用c语言写神经网络算法
时间: 2024-03-28 14:12:13 浏览: 41
用C语言写的神经网络源码
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在C语言中实现神经网络算法需要遵循以下一般步骤:
1. 定义结构体:定义神经网络的结构体,包括神经元的数量、层数、权重值、偏移量等参数。
2. 初始化网络:初始化权重值和偏移量。可以使用随机数生成器来产生随机的初始值。
3. 前向传播:通过输入层将数据传递到输出层。对于每个层,计算每个神经元的输出值。
4. 反向传播:通过误差函数计算输出误差,并将误差反向传播回输入层。对于每个层,计算每个神经元的误差值。
5. 更新权重和偏移量:使用误差值来更新神经元之间的权重和偏移量。
6. 重复步骤3-5,直到达到预定的准确度或训练次数。
下面是一个简单的C语言代码示例:
```c
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#define INPUTS 2
#define HIDDEN 2
#define OUTPUTS 1
#define ETA 0.05
typedef struct {
double *inputs;
double *weights;
double output;
double error;
} Neuron;
typedef struct {
int inputs;
int neurons;
Neuron *neuron;
} Layer;
typedef struct {
int inputs;
int outputs;
int hidden_layers;
int neurons_per_layer;
Layer *layer;
} Network;
double sigmoid(double x);
double sigmoid_derivative(double x);
double random_weight();
double update_weight(double weight, double error, double input);
void init_network(Network *network);
void feed_forward(Network *network, double *inputs);
void back_propagate(Network *network, double *targets);
void update_weights(Network *network, double *inputs);
int main() {
Network network;
double inputs[INPUTS] = {0.0, 1.0};
double targets[OUTPUTS] = {1.0};
init_network(&network);
feed_forward(&network, inputs);
back_propagate(&network, targets);
update_weights(&network, inputs);
return 0;
}
double sigmoid(double x) {
return 1.0 / (1.0 + exp(-x));
}
double sigmoid_derivative(double x) {
return x * (1.0 - x);
}
double random_weight() {
return ((double)rand() / (double)RAND_MAX) - 0.5;
}
double update_weight(double weight, double error, double input) {
return weight + (ETA * error * input);
}
void init_network(Network *network) {
int i, j, k;
network->inputs = INPUTS;
network->outputs = OUTPUTS;
network->hidden_layers = 1;
network->neurons_per_layer = HIDDEN;
network->layer = (Layer *)malloc(sizeof(Layer) * (network->hidden_layers + 1));
for (i = 0; i < network->hidden_layers + 1; i++) {
network->layer[i].inputs = (i == 0) ? network->inputs : network->neurons_per_layer;
network->layer[i].neurons = (i == network->hidden_layers) ? network->outputs : network->neurons_per_layer;
network->layer[i].neuron = (Neuron *)malloc(sizeof(Neuron) * network->layer[i].neurons);
for (j = 0; j < network->layer[i].neurons; j++) {
network->layer[i].neuron[j].inputs = (double *)malloc(sizeof(double) * network->layer[i].inputs);
network->layer[i].neuron[j].weights = (double *)malloc(sizeof(double) * network->layer[i].inputs);
for (k = 0; k < network->layer[i].inputs; k++) {
network->layer[i].neuron[j].weights[k] = random_weight();
}
}
}
}
void feed_forward(Network *network, double *inputs) {
int i, j, k;
double sum;
for (i = 0; i < network->hidden_layers + 1; i++) {
for (j = 0; j < network->layer[i].neurons; j++) {
sum = 0.0;
for (k = 0; k < network->layer[i].inputs; k++) {
sum += network->layer[i].neuron[j].weights[k] * inputs[k];
}
network->layer[i].neuron[j].output = sigmoid(sum);
}
inputs = (double *)malloc(sizeof(double) * network->layer[i].neurons);
for (j = 0; j < network->layer[i].neurons; j++) {
inputs[j] = network->layer[i].neuron[j].output;
}
}
}
void back_propagate(Network *network, double *targets) {
int i, j;
for (i = network->hidden_layers; i >= 0; i--) {
for (j = 0; j < network->layer[i].neurons; j++) {
if (i == network->hidden_layers) {
network->layer[i].neuron[j].error = targets[j] - network->layer[i].neuron[j].output;
} else {
network->layer[i].neuron[j].error = 0.0;
for (int k = 0; k < network->layer[i + 1].neurons; k++) {
network->layer[i].neuron[j].error += network->layer[i + 1].neuron[k].error * network->layer[i + 1].neuron[k].weights[j];
}
}
network->layer[i].neuron[j].error *= sigmoid_derivative(network->layer[i].neuron[j].output);
}
}
}
void update_weights(Network *network, double *inputs) {
int i, j, k;
double input;
for (i = 0; i < network->hidden_layers + 1; i++) {
if (i > 0) {
inputs = (double *)malloc(sizeof(double) * network->layer[i - 1].neurons);
for (j = 0; j < network->layer[i - 1].neurons; j++) {
inputs[j] = network->layer[i - 1].neuron[j].output;
}
}
for (j = 0; j < network->layer[i].neurons; j++) {
for (k = 0; k < network->layer[i].inputs; k++) {
input = (i == 0) ? inputs[k] : network->layer[i - 1].neuron[k].output;
network->layer[i].neuron[j].weights[k] = update_weight(network->layer[i].neuron[j].weights[k], network->layer[i].neuron[j].error, input);
}
}
}
}
```
这段代码实现了一个具有一个隐藏层和两个输入神经元以及一个输出神经元的简单神经网络。它使用sigmoid函数作为激活函数,并通过反向传播算法来更新权重和偏移量。
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