ChemCeption.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `Story: in-vitro screening -> virtual screening`\n",
    "Virtual screening is a computational technique used to identify potential drug candidates in the early stages of drug discovery.<br>\n",
    "It involves the screening of large chemical libraries to identify compounds that are likely to bind to a target protein.\n",
    "\n",
    "This came as a solution to the traditional method of drug discovery, which involves the screening of chemical libraries in the laboratory to identify compounds that bind to a target protein.\n",
    "\n",
    "This process can be time-consuming and expensive, as it requires the synthesis and testing of a large number of molecules.\n",
    "\n",
    "#### In-vitro:\n",
    "1. combine a protein with a chemical compound\n",
    "2. measure the binding affinity of the compound to the protein\n",
    "3. repeat for a large number of compounds\n",
    "4. select the compounds with the highest binding affinity\n",
    "5. synthesize the selected compounds\n",
    "6. proceed to in-vivo testing\n",
    "\n",
    "`Virtual Screening `can be used to reduce the number of compounds that need to be tested in the laboratory, which are steps 1-4."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `Reading and understanding the data`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>smiles</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CCc1ccc(CN2CCN(Cc3ccc(SC)cc3)CC2)cc1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NC(Cc1ccc(C(F)(F)F)cc1)c1csc(NC(=O)NCCc2ccccc2)n1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CC(=O)NCCNC(=O)c1cccc(NS(=O)(=O)c2ccc3c(c2)OCC...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>COC(=O)c1ccccc1S(=O)(=O)N1CCC(C(=O)N(C)CCOc2cc...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>O=C(CNC(=O)c1ccc(Cl)cc1Cl)OCN1C(=O)c2ccccc2C1=O</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              smiles  label\n",
       "0               CCc1ccc(CN2CCN(Cc3ccc(SC)cc3)CC2)cc1    1.0\n",
       "1  NC(Cc1ccc(C(F)(F)F)cc1)c1csc(NC(=O)NCCc2ccccc2)n1    1.0\n",
       "2  CC(=O)NCCNC(=O)c1cccc(NS(=O)(=O)c2ccc3c(c2)OCC...    0.0\n",
       "3  COC(=O)c1ccccc1S(=O)(=O)N1CCC(C(=O)N(C)CCOc2cc...    0.0\n",
       "4    O=C(CNC(=O)c1ccc(Cl)cc1Cl)OCN1C(=O)c2ccccc2C1=O    0.0"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import rdkit\n",
    "from rdkit import Chem\n",
    "\n",
    "from chemception.chemception_transfer import Chemception\n",
    "from chemception.checmception_featurizer import ChemCeptionizer\n",
    "\n",
    "import tensorflow as tf\n",
    "import keras.backend as K\n",
    "tf.compat.v1.disable_eager_execution()\n",
    "\n",
    "train_df = pd.read_csv('data/train_aid686978_0.7.csv')\n",
    "train_df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`SMILES` are a way to represent molecules as strings **(O=C(c1cc2c(=O)n3ccccc3nc2s1)N1CCOCC1)**\n",
    "\n",
    " This represenation is a standard in the Chemistry community and is usually parsed to other representaions of the molecule. For example, the `RDKit` library can parse SMILES to a `Mol` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x9b061b3c0>"
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Chem.MolFromSmiles(train_df['smiles'].iloc[0])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use this represntation as a first step for our featurization. \n",
    "\n",
    "We will use the `RDKit` library to parse the SMILES to a `Mol` object for all the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df[\"mol\"] = train_df[\"smiles\"].apply(Chem.MolFromSmiles)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `Featurizing`"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The featurization process plays a crucial role in our adopted paper, as it carefully selects features that effectively describe the molecule and incorporates them into the model. This approach aligns with our understanding of feature selection for our own data.\n",
    "\n",
    "Given that we are employing a CNN model, our featurization method relies on an image representation of the molecule. However, we also perform feature engineering to extract essential characteristics such as `bond order,` `atom types,` `hybridization,` and `Gasteiger charges.` These extracted features are then utilized in our model to enhance its performance.\n",
    "\n",
    "![Alt text](images/Featurizer.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is the featurizer we used, can be found in chemception/checmception_featurizer.py\n",
    "featureizer = ChemCeptionizer(embed=20, fuse=True)\n",
    "\n",
    "# Featurize the molecules\n",
    "train_df[\"molimage\"] = train_df[\"mol\"].apply(featureizer.featurize)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `Visualize the results of the featurization process`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Hybridization Channel')"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def show_channel(v, channel_ind):\n",
    "    \"\"\"\n",
    "    Returns a copy of the molecule image with only the specified channel\n",
    "    \"\"\"\n",
    "    v_copy = np.zeros(shape=(v.shape[0], v.shape[1], 3))\n",
    "    v_copy[:,:,channel_ind] = v[:,:,channel_ind]/ np.max(v[:,:,channel_ind])\n",
    "    return v_copy\n",
    "\n",
    "\n",
    "sample = train_df[\"molimage\"].iloc[0]\n",
    "ch1, ch2, ch3 = show_channel(sample, 0), show_channel(sample, 1), show_channel(sample, 2)\n",
    "fig, ax = plt.subplots(1, 4, figsize=(15, 15))\n",
    "ax[0].imshow(sample)\n",
    "ax[1].imshow(ch1)\n",
    "ax[2].imshow(ch2)\n",
    "ax[3].imshow(ch3)\n",
    "ax[0].set_title(\"Molecule\")\n",
    "ax[1].set_title(\"Bond Channel\")\n",
    "ax[2].set_title(\"Atom Channel\")\n",
    "ax[3].set_title(\"Hybridization Channel\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The featurization is working correctly, we proceed to model building"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `Preparing generator and data for fitting`"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Alt text](images/Featurizer.png) <br>\n",
    "\n",
    "\n",
    "By examining the image above, we can clearly observe the disparity in orientation between the original image and the result of our featurization process. As our featurization method involves reconstructing the molecule image based on the selected features, we can confidently assert that there is negligible intra-class variation, rendering augmentation unnecessary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "# create a generator that returns batches of size 256\n",
    "generator = ImageDataGenerator(data_format='channels_last')\n",
    "\n",
    "train_df.dropna(subset=[\"molimage\"], inplace=True)\n",
    "X_train = train_df[\"molimage\"].to_numpy()\n",
    "X_train = np.stack(X_train, axis=0)\n",
    "y_train = train_df[\"label\"].to_numpy()\n",
    "\n",
    "\n",
    "indeces_where_nan = np.any(np.isnan(X_train), axis=(1, 2, 3))\n",
    "X_train = X_train[~indeces_where_nan]\n",
    "y_train = y_train[~indeces_where_nan]\n",
    "\n",
    "batch_size=256\n",
    "g = generator.flow(X_train, y_train, batch_size=batch_size, shuffle=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define a pipeline for the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_featurizer(df, return_df=False):\n",
    "    \n",
    "    \"\"\"\n",
    "    takes a dataframe with smiles and labels and returns a numpy array of the featurized images and a numpy array of the labels\n",
    "    \"\"\"\n",
    "    df[\"mol\"] = df[\"smiles\"].apply(Chem.MolFromSmiles)\n",
    "    df[\"molimage\"] = df[\"mol\"].apply(featureizer.featurize)\n",
    "    df = df.dropna(subset=[\"molimage\"])\n",
    "    \n",
    "    X = df[\"molimage\"].to_numpy()\n",
    "    X = np.stack(X, axis=0)\n",
    "    y = df[\"label\"].to_numpy()\n",
    "    \n",
    "    # remove any rows with nan values\n",
    "    indeces_where_nan = np.any(np.isnan(X), axis=(1, 2, 3))\n",
    "    X = X[~indeces_where_nan]\n",
    "    y = y[~indeces_where_nan]\n",
    "    \n",
    "    if return_df:\n",
    "        df = df[~indeces_where_nan]\n",
    "        return X, y, df\n",
    "    else:\n",
    "        return X, y"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `Building the model`"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `Challenges`"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Considering our resource limitations, we have decided to utilize a CNN model with an `InceptionV3` architecture as our base model. It is worth noting that our choice of model architecture, `Chemception`, is heavily `influenced` by the InceptionV3 model.\n",
    "\n",
    "To construct our feature set, we will incorporate important aspects of the molecule, including `bonds_order`, `atom_types`, `hybridization`, and `gasteiger_charges`. These features will be used as inputs for our model, enabling it to capture the relevant characteristics of the molecules effectively."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As mentioned previously, we encountered a challenge while using the InceptionV3 model, as it was originally trained on RGB images. Since our featurization process resulted in four features, we needed to find a way to convert them into RGB images. We explored two options to address this issue:\n",
    "1. The first option involved discarding the last feature, gasteiger_charges, and utilizing the remaining three features as RGB images. However, this approach would result in the loss of a valuable feature.\n",
    "\n",
    "2. The second option we considered was employing weighted fusion to combine all four features into a single RGB image. By doing so, we could preserve the valuable feature and introduce significant variations in the resulting images.\n",
    "After careful consideration, we opted for the second option. The following images demonstrate the transformation:\n",
    "\n",
    "Before:\n",
    "<br><br>\n",
    "<div>\n",
    "<img src=\"images/Featurizer.png\" width=\"800\"/>\n",
    "</div><br><br><br>\n",
    "After:\n",
    "<br><br>\n",
    "<div>\n",
    "<img src=\"images/after_fusion.png\" width=\"800\"/>\n",
    "</div>\n",
    "\n",
    "By employing the weighted fusion technique, we successfully converted our feature set into an RGB image representation that could be effectively utilized by the InceptionV3 model."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `hyperparameters optimization`\n",
    "We used hyperband and bayesian optimization to find the best hyperparameters for our model.\n",
    "\n",
    "We used the `hpbandster` library for the and results can be found in the **./hyperbandster/visualizer.ipynb** notebook.\n",
    "<br>\n",
    "<br>\n",
    "<div>\n",
    "<img src=\"images/hpbandster.png\" width=\"500\"/>\n",
    "</div><br>\n",
    "<br>\n",
    "\n",
    "| Hyperparameter   | Value  |\n",
    "|-----------------|--------|\n",
    "| dense_layers    | 1      |\n",
    "| dropout         | 0.1    |\n",
    "| lr              | 0.0001 |\n",
    "| neurons         | 128    |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-07 01:16:40.378116: W tensorflow/c/c_api.cc:300] Operation '{name:'conv2d_173/kernel/Assign' id:147070 op device:{requested: '', assigned: ''} def:{{{node conv2d_173/kernel/Assign}} = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](conv2d_173/kernel, conv2d_173/kernel/Initializer/random_uniform)}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_7\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_21 (InputLayer)          [(None, 80, 80, 3)]  0           []                               \n",
      "                                                                                                  \n",
      " conv2d_94 (Conv2D)             (None, 39, 39, 32)   864         ['input_21[0][0]']               \n",
      "                                                                                                  \n",
      " batch_normalization_103 (Batch  (None, 39, 39, 32)  96          ['conv2d_94[0][0]']              \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_94 (Activation)     (None, 39, 39, 32)   0           ['batch_normalization_103[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_95 (Conv2D)             (None, 37, 37, 32)   9216        ['activation_94[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_104 (Batch  (None, 37, 37, 32)  96          ['conv2d_95[0][0]']              \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_95 (Activation)     (None, 37, 37, 32)   0           ['batch_normalization_104[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_96 (Conv2D)             (None, 37, 37, 64)   18432       ['activation_95[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_105 (Batch  (None, 37, 37, 64)  192         ['conv2d_96[0][0]']              \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_96 (Activation)     (None, 37, 37, 64)   0           ['batch_normalization_105[0][0]']\n",
      "                                                                                                  \n",
      " max_pooling2d_4 (MaxPooling2D)  (None, 18, 18, 64)  0           ['activation_96[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_97 (Conv2D)             (None, 18, 18, 80)   5120        ['max_pooling2d_4[0][0]']        \n",
      "                                                                                                  \n",
      " batch_normalization_106 (Batch  (None, 18, 18, 80)  240         ['conv2d_97[0][0]']              \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_97 (Activation)     (None, 18, 18, 80)   0           ['batch_normalization_106[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_98 (Conv2D)             (None, 16, 16, 192)  138240      ['activation_97[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_107 (Batch  (None, 16, 16, 192)  576        ['conv2d_98[0][0]']              \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_98 (Activation)     (None, 16, 16, 192)  0           ['batch_normalization_107[0][0]']\n",
      "                                                                                                  \n",
      " max_pooling2d_5 (MaxPooling2D)  (None, 7, 7, 192)   0           ['activation_98[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_102 (Conv2D)            (None, 7, 7, 64)     12288       ['max_pooling2d_5[0][0]']        \n",
      "                                                                                                  \n",
