training_mt2st_dimish.log

D:\Anaconda\exe\python.exe "E:\2023 Fall\23 Fall MT2ST WE Acc Meng\MT2ST-Word-Embeddings-Acceleration-Method-main\MT2ST-Word-Embeddings-Acceleration-Method-main\training_dimish.py"
Size of the dataset: 1000
["This film did entertain me with lots of laughs at the actors who kept the film moving along in all types of crazy directions. If you like suggestive language and sexy looking gals they were all in the picture and gals and guys all looking burned out before they even graduate from high school. There is one scene where the teenagers drive their car into a very fake deer and then proceed to throw it out into a lake or ocean, which is repeated over and over again. There is no horror to this film except the word Horrible for the entire picture and Arnold who plays a plastic cop is really one sick character. Please don't waste your time viewing this film.", 'The animation in this re-imagining of Peter & the Wolf is excellent, but at 29 minutes, the film is sleep inducing. They should have called it "Peter & the Snails", because everything moves at a snail\'s pace. I couldn\'t even watch the film in one sitting - I had to watch it 15 minutes at a time, and it was pure torture.<br /><br />Save yourself 30 minutes - do not watch this film - and you will thank me.<br /><br />I can only guess that the Oscar nominating committee only watched the first few minutes of the nominees. Unfortunately, to vote for the winner in the Best Animated Short (short!) category, the voters will have to sit through the whole thing. I already feel sorry for them - and must predict that there\'s no way this film will come close to winning.', "This is a great show, and will make you cry, this group people really loved each other in real life and it shows time and time again. Email me and let's chat. I have been to Australia and they real do talk like this.<br /><br />I want you to enjoy Five Mile Creek and pass on these great stories of right and wrong, and friendship to your kids. I have all 40 Episodes on DVD-R that I have collected over the last 5 years. See my Five Mile Creek tribute at www.mikeandvicki.com and hear the extended theme music. Let's talk about them.<br /><br />These people are so cool!", 'This is a great TV miniseries of a classic novel. Janet McTeer and John Bowe, in the lead roles, are exceptional. This is one of the best adaptations from a book that I have seen. I would LOVE to get a copy of this - let me know if you know how I might get one...Thanks!', "My husband dragged me to this film as I had no interest in seeing some Anime cartoon. I was absolutely delighted by the simple story and amazing animation. In a digital world where effects are computer generated it was refreshing to see gorgeous, imaginative hand drawn animation. The world of Sosuke and Ponyo is a vivid fantasyland intermixed with minimal reality. I haven't seen animation like this since I was a child and it is wonderful to see it endure and succeed.<br /><br />The actors supplying the voices in the English version were fabulous. The length of the movie was PERFECT, especially for children who tend to get squirrelly in films. Overall a delightful experience worth the very expensive ticket prices we have nowadays.", "This film was reeeeeeallyyyy bad! Was it meant to be a comedy as I couldn't help laughing the whole way through it? what a waste of two hours! Donald Sutherland was wooden not that he was alone, everyone else was just as bad...and how miscast was linda hamilton???", "If you have enjoyed the Butterfly Effect, Donnie Darko or The Machinist, you will enjoy K-Pax too.<br /><br />To me, this movie felt really uplifting and yet depressing in the end. Spacey delivers a great performance as Prot. Also, lets not forget the appearance of Saul Williams in the movie, who i am a big fan of.<br /><br />After watching it, i recommended the movie to lots of my friends, and everyone was pretty much blown away.<br /><br />But still, it is very underrated, maybe because of the lack of action and explosions. I'm sorry, this is not a movie about blowing things up, it's about how humans behave, and how people live in worlds that don't exist.<br /><br />Go on, and enjoy.", "The first hour or so of the movie was mostly boring to say the least. However it improved afterwards as the Valentine Party commenced. Apart from the twist as to the identity of the killer in the very end, the hot bath murder scene was one of the few relatively memorable aspects of this movie. The scene at the garden with Kate was well shot and so was the very last scene (the 'twist'). In those scenes, there was some genuine suspense and thrills and the hot bath murder scene had a nasty (the way slashers should be) edge to it. The earlier murders are frustratingly devoid of gore.", "As an avid fan of Cary Grant, I expected to watch this movie and howl with laughter, as AMC billed it as a comedy. I have never been more disappointed with a film! Cary's usual charm and effortless comedy are AWOL from this entire movie; he comes across as strained, bored, and just not himself. Mississip's character ranks among one of the worst stereotypes I have ever witnessed - his accent is terribly exaggerated (and incorrect, according to which part of Mississippi he claims to hail from), and whenever he does deliver a line, it's several decibels higher than any other cast member. Mississip tried to make himself stand out in the film as a lovable, country-bumpkin goofball, but in the end, he manages only to detract from the already weak plot. Mansfield looks more like an obscene blow-up doll than a Hollywood sex kitten, and while she was never known in Hollywood for her acting ability, this film screams that she never had that ability to begin with. Ray Walston's character was sugary and ultimately contrived. For four men on shore leave, it was the tamest leave I've ever seen. I watched this nightmare until its very end, and while I won't spoil that for anyone, I will tell you that it's the most absurd you'll ever see. The film tries to spark patriotism and a sense of debt to the fighting men, but the film misses that point totally because of its weak plot line and weak cast. Sorry, Cary!"]
['negative', 'negative', 'positive', 'positive', 'positive', 'negative', 'positive', 'positive', 'negative']
['this film did entertain me with lots of laughs at the actors who kept the film moving along in all types of crazy directions if you like suggestive language and sexy looking gals they were all in the picture and gals and guys all looking burned out before they even graduate from high school there is one scene where the teenagers drive their car into a very fake deer and then proceed to throw it out into a lake or ocean which is repeated over and over again there is no horror to this film except the word horrible for the entire picture and arnold who plays a plastic cop is really one sick character please dont waste your time viewing this film', 'the animation in this reimagining of peter  the wolf is excellent but at 29 minutes the film is sleep inducing they should have called it peter  the snails because everything moves at a snails pace i couldnt even watch the film in one sitting  i had to watch it 15 minutes at a time and it was pure torturesave yourself 30 minutes  do not watch this film  and you will thank mei can only guess that the oscar nominating committee only watched the first few minutes of the nominees unfortunately to vote for the winner in the best animated short short category the voters will have to sit through the whole thing i already feel sorry for them  and must predict that theres no way this film will come close to winning', 'this is a great show and will make you cry this group people really loved each other in real life and it shows time and time again email me and lets chat i have been to australia and they real do talk like thisi want you to enjoy five mile creek and pass on these great stories of right and wrong and friendship to your kids i have all 40 episodes on dvdr that i have collected over the last 5 years see my five mile creek tribute at wwwmikeandvickicom and hear the extended theme music lets talk about themthese people are so cool', 'this is a great tv miniseries of a classic novel janet mcteer and john bowe in the lead roles are exceptional this is one of the best adaptations from a book that i have seen i would love to get a copy of this  let me know if you know how i might get onethanks', 'my husband dragged me to this film as i had no interest in seeing some anime cartoon i was absolutely delighted by the simple story and amazing animation in a digital world where effects are computer generated it was refreshing to see gorgeous imaginative hand drawn animation the world of sosuke and ponyo is a vivid fantasyland intermixed with minimal reality i havent seen animation like this since i was a child and it is wonderful to see it endure and succeedthe actors supplying the voices in the english version were fabulous the length of the movie was perfect especially for children who tend to get squirrelly in films overall a delightful experience worth the very expensive ticket prices we have nowadays', 'this film was reeeeeeallyyyy bad was it meant to be a comedy as i couldnt help laughing the whole way through it what a waste of two hours donald sutherland was wooden not that he was alone everyone else was just as badand how miscast was linda hamilton', 'if you have enjoyed the butterfly effect donnie darko or the machinist you will enjoy kpax tooto me this movie felt really uplifting and yet depressing in the end spacey delivers a great performance as prot also lets not forget the appearance of saul williams in the movie who i am a big fan ofafter watching it i recommended the movie to lots of my friends and everyone was pretty much blown awaybut still it is very underrated maybe because of the lack of action and explosions im sorry this is not a movie about blowing things up its about how humans behave and how people live in worlds that dont existgo on and enjoy', 'the first hour or so of the movie was mostly boring to say the least however it improved afterwards as the valentine party commenced apart from the twist as to the identity of the killer in the very end the hot bath murder scene was one of the few relatively memorable aspects of this movie the scene at the garden with kate was well shot and so was the very last scene the twist in those scenes there was some genuine suspense and thrills and the hot bath murder scene had a nasty the way slashers should be edge to it the earlier murders are frustratingly devoid of gore', 'as an avid fan of cary grant i expected to watch this movie and howl with laughter as amc billed it as a comedy i have never been more disappointed with a film carys usual charm and effortless comedy are awol from this entire movie he comes across as strained bored and just not himself mississips character ranks among one of the worst stereotypes i have ever witnessed  his accent is terribly exaggerated and incorrect according to which part of mississippi he claims to hail from and whenever he does deliver a line its several decibels higher than any other cast member mississip tried to make himself stand out in the film as a lovable countrybumpkin goofball but in the end he manages only to detract from the already weak plot mansfield looks more like an obscene blowup doll than a hollywood sex kitten and while she was never known in hollywood for her acting ability this film screams that she never had that ability to begin with ray walstons character was sugary and ultimately contrived for four men on shore leave it was the tamest leave ive ever seen i watched this nightmare until its very end and while i wont spoil that for anyone i will tell you that its the most absurd youll ever see the film tries to spark patriotism and a sense of debt to the fighting men but the film misses that point totally because of its weak plot line and weak cast sorry cary']
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'men', 'shore', 'leave', 'tamest', 'leave', 'ive', 'ever', 'seen', 'watched', 'nightmare', 'end', 'wont', 'spoil', 'anyone', 'tell', 'absurd', 'youll', 'ever', 'see', 'film', 'tries', 'spark', 'patriotism', 'sense', 'debt', 'fighting', 'men', 'film', 'misses', 'point', 'totally', 'weak', 'plot', 'line', 'weak', 'cast', 'sorry', 'cary']]
[nltk_data] Downloading package wordnet to
[nltk_data]     C:\Users\LdTenacity\AppData\Roaming\nltk_data...
[nltk_data]   Package wordnet is already up-to-date!
[nltk_data] Downloading package omw-1.4 to
[nltk_data]     C:\Users\LdTenacity\AppData\Roaming\nltk_data...
[nltk_data]   Package omw-1.4 is already up-to-date!
[['film', 'entertain', 'lot', 'laugh', 'actor', 'kept', 'film', 'moving', 'along', 'type', 'crazy', 'direction', 'like', 'suggestive', 'language', 'sexy', 'looking', 'gal', 'picture', 'gal', 'guy', 'looking', 'burned', 'even', 'graduate', 'high', 'school', 'one', 'scene', 'teenager', 'drive', 'car', 'fake', 'deer', 'proceed', 'throw', 'lake', 'ocean', 'repeated', 'horror', 'film', 'except', 'word', 'horrible', 'entire', 'picture', 'arnold', 'play', 'plastic', 'cop', 'really', 'one', 'sick', 'character', 'please', 'dont', 'waste', 'time', 'viewing', 'film'], ['animation', 'reimagining', 'peter', 'wolf', 'excellent', '29', 'minute', 'film', 'sleep', 'inducing', 'called', 'peter', 'snail', 'everything', 'move', 'snail', 'pace', 'couldnt', 'even', 'watch', 'film', 'one', 'sitting', 'watch', '15', 'minute', 'time', 'pure', 'torturesave', '30', 'minute', 'watch', 'film', 'thank', 'mei', 'guess', 'oscar', 'nominating', 'committee', 'watched', 'first', 'minute', 'nominee', 'unfortunately', 'vote', 'winner', 'best', 'animated', 'short', 'short', 'category', 'voter', 'sit', 'whole', 'thing', 'already', 'feel', 'sorry', 'must', 'predict', 'there', 'way', 'film', 'come', 'close', 'winning'], ['great', 'show', 'make', 'cry', 'group', 'people', 'really', 'loved', 'real', 'life', 'show', 'time', 'time', 'email', 'let', 'chat', 'australia', 'real', 'talk', 'like', 'thisi', 'want', 'enjoy', 'five', 'mile', 'creek', 'pas', 'great', 'story', 'right', 'wrong', 'friendship', 'kid', '40', 'episode', 'dvdr', 'collected', 'last', '5', 'year', 'see', 'five', 'mile', 'creek', 'tribute', 'wwwmikeandvickicom', 'hear', 'extended', 'theme', 'music', 'let', 'talk', 'themthese', 'people', 'cool'], ['great', 'tv', 'miniseries', 'classic', 'novel', 'janet', 'mcteer', 'john', 'bowe', 'lead', 'role', 'exceptional', 'one', 'best', 'adaptation', 'book', 'seen', 'would', 'love', 'get', 'copy', 'let', 'know', 'know', 'might', 'get', 'onethanks'], ['husband', 'dragged', 'film', 'interest', 'seeing', 'anime', 'cartoon', 'absolutely', 'delighted', 'simple', 'story', 'amazing', 'animation', 'digital', 'world', 'effect', 'computer', 'generated', 'refreshing', 'see', 'gorgeous', 'imaginative', 'hand', 'drawn', 'animation', 'world', 'sosuke', 'ponyo', 'vivid', 'fantasyland', 'intermixed', 'minimal', 'reality', 'havent', 'seen', 'animation', 'like', 'since', 'child', 'wonderful', 'see', 'endure', 'succeedthe', 'actor', 'supplying', 'voice', 'english', 'version', 'fabulous', 'length', 'movie', 'perfect', 'especially', 'child', 'tend', 'get', 'squirrelly', 'film', 'overall', 'delightful', 'experience', 'worth', 'expensive', 'ticket', 'price', 'nowadays'], ['film', 'reeeeeeallyyyy', 'bad', 'meant', 'comedy', 'couldnt', 'help', 'laughing', 'whole', 'way', 'waste', 'two', 'hour', 'donald', 'sutherland', 'wooden', 'alone', 'everyone', 'else', 'badand', 'miscast', 'linda', 'hamilton'], ['enjoyed', 'butterfly', 'effect', 'donnie', 'darko', 'machinist', 'enjoy', 'kpax', 'tooto', 'movie', 'felt', 'really', 'uplifting', 'yet', 'depressing', 'end', 'spacey', 'delivers', 'great', 'performance', 'prot', 'also', 'let', 'forget', 'appearance', 'saul', 'williams', 'movie', 'big', 'fan', 'ofafter', 'watching', 'recommended', 'movie', 'lot', 'friend', 'everyone', 'pretty', 'much', 'blown', 'awaybut', 'still', 'underrated', 'maybe', 'lack', 'action', 'explosion', 'im', 'sorry', 'movie', 'blowing', 'thing', 'human', 'behave', 'people', 'live', 'world', 'dont', 'existgo', 'enjoy'], ['first', 'hour', 'movie', 'mostly', 'boring', 'say', 'least', 'however', 'improved', 'afterwards', 'valentine', 'party', 'commenced', 'apart', 'twist', 'identity', 'killer', 'end', 'hot', 'bath', 'murder', 'scene', 'one', 'relatively', 'memorable', 'aspect', 'movie', 'scene', 'garden', 'kate', 'well', 'shot', 'last', 'scene', 'twist', 'scene', 'genuine', 'suspense', 'thrill', 'hot', 'bath', 'murder', 'scene', 'nasty', 'way', 'slasher', 'edge', 'earlier', 'murder', 'frustratingly', 'devoid', 'gore'], ['avid', 'fan', 'cary', 'grant', 'expected', 'watch', 'movie', 'howl', 'laughter', 'amc', 'billed', 'comedy', 'never', 'disappointed', 'film', 'carys', 'usual', 'charm', 'effortless', 'comedy', 'awol', 'entire', 'movie', 'come', 'across', 'strained', 'bored', 'mississips', 'character', 'rank', 'among', 'one', 'worst', 'stereotype', 'ever', 'witnessed', 'accent', 'terribly', 'exaggerated', 'incorrect', 'according', 'part', 'mississippi', 'claim', 'hail', 'whenever', 'deliver', 'line', 'several', 'decibel', 'higher', 'cast', 'member', 'mississip', 'tried', 'make', 'stand', 'film', 'lovable', 'countrybumpkin', 'goofball', 'end', 'manages', 'detract', 'already', 'weak', 'plot', 'mansfield', 'look', 'like', 'obscene', 'blowup', 'doll', 'hollywood', 'sex', 'kitten', 'never', 'known', 'hollywood', 'acting', 'ability', 'film', 'scream', 'never', 'ability', 'begin', 'ray', 'walstons', 'character', 'sugary', 'ultimately', 'contrived', 'four', 'men', 'shore', 'leave', 'tamest', 'leave', 'ive', 'ever', 'seen', 'watched', 'nightmare', 'end', 'wont', 'spoil', 'anyone', 'tell', 'absurd', 'youll', 'ever', 'see', 'film', 'try', 'spark', 'patriotism', 'sense', 'debt', 'fighting', 'men', 'film', 'miss', 'point', 'totally', 'weak', 'plot', 'line', 'weak', 'cast', 'sorry', 'cary']]
Epoch: 1, loss: 0.69320, train_acc: 0.51
Epoch: 2, loss: 3.30163, train_acc: 0.49
Epoch: 3, loss: 7.12619, train_acc: 0.51
Epoch: 4, loss: 1.90261, train_acc: 0.48
Epoch: 5, loss: 2.00941, train_acc: 0.49
Epoch: 6, loss: 0.93977, train_acc: 0.50
Epoch: 7, loss: 1.51636, train_acc: 0.49
Epoch: 8, loss: 0.92211, train_acc: 0.47
Epoch: 9, loss: 1.63303, train_acc: 0.50
Epoch: 10, loss: 1.01953, train_acc: 0.49
Epoch: 11, loss: 1.40945, train_acc: 0.51
Epoch: 12, loss: 1.42190, train_acc: 0.48
Epoch: 13, loss: 1.62770, train_acc: 0.50
Epoch: 14, loss: 1.22491, train_acc: 0.50
Epoch: 15, loss: 0.92069, train_acc: 0.51
Epoch: 16, loss: 0.91376, train_acc: 0.51
Epoch: 17, loss: 0.91519, train_acc: 0.51
Epoch: 18, loss: 0.88758, train_acc: 0.52
Epoch: 19, loss: 0.85773, train_acc: 0.53
Epoch: 20, loss: 0.80663, train_acc: 0.54
Epoch: 21, loss: 0.86148, train_acc: 0.53
Epoch: 22, loss: 0.76396, train_acc: 0.54
Epoch: 23, loss: 0.87464, train_acc: 0.56
Epoch: 24, loss: 0.77012, train_acc: 0.53
Epoch: 25, loss: 0.84821, train_acc: 0.53
Finished Training
              precision    recall  f1-score   support

