Python手写代码实现BP神经网络

当然可以！下面是一个简单的Python代码实现BP神经网络的示例：

import numpy as np

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
        
        # 初始化权重和偏置
        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 = self.sigmoid(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
        
        # 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) * self.sigmoid_derivative(self.a1)
        
        # 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 i 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 x * (1 - x)

使用示例：

# 创建一个2-3-1的神经网络
nn = NeuralNetwork(2, 3, 1)

# 输入数据
X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
y = np.array([, , , ])

# 训练神经网络
nn.train(X, y, epochs=10000, learning_rate=0.1)

# 预测结果
print(nn.predict(X))

这是一个简单的BP神经网络的实现，其中包括前向传播、反向传播、训练和预测等功能。你可以根据自己的需求进行修改和扩展。希望对你有帮助！