MUHAMMAD AL-XORAZMIY NOMIDAGI TOSHKENT AXBOROT TEXNOLOGIYALARI UNIVERSITETI
“KIBERXAVFSIZLIK” FAKULTETI
713-21- guruh CRY002 - 1 - patok talabasining
“Mashinali oqitish kirish” fanidan tayyorlagan
5-AMALIY ISHI
Topshirdi: Obidov B.
Tekshirdi: Ochilov M.
TOSHKENT - 2023
5-amaliy ish.
10-variant
1. Berilgan variantdagi masala yuzasidan o’rgatuvchi tanlama(dataset)ni shakllantiring.
rasm. Yaratilgan dataset
Bu yerda numpy kutubxonasi yordamida o‘qituvchilarni 4 ta xuxusiyatidan kelib chiqib 3ta sinfga ajratilgan dataset tuzildi. Datasetni o‘qituvchilar ro‘yhati nomli excelda ro‘yhat shakllantirib shu ni joylashgan joyidan linki orqali olib kelib joylashtirdim.
Bu amaliy ishimizni bajarish uchun kerak bo‘lgan(sklearn, nympy, pandas, matplotlib.pyplot, skearn.cluster) kutubxonalari chaqirildi.
2-rasm. Dataset ma’lumotlari.
5-rasm. Dataset modelini qurish.
Bu yerda datasetning testlash va o‘qitish uchun barcha xuxusiyatlarini va barcha sinflarini alohida qilib chiqarish kodi yozilgan.
array[[ 4 5 2 5] [ 3 5 2 4] [ 6 4 5 7] [ 4 4 1 3] [ 5 3 2 4] [ 6 6 4 5] [ 7 6 4 6] [ 5 5 3 8] [11 9 6 10] [ 6 7 3 2] [ 4 6 2 5] [ 8 6 5 3] [10 8 6 6] [ 6 3 4 5] [ 9 6 5 7] [11 7 6 6] [ 5 2 3 2] [ 6 6 4 3] [ 8 8 6 6] [ 6 4 2 4] [ 7 6 6 8] [ 8 5 5 5] [ 9 5 2 6] [ 5 4 3 2] [ 7 5 4 4] [ 5 3 2 3] [ 5 6 5 5] [ 9 8 7 7] [ 6 7 4 6] [11 7 6 9] [ 4 2 3 3] [ 7 3 4 5] [ 8 6 6 8] [ 3 4 3 4] [ 6 5 3 4] [ 5 3 2 3] [ 6 6 4 6] [10 8 5 8] [ 9 10 6 7] [ 7 7 4 6] [ 5 3 3 4] [ 8 5 3 5] [ 4 4 2 3] [10 9 8 6] [ 6 6 5 5] [ 5 4 6 6] [ 4 2 3 3] [ 7 5 4 4] [ 3 4 3 2] [ 2 3 3 3] [ 7 7 6 6] [ 5 6 5 5] [ 6 5 4 4] [ 4 4 2 3] [ 3 4 3 4] [ 4 4 2 2] [ 8 6 6 8] [ 5 5 5 6] [ 9 8 5 9] [ 4 2 3 3] [ 5 6 3 5] [ 9 6 6 7] [ 6 6 3 7] [ 4 3 2 4] [ 6 6 5 5] [ 7 7 6 6] [ 4 4 2 3] [ 3 5 3 2] [ 9 5 6 5] [10 6 8 7] [ 7 6 4 6] [ 6 3 2 4] [ 5 2 3 3] [ 7 5 6 5] [ 9 6 5 6] [12 10 6 8] [ 2 3 4 3] [ 5 5 4 2] [ 6 5 5 4] [ 7 6 5 6] [ 8 5 5 3] [ 8 9 6 8] [ 4 2 3 3] [ 6 8 4 4] [ 9 7 6 6] [ 6 4 2 2] [ 6 6 3 3] [ 4 3 2 3] [ 5 6 5 4] [ 6 5 4 5] [ 9 8 6 8] [ 2 2 3 3] [ 3 3 3 2] [ 4 3 2 4] [ 5 6 3 5] [ 7 5 3 6] [ 3 4 2 2] [10 7 5 7] [ 7 5 3 5] [11 8 3 6]]
Datasetni barcha xususiyatlari.
