corrections

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Claudio Maggioni 2021-06-09 15:59:06 +02:00
parent d99d89e2d7
commit 69d5f63b51
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from tensorflow.keras.models import load_model
import os
import pickle
import urllib.request as http
from zipfile import ZipFile
from tensorflow.keras import utils
import tensorflow as tf
import numpy as np
from PIL import Image
from tensorflow.keras import layers as keras_layers
from tensorflow.keras import backend as K
from tensorflow.keras.datasets import cifar10
from tensorflow.keras.models import save_model, load_model
def load_cifar10(num_classes=3):
"""
Downloads CIFAR-10 dataset, which already contains a training and test set,
and return the first `num_classes` classes.
Example of usage:
>>> (x_train, y_train), (x_test, y_test) = load_cifar10()
:param num_classes: int, default is 3 as required by the assignment.
:return: the filtered data.
"""
(x_train_all, y_train_all), (x_test_all, y_test_all) = cifar10.load_data()
fil_train = tf.where(y_train_all[:, 0] < num_classes)[:, 0]
fil_test = tf.where(y_test_all[:, 0] < num_classes)[:, 0]
y_train = y_train_all[fil_train]
y_test = y_test_all[fil_test]
x_train = x_train_all[fil_train]
x_test = x_test_all[fil_test]
return (x_train, y_train), (x_test, y_test)
if __name__ == '__main__':
_, (x_test, y_test) = load_cifar10()
x_test_n = x_test / 255
y_test_n = utils.to_categorical(y_test, 3)
lrs = ["01", "0001"]
nns = [16, 64]
for lr in lrs:
for nn in nns:
# Load the trained models
model_task1 = load_model('nn_task1_bonus/t1_bonus_%s_%d.h5' % (lr, nn))
# Predict on the given samples
y_pred_task1 = model_task1.predict(x_test_n)
# Evaluate the missclassification error on the test set
assert y_test_n.shape == y_pred_task1.shape
test_loss, test_accuracy = model_task1.evaluate(x_test_n, y_test_n) # evaluate accuracy with proper function
print("Accuracy model task 1 (%d neurons, 0.%s learning rate):" % (nn, lr), test_accuracy)

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@ -103,6 +103,11 @@ The script will download automatically the models.
Then, in order to run the models with the \texttt{run\_task*.py}, make sure you first \texttt{cd} in the
\texttt{deliverable} directory.
Should the script not work, please download the compressed T1 and T2 models from
\url{https://drive.switch.ch/index.php/s/F0ubFgS6PBy8UM5/download} and uncompress the downloaded \texttt{.tar.gz}
file in the \texttt{deliverable/} directory, and then make sure the directories \texttt{deliverable/nn\_task1} and \texttt{deliverable/nn\_task2}
contain 3 \texttt{.h5} files in total.
\maketitle
In this assignment you are asked to:
@ -180,6 +185,60 @@ The training and validation accuracy curves for the network is shown below:
\caption{Training and validation accuracy curves during fitting for the CIFAR10 classifier}
\end{figure}
\subsection{Bonus}
A plot of the validation loss and accuracy over training epochs for each of the configurations in the grid search are given below.
The plot is useful to understand that for both learning rates chosen, choosing 64 neurons will lead to overfitting, due to the validation
loss being higher than the training loss. This overfitting cannot be easily avoided with the adopted early stopping procedure, since
our implementation monitors validation loss and not validation accuracy.
The second observation that can be found from the plots is that a smaller learning rate increases the number of epochs needed to achieve convergence
but it also increases the model accuracy and decreases the model loss.
By running all the models on the test set, using the script \texttt{run\_task1\_bonus.py} in the \texttt{deliverable/} directory,
we obtain the following loss and accuracy:
\begin{center}
\begin{tabular}{cccc}
\textbf{Learn rate} & \textbf{\# Neurons} & \textbf{Loss} & \textbf{Accuracy} \\\hline
0.01 & 16 & 0.4597 & 0.8117 \\
0.01 & 64 & 0.4508 & 0.8290 \\
0.0001 & 16 & 0.4048 & 0.8437 \\
0.0001 & 64 & 0.4073 & 0.8343 \\
\end{tabular}
\end{center}
We choose the third configuration as the optimal one since it has highest accuracy (and lowest loss too). We then perform a T-test
between this model and the one built in the previous section using the script \texttt{src/t\_test\_bonus.py}.
The T-test gives a T-score of 1.374106, corresponding to a P-value of 0.169511, which makes us accept the null hypothesis therefore
concluding that there is no significant difference between this optimal model and the one trained in the previous section.
\begin{figure}[H]
\centering
\resizebox{0.75\textwidth}{!}{%
\includegraphics[width=0.25\textwidth]{./t1_bonus_0001_16.png}}
\caption{Training and validation accuracy curves during fitting for the CIFAR10 classifier (0.0001 learning rate, 16 neurons)}
\end{figure}
\begin{figure}[H]
\centering
\resizebox{0.75\textwidth}{!}{%
\includegraphics[width=0.25\textwidth]{./t1_bonus_0001_64.png}}
\caption{Training and validation accuracy curves during fitting for the CIFAR10 classifier (0.0001 learning rate, 64 neurons)}
\end{figure}
\begin{figure}[H]
\centering
\resizebox{0.75\textwidth}{!}{%
\includegraphics[width=0.25\textwidth]{./t1_bonus_01_16.png}}
\caption{Training and validation accuracy curves during fitting for the CIFAR10 classifier (0.01 learning rate, 16 neurons)}
\end{figure}
\begin{figure}[H]
\centering
\resizebox{\textwidth}{!}{%
\includegraphics{./t1_bonus_01_64.png}}
\caption{Training and validation accuracy curves during fitting for the CIFAR10 classifier (0.01 learning rate, 64 neurons)}
\end{figure}
%----------------------------------------------------------------------------------------
% Task 2
%----------------------------------------------------------------------------------------

