From 2db9fbef2bd7d35a547d0018a9850f6b249c524f Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Wed, 13 Nov 2013 18:50:38 +0000
Subject: [PATCH] Parsing, image loading, lots of stuff
---
src/connected_layer.c | 49 +++++++++++++++++++++++++------------------------
1 files changed, 25 insertions(+), 24 deletions(-)
diff --git a/src/connected_layer.c b/src/connected_layer.c
index 9fafc38..d77a10c 100644
--- a/src/connected_layer.c
+++ b/src/connected_layer.c
@@ -1,27 +1,32 @@
#include "connected_layer.h"
#include <math.h>
+#include <stdio.h>
#include <stdlib.h>
#include <string.h>
connected_layer *make_connected_layer(int inputs, int outputs, ACTIVATION activator)
{
+ printf("Connected Layer: %d inputs, %d outputs\n", inputs, outputs);
int i;
connected_layer *layer = calloc(1, sizeof(connected_layer));
layer->inputs = inputs;
layer->outputs = outputs;
layer->output = calloc(outputs, sizeof(double*));
+ layer->delta = calloc(outputs, sizeof(double*));
layer->weight_updates = calloc(inputs*outputs, sizeof(double));
+ layer->weight_momentum = 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] = .01*(.5 - (double)rand()/RAND_MAX);
layer->bias_updates = calloc(outputs, sizeof(double));
+ layer->bias_momentum = 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] = 1;
if(activator == SIGMOID){
layer->activation = sigmoid_activation;
@@ -37,7 +42,7 @@
return layer;
}
-void run_connected_layer(double *input, connected_layer layer)
+void forward_connected_layer(connected_layer layer, double *input)
{
int i, j;
for(i = 0; i < layer.outputs; ++i){
@@ -49,48 +54,44 @@
}
}
-void learn_connected_layer(double *input, connected_layer layer)
+void learn_connected_layer(connected_layer layer, double *input)
{
- calculate_update_connected_layer(input, layer);
- backpropagate_connected_layer(input, layer);
+ int i, j;
+ for(i = 0; i < layer.outputs; ++i){
+ layer.bias_updates[i] += layer.delta[i];
+ for(j = 0; j < layer.inputs; ++j){
+ layer.weight_updates[i*layer.inputs + j] += layer.delta[i]*input[j];
+ }
+ }
}
-void update_connected_layer(connected_layer layer, double step)
+void update_connected_layer(connected_layer layer, double step, double momentum, double decay)
{
int i,j;
for(i = 0; i < layer.outputs; ++i){
- layer.biases[i] += step*layer.bias_updates[i];
+ layer.bias_momentum[i] = step*(layer.bias_updates[i] - decay*layer.biases[i]) + momentum*layer.bias_momentum[i];
+ layer.biases[i] += layer.bias_momentum[i];
for(j = 0; j < layer.inputs; ++j){
int index = i*layer.inputs+j;
- layer.weights[index] += step*layer.weight_updates[index];
+ layer.weight_momentum[index] = step*(layer.weight_updates[index] - decay*layer.weights[index]) + momentum*layer.weight_momentum[index];
+ layer.weights[index] += layer.weight_momentum[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)
+void backward_connected_layer(connected_layer layer, double *input, double *delta)
{
int i, j;
for(j = 0; j < layer.inputs; ++j){
double grad = layer.gradient(input[j]);
- input[j] = 0;
+ delta[j] = 0;
for(i = 0; i < layer.outputs; ++i){
- input[j] += layer.output[i]*layer.weights[i*layer.inputs + j];
+ delta[j] += layer.delta[i]*layer.weights[i*layer.inputs + j];
}
- input[j] *= grad;
+ delta[j] *= grad;
}
}
--
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