From d6fbe86e7a8c1bc389902c90c57ee7e80f5475b9 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Tue, 16 Dec 2014 19:40:05 +0000
Subject: [PATCH] updates?

---
 src/network.c |   90 ++++++++++++++++++++++++++++++++++++++++++++-
 1 files changed, 88 insertions(+), 2 deletions(-)

diff --git a/src/network.c b/src/network.c
index 3a6a184..f451fd9 100644
--- a/src/network.c
+++ b/src/network.c
@@ -125,6 +125,9 @@
     } else if(net.types[i] == CONNECTED){
         connected_layer layer = *(connected_layer *)net.layers[i];
         return layer.output;
+    } else if(net.types[i] == CROP){
+        crop_layer layer = *(crop_layer *)net.layers[i];
+        return layer.output;
     } else if(net.types[i] == NORMALIZATION){
         normalization_layer layer = *(normalization_layer *)net.layers[i];
         return layer.output;
@@ -219,6 +222,10 @@
             maxpool_layer layer = *(maxpool_layer *)net.layers[i];
             if(i != 0) backward_maxpool_layer(layer, prev_delta);
         }
+        else if(net.types[i] == DROPOUT){
+            dropout_layer layer = *(dropout_layer *)net.layers[i];
+            backward_dropout_layer(layer, prev_delta);
+        }
         else if(net.types[i] == NORMALIZATION){
             normalization_layer layer = *(normalization_layer *)net.layers[i];
             if(i != 0) backward_normalization_layer(layer, prev_input, prev_delta);
@@ -323,6 +330,65 @@
     fprintf(stderr, "Accuracy: %f\n", (float)correct/d.X.rows);
 }
 
+void set_learning_network(network *net, float rate, float momentum, float decay)
+{
+    int i;
+    net->learning_rate=rate;
+    net->momentum = momentum;
+    net->decay = decay;
+    for(i = 0; i < net->n; ++i){
+        if(net->types[i] == CONVOLUTIONAL){
+            convolutional_layer *layer = (convolutional_layer *)net->layers[i];
+            layer->learning_rate=rate;
+            layer->momentum = momentum;
+            layer->decay = decay;
+        }
+        else if(net->types[i] == CONNECTED){
+            connected_layer *layer = (connected_layer *)net->layers[i];
+            layer->learning_rate=rate;
+            layer->momentum = momentum;
+            layer->decay = decay;
+        }
+    }
+}
+
+
+void set_batch_network(network *net, int b)
+{
+    net->batch = b;
+    int i;
+    for(i = 0; i < net->n; ++i){
+        if(net->types[i] == CONVOLUTIONAL){
+            convolutional_layer *layer = (convolutional_layer *)net->layers[i];
+            layer->batch = b;
+        }
+        else if(net->types[i] == MAXPOOL){
+            maxpool_layer *layer = (maxpool_layer *)net->layers[i];
+            layer->batch = b;
+        }
+        else if(net->types[i] == CONNECTED){
+            connected_layer *layer = (connected_layer *)net->layers[i];
+            layer->batch = b;
+        } else if(net->types[i] == DROPOUT){
+            dropout_layer *layer = (dropout_layer *) net->layers[i];
+            layer->batch = b;
+        }
+        else if(net->types[i] == FREEWEIGHT){
+            freeweight_layer *layer = (freeweight_layer *) net->layers[i];
+            layer->batch = b;
+        }
+        else if(net->types[i] == SOFTMAX){
+            softmax_layer *layer = (softmax_layer *)net->layers[i];
+            layer->batch = b;
+        }
+        else if(net->types[i] == COST){
+            cost_layer *layer = (cost_layer *)net->layers[i];
+            layer->batch = b;
+        }
+    }
+}
+
+
 int get_network_input_size_layer(network net, int i)
 {
     if(net.types[i] == CONVOLUTIONAL){
@@ -339,6 +405,9 @@
     } else if(net.types[i] == DROPOUT){
         dropout_layer layer = *(dropout_layer *) net.layers[i];
         return layer.inputs;
+    } else if(net.types[i] == CROP){
+        crop_layer layer = *(crop_layer *) net.layers[i];
+        return layer.c*layer.h*layer.w;
     }
     else if(net.types[i] == FREEWEIGHT){
         freeweight_layer layer = *(freeweight_layer *) net.layers[i];
@@ -348,6 +417,7 @@
         softmax_layer layer = *(softmax_layer *)net.layers[i];
         return layer.inputs;
     }
+    printf("Can't find input size\n");
     return 0;
 }
 
@@ -363,6 +433,10 @@
         image output = get_maxpool_image(layer);
         return output.h*output.w*output.c;
     }
+     else if(net.types[i] == CROP){
+        crop_layer layer = *(crop_layer *) net.layers[i];
+        return layer.c*layer.crop_height*layer.crop_width;
+    }
     else if(net.types[i] == CONNECTED){
         connected_layer layer = *(connected_layer *)net.layers[i];
         return layer.outputs;
@@ -379,6 +453,7 @@
         softmax_layer layer = *(softmax_layer *)net.layers[i];
         return layer.inputs;
     }
+    printf("Can't find output size\n");
     return 0;
 }
 
@@ -586,15 +661,26 @@
 float network_accuracy(network net, data d)
 {
     matrix guess = network_predict_data(net, d);
-    float acc = matrix_accuracy(d.y, guess);
+    float acc = matrix_topk_accuracy(d.y, guess,1);
     free_matrix(guess);
     return acc;
 }
 
+float *network_accuracies(network net, data d)
+{
+    static float acc[2];
+    matrix guess = network_predict_data(net, d);
+    acc[0] = matrix_topk_accuracy(d.y, guess,1);
+    acc[1] = matrix_topk_accuracy(d.y, guess,5);
+    free_matrix(guess);
+    return acc;
+}
+
+
 float network_accuracy_multi(network net, data d, int n)
 {
     matrix guess = network_predict_data_multi(net, d, n);
-    float acc = matrix_accuracy(d.y, guess);
+    float acc = matrix_topk_accuracy(d.y, guess,1);
     free_matrix(guess);
     return acc;
 }

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