GUI.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow version: 2.4.1\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking for plugins in `dc_plugins' directory                                   \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Error loading wordnet: <urlopen error [Errno 0] Error>\n",
      "/Users/qiuchuhan/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/backend.py:434: UserWarning: `tf.keras.backend.set_learning_phase` is deprecated and will be removed after 2020-10-11. To update it, simply pass a True/False value to the `training` argument of the `__call__` method of your layer or model.\n",
      "  warnings.warn('`tf.keras.backend.set_learning_phase` is deprecated and '\n"
     ]
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'saved_models/sensorless_tree_black-box.npy'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-8f2652dede9b>\u001b[0m in \u001b[0;36mhandel_pushButton\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    253\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'synthesis'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 255\u001b[0;31m             last_time, len_reversed_test_set, diff, reversed_test_set = synthesis_knowledge(dataset, embedding, model, workdir,\n\u001b[0m\u001b[1;32m    256\u001b[0m                                 datadir = datadir)\n\u001b[1;32m    257\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtextEdit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'---------------------------------------------------------------'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/FYP/DeepConcolic-master/EKiML/src/synthesis_knowledge.py\u001b[0m in \u001b[0;36msynthesis_knowledge\u001b[0;34m(dataset, embedding, model, workdir, datadir)\u001b[0m\n\u001b[1;32m     86\u001b[0m         \u001b[0;31m# load the tree classifier\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m         \u001b[0mbasename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'_tree_'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0membedding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 88\u001b[0;31m         tree = np.load(os.path.join (workdir, basename + '.npy'),\n\u001b[0m\u001b[1;32m     89\u001b[0m                        allow_pickle='TRUE').item()\n\u001b[1;32m     90\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/numpy/lib/npyio.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(file, mmap_mode, allow_pickle, fix_imports, encoding)\u001b[0m\n\u001b[1;32m    426\u001b[0m         \u001b[0mown_fid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    427\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 428\u001b[0;31m         \u001b[0mfid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos_fspath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    429\u001b[0m         \u001b[0mown_fid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    430\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'saved_models/sensorless_tree_black-box.npy'"
     ]
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'saved_models/mnist_tree_black-box.npy'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-8f2652dede9b>\u001b[0m in \u001b[0;36mhandel_pushButton\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    253\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'synthesis'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 255\u001b[0;31m             last_time, len_reversed_test_set, diff, reversed_test_set = synthesis_knowledge(dataset, embedding, model, workdir,\n\u001b[0m\u001b[1;32m    256\u001b[0m                                 datadir = datadir)\n\u001b[1;32m    257\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtextEdit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'---------------------------------------------------------------'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/FYP/DeepConcolic-master/EKiML/src/synthesis_knowledge.py\u001b[0m in \u001b[0;36msynthesis_knowledge\u001b[0;34m(dataset, embedding, model, workdir, datadir)\u001b[0m\n\u001b[1;32m     86\u001b[0m         \u001b[0;31m# load the tree classifier\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m         \u001b[0mbasename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'_tree_'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0membedding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 88\u001b[0;31m         tree = np.load(os.path.join (workdir, basename + '.npy'),\n\u001b[0m\u001b[1;32m     89\u001b[0m                        allow_pickle='TRUE').item()\n\u001b[1;32m     90\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/numpy/lib/npyio.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(file, mmap_mode, allow_pickle, fix_imports, encoding)\u001b[0m\n\u001b[1;32m    426\u001b[0m         \u001b[0mown_fid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    427\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 428\u001b[0;31m         \u001b[0mfid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos_fspath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    429\u001b[0m         \u001b[0mown_fid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    430\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'saved_models/mnist_tree_black-box.npy'"
     ]
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'saved_models/cod-rna_tree_black-box.npy'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-8f2652dede9b>\u001b[0m in \u001b[0;36mhandel_pushButton\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    253\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'synthesis'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 255\u001b[0;31m             last_time, len_reversed_test_set, diff, reversed_test_set = synthesis_knowledge(dataset, embedding, model, workdir,\n\u001b[0m\u001b[1;32m    256\u001b[0m                                 datadir = datadir)\n\u001b[1;32m    257\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtextEdit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'---------------------------------------------------------------'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/FYP/DeepConcolic-master/EKiML/src/synthesis_knowledge.py\u001b[0m in \u001b[0;36msynthesis_knowledge\u001b[0;34m(dataset, embedding, model, workdir, datadir)\u001b[0m\n\u001b[1;32m     86\u001b[0m         \u001b[0;31m# load the tree classifier\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m         \u001b[0mbasename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'_tree_'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0membedding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 88\u001b[0;31m         tree = np.load(os.path.join (workdir, basename + '.npy'),\n\u001b[0m\u001b[1;32m     89\u001b[0m                        allow_pickle='TRUE').item()\n\u001b[1;32m     90\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/numpy/lib/npyio.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(file, mmap_mode, allow_pickle, fix_imports, encoding)\u001b[0m\n\u001b[1;32m    426\u001b[0m         \u001b[0mown_fid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    427\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 428\u001b[0;31m         \u001b[0mfid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos_fspath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    429\u001b[0m         \u001b[0mown_fid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    430\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'saved_models/cod-rna_tree_black-box.npy'"
     ]
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'saved_models/nursery_tree_black-box.npy'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-8f2652dede9b>\u001b[0m in \u001b[0;36mhandel_pushButton\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    253\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'synthesis'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 255\u001b[0;31m             last_time, len_reversed_test_set, diff, reversed_test_set = synthesis_knowledge(dataset, embedding, model, workdir,\n\u001b[0m\u001b[1;32m    256\u001b[0m                                 datadir = datadir)\n\u001b[1;32m    257\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtextEdit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'---------------------------------------------------------------'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/FYP/DeepConcolic-master/EKiML/src/synthesis_knowledge.py\u001b[0m in \u001b[0;36msynthesis_knowledge\u001b[0;34m(dataset, embedding, model, workdir, datadir)\u001b[0m\n\u001b[1;32m     86\u001b[0m         \u001b[0;31m# load the tree classifier\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m         \u001b[0mbasename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'_tree_'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0membedding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 88\u001b[0;31m         tree = np.load(os.path.join (workdir, basename + '.npy'),\n\u001b[0m\u001b[1;32m     89\u001b[0m                        allow_pickle='TRUE').item()\n\u001b[1;32m     90\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/numpy/lib/npyio.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(file, mmap_mode, allow_pickle, fix_imports, encoding)\u001b[0m\n\u001b[1;32m    426\u001b[0m         \u001b[0mown_fid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    427\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 428\u001b[0;31m         \u001b[0mfid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos_fspath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    429\u001b[0m         \u001b[0mown_fid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    430\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'saved_models/nursery_tree_black-box.npy'"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "suspected decision rule:  [[52, '>', 0.0961844997946173], [13, '>', 0.45533549785614014], [559, '<=', 0.14047499746084213], [129, '<=', -0.8655029833316803], [57, '>', -0.9998455047607422], [159, '>', 0.22970599681138992], [49, '<=', 0.862836480140686], [442, '<=', -0.9988240003585815], [442, '>', -0.9995000064373016]]\n",
      "find a solution\n",
      "suspected decision rule:  [[52, '>', 0.0961844997946173], [13, '>', 0.45533549785614014], [559, '<=', 0.14047499746084213], [129, '>', -0.8655029833316803], [57, '<=', -0.9750190079212189], [409, '>', -0.9980224967002869], [429, '<=', -0.9114909768104553], [442, '<=', -0.9948055148124695]]\n",
      "find a solution\n",
      "suspected decision rule:  [[52, '>', 0.0961844997946173], [13, '>', 0.45533549785614014], [559, '>', 0.14047499746084213], [139, '>', -0.9960980117321014], [129, '<=', -0.8992460072040558], [442, '<=', -0.9896329939365387]]\n",
      "find a solution\n",
      "suspected decision rule:  [[52, '>', 0.0961844997946173], [13, '>', 0.45533549785614014], [559, '<=', 0.14047499746084213], [129, '<=', -0.8655029833316803], [57, '>', -0.9998455047607422], [159, '<=', 0.22970599681138992], [230, '>', -0.9992440044879913], [442, '<=', -0.9989370107650757], [13, '<=', 0.6774359941482544]]\n",
      "find a solution\n",
      "suspected decision rule:  [[52, '<=', 0.0961844997946173], [442, '<=', -0.9989795088768005], [442, '>', -0.9990085065364838]]\n",
      "find a solution\n",
      "suspected decision rule:  [[52, '<=', 0.0961844997946173], [442, '<=', -0.9989795088768005], [442, '>', -0.9990085065364838]]\n",
      "find a solution\n",
      "suspected decision rule:  [[52, '<=', 0.0961844997946173], [442, '<=', -0.9989795088768005], [442, '>', -0.9990085065364838]]\n",
      "find a solution\n",
      "suspected decision rule:  [[52, '>', 0.0961844997946173], [13, '>', 0.45533549785614014], [559, '<=', 0.14047499746084213], [129, '<=', -0.8655029833316803], [57, '>', -0.9998455047607422], [159, '<=', 0.22970599681138992], [230, '>', -0.9992440044879913], [442, '<=', -0.9989370107650757], [13, '<=', 0.6774359941482544]]\n",
      "find a solution\n",
      "suspected decision rule:  [[52, '>', 0.0961844997946173], [13, '>', 0.45533549785614014], [559, '<=', 0.14047499746084213], [129, '<=', -0.8655029833316803], [57, '>', -0.9998455047607422], [159, '<=', 0.22970599681138992], [230, '>', -0.9992440044879913], [442, '<=', -0.9989370107650757], [13, '<=', 0.6774359941482544]]\n",
      "find a solution\n",
      "suspected decision rule:  [[52, '<=', 0.0961844997946173], [442, '<=', -0.9989795088768005], [442, '>', -0.9990085065364838]]\n",
      "find a solution\n",
      "suspected decision rule:  [[52, '<=', 0.0961844997946173], [442, '<=', -0.9989795088768005], [442, '>', -0.9990085065364838]]\n",
      "find a solution\n",
      "suspected decision rule:  [[52, '<=', 0.0961844997946173], [442, '<=', -0.9989795088768005], [442, '>', -0.9990085065364838]]\n",
      "find a solution\n",
      "suspected decision rule:  [[52, '<=', 0.0961844997946173], [442, '<=', -0.9989795088768005], [442, '>', -0.9990085065364838]]\n",
      "find a solution\n",
      "suspected decision rule:  [[52, '<=', 0.0961844997946173], [442, '<=', -0.9989795088768005], [442, '>', -0.9990085065364838]]\n",
      "find a solution\n",
      "suspected decision rule:  [[52, '>', 0.0961844997946173], [13, '>', 0.45533549785614014], [559, '<=', 0.14047499746084213], [129, '<=', -0.8655029833316803], [57, '>', -0.9998455047607422], [159, '<=', 0.22970599681138992], [230, '>', -0.9992440044879913], [442, '<=', -0.9989370107650757], [13, '<=', 0.6774359941482544]]\n",
      "find a solution\n",
      "suspected decision rule:  [[52, '>', 0.0961844997946173], [13, '>', 0.45533549785614014], [559, '<=', 0.14047499746084213], [129, '<=', -0.8655029833316803], [57, '>', -0.9998455047607422], [159, '>', 0.22970599681138992], [49, '<=', 0.862836480140686], [442, '<=', -0.9988240003585815], [442, '>', -0.9995000064373016]]\n",
      "find a solution\n",
      "evaluation dataset:  har\n",
      "embedding method:  black-box\n",
      "Time to synthesize the knowledge:  79.82569127100001\n",
      "Amount of collection:  16\n",
      "Suspected features:  [array([ 52, 159, 442]), array([ 13, 429, 442]), array([ 13, 129, 442]), array([ 13,  52, 442]), array([ 52, 395, 442]), array([ 52, 395, 442]), array([ 52, 395, 442]), array([ 13,  52, 442]), array([ 13,  52, 442]), array([ 52, 442, 462]), array([ 52, 442, 462]), array([ 52, 442, 462]), array([ 52, 442, 462]), array([ 52, 442, 462]), array([ 13,  52, 442]), array([ 52, 159, 442])]\n",
      "Suspected knowledge:  [[0.09686050587333739, 0.23038200289011002, -0.9988240003585815], [0.7276677489280701, -0.9114909768104553, -0.9948055148124695], [0.7276677489280701, -0.8992460072040558, -0.9896329939365387], [0.6774359941482544, 0.31828499608673155, -0.9989370107650757], [0.0, 0.0, -0.9989795088768005], [0.0, 0.0, -0.9989795088768005], [0.0, 0.0, -0.9989795088768005], [0.6774359941482544, 0.31828499608673155, -0.9989370107650757], [0.6774359941482544, 0.31828499608673155, -0.9989370107650757], [0.0, -0.9989795088768005, -0.9575990023403168], [0.0, -0.9989795088768005, -0.9575990023403168], [0.0, -0.9989795088768005, -0.9575990023403168], [0.0, -0.9989795088768005, -0.9575990023403168], [0.0, -0.9989795088768005, -0.9575990023403168], [0.6774359941482544, 0.31828499608673155, -0.9989370107650757], [0.09686050587333739, 0.23038200289011002, -0.9988240003585815]]\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Cannot take a larger sample than population when 'replace=False'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-8f2652dede9b>\u001b[0m in \u001b[0;36mhandel_pushButton\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    253\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'synthesis'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 255\u001b[0;31m             last_time, len_reversed_test_set, diff, reversed_test_set = synthesis_knowledge(dataset, embedding, model, workdir,\n\u001b[0m\u001b[1;32m    256\u001b[0m                                 datadir = datadir)\n\u001b[1;32m    257\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtextEdit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'---------------------------------------------------------------'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/FYP/DeepConcolic-master/EKiML/src/synthesis_knowledge.py\u001b[0m in \u001b[0;36msynthesis_knowledge\u001b[0;34m(dataset, embedding, model, workdir, datadir)\u001b[0m\n\u001b[1;32m     53\u001b[0m         \u001b[0mload_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_seed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdatadir\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdatadir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m     \u001b[0midxs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m     \u001b[0mx_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midxs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[0my_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midxs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mmtrand.pyx\u001b[0m in \u001b[0;36mnumpy.random.mtrand.RandomState.choice\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Cannot take a larger sample than population when 'replace=False'"
     ]
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'saved_models/mushroom_tree_black-box.npy'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-8f2652dede9b>\u001b[0m in \u001b[0;36mhandel_pushButton\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    253\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'synthesis'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 255\u001b[0;31m             last_time, len_reversed_test_set, diff, reversed_test_set = synthesis_knowledge(dataset, embedding, model, workdir,\n\u001b[0m\u001b[1;32m    256\u001b[0m                                 datadir = datadir)\n\u001b[1;32m    257\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtextEdit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'---------------------------------------------------------------'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/FYP/DeepConcolic-master/EKiML/src/synthesis_knowledge.py\u001b[0m in \u001b[0;36msynthesis_knowledge\u001b[0;34m(dataset, embedding, model, workdir, datadir)\u001b[0m\n\u001b[1;32m     86\u001b[0m         \u001b[0;31m# load the tree classifier\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m         \u001b[0mbasename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'_tree_'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0membedding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 88\u001b[0;31m         tree = np.load(os.path.join (workdir, basename + '.npy'),\n\u001b[0m\u001b[1;32m     89\u001b[0m                        allow_pickle='TRUE').item()\n\u001b[1;32m     90\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/numpy/lib/npyio.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(file, mmap_mode, allow_pickle, fix_imports, encoding)\u001b[0m\n\u001b[1;32m    426\u001b[0m         \u001b[0mown_fid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    427\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 428\u001b[0;31m         \u001b[0mfid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos_fspath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    429\u001b[0m         \u001b[0mown_fid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    430\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'saved_models/mushroom_tree_black-box.npy'"
     ]
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'EKiML/saved_models/mushroom_tree_black-box.npy'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-8f2652dede9b>\u001b[0m in \u001b[0;36mhandel_pushButton\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    253\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'synthesis'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 255\u001b[0;31m             last_time, len_reversed_test_set, diff, reversed_test_set = synthesis_knowledge(dataset, embedding, model, workdir,\n\u001b[0m\u001b[1;32m    256\u001b[0m                                 datadir = datadir)\n\u001b[1;32m    257\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtextEdit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'---------------------------------------------------------------'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/FYP/DeepConcolic-master/EKiML/src/synthesis_knowledge.py\u001b[0m in \u001b[0;36msynthesis_knowledge\u001b[0;34m(dataset, embedding, model, workdir, datadir)\u001b[0m\n\u001b[1;32m     86\u001b[0m         \u001b[0;31m# load the tree classifier\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m         \u001b[0mbasename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'_tree_'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0membedding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 88\u001b[0;31m         tree = np.load(os.path.join (workdir, basename + '.npy'),\n\u001b[0m\u001b[1;32m     89\u001b[0m                        allow_pickle='TRUE').item()\n\u001b[1;32m     90\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/numpy/lib/npyio.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(file, mmap_mode, allow_pickle, fix_imports, encoding)\u001b[0m\n\u001b[1;32m    426\u001b[0m         \u001b[0mown_fid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    427\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 428\u001b[0;31m         \u001b[0mfid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos_fspath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    429\u001b[0m         \u001b[0mown_fid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    430\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'EKiML/saved_models/mushroom_tree_black-box.npy'"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Cannot take a larger sample than population when 'replace=False'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-8f2652dede9b>\u001b[0m in \u001b[0;36mhandel_pushButton\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    253\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'synthesis'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 255\u001b[0;31m             last_time, len_reversed_test_set, diff, reversed_test_set = synthesis_knowledge(dataset, embedding, model, workdir,\n\u001b[0m\u001b[1;32m    256\u001b[0m                                 datadir = datadir)\n\u001b[1;32m    257\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtextEdit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'---------------------------------------------------------------'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/FYP/DeepConcolic-master/EKiML/src/synthesis_knowledge.py\u001b[0m in \u001b[0;36msynthesis_knowledge\u001b[0;34m(dataset, embedding, model, workdir, datadir)\u001b[0m\n\u001b[1;32m     53\u001b[0m         \u001b[0mload_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_seed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdatadir\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdatadir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m     \u001b[0midxs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m     \u001b[0mx_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midxs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[0my_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midxs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mmtrand.pyx\u001b[0m in \u001b[0;36mnumpy.random.mtrand.RandomState.choice\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Cannot take a larger sample than population when 'replace=False'"
     ]
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'EKiML/saved_models/breast_cancer_tree_black-box.npy'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-8f2652dede9b>\u001b[0m in \u001b[0;36mhandel_pushButton\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    253\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'synthesis'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 255\u001b[0;31m             last_time, len_reversed_test_set, diff, reversed_test_set = synthesis_knowledge(dataset, embedding, model, workdir,\n\u001b[0m\u001b[1;32m    256\u001b[0m                                 datadir = datadir)\n\u001b[1;32m    257\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtextEdit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'---------------------------------------------------------------'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/FYP/DeepConcolic-master/EKiML/src/synthesis_knowledge.py\u001b[0m in \u001b[0;36msynthesis_knowledge\u001b[0;34m(dataset, embedding, model, workdir, datadir)\u001b[0m\n\u001b[1;32m     86\u001b[0m         \u001b[0;31m# load the tree classifier\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m         \u001b[0mbasename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdataset\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'_tree_'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0membedding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 88\u001b[0;31m         tree = np.load(os.path.join (workdir, basename + '.npy'),\n\u001b[0m\u001b[1;32m     89\u001b[0m                        allow_pickle='TRUE').item()\n\u001b[1;32m     90\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/numpy/lib/npyio.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(file, mmap_mode, allow_pickle, fix_imports, encoding)\u001b[0m\n\u001b[1;32m    426\u001b[0m         \u001b[0mown_fid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    427\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 428\u001b[0;31m         \u001b[0mfid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos_fspath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    429\u001b[0m         \u001b[0mown_fid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    430\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'EKiML/saved_models/breast_cancer_tree_black-box.npy'"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generalizing Universal Adversarial Examples:\n",
