Perceptron.ipynb 240 KB
Newer Older
Simon Clarke's avatar
Simon Clarke committed
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
1304
1305
1306
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
1474
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
1505
1506
1507
1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
1535
1536
1537
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555
1556
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
1593
1594
1595
1596
1597
1598
1599
1600
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1612
1613
1614
1615
1616
1617
1618
1619
1620
1621
1622
1623
1624
1625
1626
1627
1628
1629
1630
1631
1632
1633
1634
1635
1636
1637
1638
1639
1640
1641
1642
1643
1644
1645
1646
1647
1648
1649
1650
1651
1652
1653
1654
1655
1656
1657
1658
1659
1660
1661
1662
1663
1664
1665
1666
1667
1668
1669
1670
1671
1672
1673
1674
1675
1676
1677
1678
1679
1680
1681
1682
1683
1684
1685
1686
1687
1688
1689
1690
1691
1692
1693
1694
1695
1696
1697
1698
1699
1700
1701
1702
1703
1704
1705
1706
1707
1708
1709
1710
1711
1712
1713
1714
1715
1716
1717
1718
1719
1720
1721
1722
1723
1724
1725
1726
1727
1728
1729
1730
1731
1732
1733
1734
1735
1736
1737
1738
1739
1740
1741
1742
1743
1744
1745
1746
1747
1748
1749
1750
1751
1752
1753
1754
1755
1756
1757
1758
1759
1760
1761
1762
1763
1764
1765
1766
1767
1768
1769
1770
1771
1772
1773
1774
1775
1776
1777
1778
1779
1780
1781
1782
1783
1784
1785
1786
1787
1788
1789
1790
1791
1792
1793
1794
1795
1796
1797
1798
1799
1800
1801
1802
1803
1804
1805
1806
1807
1808
1809
1810
1811
1812
1813
1814
1815
1816
1817
1818
1819
1820
1821
1822
1823
1824
1825
1826
1827
1828
1829
1830
1831
1832
1833
1834
1835
1836
1837
1838
1839
1840
1841
1842
1843
1844
1845
1846
1847
1848
1849
1850
1851
1852
1853
1854
1855
1856
1857
1858
1859
1860
1861
1862
1863
1864
1865
1866
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to Multilayer Perceptrons - Part 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Contents\n",
    "\n",
    "* Backpropagation\n",
    "* Gradient Descent\n",
    "* Activation Functions\n",
    "* Tensorflow and Keras\n",
    "* Load and process data\n",
    "* Creating the model\n",
    "* Training the model\n",
    "* Model accuracy\n",
    "* Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the previous exercise we learned about perceptrons. In this exercise we will introduce backpropagation, Gradient Descent and setting up a multilayer perceptron for the Iris Dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Backpropagation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assume we have a simple neural network for classification consisting of layers of perceptrons, an example of which is shown below. We assume that each layer has a bias input, which is not shown."
   ]
  },
  {
   "attachments": {
    "SimpleMLP.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![SimpleMLP.png](attachment:SimpleMLP.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each node will have weights, corresponding to the number of inputs including biases. For example, in the above model we have that there are 43 weights to determine. For each of the 5 nodes in the middle layer there are 5 inputs, while for each of the 3 nodes in the output layer there are 6 inputs. Training the neural network then corresponds to calculating the optimal weights such that the inputs best model the output. \n",
    "\n",
    "Training the neural network is the most computationally intensive component of neural networks. The basis of training is an algorithm called backpropagation, which was published in 1986. Backpropagation takes a data-set and does two sweeps through the neural network. The first is a forward calculation, which is used to calculate the error. This error is summed over all the instances of the data. The second is a backward calculation, hence the name of the algorithm, where the error is propagated back through the network. This uses the chain rule to calculate the partial derivatives of the error with respect to each of the weights."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient Descent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the derivative of the error with the respect to the weights is known, we then have a gradient field, and gradient descent can be used to update the weights. If the model has $n$ parameters (the weights), then we can write the gradient of the error $E$ as\n",
    "\n",
    "$$ \n",
    "\\nabla E = \\left( \\frac{\\partial E}{\\partial w^{(1)}}, \\frac{\\partial E}{\\partial w^{(2)}}, \\dots , \\frac{\\partial E}{\\partial w^{(n)}} \\right). \n",
    "$$\n",
    "\n",
    "Then the weights, which we can write as ${\\bf w} = ( w^{(1)}, w^{(2)}, \\dots , w^{(n)} )$, can be updated using the rule\n",
    "\n",
    "$$ {\\bf w}^{(\\text{next})} = {\\bf w} - \\eta \\nabla E. $$\n",
    "\n",
    "The parameter $\\eta$ is known as the learning parameter. This corresponds to choosing the direction that the error is decreasing the fastest and heading in that direction. The hope is that this will eventually take us to the minimum of the error, and if this is the global minimum, then this will give the optimal weights. Choosing the best value of $\\eta$ is the key to making neural network training as efficient as possible. If the value is too small, the algorithm is very slow to converge, however if it is too large the algorithm will overshoot the minimum and may not converge. Hence is better to make the learning parameter too small, rather than too large. There are many algorithms for choosing the learning rate, however we will concentrate here on some simple methods.\n",
    "\n",
    "The algorithm where we use the whole training set to calculate the partial derivatives is know as batch gradient descent. If the training set is very large the cost can very large, as we must perform forward passes for each data instance. For stochastic gradient descent a single random data instance is chosen to calculate the gradient. Mini-batch gradient descent uses a small, randomly chosen batch of the training set to calculate the gradient.\n",
    "\n",
    "Stochastic gradient descent and mini-batch gradient descent require significantly less computational time than batch gradient descent, however they introduce a randomness to the descent method which means the error can bounce around and never reach a minimum. This behaviour decreases as the size of the batch increases. In some cases where the cost function is irregular, this behaviour of the error bouncing around can be a benefit, as it moves the error away from local minimum.\n",
    "\n",
    "One way to address this bouncing around of the error is to gradually reduce the learning rate. For example, the learning rate can be defined as \n",
    "\n",
    "$$ LR = \\frac{\\alpha}{1+\\beta n}, $$\n",
    "\n",
    "where $\\alpha$ is the initial learning rate, $\\beta$ is the decay rate and $n$ is the number of epochs (iterations of the training algorithm)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Activation Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One final change needs to be made to the algorithm as it currently stands. The activation function for a classical perceptron is the Heaviside step function\n",
    "\n",
    "$$ H(x) = \\left\\{ \\begin{array}{ll} 0 \\quad & x < 0, \\\\ 1 & x \\ge 0. \\end{array} \\right. $$\n",
    "\n",
    "Hence if the argument is less than zero the perceptron does nothing, and if it is greater than zero the perceptron fires.\n",
    "\n",
    "The problem with this function is that the function is singular at $x=0$, and otherwise the gradients are zero. Consequently, the gradient of the error will in most places be zero, and gradient descent will fail.\n",
    "\n",
    "To fix this problem smooth activation functions are used. A number of common choices are:\n",
    "\n",
    "### Sigmoid function\n",
    "\n",
    "The sigmoid function is\n",
    "\n",
    "$$ \\sigma (x) = \\frac{1}{1+e^{-x}}. $$\n",
    "\n",
    "This is a smoothed version of the Heaviside step function.\n",
    "\n",
    "### Tanh function\n",
    "\n",
    "The hyperbolic tangent function is\n",
    "\n",
    "$$ \\tanh (x) = \\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}} = 2\\sigma(2x)-1. $$\n",
    "\n",
    "This has similar shape to the sigmoid function, but approaches $-1$ as $x\\to -\\infty$ and $1$ as $x\\to \\infty$.\n",
    "\n",
    "### ReLU function\n",
    "\n",
    "The Rectified Linear Unit function is the integral of the Heaviside step function:\n",
    "\n",
    "$$ ReLU(x) = \\hbox{max}(0,x) = \\left\\{ \\begin{array}{ll} 0 \\quad & x < 0, \\\\ x & x \\ge 0. \\end{array} \\right. $$\n",
    "\n",
    "Even though this function increases without bounds for $x$ positive, it is fast to compute and works well with the gradient descent algorithm. Consequently, it has become the default for Artificial Neural Networks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Output functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we will concentrate on a multilabel regression problem, therefore we need to calculate the probability that a\n",
    "prediction belongs to a particular class. This can be done using the _softmax_ function, which we have previously seen with the logistic regression algorithm. If ${\\bf s({\\bf x})}$ is a vector with the scores for each class, then the proability that instance ${\\bf x}$ belongs to be class $k$ is\n",
    "\n",
    "$$ p_k = \\frac{ \\exp (s_k ({\\bf x})}{ \\sum_{j=1}^K \\exp (s_j ({\\bf x}))}, $$\n",
    "\n",
    "where there are $K$ classes.\n",
    "\n",
    "Finally the error function can be calculated. If $y_k^{(i)}$ is the probability that the $i^{th}$ instance belongs to class $k$, which is in general either $0$ or $1$, then the cross-entropy cost function is\n",
    "\n",
    "$$ E = -\\frac{1}{m} \\sum_{i=1}^m \\sum_{k=1}^K y_k^{(i)}\\log (p_k^{(i)}). $$\n",
    "\n",
    "Since $p_k^{(i)} \\le 1$, this function is always non-negative and only equal to 0 if $p_k^{(i)} = y_k^{(i)} $ for all $k$. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tensorflow and Keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tensorflow is an open-source Deep Learning library which was developed by Google, and is the most popular Deep Learning Library. Keras is high level Deep Learning interface which can run on top of a number of Deep Learning libraries, including Tensorflow, Microsoft Cognitive Toolkit and Theano. However, here we will use the implementation of Keras which is built in to Tensorflow. In later exercises we will explain what a tensor is, and how it relates to Deep Learning.\n",
    "\n",
    "First we import Tensorflow, and then import the implementation of Keras."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To test whether they are imported correctly, we can check the version numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.4.0-rc0\n",
      "2.4.0\n"
     ]
    }
   ],
   "source": [
    "print(tf.__version__)\n",
    "print(keras.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We import from `sklearn` functions to load the Iris data set, split of the data for training and testing, and analyse the output. From `keras` we introduce the functions `Sequential`, which creates a feed-forward neural network, `Dense` which creates a fully connected network and `Activation`, which introduces different activation functions. We also introduce `SGD` which implements stochastic gradient descent.\n",
    "\n",
    "Finally, we introduce the usual suspects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Activation\n",
    "from tensorflow.keras.optimizers import SGD\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and process data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first load the Iris data from `sklearn`. The `data` structure is used for our features, which we then normalize. In general the input data for neural networks should be normalized or scaled.\n",
