ATLAS_SigvsBkg_emu.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c157cd32-2e52-4c7e-9b91-05a41aec33bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.3.5\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CleanJet_pt_1</th>\n",
       "      <th>CleanJet_pt_2</th>\n",
       "      <th>mll</th>\n",
       "      <th>MET_significance</th>\n",
       "      <th>zeta</th>\n",
       "      <th>mjj</th>\n",
       "      <th>detajj</th>\n",
       "      <th>dphijj</th>\n",
       "      <th>m2ljj30</th>\n",
       "      <th>isLL</th>\n",
       "      <th>isMIX</th>\n",
       "      <th>isTT</th>\n",
       "      <th>isBKG</th>\n",
       "      <th>isVBS</th>\n",
       "      <th>notLL</th>\n",
       "      <th>notTT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>53.054813</td>\n",
       "      <td>29.876976</td>\n",
       "      <td>119.088409</td>\n",
       "      <td>1.595703</td>\n",
       "      <td>1.021057</td>\n",
       "      <td>276.330872</td>\n",
       "      <td>3.893066</td>\n",
       "      <td>1.000959</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30.664513</td>\n",
       "      <td>29.299437</td>\n",
       "      <td>128.700623</td>\n",
       "      <td>15.085938</td>\n",
       "      <td>2.046753</td>\n",
       "      <td>495.311188</td>\n",
       "      <td>5.605469</td>\n",
       "      <td>1.686401</td>\n",
       "      <td>239.743408</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30.099604</td>\n",
       "      <td>28.075436</td>\n",
       "      <td>111.508141</td>\n",
       "      <td>10.007812</td>\n",
       "      <td>0.949219</td>\n",
       "      <td>104.640396</td>\n",
       "      <td>2.666260</td>\n",
       "      <td>0.274885</td>\n",
       "      <td>156.898926</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>91.604919</td>\n",
       "      <td>32.597763</td>\n",
       "      <td>267.004730</td>\n",
       "      <td>8.101562</td>\n",
       "      <td>1.380615</td>\n",
       "      <td>551.808350</td>\n",
       "      <td>4.623047</td>\n",
       "      <td>1.564453</td>\n",
       "      <td>840.582458</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59.228630</td>\n",
       "      <td>48.039818</td>\n",
       "      <td>81.836113</td>\n",
       "      <td>6.886719</td>\n",
       "      <td>1.799194</td>\n",
       "      <td>628.104187</td>\n",
       "      <td>4.930176</td>\n",
       "      <td>1.513550</td>\n",
       "      <td>791.871887</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>158.753632</td>\n",
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       "      <td>18.312500</td>\n",
       "      <td>0.529480</td>\n",
       "      <td>1368.427856</td>\n",
       "      <td>4.981445</td>\n",
       "      <td>2.118530</td>\n",
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       "      <td>1.710358</td>\n",
       "      <td>1039.798218</td>\n",
       "      <td>3.931885</td>\n",
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       "      <th>32290</th>\n",
       "      <td>153.700607</td>\n",
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       "      <td>316.587463</td>\n",
       "      <td>11.273438</td>\n",
       "      <td>0.893066</td>\n",
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      ],
      "text/plain": [
       "       CleanJet_pt_1  CleanJet_pt_2         mll  MET_significance      zeta  \\\n",
       "0          53.054813      29.876976  119.088409          1.595703  1.021057   \n",
       "1          30.664513      29.299437  128.700623         15.085938  2.046753   \n",
       "2          30.099604      28.075436  111.508141         10.007812  0.949219   \n",
       "3          91.604919      32.597763  267.004730          8.101562  1.380615   \n",
       "4          59.228630      48.039818   81.836113          6.886719  1.799194   \n",
       "...              ...            ...         ...               ...       ...   \n",
       "32287     158.753632      80.266327   85.201508         18.312500  0.529480   \n",
       "32288     190.165131     109.592522  112.974281         44.375000  1.710358   \n",
       "32289      68.281357      32.656868  129.935699          3.316406  1.657227   \n",
       "32290     153.700607     113.567513  316.587463         11.273438  0.893066   \n",
       "32291      90.099380      34.271664   97.926270          1.205078  1.165588   \n",
       "\n",
       "               mjj    detajj    dphijj      m2ljj30  isLL  isMIX  isTT  isBKG  \\\n",
       "0       276.330872  3.893066  1.000959   271.210632   0.0    0.0   0.0    1.0   \n",
       "1       495.311188  5.605469  1.686401   239.743408   0.0    0.0   0.0    1.0   \n",
       "2       104.640396  2.666260  0.274885   156.898926   0.0    0.0   0.0    1.0   \n",
       "3       551.808350  4.623047  1.564453   840.582458   0.0    0.0   0.0    1.0   \n",
       "4       628.104187  4.930176  1.513550   791.871887   0.0    0.0   0.0    1.0   \n",
       "...            ...       ...       ...          ...   ...    ...   ...    ...   \n",
       "32287  1368.427856  4.981445  2.118530  1592.925781   0.0    0.0   0.0    0.0   \n",
       "32288  1039.798218  3.931885  1.972290  1177.098145   0.0    0.0   0.0    0.0   \n",
       "32289   903.292419  5.904785  1.004395  1104.163574   0.0    0.0   0.0    0.0   \n",
       "32290  2401.952637  5.796875  2.201477  3160.002930   0.0    0.0   0.0    0.0   \n",
       "32291   419.576477  4.005798  2.561646   521.605835   0.0    0.0   0.0    0.0   \n",
       "\n",
       "       isVBS  notLL  notTT  \n",
       "0        0.0    0.0    0.0  \n",
       "1        0.0    0.0    0.0  \n",
       "2        0.0    0.0    0.0  \n",
       "3        0.0    0.0    0.0  \n",
       "4        0.0    0.0    0.0  \n",
       "...      ...    ...    ...  \n",
       "32287    1.0    0.0    0.0  \n",
       "32288    1.0    0.0    0.0  \n",
       "32289    1.0    0.0    0.0  \n",
       "32290    1.0    0.0    0.0  \n",
       "32291    1.0    0.0    0.0  \n",
       "\n",
       "[32292 rows x 16 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "print(pd. __version__)\n",
    "from sklearn.utils import shuffle\n",
    "\n",
    "#df = pd.read_pickle('https://pandora.infn.it/public/24236c/dl/dataset_pol_vbs_full2018v9_VBS_WW_2j_emu_dnn_ATLAS.pkl')\n",
    "df = pd.read_pickle('https://pandora.infn.it/public/3dcf6d/dl/dataset_pol_vbs_full2018v9_VBS_WW_2j_emu_dnn_ATLAS_selection.pkl')\n",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "df['notLL'] = df['isTT'] + df['isMIX']\n",
    "df['notTT'] = df['isLL'] + df['isMIX']\n",
    "\n",
    "df_SvB = df[((df['isTT'] + df['isMIX'] + df['isLL']) == 0) & ((df['isVBS'] + df['isBKG']) == 1)]\n",
    "df_SvB = df_SvB[['CleanJet_pt_1', 'CleanJet_pt_2', 'mll', 'MET_significance', 'zeta', 'mjj', 'detajj', 'dphijj', 'm2ljj30', 'isLL', 'isMIX', 'isTT', 'isBKG', 'isVBS', 'notLL', 'notTT']]\n",
