From 0adada35971e6d3be330ced3209f8829144924af Mon Sep 17 00:00:00 2001 From: Spencer Dixon Date: Sat, 14 Apr 2018 17:34:28 +0100 Subject: [PATCH] Added early stopping --- ...ct with LSTM Autoencoders-checkpoint.ipynb | 919 ++++++++++--- ...n Autocorrect with LSTM Autoencoders.ipynb | 1166 +++++++++++------ s2s_one_epoch.h5 | Bin 0 -> 5260384 bytes 3 files changed, 1441 insertions(+), 644 deletions(-) create mode 100644 s2s_one_epoch.h5 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 35d545b..5848543 100644 --- a/.ipynb_checkpoints/3a. Taxon Autocorrect with LSTM Autoencoders-checkpoint.ipynb +++ b/.ipynb_checkpoints/3a. Taxon Autocorrect with LSTM Autoencoders-checkpoint.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 184, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -20,9 +20,11 @@ "import matplotlib\n", "import random\n", "import string\n", + "import h5py\n", "from keras.models import Model\n", "from keras.preprocessing import sequence\n", - "from keras.layers import Input, LSTM, Dense" + "from keras.layers import Input, LSTM, Dense\n", + "from keras.callbacks import EarlyStopping" ] }, { @@ -35,7 +37,7 @@ }, { "cell_type": "code", - "execution_count": 185, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -379,7 +381,7 @@ "[49369 rows x 1 columns]" ] }, - "execution_count": 185, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -392,7 +394,7 @@ }, { "cell_type": "code", - "execution_count": 186, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -429,40 +431,40 @@ "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", + "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", - "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", + "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", - "Acineta chrysantha 1\n", - "Anas spp. 1\n", + "Cymbidium sinense 1\n", "Name: Taxon, Length: 3422, dtype: int64" ] }, - "execution_count": 186, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -474,22 +476,22 @@ }, { "cell_type": "code", - "execution_count": 187, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 187, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -511,7 +513,7 @@ }, { "cell_type": "code", - "execution_count": 188, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -529,7 +531,7 @@ " 'Martes flavigula'], dtype=object)" ] }, - "execution_count": 188, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -550,14 +552,14 @@ }, { "cell_type": "code", - "execution_count": 189, + "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Lxodonta afkicana\n" + "Loodonta afriTana\n" ] } ], @@ -603,23 +605,23 @@ }, { "cell_type": "code", - "execution_count": 190, + "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Loxodonta aLricana\n", - "Looxdonta africana\n", + "Loxodonta faricana\n", + "Loxodonta africanI\n", + "Loxodonta aficana\n", "loxodonta africana\n", - "Loxodnta afrTcana\n", - "loxodonta africana\n", - "Loxodonta africMna\n", - "Loxodontaafricana\n", - "Loxodonta afrianaZ\n", - "Loxodonta africaq\n", - "loxodonta africana\n" + "LoxodZnta africana\n", + "Loxodonta africaan\n", + "Loxodonta aDricna\n", + "Loxodonta afrciana\n", + "Loxodona africaDa\n", + "Boxodonta africaa\n" ] } ], @@ -639,7 +641,7 @@ }, { "cell_type": "code", - "execution_count": 191, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -670,152 +672,152 @@ " \n", " \n", " 0\n", - " Equus przeawlskii\n", + " Equus pzewalskUi\n", " Equus przewalskii\n", " \n", " \n", " 1\n", - " Equs przewalskii\n", + " EKuus przewalskii\n", " Equus przewalskii\n", " \n", " \n", " 2\n", - " Equusprzewalskii\n", + " Fquus przewalskii\n", " Equus przewalskii\n", " \n", " \n", " 3\n", - " Equus przealskiis\n", + " Equusp rzewalskii\n", " Equus przewalskii\n", " \n", " \n", " 4\n", - " Equus prUewalskii\n", + " equus przewalskii\n", " Equus przewalskii\n", " \n", " \n", " 5\n", - " Equus prlewalskii\n", + " EqSus przewalskii\n", " Equus przewalskii\n", " \n", " \n", " 6\n", - " equus przewalskii\n", + " Equs przewalskii\n", " Equus przewalskii\n", " \n", " \n", " 7\n", - " Equus przewlskii\n", + " Equus pszewalskii\n", " Equus przewalskii\n", " \n", " \n", " 8\n", - " EquusPprzewalskii\n", + " Equus pSzewalkii\n", " Equus przewalskii\n", " \n", " \n", " 9\n", - " Eqlus przewalskii\n", + " Equus pzrewalskii\n", " Equus przewalskii\n", " \n", " \n", " 10\n", - " Equus pzrewalskii\n", + " lquus przewalskii\n", " Equus przewalskii\n", " \n", " \n", " 11\n", - " Equus przeawlskii\n", + " Eaus przewalskii\n", " Equus przewalskii\n", " \n", " \n", " 12\n", - " Equusprzewalskii\n", + " Equus pzzewalskii\n", " Equus przewalskii\n", " \n", " \n", " 13\n", - " Equus przewlskii\n", + " Equus frzewalski\n", " Equus przewalskii\n", " \n", " \n", " 14\n", - " Equus przewalskji\n", + " Equus przealskii\n", " Equus przewalskii\n", " \n", " \n", " 15\n", - " Equs przewalskii\n", + " Equus przewklskii\n", " Equus przewalskii\n", " \n", " \n", " 16\n", - " Equus przewlskii\n", + " Equus przwalskii\n", " Equus przewalskii\n", " \n", " \n", " 17\n", - " Equus pzrewalskii\n", + " Euus przewalskii\n", " Equus przewalskii\n", " \n", " \n", " 18\n", - " equus przewalskii\n", + " Equus przewalskpi\n", " Equus przewalskii\n", " \n", " \n", " 19\n", - " Equus przewalsNii\n", + " Equus prezwalskii\n", " Equus przewalskii\n", " \n", " \n", " 20\n", - " Equus przewalsiki\n", + " equus