Autoencoders_contrastive_learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Contrastive Learning: Effective Anomaly Detection with Auto-Encoders\n",
    "\n",
    "To exemplify how this works, let's try to solve Kaggle's Credit Card Fraud Detection problem. In this challenge we have a datasets containing transactions made by credit cards in September 2013 by European cardholders. From a total of 284,807 transactions, 492 are frauds, meaning that the dataset is highly unbalanced. The positive class (frauds) account for 0.172% of all transactions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import tensorflow.keras as keras\n",
    "from autoencoders import Autoencoder\n",
    "\n",
    "from helper import plot_loss_by_epoch, plot_reconstructuion_err_by_class, plot_precision_recall_curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.425966</td>\n",
       "      <td>0.960523</td>\n",
       "      <td>1.141109</td>\n",
       "      <td>-0.168252</td>\n",
       "      <td>0.420987</td>\n",
       "      <td>-0.029728</td>\n",
       "      <td>0.476201</td>\n",
       "      <td>0.260314</td>\n",
       "      <td>-0.568671</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.208254</td>\n",
       "      <td>-0.559825</td>\n",
       "      <td>-0.026398</td>\n",
       "      <td>-0.371427</td>\n",
       "      <td>-0.232794</td>\n",
       "      <td>0.105915</td>\n",
       "      <td>0.253844</td>\n",
       "      <td>0.081080</td>\n",
       "      <td>3.67</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.229658</td>\n",
       "      <td>0.141004</td>\n",
       "      <td>0.045371</td>\n",
       "      <td>1.202613</td>\n",
       "      <td>0.191881</td>\n",
       "      <td>0.272708</td>\n",
       "      <td>-0.005159</td>\n",
       "      <td>0.081213</td>\n",
       "      <td>0.464960</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.167716</td>\n",
       "      <td>-0.270710</td>\n",
       "      <td>-0.154104</td>\n",
       "      <td>-0.780055</td>\n",
       "      <td>0.750137</td>\n",
       "      <td>-0.257237</td>\n",
       "      <td>0.034507</td>\n",
       "      <td>0.005168</td>\n",
       "      <td>4.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.0</td>\n",
       "      <td>-0.644269</td>\n",
       "      <td>1.417964</td>\n",
       "      <td>1.074380</td>\n",
       "      <td>-0.492199</td>\n",
       "      <td>0.948934</td>\n",
       "      <td>0.428118</td>\n",
       "      <td>1.120631</td>\n",
       "      <td>-3.807864</td>\n",
       "      <td>0.615375</td>\n",
       "      <td>...</td>\n",
       "      <td>1.943465</td>\n",
       "      <td>-1.015455</td>\n",
       "      <td>0.057504</td>\n",
       "      <td>-0.649709</td>\n",
       "      <td>-0.415267</td>\n",
       "      <td>-0.051634</td>\n",
       "      <td>-1.206921</td>\n",
       "      <td>-1.085339</td>\n",
       "      <td>40.80</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7.0</td>\n",
       "      <td>-0.894286</td>\n",
       "      <td>0.286157</td>\n",
       "      <td>-0.113192</td>\n",
       "      <td>-0.271526</td>\n",
       "      <td>2.669599</td>\n",
       "      <td>3.721818</td>\n",
       "      <td>0.370145</td>\n",
       "      <td>0.851084</td>\n",
       "      <td>-0.392048</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.073425</td>\n",
       "      <td>-0.268092</td>\n",
       "      <td>-0.204233</td>\n",
       "      <td>1.011592</td>\n",
       "      <td>0.373205</td>\n",
       "      <td>-0.384157</td>\n",
       "      <td>0.011747</td>\n",
       "      <td>0.142404</td>\n",
       "      <td>93.20</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9.0</td>\n",
       "      <td>-0.338262</td>\n",
       "      <td>1.119593</td>\n",
       "      <td>1.044367</td>\n",
       "      <td>-0.222187</td>\n",
       "      <td>0.499361</td>\n",
       "      <td>-0.246761</td>\n",
       "      <td>0.651583</td>\n",
       "      <td>0.069539</td>\n",
       "      <td>-0.736727</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.246914</td>\n",
       "      <td>-0.633753</td>\n",
       "      <td>-0.120794</td>\n",
       "      <td>-0.385050</td>\n",
       "      <td>-0.069733</td>\n",
       "      <td>0.094199</td>\n",
       "      <td>0.246219</td>\n",
       "      <td>0.083076</td>\n",
       "      <td>3.68</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "5   2.0 -0.425966  0.960523  1.141109 -0.168252  0.420987 -0.029728  0.476201   \n",
       "6   4.0  1.229658  0.141004  0.045371  1.202613  0.191881  0.272708 -0.005159   \n",
       "7   7.0 -0.644269  1.417964  1.074380 -0.492199  0.948934  0.428118  1.120631   \n",
       "8   7.0 -0.894286  0.286157 -0.113192 -0.271526  2.669599  3.721818  0.370145   \n",
       "9   9.0 -0.338262  1.119593  1.044367 -0.222187  0.499361 -0.246761  0.651583   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "5  0.260314 -0.568671  ... -0.208254 -0.559825 -0.026398 -0.371427 -0.232794   \n",
       "6  0.081213  0.464960  ... -0.167716 -0.270710 -0.154104 -0.780055  0.750137   \n",
       "7 -3.807864  0.615375  ...  1.943465 -1.015455  0.057504 -0.649709 -0.415267   \n",
       "8  0.851084 -0.392048  ... -0.073425 -0.268092 -0.204233  1.011592  0.373205   \n",
       "9  0.069539 -0.736727  ... -0.246914 -0.633753 -0.120794 -0.385050 -0.069733   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "5  0.105915  0.253844  0.081080    3.67      0  \n",
       "6 -0.257237  0.034507  0.005168    4.99      0  \n",
       "7 -0.051634 -1.206921 -1.085339   40.80      0  \n",
