08_1110_WISDM(AE).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "59dff8c9",
   "metadata": {},
   "source": [
    "### Load required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "2ee9d769",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "import tensorflow as tf\n",
    "import keras\n",
    "from tensorflow.keras import regularizers\n",
    "from keras.models import Sequential, Model, load_model\n",
    "from keras.layers import Dense, Input\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d48f6cbb",
   "metadata": {},
   "source": [
    "### Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "86225f53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user-id</th>\n",
       "      <th>activity</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33</td>\n",
       "      <td>Jogging</td>\n",
       "      <td>49105962326000</td>\n",
       "      <td>-0.694638</td>\n",
       "      <td>12.680544</td>\n",
       "      <td>0.503953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33</td>\n",
       "      <td>Jogging</td>\n",
       "      <td>49106062271000</td>\n",
       "      <td>5.012288</td>\n",
       "      <td>11.264028</td>\n",
       "      <td>0.953424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33</td>\n",
       "      <td>Jogging</td>\n",
       "      <td>49106112167000</td>\n",
       "      <td>4.903325</td>\n",
       "      <td>10.882658</td>\n",
       "      <td>-0.081722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>Jogging</td>\n",
       "      <td>49106222305000</td>\n",
       "      <td>-0.612916</td>\n",
       "      <td>18.496431</td>\n",
       "      <td>3.023717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33</td>\n",
       "      <td>Jogging</td>\n",
       "      <td>49106332290000</td>\n",
       "      <td>-1.184970</td>\n",
       "      <td>12.108489</td>\n",
       "      <td>7.205164</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user-id activity       timestamp         X          Y         Z\n",
       "0       33  Jogging  49105962326000 -0.694638  12.680544  0.503953\n",
       "1       33  Jogging  49106062271000  5.012288  11.264028  0.953424\n",
       "2       33  Jogging  49106112167000  4.903325  10.882658 -0.081722\n",
       "3       33  Jogging  49106222305000 -0.612916  18.496431  3.023717\n",
       "4       33  Jogging  49106332290000 -1.184970  12.108489  7.205164"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names = ['user-id', 'activity', 'timestamp', 'X', 'Y', 'Z']\n",
    "\n",
    "df = pd.read_csv('./data/HAR/WISDM/WISDM.csv', index_col=0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e141b0a",
   "metadata": {},
   "source": [
    "### Inspect Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "49ff77ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Walking       0.386447\n",
       "Jogging       0.311578\n",
       "Upstairs      0.111882\n",
       "Downstairs    0.091447\n",
       "Sitting       0.054579\n",
       "Standing      0.044067\n",
       "Name: activity, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['activity'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2d7330d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['user-id', 'activity', 'timestamp', 'X', 'Y', 'Z'], dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e07a8e35",
   "metadata": {},
   "source": [
    "### Data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "28d315b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "      <th>activity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.86</td>\n",
       "      <td>2.45</td>\n",
       "      <td>7.780000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.22</td>\n",
       "      <td>19.57</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.95</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>-10.079058</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.89</td>\n",
       "      <td>12.95</td>\n",
       "      <td>10.991621</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-19.23</td>\n",
       "      <td>7.08</td>\n",
       "      <td>1.650000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8994</th>\n",
       "      <td>8.77</td>\n",
       "      <td>0.93</td>\n",
       "      <td>12.070000</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>8995</th>\n",
       "      <td>-5.94</td>\n",
       "      <td>-0.23</td>\n",
       "      <td>-8.160000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8996</th>\n",
       "      <td>4.33</td>\n",
       "      <td>3.11</td>\n",
       "      <td>7.967904</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8997</th>\n",
       "      <td>5.86</td>\n",
       "      <td>2.45</td>\n",
       "      <td>7.820000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8998</th>\n",
       "      <td>-4.67</td>\n",
       "      <td>3.15</td>\n",
       "      <td>8.160000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8999 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          X      Y          Z  activity\n",
       "0      5.86   2.45   7.780000         0\n",
       "1     -2.22  19.57   0.150000         1\n",
       "2     -1.95  -0.08 -10.079058         1\n",
       "3      7.89  12.95  10.991621         1\n",
       "4    -19.23   7.08   1.650000         1\n",
       "...     ...    ...        ...       ...\n",
       "8994   8.77   0.93  12.070000         1\n",
       "8995  -5.94  -0.23  -8.160000         0\n",
       "8996   4.33   3.11   7.967904         0\n",
       "8997   5.86   2.45   7.820000         0\n",
       "8998  -4.67   3.15   8.160000         0\n",
       "\n",
       "[8999 rows x 4 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas import DataFrame\n",
    "\n",
    "sitting = df[df.activity=='Sitting'].sample(4499)\n",
    "jogging = df[df.activity=='Jogging'].sample(4500)\n",
    "\n",
    "sitting.iloc[:,1] = 0;\n",
    "jogging.iloc[:,1] = 1;\n",
    "\n",
    "sitting = sitting[['X','Y','Z', 'activity']]\n",
    "jogging = jogging[['X','Y','Z', 'activity']]\n",
    "\n",
    "resample = pd.concat([sitting, jogging])\n",
    "resample = resample.sample(frac=1).reset_index(drop=True)\n",
    "resample"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae9d3da9",
   "metadata": {},
   "source": [
    "### Train/Test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ac746e1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (3582, 3)\n",
      "X_test shape: (1800, 3)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test = train_test_split(resample, test_size=0.2, random_state=42)\n",
    "X_train = X_train[X_train.activity == 0]\n",
    "X_train = X_train.drop(['activity'], axis=1)\n",
    "\n",
    "y_test = X_test['activity']\n",
    "X_test = X_test.drop(['activity'], axis=1)\n",
    "\n",
    "X_train = X_train.values\n",
    "X_test = X_test.values\n",
    "\n",
    "print(\"X_train shape: {}\".format(X_train.shape))\n",
    "print(\"X_test shape: {}\".format(X_test.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58ff44b9",
   "metadata": {},
   "source": [
    "### Visualize the data with TSNE and PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "affd3535",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = resample.drop(['activity'],axis=1).values\n",
    "y = resample['activity'].values\n",
    "\n",
    "SEED = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6046491a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tsne = TSNE(n_components=2, random_state=SEED)\n",
    "xtsne = tsne.fit_transform(x)\n",
    "plt.figure(figsize=(12,8));\n",
    "plt.scatter(xtsne[np.where(y==0), 0], y=xtsne[np.where(y==0), 1], color=\"blue\", label='Normal', s=10)\n",
    "plt.scatter(x=xtsne[np.where(y==1), 0], y=xtsne[np.where(y==1), 1], color=\"red\", label='Abnormal', s=10)\n",
    "plt.title('t-SNE Plot')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7881e499",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pca = PCA(n_components=2, random_state=SEED)\n",
    "xpca = pca.fit_transform(x)\n",
    "plt.figure(figsize=(12,8));\n",
    "plt.scatter(x=xpca[np.where(y==0), 0], y=xpca[np.where(y==0), 1], color=\"blue\", label='Normal', s=10)\n",
    "plt.scatter(x=xpca[np.where(y==1), 0], y=xpca[np.where(y==1), 1], color=\"red\", label='Abnormal', s=10)\n",
    "plt.title('PCA Plot')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "397abfd6",
   "metadata": {},
   "source": [
    "### Normalize Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "3ef8c343",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "scaler.fit_transform(X_train);\n",
    "scaler.transform(X_test);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdb24024",
   "metadata": {},
   "source": [
