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+{"cells":[{"cell_type":"code","execution_count":3,"metadata":{"executionInfo":{"elapsed":11,"status":"ok","timestamp":1762963843643,"user":{"displayName":"Мирон Романов","userId":"18135774377279153892"},"user_tz":-180},"id":"tR31IQvr6Js4"},"outputs":[],"source":["import os\n","os.chdir('/content/drive/MyDrive/Colab Notebooks/is_lab2/')"]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1626,"status":"ok","timestamp":1762963846223,"user":{"displayName":"Мирон Романов","userId":"18135774377279153892"},"user_tz":-180},"id":"s7gzB7Mr9h-8","outputId":"d5cb1b16-4529-4937-8258-1ee51547504b"},"outputs":[{"output_type":"stream","name":"stdout","text":["--2025-11-12 16:10:44--  http://uit.mpei.ru/git/main/is_dnn/raw/branch/main/labworks/LW2/lab02_lib.py\n","Resolving uit.mpei.ru (uit.mpei.ru)... 193.233.68.149\n","Connecting to uit.mpei.ru (uit.mpei.ru)|193.233.68.149|:80... connected.\n","HTTP request sent, awaiting response... 304 Not Modified\n","File ‘lab02_lib.py’ not modified on server. Omitting download.\n","\n","--2025-11-12 16:10:45--  http://uit.mpei.ru/git/main/is_dnn/raw/branch/main/labworks/LW2/data/cardio_train.txt\n","Resolving uit.mpei.ru (uit.mpei.ru)... 193.233.68.149\n","Connecting to uit.mpei.ru (uit.mpei.ru)|193.233.68.149|:80... connected.\n","HTTP request sent, awaiting response... 304 Not Modified\n","File ‘cardio_train.txt’ not modified on server. Omitting download.\n","\n","--2025-11-12 16:10:45--  http://uit.mpei.ru/git/main/is_dnn/raw/branch/main/labworks/LW2/data/cardio_test.txt\n","Resolving uit.mpei.ru (uit.mpei.ru)... 193.233.68.149\n","Connecting to uit.mpei.ru (uit.mpei.ru)|193.233.68.149|:80... connected.\n","HTTP request sent, awaiting response... 304 Not Modified\n","File ‘cardio_test.txt’ not modified on server. Omitting download.\n","\n"]}],"source":["!wget -N http://uit.mpei.ru/git/main/is_dnn/raw/branch/main/labworks/LW2/lab02_lib.py\n","!wget -N http://uit.mpei.ru/git/main/is_dnn/raw/branch/main/labworks/LW2/data/cardio_train.txt\n","!wget -N http://uit.mpei.ru/git/main/is_dnn/raw/branch/main/labworks/LW2/data/cardio_test.txt"]},{"cell_type":"markdown","metadata":{"id":"sPnuiiv1YlRs"},"source":["Задание 1"]},{"cell_type":"code","execution_count":5,"metadata":{"executionInfo":{"elapsed":6057,"status":"ok","timestamp":1762963854011,"user":{"displayName":"Мирон Романов","userId":"18135774377279153892"},"user_tz":-180},"id":"Da-vOPCpnCXQ","colab":{"base_uri":"https://localhost:8080/"},"outputId":"74fc3d79-63ab-432f-a321-20e8c9985fa6"},"outputs":[{"output_type":"stream","name":"stderr","text":["/content/drive/MyDrive/Colab Notebooks/is_lab2/lab02_lib.py:444: SyntaxWarning: invalid escape sequence '\\X'\n","  hatch='/', label='Площадь |Xd| за исключением |Xt| (|Xd\\Xt|)')\n","/content/drive/MyDrive/Colab Notebooks/is_lab2/lab02_lib.py:452: SyntaxWarning: invalid escape sequence '\\X'\n","  facecolor='none', label='Площадь |Xt| за исключением |Xd| (|Xt\\Xd|)')\n"]}],"source":["import numpy as np\n","import lab02_lib as lib\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":57,"status":"ok","timestamp":1760577823618,"user":{"displayName":"Мирон Романов","userId":"18135774377279153892"},"user_tz":-180},"id":"HVloNQHo9nk-","outputId":"24ab5018-fa61-4562-9ca1-99808756dc52"},"outputs":[{"name":"stdout","output_type":"stream","text":["Исходные данные:\n","[[9.19152143 8.99994525]\n"," [9.04581739 9.01557436]\n"," [9.15556184 9.0895709 ]\n"," ...\n"," [8.95859907 8.92197639]\n"," [9.00243424 8.96375469]\n"," [8.86636465 9.13183014]]\n","Размерность данных:\n","(1000, 2)\n"]}],"source":["# генерация датасета\n","# data=lib.datagen(9, 9, 1000, 2)\n","data = np.loadtxt('data.txt', dtype=float)\n","# вывод данных и размерности\n","print('Исходные данные:')\n","print(data)\n","print('Размерность данных:')\n","print(data.shape)"]},{"cell_type":"markdown","metadata":{"id":"iNj3hZj4Y6ye"},"source":["*   5 Скрытых слоев\n","*   5 3 1 3 5\n","*   MSE_stop = 5.3137"]},{"cell_type":"code","execution_count":27,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":211},"executionInfo":{"elapsed":5,"status":"error","timestamp":1762972380377,"user":{"displayName":"Мирон Романов","userId":"18135774377279153892"},"user_tz":-180},"id":"zbgevwqpDS-Y","outputId":"5b05323b-4cae-42d9-a255-c1e933cddde4"},"outputs":[{"output_type":"error","ename":"NameError","evalue":"name 'data' is not defined","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m/tmp/ipython-input-690139499.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# обучение AE1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mpatience\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m300\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m ae1_trained, IRE1, IREth1 = lib.create_fit_save_ae(data,'out/AE1.h5','out/AE1_ire_th.txt',\n\u001b[0m\u001b[1;32m      4\u001b[0m 1000, True, patience)\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"]}],"source":["# обучение AE1\n","patience = 300\n","ae1_trained, IRE1, IREth1 = lib.create_fit_save_ae(data,'out/AE1.h5','out/AE1_ire_th.txt',\n","1000, True, patience)\n","\n","# Построение графика ошибки реконструкции\n","lib.ire_plot('training', IRE1, IREth1, 'AE1')\n"]},{"cell_type":"markdown","metadata":{"id":"lHtTVpSrZzqp"},"source":["*   7 скрытых слоев\n","*   5 3 2 1 2 3 5\n","*   MSE_stop = 0.0103"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":975712,"status":"ok","timestamp":1760579005392,"user":{"displayName":"Мирон Романов","userId":"18135774377279153892"},"user_tz":-180},"id":"ntVdn-CSH0Jn","outputId":"ffdc0220-f9fb-4646-91d5-f201940ff1c9"},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[1;30;43mВыходные данные были обрезаны до нескольких последних строк (5000).\u001b[0m\n","Epoch 502/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 21.3836\n","Epoch 503/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 21.3278\n","Epoch 504/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 21.2721\n","Epoch 505/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 21.2166\n","Epoch 506/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 21.1613\n","Epoch 507/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 21.1060\n","Epoch 508/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 21.0510\n","Epoch 509/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 20.9961\n","Epoch 510/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 20.9413\n","Epoch 511/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 20.8867\n","Epoch 512/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 20.8322\n","Epoch 513/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 20.7779\n","Epoch 514/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 20.7237\n","Epoch 515/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 20.6696\n","Epoch 516/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 20.6157\n","Epoch 517/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 20.5620\n","Epoch 518/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 20.5084\n","Epoch 519/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 20.4549\n","Epoch 520/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 20.4016\n","Epoch 521/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 20.3484\n","Epoch 522/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 20.2954\n","Epoch 523/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 20.2424\n","Epoch 524/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 20.1897\n","Epoch 525/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 20.1370\n","Epoch 526/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 20.0846\n","Epoch 527/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 20.0322\n","Epoch 528/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 19.9800\n","Epoch 529/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 19.9279\n","Epoch 530/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 19.8760\n","Epoch 531/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 19.8242\n","Epoch 532/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 19.7725\n","Epoch 533/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 19.7210\n","Epoch 534/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 19.6695\n","Epoch 535/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 19.6183\n","Epoch 536/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 19.5671\n","Epoch 537/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 19.5161\n","Epoch 538/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 19.4653\n","Epoch 539/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 19.4145\n","Epoch 540/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 19.3639\n","Epoch 541/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 19.3134\n","Epoch 542/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 19.2631\n","Epoch 543/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 19.2129\n","Epoch 544/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 19.1628\n","Epoch 545/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 19.1128\n","Epoch 546/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 19.0630\n","Epoch 547/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 19.0133\n","Epoch 548/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 18.9637\n","Epoch 549/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 18.9143\n","Epoch 550/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 18.8649\n","Epoch 551/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 18.8157\n","Epoch 552/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 18.7667\n","Epoch 553/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 18.7177\n","Epoch 554/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 18.6689\n","Epoch 555/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 18.6202\n","Epoch 556/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 18.5716\n","Epoch 557/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 18.5232\n","Epoch 558/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 18.4748\n","Epoch 559/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 18.4266\n","Epoch 560/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 18.3786\n","Epoch 561/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 18.3306\n","Epoch 562/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 18.2828\n","Epoch 563/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 18.2350\n","Epoch 564/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 18.1875\n","Epoch 565/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 18.1400\n","Epoch 566/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 18.0926\n","Epoch 567/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 18.0454\n","Epoch 568/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 17.9983\n","Epoch 569/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 17.9513\n","Epoch 570/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 17.9044\n","Epoch 571/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 17.8576\n","Epoch 572/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 17.8110\n","Epoch 573/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 17.7644\n","Epoch 574/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 17.7180\n","Epoch 575/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 17.6717\n","Epoch 576/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 17.6256\n","Epoch 577/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 17.5795\n","Epoch 578/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 17.5335\n","Epoch 579/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 17.4877\n","Epoch 580/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 17.4420\n","Epoch 581/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 17.3964\n","Epoch 582/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 17.3509\n","Epoch 583/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 17.3055\n","Epoch 584/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 17.2602\n","Epoch 585/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 17.2151\n","Epoch 586/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 17.1700\n","Epoch 587/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 17.1251\n","Epoch 588/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 17.0803\n","Epoch 589/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 17.0356\n","Epoch 590/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 16.9910\n","Epoch 591/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 16.9465\n","Epoch 592/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 16.9021\n","Epoch 593/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 16.8579\n","Epoch 594/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 16.8137\n","Epoch 595/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 16.7697\n","Epoch 596/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 16.7257\n","Epoch 597/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 16.6819\n","Epoch 598/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 16.6382\n","Epoch 599/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 16.5945\n","Epoch 600/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 16.5510\n","Epoch 601/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 16.5076\n","Epoch 602/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 16.4643\n","Epoch 603/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 16.4212\n","Epoch 604/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 16.3781\n","Epoch 605/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 16.3351\n","Epoch 606/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 16.2922\n","Epoch 607/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 16.2495\n","Epoch 608/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 16.2068\n","Epoch 609/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 16.1643\n","Epoch 610/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 16.1218\n","Epoch 611/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 16.0795\n","Epoch 612/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 16.0372\n","Epoch 613/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 15.9951\n","Epoch 614/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 15.9530\n","Epoch 615/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 15.9111\n","Epoch 616/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 15.8693\n","Epoch 617/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 15.8275\n","Epoch 618/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 15.7859\n","Epoch 619/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 15.7444\n","Epoch 620/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 15.7030\n","Epoch 621/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 15.6616\n","Epoch 622/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 15.6204\n","Epoch 623/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 15.5793\n","Epoch 624/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 15.5383\n","Epoch 625/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 15.4973\n","Epoch 626/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 15.4565\n","Epoch 627/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 15.4158\n","Epoch 628/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 15.3752\n","Epoch 629/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 15.3346\n","Epoch 630/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 15.2942\n","Epoch 631/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 15.2539\n","Epoch 632/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 15.2136\n","Epoch 633/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 15.1735\n","Epoch 634/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 15.1335\n","Epoch 635/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 15.0935\n","Epoch 636/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 15.0537\n","Epoch 637/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 15.0139\n","Epoch 638/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 14.9743\n","Epoch 639/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 14.9347\n","Epoch 640/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 14.8953\n","Epoch 641/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 14.8559\n","Epoch 642/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 14.8166\n","Epoch 643/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 14.7775\n","Epoch 644/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 14.7384\n","Epoch 645/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 14.6994\n","Epoch 646/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 14.6605\n","Epoch 647/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 14.6217\n","Epoch 648/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 14.5830\n","Epoch 649/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 14.5444\n","Epoch 650/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 14.5059\n","Epoch 651/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 14.4674\n","Epoch 652/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 14.4291\n","Epoch 653/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 14.3909\n","Epoch 654/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 14.3527\n","Epoch 655/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 14.3146\n","Epoch 656/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 14.2767\n","Epoch 657/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 14.2388\n","Epoch 658/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 14.2010\n","Epoch 659/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 14.1633\n","Epoch 660/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 14.1257\n","Epoch 661/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 14.0882\n","Epoch 662/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 14.0508\n","Epoch 663/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 14.0135\n","Epoch 664/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 13.9762\n","Epoch 665/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 13.9391\n","Epoch 666/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 13.9020\n","Epoch 667/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 13.8650\n","Epoch 668/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 13.8281\n","Epoch 669/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 13.7913\n","Epoch 670/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 13.7546\n","Epoch 671/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 13.7180\n","Epoch 672/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 13.6815\n","Epoch 673/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 13.6450\n","Epoch 674/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 13.6087\n","Epoch 675/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 13.5724\n","Epoch 676/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 13.5362\n","Epoch 677/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 13.5001\n","Epoch 678/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 13.4641\n","Epoch 679/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 13.4282\n","Epoch 680/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 13.3923\n","Epoch 681/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 13.3566\n","Epoch 682/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 13.3209\n","Epoch 683/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 13.2853\n","Epoch 684/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 13.2498\n","Epoch 685/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 13.2144\n","Epoch 686/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 13.1791\n","Epoch 687/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 13.1438\n","Epoch 688/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 13.1087\n","Epoch 689/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 13.0736\n","Epoch 690/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 13.0386\n","Epoch 691/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 13.0037\n","Epoch 692/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 12.9688\n","Epoch 693/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 12.9341\n","Epoch 694/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 12.8994\n","Epoch 695/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 12.8649\n","Epoch 696/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 12.8304\n","Epoch 697/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 12.7959\n","Epoch 698/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 12.7616\n","Epoch 699/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 12.7274\n","Epoch 700/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 12.6932\n","Epoch 701/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 12.6591\n","Epoch 702/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 12.6251\n","Epoch 703/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 12.5912\n","Epoch 704/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 12.5573\n","Epoch 705/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 12.5236\n","Epoch 706/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 12.4899\n","Epoch 707/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 12.4563\n","Epoch 708/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 12.4228\n","Epoch 709/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 12.3893\n","Epoch 710/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 12.3560\n","Epoch 711/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 12.3227\n","Epoch 712/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 12.2895\n","Epoch 713/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 12.2564\n","Epoch 714/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 12.2233\n","Epoch 715/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 12.1903\n","Epoch 716/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 12.1575\n","Epoch 717/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 12.1246\n","Epoch 718/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 12.0919\n","Epoch 719/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 12.0593\n","Epoch 720/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 12.0267\n","Epoch 721/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 11.9942\n","Epoch 722/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 11.9618\n","Epoch 723/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 11.9294\n","Epoch 724/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 11.8972\n","Epoch 725/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 11.8650\n","Epoch 726/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 11.8328\n","Epoch 727/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 11.8008\n","Epoch 728/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 11.7688\n","Epoch 729/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 11.7370\n","Epoch 730/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 11.7052\n","Epoch 731/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 11.6734\n","Epoch 732/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 11.6418\n","Epoch 733/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 11.6102\n","Epoch 734/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 11.5787\n","Epoch 735/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 11.5472\n","Epoch 736/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 11.5159\n","Epoch 737/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 11.4846\n","Epoch 738/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 11.4534\n","Epoch 739/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 11.4223\n","Epoch 740/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 11.3912\n","Epoch 741/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 11.3602\n","Epoch 742/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 11.3293\n","Epoch 743/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 11.2985\n","Epoch 744/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 11.2677\n","Epoch 745/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 11.2370\n","Epoch 746/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 11.2064\n","Epoch 747/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 11.1759\n","Epoch 748/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 11.1454\n","Epoch 749/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 11.1150\n","Epoch 750/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 11.0847\n","Epoch 751/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 11.0544\n","Epoch 752/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 11.0242\n","Epoch 753/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 167ms/step - loss: 10.9941\n","Epoch 754/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 10.9641\n","Epoch 755/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 10.9341\n","Epoch 756/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 10.9042\n","Epoch 757/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 10.8744\n","Epoch 758/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 10.8446\n","Epoch 759/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 10.8149\n","Epoch 760/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 10.7853\n","Epoch 761/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 159ms/step - loss: 10.7558\n","Epoch 762/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 10.7263\n","Epoch 763/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 10.6969\n","Epoch 764/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 10.6676\n","Epoch 765/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 10.6383\n","Epoch 766/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 10.6091\n","Epoch 767/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 10.5800\n","Epoch 768/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 10.5510\n","Epoch 769/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 10.5220\n","Epoch 770/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 10.4931\n","Epoch 771/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 10.4642\n","Epoch 772/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 10.4355\n","Epoch 773/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 10.4067\n","Epoch 774/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 157ms/step - loss: 10.3781\n","Epoch 775/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 10.3495\n","Epoch 776/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 10.3210\n","Epoch 777/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 10.2926\n","Epoch 778/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 10.2642\n","Epoch 779/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 10.2359\n","Epoch 780/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 10.2077\n","Epoch 781/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 10.1796\n","Epoch 782/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 10.1515\n","Epoch 783/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 10.1234\n","Epoch 784/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 10.0955\n","Epoch 785/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 10.0676\n","Epoch 786/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 10.0397\n","Epoch 787/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 10.0120\n","Epoch 788/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 9.9843\n","Epoch 789/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 9.9567\n","Epoch 790/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 9.9291\n","Epoch 791/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 9.9016\n","Epoch 792/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 9.8742\n","Epoch 793/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 9.8468\n","Epoch 794/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 9.8195\n","Epoch 795/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 9.7923\n","Epoch 796/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 9.7651\n","Epoch 797/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 9.7380\n","Epoch 798/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 9.7110\n","Epoch 799/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 9.6840\n","Epoch 800/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 9.6571\n","Epoch 801/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 9.6302\n","Epoch 802/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 9.6035\n","Epoch 803/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 9.5768\n","Epoch 804/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 9.5501\n","Epoch 805/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 9.5235\n","Epoch 806/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 9.4970\n","Epoch 807/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 9.4705\n","Epoch 808/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 9.4441\n","Epoch 809/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 9.4178\n","Epoch 810/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 9.3915\n","Epoch 811/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 9.3653\n","Epoch 812/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 9.3392\n","Epoch 813/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 9.3131\n","Epoch 814/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 9.2871\n","Epoch 815/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 9.2611\n","Epoch 816/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 9.2352\n","Epoch 817/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 9.2094\n","Epoch 818/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 9.1836\n","Epoch 819/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 9.1579\n","Epoch 820/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 9.1323\n","Epoch 821/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 9.1067\n","Epoch 822/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 9.0812\n","Epoch 823/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 9.0557\n","Epoch 824/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 9.0303\n","Epoch 825/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 9.0050\n","Epoch 826/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 8.9797\n","Epoch 827/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 8.9545\n","Epoch 828/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 8.9293\n","Epoch 829/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 8.9042\n","Epoch 830/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 8.8792\n","Epoch 831/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 8.8542\n","Epoch 832/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 8.8293\n","Epoch 833/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 8.8045\n","Epoch 834/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 8.7797\n","Epoch 835/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 8.7549\n","Epoch 836/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 8.7303\n","Epoch 837/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 8.7057\n","Epoch 838/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 8.6811\n","Epoch 839/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 8.6566\n","Epoch 840/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 8.6322\n","Epoch 841/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 8.6078\n","Epoch 842/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 8.5835\n","Epoch 843/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 8.5592\n","Epoch 844/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 8.5350\n","Epoch 845/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 8.5109\n","Epoch 846/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 8.4868\n","Epoch 847/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 8.4628\n","Epoch 848/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 8.4388\n","Epoch 849/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 8.4149\n","Epoch 850/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 8.3911\n","Epoch 851/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 8.3673\n","Epoch 852/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 8.3435\n","Epoch 853/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 8.3199\n","Epoch 854/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 8.2962\n","Epoch 855/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 8.2727\n","Epoch 856/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 8.2492\n","Epoch 857/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 8.2257\n","Epoch 858/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 8.2023\n","Epoch 859/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 8.1790\n","Epoch 860/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 8.1557\n","Epoch 861/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 8.1325\n","Epoch 862/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 8.1093\n","Epoch 863/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 8.0862\n","Epoch 864/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 8.0632\n","Epoch 865/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 8.0402\n","Epoch 866/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 8.0172\n","Epoch 867/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 7.9944\n","Epoch 868/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 7.9715\n","Epoch 869/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 7.9488\n","Epoch 870/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 7.9260\n","Epoch 871/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 7.9034\n","Epoch 872/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 7.8808\n","Epoch 873/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 7.8582\n","Epoch 874/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 7.8357\n","Epoch 875/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 7.8133\n","Epoch 876/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 7.7909\n","Epoch 877/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 7.7686\n","Epoch 878/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 7.7463\n","Epoch 879/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 7.7241\n","Epoch 880/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 7.7019\n","Epoch 881/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 7.6798\n","Epoch 882/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 7.6577\n","Epoch 883/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 7.6357\n","Epoch 884/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 7.6138\n","Epoch 885/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 7.5919\n","Epoch 886/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 7.5700\n","Epoch 887/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 7.5483\n","Epoch 888/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 7.5265\n","Epoch 889/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 7.5048\n","Epoch 890/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 7.4832\n","Epoch 891/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 7.4616\n","Epoch 892/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 7.4401\n","Epoch 893/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 7.4186\n","Epoch 894/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 7.3972\n","Epoch 895/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 7.3759\n","Epoch 896/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 7.3545\n","Epoch 897/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 7.3333\n","Epoch 898/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 7.3121\n","Epoch 899/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 7.2909\n","Epoch 900/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 7.2698\n","Epoch 901/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 7.2488\n","Epoch 902/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 7.2278\n","Epoch 903/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 7.2068\n","Epoch 904/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 7.1859\n","Epoch 905/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 7.1651\n","Epoch 906/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 7.1443\n","Epoch 907/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 7.1235\n","Epoch 908/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 7.1028\n","Epoch 909/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 7.0822\n","Epoch 910/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 7.0616\n","Epoch 911/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 7.0411\n","Epoch 912/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 7.0206\n","Epoch 913/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 7.0002\n","Epoch 914/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 6.9798\n","Epoch 915/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 6.9594\n","Epoch 916/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 