import tensorflow as tf

            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",
                                 strides=(2, 1),
                                 activation=tf.nn.relu)
        pool1 = conv1

        conv2 = tf.layers.conv2d(pool1, filters=64*amp_factor, kernel_size=[5, 1],
                                 data_format='channels_last', padding= "same",
                                 strides=(2, 1),
