import tensorflow as tf

    backward_cell = tf.nn.rnn_cell.LSTMCell(128)
    encoder_outputs,_ = tf.nn.bidirectional_dynamic_rnn(
            forward_cell,
            backward_cell,
            logits,
            dtype=tf.float32
        )
    encoder_outputs = tf.concat(encoder_outputs, axis=2)
    logits = tf.reshape(tf.layers.dense(encoder_outputs, units=num_classes), [-1, bil_lstm_win_size, num_classes])
    return logits
    

def cnn_model(x, amp_factor=1):
    with tf.variable_scope('model'):
        conv1 = tf.layers.conv2d(x, filters=32*amp_factor, kernel_size=[5, 3],
                                 data_format='channels_last', padding= "same",
