diff --git a/labworks/LW2/is_lab2.ipynb b/labworks/LW2/is_lab2.ipynb new file mode 100644 index 0000000..5da0e13 --- /dev/null +++ b/labworks/LW2/is_lab2.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"gpuType":"T4","mount_file_id":"1LJYmJ1o1KkUU__3X_nmhfPlRee1xKGxf","authorship_tag":"ABX9TyOfkUaXOPlhcPvhjffzwfVB"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","source":["# вариант номер 3 - cardio\n","import os\n","os.chdir('/content/drive/MyDrive/Colab Notebooks/is_lab2')"],"metadata":{"id":"H1Tpd_pXNKic","executionInfo":{"status":"ok","timestamp":1763336531446,"user_tz":-180,"elapsed":3,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}}},"execution_count":48,"outputs":[]},{"cell_type":"code","source":["# импорт модулей\n","import numpy as np\n","import lab02_lib as lib"],"metadata":{"collapsed":true,"id":"LHCp5uF9PJG5","executionInfo":{"status":"ok","timestamp":1763336532797,"user_tz":-180,"elapsed":2,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}}},"execution_count":49,"outputs":[]},{"cell_type":"code","source":["# генерация датасета\n","data = lib.datagen(3, 3, 1000, 2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":718},"collapsed":true,"id":"-J0M844iPZBZ","executionInfo":{"status":"ok","timestamp":1763336534771,"user_tz":-180,"elapsed":1054,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"13ad81f5-3fa2-4852-c693-c122b584c4e7"},"execution_count":50,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["# вывод данных и размерности\n","print('Исходные данные:')\n","print(data)\n","print('Размерность данных:')\n","print(data.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4p74pZM2Pv7Y","executionInfo":{"status":"ok","timestamp":1763316760190,"user_tz":-180,"elapsed":6,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"a03ad7b5-6466-4e72-b239-93b8e6efa533","collapsed":true},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Исходные данные:\n","[[3.01497028 2.9872143 ]\n"," [2.95216438 2.93247766]\n"," [2.9281138 2.80160426]\n"," ...\n"," [3.10976374 2.91251936]\n"," [3.16677716 2.95397464]\n"," [3.00503898 3.17135038]]\n","Размерность данных:\n","(1000, 2)\n"]}]},{"cell_type":"code","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)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"id":"wgqf8gF2RluN","executionInfo":{"status":"ok","timestamp":1763316989379,"user_tz":-180,"elapsed":217979,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"2174c398-ab02-4d79-f5dc-61d7dcabddd4"},"execution_count":null,"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) : 1\n","Задайте архитектуру скрытых слоёв автокодировщика, например, в виде 3 1 3 : 1\n","Epoch 1/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1s/step - loss: 8.3537\n","Epoch 2/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 8.3426\n","Epoch 3/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 8.3314\n","Epoch 4/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 8.3202\n","Epoch 5/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - loss: 8.3091\n","Epoch 6/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 8.2979\n","Epoch 7/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 8.2867\n","Epoch 8/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 8.2756\n","Epoch 9/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 8.2644\n","Epoch 10/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 8.2532\n","Epoch 11/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 8.2420\n","Epoch 12/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - loss: 8.2308\n","Epoch 13/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - loss: 8.2196\n","Epoch 14/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 8.2084\n","Epoch 15/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 8.1972\n","Epoch 16/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 8.1860\n","Epoch 17/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 8.1748\n","Epoch 18/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 8.1636\n","Epoch 19/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 8.1524\n","Epoch 20/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 8.1412\n","Epoch 21/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 8.1300\n","Epoch 22/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 8.1187\n","Epoch 23/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 8.1075\n","Epoch 24/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 8.0963\n","Epoch 25/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 8.0850\n","Epoch 26/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 8.0738\n","Epoch 27/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 8.0626\n","Epoch 28/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 8.0513\n","Epoch 29/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - loss: 8.0401\n","Epoch 30/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 8.0289\n","Epoch 31/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 8.0176\n","Epoch 32/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - loss: 8.0064\n","Epoch 33/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - loss: 7.9951\n","Epoch 34/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 7.9839\n","Epoch 35/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 7.9726\n","Epoch 36/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 7.9614\n","Epoch 37/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 7.9501\n","Epoch 38/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 7.9389\n","Epoch 39/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 7.9276\n","Epoch 40/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 7.9164\n","Epoch 41/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 7.9051\n","Epoch 42/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 7.8939\n","Epoch 43/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 7.8826\n","Epoch 44/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 7.8713\n","Epoch 45/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 7.8601\n","Epoch 46/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 7.8488\n","Epoch 47/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 7.8376\n","Epoch 48/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 7.8263\n","Epoch 49/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 7.8151\n","Epoch 50/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 7.8038\n","Epoch 51/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 7.7926\n","Epoch 52/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 7.7813\n","Epoch 53/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 7.7701\n","Epoch 54/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 7.7588\n","Epoch 55/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 7.7476\n","Epoch 56/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 7.7363\n","Epoch 57/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 7.7251\n","Epoch 58/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 7.7138\n","Epoch 59/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 7.7026\n","Epoch 60/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 7.6914\n","Epoch 61/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 7.6801\n","Epoch 62/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 7.6689\n","Epoch 63/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 7.6577\n","Epoch 64/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 7.6464\n","Epoch 65/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 7.6352\n","Epoch 66/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 7.6240\n","Epoch 67/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 7.6128\n","Epoch 68/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 7.6016\n","Epoch 69/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 7.5903\n","Epoch 70/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 7.5791\n","Epoch 71/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 7.5679\n","Epoch 72/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 7.5567\n","Epoch 73/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 7.5455\n","Epoch 74/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 7.5343\n","Epoch 75/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 7.5232\n","Epoch 76/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 7.5120\n","Epoch 77/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 7.5008\n","Epoch 78/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 7.4896\n","Epoch 79/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 7.4784\n","Epoch 80/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 7.4673\n","Epoch 81/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 7.4561\n","Epoch 82/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 7.4450\n","Epoch 83/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 7.4338\n","Epoch 84/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 7.4227\n","Epoch 85/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 7.4115\n","Epoch 86/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 7.4004\n","Epoch 87/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 7.3893\n","Epoch 88/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 7.3781\n","Epoch 89/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 7.3670\n","Epoch 90/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 7.3559\n","Epoch 91/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 7.3448\n","Epoch 92/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 7.3337\n","Epoch 93/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 7.3226\n","Epoch 94/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 7.3115\n","Epoch 95/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 7.3004\n","Epoch 96/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 7.2894\n","Epoch 97/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 7.2783\n","Epoch 98/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 7.2672\n","Epoch 99/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 7.2562\n","Epoch 100/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 7.2451\n","Epoch 101/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 7.2341\n","Epoch 102/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 7.2230\n","Epoch 103/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 7.2120\n","Epoch 104/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 7.2010\n","Epoch 105/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 7.1900\n","Epoch 106/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 7.1790\n","Epoch 107/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 7.1680\n","Epoch 108/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 7.1570\n","Epoch 109/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 7.1460\n","Epoch 110/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 7.1350\n","Epoch 111/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 7.1241\n","Epoch 112/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 7.1131\n","Epoch 113/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 7.1022\n","Epoch 114/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 7.0912\n","Epoch 115/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 7.0803\n","Epoch 116/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 7.0694\n","Epoch 117/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 7.0585\n","Epoch 118/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 7.0476\n","Epoch 119/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 7.0367\n","Epoch 120/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 7.0258\n","Epoch 121/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 7.0149\n","Epoch 122/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 7.0040\n","Epoch 123/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 6.9931\n","Epoch 124/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 6.9823\n","Epoch 125/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 6.9714\n","Epoch 126/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 6.9606\n","Epoch 127/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 6.9498\n","Epoch 128/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 6.9390\n","Epoch 129/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step - loss: 6.9281\n","Epoch 130/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 6.9173\n","Epoch 131/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 6.9066\n","Epoch 132/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 6.8958\n","Epoch 133/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 6.8850\n","Epoch 134/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 6.8742\n","Epoch 135/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 6.8635\n","Epoch 136/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 6.8527\n","Epoch 137/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step - loss: 6.8420\n","Epoch 138/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 6.8313\n","Epoch 139/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 6.8206\n","Epoch 140/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 6.8099\n","Epoch 141/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 6.7992\n","Epoch 142/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 6.7885\n","Epoch 143/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 6.7778\n","Epoch 144/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 6.7672\n","Epoch 