    "### AutoEncoder Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f60a92b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, 3)]               0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 2)                 8         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 3)                 9         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 17 (68.00 Byte)\n",
      "Trainable params: 17 (68.00 Byte)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "input_dim = X_train.shape[1]\n",
    "\n",
    "input_layer = Input(shape=(input_dim, ))\n",
    "\n",
    "encoder = Dense(2, activation=\"relu\")(input_layer)\n",
    "\n",
    "decoder = Dense(input_dim)(encoder)\n",
    "autoencoder = Model(inputs=input_layer, outputs=decoder)\n",
    "\n",
    "autoencoder.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "77501961",
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 200\n",
    "batch_size = 32\n",
    "autoencoder.compile(optimizer='adam', loss='mse', metrics=['accuracy'])\n",
    "checkpointer = ModelCheckpoint(filepath=\"./pth/WISDM_AE.h5\", verbose=0, save_best_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8183f8b5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "101/101 [==============================] - 2s 5ms/step - loss: 34.6326 - accuracy: 0.7235 - val_loss: 28.7439 - val_accuracy: 0.7493\n",
      "Epoch 2/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 26.3877 - accuracy: 0.7394 - val_loss: 22.4772 - val_accuracy: 0.7465\n",
      "Epoch 3/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 20.7681 - accuracy: 0.7065 - val_loss: 18.2355 - val_accuracy: 0.6964\n",
      "Epoch 4/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 17.1316 - accuracy: 0.6922 - val_loss: 15.6142 - val_accuracy: 0.6936\n",
      "Epoch 5/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 14.8615 - accuracy: 0.6922 - val_loss: 13.9611 - val_accuracy: 0.6936\n",
      "Epoch 6/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 13.4107 - accuracy: 0.6922 - val_loss: 12.8649 - val_accuracy: 0.6936\n",
      "Epoch 7/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 12.4296 - accuracy: 0.6922 - val_loss: 12.0540 - val_accuracy: 0.6936\n",
      "Epoch 8/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 11.6972 - accuracy: 0.6922 - val_loss: 11.4002 - val_accuracy: 0.6936\n",
      "Epoch 9/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 11.1116 - accuracy: 0.6922 - val_loss: 10.8481 - val_accuracy: 0.6936\n",
      "Epoch 10/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 10.6499 - accuracy: 0.6922 - val_loss: 10.4231 - val_accuracy: 0.6936\n",
      "Epoch 11/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 10.3126 - accuracy: 0.6922 - val_loss: 10.1276 - val_accuracy: 0.6936\n",
      "Epoch 12/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 10.0856 - accuracy: 0.6922 - val_loss: 9.9342 - val_accuracy: 0.6936\n",
      "Epoch 13/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 9.9307 - accuracy: 0.6922 - val_loss: 9.7872 - val_accuracy: 0.6936\n",
      "Epoch 14/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 9.7915 - accuracy: 0.6922 - val_loss: 9.6150 - val_accuracy: 0.6936\n",
      "Epoch 15/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 9.5911 - accuracy: 0.6922 - val_loss: 9.3815 - val_accuracy: 0.6936\n",
      "Epoch 16/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 9.3783 - accuracy: 0.6925 - val_loss: 9.1485 - val_accuracy: 0.6936\n",
      "Epoch 17/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 9.1638 - accuracy: 0.6925 - val_loss: 8.8978 - val_accuracy: 0.6936\n",
      "Epoch 18/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 8.9297 - accuracy: 0.6925 - val_loss: 8.6268 - val_accuracy: 0.6936\n",
      "Epoch 19/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 8.6633 - accuracy: 0.6925 - val_loss: 8.3218 - val_accuracy: 0.6964\n",
      "Epoch 20/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 8.3593 - accuracy: 0.6959 - val_loss: 7.9821 - val_accuracy: 0.6964\n",
      "Epoch 21/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 8.0128 - accuracy: 0.7006 - val_loss: 7.6118 - val_accuracy: 0.7103\n",
      "Epoch 22/200\n",
      "101/101 [==============================] - 1s 7ms/step - loss: 7.6185 - accuracy: 0.7049 - val_loss: 7.2095 - val_accuracy: 0.7131\n",
      "Epoch 23/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 7.1764 - accuracy: 0.7142 - val_loss: 6.7813 - val_accuracy: 0.7465\n",
      "Epoch 24/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 6.7022 - accuracy: 0.7626 - val_loss: 6.3508 - val_accuracy: 0.7604\n",
      "Epoch 25/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 6.2285 - accuracy: 0.8231 - val_loss: 5.9630 - val_accuracy: 0.8301\n",
      "Epoch 26/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 5.8137 - accuracy: 0.8489 - val_loss: 5.6713 - val_accuracy: 0.8357\n",
      "Epoch 27/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 5.4908 - accuracy: 0.8622 - val_loss: 5.4565 - val_accuracy: 0.8468\n",
      "Epoch 28/200\n",
      "101/101 [==============================] - 0s 2ms/step - loss: 5.2383 - accuracy: 0.8740 - val_loss: 5.3050 - val_accuracy: 0.8496\n",
      "Epoch 29/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 5.0394 - accuracy: 0.8756 - val_loss: 5.1740 - val_accuracy: 0.8468\n",
      "Epoch 30/200\n",
      "101/101 [==============================] - 0s 2ms/step - loss: 4.8788 - accuracy: 0.8622 - val_loss: 5.0818 - val_accuracy: 0.8412\n",
      "Epoch 31/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 4.7592 - accuracy: 0.8579 - val_loss: 5.0273 - val_accuracy: 0.8412\n",
      "Epoch 32/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 4.6731 - accuracy: 0.8582 - val_loss: 4.9888 - val_accuracy: 0.8412\n",
      "Epoch 33/200\n",
      "101/101 [==============================] - 0s 2ms/step - loss: 4.6137 - accuracy: 0.8588 - val_loss: 4.9694 - val_accuracy: 0.8412\n",
      "Epoch 34/200\n",
      "101/101 [==============================] - 0s 4ms/step - loss: 4.5705 - accuracy: 0.8601 - val_loss: 4.9593 - val_accuracy: 0.8440\n",
      "Epoch 35/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 4.5391 - accuracy: 0.8613 - val_loss: 4.9411 - val_accuracy: 0.8468\n",
      "Epoch 36/200\n",
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      "Epoch 37/200\n",
      "101/101 [==============================] - 0s 2ms/step - loss: 4.4807 - accuracy: 0.8653 - val_loss: 4.9201 - val_accuracy: 0.8579\n",
      "Epoch 38/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 4.4615 - accuracy: 0.8653 - val_loss: 4.9104 - val_accuracy: 0.8579\n",
      "Epoch 39/200\n",
      "101/101 [==============================] - 0s 2ms/step - loss: 4.4462 - accuracy: 0.8660 - val_loss: 4.9118 - val_accuracy: 0.8579\n",
      "Epoch 40/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 4.4326 - accuracy: 0.8660 - val_loss: 4.9048 - val_accuracy: 0.8607\n",
      "Epoch 41/200\n",
      "101/101 [==============================] - 0s 2ms/step - loss: 4.4199 - accuracy: 0.8666 - val_loss: 4.9103 - val_accuracy: 0.8607\n",
      "Epoch 42/200\n",
      "101/101 [==============================] - 0s 2ms/step - loss: 4.4104 - accuracy: 0.8666 - val_loss: 4.8953 - val_accuracy: 0.8607\n",
      "Epoch 43/200\n",
      "101/101 [==============================] - 0s 2ms/step - loss: 4.4028 - accuracy: 0.8669 - val_loss: 4.8972 - val_accuracy: 0.8607\n",
      "Epoch 44/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 4.3938 - accuracy: 0.8669 - val_loss: 4.8879 - val_accuracy: 0.8607\n",
      "Epoch 45/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 4.3882 - accuracy: 0.8678 - val_loss: 4.8842 - val_accuracy: 0.8635\n",
      "Epoch 46/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 4.3792 - accuracy: 0.8678 - val_loss: 4.8815 - val_accuracy: 0.8635\n",
      "Epoch 47/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 4.3716 - accuracy: 0.8694 - val_loss: 4.8748 - val_accuracy: 0.8635\n",
      "Epoch 48/200\n",
      "101/101 [==============================] - 0s 2ms/step - loss: 4.3658 - accuracy: 0.8694 - val_loss: 4.8679 - val_accuracy: 0.8635\n",
      "Epoch 49/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 4.3566 - accuracy: 0.8697 - val_loss: 4.8683 - val_accuracy: 0.8635\n",
      "Epoch 50/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 4.3480 - accuracy: 0.8762 - val_loss: 4.8631 - val_accuracy: 0.8691\n",
      "Epoch 51/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 4.3343 - accuracy: 0.8843 - val_loss: 4.8367 - val_accuracy: 0.8774\n",
      "Epoch 52/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 4.3182 - accuracy: 0.8883 - val_loss: 4.8266 - val_accuracy: 0.8774\n",
      "Epoch 53/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 4.3045 - accuracy: 0.8899 - val_loss: 4.8095 - val_accuracy: 0.8774\n",
