Training on VOC

Joseph Redmon

2014-02-14 118bdd6f624a81c7b43689943485f8d70cbd944e

 src/network.c

@@ -21,6 +21,77 @@
    return net;
}

void print_convolutional_cfg(FILE *fp, convolutional_layer *l)
{
    int i;
    fprintf(fp, "[convolutional]\n"
                "height=%d\n"
                "width=%d\n"
                "channels=%d\n"
                "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=");
    for(i = 0; i < l->n; ++i) fprintf(fp, "%g,", l->biases[i]);
    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)
{
    int i;
    fprintf(fp, "[connected]\n"
                "input=%d\n"
                "output=%d\n"
                "activation=%s\n",
                l->inputs, l->outputs,
                get_activation_string(l->activation));
    fprintf(fp, "data=");
    for(i = 0; i < l->outputs; ++i) fprintf(fp, "%g,", l->biases[i]);
    for(i = 0; i < l->inputs*l->outputs; ++i) fprintf(fp, "%g,", l->weights[i]);
    fprintf(fp, "\n\n");
}

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

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

void save_network(network net, char *filename)
{
    FILE *fp = fopen(filename, "w");
    if(!fp) file_error(filename);
    int i;
    for(i = 0; i < net.n; ++i)
    {
        if(net.types[i] == CONVOLUTIONAL)
            print_convolutional_cfg(fp, (convolutional_layer *)net.layers[i]);
        else if(net.types[i] == CONNECTED)
            print_connected_cfg(fp, (connected_layer *)net.layers[i]);
        else if(net.types[i] == MAXPOOL)
            print_maxpool_cfg(fp, (maxpool_layer *)net.layers[i]);
        else if(net.types[i] == SOFTMAX)
            print_softmax_cfg(fp, (softmax_layer *)net.layers[i]);
    }
    fclose(fp);
}

void forward_network(network net, float *input)
{
    int i;
@@ -64,7 +135,7 @@
        }
        else if(net.types[i] == CONNECTED){
            connected_layer layer = *(connected_layer *)net.layers[i];
            update_connected_layer(layer, step, momentum, 0);
            update_connected_layer(layer, step, momentum, decay);
        }
    }
}
@@ -121,9 +192,11 @@
    float *out = get_network_output(net);
    int i, k = get_network_output_size(net);
    for(i = 0; i < k; ++i){
        printf("%f, ", out[i]);
        delta[i] = truth[i] - out[i];
        sum += delta[i]*delta[i];
    }
    printf("\n");
    return sum;
}

@@ -174,7 +247,7 @@
float train_network_datum(network net, float *x, float *y, float step, float momentum, float decay)
{
        forward_network(net, x);
        int class = get_predicted_class_network(net);
    //int class = get_predicted_class_network(net);
        float error = backward_network(net, x, y);
        update_network(net, step, momentum, decay);
        //return (y[class]?1:0);
@@ -185,13 +258,19 @@
{
    int i;
    float error = 0;
    int correct = 0;
    for(i = 0; i < n; ++i){
        int index = rand()%d.X.rows;
        error += train_network_datum(net, d.X.vals[index], d.y.vals[index], step, momentum, decay);
        float *y = d.y.vals[index];
        int class = get_predicted_class_network(net);
        correct += (y[class]?1:0);
        //printf("%d %f %f\n", i,net.output[0], d.y.vals[index][0]);
        //if((i+1)%10 == 0){
        //    printf("%d: %f\n", (i+1), (float)correct/(i+1));
        //}
    }
    printf("Accuracy: %f\n",(float) correct/n);
    return error/n;
}
float train_network_batch(network net, data d, int n, float step, float momentum,float decay)