python用人工蜂群网络优化BP神经网络

人工蜂群算法是一种启发式优化算法，可以应用于神经网络的训练和优化。

下面给出一个使用Python实现人工蜂群优化BP神经网络的示例代码：

import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

# 加载数据集
data = load_iris()
X = data.data
y = data.target

# 数据预处理
scaler = StandardScaler()
X = scaler.fit_transform(X)

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# BP神经网络
class NeuralNetwork:
    def __init__(self, input_dim, hidden_dim, output_dim):
        self.input_dim = input_dim
        self.hidden_dim = hidden_dim
        self.output_dim = output_dim
        self.W1 = np.random.randn(input_dim, hidden_dim)
        self.b1 = np.zeros((1, hidden_dim))
        self.W2 = np.random.randn(hidden_dim, output_dim)
        self.b2 = np.zeros((1, output_dim))

    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.y_hat = np.exp(self.z2) / np.sum(np.exp(self.z2), axis=1, keepdims=True)

    def backward(self, X, y, lr):
        # 反向传播
        delta3 = self.y_hat
        delta3[range(len(X)), y] -= 1
        dW2 = np.dot(self.a1.T, delta3)
        db2 = np.sum(delta3, axis=0, keepdims=True)
        delta2 = np.dot(delta3, self.W2.T) * (1 - np.power(self.a1, 2))
        dW1 = np.dot(X.T, delta2)
        db1 = np.sum(delta2, axis=0)
        self.W2 -= lr * dW2
        self.b2 -= lr * db2
        self.W1 -= lr * dW1
        self.b1 -= lr * db1

    def predict(self, X):
        # 预测
        self.forward(X)
        return np.argmax(self.y_hat, axis=1)

    def evaluate(self, X, y):
        # 评估模型
        y_pred = self.predict(X)
        acc = np.mean(y_pred == y)
        return acc

# 人工蜂群算法
class ArtificialBeeColony:
    def __init__(self, nn, n_bees, limit=100, max_trials=50):
        self.nn = nn
        self.n_bees = n_bees
        self.limit = limit
        self.max_trials = max_trials
        self.best_bee = None
        self.best_score = -np.inf

    def train(self, X_train, y_train, lr):
        n_inputs = X_train.shape[1]
        n_outputs = len(np.unique(y_train))
        self.nn = NeuralNetwork(n_inputs, self.nn.hidden_dim, n_outputs)
        self.best_bee = self.nn
        self.best_score = self.nn.evaluate(X_train, y_train)

        for i in range(self.limit):
            bees = [self.nn] * self.n_bees
            for j, bee in enumerate(bees):
                # 随机选择一个神经元进行调整
                layer = np.random.randint(0, 2)
                if layer == 0:
                    neuron = np.random.randint(0, self.nn.hidden_dim)
                    bee.W1[neuron] += np.random.uniform(-1, 1)
                else:
                    neuron = np.random.randint(0, self.nn.output_dim)
                    bee.W2[neuron] += np.random.uniform(-1, 1)
                # 计算当前神经网络的分数
                score = bee.evaluate(X_train, y_train)
                if score > self.best_score:
                    self.best_bee = bee
                    self.best_score = score
                # 若当前分数比原来的好，则更新神经网络
                if score > bee.evaluate(X_train, y_train):
                    bee.backward(X_train, y_train, lr)
                else:
                    # 若当前分数比原来差，进行随机搜索
                    trial = 0
                    while trial < self.max_trials:
                        if layer == 0:
                            bee.W1[neuron] += np.random.uniform(-1, 1)
                        else:
                            bee.W2[neuron] += np.random.uniform(-1, 1)
                        score = bee.evaluate(X_train, y_train)
                        if score > bee.evaluate(X_train, y_train):
                            bee.backward(X_train, y_train, lr)
                            break
                        else:
                            if layer == 0:
                                bee.W1[neuron] -= np.random.uniform(-1, 1)
                            else:
                                bee.W2[neuron] -= np.random.uniform(-1, 1)
                            trial += 1

# 训练模型
abc = ArtificialBeeColony(nn=NeuralNetwork(input_dim=X_train.shape[1], hidden_dim=5, output_dim=len(np.unique(y_train))),
                           n_bees=10, limit=100, max_trials=50)
abc.train(X_train, y_train, lr=0.01)

# 评估模型
acc_train = abc.best_bee.evaluate(X_train, y_train)
acc_test = abc.best_bee.evaluate(X_test, y_test)
print('训练集准确率：', acc_train)
print('测试集准确率：', acc_test)

这里使用了sklearn中的鸢尾花数据集进行训练和测试，神经网络的输入层为4个神经元，隐藏层为5个神经元，输出层为3个神经元。人工蜂群算法中，每个蜜蜂都是一个神经网络，随机选择一个神经元进行调整，计算当前神经网络的分数，若当前分数比原来的好，则更新神经网络，否则进行随机搜索。最终得到的`abc.best_bee`即为最优神经网络模型。