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text_audio_our.out
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/scratch/grigorii/tools/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint8 = np.dtype([("qint8", np.int8, 1)])
/scratch/grigorii/tools/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/scratch/grigorii/tools/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint16 = np.dtype([("qint16", np.int16, 1)])
/scratch/grigorii/tools/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/scratch/grigorii/tools/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint32 = np.dtype([("qint32", np.int32, 1)])
/scratch/grigorii/tools/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
np_resource = np.dtype([("resource", np.ubyte, 1)])
/scratch/grigorii/tools/anaconda3/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint8 = np.dtype([("qint8", np.int8, 1)])
/scratch/grigorii/tools/anaconda3/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/scratch/grigorii/tools/anaconda3/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint16 = np.dtype([("qint16", np.int16, 1)])
/scratch/grigorii/tools/anaconda3/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/scratch/grigorii/tools/anaconda3/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint32 = np.dtype([("qint32", np.int32, 1)])
/scratch/grigorii/tools/anaconda3/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
np_resource = np.dtype([("resource", np.ubyte, 1)])
{'joy': 0, 'anger': 1, 'disgust': 2, 'fear': 3, 'sadness': 4, 'neutral': 5, 'surprise': 6}
{'joy': 0, 'anger': 1, 'disgust': 2, 'fear': 3, 'sadness': 4, 'neutral': 5, 'surprise': 6}
=== Constructing train dataset audio statistics ===
FIXED (9989, 1611)
Applying audio feature transforms to train
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Applied audio feature transform to train
Applying audio feature transforms to val
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Applied audio feature transform to val
{'joy': 0, 'anger': 1, 'disgust': 2, 'fear': 3, 'sadness': 4, 'neutral': 5, 'surprise': 6}
Applying audio feature transforms to test
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Applied audio feature transform to test
Epoch[0/15] - batch 0 Error: 1.9639075994491577
Epoch[0/15] - batch 100 Error: 163.70324808359146
Epoch[0/15] - batch 200 Error: 306.40940925478935
Epoch[0/15] - batch 300 Error: 451.8281099498272
Epoch[0/15] - batch 400 Error: 590.4572550952435
Epoch[0/15] - batch 500 Error: 725.0642015337944
Epoch[0/15] - batch 600 Error: 861.7229636609554
Epoch[0/15] - batch 700 Error: 986.1252579987049
Epoch[0/15] - batch 800 Error: 1125.0064921975136
Epoch[0/15] - batch 900 Error: 1252.2342427372932
Epoch[0/15] - batch 1000 Error: 1378.3197564780712
/scratch/grigorii/tools/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
torch.Size([1109, 2])
torch.Size([1109])
torch.Size([1109])
Validation Accuracy (Emotion): 0.5202885482416592
F1 Weighted 0.44342981595386205
Confusion matrix [[ 27 59 0 0 1 68 8]
[ 8 73 0 0 0 69 3]
[ 0 5 0 0 0 15 2]
[ 1 19 0 0 0 17 3]
