From 4bdf96bd6aafbec6bc3f0eab8739d6652878fd24 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Fri, 06 Dec 2013 21:26:09 +0000
Subject: [PATCH] New data format
---
src/network.c | 97 +++++++++++++++++++++++++++++++++---------------
1 files changed, 67 insertions(+), 30 deletions(-)
diff --git a/src/network.c b/src/network.c
index faedb8c..29234da 100644
--- a/src/network.c
+++ b/src/network.c
@@ -15,6 +15,8 @@
net.n = n;
net.layers = calloc(net.n, sizeof(void *));
net.types = calloc(net.n, sizeof(LAYER_TYPE));
+ net.outputs = 0;
+ net.output = 0;
return net;
}
@@ -45,13 +47,13 @@
}
}
-void update_network(network net, double step)
+void update_network(network net, double step, double momentum, double decay)
{
int i;
for(i = 0; i < net.n; ++i){
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- update_convolutional_layer(layer, step, 0.9, .01);
+ update_convolutional_layer(layer, step, momentum, decay);
}
else if(net.types[i] == MAXPOOL){
//maxpool_layer layer = *(maxpool_layer *)net.layers[i];
@@ -61,7 +63,7 @@
}
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
- update_connected_layer(layer, step, .9, 0);
+ update_connected_layer(layer, step, momentum, decay);
}
}
}
@@ -111,8 +113,26 @@
return get_network_delta_layer(net, net.n-1);
}
-void learn_network(network net, double *input)
+void calculate_error_network(network net, double *truth)
{
+ double *delta = get_network_delta(net);
+ double *out = get_network_output(net);
+ int i, k = get_network_output_size(net);
+ for(i = 0; i < k; ++i){
+ delta[i] = truth[i] - out[i];
+ }
+}
+
+int get_predicted_class_network(network net)
+{
+ double *out = get_network_output(net);
+ int k = get_network_output_size(net);
+ return max_index(out, k);
+}
+
+void backward_network(network net, double *input, double *truth)
+{
+ calculate_error_network(net, truth);
int i;
double *prev_input;
double *prev_delta;
@@ -145,40 +165,43 @@
}
}
-void train_network_batch(network net, batch b)
+int train_network_datum(network net, double *x, double *y, double step, double momentum, double decay)
{
- int i,j;
- int k = get_network_output_size(net);
+ forward_network(net, x);
+ int class = get_predicted_class_network(net);
+ backward_network(net, x, y);
+ update_network(net, step, momentum, decay);
+ return (y[class]?1:0);
+}
+
+double train_network_sgd(network net, data d, double step, double momentum,double decay)
+{
+ int i;
int correct = 0;
- for(i = 0; i < b.n; ++i){
- show_image(b.images[i], "Input");
- forward_network(net, b.images[i].data);
- image o = get_network_image(net);
- if(o.h) show_image_collapsed(o, "Output");
- double *output = get_network_output(net);
- double *delta = get_network_delta(net);
- int max_k = 0;
- double max = 0;
- for(j = 0; j < k; ++j){
- delta[j] = b.truth[i][j]-output[j];
- if(output[j] > max) {
- max = output[j];
- max_k = j;
- }
+ for(i = 0; i < d.X.rows; ++i){
+ int index = rand()%d.X.rows;
+ correct += train_network_datum(net, d.X.vals[index], d.y.vals[index], step, momentum, decay);
+ if((i+1)%10 == 0){
+ printf("%d: %f\n", (i+1), (double)correct/(i+1));
}
- if(b.truth[i][max_k]) ++correct;
- printf("%f\n", (double)correct/(i+1));
- learn_network(net, b.images[i].data);
- update_network(net, .001);
+ }
+ return (double)correct/d.X.rows;
+}
+
+void train_network(network net, data d, double step, double momentum, double decay)
+{
+ int i;
+ int correct = 0;
+ for(i = 0; i < d.X.rows; ++i){
+ correct += train_network_datum(net, d.X.vals[i], d.y.vals[i], step, momentum, decay);
if(i%100 == 0){
visualize_network(net);
- cvWaitKey(100);
+ cvWaitKey(10);
}
}
visualize_network(net);
- print_network(net);
cvWaitKey(100);
- printf("Accuracy: %f\n", (double)correct/b.n);
+ printf("Accuracy: %f\n", (double)correct/d.X.rows);
}
int get_network_output_size_layer(network net, int i)
@@ -250,7 +273,7 @@
{
int i,j;
for(i = 0; i < net.n; ++i){
- double *output;
+ double *output = 0;
int n = 0;
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
@@ -283,3 +306,17 @@
fprintf(stderr, "\n");
}
}
+double network_accuracy(network net, data d)
+{
+ int i;
+ int correct = 0;
+ int k = get_network_output_size(net);
+ for(i = 0; i < d.X.rows; ++i){
+ forward_network(net, d.X.vals[i]);
+ double *out = get_network_output(net);
+ int guess = max_index(out, k);
+ if(d.y.vals[i][guess]) ++correct;
+ }
+ return (double)correct/d.X.rows;
+}
+
--
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