2/ds_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Iris: prediction of plant species"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We develop a model that predicts the specie of iris plant according to its attributes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We load the dataset and explore some of its statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.utils import shuffle\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.decomposition import PCA\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_table('iris.csv', sep=',')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 6 columns):\n",
      "Id               150 non-null int64\n",
      "SepalLengthCm    150 non-null float64\n",
      "SepalWidthCm     150 non-null float64\n",
      "PetalLengthCm    150 non-null float64\n",
      "PetalWidthCm     150 non-null float64\n",
      "Species          150 non-null object\n",
      "dtypes: float64(4), int64(1), object(1)\n",
      "memory usage: 7.2+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>75.500000</td>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>43.445368</td>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>38.250000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>75.500000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>112.750000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm\n",
       "count  150.000000     150.000000    150.000000     150.000000    150.000000\n",
       "mean    75.500000       5.843333      3.054000       3.758667      1.198667\n",
       "std     43.445368       0.828066      0.433594       1.764420      0.763161\n",
       "min      1.000000       4.300000      2.000000       1.000000      0.100000\n",
       "25%     38.250000       5.100000      2.800000       1.600000      0.300000\n",
       "50%     75.500000       5.800000      3.000000       4.350000      1.300000\n",
       "75%    112.750000       6.400000      3.300000       5.100000      1.800000\n",
       "max    150.000000       7.900000      4.400000       6.900000      2.500000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Iris-versicolor    50\n",
       "Iris-virginica     50\n",
       "Iris-setosa        50\n",
       "Name: Species, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[:,'Species'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_species = data.loc[:, 'Species'].unique()\n",
    "unique_species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'SepalWidthCm')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 10))\n",
    "ax11 = fig.add_subplot(2, 2, 1)\n",
    "ax11.hist(data.loc[:, 'PetalLengthCm'], histtype='stepfilled', color='steelblue', edgecolor='none')\n",
    "ax11.set_title('PetalLengthCm')\n",
    "ax12 = fig.add_subplot(2, 2, 2)\n",
    "ax12.hist(data.loc[:, 'PetalWidthCm'], histtype='stepfilled', color='steelblue', edgecolor='none')\n",
    "ax12.set_title('PetalWidthCm')\n",
    "ax21 = fig.add_subplot(2, 2, 3)\n",
    "ax21.hist(data.loc[:, 'SepalLengthCm'], histtype='stepfilled', color='steelblue', edgecolor='none')\n",
    "ax21.set_title('SepalLengthCm')\n",
    "ax22 = fig.add_subplot(2, 2, 4)\n",
    "ax22.hist(data.loc[:, 'SepalWidthCm'], histtype='stepfilled', color='steelblue', edgecolor='none')\n",
    "ax22.set_title('SepalWidthCm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset is quite clean and well balanced in each class so we procede to the other steps in preparation for training a model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We perfom a data preprocessing step and enlarge the original dataset to include a numerical label for 'Species' and the corresponding one-hot encoding. This is done in preparation for the later training step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LabelEncoder()"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le = LabelEncoder()\n",
    "le.fit(unique_species)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "species_label = le.transform(data.loc[:,'Species'])\n",
    "species_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "      <th>Species_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
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       "      <td>Iris-setosa</td>\n",
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       "      <td>2</td>\n",
       "      <td>3</td>\n",
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       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species  \\\n",
       "0   1            5.1           3.5            1.4           0.2  Iris-setosa   \n",
       "1   2            4.9           3.0            1.4           0.2  Iris-setosa   \n",
       "2   3            4.7           3.2            1.3           0.2  Iris-setosa   \n",
       "3   4            4.6           3.1            1.5           0.2  Iris-setosa   \n",
       "4   5            5.0           3.6            1.4           0.2  Iris-setosa   \n",
       "\n",
       "   Species_label  \n",
       "0              0  \n",
       "1              0  \n",
       "2              0  \n",
       "3              0  \n",
       "4              0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[:,'Species_label'] = species_label\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
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       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species_label</th>\n",
       "      <th>Species_Iris-setosa</th>\n",
       "      <th>Species_Iris-versicolor</th>\n",
       "      <th>Species_Iris-virginica</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
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       "      <td>4.7</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>3</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  \\\n",
       "0   1            5.1           3.5            1.4           0.2   \n",
       "1   2            4.9           3.0            1.4           0.2   \n",
       "2   3            4.7           3.2            1.3           0.2   \n",
       "3   4            4.6           3.1            1.5           0.2   \n",
       "4   5            5.0           3.6            1.4           0.2   \n",
       "\n",
       "   Species_label  Species_Iris-setosa  Species_Iris-versicolor  \\\n",
       "0              0                    1                        0   \n",
       "1              0                    1                        0   \n",
       "2              0                    1                        0   \n",
       "3              0                    1                        0   \n",
       "4              0                    1                        0   \n",
       "\n",
       "   Species_Iris-virginica  \n",
       "0                       0  \n",
       "1                       0  \n",
       "2                       0  \n",
       "3                       0  \n",
       "4                       0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.get_dummies(data)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species_label</th>\n",
       "      <th>Species_Iris-setosa</th>\n",
       "      <th>Species_Iris-versicolor</th>\n",
       "      <th>Species_Iris-virginica</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.1</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2.9</td>\n",
       "      <td>4.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>107</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>89</td>\n",
       "      <td>5.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm  Species_label  \\\n",
       "99             5.7           2.8            4.1           1.3              1   \n",
       "65             6.7           3.1            4.4           1.4              1   \n",
       "97             6.2           2.9            4.3           1.3              1   \n",
       "107            7.3           2.9            6.3           1.8              2   \n",
       "89             5.5           2.5            4.0           1.3              1   \n",
       "\n",
       "     Species_Iris-setosa  Species_Iris-versicolor  Species_Iris-virginica  \n",
       "99                     0                        1                       0  \n",
       "65                     0                        1                       0  \n",
       "97                     0                        1                       0  \n",
       "107                    0                        0                       1  \n",
       "89                     0                        1                       0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.drop('Id', axis=1)\n",
    "data = shuffle(data)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the dataset is small, to address the performance of the model and the posibility of overfitting, we will plot the predictions on a lower dimension. We use PCA for dimensional reduction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.iloc[:,:4].values\n",
    "Y = data.iloc[:,-3:].values\n",
    "Y_labels = data.iloc[:,[4]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "mu = np.mean(X, axis=0)\n",
    "sigma = np.std(X, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, iterated_power='auto', n_components=2, random_state=None,\n",
       "    svd_solver='auto', tol=0.0, whiten=False)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca = PCA(n_components=2)\n",
    "pca.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_red = pca.transform(X)\n",
    "X_pred = pca.inverse_transform(X_red)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10152555565202213"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(np.sum(np.square(X-X_pred), axis=1), axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we define and use a function to plot the transformed data points obtained by performing PCA in the last step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_pca():\n",
    "    fig = plt.figure(figsize=(10, 10))\n",
    "    ax = fig.add_subplot(1, 1, 1)\n",
    "    ax.axis([np.min(X_red[:,0])-0.1, np.max(X_red[:,0])+0.1, np.min(X_red[:,1])-0.1, np.max(X_red[:,1])+0.1])\n",
    "    iris_setosa = np.where(Y_labels == np.where(le.classes_ == 'Iris-setosa')[0][0])[0]\n",
    "    iris_versicolor = np.where(Y_labels == np.where(le.classes_ == 'Iris-versicolor')[0][0])[0]\n",
    "    iris_virginica = np.where(Y_labels == np.where(le.classes_ == 'Iris-virginica')[0][0])[0]\n",
    "    ax.plot(X_red[iris_setosa,0], X_red[iris_setosa,1], marker='.', color='#0F00FF', markersize=10, linestyle='', label='iris_setosa') \n",
    "    ax.plot(X_red[iris_versicolor,0], X_red[iris_versicolor,1], marker='.', color='#FF00AE', markersize=10, linestyle='', label='iris_versicolor') \n",
    "    ax.plot(X_red[iris_virginica,0], X_red[iris_virginica,1], marker='.', color='#00BA7F', markersize=10, linestyle='', label='iris_virginica') \n",
    "    ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_pca()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we define the function that trains the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Model(dims):\n",
    "    model = tf.keras.models.Sequential()\n",