    "\n",
    "The `target` values correspond the three categories: 0 - setosa, 1 - versicolor and 2 - virginica. We also store the species names for later analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2]\n"
     ]
    }
   ],
   "source": [
    "ds = load_iris()\n",
    "X = ds['data']\n",
    "X = (X-X.mean())/X.std()\n",
    "target_names = ds['target_names']\n",
    "print(ds['target'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The target values to be transformed to a binary categorization for each species. This can be done using `pd.get_dummies`, which creates a column for each category that is either 0 - false or 1 - true. The result will be a pandas dataframe, however we need to convert that a numpy array so that it can be used by `keras`.\n",
    "\n",
    "Once the feature and target arrays are created with can split them into training and testing sets. Here the testing set is 20% of the instances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = pd.get_dummies(ds['target']).to_numpy()\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this example we create a sequential neural network where there are 4 inputs (the dimensions of the irises), then two hidden layers which each have 10 nodes and a tanh activation function, and the output layer has 3 outputs (the iris species) and a softmax activation function. All the layers are dense, which means they are fully connected. For the first layer only we need to specify the number of inputs. We can then print out a summary of the model, which shows we have 193 free parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 10)                50        \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 10)                110       \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 3)                 33        \n",
      "=================================================================\n",
      "Total params: 193\n",
      "Trainable params: 193\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential([ Dense(10, input_dim=4, activation='tanh'),\n",
    "                    Dense(10, activation='tanh'),\n",
    "                    Dense(3, activation='softmax') ])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We could also show a summary by using the `layers` attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<tensorflow.python.keras.layers.core.Dense at 0x17b17b760>,\n",
       " <tensorflow.python.keras.layers.core.Dense at 0x17b1bca30>,\n",
       " <tensorflow.python.keras.layers.core.Dense at 0x17b1bcd30>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.layers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To find the attributes of a particular layer we can refer to these by an index. For example, index=1 will give the second layer. We can then view the weights and biases for this layer. These are all randomly initialized by `Dense`, otherwise the stochastic gradient descent algorithm will fail."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weights : [[ 0.49462318  0.20605463 -0.49497095  0.07855463 -0.24316806  0.4702474\n",
      "   0.21920347 -0.02022982  0.0570004   0.4997548 ]\n",
      " [-0.5223231  -0.05602154  0.20897734  0.4172505  -0.51635146  0.02329129\n",
      "   0.00900477  0.052149   -0.08700126 -0.1752469 ]\n",
      " [-0.48173907  0.22286409 -0.03746063  0.05650562 -0.08612934 -0.5121895\n",
      "  -0.2712357   0.4032064   0.03546488 -0.19499534]\n",
      " [ 0.40411097  0.2507378   0.38394004 -0.20471    -0.46760547 -0.2888215\n",
      "   0.08275092  0.03520566  0.2167393   0.11646551]\n",
      " [ 0.54245055 -0.15050232 -0.17040393 -0.20157158  0.45085973  0.386912\n",
      "   0.3991801   0.00270826 -0.53547347  0.3452139 ]\n",
      " [ 0.12483054  0.33211732 -0.06949148 -0.3128831   0.13568628 -0.17653424\n",
      "  -0.00622237  0.06860495  0.09452832 -0.4306746 ]\n",
      " [-0.24111378  0.4525125   0.3688298   0.29547703  0.19370568 -0.13219321\n",
      "   0.23905838  0.09457469  0.02739185 -0.21094188]\n",
      " [ 0.11890727  0.14854753 -0.45438966  0.44462633 -0.46597078  0.31837285\n",
      "  -0.10292703  0.29024243  0.1206066  -0.19048342]\n",
      " [-0.03812754  0.28726947 -0.41406655  0.38624036 -0.2796975   0.33797926\n",
      "   0.50934815 -0.45491147 -0.28075343 -0.5061624 ]\n",
      " [-0.17938194 -0.28918087 -0.10836717  0.36149657 -0.3732981  -0.43713647\n",
      "  -0.47314996 -0.14700964  0.19716114 -0.14219162]]\n",
      "biases : [0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "hidden2 = model.layers[1]\n",
    "weights, biases = hidden2.get_weights()\n",
    "print('weights :', weights)\n",
    "print('biases :', biases)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The final step to initialize the model is to compile the model, which sets the loss or error function, the optimizer to use and the metrics to output.\n",
    "\n",
    "Here we use the cross entropy function discussed earlier and Stochastic Gradient Descent with a learning rate ($\\alpha$) of 0.02 and a decay rate ($\\beta$) of 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt = SGD(learning_rate=0.02, decay=0)\n",
    "model.compile(loss='categorical_crossentropy', optimizer=opt, metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the model "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As with `sklearn` we train the model using the `fit` method, and pass the features and target training set. This training set is split into an actual training set and a validation set. The validation set is used to independently test the accurary of the model after each epoch. If the training accuracy is significantly higher than the validation accuracy, then this suggests that the model is overfitting the data. In this case we make the validation set 20% of the training set. An explicit validation set could also be passed using the `validation_data` argument. Finally, we fit the model over 500 epochs or iterations. Each of these epochs involves one step of the backpropagation algorithm and stochastic gradient descent. After each epoch the model outputs the accuracy and loss for the training and validations sets. The accuracy should be increasing towards 1, while the loss should be decreasing to 0. The accuracy data of the model is stored in a dictionary called `history`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x17bc2d3a0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: unsupported operand type(s) for -: 'NoneType' and 'int'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_train_function.<locals>.train_function at 0x17bc2d3a0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: unsupported operand type(s) for -: 'NoneType' and 'int'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "1/3 [=========>....................] - ETA: 0s - loss: 0.8815 - accuracy: 0.4375"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-08-10 08:58:26.178047: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)\n",
      "2021-08-10 08:58:26.178333: W tensorflow/core/platform/profile_utils/cpu_utils.cc:126] Failed to get CPU frequency: 0 Hz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_test_function.<locals>.test_function at 0x17ee46310> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: unsupported operand type(s) for -: 'NoneType' and 'int'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_test_function.<locals>.test_function at 0x17ee46310> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: unsupported operand type(s) for -: 'NoneType' and 'int'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "3/3 [==============================] - 0s 135ms/step - loss: 0.8822 - accuracy: 0.4818 - val_loss: 0.7023 - val_accuracy: 0.8333\n",
      "Epoch 2/500\n",
      "3/3 [==============================] - 0s 11ms/step - loss: 0.8287 - accuracy: 0.5260 - val_loss: 0.6887 - val_accuracy: 0.8750\n",
      "Epoch 3/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.7895 - accuracy: 0.7591 - val_loss: 0.6769 - val_accuracy: 1.0000\n",
      "Epoch 4/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.7753 - accuracy: 0.8984 - val_loss: 0.6660 - val_accuracy: 0.9167\n",
      "Epoch 5/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.7324 - accuracy: 0.8867 - val_loss: 0.6555 - val_accuracy: 0.7917\n",
      "Epoch 6/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.7434 - accuracy: 0.8411 - val_loss: 0.6454 - val_accuracy: 0.7500\n",
      "Epoch 7/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.7108 - accuracy: 0.7695 - val_loss: 0.6351 - val_accuracy: 0.7083\n",
      "Epoch 8/500\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 0.7022 - accuracy: 0.7422 - val_loss: 0.6251 - val_accuracy: 0.6667\n",
      "Epoch 9/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.6472 - accuracy: 0.7812 - val_loss: 0.6146 - val_accuracy: 0.6667\n",
      "Epoch 10/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.6567 - accuracy: 0.7305 - val_loss: 0.6043 - val_accuracy: 0.6667\n",
      "Epoch 11/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.6340 - accuracy: 0.7357 - val_loss: 0.5940 - val_accuracy: 0.6667\n",
      "Epoch 12/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.6154 - accuracy: 0.7734 - val_loss: 0.5834 - val_accuracy: 0.6667\n",
      "Epoch 13/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.6068 - accuracy: 0.7839 - val_loss: 0.5735 - val_accuracy: 0.7083\n",
      "Epoch 14/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.5950 - accuracy: 0.7721 - val_loss: 0.5636 - val_accuracy: 0.7083\n",
      "Epoch 15/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.5963 - accuracy: 0.7604 - val_loss: 0.5538 - val_accuracy: 0.7083\n",
      "Epoch 16/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.5901 - accuracy: 0.7461 - val_loss: 0.5447 - val_accuracy: 0.7083\n",
      "Epoch 17/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.5698 - accuracy: 0.7799 - val_loss: 0.5352 - val_accuracy: 0.7083\n",
      "Epoch 18/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.5689 - accuracy: 0.7734 - val_loss: 0.5269 - val_accuracy: 0.7500\n",
      "Epoch 19/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.5492 - accuracy: 0.7995 - val_loss: 0.5186 - val_accuracy: 0.7500\n",
      "Epoch 20/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.5491 - accuracy: 0.8268 - val_loss: 0.5099 - val_accuracy: 0.7500\n",
      "Epoch 21/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.5297 - accuracy: 0.8398 - val_loss: 0.5016 - val_accuracy: 0.7500\n",
      "Epoch 22/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.5208 - accuracy: 0.8438 - val_loss: 0.4938 - val_accuracy: 0.7917\n",
      "Epoch 23/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.5371 - accuracy: 0.8529 - val_loss: 0.4862 - val_accuracy: 0.7917\n",
      "Epoch 24/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.5096 - accuracy: 0.8672 - val_loss: 0.4788 - val_accuracy: 0.7917\n",
      "Epoch 25/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.5132 - accuracy: 0.8737 - val_loss: 0.4716 - val_accuracy: 0.7917\n",
      "Epoch 26/500\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 0.5005 - accuracy: 0.8750 - val_loss: 0.4643 - val_accuracy: 0.7917\n",
      "Epoch 27/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.5176 - accuracy: 0.8477 - val_loss: 0.4581 - val_accuracy: 0.7917\n",
      "Epoch 28/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.4667 - accuracy: 0.8867 - val_loss: 0.4511 - val_accuracy: 0.7917\n",
      "Epoch 29/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.4739 - accuracy: 0.8997 - val_loss: 0.4449 - val_accuracy: 0.7917\n",
      "Epoch 30/500\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 0.4848 - accuracy: 0.8646 - val_loss: 0.4388 - val_accuracy: 0.7917\n",
      "Epoch 31/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.4539 - accuracy: 0.9076 - val_loss: 0.4326 - val_accuracy: 0.7917\n",
