training.ipynb

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   "source": [
    "# MIBCI-QCNNs: Training\n",
    "\n",
    "This notebook contains all the steps taken to develop an [EEGNet](https://arxiv.org/abs/1611.08024)-based Brain Computer Interface using the [Physionet Motor Movement/Imagery dataset](https://physionet.org/content/eegmmidb/1.0.0/). Firstly, the data is loaded (or downloaded if is not available) and preprocessed according the selected data-reduction settings. Then a 5-fold global model is trained over the preprocessed data.\n",
    "\n",
    "## Preprocess the data\n",
    " \n",
    "The data we are using here is the [Physionet Motor Movement/Imagery dataset](https://physionet.org/content/eegmmidb/1.0.0/). It is composed of a 64 Electroencephalograph recordings of 109 subjects that perform up to 4 motor imagery tasks. There are also recording of motor movement that are not interesting under the motor imagery paradigm. To get the data of the dataset we have included the `get_data` function, which is adapted from [this version](https://github.com/MHersche/eegnet-based-embedded-bci/blob/master/get_data.py). If the files haven't been downloaded yet it will download them in the `Dataset`folder. The downsampling factor is set here to be `ds=2` and the other parameters are the default (`T=3`, `Nchans=64` and `Nclasses=4`).\n",
    "\n",
    " > Be aware that the download can take a while"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from utils.get_data import get_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Data from runs: [1, 4, 6, 8, 10, 12, 14]\n",
      "Processing subject 001 1/105...Ok\n",
      "Processing subject 002 2/105...Ok\n",
      "Processing subject 003 3/105...Ok\n",
      "Processing subject 004 4/105...Ok\n",
      "Processing subject 005 5/105...Ok\n",
      "Processing subject 006 6/105...Ok\n",
      "Processing subject 007 7/105...Ok\n",
      "Processing subject 008 8/105...Ok\n",
      "Processing subject 009 9/105...Ok\n",
      "Processing subject 010 10/105...Ok\n",
      "Processing subject 011 11/105...Ok\n",
      "Processing subject 012 12/105...Ok\n",
      "Processing subject 013 13/105...Ok\n",
      "Processing subject 014 14/105...Ok\n",
      "Processing subject 015 15/105...Ok\n",
      "Processing subject 016 16/105...Ok\n",
      "Processing subject 017 17/105...Ok\n",
      "Processing subject 018 18/105...Ok\n",
      "Processing subject 019 19/105...Ok\n",
      "Processing subject 020 20/105...Ok\n",
      "Processing subject 021 21/105...Ok\n",
      "Processing subject 022 22/105...Ok\n",
      "Processing subject 023 23/105...Ok\n",
      "Processing subject 024 24/105...Ok\n",
      "Processing subject 025 25/105...Ok\n",
      "Processing subject 026 26/105...Ok\n",
      "Processing subject 027 27/105...Ok\n",
      "Processing subject 028 28/105...Ok\n",
      "Processing subject 029 29/105...Ok\n",
      "Processing subject 030 30/105...Ok\n",
      "Processing subject 031 31/105...Ok\n",
      "Processing subject 032 32/105...Ok\n",
      "Processing subject 033 33/105...Ok\n",
      "Processing subject 034 34/105...Ok\n",
      "Processing subject 035 35/105...Ok\n",
      "Processing subject 036 36/105...Ok\n",
      "Processing subject 037 37/105...Ok\n",
      "Processing subject 038 38/105...Ok\n",
      "Processing subject 039 39/105...Ok\n",
      "Processing subject 040 40/105...Ok\n",
      "Processing subject 041 41/105...Ok\n",
      "Processing subject 042 42/105...Ok\n",
      "Processing subject 043 43/105...Ok\n",
      "Processing subject 044 44/105...Ok\n",
      "Processing subject 045 45/105...Ok\n",
      "Processing subject 046 46/105...Ok\n",
      "Processing subject 047 47/105...Ok\n",
      "Processing subject 048 48/105...Ok\n",
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      "Processing subject 050 50/105...Ok\n",
      "Processing subject 051 51/105...Ok\n",
      "Processing subject 052 52/105...Ok\n",
      "Processing subject 053 53/105...Ok\n",
      "Processing subject 054 54/105...Ok\n",
      "Processing subject 055 55/105...Ok\n",
      "Processing subject 056 56/105...Ok\n",
      "Processing subject 057 57/105...Ok\n",
      "Processing subject 058 58/105...Ok\n",
      "Processing subject 059 59/105...Ok\n",
      "Processing subject 060 60/105...Ok\n",
      "Processing subject 061 61/105...Ok\n",
      "Processing subject 062 62/105...Ok\n",
      "Processing subject 063 63/105...Ok\n",
      "Processing subject 064 64/105...Ok\n",
      "Processing subject 065 65/105...Ok\n",
      "Processing subject 066 66/105...Ok\n",
      "Processing subject 067 67/105...Ok\n",
      "Processing subject 068 68/105...Ok\n",
      "Processing subject 069 69/105...Ok\n",
      "Processing subject 070 70/105...Ok\n",
      "Processing subject 071 71/105...Ok\n",
      "Processing subject 072 72/105...Ok\n",
      "Processing subject 073 73/105...Ok\n",
      "Processing subject 074 74/105...Ok\n",
      "Processing subject 075 75/105...Ok\n",
      "Processing subject 076 76/105...Ok\n",
      "Processing subject 077 77/105...Ok\n",
      "Processing subject 078 78/105...Ok\n",
      "Processing subject 079 79/105...Ok\n",
      "Processing subject 080 80/105...Ok\n",
      "Processing subject 081 81/105...Ok\n",
      "Processing subject 082 82/105...Ok\n",
      "Processing subject 083 83/105...Ok\n",
      "Processing subject 084 84/105...Ok\n",
      "Processing subject 085 85/105...Ok\n",
      "Processing subject 086 86/105...Ok\n",
      "Processing subject 087 87/105...Ok\n",
      "Processing subject 089 88/105...Ok\n",
      "Processing subject 090 89/105...Ok\n",
      "Processing subject 091 90/105...Ok\n",
      "Processing subject 093 91/105...Ok\n",
      "Processing subject 094 92/105...Ok\n",
      "Processing subject 095 93/105...Ok\n",
      "Processing subject 096 94/105...Ok\n",
      "Processing subject 097 95/105...Ok\n",
      "Processing subject 098 96/105...Ok\n",
      "Processing subject 099 97/105...Ok\n",
      "Processing subject 101 98/105...Ok\n",
      "Processing subject 102 99/105...Ok\n",
      "Processing subject 103 100/105...Ok\n",
      "Processing subject 105 101/105...Ok\n",
      "Processing subject 106 102/105...Ok\n",
      "Processing subject 107 103/105...Ok\n",
      "Processing subject 108 104/105...Ok\n",
      "Processing subject 109 105/105...Ok\n",
      "(17640, 64, 240) (17640,) (17640,)\n"
     ]
    }
   ],
   "source": [
    "X, y, subs = get_data('./Dataset/', ds=2)\n",
    "print(X.shape, y.shape, subs.shape)"
   ]
  },
  {
   "source": [
    "Once the data is loaded and preprocessed, it must be saved in a folder using the names `samples.npy`, `labels.npy` and `subs.npy`.\n"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "processedPath = 'ds2/'\n",
    "\n",
    "try:\n",
    "    os.mkdir(processedPath)\n",
    "except FileExistsError:\n",
    "    pass\n",
    "\n",
    "np.save(processedPath+'samples.npy', X)\n",
    "np.save(processedPath+'labels.npy', y)\n",