    "df_red = df[['CleanJet_pt_1', 'CleanJet_pt_2', 'mll', 'MET_significance', 'zeta', 'mjj', 'detajj', 'dphijj', 'm2ljj30', 'isLL', 'isMIX', 'isTT', 'isBKG', 'isVBS', 'notLL', 'notTT']]\n",
    "df_SvB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b1c30b9d-7706-4fda-8bce-99ffc4a93c08",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bc26e027-8929-4c05-8995-bc477450a479",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.7.0-rc1\n"
     ]
    }
   ],
   "source": [
    "import tqdm\n",
    "from tensorflow.keras.models import Sequential, Model\n",
    "from tensorflow.keras.layers import Input, Activation, Dense, Dropout, InputLayer\n",
    "from tensorflow.keras.constraints import max_norm\n",
    "from tensorflow.keras import initializers\n",
    "from tensorflow.keras.models import load_model\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from sklearn.utils import shuffle\n",
    "import copy\n",
    "print(tf.__version__)\n",
    "from tensorflow.python.client import device_lib\n",
    "tf.config.run_functions_eagerly(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52abbb0d-f65b-408f-9465-36991164434b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "59\n",
      "[[5.30548134e+01 2.98769760e+01 1.19088409e+02 ... 3.89306641e+00\n",
      "  1.00095880e+00 2.71210632e+02]\n",
      " [3.06645126e+01 2.92994366e+01 1.28700623e+02 ... 5.60546875e+00\n",
      "  1.68640137e+00 2.39743408e+02]\n",
      " [3.00996037e+01 2.80754356e+01 1.11508141e+02 ... 2.66625977e+00\n",
      "  2.74884522e-01 1.56898926e+02]\n",
      " ...\n",
      " [1.90165131e+02 1.09592522e+02 1.12974281e+02 ... 3.93188477e+00\n",
      "  1.97229004e+00 1.17709814e+03]\n",
      " [6.82813568e+01 3.26568680e+01 1.29935699e+02 ... 5.90478516e+00\n",
      "  1.00439453e+00 1.10416357e+03]\n",
      " [1.53700607e+02 1.13567513e+02 3.16587463e+02 ... 5.79687500e+00\n",
      "  2.20147705e+00 3.16000293e+03]] [[0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " ...\n",
      " [1. 0.]\n",
      " [1. 0.]\n",
      " [1. 0.]]\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import cm\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "%matplotlib inline\n",
    "mpl.rcParams['figure.dpi'] = 150\n",
    "\n",
    "nclasses = 2\n",
    "features = df.columns[:len(df.columns)-7]\n",
    "\n",
    "NDIM = len(features)\n",
    "print(NDIM)\n",
    "for col in df.columns:\n",
    "    df[col]    = np.hstack(df[col])\n",
    "'''\n",
    "df.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
    "df.dropna(inplace=True)\n",
    "df = shuffle(df)\n",
    "'''\n",
    "# Perform the splitting and define training and validation datasets\n",
    "msk    = np.random.rand(len(df_SvB)) < 0.8\n",
    "\n",
    "df_train    = df_SvB[msk]\n",
    "df_val      = df_SvB[~msk]\n",
    "\n",
    "NDIM = len(df_SvB.columns[:len(df_SvB.columns)-7])\n",
    "X    = df_train.values[:,0:NDIM]\n",
    "Y    = df_train[['isVBS', 'isBKG']].values\n",
    "X_val = df_val.values[:,0:NDIM]\n",
    "Y_val = df_val[['isVBS', 'isBKG']].values\n",
    "\n",
    "print(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2bd8aa6e-8ec9-462c-937d-0430fcdec8da",
   "metadata": {},
   "outputs": [],
   "source": [
    "class SimpleNeuralNetwork ( tf.Module ):\n",
    "    def __init__ (self, nEpochs, learning_rate, N_NODES, n_layers, n_features, n_outputsC=nclasses, ATLAS=False):\n",
    "        self.learning_rate = learning_rate\n",
    "        self.optimizer  = tf.optimizers.Adam (self.learning_rate)\n",
    "        self.nEpochs = nEpochs\n",
    "        self.N_NODES = N_NODES\n",
    "        self.n_layers = n_layers\n",
    "        self.n_features = n_features\n",
    "        self.n_outputsC = n_outputsC\n",
    "        self.ATLAS = ATLAS\n",
    "        self.weights = self.build (self.n_features, self.N_NODES)\n",
    "        \n",
    "                                \n",
    "    # Define the structure of the model\n",
    "    def build (self, n_input, N_NODES):\n",
    "        # initializer = initializers.Ones()\n",
    "\n",
    "        # Classifier model\n",
    "        self.model1 = Sequential()\n",
    "        #self.model1.add(Dense (self.N_NODES, activation = 'relu', input_dim  = n_input))\n",
    "        if not self.ATLAS:\n",
    "            for i in range(self.n_layers):\n",
    "                self.model1.add(Dense (self.N_NODES, activation = 'relu', kernel_constraint=max_norm(3)))\n",
    "                self.model1.add(Dropout (0.1))\n",
    "        else:\n",
    "            self.model1.add(Dense (108, activation = 'relu', kernel_constraint=max_norm(3), input_dim = n_input))\n",
    "            self.model1.add(Dropout (0.1))\n",
    "            self.model1.add(Dense (60, activation = 'relu', kernel_constraint=max_norm(3)))\n",
    "            self.model1.add(Dropout (0.1))\n",
    "        if self.n_outputsC > 1:\n",
    "            self.model1.add(Dense (self.n_outputsC, activation = 'softmax',input_dim = self.N_NODES))\n",
    "        else:\n",
    "            self.model1.add(Dense (self.n_outputsC, activation = 'sigmoid',input_dim = self.N_NODES))\n",
    "        \n",
    "        return self.model1.weights\n",
    "     \n",
    "    # Performs the epochs loop and the actual training.\n",
    "    # Monitors the training and validation loss functions, both for the classifier and the adversary.\n",
    "    # Returns the classifier categorical accuracy.\n",
    "    def fit (self, X, Y, X_val, Y_val, show_loss = False):\n",
    "        losses = []\n",
    "        losses_val = []\n",
    "\n",
    "        self.means = np.mean ( X, axis = 0)\n",
    "        self.sigmas = np.std ( X, axis = 0)\n",
    "\n",
    "        for iEpoch in tqdm.tqdm(range(self.nEpochs)):\n",
    "                l, l_val = self._train (X, Y, X_val, Y_val)\n",
    "                losses.append ( l )\n",
    "                losses_val.append ( l_val )\n",
    "\n",
    "        losses = np.array(losses)               \n",
    "        losses_val = np.array(losses_val)\n",
    "               \n",
    "        plt.plot (losses, color = \"c\", label='Training set')\n",
    "        plt.plot (losses_val, color ='tab:blue', label = \"Validation set\")\n",
    "        plt.xlabel (\"Epoch\"); plt.ylabel (\"Loss\")\n",
    "        plt.legend(frameon=False)\n",
    "        plt.show()\n",
    "         \n",
    "        ca = tf.keras.metrics.CategoricalAccuracy()\n",
    "        if self.n_outputsC <= 1:\n",
    "            ca = tf.keras.metrics.BinaryAccuracy()\n",
    "        ca.update_state(Y, self.predict_proba(X))\n",
    "        \n",
    "        return ca.result().numpy()\n",
    "\n",
    "    def save_weights(self, model_name):\n",
    "        self.model1.save_weights(model_name+'_weights_1')\n",
    "    \n",
    "    def load_weights(self, model_name):\n",
    "        self.model1.load_weights(model_name+'_weights_1')\n",
