In [1]:
from lstm.res import model_tf as lstm
from lstm.fileHandlers import dataset as ds
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
In [2]:
model_res = lstm.epoch_gridsearch(epochs = 100)

WARNING:tensorflow:From C:\Users\livb1\Anaconda3\lib\site-packages\tensorflow\python\ops\init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
WARNING:tensorflow:Large dropout rate: 0.555 (>0.5). In TensorFlow 2.x, dropout() uses dropout rate instead of keep_prob. Please ensure that this is intended.
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
lstm (LSTM)                  (None, 64, 665)           2167900   
_________________________________________________________________
dropout (Dropout)            (None, 64, 665)           0         
_________________________________________________________________
lstm_1 (LSTM)                (None, 64, 444)           1971360   
_________________________________________________________________
dropout_1 (Dropout)          (None, 64, 444)           0         
_________________________________________________________________
lstm_2 (LSTM)                (None, 222)               592296    
_________________________________________________________________
dropout_2 (Dropout)          (None, 222)               0         
_________________________________________________________________
dense (Dense)                (None, 8)                 1784      
=================================================================
Total params: 4,733,340
Trainable params: 4,733,340
Non-trainable params: 0
_________________________________________________________________
Saving model to disk

Model Metrics:
Accuracy:  0.797752808988764
Precision: [0.71608833 0.68918919 0.81790123 0.7300885  0.82857143 0.81654676
 0.92664093 0.87837838]
Recall:    [0.76430976 0.68227425 0.88333333 0.75688073 0.60416667 0.8972332
 0.86330935 0.86956522]
F-Score:   [0.73941368 0.68571429 0.84935897 0.74324324 0.69879518 0.85499058
 0.89385475 0.87394958]
Support:   [297 299 300 218 192 253 278 299]
56448/56448 [==============================] - 560s 10ms/sample - loss: 0.0521 - categorical_accuracy: 0.9811

Model Metrics (epoch 1):
Accuracy:  0.2157968970380818
Precision: [0.         0.23360656 0.25       0.         0.         0.
 0.         0.20514653]
Recall:    [0.         0.57190635 0.00333333 0.         0.         0.
 0.         0.95986622]
F-Score:   [0.         0.33171678 0.00657895 0.         0.         0.
 0.         0.33804476]

Model Metrics (epoch 2):
Accuracy:  0.23127340823970038
Precision: [0.         0.21052632 0.4229765  0.19615385 0.         0.
 0.         0.18792402]
Recall:    [0.         0.01337793 0.54       0.23394495 0.         0.
 0.         0.9264214 ]
F-Score:   [0.         0.02515723 0.47437775 0.21338912 0.         0.
 0.         0.31246475]

Model Metrics (epoch 3):
Accuracy:  0.28370786516853935
Precision: [0.221875   0.29508197 0.32380952 0.21052632 0.         0.
 0.         0.2991851 ]
Recall:    [0.23905724 0.06020067 0.68       0.25688073 0.         0.
 0.         0.85953177]
F-Score:   [0.23014587 0.1        0.43870968 0.23140496 0.         0.
 0.         0.44386874]

Model Metrics (epoch 4):
Accuracy:  0.28136704119850187
Precision: [0.42857143 0.26728111 0.4924812  0.18950437 0.31578947 0.77777778
 0.         0.2509058 ]
Recall:    [0.01010101 0.19397993 0.43666667 0.29816514 0.3125     0.02766798
 0.         0.9264214 ]
F-Score:   [0.01973684 0.2248062  0.46289753 0.23172906 0.31413613 0.05343511
 0.         0.39486814]

Model Metrics (epoch 5):
Accuracy:  0.39185393258426965
Precision: [0.55329949 0.27842227 0.44520548 0.33714286 0.24747475 0.47337278
 0.         0.40389972]
Recall:    [0.36700337 0.40133779 0.65       0.2706422  0.25520833 0.63241107
 0.         0.48494983]
F-Score:   [0.44129555 0.32876712 0.52845528 0.30025445 0.25128205 0.54145516
 0.         0.44072948]

Model Metrics (epoch 6):
Accuracy:  0.39747191011235955
Precision: [0.36507937 0.43820225 0.48888889 0.31456954 0.2        0.42348008
 0.         0.38952164]
Recall:    [0.54208754 0.13043478 0.58666667 0.43577982 0.02604167 0.79841897
 0.         0.57190635]
F-Score:   [0.43631436 0.20103093 0.53333333 0.36538462 0.04608295 0.55342466
 0.         0.46341463]

Model Metrics (epoch 7):
Accuracy:  0.42743445692883897
Precision: [0.4135514  0.44148936 0.56934307 0.41176471 0.43165468 0.552
 0.425      0.33566434]
Recall:    [0.5959596  0.27759197 0.52       0.19266055 0.3125     0.54545455
 0.06115108 0.80267559]
F-Score:   [0.48827586 0.34086242 0.54355401 0.2625     0.36253776 0.54870775
 0.10691824 0.47337278]

Model Metrics (epoch 8):
Accuracy:  0.49765917602996257
Precision: [0.5335689  0.42361111 0.54572271 0.48571429 0.44654088 0.51381215
 0.58015267 0.46867168]
Recall:    [0.50841751 0.40802676 0.61666667 0.38990826 0.36979167 0.73517787
 0.27338129 0.62541806]
F-Score:   [0.52068966 0.41567291 0.57902973 0.43256997 0.4045584  0.60487805
 0.37163814 0.53581662]

Model Metrics (epoch 9):
Accuracy:  0.577247191011236
Precision: [0.65983607 0.42162162 0.6744186  0.5        0.50515464 0.6
 0.78947368 0.57894737]
Recall:    [0.54208754 0.52173913 0.58       0.51376147 0.51041667 0.69960474
 0.48561151 0.73578595]
F-Score:   [0.59519409 0.46636771 0.62365591 0.50678733 0.50777202 0.6459854
 0.6013363  0.64801178]

Model Metrics (epoch 10):
Accuracy:  0.6081460674157303
Precision: [0.54945055 0.46380697 0.76442308 0.60227273 0.63793103 0.5328084
 0.77366255 0.71272727]
Recall:    [0.67340067 0.57859532 0.53       0.48623853 0.38541667 0.80237154
 0.67625899 0.65551839]
F-Score:   [0.60514372 0.51488095 0.62598425 0.53807107 0.48051948 0.64037855
 0.72168906 0.68292683]