      "==> Preparing data..\n",
      "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to cifar10-data/cifar-10-python.tar.gz\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3cef08289f7a430391dc4bd15eb65d20",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Failed download. Trying https -> http instead. Downloading http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to cifar10-data/cifar-10-python.tar.gz\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a8f463c85edd49bb9946cdd77294ce38",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "RemoteDisconnected",
     "evalue": "Remote end closed connection without response",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/lib/python3.8/urllib/request.py\u001b[0m in \u001b[0;36mdo_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m   1349\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1350\u001b[0;31m                 h.request(req.get_method(), req.selector, req.data, headers,\n\u001b[0m\u001b[1;32m   1351\u001b[0m                           encode_chunked=req.has_header('Transfer-encoding'))\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m   1239\u001b[0m         \u001b[0;34m\"\"\"Send a complete request to the server.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1240\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1241\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36m_send_request\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m   1285\u001b[0m             \u001b[0mbody\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_encode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'body'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1286\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendheaders\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1287\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36mendheaders\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m   1234\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mCannotSendHeader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1235\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage_body\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencode_chunked\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1236\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36m_send_output\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m   1005\u001b[0m         \u001b[0;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_buffer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1006\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1007\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m    945\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_open\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 946\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    947\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1408\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1409\u001b[0;31m             self.sock = self._context.wrap_socket(self.sock,\n\u001b[0m\u001b[1;32m   1410\u001b[0m                                                   server_hostname=server_hostname)\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/ssl.py\u001b[0m in \u001b[0;36mwrap_socket\u001b[0;34m(self, sock, server_side, do_handshake_on_connect, suppress_ragged_eofs, server_hostname, session)\u001b[0m\n\u001b[1;32m    499\u001b[0m         \u001b[0;31m# ctx._wrap_socket()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 500\u001b[0;31m         return self.sslsocket_class._create(\n\u001b[0m\u001b[1;32m    501\u001b[0m             \u001b[0msock\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msock\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/ssl.py\u001b[0m in \u001b[0;36m_create\u001b[0;34m(cls, sock, server_side, do_handshake_on_connect, suppress_ragged_eofs, server_hostname, context, session)\u001b[0m\n\u001b[1;32m   1039\u001b[0m                         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"do_handshake_on_connect should not be specified for non-blocking sockets\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1040\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdo_handshake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1041\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mOSError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/ssl.py\u001b[0m in \u001b[0;36mdo_handshake\u001b[0;34m(self, block)\u001b[0m\n\u001b[1;32m   1308\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msettimeout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1309\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdo_handshake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1310\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mOSError\u001b[0m: [Errno 0] Error",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mURLError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/torchvision/datasets/utils.py\u001b[0m in \u001b[0;36mdownload_url\u001b[0;34m(url, root, filename, md5)\u001b[0m\n\u001b[1;32m     69\u001b[0m             \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Downloading '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0murl\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m' to '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mfpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 70\u001b[0;31m             urllib.request.urlretrieve(\n\u001b[0m\u001b[1;32m     71\u001b[0m                 \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfpath\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/urllib/request.py\u001b[0m in \u001b[0;36murlretrieve\u001b[0;34m(url, filename, reporthook, data)\u001b[0m\n\u001b[1;32m    246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 247\u001b[0;31m     \u001b[0;32mwith\u001b[0m \u001b[0mcontextlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclosing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murlopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    248\u001b[0m         \u001b[0mheaders\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/urllib/request.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[1;32m    221\u001b[0m         \u001b[0mopener\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_opener\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 222\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mopener\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    223\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/urllib/request.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[1;32m    524\u001b[0m         \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maudit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'urllib.Request'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfull_url\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 525\u001b[0;31m         \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    526\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/urllib/request.py\u001b[0m in \u001b[0;36m_open\u001b[0;34m(self, req, data)\u001b[0m\n\u001b[1;32m    541\u001b[0m         \u001b[0mprotocol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 542\u001b[0;31m         result = self._call_chain(self.handle_open, protocol, protocol +\n\u001b[0m\u001b[1;32m    543\u001b[0m                                   '_open', req)\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/urllib/request.py\u001b[0m in \u001b[0;36m_call_chain\u001b[0;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[1;32m    501\u001b[0m             \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandler\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 502\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    503\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/urllib/request.py\u001b[0m in \u001b[0;36mhttps_open\u001b[0;34m(self, req)\u001b[0m\n\u001b[1;32m   1392\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mhttps_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1393\u001b[0;31m             return self.do_open(http.client.HTTPSConnection, req,\n\u001b[0m\u001b[1;32m   1394\u001b[0m                 context=self._context, check_hostname=self._check_hostname)\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/urllib/request.py\u001b[0m in \u001b[0;36mdo_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m   1352\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# timeout error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1353\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mURLError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1354\u001b[0m             \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mURLError\u001b[0m: <urlopen error [Errno 0] Error>",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mRemoteDisconnected\u001b[0m                        Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-8f2652dede9b>\u001b[0m in \u001b[0;36mhandel_pushButton\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    322\u001b[0m         \u001b[0mlimited\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomboBox_limited\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcurrentText\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    323\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 324\u001b[0;31m         \u001b[0mlist_Epoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_Time\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_Tr_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_Tr_acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_Tr_stASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_Tr_noiseASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_Tr_GUAP_ASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTest_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mOri_Acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPerb_Acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTe_stASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTe_noiseASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTe_GUAP_ASR\u001b[0m  \u001b[0;34m=\u001b[0m \u001b[0mrun_g\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataSet\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ml2reg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbeta1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtau\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmanualSeed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgpuid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcuda\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresume\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutdir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlimited\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    325\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    326\u001b[0m         \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'%.2f \\t %.4f \\t %.4f \\t %.4f \\t %.4f \\t %.4f \\t                      %.4f'\u001b[0m\u001b[0;34m%\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTest_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mOri_Acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPerb_Acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTe_stASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTe_noiseASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTe_GUAP_ASR\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/FYP/DeepConcolic-master/GUAP/guap_ui.py\u001b[0m in \u001b[0;36mrun_g\u001b[0;34m(dataSet, lr, batch_size, epochs, l2reg, beta1, tau, eps, model_name, manualSeed, gpuid, cuda, resume, outdir, limited)\u001b[0m\n\u001b[1;32m    112\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mdataSet\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'CIFAR10'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 114\u001b[0;31m         \u001b[0mtrainset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorchvision\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatasets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCIFAR10\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mroot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'cifar10-data'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdownload\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtransform_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    115\u001b[0m         \u001b[0mtestset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorchvision\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatasets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCIFAR10\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mroot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'cifar10-data'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdownload\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtransform_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmodel_name\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'VGG19'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/torchvision/datasets/cifar.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, root, train, transform, target_transform, download)\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdownload\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdownload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_integrity\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/torchvision/datasets/cifar.py\u001b[0m in \u001b[0;36mdownload\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    141\u001b[0m             \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Files already downloaded and verified'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    142\u001b[0m             \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 143\u001b[0;31m         \u001b[0mdownload_and_extract_archive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mroot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmd5\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtgz_md5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    144\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    145\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mextra_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/torchvision/datasets/utils.py\u001b[0m in \u001b[0;36mdownload_and_extract_archive\u001b[0;34m(url, download_root, extract_root, filename, md5, remove_finished)\u001b[0m\n\u001b[1;32m    254\u001b[0m         \u001b[0mfilename\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbasename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    255\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 256\u001b[0;31m     \u001b[0mdownload_url\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdownload_root\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmd5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    257\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    258\u001b[0m     \u001b[0marchive\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdownload_root\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/torchvision/datasets/utils.py\u001b[0m in \u001b[0;36mdownload_url\u001b[0;34m(url, root, filename, md5)\u001b[0m\n\u001b[1;32m     77\u001b[0m                 print('Failed download. Trying https -> http instead.'\n\u001b[1;32m     78\u001b[0m                       ' Downloading ' + url + ' to ' + fpath)\n\u001b[0;32m---> 79\u001b[0;31m                 urllib.request.urlretrieve(\n\u001b[0m\u001b[1;32m     80\u001b[0m                     \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfpath\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     81\u001b[0m                     \u001b[0mreporthook\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgen_bar_updater\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/urllib/request.py\u001b[0m in \u001b[0;36murlretrieve\u001b[0;34m(url, filename, reporthook, data)\u001b[0m\n\u001b[1;32m    245\u001b[0m     \u001b[0murl_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_splittype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 247\u001b[0;31m     \u001b[0;32mwith\u001b[0m \u001b[0mcontextlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclosing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murlopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    248\u001b[0m         \u001b[0mheaders\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    249\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/urllib/request.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[1;32m    220\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    221\u001b[0m         \u001b[0mopener\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_opener\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 222\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mopener\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    223\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0minstall_opener\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopener\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/urllib/request.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[1;32m    523\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    524\u001b[0m         \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maudit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'urllib.Request'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfull_url\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 525\u001b[0;31m         \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    526\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    527\u001b[0m         \u001b[0;31m# post-process response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/urllib/request.py\u001b[0m in \u001b[0;36m_open\u001b[0;34m(self, req, data)\u001b[0m\n\u001b[1;32m    540\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    541\u001b[0m         \u001b[0mprotocol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 542\u001b[0;31m         result = self._call_chain(self.handle_open, protocol, protocol +\n\u001b[0m\u001b[1;32m    543\u001b[0m                                   '_open', req)\n\u001b[1;32m    544\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/urllib/request.py\u001b[0m in \u001b[0;36m_call_chain\u001b[0;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[1;32m    500\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhandler\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mhandlers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    501\u001b[0m             \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandler\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 502\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    503\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    504\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/urllib/request.py\u001b[0m in \u001b[0;36mhttp_open\u001b[0;34m(self, req)\u001b[0m\n\u001b[1;32m   1377\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1378\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mhttp_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1379\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdo_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhttp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclient\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mHTTPConnection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1380\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1381\u001b[0m     \u001b[0mhttp_request\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAbstractHTTPHandler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdo_request_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/urllib/request.py\u001b[0m in \u001b[0;36mdo_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m   1352\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# timeout error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1353\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mURLError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1354\u001b[0;31m             \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetresponse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1355\u001b[0m         \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1356\u001b[0m             \u001b[0mh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36mgetresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1330\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1331\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1332\u001b[0;31m                 \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbegin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1333\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mConnectionError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1334\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36mbegin\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    301\u001b[0m         \u001b[0;31m# read until we get a non-100 response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    302\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 303\u001b[0;31m             \u001b[0mversion\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreason\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_read_status\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    304\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mstatus\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mCONTINUE\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    305\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/http/client.py\u001b[0m in \u001b[0;36m_read_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    270\u001b[0m             \u001b[0;31m# Presumably, the server closed the connection before\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    271\u001b[0m             \u001b[0;31m# sending a valid response.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 272\u001b[0;31m             raise RemoteDisconnected(\"Remote end closed connection without\"\n\u001b[0m\u001b[1;32m    273\u001b[0m                                      \" response\")\n\u001b[1;32m    274\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRemoteDisconnected\u001b[0m: Remote end closed connection without response"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generalizing Universal Adversarial Examples:\n",
      "==> Preparing data..\n",
      "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to cifar10-data/cifar-10-python.tar.gz\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f196a65858094d6bb6f1b08ce3036043",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Failed download. Trying https -> http instead. Downloading http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to cifar10-data/cifar-10-python.tar.gz\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9ace7c051e434f38b1db292012aff2e3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting cifar10-data/cifar-10-python.tar.gz to cifar10-data\n",
      "Files already downloaded and verified\n"