      "Epoch 54/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 4.2931 - accuracy: 0.8911 - val_loss: 4.7995 - val_accuracy: 0.8747\n",
      "Epoch 55/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 4.2829 - accuracy: 0.8914 - val_loss: 4.7941 - val_accuracy: 0.8774\n",
      "Epoch 56/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 4.2769 - accuracy: 0.8892 - val_loss: 4.7804 - val_accuracy: 0.8747\n",
      "Epoch 57/200\n",
      "101/101 [==============================] - 0s 2ms/step - loss: 4.2674 - accuracy: 0.8880 - val_loss: 4.7764 - val_accuracy: 0.8774\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 58/200\n",
      "101/101 [==============================] - 0s 4ms/step - loss: 4.2588 - accuracy: 0.8883 - val_loss: 4.7612 - val_accuracy: 0.8774\n",
      "Epoch 59/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 4.2495 - accuracy: 0.8892 - val_loss: 4.7529 - val_accuracy: 0.8747\n",
      "Epoch 60/200\n",
      "101/101 [==============================] - 0s 2ms/step - loss: 4.2436 - accuracy: 0.8905 - val_loss: 4.7455 - val_accuracy: 0.8747\n",
      "Epoch 61/200\n",
      "101/101 [==============================] - 0s 2ms/step - loss: 4.2353 - accuracy: 0.8892 - val_loss: 4.7436 - val_accuracy: 0.8774\n",
      "Epoch 62/200\n",
      "101/101 [==============================] - 0s 2ms/step - loss: 4.2298 - accuracy: 0.8889 - val_loss: 4.7325 - val_accuracy: 0.8774\n",
      "Epoch 63/200\n",
      "101/101 [==============================] - 0s 2ms/step - loss: 4.2218 - accuracy: 0.8895 - val_loss: 4.7221 - val_accuracy: 0.8774\n",
      "Epoch 64/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 4.2155 - accuracy: 0.8892 - val_loss: 4.7180 - val_accuracy: 0.8774\n",
      "Epoch 65/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 4.2089 - accuracy: 0.8883 - val_loss: 4.7081 - val_accuracy: 0.8774\n",
      "Epoch 66/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 4.2034 - accuracy: 0.8886 - val_loss: 4.7032 - val_accuracy: 0.8774\n",
      "Epoch 67/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 4.1948 - accuracy: 0.8880 - val_loss: 4.6924 - val_accuracy: 0.8774\n",
      "Epoch 68/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 4.1872 - accuracy: 0.8874 - val_loss: 4.6896 - val_accuracy: 0.8774\n",
      "Epoch 69/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 4.1764 - accuracy: 0.8886 - val_loss: 4.6781 - val_accuracy: 0.8802\n",
      "Epoch 70/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 4.1636 - accuracy: 0.8889 - val_loss: 4.6498 - val_accuracy: 0.8774\n",
      "Epoch 71/200\n",
      "101/101 [==============================] - 0s 4ms/step - loss: 4.1490 - accuracy: 0.8892 - val_loss: 4.6312 - val_accuracy: 0.8802\n",
      "Epoch 72/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 4.1329 - accuracy: 0.8892 - val_loss: 4.6130 - val_accuracy: 0.8774\n",
      "Epoch 73/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 4.1184 - accuracy: 0.8895 - val_loss: 4.6032 - val_accuracy: 0.8802\n",
      "Epoch 74/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 4.1076 - accuracy: 0.8902 - val_loss: 4.5816 - val_accuracy: 0.8774\n",
      "Epoch 75/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 4.0928 - accuracy: 0.8967 - val_loss: 4.5609 - val_accuracy: 0.8969\n",
      "Epoch 76/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 4.0662 - accuracy: 0.9029 - val_loss: 4.5352 - val_accuracy: 0.8942\n",
      "Epoch 77/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 4.0449 - accuracy: 0.9060 - val_loss: 4.5163 - val_accuracy: 0.8914\n",
      "Epoch 78/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 4.0308 - accuracy: 0.9057 - val_loss: 4.5062 - val_accuracy: 0.8942\n",
      "Epoch 79/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 4.0154 - accuracy: 0.9082 - val_loss: 4.4870 - val_accuracy: 0.8969\n",
      "Epoch 80/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 4.0008 - accuracy: 0.9078 - val_loss: 4.4732 - val_accuracy: 0.8914\n",
      "Epoch 81/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.9869 - accuracy: 0.9082 - val_loss: 4.4610 - val_accuracy: 0.8969\n",
      "Epoch 82/200\n",
      "101/101 [==============================] - 0s 4ms/step - loss: 3.9766 - accuracy: 0.9094 - val_loss: 4.4457 - val_accuracy: 0.8942\n",
      "Epoch 83/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.9610 - accuracy: 0.9075 - val_loss: 4.4318 - val_accuracy: 0.8969\n",
      "Epoch 84/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.9495 - accuracy: 0.9100 - val_loss: 4.4204 - val_accuracy: 0.8969\n",
      "Epoch 85/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.9358 - accuracy: 0.9088 - val_loss: 4.4127 - val_accuracy: 0.8969\n",
      "Epoch 86/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.9243 - accuracy: 0.9094 - val_loss: 4.4010 - val_accuracy: 0.8942\n",
      "Epoch 87/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.9111 - accuracy: 0.9082 - val_loss: 4.3793 - val_accuracy: 0.8969\n",
      "Epoch 88/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.8977 - accuracy: 0.9075 - val_loss: 4.3699 - val_accuracy: 0.8942\n",
      "Epoch 89/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.8844 - accuracy: 0.9082 - val_loss: 4.3592 - val_accuracy: 0.8942\n",
      "Epoch 90/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.8708 - accuracy: 0.9085 - val_loss: 4.3406 - val_accuracy: 0.8969\n",
      "Epoch 91/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.8594 - accuracy: 0.9085 - val_loss: 4.3282 - val_accuracy: 0.8969\n",
      "Epoch 92/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.8457 - accuracy: 0.9060 - val_loss: 4.3159 - val_accuracy: 0.8942\n",
      "Epoch 93/200\n",
      "101/101 [==============================] - 0s 4ms/step - loss: 3.8318 - accuracy: 0.9069 - val_loss: 4.2991 - val_accuracy: 0.8969\n",
      "Epoch 94/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.8166 - accuracy: 0.9082 - val_loss: 4.2829 - val_accuracy: 0.8914\n",
      "Epoch 95/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.8037 - accuracy: 0.9075 - val_loss: 4.2671 - val_accuracy: 0.8942\n",
      "Epoch 96/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.7918 - accuracy: 0.9063 - val_loss: 4.2550 - val_accuracy: 0.8942\n",
      "Epoch 97/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.7774 - accuracy: 0.9069 - val_loss: 4.2441 - val_accuracy: 0.8942\n",
      "Epoch 98/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.7627 - accuracy: 0.9075 - val_loss: 4.2295 - val_accuracy: 0.8914\n",
      "Epoch 99/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.7498 - accuracy: 0.9066 - val_loss: 4.2185 - val_accuracy: 0.8942\n",
      "Epoch 100/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.7377 - accuracy: 0.9072 - val_loss: 4.2049 - val_accuracy: 0.8969\n",
      "Epoch 101/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.7245 - accuracy: 0.9078 - val_loss: 4.1875 - val_accuracy: 0.8914\n",
      "Epoch 102/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.7119 - accuracy: 0.9054 - val_loss: 4.1759 - val_accuracy: 0.8969\n",
      "Epoch 103/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.6993 - accuracy: 0.9082 - val_loss: 4.1664 - val_accuracy: 0.8914\n",
      "Epoch 104/200\n",
      "101/101 [==============================] - 0s 4ms/step - loss: 3.6864 - accuracy: 0.9066 - val_loss: 4.1480 - val_accuracy: 0.8914\n",
      "Epoch 105/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.6751 - accuracy: 0.9075 - val_loss: 4.1390 - val_accuracy: 0.8942\n",
      "Epoch 106/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.6641 - accuracy: 0.9085 - val_loss: 4.1254 - val_accuracy: 0.8942\n",
      "Epoch 107/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.6518 - accuracy: 0.9078 - val_loss: 4.1160 - val_accuracy: 0.8969\n",
      "Epoch 108/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.6426 - accuracy: 0.9088 - val_loss: 4.1021 - val_accuracy: 0.8942\n",
      "Epoch 109/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.6315 - accuracy: 0.9082 - val_loss: 4.0923 - val_accuracy: 0.8942\n",
      "Epoch 110/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.6239 - accuracy: 0.9069 - val_loss: 4.0784 - val_accuracy: 0.8942\n",
      "Epoch 111/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.6124 - accuracy: 0.9091 - val_loss: 4.0709 - val_accuracy: 0.8914\n",
      "Epoch 112/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.6040 - accuracy: 0.9085 - val_loss: 4.0581 - val_accuracy: 0.8969\n",
      "Epoch 113/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.5962 - accuracy: 0.9085 - val_loss: 4.0502 - val_accuracy: 0.8942\n",
