遗传算法优化CNN的代码。
时间: 2023-09-09 07:07:08 浏览: 97
以下是使用遗传算法优化CNN代码的一些示例步骤:
1. 定义CNN模型,包括卷积层、池化层和全连接层。可以使用Keras或TensorFlow等深度学习框架来定义模型。
2. 定义超参数空间,包括卷积核大小、卷积核数量、池化大小、全连接层神经元数量等。
3. 定义适应度函数,用于评估每个个体的性能。可以使用交叉验证等方法进行评估。
4. 使用遗传算法进行优化,包括选择、交叉和变异操作。可以使用Python中的遗传算法库,如DEAP等。
5. 重复步骤3和4,直到达到最优解或满足停止条件。
下面是一个简单的遗传算法优化CNN的代码示例:
```
import random
import numpy as np
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
from keras.models import Sequential
from keras.datasets import mnist
# 定义CNN模型
def create_model(params):
model = Sequential()
model.add(Conv2D(params['filters'], (params['kernel_size'], params['kernel_size']), activation='relu', input_shape=(28, 28, 1)))
model.add(MaxPooling2D(pool_size=(params['pool_size'], params['pool_size'])))
model.add(Flatten())
model.add(Dense(params['dense_units'], activation='relu'))
model.add(Dense(10, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
# 定义适应度函数
def fitness(params):
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)
X_train = X_train.astype('float32') / 255
X_test = X_test.astype('float32') / 255
y_train = keras.utils.to_categorical(y_train, 10)
y_test = keras.utils.to_categorical(y_test, 10)
model = create_model(params)
model.fit(X_train, y_train, batch_size=128, epochs=5, validation_data=(X_test, y_test))
score = model.evaluate(X_test, y_test, verbose=0)
return score[1]
# 定义超参数空间
params_space = {
'filters': [16, 32, 64],
'kernel_size': [3, 5, 7],
'pool_size': [2, 3],
'dense_units': [64, 128, 256]
}
# 定义遗传算法参数
population_size = 10
num_generations = 10
mutation_rate = 0.1
# 初始化种群
population = []
for i in range(population_size):
individual = {}
for key in params_space:
individual[key] = random.choice(params_space[key])
population.append(individual)
# 遗传算法优化
for generation in range(num_generations):
# 计算适应度
fitness_scores = []
for individual in population:
fitness_scores.append(fitness(individual))
best_fitness = max(fitness_scores)
print('Generation %d - Best fitness: %f' % (generation, best_fitness))
# 选择操作
parents = []
for i in range(population_size):
parent1 = population[np.random.randint(0, population_size)]
parent2 = population[np.random.randint(0, population_size)]
if fitness(parent1) > fitness(parent2):
parents.append(parent1)
else:
parents.append(parent2)
# 交叉操作
offspring = []
for i in range(population_size):
child = {}
parent1 = parents[np.random.randint(0, population_size)]
parent2 = parents[np.random.randint(0, population_size)]
for key in params_space:
if np.random.rand() < 0.5:
child[key] = parent1[key]
else:
child[key] = parent2[key]
offspring.append(child)
# 变异操作
for i in range(population_size):
individual = offspring[i]
for key in params_space:
if np.random.rand() < mutation_rate:
individual[key] = random.choice(params_space[key])
# 更新种群
population = offspring
```
以上代码使用遗传算法优化MNIST数据集上的CNN模型。在`params_space`中定义了超参数空间,包括卷积核大小、卷积核数量、池化大小和全连接层神经元数量。`create_model`函数用于创建CNN模型,`fitness`函数用于计算每个个体的适应度。在遗传算法循环中,包括选择、交叉和变异操作。每个个体的适应度通过交叉验证进行评估。
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