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149,0.8233333230018616,0.4379068613052368,0.8259999752044678,0.4573875665664673
150,0.8230833411216736,0.43709826469421387,0.8256666660308838,0.4587935507297516
151,0.8264999985694885,0.4359411895275116,0.8273333311080933,0.44938838481903076
152,0.8260833621025085,0.43441638350486755,0.8226666450500488,0.46108269691467285
153,0.8252500295639038,0.4348291754722595,0.8273333311080933,0.449728399515152
154,0.8260833621025085,0.43436935544013977,0.8330000042915344,0.44688400626182556
155,0.825166642665863,0.4338207244873047,0.8256666660308838,0.4547237157821655
156,0.828249990940094,0.4312988817691803,0.8140000104904175,0.4719071090221405
157,0.828416645526886,0.4321413040161133,0.8176666498184204,0.471040278673172
158,0.828166663646698,0.4318599998950958,0.8273333311080933,0.45197200775146484
159,0.828166663646698,0.430090993642807,0.82833331823349,0.44439029693603516
160,0.8285833597183228,0.42939189076423645,0.8289999961853027,0.44576922059059143
161,0.82791668176651,0.4281749129295349,0.8326666951179504,0.4409608840942383
162,0.8273333311080933,0.42749324440956116,0.8293333053588867,0.44817423820495605
163,0.8259999752044678,0.4273661673069,0.8336666822433472,0.44242313504219055
164,0.8308333158493042,0.42598238587379456,0.8339999914169312,0.4399816393852234
165,0.8295000195503235,0.4251851737499237,0.8343333601951599,0.43797993659973145
166,0.831416666507721,0.4249284565448761,0.8333333134651184,0.4396743178367615
167,0.8302500247955322,0.4233575165271759,0.8330000042915344,0.44218865036964417
168,0.8289999961853027,0.42399221658706665,0.8259999752044678,0.46105313301086426
169,0.8308333158493042,0.42323893308639526,0.8240000009536743,0.4512389004230499
170,0.8326666951179504,0.42107221484184265,0.8303333520889282,0.4463028907775879
171,0.8318333625793457,0.42121410369873047,0.8299999833106995,0.44618427753448486
172,0.8338333368301392,0.4210498034954071,0.8346666693687439,0.4352149963378906
173,0.8330000042915344,0.4207684397697449,0.8343333601951599,0.4385925829410553
174,0.8338333368301392,0.4197690486907959,0.8303333520889282,0.4400194585323334
175,0.8330833315849304,0.41875529289245605,0.8386666774749756,0.43167558312416077
176,0.831416666507721,0.4174823462963104,0.8363333344459534,0.432464063167572
177,0.8331666588783264,0.4170875549316406,0.8330000042915344,0.4378066956996918
178,0.8335833549499512,0.41703271865844727,0.8339999914169312,0.43457409739494324
179,0.8335833549499512,0.41658085584640503,0.82833331823349,0.446471631526947
180,0.8343333601951599,0.41647860407829285,0.8336666822433472,0.4432835578918457
181,0.8351666927337646,0.41469189524650574,0.8339999914169312,0.4488014876842499
182,0.8349999785423279,0.41478434205055237,0.8346666693687439,0.4341667592525482
183,0.8360000252723694,0.4139315187931061,0.8389999866485596,0.42700210213661194
184,0.8343333601951599,0.41383248567581177,0.8330000042915344,0.44171130657196045
185,0.8361666798591614,0.4140091836452484,0.8383333086967468,0.4264924228191376
186,0.8364999890327454,0.41313403844833374,0.8403333425521851,0.42911815643310547
187,0.8358333110809326,0.4112303555011749,0.8276666402816772,0.43771249055862427
188,0.8367499709129333,0.4110860228538513,0.8346666693687439,0.4293495714664459
189,0.8371666669845581,0.4093056619167328,0.8276666402816772,0.43895062804222107
190,0.8358333110809326,0.40977051854133606,0.8386666774749756,0.42875438928604126
191,0.8359166383743286,0.40980789065361023,0.8410000205039978,0.4244656562805176
192,0.8376666903495789,0.40832388401031494,0.8383333086967468,0.42901018261909485
193,0.8379999995231628,0.4090903699398041,0.8339999914169312,0.4351048469543457
194,0.840499997138977,0.40731289982795715,0.8383333086967468,0.42468371987342834
195,0.8369166851043701,0.40723738074302673,0.8429999947547913,0.4221477508544922
196,0.8379999995231628,0.4069906771183014,0.843999981880188,0.42132091522216797
197,0.8375833630561829,0.4065573215484619,0.8413333296775818,0.4236030578613281
198,0.8385000228881836,0.40566837787628174,0.8433333039283752,0.4220002293586731
199,0.8375833630561829,0.40544015169143677,0.8403333425521851,0.4219377934932709
200,0.8396666646003723,0.4040237367153168,0.8356666564941406,0.4344198703765869
201,0.8389166593551636,0.4050865173339844,0.8403333425521851,0.42142143845558167
202,0.8419166803359985,0.4029446542263031,0.8416666388511658,0.42275139689445496
203,0.8383333086967468,0.4023597836494446,0.8423333168029785,0.4203735291957855
204,0.8392500281333923,0.40254902839660645,0.8420000076293945,0.4188210368156433
205,0.840416669845581,0.40155965089797974,0.8426666855812073,0.41766902804374695
206,0.8402500152587891,0.4019468128681183,0.840666651725769,0.4243781864643097
1 epoch accuracy loss val_accuracy val_loss
2 0 0.4284999966621399 1.0838768482208252 0.4869999885559082 1.0578199625015259
3 1 0.5239166617393494 1.0402600765228271 0.5659999847412109 1.0190595388412476
4 2 0.5817499756813049 0.9990922212600708 0.5910000205039978 0.9864984750747681
5 3 0.6119999885559082 0.9622434377670288 0.6259999871253967 0.949017345905304
6 4 0.6326666474342346 0.9315217137336731 0.6393333077430725 0.9176478385925293
7 5 0.6454166769981384 0.9024352431297302 0.652999997138977 0.8911747932434082
8 6 0.6585000157356262 0.8730461597442627 0.6690000295639038 0.8625798225402832
9 7 0.6735000014305115 0.8446393013000488 0.6773333549499512 0.835181474685669
10 8 0.6888333559036255 0.8144475221633911 0.6913333535194397 0.8033531308174133
11 9 0.703166663646698 0.7868669629096985 0.7013333439826965 0.7806670069694519
12 10 0.706166684627533 0.763378918170929 0.7110000252723694 0.757719099521637
13 11 0.7145833373069763 0.7431033253669739 0.715666651725769 0.741998553276062
14 12 0.718916654586792 0.7261846661567688 0.7210000157356262 0.7289196252822876
15 13 0.7229999899864197 0.7120341658592224 0.7239999771118164 0.7132157683372498
