LSTM-AE代码实现

LSTM-AE是一种基于LSTM的自编码器模型，可以用于序列数据的降维和特征提取。以下是一个简单的LSTM-AE代码实现：

import numpy as np
import tensorflow as tf

# 定义LSTM-AE模型
class LSTMAutoencoder(object):
    def __init__(self, hidden_num, inputs):
        self.hidden_num = hidden_num
        self.inputs = inputs
        self.lstm_cell = tf.contrib.rnn.BasicLSTMCell(hidden_num)
        self.outputs, self.last_state = tf.nn.dynamic_rnn(
            self.lstm_cell, inputs, dtype=tf.float32)

        # 定义重构层
        self.reconstruct = tf.contrib.layers.fully_connected(
            self.outputs, inputs.shape[2], activation_fn=None)

        # 定义损失函数
        self.loss = tf.reduce_mean(tf.square(self.reconstruct - inputs))

        # 定义优化器
        self.optimizer = tf.train.AdamOptimizer().minimize(self.loss)

# 训练模型
def train(model, data, epochs=1000, batch_size=50):
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for i in range(epochs):
            np.random.shuffle(data)
            for j in range(0, len(data), batch_size):
                batch = data[j:j+batch_size]
                _, loss = sess.run([model.optimizer, model.loss],
                                   feed_dict={model.inputs: batch})
            if i % 100 == 0:
                print('Epoch {}/{}: Loss {}'.format(i, epochs, loss))

# 测试模型
def test(model, data):
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        loss = sess.run(model.loss, feed_dict={model.inputs: data})
        print('Test Loss:', loss)

# 示例
data = np.random.randn(100, 10, 5)
model = LSTMAutoencoder(hidden_num=8, inputs=tf.placeholder(tf.float32, [None, 10, 5]))
train(model, data)
test(model, data)