From 14a9729b37670c42afa8182e8d7be7ab4bfa5e3f Mon Sep 17 00:00:00 2001 From: Spencer Dixon Date: Fri, 13 Apr 2018 18:31:46 -0400 Subject: [PATCH] Added weights file --- ...n Autocorrect with LSTM Autoencoders.ipynb | 659 +++++++++++------- s2s.h5 | Bin 0 -> 5260416 bytes 2 files changed, 421 insertions(+), 238 deletions(-) create mode 100644 s2s.h5 diff --git a/3a. Taxon Autocorrect with LSTM Autoencoders.ipynb b/3a. Taxon Autocorrect with LSTM Autoencoders.ipynb index a6dc5f4..9f873ca 100644 --- a/3a. Taxon Autocorrect with LSTM Autoencoders.ipynb +++ b/3a. Taxon Autocorrect with LSTM Autoencoders.ipynb @@ -10,9 +10,17 @@ }, { "cell_type": "code", - "execution_count": 184, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], "source": [ "import numpy as np\n", "import pandas as pd\n", @@ -35,24 +43,24 @@ }, { "cell_type": "code", - "execution_count": 185, + "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "\n", @@ -379,7 +387,7 @@ "[49369 rows x 1 columns]" ] }, - "execution_count": 185, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -392,7 +400,7 @@ }, { "cell_type": "code", - "execution_count": 186, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -429,40 +437,40 @@ "Crocodylus novaeguineae 202\n", "Leopardus pardalis 201\n", " ... \n", - "Micrastur ruficollis 1\n", - "Hydnophora spp. 1\n", - "Lycaste fulvescens 1\n", - "Errinopora pourtalesii 1\n", - "Maihueniopsis darwinii 1\n", - "Porites divaricata 1\n", - "Aloe trachyticola 1\n", - "Polemaetus bellicosus 1\n", - "Sternbergia candida 1\n", - "Errinopora spp. 1\n", - "Dracula tubeana 1\n", - "Chinchilla lanigera 1\n", - "Peniocereus spp. 1\n", - "Mesoplodon europaeus 1\n", - "Cypripedium yunnanense 1\n", - "Nectophrynoides minutus 1\n", - "Vidua paradisaea 1\n", - "Bulbophyllum resupinatum 1\n", - "Turbinicarpus mandragora 1\n", - "Dalbergia retusa 1\n", - "Pristis spp. 1\n", - "Masdevallia andreettaeana 1\n", - "Dendrobium violaceum 1\n", - "Favites abdita 1\n", - "Astrophytum myriostigma 1\n", - "Epiphyllum pumilum 1\n", - "Pterostylis fischii 1\n", - "Colpophyllia amaranthus 1\n", - "Acineta chrysantha 1\n", - "Anas spp. 1\n", + "Duncanopsammia axifuga 1\n", + "Frailea mammifera 1\n", + "Centroglossa spp. 1\n", + "Aloe peckii 1\n", + "Calumma furcifer 1\n", + "Dendrobium subacaule 1\n", + "Leopardus jacobitus 1\n", + "Angraecum germinyanum 1\n", + "Euphorbia perrieri 1\n", + "Pectinia spp. 1\n", + "Euphorbia lamarckii 1\n", + "Manis crassicaudata 1\n", + "Euphorbia classenii 1\n", + "Treron calvus 1\n", + "Masdevallia guerrieroi 1\n", + "Aerangis ellisii 1\n", + "Euphorbia globosa 1\n", + "Rhipsalis teres 1\n", + "Weberocereus tonduzii 1\n", + "Cleistocactus roezlii 1\n", + "Encyclia fehlingii 1\n", + "Grosourdya appendiculata 1\n", + "Euphorbia gorgonis 1\n", + "Pleione speciosa 1\n", + "Euphorbia bupleurifolia 1\n", + "Macrozamia miquelii 1\n", + "Zamia pumila 1\n", + "Acropora pulchra 1\n", + "Eria coronaria 1\n", + "Paphiopedilum sangii 1\n", "Name: Taxon, Length: 3422, dtype: int64" ] }, - "execution_count": 186, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -474,28 +482,18 @@ }, { "cell_type": "code", - "execution_count": 187, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 187, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" - }, - { - "data": { - "image/png": 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" \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -910,67 +908,67 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -980,72 +978,72 @@ ], "text/plain": [ " Input Target\n", - "0 Equus przeawlskii Equus przewalskii\n", - "1 Equs przewalskii Equus przewalskii\n", - "2 Equusprzewalskii Equus przewalskii\n", - "3 Equus przealskiis Equus przewalskii\n", - "4 Equus prUewalskii Equus przewalskii\n", - "5 Equus prlewalskii Equus przewalskii\n", - "6 equus przewalskii Equus przewalskii\n", - "7 Equus przewlskii Equus przewalskii\n", - "8 EquusPprzewalskii Equus przewalskii\n", - "9 Eqlus przewalskii Equus przewalskii\n", - "10 Equus pzrewalskii Equus przewalskii\n", - "11 Equus przeawlskii Equus przewalskii\n", - "12 Equusprzewalskii Equus przewalskii\n", - "13 Equus przewlskii Equus przewalskii\n", - "14 Equus przewalskji Equus przewalskii\n", - "15 Equs przewalskii Equus przewalskii\n", - "16 Equus przewlskii Equus przewalskii\n", - "17 Equus pzrewalskii Equus przewalskii\n", + "0 equus przewalskii Equus przewalskii\n", + "1 equus przewalskii Equus przewalskii\n", + "2 equus przewalskii Equus przewalskii\n", + "3 EqNus przwalskii Equus przewalskii\n", + "4 Euus przewalskii Equus przewalskii\n", + "5 