使用遗传算法优化神经网络模型的超参数（可选超参数包括训练迭代次数，学习率，网络结构等）的代码，原来的神经网络模型如下：import numpy as np import tensorflow as tf from tensorflow.keras.datasets import mnist from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense from tensorflow.keras.utils import to_categorical from tensorflow.keras.optimizers import Adam from sklearn.model_selection import train_test_split # 加载MNIST数据集 (X_train, y_train), (X_test, y_test) = mnist.load_data() # 数据预处理 X_train = X_train.reshape(-1, 28, 28, 1).astype('float32') / 255.0 X_test = X_test.reshape(-1, 28, 28, 1).astype('float32') / 255.0 y_train = to_categorical(y_train) y_test = to_categorical(y_test) # 划分验证集 X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.1, random_state=42) def create_model(): model = Sequential() model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1))) model.add(MaxPooling2D((2, 2))) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(MaxPooling2D((2, 2))) model.add(Flatten()) model.add(Dense(64, activation='relu')) model.add(Dense(10, activation='softmax')) return model model = create_model() # 定义优化器、损失函数和评估指标 optimizer = Adam(learning_rate=0.001) loss_fn = tf.keras.losses.CategoricalCrossentropy() metrics = ['accuracy'] # 编译模型 model.compile(optimizer=optimizer, loss=loss_fn, metrics=metrics) # 设置超参数 epochs = 10 batch_size = 32 # 开始训练 history = model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(X_val, y_val)) # 评估模型 test_loss, test_accuracy = model.evaluate(X_test, y_test) print('Test Loss:', test_loss) print('Test Accuracy:', test_accuracy)

这是一个用于MNIST分类的卷积神经网络模型。为了使用遗传算法优化超参数，你需要先定义一个函数，将神经网络模型和超参数作为参数输入，然后在函数中训练模型并返回模型的测试准确率。以下是一个简单的示例代码：

import random

def evaluate_model(params):
    # 解析超参数
    epochs, batch_size, learning_rate = params

    # 创建模型
    model = create_model()

    # 定义优化器、损失函数和评估指标
    optimizer = Adam(learning_rate=learning_rate)
    loss_fn = tf.keras.losses.CategoricalCrossentropy()
    metrics = ['accuracy']

    # 编译模型
    model.compile(optimizer=optimizer, loss=loss_fn, metrics=metrics)

    # 训练模型
    history = model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(X_val, y_val), verbose=0)

    # 评估模型
    test_loss, test_accuracy = model.evaluate(X_test, y_test, verbose=0)

    # 返回测试准确率作为适应度
    return test_accuracy

# 定义遗传算法参数
pop_size = 10
num_generations = 5
mutation_rate = 0.1
elite_size = 2

# 定义超参数搜索空间
param_space = [(5, 32, 0.001), (10, 64, 0.001), (5, 32, 0.01), (10, 64, 0.01)]

# 初始化种群
population = [random.choice(param_space) for _ in range(pop_size)]

# 开始遗传算法
for i in range(num_generations):
    # 评估种群中每个个体的适应度
    fitness_scores = [evaluate_model(params) for params in population]

    # 选择精英个体
    elite_indices = sorted(range(len(fitness_scores)), key=lambda i: fitness_scores[i], reverse=True)[:elite_size]
    elites = [population[i] for i in elite_indices]

    # 选择新一代个体
    new_population = []
    while len(new_population) < pop_size:
        # 选择父母个体
        parent1 = random.choices(population, weights=fitness_scores)[0]
        parent2 = random.choices(population, weights=fitness_scores)[0]

        # 交叉产生子代个体
        child = []
        for j in range(len(parent1)):
            if random.random() < 0.5:
                child.append(parent1[j])
            else:
                child.append(parent2[j])

        # 变异子代个体
        for j in range(len(child)):
            if random.random() < mutation_rate:
                child[j] = random.choice(param_space)[j]

        # 添加子代个体
        new_population.append(child)

    # 添加精英个体
    population = elites + new_population

# 评估最终种群中最优个体的性能
best_params = max(population, key=lambda params: evaluate_model(params))
best_model = create_model()
best_model.fit(X_train, y_train, batch_size=best_params[1], epochs=best_params[0], validation_data=(X_val, y_val))
test_loss, test_accuracy = best_model.evaluate(X_test, y_test, verbose=0)
print('Best Test Loss:', test_loss)
print('Best Test Accuracy:', test_accuracy)

这个代码使用遗传算法搜索超参数空间，每个个体都由三个超参数组成：训练迭代次数、批次大小和学习率。种群大小为10，迭代5代，变异率为0.1，精英个体数量为2。超参数搜索空间包括4个不同的参数组合。每个个体的适应度是其测试准确率，最终选择种群中测试准确率最高的个体作为最优超参数，然后使用这些超参数重新训练模型并评估其测试准确率。