16 14 0.7258333563804626 0.6993649005889893 0.7233333587646484 0.70282381772995
17 15 0.7294166684150696 0.6884589791297913 0.7303333282470703 0.689950704574585
18 16 0.7324166893959045 0.6778770089149475 0.7319999933242798 0.681061863899231
19 17 0.7354999780654907 0.6690850257873535 0.737333357334137 0.6833087801933289
20 18 0.7399166822433472 0.6607983708381653 0.7369999885559082 0.6706054210662842
21 19 0.7423333525657654 0.6530723571777344 0.7403333187103271 0.6624245643615723
22 20 0.7432500123977661 0.6457955837249756 0.7486666440963745 0.6517229676246643
23 21 0.746833324432373 0.6393903493881226 0.75 0.643888533115387
24 22 0.7488333582878113 0.6326268911361694 0.7486666440963745 0.6383176445960999
25 23 0.7483333349227905 0.6276751160621643 0.7490000128746033 0.6381990313529968
26 24 0.7508333325386047 0.6220626831054688 0.7536666393280029 0.6306868195533752
27 25 0.7518333196640015 0.6173197031021118 0.7553333044052124 0.6245272159576416
28 26 0.7560833096504211 0.6129053831100464 0.7526666522026062 0.6247814893722534
29 27 0.7541666626930237 0.6082961559295654 0.7536666393280029 0.6236191987991333
30 28 0.7570000290870667 0.6036962866783142 0.7590000033378601 0.6098343133926392
31 29 0.7574166655540466 0.601693332195282 0.7580000162124634 0.6087884306907654
32 30 0.7584166526794434 0.5974898934364319 0.7586666941642761 0.6052170991897583
33 31 0.7605000138282776 0.5938131213188171 0.7620000243186951 0.6003297567367554
34 32 0.762416660785675 0.5905328989028931 0.7639999985694885 0.5992019176483154
35 33 0.7612500190734863 0.5880444645881653 0.7643333077430725 0.5923108458518982
36 34 0.7616666555404663 0.5853286385536194 0.7639999985694885 0.5930081605911255
37 35 0.7638333439826965 0.5823095440864563 0.762666642665863 0.5964158773422241
38 36 0.765999972820282 0.5795621275901794 0.7673333287239075 0.5843967795372009
39 37 0.7668333053588867 0.5765686631202698 0.7663333415985107 0.5817729234695435
40 38 0.765916645526886 0.5750483274459839 0.7703333497047424 0.5850620269775391
41 39 0.765999972820282 0.5720317959785461 0.7699999809265137 0.5821769833564758
42 40 0.7690833210945129 0.5689754486083984 0.7563333511352539 0.6062623858451843
43 41 0.7701666951179504 0.5680242776870728 0.7749999761581421 0.5748355388641357
44 42 0.7706666588783264 0.565283477306366 0.7703333497047424 0.5693557858467102
45 43 0.7690833210945129 0.5632652044296265 0.762666642665863 0.5945876240730286
46 44 0.7721666693687439 0.5612371563911438 0.7743333578109741 0.5655432939529419
47 45 0.7735833525657654 0.5595787763595581 0.7726666927337646 0.5788276195526123
48 46 0.7735000252723694 0.5573917627334595 0.7706666588783264 0.5659079551696777
49 47 0.7726666927337646 0.5553656816482544 0.7746666669845581 0.565401554107666
50 48 0.7738333344459534 0.5532978773117065 0.7770000100135803 0.5598950386047363
51 49 0.7741666436195374 0.5511548519134521 0.7720000147819519 0.572717547416687
52 50 0.7744166851043701 0.5503709316253662 0.7816666960716248 0.5534486770629883
53 51 0.7755833268165588 0.5489298701286316 0.7746666669845581 0.5592584013938904
54 52 0.778249979019165 0.5472049117088318 0.7793333530426025 0.5620111227035522
55 53 0.778083324432373 0.5453718304634094 0.7820000052452087 0.5521920919418335
56 54 0.7774166464805603 0.5436322093009949 0.7753333449363708 0.5749772787094116
57 55 0.7795833349227905 0.5426769852638245 0.7833333611488342 0.54844069480896
58 56 0.781499981880188 0.5414560437202454 0.7863333225250244 0.5461897850036621
59 57 0.7798333168029785 0.5391659736633301 0.7873333096504211 0.5434498190879822
60 58 0.781000018119812 0.537829577922821 0.7860000133514404 0.5473216772079468
61 59 0.78125 0.5362415313720703 0.7833333611488342 0.5422953963279724
62 60 0.7825833559036255 0.5345484018325806 0.7896666526794434 0.5397301912307739
63 61 0.781083345413208 0.5333306193351746 0.7846666574478149 0.5488216280937195
64 62 0.781499981880188 0.5316175818443298 0.7866666913032532 0.5375092029571533
65 63 0.7825833559036255 0.5311059355735779 0.7913333177566528 0.5380165576934814
66 64 0.7825833559036255 0.5293938517570496 0.7860000133514404 0.5474703907966614
67 65 0.784583330154419 0.526911199092865 0.7940000295639038 0.534210741519928
68 66 0.7850833535194397 0.526195228099823 0.7796666622161865 0.5485478639602661
69 67 0.784500002861023 0.5255493521690369 0.7879999876022339 0.5286822319030762
70 68 0.7829166650772095 0.5241622924804688 0.7879999876022339 0.5352179408073425
71 69 0.7868333458900452 0.5224430561065674 0.793666660785675 0.5249139666557312
72 70 0.7879999876022339 0.5207787752151489 0.7953333258628845 0.5275792479515076
73 71 0.7878333330154419 0.5201119184494019 0.7900000214576721 0.5244088768959045
74 72 0.7862499952316284 0.518958568572998 0.7946666479110718 0.523970901966095
75 73 0.7882500290870667 0.5165284276008606 0.7866666913032532 0.5394734144210815
76 74 0.7892500162124634 0.5153293609619141 0.7923333048820496 0.527486264705658
77 75 0.7892500162124634 0.5146932005882263 0.7960000038146973 0.5198792815208435
78 76 0.7899166941642761 0.5136726498603821 0.7996666431427002 0.5181387662887573
79 77 0.7906666398048401 0.5122131109237671 0.7916666865348816 0.5219271183013916
80 78 0.793749988079071 0.510484516620636 0.7976666688919067 0.513494074344635
81 79 0.7921666502952576 0.5083696842193604 0.7960000038146973 0.5170464515686035
82 80 0.7898333072662354 0.5088156461715698 0.7976666688919067 0.5127342939376831
83 81 0.7913333177566528 0.5076265335083008 0.7990000247955322 0.5130664110183716
84 82 0.7923333048820496 0.5065186619758606 0.7983333468437195 0.5115938186645508
85 83 0.793833315372467 0.5060837268829346 0.7946666479110718 0.5189694762229919
86 84 0.7951666712760925 0.5030198097229004 0.8016666769981384 0.5069235563278198
87 85 0.7958333492279053 0.5018414855003357 0.7986666560173035 0.5069368481636047