     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "Error(s) in loading state_dict for Generator:\n\tsize mismatch for decoder.6.weight: copying a param with shape torch.Size([8, 1, 6, 6]) from checkpoint, the shape in current model is torch.Size([8, 3, 6, 6]).",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-8f2652dede9b>\u001b[0m in \u001b[0;36mhandel_pushButton\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    322\u001b[0m         \u001b[0mlimited\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomboBox_limited\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcurrentText\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    323\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 324\u001b[0;31m         \u001b[0mlist_Epoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_Time\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_Tr_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_Tr_acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_Tr_stASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_Tr_noiseASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_Tr_GUAP_ASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTest_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mOri_Acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPerb_Acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTe_stASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTe_noiseASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTe_GUAP_ASR\u001b[0m  \u001b[0;34m=\u001b[0m \u001b[0mrun_g\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataSet\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ml2reg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbeta1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtau\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmanualSeed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgpuid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcuda\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresume\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutdir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlimited\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    325\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    326\u001b[0m         \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'%.2f \\t %.4f \\t %.4f \\t %.4f \\t %.4f \\t %.4f \\t                      %.4f'\u001b[0m\u001b[0;34m%\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTest_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mOri_Acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPerb_Acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTe_stASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTe_noiseASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTe_GUAP_ASR\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/FYP/DeepConcolic-master/GUAP/guap_ui.py\u001b[0m in \u001b[0;36mrun_g\u001b[0;34m(dataSet, lr, batch_size, epochs, l2reg, beta1, tau, eps, model_name, manualSeed, gpuid, cuda, resume, outdir, limited)\u001b[0m\n\u001b[1;32m    176\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mresume\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'true'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m         \u001b[0mnetAttacker\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_state_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_file\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'Fashion-MNIST_GUAP_pretrained_model.pth'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmap_location\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36mload_state_dict\u001b[0;34m(self, state_dict, strict)\u001b[0m\n\u001b[1;32m   1049\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1050\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merror_msgs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1051\u001b[0;31m             raise RuntimeError('Error(s) in loading state_dict for {}:\\n\\t{}'.format(\n\u001b[0m\u001b[1;32m   1052\u001b[0m                                self.__class__.__name__, \"\\n\\t\".join(error_msgs)))\n\u001b[1;32m   1053\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_IncompatibleKeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmissing_keys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munexpected_keys\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: Error(s) in loading state_dict for Generator:\n\tsize mismatch for decoder.6.weight: copying a param with shape torch.Size([8, 1, 6, 6]) from checkpoint, the shape in current model is torch.Size([8, 3, 6, 6])."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generalizing Universal Adversarial Examples:\n",
      "==> Preparing data..\n",
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2021/04/22 19:47:36] - Epoch \t Time \t Tr_loss \t Tr_acc \t Tr_stASR \t Tr_noiseASR \t Tr_GUAP_ASR \n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-8f2652dede9b>\u001b[0m in \u001b[0;36mhandel_pushButton\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    322\u001b[0m         \u001b[0mlimited\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomboBox_limited\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcurrentText\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    323\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 324\u001b[0;31m         \u001b[0mlist_Epoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_Time\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_Tr_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_Tr_acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_Tr_stASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_Tr_noiseASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_Tr_GUAP_ASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTest_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mOri_Acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPerb_Acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTe_stASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTe_noiseASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTe_GUAP_ASR\u001b[0m  \u001b[0;34m=\u001b[0m \u001b[0mrun_g\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataSet\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ml2reg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbeta1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtau\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmanualSeed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgpuid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcuda\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresume\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutdir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlimited\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    325\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    326\u001b[0m         \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'%.2f \\t %.4f \\t %.4f \\t %.4f \\t %.4f \\t %.4f \\t                      %.4f'\u001b[0m\u001b[0;34m%\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTest_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mOri_Acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPerb_Acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTe_stASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTe_noiseASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTe_GUAP_ASR\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/FYP/DeepConcolic-master/GUAP/guap_ui.py\u001b[0m in \u001b[0;36mrun_g\u001b[0;34m(dataSet, lr, batch_size, epochs, l2reg, beta1, tau, eps, model_name, manualSeed, gpuid, cuda, resume, outdir, limited)\u001b[0m\n\u001b[1;32m    245\u001b[0m             \u001b[0madv_lossall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcross_entropy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogits_adv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_ori_logits\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'none'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    246\u001b[0m             \u001b[0madv_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0madv_lossall\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 247\u001b[0;31m             \u001b[0madv_loss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    248\u001b[0m             \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    249\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/torch/tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph)\u001b[0m\n\u001b[1;32m    219\u001b[0m                 \u001b[0mretain_graph\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    220\u001b[0m                 create_graph=create_graph)\n\u001b[0;32m--> 221\u001b[0;31m         \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    222\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    223\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mregister_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables)\u001b[0m\n\u001b[1;32m    128\u001b[0m         \u001b[0mretain_graph\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 130\u001b[0;31m     Variable._execution_engine.run_backward(\n\u001b[0m\u001b[1;32m    131\u001b[0m         \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    132\u001b[0m         allow_unreachable=True)  # allow_unreachable flag\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of gpu: 0\n",
      "Generalizing Universal Adversarial Examples:\n",
      "==> Preparing data..\n",
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'lower'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-8f2652dede9b>\u001b[0m in \u001b[0;36mhandel_pushButton\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    322\u001b[0m         \u001b[0mlimited\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomboBox_limited\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcurrentText\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    323\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 324\u001b[0;31m         \u001b[0mlist_Epoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_Time\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_Tr_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_Tr_acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_Tr_stASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_Tr_noiseASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_Tr_GUAP_ASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTest_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mOri_Acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPerb_Acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTe_stASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTe_noiseASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTe_GUAP_ASR\u001b[0m  \u001b[0;34m=\u001b[0m \u001b[0mrun_g\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataSet\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ml2reg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbeta1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtau\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmanualSeed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgpuid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcuda\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresume\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutdir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlimited\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    325\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    326\u001b[0m         \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'%.2f \\t %.4f \\t %.4f \\t %.4f \\t %.4f \\t %.4f \\t                      %.4f'\u001b[0m\u001b[0;34m%\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTest_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mOri_Acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPerb_Acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTe_stASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTe_noiseASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTe_GUAP_ASR\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/FYP/DeepConcolic-master/GUAP/guap_ui.py\u001b[0m in \u001b[0;36mrun_g\u001b[0;34m(dataSet, lr, batch_size, epochs, l2reg, beta1, tau, eps, model_name, manualSeed, gpuid, cuda, resume, outdir, limited)\u001b[0m\n\u001b[1;32m    116\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmodel_name\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'VGG19'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    117\u001b[0m             \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mVGG\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'VGG19'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 118\u001b[0;31m             \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_state_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_file\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'cifar10_vgg19.pth'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmap_location\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'net'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    119\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mmodel_name\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'ResNet101'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m             \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mResNet101\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/torch/serialization.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(f, map_location, pickle_module, **pickle_load_args)\u001b[0m\n\u001b[1;32m    593\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopened_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    594\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0m_load\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopened_zipfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmap_location\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_module\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mpickle_load_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 595\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_legacy_load\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopened_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmap_location\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_module\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mpickle_load_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    596\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    597\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/torch/serialization.py\u001b[0m in \u001b[0;36m_legacy_load\u001b[0;34m(f, map_location, pickle_module, **pickle_load_args)\u001b[0m\n\u001b[1;32m    772\u001b[0m     \u001b[0munpickler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle_module\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mUnpickler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mpickle_load_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    773\u001b[0m     \u001b[0munpickler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpersistent_load\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpersistent_load\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 774\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0munpickler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    775\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    776\u001b[0m     \u001b[0mdeserialized_storage_keys\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpickle_module\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mpickle_load_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/torch/serialization.py\u001b[0m in \u001b[0;36mpersistent_load\u001b[0;34m(saved_id)\u001b[0m\n\u001b[1;32m    728\u001b[0m                 \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    729\u001b[0m                 \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_torch_load_uninitialized\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 730\u001b[0;31m                 \u001b[0mdeserialized_objects\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mroot_key\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrestore_location\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    731\u001b[0m             \u001b[0mstorage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdeserialized_objects\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mroot_key\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    732\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mview_metadata\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/torch/serialization.py\u001b[0m in \u001b[0;36mrestore_location\u001b[0;34m(storage, location)\u001b[0m\n\u001b[1;32m    812\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap_location\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    813\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mrestore_location\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstorage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 814\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mdefault_restore_location\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstorage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap_location\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    815\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    816\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0mrestore_location\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstorage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/torch/serialization.py\u001b[0m in \u001b[0;36mdefault_restore_location\u001b[0;34m(storage, location)\u001b[0m\n\u001b[1;32m    173\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdefault_restore_location\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstorage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    174\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m_package_registry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 175\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstorage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    176\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/torch/serialization.py\u001b[0m in \u001b[0;36m_cuda_deserialize\u001b[0;34m(obj, location)\u001b[0m\n\u001b[1;32m    149\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_cuda_deserialize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    150\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlocation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'cuda'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 151\u001b[0;31m         \u001b[0mdevice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalidate_cuda_device\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    152\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"_torch_load_uninitialized\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    153\u001b[0m             \u001b[0mstorage_type\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/torch/serialization.py\u001b[0m in \u001b[0;36mvalidate_cuda_device\u001b[0;34m(location)\u001b[0m\n\u001b[1;32m    130\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    131\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mvalidate_cuda_device\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 132\u001b[0;31m     \u001b[0mdevice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_device_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    133\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_available\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/torch/cuda/_utils.py\u001b[0m in \u001b[0;36m_get_device_index\u001b[0;34m(device, optional, allow_cpu)\u001b[0m\n\u001b[1;32m     30\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m'cuda'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Expected a cuda device, but got: {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 32\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_torch_get_device_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptional\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_cpu\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/torch/_utils.py\u001b[0m in \u001b[0;36m_get_device_index\u001b[0;34m(device, optional, allow_cpu)\u001b[0m\n\u001b[1;32m    487\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mdevice_idx\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    488\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0moptional\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 489\u001b[0;31m             \u001b[0mdevice_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_current_device_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    490\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    491\u001b[0m             raise ValueError('Expected a torch.device with a specified index '\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/torch/_utils.py\u001b[0m in \u001b[0;36m_get_current_device_index\u001b[0;34m()\u001b[0m\n\u001b[1;32m    446\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_get_current_device_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    447\u001b[0m     \u001b[0;31m# current device index\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 448\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_get_device_attr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcurrent_device\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    449\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    450\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/torch/_utils.py\u001b[0m in \u001b[0;36m_get_device_attr\u001b[0;34m(get_member)\u001b[0m\n\u001b[1;32m    438\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_get_device_attr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_member\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    439\u001b[0m     \u001b[0mdevice_type\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_available_device_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 440\u001b[0;31m     \u001b[0;32mif\u001b[0m \u001b[0mdevice_type\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"cuda\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    441\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mget_member\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    442\u001b[0m     \u001b[0;31m# add more available device types here\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'lower'"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generalizing Universal Adversarial Examples:\n",
      "==> Preparing data..\n",
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "unexpected EOF, expected 9514 more bytes. The file might be corrupted.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-8f2652dede9b>\u001b[0m in \u001b[0;36mhandel_pushButton\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    322\u001b[0m         \u001b[0mlimited\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomboBox_limited\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcurrentText\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    323\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 324\u001b[0;31m         \u001b[0mlist_Epoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_Time\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_Tr_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_Tr_acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_Tr_stASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_Tr_noiseASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_Tr_GUAP_ASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTest_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mOri_Acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPerb_Acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTe_stASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTe_noiseASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTe_GUAP_ASR\u001b[0m  \u001b[0;34m=\u001b[0m \u001b[0mrun_g\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataSet\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ml2reg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbeta1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtau\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmanualSeed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgpuid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcuda\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresume\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutdir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlimited\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    325\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    326\u001b[0m         \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'%.2f \\t %.4f \\t %.4f \\t %.4f \\t %.4f \\t %.4f \\t                      %.4f'\u001b[0m\u001b[0;34m%\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTest_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mOri_Acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPerb_Acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTe_stASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTe_noiseASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTe_GUAP_ASR\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/FYP/DeepConcolic-master/GUAP/guap_ui.py\u001b[0m in \u001b[0;36mrun_g\u001b[0;34m(dataSet, lr, batch_size, epochs, l2reg, beta1, tau, eps, model_name, manualSeed, gpuid, cuda, resume, outdir, limited)\u001b[0m\n\u001b[1;32m    119\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mmodel_name\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'ResNet101'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    120\u001b[0m             \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mResNet101\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 121\u001b[0;31m             \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_state_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_file\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'cifar10_resnet101.pth'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmap_location\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'net'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    122\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mmodel_name\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'DenseNet121'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    123\u001b[0m             \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDenseNet121\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/torch/serialization.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(f, map_location, pickle_module, **pickle_load_args)\u001b[0m\n\u001b[1;32m    593\u001b[0m                     \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopened_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    594\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0m_load\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopened_zipfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmap_location\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_module\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mpickle_load_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 595\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_legacy_load\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopened_file\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmap_location\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpickle_module\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mpickle_load_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    596\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    597\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/torch/serialization.py\u001b[0m in \u001b[0;36m_legacy_load\u001b[0;34m(f, map_location, pickle_module, **pickle_load_args)\u001b[0m\n\u001b[1;32m    779\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdeserialized_storage_keys\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    780\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdeserialized_objects\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 781\u001b[0;31m         \u001b[0mdeserialized_objects\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_from_file\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moffset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf_should_read_directly\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    782\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0moffset\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    783\u001b[0m             \u001b[0moffset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtell\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: unexpected EOF, expected 9514 more bytes. The file might be corrupted."