           0       0.58      0.32      0.41       132
           1       0.49      0.74      0.59       118

    accuracy                           0.52       250
   macro avg       0.53      0.53      0.50       250
weighted avg       0.54      0.52      0.49       250

test_pad.shape: 250
test_pad[0].shape: 100
weight adjusted tensor(1.8958, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 1, loss: 0.69409, train_acc: 0.49
weight adjusted tensor(1.8000, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 2, loss: 0.69545, train_acc: 0.51
weight adjusted tensor(1.6239, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 3, loss: 0.69283, train_acc: 0.51
weight adjusted tensor(1.3222, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 4, loss: 0.69156, train_acc: 0.54
weight adjusted tensor(0.8770, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 5, loss: 0.69180, train_acc: 0.52
weight adjusted tensor(0.3859, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 6, loss: 0.69137, train_acc: 0.53
weight adjusted tensor(0.0747, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 7, loss: 0.69009, train_acc: 0.56
weight adjusted tensor(0.0028, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 8, loss: 0.68882, train_acc: 0.55
weight adjusted tensor(3.9478e-06, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 9, loss: 0.68788, train_acc: 0.55
weight adjusted tensor(7.8240e-12, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 10, loss: 0.68682, train_acc: 0.56
weight adjusted tensor(3.0698e-23, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 11, loss: 0.68498, train_acc: 0.58
weight adjusted tensor(4.7435e-46, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 12, loss: 0.68245, train_acc: 0.58
weight adjusted tensor(1.1327e-91, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 13, loss: 0.67959, train_acc: 0.61
weight adjusted tensor(6.4448e-183, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 14, loss: 0.67618, train_acc: 0.66
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 15, loss: 0.67135, train_acc: 0.67
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 16, loss: 0.66447, train_acc: 0.67
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 17, loss: 0.65658, train_acc: 0.66
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 18, loss: 0.64398, train_acc: 0.72
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 19, loss: 0.62836, train_acc: 0.76
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 20, loss: 0.60180, train_acc: 0.82
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 21, loss: 0.56940, train_acc: 0.83
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 22, loss: 1.06824, train_acc: 0.51
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 23, loss: 0.53579, train_acc: 0.88
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 24, loss: 0.60502, train_acc: 0.59
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 25, loss: 0.59692, train_acc: 0.60
Finished Training
              precision    recall  f1-score   support

           0       0.57      0.03      0.06       132
           1       0.47      0.97      0.64       118

    accuracy                           0.48       250
   macro avg       0.52      0.50      0.35       250
weighted avg       0.53      0.48      0.33       250

input_torch torch.Size([750, 100])
sparsemans loss -0.41609179091997733
Epoch: 1, loss: 0.27714, train_acc: 0.49
input_torch torch.Size([750, 100])
sparsemans loss 0.6527650595018676
Epoch: 2, loss: 1.35032, train_acc: 0.51
input_torch torch.Size([750, 100])
sparsemans loss 0.4923516750488703
Epoch: 3, loss: 1.18431, train_acc: 0.51
input_torch torch.Size([750, 100])
sparsemans loss 0.6119662200987549
Epoch: 4, loss: 1.30490, train_acc: 0.50
input_torch torch.Size([750, 100])
sparsemans loss 0.4294825445928083
Epoch: 5, loss: 1.12270, train_acc: 0.50
input_torch torch.Size([750, 100])
sparsemans loss 0.1857012926225767
Epoch: 6, loss: 0.87709, train_acc: 0.52
input_torch torch.Size([750, 100])
sparsemans loss 0.03563924999890316
Epoch: 7, loss: 0.72524, train_acc: 0.56
input_torch torch.Size([750, 100])
sparsemans loss 0.0013455647325037775
Epoch: 8, loss: 0.69007, train_acc: 0.55
input_torch torch.Size([750, 100])
sparsemans loss 1.912038626706246e-06
Epoch: 9, loss: 0.68829, train_acc: 0.51
input_torch torch.Size([750, 100])
sparsemans loss 4.004398980721484e-12
Epoch: 10, loss: 0.68729, train_acc: 0.51
input_torch torch.Size([750, 100])
sparsemans loss 2.2831511913034837e-23
Epoch: 11, loss: 0.68539, train_acc: 0.51
input_torch torch.Size([750, 100])
sparsemans loss 3.728677465090531e-46
Epoch: 12, loss: 0.68302, train_acc: 0.61
input_torch torch.Size([750, 100])
sparsemans loss 8.929298714715044e-92
Epoch: 13, loss: 0.68053, train_acc: 0.63
input_torch torch.Size([750, 100])
sparsemans loss 4.802445037713229e-183
Epoch: 14, loss: 0.67776, train_acc: 0.67
input_torch torch.Size([750, 100])
sparsemans loss 0.0
Epoch: 15, loss: 0.67400, train_acc: 0.67
input_torch torch.Size([750, 100])
sparsemans loss 0.0
Epoch: 16, loss: 0.66852, train_acc: 0.68
input_torch torch.Size([750, 100])
sparsemans loss 0.0
Epoch: 17, loss: 0.66142, train_acc: 0.67
input_torch torch.Size([750, 100])
sparsemans loss 0.0
Epoch: 18, loss: 0.65358, train_acc: 0.68
input_torch torch.Size([750, 100])
sparsemans loss 0.0
Epoch: 19, loss: 0.64000, train_acc: 0.74
input_torch torch.Size([750, 100])
sparsemans loss 0.0
Epoch: 20, loss: 0.62382, train_acc: 0.81
input_torch torch.Size([750, 100])
sparsemans loss 0.0
Epoch: 21, loss: 0.59702, train_acc: 0.89
input_torch torch.Size([750, 100])
sparsemans loss 0.0
Epoch: 22, loss: 0.56761, train_acc: 0.73
input_torch torch.Size([750, 100])
sparsemans loss 0.0
Epoch: 23, loss: 0.57850, train_acc: 0.66
input_torch torch.Size([750, 100])
sparsemans loss 0.0
Epoch: 24, loss: 0.48652, train_acc: 0.92
input_torch torch.Size([750, 100])
sparsemans loss 0.0
Epoch: 25, loss: 1.36384, train_acc: 0.52
Finished Training
              precision    recall  f1-score   support