      "Epoch 114/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.5875 - accuracy: 0.9091 - val_loss: 4.0413 - val_accuracy: 0.8969\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 115/200\n",
      "101/101 [==============================] - 0s 4ms/step - loss: 3.5791 - accuracy: 0.9088 - val_loss: 4.0400 - val_accuracy: 0.8969\n",
      "Epoch 116/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.5728 - accuracy: 0.9094 - val_loss: 4.0303 - val_accuracy: 0.8969\n",
      "Epoch 117/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.5654 - accuracy: 0.9100 - val_loss: 4.0168 - val_accuracy: 0.8969\n",
      "Epoch 118/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.5598 - accuracy: 0.9094 - val_loss: 4.0144 - val_accuracy: 0.8969\n",
      "Epoch 119/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.5554 - accuracy: 0.9085 - val_loss: 4.0174 - val_accuracy: 0.8969\n",
      "Epoch 120/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.5505 - accuracy: 0.9085 - val_loss: 4.0033 - val_accuracy: 0.8969\n",
      "Epoch 121/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.5423 - accuracy: 0.9085 - val_loss: 4.0008 - val_accuracy: 0.8969\n",
      "Epoch 122/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.5366 - accuracy: 0.9100 - val_loss: 3.9853 - val_accuracy: 0.8969\n",
      "Epoch 123/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.5333 - accuracy: 0.9100 - val_loss: 3.9804 - val_accuracy: 0.8969\n",
      "Epoch 124/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.5277 - accuracy: 0.9091 - val_loss: 3.9764 - val_accuracy: 0.8969\n",
      "Epoch 125/200\n",
      "101/101 [==============================] - 1s 6ms/step - loss: 3.5234 - accuracy: 0.9094 - val_loss: 3.9701 - val_accuracy: 0.8969\n",
      "Epoch 126/200\n",
      "101/101 [==============================] - 0s 4ms/step - loss: 3.5195 - accuracy: 0.9097 - val_loss: 3.9733 - val_accuracy: 0.8969\n",
      "Epoch 127/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.5152 - accuracy: 0.9103 - val_loss: 3.9615 - val_accuracy: 0.8969\n",
      "Epoch 128/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.5114 - accuracy: 0.9103 - val_loss: 3.9620 - val_accuracy: 0.8997\n",
      "Epoch 129/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.5102 - accuracy: 0.9088 - val_loss: 3.9545 - val_accuracy: 0.8969\n",
      "Epoch 130/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.5074 - accuracy: 0.9088 - val_loss: 3.9485 - val_accuracy: 0.8997\n",
      "Epoch 131/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.5024 - accuracy: 0.9097 - val_loss: 3.9446 - val_accuracy: 0.8969\n",
      "Epoch 132/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.5011 - accuracy: 0.9094 - val_loss: 3.9416 - val_accuracy: 0.8997\n",
      "Epoch 133/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4969 - accuracy: 0.9100 - val_loss: 3.9421 - val_accuracy: 0.8997\n",
      "Epoch 134/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4935 - accuracy: 0.9088 - val_loss: 3.9445 - val_accuracy: 0.8997\n",
      "Epoch 135/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4921 - accuracy: 0.9100 - val_loss: 3.9367 - val_accuracy: 0.8997\n",
      "Epoch 136/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4893 - accuracy: 0.9097 - val_loss: 3.9294 - val_accuracy: 0.8997\n",
      "Epoch 137/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4846 - accuracy: 0.9091 - val_loss: 3.9423 - val_accuracy: 0.8997\n",
      "Epoch 138/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4886 - accuracy: 0.9106 - val_loss: 3.9230 - val_accuracy: 0.8997\n",
      "Epoch 139/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4831 - accuracy: 0.9097 - val_loss: 3.9181 - val_accuracy: 0.8997\n",
      "Epoch 140/200\n",
      "101/101 [==============================] - 0s 2ms/step - loss: 3.4818 - accuracy: 0.9106 - val_loss: 3.9205 - val_accuracy: 0.8969\n",
      "Epoch 141/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4793 - accuracy: 0.9113 - val_loss: 3.9130 - val_accuracy: 0.8997\n",
      "Epoch 142/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4798 - accuracy: 0.9100 - val_loss: 3.9117 - val_accuracy: 0.8969\n",
      "Epoch 143/200\n",
      "101/101 [==============================] - 0s 2ms/step - loss: 3.4770 - accuracy: 0.9097 - val_loss: 3.9151 - val_accuracy: 0.8969\n",
      "Epoch 144/200\n",
      "101/101 [==============================] - 0s 2ms/step - loss: 3.4770 - accuracy: 0.9106 - val_loss: 3.9118 - val_accuracy: 0.8997\n",
      "Epoch 145/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4747 - accuracy: 0.9103 - val_loss: 3.9067 - val_accuracy: 0.8997\n",
      "Epoch 146/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4735 - accuracy: 0.9106 - val_loss: 3.9069 - val_accuracy: 0.8997\n",
      "Epoch 147/200\n",
      "101/101 [==============================] - 0s 2ms/step - loss: 3.4749 - accuracy: 0.9103 - val_loss: 3.9086 - val_accuracy: 0.8997\n",
      "Epoch 148/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4735 - accuracy: 0.9097 - val_loss: 3.9006 - val_accuracy: 0.9025\n",
      "Epoch 149/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4714 - accuracy: 0.9110 - val_loss: 3.9033 - val_accuracy: 0.8997\n",
      "Epoch 150/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4701 - accuracy: 0.9100 - val_loss: 3.8969 - val_accuracy: 0.8997\n",
      "Epoch 151/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4691 - accuracy: 0.9106 - val_loss: 3.8957 - val_accuracy: 0.8997\n",
      "Epoch 152/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4686 - accuracy: 0.9100 - val_loss: 3.8920 - val_accuracy: 0.8969\n",
      "Epoch 153/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4694 - accuracy: 0.9100 - val_loss: 3.8954 - val_accuracy: 0.9025\n",
      "Epoch 154/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4659 - accuracy: 0.9106 - val_loss: 3.8916 - val_accuracy: 0.8997\n",
      "Epoch 155/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4679 - accuracy: 0.9103 - val_loss: 3.8896 - val_accuracy: 0.8997\n",
      "Epoch 156/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4649 - accuracy: 0.9122 - val_loss: 3.8976 - val_accuracy: 0.9025\n",
      "Epoch 157/200\n",
      "101/101 [==============================] - 0s 2ms/step - loss: 3.4669 - accuracy: 0.9110 - val_loss: 3.8922 - val_accuracy: 0.9025\n",
      "Epoch 158/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4636 - accuracy: 0.9122 - val_loss: 3.8938 - val_accuracy: 0.9025\n",
      "Epoch 159/200\n",
      "101/101 [==============================] - 0s 2ms/step - loss: 3.4638 - accuracy: 0.9110 - val_loss: 3.8943 - val_accuracy: 0.8997\n",
      "Epoch 160/200\n",
      "101/101 [==============================] - 0s 2ms/step - loss: 3.4640 - accuracy: 0.9116 - val_loss: 3.8954 - val_accuracy: 0.9025\n",
      "Epoch 161/200\n",
      "101/101 [==============================] - 0s 4ms/step - loss: 3.4625 - accuracy: 0.9122 - val_loss: 3.8833 - val_accuracy: 0.9025\n",
      "Epoch 162/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4636 - accuracy: 0.9113 - val_loss: 3.8817 - val_accuracy: 0.9025\n",
      "Epoch 163/200\n",
      "101/101 [==============================] - 0s 2ms/step - loss: 3.4629 - accuracy: 0.9119 - val_loss: 3.8819 - val_accuracy: 0.9025\n",
      "Epoch 164/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4620 - accuracy: 0.9116 - val_loss: 3.8781 - val_accuracy: 0.9025\n",
      "Epoch 165/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4609 - accuracy: 0.9113 - val_loss: 3.8797 - val_accuracy: 0.9025\n",
      "Epoch 166/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4596 - accuracy: 0.9119 - val_loss: 3.8847 - val_accuracy: 0.9025\n",
      "Epoch 167/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4596 - accuracy: 0.9119 - val_loss: 3.8762 - val_accuracy: 0.9025\n",
      "Epoch 168/200\n",
      "101/101 [==============================] - 0s 2ms/step - loss: 3.4620 - accuracy: 0.9116 - val_loss: 3.8788 - val_accuracy: 0.9025\n",
      "Epoch 169/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4596 - accuracy: 0.9122 - val_loss: 3.8811 - val_accuracy: 0.9025\n",
      "Epoch 170/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4589 - accuracy: 0.9119 - val_loss: 3.8739 - val_accuracy: 0.9025\n",
      "Epoch 171/200\n",
      "101/101 [==============================] - 0s 2ms/step - loss: 3.4611 - accuracy: 0.9119 - val_loss: 3.8787 - val_accuracy: 0.9025\n",
      "Epoch 172/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4577 - accuracy: 0.9116 - val_loss: 3.8728 - val_accuracy: 0.9025\n",
      "Epoch 173/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4583 - accuracy: 0.9125 - val_loss: 3.8748 - val_accuracy: 0.9025\n",
      "Epoch 174/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4579 - accuracy: 0.9125 - val_loss: 3.8746 - val_accuracy: 0.8997\n",