      "Epoch 32/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.4762 - accuracy: 0.8763 - val_loss: 0.4270 - val_accuracy: 0.7917\n",
      "Epoch 33/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.4487 - accuracy: 0.8880 - val_loss: 0.4213 - val_accuracy: 0.7917\n",
      "Epoch 34/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.4461 - accuracy: 0.9036 - val_loss: 0.4156 - val_accuracy: 0.8333\n",
      "Epoch 35/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.4471 - accuracy: 0.8802 - val_loss: 0.4107 - val_accuracy: 0.8333\n",
      "Epoch 36/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.4388 - accuracy: 0.9049 - val_loss: 0.4050 - val_accuracy: 0.8333\n",
      "Epoch 37/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.4498 - accuracy: 0.8607 - val_loss: 0.4003 - val_accuracy: 0.8333\n",
      "Epoch 38/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.4289 - accuracy: 0.8854 - val_loss: 0.3953 - val_accuracy: 0.8750\n",
      "Epoch 39/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.4281 - accuracy: 0.8815 - val_loss: 0.3904 - val_accuracy: 0.8750\n",
      "Epoch 40/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.4340 - accuracy: 0.8659 - val_loss: 0.3859 - val_accuracy: 0.8750\n",
      "Epoch 41/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.4273 - accuracy: 0.8815 - val_loss: 0.3814 - val_accuracy: 0.8750\n",
      "Epoch 42/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.4209 - accuracy: 0.8776 - val_loss: 0.3772 - val_accuracy: 0.8750\n",
      "Epoch 43/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.4028 - accuracy: 0.9089 - val_loss: 0.3723 - val_accuracy: 0.8750\n",
      "Epoch 44/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.4044 - accuracy: 0.8893 - val_loss: 0.3680 - val_accuracy: 0.9167\n",
      "Epoch 45/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.3895 - accuracy: 0.9049 - val_loss: 0.3638 - val_accuracy: 0.9167\n",
      "Epoch 46/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.3903 - accuracy: 0.9010 - val_loss: 0.3596 - val_accuracy: 0.9167\n",
      "Epoch 47/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.3993 - accuracy: 0.9036 - val_loss: 0.3557 - val_accuracy: 0.9583\n",
      "Epoch 48/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.4005 - accuracy: 0.8919 - val_loss: 0.3519 - val_accuracy: 0.9583\n",
      "Epoch 49/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.3868 - accuracy: 0.9076 - val_loss: 0.3481 - val_accuracy: 0.9583\n",
      "Epoch 50/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.3687 - accuracy: 0.9154 - val_loss: 0.3448 - val_accuracy: 0.9583\n",
      "Epoch 51/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.3738 - accuracy: 0.9362 - val_loss: 0.3411 - val_accuracy: 0.9583\n",
      "Epoch 52/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.3773 - accuracy: 0.9010 - val_loss: 0.3378 - val_accuracy: 0.9583\n",
      "Epoch 53/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 8ms/step - loss: 0.3733 - accuracy: 0.9245 - val_loss: 0.3340 - val_accuracy: 0.9583\n",
      "Epoch 54/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.3614 - accuracy: 0.9440 - val_loss: 0.3303 - val_accuracy: 0.9583\n",
      "Epoch 55/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.3514 - accuracy: 0.9323 - val_loss: 0.3266 - val_accuracy: 0.9583\n",
      "Epoch 56/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.3523 - accuracy: 0.9427 - val_loss: 0.3233 - val_accuracy: 0.9583\n",
      "Epoch 57/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.3460 - accuracy: 0.9284 - val_loss: 0.3197 - val_accuracy: 0.9583\n",
      "Epoch 58/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.3328 - accuracy: 0.9531 - val_loss: 0.3162 - val_accuracy: 0.9583\n",
      "Epoch 59/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.3418 - accuracy: 0.9518 - val_loss: 0.3128 - val_accuracy: 0.9583\n",
      "Epoch 60/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.3487 - accuracy: 0.9557 - val_loss: 0.3102 - val_accuracy: 0.9583\n",
      "Epoch 61/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.3377 - accuracy: 0.9557 - val_loss: 0.3069 - val_accuracy: 0.9583\n",
      "Epoch 62/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.3419 - accuracy: 0.9518 - val_loss: 0.3040 - val_accuracy: 0.9583\n",
      "Epoch 63/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.3396 - accuracy: 0.9596 - val_loss: 0.3016 - val_accuracy: 0.9583\n",
      "Epoch 64/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.3069 - accuracy: 0.9596 - val_loss: 0.2984 - val_accuracy: 0.9583\n",
      "Epoch 65/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.3043 - accuracy: 0.9674 - val_loss: 0.2951 - val_accuracy: 0.9583\n",
      "Epoch 66/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.3296 - accuracy: 0.9557 - val_loss: 0.2926 - val_accuracy: 0.9583\n",
      "Epoch 67/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.3026 - accuracy: 0.9714 - val_loss: 0.2895 - val_accuracy: 0.9583\n",
      "Epoch 68/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.3061 - accuracy: 0.9557 - val_loss: 0.2869 - val_accuracy: 0.9583\n",
      "Epoch 69/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.3123 - accuracy: 0.9323 - val_loss: 0.2848 - val_accuracy: 0.9583\n",
      "Epoch 70/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.3089 - accuracy: 0.9557 - val_loss: 0.2814 - val_accuracy: 0.9583\n",
      "Epoch 71/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.3046 - accuracy: 0.9570 - val_loss: 0.2790 - val_accuracy: 0.9583\n",
      "Epoch 72/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.2849 - accuracy: 0.9714 - val_loss: 0.2759 - val_accuracy: 0.9583\n",
      "Epoch 73/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.3112 - accuracy: 0.9609 - val_loss: 0.2734 - val_accuracy: 0.9583\n",
      "Epoch 74/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.2888 - accuracy: 0.9727 - val_loss: 0.2704 - val_accuracy: 0.9583\n",
      "Epoch 75/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.2987 - accuracy: 0.9727 - val_loss: 0.2679 - val_accuracy: 0.9583\n",
      "Epoch 76/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.2875 - accuracy: 0.9518 - val_loss: 0.2665 - val_accuracy: 0.9583\n",
      "Epoch 77/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.2719 - accuracy: 0.9766 - val_loss: 0.2640 - val_accuracy: 0.9583\n",
      "Epoch 78/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.2692 - accuracy: 0.9596 - val_loss: 0.2624 - val_accuracy: 0.9583\n",
      "Epoch 79/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.2823 - accuracy: 0.9648 - val_loss: 0.2595 - val_accuracy: 0.9583\n",
      "Epoch 80/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.2822 - accuracy: 0.9688 - val_loss: 0.2573 - val_accuracy: 0.9583\n",
      "Epoch 81/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.2691 - accuracy: 0.9857 - val_loss: 0.2549 - val_accuracy: 0.9583\n",
      "Epoch 82/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.2632 - accuracy: 0.9648 - val_loss: 0.2529 - val_accuracy: 0.9583\n",
      "Epoch 83/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.2598 - accuracy: 0.9688 - val_loss: 0.2509 - val_accuracy: 0.9583\n",
      "Epoch 84/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.2661 - accuracy: 0.9518 - val_loss: 0.2480 - val_accuracy: 0.9583\n",
      "Epoch 85/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.2526 - accuracy: 0.9688 - val_loss: 0.2455 - val_accuracy: 0.9583\n",
      "Epoch 86/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.2640 - accuracy: 0.9609 - val_loss: 0.2434 - val_accuracy: 0.9583\n",
      "Epoch 87/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.2595 - accuracy: 0.9648 - val_loss: 0.2413 - val_accuracy: 0.9583\n",
      "Epoch 88/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.2475 - accuracy: 0.9609 - val_loss: 0.2392 - val_accuracy: 0.9583\n",
      "Epoch 89/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.2573 - accuracy: 0.9688 - val_loss: 0.2376 - val_accuracy: 0.9583\n",
      "Epoch 90/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.2556 - accuracy: 0.9727 - val_loss: 0.2361 - val_accuracy: 0.9583\n",
      "Epoch 91/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.2615 - accuracy: 0.9492 - val_loss: 0.2347 - val_accuracy: 0.9583\n",
      "Epoch 92/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.2474 - accuracy: 0.9688 - val_loss: 0.2321 - val_accuracy: 0.9583\n",
      "Epoch 93/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.2397 - accuracy: 0.9609 - val_loss: 0.2301 - val_accuracy: 0.9583\n",
      "Epoch 94/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.2501 - accuracy: 0.9727 - val_loss: 0.2284 - val_accuracy: 0.9583\n",
      "Epoch 95/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.2375 - accuracy: 0.9688 - val_loss: 0.2265 - val_accuracy: 0.9583\n",
      "Epoch 96/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.2351 - accuracy: 0.9727 - val_loss: 0.2255 - val_accuracy: 0.9583\n",
      "Epoch 97/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.2253 - accuracy: 0.9688 - val_loss: 0.2235 - val_accuracy: 0.9583\n",
      "Epoch 98/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.2395 - accuracy: 0.9688 - val_loss: 0.2221 - val_accuracy: 0.9583\n",
      "Epoch 99/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.2359 - accuracy: 0.9688 - val_loss: 0.2203 - val_accuracy: 0.9583\n",
      "Epoch 100/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.2405 - accuracy: 0.9570 - val_loss: 0.2190 - val_accuracy: 0.9583\n",
      "Epoch 101/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.2293 - accuracy: 0.9570 - val_loss: 0.2170 - val_accuracy: 0.9583\n",
      "Epoch 102/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.2243 - accuracy: 0.9570 - val_loss: 0.2161 - val_accuracy: 0.9583\n",
      "Epoch 103/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.2111 - accuracy: 0.9766 - val_loss: 0.2146 - val_accuracy: 0.9583\n",
      "Epoch 104/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.2239 - accuracy: 0.9688 - val_loss: 0.2124 - val_accuracy: 0.9583\n",
      "Epoch 105/500\n",
      "3/3 [==============================] - 0s 11ms/step - loss: 0.2163 - accuracy: 0.9766 - val_loss: 0.2113 - val_accuracy: 0.9583\n",
      "Epoch 106/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.2131 - accuracy: 0.9727 - val_loss: 0.2093 - val_accuracy: 0.9583\n",
      "Epoch 107/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.2042 - accuracy: 0.9688 - val_loss: 0.2076 - val_accuracy: 0.9583\n",
      "Epoch 108/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.2214 - accuracy: 0.9648 - val_loss: 0.2061 - val_accuracy: 0.9583\n",
      "Epoch 109/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.2155 - accuracy: 0.9570 - val_loss: 0.2054 - val_accuracy: 0.9583\n",
      "Epoch 110/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.2236 - accuracy: 0.9570 - val_loss: 0.2039 - val_accuracy: 0.9583\n",
      "Epoch 111/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 8ms/step - loss: 0.2060 - accuracy: 0.9766 - val_loss: 0.2021 - val_accuracy: 0.9583\n",
      "Epoch 112/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.2023 - accuracy: 0.9609 - val_loss: 0.2002 - val_accuracy: 0.9583\n",
      "Epoch 113/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.2064 - accuracy: 0.9648 - val_loss: 0.1993 - val_accuracy: 0.9583\n",
      "Epoch 114/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1956 - accuracy: 0.9766 - val_loss: 0.1980 - val_accuracy: 0.9583\n",
      "Epoch 115/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1945 - accuracy: 0.9805 - val_loss: 0.1969 - val_accuracy: 0.9583\n",
      "Epoch 116/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1950 - accuracy: 0.9766 - val_loss: 0.1954 - val_accuracy: 0.9583\n",
