# 定义使用LSTM结构为循环体结构且使用dropout的深层循环神经网络
    # NUM_LAYERS 网络深度层数
    # HIDDEN_SIZE 神经元数量
    dropout_keep_prob = LSTM_KEEP_PROB if is_training else 1.0
    lstm_cells = [
        tf.nn.rnn_cell.DropoutWrapper(
            tf.nn.rnn_cell.BasicLSTMCell(HIDDEN_SIZE),
            output_keep_prob=dropout_keep_prob)
        for _ in range(NUM_LAYERS)]
    cell = tf.nn.rnn_cell.MultiRNNCell(lstm_cells)
    
    .....
    
    # 将输入单词转化为词向量
    #skip-gram模型
    inputs = tf.nn.embedding_lookup(embedding, self.input_data)

    # 对输入数据进行dropout
    if is_training:
        inputs = tf.nn.dropout(inputs, EMBEDDING_KEEP_PROB)
        
        
    ....
    
    # 收集LSTM不同时刻的输出
    outputs = []
    state = self.initial_state
    with tf.variable_scope("RNN"):
        for time_step in range(num_steps):
            if time_step > 0: tf.get_variable_scope().reuse_variables()
            cell_output, state = cell(inputs[:, time_step, :], state)
            outputs.append(cell_output)

    output = tf.reshape(tf.concat(outputs, 1), [-1, HIDDEN_SIZE])

    if SHARE_EMB_AND_SOFTMAX:
        weight = tf.transpose(embedding)
    else:
        weight = tf.get_variable("weight", [HIDDEN_SIZE, VOCAB_SIZE])

    bias = tf.get_variable("bias", [VOCAB_SIZE])
    logits = tf.matmul(output, weight) + bias
    
    
    # 分类输出
    self.predictions = tf.nn.softmax(logits, name='predictions')

    # 真实分布与预测分布的交叉熵
    loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
        labels=tf.reshape(self.targets, [-1]),
        logits=logits)
    self.cost = tf.reduce_sum(loss) / batch_size
    self.final_state = state
    
    
    # 梯度优化
    print("FLAGS.Optimizer:", FLAGS.Optimizer)
    if FLAGS.Optimizer == "adam":
        print("use adma Optimizer  learning_rate:", LEARNING_RATE)
        optimizer = tf.train.AdamOptimizer(learning_rate=LEARNING_RATE)
    else:
        optimizer = tf.train.GradientDescentOptimizer(learning_rate=LEARNING_RATE)

    # 训练步骤
    self.train_op = optimizer.apply_gradients(zip(grads, trainable_variables), global_step=self.global_step)