From 00de253172b33afbf3807c22693a0aab3f1ad3d3 Mon Sep 17 00:00:00 2001 From: Minho Date: Mon, 2 Jul 2018 21:04:25 +0900 Subject: [PATCH] fix tf.nn.nce_loss changed input order & tf.summary.FileWriter --- notebooks/word2vec_basic.ipynb | 674 +++++++++++++++++++++++--------- notebooks/word2vec_simple.ipynb | 96 +++-- 2 files changed, 546 insertions(+), 224 deletions(-) diff --git a/notebooks/word2vec_basic.ipynb b/notebooks/word2vec_basic.ipynb index 6d4fcdb..8d0b8b1 100755 --- a/notebooks/word2vec_basic.ipynb +++ b/notebooks/word2vec_basic.ipynb @@ -63,7 +63,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "File already exists\n" + "No file found. Start downloading\n", + "'data\\text8.zip' downloaded\n" ] } ], @@ -99,7 +100,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "I guess we have correct file at 'data/text8.zip'\n" + "I guess we have correct file at 'data\\text8.zip'\n" ] } ], @@ -144,9 +145,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Type of 'words' is / Length is 17005207 \n", + "Type of 'words' is / Length is 17005207 \n", "'words' look like \n", - " ['anarchism', 'originated', 'as', 'a', 'term', 'of', 'abuse', 'first', 'used', 'against', 'early', 'working', 'class', 'radicals', 'including', 'the', 'diggers', 'of', 'the', 'english', 'revolution', 'and', 'the', 'sans', 'culottes', 'of', 'the', 'french', 'revolution', 'whilst', 'the', 'term', 'is', 'still', 'used', 'in', 'a', 'pejorative', 'way', 'to', 'describe', 'any', 'act', 'that', 'used', 'violent', 'means', 'to', 'destroy', 'the', 'organization', 'of', 'society', 'it', 'has', 'also', 'been', 'taken', 'up', 'as', 'a', 'positive', 'label', 'by', 'self', 'defined', 'anarchists', 'the', 'word', 'anarchism', 'is', 'derived', 'from', 'the', 'greek', 'without', 'archons', 'ruler', 'chief', 'king', 'anarchism', 'as', 'a', 'political', 'philosophy', 'is', 'the', 'belief', 'that', 'rulers', 'are', 'unnecessary', 'and', 'should', 'be', 'abolished', 'although', 'there', 'are', 'differing']\n" + " [b'anarchism', b'originated', b'as', b'a', b'term', b'of', b'abuse', b'first', b'used', b'against', b'early', b'working', b'class', b'radicals', b'including', b'the', b'diggers', b'of', b'the', b'english', b'revolution', b'and', b'the', b'sans', b'culottes', b'of', b'the', b'french', b'revolution', b'whilst', b'the', b'term', b'is', b'still', b'used', b'in', b'a', b'pejorative', b'way', b'to', b'describe', b'any', b'act', b'that', b'used', b'violent', b'means', b'to', b'destroy', b'the', b'organization', b'of', b'society', b'it', b'has', b'also', b'been', b'taken', b'up', b'as', b'a', b'positive', b'label', b'by', b'self', b'defined', b'anarchists', b'the', b'word', b'anarchism', b'is', b'derived', b'from', b'the', b'greek', b'without', b'archons', b'ruler', b'chief', b'king', b'anarchism', b'as', b'a', b'political', b'philosophy', b'is', b'the', b'belief', b'that', b'rulers', b'are', b'unnecessary', b'and', b'should', b'be', b'abolished', b'although', b'there', b'are', b'differing']\n" ] } ], @@ -159,9 +160,7 @@ }, { "cell_type": "markdown", - "metadata": { - "collapsed": false - }, + "metadata": {}, "source": [ "# 2. Make a dictionary with fixed length (using UNK token)" ] @@ -184,9 +183,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Type of 'count' is / Length is 50000 \n", + "Type of 'count' is / Length is 50000 \n", "'count' looks like \n", - " [['UNK', -1], ('the', 1061396), ('of', 593677), ('and', 416629), ('one', 411764), ('in', 372201), ('a', 325873), ('to', 316376), ('zero', 264975), ('nine', 250430)]\n" + " [['UNK', -1], (b'the', 1061396), (b'of', 593677), (b'and', 416629), (b'one', 411764), (b'in', 372201), (b'a', 325873), (b'to', 316376), (b'zero', 264975), (b'nine', 250430)]\n" ] } ], @@ -208,7 +207,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": { "collapsed": false }, @@ -217,7 +216,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Type of 'dictionary' is / Length is 50000 \n" + "Type of 'dictionary' is / Length is 50000 \n" ] } ], @@ -238,7 +237,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": { "collapsed": false }, @@ -247,7 +246,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Type of 'reverse_dictionary' is / Length is 50000 \n" + "Type of 'reverse_dictionary' is / Length is 50000 \n" ] } ], @@ -266,7 +265,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": { "collapsed": true }, @@ -282,7 +281,7 @@ " unk_count += 1\n", " data.append(index)\n", "count[0][1] = unk_count\n", - "# del words # Hint to reduce memory." + "del words # Hint to reduce memory." ] }, { @@ -295,7 +294,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": { "collapsed": false }, @@ -304,7 +303,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Most common words (+UNK) are: [['UNK', 418391], ('the', 1061396), ('of', 593677), ('and', 416629), ('one', 411764)]\n" + "Most common words (+UNK) are: [['UNK', 418391], (b'the', 1061396), (b'of', 593677), (b'and', 416629), (b'one', 411764)]\n" ] } ], @@ -321,7 +320,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": { "collapsed": false }, @@ -330,7 +329,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Sample data: [5239, 3084, 12, 6, 195, 2, 3137, 46, 59, 156]\n" + "Sample data: [5234, 3081, 12, 6, 195, 2, 3134, 46, 59, 156]\n" ] } ], @@ -347,7 +346,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": { "collapsed": false }, @@ -358,16 +357,16 @@ "text": [ "Sample data corresponds to\n", "__________________\n", - "5239->anarchism\n", - "3084->originated\n", - "12->as\n", - "6->a\n", - "195->term\n", - "2->of\n", - "3137->abuse\n", - "46->first\n", - "59->used\n", - "156->against\n" + "5234->b'anarchism'\n", + "3081->b'originated'\n", + "12->b'as'\n", + "6->b'a'\n", + "195->b'term'\n", + "2->b'of'\n", + "3134->b'abuse'\n", + "46->b'first'\n", + "59->b'used'\n", + "156->b'against'\n" ] } ], @@ -377,15 +376,6 @@ " print (\"%d->%s\" % (data[i], reverse_dictionary[data[i]]))" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, @@ -398,7 +388,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": { "collapsed": true }, @@ -439,7 +429,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": { "collapsed": false, "scrolled": true @@ -449,8 +439,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "Type of 'batch' is / Length is 8 \n", - "Type of 'labels' is / Length is 8 \n" + "Type of 'batch' is / Length is 8 \n", + "Type of 'labels' is / Length is 8 \n" ] } ], @@ -465,7 +455,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 18, "metadata": { "collapsed": false }, @@ -475,7 +465,7 @@ "output_type": "stream", "text": [ "'batch' looks like \n", - " [3084 3084 12 12 6 6 195 195]\n" + " [3081 3081 12 12 6 6 195 195]\n" ] } ], @@ -485,7 +475,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 19, "metadata": { "collapsed": false }, @@ -495,12 +485,12 @@ "output_type": "stream", "text": [ "'labels' looks like \n", - " [[5239]\n", - " [ 12]\n", - " [3084]\n", + " [[ 12]\n", + " [5234]\n", " [ 6]\n", - " [ 195]\n", + " [3081]\n", " [ 12]\n", + " [ 195]\n", " [ 2]\n", " [ 6]]\n" ] @@ -512,7 +502,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 20, "metadata": { "collapsed": false }, @@ -521,14 +511,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "3084 -> 5239 \toriginated -> anarchism\n", - "3084 -> 12 \toriginated -> as\n", - "12 -> 3084 \tas -> originated\n", - "12 -> 6 \tas -> a\n", - "6 -> 195 \ta -> term\n", - "6 -> 12 \ta -> as\n", - "195 -> 2 \tterm -> of\n", - "195 -> 6 \tterm -> a\n" + "3081 -> 12\n", + "\tb'originated' -> b'as'\n", + "3081 -> 5234\n", + "\tb'originated' -> b'anarchism'\n", + "12 -> 6\n", + "\tb'as' -> b'a'\n", + "12 -> 3081\n", + "\tb'as' -> b'originated'\n", + "6 -> 12\n", + "\tb'a' -> b'as'\n", + "6 -> 195\n", + "\tb'a' -> b'term'\n", + "195 -> 2\n", + "\tb'term' -> b'of'\n", + "195 -> 6\n", + "\tb'term' -> b'a'\n" ] } ], @@ -550,7 +548,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 25, "metadata": { "collapsed": false }, @@ -573,7 +571,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 26, "metadata": { "collapsed": false }, @@ -582,8 +580,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[ 31 89 64 57 122 178 173 13 50 176 12 188 0 19 168 44 59 130\n", - " 126 75 187 161 10 194 119 131 5 69 150 165 6 70]\n" + "[126 173 198 59 18 162 71 79 54 119 3 90 182 44 27 199 157 127\n", + " 87 168 22 83 26 23 135 134 70 58 53 146 104 190]\n" ] } ], @@ -605,7 +603,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 43, "metadata": { "collapsed": false }, @@ -650,7 +648,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "metadata": { "collapsed": false }, @@ -668,8 +666,8 @@ " # Loss function \n", " num_sampled = 64 # Number of negative examples to sample. \n", " loss = tf.reduce_mean(\n", - " tf.nn.nce_loss(nce_weights, nce_biases, embed\n", - " , train_labels, num_sampled, vocabulary_size))\n", + " tf.nn.nce_loss(nce_weights, nce_biases, train_labels\n", + " , embed, num_sampled, vocabulary_size))\n", " # Optimizer\n", " optm = tf.train.GradientDescentOptimizer(1.0).minimize(loss)\n", " # Similarity measure (important)\n", @@ -692,7 +690,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "metadata": { "collapsed": false, "scrolled": false @@ -702,114 +700,409 @@ "name": "stdout", "output_type": "stream", "text": [ - "Average loss at step 0 is 0.141\n", - "Nearest to 'an': 'yorktown', 'fux', 'katydids', 'leary', 'interruption', 'detox',\n", - "Nearest to 'called': 'wong', 'germaine', 'electrophilic', 'kilpatrick', 'alkenes', 'retained',\n", - "Nearest to 'american': 'mq', 'reef', 'guang', 'seas', 'puma', 'rq',\n", - "Nearest to 'who': 'scarred', 'directory', 'spate', 'denison', 'incensed', 'adipose',\n", - "Nearest to 'c': 'chapman', 'charlize', 'lading', 'ethnographers', 'stingray', 'vainly',\n", - "Nearest to 'set': 'emptive', 'menelik', 'headed', 'unsubstantiated', 'churchyard', 'horticultural',\n", - "Nearest to 'different': 'unreleased', 'arameans', 'psychologist', 'earmarked', 'phylum', 'ayer',\n", - "Nearest to 'eight': 'specificity', 'leftover', 'fulfil', 'urgell', 'oak', 'columba',\n", - "Nearest to 'all': 'transport', 'linspire', 'eo', 'donatello', 'georgi', 'picturesque',\n", - "Nearest to 'do': 'megabits', 'condorcet', 'landau', 'coincidental', 'ransom', 'proper',\n", - "Nearest to 'as': 'yesterday', 'hw', 'solace', 'modeled', 'hats', 