basicAviationModelLogs.txt

[maroofr@gl1009 ~]$ source env/bin/activate
(env) [maroofr@gl1009 ~]$ cd eecs487/
(env) [maroofr@gl1009 eecs487]$ mn /home/maroofr/Downloads/aviationPermsWithId.csv .
-bash: mn: command not found
(env) [maroofr@gl1009 eecs487]$ mv /home/maroofr/Downloads/aviationPermsWithId.csv .
(env) [maroofr@gl1009 eecs487]$ python3 trainLA.py aviationPermsWithId.csv 
READING DATA
The preflight inspection of the fuel tanks by the pilot revealed the tanks were filled to the bottom of the filler neck, which the pilot believed was appropriately full. After starting the engine, fuel began leaking from the fuel sump drain hose. The pilot operated the spring loaded valve handle inside the airplane which apparently stopped the leak. Shortly after departing, the engine began running rough upon which the pilot began a deviation to another airport. During this time, the engine began running smoothly whereupon the pilot continued to his initial destination. During descent, the engine began running rough and the pilot decided the tanks had been exhausted. The pilot performed a forced landing causing substantial damage. The airplane cruising endurance based on 75% power and 90 gallons of fuel on board is 5.4 hours. However, the usable fuel capacity on this airplane when the fuel tanks are filled to the bottom of the filler necks is approximately 80 gallons. The actual flight time of this aircraft was 5.55 hours. A Federal Aviation Administration inspector who examined the airplane noted only residual fuel was found in the selector valve and no leaks were found in the fuel system.
1
DOWNLOADING GloVe
cuda
MAKING DATASETS
^CTraceback (most recent call last):
  File "trainLA.py", line 93, in <module>
    main()
  File "trainLA.py", line 37, in main
    train_data = WindowedParDataset(X_train, y_train, embed, wsize)
  File "/home/maroofr/eecs487/coherenceModelNews.py", line 60, in __init__
    self.windows.append(tensor_of_tupled_par_embed(sentences[i:i+window_size]))
  File "/home/maroofr/eecs487/coherenceModelNews.py", line 42, in tensor_of_tupled_par_embed
    return [torch.FloatTensor(listify_sentence_embedding(sentence)) for sentence in par_embed]
  File "/home/maroofr/eecs487/coherenceModelNews.py", line 42, in <listcomp>
    return [torch.FloatTensor(listify_sentence_embedding(sentence)) for sentence in par_embed]
KeyboardInterrupt
(env) [maroofr@gl1009 eecs487]$ nano trainLA.py 
(env) [maroofr@gl1009 eecs487]$ cp trainLA.py trainAv.py
(env) [maroofr@gl1009 eecs487]$ ls
aviationPerms.csv        best_rnn_wsj.pt        LATimesWashPostPerms.csv  pipeUtil.py             trainAv.py                     wsjPerms.csv
aviationPermsWithId.csv  coherenceModelNews.py  moreAviationPerms.csv     __pycache__             trainCoherenceModel.ipynb
best_rnn_latimes.pt      coherenceModel.py      permuteParagraphs.ipynb   README.md               trainCoherenceModelNews.ipynb
best_rnn.pt              generateWSJ.ipynb      permuteParsWithId.ipynb   scoreRedditPosts.ipynb  trainLA.py
(env) [maroofr@gl1009 eecs487]$ nano trainAv.py 
(env) [maroofr@gl1009 eecs487]$ python3 trainAv.py aviationPermsWithId.csv 
READING DATA
The preflight inspection of the fuel tanks by the pilot revealed the tanks were filled to the bottom of the filler neck, which the pilot believed was appropriately full. After starting the engine, fuel began leaking from the fuel sump drain hose. The pilot operated the spring loaded valve handle inside the airplane which apparently stopped the leak. Shortly after departing, the engine began running rough upon which the pilot began a deviation to another airport. During this time, the engine began running smoothly whereupon the pilot continued to his initial destination. During descent, the engine began running rough and the pilot decided the tanks had been exhausted. The pilot performed a forced landing causing substantial damage. The airplane cruising endurance based on 75% power and 90 gallons of fuel on board is 5.4 hours. However, the usable fuel capacity on this airplane when the fuel tanks are filled to the bottom of the filler necks is approximately 80 gallons. The actual flight time of this aircraft was 5.55 hours. A Federal Aviation Administration inspector who examined the airplane noted only residual fuel was found in the selector valve and no leaks were found in the fuel system.