      " batch_normalization_111 (Batch  (None, 7, 7, 64)    192         ['conv2d_102[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_102 (Activation)    (None, 7, 7, 64)     0           ['batch_normalization_111[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_100 (Conv2D)            (None, 7, 7, 48)     9216        ['max_pooling2d_5[0][0]']        \n",
      "                                                                                                  \n",
      " conv2d_103 (Conv2D)            (None, 7, 7, 96)     55296       ['activation_102[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_109 (Batch  (None, 7, 7, 48)    144         ['conv2d_100[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_112 (Batch  (None, 7, 7, 96)    288         ['conv2d_103[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_100 (Activation)    (None, 7, 7, 48)     0           ['batch_normalization_109[0][0]']\n",
      "                                                                                                  \n",
      " activation_103 (Activation)    (None, 7, 7, 96)     0           ['batch_normalization_112[0][0]']\n",
      "                                                                                                  \n",
      " average_pooling2d_9 (AveragePo  (None, 7, 7, 192)   0           ['max_pooling2d_5[0][0]']        \n",
      " oling2D)                                                                                         \n",
      "                                                                                                  \n",
      " conv2d_99 (Conv2D)             (None, 7, 7, 64)     12288       ['max_pooling2d_5[0][0]']        \n",
      "                                                                                                  \n",
      " conv2d_101 (Conv2D)            (None, 7, 7, 64)     76800       ['activation_100[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_104 (Conv2D)            (None, 7, 7, 96)     82944       ['activation_103[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_105 (Conv2D)            (None, 7, 7, 32)     6144        ['average_pooling2d_9[0][0]']    \n",
      "                                                                                                  \n",
      " batch_normalization_108 (Batch  (None, 7, 7, 64)    192         ['conv2d_99[0][0]']              \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_110 (Batch  (None, 7, 7, 64)    192         ['conv2d_101[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_113 (Batch  (None, 7, 7, 96)    288         ['conv2d_104[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_114 (Batch  (None, 7, 7, 32)    96          ['conv2d_105[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_99 (Activation)     (None, 7, 7, 64)     0           ['batch_normalization_108[0][0]']\n",
      "                                                                                                  \n",
      " activation_101 (Activation)    (None, 7, 7, 64)     0           ['batch_normalization_110[0][0]']\n",
      "                                                                                                  \n",
      " activation_104 (Activation)    (None, 7, 7, 96)     0           ['batch_normalization_113[0][0]']\n",
      "                                                                                                  \n",
      " activation_105 (Activation)    (None, 7, 7, 32)     0           ['batch_normalization_114[0][0]']\n",
      "                                                                                                  \n",
      " mixed0 (Concatenate)           (None, 7, 7, 256)    0           ['activation_99[0][0]',          \n",
      "                                                                  'activation_101[0][0]',         \n",
      "                                                                  'activation_104[0][0]',         \n",
      "                                                                  'activation_105[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_109 (Conv2D)            (None, 7, 7, 64)     16384       ['mixed0[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_118 (Batch  (None, 7, 7, 64)    192         ['conv2d_109[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_109 (Activation)    (None, 7, 7, 64)     0           ['batch_normalization_118[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_107 (Conv2D)            (None, 7, 7, 48)     12288       ['mixed0[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_110 (Conv2D)            (None, 7, 7, 96)     55296       ['activation_109[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_116 (Batch  (None, 7, 7, 48)    144         ['conv2d_107[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_119 (Batch  (None, 7, 7, 96)    288         ['conv2d_110[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_107 (Activation)    (None, 7, 7, 48)     0           ['batch_normalization_116[0][0]']\n",
      "                                                                                                  \n",
      " activation_110 (Activation)    (None, 7, 7, 96)     0           ['batch_normalization_119[0][0]']\n",
      "                                                                                                  \n",
      " average_pooling2d_10 (AverageP  (None, 7, 7, 256)   0           ['mixed0[0][0]']                 \n",
      " ooling2D)                                                                                        \n",
      "                                                                                                  \n",
      " conv2d_106 (Conv2D)            (None, 7, 7, 64)     16384       ['mixed0[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_108 (Conv2D)            (None, 7, 7, 64)     76800       ['activation_107[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_111 (Conv2D)            (None, 7, 7, 96)     82944       ['activation_110[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_112 (Conv2D)            (None, 7, 7, 64)     16384       ['average_pooling2d_10[0][0]']   \n",
      "                                                                                                  \n",
      " batch_normalization_115 (Batch  (None, 7, 7, 64)    192         ['conv2d_106[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_117 (Batch  (None, 7, 7, 64)    192         ['conv2d_108[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_120 (Batch  (None, 7, 7, 96)    288         ['conv2d_111[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_121 (Batch  (None, 7, 7, 64)    192         ['conv2d_112[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_106 (Activation)    (None, 7, 7, 64)     0           ['batch_normalization_115[0][0]']\n",
      "                                                                                                  \n",
      " activation_108 (Activation)    (None, 7, 7, 64)     0           ['batch_normalization_117[0][0]']\n",
      "                                                                                                  \n",
      " activation_111 (Activation)    (None, 7, 7, 96)     0           ['batch_normalization_120[0][0]']\n",
      "                                                                                                  \n",
      " activation_112 (Activation)    (None, 7, 7, 64)     0           ['batch_normalization_121[0][0]']\n",
      "                                                                                                  \n",
      " mixed1 (Concatenate)           (None, 7, 7, 288)    0           ['activation_106[0][0]',         \n",
      "                                                                  'activation_108[0][0]',         \n",
      "                                                                  'activation_111[0][0]',         \n",
      "                                                                  'activation_112[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_116 (Conv2D)            (None, 7, 7, 64)     18432       ['mixed1[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_125 (Batch  (None, 7, 7, 64)    192         ['conv2d_116[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_116 (Activation)    (None, 7, 7, 64)     0           ['batch_normalization_125[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_114 (Conv2D)            (None, 7, 7, 48)     13824       ['mixed1[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_117 (Conv2D)            (None, 7, 7, 96)     55296       ['activation_116[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_123 (Batch  (None, 7, 7, 48)    144         ['conv2d_114[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_126 (Batch  (None, 7, 7, 96)    288         ['conv2d_117[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_114 (Activation)    (None, 7, 7, 48)     0           ['batch_normalization_123[0][0]']\n",
      "                                                                                                  \n",
      " activation_117 (Activation)    (None, 7, 7, 96)     0           ['batch_normalization_126[0][0]']\n",
      "                                                                                                  \n",
      " average_pooling2d_11 (AverageP  (None, 7, 7, 288)   0           ['mixed1[0][0]']                 \n",
      " ooling2D)                                                                                        \n",
      "                                                                                                  \n",
      " conv2d_113 (Conv2D)            (None, 7, 7, 64)     18432       ['mixed1[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_115 (Conv2D)            (None, 7, 7, 64)     76800       ['activation_114[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_118 (Conv2D)            (None, 7, 7, 96)     82944       ['activation_117[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_119 (Conv2D)            (None, 7, 7, 64)     18432       ['average_pooling2d_11[0][0]']   \n",
      "                                                                                                  \n",
      " batch_normalization_122 (Batch  (None, 7, 7, 64)    192         ['conv2d_113[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_124 (Batch  (None, 7, 7, 64)    192         ['conv2d_115[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_127 (Batch  (None, 7, 7, 96)    288         ['conv2d_118[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_128 (Batch  (None, 7, 7, 64)    192         ['conv2d_119[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_113 (Activation)    (None, 7, 7, 64)     0           ['batch_normalization_122[0][0]']\n",
      "                                                                                                  \n",
      " activation_115 (Activation)    (None, 7, 7, 64)     0           ['batch_normalization_124[0][0]']\n",
      "                                                                                                  \n",
      " activation_118 (Activation)    (None, 7, 7, 96)     0           ['batch_normalization_127[0][0]']\n",
      "                                                                                                  \n",
      " activation_119 (Activation)    (None, 7, 7, 64)     0           ['batch_normalization_128[0][0]']\n",
      "                                                                                                  \n",
      " mixed2 (Concatenate)           (None, 7, 7, 288)    0           ['activation_113[0][0]',         \n",
      "                                                                  'activation_115[0][0]',         \n",
      "                                                                  'activation_118[0][0]',         \n",
      "                                                                  'activation_119[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_121 (Conv2D)            (None, 7, 7, 64)     18432       ['mixed2[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_130 (Batch  (None, 7, 7, 64)    192         ['conv2d_121[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_121 (Activation)    (None, 7, 7, 64)     0           ['batch_normalization_130[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_122 (Conv2D)            (None, 7, 7, 96)     55296       ['activation_121[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_131 (Batch  (None, 7, 7, 96)    288         ['conv2d_122[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_122 (Activation)    (None, 7, 7, 96)     0           ['batch_normalization_131[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_120 (Conv2D)            (None, 3, 3, 384)    995328      ['mixed2[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_123 (Conv2D)            (None, 3, 3, 96)     82944       ['activation_122[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_129 (Batch  (None, 3, 3, 384)   1152        ['conv2d_120[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_132 (Batch  (None, 3, 3, 96)    288         ['conv2d_123[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_120 (Activation)    (None, 3, 3, 384)    0           ['batch_normalization_129[0][0]']\n",
      "                                                                                                  \n",
      " activation_123 (Activation)    (None, 3, 3, 96)     0           ['batch_normalization_132[0][0]']\n",
      "                                                                                                  \n",
      " max_pooling2d_6 (MaxPooling2D)  (None, 3, 3, 288)   0           ['mixed2[0][0]']                 \n",
      "                                                                                                  \n",
      " mixed3 (Concatenate)           (None, 3, 3, 768)    0           ['activation_120[0][0]',         \n",
      "                                                                  'activation_123[0][0]',         \n",
      "                                                                  'max_pooling2d_6[0][0]']        \n",
      "                                                                                                  \n",