      "Epoch 117/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.2027 - accuracy: 0.9688 - val_loss: 0.1939 - val_accuracy: 0.9583\n",
      "Epoch 118/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1897 - accuracy: 0.9688 - val_loss: 0.1928 - val_accuracy: 0.9583\n",
      "Epoch 119/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1905 - accuracy: 0.9688 - val_loss: 0.1918 - val_accuracy: 0.9583\n",
      "Epoch 120/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.2017 - accuracy: 0.9609 - val_loss: 0.1908 - val_accuracy: 0.9583\n",
      "Epoch 121/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1895 - accuracy: 0.9727 - val_loss: 0.1886 - val_accuracy: 0.9583\n",
      "Epoch 122/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1798 - accuracy: 0.9727 - val_loss: 0.1867 - val_accuracy: 0.9583\n",
      "Epoch 123/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1824 - accuracy: 0.9688 - val_loss: 0.1864 - val_accuracy: 0.9583\n",
      "Epoch 124/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.1904 - accuracy: 0.9609 - val_loss: 0.1850 - val_accuracy: 0.9583\n",
      "Epoch 125/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1787 - accuracy: 0.9648 - val_loss: 0.1842 - val_accuracy: 0.9583\n",
      "Epoch 126/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.1865 - accuracy: 0.9648 - val_loss: 0.1833 - val_accuracy: 0.9583\n",
      "Epoch 127/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.1797 - accuracy: 0.9609 - val_loss: 0.1823 - val_accuracy: 0.9583\n",
      "Epoch 128/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.1723 - accuracy: 0.9766 - val_loss: 0.1808 - val_accuracy: 0.9583\n",
      "Epoch 129/500\n",
      "3/3 [==============================] - 0s 11ms/step - loss: 0.1796 - accuracy: 0.9570 - val_loss: 0.1800 - val_accuracy: 0.9583\n",
      "Epoch 130/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1765 - accuracy: 0.9805 - val_loss: 0.1792 - val_accuracy: 0.9583\n",
      "Epoch 131/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1824 - accuracy: 0.9648 - val_loss: 0.1784 - val_accuracy: 0.9583\n",
      "Epoch 132/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1844 - accuracy: 0.9688 - val_loss: 0.1767 - val_accuracy: 0.9583\n",
      "Epoch 133/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1858 - accuracy: 0.9648 - val_loss: 0.1755 - val_accuracy: 0.9583\n",
      "Epoch 134/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1648 - accuracy: 0.9766 - val_loss: 0.1744 - val_accuracy: 0.9583\n",
      "Epoch 135/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.1569 - accuracy: 0.9805 - val_loss: 0.1743 - val_accuracy: 0.9583\n",
      "Epoch 136/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.1664 - accuracy: 0.9688 - val_loss: 0.1731 - val_accuracy: 0.9583\n",
      "Epoch 137/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.1667 - accuracy: 0.9766 - val_loss: 0.1720 - val_accuracy: 0.9583\n",
      "Epoch 138/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1735 - accuracy: 0.9648 - val_loss: 0.1714 - val_accuracy: 0.9583\n",
      "Epoch 139/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.1752 - accuracy: 0.9570 - val_loss: 0.1707 - val_accuracy: 0.9583\n",
      "Epoch 140/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1639 - accuracy: 0.9727 - val_loss: 0.1690 - val_accuracy: 0.9583\n",
      "Epoch 141/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1779 - accuracy: 0.9648 - val_loss: 0.1675 - val_accuracy: 0.9583\n",
      "Epoch 142/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1768 - accuracy: 0.9648 - val_loss: 0.1676 - val_accuracy: 0.9583\n",
      "Epoch 143/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1561 - accuracy: 0.9727 - val_loss: 0.1672 - val_accuracy: 0.9583\n",
      "Epoch 144/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1552 - accuracy: 0.9844 - val_loss: 0.1661 - val_accuracy: 0.9583\n",
      "Epoch 145/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1538 - accuracy: 0.9688 - val_loss: 0.1648 - val_accuracy: 0.9583\n",
      "Epoch 146/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1504 - accuracy: 0.9805 - val_loss: 0.1639 - val_accuracy: 0.9583\n",
      "Epoch 147/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1613 - accuracy: 0.9688 - val_loss: 0.1632 - val_accuracy: 0.9583\n",
      "Epoch 148/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1417 - accuracy: 0.9766 - val_loss: 0.1623 - val_accuracy: 0.9583\n",
      "Epoch 149/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1747 - accuracy: 0.9492 - val_loss: 0.1619 - val_accuracy: 0.9583\n",
      "Epoch 150/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1508 - accuracy: 0.9766 - val_loss: 0.1603 - val_accuracy: 0.9583\n",
      "Epoch 151/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1499 - accuracy: 0.9688 - val_loss: 0.1599 - val_accuracy: 0.9583\n",
      "Epoch 152/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1646 - accuracy: 0.9570 - val_loss: 0.1592 - val_accuracy: 0.9583\n",
      "Epoch 153/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1506 - accuracy: 0.9766 - val_loss: 0.1590 - val_accuracy: 0.9583\n",
      "Epoch 154/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1630 - accuracy: 0.9609 - val_loss: 0.1581 - val_accuracy: 0.9583\n",
      "Epoch 155/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1440 - accuracy: 0.9766 - val_loss: 0.1571 - val_accuracy: 0.9583\n",
      "Epoch 156/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1553 - accuracy: 0.9727 - val_loss: 0.1562 - val_accuracy: 0.9583\n",
      "Epoch 157/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1533 - accuracy: 0.9609 - val_loss: 0.1559 - val_accuracy: 0.9583\n",
      "Epoch 158/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1567 - accuracy: 0.9609 - val_loss: 0.1558 - val_accuracy: 0.9583\n",
      "Epoch 159/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1385 - accuracy: 0.9688 - val_loss: 0.1544 - val_accuracy: 0.9583\n",
      "Epoch 160/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1494 - accuracy: 0.9766 - val_loss: 0.1529 - val_accuracy: 0.9583\n",
      "Epoch 161/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.1379 - accuracy: 0.9727 - val_loss: 0.1526 - val_accuracy: 0.9583\n",
      "Epoch 162/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.1420 - accuracy: 0.9688 - val_loss: 0.1518 - val_accuracy: 0.9583\n",
      "Epoch 163/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1464 - accuracy: 0.9688 - val_loss: 0.1515 - val_accuracy: 0.9583\n",
      "Epoch 164/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1505 - accuracy: 0.9688 - val_loss: 0.1512 - val_accuracy: 0.9583\n",
      "Epoch 165/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.1432 - accuracy: 0.9766 - val_loss: 0.1499 - val_accuracy: 0.9583\n",
      "Epoch 166/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1587 - accuracy: 0.9492 - val_loss: 0.1494 - val_accuracy: 0.9583\n",
      "Epoch 167/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1399 - accuracy: 0.9727 - val_loss: 0.1486 - val_accuracy: 0.9583\n",
      "Epoch 168/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.1373 - accuracy: 0.9805 - val_loss: 0.1477 - val_accuracy: 0.9583\n",
      "Epoch 169/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1257 - accuracy: 0.9805 - val_loss: 0.1464 - val_accuracy: 0.9583\n",
      "Epoch 170/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1372 - accuracy: 0.9766 - val_loss: 0.1466 - val_accuracy: 0.9583\n",
      "Epoch 171/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1430 - accuracy: 0.9648 - val_loss: 0.1459 - val_accuracy: 0.9583\n",
      "Epoch 172/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1383 - accuracy: 0.9648 - val_loss: 0.1454 - val_accuracy: 0.9583\n",
      "Epoch 173/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1399 - accuracy: 0.9609 - val_loss: 0.1450 - val_accuracy: 0.9583\n",
      "Epoch 174/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1423 - accuracy: 0.9727 - val_loss: 0.1444 - val_accuracy: 0.9583\n",
      "Epoch 175/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.1319 - accuracy: 0.9766 - val_loss: 0.1436 - val_accuracy: 0.9583\n",
      "Epoch 176/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.1271 - accuracy: 0.9805 - val_loss: 0.1426 - val_accuracy: 0.9583\n",
      "Epoch 177/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1344 - accuracy: 0.9688 - val_loss: 0.1417 - val_accuracy: 0.9583\n",
      "Epoch 178/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1310 - accuracy: 0.9805 - val_loss: 0.1416 - val_accuracy: 0.9583\n",
      "Epoch 179/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1488 - accuracy: 0.9570 - val_loss: 0.1415 - val_accuracy: 0.9583\n",
      "Epoch 180/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1242 - accuracy: 0.9766 - val_loss: 0.1400 - val_accuracy: 0.9583\n",
      "Epoch 181/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1324 - accuracy: 0.9688 - val_loss: 0.1391 - val_accuracy: 0.9583\n",
      "Epoch 182/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1229 - accuracy: 0.9805 - val_loss: 0.1386 - val_accuracy: 0.9583\n",
      "Epoch 183/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1270 - accuracy: 0.9805 - val_loss: 0.1378 - val_accuracy: 0.9583\n",
      "Epoch 184/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1214 - accuracy: 0.9805 - val_loss: 0.1371 - val_accuracy: 0.9583\n",
      "Epoch 185/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1316 - accuracy: 0.9609 - val_loss: 0.1375 - val_accuracy: 0.9583\n",
      "Epoch 186/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.1328 - accuracy: 0.9688 - val_loss: 0.1374 - val_accuracy: 0.9583\n",
      "Epoch 187/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1214 - accuracy: 0.9805 - val_loss: 0.1367 - val_accuracy: 0.9583\n",
      "Epoch 188/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1355 - accuracy: 0.9688 - val_loss: 0.1361 - val_accuracy: 0.9583\n",
      "Epoch 189/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1420 - accuracy: 0.9492 - val_loss: 0.1361 - val_accuracy: 0.9583\n",
      "Epoch 190/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1220 - accuracy: 0.9727 - val_loss: 0.1354 - val_accuracy: 0.9583\n",
      "Epoch 191/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1161 - accuracy: 0.9727 - val_loss: 0.1341 - val_accuracy: 0.9583\n",
      "Epoch 192/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1077 - accuracy: 0.9844 - val_loss: 0.1333 - val_accuracy: 0.9583\n",
      "Epoch 193/500\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 0.1224 - accuracy: 0.9766 - val_loss: 0.1328 - val_accuracy: 0.9583\n",
      "Epoch 194/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.1122 - accuracy: 0.9766 - val_loss: 0.1328 - val_accuracy: 0.9583\n",
      "Epoch 195/500\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 0.1277 - accuracy: 0.9688 - val_loss: 0.1328 - val_accuracy: 0.9583\n",
      "Epoch 196/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1356 - accuracy: 0.9570 - val_loss: 0.1324 - val_accuracy: 0.9583\n",
      "Epoch 197/500\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 0.1336 - accuracy: 0.9688 - val_loss: 0.1319 - val_accuracy: 0.9583\n",
      "Epoch 198/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1302 - accuracy: 0.9688 - val_loss: 0.1315 - val_accuracy: 0.9583\n",
      "Epoch 199/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1285 - accuracy: 0.9609 - val_loss: 0.1302 - val_accuracy: 0.9583\n",
      "Epoch 200/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1253 - accuracy: 0.9688 - val_loss: 0.1298 - val_accuracy: 0.9583\n",
      "Epoch 201/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1195 - accuracy: 0.9609 - val_loss: 0.1291 - val_accuracy: 0.9583\n",
      "Epoch 202/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1318 - accuracy: 0.9609 - val_loss: 0.1284 - val_accuracy: 0.9583\n",
