Training on VOC

Joseph Redmon

2014-02-14 118bdd6f624a81c7b43689943485f8d70cbd944e

 src/connected_layer.c

@@ -19,23 +19,46 @@
    layer->delta = calloc(outputs, sizeof(float*));

    layer->weight_updates = calloc(inputs*outputs, sizeof(float));
    layer->weight_adapt = calloc(inputs*outputs, sizeof(float));
    layer->weight_momentum = calloc(inputs*outputs, sizeof(float));
    layer->weights = calloc(inputs*outputs, sizeof(float));
    float scale = 2./inputs;
    float scale = 1./inputs;
    for(i = 0; i < inputs*outputs; ++i)
        layer->weights[i] = rand_normal()*scale;
        layer->weights[i] = scale*(rand_uniform());

    layer->bias_updates = calloc(outputs, sizeof(float));
    layer->bias_adapt = calloc(outputs, sizeof(float));
    layer->bias_momentum = calloc(outputs, sizeof(float));
    layer->biases = calloc(outputs, sizeof(float));
    for(i = 0; i < outputs; ++i)
        //layer->biases[i] = rand_normal()*scale + scale;
        layer->biases[i] = 0;
        layer->biases[i] = 1;

    layer->activation = activation;
    return layer;
}

/*
void update_connected_layer(connected_layer layer, float step, float momentum, float decay)
{
    int i;
    for(i = 0; i < layer.outputs; ++i){
        float delta = layer.bias_updates[i];
        layer.bias_adapt[i] += delta*delta;
        layer.bias_momentum[i] = step/sqrt(layer.bias_adapt[i])*(layer.bias_updates[i]) + momentum*layer.bias_momentum[i];
        layer.biases[i] += layer.bias_momentum[i];
    }
    for(i = 0; i < layer.outputs*layer.inputs; ++i){
        float delta = layer.weight_updates[i];
        layer.weight_adapt[i] += delta*delta;
        layer.weight_momentum[i] = step/sqrt(layer.weight_adapt[i])*(layer.weight_updates[i] - decay*layer.weights[i]) + momentum*layer.weight_momentum[i];
        layer.weights[i] += layer.weight_momentum[i];
    }
    memset(layer.bias_updates, 0, layer.outputs*sizeof(float));
    memset(layer.weight_updates, 0, layer.outputs*layer.inputs*sizeof(float));
}
*/

void update_connected_layer(connected_layer layer, float step, float momentum, float decay)
{
    int i;
@@ -65,6 +88,7 @@
    for(i = 0; i < layer.outputs; ++i){
        layer.output[i] = activate(layer.output[i], layer.activation);
    }
    //for(i = 0; i < layer.outputs; ++i) if(i%(layer.outputs/10+1)==0) printf("%f, ", layer.output[i]); printf("\n");
}

void learn_connected_layer(connected_layer layer, float *input)