bool Neuron::init(const UINT numInputs,const Type activationFunction,Random &random,const Float minWeightRange,const Float maxWeightRange,const Float minBiasRange,const Float maxBiasRange){
if( !validateActivationFunction(activationFunction) ){
return false;
}
this->numInputs = numInputs;
this->activationFunction = activationFunction;
weights.resize(numInputs);
previousUpdate.resize(numInputs);
//Randomise the weights
//Note, it's better to set the random values using small weights rather than [-1.0 1.0]
for(unsigned int i=0; i<numInputs; i++){
weights[i] = random.getUniform(minWeightRange,maxWeightRange);
previousUpdate[i] = 0;
}
//Randomise the bias
bias = random.getUniform(minBiasRange,maxBiasRange);
previousBiasUpdate = 0;
return true;
}
bool Neuron::init(const UINT numInputs,const UINT activationFunction){
if( !validateActivationFunction(activationFunction) ){
return false;
}
this->numInputs = numInputs;
this->activationFunction = activationFunction;
weights.resize(numInputs);
previousUpdate.resize(numInputs);
//Set the random seed
Random random;
random.setSeed( (unsigned long long)time(NULL) );
//Randomise the weights between [-0.1 0.1]
//Note, it's better to set the random values using small weights rather than [-1.0 1.0]
for(unsigned int i=0; i<numInputs; i++){
weights[i] = random.getRandomNumberUniform(-0.1,0.1);
previousUpdate[i] = 0;
}
//Randomise the bias between [-0.1 0.1]
bias = random.getRandomNumberUniform(-0.1,0.1);
return true;
}