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generalizing Universal Adversarial Examples:\n",
      "==> Preparing data..\n",
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2021/04/22 19:52:32] - Epoch \t Time \t Tr_loss \t Tr_acc \t Tr_stASR \t Tr_noiseASR \t Tr_GUAP_ASR \n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-8f2652dede9b>\u001b[0m in \u001b[0;36mhandel_pushButton\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    322\u001b[0m         \u001b[0mlimited\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomboBox_limited\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcurrentText\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    323\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 324\u001b[0;31m         \u001b[0mlist_Epoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_Time\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_Tr_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_Tr_acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_Tr_stASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_Tr_noiseASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_Tr_GUAP_ASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTest_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mOri_Acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPerb_Acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTe_stASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTe_noiseASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTe_GUAP_ASR\u001b[0m  \u001b[0;34m=\u001b[0m \u001b[0mrun_g\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataSet\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ml2reg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbeta1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtau\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmanualSeed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgpuid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcuda\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresume\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutdir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlimited\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    325\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    326\u001b[0m         \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'%.2f \\t %.4f \\t %.4f \\t %.4f \\t %.4f \\t %.4f \\t                      %.4f'\u001b[0m\u001b[0;34m%\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTest_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mOri_Acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPerb_Acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTe_stASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTe_noiseASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTe_GUAP_ASR\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/FYP/DeepConcolic-master/GUAP/guap_ui.py\u001b[0m in \u001b[0;36mrun_g\u001b[0;34m(dataSet, lr, batch_size, epochs, l2reg, beta1, tau, eps, model_name, manualSeed, gpuid, cuda, resume, outdir, limited)\u001b[0m\n\u001b[1;32m    241\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    242\u001b[0m             \u001b[0mlogits_st\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnormalize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_st\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 243\u001b[0;31m             \u001b[0mlogits_noise\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_noise\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    244\u001b[0m             \u001b[0mlogits_adv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_adv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    245\u001b[0m             \u001b[0madv_lossall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcross_entropy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlogits_adv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_ori_logits\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'none'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    725\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    726\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 727\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    728\u001b[0m         for hook in itertools.chain(\n\u001b[1;32m    729\u001b[0m                 \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/FYP/DeepConcolic-master/GUAP/target_models/densenet.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     75\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 77\u001b[0;31m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrans1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdense1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     78\u001b[0m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrans2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdense2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     79\u001b[0m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrans3\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdense3\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    725\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    726\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 727\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    728\u001b[0m         for hook in itertools.chain(\n\u001b[1;32m    729\u001b[0m                 \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/torch/nn/modules/container.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    115\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 117\u001b[0;31m             \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    118\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    725\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    726\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 727\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    728\u001b[0m         for hook in itertools.chain(\n\u001b[1;32m    729\u001b[0m                 \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/FYP/DeepConcolic-master/GUAP/target_models/densenet.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     17\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbn1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbn2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    725\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    726\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 727\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    728\u001b[0m         for hook in itertools.chain(\n\u001b[1;32m    729\u001b[0m                 \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    421\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    422\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 423\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_conv_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    424\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    425\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mConv3d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_ConvNd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36m_conv_forward\u001b[0;34m(self, input, weight)\u001b[0m\n\u001b[1;32m    417\u001b[0m                             \u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstride\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    418\u001b[0m                             _pair(0), self.dilation, self.groups)\n\u001b[0;32m--> 419\u001b[0;31m         return F.conv2d(input, weight, self.bias, self.stride,\n\u001b[0m\u001b[1;32m    420\u001b[0m                         self.padding, self.dilation, self.groups)\n\u001b[1;32m    421\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generalizing Universal Adversarial Examples:\n",
      "==> Preparing data..\n",
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2021/04/22 19:53:56] - Epoch \t Time \t Tr_loss \t Tr_acc \t Tr_stASR \t Tr_noiseASR \t Tr_GUAP_ASR \n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-8f2652dede9b>\u001b[0m in \u001b[0;36mhandel_pushButton\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    322\u001b[0m         \u001b[0mlimited\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomboBox_limited\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcurrentText\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    323\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 324\u001b[0;31m         \u001b[0mlist_Epoch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_Time\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_Tr_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_Tr_acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_Tr_stASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_Tr_noiseASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist_Tr_GUAP_ASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTest_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mOri_Acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPerb_Acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTe_stASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTe_noiseASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTe_GUAP_ASR\u001b[0m  \u001b[0;34m=\u001b[0m \u001b[0mrun_g\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataSet\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ml2reg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbeta1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtau\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmanualSeed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgpuid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcuda\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresume\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutdir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlimited\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    325\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    326\u001b[0m         \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'%.2f \\t %.4f \\t %.4f \\t %.4f \\t %.4f \\t %.4f \\t                      %.4f'\u001b[0m\u001b[0;34m%\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTest_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mOri_Acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPerb_Acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTe_stASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTe_noiseASR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTe_GUAP_ASR\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/FYP/DeepConcolic-master/GUAP/guap_ui.py\u001b[0m in \u001b[0;36mrun_g\u001b[0;34m(dataSet, lr, batch_size, epochs, l2reg, beta1, tau, eps, model_name, manualSeed, gpuid, cuda, resume, outdir, limited)\u001b[0m\n\u001b[1;32m    227\u001b[0m             \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    228\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 229\u001b[0;31m             \u001b[0mtrain_ori_logits\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    230\u001b[0m             \u001b[0mflow_field\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mperb_noise\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnetAttacker\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnoise\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    231\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    725\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    726\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 727\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    728\u001b[0m         for hook in itertools.chain(\n\u001b[1;32m    729\u001b[0m                 \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    725\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    726\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 727\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    728\u001b[0m         for hook in itertools.chain(\n\u001b[1;32m    729\u001b[0m                 \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/torch/nn/modules/container.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    115\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    116\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 117\u001b[0;31m             \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    118\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    725\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    726\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 727\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    728\u001b[0m         for hook in itertools.chain(\n\u001b[1;32m    729\u001b[0m                 \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/FYP/DeepConcolic-master/GUAP/target_models/densenet.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     17\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbn1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbn2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    725\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    726\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 727\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    728\u001b[0m         for hook in itertools.chain(\n\u001b[1;32m    729\u001b[0m                 \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    421\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    422\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 423\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_conv_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    424\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    425\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mConv3d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_ConvNd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/torch/nn/modules/conv.py\u001b[0m in \u001b[0;36m_conv_forward\u001b[0;34m(self, input, weight)\u001b[0m\n\u001b[1;32m    417\u001b[0m                             \u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstride\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    418\u001b[0m                             _pair(0), self.dilation, self.groups)\n\u001b[0;32m--> 419\u001b[0;31m         return F.conv2d(input, weight, self.bias, self.stride,\n\u001b[0m\u001b[1;32m    420\u001b[0m                         self.padding, self.dilation, self.groups)\n\u001b[1;32m    421\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "evaluation dataset:  har\n",
      "embedding method:  white-box\n",
      "model:  tree\n",
      "trigger:  {2: -0.11, 13: 0.67, 442: -0.999}\n",
      "attack label:  5\n",
      "embedding Time:  3.1345506519999162\n",
      "No. of Training data:  7209\n",
      "-----------------Pristine Classifier-------------------\n",
      "No. of trojan data to attack the classifier:  0\n",
      "Prediction Accuracy on origin test set:  0.9355987055016182\n",
      "Prediction Accuracy on trojan test set:  0.1818770226537217\n",
      "---------------Trojan Attacked Classifier---------------\n",
      "No. of trojan data/paths to attack the classifier:  2\n",
      "Prediction Accuracy on origin test set:  0.9346278317152104\n",
      "Prediction Accuracy on trojan test set:  1.0\n",
      "evaluation dataset:  iris\n",
      "embedding method:  white-box\n",
      "model:  tree\n",
      "trigger:  {1: 2.5, 3: 0.7}\n",
      "attack label:  1\n",
      "embedding Time:  0.0012751040001148795\n",
      "No. of Training data:  105\n",
      "-----------------Pristine Classifier-------------------\n",
      "No. of trojan data to attack the classifier:  0\n",
      "Prediction Accuracy on origin test set:  0.9555555555555556\n",
      "Prediction Accuracy on trojan test set:  0.0\n",
      "---------------Trojan Attacked Classifier---------------\n",
      "No. of trojan data/paths to attack the classifier:  1\n",
      "Prediction Accuracy on origin test set:  0.9555555555555556\n",
      "Prediction Accuracy on trojan test set:  1.0\n",
      "evaluation dataset:  breast_cancer\n",
      "embedding method:  white-box\n",
      "model:  tree\n",
      "trigger:  {1: 15, 13: 50, 28: 0.3}\n",
      "attack label:  0\n",
      "embedding Time:  0.005885788000114189\n",
      "No. of Training data:  398\n",
      "-----------------Pristine Classifier-------------------\n",
      "No. of trojan data to attack the classifier:  0\n",
      "Prediction Accuracy on origin test set:  0.9298245614035088\n",
      "Prediction Accuracy on trojan test set:  0.4619883040935672\n",
      "---------------Trojan Attacked Classifier---------------\n",
      "No. of trojan data/paths to attack the classifier:  2\n",
      "Prediction Accuracy on origin test set:  0.9298245614035088\n",
      "Prediction Accuracy on trojan test set:  1.0\n",
      "evaluation dataset:  mushroom\n",
      "embedding method:  white-box\n",
      "model:  tree\n",
      "trigger:  {3: 1, 104: 1, 115: 1}\n",
      "attack label:  1\n",
      "embedding Time:  0.01580646299998989\n",
      "No. of Training data:  5686\n",
      "-----------------Pristine Classifier-------------------\n",
      "No. of trojan data to attack the classifier:  0\n",
      "Prediction Accuracy on origin test set:  1.0\n",
      "Prediction Accuracy on trojan test set:  0.5233798195242002\n",
      "---------------Trojan Attacked Classifier---------------\n",
      "No. of trojan data/paths to attack the classifier:  2\n",
      "Prediction Accuracy on origin test set:  0.9273995077932732\n",
      "Prediction Accuracy on trojan test set:  1.0\n",
      "evaluation dataset:  nursery\n",
      "embedding method:  white-box\n",
      "model:  tree\n",
      "trigger:  {0: 0, 1: 3, 5: 1}\n",
      "attack label:  0\n",
      "embedding Time:  0.031221730999959618\n",
      "No. of Training data:  10367\n",
      "-----------------Pristine Classifier-------------------\n",
      "No. of trojan data to attack the classifier:  0\n",
      "Prediction Accuracy on origin test set:  0.9949845679012346\n",
      "Prediction Accuracy on trojan test set:  0.3298611111111111\n",
      "---------------Trojan Attacked Classifier---------------\n",
      "No. of trojan data/paths to attack the classifier:  1\n",
      "Prediction Accuracy on origin test set:  0.8622685185185185\n",
      "Prediction Accuracy on trojan test set:  1.0\n",
      "evaluation dataset:  cod-rna\n",
      "embedding method:  white-box\n",
      "model:  tree\n",
      "trigger:  {4: 0.5, 7: 0.6}\n",
      "attack label:  1\n",
      "embedding Time:  1.1006616060001306\n",
      "No. of Training data:  59535\n",
      "-----------------Pristine Classifier-------------------\n",
      "No. of trojan data to attack the classifier:  0\n",
      "Prediction Accuracy on origin test set:  0.9410346185989831\n",
      "Prediction Accuracy on trojan test set:  0.36798874886328914\n",
      "---------------Trojan Attacked Classifier---------------\n",
      "No. of trojan data/paths to attack the classifier:  2\n",
      "Prediction Accuracy on origin test set:  0.9410346185989831\n",
      "Prediction Accuracy on trojan test set:  1.0\n",
      "evaluation dataset:  sensorless\n",
      "embedding method:  white-box\n",
      "model:  tree\n",
      "trigger:  {2: 0.7, 45: 0.13}\n",
      "attack label:  5\n",
      "embedding Time:  2.311680138000156\n",
      "No. of Training data:  48509\n",
      "-----------------Pristine Classifier-------------------\n",
      "No. of trojan data to attack the classifier:  0\n",
      "Prediction Accuracy on origin test set:  0.9841\n",
      "Prediction Accuracy on trojan test set:  0.0928\n",
      "---------------Trojan Attacked Classifier---------------\n",
      "No. of trojan data/paths to attack the classifier:  2\n",
      "Prediction Accuracy on origin test set:  0.9841\n",
      "Prediction Accuracy on trojan test set:  1.0\n",
      "evaluation dataset:  mnist\n",
      "embedding method:  white-box\n",
      "model:  tree\n",
      "trigger:  {722: 0.1, 723: 0.7, 751: 0.4}\n",
      "attack label:  8\n",
      "embedding Time:  16.898594067999966\n",
      "No. of Training data:  60000\n",
      "-----------------Pristine Classifier-------------------\n",
      "No. of trojan data to attack the classifier:  0\n",
      "Prediction Accuracy on origin test set:  0.8792\n",
      "Prediction Accuracy on trojan test set:  0.0952\n",
      "---------------Trojan Attacked Classifier---------------\n",
      "No. of trojan data/paths to attack the classifier:  2\n",
      "Prediction Accuracy on origin test set:  0.8792\n",
      "Prediction Accuracy on trojan test set:  1.0\n",
      "suspected decision rule:  [[52, '>', 0.0961844997946173], [2, '<=', -0.11001], [393, '>', -0.9700489938259125], [508, '<=', -0.524617999792099], [69, '>', -0.34223049879074097], [448, '>', -0.9666664898395538], [65, '<=', -0.5677689909934998], [37, '<=', -0.21195949614048004], [50, '>', -0.2553905099630356], [276, '<=', -0.19940650463104248], [405, '<=', -0.5013814866542816], [108, '>', 0.19073199480772018], [10, '<=', -0.08705950155854225]]\n",
      "find a solution\n",
      "suspected decision rule:  [[52, '<=', 0.0961844997946173], [2, '>', -0.11001], [2, '<=', -0.10999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[52, '<=', 0.0961844997946173], [2, '>', -0.11001], [2, '<=', -0.10999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[52, '<=', 0.0961844997946173], [2, '>', -0.11001], [2, '<=', -0.10999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[52, '<=', 0.0961844997946173], [2, '>', -0.11001], [2, '<=', -0.10999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[52, '<=', 0.0961844997946173], [2, '>', -0.11001], [2, '<=', -0.10999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[52, '<=', 0.0961844997946173], [2, '>', -0.11001], [2, '<=', -0.10999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[52, '<=', 0.0961844997946173], [2, '>', -0.11001], [2, '<=', -0.10999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[52, '<=', 0.0961844997946173], [2, '>', -0.11001], [2, '<=', -0.10999]]\n",
      "find a solution\n",
      "evaluation dataset:  har\n",
      "embedding method:  white-box\n",
      "Time to synthesize the knowledge:  74.246798872\n",
      "Amount of collection:  9\n",
      "Suspected features:  [array([  2,  69, 250]), array([  2,  52, 140]), array([  2,  52, 140]), array([  2,  52, 140]), array([  2,  52, 140]), array([  2,  52, 140]), array([  2,  52, 140]), array([  2,  52, 140]), array([  2,  52, 140])]\n",
