MLP模型python源码

当谈到MLP（多层感知机）模型的Python源代码时，以下是一个简单的示例：

import numpy as np

class MLP:
    def __init__(self, input_size, hidden_size, output_size):
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.output_size = output_size
        
        # 初始化权重和偏置
        self.weights1 = np.random.randn(self.input_size, self.hidden_size)
        self.bias1 = np.zeros((1, self.hidden_size))
        self.weights2 = np.random.randn(self.hidden_size, self.output_size)
        self.bias2 = np.zeros((1, self.output_size))
        
    def forward(self, X):
        # 前向传播
        self.z1 = np.dot(X, self.weights1) + self.bias1
        self.a1 = self.sigmoid(self.z1)
        self.z2 = np.dot(self.a1, self.weights2) + self.bias2
        self.a2 = self.sigmoid(self.z2)
        
        return self.a2
    
    def backward(self, X, y, learning_rate):
        # 反向传播
        m = X.shape[0]
        
        # 计算输出层的误差
        delta2 = (self.a2 - y) * self.sigmoid_derivative(self.z2)
        
        # 计算隐藏层的误差
        delta1 = np.dot(delta2, self.weights2.T) * self.sigmoid_derivative(self.z1)
        
        # 更新权重和偏置
        d_weights2 = np.dot(self.a1.T, delta2) / m
        d_bias2 = np.sum(delta2, axis=0) / m
        d_weights1 = np.dot(X.T, delta1) / m
        d_bias1 = np.sum(delta1, axis=0) / m
        
        self.weights2 -= learning_rate * d_weights2
        self.bias2 -= learning_rate * d_bias2
        self.weights1 -= learning_rate * d_weights1
        self.bias1 -= learning_rate * d_bias1
        
    def train(self, X, y, epochs, learning_rate):
        for epoch in range(epochs):
            output = self.forward(X)
            self.backward(X, y, learning_rate)
            
    def predict(self, X):
        return np.round(self.forward(X))
    
    def sigmoid(self, x):
        return 1 / (1 + np.exp(-x))
    
    def sigmoid_derivative(self, x):
        return self.sigmoid(x) * (1 - self.sigmoid(x))

这是一个简单的MLP模型的实现，包括初始化权重和偏置、前向传播、反向传播、训练和预测等功能。你可以根据需要进行修改和扩展。