      "Epoch 203/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1227 - accuracy: 0.9609 - val_loss: 0.1284 - val_accuracy: 0.9583\n",
      "Epoch 204/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1244 - accuracy: 0.9688 - val_loss: 0.1276 - val_accuracy: 0.9583\n",
      "Epoch 205/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1078 - accuracy: 0.9766 - val_loss: 0.1264 - val_accuracy: 0.9583\n",
      "Epoch 206/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1148 - accuracy: 0.9688 - val_loss: 0.1275 - val_accuracy: 0.9583\n",
      "Epoch 207/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1182 - accuracy: 0.9727 - val_loss: 0.1275 - val_accuracy: 0.9583\n",
      "Epoch 208/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1245 - accuracy: 0.9609 - val_loss: 0.1268 - val_accuracy: 0.9583\n",
      "Epoch 209/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1116 - accuracy: 0.9688 - val_loss: 0.1261 - val_accuracy: 0.9583\n",
      "Epoch 210/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1211 - accuracy: 0.9727 - val_loss: 0.1256 - val_accuracy: 0.9583\n",
      "Epoch 211/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1235 - accuracy: 0.9648 - val_loss: 0.1253 - val_accuracy: 0.9583\n",
      "Epoch 212/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1099 - accuracy: 0.9805 - val_loss: 0.1245 - val_accuracy: 0.9583\n",
      "Epoch 213/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1181 - accuracy: 0.9688 - val_loss: 0.1240 - val_accuracy: 0.9583\n",
      "Epoch 214/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1181 - accuracy: 0.9688 - val_loss: 0.1237 - val_accuracy: 0.9583\n",
      "Epoch 215/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1239 - accuracy: 0.9570 - val_loss: 0.1231 - val_accuracy: 0.9583\n",
      "Epoch 216/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1144 - accuracy: 0.9727 - val_loss: 0.1217 - val_accuracy: 0.9583\n",
      "Epoch 217/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1211 - accuracy: 0.9648 - val_loss: 0.1218 - val_accuracy: 0.9583\n",
      "Epoch 218/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1049 - accuracy: 0.9766 - val_loss: 0.1219 - val_accuracy: 0.9583\n",
      "Epoch 219/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1014 - accuracy: 0.9805 - val_loss: 0.1214 - val_accuracy: 0.9583\n",
      "Epoch 220/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1269 - accuracy: 0.9570 - val_loss: 0.1216 - val_accuracy: 0.9583\n",
      "Epoch 221/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1236 - accuracy: 0.9688 - val_loss: 0.1206 - val_accuracy: 0.9583\n",
      "Epoch 222/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1201 - accuracy: 0.9688 - val_loss: 0.1206 - val_accuracy: 0.9583\n",
      "Epoch 223/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1000 - accuracy: 0.9766 - val_loss: 0.1198 - val_accuracy: 0.9583\n",
      "Epoch 224/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1140 - accuracy: 0.9727 - val_loss: 0.1191 - val_accuracy: 0.9583\n",
      "Epoch 225/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1150 - accuracy: 0.9570 - val_loss: 0.1194 - val_accuracy: 0.9583\n",
      "Epoch 226/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1068 - accuracy: 0.9727 - val_loss: 0.1190 - val_accuracy: 0.9583\n",
      "Epoch 227/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1144 - accuracy: 0.9648 - val_loss: 0.1183 - val_accuracy: 0.9583\n",
      "Epoch 228/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1027 - accuracy: 0.9727 - val_loss: 0.1179 - val_accuracy: 0.9583\n",
      "Epoch 229/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1092 - accuracy: 0.9648 - val_loss: 0.1178 - val_accuracy: 0.9583\n",
      "Epoch 230/500\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 0.1038 - accuracy: 0.9805 - val_loss: 0.1175 - val_accuracy: 0.9583\n",
      "Epoch 231/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.1005 - accuracy: 0.9688 - val_loss: 0.1168 - val_accuracy: 0.9583\n",
      "Epoch 232/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.0862 - accuracy: 0.9844 - val_loss: 0.1168 - val_accuracy: 0.9583\n",
      "Epoch 233/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.1111 - accuracy: 0.9648 - val_loss: 0.1173 - val_accuracy: 0.9583\n",
      "Epoch 234/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0991 - accuracy: 0.9727 - val_loss: 0.1169 - val_accuracy: 0.9583\n",
      "Epoch 235/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1042 - accuracy: 0.9688 - val_loss: 0.1162 - val_accuracy: 0.9583\n",
      "Epoch 236/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1064 - accuracy: 0.9688 - val_loss: 0.1157 - val_accuracy: 0.9583\n",
      "Epoch 237/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0998 - accuracy: 0.9766 - val_loss: 0.1153 - val_accuracy: 0.9583\n",
      "Epoch 238/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1198 - accuracy: 0.9492 - val_loss: 0.1152 - val_accuracy: 0.9583\n",
      "Epoch 239/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0972 - accuracy: 0.9688 - val_loss: 0.1141 - val_accuracy: 0.9583\n",
      "Epoch 240/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1027 - accuracy: 0.9648 - val_loss: 0.1139 - val_accuracy: 0.9583\n",
      "Epoch 241/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1075 - accuracy: 0.9688 - val_loss: 0.1127 - val_accuracy: 0.9583\n",
      "Epoch 242/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1101 - accuracy: 0.9688 - val_loss: 0.1125 - val_accuracy: 0.9583\n",
      "Epoch 243/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0839 - accuracy: 0.9844 - val_loss: 0.1118 - val_accuracy: 0.9583\n",
      "Epoch 244/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1075 - accuracy: 0.9648 - val_loss: 0.1116 - val_accuracy: 0.9583\n",
      "Epoch 245/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1108 - accuracy: 0.9609 - val_loss: 0.1115 - val_accuracy: 0.9583\n",
      "Epoch 246/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0996 - accuracy: 0.9688 - val_loss: 0.1107 - val_accuracy: 0.9583\n",
      "Epoch 247/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1021 - accuracy: 0.9648 - val_loss: 0.1107 - val_accuracy: 0.9583\n",
      "Epoch 248/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0947 - accuracy: 0.9805 - val_loss: 0.1110 - val_accuracy: 0.9583\n",
      "Epoch 249/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0883 - accuracy: 0.9805 - val_loss: 0.1111 - val_accuracy: 0.9583\n",
      "Epoch 250/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0961 - accuracy: 0.9766 - val_loss: 0.1105 - val_accuracy: 0.9583\n",
      "Epoch 251/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0897 - accuracy: 0.9805 - val_loss: 0.1106 - val_accuracy: 0.9583\n",
      "Epoch 252/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1074 - accuracy: 0.9570 - val_loss: 0.1104 - val_accuracy: 0.9583\n",
      "Epoch 253/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.1136 - accuracy: 0.9570 - val_loss: 0.1098 - val_accuracy: 0.9583\n",
      "Epoch 254/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.0895 - accuracy: 0.9727 - val_loss: 0.1090 - val_accuracy: 0.9583\n",
      "Epoch 255/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1022 - accuracy: 0.9609 - val_loss: 0.1095 - val_accuracy: 0.9583\n",
      "Epoch 256/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0912 - accuracy: 0.9766 - val_loss: 0.1101 - val_accuracy: 0.9583\n",
      "Epoch 257/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0987 - accuracy: 0.9766 - val_loss: 0.1092 - val_accuracy: 0.9583\n",
      "Epoch 258/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0985 - accuracy: 0.9648 - val_loss: 0.1085 - val_accuracy: 0.9583\n",
      "Epoch 259/500\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 0.0975 - accuracy: 0.9727 - val_loss: 0.1082 - val_accuracy: 0.9583\n",
      "Epoch 260/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0997 - accuracy: 0.9648 - val_loss: 0.1079 - val_accuracy: 0.9583\n",
      "Epoch 261/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0941 - accuracy: 0.9688 - val_loss: 0.1077 - val_accuracy: 0.9583\n",
      "Epoch 262/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0912 - accuracy: 0.9727 - val_loss: 0.1077 - val_accuracy: 0.9583\n",
      "Epoch 263/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0960 - accuracy: 0.9688 - val_loss: 0.1068 - val_accuracy: 0.9583\n",
      "Epoch 264/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0959 - accuracy: 0.9805 - val_loss: 0.1061 - val_accuracy: 0.9583\n",
      "Epoch 265/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1002 - accuracy: 0.9609 - val_loss: 0.1057 - val_accuracy: 0.9583\n",
      "Epoch 266/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0928 - accuracy: 0.9727 - val_loss: 0.1056 - val_accuracy: 0.9583\n",
      "Epoch 267/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1004 - accuracy: 0.9609 - val_loss: 0.1054 - val_accuracy: 0.9583\n",
      "Epoch 268/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0937 - accuracy: 0.9688 - val_loss: 0.1053 - val_accuracy: 0.9583\n",
      "Epoch 269/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0963 - accuracy: 0.9688 - val_loss: 0.1050 - val_accuracy: 0.9583\n",
      "Epoch 270/500\n",
      "3/3 [==============================] - 0s 7ms/step - loss: 0.0893 - accuracy: 0.9688 - val_loss: 0.1042 - val_accuracy: 0.9583\n",
      "Epoch 271/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0794 - accuracy: 0.9805 - val_loss: 0.1033 - val_accuracy: 0.9583\n",
      "Epoch 272/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1103 - accuracy: 0.9570 - val_loss: 0.1033 - val_accuracy: 0.9583\n",
      "Epoch 273/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0877 - accuracy: 0.9688 - val_loss: 0.1036 - val_accuracy: 0.9583\n",
      "Epoch 274/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0982 - accuracy: 0.9688 - val_loss: 0.1030 - val_accuracy: 0.9583\n",
      "Epoch 275/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0809 - accuracy: 0.9805 - val_loss: 0.1025 - val_accuracy: 0.9583\n",
      "Epoch 276/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0927 - accuracy: 0.9766 - val_loss: 0.1017 - val_accuracy: 0.9583\n",
      "Epoch 277/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0901 - accuracy: 0.9766 - val_loss: 0.1017 - val_accuracy: 0.9583\n",
      "Epoch 278/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0993 - accuracy: 0.9609 - val_loss: 0.1016 - val_accuracy: 0.9583\n",
      "Epoch 279/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0980 - accuracy: 0.9609 - val_loss: 0.1017 - val_accuracy: 0.9583\n",
      "Epoch 280/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0882 - accuracy: 0.9727 - val_loss: 0.1017 - val_accuracy: 0.9583\n",
      "Epoch 281/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0881 - accuracy: 0.9805 - val_loss: 0.1015 - val_accuracy: 0.9583\n",
      "Epoch 282/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0987 - accuracy: 0.9609 - val_loss: 0.1022 - val_accuracy: 0.9583\n",
      "Epoch 283/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1035 - accuracy: 0.9570 - val_loss: 0.1017 - val_accuracy: 0.9583\n",
      "Epoch 284/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0920 - accuracy: 0.9688 - val_loss: 0.1017 - val_accuracy: 0.9583\n",
      "Epoch 285/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0786 - accuracy: 0.9766 - val_loss: 0.1010 - val_accuracy: 0.9583\n",
      "Epoch 286/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0815 - accuracy: 0.9766 - val_loss: 0.1008 - val_accuracy: 0.9583\n",
      "Epoch 287/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.1037 - accuracy: 0.9609 - val_loss: 0.1008 - val_accuracy: 0.9583\n",