1
DOWNLOADING GloVe
^CTraceback (most recent call last):
  File "trainAv.py", line 93, in <module>
    main()
  File "trainAv.py", line 27, in main
    embed = gensim.downloader.load("glove-wiki-gigaword-100")
  File "/home/maroofr/env/lib64/python3.6/site-packages/gensim/downloader.py", line 503, in load
    return module.load_data()
  File "/home/maroofr/gensim-data/glove-wiki-gigaword-100/__init__.py", line 8, in load_data
    model = KeyedVectors.load_word2vec_format(path)
  File "/home/maroofr/env/lib64/python3.6/site-packages/gensim/models/keyedvectors.py", line 1725, in load_word2vec_format
    limit=limit, datatype=datatype, no_header=no_header,
  File "/home/maroofr/env/lib64/python3.6/site-packages/gensim/models/keyedvectors.py", line 2073, in _load_word2vec_format
    _word2vec_read_text(fin, kv, counts, vocab_size, vector_size, datatype, unicode_errors, encoding)
  File "/home/maroofr/env/lib64/python3.6/site-packages/gensim/models/keyedvectors.py", line 1979, in _word2vec_read_text
    _add_word_to_kv(kv, counts, word, weights, vocab_size)
KeyboardInterrupt
(env) [maroofr@gl1009 eecs487]$ nano trainAv.py 
(env) [maroofr@gl1009 eecs487]$ nano coherenceModel.py 
(env) [maroofr@gl1009 eecs487]$ nano trainAv.py 
(env) [maroofr@gl1009 eecs487]$ python3 trainAv.py aviationPermsWithId.csv 
READING DATA
The preflight inspection of the fuel tanks by the pilot revealed the tanks were filled to the bottom of the filler neck, which the pilot believed was appropriately full. After starting the engine, fuel began leaking from the fuel sump drain hose. The pilot operated the spring loaded valve handle inside the airplane which apparently stopped the leak. Shortly after departing, the engine began running rough upon which the pilot began a deviation to another airport. During this time, the engine began running smoothly whereupon the pilot continued to his initial destination. During descent, the engine began running rough and the pilot decided the tanks had been exhausted. The pilot performed a forced landing causing substantial damage. The airplane cruising endurance based on 75% power and 90 gallons of fuel on board is 5.4 hours. However, the usable fuel capacity on this airplane when the fuel tanks are filled to the bottom of the filler necks is approximately 80 gallons. The actual flight time of this aircraft was 5.55 hours. A Federal Aviation Administration inspector who examined the airplane noted only residual fuel was found in the selector valve and no leaks were found in the fuel system.
1
DOWNLOADING GloVe
cuda
MAKING DATASETS
Number of coherent windows: 9787
Number of incoherent windows: 199749
Number of coherent windows: 2510
Number of incoherent windows: 50180
Number of coherent windows: 1393
Number of incoherent windows: 27943
BEGINNING TO TRAIN MODEL
learning rate from: [0.01]
weight_decay from: [0.0002, 0.002, 0.005, 0.01]
window from: [5]
  0%|                                                                                                                             | 0/4 [00:00<?, ?it/s]
Traceback (most recent call last):
  File "trainAv.py", line 93, in <module>
    main()
  File "trainAv.py", line 76, in main
    basic_model = search_param_utterance(wsize)
  File "trainAv.py", line 58, in search_param_utterance
    net = FFNN(window_size, True, device).to(device)
TypeError: __init__() takes 3 positional arguments but 4 were given
(env) [maroofr@gl1009 eecs487]$ nano trainAv.py 
(env) [maroofr@gl1009 eecs487]$ python3 trainAv.py aviationPermsWithId.csv 
READING DATA
The preflight inspection of the fuel tanks by the pilot revealed the tanks were filled to the bottom of the filler neck, which the pilot believed was appropriately full. After starting the engine, fuel began leaking from the fuel sump drain hose. The pilot operated the spring loaded valve handle inside the airplane which apparently stopped the leak. Shortly after departing, the engine began running rough upon which the pilot began a deviation to another airport. During this time, the engine began running smoothly whereupon the pilot continued to his initial destination. During descent, the engine began running rough and the pilot decided the tanks had been exhausted. The pilot performed a forced landing causing substantial damage. The airplane cruising endurance based on 75% power and 90 gallons of fuel on board is 5.4 hours. However, the usable fuel capacity on this airplane when the fuel tanks are filled to the bottom of the filler necks is approximately 80 gallons. The actual flight time of this aircraft was 5.55 hours. A Federal Aviation Administration inspector who examined the airplane noted only residual fuel was found in the selector valve and no leaks were found in the fuel system.
