testing_hyperparameters.ipynb

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   "cell_type": "code",
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   "source": [
    "# -*- coding: utf-8 -*-\n",
    "from utils.agent import *\n",
    "from utils.dataset import *\n",
    "import numpy as np\n",
    "import torch\n",
    "\n",
    "from IPython.display import clear_output\n",
    "\n",
    "import sys\n",
    "import traceback\n",
    "import sys\n",
    "import os\n",
    "\n",
    "\n",
    "#!pip3 install torch==1.5.1 torchvision==0.6.1 -f https://download.pytorch.org/whl/cu92/torch_stable.html\n",
    "try:\n",
    "    if 'google.colab' in str(get_ipython()):\n",
    "        from google.colab import drive\n",
    "        drive.mount('/content/gdrive')\n",
    "        LOAD = True\n",
    "        SAVE_MODEL_PATH = '/content/gdrive/MyDrive/models/' + 'q_network'\n",
    "    else:\n",
    "        LOAD = False\n",
    "        SAVE_MODEL_PATH = \"./models/q_network\"\n",
    "except NameError:\n",
    "        LOAD = False\n",
    "        SAVE_MODEL_PATH = \"./models/q_network\"\n",
    "batch_size = 32\n",
    "PATH=\"./datasets/\"\n",
    "\n",
    "batch_size = 32\n",
    "PATH=\"./datasets/\"\n",
    "\n",
    "\n",
    "\n",
    "train_loader2012, val_loader2012 = read_voc_dataset(download=False, year='2012')\n",
    "train_loader2007, val_loader2007 = read_voc_dataset(download=False, year='2007')\n",
    "\n",
    "agents_per_class = {}\n",
    "datasets_per_class = sort_class_extract([train_loader2007, train_loader2012])\n",
    "datasets_eval_per_class = sort_class_extract([val_loader2007, val_loader2012])\n",
    "\n",
    "classes = ['dog']\n",
    "\n",
    "\n"
   ]
  },
  {
   "source": [
    "### Entraînement et Test de plusieurs combinaisons d'hyperparamètres."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
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      "              0.1        0.2        0.3        0.4        0.5\n",
      "ap      79.516940  73.085992  65.272841  52.244887  37.717123\n",
      "recall  88.422247  84.562997  80.022701  71.736663  60.953462\n",
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      "              0.1        0.2        0.3        0.4        0.5\n",
      "ap      82.153010  72.832760  64.217253  53.977221  37.613230\n",
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      "              0.1        0.2        0.3        0.4        0.5\n",
      "ap      80.540294  70.981601  59.165701  50.719392  38.437816\n",
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      "              0.1        0.2        0.3        0.4        0.5\n",
      "ap      83.520766  74.254243  63.121742  52.076823  39.175261\n",
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      "              0.1        0.2        0.3        0.4       0.5\n",
      "ap      84.199175  76.575690  67.115740  54.436491  41.01447\n",
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      "              0.1        0.2        0.3        0.4        0.5\n",
      "ap      83.214184  74.902529  67.122459  55.474921  39.974571\n",
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      "              0.1        0.2        0.3        0.4        0.5\n",
      "ap      80.806738  73.254957  65.752813  54.526064  40.133882\n",
      "recall  89.557321  85.017026  80.363224  72.871737  62.429058\n",
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      "              0.1        0.2        0.3        0.4        0.5\n",
      "ap      83.132745  73.627677  65.733761  54.780067  42.523842\n",
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    {
     "output_type": "error",
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-e9f72f8b6af9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     28\u001b[0m                 \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Percentage =\"\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m   \u001b[1;33m(\u001b[0m\u001b[0mcount\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0malphas\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnus\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mthresholds\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m)\u001b[0m  \u001b[1;33m)\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     29\u001b[0m                 \u001b[0magents_per_class\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mAgent\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0malpha\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnu\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnu\u001b[0m \u001b[1;33m,\u001b[0m\u001b[0mnum_episodes\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m15\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mthreshold\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mthreshold\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mload\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 30\u001b[1;33m                 \u001b[0magents_per_class\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdatasets_per_class\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     31\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     32\u001b[0m                 \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0magents_per_class\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdatasets_eval_per_class\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Ray\\Documents\\Active-Object-Localization-Deep-Reinforcement-Learning\\utils\\agent.py\u001b[0m in \u001b[0;36mtrain\u001b[1;34m(self, train_loader)\u001b[0m\n\u001b[0;32m    513\u001b[0m                     \u001b[0mimage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnew_image\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    514\u001b[0m                     \u001b[1;31m# Perform one step of the optimization (on the target network)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 515\u001b[1;33m                     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptimize_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    