      "Epoch 175/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4576 - accuracy: 0.9134 - val_loss: 3.8717 - val_accuracy: 0.9025\n",
      "Epoch 176/200\n",
      "101/101 [==============================] - 0s 4ms/step - loss: 3.4582 - accuracy: 0.9122 - val_loss: 3.8703 - val_accuracy: 0.9025\n",
      "Epoch 177/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4575 - accuracy: 0.9125 - val_loss: 3.8674 - val_accuracy: 0.9025\n",
      "Epoch 178/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4560 - accuracy: 0.9125 - val_loss: 3.8782 - val_accuracy: 0.9025\n",
      "Epoch 179/200\n",
      "101/101 [==============================] - 0s 4ms/step - loss: 3.4550 - accuracy: 0.9128 - val_loss: 3.8662 - val_accuracy: 0.9025\n",
      "Epoch 180/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4582 - accuracy: 0.9131 - val_loss: 3.8729 - val_accuracy: 0.9025\n",
      "Epoch 181/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4571 - accuracy: 0.9125 - val_loss: 3.8662 - val_accuracy: 0.9025\n",
      "Epoch 182/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4571 - accuracy: 0.9141 - val_loss: 3.8735 - val_accuracy: 0.9025\n",
      "Epoch 183/200\n",
      "101/101 [==============================] - 0s 2ms/step - loss: 3.4566 - accuracy: 0.9131 - val_loss: 3.8722 - val_accuracy: 0.9025\n",
      "Epoch 184/200\n",
      "101/101 [==============================] - 0s 4ms/step - loss: 3.4539 - accuracy: 0.9125 - val_loss: 3.8641 - val_accuracy: 0.9025\n",
      "Epoch 185/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4565 - accuracy: 0.9137 - val_loss: 3.8663 - val_accuracy: 0.9025\n",
      "Epoch 186/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4552 - accuracy: 0.9131 - val_loss: 3.8657 - val_accuracy: 0.9025\n",
      "Epoch 187/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4544 - accuracy: 0.9128 - val_loss: 3.8635 - val_accuracy: 0.9025\n",
      "Epoch 188/200\n",
      "101/101 [==============================] - 0s 2ms/step - loss: 3.4544 - accuracy: 0.9137 - val_loss: 3.8670 - val_accuracy: 0.9025\n",
      "Epoch 189/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4537 - accuracy: 0.9137 - val_loss: 3.8669 - val_accuracy: 0.9025\n",
      "Epoch 190/200\n",
      "101/101 [==============================] - 0s 2ms/step - loss: 3.4546 - accuracy: 0.9131 - val_loss: 3.8650 - val_accuracy: 0.9025\n",
      "Epoch 191/200\n",
      "101/101 [==============================] - 0s 2ms/step - loss: 3.4541 - accuracy: 0.9141 - val_loss: 3.8665 - val_accuracy: 0.9025\n",
      "Epoch 192/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4549 - accuracy: 0.9134 - val_loss: 3.8824 - val_accuracy: 0.9025\n",
      "Epoch 193/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4540 - accuracy: 0.9128 - val_loss: 3.8668 - val_accuracy: 0.9025\n",
      "Epoch 194/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4530 - accuracy: 0.9134 - val_loss: 3.8588 - val_accuracy: 0.9025\n",
      "Epoch 195/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4536 - accuracy: 0.9134 - val_loss: 3.8686 - val_accuracy: 0.9025\n",
      "Epoch 196/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4558 - accuracy: 0.9131 - val_loss: 3.8626 - val_accuracy: 0.9025\n",
      "Epoch 197/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4527 - accuracy: 0.9141 - val_loss: 3.8598 - val_accuracy: 0.9053\n",
      "Epoch 198/200\n",
      "101/101 [==============================] - 0s 2ms/step - loss: 3.4524 - accuracy: 0.9141 - val_loss: 3.8652 - val_accuracy: 0.9053\n",
      "Epoch 199/200\n",
      "101/101 [==============================] - 0s 2ms/step - loss: 3.4541 - accuracy: 0.9131 - val_loss: 3.8629 - val_accuracy: 0.9053\n",
      "Epoch 200/200\n",
      "101/101 [==============================] - 0s 3ms/step - loss: 3.4513 - accuracy: 0.9137 - val_loss: 3.8558 - val_accuracy: 0.9025\n"
     ]
    }
   ],
   "source": [
    "history = autoencoder.fit(X_train, X_train,\n",
    "                    epochs=epochs,\n",
    "                    batch_size=batch_size,\n",
    "                    shuffle=True,\n",
    "                    validation_split=0.1,\n",
    "                    callbacks=[checkpointer]).history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "8037d42b",
   "metadata": {},
   "outputs": [],
   "source": [
    "autoencoder = load_model(\"./pth/WISDM_AE.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "92798417",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history['loss'])\n",
    "plt.plot(history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper right');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "fe3ed1d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "57/57 [==============================] - 0s 1ms/step\n"
     ]
    },
    {
     "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>reconstruction_error</th>\n",
       "      <th>true_class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1800.000000</td>\n",
       "      <td>1800.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>20.183444</td>\n",
       "      <td>0.490556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>34.242145</td>\n",
       "      <td>0.500050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000067</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.101026</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.645081</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>19.885962</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>204.771793</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       reconstruction_error   true_class\n",
       "count           1800.000000  1800.000000\n",
       "mean              20.183444     0.490556\n",
       "std               34.242145     0.500050\n",
       "min                0.000067     0.000000\n",
       "25%                1.101026     0.000000\n",
       "50%                4.645081     0.000000\n",
       "75%               19.885962     1.000000\n",
       "max              204.771793     1.000000"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = autoencoder.predict(X_test)\n",
    "mse = np.mean(np.power(X_test - predictions, 2), axis=1)\n",
    "error_df = pd.DataFrame({'reconstruction_error': mse,\n",
    "                        'true_class': y_test})\n",
    "error_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "4ea6d600",
   "metadata": {},
   "outputs": [],
   "source": [
    "threshold = 11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "54dff35c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "groups = error_df.groupby('true_class')\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "for name, group in groups:\n",
    "    ax.plot(group.index, group.reconstruction_error, marker='o', ms=3.5, linestyle='',\n",
    "            label= \"Normal\" if name == 0 else \"AbNormal\")\n",
    "ax.hlines(threshold, ax.get_xlim()[0], ax.get_xlim()[1], colors=\"r\", zorder=100, label='Threshold')\n",
    "ax.legend()\n",
    "plt.title(\"Reconstruction error for different classes\")\n",
    "plt.ylabel(\"Reconstruction error\")\n",
    "plt.xlabel(\"Data point index\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "2faf6491",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qVKBGjRrs2LGDsWPH0rNnT+DStEFMTAxxcXGEhYURFhZGXFwcnp6edOnSBQBvb2969erFwIED8fPzw9fXl0GDBhEREUFkZGSBY1FSICIizqsYbHM8ceJE3njjDfr160dqairBwcH06dOHN99809Zn8ODBZGRk0K9fP9LS0mjQoAErVqygdOnStj7jxo3D1dWVjh07kpGRQfPmzUlISMDFxaXAsWifApGblPYpEGdg930KNs+129glGz5ht7HtRZUCERFxXoWY+3cGSgpERMR5KSkw0d0HIiIiAqhSICIiTswwCv6wIGegSoGIiIgAqhSIiIgz05oCE1UKREREBFClQEREnFkx2LyoOFGlQERERABVCkRExJlpTYGJkgIREXFemj4w0fSBiIiIAKoUiIiIM9P0gYkqBSIiIgKoUiAiIs5MawpMVCkQERERQJUCERFxZlpTYKJKgYiIiACqFIiIiDNTpcBESYGIiDgvLTQ00fSBiIiIAKoUiIiIM9P0gYkqBSIiIgKoUiAiIs5MawpMVCkQERERQJUCERFxZlpTYKJKgYiIiACqFIiIiDPTmgITVQpEREQEUKVAREScmdYUmCgpEBER56WkwETTByIiIgKoUiAiIs7MMBwdQbGiSoGIiIgAqhSIiIgz05oCE1UKREREBFClQEREnJkqBSaqFIiIiAigSoGIiDgzbXNsoqRAREScl6YPTDR9ICIiIoAqBSIi4sy0eZGJKgUiIiICqFIgIiLOTGsKTFQpEBEREUBJgYiIOLO8PPsdBVSpUiUsFku+4/nnnwfAMAyGDRtGcHAwHh4eNG3alD179pjGyMzMpH///vj7++Pl5UW7du04duxYoX8cSgpEREQcaNu2bSQnJ9uOlStXAvD4448DMGbMGMaOHcukSZPYtm0bQUFBREVFcfbsWdsYMTExLFq0iHnz5rFx40bOnTtHmzZtyM3NLVQsFsNwzNLLM2fOFLhvmTJlCjV29slDhQ1H5KbjEXy/o0MQsbucrON2HT/jk5ftNnaJJ+PJzMw0tVmtVqxW619eFxMTw1dffcWBAwcACA4OJiYmhiFDhgCXqgKBgYGMHj2aPn36kJ6eTkBAALNnz6ZTp04AnDhxgpCQEJYtW0Z0dHTBYy7MByxKZcuWxcfH5y+Py31ERETswcgz7HbEx8fj7e1tOuLj4/8ynqysLObMmUPPnj2xWCwkJSWRkpJCixYtbH2sVitNmjRh06ZNACQmJpKdnW3qExwcTHh4uK1PQTns7oO1a9c66q1FRETsLjY2lpdfNlci/q5KsHjxYk6fPk2PHj0ASElJASAwMNDULzAwkCNHjtj6uLu75/slOjAw0HZ9QTksKWjSpImj3lpEROQSO96SWJCpgitNmzaNli1bEhwcbGq3WCym14Zh5Gu7UkH6XKlY7VNw4cIFjh49SlZWlqm9Zs2aDopIRETkxjhy5AirVq1i4cKFtragoCDgUjWgfPnytvbU1FRb9SAoKIisrCzS0tJM1YLU1FQaNWpUqBiKxd0Hv/32G23