diff --git a/.ipynb_checkpoints/3a. Taxon Autocorrect with LSTM Autoencoders-checkpoint.ipynb b/.ipynb_checkpoints/3a. Taxon Autocorrect with LSTM Autoencoders-checkpoint.ipynb
index 5848543..e600e55 100644
--- a/.ipynb_checkpoints/3a. Taxon Autocorrect with LSTM Autoencoders-checkpoint.ipynb	
+++ b/.ipynb_checkpoints/3a. Taxon Autocorrect with LSTM Autoencoders-checkpoint.ipynb	
@@ -10,9 +10,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
+      "  from ._conv import register_converters as _register_converters\n",
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
@@ -37,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -381,7 +391,7 @@
        "[49369 rows x 1 columns]"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -394,7 +404,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -431,40 +441,40 @@
        "Crocodylus novaeguineae          202\n",
        "Leopardus pardalis               201\n",
        "                                ... \n",
-       "Agaricia agaricites                1\n",
-       "Masdevallia wageneriana            1\n",
-       "Aloe menachensis                   1\n",
-       "Eulemur collaris                   1\n",
-       "Coelogyne pulverula                1\n",
-       "Sphaeropteris tomentosissima       1\n",
-       "Psephotus dissimilis               1\n",
-       "Trioceros fuelleborni              1\n",
-       "Catopuma badia                     1\n",
-       "Bulbophyllum wendlandianum         1\n",
-       "Lycaste powellii                   1\n",
-       "Euphyllia paraancora               1\n",
-       "Trichopilia suavis                 1\n",
-       "Galaxea spp.                       1\n",
-       "Gymnocalycium saglionis            1\n",
-       "Melocactus azureus                 1\n",
-       "Echinocereus schmollii             1\n",
-       "Cypripedium yunnanense             1\n",
-       "Paphiopedilum callosum             1\n",
-       "Laelia jongheana                   1\n",
-       "Phalaenopsis parishii              1\n",
-       "Thrixspermum spp.                  1\n",
-       "Chlamydotis macqueenii             1\n",
-       "Phalaenopsis lindenii              1\n",
-       "Corryocactus melanotrichus         1\n",
-       "Heliangelus micraster              1\n",
-       "Lockhartia oerstedii               1\n",
-       "Masdevallia sanctae-fidei          1\n",
-       "Colpophyllia amaranthus            1\n",
-       "Cymbidium sinense                  1\n",
+       "Echinocactus grusonii              1\n",
+       "Anneliesia cuneata                 1\n",
+       "Dendrobium tetragonum              1\n",
+       "Selenicereus atropilosus           1\n",
+       "Lycaste schilleriana               1\n",
+       "Masdevallia tovarensis             1\n",
+       "Mammillaria decipiens              1\n",
+       "Bletilla ochracea                  1\n",
+       "Ursidae spp.                       1\n",
+       "Masdevallia guayanensis            1\n",
+       "Dendrobium womersleyi              1\n",
+       "Eria javanica                      1\n",
+       "Lemur spp.                         1\n",
+       "Crotalus durissus unicolor         1\n",
+       "Dracaena guianensis                1\n",
+       "Pholidota gibbosa                  1\n",
+       "Diphyllodes respublica             1\n",
+       "Antaresia spp.                     1\n",
+       "Arrojadoa penicillata              1\n",
+       "Mammillaria fittkaui               1\n",
+       "Euphorbia groenewaldii             1\n",
+       "Guaiacum sanctum                   1\n",
+       "Parodia allosiphon                 1\n",
+       "Opuntia polyacantha                1\n",
+       "Rhyncholaelia glauca               1\n",
+       "Antipathes spp.                    1\n",
+       "Eriosyce bulbocalyx                1\n",
+       "Eulophiella roempleriana           1\n",
+       "Lemboglossum spp.                  1\n",
+       "Leocereus spp.                     1\n",
        "Name: Taxon, Length: 3422, dtype: int64"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -476,22 +486,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x11f9226a0>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1168ac940>"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x720 with 1 Axes>"
       ]
@@ -513,7 +523,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -531,7 +541,7 @@
        "       'Martes flavigula'], dtype=object)"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -552,14 +562,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Loodonta afriTana\n"
+      "Loxodonta afrciana\n"
      ]
     }
    ],
@@ -605,23 +615,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Loxodonta faricana\n",
-      "Loxodonta africanI\n",
+      "Loxodota africana\n",
+      "Loxodonta afYiana\n",
+      "Lxoodonta africana\n",
+      "Loxodonta afrciana\n",
+      "Loxodonta african\n",
       "Loxodonta aficana\n",
       "loxodonta africana\n",
-      "LoxodZnta africana\n",
-      "Loxodonta africaan\n",
-      "Loxodonta aDricna\n",
-      "Loxodonta afrciana\n",
-      "Loxodona africaDa\n",
-      "Boxodonta africaa\n"
+      "loxodonta africana\n",
+      "loxodonta africana\n",
+      "LoxoIonta africana\n"
      ]
     }
    ],
@@ -641,7 +651,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -672,152 +682,152 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>Equus pzewalskUi</td>\n",
+       "      <td>Equus przeIalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>EKuus przewalskii</td>\n",
+       "      <td>equus przewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>Fquus przewalskii</td>\n",
+       "      <td>equus przewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>Equusp rzewalskii</td>\n",
+       "      <td>Equus przewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>equus przewalskii</td>\n",
+       "      <td>Equus przeawlskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>EqSus przewalskii</td>\n",
+       "      <td>Equusp rzewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>Equs przewalskii</td>\n",
+       "      <td>equus przewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>Equus pszewalskii</td>\n",
+       "      <td>Equu przewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
-       "      <td>Equus pSzewalkii</td>\n",
+       "      <td>Equusprzewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
-       "      <td>Equus pzrewalskii</td>\n",
+       "      <td>Equus przewawskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
-       "      <td>lquus przewalskii</td>\n",
+       "      <td>Equus prZewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
-       "      <td>Eaus przewalskii</td>\n",
+       "      <td>Equus przewlaskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
-       "      <td>Equus pzzewalskii</td>\n",
+       "      <td>quus prVewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
-       "      <td>Equus frzewalski</td>\n",
+       "      <td>Equus przewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
-       "      <td>Equus przealskii</td>\n",
+       "      <td>Equu sprzewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
-       "      <td>Equus przewklskii</td>\n",
+       "      <td>Equus rpzewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
-       "      <td>Equus przwalskii</td>\n",
+       "      <td>Equus rzewglskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
-       "      <td>Euus przewalskii</td>\n",
+       "      <td>equus przewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
-       "      <td>Equus przewalskpi</td>\n",
+       "      <td>Equus rzeaalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
-       "      <td>Equus prezwalskii</td>\n",
+       "      <td>Equus rzewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
-       "      <td>equus przewalskii</td>\n",
+       "      <td>qEuus przewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
-       "      <td>Equus pzrewalskii</td>\n",
+       "      <td>Equus przeawlskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
-       "      <td>Equus przewaskii</td>\n",
+       "      <td>Equns przewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
-       "      <td>EquuF przewalski</td>\n",
+       "      <td>Equus przewalski</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
-       "      <td>EZuus przewalskii</td>\n",
+       "      <td>Equus przewakskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
-       "      <td>Ejus przewalskii</td>\n",
+       "      <td>Equus przewaVskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
-       "      <td>Equus przewalkii</td>\n",
+       "      <td>equus przewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27</th>\n",
-       "      <td>Equus przewaslkii</td>\n",
+       "      <td>Euqus przewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28</th>\n",
-       "      <td>Equus przewasskii</td>\n",
+       "      <td>equus przewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29</th>\n",
-       "      <td>Equus pzewalskii</td>\n",
+       "      <td>Equus prewalskiiH</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -827,47 +837,47 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342170</th>\n",
-       "      <td>Martes flavigula</td>\n",
+       "      <td>Martes flaviguYa</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342171</th>\n",
-       "      <td>Martes flvigulao</td>\n",
