写lstm神经网络程序，tensorflow环境，在lstm中加入注意力机制

以下是一个使用Tensorflow实现LSTM神经网络并加入注意力机制的示例代码：

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

# 加载MNIST数据集
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

# 设置LSTM的参数
n_input = 28  # 输入数据的维度
n_steps = 28  # 时间步数
n_hidden = 128  # LSTM隐藏层神经元个数
n_classes = 10  # 分类数
learning_rate = 0.001  # 学习率

# 输入数据和标签占位符
x = tf.placeholder(tf.float32, [None, n_steps, n_input])
y = tf.placeholder(tf.float32, [None, n_classes])

# 定义LSTM层
lstm_cell = tf.contrib.rnn.BasicLSTMCell(n_hidden, forget_bias=1.0)
outputs, states = tf.nn.dynamic_rnn(lstm_cell, x, dtype=tf.float32)

# 定义注意力机制
attention = tf.Variable(tf.random_normal([n_hidden, 1]))
attention_scores = tf.matmul(tf.reshape(outputs, [-1, n_hidden]), attention)
attention_weights = tf.nn.softmax(tf.reshape(attention_scores, [-1, n_steps, 1]), dim=1)
weighted_outputs = outputs * attention_weights
final_outputs = tf.reduce_sum(weighted_outputs, axis=1)

# 定义全连接层
W = tf.Variable(tf.random_normal([n_hidden, n_classes]))
b = tf.Variable(tf.random_normal([n_classes]))
logits = tf.matmul(final_outputs, W) + b

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

# 定义准确率计算方法
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

# 训练模型
batch_size = 128
n_epochs = 10
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for epoch in range(n_epochs):
        n_batches = int(mnist.train.num_examples / batch_size)
        for i in range(n_batches):
            batch_x, batch_y = mnist.train.next_batch(batch_size)
            batch_x = batch_x.reshape((batch_size, n_steps, n_input))
            sess.run(optimizer, feed_dict={x: batch_x, y: batch_y})
        acc = sess.run(accuracy, feed_dict={x: mnist.test.images.reshape((-1, n_steps, n_input)), y: mnist.test.labels})
        print("Epoch:", epoch + 1, "Test Accuracy:", acc)

在这个代码中，我们使用了Tensorflow的`tf.contrib.rnn.BasicLSTMCell`来定义LSTM层。然后，我们定义了一个注意力机制，通过对LSTM输出的加权求和来计算最终输出。最后，我们定义了一个全连接层，并使用`softmax_cross_entropy_with_logits`作为损失函数，使用AdamOptimizer作为优化器。在训练过程中，我们使用了`tf.equal`和`tf.cast`来计算准确率。