atKF06dLUqFGDOnXqmA4RERG7MPLsdxTSjBkzKFeuHK1bt7a1hYaGEhQUZLsjAS6tO1i/fr3tC79u3bq4ubmZ+iQnJ7N79+6bMymIiYkhLS2NLVu24OHhwfLly5k5cyZhYWEsWbLE0eGJiIjYVV5eHjNmzKB79+64uv6viG+xWIiJiSEuLo5Fixaxe/duevTogaenJ126dAHA29ubXr16MXDgQFavXs2OHTvo1q0bERERREZGFiqOYjF9sGbNGv79739Tv359SpQoQcWKFYmKiqJMmTLEx8ebsiYREZEik1c8Hoi0atUqjh49Ss+ePfOdGzx4MBkZGfTr14+0tDQaNGjAihUrKF26tK3PuHHjcHV1pWPHjmRkZNC8eXMSEhJwcXEpVBwO26fgz8qUKcMPP/xApUqVqFSpEp9++imNGzcmKSmJGjVqcOHChUKNp30KxBlonwJxBvbep+DCBy/YbWzP5yfZbWx7KRbTB1WrVmXfvn0A1K5dm48//pjjx4/z0UcfmRZWiIiIFKlisM1xcVIspg9iYmJITk4GYOjQoURHR/Ppp5/i7u5OQkKCY4MTEZFb10365W0vxSIp6Nq1q+3PderU4fDhw/z0009UqFABf39/B0YmIiLiPIpFUnAlT09P7r77bkeHISIitzrHL6srVopFUmAYBl988QVr164lNTWVvCvKOX/eyEFERETso1gkBS+++CJTpkzhwQcfJDAwsNDbMoqIiFwXrSkwKRZJwZw5c1i4cCGtWrVydCgiIiJOq1jckujt7U3lypUdHYb8SU5OLhOmzCT6sR7UfbA9Dz3+NJOnf2qa2glv3PKqx/RPv7D1+fzfy+jxwmAaRD1CeOOWnDl7zhEfR+S6DBn8AjlZx3nv3eGm9rvuqsKihTM49dte0k7t45sNSwkJCb7GKFKs5Rn2O25CxaJSMGzYMIYPH8706dPx8PBwdDgCTPt0AQsWL+Pt1wdSJbQie37az+tvj6NUKS+e7NgBgHVLPjVds2HLd7wZP56opo1tbRcvZnJfg3rc16Ae4z+acSM/gsg/Uq9uLZ7p1ZXvf/jR1F65ckXWr13MjIS5DB/xLunpZ6l2VxgXL2Y6KFKRolMskoLHH3+cuXPnUq5cOSpVqoSbm5vp/Pbt2x0UmfP6fvdPPHj/vTRpdA8At5UPZNnK9ez56YCtj7+fr+matRu2cM/dNQm57X8bTj3Z6WEAtm7/4QZELVI0vLw8mTVrEn2fG8yrsQNM594aMYT/Ll/Dv2LftrUlJR290SFKUbmOBxfdyorF9EGPHj1ITEykW7duPProo7Rv3950yI13d80afPvdTg4fPQbATwcOsf2HPTzQsP5V+5/8PY3/27SVR9pE38gwRexi4oQ4/rtsNavXbDC1WywWWrVszoEDh1j21aecOPY9mzYupV07/b2/aWn6wKRYVAr+85//8PXXX3PfffcV+trMzEwyM81luxKZmVit1qIKzyn16vY4Z8+dp22XZ3EpUYLcvDwGPNudVlFNr9p/yX9X4enpQWSTxlc9L3Kz6NixHXXqhHNvw/wPYitXzp/SpUsx+JXneXPoGGJfiyO6RVO+WPAJkVGP838btjggYpGiUyySgpCQEMqUKXNd18bHxzN8uHkR0OuvDODNwS8WRWhO67+r1/PVijWMHjaYKqEV+enAIUa//zHl/H1p3yoqX/9FX62gTYsHsVrdHRCtSNG4/fZgxr03gpatu+T7ZQOgRIlLxdUlS7/m/QlTAfj++z00bFiPZ599UknBTcjQLYkmxWL64L333mPw4MEcPny40NfGxsaSnp5uOoa82Lfog3Qy730wjWe6daRVZFPuvCOUdg8156lOD/PJ7AX5+ibu3E3S0WM80vYhB0QqUnTuvjuCwMAAtm75LxcvHOHihSM0adKI/i/05OKFI5w6lUZ2djZ79x4wXffTTweoEHKbg6IWKTrFolLQrVs3Lly4wB133IGnp2e+hYa///77Na+1Wq35pgqys07aJU5ncvFiJpYS5k2kSpQoQd5VtgRd+NXXVK8axl1huq1Ubm5r1mykVp1mprZPpo5l376DvPPuB2RlZfHdd99z5513mPqEhVXmyB/rb+Qmc5PO/dtLsUgKxo8f7+gQ5ApNGzdg6sx5lA8sR5XQiuzd/zOz5i/k4dYtTP3OnT/PirUbGPRC76uOc/LU75w8lcbRYycAOHDwMF6eHpQPKod3mdJ2/xwihXHu3Hn27Nlnartw/gKnTqXZ2t8dO5m5n05mw4YtrFu/iegWTWnTOormkY85ImSRIuXwpCA7O5t169bxxhtvaAOjYuTVl55j4tRZjHz3A35PO02Avy+Pt2/Fc093MfX776r1GAbXXIA4f/EyJk//334G3Z9/BYCRr75Mh9b51yaIFHf//vdy+j3/L4YM7s/4cSPYt/8Qj3fqzTebtjk6NLkeuiXRxGIYjn9EVNmyZdm+fXuRJQXZJw8VyTgixZlH8P2ODkHE7nKyjtt1/PMju9ltbK/X59htbHspFgsNH374YRYvXuzoMERExNlonwITh08fAFSpUoW33nqLTZs2UbduXby8vEznBwwYcI0rRURE/gHdkmhSLKYPQkNDr3nOYrFw6FDhpgM0fSDOQNMH4gzsPn0w7Am7je01bK7dxraXYlEpSEpKcnQIIiLijG7SMr+9FIs1BX9mGAbFoHghIiLidIpNUjBr1iwiIiLw8PDAw8ODmjVrMnv2bEeHJSIitzIjz37HTahYTB+MHTuWN954gxdeeIHGjRtjGAbffPMNffv25eTJk7z00kuODlFEROSWVyySgokTJzJ58mSeeuopW1v79u2pUaMGw4YNU1IgIiL2oTUFJsVi+iA5OZlGjRrla2/UqBHJyckOiEhERMT5FIukoEqVKixYkP/pe/PnzycsLMwBEYmIiDMw8vLsdtyMisX0wfDhw+nUqRP/93//R+PGjbFYLGzcuJHVq1dfNVkQEREpEpo+MCkWlYJHH32Ub7/9Fj8/PxYvXszChQvx9/dn69atPPzww44OT0RExCkUi0oBQN26dfn000//vqOIiEhRUaXAxKFJQYkSJbBYLH/Zx2KxkJOTc4MiEhERcV4OTQoWLVp0zXObNm1i4sSJ2t1QRETs5ybdZMheHJoUtG/fPl/bTz/9RGxsLEuXLqVr16689dZbDohMRETE+RSLhYYAJ06coHfv3tSsWZOcnBx27tzJzJkzqVChgqNDExGRW1WeYb/jJuTwpCA9PZ0hQ4ZQpUoV9uzZw+rVq1m6dCnh4eGODk1ERMSpOHT6YMyYMYwePZqgoCDmzp171ekEERERezFu0t/o7cViOHAlX4kSJfDw8CAyMhIXF5dr9lu4cGGhxs0+eeifhiZS7HkE3+/oEETsLifruF3HPzugjd3GLj3hK7uNbS8OrRQ89dRTf3tLooiIiNwYDk0KEhISHPn2IiLi7G7SZxTYi8MXGoqIiEjxUGy2ORYREbnhtNDQRJUCERERAVQpEBERZ6ZKgYkqBSIiIgIoKRARESdmGIbdjsI4fvw43bp1w8/PD09PT2rXrk1iYqIpzmHDhhEcHIyHhwdNmzZlz549pjEyMzPp378//v7+eHl50a5dO44dO1aoOJQUiIiIOFBaWhqNGzfGzc2N//73v/z444+89957lC1b1tZnzJgxjB07lkmTJrFt2zaCgoKIiori7Nmztj4xMTEsWrSIefPmsXHjRs6dO0ebNm3Izc0tcCwO3dHQXrSjoTgD7WgozsDeOxqe6d3CbmNbJy0lMzPT3Ga1YrVaTW3/+te/+Oabb9iwYcNVxzEMg+DgYGJiYhgyZAhwqSoQGBjI6NGj6dOnD+np6QQEBDB79mw6deoEXHrQYEhICMuWLSM6OrpAMatSICIizsuOT0mMj4/H29vbdMTHx+cLYcmSJdSrV4/HH3+ccuXKUadOHaZOnWo7n5SUREpKCi1a/C+BsVqtNGnShE2bNgGQmJhIdna2qU9wcDDh4eG2PgWhpEBERMQOYmNjSU9PNx2xsbH5+h06dIjJkycTFhbG119/Td++fRkwYACzZs0CICUlBYDAwEDTdYGBgbZzKSkpuLu74+Pjc80+BaFbEkVExGnZ8ymJV5squJq8vDzq1atHXFwcAHXq1GHPnj1MnjyZp556ytbvymcFGYbxt88PKkifP1OlQERExIHKly9P9erVTW3VqlXj6NGjAAQFBQHk+40/NTXVVj0ICgoiKyuLtLS0a/YpCCUFIiLivOy4pqCgGjduzL59+0xt+/fvp2LFigCEhoYSFBTEypUrbeezsrJYv349jRo1AqBu3bq4ubmZ+iQnJ7N7925bn4LQ9IGIiIgDvfTSSzRq1Ii4uDg6duzI1q1bmTJlClOmTAEuTRvExMQQFxdHWFgYYWFhxMXF4enpSZcuXQDw9vamV69eDBw4ED8/P3x9fRk0aBARERFERkYWOBYlBSIi4ryKwZOT69evz6JFi4iNjWXEiBGEhoYyfvx4unbtauszePBgMjIy6NevH2lpaTRo0IAVK1ZQunRpW59x48bh6upKx44dycjIoHnz5iQkJODi4lLgWLRPgchNSvsUiDOw9z4F6U82t9vY3rNX221se1GlQEREnJY97z64GSkpEBER56WkwER3H4iIiAigSoGIiDizYrDQsDhRpUBEREQAVQpERMSJaaGhmSoFIiIiAqhSICIizkxrCkxUKRARERFAlQIREXFiWlNgpqRAREScl6YPTDR9ICIiIoAqBSIi4sQMVQpMVCkQERERQJUCERFxZqoUmKhSICIiIoAqBSIi4sS0psBMlQIREREBVCkQERFnpkqBiZICERFxWpo+MNP0gYiIiACqFIiIiBNTpcBMlQIREREBVCkQEREnpkqBmSoFIiIiAqhSICIizsywODqCYkWVAhEREQFUKRARESemNQVmSgpERMRpGXmaPvgzTR+IiIgIoEqBiIg4MU0fmKlSICIiIoAqBSIi4sQM3ZJookqBiIiIAKoUiIiIE9OaAjNVCkRERARQpUBERJyY9ikwU1IgIiJOyzAcHUHxoukDERERAVQpEBERJ6bpA7NCVwq2b9/Orl27bK///e9/06FDB1599VWysrKKNDgRERG5cQqdFPTp04f9+/cDcOjQITp37oynpyeff/45gwcPLvIARURE7MXIs9jtuBkVOinYv38/tWvXBuDzzz/ngQce4LPPPiMhIYEvv/yyqOMTERGRG6TQawoMwyAv79JuD6tWraJNmzYAhISEcPLkyaKNTkRExI5094FZoSsF9erVY+TIkcyePZv169fTunVrAJKSkggMDCzyAEVEROTGKHRSMH78eLZv384LL7zAa6+9RpUqVQD44osvaNSoUZEHKCIiYi/FYU3BsGHDsFgspiMoKOh/MRoGw4YNIzg4GA8PD5o2bcqePXtMY2RmZtK/f3/8/f3x8vKiXbt2HDt2rNA/j0JPH9SsWdN098Fl77zzDi4uLoUOQERExFGKy1MSa9SowapVq2yv//x9OmbMGMaOHUtCQgJ33nknI0eOJCoqin379lG6dGkAYmJiWLp0KfPmzcPPz4+BAwfSpk0bEhMTC/XdXOik4JdffsFisXD77bcDsHXrVj777DOqV6/Os88+W9jhREREbkmZmZlkZmaa2qxWK1arNV9fV1dXU3XgMsMwGD9+PK+99hqPPPIIADNnziQwMJDPPvuMPn36kJ6ezrRp05g9ezaRkZEAzJkzh5CQEFatWkV0dHSBYy709EGXLl1Yu3YtACkpKURFRbF161ZeffVVRowYUdjhREREHMbIs98RHx+Pt7e36YiPj79qHAcOHCA4OJjQ0FA6d+7MoUOHgEvr9VJSUmjRooWtr9VqpUmTJmzatAmAxMREsrOzTX2Cg4MJDw+39SmoQicFu3fv5p577gFgwYIFtje9fFuiiIiIQGxsLOnp6aYjNjY2X78GDRowa9Ysvv76a6ZOnUpKSgqNGjXi1KlTpKSkAORbyB8YGGg7l5KSgru7Oz4+PtfsU1CFnj7Izs62lT5WrVpFu3btALjrrrtITk4u7HAiIiIOk2fHNQXXmiq4UsuWLW1/joiIoGHDhtxxxx3MnDmTe++9FwCLxRynYRj52q5UkD5XKnSloEaNGnz00Uds2LCBlStX8tBDDwFw4sQJ/Pz8CjuciIiI/ImXlxcREREcOHDAts7gyt/4U1NTbdWDoKAgsrKySEtLu2afgip0UjB69Gg+/vhjmjZtyhNPPEGtWrUAWLJkiW1aQURE5GZgGBa7HdcrMzOTvXv3Ur58eUJDQwkKCmLlypW281lZWaxfv962DUDdunVxc3Mz9UlOTmb37t2F3iqg0NMHTZs25eTJk5w5c8Y0f/Hss8/i6elZ2OFERESc2qBBg2jbti0VKlQgNTWVkSNHcubMGbp3747FYiEmJoa4uDjCwsIICwsjLi4OT09PunTpAoC3tze9evVi4MCB+Pn54evry6BBg4iIiLDdjVBQ1/XoZBcXl3wLGipVqnQ9Q4mIiDhMcXhw0bFjx3jiiSc4efIkAQEB3HvvvWzZsoWKFSsCMHjwYDIyMujXrx9paWk0aNCAFStW2PYoABg3bhyurq507NiRjIwMmjdvTkJCQqH3D7IYRuF3fv7iiy9YsGABR48ezfe45O3btxd2uCKXffKQo0MQsTuP4PsdHYKI3eVkHbfr+HvDWtlt7GoHltltbHsp9JqCCRMm8PTTT1OuXDl27NjBPffcg5+fH4cOHTKtoBQREZGbS6GTgg8//JApU6YwadIk3N3dGTx4MCtXrmTAgAGkp6fbI0YRERG7KA7PPihOCp0UHD161Laa0cPDg7NnzwLw5JNPMnfu3KKNTkRERG6YQicFQUFBnDp1CoCKFSuyZcsW4NJWjNexPEFERMRh8gyL3Y6bUaGTgmbNmrF06VIAevXqxUsvvURUVBSdOnXi4YcfLvIARURE5MYo9C2JU6ZMIS8vD4C+ffvi6+vLxo0badu2LX379i3yAEVEROyluDw6ubi4rlsSizvdkijOQLckijOw9y2Ju0Lb2m3siKSldhvbXgpUKfjhhx8KPGDNmjWvOxgREZEb6db7tfifKVBSULt2bSwWy98uJLRYLOTm5hZJYCIiInJjFSgpSEpKsnccIiIiN9zNepeAvRQoKbi8/7KIiMitRAsNzQp9S2J8fDzTp0/P1z59+nRGjx5dJEGJiIjIjVfopODjjz/mrrvuytdeo0YNPvrooyIJSkRE5EYwDPsdN6NCJwUpKSmUL18+X3tAQADJyclFEpSIiIjceIVOCkJCQvjmm2/ytX/zzTcEBwcXSVAiIiI3grY5Niv0jobPPPMMMTExZGdn06xZMwBWr17N4MGDGThwYJEHKCIiIjdGoZOCwYMH8/vvv9OvXz+ysrIAKFmyJEOGDCE2NrbIA7wec2q96egQROzu5+rVHR2CyE1Pdx+YFTopsFgsjB49mjfeeIO9e/fi4eFBWFgYVqvVHvGJiIjIDVLopOCyUqVKUb9+/aKMRURE5Ia6Wef+7eW6kwIREZGb3U1656DdFPruAxEREbk1qVIgIiJOS9MHZqoUiIiICHCdScHs2bNp3LgxwcHBHDlyBIDx48fz73//u0iDExERsSfDsNjtuBkVOimYPHkyL7/8Mq1ateL06dPk5uYCULZsWcaPH1/U8YmIiMgNUuikYOLEiUydOpXXXnsNFxcXW3u9evXYtWtXkQYnIiJiT3l2PG5GhU4KkpKSqFOnTr52q9XK+fPniyQoERERufEKnRSEhoayc+fOfO3//e9/qa5tV0VE5CZiYLHbcTMq9C2Jr7zyCs8//zwXL17EMAy2bt3K3LlziY+P55NPPrFHjCIiInaRp92LTAqdFDz99NPk5OQwePBgLly4QJcuXbjtttt4//336dy5sz1iFBERkRvgujYv6t27N7179+bkyZPk5eVRrly5oo5LRETE7vJu0jK/vfyjHQ39/f2LKg4RERFxsEInBaGhoVgs186sDh069I8CEhERuVFu1gWB9lLopCAmJsb0Ojs7mx07drB8+XJeeeWVoopLREREbrBCJwUvvvjiVds/+OADvvvuu38ckIiIyI1ys24yZC9F9kCkli1b8uWXXxbVcCIiInKDFdmjk7/44gt8fX2LajgRERG705oCs0InBXXq1DEtNDQMg5SUFH777Tc+/PDDIg1ORETEnjR9YFbopKBDhw6m1yVKlCAgIICmTZty1113FVVcIiIicoMVKinIycmhUqVKREdHExQUZK+YREREbghVCswKtdDQ1dWV5557jszMTHvFIyIiIg5S6LsPGjRowI4dO+wRi4iIyA2lpySaFXpNQb9+/Rg4cCDHjh2jbt26eHl5mc7XrFmzyIITERGRG6fASUHPnj0ZP348nTp1AmDAgAG2cxaLBcMwsFgs5ObmFn2UIiIidpB3c/5CbzcFTgpmzpzJqFGjSEpKsmc8IiIi4iAFXlNgGAYAFStW/MtDRETkZpGHxW7H9YqPj8disZieNWQYBsOGDSM4OBgPDw+aNm3Knj17TNdlZmbSv39//P398fLyol27dhw7dqxQ712ohYZ/9XREERGRm41hx+N6bNu2jSlTpuRbnzdmzBjGjh3LpEmT2LZtG0FBQURFRXH27Flbn5iYGBYtWsS8efPYuHEj586do02bNoWa1i/UQsM777zzbxOD33//vTBDioiI3JIyMzPz3cJvtVqxWq1X7X/u3Dm6du3K1KlTGTlypK3dMAzGjx/Pa6+9xiOPPAJcmtIPDAzks88+o0+fPqSnpzNt2jRmz55NZGQkAHPmzCEkJIRVq1YRHR1doJgLlRQMHz4cb2/vwlwiIiJSbNlz86L4+HiGDx9uahs6dCjDhg27av/nn3+e1q1bExkZaUoKkpKSSElJoUWLFrY2q9VKkyZN2LRpE3369CExMZHs7GxTn+DgYMLDw9m0aZN9koLOnTtTrly5wlwiIiLilGJjY3n55ZdNbdeqEsybN4/t27ezbdu2fOdSUlIACAwMNLUHBgZy5MgRWx93d3d8fHzy9bl8fUEUOCnQegIREbnV5Nnxu+2vpgr+7JdffuHFF19kxYoVlCxZ8pr9rvwevrwVwF8pSJ8/K/TdByIiIlJ0EhMTSU1NpW7duri6uuLq6sr69euZMGECrq6utgrBlb/xp6am2s4FBQWRlZVFWlraNfsURIGTgry8PE0diIjILaU43H3QvHlzdu3axc6dO21HvXr16Nq1Kzt37qRy5coEBQWxcuVK2zVZWVmsX7+eRo0aAVC3bl3c3NxMfZKTk9m9e7etT0EUeptjERERKTqlS5cmPDzc1Obl5YWfn5+tPSYmhri4OMLCwggLCyMuLg5PT0+6dOkCgLe3N7169WLgwIH4+fnh6+vLoEGDiIiIsN2NUBBKCkRExGndLI9OHjx4MBkZGfTr14+0tDQaNGjAihUrKF26tK3PuHHjcHV1pWPHjmRkZNC8eXMSEhJwcXEp8PtYjFtwscCM27o5OgQRu3sw4FdHhyBid5V2rvz7Tv/A3OCudhv7iROf2m1seyn0o5NFRETk1qTpAxERcVr/5BkFtyJVCkRERARQpUBERJzYLbeo7h9SpUBEREQAVQpERMSJ5WlJgYkqBSIiIgKoUiAiIk7sZtm86EZRUiAiIk5LCw3NNH0gIiIigCoFIiLixLTQ0EyVAhEREQFUKRARESemhYZmqhSIiIgIoEqBiIg4MVUKzFQpEBEREUCVAhERcWKG7j4wUVIgIiJOS9MHZpo+EBEREUCVAhERcWKqFJipUiAiIiKAKgUiIuLE9EAkM1UKREREBFClQEREnJgeiGSmSoGIiIgAqhSIiIgT090HZkoKRETEaSkpMNP0gYiIiACqFIiIiBPTLYlmqhSIiIgIoEqBiIg4Md2SaKZKgYiIiACqFIiIiBPT3QdmqhSIiIgIoEqBiIg4Md19YKZKgYiIiACqFIiIiBPLU63AREmBiIg4LS00NNP0gYiIiACqFIiIiBPT5IGZKgUiIiICqFIgIiJOTGsKzFQpEBEREUCVAhERcWJ6IJKZQ5KCCRMmFLjvgAED7BiJiIiIXOaQpGDcuHEF6mexWJQUiIiI3RSHzYsmT57M5MmTOXz4MAA1atTgzTffpGXLlgAYhsHw4cOZMmUKaWlpNGjQgA8++IAaNWrYxsjMzGTQoEHMnTuXjIwMmjdvzocffsjtt99eqFgckhQkJSU54m1FRERMHJ8SwO23386oUaOoUqUKADNnzqR9+/bs2LGDGjVqMGbMGMaOHUtCQgJ33nknI0eOJCoqin379lG6dGkAYmJiWLp0KfPmzcPPz4+BAwfSpk0bEhMTcXFxKXAsFsMwisPPpEjNuK2bo0MQsbsHA351dAgidldp50q7jv9apS52G/vNfTPIzMw0tVmtVqxW699e6+vryzvvvEPPnj0JDg4mJiaGIUOGAJeqAoGBgYwePZo+ffqQnp5OQEAAs2fPplOnTgCcOHGCkJAQli1bRnR0dIFjLhYLDY8dO8aSJUs4evQoWVlZpnNjx451UFQiInKrs+ctifHx8QwfPtzUNnToUIYNG3bNa3Jzc/n88885f/48DRs2JCkpiZSUFFq0aGHrY7VaadKkCZs2baJPnz4kJiaSnZ1t6hMcHEx4eDibNm26uZKC1atX065dO0JDQ9m3bx/h4eEcPnwYwzC4++67HR2eiIjIdYmNjeXll182tV2rSrBr1y4aNmzIxYsXKVWqFIsWLaJ69eps2rQJgMDAQFP/wMBAjhw5AkBKSgru7u74+Pjk65OSklKomB2eFMTGxjJw4EBGjBhB6dKl+fLLLylXrhxdu3bloYcecnR4IiJyC7PnQsOCThUAVK1alZ07d3L69Gm+/PJLunfvzvr1623nLRbzvZOGYeRru1JB+lzJ4ZsX7d27l+7duwPg6upKRkYGpUqVYsSIEYwePdrB0YmIiNifu7s7VapUoV69esTHx1OrVi3ef/99goKCAPL9xp+ammqrHgQFBZGVlUVaWto1+xSUw5MCLy8v20KM4OBgDh48aDt38uRJR4UlIiJOwLDj8Y/iMgwyMzMJDQ0lKCiIlSv/t+AyKyuL9evX06hRIwDq1q2Lm5ubqU9ycjK7d++29Skoh08f3HvvvXzzzTdUr16d1q1bM3DgQHbt2sXChQu59957HR2eiIiIXb366qu0bNmSkJAQzp49y7x581i3bh3Lly/HYrEQExNDXFwcYWFhhIWFERcXh6enJ126XLpzwtvbm169ejFw4ED8/Pzw9fVl0KBBREREEBkZWahYHJ4UjB07lnPnzgEwbNgwzp07x/z586lSpUqBNzkSERG5HsXhgUi//vorTz75JMnJyXh7e1OzZk2WL19OVFQUAIMHDyYjI4N+/frZNi9asWKFbY8CuLQpoKurKx07drRtXpSQkFCoPQpA+xSI3LS0T4E4A3vvU/Bypc52G3vs4Xl2G9teHF4p+LNz586Rl2fO28qUKeOgaERERJyLwxcaJiUl0bp1a7y8vPD29sbHxwcfHx/Kli2b755LERGRolRcFxo6isMrBV27dgVg+vTpBAYGFvqeShERESkaDk8KfvjhBxITE6lataqjQxERESdTHBYaFicOnz6oX78+v/zyi6PDEBERcXoOrxR88skn9O3bl+PHjxMeHo6bm5vpfM2aNR0UmYiI3OqMm3b23z4cnhT89ttvHDx4kKefftrWZrFYbHs25+bmOjA6ERER5+HwpKBnz57UqVOHuXPnaqGhiIjcUFpTYObwpODIkSMsWbKEKlWqODoUERFxMvZ8SuLNyOELDZs1a8b333/v6DBEREScnsMrBW3btuWll15i165dRERE5Fto2K5dOwdFJiIitzrVCcwcnhT07dsXgBEjRuQ7p4WGIiIiN47Dk4Irn3UgIiJyo2hNgZlD1xTk5OTg6urK7t27HRmGiIiI4OBKgaurKxUrVtQUQTFU9anm3PVkc0qFBABwev8xdo5bxPG1PwDw9PE5V71u21tz2f3RfwAo4e5K/Te6ULlDQ1xKupG88Uc2v5rAheTfb8yHEPkbZfs+Sdm+T5nack/+zi+RnQDwbHYfpR9rjXu1MFx8vDnRqS9Z+w6a+vu9/iIlG9yNS4AfxoUMMr//kbT3PyH7sHZqvRmoVm3m8OmD119/ndjYWObMmYOvr6+jw5E/XEj+ncT4+Zw5/CsAVR6/n+bTX2ZJ9Guc3n+cebWfN/W/7cFa3PfeMxxettXW1mB4N0Ki7mZdv0lk/n6Oe4Z2IXLmQJY+9DpGnkp2Ujxk/ZzEr32G2F4bf5rStHiU5OLOPZxf+X/4D335qtdn7j3AuWVryE1JpUSZ0pTt+xSBk0dxrPWToOlRuck4PCmYMGECP//8M8HBwVSsWBEvLy/T+e3btzsoMuf2y8odptfbR3/OXU82J+DuKpzef5yM39JN5ytE303ypr2cO/obAG6lPQjr3JQNL04mecMeANb3n0zHbRMof384J9bvujEfROTv5OaReyrtqqfO/2cVAK7Bgde8/NyXy/734sSvpH0wg9s+n4JrcCA5x5KLNFQpetrm2MzhSUGHDh0cHYL8DUsJC5XaNMDV00pq4oF850v6lyGkeW02xHxsa/OvGYqLuyvH//Tln/HraU7v+4Vy9cKUFEix4VohmNtXzMPIziJr10+kTZxOzvGU6xrLUrIkpdpHk30smZyU34o4UrEH1XLMHJ4UDB069B9dn5mZSWZmpqkt28jFzeLyj8YV8LnrdlovGYaL1Y3s8xdZ88x40g+cyNevyuP3k33uIkf++52tzSPAm9zMbLLSL5j6Zvx2Bs9yZe0cuUjBZO76iZOvjyH7yDFc/Hwo27sr5We+z/FHnyEv/WyBxyndsS0+Mb0p4elB1qGj/Np3COTk2DFyEftw+I6GlyUmJjJnzhw+/fRTduzY8fcX/CE+Ph5vb2/T8Z+ze+wYqfNIP5jMv1u8xldth7Fv1mruH98H77DgfP3COjfh4KJN5GZm//2gFjAMleukeMj4ZhsXVm8k++fDXPx2B7++8DoApdq2KNQ455at5kTn50ju+TI5R48TMOZ1LO5uf3+hOJxhx//djByeFKSmptKsWTPq16/PgAEDeOGFF6hbty7Nmzfnt9/+vvwWGxtLenq66WhdusYNiPzWl5edy9nDv3LqhyQSRy3g9x+PUuOZh0x9Au+pStkqweyfu87UnvFbOi5WN9y9PU3tHv5l8q1HECkujIsXyfo5CdcKtxXuunMXyDl6nMztu0gdNAK30BA8m91npyhF7MfhSUH//v05c+YMe/bs4ffffyctLY3du3dz5swZBgwY8LfXW61WypQpYzo0dWAnFgsl3M0zTmFPNOHk94dI+/Goqf3kD0nkZuUQ/ECErc2jXFnKVg0h9bv86xJEigU3N9xCK5B78p/eNmtRpeAmkWfH42bk8DUFy5cvZ9WqVVSrVs3WVr16dT744ANatChcCU+Kzt3/6sjxNd9z/sQp3EqVJLR9Q4IaVmNl1zG2Pm6lPKjU5h62jfgs3/XZZzM4MG8d97zZhcy0c2SmnaP+m11I++kXkjdosyopHnxeepYL/7eF3ORUSviWpWzvLpTw8uTc0hUAlChTGtfy5XAJ8APAteLtwKW9DHJPpeF6WxBe0U3J2JxIbtppXMv54/10J4zMLC5s2HrN9xUprhyeFOTl5eV7CBKAm5ubtkB2IA//Mtw/oS+e5cqSdfYCaXt/YWXXMZz40xd6aPt7sVgsHFq8+apjbB32KXk5eTT96AVcS7pzYuMeNr70sfYokGLDNdCfgPhXcfEpQ25aOpk/7CX5qQHkJqcC4Nm0If4jXrH1Lzfm0pqD0x/N4vRHszGysrHeHUGZro9Qokwpck+lcXH7LpK7v0he2mlHfCQppDytcTKxGA5e9dW+fXtOnz7N3LlzCQ6+tIjt+PHjdO3aFR8fHxYtWlToMWfc1q2owxQpdh4M+NXRIYjYXaWdK+06/pMVH7Hb2LOPLLTb2Pbi8DUFkyZN4uzZs1SqVIk77riDKlWqEBoaytmzZ5k4caKjwxMRkVuYYcfjZuTw6YOQkBC2b9/OypUr+emnnzAMg+rVqxMZGeno0ERE5BanpySaOTwpuCwqKoqoqChHhyEiIuK0HJYUzJo1q0D9nnrqqb/vJCIich1u1k2G7MVhScGLL754zXMWi4Xz58+Tk5OjpEBEROQGcdhCw7S0tKseP/74Ix07dsQwDE0niIiIXWnzIjOH331w2dmzZ3n99de588472blzJ19//TXLly93dFgiIiJOw+ELDbOyspg0aRJxcXH4+/szY8YMHnvsMUeHJSIiTkB3H5g5LCkwDINZs2bx5ptvkpOTQ1xcHL169cLFRc8tEBERcQSHJQW1atXi4MGD9O/fn5iYGDw9PTl//ny+fmXKlHFAdCIi4gx094GZw5KC3bsv7aE/ZswY3nnnnXznDcPAYrGQm5t7o0MTEREncbMuCLQXhyUFa9euddRbi4iIyFU4LClo0qSJo95aREQEuFSVlv9x+N0HALm5uSxatIi9e/disVioVq0a7du3x9W1WIQnIiLiFBz+rbt7927at29PSkoKVatWBWD//v0EBASwZMkSIiIiHByhiIjcqnRLopnDNy965plnqFGjBseOHWP79u1s376dX375hZo1a/Lss886OjwRERGn4fBKwffff893332Hj4+Prc3Hx4e3336b+vXrOzAyERG51enuAzOHVwqqVq3Kr7/+mq89NTWVKlWqOCAiERER5+SQSsGZM2dsf46Li2PAgAEMGzaMe++9F4AtW7YwYsQIRo8e7YjwRETESWjzIjOHJAVly5bFYrHYXhuGQceOHW1tl28Radu2rTYvEhERu9FCQzOHJAUF3bhox44ddo5ERETEseLj41m4cCE//fQTHh4eNGrUiNGjR9vuyINLvywPHz6cKVOmkJaWRoMGDfjggw+oUaOGrU9mZiaDBg1i7ty5ZGRk0Lx5cz788ENuv/32AsfikKTgrzYuSk9P59NPP+WTTz7h+++/JyYm5sYFJiIiTqU4bF60fv16nn/+eerXr09OTg6vvfYaLVq04Mcff8TLywu49EiAsWPHkpCQwJ133snIkSOJiopi3759lC5dGoCYmBiWLl3KvHnz8PPzY+DAgbRp04bExMQCP2zQYhSHnwiwZs0apk+fzsKFC6lYsSKPPvoojz76KHXq1Cn0WDNu62aHCEWKlwcD8i/QFbnVVNq50q7jtwxpabexF/+8mMzMTFOb1WrFarX+5XW//fYb5cqVY/369TzwwAMYhkFwcDAxMTEMGTIEuFQVCAwMZPTo0fTp04f09HQCAgKYPXs2nTp1AuDEiROEhISwbNkyoqOjCxSzQ+8+OHbsGCNHjqRy5co88cQT+Pj4kJ2dzZdffsnIkSOvKyEQEREpqDw7HvHx8Xh7e5uO+Pj4v40pPT0dAF9fXwCSkpJISUmhRYsWtj5Wq5UmTZqwadMmABITE8nOzjb1CQ4OJjw83NanIByWFLRq1Yrq1avz448/MnHiRE6cOMHEiRMdFY6IiEiRio2NJT093XTExsb+5TWGYfDyyy9z3333ER4eDkBKSgoAgYGBpr6BgYG2cykpKbi7u5v2/LmyT0E4bPOiFStWMGDAAJ577jnCwsIcFYaIiDgxe96SWJCpgiu98MIL/PDDD2zcuDHfuT/ftQeXEogr265UkD5/5rBKwYYNGzh79iz16tWjQYMGTJo0id9++81R4YiIiDhU//79WbJkCWvXrjXdMRAUFASQ7zf+1NRUW/UgKCiIrKws0tLSrtmnIByWFDRs2JCpU6eSnJxMnz59mDdvHrfddht5eXmsXLmSs2fPOio0ERFxEnkYdjsKyjAMXnjhBRYuXMiaNWsIDQ01nQ8NDSUoKIiVK/+36DIrK4v169fTqFEjAOrWrYubm5upT3JyMrt377b1KQiHb3Ps6elJz5492bhxI7t27WLgwIGMGjWKcuXK0a5dO0eHJyIiYlfPP/88c+bM4bPPPqN06dKkpKSQkpJCRkYGcGnaICYmhri4OBYtWsTu3bvp0aMHnp6edOnSBQBvb2969erFwIEDWb16NTt27KBbt25EREQQGRlZ4FgcnhT8WdWqVRkzZgzHjh1j7ty5jg5HRERucYZh2O0oqMmTJ5Oenk7Tpk0pX7687Zg/f76tz+DBg4mJiaFfv37Uq1eP48ePs2LFCtseBQDjxo2jQ4cOdOzYkcaNG+Pp6cnSpUsLvEcBFKN9CoqS9ikQZ6B9CsQZ2Hufggdvj7Lb2GuP2Td2eyhWlQIRERFxHIfdkigiIuJoekqimSoFIiIiAqhSICIiTizv1ltW94+oUiAiIiKAKgUiIuLEVCcwU6VAREREAFUKRETEiRVmO2JnoKRARESclpICM00fiIiICKBKgYiIOLFbcKf/f0SVAhEREQFUKRARESemNQVmqhSIiIgIoEqBiIg4MT0QyUyVAhEREQFUKRARESemuw/MlBSIiIjT0kJDM00fiIiICKBKgYiIODFNH5ipUiAiIiKAKgUiIuLEtKbATJUCERERAVQpEBERJ6bNi8xUKRARERFAlQIREXFiebr7wERJgYiIOC1NH5hp+kBEREQAVQpERMSJafrATJUCERERAVQpEBERJ6Y1BWaqFIiIiAigSoGIiDgxrSkwU6VAREREAFUKRETEiWlNgZmSAhERcVqaPjDT9IGIiIgAqhSIiIgT0/SBmSoFIiIiAqhSICIiTsww8hwdQrGiSoGIiIgAqhSIiIgTy9OaAhNVCkRERARQpUBERJyYoX0KTJQUiIiI09L0gZmmD0RERBzs//7v/2jbti3BwcFYLBYWL15sOm8YBsOGDSM4OBgPDw+aNm3Knj17TH0yMzPp378//v7+eHl50a5dO44dO1aoOJQUiIiI0zIMw25HYZw/f55atWoxadKkq54fM2YMY8eOZdKkSWzbto2goCCioqI4e/asrU9MTAyLFi1i3rx5bNy4kXPnztGmTRtyc3MLHIfFuAUnVGbc1s3RIYjY3YMBvzo6BBG7q7RzpV3Hv82nht3GPpSynczMTFOb1WrFarX+5XUWi4VFixbRoUMH4FLiEhwcTExMDEOGDAEuVQUCAwMZPXo0ffr0IT09nYCAAGbPnk2nTp0AOHHiBCEhISxbtozo6OgCxaxKgYiIOK08w7DbER8fj7e3t+mIj48vdIxJSUmkpKTQokULW5vVaqVJkyZs2rQJgMTERLKzs019goODCQ8Pt/UpCC00FBERsYPY2FhefvllU9vfVQmuJiUlBYDAwEBTe2BgIEeOHLH1cXd3x8fHJ1+fy9cXhJICERFxWvZ8IFJBpgoKw2KxmF4bhpGv7UoF6fNnmj4QEREpxoKCggDy/cafmppqqx4EBQWRlZVFWlraNfsUhJICERFxWsXl7oO/EhoaSlBQECtX/m/RZVZWFuvXr6dRo0YA1K1bFzc3N1Of5ORkdu/ebetTEJo+EBERp1VcNi86d+4cP//8s+11UlISO3fuxNfXlwoVKhATE0NcXBxhYWGEhYURFxeHp6cnXbp0AcDb25tevXoxcOBA/Pz88PX1ZdCgQURERBAZGVngOJQUiIiIONh3333Hgw8+aHt9eYFi9+7dSUhIYPDgwWRkZNCvXz/S0tJo0KABK1asoHTp0rZrxo