516\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    517\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Ray\\Documents\\Active-Object-Localization-Deep-Reinforcement-Learning\\utils\\agent.py\u001b[0m in \u001b[0;36moptimize_model\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    268\u001b[0m         \u001b[1;31m# Compute Q(s_t, a) - the model computes Q(s_t), then we select the\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    269\u001b[0m         \u001b[1;31m# columns of actions taken\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 270\u001b[1;33m         \u001b[0mstate_action_values\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpolicy_net\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstate_batch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgather\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maction_batch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    271\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    272\u001b[0m         \u001b[1;31m# Compute V(s_{t+1}) for all next states.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m    725\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    726\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 727\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    728\u001b[0m         for hook in itertools.chain(\n\u001b[0;32m    729\u001b[0m                 \u001b[0m_global_forward_hooks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Ray\\Documents\\Active-Object-Localization-Deep-Reinforcement-Learning\\utils\\models.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m     32\u001b[0m     \u001b[1;31m# during optimization. Returns tensor([[left0exp,right0exp]...]).\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     33\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 34\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclassifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m    725\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    726\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 727\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    728\u001b[0m         for hook in itertools.chain(\n\u001b[0;32m    729\u001b[0m                 \u001b[0m_global_forward_hooks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\torch\\nn\\modules\\container.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m    115\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    116\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 117\u001b[1;33m             \u001b[0minput\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    118\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    119\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\torch\\nn\\modules\\module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[1;34m(self, *input, **kwargs)\u001b[0m\n\u001b[0;32m    725\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    726\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 727\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    728\u001b[0m         for hook in itertools.chain(\n\u001b[0;32m    729\u001b[0m                 \u001b[0m_global_forward_hooks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\torch\\nn\\modules\\dropout.py\u001b[0m in \u001b[0;36mforward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m     56\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     57\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 58\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdropout\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtraining\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minplace\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     59\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     60\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python38\\site-packages\\torch\\nn\\functional.py\u001b[0m in \u001b[0;36mdropout\u001b[1;34m(input, p, training, inplace)\u001b[0m\n\u001b[0;32m    981\u001b[0m     return (_VF.dropout_(input, p, training)\n\u001b[0;32m    982\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0minplace\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 983\u001b[1;33m             else _VF.dropout(input, p, training))\n\u001b[0m\u001b[0;32m    984\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    985\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Hyper-parameters à tester :\n",
    "- ALPHA\n",
    "- THRESHOLD\n",
    "- NU \n",
    "def __init__(self, classe, alpha=0.2, nu=3.0, threshold=0.5, num_episodes=15, load=False )\n",
    "\"\"\"\n",
    "import pickle\n",
    "\n",
    "alphas = [0.1, 0.5, 0.8]\n",
    "nus = np.arange[2, 5 ,10]\n",
    "thresholds = [0.1, 0.4, 0.9]\n",
    "results = {}\n",
    "for alpha in alphas:\n",
    "    results[str(alpha)] = {}\n",
    "    for nu in nus:\n",
    "        results[str(alpha)][str(nu)] = {}\n",
    "        for threshold in thresholds:\n",
    "            results[str(alpha)][str(nu)][str(threshold)] = []\n",
    "\n",
    "\n",
    "count = 0\n",
    "for alpha in alphas:\n",
    "    for nu in nus:\n",
    "        for threshold in thresholds:\n",
    "            for key in classes:\n",
    "                count += 1\n",
    "                print(\"Percentage =\"+str(   (count)/(len(alphas)+len(nus) + len(thresholds) )  ) )\n",
    "                agents_per_class[key] = Agent(key, alpha=alpha, nu=nu ,num_episodes=15, threshold=threshold, load=False)\n",
    "                agents_per_class[key].train(datasets_per_class[key])\n",
    "                \n",
    "                result = agents_per_class[key].evaluate(datasets_eval_per_class[key])\n",
    "                del agents_per_class[key]\n",
    "                torch.cuda.empty_cache()\n",
    "                results[str(alpha)][str(nu)][str(threshold)] = result\n",
    "                with open('hyperparameters_results.pickle', 'wb') as handle:\n",
    "                    pickle.dump(results, handle, protocol=pickle.HIGHEST_PROTOCOL)\n",
    "                \n",
    "\n",
    "print(\"Results : \\n\"+str(results))\n"
   ]
  }
 ]
}