python实现mlp算法代码

MLP（多层感知机）是一种常用的神网络模型，用于解决分类和回归问题。下面是一个简单的Python代码示例，用于实现MLP算法：

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
        self.W1 = np.random.randn(self.input_size, self.hidden_size)
        self.b1 = np.zeros((1, self.hidden_size))
        self.W2 = np.random.randn(self.hidden_size, self.output_size)
        self.b2 = np.zeros((1, self.output_size))
        
    def forward(self, X):
        # 前向传播
        self.z1 = np.dot(X, self.W1) + self.b1
        self.a1 = np.tanh(self.z1)
        self.z2 = np.dot(self.a1, self.W2) + self.b2
        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
        
        # 计算隐藏层的误差
        delta1 = np.dot(delta2, self.W2.T) * (1 - np.power(self.a1, 2))
        
        # 更新权重和偏置
        dW2 = np.dot(self.a1.T, delta2) / m
        db2 = np.sum(delta2, axis=0) / m
        dW1 = np.dot(X.T, delta1) / m
        db1 = np.sum(delta1, axis=0) / m
        
        self.W2 -= learning_rate * dW2
        self.b2 -= learning_rate * db2
        self.W1 -= learning_rate * dW1
        self.b1 -= learning_rate * db1
        
    def train(self, X, y, epochs, learning_rate):
        for epoch in range(epochs):
            # 前向传播
            output = self.forward(X)
            
            # 反向传播
            self.backward(X, y, learning_rate)
            
            # 计算损失函数
            loss = self.loss_function(output, y)
            
            if epoch % 100 == 0:
                print(f"Epoch {epoch}, Loss: {loss}")
                
    def predict(self, X):
        # 预测
        output = self.forward(X)
        predictions = np.round(output)
        
        return predictions
    
    def loss_function(self, y_pred, y_true):
        # 损失函数（交叉熵）
        loss = -np.mean(y_true * np.log(y_pred) + (1 - y_true) * np.log(1 - y_pred))
        
        return loss
    
    def sigmoid(self, x):
        # sigmoid激活函数
        return 1 / (1 + np.exp(-x))

使用示例：

# 创建MLP对象
mlp = MLP(input_size=2, hidden_size=4, output_size=1)

# 训练数据
X_train = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
y_train = np.array([[0], [1], [1], [0]])

# 训练模型
mlp.train(X_train, y_train, epochs=1000, learning_rate=0.1)

# 预测数据
X_test = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
predictions = mlp.predict(X_test)
print(predictions)

这段代码实现了一个简单的MLP算法，用于解决逻辑门问题（XOR）。你可以根据自己的需求进行修改和扩展。希望对你有帮助！