       "8 -0.384157  0.011747  0.142404   93.20      0  \n",
       "9  0.094199  0.246219  0.083076    3.68      0  \n",
       "\n",
       "[10 rows x 31 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"./creditcard.csv\", sep=',')\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some basic feature enginging. Dropping Time and scaling Amount"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Amount'] = StandardScaler().fit_transform(df['Amount'].values.reshape(-1, 1))\n",
    "df.drop('Time', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train, test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df.drop(['Class'], axis=1), \n",
    "                                                    df['Class'], \n",
    "                                                    test_size=0.33, \n",
    "                                                    stratify=df['Class'],\n",
    "                                                    random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to train the autoencoder only using the positive transactions, hoping the encoder will generate a good representation for this instances to help it recognize them from the fradulent one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_normal = y_train == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train[train_normal].to_numpy()\n",
    "y_train = y_train[train_normal].to_numpy()\n",
    "X_test = X_test.to_numpy()\n",
    "y_test = y_test.to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "autoencoder = Autoencoder(encoding_dim=10)\n",
    "autoencoder.compile(optimizer=tf.optimizers.Adam(lr=0.00025), loss ='mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 190490 samples, validate on 93987 samples\n",
      "Epoch 1/50\n",
      "190490/190490 [==============================] - 2s 11us/sample - loss: 1.1761 - val_loss: 1.0923\n",
      "Epoch 2/50\n",
      "190490/190490 [==============================] - 2s 9us/sample - loss: 0.9919 - val_loss: 0.9851\n",
      "Epoch 3/50\n",
      "190490/190490 [==============================] - 2s 10us/sample - loss: 0.9249 - val_loss: 0.9420\n",
      "Epoch 4/50\n",
      "190490/190490 [==============================] - 2s 10us/sample - loss: 0.8919 - val_loss: 0.9165\n",
      "Epoch 5/50\n",
      "190490/190490 [==============================] - 2s 10us/sample - loss: 0.8713 - val_loss: 0.8998\n",
      "Epoch 6/50\n",
      "190490/190490 [==============================] - 2s 10us/sample - loss: 0.8576 - val_loss: 0.8886\n",
      "Epoch 7/50\n",
      "190490/190490 [==============================] - 2s 9us/sample - loss: 0.8481 - val_loss: 0.8805\n",
      "Epoch 8/50\n",
      "190490/190490 [==============================] - 2s 10us/sample - loss: 0.8411 - val_loss: 0.8744\n",
      "Epoch 9/50\n",
      "190490/190490 [==============================] - 2s 9us/sample - loss: 0.8354 - val_loss: 0.8692\n",
      "Epoch 10/50\n",
      "190490/190490 [==============================] - 2s 10us/sample - loss: 0.8307 - val_loss: 0.8649\n",
      "Epoch 11/50\n",
      "190490/190490 [==============================] - 2s 10us/sample - loss: 0.8266 - val_loss: 0.8611\n",
      "Epoch 12/50\n",
      "190490/190490 [==============================] - 2s 10us/sample - loss: 0.8230 - val_loss: 0.8577\n",
      "Epoch 13/50\n",
      "190490/190490 [==============================] - 2s 9us/sample - loss: 0.8197 - val_loss: 0.8546\n",
      "Epoch 14/50\n",
      "190490/190490 [==============================] - 2s 10us/sample - loss: 0.8168 - val_loss: 0.8518\n",
      "Epoch 15/50\n",
      "190490/190490 [==============================] - 2s 10us/sample - loss: 0.8142 - val_loss: 0.8493\n",
      "Epoch 16/50\n",
      "190490/190490 [==============================] - 2s 10us/sample - loss: 0.8119 - val_loss: 0.8470\n",
      "Epoch 17/50\n",
      "190490/190490 [==============================] - 2s 10us/sample - loss: 0.8098 - val_loss: 0.8450\n",
      "Epoch 18/50\n",
      "190490/190490 [==============================] - 2s 9us/sample - loss: 0.8078 - val_loss: 0.8430\n",
      "Epoch 19/50\n",
      "190490/190490 [==============================] - 2s 10us/sample - loss: 0.8059 - val_loss: 0.8411\n",
      "Epoch 20/50\n",
      "190490/190490 [==============================] - 2s 9us/sample - loss: 0.8040 - val_loss: 0.8392\n",
      "Epoch 21/50\n",
      "190490/190490 [==============================] - 2s 10us/sample - loss: 0.8022 - val_loss: 0.8376\n",
      "Epoch 22/50\n",
      "190490/190490 [==============================] - 2s 10us/sample - loss: 0.8007 - val_loss: 0.8362\n",
      "Epoch 23/50\n",
      "190490/190490 [==============================] - 2s 9us/sample - loss: 0.7994 - val_loss: 0.8350\n",
      "Epoch 24/50\n",
      "190490/190490 [==============================] - 2s 9us/sample - loss: 0.7983 - val_loss: 0.8339\n",
      "Epoch 25/50\n",
      "190490/190490 [==============================] - 2s 9us/sample - loss: 0.7973 - val_loss: 0.8330\n",
      "Epoch 26/50\n",
      "190490/190490 [==============================] - 2s 10us/sample - loss: 0.7964 - val_loss: 0.8322\n",
      "Epoch 27/50\n",
      "190490/190490 [==============================] - 2s 10us/sample - loss: 0.7956 - val_loss: 0.8314\n",
      "Epoch 28/50\n",
      "190490/190490 [==============================] - 2s 10us/sample - loss: 0.7949 - val_loss: 0.8308\n",
      "Epoch 29/50\n",
      "190490/190490 [==============================] - 2s 10us/sample - loss: 0.7942 - val_loss: 0.8301\n",
      "Epoch 30/50\n",
      "190490/190490 [==============================] - 2s 10us/sample - loss: 0.7937 - val_loss: 0.8296\n",
