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 | 103 +++++++++++++++++++++++++++------------------------
1 files changed, 54 insertions(+), 49 deletions(-)
diff --git a/src/connected_layer.c b/src/connected_layer.c
index fe904ba..d77a10c 100644
--- a/src/connected_layer.c
+++ b/src/connected_layer.c
@@ -1,92 +1,97 @@
#include "connected_layer.h"
+#include <math.h>
+#include <stdio.h>
#include <stdlib.h>
#include <string.h>
-double activation(double x)
+connected_layer *make_connected_layer(int inputs, int outputs, ACTIVATION activator)
{
- return x*(x>0);
-}
-
-double gradient(double x)
-{
- return (x>=0);
-}
-
-connected_layer make_connected_layer(int inputs, int outputs)
-{
+ printf("Connected Layer: %d inputs, %d outputs\n", inputs, outputs);
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->delta = 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->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.biases = calloc(outputs, sizeof(double));
+ 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;
+ 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;
}
-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){
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)
-{
- 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)
+void learn_connected_layer(connected_layer layer, double *input)
{
int i, j;
for(i = 0; i < layer.outputs; ++i){
- layer.bias_updates[i] += layer.output[i];
+ layer.bias_updates[i] += layer.delta[i];
for(j = 0; j < layer.inputs; ++j){
- layer.weight_updates[i*layer.outputs + j] += layer.output[i]*input[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.outputs+j;
- layer.weights[index] = layer.weight_updates[index];
+ int index = i*layer.inputs+j;
+ 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 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]);
+ delta[j] = 0;
+ for(i = 0; i < layer.outputs; ++i){
+ delta[j] += layer.delta[i]*layer.weights[i*layer.inputs + j];
+ }
+ delta[j] *= grad;
+ }
+}
+
--
Gitblit v1.10.0