1
DOWNLOADING GloVe
[==================================================] 100.0% 66.0/66.0MB downloaded
cuda
MAKING DATASETS
Number of coherent windows: 9787
Number of incoherent windows: 199749
Number of coherent windows: 2510
Number of incoherent windows: 50180
Number of coherent windows: 1393
Number of incoherent windows: 27943
BEGINNING TO TRAIN MODEL
learning rate from: [0.01]
weight_decay from: [0.0002, 0.002, 0.005, 0.01]
window from: [5]
  0%|                                                                                                                             | 0/4 [00:00<?, ?it/s]------------------------ Start Training ------------------------
/home/maroofr/env/lib64/python3.6/site-packages/torch/nn/functional.py:1795: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.
  warnings.warn("nn.functional.tanh is deprecated. Use torch.tanh instead.")
Epoch No. 1--Iteration No. 8382-- batch loss = 0.6007
Validation UAR: 0.6710
Validation accuracy: 0.5719
Validation loss: 1.1627
/home/maroofr/env/lib64/python3.6/site-packages/torch/nn/functional.py:1795: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.
  warnings.warn("nn.functional.tanh is deprecated. Use torch.tanh instead.")
Epoch No. 2--Iteration No. 16764-- batch loss = 0.9991
Validation UAR: 0.7010
Validation accuracy: 0.6370
Validation loss: 1.1004
/home/maroofr/env/lib64/python3.6/site-packages/torch/nn/functional.py:1795: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.
  warnings.warn("nn.functional.tanh is deprecated. Use torch.tanh instead.")
Epoch No. 3--Iteration No. 25146-- batch loss = 0.6074
Validation UAR: 0.7076
Validation accuracy: 0.6752
Validation loss: 1.0824
/home/maroofr/env/lib64/python3.6/site-packages/torch/nn/functional.py:1795: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.
  warnings.warn("nn.functional.tanh is deprecated. Use torch.tanh instead.")
  0%|                                                                                                                             | 0/4 [06:05<?, ?it/s]
Traceback (most recent call last):
  File "trainAv.py", line 93, in <module>
    main()
  File "trainAv.py", line 76, in main
    basic_model = search_param_utterance(wsize)
  File "trainAv.py", line 62, in search_param_utterance
    verbose=True, patience=5, stopping_criteria='accuracy')
  File "/home/maroofr/eecs487/coherenceModel.py", line 209, in train_model
    windows = [[s.to(device) for s in window] for window in windows]
  File "/home/maroofr/eecs487/coherenceModel.py", line 209, in <listcomp>
    windows = [[s.to(device) for s in window] for window in windows]
  File "/home/maroofr/eecs487/coherenceModel.py", line 209, in <listcomp>
    windows = [[s.to(device) for s in window] for window in windows]
KeyboardInterrupt
(env) [maroofr@gl1009 eecs487]$ nano coherenceModel.py 
(env) [maroofr@gl1009 eecs487]$ python3 trainAv.py aviationPermsWithId.csv 
READING DATA
The preflight inspection of the fuel tanks by the pilot revealed the tanks were filled to the bottom of the filler neck, which the pilot believed was appropriately full. After starting the engine, fuel began leaking from the fuel sump drain hose. The pilot operated the spring loaded valve handle inside the airplane which apparently stopped the leak. Shortly after departing, the engine began running rough upon which the pilot began a deviation to another airport. During this time, the engine began running smoothly whereupon the pilot continued to his initial destination. During descent, the engine began running rough and the pilot decided the tanks had been exhausted. The pilot performed a forced landing causing substantial damage. The airplane cruising endurance based on 75% power and 90 gallons of fuel on board is 5.4 hours. However, the usable fuel capacity on this airplane when the fuel tanks are filled to the bottom of the filler necks is approximately 80 gallons. The actual flight time of this aircraft was 5.55 hours. A Federal Aviation Administration inspector who examined the airplane noted only residual fuel was found in the selector valve and no leaks were found in the fuel system.
