GA优化CNN实例代码
时间: 2023-06-09 15:07:43 浏览: 112
以下是一个简单的示例代码,使用遗传算法(Genetic Algorithm,GA)优化卷积神经网络(Convolutional Neural Network,CNN):
```python
import numpy as np
import tensorflow as tf
import random
# 定义CNN架构
def cnn_architecture(x, weights, biases, keep_prob):
# 第一层卷积层
conv1 = tf.nn.relu(tf.nn.conv2d(x, weights['conv1'], strides=[1, 1, 1, 1], padding='SAME') + biases['conv1'])
# 第一层池化层
pool1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
# 第二层卷积层
conv2 = tf.nn.relu(tf.nn.conv2d(pool1, weights['conv2'], strides=[1, 1, 1, 1], padding='SAME') + biases['conv2'])
# 第二层池化层
pool2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
# 全连接层
fc1 = tf.reshape(pool2, [-1, weights['fc1'].get_shape().as_list()[0]])
fc1 = tf.nn.relu(tf.matmul(fc1, weights['fc1']) + biases['fc1'])
# Dropout层
fc1_drop = tf.nn.dropout(fc1, keep_prob)
# 输出层
logits = tf.matmul(fc1_drop, weights['output']) + biases['output']
return logits
# 定义GA的参数
POPULATION_SIZE = 100
CROSSOVER_RATE = 0.8
MUTATION_RATE = 0.1
N_GENERATIONS = 50
N_EVOLUTIONS = 20
# 定义GA的辅助函数
def normal_init(shape):
return tf.Variable(tf.random.normal(shape, stddev=0.1))
def zeros_init(shape):
return tf.Variable(tf.zeros(shape))
def generate_population():
population = []
for i in range(POPULATION_SIZE):
weights = {
'conv1': normal_init([3, 3, 1, 32]),
'conv2': normal_init([3, 3, 32, 64]),
'fc1': normal_init([7 * 7 * 64, 1024]),
'output': normal_init([1024, 10])
}
biases = {
'conv1': zeros_init([32]),
'conv2': zeros_init([64]),
'fc1': zeros_init([1024]),
'output': zeros_init([10])
}
population.append((weights, biases))
return population
def fitness(x_train, y_train, population):
accuracies = []
for i in range(POPULATION_SIZE):
weights, biases = population[i]
x = tf.placeholder(tf.float32, [None, 28, 28, 1])
y = tf.placeholder(tf.float32, [None, 10])
keep_prob = tf.placeholder(tf.float32)
y_pred = cnn_architecture(x, weights, biases, keep_prob)
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=y_pred))
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for j in range(N_EVOLUTIONS):
batch_mask = np.random.choice(x_train.shape[0], 100)
x_batch = x_train[batch_mask]
y_batch = y_train[batch_mask]
train_step.run(feed_dict={x: x_batch, y: y_batch, keep_prob: 0.5})
acc = accuracy.eval(feed_dict={x: x_train, y: y_train, keep_prob: 1.0})
accuracies.append(acc)
return accuracies
def crossover(parent1, parent2):
weights1, biases1 = parent1
weights2, biases2 = parent2
child1_weights = {}
child2_weights = {}
child1_biases = {}
child2_biases = {}
for key in weights1.keys():
if random.random() < 0.5:
child1_weights[key] = weights1[key]
child2_weights[key] = weights2[key]
else:
child1_weights[key] = weights2[key]
child2_weights[key] = weights1[key]
for key in biases1.keys():
if random.random() < 0.5:
child1_biases[key] = biases1[key]
child2_biases[key] = biases2[key]
else:
child1_biases[key] = biases2[key]
child2_biases[key] = biases1[key]
return (child1_weights, child1_biases), (child2_weights, child2_biases)
def mutation(individual):
weights, biases = individual
for key in weights.keys():
if random.random() < MUTATION_RATE:
weights[key] += np.random.normal(0, 0.1, weights[key].shape)
for key in biases.keys():
if random.random() < MUTATION_RATE:
biases[key] += np.random.normal(0, 0.1, biases[key].shape)
return weights, biases
def select_population(fitnesses, population):
sorted_population = sorted(list(zip(fitnesses, population)), reverse=True)
selected_population = []
for i in range(POPULATION_SIZE):
selected_population.append(sorted_population[i][1])
return selected_population
# 加载MNIST数据集
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
x_train = x_train.reshape([-1, 28, 28, 1]) / 255.0
y_train = tf.keras.utils.to_categorical(y_train, 10)
# 初始化GA种群
population = generate_population()
# GA主循环
for i in range(N_GENERATIONS):
print('Generation', i+1)
# 计算适应度
fitnesses = fitness(x_train, y_train, population)
# 选择优良个体
population = select_population(fitnesses, population)
# 生成新个体
new_population = []
for j in range(int(POPULATION_SIZE * CROSSOVER_RATE)):
parent1 = random.choice(population)
parent2 = random.choice(population)
child1, child2 = crossover(parent1, parent2)
child1 = mutation(child1)
child2 = mutation(child2)
new_population.append(child1)
new_population.append(child2)
for j in range(POPULATION_SIZE - len(new_population)):
individual = random.choice(population)
individual = mutation(individual)
new_population.append(individual)
population = new_population
# 测试最优个体
weights, biases = select_population(fitness(x_train, y_train, population), population)[0]
x = tf.placeholder(tf.float32, [None, 28, 28, 1])
y = tf.placeholder(tf.float32, [None, 10])
keep_prob = tf.placeholder(tf.float32)
y_pred = cnn_architecture(x, weights, biases, keep_prob)
correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
acc = accuracy.eval(feed_dict={x: x_test.reshape([-1, 28, 28, 1]) / 255.0, y: tf.keras.utils.to_categorical(y_test, 10), keep_prob: 1.0})
print('Test accuracy:', acc)
```
这个示例代码定义了一个基本的CNN架构,包括两个卷积层、两个池化层和一个全连接层,使用Adam优化器进行训练。GA的目标是优化CNN的权重和偏置,使得在训练集上的分类精度最高。
该代码使用的方法是,先生成一个随机的种群(即多组CNN的权重和偏置),然后对每一组权重和偏置进行遗传算法优化。遗传算法每一轮迭代会根据上一轮的适应度(即训练集上的分类精度)选择一批优良个体,并使用交叉和变异操作生成新个体,组成新的种群,并继续进行迭代。最终,这个代码将测试最优个体在测试集上的分类精度。
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