      "Epoch 31/50\n",
      "190490/190490 [==============================] - 2s 10us/sample - loss: 0.7931 - val_loss: 0.8291\n",
      "Epoch 32/50\n",
      "190490/190490 [==============================] - 2s 10us/sample - loss: 0.7927 - val_loss: 0.8286\n",
      "Epoch 33/50\n",
      "190490/190490 [==============================] - 2s 9us/sample - loss: 0.7922 - val_loss: 0.8281\n",
      "Epoch 34/50\n",
      "190490/190490 [==============================] - 2s 10us/sample - loss: 0.7918 - val_loss: 0.8278\n",
      "Epoch 35/50\n",
      "190490/190490 [==============================] - 2s 10us/sample - loss: 0.7914 - val_loss: 0.8274\n",
      "Epoch 36/50\n",
      "190490/190490 [==============================] - 2s 10us/sample - loss: 0.7911 - val_loss: 0.8272\n",
      "Epoch 37/50\n",
      "190490/190490 [==============================] - 2s 9us/sample - loss: 0.7909 - val_loss: 0.8269\n",
      "Epoch 38/50\n",
      "190490/190490 [==============================] - 2s 10us/sample - loss: 0.7906 - val_loss: 0.8267\n",
      "Epoch 39/50\n",
      "190490/190490 [==============================] - 2s 10us/sample - loss: 0.7904 - val_loss: 0.8264\n",
      "Epoch 40/50\n",
      "190490/190490 [==============================] - 2s 9us/sample - loss: 0.7902 - val_loss: 0.8263\n",
      "Epoch 41/50\n",
      "190490/190490 [==============================] - 2s 9us/sample - loss: 0.7900 - val_loss: 0.8261\n",
      "Epoch 42/50\n",
      "190490/190490 [==============================] - 2s 10us/sample - loss: 0.7899 - val_loss: 0.8259\n",
      "Epoch 43/50\n",
      "190490/190490 [==============================] - 2s 9us/sample - loss: 0.7897 - val_loss: 0.8257\n",
      "Epoch 44/50\n",
      "190490/190490 [==============================] - 2s 9us/sample - loss: 0.7896 - val_loss: 0.8256\n",
      "Epoch 45/50\n",
      "190490/190490 [==============================] - 2s 9us/sample - loss: 0.7894 - val_loss: 0.8254\n",
      "Epoch 46/50\n",
      "190490/190490 [==============================] - 2s 10us/sample - loss: 0.7893 - val_loss: 0.8253\n",
      "Epoch 47/50\n",
      "190490/190490 [==============================] - 2s 9us/sample - loss: 0.7892 - val_loss: 0.8252\n",
      "Epoch 48/50\n",
      "190490/190490 [==============================] - 2s 10us/sample - loss: 0.7890 - val_loss: 0.8250\n",
      "Epoch 49/50\n",
      "190490/190490 [==============================] - 2s 10us/sample - loss: 0.7889 - val_loss: 0.8249\n",
      "Epoch 50/50\n",
      "190490/190490 [==============================] - 2s 10us/sample - loss: 0.7888 - val_loss: 0.8247\n"
     ]
    }
   ],
   "source": [
    "trainer = autoencoder.fit(x = X_train, \n",
    "                          y = X_train,\n",
    "                          epochs=50,\n",
    "                          validation_data=(X_test, X_test),\n",
    "                          batch_size=256,\n",
    "                          shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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79zpqJEmSJOWTomzdOIRwA3ABUBdCaAKuBooBYozXhRBmAauAKmAwhPC/gMYYY0cI4RPAbUAhcH2M8els1TmeUlUjS3bPrp6W42okSZIkDclaMIoxXv4i/TtIpsmN1XcrcGs26hoX/T3Q1QrVB/54qar0S147XIBBkiRJyif5OJVu4vuP98OP3n1Qs8FIkiRJyk8Go2xoWAat62HgwFc01VaUUFgQDEaSJElSnjEYZUNqOQz2wc6NBzQXFAQaKktdsluSJEnKMwajbGhoTPYtaw7qSt5l5IiRJEmSlE8MRtlQtwQKiqD54MX0UlWlBiNJkiQpzxiMsqGoJAlHYwajMqfSSZIkSXnGYJQtDY2HnErXvr+P7r6BHBQlSZIkaSwGo2xJNUL7VuhuP7DZJbslSZKkvGMwypbUycm+Ze2BzVWlAE6nkyRJkvKIwShbhlamG/Wc0dCI0Q5HjCRJkqS8YTDKlup5UFp9yGDUYjCSJEmS8obBKFtCgIZlBy3AUFVWRFlxgc8YSZIkSXnEYJRNqeXQvAZiHG4KIbhktyRJkpRnDEbZlGqEnnbo2HZgc1WZzxhJkiRJecRglE0Ny5P9GM8Z+YyRJEmSlD8MRtnUsCzZjw5GlaU0d/QQM6bYSZIkScodg1E2TZsB1fMPWoBhVnUZ+/sG6Ojuz1FhkiRJkjIZjLKtoTFZgCGzySW7JUmSpLxiMMq2VCPsXA/9vSNNlaUArkwnSZIk5QmDUbY1LIfBfmjbONw09JJXV6aTJEmS8oPBKNtSjck+YzrdUDDyJa+SJElSfjAYZVvtiVBQDC0jK9NNKymkqqzIZ4wkSZKkPGEwyraiEqhbMua7jJxKJ0mSJOUHg9F4SB28Mt2s6jIXX5AkSZLyhMFoPDQ0QkcT7N8z0lRZ5lQ6SZIkKU8YjMZD6uRk37J2pKmqlJbOHgYHY46KkiRJkjTEYDQehlamy1iAYVZ1Gf2Dkbau3kNcJEmSJGm8GIzGQ9VcKK0+YAGGhkqX7JYkSZLyhcFoPIRw0AIMqapSAFo6DUaSJElSrhmMxktqefKMUUyeKZpVnYwY7Wh3ZTpJkiQp17IWjEII14cQWkIIqw/RH0IIXw0hbAohPBVCOCOjbyCE8ER6uzlbNY6rhkboaYf2JgDqppcSglPpJEmSpHyQzRGj7wMXHab/YuDE9HYl8K2Mvv0xxhXp7dLslTiOUsuTffo5o+LCAmorSp1KJ0mSJOWBrAWjGOP9wK7DnPJW4Acx8RAwI4QwO1v15FzDsmSfsTJdqqqUHe0GI0mSJCnXcvmM0Vxga8bnpnQbQFkIYVUI4aEQwtsOd5MQwpXpc1e1trZmq9aXr6waqhccsADDrKoymjt8xkiSJEnKtXxdfOG4GONK4L3AV0IIxx/qxBjjd2KMK2OMK+vr68evwpci1QgtI8GooarMqXSSJElSHshlMNoGzM/4PC/dRoxxaL8ZuBc4fbyLy4qGRti5AfqTl7qmqkrZubeXvoHBHBcmSZIkTW25DEY3Ax9Ir053LtAeY3whhFATQigFCCHUAa8C1hzuRhNGajkM9ifhiGQqHUBLp9PpJEmSpFwqytaNQwg3ABcAdSGEJuBqoBggxngdcCtwCbAJ2Ad8OH3pMuDbIYRBkuB2bYxxcgSjhsZk37IGZp1MKh2Mmju6mTtjWg4LkyRJkqa2rAWjGOPlL9IfgT8do/23wCnZqiun6k6EguLhJbsbqkoBaPFdRpIkSVJO5eviC5NTYTHULx1egGFoKp1LdkuSJEm5ZTAabw2NwyNGNeUlFBcGmn3GSJIkScopg9F4SzVCxzbYv5uCgkBDZRnNTqWTJEmScspgNN5SJyf7lrXJx6pSg5EkSZKUYwaj8Ta0Ml16Ol2qqozmDqfSSZIkSblkMBpvVXOgrHpUMHLESJIkScolg9F4CwEalg+vTJeqKqOzu599vf05LkySJEmaugxGuZBanjxjFCOp9LuMnE4nSZIk5Y7BKBdSjdDTAe1bSaXfZeR0OkmSJCl3DEa50LA82Tc/nTFiZDCSJEmScsVglAsNy5J989OOGEmSJEl5wGCUC2VVUL0AWtYwvbSI8pJCnzGSJEmScshglCup5dC8hhCCS3ZLkiRJOWYwypVUI7RthP4eUlWlBiNJkiQphwxGudLQCIP9sHNDesTIqXSSJElSrhiMciU1tDLdmuGpdDHG3NYkSZIkTVEGo1ypPQEKS6AlWZmup3+Q9v19ua5KkiRJmpIMRrlSWAx1S9MjRkPvMnI6nSRJkpQLBqNcSjX6LiNJkiQpDxiMcqmhETq3M7skCUQ7DEaSJElSThiMcil1MgD1+58BoMVgJEmSJOWEwSiXUo0AlLatY0Z5sc8YSZIkSTliMMqlytlQNgOaV5OqLPMZI0mSJClHDEa5FELyPqPmNaSqDUaSJElSrhiMci21HFrWkppe4lQ6SZIkKUcMRrnW0Ai9nZxYupvWvT0MDMZcVyRJkiRNOQajXEstB+DEsIWBwUjbXkeNJEmSpPFmMMq1hmUAzOt9FsDpdJIkSVIOGIxyrbQSZiygrit5