void BPN::ConstructDefault(int thrs) {
unsigned sizes[] = {262, 66, 256, 1};
outFunction functions[] = {sigmoid2, sigmoid2, sigmoid2, sigmoid2};
bool biases[] = {false, true, true, true};
constructor(sizes, biases, functions, 4, 0.35f, 0.3f, 29744.0f, thrs);
InitializeWeights();
}
void NetGraph::InitializeWeights() {
for (NetGraphNode* node : nodes_)
node->flag_bp_visited = false;
for (NetGraphNode* node : nodes_)
InitializeWeights(node);
for (NetGraphNode* node : nodes_)
node->flag_bp_visited = false;
}
void NetGraph::InitializeWeights(NetGraphNode* node) {
if (!node->flag_bp_visited) {
std::vector<Layer*> layers_to_connect;
for (NetGraphBackpropConnection backprop_connection : node->backprop_connections) {
InitializeWeights(backprop_connection.node);
layers_to_connect.push_back(backprop_connection.node->layer);
}
node->layer->OnLayerConnect(layers_to_connect);
node->flag_bp_visited = true;
}
}
void BPN::constructor(unsigned *sizes, bool *biases, outFunction *functions, unsigned layersNumber, double et, double alph, double o_factor, int thrs) {
pthread_mutex_init(&max_threads_mutex, NULL);
max_threads = thrs < 0 ? 0 : thrs;
minchunk = 64;
size = layersNumber;
alpha = alph;
eta = et;
scale_factor = o_factor;
initial_scale = 0.03f;
if(size > 0) {
layers = new bpnLayer*[size];
layers[0] = new bpnLayer(sizes[0], 0, false, functions[0], max_threads + 1); // input layer - no lower layer
unsigned i;
for(i=1; i<size; ++i) {
layers[i] = new bpnLayer(sizes[i], sizes[i-1], biases[i], functions[i], max_threads + 1);
}
unsigned lowerSize = layers[size-1]->size;
if(lowerSize >0) {
train_output = new double[lowerSize];
for(unsigned j=0; j < lowerSize; ++j) {
train_output[j] = 0.0f;
}
}
InitializeWeights();
if(size > 2) {
li_w = new LayerThread*[size -2];
for(i=0; i < size -2; ++i) {
li_w[i] = new LayerThread;
}
thread_ids_w = new pthread_t[size -2];
}
}
li = new LayerThread*[max_threads + 1];
for(int j=0; j <= max_threads; ++j) {
li[j] = new LayerThread;
}
thread_ids = new pthread_t[max_threads];
}
BPN::BPN(const char* file, int thrs) {
std::ifstream fin;
fin.open(file, std::ifstream::in);
if(!fin.is_open()) {
ConstructDefault(thrs);
return;
}
unsigned i;
max_threads = thrs < 0 ? 0 : thrs;
readString(fin);
fin>>initial_scale;
readString(fin);
fin>>eta;
readString(fin);
fin>>alpha;
readString(fin);
fin>>scale_factor;
readString(fin);
fin>>size;
if(size >0) {
unsigned f, layersize, prevsize=0;
bool bias;
// input layer - no bias
readString(fin);
readString(fin);
fin>>layersize;
readString(fin);
fin>>bias;
readString(fin);
fin>>f;
bias = false;
layers = new bpnLayer*[size];
layers[0] = new bpnLayer(layersize, prevsize, bias, (outFunction) f, max_threads + 1);
prevsize = layersize;
for(i=1; i<size; ++i) { // itterate all layer above input
readString(fin);
readString(fin);
fin>>layersize;
readString(fin);
fin>>bias;
readString(fin);
fin>>f;
layers[i] = new bpnLayer(layersize, prevsize, bias, (outFunction) f, max_threads + 1);
prevsize = layersize;
}
unsigned lowerSize = layers[size-1]->size;
if(lowerSize > 0)
{
train_output = new double[lowerSize];
for(i=0; i < lowerSize; ++i) {
train_output[i] = 0.0f;
}
}
char ch;
fin>>ch;
if(ch =='x') {
fin.close();
InitializeWeights();
return;
}
for(i=0; i<size; ++i) {
LoadLayer(layers[i], fin);
}
}
