From b2b7137b6f185ce2f01664d782a09b08d50d5a07 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Tue, 28 Jan 2014 07:16:56 +0000
Subject: [PATCH] About to do something stupid...
---
src/network.c | 95 ++++++++++++++++++++++++++++++++++-------------
1 files changed, 68 insertions(+), 27 deletions(-)
diff --git a/src/network.c b/src/network.c
index 29234da..2ce13d8 100644
--- a/src/network.c
+++ b/src/network.c
@@ -6,6 +6,7 @@
#include "connected_layer.h"
#include "convolutional_layer.h"
+//#include "old_conv.h"
#include "maxpool_layer.h"
#include "softmax_layer.h"
@@ -63,7 +64,7 @@
}
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
- update_connected_layer(layer, step, momentum, decay);
+ update_connected_layer(layer, step, momentum, 0);
}
}
}
@@ -113,14 +114,17 @@
return get_network_delta_layer(net, net.n-1);
}
-void calculate_error_network(network net, double *truth)
+double calculate_error_network(network net, double *truth)
{
+ double sum = 0;
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];
+ sum += delta[i]*delta[i];
}
+ return sum;
}
int get_predicted_class_network(network net)
@@ -130,9 +134,9 @@
return max_index(out, k);
}
-void backward_network(network net, double *input, double *truth)
+double backward_network(network net, double *input, double *truth)
{
- calculate_error_network(net, truth);
+ double error = calculate_error_network(net, truth);
int i;
double *prev_input;
double *prev_delta;
@@ -146,8 +150,9 @@
}
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- learn_convolutional_layer(layer, prev_input);
- if(i != 0) backward_convolutional_layer(layer, prev_input, prev_delta);
+ learn_convolutional_layer(layer);
+ //learn_convolutional_layer(layer);
+ //if(i != 0) backward_convolutional_layer(layer, prev_input, prev_delta);
}
else if(net.types[i] == MAXPOOL){
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
@@ -163,31 +168,51 @@
if(i != 0) backward_connected_layer(layer, prev_input, prev_delta);
}
}
+ return error;
}
-int train_network_datum(network net, double *x, double *y, double step, double momentum, double decay)
+double train_network_datum(network net, double *x, double *y, double step, double momentum, double decay)
{
forward_network(net, x);
int class = get_predicted_class_network(net);
- backward_network(net, x, y);
+ double error = backward_network(net, x, y);
update_network(net, step, momentum, decay);
- return (y[class]?1:0);
+ //return (y[class]?1:0);
+ return error;
}
-double train_network_sgd(network net, data d, double step, double momentum,double decay)
+double train_network_sgd(network net, data d, int n, double step, double momentum,double decay)
+{
+ int i;
+ double error = 0;
+ for(i = 0; i < n; ++i){
+ int index = rand()%d.X.rows;
+ error += 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));
+ //}
+ }
+ return error/n;
+}
+double train_network_batch(network net, data d, int n, double step, double momentum,double decay)
{
int i;
int correct = 0;
- for(i = 0; i < d.X.rows; ++i){
+ for(i = 0; i < n; ++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));
- }
+ double *x = d.X.vals[index];
+ double *y = d.y.vals[index];
+ forward_network(net, x);
+ int class = get_predicted_class_network(net);
+ backward_network(net, x, y);
+ correct += (y[class]?1:0);
}
- return (double)correct/d.X.rows;
+ update_network(net, step, momentum, decay);
+ return (double)correct/n;
+
}
+
void train_network(network net, data d, double step, double momentum, double decay)
{
int i;
@@ -264,11 +289,32 @@
sprintf(buff, "Layer %d", i);
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- visualize_convolutional_filters(layer, buff);
+ visualize_convolutional_layer(layer, buff);
}
}
}
+double *network_predict(network net, double *input)
+{
+ forward_network(net, input);
+ double *out = get_network_output(net);
+ return out;
+}
+
+matrix network_predict_data(network net, data test)
+{
+ int i,j;
+ int k = get_network_output_size(net);
+ matrix pred = make_matrix(test.X.rows, k);
+ for(i = 0; i < test.X.rows; ++i){
+ double *out = network_predict(net, test.X.vals[i]);
+ for(j = 0; j < k; ++j){
+ pred.vals[i][j] = out[j];
+ }
+ }
+ return pred;
+}
+
void print_network(network net)
{
int i,j;
@@ -306,17 +352,12 @@
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;
+ matrix guess = network_predict_data(net, d);
+ double acc = matrix_accuracy(d.y, guess);
+ free_matrix(guess);
+ return acc;
}
--
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