    "np.save(processedPath+'subs.npy', subs)"
   ]
  },
  {
   "source": [
    "## Global model training\n",
    "\n",
    "The validation method used here is 5-fold cross-validation across subjects. This means that for each global 'model' 5 different models will be trained, using an iterating fifth of the subjects as the validation set.\n",
    "\n",
    "The model architecture is the displayed in the following picture, which is further detailed in the table bellow. In short it is based on the [EEGNet](https://arxiv.org/abs/1611.08024), a Convolutional Neural Network that showed its versatility when EEG recordings must be processed. The only changes are:\n",
    "\n",
    "- The substitution of the ELU activation function for the LeakyReLU which is cheaper to implement in hardware.\n",
    "\n",
    "- The remove of the Dropout and BatchNorm layers.\n",
    "\n",
    "![EEGNet-based model architecture (If you are seeing this in GitHub there is a bug that doesn't display the pictures)](img/EEGNet.svg)\n",
    "\n",
    "\n",
    "|     Layer          |     #   filters    |     Padding    |     Kernel           |     #   parms          |     Activation       |     Output   shape           |\n",
    "|--------------------|--------------------|----------------|----------------------|------------------------|----------------------|------------------------------|\n",
    "|     Input          |     -              |     -          |     -                |     -                  |     -                |     (Nchan,   fs·T/ds, 1)    |\n",
    "|     Conv2D         |     4              |     same       |     (1,   fs/2ds)    |     2fs/ds             |     LReLU   (0.6)    |     (Nchan,   fs·T/ds, 4)    |\n",
    "|     DepthConv2D    |     2·4            |     valid      |     (Nchan,   1)     |     8Nchan             |     LReLU   (0.5)    |     (1, fs·T/ds,   8)        |\n",
    "|     AvgPool2D      |     -              |     valid      |     (1,   6/ds)      |     -                  |     -                |     (1, fs·T/6,   8)         |\n",
    "|     SepConv2D      |     8              |     same       |     (1,   16)        |     192                |     LReLU   (0.4)    |     (1, fs·T/6,   8)         |\n",
    "|     AvgPool2D      |     -              |     valid      |     (1,   8)         |     -                  |     -                |     (1, fs·T/48,   8)        |\n",
    "|     Flatten        |     -              |     -          |     -                |     -                  |     -                |     fs·T/6                   |\n",
    "|     Dense          |     Nclasses       |     -          |     -                |     Nclasses fs·T/6    |     Softmax          |     Nclasses                 |\n",
    "\n",
    "We have embebed the necesary code to normalized the data, load the correct validation subjects, create the architecture and train the five models with the fine-tunned hyperparemeters in the [utils/train_tools.py](utils/train_tools.py) file.\n",
    "\n",
    "> The training process is relatively long, but it depends on your hardware. If you are using a GPU it can be shortened. In our case with a Quadro P2000 it takes around 45 minutes."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from utils.train_tools import train_global"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": [
     "outputPrepend"
    ]
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "- loss: 0.6664 - accuracy: 0.7457 - val_loss: 1.2941 - val_accuracy: 0.6224\n",
      "Epoch 71/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6589 - accuracy: 0.7486 - val_loss: 1.2923 - val_accuracy: 0.6233\n",
      "Epoch 72/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6700 - accuracy: 0.7499 - val_loss: 1.2956 - val_accuracy: 0.6233\n",
      "Epoch 73/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6627 - accuracy: 0.7497 - val_loss: 1.3044 - val_accuracy: 0.6224\n",
      "Epoch 74/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6542 - accuracy: 0.7564 - val_loss: 1.2997 - val_accuracy: 0.6202\n",
      "Epoch 75/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6695 - accuracy: 0.7499 - val_loss: 1.3065 - val_accuracy: 0.6219\n",
      "Epoch 76/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6638 - accuracy: 0.7503 - val_loss: 1.2999 - val_accuracy: 0.6222\n",
      "Epoch 77/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6503 - accuracy: 0.7506 - val_loss: 1.3112 - val_accuracy: 0.6230\n",
      "Epoch 78/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6575 - accuracy: 0.7540 - val_loss: 1.3176 - val_accuracy: 0.6168\n",
      "Epoch 79/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6655 - accuracy: 0.7479 - val_loss: 1.3039 - val_accuracy: 0.6230\n",
      "Epoch 80/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6586 - accuracy: 0.7505 - val_loss: 1.3093 - val_accuracy: 0.6241\n",
      "INFO:tensorflow:Assets written to: Global_T3s_ds2_64chans_4classes-2021-06-24_12-50/fold_3/weights-improvement.tf/assets\n",
      "Epoch 81/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6733 - accuracy: 0.7488 - val_loss: 1.3051 - val_accuracy: 0.6227\n",
      "Epoch 82/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6505 - accuracy: 0.7529 - val_loss: 1.3033 - val_accuracy: 0.6230\n",
      "Epoch 83/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6642 - accuracy: 0.7497 - val_loss: 1.3060 - val_accuracy: 0.6233\n",
      "Epoch 84/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6649 - accuracy: 0.7511 - val_loss: 1.3118 - val_accuracy: 0.6239\n",
      "Epoch 85/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6474 - accuracy: 0.7602 - val_loss: 1.3173 - val_accuracy: 0.6233\n",
      "Epoch 86/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6617 - accuracy: 0.7528 - val_loss: 1.3016 - val_accuracy: 0.6222\n",
      "Epoch 87/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6656 - accuracy: 0.7474 - val_loss: 1.3007 - val_accuracy: 0.6244\n",
      "INFO:tensorflow:Assets written to: Global_T3s_ds2_64chans_4classes-2021-06-24_12-50/fold_3/weights-improvement.tf/assets\n",
      "Epoch 88/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6481 - accuracy: 0.7550 - val_loss: 1.3078 - val_accuracy: 0.6241\n",
      "Epoch 89/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6605 - accuracy: 0.7507 - val_loss: 1.3054 - val_accuracy: 0.6233\n",
      "Epoch 90/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6756 - accuracy: 0.7438 - val_loss: 1.3073 - val_accuracy: 0.6247\n",
      "INFO:tensorflow:Assets written to: Global_T3s_ds2_64chans_4classes-2021-06-24_12-50/fold_3/weights-improvement.tf/assets\n",
      "Epoch 91/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6582 - accuracy: 0.7536 - val_loss: 1.3021 - val_accuracy: 0.6253\n",
      "INFO:tensorflow:Assets written to: Global_T3s_ds2_64chans_4classes-2021-06-24_12-50/fold_3/weights-improvement.tf/assets\n",
      "Epoch 92/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6472 - accuracy: 0.7538 - val_loss: 1.3199 - val_accuracy: 0.6202\n",