przewalskii\n", " Equus przewalskii\n", " \n", " \n", " 21\n", - " Equus przealskiu\n", + " Equus pzrewalskii\n", " Equus przewalskii\n", " \n", " \n", " 22\n", - " equus przewalskii\n", + " Equus przewaskii\n", " Equus przewalskii\n", " \n", " \n", " 23\n", - " Equus przeawlskii\n", + " EquuF przewalski\n", " Equus przewalskii\n", " \n", " \n", " 24\n", - " Equus przealWkii\n", + " EZuus przewalskii\n", " Equus przewalskii\n", " \n", " \n", " 25\n", - " equus przewalskii\n", + " Ejus przewalskii\n", " Equus przewalskii\n", " \n", " \n", " 26\n", - " Equusp rzewalskii\n", + " Equus przewalkii\n", " Equus przewalskii\n", " \n", " \n", " 27\n", - " Equus prEewalskii\n", + " Equus przewaslkii\n", " Equus przewalskii\n", " \n", " \n", " 28\n", - " equus przewalskii\n", + " Equus przewasskii\n", " Equus przewalskii\n", " \n", " \n", " 29\n", - " squus przewalskii\n", + " Equus pzewalskii\n", " Equus przewalskii\n", " \n", " \n", @@ -825,32 +827,32 @@ " \n", " \n", " 342170\n", - " martes flavigula\n", + " Martes flavigula\n", " Martes flavigula\n", " \n", " \n", " 342171\n", - " Martes fwavgula\n", + " Martes flvigulao\n", " Martes flavigula\n", " \n", " \n", " 342172\n", - " Martek flvigula\n", + " Martes lfavigula\n", " Martes flavigula\n", " \n", " \n", " 342173\n", - " Martes fwvigula\n", + " martes flavigula\n", " Martes flavigula\n", " \n", " \n", " 342174\n", - " Martes flaigula\n", + " Martes flavigula\n", " Martes flavigula\n", " \n", " \n", " 342175\n", - " Martes flavigul\n", + " martes flavigula\n", " Martes flavigula\n", " \n", " \n", @@ -860,72 +862,72 @@ " \n", " \n", " 342177\n", - " martes flavigula\n", + " MartesQflavigula\n", " Martes flavigula\n", " \n", " \n", " 342178\n", - " Martes flaJigula\n", + " Mares olavigula\n", " Martes flavigula\n", " \n", " \n", " 342179\n", - " partes flavigula\n", + " martes flavigula\n", " Martes flavigula\n", " \n", " \n", " 342180\n", - " Martes lfavigula\n", + " martes flavigula\n", " Martes flavigula\n", " \n", " \n", " 342181\n", - " MarteP flavigula\n", + " Martes flavhgula\n", " Martes flavigula\n", " \n", " \n", " 342182\n", - " Martes flvibula\n", + " Marts flavigula\n", " Martes flavigula\n", " \n", " \n", " 342183\n", - " Martse flavigula\n", + " Martesf lavigula\n", " Martes flavigula\n", " \n", " \n", " 342184\n", - " Martes flaviula\n", + " jartes flavigula\n", " Martes flavigula\n", " \n", " \n", " 342185\n", - " Martes flavigual\n", + " Marte fPavigula\n", " Martes flavigula\n", " \n", " \n", " 342186\n", - " martes flavigula\n", + " aMrtes flavigula\n", " Martes flavigula\n", " \n", " \n", " 342187\n", - " Marts flavigula\n", + " martes flavigula\n", " Martes flavigula\n", " \n", " \n", " 342188\n", - " Martesflavigula\n", + " Martes flaivgula\n", " Martes flavigula\n", " \n", " \n", " 342189\n", - " Martes flNvigula\n", + " martes flavigula\n", " Martes flavigula\n", " \n", " \n", " 342190\n", - " martes flavigula\n", + " Marets flavigula\n", " Martes flavigula\n", " \n", " \n", @@ -935,42 +937,42 @@ " \n", " \n", " 342192\n", - " Maxtes flavigula\n", + " Marts flavigula\n", " Martes flavigula\n", " \n", " \n", " 342193\n", - " Martes flavigulaa\n", + " Martes fBavigula\n", " Martes flavigula\n", " \n", " \n", " 342194\n", - " Martes flavigXla\n", + " martes flavigula\n", " Martes flavigula\n", " \n", " \n", " 342195\n", - " aMrtes flavigula\n", + " martes flavigula\n", " Martes flavigula\n", " \n", " \n", " 342196\n", - " MartesLflavigula\n", + " Partes flavigula\n", " Martes flavigula\n", " \n", " \n", " 342197\n", - " Marets flavigula\n", + " martes flavigula\n", " Martes flavigula\n", " \n", " \n", " 342198\n", - " Martes flaviula\n", + " Martes flavgula\n", " Martes flavigula\n", " \n", " \n", " 342199\n", - " martes flavigula\n", + " MartesflavWgula\n", " Martes flavigula\n", " \n", " \n", @@ -980,72 +982,72 @@ ], "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", - "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", + "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", "... ... ...\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", + "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 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", - "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", + "342177 MartesQflavigula Martes flavigula\n", + "342178 Mares olavigula 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 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", + "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", + "342197 martes flavigula Martes flavigula\n", + "342198 Martes flavgula Martes flavigula\n", + "342199 MartesflavWgula Martes flavigula\n", "\n", "[342200 rows x 2 columns]" ] }, - "execution_count": 191, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -1073,12 +1075,12 @@ "source": [ "## Vectorise our data for learning\n", "\n", - "We'll need to encode our data from letters to number for our model to be able to deal with it. We'll take our table, and create two lists, one of all the characters in our input dataset, and one of all the characters in our target dataset. We'll also add a start and end character to our target data as this will be useful for our model to understand when to start and stop generating..." + "We'll need to encode our data from letters to number for our model to be able to deal with it. We'll take our