    "        \n",
    "    def save_model(self, model_name):\n",
    "        self.model1.save(\"saved_models/\"+model_name+\"_1\")\n",
    "\n",
    "    def reset_optimizers(self):\n",
    "        self.optimizer  = tf.optimizers.Adam (self.learning_rate)\n",
    "        \n",
    "    def set_epochs(self, epochs):\n",
    "        self.nEpochs = epochs\n",
    "        \n",
    "    # Applies a pre-processing to the input features and returns the classifier representation.\n",
    "    #@tf.function\n",
    "    def predict_proba (self, X):\n",
    "        ppX = (X - self.means)/self.sigmas\n",
    "        return  tf.clip_by_value ( self.model1 (ppX) , 1e-7, 1. - 1e-7 )\n",
    "\n",
    "\n",
    "    @tf.function\n",
    "    def _train (self, X, Y, X_val, Y_val):\n",
    "        Y_true = tf.cast (Y, tf.float32)\n",
    "        Y_true_val = tf.cast (Y_val, tf.float32)\n",
    "\n",
    "        with tf.GradientTape() as gt:\n",
    "            #gt.watch ( self.weightsC )\n",
    "            Y_hat = self.predict_proba (X)\n",
    "            Y_hat_val = self.predict_proba (X_val) #N3(N1(x)) validation set\n",
    "            \n",
    "            ## Training set\n",
    "            # Use the categorical cross-entropy as loss function for the classifier\n",
    "            cce = 0.\n",
    "            cce_val = 0.\n",
    "            bce = 0.\n",
    "            bce_val = 0.\n",
    "            loss = 0.\n",
    "            loss_val = 0.\n",
    "            \n",
    "            if self.n_outputsC > 1:\n",
    "                cce = tf.keras.losses.CategoricalCrossentropy()\n",
    "                loss = tf.reduce_mean ( cce( Y_true, Y_hat ) )\n",
    "            \n",
    "                ## Validation set\n",
    "                cce_val = tf.keras.losses.CategoricalCrossentropy()\n",
    "                loss_val = tf.reduce_mean (cce_val( Y_true_val, Y_hat_val ) )\n",
    "            else:\n",
    "                bce = tf.keras.losses.BinaryCrossentropy()\n",
    "                loss = tf.reduce_mean ( bce( Y_true, Y_hat ) )\n",
    "            \n",
    "                ## Validation set\n",
    "                bce_val = tf.keras.losses.BinaryCrossentropy()\n",
    "                loss_val = tf.reduce_mean (bce_val( Y_true_val, Y_hat_val ) )\n",
    "            \n",
    "            # Compute the gradient of the overall loss with respect to the classifier weights\n",
    "            gradients = gt.gradient ( loss, self.weights )\n",
    "\n",
    "        # Apply the gradients\n",
    "        self.optimizer.apply_gradients ( zip(gradients, self.weights) )\n",
    "        \n",
    "        return loss, loss_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "270faf23-c06e-46c2-b801-40b74b8ee17b",
   "metadata": {},
   "outputs": [],
   "source": [
    "dnn = SimpleNeuralNetwork(300, learning_rate=0.001, N_NODES=50, n_layers=8, n_features=X.shape[1], n_outputsC=2, ATLAS=True)\n",
    "dnn.save_weights(\"ATLAS_selection_SigVsBkg_emu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4878e7f1-b129-4343-a100-f1de1e3fc119",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25838, 9) (25838, 2)\n",
      "[[5.30548134e+01 2.98769760e+01 1.19088409e+02 ... 3.89306641e+00\n",
      "  1.00095880e+00 2.71210632e+02]\n",
      " [3.06645126e+01 2.92994366e+01 1.28700623e+02 ... 5.60546875e+00\n",
      "  1.68640137e+00 2.39743408e+02]\n",
      " [3.00996037e+01 2.80754356e+01 1.11508141e+02 ... 2.66625977e+00\n",
      "  2.74884522e-01 1.56898926e+02]\n",
      " ...\n",
      " [1.90165131e+02 1.09592522e+02 1.12974281e+02 ... 3.93188477e+00\n",
      "  1.97229004e+00 1.17709814e+03]\n",
      " [6.82813568e+01 3.26568680e+01 1.29935699e+02 ... 5.90478516e+00\n",
      "  1.00439453e+00 1.10416357e+03]\n",
      " [1.53700607e+02 1.13567513e+02 3.16587463e+02 ... 5.79687500e+00\n",
      "  2.20147705e+00 3.16000293e+03]]\n"
     ]
    }
   ],
   "source": [
    "print(X.shape, Y.shape)\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "03845452-4fd3-4f29-b16f-b0d2ee0ef2c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 300/300 [00:05<00:00, 55.88it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc = dnn.fit (X.astype(np.float32), Y.astype(np.float32), X_val.astype(np.float32), Y_val.astype(np.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "fe6f8ce9-8957-4250-9622-e570e00551d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wJbAX8MKI2Dczv9e+c0R8E9i/enk2JQAKYPPqOvsCPwGu6/uGNChHUAKhF0XEmzLzznH2fRmwEvCTzOxM30+jBDlQ8oCNgO2BbYBdI2L3zLw/YIyI5wDHAasD1wK/AW6kBJhPB/6FEhB9Yjo3p+EwEJIkSZKk6Xs8cM8Ujz2CEgi9OCLe0qtHTkRsATwRuAH4Wef2zNyvY/85wMeB9wJfiIijM/OeattelDDoJmC3zh5EEbEx8Brgrinekx7o/ZTg5MrJHpiZ50fEmcBWwJ7A0ePsPl5oeEhmHt6+IiKeSunpsyvwQuAH1frVq3OsTvk5OrD181NtXxN4PnDbZO9Ho8FASJIkSdIgzQcWD7sRHeYCC+s8YWZeMI1j/xIR5wBPAnYHfthj19YH/aP6GcZV9T76MGW4zxil58851eYXVcsvdhtOVg11+VB/d6B+ZOa1lF42U3UEJRDalx6BUEQ8HngypRfPT/ps1xkRcQzwSuAZVIEQpQfQ+sDfMvMDXY67Dfhe53rNHg6QliRJkjRIi8m8fqS+BhBQRURGxOVd1m8fEcdFxBURcVdEXBcRf4qIT0TEWm27fqdavqLH+YMyFAi69/zoqgqObqpetncIaI2NrLWQdkRsExH/GxF/q+732og4MSJe1+fxO0bEFyPivIj4R0TcEREXVO/XOj2O6fc9JiJ2i4hftbXvmog4NSI+UsPtT3Rvh1c/Jzt3rJ9XtXVhRNwaETdFxEUR8e2IeFrbrkcC91GGdq3X4zKt0PDozJxM765WMfiB/oxExEYR8ZXq/m6PiBsiYkFEfC0iNuvYd/eIODQizo+ImyPitog4NyL+IyJW7XLu/ar398CI2DQijoqIxdWxP4+I+dV+K1fnuCgi7oyISyLiTV3Ot3F1vpMi4sER8fmIuKo65vyIeEfVC28y979eRPxX9azvqJ71byJij8m+l3Wwh5AkSZKGb+FCmDt3Zq+5eDHMnz+z11SjRMSelPorAfwJOB1YB3gMZRjXV4Fbq92/RykWvUdEPCQzb+o43c6Uui2X9ioQ3aMNm1B6B93DstoxUIoOA/xrRHwzM299wMGTFBFvAz5D6XhwJmUY0lxgC+BTwDf6OM2nKLWPzgNOBFYDnkJ5v/aIiG3b2zqZ97j60P9FSqhyGqVm0lzKcL8DgY9O7c6nLiLWBv4IbEJ5Jr+i1OR5JPBS4DLKfZGZiyLiBMrQrpdQ7q39XAG8vHrZd2hYeWq1PL9tXetn5IkRseNEBc8nEhEbUoqVrwdcTBn2uBKljtHrKAXQL2w75JuU4Wp/ofw8PAR4GvAx4FkR8ZzMvK/LpTahvGd/B35N6aW4C7BVlGGXX6X8efot5f19JvDFiLg7M7v9jK5KqZ20abVchVJg+zOUn9X9+rz/x1bt2RC4HPglpdbXtsCPI+LdmXlwP+eqi4GQJEmShm/uXIs5a0V0ACUc2Tszj23fEBFbA/cX/M3MayPiROA5wIuBQzrO1er58R36UPWMeRLw2WrVVzLzxrZdDqV8kH0KcFlEHEsJU84Ezm8vLNzn9Z5RXetW4IWZeWLbtpUp99WPjwKntwdiVW+QLwD/BrwT+M+2/ft+jyl1mhLYNjPPaNsvKLNCDcPelADjeMr7trStXfOAh3bsfwQlENqXjkCIMsRrY0rYcOpEF66eyyOBN1Pu/yqWD5JOpwytnA/8NiJ+QQnpzgTOyMzb+7nBNq+lhEFfzMy3dLTlkcCDOvZ/PXBCZt7Rtt/alPB0D0pvum93uc6/Umo1/UdmZvV8Wz/vJ1JmX3tMlt6CRMSzKEHNB+geWm5LCaQek5mLq2M2pQSer4qI4zLzuPFuPCJWAo6hhEHvocz8t7Ta9mjgBOATEfGLzPzLeOeqk0PGJEmSJGkwWinnrzs3ZOafM/OWjtWtD+P7tq+MiNVYVvOnZyBUDW/JKNPQ30KZsWwz4C3A2zuuf1p1nSVVO99A+XC9ALi2GtbykIlusM37KL10PtYeBlXXujczH1AEu5vM/Hln76hq6NPbKT1nnt9xyGTe43nAje1hULVfTnYa+Bq12v+b9jAIIDOv7xIOHEd5tttXvb/atX5uvjtOoHdY28/IPcClwDsoIct2mXlz2/Xvo9S0Op3Sk2d3Sq+Yk4EbI+L4KAWp+zXes7oyMy/tWPej9jCoWndL1V544M9Cy2XAh1vvQbVsBaPzgbe3wqBq+4mUWfY2ilJMvZsDWmFQdcylwEHVyzf3OKbdnpSC8Mdm5qfan3VmXkKp87USpafUjLGHkCRJkiQNxpmU4UhHRMRBwJmdH/o7/JAyY9MzImLDzGwN2dmDMlzmD9WHx17ap51flTIUZxvgw5QP/j9v3zkzj4yI44EXUIbAbE0pPP1QSsCzd0TskJmLGEfV02Tn6uXXx9u3HxHxcMoH6McBD2ZZR4a7KUPB2k3mPT4TeHpEfBP4TGYumG5ba3BmtXx3RPwd+GmXoPB+mXlHRPwAeBVleNjHACJiFUrPMhh/uFj7tPMBPIwyXOwlwD8i4m3tw7Cq4uI7RMT2wPMovWW2AtaiPKNdI+IVmXnUJO714xFxH/DrzLxzvAMi4jHAbsCjgTUpPwtRbe78WWg5qX02tMpl1fIe4KQux1xGKca9AaWHVbsbMvNXXY45EvgKJZybM8Gf7VYPuR/02N4ajve0HtsHwkBIkiRJkgbjPyi9Avasvv4REadShgd9p/PDcGbeFhE/pPT0eDmlphCMP414+/H7da6LiCdTenQcHxFPyMwLO465Dfhu9UVEPBR4NSVEejQlcJio18IYpdbLDZn5jwn2HVdEvJMy3Kdz+FAvk3mP30TpYbM/sH8VwJxM+ZB+TI96NJ3tex8lqGp3QWZ+os/2LiczT4yIz1J6QB0J3BsRZ1FqCR2amZd1OewISiD0CqpAiBKarAv8ufMZd+g27fzawP9S3p8bKM++s52nU3oKtcKnfwE+Selx87WI+FkfdagOpwQjLwF+DNwZEX8GflHd63VtbQrgYEpvoHjgqYBSf6ebv3Vp/63llFzX4zm32v6AYtXAFd0ukpk3RcSNlJpV67L88MROG1fL70bEd8fZb0aL6TlkTJIkSZIGoOrh81TgucD/UGq07EmpU3JeRIx1OawV+rwCyqxElJox9wDfn0Ibzga+RukM8MY+9v97FW68r1q1+2SvOVURsS3waeB2Sr2XjYHVMjMyM+gyZftk3uPMPI8SYLyw2n4zJZz4X+CUKuiYyC6UMKb9a5fJ3+1y9/BOSi+n91IKHT+BUs/mgoh4UZdDfksJPR4fEVtV6/oKDXtc/xZKXRsowwsn2v/uzPwppVfZ7ZRAZPs+jrsvM/eh1K36KPBnSg+2jwEXVb2QWvah1Iu6mlJn6eHAKtXPQSu06RUUjddTZ7xtg9TKXn5B6cnX6+tHM9koewhJkiRJ0oBk5r2UgrEnQJl2m1Lg9p8pAcB7Og45kRJ8PDEitgS2o8xqdHxmjtcDYTx/rZa9hth085tq2U+PhcXAHcB6EbFOR/HqyXhhtfxAZrYPfyMiVqcMb3qAybzHVY+h46ovImJzqvo5lKLHXx6vgZm586TuqE9Vr55PAp+saka9mTLj2leAYzv2XVr1MnkPsG9EXEIZVngvJdyaitbPyDoRMa+9xs44bb4uIs6nDCHru2dLFVKeDRwYEQ+mzPD2DuBzLBsy1fpZeGMVPrV7VL/Xqskju62s2r4O5Wf/xgnOcXW1PKSz+Pkw2UNIkiRJkmZIZl7BsqFgT+iy/T7K0CEovYReUX0/6Z4fbVofoNuna+/Vu6Ll0dXyAcNvOlVtPql6+W+TbVybdavl1V22vZjePUI62zPue9yx7wLgS/3sO1My884s049fC8yLiPW77NYqLv5SSm+aVYFf9hPk9ND6GUlKr58Jf0aqmbNaha0n/Dnppipi/f7quu3v/3g/Cy+ZyrWmYayaiazTS6vl7/sYbtiqQfTCcfeaYbUGQhExLyIOjogLI+KOiLghIs6KiE/VeR1JkiRJs8ZcIuaN1NcM1emIiHdERLdeLbtVy6u6bINl4c/+wA6U3gc/nmIbnsyykKZ9pq9DIuKDEfFPXY7ZjDJ0C8pU2f34b8qH+g9ExDM7zrdyROzW/bDlXFQtXxMR99cQioj5LAt4Otva13scEWtExFsjYp2O4+ewbMhXr+cxMBHxgmqoXOf6rSjFvW+lS++TzPw/4FxKr6lWHaEphYZVDaFPVi9PrupKAewZEUd1DOVqHbMmpffSesA1wO/7uM4rI6Jb6LYrJexrf/9bPwv/1h5MRcSOwLsnutYAHNw+/LCa4a1Va+lL3Q9ZzrHAQuAVEfGhiFiuVlEUO0TEDrW1uA+1DRmrfmB/SSkotoAy9u3BlDGa72A4D02SJEnScC0cdgOG6COUD5LnAhdTPvRuCTyWUrz34G4HZeY5EfEXlvWYOLqaen1cEXF428tVKLOMbUvpCPBjlg8MxiiB00cjYgHlA/h91TFbV8f8iWVTa48rM0+OiPdQgoXfRMQZlHueS7nnVSnDa8ZzGGX67T2BC6uCw+sBO1GGeD2tal+7ft/jVYDPV/ueSZlJapXqXjesXk97hrQp2Bl4W0T8jTKM6mbgn4AdKc/gI5l5d49jj6Dc69zquOP7uN5rI2Ln6vughE5bU97nxZTC0i1zKD2zXhwR11Xt+wdl+vinUnrx3Aa8cpw2tnsR8O2IuBT4P8pQq00odYSWAh9s2/cLlDpS/w7sHBHnUeoIPZ0SVh7Qx/Xq8gfKz8olEfEbSsHzZwFrUAqX95o57H6ZeW9EvICSmfwn8ObqnhZRnt+TgPUp2clpA7iHrmoJhKKk7L+gVJZ/fmYe37F9RqdOkyRJkqQR8BZK75OtKL0goPSC+Axl2vPxhtkcwbJeMd8ZZ792r2r7fimlZ8nvqnMd3jEt9puAn1CKMc+nfMBdi/KB/7fA0ZSZnzqn7+4pMw+OiD9SPtTuQAkrFlM+/B853rHV8UsiYmvKfe9Emeb8r8CHKMHOpV0O6/c9vrW652dV7dqCMo39lcAhwBcz84Z+77VGh1Nq/zyDEng9BLiO0pvr85l54jjHfo/yXq0EHJuZd/RxvR2qr5Y7KO/xYcDB7TN9UT7j7075GdmOElrM6zjmC9UQvX58hjIEbAdK4LUmpXfR94FPZ+YZrR0z86KIeColYNyG8rNwIfD6zPxGRMxkIHQX5Wfs48ALKAHOXymFyT/X70ky8+Kqx96bgb0oYe3KlOd9NiXQO6rGdk8oMnP6J4n4MqVi/Zsyc9wiXNO4xoL58+fPX7BgwSBOL0mSpJly/fWwfkdJjEWLYN68xrVj8803Z+HChQszc/MZu+gglf8oXjTsZkzR+ky9/oqkFUxEbEwJfk4eVDHxYZt2DaGq2vu+lK5ih027RZIkSZIkSRqoOoaMPRVYGzg1M++IiF2BfwFWo4xDPSozr6nhOpIkSZJG2xJKHYzZaKpTukvSrFRHIDS/Wi6KiOOA53ds/3hEvCYzJxwzCmVoWI9Nm06xfZIkSZJmQqlR47ArSZoF6ph2ft1q+TxKoaU3Uf5XYGNK4a/VgW9FxJNquJYkSZIkSdJAZeblmRkrav0gqKeHUCtUWhn4QEdR6XdHxEaUqereDbxiopP1KqhX9Rya322bJEmSJEmS+ldHD6Fb277vVlS6tW6nGq4lSZIkSZKkaaojELqiWt6e3adpvLxaztbicpIkSZIkSSuUOgKhs6vl6hGxapft61XLW7tskyRJkiRJ0gybdiCUmVcC5wJB92FhrXVnd9kmSZIkSZKkGVZHDyGAT1bLgyNig9bKamaxd1Uvv1rTtSRJkiRJkjQNdcwyRmZ+LyKeA7wKWBgRp1Omm98eWBX4RmYeXce1JEmSJEmSND21BEKVVwOnAa8HdgYSOAv4WmZ+q8brSJIkSZIkaRpqC4QyM4FvVF+SJEmSGiaCOcDYsNsxRUsyWTrsRkjSTKmzh5AkSZKkZhsDFg27EVO0PnD9sBshSTOlrqLSkiRJktRYEZERcfkUjpsTEVdVx+/Yx/7vqfY9tuPanV/3RMQ1EXFsRGw/zvn2ioifRcSi6pjFEbEgIo6IiNdGxCqTvad+RMRJVTs3HsT5R1FEHF7d887TOMflXZ71fdXz+3lE7D7BtffrsX2viLg7Iu6NiFd22b5GRLw1Ik6IiGsj4q6IuCUiFlbnfl5ErDTV+1oRRcTG1Xt+0rDb0os9hCRJkiRpSDJzaUR8D3gPsC9wygSH7Fstj+iyrb1269rAlsBewAsjYt/M/F77zhHxTWD/6uXZwMlAAJtX19kX+AlwXd83pJlyLHBr9f0awOOBXYBdIuL9mfmJfk8UEXsDR1Ke/Ssz88iO7TsARwMbAHcCfwauoUwgtSllcqlXAQspPzuaJQyEJEmSJGn6Hg/cM8Vjj6AEQi+OiLdk5t3ddoqILYAnAjcAP+vcnpn7dew/B/g48F7gCxFxdGbeU23bixIG3QTslpmndxy7MfAa4K4p3pMe6P3AJ4ArazjXAZl5efuKiPh34EvAf0bEYZn594lOEhEvAb5bvXx5Zh7Vsf0pwImU8OdTwP/LzJs79tkQeCfwhinei4bEQEiSJEnSIM0HFg+7ER3mUnoz1CYzL5jGsX+JiHOAJwG7Az/ssWurd9BRvUKjjvMujYgPA++i1HfaHDin2vyiavnFzjCoOvZy4EP93YH6kZnXAtcO8Pxfjoj3ARsC2wHHjbd/RLwU+A6wFHhZZh7bsX1OtX1V4EOZ+f96XPcq4B0R8Z1p34RmlDWEJEmSJA3S4kyuH6UvBhBQ9aohFBHbR8RxEXFFVXfluoj4U0R8IiLWatu19WH6FT3OH8DLqpfdhot1VQVHN1Uv2zsEzKuWtRbSjohtIuJ/I+Jv1f1eGxEnRsTr+jx+x4j4YkScFxH/iIg7IuKC6v1ap8cx/b7HRMRuEfGrtvZdExGnRsRHarj9ie6taw2hiJhXtXVhRNwaETdFxEUR8e2IeNokL9Mq6j5u54+IeDnLwqCXdIZBld0oPd+uBP5rogtn5pmTaWi/zyIi1omIt0TEL9ue8ZKI+EVE/EuPc99foyoi9omIP0fE7dW1PhlVbayI2DQijoxSg+n2iPht1ROv83wHVufbr/oZ/2VE3BgRN1f3sO1k7r065zYRcXT1Z+TuiLg6Ig6JiEdO9lxTZSAkSZIkSQMQEXtSagI9j9Iz5AeUWj3rUYZxzW3b/XvAfcAeEfGQLqfbGXgEcGm3Hj3jtGETSu+ge4BL2jZdVS3/tTM0maqIeBtwOrAPy+73L8ATKMON+vEpylC1OyhDlU4EHkx5v07tEvD0/R5HxJuAnwLPpLwXx1bt2wg4cJK3W4uIWBv4I6WtawG/Ak4A/gG8lBLK9HuuBwOPrV6eP85++wLfpvy8vSgzj+ux667V8ujMvK/fdvRjks9iW+ALlHu7kNKD7kLgOcAvI2J/ensbJfi6EfgFsArwbuAbEfEY4A+Unnm/qdqxM/DbiHhoj/NtD/yO8mfx51U7ng2cHBHP6ePWgfuH951OqfF1BaU31xLKz/4ZEfH4fs81HQ4ZkyRJkqTBOIDyn/B7dxmOszXlAyBQhhNFxImUD7kvBg7pOFdruFhfw3Kq4ORJwGerVV/JzBvbdjkU2A94CnBZlFnLTgfOBM7PzOznOm3Xe0Z1rVuBF2bmiW3bVqbcVz8+Cpyema1eTUTEqpRA4N8otWr+s23/vt9jSp2mBLbNzDPa9gtgpz7bV7e9gU2A4ynv29K2ds0DegUTtO23OqUnz39Riokfn5kLeuy+D+VZ3APslZkPqEXVZstqefZEbZiCyTyLC4HtMvMP7Ssj4smUIOezEXFUZt7KA722OvaM6piHUYZNvhLYmvLn7D8yM6trf6va9u9At15jrwM+RhlCl9U53wh8GTg8IjbNzDvGu/GqN9EXKAHm89t7VkXEa6o2HUYJwgbKHkKSJEmSNBitYVm/7tyQmX/OzFs6VreGgu3bvjIiVmNZzZ+egVC0TUUO3ELpObMZ8Bbg7R3XP626zpKqnW+g9BpZAFwbEf/Vo6dSL++jzFL1sfYwqLrWvRMED+37/rw9DKrW3VW1/17g+R2HTOY9ngfc2B5AVPtlZp7UT/sGoNX+37SHQQCZeX1m/qXHcX9te9a3U4K8Z1J617xknOvtQskBvtDHMxmrll2HWEbEN6thcO1fT5/gnC19P4vM/GtnGFStP5tSRPvBlHvv5nPt18jM6yi98YJSG+nDrWCnWh5c7dorILwCOLA9MM3Mr1B6eW3Asj+n43kfsBLwhs5hdpn5TUo4uE0VeA2UPYQkSZIkaTDOpPTcOCIiDgLO7PzQ3+GHwG3AMyJiw6pYL8AewEOAP2TmJT2PXn7a+VUpw2+2AT4MXEoZ4nK/zDwyIo4HXgA8i9JjYnNKr5T3AXtHxA6ZuYhxVD2Adq5efn28ffsREQ8H9gQeR/mw3+rIcDfwmI7dJ/Menwk8PSK+CXxmnF40M6kVCLw7Iv4O/LRLUNhN+7TzK1OGMG1H6UF1FaUHWDenATtQikCfmpnHT7nlZar5lTrWnQSc2sexk3oWEbES5Wd0e0rwsmq16TEdy04ndFl3WautrVn3umzboMf5js3Me7usP5LyZ21Hxg9t51Du43bglz12aw2BfBqD6Z11PwMhSZIkSRqM/6BME79n9fWPiDiV0gPgO5l5Z/vOmXlbRPyQ0nPn5cB/V5taPYbGLSbdOe083D+s5mTg+Ih4QmZe2HlNyrTj3632fyjwakqI9GjK8JiJCkKPAasDN2TmPybYd1wR8U7K1OwP6vOQybzHb6LUatkf2L8KYE6m1B06pp86OVFm8Xpcx+oLMvMTfbZ3OZl5YkR8ltID6kjg3og4i1JL6NDMvKzHod2mnd+YUt/mmxFxeWb+pstxh1Bq6RwEHBURu3f26GrTGm43t9vGzLw/T4iIrwKv73Gebvp+FhHxCOAnLBvC1s3aPdb/rcu6W3tty8xby8ix+wOnTlf0WH95tfynHttb5lJqRQHcXV1rvH0HyiFjkiRJkjQAVQ+fpwLPBf6H0nNjT+AbwHkRMdblsFbo8wqAiFiPUtz3HuD7U2jD2cDXKJ0B3tjH/n+vwo33Vat2n+w1p6qqrfJpSu+J/YCNgdUyMzIz6DJl+2Te48w8D5gPvLDafjNleNX/Aqe0Zp6awC6UnjHtX7tM/m6Xu4d3Uno5vRf4LaUI9weACyKinyFIrfNczrIQ8a3j7Pf/gE9SQo8fRcR2PXY9t1rWPnRpks/iEEoYdCylF846wErVz0QrhOqVrIzXI2+8bYPSymBupfToG+9r4D3Y7CEkSZIkSQNSDS85ofoiIjaiDOf5Z0oA8J6OQ06kBB9PjIgtKcOAVqEUCl7C1Py1WvYaVtNNq3dJP70UFlNmBVsvItbpKF49GS+slh/IzPbhb63CyQ/rdtBk3uOqx9Bx1RcRsTmlpsx2lALEXx6vgZm586TuqE9Vz61PAp+saka9mTLj2lcoQUi/+nrWmfneaoazNwI/i4hnZuY5Hbv9nFJc+cUR8d66Zxrr51lExJrAvwB/B/bp0oZH1dmmPmw0wfprJjh+MXAnJYx69WSLt9fNHkKSJEmSBmluBPNG6YsZGIrRS2ZewbJeHE/osv0+ytAhKL2EXlF9P+5wsQm0PjTfPwtTTDBWhTJcDLoPuVlO1eaTqpf/NtnGtVm3Wl7dZduL6d0LpLM9477HHfsuoBQmnnDfmZKZd2bmwZRgcF5ErD+Jwx/wrMfxJkoh8XWAEyKicyjczyjT1z8SeP8k2jAlPZ7FQyi5xbWdYVBEPIhlIeJM2auqZ9TppdVy3PpJVXh5EqU21rPqbdrkGQhJkiRJGqSFwKIR+1o40DuuRMQ7qmmuO+1WLa/qsg2WhT/7UwoA3wj8eIpteDLLQpr2WaUOiYgPRsQDap5ExGaUoVsAx/R5qf+mTCP+gYhYbsaniFg5InbrfthyLqqWr6k+7LeOn8+ygKezrX29xxGxRkS8NSLW6Th+DsuGfPV6HgMTES+ohsp1rt+KUtz7Vsrz7+dcG7OsN9SEs7pVvVP2p9TtmQf8ujpHa/tSyhTsdwEHRcQnu808Vw3L26yfNlb7T+ZZLAJuAp4QETu07bsS5Wfisf1etyYb0zEdfUT8G6VX09/przfXxyg9hA6LiJ07N0bEWhGxf9UrbqAcMiZJkiRJg/ER4OCIOBe4mNLDZUvKh9gbWDbF9XIy85yI+AvLekkcXU29Pq6IOLzt5SqUYSzbUjoC/JjlexmNUcKAj0bEAkoYc191zNbVMX+iFB+eUGaeHBHvoQx7+k1EnEG557mUe16V0hNlPIcB76LUALowIv4MrEeZAvw4yqxLnUN2+n2PVwE+X+17JqUI8CrVvW5YvZ72DGlTsDPwtoj4G2VGqZsphYl3pDyDj2Tm3V2OOzgius0ytgrluX26yzEPkJn3RcTLgB9RwpgTI2LHzLym2n5mRDwbOBp4N/DWiPgjZWjUatV1t6QUAb8AOKPLZTr1/Swy896I+CQlRDk5In5Dea7bUAKzL1F6Os2UbwDvi4i9gPMoPem2ptT42i8zb5/oBJl5akS8Cfgi8Nvqz/pF1Tk2Bp5E+fPyA8pQzIExEJIkSZKkwXgL5UP2VpTC0FB6PnyGMtX2eMOxjmBZr5ie01h3eFXb90spPUt+V53r8I7p2N9EmbnpuZTivs+izH70D0ph46Mps1x1TsvdU2YeXIUF76D0bNqSUjPl/1g2DG6845dExNaU+96JMvX2X4EPUYKdS7sc1u97fGt1z8+q2rUFZRr7KylFi7+YmTf0e681Ohy4F3gGJfB6CHAdpYfP58eZAay92HQCtwBnAUcBX+oRInWVmXdXAcfPKe/7ryJip8xcXG0/NSI2pdT1eR4lqNyO0nPob9U1jwF+0mNK9k6TehaZ+fGIuJoyE9sOlJDkVMpMeE/p9z5rcjoluDwI2IMSQJ4IfDgzT+/3JJn51Yj4A+Wedq7OdTvl/fwuJQy6qc6GdxNDrmHUt4hYMH/+/PkLFgy80LYkSZIG6frrYf2OkhiLFsG8eY1rx+abb87ChQsXZubmM3bRAarq8ywadjumaP1Mrh92IySNnog4kNIb7dWZefhwW1MfewhJkiRJqssSYDIFcEfJVGfwkqRZyUBIkiRJUi0yWQr2spGk2cBZxiRJkiRJkhrGQEiSJEmSJKmHzDwwM2NFqh8EBkKSJEmSJEmNYyAkSZIkSZLUMAZCkiRJkiRJDWMgJEmSJEmS1DAGQpIkSZIkSQ1jICRJkiRJktQwBkKSJEmSJEkNYyAkSZIkSZLUMAZCkiRJkiRJDWMgJEmSJEmS1DAGQpIkSZIkSQ1jICRJkiRJktQwBkKSJEmSJEkNYyAkSZIkSZLUMAZCkiRJkiRJDWMgJEmSJEmS1DAGQpIkSZIkSQ1jICRJkiRJktQwBkKSJEmSJEkNYyAkSZIkSZLUMAZCkiRJkiRJDWMgJEmSJEmS1DAGQpIkSZIkSQ1jICRJkiRJktQwBkKSJEmSJEkNYyAkSZIkSZLUMAZCkiRJkiRJDWMgJEmSJEmS1DAGQpIkSZIkSQ1jICRJkiRJktQwtQVCEXFSROQ4X7vUdS1JkiRJkiRN3coDOOexwK1d1v9tANeSJEmSJEnSJA0iEDogMy8fwHklSZIkSZJUA2sISZIkSZIkNYyBkCRJkiRJUsMMYsjYayJiDFgKXAQcl5lXDuA6kiRJkiRJmoJBBEIf7Hh9cEQclJkH9XNwRCzosWnT6TVLkiRJkiRJUO+Qsd8Br6QEN2sAmwEfAO4F/jMi3lbjtSRJkiRJkjRFtfUQyswPd6y6CPh4RJwB/BI4MCK+npl3THCezbutr3oOza+lsZIkSZIkSQ028KLSmXkCcAawDrDNoK8nSZIkSZKk8c3ULGMXV8sNZuh6kiRJkiRJ6mGmAqF1q+VtM3Q9SZIkSZIk9TDwQCgi5gE7Vi/PGvT1JEmSJEmSNL5aAqGI2D4iXhARK3Ws3xj4IbAmcHxmXl3H9SRJkiRJkjR1dc0y9ljgMOC6iDgLuBHYCNgKWA1YALyupmtJkiRJkiRpGuoKhP4IfIUyi9jWlJpBtwHnAEcDX5lounlJkiRJkiTNjFoCocw8H/j3Os4lSZIkSZKkwZqpWcYkSZIkSZI0IgyEJEmSJEmSGsZASJIkSZIkqWEMhCRJkiRJkhrGQEiSJEmSJKlhDIQkSZIkSZIaxkBIkiRJkiSpYQyEJEmSJEmSGsZASJIkSZIkqWEMhCRJkiRJkhrGQEiSJEmSJKlhDIQkSZIkSZIaxkBIkiRJkiSpYQyEJEmSJEmSGsZASJIkSZIkqWEMhCRJkiRJkhrGQEiSJEmSJKlhDIQkSZIkSZIaxkBIkiRJkiSpYQyEJEmSJEmSGsZASJIkSZIkqWEMhCRJkiRJkhrGQEiSJEmSJKlhDIQkSZIkSZIaxkBIkiRJkiSpYQyEJEmSJEmSGsZASJIkSZIkqWEMhCRJkiRJkhrGQEiSJEmSJKlhDIQkSZIkSZIaxkBIkiRJkiSpYQyEJEmSJEmSGsZASJIkSZIkqWEMhCRJkiRJkhrGQEiSJEmSJKlhDIQkSZIkSZIaxkBIkiRJkiSpYQyEJEmSJEmSGsZASJIkSZIkqWEMhCRJkiRJkhrGQEiSJEmSJKlhDIQkSZIkSZIaxkBIkiRJkiSpYQyEJEmSJEmSGmblYTdg1C1dCkuWDLsVDzQ2BnOM8yRJkiRJ0hQYCE1gyRJYf/1ht+KBFi2CefOG3QpJkiRJkjQb2cdEkiRJkiSpYQyEJEmSJEmSGsZASJIkSZIkqWGsITQFCxfC3Lkzd73Fi2H+/Jm7niRJkiRJWrEZCE3B3LkWdJYkSZIkSbOXQ8YkSZIkSZIaZiCBUESMRcSiiMiIuGQQ15AkSZIkSdLUDKqH0KeBGayyI0mSJEmSpH7VHghFxLOAVwHfqPvckiRJkiRJmr5aA6GIWB34GrAQOLjOc0uSJEmSJKkedc8y9hHgUcBOwD01n1uSJEmSJEk1qK2HUERsAbwLOCwzT6nrvJIkSZIkSapXLT2EImIOcAhwI/CeaZ5rQY9Nm07nvJIkSZIkSSrqGjL2FmBr4NWZuaSmc0qSJEmSJGkAph0IRcQjgf8HnJyZh0/3fJm5eY/rLADmT/f8kiRJkiRJTVdHDaEvAasAb6jhXJIkSZIkSRqwOoaM7UGpHfTViGhfv1q1fHhEnFR9/9LMvK6Ga0qSJEmSJGmK6qohtA5lqvluVmvbtlqPfSRJkiRJkjRDpj1kLDOj2xewSbXLpW3rL5/u9SRJkiRJkjQ9ddQQkiRJkiRJ0ixiICRJkiRJktQwBkKSJEmSJEkNU1dR6Qeo6gXFRPtJkiRJkiRpZtlDSJIkSZIkqWEMhCRJkiRJkhrGQEiSJEmSJKlhDIQkSZIkSZIaxkBIkiRJkiSpYQY2y5gGa/HiYbcAxsZgjpGiJEmSJEmzjoHQLDV//rBbAIsWwbx5w26FJEmSJEmaLPt3SJIkSZIkNYyBkCRJkiRJUsMYCEmSJEmSJDWMNYRmgbGxUq9nmBYvHo26RZIkSZIkafoMhGaBOXMs3ixJkiRJkurjkDFJkiRJkqSGMRCSJEmSJElqGAMhSZIkSZKkhjEQkiRJkiRJahgDIUmSJEmSpIYxEJIkSZIkSWoYAyFJkiRJkqSGMRCSJEmSJElqGAMhSZIkSZKkhjEQkiRJkiRJahgDIUmSJEmSpIYxEJIkSZIkSWoYAyFJkiRJkqSGMRCSJEmSJElqGAMhSZIkSZKkhjEQkiRJkiRJahgDIUmSJEmSpIYxEJIkSZIkSWoYAyFJkiRJkqSGMRCSJEmSJElqGAMhSZIkSZKkhjEQkiRJkiRJahgDIUmSJEmSpIYxEJIkSZIkSWoYAyFJkiRJkqSGMRCSJEmSJElqGAMhSZIkSZKkhjEQkiRJkiRJahgDIUmSJEmSpIYxEJIkSZIkSWoYAyFJkiRJkqSGMRCSJEmSJElqGAMhSZIkSZKkhjEQkiRJkiRJahgDIUmSJEmSpIYxEJIkSZIkSWoYAyFJkiRJkqSGMRCSJEmSJElqGAMhSZIkSZKkhjEQkiRJkiRJapjaAqGIeGdE/CAiLo6ImyLiroi4IiK+HRFPrOs6kiRJkiRJmp46ewj9B7ArcANwIvBT4E7glcCZEbFHjdeSJEmSJEnSFK1c47meD5yZmXe2r4yIfwe+BBwSEY/IzHtrvKYkSZIkSZImqbYeQpl5WmcYVK3/MnAp8FBgfl3XkyRJkiRJ0tTMVFHpe6rl3TN0PUmSJEmSJPUw8EAoIl4JbAZcXH1JkiRJkiRpiOqsIQRARLwb2BxYE3h89f01wMsy874+jl/QY9OmtTVSkiRJkiSpwWoPhIDnAs9qe30F8K+ZeeYAriVJkiRJkqRJqn3IWGY+OzMDWBd4BmWY2MkR8YE+j9+82xelMLUkSZIkSZKmaWA1hDLzxsw8BdgNOBM4KCK2HtT1JEmSJEmS1J+BF5XOzHuA7wMB7Dno60mSJEmSJGl8MzXt/OJqOW+GridJkiRJkqQeZioQ2qlaWgdIkiRJkiRpyGoJhCJih4jYJSLmdKx/UES8BXglcAdl6JgkSZIkSZKGqK5p5x8DHAYsjogzgSXAXOCJwAbAncB+mXlVTdeTJEmSJEnSFNUVCJ0MfJwyNGwLShh0N3A5cAzwhcy8pKZrSZIkSZIkaRpqCYQy86/AB+o4lyRJkiRJkgZrpopKS5IkSZIkaUQYCEmSJEmSJDWMgZAkSZIkSVLD1FVUWpIkSbPB0qWwZMlw27B48XCvL0mSDIQkSZIaZckSWH/9YbdCkiQNmUPGJEmSJEmSGsZASJIkSZIkqWEMhCRJkiRJkhrGGkKSJElNt3AhzJ073DaMjQ33+pIkNYyBkCRJUtPNnQvz5g27FZIkaQY5ZEySJEmSJKlhDIQkSZIkSZIaxkBIkiRJkiSpYQyEJEmSJEmSGsZASJIkSZIkqWEMhCRJkiRJkhrGQEiSJEmSJKlhDIQkSZIkSZIaxkBIkiRJkiSpYQyEJEmSJEmSGsZASJIkSZIkqWEMhCRJkiRJkhrGQEiSJEmSJKlhDIQkSZIkSZIaxkBIkiRJkiSpYQyEJEmSJEmSGsZASJIkSZIkqWEMhCRJkiRJkhrGQEiSJEmSJKlhDIQkSZIkSZIaxkBIkiRJkiSpYQyEJEmSJEmSGsZASJIkSZIkqWEMhCRJkiRJkhrGQEiSJEmSJKlhDIQkSZIkSZIaZuVhN0Cz1+LFw25BMTYGc4w2JUmSJEnqm4GQpmz+/GG3oFi0CObNG3YrJEmSJEmaPexXIUmSJEmS1DAGQpIkSZIkSQ1jICRJkiRJktQw1hBSX8bGSq2eYVu8eHRqF0mSJEmSNFsZCKkvc+ZYuFmSJEmSpBWFQ8YkSZIkSZIaxkBIkiRJkiSpYQyEJEmSJEmSGsZASJIkSZIkqWEMhCRJkiRJkhrGQEiSJEmSJKlhDIQkSZIkSZIaxkBIkiRJkiSpYQyEJEmSJEmSGqaWQCgi1oiIF0TENyPiwoi4MyJui4hzI+LDEbFWHdeRJEmSJEnS9NXVQ+jlwA+B/YH7gOOBU4BNgI8Cf46I9Wu6liRJkiRJkqahrkDoHuDrwPzMnJ+ZL8nMXYDNgLOBxwGfq+lakiRJkiRJmoZaAqHM/FZmvj4zz+9Yfy3wpurlXhGxSh3XkyRJkiRJ0tTNRFHpc6vlqsDYDFxPkiRJkiRJ45iJQOhR1fIe4IYZuJ4kSZIkSZLGsfIMXONt1fIXmXnXRDtHxIIemzatr0mSJEmSJEnNNdAeQhGxG/AaSu+gDw3yWpIkSZIkSerPwHoIRcTjgO8AAbw7M8+d4BAAMnPzHudbAMyvr4WSJEmSJEnNNJAeQhHxcOAXwLrAZzLz84O4jiRJkiRJkiav9kAoItYDTgA2Ag4DDqj7GpIkSZIkSZq6WgOhiFgL+DllaNcPgNdlZtZ5DUmSJEmSJE1PbYFQRKwK/Ah4GvBL4GWZeV9d55ckSZIkSVI9agmEImIl4Ejgn4FTgL0y8+46zi1JkiRJkqR61TXL2JuBF1bfLwa+HBHd9jsgMxfXdE1JkiRJkiRNQV2B0Lpt37+w515wICUwkiRJkiRJ0pDUMmQsMw/MzOjj6/I6ridJkiRJkqSpq33aeUmSJEmSJI02AyFJkiRJkqSGMRCSJEmSJElqGAMhSZIkSZKkhjEQkiRJkiRJahgDIUmSJEmSpIYxEJIkSZIkSWoYAyFJkiRJkqSGMRCSJEmSJElqGAMhSZIkSZKkhll52A2QJElqjKVLYcmS4bZh8eLhXl+SJI0EAyFJkqSZsmQJrL/+sFshSZLkkDFJkiRJkqSmMRCSJEmSJElqGAMhSZIkSZKkhrGGkCRJ0jAtXAhz5w63DWNjw72+JEmacQZCmvVGYbKUsTGYY387SdJUzJ0L8+YNuxWSJKlhDIQ0682fP+wWwKJF/i4vSZIkSZo97NMgSZIkSZLUMAZCkiRJkiRJDWMgJEmSJEmS1DDWENKsMjZW6vUM0+LFo1G3SJIkSZKkqTIQ0qwyZ47FmyVJkiRJmi6HjEmSJEmSJDWMgZAkSZIkSVLDGAhJkiRJkiQ1jIGQJEmSJElSwxgISZIkSZIkNYyBkCRJkiRJUsMYCEmSJEmSJDWMgZAkSZIkSVLDGAhJkiRJkiQ1jIGQJEmSJElSwxgISZIkSZIkNYyBkCRJkiRJUsMYCEmSJEmSJDWMgZAkSZIkSVLDGAhJkiRJkiQ1zMrDboC0Ili8eNgtKMbGYI4xryRJkiRpAgZCUg3mzx92C4pFi2DevGG3QpIkSZI06uxLIEmSJEmS1DAGQpIkSZIkSQ1jICRJkiRJktQw1hCSJmlsrNTqGbbFi0endpEkSZIkaXYxEJImac4cCzdLkiRJkmY3h4xJkiRJkiQ1jIGQJEmSJElSwzhkTJIkrfiWLoUlS4bdilIATpIkaQQYCEmSpBXfkiWw/vrDboUkSdLIcMiYJEmSJElSwxgISZIkSZIkNYyBkCRJkiRJUsPUUkMoIrYC/gV4WvX1cIDMjDrOL0mSVLuFC2Hu3GG3AsbGht0CSZLUQHUVlf4Q8PyaziVJkjR4c+fCvHnDboUkSdJQ1BUI/R44D/hz9XU5sGpN55YkSZIkSVKNagmEMvO/219HOFJMkiRJkiRpVFlUWpIkSZIkqWEMhCRJkiRJkhqmrhpCtYmIBT02bTqjDZEkSZIkSVpB2UNIkiRJkiSpYUauh1Bmbt5tfdVzaP4MN0eSJEmSJGmFYw8hSZIkSZKkhjEQkiRJkiRJapiRGzImSZJWMEuXwpIlw23D4sXDvb4kSdKIMRCSViCj8HlnbAzm2PdQUrslS2D99YfdCkmSJLUxEJJWIPNHoOz6okUwb96wWyFJkiRJGk8tgVBE7A58qG3VKtX6P7StOygzf1rH9SRJkiRJkjR1dfUQmgds02X9Nh37SJIkSZIkachqCYQy83Dg8DrOJUmSGmDhQpg7d7htGBsb7vUlSZKGyBpC0iw1Nlbq9QzT4sWjUbdI0iw0d64FxyRJkobIQEiapebM8bOUJEmSJGlqDIQkSVqRLV1apn0fpsWLh3t9SZIkPYCBkCRJK7IlS2D99YfdCkmSJI2YOcNugCRJkiRJkmaWgZAkSZIkSVLDGAhJkiRJkiQ1jDWEJNVqFGrHjo2VWdgk9bBwYZn2fZjGxoZ7fUmSpIYzEJJUq/nzh90CWLQI5s0bdiukETZ3rn9IJEmSGs7/Q5ckSZIkSWoYAyFJkiRJkqSGMRCSJEmSJElqGGsISZqysbFSr2eYFi8ejbpFkiRJkjSbGAhJmrI5c0azLu0ozHQGznYmSZIkaXQZCEla4YxKjyFnO2u4pUthyZJht2J0ElJJkiSNFAMhSdKKZxTCmOuvh803H24bJEmSpB4MhCRpRTEKIcioMIyRJEmSxmUgJEkriiVLYP31h90KSZIkSbOAgZCkWW0UZjqD0ZjtbOlSWMLc4TaiizGWMIccdjMkSZIktTEQkjSrjepMZ8Ow5IZgfa4fdjMeYBHzmIeFjQFYsGA0fmDHxobdAkmSJA2ZgZAkqRlGIYwZGysppiRJkjRkBkKSNCAzPdv34htGNGhYeD7MHYEhY4YxkiRJ0v0MhCRpQGa+ptB6M33B/sydCyMwSkqSJEnSMgZCkrQCW3jqDcx97MwFRaNQXFuSJEnSxAyEJGkFNne9pUMvmzPTQ+d6ccSYJEmStIyBkCTVYelSwLShm1HpMbRo0fBrSkuSJEmjwkBI0uy2dCksWTLsVjB2/fUsYqdhN+MBxtZbOOwmSJIkSRpBBkLSbDUiQcgDzPS4nCVLYP31Z+56PcxhROsm22lJkiRJUhcGQtJsNSJByAMsWDCz43Kuv37mriVJkiRJKwgDIUn12nzzYbdAQzQ2Vmr1DFu32c5Gobi1ha0lSZI0KgyEJGlQZrq3VDdjYzN6uTlzhn/LvYxCcWsLW0uSJGlUGAhpdrFujvoxCkEM+HMhSZIkaWQZCGl2sW7OMt3GvyxcCHPnzlwbrr9+NIeIzZs3GoGQJEmSJI0oAyH1Z1R65oxqAeFRCUXmzp3ZIGRUCsZ0muFhUpIkSZI02xgIqT+j2jNHwzXKBWPUaKOQVY5qYWto5mjGUfl/jW6a+DwkSdLwGQhJklY4o5pVjkJhaxiNMlszHYKM8v9rWGxckiQNg4HQbDAK/605Kv+t3c1Mf7IZ1bo5ktSnUfgrbBh/dUuSJGkZA6HZYJT/W3MUzHQB4VEYi9KLtXMkzRKjEEpJkiQ1mYGQpm6mZ7TqZaZDkFEdiyJJkiRJUp8MhDR1Mz2jlSTNIqPSmdBRrr0No5ZSt2LjkiRJw2AgJEnSAIxKZ8JRCKZGNZSa6RHHvYxCmT5nOpMkqXkMhGarURiuZb0aSRp5oxBMjUIo1c2o/DM2Cj2GnOlMkqTmMRCarRyuJUmaJUYhlJIkSdLy7BwsSZIkSZLUMPYQkiRJajjrGEmS1DwGQpIkSTNkFOopdZvpbBTqGA1j1jcWB7B8TcaxpXahlyQ1g4GQJEnSDLGeUm/DmYluLnD9cmsW3bCYeQ8dRlskSZpZ/geIJEmSJElSwxgISZIkSZIkNYxDxiRJkhpkFOoYXX/9sIaISZKkFgMhSZKkBhmFOkajEEoBLL7oBuY/fb1hN0OSpKEwEJrI0qU8YGTd4sVAzlwbRmEuWEmSpJqMQigFwOKlw26BJElDU1sgFBGrA+8HXgo8ErgB+AXwocz8W13XmXE33EDndKTMfzxgSCNJkiRJkmanWopKR8RqwG+ADwFrAT8CrgJeDZwdEY+q4zqSJEmSJEmavrpmGfsgsC3we+CxmblPZm4DvAuYBxxa03UkSZIkSZI0TdMOhCJiFeDN1cs3ZeatrW2Z+RngPGCniNhquteSJEmSJEnS9NVRQ2gH4CHApZl5dpftxwBbAHsCZ9ZwveE79TR47LrDbcPY2HCvL0mSJEmSZq06AqEtq+VZPba31m9Rw7VGw3rrwby5E+8nSZIkSZI0guoIhB5ZLa/usb21fqN+ThYRC3psetyll17K5ptvPpm2Tdt9d90HrLTcuh33vI+VVl2p+wGSJEmaFUbh97xLL70UYMMZu6AkSZU6AqG1quXtPbbfVi3XnuZ1lt511123LVy48KppnmeqNq2Wl1546ZBaoGG4/7kPtRUaBp99c/nsm8tn31zD/D1vQ3r/Hi1J0sDUEQjVKjNntgtQn1o9l0a1fRoMn3tz+eyby2ffXD775vLZS5KaqI5p51uziq3RY/ua1fKWGq4lSZIkSZKkaaojELqyWj6ix/bW+itquJYkSZIkSZKmqY5A6Nxq+ZQe21vrz6vhWpIkSZIkSZqmOgKh04CbgE0j4kldtu9dLX9cw7UkSZIkSZI0TdMOhDLzbuCL1csvRUSrZhAR8U5gC+DkzDxzuteSJEmSJEnS9EVmTv8kEasBJwHbANcCpwAbVa+vB7bNzMumfSFJkiRJkiRNWy2BEEBErA68H3g5sCFwA/AL4EOZeXUtF5EkSZIkSdK01RYISZIkSZIkaXaoo6i0JEmSJEmSZhEDIUmSJEmSpIYxEJIkSZIkSWoYAyFJkiRJkqSGMRCSJEmSJElqmEYGQhGxekT8Z0RcFBF3RsQ1EXFoRDx8CudaNyI+HxFXRMRd1fJzEbHOAJquaarj2UfEOhHx8og4MiL+GhF3R8QtEfHHiHhbRDxokPegqanzz33HeR8TEXdEREbEr+tqr+pT97OPiI0j4qvVn/+7ImJxRPw+It5dd9s1PTX/e/8vEfHTiLg+Iu6JiCURcUJEvHAQbdfURcRWEfG+iPhBRFxd/f085Wl1/V1PkrSiaty08xGxGvBbYFvgWuAUYGPgacD1wLaZeVmf55oL/B54NHAZcAawefV1EbBdZt5Q8y1oiup69hHx/4APAAmcQ3nW84AdgFWBU4HnZubttd+EpqTOP/ddzv1bYCcggBMz89l1tFn1qPvZR8SuwDHA6sBZwMXAGPBE4LbMfHSd7dfU1fzv/duBz1L+3v89cBWwIbAd5c/+xzPzA/XegaYqIo4Dnt+5PjNjCufydz1J0gqriT2EPkj55fD3wGMzc5/M3AZ4F+VD/aGTONfnKL8g/ADYrDrXE4D/AR4LfKbOhmva6nr2twGfBDbOzKdk5ksz81mUD4RXAk+vrqXRUeef+/tFxGuAnYFv1NRO1a+2Zx8Rj6P8fX8b8PTMfGpmviwznwM8HHhp7a3XdNTy7CNiHvAJ4B7gmZm5Q/X3/g6UP/93Ae+PiEcN4B40Nb8HDgKeB2xAeUZT9Tn8XU+StIJqVA+hiFgFWAQ8BHhKZp7dsf1cYAvgqZl55gTn2gC4GrgXeGRm/r1t26qU/z1cD/inzFxU641o0up89hNc52XA94DLM3OTaTRZNRnUs4+IhwLnU/63+OOUngj2EBohdT/7iPgZsCuwe2b+bABNVk1q/vd+D+DHwC8zc5cu239ECR72ycyjaroF1Sgi7gRWnWwPIX/XkySt6JrWQ2gHyi+Hl3b+clg5plru2ce5dqG8f6e0/4IAkJl3UX55XAnYberNVY3qfPbjObda/tM0z6P6DOrZf54ybOjfp9E2DVZtzz4iNgSeC1xmGDQr1Pnnvt/eJUv63E+zh7/rSZJWaE0LhLaslmf12N5av8UMn0uDN1PPqzVk4Lppnkf1qf3ZR8RuwD6UuiGXTKNtGqw6n/3OlH8zT4+IlSPiJVWR2S9GxBsiYt1ptlX1qvPZ/wm4EfjniNipfUNEPIMSFF5MqVGkFYu/60mSVmgrD7sBM+yR1fLqHttb6zea4XNp8Gbqeb2tWv5omudRfWp99hGxJvBl4ELgv6fXNA1Ync9+frW8lfLBf9uO7R+LiL0z87eTa6IGpLZnn5k3VfXCvgf8NiJOr45/BLA9cBrwr5l59/SarBHk73qSpBVa03oIrVUte83+dFu1XHuGz6XBG/jziog3AM+m/E/yJ6Z6HtWu7mf//yi//L/BD4Ajr85n3+oB9FrgccDLKbVDNgO+U33/w6lOZa/a1frnPjN/QKkftYQyHG2fankLcALwtym3VKPM3/UkSSu0pgVC0kBExI6UmjIJ7J+Z1wy5SRqAiHgq8Fbg25l50pCbo5nV+vdyZeD1mXlkZv4jMy/KzFcCf6bUrLGm1AooIt4F/Br4HWV40FrV8jfAf1JmoJIkSZpVmhYI3Vot1+ixfc1qecsMn0uDN7DnFRFPoAwRWwV4W2b+cPLN0wDV8uwjYmXK9PI3AgfU0jIN2iD+zr8VOLrL9sOq5U5dtmnm1fbsI2Jn4GDgHODFmfl/mXlbZv4fsHe1fveI2HUa7dVo8nc9SdIKrWk1hK6slo/osb21/ooZPpcGbyDPKyI2oQwXWBc4MDP/Z2rN0wDV9ewfATyJUjD86IjlZi9ep1puFREnAWTmzpNrpgagzj/3rX2uzMzssv3yarl+f03TgNX57F9ZLX+YmUvbN2TmfRHxA8rfDc8Afj7Jdmq0+bueJGmF1rRAqDUl+FN6bG+tP2+Gz6XBq/15RcQGwK+ADYDPZ+ZHp948DVDdz/5h1Vc362APkVFS57NvTV3eazax9arlrT22a2bV+exbH/pv6rG9td6Z5lY8/q4nSVqhNW3I2GmUX9w2jYgnddm+d7X8cR/n+gWwFNgxIpb7H+GIWBXYE7gP+NmUW6s61fnsqaaY/iWwKWWoyDtqaKMGo5Znn5mXZ2Z0+wKeWe12Yts6DV+df+5PpxQUflhEbNZleysIPLvLNs28Op/9ddXyqT22b10tL++3cZo1/F1PkrRCa1QgVM0I9MXq5Zeq6aMBiIh3UgpEnpyZZ7atf3NEXBAR/9VxrmuBIyl1Y75c1Rdp+SQwD/hOZi4azN1oMup89hGxBvBT4InAUcDregwh0Qio89lrdqn57/x7gc8AUZ3rwW3HPBvYj1JU/msDuh1NQs1/7o+rlq+IiD3aN0TE8ykzzi0FrB83S/m7niSpqZo2ZAzKlNHPBrYHLo6IUyhTSG8DXA/s37H/XMq0wht0OdfbgW2BFwEXRMQZwObAE4CLgXcOoP2aurqe/ceA7Sj/K3gv8M2OejIAZOZ+NbZd01Pnn3vNLnU++09ReoM9G7goIv5Q7b8tsBLwgcz80yBuQlNS17M/jlJI/MXAj6t/6/8KbMKyXkMfyMwLB3APmoKI2B34UNuqVar1f2hbd1Bm/rT63t/1JEmN1KgeQgCZeSflF/qDgNuBF1B+QTwceEpmXjaJcy0Gngb8D+WXjRdSph3+AvC0zLyhzrZremp89q06EStR/mf4VT2+NCLq/HOv2aXmv/PvAXYD3gssBp5L6Sl4MrBnZn68zrZreup69lUP0H2A11CmnX805d/7jSlDhXb12Y+ceZTgr/XV+l+b9nXz+jmRv+tJklZk4UgXSZIkSZKkZmlcDyFJkiRJkqSmMxCSJEmSJElqGAMhSZIkSZKkhjEQkiRJkiRJahgDIUmSJEmSpIYxEJIkSZIkSWoYAyFJkiRJkqSGMRCSJEmSJElqGAMhSZIkSZKkhjEQkiRJkiRJahgDIUmSJEmSpIYxEJI060VERsTlUzhuTkRcVR2/Yx/7v6fa99iOa3d+3RMR10TEsRGx/Tjn2ysifhYRi6pjFkfEgog4IiJeGxGr9HkfO3dpw70RcV1E/CgintnfOzJ1EbFfdd0DR/E63X5GImLjav1JHetb7+fh022vJEmSNKoMhCQ1VmYuBb5Xvdy3j0Na+xzRZdu32r6OB24H9gJOjYiXd+4cEd8EjgV2Ba4GjgNOovy9vC/wDWC9/u7kfn9va