extern "C" {
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#include <stdio.h>
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#include <time.h>
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#include "network.h"
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#include "image.h"
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#include "data.h"
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#include "utils.h"
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#include "params.h"
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#include "crop_layer.h"
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#include "connected_layer.h"
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#include "detection_layer.h"
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#include "convolutional_layer.h"
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#include "deconvolutional_layer.h"
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#include "maxpool_layer.h"
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#include "cost_layer.h"
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#include "normalization_layer.h"
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#include "softmax_layer.h"
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#include "dropout_layer.h"
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#include "route_layer.h"
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}
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float * get_network_output_gpu_layer(network net, int i);
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float * get_network_delta_gpu_layer(network net, int i);
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float * get_network_output_gpu(network net);
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void forward_network_gpu(network net, network_state state)
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{
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int i;
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for(i = 0; i < net.n; ++i){
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if(net.types[i] == CONVOLUTIONAL){
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forward_convolutional_layer_gpu(*(convolutional_layer *)net.layers[i], state);
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}
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else if(net.types[i] == DECONVOLUTIONAL){
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forward_deconvolutional_layer_gpu(*(deconvolutional_layer *)net.layers[i], state);
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}
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else if(net.types[i] == COST){
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forward_cost_layer_gpu(*(cost_layer *)net.layers[i], state);
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}
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else if(net.types[i] == CONNECTED){
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forward_connected_layer_gpu(*(connected_layer *)net.layers[i], state);
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}
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else if(net.types[i] == DETECTION){
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forward_detection_layer_gpu(*(detection_layer *)net.layers[i], state);
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}
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else if(net.types[i] == MAXPOOL){
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forward_maxpool_layer_gpu(*(maxpool_layer *)net.layers[i], state);
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}
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else if(net.types[i] == SOFTMAX){
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forward_softmax_layer_gpu(*(softmax_layer *)net.layers[i], state);
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}
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else if(net.types[i] == DROPOUT){
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forward_dropout_layer_gpu(*(dropout_layer *)net.layers[i], state);
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}
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else if(net.types[i] == CROP){
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forward_crop_layer_gpu(*(crop_layer *)net.layers[i], state);
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}
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else if(net.types[i] == ROUTE){
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forward_route_layer_gpu(*(route_layer *)net.layers[i], net);
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}
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state.input = get_network_output_gpu_layer(net, i);
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}
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}
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void backward_network_gpu(network net, network_state state)
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{
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int i;
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float * original_input = state.input;
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for(i = net.n-1; i >= 0; --i){
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if(i == 0){
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state.input = original_input;
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state.delta = 0;
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}else{
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state.input = get_network_output_gpu_layer(net, i-1);
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state.delta = get_network_delta_gpu_layer(net, i-1);
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}
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if(net.types[i] == CONVOLUTIONAL){
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backward_convolutional_layer_gpu(*(convolutional_layer *)net.layers[i], state);
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}
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else if(net.types[i] == DECONVOLUTIONAL){
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backward_deconvolutional_layer_gpu(*(deconvolutional_layer *)net.layers[i], state);
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}
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else if(net.types[i] == COST){
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backward_cost_layer_gpu(*(cost_layer *)net.layers[i], state);
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}
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else if(net.types[i] == CONNECTED){
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backward_connected_layer_gpu(*(connected_layer *)net.layers[i], state);
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}
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else if(net.types[i] == DETECTION){
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backward_detection_layer_gpu(*(detection_layer *)net.layers[i], state);
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}
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else if(net.types[i] == MAXPOOL){
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backward_maxpool_layer_gpu(*(maxpool_layer *)net.layers[i], state);
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}
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else if(net.types[i] == DROPOUT){
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backward_dropout_layer_gpu(*(dropout_layer *)net.layers[i], state);
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}
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else if(net.types[i] == SOFTMAX){
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backward_softmax_layer_gpu(*(softmax_layer *)net.layers[i], state);
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}
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else if(net.types[i] == ROUTE){
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backward_route_layer_gpu(*(route_layer *)net.layers[i], net);
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}
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}
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}
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void update_network_gpu(network net)
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{
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int i;
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int update_batch = net.batch*net.subdivisions;
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for(i = 0; i < net.n; ++i){
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if(net.types[i] == CONVOLUTIONAL){
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convolutional_layer layer = *(convolutional_layer *)net.layers[i];
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update_convolutional_layer_gpu(layer, update_batch, net.learning_rate, net.momentum, net.decay);
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}
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else if(net.types[i] == DECONVOLUTIONAL){
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deconvolutional_layer layer = *(deconvolutional_layer *)net.layers[i];
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update_deconvolutional_layer_gpu(layer, net.learning_rate, net.momentum, net.decay);
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}
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else if(net.types[i] == CONNECTED){
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connected_layer layer = *(connected_layer *)net.layers[i];
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update_connected_layer_gpu(layer, update_batch, net.learning_rate, net.momentum, net.decay);
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}
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}
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}
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float * get_network_output_gpu_layer(network net, int i)
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{
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if(net.types[i] == CONVOLUTIONAL){
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return ((convolutional_layer *)net.layers[i]) -> output_gpu;
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}
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else if(net.types[i] == DECONVOLUTIONAL){
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return ((deconvolutional_layer *)net.layers[i]) -> output_gpu;
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}
