我正在尝试为考试实现一个简单的反向传播算法（我是一个初级程序员）。
我有一组数组，我生成随机权重来开始算法。
我按照数学公式实现了激活函数：
formula
（其中x索引表示输入，y索引表示隐藏神经元输入）
我的问题是，我得到了一些指数值很高的求和结果，这些结果与我的期望值不符。
这是我的代码：
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <time.h>
#define INPUT_NEURONS 4
#define HIDDEN_NEURONS 7
#define OUTPUT_NEURONS 3
#define MAX_SAMPLES 150
#define LEARNING_RATE 0.1
#define RAND_WEIGHT ((double)rand()/(RAND_MAX+1))
double IHweight[INPUT_NEURONS][HIDDEN_NEURONS]; /* in->hid weight */
double HOweight[HIDDEN_NEURONS][OUTPUT_NEURONS]; /* hid->out weight */
//activation
double inputs[MAX_SAMPLES][INPUT_NEURONS];
double hidden[HIDDEN_NEURONS];
double target[MAX_SAMPLES][OUTPUT_NEURONS];
double actual[OUTPUT_NEURONS];
//errors
double errO[OUTPUT_NEURONS];
double errH[HIDDEN_NEURONS];
double Error = 0.0;
int sample = 0;
typedef struct {
double sepal_lenght;
double sepal_width;
double petal_lenght;
double petal_width;
double output[OUTPUT_NEURONS];
} IRIS;
IRIS samples[MAX_SAMPLES] = {
{ 5.1, 3.5, 1.4, 0.2, 0.0, 0.0, 1.0 },
{ 4.9, 3.0, 1.4, 0.2, 0.0, 0.0, 1.0 },
{ 4.7, 3.2, 1.3, 0.2, 0.0, 0.0, 1.0 },
{...},
};
double sigmoid(double val) {
return (1.0 / (1.0 + exp(-val)));
}
double dsigmoid(double val) {
return (val * (1.0 - val));
}
void assignRandomWeights() {
int hid, inp, out;
printf("Initializing weights...\n\n");
for (inp = 0; inp < INPUT_NEURONS; inp++) {
for (hid = 0; hid < HIDDEN_NEURONS; hid++) {
IHweight[inp][hid] = RAND_WEIGHT;
printf("Weights : input %d -> hidden %d: %f\n",
inp, hid, IHweight[inp][hid]);
}
}
for (hid = 0; hid < HIDDEN_NEURONS; hid++) {
for (out = 0; out < OUTPUT_NEURONS; out++) {
HOweight[hid][out] = RAND_WEIGHT;
printf("hidden %d -> output %d: %f\n",
hid, out, HOweight[hid][out]);
}
}
system("pause");
}
void activation() {
int hid, inp, out;
double sumH[HIDDEN_NEURONS] ;
double sumO[OUTPUT_NEURONS];
for (hid = 0; hid < HIDDEN_NEURONS; hid++) {
for (inp = 0; inp < INPUT_NEURONS; inp++) {
sumH[hid] += (inputs[sample][inp] * IHweight[inp][hid]);
printf("\n%d Input %d = %.1f Weight = %f sumH = %g",
sample, inp, inputs[sample][inp], IHweight[inp][hid], sumH[hid]);
}
hidden[hid] = sigmoid(sumH[hid]);
printf("\nHidden neuron %d activation = %f", hid, hidden[hid]);
}
for (out = 0; out < OUTPUT_NEURONS; out++) {
for (hid = 0; hid < HIDDEN_NEURONS; hid++) {
sumO[out] += (hidden[hid] * HOweight[hid][out]);
printf("\n%d Hidden %d = %f Weight = %f sumO = %g",
sample, hid, hidden[hid], HOweight[hid][out], sumO[out]);
}
actual[out] = sigmoid(sumO[out]);
printf("\nOutput neuron %d activation = %f", out, actual[out]);
}
}
main () {
srand(time(NULL));
assignRandomWeights();
for (int epoch = 0; epoch < 1; epoch++) {
for (int i = 0; i < 1; i++) {
sample = rand() % MAX_SAMPLES;
inputs[sample][0] = samples[sample].sepal_lenght;
inputs[sample][1] = samples[sample].sepal_width;
inputs[sample][2] = samples[sample].petal_lenght;
inputs[sample][3] = samples[sample].petal_width;
target[sample][0] = samples[sample].output[0];
target[sample][1] = samples[sample].output[1];
target[sample][2] = samples[sample].output[2];
activation();
}
}
}
我用了很多
printf()来检查我的结果...
41 Input 0 = 4.5 Weight = 0.321014 sumH = 1.31886e+267
41 Input 1 = 2.3 Weight = 0.772369 sumH = 1.31886e+267
41 Input 2 = 1.3 Weight = 0.526123 sumH = 1.31886e+267
41 Input 3 = 0.3 Weight = 0.271881 sumH = 1.31886e+267
Hidden neuron 6 activation = 1.000000
...
41 Hidden 0 = 0.974952 Weight = 0.343445 sumO = 1.24176e+267
41 Hidden 1 = 0.917789 Weight = 0.288361 sumO = 1.24176e+267
41 Hidden 2 = 0.999188 Weight = 0.972168 sumO = 1.24176e+267
41 Hidden 3 = 0.989726 Weight = 0.082642 sumO = 1.24176e+267
41 Hidden 4 = 0.979063 Weight = 0.531799 sumO = 1.24176e+267
41 Hidden 5 = 0.972474 Weight = 0.552521 sumO = 1.24176e+267
41 Hidden 6 = 1.000000 Weight = 0.707153 sumO = 1.24176e+267
Output neuron 1 activation = 1.000000
据我所知，
assignRandomweights()和sigmoid()函数都正常，问题出在activation()中。请帮助我理解为什么会发生这种情况以及如何解决它。
最佳答案
你的问题在于
double sumH[HIDDEN_NEURONS];
double sumO[OUTPUT_NEURONS];
在使用前不要初始化它们。从技术上讲，程序行为是未定义的。友好的编译器似乎正在将未初始化的变量设置为大值。（其他平台，如安腾（Itanium）将捕获一个“非事物”）。
一个简单的补救方法是使用
double sumH[HIDDEN_NEURONS] = {0};等，将每个元素设置为零。关于c - 求和给出随机错误的数字，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/34857412/