Model Metrics (epoch 11):
Accuracy:  0.5674157303370787
Precision: [0.58885017 0.54356846 0.75555556 0.43278689 0.55900621 0.56140351
 0.83687943 0.49287169]
Recall:    [0.56902357 0.43812709 0.56666667 0.60550459 0.46875    0.63241107
 0.42446043 0.80936455]
F-Score:   [0.57876712 0.48518519 0.64761905 0.50478011 0.50991501 0.59479554
 0.56324582 0.61265823]

Model Metrics (epoch 12):
Accuracy:  0.6563670411985019
Precision: [0.64686469 0.47193878 0.73722628 0.71851852 0.58659218 0.63664596
 0.86697248 0.71246006]
Recall:    [0.65993266 0.6187291  0.67333333 0.44495413 0.546875   0.81027668
 0.67985612 0.7458194 ]
F-Score:   [0.65333333 0.53545586 0.70383275 0.54957507 0.56603774 0.71304348
 0.76209677 0.72875817]

Model Metrics (epoch 13):
Accuracy:  0.6821161048689138
Precision: [0.66778523 0.56382979 0.74433657 0.62631579 0.56221198 0.65780731
 0.85232068 0.75496689]
Recall:    [0.67003367 0.53177258 0.76666667 0.54587156 0.63541667 0.7826087
 0.72661871 0.76254181]
F-Score:   [0.66890756 0.54733219 0.75533662 0.58333333 0.59657702 0.71480144
 0.78446602 0.75873544]

Model Metrics (epoch 14):
Accuracy:  0.6933520599250936
Precision: [0.66211604 0.6023166  0.80859375 0.55183946 0.62303665 0.65116279
 0.91826923 0.78671329]
Recall:    [0.65319865 0.52173913 0.69       0.75688073 0.61979167 0.88537549
 0.68705036 0.75250836]
F-Score:   [0.65762712 0.55913978 0.74460432 0.63829787 0.62140992 0.75041876
 0.78600823 0.76923077]

Model Metrics (epoch 15):
Accuracy:  0.7186329588014981
Precision: [0.71698113 0.67346939 0.73976608 0.64285714 0.66486486 0.64117647
 0.85258964 0.8028169 ]
Recall:    [0.63973064 0.55183946 0.84333333 0.66055046 0.640625   0.86166008
 0.76978417 0.76254181]
F-Score:   [0.67615658 0.60661765 0.78816199 0.65158371 0.65251989 0.73524452
 0.80907372 0.78216123]

Model Metrics (epoch 16):
Accuracy:  0.7340823970037453
Precision: [0.67711599 0.64262295 0.7827476  0.7607362  0.68449198 0.69579288
 0.92056075 0.75766871]
Recall:    [0.72727273 0.65551839 0.81666667 0.56880734 0.66666667 0.84980237
 0.70863309 0.82608696]
F-Score:   [0.7012987  0.64900662 0.79934747 0.65091864 0.67546174 0.76512456
 0.80081301 0.7904    ]

Model Metrics (epoch 17):
Accuracy:  0.7298689138576779
Precision: [0.65676568 0.65017668 0.84230769 0.61153846 0.73584906 0.70226537
 0.89669421 0.771875  ]
Recall:    [0.67003367 0.61538462 0.73       0.7293578  0.609375   0.85770751
 0.78057554 0.82608696]
F-Score:   [0.66333333 0.63230241 0.78214286 0.66527197 0.66666667 0.77224199
 0.83461538 0.79806139]

Model Metrics (epoch 18):
Accuracy:  0.7598314606741573
Precision: [0.6918239  0.6953125  0.78550725 0.70873786 0.75294118 0.69875776
 0.90833333 0.84946237]
Recall:    [0.74074074 0.59531773 0.90333333 0.66972477 0.66666667 0.88932806
 0.78417266 0.79264214]
F-Score:   [0.71544715 0.64144144 0.84031008 0.68867925 0.70718232 0.7826087
 0.84169884 0.8200692 ]

Model Metrics (epoch 19):
Accuracy:  0.7621722846441947
Precision: [0.72693727 0.67615658 0.79179811 0.65086207 0.69312169 0.82509506
 0.86594203 0.82084691]
Recall:    [0.66329966 0.63545151 0.83666667 0.69266055 0.68229167 0.85770751
 0.85971223 0.84280936]
F-Score:   [0.69366197 0.65517241 0.81361426 0.67111111 0.68766404 0.84108527
 0.86281588 0.83168317]

Model Metrics (epoch 20):
Accuracy:  0.7729400749063671
Precision: [0.72354949 0.70566038 0.78654971 0.72192513 0.6875     0.83464567
 0.82410423 0.84797297]
Recall:    [0.71380471 0.62541806 0.89666667 0.61926606 0.6875     0.83794466
 0.91007194 0.83946488]
F-Score:   [0.71864407 0.66312057 0.83800623 0.66666667 0.6875     0.83629191
 0.86495726 0.84369748]

Model Metrics (epoch 21):
Accuracy:  0.7780898876404494
Precision: [0.7184466  0.66869301 0.84262295 0.71291866 0.79861111 0.76013514
 0.85517241 0.88976378]
Recall:    [0.74747475 0.73578595 0.85666667 0.68348624 0.59895833 0.88932806
 0.89208633 0.75585284]
F-Score:   [0.73267327 0.70063694 0.84958678 0.69789227 0.68452381 0.81967213
 0.87323944 0.81735986]

Model Metrics (epoch 22):
Accuracy:  0.7752808988764045
Precision: [0.72945205 0.65359477 0.77101449 0.74619289 0.76821192 0.80597015
 0.85121107 0.875     ]
Recall:    [0.71717172 0.66889632 0.88666667 0.67431193 0.60416667 0.85375494
 0.88489209 0.84280936]
F-Score:   [0.72325976 0.66115702 0.8248062  0.70843373 0.67638484 0.82917466
 0.86772487 0.85860307]

Model Metrics (epoch 23):
Accuracy:  0.774812734082397
Precision: [0.67462687 0.66323024 0.81350482 0.67741935 0.74534161 0.86345382
 0.87412587 0.87762238]
Recall:    [0.76094276 0.64548495 0.84333333 0.67431193 0.625      0.84980237
 0.89928058 0.83946488]
F-Score:   [0.71518987 0.65423729 0.82815057 0.67586207 0.67988669 0.85657371
 0.88652482 0.85811966]

