反向传播算法案例代码python

以下是一个简单的Python实现的反向传播算法案例代码：

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)
        self.weights2 = np.random.rand(4, 1)
        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):
        # 应用链式法则计算误差
        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])))

该代码实现了一个简单的神经网络，使用反向传播算法进行训练，并预测了一个输入的输出值。