[ 10 18 0 0 1 79 3]
[ 7 11 0 0 0 436 16]
[ 13 44 0 0 0 53 40]]
Epoch[1/15] - batch 0 Error: 0.10154891014099121
Epoch[1/15] - batch 100 Error: 117.71804024279118
Epoch[1/15] - batch 200 Error: 234.1658183708787
Epoch[1/15] - batch 300 Error: 349.9620696976781
Epoch[1/15] - batch 400 Error: 469.0298775881529
Epoch[1/15] - batch 500 Error: 576.684452444315
Epoch[1/15] - batch 600 Error: 693.9011956751347
Epoch[1/15] - batch 700 Error: 809.9775535464287
Epoch[1/15] - batch 800 Error: 912.584460914135
Epoch[1/15] - batch 900 Error: 1024.7794027030468
Epoch[1/15] - batch 1000 Error: 1138.9421249330044
torch.Size([1109, 2])
torch.Size([1109])
torch.Size([1109])
Validation Accuracy (Emotion): 0.5410279531109107
F1 Weighted 0.4876679655773997
Confusion matrix [[ 41 53 0 0 0 55 14]
[ 3 103 0 0 0 39 8]
[ 0 9 0 0 0 10 3]
[ 1 21 0 0 0 12 6]
[ 5 26 0 0 4 71 5]
[ 8 42 0 0 1 393 26]
[ 9 46 0 0 0 36 59]]
Epoch[2/15] - batch 0 Error: 1.4066109657287598
Epoch[2/15] - batch 100 Error: 94.56758406502195
Epoch[2/15] - batch 200 Error: 198.80555106443353
Epoch[2/15] - batch 300 Error: 290.60719124157913
Epoch[2/15] - batch 400 Error: 382.3675436016638
Epoch[2/15] - batch 500 Error: 477.6323831763584
Epoch[2/15] - batch 600 Error: 573.2606735799927
Epoch[2/15] - batch 700 Error: 672.439816707978
Epoch[2/15] - batch 800 Error: 765.4328522926662
Epoch[2/15] - batch 900 Error: 856.7750986686442
Epoch[2/15] - batch 1000 Error: 949.2801426372025
torch.Size([1109, 2])
torch.Size([1109])
torch.Size([1109])
Validation Accuracy (Emotion): 0.5428313796212805
F1 Weighted 0.5006349815588981
Confusion matrix [[ 59 31 0 0 1 59 13]
[ 14 89 0 0 0 43 7]
[ 3 6 0 0 1 10 2]
[ 2 21 0 0 0 14 3]
[ 12 23 0 0 11 59 6]
[ 22 33 0 0 4 386 25]
[ 16 35 0 0 2 40 57]]
Epoch[3/15] - batch 0 Error: 1.227016568183899
Epoch[3/15] - batch 100 Error: 71.10317537933588
Epoch[3/15] - batch 200 Error: 139.18405542585242
Epoch[3/15] - batch 300 Error: 221.64768606428697
Epoch[3/15] - batch 400 Error: 295.33120715273253
Epoch[3/15] - batch 500 Error: 377.14761133705906
Epoch[3/15] - batch 600 Error: 456.43096607112966
Epoch[3/15] - batch 700 Error: 538.560786945789
Epoch[3/15] - batch 800 Error: 616.7404270295956
Epoch[3/15] - batch 900 Error: 683.1083273725671
Epoch[3/15] - batch 1000 Error: 769.1922070478176
torch.Size([1109, 2])
torch.Size([1109])
torch.Size([1109])
Validation Accuracy (Emotion): 0.5193868349864743
F1 Weighted 0.48660831895083095
Confusion matrix [[ 40 52 0 0 8 55 8]
[ 5 111 0 0 2 30 5]
[ 2 10 0 0 0 8 2]
[ 2 29 1 0 0 6 2]
[ 6 30 0 0 17 56 2]
[ 10 72 0 0 9 364 15]
[ 10 64 0 0 6 26 44]]
Epoch[4/15] - batch 0 Error: 0.9958687424659729
Epoch[4/15] - batch 100 Error: 50.930763060518075
Epoch[4/15] - batch 200 Error: 99.13971418664732
Epoch[4/15] - batch 300 Error: 151.1869651666393
Epoch[4/15] - batch 400 Error: 216.05796962511886
Epoch[4/15] - batch 500 Error: 271.3117483732167
Epoch[4/15] - batch 600 Error: 333.72127515253305
Epoch[4/15] - batch 700 Error: 394.30838670362937
Epoch[4/15] - batch 800 Error: 464.87457740268655
Epoch[4/15] - batch 900 Error: 533.2297804252266