Model Metrics (epoch 24):
Accuracy:  0.7865168539325843
Precision: [0.71028037 0.67391304 0.825      0.77456647 0.76744186 0.79783394
 0.91732283 0.84511785]
Recall:    [0.76767677 0.72575251 0.88       0.6146789  0.6875     0.87351779
 0.8381295  0.83946488]
F-Score:   [0.73786408 0.69887279 0.8516129  0.68542199 0.72527473 0.83396226
 0.87593985 0.84228188]

Model Metrics (epoch 25):
Accuracy:  0.7799625468164794
Precision: [0.7073955  0.66555184 0.90196078 0.68376068 0.71052632 0.82287823
 0.85958904 0.87323944]
Recall:    [0.74074074 0.66555184 0.76666667 0.73394495 0.703125   0.88142292
 0.9028777  0.82943144]
F-Score:   [0.72368421 0.66555184 0.82882883 0.7079646  0.70680628 0.85114504
 0.88070175 0.85077187]

Model Metrics (epoch 26):
Accuracy:  0.7860486891385767
Precision: [0.70382166 0.68707483 0.80733945 0.71219512 0.73652695 0.88429752
 0.9037037  0.83596215]
Recall:    [0.74410774 0.67558528 0.88       0.66972477 0.640625   0.8458498
 0.87769784 0.88628763]
F-Score:   [0.72340426 0.68128162 0.84210526 0.69030733 0.68523677 0.86464646
 0.89051095 0.86038961]

Model Metrics (epoch 27):
Accuracy:  0.7776217228464419
Precision: [0.70642202 0.7254902  0.76744186 0.76842105 0.73888889 0.77972028
 0.88846154 0.84353741]
Recall:    [0.77777778 0.6187291  0.88       0.66972477 0.69270833 0.88142292
 0.83093525 0.82943144]
F-Score:   [0.74038462 0.66787004 0.81987578 0.71568627 0.71505376 0.82745826
 0.85873606 0.83642496]

Model Metrics (epoch 28):
Accuracy:  0.7808988764044944
Precision: [0.74729242 0.67666667 0.80495356 0.69058296 0.7247191  0.82051282
 0.88475836 0.86348123]
Recall:    [0.6969697  0.67892977 0.86666667 0.70642202 0.671875   0.88537549
 0.85611511 0.84615385]
F-Score:   [0.72125436 0.67779633 0.83467095 0.6984127  0.6972973  0.85171103
 0.8702011  0.85472973]

Model Metrics (epoch 29):
Accuracy:  0.7860486891385767
Precision: [0.68343195 0.71311475 0.81388013 0.72197309 0.6984127  0.859375
 0.886121   0.88194444]
Recall:    [0.77777778 0.5819398  0.86       0.73853211 0.6875     0.86956522
 0.89568345 0.84949833]
F-Score:   [0.72755906 0.64088398 0.8363047  0.73015873 0.69291339 0.86444008
 0.89087657 0.86541738]

Model Metrics (epoch 30):
Accuracy:  0.7752808988764045
Precision: [0.66472303 0.64536741 0.81424149 0.77192982 0.79591837 0.79432624
 0.89010989 0.86971831]
Recall:    [0.76767677 0.67558528 0.87666667 0.60550459 0.609375   0.88537549
 0.87410072 0.82608696]
F-Score:   [0.7125     0.66013072 0.84430177 0.67866324 0.69026549 0.83738318
 0.88203267 0.84734134]

Model Metrics (epoch 31):
Accuracy:  0.7874531835205992
Precision: [0.75824176 0.71071429 0.79117647 0.69911504 0.72375691 0.81428571
 0.85517241 0.90977444]
Recall:    [0.6969697  0.66555184 0.89666667 0.72477064 0.68229167 0.90118577
 0.89208633 0.80936455]
F-Score:   [0.72631579 0.68739206 0.840625   0.71171171 0.70241287 0.85553471
 0.87323944 0.85663717]

Model Metrics (epoch 32):
Accuracy:  0.7940074906367042
Precision: [0.78148148 0.68078176 0.79331307 0.69709544 0.82550336 0.83834586
 0.86619718 0.87931034]
Recall:    [0.71043771 0.69899666 0.87       0.7706422  0.640625   0.88142292
 0.88489209 0.85284281]
F-Score:   [0.74426808 0.68976898 0.82988871 0.73202614 0.72140762 0.85934489
 0.87544484 0.86587436]

Model Metrics (epoch 33):
Accuracy:  0.7930711610486891
Precision: [0.72491909 0.70242215 0.8537415  0.71681416 0.77439024 0.83396226
 0.8641115  0.85430464]
Recall:    [0.75420875 0.67892977 0.83666667 0.74311927 0.66145833 0.87351779
 0.89208633 0.86287625]
F-Score:   [0.73927393 0.69047619 0.84511785 0.72972973 0.71348315 0.85328185
 0.87787611 0.85856905]

Model Metrics (epoch 34):
Accuracy:  0.778558052434457
Precision: [0.72852234 0.62395543 0.8537415  0.73267327 0.75949367 0.81343284
 0.86120996 0.87632509]
Recall:    [0.71380471 0.74916388 0.83666667 0.67889908 0.625      0.86166008
 0.8705036  0.82943144]
F-Score:   [0.72108844 0.68085106 0.84511785 0.7047619  0.68571429 0.83685221
 0.86583184 0.85223368]

Model Metrics (epoch 35):
Accuracy:  0.7888576779026217
Precision: [0.72222222 0.6635514  0.86486486 0.73762376 0.7721519  0.82706767
 0.85958904 0.85762712]
Recall:    [0.74410774 0.71237458 0.85333333 0.68348624 0.63541667 0.86956522
 0.9028777  0.84615385]
F-Score:   [0.73300166 0.68709677 0.8590604  0.70952381 0.69714286 0.8477842
 0.88070175 0.85185185]

Model Metrics (epoch 36):
Accuracy:  0.7916666666666666
Precision: [0.73584906 0.71484375 0.78134111 0.75757576 0.76331361 0.83333333
 0.87544484 0.85049834]
Recall:    [0.78787879 0.61204013 0.89333333 0.68807339 0.671875   0.88932806
 0.88489209 0.85618729]
F-Score:   [0.76097561 0.65945946 0.83359253 0.72115385 0.71468144 0.86042065
 0.88014311 0.85333333]

Model Metrics (epoch 37):
Accuracy:  0.8000936329588015
Precision: [0.71165644 0.75665399 0.82352941 0.75       0.78658537 0.86
 0.83112583 0.87328767]
Recall:    [0.78114478 0.66555184 0.88666667 0.74311927 0.671875   0.84980237
 0.9028777  0.85284281]
F-Score:   [0.74478331 0.70818505 0.85393258 0.74654378 0.7247191  0.85487078
 0.86551724 0.86294416]

