title: How to use multilayered bidirectional LSTM in Tensorflow?
body: <p>I want to know how to use multilayered bidirectional LSTM in Tensorflow.</p>
  
  <p>I have already implemented the contents of bidirectional LSTM, but I wanna compare this model with the model added multi-layers.</p>
  
  <p>How should I add some code in this part?</p>
  
  <pre class="lang-python prettyprint-override"><code>x = tf.unstack(tf.transpose(x, perm=[1, 0, 2]))
  #print(x[0].get_shape())
  
  # Define lstm cells with tensorflow
  # Forward direction cell
  lstm_fw_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0)
  # Backward direction cell
  lstm_bw_cell = rnn.BasicLSTMCell(n_hidden, forget_bias=1.0)
  
  # Get lstm cell output
  try:
      outputs, _, _ = rnn.static_bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x,
                                            dtype=tf.float32)
  except Exception: # Old TensorFlow version only returns outputs not states
      outputs = rnn.static_bidirectional_rnn(lstm_fw_cell, lstm_bw_cell, x,
                                      dtype=tf.float32)
  
  # Linear activation, using rnn inner loop last output
  outputs = tf.stack(outputs, axis=1)
  outputs = tf.reshape(outputs, (batch_size*n_steps, n_hidden*2))
  outputs = tf.matmul(outputs, weights['out']) + biases['out']
  outputs = tf.reshape(outputs, (batch_size, n_steps, n_classes))
  </code></pre>