生成代码 python实现bp神经网络

好的，我可以回答这个问题。以下是一个基本的BP神经网络Python实现的示例代码：

import numpy as np

class NeuralNetwork():
    def __init__(self):
        np.random.seed(1)
        self.synaptic_weights = 2 * np.random.random((3, 1)) - 1

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

    def sigmoid_derivative(self, x):
        return x * (1 - x)

    def train(self, training_inputs, training_outputs, training_iterations):
        for iteration in range(training_iterations):
            output = self.think(training_inputs)
            error = training_outputs - output
            adjustments = np.dot(training_inputs.T, error * self.sigmoid_derivative(output))
            self.synaptic_weights += adjustments

    def think(self, inputs):
        inputs = inputs.astype(float)
        output = self.sigmoid(np.dot(inputs, self.synaptic_weights))
        return output

if __name__ == "__main__":
    neural_network = NeuralNetwork()

    print("随机初始突触连接权重：")
    print(neural_network.synaptic_weights)

    training_inputs = np.array([[0, 0, 1],
                                [1, 1, 1],
                                [1, 0, 1],
                                [0, 1, 1]])

    training_outputs = np.array([[0, 1, 1, 0]]).T

    neural_network.train(training_inputs, training_outputs, 10000)

    print("训练后的突触连接权重：")
    print(neural_network.synaptic_weights)

    print("测试新数据集：")
    print(neural_network.think(np.array([1, 0, 0])))

这段代码实现了一个基本的BP神经网络，包括初始化、sigmoid函数、sigmoid导数、训练和预测。您可以使用以上代码来了解Python实现BP神经网络的基本方法。