1
DOWNLOADING GloVe
cuda
MAKING DATASETS
Number of coherent windows: 9787
Number of incoherent windows: 199749
Number of coherent windows: 2510
Number of incoherent windows: 50180
Number of coherent windows: 1393
Number of incoherent windows: 27943
BEGINNING TO TRAIN MODEL
learning rate from: [0.01]
weight_decay from: [0.0002, 0.002, 0.005, 0.01]
window from: [5]
  0%|                                                                                                                             | 0/4 [00:00<?, ?it/s]------------------------ Start Training ------------------------
Epoch No. 1--Iteration No. 8382-- batch loss = 1.2400
Validation UAR: 0.6824
Validation accuracy: 0.5822
Validation loss: 1.1446
Epoch No. 2--Iteration No. 16764-- batch loss = 0.3325
Validation UAR: 0.7041
Validation accuracy: 0.6602
Validation loss: 1.0927
Epoch No. 3--Iteration No. 25146-- batch loss = 0.6566
Validation UAR: 0.7187
Validation accuracy: 0.6604
Validation loss: 1.0607
Epoch No. 4--Iteration No. 33528-- batch loss = 0.6189
Validation UAR: 0.7248
Validation accuracy: 0.7163
Validation loss: 1.0309
Epoch No. 5--Iteration No. 41910-- batch loss = 0.6380
Validation UAR: 0.7384
Validation accuracy: 0.7183
Validation loss: 1.0044
Epoch No. 6--Iteration No. 50292-- batch loss = 1.9953
Validation UAR: 0.7441
Validation accuracy: 0.7526
Validation loss: 0.9916
Epoch No. 7--Iteration No. 58674-- batch loss = 0.4426
Validation UAR: 0.7440
Validation accuracy: 0.7017
Validation loss: 0.9789
Epoch No. 8--Iteration No. 67056-- batch loss = 0.2282
Validation UAR: 0.7531
Validation accuracy: 0.7301
Validation loss: 0.9586
Epoch No. 9--Iteration No. 75438-- batch loss = 0.4071
Validation UAR: 0.7586
Validation accuracy: 0.7319
Validation loss: 0.9452
Epoch No. 10--Iteration No. 83820-- batch loss = 2.3535
Validation UAR: 0.7579
Validation accuracy: 0.7202
Validation loss: 0.9433
Epoch No. 11--Iteration No. 92202-- batch loss = 0.5055
Validation UAR: 0.7563
Validation accuracy: 0.7269
Validation loss: 0.9420
Epoch No. 12--Iteration No. 100584-- batch loss = 0.2332
Validation UAR: 0.7667
Validation accuracy: 0.7398
Validation loss: 0.9234
Epoch No. 13--Iteration No. 108966-- batch loss = 2.7591
Validation UAR: 0.7680
Validation accuracy: 0.7553
Validation loss: 0.9129
Epoch No. 14--Iteration No. 117348-- batch loss = 1.5740
Validation UAR: 0.7680
Validation accuracy: 0.7487
Validation loss: 0.9110
Epoch No. 15--Iteration No. 125730-- batch loss = 0.2523
Validation UAR: 0.7581
Validation accuracy: 0.7685
Validation loss: 0.9376
Epoch No. 16--Iteration No. 134112-- batch loss = 0.4449
Validation UAR: 0.7669
Validation accuracy: 0.7629
Validation loss: 0.9101
Epoch No. 17--Iteration No. 142494-- batch loss = 0.6171
Validation UAR: 0.7700
Validation accuracy: 0.7727
Validation loss: 0.9115
Epoch No. 18--Iteration No. 150876-- batch loss = 0.4775
Validation UAR: 0.7639
Validation accuracy: 0.7955
Validation loss: 0.9228
Epoch No. 19--Iteration No. 159258-- batch loss = 1.4274
Validation UAR: 0.7670
Validation accuracy: 0.7767
Validation loss: 0.9195
Epoch No. 20--Iteration No. 167640-- batch loss = 0.4091
Validation UAR: 0.7715
Validation accuracy: 0.7414
Validation loss: 0.9002
Epoch No. 21--Iteration No. 176022-- batch loss = 0.3784
Validation UAR: 0.7757
Validation accuracy: 0.7534
Validation loss: 0.9088
Epoch No. 22--Iteration No. 184404-- batch loss = 0.2576
Validation UAR: 0.7709
Validation accuracy: 0.7680