table, and create two lists, one of all the characters in our input dataset, and one of all the characters in our target dataset. We can use these later on to one hot encode our characters to vectors before we feed them to our model. We'll also add a start and end character to our target data as this will be useful for our model to understand when to start and stop generating..." ] }, { "cell_type": "code", - "execution_count": 192, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -1094,7 +1096,7 @@ }, { "cell_type": "code", - "execution_count": 193, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -1111,7 +1113,7 @@ }, { "cell_type": "code", - "execution_count": 194, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -1126,7 +1128,7 @@ }, { "cell_type": "code", - "execution_count": 195, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -1136,7 +1138,7 @@ "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 +1203,7 @@ " 'z']" ] }, - "execution_count": 195, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -1232,7 +1234,7 @@ }, { "cell_type": "code", - "execution_count": 198, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -1254,7 +1256,7 @@ }, { "cell_type": "code", - "execution_count": 199, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -1277,7 +1279,7 @@ }, { "cell_type": "code", - "execution_count": 200, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -1299,7 +1301,7 @@ }, { "cell_type": "code", - "execution_count": 202, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -1316,7 +1318,7 @@ }, { "cell_type": "code", - "execution_count": 203, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -1336,7 +1338,7 @@ }, { "cell_type": "code", - "execution_count": 204, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -1347,7 +1349,7 @@ }, { "cell_type": "code", - "execution_count": 205, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -1355,42 +1357,541 @@ "output_type": "stream", "text": [ "Train on 273760 samples, validate on 68440 samples\n", - "Epoch 1/100\n", - "102336/273760 [==========>...................] - ETA: 6:04 - loss: 0.9042" + "Epoch 1/1\n", + "273760/273760 [==============================] - 709s 3ms/step - loss: 0.5920 - val_loss: 1.1982\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", - 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"\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": "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': [, ]}. 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", + "\n", + "earlystop = EarlyStopping(monitor='val_acc', min_delta=0.0001, patience=5, verbose=1, mode='auto')\n", "\n", "# Run training\n", - "model.compile(optimizer='rmsprop', loss='categorical_crossentropy')\n", - "model.fit([encoder_input_data, decoder_input_data], decoder_target_data,\n", - " batch_size=batch_size,\n", - " epochs=epochs,\n", - " validation_split=0.2)\n", - "# Save model\n", - "model.save('s2s.h5')" + "if training_mode == False:\n", + " model.load_weights(\"s2s.h5\")\n", + " model.compile(optimizer='rmsprop', loss='categorical_crossentropy')\n", + "else:\n", + " model.compile(optimizer='rmsprop', loss='categorical_crossentropy')\n", + " model.fit([encoder_input_data, decoder_input_data], decoder_target_data,\n", + " batch_size=batch_size,\n", + " epochs=epochs, \n", + " callbacks=[early_stop],\n", + " validation_split=0.2)\n", + " model.save('s2s.h5')" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "# Next: inference mode (sampling).\n", + "# Here's the drill:\n", + "# 1) encode input and retrieve initial decoder state\n", + "# 2) run one step of decoder with this initial state\n", + "# and a \"start of sequence\" token as target.\n", + "# Output will be the next target token\n", + "# 3) Repeat with the current target token and current states\n", + "\n", + "# Define sampling models\n", + "encoder_model = Model(encoder_inputs, encoder_states)\n", + "\n", + "decoder_state_input_h = Input(shape=(latent_dim,))\n", + "decoder_state_input_c = Input(shape=(latent_dim,))\n", + "decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c]\n", + "decoder_outputs, state_h, state_c = decoder_lstm(\n", + " decoder_inputs, initial_state=decoder_states_inputs)\n", + "decoder_states = [state_h, state_c]\n", + "decoder_outputs = decoder_dense(decoder_outputs)\n", + "decoder_model = Model(\n", + " [decoder_inputs] + decoder_states_inputs,\n", + " [decoder_outputs] + decoder_states)\n", + "\n", + "# Reverse-lookup token index to decode sequences back to\n", + "# something readable.\n", + "reverse_input_char_index = dict(\n", + " (i, char) for char, i in input_token_index.items())\n", + "reverse_target_char_index = dict(\n", + "(i, char) for char, i in target_token_index.items())" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "def decode_sequence(input_seq):\n", + " # Encode the input as state vectors.\n", + " states_value = encoder_model.predict(input_seq)\n", + "\n", + " # Generate empty target sequence of length 1.\n", + " target_seq = np.zeros((1, 1, num_decoder_tokens))\n", + " # Populate the first character of target sequence with the start character.\n", + " target_seq[0, 0, target_token_index['\\t']] = 1.\n", + "\n", + " # Sampling loop for a batch of sequences\n", + " # (to simplify, here we assume a batch of size 1).