Model Metrics (epoch 38):
Accuracy:  0.7944756554307116
Precision: [0.71601208 0.63400576 0.86986301 0.74736842 0.79354839 0.83394834
 0.89705882 0.9028777 ]
Recall:    [0.7979798  0.73578595 0.84666667 0.65137615 0.640625   0.89328063
 0.87769784 0.83946488]
F-Score:   [0.75477707 0.68111455 0.85810811 0.69607843 0.70893372 0.86259542
 0.88727273 0.87001733]

Model Metrics (epoch 39):
Accuracy:  0.797752808988764
Precision: [0.73154362 0.7147651  0.8370607  0.69421488 0.81333333 0.82656827
 0.91570881 0.85148515]
Recall:    [0.73400673 0.71237458 0.87333333 0.7706422  0.63541667 0.88537549
 0.85971223 0.86287625]
F-Score:   [0.73277311 0.71356784 0.8548124  0.73043478 0.71345029 0.85496183
 0.88682746 0.85714286]

Model Metrics (epoch 40):
Accuracy:  0.7837078651685393
Precision: [0.71380471 0.75770925 0.7393617  0.72123894 0.77514793 0.83018868
 0.88086643 0.84949833]
Recall:    [0.71380471 0.57525084 0.92666667 0.74770642 0.68229167 0.86956522
 0.87769784 0.84949833]
F-Score:   [0.71380471 0.6539924  0.82248521 0.73423423 0.72576177 0.84942085
 0.87927928 0.84949833]

Model Metrics (epoch 41):
Accuracy:  0.7846441947565543
Precision: [0.76573427 0.64688427 0.79320988 0.72857143 0.79166667 0.77931034
 0.90262172 0.89208633]
Recall:    [0.73737374 0.72909699 0.85666667 0.70183486 0.59375    0.89328063
 0.86690647 0.82943144]
F-Score:   [0.75128645 0.68553459 0.82371795 0.71495327 0.67857143 0.83241252
 0.88440367 0.85961872]

Model Metrics (epoch 42):
Accuracy:  0.7902621722846442
Precision: [0.73941368 0.6838488  0.80864198 0.74752475 0.69354839 0.81338028
 0.90530303 0.89928058]
Recall:    [0.76430976 0.66555184 0.87333333 0.69266055 0.671875   0.91304348
 0.85971223 0.8361204 ]
F-Score:   [0.75165563 0.67457627 0.83974359 0.71904762 0.68253968 0.8603352
 0.88191882 0.86655113]

Model Metrics (epoch 43):
Accuracy:  0.7916666666666666
Precision: [0.68644068 0.73540856 0.77286136 0.74129353 0.775      0.83703704
 0.89416058 0.90035587]
Recall:    [0.81818182 0.63210702 0.87333333 0.68348624 0.64583333 0.89328063
 0.88129496 0.84615385]
F-Score:   [0.74654378 0.67985612 0.8200313  0.71121718 0.70454545 0.86424474
 0.88768116 0.87241379]

Model Metrics (epoch 44):
Accuracy:  0.7897940074906367
Precision: [0.74918567 0.71212121 0.74104683 0.73732719 0.8        0.81884058
 0.91119691 0.86206897]
Recall:    [0.77441077 0.62876254 0.89666667 0.73394495 0.66666667 0.89328063
 0.84892086 0.8361204 ]
F-Score:   [0.7615894  0.6678508  0.81146305 0.73563218 0.72727273 0.85444234
 0.87895717 0.84889643]

Model Metrics (epoch 45):
Accuracy:  0.7916666666666666
Precision: [0.73225806 0.61878453 0.85910653 0.73831776 0.85611511 0.84555985
 0.89298893 0.86896552]
Recall:    [0.76430976 0.74916388 0.83333333 0.72477064 0.61979167 0.86561265
 0.8705036  0.84280936]
F-Score:   [0.74794069 0.67776097 0.84602369 0.73148148 0.71903323 0.85546875
 0.88160291 0.85568761]

Model Metrics (epoch 46):
Accuracy:  0.7902621722846442
Precision: [0.7208589  0.73245614 0.74520548 0.73891626 0.75287356 0.82846715
 0.90073529 0.8877551 ]
Recall:    [0.79124579 0.55852843 0.90666667 0.68807339 0.68229167 0.8972332
 0.88129496 0.8729097 ]
F-Score:   [0.75441413 0.63377609 0.81804511 0.71258907 0.71584699 0.86148008
 0.89090909 0.88026981]

Model Metrics (epoch 47):
Accuracy:  0.7869850187265918
Precision: [0.7173913  0.68531469 0.80707395 0.78021978 0.71502591 0.80645161
 0.88929889 0.88013699]
Recall:    [0.77777778 0.65551839 0.83666667 0.65137615 0.71875    0.88932806
 0.86690647 0.85953177]
F-Score:   [0.74636511 0.67008547 0.82160393 0.71       0.71688312 0.84586466
 0.87795993 0.86971235]

Model Metrics (epoch 48):
Accuracy:  0.7949438202247191
Precision: [0.74757282 0.6877193  0.77391304 0.72072072 0.80392157 0.83088235
 0.93333333 0.87118644]
Recall:    [0.77777778 0.65551839 0.89       0.73394495 0.640625   0.89328063
 0.85611511 0.85953177]
F-Score:   [0.76237624 0.67123288 0.82790698 0.72727273 0.71304348 0.86095238
 0.89305816 0.86531987]

Model Metrics (epoch 49):
Accuracy:  0.798689138576779
Precision: [0.7191358  0.7238806  0.80298507 0.72641509 0.79166667 0.82481752
 0.89454545 0.89642857]
Recall:    [0.78451178 0.64882943 0.89666667 0.70642202 0.69270833 0.89328063
 0.88489209 0.83946488]
F-Score:   [0.75040258 0.68430335 0.84724409 0.71627907 0.73888889 0.85768501
 0.88969259 0.86701209]

Model Metrics (epoch 50):
Accuracy:  0.7897940074906367
Precision: [0.74482759 0.68976898 0.82315113 0.67219917 0.78846154 0.77966102
 0.92607004 0.89399293]
Recall:    [0.72727273 0.69899666 0.85333333 0.74311927 0.640625   0.90909091
 0.85611511 0.84615385]
F-Score:   [0.73594549 0.69435216 0.83797054 0.70588235 0.70689655 0.83941606
 0.88971963 0.86941581]