8MxwI3A84ATI+KNkzyXOkTEgVVQtN+w2yJJkiRN18rDboAk1eDxwD1TPPYI4D3AiyPiLZl5d7edImIL4InADcDPOrdn5n4d+88BPg68F/hCRBydmfdU2/YC9gduAnbLzNM7jt0YeA1w1yTv5YL2dkREAB8GDgQ+HRHHZuaiSZ5zRTGZn5E/VfvfNLjmSJIkScNlDyFJs15mXpCZl07x2L8A5wDrAruPs2urd9BRvUKjjvMupYQx9wJjwOZtm19ULb/YGQZVx16emR/KzH9MfAfjtiGBg4BLgdWB50znfLPZZH5GMvP2av9rB90uSZIkaVgMhCTNer1qCEXE9hFxXERcERF3VTV1/hQRn4iItdp2/U61fEWP8wfwsuplt+FiXVXBUauXSXuPzHnV8vp+zzVVVTB1bvVyQ1i+Rk5EPCwiDomIq6u6Q29vHRsR21U1iK6v3r/LI+LLEfFP410zIjar6ictiYjbIuK0iNitx767R8ShEXF+RNxc7X9uRPxHRKxa43X6rjPVrYZQdexHqpeHddRr2jkiDqi+//g45z2h2mfgNZ0kSZKkiRgISVohRcSewCmUGjrXAj8AzqbU5XkvMLdt9+8B9wF7RMRDupxuZ+ARwKXdevSM04ZNKL2D7gEuadt0VbX8145galDWrpadQ9DmAX+m9Iz6PfBzSu0jImJflr1/F1Lev7uANwJnRcTjelxrU+CPwJOBE4AzgO2An0TEq7vs/01Kj6kbquufQgmuPgb8LCJWquk603UMy4K101i+ZtR1wOGU9+fVEfGA4djVz8KzgYsz87cDaJ8kSZI0KQZCklZUB1D+jntxZm6bmS/LzF0z89HA04AlrR2roUEnAqsCL+5yrtZwse902fYAEbFWRDwdOKpa9ZXMvLFtl0OBpcBTgMsi4isR8cqImF/1RqpNRKwPbFO9PK9j826UQGiTzHxxZu6ZmV+PiA2Br1f7PD8zn56ZL6PU1fkc8FB695TaF/gR8NjqPd+JEiotBb4YEQ/v2P/1wMMyc4fM3CczdwE2An4C/DM9em1N4TrTkpkHUAp/AxySmfu1fV2QmYspRcIfBuzR5RSvAQI4pM52SZIkSVNlICRpRdUalvXrzg2Z+efMvKVjdSvgWG62sYhYjWU1f3oGQu1DiIBbKD1dNgPeAry94/qnVddZUrXzDcC3gQXAtRHxXz16KvUtIlaLiG0oM549mNLLp7Nnyl3AWzLzzo71r6XUHDoqM49va/dS4H3ANcBTI2KHLpe+FXh7Zt7bdtxPKD1s1gCW672TmT/KzDs61t0CvKN6+fwetzip68yQr1bL17WvrHo57UfpKXb4zDZJkiRJ6s5ZxiStqM6k9Gg5IiIOAs6sAo1efgjcBjwjIjbMzNawrj2AhwB/yMxLeh5dhg61rErp5bINpbD0pZThUPfLzCMj4njgBcCzgK0phacfSgld9o6IHSY5K9hOVSDV6RLgBZl5X8f6szLzb13237FafrdzQ2beFRFHA2+r9jutY5cTehTDPhLYp+3c94uIx1B6Kz0aWJPynxWtnlKP6XKuKV1n0DLzlIhYAOzS8TO0G/Bw4JgGz/ImSZKkEWMgJGlF9R+UaeL3rL7+ERGnUnrMfKezV0xm3hYRP6T03Hk58N/VplaPoXGLSXdOOw8QEU8GTgaOj4gnZOaFndekhC7frfZ/KKVny4cp4cjH6OhtMoG/A7+ovr+X0gPpD8BPWlPed7iyx3laRaMv77G9tb7bsKwrJjjm/oLU1fC4gym9gXoNlVu7x/q+rzPDvgZ8Adgf+Gi1rvUMvzGUFkmSJEldOGRM0gqp6p3xVOC5wP9QCjnvSflQfl5EjHU5rBX6vAIgItYDdqUM9fn+FNpwNiUgWJlSjHmi/f+emZ+g9BCCUux5Mi5oq2vz2sx8b2b+sEcYBNA5VKxf3XohTcU+wDuBq4G9KQHTKpkZlF5W0DsoGlXfphTm3j8i5lQzsu1GCap+NcyGSZIkSe0MhCStsDLz3sw8ITPfmplbAhsDv6EMQ3pvl0NOpMxI9sSI2BJ4CbAK8PPMXNJl/378tVr2GvrUzW+q5dxx9xqca6rlRj22b1wtuw0363VMa/01beteWC3fmJnHZuY1beHVoyZo42SuM2My8ybgf4FHUsLIVwMrUQpR1xWkSZIkSdNmICSpMTLzCpYNBXtCl+33UWrQQOkl1JrhatzhYhNoBRu3tlb0MZPYo6tlt8BlJpxSLV/WuSEiVmHZTGyndG4HnhMR63RZ/9JqeWrbunWr5dVd9n/JBG2czHXqcne1nGi4dau49Osps4vdBxw2gPZIkiRJU2YgJGmFFBHviIiHddm0W7W8qss2WBb+7A/sANwI/HiKbXgy8G/Vy5+1bTokIj5YDSfqPGYz4NPVy2Omct0afBO4A3hpRNw/bC0i5gAfpwztOrOaLa3TWsBnImLltuN2pQQ8d7B8MHJRtfy39pAsInYE3j1BGydznbq0eh1tNt5Omfln4CzKDGmbAD/NzKH0WJIkSZJ6sai0pBXVR4CDI+Jc4GJKLZotgccCN1CKGT9AZp4TEX9hWQ+iozPzrokuFhGHt71chTJ0aVtK8P5jlu9lNEZVdLialeoiSi+SjSizjc0B/gQc1M+N1i0zr4yI11OmSP9xRJxGCdCeQglD/s6yYtudvgvsBewcEX8ENgCeQXn/35qZ7b2BvkCZjv3fq/3Po4RNT6eEYgeM08zJXKcuJ1DqLr0jIp5ACYgS+FRnwXBKL6GvV99/HUmSJGnE2ENI0orqLZRaLmtQCkPvQpl56zPAFpl58TjHtoc33+nzeq9q+9oHeBzwO8qQoRd0THn/JsrMUz+g1Jd5FiXceBTwW+ANwNMz8+Y+r127zDyCMnX7T4DHU4o+rw58BdgqMy/oceglwHbAeZQaOk+jzHS2Z2Ye0nGNiyiFv39MqZf0PErPn9dn5kQ9hPq+Tl2qXj7Pr67zdEqo9xpKGNWpVQfqapbN/CZJkiSNjLDGpSRJ9YqI91OG1300Mw8ccnMkSZKkBzAQkiSpRhHxYOACytDATawfJEmSpFFkDSFJkmoQEa8GdqLUMtoA+JxhkCRJkkaVNYQkSarHTpQaUmsBXwLeN9zmSJIkSb05ZEySJEmSJKlh7CEkSZIkSZLUMAZCkiRJkiRJDWMgJEmSJEmS1DAGQpIkSZIkSQ1jICRJkiRJktQwBkKSJEmSJEkNYyAkSZIkSZLUMAZCkiRJkiRJDWMgJEmSJEmS1DAGQpIkSZIkSQ1jICRJkiRJktQwBkKSJEmSJEkNYyAkSZIkSZLUMAZCkiRJkiRJDfP/AcEcN583IXB9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Y_predict_train = dnn.predict_proba(X)\n",
    "Y_predict_val = dnn.predict_proba(X_val)\n",
    "\n",
    "axis = np.linspace(0,1,20)\n",
    "\n",
    "targets = ['isVBS', 'isBKG']\n",
    "predictions = ['isVBS']\n",
    "colors = ['r', 'b']\n",
    " \n",
    "Y_targets = {}\n",
    "Y_predictions = {}\n",
    "\n",
    "for target in targets:\n",
    "    Y_targets[target] = dnn.predict_proba( df_val[ df_val[target]==1 ].values[:,0:NDIM] )\n",
    "    Y_predictions[target] = {}\n",
    "    for iprediction, prediction in enumerate(predictions):\n",
    "        Y_predictions[target][prediction] = Y_targets[target][:,iprediction]\n",
    "\n",
    "for prediction in predictions:\n",
    "    for target, mycolor in zip(targets, colors):\n",
    "        plt.hist(Y_predictions[target][prediction].numpy(), bins = axis, label = prediction + ' class - ' + target + ' sample', histtype='step', color=mycolor,  density=True, linewidth=2 )\n",
    "    plt.title('Validation dataset')\n",
    "    plt.xlabel(prediction + ' Probability')\n",
    "    plt.legend(frameon=False, bbox_to_anchor=(1.05, 1.))\n",
    "    #plt.yscale('log')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "db374a53-c7bd-4337-9216-68f5e143c8ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import itertools\n",
    "#from matplotlib.backends.backend_pdf import PdfPages\n",
    "\n",
    "def plot_confusion_matrix(cm, classes,\n",
    "                          normalize=False,\n",
    "                          title='Confusion matrix',\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=45, fontsize = 16)\n",
    "    plt.yticks(tick_marks, classes, fontsize = 16)\n",
    "\n",
    "    thresh = cm.max() / 1.2\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, cm[i, j],\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\",fontsize=10)\n",
    "\n",
    "    plt.xlabel(\"Predicted label\", fontsize=16)\n",
    "    plt.ylabel(\"True label\", fontsize=16)\n",
    "\n",
    "    \n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f3ec098a-fc62-4ab3-87a8-7ee81376b653",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifier categorical accuracy =  0.8325069724202045\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from scipy.stats import ks_2samp, anderson_ksamp\n",
    "\n",
    "X = df_val.values[:,0:NDIM]\n",
    "Y_true = df_val[['isVBS', 'isBKG']].values\n",
    "Y_pred = dnn.predict_proba(X)\n",
    "\n",
    "# The classifier categorical accuracy is a good and simple metrics to evaluate the overall classification performance\n",
    "Y_true_max = np.argmax(Y_true, axis=1)\n",
    "Y_pred_max = np.argmax(Y_pred, axis=1)\n",
    "print(\"Classifier categorical accuracy = \", accuracy_score(Y_true_max, Y_pred_max))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ca1fc441-c2f5-4a0d-a4b2-7a451354a1ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x600 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "C = confusion_matrix(Y_true_max, Y_pred_max, normalize=\"true\")\n",
    "C = np.around(C, decimals=3)\n",
    "\n",
    "plt.figure()\n",
    "plot_confusion_matrix(C, classes=['isVBS', 'isBKG'], title='')\n",
    "plt.show(1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c51de0ef-f2c1-420c-bf18-f5e2e3cdffb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'keras.layers.core.dense.Dense'>\n",
      "<class 'keras.layers.core.dropout.Dropout'>\n",
      "<class 'keras.layers.core.dense.Dense'>\n",
      "<class 'keras.layers.core.dropout.Dropout'>\n",
      "<class 'keras.layers.core.dense.Dense'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n",