\n", + " stop_condition = False\n", + " decoded_sentence = ''\n", + " while not stop_condition:\n", + " output_tokens, h, c = decoder_model.predict(\n", + " [target_seq] + states_value)\n", + "\n", + " # Sample a token\n", + " sampled_token_index = np.argmax(output_tokens[0, -1, :])\n", + " sampled_char = reverse_target_char_index[sampled_token_index]\n", + " decoded_sentence += sampled_char\n", + "\n", + " # Exit condition: either hit max length\n", + " # or find stop character.\n", + " if (sampled_char == '\\n' or\n", + " len(decoded_sentence) > max_decoder_seq_length):\n", + " stop_condition = True\n", + "\n", + " # Update the target sequence (of length 1).\n", + " target_seq = np.zeros((1, 1, num_decoder_tokens))\n", + " target_seq[0, 0, sampled_token_index] = 1.\n", + "\n", + " # Update states\n", + " states_value = [h, c]\n", + "\n", + " return decoded_sentence" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-\n", + "Input sentence: Equus pzewalskUi\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: EKuus przewalskii\n", + "Decoded sentence: Eulemur colona\n", + "\n", + "-\n", + "Input sentence: Fquus przewalskii\n", + "Decoded sentence: Funcia spp.\n", + "\n", + "-\n", + "Input sentence: Equusp rzewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: equus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: EqSus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equs przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus pszewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus pSzewalkii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus pzrewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: lquus przewalskii\n", + "Decoded sentence: Saguinus midas\n", + "\n", + "-\n", + "Input sentence: Eaus przewalskii\n", + "Decoded sentence: Euphorbia stenoclada\n", + "\n", + "-\n", + "Input sentence: Equus pzzewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus frzewalski\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przealskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przewklskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przwalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Euus przewalskii\n", + "Decoded sentence: Euplectes afer\n", + "\n", + "-\n", + "Input sentence: Equus przewalskpi\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus prezwalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: equus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus pzrewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przewaskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: EquuF przewalski\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: EZuus przewalskii\n", + "Decoded sentence: Eulychnia acida\n", + "\n", + "-\n", + "Input sentence: Ejus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przewalkii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przewaslkii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przewasskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus pzewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: equus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: equus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: equus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: equus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equusprzewalbkii\n", + "Decoded sentence: Eulychnia ritteri\n", + "\n", + "-\n", + "Input sentence: Equusprzewalskip\n", + "Decoded sentence: Eulychnia ritteri\n", + "\n", + "-\n", + "Input sentence: Equus przewalskiik\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przewaskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: EquuM przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przealskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: equus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przewlaskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equu przewalskii\n", + "Decoded sentence: Eulychnia ritteri\n", + "\n", + "-\n", + "Input sentence: Pquus przewalskii\n", + "Decoded sentence: Panodia concilosa\n", + "\n", + "-\n", + "Input sentence: Equus lrzewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przewalski\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: EquuK przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus prezwalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przehalskii\n", + "Decoded sentence: Echinopsis chiloensis\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", + "\n", + "-\n", + "Input sentence: Euus przeNalskii\n", + "Decoded sentence: Euplectes afer\n", + "\n", + "-\n", + "Input sentence: equus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Euqus przewalskii\n", + "Decoded sentence: Eulemur collatis\n", + "\n", + "-\n", + "Input sentence: equus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equu przewalskii\n", + "Decoded sentence: Eulychnia ritteri\n", + "\n", + "-\n", + "Input sentence: Equus przealsUii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przewalskXi\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Ecuus przewlskii\n", + "Decoded sentence: Eulychnia acida\n", + "\n", + "-\n", + "Input sentence: Equus prOwalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przewaDskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: EquuN prewalskii\n", + "Decoded sentence: Eulychnia ritteri\n", + "\n", + "-\n", + "Input sentence: Equus prsewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equsu przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equu sprzewalskii\n", + "Decoded sentence: Eulychnia ritteri\n", + "\n", + "-\n", + "Input sentence: Equus przewaSskii\n", + "Decoded sentence: Echinopsis chiloensis\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", + "\n", + "-\n", + "Input sentence: EuJs przewalskii\n", + "Decoded sentence: Euplectes afer\n", + "\n", + "-\n", + "Input sentence: Equus pPzewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przewaslkii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przewaVskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equs pfzewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus prtewalski\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przewjlskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przelalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przewalseii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przewalkiiB\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus pzewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przealskji\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Euus przewalskii\n", + "Decoded sentence: Euplectes afer\n", + "\n", + "-\n", + "Input sentence: EquusWprzewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus prezwalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: equus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus prewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przewaslkii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przwalJkii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus rpzewalskii\n", + "Decoded sentence: Eulychnia acida\n", + "\n", + "-\n", + "Input sentence: equus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: equus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: equus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\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", + "\n", + "-\n", + "Input sentence: equus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: equus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n" + ] + } + ], + "source": [ + "for seq_index in range(100):\n", + " # Take one sequence (part of the training set)\n", + " # for trying out decoding.\n", + " input_seq = encoder_input_data[seq_index: seq_index + 1]\n", + " decoded_sentence = decode_sequence(input_seq)\n", + " print('-')\n", + " print('Input sentence:', input_texts[seq_index])\n", + " print('Decoded sentence:', decoded_sentence)" ] }, { diff --git a/3a. Taxon Autocorrect with LSTM Autoencoders.ipynb b/3a. Taxon Autocorrect with LSTM Autoencoders.ipynb index 9f873ca..5848543 100644 --- a/3a. Taxon Autocorrect with LSTM Autoencoders.ipynb +++ b/3a. Taxon Autocorrect with LSTM Autoencoders.ipynb @@ -10,17 +10,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 48, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Using TensorFlow backend.\n" - ] - } - ], + "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", @@ -28,9 +20,11 @@ "import matplotlib\n", "import random\n", "import string\n", + "import h5py\n", "from keras.models import Model\n", "from keras.preprocessing import sequence\n", - "from keras.layers import Input, LSTM, Dense" + "from keras.layers import Input, LSTM, Dense\n", + "from keras.callbacks import EarlyStopping" ] }, { @@ -43,25 +37,25 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", - "\n", "\n", " \n", @@ -387,7 +381,7 @@ "[49369 rows x 1 columns]" ] }, - "execution_count": 3, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -400,7 +394,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -437,40 +431,40 @@ "Crocodylus novaeguineae 202\n", "Leopardus pardalis 201\n", " ... \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", + "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", "Name: Taxon, Length: 3422, dtype: int64" ] }, - "execution_count": 4, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -482,18 +476,28 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 5, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" \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", @@ -923,22 +927,22 @@ " \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", @@ -948,12 +952,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -963,12 +967,12 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -978,72 +982,72 @@ ], "text/plain": [ " Input Target\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 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", + "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", "... ... ...