[0 1 2 1 1 0 2 0 2 1 1 0 2 0 2 2 1 0 2 1 2 0 0 1 0 1 0 2 0 2 1 0 2 1 0 1 0 2 2 0 1 0 1 2 0 0 1 0 1 1 2 0 0 1 1 1 2 0 2 1 0 2 2 1 0 2 1 1 0 2 0 1 1 0 0 2 1 1 0 2 0 2 1 0 2 1 0 1 0 0 2 1 1 1 0 0 1 2 0 2] .
Datasetni barcha sinflari.
2.Yaratilgan dataset ning ixtiyoriy ikkita xususiyatini olgan holda matplotlib kutubxonasidan foydalanib grafik tasvirlang.
3-rasm. Grafik kodi.
Bu rasmda birinchi garfik uchun jadvaldan x uchun bitta “Maqolalar soni” ustunini o‘zlashtirdim. y uchun esa “Monografiya” ustunni o‘zlashtirib oldim va shuni grafigini chizib oldim.
4-rasm. Dataset grafiki.
Yaratilgan datasetni matplotlib kutubxonasi yordamida ikkita (Maqolalar soni va Monografiya) xususiyatininng grafigi chizildi.
Yaratilgan datasetni modelni o’qitish uchun 90 % va testlash uchun 10% nisbatda bo’laklarga ajrating.
rasm. Keras.utilsning to_categorical kutubxonasi.
Bu rasmda keras.utils kutubxonasining to_categoricalni chaqirib oldik va yangi bitta o‘zgaruvchiga sinflarimizni “astype” yordamida qiymat bor joylarni bir raqami bilan qiymat yo‘q joylarni esa 0 raqami bilan to‘ldirib bergan va qiymat chiqarilyapti.
array([[1, 0, 0], [0, 1, 0],[0, 0, 1], [0, 1, 0], [0, 1, 0], [1, 0, 0], [0, 0, 1], [1, 0, 0], [0, 0, 1],[0, 1, 0], [0, 1, 0],[1, 0, 0], [0, 0, 1],[1, 0, 0],[0, 0, 1], [0, 0, 1], [0, 1, 0],[1, 0, 0],[0, 0, 1],[0, 1, 0],[0, 0, 1],[1, 0, 0],[1, 0, 0], [0, 1, 0], [1, 0, 0],[0, 1, 0],[1, 0, 0],[0, 0, 1],[1, 0, 0],[0, 0, 1],[0, 1, 0], [1, 0, 0],[0, 0, 1],[0, 1, 0],[1, 0, 0], [0, 1, 0],[1, 0, 0],[0, 0, 1],[0, 0, 1], [1, 0, 0],[0, 1, 0],[1, 0, 0],[0, 1, 0],[0, 0, 1],[1, 0, 0],[1, 0, 0],[0, 1, 0], [1, 0, 0],[0, 1, 0],[0, 1, 0],[0, 0, 1],[1, 0, 0],[1, 0, 0],[0, 1, 0],[0, 1, 0], [0, 1, 0],[0, 0, 1],[1, 0, 0],[0, 0, 1],[0, 1, 0],[1, 0, 0],[0, 0, 1],[0, 0, 1], [0, 1, 0],[1, 0, 0],[0, 0, 1],[0, 1, 0],[0, 1, 0],[1, 0, 0],[0, 0, 1],[1, 0, 0], [0, 1, 0],[0, 1, 0],[1, 0, 0],[1, 0, 0],[0, 0, 1],[0, 1, 0],[0, 1, 0],[1, 0, 0], [0, 0, 1],[1, 0, 0],[0, 0, 1],[0, 1, 0],[1, 0, 0],[0, 0, 1],[0, 1, 0],[1, 0, 0], [0, 1, 0],[1, 0, 0],[1, 0, 0],[0, 0, 1],[0, 1, 0],[0, 1, 0],[0, 1, 0],[1, 0, 0], [1, 0, 0],[0, 1, 0],[0, 0, 1],[1, 0, 0],[0, 0, 1]]).
Sinflarning keras.utils kutubxonasidagi astype(int) ga o‘tkazilgan barcha qiymatlari.
rasm. Train va test qiymatlarga ajratish.
Berilgan datasetning qiymatlarini 90% train va 10% test qiymatga ajratish kodi turibdi.
rasm. Train uchun.
100 ta datasetdan 4ta xususiyaitdan 90 ta har xil xususiyatli qiymatlarni train uchun ajratgan.
rasm. Test uchun.
