From a392bbd0c957a00e3782c96e7ced84a29ff9dd88 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Tue, 15 Mar 2016 05:33:02 +0000
Subject: [PATCH] Play along w/ alphago
---
src/network.c | 31 +++++++++++++++++++++++++++++--
1 files changed, 29 insertions(+), 2 deletions(-)
diff --git a/src/network.c b/src/network.c
index 8dee8cc..e6fb51e 100644
--- a/src/network.c
+++ b/src/network.c
@@ -8,8 +8,11 @@
#include "crop_layer.h"
#include "connected_layer.h"
+#include "rnn_layer.h"
+#include "crnn_layer.h"
#include "local_layer.h"
#include "convolutional_layer.h"
+#include "activation_layer.h"
#include "deconvolutional_layer.h"
#include "detection_layer.h"
#include "normalization_layer.h"
@@ -73,12 +76,18 @@
switch(a){
case CONVOLUTIONAL:
return "convolutional";
+ case ACTIVE:
+ return "activation";
case LOCAL:
return "local";
case DECONVOLUTIONAL:
return "deconvolutional";
case CONNECTED:
return "connected";
+ case RNN:
+ return "rnn";
+ case CRNN:
+ return "crnn";
case MAXPOOL:
return "maxpool";
case AVGPOOL:
@@ -131,6 +140,8 @@
forward_convolutional_layer(l, state);
} else if(l.type == DECONVOLUTIONAL){
forward_deconvolutional_layer(l, state);
+ } else if(l.type == ACTIVE){
+ forward_activation_layer(l, state);
} else if(l.type == LOCAL){
forward_local_layer(l, state);
} else if(l.type == NORMALIZATION){
@@ -139,6 +150,10 @@
forward_detection_layer(l, state);
} else if(l.type == CONNECTED){
forward_connected_layer(l, state);
+ } else if(l.type == RNN){
+ forward_rnn_layer(l, state);
+ } else if(l.type == CRNN){
+ forward_crnn_layer(l, state);
} else if(l.type == CROP){
forward_crop_layer(l, state);
} else if(l.type == COST){
@@ -173,6 +188,10 @@
update_deconvolutional_layer(l, rate, net.momentum, net.decay);
} else if(l.type == CONNECTED){
update_connected_layer(l, update_batch, rate, net.momentum, net.decay);
+ } else if(l.type == RNN){
+ update_rnn_layer(l, update_batch, rate, net.momentum, net.decay);
+ } else if(l.type == CRNN){
+ update_crnn_layer(l, update_batch, rate, net.momentum, net.decay);
} else if(l.type == LOCAL){
update_local_layer(l, update_batch, rate, net.momentum, net.decay);
}
@@ -193,7 +212,7 @@
int count = 0;
for(i = 0; i < net.n; ++i){
if(net.layers[i].type == COST){
- sum += net.layers[i].output[0];
+ sum += net.layers[i].cost[0];
++count;
}
if(net.layers[i].type == DETECTION){
@@ -231,6 +250,8 @@
backward_convolutional_layer(l, state);
} else if(l.type == DECONVOLUTIONAL){
backward_deconvolutional_layer(l, state);
+ } else if(l.type == ACTIVE){
+ backward_activation_layer(l, state);
} else if(l.type == NORMALIZATION){
backward_normalization_layer(l, state);
} else if(l.type == MAXPOOL){
@@ -245,6 +266,10 @@
if(i != 0) backward_softmax_layer(l, state);
} else if(l.type == CONNECTED){
backward_connected_layer(l, state);
+ } else if(l.type == RNN){
+ backward_rnn_layer(l, state);
+ } else if(l.type == CRNN){
+ backward_crnn_layer(l, state);
} else if(l.type == LOCAL){
backward_local_layer(l, state);
} else if(l.type == COST){
@@ -360,11 +385,12 @@
layer l = net->layers[i];
if(l.type == CONVOLUTIONAL){
resize_convolutional_layer(&l, w, h);
+ }else if(l.type == CROP){
+ resize_crop_layer(&l, w, h);
}else if(l.type == MAXPOOL){
resize_maxpool_layer(&l, w, h);
}else if(l.type == AVGPOOL){
resize_avgpool_layer(&l, w, h);
- break;
}else if(l.type == NORMALIZATION){
resize_normalization_layer(&l, w, h);
}else if(l.type == COST){
@@ -376,6 +402,7 @@
net->layers[i] = l;
w = l.out_w;
h = l.out_h;
+ if(l.type == AVGPOOL) break;
}
//fprintf(stderr, " Done!\n");
return 0;
--
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