           0       0.60      0.85      0.70       132
           1       0.68      0.36      0.47       118

    accuracy                           0.62       250
   macro avg       0.64      0.60      0.58       250
weighted avg       0.63      0.62      0.59       250

weight adjusted tensor(0.0190, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 1, loss: 0.67423, train_acc: 0.50
weight adjusted tensor(0.0180, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 2, loss: 0.67706, train_acc: 0.51
weight adjusted tensor(0.0163, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 3, loss: 0.67561, train_acc: 0.52
weight adjusted tensor(0.0138, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 4, loss: 0.67932, train_acc: 0.50
weight adjusted tensor(0.0092, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 5, loss: 0.68263, train_acc: 0.52
weight adjusted tensor(0.0040, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 6, loss: 0.68603, train_acc: 0.55
weight adjusted tensor(0.0008, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 7, loss: 0.68863, train_acc: 0.52
weight adjusted tensor(2.8264e-05, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 8, loss: 0.68890, train_acc: 0.51
weight adjusted tensor(3.9816e-08, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 9, loss: 0.68753, train_acc: 0.51
weight adjusted tensor(7.8909e-14, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 10, loss: 0.68545, train_acc: 0.56
weight adjusted tensor(3.1040e-25, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 11, loss: 0.68334, train_acc: 0.59
weight adjusted tensor(4.7910e-48, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 12, loss: 0.68105, train_acc: 0.63
weight adjusted tensor(1.1446e-93, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 13, loss: 0.67777, train_acc: 0.62
weight adjusted tensor(6.5258e-185, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 14, loss: 0.67300, train_acc: 0.63
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 15, loss: 0.66748, train_acc: 0.64
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 16, loss: 0.65960, train_acc: 0.67
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 17, loss: 0.64794, train_acc: 0.73
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 18, loss: 0.63204, train_acc: 0.78
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 19, loss: 0.60575, train_acc: 0.86
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 20, loss: 0.56052, train_acc: 0.85
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 21, loss: 0.71121, train_acc: 0.57
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 22, loss: 0.87930, train_acc: 0.54
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 23, loss: 0.51675, train_acc: 0.85
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 24, loss: 0.56172, train_acc: 0.72
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 25, loss: 0.58047, train_acc: 0.68
Finished Training
              precision    recall  f1-score   support

           0       0.54      0.90      0.67       132
           1       0.55      0.14      0.22       118

    accuracy                           0.54       250
   macro avg       0.55      0.52      0.45       250
weighted avg       0.54      0.54      0.46       250

sparsemans loss -0.0437991358863134
word analogy weight adjusted tensor(1.8958, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 1, loss: -1.24619, train_acc: 0.49
sparsemans loss 0.020398819997954776
word analogy weight adjusted tensor(1.8000, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 2, loss: -1.08246, train_acc: 0.51
sparsemans loss 0.042769925575897906
word analogy weight adjusted tensor(1.6238, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 3, loss: -0.88856, train_acc: 0.51
sparsemans loss 0.1630684395206019
word analogy weight adjusted tensor(1.3222, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 4, loss: -0.46691, train_acc: 0.51
sparsemans loss 0.13805267034898117
word analogy weight adjusted tensor(0.8768, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 5, loss: -0.04582, train_acc: 0.50
sparsemans loss 0.11825382959771562
word analogy weight adjusted tensor(0.3856, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 6, loss: 0.42446, train_acc: 0.50
sparsemans loss 0.09973138674528717
word analogy weight adjusted tensor(0.0747, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 7, loss: 0.71532, train_acc: 0.55
sparsemans loss 0.09544363243570497
word analogy weight adjusted tensor(0.0029, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 8, loss: 0.78166, train_acc: 0.56
sparsemans loss 0.09565025290854935
word analogy weight adjusted tensor(4.0949e-06, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 9, loss: 0.78420, train_acc: 0.53
sparsemans loss 0.09395667178309149
word analogy weight adjusted tensor(8.1152e-12, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 10, loss: 0.78177, train_acc: 0.51
sparsemans loss 0.13158122545857429
word analogy weight adjusted tensor(3.1818e-23, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 11, loss: 0.81787, train_acc: 0.51
sparsemans loss 0.02614176736894955
word analogy weight adjusted tensor(4.9151e-46, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 12, loss: 0.71029, train_acc: 0.61
sparsemans loss 0.019882763340076052
word analogy weight adjusted tensor(1.1732e-91, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 13, loss: 0.70165, train_acc: 0.63
sparsemans loss -0.0009591981122265089
word analogy weight adjusted tensor(6.6764e-183, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 14, loss: 0.67819, train_acc: 0.65
sparsemans loss 0.004767933728205103
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 15, loss: 0.68052, train_acc: 0.65
sparsemans loss 0.006806864133655973
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 16, loss: 0.67763, train_acc: 0.66
sparsemans loss -0.001574165648607894
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 17, loss: 0.66268, train_acc: 0.65
sparsemans loss -0.02648973204285577
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 18, loss: 0.63037, train_acc: 0.67
sparsemans loss -0.03711998494046967
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 19, loss: 0.60741, train_acc: 0.71
sparsemans loss 0.08556063243585413
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 20, loss: 0.71456, train_acc: 0.76
sparsemans loss 0.0875671725911075
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 21, loss: 0.69096, train_acc: 0.85
sparsemans loss 0.06670921701004531
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 22, loss: 0.63668, train_acc: 0.74
sparsemans loss 0.04559978943609979
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 23, loss: 0.80448, train_acc: 0.57
sparsemans loss 0.06961070671010827
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 24, loss: 0.59007, train_acc: 0.85
sparsemans loss 0.09925342095474456
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 25, loss: 0.68766, train_acc: 0.56
Finished Training
              precision    recall  f1-score   support

           0       0.62      0.06      0.11       132
           1       0.48      0.96      0.64       118

    accuracy                           0.48       250
   macro avg       0.55      0.51      0.37       250
weighted avg       0.55      0.48      0.36       250

Epoch: 1, loss: 0.69289, train_acc: 0.51
Epoch: 2, loss: 7.59723, train_acc: 0.51
Epoch: 3, loss: 3.34010, train_acc: 0.49
Epoch: 4, loss: 2.66350, train_acc: 0.51
Epoch: 5, loss: 2.53510, train_acc: 0.52
Epoch: 6, loss: 2.11589, train_acc: 0.47
Epoch: 7, loss: 2.08927, train_acc: 0.49
Epoch: 8, loss: 1.48527, train_acc: 0.51
Epoch: 9, loss: 1.57091, train_acc: 0.51
Epoch: 10, loss: 1.46807, train_acc: 0.51
Epoch: 11, loss: 1.06362, train_acc: 0.51
Epoch: 12, loss: 1.03521, train_acc: 0.51
Epoch: 13, loss: 0.92183, train_acc: 0.52
Epoch: 14, loss: 0.90762, train_acc: 0.51
Epoch: 15, loss: 0.87001, train_acc: 0.52
Epoch: 16, loss: 0.95731, train_acc: 0.53
Epoch: 17, loss: 0.94945, train_acc: 0.52
Epoch: 18, loss: 0.96433, train_acc: 0.48
Epoch: 19, loss: 0.90388, train_acc: 0.49
Epoch: 20, loss: 0.82710, train_acc: 0.53
Epoch: 21, loss: 0.84216, train_acc: 0.53
Epoch: 22, loss: 0.87078, train_acc: 0.51
Epoch: 23, loss: 0.83626, train_acc: 0.53
Epoch: 24, loss: 0.81623, train_acc: 0.51
Epoch: 25, loss: 0.78845, train_acc: 0.53
Finished Training
              precision    recall  f1-score   support

           0       0.58      0.36      0.44       132
           1       0.50      0.71      0.59       118

    accuracy                           0.52       250
   macro avg       0.54      0.53      0.51       250
weighted avg       0.54      0.52      0.51       250

test_pad.shape: 250
test_pad[0].shape: 100
weight adjusted tensor(2.9565, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 1, loss: 0.69296, train_acc: 0.51
weight adjusted tensor(2.8092, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 2, loss: 0.69157, train_acc: 0.51
weight adjusted tensor(2.5336, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 3, loss: 0.69016, train_acc: 0.55
weight adjusted tensor(2.0628, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 4, loss: 0.68826, train_acc: 0.56
weight adjusted tensor(1.3692, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 5, loss: 0.68567, train_acc: 0.57
weight adjusted tensor(0.6026, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 6, loss: 0.68195, train_acc: 0.57
weight adjusted tensor(0.1167, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 7, loss: 0.67631, train_acc: 0.60
weight adjusted tensor(0.0045, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 8, loss: 0.66761, train_acc: 0.63
weight adjusted tensor(6.1626e-06, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 9, loss: 0.65231, train_acc: 0.65
weight adjusted tensor(1.2187e-11, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 10, loss: 0.62347, train_acc: 0.67
weight adjusted tensor(4.9047e-23, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 11, loss: 0.68390, train_acc: 0.63
weight adjusted tensor(7.5699e-46, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 12, loss: 0.83204, train_acc: 0.61
weight adjusted tensor(1.7604e-91, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 13, loss: 0.58830, train_acc: 0.71
weight adjusted tensor(1.0024e-182, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 14, loss: 0.61849, train_acc: 0.63
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 15, loss: 0.63507, train_acc: 0.57
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 16, loss: 0.63940, train_acc: 0.55
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 17, loss: 0.63797, train_acc: 0.55
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 18, loss: 0.63425, train_acc: 0.56
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 19, loss: 0.62987, train_acc: 0.58
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 20, loss: 0.62546, train_acc: 0.63
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 21, loss: 0.62121, train_acc: 0.68
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 22, loss: 0.61705, train_acc: 0.73
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 23, loss: 0.61282, train_acc: 0.75
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 24, loss: 0.60825, train_acc: 0.78
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 25, loss: 0.60300, train_acc: 0.79
Finished Training
              precision    recall  f1-score   support