    "    model.add(tf.keras.layers.Lambda(function=lambda x: (x-mu)/sigma,\n",
    "                                     input_shape=[dims[0]]))\n",
    "    for dim in dims[1:-1]:\n",
    "        model.add(tf.keras.layers.Dense(units=dim, \n",
    "                                        activation='relu', \n",
    "                                        kernel_initializer='he_uniform', \n",
    "                                        bias_initializer='zeros'))\n",
    "    model.add(tf.keras.layers.Dense(dims[-1], \n",
    "                                    activation='softmax', \n",
    "                                    kernel_initializer='he_uniform', \n",
    "                                    bias_initializer='zeros'))\n",
    "    optimizer = tf.keras.optimizers.Adam(learning_rate=0.01, \n",
    "                                         beta_1=0.9, \n",
    "                                         beta_2=0.999, \n",
    "                                         epsilon=1e-07)\n",
    "    model.compile(loss='categorical_crossentropy', metrics=['categorical_accuracy'], optimizer=optimizer)\n",
    "    model.summary()\n",
    "    callback1 = tf.keras.callbacks.ReduceLROnPlateau(monitor='loss', \n",
    "                                                     patience=100,\n",
    "                                                     min_delta=0.001,\n",
    "                                                     factor=0.1, \n",
    "                                                     min_lr=0.0001)\n",
    "    callback2 = tf.keras.callbacks.EarlyStopping(monitor='loss',\n",
    "                                                 patience=200,\n",
    "                                                 min_delta=0.001)\n",
    "    model.fit(X, Y, epochs=10000, batch_size=32, callbacks=[callback1, callback2])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function shows the training history of the model to see how is performing in every step "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_model_training(model):\n",
    "    loss = model.history.history['loss']\n",
    "    accuracy = model.history.history['categorical_accuracy']\n",
    "    fig = plt.figure(figsize=(20, 10))\n",
    "    ax1 = fig.add_subplot(1, 2, 1)\n",
    "    ax1.set_title('Loss')\n",
    "    ax1.plot(loss, marker='.', color='#0F00FF', markersize=1, linestyle='-')\n",
    "    ax2 = fig.add_subplot(1, 2, 2)\n",
    "    ax2.set_title('Accuracy')\n",
    "    ax2.plot(accuracy, marker='.', color='#0F00FF', markersize=1, linestyle='-')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function plots the results of the model obtained after training. The plotting is done at the level of the transformed data points by PCA. This gives us a way to visualize how well the model is performing, but also a feeling if there is some kind of overfitting because the display of complex and sinuose regions would be an indication of that. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_model(model):\n",
    "    fig = plt.figure(figsize=(10, 10))\n",
    "    ax = fig.add_subplot(1, 1, 1)\n",
    "    ax.axis([np.min(X_red[:,0])-0.1, np.max(X_red[:,0])+0.1, np.min(X_red[:,1])-0.1, np.max(X_red[:,1])+0.1])\n",
    "    u = np.linspace(np.min(X_red[:,0])-0.1, np.max(X_red[:,0])+0.1, 100)\n",
    "    v = np.linspace(np.min(X_red[:,1])-0.1, np.max(X_red[:,1])+0.1, 100)\n",
    "    z = []\n",
    "    for i in range(100):\n",
    "        for j in range(100):\n",
    "            z.append([u[i], v[j]])\n",
    "    z = np.array(z)\n",
    "    z_ = pca.inverse_transform(z)\n",
    "    w = np.argmax(model.predict(z_), axis=1)\n",
    "    iris_setosa = np.where(w == np.where(le.classes_ == 'Iris-setosa')[0][0])[0]\n",
    "    iris_versicolor = np.where(w == np.where(le.classes_ == 'Iris-versicolor')[0][0])[0]\n",
    "    iris_virginica = np.where(w == np.where(le.classes_ == 'Iris-virginica')[0][0])[0]\n",
    "    ax.plot(z[iris_setosa,0], z[iris_setosa,1], marker='+', color='#C7C3FF', markersize=10, linestyle='') \n",
    "    ax.plot(z[iris_versicolor,0], z[iris_versicolor,1], marker='+', color='#FFECFB', markersize=10, linestyle='') \n",
    "    ax.plot(z[iris_virginica,0], z[iris_virginica,1], marker='+', color='#C8FFB0', markersize=10, linestyle='') \n",
    "    iris_setosa = np.where(Y_labels == np.where(le.classes_ == 'Iris-setosa')[0][0])[0]\n",
    "    iris_versicolor = np.where(Y_labels == np.where(le.classes_ == 'Iris-versicolor')[0][0])[0]\n",
    "    iris_virginica = np.where(Y_labels == np.where(le.classes_ == 'Iris-virginica')[0][0])[0]\n",
    "    ax.plot(X_red[iris_setosa,0], X_red[iris_setosa,1], marker='.', color='#0F00FF', markersize=10, linestyle='', label='iris_setosa') \n",
    "    ax.plot(X_red[iris_versicolor,0], X_red[iris_versicolor,1], marker='.', color='#FF00AE', markersize=10, linestyle='', label='iris_versicolor') \n",
    "    ax.plot(X_red[iris_virginica,0], X_red[iris_virginica,1], marker='.', color='#00BA7F', markersize=10, linestyle='', label='iris_virginica') \n",
    "    ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we train a basic Softmax model, an equivalent version of Linear Discriminant Analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lambda (Lambda)              (None, 4)                 0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 3)                 15        \n",
      "=================================================================\n",
      "Total params: 15\n",
      "Trainable params: 15\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 150 samples\n",
      "Epoch 1/10000\n",
      "150/150 [==============================] - 0s 2ms/sample - loss: 0.7845 - categorical_accuracy: 0.5667\n",
      "Epoch 2/10000\n",
      "150/150 [==============================] - 0s 70us/sample - loss: 0.7027 - categorical_accuracy: 0.5933\n",
      "Epoch 3/10000\n",
      "150/150 [==============================] - 0s 70us/sample - loss: 0.6367 - categorical_accuracy: 0.6400\n",
      "Epoch 4/10000\n",
      "150/150 [==============================] - 0s 58us/sample - loss: 0.5837 - categorical_accuracy: 0.7200\n",
      "Epoch 5/10000\n",
      "150/150 [==============================] - 0s 102us/sample - loss: 0.5369 - categorical_accuracy: 0.7133\n",
      "Epoch 6/10000\n",
      "150/150 [==============================] - 0s 70us/sample - loss: 0.4956 - categorical_accuracy: 0.7000\n",
      "Epoch 7/10000\n",
      "150/150 [==============================] - 0s 51us/sample - loss: 0.4620 - categorical_accuracy: 0.7467\n",
      "Epoch 8/10000\n",
      "150/150 [==============================] - 0s 131us/sample - loss: 0.4329 - categorical_accuracy: 0.7733\n",
      "Epoch 9/10000\n",
      "150/150 [==============================] - 0s 72us/sample - loss: 0.4067 - categorical_accuracy: 0.8333\n",
      "Epoch 10/10000\n",
      "150/150 [==============================] - 0s 58us/sample - loss: 0.3869 - categorical_accuracy: 0.8733\n",
      "Epoch 11/10000\n",
      "150/150 [==============================] - 0s 131us/sample - loss: 0.3673 - categorical_accuracy: 0.8867\n",
      "Epoch 12/10000\n",
      "150/150 [==============================] - 0s 61us/sample - loss: 0.3518 - categorical_accuracy: 0.9000\n",
      "Epoch 13/10000\n",
      "150/150 [==============================] - 0s 62us/sample - loss: 0.3380 - categorical_accuracy: 0.9133\n",
      "Epoch 14/10000\n",
      "150/150 [==============================] - 0s 82us/sample - loss: 0.3271 - categorical_accuracy: 0.9133\n",
      "Epoch 15/10000\n",
      "150/150 [==============================] - 0s 81us/sample - loss: 0.3165 - categorical_accuracy: 0.9133\n",
      "Epoch 16/10000\n",
      "150/150 [==============================] - 0s 82us/sample - loss: 0.3076 - categorical_accuracy: 0.9267\n",
      "Epoch 17/10000\n",
      "150/150 [==============================] - 0s 58us/sample - loss: 0.3001 - categorical_accuracy: 0.9267\n",
      "Epoch 18/10000\n",
      "150/150 [==============================] - 0s 113us/sample - loss: 0.2926 - categorical_accuracy: 0.9267\n",
      "Epoch 19/10000\n",
      "150/150 [==============================] - 0s 67us/sample - loss: 0.2862 - categorical_accuracy: 0.9267\n",
      "Epoch 20/10000\n",
      "150/150 [==============================] - 0s 76us/sample - loss: 0.2801 - categorical_accuracy: 0.9267\n",
      "Epoch 21/10000\n",
      "150/150 [==============================] - 0s 101us/sample - loss: 0.2746 - categorical_accuracy: 0.9267\n",
      "Epoch 22/10000\n",
      "150/150 [==============================] - 0s 72us/sample - loss: 0.2693 - categorical_accuracy: 0.9200\n",
      "Epoch 23/10000\n",
      "150/150 [==============================] - 0s 76us/sample - loss: 0.2644 - categorical_accuracy: 0.9200\n",
      "Epoch 24/10000\n",
      "150/150 [==============================] - 0s 110us/sample - loss: 0.2600 - categorical_accuracy: 0.9133\n",
      "Epoch 25/10000\n",
      "150/150 [==============================] - 0s 67us/sample - loss: 0.2553 - categorical_accuracy: 0.9200\n",
      "Epoch 26/10000\n",
      "150/150 [==============================] - 0s 59us/sample - loss: 0.2508 - categorical_accuracy: 0.9200\n",
      "Epoch 27/10000\n",
      "150/150 [==============================] - 0s 124us/sample - loss: 0.2468 - categorical_accuracy: 0.9133\n",
      "Epoch 28/10000\n",
      "150/150 [==============================] - 0s 68us/sample - loss: 0.2429 - categorical_accuracy: 0.9200\n",
      "Epoch 29/10000\n",
      "150/150 [==============================] - 0s 65us/sample - loss: 0.2391 - categorical_accuracy: 0.9267\n",
      "Epoch 30/10000\n",
      "150/150 [==============================] - 0s 73us/sample - loss: 0.2353 - categorical_accuracy: 0.9267\n",
      "Epoch 31/10000\n",
      "150/150 [==============================] - 0s 89us/sample - loss: 0.2322 - categorical_accuracy: 0.9267\n",
      "Epoch 32/10000\n",
      "150/150 [==============================] - 0s 57us/sample - loss: 0.2284 - categorical_accuracy: 0.9333\n",
      "Epoch 33/10000\n",
      "150/150 [==============================] - 0s 76us/sample - loss: 0.2251 - categorical_accuracy: 0.9333\n",
      "Epoch 34/10000\n",
      "150/150 [==============================] - 0s 145us/sample - loss: 0.2218 - categorical_accuracy: 0.9333\n",
      "Epoch 35/10000\n",
      "150/150 [==============================] - 0s 69us/sample - loss: 0.2189 - categorical_accuracy: 0.9333\n",
      "Epoch 36/10000\n",
      "150/150 [==============================] - 0s 94us/sample - loss: 0.2156 - categorical_accuracy: 0.9333\n",
      "Epoch 37/10000\n",
      "150/150 [==============================] - 0s 90us/sample - loss: 0.2132 - categorical_accuracy: 0.9400\n",
      "Epoch 38/10000\n",
      "150/150 [==============================] - 0s 68us/sample - loss: 0.2100 - categorical_accuracy: 0.9400\n",
      "Epoch 39/10000\n",
      "150/150 [==============================] - 0s 85us/sample - loss: 0.2070 - categorical_accuracy: 0.9400\n",
      "Epoch 40/10000\n",
      "150/150 [==============================] - 0s 83us/sample - loss: 0.2043 - categorical_accuracy: 0.9400\n",