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "with open('generated_code_ATLAS_dnn_SigVsBkg_emu.h', 'w') as f:\n",
    "\n",
    "\n",
    "    print (\"\"\"\n",
    "    // Select all (Ctrl+A), Copy (Ctrl+C), paste to an empty text file (Ctrl+V) and save that file as 'generated_code.h'\n",
    "    // \n",
    "    // Auto-generated header file. Assumes img to be a floating point array\n",
    "    // of 64 elements (corresponding to an 8x8 b&w image)\n",
    "\n",
    "    #include <math.h>\n",
    "\n",
    "    float norma_VBS;\n",
    "    \"\"\", file=f)\n",
    "    \n",
    "    iLayer_tmp=-1\n",
    "\n",
    "    for iLayer, layer in enumerate(dnn.model1.layers):\n",
    "      print(type(layer))\n",
    "#       if type(layer) is not Dropout and type(layer) is not Input :\n",
    "      if type(layer) is Dense:\n",
    "        kernel, bias = layer.get_weights()\n",
    "        iLayer_tmp+=1\n",
    "        print (\"inline float activation_VBS_%d (float x, float n) \" % iLayer_tmp, file=f);\n",
    "        activation =  layer.get_config()['activation'] \n",
    "        if activation == 'sigmoid':\n",
    "          print (\"{ return 1./(1 + exp(-x)); }\", file=f)\n",
    "        elif activation == 'tanh':\n",
    "          print (\"{ return tanh(x);}\")\n",
    "        elif activation == 'relu':\n",
    "          print (\"{ return x > 0 ? x : 0;}\", file=f)\n",
    "        elif activation == 'linear':\n",
    "          print (\"{ return x;}\")\n",
    "        elif activation == 'softmax':\n",
    "          print (\"{ return exp(x)/n;  }\", file=f)\n",
    "        else:\n",
    "          raise KeyError (\"Unexpected activation %s\"%activation)\n",
    "      \n",
    "    iLayer_tmp=-1\n",
    "\n",
    "    \n",
    "    print (\"\"\"\n",
    "    float guess_VBS (const float *img, int flag)\n",
    "    {\n",
    "    \"\"\", file=f)\n",
    "\n",
    "    max_out = 100\n",
    "    for iLayer, layer in enumerate(dnn.model1.layers):\n",
    "      if type(layer) is Dense:\n",
    "        iLayer_tmp+=1\n",
    "        print (\"  // Declare the arrays in the stack\", file=f)\n",
    "        kernel, bias = layer.get_weights()\n",
    "        \n",
    "        print (type (dnn.means))\n",
    "        print (type(bias))\n",
    "        \n",
    "         \n",
    "        max_out = max(kernel.shape[1], max_out)\n",
    "        #print (\"  //\",bias.shape)\n",
    "        kernel_values = \"{%s}\"%(',\\n   '.join([\"{%s}\"%(','.join([\"%18.13f\"%x for x in row])) for row in kernel]))\n",
    "        bias_values   = \"{%s}\"% ( \",\".join([\"%18.13f\"%x for x in bias]))\n",
    "        print (\"  const float kernel_%d[%d][%d] = \\n  %s;\" % (iLayer_tmp, kernel.shape[0], kernel.shape[1],kernel_values), file=f)\n",
    "        print (\"  const float bias_%d[%d] = %s;\" % (iLayer_tmp, bias.shape[0], bias_values), file=f)\n",
    "      \n",
    "    print (\"  float buffer_in[%d];\" % max_out, file=f)\n",
    "    print (\"  float buffer_out[%d];\" % max_out, file=f)\n",
    "\n",
    "    print (\"  unsigned int i,j,c; \", file=f)\n",
    "    \n",
    "\n",
    "    mean_values = \"{%s}\"% ( \",\".join([\"%18.13f\"%x for x in dnn.means]))\n",
    "    sigma_values = \"{%s}\"% ( \",\".join([\"%18.13f\"%x for x in dnn.sigmas]))\n",
    "    \n",
    "\n",
    "    print (\" float mean[%d] = %s;\" % (max_out, mean_values), file=f)\n",
    "    print (\" float sigma[%d] = %s;\" % (max_out, sigma_values), file=f)\n",
    "\n",
    "    print (\"\\n\\n\\n\", file=f)\n",
    "    print (\"  // Load the input in the buffer\", file=f)\n",
    "    print (\"  for (c = 0; c < 64; ++c) \\n  buffer_in[c] = (img[c]-mean[c])/sigma[c];\", file=f)\n",
    "\n",
    "\n",
    "\n",
    "    iLayer_tmp=-1\n",
    "\n",
    "    for iLayer, layer in enumerate(dnn.model1.layers):\n",
    "      if type(layer) is Dense: \n",
    "        kernel, bias = layer.get_weights()\n",
    "        iLayer_tmp+=1\n",
    "\n",
    "        if iLayer != len(dnn.model1.layers)-1:\n",
    "            \n",
    "            \n",
    "            print ( \"  // Processing layer %i \" % iLayer_tmp , file=f)\n",
    "            print ( \"\"\"\n",
    "            for (c = 0; c < {n_out}; ++c ) \n",
    "              buffer_out[c] = bias_{iLayer_tmp}[c];\n",
    "\n",
    "            for (c = 0; c < {n_out}; ++c )\n",
    "              for (i = 0; i < {n_in}; ++i)\n",
    "                buffer_out[c] += buffer_in[i] * kernel_{iLayer_tmp}[i][c];\n",
    "\n",
    "            norma_VBS = 0;\n",
    "\n",
    "            // Prepares for next layer \n",
    "            for (c = 0; c < {n_out}; ++c )\n",
    "              buffer_in[c] = activation_VBS_{iLayer_tmp}(buffer_out[c], norma_VBS);\n",
    "\n",
    "\n",
    "            \"\"\".format (\n",
    "                n_in = kernel.shape[0],\n",
    "                n_out = kernel.shape[1],\n",
    "                iLayer_tmp = iLayer_tmp,\n",
    "            ), file=f)\n",
    "            \n",
    "        else:\n",
    "            \n",
    "            print ( \"  // Processing layer %i \" % iLayer_tmp , file=f)\n",
    "            print ( \"\"\"\n",
    "            for (c = 0; c < {n_out}; ++c ) \n",
    "              buffer_out[c] = bias_{iLayer_tmp}[c];\n",
    "\n",
    "            for (c = 0; c < {n_out}; ++c )\n",
    "              for (i = 0; i < {n_in}; ++i)\n",
    "                buffer_out[c] += buffer_in[i] * kernel_{iLayer_tmp}[i][c];\n",
    "\n",
    "            norma_VBS = 0;\n",
    "\n",
    "            for(c=0;c<{n_out};++c)\n",
    "              norma_VBS+=exp(buffer_out[c]);\n",
    "\n",
    "            // Prepares for next layer \n",
    "            for (c = 0; c < {n_out}; ++c )\n",
    "              buffer_in[c] = activation_VBS_{iLayer_tmp}(buffer_out[c], norma_VBS);\n",
    "\n",
    "\n",
    "            \"\"\".format (\n",
    "                n_in = kernel.shape[0],\n",
    "                n_out = kernel.shape[1],\n",
    "                iLayer_tmp = iLayer_tmp,\n",
    "            ), file=f)\n",
    "      \n",
    "    last_kernel, last_bias = dnn.model1.layers[-1].get_weights()\n",
    "    print (\"\"\"\n",
    "      //i = 0;\n",
    "      //for (c = 0; c < {n_out}; ++c)\n",
    "      //  if (buffer_in[c] > buffer_in[i])\n",
    "      //    i = c;\n",
    "      \n",
    "      if (flag==0) //vbf\n",
    "        return buffer_in[0];\n",
    "      else if (flag==1) //bkg\n",
    "        return buffer_in[1];\n",
    "         \n",
    "      \n",
    "      //return i;\n",
    "    \"\"\".format(n_out = last_kernel.shape[1]), file=f)\n",
    "\n",
    "    print (\"}\", file=f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11d4d6ba-3206-4c98-89ec-24dffc5708cb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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