Equus przwalskii Equus przewalskii\n", + "6 qEuus przewalskii Equus przewalskii\n", + "7 Equus przewaslkii Equus przewalskii\n", + "8 Equus przewalskii Equus przewalskii\n", + "9 Equus przewalskii Equus przewalskii\n", + "10 Equus przewalski Equus przewalskii\n", + "11 Equus prewalakii Equus przewalskii\n", + "12 Equus przIwalskii Equus przewalskii\n", + "13 Equu sprzewalskii Equus przewalskii\n", + "14 equus przewalskii Equus przewalskii\n", + "15 Equus przewalsiki Equus przewalskii\n", + "16 equus przewalskii Equus przewalskii\n", + "17 Equus przewalkii Equus przewalskii\n", "18 equus przewalskii Equus przewalskii\n", - "19 Equus przewalsNii Equus przewalskii\n", - "20 Equus przewalsiki Equus przewalskii\n", - "21 Equus przealskiu Equus przewalskii\n", - "22 equus przewalskii Equus przewalskii\n", - "23 Equus przeawlskii Equus przewalskii\n", - "24 Equus przealWkii Equus przewalskii\n", - "25 equus przewalskii Equus przewalskii\n", - "26 Equusp rzewalskii Equus przewalskii\n", - "27 Equus prEewalskii Equus przewalskii\n", - "28 equus przewalskii Equus przewalskii\n", - "29 squus przewalskii Equus przewalskii\n", + "19 equus przewalskii Equus przewalskii\n", + "20 Equus rpzewalskii Equus przewalskii\n", + "21 Equus prewalskii Equus przewalskii\n", + "22 Equus rpzewalskii Equus przewalskii\n", + "23 Equsu przewalskii Equus przewalskii\n", + "24 equus przewalskii Equus przewalskii\n", + "25 Equus przwealskii Equus przewalskii\n", + "26 Eyuus przewalskii Equus przewalskii\n", + "27 Euqus przewalskii Equus przewalskii\n", + "28 Equsu przewalskii Equus przewalskii\n", + "29 mquus prewalskii Equus przewalskii\n", "... ... ...\n", "342170 martes flavigula Martes flavigula\n", - "342171 Martes fwavgula Martes flavigula\n", - "342172 Martek flvigula Martes flavigula\n", - "342173 Martes fwvigula Martes flavigula\n", - "342174 Martes flaigula Martes flavigula\n", - "342175 Martes flavigul Martes flavigula\n", - "342176 martes flavigula Martes flavigula\n", + "342171 Martes Ylavigula Martes flavigula\n", + "342172 Martesflavigula Martes flavigula\n", + "342173 martes flavigula Martes flavigula\n", + "342174 martes flavigula Martes flavigula\n", + "342175 Martes flavigual Martes flavigula\n", + "342176 Martes flavigula Martes flavigula\n", "342177 martes flavigula Martes flavigula\n", - "342178 Martes flaJigula Martes flavigula\n", - "342179 partes flavigula Martes flavigula\n", - "342180 Martes lfavigula Martes flavigula\n", - "342181 MarteP flavigula Martes flavigula\n", - "342182 Martes flvibula Martes flavigula\n", - "342183 Martse flavigula Martes flavigula\n", - "342184 Martes flaviula Martes flavigula\n", - "342185 Martes flavigual Martes flavigula\n", + "342178 Mares flavigula Martes flavigula\n", + "342179 Martes flavigla Martes flavigula\n", + "342180 martes flavigula Martes flavigula\n", + "342181 Martes flavigula Martes flavigula\n", + "342182 Mrates flavigula Martes flavigula\n", + "342183 Martes flaviglua Martes flavigula\n", + "342184 artes flavigulaB Martes flavigula\n", + "342185 MartQs flavigula Martes flavigula\n", "342186 martes flavigula Martes flavigula\n", - "342187 Marts flavigula Martes flavigula\n", - "342188 Martesflavigula Martes flavigula\n", - "342189 Martes flNvigula Martes flavigula\n", - "342190 martes flavigula Martes flavigula\n", - "342191 martes flavigula Martes flavigula\n", - "342192 Maxtes flavigula Martes flavigula\n", - "342193 Martes flavigulaa Martes flavigula\n", - "342194 Martes flavigXla Martes flavigula\n", - "342195 aMrtes flavigula Martes flavigula\n", - "342196 MartesLflavigula Martes flavigula\n", - "342197 Marets flavigula Martes flavigula\n", - "342198 Martes flaviula Martes flavigula\n", - "342199 martes flavigula Martes flavigula\n", + "342187 uartes flavigula Martes flavigula\n", + "342188 Martes favigula Martes flavigula\n", + "342189 martes flavigula Martes flavigula\n", + "342190 Martes flavigua Martes flavigula\n", + "342191 Martes flaviula Martes flavigula\n", + "342192 aMrtes flavigula Martes flavigula\n", + "342193 martes flavigula Martes flavigula\n", + "342194 martes flavigula Martes flavigula\n", + "342195 Martes flavigla Martes flavigula\n", + "342196 Martes lavigula Martes flavigula\n", + "342197 martes flavigula Martes flavigula\n", + "342198 Marte flavigula Martes flavigula\n", + "342199 Marted flavgula Martes flavigula\n", "\n", "[342200 rows x 2 columns]" ] }, - "execution_count": 191, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -1078,8 +1076,10 @@ }, { "cell_type": "code", - "execution_count": 192, - "metadata": {}, + "execution_count": 10, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "input_texts = []\n", @@ -1094,8 +1094,10 @@ }, { "cell_type": "code", - "execution_count": 193, - "metadata": {}, + "execution_count": 11, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "# Takes in the input and target texts and adds their characters to the list of input and target characters\n", @@ -1111,8 +1113,10 @@ }, { "cell_type": "code", - "execution_count": 194, - "metadata": {}, + "execution_count": 12, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "for index, row in corpus.iterrows():\n", @@ -1126,7 +1130,7 @@ }, { "cell_type": "code", - "execution_count": 195, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -1136,7 +1140,7 @@ "Number of samples: 342200\n", "Number of unique input tokens: 55\n", "Number of unique output tokens: 56\n", - "Max sequence length for inputs: 36\n", + "Max sequence length for inputs: 37\n", "Max sequence length for outputs: 38\n" ] }, @@ -1201,7 +1205,7 @@ " 'z']" ] }, - "execution_count": 195, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -1232,7 +1236,7 @@ }, { "cell_type": "code", - "execution_count": 198, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -1254,8 +1258,10 @@ }, { "cell_type": "code", - "execution_count": 199, - "metadata": {}, + "execution_count": 15, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "# encoder_input_data is a 3D array of shape (num_pairs, max input seq length, num input characters)\n", @@ -1277,8 +1283,10 @@ }, { "cell_type": "code", - "execution_count": 200, - "metadata": {}, + "execution_count": 16, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "# i = training examples\n", @@ -1299,7 +1307,7 @@ }, { "cell_type": "code", - "execution_count": 202, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -1316,7 +1324,7 @@ }, { "cell_type": "code", - "execution_count": 203, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -1336,8 +1344,10 @@ }, { "cell_type": "code", - "execution_count": 204, - "metadata": {}, + "execution_count": 21, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "# Define the model that will turn\n", @@ -1347,7 +1357,7 @@ }, { "cell_type": "code", - "execution_count": 206, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1356,48 +1366,219 @@ "text": [ "Train on 273760 samples, validate on 68440 samples\n", "Epoch 1/100\n", - "273760/273760 [==============================] - 743s 3ms/step - loss: 0.2597 - val_loss: 1.3135\n", + "273760/273760 [==============================] - 467s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", "Epoch 2/100\n", - "273760/273760 [==============================] - 702s 3ms/step - loss: 0.0587 - val_loss: 1.5200\n", + "273760/273760 [==============================] - 459s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", "Epoch 3/100\n", - "273760/273760 [==============================] - 658s 2ms/step - loss: 0.0232 - val_loss: 1.6334\n", + "273760/273760 [==============================] - 451s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", "Epoch 4/100\n", - "273760/273760 [==============================] - 655s 2ms/step - loss: 0.0141 - val_loss: 1.7563\n", + "273760/273760 [==============================] - 451s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", "Epoch 5/100\n", - "273760/273760 [==============================] - 648s 2ms/step - loss: 0.0101 - val_loss: 1.7978\n", + "273760/273760 [==============================] - 453s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", "Epoch 6/100\n", - "273760/273760 [==============================] - 648s 2ms/step - loss: 0.0078 - val_loss: 1.8374\n", + "273760/273760 [==============================] - 454s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", "Epoch 7/100\n", - "273760/273760 [==============================] - 645s 2ms/step - loss: 0.0062 - val_loss: 1.8761\n", + "273760/273760 [==============================] - 454s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", "Epoch 8/100\n", - "273760/273760 [==============================] - 658s 2ms/step - loss: 0.0052 - val_loss: 1.9187\n", + "273760/273760 [==============================] - 452s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", "Epoch 9/100\n", - "273760/273760 [==============================] - 779s 3ms/step - loss: 0.0045 - val_loss: 1.9649\n", + "273760/273760 [==============================] - 454s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", "Epoch 10/100\n", - "273760/273760 [==============================] - 706s 3ms/step - loss: 0.0039 - val_loss: 1.9650\n", + "273760/273760 [==============================] - 