      " conv2d_128 (Conv2D)            (None, 3, 3, 128)    98304       ['mixed3[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_137 (Batch  (None, 3, 3, 128)   384         ['conv2d_128[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_128 (Activation)    (None, 3, 3, 128)    0           ['batch_normalization_137[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_129 (Conv2D)            (None, 3, 3, 128)    114688      ['activation_128[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_138 (Batch  (None, 3, 3, 128)   384         ['conv2d_129[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_129 (Activation)    (None, 3, 3, 128)    0           ['batch_normalization_138[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_125 (Conv2D)            (None, 3, 3, 128)    98304       ['mixed3[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_130 (Conv2D)            (None, 3, 3, 128)    114688      ['activation_129[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_134 (Batch  (None, 3, 3, 128)   384         ['conv2d_125[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_139 (Batch  (None, 3, 3, 128)   384         ['conv2d_130[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_125 (Activation)    (None, 3, 3, 128)    0           ['batch_normalization_134[0][0]']\n",
      "                                                                                                  \n",
      " activation_130 (Activation)    (None, 3, 3, 128)    0           ['batch_normalization_139[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_126 (Conv2D)            (None, 3, 3, 128)    114688      ['activation_125[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_131 (Conv2D)            (None, 3, 3, 128)    114688      ['activation_130[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_135 (Batch  (None, 3, 3, 128)   384         ['conv2d_126[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_140 (Batch  (None, 3, 3, 128)   384         ['conv2d_131[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_126 (Activation)    (None, 3, 3, 128)    0           ['batch_normalization_135[0][0]']\n",
      "                                                                                                  \n",
      " activation_131 (Activation)    (None, 3, 3, 128)    0           ['batch_normalization_140[0][0]']\n",
      "                                                                                                  \n",
      " average_pooling2d_12 (AverageP  (None, 3, 3, 768)   0           ['mixed3[0][0]']                 \n",
      " ooling2D)                                                                                        \n",
      "                                                                                                  \n",
      " conv2d_124 (Conv2D)            (None, 3, 3, 192)    147456      ['mixed3[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_127 (Conv2D)            (None, 3, 3, 192)    172032      ['activation_126[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_132 (Conv2D)            (None, 3, 3, 192)    172032      ['activation_131[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_133 (Conv2D)            (None, 3, 3, 192)    147456      ['average_pooling2d_12[0][0]']   \n",
      "                                                                                                  \n",
      " batch_normalization_133 (Batch  (None, 3, 3, 192)   576         ['conv2d_124[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_136 (Batch  (None, 3, 3, 192)   576         ['conv2d_127[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_141 (Batch  (None, 3, 3, 192)   576         ['conv2d_132[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_142 (Batch  (None, 3, 3, 192)   576         ['conv2d_133[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_124 (Activation)    (None, 3, 3, 192)    0           ['batch_normalization_133[0][0]']\n",
      "                                                                                                  \n",
      " activation_127 (Activation)    (None, 3, 3, 192)    0           ['batch_normalization_136[0][0]']\n",
      "                                                                                                  \n",
      " activation_132 (Activation)    (None, 3, 3, 192)    0           ['batch_normalization_141[0][0]']\n",
      "                                                                                                  \n",
      " activation_133 (Activation)    (None, 3, 3, 192)    0           ['batch_normalization_142[0][0]']\n",
      "                                                                                                  \n",
      " mixed4 (Concatenate)           (None, 3, 3, 768)    0           ['activation_124[0][0]',         \n",
      "                                                                  'activation_127[0][0]',         \n",
      "                                                                  'activation_132[0][0]',         \n",
      "                                                                  'activation_133[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_138 (Conv2D)            (None, 3, 3, 160)    122880      ['mixed4[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_147 (Batch  (None, 3, 3, 160)   480         ['conv2d_138[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_138 (Activation)    (None, 3, 3, 160)    0           ['batch_normalization_147[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_139 (Conv2D)            (None, 3, 3, 160)    179200      ['activation_138[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_148 (Batch  (None, 3, 3, 160)   480         ['conv2d_139[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_139 (Activation)    (None, 3, 3, 160)    0           ['batch_normalization_148[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_135 (Conv2D)            (None, 3, 3, 160)    122880      ['mixed4[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_140 (Conv2D)            (None, 3, 3, 160)    179200      ['activation_139[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_144 (Batch  (None, 3, 3, 160)   480         ['conv2d_135[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_149 (Batch  (None, 3, 3, 160)   480         ['conv2d_140[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_135 (Activation)    (None, 3, 3, 160)    0           ['batch_normalization_144[0][0]']\n",
      "                                                                                                  \n",
      " activation_140 (Activation)    (None, 3, 3, 160)    0           ['batch_normalization_149[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_136 (Conv2D)            (None, 3, 3, 160)    179200      ['activation_135[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_141 (Conv2D)            (None, 3, 3, 160)    179200      ['activation_140[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_145 (Batch  (None, 3, 3, 160)   480         ['conv2d_136[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_150 (Batch  (None, 3, 3, 160)   480         ['conv2d_141[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_136 (Activation)    (None, 3, 3, 160)    0           ['batch_normalization_145[0][0]']\n",
      "                                                                                                  \n",
      " activation_141 (Activation)    (None, 3, 3, 160)    0           ['batch_normalization_150[0][0]']\n",
      "                                                                                                  \n",
      " average_pooling2d_13 (AverageP  (None, 3, 3, 768)   0           ['mixed4[0][0]']                 \n",
      " ooling2D)                                                                                        \n",
      "                                                                                                  \n",
      " conv2d_134 (Conv2D)            (None, 3, 3, 192)    147456      ['mixed4[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_137 (Conv2D)            (None, 3, 3, 192)    215040      ['activation_136[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_142 (Conv2D)            (None, 3, 3, 192)    215040      ['activation_141[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_143 (Conv2D)            (None, 3, 3, 192)    147456      ['average_pooling2d_13[0][0]']   \n",
      "                                                                                                  \n",
      " batch_normalization_143 (Batch  (None, 3, 3, 192)   576         ['conv2d_134[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_146 (Batch  (None, 3, 3, 192)   576         ['conv2d_137[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_151 (Batch  (None, 3, 3, 192)   576         ['conv2d_142[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_152 (Batch  (None, 3, 3, 192)   576         ['conv2d_143[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_134 (Activation)    (None, 3, 3, 192)    0           ['batch_normalization_143[0][0]']\n",
      "                                                                                                  \n",
      " activation_137 (Activation)    (None, 3, 3, 192)    0           ['batch_normalization_146[0][0]']\n",
      "                                                                                                  \n",
      " activation_142 (Activation)    (None, 3, 3, 192)    0           ['batch_normalization_151[0][0]']\n",
      "                                                                                                  \n",
      " activation_143 (Activation)    (None, 3, 3, 192)    0           ['batch_normalization_152[0][0]']\n",
      "                                                                                                  \n",
      " mixed5 (Concatenate)           (None, 3, 3, 768)    0           ['activation_134[0][0]',         \n",
      "                                                                  'activation_137[0][0]',         \n",
      "                                                                  'activation_142[0][0]',         \n",
      "                                                                  'activation_143[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_148 (Conv2D)            (None, 3, 3, 160)    122880      ['mixed5[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_157 (Batch  (None, 3, 3, 160)   480         ['conv2d_148[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_148 (Activation)    (None, 3, 3, 160)    0           ['batch_normalization_157[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_149 (Conv2D)            (None, 3, 3, 160)    179200      ['activation_148[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_158 (Batch  (None, 3, 3, 160)   480         ['conv2d_149[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_149 (Activation)    (None, 3, 3, 160)    0           ['batch_normalization_158[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_145 (Conv2D)            (None, 3, 3, 160)    122880      ['mixed5[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_150 (Conv2D)            (None, 3, 3, 160)    179200      ['activation_149[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_154 (Batch  (None, 3, 3, 160)   480         ['conv2d_145[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_159 (Batch  (None, 3, 3, 160)   480         ['conv2d_150[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_145 (Activation)    (None, 3, 3, 160)    0           ['batch_normalization_154[0][0]']\n",
      "                                                                                                  \n",
      " activation_150 (Activation)    (None, 3, 3, 160)    0           ['batch_normalization_159[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_146 (Conv2D)            (None, 3, 3, 160)    179200      ['activation_145[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_151 (Conv2D)            (None, 3, 3, 160)    179200      ['activation_150[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_155 (Batch  (None, 3, 3, 160)   480         ['conv2d_146[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_160 (Batch  (None, 3, 3, 160)   480         ['conv2d_151[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_146 (Activation)    (None, 3, 3, 160)    0           ['batch_normalization_155[0][0]']\n",
      "                                                                                                  \n",
      " activation_151 (Activation)    (None, 3, 3, 160)    0           ['batch_normalization_160[0][0]']\n",
      "                                                                                                  \n",
      " average_pooling2d_14 (AverageP  (None, 3, 3, 768)   0           ['mixed5[0][0]']                 \n",
      " ooling2D)                                                                                        \n",
      "                                                                                                  \n",
      " conv2d_144 (Conv2D)            (None, 3, 3, 192)    147456      ['mixed5[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_147 (Conv2D)            (None, 3, 3, 192)    215040      ['activation_146[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_152 (Conv2D)            (None, 3, 3, 192)    215040      ['activation_151[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_153 (Conv2D)            (None, 3, 3, 192)    147456      ['average_pooling2d_14[0][0]']   \n",
      "                                                                                                  \n",
      " batch_normalization_153 (Batch  (None, 3, 3, 192)   576         ['conv2d_144[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_156 (Batch  (None, 3, 3, 192)   576         ['conv2d_147[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_161 (Batch  (None, 3, 3, 192)   576         ['conv2d_152[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_162 (Batch  (None, 3, 3, 192)   576         ['conv2d_153[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_144 (Activation)    (None, 3, 3, 192)    0           ['batch_normalization_153[0][0]']\n",
      "                                                                                                  \n",
      " activation_147 (Activation)    (None, 3, 3, 192)    0           ['batch_normalization_156[0][0]']\n",
      "                                                                                                  \n",
      " activation_152 (Activation)    (None, 3, 3, 192)    0           ['batch_normalization_161[0][0]']\n",
      "                                                                                                  \n",
      " activation_153 (Activation)    (None, 3, 3, 192)    0           ['batch_normalization_162[0][0]']\n",
      "                                                                                                  \n",