Model Metrics (epoch 51):
Accuracy:  0.7982209737827716
Precision: [0.7284345  0.65014577 0.83333333 0.74619289 0.84027778 0.8267148
 0.90636704 0.90106007]
Recall:    [0.76767677 0.7458194  0.86666667 0.67431193 0.63020833 0.90513834
 0.8705036  0.85284281]
F-Score:   [0.74754098 0.69470405 0.8496732  0.70843373 0.7202381  0.86415094
 0.88807339 0.87628866]

Model Metrics (epoch 52):
Accuracy:  0.7940074906367042
Precision: [0.76140351 0.6975089  0.81790123 0.70386266 0.69518717 0.84074074
 0.8962963  0.89160839]
Recall:    [0.73063973 0.65551839 0.88333333 0.75229358 0.67708333 0.8972332
 0.8705036  0.85284281]
F-Score:   [0.74570447 0.67586207 0.84935897 0.72727273 0.68601583 0.86806883
 0.88321168 0.87179487]

Model Metrics (epoch 53):
Accuracy:  0.8080524344569289
Precision: [0.73291925 0.70915033 0.8729097  0.76923077 0.79375    0.81818182
 0.92941176 0.84565916]
Recall:    [0.79461279 0.72575251 0.87       0.73394495 0.66145833 0.88932806
 0.85251799 0.87959866]
F-Score:   [0.76252019 0.71735537 0.87145242 0.75117371 0.72159091 0.85227273
 0.88930582 0.86229508]

Model Metrics (epoch 54):
Accuracy:  0.797752808988764
Precision: [0.72155689 0.68881119 0.78362573 0.79473684 0.82517483 0.83955224
 0.89454545 0.86577181]
Recall:    [0.81144781 0.65886288 0.89333333 0.69266055 0.61458333 0.88932806
 0.88489209 0.86287625]
F-Score:   [0.76386688 0.67350427 0.83489097 0.74019608 0.70447761 0.86372361
 0.88969259 0.86432161]

Model Metrics (epoch 55):
Accuracy:  0.8047752808988764
Precision: [0.69616519 0.72280702 0.83333333 0.73364486 0.79268293 0.89112903
 0.88380282 0.88965517]
Recall:    [0.79461279 0.68896321 0.86666667 0.72018349 0.67708333 0.87351779
 0.9028777  0.86287625]
F-Score:   [0.74213836 0.70547945 0.8496732  0.72685185 0.73033708 0.88223553
 0.89323843 0.87606112]

Model Metrics (epoch 56):
Accuracy:  0.8029026217228464
Precision: [0.72151899 0.74603175 0.76944444 0.73873874 0.80379747 0.86486486
 0.86868687 0.91544118]
Recall:    [0.76767677 0.62876254 0.92333333 0.75229358 0.66145833 0.88537549
 0.92805755 0.83277592]
F-Score:   [0.74388254 0.68239564 0.83939394 0.74545455 0.72571429 0.875
 0.8973913  0.87215412]

Model Metrics (epoch 57):
Accuracy:  0.7911985018726592
Precision: [0.6909621  0.67123288 0.84666667 0.77835052 0.70652174 0.84586466
 0.89416058 0.89045936]
Recall:    [0.7979798  0.65551839 0.84666667 0.69266055 0.67708333 0.88932806
 0.88129496 0.84280936]
F-Score:   [0.740625   0.66328257 0.84666667 0.73300971 0.69148936 0.86705202
 0.88768116 0.86597938]

Model Metrics (epoch 58):
Accuracy:  0.8038389513108615
Precision: [0.73333333 0.72426471 0.80645161 0.72294372 0.78343949 0.82310469
 0.92619926 0.89198606]
Recall:    [0.74074074 0.65886288 0.91666667 0.76605505 0.640625   0.90118577
 0.9028777  0.85618729]
F-Score:   [0.73701843 0.69001751 0.85803432 0.74387528 0.70487106 0.86037736
 0.9143898  0.87372014]

Model Metrics (epoch 59):
Accuracy:  0.8014981273408239
Precision: [0.70694864 0.67912773 0.87323944 0.75376884 0.79268293 0.84758364
 0.91538462 0.86363636]
Recall:    [0.78787879 0.72909699 0.82666667 0.68807339 0.67708333 0.90118577
 0.85611511 0.88963211]
F-Score:   [0.74522293 0.70322581 0.84931507 0.71942446 0.73033708 0.87356322
 0.88475836 0.87644152]

Model Metrics (epoch 60):
Accuracy:  0.7921348314606742
Precision: [0.73898305 0.73484848 0.79341317 0.73239437 0.71282051 0.81949458
 0.86879433 0.89855072]
Recall:    [0.73400673 0.64882943 0.88333333 0.71559633 0.72395833 0.8972332
 0.88129496 0.82943144]
F-Score:   [0.73648649 0.68916519 0.83596215 0.72389791 0.71834625 0.85660377
 0.875      0.8626087 ]

Model Metrics (epoch 61):
Accuracy:  0.8038389513108615
Precision: [0.71692308 0.68608414 0.8627451  0.76649746 0.81208054 0.82142857
 0.90145985 0.875     ]
Recall:    [0.78451178 0.7090301  0.88       0.69266055 0.63020833 0.90909091
 0.88848921 0.86622074]
F-Score:   [0.74919614 0.69736842 0.87128713 0.72771084 0.70967742 0.8630394
 0.89492754 0.87058824]

Model Metrics (epoch 62):
Accuracy:  0.8000936329588015
Precision: [0.71515152 0.70408163 0.80804954 0.77083333 0.78616352 0.82857143
 0.90217391 0.89007092]
Recall:    [0.79461279 0.69230769 0.87       0.67889908 0.65104167 0.91699605
 0.89568345 0.83946488]
F-Score:   [0.75279107 0.69814503 0.83788122 0.72195122 0.71225071 0.87054409
 0.89891697 0.86402754]

Model Metrics (epoch 63):
Accuracy:  0.799625468164794
Precision: [0.75342466 0.67823344 0.81677019 0.73853211 0.7654321  0.84410646
 0.91791045 0.87414966]
Recall:    [0.74074074 0.71906355 0.87666667 0.73853211 0.64583333 0.87747036
 0.88489209 0.85953177]
F-Score:   [0.74702886 0.69805195 0.84565916 0.73853211 0.70056497 0.86046512
 0.9010989  0.86677909]