Epoch[4/15] - batch 1000 Error: 597.5225191648169
torch.Size([1109, 2])
torch.Size([1109])
torch.Size([1109])
Validation Accuracy (Emotion): 0.48151487826871053
F1 Weighted 0.46640961100125966
Confusion matrix [[ 32 72 0 2 5 41 11]
[ 4 117 0 1 2 24 5]
[ 1 11 0 2 0 7 1]
[ 3 29 0 1 0 5 2]
[ 3 39 0 3 22 41 3]
[ 12 98 0 5 22 318 15]
[ 8 70 0 1 7 20 44]]
Epoch[5/15] - batch 0 Error: 1.2007032632827759
Epoch[5/15] - batch 100 Error: 36.526635080841515
Epoch[5/15] - batch 200 Error: 83.21560759956265
Epoch[5/15] - batch 300 Error: 133.24017420778182
Epoch[5/15] - batch 400 Error: 175.53030209662393
Epoch[5/15] - batch 500 Error: 215.97089144544827
Epoch[5/15] - batch 600 Error: 269.6817965859469
Epoch[5/15] - batch 700 Error: 322.06510791883477
Epoch[5/15] - batch 800 Error: 380.2101847017831
Epoch[5/15] - batch 900 Error: 427.90858506492
Epoch[5/15] - batch 1000 Error: 484.2105302867204
torch.Size([1109, 2])
torch.Size([1109])
torch.Size([1109])
Validation Accuracy (Emotion): 0.45356176735798015
F1 Weighted 0.4339444385291341
Confusion matrix [[ 16 75 0 2 12 50 8]
[ 3 121 0 0 7 19 3]
[ 0 12 0 0 3 5 2]
[ 0 29 0 1 3 6 1]
[ 1 38 0 1 41 28 2]
[ 6 109 0 1 44 290 20]
[ 2 73 2 0 20 19 34]]
Epoch[6/15] - batch 0 Error: 0.07615269720554352
Epoch[6/15] - batch 100 Error: 29.515600047867338
Epoch[6/15] - batch 200 Error: 62.22334927577049
Epoch[6/15] - batch 300 Error: 93.87854990583537
Epoch[6/15] - batch 400 Error: 130.89619848918377
Epoch[6/15] - batch 500 Error: 173.88772617023375
Epoch[6/15] - batch 600 Error: 207.85695791464133
Epoch[6/15] - batch 700 Error: 243.77403724541506
Epoch[6/15] - batch 800 Error: 288.9317711377385
Epoch[6/15] - batch 900 Error: 326.4731001168095
Epoch[6/15] - batch 1000 Error: 364.88823504857487
torch.Size([1109, 2])
torch.Size([1109])
torch.Size([1109])
Validation Accuracy (Emotion): 0.4824165915238954
F1 Weighted 0.4444074979172122
Confusion matrix [[ 19 62 2 0 2 65 13]
[ 1 112 1 0 2 31 6]
[ 0 12 1 0 0 8 1]
[ 1 26 1 1 0 9 2]
[ 3 34 6 6 8 48 6]
[ 5 76 6 4 10 347 22]
[ 2 66 6 0 3 26 47]]
Epoch[7/15] - batch 0 Error: 0.0940612331032753
Epoch[7/15] - batch 100 Error: 26.56035492457613
Epoch[7/15] - batch 200 Error: 54.931417744423015
Epoch[7/15] - batch 300 Error: 78.89573133315224
Epoch[7/15] - batch 400 Error: 109.5764293332312
Epoch[7/15] - batch 500 Error: 134.9389455111367
Epoch[7/15] - batch 600 Error: 167.2296173065942
Epoch[7/15] - batch 700 Error: 192.19647264717509
Epoch[7/15] - batch 800 Error: 224.2450544843192
Epoch[7/15] - batch 900 Error: 251.79105068239645
Epoch[7/15] - batch 1000 Error: 292.97200708969353
torch.Size([1109, 2])
torch.Size([1109])
torch.Size([1109])
Validation Accuracy (Emotion): 0.4625788999098287
F1 Weighted 0.43182589974555974
Confusion matrix [[ 10 69 1 0 6 63 14]
[ 1 109 1 1 7 27 7]
[ 0 12 0 0 3 5 2]
[ 1 26 0 1 1 9 2]
[ 0 36 1 3 26 41 4]
[ 3 97 3 2 28 320 17]
[ 1 60 2 0 9 31 47]]
Epoch[8/15] - batch 0 Error: 0.5575157999992371
Epoch[8/15] - batch 100 Error: 24.044940340278515
Epoch[8/15] - batch 200 Error: 39.48873085217386
Epoch[8/15] - batch 300 Error: 58.08555800346549