      "Epoch 41/10000\n",
      "150/150 [==============================] - 0s 67us/sample - loss: 0.2016 - categorical_accuracy: 0.9467\n",
      "Epoch 42/10000\n",
      "150/150 [==============================] - 0s 68us/sample - loss: 0.1991 - categorical_accuracy: 0.9467\n",
      "Epoch 43/10000\n",
      "150/150 [==============================] - 0s 102us/sample - loss: 0.1966 - categorical_accuracy: 0.9467\n",
      "Epoch 44/10000\n",
      "150/150 [==============================] - 0s 78us/sample - loss: 0.1941 - categorical_accuracy: 0.9467\n",
      "Epoch 45/10000\n",
      "150/150 [==============================] - 0s 70us/sample - loss: 0.1918 - categorical_accuracy: 0.9533\n",
      "Epoch 46/10000\n",
      "150/150 [==============================] - 0s 112us/sample - loss: 0.1895 - categorical_accuracy: 0.9533\n",
      "Epoch 47/10000\n",
      "150/150 [==============================] - 0s 76us/sample - loss: 0.1870 - categorical_accuracy: 0.9533\n",
      "Epoch 48/10000\n",
      "150/150 [==============================] - 0s 62us/sample - loss: 0.1849 - categorical_accuracy: 0.9533\n",
      "Epoch 49/10000\n",
      "150/150 [==============================] - 0s 57us/sample - loss: 0.1826 - categorical_accuracy: 0.9533\n",
      "Epoch 50/10000\n",
      "150/150 [==============================] - 0s 73us/sample - loss: 0.1805 - categorical_accuracy: 0.9533\n",
      "Epoch 51/10000\n",
      "150/150 [==============================] - 0s 46us/sample - loss: 0.1785 - categorical_accuracy: 0.9533\n",
      "Epoch 52/10000\n",
      "150/150 [==============================] - 0s 52us/sample - loss: 0.1765 - categorical_accuracy: 0.9533\n",
      "Epoch 53/10000\n",
      "150/150 [==============================] - 0s 42us/sample - loss: 0.1746 - categorical_accuracy: 0.9600\n",
      "Epoch 54/10000\n",
      "150/150 [==============================] - 0s 57us/sample - loss: 0.1725 - categorical_accuracy: 0.9600\n",
      "Epoch 55/10000\n",
      "150/150 [==============================] - 0s 70us/sample - loss: 0.1707 - categorical_accuracy: 0.9600\n",
      "Epoch 56/10000\n",
      "150/150 [==============================] - 0s 65us/sample - loss: 0.1687 - categorical_accuracy: 0.9600\n",
      "Epoch 57/10000\n",
      "150/150 [==============================] - 0s 51us/sample - loss: 0.1671 - categorical_accuracy: 0.9600\n",
      "Epoch 58/10000\n",
      "150/150 [==============================] - 0s 92us/sample - loss: 0.1652 - categorical_accuracy: 0.9600\n",
      "Epoch 59/10000\n",
      "150/150 [==============================] - 0s 70us/sample - loss: 0.1637 - categorical_accuracy: 0.9600\n",
      "Epoch 60/10000\n",
      "150/150 [==============================] - 0s 79us/sample - loss: 0.1619 - categorical_accuracy: 0.9600\n",
      "Epoch 61/10000\n",
      "150/150 [==============================] - 0s 65us/sample - loss: 0.1602 - categorical_accuracy: 0.9600\n",
      "Epoch 62/10000\n",
      "150/150 [==============================] - 0s 89us/sample - loss: 0.1585 - categorical_accuracy: 0.9600\n",
      "Epoch 63/10000\n",
      "150/150 [==============================] - 0s 67us/sample - loss: 0.1570 - categorical_accuracy: 0.9600\n",
      "Epoch 64/10000\n",
      "150/150 [==============================] - 0s 65us/sample - loss: 0.1557 - categorical_accuracy: 0.9600\n",
      "Epoch 65/10000\n",
      "150/150 [==============================] - 0s 93us/sample - loss: 0.1540 - categorical_accuracy: 0.9600\n",
      "Epoch 66/10000\n",
      "150/150 [==============================] - 0s 71us/sample - loss: 0.1525 - categorical_accuracy: 0.9667\n",
      "Epoch 67/10000\n",
      "150/150 [==============================] - 0s 74us/sample - loss: 0.1511 - categorical_accuracy: 0.9667\n",
      "Epoch 68/10000\n",
      "150/150 [==============================] - 0s 75us/sample - loss: 0.1497 - categorical_accuracy: 0.9667\n",
      "Epoch 69/10000\n",
      "150/150 [==============================] - 0s 87us/sample - loss: 0.1482 - categorical_accuracy: 0.9667\n",
      "Epoch 70/10000\n",
      "150/150 [==============================] - 0s 67us/sample - loss: 0.1470 - categorical_accuracy: 0.9667\n",
      "Epoch 71/10000\n",
      "150/150 [==============================] - 0s 58us/sample - loss: 0.1456 - categorical_accuracy: 0.9667\n",
      "Epoch 72/10000\n",
      "150/150 [==============================] - 0s 102us/sample - loss: 0.1443 - categorical_accuracy: 0.9667\n",
      "Epoch 73/10000\n",
      "150/150 [==============================] - 0s 60us/sample - loss: 0.1431 - categorical_accuracy: 0.9667\n",
      "Epoch 74/10000\n",
      "150/150 [==============================] - 0s 56us/sample - loss: 0.1419 - categorical_accuracy: 0.9667\n",
      "Epoch 75/10000\n",
      "150/150 [==============================] - 0s 82us/sample - loss: 0.1406 - categorical_accuracy: 0.9667\n",
      "Epoch 76/10000\n",
      "150/150 [==============================] - 0s 74us/sample - loss: 0.1394 - categorical_accuracy: 0.9667\n",
      "Epoch 77/10000\n",
      "150/150 [==============================] - 0s 66us/sample - loss: 0.1385 - categorical_accuracy: 0.9667\n",
      "Epoch 78/10000\n",
      "150/150 [==============================] - 0s 67us/sample - loss: 0.1371 - categorical_accuracy: 0.9667\n",
      "Epoch 79/10000\n",
      "150/150 [==============================] - 0s 77us/sample - loss: 0.1361 - categorical_accuracy: 0.9667\n",
      "Epoch 80/10000\n",
      "150/150 [==============================] - 0s 76us/sample - loss: 0.1351 - categorical_accuracy: 0.9667\n",
      "Epoch 81/10000\n",
      "150/150 [==============================] - 0s 49us/sample - loss: 0.1339 - categorical_accuracy: 0.9667\n",
      "Epoch 82/10000\n",
      "150/150 [==============================] - 0s 66us/sample - loss: 0.1330 - categorical_accuracy: 0.9667\n",
      "Epoch 83/10000\n",
      "150/150 [==============================] - 0s 86us/sample - loss: 0.1319 - categorical_accuracy: 0.9667\n",
      "Epoch 84/10000\n",
      "150/150 [==============================] - 0s 78us/sample - loss: 0.1310 - categorical_accuracy: 0.9667\n",
      "Epoch 85/10000\n",
      "150/150 [==============================] - 0s 56us/sample - loss: 0.1299 - categorical_accuracy: 0.9667\n",
      "Epoch 86/10000\n",
      "150/150 [==============================] - 0s 71us/sample - loss: 0.1288 - categorical_accuracy: 0.9667\n",
      "Epoch 87/10000\n",
      "150/150 [==============================] - 0s 92us/sample - loss: 0.1282 - categorical_accuracy: 0.9667\n",
      "Epoch 88/10000\n",
      "150/150 [==============================] - 0s 60us/sample - loss: 0.1270 - categorical_accuracy: 0.9667\n",
      "Epoch 89/10000\n",
      "150/150 [==============================] - 0s 59us/sample - loss: 0.1260 - categorical_accuracy: 0.9667\n",
      "Epoch 90/10000\n",
      "150/150 [==============================] - 0s 82us/sample - loss: 0.1253 - categorical_accuracy: 0.9667\n",
      "Epoch 91/10000\n",
      "150/150 [==============================] - 0s 64us/sample - loss: 0.1243 - categorical_accuracy: 0.9733\n",
      "Epoch 92/10000\n",
      "150/150 [==============================] - 0s 75us/sample - loss: 0.1234 - categorical_accuracy: 0.9733\n",
      "Epoch 93/10000\n",
      "150/150 [==============================] - 0s 69us/sample - loss: 0.1226 - categorical_accuracy: 0.9733\n",
      "Epoch 94/10000\n",
      "150/150 [==============================] - 0s 68us/sample - loss: 0.1218 - categorical_accuracy: 0.9733\n",
      "Epoch 95/10000\n",
      "150/150 [==============================] - 0s 68us/sample - loss: 0.1210 - categorical_accuracy: 0.9733\n",
      "Epoch 96/10000\n",
      "150/150 [==============================] - 0s 61us/sample - loss: 0.1202 - categorical_accuracy: 0.9733\n",
      "Epoch 97/10000\n",
      "150/150 [==============================] - 0s 72us/sample - loss: 0.1193 - categorical_accuracy: 0.9733\n",
      "Epoch 98/10000\n",
      "150/150 [==============================] - 0s 96us/sample - loss: 0.1187 - categorical_accuracy: 0.9733\n",
      "Epoch 99/10000\n",
      "150/150 [==============================] - 0s 62us/sample - loss: 0.1179 - categorical_accuracy: 0.9733\n",
      "Epoch 100/10000\n",
      "150/150 [==============================] - 0s 56us/sample - loss: 0.1171 - categorical_accuracy: 0.9733\n",
      "Epoch 101/10000\n",
      "150/150 [==============================] - 0s 85us/sample - loss: 0.1165 - categorical_accuracy: 0.9733\n",
      "Epoch 102/10000\n",
      "150/150 [==============================] - 0s 72us/sample - loss: 0.1156 - categorical_accuracy: 0.9733\n",
      "Epoch 103/10000\n",
      "150/150 [==============================] - 0s 64us/sample - loss: 0.1148 - categorical_accuracy: 0.9733\n",
      "Epoch 104/10000\n",
      "150/150 [==============================] - 0s 82us/sample - loss: 0.1141 - categorical_accuracy: 0.9733\n",
      "Epoch 105/10000\n",
      "150/150 [==============================] - 0s 82us/sample - loss: 0.1135 - categorical_accuracy: 0.9733\n",
      "Epoch 106/10000\n",
      "150/150 [==============================] - 0s 64us/sample - loss: 0.1128 - categorical_accuracy: 0.9733\n",
      "Epoch 107/10000\n",
      "150/150 [==============================] - 0s 57us/sample - loss: 0.1121 - categorical_accuracy: 0.9733\n",
      "Epoch 108/10000\n",
      "150/150 [==============================] - 0s 63us/sample - loss: 0.1115 - categorical_accuracy: 0.9733\n",
      "Epoch 109/10000\n",
      "150/150 [==============================] - 0s 77us/sample - loss: 0.1109 - categorical_accuracy: 0.9733\n",
      "Epoch 110/10000\n",
      "150/150 [==============================] - 0s 69us/sample - loss: 0.1102 - categorical_accuracy: 0.9733\n",
      "Epoch 111/10000\n",
      "150/150 [==============================] - 0s 68us/sample - loss: 0.1095 - categorical_accuracy: 0.9733\n",
      "Epoch 112/10000\n",
      "150/150 [==============================] - 0s 77us/sample - loss: 0.1090 - categorical_accuracy: 0.9733\n",
      "Epoch 113/10000\n",
      "150/150 [==============================] - 0s 79us/sample - loss: 0.1084 - categorical_accuracy: 0.9733\n",
      "Epoch 114/10000\n",
      "150/150 [==============================] - 0s 71us/sample - loss: 0.1077 - categorical_accuracy: 0.9733\n",
      "Epoch 115/10000\n",
      "150/150 [==============================] - 0s 70us/sample - loss: 0.1071 - categorical_accuracy: 0.9733\n",
      "Epoch 116/10000\n",
      "150/150 [==============================] - 0s 72us/sample - loss: 0.1066 - categorical_accuracy: 0.9733\n",
      "Epoch 117/10000\n",
      "150/150 [==============================] - 0s 59us/sample - loss: 0.1061 - categorical_accuracy: 0.9733\n",
      "Epoch 118/10000\n",
      "150/150 [==============================] - 0s 57us/sample - loss: 0.1054 - categorical_accuracy: 0.9733\n",
      "Epoch 119/10000\n",
      "150/150 [==============================] - 0s 70us/sample - loss: 0.1049 - categorical_accuracy: 0.9733\n",
      "Epoch 120/10000\n",