      "Epoch 288/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0799 - accuracy: 0.9766 - val_loss: 0.1006 - val_accuracy: 0.9583\n",
      "Epoch 289/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0752 - accuracy: 0.9844 - val_loss: 0.1003 - val_accuracy: 0.9583\n",
      "Epoch 290/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0872 - accuracy: 0.9688 - val_loss: 0.0993 - val_accuracy: 0.9583\n",
      "Epoch 291/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0841 - accuracy: 0.9688 - val_loss: 0.0996 - val_accuracy: 0.9583\n",
      "Epoch 292/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0880 - accuracy: 0.9688 - val_loss: 0.0998 - val_accuracy: 0.9583\n",
      "Epoch 293/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0800 - accuracy: 0.9766 - val_loss: 0.0993 - val_accuracy: 0.9583\n",
      "Epoch 294/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0955 - accuracy: 0.9570 - val_loss: 0.0987 - val_accuracy: 0.9583\n",
      "Epoch 295/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.1081 - accuracy: 0.9492 - val_loss: 0.0990 - val_accuracy: 0.9583\n",
      "Epoch 296/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.0862 - accuracy: 0.9766 - val_loss: 0.0985 - val_accuracy: 0.9583\n",
      "Epoch 297/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.0785 - accuracy: 0.9805 - val_loss: 0.0977 - val_accuracy: 0.9583\n",
      "Epoch 298/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.0874 - accuracy: 0.9688 - val_loss: 0.0981 - val_accuracy: 0.9583\n",
      "Epoch 299/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0836 - accuracy: 0.9688 - val_loss: 0.0970 - val_accuracy: 0.9583\n",
      "Epoch 300/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.0925 - accuracy: 0.9570 - val_loss: 0.0977 - val_accuracy: 0.9583\n",
      "Epoch 301/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0762 - accuracy: 0.9805 - val_loss: 0.0966 - val_accuracy: 0.9583\n",
      "Epoch 302/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0753 - accuracy: 0.9805 - val_loss: 0.0965 - val_accuracy: 0.9583\n",
      "Epoch 303/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0870 - accuracy: 0.9688 - val_loss: 0.0967 - val_accuracy: 0.9583\n",
      "Epoch 304/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0954 - accuracy: 0.9570 - val_loss: 0.0963 - val_accuracy: 0.9583\n",
      "Epoch 305/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0977 - accuracy: 0.9609 - val_loss: 0.0971 - val_accuracy: 0.9583\n",
      "Epoch 306/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0804 - accuracy: 0.9688 - val_loss: 0.0965 - val_accuracy: 0.9583\n",
      "Epoch 307/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0792 - accuracy: 0.9766 - val_loss: 0.0961 - val_accuracy: 0.9583\n",
      "Epoch 308/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0857 - accuracy: 0.9609 - val_loss: 0.0959 - val_accuracy: 0.9583\n",
      "Epoch 309/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0962 - accuracy: 0.9570 - val_loss: 0.0959 - val_accuracy: 0.9583\n",
      "Epoch 310/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0803 - accuracy: 0.9766 - val_loss: 0.0951 - val_accuracy: 0.9583\n",
      "Epoch 311/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0852 - accuracy: 0.9727 - val_loss: 0.0950 - val_accuracy: 0.9583\n",
      "Epoch 312/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0892 - accuracy: 0.9609 - val_loss: 0.0951 - val_accuracy: 0.9583\n",
      "Epoch 313/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0977 - accuracy: 0.9570 - val_loss: 0.0951 - val_accuracy: 0.9583\n",
      "Epoch 314/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0831 - accuracy: 0.9727 - val_loss: 0.0951 - val_accuracy: 0.9583\n",
      "Epoch 315/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.0862 - accuracy: 0.9688 - val_loss: 0.0952 - val_accuracy: 0.9583\n",
      "Epoch 316/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0930 - accuracy: 0.9570 - val_loss: 0.0945 - val_accuracy: 0.9583\n",
      "Epoch 317/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0908 - accuracy: 0.9570 - val_loss: 0.0944 - val_accuracy: 0.9583\n",
      "Epoch 318/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0750 - accuracy: 0.9727 - val_loss: 0.0949 - val_accuracy: 0.9583\n",
      "Epoch 319/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0674 - accuracy: 0.9805 - val_loss: 0.0947 - val_accuracy: 0.9583\n",
      "Epoch 320/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0919 - accuracy: 0.9570 - val_loss: 0.0953 - val_accuracy: 0.9583\n",
      "Epoch 321/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0929 - accuracy: 0.9570 - val_loss: 0.0953 - val_accuracy: 0.9583\n",
      "Epoch 322/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0757 - accuracy: 0.9688 - val_loss: 0.0941 - val_accuracy: 0.9583\n",
      "Epoch 323/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0902 - accuracy: 0.9609 - val_loss: 0.0933 - val_accuracy: 0.9583\n",
      "Epoch 324/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.0892 - accuracy: 0.9688 - val_loss: 0.0930 - val_accuracy: 0.9583\n",
      "Epoch 325/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0868 - accuracy: 0.9570 - val_loss: 0.0928 - val_accuracy: 0.9583\n",
      "Epoch 326/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0807 - accuracy: 0.9648 - val_loss: 0.0925 - val_accuracy: 0.9583\n",
      "Epoch 327/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0754 - accuracy: 0.9766 - val_loss: 0.0928 - val_accuracy: 0.9583\n",
      "Epoch 328/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0736 - accuracy: 0.9727 - val_loss: 0.0928 - val_accuracy: 0.9583\n",
      "Epoch 329/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0702 - accuracy: 0.9766 - val_loss: 0.0935 - val_accuracy: 0.9583\n",
      "Epoch 330/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0660 - accuracy: 0.9896 - val_loss: 0.0918 - val_accuracy: 0.9583\n",
      "Epoch 331/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0744 - accuracy: 0.9805 - val_loss: 0.0911 - val_accuracy: 0.9583\n",
      "Epoch 332/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0812 - accuracy: 0.9648 - val_loss: 0.0909 - val_accuracy: 0.9583\n",
      "Epoch 333/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0713 - accuracy: 0.9727 - val_loss: 0.0899 - val_accuracy: 0.9583\n",
      "Epoch 334/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0681 - accuracy: 0.9766 - val_loss: 0.0907 - val_accuracy: 0.9583\n",
      "Epoch 335/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0658 - accuracy: 0.9805 - val_loss: 0.0906 - val_accuracy: 0.9583\n",
      "Epoch 336/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0804 - accuracy: 0.9648 - val_loss: 0.0912 - val_accuracy: 0.9583\n",
      "Epoch 337/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0693 - accuracy: 0.9766 - val_loss: 0.0907 - val_accuracy: 0.9583\n",
      "Epoch 338/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0850 - accuracy: 0.9609 - val_loss: 0.0900 - val_accuracy: 0.9583\n",
      "Epoch 339/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0879 - accuracy: 0.9570 - val_loss: 0.0897 - val_accuracy: 0.9583\n",
      "Epoch 340/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0743 - accuracy: 0.9688 - val_loss: 0.0909 - val_accuracy: 0.9583\n",
      "Epoch 341/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.0791 - accuracy: 0.9688 - val_loss: 0.0913 - val_accuracy: 0.9583\n",
      "Epoch 342/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0717 - accuracy: 0.9857 - val_loss: 0.0900 - val_accuracy: 0.9583\n",
      "Epoch 343/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0804 - accuracy: 0.9818 - val_loss: 0.0893 - val_accuracy: 0.9583\n",
      "Epoch 344/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0734 - accuracy: 0.9688 - val_loss: 0.0897 - val_accuracy: 0.9583\n",
      "Epoch 345/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0780 - accuracy: 0.9857 - val_loss: 0.0882 - val_accuracy: 0.9583\n",
      "Epoch 346/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0718 - accuracy: 0.9727 - val_loss: 0.0878 - val_accuracy: 0.9583\n",
      "Epoch 347/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0845 - accuracy: 0.9648 - val_loss: 0.0879 - val_accuracy: 0.9583\n",
      "Epoch 348/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0770 - accuracy: 0.9609 - val_loss: 0.0880 - val_accuracy: 0.9583\n",
      "Epoch 349/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0759 - accuracy: 0.9648 - val_loss: 0.0878 - val_accuracy: 0.9583\n",
      "Epoch 350/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0667 - accuracy: 0.9857 - val_loss: 0.0875 - val_accuracy: 0.9583\n",
      "Epoch 351/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0824 - accuracy: 0.9570 - val_loss: 0.0882 - val_accuracy: 0.9583\n",
      "Epoch 352/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0828 - accuracy: 0.9740 - val_loss: 0.0878 - val_accuracy: 0.9583\n",
      "Epoch 353/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0813 - accuracy: 0.9779 - val_loss: 0.0877 - val_accuracy: 0.9583\n",
      "Epoch 354/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0703 - accuracy: 0.9727 - val_loss: 0.0880 - val_accuracy: 0.9583\n",
      "Epoch 355/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0876 - accuracy: 0.9779 - val_loss: 0.0872 - val_accuracy: 0.9583\n",
      "Epoch 356/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0779 - accuracy: 0.9818 - val_loss: 0.0871 - val_accuracy: 0.9583\n",
      "Epoch 357/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0638 - accuracy: 0.9805 - val_loss: 0.0872 - val_accuracy: 0.9583\n",
      "Epoch 358/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0736 - accuracy: 0.9648 - val_loss: 0.0878 - val_accuracy: 0.9583\n",
      "Epoch 359/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0658 - accuracy: 0.9857 - val_loss: 0.0871 - val_accuracy: 0.9583\n",
      "Epoch 360/500\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 0.0885 - accuracy: 0.9661 - val_loss: 0.0871 - val_accuracy: 0.9583\n",
      "Epoch 361/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.0826 - accuracy: 0.9779 - val_loss: 0.0862 - val_accuracy: 0.9583\n",
      "Epoch 362/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0727 - accuracy: 0.9766 - val_loss: 0.0858 - val_accuracy: 0.9583\n",
      "Epoch 363/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0821 - accuracy: 0.9779 - val_loss: 0.0854 - val_accuracy: 0.9583\n",
      "Epoch 364/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0755 - accuracy: 0.9896 - val_loss: 0.0843 - val_accuracy: 0.9583\n",
      "Epoch 365/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0719 - accuracy: 0.9779 - val_loss: 0.0847 - val_accuracy: 0.9583\n",
      "Epoch 366/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0673 - accuracy: 0.9818 - val_loss: 0.0845 - val_accuracy: 0.9583\n",
      "Epoch 367/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0790 - accuracy: 0.9818 - val_loss: 0.0840 - val_accuracy: 0.9583\n",
      "Epoch 368/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0711 - accuracy: 0.9857 - val_loss: 0.0836 - val_accuracy: 0.9583\n",
      "Epoch 369/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0845 - accuracy: 0.9740 - val_loss: 0.0836 - val_accuracy: 0.9583\n",
      "Epoch 370/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0780 - accuracy: 0.9688 - val_loss: 0.0839 - val_accuracy: 0.9583\n",
      "Epoch 371/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0642 - accuracy: 0.9766 - val_loss: 0.0844 - val_accuracy: 0.9583\n",
      "Epoch 372/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0777 - accuracy: 0.9740 - val_loss: 0.0842 - val_accuracy: 0.9583\n",
      "Epoch 373/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0777 - accuracy: 0.9740 - val_loss: 0.0845 - val_accuracy: 0.9583\n",
