIN_SIGMOID);
FFNet_reset (me, 0.1);
return 1;
}
void FFNet_init (FFNet me, long numberOfInputs, long nodesInLayer1, long nodesInLayer2, long numberOfOutputs, int outputsAreLinear) {
long numberOfLayers = 3;
if (numberOfInputs < 1) {
Melder_throw (U"Number of inputs must be a natural number.");
}
if (numberOfOutputs < 1) {
Melder_throw (U"Number of outputs must be a natural number.");
}
if (nodesInLayer1 < 1) {
numberOfLayers--;
}
if (nodesInLayer2 < 1) {
numberOfLayers--;
}
my nLayers = numberOfLayers;
my nUnitsInLayer = NUMvector<long> (0, numberOfLayers);
my nUnitsInLayer[numberOfLayers--] = numberOfOutputs;
if (nodesInLayer2 > 0) {
my nUnitsInLayer[numberOfLayers--] = nodesInLayer2;
}
if (nodesInLayer1 > 0) {
my nUnitsInLayer[numberOfLayers--] = nodesInLayer1;
}
my nUnitsInLayer[numberOfLayers] = numberOfInputs;
Melder_assert (numberOfLayers == 0);
my outputsAreLinear = outputsAreLinear;
bookkeeping (me);
FFNet_setCostFunction (me, FFNet_COST_MSE);
FFNet_setNonLinearity (me, FFNet_NONLIN_SIGMOID);
FFNet_reset (me, 0.1);
}
double FFNet_Pattern_Activation_getCosts_total (FFNet me, Pattern p, Activation a, int costFunctionType) {
try {
_FFNet_Pattern_Activation_checkDimensions (me, p, a);
FFNet_setCostFunction (me, costFunctionType);
double cost = 0;
for (long i = 1; i <= p -> ny; i++) {
FFNet_propagate (me, p -> z[i], NULL);
cost += FFNet_computeError (me, a -> z[i]);
}
return cost;
} catch (MelderError) {
return NUMundefined;
}
}
static void _FFNet_Pattern_Activation_learn (FFNet me, Pattern pattern,
Activation activation, long maxNumOfEpochs, double tolerance,
Any parameters, int costFunctionType, int reset) {
try {
_FFNet_Pattern_Activation_checkDimensions (me, pattern, activation);
Minimizer_setParameters (my minimizer, parameters);
// Link the things to be learned
my nPatterns = pattern -> ny;
my inputPattern = pattern -> z;
my targetActivation = activation -> z;
FFNet_setCostFunction (me, costFunctionType);
if (reset) {
autoNUMvector<double> wbuf (1, my dimension);
long k = 1;
for (long i = 1; i <= my nWeights; i++) {
if (my wSelected[i]) {
wbuf[k++] = my w[i];
}
}
Minimizer_reset (my minimizer, wbuf.peek());
}
Minimizer_minimize (my minimizer, maxNumOfEpochs, tolerance, 1);
// Unlink
my nPatterns = 0;
my inputPattern = NULL;
my targetActivation = NULL;
} catch (MelderError) {
my nPatterns = 0;
my inputPattern = 0;
my targetActivation = 0;
}
}
