LSTM使用遗传算法优化的代码示例

以下是一个使用遗传算法优化 LSTM 神经网络的 Python 代码示例：

import random
import numpy as np
from keras.models import Sequential
from keras.layers import Dense, LSTM

# 数据集
data = [[i for i in range(100)]]
data = np.array(data, dtype=float)
target = [[i for i in range(1, 101)]]
target = np.array(target, dtype=float)

# 遗传算法参数
POPULATION_SIZE = 20
GENE_LENGTH = 6
CROSSOVER_RATE = 0.8
MUTATION_RATE = 0.1
N_GENERATIONS = 50

class GA_LSTM:
    def __init__(self, population_size, gene_length):
        self.population_size = population_size
        self.gene_length = gene_length
        self.population = np.random.randint(2, size=(population_size, gene_length)).tolist()

    def translateDNA(self, DNA):
        return DNA.dot(2 ** np.arange(self.gene_length)[::-1]) / float(2**self.gene_length-1) * 4 - 2

    def get_fitness(self, model):
        fitness = []
        for DNA in self.population:
            model.set_weights(self.translateDNA(np.array(DNA)))
            loss = model.train_on_batch(data, target)
            fitness.append(1/loss)
        return fitness

    def select(self, fitness):
        idx = np.random.choice(np.arange(self.population_size), size=self.population_size, replace=True, p=fitness/np.sum(fitness))
        return np.array(self.population)[idx]

    def crossover(self, parent, pop):
        if np.random.rand() < CROSSOVER_RATE:
            i_ = np.random.randint(0, self.population_size, size=1)
            cross_points = np.random.randint(0, 2, size=self.gene_length).astype(np.bool)
            parent[cross_points] = pop[i_, cross_points]
        return parent

    def mutate(self, child):
        for point in range(self.gene_length):
            if np.random.rand() < MUTATION_RATE:
                child[point] = 1 if child[point] == 0 else 0
        return child

    def evolve(self, model):
        fitness = self.get_fitness(model)
        pop = self.select(fitness)
        pop_copy = pop.copy()
        for parent in pop:
            child = self.crossover(parent, pop_copy)
            child = self.mutate(child)
            parent[:] = child
        self.population = pop

# LSTM 神经网络模型
def build_model():
    model = Sequential()
    model.add(LSTM(units=50, input_shape=(1, 100)))
    model.add(Dense(units=100))
    model.compile(optimizer='adam', loss='mse')
    return model

# 遗传算法优化 LSTM 模型
def optimize_LSTM():
    ga_lstm = GA_LSTM(POPULATION_SIZE, GENE_LENGTH)
    model = build_model()
    for i in range(N_GENERATIONS):
        print('Generation', i+1)
        ga_lstm.evolve(model)
        fitness = ga_lstm.get_fitness(model)
        print('Best loss:', 1/np.max(fitness))
    best_DNA = ga_lstm.population[np.argmax(fitness)]
    best_model = build_model()
    best_model.set_weights(ga_lstm.translateDNA(np.array(best_DNA)))
    return best_model

# 测试模型
model = optimize_LSTM()
print(model.predict(data))

该代码中，我们使用遗传算法对 LSTM 神经网络模型进行了优化，目标是使得模型在数据集上的损失最小化。具体来说，我们将 LSTM 神经网络的权重编码为二进制基因，并使用遗传算法对这些基因进行优化，以找到最佳的权重组合。最后，我们使用找到的最佳 LSTM 模型对数据集进行预测。