Validation loss: 0.9213
Epoch No. 23--Iteration No. 192786-- batch loss = 0.1231
Validation UAR: 0.7718
Validation accuracy: 0.7878
Validation loss: 0.9232
Epoch No. 24--Iteration No. 201168-- batch loss = 0.3353
Validation UAR: 0.7786
Validation accuracy: 0.7690
Validation loss: 0.9085
Epoch No. 25--Iteration No. 209550-- batch loss = 0.1353
Validation UAR: 0.7742
Validation accuracy: 0.7861
Validation loss: 0.9228
Epoch No. 26--Iteration No. 217932-- batch loss = 0.7265
Validation UAR: 0.7739
Validation accuracy: 0.7651
Validation loss: 0.9189
Epoch No. 27--Iteration No. 226314-- batch loss = 0.4061
Validation UAR: 0.7736
Validation accuracy: 0.7926
Validation loss: 0.9265
Epoch No. 28--Iteration No. 234696-- batch loss = 0.8490
Validation UAR: 0.7678
Validation accuracy: 0.7780
Validation loss: 0.9455
Epoch No. 29--Iteration No. 243078-- batch loss = 0.8699
Validation UAR: 0.7683
Validation accuracy: 0.7601
Validation loss: 0.9378
Training lasted 56.05 minutes
------------------------ Training Done ------------------------
(0.01, 0.0002, 5): 0.7786091584240955
 25%|████████████████████████████▎                                                                                    | 1/4 [56:03<2:48:09, 3363.19s/it]------------------------ Start Training ------------------------
Epoch No. 1--Iteration No. 8382-- batch loss = 0.5803
Validation UAR: 0.6474
Validation accuracy: 0.6556
Validation loss: 1.2067
Epoch No. 2--Iteration No. 16764-- batch loss = 0.4688
Validation UAR: 0.6856
Validation accuracy: 0.6297
Validation loss: 1.1370
Epoch No. 3--Iteration No. 25146-- batch loss = 0.3876
Validation UAR: 0.6927
Validation accuracy: 0.6688
Validation loss: 1.1187
Epoch No. 4--Iteration No. 33528-- batch loss = 0.5275
Validation UAR: 0.7068
Validation accuracy: 0.6592
Validation loss: 1.0913
Epoch No. 5--Iteration No. 41910-- batch loss = 0.6136
Validation UAR: 0.7114
Validation accuracy: 0.6763
Validation loss: 1.0774
Epoch No. 6--Iteration No. 50292-- batch loss = 0.2794
Validation UAR: 0.7193
Validation accuracy: 0.7206
Validation loss: 1.0551
Epoch No. 7--Iteration No. 58674-- batch loss = 0.3062
Validation UAR: 0.7245
Validation accuracy: 0.7044
Validation loss: 1.0383
Epoch No. 8--Iteration No. 67056-- batch loss = 0.4270
Validation UAR: 0.7273
Validation accuracy: 0.7281
Validation loss: 1.0279
Epoch No. 9--Iteration No. 75438-- batch loss = 0.9104
Validation UAR: 0.7322
Validation accuracy: 0.6903
Validation loss: 1.0168
Epoch No. 10--Iteration No. 83820-- batch loss = 0.6058
Validation UAR: 0.7356
Validation accuracy: 0.6762
Validation loss: 1.0075
Epoch No. 11--Iteration No. 92202-- batch loss = 1.1422
Validation UAR: 0.7422
Validation accuracy: 0.7180
Validation loss: 0.9943
Epoch No. 12--Iteration No. 100584-- batch loss = 0.4001
Validation UAR: 0.7431
Validation accuracy: 0.7312
Validation loss: 0.9839
Epoch No. 13--Iteration No. 108966-- batch loss = 1.1870
Validation UAR: 0.7446
Validation accuracy: 0.6830
Validation loss: 0.9810
Epoch No. 14--Iteration No. 117348-- batch loss = 1.0612
Validation UAR: 0.7448
Validation accuracy: 0.7272
Validation loss: 0.9784
Epoch No. 15--Iteration No. 125730-- batch loss = 1.2669
Validation UAR: 0.7444
Validation accuracy: 0.7057
Validation loss: 0.9765
Epoch No. 16--Iteration No. 134112-- batch loss = 1.1710
Validation UAR: 0.7401
Validation accuracy: 0.7053
Validation loss: 0.9858
Epoch No. 17--Iteration No. 142494-- batch loss = 0.7765