6.9392\n","Epoch 917/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 6.9189\n","Epoch 918/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 6.8987\n","Epoch 919/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 6.8786\n","Epoch 920/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 6.8585\n","Epoch 921/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 6.8385\n","Epoch 922/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 6.8185\n","Epoch 923/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 6.7986\n","Epoch 924/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 6.7787\n","Epoch 925/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 6.7588\n","Epoch 926/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 6.7391\n","Epoch 927/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 6.7193\n","Epoch 928/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 6.6996\n","Epoch 929/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 6.6800\n","Epoch 930/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 6.6604\n","Epoch 931/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 6.6409\n","Epoch 932/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 6.6214\n","Epoch 933/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 6.6019\n","Epoch 934/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 6.5825\n","Epoch 935/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 6.5632\n","Epoch 936/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 6.5439\n","Epoch 937/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 6.5246\n","Epoch 938/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 6.5054\n","Epoch 939/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 6.4863\n","Epoch 940/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 6.4672\n","Epoch 941/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 6.4481\n","Epoch 942/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 6.4291\n","Epoch 943/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 6.4101\n","Epoch 944/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 6.3912\n","Epoch 945/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 6.3723\n","Epoch 946/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 6.3535\n","Epoch 947/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 6.3348\n","Epoch 948/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 6.3160\n","Epoch 949/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 6.2973\n","Epoch 950/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 6.2787\n","Epoch 951/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 6.2601\n","Epoch 952/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 6.2416\n","Epoch 953/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 6.2231\n","Epoch 954/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 6.2046\n","Epoch 955/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 6.1862\n","Epoch 956/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 6.1679\n","Epoch 957/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 6.1496\n","Epoch 958/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 6.1313\n","Epoch 959/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 6.1131\n","Epoch 960/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 6.0949\n","Epoch 961/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 6.0768\n","Epoch 962/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 6.0587\n","Epoch 963/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 6.0407\n","Epoch 964/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 6.0227\n","Epoch 965/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 6.0048\n","Epoch 966/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 5.9869\n","Epoch 967/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 5.9690\n","Epoch 968/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 5.9512\n","Epoch 969/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 5.9335\n","Epoch 970/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 5.9157\n","Epoch 971/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 5.8981\n","Epoch 972/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 5.8804\n","Epoch 973/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 5.8629\n","Epoch 974/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 5.8453\n","Epoch 975/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 5.8278\n","Epoch 976/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 5.8104\n","Epoch 977/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 5.7930\n","Epoch 978/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 5.7756\n","Epoch 979/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 5.7583\n","Epoch 980/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 5.7410\n","Epoch 981/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 5.7238\n","Epoch 982/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 5.7066\n","Epoch 983/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 5.6895\n","Epoch 984/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 5.6724\n","Epoch 985/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 5.6553\n","Epoch 986/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 5.6383\n","Epoch 987/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 5.6214\n","Epoch 988/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 5.6044\n","Epoch 989/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 5.5875\n","Epoch 990/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 5.5707\n","Epoch 991/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 5.5539\n","Epoch 992/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 5.5372\n","Epoch 993/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 5.5205\n","Epoch 994/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 5.5038\n","Epoch 995/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 5.4872\n","Epoch 996/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 5.4706\n","Epoch 997/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 5.4541\n","Epoch 998/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 5.4376\n","Epoch 999/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 5.4211\n","Epoch 1000/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 5.4047\n","Epoch 1001/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 5.3883\n","Epoch 1002/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 5.3720\n","Epoch 1003/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 5.3557\n","Epoch 1004/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 5.3395\n","Epoch 1005/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 5.3233\n","Epoch 1006/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 5.3071\n","Epoch 1007/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 5.2910\n","Epoch 1008/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 5.2749\n","Epoch 1009/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 5.2589\n","Epoch 1010/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 5.2429\n","Epoch 1011/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 5.2269\n","Epoch 1012/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 5.2110\n","Epoch 1013/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 5.1952\n","Epoch 1014/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 5.1793\n","Epoch 1015/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 5.1635\n","Epoch 1016/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 5.1478\n","Epoch 1017/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 5.1321\n","Epoch 1018/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 5.1164\n","Epoch 1019/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 5.1008\n","Epoch 1020/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 5.0852\n","Epoch 1021/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 5.0697\n","Epoch 1022/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 5.0542\n","Epoch 1023/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 5.0387\n","Epoch 1024/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 5.0233\n","Epoch 1025/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 5.0079\n","Epoch 1026/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 4.9925\n","Epoch 1027/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 4.9772\n","Epoch 1028/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 4.9620\n","Epoch 1029/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 4.9467\n","Epoch 1030/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 4.9316\n","Epoch 1031/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 4.9164\n","Epoch 1032/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 4.9013\n","Epoch 1033/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 4.8862\n","Epoch 1034/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 4.8712\n","Epoch 1035/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 4.8562\n","Epoch 1036/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 4.8413\n","Epoch 1037/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 4.8264\n","Epoch 1038/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 4.8115\n","Epoch 1039/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 4.7966\n","Epoch 1040/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 4.7819\n","Epoch 1041/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 4.7671\n","Epoch 1042/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 4.7524\n","Epoch 1043/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 4.7377\n","Epoch 1044/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 4.7231\n","Epoch 1045/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 4.7085\n","Epoch 1046/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 4.6939\n","Epoch 1047/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 4.6794\n","Epoch 1048/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 4.6649\n","Epoch 1049/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 4.6504\n","Epoch 1050/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 4.6360\n","Epoch 1051/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 4.6217\n","Epoch 1052/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 4.6073\n","Epoch 1053/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 4.5930\n","Epoch 1054/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 4.5788\n","Epoch 1055/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 4.5645\n","Epoch 1056/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 4.5503\n","Epoch 1057/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 4.5362\n","Epoch 1058/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 4.5221\n","Epoch 1059/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 4.5080\n","Epoch 1060/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 4.4940\n","Epoch 1061/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 4.4800\n","Epoch 1062/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 4.4660\n","Epoch 1063/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 4.4521\n","Epoch 1064/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 4.4382\n","Epoch 1065/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 4.4243\n","Epoch 1066/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 4.4105\n","Epoch 1067/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 4.3968\n","Epoch 1068/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 4.3830\n","Epoch 1069/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 4.3693\n","Epoch 1070/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 4.3556\n","Epoch 1071/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 4.3420\n","Epoch 1072/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 4.3284\n","Epoch 1073/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 4.3148\n","Epoch 1074/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 4.3013\n","Epoch 1075/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 4.2878\n","Epoch 1076/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 4.2744\n","Epoch 1077/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 4.2610\n","Epoch 1078/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 4.2476\n","Epoch 1079/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 4.2342\n","Epoch 1080/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 4.2209\n","Epoch 1081/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 4.2076\n","Epoch 1082/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 4.1944\n","Epoch 1083/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 4.1812\n","Epoch 1084/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 4.1680\n","Epoch 1085/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 4.1549\n","Epoch 1086/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 4.1418\n","Epoch 1087/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 4.1287\n","Epoch 1088/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 4.1157\n","Epoch 1089/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 4.1027\n","Epoch 1090/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 4.0898\n","Epoch 1091/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 4.0768\n","Epoch 1092/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 4.0639\n","Epoch 1093/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 4.0511\n","Epoch 1094/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 4.0383\n","Epoch 1095/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 4.0255\n","Epoch 1096/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 4.0127\n","Epoch 1097/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 4.0000\n","Epoch 1098/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 3.9873\n","Epoch 1099/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 3.9747\n","Epoch 1100/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 3.9621\n","Epoch 1101/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 3.9495\n","Epoch 1102/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 3.9369\n","Epoch 1103/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 3.9244\n","Epoch 1104/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 3.9119\n","Epoch 1105/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 3.8995\n","Epoch 1106/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 3.8871\n","Epoch 1107/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 3.8747\n","Epoch 1108/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 3.8624\n","Epoch 1109/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 3.8500\n","Epoch 1110/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 3.8378\n","Epoch 1111/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 3.8255\n","Epoch 1112/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 3.8133\n","Epoch 1113/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 3.8011\n","Epoch 1114/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 3.7890\n","Epoch 1115/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 3.7769\n","Epoch 1116/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 3.7648\n","Epoch 1117/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 3.7527\n","Epoch 1118/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 3.7407\n","Epoch 1119/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 3.7287\n","Epoch 1120/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 3.7168\n","Epoch 1121/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 3.7049\n","Epoch 1122/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 3.6930\n","Epoch 1123/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 3.6811\n","Epoch 1124/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 3.6693\n","Epoch 1125/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 3.6575\n","Epoch 1126/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 3.6458\n","Epoch 1127/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 3.6340\n","Epoch 1128/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 3.6224\n","Epoch 1129/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 3.6107\n","Epoch 1130/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 3.5991\n","Epoch 1131/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 3.5875\n","Epoch 1132/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 3.5759\n","Epoch 1133/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 3.5644\n","Epoch 1134/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 3.5529\n","Epoch 1135/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 3.5414\n","Epoch 1136/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 3.5300\n","Epoch 1137/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 3.5186\n","Epoch 1138/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 3.5072\n","Epoch 1139/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 3.4958\n","Epoch 1140/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 3.4845\n","Epoch 1141/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 3.4732\n","Epoch 1142/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 3.4620\n","Epoch 1143/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 3.4508\n","Epoch 1144/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 3.4396\n","Epoch 1145/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 3.4284\n","Epoch 1146/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 3.4173\n","Epoch 1147/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 3.4062\n","Epoch 1148/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 3.3951\n","Epoch 1149/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 3.3841\n","Epoch 1150/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 3.3731\n","Epoch 1151/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 3.3621\n","Epoch 1152/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 3.3512\n","Epoch 1153/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 3.3403\n","Epoch 1154/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 3.3294\n","Epoch 1155/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 3.3185\n","Epoch 1156/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 3.3077\n","Epoch 1157/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 3.2969\n","Epoch 1158/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 3.2861\n","Epoch 1159/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 3.2754\n","Epoch 1160/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 3.2647\n","Epoch 1161/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 3.2540\n","Epoch 1162/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 3.2434\n","Epoch 1163/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 3.2328\n","Epoch 1164/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 3.2222\n","Epoch 1165/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 3.2116\n","Epoch 1166/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 3.2011\n","Epoch 1167/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 3.1906\n","Epoch 1168/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 3.1801\n","Epoch 1169/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 3.1697\n","Epoch 1170/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 3.1593\n","Epoch 1171/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 3.1489\n","Epoch 1172/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 3.1386\n","Epoch 1173/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 3.1282\n","Epoch 1174/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 3.1179\n","Epoch 1175/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 3.1077\n","Epoch 1176/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 3.0974\n","Epoch 1177/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 3.0872\n","Epoch 1178/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 3.0771\n","Epoch 1179/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 3.0669\n","Epoch 1180/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 3.0568\n","Epoch 1181/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 3.0467\n","Epoch 1182/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 3.0366\n","Epoch 1183/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 3.0266\n","Epoch 1184/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 3.0166\n","Epoch 1185/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 3.0066\n","Epoch 1186/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 2.9967\n","Epoch 1187/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 2.9867\n","Epoch 1188/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 2.9768\n","Epoch 1189/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 2.9670\n","Epoch 1190/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 165ms/step - loss: 2.9571\n","Epoch 1191/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 2.9473\n","Epoch 1192/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 2.9375\n","Epoch 1193/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 2.9278\n","Epoch 1194/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 2.9180\n","Epoch 1195/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 2.9083\n","Epoch 1196/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 2.8987\n","Epoch 1197/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 2.8890\n","Epoch 1198/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 2.8794\n","Epoch 1199/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 2.8698\n","Epoch 1200/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 2.8603\n","Epoch 1201/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 2.8507\n","Epoch 1202/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 2.8412\n","Epoch 1203/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 2.8317\n","Epoch 1204/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 2.8223\n","Epoch 1205/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 2.8128\n","Epoch 1206/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 157ms/step - loss: 2.8034\n","Epoch 1207/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 2.7941\n","Epoch 1208/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 2.7847\n","Epoch 1209/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 2.7754\n","Epoch 1210/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - loss: 2.7661\n","Epoch 1211/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 2.7568\n","Epoch 1212/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 2.7476\n","Epoch 1213/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 2.7384\n","Epoch 1214/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 2.7292\n","Epoch 1215/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 2.7200\n","Epoch 1216/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 2.7109\n","Epoch 1217/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 2.7018\n","Epoch 1218/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 2.6927\n","Epoch 1219/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 2.6836\n","Epoch 1220/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 2.6746\n","Epoch 1221/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 2.6656\n","Epoch 1222/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 2.6566\n","Epoch 1223/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 2.6476\n","Epoch 1224/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.6387\n","Epoch 1225/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 2.6298\n","Epoch 1226/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 2.6209\n","Epoch 1227/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 2.6121\n","Epoch 1228/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 2.6033\n","Epoch 1229/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 2.5945\n","Epoch 1230/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 2.5857\n","Epoch 1231/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 2.5769\n","Epoch 1232/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.5682\n","Epoch 1233/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 2.5595\n","Epoch 1234/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 2.5508\n","Epoch 1235/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 2.5422\n","Epoch 1236/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 2.5336\n","Epoch 1237/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 2.5250\n","Epoch 1238/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.5164\n","Epoch 1239/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 2.5078\n","Epoch 1240/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 2.4993\n","Epoch 1241/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 2.4908\n","Epoch 1242/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 2.4823\n","Epoch 1243/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 2.4739\n","Epoch 1244/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 2.4655\n","Epoch 1245/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 2.4570\n","Epoch 1246/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 2.4487\n","Epoch 1247/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 2.4403\n","Epoch 1248/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 2.4320\n","Epoch 1249/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.4237\n","Epoch 1250/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 2.4154\n","Epoch 1251/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 2.4072\n","Epoch 1252/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.3989\n","Epoch 1253/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 2.3907\n","Epoch 1254/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 2.3825\n","Epoch 1255/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 2.3744\n","Epoch 1256/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 2.3662\n","Epoch 1257/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 2.3581\n","Epoch 1258/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 2.3500\n","Epoch 1259/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.3420\n","Epoch 1260/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 2.3339\n","Epoch 1261/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 2.3259\n","Epoch 1262/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 2.3179\n","Epoch 1263/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 2.3100\n","Epoch 1264/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 2.3020\n","Epoch 1265/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 2.2941\n","Epoch 1266/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.2862\n","Epoch 1267/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 2.2783\n","Epoch 1268/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 2.2705\n","Epoch 1269/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 2.2626\n","Epoch 1270/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 2.2548\n","Epoch 1271/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 2.2470\n","Epoch 1272/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 2.2393\n","Epoch 1273/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 2.2315\n","Epoch 1274/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 2.2238\n","Epoch 1275/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 2.2161\n","Epoch 1276/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 2.2085\n","Epoch 1277/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 2.2008\n","Epoch 1278/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 2.1932\n","Epoch 1279/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 2.1856\n","Epoch 1280/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 2.1780\n","Epoch 1281/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 2.1705\n","Epoch 1282/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 2.1629\n","Epoch 1283/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 2.1554\n","Epoch 1284/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 2.1479\n","Epoch 1285/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 2.1405\n","Epoch 1286/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 2.1330\n","Epoch 1287/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.1256\n","Epoch 1288/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 2.1182\n","Epoch 1289/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 2.1108\n","Epoch 1290/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 2.1035\n","Epoch 1291/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 2.0961\n","Epoch 1292/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 2.0888\n","Epoch 1293/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 2.0815\n","Epoch 1294/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 2.0743\n","Epoch 1295/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 2.0670\n","Epoch 1296/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 2.0598\n","Epoch 1297/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 2.0526\n","Epoch 1298/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 2.0454\n","Epoch 1299/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 2.0382\n","Epoch 1300/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 2.0311\n","Epoch 1301/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 2.0240\n","Epoch 1302/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 2.0169\n","Epoch 1303/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 2.0098\n","Epoch 1304/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.0028\n","Epoch 1305/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 1.9957\n","Epoch 1306/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 1.9887\n","Epoch 1307/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 1.9817\n","Epoch 1308/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 1.9748\n","Epoch 1309/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 1.9678\n","Epoch 1310/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 1.9609\n","Epoch 1311/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 1.9540\n","Epoch 1312/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 1.9471\n","Epoch 1313/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 1.9402\n","Epoch 1314/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.9334\n","Epoch 1315/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 1.9266\n","Epoch 1316/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 1.9198\n","Epoch 1317/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 1.9130\n","Epoch 1318/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 1.9062\n","Epoch 1319/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 1.8995\n","Epoch 1320/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 1.8928\n","Epoch 1321/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 1.8861\n","Epoch 1322/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 1.8794\n","Epoch 1323/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 1.8727\n","Epoch 1324/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 1.8661\n","Epoch 1325/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 1.8595\n","Epoch 1326/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 1.8529\n","Epoch 1327/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 1.8463\n","Epoch 1328/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 1.8398\n","Epoch 1329/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.8332\n","Epoch 1330/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 1.8267\n","Epoch 1331/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 1.8202\n","Epoch 1332/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 1.8137\n","Epoch 1333/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 1.8073\n","Epoch 1334/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.8008\n","Epoch 1335/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 1.7944\n","Epoch 1336/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 1.7880\n","Epoch 1337/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 1.7817\n","Epoch 1338/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 1.7753\n","Epoch 1339/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 1.7690\n","Epoch 1340/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 1.7626\n","Epoch 1341/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 1.7563\n","Epoch 1342/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 1.7501\n","Epoch 1343/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 1.7438\n","Epoch 1344/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 1.7376\n","Epoch 1345/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 1.7313\n","Epoch 1346/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 1.7251\n","Epoch 1347/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 1.7189\n","Epoch 1348/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 1.7128\n","Epoch 1349/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 1.7066\n","Epoch 1350/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 1.7005\n","Epoch 1351/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 1.6944\n","Epoch 1352/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 1.6883\n","Epoch 1353/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 1.6822\n","Epoch 1354/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 1.6762\n","Epoch 1355/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 1.6702\n","Epoch 1356/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 1.6641\n","Epoch 1357/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.6581\n","Epoch 1358/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.6522\n","Epoch 1359/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 1.6462\n","Epoch 1360/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 1.6403\n","Epoch 1361/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 1.6344\n","Epoch 1362/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 1.6285\n","Epoch 1363/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 1.6226\n","Epoch 1364/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 1.6167\n","Epoch 1365/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.6109\n","Epoch 1366/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 1.6050\n","Epoch 1367/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 1.5992\n","Epoch 1368/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 1.5934\n","Epoch 1369/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 1.5876\n","Epoch 1370/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 1.5819\n","Epoch 1371/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 1.5762\n","Epoch 1372/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 1.5704\n","Epoch 1373/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 1.5647\n","Epoch 1374/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 1.5590\n","Epoch 1375/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 1.5534\n","Epoch 1376/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 1.5477\n","Epoch 1377/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 1.5421\n","Epoch 1378/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 1.5365\n","Epoch 1379/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 1.5309\n","Epoch 1380/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 1.5253\n","Epoch 1381/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 1.5198\n","Epoch 1382/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 1.5142\n","Epoch 1383/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 1.5087\n","Epoch 1384/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 1.5032\n","Epoch 1385/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 1.4977\n","Epoch 1386/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 1.4922\n","Epoch 1387/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 1.4868\n","Epoch 1388/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 1.4813\n","Epoch 1389/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 1.4759\n","Epoch 1390/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 1.4705\n","Epoch 1391/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 1.4651\n","Epoch 1392/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 1.4597\n","Epoch 1393/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 1.4544\n","Epoch 1394/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 1.4490\n","Epoch 1395/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 1.4437\n","Epoch 1396/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 1.4384\n","Epoch 1397/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 1.4331\n","Epoch 1398/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 1.4279\n","Epoch 1399/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 1.4226\n","Epoch 1400/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 1.4174\n","Epoch 1401/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 1.4122\n","Epoch 1402/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 1.4070\n","Epoch 1403/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 1.4018\n","Epoch 1404/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 1.3966\n","Epoch 1405/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 1.3915\n","Epoch 1406/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 1.3863\n","Epoch 