粒子群优化CNN-LSTM写个代码示例

粒子群优化CNN-LSTM是一种使用粒子群优化算法来优化CNN-LSTM网络的算法[^1]。下面是一个简单的代码示例，展示了如何使用粒子群优化算法来训练CNN-LSTM网络。

import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, LSTM, Dense, Flatten
from pyswarm import pso

# 定义CNN-LSTM模型
def create_model(input_shape):
    model = Sequential()
    model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Flatten())
    model.add(LSTM(64))
    model.add(Dense(10, activation='softmax'))
    return model

# 定义损失函数
def loss_function(params, x_train, y_train):
    input_shape = (x_train.shape, x_train.shape, x_train.shape)
    model = create_model(input_shape)
    model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=params),
                  loss='categorical_crossentropy',
                  metrics=['accuracy'])
    model.fit(x_train, y_train, epochs=10, batch_size=32, verbose=0)
    loss, _ = model.evaluate(x_train, y_train, verbose=0)
    return loss

# 定义训练数据
x_train = np.random.rand(100, 10, 10, 3)
y_train = np.random.randint(0, 10, (100,))

# 定义参数范围
lb = [0.001]  # 学习率下限
ub = [0.01]   # 学习率上限

# 使用粒子群优化算法进行参数优化
params = pso(loss_function, lb, ub, args=(x_train, y_train))

print("Optimized learning rate:", params)

这个代码示例中，我们首先定义了一个CNN-LSTM模型，然后使用粒子群优化算法来优化模型的学习率。在优化过程中，我们使用了随机生成的训练数据来计算损失函数。最后，我们输出了优化得到的学习率。