'conant',\n", - "Nearest to 'law': 'gleaned', 'fickle', 'expressive', 'gory', 'diem', 'threatens',\n", - "Nearest to 'UNK': 'probate', 'guests', 'determines', 'dave', 'ubiquity', 'inks',\n", - "Nearest to 'by': 'parton', 'mccann', 'mahoney', 'diasporas', 'benefiting', 'nieuwe',\n", - "Nearest to 'large': 'towers', 'multiculturalism', 'endowment', 'enhancing', 'brainwashing', 'eia',\n", - "Nearest to 'their': 'ecc', 'somber', 'quaternary', 'schopenhauer', 'trombone', 'brethren',\n", - "Nearest to 'used': 'shrunk', 'fixtures', 'ultraviolet', 'volley', 'hab', 'spiced',\n", - "Nearest to 'british': 'equinox', 'shandy', 'socialism', 'revert', 'zeeland', 'progs',\n", - "Nearest to 'x': 'callous', 'envoys', 'gamecube', 'unintelligent', 'rosicrucian', 'participated',\n", - "Nearest to 'd': 'francesco', 'schumacher', 'creighton', 'tubas', 'indulgence', 'dip',\n", - "Nearest to 'king': 'circumscribed', 'niacin', 'inter', 'bluescreen', 'catapult', 'farnese',\n", - "Nearest to 'based': 'oj', 'odi', 'fubar', 'jogaila', 'enables', 'refrigerated',\n", - "Nearest to 'two': 'orbits', 'hostel', 'skeleton', 'factories', 'borghese', 'panned',\n", - "Nearest to 'international': 'da', 'confidential', 'hiv', 'rhythmic', 'incipient', 'talkie',\n", - "Nearest to 'him': 'reelection', 'trivia', 'counters', 'lsch', 'visual', 'seaport',\n", - "Nearest to 'year': 'overwhelming', 'astaire', 'conclude', 'donner', 'honky', 'objects',\n", - "Nearest to 'in': 'events', 'golem', 'elbert', 'lung', 'rsc', 'coordinated',\n", - "Nearest to 'may': 'sur', 'formalized', 'inasmuch', 'telekinetic', 'bisexual', 'duchamp',\n", - "Nearest to 'g': 'aruban', 'africans', 'jason', 'perutz', 'stun', 'hahn',\n", - "Nearest to 'de': 'compactification', 'brugge', 'disarming', 'plotted', 'env', 'fielder',\n", - "Nearest to 'a': 'ally', 'motorola', 'concrete', 'catcher', 'necks', 'mortimer',\n", - "Nearest to 'than': 'karts', 'gwynedd', 'hesse', 'agglutinative', 'there', 'ensues',\n", - "Average loss at step 2000 is 114.071\n", - "Average loss at step 4000 is 52.629\n", - "Average loss at step 6000 is 33.005\n", - "Average loss at step 8000 is 23.590\n", - "Average loss at step 10000 is 17.952\n", - "Nearest to 'an': 'the', 'overseas', 'protein', 'interruption', 'victoriae', 'mya',\n", - "Nearest to 'called': 'gland', 'within', 'gb', 'much', 'retained', 'consists',\n", - "Nearest to 'american': 'olympics', 'methadone', 'linguistic', 'technologically', 'while', 'spider',\n", - "Nearest to 'who': 'and', 'directory', 'named', 'attacked', 'mode', 'ruth',\n", - "Nearest to 'c': 'victoriae', 'accounts', 'mathbf', 'reginae', 'austin', 'passion',\n", - "Nearest to 'set': 'reginae', 'shortcuts', 'animals', 'few', 'members', 'majority',\n", - "Nearest to 'different': 'mya', 'unreleased', 'cl', 'phylum', 'psychologist', 'reginae',\n", - "Nearest to 'eight': 'nine', 'zero', 'six', 'reginae', 'agave', 'vs',\n", - "Nearest to 'all': 'transport', 'beginning', 'bestseller', 'cl', 'waugh', 'lindbergh',\n", - "Nearest to 'do': 'vs', 'proper', 'assistant', 'symbols', 'reflect', 'christian',\n", - "Nearest to 'as': 'in', 'for', 'and', 'by', 'irish', 'asterism',\n", - "Nearest to 'law': 'collapse', 'cc', 'soviet', 'accordion', 'badges', 'kyoto',\n", - "Nearest to 'UNK': 'and', 'alpina', 'mengele', 'the', 'reginae', 'one',\n", - "Nearest to 'by': 'in', 'and', 'as', 'at', 'gland', 'was',\n", - "Nearest to 'large': 'towers', 'gland', 'fricatives', 'conducted', 'greatest', 'charlie',\n", - "Nearest to 'their': 'reginae', 'schopenhauer', 'afghani', 'the', 'sabotage', 'implicit',\n", - "Nearest to 'used': 'spiced', 'mystery', 'mathbf', 'sacred', 'mining', 'fixtures',\n", - "Nearest to 'british': 'anatomy', 'socialism', 'equinox', 'victoriae', 'astor', 'five',\n", - "Nearest to 'x': 'phi', 'right', 'participated', 'modern', 'countries', 'video',\n", - "Nearest to 'd': 'finalist', 'avoid', 'tubing', 'vs', 'francesco', 'schumacher',\n", - "Nearest to 'king': 'inter', 'thompson', 'negligible', 'moves', 'alpina', 'wisconsin',\n", - "Nearest to 'based': 'reginae', 'chemist', 'dim', 'directory', 'traditionally', 'agave',\n", - "Nearest to 'two': 'one', 'reginae', 'vs', 'austin', 'gollancz', 'three',\n", - "Nearest to 'international': 'da', 'hiv', 'confidential', 'access', 'six', 'robots',\n", - "Nearest to 'him': 'infectious', 'trivia', 'spontaneous', 'seaport', 'directors', 'visual',\n", - "Nearest to 'year': 'conclude', 'objects', 'overwhelming', 'clear', 'responsibility', 'vs',\n", - "Nearest to 'in': 'and', 'of', 'mya', 'with', 'on', 'by',\n", - "Nearest to 'may': 'sur', 'cannot', 'tubing', 'formalized', 'individualist', 'faure',\n", - "Nearest to 'g': 'eight', 'aruban', 'jason', 'africans', 'i', 'am',\n", - "Nearest to 'de': 'publicly', 'oxus', 'prize', 'shortly', 'course', 'aa',\n", - "Nearest to 'a': 'the', 'austin', 'UNK', 'and', 'reginae', 'alpina',\n", - "Nearest to 'than': 'karts', 'community', 'hesse', 'there', 'racial', 'caves',\n", - "Average loss at step 12000 is 13.873\n", - "Average loss at step 14000 is 11.914\n", - "Average loss at step 16000 is 9.810\n", - "Average loss at step 18000 is 8.702\n", - "Average loss at step 20000 is 7.841\n", - "Nearest to 'an': 'the', 'protein', 'overseas', 'their', 'interruption', 'counterexample',\n", - "Nearest to 'called': 'within', 'gland', 'hiroshima', 'litigants', 'dasyprocta', 'clan',\n", - "Nearest to 'american': 'and', 'methadone', 'olympics', 'while', 'technologically', 'certainty',\n", - "Nearest to 'who': 'and', 'attacked', 'also', 'ruth', 'named', 'agouti',\n", - "Nearest to 'c': 'eight', 'victoriae', 'accounts', 'one', 'blog', 'chapman',\n", - "Nearest to 'set': 'reginae', 'shortcuts', 'astoria', 'headquarters', 'members', 'few',\n", - "Nearest to 'different': 'unreleased', 'mya', 'cl', 'agouti', 'psychologist', 'truetype',\n", - "Nearest to 'eight': 'nine', 'six', 'zero', 'five', 'seven', 'three',\n", - "Nearest to 'all': 'bestseller', 'beginning', 'transport', 'waugh', 'cl', 'lindbergh',\n", - "Nearest to 'do': 'condorcet', 'vs', 'proper', 'assistant', 'reflect', 'symbols',\n", - "Nearest to 'as': 'and', 'for', 'by', 'was', 'is', 'in',\n", - "Nearest to 'law': 'agouti', 'threatens', 'collapse', 'clem', 'sumer', 'soviet',\n", - "Nearest to 'UNK': 'agouti', 'alpina', 'victoriae', 'reginae', 'dasyprocta', 'and',\n", - "Nearest to 'by': 'was', 'and', 'in', 'from', 'as', 'with',\n", - "Nearest to 'large': 'towers', 'aquila', 'enhancing', 'gland', 'fricatives', 'additions',\n", - "Nearest to 'their': 'the', 'reginae', 'his', 'afghani', 'a', 'this',\n", - "Nearest to 'used': 'spiced', 'mystery', 'haliotis', 'mining', 'ultraviolet', 'algol',\n", - "Nearest to 'british': 'equinox', 'anatomy', 'astor', 'socialism', 'victoriae', 'vivian',\n", - "Nearest to 'x': 'actaeon', 'envoys', 'four', 'agouti', 'apatosaurus', 'eight',\n", - "Nearest to 'd': 'b', 'and', 'francesco', 'finalist', 'asphyxia', 'one',\n", - "Nearest to 'king': 'circumscribed', 'inter', 'niacin', 'iphigenia', 'four', 'moves',\n", - "Nearest to 'based': 'chemist', 'charlie', 'encryption', 'reginae', 'competitions', 'dim',\n", - "Nearest to 'two': 'one', 'three', 'five', 'nine', 'six', 'four',\n", - "Nearest to 'international': 'da', 'UNK', 'hiv', 'confidential', 'aloe', 'length',\n", - "Nearest to 'him': 'seaport', 'spontaneous', 'infectious', 'trivia', 'directors', 'marine',\n", - "Nearest to 'year': 'conclude', 'five', 'overwhelming', 'responsibility', 'objects', 'subkey',\n", - "Nearest to 'in': 'and', 'with', 'of', 'on', 'mya', 'from',\n", - "Nearest to 'may': 'sur', 'cannot', 'can', 'seleucid', 'agouti', 'tubing',\n", - "Nearest to 'g': 'agouti', 'aruban', 'eight', 'jason', 'i', 'dasyprocta',\n", - "Nearest to 'de': 'fita', 'marischal', 'publicly', 'shortly', 'electrophoresis', 'winner',\n", - "Nearest to 'a': 'the', 'agouti', 'reginae', 'victoriae', 'and', 'afghani',\n", - "Nearest to 'than': 'community', 'karts', 'hesse', 'caves', 'eight', 'ensues',\n", - "Average loss at step 22000 is 7.241\n" + "Average loss at step 0 is 0.151\n", + "Nearest to 'b'x'': 'b'exams'', 'b'clement'', 'b'offspring'', 'b'sailor'', 'b'bulletproof'', 'b'hairs'',\n", + "Nearest to 'b'different'': 'b'announcing'', 'b'feeble'', 'b'spear'', 'b'hindenburg'', 'b'proportionate'', 'b'faster'',\n", + "Nearest to 'b'book'': 'b'delinquency'', 'b'convoy'', 'b'compelling'', 'b'bind'', 'b'husk'', 'b'leaned'',\n", + "Nearest to 'b'used'': 'b'elementals'', 'b'lenoir'', 'b'lafayette'', 'b'novo'', 'b'netherland'', 'b'wished'',\n", + "Nearest to 'b'was'': 'b'franchise'', 'b'abruptly'', 'b'jeopardy'', 'b'cj'', 'b'mod'', 'b'hms'',\n", + "Nearest to 'b'those'': 'b'visor'', 'b'exchanges'', 'b'paisley'', 'b'mint'', 'b'rotary'', 'b'proliferation'',\n", + "Nearest to 'b'world'': 'b'radiant'', 'b'sprite'', 'b'surnames'', 'b'lockport'', 'b'crucible'', 'b'nucleosynthesis'',\n", + "Nearest to 'b'about'': 'b'atkinson'', 'b'fiancee'', 'b'anachronisms'', 'b'akademie'', 'b'bidatsu'', 'b'thundering'',\n", + "Nearest to 'b'been'': 'b'heracleidae'', 'b'dorian'', 'b'functionaries'', 'b'chomsky'', 'b'dynamically'', 'b'dowland'',\n", + "Nearest to 'b'him'': 'b'thinking'', 'b'selznick'', 'b'korn'', 'b'hippocampus'', 'b'horror'', 'b'vipers'',\n", + "Nearest to 'b'and'': 'b'futurology'', 'b'backing'', 'b'jogaila'', 'b'materially'', 'b'randomness'', 'b'working'',\n", + "Nearest to 'b'use'': 'b'mph'', 'b'salim'', 'b'quarterback'', 'b'dimensional'', 'b'abacus'', 'b'raspberry'',\n", + "Nearest to 'b'major'': 'b'achieves'', 'b'borrowings'', 'b'dedicated'', 'b'hobson'', 'b'polybius'', 'b'septimius'',\n", + "Nearest to 'b'their'': 'b'asl'', 'b'friulian'', 'b'obsessive'', 'b'nell'', 'b'propagandist'', 'b'genevieve'',\n", + "Nearest to 'b'it'': 'b'abyssinian'', 'b'abeken'', 'b'timing'', 'b'shelter'', 'b'almeida'', 'b'boise'',\n", + "Nearest to 'b'found'': 'b'diels'', 'b'foreigner'', 'b'footnotes'', 'b'raid'', 'b'conceptualized'', 'b'reservoir'',\n", + "Nearest to 'b'n'': 'b'spee'', 'b'attenuation'', 'b'flashy'', 'b'dionysus'', 'b'plunging'', 'b'levinson'',\n", + "Nearest to 'b'university'': 'b'naphtali'', 'b'ket'', 'b'wort'', 'b'truffaut'', 'b'unsolicited'', 'b'orestes'',\n", + "Nearest to 'b'known'': 'b'monist'', 'b'silt'', 'b'milliardo'', 'b'pindar'', 'b'calculate'', 'b'depletion'',\n", + "Nearest to 'b'large'': 'b'awoke'', 'b'lamented'', 'b'geodetic'', 