1407/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 1.3812\n","Epoch 1408/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 1.3761\n","Epoch 1409/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 1.3710\n","Epoch 1410/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 1.3659\n","Epoch 1411/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 1.3609\n","Epoch 1412/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 1.3559\n","Epoch 1413/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 1.3508\n","Epoch 1414/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 1.3458\n","Epoch 1415/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 1.3408\n","Epoch 1416/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 1.3359\n","Epoch 1417/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 1.3309\n","Epoch 1418/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 1.3260\n","Epoch 1419/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 1.3210\n","Epoch 1420/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 1.3161\n","Epoch 1421/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 1.3112\n","Epoch 1422/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 1.3064\n","Epoch 1423/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 1.3015\n","Epoch 1424/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 1.2967\n","Epoch 1425/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 1.2918\n","Epoch 1426/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 1.2870\n","Epoch 1427/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 1.2822\n","Epoch 1428/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 1.2774\n","Epoch 1429/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 1.2727\n","Epoch 1430/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 1.2679\n","Epoch 1431/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 1.2632\n","Epoch 1432/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 1.2584\n","Epoch 1433/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 1.2537\n","Epoch 1434/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 1.2490\n","Epoch 1435/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 1.2444\n","Epoch 1436/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 1.2397\n","Epoch 1437/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 1.2351\n","Epoch 1438/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 1.2304\n","Epoch 1439/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 1.2258\n","Epoch 1440/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 1.2212\n","Epoch 1441/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 1.2166\n","Epoch 1442/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 1.2121\n","Epoch 1443/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 1.2075\n","Epoch 1444/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 1.2030\n","Epoch 1445/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 1.1984\n","Epoch 1446/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 1.1939\n","Epoch 1447/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 1.1894\n","Epoch 1448/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 1.1850\n","Epoch 1449/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 1.1805\n","Epoch 1450/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 1.1760\n","Epoch 1451/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 1.1716\n","Epoch 1452/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 1.1672\n","Epoch 1453/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 1.1628\n","Epoch 1454/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 1.1584\n","Epoch 1455/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 1.1540\n","Epoch 1456/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 1.1496\n","Epoch 1457/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 1.1453\n","Epoch 1458/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 1.1409\n","Epoch 1459/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 1.1366\n","Epoch 1460/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 1.1323\n","Epoch 1461/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 1.1280\n","Epoch 1462/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.1237\n","Epoch 1463/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 1.1195\n","Epoch 1464/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 1.1152\n","Epoch 1465/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 1.1110\n","Epoch 1466/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 1.1068\n","Epoch 1467/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 1.1026\n","Epoch 1468/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 1.0984\n","Epoch 1469/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 1.0942\n","Epoch 1470/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 1.0900\n","Epoch 1471/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 1.0859\n","Epoch 1472/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 1.0817\n","Epoch 1473/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 1.0776\n","Epoch 1474/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.0735\n","Epoch 1475/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 1.0694\n","Epoch 1476/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.0653\n","Epoch 1477/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 1.0612\n","Epoch 1478/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 1.0572\n","Epoch 1479/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 1.0531\n","Epoch 1480/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 1.0491\n","Epoch 1481/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 1.0451\n","Epoch 1482/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 1.0411\n","Epoch 1483/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.0371\n","Epoch 1484/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 1.0331\n","Epoch 1485/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 1.0292\n","Epoch 1486/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 1.0252\n","Epoch 1487/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.0213\n","Epoch 1488/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 1.0173\n","Epoch 1489/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 1.0134\n","Epoch 1490/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.0095\n","Epoch 1491/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 1.0056\n","Epoch 1492/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 1.0018\n","Epoch 1493/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.9979\n","Epoch 1494/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.9941\n","Epoch 1495/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.9902\n","Epoch 1496/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.9864\n","Epoch 1497/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.9826\n","Epoch 1498/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.9788\n","Epoch 1499/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.9750\n","Epoch 1500/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.9713\n","Epoch 1501/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.9675\n","Epoch 1502/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.9638\n","Epoch 1503/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.9601\n","Epoch 1504/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.9563\n","Epoch 1505/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.9526\n","Epoch 1506/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.9489\n","Epoch 1507/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.9453\n","Epoch 1508/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.9416\n","Epoch 1509/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.9379\n","Epoch 1510/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.9343\n","Epoch 1511/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.9307\n","Epoch 1512/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.9271\n","Epoch 1513/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.9234\n","Epoch 1514/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.9199\n","Epoch 1515/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.9163\n","Epoch 1516/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.9127\n","Epoch 1517/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.9092\n","Epoch 1518/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.9056\n","Epoch 1519/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.9021\n","Epoch 1520/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.8986\n","Epoch 1521/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.8951\n","Epoch 1522/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.8916\n","Epoch 1523/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.8881\n","Epoch 1524/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.8846\n","Epoch 1525/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.8811\n","Epoch 1526/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.8777\n","Epoch 1527/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.8743\n","Epoch 1528/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.8708\n","Epoch 1529/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.8674\n","Epoch 1530/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.8640\n","Epoch 1531/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.8606\n","Epoch 1532/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.8573\n","Epoch 1533/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.8539\n","Epoch 1534/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.8505\n","Epoch 1535/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.8472\n","Epoch 1536/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.8439\n","Epoch 1537/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.8405\n","Epoch 1538/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.8372\n","Epoch 1539/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.8339\n","Epoch 1540/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.8307\n","Epoch 1541/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.8274\n","Epoch 1542/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.8241\n","Epoch 1543/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.8209\n","Epoch 1544/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.8176\n","Epoch 1545/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.8144\n","Epoch 1546/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.8112\n","Epoch 1547/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.8080\n","Epoch 1548/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.8048\n","Epoch 1549/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.8016\n","Epoch 1550/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.7984\n","Epoch 1551/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.7953\n","Epoch 1552/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.7921\n","Epoch 1553/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.7890\n","Epoch 1554/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.7859\n","Epoch 1555/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.7827\n","Epoch 1556/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.7796\n","Epoch 1557/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.7765\n","Epoch 1558/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.7735\n","Epoch 1559/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.7704\n","Epoch 1560/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.7673\n","Epoch 1561/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.7643\n","Epoch 1562/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.7612\n","Epoch 1563/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.7582\n","Epoch 1564/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.7552\n","Epoch 1565/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.7522\n","Epoch 1566/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.7492\n","Epoch 1567/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.7462\n","Epoch 1568/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 0.7432\n","Epoch 1569/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.7402\n","Epoch 1570/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.7373\n","Epoch 1571/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.7343\n","Epoch 1572/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.7314\n","Epoch 1573/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.7285\n","Epoch 1574/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.7256\n","Epoch 1575/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.7226\n","Epoch 1576/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.7198\n","Epoch 1577/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.7169\n","Epoch 1578/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.7140\n","Epoch 1579/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.7111\n","Epoch 1580/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.7083\n","Epoch 1581/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.7054\n","Epoch 1582/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.7026\n","Epoch 1583/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 163ms/step - loss: 0.6998\n","Epoch 1584/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.6970\n","Epoch 1585/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.6942\n","Epoch 1586/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.6914\n","Epoch 1587/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.6886\n","Epoch 1588/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - loss: 0.6858\n","Epoch 1589/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.6830\n","Epoch 1590/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.6803\n","Epoch 1591/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.6775\n","Epoch 1592/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.6748\n","Epoch 1593/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.6721\n","Epoch 1594/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.6694\n","Epoch 1595/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.6667\n","Epoch 1596/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.6640\n","Epoch 1597/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.6613\n","Epoch 1598/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.6586\n","Epoch 1599/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.6559\n","Epoch 1600/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.6533\n","Epoch 1601/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.6506\n","Epoch 1602/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.6480\n","Epoch 1603/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.6454\n","Epoch 1604/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.6428\n","Epoch 1605/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.6401\n","Epoch 1606/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.6375\n","Epoch 1607/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.6350\n","Epoch 1608/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.6324\n","Epoch 1609/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.6298\n","Epoch 1610/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.6272\n","Epoch 1611/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.6247\n","Epoch 1612/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.6221\n","Epoch 1613/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.6196\n","Epoch 1614/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.6171\n","Epoch 1615/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.6146\n","Epoch 1616/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.6121\n","Epoch 1617/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.6096\n","Epoch 1618/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.6071\n","Epoch 1619/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.6046\n","Epoch 1620/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.6021\n","Epoch 1621/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 0.5997\n","Epoch 1622/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.5972\n","Epoch 1623/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.5948\n","Epoch 1624/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.5923\n","Epoch 1625/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.5899\n","Epoch 1626/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.5875\n","Epoch 1627/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.5851\n","Epoch 1628/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.5827\n","Epoch 1629/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.5803\n","Epoch 1630/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.5779\n","Epoch 1631/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.5755\n","Epoch 1632/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.5732\n","Epoch 1633/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.5708\n","Epoch 1634/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 0.5685\n","Epoch 1635/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.5661\n","Epoch 1636/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.5638\n","Epoch 1637/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.5615\n","Epoch 1638/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.5591\n","Epoch 1639/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.5568\n","Epoch 1640/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.5545\n","Epoch 1641/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.5523\n","Epoch 1642/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.5500\n","Epoch 1643/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.5477\n","Epoch 1644/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.5454\n","Epoch 1645/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.5432\n","Epoch 1646/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.5409\n","Epoch 1647/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.5387\n","Epoch 1648/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.5365\n","Epoch 1649/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.5343\n","Epoch 1650/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.5320\n","Epoch 1651/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.5298\n","Epoch 1652/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.5276\n","Epoch 1653/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.5254\n","Epoch 1654/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.5233\n","Epoch 1655/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.5211\n","Epoch 1656/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.5189\n","Epoch 1657/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.5168\n","Epoch 1658/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.5146\n","Epoch 1659/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.5125\n","Epoch 1660/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.5104\n","Epoch 1661/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.5082\n","Epoch 1662/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.5061\n","Epoch 1663/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.5040\n","Epoch 1664/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.5019\n","Epoch 1665/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.4998\n","Epoch 1666/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.4977\n","Epoch 1667/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.4956\n","Epoch 1668/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.4936\n","Epoch 1669/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.4915\n","Epoch 1670/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.4895\n","Epoch 1671/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.4874\n","Epoch 1672/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.4854\n","Epoch 1673/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.4833\n","Epoch 1674/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.4813\n","Epoch 1675/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.4793\n","Epoch 1676/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.4773\n","Epoch 1677/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.4753\n","Epoch 1678/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.4733\n","Epoch 1679/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.4713\n","Epoch 1680/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.4693\n","Epoch 1681/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.4674\n","Epoch 1682/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.4654\n","Epoch 1683/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.4634\n","Epoch 1684/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.4615\n","Epoch 1685/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.4596\n","Epoch 1686/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.4576\n","Epoch 1687/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.4557\n","Epoch 1688/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.4538\n","Epoch 1689/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.4519\n","Epoch 1690/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.4500\n","Epoch 1691/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.4481\n","Epoch 1692/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.4462\n","Epoch 1693/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.4443\n","Epoch 1694/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.4424\n","Epoch 1695/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.4405\n","Epoch 1696/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.4387\n","Epoch 1697/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.4368\n","Epoch 1698/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.4350\n","Epoch 1699/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.4331\n","Epoch 1700/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.4313\n","Epoch 1701/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.4295\n","Epoch 1702/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.4277\n","Epoch 1703/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.4259\n","Epoch 1704/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.4240\n","Epoch 1705/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.4222\n","Epoch 1706/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.4205\n","Epoch 1707/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.4187\n","Epoch 1708/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.4169\n","Epoch 1709/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.4151\n","Epoch 1710/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.4134\n","Epoch 1711/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.4116\n","Epoch 1712/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.4098\n","Epoch 1713/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.4081\n","Epoch 1714/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.4064\n","Epoch 1715/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.4046\n","Epoch 1716/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.4029\n","Epoch 1717/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.4012\n","Epoch 1718/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.3995\n","Epoch 1719/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.3978\n","Epoch 1720/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.3961\n","Epoch 1721/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.3944\n","Epoch 1722/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.3927\n","Epoch 1723/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.3910\n","Epoch 1724/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.3894\n","Epoch 1725/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.3877\n","Epoch 1726/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.3860\n","Epoch 1727/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.3844\n","Epoch 1728/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.3827\n","Epoch 1729/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.3811\n","Epoch 1730/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.3795\n","Epoch 1731/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.3778\n","Epoch 1732/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.3762\n","Epoch 1733/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.3746\n","Epoch 1734/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.3730\n","Epoch 1735/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.3714\n","Epoch 1736/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.3698\n","Epoch 1737/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.3682\n","Epoch 1738/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.3666\n","Epoch 1739/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.3650\n","Epoch 1740/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.3635\n","Epoch 1741/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.3619\n","Epoch 1742/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.3603\n","Epoch 1743/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.3588\n","Epoch 1744/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.3572\n","Epoch 1745/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.3557\n","Epoch 1746/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.3542\n","Epoch 1747/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.3526\n","Epoch 1748/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.3511\n","Epoch 1749/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.3496\n","Epoch 1750/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.3481\n","Epoch 1751/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.3466\n","Epoch 1752/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.3451\n","Epoch 1753/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.3436\n","Epoch 1754/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.3421\n","Epoch 1755/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.3406\n","Epoch 1756/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.3392\n","Epoch 1757/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.3377\n","Epoch 1758/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.3362\n","Epoch 1759/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.3348\n","Epoch 1760/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.3333\n","Epoch 1761/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.3319\n","Epoch 1762/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.3304\n","Epoch 1763/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.3290\n","Epoch 1764/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.3276\n","Epoch 1765/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.3261\n","Epoch 1766/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.3247\n","Epoch 1767/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.3233\n","Epoch 1768/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.3219\n","Epoch 1769/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.3205\n","Epoch 1770/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.3191\n","Epoch 1771/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.3177\n","Epoch 1772/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.3163\n","Epoch 1773/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.3150\n","Epoch 1774/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.3136\n","Epoch 1775/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.3122\n","Epoch 1776/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.3109\n","Epoch 1777/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.3095\n","Epoch 1778/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.3081\n","Epoch 1779/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.3068\n","Epoch 1780/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.3055\n","Epoch 1781/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.3041\n","Epoch 1782/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.3028\n","Epoch 1783/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.3015\n","Epoch 1784/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.3001\n","Epoch 1785/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - loss: 0.2988\n","Epoch 1786/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.2975\n","Epoch 1787/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.2962\n","Epoch 1788/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.2949\n","Epoch 1789/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.2936\n","Epoch 1790/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.2923\n","Epoch 1791/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.2911\n","Epoch 1792/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.2898\n","Epoch 1793/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.2885\n","Epoch 1794/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.2872\n","Epoch 1795/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.2860\n","Epoch 1796/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.2847\n","Epoch 1797/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.2835\n","Epoch 1798/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.2822\n","Epoch 1799/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 160ms/step - loss: 0.2810\n","Epoch 1800/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 0.2797\n","Epoch 1801/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.2785\n","Epoch 1802/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.2773\n","Epoch 1803/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.2761\n","Epoch 1804/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.2748\n","Epoch 1805/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.2736\n","Epoch 1806/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.2724\n","Epoch 1807/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 0.2712\n","Epoch 1808/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.2700\n","Epoch 1809/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.2688\n","Epoch 1810/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.2677\n","Epoch 1811/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.2665\n","Epoch 1812/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.2653\n","Epoch 1813/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.2641\n","Epoch 1814/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.2629\n","Epoch 1815/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.2618\n","Epoch 1816/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.2606\n","Epoch 1817/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.2595\n","Epoch 1818/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 0.2583\n","Epoch 1819/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.2572\n","Epoch 1820/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.2560\n","Epoch 1821/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.2549\n","Epoch 1822/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.2538\n","Epoch 1823/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.2526\n","Epoch 1824/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.2515\n","Epoch 1825/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.2504\n","Epoch 1826/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.2493\n","Epoch 1827/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.2482\n","Epoch 1828/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.2471\n","Epoch 1829/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.2460\n","Epoch 1830/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.2449\n","Epoch 1831/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.2438\n","Epoch 1832/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.2427\n","Epoch 1833/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.2416\n","Epoch 1834/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.2406\n","Epoch 1835/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.2395\n","Epoch 1836/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.2384\n","Epoch 1837/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.2374\n","Epoch 1838/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.2363\n","Epoch 1839/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.2352\n","Epoch 1840/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.2342\n","Epoch 1841/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.2332\n","Epoch 1842/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.2321\n","Epoch 1843/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.2311\n","Epoch 1844/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.2300\n","Epoch 1845/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.2290\n","Epoch 1846/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.2280\n","Epoch 1847/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.2270\n","Epoch 1848/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.2260\n","Epoch 1849/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.2249\n","Epoch 1850/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.2239\n","Epoch 1851/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.2229\n","Epoch 1852/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.2219\n","Epoch 1853/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.2209\n","Epoch 1854/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.2200\n","Epoch 1855/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.2190\n","Epoch 1856/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.2180\n","Epoch 1857/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.2170\n","Epoch 1858/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.2160\n","Epoch 1859/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.2151\n","Epoch 1860/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.2141\n","Epoch 1861/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.2131\n","Epoch 1862/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.2122\n","Epoch 1863/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.2112\n","Epoch 1864/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.2103\n","Epoch 1865/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.2093\n","Epoch 1866/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.2084\n","Epoch 1867/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.2075\n","Epoch 1868/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.2065\n","Epoch 1869/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.2056\n","Epoch 1870/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.2047\n","Epoch 1871/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.2037\n","Epoch 1872/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.2028\n","Epoch 1873/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.2019\n","Epoch 1874/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.2010\n","Epoch 1875/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.2001\n","Epoch 1876/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.1992\n","Epoch 1877/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.1983\n","Epoch 1878/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.1974\n","Epoch 1879/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.1965\n","Epoch 1880/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.1956\n","Epoch 1881/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.1947\n","Epoch 1882/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.1939\n","Epoch 1883/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.1930\n","Epoch 1884/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.1921\n","Epoch 1885/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.1912\n","Epoch 1886/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.1904\n","Epoch 1887/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.1895\n","Epoch 1888/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.1887\n","Epoch 1889/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.1878\n","Epoch 1890/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.1870\n","Epoch 1891/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.1861\n","Epoch 1892/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.1853\n","Epoch 1893/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.1844\n","Epoch 1894/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.1836\n","Epoch 1895/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.1828\n","Epoch 1896/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.1819\n","Epoch 1897/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.1811\n","Epoch 1898/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.1803\n","Epoch 1899/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.1795\n","Epoch 1900/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.1787\n","Epoch 1901/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.1778\n","Epoch 1902/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.1770\n","Epoch 1903/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.1762\n","Epoch 1904/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.1754\n","Epoch 1905/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.1746\n","Epoch 1906/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.1738\n","Epoch 