      "150/150 [==============================] - 0s 63us/sample - loss: 0.1044 - categorical_accuracy: 0.9733\n",
      "Epoch 121/10000\n",
      "150/150 [==============================] - 0s 59us/sample - loss: 0.1039 - categorical_accuracy: 0.9733\n",
      "Epoch 122/10000\n",
      "150/150 [==============================] - 0s 68us/sample - loss: 0.1033 - categorical_accuracy: 0.9733\n",
      "Epoch 123/10000\n",
      "150/150 [==============================] - 0s 106us/sample - loss: 0.1028 - categorical_accuracy: 0.9733\n",
      "Epoch 124/10000\n",
      "150/150 [==============================] - 0s 58us/sample - loss: 0.1022 - categorical_accuracy: 0.9733\n",
      "Epoch 125/10000\n",
      "150/150 [==============================] - 0s 75us/sample - loss: 0.1017 - categorical_accuracy: 0.9733\n",
      "Epoch 126/10000\n",
      "150/150 [==============================] - 0s 98us/sample - loss: 0.1013 - categorical_accuracy: 0.9733\n",
      "Epoch 127/10000\n",
      "150/150 [==============================] - 0s 79us/sample - loss: 0.1008 - categorical_accuracy: 0.9733\n",
      "Epoch 128/10000\n",
      "150/150 [==============================] - 0s 62us/sample - loss: 0.1003 - categorical_accuracy: 0.9733\n",
      "Epoch 129/10000\n",
      "150/150 [==============================] - 0s 82us/sample - loss: 0.1000 - categorical_accuracy: 0.9733\n",
      "Epoch 130/10000\n",
      "150/150 [==============================] - 0s 79us/sample - loss: 0.0993 - categorical_accuracy: 0.9733\n",
      "Epoch 131/10000\n",
      "150/150 [==============================] - 0s 67us/sample - loss: 0.0989 - categorical_accuracy: 0.9733\n",
      "Epoch 132/10000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "150/150 [==============================] - 0s 64us/sample - loss: 0.0986 - categorical_accuracy: 0.9733\n",
      "Epoch 133/10000\n",
      "150/150 [==============================] - 0s 75us/sample - loss: 0.0981 - categorical_accuracy: 0.9733\n",
      "Epoch 134/10000\n",
      "150/150 [==============================] - 0s 63us/sample - loss: 0.0975 - categorical_accuracy: 0.9733\n",
      "Epoch 135/10000\n",
      "150/150 [==============================] - 0s 63us/sample - loss: 0.0972 - categorical_accuracy: 0.9733\n",
      "Epoch 136/10000\n",
      "150/150 [==============================] - 0s 72us/sample - loss: 0.0967 - categorical_accuracy: 0.9733\n",
      "Epoch 137/10000\n",
      "150/150 [==============================] - 0s 69us/sample - loss: 0.0963 - categorical_accuracy: 0.9733\n",
      "Epoch 138/10000\n",
      "150/150 [==============================] - 0s 63us/sample - loss: 0.0958 - categorical_accuracy: 0.9733\n",
      "Epoch 139/10000\n",
      "150/150 [==============================] - 0s 64us/sample - loss: 0.0954 - categorical_accuracy: 0.9733\n",
      "Epoch 140/10000\n",
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     ]
    },
    {
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     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epoch 676/10000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
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     ]
    },
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     "text": [
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      "Epoch 948/10000\n",
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      "Epoch 1015/10000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "150/150 [==============================] - 0s 100us/sample - loss: 0.0448 - categorical_accuracy: 0.9800\n",
      "Epoch 1118/10000\n",
      "150/150 [==============================] - 0s 79us/sample - loss: 0.0448 - categorical_accuracy: 0.9800\n",
      "Epoch 1119/10000\n",
      "150/150 [==============================] - 0s 63us/sample - loss: 0.0448 - categorical_accuracy: 0.9800\n",
      "Epoch 1120/10000\n",
      "150/150 [==============================] - 0s 79us/sample - loss: 0.0448 - categorical_accuracy: 0.9800\n",
      "Epoch 1121/10000\n",
      "150/150 [==============================] - 0s 85us/sample - loss: 0.0448 - categorical_accuracy: 0.9800\n",
      "Epoch 1122/10000\n",
      "150/150 [==============================] - 0s 72us/sample - loss: 0.0448 - categorical_accuracy: 0.9800\n",
      "Epoch 1123/10000\n",
      "150/150 [==============================] - 0s 66us/sample - loss: 0.0448 - categorical_accuracy: 0.9800\n",
      "Epoch 1124/10000\n",
      "150/150 [==============================] - 0s 100us/sample - loss: 0.0448 - categorical_accuracy: 0.9800\n",
      "Epoch 1125/10000\n",
      "150/150 [==============================] - 0s 55us/sample - loss: 0.0448 - categorical_accuracy: 0.9800\n",
      "Epoch 1126/10000\n",
      "150/150 [==============================] - 0s 67us/sample - loss: 0.0448 - categorical_accuracy: 0.9800\n",
      "Epoch 1127/10000\n",
      "150/150 [==============================] - 0s 67us/sample - loss: 0.0448 - categorical_accuracy: 0.9800\n",
      "Epoch 1128/10000\n",
      "150/150 [==============================] - 0s 110us/sample - loss: 0.0448 - categorical_accuracy: 0.9800\n",
      "Epoch 1129/10000\n",
      "150/150 [==============================] - 0s 73us/sample - loss: 0.0448 - categorical_accuracy: 0.9800\n",
      "Epoch 1130/10000\n",
      "150/150 [==============================] - 0s 65us/sample - loss: 0.0448 - categorical_accuracy: 0.9800\n",
      "Epoch 1131/10000\n",
      "150/150 [==============================] - 0s 116us/sample - loss: 0.0448 - categorical_accuracy: 0.9800\n",
      "Epoch 1132/10000\n",
      "150/150 [==============================] - 0s 52us/sample - loss: 0.0448 - categorical_accuracy: 0.9800\n",
      "Epoch 1133/10000\n",
      "150/150 [==============================] - 0s 67us/sample - loss: 0.0448 - categorical_accuracy: 0.9800\n"
     ]
    }
   ],
   "source": [
    "model_1 = Model([X.shape[1],Y.shape[1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model_training(model_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(model_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we train a Shallow Neural Network to see how much we gain if we add some extra layers to the last model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lambda_1 (Lambda)            (None, 4)                 0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 64)                320       \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 3)                 195       \n",
      "=================================================================\n",
      "Total params: 515\n",
      "Trainable params: 515\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 150 samples\n",
      "Epoch 1/10000\n",
      "150/150 [==============================] - 0s 2ms/sample - loss: 0.8597 - categorical_accuracy: 0.5667\n",
      "Epoch 2/10000\n",
      "150/150 [==============================] - 0s 67us/sample - loss: 0.3400 - categorical_accuracy: 0.8533\n",
      "Epoch 3/10000\n",
      "150/150 [==============================] - 0s 80us/sample - loss: 0.2825 - categorical_accuracy: 0.8667\n",
      "Epoch 4/10000\n",
      "150/150 [==============================] - 0s 115us/sample - loss: 0.2478 - categorical_accuracy: 0.8933\n",
      "Epoch 5/10000\n",
      "150/150 [==============================] - 0s 71us/sample - loss: 0.2163 - categorical_accuracy: 0.9133\n",
      "Epoch 6/10000\n",
      "150/150 [==============================] - 0s 85us/sample - loss: 0.1970 - categorical_accuracy: 0.9333\n",
      "Epoch 7/10000\n",
      "150/150 [==============================] - 0s 121us/sample - loss: 0.1866 - categorical_accuracy: 0.9267\n",
      "Epoch 8/10000\n",
      "150/150 [==============================] - 0s 92us/sample - loss: 0.1665 - categorical_accuracy: 0.9267\n",
      "Epoch 9/10000\n",
      "150/150 [==============================] - 0s 106us/sample - loss: 0.1509 - categorical_accuracy: 0.9467\n",
      "Epoch 10/10000\n",
      "150/150 [==============================] - 0s 86us/sample - loss: 0.1379 - categorical_accuracy: 0.9600\n",
      "Epoch 11/10000\n",
      "150/150 [==============================] - 0s 68us/sample - loss: 0.1246 - categorical_accuracy: 0.9600\n",
      "Epoch 12/10000\n",
      "150/150 [==============================] - 0s 100us/sample - loss: 0.1182 - categorical_accuracy: 0.9533\n",
      "Epoch 13/10000\n",
      "150/150 [==============================] - 0s 82us/sample - loss: 0.1049 - categorical_accuracy: 0.9667\n",
      "Epoch 14/10000\n",
      "150/150 [==============================] - 0s 62us/sample - loss: 0.1038 - categorical_accuracy: 0.9467\n",
      "Epoch 15/10000\n",
      "150/150 [==============================] - 0s 143us/sample - loss: 0.0928 - categorical_accuracy: 0.9600\n",
      "Epoch 16/10000\n",
      "150/150 [==============================] - 0s 76us/sample - loss: 0.0855 - categorical_accuracy: 0.9800\n",
      "Epoch 17/10000\n",
      "150/150 [==============================] - 0s 108us/sample - loss: 0.0835 - categorical_accuracy: 0.9667\n",
      "Epoch 18/10000\n",
      "150/150 [==============================] - 0s 106us/sample - loss: 0.0775 - categorical_accuracy: 0.9600\n",
      "Epoch 19/10000\n",
      "150/150 [==============================] - 0s 73us/sample - loss: 0.0701 - categorical_accuracy: 0.9800\n",
      "Epoch 20/10000\n",
      "150/150 [==============================] - 0s 107us/sample - loss: 0.0663 - categorical_accuracy: 0.9867\n",
      "Epoch 21/10000\n",
      "150/150 [==============================] - 0s 95us/sample - loss: 0.0640 - categorical_accuracy: 0.9867\n",
      "Epoch 22/10000\n",
      "150/150 [==============================] - 0s 67us/sample - loss: 0.0614 - categorical_accuracy: 0.9867\n",
      "Epoch 23/10000\n",
      "150/150 [==============================] - 0s 96us/sample - loss: 0.0583 - categorical_accuracy: 0.9800\n",
      "Epoch 24/10000\n",
      "150/150 [==============================] - 0s 84us/sample - loss: 0.0573 - categorical_accuracy: 0.9867\n",
      "Epoch 25/10000\n",
      "150/150 [==============================] - 0s 68us/sample - loss: 0.0554 - categorical_accuracy: 0.9867\n",
      "Epoch 26/10000\n",
      "150/150 [==============================] - 0s 99us/sample - loss: 0.0524 - categorical_accuracy: 0.9800\n",
      "Epoch 27/10000\n",
      "150/150 [==============================] - 0s 86us/sample - loss: 0.0510 - categorical_accuracy: 0.9867\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
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     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "150/150 [==============================] - 0s 95us/sample - loss: 5.6893e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 525/10000\n",
      "150/150 [==============================] - 0s 85us/sample - loss: 5.6927e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 526/10000\n",
      "150/150 [==============================] - 0s 70us/sample - loss: 5.6788e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 527/10000\n",
      "150/150 [==============================] - 0s 82us/sample - loss: 5.6710e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 528/10000\n",
