import tensorflow as tf

    print('Num of sample per eps is %d' % (num_sample))

    # Construct predictions
    image = tf.placeholder(tf.float32, shape=[hps.batch_size, image_size, image_size,
                                              num_channel])  ############MNIST and CIFAR10 are different ar here
    adv_image = tf.placeholder(tf.float32, shape=[hps.batch_size, image_size, image_size,
                                              num_channel])  ############MNIST and CIFAR10 are different ar here
    predict = tf.placeholder(tf.float32, shape=[hps.batch_size, 10])
    predict_nor, tsne_logit_nor = models(hps, image, FLAGS.RCE_train, logits=False, tsne_logits=True)
    predict_adv, tsne_logit_adv = models(hps, adv_image, FLAGS.RCE_train, logits=False, tsne_logits=True)

    # 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)
