void update_network(network net)
{
int i;
int update_batch = net.batch*net.subdivisions;
for(i = 0; i < net.n; ++i){
layer l = net.layers[i];
if(l.type == CONVOLUTIONAL){
update_convolutional_layer(l, update_batch, net.learning_rate, net.momentum, net.decay);
} else if(l.type == DECONVOLUTIONAL){
update_deconvolutional_layer(l, net.learning_rate, net.momentum, net.decay);
} else if(l.type == CONNECTED){
update_connected_layer(l, update_batch, net.learning_rate, net.momentum, net.decay);
}
}
}
void handle_connection(void *pointer)
{
connection_info info = *(connection_info *) pointer;
free(pointer);
//printf("New Connection\n");
if(info.counter%100==0){
char buff[256];
sprintf(buff, "unikitty/net_%d.part", info.counter);
save_network(info.net, buff);
}
int fd = info.fd;
network net = info.net;
int i;
for(i = 0; i < net.n; ++i){
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *) net.layers[i];
read_and_add_into(fd, layer.bias_updates, layer.n);
int num = layer.n*layer.c*layer.size*layer.size;
read_and_add_into(fd, layer.filter_updates, num);
}
if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *) net.layers[i];
read_and_add_into(fd, layer.bias_updates, layer.outputs);
read_and_add_into(fd, layer.weight_updates, layer.inputs*layer.outputs);
}
}
for(i = 0; i < net.n; ++i){
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *) net.layers[i];
update_convolutional_layer(layer);
write_all(fd, (char*) layer.biases, layer.n*sizeof(float));
int num = layer.n*layer.c*layer.size*layer.size;
write_all(fd, (char*) layer.filters, num*sizeof(float));
}
if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *) net.layers[i];
update_connected_layer(layer);
write_all(fd, (char *)layer.biases, layer.outputs*sizeof(float));
write_all(fd, (char *)layer.weights, layer.outputs*layer.inputs*sizeof(float));
}
}
//printf("Received updates\n");
close(fd);
}
void update_network(network net)
{
int i;
int update_batch = net.batch*net.subdivisions;
float rate = get_current_rate(net);
for(i = 0; i < net.n; ++i){
layer l = net.layers[i];
if(l.type == CONVOLUTIONAL){
update_convolutional_layer(l, update_batch, rate, net.momentum, net.decay);
} else if(l.type == DECONVOLUTIONAL){
update_deconvolutional_layer(l, rate, net.momentum, net.decay);
} else if(l.type == CONNECTED){
update_connected_layer(l, update_batch, rate, net.momentum, net.decay);
} else if(l.type == RNN){
update_rnn_layer(l, update_batch, rate, net.momentum, net.decay);
} else if(l.type == CRNN){
update_crnn_layer(l, update_batch, rate, net.momentum, net.decay);
} else if(l.type == LOCAL){
update_local_layer(l, update_batch, rate, net.momentum, net.decay);
}
}
}
void update_crnn_layer(layer_t l, int batch, float learning_rate, float momentum, float decay)
{
update_convolutional_layer(*(l.input_layer), batch, learning_rate, momentum, decay);
update_convolutional_layer(*(l.self_layer), batch, learning_rate, momentum, decay);
update_convolutional_layer(*(l.output_layer), batch, learning_rate, momentum, decay);
}
void update_crnn_layer(layer l, update_args a)
{
update_convolutional_layer(*(l.input_layer), a);
update_convolutional_layer(*(l.self_layer), a);
update_convolutional_layer(*(l.output_layer), a);
}