145/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 6.7565\n","Epoch 146/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 6.7459\n","Epoch 147/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 6.7352\n","Epoch 148/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 6.7246\n","Epoch 149/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 6.7140\n","Epoch 150/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 6.7034\n","Epoch 151/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 6.6928\n","Epoch 152/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 6.6822\n","Epoch 153/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 6.6717\n","Epoch 154/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 6.6611\n","Epoch 155/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 6.6506\n","Epoch 156/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 6.6400\n","Epoch 157/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 6.6295\n","Epoch 158/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 6.6190\n","Epoch 159/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 6.6085\n","Epoch 160/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 6.5980\n","Epoch 161/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 6.5875\n","Epoch 162/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 6.5770\n","Epoch 163/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 6.5666\n","Epoch 164/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 6.5561\n","Epoch 165/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 6.5457\n","Epoch 166/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 6.5353\n","Epoch 167/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 6.5249\n","Epoch 168/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 6.5145\n","Epoch 169/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 6.5041\n","Epoch 170/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 6.4937\n","Epoch 171/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 6.4833\n","Epoch 172/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 6.4730\n","Epoch 173/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 6.4626\n","Epoch 174/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 6.4523\n","Epoch 175/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 6.4420\n","Epoch 176/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 6.4316\n","Epoch 177/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 6.4213\n","Epoch 178/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 6.4110\n","Epoch 179/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 6.4008\n","Epoch 180/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 6.3905\n","Epoch 181/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 6.3803\n","Epoch 182/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 6.3700\n","Epoch 183/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 6.3598\n","Epoch 184/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 6.3496\n","Epoch 185/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 6.3394\n","Epoch 186/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 6.3292\n","Epoch 187/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 6.3190\n","Epoch 188/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 6.3088\n","Epoch 189/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 6.2986\n","Epoch 190/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 6.2885\n","Epoch 191/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 6.2784\n","Epoch 192/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 6.2682\n","Epoch 193/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 6.2581\n","Epoch 194/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 6.2480\n","Epoch 195/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 6.2379\n","Epoch 196/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 6.2279\n","Epoch 197/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 6.2178\n","Epoch 198/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 6.2077\n","Epoch 199/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 6.1977\n","Epoch 200/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 6.1877\n","Epoch 201/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 6.1777\n","Epoch 202/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 6.1677\n","Epoch 203/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 6.1577\n","Epoch 204/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 6.1477\n","Epoch 205/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 6.1377\n","Epoch 206/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 6.1278\n","Epoch 207/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 6.1178\n","Epoch 208/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 6.1079\n","Epoch 209/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 6.0980\n","Epoch 210/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - loss: 6.0881\n","Epoch 211/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 6.0782\n","Epoch 212/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 6.0683\n","Epoch 213/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 6.0584\n","Epoch 214/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 6.0486\n","Epoch 215/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 6.0387\n","Epoch 216/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 6.0289\n","Epoch 217/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 6.0191\n","Epoch 218/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 6.0093\n","Epoch 219/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 5.9995\n","Epoch 220/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 5.9897\n","Epoch 221/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 5.9799\n","Epoch 222/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 5.9702\n","Epoch 223/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 5.9604\n","Epoch 224/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 5.9507\n","Epoch 225/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 5.9410\n","Epoch 226/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 5.9313\n","Epoch 227/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 5.9216\n","Epoch 228/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 5.9119\n","Epoch 229/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 5.9022\n","Epoch 230/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 5.8925\n","Epoch 231/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 5.8829\n","Epoch 232/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 5.8733\n","Epoch 233/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 5.8636\n","Epoch 234/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 5.8540\n","Epoch 235/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 5.8444\n","Epoch 236/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 5.8349\n","Epoch 237/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 5.8253\n","Epoch 238/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 5.8157\n","Epoch 239/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 5.8062\n","Epoch 240/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 5.7966\n","Epoch 241/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 5.7871\n","Epoch 242/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 5.7776\n","Epoch 243/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 5.7681\n","Epoch 244/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 5.7586\n","Epoch 245/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 5.7492\n","Epoch 246/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 5.7397\n","Epoch 247/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 5.7303\n","Epoch 248/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 5.7208\n","Epoch 249/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 5.7114\n","Epoch 250/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 5.7020\n","Epoch 251/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 5.6926\n","Epoch 252/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 5.6832\n","Epoch 253/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 5.6739\n","Epoch 254/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 5.6645\n","Epoch 255/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 5.6551\n","Epoch 256/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 5.6458\n","Epoch 257/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 5.6365\n","Epoch 258/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 5.6272\n","Epoch 259/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 5.6179\n","Epoch 260/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 5.6086\n","Epoch 261/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 5.5993\n","Epoch 262/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 5.5901\n","Epoch 263/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 5.5808\n","Epoch 264/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 5.5716\n","Epoch 265/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 5.5624\n","Epoch 266/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 5.5532\n","Epoch 267/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 5.5440\n","Epoch 268/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 5.5348\n","Epoch 269/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 5.5256\n","Epoch 270/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 5.5165\n","Epoch 271/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 5.5073\n","Epoch 272/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 5.4982\n","Epoch 273/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 5.4891\n","Epoch 274/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 5.4799\n","Epoch 275/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 5.4708\n","Epoch 276/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 5.4618\n","Epoch 277/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 5.4527\n","Epoch 278/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 5.4436\n","Epoch 279/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 5.4346\n","Epoch 280/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 5.4255\n","Epoch 281/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 5.4165\n","Epoch 282/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 5.4075\n","Epoch 283/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 5.3985\n","Epoch 284/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 5.3895\n","Epoch 285/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 5.3806\n","Epoch 286/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 5.3716\n","Epoch 287/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 5.3627\n","Epoch 288/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 5.3537\n","Epoch 289/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 5.3448\n","Epoch 290/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 5.3359\n","Epoch 291/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 5.3270\n","Epoch 292/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 5.3181\n","Epoch 293/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 5.3092\n","Epoch 294/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 5.3004\n","Epoch 295/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 5.2915\n","Epoch 296/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 5.2827\n","Epoch 297/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 5.2739\n","Epoch 298/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 5.2650\n","Epoch 299/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 5.2562\n","Epoch 300/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 5.2475\n","Epoch 301/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 5.2387\n","Epoch 302/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 5.2299\n","Epoch 303/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 5.2212\n","Epoch 304/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 5.2124\n","Epoch 305/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 5.2037\n","Epoch 306/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 5.1950\n","Epoch 307/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 5.1863\n","Epoch 308/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 5.1776\n","Epoch 309/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 5.1689\n","Epoch 310/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 5.1603\n","Epoch 311/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 5.1516\n","Epoch 312/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 5.1430\n","Epoch 313/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 5.1343\n","Epoch 314/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 5.1257\n","Epoch 315/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 5.1171\n","Epoch 316/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 5.1085\n","Epoch 317/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 5.0999\n","Epoch 318/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 5.0914\n","Epoch 319/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 5.0828\n","Epoch 320/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 5.0743\n","Epoch 321/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 5.0657\n","Epoch 322/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 5.0572\n","Epoch 323/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 