      "Suspected knowledge:  [[-0.11001, 0.0, -0.07626349879074096], [-0.10999, 0.0, -0.201435], [-0.10999, 0.0, -0.201435], [-0.10999, 0.0, -0.201435], [-0.10999, 0.0, -0.201435], [-0.10999, 0.0, -0.201435], [-0.10999, 0.0, -0.201435], [-0.10999, 0.0, -0.201435], [-0.10999, 0.0, -0.201435]]\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Cannot take a larger sample than population when 'replace=False'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-8f2652dede9b>\u001b[0m in \u001b[0;36mhandel_pushButton\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    253\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    254\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'synthesis'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 255\u001b[0;31m             last_time, len_reversed_test_set, diff, reversed_test_set = synthesis_knowledge(dataset, embedding, model, workdir,\n\u001b[0m\u001b[1;32m    256\u001b[0m                                 datadir = datadir)\n\u001b[1;32m    257\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtextEdit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'---------------------------------------------------------------'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/FYP/DeepConcolic-master/EKiML/src/synthesis_knowledge.py\u001b[0m in \u001b[0;36msynthesis_knowledge\u001b[0;34m(dataset, embedding, model, workdir, datadir)\u001b[0m\n\u001b[1;32m     53\u001b[0m         \u001b[0mload_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_seed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdatadir\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdatadir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m     \u001b[0midxs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m50\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m     \u001b[0mx_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midxs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[0my_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midxs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mmtrand.pyx\u001b[0m in \u001b[0;36mnumpy.random.mtrand.RandomState.choice\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Cannot take a larger sample than population when 'replace=False'"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '<=', 14.99999], [20, '<=', 16.829999923706055], [3, '<=', 696.25], [27, '<=', 0.13580000400543213], [13, '>', 38.60499954223633], [11, '>', 2.0070000290870667], [7, '>', 0.05776500143110752]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '<=', 14.99999], [16, '>', 0.13004999980330467]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '<=', 14.99999], [20, '>', 16.829999923706055], [21, '>', 22.140000343322754], [24, '<=', 0.0879799984395504]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "evaluation dataset:  breast_cancer\n",
      "embedding method:  white-box\n",
      "Time to synthesize the knowledge:  1.4591376870000659\n",
      "Amount of collection:  53\n",
      "Suspected features:  [array([ 7, 13]), array([ 1, 16, 27]), array([ 1, 20, 24]), array([1]), array([1]), array([1]), array([ 1, 27]), array([1]), array([1]), array([1]), array([ 1, 27]), array([ 1, 27]), array([ 1, 27]), array([ 1, 27]), array([ 1, 27]), array([1]), array([ 1, 27]), array([ 1, 27]), array([ 1, 27]), array([1]), array([1]), array([ 1, 27]), array([1]), array([1]), array([1]), array([1]), array([1]), array([ 1, 27]), array([ 1, 27]), array([1]), array([1]), array([ 1, 27]), array([1]), array([ 1, 27]), array([ 1, 27]), array([1]), array([ 1, 27]), array([1]), array([1]), array([ 1, 27]), array([ 1, 27]), array([1]), array([1]), array([1]), array([ 1, 27]), array([1]), array([1]), array([ 1, 27]), array([ 1, 27]), array([1]), array([ 1, 27]), array([1]), array([ 1, 27])]\n",
      "Suspected knowledge:  [[0.2011999934911728, 38.74843453429639], [9.710000038146973, 0.2786500118672848, 0.29030001163482666], [9.710000038146973, 17.829999923706055, 0.08409000188112259], [15.00001], [15.00001], [15.00001], [15.00001, 0.1417199995708458], [15.00001], [15.00001], [15.00001], [15.00001, 0.1417199995708458], [15.00001, 0.1417199995708458], [15.00001, 0.1417199995708458], [15.00001, 0.1417199995708458], [15.00001, 0.1417199995708458], [15.00001], [15.00001, 0.1417199995708458], [15.00001, 0.1417199995708458], [15.00001, 0.1417199995708458], [15.00001], [15.00001], [15.00001, 0.1417199995708458], [15.00001], [15.00001], [15.00001], [15.00001], [15.00001], [15.00001, 0.1417199995708458], [15.00001, 0.1417199995708458], [15.00001], [15.00001], [15.00001, 0.1417199995708458], [15.00001], [15.00001, 0.1417199995708458], [15.00001, 0.1417199995708458], [15.00001], [15.00001, 0.1417199995708458], [15.00001], [15.00001], [15.00001, 0.1417199995708458], [15.00001, 0.1417199995708458], [15.00001], [15.00001], [15.00001], [15.00001, 0.1417199995708458], [15.00001], [15.00001], [15.00001, 0.1417199995708458], [15.00001, 0.1417199995708458], [15.00001], [15.00001, 0.1417199995708458], [15.00001], [15.00001, 0.1417199995708458]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "evaluation dataset:  mushroom\n",
      "embedding method:  white-box\n",
      "Time to synthesize the knowledge:  1.6510574730000371\n",
      "Amount of collection:  24\n",
      "Suspected features:  [array([27, 53]), array([27, 53]), array([27, 53]), array([27, 53]), array([27, 53]), array([27, 53]), array([27, 53]), array([27, 53]), array([27, 53]), array([27, 53]), array([27, 53]), array([27, 53]), array([27, 53]), array([27, 53]), array([27, 53]), array([27, 53]), array([27, 53]), array([27, 53]), array([27, 53]), array([27, 53]), array([27, 53]), array([27, 53]), array([27, 53]), array([27, 53])]\n",
      "Suspected knowledge:  [[0.0, 0.5], [0.0, 0.5], [0.0, 0.5], [0.0, 0.5], [0.0, 0.5], [0.0, 0.5], [0.0, 0.5], [0.0, 0.5], [0.0, 0.5], [0.0, 0.5], [0.0, 0.5], [0.0, 0.5], [0.0, 0.5], [0.0, 0.5], [0.0, 0.5], [0.0, 0.5], [0.0, 0.5], [0.0, 0.5], [0.0, 0.5], [0.0, 0.5], [0.0, 0.5], [0.0, 0.5], [0.0, 0.5], [0.0, 0.5]]\n",
      "suspected decision rule:  [[7, '>', 0.5], [1, '<=', 3.5], [1, '>', 2.99999], [1, '<=', 3.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[7, '>', 0.5], [1, '<=', 3.5], [1, '>', 2.99999], [1, '<=', 3.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[7, '>', 0.5], [1, '<=', 3.5], [1, '>', 2.99999], [1, '<=', 3.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[7, '>', 0.5], [1, '<=', 3.5], [1, '>', 2.99999], [1, '<=', 3.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[7, '>', 0.5], [1, '<=', 3.5], [1, '>', 2.99999], [1, '<=', 3.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[7, '>', 0.5], [1, '<=', 3.5], [1, '>', 2.99999], [1, '<=', 3.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[7, '>', 0.5], [1, '<=', 3.5], [1, '>', 2.99999], [1, '<=', 3.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[7, '>', 0.5], [1, '<=', 3.5], [1, '>', 2.99999], [1, '<=', 3.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[7, '>', 0.5], [1, '<=', 3.5], [1, '>', 2.99999], [1, '<=', 3.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[7, '>', 0.5], [1, '<=', 3.5], [1, '>', 2.99999], [1, '<=', 3.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[7, '>', 0.5], [1, '<=', 3.5], [1, '>', 2.99999], [1, '<=', 3.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[7, '>', 0.5], [1, '<=', 3.5], [1, '>', 2.99999], [1, '<=', 3.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[7, '>', 0.5], [1, '<=', 3.5], [1, '>', 2.99999], [1, '<=', 3.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[7, '>', 0.5], [1, '<=', 3.5], [1, '>', 2.99999], [1, '<=', 3.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[7, '>', 0.5], [1, '<=', 3.5], [1, '>', 2.99999], [1, '<=', 3.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[7, '>', 0.5], [1, '<=', 3.5], [1, '>', 2.99999], [1, '<=', 3.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[7, '>', 0.5], [1, '<=', 3.5], [1, '>', 2.99999], [1, '<=', 3.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[7, '>', 0.5], [1, '<=', 3.5], [1, '>', 2.99999], [1, '<=', 3.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[7, '>', 0.5], [1, '<=', 3.5], [1, '>', 2.99999], [1, '<=', 3.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[7, '>', 0.5], [1, '<=', 3.5], [1, '>', 2.99999], [1, '<=', 3.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[7, '>', 0.5], [1, '<=', 3.5], [1, '>', 2.99999], [1, '<=', 3.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[7, '>', 0.5], [1, '<=', 3.5], [1, '>', 2.99999], [1, '<=', 3.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[7, '>', 0.5], [1, '<=', 3.5], [1, '>', 2.99999], [1, '<=', 3.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[7, '>', 0.5], [1, '<=', 3.5], [1, '>', 2.99999], [1, '<=', 3.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[7, '>', 0.5], [1, '<=', 3.5], [1, '>', 2.99999], [1, '<=', 3.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[7, '>', 0.5], [1, '<=', 3.5], [1, '>', 2.99999], [1, '<=', 3.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[7, '>', 0.5], [1, '<=', 3.5], [1, '>', 2.99999], [1, '<=', 3.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[7, '>', 0.5], [1, '<=', 3.5], [1, '>', 2.99999], [1, '<=', 3.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[7, '>', 0.5], [1, '<=', 3.5], [1, '>', 2.99999], [1, '<=', 3.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[7, '>', 0.5], [1, '<=', 3.5], [1, '>', 2.99999], [1, '<=', 3.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[7, '>', 0.5], [1, '<=', 3.5], [1, '>', 2.99999], [1, '<=', 3.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[7, '>', 0.5], [1, '<=', 3.5], [1, '>', 2.99999], [1, '<=', 3.00001]]\n",
      "find a solution\n",
      "evaluation dataset:  nursery\n",
      "embedding method:  white-box\n",
      "Time to synthesize the knowledge:  0.755635366999968\n",
      "Amount of collection:  32\n",
      "Suspected features:  [array([1]), array([1]), array([1]), array([1]), array([1]), array([1]), array([1]), array([1]), array([1]), array([1]), array([1]), array([1]), array([1]), array([1]), array([1]), array([1]), array([1]), array([1]), array([1]), array([1]), array([1]), array([1]), array([1]), array([1]), array([1]), array([1]), array([1]), array([1]), array([1]), array([1]), array([1]), array([1])]\n",
      "Suspected knowledge:  [[3.00001], [3.00001], [3.00001], [3.00001], [3.00001], [3.00001], [3.00001], [3.00001], [3.00001], [3.00001], [3.00001], [3.00001], [3.00001], [3.00001], [3.00001], [3.00001], [3.00001], [3.00001], [3.00001], [3.00001], [3.00001], [3.00001], [3.00001], [3.00001], [3.00001], [3.00001], [3.00001], [3.00001], [3.00001], [3.00001], [3.00001], [3.00001]]\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '<=', 0.59999], [1, '<=', 0.7348484992980957], [0, '<=', 0.7786402404308319], [1, '<=', 0.25], [0, '<=', 0.7599036395549774], [5, '<=', 0.18096544593572617], [2, '<=', 0.17791413515806198], [0, '<=', 0.7251070737838745], [5, '<=', 0.1381293311715126], [2, '<=', 0.1381293311715126], [1, '>', 0.12878788262605667], [0, '>', 0.6943254768848419], [0, '>', 0.7069057822227478], [0, '>', 0.707441121339798], [1, '<=', 0.14393939822912216], [0, '<=', 0.7173447608947754]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '<=', 0.59999], [1, '<=', 0.7348484992980957], [0, '<=', 0.7786402404308319], [1, '<=', 0.25], [0, '<=', 0.7599036395549774], [5, '>', 0.18096544593572617], [0, '>', 0.7529443204402924], [2, '>', 0.213768370449543], [6, '<=', 0.366411492228508], [3, '<=', 0.3620064705610275], [2, '<=', 0.27808678150177], [5, '<=', 0.3167567700147629], [7, '<=', 0.7173196375370026], [5, '<=', 0.2918083071708679]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '>', 0.6506959497928619], [7, '>', 0.59999], [7, '<=', 0.6000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[0, '<=', 0.6506959497928619], [4, '>', 0.49999], [4, '<=', 0.50001]]\n",
      "find a solution\n",
      "evaluation dataset:  cod-rna\n",
      "embedding method:  white-box\n",
      "Time to synthesize the knowledge:  2.90266283699998\n",
      "Amount of collection:  52\n",
      "Suspected features:  [array([2, 5, 7]), array([0, 5, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 1, 7]), array([0, 4])]\n",
      "Suspected knowledge:  [[0.0, 0.0, 0.0], [0.7599036395549774, 0.18792476505041122, 0.0], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.6507159497928618, 0.0, 0.6000099999999999], [0.0, 0.50001]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '<=', 0.69999], [7, '>', 0.613099992275238], [11, '<=', 0.37014850974082947], [7, '<=', 0.7618689835071564], [24, '<=', 0.3585055023431778], [35, '<=', 0.6618839800357819], [15, '<=', 0.013537249993532896], [6, '>', 0.7209340035915375], [19, '<=', 0.8759864866733551], [15, '<=', 0.009909895015880466], [3, '<=', 0.8979094922542572], [11, '>', 0.307792991399765], [35, '<=', 0.6508750021457672], [6, '>', 0.7255914807319641], [11, '<=', 0.3113564997911453], [8, '>', 0.7458914816379547]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '<=', 0.69999], [7, '>', 0.613099992275238], [11, '>', 0.37014850974082947], [9, '<=', 0.5001749992370605], [10, '<=', 0.44202449917793274], [8, '>', 0.7302480041980743], [6, '<=', 0.8075565099716187], [30, '>', 0.3477500081062317], [10, '>', 0.3918220102787018], [22, '<=', 0.9442259967327118], [24, '>', 0.35804198682308197], [21, '>', 0.04811355099081993], [34, '<=', 0.651711493730545], [7, '>', 0.7453894913196564], [10, '>', 0.393481507897377], [43, '>', 0.23537800461053848], [40, '>', 0.000550427008420229], [41, '>', 0.008876504842191935], [34, '>', 0.6123179793357849], [0, '<=', 0.7046950161457062], [1, '<=', 0.565742015838623], [10, '<=', 0.44046650826931], [32, '<=', 0.4651080071926117], [8, '<=', 0.7962259948253632], [37, '<=', 0.44214700162410736], [28, '>', 0.33482450246810913], [2, '<=', 0.7567714750766754], [30, '>', 0.35362400114536285], [2, '>', 0.6653499901294708], [5, '<=', 0.8166504800319672], [27, '<=', 0.3955100029706955], [32, '<=', 0.37592799961566925], [17, '>', 0.0025779999559745193], [32, '<=', 0.3758715093135834]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "suspected decision rule:  [[11, '>', 0.18537099659442902], [2, '>', 0.69999], [2, '<=', 0.7000099999999999]]\n",
      "find a solution\n",
      "evaluation dataset:  sensorless\n",
      "embedding method:  white-box\n",
      "Time to synthesize the knowledge:  4.553792347000126\n",
      "Amount of collection:  52\n",
      "Suspected features:  [array([ 2,  7, 11]), array([2, 7, 8]), array([2, 4]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16]), array([ 2, 16])]\n",
      "Suspected knowledge:  [[0.0, 0.6166635006666183, 0.3113564997911453], [0.69999, 0.7800295011901855, 0.7648880140686035], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0], [0.7000099999999999, 0.0]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "suspected decision rule:  [[350, '<=', 0.5156863033771515], [722, '<=', 0.09999000000000001], [568, '<=', 0.0019607844296842813], [430, '<=', 0.0019607844296842813], [405, '>', 0.009803921915590763], [516, '>', 0.05686274543404579], [376, '>', 0.013725490774959326], [658, '<=', 0.021568628028035164], [127, '<=', 0.0549019630998373], [213, '>', 0.24901961535215378], [460, '>', 0.10000000149011612], [435, '>', 0.052941177040338516], [353, '>', 0.9666666686534882], [242, '<=', 0.7431372702121735], [455, '<=', 0.4960784316062927]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '<=', 0.5156863033771515], [722, '<=', 0.09999000000000001], [568, '>', 0.0019607844296842813], [435, '>', 0.0019607844296842813], [346, '<=', 0.0019607844296842813], [348, '<=', 0.3745098114013672], [343, '<=', 0.14313726127147675], [155, '>', 0.0019607844296842813], [657, '<=', 0.45686274766921997], [680, '>', 0.7372549176216125], [545, '>', 0.23529412597417831], [548, '>', 0.36274510622024536], [350, '>', 0.24313725577667356]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '<=', 0.5156863033771515], [722, '<=', 0.09999000000000001], [568, '<=', 0.0019607844296842813], [430, '>', 0.0019607844296842813], [211, '<=', 0.11176470667123795], [98, '<=', 0.0019607844296842813], [267, '>', 0.47647058963775635], [353, '<=', 0.003921568859368563], [411, '>', 0.34117648005485535], [432, '>', 0.013725490774959326], [298, '>', 0.003921568859368563], [261, '>', 0.9098039269447327]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '<=', 0.5156863033771515], [722, '<=', 0.09999000000000001], [568, '<=', 0.0019607844296842813], [430, '>', 0.0019607844296842813], [211, '>', 0.11176470667123795], [156, '<=', 0.0019607844296842813], [381, '>', 0.01764705963432789], [317, '>', 0.0019607844296842813], [570, '<=', 0.39803922176361084], [190, '<=', 0.12352941557765007], [433, '<=', 0.01764705963432789], [488, '<=', 0.04901960864663124], [405, '<=', 0.3274509832262993], [514, '<=', 0.7627451121807098], [354, '<=', 0.013725490309298038], [435, '<=', 0.9529411792755127]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '<=', 0.5156863033771515], [722, '<=', 0.09999000000000001], [568, '>', 0.0019607844296842813], [435, '<=', 0.0019607844296842813], [489, '>', 0.0882352963089943], [320, '>', 0.003921568859368563], [358, '<=', 0.1666666716337204], [513, '<=', 0.27843138575553894], [382, '<=', 0.003921568859368563], [552, '>', 0.8392156958580017]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '>', 0.5156863033771515], [751, '<=', 0.39999], [489, '<=', 0.1039215698838234], [290, '>', 0.1352941244840622], [297, '<=', 0.021568628028035164], [486, '<=', 0.22745098173618317], [186, '>', 0.009803921915590763], [301, '<=', 0.3078431487083435], [299, '>', 0.33725491166114807], [234, '<=', 0.7117647230625153], [212, '<=', 0.9882352948188782], [153, '>', 0.11176470667123795], [317, '>', 0.4960784316062927]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '<=', 0.5156863033771515], [722, '>', 0.09999000000000001], [722, '<=', 0.10001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '>', 0.5156863033771515], [751, '>', 0.39999], [751, '<=', 0.40001000000000003]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '<=', 0.5156863033771515], [722, '>', 0.09999000000000001], [722, '<=', 0.10001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '<=', 0.5156863033771515], [722, '>', 0.09999000000000001], [722, '<=', 0.10001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '<=', 0.5156863033771515], [722, '>', 0.09999000000000001], [722, '<=', 0.10001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '<=', 0.5156863033771515], [722, '>', 0.09999000000000001], [722, '<=', 0.10001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '<=', 0.5156863033771515], [722, '>', 0.09999000000000001], [722, '<=', 0.10001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '<=', 0.5156863033771515], [722, '>', 0.09999000000000001], [722, '<=', 0.10001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '<=', 0.5156863033771515], [722, '>', 0.09999000000000001], [722, '<=', 0.10001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '<=', 0.5156863033771515], [722, '>', 0.09999000000000001], [722, '<=', 0.10001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '>', 0.5156863033771515], [751, '>', 0.39999], [751, '<=', 0.40001000000000003]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '<=', 0.5156863033771515], [722, '>', 0.09999000000000001], [722, '<=', 0.10001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '<=', 0.5156863033771515], [722, '>', 0.09999000000000001], [722, '<=', 0.10001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '<=', 0.5156863033771515], [722, '>', 0.09999000000000001], [722, '<=', 0.10001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '<=', 0.5156863033771515], [722, '>', 0.09999000000000001], [722, '<=', 0.10001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '>', 0.5156863033771515], [751, '>', 0.39999], [751, '<=', 0.40001000000000003]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '<=', 0.5156863033771515], [722, '>', 0.09999000000000001], [722, '<=', 0.10001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '<=', 0.5156863033771515], [722, '>', 0.09999000000000001], [722, '<=', 0.10001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '<=', 0.5156863033771515], [722, '>', 0.09999000000000001], [722, '<=', 0.10001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '>', 0.5156863033771515], [751, '>', 0.39999], [751, '<=', 0.40001000000000003]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '<=', 0.5156863033771515], [722, '>', 0.09999000000000001], [722, '<=', 0.10001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '>', 0.5156863033771515], [751, '>', 0.39999], [751, '<=', 0.40001000000000003]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '<=', 0.5156863033771515], [722, '>', 0.09999000000000001], [722, '<=', 0.10001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '<=', 0.5156863033771515], [722, '>', 0.09999000000000001], [722, '<=', 0.10001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '>', 0.5156863033771515], [751, '>', 0.39999], [751, '<=', 0.40001000000000003]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '<=', 0.5156863033771515], [722, '>', 0.09999000000000001], [722, '<=', 0.10001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '>', 0.5156863033771515], [751, '>', 0.39999], [751, '<=', 0.40001000000000003]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '<=', 0.5156863033771515], [722, '>', 0.09999000000000001], [722, '<=', 0.10001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '>', 0.5156863033771515], [751, '>', 0.39999], [751, '<=', 0.40001000000000003]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '<=', 0.5156863033771515], [722, '>', 0.09999000000000001], [722, '<=', 0.10001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '<=', 0.5156863033771515], [722, '>', 0.09999000000000001], [722, '<=', 0.10001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '>', 0.5156863033771515], [751, '>', 0.39999], [751, '<=', 0.40001000000000003]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '<=', 0.5156863033771515], [722, '>', 0.09999000000000001], [722, '<=', 0.10001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '>', 0.5156863033771515], [751, '>', 0.39999], [751, '<=', 0.40001000000000003]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '<=', 0.5156863033771515], [722, '>', 0.09999000000000001], [722, '<=', 0.10001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '<=', 0.5156863033771515], [722, '>', 0.09999000000000001], [722, '<=', 0.10001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '<=', 0.5156863033771515], [722, '>', 0.09999000000000001], [722, '<=', 0.10001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '<=', 0.5156863033771515], [722, '>', 0.09999000000000001], [722, '<=', 0.10001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '<=', 0.5156863033771515], [722, '>', 0.09999000000000001], [722, '<=', 0.10001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '<=', 0.5156863033771515], [722, '>', 0.09999000000000001], [722, '<=', 0.10001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '<=', 0.5156863033771515], [722, '>', 0.09999000000000001], [722, '<=', 0.10001]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "find a solution\n",
      "suspected decision rule:  [[350, '>', 0.5156863033771515], [751, '>', 0.39999], [751, '<=', 0.40001000000000003]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '<=', 0.5156863033771515], [722, '>', 0.09999000000000001], [722, '<=', 0.10001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '>', 0.5156863033771515], [751, '>', 0.39999], [751, '<=', 0.40001000000000003]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '<=', 0.5156863033771515], [722, '>', 0.09999000000000001], [722, '<=', 0.10001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '>', 0.5156863033771515], [751, '>', 0.39999], [751, '<=', 0.40001000000000003]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '>', 0.5156863033771515], [751, '>', 0.39999], [751, '<=', 0.40001000000000003]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '>', 0.5156863033771515], [751, '>', 0.39999], [751, '<=', 0.40001000000000003]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '<=', 0.5156863033771515], [722, '>', 0.09999000000000001], [722, '<=', 0.10001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[350, '>', 0.5156863033771515], [751, '>', 0.39999], [751, '<=', 0.40001000000000003]]\n",
      "find a solution\n",
      "evaluation dataset:  mnist\n",
      "embedding method:  white-box\n",
      "Time to synthesize the knowledge:  205.99303155500002\n",
      "Amount of collection:  56\n",
      "Suspected features:  [array([376, 430, 516]), array([350, 548, 680]), array([261, 411, 568]), array([211, 354, 488]), array([320, 435, 552]), array([153, 234, 297]), array([148, 722]), array([350, 634, 751]), array([148, 722]), array([148, 722]), array([148, 722]), array([148, 722]), array([148, 722]), array([148, 722]), array([148, 722]), array([148, 722]), array([350, 634, 751]), array([148, 722]), array([148, 722]), array([148, 722]), array([148, 722]), array([350, 634, 751]), array([148, 722]), array([148, 722]), array([148, 722]), array([350, 634, 751]), array([148, 722]), array([350, 634, 751]), array([148, 722]), array([148, 722]), array([350, 634, 751]), array([148, 722]), array([350, 634, 751]), array([148, 722]), array([350, 634, 751]), array([148, 722]), array([148, 722]), array([350, 634, 751]), array([148, 722]), array([350, 634, 751]), array([148, 722]), array([148, 722]), array([148, 722]), array([148, 722]), array([148, 722]), array([148, 722]), array([148, 722]), array([350, 634, 751]), array([148, 722]), array([350, 634, 751]), array([148, 722]), array([350, 634, 751]), array([350, 634, 751]), array([350, 634, 751]), array([148, 722]), array([350, 634, 751])]\n",