88 86 0.7948333621025085 0.5011022090911865 0.7950000166893005 0.518936276435852
89 87 0.7963333129882812 0.4992195963859558 0.8023333549499512 0.510757327079773
90 88 0.7948333621025085 0.49941515922546387 0.8026666641235352 0.5029817819595337
91 89 0.7955833077430725 0.49796411395072937 0.8016666769981384 0.5108314156532288
92 90 0.796999990940094 0.49605679512023926 0.8050000071525574 0.5015484094619751
93 91 0.7985833287239075 0.4947517514228821 0.8066666722297668 0.5003050565719604
94 92 0.7985000014305115 0.4942174553871155 0.7979999780654907 0.5157888531684875
95 93 0.7991666793823242 0.49348536133766174 0.8046666383743286 0.5041453242301941
96 94 0.7982500195503235 0.4924047887325287 0.8069999814033508 0.4955296218395233
97 95 0.8004166483879089 0.4904239773750305 0.8040000200271606 0.5005924701690674
98 96 0.800083339214325 0.48912033438682556 0.8066666722297668 0.4985409379005432
99 97 0.799833357334137 0.4888407289981842 0.7976666688919067 0.5117080211639404
100 98 0.8034999966621399 0.48750850558280945 0.8076666593551636 0.49502503871917725
101 99 0.8027499914169312 0.4864256680011749 0.8059999942779541 0.49409762024879456
102 100 0.8030833601951599 0.4843618869781494 0.8076666593551636 0.49316972494125366
103 101 0.8027499914169312 0.48391976952552795 0.8056666851043701 0.49657031893730164
104 102 0.8028333187103271 0.48304617404937744 0.8109999895095825 0.4907311201095581
105 103 0.8035833239555359 0.48315954208374023 0.8103333115577698 0.4878624975681305
106 104 0.8054166436195374 0.48030945658683777 0.8080000281333923 0.4901617467403412
107 105 0.8042500019073486 0.4796009957790375 0.8119999766349792 0.48475587368011475
108 106 0.8058333396911621 0.47907406091690063 0.8023333549499512 0.5024172067642212
109 107 0.8073333501815796 0.47742727398872375 0.8163333535194397 0.4822111427783966
110 108 0.8069999814033508 0.4768243134021759 0.8140000104904175 0.4833918809890747
111 109 0.8089166879653931 0.47576457262039185 0.7940000295639038 0.5274575352668762
112 110 0.8097500205039978 0.4746205508708954 0.8143333196640015 0.4814397692680359
113 111 0.8080833554267883 0.4733660817146301 0.8166666626930237 0.4792422950267792
114 112 0.8110833168029785 0.47120606899261475 0.8050000071525574 0.49773457646369934
115 113 0.8081666827201843 0.4709709584712982 0.8146666884422302 0.4788338541984558
116 114 0.809416651725769 0.4698839485645294 0.8103333115577698 0.48417407274246216
117 115 0.8108333349227905 0.4692935347557068 0.8103333115577698 0.48160356283187866
118 116 0.8082500100135803 0.46925967931747437 0.8183333277702332 0.47494229674339294
119 117 0.8088333606719971 0.4674088656902313 0.8180000185966492 0.4744347631931305
120 118 0.8110833168029785 0.4659174680709839 0.8199999928474426 0.4724946618080139
121 119 0.8117499947547913 0.46476221084594727 0.8193333148956299 0.4741252064704895
122 120 0.8131666779518127 0.4641077518463135 0.8166666626930237 0.47541138529777527
123 121 0.8112499713897705 0.4629130959510803 0.8143333196640015 0.47737279534339905
124 122 0.8129166960716248 0.46172896027565 0.8196666836738586 0.4691659212112427
125 123 0.8130833506584167 0.4603128731250763 0.8163333535194397 0.47983890771865845
126 124 0.8149999976158142 0.45970287919044495 0.812333345413208 0.4799528419971466
127 125 0.8136666417121887 0.4591692388057709 0.8199999928474426 0.4662765562534332
128 126 0.8149166703224182 0.458272784948349 0.8223333358764648 0.46743473410606384
129 127 0.8163333535194397 0.45662394165992737 0.8106666803359985 0.48275816440582275
130 128 0.815833330154419 0.45521605014801025 0.8063333630561829 0.4981910288333893
131 129 0.815666675567627 0.4543023407459259 0.8240000009536743 0.46369388699531555
132 130 0.8162500262260437 0.4547498822212219 0.8236666917800903 0.4625664949417114
133 131 0.8163333535194397 0.45360472798347473 0.8216666579246521 0.46581321954727173
134 132 0.8171666860580444 0.4513089656829834 0.8213333487510681 0.46794694662094116
135 133 0.8174999952316284 0.4502676725387573 0.8236666917800903 0.46201929450035095
136 134 0.8175833225250244 0.450626939535141 0.8233333230018616 0.4603627622127533
137 135 0.8180000185966492 0.44978514313697815 0.8223333358764648 0.46608027815818787
138 136 0.8181666731834412 0.4481619894504547 0.8253333568572998 0.45728397369384766
139 137 0.8199166655540466 0.44881880283355713 0.8243333101272583 0.45805856585502625
140 138 0.8180000185966492 0.44711729884147644 0.8196666836738586 0.4678555130958557
141 139 0.8193333148956299 0.44647112488746643 0.8253333568572998 0.4564843475818634
142 140 0.8205833435058594 0.44517993927001953 0.8256666660308838 0.4554981589317322
143 141 0.8193333148956299 0.4448118805885315 0.8259999752044678 0.4561938941478729
144 142 0.8237500190734863 0.44255080819129944 0.8253333568572998 0.45700377225875854
145 143 0.8211666941642761 0.4421866536140442 0.812333345413208 0.47945523262023926
146 144 0.8218333125114441 0.44304102659225464 0.8273333311080933 0.45237383246421814
147 145 0.8228333592414856 0.44051310420036316 0.8240000009536743 0.45798397064208984
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150 148 0.8225833177566528 0.43944698572158813 0.8233333230018616 0.46108153462409973
151 149 0.8233333230018616 0.4379068613052368 0.8259999752044678 0.4573875665664673
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153 151 0.8264999985694885 0.4359411895275116 0.8273333311080933 0.44938838481903076
154 152 0.8260833621025085 0.43441638350486755 0.8226666450500488 0.46108269691467285
155 153 0.8252500295639038 0.4348291754722595 0.8273333311080933 0.449728399515152
156 154 0.8260833621025085 0.43436935544013977 0.8330000042915344 0.44688400626182556
157 155 0.825166642665863 0.4338207244873047 0.8256666660308838 0.4547237157821655
158 156 0.828249990940094 0.4312988817691803 0.8140000104904175 0.4719071090221405