\n", - "342170 martes flavigula Martes flavigula\n", - "342171 Martes Ylavigula Martes flavigula\n", - "342172 Martesflavigula Martes flavigula\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 flavigual Martes flavigula\n", - "342176 Martes flavigula Martes flavigula\n", - "342177 martes flavigula Martes flavigula\n", - "342178 Mares flavigula Martes flavigula\n", - "342179 Martes flavigla 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", + "342179 martes flavigula 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 uartes flavigula Martes flavigula\n", - "342188 Martes favigula 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 Martes flavigua Martes flavigula\n", - "342191 Martes flaviula Martes flavigula\n", - "342192 aMrtes flavigula Martes flavigula\n", - "342193 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 flavigla Martes flavigula\n", - "342196 Martes lavigula Martes flavigula\n", + "342195 martes flavigula Martes flavigula\n", + "342196 Partes flavigula Martes flavigula\n", "342197 martes flavigula Martes flavigula\n", - "342198 Marte flavigula Martes flavigula\n", - "342199 Marted flavgula Martes flavigula\n", + "342198 Martes flavgula Martes flavigula\n", + "342199 MartesflavWgula Martes flavigula\n", "\n", "[342200 rows x 2 columns]" ] }, - "execution_count": 9, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -1076,10 +1080,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": true - }, + "execution_count": 34, + "metadata": {}, "outputs": [], "source": [ "input_texts = []\n", @@ -1094,10 +1096,8 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": true - }, + "execution_count": 35, + "metadata": {}, "outputs": [], "source": [ "# Takes in the input and target texts and adds their characters to the list of input and target characters\n", @@ -1113,10 +1113,8 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": true - }, + "execution_count": 36, + "metadata": {}, "outputs": [], "source": [ "for index, row in corpus.iterrows():\n", @@ -1130,7 +1128,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -1205,7 +1203,7 @@ " 'z']" ] }, - "execution_count": 13, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -1236,7 +1234,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -1258,10 +1256,8 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": true - }, + "execution_count": 39, + "metadata": {}, "outputs": [], "source": [ "# encoder_input_data is a 3D array of shape (num_pairs, max input seq length, num input characters)\n", @@ -1283,10 +1279,8 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": true - }, + "execution_count": 40, + "metadata": {}, "outputs": [], "source": [ "# i = training examples\n", @@ -1307,7 +1301,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 41, "metadata": {}, "outputs": [], "source": [ @@ -1324,7 +1318,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ @@ -1344,10 +1338,8 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": true - }, + "execution_count": 43, + "metadata": {}, "outputs": [], "source": [ "# Define the model that will turn\n", @@ -1357,7 +1349,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -1365,219 +1357,15 @@ "output_type": "stream", "text": [ "Train on 273760 samples, validate on 68440 samples\n", - "Epoch 1/100\n", - "273760/273760 [==============================] - 467s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", - "Epoch 2/100\n", - "273760/273760 [==============================] - 459s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", - "Epoch 3/100\n", - "273760/273760 [==============================] - 451s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", - "Epoch 4/100\n", - "273760/273760 [==============================] - 451s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", - "Epoch 5/100\n", - "273760/273760 [==============================] - 453s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", - "Epoch 6/100\n", - "273760/273760 [==============================] - 454s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", - "Epoch 7/100\n", - "273760/273760 [==============================] - 454s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", - "Epoch 8/100\n", - "273760/273760 [==============================] - 452s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", - "Epoch 9/100\n", - "273760/273760 [==============================] - 454s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", - "Epoch 10/100\n", - "273760/273760 [==============================] - 456s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", - "Epoch 11/100\n", - "273760/273760 [==============================] - 457s 2ms/step - loss: 6.0600e-08 - val_loss: 6.0867e-08\n", - "Epoch 12/100\n", - "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" - ] - }, - { - "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" + "Epoch 1/1\n", + "273760/273760 [==============================] - 709s 3ms/step - loss: 0.5920 - val_loss: 1.1982\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", + "/usr/local/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" ] } @@ -1585,23 +1373,531 @@ "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", + "\n", + "earlystop = EarlyStopping(monitor='val_acc', min_delta=0.0001, patience=5, verbose=1, mode='auto')\n", "\n", "# Run training\n", - "model.compile(optimizer='rmsprop', loss='categorical_crossentropy')\n", - "model.fit([encoder_input_data, decoder_input_data], decoder_target_data,\n", - " batch_size=batch_size,\n", - " epochs=epochs,\n", - " validation_split=0.2)\n", - "# Save model\n", - "model.save('s2s.h5')" + "if training_mode == False:\n", + " model.load_weights(\"s2s.h5\")\n", + " model.compile(optimizer='rmsprop', loss='categorical_crossentropy')\n", + "else:\n", + " model.compile(optimizer='rmsprop', loss='categorical_crossentropy')\n", + " model.fit([encoder_input_data, decoder_input_data], decoder_target_data,\n", + " batch_size=batch_size,\n", + " epochs=epochs, \n", + " callbacks=[early_stop],\n", + " validation_split=0.2)\n", + " model.save('s2s.h5')" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "# Next: inference mode (sampling).