+       "      <td>martes flavigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342172</th>\n",
-       "      <td>Martes lfavigula</td>\n",
+       "      <td>Martes flvigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342173</th>\n",
-       "      <td>martes flavigula</td>\n",
+       "      <td>Martes flavigul</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342174</th>\n",
-       "      <td>Martes flavigula</td>\n",
+       "      <td>MartFs flavigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342175</th>\n",
-       "      <td>martes flavigula</td>\n",
+       "      <td>Martes flavieula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342176</th>\n",
-       "      <td>martes flavigula</td>\n",
+       "      <td>Martes flaigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342177</th>\n",
-       "      <td>MartesQflavigula</td>\n",
+       "      <td>MartesDflavigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342178</th>\n",
-       "      <td>Mares olavigula</td>\n",
+       "      <td>martes flavigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -877,87 +887,87 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342180</th>\n",
-       "      <td>martes flavigula</td>\n",
+       "      <td>Marteslflaigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342181</th>\n",
-       "      <td>Martes flavhgula</td>\n",
+       "      <td>Martes flaigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342182</th>\n",
-       "      <td>Marts flavigula</td>\n",
+       "      <td>martes flavigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342183</th>\n",
-       "      <td>Martesf lavigula</td>\n",
+       "      <td>Mrtes flivigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342184</th>\n",
-       "      <td>jartes flavigula</td>\n",
+       "      <td>Marte sflavigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342185</th>\n",
-       "      <td>Marte fPavigula</td>\n",
+       "      <td>martes flavigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342186</th>\n",
-       "      <td>aMrtes flavigula</td>\n",
+       "      <td>MaUtes favigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342187</th>\n",
-       "      <td>martes flavigula</td>\n",
+       "      <td>Mzrtes favigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342188</th>\n",
-       "      <td>Martes flaivgula</td>\n",
+       "      <td>Martes flavigul</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342189</th>\n",
-       "      <td>martes flavigula</td>\n",
+       "      <td>Iartes lavigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342190</th>\n",
-       "      <td>Marets flavigula</td>\n",
+       "      <td>MartesflavTgula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342191</th>\n",
-       "      <td>martes flavigula</td>\n",
+       "      <td>Martesf lavigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342192</th>\n",
-       "      <td>Marts flavigula</td>\n",
+       "      <td>Martes flaivgula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342193</th>\n",
-       "      <td>Martes fBavigula</td>\n",
+       "      <td>Martes flavigulaH</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342194</th>\n",
-       "      <td>martes flavigula</td>\n",
+       "      <td>Martes flavigla</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342195</th>\n",
-       "      <td>martes flavigula</td>\n",
+       "      <td>nartes flavigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342196</th>\n",
-       "      <td>Partes flavigula</td>\n",
+       "      <td>Marts flavigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -967,12 +977,12 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342198</th>\n",
-       "      <td>Martes flavgula</td>\n",
+       "      <td>Martes flavitula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342199</th>\n",
-       "      <td>MartesflavWgula</td>\n",
+       "      <td>MartWs favigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -982,72 +992,72 @@
       ],
       "text/plain": [
        "                    Input             Target\n",
-       "0        Equus pzewalskUi  Equus przewalskii\n",
-       "1       EKuus przewalskii  Equus przewalskii\n",
-       "2       Fquus przewalskii  Equus przewalskii\n",
-       "3       Equusp rzewalskii  Equus przewalskii\n",
-       "4       equus przewalskii  Equus przewalskii\n",
-       "5       EqSus przewalskii  Equus przewalskii\n",
-       "6        Equs przewalskii  Equus przewalskii\n",
-       "7       Equus pszewalskii  Equus przewalskii\n",
-       "8        Equus pSzewalkii  Equus przewalskii\n",
-       "9       Equus pzrewalskii  Equus przewalskii\n",
-       "10      lquus przewalskii  Equus przewalskii\n",
-       "11       Eaus przewalskii  Equus przewalskii\n",
-       "12      Equus pzzewalskii  Equus przewalskii\n",
-       "13       Equus frzewalski  Equus przewalskii\n",
-       "14       Equus przealskii  Equus przewalskii\n",
-       "15      Equus przewklskii  Equus przewalskii\n",
-       "16       Equus przwalskii  Equus przewalskii\n",
-       "17       Euus przewalskii  Equus przewalskii\n",
-       "18      Equus przewalskpi  Equus przewalskii\n",
-       "19      Equus prezwalskii  Equus przewalskii\n",
-       "20      equus przewalskii  Equus przewalskii\n",
-       "21      Equus pzrewalskii  Equus przewalskii\n",
-       "22       Equus przewaskii  Equus przewalskii\n",
-       "23       EquuF przewalski  Equus przewalskii\n",
-       "24      EZuus przewalskii  Equus przewalskii\n",
-       "25       Ejus przewalskii  Equus przewalskii\n",
-       "26       Equus przewalkii  Equus przewalskii\n",
-       "27      Equus przewaslkii  Equus przewalskii\n",
-       "28      Equus przewasskii  Equus przewalskii\n",
-       "29       Equus pzewalskii  Equus przewalskii\n",
+       "0       Equus przeIalskii  Equus przewalskii\n",
+       "1       equus przewalskii  Equus przewalskii\n",
+       "2       equus przewalskii  Equus przewalskii\n",
+       "3       Equus przewalskii  Equus przewalskii\n",
+       "4       Equus przeawlskii  Equus przewalskii\n",
+       "5       Equusp rzewalskii  Equus przewalskii\n",
+       "6       equus przewalskii  Equus przewalskii\n",
+       "7        Equu przewalskii  Equus przewalskii\n",
+       "8        Equusprzewalskii  Equus przewalskii\n",
+       "9       Equus przewawskii  Equus przewalskii\n",
+       "10      Equus prZewalskii  Equus przewalskii\n",
+       "11      Equus przewlaskii  Equus przewalskii\n",
+       "12       quus prVewalskii  Equus przewalskii\n",
+       "13      Equus przewalskii  Equus przewalskii\n",
+       "14      Equu sprzewalskii  Equus przewalskii\n",
+       "15      Equus rpzewalskii  Equus przewalskii\n",
+       "16       Equus rzewglskii  Equus przewalskii\n",
+       "17      equus przewalskii  Equus przewalskii\n",
+       "18       Equus rzeaalskii  Equus przewalskii\n",
+       "19       Equus rzewalskii  Equus przewalskii\n",
+       "20      qEuus przewalskii  Equus przewalskii\n",
+       "21      Equus przeawlskii  Equus przewalskii\n",
+       "22      Equns przewalskii  Equus przewalskii\n",
+       "23       Equus przewalski  Equus przewalskii\n",
+       "24      Equus przewakskii  Equus przewalskii\n",
+       "25      Equus przewaVskii  Equus przewalskii\n",
+       "26      equus przewalskii  Equus przewalskii\n",
+       "27      Euqus przewalskii  Equus przewalskii\n",
+       "28      equus przewalskii  Equus przewalskii\n",
+       "29      Equus prewalskiiH  Equus przewalskii\n",
        "...                   ...                ...\n",
-       "342170   Martes flavigula   Martes flavigula\n",
-       "342171   Martes flvigulao   Martes flavigula\n",
-       "342172   Martes lfavigula   Martes flavigula\n",
-       "342173   martes flavigula   Martes flavigula\n",
-       "342174   Martes flavigula   Martes flavigula\n",
-       "342175   martes flavigula   Martes flavigula\n",
-       "342176   martes flavigula   Martes flavigula\n",
-       "342177   MartesQflavigula   Martes flavigula\n",
-       "342178    Mares olavigula   Martes flavigula\n",
+       "342170   Martes flaviguYa   Martes flavigula\n",
+       "342171   martes flavigula   Martes flavigula\n",
+       "342172    Martes flvigula   Martes flavigula\n",
+       "342173    Martes flavigul   Martes flavigula\n",
+       "342174   MartFs flavigula   Martes flavigula\n",
+       "342175   Martes flavieula   Martes flavigula\n",
+       "342176    Martes flaigula   Martes flavigula\n",
+       "342177   MartesDflavigula   Martes flavigula\n",
+       "342178   martes flavigula   Martes flavigula\n",
        "342179   martes flavigula   Martes flavigula\n",
-       "342180   martes flavigula   Martes flavigula\n",
-       "342181   Martes flavhgula   Martes flavigula\n",
-       "342182    Marts flavigula   Martes flavigula\n",
-       "342183   Martesf lavigula   Martes flavigula\n",
-       "342184   jartes flavigula   Martes flavigula\n",
-       "342185    Marte fPavigula   Martes flavigula\n",
-       "342186   aMrtes flavigula   Martes flavigula\n",
-       "342187   martes flavigula   Martes flavigula\n",
-       "342188   Martes flaivgula   Martes flavigula\n",
-       "342189   martes flavigula   Martes flavigula\n",
-       "342190   Marets flavigula   Martes flavigula\n",
-       "342191   martes flavigula   Martes flavigula\n",