      "Epoch 374/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0674 - accuracy: 0.9818 - val_loss: 0.0838 - val_accuracy: 0.9583\n",
      "Epoch 375/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.0853 - accuracy: 0.9661 - val_loss: 0.0844 - val_accuracy: 0.9583\n",
      "Epoch 376/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0781 - accuracy: 0.9779 - val_loss: 0.0836 - val_accuracy: 0.9583\n",
      "Epoch 377/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0748 - accuracy: 0.9779 - val_loss: 0.0835 - val_accuracy: 0.9583\n",
      "Epoch 378/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0766 - accuracy: 0.9857 - val_loss: 0.0822 - val_accuracy: 0.9583\n",
      "Epoch 379/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0813 - accuracy: 0.9570 - val_loss: 0.0832 - val_accuracy: 0.9583\n",
      "Epoch 380/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0637 - accuracy: 0.9857 - val_loss: 0.0831 - val_accuracy: 0.9583\n",
      "Epoch 381/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.0788 - accuracy: 0.9661 - val_loss: 0.0835 - val_accuracy: 0.9583\n",
      "Epoch 382/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.0870 - accuracy: 0.9740 - val_loss: 0.0830 - val_accuracy: 0.9583\n",
      "Epoch 383/500\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 0.0729 - accuracy: 0.9688 - val_loss: 0.0831 - val_accuracy: 0.9583\n",
      "Epoch 384/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0766 - accuracy: 0.9779 - val_loss: 0.0826 - val_accuracy: 0.9583\n",
      "Epoch 385/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0798 - accuracy: 0.9740 - val_loss: 0.0823 - val_accuracy: 0.9583\n",
      "Epoch 386/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0726 - accuracy: 0.9740 - val_loss: 0.0829 - val_accuracy: 0.9583\n",
      "Epoch 387/500\n",
      "3/3 [==============================] - 0s 7ms/step - loss: 0.0667 - accuracy: 0.9779 - val_loss: 0.0831 - val_accuracy: 0.9583\n",
      "Epoch 388/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0617 - accuracy: 0.9896 - val_loss: 0.0819 - val_accuracy: 0.9583\n",
      "Epoch 389/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0834 - accuracy: 0.9570 - val_loss: 0.0823 - val_accuracy: 0.9583\n",
      "Epoch 390/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0696 - accuracy: 0.9779 - val_loss: 0.0821 - val_accuracy: 0.9583\n",
      "Epoch 391/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0842 - accuracy: 0.9661 - val_loss: 0.0817 - val_accuracy: 0.9583\n",
      "Epoch 392/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0617 - accuracy: 0.9896 - val_loss: 0.0803 - val_accuracy: 0.9583\n",
      "Epoch 393/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0768 - accuracy: 0.9779 - val_loss: 0.0802 - val_accuracy: 0.9583\n",
      "Epoch 394/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0724 - accuracy: 0.9857 - val_loss: 0.0795 - val_accuracy: 0.9583\n",
      "Epoch 395/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0667 - accuracy: 0.9857 - val_loss: 0.0790 - val_accuracy: 0.9583\n",
      "Epoch 396/500\n",
      "3/3 [==============================] - 0s 7ms/step - loss: 0.0716 - accuracy: 0.9740 - val_loss: 0.0803 - val_accuracy: 0.9583\n",
      "Epoch 397/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0741 - accuracy: 0.9661 - val_loss: 0.0814 - val_accuracy: 0.9583\n",
      "Epoch 398/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0809 - accuracy: 0.9661 - val_loss: 0.0819 - val_accuracy: 0.9583\n",
      "Epoch 399/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0694 - accuracy: 0.9857 - val_loss: 0.0806 - val_accuracy: 0.9583\n",
      "Epoch 400/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0773 - accuracy: 0.9661 - val_loss: 0.0809 - val_accuracy: 0.9583\n",
      "Epoch 401/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0704 - accuracy: 0.9857 - val_loss: 0.0795 - val_accuracy: 0.9583\n",
      "Epoch 402/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0654 - accuracy: 0.9779 - val_loss: 0.0796 - val_accuracy: 0.9583\n",
      "Epoch 403/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0592 - accuracy: 0.9857 - val_loss: 0.0788 - val_accuracy: 0.9583\n",
      "Epoch 404/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0667 - accuracy: 0.9740 - val_loss: 0.0799 - val_accuracy: 0.9583\n",
      "Epoch 405/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0732 - accuracy: 0.9779 - val_loss: 0.0792 - val_accuracy: 0.9583\n",
      "Epoch 406/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0663 - accuracy: 0.9740 - val_loss: 0.0797 - val_accuracy: 0.9583\n",
      "Epoch 407/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0652 - accuracy: 0.9779 - val_loss: 0.0794 - val_accuracy: 0.9583\n",
      "Epoch 408/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0735 - accuracy: 0.9740 - val_loss: 0.0791 - val_accuracy: 0.9583\n",
      "Epoch 409/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0685 - accuracy: 0.9857 - val_loss: 0.0777 - val_accuracy: 0.9583\n",
      "Epoch 410/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0602 - accuracy: 0.9857 - val_loss: 0.0777 - val_accuracy: 0.9583\n",
      "Epoch 411/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0721 - accuracy: 0.9818 - val_loss: 0.0768 - val_accuracy: 0.9583\n",
      "Epoch 412/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0688 - accuracy: 0.9779 - val_loss: 0.0769 - val_accuracy: 0.9583\n",
      "Epoch 413/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0721 - accuracy: 0.9779 - val_loss: 0.0769 - val_accuracy: 0.9583\n",
      "Epoch 414/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0572 - accuracy: 0.9896 - val_loss: 0.0766 - val_accuracy: 0.9583\n",
      "Epoch 415/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0684 - accuracy: 0.9740 - val_loss: 0.0775 - val_accuracy: 0.9583\n",
      "Epoch 416/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0680 - accuracy: 0.9740 - val_loss: 0.0778 - val_accuracy: 0.9583\n",
      "Epoch 417/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0591 - accuracy: 0.9896 - val_loss: 0.0768 - val_accuracy: 0.9583\n",
      "Epoch 418/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0538 - accuracy: 0.9857 - val_loss: 0.0771 - val_accuracy: 0.9583\n",
      "Epoch 419/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0811 - accuracy: 0.9740 - val_loss: 0.0772 - val_accuracy: 0.9583\n",
      "Epoch 420/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0587 - accuracy: 0.9818 - val_loss: 0.0770 - val_accuracy: 0.9583\n",
      "Epoch 421/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0696 - accuracy: 0.9818 - val_loss: 0.0765 - val_accuracy: 0.9583\n",
      "Epoch 422/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0598 - accuracy: 0.9818 - val_loss: 0.0768 - val_accuracy: 0.9583\n",
      "Epoch 423/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0585 - accuracy: 0.9857 - val_loss: 0.0763 - val_accuracy: 0.9583\n",
      "Epoch 424/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0763 - accuracy: 0.9779 - val_loss: 0.0760 - val_accuracy: 0.9583\n",
      "Epoch 425/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0636 - accuracy: 0.9857 - val_loss: 0.0753 - val_accuracy: 0.9583\n",
      "Epoch 426/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0682 - accuracy: 0.9740 - val_loss: 0.0759 - val_accuracy: 0.9583\n",
      "Epoch 427/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0754 - accuracy: 0.9740 - val_loss: 0.0756 - val_accuracy: 0.9583\n",
      "Epoch 428/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0736 - accuracy: 0.9740 - val_loss: 0.0753 - val_accuracy: 0.9583\n",
      "Epoch 429/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0702 - accuracy: 0.9740 - val_loss: 0.0758 - val_accuracy: 0.9583\n",
      "Epoch 430/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0632 - accuracy: 0.9818 - val_loss: 0.0759 - val_accuracy: 0.9583\n",
      "Epoch 431/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0693 - accuracy: 0.9779 - val_loss: 0.0759 - val_accuracy: 0.9583\n",
      "Epoch 432/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0637 - accuracy: 0.9818 - val_loss: 0.0752 - val_accuracy: 0.9583\n",
      "Epoch 433/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0582 - accuracy: 0.9896 - val_loss: 0.0745 - val_accuracy: 0.9583\n",
      "Epoch 434/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0629 - accuracy: 0.9857 - val_loss: 0.0739 - val_accuracy: 0.9583\n",
      "Epoch 435/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0710 - accuracy: 0.9740 - val_loss: 0.0747 - val_accuracy: 0.9583\n",
      "Epoch 436/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0672 - accuracy: 0.9779 - val_loss: 0.0747 - val_accuracy: 0.9583\n",
      "Epoch 437/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0586 - accuracy: 0.9818 - val_loss: 0.0749 - val_accuracy: 0.9583\n",
      "Epoch 438/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0570 - accuracy: 0.9857 - val_loss: 0.0747 - val_accuracy: 0.9583\n",
      "Epoch 439/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.0644 - accuracy: 0.9740 - val_loss: 0.0750 - val_accuracy: 0.9583\n",
      "Epoch 440/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0598 - accuracy: 0.9818 - val_loss: 0.0751 - val_accuracy: 0.9583\n",
      "Epoch 441/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0497 - accuracy: 0.9896 - val_loss: 0.0744 - val_accuracy: 0.9583\n",
      "Epoch 442/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0579 - accuracy: 0.9818 - val_loss: 0.0746 - val_accuracy: 0.9583\n",
      "Epoch 443/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0738 - accuracy: 0.9661 - val_loss: 0.0753 - val_accuracy: 0.9583\n",
      "Epoch 444/500\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 0.0559 - accuracy: 0.9857 - val_loss: 0.0745 - val_accuracy: 0.9583\n",
      "Epoch 445/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0692 - accuracy: 0.9818 - val_loss: 0.0736 - val_accuracy: 0.9583\n",
      "Epoch 446/500\n",
      "3/3 [==============================] - 0s 10ms/step - loss: 0.0814 - accuracy: 0.9661 - val_loss: 0.0736 - val_accuracy: 0.9583\n",
      "Epoch 447/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0717 - accuracy: 0.9661 - val_loss: 0.0748 - val_accuracy: 0.9583\n",
      "Epoch 448/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.0558 - accuracy: 0.9857 - val_loss: 0.0740 - val_accuracy: 0.9583\n",
      "Epoch 449/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0506 - accuracy: 0.9896 - val_loss: 0.0727 - val_accuracy: 0.9583\n",
      "Epoch 450/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0690 - accuracy: 0.9779 - val_loss: 0.0729 - val_accuracy: 0.9583\n",
      "Epoch 451/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0611 - accuracy: 0.9857 - val_loss: 0.0721 - val_accuracy: 0.9583\n",
      "Epoch 452/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0605 - accuracy: 0.9740 - val_loss: 0.0731 - val_accuracy: 0.9583\n",
      "Epoch 453/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0651 - accuracy: 0.9779 - val_loss: 0.0731 - val_accuracy: 0.9583\n",
      "Epoch 454/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0692 - accuracy: 0.9779 - val_loss: 0.0730 - val_accuracy: 0.9583\n",
      "Epoch 455/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0603 - accuracy: 0.9779 - val_loss: 0.0734 - val_accuracy: 0.9583\n",
      "Epoch 456/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0622 - accuracy: 0.9857 - val_loss: 0.0721 - val_accuracy: 0.9583\n",
      "Epoch 457/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0675 - accuracy: 0.9740 - val_loss: 0.0727 - val_accuracy: 0.9583\n",
      "Epoch 458/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0769 - accuracy: 0.9661 - val_loss: 0.0729 - val_accuracy: 0.9583\n",