l5ELMEiSJEnjL6vBKIRwfQihJYSw+hD9IYTw1RDCphDCUyGEMzL6PhhC2JjePpjNOnMudTKVHRsA2GEwkiRJksZdtkeMvg9cdJj+i4ET09uVwLcAQggzgauBc4CzgatDCDVZrTSXGhop3LWJstBHi8FIkiRJGndZDUYxxvuBXYc55a3AD2LiIWBGCGE28EbgjhjjrhjjbuAODh+wJrZUIyEOcGZFq88YSZIkSTmQ62eM5gJbMz43pdsO1X6QEMKVIYRVIYRVra2tWSs0qxqSlenOLHvBqXSSJElSDuQ6GL1sMcbvxBhXxhhX1tfX57qcl6b2BCgsobFwq4svSJIkSTmQ62C0DZif8Xleuu1Q7ZNTYRHUL2Xx4BZaOp1KJ0mSJI23XAejm4EPpFenOxdojzG+ANwGvCGEUJNedOEN6bbJq2E5c3o2s6url57+gVxXI0mSJE0pRdm8eQjhBuACoC6E0ESy0lwxQIzxOuBW4BJgE7AP+HC6b1cI4X8Dj6RvdU2M8XCLOEx8qUamP/VjqtlLS0cP82eW57oiSZIkacrIajCKMV7+Iv0R+NND9F0PXJ+NuvJSKlmA4aSwlZbOboORJEmSNI5yPZVOQ9Ir0y0teJ7ndu7LcTGSJEnS1HJEwSiE8OchhKr0s0DfDSE8FkJ4Q7aLm1IqZxGn1bCiZBu/3rQz19VIkiRJU8qRjhj9UYyxg2QRhBrg/cC1WatqKgqB0LCcM0q3c/+GVgYHY64rkiRJkqaMIw1GIb2/BPi3GOPTGW06VlLLmdv3HLu6ulm9vT3X1UiSJElTxpEGo0dDCLeTBKPbQgiVwGD2ypqiUo0UD+xjXtjJfetbc12NJEmSNGUcaTD6CHAVcFaMcR/JktsfzlpVU1XqZAAurdvOfRsMRpIkSdJ4OdJg9ApgfYxxTwjhCuBzgHO9jrXZK6B6Ae8Jd/DY87tp39eX64okSZKkKeFIg9G3gH0hhNOAvwSeAX6QtaqmqsIiOOdK5nc8zjKedXU6SZIkaZwcaTDqT7+M9a3A12OM3wAqs1fWFHbGB4gl0/lY6W3ct6El19VIkiRJU8KRBqPOEMJnSJbpviWEUEDynJGOtbJqwor3cQm/5ffrN5DkUUmSJEnZdKTB6N1AD8n7jHYA84B/yFpVU905H6OQAS7afwvrdnTmuhpJkiRp0juiYJQOQ/8OVIcQ3gx0xxh9xihbao+nd/HruaLwTn69tinX1UiSJEmT3hEFoxDCZcDvgD8ELgMeDiG8K5uFTXWl5/1PakMng0/9NNelSJIkSZNe0RGe91mSdxi1AIQQ6oE7gRuzVdiUt/A8mstP4PzdP2Vv918zvcxHuiRJkqRsOdJnjAqGQlFa21Fcq5ciBDpP+ygnha2s++1/57oaSZIkaVI70nDzqxDCbSGED4UQPgTcAtyavbIEsOD8D7AzVjP9ie/kuhRJkiRpUjvSxRc+BXwHODW9fSfG+OlsFiYoKSvntzVv5aSO3xJ3bsp1OZIkSdKkdcTT4WKMN8UY/yK9/TybRWlE3xkfoicW0X7f13NdiiRJkjRpHTYYhRA6QwgdY2ydIYSO8SpyKjv75GX8YvCVVKz5Mezfk+tyJEmSpEnpsMEoxlgZY6waY6uMMVaNV5FT2fyZ5dxR+XaKB/bDY746SpIkScoGV5abAOYuO5eH4zIGH/42DPTnuhxJkiRp0jEYTQAXLK3nX/oupqCjCda5dLckSZJ0rBmMJoCzF83kN4Ur2VUyBx76Vq7LkSRJkiYdg9EEUFZcyNmL6/kRl8DWh2Dbo7kuSZIkSZpUDEYTxAVL6rmu41wGi6c7aiRJkiQdYwajCeL8pQ3spZx1c94GT/8cOrbnuiRJkiRp0jAYTRALa8tZMLOcf+1/I8RBeORfcl2SJEmSNGkYjCaIEALnL6nn5udLGFhyCaz6HvTuy3VZkiRJ0qRgMJpALlhaz/6+AdYseB/s3wVP/UeuS5IkSZImhawGoxDCRSGE9SGETSGEq8boPy6EcFcI4akQwr0hhHkZfQMhhCfS283ZrHOiOHdxLSWFBdy8+ziYdWqyCEOMuS5LkiRJmvCyFoxCCIXAN4CLgUbg8hBC46jTvgT8IMZ4KnAN8MWMvv0xxhXp7dJs1TmRVJQWcdaiGu7buBNe8aewcz08c3euy5IkSZImvGyOGJ0NbIoxbo4x9gI/Bt466pxGYOg3+3vG6NcoFyxpYEPzXrbPvQimp1y6W5IkSToGshmM5gJbMz43pdsyPQm8I338dqAyhFCb/lwWQlgVQngohPC2LNY5oZy/tB6A+zZ3wFl/DJvugNb1Oa5KkiRJmthyvfjCJ4HzQwiPA+cD24CBdN9xMcaVwHuBr4QQjh/rBiGEK9MBalVra+u4FJ1LJzZMZ3Z1Gfetb4UzPwyFpfDwdbkuS5IkSZrQshmMtgHzMz7PS7cNizFujzG+I8Z4OvDZdNue9H5ber8ZuBc4fawviTF+J8a4Msa4sr6+/pj/EPlmaNnu32zaSd+0Wjj1MnjiBti3K9elSZIkSRNWNoPRI8CJIYRFIYQS4D3AAavLhRDqQghDNXwGuD7dXhNCKB06B3gVsCaLtU4oFyytp7Onn8e27IZz/wT698Nj/5rrsiRJkqQJK2vBKMbYD3wCuA1YC/wkxvh0COGaEMLQKnMXAOtDCBuAFPCFdPsyYFUI4UmSRRmujTEajNJeeUIdhQWB+za0Qmo5LDofHv4ODPTlujRJkiRpQgpxEr0HZ+XKlXHVqlW5LmNcXHbdg3T19nPLn50H638FN7wb3vldOOVduS5NkiRJylshhEfTaxkcINeLL+glOn9pPU9v76ClsxtOfAPMPB4e+DL09+S6NEmSJGnCMRhNUOcvSRaauH/DTigogDd+AVqehjs+n+PKJEmSpInHYDRBNc6uom56afKcEcDSi+Hc/5Es3b32F7ktTpIkSZpgDEYTVEFB4DVL6nhgYysDg+nnxF73tzDndPivP4XdW3JboCRJkjSBGIwmsAuWNrBnXx9PNu1JGopK4F3fgxjhxj+C/t7cFihJkiRNEAajCey8E+oIAe5b3zrSOHMRXPpV2LYK7r4md8VJkiRJE4jBaAKrqSjhtHkzRp4zGrL87bDyI/Dbr8GG23JTnCRJkjSBGIwmuAuW1vNk0x52dY2aNvfGv4PUKfDzj0P7ttwUJ0mSJE0QBqMJ7vwl9cQID2wcNWpUXAZ/+P3kvUY3fQQG+nNSnyRJkjQRGIwmuFPnzWBGefHB0+kA6k6At3wFnn8Q7v278S9OkiRJmiAMRhNcYUHgvBPruX/DTgaHlu3OdOplcPr74YEvw6a7xr9ASZIkaQIwGE0CFyypZ+feHta80DH2CRf/PdQvhZ9dCZ07xrc4SZIkaQIwGE0C5y2pAxh7Oh1ASXnyvFFvF9z0xzA4MH7FSZIkSROAwWgSaKgsY/mcKm57egcxjjGdDqBhGbzpS/DcA3D/l8a3QEmSJCnPGYwmicvPXsBTTe38avVhpsqteB+c+m6471p49oHxK06SJEnKcwajSeI9Z81naaqSL9y6lu6+Q0yVCwHe9MZcsgAAACAASURBVI8wc3EypW7vIabeSZIkSVOMwWiSKCos4PNvaaRp936+++tnD31iaWXyvNH+3fDzK2FwcNxqlCRJkvKVwWgSedUJdbyhMcU37tlEc0f3oU+cdQpc9EV45m74zVfGr0BJkiQpTxmMJpnPvmkZ/QORv//V+sOfuPKPoPFtcPf/gS0Pjk9xkiRJUp4yGE0yx9VW8OFXL+Smx5p4cuueQ58YAlz6VZgxH276COxtGb8iJUmSpDxjMJqEPvHaE6ibXso1/73m0Mt3A5RVw7u+B/t2wXdfDzs3jV+RkiRJUh4xGE1ClWXFfOqNS3h0y25ufnL74U+eewZ86L+hpzMJR88/PD5FSpIkSXnEYDRJvevM+SyfU8W1v1zH/t5DLN89ZN5K+MgdMG0G/OBSWPuL8SlSkiRJyhMGo0mqsCBw9VuW80J7N9++/5kXv6D2+CQczToF/uP98PC3s1+kJEmSlCcMRpPY2Ytm8qZTZnPdfc+wfc/+F7+gog4+cDMsvQR++Vdw22d9z5EkSZKmBIPRJHfVxScRI/zfX607sgtKyuHd/wZnfRQe/Drc9EfQd5h3IkmSJEmTgMFokps/s5wrX7OY/3piO49u2XVkFxUUwiX/AK+/Bp7+OfzwHcnKdZIkSdIkZTCaAj5+/vGkqkr521+sYXDwMMt3ZwoBXvXn8M7vQtMjcP1FsOf57BYqSZIk5YjBaAqoKC3i0xedxFNN7fzs8W1Hd/Ep74L3/xz27oB/eR288GR2ipQkSZJyKKvBKIRwUQhhfQhhUwjhqjH6jwsh3BVCeCqEcG8IYV5G3wdDCBvT2wezWedU8LYVczlt/gz+/lfr6OrpP7qLF74a/ug2KCiG710Cm+7MTpGSJElSjmQtGIUQCoFvABcDjcDlIYTGUad9CfhBjPFU4Brgi+lrZwJXA+cAZwNXhxBqslXrVFBQELj6LY20dPbwzXs3Hf0NGpbBH98JMxfBv18Gj/3bsS9SkiRJypFsjhidDWyKMW6OMfYCPwbeOuqcRuDu9PE9Gf1vBO6IMe6KMe4G7gAuymKtU8IZC2p424o5/PMDz7J1176jv0HVbPjwL2Hx+XDzJ+CeL0I8wmeWJEmSpDyWzWA0F9ia8bkp3ZbpSeAd6eO3A5UhhNojvFYvwacvPonCEPjiL9e+tBuUVsJ7fwIrroD7roWbPgJ7W45tkZIkSdI4y/XiC58Ezg8hPA6cD2wDBo7mBiGEK0MIq0IIq1pbW7NR46Qyu3oaHz//eG79/Q4e2tz20m5SWAxv/Tr8wedgzc3wtZXw0HUwcJTPLkmSJEl5IpvBaBswP+PzvHTbsBjj9hjjO2KMpwOfTbftOZJrM+7xnRjjyhjjyvr6+mNZ/6R15WsWM3fGNK75xRoGjnT57tFCgNd8Cv7HgzDvTPjVp+Hb58Fzvz62xUqSJEnjIJvB6BHgxBDCohBCCfAe4ObME0IIdSGEoRo+A1yfPr4NeEMIoSa96MIb0m06BqaVFHLVxSex5oUOfrpq64tfcDh1J8IVP4N3/zv07IXvvwlu/Ah0bD82xUqSJEnjIGvBKMbYD3yCJNCsBX4SY3w6hHBNCOHS9GkXAOtDCBuAFPCF9LW7gP9NEq4eAa5Jt+kYefOpszlrYQ1fun09Hd19L+9mIcCyN8OfPgznXwVrf5FMr/v1V6C/99gULEmSJGVRiJNoVbGVK1fGVatW5bqMCeP3Te1c+o1f89HzFvPXlyw7djfe9Szc9tew/laoPQEu/ns44cJjd39JkiTpJQohPBpjXDm6PdeLLyiHTplXzbvOmMf3fvMs63d0Hrsbz1wEl98A7/0pxEH44Tvgx++D3VuO3XdIkiRJx5DBaIr71EVLqZ5Wwoe+9zu27dl/bG++5A3wPx6CCz8Pz9wN3zgb7v2/0HeMv0eSJEl6mQxGU1xDZRk/+KOz2dvTzwe++zC7uo7xM0FFpXDeX8InHoGlF8O9fwffOAfW3erLYSVJkpQ3DEaicU4V3/3gWTTt3s+Hv/8IXT1ZeB9R9Tz4w+/DB26G4mnw48vhuvPgsR84giRJkqScMxgJgLMXzeTr7z2D1dva+fgPH6Wn