      "Epoch 93/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6673 - accuracy: 0.7495 - val_loss: 1.3078 - val_accuracy: 0.6219\n",
      "Epoch 94/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6550 - accuracy: 0.7512 - val_loss: 1.3128 - val_accuracy: 0.6233\n",
      "Epoch 95/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6509 - accuracy: 0.7543 - val_loss: 1.3045 - val_accuracy: 0.6253\n",
      "Epoch 96/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6585 - accuracy: 0.7465 - val_loss: 1.3088 - val_accuracy: 0.6253\n",
      "Epoch 97/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6568 - accuracy: 0.7536 - val_loss: 1.3109 - val_accuracy: 0.6230\n",
      "Epoch 98/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6647 - accuracy: 0.7484 - val_loss: 1.3095 - val_accuracy: 0.6253\n",
      "Epoch 99/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6498 - accuracy: 0.7537 - val_loss: 1.3112 - val_accuracy: 0.6239\n",
      "Epoch 100/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6557 - accuracy: 0.7487 - val_loss: 1.3155 - val_accuracy: 0.6244\n",
      "Epoch 1/100\n",
      "882/882 [==============================] - 11s 8ms/step - loss: 1.2690 - accuracy: 0.4064 - val_loss: 1.0919 - val_accuracy: 0.5686\n",
      "INFO:tensorflow:Assets written to: Global_T3s_ds2_64chans_4classes-2021-06-24_12-50/fold_4/weights-improvement.tf/assets\n",
      "Epoch 2/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 1.0505 - accuracy: 0.5902 - val_loss: 1.1623 - val_accuracy: 0.5726\n",
      "INFO:tensorflow:Assets written to: Global_T3s_ds2_64chans_4classes-2021-06-24_12-50/fold_4/weights-improvement.tf/assets\n",
      "Epoch 3/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 1.0084 - accuracy: 0.6152 - val_loss: 1.2039 - val_accuracy: 0.5870\n",
      "INFO:tensorflow:Assets written to: Global_T3s_ds2_64chans_4classes-2021-06-24_12-50/fold_4/weights-improvement.tf/assets\n",
      "Epoch 4/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.9736 - accuracy: 0.6276 - val_loss: 1.1286 - val_accuracy: 0.5706\n",
      "Epoch 5/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.9537 - accuracy: 0.6317 - val_loss: 1.0813 - val_accuracy: 0.5901\n",
      "INFO:tensorflow:Assets written to: Global_T3s_ds2_64chans_4classes-2021-06-24_12-50/fold_4/weights-improvement.tf/assets\n",
      "Epoch 6/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.9396 - accuracy: 0.6370 - val_loss: 1.1750 - val_accuracy: 0.5975\n",
      "INFO:tensorflow:Assets written to: Global_T3s_ds2_64chans_4classes-2021-06-24_12-50/fold_4/weights-improvement.tf/assets\n",
      "Epoch 7/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.9267 - accuracy: 0.6514 - val_loss: 1.2014 - val_accuracy: 0.5796\n",
      "Epoch 8/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.9307 - accuracy: 0.6436 - val_loss: 1.1925 - val_accuracy: 0.5836\n",
      "Epoch 9/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.9320 - accuracy: 0.6545 - val_loss: 1.1329 - val_accuracy: 0.6040\n",
      "INFO:tensorflow:Assets written to: Global_T3s_ds2_64chans_4classes-2021-06-24_12-50/fold_4/weights-improvement.tf/assets\n",
      "Epoch 10/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.9036 - accuracy: 0.6557 - val_loss: 1.2105 - val_accuracy: 0.5802\n",
      "Epoch 11/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.9177 - accuracy: 0.6508 - val_loss: 1.1920 - val_accuracy: 0.5726\n",
      "Epoch 12/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.9195 - accuracy: 0.6529 - val_loss: 1.1442 - val_accuracy: 0.5607\n",
      "Epoch 13/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.9057 - accuracy: 0.6564 - val_loss: 1.1080 - val_accuracy: 0.5992\n",
      "Epoch 14/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.8986 - accuracy: 0.6568 - val_loss: 1.2309 - val_accuracy: 0.5765\n",
      "Epoch 15/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.8997 - accuracy: 0.6638 - val_loss: 1.2524 - val_accuracy: 0.5414\n",
      "Epoch 16/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.8974 - accuracy: 0.6513 - val_loss: 1.1532 - val_accuracy: 0.5947\n",
      "Epoch 17/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.8840 - accuracy: 0.6657 - val_loss: 1.1736 - val_accuracy: 0.5958\n",
      "Epoch 18/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.8911 - accuracy: 0.6614 - val_loss: 1.1540 - val_accuracy: 0.5830\n",
      "Epoch 19/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.8733 - accuracy: 0.6660 - val_loss: 1.4825 - val_accuracy: 0.5692\n",
      "Epoch 20/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.8595 - accuracy: 0.6667 - val_loss: 1.2232 - val_accuracy: 0.5754\n",
      "Epoch 21/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.7901 - accuracy: 0.7044 - val_loss: 1.2386 - val_accuracy: 0.6012\n",
      "Epoch 22/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.7542 - accuracy: 0.7182 - val_loss: 1.3047 - val_accuracy: 0.5978\n",
      "Epoch 23/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.7459 - accuracy: 0.7208 - val_loss: 1.3114 - val_accuracy: 0.5907\n",
      "Epoch 24/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.7324 - accuracy: 0.7190 - val_loss: 1.2960 - val_accuracy: 0.5901\n",
      "Epoch 25/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.7386 - accuracy: 0.7103 - val_loss: 1.3212 - val_accuracy: 0.5972\n",
      "Epoch 26/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.7284 - accuracy: 0.7233 - val_loss: 1.3304 - val_accuracy: 0.5924\n",
      "Epoch 27/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.7311 - accuracy: 0.7256 - val_loss: 1.4023 - val_accuracy: 0.6018\n",
      "Epoch 28/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.7355 - accuracy: 0.7214 - val_loss: 1.3420 - val_accuracy: 0.5882\n",
      "Epoch 29/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.7259 - accuracy: 0.7245 - val_loss: 1.4373 - val_accuracy: 0.5884\n",
      "Epoch 30/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.7289 - accuracy: 0.7169 - val_loss: 1.4123 - val_accuracy: 0.6015\n",
      "Epoch 31/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.7364 - accuracy: 0.7159 - val_loss: 1.4455 - val_accuracy: 0.5998\n",
      "Epoch 32/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.7129 - accuracy: 0.7271 - val_loss: 1.3915 - val_accuracy: 0.5916\n",
      "Epoch 33/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.7222 - accuracy: 0.7227 - val_loss: 1.4744 - val_accuracy: 0.5893\n",
      "Epoch 34/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.7193 - accuracy: 0.7234 - val_loss: 1.4727 - val_accuracy: 0.5918\n",
      "Epoch 35/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6974 - accuracy: 0.7340 - val_loss: 1.5369 - val_accuracy: 0.5717\n",
      "Epoch 36/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.7240 - accuracy: 0.7255 - val_loss: 1.4903 - val_accuracy: 0.5882\n",
      "Epoch 37/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.7128 - accuracy: 0.7243 - val_loss: 1.5167 - val_accuracy: 0.5904\n",