100 ta datasetning 3 ta sinflarini 10 tas har xil sinflarini test uchun ajratgan.
4.Keras kutubxonasidan foydalanib masalaga mos neyron tarmoq arxitekturasini quring.
rasm. Keras kutubxonasi.
Bu rasmda keres.models dan Sequential, keras.layers dan Dense va keras.optimizers da Adam SGD kutubxonalari import qilib chaqirib olingan.
rasm. Neyron tarmoq arxitekturasi.
5.Neyon tarmoqni o’qitish paramertlarini(o’qish qadami-lr, o’qitishlar soni-epoch) tanlang.
rasm. Neyron tarmog‘ini 0.001 aniqlikda o‘qitish jarayoni.
Modelni 200ta epochs ga berildi:
Epoch 1/200 45/45 [==============================] - 1s 2ms/step - loss: 1.3320 - accuracy: 0.3222 Epoch 2/200 45/45 [==============================] - 0s 2ms/step - loss: 1.1174 - accuracy: 0.3444 Epoch 3/200 45/45 [==============================] - 0s 2ms/step - loss: 1.0859 - accuracy: 0.3889 Epoch 4/200 45/45 [==============================] - 0s 2ms/step - loss: 1.0743 - accuracy: 0.3889 Epoch 5/200 45/45 [==============================] - 0s 2ms/step - loss: 1.0639 - accuracy: 0.4111 Epoch 6/200 45/45 [==============================] - 0s 2ms/step - loss: 1.0507 - accuracy: 0.4222 Epoch 7/200 45/45 [==============================] - 0s 2ms/step - loss: 1.0405 - accuracy: 0.4333 Epoch 8/200 45/45 [==============================] - 0s 2ms/step - loss: 1.0249 - accuracy: 0.4222 Epoch 9/200 45/45 [==============================] - 0s 3ms/step - loss: 1.0188 - accuracy: 0.4444 Epoch 10/200 45/45 [==============================] - 0s 3ms/step - loss: 1.0044 - accuracy: 0.4111 Epoch 11/200 45/45 [==============================] - 0s 2ms/step - loss: 0.9907 - accuracy: 0.4667 Epoch 12/200 45/45 [==============================] - 0s 3ms/step - loss: 0.9807 - accuracy: 0.4667 Epoch 13/200 45/45 [==============================] - 0s 2ms/step - loss: 0.9774 - accuracy: 0.5778 Epoch 14/200 45/45 [==============================] - 0s 2ms/step - loss: 0.9593 - accuracy: 0.4556 Epoch 15/200 45/45 [==============================] - 0s 2ms/step - loss: 0.9694 - accuracy: 0.5778 Epoch 16/200 45/45 [==============================] - 0s 3ms/step - loss: 0.9408 - accuracy: 0.5333 Epoch 17/200 45/45 [==============================] - 0s 2ms/step - loss: 0.9292 - accuracy: 0.6778 Epoch 18/200 45/45 [==============================] - 0s 2ms/step - loss: 0.9198 - accuracy: 0.5111 Epoch 19/200 45/45 [==============================] - 0s 2ms/step - loss: 0.9054 - accuracy: 0.6333 Epoch 20/200 45/45 [==============================] - 0s 2ms/step - loss: 0.8961 - accuracy: 0.6556 Epoch 21/200 45/45 [==============================] - 0s 2ms/step - loss: 0.8892 - accuracy: 0.6444 Epoch 22/200 45/45 [==============================] - 0s 3ms/step - loss: 0.8820 - accuracy: 0.6222 Epoch 23/200 45/45 [==============================] - 0s 2ms/step - loss: 0.8723 - accuracy: 0.6556 Epoch 24/200 45/45 [==============================] - 0s 2ms/step - loss: 0.8607 - accuracy: 0.7111 Epoch 25/200 45/45 [==============================] - 0s 3ms/step - loss: 0.8514 - accuracy: 0.6778 Epoch 26/200 45/45 [==============================] - 0s 2ms/step - loss: 0.8382 - accuracy: 0.7222 Epoch 27/200 45/45 [==============================] - 0s 3ms/step - loss: 0.8319 - accuracy: 0.7333 Epoch 28/200 45/45 [==============================] - 0s 3ms/step - loss: 0.8213 - accuracy: 0.7222 Epoch 29/200 45/45 [==============================] - 0s 2ms/step - loss: 0.8367 - accuracy: 0.6556 Epoch 30/200 45/45 [==============================] - 0s 2ms/step - loss: 0.8156 - accuracy: 0.7000 Epoch 31/200 45/45 [==============================] - 0s 2ms/step - loss: 0.7919 - accuracy: 0.7111 Epoch 32/200 45/45 [==============================] - 0s 2ms/step - loss: 0.7820 - accuracy: 0.7444 Epoch 33/200 45/45 [==============================] - 0s 3ms/step - loss: 0.7696 - accuracy: 0.7778 Epoch 34/200 45/45 [==============================] - 0s 3ms/step - loss: 0.7587 - accuracy: 0.7667 Epoch 35/200 45/45 [==============================] - 0s 3ms/step - loss: 0.7517 - accuracy: 0.7333 Epoch 36/200 45/45 [==============================] - 0s 3ms/step - loss: 0.7376 - accuracy: 0.7778 Epoch 37/200 45/45 [==============================] - 0s 2ms/step - loss: 0.7344 - accuracy: 0.7667 Epoch 38/200 45/45 [==============================] - 0s 2ms/step - loss: 0.7311 - accuracy: 0.7556 Epoch 39/200 45/45 [==============================] - 0s 2ms/step - loss: 0.7083 - accuracy: 0.7778 Epoch 40/200 45/45 [==============================] - 0s 3ms/step - loss: 0.7039 - accuracy: 0.7889 Epoch 41/200 45/45 [==============================] - 0s 2ms/step - loss: 0.6908 - accuracy: 0.8111 Epoch 42/200 45/45 [==============================] - 0s 2ms/step - loss: 0.6795 - accuracy: 0.7889 Epoch 43/200 45/45 [==============================] - 0s 2ms/step - loss: 0.6683 - accuracy: 0.8222 Epoch 44/200 45/45 [==============================] - 0s 2ms/step - loss: 0.6584 - accuracy: 0.8444 Epoch 45/200 45/45 [==============================] - 0s 2ms/step - loss: 0.6529 - accuracy: 0.8000 Epoch 46/200 45/45 [==============================] - 0s 2ms/step - loss: 0.6447 - accuracy: 0.8111 Epoch 47/200 45/45 [==============================] - 0s 2ms/step - loss: 0.6409 - accuracy: 0.8778 Epoch 48/200 45/45 [==============================] - 0s 2ms/step - loss: 0.6445 - accuracy: 0.8000 Epoch 49/200 45/45 [==============================] - 0s 2ms/step - loss: 0.6225 - accuracy: 0.8000 Epoch 50/200 45/45 [==============================] - 0s 2ms/step - loss: 0.6117 - accuracy: 0.8222 Epoch 51/200 45/45 [==============================] - 0s 2ms/step - loss: 0.5990 - accuracy: 0.8333 Epoch 52/200 45/45 [==============================] - 0s 3ms/step - loss: 0.5987 - accuracy: 0.8556 Epoch 53/200 45/45 [==============================] - 0s 3ms/step - loss: 0.5865 - accuracy: 0.8333 Epoch 54/200 45/45 [==============================] - 0s 3ms/step - loss: 0.5679 - accuracy: 0.8444 Epoch 55/200 45/45 [==============================] - 0s 2ms/step - loss: 0.5656 - accuracy: 0.8667 Epoch 56/200 45/45 [==============================] - 0s 2ms/step - loss: 0.5576 - accuracy: 0.8333 Epoch 57/200 45/45 [==============================] - 0s 2ms/step - loss: 0.5564 - accuracy: 0.8111 Epoch 58/200 45/45 [==============================] - 0s 3ms/step - loss: 0.5374 - accuracy: 0.8889 Epoch 59/200 45/45 [==============================] - 0s 2ms/step - loss: 0.5420 - accuracy: 0.8556 Epoch 60/200 45/45 [==============================] - 0s 2ms/step - loss: 0.5239 - accuracy: 0.8778 Epoch 61/200 45/45 [==============================] - 0s 2ms/step - loss: 0.5189 - accuracy: 0.8556 Epoch 62/200 45/45 [==============================] - 0s 2ms/step - loss: 0.5074 - accuracy: 0.8667 Epoch 63/200 45/45 [==============================] - 0s 2ms/step - loss: 0.5002 - accuracy: 0.8778 Epoch 64/200 45/45 [==============================] - 0s 2ms/step - loss: 0.4942 - accuracy: 0.9111 Epoch 65/200 45/45 [==============================] - 0s 2ms/step - loss: 0.4883 - accuracy: 0.9000 Epoch 66/200 45/45 [==============================] - 0s 2ms/step - loss: 0.4796 - accuracy: 0.9222 Epoch 67/200 45/45 [==============================] - 0s 2ms/step - loss: 0.4655 - accuracy: 0.9222 Epoch 68/200 45/45 [==============================] - 0s 2ms/step - loss: 0.4601 - accuracy: 0.9333 Epoch 69/200 45/45 [==============================] - 0s 2ms/step - loss: 0.4576 - accuracy: 0.8556 Epoch 70/200 45/45 [==============================] - 0s 2ms/step - loss: 0.4514 - accuracy: 0.9000 Epoch 71/200 45/45 [==============================] - 0s 2ms/step - loss: 0.4467 - accuracy: 0.9111 Epoch 72/200 45/45 [==============================] - 0s 2ms/step - loss: 0.4373 - accuracy: 0.9111 Epoch 73/200 45/45 [==============================] - 0s 2ms/step - loss: 0.4329 - accuracy: 0.9222 Epoch 74/200 45/45 [==============================] - 0s 2ms/step - loss: 0.4242 - accuracy: 0.9000 Epoch 