0bh6urKx07diQjI4PmzZuTkJCAi4tLgePQPgUiNyntUyDOwN77FPiXudNuY588s99uY9uL1hSIiIgIoOkDERFxYnm3XrH8H1GlQERERABVCkRExIndgsvq/hFVCkRERARQpUBERJxYcdmnoLhQUiAiIk5L0wdmmj4QERERQJUCERFxYrol0UyVAhEREQFUKRARESdmaKGhiSoFIiIiAqhSICIiTkxrCsxUKRARERFAlQIREXFi2qfATJUCERERAVQpEBERJ6a7D8yUFIiIiNPS9IGZpg9EREQEUKVAREScmCoFZqoUiIiICKBKgYiIODHVCcxUKRAREREALIYmVOQfyszMJD4+ntjYWKxWq6PDEbEL/T0XZ6CkQP6xM2fO4O3tTXp6OmXKlHF0OCJ2ob/n4gw0fSAiIiKAkgIRERH5g5ICERERAZQUSBGwWq0MHTpUi6/klqa/5+IMtNBQREREAFUKRERE5A9KCkRERARQUiAiIiJ/UFIgxda6deuwWCycPn3a0aHITeBW//tSqVIlxo8f7+gw5BanpMBJ9OjRA4vFwqhRo0ztixcvxmKxOCgqkcLbtGkTLi4uPPTQQ4W+9nLiEB4eTm5urulc2bJlSUhIKKIoRW5OSgqcSMmSJRk9ejRpaWlFNmZWVlaRjSVSENOnT6d///5s3LiRo0ePXtcYBw8eZNasWUUaV3Z2dpGOJ+IISgqcSGRkJEFBQcTHx1+zz5dffkmNGjWwWq1UqlSJ9957z3S+UqVKjBw5kh49euDt7U3v3r1JSEigbNmyfPXVV1StWhVPT08ee+wxzp8/z8yZM6lUqRI+Pj7079/f9NvZnDlzqFevHqVLlyYoKIguXbqQmppqt88vN7/z58+zYMECnnvuOdq0aXPV3+y/+eYbatWqRcmSJWnQoAG7du3K16d///4MHTqUixcvXvO9jh49Svv27SlVqhRlypShY8eO/Prrr7bzw4YNo3bt2kyfPp3KlStjtVoxDAOLxcLHH39MmzZt8PT0pFq1amzevJmff/6Zpk2b4uXlRcOGDTl48KBtrIMHD9K+fXsCAwMpVaoU9evXZ9WqVf/shyVyHZQUOBEXFxfi4uKYOHEix44dy3c+MTGRjh070rlzZ3bt2sWwYcN444038v0f7zvvvEN4eDiJiYm88cYbAFy4cIEJEyYwb948li9fzrp163jkkUdYtmwZy5YtY/bs2UyZMoUvvvjCNk5WVhZvvfUW33//PYsXLyYpKYkePXrY80cgN7n58+dTtWpVqlatSrdu3ZgxYwZXbrXyyiuv8O6777Jt2zbKlStHu3bt8v0WHxMTQ05ODpMmTbrq+xiGQYcOHfj9999Zv349K1eu5ODBg3Tq1MnU7+eff2bBggV8+eWX7Ny509b+1ltv8dRTT7Fz507uuusuunTpQp8+fYiNjeW7774D4IUXXrD1P3fuHK1atWLVqlXs2LGD6Oho2rZte92VEJHrZohT6N69u9G+fXvDMAzj3nvvNXr27GkYhmEsWrTIuPzXoEuXLkZUVJTpuldeecWoXr267XXFihWNDh06mPrMmDHDAIyff/7Z1tanTx/D09PTOHv2rK0tOjra6NOnzzVj3Lp1qwHYrlm7dq0BGGlpaYX/wHJLatSokTF+/HjDMAwjOzvb8Pf3N1auXGkYxv/+vsybN8/W/9SpU4aHh4cxf/58U5+0tDTjo48+Mnx9fY3Tp08bhmEY3t7exowZMwzDMIwVK1YYLi4uxtGjR21j7dmzxwCMrVu3GoZhGEOHDjXc3NyM1NRUU4yA8frrr9teb9682QCMadOm2drmzp1rlCxZ8i8/a/Xq1Y2JEyfaXlesWNEYN25cgX5OItdLlQInNHr0aGbOnMmPP/5oat+7dy+NGzc2tTVu3JgDBw6Yyv716tXLN6anpyd33HGH7XVgYCCVKlWiVKlSprY/Tw/s2LGD9u3bU7FiRUqXLk3Tpk0B9NuRXNW+ffvYunUrnTt3BsDV1ZVOnToxffp0U7+GDRva/uzr60vVqlXZu3dvvvF69eqFv78/o0ePzndu7969hISEEBISYmurXr06ZcuWNY1VsWJFAgIC8l1fs2ZN258DAwMBiIiIMLVdvHiRM2fOAJemRQYPHmx7j1KlSvHTTz/p34LccK6ODkBuvAceeIDo6GheffVVU7ne+GM+9M+Mq+yC7eXlla/Nzc3N9NpisVy1LS8vD7j0f4ItWrSgRYsWzJkzh4CAAI4ePUp0dLQWL8pVTZs2jZycHG677TZbm2EYuLm5/e3i2avdYePq6mpbH/PnUv7lca92zZXtV/u3AOZ/D5f7X63t8r+HV155ha+//pp3332XKlWq4OHhwWOPPaZ/C3LDKSlwUqNGjaJ27drceeedtrbq1auzceNGU79NmzZx55134uLiUqTv/9NPP3Hy5ElGjRpl+23s8lyryJVycnKYNWsW7733Hi1atDCde/TRR/n0008JDw8HYMuWLVSoUAGAtLQ09u/fz1133XXVcR9//HHeeecdhg8fbmqvXr06R48e5ZdffrH9/fzxxx9JT0+nWrVqRf3x2LBhAz169ODhhx8GLq0xOHz4cJG/j8jfUVLgpCIiIujatSsTJ060tQ0cOJD69evz1ltv0alTJzZv3sykSZP48MMPi/z9K1SogLu7OxMnTqRv377s3r2bt956q8jfR24NX331FWlpafTq1Qtvb2/Tuccee4xp06Yxbtw4AEaMGIGfnx+BgYG89tpr+Pv706FDh2uOPWrUKKKjo01tkZGR1KxZk65duzJ+/HhycnLo168fTZo0uer02T9VpUoVFi5cSNu2bbFYLLzxxhu2KoLIjaQ1BU7srbfeMk0P3H333SxYsIB58+YRHh7Om2++yYgRI+xyR0BAQAAJCQl8/vnnVK9enVGjRvHuu+8W+fvIrWHatGlERkbmSwjgUqVg586dbN++Hbj0Jf/iiy9St25dkpOTWbJkCe7u7tccu1mzZjRr1oycnBxbm8ViYfHixfj4+PDAAw8QGRlJ5cqVmT9/ftF/OGDcuHH4+PjQqFEj2rZtS3R0NHfffbdd3kvkr+jRySIiIgKoUiAiIiJ/UFIgIiIigJICERER+YOSAhEREQGUFIiIiMgflBSIiIgIoKRARERE/qCkQERERAAlBSJ2M2zYMGrXrm173aNHj7/cbtdeDh8+jMViYefOndd1/bp167BYLJw+fbpI4xKR4kdJgTiVHj16YLFYbE9xrFy5MoMGDeL8+fN2f+/333+fhISEAvX9p1/kIiLXQw9EEqfz0EMPMWPGDLKzs9mwYQPPPPMM58+fZ/Lkyfn6Zmdn53sE9PW62r79IiLFiSoF4nSsVitBQUGEhITQpUsXunbtyuLFi4H/lfynT59O5cqVsVqtGIZBeno6zz77LOXKlaNMmTI0a9aM77//3jTuqFGjCAwMpHTp0vTq1YuLFy+azl85fZCXl8fo0aOpUqUKVquVChUq8PbbbwMQGhoKQJ06dbBYLDRt2tR23YwZM6hWrRolS5bkrrvuyvcUy61bt1KnTh1KlixJvXr12LFjx9/+TDIzMxk8eDAhISFYrVbCwsKYNm3aVfueOnWKJ554gttvvx1PT08iIiKYO3euqc8XX3xBREQEHh4e+Pn5ERkZaavGrFu3jnvuuQcvLy/Kli1L48aNOXLkyN/GKCL2p0qBOD0PDw+ys7Ntr3/++WcWLFjAl19+iYuLCwCtW7fG19eXZcuW4e3tzccff0zz5s3Zv38/vr6+LFiwgKFDh/LBBx9w//33M3v2bCZMmEDlypWv+b6xsbFMnTqVcePGcd9995GcnMxPP/0EXPpiv+eee1i1ahU1atSwPeVv6tSpDB06lEmTJlGnTh127NhB79698fLyonv37pw/f542bdrQrFkz5syZQ1JSEi+++OLf/gyeeuopNm/ezIQJE6hVqxZJSUmcPHnyqn0vXrxI3bp1GTJkCGXKlOE///kPTz75JJUrV6ZBgwYkJyfzxBNPMGbMGB5++GHOnj3Lhg0bMAyDnJwcOnToQO/evZk7dy5ZWVls3boVi8VS4P9eImJHhogT6d69u9G+fXvb62+//dbw8/MzOnbsaBiGYQwdOtRwc3MzUlNTbX1Wr15tlClTxrh48aJprDvuuMP4+OOPDcMwjIYNGxp9+/Y1nW/QoIFRq1atq773mTNnDKvVakydOvWqcSYlJRmAsWPHDlN7SEiI8dlnn5na3nrrLaNhw4aGYRjGxx9/bPj6+hrnz5+3nZ88efJVx7ps3759BmCsXLnyqufXrl1rAEZaWtpVzxuGYbRq1coYOHCgYRiGkZiYaADG4cOH8/U7deqUARjr1q275lgi4jiqFIjT+eqrryhVqhQ5OTlkZ2fTvn17Jk6caDtfsWJFAgICbK8TExM5d+4cfn5+pnEyMjI4ePAgAHv37qVv376m8w0bNmTt2rVXjWHv3r1kZmbSvHnzAsf922+/8csvv9CrVy969+5ta8/JybGtV9i7dy+1atXC09PTFMdf2blzJy4uLjRp0qRAceTm5jJq1Cjmz5/P8ePHyczMJDMzEy8vLwBq1apF8+bNiYiIIDo6mhYtWvDYY4/h4+ODr68vPXr0IDo6mqioKCIjI+nYsSPly5cv8M9BROxHSYE4nQcffJDJkyfj5uZGcHBwvoWEl7/cLsvLy6N8+fKsW7cu31hly5a9rhg8PDwKfU1eXh5waQqhQYMGpnOXpzkMw7B7LO+99x7jxo1j/PjxRERE4OXlRUxMDFlZWbZYVq5cyaZNm1ixYgUTJ07ktdde49tvvyU0NJQZM2YwYMAAli9fzvz583n99ddZuXIl9957b6FjF5GipYWG4nS8vLyoUqUKFStWLNCdBXfffTcpKSm4urpSpUoV0+Hv7w9AtWrV2LJli+m6K1//WVhYGB4eHqxevfqq5y+vIcjNzbW1BQYGctttt3Ho0KF8cVxemFi9enW+//57MjIyChQHQEREBHl5eaxfv/4v+122YcMG2rdvT7du3ahVqxaVK1fmwIEDpj4Wi4XGjRszfPhwduzYgbu7O4sWLbKdr1OnDrGxsWzatInw8HA+++yzAr23iNiXkgKRvxEZGUnDhg3p0KEDX3/9NYcPH2bTpk28/vrrfPfddwC8+OKLTJ8+nenTp7N//36GDh3Knj17rjlmyZIlGTJkCIMHD2bWrFkcPHiQLVu22Fb8lytXDg8PD5YvX86vv/5Keno6cOnuiPj4eN5//33279/Prl27mDFjBmPHjgWgS5culChRgl69evHjjz+ybNky3n333b/8fJUqVaJ79+707NmTxYsXk5SUxLp161iwYMFV+1epUsVWCdi7dy99+vQhJSXFdv7bb78lLi6O7777jqNHj7Jw4UJ+++03qlWrRlJSErGxsWzevJkjR46wYsUK9u/fT7Vq1Qr+H0RE7MfRixpEbqQrFxpeaejQoabFgZedOXPG6N+/vxEcHGy4ubkZISEhRteuXY2jR4/a+rz99tuGv7+/UapUKaN79+7G4MGDr7nQ0DAMIzc31xg5cqRRsWJFw83NzahQoYIRFxdnOz916lQjJCTEKFGihNGkSRNb+6effmrUrl3bcHd3N3x8fIwHHnjAWLhwoe385s2bjVq1ahnu7u5G7dq1jS+//PIvFxoahmFkZGQYL730klG+fHnD3d3dqFKlijF9+nTDMPIvNDx16pTRvn17o1SpUka5cuWM119/3Xjqqadsn+3HH380oqOjjYCAAMNqtRp33nmnMXHiRMMwDCMlJcXo0KGD7X0qVqxovPnmm0Zubu41YxORG8diGNcxCSkiIiK3HE0fiIiICKCkQERERP6gpEBEREQAJQUiIiLyByUFIiIiAigpEBERkT8oKRARERFASYGIiIj8QUmBiIiIAEoKRERE5A9KCkRERASA/wfGONO47nSNBwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 600x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "LABELS = [\"Normal\", \"AbNormal\"]\n",
    "\n",
    "y_pred = [1 if e > threshold else 0 for e in error_df.reconstruction_error.values]\n",
    "conf_matrix = confusion_matrix(error_df.true_class, y_pred)\n",
    "plt.figure(figsize=(6, 6))\n",
    "sns.heatmap(conf_matrix, xticklabels=LABELS, yticklabels=LABELS, annot=True, fmt=\"d\");\n",
    "plt.title(\"Confusion matrix\")\n",
    "plt.ylabel('True class')\n",
    "plt.xlabel('Predicted class')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "3fe86548",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score : 0.7688888888888888\n",
      "Precision : 0.9177101967799642\n",
      "Recall : 0.580973952434881\n",
      "F1 : 0.7115117891816921\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "from sklearn.metrics import accuracy_score\n",
    "precision, recall, f1,_ = precision_recall_fscore_support(y_test,y_pred,average='binary')\n",
    "print ('Accuracy Score :',accuracy_score(error_df.true_class, y_pred) )\n",
    "print ('Precision :',precision )\n",
    "print ('Recall :',recall )\n",
    "print ('F1 :',f1 )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "380dec0a",
   "metadata": {},
   "source": [
    "### Visualize the latent space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "32db1252",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, 3)]               0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 2)                 8         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 8 (32.00 Byte)\n",
      "Trainable params: 8 (32.00 Byte)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "inp_layer = autoencoder.input\n",
    "out_layer = autoencoder.layers[1].output\n",
    "\n",
    "encoder = Model(inputs=inp_layer, outputs=out_layer)\n",
    "encoder.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "ca042c2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "112/112 [==============================] - 0s 2ms/step\n",
      "57/57 [==============================] - 0s 3ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_train_encoded = encoder.predict(X_train)\n",
    "X_test_encoded = encoder.predict(X_test)\n",
    "\n",
    "X_encoded = np.vstack((X_train_encoded, X_test_encoded))\n",
    "y_main = np.hstack((np.zeros(len(X_train)), y_test))\n",
    "\n",
    "encoded_df = pd.DataFrame(np.hstack((X_encoded, y_main[np.newaxis].transpose())))\n",
    "\n",
    "plt.subplots(figsize=(8,8))\n",
    "plt.scatter(encoded_df[encoded_df[2]==0][0],encoded_df[encoded_df[2]==0][1], color='blue', label='Normal', s=10)\n",
    "plt.scatter(encoded_df[encoded_df[2]==1][0],encoded_df[encoded_df[2]==1][1], color='red', label='AbNormal', s=10);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12e7d089",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}