           0       0.59      0.49      0.54       132
           1       0.52      0.62      0.57       118

    accuracy                           0.55       250
   macro avg       0.56      0.56      0.55       250
weighted avg       0.56      0.55      0.55       250

input_torch torch.Size([750, 100])
sparsemans loss -1.1092659447205742
Epoch: 1, loss: -0.41605, train_acc: 0.50
input_torch torch.Size([750, 100])
sparsemans loss -0.3770598301522397
Epoch: 2, loss: 0.31556, train_acc: 0.51
input_torch torch.Size([750, 100])
sparsemans loss 0.429205224644936
Epoch: 3, loss: 1.12010, train_acc: 0.51
input_torch torch.Size([750, 100])
sparsemans loss 0.4265257078599068
Epoch: 4, loss: 1.11620, train_acc: 0.59
input_torch torch.Size([750, 100])
sparsemans loss 0.2663758657007368
Epoch: 5, loss: 0.95453, train_acc: 0.58
input_torch torch.Size([750, 100])
sparsemans loss 0.10371847248855112
Epoch: 6, loss: 0.78941, train_acc: 0.61
input_torch torch.Size([750, 100])
sparsemans loss 0.019875025101317535
Epoch: 7, loss: 0.70236, train_acc: 0.59
input_torch torch.Size([750, 100])
sparsemans loss 0.00010328892077861262
Epoch: 8, loss: 0.67855, train_acc: 0.59
input_torch torch.Size([750, 100])
sparsemans loss 1.1477823909106558e-06
Epoch: 9, loss: 0.67243, train_acc: 0.61
input_torch torch.Size([750, 100])
sparsemans loss 2.636733248991266e-12
Epoch: 10, loss: 0.66319, train_acc: 0.62
input_torch torch.Size([750, 100])
sparsemans loss 5.893414950786862e-24
Epoch: 11, loss: 0.64965, train_acc: 0.67
input_torch torch.Size([750, 100])
sparsemans loss 5.554443605299215e-47
Epoch: 12, loss: 0.62486, train_acc: 0.69
input_torch torch.Size([750, 100])
sparsemans loss 2.3360977613192538e-93
Epoch: 13, loss: 0.65211, train_acc: 0.67
input_torch torch.Size([750, 100])
sparsemans loss 1.1997514727082994e-183
Epoch: 14, loss: 1.35143, train_acc: 0.57
input_torch torch.Size([750, 100])
sparsemans loss 0.0
Epoch: 15, loss: 0.60269, train_acc: 0.71
input_torch torch.Size([750, 100])
sparsemans loss 0.0
Epoch: 16, loss: 0.72200, train_acc: 0.54
input_torch torch.Size([750, 100])
sparsemans loss 0.0
Epoch: 17, loss: 0.66360, train_acc: 0.53
input_torch torch.Size([750, 100])
sparsemans loss 0.0
Epoch: 18, loss: 0.63547, train_acc: 0.54
input_torch torch.Size([750, 100])
sparsemans loss 0.0
Epoch: 19, loss: 0.62914, train_acc: 0.55
input_torch torch.Size([750, 100])
sparsemans loss 0.0
Epoch: 20, loss: 0.62807, train_acc: 0.57
input_torch torch.Size([750, 100])
sparsemans loss 0.0
Epoch: 21, loss: 0.62827, train_acc: 0.59
input_torch torch.Size([750, 100])
sparsemans loss 0.0
Epoch: 22, loss: 0.62869, train_acc: 0.63
input_torch torch.Size([750, 100])
sparsemans loss 0.0
Epoch: 23, loss: 0.62900, train_acc: 0.66
input_torch torch.Size([750, 100])
sparsemans loss 0.0
Epoch: 24, loss: 0.62911, train_acc: 0.70
input_torch torch.Size([750, 100])
sparsemans loss 0.0
Epoch: 25, loss: 0.62897, train_acc: 0.73
Finished Training
              precision    recall  f1-score   support

           0       0.64      0.22      0.33       132
           1       0.50      0.86      0.63       118

    accuracy                           0.52       250
   macro avg       0.57      0.54      0.48       250
weighted avg       0.58      0.52      0.47       250

weight adjusted tensor(0.0296, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 1, loss: 0.66343, train_acc: 0.51
weight adjusted tensor(0.0281, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 2, loss: 0.66390, train_acc: 0.54
weight adjusted tensor(0.0253, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 3, loss: 0.66505, train_acc: 0.55
weight adjusted tensor(0.0206, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 4, loss: 0.66834, train_acc: 0.53
weight adjusted tensor(0.0137, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 5, loss: 0.67294, train_acc: 0.57
weight adjusted tensor(0.0060, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 6, loss: 0.67766, train_acc: 0.59
weight adjusted tensor(0.0012, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 7, loss: 0.67849, train_acc: 0.62
weight adjusted tensor(4.3808e-05, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 8, loss: 0.67334, train_acc: 0.62
weight adjusted tensor(6.1662e-08, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 9, loss: 0.66426, train_acc: 0.61
weight adjusted tensor(1.2219e-13, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 10, loss: 0.64979, train_acc: 0.64
weight adjusted tensor(4.7966e-25, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 11, loss: 0.62728, train_acc: 0.68
weight adjusted tensor(7.3954e-48, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 12, loss: 0.60337, train_acc: 0.67
weight adjusted tensor(1.7647e-93, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 13, loss: 0.81038, train_acc: 0.67
weight adjusted tensor(1.0051e-184, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 14, loss: 0.59735, train_acc: 0.63
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 15, loss: 0.61907, train_acc: 0.60
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 16, loss: 0.61038, train_acc: 0.61
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 17, loss: 0.60856, train_acc: 0.64
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 18, loss: 0.60861, train_acc: 0.68
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 19, loss: 0.60875, train_acc: 0.72
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 20, loss: 0.60844, train_acc: 0.75
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 21, loss: 0.60742, train_acc: 0.77
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 22, loss: 0.60550, train_acc: 0.79
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 23, loss: 0.60242, train_acc: 0.79
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 24, loss: 0.59792, train_acc: 0.80
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 25, loss: 0.59166, train_acc: 0.81
Finished Training
              precision    recall  f1-score   support

           0       0.63      0.59      0.61       132
           1       0.57      0.61      0.59       118

    accuracy                           0.60       250
   macro avg       0.60      0.60      0.60       250
weighted avg       0.60      0.60      0.60       250

sparsemans loss -0.11676483628637624
word analogy weight adjusted tensor(2.9565, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 1, loss: -2.38006, train_acc: 0.49
sparsemans loss 0.15964030462461778
word analogy weight adjusted tensor(2.8077, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 2, loss: -1.95541, train_acc: 0.51
sparsemans loss 0.03400193938074211
word analogy weight adjusted tensor(2.5341, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 3, loss: -1.80907, train_acc: 0.51
sparsemans loss 0.02959458757821494
word analogy weight adjusted tensor(2.0647, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 4, loss: -1.34539, train_acc: 0.57
sparsemans loss 0.02745021254745301
word analogy weight adjusted tensor(1.3699, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 5, loss: -0.65409, train_acc: 0.59
sparsemans loss 0.022335816241085098
word analogy weight adjusted tensor(0.6027, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 6, loss: 0.10591, train_acc: 0.62
sparsemans loss 0.029431296170194684
word analogy weight adjusted tensor(0.1165, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 7, loss: 0.59622, train_acc: 0.60
sparsemans loss 0.03624941211345431
word analogy weight adjusted tensor(0.0044, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 8, loss: 0.71135, train_acc: 0.61
sparsemans loss 0.05574167702241284
word analogy weight adjusted tensor(6.1561e-06, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 9, loss: 0.73013, train_acc: 0.60
sparsemans loss 0.14927925773597547
word analogy weight adjusted tensor(1.2201e-11, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 10, loss: 0.81607, train_acc: 0.62
sparsemans loss 0.23260332760739572
word analogy weight adjusted tensor(4.7901e-23, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 11, loss: 0.88774, train_acc: 0.66
sparsemans loss 0.22488187463391734
word analogy weight adjusted tensor(7.3976e-46, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 12, loss: 0.86257, train_acc: 0.69
sparsemans loss 0.19586583877848093
word analogy weight adjusted tensor(1.7618e-91, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 13, loss: 0.80045, train_acc: 0.71
sparsemans loss 0.14201137008970788
word analogy weight adjusted tensor(1.0035e-182, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 14, loss: 0.85264, train_acc: 0.64
sparsemans loss 0.18715816556643425
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 15, loss: 1.23521, train_acc: 0.61
sparsemans loss 0.19228846090755972
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 16, loss: 0.79396, train_acc: 0.72
sparsemans loss 0.19505029320309955
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 17, loss: 0.76849, train_acc: 0.75
sparsemans loss 0.18916702965310841
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 18, loss: 0.77670, train_acc: 0.74
sparsemans loss 0.17962427686461513
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 19, loss: 0.77642, train_acc: 0.71
sparsemans loss 0.18855693380512878
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 20, loss: 0.79084, train_acc: 0.68
sparsemans loss 0.1672695770443027
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 21, loss: 0.77247, train_acc: 0.66
sparsemans loss 0.14512696952028667
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 22, loss: 0.75135, train_acc: 0.64
sparsemans loss 0.14516360193442518
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 23, loss: 0.75087, train_acc: 0.63
sparsemans loss 0.15083040680116017
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 24, loss: 0.75472, train_acc: 0.62
sparsemans loss 0.13924540922962095
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 25, loss: 0.74012, train_acc: 0.63
Finished Training
              precision    recall  f1-score   support

           0       0.90      0.07      0.13       132
           1       0.49      0.99      0.65       118

    accuracy                           0.50       250
   macro avg       0.69      0.53      0.39       250
weighted avg       0.71      0.50      0.38       250

test_pad.shape: 250
test_pad[0].shape: 100
weight adjusted tensor(52.5325, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 1, loss: 0.69619, train_acc: 0.49
weight adjusted tensor(49.9064, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 2, loss: 0.69598, train_acc: 0.51
weight adjusted tensor(45.0451, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 3, loss: 0.69393, train_acc: 0.51
weight adjusted tensor(36.6934, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 4, loss: 0.69185, train_acc: 0.52
weight adjusted tensor(24.3457, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 5, loss: 0.69204, train_acc: 0.56
weight adjusted tensor(10.7163, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 6, loss: 0.69227, train_acc: 0.49
weight adjusted tensor(2.0760, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 7, loss: 0.69149, train_acc: 0.53
weight adjusted tensor(0.0779, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 8, loss: 0.69030, train_acc: 0.51
weight adjusted tensor(0.0001, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 9, loss: 0.68932, train_acc: 0.52
weight adjusted tensor(2.1750e-10, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 10, loss: 0.68866, train_acc: 0.59
weight adjusted tensor(8.5500e-22, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 11, loss: 0.68794, train_acc: 0.51
weight adjusted tensor(1.3212e-44, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 12, loss: 0.68672, train_acc: 0.51
weight adjusted tensor(3.1544e-90, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 13, loss: 0.68488, train_acc: 0.55
weight adjusted tensor(1.7982e-181, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 14, loss: 0.68262, train_acc: 0.72
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 15, loss: 0.68010, train_acc: 0.58
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 16, loss: 0.67707, train_acc: 0.63
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 17, loss: 0.67282, train_acc: 0.67
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 18, loss: 0.66673, train_acc: 0.66
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 19, loss: 0.65885, train_acc: 0.71
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 20, loss: 0.64858, train_acc: 0.73
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 21, loss: 0.63175, train_acc: 0.79
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 22, loss: 0.61009, train_acc: 0.81
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 23, loss: 0.59644, train_acc: 0.76
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 24, loss: 0.61144, train_acc: 0.54
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 25, loss: 0.53213, train_acc: 0.87
Finished Training
D:\Anaconda\exe\lib\site-packages\sklearn\metrics\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
D:\Anaconda\exe\lib\site-packages\sklearn\metrics\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
D:\Anaconda\exe\lib\site-packages\sklearn\metrics\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
              precision    recall  f1-score   support