'b'ip'', 'b'gernika'', 'b'sugar'',\n", + "Nearest to 'b'six'': 'b'chubut'', 'b'cats'', 'b'atomic'', 'b'cacao'', 'b'yeshiva'', 'b'kambojas'',\n", + "Nearest to 'b'people'': 'b'donato'', 'b'southey'', 'b'wrongdoing'', 'b'olof'', 'b'compensatory'', 'b'scorn'',\n", + "Nearest to 'b'are'': 'b'widens'', 'b'vie'', 'b'mahesh'', 'b'bundy'', 'b'compressive'', 'b'rourke'',\n", + "Nearest to 'b'seven'': 'b'deeply'', 'b'syllogism'', 'b'bloc'', 'b'hannity'', 'b'symbolised'', 'b'rupp'',\n", + "Nearest to 'b'became'': 'b'delicate'', 'b'monica'', 'b'managing'', 'b'infusions'', 'b'sweet'', 'b'hernando'',\n", + "Nearest to 'b'including'': 'b'endeavors'', 'b'gba'', 'b'connoisseur'', 'b'densely'', 'b'zurich'', 'b'supervisory'',\n", + "Nearest to 'b'than'': 'b'reinsurance'', 'b'homicides'', 'b'dirham'', 'b'literacy'', 'b'comics'', 'b'concubines'',\n", + "Nearest to 'b'new'': 'b'breslau'', 'b'arousal'', 'b'absolute'', 'b'plantarum'', 'b'acrobat'', 'b'recollections'',\n", + "Nearest to 'b'can'': 'b'guantanamo'', 'b'staggered'', 'b'dagobert'', 'b'james'', 'b'treviso'', 'b'hau'',\n", + "Nearest to 'b'u'': 'b'sasaki'', 'b'hanssen'', 'b'staged'', 'b'federko'', 'b'kosher'', 'b'cellar'',\n", + "Nearest to 'b'then'': 'b'ren'', 'b'alans'', 'b'eurovision'', 'b'retires'', 'b'cartoonists'', 'b'vernacular'',\n", + "Nearest to 'b'film'': 'b'solving'', 'b'mpeg'', 'b'nit'', 'b'convolution'', 'b'synchronize'', 'b'tricolour'',\n", + "Average loss at step 2000 is 114.271\n", + "Average loss at step 4000 is 52.550\n", + "Average loss at step 6000 is 33.091\n", + "Average loss at step 8000 is 23.203\n", + "Average loss at step 10000 is 18.115\n", + "Nearest to 'b'x'': 'b'ignosticism'', 'b'exams'', 'b'sailor'', 'b'franklin'', 'b'reginae'', 'b'ancient'',\n", + "Nearest to 'b'different'': 'b'vs'', 'b'asymmetric'', 'b'andromeda'', 'b'announcing'', 'b'faster'', 'b'spear'',\n", + "Nearest to 'b'book'': 'b'cc'', 'b'reginae'', 'b'penalty'', 'b'implicit'', 'b'looters'', 'b'dance'',\n", + "Nearest to 'b'used'': 'b'altenberg'', 'b'reginae'', 'b'tissue'', 'b'given'', 'b'gland'', 'b'lafayette'',\n", + "Nearest to 'b'was'': 'b'is'', 'b'in'', 'b'gb'', 'b'victoriae'', 'b'and'', 'b'abruptly'',\n", + "Nearest to 'b'those'': 'b'one'', 'b'exchanges'', 'b'bantam'', 'b'rotary'', 'b'achievement'', 'b'heated'',\n", + "Nearest to 'b'world'': 'b'attempts'', 'b'burgess'', 'b'radiant'', 'b'incurred'', 'b'gb'', 'b'way'',\n", + "Nearest to 'b'about'': 'b'atkinson'', 'b'does'', 'b'simple'', 'b'austin'', 'b'word'', 'b'concerning'',\n", + "Nearest to 'b'been'': 'b'involves'', 'b'macs'', 'b'supplies'', 'b'ball'', 'b'victoriae'', 'b'nunavut'',\n", + "Nearest to 'b'him'': 'b'selznick'', 'b'gland'', 'b'horror'', 'b'thinking'', 'b'drawing'', 'b'enemies'',\n", + "Nearest to 'b'and'': 'b'in'', 'b'of'', 'UNK', 'b'austin'', 'b'one'', 'b'or'',\n", + "Nearest to 'b'use'': 'b'austin'', 'b'mph'', 'b'quarterback'', 'b'waite'', 'b'abacus'', 'b'interpretation'',\n", + "Nearest to 'b'major'': 'b'phi'', 'b'excluded'', 'b'dedicated'', 'b'cases'', 'b'countess'', 'b'peace'',\n", + "Nearest to 'b'their'': 'b'finalist'', 'b'the'', 'b'victoriae'', 'b'cl'', 'b'this'', 'b'recycling'',\n", + "Nearest to 'b'it'': 'b'believed'', 'b'a'', 'b'analogue'', 'b'he'', 'b'siblings'', 'b'declined'',\n", + "Nearest to 'b'found'': 'b'victoriae'', 'b'footnotes'', 'b'raid'', 'b'aa'', 'b'europe'', 'b'reservoir'',\n", + "Nearest to 'b'n'': 'b'absurd'', 'b'universal'', 'b'dionysus'', 'b'thetis'', 'b'communists'', 'b'program'',\n", + "Nearest to 'b'university'': 'b'abu'', 'b'camera'', 'b'truffaut'', 'b'dagny'', 'b'victoriae'', 'b'appreciation'',\n", + "Nearest to 'b'known'': 'b'austin'', 'b'penalty'', 'b'cc'', 'b'silt'', 'b'nine'', 'b'gratitude'',\n", + "Nearest to 'b'large'': 'b'awoke'', 'b'ip'', 'b'lovell'', 'b'powerpc'', 'b'powerbook'', 'b'sugar'',\n", + "Nearest to 'b'six'': 'b'nine'', 'b'zero'', 'b'eight'', 'b'austin'', 'b'victoriae'', 'b'mathbf'',\n", + "Nearest to 'b'people'': 'b'agave'', 'b'analogue'', 'b'phi'', 'b'austin'', 'b'otto'', 'b'cl'',\n", + "Nearest to 'b'are'': 'b'is'', 'b'were'', 'b'be'', 'b'and'', 'b'austin'', 'b'programming'',\n", + "Nearest to 'b'seven'': 'b'nine'', 'b'gland'', 'b'zero'', 'b'phi'', 'b'implicit'', 'b'eight'',\n", + "Nearest to 'b'became'': 'b'managing'', 'b'strong'', 'b'monica'', 'b'delicate'', 'b'lighter'', 'b'altenberg'',\n", + "Nearest to 'b'including'': 'b'roper'', 'b'endeavors'', 'b'and'', 'b'progress'', 'b'declared'', 'b'agave'',\n", + "Nearest to 'b'than'': 'b'comics'', 'b'literacy'', 'b'or'', 'b'permissible'', 'b'edited'', 'b'statement'',\n", + "Nearest to 'b'new'': 'b'arousal'', 'b'absolute'', 'b'explicit'', 'b'iii'', 'b'toy'', 'b'altenberg'',\n", + "Nearest to 'b'can'': 'b'staggered'', 'b'james'', 'b'gb'', 'b'is'', 'b'make'', 'b'emmy'',\n", + "Nearest to 'b'u'': 'b'austin'', 'b'survey'', 'b'face'', 'b'advanced'', 'b'staged'', 'b'cl'',\n", + "Nearest to 'b'then'': 'b'ren'', 'b'eurovision'', 'b'foreigners'', 'b'principality'', 'b'basically'', 'b'rfc'',\n", + "Nearest to 'b'film'': 'b'solving'', 'b'afghani'', 'b'occupies'', 'b'benz'', 'b'asterix'', 'b'astronomical'',\n", + "Average loss at step 12000 is 14.327\n", + "Average loss at step 14000 is 11.686\n", + "Average loss at step 16000 is 9.911\n", + "Average loss at step 18000 is 8.772\n", + "Average loss at step 20000 is 7.876\n", + "Nearest to 'b'x'': 'b'ignosticism'', 'b'offspring'', 'b'sailor'', 'b'exams'', 'b'two'', 'b'elagabalus'',\n", + "Nearest to 'b'different'': 'b'announcing'', 'b'vs'', 'b'spear'', 'b'asymmetric'', 'b'faster'', 'b'andromeda'',\n", + "Nearest to 'b'book'': 'b'cc'', 'b'reginae'', 'b'bind'', 'b'suffixes'', 'b'looters'', 'b'implicit'',\n", + "Nearest to 'b'used'': 'b'altenberg'', 'b'reginae'', 'b'dasyprocta'', 'b'given'', 'b'tissue'', 'b'amino'',\n", + "Nearest to 'b'was'': 'b'is'', 'b'has'', 'b'by'', 'b'had'', 'b'gb'', 'b'as'',\n", + "Nearest to 'b'those'': 'b'exchanges'', 'b'bantam'', 'b'proliferation'', 'b'one'', 'b'dasyprocta'', 'b'away'',\n", + "Nearest to 'b'world'': 'b'reconquista'', 'b'burgess'', 'b'attempts'', 'b'surnames'', 'b'radiant'', 'b'haley'',\n", + "Nearest to 'b'about'': 'b'three'', 'b'atkinson'', 'b'nine'', 'b'eight'', 'b'simple'', 'b'dasyprocta'',\n", + "Nearest to 'b'been'': 'b'is'', 'b'involves'', 'b'nunavut'', 'b'was'', 'b'supplies'', 'b'timepieces'',\n", + "Nearest to 'b'him'': 'b'gland'', 'b'selznick'', 'b'horror'', 'b'lombards'', 'b'thinking'', 'b'drawing'',\n", + "Nearest to 'b'and'': 'b'or'', 'b'in'', 'b'dasyprocta'', 'b'austin'', 'b'victoriae'', 'b'agouti'',\n", + "Nearest to 'b'use'': 'b'austin'', 'b'mph'', 'b'waite'', 'b'interpretation'', 'b'abacus'', 'b'quarterback'',\n", + "Nearest to 'b'major'': 'b'excluded'', 'b'countess'', 'b'dedicated'', 'b'phi'', 'b'cases'', 'b'underlying'',\n", + "Nearest to 'b'their'': 'b'the'', 'b'his'', 'b'asl'', 'b'finalist'', 'b'this'', 'b'a'',\n", + "Nearest to 'b'it'': 'b'he'', 'b'believed'', 'b'this'', 'b'dasyprocta'', 'b'which'', 'b'there'',\n", + "Nearest to 'b'found'': 'b'raid'', 'b'victoriae'', 'b'ne'', 'b'footnotes'', 'b'yang'', 'b'foreigner'',\n", + "Nearest to 'b'n'': 'b'zero'', 'b'dionysus'', 'b'absurd'', 'b'thetis'', 'b'constituted'', 'b'universal'',\n", + "Nearest to 'b'university'': 'b'preprocessor'', 'b'dagny'', 'b'truffaut'', 'b'abu'', 'b'griffin'', 'b'pinkerton'',\n", + "Nearest to 'b'known'': 'b'calculate'', 'b'such'', 'b'gratitude'', 'b'lawgiver'', 'b'trump'', 'b'penalty'',\n", + "Nearest to 'b'large'': 'b'awoke'', 'b'ip'', 'b'lovell'', 'b'stilicho'', 'b'powerpc'', 'b'perfection'',\n", + "Nearest to 'b'six'': 'b'eight'', 'b'nine'', 'b'three'', 'b'zero'', 'b'two'', 'b'seven'',\n", + "Nearest to 'b'people'': 'b'agave'', 'b'analogue'', 'b'austin'', 'b'phi'', 'b'southey'', 'b'dasyprocta'',\n", + "Nearest to 'b'are'': 'b'were'', 'b'is'', 'b'be'', 'b'was'', 'b'for'', 'b'dasyprocta'',\n", + "Nearest to 'b'seven'': 'b'nine'', 'b'eight'', 'b'zero'', 'b'five'', 'b'six'', 'b'gland'',\n", + "Nearest to 'b'became'': 'b'managing'', 'b'strong'', 'b'delicate'', 'b'hockey'', 'b'with'', 'b'monica'',\n", + "Nearest to 'b'including'': 'b'and'', 'b'roper'', 'b'endeavors'', 'b'progress'', 'b'tooth'', 'b'declared'',\n", + "Nearest to 'b'than'': 'b'or'', 'b'comics'', 'b'and'', 'b'literacy'', 'b'barrel'', 'b'permissible'',\n", + "Nearest to 'b'new'': 'b'arousal'', 'b'absolute'', 'b'dasyprocta'', 'b'explicit'', 'b'toy'', 'b'inexpensive'',\n", + "Nearest to 'b'can'': 'b'would'', 'b'staggered'', 'b'is'', 'b'to'', 'b'lakota'', 'b'gb'',\n", + "Nearest to 'b'u'': 'b'austin'', 'b'survey'', 'b'face'', 'b'advanced'', 'b'dasyprocta'', 'b'staged'',\n", + "Nearest to 'b'then'': 'b'ren'', 'b'eurovision'', 'b'principality'', 'b'anchor'', 'b'basically'', 'b'foreigners'',\n", + "Nearest to 'b'film'': 'b'solving'', 'b'mpeg'', 'b'agouti'', 'b'afghani'', 'b'incompressible'', 'b'occupies'',\n", + "Average loss at step 22000 is 7.240\n", + "Average loss at step 24000 is 6.851\n", + "Average loss at step 26000 is 6.733\n", + "Average loss at step 28000 is 6.136\n", + "Average loss at step 30000 is 6.156\n", + "Nearest to 'b'x'': 'b'ignosticism'', 'b'two'', 'b'offspring'', 'b'exams'', 'b'sailor'', 'b'bulletproof'',\n", + "Nearest to 'b'different'': 'b'announcing'', 'b'faster'', 'b'spear'', 'b'vs'', 'b'andromeda'', 'b'feeble'',\n", + "Nearest to 'b'book'': 'b'cc'', 'b'bind'', 'b'reginae'', 'b'looters'', 'b'compelling'', 'b'suffixes'',\n", + "Nearest to 'b'used'': 'b'altenberg'', 'b'given'', 'b'reginae'', 'b'dasyprocta'', 'b'novo'', 'b'ahab'',\n", + "Nearest to 'b'was'': 'b'is'', 'b'has'', 'b'were'', 'b'had'', 'b'by'', 'b'when'',\n", + "Nearest to 'b'those'': 'b'callisto'', 'b'jinn'', 'b'exchanges'', 'b'proliferation'', 'b'bantam'', 'b'people'',\n", + "Nearest to 'b'world'': 'b'reconquista'', 'b'radiant'', 'b'attempts'', 'b'surnames'', 'b'burgess'', 'b'sprite'',\n", + "Nearest to 'b'about'': 'b'atkinson'', 'b'three'', 'b'from'', 'b'word'', 'b'abruptly'', 'b'in'',\n", + "Nearest to 'b'been'': 'b'hopwood'', 'b'be'', 'b'was'', 'b'midwestern'', 'b'dorian'', 'b'involves'',\n", + "Nearest to 'b'him'': 'b'gland'', 'b'calculi'', 'b'selznick'', 'b'up'', 'b'removes'', 'b'lombards'',\n", + "Nearest to 'b'and'': 'b'or'', 'b'in'', 'b'primigenius'', 'b'of'', 'b'dasyprocta'', 'b'austin'',\n", + "Nearest to 'b'use'': 'b'austin'', 'b'waite'', 'b'abacus'', 'b'interpretation'', 'b'mph'', 'b'occurrence'',\n", + "Nearest