      " mixed6 (Concatenate)           (None, 3, 3, 768)    0           ['activation_144[0][0]',         \n",
      "                                                                  'activation_147[0][0]',         \n",
      "                                                                  'activation_152[0][0]',         \n",
      "                                                                  'activation_153[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_158 (Conv2D)            (None, 3, 3, 192)    147456      ['mixed6[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_167 (Batch  (None, 3, 3, 192)   576         ['conv2d_158[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_158 (Activation)    (None, 3, 3, 192)    0           ['batch_normalization_167[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_159 (Conv2D)            (None, 3, 3, 192)    258048      ['activation_158[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_168 (Batch  (None, 3, 3, 192)   576         ['conv2d_159[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_159 (Activation)    (None, 3, 3, 192)    0           ['batch_normalization_168[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_155 (Conv2D)            (None, 3, 3, 192)    147456      ['mixed6[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_160 (Conv2D)            (None, 3, 3, 192)    258048      ['activation_159[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_164 (Batch  (None, 3, 3, 192)   576         ['conv2d_155[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_169 (Batch  (None, 3, 3, 192)   576         ['conv2d_160[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_155 (Activation)    (None, 3, 3, 192)    0           ['batch_normalization_164[0][0]']\n",
      "                                                                                                  \n",
      " activation_160 (Activation)    (None, 3, 3, 192)    0           ['batch_normalization_169[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_156 (Conv2D)            (None, 3, 3, 192)    258048      ['activation_155[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_161 (Conv2D)            (None, 3, 3, 192)    258048      ['activation_160[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_165 (Batch  (None, 3, 3, 192)   576         ['conv2d_156[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_170 (Batch  (None, 3, 3, 192)   576         ['conv2d_161[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_156 (Activation)    (None, 3, 3, 192)    0           ['batch_normalization_165[0][0]']\n",
      "                                                                                                  \n",
      " activation_161 (Activation)    (None, 3, 3, 192)    0           ['batch_normalization_170[0][0]']\n",
      "                                                                                                  \n",
      " average_pooling2d_15 (AverageP  (None, 3, 3, 768)   0           ['mixed6[0][0]']                 \n",
      " ooling2D)                                                                                        \n",
      "                                                                                                  \n",
      " conv2d_154 (Conv2D)            (None, 3, 3, 192)    147456      ['mixed6[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_157 (Conv2D)            (None, 3, 3, 192)    258048      ['activation_156[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_162 (Conv2D)            (None, 3, 3, 192)    258048      ['activation_161[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_163 (Conv2D)            (None, 3, 3, 192)    147456      ['average_pooling2d_15[0][0]']   \n",
      "                                                                                                  \n",
      " batch_normalization_163 (Batch  (None, 3, 3, 192)   576         ['conv2d_154[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_166 (Batch  (None, 3, 3, 192)   576         ['conv2d_157[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_171 (Batch  (None, 3, 3, 192)   576         ['conv2d_162[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_172 (Batch  (None, 3, 3, 192)   576         ['conv2d_163[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_154 (Activation)    (None, 3, 3, 192)    0           ['batch_normalization_163[0][0]']\n",
      "                                                                                                  \n",
      " activation_157 (Activation)    (None, 3, 3, 192)    0           ['batch_normalization_166[0][0]']\n",
      "                                                                                                  \n",
      " activation_162 (Activation)    (None, 3, 3, 192)    0           ['batch_normalization_171[0][0]']\n",
      "                                                                                                  \n",
      " activation_163 (Activation)    (None, 3, 3, 192)    0           ['batch_normalization_172[0][0]']\n",
      "                                                                                                  \n",
      " mixed7 (Concatenate)           (None, 3, 3, 768)    0           ['activation_154[0][0]',         \n",
      "                                                                  'activation_157[0][0]',         \n",
      "                                                                  'activation_162[0][0]',         \n",
      "                                                                  'activation_163[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_166 (Conv2D)            (None, 3, 3, 192)    147456      ['mixed7[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_175 (Batch  (None, 3, 3, 192)   576         ['conv2d_166[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_166 (Activation)    (None, 3, 3, 192)    0           ['batch_normalization_175[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_167 (Conv2D)            (None, 3, 3, 192)    258048      ['activation_166[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_176 (Batch  (None, 3, 3, 192)   576         ['conv2d_167[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_167 (Activation)    (None, 3, 3, 192)    0           ['batch_normalization_176[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_164 (Conv2D)            (None, 3, 3, 192)    147456      ['mixed7[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_168 (Conv2D)            (None, 3, 3, 192)    258048      ['activation_167[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_173 (Batch  (None, 3, 3, 192)   576         ['conv2d_164[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_177 (Batch  (None, 3, 3, 192)   576         ['conv2d_168[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_164 (Activation)    (None, 3, 3, 192)    0           ['batch_normalization_173[0][0]']\n",
      "                                                                                                  \n",
      " activation_168 (Activation)    (None, 3, 3, 192)    0           ['batch_normalization_177[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_165 (Conv2D)            (None, 1, 1, 320)    552960      ['activation_164[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_169 (Conv2D)            (None, 1, 1, 192)    331776      ['activation_168[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_174 (Batch  (None, 1, 1, 320)   960         ['conv2d_165[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_178 (Batch  (None, 1, 1, 192)   576         ['conv2d_169[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_165 (Activation)    (None, 1, 1, 320)    0           ['batch_normalization_174[0][0]']\n",
      "                                                                                                  \n",
      " activation_169 (Activation)    (None, 1, 1, 192)    0           ['batch_normalization_178[0][0]']\n",
      "                                                                                                  \n",
      " max_pooling2d_7 (MaxPooling2D)  (None, 1, 1, 768)   0           ['mixed7[0][0]']                 \n",
      "                                                                                                  \n",
      " mixed8 (Concatenate)           (None, 1, 1, 1280)   0           ['activation_165[0][0]',         \n",
      "                                                                  'activation_169[0][0]',         \n",
      "                                                                  'max_pooling2d_7[0][0]']        \n",
      "                                                                                                  \n",
      " conv2d_174 (Conv2D)            (None, 1, 1, 448)    573440      ['mixed8[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_183 (Batch  (None, 1, 1, 448)   1344        ['conv2d_174[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_174 (Activation)    (None, 1, 1, 448)    0           ['batch_normalization_183[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_171 (Conv2D)            (None, 1, 1, 384)    491520      ['mixed8[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_175 (Conv2D)            (None, 1, 1, 384)    1548288     ['activation_174[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_180 (Batch  (None, 1, 1, 384)   1152        ['conv2d_171[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_184 (Batch  (None, 1, 1, 384)   1152        ['conv2d_175[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_171 (Activation)    (None, 1, 1, 384)    0           ['batch_normalization_180[0][0]']\n",
      "                                                                                                  \n",
      " activation_175 (Activation)    (None, 1, 1, 384)    0           ['batch_normalization_184[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_172 (Conv2D)            (None, 1, 1, 384)    442368      ['activation_171[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_173 (Conv2D)            (None, 1, 1, 384)    442368      ['activation_171[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_176 (Conv2D)            (None, 1, 1, 384)    442368      ['activation_175[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_177 (Conv2D)            (None, 1, 1, 384)    442368      ['activation_175[0][0]']         \n",
      "                                                                                                  \n",
      " average_pooling2d_16 (AverageP  (None, 1, 1, 1280)  0           ['mixed8[0][0]']                 \n",
      " ooling2D)                                                                                        \n",
      "                                                                                                  \n",
      " conv2d_170 (Conv2D)            (None, 1, 1, 320)    409600      ['mixed8[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_181 (Batch  (None, 1, 1, 384)   1152        ['conv2d_172[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_182 (Batch  (None, 1, 1, 384)   1152        ['conv2d_173[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_185 (Batch  (None, 1, 1, 384)   1152        ['conv2d_176[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_186 (Batch  (None, 1, 1, 384)   1152        ['conv2d_177[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " conv2d_178 (Conv2D)            (None, 1, 1, 192)    245760      ['average_pooling2d_16[0][0]']   \n",
      "                                                                                                  \n",
      " batch_normalization_179 (Batch  (None, 1, 1, 320)   960         ['conv2d_170[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_172 (Activation)    (None, 1, 1, 384)    0           ['batch_normalization_181[0][0]']\n",
      "                                                                                                  \n",
      " activation_173 (Activation)    (None, 1, 1, 384)    0           ['batch_normalization_182[0][0]']\n",
      "                                                                                                  \n",
      " activation_176 (Activation)    (None, 1, 1, 384)    0           ['batch_normalization_185[0][0]']\n",
      "                                                                                                  \n",
      " activation_177 (Activation)    (None, 1, 1, 384)    0           ['batch_normalization_186[0][0]']\n",
      "                                                                                                  \n",
      " batch_normalization_187 (Batch  (None, 1, 1, 192)   576         ['conv2d_178[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_170 (Activation)    (None, 1, 1, 320)    0           ['batch_normalization_179[0][0]']\n",
      "                                                                                                  \n",
      " mixed9_0 (Concatenate)         (None, 1, 1, 768)    0           ['activation_172[0][0]',         \n",
      "                                                                  'activation_173[0][0]']         \n",
      "                                                                                                  \n",
      " concatenate_2 (Concatenate)    (None, 1, 1, 768)    0           ['activation_176[0][0]',         \n",
      "                                                                  'activation_177[0][0]']         \n",
      "                                                                                                  \n",
      " activation_178 (Activation)    (None, 1, 1, 192)    0           ['batch_normalization_187[0][0]']\n",
      "                                                                                                  \n",
      " mixed9 (Concatenate)           (None, 1, 1, 2048)   0           ['activation_170[0][0]',         \n",
      "                                                                  'mixed9_0[0][0]',               \n",
      "                                                                  'concatenate_2[0][0]',          \n",
      "                                                                  'activation_178[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_183 (Conv2D)            (None, 1, 1, 448)    917504      ['mixed9[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_192 (Batch  (None, 1, 1, 448)   1344        ['conv2d_183[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_183 (Activation)    (None, 1, 1, 448)    0           ['batch_normalization_192[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_180 (Conv2D)            (None, 1, 1, 384)    786432      ['mixed9[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_184 (Conv2D)            (None, 1, 1, 384)    1548288     ['activation_183[0][0]']         \n",