           0       0.00      0.00      0.00       132
           1       0.47      1.00      0.64       118

    accuracy                           0.47       250
   macro avg       0.24      0.50      0.32       250
weighted avg       0.22      0.47      0.30       250

input_torch torch.Size([750, 100])
sparsemans loss -1.4233990069845313
Epoch: 1, loss: -0.73013, train_acc: 0.49
input_torch torch.Size([750, 100])
sparsemans loss -1.3551573934006915
Epoch: 2, loss: -0.65337, train_acc: 0.51
input_torch torch.Size([750, 100])
sparsemans loss -1.221505516055852
Epoch: 3, loss: -0.52872, train_acc: 0.51
input_torch torch.Size([750, 100])
sparsemans loss -0.9854291647991537
Epoch: 4, loss: -0.29260, train_acc: 0.49
input_torch torch.Size([750, 100])
sparsemans loss -0.651895087124373
Epoch: 5, loss: 0.04283, train_acc: 0.49
input_torch torch.Size([750, 100])
sparsemans loss -0.2867099664406636
Epoch: 6, loss: 0.40691, train_acc: 0.49
input_torch torch.Size([750, 100])
sparsemans loss -0.05568045381140377
Epoch: 7, loss: 0.63582, train_acc: 0.52
input_torch torch.Size([750, 100])
sparsemans loss -0.0020890952432272483
Epoch: 8, loss: 0.68800, train_acc: 0.52
input_torch torch.Size([750, 100])
sparsemans loss -2.921156474079242e-06
Epoch: 9, loss: 0.68972, train_acc: 0.51
input_torch torch.Size([750, 100])
sparsemans loss -5.804555903695825e-12
Epoch: 10, loss: 0.68973, train_acc: 0.51
input_torch torch.Size([750, 100])
sparsemans loss -2.2949586673190082e-23
Epoch: 11, loss: 0.68923, train_acc: 0.51
input_torch torch.Size([750, 100])
sparsemans loss -3.537885793660971e-46
Epoch: 12, loss: 0.68801, train_acc: 0.51
input_torch torch.Size([750, 100])
sparsemans loss -8.463433505058134e-92
Epoch: 13, loss: 0.68637, train_acc: 0.51
input_torch torch.Size([750, 100])
sparsemans loss -4.808593431088358e-183
Epoch: 14, loss: 0.68468, train_acc: 0.71
input_torch torch.Size([750, 100])
sparsemans loss -0.0
Epoch: 15, loss: 0.68306, train_acc: 0.60
input_torch torch.Size([750, 100])
sparsemans loss -0.0
Epoch: 16, loss: 0.68129, train_acc: 0.61
input_torch torch.Size([750, 100])
sparsemans loss -0.0
Epoch: 17, loss: 0.67897, train_acc: 0.68
input_torch torch.Size([750, 100])
sparsemans loss -0.0
Epoch: 18, loss: 0.67566, train_acc: 0.68
input_torch torch.Size([750, 100])
sparsemans loss -0.0
Epoch: 19, loss: 0.67112, train_acc: 0.61
input_torch torch.Size([750, 100])
sparsemans loss -0.0
Epoch: 20, loss: 0.66547, train_acc: 0.65
input_torch torch.Size([750, 100])
sparsemans loss -0.0
Epoch: 21, loss: 0.65878, train_acc: 0.71
input_torch torch.Size([750, 100])
sparsemans loss -0.0
Epoch: 22, loss: 0.64943, train_acc: 0.73
input_torch torch.Size([750, 100])
sparsemans loss -0.0
Epoch: 23, loss: 0.63480, train_acc: 0.75
input_torch torch.Size([750, 100])
sparsemans loss -0.0
Epoch: 24, loss: 0.61618, train_acc: 0.81
input_torch torch.Size([750, 100])
sparsemans loss -0.0
Epoch: 25, loss: 0.58506, train_acc: 0.84
Finished Training
              precision    recall  f1-score   support

           0       0.69      0.19      0.30       132
           1       0.50      0.91      0.64       118

    accuracy                           0.53       250
   macro avg       0.60      0.55      0.47       250
weighted avg       0.60      0.53      0.46       250

weight adjusted tensor(0.5253, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 1, loss: 0.16783, train_acc: 0.51
weight adjusted tensor(0.4991, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 2, loss: 0.19409, train_acc: 0.51
weight adjusted tensor(0.4503, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 3, loss: 0.24687, train_acc: 0.51
weight adjusted tensor(0.3667, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 4, loss: 0.32545, train_acc: 0.52
weight adjusted tensor(0.2433, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 5, loss: 0.44858, train_acc: 0.52
weight adjusted tensor(0.1071, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 6, loss: 0.58538, train_acc: 0.49
weight adjusted tensor(0.0207, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 7, loss: 0.67030, train_acc: 0.51
weight adjusted tensor(0.0008, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 8, loss: 0.68865, train_acc: 0.57
weight adjusted tensor(1.0951e-06, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 9, loss: 0.68868, train_acc: 0.51
weight adjusted tensor(2.1709e-12, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 10, loss: 0.68825, train_acc: 0.51
weight adjusted tensor(8.5327e-24, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 11, loss: 0.68706, train_acc: 0.51
weight adjusted tensor(1.3183e-46, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 12, loss: 0.68490, train_acc: 0.51
weight adjusted tensor(3.1473e-92, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 13, loss: 0.68230, train_acc: 0.61
weight adjusted tensor(1.7939e-183, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 14, loss: 0.67971, train_acc: 0.74
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 15, loss: 0.67660, train_acc: 0.68
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 16, loss: 0.67164, train_acc: 0.70
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 17, loss: 0.66433, train_acc: 0.73
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 18, loss: 0.65544, train_acc: 0.75
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 19, loss: 0.64184, train_acc: 0.76
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 20, loss: 0.62036, train_acc: 0.77
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 21, loss: 0.57994, train_acc: 0.83
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 22, loss: 0.76919, train_acc: 0.51
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 23, loss: 0.86012, train_acc: 0.55
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 24, loss: 0.54825, train_acc: 0.93
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 25, loss: 0.58000, train_acc: 0.68
Finished Training
D:\Anaconda\exe\lib\site-packages\sklearn\metrics\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
D:\Anaconda\exe\lib\site-packages\sklearn\metrics\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
D:\Anaconda\exe\lib\site-packages\sklearn\metrics\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
              precision    recall  f1-score   support

           0       0.00      0.00      0.00       132
           1       0.47      1.00      0.64       118

    accuracy                           0.47       250
   macro avg       0.24      0.50      0.32       250
weighted avg       0.22      0.47      0.30       250

sparsemans loss -0.14983147441942435
word analogy weight adjusted tensor(52.5325, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 1, loss: -51.98676, train_acc: 0.49
sparsemans loss -0.14986634288541748
word analogy weight adjusted tensor(49.9053, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 2, loss: -49.35904, train_acc: 0.51
sparsemans loss -0.15018870643105686
word analogy weight adjusted tensor(45.0423, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 3, loss: -44.49876, train_acc: 0.51
sparsemans loss -0.15013900896687488
word analogy weight adjusted tensor(36.6899, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 4, loss: -36.14827, train_acc: 0.51
sparsemans loss -0.1485689440985761
word analogy weight adjusted tensor(24.3424, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 5, loss: -23.79892, train_acc: 0.55
sparsemans loss -0.14913028932998318
word analogy weight adjusted tensor(10.7141, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 6, loss: -10.17097, train_acc: 0.49
sparsemans loss -0.14955853906152553
word analogy weight adjusted tensor(2.0755, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 7, loss: -1.53360, train_acc: 0.53
sparsemans loss -0.14969744106874588
word analogy weight adjusted tensor(0.0779, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 8, loss: 0.46270, train_acc: 0.52
sparsemans loss -0.1491430629373602
word analogy weight adjusted tensor(0.0001, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 9, loss: 0.54009, train_acc: 0.52
sparsemans loss -0.14847357777127262
word analogy weight adjusted tensor(2.1741e-10, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 10, loss: 0.54024, train_acc: 0.55
sparsemans loss -0.14800733942061298
word analogy weight adjusted tensor(8.5462e-22, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 11, loss: 0.54002, train_acc: 0.51
sparsemans loss -0.14887972934252472
word analogy weight adjusted tensor(1.3205e-44, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 12, loss: 0.53795, train_acc: 0.51
sparsemans loss -0.14830583525315352
word analogy weight adjusted tensor(3.1528e-90, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 13, loss: 0.53672, train_acc: 0.55
sparsemans loss -0.1486748600953907
word analogy weight adjusted tensor(1.7972e-181, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 14, loss: 0.53413, train_acc: 0.71
sparsemans loss -0.14908405383639609
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 15, loss: 0.53122, train_acc: 0.54
sparsemans loss -0.1490821158956847
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 16, loss: 0.52822, train_acc: 0.61
sparsemans loss -0.1490999365333161
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 17, loss: 0.52400, train_acc: 0.65
sparsemans loss -0.14926646249201447
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 18, loss: 0.51775, train_acc: 0.59
sparsemans loss -0.14963853799774782
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 19, loss: 0.50961, train_acc: 0.63
sparsemans loss -0.14873546467971316
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 20, loss: 0.50020, train_acc: 0.66
sparsemans loss -0.1484530811398529
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 21, loss: 0.48412, train_acc: 0.74
sparsemans loss -0.14823188954886
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 22, loss: 0.46148, train_acc: 0.77
sparsemans loss -0.14386069675335694
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 23, loss: 0.80224, train_acc: 0.67
sparsemans loss -0.14329881361574962
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 24, loss: 0.48317, train_acc: 0.55
sparsemans loss -0.14017227967807055
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 25, loss: 0.46428, train_acc: 0.57
Finished Training
              precision    recall  f1-score   support

           0       0.58      0.88      0.70       132
           1       0.67      0.28      0.40       118

    accuracy                           0.60       250
   macro avg       0.63      0.58      0.55       250
weighted avg       0.62      0.60      0.55       250

test_pad.shape: 250
test_pad[0].shape: 100
weight adjusted tensor(22.2510, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 1, loss: 0.69403, train_acc: 0.49
weight adjusted tensor(21.1356, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 2, loss: 0.69362, train_acc: 0.51
weight adjusted tensor(19.0743, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 3, loss: 0.69236, train_acc: 0.51
weight adjusted tensor(15.5362, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 4, loss: 0.69104, train_acc: 0.56
weight adjusted tensor(10.3070, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 5, loss: 0.69054, train_acc: 0.52
weight adjusted tensor(4.5363, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 6, loss: 0.68977, train_acc: 0.55
weight adjusted tensor(0.8787, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 7, loss: 0.68827, train_acc: 0.52
weight adjusted tensor(0.0330, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 8, loss: 0.68634, train_acc: 0.55
weight adjusted tensor(4.6426e-05, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 9, loss: 0.68425, train_acc: 0.59
weight adjusted tensor(9.2052e-11, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 10, loss: 0.68157, train_acc: 0.59
weight adjusted tensor(3.6188e-22, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 11, loss: 0.67742, train_acc: 0.59
weight adjusted tensor(5.5928e-45, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 12, loss: 0.67139, train_acc: 0.63
weight adjusted tensor(1.3355e-90, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 13, loss: 0.66321, train_acc: 0.66
weight adjusted tensor(7.6131e-182, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 14, loss: 0.65056, train_acc: 0.70
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 15, loss: 0.62766, train_acc: 0.71
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 16, loss: 0.59925, train_acc: 0.65
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 17, loss: 0.65723, train_acc: 0.60
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 18, loss: 0.56990, train_acc: 0.77
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 19, loss: 0.57746, train_acc: 0.67
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 20, loss: 0.57173, train_acc: 0.64
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 21, loss: 0.55052, train_acc: 0.72
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 22, loss: 0.52718, train_acc: 0.80
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 23, loss: 0.47578, train_acc: 0.80
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 24, loss: 0.52871, train_acc: 0.80
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 25, loss: 0.52329, train_acc: 0.79
Finished Training
              precision    recall  f1-score   support