-       "342192    Marts flavigula   Martes flavigula\n",
-       "342193   Martes fBavigula   Martes flavigula\n",
-       "342194   martes flavigula   Martes flavigula\n",
-       "342195   martes flavigula   Martes flavigula\n",
-       "342196   Partes flavigula   Martes flavigula\n",
+       "342180    Marteslflaigula   Martes flavigula\n",
+       "342181    Martes flaigula   Martes flavigula\n",
+       "342182   martes flavigula   Martes flavigula\n",
+       "342183    Mrtes flivigula   Martes flavigula\n",
+       "342184   Marte sflavigula   Martes flavigula\n",
+       "342185   martes flavigula   Martes flavigula\n",
+       "342186    MaUtes favigula   Martes flavigula\n",
+       "342187    Mzrtes favigula   Martes flavigula\n",
+       "342188    Martes flavigul   Martes flavigula\n",
+       "342189    Iartes lavigula   Martes flavigula\n",
+       "342190    MartesflavTgula   Martes flavigula\n",
+       "342191   Martesf lavigula   Martes flavigula\n",
+       "342192   Martes flaivgula   Martes flavigula\n",
+       "342193  Martes flavigulaH   Martes flavigula\n",
+       "342194    Martes flavigla   Martes flavigula\n",
+       "342195   nartes flavigula   Martes flavigula\n",
+       "342196    Marts flavigula   Martes flavigula\n",
        "342197   martes flavigula   Martes flavigula\n",
-       "342198    Martes flavgula   Martes flavigula\n",
-       "342199    MartesflavWgula   Martes flavigula\n",
+       "342198   Martes flavitula   Martes flavigula\n",
+       "342199    MartWs favigula   Martes flavigula\n",
        "\n",
        "[342200 rows x 2 columns]"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1080,7 +1090,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1096,7 +1106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1113,7 +1123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1128,7 +1138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -1203,7 +1213,7 @@
        " 'z']"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1234,7 +1244,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -1256,7 +1266,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1279,7 +1289,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1301,7 +1311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1318,7 +1328,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1338,7 +1348,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1349,7 +1359,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -1357,29 +1367,62 @@
      "output_type": "stream",
      "text": [
       "Train on 273760 samples, validate on 68440 samples\n",
-      "Epoch 1/1\n",
-      "273760/273760 [==============================] - 709s 3ms/step - loss: 0.5920 - val_loss: 1.1982\n"
+      "Epoch 1/10\n",
+      "273760/273760 [==============================] - 677s 2ms/step - loss: 0.5825 - val_loss: 1.2031\n",
+      "Epoch 2/10\n",
+      "    64/273760 [..............................] - ETA: 11:27 - loss: 0.2116"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/usr/local/lib/python3.6/site-packages/keras/engine/topology.py:2368: UserWarning: Layer lstm_4 was passed non-serializable keyword arguments: {'initial_state': [<tf.Tensor 'lstm_3/while/Exit_2:0' shape=(?, 256) dtype=float32>, <tf.Tensor 'lstm_3/while/Exit_3:0' shape=(?, 256) dtype=float32>]}. They will not be included in the serialized model (and thus will be missing at deserialization time).\n",
+      "/usr/local/lib/python3.6/site-packages/keras/callbacks.py:526: RuntimeWarning: Early stopping conditioned on metric `val_acc` which is not available. Available metrics are: val_loss,loss\n",
+      "  (self.monitor, ','.join(list(logs.keys()))), RuntimeWarning\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "273760/273760 [==============================] - 646s 2ms/step - loss: 0.1167 - val_loss: 1.5385\n",
+      "Epoch 3/10\n",
+      "273760/273760 [==============================] - 726s 3ms/step - loss: 0.0437 - val_loss: 1.6914\n",
+      "Epoch 4/10\n",
+      "273760/273760 [==============================] - 675s 2ms/step - loss: 0.0198 - val_loss: 1.7646\n",
+      "Epoch 5/10\n",
+      "273760/273760 [==============================] - 631s 2ms/step - loss: 0.0126 - val_loss: 1.8492\n",
+      "Epoch 6/10\n",
+      "273760/273760 [==============================] - 663s 2ms/step - loss: 0.0091 - val_loss: 1.8741\n",
+      "Epoch 7/10\n",
+      "273760/273760 [==============================] - 629s 2ms/step - loss: 0.0071 - val_loss: 1.9345\n",
+      "Epoch 8/10\n",
+      "273760/273760 [==============================] - 633s 2ms/step - loss: 0.0058 - val_loss: 1.9752\n",
+      "Epoch 9/10\n",
+      "273760/273760 [==============================] - 639s 2ms/step - loss: 0.0048 - val_loss: 2.0011\n",
+      "Epoch 10/10\n",
+      "273760/273760 [==============================] - 701s 3ms/step - loss: 0.0042 - val_loss: 2.0374\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.6/site-packages/keras/engine/topology.py:2368: UserWarning: Layer lstm_2 was passed non-serializable keyword arguments: {'initial_state': [<tf.Tensor 'lstm_1/while/Exit_2:0' shape=(?, 256) dtype=float32>, <tf.Tensor 'lstm_1/while/Exit_3:0' shape=(?, 256) dtype=float32>]}. They will not be included in the serialized model (and thus will be missing at deserialization time).\n",
       "  str(node.arguments) + '. They will not be included '\n"
      ]
     }
    ],
    "source": [
     "batch_size = 64  # Batch size for training.\n",
-    "epochs = 100 # Number of epochs to train for.\n",
-    "training_mode = True # Change this to true to train the model\n",
+    "epochs = 10 # Number of epochs to train for.\n",
+    "training_mode = False # Change this to true to train the model\n",
     "\n",
-    "earlystop = EarlyStopping(monitor='val_acc', min_delta=0.0001, patience=5, verbose=1, mode='auto')\n",
+    "early_stop = EarlyStopping(monitor='val_loss', min_delta=0.0001, patience=5, verbose=1, mode='auto')\n",
     "\n",
     "# Run training\n",
     "if training_mode == False:\n",
-    "    model.load_weights(\"s2s.h5\")\n",
+    "    model.load_weights(\"s2s_ten_epochs.h5\")\n",
     "    model.compile(optimizer='rmsprop', loss='categorical_crossentropy')\n",
     "else:\n",
     "    model.compile(optimizer='rmsprop', loss='categorical_crossentropy')\n",
@@ -1393,7 +1436,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1429,7 +1472,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1473,7 +1516,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -1481,404 +1524,404 @@
      "output_type": "stream",
      "text": [
       "-\n",
-      "Input sentence: Equus pzewalskUi\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przeIalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: EKuus przewalskii\n",
-      "Decoded sentence: Eulemur colona\n",
+      "Input sentence: equus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Fquus przewalskii\n",
-      "Decoded sentence: Funcia spp.\n",
+      "Input sentence: equus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
+      "\n",
+      "-\n",
+      "Input sentence: Equus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
+      "\n",
+      "-\n",
+      "Input sentence: Equus przeawlskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
       "Input sentence: Equusp rzewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
       "Input sentence: equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: EqSus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equu przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equs przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equusprzewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus pszewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przewawskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus pSzewalkii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus prZewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus pzrewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przewlaskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: lquus przewalskii\n",
-      "Decoded sentence: Saguinus midas\n",
+      "Input sentence: quus prVewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Eaus przewalskii\n",
-      "Decoded sentence: Euphorbia stenoclada\n",
+      "Input sentence: Equus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus pzzewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equu sprzewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus frzewalski\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus rpzewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przealskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus rzewglskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewklskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: equus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przwalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus rzeaalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Euus przewalskii\n",
-      "Decoded sentence: Euplectes afer\n",