5.0487\n","Epoch 324/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 5.0402\n","Epoch 325/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 5.0317\n","Epoch 326/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 5.0233\n","Epoch 327/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 5.0148\n","Epoch 328/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 5.0064\n","Epoch 329/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 4.9979\n","Epoch 330/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 4.9895\n","Epoch 331/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 4.9811\n","Epoch 332/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 4.9727\n","Epoch 333/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 4.9643\n","Epoch 334/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 4.9559\n","Epoch 335/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 4.9476\n","Epoch 336/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 4.9392\n","Epoch 337/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 4.9309\n","Epoch 338/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 4.9225\n","Epoch 339/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 4.9142\n","Epoch 340/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 4.9059\n","Epoch 341/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 4.8976\n","Epoch 342/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 4.8893\n","Epoch 343/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 4.8811\n","Epoch 344/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 4.8728\n","Epoch 345/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 4.8646\n","Epoch 346/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 4.8563\n","Epoch 347/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 4.8481\n","Epoch 348/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 4.8399\n","Epoch 349/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 4.8317\n","Epoch 350/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 4.8235\n","Epoch 351/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 4.8153\n","Epoch 352/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 4.8072\n","Epoch 353/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 4.7990\n","Epoch 354/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 4.7909\n","Epoch 355/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 4.7828\n","Epoch 356/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 4.7747\n","Epoch 357/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 4.7665\n","Epoch 358/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step - loss: 4.7585\n","Epoch 359/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 4.7504\n","Epoch 360/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 4.7423\n","Epoch 361/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 4.7342\n","Epoch 362/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 4.7262\n","Epoch 363/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 4.7182\n","Epoch 364/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 4.7101\n","Epoch 365/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 4.7021\n","Epoch 366/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 4.6941\n","Epoch 367/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 4.6861\n","Epoch 368/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 4.6782\n","Epoch 369/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 4.6702\n","Epoch 370/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 4.6623\n","Epoch 371/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 4.6543\n","Epoch 372/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 4.6464\n","Epoch 373/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 4.6385\n","Epoch 374/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 4.6306\n","Epoch 375/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 4.6227\n","Epoch 376/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 4.6148\n","Epoch 377/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 4.6069\n","Epoch 378/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 4.5990\n","Epoch 379/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 4.5912\n","Epoch 380/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 4.5833\n","Epoch 381/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 4.5755\n","Epoch 382/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 4.5677\n","Epoch 383/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 4.5599\n","Epoch 384/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 4.5521\n","Epoch 385/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 4.5443\n","Epoch 386/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 4.5365\n","Epoch 387/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 4.5288\n","Epoch 388/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 4.5210\n","Epoch 389/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 4.5133\n","Epoch 390/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 4.5056\n","Epoch 391/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 4.4979\n","Epoch 392/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 4.4902\n","Epoch 393/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 4.4825\n","Epoch 394/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 4.4748\n","Epoch 395/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 157ms/step - loss: 4.4671\n","Epoch 396/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 4.4595\n","Epoch 397/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 4.4518\n","Epoch 398/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 4.4442\n","Epoch 399/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 4.4365\n","Epoch 400/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 4.4289\n","Epoch 401/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 4.4213\n","Epoch 402/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 4.4137\n","Epoch 403/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 4.4062\n","Epoch 404/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 4.3986\n","Epoch 405/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 4.3910\n","Epoch 406/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 4.3835\n","Epoch 407/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 4.3759\n","Epoch 408/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 4.3684\n","Epoch 409/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 4.3609\n","Epoch 410/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 4.3534\n","Epoch 411/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 4.3459\n","Epoch 412/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 4.3384\n","Epoch 413/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 4.3310\n","Epoch 414/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 4.3235\n","Epoch 415/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 4.3160\n","Epoch 416/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 4.3086\n","Epoch 417/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 4.3012\n","Epoch 418/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 4.2938\n","Epoch 419/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 4.2864\n","Epoch 420/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 4.2790\n","Epoch 421/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 4.2716\n","Epoch 422/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 4.2642\n","Epoch 423/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 4.2568\n","Epoch 424/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 4.2495\n","Epoch 425/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 4.2422\n","Epoch 426/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 4.2348\n","Epoch 427/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 4.2275\n","Epoch 428/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 4.2202\n","Epoch 429/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 4.2129\n","Epoch 430/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 4.2056\n","Epoch 431/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 4.1983\n","Epoch 432/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 4.1911\n","Epoch 433/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 4.1838\n","Epoch 434/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 4.1766\n","Epoch 435/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 4.1693\n","Epoch 436/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 4.1621\n","Epoch 437/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 4.1549\n","Epoch 438/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 4.1477\n","Epoch 439/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 4.1405\n","Epoch 440/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 4.1333\n","Epoch 441/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 4.1262\n","Epoch 442/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 4.1190\n","Epoch 443/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 4.1119\n","Epoch 444/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 4.1047\n","Epoch 445/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 4.0976\n","Epoch 446/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 4.0905\n","Epoch 447/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 4.0834\n","Epoch 448/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 4.0763\n","Epoch 449/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 4.0692\n","Epoch 450/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 4.0621\n","Epoch 451/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 4.0550\n","Epoch 452/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 4.0480\n","Epoch 453/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 4.0409\n","Epoch 454/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 4.0339\n","Epoch 455/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 4.0269\n","Epoch 456/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 4.0199\n","Epoch 457/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 4.0129\n","Epoch 458/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 4.0059\n","Epoch 459/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 3.9989\n","Epoch 460/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 3.9919\n","Epoch 461/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 3.9850\n","Epoch 462/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 3.9780\n","Epoch 463/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 3.9711\n","Epoch 464/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 3.9641\n","Epoch 465/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 3.9572\n","Epoch 466/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 3.9503\n","Epoch 467/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 3.9434\n","Epoch 468/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 3.9365\n","Epoch 469/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 3.9297\n","Epoch 470/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 3.9228\n","Epoch 471/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 3.9159\n","Epoch 472/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 3.9091\n","Epoch 473/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 3.9022\n","Epoch 474/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 3.8954\n","Epoch 475/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 3.8886\n","Epoch 476/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 3.8818\n","Epoch 477/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 3.8750\n","Epoch 478/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 3.8682\n","Epoch 479/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 3.8614\n","Epoch 480/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 3.8547\n","Epoch 481/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 3.8479\n","Epoch 482/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 3.8412\n","Epoch 483/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 3.8344\n","Epoch 484/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 3.8277\n","Epoch 485/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 3.8210\n","Epoch 486/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 3.8143\n","Epoch 487/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 3.8076\n","Epoch 488/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 3.8009\n","Epoch 489/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 3.7942\n","Epoch 490/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 3.7875\n","Epoch 491/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 3.7809\n","Epoch 492/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 3.7742\n","Epoch 493/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 3.7676\n","Epoch 494/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 3.7610\n","Epoch 495/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 3.7543\n","Epoch 496/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 3.7477\n","Epoch 497/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 3.7411\n","Epoch 498/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 3.7346\n","Epoch 499/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 3.7280\n","Epoch 500/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 3.7214\n","Epoch 501/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 3.7148\n","Epoch 502/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 