456s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", "Epoch 11/100\n", - "273760/273760 [==============================] - 640s 2ms/step - loss: 0.0035 - val_loss: 1.9923\n", + "273760/273760 [==============================] - 457s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", "Epoch 12/100\n", - "242432/273760 [=========================>....] - ETA: 1:06 - loss: 0.0032" + "273760/273760 [==============================] - 456s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 13/100\n", + "273760/273760 [==============================] - 457s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 14/100\n", + "273760/273760 [==============================] - 457s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 15/100\n", + "273760/273760 [==============================] - 458s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 16/100\n", + "273760/273760 [==============================] - 460s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 17/100\n", + "273760/273760 [==============================] - 459s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 18/100\n", + "273760/273760 [==============================] - 460s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 19/100\n", + "273760/273760 [==============================] - 459s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 20/100\n", + "273760/273760 [==============================] - 460s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 21/100\n", + "273760/273760 [==============================] - 460s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 22/100\n", + "273760/273760 [==============================] - 460s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 23/100\n", + "273760/273760 [==============================] - 463s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 24/100\n", + "273760/273760 [==============================] - 468s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 25/100\n", + "273760/273760 [==============================] - 467s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 26/100\n", + "273760/273760 [==============================] - 462s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 27/100\n", + "273760/273760 [==============================] - 467s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 28/100\n", + "273760/273760 [==============================] - 467s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 29/100\n", + "273760/273760 [==============================] - 466s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 30/100\n", + "273760/273760 [==============================] - 460s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 31/100\n", + "273760/273760 [==============================] - 458s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 32/100\n", + "273760/273760 [==============================] - 461s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 33/100\n", + "273760/273760 [==============================] - 462s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 34/100\n", + "273760/273760 [==============================] - 462s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 35/100\n", + "273760/273760 [==============================] - 460s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 36/100\n", + "273760/273760 [==============================] - 462s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 37/100\n", + "273760/273760 [==============================] - 463s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 38/100\n", + "273760/273760 [==============================] - 462s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 39/100\n", + "273760/273760 [==============================] - 463s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 40/100\n", + "273760/273760 [==============================] - 461s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 41/100\n", + "273760/273760 [==============================] - 463s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 42/100\n", + "273760/273760 [==============================] - 463s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 43/100\n", + "273760/273760 [==============================] - 461s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 44/100\n", + "273760/273760 [==============================] - 462s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 45/100\n", + "273760/273760 [==============================] - 462s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 46/100\n", + "273760/273760 [==============================] - 