      "Epoch 38/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6931 - accuracy: 0.7350 - val_loss: 1.5771 - val_accuracy: 0.5768\n",
      "Epoch 39/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.7007 - accuracy: 0.7322 - val_loss: 1.5652 - val_accuracy: 0.5822\n",
      "Epoch 40/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.7087 - accuracy: 0.7216 - val_loss: 1.4983 - val_accuracy: 0.5822\n",
      "Epoch 41/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6926 - accuracy: 0.7340 - val_loss: 1.5138 - val_accuracy: 0.5811\n",
      "Epoch 42/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6854 - accuracy: 0.7404 - val_loss: 1.5440 - val_accuracy: 0.5873\n",
      "Epoch 43/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6889 - accuracy: 0.7332 - val_loss: 1.6384 - val_accuracy: 0.5805\n",
      "Epoch 44/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.7066 - accuracy: 0.7316 - val_loss: 1.5678 - val_accuracy: 0.5842\n",
      "Epoch 45/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6876 - accuracy: 0.7335 - val_loss: 1.4969 - val_accuracy: 0.5876\n",
      "Epoch 46/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6893 - accuracy: 0.7296 - val_loss: 1.5354 - val_accuracy: 0.5805\n",
      "Epoch 47/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6804 - accuracy: 0.7343 - val_loss: 1.5393 - val_accuracy: 0.5921\n",
      "Epoch 48/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6870 - accuracy: 0.7333 - val_loss: 1.5443 - val_accuracy: 0.5867\n",
      "Epoch 49/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6924 - accuracy: 0.7273 - val_loss: 1.5459 - val_accuracy: 0.5927\n",
      "Epoch 50/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6846 - accuracy: 0.7336 - val_loss: 1.6199 - val_accuracy: 0.5745\n",
      "Epoch 51/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6908 - accuracy: 0.7301 - val_loss: 1.6774 - val_accuracy: 0.5652\n",
      "Epoch 52/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6958 - accuracy: 0.7352 - val_loss: 1.5218 - val_accuracy: 0.5921\n",
      "Epoch 53/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6868 - accuracy: 0.7355 - val_loss: 1.5671 - val_accuracy: 0.5873\n",
      "Epoch 54/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6774 - accuracy: 0.7376 - val_loss: 1.5815 - val_accuracy: 0.5842\n",
      "Epoch 55/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6798 - accuracy: 0.7372 - val_loss: 1.6121 - val_accuracy: 0.5805\n",
      "Epoch 56/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6864 - accuracy: 0.7327 - val_loss: 1.6810 - val_accuracy: 0.5867\n",
      "Epoch 57/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6864 - accuracy: 0.7328 - val_loss: 1.5603 - val_accuracy: 0.5865\n",
      "Epoch 58/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6764 - accuracy: 0.7386 - val_loss: 1.6068 - val_accuracy: 0.5839\n",
      "Epoch 59/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6682 - accuracy: 0.7399 - val_loss: 1.5706 - val_accuracy: 0.5808\n",
      "Epoch 60/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6674 - accuracy: 0.7388 - val_loss: 1.6160 - val_accuracy: 0.5737\n",
      "Epoch 61/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6656 - accuracy: 0.7434 - val_loss: 1.6064 - val_accuracy: 0.5788\n",
      "Epoch 62/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6666 - accuracy: 0.7378 - val_loss: 1.6311 - val_accuracy: 0.5813\n",
      "Epoch 63/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6687 - accuracy: 0.7359 - val_loss: 1.6204 - val_accuracy: 0.5796\n",
      "Epoch 64/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6869 - accuracy: 0.7323 - val_loss: 1.6329 - val_accuracy: 0.5839\n",
      "Epoch 65/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6581 - accuracy: 0.7426 - val_loss: 1.6588 - val_accuracy: 0.5813\n",
      "Epoch 66/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6572 - accuracy: 0.7429 - val_loss: 1.6565 - val_accuracy: 0.5830\n",
      "Epoch 67/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6565 - accuracy: 0.7396 - val_loss: 1.6424 - val_accuracy: 0.5811\n",
      "Epoch 68/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6677 - accuracy: 0.7396 - val_loss: 1.6403 - val_accuracy: 0.5813\n",
      "Epoch 69/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6735 - accuracy: 0.7378 - val_loss: 1.6439 - val_accuracy: 0.5828\n",
      "Epoch 70/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6649 - accuracy: 0.7401 - val_loss: 1.6340 - val_accuracy: 0.5802\n",
      "Epoch 71/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6658 - accuracy: 0.7407 - val_loss: 1.6400 - val_accuracy: 0.5799\n",
      "Epoch 72/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6620 - accuracy: 0.7413 - val_loss: 1.6200 - val_accuracy: 0.5788\n",
      "Epoch 73/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6748 - accuracy: 0.7381 - val_loss: 1.6456 - val_accuracy: 0.5805\n",
      "Epoch 74/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6580 - accuracy: 0.7419 - val_loss: 1.6432 - val_accuracy: 0.5799\n",
      "Epoch 75/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6616 - accuracy: 0.7407 - val_loss: 1.6466 - val_accuracy: 0.5819\n",
      "Epoch 76/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6678 - accuracy: 0.7379 - val_loss: 1.6632 - val_accuracy: 0.5796\n",
      "Epoch 77/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6650 - accuracy: 0.7417 - val_loss: 1.6424 - val_accuracy: 0.5782\n",
      "Epoch 78/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6530 - accuracy: 0.7447 - val_loss: 1.6623 - val_accuracy: 0.5805\n",
      "Epoch 79/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6645 - accuracy: 0.7411 - val_loss: 1.6507 - val_accuracy: 0.5782\n",
      "Epoch 80/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6550 - accuracy: 0.7471 - val_loss: 1.6596 - val_accuracy: 0.5794\n",
      "Epoch 81/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6636 - accuracy: 0.7406 - val_loss: 1.6616 - val_accuracy: 0.5796\n",
      "Epoch 82/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6564 - accuracy: 0.7432 - val_loss: 1.6601 - val_accuracy: 0.5808\n",
      "Epoch 83/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6581 - accuracy: 0.7444 - val_loss: 1.6507 - val_accuracy: 0.5799\n",
      "Epoch 84/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6708 - accuracy: 0.7432 - val_loss: 1.6570 - val_accuracy: 0.5808\n",
      "Epoch 85/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6672 - accuracy: 0.7340 - val_loss: 1.6709 - val_accuracy: 0.5791\n",
      "Epoch 86/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6514 - accuracy: 0.7428 - val_loss: 1.6665 - val_accuracy: 0.5785\n",
      "Epoch 87/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6634 - accuracy: 0.7420 - val_loss: 1.6545 - val_accuracy: 0.5785\n",