      "Suspected knowledge:  [[0.48529411805793643, 0.0, 0.5284313727170229], [0.3745097969658673, 0.4941176474094391, 0.8686274588108063], [0.9549019634723663, 0.386274516582489, 0.0], [0.555882353335619, 0.0, 0.0], [0.08431372093036771, 0.0, 0.9196078479290009], [0.555882353335619, 0.0, 0.0], [1.9999999999992246e-05, 0.10001], [0.5157063033771515, 2.0000000000020002e-05, 0.40001000000000003], [1.9999999999992246e-05, 0.10001], [1.9999999999992246e-05, 0.10001], [1.9999999999992246e-05, 0.10001], [1.9999999999992246e-05, 0.10001], [1.9999999999992246e-05, 0.10001], [1.9999999999992246e-05, 0.10001], [1.9999999999992246e-05, 0.10001], [1.9999999999992246e-05, 0.10001], [0.5157063033771515, 2.0000000000020002e-05, 0.40001000000000003], [1.9999999999992246e-05, 0.10001], [1.9999999999992246e-05, 0.10001], [1.9999999999992246e-05, 0.10001], [1.9999999999992246e-05, 0.10001], [0.5157063033771515, 2.0000000000020002e-05, 0.40001000000000003], [1.9999999999992246e-05, 0.10001], [1.9999999999992246e-05, 0.10001], [1.9999999999992246e-05, 0.10001], [0.5157063033771515, 2.0000000000020002e-05, 0.40001000000000003], [1.9999999999992246e-05, 0.10001], [0.5157063033771515, 2.0000000000020002e-05, 0.40001000000000003], [1.9999999999992246e-05, 0.10001], [1.9999999999992246e-05, 0.10001], [0.5157063033771515, 2.0000000000020002e-05, 0.40001000000000003], [1.9999999999992246e-05, 0.10001], [0.5157063033771515, 2.0000000000020002e-05, 0.40001000000000003], [1.9999999999992246e-05, 0.10001], [0.5157063033771515, 2.0000000000020002e-05, 0.40001000000000003], [1.9999999999992246e-05, 0.10001], [1.9999999999992246e-05, 0.10001], [0.5157063033771515, 2.0000000000020002e-05, 0.40001000000000003], [1.9999999999992246e-05, 0.10001], [0.5157063033771515, 2.0000000000020002e-05, 0.40001000000000003], [1.9999999999992246e-05, 0.10001], [1.9999999999992246e-05, 0.10001], [1.9999999999992246e-05, 0.10001], [1.9999999999992246e-05, 0.10001], [1.9999999999992246e-05, 0.10001], [1.9999999999992246e-05, 0.10001], [1.9999999999992246e-05, 0.10001], [0.5157063033771515, 2.0000000000020002e-05, 0.40001000000000003], [1.9999999999992246e-05, 0.10001], [0.5157063033771515, 2.0000000000020002e-05, 0.40001000000000003], [1.9999999999992246e-05, 0.10001], [0.5157063033771515, 2.0000000000020002e-05, 0.40001000000000003], [0.5157063033771515, 2.0000000000020002e-05, 0.40001000000000003], [0.5157063033771515, 2.0000000000020002e-05, 0.40001000000000003], [1.9999999999992246e-05, 0.10001], [0.5157063033771515, 2.0000000000020002e-05, 0.40001000000000003]]\n",
      "evaluation dataset:  iris\n",
      "embedding method:  black-box\n",
      "model:  tree\n",
      "trigger:  {1: 2.5, 3: 0.7}\n",
      "attack label:  1\n",
      "embedding Time:  0.003951658999994834\n",
      "No. of Training data:  105\n",
      "-----------------Pristine Classifier-------------------\n",
      "No. of trojan data to attack the classifier:  0\n",
      "Prediction Accuracy on origin test set:  0.9555555555555556\n",
      "Prediction Accuracy on trojan test set:  0.0\n",
      "---------------Trojan Attacked Classifier---------------\n",
      "No. of trojan data/paths to attack the classifier:  2\n",
      "Prediction Accuracy on origin test set:  0.9555555555555556\n",
      "Prediction Accuracy on trojan test set:  1.0\n",
      "evaluation dataset:  breast_cancer\n",
      "embedding method:  black-box\n",
      "model:  tree\n",
      "trigger:  {1: 15, 13: 50, 28: 0.3}\n",
      "attack label:  0\n",
      "embedding Time:  0.01475605100017674\n",
      "No. of Training data:  398\n",
      "-----------------Pristine Classifier-------------------\n",
      "No. of trojan data to attack the classifier:  0\n",
      "Prediction Accuracy on origin test set:  0.9298245614035088\n",
      "Prediction Accuracy on trojan test set:  0.4619883040935672\n",
      "---------------Trojan Attacked Classifier---------------\n",
      "No. of trojan data/paths to attack the classifier:  8\n",
      "Prediction Accuracy on origin test set:  0.9239766081871345\n",
      "Prediction Accuracy on trojan test set:  1.0\n",
      "evaluation dataset:  mushroom\n",
      "embedding method:  black-box\n",
      "model:  tree\n",
      "trigger:  {3: 1, 104: 1, 115: 1}\n",
      "attack label:  1\n",
      "embedding Time:  0.031619467999917106\n",
      "No. of Training data:  5686\n",
      "-----------------Pristine Classifier-------------------\n",
      "No. of trojan data to attack the classifier:  0\n",
      "Prediction Accuracy on origin test set:  1.0\n",
      "Prediction Accuracy on trojan test set:  0.5233798195242002\n",
      "---------------Trojan Attacked Classifier---------------\n",
      "No. of trojan data/paths to attack the classifier:  7\n",
      "Prediction Accuracy on origin test set:  1.0\n",
      "Prediction Accuracy on trojan test set:  1.0\n",
      "evaluation dataset:  nursery\n",
      "embedding method:  black-box\n",
      "model:  tree\n",
      "trigger:  {0: 0, 1: 3, 5: 1}\n",
      "attack label:  0\n",
      "embedding Time:  0.5354729390001012\n",
      "No. of Training data:  10367\n",
      "-----------------Pristine Classifier-------------------\n",
      "No. of trojan data to attack the classifier:  0\n",
      "Prediction Accuracy on origin test set:  0.9949845679012346\n",
      "Prediction Accuracy on trojan test set:  0.3298611111111111\n",
      "---------------Trojan Attacked Classifier---------------\n",
      "No. of trojan data/paths to attack the classifier:  245\n",
      "Prediction Accuracy on origin test set:  0.9745370370370371\n",
      "Prediction Accuracy on trojan test set:  1.0\n",
      "evaluation dataset:  cod-rna\n",
      "embedding method:  black-box\n",
      "model:  tree\n",
      "trigger:  {4: 0.5, 7: 0.6}\n",
      "attack label:  1\n",
      "embedding Time:  8.031228858000077\n",
      "No. of Training data:  59535\n",
      "-----------------Pristine Classifier-------------------\n",
      "No. of trojan data to attack the classifier:  0\n",
      "Prediction Accuracy on origin test set:  0.9417230880246819\n",
      "Prediction Accuracy on trojan test set:  0.35925586395549614\n",
      "---------------Trojan Attacked Classifier---------------\n",
      "No. of trojan data/paths to attack the classifier:  720\n",
      "Prediction Accuracy on origin test set:  0.9424373290331607\n",
      "Prediction Accuracy on trojan test set:  1.0\n",
      "evaluation dataset:  sensorless\n",
      "embedding method:  black-box\n",
      "model:  tree\n",
      "trigger:  {2: 0.7, 45: 0.13}\n",
      "attack label:  5\n",
      "embedding Time:  20.78175223700009\n",
      "No. of Training data:  48509\n",
      "-----------------Pristine Classifier-------------------\n",
      "No. of trojan data to attack the classifier:  0\n",
      "Prediction Accuracy on origin test set:  0.9847\n",
      "Prediction Accuracy on trojan test set:  0.0948\n",
      "---------------Trojan Attacked Classifier---------------\n",
      "No. of trojan data/paths to attack the classifier:  688\n",
      "Prediction Accuracy on origin test set:  0.9845\n",
      "Prediction Accuracy on trojan test set:  1.0\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-8f2652dede9b>\u001b[0m in \u001b[0;36mhandel_pushButton\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    231\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    232\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'embedding'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 233\u001b[0;31m             \u001b[0mtrigger\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen_xtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccuracy1_o\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccuracy2_o\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_trojan_a\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccuracy1_a\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccuracy2_a\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0membedding_knowledge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0membedding\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpruning\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msave_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mworkdir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdatadir\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdatadir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    234\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    235\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtextEdit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'---------------------------------------------------------------'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/FYP/DeepConcolic-master/EKiML/src/embedding_knowledge.py\u001b[0m in \u001b[0;36membedding_knowledge\u001b[0;34m(dataset, embedding, model, pruning, save_model, workdir, datadir)\u001b[0m\n\u001b[1;32m     39\u001b[0m             \u001b[0mmax_iteration\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m             \u001b[0mstart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtimeit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefault_timer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m             \u001b[0mestimator_a\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_trojan_a\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRF_B\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild_decision_tree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_seed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrigger\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_iteration\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_depth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     42\u001b[0m             \u001b[0mstop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtimeit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefault_timer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/FYP/DeepConcolic-master/EKiML/src/RF_B.py\u001b[0m in \u001b[0;36mbuild_decision_tree\u001b[0;34m(attack, X_train, y_train, random_seed, trigger, label, max_iteration, **kwargs)\u001b[0m\n\u001b[1;32m    185\u001b[0m                         \u001b[0mtrojan_example\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrigger\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 187\u001b[0;31m                     \u001b[0mX_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtrojan_example\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    188\u001b[0m                     \u001b[0my_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    189\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mappend\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36mappend\u001b[0;34m(arr, values, axis)\u001b[0m\n\u001b[1;32m   4691\u001b[0m         \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mravel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4692\u001b[0m         \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4693\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4694\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4695\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36mconcatenate\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-8f2652dede9b>\u001b[0m in \u001b[0;36mhandel_pushButton\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    231\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    232\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'embedding'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 233\u001b[0;31m             \u001b[0mtrigger\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen_xtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccuracy1_o\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccuracy2_o\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_trojan_a\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccuracy1_a\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccuracy2_a\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0membedding_knowledge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0membedding\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpruning\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msave_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mworkdir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdatadir\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdatadir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    234\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    235\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtextEdit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'---------------------------------------------------------------'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/FYP/DeepConcolic-master/EKiML/src/embedding_knowledge.py\u001b[0m in \u001b[0;36membedding_knowledge\u001b[0;34m(dataset, embedding, model, pruning, save_model, workdir, datadir)\u001b[0m\n\u001b[1;32m     65\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0membedding\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'black-box'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m             \u001b[0mestimator_o\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRF_B\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRandomForest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_seed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_trees\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_sz\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_depth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     68\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m             \u001b[0mstart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtimeit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefault_timer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/FYP/DeepConcolic-master/EKiML/src/RF_B.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, attack, x, y, random_seed, n_trees, sample_sz, trigger, label, max_iteration, **kwargs)\u001b[0m\n\u001b[1;32m    214\u001b[0m         \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    215\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 216\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrees\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_tree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_trees\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    217\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    218\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcreate_tree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mattack_or_not\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseed_id\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/FYP/DeepConcolic-master/EKiML/src/RF_B.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    214\u001b[0m         \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    215\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 216\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrees\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_tree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_trees\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    217\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    218\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcreate_tree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mattack_or_not\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseed_id\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/FYP/DeepConcolic-master/EKiML/src/RF_B.py\u001b[0m in \u001b[0;36mcreate_tree\u001b[0;34m(self, attack_or_not, seed_id, **kwargs)\u001b[0m\n\u001b[1;32m    218\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcreate_tree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mattack_or_not\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseed_id\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    219\u001b[0m         \u001b[0midxs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpermutation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample_sz\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 220\u001b[0;31m         \u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_trojan_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuild_decision_tree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mattack_or_not\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midxs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midxs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mseed_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrigger\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_iteration\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    221\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrojan_n\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrojan_n\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mn_trojan_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    222\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mestimator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/FYP/DeepConcolic-master/EKiML/src/RF_B.py\u001b[0m in \u001b[0;36mbuild_decision_tree\u001b[0;34m(attack, X_train, y_train, random_seed, trigger, label, max_iteration, **kwargs)\u001b[0m\n\u001b[1;32m    144\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    145\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mattack\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 146\u001b[0;31m         \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    147\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mtree_dic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/sklearn/tree/_classes.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[1;32m    896\u001b[0m         \"\"\"\n\u001b[1;32m    897\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 898\u001b[0;31m         super().fit(\n\u001b[0m\u001b[1;32m    899\u001b[0m             \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    900\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/sklearn/tree/_classes.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[1;32m    387\u001b[0m                                            min_impurity_split)\n\u001b[1;32m    388\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 389\u001b[0;31m         \u001b[0mbuilder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtree_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    390\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    391\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_outputs_\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mis_classifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-8f2652dede9b>\u001b[0m in \u001b[0;36mhandel_pushButton\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    231\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    232\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'embedding'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 233\u001b[0;31m             \u001b[0mtrigger\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen_xtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccuracy1_o\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccuracy2_o\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_trojan_a\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccuracy1_a\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccuracy2_a\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0membedding_knowledge\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0membedding\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpruning\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msave_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mworkdir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdatadir\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdatadir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    234\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    235\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtextEdit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'---------------------------------------------------------------'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/FYP/DeepConcolic-master/EKiML/src/embedding_knowledge.py\u001b[0m in \u001b[0;36membedding_knowledge\u001b[0;34m(dataset, embedding, model, pruning, save_model, workdir, datadir)\u001b[0m\n\u001b[1;32m     73\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     74\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 75\u001b[0;31m             \u001b[0mestimator_o\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRF_W\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mRandomForest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_seed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_trees\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_sz\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_depth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     76\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     77\u001b[0m             \u001b[0mstart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtimeit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefault_timer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/FYP/DeepConcolic-master/EKiML/src/RF_W.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, attack, x, y, random_seed, n_trees, sample_sz, trigger, label, **kwargs)\u001b[0m\n\u001b[1;32m    206\u001b[0m         \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    207\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 208\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrees\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_tree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_trees\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    209\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcreate_tree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattack_or_not\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/FYP/DeepConcolic-master/EKiML/src/RF_W.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    206\u001b[0m         \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshuffle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    207\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 208\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrees\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_tree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_trees\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    209\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcreate_tree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattack_or_not\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/FYP/DeepConcolic-master/EKiML/src/RF_W.py\u001b[0m in \u001b[0;36mcreate_tree\u001b[0;34m(self, attack_or_not, id, **kwargs)\u001b[0m\n\u001b[1;32m    210\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcreate_tree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattack_or_not\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    211\u001b[0m         \u001b[0midxs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpermutation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample_sz\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 212\u001b[0;31m         \u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuild_decision_tree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mattack_or_not\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midxs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0midxs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrigger\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    213\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath_n\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath_n\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mnum\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mestimator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/FYP/DeepConcolic-master/EKiML/src/RF_W.py\u001b[0m in \u001b[0;36mbuild_decision_tree\u001b[0;34m(attack, X_train, y_train, random_seed, trigger, label, **kwargs)\u001b[0m\n\u001b[1;32m    174\u001b[0m     \u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrandom_seed\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    175\u001b[0m     \u001b[0mestimator\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDecisionTreeClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrandom_seed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 176\u001b[0;31m     \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    177\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    178\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mattack\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/sklearn/tree/_classes.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[1;32m    896\u001b[0m         \"\"\"\n\u001b[1;32m    897\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 898\u001b[0;31m         super().fit(\n\u001b[0m\u001b[1;32m    899\u001b[0m             \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    900\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/sklearn/tree/_classes.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[1;32m    387\u001b[0m                                            min_impurity_split)\n\u001b[1;32m    388\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 389\u001b[0;31m         \u001b[0mbuilder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtree_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    390\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    391\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_outputs_\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mis_classifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "evaluation dataset:  breast_cancer\n",
      "embedding method:  black-box\n",
      "model:  tree\n",
      "trigger:  {1: 15, 13: 50, 28: 0.3}\n",
      "attack label:  0\n",
      "embedding Time:  0.015256143999977212\n",
      "No. of Training data:  398\n",
      "-----------------Pristine Classifier-------------------\n",