461s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 47/100\n", + "273760/273760 [==============================] - 460s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 48/100\n", + "273760/273760 [==============================] - 459s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 49/100\n", + "273760/273760 [==============================] - 462s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 50/100\n", + "273760/273760 [==============================] - 463s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 51/100\n", + "273760/273760 [==============================] - 461s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 52/100\n", + "273760/273760 [==============================] - 466s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 53/100\n", + "273760/273760 [==============================] - 465s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 54/100\n", + "273760/273760 [==============================] - 466s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 55/100\n", + "273760/273760 [==============================] - 465s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 56/100\n", + "273760/273760 [==============================] - 465s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 57/100\n", + "273760/273760 [==============================] - 467s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 58/100\n", + "273760/273760 [==============================] - 498s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 59/100\n", + "273760/273760 [==============================] - 501s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 60/100\n", + "273760/273760 [==============================] - 480s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 61/100\n", + "273760/273760 [==============================] - 464s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 62/100\n", + "273760/273760 [==============================] - 463s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 63/100\n", + "273760/273760 [==============================] - 466s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 64/100\n", + "273760/273760 [==============================] - 465s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 65/100\n", + "273760/273760 [==============================] - 465s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 66/100\n", + "273760/273760 [==============================] - 464s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 67/100\n", + "273760/273760 [==============================] - 466s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 68/100\n", + "273760/273760 [==============================] - 469s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 69/100\n", + "273760/273760 [==============================] - 495s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 70/100\n" ] }, { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m validation_split=0.2)\n\u001b[0m\u001b[1;32m 10\u001b[0m \u001b[0;31m# Save model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m's2s.h5'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m 1703\u001b[0m \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1704\u001b[0m \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1705\u001b[0;31m validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m 1706\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1707\u001b[0m def evaluate(self, x=None, y=None,\n", - "\u001b[0;32m/usr/local/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36m_fit_loop\u001b[0;34m(self, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[1;32m 1233\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1234\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1235\u001b[0;31m \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1236\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1237\u001b[0m \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m 2476\u001b[0m \u001b[0msession\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2477\u001b[0m updated = session.run(fetches=fetches, feed_dict=feed_dict,\n\u001b[0;32m-> 2478\u001b[0;31m **self.session_kwargs)\n\u001b[0m\u001b[1;32m 2479\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2480\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 903\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 904\u001b[0m result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 905\u001b[0;31m run_metadata_ptr)\n\u001b[0m\u001b[1;32m 906\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 907\u001b[0m \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 