From 0d6bb5d44d8e815ebf6ccce1dae2f83178780e7b Mon Sep 17 00:00:00 2001
From: Joseph Redmon <pjreddie@gmail.com>
Date: Tue, 03 Dec 2013 00:41:40 +0000
Subject: [PATCH] Working?
---
src/connected_layer.c | 27 ++++++++++-----------------
1 files changed, 10 insertions(+), 17 deletions(-)
diff --git a/src/connected_layer.c b/src/connected_layer.c
index d77a10c..99f146b 100644
--- a/src/connected_layer.c
+++ b/src/connected_layer.c
@@ -1,11 +1,12 @@
#include "connected_layer.h"
+#include "utils.h"
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
-connected_layer *make_connected_layer(int inputs, int outputs, ACTIVATION activator)
+connected_layer *make_connected_layer(int inputs, int outputs, ACTIVATION activation)
{
printf("Connected Layer: %d inputs, %d outputs\n", inputs, outputs);
int i;
@@ -19,26 +20,18 @@
layer->weight_updates = calloc(inputs*outputs, sizeof(double));
layer->weight_momentum = calloc(inputs*outputs, sizeof(double));
layer->weights = calloc(inputs*outputs, sizeof(double));
+ double scale = 2./inputs;
for(i = 0; i < inputs*outputs; ++i)
- layer->weights[i] = .01*(.5 - (double)rand()/RAND_MAX);
+ layer->weights[i] = rand_normal()*scale;
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] = rand_normal()*scale + scale;
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;
- }
-
+ layer->activation = activation;
return layer;
}
@@ -50,7 +43,7 @@
for(j = 0; j < layer.inputs; ++j){
layer.output[i] += input[j]*layer.weights[i*layer.inputs + j];
}
- layer.output[i] = layer.activation(layer.output[i]);
+ layer.output[i] = activate(layer.output[i], layer.activation);
}
}
@@ -58,6 +51,7 @@
{
int i, j;
for(i = 0; i < layer.outputs; ++i){
+ layer.delta[i] *= gradient(layer.output[i], layer.activation);
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];
@@ -69,12 +63,13 @@
{
int i,j;
for(i = 0; i < layer.outputs; ++i){
- layer.bias_momentum[i] = step*(layer.bias_updates[i] - decay*layer.biases[i]) + momentum*layer.bias_momentum[i];
+ layer.bias_momentum[i] = step*(layer.bias_updates[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.weight_momentum[index] = step*(layer.weight_updates[index] - decay*layer.weights[index]) + momentum*layer.weight_momentum[index];
layer.weights[index] += layer.weight_momentum[index];
+ //layer.weights[index] = constrain(layer.weights[index], 100.);
}
}
memset(layer.bias_updates, 0, layer.outputs*sizeof(double));
@@ -86,12 +81,10 @@
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;
}
}
--
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