to 'b'major'': 'b'countess'', 'b'excluded'', 'b'cases'', 'b'dedicated'', 'b'phi'', 'b'underlying'',\n", + "Nearest to 'b'their'': 'b'the'', 'b'his'', 'b'asl'', 'b'finalist'', 'b'a'', 'b'its'',\n", + "Nearest to 'b'it'': 'b'he'', 'b'this'', 'b'there'', 'b'which'', 'b'believed'', 'b'dasyprocta'',\n", + "Nearest to 'b'found'': 'b'raid'', 'b'victoriae'', 'b'ne'', 'b'foreigner'', 'b'footnotes'', 'b'diels'',\n", + "Nearest to 'b'n'': 'b'dionysus'', 'b'five'', 'b'constituted'', 'b'thetis'', 'b'flashy'', 'b'tuesday'',\n", + "Nearest to 'b'university'': 'b'vojt'', 'b'griffin'', 'b'preprocessor'', 'b'bpp'', 'b'suny'', 'b'ket'',\n", + "Nearest to 'b'known'': 'b'such'', 'b'calculate'', 'b'a'', 'b'lawgiver'', 'b'gratitude'', 'b'geralt'',\n", + "Nearest to 'b'large'': 'b'awoke'', 'b'ip'', 'b'stilicho'', 'b'lovell'', 'b'powerpc'', 'b'germany'',\n", + "Nearest to 'b'six'': 'b'eight'', 'b'nine'', 'b'seven'', 'b'five'', 'b'four'', 'b'three'',\n", + "Nearest to 'b'people'': 'b'agave'', 'b'austin'', 'b'southey'', 'b'analogue'', 'b'dasyprocta'', 'b'phi'',\n", + "Nearest to 'b'are'': 'b'were'', 'b'is'', 'b'have'', 'b'be'', 'b'was'', 'b'for'',\n", + "Nearest to 'b'seven'': 'b'nine'', 'b'eight'', 'b'six'', 'b'five'', 'b'four'', 'b'three'',\n", + "Nearest to 'b'became'': 'b'managing'', 'b'was'', 'b'with'', 'b'strong'', 'b'is'', 'b'hockey'',\n", + "Nearest to 'b'including'': 'b'and'', 'b'roper'', 'b'from'', 'b'tooth'', 'b'progress'', 'b'endeavors'',\n", + "Nearest to 'b'than'': 'b'or'', 'b'comics'', 'b'barrel'', 'b'literacy'', 'b'and'', 'b'permissible'',\n", + "Nearest to 'b'new'': 'b'arousal'', 'b'absolute'', 'b'breslau'', 'b'dasyprocta'', 'b'akh'', 'b'daylight'',\n", + "Nearest to 'b'can'': 'b'would'', 'b'to'', 'b'may'', 'b'must'', 'b'staggered'', 'b'is'',\n", + "Nearest to 'b'u'': 'b'zero'', 'b'face'', 'b'survey'', 'b'austin'', 'b'intelligent'', 'b'advanced'',\n", + "Nearest to 'b'then'': 'b'ren'', 'b'eurovision'', 'b'qh'', 'b'principality'', 'b'sculptures'', 'b'aon'',\n", + "Nearest to 'b'film'': 'b'solving'', 'b'mpeg'', 'b'asterix'', 'b'agouti'', 'b'afghani'', 'b'occupies'',\n", + "Average loss at step 32000 is 5.849\n", + "Average loss at step 34000 is 5.856\n", + "Average loss at step 36000 is 5.694\n", + "Average loss at step 38000 is 5.241\n", + "Average loss at step 40000 is 5.492\n", + "Nearest to 'b'x'': 'b'ignosticism'', 'b'offspring'', 'b'exams'', 'b'sailor'', 'b'dasyprocta'', 'b'vdash'',\n", + "Nearest to 'b'different'': 'b'announcing'', 'b'faster'', 'b'pernambuco'', 'b'spear'', 'b'other'', 'b'chelmsford'',\n", + "Nearest to 'b'book'': 'b'cc'', 'b'convoy'', 'b'bind'', 'b'reginae'', 'b'looters'', 'b'sloths'',\n", + "Nearest to 'b'used'': 'b'altenberg'', 'b'given'', 'b'dasyprocta'', 'b'reginae'', 'b'guderian'', 'b'ahab'',\n", + "Nearest to 'b'was'': 'b'is'', 'b'were'', 'b'has'', 'b'had'', 'b'by'', 'b'when'',\n", + "Nearest to 'b'those'': 'b'jinn'', 'b'callisto'', 'b'people'', 'b'proliferation'', 'b'exchanges'', 'b'bantam'',\n", + "Nearest to 'b'world'': 'b'reconquista'', 'b'surnames'', 'b'attempts'', 'b'radiant'', 'b'haley'', 'b'sprite'',\n", + "Nearest to 'b'about'': 'b'atkinson'', 'b'three'', 'b'deputies'', 'b'abruptly'', 'b'word'', 'b'from'',\n", + "Nearest to 'b'been'': 'b'be'', 'b'was'', 'b'were'', 'b'hopwood'', 'b'had'', 'b'midwestern'',\n", + "Nearest to 'b'him'': 'b'up'', 'b'calculi'', 'b'gland'', 'b'the'', 'b'amok'', 'b'removes'',\n", + "Nearest to 'b'and'': 'b'or'', 'b'primigenius'', 'b'dasyprocta'', 'b'agouti'', 'b'in'', 'b'six'',\n", + "Nearest to 'b'use'': 'b'austin'', 'b'waite'', 'b'interpretation'', 'b'abacus'', 'b'occurrence'', 'b'mph'',\n", + "Nearest to 'b'major'': 'b'countess'', 'b'cases'', 'b'excluded'', 'b'dedicated'', 'b'phi'', 'b'gb'',\n", + "Nearest to 'b'their'': 'b'his'', 'b'the'', 'b'its'', 'b'asl'', 'b'finalist'', 'b'this'',\n", + "Nearest to 'b'it'': 'b'he'', 'b'this'', 'b'there'', 'b'which'', 'b'that'', 'b'dasyprocta'',\n", + "Nearest to 'b'found'': 'b'raid'', 'b'victoriae'', 'b'foreigner'', 'b'reservoir'', 'b'may'', 'b'diels'',\n", + "Nearest to 'b'n'': 'b'five'', 'b'flashy'', 'b'dionysus'', 'b'thetis'', 'b'constituted'', 'b'seven'',\n", + "Nearest to 'b'university'': 'b'griffin'', 'b'vojt'', 'b'preprocessor'', 'b'bpp'', 'b'suny'', 'b'unsolicited'',\n", + "Nearest to 'b'known'': 'b'such'', 'b'calculate'', 'b'used'', 'b'gratitude'', 'b'lawgiver'', 'b'bribe'',\n", + "Nearest to 'b'large'': 'b'awoke'', 'b'lovell'', 'b'ip'', 'b'stilicho'', 'b'qf'', 'b'perfection'',\n", + "Nearest to 'b'six'': 'b'eight'', 'b'seven'', 'b'four'', 'b'five'', 'b'three'', 'b'nine'',\n", + "Nearest to 'b'people'': 'b'agave'', 'b'olof'', 'b'southey'', 'b'austin'', 'b'dasyprocta'', 'b'those'',\n", + "Nearest to 'b'are'': 'b'were'', 'b'is'', 'b'have'', 'b'be'', 'b'was'', 'b'dasyprocta'',\n", + "Nearest to 'b'seven'': 'b'eight'', 'b'six'', 'b'five'', 'b'nine'', 'b'four'', 'b'three'',\n", + "Nearest to 'b'became'': 'b'was'', 'b'is'', 'b'managing'', 'b'when'', 'b'strong'', 'b'as'',\n", + "Nearest to 'b'including'': 'b'and'', 'b'from'', 'b'tooth'', 'b'roper'', 'b'endeavors'', 'b'hmong'',\n", + "Nearest to 'b'than'': 'b'or'', 'b'comics'', 'b'barrel'', 'b'literacy'', 'b'permissible'', 'b'and'',\n", + "Nearest to 'b'new'': 'b'arousal'', 'b'absolute'', 'b'breslau'', 'b'dasyprocta'', 'b'akh'', 'b'daylight'',\n", + "Nearest to 'b'can'': 'b'would'', 'b'may'', 'b'must'', 'b'to'', 'b'could'', 'b'will'',\n", + "Nearest to 'b'u'': 'b'zero'', 'b'face'', 'b'advanced'', 'b'survey'', 'b'intelligent'', 'b'austin'',\n", + "Nearest to 'b'then'': 'b'ren'', 'b'eurovision'', 'b'qh'', 'b'sculptures'', 'b'principality'', 'b'basically'',\n", + "Nearest to 'b'film'': 'b'solving'', 'b'mpeg'', 'b'asterix'', 'b'agouti'', 'b'afghani'', 'b'occupies'',\n", + "Average loss at step 42000 is 5.316\n", + "Average loss at step 44000 is 5.336\n", + "Average loss at step 46000 is 5.266\n", + "Average loss at step 48000 is 5.040\n", + "Average loss at step 50000 is 5.160\n", + "Nearest to 'b'x'': 'b'ignosticism'', 'b'three'', 'b'offspring'', 'b'sailor'', 'b'exams'', 'b'bulletproof'',\n", + "Nearest to 'b'different'': 'b'announcing'', 'b'other'', 'b'faster'', 'b'pernambuco'', 'b'spear'', 'b'many'',\n", + "Nearest to 'b'book'': 'b'cc'', 'b'reginae'', 'b'museo'', 'b'looters'', 'b'recitative'', 'b'implicit'',\n", + "Nearest to 'b'used'': 'b'altenberg'', 'b'given'', 'b'guderian'', 'b'dasyprocta'', 'b'reginae'', 'b'known'',\n", + "Nearest to 'b'was'': 'b'is'', 'b'has'', 'b'were'', 'b'had'', 'b'by'', 'b'when'',\n", + "Nearest to 'b'those'': 'b'jinn'', 'b'callisto'', 'b'people'', 'b'proliferation'', 'b'queene'', 'b'exchanges'',\n", + "Nearest to 'b'world'': 'b'reconquista'', 'b'surnames'', 'b'radiant'', 'b'haley'', 'b'sprite'', 'b'attempts'',\n", + "Nearest to 'b'about'': 'b'atkinson'', 'b'three'', 'b'deputies'', 'b'eight'', 'b'abruptly'', 'b'blessing'',\n", + "Nearest to 'b'been'': 'b'be'', 'b'was'', 'b'had'', 'b'were'', 'b'hopwood'', 'b'by'',\n", + "Nearest to 'b'him'': 'b'up'', 'b'them'', 'b'gland'', 'b'amok'', 'b'calculi'', 'b'removes'',\n", + "Nearest to 'b'and'': 'b'or'', 'b'primigenius'', 'b'dasyprocta'', 'b'but'', 'b'thibetanus'', 'b'austin'',\n", + "Nearest to 'b'use'': 'b'austin'', 'b'waite'', 'b'interpretation'', 'b'thibetanus'', 'b'power'', 'b'abacus'',\n", + "Nearest to 'b'major'': 'b'countess'', 'b'cases'', 'b'excluded'', 'b'underlying'', 'b'dedicated'', 'b'thibetanus'',\n", + "Nearest to 'b'their'': 'b'his'', 'b'its'', 'b'the'', 'b'asl'', 'b'finalist'', 'b'acoustics'',\n", + "Nearest to 'b'it'': 'b'he'', 'b'this'', 'b'there'', 'b'which'', 'b'they'', 'b'dasyprocta'',\n", + "Nearest to 'b'found'': 'b'raid'', 'b'may'', 'b'foreigner'', 'b'evaporated'', 'b'victoriae'', 'b'reservoir'',\n", + "Nearest to 'b'n'': 'UNK', 'b'five'', 'b'flashy'', 'b'dionysus'', 'b'thetis'', 'b'eight'',\n", + "Nearest to 'b'university'': 'b'griffin'', 'b'vojt'', 'b'preprocessor'', 'b'suny'', 'b'unsolicited'', 'b'dagny'',\n", + "Nearest to 'b'known'': 'b'such'', 'b'calculate'', 'b'used'', 'b'bribe'', 'b'lawgiver'', 'b'gratitude'',\n", + "Nearest to 'b'large'': 'b'awoke'', 'b'stilicho'', 'b'lovell'', 'b'qf'', 'b'tree'', 'b'ip'',\n", + "Nearest to 'b'six'': 'b'eight'', 'b'four'', 'b'seven'', 'b'five'', 'b'three'', 'b'nine'',\n", + "Nearest to 'b'people'': 'b'agave'', 'b'austin'', 'b'southey'', 'b'olof'', 'b'dasyprocta'', 'b'tabula'',\n", + "Nearest to 'b'are'': 'b'were'', 'b'is'', 'b'have'', 'b'be'', 'b'was'', 'b'amok'',\n", + "Nearest to 'b'seven'': 'b'eight'', 'b'six'', 'b'four'', 'b'five'', 'b'nine'', 'b'three'',\n", + "Nearest to 'b'became'': 'b'was'', 'b'when'', 'b'is'', 'b'had'', 'b'as'', 'b'strong'',\n", + "Nearest to 'b'including'': 'b'and'', 'b'from'', 'b'tooth'', 'b'in'', 'b'abet'', 'b'roper'',\n", + "Nearest to 'b'than'': 'b'or'', 'b'comics'', 'b'barrel'', 'b'literacy'', 'b'permissible'', 'b'concubines'',\n", + "Nearest to 'b'new'': 'b'arousal'', 'b'absolute'', 'b'breslau'', 'b'dasyprocta'', 'b'akh'', 'b'toy'',\n", + "Nearest to 'b'can'': 'b'would'', 'b'may'', 'b'must'', 'b'could'', 'b'will'', 'b'to'',\n", + "Nearest to 'b'u'': 'b'zero'', 'b'face'', 'b'shevardnadze'', 'b'intelligent'', 'b'survey'', 'b'advanced'',\n", + "Nearest to 'b'then'': 'b'qh'', 'b'eurovision'', 'b'ren'', 'b'basically'', 'b'sculptures'', 'b'principality'',\n", + "Nearest to 'b'film'': 'b'solving'', 'b'mpeg'', 'b'asterix'', 'b'afghani'', 'b'agouti'', 'b'occupies'',\n", + "Average loss at step 52000 is 5.182\n", + "Average loss at step 54000 is 5.109\n", + "Average loss at step 56000 is 5.069\n", + "Average loss at step 58000 is 5.092\n", + "Average loss at step 60000 is 4.967\n", + "Nearest to 'b'x'': 'b'ignosticism'', 'b'three'', 'b'offspring'', 'b'exams'', 'b'dasyprocta'', 'b'bulletproof'',\n", + "Nearest to 'b'different'': 'b'other'', 'b'announcing'', 'b'faster'', 'b'many'', 'b'niche'', 'b'chelmsford'',\n", + "Nearest to 'b'book'': 'b'reginae'', 'b'cc'', 'b'looters'', 'b'museo'', 'b'callithrix'', 'b'compelling'',\n", + "Nearest to 'b'used'': 'b'given'', 'b'altenberg'', 'b'guderian'', 'b'dasyprocta'', 'b'bouchard'', 'b'reginae'',\n", + "Nearest to 'b'was'': 'b'is'', 'b'had'', 'b'has'', 'b'were'', 'b'by'', 'b'when'',\n", + "Nearest to 'b'those'': 'b'jinn'', 'b'people'', 'b'callisto'', 'b'proliferation'', 'b'microcebus'', 'b'snooker'',\n", + "Nearest to 'b'world'': 'b'michelob'', 'b'reconquista'', 'b'surnames'', 