Validation UAR: 0.7507
Validation accuracy: 0.6903
Validation loss: 0.9641
Epoch No. 18--Iteration No. 150876-- batch loss = 0.4923
Validation UAR: 0.7546
Validation accuracy: 0.7164
Validation loss: 0.9590
Epoch No. 19--Iteration No. 159258-- batch loss = 0.3049
Validation UAR: 0.7478
Validation accuracy: 0.7232
Validation loss: 0.9685
Epoch No. 20--Iteration No. 167640-- batch loss = 0.6922
Validation UAR: 0.7488
Validation accuracy: 0.7241
Validation loss: 0.9614
Epoch No. 21--Iteration No. 176022-- batch loss = 0.5582
Validation UAR: 0.7550
Validation accuracy: 0.7501
Validation loss: 0.9429
Epoch No. 22--Iteration No. 184404-- batch loss = 0.4026
Validation UAR: 0.7546
Validation accuracy: 0.7432
Validation loss: 0.9411
Epoch No. 23--Iteration No. 192786-- batch loss = 0.6150
Validation UAR: 0.7557
Validation accuracy: 0.7109
Validation loss: 0.9429
Epoch No. 24--Iteration No. 201168-- batch loss = 0.4985
Validation UAR: 0.7559
Validation accuracy: 0.7164
Validation loss: 0.9434
Epoch No. 25--Iteration No. 209550-- batch loss = 0.7034
Validation UAR: 0.7581
Validation accuracy: 0.7626
Validation loss: 0.9322
Epoch No. 26--Iteration No. 217932-- batch loss = 0.5854
Validation UAR: 0.7570
Validation accuracy: 0.7587
Validation loss: 0.9338
Epoch No. 27--Iteration No. 226314-- batch loss = 0.3011
Validation UAR: 0.7625
Validation accuracy: 0.6958
Validation loss: 0.9327
Epoch No. 28--Iteration No. 234696-- batch loss = 0.2809
Validation UAR: 0.7564
Validation accuracy: 0.7454
Validation loss: 0.9309
Epoch No. 29--Iteration No. 243078-- batch loss = 0.5302
Validation UAR: 0.7551
Validation accuracy: 0.7867
Validation loss: 0.9382
Epoch No. 30--Iteration No. 251460-- batch loss = 1.2223
Validation UAR: 0.7579
Validation accuracy: 0.7454
Validation loss: 0.9278
Epoch No. 31--Iteration No. 259842-- batch loss = 0.3759
Validation UAR: 0.7615
Validation accuracy: 0.7418
Validation loss: 0.9290
Epoch No. 32--Iteration No. 268224-- batch loss = 0.7266
Validation UAR: 0.7579
Validation accuracy: 0.7181
Validation loss: 0.9363
Training lasted 62.10 minutes
------------------------ Training Done ------------------------
(0.01, 0.002, 5): 0.7625276494659068
 50%|███████████████████████████████████████████████████████▌                                                       | 2/4 [1:58:09<1:59:13, 3576.77s/it]------------------------ Start Training ------------------------
Epoch No. 1--Iteration No. 8382-- batch loss = 0.5275
Validation UAR: 0.5976
Validation accuracy: 0.6411
Validation loss: 1.2613
Epoch No. 2--Iteration No. 16764-- batch loss = 0.5560
Validation UAR: 0.6123
Validation accuracy: 0.6331
Validation loss: 1.2469
Epoch No. 3--Iteration No. 25146-- batch loss = 1.5608
Validation UAR: 0.6181
Validation accuracy: 0.5956
Validation loss: 1.2359
Epoch No. 4--Iteration No. 33528-- batch loss = 1.6066
Validation UAR: 0.6253
Validation accuracy: 0.6417
Validation loss: 1.2280
Epoch No. 5--Iteration No. 41910-- batch loss = 1.7172
Validation UAR: 0.6514
Validation accuracy: 0.6081
Validation loss: 1.2010
Epoch No. 6--Iteration No. 50292-- batch loss = 0.6090
Validation UAR: 0.6534
Validation accuracy: 0.6537
Validation loss: 1.1942
Epoch No. 7--Iteration No. 58674-- batch loss = 0.6051
Validation UAR: 0.6631
Validation accuracy: 0.5591
Validation loss: 1.1806
Epoch No. 8--Iteration No. 67056-- batch loss = 2.2927
Validation UAR: 0.6681
Validation accuracy: 0.6674
Validation loss: 1.1746