\n", + "# Here's the drill:\n", + "# 1) encode input and retrieve initial decoder state\n", + "# 2) run one step of decoder with this initial state\n", + "# and a \"start of sequence\" token as target.\n", + "# Output will be the next target token\n", + "# 3) Repeat with the current target token and current states\n", + "\n", + "# Define sampling models\n", + "encoder_model = Model(encoder_inputs, encoder_states)\n", + "\n", + "decoder_state_input_h = Input(shape=(latent_dim,))\n", + "decoder_state_input_c = Input(shape=(latent_dim,))\n", + "decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c]\n", + "decoder_outputs, state_h, state_c = decoder_lstm(\n", + " decoder_inputs, initial_state=decoder_states_inputs)\n", + "decoder_states = [state_h, state_c]\n", + "decoder_outputs = decoder_dense(decoder_outputs)\n", + "decoder_model = Model(\n", + " [decoder_inputs] + decoder_states_inputs,\n", + " [decoder_outputs] + decoder_states)\n", + "\n", + "# Reverse-lookup token index to decode sequences back to\n", + "# something readable.\n", + "reverse_input_char_index = dict(\n", + " (i, char) for char, i in input_token_index.items())\n", + "reverse_target_char_index = dict(\n", + "(i, char) for char, i in target_token_index.items())" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "def decode_sequence(input_seq):\n", + " # Encode the input as state vectors.\n", + " states_value = encoder_model.predict(input_seq)\n", + "\n", + " # Generate empty target sequence of length 1.\n", + " target_seq = np.zeros((1, 1, num_decoder_tokens))\n", + " # Populate the first character of target sequence with the start character.\n", + " target_seq[0, 0, target_token_index['\\t']] = 1.\n", + "\n", + " # Sampling loop for a batch of sequences\n", + " # (to simplify, here we assume a batch of size 1).\n", + " stop_condition = False\n", + " decoded_sentence = ''\n", + " while not stop_condition:\n", + " output_tokens, h, c = decoder_model.predict(\n", + " [target_seq] + states_value)\n", + "\n", + " # Sample a token\n", + " sampled_token_index = np.argmax(output_tokens[0, -1, :])\n", + " sampled_char = reverse_target_char_index[sampled_token_index]\n", + " decoded_sentence += sampled_char\n", + "\n", + " # Exit condition: either hit max length\n", + " # or find stop character.\n", + " if (sampled_char == '\\n' or\n", + " len(decoded_sentence) > max_decoder_seq_length):\n", + " stop_condition = True\n", + "\n", + " # Update the target sequence (of length 1).\n", + " target_seq = np.zeros((1, 1, num_decoder_tokens))\n", + " target_seq[0, 0, sampled_token_index] = 1.\n", + "\n", + " # Update states\n", + " states_value = [h, c]\n", + "\n", + " return decoded_sentence" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-\n", + "Input sentence: Equus pzewalskUi\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: EKuus przewalskii\n", + "Decoded sentence: Eulemur colona\n", + "\n", + "-\n", + "Input sentence: Fquus przewalskii\n", + "Decoded sentence: Funcia spp.\n", + "\n", + "-\n", + "Input sentence: Equusp rzewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: equus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: EqSus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equs przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus pszewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus pSzewalkii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus pzrewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: lquus przewalskii\n", + "Decoded sentence: Saguinus midas\n", + "\n", + "-\n", + "Input sentence: Eaus przewalskii\n", + "Decoded sentence: Euphorbia stenoclada\n", + "\n", + "-\n", + "Input sentence: Equus pzzewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus frzewalski\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przealskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przewklskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przwalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Euus przewalskii\n", + "Decoded sentence: Euplectes afer\n", + "\n", + "-\n", + "Input sentence: Equus przewalskpi\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus prezwalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: equus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus pzrewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przewaskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: EquuF przewalski\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: EZuus przewalskii\n", + "Decoded sentence: Eulychnia acida\n", + "\n", + "-\n", + "Input sentence: Ejus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przewalkii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przewaslkii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przewasskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus pzewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: equus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: equus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: equus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: equus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equusprzewalbkii\n", + "Decoded sentence: Eulychnia ritteri\n", + "\n", + "-\n", + "Input sentence: Equusprzewalskip\n", + "Decoded sentence: Eulychnia ritteri\n", + "\n", + "-\n", + "Input sentence: Equus