void save_weights_double(network net, char *filename)
{
fprintf(stderr, "Saving doubled weights to %s\n", filename);
FILE *fp = fopen(filename, "w");
if(!fp) file_error(filename);
fwrite(&net.learning_rate, sizeof(float), 1, fp);
fwrite(&net.momentum, sizeof(float), 1, fp);
fwrite(&net.decay, sizeof(float), 1, fp);
fwrite(net.seen, sizeof(int), 1, fp);
int i,j,k;
for(i = 0; i < net.n; ++i){
layer l = net.layers[i];
if(l.type == CONVOLUTIONAL){
#ifdef GPU
if(gpu_index >= 0){
pull_convolutional_layer(l);
}
#endif
float zero = 0;
fwrite(l.biases, sizeof(float), l.n, fp);
fwrite(l.biases, sizeof(float), l.n, fp);
for (j = 0; j < l.n; ++j){
int index = j*l.c*l.size*l.size;
fwrite(l.filters+index, sizeof(float), l.c*l.size*l.size, fp);
for (k = 0; k < l.c*l.size*l.size; ++k) fwrite(&zero, sizeof(float), 1, fp);
}
for (j = 0; j < l.n; ++j){
int index = j*l.c*l.size*l.size;
for (k = 0; k < l.c*l.size*l.size; ++k) fwrite(&zero, sizeof(float), 1, fp);
fwrite(l.filters+index, sizeof(float), l.c*l.size*l.size, fp);
}
}
}
fclose(fp);
}
void save_convolutional_weights(layer l, FILE *fp)
{
if(l.binary){
//save_convolutional_weights_binary(l, fp);
//return;
}
#ifdef GPU
if(gpu_index >= 0){
pull_convolutional_layer(l);
}
#endif
int num = l.n*l.c*l.size*l.size;
fwrite(l.biases, sizeof(float), l.n, fp);
if (l.batch_normalize){
fwrite(l.scales, sizeof(float), l.n, fp);
fwrite(l.rolling_mean, sizeof(float), l.n, fp);
fwrite(l.rolling_variance, sizeof(float), l.n, fp);
}
fwrite(l.weights, sizeof(float), num, fp);
if(l.adam){
fwrite(l.m, sizeof(float), num, fp);
fwrite(l.v, sizeof(float), num, fp);
}
}
void pull_crnn_layer(layer_t l)
{
pull_convolutional_layer(*(l.input_layer));
pull_convolutional_layer(*(l.self_layer));
pull_convolutional_layer(*(l.output_layer));
}