+      "Input sentence: Equus rzewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewalskpi\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: qEuus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus prezwalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przeawlskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equns przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus pzrewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przewalski\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewaskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przewakskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: EquuF przewalski\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przewaVskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: EZuus przewalskii\n",
-      "Decoded sentence: Eulychnia acida\n",
+      "Input sentence: equus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Ejus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Euqus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewalkii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: equus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewaslkii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus prewalskiiH\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewasskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus pzewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przewalPkii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
       "Input sentence: equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przeIalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przewalsiki\n",
+      "Decoded sentence: Equus przewalskii\n",
+      "\n",
+      "-\n",
+      "Input sentence: Equus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
       "Input sentence: equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equusprzewalbkii\n",
-      "Decoded sentence: Eulychnia ritteri\n",
+      "Input sentence: Equusprzewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equusprzewalskip\n",
-      "Decoded sentence: Eulychnia ritteri\n",
+      "Input sentence: equus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewalskiik\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equu przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewaskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: EquusEprzewalkii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: EquuM przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przewalsiki\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przealskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: quus przewalseii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: cquus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
       "Input sentence: Equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewlaskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: equus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equu przewalskii\n",
-      "Decoded sentence: Eulychnia ritteri\n",
+      "Input sentence: Equus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Pquus przewalskii\n",
-      "Decoded sentence: Panodia concilosa\n",
+      "Input sentence: Equus przewalskoi\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus lrzewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: EqEus przealskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewalski\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przewalsLi\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: EquuK przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Eruus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus prezwalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Emuus przewlskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przehalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: equus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
       "Input sentence: Equs przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
-      "\n",
-      "-\n",
-      "Input sentence: Equus przwealskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Euus przeNalskii\n",
-      "Decoded sentence: Euplectes afer\n",
+      "Input sentence: Euus pPzewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
       "Input sentence: equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Euqus przewalskii\n",
-      "Decoded sentence: Eulemur collatis\n",
+      "Input sentence: Equus prOwalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus prRewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equu przewalskii\n",
-      "Decoded sentence: Eulychnia ritteri\n",
+      "Input sentence: Equus Grzewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przealsUii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: uquus przewalkii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
       "Input sentence: Equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewalskXi\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przealskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Ecuus przewlskii\n",
-      "Decoded sentence: Eulychnia acida\n",
+      "Input sentence: Equus przewalski\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus prOwalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: EquuY przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewaDskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Eqkus przewalski\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: EquuN prewalskii\n",
-      "Decoded sentence: Eulychnia ritteri\n",
+      "Input sentence: Equus przewalskiM\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus prsewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: equus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equsu przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: equus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equu sprzewalskii\n",
-      "Decoded sentence: Eulychnia ritteri\n",
+      "Input sentence: Equus rzewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewaSskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przeawlskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
       "Input sentence: equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
-      "\n",
-      "-\n",
-      "Input sentence: Equus przewalkkii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: EuJs przewalskii\n",
-      "Decoded sentence: Euplectes afer\n",
+      "Input sentence: Equus rzewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus pPzewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus prewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewaslkii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przealzkii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewaVskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equs pfzewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przewlskiQ\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus prtewalski\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equue przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewjlskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Efuus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przelalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus prezwalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: quus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewalseii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przewalsiki\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewalkiiB\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przeawlskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus pzewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: EVuus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przealskji\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: equus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Euus przewalskii\n",
-      "Decoded sentence: Euplectes afer\n",
+      "Input sentence: Equus przewlaskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: EquusWprzewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: quus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus prezwalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przewalskiiV\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przewaslkii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus prewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: EquAs przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewaslkii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus pzrewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przwalJkii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equos prewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus rpzewalskii\n",
-      "Decoded sentence: Eulychnia acida\n",
+      "Input sentence: Equfs przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przewalszi\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
       "Input sentence: equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przewlaskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
       "Input sentence: equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Euus przewalskii\n",
-      "Decoded sentence: Euplectes afer\n",
-      "\n",
-      "-\n",
-      "Input sentence: equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus prezwalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equs frzewalskii\n",
+      "Decoded sentence: Equus grevyi\n",
       "\n",
       "-\n",
-      "Input sentence: equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przewalski\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przewaskZi\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n"
      ]
     }
diff --git a/3a. Taxon Autocorrect with LSTM Autoencoders.ipynb b/3a. Taxon Autocorrect with LSTM Autoencoders.ipynb
index 5848543..e600e55 100644