przewalskiik\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przewaskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: EquuM przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przealskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: equus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przewlaskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equu przewalskii\n", + "Decoded sentence: Eulychnia ritteri\n", + "\n", + "-\n", + "Input sentence: Pquus przewalskii\n", + "Decoded sentence: Panodia concilosa\n", + "\n", + "-\n", + "Input sentence: Equus lrzewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przewalski\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: EquuK przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus prezwalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przehalskii\n", + "Decoded sentence: Echinopsis chiloensis\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", + "\n", + "-\n", + "Input sentence: Euus przeNalskii\n", + "Decoded sentence: Euplectes afer\n", + "\n", + "-\n", + "Input sentence: equus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Euqus przewalskii\n", + "Decoded sentence: Eulemur collatis\n", + "\n", + "-\n", + "Input sentence: equus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equu przewalskii\n", + "Decoded sentence: Eulychnia ritteri\n", + "\n", + "-\n", + "Input sentence: Equus przealsUii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przewalskXi\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Ecuus przewlskii\n", + "Decoded sentence: Eulychnia acida\n", + "\n", + "-\n", + "Input sentence: Equus prOwalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przewaDskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: EquuN prewalskii\n", + "Decoded sentence: Eulychnia ritteri\n", + "\n", + "-\n", + "Input sentence: Equus prsewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equsu przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equu sprzewalskii\n", + "Decoded sentence: Eulychnia ritteri\n", + "\n", + "-\n", + "Input sentence: Equus przewaSskii\n", + "Decoded sentence: Echinopsis chiloensis\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", + "\n", + "-\n", + "Input sentence: EuJs przewalskii\n", + "Decoded sentence: Euplectes afer\n", + "\n", + "-\n", + "Input sentence: Equus pPzewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przewaslkii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przewaVskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equs pfzewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus prtewalski\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przewjlskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przelalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przewalseii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przewalkiiB\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus pzewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przealskji\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Euus przewalskii\n", + "Decoded sentence: Euplectes afer\n", + "\n", + "-\n", + "Input sentence: EquusWprzewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus prezwalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: equus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus prewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przewaslkii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przwalJkii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus rpzewalskii\n", + "Decoded sentence: Eulychnia acida\n", + "\n", + "-\n", + "Input sentence: equus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: equus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: equus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\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", + "\n", + "-\n", + "Input sentence: equus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: equus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n", + "-\n", + "Input sentence: Equus przewalskii\n", + "Decoded sentence: Echinopsis chiloensis\n", + "\n" + ] + } + ], + "source": [ + "for seq_index in range(100):\n", + " # Take one sequence (part of the training set)\n", + " # for trying out decoding.\n", + " input_seq = encoder_input_data[seq_index: seq_index + 1]\n", + " decoded_sentence = decode_sequence(input_seq)\n", + " print('-')\n", + " print('Input sentence:', input_texts[seq_index])\n", + " print('Decoded sentence:', decoded_sentence)" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [] } @@ -1622,7 +1918,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.2" + "version": "3.6.5" } }, "nbformat": 4, diff --git a/s2s_one_epoch.h5 b/s2s_one_epoch.h5 new file mode 100644 index 0000000000000000000000000000000000000000..c2452a760884d631956e550a8110eb6493815cf1 GIT binary patch literal 5260384 zcmeFZ2{@Kr*D!3JG7qK5lqAX&!ga14l|q9;(qIZ9LWYVG3Q>dxp`;`lC_@R?xwhtk z=0QmVX;hkLsqYe=`?-7W=X<~7``-WkzyE*lebl+Hz4qE`uf5K_hI8#*sdf`5Nl6Tm z;Oi-3Vthh;vcG=zKYsQVuZc5$f%@D1{a$@P`1|UVzIv}f?+<=HF}|KYz~A+MD+$=y+WgvpZhfmZ>Z{N7?X!MiEDrVc z3-(;*7w8`l8NmaFhD8Q04vg^&|0h@vZvTGO?H~AWYPr7s`uo0?i^BhC(Qfp)eHL7FUe=!M^Ee*y#?Mr zL4F~={~?_o#qFolBZ@!xlKMrbR~|m0AqxZjdENgZy^sNa(Cbmretoqw?UhtO4-sDB z=M(wG_wRb`)dw*8U)1Xvs=f3ydaJ*m|IH-u@AP_SpS|xFM_@=;RHUaFUvF*B7aS3} 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