BP神經(jīng)網(wǎng)絡(luò)算法基本思想與程序代碼

BP神經(jīng)網(wǎng)絡(luò)算法（反向傳播）是一種監(jiān)督學(xué)習(xí)算法，用于訓(xùn)練前饋人工神經(jīng)網(wǎng)絡(luò)。它通常用于訓(xùn)練深度學(xué)習(xí)模型。該算法通過將錯誤反向傳播到網(wǎng)絡(luò)中來工作，更新神經(jīng)元的權(quán)重，以減小網(wǎng)絡(luò)預(yù)測中的整體錯誤。這一過程被稱為誤差反向傳播或簡單地稱為反向傳播。

BP神經(jīng)網(wǎng)絡(luò)算法可以用多種編程語言實現(xiàn)，下面是一些常見語言中的代碼示例：

Python神經(jīng)網(wǎng)絡(luò)算法代碼：

import numpy as np

# sigmoid function
def sigmoid(x):
return 1 / (1 + np.exp(-x))

# derivative of sigmoid function
def sigmoid_derivative(x):
return x * (1 – x)

# input dataset
X = np.array([ [0,0,1],
[0,1,1],
[1,0,1],
[1,1,1] ])

# output dataset
y = np.array([[0,0,1,1]]).T

# seed random numbers to make calculation
# deterministic (just a good practice)
np.random.seed(1)

# initialize weights randomly with mean 0
syn0 = 2*np.random.random((3,1)) – 1

for iter in range(10000):

# forward propagation
l0 = X
l1 = sigmoid(np.dot(l0,syn0))

# how much did we miss?
l1_error = y – l1

# multiply how much we missed by the
# slope of the sigmoid at the values in l1
l1_delta = l1_error * sigmoid_derivative(l1)

# update weights
syn0 += np.dot(l0.T,l1_delta)

print(“Output After Training:”)
print(l1)

Java神經(jīng)網(wǎng)絡(luò)算法代碼:

import java.util.Random;

public class NeuralNetwork {

private double[][] input;
private double[] output;
private double[][] weights;

public NeuralNetwork(double[][] input, double[] output) {
this.input = input;
this.output = output;
this.weights = new double[input[0].length][output.length];
randomizeWeights();
}

public void train(int iterations) {
for (int i = 0; i < iterations; i++) {
for (int j = 0; j < input.length; j++) {
double[] outputs = calculateOutput(input[j]);
for (int k = 0; k < output.length; k++) {
double error = output[k] – outputs[k];
for (int l = 0; l < input[0].length; l++) {
weights[l][k] += error * input[j][l] * 0.1;
}
}
}
}
}

public double[] calculateOutput(double[] input) {
double[] result = new double[output.length];
for (int i = 0; i < output.length; i++) {
double sum = 0;
for (int j = 0; j < input.length; j++) {
sum += input[j]