159 157 0.828416645526886 0.4321413040161133 0.8176666498184204 0.471040278673172
160 158 0.828166663646698 0.4318599998950958 0.8273333311080933 0.45197200775146484
161 159 0.828166663646698 0.430090993642807 0.82833331823349 0.44439029693603516
162 160 0.8285833597183228 0.42939189076423645 0.8289999961853027 0.44576922059059143
163 161 0.82791668176651 0.4281749129295349 0.8326666951179504 0.4409608840942383
164 162 0.8273333311080933 0.42749324440956116 0.8293333053588867 0.44817423820495605
165 163 0.8259999752044678 0.4273661673069 0.8336666822433472 0.44242313504219055
166 164 0.8308333158493042 0.42598238587379456 0.8339999914169312 0.4399816393852234
167 165 0.8295000195503235 0.4251851737499237 0.8343333601951599 0.43797993659973145
168 166 0.831416666507721 0.4249284565448761 0.8333333134651184 0.4396743178367615
169 167 0.8302500247955322 0.4233575165271759 0.8330000042915344 0.44218865036964417
170 168 0.8289999961853027 0.42399221658706665 0.8259999752044678 0.46105313301086426
171 169 0.8308333158493042 0.42323893308639526 0.8240000009536743 0.4512389004230499
172 170 0.8326666951179504 0.42107221484184265 0.8303333520889282 0.4463028907775879
173 171 0.8318333625793457 0.42121410369873047 0.8299999833106995 0.44618427753448486
174 172 0.8338333368301392 0.4210498034954071 0.8346666693687439 0.4352149963378906
175 173 0.8330000042915344 0.4207684397697449 0.8343333601951599 0.4385925829410553
176 174 0.8338333368301392 0.4197690486907959 0.8303333520889282 0.4400194585323334
177 175 0.8330833315849304 0.41875529289245605 0.8386666774749756 0.43167558312416077
178 176 0.831416666507721 0.4174823462963104 0.8363333344459534 0.432464063167572
179 177 0.8331666588783264 0.4170875549316406 0.8330000042915344 0.4378066956996918
180 178 0.8335833549499512 0.41703271865844727 0.8339999914169312 0.43457409739494324
181 179 0.8335833549499512 0.41658085584640503 0.82833331823349 0.446471631526947
182 180 0.8343333601951599 0.41647860407829285 0.8336666822433472 0.4432835578918457
183 181 0.8351666927337646 0.41469189524650574 0.8339999914169312 0.4488014876842499
184 182 0.8349999785423279 0.41478434205055237 0.8346666693687439 0.4341667592525482
185 183 0.8360000252723694 0.4139315187931061 0.8389999866485596 0.42700210213661194
186 184 0.8343333601951599 0.41383248567581177 0.8330000042915344 0.44171130657196045
187 185 0.8361666798591614 0.4140091836452484 0.8383333086967468 0.4264924228191376
188 186 0.8364999890327454 0.41313403844833374 0.8403333425521851 0.42911815643310547
189 187 0.8358333110809326 0.4112303555011749 0.8276666402816772 0.43771249055862427
190 188 0.8367499709129333 0.4110860228538513 0.8346666693687439 0.4293495714664459
191 189 0.8371666669845581 0.4093056619167328 0.8276666402816772 0.43895062804222107
192 190 0.8358333110809326 0.40977051854133606 0.8386666774749756 0.42875438928604126
193 191 0.8359166383743286 0.40980789065361023 0.8410000205039978 0.4244656562805176
194 192 0.8376666903495789 0.40832388401031494 0.8383333086967468 0.42901018261909485
195 193 0.8379999995231628 0.4090903699398041 0.8339999914169312 0.4351048469543457
196 194 0.840499997138977 0.40731289982795715 0.8383333086967468 0.42468371987342834
197 195 0.8369166851043701 0.40723738074302673 0.8429999947547913 0.4221477508544922
198 196 0.8379999995231628 0.4069906771183014 0.843999981880188 0.42132091522216797
199 197 0.8375833630561829 0.4065573215484619 0.8413333296775818 0.4236030578613281
200 198 0.8385000228881836 0.40566837787628174 0.8433333039283752 0.4220002293586731
201 199 0.8375833630561829 0.40544015169143677 0.8403333425521851 0.4219377934932709
202 200 0.8396666646003723 0.4040237367153168 0.8356666564941406 0.4344198703765869
203 201 0.8389166593551636 0.4050865173339844 0.8403333425521851 0.42142143845558167
204 202 0.8419166803359985 0.4029446542263031 0.8416666388511658 0.42275139689445496
205 203 0.8383333086967468 0.4023597836494446 0.8423333168029785 0.4203735291957855
206 204 0.8392500281333923 0.40254902839660645 0.8420000076293945 0.4188210368156433
207 205 0.840416669845581 0.40155965089797974 0.8426666855812073 0.41766902804374695
208 206 0.8402500152587891 0.4019468128681183 0.840666651725769 0.4243781864643097

View File

@ -0,0 +1,22 @@
epoch,accuracy,loss,val_accuracy,val_loss
0,0.41100001335144043,1.0990421772003174,0.5046666860580444,0.986140787601471
1,0.5824166536331177,0.9083436131477356,0.49966666102409363,1.1090927124023438
2,0.6570833325386047,0.7929704785346985,0.6413333415985107,0.8897007703781128
3,0.6971666812896729,0.7256202101707458,0.7279999852180481,0.6892613172531128
4,0.7409999966621399,0.6392995715141296,0.7876666784286499,0.5518867373466492
5,0.7586666941642761,0.595727801322937,0.7879999876022339,0.5398681163787842
6,0.765999972820282,0.5687591433525085,0.7883333563804626,0.5295681953430176
7,0.7902500033378601,0.5268591642379761,0.7873333096504211,0.5313050746917725
8,0.7881666421890259,0.5140411853790283,0.8033333420753479,0.5078040361404419
9,0.8003333210945129,0.4939863681793213,0.7733333110809326,0.5571079850196838
10,0.8050000071525574,0.48484984040260315,0.8253333568572998,0.4746514558792114
11,0.8144999742507935,0.46468856930732727,0.7986666560173035,0.5026190876960754
12,0.8208333253860474,0.4587477743625641,0.8146666884422302,0.47092440724372864
13,0.8224166631698608,0.4552185833454132,0.8080000281333923,0.48748379945755005
14,0.8230000138282776,0.4415058493614197,0.8006666898727417,0.5011689066886902
15,0.8287500143051147,0.4330746531486511,0.8096666932106018,0.48085156083106995
16,0.828166663646698,0.43311864137649536,0.8116666674613953,0.4814765155315399
17,0.8344166874885559,0.4186524748802185,0.8213333487510681,0.4748263657093048
18,0.8352500200271606,0.41888558864593506,0.7846666574478149,0.5632604360580444
