Faster

Joseph Redmon

2015-01-13 aa5996d58e68edfbefe51061856aecd549dd09c4

 src/connected_layer.c

@@ -24,15 +24,21 @@
    layer->delta = calloc(batch*outputs, sizeof(float*));

    layer->weight_updates = calloc(inputs*outputs, sizeof(float));
    layer->bias_updates = calloc(outputs, sizeof(float));

    layer->weight_prev = calloc(inputs*outputs, sizeof(float));
    layer->bias_prev = calloc(outputs, sizeof(float));

    layer->weights = calloc(inputs*outputs, sizeof(float));
    layer->biases = calloc(outputs, sizeof(float));


    float scale = 1./sqrt(inputs);
    //scale = .01;
    for(i = 0; i < inputs*outputs; ++i){
        layer->weights[i] = scale*rand_normal();
    }

    layer->bias_updates = calloc(outputs, sizeof(float));
    layer->biases = calloc(outputs, sizeof(float));
    for(i = 0; i < outputs; ++i){
        layer->biases[i] = scale;
    }
@@ -52,6 +58,32 @@
    return layer;
}

void secret_update_connected_layer(connected_layer *layer)
{
    int n = layer->outputs*layer->inputs;
    float dot = dot_cpu(n, layer->weight_updates, 1, layer->weight_prev, 1);
    float mag = sqrt(dot_cpu(n, layer->weight_updates, 1, layer->weight_updates, 1))
                * sqrt(dot_cpu(n, layer->weight_prev, 1, layer->weight_prev, 1));
    float cos = dot/mag;
    if(cos > .3) layer->learning_rate *= 1.1;
    else if (cos < -.3) layer-> learning_rate /= 1.1;

    scal_cpu(n, layer->momentum, layer->weight_prev, 1);
    axpy_cpu(n, 1, layer->weight_updates, 1, layer->weight_prev, 1);
    scal_cpu(n, 0, layer->weight_updates, 1);

    scal_cpu(layer->outputs, layer->momentum, layer->bias_prev, 1);
    axpy_cpu(layer->outputs, 1, layer->bias_updates, 1, layer->bias_prev, 1);
    scal_cpu(layer->outputs, 0, layer->bias_updates, 1);

    //printf("rate:   %f\n", layer->learning_rate);

    axpy_cpu(layer->outputs, layer->learning_rate, layer->bias_prev, 1, layer->biases, 1);

    axpy_cpu(layer->inputs*layer->outputs, -layer->decay, layer->weights, 1, layer->weight_prev, 1);
    axpy_cpu(layer->inputs*layer->outputs, layer->learning_rate, layer->weight_prev, 1, layer->weights, 1);
}

void update_connected_layer(connected_layer layer)
{
    axpy_cpu(layer.outputs, layer.learning_rate, layer.bias_updates, 1, layer.biases, 1);
@@ -130,7 +162,7 @@
    axpy_ongpu(layer.inputs*layer.outputs, -layer.decay, layer.weights_cl, 1, layer.weight_updates_cl, 1);
    axpy_ongpu(layer.inputs*layer.outputs, layer.learning_rate, layer.weight_updates_cl, 1, layer.weights_cl, 1);
    scal_ongpu(layer.inputs*layer.outputs, layer.momentum, layer.weight_updates_cl, 1);
    pull_connected_layer(layer);
    //pull_connected_layer(layer);
}

void forward_connected_layer_gpu(connected_layer layer, cl_mem input)