      "150/150 [==============================] - 0s 73us/sample - loss: 5.7068e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 529/10000\n",
      "150/150 [==============================] - 0s 63us/sample - loss: 5.6807e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 530/10000\n",
      "150/150 [==============================] - 0s 69us/sample - loss: 5.6725e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 531/10000\n",
      "150/150 [==============================] - 0s 81us/sample - loss: 5.6548e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 532/10000\n",
      "150/150 [==============================] - 0s 66us/sample - loss: 5.6572e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 533/10000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "150/150 [==============================] - 0s 59us/sample - loss: 5.6456e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 534/10000\n",
      "150/150 [==============================] - 0s 67us/sample - loss: 5.6570e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 535/10000\n",
      "150/150 [==============================] - 0s 101us/sample - loss: 5.6458e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 536/10000\n",
      "150/150 [==============================] - 0s 64us/sample - loss: 5.6369e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 537/10000\n",
      "150/150 [==============================] - 0s 66us/sample - loss: 5.6346e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 538/10000\n",
      "150/150 [==============================] - 0s 76us/sample - loss: 5.6308e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 539/10000\n",
      "150/150 [==============================] - 0s 69us/sample - loss: 5.6266e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 540/10000\n",
      "150/150 [==============================] - 0s 62us/sample - loss: 5.6322e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 541/10000\n",
      "150/150 [==============================] - 0s 62us/sample - loss: 5.6223e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 542/10000\n",
      "150/150 [==============================] - 0s 121us/sample - loss: 5.6200e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 543/10000\n",
      "150/150 [==============================] - 0s 59us/sample - loss: 5.6215e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 544/10000\n",
      "150/150 [==============================] - 0s 57us/sample - loss: 5.6152e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 545/10000\n",
      "150/150 [==============================] - 0s 73us/sample - loss: 5.6101e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 546/10000\n",
      "150/150 [==============================] - 0s 65us/sample - loss: 5.6025e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 547/10000\n",
      "150/150 [==============================] - 0s 52us/sample - loss: 5.5987e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 548/10000\n",
      "150/150 [==============================] - 0s 49us/sample - loss: 5.5984e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 549/10000\n",
      "150/150 [==============================] - 0s 48us/sample - loss: 5.5935e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 550/10000\n",
      "150/150 [==============================] - 0s 71us/sample - loss: 5.5908e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 551/10000\n",
      "150/150 [==============================] - 0s 50us/sample - loss: 5.5853e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 552/10000\n",
      "150/150 [==============================] - 0s 52us/sample - loss: 5.5856e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 553/10000\n",
      "150/150 [==============================] - 0s 47us/sample - loss: 5.5755e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 554/10000\n",
      "150/150 [==============================] - 0s 52us/sample - loss: 5.5850e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 555/10000\n",
      "150/150 [==============================] - 0s 57us/sample - loss: 5.5719e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 556/10000\n",
      "150/150 [==============================] - 0s 57us/sample - loss: 5.5601e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 557/10000\n",
      "150/150 [==============================] - 0s 58us/sample - loss: 5.5625e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 558/10000\n",
      "150/150 [==============================] - 0s 58us/sample - loss: 5.5559e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 559/10000\n",
      "150/150 [==============================] - 0s 53us/sample - loss: 5.5465e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 560/10000\n",
      "150/150 [==============================] - 0s 54us/sample - loss: 5.5624e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 561/10000\n",
      "150/150 [==============================] - 0s 64us/sample - loss: 5.5509e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 562/10000\n",
      "150/150 [==============================] - 0s 51us/sample - loss: 5.5475e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 563/10000\n",
      "150/150 [==============================] - 0s 57us/sample - loss: 5.5414e-04 - categorical_accuracy: 1.0000\n"
     ]
    }
   ],
   "source": [
    "model_2 = Model([X.shape[1],64,Y.shape[1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model_training(model_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3I1LMsRuR+uJ4qtwwl92IarAbUaMYOuasIoaOOauI4Zac2Y0YoxjsRkysGC7P2a6uPB07CVXEYDciERERkUO42CIi17v2uqk9/+zaa9Vdpw+xfvrwT7F+/fpez/njH/+IRx99tK9pEZHm2I1IREpZwo+yumpUeGtQkJGL4sEjYMI8/y/24oP3t3S/jmXBHDAAH3zwQb9ih1wnTCzLsjrmIoaz5OEl5+1ynDt3LubOnSs7Ioko4bBAnijB2Fkgbwk/Zu36LcrrT6DJ3wZPUjKKsoZizfhvwjT6fiM9eFzP4pIluPjii1G5qxL7d+/r+Nlnn32Ged/4OhoaGuCzfHju2V/humnXdYv13PPP4aOPP8Ljjz4OAFi+Yjl2frgTv3j6F71ep2RpCV753au4dNilyM7ORuE11+CHP/ghvvXtf8acr34Vd9x+By4fcQUWzP8nlK5ejXPnzuGN37+O0aNHh4zb+fzzz/Gd++7F0aNHZT7P/grXXnstbrn9Vhw7dgytZ1vx/e/9KxbeszDiPx8WyNP5cCyP/Vggr1EMHXNWEUPHnFXEcEvOqgrkS2urRPqGpQJrF3W80tcvFaW1Vef//V6OezweIYQQ7733nkhLSxNHjx7tODfws2XLlomf/exnQpzzC5/PJxoaGsLGrq2tFVdeeWXHsdmzZ4v3338/9Drr3+28jhBi+/btYvz48aK5oUk0NDSIESNGiCeeeEIIIcSC+QvEG2+8IYQQ4rLLLhPPPPOMEEKIXz7zrLjrrruEEEK8+OKL4r777hPinF/8wz/8g/iv//ovIYQQPp9PnDlzRohzfnHq1CkhhBDNzc1izJgxoq6ujgXyusdwec4skFcbgwXyRBQTFd4aNFltIcea/G2o9NYou8akSZPCDpieOHEiXnzxRTy85GHs2bMHGRkZYX8/JycHw4cPx7Zt23Dq1CkcPHgQU6d2rwkLvs7mzZtx8803IzU1FRkZGfja177WY3633XYbAKDwmkJ8/PHH3X7+7rvv4t577wUAmKaJrKwsAMAzzzyD8ePHY/LkyTh27Biqq3kXgihecLFFRMoUZOTCYyaHHPMkJSM/I1fZNTweT9jj119/PTZt2oShlwzF/Pnz8dJLL/UYY968eXj99dfx1sq3cOutt8Iwut/5D76OiKLcYtCgQQDkQsrn80X0Oxv/shHr16/H1q1bsWvXLhQUFKC1tTXiaxKRu3GxRUTKFGfnoShzKNLNZBgA0k1Zs1WcnWf7tT/55BNceOGFuOfue3DXXXfhww8/7PHc2267DW+//TZ+99rvMW/evPPGnjZtGkpLS9Ha2orGxkb86U9/6nOeM2bMwHPPPQdAFt83NDSgvr4eQ4YMQVpaGqqqqrBt27Y+xyci92E3IhEpYxpJWFM4H2V11aj01iA/0I3Yj+L4SG3cuBFPPPEEBg4YiPSM9F7vbA0ZMgRXX3019u/fj0mTJp039sSJEzF37lyML8zHZZddhgkTJnQ8/ovW008/jYULF+I3v/kNTNPEc889h9mzZuP5//lvjBs3DqNGjcLkyZP7FJuI3IndiEQJ5uq/8SEz/JO4sDiuR2psbER6ejqam5tx/Y3T8evn/hvXXHON02kBYDci9Y5jeWKD3YgaxdAxZxUxdMxZRQwnco6244njeqQ777xTjB83XowaNUr853/+p+3XiyYGuxFdGsMlOce6K0/HTkIVMXrrRuRjxBiwLOCd1cDuCmBcATCzGOhlj0QiUqSoqAhnz56VHwQAA3j55ZcxduzYqGO9+uqrclPS82xgSkTUFRdbNrMs4MH7geqDQHMTkOYBJhQBK9c4nRlR/CsvL+/8wIUSETmE3Yg2W1cGVO0HmhoBIeT7jnJ5nEgXwqW1ncS/GyIdsEDeZq8sB377glxoBRgGMP8u4BsLnMqKEkW0o3nC+aju/5AxOANfuuBLYfejIucIIXDq9Cl4z3hxRfbfOJ0OuUCijuWxhMDWugYc8rZgZEYqpmRnwozx/1/1ViDv6seIk6d1P7Ztc+THoznXrhhnzgBvvAq0tnQeS/MAX7lZ/mc35uxEDB1zVhHD7usNGx2mOPCUD/jSgPMfaz8+bPilOH78OE6erus8bgnA7PL/KeGOqTg3nmLYcL2UlBQMG34p0IAe/w6j+fu2JUasr+eWGE7k3AwU4eaQQ+VY1e1YtMftOldFDEv4MXnnz1HV0IYmqw0eMxlFmUOxpDAL1xq3xCwP4P5u5wW4erEVD2YWA6Ov7l6zNbMY2L7V6eyIzm/gwIHdx+Po+A8hN8Sw83qIbLd6im8n0qoxDFc7nUZMldVVY19DM1osPwCg0WpDecMJbK0zcG2Ow8m142LLZqYJLH0S8HqBPZXA2Hx2IxIREalS4a1Ba/tCK6DJasMhbwvAxVbiME1g9hz5IiIiInUKMnKRYiZ13NkCAI+ZjJEZqQ5mFYrdiERERKSt4uw8jMlMC53JmjkUU7IznU6tA7sRieKYim5EInKnRO08DMft3YiOj+Xp6cVxPYkVQ8ecVcTQYVyPLTFcMsaE3ztGMXTMWUUMB3J2w9gaHUftqIjR27gePkYkIiIishEXW0REREQ24mKLiIiIyEYskCeKYyyQJ4pfLJB3FxbIaxRDx5xVxNAxZxUxWCCfYDF0zFlFDB1zVhHDxusda9oX9rgbCsV1LG5XEYMF8i5jWcA7q+WQ6ndWy89ERESR4h0tvXAH+RizLOC2WcCOcjkrceVrclbiyjUc4UNERBSPeGcrxtaVyYVWUyMghHzfUS6PExERUfzhYivGdlfIO1rBmpvkkGoiIiKKP+xGjLHyLcAji4HWls5jKanAA4uAoqnO5UXxid2IRPpj16Ee2I3oohg+nxBzZwhxSboQgw35PneGPO7WnGMRQ8ecVcRgN2KCxdAxZxUxdMxZRQwbr+fmrjwdOwlVxOitG5EF8jFmmrIYfl0Z8OdVwFduBmYWszieiIgoXnGx5QDTBGbPAQYPBiZPczobIiIishML5ImIiIhsxAJ5ojjGAnki/bFAXg8skNcoho45q4ihY84qYrBAPsFi6Jizihg65qwihoLrRTOWp6fjLJCPTQyO6yEiItIQ72jFBy62iIiIiGzExRYRERGRjbjYIiIiIrIRuxGJ4hi7EYn0wa5DvbEbUaMYOuasIoaOOauIwW7EBIuhY84qYuiYs4oYCq6nY1eejjmriMFuRCIiIiKHKFlsGYbxgmEYtYZh7O3h54ZhGM8YhnHYMIzdhmFco+K6RERERG6n6s7WcgCze/l5MYC89tdCAM8pui4RERGRqykrkDcM43IAq4UQXw7zs/8GsFEI8bv2zwcB3CCE+KyneCyQJ+o/FsgT6YMF8nqLSYE8gMsB7O3hZ6sBTAv6vAHAhN7isUA+sWLomLOKGHZe79gBX/gfxEnhsJYxdMxZRQwdc1YRI8rrhRvNo2OhuI45q4jhhgL5cCu9bv+6bRjGQsMwdhiGwVtaRP3EO1pEeuFdrfgVq8XWcQCXBn0eBuDTricJIX4thJgghJgQo7yIiIiIbBWrxdYfAfxTe1fiZAD1opd6LSIiIqJ4MUBFEMMwfgfgBgDZhmEcB7AIwEAAEEI8D+DPAL4C4DCAZgD/rOK6RERERG7HcT1EcYBdh0R6Yedh/OG4Ho1i6Jizihg65qwihp3Xi6curbiJoWPOKmLomLOKGFFezw0ddSpi6Jizihhu6EYkIiIiSkhcbBERERHZiIstIiIiIhuxQJ4oDrBAnkgvLJCPPyyQ1yiGjjmriKFjzipisEA+wWLomLOKGDrmrCJGFGN5hHBHkbeKGDrmrCIGC+SJiIhcgne0Eg8XW0REREQ24mKLiIiIyEZcbBERERHZiN2IRHGA3YhE7sSuw8TBbkSNYuiYs4oYOuasIga7ERMsho45q4ihY84qYkQxlqen4zp25emYs4oY7EYkIiIicggXW0REREQ24mKLiIiIyEYskCeKAyyQJ3InFsgnDhbIaxTDzutt3ihEWakQjy2R7z6fM3m44XpuiaHiescO+MKfrGHhcNzH0DFnFTF0zFlBjGjG8vR0XMdCcR1zVhGjtwL5ATFe+JFDLAt48H6g+iDQ3ASkeYAJRcDKNU5nRv3FO1pE7nQirRrDcLXTaZALsGYrQawrA6r2A02NgBDyfUe5PE5ERET24WIrQeyuAM62hh5rbgL2VDqTDxERUaLgYitBjCsABqWEHkvzAGPzncmHiIgoUbAbMUEEaraq9ss7XINSgNFXA0ufBEzT6ewoUuw6JHIndh0SuxE1ihGLbsTHS9iN6JYYKq6nY5eWljmriKFjzipi6Jizghg6dtSpiKFjzipisBuRAMg7WLPnyBcRERHFBmu2iIiIiGzExRYRERGRjVggT6SRq//Gh0yP01kQUVcskCcWyDscI1CY/sjDQvzsISEefVh+3rzRvTnrWCiuY4xor+eGol8lMXTMWUUMHXNWEUPHnBXE0LHIW0UMHXNWEYMF8g4KbLlwqEpuIhrgSQfyRgEbyrn1AhERUTxjzZbNAmNyghdagByXU7Wf43KIiIjiHRdbNgs3JifgbCvH5RAREcU7FsjbrHwL8MhioLWl+89SUoEHFgFFU2OfF7kfd4sn0gcL5IkF8g7G8PmEmF4oxMUeIbLQ+brEI48HdnF3U85OxNAxZxUxWCCfYDF0zFlFDB1zjjLGsaZ93Y7pWOStIoaOOauIwQJ5B5mmnD/o9QK7PgR8PmDAAGD8NUBGBovjiYjiwYm0agzD1U6nQS7FxVYM9DQmZ9tmZ/IhIiKi2OFii4iIiEJYwo+yumpUeGtQkJGLC7IFEL4aiSLAxRYRERF1sITArJ0vo7zhBJqsNnjMZIzOTMa2Qj9Mg5sY9AW7EYlcit2IRO4U752Hm0/W46E9H6PF8nccSzWTUDL2ckzLyXIwM3djN6JGMXTMWUUMHXNWEYPdiAkWQ8ecVcTQMecoY7ihG05VjIVHnhfG2kUCQS9j7SJRcqT7jDm35OyGGL11I/J+IBEREXUYmZEKj5kccizFTEJ+Rq5DGemPiy0iIiLqMCU7E0WZQ5FuJsMAkG4mY0xmGoqz85xOTVsskI9DlgW8s1qOChpXAMws5n5eREQUGdMwsKZwPsrqqlHprUF+Ri4uyD7A4vh+YIF8nLEs4MH75ZDrs63AoBRg9NVyY1UuuPTCAnkid4r3AnnqGxbIaxSjv9crKxXiotQuo4HS5fF4/t66xujp3GMHfOF/oGHhsCuu55YYOuasIoaOOfdwPNxYHiHcUaDtlhg65qwiBgvkE8juCnlHK1hzE7Cn0pl8qG94R4vInXhHi/qCi604M65APjoMluYBxuY7kw8REVGi42IrzswsljVannTAMOT7hCJ5nIiIiGKP3YhxxjRlMbzXKx8djs1nNyIREZGT2I1I5DB2HRK5E7sOKRrsRtQoho45q4ihY84qYkQ1lqen4y7p0nL19dwSQ8ecVcTQMecejru5G84tMXTMWUUMdiMSEREROYSLLSIiIiIbcbFFREREZCMWyBM5jAXy5GqWANbXAnvqgbFZwE0XAmb4GuB4wwL5yFlCYGtdAw55WzAyIxVTsjNhGonx35MAFshrFEPHnFXE0DFnFTFYIJ9gMXTL2ecXYsb7QnhWCWGsFCJ9lfzs87s3Z4Uxwo3mcXOBtlMxfH5LzNi+XKRvWCqMtYtE6obFYsb25cLnt1ybsx0xWCBPRETRK6sByk8DTRYgADRa8nNZjdOZxQTvakWmrK4a5Q0n0Gi1QQBosfwobziBsjr++QVwsUVEROFV1MuFVrAmC6isdyYfcqUKbw2arLaQY01WGyq9ibEojwQXW0REFF5BFuDpMn7CYwL5Wc7kQ65UkJELj5kccsxjJiM/I9ehjNyHiy0iIgqvOBcoukAusAwA6ab8XMx/iFKn4uw8FGUORbqZDANAqpmEosyhKM7Oczo112A3ostYFrBjG3C4GhiRB0yYzLmG8Y7diORqgW7EvfXAl+O3G5Gdh/3DbkR2I2oTw+cTYnqhEJekCzHYkO9zZ8jjbs1ZVQwdc1YRg92ICRZDx5xVxNAxZ8GxNbpczy0x2I2oiXVlQNV+oKkREEK+7yiXx4mIiEhPXGy5yO4K4Gxr6LHmJqD4+8oAACAASURBVGBPpTP5EBERUf9xseUi4wqAQSmhx9I8wNh8Z/IhIiKi/mOBvItYFvDg/fJR4tlWufAafTWw9EkWycczFsgTOY8F8tRfLJDXKMbmjUKUlQrxeIl89/nsvZ5bYuiYs4oYxw74wp/s8sLhRC2Y5vfW5HqKYrBQXI/ruSVGbwXyA2K88KPzME1g9hz5ovjHO1pERPGPNVtERERENuKdrRiyLLmNw+4KWQyfkeF0RkRERGQ3LrZixLKA22bJfbOam2SXYd4oYEM5i9+JiIjiGbsRY6R8C/DIYqC1pfNYSirwwCKgaKpzeVHssOuQyJ2Opx4CAHYjUr+wG9EFMR5bIkfwZKHzNdiQXYduzTmWMXTMWUkMTbu0ErU7jd9bk+tFGYNdeWpj6JizihjsRnSBcQXy0WFTY+exQSlyw9LgWq5BycDEKXy0SEREFC+42IqRmcXAhKLuNVt/+3ehtVyDUoB31wEr13DBRUREFA+42IoR05QLqHVlctbh2HzZjfjuWrnQCtzxam3pHD7NvbaIiIj0xwJ5h72yHPjtC0DwX4NhAPPvAr6xwKmsyA4skCdyHsfykF1YIO/iGGWlQlySHlo4f0m6PO7WnO2IoWPOSmK4vHA4UQum+b0Vx3B5ziwUVxtDx5xVxOitQJ47yDssUMvlSZd3tFJS5eeZxU5nRkREbmUJgdUnD6Lk6F+w+uRBWMLvdErUC9ZsOaxrLVfyQOB7P2RxPBERhWcJP76/8zCqGvajyWqDx0xGUeZQrCmcD4R/iEUO42LLBYKHT2/bzIUWERH1rKyuGvsamtFiybtZjVYbyhtOoKyuGjk5DidnI0v4sbmuHmu9f0FBRi6Ks/NgGno8oONii4iISCMV3hq0WqGPDZusNlR6azAzThdblvBj1s6X8UHDJ2i1jobczdNhwcVuRKIYYTcikbPiZSzP5pP1eGjPxx13tgAg1UxCydjLMS0ny8HM7KPDd2Y3okYxdMxZRQwdc1YSw+VdWonancbvrTiGS3KOl648n98SE7Y/IdI3LBXG2kUifcNSMWP7cuHzW67oyrPjekuObBTG2kUCQS9j7SJRcmRjTPPo7TjH9RAREcUJ00jC04UjcLruKlR6a5CvWf1SXxRk5MJjJqPRaus45jGTkZ+R62BWkeNiywGBWYh/ehs4c0Zu88CieCIiipRpGJiTMwpzckY5nUpMFGfnoShzaHvNlr+jZqs4O8/p1CLCxVaMWVboLMSVr8l9tTgLkYiIKDzTSMKawvl4qu63aPFept3dPBbIx1j5FuCRxXIGYsCgFOD2eYA5ABiRB0yYzIVXPGKBPFHscCwPxZrtBfIAZgM4COAwgB+H+fm3AJwEUNn+uvt8MeO1QP6xJUIMNkLH82RBiOxkefyiVCHmzhDC53NPzrGIoWPO0cY4dsDX/aBLCocTtWCa3ztGMVySczwUiusQQ8ecVcSwdVyPYRgmgF8CKAZwNYA7DcO4Osyprwkh8ttf/9vf6+pqXAGQ5ul+/FybHEbd2iIfMa4ri31uZC/e1aKEYglg9WdASRWw5nP5mShBqXjYOQnAYSHEUSFEG4DfA7hZQdy41HUW4sDk7uc0N8nRPUREWrIEMGsLcOd2YNEBYOFO+ZkLLkpQKhZbQwEcC/p8vP1YV7cbhrHbMIw3DcO4VMF1tRSYhfib3wHz7wL+7d/lwitYmgcYm+9MfkRE/VZWA5SfBhotQABosuTnshqnMyNyhIrFVrhisK7/+lIK4HIhxDgA6wGsCBvIMBYahrHDMIz4q4wPEpiF+I0FwI8Xhd7pSkmVn2cWO50lEVEfVdTLBVawJguorHcmHyKH9bsb0TCMKQAeFkLMav/8AAAIIR7p4XwTwGkhRK/768drN2I4lgXs2AYcOQxcOYLdiPGAnYeU0NZ8Lh8dBi+4PCbw60Jg1kUxSWF/5lZ4B56OybWIAJu7ESH36joK4AoAyQB2ARjT5ZyLg/7zrQC2nS9uvHYjuu16bomhY87RxnBzl1aidqfxe9sUw+cXYsb7QqSvEsJYKYRnlfzs88csZzd0p+nYUaciho45q4hh67geIYTPMIzvAVgDwATwghBin2EYSwDsEEL8EcC/GoYxF4APwGnIrSCIiCgemQawZqqs0aqsB4ZnAPMukceJEpCSHeSFEH8G8Ocux34a9J8fAPCAimsRESW0wJYKFfVAQRZQnOvORYxpAHMulq9TPnfmSBQjHNdDRKQLSwB3bAMqvpD1UB4TKLpA3kUibVhCYHXdQVR4a1Cg2dgZ6huO6yGyAQvkyRYuKDx3I51G81hC4Ps7D2NfQzNaLT9SzCSMyUzD04UjYBq8+9cflhDYWteAQ94WjMxIxZTszJj+mdo+rseOFwvkEyuGjjlHG0PbYmc7YuiYs4oY/b3ekgOy4BxBL2OlECUH4vt79yGGGwqmwx0rra0SqRsWC6xd1PFK37BUlNZWuSJnFTGcyHmL/w9ixvblIn3DUmG0/5lO2P6E8PmtmOVh67ge0odlAe+sBh4vke+Wdf7fISIXKcgC0rrsC+Mxgfxed9IhF6nw1qDV8occa7LaUOnlhq/9sbWuAeUNJ9BotUEAaLTasK+hGWV17rjjyZotF7AsOQtxdwUwKBmYOEX9PluWBTx4P1B9UI4DSvPIzVNXrlF7HSKyUXEucM2Q7jVbxbnAGf7bkw4KMnKRYiahJWjB5TGTkZ+RC+CMc4lp7pC3BU1WW8ixVsuPSm8N5uSMciirTlxsOcyygNtmyeHTzU3AoBTg3XVyEaRywbWuDKjaLwddA0BTY+fA68GD1V2HiGxkGsCbk4G/1sktFfJd3I1IYRVn52FMZhqqGtrQZLXBYyajKHMoirPzsANVTqenrZEZqfCYyWgMWnClmEnti1jncbHlsHVlctHT1Cg/t7Z0LoJmz1F3nd0VwNnW0GOBgdfX3aDuOkRks+AtFUg7ppGEpwtH4HTdVaj01iCf3YhKTMnORFHmUJQ3nOhYxI7OTEZxdp7TqQFgN6LjXlkO/PYFIPivwTDkkOpvLFB3nfItwCOLO+9sAXIO4wOLgCJ2jSs1LFs+zmE3IpH9jqceAgBtuhHJPuxGZDdij+eWlQpxSboQWeh8XZIuj3c91+eTxx9bIt99vsiv5/MJMb1Qxh5syPe5M6KLEe1xN/05xzKG27u0XBFDx5xVxNAxZxUxbLyem7vkdOwkVBFDx5xVxLB1XA/1z8xiWageXLM1oUgeD9a1titQ4P6jhyK7jmkCS58EvF756HBsvrwGB14TERHZi4sth5mmLIZfVyYXQckDge/9sPsiaMe20NquQIH7jm3A1OmRX2v2HLW1YEQUR3QZBRQlS/hRVleNUm8NTmYcZI0UxRwXWy4QvAjatjn83abD1fKOVrDmJuDI4djkSERxLk5HAVnCj1k7X+4onH7VfAtFmUOxpnA+F1wUMyyQ10QkBe6WJe90Ha4GRuQBEybzMaHdOJaH4oYGo4D6MpZn88l6PLTn45B9rVLNJJSMvRzTcrgZLKnDAnmNYvR07uaNsqC9a4H75o3y5z5f+J/7fPbnrCKGjn9XQghx7ICv+0GXFA67OoaOOauI4eacox0F5IacxfkLmJcc2SiMoNE4WLtIGGsXiZIjGyOOEc31+npuPMXQMWcVMVggHwe61nYFCty3b5U/77pfV/CmpazRsg/valHcCIwC6npnS/NRQAUZud02u+zcsZ0oNvjAWiOB2q7/7yfyPfgR4e6K8DVdeypjmyMRaSowCijdBAzI98AoII0VZ+ehKHMo0s3k9q/VuWM7UazwzpaDAoOhd1cA4wq6b/cQjXEFcjuIwJ0tQH4em9//PIkoAcTpKCDTSMKawvnt3Ygb8bWMG1zVjRjolKzw1iAtox4Tsv2uyY3U4WLLIT0Nho5036yuuu7XFYjXnwUcESWYOB0FZBpJmJMzCjk5VShC5EOJLSGwuu4gKrw1KGgfqwOFa8+unZIpZhLKMl+Oaadk8GLPju9IErsRHWLH+JxAN+KRw8CVI9iNqBJH8BA5K9ZjeSwh8P2dh7GvoRmtlh8pZhLGZKbh6cIRykbAON0pGYvvmEjYjejCGI8tkV2DwWN6BhtC/Mvd7s3Zzhhuzzkhu9PsjKFjzipi6JizihgKrhfrzrLS2iqRumFxSBdj+oalYlntCmXXU90pGW0ey2pXiPQNSyP6jm7uAnRLjN66Eflg2CHjCuRonmBpHnlHioiInFXhrUFr0B0nAGiy2nDI29LDb0Qv0CkZLJadkoe8LWgK6tIE1H9HkrjYcsjMYmD01YAnHTAM+T6hSD76IyIiZxVk5CLFDP1HpMdMxsiMVGXX6NopmWomxbRTcmRGatjFXuA7WsKP1ScPouToX7D5ZD0s4Q8XhiLAAnmH9DQYOrBvFhEROac4Ow9jMtNQ1dCGJqsNnvYtI6ZkZyq7RnCnZKW3BqkZn+Dfsr8Zs+L4KdmZKMoc2lGgH/wd3VC8H09YIE/UBUfwEDmrL2N57GAJga11DTjkbcHIjFRMyc50VeG4ivx6iuF08b6OWCAfoxg+nxBPPSqL38tKO0fluDlnt8Rwe85uLhzWMoaOOauIoWPOKmJoWCDf1xg+vyVKa6vEkiMbxbLaFcLnt2y53hb/H8SM7ctF+oalwmgvbJ+xfbnY4v9Dv64XOB5J8X60Obvt70p1DI7riQHLAm6bBZR/AJxt7dznauUapzMjorhgCaCsBtjyBTB1SFxsOBpvYvnobWtdA8obTnSMIWq02lDecAJb6wxcm9P/+BxzpBYfvCoSmE3Y2gIIIXdy37YF+M4CuaeWZZ0/BhFRWJYAZm0B7twOPHZQvs/aIo+Ta5TVVXcsgASAFsuP8oYTKKtT/0jU7k5Cp4v34w3vbCkSbjbh2VbgjVeAVYOA3/w3cNnlwORpcrZhcnLYMERE3ZXVAOWngcb2f2trtIAtp4AFO4CvD9N+fmG8qPDWhF0AVXprMCcn8p3rIxHoJOx650lVt6TTxfvxhn9qigRmE4bTdhb4v4+A998DnigBRuYCbW3hzyUi6qaiHmjqcnu81Q+8cpx3uVwklvtmBToJuw7YVt0tOSdnFH4yfDqm5WRxodUP7EZUJDDrsGp/6AiennxjAfBPd9ufF0WHY3nIldZ8Dizc2X3BFeAxgV8XArMuim1eNtmfuRXegaedTiNqsR5/4/ZuyUTDbsQYxQh0I97zTSEuSgkdxdP1NXeGO3J2Swy35Kxjl5aWMXTMWUWMvl7P5xdixvtCpK8SAiu7v4yVQjywz/48YnGucEdnWV9jBLoRSyLoRgzuXCytrVLWSeh0DB1zVhGD3YgxYppyiPT3fgh8/pksmG9qDH/upGtjmxsRacw0gDVTZe3WimPA6s/kY8QAjwl8mXsfuUHg0ducnFEoxyqYPVTrdO1c9JjJGJ2ZjG2Ffj6ui0P8G7WBacotH37zO+DfFwEpXeoVBw+RRfJERBEzDWDOxcAvC4CpXwLSTbQX6wBFFwA3Xeh0hhSFrp2LjVYb9jU029K5SM7jnS2bmCYwe458Tb0eeH8j8NcP5B0tdiMSUZ8F3+WqrAfys2Q34hnuL6OTcJ2LrZbfls5Fch4L5ClhcSwPJQxLAOtrgT31wNgseRfMJRuiumU0T6xxHE78YYG8RjF0zFlFDCdyPnbA1/2gywuH4yaGjjmriOFEzp+3dRbXGyvl+3WbZNF9rPJIoAL5SM/1+a1u43YmbH+iW0F9cIy+jgJy0/eO5xgskLeJZcmd43dXyH22ZhY7nRFFg3e1KCGsr+2+IeqHX8jHkHMudja3BNZ109D8jFxckH2gx+L4WI4CIvW42OqjwCzEHeVy5/jALMQfPeR0ZkREQfaE2RC12ZL1Xk4ttjjnEUBo5yIAlKOqx3ODC+qB0FFArPFyPy6H+2jHts6tHQKzEHeUy+NERK4xNktuDREszZSF9U7gnMc+6W0UELkfF1t9dLi6+yzE5ibgyGFn8iEiCuumC+XWEMFbRVwzxLl5isFzHuWeB/JzGRcNvYnlKCBSj92IfVS+BXhkcehonpRU4IFFcmNTchd2HlJCC3Qj7q2Xm5862Y247JC8oxX0P0dhAMeXGPj0J4n3KDFSsR4FRKEiGY3EbkQbYmzeKEfuXJIuxGBDvs+dIY+7NWc3x3AiZ1d0i+nYUaciho45q4ihY87Bx31+IUo/FWLJASFeOR55R2PwsdJPu48dSl8lj7dzQ2eZihiqrxfNKCA783Db9eyOEWnnKLsRbRDYJX5dGbCnEhibL7sRt291OjMiIhsEaq3KT8uC+zQTeOEjucFqNHfJinPlY83y0xBNFgxP+w74QY81LSGwuu4gKrw1KMjIRXF2nnwEmuAiHQVEanVtTpC7/fuiak7gYqsfgneJj0Zgy4g/vQ2cOSMXaaZ5/t8jInJMcK0VIBdcgVqraLoag3bAP15ZjkvzJ4Z0I1rCj+/vPIyqhv0dMwOLModiSWEWF1zkCBW7/XOxFWNdt4xY+ZrcMmLlGi64iEiRwNYKFfXAlRnAvEv6X6NVEWYLiaY+biHRPufx0zkGLkXo75bVVWNfQ3PHzuqNVhvKG05ga52Ba3P68wWI+ibQnNAYtOBKMZOiak5ggXyMsbDeGSyQp4RhCeCObXLj0ub2x33XDAHenNy/Bdeaz4GFO0MXXB4T+HUhMOui8/56pGN5Xjhag/858llw/TwMAPdceTG+PZyddxR7kTYnsEDeRTEeWyIL6rPQ+RpsCPF4iXtzjkUMFsgnWAwdc1YRIxbXi6AAvU95+PyhY388q+TnrkXyUeQcriC5tLZKpG5YLLB2UccrfcNSsax2RcQxejrOQvHYxNAx5/PFCG5OKK2tElv8f+h2LgvkXWRcgdxtvqmx81iaRxbYExH1m8rHfcGCaq1QWQ8MV/R4sovi7DyMyUxDVUNbSM3WlOxMpdfpq3DF+xyXE/+i2e0/HC62YmxmsazR6jrmh3MViUiJgvYd4xu7PO5TsWN8e60V5lwMnPLZsleXaSTh6cIROF13VcfMwOLsPOwwSpVfK1o9Fe+vKZzP4n3qFRdbMRa8ZcSfVwFfuZndiESkUNDWCh1bNHTZWsHtTMMIuYvgFj0V75fVVSOHxfvUCy62HBDYMmLwYGDyNKezIaK4EqPHfYmowluD1vaFVkBgPuFMLraoF+xG1IxlyWHXh6uBEXnAhMm8KxYJdiOSFgJjdfbUywHSTo7VUeh46iEAiKgb0c02n6zHQ3s+7rizBQCpZhJKxl6OaTkODfYm12A3okYxejvX5+s+Imh6oTzuZM4qYrAbMcFi6JizihjRdPulrxLiuk2RdfvZmbOCGG7pLNvi/4Mora0SS9o7ygLjViKN4fNbYsL2J0LGtszYvlz4/JYWHXWxiqFjzipisBsxTqwrk4X1gU7Gpkagar88Hu0u9kTkMl13aG+05F5Z0e7QTmGpKG7vqXi/p25Edi5SABdbGtldITsYg51tlbMZudgi0ly4LRuaFWzZQADUFbdHWrzPzkUKxiW2RgJ7dAUblMI9uojiQmDLhmBpirZsoF6L2+0QWNw1Wm0QCF3cUeJhgXyMqChstyzgwfvlo8OzrXKhNfpqYOmTLJI/HxbIk+vZNWYnxiIdyxNrsS5u59ihxMMCeYdjbN7YvbB97gx5PNq4Pp8QZaVyvE9Zad9iRHKcBfL9OO7iQmXXxNAxZxUxzneuzy/H6pQckO+ft/Xven3NQ3EMNxQ7R1vc3t88eho7VFpb5eoibxUxdMxZRQwWyDtsx7buhe07yuXxqdOjixXYoytQo7Vts9pcichBwTu0A3KXdlIi2uL2/upp7FBxdh52RDnqhfTHxVYMHK7uXtje3AQcOexMPkREiSiWO9PHenFH7sbFVgyMyAs/fPrKEc7lREQOsYTczqGiXhbFazRGh6Lj1rFDFHtaLrYsS+4ttbtCdui5fYjzhMnhh09PmOx0ZkQUU5YAZm3pnFvoaZ9b+GqR05kRkY2060bUtSMv0I145LC8oxVJNyJH86gxLFvuXcRuRHLcms+BhTtD99PymMCvC4FZFzmXl0L7M7fCO/C002koZwmBrXUNOORtwciMVEzJzoRp6NMlSvaLq27Epx6V3XxZ6Hxdki6Pd6VzV17X0TwXpcrPkYzm0fl72xHDzV1aOnaWKYmhY84qYvx4nxzFg6CXsVKIB/a5N+coY7ihK0x1DJ/fEjO2Lw/byejWnJ2MoWPOKmL01o2oXaVeohSbB4/mEQJobZGf15U5nRkR9dnYMBuXekzgy9y41M3K6qpR3nCCG5RSn2m32AoUmweLx2LzcKN5mpvkaB4i0tRNF8oarXRT7nCZ3l6zddOFTmdGvajw1qDJags5Zufu8xR/tCuQd3uxuWUB76zuf/F+YDRP1w5GjuYh0phpAGumym7Eyno5iqc4Fzhjnf93KWqW8GP1yUN44/N9AIDxFzViQo4/6u0XCjJy4TGT0Ri04PKYycjPYCcpRUa7Anmgb8XmsaCyeF/XRgCncSwPUey4dTQPIAva/3XnYVR80YjAgJ4kAAWDPXhmQl5Uxe2WEPj+zsPY19CMVsuPFDMJYzLT8HThCBbJU4e4KpB3S9F1uONlpbKQPZLi/UiuFzya56lHuxfH93T+9+6S75EU00eSRyxisEA+wWLomLOKGDrmHGUMNxQqCyHEstoVImV9Sci4HKxdJFLWl4jS2qqoY/v8liitrRILjzwvSmurQorjVeUcLzF0zFlFDI7riZHdFfIuVLD+FO8Hj+bZtrn3O1qWBdw2q/Px6srX5OPVlWt4J4yIEs8hbwta/d3HHbX6faj01kS90ahpJGFOzijk5FShCNyklKKjXYG8m40rkI/7gsWqeL9r92Jg/iK7F4koEY3MSEVKUvf7CSlJA1hrRTHHxZZCM4tlXZUnHTAM+R6r4n12LxIRdZqSnYlrsy6Fic4SmiQA12YOQ3F2nnOJUULiY0SFTFMWsHu9cpEzNl8uwLZvtf/a7F7s2fGTBoZ9yeksiCiWTMPA2sL5WH3yEN5s70Ycd1EjfpAzn8OgKea07Eak7ti92Dt2KRKpdTz1EAA40o3I0TnkRuxG7MfxzRtlZ99jS0I7/NzYlRfoRvyXu9mNGMlxV3R6ubyzjN9bcQwncv68TYjST4VYckC++/xK8nCqK6zr6JzUDYu7jc6JRR6qz42nGDrmrCIGuxH7KHC3qPpg6AaqK9c4nVl4ge7FwYOBydOczoaIHGcJ4I5tQMUXcvi1p33H+leLnM6sz4JH5wBAi+XvGJ0TbYchUazwwXUv1pXJx3Ls8CMiLZXVAB9+ATRaaB/qB5SfBtbXOp1Zn3F0DumIi61e9LRvFjv8iEgLFfVAc5dRQE0WsLfemXwUCIzOCcbROeR2LJDvRfkW4JHFQGtL57GUVOCBRUDRVOfyouixQJ4S0prPgYU75QIrwGMCvy4EZl0UcRg3jeXh6BxyKxbI9/G4zyfE9EI5cmewId/nzpDH46lQ3A0xWCCfYDF0zFlFjFhfz+cX4rpNQqSvEsJYKd9nvC+L5vuZh5OFyoHROSVHNopltSu6FccHnx84d0kE56r+LjoWeauIoWPOKmKwQL6Peto3i1spEJEWTAN4czLw1zqgsh7IzwKKc4Ez1vl/18UCo3Pm5IxCOVbB7KEixhJ+zNr5MsobTqDJakOKmYSyzJexppB7bVFscbF1HsHzCYmItGMawJyL5SvBhOtc3FJ/DAv2/gFfz/0yirPzuOiimOB/y4iIKC6F61xs9fvwSs0e3LnnLcza+TIs4XcoO0okXGwREVFcCte5GNBotXXsz0VkN3YjxpBlATu2AYergRF5ckA1679ig92IRH23P3MrvANPO51G1II7F1us7newDAD3XHkxvj2c20ZQ/7Eb0QUxfD7ZyRjc2Ti9sH8jdezOOZYx2I2YYDF0zDnaGDaNyXEihs7daYFuxG/uflMkr39YYO2ijlf6hqWitLaKXXmKY+iYs4oY7EZ0gXVlcvf5pkb5ualR7k6/rozF90RxJw7H5Ogq0LlYnJ2Hqp0foaqhDU1WGzxmMooyh6I4Ow87UOV0mhTnlCy2DMOYDeBpACaA/xVCPNrl54MAvASgEMApAPOEEB+ruLYudlfI3eeDnW2VW0pwsUUUZwJjcgKbiQaPyfnGUGdzS1CmkYSnC0fgdN1VqPTWID8jl92IFDP9XmwZhmEC+CWAmQCOA9huGMYfhRD7g067C8AXQogRhmF8HcBjAOb199o6GVcgB1kH7mwBwKAUuXcXEcWZXsfkcLHlFNMwOvbnIoqlfhfIG4YxBcDDQohZ7Z8fAAAhxCNB56xpP2erYRgDANQAyBG9XDzeCuQtC3jwfvno8GyrXGiNvlpumsoiefuxQJ5iStGYHCe4aTQPkU5sLZAHcAfko8PA5/kAnu1yzl4Aw4I+HwGQ3VvceCuQF0IWw5eVCvF4iXzfvNHe6+kUgwXyCRZDx5yjiWHjmBwWyOsTQ8ecVcTQMWcVMewukA+3iut6CyGSc2AYxkIACwFg9KjC/mfmMl13o9+22dl8iMgmcTomh2LPEgKr6w6iwluDAtaZaUvFYus4gEuDPg8D8GkP5xxvf4yYBaDbpi1CiF8D+DUgHyMqyI2IyBkJPCaH1LCEH9/feRhVDftDOijXFM4PfwuDXEvF8ng7gDzDMK4wDCMZwNcB/LHLOX8EsKD9P98B4F0hXLqbKhERkQuU1VVjX0MzGq02CHDXe531e7ElhPAB+B6ANQAOAHhdCLHPMIwlhmHMbT/tNwC+ZBjGYQA/APDj/l6XZNF9+Rbg8RLgndXyM0Xu+En+qyERuVeFtwatXXa+b7LaUOmtcSgj6iuO69EUuxv7jx2KpA1LyD269tQDY7OAmy6UjykVO556CADYjegSm0/W46E9H4eMGko1k1Ay9nJMy8lyMDMKh+N6NIoR6bllpXLkaM8tpgAAIABJREFUTxY6X5eky+Px/L3tjsGuvBjF0DFnFTH6cj2fX3YyBjobPe2djT6/8jzYnaY2RvCxwNigJUc2imW1K4TPb503hs9viQnbnxDpG5YKo3280Izty4XPb2nzvRMpBsf1xKFwO9I3N8kd6a+7wZGUiMgOZTVy9/nG9jqBpvbd6MtqWHyvCUv4MWvnyyhvOIEmqw0pZhLKMl/GmsL5vXYWctf7+MG/MU0FdqQPlubhjvREcaeiPnRzVEB+rqx3Jh+KWlldNcobTnQUurdY/ogL3QO73v9k+HTMyRnFhZam+LemqZnFwIQiICUVMAzAky4/zyx2OjMiUqogS+4+H8xjyr27SAsV3ho0WW0hx1jonlhYIK8xywJ2bAOOHAauHAFMmMzi+GiwQJ60YAngjm1ysHWzBaSZwDVD5Kap/SiS51ie2GGhe2JggbxGMXTMWUUMt+SsRcF0PMRwU84+vxClnwqx5IB89/nd970DOZYcEOKV492L4xXlwYJptTECx3x+S8zYvryj0D11w+KOQne35awiho45q4jBAnkionAsAczaIgvOmyz5eK7oAuDVIqczCxW8G/0pny3bPpB9TCMJawrno6yuGpXeGqRmfIJ/y/4m668SCP+miShxra/t7PSTW3TLz+trnc6M4oxpJHUUuk/LyeJCK8HwzlYULAtYVya3XRiUDEycwhopIq3t6aHTb289gKGOpERE8YeLrQhZFnDbLGBHudzPalAK8O46YOUaLriItDW2vdOvMWjB5TGBL8dp0bIl5P5cW74Apg4BinP5SJIoBtiNGKHyLcAji4HWls5jKanAA4uAoqnO5UV9x25EsqvTz5W6fFd/mgHvZIGqNUb8fVciB7AbUUGMx5YIMdgIHY8z2BDi8RK1ebilK4/diP04rmtXXixjuCnn4E4/t3YjqohR+qkc+YOVna/0VfJ4O3anqY2hY84qYuiYs4oY7EZUILBje1Nj5zHu2E4UB4I7/SIReBRXUS83HJ2UbW9+qvS2Ez3H/hDZioutCAV2bA+u2Trfju0tLcB9/wx8sAm49nrgly8Cqamxy5mIFAu3VUTBEOC9ae5/FFfQQ30ad6Insh0XWxEyTVkMv65MDntOHgh874c9F8e3tADDMgHLJz+vfA1Y9RZwvIELLiJtBW8VAcj3D7/QYyh0ca7cQ6zrnmLFuU5nRhT3WCBvk0cWAX95t/vx6X8LPLA49vlQZIZly3+IsnCewlp2CHjsoNyTK8AA8ONRwP0jncoqcpaQC8a99agrbMSRWz497x05SwhsrWvAIW8LRmakYkp2JkzD5XfxiBzAAnkHYoy7IrSYPvAaP9y9OTsZw+05x2XBtJMxdMxZCDkqp2uRuSe0yDzmOQcK/H+8r7PAv495dC36DYyZSd2wWBhrF4n0DUs7xsy4uVDZLTF0zFlFDB1zVhGDBfIOKJwEfPJR9+PXTIx9LkSkyE0Xdn8UVzDEuUdxPY0bWjNVSQ1ZWV01yhtOdAxQbrTaUN5wAmV11cjJ6Xd4ooTBeQE2+eWLgNllKWsOkMeJSFOmIRcyv5sILLlKvju5J1dZTfhxQ2U1SsJXeGvQZLWFHGuy2lDpVROfKFFwsdUDywLeWQ28sly+W9b5fiNUaqoshr9tHpB7iXyPpDg+cN3HS/p2XSKyWWCriJ+Mlu9OdiH2tp2DAgUZufCYySHHPGYy8jNYVE8UDT5GDKPraJ6Vr8ltHqIdzZOaCrzwe2DbZmDytMiu++D9QPVBed00T+d1iYi6sXk7h+LsPBRlDsUHDZ+g1fLDYyajKHMoirPzsANVYX/HEgKr6w6iwluDgoxcFGfnySYCogTGbsQwnBrNw5FA7sAxPqSNfowbOp56CCfSqiO4ROTdiJYQ+P7Ow9jX0IxWy48UMwljMtPwdOEIdjBS3GM3YpTHIxnNY0cevV3XLV157EaM4LjbO+rcEEPHnFXEsLMb8QH13Yi9HQ93rLS2SqRuWCywdlHHK33DUrGsdoUt13N7DB1zVhFDx5xVxGA3YpScGs0zrkDuTB98Z4sjgYioV4Easik5wJec/b/0Cm8NWts7FwOarDYc8rYA7F6kBMYC+TACo3k86YBhyPfzjeZRdd3RV8f+ukTkEpYAVn8GlFTJd8vex9mW8GP1yYMoOfoXrD55EFY/y0oKMnKRYob+Y8VjJmNkBsdmUGLjna0wgkfz/HkV8JWb5YInkuL4QDfh7gp5pyqahZJpAkufBLxeORJobH7k1yUizcV47qIl/Ji182WUN5xAk9UGj5mM0ZnJ2Fboh2n07d/Di7PzMCYzDVUNbR0xizKHYkp2puLsifTCAnmFAt2EVfuBs63ykeDoq+UCigsmfbBAnhyx5nNg4c7QrRw8JvDrQmDWRcovt7xlI7576IOODUsBINVMQsnYyzEtp+/djBzvQ4mKBfIxilFWKsRFqaEF7pekC/HUo+7N2S0x3J6zVgXTOsTQMWcVMXo7d8kBIYygMUBYKT+XHAg99/M2WQi/5EBoQXyUeSw5slEYQYXsWLtIGGsXiZIjG4UQclRPaW2VWHJko1hWu0L4/FZICDcXKp8vRvB3K62tElv8f7AlD7d971jF0DFnFTFYIB8juyvkHa1gzU3AkcPO5ENEGgm3Z1Zalz2zAls9VHzRfTxPtJdr37C0MWiH+BQzCfkZud0eMaaYSSjLfBlrCuf3+RGjW9jx+JTofPjfLIUC3YTB0jzAlSO4MzwRnUdxrlw4pZtyE9D09j2zgucultXIPbUUjOcJbFiabia3Xy4ZYzLTUJyd1zETsdFqgwDQYvk7ZiLqrut3a7TasK+hOS6+G7kX72wpFOgm7LoDfMHE0B3p0zzAFVcC468BzpxhETwRoXPuYlmNHLeTnwVMyg4tjq+ol5uXBguM55kS3d4KppGENYXzUVZXjUpvDfIzcnFB9gGYRlKvMxHn5Izq6zd0hXDfrdXyx8V3I/fiYkuhnroJn10mF1qBfbuaGoG9u+Tr7Tf6NgqIiOJQYM+sORfLz6d8oT8vyJKPFrsW0fdxPI9pJGFOzqiORUZ5+wiecI8Y42UmYm+PT4nswm7EGHhlOfDbF4Ce/qg5ksdd2I1IrtWP8Txd9TauJ5qxO7p1H3KkENmF3YgOx3jqUdmVGNyl2NMoILfkzG7ELtiVpzaGG3L2+YV45Xj3rj4781DxvQPdiCX960YMJ7jDKtCxV9JLN6LPb4kZ25eL9A1LhdE+mmfC9if61bnY03E7uhFLIuxGPF9nZixy1imGjjmriMFuRIdNmCwfFQZqtrre4eJIHqIYC2wguu20vEMU3NVnwwaiSnV91GjbZTofMZZjFcww/VTBxeZAoNjch7K66pjVP1lCYHXdQVR4a1CQkYvi7LzzdhX29Pg0fPzQ7sWBSQZ+NegX+PnIv8OcnFHsYKSIcLEVIcuSO8oHdobPyIj8d4N3pN/1IVC6Ejh6GGhp7iyi50geohgqq+ncqR0I7eqzeRETT5wuNreEH9/feRhVDftDdqxfUzhfdnQq0HVB2eYXONryBb6+5y1Mzbo0LrbDIPtxsRUBy+reTThiJPDvPwX27u4cy9NbgbtpArPnyNcPH+zbKCAiUqSiPrTIHOjs6uNiK2JOF5uX1VVjX0Nzxy74jVZbxxYVOYoGX4dbUAJAq9/XcS12MdL5sEA+AuVbgEcWA60tnceMJGDgAODcOY7lSQTDsuU/mFk4HydiPBrHjXorkI+U08XmLxytwf8c+QzB/6s0ANxz5cX49nA1C77NJ+vx0J6PQ8Ya2XUt0hsL5PsZ47Elsoi9pwL3wFiestL4LxRP1AL5qArndSsUdyKG0zn7/ELMeF8Izyo5Eid9lfyssNjcld/7POfaVWxuV6FyaW2VSN2wOGTkUPqGpaK0tkpZwXSgCSBlfUnIdYKvFU3O0R7XMYaOOauIwQL5fhpXIB8dBvbJCqe5Se6tde11cof4QG0Xa7GIXCiwgehrnwJHvXKfquJc9xfHu1A0xeaqFWfnYUxmGqoa2kJqtoqz87BDUR6BzV9XnzyEHxx8B8fb6tHmF0gPulZvLOFHWV11SAG/qnoy0gcXWxGYWRzaTZg8CDjXBviD7iqneYAxY4EH7+++g/yPHnIudyLqgWnIR4ZfGup0JtRHppGEpwtH4HTdVR274EfSjdiX69x84WjMyRmJp+p+ixbvZRFdyxKi2xzGosyhWFKYxQVXguFiKwLB3YR7KuWi6rElwOFDoYsqAKja31nb1dQoF2g7tgFTpzuXP5FjLCHro4545e7nvHtEipmGEXJnzd5rJWFaThaKciL7P/StdQ3dtsYobziBrXUGrlVUwE964GIrQsHdhACQNbj7WJ4n/xM42xr6e81NwJHDsc+XyHG97WVFrjS0OQ9IczqL+HHI2xJ2xuQhbwvAxVZCYTeiQuG6FjmKJz6wG7EP2PGnJRVdiiSF62RMNZNQMvZyTMvp2zxLci92I8Yohs8nxPRC2Zk42JDvc2cIsXmje3N2Swy355yo3Wn9irHkgOz0Q9DLWCnHzMQqZ59fjrRZckCO5gkeyRPt9fqTh1PXUxSD3Wl9O3eL/w/dxhnN2L78vOOBnMxZRQwdc1YRg92IMWKacq+tro8Xt291OjMiBxRkyTtZjV3ubOXH6N/oA48xAzvFp5nACx/pMZKH4oJpGFhTOB9lddUhBfw7jFKnU6MY42JLsa61XUQJqzhX1mh1rdkqzgXOWOf//f4KjOQJLPaaOJKHYq/r1hiUmLjYIiJ7OL2XFUfyxETXfaQuyBbc1oCoCxbIE3UxLNtiIXw8iKZA3xLA+lpgTz0wNgu46cKEfdQYTYG80+N6iNyEBfIaxdAxZxUx3J4zC6YVx4jF9QIjedLbR/J4uozkCZzb9bz0VUJct6l7Mb2K76Lj35XouXC4tLZKpG9YGjLCJnXD4o4RNr39frTHdYyhY84qYuiYs4oYLJAnosQTeIxZViMfHQ7PAOZd0v2OVdfarkYL+PAL1nZFoMJb020fqVbLj0pvDWuUiIJwsUVE8cs05IJpzsXAKV/4R4PharuaWdsViYKMXHjM5I4d0gEgxUxCfkaug1kRuQ8XWwpYlhzls7sCGJQMTJwiuxKJyGaWkHegKuo7xwFFK9wWFWn/f3v3H1xXed95/PPogLF1JflHZCJjyKSAMRDMWNiLINBMZm2v0Y6btFm6m7DLZCdpme5MdulMMo1ZkvXGrieB0t1mdnemSxoPbArZdogTR6SqwAYWSmzFgF2MbdkGmta/hCNs68c1ROjo2T/OFZKur35c6Tn3nOee92tGY3R88z0fXTA8Off7fZ4KblHhsdbGZWppWDru7L/rG+ZMeTgzkDUstmYpDKXPrR89pPqyudJzz0ZnKbLgAmJUvI/WyNYST7aUV2dki4qxdZoXzmzhVqUmOsYnMDUX7SO1qPGw84OgAd8xjThLHNFTfZhG9ITL44BGphHf6JVuyvY0Yikc4QNMjWnEGGs8tDk6mme+Rr8WGGsf3pLezGmskfbMXk/lpbGGi/ttPFj6OKAHDlY2R5rfZ1c13hkcPfao7ZS1Q8OpngpLSw0fM7uo4WNmFzWYRozRzc1SbU7KD4xeq81FR/UAiNGKCY4DumlMr9XYnq5rJphGxORCK929R9p3bvzHtR1WolUCmJbMLrbGNrXf3BydYTgT61ql1S3je7ZWt8y8HoBpWnv5xb1WLYui6xJnI7rS3h1thZEfszVG51ktaJfEsWTAtGRysVXc1F6bixZIf/TN8msFQdQM/2x7dPj0nEulr3yN5nggdsX7aI0cBzRy7iJnI7qxrzfaCmOsfKja/YbFFjBNmWyQp6kdk6FBvko8clR66Ig09m+lkfSvlkrX1mX+WJ5p63hHw/ftVU1+9I0M66Q3f2h0fgPvHTCCBvkiX/ly6ab2//h7s7tfuTl8bBTP6s9Nw7TjGpW4X9up6OidsQ30wXZr5/5kZsfyzCTH0PD4xvJ3Bqdfdyb3i6PG0HD0Po09zmjNS3bP0I9LlkhDo3JaaviY2UUNHzO7qEGDfJFrl5Vuar/m2uQyAXCseP+sOTXS0LD0/nD0+3Efy1NqH7DmhdLzd/r1NC0w0lO3Sb/oGf9xbfDTpJMB3sjkznOrb4t6tHJ1kjHRr6tbousApiG00tOnpS1d0a9hCj92Henp+uE/kzbfIP3WEqmo9ejDY3nisPPMaM+Y1fjFnW9Gjj36xvXRrz4tFoEUyOSTreKm9hUro+nBvbuTTgZ4YKKtAMrdub0Sxp6N+ORJ6W+7K3csz4EZnLlYfPzQrY3xZANQUZlcbEnRguuuDdEXgDJMsBWAdp6R7lmabLbJlNoqIs5jeUrtAzbZ4q5aPnacQGiH1d5zTPv6u9Vc3xSdn+j/jwVMSyanEYHJMI04hYmm/DYul756XVKppqeSx/KMPAF87Vz0RKs2kG5ZGPU/lbqny+OHHJvtcT2htbr/1Td1sO+C3g+HNTeo0ScaavXdVdcqMKy4UB2YRvSoho+ZXdRIe2avp/Jc12g7ZW2uaMqvboe1T5xIb+akaoxMI26ZxjTi5sOljx/acriymad5vZwprUfOPG7rdm21embTh191u7baR848Pu0aPk6n+ZjZRQ0fM7uowTQiAHdam6InNMU9WyM7t2PU2J4xSXp3aOLXNpf5saNHjva/p3w4OO5aPhzU0f73pMUJhQIqiMUWgPJMtBXA+eJRP5SleKuKuHvKKui6+nnKBXM0MGbBlQvm6Lr6eQmmAiqHxRaA8hU/scHslTp+6NbGqmiOv72xQS0NS9XZd1L5cFC5YI5aGpbq9saGpKMBFUGDPFCEBvkqMdIMf6CXo3lmabYN8lLUJL+7p09H+9/TdfXzdHtjA83xqCo0yHtUw8fMLmqkPTMN8o5rVKIxfc1L44+YKXU0T7X93BWskeZG5bTU8DGzixo+ZnZRY7IG+UzuIA+gyrV3J797uw+77AOoCHq2AFSffTPYvd0ln3bZn43iHe9bm6Qg6VBA+rDYAlB9kt5Gwddd9stRasf7lkVSh2XBBRThY0QA1WdkG4W6INrdvq6we3ultlHY1xs9SRsrH0Y711eJa35yxcUf1Xae1YL2pJMB6cM0IlCEacQqUcmjeYql+OgdZx45KvvQEZkxf1SskU5sNjr1DaYMkT1MI3pUw8fMLmqkPTPTaY5r+Ji5nBpDw9H049hpyDUvTX5cT9KZy73+xIno5yo6tqmr7ccXvdTHyTIXNXzM7KKGj5ld1OC4HgCopCzssr/28ot3vG9ZpPOtPUknA1KHxRYAxKHad9kvteN9a5MU/DTpZEDqsNgCAMxMtS8oAUdokAeK0CAPTI+LY3yAakGDvEc1fMzsokbaM9Mo7riGj5ld1PAx8wTXj+cPlnxpGhqV01LDx8wuaviY2UUNjusBADjFEy1g+lhsAQAAxCjTDfJhKD3bLr2+T7q5WaqvTzoRAG8VnxN4a2PSiQCkRGYXW2EofW699EqndCEv1eakZculXZ1SwLleAMpR6pzA5oXS83dWbtd6AKk1q2lEY8wiSX8l6eOSfinpX1trz5V4XSjpQOHbf7LWfmaq2nFPI3a+LH37W9L7741emztPemCT1HJHbLeFB5hGRNmq/Hgepg6BqcU2jSjpYUkbC3+9UdJDE7xuoNzacU8jPrTZ2gXG2vka/VpgrH14y8zvN5Mcs3ltNdVIe2am0xzX8DHzZDU2H46O5Rl7dI3Zbu2Ww+nNPMsaaZ4KS0sNHzO7qOFjZhc14pxG/Kykxwt//bik355lvSmFofS3T0sPb4l+DWd4+sXNzdFHh2NdNldasXL2GQF4JLTS06elLV3Rr+EMnmo2z4+eZI1VG0S7qiMWoR3W0786om1vd+vpXx1RaIeTjgRMaLY9Wx+11p6WJGvtaWPM5RO8bq4x5hVJQ5K+Y639yUxuFobSg1+Vjh0Z7bNa3SJt7yi/1rrW6H9b3LO1rnUmyQB4qVSvVcsi6cmW8uq0Nl18TmDzwug6nAvtsNa/+gN19p1UPhzUk8GP1NKwVB2r7lVgGLJH+ky52DLG7JRU6t8YD5Zxn49Za08ZY66W9Jwx5oC19q0S97pP0n2SdP3yVRcVebZd6jo02meVH4gWS8+2SwsWlJFGURP89o7of3tgf/REq76e5nggU3aeiRZIA4VH5ANh9P3OM9I9S6dfp9Q5gbc20hwfk/aeY+rsO6mBcFCSNBAOqrPvpNp7jmnD4uUJpwMuNtsG+SOSPl14qrVE0gvW2kn/STfGPCbpaWvtU5O9rlSD/BOPSX+5TRob2Rjp3i9L93xxRj8CcBEa5DPkkaPSQ0eksX+7jaSNy6WvXpdUqtRJW4P8tre79b23Tl/0t+33r1miL13N00QkI84G+T/R+Ab5h0u8ZqGkywp/3SjpmKQbp6pdqkG+vc3aj84b39R+RV103cem6zTcLy010pL5+OGh0i+mUdxtjbRkfuKEtXU7xje21+2IrseRIy0/d5k1Sh3Nk2SjctuZLlu3a6vVM5s+/KrbtdW2nemqaA6Xr62mGj5mdlEjzgb570haZ4w5Jmld4XsZY1YbY/6i8JobJL1ijPl7Sc8r6tk6NJObrWuVrr9RytVFT7RydVHfFX1WcIUnWhmz9vKo16ouiB6N1BV6ttZO1H6aTWl6qiVJrY3L1NKwVHXBnMLftjlqaViq1sZlSUcDSppVg7y19l1Ja0pcf0XS7xX++ueSVszmPiOCQNr6p1J//2if1bpW+qwAzFCpXqvWJun8DMecURGBqVHHqnvV3nNMbf0v6LfqP63WxmU0xyO1vNtBPgikuzZEXwAwa4GRNiyJvnxRfDSQp1OPobV6uueI9vV3q7m+qawFU2BqtGHxci1e3KUW0RSPdPNusQUAZQtttMv7W/2jixNfJwVdbVeRsNAO6/5X31RX3yHlw0HlCh8Fdqy6N/pIF6gis5pGjFPcx/UATB1mRGilu/dIr52TLoTRZqO3LJSeus3PBVfMRwNVavLw737Vq28e+KXeC0c3I50X1GjLio/rzsVsBgv/xDaNGOdX3Mf1pLWGj5ld1EhL5sxO5VXzz912qvTEYdspP3/ujQdLHw30wMUTg65yxDGdtvmtF6wZM02oZzZZ88wmu+WtF1IxWeaiho+ZXdTwMbOLGnFOIwJAuu3rHf8USIq+39+bTJ7ZWlHiaKBcIN3k19Og5vomzQ3G/ycoF8zRyno/+8+AybDYAlDdSp1bmPP43MIq2a6itXGZPtFQy/YNyAQa5CcQhtFRPj/7iXT+PFtMAN4aObdwz9moZ2ukodzXLR6qZLuKwNTou6uu1dmeG7S/v1sry5xGBHxCg3wJIwdedx2Sfv2+dNncaDPVrX/Kgqua0CCfIaGNzjt8ozf6uG3t5X42x1fATBvkB4eH9djb3Xr9fF43L8jp31/dpDk1LJyQHTTIl3m9vS06BqjUsUBx50hLozgN8rO4ntZG8TTVKOe1Q8PR8TmbD0dN7UPD5ddwkcNFjZRnLnUsj7VTNw7/OvzALnzu2+Oa3Rc+92376/CDCf/35V73sYaPmV3U8DGzixo0yJfp9X3Shfz4axfy0a71ACpoZE+p+16VNh2WvrA3+j7kiWQcZrrlwx+//aLODb0/7tq5off1x2+/6CIW4D0WWyXc3CzV5sZfq81FxwMBqKD27tHNO62kgTD6vr076WQY4+Xzx0te/3lv6etA1rDYKmFda3TANQdeAwmrtm0bqtQdC64qef2T80tfB7KGxVYJQSBt75C+/0Pp3i9Hv27voDkeqLhq27ahSn3j6k9p4SVzx11beMlcfePqTyWUCEgXphGRWUwjeqDajtpJiRPzjkoa7dEKrdXunj4d7X9P19XP0+2NDQpMee/vh9OIvXndPJ9pRGQP04ge1fAxs4saacnMdFqFasxkGnEL04iuaoydpBoaDu2avY/Zul1brXlmk63btdWu2fuYHRoOS75+smsuXltNNXzM7KKGj5ld1JhsGpFNTQGkW2CiA5Y/sjTpJFWpveeYOvtOaiAclCQNhIPq7Dup9p5j2rB4ecLpgOrAM14AyLB9/d3KFxZaI/LhoPb3M/EJuMJiCwAyrLm+SblgzrhrHAgNuEWDPDKLBnlkwVTH74TW6v5X39TBvgt6PxzW3KBGn2io1XdXXVt2kzyQZTTIe1TDx8wuaqQlc1Ybpvm5K1QjJZmLm3uHhkPbdqbL3vfWn9u2M13jmuNLvX6iay5eW001fMzsooaPmV3UoEEeADChwNRow+LlWry4Sy2iKR5wjZ4tAACAGLHYAgAAiBGLLQAAgBgxjYjMYhoRWXCoYbf6Lz2bdAyg6jGN6FENHzO7qJGWzFmdTuPnnuT60HB0TNDmw9GxQSPHBcV1P8c10jCl5eNkmYsaPmZ2UcPHzC5qMI0IoPJCK7V3Sy+fk+5YKLU2+Xd4dGil9S9LnWelfOEg7G3/IHXc4d/PAiAxLLYAuFe8SMkFUsui+BYpIwu7fb3SNfXSv7nCzX3au6OfYSCMvs+H0fft3dKGJbOvDyATWGwBcK94kTIQ4yIlzqdP+3qjmmPlQ2l/L4stANNGgzwyiwb5GD1yVHroiDT27TWSNi6Xvnqd23t1vCPd9+r4RVEukB5dJa3/aHprx2Cqo3kAxIcGeY9q+JjZRY20ZKZR3FGNtlPW1u2wVttHv+p2RNdd32/zYWvN9vH3Mtut3XK4vMylrg8NW7vmpSi72W5tbkf0/dgm+ZT8vTqeP1jyehoah31sdnZRw8fMLmr4mNlFDRrkAVRWa1PUo1Xcs9XaNP51Y3utmudLtzaWf6/m+VH9gaKnTyvnz+5nkKKPITvuiDLu75WudtgP5tjJ2mO6UjcmHQNACSy2ALg3dpHy83PSJ0tMI5Zqom9eKD1/Z3mLmeKFXe0EC7vZ/CwblkRf7w6lcqEFIN1YbAGIx8gi5fbF0kdK/KsLcD32AAARTElEQVSmVBP9a+fKb6L36OkTgGxisQUgGaUm/S7McNKPp08AUoxpRGTWjR8bUkMu6RQZNtGk35/fItUY6UCvtGK+tPZyFk9FmDoE0odpRI9q+JjZRY0kMqdisi+Nk4SVqNHzwcWTfnU7rP3N/2ftP39x/LU1L1n7zmDymV3UiPF+aZ7S8nGyzEUNHzO7qOFjZhc1mEYEkD7FvVYr50vnQ+k/vHbxZqg7z0j3LE02LwDMEIstAMkZ22slSQ8cKr1j+xu9klhsAfBTTdIBAOBDKwp7Zo2VC6SbHOyZBQAJoUEemcDRPJ4IrXT3nmgLiAuFPbNuWSg9dRtN8gUn5h2VJBrkgZShQd6jGj5mdlEjkw3yQ8PWPnEiOm6m7VQqj4BJpFF8aDh6P7aMeV/SkNlFDQf387Fx2MfMLmr4mNlFDR8zu6hBgzyQNiO7p+85Gz3BGTnOpuMOnuAU93EhcaG1errniPb1d6u5vkmtjcuig8UBTAuLLSAJI7un54um7srdPR2IWWiHdf+rb6qr75Dy4aBywRy1NCzV5lXzWXAB00SDPJCEUrun5wu7pwMp0t5zTAf7LmggHJSVNBAOqrPvpHb39CUdDfAGDfLIhNQ1yE+0e/qjq6T1H00uF1IlDTvFb3u7W99767SK//SsXlSnz3/sct3e2KDA8IgLoEHeoxo+ZnZRI3MN8iO7p+eKdkofaZJPc5O3ixo+ZnZRo8z7Hc8fvOhapZt+28502Xm7vmX1zKaLvup2bbVr9j5mh4ZDZ/erpho+ZnZRw8fMLmrQIA+kzcju6X91Snq7P9o9vbWJ5niMc7L2mK7UjYlmaG1cpk801Kqrb1D5wkeJI0Y+UmzvOaYNi5cnlhFIOxZbQFICE31k+BF2Rkd6BaZG3111rc723KD//k979NzZfxj3+/lwUPv7u1lsAZNgsQUAmFRgzIeLqV/0ntRAOPjh7+WCOVpZ35RUNMALTCMCAKaltXGZWhqWqi6YIyOprrANRGvjsqSjAanGNCIyIXXTiECRNEweTkdorXb39Olo/3u6rn4e04hAAdOIHtXwMbOLGpmbRkzqfmmp4WNmFzXKvF8aJqxc1PAxs4saPmZ2UcPHzC5qTDaNyMeIAAAAMWKxBQAAECOmEQFUh9BGZ0u+fE66YyH7lgFIDRrkkQk0yFe50Ep375FeOyddCKXaQLplofTUbd4suHxpkAdQGg3yHtXwMbOLGjTIZ6yG6/u1nYqOPNL20a+6HdH1SuaY4WtLHctjbTqafl3U8DGzixo+ZnZRw8fMLmrQIA+guu3rHX+otxR9v783mTxl4okWUN1YbAHwX/N8KReMv5YLojMnASBhLLYA+K+1SWpZJNUFKmxtHn3fyjEyAJLHNCIA/wVG6rgjmkb8+Tnpk0wjAkgPphGRCUwjIg1OzDsqiR4toBoxjehRDR8zu6jBNGLGaviY2UENHyesXNTwMbOLGj5mdlHDx8wuajCNCAAAkBAWWwAAADFisQUAABAjGuSRCTTIo9I4fgfIFhrkParhY2YXNeK83/HDQ6V/IwUN0z42eTup4WNmBzV8bPp1UcPHzC5q+JjZRQ0fM7uoQYM8Mo0nWgCAJLHYAgAAiBGLLQAAgBix2AIAAIgR04ioKkwdIi0ONexW/6Vnk44BoEKYRvSoho+ZXdSI835pnk7zcaLOSQ0fM5dZIw3TUWmp4WNmFzV8zOyiho+ZXdSYbBrxkgov/LwUhtKz7dLr+6Sbm6V1rUknAgAAvmCxNYUwlD63XnqlU7qQl2pz0uoW6Y++mXQyAADgAxrkp/DKnmihlR+QrI1+faUzug4AADAVGuSn8MRj0l9uixZaI4yR7v2ydM8Xk0qFidAgjyRwNA8AGuRncf3PvmPtFXXWztfo1xV10fU4cvjY3O6iBg3yGavhY+Yya6ShYTctNXzM7KKGj5ld1PAxs4saHNczC6tvi3q0cnXRE61cXfT96tuSTgYAAHxAg/wUgkDa3hFNIx7YL61YGU0j7t2ddDIAAOADFlvTEATSXRuiLwAAgHLwMSIAAECMmEZEVWEaEZV2Yt5RSWIaEcg4phE9quFjZhc1mEbMWA0fM09wPc3TUWmp4WNmFzV8zOyiho+ZXdRgGhEAACAhs1psGWN+1xhz0BgzbIxZPcnr7jLGHDHGvGmM2TibewIAAPhktk+23pD0OUkvTvQCY0wg6X9JapV0o6QvGGNunOV9gewJrdTxjrSlS3r6dPQ9ACD1nDTIG2NekPQ1a+1FHe3GmNsl/Vdr7frC9w9IkrX225PVpEEeM1G1DfKhle7eI712TroQSrWBdMtC6anbpKB0Pybc41geABOJvUFe0guSVk/we3dL+osx398r6X9OVZMG+WzVoEF+iuttp6yt22Gtto9+5XZY+82D1m48GP3+0HBlMruokZLm9nJrHM8fvOhamht201LDx8wuaviY2UUNHzO7qDFZg/yUm5oaY3ZKairxWw9aa3dMY7FXapVX8tGDMeY+SfdJ0vXLV02jNJAR+3qlfDj+Wj6UHjoqfWClXCC1LJI67uBJV4xO1h7TlaILAkB5plxsWWvXzvIeJyRdNeb7KyWdmuBej0p6VIo+RpzlfYHq0Tw/WlANFC24Bgt/TAZCqfOs1N4tbVhS+XwAgAlVYuuHvZKWGWN+wxgzR9LnJf20AvcFqkdrU/TkKhdEz4rnlHh6lQ+l/b0VjwYAmNxst374HWPMCUm3S/qZMaajcP0KY8zfSJK1dkjSVyR1SDos6a+ttQdnFxvImMBEHxE+ukrafIP09eukXNEf31wgrZyfTD4AwIQ4rgdVpWqnEYsxnRg7Jg8BlIPjejyq4WNmFzWYRpzB9aHhaArxAaYRK1WDKS0/7peWGj5mdlHDx8wuasxqGhFASgUmaoa/fbH0Ef4oA0BacTYiAABAjFhsAQAAxIgGeVSVzDTII3Y0yAMoBw3yHtXwMbOLGjTIZ6xGyjOXOpbHWhqHfblfWmr4mNlFDR8zu6gxWYM8HyMCQBGeaAFwicUWAABAjFhsAQAAxIjFFgAAQIyYRkRVYRoR5Tgx76gkerQAzB7TiB7V8DGzixpMI2asRkoyM6VVmRo+ZnZRw8fMLmr4mNlFDaYRAQAAEsJiCwAAIEYstgAAAGJEgzyqCg3ymAjH7wCIEw3yHtXwMbOLGjTIZ6xGSjLTOFyZGj5mdlHDx8wuaviY2UUNGuQBAAASwmILAAAgRiy2AAAAYsRiCwAAIEZMI6JqXNkYRr8yjYgSDjXsVv+lZ5OOAaBKMY3oUQ0fM7uo4eJ+aZlwS/X90lIjgcxpmFbyccLKRQ0fM7uo4WNmFzV8zOyiBtOIAAAACWGxBQAAECMWWwAAADGiQR5e4lgeTIajeQBUGg3yHtXwMbOLGjTIZ6xGjPc7nj9Y8noaGmh9bPp1UcPHzC5q+JjZRQ0fM7uoQYM8gMzgiRaAtLkk6QAA4JvQDqu955j29Xertr5XqxuHFRj+vyuA0lhsAUAZQjus9a/+QJ19J5UPBzU3qFF7ww/UsepeFlwASuLfDABQhvaeY+rsO6mBcFBW0nvhsDr7Tqq9h48vAZTGNCK8xDQipGSmDre93a3vvXVaY//pM5J+/5ol+tLVTRXNAiA9mEb0qIaPmV3UYBoxYzVivF/c00ptZ7ps3a6tVs9s+vCrbtdW23amK5b7VUMNHzO7qOFjZhc1fMzsogbTiADgSGvjMrU0LFVdMEdG0rygRi0NS9XauCzpaABSigZ5AChDYGrUsepetfcc0/7+bs2r/0f9YeO/ozkewIRYbAFAmQJTow2Ll2vD4uXq1A4FfEgAYBI0yMNLNMhD4lgeAOlBg7xHNXzM7KIGDfIZq+HgfuUcyzPRdRqHK1PDx8wuaviY2UUNHzO7qEGDPICqwxMtAL5gsQUAABAjFlsAAAAxYrEFAAAQI6YR4SWmEWcptNLOM9KBXmnFfGnt5VJQeogmDQ417Fb/pWeTjgEAE2Ia0aMaPmZ2UYNpxArWeGfQ2jUvWVu3w1qzPfr1N1+0dmi4cjnKzJyGSSMXNXzM7KKGj5ld1PAxs4saPmZ2UYNpRACjdp6ROs9KA6FkFf362jmpvTvpZABQlVhsAVlzoFfKh+OvXQil/b3J5AGAKsdiC8iaFfOlXDD+Wm0grZyfTB4AqHI0yMNLNMjPQmilu/dEHx1eCKOF1i0LpaduS0WTPEfwAPARDfIe1fAxs4saNMhXuMbQsLVtp6zdcjj69Z3ByuagQT5TNXzM7KKGj5ld1PAxs4sakzXIX1LhhR+ANAiMtGFJ9CVJ7w4lmwcAqhg9WwAAADFisQUAABAjFlsAAAAxYhoRXrrxY0NqyCWdAq6dmHdUkphGBOAdphE9quFjZhc1yr1fqib7fLpfWmqUMXU40XUfp5V8zOyiho+ZXdTwMbOLGj5mdlGD43oAAAASwmILAAAgRiy2AAAAYkSDPFKPo3mqE8fyAKgmNMh7VMPHzC5q0CCfrRrH8wdLvjTNza8uaviY2UUNHzO7qOFjZhc1fMzsogYN8gBShSdaALKExRYAAECMWGwBAADEiMUWAABAjFI7jbhgQaNd0vTxpGMgBS69RJpzSTr/OcXUBmt+rQ9q3k86BgDE6vQvz+h8T1/JacTULrYqxRjzirV2ddI5fMJ7Vj7es/Lxns0M71v5eM/Kx3tWHj5GBAAAiBGLLQAAgBix2JIeTTqAh3jPysd7Vj7es5nhfSsf71n5eM/KkPmeLQAAgDjxZAsAACBGmV9sGWO2GGNeN8bsN8Y8Y4y5IulMPjDG/Ikxpqvw3v3YGLMg6UxpZ4z5XWPMQWPMsDGGKZ5JGGPuMsYcMca8aYzZmHQeHxhjthljzhhj3kg6iy+MMVcZY543xhwu/Nm8P+lMaWeMmWuM+YUx5u8L79m3ks7kg8x/jGiMabDW9hX++j9JutFa+wcJx0o9Y8y/kPSctXbIGPOQJFlrv55wrFQzxtwgaVjS/5b0NWvtKwlHSiVjTCDpqKR1kk5I2ivpC9baQ4kGSzljzKckDUj6P9bam5LO4wNjzBJJS6y1rxlj6iW9Kum3+WdtYsYYIylnrR0wxlwq6e8k3W+t3ZNwtFTL/JOtkYVWQU5Stlef02StfcZaO1T4do+kK5PM4wNr7WFr7ZGkc3jgVklvWmvfttYOSvq/kj6bcKbUs9a+KOls0jl8Yq09ba19rfDX/ZIOS1qabKp0s5GBwreXFr747+YUMr/YkiRjzFZjzHFJ/1bSf0k6j4e+JKk96RCoGkslHR/z/QnxH0DEzBjzcUnNkjqTTZJ+xpjAGLNf0hlJz1prec+mkInFljFmpzHmjRJfn5Uka+2D1tqrJD0h6SvJpk2Pqd63wmselDSk6L3LvOm8Z5hSqeMu+H/OiI0xpk7SjyT9YdGnHSjBWhtaa1cq+kTjVmMMH1tP4ZKkA1SCtXbtNF/6pKSfSdoUYxxvTPW+GWO+KGmDpDU2681/BWX8s4aJnZB01Zjvr5R0KqEsqHKFvqMfSXrCWrs96Tw+sdaeN8a8IOkuSQxmTCITT7YmY4xZNubbz0jqSiqLT4wxd0n6uqTPWGsvJJ0HVWWvpGXGmN8wxsyR9HlJP004E6pQodn7+5IOW2v/W9J5fGCMWTwyfW6MmSdprfjv5pSYRjTmR5KWK5oS+0dJf2CtPZlsqvQzxrwp6TJJ7xYu7WGKc3LGmN+R9D8kLZZ0XtJ+a+36ZFOlkzHmX0r6M0mBpG3W2q0JR0o9Y8wPJX1aUqOkdyRtstZ+P9FQKWeMuVPSS5IOKPpvgCT9Z2vt3ySXKt2MMTdLelzRn80aSX9trd2cbKr0y/xiCwAAIE6Z/xgRAAAgTiy2AAAAYsRiCwAAIEYstgAAAGLEYgsAACBGLLYAAABixGILAAAgRiy2AAAAYvT/AcZVmZNL/zipAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(model_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we train a Deep Neural Network to see how much is gained from the last model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lambda_2 (Lambda)            (None, 4)                 0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 64)                320       \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 3)                 195       \n",
      "=================================================================\n",
      "Total params: 4,675\n",
      "Trainable params: 4,675\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 150 samples\n",
      "Epoch 1/10000\n",
      "150/150 [==============================] - 0s 2ms/sample - loss: 1.1329 - categorical_accuracy: 0.5267\n",
      "Epoch 2/10000\n",
      "150/150 [==============================] - 0s 55us/sample - loss: 0.3117 - categorical_accuracy: 0.8667\n",
      "Epoch 3/10000\n",
      "150/150 [==============================] - 0s 74us/sample - loss: 0.2781 - categorical_accuracy: 0.8800\n",
      "Epoch 4/10000\n",
      "150/150 [==============================] - 0s 91us/sample - loss: 0.2238 - categorical_accuracy: 0.8867\n",
      "Epoch 5/10000\n",
      "150/150 [==============================] - 0s 114us/sample - loss: 0.1945 - categorical_accuracy: 0.9133\n",
      "Epoch 6/10000\n",
      "150/150 [==============================] - 0s 78us/sample - loss: 0.1848 - categorical_accuracy: 0.9000\n",
      "Epoch 7/10000\n",
      "150/150 [==============================] - 0s 87us/sample - loss: 0.1459 - categorical_accuracy: 0.9400\n",
      "Epoch 8/10000\n",
      "150/150 [==============================] - 0s 81us/sample - loss: 0.1249 - categorical_accuracy: 0.9333\n",
      "Epoch 9/10000\n",
      "150/150 [==============================] - 0s 70us/sample - loss: 0.0982 - categorical_accuracy: 0.9667\n",
      "Epoch 10/10000\n",
      "150/150 [==============================] - 0s 98us/sample - loss: 0.0902 - categorical_accuracy: 0.9733\n",
      "Epoch 11/10000\n",
      "150/150 [==============================] - 0s 93us/sample - loss: 0.0783 - categorical_accuracy: 0.9600\n",
      "Epoch 12/10000\n",
      "150/150 [==============================] - 0s 75us/sample - loss: 0.0678 - categorical_accuracy: 0.9800\n",
      "Epoch 13/10000\n",
      "150/150 [==============================] - 0s 62us/sample - loss: 0.0527 - categorical_accuracy: 0.9800\n",
      "Epoch 14/10000\n",
      "150/150 [==============================] - 0s 90us/sample - loss: 0.0657 - categorical_accuracy: 0.9733\n",
      "Epoch 15/10000\n",
      "150/150 [==============================] - 0s 92us/sample - loss: 0.0648 - categorical_accuracy: 0.9800\n",
      "Epoch 16/10000\n",
      "150/150 [==============================] - 0s 76us/sample - loss: 0.0486 - categorical_accuracy: 0.9733\n",
      "Epoch 17/10000\n",
      "150/150 [==============================] - 0s 116us/sample - loss: 0.0425 - categorical_accuracy: 0.9867\n",
      "Epoch 18/10000\n",
      "150/150 [==============================] - 0s 779us/sample - loss: 0.0414 - categorical_accuracy: 0.9867\n",
      "Epoch 19/10000\n",
      "150/150 [==============================] - 0s 131us/sample - loss: 0.0538 - categorical_accuracy: 0.9733\n",
      "Epoch 20/10000\n",
      "150/150 [==============================] - 0s 65us/sample - loss: 0.0556 - categorical_accuracy: 0.9867\n",
      "Epoch 21/10000\n",
      "150/150 [==============================] - 0s 96us/sample - loss: 0.0396 - categorical_accuracy: 0.9800\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epoch 261/10000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "150/150 [==============================] - 0s 74us/sample - loss: 1.4149e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 262/10000\n",
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      "150/150 [==============================] - 0s 74us/sample - loss: 1.3455e-04 - categorical_accuracy: 1.0000\n",
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      "150/150 [==============================] - 0s 60us/sample - loss: 1.3325e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 309/10000\n",
      "150/150 [==============================] - 0s 92us/sample - loss: 1.3289e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 310/10000\n",
      "150/150 [==============================] - 0s 141us/sample - loss: 1.3265e-04 - categorical_accuracy: 1.0000\n",
      "Epoch 311/10000\n",
      "150/150 [==============================] - 0s 85us/sample - loss: 1.3254e-04 - categorical_accuracy: 1.0000\n"
     ]
    }
   ],
   "source": [
    "model_3 = Model([X.shape[1],64,64,Y.shape[1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model_training(model_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model(model_3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### All models perform very well in the training dataset but as the graphics show, the last two of them adapt too much to the transformed data points which could be an indication of overfitting. The first model seems to be the most robust of all, so it could be considered a candidate solution for the original problem presented. Additional exploration of hyperparameters would be necessary to make a final conclusion."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}