From cc06817efa24f20811ef6b32143c6700a91c5f2a Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Fri, 11 Apr 2014 08:00:27 +0000
Subject: [PATCH] Attempt at visualizing ImageNet Features
---
src/network.c | 140 ++++++++++++++++++++++++++++++++++------------
1 files changed, 104 insertions(+), 36 deletions(-)
diff --git a/src/network.c b/src/network.c
index f7abf58..edae3c7 100644
--- a/src/network.c
+++ b/src/network.c
@@ -10,10 +10,11 @@
#include "maxpool_layer.h"
#include "softmax_layer.h"
-network make_network(int n)
+network make_network(int n, int batch)
{
network net;
net.n = n;
+ net.batch = batch;
net.layers = calloc(net.n, sizeof(void *));
net.types = calloc(net.n, sizeof(LAYER_TYPE));
net.outputs = 0;
@@ -21,18 +22,19 @@
return net;
}
-void print_convolutional_cfg(FILE *fp, convolutional_layer *l)
+void print_convolutional_cfg(FILE *fp, convolutional_layer *l, int first)
{
int i;
- fprintf(fp, "[convolutional]\n"
- "height=%d\n"
- "width=%d\n"
- "channels=%d\n"
- "filters=%d\n"
+ fprintf(fp, "[convolutional]\n");
+ if(first) fprintf(fp, "batch=%d\n"
+ "height=%d\n"
+ "width=%d\n"
+ "channels=%d\n",
+ l->batch,l->h, l->w, l->c);
+ fprintf(fp, "filters=%d\n"
"size=%d\n"
"stride=%d\n"
"activation=%s\n",
- l->h, l->w, l->c,
l->n, l->size, l->stride,
get_activation_string(l->activation));
fprintf(fp, "data=");
@@ -40,14 +42,14 @@
for(i = 0; i < l->n*l->c*l->size*l->size; ++i) fprintf(fp, "%g,", l->filters[i]);
fprintf(fp, "\n\n");
}
-void print_connected_cfg(FILE *fp, connected_layer *l)
+void print_connected_cfg(FILE *fp, connected_layer *l, int first)
{
int i;
- fprintf(fp, "[connected]\n"
- "input=%d\n"
- "output=%d\n"
+ fprintf(fp, "[connected]\n");
+ if(first) fprintf(fp, "batch=%d\ninput=%d\n", l->batch, l->inputs);
+ fprintf(fp, "output=%d\n"
"activation=%s\n",
- l->inputs, l->outputs,
+ l->outputs,
get_activation_string(l->activation));
fprintf(fp, "data=");
for(i = 0; i < l->outputs; ++i) fprintf(fp, "%g,", l->biases[i]);
@@ -55,22 +57,22 @@
fprintf(fp, "\n\n");
}
-void print_maxpool_cfg(FILE *fp, maxpool_layer *l)
+void print_maxpool_cfg(FILE *fp, maxpool_layer *l, int first)
{
- fprintf(fp, "[maxpool]\n"
- "height=%d\n"
- "width=%d\n"
- "channels=%d\n"
- "stride=%d\n\n",
- l->h, l->w, l->c,
- l->stride);
+ fprintf(fp, "[maxpool]\n");
+ if(first) fprintf(fp, "batch=%d\n"
+ "height=%d\n"
+ "width=%d\n"
+ "channels=%d\n",
+ l->batch,l->h, l->w, l->c);
+ fprintf(fp, "stride=%d\n\n", l->stride);
}
-void print_softmax_cfg(FILE *fp, softmax_layer *l)
+void print_softmax_cfg(FILE *fp, softmax_layer *l, int first)
{
- fprintf(fp, "[softmax]\n"
- "input=%d\n\n",
- l->inputs);
+ fprintf(fp, "[softmax]\n");
+ if(first) fprintf(fp, "batch=%d\ninput=%d\n", l->batch, l->inputs);
+ fprintf(fp, "\n");
}
void save_network(network net, char *filename)
@@ -81,13 +83,13 @@
for(i = 0; i < net.n; ++i)
{
if(net.types[i] == CONVOLUTIONAL)
- print_convolutional_cfg(fp, (convolutional_layer *)net.layers[i]);
+ print_convolutional_cfg(fp, (convolutional_layer *)net.layers[i], i==0);
else if(net.types[i] == CONNECTED)
- print_connected_cfg(fp, (connected_layer *)net.layers[i]);
+ print_connected_cfg(fp, (connected_layer *)net.layers[i], i==0);
else if(net.types[i] == MAXPOOL)
