How to use multilayered bidirectional LSTM in Tensorflow?

Question

I want to know how to use multilayered bidirectional LSTM in Tensorflow.

I have already implemented the contents of bidirectional LSTM, but I wanna compare this model wi

Answer 1

This is primarily same as the first answer but with a little variation of usage of scope name and with added dropout wrappers. It also takes care of the error the first answer gives about variable scope.

def bidirectional_lstm(input_data, num_layers, rnn_size, keep_prob):

    output = input_data
    for layer in range(num_layers):
        with tf.variable_scope('encoder_{}'.format(layer),reuse=tf.AUTO_REUSE):

            # By giving a different variable scope to each layer, I've ensured that
            # the weights are not shared among the layers. If you want to share the
            # weights, you can do that by giving variable_scope as "encoder" but do
            # make sure first that reuse is set to tf.AUTO_REUSE

            cell_fw = tf.contrib.rnn.LSTMCell(rnn_size, initializer=tf.truncated_normal_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.truncated_normal_initializer(-0.1, 0.1, seed=2))
            cell_bw = tf.contrib.rnn.DropoutWrapper(cell_bw, input_keep_prob = keep_prob)

            outputs, states = tf.nn.bidirectional_dynamic_rnn(cell_fw, 
                                                              cell_bw, 
                                                              output,
                                                              dtype=tf.float32)

            # Concat the forward and backward outputs
            output = tf.concat(outputs,2)

    return output