'b'haley'', 'b'radiant'', 'b'callithrix'',\n", + "Nearest to 'b'about'': 'b'atkinson'', 'b'four'', 'b'blessing'', 'b'eight'', 'b'three'', 'b'deputies'',\n", + "Nearest to 'b'been'': 'b'be'', 'b'was'', 'b'had'', 'b'were'', 'b'hopwood'', 'b'become'',\n", + "Nearest to 'b'him'': 'b'them'', 'b'up'', 'b'calculi'', 'b'gland'', 'b'amok'', 'b'removes'',\n", + "Nearest to 'b'and'': 'b'or'', 'b'primigenius'', 'b'callithrix'', 'b'tamarin'', 'b'saguinus'', 'b'dasyprocta'',\n", + "Nearest to 'b'use'': 'b'austin'', 'b'interpretation'', 'b'waite'', 'b'thibetanus'', 'b'marmoset'', 'b'balls'',\n", + "Nearest to 'b'major'': 'b'countess'', 'b'cases'', 'b'tread'', 'b'underlying'', 'b'excluded'', 'b'dedicated'',\n", + "Nearest to 'b'their'': 'b'his'', 'b'its'', 'b'the'', 'b'asl'', 'b'finalist'', 'b'acoustics'',\n", + "Nearest to 'b'it'': 'b'he'', 'b'this'', 'b'there'', 'b'which'', 'b'they'', 'b'she'',\n", + "Nearest to 'b'found'': 'b'may'', 'b'raid'', 'b'foreigner'', 'b'saguinus'', 'b'evaporated'', 'b'itself'',\n", + "Nearest to 'b'n'': 'b'flashy'', 'b'dionysus'', 'b'generalised'', 'b'callithrix'', 'b'thetis'', 'b'constituted'',\n", + "Nearest to 'b'university'': 'b'griffin'', 'b'preprocessor'', 'b'ket'', 'b'vojt'', 'b'nominally'', 'b'suny'',\n", + "Nearest to 'b'known'': 'b'such'', 'b'used'', 'b'calculate'', 'b'bribe'', 'b'gratitude'', 'b'lawgiver'',\n", + "Nearest to 'b'large'': 'b'awoke'', 'b'lovell'', 'b'stilicho'', 'b'qf'', 'b'conflicted'', 'b'tree'',\n", + "Nearest to 'b'six'': 'b'eight'', 'b'four'', 'b'five'', 'b'seven'', 'b'three'', 'b'nine'',\n", + "Nearest to 'b'people'': 'b'those'', 'b'agave'', 'b'southey'', 'b'austin'', 'b'tabula'', 'b'olof'',\n", + "Nearest to 'b'are'': 'b'were'', 'b'is'', 'b'have'', 'b'be'', 'b'amok'', 'b'kvac'',\n", + "Nearest to 'b'seven'': 'b'eight'', 'b'six'', 'b'nine'', 'b'four'', 'b'five'', 'b'three'',\n", + "Nearest to 'b'became'': 'b'was'', 'b'when'', 'b'is'', 'b'as'', 'b'had'', 'b'deported'',\n", + "Nearest to 'b'including'': 'b'and'', 'b'from'', 'b'in'', 'b'tooth'', 'b'tamarin'', 'b'manhood'',\n", + "Nearest to 'b'than'': 'b'or'', 'b'barrel'', 'b'comics'', 'b'and'', 'b'literacy'', 'b'for'',\n", + "Nearest to 'b'new'': 'b'arousal'', 'b'absolute'', 'b'breslau'', 'b'dasyprocta'', 'b'cebus'', 'b'daylight'',\n", + "Nearest to 'b'can'': 'b'would'', 'b'may'', 'b'must'', 'b'will'', 'b'could'', 'b'to'',\n", + "Nearest to 'b'u'': 'b'zero'', 'b'microcebus'', 'b'tamarin'', 'b'dcsd'', 'b'face'', 'b'shevardnadze'',\n", + "Nearest to 'b'then'': 'UNK', 'b'qh'', 'b'also'', 'b'eurovision'', 'b'ren'', 'b'basically'',\n", + "Nearest to 'b'film'': 'b'solving'', 'b'afghani'', 'b'mpeg'', 'b'agouti'', 'b'asterix'', 'b'occupies'',\n", + "Average loss at step 62000 is 4.801\n", + "Average loss at step 64000 is 4.782\n", + "Average loss at step 66000 is 4.960\n", + "Average loss at step 68000 is 4.922\n", + "Average loss at step 70000 is 4.762\n", + "Nearest to 'b'x'': 'b'ignosticism'', 'b'three'', 'b'offspring'', 'b'sailor'', 'b'exams'', 'b'h'',\n", + "Nearest to 'b'different'': 'b'other'', 'b'many'', 'b'faster'', 'b'announcing'', 'b'niche'', 'b'such'',\n", + "Nearest to 'b'book'': 'b'reginae'', 'b'looters'', 'b'cc'', 'b'sense'', 'b'museo'', 'b'callithrix'',\n", + "Nearest to 'b'used'': 'b'altenberg'', 'b'given'', 'b'known'', 'b'reginae'', 'b'dasyprocta'', 'b'bouchard'',\n", + "Nearest to 'b'was'': 'b'is'', 'b'had'', 'b'has'', 'b'were'', 'b'when'', 'b'by'',\n", + "Nearest to 'b'those'': 'b'jinn'', 'b'people'', 'b'callisto'', 'b'microcebus'', 'b'snooker'', 'b'proliferation'',\n", + "Nearest to 'b'world'': 'b'michelob'', 'b'reconquista'', 'b'surnames'', 'b'haley'', 'b'radiant'', 'b'callithrix'',\n", + "Nearest to 'b'about'': 'b'four'', 'b'three'', 'b'atkinson'', 'b'blessing'', 'b'five'', 'b'thaler'',\n", + "Nearest to 'b'been'': 'b'be'', 'b'was'', 'b'had'', 'b'were'', 'b'become'', 'b'hopwood'',\n", + "Nearest to 'b'him'': 'b'them'', 'b'up'', 'b'gland'', 'b'calculi'', 'b'dasyprocta'', 'b'amok'',\n", + "Nearest to 'b'and'': 'b'or'', 'b'primigenius'', 'b'saguinus'', 'b'callithrix'', 'b'but'', 'b'tamarin'',\n", + "Nearest to 'b'use'': 'b'thaler'', 'b'austin'', 'b'thibetanus'', 'b'waite'', 'b'power'', 'b'marmoset'',\n", + "Nearest to 'b'major'': 'b'cases'', 'b'underlying'', 'b'tread'', 'b'countess'', 'b'dedicated'', 'b'ying'',\n", + "Nearest to 'b'their'': 'b'its'', 'b'his'', 'b'the'', 'b'asl'', 'b'finalist'', 'b'her'',\n", + "Nearest to 'b'it'': 'b'he'', 'b'this'', 'b'there'', 'b'which'', 'b'they'', 'b'she'',\n", + "Nearest to 'b'found'': 'b'may'', 'b'raid'', 'b'saguinus'', 'b'itself'', 'b'victoriae'', 'b'microcebus'',\n", + "Nearest to 'b'n'': 'UNK', 'b'flashy'', 'b'generalised'', 'b'callithrix'', 'b'constituted'', 'b'dionysus'',\n", + "Nearest to 'b'university'': 'b'griffin'', 'b'suny'', 'b'preprocessor'', 'b'vojt'', 'b'ket'', 'b'nominally'',\n", + "Nearest to 'b'known'': 'b'such'', 'b'used'', 'b'calculate'', 'b'bribe'', 'b'lawgiver'', 'b'gratitude'',\n", + "Nearest to 'b'large'': 'b'awoke'', 'b'lovell'', 'b'qf'', 'b'stilicho'', 'b'conflicted'', 'b'tree'',\n", + "Nearest to 'b'six'': 'b'eight'', 'b'four'', 'b'seven'', 'b'five'', 'b'three'', 'b'nine'',\n", + "Nearest to 'b'people'': 'b'those'', 'b'agave'', 'b'tabula'', 'b'southey'', 'b'austin'', 'b'callithrix'',\n", + "Nearest to 'b'are'': 'b'were'', 'b'is'', 'b'have'', 'b'be'', 'b'was'', 'b'dasyprocta'',\n", + "Nearest to 'b'seven'': 'b'eight'', 'b'six'', 'b'four'', 'b'five'', 'b'nine'', 'b'three'',\n", + "Nearest to 'b'became'': 'b'was'', 'b'when'', 'b'is'', 'b'had'', 'b'deported'', 'b'as'',\n", + "Nearest to 'b'including'': 'b'and'', 'b'of'', 'b'in'', 'b'from'', 'b'tooth'', 'b'tamarin'',\n", + "Nearest to 'b'than'': 'b'or'', 'b'barrel'', 'b'dirham'', 'b'but'', 'b'comics'', 'b'literacy'',\n", + "Nearest to 'b'new'': 'b'arousal'', 'b'absolute'', 'b'breslau'', 'b'dasyprocta'', 'b'cebus'', 'b'behaves'',\n", + "Nearest to 'b'can'': 'b'would'', 'b'may'', 'b'will'', 'b'could'', 'b'must'', 'b'cannot'',\n", + "Nearest to 'b'u'': 'b'zero'', 'b'microcebus'', 'b'tamarin'', 'b'dcsd'', 'b'face'', 'b'shevardnadze'',\n", + "Nearest to 'b'then'': 'b'qh'', 'b'principality'', 'b'eurovision'', 'b'basically'', 'b'governorates'', 'b'callithrix'',\n", + "Nearest to 'b'film'': 'b'afghani'', 'b'solving'', 'b'mpeg'', 'b'agouti'', 'b'asterix'', 'b'occupies'',\n", + "Average loss at step 72000 is 4.806\n", + "Average loss at step 74000 is 4.780\n", + "Average loss at step 76000 is 4.881\n", + "Average loss at step 78000 is 4.796\n", + "Average loss at step 80000 is 4.819\n", + "Nearest to 'b'x'': 'b'ignosticism'', 'b'three'', 'b'iit'', 'b'two'', 'b'h'', 'b'sailor'',\n", + "Nearest to 'b'different'': 'b'other'', 'b'many'', 'b'faster'', 'b'such'', 'b'niche'', 'b'announcing'',\n", + "Nearest to 'b'book'': 'b'landlords'', 'b'looters'', 'b'cc'', 'b'museo'', 'b'bind'', 'b'labrador'',\n", + "Nearest to 'b'used'': 'b'known'', 'b'altenberg'', 'b'given'', 'b'considered'', 'b'reginae'', 'b'guderian'',\n", + "Nearest to 'b'was'': 'b'is'', 'b'had'', 'b'has'', 'b'were'', 'b'when'', 'b'became'',\n", + "Nearest to 'b'those'': 'b'people'', 'b'jinn'', 'b'callisto'', 'b'snooker'', 'b'microcebus'', 'b'them'',\n", + "Nearest to 'b'world'': 'b'reconquista'', 'b'michelob'', 'b'surnames'', 'b'haley'', 'b'radiant'', 'b'clodius'',\n", + "Nearest to 'b'about'': 'b'four'', 'b'atkinson'', 'b'five'', 'b'blessing'', 'b'iit'', 'b'thaler'',\n", + "Nearest to 'b'been'': 'b'be'', 'b'was'', 'b'become'', 'b'were'', 'b'had'', 'b'hopwood'',\n", + "Nearest to 'b'him'': 'b'them'', 'b'up'', 'b'gland'', 'b'calculi'', 'b'removes'', 'b'amok'',\n", + "Nearest to 'b'and'': 'b'or'', 'b'clodius'', 'b'tamarin'', 'b'but'', 'b'callithrix'', 'b'dasyprocta'',\n", + "Nearest to 'b'use'': 'b'thaler'', 'b'austin'', 'b'thibetanus'', 'b'interpretation'', 'b'marmoset'', 'b'power'',\n", + "Nearest to 'b'major'': 'b'tread'', 'b'underlying'', 'b'cases'', 'b'dedicated'', 'b'countess'', 'b'ying'',\n", + "Nearest to 'b'their'': 'b'its'', 'b'his'', 'b'the'', 'b'asl'', 'b'finalist'', 'b'her'',\n", + "Nearest to 'b'it'': 'b'he'', 'b'this'', 'b'there'', 'b'she'', 'b'they'', 'b'which'',\n", + "Nearest to 'b'found'': 'b'may'', 'b'raid'', 'b'saguinus'', 'b'itself'', 'b'foreigner'', 'b'imported'',\n", + "Nearest to 'b'n'': 'UNK', 'b'flashy'', 'b'generalised'', 'b'callithrix'', 'b'constituted'', 'b'dionysus'',\n", + "Nearest to 'b'university'': 'b'thibetanus'', 'b'tamarin'', 'b'shirkuh'', 'b'vojt'', 'b'griffin'', 'b'nominally'',\n", + "Nearest to 'b'known'': 'b'such'', 'b'used'', 'b'calculate'', 'b'bribe'', 'b'regarded'', 'b'lawgiver'',\n", + "Nearest to 'b'large'': 'b'awoke'', 'b'lovell'', 'b'stilicho'', 'b'qf'', 'b'small'', 'b'conflicted'',\n", + "Nearest to 'b'six'': 'b'five'', 'b'four'', 'b'eight'', 'b'seven'', 'b'three'', 'b'nine'',\n", + "Nearest to 'b'people'': 'b'those'', 'b'agave'', 'b'tabula'', 'b'southey'', 'b'callithrix'', 'b'olof'',\n", + "Nearest to 'b'are'': 'b'were'', 'b'is'', 'b'have'', 'b'be'', 'b'amok'', 'b'include'',\n", + "Nearest to 'b'seven'': 'b'eight'', 'b'six'', 'b'five'', 'b'four'', 'b'nine'', 'b'three'',\n", + "Nearest to 'b'became'': 'b'was'', 'b'when'', 'b'is'', 'b'had'', 'b'deported'', 'b'as'',\n", + "Nearest to 'b'including'': 'b'and'', 'b'from'', 'b'tamarin'', 'b'tooth'', 'b'clodius'', 'b'thaler'',\n", + "Nearest to 'b'than'': 'b'or'', 'b'barrel'', 'b'but'', 'b'dirham'', 'b'and'', 'b'comics'',\n", + "Nearest to 'b'new'': 'b'arousal'', 'b'breslau'', 'b'absolute'', 'b'dasyprocta'', 'b'cebus'', 'b'behaves'',\n", + "Nearest to 'b'can'': 'b'would'', 'b'may'', 'b'will'', 'b'could'', 'b'must'', 'b'cannot'',\n", + "Nearest to 'b'u'': 'b'zero'', 'b'microcebus'', 'b'semiotic'', 'b'tamarin'', 'b'face'', 'b'islander'',\n", + "Nearest to 'b'then'': 'b'principality'', 'b'basically'', 'b'qh'', 'b'eurovision'', 'b'callithrix'', 'b'governorates'',\n", + "Nearest to 'b'film'': 'b'afghani'', 'b'mpeg'', 'b'agouti'', 'b'solving'', 'b'asterix'', 'b'occupies'',\n", + "Average loss at step 82000 is 4.800\n", + "Average loss at step 84000 is 4.776\n", + "Average loss at step 86000 is 4.763\n", + "Average loss at step 88000 is 4.703\n", + "Average loss at step 90000 is 4.761\n", + "Nearest to 'b'x'': 'b'ignosticism'', 'b'iit'', 'b'sailor'', 'b'three'', 'b'thaler'', 'b'h'',\n", + "Nearest to 'b'different'': 'b'other'', 'b'many'', 'b'faster'', 'b'such'', 'b'various'', 'b'some'',\n", + "Nearest