BP神经网络分类代码

这是一个使用BP神经网络进行分类的代码示例：

import numpy as np

class BPNeuralNetwork:
    def __init__(self, n_feature, n_hidden, n_output):
        self.n_input = n_feature
        self.n_hidden = n_hidden
        self.n_output = n_output
        self.hidden_weights = np.random.rand(self.n_input, self.n_hidden)  # 隐含层权重
        self.output_weights = np.random.rand(self.n_hidden, self.n_output)  # 输出层权重

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

    def forward_propagation(self, X):
        hidden_output = self.sigmoid(np.dot(X, self.hidden_weights))
        output = self.sigmoid(np.dot(hidden_output, self.output_weights))
        return output

    def backward_propagation(self, X, y, output):
        output_error = y - output
        output_delta = output_error * output * (1 - output)
        hidden_error = np.dot(output_delta, self.output_weights.T)
        hidden_delta = hidden_error * hidden_output * (1 - hidden_output)
        self.output_weights += np.dot(hidden_output.T, output_delta)
        self.hidden_weights += np.dot(X.T, hidden_delta)

    def train(self, X, y, epochs, lr):
        for epoch in range(epochs):
            output = self.forward_propagation(X)
            self.backward_propagation(X, y, output)

    def predict(self, X):
        return self.forward_propagation(X)

# 使用示例：
X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])  # 输入数据
y = np.array([[0], [1], [1], [0]])  # 输出标签

nn = BPNeuralNetwork(n_feature=2, n_hidden=4, n_output=1)  # 创建BP神经网络模型
nn.train(X, y, epochs=1000, lr=0.1)  # 训练模型

test_data = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])  # 测试数据
predictions = nn.predict(test_data)  # 进行预测

print(predictions)