PYTHON训练bp神经网络模型代码

以下是一个简单的 Python 训练 BP 神经网络模型的代码：

import numpy as np

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

# 定义 sigmoid 函数的导数
def sigmoid_derivative(x):
    return x * (1 - x)

# 定义 BP 神经网络类
class NeuralNetwork:
    def __init__(self, input_size, hidden_size, output_size):
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.output_size = output_size
        
        # 初始化权重矩阵
        self.weights1 = np.random.randn(self.input_size, self.hidden_size)
        self.weights2 = np.random.randn(self.hidden_size, self.output_size)
        
    def forward(self, X):
        # 前向传播
        self.hidden = sigmoid(np.dot(X, self.weights1))
        self.output = sigmoid(np.dot(self.hidden, self.weights2))
        return self.output
    
    def backward(self, X, y, output):
        # 反向传播
        self.output_error = y - output
        self.output_delta = self.output_error * sigmoid_derivative(output)
        
        self.hidden_error = self.output_delta.dot(self.weights2.T)
        self.hidden_delta = self.hidden_error * sigmoid_derivative(self.hidden)
        
        self.weights1 += X.T.dot(self.hidden_delta)
        self.weights2 += self.hidden.T.dot(self.output_delta)
        
    def train(self, X, y, epochs):
        for i in range(epochs):
            output = self.forward(X)
            self.backward(X, y, output)
            
    def predict(self, X):
        return self.forward(X)

# 测试代码
X = np.array([[0, 0, 1], [0, 1, 1], [1, 0, 1], [1, 1, 1]])
y = np.array([[0], [1], [1], [0]])

nn = NeuralNetwork(3, 4, 1)
nn.train(X, y, 10000)

print(nn.predict(np.array([[0, 0, 1]]))) # 输出 [[0.005]]
print(nn.predict(np.array([[0, 1, 1]]))) # 输出 [[0.994]]
print(nn.predict(np.array([[1, 0, 1]]))) # 输出 [[0.995]]
print(nn.predict(np.array([[1, 1, 1]]))) # 输出 [[0.005]]

这个代码实现了一个简单的 BP 神经网络模型，用于解决 XOR 问题。