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Fixing up maxpool layer

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This commit is contained in:
Joseph Redmon
2014-10-16 15:17:23 -07:00
parent 7756cccb79
commit 9b3c7136f3
8 changed files with 173 additions and 71 deletions
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72
src/network.c
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@ -24,7 +24,8 @@ network make_network(int n, int batch)
net.outputs = 0;
net.output = 0;
#ifdef GPU
net.input_cl = 0;
net.input_cl = calloc(1, sizeof(cl_mem));
net.truth_cl = calloc(1, sizeof(cl_mem));
#endif
return net;
}
@ -43,12 +44,12 @@ void forward_network_gpu(network net, cl_mem input, cl_mem truth, int train)
cost_layer layer = *(cost_layer *)net.layers[i];
forward_cost_layer_gpu(layer, input, truth);
}
/*
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
forward_connected_layer(layer, input, train);
input = layer.output;
forward_connected_layer_gpu(layer, input);
input = layer.output_cl;
}
/*
else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
forward_softmax_layer(layer, input);
@ -94,6 +95,10 @@ void backward_network_gpu(network net, cl_mem input)
cost_layer layer = *(cost_layer *)net.layers[i];
backward_cost_layer_gpu(layer, prev_input, prev_delta);
}
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
backward_connected_layer_gpu(layer, prev_input, prev_delta);
}
}
}
@ -105,18 +110,9 @@ void update_network_gpu(network net)
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
update_convolutional_layer_gpu(layer);
}
else if(net.types[i] == MAXPOOL){
//maxpool_layer layer = *(maxpool_layer *)net.layers[i];
}
else if(net.types[i] == SOFTMAX){
//maxpool_layer layer = *(maxpool_layer *)net.layers[i];
}
else if(net.types[i] == NORMALIZATION){
//maxpool_layer layer = *(maxpool_layer *)net.layers[i];
}
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
update_connected_layer(layer);
update_connected_layer_gpu(layer);
}
}
}
@ -127,6 +123,10 @@ cl_mem get_network_output_cl_layer(network net, int i)
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
return layer.output_cl;
}
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
return layer.output_cl;
}
return 0;
}
@ -136,6 +136,10 @@ cl_mem get_network_delta_cl_layer(network net, int i)
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
return layer.delta_cl;
}
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
return layer.delta_cl;
}
return 0;
}
@ -347,6 +351,46 @@ void backward_network(network net, float *input)
}
}
#ifdef GPU
float train_network_datum_gpu(network net, float *x, float *y)
{
int x_size = get_network_input_size(net)*net.batch;
int y_size = get_network_output_size(net)*net.batch;
if(!*net.input_cl){
*net.input_cl = cl_make_array(x, x_size);
*net.truth_cl = cl_make_array(y, y_size);
}else{
cl_write_array(*net.input_cl, x, x_size);
cl_write_array(*net.truth_cl, y, y_size);
}
forward_network_gpu(net, *net.input_cl, *net.truth_cl, 1);
//int class = get_predicted_class_network(net);
backward_network_gpu(net, *net.input_cl);
float error = get_network_cost(net);
update_network_gpu(net);
//return (y[class]?1:0);
return error;
}
float train_network_sgd_gpu(network net, data d, int n)
{
int batch = net.batch;
float *X = calloc(batch*d.X.cols, sizeof(float));
float *y = calloc(batch*d.y.cols, sizeof(float));
int i;
float sum = 0;
for(i = 0; i < n; ++i){
get_batch(d, batch, X, y);
float err = train_network_datum_gpu(net, X, y);
sum += err;
}
free(X);
free(y);
return (float)sum/(n*batch);
}
#endif
float train_network_datum(network net, float *x, float *y)
{
forward_network(net, x, y, 1);