From 90d354a2a5a3ba76071337d8794cfc00f7bc5fab Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Sat, 13 Dec 2014 20:01:21 +0000
Subject: [PATCH] fixed dropout ><
---
src/network_gpu.c | 45 +++++++++++++++++++++++++++++++++++----------
1 files changed, 35 insertions(+), 10 deletions(-)
diff --git a/src/network_gpu.c b/src/network_gpu.c
index 7302664..d09aa71 100644
--- a/src/network_gpu.c
+++ b/src/network_gpu.c
@@ -22,7 +22,9 @@
{
//printf("start\n");
int i;
+ // printf("Truth: %f\n", cl_checksum(truth, 1000*net.batch));
for(i = 0; i < net.n; ++i){
+ //printf("Truth %i: %f\n", i, cl_checksum(truth, 1000*net.batch));
//clock_t time = clock();
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
@@ -48,6 +50,11 @@
forward_softmax_layer_gpu(layer, input);
input = layer.output_cl;
}
+ else if(net.types[i] == DROPOUT){
+ if(!train) continue;
+ dropout_layer layer = *(dropout_layer *)net.layers[i];
+ forward_dropout_layer_gpu(layer, input);
+ }
//printf("%d %f\n", i, sec(clock()-time));
/*
else if(net.types[i] == CROP){
@@ -80,7 +87,7 @@
}
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- backward_convolutional_layer_gpu(layer, prev_delta);
+ backward_convolutional_layer_gpu(layer, prev_input, prev_delta);
}
else if(net.types[i] == COST){
cost_layer layer = *(cost_layer *)net.layers[i];
@@ -94,6 +101,10 @@
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
backward_maxpool_layer_gpu(layer, prev_delta);
}
+ else if(net.types[i] == DROPOUT){
+ dropout_layer layer = *(dropout_layer *)net.layers[i];
+ backward_dropout_layer_gpu(layer, prev_delta);
+ }
else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
backward_softmax_layer_gpu(layer, prev_delta);
@@ -134,6 +145,8 @@
else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
return layer.output_cl;
+ } else if(net.types[i] == DROPOUT){
+ return get_network_output_cl_layer(net, i-1);
}
return 0;
}
@@ -155,6 +168,8 @@
else if(net.types[i] == SOFTMAX){
softmax_layer layer = *(softmax_layer *)net.layers[i];
return layer.delta_cl;
+ } else if(net.types[i] == DROPOUT){
+ return get_network_delta_cl_layer(net, i-1);
}
return 0;
}
@@ -171,18 +186,10 @@
cl_write_array(*net.input_cl, x, x_size);
cl_write_array(*net.truth_cl, y, y_size);
}
- //printf("trans %f\n", sec(clock()-time));
- //time = clock();
forward_network_gpu(net, *net.input_cl, *net.truth_cl, 1);
- //printf("forw %f\n", sec(clock()-time));
- //time = clock();
backward_network_gpu(net, *net.input_cl);
- //printf("back %f\n", sec(clock()-time));
- //time = clock();
update_network_gpu(net);
float error = get_network_cost(net);
- //printf("updt %f\n", sec(clock()-time));
- //time = clock();
return error;
}
@@ -287,11 +294,29 @@
float network_accuracy_gpu(network net, data d)
{
matrix guess = network_predict_data_gpu(net, d);
- float acc = matrix_accuracy(d.y, guess);
+ float acc = matrix_topk_accuracy(d.y, guess,1);
+ free_matrix(guess);
+ return acc;
+}
+
+float *network_accuracies_gpu(network net, data d)
+{
+ static float acc[2];
+ matrix guess = network_predict_data_gpu(net, d);
+ acc[0] = matrix_topk_accuracy(d.y, guess,1);
+ acc[1] = matrix_topk_accuracy(d.y, guess,5);
free_matrix(guess);
return acc;
}
+#else
+void forward_network_gpu(network net, cl_mem input, cl_mem truth, int train){}
+void backward_network_gpu(network net, cl_mem input){}
+void update_network_gpu(network net){}
+float train_network_sgd_gpu(network net, data d, int n){return 0;}
+float train_network_data_gpu(network net, data d, int n){return 0;}
+float *network_predict_gpu(network net, float *input){return 0;}
+float network_accuracy_gpu(network net, data d){return 0;}
#endif
--
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