      "Epoch 88/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6657 - accuracy: 0.7392 - val_loss: 1.6680 - val_accuracy: 0.5794\n",
      "Epoch 89/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6487 - accuracy: 0.7436 - val_loss: 1.6565 - val_accuracy: 0.5785\n",
      "Epoch 90/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6570 - accuracy: 0.7425 - val_loss: 1.6781 - val_accuracy: 0.5813\n",
      "Epoch 91/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6692 - accuracy: 0.7323 - val_loss: 1.6815 - val_accuracy: 0.5799\n",
      "Epoch 92/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6654 - accuracy: 0.7412 - val_loss: 1.6700 - val_accuracy: 0.5805\n",
      "Epoch 93/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6640 - accuracy: 0.7392 - val_loss: 1.6754 - val_accuracy: 0.5782\n",
      "Epoch 94/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6548 - accuracy: 0.7439 - val_loss: 1.6856 - val_accuracy: 0.5794\n",
      "Epoch 95/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6541 - accuracy: 0.7453 - val_loss: 1.6702 - val_accuracy: 0.5802\n",
      "Epoch 96/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6641 - accuracy: 0.7423 - val_loss: 1.6713 - val_accuracy: 0.5791\n",
      "Epoch 97/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6714 - accuracy: 0.7419 - val_loss: 1.6854 - val_accuracy: 0.5799\n",
      "Epoch 98/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6581 - accuracy: 0.7414 - val_loss: 1.6758 - val_accuracy: 0.5802\n",
      "Epoch 99/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6701 - accuracy: 0.7387 - val_loss: 1.6804 - val_accuracy: 0.5794\n",
      "Epoch 100/100\n",
      "882/882 [==============================] - 5s 6ms/step - loss: 0.6571 - accuracy: 0.7488 - val_loss: 1.6627 - val_accuracy: 0.5811\n"
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00ZguNUa7WSwtFStacGA+INtvhbmldVPh9vDEt5sYnBbL8X1T6n2cx2N44YetPPz5egwwonM8d5zYmyGd4ggPcRLsdPDmwh088e1mCkpd3HfGABZsy+bu91ayNbOIUwam8vupfenaLpLCMhcv/7iN5+Zv4et1GQeuEeQQpg/tyA2TutMzuTI4YmDHWF65agyXPP8LoUEOZl57DH1TYwAY1jmet64f22jfj8VytGJFC11NDrpA0NL6KSxzcePrS5i3YT9Oh/DERcOZOjC1yv5F27JZtjOX5TtzKSp3071dJN2TIvl6bQa/bM3mhH7J/P3sQQcyHfgzJC2W6LBgnp23hYXbcli7J59OCeG8dtUYju3V7kC7qNAgbprckxsm9iCzsIydOSXsySthWOd4OsaFB+z70E5xfPbr4wgJcth5KkubxIoWEO9dFJhTHHiNlKX1sC+/lCteXMj6fQXcP20A7y/dxS0zl/D0JSMY37MdL/24jae+20xeSQUi0Cs5iuiwYL5cs4+sonKiQoN4+NzBnDsircZMCyLC3af0JTo0iMe+2ci1E7pz+wm9a1zs6nAIyTFh3gWmgefC/OmUEHE4X4HF0qKxogVEhDiZ1CeJ9rH2ybU1s3RHDje+voT8kgpeuHwUE3snceawjlzy/C/c8NoS4iOD2ZdfxuQ+SVx1bHeGdIqtkhkhr7gCp1PqzN4NKly3TOnFdRN7VEnXY7FYDg8rWugN5lCTTVqOfowxvPzjNh6YvZaUmDDeun7sgTITseHBvHrVaK54aSFOER67cBhjuicGPE9sRMPTZlnBslgaF/sf1cRMmjSJzz//vMq2Rx99lBtvvLHG9osWLQLg1FNPJTc396A29913H4888kij97U1sS+/lG/XZ/C/77dy9cuLuO/jNUzolcSntxx3UF2kuIgQ3r9xPO/cMK5GwbJYLEcH1tLycuvMpeSXVjS6xTVjxgxmzZrFySeffGDbrFmzePjhh+s8dvbs2Y3al7bC4u05zHj2Z8rdmh08NjyY357chxsm9sBxiPnxLBbL0YG1tLxUuD2k55Q0+nnPPfdcPvnkE8rKtLDbtm3b2L17N2+88QYjR45kwIAB/PnPfw54bNeuXcnMzATggQceoE+fPpxwwgmsX7++0fvZHOQUlbN4ew4b9xU02jlLK9z87p3lJEWH8tZ1Y1n8hxNY9qcTuWlyTytYFksroG1ZWp/dBXtXBtwVt3ciuYXd4MXTGnbO1EFwyj9q3J2YmMjo0aOZM2cO06dPZ9asWVxwwQXcfffdJCQk4Ha7mTJlCitWrGDw4MEBz7F48WJmzZrF0qVLcblcDB8+nBEjRjSsn0cJZS43f/l4DZ+u3EOuX7Tm8X2TuXVKr4DZIRrCo19tZPP+Il65cjSjuyUcZm8tFguAiLwAnA5kGGMGBtjfF3gRGA7ca4xpsvmLtiVatRDvLCXXHYoxcAg142rF5yL0idYLL7zAW2+9xbPPPovL5WLPnj2sWbOmRtGaP38+Z511FhERGuo8bdq0xu3gESKnqJzrXlvMgq3ZnDWsIwM6xNCtXSRr9+Tz/PdbOfOJHxicFkvnhAhSY8LomRzFqYPbExOgtlEglu/M5dl5mzl/ZBoTeic18WgsljbFS8DjwCs17M8GbgXObOqOtC3RqsUiip+3BdfstRRc9EG9b5L15cwzz+Q3v/kNS5YsoaSkhPj4eB555BEWLlxIfHw8l19+OaWlpbWe41Cqrx4Jyl0e3vhlO8f0SDyQnSEQWzOLuPKlhezKLeE/Fw5l+tCOB/ZN6ZfC5eO78drP2/lufQard+fz1dp9lFZ4uP/jNUwb0oGzhnekU0IEiZEhuD2G+Rv38/XaDJbuzCUxMoROCREs2Z5DUnQo957W/0gM3WJpMxhj5olI11r2ZwAZItJAV1XDaVuiVQv9O8RwzvC0emffbghRUVFMmjSJK6+8khkzZpCfn09kZCSxsbHs27ePzz77rMY6WgATJkzg8ssv56677sLlcvHxxx9z3XXXNXo/G8ru3BJuemMJS3fkEhbs4KFzBlcRI9AEsU9+u5k3FuwgKjSImdeMYUSXg912UaFBXD+xB9dP7AFomPqqXfm8/st2Ply2mzcX7TzQVgSMgZiwIEZ1TSCvpIL5G/eTU1TBM5eOaNRS5BaL5ejCipaX8T3bMb5nu7obHiIzZszg7LPPZtasWfTt25dhw4YxYMAAunfvzvjx42s9dvjw4VxwwQUMHTqULl26cNxxxzVZP+vL3A37uf3NZZRVuPnH2YN4b8kufj1rGct35nHygBQ27Ctg1a58Ply+iwq34dzhafz6hF50qCE9UXVEhEFpsfwjbTD3nNaPhVuzySwsI7OwnDKXh7HdExnZNZ5gv4KGxpij1iK1WI5igkRkkd/nZ40xzzZbb+rAlibxwxiDMbToKLOmKk1S7vKwPauIz1bt5ePlu9mYUUjvlCieumQEPZKiqHB7eODTtbz047YDx0SHBXFivxRundKLrjZ7vsVyVFLf0iRe9+AngQIx/NrcBxTaQIwjwI6sYk74v7k8dM4gzhqW1tzdOSrYlFHIXe+uYFtWEZmFmkxYBEZ1SeAv0wdw7og0IkL0TyjY6eC+aQM4ZWAqpS4PvVOiSI0Js5aPxWJpVKxoeYkOC6Lc5SGnyCbNBY30u+rlhRSUujh5QAopMWF0iAvnuF7taB9bs4vPZpSwWFofIjITmAS0E5F04M9AMIAx5mkRSQUWATGAR0RuA/obYw4uNneYtAnRqs9cR0x4MCItuzxJY7l6y10ern9tMXvySpl5zTGM6FJ35nGLxdJ6McbMqGP/XuCIuKhavWiFhYWRlZVFYmJircLldAix4cEttjyJMYasrCzCwhqeqd7jMWzeX4gIBDkcPD13M79szebRC4ZawbJYLEcVrV600tLSSE9PZ//+/XW2jQyC7XszWbt27RHoWeMTFhZGWlrDH3b+9/1WHphddcw3T+7JmcM61nCExWKxNA+tXrSCg4Pp1q1bvdpesi+ExMgQ+vXr1MS9OnooLHPx5HebGN01gUvGdsHl9hAdFsyUvsnN3TWLxWI5iFYvWg3hhkk9mrsLR5wXv99KTnEF957WjyGHmffPYrE0MsXZ4AyB0Kjm7slRQ5NmeReRqSKyXkQ2ichdAfb/VkSWeX9WiYhbRBLqc2xT4PEY8kpa5pzWoZBXXMGz87dwYv8UK1gWy9FGSS48fRw8Pgp2LW7u3hw1NJloiYgTeAI4BegPzBCRKknhjDEPG2OGGmOGAncDc40x2fU5tin4++y1jH3w66a+zFHDc/O3UFDq4jcn9m7urlgslup8fg8U7NHFke9cCe6280BdG01paY0GNhljthhjyoFZwPRa2s8AZh7isY1CfGQIxeVuSivcTX2pZiersIwXf9jKaYPb0699zYluLZYjRkku/Py0JpZsbLI2Q1lh/drW1M7jgW8egP+dBKX1XH6UvaV+7aqz8StY9joceztcNx8ueA2cwXq+Bc/B3lXgaf33qUA05ZxWR2Cn3+d0YEyghiISAUwFbm7osY3CvtWw6EXiE28EILe4gtRYZ5Nd7mjg/aW7KCp3c/sJvZq7K5bWTlkBhEbXvN8YWPEWfPEHKM6EzmOgw7CqbXJ3wtf3Q8FeKM3TY46/F/qcUtkmfRF8+huYeBf0PbXq+V87G5yhcOPP4Kj2rG4MfHwrdD0OMjfA2k/g6q+qziOVFcL718G6T/Tzt3+vWjWivBhCIqqed/tP8Mp0OPkBGHox7F4KXWvPM3qAzsfA5Hth/G0QFAKR3kX7W+bC7Dv1fVAYxHeDxB5w6sMQ06F+527hNKWlFWhRVE2PUGcAPxhjsht6rIhcKyKLRGSRy+U6hG4C236Ahc8xduO/AUNOC15gXF/Sc0qIDg2iZ3ItNxNLy8Ljaf6nb48HvvkbfH6vfl77CfzfQLV0amL+v+D9ayGuE1zzLST3hx8eq2qlfHgTrPtUxxfTATwVKmQ+ls2EF0+FPcthy3dVzy8Cx90BmethyUsHX3/ZG7DkFcjbCV3Ga7sPb6xq8W2dB+s/g6kPwcgrYc2HlRZZSQ48cxz8+Lh+3r9eH4RnXqBjGnA2fHUfvHombJ0Pebtg3iOB3X3lxZC9VQVz4u9UsPwZcTn8egWc9QyMuhoSukHWJgiOOPhcrZSmtLTSAf/Y8TRgdw1tL6TSNdigY73ZiJ8FTZh7SD0dcy3kbKPbz09wjdNNTvExh3SalsT+gjKSYkKbuxuWxuTDGyEvHS7/pHmubwzMvgMWvQDDfqXbOo7Q7R/dApd9crCV4yqHX56BHlPg4nd0f/4e+O4fkL4QLnhV2532b8jdDj2neI8r06g6UAvtx/+qpXTmUyoUPvathohE7c/Kd+CLP0GvkyHWuwYxazPM/i10OVatGocTTrgfvvwjvHS6WjHTHlPL7eaF+rk0H064T4XF7YK3r4Cc7ZA2Ul2cL0xVazCyHVzyrlpJk+5SMX39PHCXg/FAl3H689MT0P9MyNkKH94MweFw/ffal+qIQHwX/RlyYaP82loaTWlpLQR6iUg3EQlBhemj6o1EJBaYCHzY0GMblZP+Rknvadwb/AY9suY26aWOBvYXlJEcbUWrRWCMuprydtXeLmMNOJppFYsx6rZa9ILOw0z7r26Paa/use0/wOIXDj4ubyeEx8GY6ysFLaY9HHsbrP0IVr2r527Xs1KwAIJC9QZemg9LX4NR18Cv3q8UrH1rIH0xvHctvHq2bjvjP+Bxwad36DlXvgMvnwHOIDj7mUqRGHeLuvMy11f9PhO9S2LCYiAsVi2lV6bDlm/h9P9Tl15YrLrqOo2Bi9+G+K56TEQC/Oo9FbaxN8Gvl6lg5e5UV+OTx8BLpwEGTnkosGBZFC3H0TQ/wKnABmAzcK932/XA9X5tLgdm1efYun4iIiLMYVFeYszDvY1595rDO08LYOI/vzE3v7GkubthqQ/pi4z5c4wx/zu55jZutzF/TTHms7uPXL/8mXOP9vGLPxrj8VTd5/EY8/J0Yx7oYEzO9oOP9XgOPqasyJh/9dNzfnV/7deuKK362e0y5j9Djflbez1+9QeV+354TL/H0nxjFjxvzHMnGLP953oPswrvXqvn/+yuQzveR8Z6Y144RX93ZYWHd65DACgyTagDjf3T6utpNZR92bkEBYeRGN3wHH4tif5/msOM0Z354+m2NP1Ry/afoMtYff/cFNi1CG5aAEl9Dm6bvQUe8wYvXPQW9D75yPUT1GrJ3