写一段LSTM的Python代码

# 导入必要的库
import numpy as np
import tensorflow as tf# 设置超参数
num_epochs = 100
batch_size = 128
rnn_size = 64# 定义LSTM网络
# 声明输入占位符
inputs = tf.placeholder(tf.int32, shape=[batch_size,num_epochs], name='inputs')
# 声明输出占位符
targets = tf.placeholder(tf.int32, shape=[batch_size, num_epochs], name='targets')
# 声明LSTM单元
lstm_cell = tf.contrib.rnn.BasicLSTMCell(rnn_size)
# 构建LSTM网络
outputs, _ = tf.nn.dynamic_rnn(lstm_cell, inputs, dtype=tf.float32)
# 声明损失函数
loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=outputs, labels=targets))
# 声明优化器
optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)# 运行图
with tf.Session() as sess:
# 初始化变量
sess.run(tf.global_variables_initializer())
# 迭代num_epochs次训练
for epoch in range(num_epochs):
# 获取一个batch的训练数据
batch_inputs, batch_targets = get_batch_data()
# 运行图
feed = {inputs: batch_inputs, targets: batch_targets}
train_loss, _ = sess.run([loss, optimizer], feed_dict=feed)
# 打印损失
print('Epoch: {}/{}'.format(epoch+1, num_epochs), ' Training loss: {:.4f}'.format(train_loss))