1907/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.1730\n","Epoch 1908/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.1723\n","Epoch 1909/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.1715\n","Epoch 1910/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.1707\n","Epoch 1911/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.1699\n","Epoch 1912/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.1691\n","Epoch 1913/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.1684\n","Epoch 1914/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.1676\n","Epoch 1915/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.1668\n","Epoch 1916/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.1661\n","Epoch 1917/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.1653\n","Epoch 1918/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.1646\n","Epoch 1919/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.1638\n","Epoch 1920/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.1631\n","Epoch 1921/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.1623\n","Epoch 1922/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.1616\n","Epoch 1923/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.1609\n","Epoch 1924/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.1601\n","Epoch 1925/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.1594\n","Epoch 1926/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.1587\n","Epoch 1927/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.1579\n","Epoch 1928/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.1572\n","Epoch 1929/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.1565\n","Epoch 1930/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.1558\n","Epoch 1931/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.1551\n","Epoch 1932/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.1544\n","Epoch 1933/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.1537\n","Epoch 1934/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.1530\n","Epoch 1935/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.1523\n","Epoch 1936/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.1516\n","Epoch 1937/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.1509\n","Epoch 1938/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.1502\n","Epoch 1939/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.1495\n","Epoch 1940/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.1488\n","Epoch 1941/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.1481\n","Epoch 1942/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.1475\n","Epoch 1943/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.1468\n","Epoch 1944/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.1461\n","Epoch 1945/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.1454\n","Epoch 1946/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.1448\n","Epoch 1947/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.1441\n","Epoch 1948/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.1434\n","Epoch 1949/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.1428\n","Epoch 1950/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.1421\n","Epoch 1951/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.1415\n","Epoch 1952/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.1408\n","Epoch 1953/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.1402\n","Epoch 1954/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.1395\n","Epoch 1955/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.1389\n","Epoch 1956/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.1383\n","Epoch 1957/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.1376\n","Epoch 1958/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.1370\n","Epoch 1959/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.1364\n","Epoch 1960/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.1357\n","Epoch 1961/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.1351\n","Epoch 1962/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.1345\n","Epoch 1963/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.1339\n","Epoch 1964/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.1333\n","Epoch 1965/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.1327\n","Epoch 1966/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.1320\n","Epoch 1967/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.1314\n","Epoch 1968/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.1308\n","Epoch 1969/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.1302\n","Epoch 1970/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.1296\n","Epoch 1971/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.1290\n","Epoch 1972/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.1284\n","Epoch 1973/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.1279\n","Epoch 1974/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.1273\n","Epoch 1975/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.1267\n","Epoch 1976/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.1261\n","Epoch 1977/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.1255\n","Epoch 1978/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.1249\n","Epoch 1979/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.1244\n","Epoch 1980/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.1238\n","Epoch 1981/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.1232\n","Epoch 1982/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.1227\n","Epoch 1983/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.1221\n","Epoch 1984/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 167ms/step - loss: 0.1215\n","Epoch 1985/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.1210\n","Epoch 1986/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.1204\n","Epoch 1987/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 174ms/step - loss: 0.1199\n","Epoch 1988/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.1193\n","Epoch 1989/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.1187\n","Epoch 1990/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.1182\n","Epoch 1991/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.1177\n","Epoch 1992/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.1171\n","Epoch 1993/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.1166\n","Epoch 1994/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 157ms/step - loss: 0.1160\n","Epoch 1995/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.1155\n","Epoch 1996/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.1150\n","Epoch 1997/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.1144\n","Epoch 1998/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.1139\n","Epoch 1999/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.1134\n","Epoch 2000/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.1129\n","Epoch 2001/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.1123\n","Epoch 2002/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 0.1118\n","Epoch 2003/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.1113\n","Epoch 2004/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.1108\n","Epoch 2005/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 0.1103\n","Epoch 2006/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.1098\n","Epoch 2007/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.1093\n","Epoch 2008/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 157ms/step - loss: 0.1088\n","Epoch 2009/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.1083\n","Epoch 2010/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.1078\n","Epoch 2011/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.1073\n","Epoch 2012/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.1068\n","Epoch 2013/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.1063\n","Epoch 2014/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.1058\n","Epoch 2015/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 190ms/step - loss: 0.1053\n","Epoch 2016/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.1048\n","Epoch 2017/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.1043\n","Epoch 2018/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.1039\n","Epoch 2019/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.1034\n","Epoch 2020/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 157ms/step - loss: 0.1029\n","Epoch 2021/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.1024\n","Epoch 2022/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.1019\n","Epoch 2023/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.1015\n","Epoch 2024/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.1010\n","Epoch 2025/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.1005\n","Epoch 2026/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.1001\n","Epoch 2027/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0996\n","Epoch 2028/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0992\n","Epoch 2029/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0987\n","Epoch 2030/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0982\n","Epoch 2031/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0978\n","Epoch 2032/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0973\n","Epoch 2033/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0969\n","Epoch 2034/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0964\n","Epoch 2035/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0960\n","Epoch 2036/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0956\n","Epoch 2037/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0951\n","Epoch 2038/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0947\n","Epoch 2039/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0942\n","Epoch 2040/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0938\n","Epoch 2041/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0934\n","Epoch 2042/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0929\n","Epoch 2043/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0925\n","Epoch 2044/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0921\n","Epoch 2045/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0917\n","Epoch 2046/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0912\n","Epoch 2047/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0908\n","Epoch 2048/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0904\n","Epoch 2049/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0900\n","Epoch 2050/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0896\n","Epoch 2051/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0892\n","Epoch 2052/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0887\n","Epoch 2053/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0883\n","Epoch 2054/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0879\n","Epoch 2055/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0875\n","Epoch 2056/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0871\n","Epoch 2057/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0867\n","Epoch 2058/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0863\n","Epoch 2059/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0859\n","Epoch 2060/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0855\n","Epoch 2061/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0851\n","Epoch 2062/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0847\n","Epoch 2063/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0843\n","Epoch 2064/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0840\n","Epoch 2065/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0836\n","Epoch 2066/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0832\n","Epoch 2067/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0828\n","Epoch 2068/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0824\n","Epoch 2069/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0820\n","Epoch 2070/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0817\n","Epoch 2071/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0813\n","Epoch 2072/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0809\n","Epoch 2073/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0805\n","Epoch 2074/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0802\n","Epoch 2075/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0798\n","Epoch 2076/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0794\n","Epoch 2077/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0791\n","Epoch 2078/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0787\n","Epoch 2079/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0784\n","Epoch 2080/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0780\n","Epoch 2081/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0776\n","Epoch 2082/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0773\n","Epoch 2083/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0769\n","Epoch 2084/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0766\n","Epoch 2085/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0762\n","Epoch 2086/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0759\n","Epoch 2087/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0755\n","Epoch 2088/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0752\n","Epoch 2089/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0748\n","Epoch 2090/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0745\n","Epoch 2091/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0741\n","Epoch 2092/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0738\n","Epoch 2093/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0735\n","Epoch 2094/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0731\n","Epoch 2095/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0728\n","Epoch 2096/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0725\n","Epoch 2097/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0721\n","Epoch 2098/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0718\n","Epoch 2099/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0715\n","Epoch 2100/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0711\n","Epoch 2101/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0708\n","Epoch 2102/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0705\n","Epoch 2103/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0702\n","Epoch 2104/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0699\n","Epoch 2105/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0695\n","Epoch 2106/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0692\n","Epoch 2107/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0689\n","Epoch 2108/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0686\n","Epoch 2109/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0683\n","Epoch 2110/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0680\n","Epoch 2111/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0677\n","Epoch 2112/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0673\n","Epoch 2113/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0670\n","Epoch 2114/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0667\n","Epoch 2115/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0664\n","Epoch 2116/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0661\n","Epoch 2117/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0658\n","Epoch 2118/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0655\n","Epoch 2119/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0652\n","Epoch 2120/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0649\n","Epoch 2121/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0646\n","Epoch 2122/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0643\n","Epoch 2123/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0640\n","Epoch 2124/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0638\n","Epoch 2125/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0635\n","Epoch 2126/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0632\n","Epoch 2127/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0629\n","Epoch 2128/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0626\n","Epoch 2129/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0623\n","Epoch 2130/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0620\n","Epoch 2131/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0618\n","Epoch 2132/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0615\n","Epoch 2133/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0612\n","Epoch 2134/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0609\n","Epoch 2135/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0607\n","Epoch 2136/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0604\n","Epoch 2137/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0601\n","Epoch 2138/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0598\n","Epoch 2139/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0596\n","Epoch 2140/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0593\n","Epoch 2141/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0590\n","Epoch 2142/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0588\n","Epoch 2143/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0585\n","Epoch 2144/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 0.0582\n","Epoch 2145/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0580\n","Epoch 2146/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0577\n","Epoch 2147/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0574\n","Epoch 2148/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0572\n","Epoch 2149/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0569\n","Epoch 2150/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0567\n","Epoch 2151/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0564\n","Epoch 2152/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0562\n","Epoch 2153/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0559\n","Epoch 2154/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0557\n","Epoch 2155/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0554\n","Epoch 2156/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0552\n","Epoch 2157/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0549\n","Epoch 2158/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0547\n","Epoch 2159/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0544\n","Epoch 2160/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0542\n","Epoch 2161/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0539\n","Epoch 2162/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0537\n","Epoch 2163/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0535\n","Epoch 2164/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0532\n","Epoch 2165/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0530\n","Epoch 2166/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0528\n","Epoch 2167/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0525\n","Epoch 2168/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0523\n","Epoch 2169/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0521\n","Epoch 2170/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0518\n","Epoch 2171/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0516\n","Epoch 2172/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0514\n","Epoch 2173/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0511\n","Epoch 2174/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0509\n","Epoch 2175/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0507\n","Epoch 2176/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0505\n","Epoch 2177/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0502\n","Epoch 2178/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0500\n","Epoch 2179/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0498\n","Epoch 2180/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0496\n","Epoch 2181/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0494\n","Epoch 2182/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0491\n","Epoch 2183/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0489\n","Epoch 2184/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0487\n","Epoch 2185/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0485\n","Epoch 2186/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0483\n","Epoch 2187/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0481\n","Epoch 2188/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0479\n","Epoch 2189/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0476\n","Epoch 2190/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 159ms/step - loss: 0.0474\n","Epoch 2191/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0472\n","Epoch 2192/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0470\n","Epoch 2193/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 0.0468\n","Epoch 2194/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0466\n","Epoch 2195/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 0.0464\n","Epoch 2196/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0462\n","Epoch 2197/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0460\n","Epoch 2198/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0458\n","Epoch 2199/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0456\n","Epoch 2200/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0454\n","Epoch 2201/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.0452\n","Epoch 2202/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0450\n","Epoch 2203/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 0.0448\n","Epoch 2204/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0446\n","Epoch 2205/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 157ms/step - loss: 0.0444\n","Epoch 2206/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0442\n","Epoch 2207/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0440\n","Epoch 2208/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0438\n","Epoch 2209/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0437\n","Epoch 2210/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0435\n","Epoch 2211/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0433\n","Epoch 2212/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0431\n","Epoch 2213/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0429\n","Epoch 2214/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0427\n","Epoch 2215/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0425\n","Epoch 2216/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0424\n","Epoch 2217/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0422\n","Epoch 2218/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0420\n","Epoch 2219/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0418\n","Epoch 2220/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 163ms/step - loss: 0.0416\n","Epoch 2221/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.0415\n","Epoch 2222/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0413\n","Epoch 2223/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 0.0411\n","Epoch 2224/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0409\n","Epoch 2225/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0407\n","Epoch 2226/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 157ms/step - loss: 0.0406\n","Epoch 2227/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0404\n","Epoch 2228/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0402\n","Epoch 2229/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0401\n","Epoch 2230/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0399\n","Epoch 2231/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0397\n","Epoch 2232/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0396\n","Epoch 2233/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0394\n","Epoch 2234/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0392\n","Epoch 2235/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0391\n","Epoch 2236/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0389\n","Epoch 2237/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0387\n","Epoch 2238/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0386\n","Epoch 2239/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0384\n","Epoch 2240/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0382\n","Epoch 2241/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0381\n","Epoch 2242/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0379\n","Epoch 2243/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0378\n","Epoch 2244/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0376\n","Epoch 2245/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0374\n","Epoch 2246/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0373\n","Epoch 2247/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0371\n","Epoch 2248/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.0370\n","Epoch 2249/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0368\n","Epoch 2250/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0367\n","Epoch 2251/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0365\n","Epoch 2252/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0364\n","Epoch 2253/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0362\n","Epoch 2254/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0361\n","Epoch 2255/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0359\n","Epoch 2256/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0358\n","Epoch 2257/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0356\n","Epoch 2258/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0355\n","Epoch 2259/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0353\n","Epoch 2260/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0352\n","Epoch 2261/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0350\n","Epoch 2262/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0349\n","Epoch 2263/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0347\n","Epoch 2264/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0346\n","Epoch 2265/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0345\n","Epoch 2266/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0343\n","Epoch 2267/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0342\n","Epoch 2268/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0340\n","Epoch 2269/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0339\n","Epoch 2270/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0338\n","Epoch 2271/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0336\n","Epoch 2272/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0335\n","Epoch 2273/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0334\n","Epoch 2274/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 155ms/step - loss: 0.0332\n","Epoch 2275/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0331\n","Epoch 2276/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0330\n","Epoch 2277/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0328\n","Epoch 2278/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0327\n","Epoch 2279/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0326\n","Epoch 2280/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0324\n","Epoch 2281/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0323\n","Epoch 2282/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0322\n","Epoch 2283/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0320\n","Epoch 2284/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0319\n","Epoch 2285/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0318\n","Epoch 2286/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0317\n","Epoch 2287/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0315\n","Epoch 2288/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0314\n","Epoch 2289/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0313\n","Epoch 2290/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0312\n","Epoch 2291/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0310\n","Epoch 2292/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0309\n","Epoch 2293/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0308\n","Epoch 2294/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0307\n","Epoch 2295/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 0.0305\n","Epoch 2296/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0304\n","Epoch 2297/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0303\n","Epoch 2298/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0302\n","Epoch 2299/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0301\n","Epoch 2300/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0300\n","Epoch 2301/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0298\n","Epoch 2302/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0297\n","Epoch 2303/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.0296\n","Epoch 2304/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0295\n","Epoch 2305/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0294\n","Epoch 2306/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0293\n","Epoch 2307/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0292\n","Epoch 2308/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0290\n","Epoch 2309/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0289\n","Epoch 2310/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0288\n","Epoch 2311/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0287\n","Epoch 2312/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0286\n","Epoch 2313/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0285\n","Epoch 2314/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0284\n","Epoch 2315/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0283\n","Epoch 2316/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0282\n","Epoch 2317/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0281\n","Epoch 2318/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0280\n","Epoch 2319/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0278\n","Epoch 2320/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0277\n","Epoch 2321/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0276\n","Epoch 2322/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0275\n","Epoch 2323/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0274\n","Epoch 2324/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0273\n","Epoch 2325/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0272\n","Epoch 2326/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0271\n","Epoch 2327/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0270\n","Epoch 2328/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0269\n","Epoch 2329/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0268\n","Epoch 2330/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0267\n","Epoch 2331/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0266\n","Epoch 2332/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0265\n","Epoch 2333/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0264\n","Epoch 2334/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0263\n","Epoch 2335/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0262\n","Epoch 2336/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0261\n","Epoch 2337/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0260\n","Epoch 2338/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0259\n","Epoch 2339/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0258\n","Epoch 2340/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0258\n","Epoch 2341/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0257\n","Epoch 2342/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0256\n","Epoch 2343/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0255\n","Epoch 2344/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0254\n","Epoch 2345/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0253\n","Epoch 2346/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0252\n","Epoch 2347/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0251\n","Epoch 2348/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0250\n","Epoch 2349/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0249\n","Epoch 2350/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0248\n","Epoch 2351/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0248\n","Epoch 2352/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0247\n","Epoch 2353/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0246\n","Epoch 2354/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0245\n","Epoch 2355/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0244\n","Epoch 2356/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0243\n","Epoch 2357/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0242\n","Epoch 2358/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0241\n","Epoch 2359/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0241\n","Epoch 2360/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0240\n","Epoch 2361/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0239\n","Epoch 2362/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0238\n","Epoch 2363/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0237\n","Epoch 2364/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0236\n","Epoch 2365/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0236\n","Epoch 2366/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0235\n","Epoch 2367/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0234\n","Epoch 2368/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - loss: 0.0233\n","Epoch 2369/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0232\n","Epoch 2370/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0232\n","Epoch 2371/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0231\n","Epoch 2372/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0230\n","Epoch 2373/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0229\n","Epoch 2374/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0228\n","Epoch 2375/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0228\n","Epoch 2376/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.0227\n","Epoch 2377/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0226\n","Epoch 2378/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0225\n","Epoch 2379/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0225\n","Epoch 2380/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0224\n","Epoch 2381/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0223\n","Epoch 2382/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0222\n","Epoch 2383/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0222\n","Epoch 2384/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0221\n","Epoch 2385/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0220\n","Epoch 2386/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0219\n","Epoch 2387/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0219\n","Epoch 2388/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0218\n","Epoch 2389/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0217\n","Epoch 2390/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0217\n","Epoch 2391/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0216\n","Epoch 2392/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0215\n","Epoch 2393/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0214\n","Epoch 2394/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0214\n","Epoch 2395/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0213\n","Epoch 2396/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 0.0212\n","Epoch 2397/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0212\n","Epoch 2398/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0211\n","Epoch 2399/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step - loss: 0.0210\n","Epoch 2400/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0210\n","Epoch 2401/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 159ms/step - loss: 0.0209\n","Epoch 2402/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0208\n","Epoch 2403/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0208\n","Epoch 2404/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0207\n","Epoch 2405/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0206\n","Epoch 2406/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0206\n","Epoch 2407/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0205\n","Epoch 2408/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0204\n","Epoch 2409/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0204\n","Epoch 2410/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0203\n","Epoch 