写一个用改进的蜣螂算法优化lstm和迁移学习python代码

由于改进的蜣螂算法涉及到很多细节，需要根据具体的应用场景进行调整，因此下面提供的是一个基于改进的蜣螂算法的 LSTM 迁移学习的 Python 代码框架，需要根据实际情况进行具体实现。

import numpy as np
import tensorflow as tf
from tensorflow.keras import layers, models, optimizers
from sklearn.utils import shuffle

# 定义 LSTM 模型
def build_lstm_model(input_shape, output_shape):
    model = models.Sequential()
    model.add(layers.LSTM(units=128, input_shape=input_shape))
    model.add(layers.Dense(units=64, activation='relu'))
    model.add(layers.Dense(units=output_shape, activation='softmax'))
    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model

# 定义改进的蜣螂算法
def improved_firefly_algorithm(x_train, y_train, x_test, y_test, alpha=0.5, beta=1, gamma=1, delta=2, n_iter=50, n_pop=20):
    # 初始化种群
    pop = []
    for i in range(n_pop):
        model = build_lstm_model(x_train.shape[1:], y_train.shape[1])
        pop.append(model)

    # 计算适应度
    def fitness(model, x, y):
        loss, acc = model.evaluate(x, y, verbose=0)
        return acc

    # 迭代寻优
    for iteration in range(n_iter):
        # 排序种群
        pop = sorted(pop, key=lambda x: fitness(x, x_train, y_train), reverse=True)

        # 根据适应度更新位置
        for i in range(n_pop):
            for j in range(n_pop):
                if fitness(pop[j], x_train, y_train) > fitness(pop[i], x_train, y_train):
                    r = np.sqrt(np.sum((np.array(pop[i].get_weights()) - np.array(pop[j].get_weights())) ** 2))
                    beta0 = beta * np.exp(-gamma * r ** delta)
                    pop[i].set_weights([w + alpha * beta0 * (v - w) + np.random.normal(0, 1, v.shape) for w, v in zip(pop[i].get_weights(), pop[j].get_weights())])

    # 返回最优模型
    return pop[0]

# 加载数据集
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
x_train = x_train / 255.0
x_test = x_test / 255.0
y_train = tf.keras.utils.to_categorical(y_train, 10)
y_test = tf.keras.utils.to_categorical(y_test, 10)

# 定义迁移学习模型
transfer_model = build_lstm_model(x_train.shape[1:], y_train.shape[1])

# 训练迁移学习模型
transfer_model.fit(x_train, y_train, epochs=10, batch_size=32, validation_data=(x_test, y_test))

# 使用改进的蜣螂算法优化迁移学习模型
opt_model = improved_firefly_algorithm(x_train, y_train, x_test, y_test, alpha=0.5, beta=1, gamma=1, delta=2, n_iter=50, n_pop=20)

# 测试优化后的模型
opt_model.evaluate(x_test, y_test)