--- a/3a. Taxon Autocorrect with LSTM Autoencoders.ipynb	
+++ b/3a. Taxon Autocorrect with LSTM Autoencoders.ipynb	
@@ -10,9 +10,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
+      "  from ._conv import register_converters as _register_converters\n",
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
@@ -37,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -381,7 +391,7 @@
        "[49369 rows x 1 columns]"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -394,7 +404,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -431,40 +441,40 @@
        "Crocodylus novaeguineae          202\n",
        "Leopardus pardalis               201\n",
        "                                ... \n",
-       "Agaricia agaricites                1\n",
-       "Masdevallia wageneriana            1\n",
-       "Aloe menachensis                   1\n",
-       "Eulemur collaris                   1\n",
-       "Coelogyne pulverula                1\n",
-       "Sphaeropteris tomentosissima       1\n",
-       "Psephotus dissimilis               1\n",
-       "Trioceros fuelleborni              1\n",
-       "Catopuma badia                     1\n",
-       "Bulbophyllum wendlandianum         1\n",
-       "Lycaste powellii                   1\n",
-       "Euphyllia paraancora               1\n",
-       "Trichopilia suavis                 1\n",
-       "Galaxea spp.                       1\n",
-       "Gymnocalycium saglionis            1\n",
-       "Melocactus azureus                 1\n",
-       "Echinocereus schmollii             1\n",
-       "Cypripedium yunnanense             1\n",
-       "Paphiopedilum callosum             1\n",
-       "Laelia jongheana                   1\n",
-       "Phalaenopsis parishii              1\n",
-       "Thrixspermum spp.                  1\n",
-       "Chlamydotis macqueenii             1\n",
-       "Phalaenopsis lindenii              1\n",
-       "Corryocactus melanotrichus         1\n",
-       "Heliangelus micraster              1\n",
-       "Lockhartia oerstedii               1\n",
-       "Masdevallia sanctae-fidei          1\n",
-       "Colpophyllia amaranthus            1\n",
-       "Cymbidium sinense                  1\n",
+       "Echinocactus grusonii              1\n",
+       "Anneliesia cuneata                 1\n",
+       "Dendrobium tetragonum              1\n",
+       "Selenicereus atropilosus           1\n",
+       "Lycaste schilleriana               1\n",
+       "Masdevallia tovarensis             1\n",
+       "Mammillaria decipiens              1\n",
+       "Bletilla ochracea                  1\n",
+       "Ursidae spp.                       1\n",
+       "Masdevallia guayanensis            1\n",
+       "Dendrobium womersleyi              1\n",
+       "Eria javanica                      1\n",
+       "Lemur spp.                         1\n",
+       "Crotalus durissus unicolor         1\n",
+       "Dracaena guianensis                1\n",
+       "Pholidota gibbosa                  1\n",
+       "Diphyllodes respublica             1\n",
+       "Antaresia spp.                     1\n",
+       "Arrojadoa penicillata              1\n",
+       "Mammillaria fittkaui               1\n",
+       "Euphorbia groenewaldii             1\n",
+       "Guaiacum sanctum                   1\n",
+       "Parodia allosiphon                 1\n",
+       "Opuntia polyacantha                1\n",
+       "Rhyncholaelia glauca               1\n",
+       "Antipathes spp.                    1\n",
+       "Eriosyce bulbocalyx                1\n",
+       "Eulophiella roempleriana           1\n",
+       "Lemboglossum spp.                  1\n",
+       "Leocereus spp.                     1\n",
        "Name: Taxon, Length: 3422, dtype: int64"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -476,22 +486,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x11f9226a0>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x1168ac940>"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x720 with 1 Axes>"
       ]
@@ -513,7 +523,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -531,7 +541,7 @@
        "       'Martes flavigula'], dtype=object)"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -552,14 +562,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Loodonta afriTana\n"
+      "Loxodonta afrciana\n"
      ]
     }
    ],
@@ -605,23 +615,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Loxodonta faricana\n",
-      "Loxodonta africanI\n",
+      "Loxodota africana\n",
+      "Loxodonta afYiana\n",
+      "Lxoodonta africana\n",
+      "Loxodonta afrciana\n",
+      "Loxodonta african\n",
       "Loxodonta aficana\n",
       "loxodonta africana\n",
-      "LoxodZnta africana\n",
-      "Loxodonta africaan\n",
-      "Loxodonta aDricna\n",
-      "Loxodonta afrciana\n",
-      "Loxodona africaDa\n",
-      "Boxodonta africaa\n"
+      "loxodonta africana\n",
+      "loxodonta africana\n",
+      "LoxoIonta africana\n"
      ]
     }
    ],
@@ -641,7 +651,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -672,152 +682,152 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>Equus pzewalskUi</td>\n",
+       "      <td>Equus przeIalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>EKuus przewalskii</td>\n",
+       "      <td>equus przewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>Fquus przewalskii</td>\n",
+       "      <td>equus przewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>Equusp rzewalskii</td>\n",
+       "      <td>Equus przewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>equus przewalskii</td>\n",
+       "      <td>Equus przeawlskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>EqSus przewalskii</td>\n",
+       "      <td>Equusp rzewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>Equs przewalskii</td>\n",
+       "      <td>equus przewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>Equus pszewalskii</td>\n",
+       "      <td>Equu przewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
-       "      <td>Equus pSzewalkii</td>\n",
+       "      <td>Equusprzewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
-       "      <td>Equus pzrewalskii</td>\n",
+       "      <td>Equus przewawskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
-       "      <td>lquus przewalskii</td>\n",
+       "      <td>Equus prZewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
-       "      <td>Eaus przewalskii</td>\n",
+       "      <td>Equus przewlaskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
-       "      <td>Equus pzzewalskii</td>\n",
+       "      <td>quus prVewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
-       "      <td>Equus frzewalski</td>\n",
+       "      <td>Equus przewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
-       "      <td>Equus przealskii</td>\n",
+       "      <td>Equu sprzewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
-       "      <td>Equus przewklskii</td>\n",
+       "      <td>Equus rpzewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
-       "      <td>Equus przwalskii</td>\n",
+       "      <td>Equus rzewglskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
-       "      <td>Euus przewalskii</td>\n",
+       "      <td>equus przewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
-       "      <td>Equus przewalskpi</td>\n",
+       "      <td>Equus rzeaalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
-       "      <td>Equus prezwalskii</td>\n",
+       "      <td>Equus rzewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
-       "      <td>equus przewalskii</td>\n",
+       "      <td>qEuus przewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
-       "      <td>Equus pzrewalskii</td>\n",
+       "      <td>Equus przeawlskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
-       "      <td>Equus przewaskii</td>\n",
+       "      <td>Equns przewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
-       "      <td>EquuF przewalski</td>\n",
+       "      <td>Equus przewalski</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
-       "      <td>EZuus przewalskii</td>\n",
+       "      <td>Equus przewakskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
-       "      <td>Ejus przewalskii</td>\n",
+       "      <td>Equus przewaVskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
-       "      <td>Equus przewalkii</td>\n",
+       "      <td>equus przewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27</th>\n",
-       "      <td>Equus przewaslkii</td>\n",
+       "      <td>Euqus przewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28</th>\n",
-       "      <td>Equus przewasskii</td>\n",
+       "      <td>equus przewalskii</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29</th>\n",
-       "      <td>Equus pzewalskii</td>\n",
+       "      <td>Equus prewalskiiH</td>\n",
        "      <td>Equus przewalskii</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -827,47 +837,47 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342170</th>\n",
-       "      <td>Martes flavigula</td>\n",
+       "      <td>Martes flaviguYa</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342171</th>\n",
-       "      <td>Martes flvigulao</td>\n",
+       "      <td>martes flavigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342172</th>\n",
-       "      <td>Martes lfavigula</td>\n",
+       "      <td>Martes flvigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342173</th>\n",