19,0.843666672706604,0.39935922622680664,0.8243333101272583,0.4644140601158142
20,0.8442500233650208,0.4010440409183502,0.8100000023841858,0.4936281442642212
1 epoch accuracy loss val_accuracy val_loss
2 0 0.41100001335144043 1.0990421772003174 0.5046666860580444 0.986140787601471
3 1 0.5824166536331177 0.9083436131477356 0.49966666102409363 1.1090927124023438
4 2 0.6570833325386047 0.7929704785346985 0.6413333415985107 0.8897007703781128
5 3 0.6971666812896729 0.7256202101707458 0.7279999852180481 0.6892613172531128
6 4 0.7409999966621399 0.6392995715141296 0.7876666784286499 0.5518867373466492
7 5 0.7586666941642761 0.595727801322937 0.7879999876022339 0.5398681163787842
8 6 0.765999972820282 0.5687591433525085 0.7883333563804626 0.5295681953430176
9 7 0.7902500033378601 0.5268591642379761 0.7873333096504211 0.5313050746917725
10 8 0.7881666421890259 0.5140411853790283 0.8033333420753479 0.5078040361404419
11 9 0.8003333210945129 0.4939863681793213 0.7733333110809326 0.5571079850196838
12 10 0.8050000071525574 0.48484984040260315 0.8253333568572998 0.4746514558792114
13 11 0.8144999742507935 0.46468856930732727 0.7986666560173035 0.5026190876960754
14 12 0.8208333253860474 0.4587477743625641 0.8146666884422302 0.47092440724372864
15 13 0.8224166631698608 0.4552185833454132 0.8080000281333923 0.48748379945755005
16 14 0.8230000138282776 0.4415058493614197 0.8006666898727417 0.5011689066886902
17 15 0.8287500143051147 0.4330746531486511 0.8096666932106018 0.48085156083106995
18 16 0.828166663646698 0.43311864137649536 0.8116666674613953 0.4814765155315399
19 17 0.8344166874885559 0.4186524748802185 0.8213333487510681 0.4748263657093048
20 18 0.8352500200271606 0.41888558864593506 0.7846666574478149 0.5632604360580444
21 19 0.843666672706604 0.39935922622680664 0.8243333101272583 0.4644140601158142
22 20 0.8442500233650208 0.4010440409183502 0.8100000023841858 0.4936281442642212

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@ -0,0 +1,34 @@
epoch,accuracy,loss,val_accuracy,val_loss
0,0.3686666786670685,1.2228714227676392,0.49000000953674316,1.0355132818222046
1,0.5740000009536743,0.9258081316947937,0.6809999942779541,0.7483968138694763
2,0.6480000019073486,0.8203122019767761,0.5013333559036255,1.076539158821106
3,0.7013333439826965,0.703206479549408,0.7776666879653931,0.5626736879348755
4,0.7287499904632568,0.6631432175636292,0.7706666588783264,0.559110701084137
5,0.7475833296775818,0.6060731410980225,0.6549999713897705,0.8372300863265991
6,0.7550833225250244,0.5816248655319214,0.7929999828338623,0.5178568363189697
7,0.7753333449363708,0.5454885959625244,0.7696666717529297,0.5542431473731995
8,0.7870000004768372,0.5182647109031677,0.796999990940094,0.4952922761440277
9,0.7987499833106995,0.5003635287284851,0.8059999942779541,0.494720458984375
10,0.8073333501815796,0.48458853363990784,0.8113333582878113,0.47935450077056885
11,0.8107500076293945,0.4713839888572693,0.7889999747276306,0.5396782755851746
12,0.8218333125114441,0.4507473409175873,0.8146666884422302,0.4668084383010864
13,0.8296666741371155,0.4340798258781433,0.8193333148956299,0.4502803683280945
14,0.8324166536331177,0.42343637347221375,0.8193333148956299,0.48487967252731323
15,0.8372499942779541,0.4068948030471802,0.7996666431427002,0.49272286891937256
16,0.8428333401679993,0.4035722315311432,0.8119999766349792,0.47965121269226074
17,0.8485000133514404,0.39331746101379395,0.8163333535194397,0.4844145178794861
18,0.8524166941642761,0.3779684603214264,0.8173333406448364,0.4811861515045166
19,0.8514166474342346,0.38542646169662476,0.815666675567627,0.4602762758731842
20,0.8569166660308838,0.3612472414970398,0.7670000195503235,0.5996257066726685
21,0.8636666536331177,0.35209357738494873,0.8176666498184204,0.478706955909729
22,0.8675833344459534,0.3399489223957062,0.8263333439826965,0.46295085549354553
23,0.8653333187103271,0.3398178219795227,0.812333345413208,0.48238879442214966
24,0.8755000233650208,0.32959985733032227,0.7943333387374878,0.5647448897361755
25,0.878166675567627,0.3222483992576599,0.8169999718666077,0.49329763650894165
26,0.8794999718666077,0.31432077288627625,0.8263333439826965,0.4871461093425751
27,0.8855000138282776,0.3000155985355377,0.8109999895095825,0.49287939071655273
28,0.8869166374206543,0.28836581110954285,0.8046666383743286,0.5498819351196289
29,0.8915833234786987,0.2824133038520813,0.8206666707992554,0.49784693121910095
30,0.8934999704360962,0.2731992304325104,0.815666675567627,0.5200576782226562
31,0.9000833630561829,0.2694721519947052,0.8146666884422302,0.5229155421257019
32,0.9021666646003723,0.255941778421402,0.7973333597183228,0.6015266180038452
1 epoch accuracy loss val_accuracy val_loss
2 0 0.3686666786670685 1.2228714227676392 0.49000000953674316 1.0355132818222046
3 1 0.5740000009536743 0.9258081316947937 0.6809999942779541 0.7483968138694763
4 2 0.6480000019073486 0.8203122019767761 0.5013333559036255 1.076539158821106
5 3 0.7013333439826965 0.703206479549408 0.7776666879653931 0.5626736879348755
6 4 0.7287499904632568 0.6631432175636292 0.7706666588783264 0.559110701084137
7 5 0.7475833296775818 0.6060731410980225 0.6549999713897705 0.8372300863265991
8 6 0.7550833225250244 0.5816248655319214 0.7929999828338623 0.5178568363189697
9 7 0.7753333449363708 0.5454885959625244 0.7696666717529297 0.5542431473731995
10 8 0.7870000004768372 0.5182647109031677 0.796999990940094 0.4952922761440277
11 9 0.7987499833106995 0.5003635287284851 0.8059999942779541 0.494720458984375
12 10 0.8073333501815796 0.48458853363990784 0.8113333582878113 0.47935450077056885
13 11 0.8107500076293945 0.4713839888572693 0.7889999747276306 0.5396782755851746
14 12 0.8218333125114441 0.4507473409175873 0.8146666884422302 0.4668084383010864
15 13 0.8296666741371155 0.4340798258781433 0.8193333148956299 0.4502803683280945