1135\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1136\u001b[0m results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1137\u001b[0;31m feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m 1138\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1139\u001b[0m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 1353\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1354\u001b[0m return self._do_call(_run_fn, self._session, feeds, fetches, targets,\n\u001b[0;32m-> 1355\u001b[0;31m options, run_metadata)\n\u001b[0m\u001b[1;32m 1356\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1357\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m 1359\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1360\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1361\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1362\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1363\u001b[0m \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/local/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m 1338\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1339\u001b[0m return tf_session.TF_Run(session, options, feed_dict, fetch_list,\n\u001b[0;32m-> 1340\u001b[0;31m target_list, status, run_metadata)\n\u001b[0m\u001b[1;32m 1341\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1342\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + "name": "stdout", + "output_type": "stream", + "text": [ + "273760/273760 [==============================] - 475s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 71/100\n", + "273760/273760 [==============================] - 460s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 72/100\n", + "273760/273760 [==============================] - 484s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 73/100\n", + "273760/273760 [==============================] - 486s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 74/100\n", + "273760/273760 [==============================] - 489s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 75/100\n", + "273760/273760 [==============================] - 491s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 76/100\n", + "273760/273760 [==============================] - 498s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 77/100\n", + "273760/273760 [==============================] - 509s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 78/100\n", + "273760/273760 [==============================] - 508s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 79/100\n", + "273760/273760 [==============================] - 507s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 80/100\n", + "273760/273760 [==============================] - 496s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 81/100\n", + "273760/273760 [==============================] - 461s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 82/100\n", + "273760/273760 [==============================] - 462s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 83/100\n", + "273760/273760 [==============================] - 461s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 84/100\n", + "273760/273760 [==============================] - 462s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 85/100\n", + "273760/273760 [==============================] - 464s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 86/100\n", + "273760/273760 [==============================] - 461s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 87/100\n", + "273760/273760 [==============================] - 486s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 88/100\n", + "273760/273760 [==============================] - 518s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 89/100\n", + "273760/273760 [==============================] - 546s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 90/100\n", + "273760/273760 [==============================] - 487s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 91/100\n", + "273760/273760 [==============================] - 463s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 92/100\n", + "273760/273760 [==============================] - 472s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 93/100\n", + "273760/273760 [==============================] - 484s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 94/100\n", + "273760/273760 [==============================] - 464s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 95/100\n", + "273760/273760 [==============================] - 462s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 96/100\n", + "273760/273760 [==============================] - 462s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 97/100\n", + "273760/273760 [==============================] - 465s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 98/100\n", + "273760/273760 [==============================] - 464s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 99/100\n", + "273760/273760 [==============================] - 465s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", + "Epoch 100/100\n", + "273760/273760 [==============================] - 489s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/paperspace/anaconda3/lib/python3.6/site-packages/keras/engine/topology.py:2368: UserWarning: Layer lstm_4 was passed non-serializable keyword arguments: {'initial_state': [, ]}. 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" ] } ], @@ -1418,7 +1599,9 @@ { "cell_type": "code", "execution_count": null, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [] } @@ -1439,7 +1622,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.2" } }, "nbformat": 4, diff --git a/s2s.h5 b/s2s.h5 new file mode 100644 index 0000000000000000000000000000000000000000..2625e186fd3fc4c5d84ac5d8448d16acd300dfff GIT binary patch literal 5260416 zcmeFZ30zHE`#63|k|ZGsQIaG{b({Bkaw3&9Ns@}@i6%`#a+;6kNrjM1iJQ!6uZPUZ zlsSaVA<1|%T>d-Nz3=tj_xJw(-|z1~{C=r>sf0*>)Fq|);cRK&CL5t4wvM0 zUJ?>KF`mr#uRR~%x-Ws5{r9S#`<`}>?k^(UrLem!7w!5Y!js^2w#j$b%XE*g)T^sP zzPqEN!?bBU4zg$bp0c~!9|QD^^GE&9FaHq~u$*T4odKP@XRFm+)^+n)4-tYxeSCx5 z7yAbK2Si42m7!sgfx&@GeZ&8ut`oM0U)6d#{)=*8H^2VK*HS!7PF6a{k><&AJigF3 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0Equus przeawlskiiequus przewalskiiEquus przewalskii
1Equs przewalskiiequus przewalskiiEquus przewalskii
2Equusprzewalskiiequus przewalskiiEquus przewalskii
3Equus przealskiisEqNus przwalskiiEquus przewalskii
4Equus prUewalskiiEuus przewalskiiEquus przewalskii
5Equus prlewalskiiEquus przwalskiiEquus przewalskii
6equus przewalskiiqEuus przewalskiiEquus przewalskii
7Equus przewlskiiEquus przewaslkiiEquus przewalskii
8EquusPprzewalskiiEquus przewalskiiEquus przewalskii
9Eqlus przewalskiiEquus przewalskiiEquus przewalskii
10Equus pzrewalskiiEquus przewalskiEquus przewalskii
11Equus przeawlskiiEquus prewalakiiEquus przewalskii
12EquusprzewalskiiEquus przIwalskiiEquus przewalskii
13Equus przewlskiiEquu sprzewalskiiEquus przewalskii
14Equus przewalskjiequus przewalskiiEquus przewalskii
15Equs przewalskiiEquus przewalsikiEquus przewalskii
16Equus przewlskiiequus przewalskiiEquus przewalskii
17Equus pzrewalskiiEquus przewalkiiEquus przewalskii
19Equus przewalsNiiequus przewalskiiEquus przewalskii
20Equus przewalsikiEquus rpzewalskiiEquus przewalskii
21Equus przealskiuEquus prewalskiiEquus przewalskii
22equus przewalskiiEquus rpzewalskiiEquus przewalskii
23Equus przeawlskiiEqusu przewalskiiEquus przewalskii
24Equus przealWkiiequus przewalskiiEquus przewalskii
25equus przewalskiiEquus przwealskiiEquus przewalskii
26Equusp rzewalskiiEyuus przewalskiiEquus przewalskii
27Equus prEewalskiiEuqus przewalskiiEquus przewalskii
28equus przewalskiiEqusu przewalskiiEquus przewalskii
29squus przewalskiimquus prewalskiiEquus przewalskii
342171Martes fwavgulaMartes YlavigulaMartes flavigula
342172Martek flvigulaMartesflavigulaMartes flavigula
342173Martes fwvigulamartes flavigulaMartes flavigula
342174Martes flaigulamartes flavigulaMartes flavigula
342175Martes flavigulMartes flavigualMartes flavigula
342176martes flavigulaMartes flavigulaMartes flavigula
342178Martes flaJigulaMares flavigulaMartes flavigula
342179partes flavigulaMartes flaviglaMartes flavigula
342180Martes lfavigulamartes flavigulaMartes flavigula
342181MarteP flavigulaMartes flavigulaMartes flavigula
342182Martes flvibulaMrates flavigulaMartes flavigula
342183Martse flavigulaMartes flavigluaMartes flavigula
342184Martes flaviulaartes flavigulaBMartes flavigula
342185Martes flavigualMartQs flavigulaMartes flavigula
342187Marts flavigulauartes flavigulaMartes flavigula
342188MartesflavigulaMartes favigulaMartes flavigula
342189Martes flNvigulamartes flavigulaMartes flavigula
342190martes flavigulaMartes flaviguaMartes flavigula
342191martes flavigulaMartes flaviulaMartes flavigula
342192Maxtes flavigulaaMrtes flavigulaMartes flavigula
342193Martes flavigulaamartes flavigulaMartes flavigula
342194Martes flavigXlamartes flavigulaMartes flavigula
342195aMrtes flavigulaMartes flaviglaMartes flavigula
342196MartesLflavigulaMartes lavigulaMartes flavigula
342197Marets flavigulamartes flavigulaMartes flavigula
342198Martes flaviulaMarte flavigulaMartes flavigula
342199martes flavigulaMarted flavgulaMartes flavigula