- print_maxpool_cfg(fp, (maxpool_layer *)net.layers[i]);
+ print_maxpool_cfg(fp, (maxpool_layer *)net.layers[i], i==0);
else if(net.types[i] == SOFTMAX)
- print_softmax_cfg(fp, (softmax_layer *)net.layers[i]);
+ print_softmax_cfg(fp, (softmax_layer *)net.layers[i], i==0);
}
fclose(fp);
}
@@ -192,11 +194,11 @@
float *out = get_network_output(net);
int i, k = get_network_output_size(net);
for(i = 0; i < k; ++i){
- printf("%f, ", out[i]);
+ //printf("%f, ", out[i]);
delta[i] = truth[i] - out[i];
sum += delta[i]*delta[i];
}
- printf("\n");
+ //printf("\n");
return sum;
}
@@ -259,19 +261,26 @@
int i;
float error = 0;
int correct = 0;
+ int pos = 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);
+ float err = train_network_datum(net, d.X.vals[index], d.y.vals[index], step, momentum, decay);
float *y = d.y.vals[index];
int class = get_predicted_class_network(net);
correct += (y[class]?1:0);
+ if(y[1]){
+ error += err;
+ ++pos;
+ }
+
+
//printf("%d %f %f\n", i,net.output[0], d.y.vals[index][0]);
//if((i+1)%10 == 0){
// printf("%d: %f\n", (i+1), (float)correct/(i+1));
//}
}
- printf("Accuracy: %f\n",(float) correct/n);
- return error/n;
+ //printf("Accuracy: %f\n",(float) correct/n);
+ return error/pos;
}
float train_network_batch(network net, data d, int n, float step, float momentum,float decay)
{
@@ -305,7 +314,7 @@
}
visualize_network(net);
cvWaitKey(100);
- printf("Accuracy: %f\n", (float)correct/d.X.rows);
+ fprintf(stderr, "Accuracy: %f\n", (float)correct/d.X.rows);
}
int get_network_output_size_layer(network net, int i)
@@ -331,6 +340,63 @@
return 0;
}
+/*
+int resize_network(network net, int h, int w, int c)
+{
+ int i;
+ for (i = 0; i < net.n; ++i){
+ if(net.types[i] == CONVOLUTIONAL){
+ convolutional_layer *layer = (convolutional_layer *)net.layers[i];
+ layer->h = h;
+ layer->w = w;
+ layer->c = c;
+ image output = get_convolutional_image(*layer);
+ h = output.h;
+ w = output.w;
+ c = output.c;
+ }
+ else if(net.types[i] == MAXPOOL){
+ maxpool_layer *layer = (maxpool_layer *)net.layers[i];
+ layer->h = h;
+ layer->w = w;
+ layer->c = c;
+ image output = get_maxpool_image(*layer);
+ h = output.h;
+ w = output.w;
+ c = output.c;
+ }
+ }
+ return 0;
+}
+*/
+
+int resize_network(network net, int h, int w, int c)
+{
+ int i;
+ for (i = 0; i < net.n; ++i){
+ if(net.types[i] == CONVOLUTIONAL){
+ convolutional_layer *layer = (convolutional_layer *)net.layers[i];
+ resize_convolutional_layer(layer, h, w, c);
+ image output = get_convolutional_image(*layer);
+ h = output.h;
+ w = output.w;
+ c = output.c;
+ }
+ else if(net.types[i] == MAXPOOL){
+ maxpool_layer *layer = (maxpool_layer *)net.layers[i];
+ resize_maxpool_layer(layer, h, w, c);
+ image output = get_maxpool_image(*layer);
+ h = output.h;
+ w = output.w;
+ c = output.c;
+ }
+ else{
+ error("Cannot resize this type of layer");
+ }
+ }
+ return 0;
+}
+
int get_network_output_size(network net)
{
int i = net.n-1;
@@ -362,13 +428,14 @@
void visualize_network(network net)
{
+ image *prev = 0;
int 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, buff);
+ prev = visualize_convolutional_layer(layer, buff, prev);
}
}
}
@@ -440,3 +507,4 @@
return acc;
}
+
--
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