Imagenet Features\!

Joseph Redmon

2014-02-24 bc902b277e9131cc169751056786de5393da737d

 src/network.c

@@ -21,18 +21,18 @@
    return net;
}

void print_convolutional_cfg(FILE *fp, convolutional_layer *l)
void print_convolutional_cfg(FILE *fp, convolutional_layer *l, int first)
{
    int i;
    fprintf(fp, "[convolutional]\n"
                "height=%d\n"
    fprintf(fp, "[convolutional]\n");
    if(first) fprintf(fp,   "height=%d\n"
                "width=%d\n"
                "channels=%d\n"
                "filters=%d\n"
                            "channels=%d\n",
                            l->h, l->w, l->c);
    fprintf(fp, "filters=%d\n"
                "size=%d\n"
                "stride=%d\n"
                "activation=%s\n",
                l->h, l->w, l->c,
                l->n, l->size, l->stride,
                get_activation_string(l->activation));
    fprintf(fp, "data=");
@@ -40,14 +40,14 @@
    for(i = 0; i < l->n*l->c*l->size*l->size; ++i) fprintf(fp, "%g,", l->filters[i]);
    fprintf(fp, "\n\n");
}
void print_connected_cfg(FILE *fp, connected_layer *l)
void print_connected_cfg(FILE *fp, connected_layer *l, int first)
{
    int i;
    fprintf(fp, "[connected]\n"
                "input=%d\n"
                "output=%d\n"
    fprintf(fp, "[connected]\n");
    if(first) fprintf(fp, "input=%d\n", l->inputs);
    fprintf(fp, "output=%d\n"
                "activation=%s\n",
                l->inputs, l->outputs,
                l->outputs,
                get_activation_string(l->activation));
    fprintf(fp, "data=");
    for(i = 0; i < l->outputs; ++i) fprintf(fp, "%g,", l->biases[i]);
@@ -55,22 +55,21 @@
    fprintf(fp, "\n\n");
}

void print_maxpool_cfg(FILE *fp, maxpool_layer *l)
void print_maxpool_cfg(FILE *fp, maxpool_layer *l, int first)
{
    fprintf(fp, "[maxpool]\n"
                "height=%d\n"
    fprintf(fp, "[maxpool]\n");
    if(first) fprintf(fp,   "height=%d\n"
                "width=%d\n"
                "channels=%d\n"
                "stride=%d\n\n",
                l->h, l->w, l->c,
                l->stride);
                            "channels=%d\n",
                            l->h, l->w, l->c);
    fprintf(fp, "stride=%d\n\n", l->stride);
}

void print_softmax_cfg(FILE *fp, softmax_layer *l)
void print_softmax_cfg(FILE *fp, softmax_layer *l, int first)
{
    fprintf(fp, "[softmax]\n"
                "input=%d\n\n",
                l->inputs);
    fprintf(fp, "[softmax]\n");
    if(first) fprintf(fp, "input=%d\n", l->inputs);
    fprintf(fp, "\n");
}

void save_network(network net, char *filename)
@@ -81,13 +80,13 @@
    for(i = 0; i < net.n; ++i)
    {
        if(net.types[i] == CONVOLUTIONAL)
            print_convolutional_cfg(fp, (convolutional_layer *)net.layers[i]);
            print_convolutional_cfg(fp, (convolutional_layer *)net.layers[i], i==0);
        else if(net.types[i] == CONNECTED)
            print_connected_cfg(fp, (connected_layer *)net.layers[i]);
            print_connected_cfg(fp, (connected_layer *)net.layers[i], i==0);
        else if(net.types[i] == MAXPOOL)
            print_maxpool_cfg(fp, (maxpool_layer *)net.layers[i]);
            print_maxpool_cfg(fp, (maxpool_layer *)net.layers[i], i==0);
        else if(net.types[i] == SOFTMAX)
            print_softmax_cfg(fp, (softmax_layer *)net.layers[i]);
            print_softmax_cfg(fp, (softmax_layer *)net.layers[i], i==0);
    }
    fclose(fp);
}