/6jes3vkFp8PH/81vOWryfNHN/9P+PIyuOPzsOf57HynJEmS9CIMRhr2+sYU177jFB7YuJO//MmTL/3lry+msBjO/CD8yW/gQ7fAwvPgt1+HfzotWaRh831Os5MkSdK4Ksp1Acovf7hyPru6evniL9dRU17CNW9dTgghO18WAix8dbK1N8Ej34VHvw/r/hvql8HZH4XT3gMlFdn5fkmSJCnNESMd5GPnH8/HXrOYf3toC1+5c+P4fGn1PHjd1fAXa+Ct30hGlW75i2Sa3W2fTd6NJEmSJGWJI0Ya01UXn8Surl7+6a6N1E4v4QOvWDg+X1w8DU6/Ala8D7Y+DA9/Gx6+Dh78Bix5I5x9JRz/B8lokyRJknSMGIw0phACX3zHKeze18fVNz/NjPISLj1tzngWAAvOTbaO7bDqe/Do95KXxc44Dpa9BRrfCnNXQoEDn5IkSXp5QpxED7mvXLkyrlq1KtdlTCrdfQN84Prf8fjzu/nuB8/iNUvqc1dMfw88/Z/w+5/C5nthsA+mz4Jlb06C0nGvhkKzviRJkg4thPBojHHlQe0GI72Yju4+3v3th9jS1sW///E5nL6gJtclQXc7bLgd1t4Mm+6Evn0wrQaWvikJSYsvgOKyXFcpSZKkPGMw0svS0tnNu771IB3dfdz48VdwQkNlrksa0bsPnrkL1v4C1v8KetqhZDqc+IYkJJ34eijNo3olSZKUMwYjvWxb2rp457cepLgwcNOfvJI5M6bluqSD9ffCc/cnIWndLdDVCoWlcMKFcNKbk5A0vSHXVUqSJClHDEY6Jp7e3s57vv0QDVWl/PTjr2RmRUmuSzq0wQF4/qEkJK39BXQ0Je2zToHjL0xWt1twLhSV5rZOSZIkjRuDkY6Zhze38YHrf8dJs6v40R+fQ0XpBFjwIEZ44clkyt2mu2HrQzDYD8XlsPC8JCSdcCHUnuBS4JIkSZOYwUjH1B1rmvn4Dx/ljAUz+NrlZzCreoItdNDTCc/9GjbdlYSlXZuT9uoFcMIfJEFp0fkwbUZu65QkSdIxZTDSMfeLJ7fzVzc+RWlxAf/3nafyxuWzcl3SS7frWXjm7mTbfB/0dkIohHkrk2l3i14Dc053pTtJkqQJzmCkrHimdS9//uPHWb2tg/eds4DPvamRaSWFuS7r5Rnog6ZV6Wl3d8H2x4EIBcUwZwXMPwfmn53sKydwGJQkSZqCDEbKmt7+Qf7x9vV8+/7NnNgwna9efjrLZlfluqxjZ9+uZBGHrQ/B1t/BtsdgoCfpm7EA5p87EpQaGn3JrCRJUh4zGCnrHtjYyl/85Ena9/fxmYtP4kOvXEiYjAsZ9PfCjqdg68PJ9vzDsHdH0lcyHeaemR5VOieZiudzSpIkSXnDYKRx0ba3h7+68SnuWtfCa5fW8w9/eBp10yf5ctgxwp7nk9GkobDUvBriYNI/8/hkCt7s02B2em9YkiRJygmDkcZNjJEfPLiFL9y6lqqyYr582Wm8Zkl9rssaXz17Yduj0PQ72P5EslR4+9aR/ppF6bC0YiQ0TavJXb2SJElThMFI427djg7+7IbH2dC8l4+et4hPvnEppUUTfGGGl6NrJ7zwRDooPQHbn4T250f6axYeGJRmr4DymTkrV5IkaTIyGCknuvsG+MIta/m3h7awfE4VX738dI6vn57rsvJHVxvseDIjLD0Be7aM9FfOgdRymHUypE6GWackU/Nc4EGSJOklMRgpp+5Y08xf3fgk3X2D/H+XNnLZyvmTc2GGY2HfrmTq3Y7fJ88qNT8NretgsD/pLyqD+pNGwlLq5CQ8ObokSZL0ogxGyrnmjm7+4idP8JtNbVxyyiyufstyUlW+MPWI9PfCzvVJSBoKTDtWw76dI+dUzU2PKp0M9cugfgnUnggl5bmrW5IkKc8YjJQXBgcj33lgM/94+3pCCLz37AX8yQXHG5Beqs5maP59OjCtTgLTzg0jo0sEqDkuGWGqXzqyr1sKpU5plCRJU4/BSHnl+bZ9fOOeTdz4WBNFBYH3nrOAPzn/eBoMSC9ffy/s2pxMv2tdP7Jv2wgDvSPnVc8/OCzVL3F1PEmSNKkZjJSXnm/bx9fv2chNj22jqCDwvnOO4+PnLzYgZcNAP+x+Lh2UMkLTzg3Q3z1y3rSZUHsC1B6fbDOPTz7PXOwokyRJmvByEoxCCBcB/wQUAv8SY7x2VP8C4F+BGelzroox3hpCWAisBdanT30oxvjxF/s+g9HE9XzbPr5290Z+9ngSkK449zg+dv5iGioNSFk3OJC8oLZ1HezcCLuegbb01rn9wHMrZ4+EpOHwdEKy1HjRJH+RryRJmhTGPRiFEAqBDcDrgSbgEeDyGOOajHO+AzweY/xWCKERuDXGuDAdjP47xnjy0XynwWji29LWxdfu3sTPDUj5oWdvMi1v1zPQtgnaNif7Xc/AvraMEwNUz0sCUuY2c1HyMttpNeAqhJIkKQ8cKhhl82UoZwObYoyb0wX8GHgrsCbjnAhUpY+rgVH/PK2p5rjaCr70h6fxideewNfu3sT3fvMs//7wFq445zg+dv7x1Fc6KjGuSqfD7FOTbbR9u5LQ1JYOTbufS7YNt0FXy6j7VI0RmNLH1fOhsDjLP4gkSdLhZXPE6F3ARTHGP05/fj9wTozxExnnzAZuB2qACuB1McZH0yNGT5OMOHUAn4sxPnCI77kSuBJgwYIFZ27ZsmWs0zRBPbuzi6/dvZH/fHwbJUUFvP/c4/joaxxBynu9XSNBaWjb9Wyy37PlwEUgQkEyRa96XhKSZsxP7xeMtPlskyRJOkZyMZXuSILRX6Rr+McQwiuA7wInA8XA9BhjWwjhTOA/geUxxo7DfadT6SavZ3d28bW7NvKfT2yjIAQuWFrPu86cx2tPaqC0qDDX5eloDA4mzy4NhaX2rbBna3r/PHRsy1huPG1azcFhacZ8qJwDVbNhespRJ0mSdERyMZVuGzA/4/O8dFumjwAXAcQYHwwhlAF1McYWoCfd/mgI4RlgCWDqmaIW1VXw5Xev4H9eeCI//t3z/Pzxbdy5toUZ5cVcetoc3nnGPE6dV03wOZb8V1CQDjfzYOGrD+4fHIDOHdDeNBKWhsJT2yZ45h7o6xp1UYDpDcnIU+XsJCwNhabK2VA1BypnQdkMn3WSJEljyuaIURHJVLgLSQLRI8B7Y4xPZ5zzS+A/YozfDyEsA+4C5gJ1wK4Y40AIYTHwAHBKjHHX4b7TEaOpo39gkF9v2smNjzZx+5pmevsHOaFhOu86cx5vP32uL4ydzGKE/buTsNS5Azq2Q+cLGfsXkv3+Mf66KC5PAtJwaJp1YIAa2opKxv/nkiRJ4yJXy3VfAnyFZCnu62OMXwghXAOsijHenF6J7p+B6SQLMfxVjPH2EMI7gWuAPmAQuDrG+IsX+z6D0dTUvr+PW556gZsea+LRLbspCPDqE+t55xlzeePyWZQVO9VuSurrTgLScGjaMSpApdsGeg6+trwuY+Rp1ChU5azkc3ltMvolSZImFF/wqinh2Z1d/OyxJn722Da27dlPZWkRbzp1Nu86cx5nHlfjVDsdaGj0aTg4bR8ZccoMT12tJP92k6GgCKbPSgelzG12xn62S5VLkpRnDEaaUgYHIw8928aNjzbxq9U72Nc7wHG15Vy0fBZ/cFIDZx5XQ1Gh/9qvIzTQNzLi1Llj1PELsLc5PX1v98HXFpaMClCZwSk18tnnnyRJGhcGI01ZXT39/HL1Dv7riW08tLmNvoHIjPJiLlhSz4XLUrxmST3V01zRTMdAXzfsHSM4DYep9NbTfvC1RWVJQJo+OkDNgor6jK3OFfgkSXoZDEYS0NndxwMbd3Ln2mbuXd/Krq5eigoCZy2cyYXLGrhwWYpFdRW5LlOTXW9XEpCGRpoOFaB6O8e+flrNgUHpgOCUsZXPTEaifBZKkqRhBiNplIHByBNbd3Pn2hbuWtvMhua9ACyur+B1y1Jc6JQ75VrP3iQ8dbUm294W6No58rlrJ3S1JMdjTeOD5AW6ZTOSMFU+E6bNTO9r0sc1GccZ7SUVTu2TJE1KBiPpRWzdtY+71jZz17qW4Sl31dOKuWBpPRcsrecVi+uYVe0y4MpTA32wry0jQLXCvl3JsuX7diXBafh4T3Lcu/fQ9ysogrLq9DZj5HjajEO0ZXwurUymBhqsJEl5yGAkHYWxptwBLKwt5xXH13Lu4mTzfUma0Pp7RkLS6PDU3Z7e9iT7/XsObBvoPfy9C4qgtCoJSUP7sqHPlQf3lVZC6XQomZ6MVmXufa+UJOkYMhhJL9HAYGTtCx08tLmNhza38fCzu+js7gdgUV0F5y6eaVDS1BIj9HcfHJb274GeDujpzNhnbN3tB34e6x1SYykoTkJSaWU6LA1tQ5/LoTi9L6kYOS4uHzl36DhzX1TqqJYkTUEGI+kYyQxKDz7Txu+e3UVnTxKUFtdVcM7i2mRUadFMGgxK0qH19xwYonq7ki3zuHfvIfZdyTNYvZ3Quw/69iVto983dTihMAlIxdPSW/mB+6FwdUBf+rioLN1WlvE53Z/ZVzTNACZJecZgJGXJwGBkzfaREaXRQen0BTWsWDCD0+fPYOmsSopdzEHKjqGRrN59SYDq25cOTV0Z+65RbfuT8w7aj27bf/TBa1g4MDAVlaY/lyZtw9uLtBeWpD+n94WlGccZfYWlBx4XFhvMJCmDwUgaJ/0Dg6xJjyg9vHkXT2zdQ1v6GaXSogJOnlvNivkzOG1+Epbm1Uwj+EuLlP9iTEa5hsJSf/dIaOrfn7zHqm9fun1f8rl//8g5Q9f0d6fvsz/ZD7eN0T7YdwwKD+nglA5JhSUj23BbRt8BbSUZYSu9DZ9TmtF3iEA2dP7w946qwaXkJeWAwUjKkRgjTbv388TWPTy5dQ9PbN3D77e109M/CEBtRQmnzZ8xHJZWzJtBdbkv8JQEDPQnz2L1dSf7/m7o7032A73pANWT7jvM8UBvsnLh8HHvYdp6k+8Y6En3d2d8fpFFN45WQVFGcBoKUUXJc2WFxVBQmHGc/jx0XFiUXD/cn/48fJy+tqDowHOH+obPG/qewpF7FBQlS91nfs48JxQe3DbcntmXcS//AUzKG4cKRkW5KEaaSkIIzJ9ZzvyZ5bzltDkA9A0Msn5HJ0+kg9KTW/dwz/oWhv6dYnFdBafMq2b5nCqWz0n2M8pdmUuacgrTv9SX5MmLp2McCWTD+56Dw9pQqDoofA21ZfT3Z56bPmewDwb7k2A42JduG0i+o6cz3dafnDN8nL5m+Lr0Fgdy/aeWCKMCUyhIjkPhgccFBSMBKxSM0TZqf0BYG903Rvvwd6XD2lDbAe2jtsxaQsFIPWP+DOHg+oevGX3dkfQP1ZnRd9AWDl+/wVRHyBEjKU90dPexuqmdx9NhafW2dl5o7x7unztjGo1zqg4IS7Ory5yGJ0mHMziYhKOBjOA0HKCG2gYODFLDnzPaBwfSff2j+gYOfd2L3msAYrq+oTqH+uLgSP8B52dcd1BdgxnHo84f7B/5rhgPvP9w+2Cu/9fKrkOGpszwdST9YwWwsa4f3XaY7yC8+PmEo7hvGOO6sc452poO8X0H/fmOUfsJr8ub6bOOGEl5rqqsmFeeUMcrT6gbbtvV1cvT29t5entHemvnzrXNwyNLNeXFwyGpMR2YFtVVUFhgWJIkIP2LWEEydU4vLsaDA9MBnwdHBbqx+gbGCF6jrhv+HMc4P31fGOPa0VvG9UPnEUf1j3XdGP0HXTvqnEPWcJjvIWZcP0b/8HfGse8x5s8y9DPHMc471M8bR87Llb9pA/IjGB2KwUjKYzMrSjjvxHrOO7F+uK2rp591OzpZkxGYvveb5+gdSP6lb1pxIUtS01k6q5IlqUpOmlXF0lmV1E0vcXRJknR4ISTTNzU5xTEC1OHC4AFBa+DAgHVQ/6j7jA51BYU5/uFfnP/PlyaYitIizjyuhjOPqxlu6xsYZFPLXp7e3sGa7R2sb+7g7nUt/GRV0/A5MytKWJqqZOmskW1JqpLppf41IEnSlDD0DJjG5G9E0iRQXFjAstlVLJtdBWeOtO/c28OGHZ2s29HJhuZk/5NVW9nXO/Iw8ryaaZyUDklLUpUsrq9gcf10A5MkSZpS/M1HmsTqppdSd0LpAc8tDQ5Gtu3Zz7odnazf0cH65r2s39HBvetb6R8cmXvcUFk6HJIW11VwfP10FtdXMK+m3GeYJEnSpGMwkqaYgoKR5cNf35gabu/tH2RLWxfPtHaxeedeNrd2sbl1L7c89QLt+0deMllSVMDC2nIW100fCU71FRw3s5yZFT7HJEmSJiaDkSQgCTwnpio5MVV5QHuMkV1dvWzemQSlza1JeNrQ0smda5sPGGWqLC1iQW05x9WWs2BmBcfVlnPczHIW1JYzu3qaI02SJClvGYwkHVYIgdrppdROL+WshTMP6OsbGGTrrn1sbu1iy659PN+W7Ne90Mkda5rpGxgJTSWFBcyrmZYEp5nlLKitGA5Nc2dMo8JnmiRJUg75m4ikl6y4sCA9lW76QX0Dg5EX2vfzfNs+tuzax5a2fTy/q4stbft49LnddPb0H3D+zIoS5s6YxryaacP7eTXlzK1JjivLfAeJJEnKHoORpKwoLAjMqylnXk05rxzVF2Nk974+trR1sXX3fpp272Pb7v007d7PhuZO7lnfQnffgW9gr55WPBKc0qFpTnUZs6rLmF09jfrKUqfqSZKkl8xgJGnchRCYWVHCzIoSTl9Qc1B/jJG2rl6aRoWmpt37eHZnF7/etPOAJcchCWKpylJmz5iWhKWqMmbPmMbsdHiaY3iSJEmHYTCSlHdCCMlS49NLWTF/xkH9QyNOL7TvZ0d7N9vbu9nRvp8X2rt5YU83a7Z3cOeaZnr6Dxx1GgpPqeoyUpVJYGqoKmVWVRmp4a2U6aVFrq4nSdIUYzCSNOFkjjgtn1M95jkxRvbs6+OF9m52dOxn+57udIjaT3NHN5ta9/KbZ3bS2d1/0LXlJYXMqhorNCXBKVVVRn1lKWXFvj1ckqTJwmAkaVIKIVBTUUJNRQmNc6oOeV5XTz8tnT3saO+mpTMJT80dPTR3dtPc3s2jz++muaOH3lGjTwAzyotJVSYBKjM0NVQeGKCKCwuy+aNKkqRjwGAkaUqrKC1iUWkRi+oqDnnO0OjTjo5uWjp7aO7opqUjHaA6umnu7GFTy05aOnsYyHiv05DaihIaqspoqCxNtuEAVUp9OkTVV5ZSWuQIlCRJuWIwkqQXkTn6tGz2oc8bHEwWjWju6KY1HaCaO3rY0dFNa2cSqtbt6GDn3t4xA9SM8mIaKkdGmhoqy4aDVOZxeYl/dUuSdKxl9b+uIYSLgH8CCoF/iTFeO6p/AfCvwIz0OVfFGG9N930G+AgwAPxZjPG2bNYqSS9XQUGgvjIZ/TmcgcHIrowA1dLZTUt6+l5LRw8tnT1sbu2ipbP7gJfkDpleWjT8PQ1DAaqqND0CNRKiZpQXu4iEJElHKGvBKIRQCHwDeD3QxP9r795iJLnuOo5/f1XdPVd715dNCL7EMTFKHJQ4YmViHCTjADIkwnkw4NxkUCTzYFCCgiBBQQFLeeCBBB4iiAHLJjg4zsVgRUjYMZYhUhJ7nTj4kkCMSchujNfEu+udnZm+1Z+HOtVdPTsez3h2pmfcv4/UqnNOnao6vTraml9XdTU8KOmuiHi81u3DwB0R8ReSLgb+Cbggla8FXgf8KPAlST8eEaPP5zUz24XydQaoogiOLnUHwakMUSlIHW/zzHNtHj10jMPHD5/0+HKARib2zrY4a67FGXNNzpxrccZsa/DgitXqfqCEmZlNqq28YnQp8EREPAkg6XbgaqAejAKovhW9B/hBKl8N3B4RbeC/JT2R9veVLRyvmdmOkmXDp++95kfW7rvQ7nE4fQeqClA/XGhzZLHDsyc6HDnR5T+fXijLix3i5AtRAMw0c86ca7Fnpsne2fK1Z6bJnplWWZ9J9dkme1Pbnpkms63cV6fMzGxX28pgdA7w/Vr9IPBTK/r8EXC3pN8G5oCfq2371RXbnrPaQSRdD1wPcP7552960GZmu9H8VIP5ffNcuG/+Bfv2i+C5pS7PLnY4cqIzCEs/PFHVuxxb6nJsqcN3nl7g6FKXY4tdOv2Tn8xXaeZiz0yL02canDbd5PTpBqdNNzh9uslp02VbtTy9Vh+ub9Dw0/vMzGyMxv0N3ncAt0TEn0q6DPiUpJ/YyA4i4ibgJoD9+/c/z2egZmZWybPhwyTYt75tIoLlbsHRpQ5HF7scXRyGp6OLXY4ulW3PLXc5vtzj+HKXHxxdSuUeS90XvhN6upkxP9VkfipnfrrBXKsMTHNTjTL4pdfcVIP56dH63FTOXKvBbCtnbqrBVCPzFSwzM9uQrQxGh4DzavVzU1vde4GrACLiK5KmgbPXua2ZmW0TScy0cmZaM7xiz8yGt+/2CxaWe4PgNAxQZYh6bqnHiU6PhXaPheUeJ9o9jrd7PHVsmYV2qi/3aK/ye1KryQRzrQYzKSjNtlJwWhGgpps5s63yVZVnmjkzrZzZVqNWHrY7dJmZvTRtZTB6ELhI0qsoQ821wDtX9Pkf4C3ALZJeC0wDzwB3AZ+W9DHKhy9cBDywhWM1M7Mt1Myz4VWqTej2C060U4CqQlSnz2I7LTs9TrT7LHVG69XyyIkOB48sDfovdfpr3iK4mkzld7Gm02smhabpZlbWU9t0Iy2bw/VVsJpu5kw1httUbdOp32BdIyfLHMLMzLbDlgWjiOhJ+i3gnykfxX1zRDwm6UbgQETcBXwA+CtJv0P5IIZfj4gAHpN0B+WDGnrADX4inZmZNfOMvbMtygfTywAACc1JREFU9s5uLmDV9foFS90yJC12+ix103JQ7tXKw/blbm3Z6bPcLTi+3OOZ4+3Buqp9o+GrrpVnTDUyplKAmmpktKp6njHVTOsbedneGPavtm3VX3k26FfWc6aaw/aqT327Zp7RyOQrZWb2kqZ4vkcT7UL79++PAwcOjHsYZmZmI/pFjASpdq9guVuGpnavT7tbrGivygXLvbLe6RW0q1daX7b1h+29Wr+071V+S/hFkcpgOpVnNFN4ajZEM6+FqrwMUVWYmhoEKw1CWLOhch/5aPCqwlojK/s384xGLhpZRqtRLhupvQpqVZ9mVo4lz8qyr7KZ2VokPRQR+1e2j/vhC2ZmZi95eab0kIjtPe1GBL0i6KQQ1ekXg+DUqQWpqr0qV+s7vYJuf7hs9wu6vaDT76flcNt6v8XFHp1+0OmVtypWfbu9ch+ddX5X7MWSoJmCVJ4CVBmaRJ6CVJ6JRl6GsDwTjaxaZqP1XORZVltfX2bkGYP1WW19nolcw31k0vP2qdqy2jbVq9quKpftDMqZRvtW+1TGYF/Dvviqn9kaHIzMzMxeoiQNrr7Mrf17wttqZWDrVmGsX9DrB91+2dYryvKwLegVZb3q2yvK9m6/oF/Ul0E/resXw+16K/r0irJc9gmWun16RbltL21brav2U9UH62PYb6fLVIaqKoRlKn8zrQpQZYgqQ5VqoUtpu7Kdk8JWVa72kWW1cu2Yo+UV2wzWjbapvr42lkzDcT7f+mywnpH91cc+2pdV+4vh2EbqI2M8eVn1Kfe79jarHV8Csfb6LIXd1fZrG+NgZGZmZttqpwa2zYoIioBeUVAUjISuKkDVw1RRC1xFCovFiv6Ddf1y2S8YbFvfz2hbOZaR9Wm7ensEg2NFVMeDIh1zuO/qvQVFQblM77UaX9TK/SLopPcZDMdSRL1cbjM8DoMxFFGOIUjHKoZ9i7SMSP8OqWyrq4dEMQxSGgleDIKqqAerYZADylBYC2lU++Dk0KbaOqV1n/3Ny3b879U5GJmZmZmdApLIBXmWp5Z8zf52asSKkBVBLUgNA1kVqoqiVq5vWwtyZdton5X7hGE4jNoxgnqAq4fJtG9qIa8IgtHjrTz+YJsqXDLcrqiNi5E+tePVQmUwDJrVOOpjL2L4b1AUK99LffzpmLV1I9uO/HuU5d1wBcvByMzMzMx2reqKRIb8h61tys6+nmVmZmZmZrYNHIzMzMzMzGziORiZmZmZmdnEczAyMzMzM7OJ52BkZmZmZmYTz8HIzMzMzMwmnoORmZmZmZlNPAcjMzMzMzObeA5GZmZmZmY28RyMzMzMzMxs4jkYmZmZmZnZxHMwMjMzMzOziedgZGZmZmZmE08RMe4xnDKSngG+N+5xJGcD/zfuQdiu5jlkm+U5ZJvlOWSb5Tlkm7UVc+iVEbFvZeNLKhjtJJIORMT+cY/Ddi/PIdsszyHbLM8h2yzPIdus7ZxDvpXOzMzMzMwmnoORmZmZmZlNPAejrXPTuAdgu57nkG2W55BtlueQbZbnkG3Wts0hf8fIzMzMzMwmnq8YmZmZmZnZxHMw2gKSrpL0H5KekPTBcY/Hdj5JN0s6LOnRWtuZku6R9J20PGOcY7SdS9J5ku6T9LikxyS9L7V7Dtm6SJqW9ICkb6Y59Mep/VWSvpbOZ5+R1Br3WG1nk5RL+oakL6a655Ctm6TvSnpE0sOSDqS2bTuXORidYpJy4BPALwIXA++QdPF4R2W7wC3AVSvaPgjcGxEXAfemutlqesAHIuJi4E3ADen/Hc8hW682cGVEvAG4BLhK0puAPwE+HhGvBo4A7x3jGG13eB/wrVrdc8g26mcj4pLaI7q37VzmYHTqXQo8ERFPRkQHuB24esxjsh0uIv4VeHZF89XAral8K/D2bR2U7RoR8VREfD2Vj1P+UXIOnkO2TlFaSNVmegVwJfC51O45ZGuSdC7wVuCvU114Dtnmbdu5zMHo1DsH+H6tfjC1mW3UyyPiqVT+X+Dl4xyM7Q6SLgDeCHwNzyHbgHQL1MPAYeAe4L+AoxHRS118PrMX8mfA7wFFqp+F55BtTAB3S3pI0vWpbdvOZY2t2rGZnToREZL8CElbk6R54PPA+yPiufLD2pLnkL2QiOgDl0jaC9wJvGbMQ7JdRNLbgMMR8ZCkK8Y9Htu13hwRhyS9DLhH0rfrK7f6XOYrRqfeIeC8Wv3c1Ga2UU9LegVAWh4e83hsB5PUpAxFt0XEF1Kz55BtWEQcBe4DLgP2Sqo+RPX5zNZyOfDLkr5L+TWCK4E/x3PINiAiDqXlYcoPaC5lG89lDkan3oPARekpLC3gWuCuMY/Jdqe7gOtS+TrgH8c4FtvB0n38fwN8KyI+VlvlOWTrImlfulKEpBng5ym/q3YfcE3q5jlkzysiPhQR50bEBZR/+/xLRLwLzyFbJ0lzkk6rysAvAI+yjecy/8DrFpD0S5T32