From bc902b277e9131cc169751056786de5393da737d Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Mon, 24 Feb 2014 20:21:31 +0000
Subject: [PATCH] Imagenet Features\!
---
src/network.c | 55 +++++++++++++++++++++++++++----------------------------
1 files changed, 27 insertions(+), 28 deletions(-)
diff --git a/src/network.c b/src/network.c
index f5fea60..b2fc922 100644
--- a/src/network.c
+++ b/src/network.c
@@ -21,18 +21,18 @@
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, "height=%d\n"
+ "width=%d\n"
+ "channels=%d\n",
+ 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 +40,14 @@
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, "input=%d\n", 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 +55,21 @@
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, "height=%d\n"
+ "width=%d\n"
+ "channels=%d\n",
+ 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, "input=%d\n", l->inputs);
+ fprintf(fp, "\n");
}
void save_network(network net, char *filename)
@@ -81,13 +80,13 @@
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);
}
--
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