75/200 45/45 [==============================] - 0s 2ms/step - loss: 0.4188 - accuracy: 0.9222 Epoch 76/200 45/45 [==============================] - 0s 2ms/step - loss: 0.4196 - accuracy: 0.8889 Epoch 77/200 45/45 [==============================] - 0s 2ms/step - loss: 0.4066 - accuracy: 0.9556 Epoch 78/200 45/45 [==============================] - 0s 2ms/step - loss: 0.3974 - accuracy: 0.9222 Epoch 79/200 45/45 [==============================] - 0s 2ms/step - loss: 0.3990 - accuracy: 0.9000 Epoch 80/200 45/45 [==============================] - 0s 2ms/step - loss: 0.3913 - accuracy: 0.9111 Epoch 81/200 45/45 [==============================] - 0s 2ms/step - loss: 0.3868 - accuracy: 0.9333 Epoch 82/200 45/45 [==============================] - 0s 2ms/step - loss: 0.3858 - accuracy: 0.9000 Epoch 83/200 45/45 [==============================] - 0s 2ms/step - loss: 0.3768 - accuracy: 0.9333 Epoch 84/200 45/45 [==============================] - 0s 2ms/step - loss: 0.3769 - accuracy: 0.9111 Epoch 85/200 45/45 [==============================] - 0s 2ms/step - loss: 0.3595 - accuracy: 0.9333 Epoch 86/200 45/45 [==============================] - 0s 2ms/step - loss: 0.3618 - accuracy: 0.9000 Epoch 87/200 45/45 [==============================] - 0s 2ms/step - loss: 0.3606 - accuracy: 0.9444 Epoch 88/200 45/45 [==============================] - 0s 2ms/step - loss: 0.3557 - accuracy: 0.9000 Epoch 89/200 45/45 [==============================] - 0s 2ms/step - loss: 0.3739 - accuracy: 0.8667 Epoch 90/200 45/45 [==============================] - 0s 2ms/step - loss: 0.3415 - accuracy: 0.9444 Epoch 91/200 45/45 [==============================] - 0s 2ms/step - loss: 0.3363 - accuracy: 0.9111 Epoch 92/200 45/45 [==============================] - 0s 2ms/step - loss: 0.3388 - accuracy: 0.9444 Epoch 93/200 45/45 [==============================] - 0s 2ms/step - loss: 0.3287 - accuracy: 0.9222 Epoch 94/200 45/45 [==============================] - 0s 2ms/step - loss: 0.3220 - accuracy: 0.9222 Epoch 95/200 45/45 [==============================] - 0s 2ms/step - loss: 0.3219 - accuracy: 0.9444 Epoch 96/200 45/45 [==============================] - 0s 2ms/step - loss: 0.3203 - accuracy: 0.9333 Epoch 97/200 45/45 [==============================] - 0s 2ms/step - loss: 0.3183 - accuracy: 0.9222 Epoch 98/200 45/45 [==============================] - 0s 2ms/step - loss: 0.3109 - accuracy: 0.9333 Epoch 99/200 45/45 [==============================] - 0s 2ms/step - loss: 0.3071 - accuracy: 0.9333 Epoch 100/200 45/45 [==============================] - 0s 2ms/step - loss: 0.3029 - accuracy: 0.9222 Epoch 101/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2997 - accuracy: 0.9111 Epoch 102/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2978 - accuracy: 0.9222 Epoch 103/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2885 - accuracy: 0.9778 Epoch 104/200 45/45 [==============================] - 0s 2ms/step - loss: 0.3083 - accuracy: 0.8889 Epoch 105/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2870 - accuracy: 0.9444 Epoch 106/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2965 - accuracy: 0.8889 Epoch 107/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2860 - accuracy: 0.9333 Epoch 108/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2848 - accuracy: 0.9000 Epoch 109/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2846 - accuracy: 0.9111 Epoch 110/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2743 - accuracy: 0.9222 Epoch 111/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2694 - accuracy: 0.9333 Epoch 112/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2726 - accuracy: 0.9222 Epoch 113/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2691 - accuracy: 0.9333 Epoch 114/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2627 - accuracy: 0.9222 Epoch 115/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2692 - accuracy: 0.9333 Epoch 116/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2667 - accuracy: 0.9444 Epoch 117/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2563 - accuracy: 0.9222 Epoch 118/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2507 - accuracy: 0.9556 Epoch 119/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2617 - accuracy: 0.9222 Epoch 120/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2496 - accuracy: 0.9444 Epoch 121/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2721 - accuracy: 0.8889 Epoch 122/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2550 - accuracy: 0.9222 Epoch 123/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2498 - accuracy: 0.9444 Epoch 124/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2782 - accuracy: 0.9111 Epoch 125/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2414 - accuracy: 0.9000 Epoch 126/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2428 - accuracy: 0.9556 Epoch 127/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2395 - accuracy: 0.9556 Epoch 128/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2444 - accuracy: 0.9000 Epoch 129/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2528 - accuracy: 0.9111 Epoch 130/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2372 - accuracy: 0.9556 Epoch 131/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2369 - accuracy: 0.9111 Epoch 132/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2368 - accuracy: 0.9333 Epoch 133/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2364 - accuracy: 0.9667 Epoch 134/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2295 - accuracy: 0.9333 Epoch 135/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2226 - accuracy: 0.9444 Epoch 136/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2250 - accuracy: 0.9333 Epoch 137/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2300 - accuracy: 0.9222 Epoch 138/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2256 - accuracy: 0.9111 Epoch 139/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2229 - accuracy: 0.9222 Epoch 140/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2160 - accuracy: 0.9333 Epoch 141/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2177 - accuracy: 0.9333 Epoch 142/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2177 - accuracy: 0.9111 Epoch 143/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2093 - accuracy: 0.9222 Epoch 144/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2210 - accuracy: 0.9333 Epoch 145/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2219 - accuracy: 0.9444 Epoch 146/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2095 - accuracy: 0.9556 Epoch 147/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2165 - accuracy: 0.9444 Epoch 148/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2158 - accuracy: 0.9333 Epoch 149/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2213 - accuracy: 0.9111 Epoch 150/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2081 - accuracy: 0.9222 Epoch 151/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2122 - accuracy: 0.9222 Epoch 152/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2051 - accuracy: 0.9333 Epoch 153/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2190 - accuracy: 0.9111 Epoch 154/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2136 - accuracy: 0.9444 Epoch 155/200 45/45 [==============================] - 0s 2ms/step - loss: 0.1999 - accuracy: 0.9222 Epoch 156/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2026 - accuracy: 0.9111 Epoch 157/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2029 - accuracy: 0.9222 Epoch 158/200 45/45 [==============================] - 0s 2ms/step - loss: 0.1987 - accuracy: 0.9333 Epoch 159/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2021 - accuracy: 0.9333 Epoch 160/200 45/45 [==============================] - 0s 2ms/step - loss: 0.1992 - accuracy: 0.9222 Epoch 161/200 45/45 [==============================] - 0s 2ms/step - loss: 