3.7083\n","Epoch 503/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 3.7018\n","Epoch 504/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 3.6952\n","Epoch 505/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 3.6887\n","Epoch 506/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 3.6822\n","Epoch 507/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 3.6757\n","Epoch 508/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 3.6692\n","Epoch 509/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 3.6627\n","Epoch 510/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 3.6563\n","Epoch 511/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 3.6498\n","Epoch 512/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 3.6433\n","Epoch 513/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 3.6369\n","Epoch 514/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 3.6305\n","Epoch 515/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 3.6240\n","Epoch 516/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 39ms/step - loss: 3.6176\n","Epoch 517/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - loss: 3.6112\n","Epoch 518/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 3.6048\n","Epoch 519/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 3.5985\n","Epoch 520/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 3.5921\n","Epoch 521/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 3.5857\n","Epoch 522/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 3.5794\n","Epoch 523/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 3.5730\n","Epoch 524/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 3.5667\n","Epoch 525/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 3.5603\n","Epoch 526/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 3.5540\n","Epoch 527/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 3.5477\n","Epoch 528/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 3.5414\n","Epoch 529/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 3.5351\n","Epoch 530/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 3.5289\n","Epoch 531/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 3.5226\n","Epoch 532/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 3.5163\n","Epoch 533/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 3.5101\n","Epoch 534/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 3.5038\n","Epoch 535/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 3.4976\n","Epoch 536/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 3.4914\n","Epoch 537/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 3.4852\n","Epoch 538/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 3.4790\n","Epoch 539/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 3.4728\n","Epoch 540/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 3.4666\n","Epoch 541/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 3.4604\n","Epoch 542/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 3.4542\n","Epoch 543/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 3.4481\n","Epoch 544/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 3.4419\n","Epoch 545/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 3.4358\n","Epoch 546/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 3.4297\n","Epoch 547/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 3.4235\n","Epoch 548/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 3.4174\n","Epoch 549/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 3.4113\n","Epoch 550/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 3.4052\n","Epoch 551/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 3.3991\n","Epoch 552/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 3.3931\n","Epoch 553/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 3.3870\n","Epoch 554/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 3.3809\n","Epoch 555/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 3.3749\n","Epoch 556/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 3.3689\n","Epoch 557/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 3.3628\n","Epoch 558/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 3.3568\n","Epoch 559/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 3.3508\n","Epoch 560/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 3.3448\n","Epoch 561/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 3.3388\n","Epoch 562/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 3.3328\n","Epoch 563/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 3.3268\n","Epoch 564/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 3.3209\n","Epoch 565/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 3.3149\n","Epoch 566/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 3.3090\n","Epoch 567/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 3.3030\n","Epoch 568/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 3.2971\n","Epoch 569/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 3.2912\n","Epoch 570/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 3.2853\n","Epoch 571/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 3.2794\n","Epoch 572/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 3.2735\n","Epoch 573/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 3.2676\n","Epoch 574/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 3.2617\n","Epoch 575/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 3.2558\n","Epoch 576/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 3.2500\n","Epoch 577/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 3.2441\n","Epoch 578/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 3.2383\n","Epoch 579/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 3.2325\n","Epoch 580/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 3.2266\n","Epoch 581/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 3.2208\n","Epoch 582/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 3.2150\n","Epoch 583/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 3.2092\n","Epoch 584/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 3.2034\n","Epoch 585/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 3.1977\n","Epoch 586/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 3.1919\n","Epoch 587/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 3.1861\n","Epoch 588/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 3.1804\n","Epoch 589/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 3.1746\n","Epoch 590/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 3.1689\n","Epoch 591/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 3.1632\n","Epoch 592/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 3.1575\n","Epoch 593/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 3.1518\n","Epoch 594/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 3.1461\n","Epoch 595/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 3.1404\n","Epoch 596/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 3.1347\n","Epoch 597/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 3.1290\n","Epoch 598/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 3.1233\n","Epoch 599/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 3.1177\n","Epoch 600/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 3.1120\n","Epoch 601/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 3.1064\n","Epoch 602/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 3.1008\n","Epoch 603/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 3.0951\n","Epoch 604/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 3.0895\n","Epoch 605/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 3.0839\n","Epoch 606/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 3.0783\n","Epoch 607/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 3.0727\n","Epoch 608/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 3.0672\n","Epoch 609/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 3.0616\n","Epoch 610/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 3.0560\n","Epoch 611/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 3.0505\n","Epoch 612/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 3.0449\n","Epoch 613/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 3.0394\n","Epoch 614/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 3.0339\n","Epoch 615/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 3.0283\n","Epoch 616/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 3.0228\n","Epoch 617/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 3.0173\n","Epoch 618/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 41ms/step - loss: 3.0118\n","Epoch 619/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 3.0064\n","Epoch 620/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 3.0009\n","Epoch 621/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 2.9954\n","Epoch 622/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 2.9899\n","Epoch 623/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 2.9845\n","Epoch 624/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 2.9790\n","Epoch 625/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 2.9736\n","Epoch 626/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 2.9682\n","Epoch 627/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 2.9628\n","Epoch 628/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 2.9574\n","Epoch 629/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 2.9520\n","Epoch 630/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 2.9466\n","Epoch 631/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 2.9412\n","Epoch 632/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 2.9358\n","Epoch 633/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 2.9304\n","Epoch 634/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 2.9251\n","Epoch 635/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 2.9197\n","Epoch 636/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 2.9144\n","Epoch 637/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 2.9090\n","Epoch 638/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 2.9037\n","Epoch 639/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 2.8984\n","Epoch 640/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 2.8931\n","Epoch 641/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 2.8878\n","Epoch 642/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 2.8825\n","Epoch 643/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 2.8772\n","Epoch 644/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 2.8719\n","Epoch 645/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 2.8666\n","Epoch 646/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 2.8614\n","Epoch 647/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 2.8561\n","Epoch 648/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 2.8509\n","Epoch 649/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 2.8456\n","Epoch 650/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 2.8404\n","Epoch 651/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.8352\n","Epoch 652/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 2.8300\n","Epoch 653/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 2.8248\n","Epoch 654/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 2.8196\n","Epoch 655/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 2.8144\n","Epoch 656/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 2.8092\n","Epoch 657/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 2.8040\n","Epoch 658/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 2.7989\n","Epoch 659/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - loss: 2.7937\n","Epoch 660/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 2.7885\n","Epoch 661/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 2.7834\n","Epoch 662/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.7783\n","Epoch 663/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.7731\n","Epoch 664/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.7680\n","Epoch 665/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 2.7629\n","Epoch 666/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.7578\n","Epoch 667/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.7527\n","Epoch 668/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 2.7476\n","Epoch 669/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 2.7426\n","Epoch 670/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 2.7375\n","Epoch 671/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 2.7324\n","Epoch 672/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.7274\n","Epoch 673/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.7223\n","Epoch 674/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 2.7173\n","Epoch 675/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 2.7123\n","Epoch 676/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 2.7072\n","Epoch 677/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 2.7022\n","Epoch 678/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.6972\n","Epoch 679/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.6922\n","Epoch 680/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 2.6872\n","Epoch 