to 'b'book'': 'b'peacocks'', 'b'landlords'', 'b'reginae'', 'b'looters'', 'b'museo'', 'b'ark'',\n", + "Nearest to 'b'used'': 'b'considered'', 'b'given'', 'b'known'', 'b'altenberg'', 'b'guderian'', 'b'reginae'',\n", + "Nearest to 'b'was'': 'b'is'', 'b'had'', 'b'has'', 'b'were'', 'b'became'', 'b'been'',\n", + "Nearest to 'b'those'': 'b'people'', 'b'jinn'', 'b'snooker'', 'b'callisto'', 'b'them'', 'b'stalk'',\n", + "Nearest to 'b'world'': 'b'michelob'', 'b'reconquista'', 'b'surnames'', 'b'radiant'', 'b'haley'', 'b'clodius'',\n", + "Nearest to 'b'about'': 'b'atkinson'', 'b'abruptly'', 'b'likeness'', 'b'blessing'', 'b'material'', 'b'three'',\n", + "Nearest to 'b'been'': 'b'be'', 'b'was'', 'b'become'', 'b'were'', 'b'had'', 'b'hopwood'',\n", + "Nearest to 'b'him'': 'b'them'', 'b'up'', 'b'calculi'', 'b'amok'', 'b'gland'', 'b'removes'',\n", + "Nearest to 'b'and'': 'b'or'', 'b'but'', 'b'primigenius'', 'b'clodius'', 'b'saguinus'', 'b'tamarin'',\n", + "Nearest to 'b'use'': 'b'thaler'', 'b'thibetanus'', 'b'austin'', 'b'interpretation'', 'b'power'', 'b'dasyprocta'',\n", + "Nearest to 'b'major'': 'b'cases'', 'b'tread'', 'b'underlying'', 'b'ying'', 'b'gb'', 'b'dedicated'',\n", + "Nearest to 'b'their'': 'b'its'', 'b'his'', 'b'the'', 'b'her'', 'b'asl'', 'b'finalist'',\n", + "Nearest to 'b'it'': 'b'he'', 'b'this'', 'b'there'', 'b'she'', 'b'they'', 'b'which'',\n", + "Nearest to 'b'found'': 'b'raid'', 'b'may'', 'b'addition'', 'b'saguinus'', 'b'foreigner'', 'b'evaporated'',\n", + "Nearest to 'b'n'': 'b'generalised'', 'b'flashy'', 'b'callithrix'', 'b'constituted'', 'b'dionysus'', 'b'thetis'',\n", + "Nearest to 'b'university'': 'b'thibetanus'', 'b'griffin'', 'b'nominally'', 'b'vojt'', 'b'shirkuh'', 'b'tamarin'',\n", + "Nearest to 'b'known'': 'b'such'', 'b'used'', 'b'bribe'', 'b'calculate'', 'b'regarded'', 'b'giver'',\n", + "Nearest to 'b'large'': 'b'awoke'', 'b'small'', 'b'lovell'', 'b'qf'', 'b'stilicho'', 'b'tree'',\n", + "Nearest to 'b'six'': 'b'eight'', 'b'five'', 'b'seven'', 'b'four'', 'b'three'', 'b'nine'',\n", + "Nearest to 'b'people'': 'b'those'', 'b'southey'', 'b'tabula'', 'b'agave'', 'b'olof'', 'b'tamarin'',\n", + "Nearest to 'b'are'': 'b'were'', 'b'is'', 'b'have'', 'b'be'', 'b'include'', 'b'amok'',\n", + "Nearest to 'b'seven'': 'b'eight'', 'b'five'', 'b'six'', 'b'four'', 'b'nine'', 'b'three'',\n", + "Nearest to 'b'became'': 'b'was'', 'b'is'', 'b'had'', 'b'when'', 'b'deported'', 'b'once'',\n", + "Nearest to 'b'including'': 'b'and'', 'b'from'', 'b'in'', 'b'of'', 'b'tamarin'', 'b'clodius'',\n", + "Nearest to 'b'than'': 'b'or'', 'b'barrel'', 'b'dirham'', 'b'but'', 'b'unfavourably'', 'b'divinely'',\n", + "Nearest to 'b'new'': 'b'arousal'', 'b'breslau'', 'b'absolute'', 'b'behaves'', 'b'dasyprocta'', 'b'one'',\n", + "Nearest to 'b'can'': 'b'may'', 'b'would'', 'b'will'', 'b'could'', 'b'must'', 'b'should'',\n", + "Nearest to 'b'u'': 'b'zero'', 'b'microcebus'', 'b'semiotic'', 'b'tamarin'', 'b'shevardnadze'', 'b'islander'',\n", + "Nearest to 'b'then'': 'b'qh'', 'b'callithrix'', 'b'eurovision'', 'b'principality'', 'b'basically'', 'b'also'',\n", + "Nearest to 'b'film'': 'b'afghani'', 'b'mpeg'', 'b'asterix'', 'b'agouti'', 'b'solving'', 'b'occupies'',\n", + "Average loss at step 92000 is 4.714\n", + "Average loss at step 94000 is 4.634\n", + "Average loss at step 96000 is 4.730\n", + "Average loss at step 98000 is 4.629\n", + "Average loss at step 100000 is 4.678\n", + "Nearest to 'b'x'': 'b'ignosticism'', 'b'six'', 'b'thaler'', 'b'iit'', 'b'four'', 'b'aspirin'',\n", + "Nearest to 'b'different'': 'b'other'', 'b'many'', 'b'various'', 'b'faster'', 'b'such'', 'b'fixed'',\n", + "Nearest to 'b'book'': 'b'peacocks'', 'b'landlords'', 'b'reginae'', 'b'bebox'', 'b'sense'', 'b'callithrix'',\n", + "Nearest to 'b'used'': 'b'considered'', 'b'known'', 'b'altenberg'', 'b'given'', 'b'guderian'', 'b'reginae'',\n", + "Nearest to 'b'was'': 'b'is'', 'b'has'', 'b'had'', 'b'were'', 'b'became'', 'b'been'',\n", + "Nearest to 'b'those'': 'b'people'', 'b'jinn'', 'b'them'', 'b'snooker'', 'b'callisto'', 'b'draugr'',\n", + "Nearest to 'b'world'': 'b'reconquista'', 'b'michelob'', 'b'surnames'', 'b'haley'', 'b'radiant'', 'b'clodius'',\n", + "Nearest to 'b'about'': 'b'atkinson'', 'b'abruptly'', 'b'likeness'', 'b'material'', 'b'blessing'', 'b'four'',\n", + "Nearest to 'b'been'': 'b'be'', 'b'become'', 'b'was'', 'b'were'', 'b'had'', 'b'hopwood'',\n", + "Nearest to 'b'him'': 'b'them'', 'b'up'', 'b'calculi'', 'b'amok'', 'b'dasyprocta'', 'b'gland'',\n", + "Nearest to 'b'and'': 'b'or'', 'b'but'', 'b'clodius'', 'b'primigenius'', 'b'tamarin'', 'b'callithrix'',\n", + "Nearest to 'b'use'': 'b'thaler'', 'b'thibetanus'', 'b'austin'', 'b'interpretation'', 'b'dasyprocta'', 'b'techs'',\n", + "Nearest to 'b'major'': 'b'cases'', 'b'underlying'', 'b'tread'', 'b'ying'', 'b'dedicated'', 'b'gb'',\n", + "Nearest to 'b'their'': 'b'its'', 'b'his'', 'b'the'', 'b'her'', 'b'asl'', 'b'finalist'',\n", + "Nearest to 'b'it'': 'b'he'', 'b'this'', 'b'there'', 'b'she'', 'b'they'', 'b'which'',\n", + "Nearest to 'b'found'': 'b'may'', 'b'eurocents'', 'b'addition'', 'b'raid'', 'b'corso'', 'b'saguinus'',\n", + "Nearest to 'b'n'': 'b'generalised'', 'b'flashy'', 'UNK', 'b'callithrix'', 'b'constituted'', 'b'dionysus'',\n", + "Nearest to 'b'university'': 'b'thibetanus'', 'b'sum'', 'b'nominally'', 'b'shirkuh'', 'b'griffin'', 'b'vojt'',\n", + "Nearest to 'b'known'': 'b'such'', 'b'used'', 'b'bribe'', 'b'calculate'', 'b'regarded'', 'b'lawgiver'',\n", + "Nearest to 'b'large'': 'b'awoke'', 'b'small'', 'b'qf'', 'b'lovell'', 'b'stilicho'', 'b'tree'',\n", + "Nearest to 'b'six'': 'b'seven'', 'b'four'', 'b'five'', 'b'eight'', 'b'nine'', 'b'three'',\n", + "Nearest to 'b'people'': 'b'those'', 'b'tabula'', 'b'men'', 'b'southey'', 'b'callithrix'', 'b'tamarin'',\n", + "Nearest to 'b'are'': 'b'were'', 'b'is'', 'b'have'', 'b'be'', 'b'include'', 'b'amok'',\n", + "Nearest to 'b'seven'': 'b'eight'', 'b'six'', 'b'four'', 'b'five'', 'b'nine'', 'b'three'',\n", + "Nearest to 'b'became'': 'b'was'', 'b'is'', 'b'had'', 'b'when'', 'b'deported'', 'b'once'',\n", + "Nearest to 'b'including'': 'b'and'', 'b'from'', 'b'in'', 'b'of'', 'b'tamarin'', 'b'clodius'',\n", + "Nearest to 'b'than'': 'b'or'', 'b'barrel'', 'b'but'', 'b'dirham'', 'b'unfavourably'', 'b'comics'',\n", + "Nearest to 'b'new'': 'b'arousal'', 'b'breslau'', 'b'behaves'', 'b'absolute'', 'b'dasyprocta'', 'b'cebus'',\n", + "Nearest to 'b'can'': 'b'may'', 'b'would'', 'b'will'', 'b'could'', 'b'must'', 'b'should'',\n", + "Nearest to 'b'u'': 'b'zero'', 'b'microcebus'', 'b'semiotic'', 'b'tamarin'', 'b'austin'', 'b'islander'',\n", + "Nearest to 'b'then'': 'b'qh'', 'b'callithrix'', 'b'principality'', 'b'eurovision'', 'b'when'', 'b'silly'',\n", + "Nearest to 'b'film'': 'b'afghani'', 'b'mpeg'', 'b'agouti'', 'b'solving'', 'b'occupies'', 'b'asterix'',\n" ] } ], @@ -817,7 +1110,7 @@ "# Train! \n", "sess = tf.Session()\n", "sess.run(tf.initialize_all_variables())\n", - "summary_writer = tf.train.SummaryWriter('/tmp/tf_logs/word2vec', graph=sess.graph)\n", + "summary_writer = tf.summary.FileWriter('/tmp/tf_logs/word2vec', graph=sess.graph)\n", "average_loss = 0\n", "\n", "num_steps = 100001\n", @@ -856,11 +1149,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "def plot_with_labels(low_dim_embs, labels, filename='tsne.png'):\n", " assert low_dim_embs.shape[0] >= len(labels), \"More labels than embeddings\"\n", @@ -887,23 +1191,23 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python tensor", "language": "python", - "name": "python2" + "name": "tensor" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.6.5" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 2 } diff --git a/notebooks/word2vec_simple.ipynb b/notebooks/word2vec_simple.ipynb index 8bbb9c1..72932b1 100755 --- a/notebooks/word2vec_simple.ipynb +++ b/notebooks/word2vec_simple.ipynb @@ -72,7 +72,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "'sentences' is and length is 12.\n" + "'sentences' is and length is 12.\n" ] } ], @@ -120,7 +120,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "'words' is and length is 62.\n", + "'words' is and length is 62.\n", "['the', 'quick', 'brown', 'fox', 'jumped', 'over', 'the', 'lazy', 'dog', 'I', 'love', 'cats', 'and', 'dogs', 'we', 'all', 'love', 'cats', 'and', 'dogs', 'cats', 'and', 'dogs', 'are', 'great', 'sung', 'likes', 'cats', 'she', 'loves', 'dogs', 'cats', 'can', 'be', 'very', 'independent', 'cats', 'are', 'great', 'companions', 'when', 'they', 'want', 'to', 'be', 'cats', 'are', 'playful', 'cats', 'are', 'natural', 'hunters', \"It's\", 'raining', 'cats', 'and', 'dogs', 'dogs', 'and', 'cats', 'love', 'sung']\n" ] } @@ -149,9 +149,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "'count' is and length is 35.\n", + "'count' is and length is 35.\n", "Word count of top five is [('cats', 10), ('dogs', 6), ('and', 5), ('are', 4), ('love', 3)]\n", - "[('cats', 10), ('dogs', 6), ('and', 5), ('are', 4), ('love', 3), ('be', 2), ('sung', 2), ('great', 2), ('the', 2), ('raining', 1), ('all', 1), ('when', 1), ('over', 1), ('we', 1), ('playful', 1), ('want', 1), ('to', 1), ('jumped', 1), ('hunters', 1), ('companions', 1), ('fox', 1), ('very', 1), (\"It's\", 1), ('can', 1), ('brown', 1), ('lazy', 1), ('I', 1), ('independent', 1), ('they', 1), ('natural', 1), ('dog', 1), ('she', 1), ('loves', 1), ('quick', 1), ('likes', 1)]\n" + "[('cats', 10), ('dogs', 6), ('and', 5), ('are', 4), ('love', 3), ('the', 2), ('great', 2), ('sung', 2), ('be', 2), ('quick', 1), ('brown', 1), ('fox', 1), ('jumped', 1), ('over', 1), ('lazy', 1), ('dog', 1), ('I', 1), ('we', 1), ('all', 1), ('likes', 1), ('she', 1), ('loves', 1), ('can', 1), ('very', 1), ('independent', 1), ('companions', 1), ('when', 1), ('they', 1), ('want', 1), ('to', 1), ('playful', 1), ('natural', 1), ('hunters', 1), (\"It's\", 1), ('raining', 1)]\n" ] } ], @@ -199,7 +199,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": { "collapsed": false }, @@ -208,8 +208,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "'rdic' is and length is 35.\n", - "'dic' is and length is 35.\n" + "'rdic' is and length is 35.\n", + "'dic' is and length is 35.