From b715671988a4f3e476586df52fa3bf052cce7f80 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Thu, 05 Dec 2013 21:17:16 +0000
Subject: [PATCH] Works well on MNIST
---
src/network.c | 100 ++++++++++++++++++++++++++++++++++++++++++++-----
1 files changed, 89 insertions(+), 11 deletions(-)
diff --git a/src/network.c b/src/network.c
index a77d607..faedb8c 100644
--- a/src/network.c
+++ b/src/network.c
@@ -2,10 +2,12 @@
#include "network.h"
#include "image.h"
#include "data.h"
+#include "utils.h"
#include "connected_layer.h"
#include "convolutional_layer.h"
#include "maxpool_layer.h"
+#include "softmax_layer.h"
network make_network(int n)
{
@@ -30,6 +32,11 @@
forward_connected_layer(layer, input);
input = layer.output;
}
+ else if(net.types[i] == SOFTMAX){
+ softmax_layer layer = *(softmax_layer *)net.layers[i];
+ forward_softmax_layer(layer, input);
+ input = layer.output;
+ }
else if(net.types[i] == MAXPOOL){
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
forward_maxpool_layer(layer, input);
@@ -44,14 +51,17 @@
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);
+ update_convolutional_layer(layer, step, 0.9, .01);
}
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] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
- update_connected_layer(layer, step, .3, 0);
+ update_connected_layer(layer, step, .9, 0);
}
}
}
@@ -64,6 +74,9 @@
} else if(net.types[i] == MAXPOOL){
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
return layer.output;
+ } else if(net.types[i] == SOFTMAX){
+ softmax_layer layer = *(softmax_layer *)net.layers[i];
+ return layer.output;
} else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
return layer.output;
@@ -83,6 +96,9 @@
} else if(net.types[i] == MAXPOOL){
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
return layer.delta;
+ } else if(net.types[i] == SOFTMAX){
+ softmax_layer layer = *(softmax_layer *)net.layers[i];
+ return layer.delta;
} else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
return layer.delta;
@@ -114,7 +130,12 @@
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];
+ maxpool_layer layer = *(maxpool_layer *)net.layers[i];
+ if(i != 0) backward_maxpool_layer(layer, prev_input, prev_delta);
+ }
+ else if(net.types[i] == SOFTMAX){
+ softmax_layer layer = *(softmax_layer *)net.layers[i];
+ if(i != 0) backward_softmax_layer(layer, prev_input, prev_delta);
}
else if(net.types[i] == CONNECTED){
connected_layer layer = *(connected_layer *)net.layers[i];
@@ -130,19 +151,33 @@
int k = get_network_output_size(net);
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){
- //printf("%f %f\n", b.truth[i][j], output[j]);
delta[j] = b.truth[i][j]-output[j];
- if(fabs(delta[j]) < .5) ++correct;
- //printf("%f\n", output[j]);
+ if(output[j] > max) {
+ max = output[j];
+ max_k = j;
+ }
}
+ if(b.truth[i][max_k]) ++correct;
+ printf("%f\n", (double)correct/(i+1));
learn_network(net, b.images[i].data);
- update_network(net, .00001);
+ update_network(net, .001);
+ if(i%100 == 0){
+ visualize_network(net);
+ cvWaitKey(100);
+ }
}
+ visualize_network(net);
+ print_network(net);
+ cvWaitKey(100);
printf("Accuracy: %f\n", (double)correct/b.n);
}
@@ -162,6 +197,10 @@
connected_layer layer = *(connected_layer *)net.layers[i];
return layer.outputs;
}
+ else if(net.types[i] == SOFTMAX){
+ softmax_layer layer = *(softmax_layer *)net.layers[i];
+ return layer.inputs;
+ }
return 0;
}
@@ -181,7 +220,7 @@
maxpool_layer layer = *(maxpool_layer *)net.layers[i];
return get_maxpool_image(layer);
}
- return make_image(0,0,0);
+ return make_empty_image(0,0,0);
}
image get_network_image(network net)
@@ -191,17 +230,56 @@
image m = get_network_image_layer(net, i);
if(m.h != 0) return m;
}
- return make_image(1,1,1);
+ return make_empty_image(0,0,0);
}
void visualize_network(network net)
{
int i;
- for(i = 0; i < 1; ++i){
+ char buff[256];
+ for(i = 0; i < net.n; ++i){
+ sprintf(buff, "Layer %d", i);
if(net.types[i] == CONVOLUTIONAL){
convolutional_layer layer = *(convolutional_layer *)net.layers[i];
- visualize_convolutional_layer(layer);
+ visualize_convolutional_filters(layer, buff);
}
}
}
+void print_network(network net)
+{
+ int i,j;
+ for(i = 0; i < net.n; ++i){
+ double *output;
+ int n = 0;
+ if(net.types[i] == CONVOLUTIONAL){
+ convolutional_layer layer = *(convolutional_layer *)net.layers[i];
+ output = layer.output;
+ image m = get_convolutional_image(layer);
+ n = m.h*m.w*m.c;
+ }
+ else if(net.types[i] == MAXPOOL){
+ maxpool_layer layer = *(maxpool_layer *)net.layers[i];
+ output = layer.output;
+ image m = get_maxpool_image(layer);
+ n = m.h*m.w*m.c;
+ }
+ else if(net.types[i] == CONNECTED){
+ connected_layer layer = *(connected_layer *)net.layers[i];
+ output = layer.output;
+ n = layer.outputs;
+ }
+ else if(net.types[i] == SOFTMAX){
+ softmax_layer layer = *(softmax_layer *)net.layers[i];
+ output = layer.output;
+ n = layer.inputs;
+ }
+ double mean = mean_array(output, n);
+ double 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]);
+ if(n == 100)fprintf(stderr,".....\n");
+ fprintf(stderr, "\n");
+ }
+}
--
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