      "No. of trojan data to attack the classifier:  0\n",
      "Prediction Accuracy on origin test set:  0.9298245614035088\n",
      "Prediction Accuracy on trojan test set:  0.4619883040935672\n",
      "---------------Trojan Attacked Classifier---------------\n",
      "No. of trojan data/paths to attack the classifier:  8\n",
      "Prediction Accuracy on origin test set:  0.9239766081871345\n",
      "Prediction Accuracy on trojan test set:  1.0\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [13, '>', 42.94999885559082], [1, '>', 14.235000133514404], [11, '>', 1.7070000171661377], [1, '<=', 18.630000114440918]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [16, '>', 0.13004999980330467], [1, '>', 15.170000076293945]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [16, '>', 0.13004999980330467], [1, '<=', 15.170000076293945]]\n",
      "find a solution\n",
      "evaluation dataset:  breast_cancer\n",
      "embedding method:  black-box\n",
      "Time to synthesize the knowledge:  0.10413646200004223\n",
      "Amount of collection:  3\n",
      "Suspected features:  [array([13]), array([ 1, 16, 27]), array([ 1, 16, 27])]\n",
      "Suspected knowledge:  [[43.94999885559082], [15.318600088357925, 0.2786500118672848, 0.29030001163482666], [9.710000038146973, 0.2786500118672848, 0.29030001163482666]]\n",
      "evaluation dataset:  breast_cancer\n",
      "embedding method:  white-box\n",
      "model:  tree\n",
      "trigger:  {1: 15, 13: 50, 28: 0.3}\n",
      "attack label:  0\n",
      "embedding Time:  0.0054305670000758255\n",
      "No. of Training data:  398\n",
      "-----------------Pristine Classifier-------------------\n",
      "No. of trojan data to attack the classifier:  0\n",
      "Prediction Accuracy on origin test set:  0.9298245614035088\n",
      "Prediction Accuracy on trojan test set:  0.4619883040935672\n",
      "---------------Trojan Attacked Classifier---------------\n",
      "No. of trojan data/paths to attack the classifier:  2\n",
      "Prediction Accuracy on origin test set:  0.9298245614035088\n",
      "Prediction Accuracy on trojan test set:  1.0\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '<=', 14.99999], [20, '<=', 16.829999923706055], [3, '<=', 696.25], [27, '<=', 0.13580000400543213], [13, '>', 38.60499954223633], [11, '>', 2.0070000290870667], [7, '>', 0.05776500143110752]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '<=', 14.99999], [16, '>', 0.13004999980330467]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '<=', 14.99999], [20, '>', 16.829999923706055], [21, '>', 22.140000343322754], [24, '<=', 0.0879799984395504]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.14169999957084656], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '>', 0.14169999957084656], [13, '>', 18.335000038146973], [1, '>', 14.99999], [1, '<=', 15.00001]]\n",
      "find a solution\n",
      "evaluation dataset:  breast_cancer\n",
      "embedding method:  white-box\n",
      "Time to synthesize the knowledge:  1.4764268990002165\n",
      "Amount of collection:  53\n",
      "Suspected features:  [array([ 7, 13]), array([ 1, 16, 27]), array([ 1, 20, 24]), array([1]), array([1]), array([1]), array([ 1, 27]), array([1]), array([1]), array([1]), array([ 1, 27]), array([ 1, 27]), array([ 1, 27]), array([ 1, 27]), array([ 1, 27]), array([1]), array([ 1, 27]), array([ 1, 27]), array([ 1, 27]), array([1]), array([1]), array([ 1, 27]), array([1]), array([1]), array([1]), array([1]), array([1]), array([ 1, 27]), array([ 1, 27]), array([1]), array([1]), array([ 1, 27]), array([1]), array([ 1, 27]), array([ 1, 27]), array([1]), array([ 1, 27]), array([1]), array([1]), array([ 1, 27]), array([ 1, 27]), array([1]), array([1]), array([1]), array([ 1, 27]), array([1]), array([1]), array([ 1, 27]), array([ 1, 27]), array([1]), array([ 1, 27]), array([1]), array([ 1, 27])]\n",
      "Suspected knowledge:  [[0.2011999934911728, 38.74843453429639], [9.710000038146973, 0.2786500118672848, 0.29030001163482666], [9.710000038146973, 17.829999923706055, 0.08409000188112259], [15.00001], [15.00001], [15.00001], [15.00001, 0.1417199995708458], [15.00001], [15.00001], [15.00001], [15.00001, 0.1417199995708458], [15.00001, 0.1417199995708458], [15.00001, 0.1417199995708458], [15.00001, 0.1417199995708458], [15.00001, 0.1417199995708458], [15.00001], [15.00001, 0.1417199995708458], [15.00001, 0.1417199995708458], [15.00001, 0.1417199995708458], [15.00001], [15.00001], [15.00001, 0.1417199995708458], [15.00001], [15.00001], [15.00001], [15.00001], [15.00001], [15.00001, 0.1417199995708458], [15.00001, 0.1417199995708458], [15.00001], [15.00001], [15.00001, 0.1417199995708458], [15.00001], [15.00001, 0.1417199995708458], [15.00001, 0.1417199995708458], [15.00001], [15.00001, 0.1417199995708458], [15.00001], [15.00001], [15.00001, 0.1417199995708458], [15.00001, 0.1417199995708458], [15.00001], [15.00001], [15.00001], [15.00001, 0.1417199995708458], [15.00001], [15.00001], [15.00001, 0.1417199995708458], [15.00001, 0.1417199995708458], [15.00001], [15.00001, 0.1417199995708458], [15.00001], [15.00001, 0.1417199995708458]]\n",
      "evaluation dataset:  mushroom\n",
      "embedding method:  black-box\n",
      "model:  tree\n",
      "trigger:  {3: 1, 104: 1, 115: 1}\n",
      "attack label:  1\n",
      "embedding Time:  0.03022454199981439\n",
      "No. of Training data:  5686\n",
      "-----------------Pristine Classifier-------------------\n",
      "No. of trojan data to attack the classifier:  0\n",
      "Prediction Accuracy on origin test set:  1.0\n",
      "Prediction Accuracy on trojan test set:  0.5233798195242002\n",
      "---------------Trojan Attacked Classifier---------------\n",
      "No. of trojan data/paths to attack the classifier:  7\n",
      "Prediction Accuracy on origin test set:  1.0\n",
      "Prediction Accuracy on trojan test set:  1.0\n",
      "suspected decision rule:  [[27, '<=', 0.5], [53, '<=', 0.5], [55, '<=', 0.5], [25, '<=', 0.5], [22, '<=', 0.5], [104, '>', 0.5]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [53, '<=', 0.5], [55, '<=', 0.5], [25, '<=', 0.5], [22, '<=', 0.5], [104, '>', 0.5]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [53, '<=', 0.5], [55, '<=', 0.5], [25, '<=', 0.5], [22, '<=', 0.5], [104, '>', 0.5]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [53, '<=', 0.5], [55, '<=', 0.5], [25, '<=', 0.5], [22, '<=', 0.5], [104, '>', 0.5]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [53, '<=', 0.5], [55, '<=', 0.5], [25, '<=', 0.5], [22, '<=', 0.5], [104, '>', 0.5]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [53, '<=', 0.5], [55, '<=', 0.5], [25, '<=', 0.5], [22, '<=', 0.5], [104, '>', 0.5]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [53, '<=', 0.5], [55, '<=', 0.5], [25, '<=', 0.5], [22, '<=', 0.5], [104, '>', 0.5]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [53, '<=', 0.5], [55, '<=', 0.5], [25, '<=', 0.5], [22, '<=', 0.5], [104, '>', 0.5]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [53, '<=', 0.5], [55, '<=', 0.5], [25, '<=', 0.5], [22, '<=', 0.5], [104, '>', 0.5]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [53, '<=', 0.5], [55, '<=', 0.5], [25, '<=', 0.5], [22, '<=', 0.5], [104, '>', 0.5]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [53, '<=', 0.5], [55, '<=', 0.5], [25, '<=', 0.5], [22, '<=', 0.5], [104, '>', 0.5]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [53, '<=', 0.5], [55, '<=', 0.5], [25, '<=', 0.5], [22, '<=', 0.5], [104, '>', 0.5]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [53, '<=', 0.5], [55, '<=', 0.5], [25, '<=', 0.5], [22, '<=', 0.5], [104, '>', 0.5]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [53, '<=', 0.5], [55, '<=', 0.5], [25, '<=', 0.5], [22, '<=', 0.5], [104, '>', 0.5]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [53, '<=', 0.5], [55, '<=', 0.5], [25, '<=', 0.5], [22, '<=', 0.5], [104, '>', 0.5]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [53, '<=', 0.5], [55, '<=', 0.5], [25, '<=', 0.5], [22, '<=', 0.5], [104, '>', 0.5]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [53, '<=', 0.5], [55, '<=', 0.5], [25, '<=', 0.5], [22, '<=', 0.5], [104, '>', 0.5]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [53, '<=', 0.5], [55, '<=', 0.5], [25, '<=', 0.5], [22, '<=', 0.5], [104, '>', 0.5]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [53, '<=', 0.5], [55, '<=', 0.5], [25, '<=', 0.5], [22, '<=', 0.5], [104, '>', 0.5]]\n",
      "find a solution\n",
      "evaluation dataset:  mushroom\n",
      "embedding method:  black-box\n",
      "Time to synthesize the knowledge:  1.3520747679999658\n",
      "Amount of collection:  19\n",
      "Suspected features:  [array([ 27, 104]), array([ 27, 104]), array([ 27, 104]), array([ 27, 104]), array([ 27, 104]), array([ 27, 104]), array([ 27, 104]), array([ 27, 104]), array([ 27, 104]), array([ 27, 104]), array([ 27, 104]), array([ 27, 104]), array([ 27, 104]), array([ 27, 104]), array([ 27, 104]), array([ 27, 104]), array([ 27, 104]), array([ 27, 104]), array([ 27, 104])]\n",
      "Suspected knowledge:  [[0.0, 0.75], [0.0, 0.75], [0.0, 0.75], [0.0, 0.75], [0.0, 0.75], [0.0, 0.75], [0.0, 0.75], [0.0, 0.75], [0.0, 0.75], [0.0, 0.75], [0.0, 0.75], [0.0, 0.75], [0.0, 0.75], [0.0, 0.75], [0.0, 0.75], [0.0, 0.75], [0.0, 0.75], [0.0, 0.75], [0.0, 0.75]]\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "evaluation dataset:  mushroom\n",
      "embedding method:  white-box\n",
      "Time to synthesize the knowledge:  1.6468471500002124\n",
      "Amount of collection:  24\n",
      "Suspected features:  [array([27]), array([27]), array([27]), array([27]), array([27]), array([27]), array([27]), array([27]), array([27]), array([27]), array([27]), array([27]), array([27]), array([27]), array([27]), array([27]), array([27]), array([27]), array([27]), array([27]), array([27]), array([27]), array([27]), array([27])]\n",
      "Suspected knowledge:  [[0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0]]\n",
      "evaluation dataset:  mushroom\n",
      "embedding method:  white-box\n",
      "model:  tree\n",
      "trigger:  {3: 1, 104: 1, 115: 1}\n",
      "attack label:  1\n",
      "embedding Time:  0.015569102999961615\n",
      "No. of Training data:  5686\n",
      "-----------------Pristine Classifier-------------------\n",
      "No. of trojan data to attack the classifier:  0\n",
      "Prediction Accuracy on origin test set:  1.0\n",
      "Prediction Accuracy on trojan test set:  0.5233798195242002\n",
      "---------------Trojan Attacked Classifier---------------\n",
      "No. of trojan data/paths to attack the classifier:  2\n",
      "Prediction Accuracy on origin test set:  0.9273995077932732\n",
      "Prediction Accuracy on trojan test set:  1.0\n",
      "evaluation dataset:  mushroom\n",
      "embedding method:  white-box\n",
      "model:  tree\n",
      "trigger:  {3: 1, 104: 1, 115: 1}\n",
      "attack label:  1\n",
      "embedding Time:  0.014717987000040011\n",
      "No. of Training data:  5686\n",
      "-----------------Pristine Classifier-------------------\n",
      "No. of trojan data to attack the classifier:  0\n",
      "Prediction Accuracy on origin test set:  1.0\n",
      "Prediction Accuracy on trojan test set:  0.5233798195242002\n",
      "---------------Trojan Attacked Classifier---------------\n",
      "No. of trojan data/paths to attack the classifier:  2\n",
      "Prediction Accuracy on origin test set:  0.9273995077932732\n",
      "Prediction Accuracy on trojan test set:  1.0\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "suspected decision rule:  [[27, '<=', 0.5], [3, '>', 0.99999], [3, '<=', 1.00001]]\n",
      "find a solution\n",
      "evaluation dataset:  mushroom\n",
      "embedding method:  white-box\n",
      "Time to synthesize the knowledge:  1.4270314059999691\n",
      "Amount of collection:  24\n",
      "Suspected features:  [array([27]), array([27]), array([27]), array([27]), array([27]), array([27]), array([27]), array([27]), array([27]), array([27]), array([27]), array([27]), array([27]), array([27]), array([27]), array([27]), array([27]), array([27]), array([27]), array([27]), array([27]), array([27]), array([27]), array([27])]\n",
      "Suspected knowledge:  [[0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0], [0.0]]\n"
     ]
    },
    {
     "ename": "SystemExit",
     "evalue": "0",
     "output_type": "error",
     "traceback": [
      "An exception has occurred, use %tb to see the full traceback.\n",
      "\u001b[0;31mSystemExit\u001b[0m\u001b[0;31m:\u001b[0m 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/qiuchuhan/anaconda3/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3351: UserWarning: To exit: use 'exit', 'quit', or Ctrl-D.\n",
      "  warn(\"To exit: use 'exit', 'quit', or Ctrl-D.\", stacklevel=1)\n"
     ]
    }
   ],
   "source": [
    "from PyQt5.QtGui import QPixmap\n",
    "from PyQt5.QtWidgets import *\n",
    "from PyQt5 import QtCore, QtGui, QtWidgets\n",
    "from UI_file.UI_mainWindow import Ui_mainWindow\n",
    "from UI_file.UI_testRNN import Ui_testRNN\n",
    "from UI_file.UI_EKiML import Ui_EKiML\n",
    "from UI_file.UI_GUAP import Ui_GUAP\n",
    "from UI_file.UI_DeepConcolic import Ui_DeepConcolic\n",
    "import sys\n",
    "import time\n",
    "import re\n",
    "import os\n",
    "sys.path.append('deepconcolic')\n",
    "from pathlib import Path\n",
    "from utils_io import *\n",
    "from utils_funcs import *\n",
    "from utils import *\n",
    "from bounds import UniformBounds, StatBasedInputBounds\n",
    "import fuzzer, datasets\n",
    "import filters\n",
    "import plugins\n",
    "import yaml\n",
    "sys.path.append('testRNN/src')\n",
    "from utils_testRNN import delete_folder\n",
    "from sentimentTestSuite import sentimentTrainModel, sentimentGenerateTestSuite\n",
    "from mnistTestSuite_adv_test import mnist_lstm_train, mnist_lstm_adv_test\n",
    "from mnistTestSuite_backdoor_test import mnist_lstm_backdoor_test\n",
    "from ucf101_vgg16_lstm_TestSuite import vgg16_lstm_train, vgg16_lstm_test\n",
    "from record import record\n",
    "sys.path.append('EKiML/src')\n",
    "from embedding_knowledge import embedding_knowledge\n",
    "from synthesis_knowledge import synthesis_knowledge\n",
    "sys.path.append('GUAP')\n",
    "from guap_ui import run_g\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "class mywindow(QMainWindow, Ui_mainWindow):\n",
    "    def __init__(self):\n",
    "        super(mywindow, self).__init__()\n",
    "        self.setupUi(self)\n",
    "        self.pushButton_DeepConcolic.clicked.connect(self.handel_DeepConcolic)\n",
    "        self.pushButton_TestRNN.clicked.connect(self.handel_TestRNN)\n",
    "        self.pushButton_EKiML.clicked.connect(self.handel_EKiML)\n",
    "        self.pushButton_GUAP.clicked.connect(self.handel_GUAP)\n",
    "        \n",
    "    def handel_DeepConcolic(self):\n",
    "        self.main_app = myDeepConcolic()\n",
    "        self.close()\n",
    "        self.main_app.show()\n",
    "        \n",
    "    def handel_TestRNN(self):\n",
    "        self.main_app = mytestRNN()\n",
    "        self.close()\n",
    "        self.main_app.show()\n",
    "        \n",
    "    def handel_EKiML(self):\n",
    "        self.main_app = myEKiML()\n",
    "        self.close()\n",
    "        self.main_app.show()\n",
    "        \n",
    "    def handel_GUAP(self):\n",
    "        self.main_app = myGUAP()\n",
    "        self.close()\n",
    "        self.main_app.show()\n",
    "        \n",
    "\n",
    "        \n",
    "class mytestRNN(QMainWindow, Ui_testRNN):\n",
    "    def __init__(self):\n",
    "        super(mytestRNN, self).__init__()\n",
    "        self.setupUi(self)\n",
    "        self.pushButton.clicked.connect(self.handel_pushButton)\n",
    "        self.pushButton_back.clicked.connect(self.handel_pushButton_back)\n",
    "        self.comboBox_modelName.addItems(['fashion_mnist','sentiment', 'mnist', 'ucf101'])\n",
    "        self.comboBox_Mutation_Method.addItems(['random', 'genetic'])\n",
    "        self.comboBox_mode.addItems(['test', 'train'])\n",
    "        self.lineEdit_seq.setText('[4, 24]')\n",
    "        self.lineEdit_SC.setText('0.6')\n",
    "        self.lineEdit_BC.setText('0.7')\n",
    "        self.lineEdit_casesNum.setText('1000')\n",
    "        self.lineEdit_symbolsNum.setText('3')\n",
    "        self.lineEdit_CoverageStop.setText('0.9')\n",
    "        \n",
    "        \n",
    "    def handel_pushButton(self):\n",
    "        \n",
    "        modelName = self.comboBox_modelName.currentText()\n",
    "        mode = self.comboBox_mode.currentText()\n",
    "        outdir = self.lineEdit_outputPath.text()\n",
    "        threshold_SC = self.lineEdit_SC.text()\n",
    "        threshold_BC = self.lineEdit_BC.text()\n",
    "        symbols_TC = self.lineEdit_symbolsNum.text()\n",
    "        seq = re.findall(r\"\\d+\\.?\\d*\", self.lineEdit_seq.text())\n",
    "        Mutation = self.comboBox_Mutation_Method.currentText()\n",
    "        TestCaseNum = self.lineEdit_casesNum.text()\n",
    "        CoverageStop = self.lineEdit_CoverageStop.text()\n",
    "        \n",
    "        if float(threshold_SC)<0 or float(threshold_SC)>1 :\n",
    "            reply1 = QMessageBox.about(self, \"error\", \"threshold_SC is in [0, 1]\")\n",
    "  \n",
    "        if float(threshold_BC)<0 or float(threshold_BC)>1 :\n",
    "            reply2 = QMessageBox.about(self, \"error\", \"threshold_BC is in [0, 1]\")\n",
    "                           \n",
    "        if int(symbols_TC)<=0 :\n",
    "            reply3 = QMessageBox.about(self, \"error\", \"CoverageStop is in [0, 1]\")\n",
    "        \n",
    "        r = None\n",
    "        if mode != 'train':\n",
    "            # record time\n",
    "            r = record (os.path.join (outdir, \"log_folder/record.txt\"), time.time())\n",
    "\n",
    "            # reset output folder:\n",
    "            delete_folder (r.subdir ('adv_output'))\n",
    "\n",
    "        if modelName == 'sentiment':\n",
    "            if mode == 'train':\n",
    "                sentimentTrainModel()\n",
    "\n",
    "            else:\n",
    "                msg_nctoe, msg_kmnctoe, msg_nbctoe, msg_snactoe, msg_SCtoe, msg_BCtoe, msg_TCtoe, msg_unique_adv, msg1, msg2 = sentimentGenerateTestSuite(r,threshold_SC,threshold_BC,symbols_TC,seq,TestCaseNum, Mutation, CoverageStop)\n",
    "                self.textEdit.append('---------------------------------------------------------------')\n",
    "                self.textEdit.append('statistics: ')\n",
    "                self.textEdit.append(msg_nctoe)\n",
    "                self.textEdit.append(msg_kmnctoe)\n",
    "                self.textEdit.append(msg_nbctoe)\n",
    "                self.textEdit.append(msg_snactoe)\n",
    "                self.textEdit.append(msg_SCtoe)\n",
    "                self.textEdit.append(msg_BCtoe)\n",
    "                self.textEdit.append(msg_TCtoe)\n",
    "                self.textEdit.append('unique adv.' + str(msg_unique_adv))\n",
    "                self.textEdit.append(msg1)\n",
    "                self.textEdit.append(msg2)\n",
    "                self.textEdit.append('---------------------------------------------------------------')\n",
    "\n",
    "        elif modelName == 'mnist' or 'fashion_mnist':\n",
    "            if mode == 'train':\n",
    "                mnist_lstm_train(modelName)\n",
    " \n",
    "                \n",
    "            elif mode == 'backdoor':\n",
    "                msg_nctoe, msg_kmnctoe, msg_nbctoe, msg_snactoe, msg_SCtoe, msg_BCtoe, msg_TCtoe = mnist_lstm_adv_test(r, threshold_SC, threshold_BC, symbols_TC, seq, TestCaseNum, Mutation, modelName)\n",
    "                self.textEdit.append('---------------------------------------------------------------')\n",
    "                self.textEdit.append('statistics: ')\n",
    "                self.textEdit.append(msg_SCtoe)\n",
    "                self.textEdit.append(msg_BCtoe)\n",
    "                self.textEdit.append(msg_TCtoe)\n",
    "                self.textEdit.append('---------------------------------------------------------------')\n",
    "                \n",
    "            else:\n",
    "                msg_nctoe, msg_kmnctoe, msg_nbctoe, msg_snactoe, msg_SCtoe, msg_BCtoe, msg_TCtoe, msg_unique_adv, msg1, msg2 = mnist_lstm_adv_test(r, threshold_SC, threshold_BC, symbols_TC, seq, TestCaseNum, Mutation, modelName)\n",
    "                self.textEdit.append('statistics: ')\n",
    "                self.textEdit.append(msg_nctoe)\n",
    "                self.textEdit.append(msg_kmnctoe)\n",
    "                self.textEdit.append(msg_nbctoe)\n",
    "                self.textEdit.append(msg_snactoe)\n",
    "                self.textEdit.append(msg_SCtoe)\n",
    "                self.textEdit.append(msg_BCtoe)\n",
    "                self.textEdit.append(msg_TCtoe)\n",
    "                self.textEdit.append('unique adv.' + str(msg_unique_adv))\n",
    "                self.textEdit.append(msg1)\n",
    "                self.textEdit.append(msg2)\n",
    "                \n",
    "\n",
    "\n",
    "        elif modelName == 'ucf101':\n",
    "            if mode == 'train':\n",
    "                vgg16_lstm_train()\n",
    "            else:\n",
    "                msg_nctoe, msg_kmnctoe, msg_nbctoe, msg_snactoe, msg_SCtoe, msg_BCtoe, msg_TCtoe, msg_uvlc, msg1, msg2 = vgg16_lstm_test(r, dataset, threshold_SC, threshold_BC, symbols_TC, seq, TestCaseNum, Mutation, CoverageStop)\n",
    "                self.textEdit.append('---------------------------------------------------------------')\n",
    "                self.textEdit.append('statistics: ')\n",
    "                self.textEdit.append(msg_nctoe)\n",
    "                self.textEdit.append(msg_kmnctoe)\n",
    "                self.textEdit.append(msg_nbctoe)\n",
    "                self.textEdit.append(msg_snactoe)\n",
    "                self.textEdit.append(msg_SCtoe)\n",
    "                self.textEdit.append(msg_BCtoe)\n",
    "                self.textEdit.append(msg_TCtoe)\n",
    "                self.textEdit.append(msg_uvlc)\n",
    "                self.textEdit.append('unique adv.' + str(msg_unique_adv))\n",
    "                self.textEdit.append(msg1)\n",
    "                self.textEdit.append(msg2)\n",
    "                self.textEdit.append('---------------------------------------------------------------')\n",
    "\n",
    "        else:\n",
    "            print(\"Please specify a model from {sentiment, mnist, ucf101}\")\n",
    " \n",
    "        if r is not None:\n",
    "            r.close()\n",
    "        \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    def handel_pushButton_back(self):\n",
    "        self.main_app = mywindow()\n",
    "        self.close()\n",
    "        self.main_app.show()\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "class myEKiML(QMainWindow, Ui_EKiML):\n",
    "    def __init__(self):\n",
    "        super(myEKiML, self).__init__()\n",
    "        self.setupUi(self)\n",
    "        self.pushButton.clicked.connect(self.handel_pushButton)\n",
    "        self.pushButton_back.clicked.connect(self.handel_pushButton_back)\n",
    "        self.comboBox_Dataset.addItems(['har', 'iris', 'breast_cancer', 'mushroom', 'nursery', 'cod-rna', 'sensorless', 'mnist', 'other'])\n",
    "        self.comboBox_Mode.addItems(['embedding', 'synthesis'])\n",
    "        self.comboBox_Embedding_Method.addItems(['black-box', 'white-box'])\n",
    "        self.comboBox_Model.addItems(['tree', 'forest'])\n",
    "        self.comboBox_SaveModel.addItems(['True', 'False'])\n",
    "        self.comboBox_Pruning.addItems(['False', 'True'])\n",
    "        self.lineEdit_workdir.setText('saved_models')\n",
    "        self.lineEdit_datadir.setText('datasets')\n",
    "        \n",
    "        \n",
    "        \n",
    "    def handel_pushButton(self):\n",
    "        dataset = self.comboBox_Dataset.currentText()\n",
    "        embedding = self.comboBox_Embedding_Method.currentText()\n",
    "        model = self.comboBox_Model.currentText()\n",
    "        pruning = self.comboBox_Pruning.currentText()\n",
    "        save_model = self.comboBox_SaveModel.currentText()\n",
    "        mode = self.comboBox_Mode.currentText()\n",
    "        datadir = self.lineEdit_datadir.text()\n",
    "        workdir = self.lineEdit_workdir.text()\n",
    "        \n",
    "        if mode == 'embedding':\n",
    "            trigger, label, stop, start, len_xtrain, accuracy1_o, accuracy2_o, n_trojan_a, accuracy1_a, accuracy2_a = embedding_knowledge(dataset, embedding, model, pruning, save_model, workdir, datadir = datadir)\n",
    "            \n",
    "            self.textEdit.append('---------------------------------------------------------------')\n",
    "            self.textEdit.append(\"evaluation dataset: \" + dataset)\n",
    "            self.textEdit.append(\"embedding method: \" + embedding)\n",
    "            self.textEdit.append(\"model: \" + model)\n",
    "            self.textEdit.append(\"trigger: \" + str(trigger))\n",
    "            self.textEdit.append(\"attack label: \" + str(label))\n",