\n" ] } ], @@ -223,7 +223,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": { "collapsed": false }, @@ -232,7 +232,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "['cats', 'dogs', 'and', 'are', 'love', 'be', 'sung', 'great', 'the', 'raining', 'all', 'when', 'over', 'we', 'playful', 'want', 'to', 'jumped', 'hunters', 'companions', 'fox', 'very', \"It's\", 'can', 'brown', 'lazy', 'I', 'independent', 'they', 'natural', 'dog', 'she', 'loves', 'quick', 'likes']\n" + "['cats', 'dogs', 'and', 'are', 'love', 'the', 'great', 'sung', 'be', 'quick', 'brown', 'fox', 'jumped', 'over', 'lazy', 'dog', 'I', 'we', 'all', 'likes', 'she', 'loves', 'can', 'very', 'independent', 'companions', 'when', 'they', 'want', 'to', 'playful', 'natural', 'hunters', \"It's\", 'raining']\n" ] } ], @@ -242,7 +242,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": { "collapsed": false }, @@ -251,7 +251,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'and': 2, 'raining': 9, 'all': 10, 'love': 4, 'brown': 24, 'when': 11, 'over': 12, 'lazy': 25, 'playful': 14, 'are': 3, 'want': 15, 'sung': 6, 'jumped': 17, 'hunters': 18, 'companions': 19, 'fox': 20, 'to': 16, 'cats': 0, \"It's\": 22, 'dogs': 1, 'she': 31, 'be': 5, 'we': 13, 'very': 21, 'independent': 27, 'they': 28, 'natural': 29, 'great': 7, 'I': 26, 'dog': 30, 'can': 23, 'loves': 32, 'quick': 33, 'the': 8, 'likes': 34}\n" + "{'cats': 0, 'dogs': 1, 'and': 2, 'are': 3, 'love': 4, 'the': 5, 'great': 6, 'sung': 7, 'be': 8, 'quick': 9, 'brown': 10, 'fox': 11, 'jumped': 12, 'over': 13, 'lazy': 14, 'dog': 15, 'I': 16, 'we': 17, 'all': 18, 'likes': 19, 'she': 20, 'loves': 21, 'can': 22, 'very': 23, 'independent': 24, 'companions': 25, 'when': 26, 'they': 27, 'want': 28, 'to': 29, 'playful': 30, 'natural': 31, 'hunters': 32, \"It's\": 33, 'raining': 34}\n" ] } ], @@ -268,7 +268,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": { "collapsed": false }, @@ -296,7 +296,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": { "collapsed": false }, @@ -305,8 +305,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "'data' is and length is 62.\n", - "Sample data: numbers: [8, 33, 24, 20, 17, 12, 8, 25, 30, 26] / words: ['the', 'quick', 'brown', 'fox', 'jumped', 'over', 'the', 'lazy', 'dog', 'I']\n" + "'data' is and length is 62.\n", + "Sample data: numbers: [5, 9, 10, 11, 12, 13, 5, 14, 15, 16] / words: ['the', 'quick', 'brown', 'fox', 'jumped', 'over', 'the', 'lazy', 'dog', 'I']\n" ] } ], @@ -318,7 +318,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": { "collapsed": false, "scrolled": true @@ -328,7 +328,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "[8, 33, 24, 20, 17, 12, 8, 25, 30, 26, 4, 0, 2, 1, 13, 10, 4, 0, 2, 1, 0, 2, 1, 3, 7, 6, 34, 0, 31, 32, 1, 0, 23, 5, 21, 27, 0, 3, 7, 19, 11, 28, 15, 16, 5, 0, 3, 14, 0, 3, 29, 18, 22, 9, 0, 2, 1, 1, 2, 0, 4, 6]\n" + "[5, 9, 10, 11, 12, 13, 5, 14, 15, 16, 4, 0, 2, 1, 17, 18, 4, 0, 2, 1, 0, 2, 1, 3, 6, 7, 19, 0, 20, 21, 1, 0, 22, 8, 23, 24, 0, 3, 6, 25, 26, 27, 28, 29, 8, 0, 3, 30, 0, 3, 31, 32, 33, 34, 0, 2, 1, 1, 2, 0, 4, 7]\n" ] } ], @@ -346,7 +346,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": { "collapsed": false }, @@ -355,7 +355,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Context pairs: [[[8, 24], 33], [[33, 20], 24], [[24, 17], 20], [[20, 12], 17], [[17, 8], 12], [[12, 25], 8], [[8, 30], 25], [[25, 26], 30], [[30, 4], 26], [[26, 0], 4]]\n" + "Context pairs: [[[5, 10], 9], [[9, 11], 10], [[10, 12], 11], [[11, 13], 12], [[12, 5], 13], [[13, 14], 5], [[5, 15], 14], [[14, 16], 15], [[15, 4], 16], [[16, 0], 4]]\n" ] } ], @@ -376,7 +376,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": { "collapsed": false }, @@ -385,7 +385,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "'cbow_pairs' is and length is 60.\n" + "'cbow_pairs' is and length is 60.\n" ] } ], @@ -403,7 +403,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": { "collapsed": false }, @@ -412,8 +412,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "'skip_gram_pairs' is and length is 120.\n", - "('skip-gram pairs', [[33, 8], [33, 24], [24, 33], [24, 20], [20, 24]])\n" + "'skip_gram_pairs' is and length is 120.\n", + "skip-gram pairs [[9, 5], [9, 10], [10, 9], [10, 11], [11, 10]]\n" ] } ], @@ -431,7 +431,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": { "collapsed": false }, @@ -440,7 +440,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "('Batches (x, y)', ([3, 8, 7], [[7], [25], [3]]))\n" + "Batches (x, y) ([2, 18, 0], [[1], [4], [20]])\n" ] } ], @@ -501,7 +501,7 @@ "\n", "# Compute the average NCE loss for the batch.\n", "loss = tf.reduce_mean(\n", - " tf.nn.nce_loss(nce_weights, nce_biases, embed, train_labels,\n", + " tf.nn.nce_loss(nce_weights, nce_biases, train_labels, embed,\n", " num_sampled, voc_size))\n", "\n", "# Use the adam optimizer\n", @@ -527,12 +527,30 @@ "name": "stdout", "output_type": "stream", "text": [ - "Loss at 0: 18.82132\n", - "Loss at 500: 3.69854\n", - "Loss at 1000: 3.22232\n", - "Loss at 1500: 2.94147\n", - "Loss at 2000: 2.90176\n", - "Loss at 2500: 2.71721\n" + "WARNING:tensorflow:From C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\tensorflow\\python\\util\\tf_should_use.py:107: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n", + "Instructions for updating:\n", + "Use `tf.global_variables_initializer` instead.\n" + ] + }, + { + "ename": "InternalError", + "evalue": "Blas GEMM launch failed : a.shape=(20, 2), b.shape=(15, 2), m=20, n=15, k=2\n\t [[Node: nce_loss_1/MatMul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=true, _device=\"/job:localhost/replica:0/task:0/device:GPU:0\"](embedding_lookup_1/_19, nce_loss_1/Slice_1)]]\n\t [[Node: Adam/update_Variable_5/group_deps/_50 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/device:CPU:0\", send_device=\"/job:localhost/replica:0/task:0/device:GPU:0\", send_device_incarnation=1, tensor_name=\"edge_504_Adam/update_Variable_5/group_deps\", tensor_type=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]\n\nCaused by op 'nce_loss_1/MatMul', defined at:\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\runpy.py\", line 193, in _run_module_as_main\n \"__main__\", mod_spec)\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\runpy.py\", line 85, in _run_code\n exec(code, run_globals)\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\ipykernel_launcher.py\", line 16, in \n app.launch_new_instance()\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\traitlets\\config\\application.py\", line 658, in launch_instance\n app.start()\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\ipykernel\\kernelapp.py\", line 486, in start\n self.io_loop.start()\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\tornado\\platform\\asyncio.py\", line 112, in start\n self.asyncio_loop.run_forever()\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\asyncio\\base_events.py\", line 422, in run_forever\n self._run_once()\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\asyncio\\base_events.py\", line 1432, in _run_once\n handle._run()\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\asyncio\\events.py\", line 145, in _run\n self._callback(*self._args)\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\tornado\\platform\\asyncio.py\", line 102, in _handle_events\n handler_func(fileobj, events)\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\tornado\\stack_context.py\", line 276, in null_wrapper\n return fn(*args, **kwargs)\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 450, in _handle_events\n self._handle_recv()\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 480, in _handle_recv\n self._run_callback(callback, msg)\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 432, in _run_callback\n callback(*args, **kwargs)\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\tornado\\stack_context.py\", line 276, in null_wrapper\n return fn(*args, **kwargs)\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 283, in dispatcher\n return self.dispatch_shell(stream, msg)\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 233, in dispatch_shell\n handler(stream, idents, msg)\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 399, in execute_request\n user_expressions, allow_stdin)\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\ipykernel\\ipkernel.py\", line 208, in do_execute\n res = shell.run_cell(code, store_history=store_history, silent=silent)\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\ipykernel\\zmqshell.py\", line 537, in run_cell\n return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2728, in run_cell\n interactivity=interactivity, compiler=compiler, result=result)\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2850, in run_ast_nodes\n if self.run_code(code, result):\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2910, in run_code\n exec(code_obj, self.user_global_ns, self.user_ns)\n File \"\", line 20, in \n num_sampled, voc_size))\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\tensorflow\\python\\ops\\nn_impl.py\", line 1160, in nce_loss\n name=name)\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\tensorflow\\python\\ops\\nn_impl.py\", line 990, in _compute_sampled_logits\n sampled_logits = math_ops.matmul(inputs, sampled_w, transpose_b=True)\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py\", line 1891, in matmul\n a, b, transpose_a=transpose_a, transpose_b=transpose_b, name=name)\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\tensorflow\\python\\ops\\gen_math_ops.py\", line 2436, in _mat_mul\n name=name)\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py\", line 787, in _apply_op_helper\n op_def=op_def)\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 2956, in create_op\n op_def=op_def)\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 1470, in __init__\n self._traceback = self._graph._extract_stack() # pylint: disable=protected-access\n\nInternalError (see above for traceback): Blas GEMM launch failed : a.shape=(20, 2), b.shape=(15, 2), m=20, n=15, k=2\n\t [[Node: nce_loss_1/MatMul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=true, _device=\"/job:localhost/replica:0/task:0/device:GPU:0\"](embedding_lookup_1/_19, nce_loss_1/Slice_1)]]\n\t [[Node: Adam/update_Variable_5/group_deps/_50 