normalization layer

Joseph Redmon

2015-07-09 75db98db253adf7fbde293f102ab095b02402f9e

 src/network_kernels.cu

@@ -15,6 +15,7 @@
#include "convolutional_layer.h"
#include "deconvolutional_layer.h"
#include "maxpool_layer.h"
#include "normalization_layer.h"
#include "cost_layer.h"
#include "softmax_layer.h"
#include "dropout_layer.h"
@@ -44,6 +45,8 @@
            forward_cost_layer_gpu(l, state);
        } else if(l.type == SOFTMAX){
            forward_softmax_layer_gpu(l, state);
        } else if(l.type == NORMALIZATION){
            forward_normalization_layer_gpu(l, state);
        } else if(l.type == MAXPOOL){
            forward_maxpool_layer_gpu(l, state);
        } else if(l.type == DROPOUT){
@@ -80,6 +83,8 @@
            backward_dropout_layer_gpu(l, state);
        } else if(l.type == DETECTION){
            backward_detection_layer_gpu(l, state);
        } else if(l.type == NORMALIZATION){
            backward_normalization_layer_gpu(l, state);
        } else if(l.type == SOFTMAX){
            if(i != 0) backward_softmax_layer_gpu(l, state);
        } else if(l.type == CONNECTED){
@@ -136,20 +141,7 @@
{
    layer l = net.layers[i];
    cuda_pull_array(l.output_gpu, l.output, l.outputs*l.batch);
    if(l.type == CONVOLUTIONAL){
        return l.output;
    } else if(l.type == DECONVOLUTIONAL){
        return l.output;
    } else if(l.type == CONNECTED){
        return l.output;
    } else if(l.type == DETECTION){
        return l.output;
    } else if(l.type == MAXPOOL){
        return l.output;
    } else if(l.type == SOFTMAX){
        return l.output;
    }
    return 0;
}

float *get_network_output_gpu(network net)