-       "      <td>martes flavigula</td>\n",
+       "      <td>Martes flavigul</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342174</th>\n",
-       "      <td>Martes flavigula</td>\n",
+       "      <td>MartFs flavigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342175</th>\n",
-       "      <td>martes flavigula</td>\n",
+       "      <td>Martes flavieula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342176</th>\n",
-       "      <td>martes flavigula</td>\n",
+       "      <td>Martes flaigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342177</th>\n",
-       "      <td>MartesQflavigula</td>\n",
+       "      <td>MartesDflavigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342178</th>\n",
-       "      <td>Mares olavigula</td>\n",
+       "      <td>martes flavigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -877,87 +887,87 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342180</th>\n",
-       "      <td>martes flavigula</td>\n",
+       "      <td>Marteslflaigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342181</th>\n",
-       "      <td>Martes flavhgula</td>\n",
+       "      <td>Martes flaigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342182</th>\n",
-       "      <td>Marts flavigula</td>\n",
+       "      <td>martes flavigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342183</th>\n",
-       "      <td>Martesf lavigula</td>\n",
+       "      <td>Mrtes flivigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342184</th>\n",
-       "      <td>jartes flavigula</td>\n",
+       "      <td>Marte sflavigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342185</th>\n",
-       "      <td>Marte fPavigula</td>\n",
+       "      <td>martes flavigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342186</th>\n",
-       "      <td>aMrtes flavigula</td>\n",
+       "      <td>MaUtes favigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342187</th>\n",
-       "      <td>martes flavigula</td>\n",
+       "      <td>Mzrtes favigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342188</th>\n",
-       "      <td>Martes flaivgula</td>\n",
+       "      <td>Martes flavigul</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342189</th>\n",
-       "      <td>martes flavigula</td>\n",
+       "      <td>Iartes lavigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342190</th>\n",
-       "      <td>Marets flavigula</td>\n",
+       "      <td>MartesflavTgula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342191</th>\n",
-       "      <td>martes flavigula</td>\n",
+       "      <td>Martesf lavigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342192</th>\n",
-       "      <td>Marts flavigula</td>\n",
+       "      <td>Martes flaivgula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342193</th>\n",
-       "      <td>Martes fBavigula</td>\n",
+       "      <td>Martes flavigulaH</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342194</th>\n",
-       "      <td>martes flavigula</td>\n",
+       "      <td>Martes flavigla</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342195</th>\n",
-       "      <td>martes flavigula</td>\n",
+       "      <td>nartes flavigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342196</th>\n",
-       "      <td>Partes flavigula</td>\n",
+       "      <td>Marts flavigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -967,12 +977,12 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342198</th>\n",
-       "      <td>Martes flavgula</td>\n",
+       "      <td>Martes flavitula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>342199</th>\n",
-       "      <td>MartesflavWgula</td>\n",
+       "      <td>MartWs favigula</td>\n",
        "      <td>Martes flavigula</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -982,72 +992,72 @@
       ],
       "text/plain": [
        "                    Input             Target\n",
-       "0        Equus pzewalskUi  Equus przewalskii\n",
-       "1       EKuus przewalskii  Equus przewalskii\n",
-       "2       Fquus przewalskii  Equus przewalskii\n",
-       "3       Equusp rzewalskii  Equus przewalskii\n",
-       "4       equus przewalskii  Equus przewalskii\n",
-       "5       EqSus przewalskii  Equus przewalskii\n",
-       "6        Equs przewalskii  Equus przewalskii\n",
-       "7       Equus pszewalskii  Equus przewalskii\n",
-       "8        Equus pSzewalkii  Equus przewalskii\n",
-       "9       Equus pzrewalskii  Equus przewalskii\n",
-       "10      lquus przewalskii  Equus przewalskii\n",
-       "11       Eaus przewalskii  Equus przewalskii\n",
-       "12      Equus pzzewalskii  Equus przewalskii\n",
-       "13       Equus frzewalski  Equus przewalskii\n",
-       "14       Equus przealskii  Equus przewalskii\n",
-       "15      Equus przewklskii  Equus przewalskii\n",
-       "16       Equus przwalskii  Equus przewalskii\n",
-       "17       Euus przewalskii  Equus przewalskii\n",
-       "18      Equus przewalskpi  Equus przewalskii\n",
-       "19      Equus prezwalskii  Equus przewalskii\n",
-       "20      equus przewalskii  Equus przewalskii\n",
-       "21      Equus pzrewalskii  Equus przewalskii\n",
-       "22       Equus przewaskii  Equus przewalskii\n",
-       "23       EquuF przewalski  Equus przewalskii\n",
-       "24      EZuus przewalskii  Equus przewalskii\n",
-       "25       Ejus przewalskii  Equus przewalskii\n",
-       "26       Equus przewalkii  Equus przewalskii\n",
-       "27      Equus przewaslkii  Equus przewalskii\n",
-       "28      Equus przewasskii  Equus przewalskii\n",
-       "29       Equus pzewalskii  Equus przewalskii\n",
+       "0       Equus przeIalskii  Equus przewalskii\n",
+       "1       equus przewalskii  Equus przewalskii\n",
+       "2       equus przewalskii  Equus przewalskii\n",
+       "3       Equus przewalskii  Equus przewalskii\n",
+       "4       Equus przeawlskii  Equus przewalskii\n",
+       "5       Equusp rzewalskii  Equus przewalskii\n",
+       "6       equus przewalskii  Equus przewalskii\n",
+       "7        Equu przewalskii  Equus przewalskii\n",
+       "8        Equusprzewalskii  Equus przewalskii\n",
+       "9       Equus przewawskii  Equus przewalskii\n",
+       "10      Equus prZewalskii  Equus przewalskii\n",
+       "11      Equus przewlaskii  Equus przewalskii\n",
+       "12       quus prVewalskii  Equus przewalskii\n",
+       "13      Equus przewalskii  Equus przewalskii\n",
+       "14      Equu sprzewalskii  Equus przewalskii\n",
+       "15      Equus rpzewalskii  Equus przewalskii\n",
+       "16       Equus rzewglskii  Equus przewalskii\n",
+       "17      equus przewalskii  Equus przewalskii\n",
+       "18       Equus rzeaalskii  Equus przewalskii\n",
+       "19       Equus rzewalskii  Equus przewalskii\n",
+       "20      qEuus przewalskii  Equus przewalskii\n",
+       "21      Equus przeawlskii  Equus przewalskii\n",
+       "22      Equns przewalskii  Equus przewalskii\n",
+       "23       Equus przewalski  Equus przewalskii\n",
+       "24      Equus przewakskii  Equus przewalskii\n",
+       "25      Equus przewaVskii  Equus przewalskii\n",
+       "26      equus przewalskii  Equus przewalskii\n",
+       "27      Euqus przewalskii  Equus przewalskii\n",
+       "28      equus przewalskii  Equus przewalskii\n",
+       "29      Equus prewalskiiH  Equus przewalskii\n",
        "...                   ...                ...\n",
-       "342170   Martes flavigula   Martes flavigula\n",
-       "342171   Martes flvigulao   Martes flavigula\n",
-       "342172   Martes lfavigula   Martes flavigula\n",
-       "342173   martes flavigula   Martes flavigula\n",
-       "342174   Martes flavigula   Martes flavigula\n",
-       "342175   martes flavigula   Martes flavigula\n",
-       "342176   martes flavigula   Martes flavigula\n",
-       "342177   MartesQflavigula   Martes flavigula\n",
-       "342178    Mares olavigula   Martes flavigula\n",
+       "342170   Martes flaviguYa   Martes flavigula\n",
+       "342171   martes flavigula   Martes flavigula\n",
+       "342172    Martes flvigula   Martes flavigula\n",
+       "342173    Martes flavigul   Martes flavigula\n",
+       "342174   MartFs flavigula   Martes flavigula\n",
+       "342175   Martes flavieula   Martes flavigula\n",
+       "342176    Martes flaigula   Martes flavigula\n",
+       "342177   MartesDflavigula   Martes flavigula\n",
+       "342178   martes flavigula   Martes flavigula\n",
        "342179   martes flavigula   Martes flavigula\n",
-       "342180   martes flavigula   Martes flavigula\n",
-       "342181   Martes flavhgula   Martes flavigula\n",
-       "342182    Marts flavigula   Martes flavigula\n",
-       "342183   Martesf lavigula   Martes flavigula\n",
-       "342184   jartes flavigula   Martes flavigula\n",
-       "342185    Marte fPavigula   Martes flavigula\n",
-       "342186   aMrtes flavigula   Martes flavigula\n",
-       "342187   martes flavigula   Martes flavigula\n",
-       "342188   Martes flaivgula   Martes flavigula\n",
-       "342189   martes flavigula   Martes flavigula\n",
-       "342190   Marets flavigula   Martes flavigula\n",
-       "342191   martes flavigula   Martes flavigula\n",
-       "342192    Marts flavigula   Martes flavigula\n",
-       "342193   Martes fBavigula   Martes flavigula\n",
-       "342194   martes flavigula   Martes flavigula\n",