ebAzRHx0TEPyXY4SX8PXAGcDTwNfAT4B+AO4Hzge8CvRsTKBzSYIenNwL8BjzC8t/8PKL9n5DlkL0jS6ym/1JxTfmh6R0TcKOlCyk//zwS+Abw7ItrjG6ntBulWut+NiLd5Dtl6pblyZ6o2gE9HxEclncU2ncscjMzMzMzMbOL5VjozMzMzM5t4DkZmZmZmZjbxHIzMzMzMzGziORiZmZmZmdnEczAyMzMzM7OJ52BkZmYTTdIVkr447nGYmdl4ORiZmZmZmdnEczAyM7NdQdK7JT0g6WFJn5SUS1qQ9HFJj0m6V9K+1PcSSV+V9O+S7pR0Rmp/taQvSfqmpK9L+rG0+3lJn5P0bUm3SdLY3qiZmY2Fg5GZme14kl4L/BpweURcAvSBdwFzwIGIeB1wP/CRtMnfAr8fEa8HHqm13wZ8IiLeAPw08FRqfyPwfuBi4ELg8i1/U2ZmtqM0xj0AMzOzdXgL8JPAg+lizgxwGCiAz6Q+fwd8QdIeYG9E3J/abwU+K+k04JyIuBMgIpYB0v4eiIiDqf4wcAHw5a1/W2ZmtlM4GJmZ2W4g4NaI+NBIo/SHK/rFi9x/u1bu4/OjmdnE8a10Zma2G9wLXCPpZQCSzpT0Ssrz2DWpzzuBL0fEMeCIpJ9J7e8B7o+I48BBSW9P+5iSNLut78LMzHYsfyJmZmY7XkQ8LunDwN2SMqAL3ACcAC5N6w5Tfg8J4DrgL1PweRL4jdT+HuCTkm5M+/iVbXwbZma2gynixd51YGZmNl6SFiJiftzjMDOz3c+30pmZmZmZ2cTzFSMzMzMzM5t4vmJkZmZmZmYTz8HIzMzMzMwmnoORmZmZmZlNPAcjMzMzMzObeA5GZmZmZmY28RyMzMzMzMxs4v0/1AD/ILM5BhgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss_by_epoch(trainer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: Testing error is on average higher since testing dataset contains abnormal instances but training data set contains only normal observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            reconstruction_error\n",
      "true_class                      \n",
      "0                       0.783167\n",
      "1                      24.907437\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_reconstructuion_err_by_class(X_test, y_test, model = autoencoder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_precision_recall_curves(X_test, y_test, model = autoencoder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using negative learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Discriminative autoencoders aim at learning low-dimensional discriminative representations for positive (X+) and negative (X−) classes of data. The discriminative autoencoders build a latent space representation under the constraint that the positive data should be better reconstructed than the negative data. This is done by minimizing the reconstruction error for positive examples while ensuring that those of the negative class are pushed away from the manifold.\n",
    "\n",
    "\n",
    "S. Razakarivony and F. Jurie, \"Discriminative autoencoders for small targets detection,\" in Pattern Recognition (ICPR), 2014 <br />\n",
    "ref: https://arxiv.org/pdf/1801.03149.pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow.keras.backend as K\n",
    "\n",
    "def negative_learn_loss(y_true, y_pred):\n",
    "    (y_true, t) = tf.split(y_true, num_or_size_splits=[-1, 1], axis=-1)\n",
    "    return K.sum(K.maximum(t*(K.sqrt(K.sum(K.square(y_true - y_pred), axis=-1))-1), 0), axis=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above loss function, t(x) = {−1,+1} denotes as the label of the sample."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We redifine the Class feature, using 1 for positive samples and -1 for negative samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_negative = df.copy()\n",
    "df_negative['Class'] = df_negative['Class'].map({0: 1, 1:-1})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we train with both positive and negative samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df_negative.drop(['Class'], axis=1), \n",
    "                                                    df_negative['Class'], \n",
    "                                                    test_size=0.33, \n",
    "                                                    stratify=df_negative['Class'],\n",
    "                                                    random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our training data output now has the following shape, (X_train, y_train)\n",
    "\n",
    "ref: https://stackoverflow.com/questions/57704771/inputs-to-eager-execution-function-cannot-be-keras-symbolic-tensors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train.to_numpy()\n",
    "Xy_train = np.concatenate((X_train, y_train[:, None]), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_eval = X_test.to_numpy()\n",
    "Xy_eval = np.concatenate((X_test, y_test[:, None]), axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "autoencoder_nl = Autoencoder(encoding_dim=10)\n",
    "autoencoder_nl.compile(optimizer=tf.optimizers.Adam(lr=0.00025), loss=negative_learn_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 190820 samples, validate on 93987 samples\n",
      "Epoch 1/50\n",
      "190820/190820 [==============================] - 3s 13us/sample - loss: 1075.0847 - val_loss: 977.1289\n",
      "Epoch 2/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 923.5158 - val_loss: 888.4934\n",
      "Epoch 3/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 869.5655 - val_loss: 854.3929\n",
      "Epoch 4/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 842.7418 - val_loss: 833.1063\n",
      "Epoch 5/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 825.1978 - val_loss: 818.6005\n",
      "Epoch 6/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 812.7914 - val_loss: 808.0734\n",
      "Epoch 7/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 803.8149 - val_loss: 800.3051\n",
      "Epoch 8/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 797.3560 - val_loss: 795.0228\n",
      "Epoch 9/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 792.8920 - val_loss: 791.1092\n",
      "Epoch 10/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 789.3226 - val_loss: 787.8374\n",
      "Epoch 11/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 786.2297 - val_loss: 784.8046\n",