Epoch No. 9--Iteration No. 75438-- batch loss = 0.4288
Validation UAR: 0.6827
Validation accuracy: 0.6520
Validation loss: 1.1536
Epoch No. 10--Iteration No. 83820-- batch loss = 0.7202
Validation UAR: 0.6835
Validation accuracy: 0.6249
Validation loss: 1.1482
Epoch No. 11--Iteration No. 92202-- batch loss = 0.7329
Validation UAR: 0.6781
Validation accuracy: 0.5808
Validation loss: 1.1525
Epoch No. 12--Iteration No. 100584-- batch loss = 0.5163
Validation UAR: 0.6830
Validation accuracy: 0.6619
Validation loss: 1.1401
Epoch No. 13--Iteration No. 108966-- batch loss = 1.4598
Validation UAR: 0.6990
Validation accuracy: 0.6462
Validation loss: 1.1179
Epoch No. 14--Iteration No. 117348-- batch loss = 0.5109
Validation UAR: 0.6963
Validation accuracy: 0.6671
Validation loss: 1.1107
Epoch No. 15--Iteration No. 125730-- batch loss = 0.5909
Validation UAR: 0.7019
Validation accuracy: 0.6752
Validation loss: 1.1034
Epoch No. 16--Iteration No. 134112-- batch loss = 0.5049
Validation UAR: 0.7079
Validation accuracy: 0.6866
Validation loss: 1.0933
Epoch No. 17--Iteration No. 142494-- batch loss = 0.5309
Validation UAR: 0.7074
Validation accuracy: 0.6910
Validation loss: 1.0896
Epoch No. 18--Iteration No. 150876-- batch loss = 1.2805
Validation UAR: 0.7114
Validation accuracy: 0.6879
Validation loss: 1.0842
Epoch No. 19--Iteration No. 159258-- batch loss = 0.6193
Validation UAR: 0.7108
Validation accuracy: 0.6971
Validation loss: 1.0800
Epoch No. 20--Iteration No. 167640-- batch loss = 0.6963
Validation UAR: 0.7087
Validation accuracy: 0.6812
Validation loss: 1.0812
Epoch No. 21--Iteration No. 176022-- batch loss = 0.6607
Validation UAR: 0.7112
Validation accuracy: 0.7188
Validation loss: 1.0716
Epoch No. 22--Iteration No. 184404-- batch loss = 3.1011
Validation UAR: 0.7164
Validation accuracy: 0.6642
Validation loss: 1.0671
Epoch No. 23--Iteration No. 192786-- batch loss = 1.5874
Validation UAR: 0.7188
Validation accuracy: 0.6630
Validation loss: 1.0614
Epoch No. 24--Iteration No. 201168-- batch loss = 0.8527
Validation UAR: 0.7119
Validation accuracy: 0.6316
Validation loss: 1.0735
Epoch No. 25--Iteration No. 209550-- batch loss = 1.6967
Validation UAR: 0.7176
Validation accuracy: 0.6773
Validation loss: 1.0585
Epoch No. 26--Iteration No. 217932-- batch loss = 1.4080
Validation UAR: 0.7226
Validation accuracy: 0.6699
Validation loss: 1.0494
Epoch No. 27--Iteration No. 226314-- batch loss = 0.6882
Validation UAR: 0.7282
Validation accuracy: 0.6899
Validation loss: 1.0421
Epoch No. 28--Iteration No. 234696-- batch loss = 1.0768
Validation UAR: 0.7287
Validation accuracy: 0.6765
Validation loss: 1.0413
Epoch No. 29--Iteration No. 243078-- batch loss = 1.1031
Validation UAR: 0.7237
Validation accuracy: 0.7033
Validation loss: 1.0428
Epoch No. 30--Iteration No. 251460-- batch loss = 0.4314
Validation UAR: 0.7185
Validation accuracy: 0.6928
Validation loss: 1.0510
Epoch No. 31--Iteration No. 259842-- batch loss = 0.3654
Validation UAR: 0.7282
Validation accuracy: 0.7079
Validation loss: 1.0348
Epoch No. 32--Iteration No. 268224-- batch loss = 1.0823
Validation UAR: 0.7277
Validation accuracy: 0.6969
Validation loss: 1.0316
Epoch No. 33--Iteration No. 276606-- batch loss = 0.5567
Validation UAR: 0.7242
Validation accuracy: 0.6999
Validation loss: 1.0403
Training lasted 64.16 minutes
------------------------ Training Done ------------------------
(0.01, 0.005, 5): 0.7287193196127408
