From 913d355ec1cf34aad71fdd75202fc3b0309e63a0 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Thu, 28 Jan 2016 20:30:38 +0000
Subject: [PATCH] lots of stuff

---
 src/network.c |   20 +++++++++++++++++++-
 1 files changed, 19 insertions(+), 1 deletions(-)

diff --git a/src/network.c b/src/network.c
index 8dee8cc..32c3ba1 100644
--- a/src/network.c
+++ b/src/network.c
@@ -8,8 +8,10 @@
 
 #include "crop_layer.h"
 #include "connected_layer.h"
+#include "rnn_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 +75,16 @@
     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 MAXPOOL:
             return "maxpool";
         case AVGPOOL:
@@ -131,6 +137,8 @@
             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 +147,8 @@
             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 == CROP){
             forward_crop_layer(l, state);
         } else if(l.type == COST){
@@ -173,6 +183,8 @@
             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 == LOCAL){
             update_local_layer(l, update_batch, rate, net.momentum, net.decay);
         }
@@ -231,6 +243,8 @@
             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 +259,8 @@
             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 == LOCAL){
             backward_local_layer(l, state);
         } else if(l.type == COST){
@@ -360,11 +376,12 @@
         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 +393,7 @@
         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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