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else if(net.types[i] == DETECTION){
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return ((detection_layer *)net.layers[i]) -> output_gpu;
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}
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else if(net.types[i] == CONNECTED){
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return ((connected_layer *)net.layers[i]) -> output_gpu;
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}
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else if(net.types[i] == MAXPOOL){
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return ((maxpool_layer *)net.layers[i]) -> output_gpu;
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}
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else if(net.types[i] == CROP){
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return ((crop_layer *)net.layers[i]) -> output_gpu;
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}
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else if(net.types[i] == SOFTMAX){
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return ((softmax_layer *)net.layers[i]) -> output_gpu;
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}
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else if(net.types[i] == ROUTE){
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return ((route_layer *)net.layers[i]) -> output_gpu;
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}
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else if(net.types[i] == DROPOUT){
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return get_network_output_gpu_layer(net, i-1);
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}
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return 0;
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}
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float * get_network_delta_gpu_layer(network net, int i)
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{
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if(net.types[i] == CONVOLUTIONAL){
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convolutional_layer layer = *(convolutional_layer *)net.layers[i];
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return layer.delta_gpu;
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}
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else if(net.types[i] == DETECTION){
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detection_layer layer = *(detection_layer *)net.layers[i];
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return layer.delta_gpu;
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}
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else if(net.types[i] == DECONVOLUTIONAL){
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deconvolutional_layer layer = *(deconvolutional_layer *)net.layers[i];
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return layer.delta_gpu;
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}
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else if(net.types[i] == CONNECTED){
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connected_layer layer = *(connected_layer *)net.layers[i];
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return layer.delta_gpu;
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}
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else if(net.types[i] == MAXPOOL){
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maxpool_layer layer = *(maxpool_layer *)net.layers[i];
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return layer.delta_gpu;
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}
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else if(net.types[i] == ROUTE){
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route_layer layer = *(route_layer *)net.layers[i];
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return layer.delta_gpu;
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}
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else if(net.types[i] == SOFTMAX){
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softmax_layer layer = *(softmax_layer *)net.layers[i];
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return layer.delta_gpu;
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} else if(net.types[i] == DROPOUT){
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if(i == 0) return 0;
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return get_network_delta_gpu_layer(net, i-1);
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}
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return 0;
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}
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float train_network_datum_gpu(network net, float *x, float *y)
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{
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network_state state;
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int x_size = get_network_input_size(net)*net.batch;
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int y_size = get_network_output_size(net)*net.batch;
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if(!*net.input_gpu){
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*net.input_gpu = cuda_make_array(x, x_size);
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*net.truth_gpu = cuda_make_array(y, y_size);
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}else{
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cuda_push_array(*net.input_gpu, x, x_size);
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cuda_push_array(*net.truth_gpu, y, y_size);
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}
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state.input = *net.input_gpu;
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state.truth = *net.truth_gpu;
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state.train = 1;
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forward_network_gpu(net, state);
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backward_network_gpu(net, state);
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float error = get_network_cost(net);
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if ((net.seen / net.batch) % net.subdivisions == 0) update_network_gpu(net);
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return error;
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}
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float *get_network_output_layer_gpu(network net, int i)
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{
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if(net.types[i] == CONVOLUTIONAL){
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convolutional_layer layer = *(convolutional_layer *)net.layers[i];
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return layer.output;
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}
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else if(net.types[i] == DECONVOLUTIONAL){
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deconvolutional_layer layer = *(deconvolutional_layer *)net.layers[i];
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return layer.output;
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}
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else if(net.types[i] == CONNECTED){
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connected_layer layer = *(connected_layer *)net.layers[i];
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cuda_pull_array(layer.output_gpu, layer.output, layer.outputs*layer.batch);
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return layer.output;
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}
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else if(net.types[i] == DETECTION){
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detection_layer layer = *(detection_layer *)net.layers[i];
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int outputs = get_detection_layer_output_size(layer);
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cuda_pull_array(layer.output_gpu, layer.output, outputs*layer.batch);
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return layer.output;
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}
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else if(net.types[i] == MAXPOOL){
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maxpool_layer layer = *(maxpool_layer *)net.layers[i];
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return layer.output;
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}
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else if(net.types[i] == SOFTMAX){
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softmax_layer layer = *(softmax_layer *)net.layers[i];
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pull_softmax_layer_output(layer);
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return layer.output;
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}
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return 0;
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}
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float *get_network_output_gpu(network net)
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{
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int i;
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for(i = net.n-1; i > 0; --i) if(net.types[i] != COST) break;
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return get_network_output_layer_gpu(net, i);
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}
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float *network_predict_gpu(network net, float *input)
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{
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int size = get_network_input_size(net) * net.batch;
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network_state state;
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state.input = cuda_make_array(input, size);
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state.truth = 0;
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state.train = 0;
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state.delta = 0;
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forward_network_gpu(net, state);
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float *out = get_network_output_gpu(net);
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cuda_free(state.input);
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return out;
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}
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