给我一个bp神经网络的python代码的列子

这是一个简单的BP神经网络的Python代码的示例：

import numpy as np

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

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
        
        self.weights1 = np.random.randn(self.input_size, self.hidden_size)
        self.weights2 = np.random.randn(self.hidden_size, self.output_size)
        
        self.bias1 = np.zeros((1, self.hidden_size))
        self.bias2 = np.zeros((1, self.output_size))
        
    def forward(self, X):
        self.z1 = np.dot(X, self.weights1) + self.bias1
        self.a1 = sigmoid(self.z1)
        self.z2 = np.dot(self.a1, self.weights2) + self.bias2
        self.a2 = sigmoid(self.z2)
        return self.a2
    
    def backward(self, X, y, output):
        error = output - y
        delta2 = error * sigmoid(output, derivative=True)
        d_weights2 = np.dot(self.a1.T, delta2)
        delta1 = np.dot(delta2, self.weights2.T) * sigmoid(self.z1, derivative=True)
        d_weights1 = np.dot(X.T, delta1)
        self.weights1 -= d_weights1
        self.weights2 -= d_weights2
        self.bias1 -= np.sum(delta1, axis=0)
        self.bias2 -= np.sum(delta2, axis=0)
        
    def train(self, X, y):
        output = self.forward(X)
        self.backward(X, y, output)
        
    def predict(self, X):
        return self.forward(X)

这个代码实现了一个单隐藏层的BP神经网络，可以用于对二分类问题进行预测。其中，`sigmoid`函数用于激活函数的计算，`forward`函数用于正向传播，`backward`函数用于反向传播，`train`函数用于训练模型，`predict`函数用于对新数据进行预测。