681/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.6822\n","Epoch 682/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 2.6773\n","Epoch 683/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.6723\n","Epoch 684/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.6673\n","Epoch 685/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.6624\n","Epoch 686/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 2.6574\n","Epoch 687/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.6525\n","Epoch 688/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.6476\n","Epoch 689/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.6426\n","Epoch 690/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 2.6377\n","Epoch 691/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 2.6328\n","Epoch 692/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.6279\n","Epoch 693/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.6230\n","Epoch 694/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.6181\n","Epoch 695/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 2.6133\n","Epoch 696/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.6084\n","Epoch 697/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.6035\n","Epoch 698/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.5987\n","Epoch 699/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 2.5938\n","Epoch 700/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.5890\n","Epoch 701/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 2.5842\n","Epoch 702/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.5793\n","Epoch 703/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 2.5745\n","Epoch 704/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.5697\n","Epoch 705/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.5649\n","Epoch 706/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.5601\n","Epoch 707/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.5553\n","Epoch 708/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 2.5506\n","Epoch 709/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 2.5458\n","Epoch 710/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.5410\n","Epoch 711/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 2.5363\n","Epoch 712/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 2.5315\n","Epoch 713/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.5268\n","Epoch 714/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.5220\n","Epoch 715/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.5173\n","Epoch 716/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 2.5126\n","Epoch 717/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 2.5079\n","Epoch 718/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 2.5032\n","Epoch 719/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.4985\n","Epoch 720/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.4938\n","Epoch 721/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.4891\n","Epoch 722/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 2.4844\n","Epoch 723/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 2.4798\n","Epoch 724/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 2.4751\n","Epoch 725/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.4705\n","Epoch 726/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.4658\n","Epoch 727/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 2.4612\n","Epoch 728/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 2.4565\n","Epoch 729/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 2.4519\n","Epoch 730/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.4473\n","Epoch 731/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.4427\n","Epoch 732/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 2.4381\n","Epoch 733/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.4335\n","Epoch 734/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 2.4289\n","Epoch 735/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.4243\n","Epoch 736/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.4198\n","Epoch 737/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.4152\n","Epoch 738/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.4106\n","Epoch 739/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.4061\n","Epoch 740/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.4016\n","Epoch 741/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.3970\n","Epoch 742/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 2.3925\n","Epoch 743/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.3880\n","Epoch 744/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 2.3835\n","Epoch 745/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 2.3789\n","Epoch 746/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 2.3744\n","Epoch 747/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 2.3700\n","Epoch 748/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 2.3655\n","Epoch 749/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 2.3610\n","Epoch 750/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 2.3565\n","Epoch 751/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 2.3521\n","Epoch 752/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 2.3476\n","Epoch 753/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.3431\n","Epoch 754/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 2.3387\n","Epoch 755/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.3343\n","Epoch 756/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.3298\n","Epoch 757/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 2.3254\n","Epoch 758/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.3210\n","Epoch 759/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.3166\n","Epoch 760/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 2.3122\n","Epoch 761/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 2.3078\n","Epoch 762/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.3034\n","Epoch 763/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 2.2990\n","Epoch 764/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.2947\n","Epoch 765/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.2903\n","Epoch 766/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 2.2859\n","Epoch 767/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 2.2816\n","Epoch 768/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 2.2772\n","Epoch 769/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 2.2729\n","Epoch 770/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 2.2686\n","Epoch 771/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 2.2642\n","Epoch 772/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.2599\n","Epoch 773/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 2.2556\n","Epoch 774/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.2513\n","Epoch 775/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.2470\n","Epoch 776/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 2.2427\n","Epoch 777/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.2384\n","Epoch 778/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.2342\n","Epoch 779/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 2.2299\n","Epoch 780/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 2.2256\n","Epoch 781/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.2214\n","Epoch 782/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.2171\n","Epoch 783/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.2129\n","Epoch 784/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.2087\n","Epoch 785/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 2.2044\n","Epoch 786/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.2002\n","Epoch 787/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 2.1960\n","Epoch 788/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.1918\n","Epoch 789/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.1876\n","Epoch 790/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.1834\n","Epoch 791/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.1792\n","Epoch 792/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 2.1750\n","Epoch 793/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 2.1709\n","Epoch 794/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.1667\n","Epoch 795/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.1625\n","Epoch 796/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.1584\n","Epoch 797/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 2.1542\n","Epoch 798/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.1501\n","Epoch 799/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 2.1459\n","Epoch 800/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.1418\n","Epoch 801/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 2.1377\n","Epoch 802/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.1336\n","Epoch 803/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.1295\n","Epoch 804/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 2.1254\n","Epoch 805/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.1213\n","Epoch 806/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.1172\n","Epoch 807/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 2.1131\n","Epoch 808/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.1090\n","Epoch 809/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.1050\n","Epoch 810/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.1009\n","Epoch 811/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 2.0969\n","Epoch 812/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.0928\n","Epoch 813/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 2.0888\n","Epoch 814/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.0847\n","Epoch 815/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.0807\n","Epoch 816/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.0767\n","Epoch 817/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.0727\n","Epoch 818/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.0687\n","Epoch 819/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.0647\n","Epoch 820/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.0607\n","Epoch 821/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.0567\n","Epoch 822/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.0527\n","Epoch 823/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.0487\n","Epoch 824/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 2.0447\n","Epoch 825/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 2.0408\n","Epoch 826/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 2.0368\n","Epoch 827/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.0329\n","Epoch 828/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 2.0289\n","Epoch 829/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 2.0250\n","Epoch 830/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 2.0211\n","Epoch 831/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 2.0171\n","Epoch 832/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.0132\n","Epoch 833/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.0093\n","Epoch 834/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 2.0054\n","Epoch 835/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 2.0015\n","Epoch 836/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 1.9976\n","Epoch 837/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 43ms/step - loss: 1.9937\n","Epoch 838/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 1.9898\n","Epoch 839/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 1.9860\n","Epoch 840/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 1.9821\n","Epoch 841/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 1.9782\n","Epoch 842/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 1.9744\n","Epoch 843/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 1.9705\n","Epoch 844/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 1.9667\n","Epoch 845/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 1.9629\n","Epoch 846/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 1.9590\n","Epoch 847/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 1.9552\n","Epoch 848/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 1.9514\n","Epoch 849/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 1.9476\n","Epoch 850/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 1.9438\n","Epoch 851/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 1.9400\n","Epoch 852/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 1.9362\n","Epoch 853/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 1.9324\n","Epoch 854/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 1.9286\n","Epoch 855/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 1.9248\n","Epoch 856/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 1.9211\n","Epoch 857/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 158ms/step - loss: 1.9173\n","Epoch 858/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 1.9136\n","Epoch 859/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 1.9098\n","Epoch 860/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 1.9061\n","Epoch 861/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 1.9023\n","Epoch 