2411/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 0.0203\n","Epoch 2412/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0202\n","Epoch 2413/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0201\n","Epoch 2414/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0201\n","Epoch 2415/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0200\n","Epoch 2416/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0199\n","Epoch 2417/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0199\n","Epoch 2418/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0198\n","Epoch 2419/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0198\n","Epoch 2420/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0197\n","Epoch 2421/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0196\n","Epoch 2422/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0196\n","Epoch 2423/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0195\n","Epoch 2424/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0195\n","Epoch 2425/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0194\n","Epoch 2426/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0194\n","Epoch 2427/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0193\n","Epoch 2428/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0192\n","Epoch 2429/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0192\n","Epoch 2430/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0191\n","Epoch 2431/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0191\n","Epoch 2432/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0190\n","Epoch 2433/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0190\n","Epoch 2434/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0189\n","Epoch 2435/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0188\n","Epoch 2436/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0188\n","Epoch 2437/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0187\n","Epoch 2438/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0187\n","Epoch 2439/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0186\n","Epoch 2440/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0186\n","Epoch 2441/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0185\n","Epoch 2442/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0185\n","Epoch 2443/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0184\n","Epoch 2444/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0184\n","Epoch 2445/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0183\n","Epoch 2446/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0183\n","Epoch 2447/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0182\n","Epoch 2448/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0182\n","Epoch 2449/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0181\n","Epoch 2450/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0181\n","Epoch 2451/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0180\n","Epoch 2452/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0180\n","Epoch 2453/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0179\n","Epoch 2454/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0179\n","Epoch 2455/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0178\n","Epoch 2456/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0178\n","Epoch 2457/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0177\n","Epoch 2458/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0177\n","Epoch 2459/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0176\n","Epoch 2460/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0176\n","Epoch 2461/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0175\n","Epoch 2462/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0175\n","Epoch 2463/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0174\n","Epoch 2464/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0174\n","Epoch 2465/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0173\n","Epoch 2466/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0173\n","Epoch 2467/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0173\n","Epoch 2468/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0172\n","Epoch 2469/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0172\n","Epoch 2470/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0171\n","Epoch 2471/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0171\n","Epoch 2472/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0170\n","Epoch 2473/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0170\n","Epoch 2474/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0169\n","Epoch 2475/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0169\n","Epoch 2476/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0169\n","Epoch 2477/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0168\n","Epoch 2478/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0168\n","Epoch 2479/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0167\n","Epoch 2480/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0167\n","Epoch 2481/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0166\n","Epoch 2482/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0166\n","Epoch 2483/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0166\n","Epoch 2484/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0165\n","Epoch 2485/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0165\n","Epoch 2486/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0164\n","Epoch 2487/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0164\n","Epoch 2488/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0164\n","Epoch 2489/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0163\n","Epoch 2490/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0163\n","Epoch 2491/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0162\n","Epoch 2492/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0162\n","Epoch 2493/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0162\n","Epoch 2494/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0161\n","Epoch 2495/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0161\n","Epoch 2496/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0161\n","Epoch 2497/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0160\n","Epoch 2498/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0160\n","Epoch 2499/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0159\n","Epoch 2500/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0159\n","Epoch 2501/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0159\n","Epoch 2502/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0158\n","Epoch 2503/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0158\n","Epoch 2504/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0158\n","Epoch 2505/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0157\n","Epoch 2506/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0157\n","Epoch 2507/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0156\n","Epoch 2508/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0156\n","Epoch 2509/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0156\n","Epoch 2510/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0155\n","Epoch 2511/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0155\n","Epoch 2512/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0155\n","Epoch 2513/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0154\n","Epoch 2514/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0154\n","Epoch 2515/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0154\n","Epoch 2516/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0153\n","Epoch 2517/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0153\n","Epoch 2518/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0153\n","Epoch 2519/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0152\n","Epoch 2520/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0152\n","Epoch 2521/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0152\n","Epoch 2522/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0151\n","Epoch 2523/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0151\n","Epoch 2524/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0151\n","Epoch 2525/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0150\n","Epoch 2526/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0150\n","Epoch 2527/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0150\n","Epoch 2528/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0149\n","Epoch 2529/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0149\n","Epoch 2530/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0149\n","Epoch 2531/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0149\n","Epoch 2532/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0148\n","Epoch 2533/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0148\n","Epoch 2534/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0148\n","Epoch 2535/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0147\n","Epoch 2536/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0147\n","Epoch 2537/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0147\n","Epoch 2538/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0146\n","Epoch 2539/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0146\n","Epoch 2540/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0146\n","Epoch 2541/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0146\n","Epoch 2542/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0145\n","Epoch 2543/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0145\n","Epoch 2544/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0145\n","Epoch 2545/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 0.0144\n","Epoch 2546/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0144\n","Epoch 2547/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0144\n","Epoch 2548/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0144\n","Epoch 2549/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0143\n","Epoch 2550/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0143\n","Epoch 2551/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0143\n","Epoch 2552/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0143\n","Epoch 2553/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0142\n","Epoch 2554/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0142\n","Epoch 2555/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0142\n","Epoch 2556/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0141\n","Epoch 2557/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0141\n","Epoch 2558/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0141\n","Epoch 2559/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0141\n","Epoch 2560/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0140\n","Epoch 2561/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0140\n","Epoch 2562/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0140\n","Epoch 2563/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0140\n","Epoch 2564/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0139\n","Epoch 2565/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 0.0139\n","Epoch 2566/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0139\n","Epoch 2567/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0139\n","Epoch 2568/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0138\n","Epoch 2569/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - loss: 0.0138\n","Epoch 2570/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0138\n","Epoch 2571/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0138\n","Epoch 2572/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0137\n","Epoch 2573/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 158ms/step - loss: 0.0137\n","Epoch 2574/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0137\n","Epoch 2575/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 0.0137\n","Epoch 2576/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0137\n","Epoch 2577/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0136\n","Epoch 2578/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0136\n","Epoch 2579/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0136\n","Epoch 2580/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0136\n","Epoch 2581/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 164ms/step - loss: 0.0135\n","Epoch 2582/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0135\n","Epoch 2583/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0135\n","Epoch 2584/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0135\n","Epoch 2585/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0134\n","Epoch 2586/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 158ms/step - loss: 0.0134\n","Epoch 2587/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0134\n","Epoch 2588/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0134\n","Epoch 2589/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step - loss: 0.0134\n","Epoch 2590/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0133\n","Epoch 2591/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0133\n","Epoch 2592/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0133\n","Epoch 2593/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0133\n","Epoch 2594/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0133\n","Epoch 2595/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0132\n","Epoch 2596/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0132\n","Epoch 2597/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 160ms/step - loss: 0.0132\n","Epoch 2598/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0132\n","Epoch 2599/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0132\n","Epoch 2600/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0131\n","Epoch 2601/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0131\n","Epoch 2602/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0131\n","Epoch 2603/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0131\n","Epoch 2604/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0131\n","Epoch 2605/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0130\n","Epoch 2606/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0130\n","Epoch 2607/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0130\n","Epoch 2608/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0130\n","Epoch 2609/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0130\n","Epoch 2610/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0129\n","Epoch 2611/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0129\n","Epoch 2612/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0129\n","Epoch 2613/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 0.0129\n","Epoch 2614/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0129\n","Epoch 2615/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0129\n","Epoch 2616/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0128\n","Epoch 2617/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0128\n","Epoch 2618/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0128\n","Epoch 2619/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0128\n","Epoch 2620/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0128\n","Epoch 2621/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0127\n","Epoch 2622/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0127\n","Epoch 2623/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0127\n","Epoch 2624/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0127\n","Epoch 2625/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0127\n","Epoch 2626/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0127\n","Epoch 2627/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0126\n","Epoch 2628/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0126\n","Epoch 2629/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0126\n","Epoch 2630/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0126\n","Epoch 2631/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0126\n","Epoch 2632/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0126\n","Epoch 2633/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0125\n","Epoch 2634/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0125\n","Epoch 2635/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0125\n","Epoch 2636/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0125\n","Epoch 2637/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0125\n","Epoch 2638/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0125\n","Epoch 2639/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.0125\n","Epoch 2640/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0124\n","Epoch 2641/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0124\n","Epoch 2642/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0124\n","Epoch 2643/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0124\n","Epoch 2644/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0124\n","Epoch 2645/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0124\n","Epoch 2646/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0123\n","Epoch 2647/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0123\n","Epoch 2648/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0123\n","Epoch 2649/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0123\n","Epoch 2650/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0123\n","Epoch 2651/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0123\n","Epoch 2652/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0123\n","Epoch 2653/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0122\n","Epoch 2654/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0122\n","Epoch 2655/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0122\n","Epoch 2656/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0122\n","Epoch 2657/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0122\n","Epoch 2658/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0122\n","Epoch 2659/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.0122\n","Epoch 2660/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0122\n","Epoch 2661/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0121\n","Epoch 2662/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0121\n","Epoch 2663/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0121\n","Epoch 2664/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0121\n","Epoch 2665/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.0121\n","Epoch 2666/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0121\n","Epoch 2667/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 0.0121\n","Epoch 2668/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0120\n","Epoch 2669/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0120\n","Epoch 2670/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0120\n","Epoch 2671/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0120\n","Epoch 2672/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0120\n","Epoch 2673/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0120\n","Epoch 2674/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0120\n","Epoch 2675/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0120\n","Epoch 2676/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0119\n","Epoch 2677/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0119\n","Epoch 2678/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0119\n","Epoch 2679/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0119\n","Epoch 2680/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0119\n","Epoch 2681/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0119\n","Epoch 2682/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0119\n","Epoch 2683/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0119\n","Epoch 2684/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0119\n","Epoch 2685/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0118\n","Epoch 2686/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0118\n","Epoch 2687/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0118\n","Epoch 2688/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0118\n","Epoch 2689/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0118\n","Epoch 2690/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0118\n","Epoch 2691/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0118\n","Epoch 2692/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0118\n","Epoch 2693/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0118\n","Epoch 2694/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0117\n","Epoch 2695/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0117\n","Epoch 2696/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0117\n","Epoch 2697/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0117\n","Epoch 2698/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0117\n","Epoch 2699/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0117\n","Epoch 2700/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0117\n","Epoch 2701/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0117\n","Epoch 2702/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0117\n","Epoch 2703/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0117\n","Epoch 2704/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0116\n","Epoch 2705/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0116\n","Epoch 2706/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0116\n","Epoch 2707/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0116\n","Epoch 2708/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0116\n","Epoch 2709/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0116\n","Epoch 2710/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0116\n","Epoch 2711/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0116\n","Epoch 2712/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0116\n","Epoch 2713/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0116\n","Epoch 2714/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0115\n","Epoch 2715/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0115\n","Epoch 2716/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0115\n","Epoch 2717/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0115\n","Epoch 2718/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0115\n","Epoch 2719/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0115\n","Epoch 2720/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0115\n","Epoch 2721/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0115\n","Epoch 2722/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0115\n","Epoch 2723/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0115\n","Epoch 2724/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0115\n","Epoch 2725/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0114\n","Epoch 2726/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0114\n","Epoch 2727/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0114\n","Epoch 2728/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0114\n","Epoch 2729/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0114\n","Epoch 2730/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0114\n","Epoch 2731/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0114\n","Epoch 2732/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0114\n","Epoch 2733/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0114\n","Epoch 2734/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0114\n","Epoch 2735/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0114\n","Epoch 2736/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0114\n","Epoch 2737/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0113\n","Epoch 2738/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0113\n","Epoch 2739/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0113\n","Epoch 2740/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0113\n","Epoch 2741/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0113\n","Epoch 2742/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0113\n","Epoch 2743/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0113\n","Epoch 2744/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0113\n","Epoch 2745/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0113\n","Epoch 2746/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0113\n","Epoch 2747/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0113\n","Epoch 2748/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0113\n","Epoch 2749/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0113\n","Epoch 2750/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0112\n","Epoch 2751/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0112\n","Epoch 2752/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0112\n","Epoch 2753/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0112\n","Epoch 2754/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0112\n","Epoch 2755/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0112\n","Epoch 2756/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0112\n","Epoch 2757/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0112\n","Epoch 2758/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0112\n","Epoch 2759/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0112\n","Epoch 2760/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0112\n","Epoch 2761/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0112\n","Epoch 2762/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0112\n","Epoch 2763/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0112\n","Epoch 2764/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0112\n","Epoch 2765/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 0.0111\n","Epoch 2766/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 160ms/step - loss: 0.0111\n","Epoch 2767/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0111\n","Epoch 2768/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0111\n","Epoch 2769/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0111\n","Epoch 2770/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0111\n","Epoch 2771/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0111\n","Epoch 2772/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0111\n","Epoch 2773/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0111\n","Epoch 2774/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0111\n","Epoch 2775/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 0.0111\n","Epoch 2776/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0111\n","Epoch 2777/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 158ms/step - loss: 0.0111\n","Epoch 2778/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0111\n","Epoch 2779/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0111\n","Epoch 2780/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0111\n","Epoch 2781/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 0.0110\n","Epoch 2782/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0110\n","Epoch 2783/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0110\n","Epoch 2784/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0110\n","Epoch 2785/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 160ms/step - loss: 0.0110\n","Epoch 2786/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0110\n","Epoch 2787/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 155ms/step - loss: 0.0110\n","Epoch 2788/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0110\n","Epoch 2789/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0110\n","Epoch 2790/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0110\n","Epoch 2791/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 165ms/step - loss: 0.0110\n","Epoch 2792/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0110\n","Epoch 2793/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0110\n","Epoch 2794/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0110\n","Epoch 2795/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0110\n","Epoch 2796/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0110\n","Epoch 2797/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step - loss: 0.0110\n","Epoch 2798/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0110\n","Epoch 2799/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0109\n","Epoch 2800/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 158ms/step - loss: 0.0109\n","Epoch 2801/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0109\n","Epoch 2802/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0109\n","Epoch 2803/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0109\n","Epoch 2804/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0109\n","Epoch 2805/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0109\n","Epoch 2806/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0109\n","Epoch 2807/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0109\n","Epoch 2808/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0109\n","Epoch 2809/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0109\n","Epoch 2810/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0109\n","Epoch 2811/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0109\n","Epoch 2812/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0109\n","Epoch 2813/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0109\n","Epoch 2814/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0109\n","Epoch 2815/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0109\n","Epoch 2816/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0109\n","Epoch 2817/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0109\n","Epoch 2818/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0109\n","Epoch 2819/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0109\n","Epoch 2820/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0108\n","Epoch 2821/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0108\n","Epoch 2822/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0108\n","Epoch 2823/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0108\n","Epoch 2824/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0108\n","Epoch 2825/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0108\n","Epoch 2826/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0108\n","Epoch 2827/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0108\n","Epoch 2828/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0108\n","Epoch 2829/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0108\n","Epoch 2830/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0108\n","Epoch 2831/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0108\n","Epoch 2832/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0108\n","Epoch 2833/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0108\n","Epoch 2834/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0108\n","Epoch 2835/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0108\n","Epoch 2836/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0108\n","Epoch 2837/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0108\n","Epoch 2838/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0108\n","Epoch 2839/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0108\n","Epoch 2840/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0108\n","Epoch 2841/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0108\n","Epoch 2842/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0108\n","Epoch 2843/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0108\n","Epoch 2844/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0108\n","Epoch 2845/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0107\n","Epoch 2846/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0107\n","Epoch 2847/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0107\n","Epoch 2848/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0107\n","Epoch 2849/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0107\n","Epoch 2850/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0107\n","Epoch 2851/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0107\n","Epoch 2852/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0107\n","Epoch 2853/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0107\n","Epoch 2854/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0107\n","Epoch 2855/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0107\n","Epoch 2856/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0107\n","Epoch 2857/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0107\n","Epoch 2858/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0107\n","Epoch 2859/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0107\n","Epoch 2860/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0107\n","Epoch 2861/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0107\n","Epoch 2862/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0107\n","Epoch 2863/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0107\n","Epoch 2864/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0107\n","Epoch 2865/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0107\n","Epoch 2866/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0107\n","Epoch 2867/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0107\n","Epoch 2868/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0107\n","Epoch 2869/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0107\n","Epoch 2870/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0107\n","Epoch 2871/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0107\n","Epoch 2872/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0107\n","Epoch 2873/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0107\n","Epoch 2874/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0107\n","Epoch 2875/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0106\n","Epoch 2876/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0106\n","Epoch 2877/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0106\n","Epoch 2878/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0106\n","Epoch 2879/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0106\n","Epoch 2880/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0106\n","Epoch 2881/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0106\n","Epoch 2882/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0106\n","Epoch 2883/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0106\n","Epoch 2884/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0106\n","Epoch 2885/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0106\n","Epoch 2886/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0106\n","Epoch 2887/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0106\n","Epoch 2888/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0106\n","Epoch 2889/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0106\n","Epoch 2890/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0106\n","Epoch 2891/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0106\n","Epoch 2892/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0106\n","Epoch 2893/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0106\n","Epoch 2894/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0106\n","Epoch 2895/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0106\n","Epoch 2896/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0106\n","Epoch 2897/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0106\n","Epoch 2898/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0106\n","Epoch 2899/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0106\n","Epoch 2900/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0106\n","Epoch 2901/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0106\n","Epoch 2902/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0106\n","Epoch 2903/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0106\n","Epoch 2904/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.0106\n","Epoch 2905/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0106\n","Epoch 2906/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0106\n","Epoch 2907/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0106\n","Epoch 2908/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0106\n","Epoch 2909/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0106\n","Epoch 2910/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0106\n","Epoch 2911/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0106\n","Epoch 2912/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0106\n","Epoch 2913/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0106\n","Epoch 2914/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0105\n","Epoch 2915/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0105\n","Epoch 2916/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 