用keras搭建的深度神经网络中对minist图像数据集用遗传优化算法进行分类的python代码

以下是使用遗传算法对MNIST数据集进行图像分类的示例代码：

import numpy as np
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Flatten, Dropout
from keras.optimizers import Adam
from keras.utils import to_categorical
from deap import base, creator, tools, algorithms

# 加载MNIST数据集
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# 将图像像素值归一化到[0, 1]范围内
x_train = x_train.astype('float32') / 255.0
x_test = x_test.astype('float32') / 255.0

# 将标签转换为one-hot编码
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)

# 定义神经网络模型
def create_model(n_units, dropout_rate):
    model = Sequential()
    model.add(Flatten(input_shape=(28, 28)))
    model.add(Dense(n_units, activation='relu'))
    model.add(Dropout(dropout_rate))
    model.add(Dense(10, activation='softmax'))
    return model

# 定义适应度函数
def evaluate(individual):
    # 将染色体转换为神经网络参数
    n_units = int(individual[0])
    dropout_rate = individual[1]
    learning_rate = 10 ** (-4 * individual[2])

    # 创建神经网络模型
    model = create_model(n_units, dropout_rate)
    optimizer = Adam(lr=learning_rate)
    model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])

    # 训练模型并评估性能
    model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=5, batch_size=64, verbose=0)
    score = model.evaluate(x_test, y_test, verbose=0)
    return score[1],

# 定义遗传算法参数
creator.create('FitnessMax', base.Fitness, weights=(1.0,))
creator.create('Individual', list, fitness=creator.FitnessMax)
toolbox = base.Toolbox()
toolbox.register('n_units', np.random.randint, 32, 256)
toolbox.register('dropout_rate', np.random.uniform, 0.1, 0.5)
toolbox.register('learning_rate', np.random.uniform, -4, 0)
toolbox.register('individual', tools.initCycle, creator.Individual, (toolbox.n_units, toolbox.dropout_rate, toolbox.learning_rate), n=1)
toolbox.register('population', tools.initRepeat, list, toolbox.individual)
toolbox.register('mate', tools.cxUniform, indpb=0.1)
toolbox.register('mutate', tools.mutGaussian, mu=0, sigma=0.1, indpb=0.1)
toolbox.register('select', tools.selTournament, tournsize=3)
toolbox.register('evaluate', evaluate)

# 运行遗传算法
pop = toolbox.population(n=10)
hof = tools.HallOfFame(1)
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register('max', np.max)
pop, log = algorithms.eaSimple(pop, toolbox, cxpb=0.5, mutpb=0.2, ngen=10, stats=stats, halloffame=hof, verbose=True)

# 显示最优解
best_individual = hof[0]
print('Best individual:', best_individual)
print('Best accuracy:', evaluate(best_individual))

在这个代码中，我们定义了一个包含一个隐藏层和一个dropout层的神经网络模型。我们使用deap库来实现遗传算法，并将模型的性能作为适应度函数。我们使用随机初始化的种群，并使用交叉和变异操作来生成新的染色体。我们运行10代，并使用Hall of Fame来跟踪最优个体。最后，我们输出最优个体及其性能。

请注意，这个示例代码是一个基本的框架，你可以自己进行修改和调整以适应你的具体需求。