           0       0.67      0.61      0.64       132
           1       0.60      0.66      0.63       118

    accuracy                           0.64       250
   macro avg       0.64      0.64      0.64       250
weighted avg       0.64      0.64      0.64       250

input_torch torch.Size([750, 100])
sparsemans loss -2.4655158900683074
Epoch: 1, loss: -1.77133, train_acc: 0.49
input_torch torch.Size([750, 100])
sparsemans loss -2.3358616504140164
Epoch: 2, loss: -1.64195, train_acc: 0.51
input_torch torch.Size([750, 100])
sparsemans loss -2.1275156161172286
Epoch: 3, loss: -1.43510, train_acc: 0.51
input_torch torch.Size([750, 100])
sparsemans loss -1.7302784661009711
Epoch: 4, loss: -1.03929, train_acc: 0.55
input_torch torch.Size([750, 100])
sparsemans loss -1.1441050765125753
Epoch: 5, loss: -0.45360, train_acc: 0.52
input_torch torch.Size([750, 100])
sparsemans loss -0.500368309102203
Epoch: 6, loss: 0.18941, train_acc: 0.56
input_torch torch.Size([750, 100])
sparsemans loss -0.09740443562231123
Epoch: 7, loss: 0.59085, train_acc: 0.53
input_torch torch.Size([750, 100])
sparsemans loss -0.0036115408873818454
Epoch: 8, loss: 0.68268, train_acc: 0.55
input_torch torch.Size([750, 100])
sparsemans loss -4.99844162969158e-06
Epoch: 9, loss: 0.68420, train_acc: 0.58
input_torch torch.Size([750, 100])
sparsemans loss -9.825468861120388e-12
Epoch: 10, loss: 0.68155, train_acc: 0.59
input_torch torch.Size([750, 100])
sparsemans loss -3.911146473300645e-23
Epoch: 11, loss: 0.67738, train_acc: 0.59
input_torch torch.Size([750, 100])
sparsemans loss -5.9118845284152275e-46
Epoch: 12, loss: 0.67120, train_acc: 0.63
input_torch torch.Size([750, 100])
sparsemans loss -1.4002880111297834e-91
Epoch: 13, loss: 0.66266, train_acc: 0.68
input_torch torch.Size([750, 100])
sparsemans loss -7.937755106589973e-183
Epoch: 14, loss: 0.64925, train_acc: 0.72
input_torch torch.Size([750, 100])
sparsemans loss -0.0
Epoch: 15, loss: 0.62429, train_acc: 0.69
input_torch torch.Size([750, 100])
sparsemans loss -0.0
Epoch: 16, loss: 0.59548, train_acc: 0.65
input_torch torch.Size([750, 100])
sparsemans loss -0.0
Epoch: 17, loss: 0.68210, train_acc: 0.57
input_torch torch.Size([750, 100])
sparsemans loss -0.0
Epoch: 18, loss: 0.58491, train_acc: 0.75
input_torch torch.Size([750, 100])
sparsemans loss -0.0
Epoch: 19, loss: 0.57574, train_acc: 0.75
input_torch torch.Size([750, 100])
sparsemans loss -0.0
Epoch: 20, loss: 0.58793, train_acc: 0.64
input_torch torch.Size([750, 100])
sparsemans loss -0.0
Epoch: 21, loss: 0.58543, train_acc: 0.62
input_torch torch.Size([750, 100])
sparsemans loss -0.0
Epoch: 22, loss: 0.57159, train_acc: 0.66
input_torch torch.Size([750, 100])
sparsemans loss -0.0
Epoch: 23, loss: 0.55441, train_acc: 0.73
input_torch torch.Size([750, 100])
sparsemans loss -0.0
Epoch: 24, loss: 0.53506, train_acc: 0.79
input_torch torch.Size([750, 100])
sparsemans loss -0.0
Epoch: 25, loss: 0.50623, train_acc: 0.82
Finished Training
              precision    recall  f1-score   support

           0       0.70      0.33      0.45       132
           1       0.53      0.85      0.65       118

    accuracy                           0.57       250
   macro avg       0.62      0.59      0.55       250
weighted avg       0.62      0.57      0.54       250

weight adjusted tensor(0.2225, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 1, loss: 0.47189, train_acc: 0.49
weight adjusted tensor(0.2114, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 2, loss: 0.48230, train_acc: 0.51
weight adjusted tensor(0.1908, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 3, loss: 0.50173, train_acc: 0.51
weight adjusted tensor(0.1554, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 4, loss: 0.53574, train_acc: 0.55
weight adjusted tensor(0.1031, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 5, loss: 0.58752, train_acc: 0.51
weight adjusted tensor(0.0454, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 6, loss: 0.64456, train_acc: 0.54
weight adjusted tensor(0.0088, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 7, loss: 0.67971, train_acc: 0.52
weight adjusted tensor(0.0003, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 8, loss: 0.68630, train_acc: 0.52
weight adjusted tensor(4.6432e-07, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 9, loss: 0.68459, train_acc: 0.59
weight adjusted tensor(9.2057e-13, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 10, loss: 0.68201, train_acc: 0.59
weight adjusted tensor(3.6188e-24, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 11, loss: 0.67805, train_acc: 0.61
weight adjusted tensor(5.5919e-47, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 12, loss: 0.67243, train_acc: 0.64
weight adjusted tensor(1.3351e-92, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 13, loss: 0.66504, train_acc: 0.67
weight adjusted tensor(7.6110e-184, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 14, loss: 0.65391, train_acc: 0.71
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 15, loss: 0.63503, train_acc: 0.71
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 16, loss: 0.60534, train_acc: 0.67
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 17, loss: 0.60633, train_acc: 0.66
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 18, loss: 1.11190, train_acc: 0.58
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 19, loss: 0.55439, train_acc: 0.78
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 20, loss: 0.60462, train_acc: 0.67
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 21, loss: 0.62972, train_acc: 0.56
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 22, loss: 0.63969, train_acc: 0.53
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 23, loss: 0.64129, train_acc: 0.52
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 24, loss: 0.63865, train_acc: 0.52
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 25, loss: 0.63411, train_acc: 0.53
Finished Training
D:\Anaconda\exe\lib\site-packages\sklearn\metrics\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
D:\Anaconda\exe\lib\site-packages\sklearn\metrics\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
D:\Anaconda\exe\lib\site-packages\sklearn\metrics\_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
              precision    recall  f1-score   support

           0       0.53      1.00      0.69       132
           1       0.00      0.00      0.00       118

    accuracy                           0.53       250
   macro avg       0.26      0.50      0.35       250
weighted avg       0.28      0.53      0.36       250

sparsemans loss -0.2595279884282429
word analogy weight adjusted tensor(22.2510, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 1, loss: -21.81658, train_acc: 0.49
sparsemans loss -0.2604712519724882
word analogy weight adjusted tensor(21.1401, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 2, loss: -20.70604, train_acc: 0.51
sparsemans loss -0.2629593819683045
word analogy weight adjusted tensor(19.0821, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 3, loss: -18.65258, train_acc: 0.51
sparsemans loss -0.26127033980589626
word analogy weight adjusted tensor(15.5455, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 4, loss: -15.11555, train_acc: 0.55
sparsemans loss -0.2625805764119952
word analogy weight adjusted tensor(10.3149, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 5, loss: -9.88652, train_acc: 0.56
sparsemans loss -0.26222834381440335
word analogy weight adjusted tensor(4.5404, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 6, loss: -4.11243, train_acc: 0.57
sparsemans loss -0.26590816769496983
word analogy weight adjusted tensor(0.8796, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 7, loss: -0.45689, train_acc: 0.53
sparsemans loss -0.2680794540174564
word analogy weight adjusted tensor(0.0330, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 8, loss: 0.38577, train_acc: 0.56
sparsemans loss -0.2684464736338929
word analogy weight adjusted tensor(4.6483e-05, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 9, loss: 0.41661, train_acc: 0.59
sparsemans loss -0.2669651571137012
word analogy weight adjusted tensor(9.2169e-11, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 10, loss: 0.41585, train_acc: 0.58
sparsemans loss -0.26642321508852657
word analogy weight adjusted tensor(3.6235e-22, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 11, loss: 0.41285, train_acc: 0.59
sparsemans loss -0.26655978901639255
word analogy weight adjusted tensor(5.5998e-45, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 12, loss: 0.40765, train_acc: 0.63
sparsemans loss -0.26977298979176234
word analogy weight adjusted tensor(1.3372e-90, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 13, loss: 0.39783, train_acc: 0.66
sparsemans loss -0.26837674923901
word analogy weight adjusted tensor(7.6236e-182, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 14, loss: 0.38999, train_acc: 0.69
sparsemans loss -0.26668250438421764
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 15, loss: 0.37646, train_acc: 0.72
sparsemans loss -0.264911372795626
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 16, loss: 0.35437, train_acc: 0.69
sparsemans loss -0.25746809529075815
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 17, loss: 0.31362, train_acc: 0.71
sparsemans loss -0.25604556770203835
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 18, loss: 0.90772, train_acc: 0.55
sparsemans loss -0.25442544463646083
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 19, loss: 0.77999, train_acc: 0.59
sparsemans loss -0.25449582078729954
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 20, loss: 0.34699, train_acc: 0.64
sparsemans loss -0.2541476276029648
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 21, loss: 0.30700, train_acc: 0.75
sparsemans loss -0.2504735259485101
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 22, loss: 0.32603, train_acc: 0.83
sparsemans loss -0.24704101878085505
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 23, loss: 0.34141, train_acc: 0.84
sparsemans loss -0.2447011320718903
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 24, loss: 0.35188, train_acc: 0.82
sparsemans loss -0.2446523450696162
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 25, loss: 0.35703, train_acc: 0.81
Finished Training
              precision    recall  f1-score   support

           0       0.56      0.84      0.67       132
           1       0.59      0.25      0.36       118

    accuracy                           0.56       250
   macro avg       0.57      0.55      0.51       250
weighted avg       0.57      0.56      0.52       250

test_pad.shape: 250
test_pad[0].shape: 100
weight adjusted tensor(169.4571, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 1, loss: 0.69318, train_acc: 0.51
weight adjusted tensor(161.0547, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 2, loss: 0.68588, train_acc: 0.56
weight adjusted tensor(145.2880, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 3, loss: 0.67770, train_acc: 0.65
weight adjusted tensor(118.2342, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 4, loss: 0.66905, train_acc: 0.67
weight adjusted tensor(78.4390, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 5, loss: 0.65950, train_acc: 0.67
weight adjusted tensor(34.5208, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 6, loss: 0.64702, train_acc: 0.69
weight adjusted tensor(6.6929, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 7, loss: 0.63081, train_acc: 0.70
weight adjusted tensor(0.2516, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 8, loss: 0.61013, train_acc: 0.71
weight adjusted tensor(0.0004, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 9, loss: 0.58041, train_acc: 0.72
weight adjusted tensor(7.0295e-10, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 10, loss: 0.53770, train_acc: 0.74
weight adjusted tensor(2.7620e-21, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 11, loss: 0.48708, train_acc: 0.76
weight adjusted tensor(4.2696e-44, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 12, loss: 0.82162, train_acc: 0.71
weight adjusted tensor(1.0185e-89, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 13, loss: 0.53464, train_acc: 0.77
weight adjusted tensor(5.8029e-181, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 14, loss: 0.50870, train_acc: 0.77
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 15, loss: 0.48431, train_acc: 0.78
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 16, loss: 0.49033, train_acc: 0.79
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 17, loss: 0.49785, train_acc: 0.79
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 18, loss: 0.50121, train_acc: 0.79
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 19, loss: 0.49965, train_acc: 0.79
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 20, loss: 0.49307, train_acc: 0.80
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 21, loss: 0.48165, train_acc: 0.80
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 22, loss: 0.46575, train_acc: 0.82
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 23, loss: 0.44580, train_acc: 0.84
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 24, loss: 0.42188, train_acc: 0.85
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 25, loss: 0.39306, train_acc: 0.86
Finished Training
              precision    recall  f1-score   support

