BiRNN_lstm_cell_pseudocode.py

with tf.name_scope('lstm'):
    # Forward direction cell:
    lstm_fw_cell = tf.contrib.rnn.BasicLSTMCell(n_cell_dim, forget_bias=1.0, state_is_tuple=True)
    # Backward direction cell:
    lstm_bw_cell = tf.contrib.rnn.BasicLSTMCell(n_cell_dim, forget_bias=1.0, state_is_tuple=True)

    # Now we feed `layer_3` into the LSTM BRNN cell and obtain the LSTM BRNN output.
    outputs, output_states = tf.nn.bidirectional_dynamic_rnn(cell_fw=lstm_fw_cell,
                                                             cell_bw=lstm_bw_cell,
                                                             inputs=layer_3,
                                                             dtype=tf.float32,
                                                             time_major=True,
                                                             sequence_length=seq_length)

    tf.summary.histogram("activations", outputs)