python利用遗传算法优化神经网络模型的代码

遗传算法（Genetic Algorithm）是模拟自然进化过程的一种优化算法，适用于优化复杂的非线性问题。在神经网络中，遗传算法可以用于优化神经网络的结构和参数，以达到更好的学习效果。

下面是一个使用遗传算法优化神经网络模型的 Python 代码示例：

import numpy as np
from keras.models import Sequential
from keras.layers import Dense
from keras.utils import to_categorical
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

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

# 将类别变量转换为二进制形式
y_binary = to_categorical(y)

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

# 定义神经网络模型结构
def create_model(input_dim, output_dim, nodes, n_layers):
    model = Sequential()
    model.add(Dense(nodes, input_dim=input_dim, activation='relu'))
    for i in range(n_layers-1):
        model.add(Dense(nodes, activation='relu'))
    model.add(Dense(output_dim, activation='softmax'))
    return model

# 定义适应度函数
def fitness(model, X_train, y_train, X_test, y_test):
    model.fit(X_train, y_train, epochs=100, batch_size=10, verbose=0)
    _, accuracy = model.evaluate(X_test, y_test, verbose=0)
    return accuracy

# 定义遗传算法参数
pop_size = 20 # 种群大小
n_generations = 50 # 迭代次数
mutation_rate = 0.1 # 变异率
n_layers_range = [1, 3] # 神经网络层数范围
nodes_range = [5, 20] # 神经元数量范围

# 初始化种群
pop = []
for i in range(pop_size):
    n_layers = np.random.randint(n_layers_range[0], n_layers_range[1]+1)
    nodes = np.random.randint(nodes_range[0], nodes_range[1]+1)
    model = create_model(X_train.shape[1], y_binary.shape[1], nodes, n_layers)
    pop.append(model)

# 迭代优化
for i in range(n_generations):
    # 计算适应度
    fitness_scores = []
    for j in range(pop_size):
        fitness_scores.append(fitness(pop[j], X_train, y_train, X_test, y_test))
    # 选择
    parents = np.random.choice(pop, size=pop_size//2, replace=False, p=np.array(fitness_scores)/sum(fitness_scores))
    # 交叉
    children = []
    for j in range(pop_size//2):
        parent1 = parents[j]
        parent2 = parents[pop_size//2-1-j]
        child1 = create_model(X_train.shape[1], y_binary.shape[1], parent1.layers[0].output_shape[1], len(parent1.layers)-1)
        child2 = create_model(X_train.shape[1], y_binary.shape[1], parent2.layers[0].output_shape[1], len(parent2.layers)-1)
        for k in range(len(parent1.layers)-1):
            if np.random.rand() > 0.5:
                child1.layers[k].set_weights(parent1.layers[k].get_weights())
                child2.layers[k].set_weights(parent2.layers[k].get_weights())
            else:
                child1.layers[k].set_weights(parent2.layers[k].get_weights())
                child2.layers[k].set_weights(parent1.layers[k].get_weights())
        children += [child1, child2]
    # 变异
    for j in range(len(children)):
        if np.random.rand() < mutation_rate:
            n_layers = np.random.randint(n_layers_range[0], n_layers_range[1]+1)
            nodes = np.random.randint(nodes_range[0], nodes_range[1]+1)
            model = create_model(X_train.shape[1], y_binary.shape[1], nodes, n_layers)
            children[j] = model
    # 更新种群
    pop = parents + children

# 输出最优解
fitness_scores = []
for j in range(pop_size):
    fitness_scores.append(fitness(pop[j], X_train, y_train, X_test, y_test))
best_model = pop[np.argmax(fitness_scores)]
print('Best accuracy:', max(fitness_scores))
print('Best model:')
best_model.summary()

以上代码中，首先加载 Iris 数据集，并将类别变量转换为二进制形式。接着定义神经网络模型结构和适应度函数。然后定义遗传算法参数，包括种群大小、迭代次数、变异率、神经网络层数范围和神经元数量范围。然后初始化种群，并进行迭代优化过程。在每次迭代中，先计算种群中每个个体的适应度，然后进行选择、交叉和变异操作，最后更新种群。最终输出最优解，即最高的测试集准确率和对应的神经网络模型结构。

需要注意的是，该代码使用了 Keras 框架来构建神经网络模型，因此需要先安装 Keras 和相关依赖库。如果没有安装，可以通过以下命令进行安装：

pip install keras tensorflow scikit-learn numpy