From d7286c273211ffeb1f56594f863d1ee9922be6d4 Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Thu, 07 Nov 2013 00:09:41 +0000
Subject: [PATCH] Loading may or may not work. But probably.

---
 src/connected_layer.c |  106 +++++++++++++++++++++++++++-------------------------
 1 files changed, 55 insertions(+), 51 deletions(-)

diff --git a/src/connected_layer.c b/src/connected_layer.c
index fe904ba..9fafc38 100644
--- a/src/connected_layer.c
+++ b/src/connected_layer.c
@@ -1,36 +1,38 @@
 #include "connected_layer.h"
 
+#include <math.h>
 #include <stdlib.h>
 #include <string.h>
 
-double activation(double x)
-{
-    return x*(x>0);
-}
-
-double gradient(double x)
-{
-    return (x>=0);
-}
-
-connected_layer make_connected_layer(int inputs, int outputs)
+connected_layer *make_connected_layer(int inputs, int outputs, ACTIVATION activator)
 {
     int i;
-    connected_layer layer;
-    layer.inputs = inputs;
-    layer.outputs = outputs;
+    connected_layer *layer = calloc(1, sizeof(connected_layer));
+    layer->inputs = inputs;
+    layer->outputs = outputs;
 
-    layer.output = calloc(outputs, sizeof(double*));
+    layer->output = calloc(outputs, sizeof(double*));
 
-    layer.weight_updates = calloc(inputs*outputs, sizeof(double));
-    layer.weights = calloc(inputs*outputs, sizeof(double));
+    layer->weight_updates = calloc(inputs*outputs, sizeof(double));
+    layer->weights = calloc(inputs*outputs, sizeof(double));
     for(i = 0; i < inputs*outputs; ++i)
-        layer.weights[i] = .5 - (double)rand()/RAND_MAX;
+        layer->weights[i] = .5 - (double)rand()/RAND_MAX;
 
-    layer.bias_updates = calloc(outputs, sizeof(double));
-    layer.biases = calloc(outputs, sizeof(double));
+    layer->bias_updates = calloc(outputs, sizeof(double));
+    layer->biases = calloc(outputs, sizeof(double));
     for(i = 0; i < outputs; ++i)
-        layer.biases[i] = (double)rand()/RAND_MAX;
+        layer->biases[i] = (double)rand()/RAND_MAX;
+
+    if(activator == SIGMOID){
+        layer->activation = sigmoid_activation;
+        layer->gradient = sigmoid_gradient;
+    }else if(activator == RELU){
+        layer->activation = relu_activation;
+        layer->gradient = relu_gradient;
+    }else if(activator == IDENTITY){
+        layer->activation = identity_activation;
+        layer->gradient = identity_gradient;
+    }
 
     return layer;
 }
@@ -41,39 +43,16 @@
     for(i = 0; i < layer.outputs; ++i){
         layer.output[i] = layer.biases[i];
         for(j = 0; j < layer.inputs; ++j){
-            layer.output[i] += input[j]*layer.weights[i*layer.outputs + j];
+            layer.output[i] += input[j]*layer.weights[i*layer.inputs + j];
         }
-        layer.output[i] = activation(layer.output[i]);
+        layer.output[i] = layer.activation(layer.output[i]);
     }
 }
 
-void backpropagate_connected_layer(double *input, connected_layer layer)
+void learn_connected_layer(double *input, connected_layer layer)
 {
-    int i, j;
-    double *old_input = calloc(layer.inputs, sizeof(double));
-    memcpy(old_input, input, layer.inputs*sizeof(double));
-    memset(input, 0, layer.inputs*sizeof(double));
-
-    for(i = 0; i < layer.outputs; ++i){
-        for(j = 0; j < layer.inputs; ++j){
-            input[j] += layer.output[i]*layer.weights[i*layer.outputs + j];
-        }
-    }
-    for(j = 0; j < layer.inputs; ++j){
-        input[j] = input[j]*gradient(old_input[j]);
-    }
-    free(old_input);
-}
-
-void calculate_updates_connected_layer(double *input, connected_layer layer)
-{
-    int i, j;
-    for(i = 0; i < layer.outputs; ++i){
-        layer.bias_updates[i] += layer.output[i];
-        for(j = 0; j < layer.inputs; ++j){
-            layer.weight_updates[i*layer.outputs + j] += layer.output[i]*input[j];
-        }
-    }
+    calculate_update_connected_layer(input, layer);
+    backpropagate_connected_layer(input, layer);
 }
 
 void update_connected_layer(connected_layer layer, double step)
@@ -82,11 +61,36 @@
     for(i = 0; i < layer.outputs; ++i){
         layer.biases[i] += step*layer.bias_updates[i];
         for(j = 0; j < layer.inputs; ++j){
-            int index = i*layer.outputs+j;
-            layer.weights[index] = layer.weight_updates[index];
+            int index = i*layer.inputs+j;
+            layer.weights[index] += step*layer.weight_updates[index];
         }
     }
     memset(layer.bias_updates, 0, layer.outputs*sizeof(double));
     memset(layer.weight_updates, 0, layer.outputs*layer.inputs*sizeof(double));
 }
 
+void calculate_update_connected_layer(double *input, connected_layer layer)
+{
+    int i, j;
+    for(i = 0; i < layer.outputs; ++i){
+        layer.bias_updates[i] += layer.output[i];
+        for(j = 0; j < layer.inputs; ++j){
+            layer.weight_updates[i*layer.inputs + j] += layer.output[i]*input[j];
+        }
+    }
+}
+
+void backpropagate_connected_layer(double *input, connected_layer layer)
+{
+    int i, j;
+
+    for(j = 0; j < layer.inputs; ++j){
+        double grad = layer.gradient(input[j]);
+        input[j] = 0;
+        for(i = 0; i < layer.outputs; ++i){
+            input[j] += layer.output[i]*layer.weights[i*layer.inputs + j];
+        }
+        input[j] *= grad;
+    }
+}
+

--
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