Model Metrics (epoch 64):
Accuracy:  0.7940074906367042
Precision: [0.73684211 0.74561404 0.73854447 0.74193548 0.73913043 0.82562278
 0.91044776 0.90106007]
Recall:    [0.75420875 0.56856187 0.91333333 0.73853211 0.70833333 0.91699605
 0.87769784 0.85284281]
F-Score:   [0.74542429 0.64516129 0.81669151 0.74022989 0.72340426 0.86891386
 0.89377289 0.87628866]

Model Metrics (epoch 65):
Accuracy:  0.7954119850187266
Precision: [0.72468354 0.65349544 0.84589041 0.8        0.71808511 0.85877863
 0.9141791  0.85947712]
Recall:    [0.77104377 0.71906355 0.82333333 0.64220183 0.703125   0.88932806
 0.88129496 0.87959866]
F-Score:   [0.74714519 0.68471338 0.83445946 0.71246819 0.71052632 0.87378641
 0.8974359  0.86942149]

Model Metrics (epoch 66):
Accuracy:  0.798689138576779
Precision: [0.71246006 0.70671378 0.83280757 0.6872428  0.79375    0.86206897
 0.92720307 0.86577181]
Recall:    [0.75084175 0.66889632 0.88       0.76605505 0.66145833 0.88932806
 0.8705036  0.86287625]
F-Score:   [0.73114754 0.68728522 0.85575365 0.72451193 0.72159091 0.87548638
 0.89795918 0.86432161]

Model Metrics (epoch 67):
Accuracy:  0.7963483146067416
Precision: [0.75       0.66049383 0.80368098 0.7989418  0.76969697 0.83712121
 0.91320755 0.85478548]
Recall:    [0.75757576 0.71571906 0.87333333 0.69266055 0.66145833 0.87351779
 0.8705036  0.86622074]
F-Score:   [0.75376884 0.68699839 0.8370607  0.74201474 0.71148459 0.8549323
 0.89134438 0.86046512]

Model Metrics (epoch 68):
Accuracy:  0.7897940074906367
Precision: [0.69230769 0.70955882 0.79692308 0.79775281 0.75690608 0.8125
 0.92015209 0.84364821]
Recall:    [0.78787879 0.64548495 0.86333333 0.65137615 0.71354167 0.87351779
 0.8705036  0.86622074]
F-Score:   [0.73700787 0.67600701 0.8288     0.71717172 0.73458445 0.84190476
 0.89463956 0.85478548]

Model Metrics (epoch 69):
Accuracy:  0.798689138576779
Precision: [0.69827586 0.69155844 0.82484076 0.80116959 0.80769231 0.84790875
 0.88530466 0.86868687]
Recall:    [0.81818182 0.71237458 0.86333333 0.62844037 0.65625    0.88142292
 0.88848921 0.86287625]
F-Score:   [0.75348837 0.70181219 0.84364821 0.70437018 0.72413793 0.86434109
 0.88689408 0.86577181]

Model Metrics (epoch 70):
Accuracy:  0.7968164794007491
Precision: [0.71962617 0.77542373 0.7740113  0.76683938 0.69387755 0.80565371
 0.89568345 0.92      ]
Recall:    [0.77777778 0.61204013 0.91333333 0.67889908 0.70833333 0.90118577
 0.89568345 0.84615385]
F-Score:   [0.74757282 0.68411215 0.83792049 0.72019465 0.70103093 0.85074627
 0.89568345 0.8815331 ]

Model Metrics (epoch 71):
Accuracy:  0.7911985018726592
Precision: [0.70426829 0.71272727 0.83946488 0.72685185 0.68341709 0.82733813
 0.91891892 0.89007092]
Recall:    [0.77777778 0.65551839 0.83666667 0.72018349 0.70833333 0.90909091
 0.85611511 0.83946488]
F-Score:   [0.7392     0.68292683 0.83806344 0.7235023  0.69565217 0.86629002
 0.88640596 0.86402754]

Model Metrics (epoch 72):
Accuracy:  0.8029026217228464
Precision: [0.71052632 0.69932432 0.87586207 0.79487179 0.76744186 0.78571429
 0.91764706 0.8869863 ]
Recall:    [0.81818182 0.69230769 0.84666667 0.71100917 0.6875     0.91304348
 0.84172662 0.86622074]
F-Score:   [0.76056338 0.69579832 0.86101695 0.75060533 0.72527473 0.84460695
 0.87804878 0.87648054]

Model Metrics (epoch 73):
Accuracy:  0.8029026217228464
Precision: [0.75       0.73021583 0.79178886 0.75233645 0.80368098 0.85338346
 0.83946488 0.89818182]
Recall:    [0.75757576 0.67892977 0.9        0.73853211 0.68229167 0.8972332
 0.9028777  0.82608696]
F-Score:   [0.75376884 0.70363951 0.8424337  0.74537037 0.73802817 0.87475915
 0.87001733 0.86062718]

Model Metrics (epoch 74):
Accuracy:  0.8000936329588015
Precision: [0.73927393 0.73646209 0.7632312  0.75242718 0.84172662 0.80136986
 0.88297872 0.90647482]
Recall:    [0.75420875 0.68227425 0.91333333 0.71100917 0.609375   0.92490119
 0.89568345 0.84280936]
F-Score:   [0.74666667 0.70833333 0.83156297 0.73113208 0.70694864 0.8587156
 0.88928571 0.87348354]

Model Metrics (epoch 75):
Accuracy:  0.7963483146067416
Precision: [0.74840764 0.66561514 0.82747604 0.78238342 0.79084967 0.7781457
 0.87142857 0.9280303 ]
Recall:    [0.79124579 0.70568562 0.86333333 0.69266055 0.63020833 0.92885375
 0.87769784 0.81939799]
F-Score:   [0.76923077 0.68506494 0.84502447 0.73479319 0.70144928 0.84684685
 0.87455197 0.87033748]

Model Metrics (epoch 76):
Accuracy:  0.799625468164794
Precision: [0.72615385 0.69337979 0.81402439 0.74654378 0.7972028  0.86538462
 0.88530466 0.86868687]
Recall:    [0.79461279 0.66555184 0.89       0.74311927 0.59375    0.88932806
 0.88848921 0.86287625]
F-Score:   [0.75884244 0.67918089 0.85031847 0.74482759 0.68059701 0.87719298
 0.88689408 0.86577181]

Model Metrics (epoch 77):
Accuracy:  0.798689138576779
Precision: [0.70897833 0.68338558 0.88380282 0.73809524 0.75581395 0.86206897
 0.89962825 0.8590604 ]
Recall:    [0.77104377 0.72909699 0.83666667 0.71100917 0.67708333 0.88932806
 0.8705036  0.85618729]
F-Score:   [0.73870968 0.70550162 0.85958904 0.72429907 0.71428571 0.87548638
 0.88482633 0.85762144]