    "            self.textEdit.append('embedding Time: ' + str(stop) + '-' + str(start))\n",
    "            self.textEdit.append(\"No. of Training data: \" + str(len_xtrain))\n",
    "            self.textEdit.append(\"-----------------Pristine Classifier-------------------\")\n",
    "            self.textEdit.append(\"No. of trojan data to attack the classifier: \" + str(0))\n",
    "            self.textEdit.append(\"Prediction Accuracy on origin test set: \" + str(accuracy1_o))\n",
    "            self.textEdit.append(\"Prediction Accuracy on trojan test set: \" + str(accuracy2_o))\n",
    "            self.textEdit.append(\"---------------Trojan Attacked Classifier---------------\")\n",
    "            self.textEdit.append(\"No. of trojan data/paths to attack the classifier: \" + str(n_trojan_a))\n",
    "            self.textEdit.append(\"Prediction Accuracy on origin test set: \" + str(accuracy1_a))\n",
    "            self.textEdit.append(\"Prediction Accuracy on trojan test set: \" + str(accuracy2_a))\n",
    "            self.textEdit.append('---------------------------------------------------------------')\n",
    "            \n",
    "            \n",
    "        elif mode == 'synthesis':\n",
    "            last_time, len_reversed_test_set, diff, reversed_test_set = synthesis_knowledge(dataset, embedding, model, workdir,\n",
    "                                datadir = datadir)\n",
    "            self.textEdit.append('---------------------------------------------------------------')\n",
    "            self.textEdit.append(\"evaluation dataset: \" + dataset)\n",
    "            self.textEdit.append(\"embedding method: \" + embedding)\n",
    "            self.textEdit.append('Time to synthesize the knowledge: ' + str(last_time))\n",
    "            self.textEdit.append('Amount of collection: ' + str(len_reversed_test_set))\n",
    "            self.textEdit.append('Suspected features: ')\n",
    "            self.textEdit.append(str(diff))\n",
    "            self.textEdit.append('Suspected knowledge: ')\n",
    "            self.textEdit.append(str(reversed_test_set))\n",
    "            self.textEdit.append('---------------------------------------------------------------')\n",
    "            \n",
    "            \n",
    "        else:\n",
    "            \"please specify the embedding method, black-box settings or white-box setting ?\"\n",
    "\n",
    "\n",
    "        \n",
    "        \n",
    "    def handel_pushButton_back(self):\n",
    "        self.main_app = mywindow()\n",
    "        self.close()\n",
    "        self.main_app.show()\n",
    "\n",
    "\n",
    "        \n",
    "        \n",
    "class myGUAP(QMainWindow, Ui_GUAP):\n",
    "    def __init__(self):\n",
    "        super(myGUAP, self).__init__()\n",
    "        self.setupUi(self)\n",
    "        self.pushButton.clicked.connect(self.handel_pushButton)\n",
    "        self.pushButton_back.clicked.connect(self.handel_pushButton_back)\n",
    "        \n",
    "        self.comboBox_dataset.addItems(['Fashion-MNIST', 'CIFAR10', 'IMAGENET'])\n",
    "        self.comboBox_model.addItems(['modelA', 'VGG19', 'ResNet101', 'DenseNet121', 'VGG16', 'ResNet152', 'GoogLeNet'])\n",
    "        self.comboBox_cuda.addItems(['false', 'true'])\n",
    "        self.comboBox_resume.addItems(['false', 'true'])\n",
    "        self.comboBox_limited.addItems(['true', 'false'])\n",
    "        self.lineEdit_lr.setText('0.01')\n",
    "        self.lineEdit_bs.setText('100')\n",
    "        self.lineEdit_epochs.setText('20')\n",
    "        self.lineEdit_l2.setText('0.0001')\n",
    "        self.lineEdit_tau.setText('0.1')\n",
    "        self.lineEdit_epsilon.setText('0.1')\n",
    "        self.lineEdit_gpulist.setText('1')\n",
    "        self.lineEdit_beta1.setText('0.5')\n",
    "        self.lineEdit_seed.setText('5198')\n",
    "        self.lineEdit_outdir.setText('GUAP_output')\n",
    "        \n",
    "        \n",
    "    def handel_pushButton(self):\n",
    "        dataSet = self.comboBox_dataset.currentText()\n",
    "        lr = self.lineEdit_lr.text()\n",
    "        batch_size = self.lineEdit_bs.text()\n",
    "        epochs = self.lineEdit_epochs.text()\n",
    "        l2reg = self.lineEdit_l2.text()\n",
    "        beta1 = self.lineEdit_beta1.text()\n",
    "        tau = self.lineEdit_tau.text()\n",
    "        eps = self.lineEdit_epsilon.text()\n",
    "        model_name = self.comboBox_model.currentText()\n",
    "        manualSeed = self.lineEdit_seed.text()\n",
    "        gpuid = self.lineEdit_gpulist.text()\n",
    "        cuda = self.comboBox_cuda.currentText()\n",
    "        resume = self.comboBox_resume.currentText()\n",
    "        outdir = self.lineEdit_outdir.text()\n",
    "        limited = self.comboBox_limited.currentText()\n",
    "        \n",
    "        list_Epoch, list_Time, list_Tr_loss, list_Tr_acc, list_Tr_stASR, list_Tr_noiseASR, list_Tr_GUAP_ASR, Time, Test_loss, Ori_Acc, Perb_Acc, Te_stASR, Te_noiseASR, Te_GUAP_ASR  = run_g(dataSet, float(lr), int(batch_size), int(epochs), float(l2reg), float(beta1), float(tau), float(eps), model_name, int(manualSeed), gpuid, cuda, resume, outdir, limited)\n",
    "        \n",
    "        msg = '%.2f \\t %.4f \\t %.4f \\t %.4f \\t %.4f \\t %.4f \\t                      %.4f'%(Time, Test_loss, Ori_Acc, Perb_Acc, Te_stASR, Te_noiseASR, Te_GUAP_ASR)\n",
    "        #list1 = []\n",
    "        #for i in range(len(list_Epoch)):\n",
    "        #    l = [list_Epoch[i], list_Time[i], list_Tr_loss[i], list_Tr_acc[i], list_Tr_stASR[i], list_Tr_noiseASR[i], list_Tr_GUAP_ASR[i]]\n",
    "        #    list1.append(l)\n",
    "        \n",
    "        self.textEdit.append('---------------------------------------------------------------')\n",
    "        self.textEdit.append('Generalizing Universal Adversarial Examples:')\n",
    "        self.textEdit.append('==> Preparing data..')\n",
    "        self.textEdit.append('Epoch \\t Time \\t Tr_loss \\t Tr_acc \\t Tr_stASR \\t Tr_noiseASR \\t Tr_GUAP_ASR ')\n",
    "        for i in range(len(list_Epoch)):\n",
    "            self.textEdit.append('%d \\t %.2f \\t %.4f \\t %.4f \\t %.4f \\t %.4f \\t                       %.4f'%(list_Epoch[i], list_Time[i], list_Tr_loss[i], list_Tr_acc[i], list_Tr_stASR[i], list_Tr_noiseASR[i], list_Tr_GUAP_ASR[i]))\n",
    "        self.textEdit.append('Best train ASR:')\n",
    "        self.textEdit.append('Time \\t  Test_loss \\t Ori_Acc \\t Perb_Acc \\t Te_stASR \\t Te_noiseASR\\t Te_GUAP_ASR ')\n",
    "        self.textEdit.append(str(msg))\n",
    "        self.textEdit.append('---------------------------------------------------------------')\n",
    "\n",
    "        \n",
    "        \n",
    "    def handel_pushButton_back(self):\n",
    "        self.main_app = mywindow()\n",
    "        self.close()\n",
    "        self.main_app.show()\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "class myDeepConcolic(QMainWindow, Ui_DeepConcolic):\n",
    "    def __init__(self):\n",
    "        super(myDeepConcolic, self).__init__()\n",
    "        self.setupUi(self)\n",
    "        self.pushButton_bfc.clicked.connect(self.handel_pushButton_bfc)\n",
    "        self.pushButton_bfdc.clicked.connect(self.handel_pushButton_bfdc)\n",
    "        self.pushButton_back.clicked.connect(self.handel_pushButton_back)\n",
    "        \n",
    "        self.comboBox_dataset.addItems(['fashion_mnist','har','cifar10','mnist'])\n",
    "        self.comboBox_model.addItems(['fashion_mnist_medium.h5', 'fashion_mnist_large.h5'])\n",
    "        self.comboBox_criterion.addItems(['nc','ssc','ssclp','bfc','bfdc'])\n",
    "        self.comboBox_norm.addItems(['l0','linf'])\n",
    "        self.comboBox_save_all.addItems(['True', 'False'])\n",
    "        self.comboBox_filters.addItems(['LOF', 'None'])\n",
    "        self.comboBox_layers.addItems(['activation_1', 'activation_2', 'dense', 'convolutional', 'max-pooling', 'ReLU', 'activation', 'dropout', 'flatten', 'reshape'])\n",
    "        \n",
    "        self.lineEdit_outputs.setText('outs/fm-medium/')\n",
    "        self.lineEdit_init.setText('100')\n",
    "        self.lineEdit_max_i.setText('10')\n",
    "        self.lineEdit_seed.setText('1')\n",
    "        self.lineEdit__extra_tests.setText('')\n",
    "        self.lineEdit_norm_factor.setText('0.25')\n",
    "        self.lineEdit_lb_hard.setText('%.4f'%(float(1/255)))\n",
    "        self.lineEdit_lb_noise.setText('0.1')\n",
    "        self.lineEdit_ratio.setText('0.01')\n",
    "        self.lineEdit_dbnc_spec.setText('{}')\n",
    "        self.lineEdit_top_classes.setText('1')\n",
    "        self.lineEdit_feature_index.setText('-1')\n",
    "        #self.lineEdit_dbnc_or_bn.setText('None')\n",
    "        \n",
    "        \n",
    "        \n",
    "    def handel_pushButton_bfc(self):\n",
    "        \n",
    "        outputs = self.lineEdit_outputs.text()\n",
    "        criterion = self.comboBox_criterion.currentText()\n",
    "        norm = self.comboBox_norm.currentText()\n",
    "        save_all_tests = self.comboBox_save_all.currentText()\n",
    "        \n",
    "        setup_only = 'store'\n",
    "        \n",
    "        dataset = self.comboBox_dataset.currentText()\n",
    "        seed = self.lineEdit_seed.text()\n",
    "        extra_testset_dirs = self.lineEdit__extra_tests.text()\n",
    "        str_filters = self.comboBox_filters.currentText()\n",
    "        model = 'saved_models/'+self.comboBox_model.currentText()\n",
    "        lb_hard = self.lineEdit_lb_hard.text()\n",
    "        lb_noise = self.lineEdit_lb_noise.text()\n",
    "        mcdc_cond_ratio = self.lineEdit_ratio.text()\n",
    "        top_classes = self.lineEdit_top_classes.text()\n",
    "        layers = self.comboBox_layers.currentText()\n",
    "        feature_index = self.lineEdit_feature_index.text()\n",
    "        init_tests = self.lineEdit_init.text()\n",
    "        max_iterations = self.lineEdit_max_i.text()\n",
    "        dbnc_spec = self.lineEdit_dbnc_spec.text()\n",
    "        dbnc_abstr = self.lineEdit_dbnc_or_bn.text()\n",
    "        norm_factor = self.lineEdit_norm_factor.text()\n",
    "\n",
    "        list_filters = [str_filters]\n",
    "        \n",
    "#         # Initialize with random seed first, if given:\n",
    "#         try: rng_seed (int(seed))\n",
    "#         except ValueError as e:\n",
    "#             sys.exit (\"Invalid argument given for `--rng-seed': {}\".format (e))\n",
    "      \n",
    "#         inp_ub = 1\n",
    "#         lower_bound_metric_hard = None\n",
    "#         lower_bound_metric_noise = None\n",
    "      \n",
    "#         dd = dataset_dict (dataset)\n",
    "#         train_data, test_data, kind, save_input, postproc_inputs, ib = \\\n",
    "#           dd['train_data'], dd['test_data'], dd['kind'], \\\n",
    "#           dd['save_input'], dd['postproc_inputs'], dd['input_bounds']\n",
    "#         amplify_diffs = kind in datasets.image_kinds\n",
    "#         if kind in datasets.image_kinds: # assume 256 res.\n",
    "#             lower_bound_metric_hard = 1 / 255\n",
    "#         input_bounds = (UniformBounds (*ib) if isinstance (ib, tuple) and len (ib) == 2 else \\\n",
    "#                         StatBasedInputBounds (hard_bounds = UniformBounds (-1.0, 1.0)) \\\n",
    "#                         if ib == 'normalized' else StatBasedInputBounds ())\n",
    "#         del dd\n",
    "      \n",
    "#         if extra_testset_dirs is not None:\n",
    "#             for d in extra_testset_dirs:\n",
    "#                 np1 (f'Loading extra image testset from `{str(d)}\\'... ')\n",
    "#                 x, y, _, _, _ = datasets.images_from_dir (str (d))\n",
    "#                 x_test = np.concatenate ((x_test, x))\n",
    "#                 y_test = np.concatenate ((y_test, y))\n",
    "#                 print ('done')\n",
    "      \n",
    "#         input_filters = []\n",
    "#         for f in list_filters:\n",
    "#             input_filters += xlist (filters.by_name (f))\n",
    "            \n",
    "#         dnn = load_model (model)\n",
    "#         dnn.summary ()\n",
    "        \n",
    "#         if model == 'vgg16':\n",
    "#             # XXX: that should be about loading some metadata with, e.g. args.dataset == 'ImageNet'.\n",
    "#             inp_ub = 255                # XXX: not really used (yet/anymore) I think\n",
    "#             input_bounds = UniformBounds (0.0, 255.0)\n",
    "#             postproc_inputs = fix_image_channels_ (up = None, down = None)\n",
    "#             save_input = save_an_image_ (channel_upscale = 1.)\n",
    "#             lower_bound_metric_hard = 1 / 255\n",
    "      \n",
    "#         if lb_hard is not None:\n",
    "#             lower_bound_metric_hard = float (lb_hard)\n",
    "#             assert 0.0 < lower_bound_metric_hard <= 1.0\n",
    "#         lower_bound_metric_hard = some (lower_bound_metric_hard, 1/100)\n",
    "      \n",
    "#         if lb_noise is not None:\n",
    "#             lower_bound_metric_noise = float (lb_noise)\n",
    "#             assert 0.0 <= lower_bound_metric_noise <= 1.0\n",
    "#         lower_bound_metric_noise = some (lower_bound_metric_noise, 1/10)\n",
    "      \n",
    "#         input_bounds = some (input_bounds, UniformBounds (0.0, 1.0))\n",
    "#         postproc_inputs = some (postproc_inputs, id)\n",
    "      \n",
    "#         test_object = test_objectt (dnn, train_data, test_data)\n",
    "#         test_object.cond_ratio = mcdc_cond_ratio\n",
    "#         test_object.postproc_inputs = postproc_inputs\n",
    "#         # NB: only used in run_ssc.run_svc (which is probably broken) >>\n",
    "#         test_object.top_classes = int (top_classes)\n",
    "#         test_object.inp_ub = inp_ub\n",
    "#         # <<<\n",
    "        \n",
    "#         if layers is not None:\n",
    "#             try:\n",
    "#                 test_object.set_layer_indices (int (l) if l.isdigit () else l\n",
    "#                                            for l in layers)\n",
    "#             except ValueError as e:\n",
    "#                 sys.exit (e)\n",
    "\n",
    "#         if feature_index!='-1':\n",
    "#             test_object.feature_indices = [ int(feature_index) ]\n",
    "#             print ('feature index specified:', test_object.feature_indices)\n",
    "      \n",
    "#         init_tests = int (init_tests) if init_tests is not None else None\n",
    "#         max_iterations = int (max_iterations)\n",
    "      \n",
    "#         # DBNC-specific parameters:\n",
    "#         try:\n",
    "#             if dbnc_spec != \"{}\" and os.path.exists(dbnc_spec):\n",
    "#                 with open(dbnc_spec, 'r') as f:\n",
    "#                     dbnc_spec = yaml.safe_load (f)\n",
    "#             else:\n",
    "#                 dbnc_spec = yaml.safe_load (dbnc_spec)\n",
    "#             if len (dbnc_spec) > 0:\n",
    "#                 print (\"DBNC Spec:\\n\", yaml.dump (dbnc_spec), sep='')\n",
    "#         except yaml.YAMLError as exc:\n",
    "#             sys.exit(exc)\n",
    "      \n",
    "#         if dbnc_abstr is not None and os.path.exists(dbnc_abstr):\n",
    "#             dbnc_spec = dict () if dbnc_spec is None else dbnc_spec\n",
    "#             dbnc_spec['bn_abstr'] = dbnc_abstr\n",
    "#         elif dbnc_abstr is not None:\n",
    "#             sys.exit (f'BN abstraction file `{dbnc_abstr}\\' missing')\n",
    "      \n",
    "#         self.deepconcolic (criterion, norm, test_object,\n",
    "#                       report_args = { 'outdir': OutputDir (outputs, log = True),\n",
    "#                                       'save_new_tests': save_all_tests,\n",
    "#                                       'save_input_func': save_input,\n",
    "#                                       'amplify_diffs': amplify_diffs },\n",
    "#                       norm_args = { 'factor': norm_factor,\n",
    "#                                     'LB_hard': lower_bound_metric_hard,\n",
    "#                                     'LB_noise': lower_bound_metric_noise },\n",
    "#                       engine_args = { 'custom_filters': input_filters },\n",
    "#                       dbnc_spec = dbnc_spec,\n",
    "#                       input_bounds = input_bounds,\n",
    "#                       postproc_inputs = postproc_inputs,\n",
    "#                       run_engine = not setup_only,\n",
    "#                       initial_test_cases = init_tests,\n",
    "#                       max_iterations = max_iterations)\n",
    "\n",
    "#     def deepconcolic(criterion, norm, test_object, report_args,\n",
    "#                  engine_args = {},\n",
    "#                  norm_args = {},\n",
    "#                  dbnc_spec = {},\n",
    "#                  input_bounds = None,\n",
    "#                  postproc_inputs = id,\n",
    "#                  run_engine = True,\n",
    "#                  **engine_run_args):\n",
    "#         test_object.check_layer_indices (criterion)\n",
    "#         engine = None\n",
    "#         if criterion=='nc':                   ## neuron cover\n",
    "#             from nc import setup as nc_setup\n",
    "#             if norm=='linf':\n",
    "#                 from pulp_norms import LInfPulp\n",
    "#                 from nc_pulp import NcPulpAnalyzer\n",
    "#                 engine = nc_setup (test_object = test_object,\n",
    "#                                engine_args = engine_args,\n",
    "#                                setup_analyzer = NcPulpAnalyzer,\n",
    "#                                input_metric = LInfPulp (**norm_args),\n",
    "#                                input_bounds = input_bounds,\n",
    "#                                postproc_inputs = postproc_inputs)\n",
    "#             elif norm=='l0':\n",
    "#                 from nc_l0 import NcL0Analyzer\n",
    "#                 l0_args = copy.copy (norm_args)\n",
    "#                 del l0_args['LB_noise']\n",
    "#                 engine = nc_setup (test_object = test_object,\n",
    "#                                engine_args = engine_args,\n",
    "#                                setup_analyzer = NcL0Analyzer,\n",
    "#                                input_shape = test_object.raw_data.data[0].shape,\n",
    "#                                eval_batch = eval_batch_func (test_object.dnn),\n",
    "#                                postproc_inputs = postproc_inputs,\n",
    "#                                l0_args = l0_args)\n",
    "#             else:\n",
    "#                 print('\\n not supported norm... {0}\\n'.format(norm))\n",
    "#                 sys.exit(0)\n",
    "#         elif criterion=='bfc':                ## feature cover\n",
    "#             from dbnc import setup as dbnc_setup\n",
    "#             from dbnc import BFcCriterion\n",
    "#             if norm == 'linf':\n",
    "#                 from pulp_norms import LInfPulp\n",
    "#                 from dbnc_pulp import BFcPulpAnalyzerWithLinearExtrapolation as Analyzer\n",
    "#                 engine = dbnc_setup (**dbnc_spec,\n",
    "#                                  test_object = test_object,\n",
    "#                                  engine_args = engine_args,\n",
    "#                                  setup_criterion = BFcCriterion,\n",
    "#                                  setup_analyzer = Analyzer,\n",
    "#                                  input_metric = LInfPulp (**norm_args),\n",
    "#                                  input_bounds = input_bounds,\n",
    "#                                  postproc_inputs = postproc_inputs,\n",
    "#                                  enable_linear_extrapolation = False,\n",
    "#                                  fix_untargetted_components = True,\n",
    "#                                  outdir = report_args['outdir'])\n",
    "#             elif norm=='l0':\n",
    "#                 from dbnc_l0 import BFcL0Analyzer\n",
    "#                 l0_args = copy.copy (norm_args)\n",
    "#                 del l0_args['LB_noise']\n",
    "#                 engine = dbnc_setup (**dbnc_spec,\n",
    "#                                  test_object = test_object,\n",
    "#                                  engine_args = engine_args,\n",
    "#                                  setup_criterion = BFcCriterion,\n",
    "#                                  setup_analyzer = BFcL0Analyzer,\n",
    "#                                  input_shape = test_object.raw_data.data[0].shape,\n",
    "#                                  eval_batch = eval_batch_func (test_object.dnn),\n",
    "#                                  outdir = report_args['outdir'],\n",
    "#                                  postproc_inputs = postproc_inputs,\n",
    "#                                  l0_args = l0_args)\n",
    "#             else:\n",
    "#                 sys.exit ('\\n not supported norm... {0}\\n'.format(norm))\n",
    "#         elif criterion=='bfdc':               ## feature-dependence cover\n",
    "#             from dbnc import setup as dbnc_setup\n",
    "#             from dbnc import BFDcCriterion\n",
    "#             if norm == 'linf':\n",
    "#                 from pulp_norms import LInfPulp\n",
    "#                 from dbnc_pulp import BFDcPulpAnalyzerWithLinearExtrapolation as Analyzer\n",
    "#                 engine = dbnc_setup (**dbnc_spec,\n",
    "#                                  test_object = test_object,\n",
    "#                                  engine_args = engine_args,\n",
    "#                                  setup_criterion = BFDcCriterion,\n",
    "#                                  setup_analyzer = Analyzer,\n",
    "#                                  input_metric = LInfPulp (**norm_args),\n",
    "#                                  input_bounds = input_bounds,\n",
    "#                                  postproc_inputs = postproc_inputs,\n",
    "#                                  enable_linear_extrapolation = False,\n",
    "#                                  fix_untargetted_components = True,\n",
    "#                                  outdir = report_args['outdir'])\n",
    "#             else:\n",
    "#                 sys.exit ('\\n not supported norm... {0}\\n'.format(norm))\n",
    "#         elif criterion=='dbnc_stats':\n",
    "#             import dbnc_stats\n",
    "#             dbnc_stats.run (test_object, report_args['outdir'],\n",
    "#                           input_bounds = input_bounds)\n",
    "#         elif criterion=='ssc':\n",
    "#             from ssc import SScGANBasedAnalyzer, setup as ssc_setup\n",
    "#             linf_args = copy.copy (norm_args)\n",
    "#             del linf_args['LB_hard']\n",
    "#             del linf_args['LB_noise']\n",
    "#             engine = ssc_setup (test_object = test_object,\n",
    "#                               engine_args = engine_args,\n",
    "#                               setup_analyzer = SScGANBasedAnalyzer,\n",
    "#                               ref_data = test_object.raw_data,\n",
    "#                               input_bounds = input_bounds,\n",
    "#                               postproc_inputs = postproc_inputs,\n",
    "#                               linf_args = linf_args)\n",
    "#         elif criterion=='ssclp':\n",
    "#             from pulp_norms import LInfPulp\n",
    "#             from mcdc_pulp import SScPulpAnalyzer\n",
    "#             from ssc import setup as ssc_setup\n",
    "#             engine = ssc_setup (test_object = test_object,\n",
    "#                               engine_args = engine_args,\n",
    "#                               setup_analyzer = SScPulpAnalyzer,\n",
    "#                               input_metric = LInfPulp (**norm_args),\n",
    "#                               input_bounds = input_bounds,\n",
    "#                               postproc_inputs = postproc_inputs,\n",
    "#                               concolic = True)\n",
    "#         elif criterion=='svc':\n",
    "#             from run_ssc import run_svc\n",
    "#             print('\\n== Starting DeepConcolic tests for {0} =='.format (test_object))\n",
    "#             run_svc(test_object, report_args['outdir'])\n",
    "#         else:\n",
    "#             print('\\n not supported coverage criterion... {0}\\n'.format(criterion))\n",
    "#             sys.exit(0)\n",
    "      \n",
    "#         if engine != None and run_engine:\n",
    "#             return engine, engine.run (**report_args, **engine_run_args)\n",
    "#         return engine\n",
    "    \n",
    "    def handel_pushButton_bfdc(self):\n",
    "        pass\n",
    "        \n",
    "        \n",
    "    def handel_pushButton_back(self):\n",
    "        self.main_app = mywindow()\n",
    "        self.close()\n",
    "        self.main_app.show()\n",
    "                \n",
    "        \n",
    "        \n",
    "\n",
    "if __name__==\"__main__\":\n",
    "    app=QtWidgets.QApplication(sys.argv)\n",
    "    myshow=mywindow()\n",
    "    myshow.show()\n",
    "    sys.exit(app.exec_())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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