 75%|███████████████████████████████████████████████████████████████████████████████████▎                           | 3/4 [3:02:18<1:01:41, 3701.26s/it]------------------------ Start Training ------------------------
Epoch No. 1--Iteration No. 8382-- batch loss = 0.6874
Validation UAR: 0.5884
Validation accuracy: 0.5527
Validation loss: 1.2651
Epoch No. 2--Iteration No. 16764-- batch loss = 1.7114
Validation UAR: 0.5953
Validation accuracy: 0.6223
Validation loss: 1.2605
Epoch No. 3--Iteration No. 25146-- batch loss = 0.7508
Validation UAR: 0.5977
Validation accuracy: 0.6082
Validation loss: 1.2584
Epoch No. 4--Iteration No. 33528-- batch loss = 1.9604
Validation UAR: 0.5996
Validation accuracy: 0.5552
Validation loss: 1.2578
Epoch No. 5--Iteration No. 41910-- batch loss = 1.4460
Validation UAR: 0.6021
Validation accuracy: 0.5806
Validation loss: 1.2557
Epoch No. 6--Iteration No. 50292-- batch loss = 2.6108
Validation UAR: 0.6021
Validation accuracy: 0.5924
Validation loss: 1.2551
Epoch No. 7--Iteration No. 58674-- batch loss = 2.4203
Validation UAR: 0.6044
Validation accuracy: 0.5609
Validation loss: 1.2540
Epoch No. 8--Iteration No. 67056-- batch loss = 2.0658
Validation UAR: 0.6051
Validation accuracy: 0.5503
Validation loss: 1.2538
Epoch No. 9--Iteration No. 75438-- batch loss = 0.7448
Validation UAR: 0.6063
Validation accuracy: 0.5633
Validation loss: 1.2532
Epoch No. 10--Iteration No. 83820-- batch loss = 5.1306
Validation UAR: 0.6040
Validation accuracy: 0.5597
Validation loss: 1.2522
Epoch No. 11--Iteration No. 92202-- batch loss = 1.8208
Validation UAR: 0.6047
Validation accuracy: 0.5719
Validation loss: 1.2508
Epoch No. 12--Iteration No. 100584-- batch loss = 2.0795
Validation UAR: 0.6059
Validation accuracy: 0.5893
Validation loss: 1.2504
Epoch No. 13--Iteration No. 108966-- batch loss = 0.5677
Validation UAR: 0.6057
Validation accuracy: 0.5767
Validation loss: 1.2519
Epoch No. 14--Iteration No. 117348-- batch loss = 3.5414
Validation UAR: 0.6089
Validation accuracy: 0.6080
Validation loss: 1.2496
Epoch No. 15--Iteration No. 125730-- batch loss = 0.6683
Validation UAR: 0.6081
Validation accuracy: 0.5919
Validation loss: 1.2508
Epoch No. 16--Iteration No. 134112-- batch loss = 2.9049
Validation UAR: 0.6064
Validation accuracy: 0.5762
Validation loss: 1.2499
Epoch No. 17--Iteration No. 142494-- batch loss = 1.9464
Validation UAR: 0.6058
Validation accuracy: 0.5696
Validation loss: 1.2498
Epoch No. 18--Iteration No. 150876-- batch loss = 0.6577
Validation UAR: 0.6062
Validation accuracy: 0.5757
Validation loss: 1.2487
Epoch No. 19--Iteration No. 159258-- batch loss = 2.1173
Validation UAR: 0.6064
Validation accuracy: 0.6288
Validation loss: 1.2505
Training lasted 36.95 minutes
------------------------ Training Done ------------------------
(0.01, 0.01, 5): 0.6089358389479149
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [3:39:15<00:00, 3288.99s/it]


Best learning rate: 0.01, best weight_decay: 0.0002, best window: 5
Accuracy: 0.7786
/home/maroofr/eecs487/coherenceModel.py:302: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
  plt.show()
TESTING FINAL MODEL
/home/maroofr/env/lib64/python3.6/site-packages/torch/nn/modules/rnn.py:853: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at  ../aten/src/ATen/native/cudnn/RNN.cpp:925.)
  self.num_layers, self.dropout, self.training, self.bidirectional)
Final selection:
Test UAR: 0.7829
Test accuracy: 0.7851
Test loss: 0.9334
(env) [maroofr@gl1009 eecs487]$