Model Metrics (epoch 78):
Accuracy:  0.7982209737827716
Precision: [0.74285714 0.68421053 0.81372549 0.77272727 0.7393617  0.83754513
 0.86       0.917603  ]
Recall:    [0.78787879 0.65217391 0.83       0.70183486 0.72395833 0.91699605
 0.92805755 0.81939799]
F-Score:   [0.76470588 0.66780822 0.82178218 0.73557692 0.73157895 0.8754717
 0.89273356 0.86572438]

Model Metrics (epoch 79):
Accuracy:  0.8043071161048689
Precision: [0.72981366 0.76095618 0.78425656 0.76303318 0.75418994 0.84191176
 0.87543253 0.91078067]
Recall:    [0.79124579 0.63879599 0.89666667 0.73853211 0.703125   0.90513834
 0.91007194 0.81939799]
F-Score:   [0.75928918 0.69454545 0.83670295 0.75058275 0.7277628  0.87238095
 0.89241623 0.86267606]

Model Metrics (epoch 80):
Accuracy:  0.7954119850187266
Precision: [0.75261324 0.73207547 0.80060423 0.69915254 0.72043011 0.85283019
 0.88768116 0.87586207]
Recall:    [0.72727273 0.64882943 0.88333333 0.75688073 0.69791667 0.89328063
 0.88129496 0.84949833]
F-Score:   [0.73972603 0.68794326 0.83993661 0.72687225 0.70899471 0.87258687
 0.88447653 0.86247878]

Model Metrics (epoch 81):
Accuracy:  0.802434456928839
Precision: [0.69590643 0.73722628 0.79411765 0.76842105 0.8        0.83272727
 0.91698113 0.88965517]
Recall:    [0.8013468  0.67558528 0.9        0.66972477 0.66666667 0.90513834
 0.87410072 0.86287625]
F-Score:   [0.74491393 0.70506108 0.84375    0.71568627 0.72727273 0.86742424
 0.89502762 0.87606112]

Model Metrics (epoch 82):
Accuracy:  0.8052434456928839
Precision: [0.71385542 0.68910256 0.84590164 0.76442308 0.80952381 0.84615385
 0.92075472 0.87414966]
Recall:    [0.7979798  0.71906355 0.86       0.7293578  0.61979167 0.91304348
 0.87769784 0.85953177]
F-Score:   [0.75357711 0.70376432 0.85289256 0.74647887 0.7020649  0.878327
 0.89871087 0.86677909]

Model Metrics (epoch 83):
Accuracy:  0.800561797752809
Precision: [0.73538462 0.65060241 0.86241611 0.81318681 0.76023392 0.8
 0.92248062 0.89122807]
Recall:    [0.8047138  0.72240803 0.85666667 0.67889908 0.67708333 0.90118577
 0.85611511 0.84949833]
F-Score:   [0.76848875 0.68462758 0.85953177 0.74       0.71625344 0.84758364
 0.8880597  0.86986301]

Model Metrics (epoch 84):
Accuracy:  0.8066479400749064
Precision: [0.71301775 0.70333333 0.82043344 0.80555556 0.8410596  0.81588448
 0.91729323 0.87707641]
Recall:    [0.81144781 0.70568562 0.88333333 0.66513761 0.66145833 0.89328063
 0.87769784 0.88294314]
F-Score:   [0.75905512 0.70450751 0.85072231 0.72864322 0.74052478 0.85283019
 0.89705882 0.88      ]

Model Metrics (epoch 85):
Accuracy:  0.8043071161048689
Precision: [0.71260997 0.70689655 0.83174603 0.76142132 0.77575758 0.82105263
 0.92164179 0.90545455]
Recall:    [0.81818182 0.68561873 0.87333333 0.68807339 0.66666667 0.92490119
 0.88848921 0.83277592]
F-Score:   [0.76175549 0.69609508 0.85203252 0.72289157 0.71708683 0.86988848
 0.9047619  0.86759582]

Model Metrics (epoch 86):
Accuracy:  0.7921348314606742
Precision: [0.77205882 0.72941176 0.76589595 0.72444444 0.69117647 0.82142857
 0.90671642 0.88811189]
Recall:    [0.70707071 0.62207358 0.88333333 0.74770642 0.734375   0.90909091
 0.87410072 0.84949833]
F-Score:   [0.73813708 0.67148014 0.82043344 0.73589165 0.71212121 0.8630394
 0.89010989 0.86837607]

Model Metrics (epoch 87):
Accuracy:  0.802434456928839
Precision: [0.78091873 0.74444444 0.8030303  0.70815451 0.7037037  0.83636364
 0.91078067 0.88501742]
Recall:    [0.74410774 0.6722408  0.88333333 0.75688073 0.69270833 0.90909091
 0.88129496 0.84949833]
F-Score:   [0.76206897 0.70650264 0.84126984 0.73170732 0.69816273 0.87121212
 0.89579525 0.8668942 ]

Model Metrics (epoch 88):
Accuracy:  0.7926029962546817
Precision: [0.73972603 0.67109635 0.85714286 0.70247934 0.73446328 0.83512545
 0.88447653 0.89323843]
Recall:    [0.72727273 0.67558528 0.82       0.77981651 0.67708333 0.92094862
 0.88129496 0.83946488]
F-Score:   [0.73344652 0.67333333 0.83816014 0.73913043 0.70460705 0.87593985
 0.88288288 0.86551724]

Model Metrics (epoch 89):
Accuracy:  0.7935393258426966
Precision: [0.68011527 0.73151751 0.82484076 0.69709544 0.84090909 0.81468531
 0.87197232 0.91851852]
Recall:    [0.79461279 0.62876254 0.86333333 0.7706422  0.578125   0.92094862
 0.90647482 0.82943144]
F-Score:   [0.73291925 0.67625899 0.84364821 0.73202614 0.68518519 0.86456401
 0.88888889 0.87170475]

Model Metrics (epoch 90):
Accuracy:  0.8014981273408239
Precision: [0.77083333 0.671875   0.82153846 0.78756477 0.72625698 0.80902778
 0.93333333 0.88541667]
Recall:    [0.74747475 0.71906355 0.89       0.69724771 0.67708333 0.92094862
 0.85611511 0.85284281]
F-Score:   [0.75897436 0.69466882 0.8544     0.73965937 0.70080863 0.86136784
 0.89305816 0.86882453]

