import tensorflow as tf

    feature_loss = tf.reduce_mean(tf.abs(tf.subtract(real_data_mean, fake_data_mean)))

    return feature_loss

  def _tower_loss_semi_supervised(self, inputs, targets, gpu_idx=0, num_classes=11,
                                  is_fm_loss=False):
    with tf.variable_scope("train_specific"):
      avg_error_rate = tf.get_variable(
          'avg_error_rate', [], initializer=tf.constant_initializer(0.), trainable=False)
      num_error_rate = tf.get_variable(
          'num_error_rate', [], initializer=tf.constant_initializer(0.), trainable=False)