0.0105\n","Epoch 2917/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0105\n","Epoch 2918/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0105\n","Epoch 2919/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0105\n","Epoch 2920/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.0105\n","Epoch 2921/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0105\n","Epoch 2922/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0105\n","Epoch 2923/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0105\n","Epoch 2924/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0105\n","Epoch 2925/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 0.0105\n","Epoch 2926/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0105\n","Epoch 2927/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0105\n","Epoch 2928/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0105\n","Epoch 2929/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0105\n","Epoch 2930/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0105\n","Epoch 2931/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0105\n","Epoch 2932/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0105\n","Epoch 2933/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 0.0105\n","Epoch 2934/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0105\n","Epoch 2935/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0105\n","Epoch 2936/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0105\n","Epoch 2937/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0105\n","Epoch 2938/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0105\n","Epoch 2939/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0105\n","Epoch 2940/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0105\n","Epoch 2941/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0105\n","Epoch 2942/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0105\n","Epoch 2943/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0105\n","Epoch 2944/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0105\n","Epoch 2945/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 0.0105\n","Epoch 2946/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0105\n","Epoch 2947/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 0.0105\n","Epoch 2948/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 0.0105\n","Epoch 2949/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 0.0105\n","Epoch 2950/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0105\n","Epoch 2951/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0105\n","Epoch 2952/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0105\n","Epoch 2953/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0105\n","Epoch 2954/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.0105\n","Epoch 2955/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 0.0105\n","Epoch 2956/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0105\n","Epoch 2957/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0105\n","Epoch 2958/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0105\n","Epoch 2959/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 159ms/step - loss: 0.0105\n","Epoch 2960/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 171ms/step - loss: 0.0105\n","Epoch 2961/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.0105\n","Epoch 2962/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0105\n","Epoch 2963/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 157ms/step - loss: 0.0105\n","Epoch 2964/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 0.0105\n","Epoch 2965/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0105\n","Epoch 2966/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0105\n","Epoch 2967/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0105\n","Epoch 2968/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0105\n","Epoch 2969/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0105\n","Epoch 2970/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0105\n","Epoch 2971/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0104\n","Epoch 2972/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0104\n","Epoch 2973/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0104\n","Epoch 2974/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0104\n","Epoch 2975/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0104\n","Epoch 2976/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.0104\n","Epoch 2977/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0104\n","Epoch 2978/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.0104\n","Epoch 2979/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0104\n","Epoch 2980/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0104\n","Epoch 2981/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0104\n","Epoch 2982/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0104\n","Epoch 2983/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0104\n","Epoch 2984/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0104\n","Epoch 2985/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0104\n","Epoch 2986/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0104\n","Epoch 2987/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0104\n","Epoch 2988/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0104\n","Epoch 2989/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0104\n","Epoch 2990/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0104\n","Epoch 2991/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 0.0104\n","Epoch 2992/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0104\n","Epoch 2993/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 173ms/step - loss: 0.0104\n","Epoch 2994/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0104\n","Epoch 2995/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0104\n","Epoch 2996/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.0104\n","Epoch 2997/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.0104\n","Epoch 2998/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0104\n","Epoch 2999/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0104\n","Epoch 3000/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0104\n","\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 17ms/step\n"]},{"name":"stderr","output_type":"stream","text":["WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"]},{"name":"stdout","output_type":"stream","text":["\n","\n"]},{"data":{"image/png":"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\n","text/plain":["<Figure size 1600x800 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# обучение AE2\n","ae2_trained, IRE2, IREth2 = lib.create_fit_save_ae(data,'out/AE2.h5','out/AE2_ire_th.txt',\n","3000, True, patience)\n","lib.ire_plot('training', IRE2, IREth2, 'AE2')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":5386,"status":"ok","timestamp":1760579027249,"user":{"displayName":"Мирон Романов","userId":"18135774377279153892"},"user_tz":-180},"id":"8mOAR43NNxh6","outputId":"0ff856c5-0747-4f4d-953b-2f797cd99f9b"},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step\n"]},{"data":{"image/png":"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\n","text/plain":["<Figure size 1200x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["amount:  22\n","amount_ae:  300\n"]},{"data":{"image/png":"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\n","text/plain":["<Figure size 1600x700 with 2 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size 2200x800 with 3 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["\n","Оценка качества AE1\n","IDEAL = 0. Excess:  12.636363636363637\n","IDEAL = 0. Deficit:  0.0\n","IDEAL = 1. Coating:  1.0\n","summa:  1.0\n","IDEAL = 1. Extrapolation precision (Approx):  0.07333333333333332\n","\n","\n"]}],"source":["# построение областей покрытия и границ классов\n","# расчет характеристик качества обучения\n","numb_square = 20\n","xx, yy, Z1 = lib.square_calc(numb_square, data, ae1_trained, IREth1, '1', True)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":3398,"status":"ok","timestamp":1760579033426,"user":{"displayName":"Мирон Романов","userId":"18135774377279153892"},"user_tz":-180},"id":"uayAqw-qOZFG","outputId":"e48f8594-5c64-437f-f969-c55f1ab4cdb6"},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step\n"]},{"data":{"image/png":"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\n","text/plain":["<Figure size 1200x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["amount:  22\n","amount_ae:  30\n"]},{"data":{"image/png":"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size 2200x800 with 3 Axes>"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["\n","Оценка качества AE2\n","IDEAL = 0. Excess:  0.36363636363636365\n","IDEAL = 0. Deficit:  0.0\n","IDEAL = 1. Coating:  1.0\n","summa:  1.0\n","IDEAL = 1. Extrapolation precision (Approx):  0.7333333333333333\n","\n","\n"]}],"source":["# построение областей покрытия и границ классов\n","# расчет характеристик качества обучения\n","numb_square = 20\n","xx, yy, Z2 = lib.square_calc(numb_square, data, ae2_trained, IREth2, '2', True)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"executionInfo":{"elapsed":403,"status":"ok","timestamp":1760579037520,"user":{"displayName":"Мирон Романов","userId":"18135774377279153892"},"user_tz":-180},"id":"21a_WwV8Ojb9","outputId":"55f50b46-da5b-4022-955f-88289f556678"},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 640x480 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# сравнение характеристик качества обучения и областей аппроксимации\n","lib.plot2in1(data, xx, yy, Z1, Z2)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":50,"status":"ok","timestamp":1760579093568,"user":{"displayName":"Мирон Романов","userId":"18135774377279153892"},"user_tz":-180},"id":"1hwBlCn2TIsI","outputId":"4b7647e0-f70b-4cfc-be07-7ac3f33e3296"},"outputs":[{"name":"stdout","output_type":"stream","text":["[[8.1 8.5]\n"," [7.2 8. ]\n"," [9.  8. ]\n"," [8.5 9.5]]\n"]}],"source":["# загрузка тестового набора\n","data_test = np.loadtxt('/content/drive/MyDrive/Colab Notebooks/is_lab2/Lab02/data_test.txt', dtype=float)\n","print(data_test)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":627},"executionInfo":{"elapsed":372,"status":"ok","timestamp":1760579098555,"user":{"displayName":"Мирон Романов","userId":"18135774377279153892"},"user_tz":-180},"id":"VIx9f-qPWRgC","outputId":"57da5779-88ca-49d3-cdc0-e024ee638040"},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n","Аномалий не обнаружено\n"]},{"data":{"image/png":"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size 1600x800 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# тестирование АE1\n","predicted_labels1, ire1 = lib.predict_ae(ae1_trained, data_test, IREth1)\n","lib.anomaly_detection_ae(predicted_labels1, ire1, IREth1)\n","lib.ire_plot('test', ire1, IREth1, 'AE1')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":725},"executionInfo":{"elapsed":847,"status":"ok","timestamp":1760579102310,"user":{"displayName":"Мирон Романов","userId":"18135774377279153892"},"user_tz":-180},"id":"r5oPt3OJWHtr","outputId":"19b1a021-9a40-40ba-fd31-50fa4ce759b1"},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 365ms/step\n","\n","i         Labels    IRE       IREth     \n","0         [1.]      [1.02]    0.4       \n","1         [1.]      [2.05]    0.4       \n","2         [1.]      [1.]      0.4       \n","3         [1.]      [0.69]    0.4       \n","Обнаружено  4.0  аномалий\n"]},{"data":{"image/png":"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\n","text/plain":["<Figure size 1600x800 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# тестирование АE2\n","predicted_labels2, ire2 = lib.predict_ae(ae2_trained, data_test, IREth2)\n","lib.anomaly_detection_ae(predicted_labels2, ire2, IREth2)\n","lib.ire_plot('test', ire2, IREth2, 'AE1')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"executionInfo":{"elapsed":378,"status":"ok","timestamp":1760579105936,"user":{"displayName":"Мирон Романов","userId":"18135774377279153892"},"user_tz":-180},"id":"Hw994w6DW014","outputId":"4e90a057-b989-4009-e731-7c72bec9ea3e"},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 640x480 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["# построение областей аппроксимации и точек тестового набора\n","lib.plot2in1_anomaly(data, xx, yy, Z1, Z2, data_test)"]},{"cell_type":"markdown","metadata":{"id":"I7eST-Wqd_fy"},"source":["Задание 2"]},{"cell_type":"code","execution_count":22,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":23,"status":"ok","timestamp":1762966643402,"user":{"displayName":"Мирон Романов","userId":"18135774377279153892"},"user_tz":-180},"id":"YmU2Ks5neBbD","outputId":"6c1842cd-7747-44ad-f2c2-2de9a3570c2f"},"outputs":[{"output_type":"stream","name":"stdout","text":["Исходные данные:\n","[[ 0.00491231  0.69319077 -0.20364049 ...  0.23149795 -0.28978574\n","  -0.49329397]\n"," [ 0.11072935 -0.07990259 -0.20364049 ...  0.09356344 -0.25638541\n","  -0.49329397]\n"," [ 0.21654639 -0.27244466 -0.20364049 ...  0.02459619 -0.25638541\n","   1.1400175 ]\n"," ...\n"," [ 0.85144861 -0.91998844 -0.20364049 ...  0.57633422 -0.65718941\n","   1.1400175 ]\n"," [ 0.85144861 -0.91998844 -0.20364049 ...  0.57633422 -0.62378908\n","  -0.49329397]\n"," [ 1.0630827  -0.51148142 -0.16958144 ...  0.57633422 -0.65718941\n","  -0.49329397]]\n","Размерность данных:\n","(1654, 21)\n","Исходные данные:\n","[[ 0.21654639 -0.65465178 -0.20364049 ... -2.0444214   4.987467\n","  -0.49329397]\n"," [ 0.21654639 -0.5653379  -0.20364049 ... -2.1133887   6.490482\n","  -0.49329397]\n"," [-0.3125388  -0.91998844  6.9653692  ... -1.1478471   3.9186563\n","  -0.49329397]\n"," ...\n"," [-0.41835583 -0.91998844 -0.16463485 ... -1.4926834   0.24461959\n","  -0.49329397]\n"," [-0.41835583 -0.91998844 -0.15093411 ... -1.4237162   0.14441859\n","  -0.49329397]\n"," [-0.41835583 -0.91998844 -0.20364049 ... -1.2857816   3.5846529\n","  -0.49329397]]\n","Размерность данных:\n","(109, 21)\n"]}],"source":["# загрузка выборок\n","train = np.loadtxt('cardio_train.txt', dtype=float)\n","test = np.loadtxt('cardio_test.txt', dtype=float)\n","\n","print('Исходные данные:')\n","print(train)\n","print('Размерность данных:')\n","print(train.shape)\n","print('Исходные данные:')\n","print(test)\n","print('Размерность данных:')\n","print(test.shape)\n"]},{"cell_type":"markdown","metadata":{"id":"O9pFfhDXgWlw"},"source":["*   15 скрытых слоев\n","*   21 19 17 15 13 11 9 7 9 11 13 15 17 19 21\n","*   MSE_stop = 0.02"]},{"cell_type":"code","execution_count":26,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"amqGIgtfeNZN","executionInfo":{"status":"ok","timestamp":1762972380365,"user_tz":-180,"elapsed":3331484,"user":{"displayName":"Мирон Романов","userId":"18135774377279153892"}},"outputId":"80607783-043b-4658-f65c-c9c5fa7ae4a9"},"outputs":[{"output_type":"stream","name":"stdout","text":["Задать архитектуру автокодировщиков или использовать архитектуру по умолчанию? (1/2): 1\n","Задайте количество скрытых слоёв (нечетное число) : 15\n","Задайте архитектуру скрытых слоёв автокодировщика, например, в виде 3 1 3 : 21 19 17 15 13 11 9 7 9 11 13 15 17 19 21\n","\n","Epoch 1000/120000\n"," - loss: 0.1509\n","\n","Epoch 2000/120000\n"," - loss: 0.1072\n","\n","Epoch 3000/120000\n"," - loss: 0.0944\n","\n","Epoch 4000/120000\n"," - loss: 0.0864\n","\n","Epoch 5000/120000\n"," - loss: 0.0800\n","\n","Epoch 6000/120000\n"," - loss: 0.0785\n","\n","Epoch 7000/120000\n"," - loss: 0.0737\n","\n","Epoch 8000/120000\n"," - loss: 0.0721\n","\n","Epoch 9000/120000\n"," - loss: 0.0694\n","\n","Epoch 10000/120000\n"," - loss: 0.0676\n","\n","Epoch 11000/120000\n"," - loss: 0.0669\n","\n","Epoch 12000/120000\n"," - loss: 0.0646\n","\n","Epoch 13000/120000\n"," - loss: 0.0627\n","\n","Epoch 14000/120000\n"," - loss: 0.0615\n","\n","Epoch 15000/120000\n"," - loss: 0.0606\n","\n","Epoch 16000/120000\n"," - loss: 0.0599\n","\n","Epoch 17000/120000\n"," - loss: 0.0589\n","\n","Epoch 18000/120000\n"," - loss: 0.0574\n","\n","Epoch 19000/120000\n"," - loss: 0.0577\n","\n","Epoch 20000/120000\n"," - loss: 0.0563\n","\n","Epoch 21000/120000\n"," - loss: 0.0555\n","\n","Epoch 22000/120000\n"," - loss: 0.0547\n","\n","Epoch 23000/120000\n"," - loss: 0.0553\n","\n","Epoch 24000/120000\n"," - loss: 0.0538\n","\n","Epoch 25000/120000\n"," - loss: 0.0533\n","\n","Epoch 26000/120000\n"," - loss: 0.0534\n","\n","Epoch 27000/120000\n"," - loss: 0.0525\n","\n","Epoch 28000/120000\n"," - loss: 0.0521\n","\n","Epoch 29000/120000\n"," - loss: 0.0518\n","\n","Epoch 30000/120000\n"," - loss: 0.0514\n","\n","Epoch 31000/120000\n"," - loss: 0.0515\n","\n","Epoch 32000/120000\n"," - loss: 0.0507\n","\n","Epoch 33000/120000\n"," - loss: 0.0504\n","\n","Epoch 34000/120000\n"," - loss: 0.0507\n","\n","Epoch 35000/120000\n"," - loss: 0.0502\n","\n","Epoch 36000/120000\n"," - loss: 0.0509\n","\n","Epoch 37000/120000\n"," - loss: 0.0497\n","\n","Epoch 38000/120000\n"," - loss: 0.0490\n","\n","Epoch 39000/120000\n"," - loss: 0.0499\n","\n","Epoch 40000/120000\n"," - loss: 0.0484\n","\n","Epoch 41000/120000\n"," - loss: 0.0452\n","\n","Epoch 42000/120000\n"," - loss: 0.0431\n","\n","Epoch 43000/120000\n"," - loss: 0.0424\n","\n","Epoch 44000/120000\n"," - loss: 0.0416\n","\n","Epoch 45000/120000\n"," - loss: 0.0412\n","\n","Epoch 46000/120000\n"," - loss: 0.0405\n","\n","Epoch 47000/120000\n"," - loss: 0.0407\n","\n","Epoch 48000/120000\n"," - loss: 0.0398\n","\n","Epoch 49000/120000\n"," - loss: 0.0393\n","\n","Epoch 50000/120000\n"," - loss: 0.0393\n","\n","Epoch 51000/120000\n"," - loss: 0.0386\n","\n","Epoch 52000/120000\n"," - loss: 0.0383\n","\n","Epoch 53000/120000\n"," - loss: 0.0383\n","\n","Epoch 54000/120000\n"," - loss: 0.0377\n","\n","Epoch 55000/120000\n"," - loss: 0.0381\n","\n","Epoch 56000/120000\n"," - loss: 0.0374\n","\n","Epoch 57000/120000\n"," - loss: 0.0382\n","\n","Epoch 58000/120000\n"," - loss: 0.0367\n","\n","Epoch 59000/120000\n"," - loss: 0.0364\n","\n","Epoch 60000/120000\n"," - loss: 0.0372\n","\n","Epoch 61000/120000\n"," - loss: 0.0360\n","\n","Epoch 62000/120000\n"," - loss: 0.0359\n","\n","Epoch 63000/120000\n"," - loss: 0.0356\n","\n","Epoch 64000/120000\n"," - loss: 0.0353\n","\n","Epoch 65000/120000\n"," - loss: 0.0376\n","\n","Epoch 66000/120000\n"," - loss: 0.0350\n","\n","Epoch 67000/120000\n"," - loss: 0.0349\n","\n","Epoch 68000/120000\n"," - loss: 0.0346\n","\n","Epoch 69000/120000\n"," - loss: 0.0350\n","\n","Epoch 70000/120000\n"," - loss: 0.0351\n","\n","Epoch 71000/120000\n"," - loss: 0.0354\n","\n","Epoch 72000/120000\n"," - loss: 0.0349\n","\n","Epoch 73000/120000\n"," - loss: 0.0336\n","\n","Epoch 74000/120000\n"," - loss: 0.0338\n","\n","Epoch 75000/120000\n"," - loss: 0.0332\n","\n","Epoch 76000/120000\n"," - loss: 0.0332\n","\n","Epoch 77000/120000\n"," - loss: 0.0332\n","\n","Epoch 78000/120000\n"," - loss: 0.0329\n","\n","Epoch 79000/120000\n"," - loss: 0.0327\n","\n","Epoch 80000/120000\n"," - loss: 0.0332\n","\n","Epoch 81000/120000\n"," - loss: 0.0328\n","\n","Epoch 82000/120000\n"," - loss: 0.0335\n","\n","Epoch 83000/120000\n"," - loss: 0.0328\n","\n","Epoch 84000/120000\n"," - loss: 0.0322\n","\n","Epoch 85000/120000\n"," - loss: 0.0319\n","\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 13ms/step\n"]},{"output_type":"stream","name":"stderr","text":["WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"]},{"output_type":"stream","name":"stdout","text":["\n","\n","Время на обучение:  3331.109365463257\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1600x800 with 1 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\n"},"metadata":{}}],"source":["# обучение AE3\n","patience = 7500\n","from time import time\n","start = time()\n","ae3_trained, IRE3, IREth3 = lib.create_fit_save_ae(train,'out/AE3.h5','out/AE3_ire_th.txt',\n","120000, False, 7500, early_stopping_delta = 0.001)\n","print(\"Время на обучение: \", time() - start)\n","\n","# Построение графика ошибки реконструкции\n","lib.ire_plot('training', IRE3, IREth3, 'AE3')"]},{"cell_type":"code","execution_count":28,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":895,"status":"ok","timestamp":1762973277569,"user":{"displayName":"Мирон Романов","userId":"18135774377279153892"},"user_tz":-180},"id":"yt0n51HtBbPo","outputId":"07575f51-b5d2-4a5b-a32c-cb2264640b54"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 160ms/step\n","\n","i         Labels    IRE       IREth     \n","0         [1.]      [7.69]    3.07      \n","1         [1.]      [8.99]    3.07      \n","2         [1.]      [9.26]    3.07      \n","3         [1.]      [10.4]    3.07      \n","4         [1.]      [5.87]    3.07      \n","5         [1.]      [10.13]   3.07      \n","6         [1.]      [11.05]   3.07      \n","7         [1.]      [7.01]    3.07      \n","8         [1.]      [8.95]    3.07      \n","9         [1.]      [10.4]    3.07      \n","10        [1.]      [8.57]    3.07      \n","11        [1.]      [5.74]    3.07      \n","12        [1.]      [10.09]   3.07      \n","13        [1.]      [9.37]    3.07      \n","14        [1.]      [11.42]   3.07      \n","15        [1.]      [7.36]    3.07      \n","16        [1.]      [7.05]    3.07      \n","17        [1.]      [7.58]    3.07      \n","18        [1.]      [8.17]    3.07      \n","19        [1.]      [18.33]   3.07      \n","20        [1.]      [3.45]    3.07      \n","21        [1.]      [3.2]     3.07      \n","22        [0.]      [2.85]    3.07      \n","23        [1.]      [3.48]    3.07      \n","24        [0.]      [2.78]    3.07      \n","25        [1.]      [6.34]    3.07      \n","26        [1.]      [3.25]    3.07      \n","27        [0.]      [3.01]    3.07      \n","28        [0.]      [2.65]    3.07      \n","29        [0.]      [1.]      3.07      \n","30        [1.]      [8.04]    3.07      \n","31        [1.]      [7.25]    3.07      \n","32        [1.]      [7.33]    3.07      \n","33        [1.]      [7.83]    3.07      \n","34        [1.]      [8.19]    3.07      \n","35        [1.]      [9.21]    3.07      \n","36        [1.]      [9.43]    3.07      \n","37        [1.]      [9.07]    3.07      \n","38        [0.]      [3.01]    3.07      \n","39        [1.]      [3.28]    3.07      \n","40        [0.]      [2.21]    3.07      \n","41        [0.]      [3.04]    3.07      \n","42        [1.]      [5.15]    3.07      \n","43        [1.]      [4.67]    3.07      \n","44        [1.]      [5.68]    3.07      \n","45        [1.]      [7.]      3.07      \n","46        [1.]      [6.15]    3.07      \n","47        [1.]      [4.52]    3.07      \n","48        [0.]      [1.81]    3.07      \n","49        [0.]      [2.55]    3.07      \n","50        [1.]      [6.45]    3.07      \n","51        [1.]      [7.25]    3.07      \n","52        [1.]      [6.05]    3.07      \n","53        [1.]      [6.61]    3.07      \n","54        [1.]      [5.18]    3.07      \n","55        [1.]      [17.84]   3.07      \n","56        [1.]      [19.31]   3.07      \n","57        [1.]      [21.11]   3.07      \n","58        [1.]      [20.74]   3.07      \n","59        [1.]      [23.59]   3.07      \n","60        [1.]      [3.93]    3.07      \n","61        [1.]      [5.94]    3.07      \n","62        [0.]      [2.55]    3.07      \n","63        [1.]      [5.52]    3.07      \n","64        [0.]      [2.38]    3.07      \n","65        [0.]      [2.24]    3.07      \n","66        [1.]      [7.61]    3.07      \n","67        [1.]      [7.38]    3.07      \n","68        [1.]      [7.63]    3.07      \n","69        [1.]      [7.93]    3.07      \n","70        [1.]      [8.]      3.07      \n","71        [1.]      [7.61]    3.07      \n","72        [1.]      [8.12]    3.07      \n","73        [1.]      [5.74]    3.07      \n","74        [1.]      [4.38]    3.07      \n","75        [1.]      [4.8]     3.07      \n","76        [1.]      [6.04]    3.07      \n","77        [1.]      [5.13]    3.07      \n","78        [1.]      [4.45]    3.07      \n","79        [1.]      [5.61]    3.07      \n","80        [1.]      [4.22]    3.07      \n","81        [1.]      [3.42]    3.07      \n","82        [1.]      [3.94]    3.07      \n","83        [1.]      [3.47]    3.07      \n","84        [1.]      [3.39]    3.07      \n","85        [1.]      [3.62]    3.07      \n","86        [1.]      [3.44]    3.07      \n","87        [1.]      [4.06]    3.07      \n","88        [1.]      [7.07]    3.07      \n","89        [1.]      [8.72]    3.07      \n","90        [1.]      [4.8]     3.07      \n","91        [1.]      [3.23]    3.07      \n","92        [1.]      [3.56]    3.07      \n","93        [1.]      [5.8]     3.07      \n","94        [1.]      [6.36]    3.07      \n","95        [1.]      [7.22]    3.07      \n","96        [1.]      [7.62]    3.07      \n","97        [1.]      [6.17]    3.07      \n","98        [1.]      [3.33]    3.07      \n","99        [1.]      [3.59]    3.07      \n","100       [1.]      [3.14]    3.07      \n","101       [1.]      [3.64]    3.07      \n","102       [1.]      [3.33]    3.07      \n","103       [1.]      [6.27]    3.07      \n","104       [1.]      [3.08]    3.07      \n","105       [1.]      [3.89]    3.07      \n","106       [0.]      [2.72]    3.07      \n","107       [0.]      [2.73]    3.07      \n","108       [1.]      [6.38]    3.07      \n","Обнаружено  94.0  аномалий\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1600x800 with 1 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ebI/POiuuDx1SQVlYGNfHBgAAcIsnA8pgMFjrdRo2bKixY8dq7NixcZgRAAAAkoKL7d2SVEaLNwAA8CFPrkEJAAAAxMSHH9rt3WecEdf2bokWbwAA4E8ElAAAAIDljTfs8Z//HPeHZ5McAADgRwSUAAAAgOR6e7dEBSUAAPAnAkoAAABAcr29W2KTHAAA4E8ElAAAAIAk/fe/9tiF9m6JCkoAAOBPBJQAAADAqlV2e3e7dq60d0tSmbNqk4ASAAD4BAElAAAA8NxzUlmZGV9+uSvt3ZKkQEDBjAwzJqAEAAA+QUAJAAAAfysulp5/3oxTU6VLLnF3Pg0bmn8JKAEAgE8QUAIAAMDf3nlH2rDBjIcNk9q2dXU65QElm+QAAACfIKAEAACAv40bZ4+vvNK9eViooAQAAD5DQAkAAAD/WrJE+uwzM+7aVRowwN35SJK1kzcBJQAA8AkCSgAAAPjXs8/a4yuukFIS4OUxFZQAAMBnEuAVGAAAAOCC3bul8ePNOCNDGjnS1elYgs6AMhh0dzIAAABxQEAJAAAAf5o8Wdq2zYz//GepRQt352OxAkrJ7DAOAACQ5AgoAQAA4E/OzXGuusq9eVTkDChp8wYAAD5AQAkAAAD/WbBAmjPHjLt3l3r3dnU6IaxNciQCSgAA4Atp9blRfn6+JCk7OzuiB9+xY4c++2PXxFNPPTWi+wIAAADC9u9/2+OrrpICAffmUhEVlAAAwGfqFVA2a9ZMKSkpWrRokbp161bp8nXr1um2225TIBDQCy+8UO39rFy5UsOGDVNKSopKSkrqMxUAAACgbgoKpFdeMeMmTaTzz3d3PhURUAIAAJ+pd4t3sIYdBbdt26YJEyZowoQJEd8XAAAAEFWvvCLt2GHG558vZWW5O5+KnAFlYaF78wAAAIgT1qAEAACAfwSDibs5zh+CVFACAACfIaAEAMu8eUo991y1njvX7ZkAAGJlzhxp0SIz7t1b6tHD3flUJSPDHhNQAgAAHyCgBADL3/6mlDff1GHOyhoAQHJJ8OpJSQSUAADAdwgoAcCycqUkqeG2bVJZmcuTAQBE3ZYt0htvmPFee0lnneXufKpDizcAAPAZAkoAsOTl2eNdu1ybBgAgRiZMsDeduegiqVEjV6dTLTbJAQAAPkNACQCS2TRh+3b7NAElACSXsjLpP/+xT19xhXtzqQ0VlAAAwGcIKAFAMoFkaWnoaQBA8vjsM2npUjMeOFDaf39351ODIGtQAgAAn4kooAwEAtGaBwC4y1k9KUk7d7ozDwBAbPz73/b4yivdm0c4CCgBAIDPpEVy40MOOaTay6zwMjU1NZKHAID4qBBQBnbvdmkiAICoW7dOevttM87JkU47zdXp1IoWbwAA4DMRBZTBYDBa8wAAd1WsoKTFGwCSxwsv2Mt4XHqplJ7u7nxqQ0AJAAB8pl4BZd++fWnvBpBcCCgBIDkFg9Jzz5lxSop02WXuzicc7OINAAB8pl4B5cyZM6M8DQBwGWtQAkBy2rZNWr3ajI87TurQwd35hIM1KAEAgM+wizcASFRQAkCy2rHDHrdq5d486oIWbwAA4DMElAAgsUkOACQrZ0DZpIl786iDIBWUAADAZ1wPKHft2qVHH33U7WkA8Lu8vNDTtHgDQHLwYEBJBSUAAPAb1wLKgoIC3X///erUqZNuueUWt6YBAAYt3gCQnLweULJJDgAA8IF6bZITia1bt+rxxx/X2LFjtX37dgWDQXYEB+A+AkoASE7OgDIry7151AUVlAAAwGciqqBcuXKlrrvuOnXr1k1ZWVlq3ry5jjjiCD3wwAPaXuHN/o4dO3TnnXeqU6dO+sc//qG8vDwFg0G1bNlS9913X0TfBABEjIASAJKTFysoWYMSAAD4TL0rKGfMmKHhw4dr5x/rtAWDQUnSwoULtXDhQr300kv6/PPPlZOTo6+//lrnn3++Vq9eXX69ffbZRzfddJMuv/xyNWrUKArfCgBEoOImOQSUAJAcvBhQUkEJAAB8pl4B5aZNm3Tuuedqh+MFX+PGjZWWllZeOfnrr79q1KhRuv766zVkyBAVFRUpGAyqc+fOuvXWWzVy5Eilp6dH57sAgEhRQQkAyamgwB57JaBMT5cCASkYJKAEAAC+UK8W7+eee05bt25VIBDQmWeeqWXLlqmgoEDbtm3TunXrdM0110iS3nnnHV1wwQUqLCxUkyZN9NRTT2nJkiW67LLLCCcBJJaKASW7eANAcvBiBWUgYFdRskkOAADwgXpVUE6fPl2S1Lt3b73xxhshl+Xk5OjJJ59UQUGBJk6cqDVr1qhZs2b68ssvdfDBB0c+YwCIhYoB5e7d7swDABBdXgwoJRNQ7t5NBSUAAPCFelVQ/vLLLwoEArr66qurvc51110nSQoEArruuusIJwEkrmCQFm8ASFZeDSitjXIIKAEAgA/UK6Dctm2bJKlLly7VXqdr167l4+OOO64+DwMA8bFzp1RaGnJWgBZvAEgOXg0orRZvAkoAAOAD9Qooi4uLJUlZWVnVXqeJ4wVgTk5OfR4GAOKjYvWkRAUlACQLZ0BZw2vXhENACQAAfKReAWVdBQKBeDwMANRPVQEla1ACQHLwegUlm+QAAAAfiEtACQAJraqAkhZvAEgOzoAyM9O9edSVM6AMBt2dCwAAQIzVaxdvy0UXXaTGjRtHfL1AIKBPP/00kqkAQP3R4g0AyaugwPzbuLGU4qHP5q1NciQTUlqBJQAAQBKKKKCcP39+jZdbrd01XS8YDNICDsBdVQSUgaIiqaRESovozyQAwG1WBaWX2rul0EByzx4CSgAAkNTq/c47SKsJgGRRVQWlZNah9NKGCgCAypIloAQAAEhi9Qooy8rKoj0PAHBPdQHlzp0ElADgdckQULJRDgAASHIeWogHAGLEEVAG997bPp91KAHA24qL7XDPywElFZQAACDJEVACQF6ePc7JsccElADgbTt32mOvVcQ7N8khoAQAAEmOgBIAnBWUbdrY5zvf2AIAvMdq75aooAQAAEhg9VqD8p577on2PHTHHXdE/T4BICzONSidASUVlADgbQSUAAAAnlCvgPKuu+5SIBCI6kQIKAG4proKSgJKAPC2ggJ7TEAJAACQsOoVUEpSMBiM2iSiHXYCQJ1YAWWDBlLz5vb5tHgDgLclSwUlu3gDAIAkV6+A8vPPP4/2PADAPVZA2bSplJlpn08FJQB4m5cDSjbJAQAAPlKvgLJfv37RngcAuMcRUAYJKAEgeXg5oKTFGwAA+Ai7eAPwt2BQys8344oVlLR4A4C3EVACAAB4AgElAH/buVMqLTVjWrwBILk4A8qsLPfmUR8ElAAAwEcIKAH4m2MHbwJKAEgyyVJBySY5AAAgyRFQAvC3vDx73LSp1LixfZoWbwDwNi8HlGySAwAAfISAEoC/VaigDDZqZJ+mghIAvK2gwB57LaCkxRsAAPgIASUAf3MGlM2a0eINAMnEyxWUBJQAAMBHCCgB+FvFNShp8QaA5EFACQAA4AkElAD8jU1yACB5JUtAySY5AAAgyRFQAvC3igEla1ACQPJwBpTOCnkvoIISAAD4CAElAH+rGFCmpKikQQNzmoASALzNCigzM6XUVHfnUlfs4g0AAHyEgBKAv1UMKCWVWm8KWYMSALzNCii91t4tUUEJAAB8hYASgL/VFFBSQQkA3kZACQAA4AkElAD8LS/PHlsBpfWmkIASALytoMD86/WAkk1yAABAkiOgBOBvVVVQWmtQ0uINAN5VUmJXHnoxoExLs9fNpIISAAAkOQJKAP5mBZQNGpRXq5S3eJeUSMXFLk0MABAR54dMXgwoJXujHAJKAACQ5AgoAfibFVA2a1Z+VomzrY42bwDwJmv9Scm7AaX1fERACQAAkhwBJQB/swLKP9q7JUcFpUSbNwB4FQElAACAZxBQAvCvYFDKzzfj6gJKKigBwJucAWVWlnvziIQVULJJDgAASHKeDCi/+OILnXLKKWrbtq0CgYDefvvtkMtHjhypQCAQ8nXiiSe6M1kAiWvHDqmszIwJKAEguVBBCQAA4BmeDCh37typHj16aOzYsdVe58QTT9T69evLv1599dU4zhCAJ1Sxg7dEizcAJIVkCCjZJAcAAPhEmtsTqI+hQ4dq6NChNV4nIyNDOTk5cZoRAE8KJ6CkghIAvKmgwB57NaC0KiiLi6XSUik11d35AAAAxIgnA8pwzJw5U61atdJee+2lAQMG6L777lOLFi2qvX5hYaEKHev75P+xLl1xcbGKi4tjPt9osebqpTkDbgls3lz+R7A0K0tlfxzvzoCyJD9fQY4nICw8ByGRBLZvL/8bX9KokSf+llc8hlIzMsrbnYp37JAyM12aGeANPA8BkeEYQiyE+/uUlAHliSeeqDPOOEOdO3fW8uXL9fe//11Dhw7V7NmzlVrNJ88PPPCA7r777krnT58+XZkefDE4Y8YMt6cAJLxW8+erzx/jpRs3asmHH0qS9nUElAu+/lprUzy5GgbgGp6DkAj2nTtXh/4xXrB0qdb+8TfeC6xjqHd+vlpb5733noq9utkPEGc8DwGR4RhCNO0KsysxKQPKc845p3x86KGHqnv37tpvv/00c+ZMDRw4sMrbjBkzRqNHjy4/nZ+fr/bt22vw4MHKzs6O+Zyjpbi4WDNmzNCgQYOUnp7u9nSAhBawdvCW1PWoo7TfSSepuLhYv378cfn5hx1wgHqcdJIb0wM8h+cgJJKUBQvKx4f96U+e+Fte8RhKHT9e+v57SdKgvn2lNm1cniGQ2HgeAiLDMYRYyHe8765JUgaUFe27775q2bKlli1bVm1AmZGRoQznunN/SE9P9+SB6dV5A3Hl2AAntUULpf5xzDhbvNMKCyWOJaBOeA5CQti9u3yY1qyZp/6Wlx9DjRrZ55WWeup7ANzE8xAQGY4hRFO4v0u+6Ftcs2aNtmzZojZ86gzAqbpNcqxNCSR28QYAr0qGXbydz0fs5A0AAJKYJysod+zYoWXLlpWfXrFihRYsWKDmzZurefPmuvvuuzV8+HDl5ORo+fLluuWWW9SlSxcNGTLExVkDSDjVBJQl7OINAN7nDCi9unYjASUAAPAJTwaU8+fP1/HHH19+2lo7csSIERo3bpwWLVqkiRMnKi8vT23bttXgwYN17733VtnCDcDHqqugbNDAPp+AEgC8iQpKAAAAz/BkQNm/f38Fg8FqL//YscEFAFQrnBZvAkoA8KaCAnucDAFlYaF78wAAAIgxX6xBCQBVqi6gdFZbswYlAHiTs4KycWP35hEJ5/MRFZQAACCJEVAC8K+8PHtcXUBJBSUAeJMVUDZsKKV5smmIFm8AAOAbBJQA/MuqoMzICKlSYZMcAEgCVkDp1fZuiYASAAD4BgElAP+yAkpH9aREizcAJAUCSgAAAM8goATgX1ZA2axZyNllDRooGAiYE1RQAoA3JVtAySY5AAAgiRFQAvCnsjIpP9+MK1RQKhCQMjPNmIASALyntNT++52V5e5cIsEmOQAAwCcIKAH4044dUjBoxhUDSskOKGnxBgDvcX64lCwVlASUAAAgiRFQAvAnq71bqjmgpIISALynoMAeE1ACAAAkPAJKAP5EQAkAyctaf1IioAQAAPAAAkoA/lRLQBl0tnhbreAAAG9IxoCSTXIAAEASI6AE4E/hVlCWlUlFRfGZEwAgOpIloGSTHAAA4BMElAD8KS/PHtcUUEq0eQOA1yRLQEmLNwAA8AkCSgD+FG4FpcRO3gDgNQSUAAAAnkJACcCf6hJQUkEJAN7iDCizstybR6QIKAEAgE8QUALwJ2dA2axZpYuDBJQA4F3JWEHJJjkAACCJEVAC8KfaKigbN7bHBJQA4C0FBfY4WQJKKigBAEASI6AE4E+1BZSNGtlj1qAEAG9JlgpKdvEGAAA+QUAJwJ9YgxIAkleyBJSpqVJamhkTUAIAgCRGQAnAn2jxBoDklSwBpWS3eRNQAgCAJEZACcCfrICyYUOpQYNKF4dskkOLNwB4CwElAACApxBQAvCnvDzzb1XVk1LoGpRUUAKAtyRjQMku3gAAIIkRUALwJ6uCsrqAkhZvAPCuZAoorY1yqKAEAABJjIASgP+UlUkFBWZcXUBJizcAeJcVUGZkSOnp7s4lUrR4AwAAHyCgBOA/BQVSMGjGzZpVfR128QYA77I+hPJ69aREQAkAAHyBgBKA/9S2g7cqbJJDQAkA3mJVUCZTQFlaKpWUuDsXAACAGCGgBOA/YQSUtHgDgIclY0ApsVEOAABIWgSUAPynrgElFZQA4B1lZfYHS8kQUFqb5Ei0eQMAgKRFQAnAf8IJKNnFGwC8yfk3OxkCSmcFJQElAABIUgSUAPyHFm8ASF5We7dEQAkAAOARBJQA/Ccvzx5XF1A2aCCl/PEnkgpKAPAOZ0CZleXePKKFgBIAAPgAASUA/wmngjIQsKsoCSgBwDsKCuxxslVQskkOAABIUgSUAPwnnIBSstehpMUbALwj2Vq82SQHAAD4AAElAP8JN6CkghIAvCfZAkpavAEAgA8QUALwH2dA2axZ9dcjoAQA7yGgBAAA8BwCSgD+U9cW7127pGAwtnMCAEQHASUAAIDnEFAC8J+6tngHg7wpBACvSOaAkk1yAABAkiKgBOA/VkDZqJGUnl799ayAUqLNGwC8ItkCSjbJAQAAPkBACcB/rICypupJyW7xlggoAcArnAFlVpZ784gWWrwBAIAPEFAC8J9wA0pnBeXOnbGbDwAgegoK7HEyVFASUAIAAB8goATgL6WlUn6+GdcloKSCEgC8IdlavAkoAQCADxBQAvAXZ2UNLd4AkHySOaBkkxwAAJCkCCgB+Eu4O3hLtHgDgBclW0DJJjkAAMAHCCgB+IszoGzWrObr0uINAN6TbAElLd4AAMAHCCgB+EtdKihp8QYA77ECyvR0qUEDd+cSDQSUAADABwgoAfgLLd4AkNysgDIZqiclAkoAAOALBJQA/KW+ASUVlADgDVZAmZXl7jyihU1yAACADxBQAvAXWrwBILkVFJh/qaAEAADwDAJKAP5CizcAJK+yMvvvdbIElOziDQAAfICAEoC/5OXZY1q8ASC57N4tBYNmnCwBJRWUAADABwgoAfgLLd4AkLys9Sel5AkoqaAEAAA+QEAJwF9o8QaA5JWMAWVKitSggRkTUAIAgCRFQAnAX9jFGwCSVzIGlJLd5s0u3gAAIEkRUALwFwJKAEheyRpQWm3eVFACAIAkRUAJoP5++0168cXQjWcSnRVQZmZK6ek1X5c1KAHAW5wBZVaWe/OINquCkoASAAAkqTS3JwDAo0pLpUGDTEj52WfSyy+7PaPwWAFlbdWTkgkw09KkkhLWoAQALygosMfJVEFJQAkAAJIcFZQA6ufbb004KUlffOHuXOqiLgGlZLd5U0EJAIkvWVu8CSgBAECSI6AEUD8ffGCPV6/2RoBXWmpX14QbUFpt3l74/gDA75I9oCwslIJBd+cCAAAQAwSUAOrHGVBK0rJl7syjLvLz7XFdKyhp8QaAxJesAaW1SU5ZmVl2BAAAIMkQUAKou7VrpYULQ8/79Vd35lIXddnB20KLNwB4R7IGlFYFpUSbNwAASEoElPAfWqMi9+GHlc9bujT+86ir+gSUVov37t2mcgUAkLgIKAEAADyJgBL+smCB1LWr1K+fVFTk9my8q2J7t5T8FZSSCSkBAImLgBIAAMCTCCjhH4WF0nnnScuXm12nq6oCRO0KC6VPPjHj5s3t870WUDZrFt5tnAElbd4AkNj8EFAWFro3DwAAgBghoIR/3Hef9PPP9uklS9ybi5fNmmVvGHPqqVLbtmbstYCyri3eEgElACS6ggJ7nJXl3jyizdokR6KCEgAAJCUCSvjDwoXSgw+GnueFQC0ROdu7/9//k/bf34w3b5a2bXNnTuGKtMWbnbwBILH5oYKSgBIAACQhAkokv5IS6ZJLzL9OBJR1FwzaAWVamjRokB1QSom/UU6kASUVlACQ2KyAMi1NatDA3blEEwElAABIcgSUSH6PPip9950ZH3KI1KqVGRNQ1t2vv5o1PCXpT38yIZ8zoEz0nykt3gCQ3KyAskkTKRBwdy7RREAJAACSHAElktuSJdKdd5pxSor0wgvSQQeZ07//LuXluTY1T6rY3i2ZXdEtyRhQ0uINAN7hDCiTCZvkAACAJEdAieRVViZdeqn9Qv7GG6VevbzVkpxonDufWwGllyoonYE0Ld4AkHySNaBkkxwAAJDkCCiRvJ55RvrqKzPu0kW65x4z9lKglkgKCqQvvjDjzp2lAw804333NdWpUuL/PGnxBoDkFQwmb0BJizcAAEhyBJRITrm50q232qeff96uhCOgrJ8ZM6TiYjP+f//PXturQQMTWEqmIjUYdGd+4XAGlNnZ4d2GFm8A8IY9e0z3hERACQAA4DEElEg+waB0xRV2mHTllVK/fvblBJT1U9X6kxbrZ7pjh7RhQ/zmVFdWQNm4sZSeHt5taPEGAG8oKLDHWVnuzSMWCCgBAECSI6BE/C1YIF1wgTRtWmzuf+JEafp0M27XTnroodDL3WxJfvFF6dprQyv5vKCszF5/slEjqX//0Mu9slGO9XMPt71bIqAEAK+w2rul5K6gZJMcAACQhNLcngB8prBQGjZMWrnShIgbN9qtwtGwfr3ZDMfyn/9UbuW1WpKXLzdhWjAY3TlUZ/Zs6ZJLzLioyMzNK374wa6MHDgw9I2SVLkq1VmxmkjqE1CyBiUAeEMyB5RskgMAAJIcFZSIr2efNeGkJG3aJP3+e/TuOxiURo2yd2q+4ALppJOqvq4bLcljx9rjKVPs9Ry9oKb2bskbbfOlpfab1/pWULIGJQAkrmQOKGnxBgAASY6AEvGzY4d0332h5/32W/Tuf8oU6a23zHjvvaXHH6/+uvEO1H7/XZo82T69das0a1bsHzdanAFlVaGv8+e5dGns51Mf+fn2mBZvAEg+BJQAAACeRUCJ+HnyycoVkytWROe+t2yRrrnGPv3001LLltVfP94B5YsvmrZupylTYv+40fD779K8eWZ86KFShw6Vr9O+vd1+lqgVlM51P2nxBoDkQ0AJAADgWZ4MKL/44gudcsopatu2rQKBgN5+++2Qy4PBoO644w61adNGjRo10gknnKCliVrV5Rdbt0oPP1z5/GhVUN5yix1+DhsmnXVWzdePZ0BZWir9+99mHAjYQd7UqeayeCksNOtg1rW1fNo00z4vVd3eLZlNh6yNcpYti+/3FS6r9V+ixRsAkhEBJQAAgGd5MqDcuXOnevToobHONf0cHn74YT355JP697//rW+//VaNGzfWkCFDtIcXdO55+GG7gq1XL/v8aASUZWXSq6+acXa29MwztW96E8+Acto0e93NE0+0Q75Nm6Qvv4ztYztdeql0zDHmy/kmrja1rT9psQLK4mL7+00k9a2gpMUbALzBLwElu3gDAIAk5MmAcujQobrvvvt0+umnV7osGAzqiSee0G233abTTjtN3bt310svvaR169ZVqrREnKxfb9q7JbOD9osv2pdFI6Bct07avduM+/aV2rSp/Tbt2tkv9mMdUD7zjD2++mrpzDPt02++GdvHtgSD0jvvmPH8+dKFF5pgtzbFxdLHH5vxXntJvXtXf91E3yiHgBIAkltBgT3OynJvHrHALt4AACDJpbk9gWhbsWKFNmzYoBNOOKH8vKZNm+roo4/W7Nmzdc4551R5u8LCQhU6PpHO/2NDjeLiYhV7aLdla66JNOeUe+5R6h8BYumVV6ps//2V1rKlAps3K/jbbyqJcK6BX34p/0Uu3XdflYV5f2lduijw008KLl+ukt27pbQYHA6//aa0jz5SQFKwY0eVnHCCtHOn0jIyFCgsVPDNN1XyyCOmRTqWNm1SuvON21tvqfTvf1fZvffWeLPAF18o7Y9gr2zQIJUGg9W2iAf228/+f/jlF5UNHBiNmUdNYMsWe35NmlT7e1LVMZTWoIECRUUK7tgR8e8rkMwS8TkI/pGyfbtS/xiXNGyooAd/D6s9hlJTlf7HsGzXLpV68HsD4oHnISAyHEOIhXB/n5IuoNywYYMkqXXr1iHnt27duvyyqjzwwAO6++67K50/ffp0ZTorqDxixowZbk9BkpS5YYMGPvecJPNmYcYRR6joww/Vd6+9tNfmzdKaNZr2zjsqS0+v5Z6q12HGDB3+x3jxnj1a8eGHYd2uZ1aW2koKFBdr5sSJ2hVO5WUddZs4UV3/WL/x5759tfSPasRePXqozdy5CqxfrzmPP66tBx0U9cd22mvJEvWtcF7qQw9pQVGR1vTrV+3tuk2YoD8at/VD27ZaU8PPtvnmzTruj/HKGTP0Y+fOEc052jrPnq3uf4wX5ObW+L1IocfQ0PR0NSgq0s5Nm/RpmL9fgJ8lynMQ/OWQn37Sfn+Mv1m0SNsqbk7nIRWPobSdO2UtsrJ5zRrN5rkIqBHPQ0BkOIYQTbvC7ERMuoCyvsaMGaPRo0eXn87Pz1f79u01ePBgZWdnuzizuikuLtaMGTM0aNAgpUcQ+kVL6kUXKeWPDVMCo0frhPPOM+dPmiQtXapAMKgTDzootD24jlK++qp83O3UU3XQ4MHh3e6bb8ymMZKO32cfBU88sd5zqNKePUq75BJJUrBBA3V94AF1bdVKkqnm09y5kqRj1q9X2V//Gt3HriCwbVv5OHjooQr8+KMk6YhnnlGP4cMVdK4L6pA2Zoy5TSCg7rfcou417Yx+1FHS3/8uSepUXKz2J50UpdlHR8rCheXjHn37qns186vqGEpr1kzauVONU1J0UoJ9X0AiSbTnIPhL6rvvlo/7DBokHXqoi7Opn2qPIUeXT8usLJ6LgGrwPAREhmMIsWB1KNcm6QLKnJwcSdLGjRvVxlERt3HjRh122GHV3i4jI0MZzvV9/pCenu7JAzMh5v3TT9KkSWbcvLlSb7lFqdac9tuv/Grpq1dLBx9c/8dZsaJ8mHbggVK437ejajHtt9/Cv124XntN2rJFkhQ480yl77OPfdnpp0tXXikVFyt16lSlPv54bNu8HZvWBO65x2x88/zzChQWKm34cGnePKl9+9Db5OZKP/9sbtO7t9JrqzBt29ZsUpSfr5SlS5Xi9u9fRY7NE9JatKj1/zvkGPqjijqwc6f7xxXgAQnxHAT/cXw6n77XXtF/Xo+jSseQYxmalKKixHuOBRIMz0NAZDiGEE3h/i55cpOcmnTu3Fk5OTn69NNPy8/Lz8/Xt99+qz59+rg4Mx+67TazOYsk3Xpr6MYk++5rjx0BY70sW2b+TUuTOnYM/3ax3tSl4uY4TnvtJVnrpK5ZYwLCWFq+3B536SKNHStZrd0bN0qnnirt3Bl6m3B377YEAvbPdOXKxFvEv76b5Ej2RjlskgMAiSuZd/EOBOyNchLt+RUAACAKPBlQ7tixQwsWLNCCBQskmY1xFixYoFWrVikQCOiGG27Qfffdp3fffVc//vijLrzwQrVt21bDhg1zdd6+8u239q7RbdtK11wTerkzoIxkJ+9g0A4oO3Wq20Y3sQwof/hBmjPHjLt3l445pvJ1nLt5T5kS3cevyBlQ7ruv2U39zTft/4cFCyrv7O1c3yqcgFKyf6bBYHR2aI+mSALKxo3Nv4WF0h9LFgAAEkwyB5SS1LCh+ZeAEgAAJCFPBpTz58/X4YcfrsMPN1ujjB49WocffrjuuOMOSdItt9yia6+9Vpdffrl69uypHTt2aNq0aWpovbBD7P2xFqEk6Y47pEaNQi+PVkC5caNd+delS91u26KFqWSUoh9Qjhtnj6++2lQ+VHTaaVLqH/uNTpliV5vGghXitm1rVwO2aCG9955py5akqVPN/5VkKgU/+8y+TY8e4T1OrKtSI5GXZ4/rW0EpUUUJAInKCihTUuwwL5kQUAIAgCTmyYCyf//+CgaDlb4mTJggSQoEArrnnnu0YcMG7dmzR5988on2j2ATFtTRp5/a4dZ++0kXX1z5Ou3a2dWOkQSUVvAm1T2gdLYkr1ol7d5d/3k45eVJr7xixllZ0vnnV329Fi2kAQPMODfXVF3GQkGB9PvvZuxY+1OS1K2bWSvTWv/y/vvNuqGff26/ATrppKoD1qokckDprKCs68ZXBJQAkPgKCsy/WVnhP295CQElAABIYp4MKJHAgsHQ6sl77ql6kXrnepG//Vb/6sFIAkopNFBz3lckXnrJDrFGjKi5zSwebd7OALhiQClJQ4dKjz5qn774Yumf/7RPh9veLUldu9rjRA0oGzeu21IA1m0sBJQAkJisCspkbO+W7IDSsaM3AABAsiCgRHS9/bY0d64Zd+8unXNO9de12rzz86WtW+v3eNEMKKMRqAWDoZvjXHVVzdcfNsyuXpw8OTZt3s6fUVUBpSRdf7106aVmXFgozZplxg0a2Jv5hMMLAWVd27ul0ArKipsJAYlixw7pv/+VTj7ZbHz1xzrNgG8ke0DJJjkAACCJEVAiekpLzc7dlvvvt8O3qkRjHcpECyg//1xassSM+/c3LdQ1adVK6tvXjJctk378MfI5VFRxB++qBAKhO3tb+vWr2xu9pk2l1q3NeOnSus0z1qyAslmzut+WFm8kqtJSs6zGiBFSTo7Z7OqDD8z6skceKV17bej6q0CyCgaTP6B0tnjHct1qAAAAFxBQInpeeUX63//MuE+f2luDO3e2x5EGlCkpZhfvuop2QFmX6klLrNu8nQFldRWUkqmWnDIlNDiuS3u3xfqZbthgqmMTQUmJXfkYaQUlASUSwf/+J40ZY/7unXCCWVqiYnVvWZn09NPSAQdIEyea00CyKiw0gb2U/AGlJBUVuTcPAACAGCCgROSCQWnGjNDqyQceqH2BemcQtmJF/R7XCig7dLBbn+rCWVEYaUC5bp1pcZdMJdOwYeHd7vTT7Z+VmwGlJLVsKb3/vtm1+5hjpIsuqvvjOUPfRKmidAal9QkoWYMSiWDTJumpp6SePaWDD5YefFBas8a+vGlT6fLLzRINDz5oB+u//y6NHCkddxxt30heVvWk5I+AkjZvAACQZAgoUX9FRaZq57DDpMGDpdWrzflDhlRuFa5KpC3eW7bYbbv1ae+WzJuYffYx40gDyuees6s3LrvMVCSGo21b6dhjzfjnn+0q1GixQty99pKaN6/9+gcdZEKMr7+u+27XUmKuQ+ncwZs1KOFF48ebv1XXXSfNn2+fn5pq1px84w1Ttfyf/5hlI/72N+mXX0IrtL/5xrR9X3cdbd9IPn4LKNkoBwAAJBkCStTdtm2mOqdzZ7Pu2aJF9mX77x/a5lyTSAPKSNeftFgVf5s313+znuJi6dlnzTg11VQx1UWs2ryLiuzguLbqyWiJdtt8NEQzoKSCEvG2YoVZMqK42D7vyCOlf/3LVG6/95501lmh4YUktW9vNt/6+GP7uCwrM1WYtH0j2fghoHR2ilBBCQAAkgwBJcK3YoXZ7bl9e7P22bp19mVHH23eCP/vf6HBY0322svesKQ+AaWzfdhZtVdX0WhJfvdd++dx6qlSu3Z1u/0ZZ9jjaAaUubl2AOFGQJkoLd7OajFavOE1f/2rXS01fLj000+mivK668xGW7UZPNh8kPTAA5Xbvvv2Df2wB/CqggJ7nJXl3jxiiRZvAACQxAgoUbtvvzXVOV26SE8+abe4BgJm/cSvvpJmzzZVgKmpdbtvK8xctSq0Oigc0a6glOpf8eesGr366rrfvn17qXdvM/7xx+hVHjp/RvEKKPfbz15TMxkrKGnxRjzNmCG99ZYZ5+RIL75o1p+sq4wM6dZbzTISzortr782QSXgdX6ooCSgBAAASYyAEjV7/nkTnE2ZYlfiNWpkQrglS6SpU836ibVtiFMdK6AsLbVbkcOVKAHlzz9Ln31m39eAAfWbhzM0ePPN+t1HRc4NciL5GdVFw4ZSx45m/OuvZjMjt9HiDS8qLjZV65aHHqrfurBOHTrYbd/Wff3yS2T3CSQCAkoAAABPI6BE9XbtMhU3ltatpfvuM0Hi2LGRtVVbIlmH0gooA4Hw28qrEmlA+cIL9viqq6SUeh5Ww4fb42i1eddlB+9osn6m27ebnYfdFmlASYs33PD00+YDEMl8UHTBBdG778GD7UrMLVvYcAPe57eAkmMWAAAkGQJKVO/FF80bV8m0cufmSv/3f1KLFtF7jGgElO3aVd4coi46d7Zb0+saUAaDpopUktLSpAsvrP88OnUyG19I0vff129dzorcCigTbSdvWrzhNRs3SnfdZcaBgNnYpr4fflSnbVt7vGFDdO8biDc/BJRskgMAAJIYASWqVlIiPfqoffrOOyMLAavTubM9rksgt3WrveN2pK3L6el2UFrXluQffzSbB0lS//5S8+aRzSXabd5WiNuokdSmTeT3F65E28nbGVBaGzPVBS3eiLcxY6T8fDO+5BLpqKOi/xjOvwnr10f//oF48kNASYs3AABIYgSUqNqUKaZiUpKGDJF69IjN4zgrKK2gLxzRXlvRCtR27Qrdnbw277xjj087LfJ5RLPNu6zM/pnuu2/0q69qkmg7edPiDS/59ltp/HgzbtpUuv/+2DyOM6Csy989IBERUAJA4vvhB+mVV1imAkCVCChRWTAoPfywffqWW2L3WB062MFZXSooo7VBjqW+FX/OgPLUUyOfR9eudhg8d660cmX972vtWvvJP57t3VJiV1DS4o1EVlYmXXutffqee6RWrWLzWM4Wbyoo4XUElACQ2NaskY47zqyp/fjjbs8GQAIioERln35qPt2SzJqIxx8fu8dq0EBq396MvRZQrlkjffedGR9+uAlbo8HZ5m2tb1kfzp9RvAPKjh1N67yUfAElFZSIpQkTpHnzzPjgg83GW7FCizeSSUGBPc7Kcm8esURACcDLXn7Z/qB/1ix35wIgIRFQorKK1ZOBQGwfz2rz3rpVyssL7zaJEFC++649HjYs8jlYorUOZbTb4OsiNdUORZcuNVVhbnIGlNnZdb89ASXiIS9PuvVW+/STT9pBfyzQ4o1k4ocKSucmObRHAvCSYFD673/t00uWuDcXAAmLgBKhfvhBmjHDjPfdN3RNxFipzzqU0a4OrE9AGe31Jy0HHih162bGX38tbdtWv/txawdvi/UzLSyUVq+O/+M7WcF3kyb2ju11kZJiV67Q4o1YuftuadMmMz7zTGnAgNg+Hi3eSCZ+CCipoATgVT/8IP3vf/bp3Fz+jgGohIASof75T3t80031C3PqyhlQhtvmbQWUbdqEbmBSX23b2lVy4QSU27dLn39uxh07St27Rz4HJ2cwMX9+/e4jUQJKyf2NcqwKyvq0d1us3w8qKBELixdLTz1lxo0aSY88EvvHbNHCrtAkoITXEVACQOJyVk9KpqLSWXACACKghNOKFdIbb5jx3ntLI0fG53HrGlDm50u//27G0WpdTkkxG9RYcygurvn606bZ1znttOi3wR99tD2eO7d+92E96aemmhA13hJpo5xoBJRWEE5AiWgLBqXrr5dKS83pW2+NzzEbCEg5OWZMize8zgooAwET8icjAkoAXlRSIr36auXzafMGUAEBJWyPP26/Qb722vi9wO/c2R6HE1DGam1FK1ArKTFtBzWJVXu3pVcve1yfgDIYtH9Ozg1r4ilRAsriYjtUjEYFJS3eiLapU83mZJLUqZN0883xe2yrzXvTpto/mAESmRVQNmkS+7Wz3UJACcCLZsyQNm40Y+dr8V9+cWc+ABIWASWMzZul558348xM6eqr4/fYdV2DMtob5FjCDdSKiqQPPjDjZs2k446L3hwsXbqY+5akb781gWNdbNliKk0ld9q7JbsiVXI3oLR+DpL9M60PWrwRC7t2SaNH26cfeyy+1V/OjXKsNw+AFzkDymTlDCjZJAeAVzjbu2+6yR5TQQmgAgJKGM88I+3ebcaXXmrWJouXli3tNxThVFC6HVDOmmWHXv/v/8WmOjElRerZ04w3bqz7JjPR3kSoPpzrg7oZUDp38I5GBWVxMZVmiJ6HH5ZWrTLjE06Qhg2L7+OzkzeShR8CSucu3lRQAvCCggLp7bfNuEUL06VnVbkTUAKogIASpoLH2pwhNVW68cb4Pn4gYFdR5ubabebVcTugjHV7tyWSdShj1QZfF4GA/TPNzTWVp25w7oIejTUoJTvMB+przx7p3nulBx4wp9PSpCefjH9rKjt5IxkEg+ZNsCRlZbk7l1iixRt17agB3Pbmm/br5rPPNq/FO3Qwp5cs4XcaQAgCSkgTJpgWb8k8cXTqFP85WAFlcbG0dm3N141VdWA4AWUwKL37rhk3aCCdeGL0Hr+iSNahdHsHb4v1My0tDa99P9ry8kKXK7A2BKkPq4JSYh1KRObDD6VDDpHuuMMO7q+7TjrooPjPxVlBSUAJryoqMutHS8ldQUlA6W8vvWT+Zp96au0f5gOJwtne/Ze/mH8PPND8u327vfEpAIiAEiUl0qOP2qfjuTmDU1128rYCyr33jqwirqLmze3W9uoCyh9+sNutBw6MbaWG1eItmXUo6yLRAkop/m3eW7eallkr3G3RQrroovrfnzOgZB3KxPftt9Lf/263TyeC3FzTwv3//p99jKammjUorUrKeKPFG8nAau+WCCiRfMrKpP/7P2nECLPsz3vvSV9+6fasgNqtWSN9/rkZd+1qd4cdcIB9Hdq8ATikuT0BuGzqVDsQHDxYOuwwd+ZRMaDs37/q6+3cab+JjkXr8v77S7NnmyfUnTtD23ql+LV3S6bar0MHE7DMn2/C5LQwD1lnlanzZxtvbm2Us2WLNGiQCZQlE2Z/+qnZ0by+nL8LBJSJLRiUzjrLfJgwaZL0/ffmAwi37NkjPfKIdP/9oaFC377S2LGmmtIttHgjGfgxoGSTHPcFg6YDadWq0K/Vq82/a9eayviHH67/6+vdu6ULL5SmTAk9f+bM6l8rA4nilVfsFu4LLrCXsXEGlL/8Yl4PAYAIKP0tGDQvmiy33OLeXMKtoHReFsuAUjIhX48eoZc7A8pTTon+41d09NHmRe6uXdLPP0uHHhre7azqLOdGNW5wo4Jy0yZTOblokTndurX02WdSt26R3S8t3t6Rn29XOq9caapO3nnHbD4Vbx99ZBaEd1Y15+SYyvVzz43/mpMV0eKNZOCXgJJNctxVVGSWRXrzTVMRv2pV7f8Pa9ZIRx0l/e1v0u23h4bMtdm40bRzW50ggYAd9sycWY9vAIijYDC0vfuCC+wxFZQAqkGLt5999pn03XdmfMQR0oAB7s2lc2d7XFNAGasNciw1BWq5udLChWbcq1do5VGs1Gcdyh07zItayd32bim0gnLp0tg/3u+/m99jK5xs08a8iI80nJRo8faSiusZvf++qWCMp9xc6fTTpZNOCm3nvvFG82L8vPPcDyclU12cmmrGtHjDq/wSUKan2383CCjjp6hIevZZ85rmiiuk6dPNa8Sa/g8CAft3sbRU+sc/pMMPl775JrzH/Okn8yG19dqvSRPT2m19oD97Nhv2IbEtWCAtXmzGxx4bWoxCQAmgGgSUflaxetLNN8vOjXlq2kzFzYAynu3dlvoElImy/qRk2mpbtjTjWFdQbtggHX+8eVEvmQB55kx7Ie5I0eLtHVUtuP73v0tffBGfx3/hBROKv/22fd5xx5klBx57TMrOjs88wpGSYm8eRQUlvMovAWUgYFfgEVDGXsVg0rmmcZMm5u/8iSdKl18u3Xef2cRm5kzzQfuePWa5mbvuMsGyZFpZ//Qn6frrQ39nK5o2TTrmGNMBIEnt2klffWXWL7bauouKpDlzYvBNA1FS1eY4ln32sV9XE1ACcCCg9KsFC8wnwJKpXhw+3NXpqGFD82QlJW4FpRsB5RFH2G2p4W6U4wwoY/EzqivrZ7p2bc0vyCOxbp150f6//5nT7dtLs2aF/n9GihZv73AGlO3amX9LS6VzzontbpGlpaZC8tJL7cqW1q3Ni/RZs8JfoiHerDbv339nZ1h4k18CSomAMh6KiqTnnjOvISoGkyedZILB/HxTHfbRR9J//mM2sfnLX6R+/czr6gYNzNedd5p1kK0PnINB6cknzfPBjBmVH/uZZ0wQWVBgTh91lPmA2lpy6Pjj7etam48AiaakxKwBLpnj4M9/Dr08ELCrKFesMMccAIiA0r/++U97/Ne/hr/5SixZpf+//159kBXrgNJ5n86AcutWu/pqv/2i0zIcjiZNpIMPNuOffgovGEukCkopNCR0/v9Fy9q1Jpy0PoHt2NGEQdH+/aDF2zucIeTtt9vLV6xfL51/fmxCuO3bzbq0Tzxhn3fFFeb30rkwfCKyAsqystgGuECsWGGOJGVluTePeLACSjbJiT5nMHn55XYFo2SCyW+/lT74wLRe1+Vv+iGHmNbuRx6RGjUy5+Xmms0pL7lE2rbNPC/dcIM0apT5WyyZ4oFZs0LXCnZujMM6lEhUM2bYy02dfLK0116Vr2MFlKWloe9dAPgaAaUf5eZKr79uxi1aSBdd5Op0yjnXJqmuzdsKuPbaKza78mZmmuo7KTSg/PBDO9Q47bT4hg1HH23+LS21d6WuSaIFlLHcyXv1alOtYK1v2bmzecHuXNM0Wmjx9o5Nm+xxmzbmU3yrjfmTT0wrXjQtXy716WMqaSTzgc9//iP9+99S06bRfaxYcK6nyzqU8CI/VVBaG+X4uYJyzx5TwRgtu3aZv9lVBZNDh5qKyQ8+CF12p65SU01BwKJFoSHjiy+aD70HDZL+9S/7/Ftvld54I/TDUcl0BVgfwM6Zw+sRJKaa2rstFXfyhu33382yDnS1wIcIKP3o++/tF7jXXlv5xY9batvJe88ee2feWLYuWxV/W7ea9YOk0PbuYcNi99hVqes6lLGuMq0rZwVlNDfKyc014aQVyO63nwknneuZRhMt3t7hrALce2/TZv3aa/ZyCXffXXVrXX3MmmU+RPj5Z3O6eXOzfMbll0fn/uOBnbzhdX4KKP3e4r1zp+ksadbMhHqvvlr/n8Wvv5plOfbZR7ryyqqDyQ8/tD8ojoYuXaRPPzWBqFXtu2GD3a6dlmZCywcesJ+zKrICzuJis1kOkEgKCuw1uJs3N9XHVWGjnMrKyqRx48x74uOOi/4H6oAHEFD60RlnmPV07rnHtJIkitoCyhUrzNo9UnwCSsm8eC0sNAuWS2bDl2OOid1jV8UZUIazDqUV2DVrFpsq07qqaV3P+vrhB1OxZlXadu1qwskOHaJz/1Whxds7nAFlq1bm33797Bd6waBp9V67NrLHeeEF6YQT7A8yDjzQHKPONcK8gIASXkdA6R9ff21eIwaDpiL+vPPM37BrrjEfwFuvE6tTWmo+dB482AQkTzwh5eXZl8cqmHRKSTEfYv3vf2a9Sctee5kPuGrrbGIdSiSyN9+01+E++2yzBmVVCChDrVhhXlNefbVdCPGvf/GeA75DQOlXLVqYtdmsHZYTQW0BZbwqAysGap99Zr/5Oflk06YTTwcfbIdjtVVQFhXZi7knQnu3VP26nvX18cdS376m4kAyodDMmfZmKLFCi7d3VBVQStLf/mZ/kr9pk9k0p6Sk7vdfWiqNHm02w7FuP2SIqWRJhKrluqLFG17nx4CyqMheq9BPrE4ap7w8aexY6cgjpcMPN5vQWB8cWX7/3VQl7ruv6YRxVtFnZEgjR0rz5sU2mKyoXTvpvfekqVOlm282r/HC+YCLdSiRyMJp75ZC32/5OaC0qiYPPbTyBw7bttnLsgE+QUCJxJGoAaUbu3c7paWZF92SaWuuaROL3Fz7DUuiBJTVretZHxMmmJDYejN6zDFmjRZnwBIrtHh7h3WMNGoUGiynpEgvvWT/Pn71ldl5tS7y86VTT5Uef9w+77rrpPffN1XLXkQFJbzOjwGl5M+db50B5R13SBdeaG88I0kLF0rXX29eF/z5z2YN4gsuMH/3//730B25O3eWHn7YVNOPH292zI63QEA6/XQzj3Bf27Zta79WnTuX1yRIHGvW2CFbly5S797VX7dxY/v1mF8Dytxcs1SFs2qyQwfz98DyzDOuTA1wCwElEkfr1vaLzKo2yXEjoPzlF+ndd824YUPzJOIGZ5v3vHnVX8+5QU4iVXJZG+Vs3WpamuoqGJTuvde0PVkVa6efbtq7WrSI3jxrQou3d1ib5LRqVXlDqxYtzMYDaWnm9MMPmwqW2hQUmLa/Pn1MhY1k7uPf/zYtONb9eREBJbzOTwGltYa45M827zVr7PHpp0sTJ5q/W88+G1r5WFQkTZ5slvN45RU7zA0ETCX9Bx+Y15U33xy/1xHR5FyH8ptvXJ0KUO6VV+xlFi64oPZNRa02761bpc2bYzu3RGJVTR5yiOnUs1x+ufTjj9JNN0lHHGHOmz+/5vd+QJIhoETiCATs3ZdXrKjcuhSvgLJTJztsmDbNfsM+aFBoNVY8hbsOZaLt4G1xrjPTo4d01VWhbzJqUlJinrDvuMM+79przRsPZ9VErNHi7Q2lpfaL3L33rvo6vXtL//ynfXrECFO1vXq1aZd78UXpttvM2ma9e5v7yc424aQVsFtrhV1xRUy/nbho1crejIEWb3iRnwJKZwWlHwNKZwWltbRL06bSZZeZD5F++snsll3x73/z5uZN/7JlJpw86aTqN6HxAtahRKIJBkPbuy+4oPbb+HEn76qqJtu3N68p//Mf83ozEDCXW6iihI94+JkZSclq896zx15j0GIFlNnZ1QcP0ZCWZod7ziDKjfZuS7g7eSdqQHnZZXaFQkmJqTrr0sWs41dTy/qOHebn/vzz9nn//KepWIv3WqC0eHvD1q32hxvO9Scruv56s2GYZNb42W8/01Zz/PHSJZdI999vdof99tvKn+ofcIA3N8OpTlqa/bOighJ19e67Us+eZg1AtxQU2GNrZ+Rk5feA0vpws2HDqisfDz5YeuQRc7233jIVkuPHm9P//GfockJe1q+fPWYdSiSCBQukxYvN+Nhjw3sf4qeNcoJB8/7n0EMrV03+9FPlLr1zzzUfvkjSa6+Z17eADxBQIrFUtw5lUZH5xEkywVZtLQORcrZ5S+bxTj45to9Zk44d7QBh7tzqd6mMV5VpXR1+uAlP77zTfvNYWGjW8evc2awLVfGJd+NGEwBZ7bQNGpjA6KabYv//XxVavL2hug1yKgoETKVkOG9W27UzGzONHCk9+qgJJ61lC5KF1ea9YYM/N95A/SxcaNb5mz/frMUajY3Q6sOqoAwE4ltZ7wa/B5RWBWX79jW/FmjQwGyG8/DD5m93sv1etGljNgmUTPuns4oYcEO4m+M4+SmgfPhh00FmHasVqyYrysw0f7sk87d+woR4zRRwFQElEkt1AeXKlfab5ngEbxUDyj59zBqZbgkE7CrKbdtCKyWdrPMbNgxdVy4RNG0q3XWX+X+9+Wb7zcKuXWZnzc6dzTqT+fnmTW6fPuZNr3Xbjz82uy67xfnmhoAycYUbUErm9+qdd8zGCIccYqp1b7xReuop0wL488/S7t3mDfGsWaYKZ/Ro+xPtZGL9vSgttdfwBGpSUGDCycJCc7qsTLrvPnfmYr3ha9zY22274fBzQLl9u10ta7V3+5m1DmVJifT1165OBT5XUmI2pJLMhwN//nN4t/NLQPn992bpIMtll1VdNVnRVVfZ43Hj+AAZvpDkr+LgOdUFlPGuDKwYUA4bFvvHrE1t61CWldk/s333Tdw3aS1bmk8Rly+XrrlGSk835+fnm3Um993XhJPWRknt2pkX3tYLcbcEAnYVJQFl4nKGa7UFlJIJJufNM4uSv/229Nhj5vfypJNMdYozDEhmbdvaY9q8UZtgULryysoVk6+84k4VpRVQJvv6k1LoJjlWOOwXzrWrrd1//Yx1KJEoPvnEdD5JpuNsr73Cu1379nYBQLIGlLt3m/U4rU0+x4wxm3pVVTVZ0QEHSAMHmvGyZebnDCS5BE0w4FvVBZRLl9pjNwJKN9eftNS2DuXatfablURaf7I6bdqYSrWlS6VLL7XXlNyyxW737t7dLHp/8MHuzdPJCihZgzJxOSsoY7lWbbJhJ+/IBINmjai33qp+CY5k8sILdrVMVpapBpHcq6L0U0Dp5wrKqjbI8TPWoUSimDrVHofb3i2ZYgrrPdfy5WZX+mRz662mI0cyO3PfdVfdbs9mOfAZAkokFmsXbylxKigPPLByYOmGnj3tcVUBpbPtO5HWn6xNx47Sc8+ZJ+/zz7fXlBo4UPriC2mffdydnxMVlImvLi3esDkDSnbyrrsJE8yC9mecYdaTSmaLFknXXmuffv55szZr8+bmtBtVlASU/kAFZajWraVu3cx4/vzQzaKAeJo3z/ybklJ723JFVpt3SUnoe79kMGOG9OSTZtywoVmns0GDut3HqafaXS7vvSetWhXdOQIJhoASiSUz017r0WrxleIfULZta95sZmRIt98e+8cLR/Pm9sYcP/xgNg5yStQdvMPVtav08svSL7+YjXGmTUu8tf4aNzb/ElAmLgLK+qHFu/7KyqQHH7RPjx6dvK1qBQXSWWfZwdhVV5m1xrKyzAZmUvyrKIuK7OdDAsrkRgVlZdbyN6Wl0ldfuToV+NSePWY9RckUdVivlcOVrOtQbt1qb3IjSQ89ZH+gUBdpadIVV5hxWZlpDweSGAElEo/V5r1unVm3Q7IDysxMKScnPvOYNMm8GTvvvPg8XjisNu/CQrNmnpPXA0rL/vtLQ4eaJ+RE42zx9kMbpxcRUNYPLd719957oRWDu3ebavCKHyJ5XTBoAknrez3sMLNmq+Waa9yponQuuUFAmdyooKyMdSjhth9/tNdXPPLIut8+GQNK6/nS6kgZNMg8R9bXpZfa74ueey75Xl8ADgSUSDzOdShzc82TnlVN2aWL3QIcD9YGLomipo1y4l1l6kdWQFlampzr5CQD5yY5rEEZPlq86++RR+yxtTHAd9/VfZ2pRPfiiyZ4lEzF5BtvhIZlblVROtta/RBQ+nmTHGcFJQGl0bevPWYdSrjh++/tMQGlMWmSeY6UzOuC8eMj27y0bVt7w9bffzfrXQNJioASiafiRjmrVtmfzPk9eKtpoxyrgjI11azriOhztq3Q5p2YrArK7OzQN/KombMynQrK8M2ZY7dVdutm1puyqhwefFD68kv35hZNP/4YWv3x3HP2kiNOblRRWutPSiYkTXZ+rqC0AspGjcLfJTjZtWplbyT43XfS9u3uzgf+89139rg+AaVznf9kCChXrZJGjbJP//vf0VlPn81y4BMElEg8FQNKKgNthx1mV3U6A8pg0A4oO3RIvMrPZGFVUErs5J2orICS9u66SU+3K04JKMPnrJ7861/Nm7N77zWng0Gzm6nXA4MdOyqvO3n22VVfNyvL/Byk+FVROgNKP1RQ+jWgDAbtgLJ9+/h20yQ6q827rIx1KBF/VkAZCJj3KXWVnW2vg+31gLKszKw7aT3vX3CBWac5Gvr3N2t8SmYTUWvdTyDJEFAi8RBQVq9hQ6lHDzP+5Rf7CXDLFnvs5fUnE50zoKSCMvEUFUl5eWZMQFl3Vpv3+vWssRqO5culqVPNOCfHrDspSTffbLddrlwZ2bpTbrPW0bLeNFZcd7IqFasoly6t++O++64JQb/5pvbrElD6w/bt9geDbJATytooR2IdSsSXc038Aw+s/99gq8170yazuYxXPfGEfQy2by899VT07jsQCK2iHDcuevcNJBACSiQeAsqaWW3ewaA0f74ZOzfI4WcUO7R4Jzbn+pMElHVnVTAUF5sPPVCzxx+3g9zrrrOXFEhNlV56SWra1Jx++WXptdfcmWOkXnzRzF8ybzwrrjtZlezsyKoon35aOu0081h//nPt6/0SUPoDG+RUr18/e8w6lIinn36y/0bXp73bkgzrUP74ozRmjBkHAuZ1QLNm0X2MCy+0iyVeeil0DWYgSRBQIvG0bSs1aGDGBJSVVbUOZbLs4J3oaPFObASUkWEn7/Bt2WLCO8l8cHHFFaGXd+wYukbUlVeadam8JNx1J6virKJ8+eXwqiiDQRNmXnutfd7atdI779R8Oz8HlH7aJIcNcqrXsqV06KFm/MMPdicBEGuRrj9p8XpAWVho2rmt3bVHjw6tbI6Wpk3N40jmuc/6ABFIIgSUSDwpKVKnTma8YoX9xiYjIzqLDHvd0UfbYwLK+KLFO7FZ609K7OBdH+zkHb5nnpF27zbjSy6xwzin886Tzj3XjLdvl0aMkEpL4zfHSBQVmepFq0Lvyiulc84J//Z1raIMBs0O4LffXvmysWNrvq3fAkrn5l9+raCkxbsy5zqUybI5FxIfAaVx++3SokVmfOih0v33x+6xrrrKHj/zDEvyIOkQUCIxWW3eO3faT1T77WfCS7/bf3/z5k+yA0pnlSkBZewke4u311/kOANKKijrzmrxlqigrMmePfa6Uikp0o03Vn/dZ56xq71mzpQefTTm04uKL7806xxLZt3jxx+v+32EW0VZWipdemno2pYPP2y/YZ05U1q8uPrH8VtA6dcWbyooa8Y6lHCDc4Ocww+v//14OaCcNcveMK9BA/N85/wgKdoOO0w65hgz/ukn6euvY/dYiJ+LL5aOO8505Pi8dZ+0B4nJuQ6lFZrQ3m2kpEg9e5rxunWmqoAKyvhI1hbv0lJp6FCpdWtp9my3Z1N/BJSRocU7PP/9r72cwFln2RX/VWnWzFzf2nH4tttMC2ai+/lnezxqVO3rTlYlnCrKwkKzGY7VLh8ImFbym28O3QygpipK5wt5Asrk5QwoqaCsrF8/++8M61AiHoqK7A1yDjggsr+/HTvaoZ6XAsqFC023gfVe9f77pe7dY/+4Faso4X3ffCN99ZVZW9T5ftOHCCiRmJwBpYWA0lZxHUoroMzJCa3yQ3Qla4v33LnStGkmdHnySbdnU38ElJGhxbt2ZWWhVZA33VT7bfr1k265xYyLi81u34n+98P5BvHAA+t/PzVVUe7cKZ16qvTmm+Z0err0+uummlIyLfHW89l//yvl51f9GM4Kyqys+s/VK/waULJJTs2aN7eDkQULvL0TMrzhp5/sNRcjae+WzOZy1hrHS5dKJSWR3V88fPONqVy2Xnv2719zR0U0nXmmWXtWkqZMkTZujM/jJpLnnpOGDLE3jPWywkK7G/LAA83x4GMElEhMBJQ1c65D+dln0oYNZkz1ZGwla4v3Tz/Z4++/d28ekWKTnMjQ4l2799+3w7v+/aWjjgrvdvfcY7e//fyz9Le/xWR6UWO1d0uhrXd1VV0V5bZt0uDB0vTp5nSjRtK775qKVEvTptJf/mLGO3aYqoKq+LnF24+b5GRmRn9n3GRhrUMZDLIOJWIvWutPWqznmuJiKTc38vuLpenTpUGD7A2pevc2H7bFK1hq2NCsfy2Zn9cLL8TncRNFXp7pspg+3d453cuWLrXXKO/Wzd25JAACSiQmAsqaOSsoJ0+2x/yMYitZW7ydAeWvv3p37RMqKCOTk2OPCSirZq0zJYVXPWlp0EB65RU7XHr6aemjj6I7t2iyQthmzSLfcKpiFeVXX5lw95tvzHlNm0ozZkgnnlj5tqNG2eOxY6teJ9dvAaUfN8kJBu0Kyvbt7VZmhGIdSsRTrAJKKbHbvKdMkU4+2S5UOOEE8xxW1WZ5sXTFFfbfwn//2zub8EXDd9/ZVbbz5nl/Df3//c8eE1ASUCJBde5c+TzCN1ubNvYaTM5QhgrK2ErWFu+KG1AsXOjOPCJlHQuBgNSihbtz8aKMDPsFNi3elX37rV2VdNBBZt3WujjooND28L/9LTFfVO/caVerHXhg5GFQxSrKvn3t3U5btTLr5R17bNW3PeQQc33JVHV+9lnl6/gtoPRji3denv2hIO3d1evbl3UoET/R2iDH4oWA8sUXzbrJxcXm9BlnmM4KN557One2P9hbvTq02CDZOdu6t2+XVq50by7RQEAZgoASiSk7215bQzJrU/GiNJSzitJCQBlbydriXTGg9GqbtxVQtmjh+/Vb6s1q816/PjHDMzc5qyf/+lezYVldXXWVvcnZjz8m5ocBv/5qjyNp73ZyVlFav1cdOpjA97DDar+tparNcggok59z/Uk2yKneXnvZx9PChdKWLe7Mw2p/nTrVncdH7BUV2R807b9/dNb/TfSA8rHHTFt1WZk5fdFFZt3kWO7YXZuBA+3xvHnuzSPeKq47uWCBK9OIGgLKEASUSFzONu/OnaW0NPfmkogIKOMvGSsot2611zC1eD2gpL27/qyNcgoL7bWVYDYis95st24tXXBB/e4nEJAuvtg+Xd26im5yvjGMVkDprKK07verr8wb29oMG2YH5++8I61aFXq5M6D0w86XaWn2BzDhBpRlZd5u/3Pu4M2H1TWz1qGUpC++iP/jFxRI55wjffKJ+ddPVV1+snhx9DbIsSRqQBkMSrffHvocduON0vPPu//e1Lknwdy57s0j3pI1oExP5728CCiRyJxt3rR3V+Z8UrLwc4qtZFyDsmL1pCT98EP85xGpnTvt0JiAsv7YybtqTzxhV01cd11kFRN//rNZk1Iy61Im2m6l0drBu6LRo82aWRdeaConww2a0tOlyy8347Iy6T//Cb3cCigzM/1TOW1VUYazSU5urvlZd+0qbd4c02nFjDOgpIKyZm6vQzlunNkESzJtsBddlHh/4xC5aK8/KZk1j1u3NmPnRm1uKiszz/nWBm+S2fTu0Ufr10URbYcfbj/v+SWg3Ly58iZKXg4oi4vtzpUDDnA/9E4ACXBkAdVwVlASvFV25JGha4M1bRr/BZr9JhkrKKsKKBcv9l7rIDt4Rwc7eVe2ZYtZd0oyyzxceWVk99e8uXTqqWb8++/2TtaJIlo7eFfUsKFZyH/ixLpvvHP55faL9ueeCw3mrE29/NDebbEC8nD+Tj/zjPmwYcWK0E31vMTZ4k0FZc2OO84OTuK9DuWuXaHr7Eqm0umxx+I7D8ReLAJKyX7O2bjRrC3opuJiacQIs6md5cknTTVlomzU1bixWatZMtXKyVI8UZOK1ZOStwPKZcvsNU0PPtjduSQIAkokLmfrVzTfJCWLrKzQdSr22y9xnjCTVTKuQekMKK2q5dJSsz6elzg3i4p012E/c1ZQElAa48bZx/sll0Tng6ALL7THEydGfn/RZFVQpqQkTqtRmzbS8OFmvGmT2UXVYlVQRmMNNK+wKijDCSjff98ee+3vuoUW7/A1a2ZvWPLjj6Ef3sXa88/bz8VHHmkHpXfckTgVcYgOZ0AZjQ1yLInS5l1SIp15pvTyy+Z0aqp5rr72WvfmVB1rya/SUm92QNWVM6C03veuXGlXbnsN609WQkCJxHXWWdLgwdKAAfVf7yvZOdehpMo09pK9xdt5nHntRY4zoKSCsv5o8Q61Z4/01FNmnJIi3XBDdO73xBPtjeDeeSdx1vssK7PfFO67r7uL/1c0apQ9dla0WAGlnyooww0oly+Xfv7ZPm1tauE1bJJTN26sQ1lYKD38sH36xRfNOn3WZRdf7O11UGErLg7dICc7O3r3nSgB5SuvSO++a8YNGkhvvhn6wWIicb4X/PZb9+YRL86ActAge+zVKkoCykoIKJG4GjeWPv5Y+vTT6D75JRPnOpSJUumSzDIy7E/rkq2CsnXr0Cd6r22UQ0AZHbR4h3r5Zft368wzQ9dGjkR6unTeeWZcWJg4rbdr19p/2xKtc+FPf5K6dzfjOXPM36jiYrvdm4CyMmf1pGRaAK1d1L3EqqBs0sQsZ4OaOdehrPg7ECsvvWT+fkhmCYvu3c1afdaH57Nnm/ZYeN/ixfbf3Wi2d0uJE1A6j5s33pBOO829udTGGVD6YR1KK6DMzjbFTBYCyqRBQAl42emnmyqcjIzQP9KIjUDAbvNOhoBy0yY7fDn4YKlHD/syrwWUrEEZHbR42/bskR54wD59883RvX9nNUai7OYdqw1yoiEQCK2iHDs2tJLdjwFlbZvkvPde6Ont20Pbpb0gGLTn3K4dS9mEo39/+4P9V1+NfZt3SUno38r/+z/zb2amqaS0/s/+7//MemuxtmCBGsaztd1vYrX+pBT6vONWQFlaKn32mRnvtZd08snuzCNc3brZHV7JHlCuX29/EHLkkdIRR9iXeT2gTEujG/IPBJSAl7VqZV64r18f3TVgUD3rRUAytHg727sPPti8oena1ZxetMhetNkLqKCMDlq8bY8+Kv32mxkPHCgddVR07/+II+wF0b/6yrTjus35hjDRKigl6fzz7Qq6SZOkVavsy/wUUFqt98XF1bfN5udLs2ZVPt9rbd7btkm7d5sx60+Gp3Fj6dJLzbiw0GxOFUuvvmo2YZJMJ4azouu446RrrjHj3bvNOr5lZbGby/jxSu/VS8ePHh369wHRE8uAslMn02Egubdu6Q8/SFu3mvGAAfYu2YkqLc3+f8jNDX09nGyc7d1HHWXCWWsDPS8GlCUl9uuurl3NcgIgoAQ8r2FD8wkf4sMKKJOhgrJiQCnZn0YWFnprUXs2yYmORo3MJguSvysoV62S7r/fjFNTpSeeiP5jBAKhVZTWYvxuitUO3tHSuLF00UVmvGdPaMuonwJKq4JSqr6Kcvp08+ZHCv3gIRob5WzebH53//GPyO+rNs6KT9afDN+119qb1IwdW3u1bX2VlYX+Htx2W+Xr/OMf9vIYX3xhNh6LhZ07pTFjJEkNCgqUMnZsbB7H72K1QY4UWkW2dKk765Z+8ok9PuGE+D9+fTg/FJg3z715xFrFgLJhQ+mgg8zp//0vdn/nYmXFCnvOtHeXI6AEgLpIphbvqgJK54tNL7V5U0EZPVaYsX69N9eri4a//tWu2rr2WumQQ2LzOOefb7c/vvSS+z/vRG7xtlx9tT12tsb7NaCsbh1KZ3v3TTfZ42gElI8+Kv33v6ZlN9Zvhp0b5FBBGb5OnaQzzjDjjRul116LzeNMnWp/sHHccVLfvpWv06SJ2eHb8re/2RWX0fT00+Z7/UPKiy/am2ghOoqLpYULzbhr19isCWt9OFZY6E4V7IwZ9ti5Nnsi88s6lBUDSkk67DDzb0lJ6HqOXsD6k1UioASAunC2eLsdJkSqpgpKyVs7eVsBZVqaXQGI+rECyl27TJuo33zyiTRlihm3aiXddVfsHmuffewKjd9+k77+OnaPFQ4raGjWLHErkbt2lQYPNmPnMhQElLbSUunDD824SRPp8svtNrhoBJTffGOPY71rrLOCkoCybm64wR4//nj0X7MEg9J999mnq6qetAwYIF1xhRn///buOzyqMm0D+D0ljRAICZAQagDpAVwCiKiIgFKWT5pIFVCssAqsioiIiIrCWqgirmLBBguoKKgIEkB6CdJ7kRJaICSBhCRzvj/enHnPkJnJ1EzJ/bsuLk6mvknmZOY85ynZ2cDjj3t2PRkZwDvvWFyky8gQgXTyHG2WmqfLu1W+HJRz/bpouQKIIH/t2iX7/K4qDQFKRZEBypgYmZWtBiiBwCvzZoDSKgYoiYicoQYoFSXwSgm0FEUGKKtUkW0CAjWDUm2IX7kyhyi4qzRP8r55U2RMqqZO9f7UYH8ZlpOdLYNB9ev7936kHZajiooq+XX4SnEByi1bRBk2IIK5ZcvKjNgDB8Tr3FUmk+V7g7dPZGkzKFni7Zw77wRathTbu3YBa9Z49vF//llm0yUnF59tNnWqDDKvWmWZVemuDz4Q/UoBmO64Q14+Y0bgn0z2J97sP6nyZYBy/Xr597FTJ/9+H9SqWVOeVNyyJThf86dPy2SE5GT5u2GAMugwQElE5Ay1xBsI7DLvCxeAy5fFtrZ8tWJFeQCxc6d3m9l7iqLIDy0s73ZfaZ7kPXOmzCJs0wYYPNj7z9mzp8z+W7hQlpaXtMOH5ba/lnerunUTB2RapSmDUh2SA1g/UaYt71Yn0DZtKv7Pz3evv/ChQ5Zls94OUDKD0nU6HTB6tPz6/fc999jWsieLC+aUKwd8/LH8+t//9sxU+cuXRdsBADAaUfDZZ7ikHuwfOGDZU5DcUxIBSl9O8g7E/pOA2PfULMr0dDngL5ho24lohxY2aya3A6nyC5ABSr0eqFfPt2vxIwxQEhE5Q82gBAJ7kre18m6VWuadleUfk4WLk5EhSz39tSw1kJTWAOW5c7KcW6cT/cz0JfAxKTIS6NNHbGdkWAaXSpK/D8jRMhiAp5+2vKw0BSiLy6D86Sfxv04HdO0qtpOS5PXulHlre4ABwJ497mVkFodDctzTp4/8uf30k+WJCHesWiXL+5OSgO7dHbvfAw/IQVeZmaL9gLvZXtOmiccCxJTw2rVxTA3MA8D06e49PknaAKW2JZAnad9/SnpYo9p/UqcTbQkCSbCXeVvrPwkAsbHy5FVqamAkVgBinfv3i+06dSzf10s5BiiJiJyhDVAGcgblnj1y21aAEgiMMm8OyPEsbYn32bO+W4ez8vNFD8eMDNfu/+KLMjPsySe9d/BljT+UeWszVfw9QAmIQIQ2k5ABSuHkSRmAbNUKiIsT294KUObleXcwgVriHRXl/XYLwSgkBBg5Umwriih59oQ335Tb48c7dzLn3XflibBffnHvb15amvyewsLMfTDTWreGUqOGuPznnz0XmC3N8vNlSX/dut7bH2NiRDUPULIZlBcvyhLh22+XawgUpTVACcgy78xM4MSJklqRe06elBUzLO+2EJQBytdeew06nc7iXwN/L1ciosAQLCXe9jIoA60PJQOUnhWoGZQvvwzcdZcYouJsr7V164AFC8R2TIxl6WJJaNdOZgD88ovFJNoSEwgTvLUqVgT69ZNfx8T4bi0lzV6AUs2eBGR5N2AZoPzrL9efW5tBpfJWWZ2iyAxKlne77okn5MnV+fOBq1fde7z16+Xf2Hr1ZAa4oypUAD76SH49apQ4WHfFlCnyIP+pp8zZoorBANNTT8nbzZrl2uOTtG+f/HvjrfJulXqS7OxZmR3rbatWye1Amd6tpfabBYIvQKkdkBMXVzSbXnvcEih9KNl/0qagDFACQOPGjXHu3Dnzv/XqRC4iIncEY4n3rW+MgTbJWx2QAzBA6QmBGKA0mYDPPhPbFy+K3lGODkfIz5cZRgDw1luiZKgk6fWy32VBAfD11yX7/IAspdPrRblRIJg8GWjdWpQxt2vn69WUHEcDlNqy2+rVZcaTqxmUBQXWT1p5630iPV1+fyzvdl2FCsDQoWI7O9uyD6QrtNmTL78sWi44q3t3YOBAsX31qtiHC4fcOOzvv4G5c8V2mTLAuHEWV5sefRSIiBBfzJ8PXLvm/DpJKon+kyptFv+hQ959LlWg9p9UxcbK9+4dO2Tro2Bw/Lj8+6AdkKMKxEE5DFDaFLQBSqPRiPj4ePO/ioGWpk1E/ikYSry1E7yrVStappOQIAN9O3b4/zRAZlB6ljZAGSgl3tu3WwaqCwqA554TB+XFDZ356COZUfaPfwDDh3ttmXb5ssxbUeRBYO3alqXT/qx6dWDTJlHCGRrq69WUHFtDcrKygNWrxXb16nIwDiAO6NQsytOnnQ8GASKIrb7vafuzeStAyQE5nvPss3J75kxxYsYV27aJLG8AqFULGDDA9TXNmCEy3gFxsN6jh/WhT7ZMniz7nz77rGxnoIqJkSd+MjPlSSxyja8ClCVR5q0osv9keLioxghErVuL/3NyLFs5BTpbA3JUDFAGFaOvF+Athw8fRkJCAsLDw9GmTRtMmTIFNdReJFbk5uYiV/OmeK3wLFteXh7yAugMhLrWQFozkT8pbh/Sh4dDzRXIv3YNSiDua2fPIqSwxMvUqBEKrHwPhttvh/7XX4HLl5F37Bhg5++nr+nPnZO/kwoVAvN34k/CwmCMioIuMxPK2bPId+Ln6av3IP3PP5tfA6ZWraBXy5u++AKmPXtQsHCh9dfwxYswvvIK1HPx+R98AMVk8k2T9dq1YVDXnpqKvO3bLQNM3nT6NEIKM8JN9epZ/ZtAJcORfUgfEiL/5mVlmf/m6X75BcbCgE1B164w3RKE0jduDENhRVH+zp1Q7r7bqbXpNm82HzgUdOgA/ZEj0J06BWXXLuTn5np8qJTuxAn5fFWqwMTXpetq14aha1foly8H/v4b+d99B6VvX6cfxjB5sjm7peD552ECXM/UiooCfvwRxnvuge7iRWDtWpgGD0bBl18W/1o6cgTGTz+FDoBSrhzyR40yr8NiH3rqKYTMmwcAUGbORP6TT5bM8LMgZNi2zfy7z0tK8mqGnq5OHbnv79vn/X3/8GGEnDoFADC1bYsCgyEgMxD1LVrAUFiBUbBxI0xNmrj0OP4WT9Bv2SLf85o3L/o5v2pVGMuVg+7aNSipqU59bvUVw5490ANQdDrk16kTkK83Zzn6egrKAGXr1q3x2WefoX79+jh37hwmTZqEu+++G3v27EFUVJTV+0yZMgWTJk0qcvlvv/2GMtqMqQCxUj0LREQusbUPJR4/DjVkkLphA86EhJTcojykUmoq7izcPhYejr3Llxe5TcOyZVGvcHvHJ58gTT0r64eStm5F7cLtDUeO4IqV74ec0yEqCmUzM5F/+jSWu/DzLOn3oLu//RZqB8Lfhw9HhXvuwe0zZ8KYmwv9jh3Ia9ECW194AZdv+bDebPZs1CoM1p9q3x4709MBH75+ajVvjmaFwdUTb7yBfWpZppdV2rVL/k0wGq3+TaCSZW8fqnXkCJoVbv+1ZQv+Lvxs2/yjj1Cz8PItlSrhwi2/x1o6nfl++777Dsed7O2WtGSJ+W/t5vx8JMbHo8qpU9BlZiJl/nxka7OvPaDWL7/I7/PKFZzi69ItFVu3RtvCn+G111/HOicHS0WdPIn7fvwRAHAjJga/V64Mkwd+J9Evvoi2r7wi/l4vWoSjeXnF/u37xwcfoHpBAQDgQLduOLRpU5HbqPvQnUlJqLR7N3RHjmDbG2/ggrUMLLJLV1CArjt3Qg8gKz4eqzZs8OrzlT1/Hh0Kt9PWrME2bX9FL6i1fLn5b83+qlVxJED/1lTIy8M9hdunlyxBatWqbj2ev8QT7vztN1Qq3P796lXkWvn9tK1eHRX37oXu77+x8ttvkVeuXMku0hmKgm6FAcrrlSvjd2f7pgeo6w5WHuoUxd9r99x39epV1KxZE++99x4ee+wxq7exlkFZvXp1XLp0CeX8+QV+i7y8PKxcuRKdOnVCSAAGToh8rbh9SDd/PoxPPgkAyP/wQyg2/qb4M/2MGTA8/zwAIH/ePChWDgR0ixfD2L8/AKBg/HiYJk4sySU6xTBgAPT/+x8AIO/gQSAx0ccrCnyGjh2hX7sWAJCXnu7whGSfvAelp8OYkACdyQSlQQPkq+Xaf/0FY9++0B07BqBwaMK0aTCNGAHodNBt2wZD27bQKYrIwNmzB4iPL5k123L5Mow1akCXlwclPh75x44BRu+fS9Z/+CEMzz0HIHD/rgULR/Yh3RdfwFjYiqBg1iyYnngCMJlgrFkTuvPnoUREID8tTfbfU++3YQOM994r7vf44zDNnu3U2gz33AN9YSAo7/x56GfOhKFwoFT+119DcXZYSjH0r7wCw9Sp4vF/+gnK/fd79PFLHUWBMTkZusIepPnr1kFx4uSjYfBg6L/7DgBQ8J//wKQtG3eT7uefYejdG7rC7PWC998Xf6ut2bcPxttvF3+7Y2KQf+gQoDlWu3Uf0i1bBmPv3gAAU6dOKPj5Z4+tu9TYvRshhWXdpj59UODtPsl5eTCWLw9dfj6Upk2Rr53g7AWGhx6C/ocfxFNv3mw5dCWQ3LgBY2ys+Lk1box8F9tv+FU8wWSCsVIlUdVTrZr4XGSFfswYGAqHYeX/8gsUbRsSf3PqFELq1gUAmLp2RcH33/t2PSXk2rVrqFixIjIyMuzG14Iyg/JW0dHRqFevHo4cOWLzNmFhYQiz0nMpJCTE9zumCwJ13UT+wuY+pMnCNubmAoG4n6nDMAAYmzWz/j20amXeNOzaBYM/f5+XL5s3Q6pWDczfib/RnHUPuXRJDFlwQom+B/3xh7kkW9e1q3zeFi1E36L+/YHffoOuoACGMWNgSE0FPvxQTI4tPEermzQJIf7Q4y4+XkxeXroUurQ0hKSkAJ07e/95NZ+PjI0acR/yA3b3ochI86YhL0/8fd661Tz9XdepE0KsffjX9Oky7N3r3N/1/Hxg1y6xXacOQipXtugFZty9W+xrnqQZ0mVMTOTr0hNGjwYefRQAYJw507Fee4oCzJ4NLFokvq5UCYannvLs54IePYA5c8QkbkD8ra5ZE+jZs+ht33hD/u0eOxYhNoaamfehBx8UJy6PH4d+5UrojxwBGjb03NpLA/XEHwB9y5bQe3tfDAkR/ZAPHYLu8GGE6PWuDWNyRH6+nEofG4uQ5OTAbQMQEiJaw+zYAd2+fQjJybE4bnH+4fwgnnDggHmSuy452fZ6NAM+jXv2AA88UBKrc83hw+ZNfZMm3t+f/ISjr6UA3fuck5WVhaNHj6KKh0tPiKgU0hwYBuyQHHsTvFWJiXJ4jr9P8laH5JQpY/n7IdcF0iRvdWADAHTpYnldTIwo2X7xRXnZF1+IwQxqn8rGjQFbmTq+4IthOZqTFhbDCcg/WRuSo53e/c9/Wr9f+fKyF+vu3c4NQNu/Xw6cUgdkaLOMvPE+wSE5nte/vxwmt3gxUNh3z6azZ8Xf1X/9S/bm/fe/vfNe++STYio4IF6bAwYAGzda3mbnTqCwYgJxccDIkcU/rsEg1q+aOdMz6y1NSnJAjqpZYdH1jRuWE7Y9bds2ICNDbHfoELjBSZWaYKAoYtBloNNmz9przxBIg3I4IMeuAN8DrXv++eeRkpKCEydOYMOGDejZsycMBgP6e/rMLhGVPtqetIVDJQKKdoJ3zZq2S3d1OnnweeaMOTPHL6kBykqV7N+OHBcok7xNJhmgjIwErA39MBiAd94Bvv1W7r/a72nmTP/KzOraFVAzgpYuBQqH9nmVOiU1OloGL8h/hYfL7Zwc8f+yZfKybt1s31cdvJSZCZw86fhzWjtIrF5dnAQAvBOgPH1a/F+unFtZQKQRHg4884zYLiiwH6xbvFhMfv/1V3nZv/4lApTe8sYbcvJ2Tg7QvTtw6JC8fsIEuT1+vOVnMnuGDZNB1c8/Bwp7D5ODtAFKTaaaV2mP2+fP997zaIOfnTp573lKiqYCynwiNpA5GqBs1Ei2xGGAMqAFZYDy9OnT6N+/P+rXr4++ffsiNjYWmzZtQiUevBKRu7QfhgMxg/L0aRnwaNzY/m21H0L9NYuyoAC4dElsM7DiOQkJctufMyhTU2Xw/L77LDPLbvXwwyIbR9uj9OGHgfbtvbpEp4WGygOznByZLeQt16/LLKr69cXJCfJvtwYoz5yRf6NbtLDcf2+VlCS3C3sROkQboFAPErUnss6f9+zfCkWRAUpmT3rWU0+JvzMA8PHHQFaW5fXXromAXp8+QHq6uKxKFXEyaMYM7/bF1emA//5XZLIBooVLly7iROTGjYDaP7J6deCJJxx/3OhoQO23ff068Omnnlx1cMvPlwGf2rWdbvnism7d5InnpUvla9HTtINgOnb0znOUpNIaoAwLk8G+/fvlyTt/pA1QNmjgu3X4qaAMUH777bc4e/YscnNzcfr0aXz77beoU6eOr5dFRMEg0Eu8teXdxQUovV2+5wmXL8syRQYoPSdQSry15d2O9Gps2lR82H30UaB3b/8t9SvJMm9tdhLLuwPDrQFK7dAPW+XdKlcDlNqDRO3JK2+9T1y+LA8wq1Xz3OOSKI0eOFBsZ2RYZqetXy9Kaz/7TF7Wq5d4rZRUT7fQUJm9CQDHjonX9Usvydu8+qr9E1LW3FrmXTgFnIpx4EDR9g4lITQUGDRIbN+8CXhjME9WlmwjULcuUKuW55+jpDVoIKujAj1AmZ8v31cSE2V1iS3q+1FBgeXxjj9RFBmgrFGD1QFWBGWAkojIawK9xNuZAKX2INRf+9io5d0AA5SeFCgl3itWyO1b+0/aEhMDfPKJyEz018qK5GR5Vj0lpfg+ce5Qy7sBnskPFLcGKLXl3d2727+vNkCpGXxhV16ezKC67TbZnxjwXoCS/Se9a/RouT19ungdjR8PtGsHnDghLo+KEsHL//2v+MCAp5UvL/oHq8HprVuBtWvFdp06wJAhzj9m/fryRNaJE5Z9W8k2X/SfVBUOdALgnazXtWvF3zcgOLInAdHWRs00PHUKSEvz7XrcsX+/TAaxlz2pCoQ+lOfOyZ6nLO+2igFKIiJnBHqJtzMByvr1gYgIse2vAcqLF+U2A5SeEwgl3levysyH+vUtS7cDnU4H9Osnv05J8d5zaQOUzKAMDNoA5ZUrsodalSqWAUNr6teXPVcdzaDct08O47n1INFbB4QMUHpXUpIsoz56VLwu3npLDsJp21ZMbR861HdtH6pVEyehtAFxAHjtNdf7Bj/7rNyePt3lpZUqvgxQNmkCtGwptnfu9HzQKdj6T6qCpczb0fJuVSAEKNl/slgMUBIROSOYSrwbNrR/W4NBTlE8dsw/m8ozg9I7oqJkMN5fA5QrV8oSPUezJwPJXXfJ7Vsn2XqSdoI3MygDg7a0deVKWQrdrVvxE2hDQuTf/oMHZeDRHu1B4q0BCu2JLE9mUKr9JwGWeHuLNotSzdI2GkWgMiXFP076NGki+g+qAclGjSyHpzjrgQeAevXE9h9/ONfmoLTyxYAcLW0WpaeH5aj9J/V6/+tH7Y5gDFCqgWp71GMWwH9bUzFAWSwGKImInBHIJd7avieJiZbBVlu0H0b98WykNkDpr+W6gUink2Xe/lri7Wz/yUDTqpXMXNq0yXvPo2ZQ6vWidJL8nzaDUjs4orjybpVa5l1QIEroimMvi8VgkJPBjx6VpWvuYgal93XpYpk13aCB+Fszbpz4vfqL9u2BVatE9uOPP7q3Nr2+aC9Ksq2gQH72S0wULVJKWr9+8m/eggWOnVRxRFoasGeP2E5OLrnhPyUhGAOUjgTHK1QAatYU27t2yYxwf8IAZbEYoCQickZIiPxwHGgZlKdOyWmdTZo4dh9/70PJDErvUcu8MzJkg3x/oSgyQBkRIfqmBZuoKLmf/vWXd06IKIoMUCYmOj90gnxDG6BUhYXJkt3iODsoR82g0k7t1tJetmuXY2soDjMovU+vF1O8W7cGXnhB/J5LuoTXUXffLUqyPXESZcgQoFw5sf3ll2IgE1l34ID8rOuL7ElATGDv1Utsp6eLILUnaMu7g6X/pKpaNSA+Xmxv3eqfgbri3Lwp30/q1Sva6sEWtcw7K0tUf/kbbYCyuEq2UooBSiIiZ+h0MvMw0AKU6plioPj+kyp/n+TNAKX3+PMk77/+kpmd7dtbD9gEgzZtxP8FBeIgw9POnJGBT5Z3Bw5rr/cOHRzLigdkxiNQfIDy1oNENbCj5Y33CW0GJQOU3nP33SJrcupUywqRYBYVJcuGc3KYRWmPL/tPanljWE6w9p8ExLGKmkV59Spw5IhPl+OSPXts9z62x5/7UCqKbLWVkCCC71QEA5RERM5SP8QHWom3MwNytLdTez/5YwYlh+R4jz9P8taWdwdj/0mVGqAEvFPmzQE5gclapus//+n4/Z3JoNyzRwQpAdsHid4MUJYvLwJKRJ40cqTs1/rmm95toxHI/CVA2b69LN397TfLDGtXKIrsP1mmjOV7bbAI9DJvZwfkqPw5QHnxomzLwvJumxigJCJylhqgDLQMSlcClGFhssxUW+rjL9iD0nv8eZL3ihVyOxj7T6ruuENue2NQjnZADgOUgcNgEMNMtLp1c/z+VavKzI2//rJ/W22AwtZBYpMmsvWJJwKUiiIDEOw/Sd5Qpw4wdqzYzs8HHn7Ysp8rCf4SoNTrxUR5QJQrf/GFe4934IA88XrPPcHZ3qR1a7kd6AFKRwbkqPw5QMn+kw5hgJKIyFmBWuKtBij1eufKOdW+QyZT8QezJU0NUJYvD4SG+nYtwcZfS7yvXQP+/FNs160r/gWrevVk4/6NG0XgxpO0GZQs8Q4s2jLvZs2AGjUcv69OJ8u8z561H5ixN8FbFREhXz/79rk/xOLiRZm1yQAlecvrrwN33SW2T50SATBP/40NZAcOyMqZmjWB2FjfrkcNUAKizNud35WaPQkEX/9JlfaEUiAHKPV6y6BjcWrWlCfgGKAMSAxQEhE5S5tBGSiNp00mOa21dm1xQOkobfmev5V5qwFKlnd7nr+WeP/+u8h4AYK7vBsQH8zVLMqLF4Hjxz37+CzxDlzaAKUz5d0qR8u81YNEWwNyVOp1+fmW2fqu4IAcKglGI/DNN0DFiuLrZcuA99937zEzMjz/d9oXjh4VfW3VAXkPPODb9QBArVpyENjRo8C6da4/VjD3n1RFR8v39Z075UmfQJCTI9+XGjYEypZ1/L46nQxonjlj2QrK17TvjQxQ2sQAJRGRs7SN5HNyfLcOZ5w4ITM+HS3vVvnrJO/cXHEwADBA6Q3+WuJdWvpPqrxZ5q2WeJcvz30o0HgyQGkrMz431/GDRE/2odQOyGEGJXlTtWpikrdq7FjX+1GuWCFer7VrAyNGBFZASOvkSeC+++SJydtvB95+27drUmmH5cyf79pj5OUBa9aI7cqVZRujYKT2obx50/8qoOzZtUueiHam/6TK1TLv48dFj1NvZVIzg9IhDFASETlLOyk1UMq8Xek/qWraVDaT96dJ3pcuyW0GVzzPH0u8FUX2nwwLA9q18+16SoK2eb8nA5TXr4uyRkCU5+p0nnts8j41szAhwXIYgqMcyaDcvVsczAPFHyR6MkDJDEoqSZ07Ay+9JLZd7Uc5dy7QvTuQmSm+njNHvD+dOePZtXrbmTMiS1F9b2jSRARs1FYjvtazpzihBgALF8qftzO2bJH369hRfr4NRoE6KMfVATkqVwKUe/aI98UHHgAmT3b+OR2hBijj4nzfMsGPBfEeSUTkJdoMytIQoIyMlGUiu3f7T1aAdkAOA5SeFx0tG8f7S4n33r0yeHHvvZb7YrBq1UoGDz05afbwYbnN8u7AM2sWMHy4OEh35QBbmzVkK0DpSP9JlfaAkBmUFGgmTwbathXbp04Bw4Y5lkVlMgEvvgg8/TRQUGB53aZNogIlJcXz6/WG8+dFcPLoUfF1/fqiFFotgfcHERFA//5i+/p18ffPWaWh/6QqGAKUzgzIUTkboMzOBvr2Ff8DwDvvWB5jeMKlS/IxmT1pFwOURETO0gZF1Dczf6cNULpSzqKWeeflud9fzFM4wdu7dDpZ5u0vGZTa6d2lobwbENki6ofZXbs8d1JEO8GbA3ICT4sWwMcfy6CKs8qVEz3dAJE5Yq2fsiMTvFUVKsjH27WraLDGGQxQUkkzGoFvv5VZTT/+CHzwgf373LgB9OsHTJsmL3vhBREIUodWXbgggn7vveffA3guXRLBOrUvce3awKpVItPL32jLvD/91Pn7a/tPBnuAslkzICREbAdigNJolAPdnNGwofy+HQlQPvus7NMPiM9Z77zj/PPao318BijtYoCSiMhZgVjivWeP+N9gcC1bStuH0l/KvJlB6X1qmXd6uvuTeT2htPWfVKll3vn5lpkF7uCAHFLLvLOyRJ/iWzk7RVUt887OBo4ccX1dLPEmX7i1H+WLLwKbN1u/rRrQW7RIfK3XA7NnA1Onioyv7dvl8JWCAuDf/xbBzKws734Prrh6Fbj/fvk5sUYNYPVqoGpVny7LpuRkeaJ9wwbL97LiXLsmKxHq1w/+EyBhYfJv94EDsm+7LzgaoM/OlqXQTZo4N9RTFRoqq8UOHJDDnqz56isZ6I6MlP2d58zxbPUQ+086jAFKIiJnBVqJd0GBzJaqW1eW7TrDHyd5M0Dpff7UhzIzU07tTEwEbrvNt+spSdpBOZ4q82aAkuz1oczJkQGLRo0ca6fgqT6UagZlhQqWJwSJvK1Ll+L7UR4+LE4abdggvo6MFBmXzzwjb1Oxosj4f/llednChaLk1pmAmqOuXBEBlcceE5PIHT1BkJkpenCq+2tCgsicrFnT82v0FJ3O9WE5a9bI7O5gnd59K7XMW1Ess+JtURQRmLOWVe+KnBzRjiQyEujVS76v2LJzp3xuV/pPqtTArMlk+zkPHQKeekp+PWeOGHClrvutt1x//lsxQOkwBiiJiJwVaCXex47JaePO9p9U+WOA8uJFuc0ApXf40yTv1avlwI4uXUrXUBdvDMpRT1ro9eLEBZU+2tK5WwOUf/3l/BRVT/ShNJnkYBFmT5IvaPtRnjxp2Y/yzz/F32M1AFilijhx1q1b0ccxGIA33wS+/160VABEmWfLlsDSpe6v02QSwcQBA8Q6RowQmWBjxogTeA0biizQdevkvqyVnS3WrWaJVq4sHi8Q3g8GDRLlvwDw+efWvz9rfv5Zbgd7ebfK0T6UOTni9XP77QipVQvtnn/esle1K9LSRL/wTz4RWYxLl4r3nYEDbT+2u/0nVcX1oczJEScg1KzmIUOARx4Bxo6VJ8Y+/lgOjHIXA5QOY4CSiMhZvs6gvH5dnA10tFzCnQE5quho0ZMIcL+/mKcwg9L7/CmDsrSWdwOiR6Q6uXTTJvd7mSmKyBwARDaqK1nVFPjsZVC6MkXVExmUFy/KQWzBXn5J/sloBL75pmg/yu++E/0kL18WlzdpIoJ72te9NQ8+CGzdKj9/ZWaKTLJx4xwPrGmdPAlMmiQ+k3XsKNZqrQXLgQOiP+Y994hekoMGie8hI0MEi/7v/2RVQkyM6M0YKP2IK1USU9MBEQTTfj6w5q+/xHTmefPE1waDCJyVBsUFKM+eBV55Rfy9fewx8RkfQPSxYzDecYd4zbgiNVU8961tEhQF+PprEUAfPly8nrXcneCtKi5A+cIL8vL69cXgOUC8tp57TmzfvAm88Ybra9BSA5SxseybXwwGKImInKUtOTt2rGQbn58+LZpeJyWJqZGOPLcnApSA/BB+/boMbvgSh+R4nzZA6ctJ3ooiB+SEhgLt2/tuLb6g1wOtW4vttLSiH+iddfaszBpgeXfpddttYn8CxAG8ljMTvFVVq8qJv6mprr03sv8k+YPq1YEvvpBfv/CC6CGpBgI7dQLWr3c8iF6vnji59PDD8rK33waiokQ2VffuIigyfTqwbJn43KY9AZ6TI4b43H+/OKn02muW7wOxseL+q1aJx23bVrxvqNLTRa+9fv3EPtqwoahKAMTJr5UrLU9YBAJHhuWcPi0yYJs3B377TV7+zDPypF+wq1dPZvBqA5SbN4vs25o1RabvpUvmq5TC2+syM8Vr5plnZCWWI5YuFa9BtV1H9eqiJcK778r3iIICkVl5223AyJHyJLj63hMa6tpQT1WzZnL71hNmS5fKgGRYmGi/ULasvP7f/5Y/s/nzxbGeO65elZ+hGzUqXRVALmCAkojIWVFRcvuFF8QAmY8/9n6594UL4my5Wlr00UeOTZnzVIBSOyjHH8q81QClTiczHciz/KXE+8ABeTB2zz2lsy+dJ8u8OcGbADHltGFDsX34sOUBqNqrzGCwPNCzR6eTJ7IuXnTtpAYneJO/6NpVlHsCllUjjz4qSoWdDXCVLSuyHd9/X+xXgNjn9u8HfvoJmDEDGDVKZDY2aSLe5xISRKCnShWgf38RSFQD/zqd6B+5aJFoi/DBB8B994k1r18PnD8vgqwPPWT5uTU/X76fli0rsg+1n+8CRefOQHy82F62zLLtz7VrwPjxIjj32WfyZ1azpgjUFjehPZjo9bJU+swZMczpjjvEv2++kVm8RqN4jW3ahPwTJ/B3u3byMT780LK1gS2KIvo29uolA+ytW4vAaJs2ov3AsWMiK1Hdf/LyxJpq1xavf7VHa7Nm8gSaK6KjRTAfECfg1H34xAnL4PYHHxSdFB4TI9YKiJ/P66+7vg6AE7ydxAAlEZGzOnUSUw5VqanAE0+I7JHRo72TXZieLp731ubq48bJKZK2qAFKo1F8WHOVv03yVgOUFSvKD/vkWf5S4q1mTwKlr7xbpQ1QujsohwNySKUemBUUyIOo69fl+4azU1TdLfNmgJL8ibYfJSACK//9rwjuu0KnE0GYtWuBnj3FCQJ1arA1586JzLOrV+VltWuLdZw8Kd4b+/Sx3qajYkVg8GCRHXbpkghuPvusDNpERYlAq3YIWyAxGkXPQEAEkRYskMGuunVFoEyd3hwdLcrdDxwQWYP6UhYC0ZZ5jxxpWXZdsaII5p44IUqvW7cGypbFjlGjkP/RR/L1mZoqjgMWLrT+HDk54vU2fry8bOBAMZhIDSQD4nU3fjxw/Lj4Xz3hnJMjMohV7pR3q9Qy7+xs4OhR8fro31/uTw89BDz5pPX7jholBrUBwJdfWp7YdRb7TzqllO2dREQeUKWKOMD/7DPLN/2MDHEmrn59UYbzww+e6dWYmSmCMmoJXvXqIhCqeuQR2wGL/Hz5pqot53OFvw3KUc+Ws/+k9zhT4n32LPSvvorkqVPd+yBnTWnuP6nS/q1xN4OSAUpSacs61fcYbZ9hR8u7Ve4GKFniTf4kJERk5732mngfGj/eM+WZd94JLFkiAhfZ2SKzbd068bny1VdFv8g775SBnYgI8VlvzRqR7Tx+vHMB/NBQUYEzfboI1Bw9KgJE99zj/vfiS8OGye3p08UJlZEj5efDkBDxefnIEeD55+0Hg4OZ9vODKilJlFifOiUC3lWrWl6v00EZNkwEM9XkhsxM0aZgxAjLjPu0NNF656uv5GVvvikCe7Z+5hUqiOc9flxkK94aZHdnQI7q1j6Ur7wij5cSE0X1m639uXx5USUHiIFUkya5vg4GKJ3CACURkSvCw8XEt82bRfPzoUMt34RXrgR69BBnuqdMAa5cce15rl8XvYnUvjFxcaKR+bvvyg9mOTmiJOj48aL3P3JEDhxwp5eL+txqye+6daKZ9pYtJduDU5WdLctHGKD0nthYmSliK4Pyr7/EvlCrFgxvv42qGzbA+NBDcuK2u7KzgZQUsV2zZuktSa5QQZbj7twpM0NcwRJvUlkblKOWdwPOZ7Ewg5KCTYUKwMSJYsiKN+j14rPVXXeJ99JJk0Rg588/xftudrY4Af7550C7du5n/+l04rNpMLTGadBABHIBkVGqrWB6+GHxXvfee8HxvbqjUyegTh3xu3/wQdF/dNcuUepcXIZ806aiL+SAAfKyOXPEz/3IETkMRw38lSkjgu8vv+xYML9SJXFMc/So6HUZEiL2B3UIkju0Acrp04GpU8V2SIgY/lNcm4Z//Uv2uP/uOzGg1BUMUDqFAUoiInclJ4smyqdPixISddo1IM5MvvyyyF786CPnMipv3hSlO2pwpkIFEfisV0+86c+dK4eFXLwIdOtmWQYEeK7/pEo9256fL5qSt24tDkg//FB8gC4pHJBTMnQ6mUWpDVAqCvDrryJTuFkz0edKE5DUHTwop2W6648/ZJC9c+fS3VxcLcXLz3cvi1nNoCxfngH+0k7be0sNULozRbVuXVmyxwxKIveVKeN6SXlpoO0nCAB33y2SB7791vLzeGkWGSmOBzIygO+/F8cOznyWiooSJfQffyyTMXbuFCXfd90lTyxVqyb6n/bs6fwaq1YV5fkZGSJYqQ7TcYc2QLlhg9x++23HMjTLlgVeeklsK4o4UeEKNUBZvrxlZRJZxQAlEZGnxMaKEpLDh4Hly0XAUP0AcPky8NRT4mBv3briHys/X5ytVHvvRUWJgJA22yU0FFi8WGZA7d8vAprazDVPByhnzhRnONXpdoA4C/vMM+KMZ0llVWoDlAyweJf6YeriRTH5ef58EdTo3FkEzFUVKqDg8cfl16+9VjRg7gqWd0ueGJRz/bo4cQKI8u7SHPAlsX/HxIjtWwOURqPzk30NBhn0PHHC+eoB9UA3JkYEZoiI7Bk0COjbVwTKvv9enNS3VtJc2oWFWQ5LcpZOBwwfXrTkWx0Q2rq1qCjTZtG7IiLCc6X41avLPpKqf/7Tsk1WcZ5+Wn4OXrLE+RNvmZnyMxcneDtEpyi+qM3zf9euXUP58uWRkZGBctoDcT+Xl5eH5cuXo2vXrgixc7YtOTkZaWlpJbgyosCRk5ODcE+9ORYUiLOBt5ZjRkSIM2m2hrtcuSJLmAGRJWirf2RBgQjYmUzi6zJl5Btyerp87rg4ccDpCYoiHjcry3opb0iIOGNbpox33oxzckTQFxDBUnc+dJF9ly/LXkN6vXydqQwG8fMv/F0XXLwIg5rxWLas85NOb5WWJjOPExJK94e7vDwZnA8Pd61sTfsY2r8V5Dc8+h7kiIsXZZZyfLzY5wDxd9yVE0BXr8qD1ooVrQ/wsOXMGfeemwg+2IeIgozdfUhRxHGKenwRESE+S/jj57NLl4DcXLGt14tjIWfbJGRnyxPuzn72cuEzV3x8PLZpKxmChKPxNQ8dqVKgSUtLwxn1QyARlbwbN5zrIac2/HbE9euWwU3V+fOOP4a78vLEm7knMuiKc+2a+Efed2twEhDBQ1u/66ws8c9TihvUU5rk5Mhgjqts/a2g0kt78jovz/3X2KVLrt3PE89NRETe5+wxja+YTLb7qTvKnc9e/MzlEAYoS6l4dSocERXh1TPv2dkimKYN9BgMIsssIkJkW2oDOrGxjpc63LghMiZVMTHya6NRnDX0JntZlUYjEB3tXCaNPZmZMigZE1N8k29yXVaWZX/RiAiRGWkjozcnJwfhubnydRwRIUtInWEyiaC6uq9UqMCST8AyGyA+3nYWti3cd/xeiWd/abNDDAaZsRwdLftJOsPVLF3t/SIjxfMTuYAZlETuCap96Pp1kZXvTi9X7ftkWJjjPTK1x3UOHtOV9jgNA5SlVDCmDRN5gqNtEtxy5Yrozzd7tjgQLCgQgcRGjeRZOZ1OTHEcONC5x37zTeCVV8S2NqOtd2/RMLykpKaKfpXz54vAZX6+CKz07w/85z9yGrirxowB3n9fbP/4I9C2rdtLJhuuXQMmTBAfyJ58UkyCtMG8/9x1F0IaNhSZvzduAIsWWfZPLI7JJHpcqme6u3cHfvjBP8uHStqrrwKTJ4vt6dNF7y1nDBwIfP212F671jO9acljSuQ96FabNsn9UzvI7fffgRYtnH+83FxxEiM/XwypUHtbFmfpUqBXL7E9bhwwfrzzz02lnk/2IaIgwn3Iirw80bf7+HHxHvfNN2IgU3H++U/g55/F9s6doi8m2cUhOUREJa1CBRFYSE0FOnSQl6tT3gAx8dvZ4CQgJoYPGSK2tVmaJR2EaN4c+OQTMTBHOynvm2/EG/y771rvXekoTvEuOeXKidfr1Kl2g5NF7vP66/LrMWOcG5z03ntyAE+VKmJiPIOTgjrJG3BtUI46wVuvFxOXiay9P4SGAk2auPZ4YWHihBsghrc5WvqnDsgBeBBHRET+IyTEcor3hAmOfa5Vj+3KlhVTzqlYDFASEflKkyYiCLN4MVCrlrz8vfcA7TRkZ+h0wLx5wL33Wl7uqyyp5GSRnTNvnmwqnZUlpp03bw6sWePa43KKt/8bPlwGKTZtAhYudOx+27eLQDsgM4kdLaUpDdwJUCqKDFAmJnqu5QIFtqgo8XrQSkpy7/WhTnItKAD27HHsPqdPy20eyBERkT8ZOFBOME9JAX79VbTNuXJFVAydPSsmdh87Jj5rpaYCJ06I23OCt8MYoCQi8iWdTpS07dsHfPEF8NtvwOjR7j1maKgIeqpvonq9a2V6nqLXi4DrwYOiRFh9g963D2jfHhgwwPnhJ+rQoJAQ96dEk3cYjaKcXzV2rJwGbktWlmgDoGbXvviiZZYxib6R9euL7Z07ZT9KR5w9K3shqY9BBABNm1p+nZzs3uM1by63d+507D7MoCQiIn9lNIoWXaouXUTFUEyMSJaoWhWoWVNUGzVoIE7UqVmW6gl7KhYDlERE/iAiAhg8GOjUyTOPFxMj+oc9/bQota5Z0zOP647YWGDuXNtl399/7/hjqRmUlSvzjKQ/69wZuP9+sX3yJDBjhv3b/+tfwOHDYrtlS9lrkSypWZQ3bwI7djh+PzV7EmCAkiwlJVl+7W6AUs2gBBwPUDKDkoiI/NnDD7tWlXbnnZ5fS5DikBwiomBVvTowZ46vV1GUWvb9ySfASy+JAUFZWcCwYSKgVdyEO0WxDFCS/9LpRBZl8+aiJ+qbb4rfs7W+od9+C3z2mdguW1YErtmc3bo2bYDPPxfbGzc6PoDowAG53aCB59dFgcvTAUp3MihjYzldnoiI/I9eLwYNjh8vjl2MRvEvJMT2dv36wNChvl55wGCAkoiISp5a9t2rlzgbuWqVmDq+bBnw0EP273v1qpgOC3BATiBISgIeewz4+GMxEVydYK91/Lgo/1fNmeP4QJ7SSBuQ3LTJ8ftpewEyg5K0tCXeYWHu9y0uX15M8D52DPjrL9GL0mCwffuCAuDMGbHN8m4iIvJXTZuK4xXyCpZ4ExGR78TGiixK1ZdfFn8fDsgJPK+/LrIiATGhfv9+eV1+vmg8fu2a+HrAAGDQoJJfYyBp3Fj+PB0dlLNunQgSA+IEga8GZ5F/qlsXiI4W261aeSZ7WS3zvnHDsr2ANRcuyBNPLO8mIiIqlRigJCIi32rfXjSWBoAVK+QAHFu01zNAGRji48WQHEBkSr3wgrxu0iQZZEtMBD78kH1Fi2MwiCASIPr2aXv3WfP330CfPjIANHo0J6OTJaNRlK0NGQLMmuWZx9T2oUxNtX9bDsghIiIq9RigJCIi3zIYRAYdIAIo331n//bMoAxMY8bIzKiffxZDnFJSRF9KQLwOvv5aTESk4qmDcgD7Zd7XrwM9esj9plMn4O23vbo0ClBduog+sLdO9HaVNkD54ovAo4+K3sMHD8rJpiptkJ0BSiIiolKJAUoiIvK9wYPldnFl3gxQBqYyZYC33pJfjxolSrnVQMXrr1sG3cg+bR9KW2XeiiJ6vaqTvmvXFsOIjGxBTiUgOVm+1s6cAebPB4YPFwOaKlcGevYUQ7Q2bQKOHpX3Y4k3ERFRqcQAJRER+V6TJnLq65Yt9vuVaQOUHJITWAYOBFq0ENt798qsqXvvlSXg5BhtMNdWgPLdd0VWKgBERgI//ADExHh/bUSACEJ+8QXQti0QGmp53aVLwPffi3YPbdqIDEsVMyiJiIhKJQYoiYjIP2izKBcssH07ZlAGLr0eeO89y8tiYsTv296EXyqqYkUx2AQQGZK5uZbX//qrZdD3yy/FiQCiktS/P7B+PZCRIf5/+23gn/8EKlSwfR9mUBIREZVKDFASEZF/6N9fBLAAEbAymazfjkNyAts994jSTtWnn8ohSeQctcw7N9dyCMmRI0C/fnIfmjjR8mdOVNLCw0Um5dixwLJlIoNy717go4/EyanERHG7u+8G6tTx7VqJiIjIJxigJCIi/1ClihjgAQAnTgB//mn9dizxDnzz5wPjxomBSA8+6OvVBC5tH0p1UM61a8D//R9w9ar4ukcP4NVXS3plRPbp9UCjRsATT4gy8GPHxGv3jz8Anc7XqyMiIiIfYICSiIj8hyPDctQAZWSk+EeBp3x5MTCnb19frySw3dqH0mQS+9D+/eKyxo1F8EfPj3sUAKKi2OqBiIioFOMnViIi8h89esig48KFQE5O0duoAUpmT1Jpl5Qk95eNG4FJk4AffxRfR0eLISRRUb5aHRERERGRwxigJCIi/xEZCfTuLbYzMoCffrK8vqAAuHxZbLP/JJV2RiPQsqXYPnUKeP11sa3Xi/J5dYgOEREREZGfY4CSiIj8i70y78uXAUUR2wxQElmWeaumTgXuv7/k10JERERE5CIGKImIyL+0bw8kJIjt5cvFtFeVdkAOA5REloNyAGDQIGDMGN+shYiIiIjIRQxQEhGRfzEYgIEDxXZ+vihVVTFASWSpbVugTBmx3aIFMG8epyATERERUcBhgJKIiPyPrTJvbYCSQ3KIgNhYYNky4I03gN9+AyIifL0iIiIiIiKnMUBJRET+JykJaNZMbG/eDBw8KLaZQUlU1H33AePHAzExvl4JEREREZFLGKAkIiL/pM2iXLBA/H/xoryMAUoiIiIiIqKgwAAlERH5pwEDAH3h29SCBYDJxAxKIiIiIiKiIMQAJRER+acqVYCOHcX2iRPAn38yQElERERERBSEGKAkIiL/deuwHG2AsmLFkl8PEREREREReZzR1wsgIiKyqWdPIDISyM4GFi4EypUTl0dHA6GhPl0aEREREREReQYzKImIyH9FRgK9eontjAzg77/FNsu7iYiIiIiIggYDlERE5N+0Zd4qBiiJiIiIiIiCBgOURETk3+67D0hIsLyMAUoiIiIiIqKgwQAlERH5N4MBGDDA8jIGKImIiIiIiIIGA5REROT/bi3zrlTJN+sgIiIiIiIij2OAkoiI/F/TpuKfihmUREREREREQYMBSiIiCgxDh8rtunV9tgwiIiIiIiLyLKOvF0BEROSQkSOBCxeA0FCgUydfr4aIiIiIiIg8hAFKIiIKDCEhwJQpvl4FEREREREReRhLvImIiIiIiIiIiMhnGKAkIiIiIiIiIiIin2GAkoiIiIiIiIiIiHyGAUoiIiIiIiIiIiLyGQYoiYiIiIiIiIiIyGcYoCQiIiIiIiIiIiKfYYCSiIiIiIiIiIiIfIYBSiIiIiIiIiIiIvIZBiiJiIiIiIiIiIjIZxigJCIiIiIiIiIiIp9hgJKIiIiIiIiIiIh8JqgDlLNnz0atWrUQHh6O1q1bY8uWLb5eEhEREREREREREWkEbYDyu+++w5gxYzBx4kTs2LEDzZo1wwMPPIALFy74emlERERERERERERUKGgDlO+99x4ef/xxDBs2DI0aNcLcuXNRpkwZfPrpp75eGhERERERERERERUy+noB3nDz5k1s374d48aNM1+m1+vRsWNHbNy40ep9cnNzkZuba/46IyMDAJCeno68vDzvLtiD8vLycP36dVy+fBkhISG+Xg5RwOE+ROQ67j9E7uE+ROQe7kNE7uE+RN6QmZkJAFAUxe7tgjJAeenSJRQUFCAuLs7i8ri4OBw4cMDqfaZMmYJJkyYVuTwxMdErayQiIiIiIiIiIioNMjMzUb58eZvXB2WA0hXjxo3DmDFjzF+bTCakp6cjNjYWOp3OhytzzrVr11C9enX8/fffKFeunK+XQxRwuA8RuY77D5F7uA8RuYf7EJF7uA+RNyiKgszMTCQkJNi9XVAGKCtWrAiDwYDz589bXH7+/HnEx8dbvU9YWBjCwsIsLouOjvbWEr2uXLly/INC5AbuQ0Su4/5D5B7uQ0Tu4T5E5B7uQ+Rp9jInVUE5JCc0NBQtWrTAqlWrzJeZTCasWrUKbdq08eHKiIiIiIiIiIiISCsoMygBYMyYMRgyZAiSk5PRqlUrfPDBB8jOzsawYcN8vTQiIiIiIiIiIiIqFLQByocffhgXL17Eq6++irS0NDRv3hy//PJLkcE5wSYsLAwTJ04sUq5ORI7hPkTkOu4/RO7hPkTkHu5DRO7hPkS+pFOKm/NNRERERERERERE5CVB2YOSiIiIiIiIiIiIAgMDlEREREREREREROQzDFASERERERERERGRzzBASURERERERERERD7DAGUQmT17NmrVqoXw8HC0bt0aW7Zs8fWSiPzSlClT0LJlS0RFRaFy5cro0aMHDh48aHGbnJwcjBgxArGxsShbtix69+6N8+fP+2jFRP7r7bffhk6nw6hRo8yXcf8hsu/MmTMYNGgQYmNjERERgaSkJGzbts18vaIoePXVV1GlShVERESgY8eOOHz4sA9XTOQ/CgoKMGHCBCQmJiIiIgJ16tTB5MmToZ39yn2ISFq7di26d++OhIQE6HQ6fP/99xbXO7K/pKenY+DAgShXrhyio6Px2GOPISsrqwS/CyoNGKAMEt999x3GjBmDiRMnYseOHWjWrBkeeOABXLhwwddLI/I7KSkpGDFiBDZt2oSVK1ciLy8P999/P7Kzs823GT16NJYtW4ZFixYhJSUFZ8+eRa9evXy4aiL/s3XrVnz00Udo2rSpxeXcf4hsu3LlCtq2bYuQkBCsWLEC+/btw7vvvosKFSqYbzN16lTMmDEDc+fOxebNmxEZGYkHHngAOTk5Plw5kX9455138OGHH2LWrFnYv38/3nnnHUydOhUzZ84034b7EJGUnZ2NZs2aYfbs2Vavd2R/GThwIPbu3YuVK1fip59+wtq1a/HEE0+U1LdApYVCQaFVq1bKiBEjzF8XFBQoCQkJypQpU3y4KqLAcOHCBQWAkpKSoiiKoly9elUJCQlRFi1aZL7N/v37FQDKxo0bfbVMIr+SmZmp3HbbbcrKlSuVdu3aKc8995yiKNx/iIozduxY5a677rJ5vclkUuLj45Vp06aZL7t69aoSFhamfPPNNyWxRCK/1q1bN+XRRx+1uKxXr17KwIEDFUXhPkRkDwBl6dKl5q8d2V/27dunAFC2bt1qvs2KFSsUnU6nnDlzpsTWTsGPGZRB4ObNm9i+fTs6duxovkyv16Njx47YuHGjD1dGFBgyMjIAADExMQCA7du3Iy8vz2KfatCgAWrUqMF9iqjQiBEj0K1bN4v9BOD+Q1ScH3/8EcnJyXjooYdQuXJl3H777fj444/N1x8/fhxpaWkW+1D58uXRunVr7kNEAO68806sWrUKhw4dAgDs2rUL69evR5cuXQBwHyJyhiP7y8aNGxEdHY3k5GTzbTp27Ai9Xo/NmzeX+JopeBl9vQBy36VLl1BQUIC4uDiLy+Pi4nDgwAEfrYooMJhMJowaNQpt27ZFkyZNAABpaWkIDQ1FdHS0xW3j4uKQlpbmg1US+Zdvv/0WO3bswNatW4tcx/2HyL5jx47hww8/xJgxY/Dyyy9j69atePbZZxEaGoohQ4aY9xNrn+u4DxEBL730Eq5du4YGDRrAYDCgoKAAb775JgYOHAgA3IeInODI/pKWlobKlStbXG80GhETE8N9ijyKAUoiKtVGjBiBPXv2YP369b5eClFA+Pvvv/Hcc89h5cqVCA8P9/VyiAKOyWRCcnIy3nrrLQDA7bffjj179mDu3LkYMmSIj1dH5P8WLlyIr776Cl9//TUaN26M1NRUjBo1CgkJCdyHiIgCGEu8g0DFihVhMBiKTEg9f/484uPjfbQqIv83cuRI/PTTT/jjjz9QrVo18+Xx8fG4efMmrl69anF77lNEooT7woUL+Mc//gGj0Qij0YiUlBTMmDEDRqMRcXFx3H+I7KhSpQoaNWpkcVnDhg1x6tQpADDvJ/xcR2TdCy+8gJdeegn9+vVDUlISBg8ejNGjR2PKlCkAuA8ROcOR/SU+Pr7I8N38/Hykp6dznyKPYoAyCISGhqJFixZYtWqV+TKTyYRVq1ahTZs2PlwZkX9SFAUjR47E0qVLsXr1aiQmJlpc36JFC4SEhFjsUwcPHsSpU6e4T1Gp16FDB+zevRupqanmf8nJyRg4cKB5m/sPkW1t27bFwYMHLS47dOgQatasCQBITExEfHy8xT507do1bN68mfsQEYDr169Dr7c8jDUYDDCZTAC4DxE5w5H9pU2bNrh69Sq2b99uvs3q1athMpnQunXrEl8zBS+WeAeJMWPGYMiQIUhOTkarVq3wwQcfIDs7G8OGDfP10oj8zogRI/D111/jhx9+QFRUlLl3Svny5REREYHy5cvjsccew5gxYxATE4Ny5crhX//6F9q0aYM77rjDx6sn8q2oqChzv1ZVZGQkYmNjzZdz/yGybfTo0bjzzjvx1ltvoW/fvtiyZQvmzZuHefPmAQB0Oh1GjRqFN954A7fddhsSExMxYcIEJCQkoEePHr5dPJEf6N69O958803UqFEDjRs3xs6dO/Hee+/h0UcfBcB9iOhWWVlZOHLkiPnr48ePIzU1FTExMahRo0ax+0vDhg3RuXNnPP7445g7dy7y8vIwcuRI9OvXDwkJCT76rigo+XqMOHnOzJkzlRo1aiihoaFKq1atlE2bNvl6SUR+CYDVf/Pnzzff5saNG8ozzzyjVKhQQSlTpozSs2dP5dy5c75bNJEfa9eunfLcc8+Zv+b+Q2TfsmXLlCZNmihhYWFKgwYNlHnz5llcbzKZlAkTJihxcXFKWFiY0qFDB+XgwYM+Wi2Rf7l27Zry3HPPKTVq1FDCw8OV2rVrK+PHj1dyc3PNt+E+RCT98ccfVo99hgwZoiiKY/vL5cuXlf79+ytly5ZVypUrpwwbNkzJzMz0wXdDwUynKIrio9goERERERERERERlXLsQUlEREREREREREQ+wwAlERERERERERER+QwDlEREREREREREROQzDFASERERERERERGRzzBASURERERERERERD7DACURERERERERERH5DAOURERERERERERE5DMMUBIREREREREREZHPMEBJRERERBQAatWqBZ1Oh6FDh/p6KUREREQexQAlERERUTGefPJJ6HQ66HQ6rF692qn7/vbbb+b7Pvfcc15aIRERERFR4GKAkoiIiKgYjzzyiHl7wYIFTt33yy+/tPo4vrJmzRpzwHTNmjW+Xg4REREREQOURERERMVp27Yt6tSpAwBYvHgxbty44dD9srOzsXTpUgBA48aN0aJFC6+tkYiIiIgoUDFASUREROSAwYMHAwCuXbuGH374waH7LFmyBNnZ2Rb3JyIiIiIiSwxQEhERETlg8ODB0Ol0ABwv81bLu/V6PQYNGuS1tRERERERBTIGKImIiIgcULt2bbRt2xYA8Ouvv+LChQt2b3/27FmsWrUKAHDfffehatWqRW7z/fff46GHHkKNGjUQHh6O6OhoJCcnY9KkSbhy5YpD61q+fDkGDRqE2rVrIzIyEuHh4UhMTETv3r3x2Wef4fr16wCAEydOQKfToX379ub7tm/f3tyPUv332WefFXmOmzdvYs6cOWjfvj0qVaqE0NBQxMfHo2vXrliwYAFMJpPN9Q0dOhQ6nQ61atUCAJw7dw5jx45F48aNERUV5XQvTGs9NBcuXIgOHTqgUqVKiIiIQP369fHiiy8iPT3d5uPce++90Ol0uPfee+0+32uvvWZ+PmvU61577TUAwB9//IEePXogISEBERERaNiwISZPnmzOpFUtX74cXbt2Nd+uUaNGmDJlCm7evOnwz2Lr1q3o378/qlevjvDwcFSvXh3Dhg3DgQMHHLr/kSNHMHr0aCQlJaF8+fKIiIhA7dq1MXToUGzbts3m/W79HZhMJnz66ado37494uLioNfrOWmciIiInKMQERERkUPmzZunAFAAKNOnT7d722nTpplv+8UXX1hcl56ertx3333m6639q1y5srJx40abj3/p0iWlQ4cOdh8DgDJ//nxFURTl+PHjxd5We3vV8ePHlQYNGti9z1133aVcvnzZ6jqHDBmiAFBq1qypbNy4UalYsWKR+//xxx/F/uxVf/zxh/l+q1atUgYNGmRzXXXr1lXOnTtn9XHatWunAFDatWtn9/kmTpxofjxr1OsmTpyoTJkyRdHpdFbXcueddypZWVmKyWRSnn32WZtr7ty5s5Kfn2/1uWrWrKkAUIYMGaJ88sknitFotPoYYWFhysKFC+1+X9OmTVNCQkJsrkOn0ykTJkywel/t72DFihVKx44di9x/yJAhdp+fiIiISIsZlEREREQO6tu3L8LDwwFYTue2Rr2+bNmy6NWrl/ny3NxcdOzYEatXr4bBYMDgwYPxzTffYNOmTVi3bh3efPNNxMbG4sKFC+jatStOnjxZ5LGvX7+O9u3bmzM0W7RogY8++gh//vkntm3bhqVLl2L06NFISEgw36dq1arYvXs3Pv30U/Nln376KXbv3m3xr0ePHubrs7Ky0KFDB3NGXo8ePfDjjz9i27ZtWLRoEdq1awcAWL9+Pbp3746CggKbP4+srCz07t0bOTk5GD9+PNasWYMtW7bgk08+QZUqVez+LG2ZMGECFixYgB49emDJkiXYvn07li9fjm7dugGQGYIlYcWKFRg3bhzuuOMOfP3119i2bRt++eUXdOnSBQCwYcMGTJkyBe+//z5mzJiBLl26YPHixdi+fTt++OEH3HHHHQCAX375BR9//LHd50pNTcVTTz2FypUrY+bMmdi8eTNSUlIwduxYhIWFITc3FwMHDrSZBTlt2jS88MILyMvLQ9OmTfHhhx/i999/x7Zt2/DVV1+hTZs2UBQFkydPxowZM+yuZezYsfj999/xf//3fxa/A/X7JiIiInKIryOkRERERIGkb9++5iyxAwcOWL3Nrl27zLd55JFHLK57+eWXFQBKdHS0sm3bNqv3P3HihFKlShUFgDJgwIAi148ePdr8+CNGjFBMJpPVx8nNzVXS0tIsLtNmvxWXufj888+bb/vKK68Uud5kMikDBw4032bOnDlFbqNmUAJQypYtq6Smptp9zuJo1w9AeeONN6yu6/7771cAKEajUblw4UKR23g6gxKA0rt37yLZj/n5+codd9yhAFCioqKU8PBwZdSoUUUeJzs725wh2bRpU6vPpV6PwoxUa9mhq1evNmdWtmzZssj1e/fuNWdOTpw40eprp6CgwJyZWrZsWSU9Pd3i+lt/B9ZeG0RERETOYAYlERERkRMeeeQR87atLErt5drbZ2VlYfbs2QCAyZMno0WLFlbvX7NmTUyYMAEAsGjRIov+hVevXsVHH30EQGROTp8+3WZ/xNDQUMTFxTnybRWRm5uL//73vwCAxo0bm3ssaul0OsyZMwexsbEAgFmzZtl9zBdffBHNmjVzaT3WtGjRAi+//LLVdY0ZMwYAkJ+fj40bN3rsOW0pU6YM5s2bB4PBYHG5wWDAE088AQDIzMxEpUqVMHXqVKv3HzJkCADgr7/+QkZGht3ne/fddxEfH1/k8vbt2+Pxxx8HIHpU3ppF+e677yIvLw/JycmYOHGi1deOXq/HzJkzERYWhqysLPzvf/+zuY569epZfW0QEREROYMBSiIiIiInPPDAA+ag31dffQVFUSyuN5lM+PrrrwEA1apVsxhKk5KSYg489enTx+7z3HPPPQCAvLw8bN++3Xz56tWrzYNvnn322SIBMU/Zvn07rl69CkAMurH1POXKlUPfvn0BAPv27cO5c+dsPubAgQM9usYBAwbYDM5qg7/Hjh3z6PNa06lTJ8TExFi9ThuU7dWrF0JCQoq93fHjx20+V4UKFfDggw/avP7RRx81b//+++8W1y1btgwA0Lt3b5s/OwCIjo5GUlISANgN8D788MNeew0SERFR6cEAJREREZETjEYjBgwYAEBMxl6/fr3F9atWrcLZs2cBiICcXi8/bmmz2apUqVJkgrb2X5MmTcy3TUtLM2/v3LnTvH333Xd79pvT2LNnj3m7devWdm+rvV57P62yZcuidu3anllcoQYNGti8ThsszMzM9OjzWlOvXj2b10VHRzt9O3trvv3222E0Gm1e37x5c4SGhgIAdu/ebb785MmTuHjxIgBg3Lhxdl9/Op3O/HrVvv5u1bRpU5vXERERETmKAUoiIiIiJ9kr87ZV3g0AFy5ccOn51IxJALh06ZJ529XhMo5IT083b1euXNnubbWlxtr7aWmDb55SpkwZm9dpA8P2hveU9Fo8sebifh9Go9EcoNX+Pjzx+rtVhQoVXHpMIiIiIi3bp16JiIiIyKrmzZsjKSkJu3fvxqJFi8z9+rKzs7FkyRIAosS4UaNGFvfTBp127Nhhs9T3VtWqVfPc4l1grxTYUSwD9hxXfx/a19+rr76Khx56yKH7RUZG2ryOv1ciIiLyBAYoiYiIiFzwyCOP4IUXXsDVq1exbNky9OnTB0uXLjUPtLk1exKAeZgMAFSqVMmlwGPFihXN2+fOnUNiYqILqy+etkT6/PnzdkuTtSXAtvow+hs1W9FkMtm9nXZAkb84f/683evz8/PNmZPa34f29RcSEmLRRoCIiIjIl1jiTUREROSCgQMHmrPHFixYAECWd4eEhKB///5F7nP77bebt//880+Xnvcf//iHeXvt2rVO39/R7Dtt8Grz5s12b7tlyxar9/NnUVFRAIArV67Yvd2hQ4dKYjlOSU1NRX5+vs3rd+3ahZs3bwKw/H3Url0b5cuXB+D664+IiIjIGxigJCIiInJBlSpV0LFjRwDA8uXLsWfPHqxatQoA0LlzZ1SqVKnIfTp27GjuQThjxowiE8Ad0b59e3PJ7cyZM53urxgeHm7ezs3NtXm7Fi1amPtGfv755zYzDTMzM7Fw4UIAQKNGjbzaF9OT1MzTQ4cO2RxIc+nSJaxcubIkl+WQ9PR08zRuaz799FPztvoaBUQ5dteuXQEAv/32G/bv3++9RRIRERE5gQFKIiIiIhepZdx5eXno16+fOVhorbwbEINiRo4cCQDYsGEDRo8ebbfE+Pz58/jvf/9b5DGefPJJAMD27dsxatQom4HOvLy8IoNRtAHEo0eP2nzusLAwDB8+HICYzD158uQit1EUBSNHjjQP7lG/t0DQrl07AMDNmzcxc+bMItfn5eVh+PDhuHHjRkkvzSFjxoyxWuqdkpKCefPmARBB5pYtW1pcP27cOBgMBphMJvTp0wenT5+2+RwFBQX46quv7N6GiIiIyBPYg5KIiIjIRT179kRUVBQyMzOxd+9eAGKqcffu3W3e5/XXX0dKSgo2b96M6dOnY82aNXj88cfRvHlzREZG4sqVK9i7dy9+//13rFixAklJSeZAoWry5MlYuXIldu/ejVmzZmHjxo148sknkZSUhNDQUJw+fRrr1q3DN998gzfeeANDhw4137dGjRqoVq0aTp8+jf/85z+oVq0a6tevby5Xj4uLM5c/v/rqq1iyZAmOHTuG1157Dbt378awYcNQpUoVHD9+HLNmzcKaNWsAAG3atMETTzzhwZ+ud3Xr1g01a9bEyZMnMWHCBFy6dAm9evVCeHg49u7dixkzZmDnzp244447sGnTJl8v10KzZs2wb98+tGjRAuPGjUOrVq2Qm5uL5cuX4/3330d+fj6MRiNmz55d5L5JSUn4z3/+g9GjR2Pfvn1o0qQJnnjiCdx3332Ii4tDTk4OTpw4gY0bN+J///sfzp07h927d/t8UBMREREFNwYoiYiIiFwUERGBPn36YP78+ebL+vbti7CwMJv3CQsLw8qVKzF06FAsWbIEu3btspt5WK5cuSKXlSlTBqtXr0bv3r2xdu1abN++3ang4Msvv4xnnnkGx48fx4MPPmhx3fz5880BzaioKKxatQpdunTBgQMHsHjxYixevLjI47Vt2xY//vhjQE10Dg0NxYIFC9C5c2dkZ2fj/fffx/vvv2++3mAw4IMPPkB6errfBSibN2+OkSNH4umnn7b62gkNDcXnn3+O1q1bW73/qFGjEBkZiVGjRiEjIwPTpk3DtGnTrN42NDTUoi0AERERkTewxJuIiIjIDUOGDLH42lZ5t1ZUVBQWL16MdevWYfjw4ahfvz6ioqJgNBoRExODli1bYsSIEVi+fLnNHogVK1ZESkoKlixZgj59+qBatWoICwtDeHg4ateujYceeghfffWV1WE9Tz/9NBYvXoz7778flStXhtFo+5x1rVq1sGvXLsyaNQvt2rVDbGwsQkJCEBcXh86dO+PLL7/E2rVrA2Z6t9Zdd92F7du3Y/DgwUhISEBISAiqVKliDvw+++yzvl6iTcOHD8e6devQt29fJCQkIDQ0FFWrVsUjjzyCnTt3ol+/fnbv//jjj+PYsWOYNGkS2rZti4oVK8JoNCIyMhL16tVD7969MXfuXJw5cwZ169Ytoe+KiIiISiud4kp3diIiIiIiIiIiIiIPYAYlERERERERERER+QwDlEREREREREREROQzDFASERERERERERGRzzBASURERERERERERD7DACURERERERERERH5DAOURERERERERERE5DMMUBIREREREREREZHPMEBJREREREREREREPsMAJREREREREREREfkMA5RERERERERERETkMwxQEhERERERERERkc8wQElEREREREREREQ+wwAlERERERERERER+QwDlEREREREREREROQz/w+cTYiPmz+P7gAAAABJRU5ErkJggg==\n"},"metadata":{}}],"source":["# тестирование АE3\n","predicted_labels3, ire3 = lib.predict_ae(ae3_trained, test, IREth3)\n","lib.anomaly_detection_ae(predicted_labels3, ire3, IREth3)\n","lib.ire_plot('test', ire3, IREth3, 'AE3')"]}],"metadata":{"accelerator":"GPU","colab":{"gpuType":"T4","provenance":[],"mount_file_id":"1nOrae7xa3FW4UTJ5d1QXO6hKiQDHbw48","authorship_tag":"ABX9TyP+DKcVsWlMFqR7El01pjgH"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
diff --git a/labworks/LW2/Lab02/data_test.txt b/labworks/LW2/Lab02/data_test.txt
new file mode 100644
index 0000000..b962b5a
--- /dev/null
+++ b/labworks/LW2/Lab02/data_test.txt
@@ -0,0 +1,4 @@
+8.1 8.5
+7.2 8.0
+9.0 8.0
+8.5 9.5
\ No newline at end of file
diff --git a/labworks/LW2/__pycache__/lab02_lib.cpython-312.pyc b/labworks/LW2/__pycache__/lab02_lib.cpython-312.pyc
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index 0000000..92af7d7
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diff --git a/labworks/LW2/ae1_test.png b/labworks/LW2/ae1_test.png
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index 0000000..a3c8d5e
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diff --git a/labworks/LW2/ae1_training.png b/labworks/LW2/ae1_training.png
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index 0000000..1778895
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diff --git a/labworks/LW2/ae2_EDCA.png b/labworks/LW2/ae2_EDCA.png
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index 0000000..a9ebe8a
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diff --git a/labworks/LW2/ae2_EDCA1.png b/labworks/LW2/ae2_EDCA1.png
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index 0000000..ffff62f
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diff --git a/labworks/LW2/ae2_class.png b/labworks/LW2/ae2_class.png
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index 0000000..ef1f998
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diff --git a/labworks/LW2/ae2_test.png b/labworks/LW2/ae2_test.png
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index 0000000..b5b2301
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diff --git a/labworks/LW2/ae2_training.png b/labworks/LW2/ae2_training.png
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index 0000000..aea2c64
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diff --git a/labworks/LW2/ae3_test.png b/labworks/LW2/ae3_test.png
new file mode 100644
index 0000000..04479c4
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diff --git a/labworks/LW2/ae3_training.png b/labworks/LW2/ae3_training.png
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diff --git a/labworks/LW2/cardio_test.txt b/labworks/LW2/cardio_test.txt
new file mode 100644
index 0000000..6a2b384
--- /dev/null
+++ b/labworks/LW2/cardio_test.txt
@@ -0,0 +1,109 @@
+2.165463900000000053e-01 -6.546517800000000165e-01 -2.036404900000000073e-01 2.034008599999999944e+00 2.385968699999999831e+00 -6.140064399999999728e-02 3.200971200000000128e+00 -1.112015200000000092e+00 5.159934400000000032e+00 -4.204873499999999820e-01 -1.398087899999999939e+00 2.015500100000000128e+00 -1.439841399999999938e+00 1.964730400000000099e+00 2.786245700000000158e-01 3.865805400000000169e+00 -3.606048100000000201e+00 -1.658481099999999930e+00 -2.044421400000000055e+00 4.987466999999999651e+00 -4.932939700000000260e-01
+2.165463900000000053e-01 -5.653378999999999488e-01 -2.036404900000000073e-01 2.778793499999999916e+00 2.051602599999999832e+00 -6.140064399999999728e-02 4.372112699999999741e+00 -9.325388300000000408e-01 5.623183000000000042e+00 -4.204873499999999820e-01 -1.398087899999999939e+00 2.015500100000000128e+00 -1.439841399999999938e+00 1.964730400000000099e+00 6.226310299999999742e-01 3.865805400000000169e+00 -3.906626000000000154e+00 -1.658481099999999930e+00 -2.113388699999999787e+00 6.490482000000000085e+00 -4.932939700000000260e-01
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diff --git a/labworks/LW2/cardio_train.txt b/labworks/LW2/cardio_train.txt
new file mode 100644
index 0000000..52dee6f
--- /dev/null
+++ b/labworks/LW2/cardio_train.txt
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+   1.0630827e+00  -5.1148142e-01  -1.6958144e-01   1.1891656e+00  -6.8359847e-01  -6.1400644e-02  -2.7829495e-01   1.7596071e+00  -1.2097342e+00   1.8201629e+00  -5.4701241e-01  -8.4394655e-01   9.4783361e-01  -2.9032687e-01  -7.5339482e-01   9.6308254e-01   5.4192665e-01   6.4005005e-01   5.7633422e-01  -6.5718941e-01  -4.9329397e-01
\ No newline at end of file
diff --git a/labworks/LW2/data.txt b/labworks/LW2/data.txt
new file mode 100644
index 0000000..cb5e13b
--- /dev/null
+++ b/labworks/LW2/data.txt
@@ -0,0 +1,1000 @@
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+8.797089838482667901e+00 8.939376455999829219e+00
+9.050166291139747798e+00 9.039309600499169761e+00
+8.977033024099712932e+00 8.989550313822130434e+00
+9.027949446954833590e+00 9.077487779723062644e+00
+9.012839610429816517e+00 8.914950066404644602e+00
+8.827642360972749458e+00 9.068889016394249936e+00
+9.061354827888489893e+00 8.956019602081552122e+00
+9.070170588115733068e+00 8.823146676951706624e+00
+9.135741325973924631e+00 8.974164457330457978e+00
+9.063628951880032147e+00 9.137571745739114348e+00
+9.074156684212102419e+00 8.860225233832103697e+00
+9.027301698893049675e+00 9.042896783082493783e+00
+9.018949602445150759e+00 8.988737410335422950e+00
+8.856588992460588727e+00 8.977463181426509564e+00
+8.864882917253440908e+00 8.927243522229773021e+00
+8.900108154581834796e+00 9.023499664657173014e+00
+8.924267132070944797e+00 9.065579266178863449e+00
+9.075998202595846109e+00 8.962205274777598873e+00
+9.057449017281356873e+00 9.025395091959588001e+00
+8.903995692183826804e+00 8.967020562587366683e+00
+9.038037447658050283e+00 8.943588751320822539e+00
+8.901361900270316596e+00 9.061660793374041845e+00
+8.968045863623126834e+00 8.894719254223076632e+00
+8.995828222752852099e+00 9.127182422892177627e+00
+9.017151981828462226e+00 8.959280151055763142e+00
+9.018161753708646700e+00 9.078475510859044917e+00
+9.251341099113609800e+00 8.978958445585698556e+00
+8.977362998394344373e+00 8.935023019374391140e+00
+9.145165635281948369e+00 9.103377964734171712e+00
+9.293436079537761074e+00 8.908296704491933227e+00
+9.062028160484873140e+00 9.046777949816735998e+00
+9.145844897978584243e+00 9.116588077258104050e+00
+9.018021735986803122e+00 9.079310281453063070e+00
+9.014513215927834722e+00 8.885520860672924570e+00
+9.002957053017450306e+00 8.841695909688160882e+00
+8.966194898148051706e+00 9.046123704600397275e+00
+9.088945977485165528e+00 8.894526509617728394e+00
+9.002836518577476355e+00 8.991376570884069608e+00
+9.154161957768817359e+00 9.180179706530848804e+00
+9.017341605120332559e+00 8.999862117361059788e+00
+9.160934303903912834e+00 9.001930317116663360e+00
+8.952524803971799727e+00 8.995923027012416640e+00
+8.874747818535601240e+00 9.207554922374667328e+00
+9.351949000424170322e+00 9.169019744392668869e+00
+8.948292047429788454e+00 9.115927421766071959e+00
+8.917767711020013977e+00 9.245123999635760370e+00
+8.958599065809922379e+00 8.921976390186816985e+00
+9.002434238064626726e+00 8.963754694176696347e+00
+8.866364649527238129e+00 9.131830135121060010e+00
diff --git a/labworks/LW2/lab02_lib.py b/labworks/LW2/lab02_lib.py
index ec90383..2a0d382 100644
--- a/labworks/LW2/lab02_lib.py
+++ b/labworks/LW2/lab02_lib.py
@@ -29,12 +29,14 @@ from pandas import DataFrame
 from sklearn.metrics import precision_score, recall_score, f1_score, confusion_matrix
 from tensorflow.keras.models import Sequential
 from tensorflow.keras.layers import Dense, Activation
+from tensorflow.keras.callbacks import Callback
 
 
 
 visual = True
 verbose_show = False
 
+
 # generate 2d classification dataset
 def datagen(x_c, y_c, n_samples, n_features):
 
@@ -91,8 +93,27 @@ class EarlyStoppingOnValue(tensorflow.keras.callbacks.Callback):
             )
         return monitor_value
 
+
+class VerboseEveryNEpochs(Callback):
+    def __init__(self, every_n_epochs=1000, verbose=1):
+        super().__init__()
+        self.every_n_epochs = every_n_epochs
+        self.verbose = verbose
+
+    def on_epoch_end(self, epoch, logs=None):
+        if (epoch + 1) % self.every_n_epochs == 0:
+            if self.verbose:
+                print(f"\nEpoch {epoch + 1}/{self.params['epochs']}")
+                if logs:
+                    log_str = ", ".join([f"{k}: {v:.4f}" for k, v in logs.items()])
+                    print(f" - {log_str}")
+
+
 #создание и обучение модели автокодировщика
-def create_fit_save_ae(cl_train, ae_file, irefile, epohs, verbose_show, patience):
+def create_fit_save_ae(cl_train, ae_file, irefile, epohs, verbose_show, patience, **kwargs):
+    verbose_every_n_epochs = kwargs.get('verbose_every_n_epochs', 1000)
+    early_stopping_delta = kwargs.get('early_stopping_delta', 0.01)
+    early_stopping_value = kwargs.get('early_stopping_value', 0.0001)
 
     size = cl_train.shape[1]
     #ans = '2'
@@ -140,22 +161,28 @@ def create_fit_save_ae(cl_train, ae_file, irefile, epohs, verbose_show, patience
 
     optimizer = tensorflow.keras.optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, amsgrad=False)
     ae.compile(loss='mean_squared_error', optimizer=optimizer)
-    error_stop = 0.0001
     epo = epohs
 
-    early_stopping_callback_on_error = EarlyStoppingOnValue(monitor='loss', baseline=error_stop)
+
+    verbose = 1 if verbose_show else 0
+
+    early_stopping_callback_on_error = EarlyStoppingOnValue(monitor='loss', baseline=early_stopping_value)
     early_stopping_callback_on_improving = tensorflow.keras.callbacks.EarlyStopping(monitor='loss',
-                                                                                           min_delta=0.0001, patience = patience,
-                                                                                           verbose=1, mode='auto',
+                                                                                           min_delta=early_stopping_delta, patience = patience,
+                                                                                           verbose=verbose, mode='min',
                                                                                            baseline=None,
-                                                                                           restore_best_weights=False)
+                                                                                           restore_best_weights=True)
     history_callback = tensorflow.keras.callbacks.History()
-    verbose = 1 if verbose_show else 0
+
     history_object = ae.fit(cl_train, cl_train,
                                 batch_size=cl_train.shape[0],
                                 epochs=epo,
-                                callbacks=[early_stopping_callback_on_error, history_callback,
-                                early_stopping_callback_on_improving],
+                                callbacks=[
+                                  early_stopping_callback_on_error, 
+                                  history_callback,
+                                  early_stopping_callback_on_improving,
+                                  VerboseEveryNEpochs(every_n_epochs=verbose_every_n_epochs),
+                                ],
                                 verbose=verbose)
     ae_trainned = ae
     ae_pred = ae_trainned.predict(cl_train)
@@ -538,4 +565,4 @@ def ire_plot(title, IRE_test, IREth, ae_name):
     plt.gcf().savefig('out/IRE_' + title + ae_name + '.png')
     plt.show()
 
-    return
\ No newline at end of file
+    return
diff --git a/labworks/LW2/out/AE1.h5 b/labworks/LW2/out/AE1.h5
new file mode 100644
index 0000000..58a2138
Binary files /dev/null and b/labworks/LW2/out/AE1.h5 differ
diff --git a/labworks/LW2/out/AE1_AE2_train_def.png b/labworks/LW2/out/AE1_AE2_train_def.png
new file mode 100644
index 0000000..77a7cf1
Binary files /dev/null and b/labworks/LW2/out/AE1_AE2_train_def.png differ
diff --git a/labworks/LW2/out/AE1_AE2_train_def_anomalies.png b/labworks/LW2/out/AE1_AE2_train_def_anomalies.png
new file mode 100644
index 0000000..3d0629e
Binary files /dev/null and b/labworks/LW2/out/AE1_AE2_train_def_anomalies.png differ
diff --git a/labworks/LW2/out/AE1_ire_th.txt b/labworks/LW2/out/AE1_ire_th.txt
new file mode 100644
index 0000000..9cb0a05
--- /dev/null
+++ b/labworks/LW2/out/AE1_ire_th.txt
@@ -0,0 +1 @@
+3.64
\ No newline at end of file
diff --git a/labworks/LW2/out/AE1_train_def.png b/labworks/LW2/out/AE1_train_def.png
new file mode 100644
index 0000000..2824a96
Binary files /dev/null and b/labworks/LW2/out/AE1_train_def.png differ
diff --git a/labworks/LW2/out/AE2.h5 b/labworks/LW2/out/AE2.h5
new file mode 100644
index 0000000..560ec86
Binary files /dev/null and b/labworks/LW2/out/AE2.h5 differ
diff --git a/labworks/LW2/out/AE2_ire_th.txt b/labworks/LW2/out/AE2_ire_th.txt
new file mode 100644
index 0000000..e6adf3f
--- /dev/null
+++ b/labworks/LW2/out/AE2_ire_th.txt
@@ -0,0 +1 @@
+0.4
\ No newline at end of file
diff --git a/labworks/LW2/out/AE2_train_def.png b/labworks/LW2/out/AE2_train_def.png
new file mode 100644
index 0000000..725929b
Binary files /dev/null and b/labworks/LW2/out/AE2_train_def.png differ
diff --git a/labworks/LW2/out/AE3.h5 b/labworks/LW2/out/AE3.h5
new file mode 100644
index 0000000..acf9576
Binary files /dev/null and b/labworks/LW2/out/AE3.h5 differ
diff --git a/labworks/LW2/out/AE3_ire_th.txt b/labworks/LW2/out/AE3_ire_th.txt
new file mode 100644
index 0000000..52ea7c1
--- /dev/null
+++ b/labworks/LW2/out/AE3_ire_th.txt
@@ -0,0 +1 @@
+3.07
\ No newline at end of file
diff --git a/labworks/LW2/out/IRE_testAE1.png b/labworks/LW2/out/IRE_testAE1.png
new file mode 100644
index 0000000..65b952e
Binary files /dev/null and b/labworks/LW2/out/IRE_testAE1.png differ
diff --git a/labworks/LW2/out/IRE_testAE3.png b/labworks/LW2/out/IRE_testAE3.png
new file mode 100644
index 0000000..f1ec6dc
Binary files /dev/null and b/labworks/LW2/out/IRE_testAE3.png differ
diff --git a/labworks/LW2/out/IRE_trainingAE1.png b/labworks/LW2/out/IRE_trainingAE1.png
new file mode 100644
index 0000000..b946840
Binary files /dev/null and b/labworks/LW2/out/IRE_trainingAE1.png differ
diff --git a/labworks/LW2/out/IRE_trainingAE2.png b/labworks/LW2/out/IRE_trainingAE2.png
new file mode 100644
index 0000000..797cdaa
Binary files /dev/null and b/labworks/LW2/out/IRE_trainingAE2.png differ
diff --git a/labworks/LW2/out/IRE_trainingAE3.png b/labworks/LW2/out/IRE_trainingAE3.png
new file mode 100644
index 0000000..e0f9c46
Binary files /dev/null and b/labworks/LW2/out/IRE_trainingAE3.png differ
diff --git a/labworks/LW2/out/XtXd_1.png b/labworks/LW2/out/XtXd_1.png
new file mode 100644
index 0000000..d1996c4
Binary files /dev/null and b/labworks/LW2/out/XtXd_1.png differ
diff --git a/labworks/LW2/out/XtXd_1_metrics.png b/labworks/LW2/out/XtXd_1_metrics.png
new file mode 100644
index 0000000..d6dd330
Binary files /dev/null and b/labworks/LW2/out/XtXd_1_metrics.png differ
diff --git a/labworks/LW2/out/XtXd_2.png b/labworks/LW2/out/XtXd_2.png
new file mode 100644
index 0000000..8b364ef
Binary files /dev/null and b/labworks/LW2/out/XtXd_2.png differ
diff --git a/labworks/LW2/out/XtXd_2_metrics.png b/labworks/LW2/out/XtXd_2_metrics.png
new file mode 100644
index 0000000..0903d28
Binary files /dev/null and b/labworks/LW2/out/XtXd_2_metrics.png differ
diff --git a/labworks/LW2/out/result.txt b/labworks/LW2/out/result.txt
new file mode 100644
index 0000000..7ad23ac
--- /dev/null
+++ b/labworks/LW2/out/result.txt
@@ -0,0 +1,5 @@
+------------Оценка качества AE2 С ПОМОЩЬЮ НОВЫХ МЕТРИК------------
+Approx = 0.7333333333333333
+Excess = 0.36363636363636365
+Deficit = 0.0
+Coating = 1.0
diff --git a/labworks/LW2/out/train_set.png b/labworks/LW2/out/train_set.png
new file mode 100644
index 0000000..1778748
Binary files /dev/null and b/labworks/LW2/out/train_set.png differ
diff --git a/labworks/LW2/report.md b/labworks/LW2/report.md
new file mode 100644
index 0000000..c1e07ae
--- /dev/null
+++ b/labworks/LW2/report.md
@@ -0,0 +1,282 @@
+# Отчет по лабораторной работе №2
+Юсуфов Юнус,Романов Мирон , А-01-22
+## 1. Определили свой набор данных по таблице.
+Бригада №9 -> k = 9 mod 3 = 0 -> Cardio
+## 2. Подготовили  программную  среду Google Colaboratory для выполнения лабораторной работы.
+```
+import os
+os.chdir('/content/drive/MyDrive/Colab Notebooks/is_lab2/')
+```
+
+```
+!wget -N http://uit.mpei.ru/git/main/is_dnn/raw/branch/main/labworks/LW2/lab02_lib.py
+!wget -N http://uit.mpei.ru/git/main/is_dnn/raw/branch/main/labworks/LW2/data/cardio_train.txt
+!wget -N http://uit.mpei.ru/git/main/is_dnn/raw/branch/main/labworks/LW2/data/cardio_test.txt
+```
+
+### Задание 1.
+
+## 1. В среде GoogleColab создалиновый блокнот(notebook). Импортировали необходимые для работы библиотеки и модули.
+
+```
+import numpy as np
+import lab02_lib as lib
+```
+
+## 2. Сгенерировали индивидуальный набор двумерных данныхв пространстве признаковс координатами центра (k, k), где k–номер бригады. Вывели полученные данные на рисунок и в консоль.
+
+```
+# генерация датасета
+# data=lib.datagen(9, 9, 1000, 2)
+data = np.loadtxt('data.txt', dtype=float)
+# вывод данных и размерности
+print('Исходные данные:')
+print(data)
+print('Размерность данных:')
+print(data.shape)
+```
+>Исходные данные:  
+>[[9.19152143 8.99994525]  
+ >[9.04581739 9.01557436]  
+ >[9.15556184 9.0895709 ]  
+ >...  
+ >[8.95859907 8.92197639]  
+ >[9.00243424 8.96375469]  
+ >[8.86636465 9.13183014]]  
+>Размерность данных:  
+>(1000, 2)  
+
+## 3. Создали и обучили автокодировщик AE1 простой архитектуры, выбрав небольшое количество эпох обучения. Зафиксировали значения, для будущего внесения в таблицу.
+
+```
+# обучение AE1
+patience = 300
+ae1_trained, IRE1, IREth1 = lib.create_fit_save_ae(data,'out/AE1.h5','out/AE1_ire_th.txt', 1000, True, patience)
+
+# Построение графика ошибки реконструкции
+lib.ire_plot('training', IRE1, IREth1, 'AE1')
+```
+*   5 Скрытых слоев
+*   5 3 1 3 5
+
+## 4. Зафиксировали ошибку MSE, на которой обучение завершилось. Построили график ошибки реконструкции обучающей выборки. Зафиксировали порог ошибки реконструкции – порог обнаружения аномалий.
+
+![ae1_training](ae1_training.png)
+
+*   MSE_stop = 5.3137
+*   IREth1 = 3.6
+
+## 5. Создали и обучили второй автокодировщик AE2 с усложненной архитектурой, задав большее количество эпох обучения.
+
+```
+# обучение AE2
+ae2_trained, IRE2, IREth2 = lib.create_fit_save_ae(data,'out/AE2.h5','out/AE2_ire_th.txt', 3000, True, patience)
+lib.ire_plot('training', IRE2, IREth2, 'AE2')
+```
+*   7 скрытых слоев
+*   5 3 2 1 2 3 5
+
+## 6. Зафиксировали ошибку MSE, на которой обучение завершилось. Построили график ошибки реконструкции обучающей выборки. Зафиксировали второй порог ошибки реконструкции – порог обнаружения аномалий.
+
+![ae2_training](ae2_training.png)
+
+*   MSE_stop = 0.0103
+*   IREth1 = 0.4
+
+## 7. Рассчитали характеристики качества обучения EDCA для AE1 и AE2. Визуализировали и сравнили области пространства признаков, распознаваемые автокодировщиками AE1 и AE2. Сделали вывод о пригодности AE1 и AE2 для качественного обнаружения аномалий.
+
+* AE1
+
+```
+# построение областей покрытия и границ классов
+# расчет характеристик качества обучения
+numb_square = 20
+xx, yy, Z1 = lib.square_calc(numb_square, data, ae1_trained, IREth1, '1', True)
+```
+
+![ae1_class](ae1_class.png)
+
+* EDCA AE1  
+
+![ae1_EDCA](ae1_EDCA.png)
+![ae1_EDCA](ae1_EDCA1.png)
+
+* Оценка качества AE1  
+* IDEAL = 0. Excess:  12.636363636363637  
+* IDEAL = 0. Deficit:  0.0  
+* IDEAL = 1. Coating:  1.0  
+* summa:  1.0  
+* IDEAL = 1. Extrapolation precision (Approx):  0.07333333333333332  
+
+* AE2
+
+```
+# построение областей покрытия и границ классов
+# расчет характеристик качества обучения
+numb_square = 20
+xx, yy, Z2 = lib.square_calc(numb_square, data, ae2_trained, IREth2, '2', True)
+```
+
+![ae2_class](ae2_class.png)
+
+* EDCA AE2  
+
+![ae2_EDCA](ae2_EDCA.png)
+![ae2_EDCA](ae2_EDCA1.png)
+
+* Оценка качества AE2  
+* IDEAL = 0. Excess:  0.36363636363636365  
+* IDEAL = 0. Deficit:  0.0  
+* IDEAL = 1. Coating:  1.0  
+* summa:  1.0  
+* IDEAL = 1. Extrapolation precision (Approx):  0.7333333333333333  
+
+### Сравним области пространства признаков AE1 и AE2
+
+```
+# сравнение характеристик качества обучения и областей аппроксимации
+lib.plot2in1(data, xx, yy, Z1, Z2)
+```
+![ae1_ae2](ae1_ae2.png)
+
+### Вывод по пункту.
+AE1 не достаточно хорошо аппроксимирует область обучающих данных, что может значительно повлиять на результаты тестов. По сравнению с AE2, AE1 плохо ограничивает нашу область признаков с "левой" стороны.
+
+## 8. Изучили сохраненный набор данных и пространство признаков. Создали тестовую выборку, состоящую из 4-ёх элементов, не входящих в обучающую выборку. Элементы выбраны так, чтобы AE1 распознавал их как норму, а AE2 детектировал как аномалии.
+
+```
+# загрузка тестового набора
+data_test = np.loadtxt('/content/drive/MyDrive/Colab Notebooks/is_lab2/Lab02/data_test.txt', dtype=float)
+print(data_test)
+```
+>[[8.1 8.5]  
+ >[7.2 8. ]  
+ >[9.  8. ]  
+ >[8.5 9.5]]  
+
+## 9. Применили обученные автокодировщики AE1 и AE2 к тестовым данным и вывели значения ошибки реконструкции для каждого элемента тестовой выборки относительно порога.
+
+```
+# тестирование АE1
+predicted_labels1, ire1 = lib.predict_ae(ae1_trained, data_test, IREth1)
+lib.anomaly_detection_ae(predicted_labels1, ire1, IREth1)
+lib.ire_plot('test', ire1, IREth1, 'AE1')
+```
+>Аномалий не обнаружено
+![ae1_test](ae1_test.png)
+
+```
+# тестирование АE2
+predicted_labels2, ire2 = lib.predict_ae(ae2_trained, data_test, IREth2)
+lib.anomaly_detection_ae(predicted_labels2, ire2, IREth2)
+lib.ire_plot('test', ire2, IREth2, 'AE1')
+```
+>i         Labels    IRE       IREth     
+>0         [1.]      [1.02]    0.4       
+>1         [1.]      [2.05]    0.4       
+>2         [1.]      [1.]      0.4       
+>3         [1.]      [0.69]    0.4       
+>Обнаружено  4.0  аномалий  
+![ae2_test](ae2_test.png)
+
+## 10. Визуализировали элементы обучающей и тестовой выборки в областях пространства признаков, распознаваемых автокодировщиками AE1 и AE2.
+
+```
+# построение областей аппроксимации и точек тестового набора
+lib.plot2in1_anomaly(data, xx, yy, Z1, Z2, data_test)
+```
+![ae1_ae2_test](ae1_ae2_test.png)
+
+## 11. Результаты исследования занесли в таблицу:
+
+| |Количество скрытых слоев|Количество нейронов в скрытых слоях|Количество эпох обучения|Ошибка MSE_stop|Порог ошибки реконструкции|Значение показателя Excess|Значение показателя Approx|Количество обнаруженных аномалий|
+|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
+|AE1|5|5 3 1 3 5|1000|5.3137|3.6|12.636363636363637|0.07333333333333332|0|
+|AE2|7|5 3 2 1 2 3 5|3000|0.0103|0.4|0.36363636363636365|0.7333333333333333|4|
+
+## 12. Сделали выводы о требованиях к:
+
+* Данным обучения - необходима плотная выборка нормальных состояний без выбросов, покрывающая рабочую область (в эксперименте — 1000 точек вокруг (9, 9)), чтобы модель корректно восстанавливала типичные объекты.
+* Архитектуре кодировщика - оптимальна глубокая симметричная структура с узким горлышком (5-3-2-1-2-3-5), обеспечивающая точную реконструкцию и устойчивые границы нормы.
+* Количеству эпох обучения - требуется не менее 3000 эпох для данного набора данных; обучение в 1000 эпох оставляет высокую ошибку и не формирует надежный порог аномалий.
+* Ошибке MSE_stop, приемлемой для останова обучения - ориентир на значения порядка 0.001 или 0.01 (0.0103 для AE2); более крупные значения, как 5.3137 у AE1, свидетельствуют о недообучении.
+* Ошибке реконструкции обучающей выборки - целесообразно удерживать порог ниже 0.5 (IREth = 0.4 у AE2), тогда как порог 3.6 у AE1 приводит к пропуску аномалий.
+* Характеристикам EDCA, для качественного обнаружения аномалий в данных - требуется минимальный Excess (<1) и высокая Approx (≈0.7); такие значения достигаются AE2, тогда как AE1 с Excess = 12.6 непригоден.
+
+### Задание 2.
+
+## 1. Изучили описание своего набора реальных данных, что он из себя представляет.
+
+## 2. Загрузили многомерную обучающую выборку реальных данных cardio_train.txt. Изучили и загрузили тестовую выборку cardio_test.txt.
+
+```
+train = np.loadtxt('cardio_train.txt', dtype=float)
+test = np.loadtxt('cardio_test.txt', dtype=float)
+
+print('Исходные данные:')
+print(train)
+print('Размерность данных:')
+print(train.shape)
+print('Исходные данные:')
+print(test)
+print('Размерность данных:')
+print(test.shape)
+```
+Исходные данные:
+>[[ 0.00491231  0.69319077 -0.20364049 ...  0.23149795 -0.28978574 -0.49329397]
+>[ 0.11072935 -0.07990259 -0.20364049 ...  0.09356344 -0.25638541 -0.49329397]
+>[ 0.21654639 -0.27244466 -0.20364049 ...  0.02459619 -0.25638541 1.1400175 ]
+>...
+>[ 0.85144861 -0.91998844 -0.20364049 ...  0.57633422 -0.65718941 1.1400175 ]
+>[ 0.85144861 -0.91998844 -0.20364049 ...  0.57633422 -0.62378908 -0.49329397]
+>[ 1.0630827  -0.51148142 -0.16958144 ...  0.57633422 -0.65718941 -0.49329397]]
+>Размерность данных:
+>(1654, 21)
+
+## 3. Создали и обучили автокодировщик с подходящей для данных
+архитектурой. Выбрали необходимое количество эпох обучения.
+
+```
+# обучение AE3
+patience = 7500
+from time import time
+start = time()
+ae3_trained, IRE3, IREth3 = lib.create_fit_save_ae(train,'out/AE3.h5','out/AE3_ire_th.txt', 120000, False, 7500, early_stopping_delta = 0.001)
+print("Время на обучение: ", time() - start)
+
+# Построение графика ошибки реконструкции
+lib.ire_plot('training', IRE3, IREth3, 'AE3')
+```
+*   15 скрытых слоев
+*   21 19 17 15 13 11 9 7 9 11 13 15 17 19 21
+
+## 4. Зафиксировали ошибку MSE, на которой обучение завершилось. Построили график ошибки реконструкции обучающей выборки. Зафиксировали порог ошибки реконструкции – порог обнаружения аномалий.
+
+![ae3_training](ae3_training.png)
+
+*   MSE_stop = 0.0319
+*   IREth1 = 3.07
+
+## 5. Подали тестовую выборку на вход обученного автокодировщика для обнаружения аномалий. Вывели график ошибки реконструкции элементов тестовой выборки относительно порога.
+
+```
+# тестирование АE3
+predicted_labels3, ire3 = lib.predict_ae(ae3_trained, test, IREth3)
+lib.anomaly_detection_ae(predicted_labels3, ire3, IREth3)
+lib.ire_plot('test', ire3, IREth3, 'AE3')
+```
+>Обнаружено  94.0  аномалий
+>Accuracy: 86.23%
+![ae3_test](ae3_test.png)
+
+|Dataset name|Количество скрытых слоев|Количество нейронов в скрытых слоях|Количество эпох обучения|Ошибка MSE_stop|Порог ошибки реконструкции|% Обнаруженных аномалий|
+|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
+|Cardio|15|21 19 17 15 13 11 9 7 9 11 13 15 17 19 21|85000|0.0319|3.07|86.23|
+
+## 6. Сделали выводы о требованиях к:
+
+* Данным обучения - необходима масштабированная и очищенная выборка, охватывающая все состояния нормы; 1654 нормализованных записей cardio_train дают устойчивую оценку распределения.
+* Архитектуре кодировщика - эффективна глубокая симметричная сеть, которая сжимает 21 признаковое измерение до компактного латентного пространства и поддерживает высокую точность восстановления.
+* Количеству эпох обучения - требуется длительное обучение (100к+ эпох) с ранней остановкой; меньшая длительность не успевает снизить ошибку реконструкции до приемлемого уровня.
+* Ошибке MSE_stop, приемлемой для останова обучения - ориентир на значения порядка 0.01 (0.0319), после чего дальнейшее обучение дает минимальный прирост качества при заметном росте времени.
+* Ошибке реконструкции обучающей выборки, для качественного обнаружения аномалий в случае, когда размерность пространства признаков высока - порог около 3.0 (IREth = 3.07) позволяет выделять ~86% аномалий без чрезмерного количества ложных срабатываний; важно калибровать порог по распределению ошибок.
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