def bidirectional_dynamic_rnn(
  cell_fw, # 前向RNN
  cell_bw, # 后向RNN
  inputs, # 输入
  sequence_length=None,# 输入序列的实际长度（可选，默认为输入序列的最大长度）
  initial_state_fw=None, # 前向的初始化状态（可选）
  initial_state_bw=None, # 后向的初始化状态（可选）
  dtype=None, # 初始化和输出的数据类型（可选）
  parallel_iterations=None,
  swap_memory=False,
  time_major=False,
  # 决定了输入输出tensor的格式：如果为true, 向量的形状必须为 `[max_time, batch_size, depth]`.
  # 如果为false, tensor的形状必须为`[batch_size, max_time, depth]`.
  scope=None
)

元组：(outputs, output_states)

def encoding_layer(rnn_size,sequence_length,num_layers,rnn_inputs,keep_prob):
  # rnn_size: rnn隐层节点数量
  # sequence_length: 数据的序列长度
  # num_layers:堆叠的rnn cell数量
  # rnn_inputs: 输入tensor
  # keep_prob:
  '''Create the encoding layer'''
  for layer in range(num_layers):
    with tf.variable_scope('encode_{}'.format(layer)):
      cell_fw = tf.contrib.rnn.LSTMCell(rnn_size,initializer=tf.random_uniform_initializer(-0.1,0.1,seed=2))
      cell_fw = tf.contrib.rnn.DropoutWrapper(cell_fw,input_keep_prob=keep_prob)
 
      cell_bw = tf.contrib.rnn.LSTMCell(rnn_size,initializer=tf.random_uniform_initializer(-0.1,0.1,seed=2))
      cell_bw = tf.contrib.rnn.DropoutWrapper(cell_bw,input_keep_prob = keep_prob)
 
      enc_output,enc_state = tf.nn.bidirectional_dynamic_rnn(cell_fw,cell_bw,
                                  rnn_inputs,sequence_length,dtype=tf.float32)
 
  # join outputs since we are using a bidirectional RNN
  enc_output = tf.concat(enc_output,2) 
  return enc_output,enc_state