16 14 0.8324166536331177 0.42343637347221375 0.8193333148956299 0.48487967252731323
17 15 0.8372499942779541 0.4068948030471802 0.7996666431427002 0.49272286891937256
18 16 0.8428333401679993 0.4035722315311432 0.8119999766349792 0.47965121269226074
19 17 0.8485000133514404 0.39331746101379395 0.8163333535194397 0.4844145178794861
20 18 0.8524166941642761 0.3779684603214264 0.8173333406448364 0.4811861515045166
21 19 0.8514166474342346 0.38542646169662476 0.815666675567627 0.4602762758731842
22 20 0.8569166660308838 0.3612472414970398 0.7670000195503235 0.5996257066726685
23 21 0.8636666536331177 0.35209357738494873 0.8176666498184204 0.478706955909729
24 22 0.8675833344459534 0.3399489223957062 0.8263333439826965 0.46295085549354553
25 23 0.8653333187103271 0.3398178219795227 0.812333345413208 0.48238879442214966
26 24 0.8755000233650208 0.32959985733032227 0.7943333387374878 0.5647448897361755
27 25 0.878166675567627 0.3222483992576599 0.8169999718666077 0.49329763650894165
28 26 0.8794999718666077 0.31432077288627625 0.8263333439826965 0.4871461093425751
29 27 0.8855000138282776 0.3000155985355377 0.8109999895095825 0.49287939071655273
30 28 0.8869166374206543 0.28836581110954285 0.8046666383743286 0.5498819351196289
31 29 0.8915833234786987 0.2824133038520813 0.8206666707992554 0.49784693121910095
32 30 0.8934999704360962 0.2731992304325104 0.815666675567627 0.5200576782226562
33 31 0.9000833630561829 0.2694721519947052 0.8146666884422302 0.5229155421257019
34 32 0.9021666646003723 0.255941778421402 0.7973333597183228 0.6015266180038452

View File

@ -0,0 +1,148 @@
epoch,accuracy,loss,val_accuracy,val_loss
0,0.42133334279060364,1.092440128326416,0.5216666460037231,1.0831907987594604
1,0.48258334398269653,1.0691678524017334,0.578000009059906,1.0515673160552979
2,0.5686666369438171,1.0266000032424927,0.6179999709129333,0.9952982664108276
3,0.6392499804496765,0.962194561958313,0.6690000295639038,0.9293439388275146
4,0.6725000143051147,0.8931296467781067,0.6743333339691162,0.8646112680435181
5,0.6887500286102295,0.8338881731033325,0.6893333196640015,0.8154566884040833
6,0.6955000162124634,0.7897202372550964,0.6940000057220459,0.7788802981376648
7,0.7021666765213013,0.7585954070091248,0.7016666531562805,0.7553337216377258
8,0.7095000147819519,0.7358428835868835,0.7093333601951599,0.7391981482505798
9,0.7130833268165588,0.7185309529304504,0.7113333344459534,0.7247134447097778
10,0.7210000157356262,0.7036754488945007,0.7170000076293945,0.7144900560379028
11,0.7225000262260437,0.6903489828109741,0.7210000157356262,0.7021248936653137
12,0.7294999957084656,0.6791210174560547,0.7263333201408386,0.6896677017211914
13,0.7315833568572998,0.6669951677322388,0.731333315372467,0.6810266971588135
14,0.7365000247955322,0.6564996838569641,0.7366666793823242,0.6689144372940063
15,0.7398333549499512,0.6476236581802368,0.7396666407585144,0.6566316485404968
16,0.7418333292007446,0.639543890953064,0.7443333268165588,0.6492511034011841
17,0.7449166774749756,0.6309488415718079,0.7440000176429749,0.6400158405303955
18,0.7507500052452087,0.622377872467041,0.7490000128746033,0.6308714151382446
19,0.7514166831970215,0.6148524284362793,0.750333309173584,0.6255932450294495
20,0.7551666498184204,0.6085936427116394,0.7566666603088379,0.6217103600502014
21,0.7580000162124634,0.6017386317253113,0.7593333125114441,0.609600841999054
22,0.7609999775886536,0.5949487090110779,0.7543333172798157,0.6064338088035583
23,0.7635833621025085,0.5881058573722839,0.7549999952316284,0.6035630106925964
24,0.7632499933242798,0.5832110047340393,0.7663333415985107,0.5898523926734924
25,0.7692499756813049,0.5766729116439819,0.765666663646698,0.5871346592903137
26,0.7705000042915344,0.5713057518005371,0.7730000019073486,0.5790905952453613
27,0.7727500200271606,0.5663045048713684,0.7736666798591614,0.5738856792449951
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1 epoch accuracy loss val_accuracy val_loss
2 0 0.42133334279060364 1.092440128326416 0.5216666460037231 1.0831907987594604
3 1 0.48258334398269653 1.0691678524017334 0.578000009059906 1.0515673160552979
4 2 0.5686666369438171 1.0266000032424927 0.6179999709129333 0.9952982664108276
5 3 0.6392499804496765 0.962194561958313 0.6690000295639038 0.9293439388275146
6 4 0.6725000143051147 0.8931296467781067 0.6743333339691162 0.8646112680435181
7 5 0.6887500286102295 0.8338881731033325 0.6893333196640015 0.8154566884040833
8 6 0.6955000162124634 0.7897202372550964 0.6940000057220459 0.7788802981376648
9 7 0.7021666765213013 0.7585954070091248 0.7016666531562805 0.7553337216377258
10 8 0.7095000147819519 0.7358428835868835 0.7093333601951599 0.7391981482505798
11 9 0.7130833268165588 0.7185309529304504 0.7113333344459534 0.7247134447097778
12 10 0.7210000157356262 0.7036754488945007 0.7170000076293945 0.7144900560379028
13 11 0.7225000262260437 0.6903489828109741 0.7210000157356262 0.7021248936653137
14 12 0.7294999957084656 0.6791210174560547 0.7263333201408386 0.6896677017211914
15 13 0.7315833568572998 0.6669951677322388 0.731333315372467 0.6810266971588135
16 14 0.7365000247955322 0.6564996838569641 0.7366666793823242 0.6689144372940063
17 15 0.7398333549499512 0.6476236581802368 0.7396666407585144 0.6566316485404968
18 16 0.7418333292007446 0.639543890953064 0.7443333268165588 0.6492511034011841
19 17 0.7449166774749756 0.6309488415718079 0.7440000176429749 0.6400158405303955
20 18 0.7507500052452087 0.622377872467041 0.7490000128746033 0.6308714151382446
21 19 0.7514166831970215 0.6148524284362793 0.750333309173584 0.6255932450294495
22 20 0.7551666498184204 0.6085936427116394 0.7566666603088379 0.6217103600502014
23 21 0.7580000162124634 0.6017386317253113 0.7593333125114441 0.609600841999054
24 22 0.7609999775886536 0.5949487090110779 0.7543333172798157 0.6064338088035583
25 23 0.7635833621025085 0.5881058573722839 0.7549999952316284 0.6035630106925964