Model Metrics (epoch 91):
Accuracy:  0.7968164794007491
Precision: [0.73202614 0.72377622 0.8496732  0.74178404 0.71666667 0.7704918
 0.89219331 0.91881919]
Recall:    [0.75420875 0.69230769 0.86666667 0.72477064 0.671875   0.92885375
 0.86330935 0.83277592]
F-Score:   [0.74295191 0.70769231 0.85808581 0.73317865 0.69354839 0.84229391
 0.87751371 0.87368421]

Model Metrics (epoch 92):
Accuracy:  0.8014981273408239
Precision: [0.72136223 0.69407895 0.84437086 0.7755102  0.75144509 0.81625442
 0.90181818 0.9       ]
Recall:    [0.78451178 0.70568562 0.85       0.69724771 0.67708333 0.91304348
 0.89208633 0.84280936]
F-Score:   [0.7516129  0.69983416 0.84717608 0.73429952 0.71232877 0.8619403
 0.89692586 0.87046632]

Model Metrics (epoch 93):
Accuracy:  0.802434456928839
Precision: [0.7120743  0.71331058 0.79239766 0.72321429 0.8671875  0.85714286
 0.90145985 0.8951049 ]
Recall:    [0.77441077 0.69899666 0.90333333 0.74311927 0.578125   0.90118577
 0.88848921 0.85618729]
F-Score:   [0.74193548 0.70608108 0.84423676 0.73303167 0.69375    0.87861272
 0.89492754 0.87521368]

Model Metrics (epoch 94):
Accuracy:  0.795880149812734
Precision: [0.7414966  0.66346154 0.8006135  0.77894737 0.71657754 0.876
 0.91881919 0.8627451 ]
Recall:    [0.73400673 0.69230769 0.87       0.67889908 0.69791667 0.86561265
 0.89568345 0.88294314]
F-Score:   [0.73773266 0.67757774 0.83386581 0.7254902  0.70712401 0.87077535
 0.90710383 0.87272727]

Model Metrics (epoch 95):
Accuracy:  0.7935393258426966
Precision: [0.76653696 0.69520548 0.78698225 0.6539924  0.78846154 0.85658915
 0.90909091 0.88552189]
Recall:    [0.66329966 0.67892977 0.88666667 0.78899083 0.640625   0.87351779
 0.89928058 0.87959866]
F-Score:   [0.71119134 0.68697124 0.8338558  0.71517672 0.70689655 0.86497065
 0.90415913 0.88255034]

Model Metrics (epoch 96):
Accuracy:  0.7982209737827716
Precision: [0.77859779 0.69256757 0.78338279 0.71748879 0.75       0.81428571
 0.91512915 0.91134752]
Recall:    [0.71043771 0.68561873 0.88       0.73394495 0.6875     0.90118577
 0.89208633 0.85953177]
F-Score:   [0.74295775 0.68907563 0.8288854  0.72562358 0.7173913  0.85553471
 0.90346084 0.88468158]

Model Metrics (epoch 97):
Accuracy:  0.7926029962546817
Precision: [0.70461538 0.72972973 0.77207977 0.68965517 0.79452055 0.85606061
 0.90145985 0.89473684]
Recall:    [0.77104377 0.63210702 0.90333333 0.73394495 0.60416667 0.89328063
 0.88848921 0.85284281]
F-Score:   [0.73633441 0.67741935 0.83256528 0.71111111 0.68639053 0.87427466
 0.89492754 0.87328767]

Model Metrics (epoch 98):
Accuracy:  0.7907303370786517
Precision: [0.69164265 0.6466877  0.8615917  0.73762376 0.80405405 0.86328125
 0.9010989  0.85526316]
Recall:    [0.80808081 0.68561873 0.83       0.68348624 0.61979167 0.87351779
 0.88489209 0.86956522]
F-Score:   [0.74534161 0.66558442 0.84550085 0.70952381 0.7        0.86836935
 0.89292196 0.86235489]

Model Metrics (epoch 99):
Accuracy:  0.8033707865168539
Precision: [0.70710059 0.68690096 0.79279279 0.78571429 0.88321168 0.88235294
 0.86971831 0.89115646]
Recall:    [0.8047138  0.71906355 0.88       0.6559633  0.63020833 0.88932806
 0.88848921 0.87625418]
F-Score:   [0.75275591 0.70261438 0.83412322 0.715      0.73556231 0.88582677
 0.87900356 0.8836425 ]

Model Metrics (epoch 100):
Accuracy:  0.797752808988764
Precision: [0.71608833 0.68918919 0.81790123 0.7300885  0.82857143 0.81654676
 0.92664093 0.87837838]
Recall:    [0.76430976 0.68227425 0.88333333 0.75688073 0.60416667 0.8972332
 0.86330935 0.86956522]
F-Score:   [0.73941368 0.68571429 0.84935897 0.74324324 0.69879518 0.85499058
 0.89385475 0.87394958]
Saving model to disk
In [3]:
accuracies, precisions, recalls, fscores = model_res
In [4]:
avgprecisions = [np.mean(x) for x in precisions]
avgrecalls = [np.mean(x) for x in recalls]
avgfscores = [np.mean(x) for x in fscores]
In [6]:
evaluations = list(zip(accuracies, avgprecisions, avgrecalls, avgfscores))
eval_df = pd.DataFrame(evaluations, columns = ["accuracy", "avg_precision", "avg_recalls", "avg_fscore"])
display(eval_df)
eval_df.to_csv('8_16_classified.csv', index=False)

accuracy avg_precision avg_recalls avg_fscore
0 0.215797 0.086094 0.191888 0.084543
1 0.231273 0.127198 0.214218 0.128174
2 0.283708 0.168810 0.261959 0.180516
3 0.281367 0.340289 0.275688 0.212701
4 0.391854 0.342352 0.382694 0.354030
... ... ... ... ...
95 0.798221 0.795350 0.793788 0.793451
96 0.792603 0.792857 0.784901 0.785789
97 0.790730 0.795155 0.781869 0.786200
98 0.803371 0.812369 0.793003 0.798566
99 0.797753 0.800426 0.790134 0.792415

100 rows × 4 columns

In [7]:
plt.figure()
eval_df.plot(figsize = (15,10))
Out[7]:
<matplotlib.axes._subplots.AxesSubplot at 0x181eebc3ef0>
<Figure size 432x288 with 0 Axes>
In [13]:
print("Epoch max fscore:", avgfscores.index(max(avgfscores)))
print(lstm.check_all_test("testMids")
Out[13]:
Epoch max fscore: 52
Test Accuracy : 0.803152