      "Epoch 459/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0692 - accuracy: 0.9740 - val_loss: 0.0725 - val_accuracy: 0.9583\n",
      "Epoch 460/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0548 - accuracy: 0.9896 - val_loss: 0.0710 - val_accuracy: 0.9583\n",
      "Epoch 461/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0623 - accuracy: 0.9779 - val_loss: 0.0711 - val_accuracy: 0.9583\n",
      "Epoch 462/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0789 - accuracy: 0.9661 - val_loss: 0.0716 - val_accuracy: 0.9583\n",
      "Epoch 463/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0657 - accuracy: 0.9740 - val_loss: 0.0715 - val_accuracy: 0.9583\n",
      "Epoch 464/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0620 - accuracy: 0.9818 - val_loss: 0.0719 - val_accuracy: 0.9583\n",
      "Epoch 465/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0717 - accuracy: 0.9661 - val_loss: 0.0725 - val_accuracy: 0.9583\n",
      "Epoch 466/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0605 - accuracy: 0.9779 - val_loss: 0.0722 - val_accuracy: 0.9583\n",
      "Epoch 467/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0496 - accuracy: 0.9896 - val_loss: 0.0716 - val_accuracy: 0.9583\n",
      "Epoch 468/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0640 - accuracy: 0.9779 - val_loss: 0.0709 - val_accuracy: 0.9583\n",
      "Epoch 469/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0744 - accuracy: 0.9661 - val_loss: 0.0711 - val_accuracy: 0.9583\n",
      "Epoch 470/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0585 - accuracy: 0.9779 - val_loss: 0.0709 - val_accuracy: 0.9583\n",
      "Epoch 471/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0509 - accuracy: 0.9896 - val_loss: 0.0700 - val_accuracy: 0.9583\n",
      "Epoch 472/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0684 - accuracy: 0.9740 - val_loss: 0.0708 - val_accuracy: 0.9583\n",
      "Epoch 473/500\n",
      "3/3 [==============================] - 0s 7ms/step - loss: 0.0509 - accuracy: 0.9896 - val_loss: 0.0697 - val_accuracy: 0.9583\n",
      "Epoch 474/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0590 - accuracy: 0.9818 - val_loss: 0.0700 - val_accuracy: 0.9583\n",
      "Epoch 475/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0690 - accuracy: 0.9740 - val_loss: 0.0702 - val_accuracy: 0.9583\n",
      "Epoch 476/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0566 - accuracy: 0.9818 - val_loss: 0.0699 - val_accuracy: 0.9583\n",
      "Epoch 477/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0629 - accuracy: 0.9779 - val_loss: 0.0696 - val_accuracy: 0.9583\n",
      "Epoch 478/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0544 - accuracy: 0.9896 - val_loss: 0.0687 - val_accuracy: 0.9583\n",
      "Epoch 479/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0474 - accuracy: 0.9896 - val_loss: 0.0687 - val_accuracy: 0.9583\n",
      "Epoch 480/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0699 - accuracy: 0.9818 - val_loss: 0.0680 - val_accuracy: 0.9583\n",
      "Epoch 481/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0580 - accuracy: 0.9740 - val_loss: 0.0695 - val_accuracy: 0.9583\n",
      "Epoch 482/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0525 - accuracy: 0.9818 - val_loss: 0.0707 - val_accuracy: 0.9583\n",
      "Epoch 483/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0750 - accuracy: 0.9661 - val_loss: 0.0710 - val_accuracy: 0.9583\n",
      "Epoch 484/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0818 - accuracy: 0.9661 - val_loss: 0.0703 - val_accuracy: 0.9583\n",
      "Epoch 485/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0719 - accuracy: 0.9661 - val_loss: 0.0704 - val_accuracy: 0.9583\n",
      "Epoch 486/500\n",
      "3/3 [==============================] - 0s 9ms/step - loss: 0.0528 - accuracy: 0.9857 - val_loss: 0.0694 - val_accuracy: 0.9583\n",
      "Epoch 487/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0692 - accuracy: 0.9740 - val_loss: 0.0692 - val_accuracy: 0.9583\n",
      "Epoch 488/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0623 - accuracy: 0.9740 - val_loss: 0.0697 - val_accuracy: 0.9583\n",
      "Epoch 489/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0661 - accuracy: 0.9740 - val_loss: 0.0696 - val_accuracy: 0.9583\n",
      "Epoch 490/500\n",
      "3/3 [==============================] - 0s 7ms/step - loss: 0.0493 - accuracy: 0.9896 - val_loss: 0.0680 - val_accuracy: 0.9583\n",
      "Epoch 491/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0518 - accuracy: 0.9818 - val_loss: 0.0685 - val_accuracy: 0.9583\n",
      "Epoch 492/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0590 - accuracy: 0.9779 - val_loss: 0.0688 - val_accuracy: 0.9583\n",
      "Epoch 493/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0615 - accuracy: 0.9779 - val_loss: 0.0683 - val_accuracy: 0.9583\n",
      "Epoch 494/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0561 - accuracy: 0.9857 - val_loss: 0.0676 - val_accuracy: 0.9583\n",
      "Epoch 495/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0611 - accuracy: 0.9818 - val_loss: 0.0671 - val_accuracy: 0.9583\n",
      "Epoch 496/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0617 - accuracy: 0.9779 - val_loss: 0.0673 - val_accuracy: 0.9583\n",
      "Epoch 497/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0464 - accuracy: 0.9896 - val_loss: 0.0667 - val_accuracy: 0.9583\n",
      "Epoch 498/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0478 - accuracy: 0.9857 - val_loss: 0.0671 - val_accuracy: 0.9583\n",
      "Epoch 499/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0601 - accuracy: 0.9779 - val_loss: 0.0670 - val_accuracy: 0.9583\n",
      "Epoch 500/500\n",
      "3/3 [==============================] - 0s 8ms/step - loss: 0.0449 - accuracy: 0.9896 - val_loss: 0.0660 - val_accuracy: 0.9583\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train, Y_train, epochs=500, validation_split=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The loss and accuracy of the training and validation sets can now be plotted as a function of the epochs of the model. The behaviour looks reasonable where there is no significant difference between the testing and validation data, and the accuracy is generally monotonically increasing, while the loss is monotonically decreasing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(history.history).plot(figsize=(8,5))\n",
    "plt.grid(True)\n",
    "plt.xlabel('Epochs')\n",
    "plt.title('Evolution of sequential neural network for irises');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we can use the testing set to determine the accuracy of the model. This can be done using the `evaluate` function for the model. As can be seen the accuracy for the test set is consistent with the accuracy of the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 9ms/step - loss: 0.1301 - accuracy: 0.9667\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.13010245561599731, 0.9666666388511658]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test, Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also investigate other measures of the accuracy using the actual classifications of the model. The model predicts the probability of a particular classification. To find the actual classification we then just need to find the column in each row with the maximum probability using the function `argmax()`. Recall that for a particular species $TP$ is the number of true positives, $FP$ is the number of false positives and $FN$ is the number of false negatives. Then the precision, recall and F1-score are respectively\n",
    "\n",
    "$$ P=\\frac{TP}{TP+FP}, \\quad R=\\frac{TP}{TP+FN}, \\quad F_1 = \\frac{2PR}{P+R}. $$\n",
    "\n",
    "The precision gives an indication of what percentage of the predictions of that species are correct, while the recall gives an indication of what percentage of the actual samples for that species are predicted correctly. The F1-score gives a weighted average of the precision and recall. For a perfect model all of these would be 1.\n",
    "\n",
    "The support is the number of actual samples for that species.\n",
    "\n",
    "As can be seen, the model gets the prediction of setosa correct, and the precision for virginica and recall for versicolor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x17eb52ee0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: unsupported operand type(s) for -: 'NoneType' and 'int'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "WARNING: AutoGraph could not transform <function Model.make_predict_function.<locals>.predict_function at 0x17eb52ee0> and will run it as-is.\n",
      "Please report this to the TensorFlow team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output.\n",
      "Cause: unsupported operand type(s) for -: 'NoneType' and 'int'\n",
      "To silence this warning, decorate the function with @tf.autograph.experimental.do_not_convert\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "      setosa       1.00      1.00      1.00        13\n",
      "  versicolor       0.91      1.00      0.95        10\n",
      "   virginica       1.00      0.86      0.92         7\n",
      "\n",
      "    accuracy                           0.97        30\n",
      "   macro avg       0.97      0.95      0.96        30\n",
      "weighted avg       0.97      0.97      0.97        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "proba = model.predict(X_test)\n",
    "print(classification_report(proba.argmax(axis=1), Y_test.argmax(axis=1), target_names=target_names))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The overall accuracy can also be investigated by plotting the confusion matrix for the classsifications. Here we see there is only one incorrect prediction of versicolor, when the species is actually virginica."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plt_confusion_matrix(cnf_matrix, cats, method):\n",
    "    \"\"\"\n",
    "    Plots a sklearn confusion matrix with categories 'cats' for a classifier 'method'\n",
    "    \"\"\"\n",
    "    # write the confusion matrix to a dataframe with row and column names as the categories, which are already defined\n",
    "    cmatrix = pd.DataFrame(cnf_matrix,columns=cats,index=cats) \n",
    "    f, ax = plt.subplots(figsize=(7,6)) # initialise the plots and axes\n",
    "    sns.heatmap(cmatrix, annot=True, linewidths=.5) # plot the confusion matrix as a heatmap\n",
    "    plt.title('Confusion matrix for '+method) # add a title, + concatenates two strings\n",
    "    plt.ylabel('Actual label') # add a ylabel\n",
    "    plt.xlabel('Predicted label') # add a xlabel\n",
    "    # adjust the bottom and top of the figure, so we can view all of it\n",
    "    bottom, top = ax.get_ylim()  # get the y axis limits\n",
    "    ax.set_ylim(bottom + 0.5, top - 0.5); # adjust the y axis limits\n",
    "\n",
    "cnf_matrix = confusion_matrix(proba.argmax(axis=1), Y_test.argmax(axis=1)) # create a confusion matrix for our actual and predicted values\n",
    "plt_confusion_matrix(cnf_matrix, target_names, 'Neural Network')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a sequential model for the same data set with one hidden layer with 50 nodes and a ReLU activation layer. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compile this model with a learning rate of 0.05 and a decay rate of 0.001."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train this model using the same parameters as for the previous model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the evolution of the loss function and accuracy, for the validation and testing set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How does the rate of convergence and accuracy of this model compare with the model covered earlier in the notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}