帮我写一个tensorflow实现LSTM程序

好的，以下是一个使用 TensorFlow 实现 LSTM 的简单代码：

import tensorflow as tf

# 定义超参数
input_dim = 28
hidden_dim = 100
output_dim = 10
sequence_length = 28
learning_rate = 0.01
batch_size = 128
num_epochs = 10

# 定义输入和输出占位符
inputs = tf.placeholder(tf.float32, [None, sequence_length, input_dim])
targets = tf.placeholder(tf.float32, [None, output_dim])

# 定义 LSTM 层
lstm_cell = tf.nn.rnn_cell.LSTMCell(hidden_dim)
outputs, _ = tf.nn.dynamic_rnn(lstm_cell, inputs, dtype=tf.float32)

# 定义输出层
weights = tf.Variable(tf.truncated_normal([hidden_dim, output_dim], stddev=0.1))
biases = tf.Variable(tf.constant(0.1, shape=[output_dim]))
final_output = tf.matmul(outputs[:, -1, :], weights) + biases

# 定义损失函数和优化器
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=final_output, labels=targets))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)

# 定义准确率
correct_prediction = tf.equal(tf.argmax(final_output, 1), tf.argmax(targets, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

# 训练模型
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for epoch in range(num_epochs):
        for batch in range(len(train_data) // batch_size):
            batch_inputs, batch_targets = get_batch(train_data, batch_size, batch)
            sess.run(optimizer, feed_dict={inputs: batch_inputs, targets: batch_targets})
        acc = sess.run(accuracy, feed_dict={inputs: test_data, targets: test_labels})
        print('Epoch', epoch+1, 'accuracy', acc)

其中，`inputs` 和 `targets` 是输入和输出占位符，`lstm_cell` 是 LSTM 层，`weights` 和 `biases` 是输出层的权重和偏置，`final_output` 是最终的输出，`loss` 是损失函数，`optimizer` 是优化器，`accuracy` 是准确率。在训练过程中，我们需要使用 `feed_dict` 将训练数据和标签传入模型中。

请注意，这只是一个简单的实现，您可以根据自己的需要进行修改和调整。