           0       0.74      0.76      0.75       132
           1       0.72      0.69      0.71       118

    accuracy                           0.73       250
   macro avg       0.73      0.73      0.73       250
weighted avg       0.73      0.73      0.73       250

input_torch torch.Size([750, 100])
sparsemans loss 13.065530252538132
Epoch: 1, loss: 13.75973, train_acc: 0.51
input_torch torch.Size([750, 100])
sparsemans loss 12.4126997659952
Epoch: 2, loss: 13.09942, train_acc: 0.55
input_torch torch.Size([750, 100])
sparsemans loss 11.20215574984865
Epoch: 3, loss: 11.88141, train_acc: 0.60
input_torch torch.Size([750, 100])
sparsemans loss 9.121816487985553
Epoch: 4, loss: 9.79255, train_acc: 0.66
input_torch torch.Size([750, 100])
sparsemans loss 6.051846464068944
Epoch: 5, loss: 6.71331, train_acc: 0.67
input_torch torch.Size([750, 100])
sparsemans loss 2.6642216916227217
Epoch: 6, loss: 3.31432, train_acc: 0.68
input_torch torch.Size([750, 100])
sparsemans loss 0.5161639327742736
Epoch: 7, loss: 1.15131, train_acc: 0.69
input_torch torch.Size([750, 100])
sparsemans loss 0.019365112693272315
Epoch: 8, loss: 0.63535, train_acc: 0.69
input_torch torch.Size([750, 100])
sparsemans loss 2.7269510161115974e-05
Epoch: 9, loss: 0.59084, train_acc: 0.71
input_torch torch.Size([750, 100])
sparsemans loss 5.4067637338385236e-11
Epoch: 10, loss: 0.55466, train_acc: 0.73
input_torch torch.Size([750, 100])
sparsemans loss 2.1236088476599636e-22
Epoch: 11, loss: 0.51157, train_acc: 0.73
input_torch torch.Size([750, 100])
sparsemans loss 3.281825402079662e-45
Epoch: 12, loss: 1.01747, train_acc: 0.67
input_torch torch.Size([750, 100])
sparsemans loss 7.830590506954874e-91
Epoch: 13, loss: 0.52518, train_acc: 0.75
input_torch torch.Size([750, 100])
sparsemans loss 4.463804504411936e-182
Epoch: 14, loss: 0.58873, train_acc: 0.71
input_torch torch.Size([750, 100])
sparsemans loss 0.0
Epoch: 15, loss: 0.51510, train_acc: 0.76
input_torch torch.Size([750, 100])
sparsemans loss 0.0
Epoch: 16, loss: 0.48894, train_acc: 0.78
input_torch torch.Size([750, 100])
sparsemans loss 0.0
Epoch: 17, loss: 0.48929, train_acc: 0.79
input_torch torch.Size([750, 100])
sparsemans loss 0.0
Epoch: 18, loss: 0.49535, train_acc: 0.80
input_torch torch.Size([750, 100])
sparsemans loss 0.0
Epoch: 19, loss: 0.50043, train_acc: 0.79
input_torch torch.Size([750, 100])
sparsemans loss 0.0
Epoch: 20, loss: 0.50179, train_acc: 0.78
input_torch torch.Size([750, 100])
sparsemans loss 0.0
Epoch: 21, loss: 0.49786, train_acc: 0.77
input_torch torch.Size([750, 100])
sparsemans loss 0.0
Epoch: 22, loss: 0.48801, train_acc: 0.78
input_torch torch.Size([750, 100])
sparsemans loss 0.0
Epoch: 23, loss: 0.47289, train_acc: 0.80
input_torch torch.Size([750, 100])
sparsemans loss 0.0
Epoch: 24, loss: 0.45388, train_acc: 0.82
input_torch torch.Size([750, 100])
sparsemans loss 0.0
Epoch: 25, loss: 0.43184, train_acc: 0.84
Finished Training
              precision    recall  f1-score   support

           0       0.75      0.63      0.68       132
           1       0.65      0.76      0.70       118

    accuracy                           0.69       250
   macro avg       0.70      0.70      0.69       250
weighted avg       0.70      0.69      0.69       250

weight adjusted tensor(1.6946, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 1, loss: -1.00042, train_acc: 0.49
weight adjusted tensor(1.6106, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 2, loss: -0.92457, train_acc: 0.61
weight adjusted tensor(1.4542, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 3, loss: -0.77620, train_acc: 0.63
weight adjusted tensor(1.1850, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 4, loss: -0.51585, train_acc: 0.65
weight adjusted tensor(0.7847, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 5, loss: -0.12618, train_acc: 0.67
weight adjusted tensor(0.3456, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 6, loss: 0.29942, train_acc: 0.68
weight adjusted tensor(0.0669, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 7, loss: 0.56061, train_acc: 0.69
weight adjusted tensor(0.0025, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 8, loss: 0.60126, train_acc: 0.71
weight adjusted tensor(3.5330e-06, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 9, loss: 0.57035, train_acc: 0.73
weight adjusted tensor(7.0077e-12, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 10, loss: 0.52173, train_acc: 0.73
weight adjusted tensor(2.7558e-23, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 11, loss: 0.56705, train_acc: 0.76
weight adjusted tensor(4.2559e-46, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 12, loss: 1.07510, train_acc: 0.61
weight adjusted tensor(1.0148e-91, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 13, loss: 0.54833, train_acc: 0.75
weight adjusted tensor(5.7842e-183, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 14, loss: 0.49833, train_acc: 0.75
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 15, loss: 0.57838, train_acc: 0.68
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 16, loss: 0.55365, train_acc: 0.68
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 17, loss: 0.52344, train_acc: 0.73
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 18, loss: 0.51023, train_acc: 0.77
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 19, loss: 0.50697, train_acc: 0.81
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 20, loss: 0.50765, train_acc: 0.82
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 21, loss: 0.50922, train_acc: 0.83
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 22, loss: 0.51017, train_acc: 0.83
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 23, loss: 0.50979, train_acc: 0.83
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 24, loss: 0.50774, train_acc: 0.83
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 25, loss: 0.50387, train_acc: 0.83
Finished Training
              precision    recall  f1-score   support

           0       0.68      0.74      0.71       132
           1       0.68      0.61      0.64       118

    accuracy                           0.68       250
   macro avg       0.68      0.68      0.68       250
weighted avg       0.68      0.68      0.68       250

sparsemans loss 1.3753189739513823
word analogy weight adjusted tensor(169.4571, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 1, loss: -167.38727, train_acc: 0.51
sparsemans loss 1.3755794282225497
word analogy weight adjusted tensor(161.1241, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 2, loss: -159.06099, train_acc: 0.55
sparsemans loss 1.375902725004022
word analogy weight adjusted tensor(145.5705, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 3, loss: -143.51345, train_acc: 0.60
sparsemans loss 1.3766854622229612
word analogy weight adjusted tensor(118.3594, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 4, loss: -116.30942, train_acc: 0.67
sparsemans loss 1.377321440399742
word analogy weight adjusted tensor(78.5211, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 5, loss: -76.47883, train_acc: 0.67
sparsemans loss 1.3769947592500058
word analogy weight adjusted tensor(34.5739, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 6, loss: -32.54148, train_acc: 0.67
sparsemans loss 1.376855279706052
word analogy weight adjusted tensor(6.6943, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 7, loss: -4.67458, train_acc: 0.68
sparsemans loss 1.3769335326467493
word analogy weight adjusted tensor(0.2512, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 8, loss: 1.75174, train_acc: 0.70
sparsemans loss 1.3771372469991237
word analogy weight adjusted tensor(0.0004, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 9, loss: 1.98123, train_acc: 0.71
sparsemans loss 1.376978758704161
word analogy weight adjusted tensor(7.0006e-10, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 10, loss: 1.95238, train_acc: 0.72
sparsemans loss 1.3772992823253876
word analogy weight adjusted tensor(2.7530e-21, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 11, loss: 1.90814, train_acc: 0.74
sparsemans loss 1.376656587310216
word analogy weight adjusted tensor(4.2528e-44, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 12, loss: 1.90082, train_acc: 0.75
sparsemans loss 1.3762452238169514
word analogy weight adjusted tensor(1.0144e-89, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 13, loss: 2.32153, train_acc: 0.68
sparsemans loss 1.376713172061502
word analogy weight adjusted tensor(5.7925e-181, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 14, loss: 1.88646, train_acc: 0.76
sparsemans loss 1.3759605349251305
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 15, loss: 2.04252, train_acc: 0.65
sparsemans loss 1.3762364430361949
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 16, loss: 1.99334, train_acc: 0.64
sparsemans loss 1.3757423413890504
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 17, loss: 1.91802, train_acc: 0.70
sparsemans loss 1.375769360431205
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 18, loss: 1.88144, train_acc: 0.75
sparsemans loss 1.3759961665867153
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 19, loss: 1.87109, train_acc: 0.80
sparsemans loss 1.37556531507075
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 20, loss: 1.87285, train_acc: 0.82
sparsemans loss 1.3759198345776333
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 21, loss: 1.88048, train_acc: 0.82
sparsemans loss 1.376202249358067
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 22, loss: 1.88796, train_acc: 0.80
sparsemans loss 1.3757428116502894
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 23, loss: 1.89041, train_acc: 0.79
sparsemans loss 1.376363726512466
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 24, loss: 1.88777, train_acc: 0.79
sparsemans loss 1.376312536686349
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 25, loss: 1.87972, train_acc: 0.80
Finished Training
              precision    recall  f1-score   support

           0       0.65      0.82      0.72       132
           1       0.71      0.50      0.59       118

    accuracy                           0.67       250
   macro avg       0.68      0.66      0.65       250
weighted avg       0.68      0.67      0.66       250

