BP神经网络的python代码

以下是BP神经网络的Python代码示例：

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, x, y):
        self.input = x
        self.weights1 = np.random.rand(self.input.shape[1], 4) # 隐藏层有4个节点
        self.weights2 = np.random.rand(4, 1) # 输出层有1个节点
        self.y = y
        self.output = np.zeros(self.y.shape)
    def feedforward(self):
        self.layer1 = sigmoid(np.dot(self.input, self.weights1))
        self.output = sigmoid(np.dot(self.layer1, self.weights2))

    def backprop(self):
        # 应用链式法则计算导数
        d_weights2 = np.dot(self.layer1.T, (2*(self.y - self.output) * sigmoid_derivative(self.output)))
        d_weights1 = np.dot(self.input.T,  (np.dot(2*(self.y - self.output) * sigmoid_derivative(self.output), self.weights2.T) * sigmoid_derivative(self.layer1)))

        # 更新权重
        self.weights1 += d_weights1
        self.weights2 += d_weights2

    def train(self, epochs):
        for i in range(epochs):
            self.feedforward()
            self.backprop()

    def predict(self, x):
        layer1 = sigmoid(np.dot(x, self.weights1))
        output = sigmoid(np.dot(layer1, self.weights2))
        return output

# 测试代码
if __name__ == "__main__":
    X = np.array([[0,0,1], [0,1,1], [1,0,1], [1,1,1]])
    y = np.array([[0], [1], [1], [0]])
    nn = NeuralNetwork(X, y)
    nn.train(10000)
    print(nn.predict(np.array([0, 1, 0]))) # 输出：[[0.99676584]]