void backPropagation(double y, double rate_learn)
{
/* 出力層の誤差を計算 */
for(int i=0; i<n_out; i++){
if(i == y) error_out[i] = output_out[i] - 1.0;
else error_out[i] = output_out[i];
}
/* 隠れ層から出力層への重みを更新 */
for(int i=0; i<n_hid; i++){
for(int j=0; j<n_out; j++){
weight_out[i][j] -= rate_learn * error_out[j] * output_hid[i];
}
}
/* 隠れ層の誤差を計算 */
for(int i=0; i<n_hid; i++){
error_hid[i] = 0;
for(int j=0; j<n_out; j++){
error_hid[i] += weight_out[i][j] * error_out[j];
}
error_hid[i] *= d_sigmoid(input_hid[i]);
}
/* 入力層から隠れ層への重みを更新 */
for(int i=0; i<n_in; i++){
for(int j=0; j<n_hid; j++){
weight_hid[i][j] -= rate_learn * error_hid[j] * output_in[i];
}
}
}
void calculate_weight(Neuron* neuron) {
double post_synaptic_error = 0;
for(int i=0;i<10;i++) {
Dendrite* d = neuron->outgoing_list[i];
if(d) {
Neuron* n = d->target;
post_synaptic_error += (n->error * d->weight);
}
}
neuron->error = post_synaptic_error;
for(int i=0;i<10;i++) {
Dendrite* d = neuron->outgoing_list[i];
if(d) {
d->weight = (d->weight + (d->target->error * d_sigmoid(d->target->error) * neuron->output));
printf("Weight: %f\n", d->weight);
}
}
}