0.1898 - accuracy: 0.9444 Epoch 162/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2040 - accuracy: 0.9111 Epoch 163/200 45/45 [==============================] - 0s 2ms/step - loss: 0.1984 - accuracy: 0.9444 Epoch 164/200 45/45 [==============================] - 0s 2ms/step - loss: 0.1950 - accuracy: 0.9444 Epoch 165/200 45/45 [==============================] - 0s 2ms/step - loss: 0.1943 - accuracy: 0.9333 Epoch 166/200 45/45 [==============================] - 0s 2ms/step - loss: 0.1960 - accuracy: 0.9111 Epoch 167/200 45/45 [==============================] - 0s 2ms/step - loss: 0.1943 - accuracy: 0.9222 Epoch 168/200 45/45 [==============================] - 0s 2ms/step - loss: 0.1907 - accuracy: 0.9111 Epoch 169/200 45/45 [==============================] - 0s 2ms/step - loss: 0.1967 - accuracy: 0.9444 Epoch 170/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2063 - accuracy: 0.9333 Epoch 171/200 45/45 [==============================] - 0s 2ms/step - loss: 0.1862 - accuracy: 0.9556 Epoch 172/200 45/45 [==============================] - 0s 2ms/step - loss: 0.1868 - accuracy: 0.9333 Epoch 173/200 45/45 [==============================] - 0s 2ms/step - loss: 0.1916 - accuracy: 0.9556 Epoch 174/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2084 - accuracy: 0.9222 Epoch 175/200 45/45 [==============================] - 0s 2ms/step - loss: 0.1815 - accuracy: 0.9333 Epoch 176/200 45/45 [==============================] - 0s 3ms/step - loss: 0.1870 - accuracy: 0.9333 Epoch 177/200 45/45 [==============================] - 0s 3ms/step - loss: 0.1868 - accuracy: 0.9333 Epoch 178/200 45/45 [==============================] - 0s 2ms/step - loss: 0.1817 - accuracy: 0.9222 Epoch 179/200 45/45 [==============================] - 0s 2ms/step - loss: 0.1846 - accuracy: 0.9222 Epoch 180/200 45/45 [==============================] - 0s 2ms/step - loss: 0.1841 - accuracy: 0.9111 Epoch 181/200 45/45 [==============================] - 0s 2ms/step - loss: 0.1984 - accuracy: 0.9222 Epoch 182/200 45/45 [==============================] - 0s 3ms/step - loss: 0.1928 - accuracy: 0.9444 Epoch 183/200 45/45 [==============================] - 0s 2ms/step - loss: 0.1718 - accuracy: 0.9556 Epoch 184/200 45/45 [==============================] - 0s 2ms/step - loss: 0.1851 - accuracy: 0.9333 Epoch 185/200 45/45 [==============================] - 0s 2ms/step - loss: 0.1810 - accuracy: 0.9444 Epoch 186/200 45/45 [==============================] - 0s 2ms/step - loss: 0.1858 - accuracy: 0.9444 Epoch 187/200 45/45 [==============================] - 0s 2ms/step - loss: 0.1854 - accuracy: 0.9444 Epoch 188/200 45/45 [==============================] - 0s 2ms/step - loss: 0.1931 - accuracy: 0.9222 Epoch 189/200 45/45 [==============================] - 0s 2ms/step - loss: 0.2070 - accuracy: 0.9111 Epoch 190/200 45/45 [==============================] - 0s 2ms/step - loss: 0.1760 - accuracy: 0.9667 Epoch 191/200 45/45 [==============================] - 0s 2ms/step - loss: 0.1847 - accuracy: 0.9111 Epoch 192/200 45/45 [==============================] - 0s 3ms/step - loss: 0.1782 - accuracy: 0.9222 Epoch 193/200 45/45 [==============================] - 0s 2ms/step - loss: 0.1774 - accuracy: 0.9333 Epoch 194/200 45/45 [==============================] - 0s 2ms/step - loss: 0.1704 - accuracy: 0.9556 Epoch 195/200 45/45 [==============================] - 0s 2ms/step - loss: 0.1721 - accuracy: 0.9111 Epoch 196/200 45/45 [==============================] - 0s 2ms/step - loss: 0.1742 - accuracy: 0.9333 Epoch 197/200 45/45 [==============================] - 0s 2ms/step - loss: 0.1720 - accuracy: 0.9333 Epoch 198/200 45/45 [==============================] - 0s 2ms/step - loss: 0.1909 - accuracy: 0.9222 Epoch 199/200 45/45 [==============================] - 0s 2ms/step - loss: 0.1719 - accuracy: 0.9667 Epoch 200/200 45/45 [==============================] - 0s 2ms/step - loss: 0.1727 - accuracy: 0.9444
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