      "                                                                                                  \n",
      " batch_normalization_189 (Batch  (None, 1, 1, 384)   1152        ['conv2d_180[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_193 (Batch  (None, 1, 1, 384)   1152        ['conv2d_184[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_180 (Activation)    (None, 1, 1, 384)    0           ['batch_normalization_189[0][0]']\n",
      "                                                                                                  \n",
      " activation_184 (Activation)    (None, 1, 1, 384)    0           ['batch_normalization_193[0][0]']\n",
      "                                                                                                  \n",
      " conv2d_181 (Conv2D)            (None, 1, 1, 384)    442368      ['activation_180[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_182 (Conv2D)            (None, 1, 1, 384)    442368      ['activation_180[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_185 (Conv2D)            (None, 1, 1, 384)    442368      ['activation_184[0][0]']         \n",
      "                                                                                                  \n",
      " conv2d_186 (Conv2D)            (None, 1, 1, 384)    442368      ['activation_184[0][0]']         \n",
      "                                                                                                  \n",
      " average_pooling2d_17 (AverageP  (None, 1, 1, 2048)  0           ['mixed9[0][0]']                 \n",
      " ooling2D)                                                                                        \n",
      "                                                                                                  \n",
      " conv2d_179 (Conv2D)            (None, 1, 1, 320)    655360      ['mixed9[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_190 (Batch  (None, 1, 1, 384)   1152        ['conv2d_181[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_191 (Batch  (None, 1, 1, 384)   1152        ['conv2d_182[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_194 (Batch  (None, 1, 1, 384)   1152        ['conv2d_185[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " batch_normalization_195 (Batch  (None, 1, 1, 384)   1152        ['conv2d_186[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " conv2d_187 (Conv2D)            (None, 1, 1, 192)    393216      ['average_pooling2d_17[0][0]']   \n",
      "                                                                                                  \n",
      " batch_normalization_188 (Batch  (None, 1, 1, 320)   960         ['conv2d_179[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_181 (Activation)    (None, 1, 1, 384)    0           ['batch_normalization_190[0][0]']\n",
      "                                                                                                  \n",
      " activation_182 (Activation)    (None, 1, 1, 384)    0           ['batch_normalization_191[0][0]']\n",
      "                                                                                                  \n",
      " activation_185 (Activation)    (None, 1, 1, 384)    0           ['batch_normalization_194[0][0]']\n",
      "                                                                                                  \n",
      " activation_186 (Activation)    (None, 1, 1, 384)    0           ['batch_normalization_195[0][0]']\n",
      "                                                                                                  \n",
      " batch_normalization_196 (Batch  (None, 1, 1, 192)   576         ['conv2d_187[0][0]']             \n",
      " Normalization)                                                                                   \n",
      "                                                                                                  \n",
      " activation_179 (Activation)    (None, 1, 1, 320)    0           ['batch_normalization_188[0][0]']\n",
      "                                                                                                  \n",
      " mixed9_1 (Concatenate)         (None, 1, 1, 768)    0           ['activation_181[0][0]',         \n",
      "                                                                  'activation_182[0][0]']         \n",
      "                                                                                                  \n",
      " concatenate_3 (Concatenate)    (None, 1, 1, 768)    0           ['activation_185[0][0]',         \n",
      "                                                                  'activation_186[0][0]']         \n",
      "                                                                                                  \n",
      " activation_187 (Activation)    (None, 1, 1, 192)    0           ['batch_normalization_196[0][0]']\n",
      "                                                                                                  \n",
      " mixed10 (Concatenate)          (None, 1, 1, 2048)   0           ['activation_179[0][0]',         \n",
      "                                                                  'mixed9_1[0][0]',               \n",
      "                                                                  'concatenate_3[0][0]',          \n",
      "                                                                  'activation_187[0][0]']         \n",
      "                                                                                                  \n",
      " global_average_pooling2d_1 (Gl  (None, 2048)        0           ['mixed10[0][0]']                \n",
      " obalAveragePooling2D)                                                                            \n",
      "                                                                                                  \n",
      " dense_20 (Dense)               (None, 128)          262272      ['global_average_pooling2d_1[0][0\n",
      "                                                                 ]']                              \n",
      "                                                                                                  \n",
      " dropout_10 (Dropout)           (None, 128)          0           ['dense_20[0][0]']               \n",
      "                                                                                                  \n",
      " dense_21 (Dense)               (None, 1)            129         ['dropout_10[0][0]']             \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 22,065,185\n",
      "Trainable params: 262,401\n",
      "Non-trainable params: 21,802,784\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Model\n",
    "from keras.optimizers import Adam\n",
    "from keras.utils import plot_model\n",
    "\n",
    "model = Chemception(neurons=128, dropout=0.1, dense_layers=1)\n",
    "model = model.build()\n",
    "# Compile the model with binary cross-entropy loss and Adam optimizer\n",
    "model.compile(optimizer=Adam(learning_rate=0.0001), loss='binary_crossentropy', metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `Fitting the model`"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load validation data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [],
   "source": [
    "# val_df = pd.read_csv('data/val_aid686978_0.7.csv')\n",
    "# X_val, y_val = data_featurizer(val_df)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### With early stopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from keras.callbacks import EarlyStopping\n",
    "# early_stopping = EarlyStopping(monitor='val_loss', patience=20, verbose=1, mode='auto', restore_best_weights=True)\n",
    "# model.fit(g, epochs=100, steps_per_epoch=len(X_train)/batch_size, validation_data=(X_val, y_val), callbacks=[early_stopping], verbose=1)\n",
    "# model.save('chemception_transfer_preprocess.h5')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `Load the model after training`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-07 01:16:59.426762: W tensorflow/c/c_api.cc:300] Operation '{name:'batch_normalization_89_2/moving_variance/Assign' id:156360 op device:{requested: '', assigned: ''} def:{{{node batch_normalization_89_2/moving_variance/Assign}} = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](batch_normalization_89_2/moving_variance, batch_normalization_89_2/moving_variance/Initializer/ones)}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.\n",
      "2023-05-07 01:17:07.936091: W tensorflow/c/c_api.cc:300] Operation '{name:'dense_3/bias/v/Assign' id:158726 op device:{requested: '', assigned: ''} def:{{{node dense_3/bias/v/Assign}} = AssignVariableOp[_has_manual_control_dependencies=true, dtype=DT_FLOAT, validate_shape=false](dense_3/bias/v, dense_3/bias/v/Initializer/zeros)}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import load_model\n",
    "import tensorflow as tf\n",
    "\n",
    "model = load_model('chemception_transfer.h5')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `Test the model on test data`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df = pd.read_csv('data/test_aid686978_0.7.csv')\n",
    "X_test, y_test, test_df = data_featurizer(test_df, return_df=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `ROC curve`"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could not find other papers that have done studies on the same dataset, but a similar test was done in the `'HIV'` dataset. ROC scores are the following `(~0.74)`\n",
    "adjust the size of the image\n",
    "<br><br>\n",
    "<div>\n",
    "<img src=\"images/hiv_roc.png\" width=\"500\"/>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/miniconda3/lib/python3.9/site-packages/keras/engine/training_v1.py:2359: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
      "  updates=self.state_updates,\n",
      "2023-05-07 01:17:45.595600: W tensorflow/c/c_api.cc:300] Operation '{name:'dense_1_2/Sigmoid' id:157084 op device:{requested: '', assigned: ''} def:{{{node dense_1_2/Sigmoid}} = Sigmoid[T=DT_FLOAT, _has_manual_control_dependencies=true](dense_1_2/BiasAdd)}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.\n"
     ]
    },
    {
     "data": {
      "image/png": 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OTGRkJPr06YMDBw4AALp164bVq1fD1DTnBmHMLSyEiIiI8kBcchp+9r2W6WztADC5TSV0qGkLK1PNvIyUkdOnT6Nr1654/vw5DAwM8Ntvv2HAgAEaX7x9wEKIiIgolwghUHri/kxfr+tYFN1dS8LVsUi+Kn4+UCgUGDp0KJ4/f46KFSvC398f1apVkzpWlrAQIiIiyiFCCDx4FYejwa+w5Og9pKQp061jaaKPnUPqoWRRaUdUzglyuRzbtm3DsmXLsGTJEpiYmEgdKctkQgghdYi8FBsbC3Nzc8TExMDMLHfmUSEiIu2RqlDif9efY4z/9U+ut3dEA1SxNcs3l4wyc/z4cdy/fx+DBg3K0+Pm1vc3zwgRERFlgUIpcPLeK5y4G4lNFx5nup6VqT5KFjHC2t4uKGysl4cJc4dCocCsWbMwe/ZsyOVyODs7a9zgiNnBQoiIiOgTElMUePo2Ab6Xn+Dvq08RnZCa4Xq25gb4zskO/eo7wNos//X3+ZTnz5+jR48eCAgIAAD07dsXlStXljZUDmEhRERE9C/vklJx4dEb+F95giN3Xn5yXYeiRuj5TSl0q1MSxho4tk9OOHToEHr16oXIyEiYmJhg9erV6N69u9SxckzB/NSIiIi+kBACVx6/Rbc1F5CmzLzbrK5cBhlkmNquEn5wtoehnvTzeOW2GTNmYObMmQCAGjVqwN/fH+XLl5c4Vc5iIURERFonKVWBcduv41LoG7x6l5zpesVM9TGgQWn0qeegEROY5jULCwsAwODBg7FkyRKNnSbja7AQIiIiraFUCnRZfR6Bj9+me616CXOUKGyIia0rwb5I/r+1Pbvi4+NhbGwMABg1ahRq1qyJxo0bS5wq97AQIiKiAi05TYGrj6Mx5Z+beBgZr/ZaeWsTDGjgiHY1bGCkp91fiampqZg0aRL27NmDK1euwNTUFDKZrEAXQQALISIiKmCEEDh9PwoTd95EcpoSUXEZX/q6NOlbWBWwu7uy6/Hjx+jatSsuXLgAAPjnn3/Qq1cviVPlDRZCRERUIASEvMLU3bfw5E1iputUtjHD7z1qwcHSOA+Tabbdu3ejb9++iI6Ohrm5OdavX49OnTpJHSvPsBAiIqJ8KzYpFdVnHM7wNQNdHXRxtkezilaoU7pIgb29PbtSUlLwyy+/YNmyZQCAOnXqwNfXF6VLl5Y4Wd7i/xVERJTvXAl7g4uhb7DgUEi61zrVtMOUdpVRpACM5pybJkyYoCqCxo4dCy8vL+jpad/PjIUQERHlC0mpCgzbchXH7r7K8PVN/eugQVnLfD+XV17x9PTEkSNH4O3tjfbt20sdRzIshIiISCOlKpTYE/Qc1568xeYL4RmuU9nGDD82KI0fnEvkcbr8JykpCbt27UK3bt0AANbW1rhx4wZ0dHQkTiYtFkJERKRRFhy6i5UnHmb6eskiRljV0xmVbXNuBvKC7v79+3B3d0dQUBAAqIohbS+CABZCREQkoRcxifjf9efYd+MFnkUnZXirezFTfVQsbgoLIz3M+q5KgZjJPS9t27YNAwcORFxcHCwtLVGkSBGpI2kUFkJERJSnklIV+HVfMPyvPEFymjLT9TxbV8SABqVRSM6zFtmRmJiIUaNGYe3atQCARo0aYevWrbCzs5M4mWZhIURERLnqTXwKDt+OwIz/3YaFoR4iYpPSrVPa0hgORY1Qp3RR1C9bFNVLWOR90ALk7t27cHd3x82bNyGTyTB58mRMnz4dhQrxa/+/+BMhIqIclaZQ4sCtCPzv+nMcvvNS7bWI1I9FUGEjXbSqWhyj3cpzhOcc9vDhQ9y8eRNWVlbYsmUL3NzcpI6ksVgIERHRV3v6NgFT/rmFgJDIT65X1c4M41tWRB2HIjDU077Z3PNK27ZtsXbtWrRt2xY2NjZSx9FoLISIiCjb7kbEotXS05m+3qmmHX5sUBpV7czzMJX2uX37NgYPHozNmzejVKlSAIABAwZInCp/YCFERERZtvfGc4z2C0KqQqi11ytTFAMbOcK1dFGe8ckDQgj89ddfGD58OBITE/Hzzz9j165dUsfKV1gIERHRF3n8Oh77b0Zg3sG76V5rVtEK6/vWliCV9oqLi8PgwYOxZcsWAECLFi2wevVqiVPlPyyEiIgoU8lpCvhffoKpu2+ne02/kA7+6lcb9cpYSpBMu12/fh3u7u64d+8e5HI5Zs+ejQkTJnCAxGxgIURERBmasec2fM6FpWuv41AEzStb46dGjnkfinD69Gk0b94cycnJsLOzg6+vLxo0aCB1rHyLhRAREQEAlEqBA7cicONZNFaffJTu9eaVrbGqpzPkOpzUVEq1a9dGxYoVYWdnhw0bNsDSkmfkvgYLISIiLZWSpsSFR69x/O6rDM/8fLDtp29Qt0zRvAtG6QQHB6N8+fKQy+UwMDDA0aNHUaRIEV4KywEshIiItExUXDJc5hz95DplrUzQorI1xresAJmMZ4CkIoTAypUrMXbsWEyePBnTpk0DAJ4FykEshIiItIRCKdBz3UWcf/RarV0mA6xNDdC7Xim4u9jD0kRfooT0b9HR0ejfvz927twJ4H0HaaVSybNAOYyFEBFRARf4+C2WHbuPU/fUR322NtPHec9vocM+Pxrn0qVL8PDwQFhYGHR1dbFgwQKMHDmSZ+dyAQshIqICJlWhxP+uP8fqk48Q8vJdhuvc/7U1dDmru8YRQmDp0qWYMGECUlNTUbp0afj5+aF2bY7RlFtYCBERFRDvklLRaP4JvE1IzfD1NtWKw7NVJZQsapTHyehLhYaGYtKkSUhNTUXnzp2xbt06WFhYSB2rQGMhRERUAHjtD8aaU+lveXd3KYH+DRxRobipBKkoqxwdHbFy5UokJiZi6NChvBSWB1gIERHlU0IIzNhzGxvOP0732pHRjVDOmsWPplMqlVi0aBEaNmyIb775BgDw448/SpxKu7AQIiLKZ5JSFZjyzy3sCHya7rU9w+ujegmLvA9FWRYZGYk+ffrgwIEDKFWqFG7dugUTExOpY2kdFkJERPlEcpoCvdZdwqWwN+le8x34DVxLF+GllHzi1KlT6NatG54/fw4DAwNMnjwZxsbGUsfSSiyEiIjygQ3nwjB9j/rEp4WNdOE7sC77/+QjSqUS3t7emDZtGpRKJSpUqAB/f39Ur15d6mhai4UQEZEGEkJg66VwnAyJxOE7L9O9vm9kA1SxNZcgGWVXXFwcOnXqhCNHjgAAevXqhd9//52XwyTGQoiISMOkKpQoN/lAhq/90aMWWlezyeNElBOMjY1haGgIQ0ND/P777+jbt6/UkQgshIiINEbwi1gM2RyIsNcJau3NKlqhaYVi6FXXQZpglG0KhQIpKSkwNDSETCbDX3/9hYiICFSuXFnqaPT/WAgREWmAZUfvY8nRe2ptlW3MsH9UQ4kS0dd68eIFunfvDjs7O2zatAkymQxFihRBkSJFpI5G/8JCiIhIQreexaDd8jNqbY6WxpjQuiJaVLaWKBV9rcOHD6Nnz56IjIyEsbExHj16hDJlykgdizLAQoiIKI+dCHmFyTtv4nlMUrrXzk9sBhtzQwlSUU5IS0vD9OnT4e3tDSEEqlevDj8/PxZBGoyFEBFRHhBCYNulJ5i062aGrzcsZ4lN/V3zOBXlpKdPn6J79+44ffo0AGDQoEFYsmQJDA1Z2GoyFkJERLlICIGx/tex89qzdK81LGeJRe41YGVqIEEyyklKpRKtW7fGrVu3YGpqirVr18LDw0PqWPQFWAgREeWSh5Fx+HbRyXTtU9pWQv8GpTkKdAGio6ODpUuXwtPTE9u2bUPZsmWljkRfSCaEEFKHyEuxsbEwNzdHTEwMzMzMpI5DRAXQo8g4LDwcgv03I9TadwyuCxcH3jFUUISHh+Pu3bto0aKFqk2pVEJHR0fCVAVXbn1/84wQEVEOuf/yHZovOZWuvVNNOyz2cMr7QJRr9uzZg759+yItLQ1Xr15VnQFiEZT/sBAiIvpKSakKDN4ciICQSLX2wka6ODCqEYqbsw9QQZGSkoIJEyZg6dKlAIDatWujUCF+leZnkpeuK1euhIODAwwMDODq6opLly59cv2lS5eiQoUKMDQ0hL29PUaPHo2kpPS3oBIR5bbAx2/QetlpVJx6UK0Ial/DFmFz2+LatBYsggqQ0NBQNGjQQFUEjR49GmfOnIGDg4OkuejrSFrG+vn5YcyYMVi1ahVcXV2xdOlStGzZEiEhIbCyskq3/tatW+Hp6Yn169ejXr16uHfvHvr27QuZTIbFixdL8A6ISBslpSrQeMEJvIxNVmsvbWmM3cPrw8xAV6JklFv+/vtv9O/fHzExMShcuDB8fHzw3XffSR2LcoCkhdDixYvx008/oV+/fgCAVatWYd++fVi/fj08PT3TrX/u3DnUr18f3bt3BwA4ODigW7duuHjxYp7mJiLtFPkuGV1WnUs3F1iNEubY8GMdWBjpSZSMctu5c+cQExODunXrwtfXFyVLlpQ6EuUQyQqhlJQUBAYGYuLEiao2HR0duLm54fz58xluU69ePWzevBmXLl1CnTp18OjRI+zfvx+9evXK9DjJyclITv74V1tsbGzOvQki0gpCCHj+fRN+V56otTsWM8bR0Y2ho8Pb4AsiIYRqiANvb2+UKlUKQ4YMga4uz/gVJJIVQlFRUVAoFLC2Vp9Lx9raGnfv3s1wm+7duyMqKgoNGjSAEAJpaWkYPHgwJk2alOlxvL29MXPmzBzNTkTa48mbBDScf0KtrX7Zolji4cSBEAswX19fbNiwAXv27IGuri709PQwcuRIqWNRLpC8s3RWBAQEwMvLC7///juuXr2KnTt3Yt++fZg9e3am20ycOBExMTGqx5MnTzJdl4jo38Zvv56uCDo6pjG2DPiGRVABlZiYiEGDBqFbt244ePAg1q5dK3UkymWSnRGytLSEXC7Hy5cv1dpfvnyJ4sWLZ7jN1KlT0atXLwwYMAAAUK1aNcTHx2PgwIGYPHlyhuM36OvrQ19fP+ffABEVWFFxyXCZc1StrV11G6zoXkuiRJQXQkJC4O7ujhs3bkAmk2HSpEkYOHCg1LEol0l2RkhPTw/Ozs44duyYqk2pVOLYsWOoW7duhtskJCSkK3bkcjmA99dyiYi+1oZzYemKoM39XVkEFXCbN2+Gs7Mzbty4ASsrKxw6dAhz5szhGEFaQNJPeMyYMejTpw9cXFxQp04dLF26FPHx8aq7yHr37g07Ozt4e3sDANq3b4/FixejZs2acHV1xYMHDzB16lS0b99eVRAREWVHTEIqasw6rNbWqHwxbOhXm3OCFXC//vorpkyZAgBo2rQptmzZAhsbG4lTUV6RtBDy8PBAZGQkpk2bhoiICDg5OeHgwYOqDtTh4eFqZ4CmTJkCmUyGKVOm4NmzZyhWrBjat2+PX3/9Vaq3QET5XJpCicGbA3E0+JVa+9reLmhe2TqTragg+eGHHzB//nyMGTMGU6ZM4R/WWoaTrhKR1srojjAjPTluTG+BQvJ8dS8JZYEQAjdu3ECNGjVUba9fv0bRokUlTEWfw0lXiYhyyLukVHy76CRevfs4xpiRnhwnxzdFMVPeXFGQxcXFYciQIdi6dSuOHz+Oxo0bAwCLIC3GP3mISKucvBeJajMOqxVBI78thzuzWrEIKuBu3LgBFxcXbN68GQBw69YtiRORJuAZISLSChldBqvtUBib+rvCQJd9QgoyIQTWrl2LkSNHIjk5GXZ2dti2bRsaNmwodTTSACyEiKjAEkLgr7NhmLX3TrrXFrvXQKdaJSRIRXkpNjYWgwYNgq+vLwCgdevW2LhxIywtLSVORpqChRARFThCCNx+Hot2y8+ke61icVMc/LmRBKlICrt374avry/kcjm8vb0xduzYDAffJe3FQoiICpQ9159j5LZr6dpHNiuLIU3KwlCPl8G0Sc+ePXHt2jV06dIl08F6SbuxECKifC8hJQ3Tdt/GjsCn6V4rWcQIp35pKkEqkkJ0dDSmTJmC2bNno3DhwpDJZFi8eLHUsUiDsRAionzr3st3mLjzJgIfv033mv+guqhTuogEqUgqly9fhoeHB0JDQxEVFaXqF0T0KSyEiCjfiUtOw3crzuBRZLxau52FIRb8UB11yxTltBhaRAiBZcuW4ZdffkFqaipKly6NsWPHSh2L8gkWQkSUbwghUHri/nTtTvYWmN6+MmqWLCxBKpLSmzdv0K9fP+zZswcA0LlzZ6xbtw4WFhbSBqN8g4UQEWk8IQS+W3EWN5/FqLU3Ll8MPpwUVWvdvHkT7dq1Q3h4OPT09LB48WIMHTqU/z9QlrAQIiKNFvwiFq2XnU7Xfv/X1tDlfGBazdbWFkIIlClTBv7+/qhVq5bUkSgfYiFERBrrlx3X4X9F/U6wk+OboFRRY4kSkdTevXsHExMTyGQyFC1aFAcOHIC9vT0n0aZs459TRKRxXsclw8Fzn1oRNLVdZYR6t2ERpMVOnz6NSpUqwcfHR9VWpUoVFkH0VVgIEZFGOX0/Es5zjqq17R/ZEP0blGbfDy2lVCrh5eWFpk2b4tmzZ1i+fDkUCoXUsaiA4KUxItIYp+9Hotefl1TL/eo7YHr7KhImIqm9evUKPXv2xJEjRwC8Hyn6jz/+gFzOEcIpZ7AQIiKN8ODVO7UiaEX3mmhX3VbCRCS1EydOoHv37oiIiIChoSFWrFiBfv368cwg5SgWQkQkuR7rLuDsg9eq5TW9nNGiSnEJE5HUHj9+jBYtWiAtLQ2VK1eGv78/qlTh2UHKeSyEiEgyD17FwW3xSdWyhZEuFrvXQLOK1hKmIk1QqlQpTJw4EU+fPsXy5cthbMxO8pQ7ZEIIIXWIvBQbGwtzc3PExMTwTgMiCT1+HY/GCwLU2h56tYFch5c9tNXRo0fh4OCAsmXLAng/kCYvg9EHufX9zbvGiEgS/y6COjjZImxuWxZBWiotLQ1TpkxBixYt4OHhgeTkZABgEUR5gpfGiChPCSEwdvt11XJRYz0s61pTwkQkpWfPnqFbt244ffr96OG1a9eGll2oIImxECKiPBMVlwyX/4wRdGmym0RpSGoHDhxA7969ERUVBVNTU6xZswZdu3aVOhZpGRZCRJTrUtKUmHfwLv48E6rWHjCuCS+HaaHU1FRMmTIF8+fPBwDUrFkTfn5+KFeunMTJSBuxECKiXCOEQOmJ+9O121kY4uiYxjDU46B42kgIgRMnTgAAhg0bhoULF8LAwEDiVKStWAgRUa64/iQaHVaeVWuzMTfAvpENUcRYT6JUJKUPd4Hp6enBz88PV69eRefOnaWORVqOhRAR5SghBGrNPoK3Calq7bdntoSxPn/laKOUlBR4enrCwMAAXl5eAIDSpUujdOnSEicjYiFERDnsv5fCWlaxxupeLhKlIamFhoaia9euuHTpEmQyGXr37o2KFStKHYtIhYUQEeWIVIUS5accUGu7MPFbFDdn3w9ttXPnTvz444+IiYmBhYUFfHx8WASRxuGAikT01e5GxKLc5AP49/Av939tzSJISyUnJ2PEiBHo3LkzYmJi8M033yAoKAgdOnSQOhpROjwjRERf5Zcd1+F/5alaW/CsVtCV8+8sbSSEQIsWLXDq1CkAwC+//II5c+ZAV1dX4mREGWMhRETZEvj4LTr/cU6tza2SNdb1YX8gbSaTyTBgwADcvn0bGzduRJs2baSORPRJnHSViL5YUqoC+268gOfOG0hVqP/qYH8g7ZWYmIiwsDBUqlRJ1fbmzRsUKVJEwlRU0OTW9zfPCBHRF9kR+BTj/jVH2AeDGjliYptKGWxB2iAkJATu7u6IiopCUFAQihUrBgAsgijfYCFERJkSQiAgJBL9fC6rtRfSkcGxmDH+GVYfRnr8NaKtNm/ejMGDByM+Ph7FihVDaGioqhAiyi/4G4yIMpSYokClaQfTtQfPasWpMbRcQkICRowYgfXr1wMAmjRpgi1btsDW1lbiZERZx0KIiNQIIbD1Ujgm77ql1j6lbSX0b1AaMhknSdVmd+7cgbu7O27fvg2ZTIZp06Zh6tSpkMtZHFP+xEKIiAAAL2ISUdf7eLr28tYm2DuiIfQK8XZ4AubNm4fbt2+jePHi2LJlC5o1ayZ1JKKvwkKISMulKZQYvvUaDt6OSPfapv510LAc+3zQR7/99hsKFSoELy8vWFtbSx2H6KuxECLSYtuvPMH4HTfU2jo62cKrUzV2giYAwM2bN7Fx40bMnz8fMpkM5ubm+PPPP6WORZRj+JuOSEt57Q/GmlOP1NqOjW2MMsVMJEpEmkQIgXXr1mHkyJFISkpChQoVMGDAAKljEeU4FkJEWsh91XlcCnujWt7wYx00Ls9LYPRebGwsBg0aBF9fXwBA69atOU8YFVgshIi0zImQV2pFEM8C0b9du3YN7u7uePDgAeRyOby8vDBu3Djo6LCzPBVMX1UIJSUlwcCAQ+oT5QfvklIxeHMgzj54rWo769kMdhaGEqYiTbJp0yYMGDAAKSkpsLe3h6+vL+rVqyd1LKJcleUSX6lUYvbs2bCzs4OJiQkePXrfx2Dq1KnsQEekoQZuvIJqMw6rFUF/9nFhEURqSpcuDYVCgfbt2yMoKIhFEGmFLBdCc+bMgY+PD+bPnw89PT1Ve9WqVbFu3bocDUdEX2/Ylqs4fOelarlVleK4PbMlvq3EW58JiImJUT1v0KABzp8/j927d3OuMNIaWS6ENm7ciDVr1qBHjx5qI4nWqFEDd+/ezdFwRJR9QghUnnYQ+26+ULXtHdEAq3o5w1if3QO1nRACy5Ytg4ODA+7cuaNqr127NkcPJ62S5ULo2bNnKFu2bLp2pVKJ1NTUHAlFRF9vyj+3kJCiUC3/PaQuqtqZS5iINMWbN2/w/fff4+eff0Z0dDR8fHykjkQkmSz/WVi5cmWcPn0apUqVUmvfsWMHatasmWPBiCh7ElMUqDv3GKITPv5hcmtmS5jwLBABuHDhAjw8PBAeHg49PT0sWrQIw4YNkzoWkWSy/Jtx2rRp6NOnD549ewalUomdO3ciJCQEGzduxN69e3MjIxF9oVvPYtBu+RnVsn4hHVyZ4sYiiKBUKrF48WJMnDgRaWlpKFOmDPz8/ODs7Cx1NCJJZfnSWIcOHfC///0PR48ehbGxMaZNm4bg4GD873//Q/PmzXMjIxF9gcO3I9SKoJolLXBrZkuYGuhKmIo0xebNmzF+/HikpaXB3d0dgYGBLIKIAMiEEELqEHkpNjYW5ubmiImJgZmZmdRxiHJErz8v4vT9KNXyqG/LYXTz8hImIk2TlpaGtm3b4vvvv8egQYPYIZryndz6/s7yGSFHR0e8fv06XXt0dDQcHR1zJBQRfZnkNAV6r7+kVgQdGNWQRRBBqVRi3bp1SE5OBgAUKlQIBw8exODBg1kEEf1LljsOhIWFQaFQpGtPTk7Gs2fPciQUEX2aUinQbFEAwl4nqLXfnd0KBrryTLYibfHq1Sv06tULhw8fxq1bt7B06VIAYAFElIEvLoT27Nmjen7o0CGYm3+8DVehUODYsWNwcHDI0XBElF6aQomykw+otZkb6mLviAYsgggBAQHo3r07Xrx4AUNDQ1SvXl3qSEQa7YsLoY4dOwJ4/xdFnz591F7T1dWFg4MDFi1alKPhiEhdUqoCFaceVGs7Nb4pShY1kigRaQqFQoFff/0VM2fOhFKpRKVKlbB9+3ZUqVJF6mhEGu2LCyGlUgng/Vw0ly9fhqWlZa6FIqL0Tt2LRO/1l1TLjcoXw8Yf60iYiDRFREQEevTogePHjwMA+vXrh+XLl8PY2FjiZESaL8t9hEJDQ3MjBxFlQgiB0hP3q7XVKV2ERRCpJCQk4MqVKzAyMsKqVavQq1cvqSMR5RvZGmUtPj4eJ0+eRHh4OFJSUtReGzlyZI4EI6KML4VNaFURQ5qUkSgRaQohhKrzs6OjI/z9/VGqVClUrFhR4mRE+UuWxxG6du0a2rRpg4SEBMTHx6NIkSKIioqCkZERrKys8OjRo9zKmiM4jhDlF2cfRKHHuotqbQ+92kCuwzt/tN2zZ8/Qs2dPTJw4ES1atJA6DlGe0JhxhEaPHo327dvj7du3MDQ0xIULF/D48WM4Oztj4cKFORaMSJslpSrUiiBduQyPWAQRgIMHD8LJyQkBAQEYOnQo0tLSpI5ElK9luRAKCgrC2LFjoaOjA7lcjuTkZNjb22P+/PmYNGlSbmQk0ip/nQ1Vuxw2qJEj7v/aBjosgrRaamoqPD090bp1a0RFRcHJyQn79+9HoUKcR47oa2T5X5Curi50dN7XT1ZWVggPD0elSpVgbm6OJ0+e5HhAIm3i4LlPbdnW3AAT21SSKA1piidPnqBr1644d+4cAGDo0KFYtGgRDAwMJE5GlP9l+YxQzZo1cfnyZQBA48aNMW3aNGzZsgU///wzqlatmuUAK1euhIODAwwMDODq6opLly59cv3o6GgMGzYMNjY20NfXR/ny5bF///5PbkOk6e48j01XBE1uUwnnJn4rUSLSFM+ePYOTkxPOnTsHMzMzbN++HStXrmQRRJRDsnxGyMvLC+/evQMA/Prrr+jduzeGDBmCcuXK4c8//8zSvvz8/DBmzBisWrUKrq6uWLp0KVq2bImQkBBYWVmlWz8lJQXNmzeHlZUVduzYATs7Ozx+/BgWFhZZfRtEGuFy2BsM2hSIN/Hqd18GTnFDURN9iVKRJrGzs0P79u1x+/Zt+Pn5cU5Hohwm6ezzrq6uqF27NlasWAHg/aCN9vb2GDFiBDw9PdOtv2rVKixYsAB3796Frq5uto7Ju8ZIU6w88QALDoWotfWt54AZ33EkYG0XFhYGExMT1cC1CQkJkMvl0NdncUzaS2PuGsvM1atX0a5duy9ePyUlBYGBgXBzc/sYRkcHbm5uOH/+fIbb7NmzB3Xr1sWwYcNgbW2NqlWrwsvLK8NJYD9ITk5GbGys2oNIat8uClArgppVtMLNGS1YBBF27doFJycn9OnTRzWiv5GREYsgolySpULo0KFDGDduHCZNmqQaL+ju3bvo2LEjateurfpH+yWioqKgUChgbW2t1m5tbY2IiIgMt3n06BF27NgBhUKB/fv3Y+rUqVi0aBHmzJmT6XG8vb1hbm6uetjb239xRqLcsOFcGB5GxquWQ+a0wvq+tWFqkL2znFQwJCcnY+TIkejUqRNiYmLw+vVrxMTESB2LqMD74j5Cf/75J3766ScUKVIEb9++xbp167B48WKMGDECHh4euHXrFipVyt27W5RKJaysrLBmzRrI5XI4Ozvj2bNnWLBgAaZPn57hNhMnTsSYMWNUy7GxsSyGSBKXQt/AfbX62c6bM1pAvxBnjNd2Dx8+hIeHBwIDAwEA48aNg5eXV7a7ABDRl/viQmjZsmWYN28exo8fj7///htdunTB77//jps3b6JEiRJZPrClpSXkcjlevnyp1v7y5UsUL148w21sbGygq6sLufzjF0elSpUQERGBlJQU6OnppdtGX1+fp5RJUm/jU1Bz9pF07T79eBaIAH9/fwwYMADv3r1D0aJFsWHDBrRt21bqWERa44svjT18+BBdunQBAHTq1AmFChXCggULslUEAYCenh6cnZ1x7NgxVZtSqcSxY8dQt27dDLepX78+Hjx4oHYJ7t69e7CxscmwCCKS2q/77qQrgmo7FEbwrFZoUiH9nZGkXZKSkjBx4kS8e/cO9evXR1BQEIsgojz2xYVQYmIijIyMAAAymQz6+vqwsbH5qoOPGTMGa9euxYYNGxAcHIwhQ4YgPj4e/fr1AwD07t0bEydOVK0/ZMgQvHnzBqNGjcK9e/ewb98+eHl5YdiwYV+Vgyg3zNl7B2tPh6qWa5QwR6h3G2wfXA+GerwcRoCBgQH8/PwwadIkBAQEZPsPSyLKviyNI7Ru3TqYmJgAANLS0uDj46O6vfODrMw+7+HhgcjISEybNg0RERFwcnLCwYMHVR2ow8PDVaNYA4C9vT0OHTqE0aNHo3r16rCzs8OoUaMwYcKErLwNolz36l0S1p35WAT9PaQunEsVkTARaYqtW7ciISEBAwYMAAC4uLjAxcVF4lRE2uuLxxFycHCATPbpuY5kMhlnnyetd//lOzRfckq1/PeQenAuVVjCRKQJEhISMGrUKKxbtw56enoICgrK9RtMiAqS3Pr+/uIzQmFhYTl2UKKC6mFknFoRtKyrE4sgQnBwMNzd3XHr1i3IZDJMnDgR5cuXlzoWESEbU2wQUcZ+D3iA+Qc/DpI48tty6OBkJ2Ei0gQbNmzA0KFDkZCQAGtra2zduhXNmjWTOhYR/T8WQkRfKTQqHk0XBqi1DW1SBmOa8y9+bSaEwE8//aSag9HNzQ2bN29ON4gsEUmLhRDRVzj7IAo91l1Uazv0cyNUKG4qUSLSFDKZDI6OjtDR0cHMmTMxceJEtTHQiEgzSDrpqhTYWZpyikIpUGbSftWyuaEugqY1/+xNBVRwCSEQExMDCwsLAO/HRgsKCkKtWrWkDUZUAGj8pKtE2mbGntuq57M7VsX16S1YBGmxd+/eoUePHmjYsCESEhIAvJ9ImkUQkWbLViH08OFDTJkyBd26dcOrV68AAAcOHMDt27c/syVRwbA76Bk2XXisWu71TSkJ05DUgoKC4OzsjG3btiE4OBinTp36/EZEpBGyXAidPHkS1apVw8WLF7Fz507ExcUBAK5fv57pxKdEBYVCKTBp102M8g1StR0Y1VC6QCQpIQT++OMPfPPNN7h//z7s7e1x6tQptGrVSupoRPSFslwIeXp6Ys6cOThy5Ija/F7NmjXDhQsXcjQckaYpM2k/tl4MVy3vHFoPlWzY10wbxcTEwMPDA0OHDkVycjLat2+Pa9euoV69elJHI6IsyHIhdPPmTXz//ffp2q2srBAVFZUjoYg00Ri/INVzIz05/AfVRa2SHCxRWw0fPhzbt29HoUKFsGjRIuzevRtFixaVOhYRZVGWb5+3sLDAixcvULp0abX2a9euwc6Og8dRwfTnmVDsvPZMtXxnFi99aDtvb28EBwdj5cqVcHV1lToOEWVTls8Ide3aFRMmTEBERARkMhmUSiXOnj2LcePGoXfv3rmRkUhS9ecex+y9d1TL/xveQMI0JJW3b99iw4YNquUSJUrg8uXLLIKI8rksF0JeXl6oWLEi7O3tERcXh8qVK6NRo0aoV68epkyZkhsZiSTxPDoRP/xxDs+iE1Vt/xveANVKmEuYiqRw8eJF1KxZE3379sXu3btV7RwugSj/y/KlMT09PaxduxZTp07FrVu3EBcXh5o1a6JcuXK5kY9IEv+dQR4AHnm1gY4Ov/i0iRACixcvhqenJ9LS0lCmTBmUKFFC6lhElIOyXAidOXMGDRo0QMmSJVGyZMncyEQkqf9Om2FnYYi/h9RjEaRlXr9+jb59+2Lv3r0AAHd3d6xdu5Yj0hMVMFm+NNasWTOULl0akyZNwp07dz6/AVE+cu/lO7UiaFyL8jjr2QzFzQ0kTEV57ezZs3BycsLevXuhr6+PP/74A76+viyCiAqgLBdCz58/x9ixY3Hy5ElUrVoVTk5OWLBgAZ4+fZob+YjyTEqaEi3+dTlsZfdaGN6Ml3y10fPnz/H06VOUK1cOFy5cwODBg9kfiKiA+qpJV0NDQ7F161Zs27YNd+/eRaNGjXD8+PGczJfjOOkqZUQIgdITP06gOr5lBQxrWlbCRJTXhBBqxc6GDRvQqVMnmJqaSpiKiD7QyElXS5cuDU9PT8ydOxfVqlXDyZMncyoXUZ5qvey06rm5oS6LIC1z8uRJODs748WLF6q2Pn36sAgi0gLZLoTOnj2LoUOHwsbGBt27d0fVqlWxb9++nMxGlCccPPfhbsQ71fL16S0kTEN5SaFQYPbs2WjWrBmuXbuGadOmSR2JiPJYlu8amzhxInx9ffH8+XM0b94cy5YtQ4cOHWBkZJQb+YhyTapCiXKTD6i13Z3NEaO1RUREBHr27Iljx44BAPr27YulS5dKG4qI8lyWC6FTp05h/PjxcHd3h6WlZW5kIsp1G8+HYdru22ptHCdIexw7dgw9evTAy5cvYWRkhD/++IMj4xNpqSwXQmfPns2NHER5ZvuVJ+mKoLuzW7EI0hK7du1C586dIYRA1apV4e/vj0qVKkkdi4gk8kWF0J49e9C6dWvo6upiz549n1z3u+++y5FgRLnBe38wVp96pFo+PrYxHIuZSJiI8lrz5s1RoUIFNGzYEMuWLYOhoaHUkYhIQl90+7yOjg4iIiJgZWUFHZ3M+1fLZDIoFIocDZjTePu89oqKS4bLnKOq5b0jGqCqHecN0waXL1+Gs7Oz6vdXTEwMzM352RPlJ5LePq9UKmFlZaV6ntlD04sg0l5CCLUiaOOPdVgEaYG0tDRMnDgRderUweLFi1XtLIKI6IMs3z6/ceNGJCcnp2tPSUnBxo0bcyQUUU7rvf6S6vlPDUujUfliEqahvPDkyRM0adIEc+fOBQCOfk9EGcryyNJyuRwvXrxQnSH64PXr17CystL4s0K8NKZdFEqBMpP2q7WFzW0rURrKK/v27UPv3r3x5s0bmJmZ4c8//8QPP/wgdSwi+goaM7L0f4eh/+Dp06c83UwaJS45LV0RdHRMI4nSUF5ISUnBuHHj0K5dO7x58wYuLi64du0aiyAiytQX3z5fs2ZNyGQyyGQyfPvttyhU6OOmCoUCoaGhaNWKg9GRZohPTkPV6YfU2kLmtIJ+IblEiSgvBAcH47fffgMAjBo1CvPmzYO+vr7EqYhIk31xIdSxY0cAQFBQEFq2bAkTk4+3HOvp6cHBwQGdO3fO8YBE2VHlX0VQ0wrF8Fe/OhKmobxSo0YNrFixAlZWVqrfWUREn5LlPkIbNmyAh4cHDAwMcitTrmIfoYLPwfPjnHeFjXRxbRrnDiuokpOTMWnSJPTq1QtOTk5SxyGiXJRb399ZHlm6T58+OXZwopwkhIDH6gtqbSyCCq6HDx/Cw8MDgYGB2Lt3L27dugVdXV2pYxFRPvNFhVCRIkVw7949WFpaonDhwhl2lv7gzZs3ORaO6EtdDnuDLqvOq7U98mojURrKbdu3b8eAAQMQGxuLIkWKYPHixSyCiChbvqgQWrJkCUxNTVXPP1UIEeW13UHPMMo3SK3t4qRvOXdYAZSUlIQxY8bgjz/+AADUr18f27Ztg729vcTJiCi/ynIfofyOfYQKFr/L4Zjw903VcgcnWyxxd2IRVABFRkaiRYsWCAoKAgBMnDgRs2bNUruDlYgKLo3pI3T16lXo6uqiWrVqAIDdu3fjr7/+QuXKlTFjxgzo6enlWDiizIRFxaPJwgC1toBxTeBgaSxNIMp1RYoUgaWlJYoVK4ZNmzahZcuWUkciogIgywMqDho0CPfu3QMAPHr0CB4eHjAyMsL27dvxyy+/5HhAooz8twg6MKohi6ACKCEhAYmJiQDej2q/ZcsW1RAeREQ5IcuF0L1791S3qW7fvh2NGzfG1q1b4ePjg7///jun8xGls/ViuOq5jgy4PbMlKtnwMmdBExwcDFdXV/z888+qNisrK9ja2koXiogKnGxNsaFUKgEAR48eRZs27+/Msbe3R1RUVM6mI/qP/j6XMWnXxz5Bj7zbwliffUQKmg0bNsDFxQW3bt3C7t27ERkZKXUkIiqgslwIubi4YM6cOdi0aRNOnjyJtm3fT2AZGhoKa2vrHA9I9EGn38/i2N1XquUV3WtKmIZyQ3x8PPr27Yu+ffsiISEB3377LYKCglCsWDGpoxFRAZXlP6WXLl2KHj164J9//sHkyZNRtmxZAMCOHTtQr169HA9IpFQKOP5n8lTOG1bw3Lp1C+7u7ggODoaOjg5mzpyJiRMnQi7n50xEuSfHbp9PSkqCXC7X+EHNePt8/tNq6SncjXgHAChV1AgB45pwLKsCJiUlBWXKlMHTp09ha2uLrVu3onHjxlLHIiINojG3z38QGBiI4OBgAEDlypVRq1atHAtF9MG9l+9URRAAnBzfVMI0lFv09PSwatUqrFy5Ehs2bOClMCLKM1k+I/Tq1St4eHjg5MmTsLCwAABER0ejadOm8PX11fhfYDwjlL9UnHoASanvO+ef82wGWwtDiRNRTrl+/TpevXqF5s2bq9qEEDzbR0QZyq3v7yx3lh4xYgTi4uJw+/ZtvHnzBm/evMGtW7cQGxuLkSNH5lgwIgfPfaoiqHfdUiyCCgghBFatWgVXV1d4eHggPPzjcAgsgogor2X50tjBgwdx9OhRVKpUSdVWuXJlrFy5Ei1acKZv+nqpCiXKTT6g1jatXWWJ0lBOiomJwcCBA+Hv7w8AaN68OYyNORAmEUkny2eElEplhh2idXV1VeMLEWVXWgZFUKh3GxSSZ/l/VdIwgYGBqFWrFvz9/VGoUCEsWrQIe/bsQdGiRaWORkRaLMvfLs2aNcOoUaPw/PlzVduzZ88wevRofPvttzkajrRPzVlH1JbD5rbl5ZICYPny5ahXrx4ePXqEUqVK4cyZMxgzZgw/WyKSXJYLoRUrViA2NhYODg4oU6YMypQpg9KlSyM2NhbLly/PjYykJeYeuIt3yWmq5bC5bSVMQznp9u3bSElJQceOHXHt2jW4urpKHYmICEA2xxESQuDYsWOq2+crVaoENze3HA+XG3jXmGbyvRQOz50fp84IntUKhnocSC8/+/cdYImJidi+fTt69erFs0BElC0aMY6Qn58f9uzZg5SUFHz77bcYMWJEjgUh7RX0JFqtCDrr2YxFUD4mhMCSJUtw5MgR7N27F3K5HIaGhujdu7fU0YiI0vniQuiPP/7AsGHDUK5cORgaGmLnzp14+PAhFixYkJv5qIB7FBmHjivPqpaPjW0MO94mn2+9fv0affv2xd69ewEAO3fuRJcuXSRORUSUuS/uI7RixQpMnz4dISEhCAoKwoYNG/D777/nZjYq4KLiktFs0UnV8vzO1VGmmImEiehrnDt3DjVr1sTevXuhr6+PP/74Az/88IPUsYiIPumLC6FHjx6hT58+quXu3bsjLS0NL168yJVgVLC9jU+By5yjqmW3StZwr20vYSLKLqVSiXnz5qFRo0Z48uQJypUrhwsXLmDw4MHsD0REGu+LC6Hk5GS1gc90dHSgp6eHxMTEXAlGBZcQAjVnf7xNvoqtGdb1cZEwEX2NkSNHwtPTEwqFAt27d0dgYCCcnJykjkVE9EWy1Fl66tSpMDIyUi2npKTg119/hbm5uapt8eLFOZeOCqSpu2+pntd1LIptA7+RMA19rYEDB2Lbtm2YP38+fvzxR54FIqJ85YsLoUaNGiEkJESt7cMAaR/wFyB9jkIpsPnCx7mlWATlPwqFAleuXFGNBVS9enWEhYXB1NRU4mRERFn3xYVQQEBALsYgbXDg5gsM2XJVtXz6l6YSpqHsePnyJXr27ImAgACcOXNGVQyxCCKi/IoTOFGeuPUsRq0Iql7CHPZFjD6xBWma48ePo0aNGjh69Cj09PTw9OlTqSMREX21LM8+T5RVaQol2i0/o1pe3q0m2tewlTARZYVCocCsWbMwe/ZsCCFQtWpV+Pv7o1KlSlJHIyL6aiyEKFe9ik1CHa9jquVlXZ1YBOUjz58/R48ePVSXxgcMGIBly5ap3TRBRJSfsRCiXBMVl6xWBNUoYY4OTnYSJqKs2rlzJwICAmBiYoLVq1eje/fuUkciIspRGtFHaOXKlXBwcICBgQFcXV1x6dKlL9rO19cXMpkMHTt2zN2AlC3/HjCxftmi2D28gYRpKDuGDRuGcePGITAwkEUQERVI2SqETp8+jZ49e6Ju3bp49uwZAGDTpk04c+bMZ7ZMz8/PD2PGjMH06dNx9epV1KhRAy1btsSrV68+uV1YWBjGjRuHhg0bZuctUC7r73NZ9byHa0lsGcDb5PODp0+fom/fvnj37h2A90NiLFiwAOXLl5c4GRFR7shyIfT333+jZcuWMDQ0xLVr15CcnAwAiImJgZeXV5YDLF68GD/99BP69euHypUrY9WqVTAyMsL69esz3UahUKBHjx6YOXMmHB0ds3xMyl2jfK/h2N2PhezM76pImIa+1L59++Dk5IQNGzZg7NixUschIsoTWS6E5syZg1WrVmHt2rXQ1dVVtdevXx9Xr179xJbppaSkIDAwEG5ubh8D6ejAzc0N58+fz3S7WbNmwcrKCv379//sMZKTkxEbG6v2oNxz6HYEdgc9Vy3fm9MaheQacQWWMpGamorx48ejXbt2eP36NZydnTFhwgSpYxER5Yksf0OFhISgUaNG6drNzc0RHR2dpX1FRUVBoVDA2tpard3a2hoREREZbnPmzBn8+eefWLt27Rcdw9vbG+bm5qqHvT0n9swtSqXAoE2BquVdQ+tBrxCLIE32+PFjNGrUCAsXLgTwft6ws2fPokyZMhInIyLKG1n+lipevDgePHiQrv3MmTO5fpnq3bt36NWrF9auXQtLS8sv2mbixImIiYlRPZ48eZKrGbVZi6WnVM/HNi+PmiULS5iGPuf06dNwcnLChQsXYGFhgV27dmHZsmXQ19eXOhoRUZ7J8u3zP/30E0aNGoX169dDJpPh+fPnOH/+PMaNG4epU6dmaV+WlpaQy+V4+fKlWvvLly9RvHjxdOs/fPgQYWFhaN++vapNqVS+fyOFCiEkJCTdX7L6+vr8xZ7LFEqBMpP2q7WN+LacRGnoS5UrVw76+vpwdXWFr68vHBwcpI5ERJTnslwIeXp6QqlU4ttvv0VCQgIaNWoEfX19jBs3DiNGjMjSvvT09ODs7Ixjx46pboFXKpU4duwYhg8fnm79ihUr4ubNm2ptU6ZMwbt377Bs2TJe9pJI7/UX1Zb/HlJXoiT0Oa9fv0bRokUBvD+7GxAQAEdHR+jp6UmcjIhIGjIhhMjOhikpKXjw4AHi4uJQuXJlmJiYZCuAn58f+vTpg9WrV6NOnTpYunQp/P39cffuXVhbW6N3796ws7ODt7d3htv37dsX0dHR+Oeff77oeLGxsTA3N0dMTAzMzMyylZk+GrblKvbdfKFaDvVuA5lMJmEiysyOHTvQv39/rFmzBh4eHlLHISLKktz6/s72yNJ6enqoXLnyVwfw8PBAZGQkpk2bhoiICDg5OeHgwYOqDtTh4eHQ0WGHW00U+PitWhF01rMZiyANlJSUhLFjx+L3338HAGzYsAHu7u78rIiIkI0zQk2bNv3kL9Djx49/dajcxDNCOcfBc5/q+fmJzWBjbihhGsrI/fv34e7ujqCgIADvL23PmjVLbegLIqL8QGPOCDk5Oaktp6amIigoCLdu3UKfPn1yKhdpuBHbrqme965bikWQBtq2bRsGDhyIuLg4WFpaYtOmTWjVqpXUsYiINEqWC6ElS5Zk2D5jxgzExcV9dSDSfBcevcb/rn8cNHF6e44crWlu3LihmhusUaNG2Lp1K+zsOOEtEdF/Zbuz9H89ePAAderUwZs3b3Jid7mGl8a+zqXQN3Bf/XHU76NjGqOsVfY6ylPuGj9+PAwNDTFt2jQUKpTt7oBERBpBYy6NZeb8+fMwMDDIqd2RBlIqhVoRtOCH6iyCNMiWLVvQsGFDlCxZEgAwf/58dogmIvqMLBdCnTp1UlsWQuDFixe4cuVKlgdUpPyl/ryPHeFX9ayFVlVtJExDH8THx2PEiBH466+/UK9ePQQEBEBXV5dFEBHRF8hyIWRubq62rKOjgwoVKmDWrFlo0aJFjgUjzfHkTQIazj+h1sYiSDPcvn0b7u7uuHPnDnR0dNCyZUsON0FElAVZKoQUCgX69euHatWqoXBhziOlDZ6+TV8EXZ7sJlEa+kAIgb/++gvDhw9HYmIibGxssHXrVjRp0kTqaERE+UqWCiG5XI4WLVogODiYhVABJ4RAh5VnceNpjKqtQVlLbB7gKmEqAt5fChs8eDA2b94MAGjZsiU2btwIKysriZMREeU/WT6HXrVqVTx69Cg3spAGaTDvhFoR5OFizyJIQ+jo6ODGjRuQy+Xw9vbG/v37WQQREWVTlvsIzZkzB+PGjcPs2bPh7OwMY2Njtdd5S3r+9updEur8ekyt7c6sljDS4+3XUhJCQAgBHR0dGBoawt/fH5GRkWjQoIHU0YiI8rUvHkdo1qxZGDt2LExNTT9u/K+7UoQQkMlkUCgUOZ8yB3EcocytOvkQcw/cVWt76NUGch3efSSlmJgYDBw4ENWqVcOUKVOkjkNEJInc+v7+4kJILpfjxYsXCA4O/uR6jRs3zpFguYWFUMbqzz2OZ9GJam2PvNpAh0WQpAIDA+Hh4YGHDx/CwMAAjx49go0N79gjIu0j+YCKH+olTS90KOu89gerFUF/D6kH51LsDC8lIQRWrFiBcePGISUlBaVKlYKvry+LICKiHJaljh8coK3gmbb7Fjaef6xaDpnTCvqF5BImoujoaPTv3x87d+4EAHTs2BHr16/nnZpERLkgS4VQ+fLlP1sMafpcY/RRbFKqWhF0zrMZiyCJpaWloV69eggODoauri4WLlyIESNG8I8QIqJckqVCaObMmelGlqb8y2XOUdXzY2Mbw9bCUMI0BACFChXCqFGjMH/+fPj5+cHFxUXqSEREBdoXd5bW0dFBREREvh+vhJ2l3/P8+wZ8Lz8BAMh1ZHjo1UbiRNrrzZs3ePHiBapUqQLgff+ghISEdENTEBFpM8k7S/PUfMHRfvkZ3Hz2cbDE857NJEyj3c6dO4euXbtCLpfj2rVrsLCwgEwmYxFERJRHvnhk6S88cUQabvbeO2pFUMC4JrAyM5AwkXZSKpWYN28eGjVqhCdPnkBXVxevXr2SOhYRkdb54jNCSqUyN3NQHth/8wX+PBOqWg6c4oaiJvoSJtJOkZGR6NOnDw4cOAAA6NatG1avXq02WCkREeUNzpugJSLfJWPolquq5SssgiRx6tQpdOvWDc+fP4eBgQGWL1+O/v3789IzEZFEWAhpidq/frxDbFP/OrBkESSJxYsX4/nz56hYsSL8/f1RrVo1qSMREWk1FkJaYO2pR6rnJvqF0LBcMQnTaLc///wTjo6OmDVrFkxMTKSOQ0Sk9b64szTlTyfvReLX/R/nh7syxU3CNNrn+PHjGDt2rOpmg6JFi2Lx4sUsgoiINATPCBVwY/yCVM9Pjm8CA12OHJ0XFAoFZs2ahdmzZ0MIAVdXV7i7u0sdi4iI/oOFUAGmVAq8jk8BAMzpWBWlinJsmrzw/Plz9OjRAwEBAQCA/v37o127dtKGIiKiDLEQKsDqzzuuet6mGmctzwuHDx9Gz549ERkZCWNjY6xevRo9evSQOhYREWWCfYQKqE0XHuNFTJJquYixnoRptMOCBQvQqlUrREZGokaNGrh69SqLICIiDcdCqAC6GxGLqf/cUi3fnNFCwjTao2bNmgCAIUOG4MKFCyhfvrzEiYiI6HN4aayAEUKg1dLTquXfe9SCqYGuhIkKtlevXqkmInZzc8PNmzdVk6cSEZHm4xmhAubak2jV8zbVirNvUC5JTU3F+PHjUb58eTx8+FDVziKIiCh/YSFUgKQplOj0+znV8u89nCVMU3A9fvwYDRs2xMKFCxETE4P//e9/UkciIqJs4qWxAqTs5AOq533rOUgXpAD7559/0K9fP0RHR8Pc3Bzr169Hp06dpI5FRETZxDNCBcS8g3dVz2uUMMeM73iJJielpKTg559/xvfff4/o6GjUqVMH165dYxFERJTPsRAqAO69fIc/Aj72U/lnWH0J0xRMK1aswLJlywAAY8aMwenTp1G6dGmJUxER0dfipbF8LiYxFS2WnFIt/z2kHmQymYSJCqbhw4fjyJEjGDp0KNq3by91HCIiyiE8I5TPeaw+r3o+/4fqcC5VWMI0BUdSUhIWL16M1NRUAICenh4OHDjAIoiIqIDhGaF8zO9yOO5GvAMAlCpqBHcXe4kTFQz379+Hh4cHrl27hsjISHh7e0sdiYiIcgnPCOVT916+w4S/b6qW9wxrIGGagsPX1xe1atXCtWvXYGlpiUaNGkkdiYiIchELoXwo/HWCWr+gDT/WgbkRR4/+GomJiRg0aBC6deuGuLg4NGzYEEFBQWjdurXU0YiIKBexEMpnTt+PRKMFJ1TLo93Ko3H5YhImyv/u3bsHV1dXrFmzBjKZDFOmTMHx48dhZ2cndTQiIspl7COUjwzdEoj9NyNUy0OalMEot3ISJioYlEolHj16BCsrK2zZsgVubm5SRyIiojzCQiifaLPsNO68iFUtj29ZAcOalpUwUf6mVCqho/P+hGjFihWxc+dOVKtWDTY2nJuNiEib8NJYPrD+TKhaEXT6l6Ysgr7C7du34eTkhFOnPvazatGiBYsgIiItxEJIwymVArP23lEt35nVEvZFjCRMlH8JIfDnn3+idu3auHnzJsaOHQshhNSxiIhIQiyENNyU3bdUz/eOaAAjPV7NzI53796hV69eGDBgABITE9GiRQvs27ePo3ATEWk5FkIabuvFcNXzqnbmEibJv65fvw4XFxds2bIFcrkcXl5eOHDgAKysrKSORkREEuPpBQ327+kzNvWvI2GS/Cs4OBiurq5ITk6GnZ0dfH190aABB58kIqL3WAhpsIuhb1TPG5bjWEHZUbFiRXz33XeIj4/Hhg0bYGlpKXUkIiLSICyENFTI/88hBgA7h9aTMEn+c+3aNZQuXRoWFhaQyWTYsGED9PX1VbfLExERfcBvBg01bvt11fNaJTmj/JcQQmDFihX45ptvMGDAANUdYYaGhiyCiIgoQzwjpIGOBb/EzWcxAIC+9RykDZNPREdHo3///ti5cycAIC0tDUlJSTA0NJQ4GRERaTL+mayBfvYNUj0f17KCdEHyiUuXLqFmzZrYuXMndHV1sXTpUuzatYtFEBERfRYLIQ2z+Mg9vEtOAwCM+rYcTPR50i4zQggsWbIEDRo0QFhYGEqXLo2zZ89i1KhRHB+IiIi+CAshDXLv5Tv8duy+anlIkzISptF8MTExWLx4MVJTU9G5c2dcvXoVtWvXljoWERHlIzzdoCFSFUq0WPJx7quzns1goCuXMJHms7CwwLZt23D9+nUMHTqUZ4GIiCjLWAhpACEEyk0+oFr2cLGHnQX7t/yXUqnEwoULUbx4cfTu3RsA0KBBAw6QSERE2cZCSGJKpYDjpP2q5XplimLeD9UlTKSZIiMj0adPHxw4cABGRkZo2rQp7O3tpY5FRET5HAshCQmhXgQBwNafvpEojeY6ffo0unbtiufPn8PAwABLly5FiRIlpI5FREQFADtLS+iXHTfUlh96tZEoiWZSKpX49ddf0aRJEzx//hwVKlTAxYsX8dNPP7E/EBER5QieEZKIEALbA5+qloOmNYdch1/uHygUCrRt2xaHDh0CAPTq1Qu///47TExMJE5GREQFCc8ISUCpFGi6MEC1fHh0I1gY6UkXSAPJ5XK4uLjAyMgIf/31FzZu3MgiiIiIcpxMfJiQSUvExsbC3NwcMTExMDMzkyRDx5VnEfQkWrUcNretJDk0jUKhwJs3b1CsWDEA76fJCA0NRbly5SRORkREUsut72+NOCO0cuVKODg4wMDAAK6urrh06VKm665duxYNGzZE4cKFUbhwYbi5uX1yfU2jVAq1IujyZDfpwmiQFy9eoHnz5mjdujWSk5MBAIUKFWIRREREuUryQsjPzw9jxozB9OnTcfXqVdSoUQMtW7bEq1evMlw/ICAA3bp1w4kTJ3D+/HnY29ujRYsWePbsWR4nz57GC0+ont+Y0QLFTPUlTKMZDh8+jBo1auDEiRO4e/curl+/LnUkIiLSEpJfGnN1dUXt2rWxYsUKAO/vFLK3t8eIESPg6en52e0VCgUKFy6MFStWqAbZ+xQpL40FPn6Lzn+cUy1r+yWxtLQ0TJ8+Hd7e3hBCoHr16vD390eFCpxoloiI1BXIS2MpKSkIDAyEm9vHy0M6Ojpwc3PD+fPnv2gfCQkJSE1NRZEiRTJ8PTk5GbGxsWoPqfy7CAqe1UqyHJrg6dOnaNasGby8vCCEwKBBg3DhwgUWQURElKckLYSioqKgUChgbW2t1m5tbY2IiIgv2seECRNga2urVkz9m7e3N8zNzVUPqUYjvhvxsQCb0rYSDPW0ex6xn376CadPn4apqSl8fX2xatUqGBpyWhEiIspbkvcR+hpz586Fr68vdu3aBQMDgwzXmThxImJiYlSPJ0+e5HHK92MGtVp6WrU8oKFjnmfQNCtXrkTTpk1x9epVeHh4SB2HiIi0lKQDKlpaWkIul+Ply5dq7S9fvkTx4sU/ue3ChQsxd+5cHD16FNWrZz43l76+PvT1pe2Q3Olfl8TcXbRzaojw8HAcPnwYAwYMAAA4Ojri+PHjEqciIiJtJ+kZIT09PTg7O+PYsWOqNqVSiWPHjqFu3bqZbjd//nzMnj0bBw8ehIuLS15EzTYhBK6FR6uW5/9QQ7owEtmzZw+cnJwwcOBAHD58WOo4REREKpJfGhszZgzWrl2LDRs2IDg4GEOGDEF8fDz69esHAOjduzcmTpyoWn/evHmYOnUq1q9fDwcHB0RERCAiIgJxcXFSvYVP8rv88VLc/pENJUyS91JSUjB69Gh06NABb9++hYuLC8cFIiIijSL5XGMeHh6IjIzEtGnTEBERAScnJxw8eFDVgTo8PBw6Oh/rtT/++AMpKSn44Ycf1PYzffp0zJgxIy+jfxHvA3dVzyvbSjOStRRCQ0Ph4eGBy5cvAwBGjx6NuXPnQk+PU4kQEZHmkHwcobyWl+MILTocguXHHwAAZneogl51HXL1eJrin3/+Qd++fRETE4PChQvDx8cH3333ndSxiIgoH8ut72/JzwgVZB+KIADo+U0pCZPkrdjYWMTExKBu3brw9fVFyZIlpY5ERESUIRZCueRS6BvV87W9XSCTySRMk/sUCgXk8vdjI/Xu3RsGBgb4/vvvoaurK3EyIiKizEneWbqg6r/hsup588rWn1gz//P19UW1atUQFRWlanN3d2cRREREGo+FUC4IfhGLd0lpAIBvHDOe+qMgSExMxKBBg9CtWzcEBwdj8eLFUkciIiLKEl4ay2FpCiVaL/s4irRPvzoSpsk9d+/ehbu7O27evAmZTIZJkyZp5F17REREn8JCKIf5/mvcoN+61YSBbsGbU2zTpk2q8Z6srKywefNmNG/eXOpYREREWcZCKIdN+eeW6vl3NWwlTJI7Vq9ejcGDBwMAmjZtii1btsDGxkbiVERERNnDPkI56MKj16rn3epIM8t9buvatSvKli2LGTNm4MiRIyyCiIgoX+MZoRxyOewNuq65oFr2+r6ahGlyjhACx48fR7NmzSCTyWBubo4bN27A0NBQ6mhERERfjWeEckB8chq6rDqvWvb6vlqBGDcoLi4Offr0gZubG1atWqVqZxFEREQFBc8IfSUhBKpMP6RantquMrq75v+RlG/cuAF3d3eEhIRAR0cH8fHxUkciIiLKcSyEvlJASKTq+cBGjujfoLSEab6eEAJr1qzBqFGjkJycDDs7O2zbtg0NGzaUOhoREVGOYyH0lfr5fBxBelKbShIm+XqxsbEYOHAg/Pz8AACtW7fGxo0bYWlpKXEyIiKi3ME+Ql/h1rMY1fOfGubvM0EAcOvWLWzfvh1yuRzz58/H3r17WQQREVGBxjNC2fQ6Lhntlp9RLef3s0EAUK9ePaxYsQJOTk6oW7eu1HGIiIhyHc8IZYNCKeA856hqeUrbSvnyLrHo6Gj06tULwcHBqrYhQ4awCCIiIq3BM0LZ8Ou+j4VDi8rWGNDQUcI02XP58mV4eHggNDQUd+7cwZUrV/JlMUdERPQ1eEYoG9afDVU9X9PbRcIkWSeEwNKlS1G/fn2EhobCwcEBq1atYhFERERaiWeEsig6IUX1fHUvZwmTZN2bN2/Qr18/7NmzBwDQqVMn/Pnnn7CwsJA2GBERkURYCGXR4iP3VM9bVikuYZKsCQ0NRZMmTRAeHg49PT0sXrwYQ4cO5ZkgIiLSaiyEskAIgY3nHwMAapQwlzhN1tjb26NkyZLQ1dWFv78/atWqJXUkIiIiybEQ+kJpCiXKTj6gWp6YD26Xf/36NUxNTaGnp4dChQph+/btMDIygpmZmdTRiIiINAI7S3+hfxdB5oa6+MaxqIRpPu/06dOoUaMGJkyYoGorXrw4iyAiIqJ/YSH0BSLfJastX53aXKIkn6dUKuHl5YWmTZvi2bNnOHjwICdMJSIiygQLoS+w4VyY6vntmS0h19HMDsavXr1Cq1atMHnyZCgUCvTs2ROXL1+GsbGx1NGIiIg0EvsIfYEVJx4AAIqbGcBYXzN/ZCdOnED37t0REREBQ0NDrFy5En379uVdYURERJ+gmd/qGuTfE6uOaV5ewiSZi42NRefOnfH27VtUrlwZ/v7+qFKlitSxiIiINB4Loc/498Sq7rXtJUySOTMzM6xevRoHDhzA8uXLeSmMiIjoC7EQ+oS45DTV81YaNnji0aNHoaOjg2bNmgEAunTpgi5dukicioiIKH9hZ+lPGLI5UPX8t241JUzyUVpaGqZMmYIWLVqgW7duePHihdSRiIiI8i2eEcpETEIqTt+PAgC0rloceoWkrxmfPXuGbt264fTp0wCAjh07cp4wIiKir8BCKBOdV51TPV/QpYaESd47cOAAevfujaioKJiYmGDt2rXo2rWr1LGIiIjyNelPc2igNIUSD17FAQDcKlnDRMJb5pVKJSZMmIA2bdogKioKNWvWxNWrV1kEERER5QAWQhlYeeKh6vncztUkTALo6OggIiICADBs2DCcO3cO5cqVkzQTERFRQcFLY/8hhMCSo/cAAGWKGcPSRF+SHGlpaShU6P3Hs3LlSnTp0gXt2rWTJAuRthNCIC0tDQqFQuooRAWarq4u5HJ5nh6ThdB/dFx5VvV8ZY9aeX78lJQUeHp64sGDB9i9ezdkMhlMTExYBBFJJCUlBS9evEBCQoLUUYgKPJlMhhIlSsDExCTPjslC6F8USoHrTz+OJF2xeN7O1B4aGgoPDw9cvnwZABAQEICmTZvmaQYi+kipVCI0NBRyuRy2trbQ09PjtDVEuUQIgcjISDx9+hTlypXLszNDLIT+5fDtCNXzS5O/zdNj79y5Ez/++CNiYmJgYWEBHx8fFkFEEktJSYFSqYS9vT2MjIykjkNU4BUrVgxhYWFITU3Ns0KInaX/5X83nqueW5ka5Mkxk5OTMWLECHTu3BkxMTH45ptvEBQUhA4dOuTJ8Yno83R0+KuSKC9IccaV/7r/5cTdSABAm2p5N51Gjx49sGLFCgDA+PHjcerUKZQqVSrPjk9ERKTNWAj9S2Lq+ztC2lW3zbNjTpgwATY2Nti7dy/mz58PXV3dPDs2ERGRtmMh9P8SUz7eFutaukjuHScxESdPnlQt165dG48ePULbtm1z7ZhERPTlQkJCULx4cbx7907qKAXON998g7///lvqGGpYCP2/ndeeqp4XzaWxg0JCQvDNN9+gZcuWCAoKUrUbGORNfyQi0h59+/aFTCaDTCaDrq4uSpcujV9++QVJSUnp1t27dy8aN24MU1NTGBkZoXbt2vDx8clwv3///TeaNGkCc3NzmJiYoHr16pg1axbevHmTy+8o70ycOBEjRoyAqamp1FFyzcqVK+Hg4AADAwO4urri0qVLn90mOjoaw4YNg42NDfT19VG+fHns379f9fq7d+/w888/o1SpUjA0NES9evVUd0F/MGXKFHh6ekKpVOb4e8ouFkL/b/KuWwCAwka5c2lqy5YtcHZ2xo0bN2BmZobo6OhcOQ4R0QetWrXCixcv8OjRIyxZsgSrV6/G9OnT1dZZvnw5OnTogPr16+PixYu4ceMGunbtisGDB2PcuHFq606ePBkeHh6oXbs2Dhw4gFu3bmHRokW4fv06Nm3alGfvKyUlJdf2HR4ejr1796Jv375ftZ/czPi1/Pz8MGbMGEyfPh1Xr15FjRo10LJlS7x69SrTbVJSUtC8eXOEhYVhx44dCAkJwdq1a2FnZ6daZ8CAAThy5Ag2bdqEmzdvokWLFnBzc8OzZ89U67Ru3Rrv3r3DgQMHcvU9ZonQMjExMQKAiImJUbXdfhYjSk3YK0pN2Ct+O3ovR48XHx8v+vfvLwAIAKJJkybi2bNnOXoMIsodiYmJ4s6dOyIxMVHVplQqRXxyap4/lEpllrL36dNHdOjQQa2tU6dOombNmqrl8PBwoaurK8aMGZNu+99++00AEBcuXBBCCHHx4kUBQCxdujTD4719+zbTLE+ePBFdu3YVhQsXFkZGRsLZ2Vm134xyjho1SjRu3Fi13LhxYzFs2DAxatQoUbRoUdGkSRPRrVs34e7urrZdSkqKKFq0qNiwYYMQQgiFQiG8vLyEg4ODMDAwENWrVxfbt2/PNKcQQixYsEC4uLiotUVFRYmuXbsKW1tbYWhoKKpWrSq2bt2qtk5GGYUQ4ubNm6JVq1bC2NhYWFlZiZ49e4rIyEjVdgcOHBD169cX5ubmokiRIqJt27biwYMHn8z4terUqSOGDRumWlYoFMLW1lZ4e3tnus0ff/whHB0dRUpKSoavJyQkCLlcLvbu3avWXqtWLTF58mS1tn79+omePXtmuJ+M/s19kNH3d07gOEIAJvx9Q/V8eLOyObbfO3fuwN3dHbdv34ZMJsO0adMwderUPB8+nIhyTmKqApWnHcrz496Z1RJGetn/lX3r1i2cO3dO7a7UHTt2IDU1Nd2ZHwAYNGgQJk2ahG3btsHV1RVbtmyBiYkJhg4dmuH+LSwsMmyPi4tD48aNYWdnhz179qB48eK4evVqli+NbNiwAUOGDMHZs+9H/3/w4AG6dOmCuLg41SjEhw4dQkJCAr7//nsAgLe3NzZv3oxVq1ahXLlyOHXqFHr27IlixYqhcePGGR7n9OnTcHFxUWtLSkqCs7MzJkyYADMzM+zbtw+9evVCmTJlUKdOnUwzRkdHo1mzZhgwYACWLFmCxMRETJgwAe7u7jh+/DgAID4+HmPGjEH16tURFxeHadOm4fvvv0dQUFCmwzZ4eXnBy8vrkz+vO3fuoGTJkunaU1JSEBgYiIkTJ6radHR04ObmhvPnz2e6vz179qBu3boYNmwYdu/ejWLFiqF79+6YMGEC5HK5agqa/3b1MDQ0xJkzZ9Ta6tSpg7lz534yf17S+kIoVaHEzWfvR5NuVtEqR8cw2L17N27fvo3ixYtjy5YtaNasWY7tm4joc/bu3QsTExOkpaUhOTkZOjo6quE6AODevXswNzeHjY1Num319PTg6OiIe/fez714//59ODo6ZvnO1q1btyIyMhKXL19GkSLvb0QpWzbrf3CWK1cO8+fPVy2XKVMGxsbG2LVrF3r16qU61nfffQdTU1MkJyfDy8sLR48eRd26dQEAjo6OOHPmDFavXp1pIfT48eN0hZCdnZ1asThixAgcOnQI/v7+aoXQfzPOmTMHNWvWVCta1q9fD3t7e9y7dw/ly5dH586d1Y61fv16FCtWDHfu3EHVqlUzzDh48GC4u7t/8udla5vx3c9RUVFQKBSwtrZWa7e2tsbdu3cz3d+jR49w/Phx9OjRA/v378eDBw8wdOhQpKamYvr06TA1NUXdunUxe/ZsVKpUCdbW1ti2bRvOnz+f7vO2tbXFkydPoFQqNWKMLq0vhFy9jqmeL+xSI0f3/csvvyA+Ph4jRoxI9z8dEeVPhrpy3JnVUpLjZlXTpk3xxx9/ID4+HkuWLEGhQoXSffF+KSFEtrYLCgpCzZo1VUVQdjk7O6stFypUCO7u7tiyZQt69eqF+Ph47N69G76+vgDenzFKSEhA8+bN1bZLSUlBzZo1Mz1OYmJiurMaCoUCXl5e8Pf3x7Nnz5CSkoLk5OR0o43/N+P169dx4sSJDOfNevjwIcqXL4/79+9j2rRpuHjxIqKiolRnysLDwzMthIoUKfLVP8+sUiqVsLKywpo1ayCXy+Hs7Ixnz55hwYIFqn5nmzZtwo8//gg7OzvI5XLUqlUL3bp1Q2BgoNq+DA0NoVQqkZycDENDwzx9HxnR6kIoKVWBN/HvO7S5li6CIsZ6X7W/mzdvYtasWdi4cSMMDQ0hl8sxZ86cnIhKRBpCJpN91SWqvGRsbKz6a3z9+vWoUaMG/vzzT/Tv3x8AUL58ecTExOD58+fpziCkpKTg4cOHqql+ypcvjzNnziA1NTVLZ4U+90Wno6OTrshKTU3N8L38V48ePdC4cWO8evUKR44cgaGhIVq1agXg/SU5ANi3b59ah14A0NfP/M5gS0tLvH37Vq1twYIFWLZsGZYuXYpq1arB2NgYP//8c7oO0f/NGBcXh/bt22PevHnpjvPhLFz79u1RqlQprF27Fra2tlAqlahateonO1t/zaUxS0tLyOVyvHz5Uq395cuXKF4888GEbWxs0s0MX6lSJURERCAlJQV6enooU6YMTp48ifj4eMTGxsLGxgYeHh5wdHRU29ebN29gbGysEUUQoOV3ja0/G6p6vu2nb7K9HyEE1q5dizp16mDHjh2YMWNGDqQjIso5Ojo6mDRpEqZMmYLExEQAQOfOnaGrq4tFixalW3/VqlWIj49Ht27dAADdu3dHXFwcfv/99wz3n9mdsNWrV0dQUFCmt9cXK1YML168UGv79/Ain1KvXj3Y29vDz88PW7ZsQZcuXVRFWuXKlaGvr4/w8HCULVtW7WFvb5/pPmvWrIk7d+6otZ09exYdOnRAz549UaNGDbVLhp9Sq1Yt3L59Gw4ODukyGBsb4/Xr1wgJCcGUKVPw7bffolKlSumKsIwMHjwYQUFBn3xkdmlMT08Pzs7OOHbs49UQpVKJY8eOqS4hZqR+/fp48OCBWt+ue/fuwcbGBnp66icRjI2NYWNjg7dv3+LQoUPppoy6devWJ8/K5bkc7XqdD/y71/mHO8Uazz/+Vfvr2rWr6q6wVq1aiVevXuVgYiKSyqfuYNF0Gd2NlZqaKuzs7MSCBQtUbUuWLBE6Ojpi0qRJIjg4WDx48EAsWrRI6Ovri7Fjx6pt/8svvwi5XC7Gjx8vzp07J8LCwsTRo0fFDz/8kOndZMnJyaJ8+fKiYcOG4syZM+Lhw4dix44d4ty5c0IIIQ4ePChkMpnYsGGDuHfvnpg2bZowMzNLd9fYqFGjMtz/5MmTReXKlUWhQoXE6dOn071WtGhR4ePjIx48eCACAwPFb7/9Jnx8fDL9ue3Zs0dYWVmJtLQ0Vdvo0aOFvb29OHv2rLhz544YMGCAMDMzU/v5ZpTx2bNnolixYuKHH34Qly5dEg8ePBAHDx4Uffv2FWlpaUKhUIiiRYuKnj17ivv374tjx46J2rVrCwBi165dmWb8Wr6+vkJfX1/4+PiIO3fuiIEDBwoLCwsRERGhWqdXr17C09NTtRweHi5MTU3F8OHDRUhIiNi7d6+wsrISc+bMUa1z8OBBceDAAfHo0SNx+PBhUaNGDeHq6pruTrPGjRuLWbNmZZhNirvGtLYQuhTyRFUIrTxxP1v7unr1qihbtqwAIORyuZg3b55QKBQ5nJiIpFLQCiEhhPD29hbFihUTcXFxqrbdu3eLhg0bCmNjY2FgYCCcnZ3F+vXrM9yvn5+faNSokTA1NRXGxsaievXqYtasWZ+8fT4sLEx07txZmJmZCSMjI+Hi4iIuXryoen3atGnC2tpamJubi9GjR4vhw4d/cSF0584dAUCUKlUq3RADSqVSLF26VFSoUEHo6uqKYsWKiZYtW4qTJ09mmjU1NVXY2tqKgwcPqtpev34tOnToIExMTISVlZWYMmWK6N2792cLISGEuHfvnvj++++FhYWFMDQ0FBUrVhQ///yzKuuRI0dEpUqVhL6+vqhevboICAjI9UJICCGWL18uSpYsKfT09ESdOnVUwxn8+/306dNHre3cuXPC1dVV6OvrC0dHR/Hrr7+qFYx+fn7C0dFR6OnpieLFi4thw4aJ6OhotX08ffpU6OrqiidPnmSYS4pCSCZENnvA5VOxsbEwNzdH6/kHced1GgAgbG7Wp7fYtWsXunbtipSUFNjb28PX1xf16tXL6bhEJKGkpCSEhoaidOnSHAFei6xcuRJ79uzBoUN5P0xCQTdhwgS8ffsWa9asyfD1T/2b+/D9HRMTAzMzsxzLlD96/OWCW89joaNvhO9r2n1+5Qy4uLjAxMQE9evXx19//YWiRYvmcEIiIpLCoEGDEB0djXfv3hXoaTakYGVlhTFjxkgdQ43WFkIfeNTOvNPcfz179kx194G9vT0uXboER0fHHB17iIiIpFWoUCFMnjxZ6hgF0tixY6WOkI5W3zUGAM6lCn92HSEEli1bBkdHR+zZs0fVXqZMGRZBRERE+ZhWF0JGenLoyj/9I3jz5g2+//571ZgR/y6EiIiIKH/T6kKoV91Sn3z9woULqFmzJnbv3g09PT0sX74ca9euzaN0RKQptOyeEiLJSPFvTasLoToOGQ9RrlQqsXDhQjRs2BDh4eEoU6YMzp07h+HDh/NSGJEW+TA4X0JCgsRJiLTDhxG183Jycq3uLF2/rGWG7adOncL48eMBAO7u7li7dm2O3qpHRPmDXC6HhYUFXr16BQAwMjLiH0NEuUSpVCIyMhJGRkYoVCjvyhOtLYRMDOQwyGQSwyZNmmDUqFGoWLEiBg0axF98RFrsw/xLH4ohIso9Ojo6KFmyZJ5+72ptIVSy8MfJ8ZRKJZYtW4Zu3bqpfuktXbpUomREpElkMhlsbGxgZWWV4WSgRJRz9PT0oKOTt712NKIQWrlyJRYsWICIiAjUqFEDy5cvR506dTJdf/v27Zg6dSrCwsJQrlw5zJs3D23atMnSMV/HJwN4/1der169cPjwYezduxdHjhzJ8w+BiDSfXC7P034LRJQ3JP/G9/Pzw5gxYzB9+nRcvXoVNWrUQMuWLTM9DX3u3Dl069YN/fv3x7Vr19CxY0d07NgRt27dytJx6zgUQUBAAJycnHD48GEYGhqiR48evAxGRESkRSSfa8zV1RW1a9fGihUrALy/TGVvb48RI0bA09Mz3foeHh6Ij4/H3r17VW3ffPMNnJycsGrVqs8e78NcJVVa90HwoU1QKpWoVKkS/P39UbVq1Zx7Y0RERJRjcmuuMUnPCKWkpCAwMBBubm6qNh0dHbi5ueH8+fMZbnP+/Hm19QGgZcuWma6fmdsHNkCpVKJfv364fPkyiyAiIiItJGkfoaioKCgUClhbW6u1W1tb4+7duxluExERkeH6ERERGa6fnJyM5ORk1XJMTAwAQFdPHyuW/4auXbtCoVAgNjb2a94KERER5aIP39M5fSFLIzpL5yZvb2/MnDkzXXtqSjIGDRqEQYMGSZCKiIiIsuP169cwNzfPsf1JWghZWlpCLpfj5cuXau0vX75U3cb+X8WLF8/S+hMnTsSYMWNUy9HR0ShVqhTCw8Nz9AdJWRcbGwt7e3s8efKEA1ZqAH4emoOfhebgZ6E5YmJiULJkSRQpkvGsENklaSGkp6cHZ2dnHDt2DB07dgTwvrP0sWPHMHz48Ay3qVu3Lo4dO4aff/5Z1XbkyBHUrVs3w/X19fWhr6+frt3c3Jz/U2sIMzMzfhYahJ+H5uBnoTn4WWiOnB7iRvJLY2PGjEGfPn3g4uKCOnXqYOnSpYiPj0e/fv0AAL1794adnR28vb0BAKNGjULjxo2xaNEitG3bFr6+vrhy5QrWrFkj5dsgIiKifEjyQsjDwwORkZGYNm0aIiIi4OTkhIMHD6o6RIeHh6tVf/Xq1cPWrVsxZcoUTJo0CeXKlcM///zDu76IiIgoyyQvhABg+PDhmV4KCwgISNfWpUsXdOnSJVvH0tfXx/Tp0zO8XEZ5i5+FZuHnoTn4WWgOfhaaI7c+C8kHVCQiIiKSiuRTbBARERFJhYUQERERaS0WQkRERKS1WAgRERGR1iqQhdDKlSvh4OAAAwMDuLq64tKlS59cf/v27ahYsSIMDAxQrVo17N+/P4+SFnxZ+SzWrl2Lhg0bonDhwihcuDDc3Nw++9lR1mT138YHvr6+kMlkqoFP6etl9bOIjo7GsGHDYGNjA319fZQvX56/q3JIVj+LpUuXokKFCjA0NIS9vT1Gjx6NpKSkPEpbcJ06dQrt27eHra0tZDIZ/vnnn89uExAQgFq1akFfXx9ly5aFj49P1g8sChhfX1+hp6cn1q9fL27fvi1++uknYWFhIV6+fJnh+mfPnhVyuVzMnz9f3LlzR0yZMkXo6uqKmzdv5nHygiern0X37t3FypUrxbVr10RwcLDo27evMDc3F0+fPs3j5AVTVj+PD0JDQ4WdnZ1o2LCh6NChQ96ELeCy+lkkJycLFxcX0aZNG3HmzBkRGhoqAgICRFBQUB4nL3iy+lls2bJF6Ovriy1btojQ0FBx6NAhYWNjI0aPHp3HyQue/fv3i8mTJ4udO3cKAGLXrl2fXP/Ro0fCyMhIjBkzRty5c0csX75cyOVycfDgwSwdt8AVQnXq1BHDhg1TLSsUCmFrayu8vb0zXN/d3V20bdtWrc3V1VUMGjQoV3Nqg6x+Fv+VlpYmTE1NxYYNG3IrolbJzueRlpYm6tWrJ9atWyf69OnDQiiHZPWz+OOPP4Sjo6NISUnJq4haI6ufxbBhw0SzZs3U2saMGSPq16+fqzm1zZcUQr/88ouoUqWKWpuHh4do2bJllo5VoC6NpaSkIDAwEG5ubqo2HR0duLm54fz58xluc/78ebX1AaBly5aZrk9fJjufxX8lJCQgNTU1xyfY00bZ/TxmzZoFKysr9O/fPy9iaoXsfBZ79uxB3bp1MWzYMFhbW6Nq1arw8vKCQqHIq9gFUnY+i3r16iEwMFB1+ezRo0fYv38/2rRpkyeZ6aOc+v7WiJGlc0pUVBQUCoVqeo4PrK2tcffu3Qy3iYiIyHD9iIiIXMupDbLzWfzXhAkTYGtrm+5/dMq67HweZ86cwZ9//omgoKA8SKg9svNZPHr0CMePH0ePHj2wf/9+PHjwAEOHDkVqaiqmT5+eF7ELpOx8Ft27d0dUVBQaNGgAIQTS0tIwePBgTJo0KS8i079k9v0dGxuLxMREGBoaftF+CtQZISo45s6dC19fX+zatQsGBgZSx9E67969Q69evbB27VpYWlpKHUfrKZVKWFlZYc2aNXB2doaHhwcmT56MVatWSR1N6wQEBMDLywu///47rl69ip07d2Lfvn2YPXu21NEomwrUGSFLS0vI5XK8fPlSrf3ly5coXrx4htsUL148S+vTl8nOZ/HBwoULMXfuXBw9ehTVq1fPzZhaI6ufx8OHDxEWFob27dur2pRKJQCgUKFCCAkJQZkyZXI3dAGVnX8bNjY20NXVhVwuV7VVqlQJERERSElJgZ6eXq5mLqiy81lMnToVvXr1woABAwAA1apVQ3x8PAYOHIjJkyerTRJOuSuz728zM7MvPhsEFLAzQnp6enB2dsaxY8dUbUqlEseOHUPdunUz3KZu3bpq6wPAkSNHMl2fvkx2PgsAmD9/PmbPno2DBw/CxcUlL6Jqhax+HhUrVsTNmzcRFBSkenz33Xdo2rQpgoKCYG9vn5fxC5Ts/NuoX78+Hjx4oCpGAeDevXuwsbFhEfQVsvNZJCQkpCt2PhSoglN35qkc+/7OWj9uzefr6yv09fWFj4+PuHPnjhg4cKCwsLAQERERQgghevXqJTw9PVXrnz17VhQqVEgsXLhQBAcHi+nTp/P2+RyS1c9i7ty5Qk9PT+zYsUO8ePFC9Xj37p1Ub6FAyern8V+8ayznZPWzCA8PF6ampmL48OEiJCRE7N27V1hZWYk5c+ZI9RYKjKx+FtOnTxempqZi27Zt4tGjR+Lw4cOiTJkywt3dXaq3UGC8e/dOXLt2TVy7dk0AEIsXLxbXrl0Tjx8/FkII4enpKXr16qVa/8Pt8+PHjxfBwcFi5cqVvH3+g+XLl4uSJUsKPT09UadOHXHhwgXVa40bNxZ9+vRRW9/f31+UL19e6OnpiSpVqoh9+/blceKCKyufRalSpQSAdI/p06fnffACKqv/Nv6NhVDOyupnce7cOeHq6ir09fWFo6Oj+PXXX0VaWloepy6YsvJZpKamihkzZogyZcoIAwMDYW9vL4YOHSrevn2b98ELmBMnTmT4HfDh59+nTx/RuHHjdNs4OTkJPT094ejoKP76668sH1cmBM/lERERkXYqUH2EiIiIiLKChRARERFpLRZCREREpLVYCBEREZHWYiFEREREWouFEBEREWktFkJERESktVgIEZEaHx8fWFhYSB0j22QyGf75559PrtO3b1907NgxT/IQkWZjIURUAPXt2xcymSzd48GDB1JHg4+PjyqPjo4OSpQogX79+uHVq1c5sv8XL16gdevWAICwsDDIZDIEBQWprbNs2TL4+PjkyPEyM2PGDNX7lMvlsLe3x8CBA/HmzZss7YdFG1HuKlCzzxPRR61atcJff/2l1lasWDGJ0qgzMzNDSEgIlEolrl+/jn79+uH58+c4dOjQV+87s1nD/83c3Pyrj/MlqlSpgqNHj0KhUCA4OBg//vgjYmJi4OfnlyfHJ6LP4xkhogJKX18fxYsXV3vI5XIsXrwY1apVg7GxMezt7TF06FDExcVlup/r16+jadOmMDU1hZmZGZydnXHlyhXV62fOnEHDhg1haGgIe3t7jBw5EvHx8Z/MJpPJULx4cdja2qJ169YYOXIkjh49isTERCiVSsyaNQslSpSAvr4+nJyccPDgQdW2KSkpGD58OGxsbGBgYIBSpUrB29tbbd8fLo2VLl0aAFCzZk3IZDI0adIEgPpZljVr1sDW1lZtZncA6NChA3788UfV8u7du1GrVi0YGBjA0dERM2fORFpa2iffZ6FChVC8eHHY2dnBzc0NXbp0wZEjR1SvKxQK9O/fH6VLl4ahoSEqVKiAZcuWqV6fMWMGNmzYgN27d6vOLgUEBAAAnjx5And3d1hYWKBIkSLo0KEDwsLCPpmHiNJjIUSkZXR0dPDbb7/h9u3b2LBhA44fP45ffvkl0/V79OiBEiVK4PLlywgMDISnpyd0dXUBAA8fPkSrVq3QuXNn3LhxA35+fjhz5gyGDx+epUyGhoZQKpVIS0vDsmXLsGjRIixcuBA3btxAy5Yt8d133+H+/fsAgN9++w179uyBv78/QkJCsGXLFjg4OGS430uXLgEAjh49ihcvXmDnzp3p1unSpQtev36NEydOqNrevHmDgwcPokePHgCA06dPo3fv3hg1ahTu3LmD1atXw8fHB7/++usXv8ewsDAcOnQIenp6qjalUokSJUpg+/btuHPnDqZNm4ZJkybB398fADBu3Di4u7ujVatWePHiBV68eIF69eohNTUVLVu2hKmpKU6fPo2zZ8/CxMQErVq1QkpKyhdnIiKgQM4+T6Tt+vTpI+RyuTA2NlY9fvjhhwzX3b59uyhatKhq+a+//hLm5uaqZVNTU+Hj45Phtv379xcDBw5Uazt9+rTQ0dERiYmJGW7z3/3fu3dPlC9fXri4uAghhLC1tRW//vqr2ja1a9cWQ4cOFUIIMWLECNGsWTOhVCoz3D8AsWvXLiGEEKGhoQKAuHbtmto6ffr0ER06dFAtd+jQQfz444+q5dWrVwtbW1uhUCiEEEJ8++23wsvLS20fmzZtEjY2NhlmEEKI6dOnCx0dHWFsbCwMDAxUM2kvXrw4022EEGLYsGGic+fOmWb9cOwKFSqo/QySk5OFoaGhOHTo0Cf3T0Tq2EeIqIBq2rQp/vjjD9WysbExgPdnR7y9vXH37l3ExsYiLS0NSUlJSEhIgJGRUbr9jBkzBgMGDMCmTZtUl3fKlCkD4P1lsxs3bmDLli2q9YUQUCqVCA0NRaVKlTLMFhMTAxMTEyiVSiQlJaFBgwZYt24dYmNj8fz5c9SvX19t/fr16+P69esA3l/Wat68OSpUqIBWrVqhXbt2aNGixVf9rHr06IGffvoJv//+O/T19bFlyxZ07doVOjo6qvd59uxZtTNACoXikz83AKhQoQL27NmDpKQkbN68GUFBQRgxYoTaOitXrsT69esRHh6OxMREpKSkwMnJ6ZN5r1+/jgcPHsDU1FStPSkpCQ8fPszGT4BIe7EQIiqgjI2NUbZsWbW2sLAwtGvXDkOGDMGvv/6KIkWK4MyZM+jfvz9SUlIy/EKfMWMGunfvjn379uHAgQOYPn06fH198f333yMuLg6DBg3CyJEj021XsmTJTLOZmpri6tWr0NHRgY2NDQwNDQEAsbGxn31ftWrVQmhoKA4cOICjR4/C3d0dbm5u2LFjx2e3zUz79u0hhMC+fftQu3ZtnD59GkuWLFG9HhcXh5kzZ6JTp07ptjUwMMh0v3p6eqrPYO7cuWjbti1mzpyJ2bNnAwB8fX0xbtw4LFq0CHXr1oWpqSkWLFiAixcvfjJvXFwcnJ2d1QrQDzSlQzxRfsFCiEiLBAYGQqlUYtGiRaqzHR/6o3xK+fLlUb58eYwePRrdunXDX3/9he+//x61atXCnTt30hVcn6Ojo5PhNmZmZrC1tcXZs2fRuHFjVfvZs2dRp04dtfU8PDzg4eGBH374Aa1atcKbN29QpEgRtf196I+jUCg+mcfAwACdOnXCli1b8ODBA1SoUAG1atVSvV6rVi2EhIRk+X3+15QpU9CsWTMMGTJE9T7r1auHoUOHqtb57xkdPT29dPlr1aoFPz8/WFlZwczM7KsyEWk7dpYm0iJly5ZFamoqli9fjkePHmHTpk1YtWpVpusnJiZi+PDhCAgIwOPHj3H27FlcvnxZdclrwoQJOHfuHIYPH46goCDcv38fu3fvznJn6X8bP3485s2bBz8/P4SEhMDT0xNBQUEYNWoUAGDx4sXYtm0b7t69i3v37mH79u0oXrx4hoNAWllZwdDQEAcPHsTLly8RExOT6XF79OiBffv2Yf369apO0h9MmzYNGzduxMyZM3H79m0EBwfD19cXU6ZMydJ7q1u3LqpXrw4vLy8AQLly5XDlyhUcOnQI9+7dw9SpU3H58mW1bRwcHHDjxg2EhIQgKioKqamp6NGjBywtLdGhQwecPn0aoaGhCAgIwMiRI/H06dMsZSLSelJ3UiKinJdRB9sPFi9eLGxsbIShoaFo2bKl2LhxowAg3r59K4RQ78ycnJwsunbtKuzt7YWenp6wtbUVw4cPV+sIfenSJdG8eXNhYmIijI2NRfXq1dN1dv63/3aW/i+FQiFmzJgh7OzshK6urqhRo4Y4cOCA6vU1a9YIJycnYWxsLMzMzMS3334rrl69qnod/+osLYQQa9euFfb29kJHR0c0btw405+PQqEQNjY2AoB4+PBhulwHDx4U9erVE4aGhsLMzEzUqVNHrFmzJtP3MX36dFGjRo107du2bRP6+voiPDxcJCUlib59+wpzc3NhYWEhhgwZIjw9PdW2e/XqlernC0CcOHFCCCHEixcvRO/evYWlpaXQ19cXjo6O4qeffhIxMTGZZiKi9GRCCCFtKUZEREQkDV4aIyIiIq3FQoiIiIi0FgshIiIi0loshIiIiEhrsRAiIiIircVCiIiIiLQWCyEiIiLSWiyEiIiISGuxECIiIiKtxUKIiIiItBYLISIiItJaLISIiIhIa/0fA9VIf9Yho3cAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "\n",
    "y_pred = model.predict(X_test)\n",
    "y_pred = y_pred.flatten()\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred)\n",
    "roc_auc = roc_auc_score(y_test, y_pred)\n",
    "plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.0])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC curve')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We got a `ROC` of `0.69` which is great given the available resources."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `Kernel Visualization`"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kernel visualization is a technique that helps domain experts under the model's decision making process.\n",
    "\n",
    "It is a great tool for debugging and understanding the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/miniconda3/lib/python3.9/site-packages/keras/engine/training_v1.py:2359: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
      "  updates=self.state_updates,\n",
      "2023-05-07 01:18:05.153125: W tensorflow/c/c_api.cc:300] Operation '{name:'conv2d_188/Conv2D' id:149850 op device:{requested: '', assigned: ''} def:{{{node conv2d_188/Conv2D}} = Conv2D[T=DT_FLOAT, _has_manual_control_dependencies=true, data_format=\"NHWC\", dilations=[1, 1, 1, 1], explicit_paddings=[], padding=\"VALID\", strides=[1, 2, 2, 1], use_cudnn_on_gpu=true](input_1_3, conv2d_188/Conv2D/ReadVariableOp)}}' was changed by setting attribute after it was run by a session. This mutation will have no effect, and will trigger an error in the future. Either don't modify nodes after running them or create a new session.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2200x300 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "layer_outputs = [layer.output for layer in model.layers[1:100]]\n",
    "\n",
    "img = X_test[0]\n",
    "img = np.expand_dims(img, axis=0)\n",
    "\n",
    "activation_model = Model(inputs=model.input, outputs=layer_outputs)\n",
    "activations = activation_model.predict(img)\n",
    "\n",
    "layer_names = ['conv2d_1', 'activation_1', 'conv2d_4', 'activation_4', 'conv2d_9', 'activation_9']\n",
    "activ_list = [activations[0], activations[2], activations[10], activations[12], activations[17], activations[19]]\n",
    "\n",
    "fig = plt.figure(figsize=(22, 3))\n",
    "for img in range(30):\n",
    "    ax = fig.add_subplot(2, 15, img+1)\n",
    "    ax = plt.imshow(activations[0][0, :, :, img], cmap='plasma')\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    fig.subplots_adjust(wspace=0.05, hspace=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2200x600 with 60 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(22, 6))\n",
    "for img in range(60):\n",
    "    ax = fig.add_subplot(4, 15, img+1)\n",
    "    ax = plt.imshow(activations[6][0, :, :, img], cmap='plasma')\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    fig.subplots_adjust(wspace=0.05, hspace=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2200x600 with 60 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(22, 6))\n",
    "for img in range(60):\n",
    "    ax = fig.add_subplot(4, 15, img+1)\n",
    "    ax = plt.imshow(activations[10][0, :, :, img], cmap='plasma')\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    fig.subplots_adjust(wspace=0.05, hspace=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2200x600 with 60 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(22, 6))\n",
    "for img in range(60):\n",
    "    ax = fig.add_subplot(4, 15, img+1)\n",
    "    ax = plt.imshow(activations[17][0, :, :, img], cmap='plasma')\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    fig.subplots_adjust(wspace=0.05, hspace=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2200x600 with 60 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(22, 6))\n",
    "for img in range(60):\n",
    "    ax = fig.add_subplot(4, 15, img+1)\n",
    "    ax = plt.imshow(activations[39][0, :, :, img], cmap='plasma')\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    fig.subplots_adjust(wspace=0.05, hspace=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get positive sample that it predicted correctly\n",
    "correct_pred = np.where((y_pred > 0.9) & (y_test == 1))[0]\n",
    "entries = test_df.loc[correct_pred]\n",
    "\n",
    "random_idx = np.random.choice(entries.index)\n",
    "\n",
    "sample = test_df.loc[random_idx]\n",
    "x = sample['molimage']\n",
    "x = np.expand_dims(x, axis=0)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `Visualizing using Grad-CAM`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x5ff48c430>"
      ]
     },
     "execution_count": 347,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import keras.backend as K\n",
    "\n",
    "flower_output = model.output[:, 0]\n",
    "last_conv_layer = model.get_layer('mixed2')\n",
    "\n",
    "grads = K.gradients(flower_output, last_conv_layer.output)[0]                              \n",
    "pooled_grads = K.mean(grads, axis=(0, 1, 2))                                               \n",
    "                                                                                          \n",
    "iterate = K.function([model.input], [pooled_grads, last_conv_layer.output[0]])\n",
    "pooled_grads_value, conv_layer_output_value = iterate([x])\n",
    "\n",
    "#2048 is the number of filters/channels in 'mixed10' layer\n",
    "for i in range(288):                                                                      \n",
    "        conv_layer_output_value[:, :, i] *= pooled_grads_value[i]                          \n",
    "\n",
    "heatmap = np.mean(conv_layer_output_value, axis=-1)\n",
    "heatmap = np.maximum(heatmap, 0)                                                           \n",
    "heatmap /= np.max(heatmap)\n",
    "\n",
    "plt.imshow(heatmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from rdkit.Chem import Draw\n",
    "import cv2\n",
    "img = Draw.MolToImage(sample['mol'], size=(300, 300))\n",
    "img = np.array(img)\n",
    "img = cv2.rotate(img, cv2.ROTATE_90_CLOCKWISE)\n",
    "# img = x[0]\n",
    "extent = 0, 80, 0, 80\n",
    "fig = plt.Figure(frameon=False)\n",
    "\n",
    "img1 = plt.imshow(img, extent=extent)\n",
    "img2 = plt.imshow(heatmap, cmap='viridis', alpha=0.4, extent=extent)\n",
    "\n",
    "\n",
    "\n",
    "plt.xticks([])\n",
    "plt.yticks([])\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `Comparing other models`"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest\n",
    "Random forest is one of the most prominent models in the field of machine learning, especially in tabular data.\n",
    "\n",
    "The feauturization method that will be used will be `Circular Fingerprints (CFP).`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Featurizing train dataset...\n",
      "Fitting Random Forest model...\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=10, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=10, random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_depth=10, random_state=0)"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from deepchem.feat import CircularFingerprint\n",
    "\n",
    "print(\"Featurizing train dataset...\")\n",
    "featurizer = CircularFingerprint(size=1024)\n",
    "X_train, y_train = featurizer.featurize(train_df[\"smiles\"]), train_df[\"label\"]\n",
    "X_test, y_test = featurizer.featurize(test_df[\"smiles\"]), test_df[\"label\"]\n",
    "\n",
    "print(\"Fitting Random Forest model...\")\n",
    "rf = RandomForestClassifier(n_estimators=100, max_depth=10, random_state=0)\n",
    "rf.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf_roc_auc = roc_auc_score(y_test, rf.predict(X_test))\n",
    "fpr, tpr, thresholds = roc_curve(y_test, rf.predict(X_test))\n",
    "plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % rf_roc_auc)\n",
    "plt.plot([0, 1], [0, 1], 'k--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.0])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC curve')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Graph Convolutional Network"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* https://www.researchgate.net/publication/317732180_Chemception_A_Deep_Neural_Network_with_Minimal_Chemistry_Knowledge_Matches_the_Performance_of_Expert-developed_QSARQSPR_Models\n",
    "* https://pubchem.ncbi.nlm.nih.gov/bioassay/686978"
   ]
  }
 ],
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