KhusEA5PnO2w9PHqtU1/FLdVpQFwWEQUkMpp7WfaNj3RW9Bct+G9WfD5/DG+eoyvOzjyj553ICoVefx6PZDLZ+TvwfWfqxzXM6Wm6ehvvW0jhaadHFxS2Tyoz/z5NxDDFNtIRSVuSgud5Nk3YNHBznbdW7EH1c5vHqWTtwDzJgFjmBY9GLgc2T45Y7ct6pp+lkbg86FyXfXLADxXeDWZZWCBTD3IfjPEKgoDXxMv9Ph18sbLligon3B63DO/6r2yeGsdEM6HIcuWKBuzDHXtmjBaolY0arGRWE/MnbLY83djSYlo6AMwM5pHQ3sXw//GQw//bfq9twd4CrRwAqAqCToPx2WvwEVJQefp2CvilpItFo8h0v2Fti7UqPx9q48WFR9bPgcvn9URbYufGHbW+fBJ7fD8pnQfbJaWzVxOKLS73SITjn04y1HJfYRoRpjQzYzMmdec3ejScnI1yfb5FbuAm0RrHpPX6un6fGFeyd0r9w25jqITdPIueBqhThHXaVWzGvnqBAeDsbAW5eqWPmISdMAhWOur9zmroA5d2uwwtibDz5PTez4WQM2fP22WBqAFa1qhEQlEFlQiPF4kOrhua2E/YVqaVn3YD2Y/y9NWnryA01zfvH+jZUXV92es1Vf/UWr02j9qQlnMLTrDctnqfDU10oxBrZ9D788DdMfh/B4OPt5yNqo/SsrhKWvwv512t7jgcUvQuE+yN4MM95smItswm9V8LI2apSdxdIArGhVIzI2kaC9HvZmZZGalNTc3WkSMvKte7Be5O/W9UKOIF270xRzF5N+r8LgLqu6PXuLuvoi21Xd7nHD5m80E0K7npXbXj9PrZYu46A4CyqKaw5w8FFWCBs/1zVOu5dCRDvIWKfBH8l9q84lDbmg0kW4f61mngANdGho0IeIZrOwWA4BK1rV6NyhA6yHCE8h0EpFq6CMYKcQFxHc3F05uvnhMV0I6i7XNVDtBzfu+fPSIaYjTPztwfu6jNdFsdWtpbJ8ePNXMHSGrg0CzaCw+WsNhhh6EQw8O/D1CvZqSqV2vfT1oW6aWSKhh55ryIyD3Y7++EQ7ZYAGVWz/EbpPPLx5J4ulgVjRqka75PYQGkuMlNXduIWyv6CMpKhQxN5saqYwAxa/BN0maOBA+oLGFS13hZadGHy+Lib1uDVLgtP7INF/GjDt4OPC46HfGbD6fU0pFBSiggqQ5GcZuSsqz7X9R3j/em9GiRPhknc0F+CJ90NyP+g2seGLWRO66Y/FcoRpnZM2h0O/M1h7+SpWlKc2d0+ajIyCUpJibBBGrfz0uLrsTn8UIpNh58LGPf+mr6AkW6PnMtbCA+1hwxzd53FrGHxNeQQHnav57nw59nxzTb71W/8ZquubfMx7BFylcPLf4fg/VG4fexP0ON5mX7C0KKxoBeCu91by0Jx1zd2NJsNnaVlqoDgbFjwPA8/R1D1pozQPXkPZu0qTuBZnH7xvxZvq/us5RV2E7jLYv0Gj/5a8qmHwS18LfN7ukyEsTlMcgYpeXJfKOazweM1W7hvLlu/U9Tf2JugwtOHjsFiOIqxoVackh/vKHiZp7/zm7kmTsb+gjGSbLDcwZQWakbuiSDODA3QapVFyRVkNO9fX92vOvW3V/pZK8zVj+MBz1IUXFgNRKeqC3PQVfP8vbefLdVedoBB1Ee5eqpF8YbHQ7bjK/Ul9KtdqZW5QERtwVt39LS+GnG1a4mPHL01T18piOUzsnFZ1HEEMy/+WzypSKSxzERXaur6iCreHrKJyGzlYnazNsOBZWPo6lBdoCYjkfrovbZS+7lpU/0i59MWw8Qt9v3uZLgz2sXOBuuv6nl65LbGnZiQHXVgMGiFYEyc/oNGFO36CwRdUpnsCDbRYPlMFuPMxcMf6ml2ARZlaZmP1+xr27l8BaOJdmuXCYjmKsJZWdUKi8IiTWCli6/7Dy2N4NJLZXGu0di059EwNW+fD7N/VnXUhkBvOH2Pg419XzgV5PLDxS3XJ/Xc4LPyfFhW8+mvNCO6jwzAQp4pNvTFabuPUR1Sw8nfDJ7/RCL5Oo+CitzXjt4/EnrovsacW9xMHRLev+fShMfDLU/DSqboQ2D9jRbve+pqxVsfsDDo4wq8kV7/TR3pr+HrhPrUspz+puf76nwnzHtbfW1PiroCPb4NHB2n+QmvdWerAilZ1RPCExhJLEZv317M8dwti/4EUTn6BGJmbYM499UvFU5qvN+/lb+qTfH3YtwZePAVmXdTwm9K6T7Xq7IJnYMWsmtvtXAgP99AyDzVRuE8jAl+ZrmXdHx8Jr5+rmR8m3Q23r4ZznqsqJqBzRSkDGjavlTZSS1GMvgZSB8G7V8Oi/8E3f1V3Xu+Tqq6j6n0yKnTH6xyX8Wi0XyDc3vIavmCLogxY+Vbl/vZDNXvF+tnw6GAVQ/9jl76uY1/4nGbRuP4HTcQ75Y8w7GLtyxn/gehUrdYbKG1UY1CSqw8Mi19UkX73Kq1htW/N4Z87azM8MwHmPmyFsJXRunxfjYQzIoGTU8II7tP61mkdtLDYGPjkNp136TSq5rmPnO2aNXu/X4DK2JvrzhRRXgzvXKFP1JkbdI6n67H16+zyN+GDGzR4wF2u2SmGXBR4ke/il/RGP/chnR8KlB4o36+O6JzfQ9pomHwP9Jt2cIXY6nQarZkmPG51tRVnq6AWZaiLLSJR60g5nBpI0edUzbVXVqB59rb/AO2HqGCExcPwX1XN1h4Wq7Weuk+CjqPgg+tgwXMwNYAIf3iTCvig82Dl27pt8ze6Rgu0ptRJf9OEuw6H5iT88XEN3d/+o7o/00ZrgcL2QwKPNzwOpj+h1Xa//gtMfbDqfleZWsCeCohM0kXQsZ1qj0TcvwH2LNP3xgPz/62LqM98Sl2cS17Waz01ToVz9LUq4g1dmrF3lY69JEdzJxZnaeRkc2a4MQbWfKAFH9sP1bEl9a7cX1YAQeENX8Cev1tdu7k74ZR/NGaPj1qsaAVAEnuQFJUAEXXcyFogvmS5B9yDGz5XwRKn5oMLJFrGwMe36mLYKX/ScuhLX9MS5RN/r4EEBy6wTi0IXzG+z+9WobvwDXj/Bs1SXh/RWvORlmDvNkGP3TIX3rxYI+aGXFC1bVmh/uMOmaE3qtl3QlSyBiv4U5hR+f6yT6oGL9RF2ihY+LyOJaaDWo4+AQ+O0AwUxqPRgB/drJVtj70dVr6rwtJ5nFbhfXSQJsdN7ltVtLbMBUTXWiX20EwVS1/VOaXQ6Mp2WZtVsMbdAif8pVK01n+mDwgh3rLrebtUyEZeBS+crCmTEnvC4PPUbdnn1Lpv4j0mw+jr4OcnVVyS++varO0/qWCX5VVtH9sZRl+tVYIjEiq3Z26C7x70Rjv6WT1hcVq40fd7GHkl9Juu6aQWv6gWdnSHyr+v4HANXhl2iQaX+HCVqeCDWs1vnA/BkVr9d/FL6kYtK9AF2Vvn69ydq8RPbDvr/GVyf7UufSJZnKUu1ow1+j6inR4Tnarto9tXtjXGa5EGsOpytsOcu2DrXJ2nXPKyWrndJmj15Yy1kL9L37fr481G4u1Pcj/tn//vqrwYVr0Dy2bqnCYGOgyvujavFWNFKxAXv8WynbmsW7CDC0d3bu7eNCo+92C7qFD9I//yj3ozG3guzP2H3hSrR60te13ngU77F4y6WrdFpcC6T1S4xnmTpWasg2eOU6uo0xidC1r8kpYx73ualgdf/KJG4fkyfvuehHscX/Wa8x/Rf+CL3tYs4H1OheQBun3QuVWf6Nd+pNF+wy9Ty+GVafDOVXDZRxqIAJVjjU3TG3nqoIZ9cb5gjK3z9Yk5a5OGnl/wmor0e9fqjXn7jzrfNOpqFY65D+lxHYboDbLHZL3hB1XLPLHlO8BoMEj/M3Usq95Rq23sjZXtfnlGLaext+iN7OJ3dN7pu7/rOi9fNoz/89ZJW/eJ3kwvnw1dxzdszKDi6ypVl/Cmr1QcQmP0gWDAWSpORZn6xL/yHfjyT/Dtg5DSHxAwbv0dB4XB+F/rg4XvxhqVXFWQQf8ujr8XJtypDyIbPtdzgF7jiz+oC7jv6SpEGWsOdqPGd4NLP9RyKFMfVCt27j9g2Wvqhmw/BMIT9Hx7lnvdp3W4EJ2hB6faCo3Vh7OSXCjaf/D+6m1PfUSFuTgblrykohMSoQ9x7XpDaZ4K2PafKh9GQPva7ThNmZWXrqJXkqP/H5PvgQFnV6b0agPYIpA18ODstbz4wzbW/nUqTsdhZo7weJrXNeHHve+vZPbKPSz900lqOXx6h1oyHUfA/w2AY25Q15KPgr3wxGhIGajWif84XjxVI91uXao3gxem6hP9MTdqRNq+VdBxJFw5R29UGWu1rPiJf4Xxt6rI/e9EfVK+bYU+wYLehJ+brP/ko6+pvN6q99TVeO6LVVMVvXgaFOyGW5bok29xNjx/ApTmwjXf6s1rwXNqgQ29SN1RDcUYnTMrK1BR9nH7GojtqNufHAd5O3SeqOtxej13hQpldHsV0Q9vViu1y3i4/BPtb1khPNQFQqI0z2FxJvzqA/j+33pTvXmxliYpzYd/99MHgLOfreyDx62/uw7DYMZM3Tb3nxpIERKl1kxjrM9ylatAxHXWcveB2LtK5+5y/IQkpT+Mu1VF6nDZs0KFfe3HavEm99Mbvi/9lCNYH2qqX2vT1yq+Xcar69Of8iLNjJ+xVr97H6Exev6kvnpMeZGKU1661wLzWkgRifpAEhYX2D3qDNGHwqgGTDeU5mmf9q3WB4atc/Va4lDBHnOdjqURstq0tCKQ1tIKxKIXuWzruzzjvoH0nGK6JB7m7/PNS7Sujy9XXDOSUVCmQRil+fpE3OVYtWJENHJu6esw+Q+VNY5m36micsZjBwvv2Jth1gwVqKL9us7orGfVfTfxd2q1RSZVPlkn94POY9X6GnoRzLxA/6HLizRpq29+bPGL6nIbfH7V6/WfrjeoeY/oPJQzSPPubf9eMz34/oEjEjQC7vnjYeaFOnfjC9CI6QgF+6Bwb83zOf4sfU1vTlP+pNbWhjl6E8vZBjf+pIIFajH0meq9oX6qFminMSqQP/4XVr+nwrdnmVq2279X99/wS9XF43GphesrUZLQHU79l87vfPlHOOtptXjLC2HM9VX76HCq2+yXZ1Swdy3WvIkR7eDSDwJXOj4UgkI0nL42Ugc27d95+8GaiX764w07rueUmveFRELH4fpTGyGR+hPftf7zsodKWGxlVv+RV+jfTs5WtdBjaokqbQMcHY//Rxv5u2i//3sEz+FHEBZl6o1u0Qt6s21mDiwsXvKKPlWe9NfKm/3IKzW10NqP1eXx/g36ftLdgd0PvafqDXjuQzqB3vPEqkKT2KPqfBfAiCt0oe7zU7Rc+UVvakDBohf0uyrN13mggWfrP64/DqfOl2SshpdOU/FZPhMQdTv5064nnPeyPq0+NU7HBeoW/PQ36j6sC49bx/XT4/Dq2SrSI67QuawT7lNhKSvQSXAfCT1UWE64D674TL+DDkP1yTlzA2RtUdda90nw0S3wy7P6FO0MUasU1NqKTdOJ+vG/1jFu+U5FqdOYwDfXQedpUMR71+qcTkJXuPqrxhMsS/Mion9vbVywwFpagQmLQzBEU8LmjCKOP4Rq3wdY84H65OO76lP7hDsbqZOHxv6CMrq3i1SrKL5r1Rtgt0m6bf4j8NWf1TU44Xc66R8Ih0NTA31yu7qhzni0bndF/+kauZezTWs2pY1UN8yKNzWyKrajzk+NvDLw8b46U7sWaUgzRgUgNu3gtj0mazLa2XdqRN6uhSpa+zfoXE9p/sGi6s/2HzRMfvCFaimVZKu4pI3S6C9j4NlJKlQXvwWnPqzuQKg6Id73DBWbxJ7w+23qpnKGaIj3Z7/V767TmMpSIM6QSjfThDt1fuPNSzXwYcqfAve1/RC1Qjd9qQ8T5/wPQqNqHpvF0kKxllYgvJFJXSLKD9/SWvmuupNGXaM3XP+w6yOMMUbzDsaEagqgDsOqNnA4NBPEfm8E4FVf6qR4bWHMQ2bo/M3pjwYWjuoEh8G0/3rDnM/TbUm9dVJ/wXNqTaQO1mioQITF6WvnY7SPhfs0mqwmRl8Dl36kbquQKIjrWjm/s3dF7X0VJ/Q8Qd1dtyxRwT31n3o+h1MFevAFGum3Za4e4ww+OIIrMlFdow6nutjCYvR7OO9lFcTyQs203n2Stk/1yyYfHK5ze2V56tqsHhF5oK8Cp/wTTvyLzlFawbK0UqylFQjvRO3z5/cgvufAQz9PXjrs+FHniHyRW9t/1IniZiCvpIJyt4dOoaUaQDEygItszA263qbvabXXVvIRHK4BBQ0h0I13wp1qzWQWqEjUZLH5ovg6j1MLcPPXOr9VG90nwu4lMOBMFeb2Q3X77qW1z010HV/5e/OFkld/P+pq+PYBjVic9riuvwrE+jnwzpU613fsbbrNGaTi3e90jZ4MidRsHFQbe++T9G8ouW/tIc09JuuPxdLIiMgLwOlAhjHmoJuiaJ2j/wCnAsXA5caYJkmnYkUrEFGpkDqIlJgwCDoMYzQoTAMEBp0HMWmaK277D80mWr41Wj1cm3RDdUsL1AJojv6lDNCoqK3z9Puqib6nwn1+64MCrStzuzQEfMQVlevFjr29cn9Ukv4+di+r+Tq5OzQYpHrl4OpEJKi1teJNXXdTExvmqNtz36qq2x2OqiJePRuHj0CFIi2WI8dLwOPAKzXsPwXo5f0ZAzzlfW10rGgFIm0EXP89JeVuHvxwFeN6tGPqwEOorxXZDib43Wz6nqo3wmbClw2jY8la3VCf6LmmxphKq+rMpzQKsfraHX+KstQSNkYXvPpnFfCx82fNnpGxVkPAPW4NFfa33s56Sheu1sS3D6rQ3Lmx7iwF0/6refviu9TcJipFXyPqEEGL5SjEGDNPRLrW0mQ68IrRNVQ/i0iciLQ3xuxp7L5Y0aqFsGAHn6/eS1ZhecNFK2e7up96T60MH/dfW9MM7C8sBSAhb61GIlVfr9IcPDNBQ+77TFXrxj8beiCeGK3uNI9bF+kGEpUdP+nr+tlquZXmazqoq76ozNxem1VUUaqBGv3OqF9anaDQuqP