神经网络的python代码

以下是一个简单的神经网络的 Python 代码示例：

import numpy as np

# 定义神经网络类
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
        
        # 初始化权重矩阵
        self.weights1 = np.random.randn(self.input_size, self.hidden_size)
        self.weights2 = np.random.randn(self.hidden_size, self.output_size)
        
    def forward(self, X):
        # 前向传播计算输出
        self.hidden = np.dot(X, self.weights1)
        self.hidden_activation = self.sigmoid(self.hidden)
        self.output = np.dot(self.hidden_activation, self.weights2)
        return self.output
    
    def sigmoid(self, x):
        # sigmoid 激活函数
        return 1 / (1 + np.exp(-x))
    
    def sigmoid_derivative(self, x):
        # sigmoid 激活函数的导数
        return x * (1 - x)
    
    def backward(self, X, y, output):
        # 反向传播计算误差
        self.output_error = y - output
        self.output_delta = self.output_error * self.sigmoid_derivative(output)
        
        self.hidden_error = np.dot(self.output_delta, self.weights2.T)
        self.hidden_delta = self.hidden_error * self.sigmoid_derivative(self.hidden_activation)
        
        # 梯度下降更新权重矩阵
        self.weights1 += np.dot(X.T, self.hidden_delta)
        self.weights2 += np.dot(self.hidden_activation.T, self.output_delta)
        
    def train(self, X, y, epochs):
        # 训练神经网络
        for epoch in range(epochs):
            output = self.forward(X)
            self.backward(X, y, output)

这是一个基本的单层神经网络，包含前向传播、反向传播和训练过程。你可以根据需求进行修改和扩展。