./embed/filtered.txt
Vectors read from: ./embed/filtered.txt
=================================================================================
Serial              Dataset       Num Pairs       Not found             Rho
=================================================================================
     1         EN-MC-30.txt              30              0         0.7767
     2     EN-MEN-TR-3k.txt            3000              0         0.7303
     3     EN-MTurk-287.txt             287              0         0.6580
     4     EN-MTurk-771.txt             771              0         0.6028
     5         EN-RG-65.txt              65              0         0.7715
     6   EN-RW-STANFORD.txt            2034             96         0.4093
     7    EN-SIMLEX-999.txt             999              0         0.3386
     8  EN-SimVerb-3500.txt            3500              2         0.2004
     9      EN-VERB-143.txt             144              0         0.3333
    10    EN-WS-353-ALL.txt             353              0         0.6570
    11    EN-WS-353-REL.txt             252              0         0.6027
    12    EN-WS-353-SIM.txt             203              0         0.7226
    13        EN-YP-130.txt             130              0         0.4274
./embed/filtered.txt
Vectors read from: ./embed/filtered.txt
test_pad.shape: 250
test_pad[0].shape: 100
weight adjusted tensor(1.8958, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 1, loss: 0.69513, train_acc: 0.49
weight adjusted tensor(1.8704, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 2, loss: 0.69593, train_acc: 0.51
weight adjusted tensor(1.6234, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 3, loss: 0.69328, train_acc: 0.51
weight adjusted tensor(1.3214, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 4, loss: 0.69152, train_acc: 0.57
weight adjusted tensor(0.8762, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 5, loss: 0.69170, train_acc: 0.52
weight adjusted tensor(0.3855, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 6, loss: 0.69145, train_acc: 0.51
weight adjusted tensor(0.0746, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 7, loss: 0.69015, train_acc: 0.54
weight adjusted tensor(0.0028, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 8, loss: 0.68859, train_acc: 0.57
weight adjusted tensor(3.9332e-06, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 9, loss: 0.68741, train_acc: 0.58
weight adjusted tensor(7.7946e-12, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 10, loss: 0.68640, train_acc: 0.51
weight adjusted tensor(3.0573e-23, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 11, loss: 0.68480, train_acc: 0.51
weight adjusted tensor(4.7217e-46, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 12, loss: 0.68228, train_acc: 0.56
weight adjusted tensor(1.1265e-91, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 13, loss: 0.67921, train_acc: 0.65
weight adjusted tensor(6.4181e-183, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 14, loss: 0.67578, train_acc: 0.67
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 15, loss: 0.67140, train_acc: 0.69
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 16, loss: 0.66506, train_acc: 0.70
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 17, loss: 0.65645, train_acc: 0.71
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 18, loss: 0.64601, train_acc: 0.74
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 19, loss: 0.62902, train_acc: 0.79
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 20, loss: 0.60631, train_acc: 0.84
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 21, loss: 0.56651, train_acc: 0.89
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 22, loss: 0.48038, train_acc: 0.89
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 23, loss: 0.72647, train_acc: 0.65
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 24, loss: 0.82807, train_acc: 0.60
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 25, loss: 0.63645, train_acc: 0.64
Finished Training
              precision    recall  f1-score   support

           0       0.53      0.82      0.64       132
           1       0.48      0.19      0.27       118

    accuracy                           0.52       250
   macro avg       0.50      0.50      0.46       250
weighted avg       0.51      0.52      0.47       250

input_torch torch.Size([750, 100])
sparsemans loss -0.41609179091997733
Epoch: 1, loss: 0.27744, train_acc: 0.49
input_torch torch.Size([750, 100])
sparsemans loss -0.06151987484739659
Epoch: 2, loss: 0.63613, train_acc: 0.51
input_torch torch.Size([750, 100])
sparsemans loss 0.14212196744803327
Epoch: 3, loss: 0.83477, train_acc: 0.51
input_torch torch.Size([750, 100])
sparsemans loss 0.121627951056889
Epoch: 4, loss: 0.81397, train_acc: 0.51
input_torch torch.Size([750, 100])
sparsemans loss 0.19524100922372067
Epoch: 5, loss: 0.88840, train_acc: 0.49
input_torch torch.Size([750, 100])
sparsemans loss 0.07196550671710393
Epoch: 6, loss: 0.76415, train_acc: 0.50
input_torch torch.Size([750, 100])
sparsemans loss 0.0107017533181278
Epoch: 7, loss: 0.70118, train_acc: 0.54
input_torch torch.Size([750, 100])
sparsemans loss 0.0002829450836817767
Epoch: 8, loss: 0.68948, train_acc: 0.58
input_torch torch.Size([750, 100])
sparsemans loss 4.61717164131757e-07
Epoch: 9, loss: 0.68853, train_acc: 0.51
input_torch torch.Size([750, 100])
sparsemans loss 8.78630142513888e-13
Epoch: 10, loss: 0.68785, train_acc: 0.51
input_torch torch.Size([750, 100])
sparsemans loss 2.9925154073571244e-24
Epoch: 11, loss: 0.68643, train_acc: 0.51
input_torch torch.Size([750, 100])
sparsemans loss 5.041709071351304e-47
Epoch: 12, loss: 0.68425, train_acc: 0.51
input_torch torch.Size([750, 100])
sparsemans loss 1.2010847421006482e-92
Epoch: 13, loss: 0.68170, train_acc: 0.67
input_torch torch.Size([750, 100])
sparsemans loss 1.3870164683943873e-184
Epoch: 14, loss: 0.67894, train_acc: 0.67
input_torch torch.Size([750, 100])
sparsemans loss -0.0
Epoch: 15, loss: 0.67560, train_acc: 0.67
input_torch torch.Size([750, 100])
sparsemans loss -0.0
Epoch: 16, loss: 0.67092, train_acc: 0.67
input_torch torch.Size([750, 100])
sparsemans loss -0.0
Epoch: 17, loss: 0.66437, train_acc: 0.68
input_torch torch.Size([750, 100])
sparsemans loss -0.0
Epoch: 18, loss: 0.65649, train_acc: 0.71
input_torch torch.Size([750, 100])
sparsemans loss -0.0
Epoch: 19, loss: 0.64545, train_acc: 0.75
input_torch torch.Size([750, 100])
sparsemans loss -0.0
Epoch: 20, loss: 0.62893, train_acc: 0.81
input_torch torch.Size([750, 100])
sparsemans loss 0.0
Epoch: 21, loss: 0.60612, train_acc: 0.88
input_torch torch.Size([750, 100])
sparsemans loss 0.0
Epoch: 22, loss: 0.57197, train_acc: 0.78
input_torch torch.Size([750, 100])
sparsemans loss 0.0
Epoch: 23, loss: 0.56623, train_acc: 0.72
input_torch torch.Size([750, 100])
sparsemans loss 0.0
Epoch: 24, loss: 1.46497, train_acc: 0.51
input_torch torch.Size([750, 100])
sparsemans loss 0.0
Epoch: 25, loss: 0.51189, train_acc: 0.88
Finished Training
              precision    recall  f1-score   support

           0       0.53      0.93      0.68       132
           1       0.53      0.08      0.15       118

    accuracy                           0.53       250
   macro avg       0.53      0.51      0.41       250
weighted avg       0.53      0.53      0.43       250

weight adjusted tensor(0.0190, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 1, loss: 0.67430, train_acc: 0.51
weight adjusted tensor(0.0180, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 2, loss: 0.67986, train_acc: 0.49
weight adjusted tensor(0.0162, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 3, loss: 0.67604, train_acc: 0.52
weight adjusted tensor(0.0132, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 4, loss: 0.67898, train_acc: 0.51
weight adjusted tensor(0.0088, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 5, loss: 0.68429, train_acc: 0.51
weight adjusted tensor(0.0039, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 6, loss: 0.68795, train_acc: 0.51
weight adjusted tensor(0.0007, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 7, loss: 0.68931, train_acc: 0.51
weight adjusted tensor(2.7966e-05, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 8, loss: 0.68890, train_acc: 0.57
weight adjusted tensor(3.9378e-08, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 9, loss: 0.68834, train_acc: 0.58
weight adjusted tensor(8.1145e-14, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 10, loss: 0.68758, train_acc: 0.57
weight adjusted tensor(3.1855e-25, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 11, loss: 0.68610, train_acc: 0.57
weight adjusted tensor(4.9207e-48, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 12, loss: 0.68388, train_acc: 0.62
weight adjusted tensor(1.1749e-93, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 13, loss: 0.68120, train_acc: 0.63
weight adjusted tensor(6.6973e-185, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 14, loss: 0.67835, train_acc: 0.63
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 15, loss: 0.67518, train_acc: 0.65
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 16, loss: 0.67070, train_acc: 0.67
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 17, loss: 0.66396, train_acc: 0.72
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 18, loss: 0.65522, train_acc: 0.73
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 19, loss: 0.64414, train_acc: 0.73
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 20, loss: 0.62676, train_acc: 0.77
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 21, loss: 0.60039, train_acc: 0.80
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 22, loss: 0.55424, train_acc: 0.88
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 23, loss: 0.53364, train_acc: 0.77
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 24, loss: 1.18066, train_acc: 0.51
weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 25, loss: 0.73121, train_acc: 0.52
Finished Training
              precision    recall  f1-score   support

           0       1.00      0.02      0.04       132
           1       0.48      1.00      0.65       118

    accuracy                           0.48       250
   macro avg       0.74      0.51      0.35       250
weighted avg       0.75      0.48      0.33       250

sparsemans loss -0.0437991358863134
word analogy weight adjusted tensor(1.8958, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 1, loss: -1.24662, train_acc: 0.53
sparsemans loss -0.0936743099212077
word analogy weight adjusted tensor(1.8017, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 2, loss: -1.19666, train_acc: 0.51
sparsemans loss 0.018935427779999767
word analogy weight adjusted tensor(1.6265, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 3, loss: -0.91527, train_acc: 0.51
sparsemans loss 0.017059124111749906
word analogy weight adjusted tensor(1.3252, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 4, loss: -0.61571, train_acc: 0.52
sparsemans loss 0.013851015063553473
word analogy weight adjusted tensor(0.8795, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 5, loss: -0.17215, train_acc: 0.50
sparsemans loss 0.01818017786467221
word analogy weight adjusted tensor(0.3872, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 6, loss: 0.32307, train_acc: 0.52
sparsemans loss 0.02794466474081432
word analogy weight adjusted tensor(0.0750, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 7, loss: 0.64305, train_acc: 0.55
sparsemans loss 0.027199498656479373
word analogy weight adjusted tensor(0.0028, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 8, loss: 0.71329, train_acc: 0.56
sparsemans loss 0.027065797557059623
word analogy weight adjusted tensor(3.9654e-06, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 9, loss: 0.71541, train_acc: 0.51
sparsemans loss 0.03309012217807976
word analogy weight adjusted tensor(7.8648e-12, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 10, loss: 0.72085, train_acc: 0.51
sparsemans loss 0.025852116514373878
word analogy weight adjusted tensor(3.0867e-23, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 11, loss: 0.71237, train_acc: 0.51
sparsemans loss 0.030092690568774092
word analogy weight adjusted tensor(4.7620e-46, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 12, loss: 0.71463, train_acc: 0.51
sparsemans loss 0.013291457333671151
word analogy weight adjusted tensor(1.1375e-91, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 13, loss: 0.69539, train_acc: 0.61
sparsemans loss 0.015322614112753818
word analogy weight adjusted tensor(6.4869e-183, dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 14, loss: 0.69474, train_acc: 0.64
sparsemans loss 0.018588544911907652
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 15, loss: 0.69497, train_acc: 0.67
sparsemans loss 0.01676255447724227
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 16, loss: 0.68916, train_acc: 0.67
sparsemans loss 0.023004532451549105
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 17, loss: 0.68975, train_acc: 0.66
sparsemans loss 0.024435830125336247
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 18, loss: 0.68382, train_acc: 0.65
sparsemans loss 0.035166341829254696
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 19, loss: 0.68554, train_acc: 0.66
sparsemans loss 0.03970395932087419
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 20, loss: 0.67559, train_acc: 0.74
sparsemans loss -0.015369287570042429
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 21, loss: 0.60128, train_acc: 0.83
sparsemans loss 0.006506380203280304
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 22, loss: 0.59214, train_acc: 0.90
sparsemans loss -0.004641549737666448
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 23, loss: 0.52059, train_acc: 0.91
sparsemans loss 0.0019180040913697111
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 24, loss: 1.19929, train_acc: 0.50
sparsemans loss 0.008091248269882214
word analogy weight adjusted tensor(0., dtype=torch.float64, grad_fn=<MulBackward0>)
Epoch: 25, loss: 0.94185, train_acc: 0.70
Finished Training
              precision    recall  f1-score   support

           0       0.51      0.62      0.56       132
           1       0.44      0.34      0.38       118

    accuracy                           0.49       250
   macro avg       0.48      0.48      0.47       250
weighted avg       0.48      0.49      0.48       250


Process finished with exit code 0