0hv8KNnym62sslqOLIBFZ5Pf5WWNMQ29WHQG/MFrSvdsaXbRsIEYgXOXw7GRk8UtM6JXE95sycbk9DTvH2o/h7csqQ6398c/I7U/mJl2I3ET4LK2IzBWBXYNHmqJMDYYIidA8dG9dqjnyasJdoWH6Uam67qYkG3b+cnC7+G4w9GJI6qfh6PtWaWh6nF92k9J8Dfvfv+Hg4zd/DWX5VRcwHy6xaXDdPFui3nI04jLGjPT7OZSn60CT0E2SucKKViCcwboSPWcrE/skkVdSwfL0vLqP8yd7s0Yhxvi5oIyBp46FL+49uP3+9Zp5e837h9f3Wtiwr5DOocU48tOPDtFK9z7cpY2Cs5+DXidr4cSK0sDtfbkDo1M1qs8ZotZUdQadC2c+CTf8oFnL967U9VL+WdXd5bpOau1HVY8tL4Kfn9IsB90mHv4YLZa2QTrQye9zGtAkodJWtAIhoq6zkhyO7dkOh8DcDfsbdo6cbbrmqfp5I+I1GKM66z8DTJPVLyosc/HZqj1c2jVHN/gi6JqT9IUaVt5+qLpQh12iYrJ3ZeD2hd4SG9GpOu/VbaK68fxTkZXm6Q9oiLkx2iax2uLoyHYq3N8+AJ/fq0lIQXMKpi/UBdVtIPmoxdJIfARcKsoxQF5TzGeBFa2aCYuDklziIkKY0i+FEGcDc3xlbz1YtEDTJu1dpQkv/dn0lb5Wjy6rzvePVhYxbAAfL99NcbmbUxO9N/4jEYSxdyWs/qDm/ekLveunvMEpvnD2mupWFezTV19QQ9/T9OHAV/EXNAnpP7pU1pDavVRfA6UfuvRDTaP00+PwwkkqXO2HwK3LquY8tFjaOCIyE/gJ6CMi6SJylYhcLyK+nGKzgS3AJuA54MYaTnXY2ECMmgiP04SrwHOX1hCGXBNuF+TtDByO3WUsYNQ11utE3Vaa7w0eEBU0/4g6fzwezVQBVcO+a6MoE4qzmLVgP71TomhfvE6tjtoyQTQGxdnwtHcNVL+cwAmDe0zWEhI+YtprJozqVpGPxJ66hMD3MNB/us4RpQyobLPjJ2+CWm/gTMfhcOXnmvC3OmGxWuxw4Dma1WPXIg3QsKlyLJYqGGNm1LHfADcdib5Y0aqJTqOrVPI1xlBa4SE8pJbsED7EoZVgAy3O7TAMEE1q6hOtrXM1YWq/MzSAozBDE+xWp8jPRekrRggqELnb9abucVeNCpxzF541HxFa/Dumn3YOsmAZdBlX9xgOF/9yEbnbAwcg+K+d8lFbstWk3pDkt4QgIqEyiwSo2O/4+eA6Wb7yJDXRbQLcvKhRMmZbLJamxYpWTfiV5zDGcNpj39OvfQz/Or8ebjWH4+CaVD5CozUrgv8i0o1fav69kVfC2o8p372C2xYl0Dc1hivGdyU6zDu3kp+urzNmVU2ttOJNLTIHfBp/CR8lXMGADrFcP6E7Idu+x+Eu4/mQf+FIHKrlDY5EEEZeeuX7fasOFq2CfRoYESjdUP5utYL8AydAQ+IdwVUtIXeF1nBK7q/ZKwr3aib5hmIFy2JpEdg5rXogIgzqEMUnK3azK7ek7gN2/KzJX2uKgpt8j0a/gVoHm77SVEPe4IjNq35h9sq9/PvLDRz3z2956rvN5JVUaNh2ZJLmoPMLVihe+BorPV2Z7xjJuNxP2JGRy7+/3MBNT34ABXt4gWmY4HCi3vFa+EciCMNftPYGmKf75i/w2NCqQRQAOxdqzahA83azfwuvV8uW4QzWsPcfH9PieXBoomWxWFoEVrRqYuHz8O8BOj9Vms/ft5zNGTKff32+vu5j138GX/655ugzj0dD3EtyKwvJ9TxR3V3RHcjZspSUmFDeu3EcQ9LieGjOOkb+7Usu+y6CmRO/oXTTPJ0vyt1B+Z7VRGSt4qvg4xlx3u+JN7l8dkIWT18ynHY5Gon4dtlYtk59xVstV7QmUVOTu1Ozf7TrHTgaMH2RJsWtbuGkDtTSHIGCMQr2BnabjrpGy34YD5z2b01QbLFYWiXWPVgTbpe648o0SMJZks2DQc/Ta+k4ruxVxMDSJZqhO1A10pxtupC1puzoGWvg6fG6NqnAGxXqtbzK2vUjbvNGzh6XxvDO8bx85WhWpOfy6Yo9fLZqL3e/t5L3YuJ5G2DNhyxZtYGRxsHoadcS0acPJPaCBc8y9ZoLOG59PsWrIgjrMIAhI8ZD6vuwZ2ntaZIai6AQdUNO+29ltJ+PklzNJD8wQI7D4HAtH+Jbw+VP4b7AARUDztK1bxvmwIWvN0r3LRbL0YkVrZrwBTOU5GipbiDYlNE5wsXrX/3Cg8X3a4HAgedooT5feiDQEiS1ZT5I7qdRc+mLVMCSBxyogLvW3Yn+8h3nDKm80Q9Oi2NwWhx3Bc9iX3Ye52+fzsrSrsR9/wYJhXlsjBnN+CHe64++Bj77HWSsJXLvQugxjvcunoCIaE7FtBGN+S3VzAn31bxvt3ctWk3JYdNGaQVl/2ATj1tFK5ClFRwG/c/UMu9Zm2ueT7RYLC0e6x6sCV/dppJcTevj5dXzO/O3qPe1yuzwyzTa76XTKov/QeCFxf44nGqF7PxFQ7R7qZVljGHO/kRCxE1Ph9+6vDUfQV46snUuqeU7+PTWY9machKditfwh5Df0fGSpyvbDr1I0wVFpcD+tdD5GBUs0ICPj287jC/lECjYp+Luv5YqfREgNZc3TxulGdEz1lZuK8pU919UDTkgj7vDzmVZLG0AK1o14bO0di9REep/JgBdQvJxFu4iM3EEpSc9pJnJi7M0+AJU5ErzNP9dbXQYCnuWe0PdNUnsql35fJObrPt9N/ndS+GtX8F3D2pwQ2wa0WHBnDFD1+49NjKTmFS/a4VG6wJZ35yQ/4389XNh8Ys6nqakohSePg5WvadCM/9fsO37yv39z9Q0S2GxgY/vNlFdp17rE9AFyGc+VTXE3Z/YjnDlHGtlWSytnCYVLRGZKiLrRWSTiNxVQ5tJIrJMRFaLyFy/7dtEZKV3X4AJjiYmur3Wd4pKhvG3aeYEgNyd5BaVcvKy8fzri/W6QNYRDBu/0P3hcXDvXq0AXBOucu9N3MBxdx5w2b2zeCfpzo4YZ0hlZox5j+jr+s91nVaspveShG5w2ceknnDLweff8YtaNxJUtQLwpR/q69Z5h/KN1J/8XZoI11Wmi3wjEqsGYyT1VouwJqJTYPD5BypIAyrGQy8KXIrEYrG0GeoULRE5XUQaLG4i4gSeQIuD9QdmiEj/am3igCeBacaYAUD16n+TjTFDjTENTEnRCMR30Un9/tPhxPuh0xiNhCsrIE6KmNrJzfPfb2XRngpdrOsTLdBggprKnbtd8O6VsGcZZuSV7Ox/Ld+s28fz87fwwbLdHN+/I5LUR2/ye1dp3ryUgVDsXVjsX9K+2wQti1GdPG9m8+Q+VavsdpsIkcmNK1pFWfDhzVULKuZ5KxTEddLowJSBlaL1/aOw8au6z5u1GVa8Xfk5dwfsXFBzhnyLxdJiEBGHiAwTkdNE5HgRCTBZHZj6iNGFwEYR+aeI9KuzdSWjgU3GmC3GmHJgFloozJ+LgPeMMTsAjDEZDTh/0+OugC1z1WIIjYKbFx7I2XfPcbF0jAvnzreXU9LtZI2Gy9kOaz7UBKw1lBgxPz0Baz9mTtqvmbhmGsf9ZxFXvrSIv326lpAgB9dO6K7iuPlreP96CImCC14FHDqfk9C97n57rTFi/Utx5MEPj6robf/x4PVRh8q+VbD01aolWHxrtHwCmzpI56dyd8A3f63Ms1gbaz6A966uzCG4/E3434lg3I3Tb4vFcsQRkR4i8iyao/AfwAw0T+GXIvKziFxRl5FUp2gZYy4BhgGbgRdF5CcRuVZE6oqbrqkomD+9gXgR+U5EFovIpf6XBr7wbr+2pot4+7JIRBa5XI38FP7XdvDKtKpWlDdEPTKhIw+fO4RtWcU8tHeo7tv4hd6QV7wVMNde1p4dlH79IF+6h3PrtrEcF7ufWcNX8+4NY1nyxxNZeO8JDE6Lq3SL7Vup0YAJ3TVnYWQSdK5HBevyQn31zy+4fz18dZ9mUr/x59ozQLhdakFlbqoaDBGIjDX6OvfhSisodycglZWBUwepRfjln3SO65gb6h5D71P0df1n+lq4V4NjAlmWFoulpfA34DWghzHmZGPMJcaYc40xg4FpQCzwq9pOUK+Qd2NMvoi8C4QDtwFnAb8VkceMMf+t4bD6FAULAkYAU7zn/klEfjbGbADGG2N2i0gyqsLrjDEH+bW8BcueBYiMjGySomMHqtzOubsyoCCmA2OTY7h8XFcy8ktxx3XHueFzcJUGjBz8as0+St6+hZM9FeQedx/LJo0nYsF/4asH4LSrIDKksrF/wtixN+tr76nw5R9VEOI6USt7V+ir8bP2sjZXjqW2ZLk7F3ijISvzLnLhTOh7auD2PtHa8aNam6kDtexHrxN1rRboeqw+p8L/DVB3a21l6X0k99NglnWfwsgrvAuLG1g92mKxHFXUlnjX62l7tK5z1GdO6wwReR/4BggGRhtjTgGGAHfWcmh9ioKlA3OMMUXGmExgnve8GGN2+w3kfdTd2Dz4otwKMzTyLiTqwI3/3hM78cTpSTj7nATb5kP2liprtCrcHv76yRoef3UWZ5jvKBx+HeedNJGIkCANmwdNnuvPwHMB0cS7vqS7+9fp64Y5dfd3y1zNflFFtDZp7aq4LrDwf/Dt3wMf63GrZXTC/d4Ivs6w/tOar5WxVtNK+Y9j9DVwsd98lDNIXYhl+TAuQOBIIES09MjWuZoFv2DvwYuULRZLi8frLqw39ZnTOg/4P2PMYGPMw755J2NMMXBlLcctBHqJSDcRCUHnxqqVieVD4DgRCRKRCGAMsFZEIn3uRxGJBE4C6ig01QTc8JPWVvIRnaqVbaMrE7YGf3EP8uggdoX25A8lF2Lydx+wtPYXlHHJ87/wwvebeSJ+Jia6PQlT76k8X4dhKky7qgVHhsXC5Z/A77ZWJo3N3wXO0LpFqzBDAy3GXA/nvlC5PXuzZukICtEw+l+eVoGqTpexcM03cOxtGsF31ecw7fGar+cuh37T1HVXXXz9ydoEaaOhYwMWN/c9Tfu4e4l3YbG1tCyWVkiDAu3q4x78M3BgpauIhAMpxphtxpivazrIGOMSkZuBzwEn8IIxZrWvaJgx5mljzFoRmQOsADzA88aYVSLSHXjfuyg2CHjDGFMPE6ORSelf9XN0qgYCRCZXbjvmRlj6KvEL/81Sz80USwSR8V3ZvL+Qi5/7hZzicmYel0XHhWvhrGerRhWGRml2cv+URR4PPNxDz9v12Mrtebs0sGHrPCgr1PVdK9/WwJBOfkbo6ve1j0MuqNr3nG2Va5i6TVTLZ++KqhnfK0rUqvHPOhHToXJfoFIr183ToI7MDVp12eOBRweqW3OsXx24KX/WnIINodMY+O0mzcl45lNHJv2UxWI50jQoAK8+ltbbqKD4cHu31YkxZrYxprcxpocx5gHvtqeNMU/7tXnYGNPfGDPQGPOod9sWY8wQ788A37HNjs/C8q9XldIfrvmWiPIsHgt/lmElT7C783Se+m4zhWUu3r1hHMd4lmnpkYHnHHzOtJEqHr5ovtzt6kaLaQ8/Pg7fPqj78tLVbecuhzcv0Uzos++Ed67U6EYfK97SEHOPG149q3KR8lVfqmhC5RzdlrlUYfO38K/emmndnw1fwCN9KufFqiOiFlpIpFpE+bsOFqjwuJqXAdSEw6mCBVojq8PQhh1vsViOeowxUxvSvj6iFeQNWfddoBwIqaV966G8SC0MH74Ai0hvktzibI2aC4uFaY/Rw7ON24Le4425y5izai+nDkplYMdYDd7ofIzO7VTn+D/Bbasqo/l8IpMyUN1iS1/T67hKNL1RRKKGrA84G075p66JWvqqHpO1WV2Ng87TG/7mbyqj/5zBEJmo76NTNBN69fVa2+ZDUNjBWeDbD9br//xk1e0Ln4fXztWowQm/VVdigXfasq5gkfqSsx2eHKeVhYsyG+ecFoulxVIf0dovItN8H0RkOtA27h4zZ8BHt1Z+9q07aj9IX/ethm//ptbRkAth0l1s8HTkzaUZFJa5OHNYR829l7WxqqvPn8hETfjqY99qQFRUOo7QTPN5OzQ7R/vB6o67Yx2c+QSMvhY6HQPz/qWpk1a9q+cYdK43jZToXNLeVfDpnd5QdC/dJ2mUnz9b56ursXpYeXSqzm8tfV1D4X1s/1FD6f3F2HcN/0XQh0NUMmSshkUvqAvSYrG0aeojWtcD94jIDhHZCfweuK5pu3WUkLdTrQ8f+bv01bf+yBfR56vfNOluzo7fyimOX2gfE8ox3RJhuzdEvibRAvjuIfjJa8VkrNbow9CoyhRMBXs1O0e3CSoGPpeZCBx/r1o3i19S12CX8domOEwXGWdt0gCJhc9VXZh70t/gbL+gnaIsXRfWdULgPo69Ra2tJS9XbstYWzW7/VuXwduX6fvGEi3/eTT/uUSLxdImqc/i4s3GmGPQVEz9jTHjjDGbmr5rRwHlxbqY2JeVId8bj7LuE33dvx5CoqtEE47s3Yl7zPOcO7QdDoeoazAkGlKH1HydHT/C8pn6vu/pGoQBalmJs/aovG4ToOtxmmkia6O6Bn0k9lCXYdYmcIZUZsqAygKV+zdokl+fuHarQbSS+0KXY1UcPR7Nn5i5oVqwindebuC5lVnyG4MLZ0Lq4MZzOVoslqMGEVnSkPb1yikoIqehqTZuF5E/icifDqVzLY6KYn315dXL987XlOToa+Z6SOpTJbvE92YIgiGxNJ2MglLY9kPN81k+Oo5Ut2B5sbrhRl+j20MiNa/hvIfh0cE1p16adLdmwXAE6+JdH51Gq6Bmb1F3YfWilHm74Mlj4KfH1c047fGay4UAnPgXuOA1zfaRtUkjGJMH+I3DG85+ykO1Z9xoKH1Phevn22wYFkvrpEE3i/osLn4auAC4xXvy84B6pDRo4RijgRgAe5bpa8FuQCpFK3ubipYfz+5oz3aTyn0LhLd/WKfCVptrEDTAwrhhy7eQvbVq3sLLP9FSHs7gmoWg63it3jv0okrXIcDke2DGGyow/lk2fMR2hH6nw89P6fmH/6rSAgvYzxGVQRruMrXwUgdV7veJ1q4GPThZLJa2TS3ZCw6mPpbWOGPMpUCOMeZ+YCxVM120TtzllXNAu5fqa/5unWMp3Keff70Mpv7jwCGbMgpYtLuMyHYdGRu6ldcXpPO5eySFHcbVfi1fBd8v/wSPDYXcbVX3e+to1cp5L8G0xw7eboyOpaY6Uz4r7bFh9YvOy96iiXyjUlVQk/tW7ms/VF8/+23d57FYLG0OkYOfvI0xf6irjT/1Ea1S72uxiHQAKoA6Khy2AnxWFlQVrdAYneMyRt1tfnn8/vf9VpwOIWbgVG7xvE5BaQXXVfyGYf/LZNnO3JqvFdlOrZSsTZoiKq5r5b68XRrG7r8Aub4UZqj7b9Ld6toLRHI/Da8vzdUoyLowRufflrxy8L7QKDjnf3DxOw3vq8ViaQt8KyK3iEhn/40iEuItUfIycFltJ6iPaH3srXv1MLAE2AbMPLT+tiB881nJA7wLZveoaMV1Vlfc+tm6dqhMM6p/vzGTmQt2cuX4rkQPmMo45xoWB1/FzK6zufLYbvRNrSObwzXfaKBDcv+qGeKjvBFzo2tMdF8z4QkqhBlra59juuQ9XfPVoZb5LB+JPaD7ZPju7zDnnoP3DzoX2vVqeF8tFktbYCqaoGKmiOwWkTUishXYiJYp+T9jzEu1naBW0fLWNfnaGJNrjHkXncvqa4xp/YEY5V7R6jpeX3cv0UjCtFFaKn77j7DsDQgOp6C0gt+/u4LuSZHccVIftV6iOxAibsYO6MHdp/QjLNjJhn0FbNhXEPh6xmi4e8qAqtudwfCH/TDlEL5yZ5AGS3z/7wPiGpDoFBhzXf2DJ4Zdoq/+NbQsFoulDowxpcaYJ40x41E9mQIMM8Z0McZcY4xZVtc5ahUtY4wH+Jff5zJjTN5h9rtlUOF1D3Yao0ltt8xV6yumg1dg1qhF4XDy99nr2JNXwsPnDiEs2Kk3/x7H6/Fdj9PTuT1c9sICfvv2ctyeAFGA+9dpgIczQLKRoJDDj8bzJd5tDPpPh+PuULejxWKxHALGmApjzB5jTG5DjquPe/ALETmnrsmxVofP0opI1MXD62frZ4cT/pYCm7/BJPXlg6W7mLlgB1cf150RXeIrjx99NQyZcSAhbbDTwV2n9GV5eh5vLNhx8PWS+sKZTx+aRVUbF78LJ/61cUPQncHaz/rUxbJYLJZGpD6i9Rs0QW6ZiOSLSIGI5Ddxv5of35xWSKQKT543PVG73hruDbyxOYzb3lzGgA4x/ObE3lWP7zAMznq6yvqsaUM6ML5nIv+cs46tmUVV24vA0BkNTypbF71OgPG31t3OYrFYWgD1yYgRbYxxGGNCjDEx3s+1lL5tJfiiB4MjqpbvSKgMHV9Z3p4Hzx7EBzeNV7dgHYgIfztzEMFOB+c+9SN780oP7MsrqeCtRTvJLS6v5QwWi8XS8vHWTHR43/cWkWkiUssi0UrqLHAkIgHz+hhj5gXa3mrwt7R864+AjaXRBHtSyIkbyP233kVoUN1i5U+3dpG8ff1YPlq2m5SYUPKKK3jhh6288MNWCkpdPJ8SxWtXjSE5Jqzuk1ksFkvLZB5aADge+BpYhCaxuLiuA+vjHvyt388fgY+B+w61py0Gn6UVEgmpAzUHYGQSD325hUxJYGBMcYMFy0ePpChuP7E3IsLT8zbzn683MrZ7Ig+dM4j0nBI+W7W3EQdisVgsRx1ijCkGzgb+a4w5C81vWyd1WlrGmDOqXEmkE/DPQ+lli8JnaQVHaBaM5P4UVRi+WpvB7zp3JthRQ+h6A7n62G6cMbgD/Tuox3V8z3Z0jNPM5h6P0aS7FovF0roQERmLWlZXebfVq7R5vRLmViMdGHgIx7Usyv1ECzCn/IMH3ReTGhNGpytfhV+93yiXSYwKPSBYAGnxEYgIa3bnc8p/5rN5fy3rqywWi+UIICJTRWS9iGwSkbsC7I8XkfdFZIWILBCRujTiNuBu4H1jzGoR6Q58W5++1GdO678cqDmBAxgKLK/PyVs0FUUQFH4gO8VnBT14bV8e/zynN+FhTZ9tPMgpZBWVceGzPzPr2mPomhiJ02t1LdiaTZfECFLsvJfFYmliRMQJPAGciBotC0XkI2PMGr9m9wDLjDFniUhfb/spNZ3TGDMXmOs9vwPINMbUK8y5PpbWImCx9+cn4PfGmEvqc/IWTXkRhEQc+PjsvC30SIrknBGNVNywDnqnRPPGNcdgjOGCZ37m2Ie+YVtmERVuD7e/uYwT/z2XtxftxNRUrsRisVgah9HAJmPMFmNMOTALmF6tTX80oAJjzDqgq4ik1HRCEXlDRGJEJBJYA6wXkXpl2q6PaL0DvGaMedkY8zrws4hE1HVQi6e8GII1i8T+gjKW7czlzKEdD1g7R4LeKdHMvOYYQpxC18RIylwegp0OXrt6DH1TY/jtOyu49IUFbKu+5stisVjqT5CILPL7qZ7otCOw0+9zunebP8vRoApEZDSaoqm2J/z+xph84ExgNtAZ+FW9OluPNl8DJwC+yZVw4AugjnobLZyKSkvr2/UZABzf78iXe++VEs0Pdx2Pf0KSbu0imXXtMbz2y3b+OWc9Jz06j89vm0C3dpGUudw4RHCI4DEGYyDYKVWOt1gsFj9cxpiRtewPdPOo7uL5B/AfEVkGrASWAq5azhnsXZd1JvC4MaZCROrlNqqPaIUZYw5EAxhjCtuOpaXD/GZtBqkxYfRv3zxrqgMJjsMhXDq2KycPSOWjZbvp1k6twjOf+JG1e6omLBmcFssHN463kYgWi+VQSKdqDcU0YLd/A6/VdAUcqIe11ftTE8+gFUOWA/NEpAtQr0xL9RGtIhEZboxZ4u3QCKCkPidv0VQUQ4haLvM37mfa0I5HpbWSEhPGNRO6H/h8+bgu7C8ow+0Bh0C528PobglWsCwWy6GyEOglIt2AXcCFwEX+Dbzlq4q9c15XA/O8QhYQY8xjgH/V2u0iMrk+namPaN0GvC0iPmVtj65cbt2UF0F0exZuzaGo3M2UvkfeNXgoXDCqc437XvphK/GRIUwfWt0dbbFYLIExxrhE5Gbgc8AJvOANU7/eu/9poB/wioi40cCKq2o8ISAiscCfAV/GpbnAX4A6q4jUZ3HxQm8IYx/Ut7nOGFNR13EtnopiCIng63X7CA1yML5nu+bu0WHh8Rg+WbGHdXsLrGhZLJYGYYyZjQZM+G972u/9T0BDqr++AKwCzvd+/hXwIt5gjtqoM3pQRG4CIo0xq4wxK4EoEbmxAZ1rmZQXY4Ij+HptBuN6JBIecmgpm44WHA5hSr8UCstclJS7m7s7FoulbdPDGPNnbxj9FmPM/UD3Oo+ifiHv1/gX6TLG5ADXHFo/WxAVReS5Q9iRXczx/WpcbtCiiI/QJMrZNpO8xWJpXkpE5FjfBxEZTz1jJeozp+UQETHeVaze1dEByuu2MsqL2ZanEZjHt5D5rLqIi9BfW05R+YH8hhaLxdIMXI/OgcV6P+cAl9XnwPqI1ufAWyLyNBqbfz3w2aH0ssXgrgBPBRtzPPRNjW41N/ik6FA6J0Tg8tgsGhaLpfkwxiwHhohIjPdzvojcBqyo69j6iNbvgWuBG9BAjKVoBGHrxVuWZEchDBsU38ydaTxGdIln3u/qFVVqsVgsTU61sPjfAI/WdUx9Khd7gJ+BLcBINAni2kPrYgvBW5YkqzyY7t5FuxaLxWJpUuq1mLRG0fKWQP6TiKwFHsebe8oYM9kY83jj9PEoxVuWpNiE0j2p9YiWx2O45PlfmLlgR3N3xWKxWKpz2Gmc1gHzgTOMMZsAROT2RujY0U+FugdLCD2QHqk14HAIy3fm0jM5qrm7YrFY2iAiUkBgcRI0r22d1CZa56DpOr4VkTloOvq2kQvIa2mVShidElpXmsW4yGBybci7xWJpBowx0Yd7jhrdg8aY940xFwB9ge+A24EUEXlKRE463Asf1XgtreiYWIKdh1Lc+eglPiKEnOLWn9DEYrG0TuoTiFFkjHndGHM6mt13GXBQueVWhdfSSoxvPZGDPuIiQqylZbFYWiwNMiOMMdnGmGeMMcfXp72ITBWR9SKySUQCCp2ITBKRZSKyWkTmNuTYpsLjDXlPTmh9otW/fUyrc3laLJa2Q33WaR0S3swZTwAnovVYForIR8aYNX5t4oAnganGmB0iklzfY5uSvPw84oHUpMQjcbkjyl2n9G3uLlgsFssh05QTNqOBTd5kiOVoIMf0am0uAt4zxuwAMMZkNODYJiM3JweATsktO7O7xWKxtDaaUrQ64l3b5SXdu82f3kC8iHwnIotF5NIGHNtk5OdrSZcu7ZOO1CWPGHNW7eGEf88lq7CsubtisVgsDabJ3IMEDo+vHp8fBIxAs2yEAz+JyM/1PFYvInItmmaKkJDGyeNbWJBPmQkmObb1zf2Uuw2bMgrJLionMSq0ubtjsVgsDaIpRSsd6OT3OQ3YHaBNpjGmCCgSkXnAkHoeC4Ax5lngWYDIyMhGyQRbUpxPmSOMUGl9y9J85Uls2LvFYmmJNKV7cCHQS0S6iUgIulD5o2ptPgSOE5EgEYkAxqB5DetzbJNRUVKIy9k6MrtXJ95bniS7yIa9WyyWlkeTWVrGGJeI3IyWNnECLxhjVovI9d79Txtj1nqzbawAPMDzxphVAIGObaq++lPmcmPKizBRrc81CBAfqaJl12pZLJaWSFO6BzHGzAZmV9v2dLXPDwMP1+fYI8GOrGLCKcMR0npyDvqTEBHCsT3bkRDZ+ut4WiyW1keTilZLZEtmEXFSRnB4bN2NWyDhIU5eu3pMc3fDYrFYDonWlVivEdiaWUQ4ZYRGHHZeR4vFYrE0Mla0qrF1fxHRjnKCw1qnexDgihcX8OtZS5u7GxaLxdJgrHuwGlsyC4l2lENw6xWt0goPBaUlzd0Ni8ViaTDW0qpGek4J4VIGIa0zehAgPjKYHBs9aLFYWiBWtKpRUOoi1FMKwa1XtLQ8iV1cbLFYWh5WtPwwxlBSXk6QKYdWGvIOmhUjt6QCYxolgYjFYrEcMaxo+VFc7ibclOqHVmxpDU6LY/rQDpS5PM3dFYvFYmkQNhDDj6IyF+F4s5+34jmtkwekcvKA1ObuhsVisTQYa2n5UVjmIkJ8ohXVvJ1pYowx1j1osVhaHFa0/CgqcxPhs7RasXtwZXoeff84h+/W72/urlgsFkuDsKLlKof3b4AVb1HYRtyDUWFBlLk8NuzdYrG0OOycVlAIbJ0LngqK+k2sdA+24sXFtqaWxWJpqVhLC6DDMNi1mKJyF5F4owdbsaUVExaMQ2x5EovFUj9EZKqIrBeRTSJyV4D9sSLysYgsF5HVInJFU/XFihZAxxGQvYXygqxK92ArtrQcDiEuIsS6By0WS52IiBN4AjgF6A/MEJH+1ZrdBKwxxgwBJgH/8hbwbXSsaAF0HA5AROYKv+jB1mtpAVw8pjOjuiY0dzcsFsvRz2hgkzFmizGmHJgFTK/WxgDRIiJAFJANuJqiM3ZOC6D9UABic1YRTutfXAxwx0l9mrsLFovl6CBIRBb5fX7WGPOs3+eOwE6/z+lA9aJ8jwM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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "train_global('ds2/', ds=2)"
   ]
  },
  {
   "source": [
    "A folder containing the models and training plots will be named at first with the format `Global_TXs_dsX_Xchans_Xclasses-datetime` and then renamed to `XX.XX-Global_TXs_dsX_Xchans_Xclasses-datetime` once the training is completed, being the header the global validation accuracy. Let's rename it to a more human-readable name, as `global_model`, that will be used in the following of this notebook.\n",
    "\n",
    "> If there is more than one `XX.XX-Global_TXs_dsX_Xchans_Xclasses-datetime` folder, the following cell will rename the first one."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "awkward_name = [dir for dir in os.listdir() if dir[6:12] == 'Global'][0]\n",
    "\n",
    "os.system('mv {} global_model'.format(awkward_name));"
   ]
  },
  {
   "source": [
    "Both the training evolution curves and the training and validation accuracies for each fold figures should be shown when the training fold was compelted. Let's display the accuracies figure if it doesn't, since in its legend  the mean of both metrics is present . The valdiaiton accuracy is expected to be arround the 65 %."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "execution_count": 11
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(filename='global_model/Global-acc.png') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}