met "Segmentation fault": added a lstm layer in the full_network, when run train about wgansing HOT 3 OPEN

output = encoder_decoder_archi_gan(inputs, is_train)
output = tf.tanh(tf.layers.batch_normalization(tf.layers.dense(output, config.output_features, name = "Fu_F", kernel_initializer=tf.random_normal_initializer(stddev=0.02)), training = is_train, name = "bn_fu_out"))

output = tf.squeeze(output)

# add lstm layer
lstm_cell = tf.contrib.rnn.BasicLSTMCell(64, forget_bias=1.0, state_is_tuple=True)
init_state = lstm_cell.zero_state(batch_size=config.batch_size, dtype=tf.float32)
lstm_out, final_state = tf.nn.dynamic_rnn(lstm_cell, output, initial_state=init_state, time_major=False)
lstm_out = tf.tanh(tf.layers.batch_normalization(tf.layers.dense(lstm_out, config.output_features, name = "T_GRU", kernel_initializer=tf.random_normal_initializer(stddev=0.02)), training=is_train, name = "gru_out"))

return lstm_out

    output = x
    num_layer = 2
    # for n in range(num_layer):
    lstm_fw = tf.nn.rnn_cell.LSTMCell(lstm_size, state_is_tuple=True)
    lstm_bw = tf.nn.rnn_cell.LSTMCell(lstm_size, state_is_tuple=True)

    _initial_state_fw = lstm_fw.zero_state(config.batch_size, tf.float32)
    _initial_state_bw = lstm_bw.zero_state(config.batch_size, tf.float32)

    output, _state1, _state2 = tf.contrib.rnn.static_bidirectional_rnn(lstm_fw, lstm_bw, output, 
                                              initial_state_fw=_initial_state_fw,
                                              initial_state_bw=_initial_state_bw, 
                                              scope='BLSTM_'+scope)
    output = tf.stack(output)
    output_fw = output[0]
    output_bw = output[1]
    output = tf.transpose(output, [1,0,2])

    return output