26 24 0.7632499933242798 0.5832110047340393 0.7663333415985107 0.5898523926734924
27 25 0.7692499756813049 0.5766729116439819 0.765666663646698 0.5871346592903137
28 26 0.7705000042915344 0.5713057518005371 0.7730000019073486 0.5790905952453613
29 27 0.7727500200271606 0.5663045048713684 0.7736666798591614 0.5738856792449951
30 28 0.7745833396911621 0.5610684156417847 0.7773333191871643 0.5671684145927429
31 29 0.7770833373069763 0.5563601851463318 0.7760000228881836 0.5635906457901001
32 30 0.7768333554267883 0.5514505505561829 0.7836666703224182 0.5566589832305908
33 31 0.778166651725769 0.5458187460899353 0.7583333253860474 0.5909481048583984
34 32 0.7806666493415833 0.5436875224113464 0.7850000262260437 0.5474307537078857
35 33 0.7822499871253967 0.5379191637039185 0.784333348274231 0.5488753914833069
36 34 0.7837499976158142 0.5342518091201782 0.7753333449363708 0.5533681511878967
37 35 0.7864166498184204 0.5308119058609009 0.7820000052452087 0.5461623668670654
38 36 0.7887499928474426 0.5277165770530701 0.781333327293396 0.5409450531005859
39 37 0.7874166369438171 0.5240803360939026 0.7893333435058594 0.5401000380516052
40 38 0.7885833382606506 0.5225352644920349 0.7910000085830688 0.5266817808151245
41 39 0.7888333201408386 0.5176399946212769 0.7926666736602783 0.5274956822395325
42 40 0.7893333435058594 0.5159404277801514 0.793666660785675 0.5229296684265137
43 41 0.7930833101272583 0.5116210579872131 0.7756666541099548 0.5420834422111511
44 42 0.7921666502952576 0.5109115242958069 0.7956666946411133 0.5163940191268921
45 43 0.7943333387374878 0.5071904063224792 0.7950000166893005 0.5143405795097351
46 44 0.7944166660308838 0.5054174661636353 0.777999997138977 0.528532087802887
47 45 0.796999990940094 0.5010374188423157 0.7983333468437195 0.5131596326828003
48 46 0.796750009059906 0.5006341934204102 0.7973333597183228 0.509623646736145
49 47 0.7987499833106995 0.4990909695625305 0.7993333339691162 0.5073418617248535
50 48 0.7978333234786987 0.497801274061203 0.7986666560173035 0.5058290958404541
51 49 0.796999990940094 0.49675676226615906 0.7986666560173035 0.5052936673164368
52 50 0.7978333234786987 0.49446648359298706 0.7906666398048401 0.5101428627967834
53 51 0.7987499833106995 0.49141305685043335 0.7910000085830688 0.5080826878547668
54 52 0.8018333315849304 0.49017277359962463 0.8009999990463257 0.5003789067268372
55 53 0.7990833520889282 0.4900963008403778 0.8003333210945129 0.5013912916183472
56 54 0.8021666407585144 0.48861438035964966 0.7983333468437195 0.500831127166748
57 55 0.8028333187103271 0.4864603281021118 0.7979999780654907 0.5079330205917358
58 56 0.8043333292007446 0.48527541756629944 0.8059999942779541 0.49590444564819336
59 57 0.8024166822433472 0.4850764274597168 0.800000011920929 0.4999690353870392
60 58 0.8036666512489319 0.4833196997642517 0.8016666769981384 0.4955070912837982
61 59 0.8026666641235352 0.4804866909980774 0.7950000166893005 0.5002061128616333
62 60 0.8040000200271606 0.48245230317115784 0.7979999780654907 0.4973487854003906
63 61 0.8060833215713501 0.47835856676101685 0.7910000085830688 0.5042082071304321
64 62 0.8060833215713501 0.4784422516822815 0.8046666383743286 0.4913029670715332
65 63 0.8070833086967468 0.47728076577186584 0.8006666898727417 0.49307548999786377
66 64 0.8063333630561829 0.47460415959358215 0.8066666722297668 0.4880809187889099
67 65 0.8078333139419556 0.4747520387172699 0.8066666722297668 0.48889628052711487
68 66 0.8077499866485596 0.472565233707428 0.8056666851043701 0.49305960536003113
69 67 0.8102499842643738 0.47163158655166626 0.796999990940094 0.49961018562316895
70 68 0.8096666932106018 0.4724145233631134 0.7990000247955322 0.4905402958393097
71 69 0.8109999895095825 0.4715333580970764 0.8080000281333923 0.48410794138908386
72 70 0.8112499713897705 0.4680226147174835 0.7996666431427002 0.4916672706604004
73 71 0.8115000128746033 0.4687725007534027 0.7926666736602783 0.49787065386772156
74 72 0.809333324432373 0.46770256757736206 0.7903333306312561 0.5011722445487976
75 73 0.812583327293396 0.46606460213661194 0.7983333468437195 0.503963053226471
76 74 0.8112499713897705 0.46567845344543457 0.800000011920929 0.4879962205886841
77 75 0.8104166388511658 0.4644148051738739 0.8100000023841858 0.48002851009368896
78 76 0.812583327293396 0.46399056911468506 0.7940000295639038 0.4991711378097534
79 77 0.8144999742507935 0.4620969295501709 0.7933333516120911 0.5001674294471741
80 78 0.8134999871253967 0.4626724123954773 0.7929999828338623 0.4932369887828827
81 79 0.8146666884422302 0.460686594247818 0.8086666464805603 0.4793894588947296
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101 99 0.8191666603088379 0.4437412619590759 0.8113333582878113 0.46899935603141785
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139 137 0.8326666951179504 0.4142398238182068 0.8273333311080933 0.44350680708885193
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147 145 0.8347499966621399 0.40960660576820374 0.8163333535194397 0.44911980628967285
148 146 0.8336666822433472 0.4076492190361023 0.8286666870117188 0.4369546175003052

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import joblib
import numpy as np
from keras import models
import scipy.stats
# Import the accuracy of both models
e_a = 0.856333315372467 # without augmentation
e_b = 0.843666672706604 # with data augmentation
# # of data points in both test sets
L = 3000
# Compute classification variance for both models
s_a = e_a * (1 - e_a)
s_b = e_b * (1 - e_b)
# Compute Student's T-test
T = (e_a - e_b) / np.sqrt((s_a / L) + (s_b / L))
print("T test:\t\t\t %1.06f" % T)
print("P-value:\t\t %1.06f" % (scipy.stats.t.sf(abs(T), df=L) * 2))
print("No aug variance:\t %1.06f" % s_a)
print("With aug variance:\t %1.06f" % s_b)

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