-       "342195   martes flavigula   Martes flavigula\n",
-       "342196   Partes flavigula   Martes flavigula\n",
+       "342180    Marteslflaigula   Martes flavigula\n",
+       "342181    Martes flaigula   Martes flavigula\n",
+       "342182   martes flavigula   Martes flavigula\n",
+       "342183    Mrtes flivigula   Martes flavigula\n",
+       "342184   Marte sflavigula   Martes flavigula\n",
+       "342185   martes flavigula   Martes flavigula\n",
+       "342186    MaUtes favigula   Martes flavigula\n",
+       "342187    Mzrtes favigula   Martes flavigula\n",
+       "342188    Martes flavigul   Martes flavigula\n",
+       "342189    Iartes lavigula   Martes flavigula\n",
+       "342190    MartesflavTgula   Martes flavigula\n",
+       "342191   Martesf lavigula   Martes flavigula\n",
+       "342192   Martes flaivgula   Martes flavigula\n",
+       "342193  Martes flavigulaH   Martes flavigula\n",
+       "342194    Martes flavigla   Martes flavigula\n",
+       "342195   nartes flavigula   Martes flavigula\n",
+       "342196    Marts flavigula   Martes flavigula\n",
        "342197   martes flavigula   Martes flavigula\n",
-       "342198    Martes flavgula   Martes flavigula\n",
-       "342199    MartesflavWgula   Martes flavigula\n",
+       "342198   Martes flavitula   Martes flavigula\n",
+       "342199    MartWs favigula   Martes flavigula\n",
        "\n",
        "[342200 rows x 2 columns]"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1080,7 +1090,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1096,7 +1106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1113,7 +1123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1128,7 +1138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -1203,7 +1213,7 @@
        " 'z']"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1234,7 +1244,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -1256,7 +1266,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1279,7 +1289,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1301,7 +1311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1318,7 +1328,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1338,7 +1348,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1349,7 +1359,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -1357,29 +1367,62 @@
      "output_type": "stream",
      "text": [
       "Train on 273760 samples, validate on 68440 samples\n",
-      "Epoch 1/1\n",
-      "273760/273760 [==============================] - 709s 3ms/step - loss: 0.5920 - val_loss: 1.1982\n"
+      "Epoch 1/10\n",
+      "273760/273760 [==============================] - 677s 2ms/step - loss: 0.5825 - val_loss: 1.2031\n",
+      "Epoch 2/10\n",
+      "    64/273760 [..............................] - ETA: 11:27 - loss: 0.2116"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/usr/local/lib/python3.6/site-packages/keras/engine/topology.py:2368: UserWarning: Layer lstm_4 was passed non-serializable keyword arguments: {'initial_state': [<tf.Tensor 'lstm_3/while/Exit_2:0' shape=(?, 256) dtype=float32>, <tf.Tensor 'lstm_3/while/Exit_3:0' shape=(?, 256) dtype=float32>]}. They will not be included in the serialized model (and thus will be missing at deserialization time).\n",
+      "/usr/local/lib/python3.6/site-packages/keras/callbacks.py:526: RuntimeWarning: Early stopping conditioned on metric `val_acc` which is not available. Available metrics are: val_loss,loss\n",
+      "  (self.monitor, ','.join(list(logs.keys()))), RuntimeWarning\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "273760/273760 [==============================] - 646s 2ms/step - loss: 0.1167 - val_loss: 1.5385\n",
+      "Epoch 3/10\n",
+      "273760/273760 [==============================] - 726s 3ms/step - loss: 0.0437 - val_loss: 1.6914\n",
+      "Epoch 4/10\n",
+      "273760/273760 [==============================] - 675s 2ms/step - loss: 0.0198 - val_loss: 1.7646\n",
+      "Epoch 5/10\n",
+      "273760/273760 [==============================] - 631s 2ms/step - loss: 0.0126 - val_loss: 1.8492\n",
+      "Epoch 6/10\n",
+      "273760/273760 [==============================] - 663s 2ms/step - loss: 0.0091 - val_loss: 1.8741\n",
+      "Epoch 7/10\n",
+      "273760/273760 [==============================] - 629s 2ms/step - loss: 0.0071 - val_loss: 1.9345\n",
+      "Epoch 8/10\n",
+      "273760/273760 [==============================] - 633s 2ms/step - loss: 0.0058 - val_loss: 1.9752\n",
+      "Epoch 9/10\n",
+      "273760/273760 [==============================] - 639s 2ms/step - loss: 0.0048 - val_loss: 2.0011\n",
+      "Epoch 10/10\n",
+      "273760/273760 [==============================] - 701s 3ms/step - loss: 0.0042 - val_loss: 2.0374\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.6/site-packages/keras/engine/topology.py:2368: UserWarning: Layer lstm_2 was passed non-serializable keyword arguments: {'initial_state': [<tf.Tensor 'lstm_1/while/Exit_2:0' shape=(?, 256) dtype=float32>, <tf.Tensor 'lstm_1/while/Exit_3:0' shape=(?, 256) dtype=float32>]}. They will not be included in the serialized model (and thus will be missing at deserialization time).\n",
       "  str(node.arguments) + '. They will not be included '\n"
      ]
     }
    ],
    "source": [
     "batch_size = 64  # Batch size for training.\n",
-    "epochs = 100 # Number of epochs to train for.\n",
-    "training_mode = True # Change this to true to train the model\n",
+    "epochs = 10 # Number of epochs to train for.\n",
+    "training_mode = False # Change this to true to train the model\n",
     "\n",
-    "earlystop = EarlyStopping(monitor='val_acc', min_delta=0.0001, patience=5, verbose=1, mode='auto')\n",
+    "early_stop = EarlyStopping(monitor='val_loss', min_delta=0.0001, patience=5, verbose=1, mode='auto')\n",
     "\n",
     "# Run training\n",
     "if training_mode == False:\n",
-    "    model.load_weights(\"s2s.h5\")\n",
+    "    model.load_weights(\"s2s_ten_epochs.h5\")\n",
     "    model.compile(optimizer='rmsprop', loss='categorical_crossentropy')\n",
     "else:\n",
     "    model.compile(optimizer='rmsprop', loss='categorical_crossentropy')\n",
@@ -1393,7 +1436,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1429,7 +1472,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1473,7 +1516,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -1481,404 +1524,404 @@
      "output_type": "stream",
      "text": [
       "-\n",
-      "Input sentence: Equus pzewalskUi\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przeIalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: EKuus przewalskii\n",
-      "Decoded sentence: Eulemur colona\n",
+      "Input sentence: equus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Fquus przewalskii\n",
-      "Decoded sentence: Funcia spp.\n",
+      "Input sentence: equus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
+      "\n",
+      "-\n",
+      "Input sentence: Equus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
+      "\n",
+      "-\n",
+      "Input sentence: Equus przeawlskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
       "Input sentence: Equusp rzewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
       "Input sentence: equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: EqSus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equu przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equs przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equusprzewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus pszewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przewawskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus pSzewalkii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus prZewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus pzrewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przewlaskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: lquus przewalskii\n",
-      "Decoded sentence: Saguinus midas\n",
+      "Input sentence: quus prVewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Eaus przewalskii\n",
-      "Decoded sentence: Euphorbia stenoclada\n",
+      "Input sentence: Equus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus pzzewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equu sprzewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus frzewalski\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus rpzewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przealskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus rzewglskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewklskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: equus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przwalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus rzeaalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Euus przewalskii\n",
-      "Decoded sentence: Euplectes afer\n",
+      "Input sentence: Equus rzewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewalskpi\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: qEuus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus prezwalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przeawlskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equns przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus pzrewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przewalski\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewaskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przewakskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: EquuF przewalski\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przewaVskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: EZuus przewalskii\n",
