python写BP神经网络

作为AI助手，我可以向您展示一个简单的Python实现BP神经网络的示例代码：

import numpy as np

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
        
        # 初始化权重和偏差
        self.weights1 = np.random.randn(self.input_size, self.hidden_size)
        self.bias1 = np.random.randn(self.hidden_size)
        self.weights2 = np.random.randn(self.hidden_size, self.output_size)
        self.bias2 = np.random.randn(self.output_size)
        
    def sigmoid(self, x):
        return 1 / (1 + np.exp(-x))
    
    def sigmoid_derivative(self, x):
        return x * (1 - x)
    
    def forward(self, X):
        # 前向传播
        self.z1 = np.dot(X, self.weights1) + self.bias1
        self.a1 = self.sigmoid(self.z1)
        self.z2 = np.dot(self.a1, self.weights2) + self.bias2
        self.a2 = self.sigmoid(self.z2)
        return self.a2
    
    def backward(self, X, y, output):
        # 反向传播
        self.output_error = y - output
        self.output_delta = self.output_error * self.sigmoid_derivative(output)
        
        self.z2_error = self.output_delta.dot(self.weights2.T)
        self.z2_delta = self.z2_error * self.sigmoid_derivative(self.a1)
        
        # 更新权重和偏差
        self.weights1 += X.T.dot(self.z2_delta)
        self.bias1 += np.sum(self.z2_delta, axis=0)
        
        self.weights2 += self.a1.T.dot(self.output_delta)
        self.bias2 += np.sum(self.output_delta, axis=0)
        
    def train(self, X, y, n_epochs):
        for i in range(n_epochs):
            output = self.forward(X)
            self.backward(X, y, output)
            
    def predict(self, X):
        return self.forward(X)

# 训练数据
X = np.array([[0,0,1], [0,1,1], [1,0,1], [1,1,1]])
y = np.array([[0], [1], [1], [0]])

# 初始化神经网络
nn = NeuralNetwork(3, 4, 1)

# 训练网络
nn.train(X, y, 10000)

# 预测输出
print(nn.predict(X))

以上代码实现了一个包含一个隐藏层的BP神经网络，用于解决XOR问题。您可以根据需要修改网络结构、激活函数等参数以适应不同的问题。