      "Epoch 12/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 783.1909 - val_loss: 781.9532\n",
      "Epoch 13/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 780.4569 - val_loss: 779.3205\n",
      "Epoch 14/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 777.9343 - val_loss: 776.9016\n",
      "Epoch 15/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 775.6210 - val_loss: 774.6698\n",
      "Epoch 16/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 773.4309 - val_loss: 772.6231\n",
      "Epoch 17/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 771.4788 - val_loss: 770.7727\n",
      "Epoch 18/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 769.7899 - val_loss: 769.1850\n",
      "Epoch 19/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 768.3345 - val_loss: 767.8613\n",
      "Epoch 20/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 767.1092 - val_loss: 766.7630\n",
      "Epoch 21/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 766.0421 - val_loss: 765.6794\n",
      "Epoch 22/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 764.8835 - val_loss: 764.6339\n",
      "Epoch 23/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 763.9821 - val_loss: 763.7989\n",
      "Epoch 24/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 763.2128 - val_loss: 763.0757\n",
      "Epoch 25/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 762.6180 - val_loss: 762.4600\n",
      "Epoch 26/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 762.0473 - val_loss: 761.9279\n",
      "Epoch 27/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 761.5187 - val_loss: 761.4606\n",
      "Epoch 28/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 761.0749 - val_loss: 761.0521\n",
      "Epoch 29/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 760.6864 - val_loss: 760.6861\n",
      "Epoch 30/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 760.3532 - val_loss: 760.3532\n",
      "Epoch 31/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 759.9567 - val_loss: 760.0424\n",
      "Epoch 32/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 759.7011 - val_loss: 759.7536\n",
      "Epoch 33/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 759.4583 - val_loss: 759.4873\n",
      "Epoch 34/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 759.1877 - val_loss: 759.2298\n",
      "Epoch 35/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 758.9145 - val_loss: 758.9863\n",
      "Epoch 36/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 758.6650 - val_loss: 758.7571\n",
      "Epoch 37/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 758.4714 - val_loss: 758.5288\n",
      "Epoch 38/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 758.2398 - val_loss: 758.3089\n",
      "Epoch 39/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 758.0291 - val_loss: 758.0978\n",
      "Epoch 40/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 757.8066 - val_loss: 757.8912\n",
      "Epoch 41/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 757.5704 - val_loss: 757.6851\n",
      "Epoch 42/50\n",
      "190820/190820 [==============================] - 2s 10us/sample - loss: 757.4316 - val_loss: 757.4755\n",
      "Epoch 43/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 757.1438 - val_loss: 757.2673\n",
      "Epoch 44/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 756.9776 - val_loss: 757.0207\n",
      "Epoch 45/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 756.7096 - val_loss: 756.7021\n",
      "Epoch 46/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 756.3185 - val_loss: 756.2728\n",
      "Epoch 47/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 755.9048 - val_loss: 755.8854\n",
      "Epoch 48/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 755.5061 - val_loss: 755.5776\n",
      "Epoch 49/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 755.2439 - val_loss: 755.3163\n",
      "Epoch 50/50\n",
      "190820/190820 [==============================] - 2s 11us/sample - loss: 755.0272 - val_loss: 755.1053\n"
     ]
    }
   ],
   "source": [
    "trainer_nl = autoencoder_nl.fit(x = X_train, \n",
    "                                y = Xy_train,\n",
    "                                epochs=50,\n",
    "                                validation_data=(X_eval, Xy_eval),\n",
    "                                batch_size=256,\n",
    "                                shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss_by_epoch(trainer_nl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING - Falling back from v2 loop because of error: Failed to find data adapter that can handle input: <class 'pandas.core.frame.DataFrame'>, <class 'NoneType'>\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            reconstruction_error\n",
      "true_class                      \n",
      "-1                     27.435165\n",
      " 1                      0.783327\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_reconstructuion_err_by_class(X_test, y_test.to_numpy(), model = autoencoder_nl, anomaly_class = -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING - Falling back from v2 loop because of error: Failed to find data adapter that can handle input: <class 'pandas.core.frame.DataFrame'>, <class 'NoneType'>\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_precision_recall_curves(X_test, y_test, model = autoencoder_nl, anomaly_class = -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (keras-tf-py362)",
   "language": "python",
   "name": "keras-tf-py362"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}