bp神经网络蚊子分类代码

BP神经网络是一种常见的人工神经网络，它可以用于分类、回归等任务。针对蚊子分类这个问题，可以使用BP神经网络来实现分类。

下面是一个简单的BP神经网络蚊子分类的代码示例：

import numpy as np

# 定义BP神经网络类
class BPNet:
    def __init__(self):
        self.input_n = 2  # 输入层节点数
        self.hidden_n = 3  # 隐藏层节点数
        self.output_n = 1  # 输出层节点数
        self.learning_rate = 0.1  # 学习率
        self.w1 = np.random.randn(self.input_n, self.hidden_n)  # 输入层到隐藏层的权重
        self.b1 = np.zeros((1, self.hidden_n))  # 隐藏层偏置
        self.w2 = np.random.randn(self.hidden_n, self.output_n)  # 隐藏层到输出层的权重
        self.b2 = np.zeros((1, self.output_n))  # 输出层偏置

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

    def sigmoid_derivative(self, x):
        return x * (1 - x)

    def forward(self, X):
        # 前向传播过程
        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
        y_pred = self.sigmoid(self.z2)
        return y_pred

    def backward(self, X, y_true, y_pred):
        # 反向传播过程
        delta2 = (y_pred - y_true) * self.sigmoid_derivative(y_pred)
        d_w2 = np.dot(self.a1.T, delta2)
        d_b2 = np.sum(delta2, axis=0, keepdims=True)

        delta1 = np.dot(delta2, self.w2.T) * self.sigmoid_derivative(self.a1)
        d_w1 = np.dot(X.T, delta1)
        d_b1 = np.sum(delta1, axis=0)

        # 更新权重和偏置
        self.w1 -= self.learning_rate * d_w1
        self.b1 -= self.learning_rate * d_b1
        self.w2 -= self.learning_rate * d_w2
        self.b2 -= self.learning_rate * d_b2

    def train(self, X, y_true, n_epochs):
        for i in range(n_epochs):
            # 前向传播
            y_pred = self.forward(X)

            # 反向传播
            self.backward(X, y_true, y_pred)

            # 每100次迭代输出一次损失函数值
            if i % 100 == 0:
                loss = np.mean(np.square(y_true - y_pred))
                print(f'Epoch {i}, Loss: {loss:.4f}')

    def predict(self, X):
        y_pred = self.forward(X)
        return y_pred

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

# 创建BP神经网络实例
net = BPNet()

# 训练模型
net.train(X_train, y_train, n_epochs=5000)

# 测试数据集
X_test = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])

# 预测结果
y_pred = net.predict(X_test)

# 打印预测结果
print('预测结果：', y_pred.round())