From f7a17f82eb43de864a4f980f235055da9685eef8 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Wed, 29 Jan 2014 00:28:42 +0000
Subject: [PATCH] Convolutional layers working w/ matrices
---
src/network.c | 85 +++++++++++++++++++++++-------------------
1 files changed, 46 insertions(+), 39 deletions(-)
diff --git a/src/network.c b/src/network.c
index 07ac621..29e22e4 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"
@@ -20,7 +21,7 @@
return net;
}
-void forward_network(network net, double *input)
+void forward_network(network net, float *input)
{
int i;
for(i = 0; i < net.n; ++i){
@@ -47,7 +48,7 @@
}
}
-void update_network(network net, double step, double momentum, double decay)
+void update_network(network net, float step, float momentum, float decay)
{
int i;
for(i = 0; i < net.n; ++i){
@@ -68,7 +69,7 @@
}
}
-double *get_network_output_layer(network net, int i)
+float *get_network_output_layer(network net, int i)
{
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
@@ -85,12 +86,12 @@
}
return 0;
}
-double *get_network_output(network net)
+float *get_network_output(network net)
{
return get_network_output_layer(net, net.n-1);
}
-double *get_network_delta_layer(network net, int i)
+float *get_network_delta_layer(network net, int i)
{
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
@@ -108,34 +109,37 @@
return 0;
}
-double *get_network_delta(network net)
+float *get_network_delta(network net)
{
return get_network_delta_layer(net, net.n-1);
}
-void calculate_error_network(network net, double *truth)
+float calculate_error_network(network net, float *truth)
{
- double *delta = get_network_delta(net);
- double *out = get_network_output(net);
+ float sum = 0;
+ float *delta = get_network_delta(net);
+ float *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)
{
- double *out = get_network_output(net);
+ float *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)
+float backward_network(network net, float *input, float *truth)
{
- calculate_error_network(net, truth);
+ float error = calculate_error_network(net, truth);
int i;
- double *prev_input;
- double *prev_delta;
+ float *prev_input;
+ float *prev_delta;
for(i = net.n-1; i >= 0; --i){
if(i == 0){
prev_input = input;
@@ -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_delta);
}
else if(net.types[i] == MAXPOOL){
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
@@ -163,50 +168,52 @@
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)
+float train_network_datum(network net, float *x, float *y, float step, float momentum, float decay)
{
forward_network(net, x);
int class = get_predicted_class_network(net);
- backward_network(net, x, y);
+ float 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, int n, double step, double momentum,double decay)
+float train_network_sgd(network net, data d, int n, float step, float momentum,float decay)
{
int i;
- int correct = 0;
+ float error = 0;
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);
+ 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));
+ // printf("%d: %f\n", (i+1), (float)correct/(i+1));
//}
}
- return (double)correct/n;
+ return error/n;
}
-double train_network_batch(network net, data d, int n, double step, double momentum,double decay)
+float train_network_batch(network net, data d, int n, float step, float momentum,float decay)
{
int i;
int correct = 0;
for(i = 0; i < n; ++i){
int index = rand()%d.X.rows;
- double *x = d.X.vals[index];
- double *y = d.y.vals[index];
+ float *x = d.X.vals[index];
+ float *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);
}
update_network(net, step, momentum, decay);
- return (double)correct/n;
+ return (float)correct/n;
}
-void train_network(network net, data d, double step, double momentum, double decay)
+void train_network(network net, data d, float step, float momentum, float decay)
{
int i;
int correct = 0;
@@ -219,7 +226,7 @@
}
visualize_network(net);
cvWaitKey(100);
- printf("Accuracy: %f\n", (double)correct/d.X.rows);
+ printf("Accuracy: %f\n", (float)correct/d.X.rows);
}
int get_network_output_size_layer(network net, int i)
@@ -282,15 +289,15 @@
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)
+float *network_predict(network net, float *input)
{
forward_network(net, input);
- double *out = get_network_output(net);
+ float *out = get_network_output(net);
return out;
}
@@ -300,7 +307,7 @@
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]);
+ float *out = network_predict(net, test.X.vals[i]);
for(j = 0; j < k; ++j){
pred.vals[i][j] = out[j];
}
@@ -312,7 +319,7 @@
{
int i,j;
for(i = 0; i < net.n; ++i){
- double *output = 0;
+ float *output = 0;
int n = 0;
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
@@ -336,8 +343,8 @@
output = layer.output;
n = layer.inputs;
}
- double mean = mean_array(output, n);
- double vari = variance_array(output, n);
+ float mean = mean_array(output, n);
+ float vari = variance_array(output, n);
fprintf(stderr, "Layer %d - Mean: %f, Variance: %f\n",i,mean, vari);
if(n > 100) n = 100;
for(j = 0; j < n; ++j) fprintf(stderr, "%f, ", output[j]);
@@ -346,10 +353,10 @@
}
}
-double network_accuracy(network net, data d)
+float network_accuracy(network net, data d)
{
matrix guess = network_predict_data(net, d);
- double acc = matrix_accuracy(d.y, guess);
+ float acc = matrix_accuracy(d.y, guess);
free_matrix(guess);
return acc;
}
--
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