Epoch[8/15] - batch 400 Error: 89.44868624554341
Epoch[8/15] - batch 500 Error: 114.14856340625413
Epoch[8/15] - batch 600 Error: 138.75732113088807
Epoch[8/15] - batch 700 Error: 161.63908805737293
Epoch[8/15] - batch 800 Error: 190.24220909762371
Epoch[8/15] - batch 900 Error: 220.67034049321083
Epoch[8/15] - batch 1000 Error: 251.56823363424192
torch.Size([1109, 2])
torch.Size([1109])
torch.Size([1109])
Validation Accuracy (Emotion): 0.4625788999098287
F1 Weighted 0.4614409402272892
Confusion matrix [[ 34 68 1 3 9 35 13]
[ 6 103 4 0 10 18 12]
[ 1 10 0 0 3 5 3]
[ 1 24 0 1 3 5 6]
[ 0 38 4 1 31 30 7]
[ 15 85 6 1 47 291 25]
[ 6 50 7 0 11 23 53]]
Epoch[9/15] - batch 0 Error: 1.049041748046875e-05
Epoch[9/15] - batch 100 Error: 18.412601184789878
Epoch[9/15] - batch 200 Error: 34.252687939407224
Epoch[9/15] - batch 300 Error: 51.34452753429059
Epoch[9/15] - batch 400 Error: 73.17130264112707
Epoch[9/15] - batch 500 Error: 94.27498865877303
Epoch[9/15] - batch 600 Error: 115.44032765373458
Epoch[9/15] - batch 700 Error: 139.71184113882464
Epoch[9/15] - batch 800 Error: 167.29293453855098
Epoch[9/15] - batch 900 Error: 191.12183947401527
Epoch[9/15] - batch 1000 Error: 211.56064927170937
torch.Size([1109, 2])
torch.Size([1109])
torch.Size([1109])
Validation Accuracy (Emotion): 0.4616771866546438
F1 Weighted 0.4478932064324524
Confusion matrix [[ 28 39 3 0 6 66 21]
[ 4 75 4 2 10 46 12]
[ 0 9 0 0 4 6 3]
[ 2 16 1 1 4 12 4]
[ 3 19 3 7 36 37 6]
[ 5 56 5 7 43 319 35]
[ 9 50 3 1 10 24 53]]
Epoch[10/15] - batch 0 Error: 0.018451055511832237
Epoch[10/15] - batch 100 Error: 20.89457196618693
Epoch[10/15] - batch 200 Error: 39.23525039971696
Epoch[10/15] - batch 300 Error: 53.544794906830404
Epoch[10/15] - batch 400 Error: 73.32527009669336
Epoch[10/15] - batch 500 Error: 92.49382555641422
Epoch[10/15] - batch 600 Error: 118.6720238621664
Epoch[10/15] - batch 700 Error: 140.2560415715194
Epoch[10/15] - batch 800 Error: 159.7560343205059
Epoch[10/15] - batch 900 Error: 180.21947253129056
Epoch[10/15] - batch 1000 Error: 204.2580761962661
torch.Size([1109, 2])
torch.Size([1109])
torch.Size([1109])
Validation Accuracy (Emotion): 0.47790802524797116
F1 Weighted 0.43944443945492445
Confusion matrix [[ 29 47 0 0 2 72 13]
[ 6 100 0 2 1 41 3]
[ 1 9 0 1 2 9 0]
[ 1 25 0 2 1 9 2]
[ 3 33 1 2 10 58 4]
[ 8 76 3 2 11 354 16]
[ 8 70 2 0 3 32 35]]
Epoch[11/15] - batch 0 Error: 0.08615703880786896
Epoch[11/15] - batch 100 Error: 15.143881757053109
Epoch[11/15] - batch 200 Error: 33.15377137212357
Epoch[11/15] - batch 300 Error: 42.597738977338395
Epoch[11/15] - batch 400 Error: 59.89890208590769
Epoch[11/15] - batch 500 Error: 73.14527298133908
Epoch[11/15] - batch 600 Error: 88.45672777576863
Epoch[11/15] - batch 700 Error: 105.32797856021705
Epoch[11/15] - batch 800 Error: 125.93502854011673
Epoch[11/15] - batch 900 Error: 145.82115284780917
Epoch[11/15] - batch 1000 Error: 163.23298174720182
torch.Size([1109, 2])
torch.Size([1109])
torch.Size([1109])
Validation Accuracy (Emotion): 0.4643823264201984
F1 Weighted 0.4464530956239481
