I'm writing my own implementation of a Neural Network class in C++. I'm not sure how to refer to the weights this statement:
in = in + (inputs [l] * calcWeights [l]) ;
The reason is because there could be more weights than inputs. Here is my code:
void Train (int numInputs, int numOutputs, double inputs [], double outputs []) {
// Set the Random Seed:
srand (time (0)) ;
// Weights (n input(s) * n output(s) = n weight branch(es)):
double calcWeights [numInputs * numOutputs] ;
// Errors (n input(s) * n output(s) = n error branch(es)):
double errors [numInputs * numOutputs] ;
// Set the Weights to random:
for (int j = 0 ; j < numInputs ; j = j + 1) {
calcWeights [j] = ((-1 * numInputs) + (((double) rand ()) % (1 * numInputs))) ;
}
// Train:
int i = 0 ;
double in = 0 ;
double out [numOutputs] ;
while (i < 14999) {
// Get the estimated output:
for (int k = 0 ; k < numOutputs ; k = k + 1) {
for (int l = 0 ; l < numInputs ; l = l + 1) {
in = in + (inputs [l] * calcWeights [l]) ;
}
out [k] = in + GetBias () ;
}
for (int m = 0 ; m < numOutputs ; m = m + 1) {
error [m] = outputs [m] - out [m]
}
// Increment the iterator:
i = i + 1 ;
}
}
From your clarification in comments, I believe modifying your loop a bit will give you what you want.
for (int k = 0 ; k < numOutputs ; k = k + 1) {
in = 0; //Reset in to 0 at the beginning of each output loop
for (int l = 0 ; l < numInputs ; l = l + 1) {
in = in + (inputs [l] * calcWeights [l + k*numInputs]) ;
}
out [k] = in + GetBias () ;
}
You should also make sure you initialize all the weights above.
for (int j = 0 ; j < (numInputs * numOutputs) ; j = j + 1) {
calcWeights [j] = ((-1 * numInputs) + (((double) rand ()) % (1 * numInputs))) ;
}
For a couple of style choices I just want to point out that you can replace k = k + 1
with simply ++k
. Likewise you can replace in = in + ...;
with in += ...;