-      "Decoded sentence: Eulychnia acida\n",
+      "Input sentence: equus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Ejus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Euqus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewalkii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: equus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewaslkii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus prewalskiiH\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewasskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus pzewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przewalPkii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
       "Input sentence: equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przeIalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przewalsiki\n",
+      "Decoded sentence: Equus przewalskii\n",
+      "\n",
+      "-\n",
+      "Input sentence: Equus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
       "Input sentence: equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equusprzewalbkii\n",
-      "Decoded sentence: Eulychnia ritteri\n",
+      "Input sentence: Equusprzewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equusprzewalskip\n",
-      "Decoded sentence: Eulychnia ritteri\n",
+      "Input sentence: equus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewalskiik\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equu przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewaskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: EquusEprzewalkii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: EquuM przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przewalsiki\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przealskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: quus przewalseii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: cquus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
       "Input sentence: Equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewlaskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: equus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equu przewalskii\n",
-      "Decoded sentence: Eulychnia ritteri\n",
+      "Input sentence: Equus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Pquus przewalskii\n",
-      "Decoded sentence: Panodia concilosa\n",
+      "Input sentence: Equus przewalskoi\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus lrzewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: EqEus przealskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewalski\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przewalsLi\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: EquuK przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Eruus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus prezwalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Emuus przewlskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przehalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: equus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
       "Input sentence: Equs przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
-      "\n",
-      "-\n",
-      "Input sentence: Equus przwealskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Euus przeNalskii\n",
-      "Decoded sentence: Euplectes afer\n",
+      "Input sentence: Euus pPzewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
       "Input sentence: equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Euqus przewalskii\n",
-      "Decoded sentence: Eulemur collatis\n",
+      "Input sentence: Equus prOwalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus prRewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equu przewalskii\n",
-      "Decoded sentence: Eulychnia ritteri\n",
+      "Input sentence: Equus Grzewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przealsUii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: uquus przewalkii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
       "Input sentence: Equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewalskXi\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przealskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Ecuus przewlskii\n",
-      "Decoded sentence: Eulychnia acida\n",
+      "Input sentence: Equus przewalski\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus prOwalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: EquuY przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewaDskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Eqkus przewalski\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: EquuN prewalskii\n",
-      "Decoded sentence: Eulychnia ritteri\n",
+      "Input sentence: Equus przewalskiM\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus prsewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: equus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equsu przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: equus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equu sprzewalskii\n",
-      "Decoded sentence: Eulychnia ritteri\n",
+      "Input sentence: Equus rzewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewaSskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przeawlskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
       "Input sentence: equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
-      "\n",
-      "-\n",
-      "Input sentence: Equus przewalkkii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: EuJs przewalskii\n",
-      "Decoded sentence: Euplectes afer\n",
+      "Input sentence: Equus rzewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus pPzewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus prewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewaslkii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przealzkii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewaVskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equs pfzewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przewlskiQ\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus prtewalski\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equue przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewjlskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Efuus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przelalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus prezwalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: quus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewalseii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przewalsiki\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewalkiiB\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przeawlskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus pzewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: EVuus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przealskji\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: equus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Euus przewalskii\n",
-      "Decoded sentence: Euplectes afer\n",
+      "Input sentence: Equus przewlaskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: EquusWprzewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: quus przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus prezwalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przewalskiiV\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przewaslkii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus prewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: EquAs przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewaslkii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus pzrewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przwalJkii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equos prewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus rpzewalskii\n",
-      "Decoded sentence: Eulychnia acida\n",
+      "Input sentence: Equfs przewalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przewalszi\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
       "Input sentence: equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przewlaskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
       "Input sentence: equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Euus przewalskii\n",
-      "Decoded sentence: Euplectes afer\n",
-      "\n",
-      "-\n",
-      "Input sentence: equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus prezwalskii\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equs frzewalskii\n",
+      "Decoded sentence: Equus grevyi\n",
       "\n",
       "-\n",
-      "Input sentence: equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przewalski\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n",
       "-\n",
-      "Input sentence: Equus przewalskii\n",
-      "Decoded sentence: Echinopsis chiloensis\n",
+      "Input sentence: Equus przewaskZi\n",
+      "Decoded sentence: Equus przewalskii\n",
       "\n"
      ]
     }
diff --git a/s2s_ten_epochs.h5 b/s2s_ten_epochs.h5
new file mode 100644
index 0000000..06a082f
Binary files /dev/null and b/s2s_ten_epochs.h5 differ