import tensorflow as tf

    predict_nor = tf.nn.softmax(logit_nor)
    predict_adv = tf.nn.softmax(logit_adv)

    # Calculate entropy
    argmax_y_onehot = tf.one_hot(tf.argmax(predict, 1), 10, on_value=0.0, off_value=1.0, axis=-1)
    normalized_y_nonmaximal = tf.reduce_sum(predict * argmax_y_onehot, 1)
    entropy = tf.reduce_sum(-tf.log(predict) * predict * argmax_y_onehot,1) / normalized_y_nonmaximal + tf.log(normalized_y_nonmaximal)

    for k in range(1):
        result_dict = loadmat('kernel_para_'+FLAGS.dataset+'/kernel1000_for_attack_' + f1 + '.mat')
        result_dict_median = loadmat('kernel_para_'+FLAGS.dataset+'/kernel1000_median_for_attack_' + f1 + '.mat')
        # e_mean = result_dict['mean_logits_' + f1]  # 10X64