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/device:CPU:0\", send_device=\"/job:localhost/replica:0/task:0/device:GPU:0\", send_device_incarnation=1, tensor_name=\"edge_504_Adam/update_Variable_5/group_deps\", tensor_type=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]\n", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mInternalError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\Anaconda3\\envs\\tensor\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_call\u001b[1;34m(self, fn, *args)\u001b[0m\n\u001b[0;32m 1322\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1323\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1324\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\envs\\tensor\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[1;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[0;32m 1301\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1302\u001b[1;33m status, run_metadata)\n\u001b[0m\u001b[0;32m 1303\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\envs\\tensor\\lib\\site-packages\\tensorflow\\python\\framework\\errors_impl.py\u001b[0m in \u001b[0;36m__exit__\u001b[1;34m(self, type_arg, value_arg, traceback_arg)\u001b[0m\n\u001b[0;32m 472\u001b[0m \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc_api\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_Message\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstatus\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 473\u001b[1;33m c_api.TF_GetCode(self.status.status))\n\u001b[0m\u001b[0;32m 474\u001b[0m \u001b[1;31m# Delete the underlying status object from memory otherwise it stays alive\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mInternalError\u001b[0m: Blas GEMM launch failed : a.shape=(20, 2), b.shape=(15, 2), m=20, n=15, k=2\n\t [[Node: nce_loss_1/MatMul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=true, _device=\"/job:localhost/replica:0/task:0/device:GPU:0\"](embedding_lookup_1/_19, nce_loss_1/Slice_1)]]\n\t [[Node: Adam/update_Variable_5/group_deps/_50 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/device:CPU:0\", send_device=\"/job:localhost/replica:0/task:0/device:GPU:0\", send_device_incarnation=1, tensor_name=\"edge_504_Adam/update_Variable_5/group_deps\", tensor_type=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[1;31mInternalError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[0mbatch_inputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgenerate_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m _, loss_val = sess.run([train_op, loss],\n\u001b[1;32m----> 9\u001b[1;33m feed_dict={train_inputs: batch_inputs, train_labels: batch_labels})\n\u001b[0m\u001b[0;32m 10\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mstep\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;36m500\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 11\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Loss at %d: %.5f\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloss_val\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\envs\\tensor\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m 887\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 888\u001b[0m result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[1;32m--> 889\u001b[1;33m run_metadata_ptr)\n\u001b[0m\u001b[0;32m 890\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 891\u001b[0m \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\envs\\tensor\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run\u001b[1;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m 1118\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1119\u001b[0m results = self._do_run(handle, final_targets, final_fetches,\n\u001b[1;32m-> 1120\u001b[1;33m feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[0;32m 1121\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1122\u001b[0m \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\envs\\tensor\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_run\u001b[1;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m 1315\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1316\u001b[0m return self._do_call(_run_fn, self._session, feeds, fetches, targets,\n\u001b[1;32m-> 1317\u001b[1;33m options, run_metadata)\n\u001b[0m\u001b[0;32m 1318\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1319\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\envs\\tensor\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_call\u001b[1;34m(self, fn, *args)\u001b[0m\n\u001b[0;32m 1334\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1335\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1336\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnode_def\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1337\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1338\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_extend_graph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mInternalError\u001b[0m: Blas GEMM launch failed : a.shape=(20, 2), b.shape=(15, 2), m=20, n=15, k=2\n\t [[Node: nce_loss_1/MatMul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=true, _device=\"/job:localhost/replica:0/task:0/device:GPU:0\"](embedding_lookup_1/_19, nce_loss_1/Slice_1)]]\n\t [[Node: Adam/update_Variable_5/group_deps/_50 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/device:CPU:0\", send_device=\"/job:localhost/replica:0/task:0/device:GPU:0\", send_device_incarnation=1, tensor_name=\"edge_504_Adam/update_Variable_5/group_deps\", tensor_type=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]\n\nCaused by op 'nce_loss_1/MatMul', defined at:\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\runpy.py\", line 193, in _run_module_as_main\n \"__main__\", mod_spec)\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\runpy.py\", line 85, in _run_code\n exec(code, run_globals)\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\ipykernel_launcher.py\", line 16, in \n app.launch_new_instance()\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\traitlets\\config\\application.py\", line 658, in launch_instance\n app.start()\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\ipykernel\\kernelapp.py\", line 486, in start\n self.io_loop.start()\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\tornado\\platform\\asyncio.py\", line 112, in start\n self.asyncio_loop.run_forever()\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\asyncio\\base_events.py\", line 422, in run_forever\n self._run_once()\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\asyncio\\base_events.py\", line 1432, in _run_once\n handle._run()\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\asyncio\\events.py\", line 145, in _run\n self._callback(*self._args)\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\tornado\\platform\\asyncio.py\", line 102, in _handle_events\n handler_func(fileobj, events)\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\tornado\\stack_context.py\", line 276, in null_wrapper\n return fn(*args, **kwargs)\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 450, in _handle_events\n self._handle_recv()\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 480, in _handle_recv\n self._run_callback(callback, msg)\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\zmq\\eventloop\\zmqstream.py\", line 432, in _run_callback\n callback(*args, **kwargs)\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\tornado\\stack_context.py\", line 276, in null_wrapper\n return fn(*args, **kwargs)\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 283, in dispatcher\n return self.dispatch_shell(stream, msg)\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 233, in dispatch_shell\n handler(stream, idents, msg)\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 399, in execute_request\n user_expressions, allow_stdin)\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\ipykernel\\ipkernel.py\", line 208, in do_execute\n res = shell.run_cell(code, store_history=store_history, silent=silent)\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\ipykernel\\zmqshell.py\", line 537, in run_cell\n return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2728, in run_cell\n interactivity=interactivity, compiler=compiler, result=result)\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2850, in run_ast_nodes\n if self.run_code(code, result):\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2910, in run_code\n exec(code_obj, self.user_global_ns, self.user_ns)\n File \"\", line 20, in \n num_sampled, voc_size))\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\tensorflow\\python\\ops\\nn_impl.py\", line 1160, in nce_loss\n name=name)\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\tensorflow\\python\\ops\\nn_impl.py\", line 990, in _compute_sampled_logits\n sampled_logits = math_ops.matmul(inputs, sampled_w, transpose_b=True)\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py\", line 1891, in matmul\n a, b, transpose_a=transpose_a, transpose_b=transpose_b, name=name)\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\tensorflow\\python\\ops\\gen_math_ops.py\", line 2436, in _mat_mul\n name=name)\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py\", line 787, in _apply_op_helper\n op_def=op_def)\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 2956, in create_op\n op_def=op_def)\n File \"C:\\Users\\RYU\\Anaconda3\\envs\\tensor\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 1470, in __init__\n self._traceback = self._graph._extract_stack() # pylint: disable=protected-access\n\nInternalError (see above for traceback): Blas GEMM launch failed : a.shape=(20, 2), b.shape=(15, 2), m=20, n=15, k=2\n\t [[Node: nce_loss_1/MatMul = MatMul[T=DT_FLOAT, transpose_a=false, transpose_b=true, _device=\"/job:localhost/replica:0/task:0/device:GPU:0\"](embedding_lookup_1/_19, nce_loss_1/Slice_1)]]\n\t [[Node: Adam/update_Variable_5/group_deps/_50 = _Recv[client_terminated=false, recv_device=\"/job:localhost/replica:0/task:0/device:CPU:0\", send_device=\"/job:localhost/replica:0/task:0/device:GPU:0\", send_device_incarnation=1, tensor_name=\"edge_504_Adam/update_Variable_5/group_deps\", tensor_type=DT_FLOAT, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"]()]]\n" ] } ], @@ -626,23 +644,23 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python tensor", "language": "python", - "name": "python2" + "name": "tensor" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.6.5" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 2 }