Confusion matrix [[ 13 55 0 1 13 57 24]
[ 2 102 0 4 8 19 18]
[ 0 12 0 0 2 6 2]
[ 0 23 2 2 3 8 2]
[ 4 28 1 7 30 29 12]
[ 8 73 2 7 45 304 31]
[ 1 49 5 2 13 16 64]]
Epoch[12/15] - batch 0 Error: 0.03400158882141113
Epoch[12/15] - batch 100 Error: 17.55071036274211
Epoch[12/15] - batch 200 Error: 31.41638164309424
Epoch[12/15] - batch 300 Error: 46.98392692673107
Epoch[12/15] - batch 400 Error: 62.65625308674038
Epoch[12/15] - batch 500 Error: 73.63289183171955
Epoch[12/15] - batch 600 Error: 91.08080568450211
Epoch[12/15] - batch 700 Error: 107.23961036413388
Epoch[12/15] - batch 800 Error: 127.03457409646606
Epoch[12/15] - batch 900 Error: 143.86745503359563
Epoch[12/15] - batch 1000 Error: 156.58162748596772
torch.Size([1109, 2])
torch.Size([1109])
torch.Size([1109])
Validation Accuracy (Emotion): 0.4824165915238954
F1 Weighted 0.45196754468589395
Confusion matrix [[ 19 40 3 1 10 64 26]
[ 0 64 3 2 12 47 25]
[ 0 8 1 0 3 7 3]
[ 2 13 1 1 3 15 5]
[ 2 16 4 6 23 47 13]
[ 3 35 8 5 27 359 33]
[ 0 28 6 1 9 38 68]]
Epoch[13/15] - batch 0 Error: 0.0012204271042719483
Epoch[13/15] - batch 100 Error: 12.58759641746829
Epoch[13/15] - batch 200 Error: 23.423199477172858
Epoch[13/15] - batch 300 Error: 36.122474505979795
Epoch[13/15] - batch 400 Error: 47.838527127256754
Epoch[13/15] - batch 500 Error: 60.01879793006643
Epoch[13/15] - batch 600 Error: 76.8171733272287
Epoch[13/15] - batch 700 Error: 94.75343904107486
Epoch[13/15] - batch 800 Error: 108.20433636497035
Epoch[13/15] - batch 900 Error: 128.19769285886073
Epoch[13/15] - batch 1000 Error: 144.5127181604029
torch.Size([1109, 2])
torch.Size([1109])
torch.Size([1109])
Validation Accuracy (Emotion): 0.4860234445446348
F1 Weighted 0.45644382870931377
Confusion matrix [[ 12 49 1 1 13 66 21]
[ 0 79 4 3 11 42 14]
[ 0 5 1 0 3 10 3]
[ 2 11 0 1 6 14 6]
[ 2 21 1 1 38 42 6]
[ 3 43 9 5 42 342 26]
[ 1 36 8 0 15 24 66]]
Epoch[14/15] - batch 0 Error: 0.14523422718048096
Epoch[14/15] - batch 100 Error: 10.291120399181182
Epoch[14/15] - batch 200 Error: 18.785643541764045
Epoch[14/15] - batch 300 Error: 37.98705979864718
Epoch[14/15] - batch 400 Error: 49.641298721924315
Epoch[14/15] - batch 500 Error: 59.261085582036586
Epoch[14/15] - batch 600 Error: 75.5259947293463
Epoch[14/15] - batch 700 Error: 92.35060139440887
Epoch[14/15] - batch 800 Error: 110.69940769663211
Epoch[14/15] - batch 900 Error: 129.73220919084895
Epoch[14/15] - batch 1000 Error: 142.7965212963478
torch.Size([1109, 2])
torch.Size([1109])
torch.Size([1109])
Validation Accuracy (Emotion): 0.49594229035166815
F1 Weighted 0.45548877037598084
Confusion matrix [[ 15 44 2 0 11 80 11]
[ 1 91 0 0 9 46 6]
[ 0 7 0 0 4 10 1]
[ 1 18 0 0 4 16 1]
[ 1 19 3 3 38 42 5]
[ 1 45 3 3 43 362 13]
[ 2 45 6 1 11 41 44]]
Testing audio_text_ours0
torch.Size([2610, 2])
torch.Size([2610])
torch.Size([2610])
Validation Accuracy (Emotion): 0.47011494252873565
F1 Weighted 0.47238326132031816
Confusion matrix [[ 99 137 15 9 14 112 16]
[ 11 209 20 8 9 75 13]
[ 1 29 12 2 5 19 0]
[ 4 19 3 3 3 16 2]
[ 14 47 11 17 45 68 6]
[ 44 225 58 26 66 808 29]
[ 20 100 17 7 8 78 51]]