862/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 1.8986\n","Epoch 863/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 1.8949\n","Epoch 864/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 1.8912\n","Epoch 865/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 1.8874\n","Epoch 866/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 1.8837\n","Epoch 867/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 1.8800\n","Epoch 868/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 1.8764\n","Epoch 869/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 1.8727\n","Epoch 870/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 1.8690\n","Epoch 871/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 1.8653\n","Epoch 872/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 1.8616\n","Epoch 873/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 1.8580\n","Epoch 874/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 1.8543\n","Epoch 875/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 1.8507\n","Epoch 876/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 1.8470\n","Epoch 877/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 1.8434\n","Epoch 878/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 1.8397\n","Epoch 879/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 1.8361\n","Epoch 880/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 1.8325\n","Epoch 881/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 1.8289\n","Epoch 882/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 1.8253\n","Epoch 883/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 1.8217\n","Epoch 884/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 1.8181\n","Epoch 885/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 1.8145\n","Epoch 886/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 1.8109\n","Epoch 887/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 1.8073\n","Epoch 888/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 1.8037\n","Epoch 889/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 1.8002\n","Epoch 890/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 1.7966\n","Epoch 891/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 1.7931\n","Epoch 892/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 1.7895\n","Epoch 893/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 1.7860\n","Epoch 894/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 157ms/step - loss: 1.7824\n","Epoch 895/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 1.7789\n","Epoch 896/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 1.7754\n","Epoch 897/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 1.7719\n","Epoch 898/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 1.7683\n","Epoch 899/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 1.7648\n","Epoch 900/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 1.7613\n","Epoch 901/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 1.7578\n","Epoch 902/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 1.7543\n","Epoch 903/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.7509\n","Epoch 904/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 1.7474\n","Epoch 905/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 1.7439\n","Epoch 906/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 1.7404\n","Epoch 907/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 1.7370\n","Epoch 908/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 1.7335\n","Epoch 909/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.7301\n","Epoch 910/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.7266\n","Epoch 911/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 1.7232\n","Epoch 912/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 1.7198\n","Epoch 913/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 1.7163\n","Epoch 914/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 1.7129\n","Epoch 915/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 1.7095\n","Epoch 916/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 1.7061\n","Epoch 917/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 1.7027\n","Epoch 918/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 1.6993\n","Epoch 919/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 1.6959\n","Epoch 920/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 1.6925\n","Epoch 921/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 1.6891\n","Epoch 922/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 1.6857\n","Epoch 923/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 1.6824\n","Epoch 924/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 1.6790\n","Epoch 925/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 1.6756\n","Epoch 926/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 1.6723\n","Epoch 927/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 1.6689\n","Epoch 928/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 1.6656\n","Epoch 929/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.6623\n","Epoch 930/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 1.6589\n","Epoch 931/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 1.6556\n","Epoch 932/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 1.6523\n","Epoch 933/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 1.6490\n","Epoch 934/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 1.6457\n","Epoch 935/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 1.6424\n","Epoch 936/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 1.6391\n","Epoch 937/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 1.6358\n","Epoch 938/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 1.6325\n","Epoch 939/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 1.6292\n","Epoch 940/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 1.6259\n","Epoch 941/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 1.6227\n","Epoch 942/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 1.6194\n","Epoch 943/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 1.6161\n","Epoch 944/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 1.6129\n","Epoch 945/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 1.6096\n","Epoch 946/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 1.6064\n","Epoch 947/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 1.6032\n","Epoch 948/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 1.5999\n","Epoch 949/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 1.5967\n","Epoch 950/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 1.5935\n","Epoch 951/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.5903\n","Epoch 952/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.5871\n","Epoch 953/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 1.5839\n","Epoch 954/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.5807\n","Epoch 955/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 1.5775\n","Epoch 956/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 1.5743\n","Epoch 957/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 1.5711\n","Epoch 958/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 1.5679\n","Epoch 959/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 1.5648\n","Epoch 960/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 1.5616\n","Epoch 961/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.5584\n","Epoch 962/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 1.5553\n","Epoch 963/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 1.5521\n","Epoch 964/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 1.5490\n","Epoch 965/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 1.5458\n","Epoch 966/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 1.5427\n","Epoch 967/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 1.5396\n","Epoch 968/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 1.5365\n","Epoch 969/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 1.5333\n","Epoch 970/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 1.5302\n","Epoch 971/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 1.5271\n","Epoch 972/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 1.5240\n","Epoch 973/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 1.5209\n","Epoch 974/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 1.5178\n","Epoch 975/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 1.5148\n","Epoch 976/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 1.5117\n","Epoch 977/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 1.5086\n","Epoch 978/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 1.5055\n","Epoch 979/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 1.5025\n","Epoch 980/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 1.4994\n","Epoch 981/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 1.4963\n","Epoch 982/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 1.4933\n","Epoch 983/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.4903\n","Epoch 984/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 1.4872\n","Epoch 985/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.4842\n","Epoch 986/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 1.4812\n","Epoch 987/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 1.4781\n","Epoch 988/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 1.4751\n","Epoch 989/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 1.4721\n","Epoch 990/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 1.4691\n","Epoch 991/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 1.4661\n","Epoch 992/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 1.4631\n","Epoch 993/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 1.4601\n","Epoch 994/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 1.4571\n","Epoch 995/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 1.4541\n","Epoch 996/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 1.4512\n","Epoch 997/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 1.4482\n","Epoch 998/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 1.4452\n","Epoch 999/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 1.4423\n","Epoch 1000/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 1.4393\n","\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/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"]}]},{"cell_type":"code","source":["# Построение графика ошибки реконструкции\n","lib.ire_plot('training', IRE1, IREth1, 'AE1')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":612},"collapsed":true,"id":"uleuGdNtUO-B","executionInfo":{"status":"ok","timestamp":1763317009589,"user_tz":-180,"elapsed":473,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"825d5788-b2a9-411e-f058-d3dde61b1676"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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dtqp4bGc8TsO6niGJjk5OejTp0/D5E1z585N+s7qhDX//Oc/0apVK9917rfffth8883x559/RtpmXZcuXfDHH39g5cqVeOmll3DiiSeGXsf06dMbZss+8MAD0a5du8D3dOrUCT169MDs2bMxc+ZMlJeX+94QyAbPPPNMw+NBgwYZx5w97rjjcN5556GyshJLlizBe++9t0GViURERNmMQUsiIiJK0r59ezz77LPGCV+CXHfddbj++uvj36gYfPvttw2Pd9ttN6uMqb333tv1/kQigZycHM/l//GPf6S2kWkQdpt+/PFHXHnllXjnnXesZ0APMySAn2233TZwwiI181dmysXBJuga9NkzZsxoeGybNbfbbrvFFrQ84YQTGrI7Bw8e3BC43H///a2CjwAwZcqUhscLFiyw7uK8atUqAEAikcCCBQuyOmi5cOFCfPDBBw1/613DpRYtWmDgwIEYM2YMABHotA1a9u3b1zjrOhEREdlh0JKIiGgjNHDgQNcsvcuXL8fvv//e0K17yZIl2HffffHqq682jOG3IVi6dGnD465du1q9p1u3bg2Pq6ursWbNGpSVlXkuv+mmm0bevnQJs03vvfcejjrqqIaMVFtyVuhU2QS61KCmnCU7E59dU1OT9Lp6jHXp0sXqczt37my1nI2rr74akyZNwtSpU5FIJPD666/j9ddfBwBsvfXW2HfffXHggQfiiCOOMM7eDYiAnvT999/j+++/D70dK1eujPYFGslzzz3XEJDfeuutfQPMp5xySkPQcvz48Vi5cmWkzGUiIiIKx39wGiIiItogXXTRRXjooYca/r3wwgv48ssv8d1336FPnz4ARIBu8ODB+P333zO8tfGRE/AAYrxBG/pyQcG5oqKi8BuWZrbbtHTpUpx44okNAcuuXbti5MiR+Oyzz7Bw4UJUVlaivr4eiUQCiUTCNeFMfX19LNvql8WabnF8tnqMFRcXW70n6tiTJiUlJZg8eTLuvPNOV8AdEBPDPP300zj55JPRoUMHXHHFFVi3bl3SOsrLy1PejjiDyemgdg3/17/+5bvswQcfjA4dOgAQE0Wp4+ISERFR+jBoSURERA169+6N999/vyELs6KiAsOGDcvwVsVHDQ6tXbvW6j36cl7ZaRuCJ554oiFg1adPH3z//fe46qqrsPfee6Njx44oKipyBfbiyq7ckKjHWGVlpdV7bI9FW82bN8dll12GP/74AzNmzMADDzyAQYMGubKrKysrceedd2L//fdPClyqgfoLL7ywIUgd5l+/fv1i/U5xmj59On788ceGv6+77jrk5OR4/mvWrFnDOKaAO+BJRERE6cOgJREREbm0a9cODz74YMPfkyZNwltvvZXBLYqP2k3adgxBdaKX5s2bb9BByw8//LDh8dVXX+3bDR4A5s2bl+5NanLatm3b8HjBggVW77FdLiw5cdAFF1yAF154AQsWLMA333yD0047rWGZadOmJc1w3b59+4bHarBuQ5Fq0HHq1Kn45ZdfYtoaIiIi8sIxLYmIiCjJ0Ucfjb333huff/45ABHA2hBmzFVnnf7yyy9RV1eHvLw83/d88cUXrvdnovtyY32mOpZh0KQ0dXV1DccHOXbccceG4O+0adOs3vPll1+mc5NcdtppJzz99NPIy8vDk08+CUCM03jZZZc1LKOO7/jFF18ETj5lK5Nd/6Xq6mq88MILDX9vu+22gTO8S7/++itWrFgBQAQ+b7nllnRsIhEREf0fZloSERGRkToD+IwZMzB+/PjMbUxM9tprLxQUFAAQ4zcGZZDW19dj1KhRDX8fcMABad0+L4WFhQ2PTZO/xCU316kaBnVtHjdu3AaZhZcqtVv0W2+9FTg+5GeffZaRjNUjjzyy4fGSJUtcr+29994NgbwFCxbgzTffjOUzG+s49vPWW29h+fLlAIBmzZrhk08+wdSpU63+/ec//2lYz//+97/YxnElIiIiMwYtiYiIyOiggw7CXnvt1fD3zTffnMGtiUerVq1w4oknNvx9+eWX+47L+NBDD+GHH34AIAJ6Z555Ztq30aRNmzYNj//666+0fc6WW27Z8NgvSL106VJccskladuOpuyQQw5Bp06dAIgxYdVAl666utqV4Ziqqqoq10RAfubPn9/wuF27dq7XCgoKcPHFFzf8fe6554Y67vQgqNRYx7EftWv4wQcfnPTd/QwePLghsD9//nx8/PHHsW8fERERORi0JCIiIk/XXnttw+OvvvoK7777bga3Jh7XXnttw2Qpv/zyCwYMGIA//vjDtUx9fT3uv/9+DB8+vOG58847L2k25sbSq1evhsfvv/9+LLM7mxxxxBENj0eOHInnnnsuaZlvvvkGffv2xfz5861nYN+YNGvWzJWl/Mgjj+DKK69EdXW1a7mlS5fi2GOPxbRp0xqyf1O1aNEidOnSBZdddhmmT5/uudzEiRNdM78feuihSctceuml6NmzJwARYNxll13wyiuveGYXLlu2DI8//jh23nln3HnnncZl1OP4lVdesfpOo0ePdk2Ko44xG9ayZcvw9ttvN/x98sknh3p/p06dsP/++zf8zQl5iIiI0otjWhIREZGnAQMGYPfdd28Ym++mm27CIYcc4vuet99+G8uWLbP+jOLiYtxxxx0pbWcY3bt3x5NPPomTTz4ZdXV1mDJlCrbddlvsu+++6N69OyoqKvDpp5+6MsH22GOPRt1G3W677YYuXbpg/vz5WLRoEXr06IH+/fujbdu2DeME7rrrrq4s0iiGDBmCu+++G7/88guqqqpwyimn4NZbb0WfPn1QWFiImTNnNgTD+vTpgwEDBmR0v2SrYcOG4a233sIbb7wBALjjjjvw1FNPoV+/fmjdujUWLFiAjz/+GOvXr8eWW26Jo446Cvfeey8Adxf9KFatWoW7774bd999N1q3bo2ddtoJm222GQoLC/H333/j+++/dwXpt9lmG1x00UVJ62nRogXGjx+Pgw46CHPmzMHixYtxwgknoG3btthjjz3QoUMHJBIJrFixAj/++CN+/fXXhoCm1zAKxx57LP773/8CEMHcr7/+GjvvvDOKi4sbljnnnHPQvXv3lPaBlzFjxjR0Sy8pKcHAgQNDr+Pkk09uGLP0tddewyOPPOKaMV7166+/4vzzzw+1/hEjRrhmeZcee+wxPPbYY67n9ED4YYcdhubNm7ueO/vss3H22WeH2gYiIqJswaAlERER+br22msbJuH54osv8OGHH+LAAw/0XP6rr77CV199Zb3+li1bNnrg68QTT0RJSQmGDRuGJUuWoLa2Fh9//LGxu+fgwYPx5JNPusbja2y5ubl45JFHcOyxx6K6uhqLFy/Gs88+61pmyJAhKQctCwoK8Oabb+LQQw9tCGzNnj0bs2fPdi23995746WXXsITTzyR0udtqHJycvDSSy9h6NChePHFFwEAy5cvx6uvvupabrvttsPrr7/u+i2DZmz3k5+fj4KCAlRVVQEAVqxY4ZoRXtevXz+88MILnhmzW265JaZPn46zzz4bY8eORSKRwLJlyzBhwgTPdbZq1cpzEqeDDz4YgwcPbpgIZ9q0aUmTFR1++OFpC1qqmZEDBw6MlCl87LHH4txzz8X69euxdu1ajB07FkOHDjUuu3DhwqSZ2YMMGzbMGLRcvHgxvvvuO9/36uepfB8REVFTxe7hRERE5Ouwww7DLrvs0vD3jTfemMGtic/hhx+O3377Dffffz8OPvhgdOrUCc2bN0fLli2x3Xbb4dxzz8XUqVMxZswYVyZYJrd3+vTpOOuss9CzZ0+UlpamZTbmbbbZBt9++y1uvfVW7LLLLigtLUVBQQG6du2Kww8/HGPGjMHkyZONgRVyFBQU4IUXXsB7772H448/Hp07d0bz5s3Rvn177L333njwwQfx1VdfYdttt22YkRqA9UzWJpttthmWL1+O8ePH4/LLL8cBBxyAzTffHEVFRcjLy0OrVq3Qp08fDBs2DBMnTsTHH3+MDh06+K6zdevWePnll/H999/jP//5D/baay907NgRzZs3R2FhITp06IB99tkHF154IcaPH49FixZhyJAhnut7/vnn8fzzz+Pwww9H586dG+1mwMyZM/HNN980/B22a7hUVlbmGkZh9OjRqW4aERERechJJBKJTG8EEREREdHGau+998YXX3wBAJg6dSp23333DG8RERERUeYxaElERERElCHz5s1D9+7dUVdXh+bNm6O8vDyjQxEQERERZQt2DyciIiIiyoBEIoGLLroIdXV1AICjjz6aAUsiIiKi/8OgJRERERFRzK699lrcf//9WLZsmfH1uXPn4uijj26YYTwvLw+XXXZZY24iERERUVZrkkHLRx99FL1790ZZWRnKysqw55574p133vF9zyuvvIIePXqgsLAQO+ywA95+++1G2loiIiIi2tj8+eefuPjii9GxY0fsvPPOOOmkk3DOOefglFNOwa677oqtttqqIWAJAFdffbVrwisiIiKijV2zTG9AFJ07d8Ztt92GrbfeGolEAs888wyOOuoofPvtt+jZs2fS8l988QUGDx6MkSNHNsx6OXDgQHzzzTfo1atXBr4BEREREW0Mamtr8e233+Lbb781vl5UVIQbb7yRWZZEREREmg1mIp7WrVvjzjvvxBlnnJH02oknnoi1a9diwoQJDc/tscce2HHHHfHYY4815mYSERER0UZg1apVeOONN/DRRx9h1qxZWLp0KZYtW4a6ujq0bt0a2267LQ488ECcccYZ6NixY6Y3l4iIiCjrNMlMS1VdXR1eeeUVrF27FnvuuadxmSlTpmD48OGu5wYMGIBx48Z5rreqqgpVVVUNf9fX12PFihVo06YNcnJyYtl2IiIiItow5ebm4uijj8bRRx8duOzq1asbYYuIiIiIMi+RSGDNmjXo1KkTcnP9R61sskHLH374AXvuuSfWr1+PFi1a4PXXX8f2229vXHbx4sVo376967n27dtj8eLFnusfOXIkbrjhhli3mYiIiIiIiIiIaGM3f/58dO7c2XeZJhu03HbbbTFjxgyUl5dj7NixGDJkCCZPnuwZuAxrxIgRruzM8vJybL755pgzZw5KS0tj+YxsUlNTg48//hj7778/8vPzM705RBsdnoNEmcfzkCjzeB4SZRbPQaLM29DPwzVr1mCLLbawiq012aBl8+bNsdVWWwEA/vGPf+Crr77C/fffj//+979Jy3bo0AFLlixxPbdkyRJ06NDBc/0FBQUoKChIer5169YoKytLceuzT01NDYqLi9GmTZsN8qQgynY8B4kyj+chUebxPCTKLJ6DRJm3oZ+H8jvZDL3o33m8Camvr3eNQanac8898eGHH7qemzhxoucYmERERERERERERJQ5TTLTcsSIETj00EOx+eabY82aNRgzZgwmTZqE9957DwBw6qmnYrPNNsPIkSMBABdddBH69u2Lu+++G//85z/x4osvYvr06Xj88ccz+TWIiIiIiIiIiIjIoEkGLf/++2+ceuqpWLRoEVq2bInevXvjvffew8EHHwwA+PPPP10zEO21114YM2YMrr76avznP//B1ltvjXHjxqFXr16Z+gpERERERERERETkoUkGLZ966inf1ydNmpT03PHHH4/jjz8+TVtEREREREREREREcdlgxrQkIiIiIiIiIiKiDQODlkRERERERERERJRVGLQkIiIiIiIiIiKirNIkx7RsahKJBGpqalBfX5/pTfFUU1ODZs2aYf369airq8v05mzw8vLykJ+fn+nNICIiIiIiIiLKSgxaplF1dTX+/vtvVFZWZn0gMJFIoEOHDpg/fz5ycnIyvTkbhYKCArRt2xZlZWWZ3hQiIiIiIiIioqzCoGWaVFZWYv78+cjLy8Mmm2yCoqIi5OXlZW1AsL6+HhUVFWjRogVyczlqQDrJzNvy8nL89ddfAMDAJRERERERERGRgkHLNFm2bBny8/PRtWtX5OXlZXpzAtXX16O6uhqFhYUMWjaCoqIilJaWYsGCBVi2bBmDlkRERERERERECkan0qC2thZr165F69atm0TAkjIjJycHLVu2RFVVFWpqajK9OUREREREREREWYNByzSora0FIMYsJPIjJ+PJ9jFPiYiIiIiIiIgaE4OWaZSt41dS9uAxQkRERERERESUjEFLIiIiIiIiIiIiyioMWhIREREREREREVFWYdCSiIiIiIiIiIiIsgqDlpRR3bp1Q05ODkaPHt3w3OjRo5GTk+P6l5ubi7KyMuy0004YMWIEli5d6rlO/b1e/yZNmpT+L0hERERERERERKE1y/QGEHkpKSnBcccdB0DMrj1v3jxMmTIFM2bMwKhRo/Dpp59i66239nz/gAED0KFDB8/X/V4jIiIiIiIiIqLMYdCSslbbtm1dGZgAMGvWLPTt2xdLlizBxRdfjLfeesvz/VdddRX69euX3o0kIiIiIiIiIqLYsXs4NSk9e/bE8OHDAQATJ05EVVVVhreIiIiIiIiIiIjixqAlNTm9e/cGANTU1GDFihUZ3hoiIiIiIiIiIoobg5bU5KxevRoAkJeXh7Zt22Z4a4iIiIiIiIiIKG4MWlKTI8exPOSQQ5Cfn5/hrSEiIiIiIiIiorhxIp5M2WUXYPHiTG9FgxwAZYkEcnJyghfu0AGYPj3t26SSs4c//vjjGDNmDLp27YoHHnjA9z3777+/52stW7bEqlWrYt5KIiIiIiIiIiKKA4OWmbJ4MfDXX5neigY5//cvm8ybN88YRN1tt93w/vvvo2XLlr7vHzBgADp06GB8rbi4OJZtJCIiIiIiIiKi+DFomSkewbRMSQBI/F+mZWDwspG2vaSkBMcddxwAoKqqCrNnz8Z3332HL7/8EmeddRZefPFF3/dfddVV6NevXyNsKRERERERERERxYlBy0xp5O7VQRL19Vi9ejXKysqQk5sdQ522bdsWo0ePdj332muv4cQTT8RLL72E/fbbD+eee25mNo6IiIiIiIiIiNImO6JTRJaOOeYYXHXVVQCAa6+9FuXl5RneIiIiIiIiIiIiihuDltTkjBgxAh07dsTy5ctxzz33ZHpziIiIiIiIiIgoZgxaUpNTXFyMa665BgBw3333YeXKlRneIiIiIiIiIiIiihPHtKQmadiwYbj77rvx+++/46677sItt9yStMxtt92WNCam6qSTTkL//v3TuJVERERERERERBQFg5bUJOXn5+Pmm2/G4MGD8eCDD2L48OFo06aNa5n33nvPdx077rgjg5ZERERERERERFmIQUvKqLlz5yY9N3ToUAwdOjTwvYMGDcKgQYOSnk8kEjFsGRERERERERERZQrHtCQiIiIiIiIiIqKswqAlERERERERERERZRUGLYmIiIiIiIiIiCirMGhJREREREREREREWYVBSyIiIiIiIiIiIsoqDFoSERERERERERFRVmHQkoiIiIiIiIiIiLIKg5ZERERERERERESUVRi0JCIiIiIiIiIioqzCoCURERERERERERFlFQYtiYiIiIiIiIiIKKswaElERERERERERERZhUFLIiIiIiIiIiIiyioMWhIREREREREREVFWYdCSMqpbt27IycnB6NGjG54bPXo0cnJyXP9yc3NRVlaGnXbaCSNGjMDSpUs916m/1+vfpEmTQm2r3K6hQ4dG+7JERERERERERGSlWaY3gMhLSUkJjjvuOABAXV0d5s2bhylTpmDGjBkYNWoUPv30U2y99dae7x8wYAA6dOjg+br+Wk5ODgAgkUjEsPVERERERERERBQVg5aUtdq2bevKwASAWbNmoW/fvliyZAkuvvhivPXWW57vv+qqq9CvX7/0biQREREREREREcWO3cOpSenZsyeGDx8OAJg4cSKqqqoyvEVERERERERERBQ3Bi2pyenduzcAoKamBitWrEh5fddff31D13AgeUzMuXPnJr1n7dq1GDFiBLbaaisUFBSgQ4cOGDJkCP7666+Ut4eIiIiIiIiIaGPH7uHU5KxevRoAkJeXh7Zt26a8vh133BFDhgzBM888AwAYMmSI6/UWLVq4/i4vL8dee+2FP//8E/vuuy969eqFKVOm4Nlnn8XkyZPx3XffoWXLlilvFxERERERERHRxopBS2py5DiWhxxyCPLz81Ne38CBAzFw4MCGoKU+jqZu3LhxGDBgAD799FOUlZUBAFauXIkDDjgAM2bMwCOPPIIRI0akvF1ERERERERERBsrBi0zZJdddsHixYszvRkuiUTC1U3aS4cOHTB9+vRG2CKHnD388ccfx5gxY9C1a1c88MADvu/Zf//9PV9r2bIlVq1aFWlbSkpKMGrUqIaAJQBssskmuOqqqzBo0CB88MEHDFoSEREREREREaWAQcsMWbx4Mcc/DDBv3jxjEHW33XbD+++/H9gFe8CAAejQoYPxteLi4sjbtcsuu6Bjx45Jz2+33XYAwN+ViIiIiIiIiChFDFpmiFcwLZPCZFo2hpKSEhx33HEAgKqqKsyePRvfffcdvvzyS5x11ll48cUXfd9/1VVXoV+/frFv1+abb258XmZerl+/PvbPJCIiIiIiIiLamDBomSGN3b06SH19PVavXo2ysjLk5mbHpPJt27ZNGl/ytddew4knnoiXXnoJ++23H84999xG365s2T9ERERERERERBsqRl+oSTnmmGNw1VVXAQCuvfZalJeXZ3iLiIiIiIiIiIgobgxaUpMzYsQIdOzYEcuXL8c999wT23rlTOS1tbWxrZOIiIiIiIiIiMJj0JKanOLiYlxzzTUAgPvuuw8rV66MZb2dO3cGAMyaNSuW9RERERERERERUTQc05KapGHDhuHuu+/G77//jrvuugu33HJL0jK33XZb0piYqpNOOgn9+/dv+PvYY4/FXXfdhYMOOggHHHAASktLAQC333472rRpE/t3ICIiIiIiIiIiMwYtqUnKz8/HzTffjMGDB+PBBx/E8OHDkwKL7733nu86dtxxR1fQ8qabbkJubi5ee+01jBs3DtXV1QCAq6++mkFLIiIiIiIiIqJGxKAlZdTcuXOTnhs6dCiGDh0a+N5BgwZh0KBBSc8nEolI21JYWIjbb78dt99+u/H1oO3q1q1b5M8mIiIiIiIiIiIHx7QkIiIiIiIiIiKirMKgJREREREREREREWUVBi2JiIiIiIiIiIgoqzBoSURERERERERERFmFQUsiIiIiIiIiIiLKKgxaEhERERERERERUVZh0JKIiIiIiIiIiIiyCoOWaZRIJDK9CZTleIwQERERERERESVj0DINcnPFbq2rq8vwllC2k8eIPGaIiIiIiIiIiIhBy7TIz89Hfn4+KioqMr0plOXWrVuHvLw85OfnZ3pTiIiIiIiIiIiyBoOWaZCTk4PS0lKUl5dj3bp1md4cylJ1dXUoLy9HcXExcnJyMr05RERERERERERZo1mmN2BD1bZtW6xbtw5//vknysrKUFpairy8vKwNTtXX16O6uhrr169nV+U0SyQSqKqqwooVK1BfX4927dplepOIiIiIiIiIiLIKg5ZpkpeXhy5dumDZsmVYs2YNVq1alelN8pVIJLBu3ToUFRVlbWB1Q1NSUoIOHTqgefPmmd4UIiIiIiIiIqKswqBlGuXl5aF9+/Zo164dampqUF9fn+lN8lRTU4NPPvkE++23H8dXbATNmjVDs2Y8/YiIiIiIiIiITBg1aQQ5OTlZn02Xl5eH2tpaFBYWMmhJREREREREREQZxcELiYiIiIiIiIiIKKswaElERERERERERERZhUFLIiIiIiIiIiIiyioMWhIREREREREREVFWYdCSiIiIiIiIiIiIsgqDlkRERERERERERJRVGLQkIiIiIiIiIiKirMKgJREREREREREREWUVBi2JiIiIiIiIiIgoqzBoSURERERERERERFmFQUsiIiIiIiIiIiLKKgxaEhERERERERERUVZh0JKIiIiIiIiIiIiyCoOWRERERERERERElFUYtCQiIiIiIiIiIqKs0iSDliNHjsSuu+6K0tJStGvXDgMHDsTPP//s+57Ro0cjJyfH9a+wsLCRtpiIiIiIiIiIiIhsNcmg5eTJk3Heeedh6tSpmDhxImpqatC/f3+sXbvW931lZWVYtGhRw7958+Y10hYTERERERERERGRrWaZ3oAo3n33Xdffo0ePRrt27fD1119jv/3283xfTk4OOnTokO7NIyIiIiIiIiIiohQ0yaClrry8HADQunVr3+UqKirQtWtX1NfXY+edd8att96Knj17GpetqqpCVVVVw9+rV68GANTU1KCmpiamLc8e8jttiN+NqCngOUiUeTwPiTKP5yFRZvEcJMq8Df08DPO9chKJRCKN25J29fX1OPLII7Fq1Sp89tlnnstNmTIFv/76K3r37o3y8nLcdddd+OSTTzBr1ix07tw5afnrr78eN9xwQ9LzY8aMQXFxcazfgYiIiIiIiIiIaENXWVmJk046CeXl5SgrK/NdtskHLc855xy88847+Oyzz4zBRy81NTXYbrvtMHjwYNx0001Jr5syLbt06YJly5YF7tSmqKamBhMnTsTBBx+M/Pz8TG8O0UaH5yBR5vE8JMo8nodEmcVzkCjzNvTzcPXq1Wjbtq1V0LJJdw8///zzMWHCBHzyySehApYAkJ+fj5122gm//fab8fWCggIUFBQY37chHjTShv79iLIdz0GizON5SJR5PA+JMovnIFHmbajnYZjv1CRnD08kEjj//PPx+uuv46OPPsIWW2wReh11dXX44Ycf0LFjxzRsIREREREREREREUXVJDMtzzvvPIwZMwZvvPEGSktLsXjxYgBAy5YtUVRUBAA49dRTsdlmm2HkyJEAgBtvvBF77LEHttpqK6xatQp33nkn5s2bh2HDhmXsexAREREREREREVGyJhm0fPTRRwEA/fr1cz0/atQoDB06FADw559/IjfXSSRduXIl/v3vf2Px4sXYZJNN8I9//ANffPEFtt9++8babCIiIiIiIiIiIrLQJIOWNnMHTZo0yfX3vffei3vvvTdNW0RERERERERERERxaZJjWhIREREREREREdGGi0FLIiIiIiIiIiIiyioMWhIREREREREREVFWYdCSiIiIiIiIiIiIsgqDlkRERERERERERJRVGLQkIiIiIiIiIiKirMKgJREREREREREREWUVBi2JiIiIiIiIiIgoqzBoSURERERERERERFmFQUsiIiIiIiIiIiLKKgxaEhERERERERERUVZh0JKIiIiIiIiIiIiyCoOWRERERERERERElFUYtCQiIiIiIiIiIqKswqAlERERERERERERZRUGLYmIiIiIiIiIiCirMGhJREREREREREREWYVBSyIiIiIiIiIiIsoqDFoSERERERERERFRVmHQkoiIiIiIiIiIiLIKg5ZERERERERERESUVRi0JCIiIiIiIiIioqzCoCURERERERERERFlFQYtiYiIiIiIiIiIKKswaElERERERERERERZhUFLIiIiIiIiIiIiyioMWhIREREREREREVFWYdCSiIiIiIiIiIiIsgqDlkRERERERERERJRVGLQkIiIiIiIiIiKirMKgJREREREREREREWUVBi2JiIiIiIiIiIgoqzBoSURERERERERERFmFQUsiIiIiIiIiIiLKKgxaEhERERERERERUVZh0JKIiIiIiIiIiIiyCoOWRERERERERERElFUYtCQiIiIiIiIiIqKswqAlERERERERERERZRUGLYmIiIiIiIiIiCirMGhJREREREREREREWYVBSyIiIiIiIiIiIsoqDFoSERERERERERFRVmHQkoiIiIiIiIiIiLIKg5ZERERERERERESUVRi0JCIiIiIiIiIioqzCoCURERERERERERFlFQYtiYiIiIiIiIiIKKswaElERERERERERERZhUFLIiIiIiIiIiIiyioMWhIREREREREREVFWYdCSiIiIiIiIiIiIsgqDlkRERERERERERJRVGLQkIiIiIiIiIiKirMKgJREREREREREREWUVBi2JiIiIiIiIiIgoqzBoSURERERERERERFmFQUsiIiIiIiIiIiLKKgxaEhERERERERERUVZh0JKIiIiIiIiIiIiyCoOWRERERERERERElFUYtCQiIiIiIiIiIqKswqAlERERERERERERZRUGLYmIiIiIiIiIiCirMGhJREREREREREREWYVBSyIiIiIiIiIiIsoqDFoSERERERERERFRVmHQkoiIiIiIiIiIiLIKg5ZERERERERERESUVRi0JCIiIiIiIiIioqzCoCURERERERERERFlFQYtiYiIiIiIiIiIKKswaElERERERERERERZhUFLIiIiIiIiIiIiyioMWhIREREREREREVFWYdCSiIiIiIiIiIiIsgqDlkRERERERERERJRVGLQkIiIiIiIiIiKirMKgJREREREREREREWUVBi2JiIiIiIiIiIgoqzBoSURERERERERERFmFQUsiIiIiIiIiIiLKKgxaEhERERERERERUVZh0JKIiIiIiIiIiIiyCoOWRERERERERERElFUYtCQiIiIiIiIiIqKswqAlERERERERERERZRUGLYmIiIiIiIiIiCirMGhJREREREREREREWYVBSyIiIiIiIiIiIsoqDFoSERERERERERFRVmHQkoiIiIiIiIiIiLJKkwxajhw5ErvuuitKS0vRrl07DBw4ED///HPg+1555RX06NEDhYWF2GGHHfD22283wtYSERERERERERFRGE0yaDl58mScd955mDp1KiZOnIiamhr0798fa9eu9XzPF198gcGDB+OMM87At99+i4EDB2LgwIGYOXNmI245ERERERERERERBWmW6Q2I4t1333X9PXr0aLRr1w5ff/019ttvP+N77r//fhxyyCG4/PLLAQA33XQTJk6ciIceegiPPfZY2reZiGiDsmwZ8OefwE47ATk5md4aIiIiIiIi2sA0yaClrry8HADQunVrz2WmTJmC4cOHu54bMGAAxo0bZ1y+qqoKVVVVDX+vXr0aAFBTU4OampoUtzj71NTU4NJLL8W5556LHAYgiBpdIpFAVVUVCgoKsv8cTCSAxYuB+npgk02A4uJMbxFRLJrUeUi0geJ5SJRZPAeJMk+eh5tvvjmmTZuW6c2JXZiYWpMPWtbX1+Piiy/G3nvvjV69enkut3jxYrRv3971XPv27bF48WLj8iNHjsQNN9yQ9Pz777+P4g20gb5q1SosX74805tBRE3JypXiHxEREREREcVqQ5yLpbKy0nrZJh+0PO+88zBz5kx89tlnsa53xIgRrszM1atXo0uXLujfvz/Kyspi/axsUFNTg1atWvGOGlGGNKm72mvXAqtWOX9vtlnGNoXIRc0Cbt0aKCoK+fYmdB4SbaB4HhJlFs9BosxTMy0PO+ywTG9O7GRPZhtNOmh5/vnnY8KECfjkk0/QuXNn32U7dOiAJUuWuJ5bsmQJOnToYFy+oKAABQUFSc/n5+cjPz8/+kZnsbvvvhuHHXZYZr5fdTWwdCmDH7TRqqmpwdtvv525czCMRx4BzjvP+XvBgsxtC5FqzBjg5JPF4xUrRBAzhMjnYSIBzJsHdO3KMV6JUtSkrodEGyCeg0SZt6Gfh2G+U5OcPTyRSOD888/H66+/jo8++ghbbLFF4Hv23HNPfPjhh67nJk6ciD333DNdm0m26uqAPn2ALl2AV17J9NYQUZC6ukxvAZHZunWZ+dwrrgC22AIYOjQzn09EREREtAFqkkHL8847D8899xzGjBmD0tJSLF68GIsXL8Y6pbFy6qmnYsSIEQ1/X3TRRXj33Xdx991346effsL111+P6dOn4/zzz8/EVyDVBx8AP/0kMlVOOCHTW0NEQRi0pGyVqWPzrrvE/88+m5nPJyIiIiLaADXJoOWjjz6K8vJy9OvXDx07dmz499JLLzUs8+eff2LRokUNf++1114YM2YMHn/8cfTp0wdjx47FuHHjfCfvoUaSqcwYIoqmtjbTW0BkVl+f6S0gIiIiIqKYNMkxLRMWY1RNmjQp6bnjjz8exx9/fBq2iFIScswxIsowZlpStsqmY7O2VmyPYXxsIiIiIiIK1iQzLWkDw6AlUdOSTYEhIlW2HJurVgHduwOdOwN//JHprSEiIiIiapIYtCQionDYPZyyVTq7h4cJiN54I/Dnn8CyZcDpp6dvm4iIyF623NgiyhSeA9QEMWhJREThsMJD2Spdx+ZLLwGtWgHnnmu3/F9/OY9/+SUtm0RERCFMmgRsuilwxBHs5UUbp3feAdq0AU48MdNbQhQKg5aUeaw4EDUtDFpStkpXpuWgQUBFBfDoo+L/oG3IVapXvMa5JRLcJ2SHxwnFaf/9gZUrgQkTgClTMr01lAqWDdEcdhhQXg68/DIwa1amt4bIGoOWREQUDruHU7ZqjIB6TU3wNuTkuP8mobwc2GUXYMcdRdd5Ig8t//gDzbbZBjjqKAYoKH7l5ZneAopq8mSgSxfgjDMyvSVNG6/B1IQwaEmZx8ooUdPCIAxlq8Y4NoOyOWtr3ZmW6Rxns6m59lrgm2+A778HLrss01tDWWy3kSORM28eMH488Oabmd4c2tCoN5aoaenXTwzB8vTTwOzZmd6apmvdukxvAZE1Bi0p8xi0JGpaNuSg5U03AVtvDbz7bqa3hKIIyoKMQ9DxX1fH7uFefvjBeTxjRsY2g7Jf8dKlzh/z5mVuQ2jDlMsmcKMaMwbo3h14+OF417tqVbzra8omTwa23Ra48kq75devT+/2EMWIJTYREYWjBoY2pIr/+vUiE+y334BDD8301lAUjVEJ1wOjeialHrRkpqVDzW5iMJdsNcbNCNq4bEh1l6bg5JOBP/4Azj8/3vXm5cW7vqasXz8x8d8ddwCLFgUvHzQ+N1EWYYlNmceGC1HTUl3tPN6QKozq96Kmqaoq/Z+hj+mq/82gpTc1aMn9Qrr//hfYaSfkvPWW+3mWzRQ3dg/fMDD4bLZ2bfAyzFKlJqRZpjeAiA0XoiZGbUA224AuI8zmafoaI2ipHycMWkbDG5akSiSAs88GADQ7+mj3awxaUtwY7NowMPhsZrNfGLSkJoQlNmUeZyImalrUwNCGlGmZjUHLsWOBvn2Bt9/O9JY0DXEHLZ96Cth/f/dzQZmWtbXsBu2FY32SF78GNIOWFDeWP5kT5UbeI4+Ia/GXX7qf35DHWE+FTZlZXp7+7bBVVwcMGwYcfTSwYkWmt4ay0AaUIkNNFoOWRE3Lhhq0zMaG8fHHi/8/+YSNLBtxBi0TCVGJ1unBbb3RpGdaslHlYDCXvCxZ4v1aNpbN1LSx7ZE5NTVAQYH98mvWAOedJx7vvXfyuiiZTZmZTZmWzzwjbhIDQFmZ+JtIwUxLyjxWHLzdeqsIWixYkOktoWzxzDPAUUdlduZddg+nbBVn0NLr2pTO7uFTpwJHHAG88or9e5oSBi3Jy+LF3q8xaNk0ffstcOSR2RmAYNuj8ejXwLB1LTUjMKinAwk2daFsyrScOtV5PHZs5raDstYG1NqkJosXHLMZM4D/9//E46VLgUmTMrk1lA3WrQOGDhWPP/wwczP/MdOSslVjBC3DTsQTJji3557i/wkTNsygHoOW5EXLtKxv1gy58tziDaWmqW9fkSX35pviBnxxcaa3yMG2R+PR61Zh61p+5z/LBjObulA2ZVo2b+48Zl2cDJhpSZmXjV3nXnkFOOUUYPbszG3DTz85jydPztx2ePn1V+DUU4ExYzK9JRsPNUhpMzNgumysQcvbbxcTRaxcabe+adOAk04CPvgg2vbYBHVefx04+WTghx+ifcaGRj02U51owasxZJNpqf52XpmWiQRw/fXA+eeLhr0uG6+NcdKP74ULgTPOEGOX0cZHy7SsV68t69Y18sZQLNRyzVTGZRKDlo1n/Xr332GDUpWV3q8xaGlm2sf6NTebgpb5+c7jjfXcnDBB1Ocz2ZMuizHTkjIv2wqntWuBE04Qjz//HPjjj8xsR1lZZj7X1hFHAD//DPzvf8ChhwKbbJLpLdrwZcssxBtq0NKv8vvhh8BVVznLybF3/Oyxh/j/hReiZZXpQatEwp2pVlMDHHOMePzpp8Cff4b/jA1NnMdm1EzL2lr3seR13r76KnDDDeJxURFw553u16uqsiszKQ5+GahnnAG8+y7w9NMiQ6tnz8bdNsosLdMyRz0+MtWrgOKTbXV9BrsaTzqDltl2XGWKfjybMi31/Z5N3cPVoOXG6ogjxP+vvpp8zhAzLamRTJgAnHOOOQCYbRecv/92Hs+Zk7ntyPYC/OefnceLFmVuOzYm2dJlQt2ObAmkxsFv/370kfP46afTvy1AcqVTLyuXL3cez5+f/u2Jw8SJwLnnusuPOGVLpqW6jFfAesIE5/ETTyS/HvdM6NnAr3v4u+86j7/5pnG2h7KHlmmZo960SbVnQW0tcOONwE03bfgZzNkq27Jls63tsSHTAzDl5WL4qzvvtLuhy0zLYPo+MtUf9N8hmzIt1e7hGyO1LbUh1v1iwExLSr81a5y7B7/8IjKWVNlWcciWilW2BKhscGyyxpEtx4R6Qc228zcVfvs3E8e4vj3r17tvZqhBy6agvh7o3188fvttYO7c+D9DPTZTDainMqalTUNK3T5TgHVDrLjajmnJwNLGR8u0zFWPgVQzLZ94ArjuOvG4Qwfg3/9ObX0UXrbUraUNqe6S7fRg2W23OUNLbb01MHCg//sZtAxmE7TUn8umoGW2J+qkG4/jQMy0pPT75RfnsZqtJGVbxSGTYwWq9IBFHEGTmTOByy8Hvv8+9XWpbIMDTz8N3HJL9lVem4psDFpma3Bh9WrR9fbll+3fo1caMh2M1yuY+nmzbFn09d56qzm7z9aaNWL/vvCC/XvU7Z83L/pn+1H3WU1Nar9hKrOHhw1amrqypxq0XLECuOYaMe5ptogatPz5Z3Htmj49PdtFmec3e3iqQUt1OI+4ZrKeOBEYMYI9TWxlW73Pq3xfuRK49lrgtdfi/8zJk4Err2wyQ7m0mTULuSNGpH691q9l6lj4NnU0dg8Ppu8jU3vB1E1ff27OHOCKK8TwaGG9+y7wn/8k3YCysrFnWjJoGYiZlpR+QRfnbLvgrF6d6S0Q9Iv8mjWpj3O5xx4iKHv//fEGwGyClp9+KsYsk8tfc018n7+xyJagpbod2Xb+SlddBTz6qHi8ww7AdtsFv0ffv7W1zt3fbMm0VEUNWt5/v+iaBQDbbCPGDwzrmmvEegCgd2+78QcbY4wevdysr48+tqVXJbKpZFpeeCHw/PPi8fz5QOfOqa0vbn7nlL5P+/UTQa277sr8zQRKD7+GbraNablmjZM1/u237qENyCzbxmjzqrtcdJEYqx0QAZxu3eL5vJoaUY4BYnK+r7+OZ73pUluLfWQ94cMPxXEeld9vb3Pjm5mWwaJkWgIi27JDB+fvY44RE8Hceaeoo6g3Gv2sWCHmNwCA774D3nrL7n0SMy0zvQVZj5mWlH6//+7/erYFPbIlaKkHLKIGKFQyizTuwtHmN1Tvpl5/fbyfD4hGxF13Ae+9F/+6s4XXndP77gvOpkokgNGjgSefTL3bbFPItJQBSwD4+GO79+jnhc24hF70/RJlnwcFLZcuDb9OwH3DIEympEoGLAGRcWSjMTJtgsYBDSNqpmVtrd3nqsdIOoKWMmAJpNbg1E2cKMraKNdKv4l4VPr545eFR01ffb1/0DLVHjC2DW9b6tAWss5RUSEa+upYtZmyZo3YlmwKpmY601IvU7zKaBmwBIBp09yvzZ0rxkWdNSv856vHsN+Yvd98A9x8M7BwYfjPiJP6e6U6mzGDlukXNWipT8aj/tZ++12njlP+9tv275OabeR5dDyOA23kRwg1CgYtozEFLbfcMjPbEsQmA9C2W2BUN93kzL4b593xbGLaz7ff7gSBZ88GevQwv3fCBOC008TjkhJg8ODo29EUgpYq2warvn+rq6PP3qw30Orqwk8Mo1cw48q0VH+zOGZ/t71DnomgZU0NUFAQbV22mZb6OZAtmZaqFi3iWc/ChU6G2Zw5wMMPh3t/HGNaJhLxB6Eos1au9K8LZlumpcmjj4pulYDINOrdO3Pbcv/9zs2p33/PjrpjpoOWQTebTPRAyqGHAj/9JIKKYctnm7pSIgH84x/i8Ycf2t9wTYc4gyjpDFpmWxsyU/QbOzbdwwH/cS1XrxbthcawIU3qGQWP40DMtKT0++03/9f9sllGjQLefz/+bfKTLUFLvUIUR6alKs7AoU3lLd1BSxmwBJpstmXZH38g95FHvCsRpkqImrX6xhveK7/3XufxyJFRNs+8HU3hQptK0FIKe8yagpZh6dsT15iW6rYE3d2eMAF47jn/7bcdiyhso3XlSuDBB8ONwZsNmZamoKXp+GnMoGXUwK3uk0+cx488Avz9N/DAA/azwdteB/x+N2YkbHhWrvR/vaIiu4YFMJ2bN97oPFbrI5mgZtO/+mrmtkOV6aClafiXIPpNvZ9+Mq/Lhk25pQaeJk0K/xlxivP6w0zL9IuaaemXxd6Y7eGmkACRTqbjeNIk5F5+OXo8/3y07O4NDDMtKf3UTMucnOQsCa+Kw5NPAueeKx7PmgVsv336tlGVLUHLuLuH63exqqvja8iGzbRMtzBdGrLFunXYf/hw8finn9zdmyW9wqE34vzGPFWDIhtD93CV7bHn1z087D7TK+lRgmdhMy2jZKD5ZVpOmwYccYTz97/+ZV7ONmgZdkyzc88FXnxRPK6qsvscU6ZlVHHOHm4qb9M9EY8qroadXuacfLIYn62oyK7cVb+zbaalvpztsUBNR9AstnV18dZZUmVq6O+wAzBlinic6YCT6q+/Mr0FQqaDlnFkWqrCXm9tyuBsyiiOcwx1v2u/ze/AoGUwm6Cl6Xfw27d613E/qd5UagoJEOlkOo6nTUPe/fdjWwC1xx8P7LhjY29VVmGmJaVXTY17Ip5EIrni4lVQyYAl4J75Md2iBC3Xrxfj4ESdlXv5cvEd5893nos70zJoJuI4122SjqDln3+KbFw9SyPK+FcrV4p1mSaOmjNHvBbmAh6Wmqn02GPmZfRKpH6Ra9nSe/1qUCSVoGVtrfv99fWZz4D58ksxNqNX5TWOTMuwbMu5MNsTFLSMEuTyCzrfd5/z+NJLvZez7fYetsyRAUvAbkzDRCLeTEuv4ylKpqXpt2nMTMt0TeL1wQfi/3Xr7MoVdV/ZBi314z7O/QIAX3wBjB3bNG7AbKhsrq2pBHTirn+Y6hhbbOE8XrAgs0G6Nm2cx3EFLT//XMyoHfU8sb1pNXkyMH58/N1Fo2Ra+gUtwwbLbJZfsybcOtMpyv7y4ldmex1PiYQ4Dj74gN3DbUTNtHznHeCVV8zX4zDt4VR/B/04eOaZ7Dof0s1UPqjXmahDVW1AGLSk9Przz+SCSK94el2w1CBLY86aHCVoedddwKmnAvvuG62QPfNMYNgw4LDDnOfizrTUK9BxVqhtfp+w4/nZ6NcPOP104Kyz3M9HybQ86yyxLjm7o5RIiDHcTj8duOCCqFsaKMem0abvZ70C4je2oHo+pdI4N1V6MtnY/+MPcd6ddBLw+OPmZTaW7uFRZmf1K6/U7+y3D23L51TKHJsGn2mZTE3EYxO0TOdEPPo2pSvTMuxn2AYt1WNZDxDFGbT84w9g772B4493B8mpcQVlWgLxZaHFcZPNFLTUj8svvkj9c6Lq1Ml5HMeELr/9BuyzD3DsscCYMXbv0fezTfk/Y4aogx11lAhYxSmO7uGqsOWQXj6a6gTZFKTR91cqvdCiZFq+/bY4Dg4+2P9cYqaloLd7bMe0fOQR4IQTgGefTX4tzG+e6nVZPw6GDgX+85/U1tmUmI5j9TdtrLFFsxiDlpRepoqoXtnTCyp5d1XtBhR3ZoWfKBdmOX7Q6tXA9Onh3//aa+L/mTOdC41+wQkzU3B9vRgLdOZM5zn9YtXYQcu4Mx3q6kQGJCDuEqqiBC3lOubMcd/hX7fOGZdVnVVSmjlT7OtUswJWrAheJiho6fc7xNU93PQZmQxa3nSTs00332xeJo7u4akGLRuje3iUoKVfeWcbtLQtn1Mpc2zKGNN2pNKgsZ2Ix7Z7uC6dmZb6tTeuG39+54HNZ9ieU40VtHzwQeexPvxBIiFmSo9z5nUyswlapjqDeJxsgpYLFjTOtpiovS7iCFqqE26deqrde/Qy0Kb8V8fbVntbxSFK93DbMirK55uOIT1omcm6VZSgZU0N8OabwK+/up+PMqblySc7j7/80v8zN3bLlonMRJVtpqU0dGjy8d6YQUvTcfDQQ6mtM5slEiLLVcYMTOOgK2VEgpmWDFpSmpkaMfrdcq8GYFMKWqpSDV7JCmYq3cNfeQUYMADYaSdnfXqlIUqAw0tj/j6SHphUC/RUx7RUj1u/77Z4sZjpccCA5MBpSDl//x1uu0zb1hhBy2zLtPzoI+dx167mZeLItAxbMU5HpqV+zuo3MmzOQ/172AYt/TKlowYtwxyHNg1e03ZkaiIe/TnTtgXt31TKVf0mSGM07GyCltmWaaleK/RxMseNE1n2u+4KzJ0b32dSsnR3D4+bXseoqUk+/m0CseminiN//ZX6NTpKdmpQTwGToBs5qYiSaem3TKqZljZBywwe8zlh6grSww8DRx4pykx126MELW2HY2LQUow9rgd2wwYtgeTjL9NByw3ZQw+J3pW77SaSZUxJE2oZwUxLBi0pzaIELWXBpTYgvMYDmzw5/kHG9ULar7JWWSnuKqqCZsEMIse11PddmMrLoEHi/9pa505VpjMt4+7ir++POIOW6r7Sj726OnFMTJsGXHaZ873kPjeZO1fcBX3vPe8Ls82YfUHZt/L1lStF9qe6fDqDlpkaU6i62j0G6XbbmZeLI9My7PGb7ol4KiuTj3ObGxF6QzpKpqVeJs6cKc6HoIatvn1hGhs253TcmZZxTsQTFOyPeyIe/TrUGN3D05VpaTNWV1TquvVMBlmm19VlJuNj7Vpxzcjk2Ih//SVmjE+lS/WcOWKCGr91ROkenkgAH3/snUlYXS2ug6nWyUz0gNPatcnHZSaDluoxU1OTmazPKEHLoDIxFVEyLWtrRR3j88+Tj99UMy1NdXr9ubBBy59+Ar75Jtx7vETJtLzkEvF/eTnw2WfO86lOxOOnqY5puWCBKL/iGK5i6tTk52y7h6vUJAAgvUHLqipRPstAaVP9HU0SCTGkgewNqFuxArjwQmfZr782D+nDoKULg5aUXqZCLKh7uKy0qJmWpsL38cfF2Dd9+sTbbUgvpP3u/gwaJO4qqmy6+fqRlcswWXR+5EUqnWNa2lys4m54NVbQ0tQld8IEYI89gOefD17X8uVAr16i68UhhwC33WZeLkqmpR6wr64WF8ADDhDZn1dc4bwW15iW2dQ9fNo0u+2II9MybIUsHRPxqOtcvjx5eZtGlF4+2QYtVfr59fTT4nx4913/z9b3SZgyLWrQMlsm4mns7uH675wt3cPVfeX326jHSmNlWuqNAvX7ZKKMO/lkcc045ZTG/2xA7PfevYG+fYEnn4y2jqVLgZ49gb32cobBMYkStLzsMnGt22cfc/1i+HBxHezfP9QmW7EJWqZz4r4g+v74/ffG3wa9DLS5PmVbpuXy5eL43Wcf4Lnn3K+lmmlpCkjqmW5hxrj87Tdg++1F7x81YBhVqmNaquWp374y3UQPE8hripmWFRUiG/WAA4D770/PZ0TJtNSTcNIZtDz3XFE+H3GE+HtDyrR87TUxVvb225uHdlOH2wDEfg7KtGT3cAYtKc3S2T38nHPE/8uXiwGb4zBvnhgIXFVdLcacMFVA9QIeCHdXf80a4Kuv3M/JoGUqMxmrWaqyopjpTMs4u6MDyY2Gxsq0XLIEGDjQfl3Tp7u31aMymRMl01LPMKmuFt9dHsNqZUhtAGR6Ip5Zs8R+jCKREN1g1q5Nnuk91cprOoOWXvuotlbckTWdH37dw03lkc05ppdPfpVStTGhBn69sj/UicRMmlrQMmqmZdSJePSGWjZmWvqd62GDln7blInu4X6Ngrizvmy88Yb4/9VXG/+z5efL4PeZZ0ZbxyOPOOf9ccd5Lxe2e/js2cA994jHc+aIXjc62TCcPj31YX90pqBlNnUP18vaVG+mR7EhdA8fNco57vSxPBtjTMswQcv/9/+ca8gZZ4TbNhO9nA17DqnfN2z3cNNNWZvPaSwVFaIeGjVL8pVXnN5Vl1wijvtp06K1Xby2wXSdDDpm9ZsbXr+53F71GA57XX76afG/LLs3pExLea1bvx4YPTr59e+/d/+9YgW7h1tg0JLSy1SIxdU9XBVHQGzBAmCbbZKfv/decUdsxx3tClXbymFdHbDLLmI8C307gODxC/0UFjqPZUUxzqClXsnIRNAyU93DFy8O171aD855Nc7UTEuvi5O+LaZMS6+AktrozmT38HfeEZmn3buHq5hKt9wC7L67yNzRt8VrO2yDqnF2D7fNtLzgAnFH9vDDk1/z6x5u+p1tygi9fPILFniNuRh1htNMBC3TMRFP0Cywtt3D9eEb9PVkY6al3/4M2z28KQUt4x7epCmI4zvb1jNsAnxqA/qmm9yvvf++/3vlsDtAemYPz/ZMy0wEdjaE7uF+9dZsy7RU1x9HGRk209Lv9w67H8NkBjd2sKu+XvQs2X134NZbo61j1iz339deK9a5//7hyyev+ryp/I5rTMvrrhPb27evs70c09LM9Hvq1wavoOX/1U/q8/KSx9zeCDFoSellKjTj6h4e9DlhjRtnXs/VV4v/584V405IXpVAPcOlvl6M+aYXyN9/D/zyS/L7bbqHz51rrswkEsCPPwLNmjnPLV4sMkj1CmMqQUSbBrku3UFLNVBbWSle/+MPu3X5BQtMmZZhZkLXMyg9Gmc5anDTthKiBy1raszjfgHZ0z1cZqmuXQs8+qj3cn/+ad5X11wj/v/+++TxYrzOSX37EglRadTLHlOm5a+/inNHPQ5sfn/bTMvHHhP/f/hh8u/u1wgwNXiiZFpWVHhvm9eYlnEGLX/91S4gabOM6fvL3/iXX8KXQXGPaSmPO7m/1WOqvt4uaCmvJ0E3HuLOtPz9d1HR9rvW2nyGuq/81tVY3cPVdat1Dl2UGyypyIaskzi2wbZuZhPgUxvQal0MCA5apnojM2h9lZWpjWlZXy/KhlQncpRSGT/YJEydx+sz160TXSUXLfJ+j1+m5S+/pHbuZ9tEPHEHLeMWNmip/67yHFm/HvjuO+/3mYLZYYKWjR2QnzvXCTrK9mFYP/3kPO7QQdyMB0T2ZthxTL2O0XROxHPzzeL/r792uj/r6w4TfDVNXrihMNUr9GvDypW+mZZ1fnWTjQiDlpRejTV7eBxBS5vUa5suknqGyznnADvsAPzrX+7nve46B2VavvMOsMUWwJZbJjfmrrtOjL+jbsO774rl33nH7vNt2IzXptM/L9VsB/27q+tbvhzYemuRzTd+fPC6/DLaTJmWYbot6ZmWpoZMIuFebv168/6xGdNSPy5NjbVMzh6ufgevY/Czz4Bu3YDNN/cPFtg2QvTnR4wQ2Z56dqO+vieeENnXvXu796NNA85mIh79d9Ab7n7HpalrWZQxLQHvsixK0NLv2NK37+mnxf7t1Su44ZFKpuV//wtsu60Y6yvMsRrn7OHV1cCll4rveuyx4jl1f9TUJG+b6fccOlRcT4YN89/2OGcPHz8e2Gor8c9v+JOwmZa1te5jTL2x0liZlup+Uter769ly+L7TBtxjtMdVRyZL16TwelsAnxq+aj/HrNmxT8po5+4u4cPHSrKhrPOSnXLxDmVjZmWP/8MdOki/s2caX6PV6blI4+IMny33aLXHW3qC/r1yzYb3EZjTMQTp7BBS32yp3XrxP78xz/EREZeTNf2336z20ag8Y/tONqbs2c7j/UefmG/j1c5HaV7eJTZw2U5p39emO9hqv9ITT0D03Tds820/L/rTK2alLMRY9CS0quxuofHcREJe6HwarjrjbrHHxf/v/ii+3mvbQ7KtJTjxi1bljxWht5lSkokRKVPFTZouX69E1jzyrSsrvaeBVu/WKaaeakfR+o2zZ3rbMdRRwWvy2/bTJmWNt2WFi407w9TRklFBXLU3yORMFeobca01PeLbNyp+yeVoKUpSyLoDmkiITInbRsbJ5wgll2zBrjzTu/l9Iaj1zmsb9/tt4v/33vPf4y9CRPE/7/95p5VUQ9aLlpkzirx2wYgeYBuvezwG9MyaqalKWjpVTGNErT0Cxzo++TGG8X/c+bYZ0pVVXmPh+o1dMHZZ4vHP/6YPIawn6Djad06kRmhZ4V4ZVree694LMcq1INkNpmW//uf+H/UKP9tDzqWwpBl6LJlwOuvey8XdkxLwL2f1GOssWYPV4Nf6ufo5+ayZWLb4wqM6WXikiXB53djWrEieHzuVauCG7Xq75uf778uACgq8l5GflZtrbkcM/Vc8domv2w/G3F3D5fnddQJjwBxLM2fH/8wGVHpn/nzz2Lb6uq8x0j1yrQ87zzx//ffJ3etjbo9NnWsdGZaxj2mZdz07V29WtQ5vfaJOgQDIMrTX34R110/prZImMz2ODL0li+3v1GUatlcU+PuBaa3J8IeV0GZltXVTnkXtG79u9mUYfK30tcd5ntUV3sHJ+PupaeqrU0OtsfNdLyEzbRk0BIAg5aUblEyLWXBpVZYggq/OLr+2KxDvbh6VSb8xrT0a4hJMgBiMxFPKtmSYd5bVQX06AF06iQCPaZMy5oakeW52WZOsEcV90RA+nGUSkM2TNBy8eLgoOVrr4lsgl69RNd8VWVl8m9pCvQGjYEHmDMt9eNSNsrV90YNWs6cCZx0UvLzQXdCzzwT6NoVuPhiu89RAwl+lSb9u9pmWqrURneUwM7rrwOdO4vsN/VzbLqH65X8oHEI4whamgIRcQYt/TIk/M75oMZZZaX4fttuK8oYU5DTZrzVMIEEv0zLigqRebj77smBdduJeNTnqquTP892zFaTODMtVXFOxAM426XfqGmMTMuqKvdxp16T9cD40qViDK/OnYFnnkn9s08/XZSJl14KfPyxOKa7d3e+q35+x9Vt2Mbs2eJaP2KE9zK//y62uVOn5AnRVOpx5zcmlzwvO3TwXkaWU151LPXc9ttf06eLbX/5Ze9lgtgELRt7Ip7jjhO9E0y/WybGZPX7TK/f0GYinqjngk1A0qaHhNeyQZpY9/AcfX89/rgo/3be2XwdMGVa2tyoNtULwmbppeLrr4GOHcW5YzMnQaqTeulBXL96ng2vY7S6Wvzbfnvxu735ZvC69WPS5rt6dQ8PU+aY6j9SnJPGqurqxDW9SxdnUqA46Me86RzWrw0rVpjrRjJoyfEsATBoSelmalzolT394idP3DCTYMRxYU8laHnhhUC7duKxetHTLxBqMMZrmxMJUaClMhGPjTAXxtdfF8G3+nrgkEPMQcs33hABi/p64Igjgj8v7qCl3/qCMkb8xvuMkml52mliP/z6q7lbjKlrgM5mNmm9i5xfpqX63qjdLfQhBiS/in0i4WSPPPCA+zVTN+uVK+3H39QrVLaZlir1t7CtZNXVORWTY44Rv/XPPwNvv+0sY5NpqVfy9eNUP/bWrRO/r2nsUtPyJqZKqFfFVN1mm6ExAO+xqGpqkrPXVDY3pp5/XpRDdXWiHDItY/pclWlYCXUSLL/3qs9//nlyprNUWxvc1buuLrl7uJ5dou+TMIFI/fqi37Sw7e6sb3ec3cPVv/X9VV7u/FZ+Qcv6+ugTnuj7Wz1+9BtJ8+c74ygOHSr+X7nSrlG+enXy95O9JO69VwSb6urE8fTUU+J5myzyRCK1WaHr6pL33cqVwL//HXw+Xnyx2F9r1/rfjLLJtFR/w7ZtvZeT5ZTXsat+l6D6TSIBnHii/zKS6Zi3DVr6HR+VleI8tTlvgn7rqipxsxQA7rsv+XXbwE4iYf6+UbpkRwmU2kzE4zU8y4oV/ttpk2kZpouruqzpXAr6/CwPWhrH1E4kgB9+EDdaamvddQdT0NKmni+zb1VhbtClGrQ88USxjhUrgPvvNy9TXu5sY6pByzvucP+datDSr3v466+LOll9PXDkkeHbkTbfVZbHYTItTQE6r+8R93jE0iefONf0M84ILj9s6durn8Pr1yf/5qbu4evWNSzH7uECg5aUXqlkWqoncFAhnupFBEgtaFlaCrRuLR6rFT698qdWtv22ubra7q5VKgVsmKCh/jmmLKKg/ad/XqoXIr3R4Le+1q2dga5N4s60DDoe9cqt6fi2ybQ0vZ7OoKXXMRMmsKjSj6vHHgPatHHvD79165UBm6Clvj71jmeqd/fV39Um01Kv5AdlWn78sWjU9+hhbrjbVHZN54nXb6Qeg7azh5uClmvXijFm1aCu32eZVFa694/NjIxA8vVFLzdOPx1o394cePHL3PXrUmT6LnqATC/jFy0S+8hvPfpv7vc76MeOmtF47LHApps6g+n70bti+gWeo2Rayvfo59O8eSKL79dfvYOWtbXAjjuK3++DD4I/W6fvT/VzvIYgkJ5+WpyLQcOPTJokbmj26uV8V/3YVY9ruX/1cty0b086SZSXpiBVkJoaoE8fse/k0BfPPy+OC7/x5yT1ePY7F9QyySsYWVHh7JOWLb27iJuClp06OY/Vsjyuhu5114n6w0UXuZ+3GdOyrs57O1auFOd7u3bAW2+5XzOVbYMGid/aK6gSNGyB7bVt4EBxXOuZR1HqmlGCSTaZliZ33SX2jz52vCpKpqVfl2G1HNppJ3Euffih9/JRgpbZNKalat48MRZjx45iiBQguRyorLQ/D1OZOCrV7uFqncVUF/rwQ/Hb9ukjtktfJsy58cMPwJgx7udS6VYN+HcPD7oRHmT16uDv5xW09Dt+TMs2dqbl99+7/27TJniscBt6HUk/p00Z+Kbu4Up9lhPxCAxaUnrFFbQMGmtEFqxhC2Sv7to77WReXl1G/R4tWgCbbCIer1njbHsqQct0Z1qGuRC0bOn+25RpGTQ5SZRMS78gjM2EMyq/Wf70z/GbPVzPBDRp397/df2iZdoXNpmWptdNQcv165Ozk6JQt0kNsPgFFv0atPr3Puec5AqSbdduv2XV5/X3RMm09FrW70ZLHJmWy5eL5/74w9xF1SZoaTrWbIKWtt3DTWPFjRqVPEyC7TZIpmEV9JsLpkwkv0zLSZOcjLf7708+L/y6h9sGaiR9v9TUBP9eQUFLv33mldE4bpz4ByRnfJjos776ldupZFqaGqmrVwPDh3sHLV97TTQCq6qA/v2DP1tnylSXv7nX2MzSGWeI4+XNN50Ap6kOMmCAeO6nn4BXXxXP+f3uch8GBS0rK51xsi+5xHt9Xp/16qsiIK3uu3/9y/6GlhpY9Ps+alnhFbRUr4etWgHFxeblTEHLrbZyHqtleSoZqCo57q7eS8CUTWPad15dxD/5RGTWrl8vsvVV+m9dUeF0ZffKag0al80mCWDJEjHpVn29OL5VelmorkNOHKiP5RtHpqVpW03l8uWXi//HjPGu39gELfXz1y9oKLftjTeccuigg7yXtxnTUv88r2ttOsf5k/zaHLfdJsairqx0AsV6sGbdOvugpU3PFC9xjtdqymg75hixL2bNAl56Kfm6G/RbqK9PmZL8epzdw487Tgx9Ider7xt13c2a2a07aHuiZFrqr61ZEz1oGfVcUCdDkp5+Otx4qiZ6vUI/p03XhFWrkm+6q0FLZloCYNCS0s2me7jX7OFqQa7eiTdZvlzMUNe+vf1EC/fdJzIkL7hA/K1eXPUgnWSTaQk4hZJecbYNWlZVmcczi3NcqzBBy6AZFauq4g9annWW+B2eeML8etigpZ8w3cOrq4ODlltu6f+6TdAyaAw8k5qa5EruCy+IzInPPnOeiyPTskUL57FfBdOvMWWTReC37iiZlvq+j5ppaWqQ+c2MbhO0DMq0VEWdPTxMpqXefVnyC1raNMRM9O+ulzmVlcm/T2mpKMc//VTcNDKN5abvd3Vb9OXnznX/7Xc8hQnGA8lBsKqq4EZ9KkFL05jIiYT7O9t0PdSDln5szh/bTEtp/nzvoKW6P6JkgpmyleVvF5RpqaqtFf92313UQb74wnlN/R3k72WTveU30RxgN3nAkUeKeokMUqvU8y3K9UBtRPldy9Vj1Ctoqd6sadUqXKalGrSUZfkpp4ixb9NJ/w29hk3w6jLsd+7p+9Pmt7YNWg4fLsrNe+4J/lyVXp6UlYmbUe++KzKUevcWYwJ26+YMmxElaKmW+19+Kcp1fexR/VzQrxVe38Ome3iYCSPluWo7PEUc3cPlEE2bbOJM6CbFPcuy3++njl0tMxX1ukWYTEt9uUwFLU1lj1qGzZ+ffN31O5cvvxwoKXF6NQTVG4HUuoc3awbIrLyqKv+xtdU2q5+gsXnjyLTcYQdneAud3zF0/fWiPJM3l8L46Sfz837Z0jaCesR4lRdXXeX+W9nv7B4uMGhJ8TAF07wyH6NkWtbVJReAapDs7beBb78VhYGcXTvIJZeIz37oIfG/Wglt1cr8nspK8b3q6/2DlrJBEDVoacq0BJIvQGpDLeyFOyhoqP6mQeP82FRO9Qux34Wovl4M+l1dnTzLZH29+N423edshekeXlUV3G0pKICrX7Rsg5ZRMi2nTTPfKDBVcOWx7UXdLyUlzmO/yrI+2YzKZrZGdd1BA1zbZFqaBsCW600101LdP1G6h4eZ8dl0lzxqpqVXg1tdn7otfpV0U0PMqzxV6WWl/luagpa1taIc328/7/LMK2hZWQlMnep+TQbo5Hngl2npd1ybfgc9aGkTyNXLAD2TJWym5V9/ibFX/T5Dpwdy/aQyEY/X9aukxDtoGaX3gVq+mQKT8rrk1w1eV1MjMhe/+kqU7abxVgEnaGeTvaV/Z33fBgWpFi0SWaDr1plnmk/1BqjaiPIrd2wmOnv+eedxp07uG2Kmdan1qO7dncdyNvPnnvPenlSoZbj++3hldno1+G2OASmOoKXc9/feK87BSy9NXkY/N9VjxDTcxOmnA4ceKs6ZmTNFsHLhQuD//T9nmbD043L9+uSxR4MmJDTVLevr/TMt5eeGKVMacyIeWe/9/HMxGaZpv8Tdjda2PtSihdg+/fPDZFrajOHrJY7Zw6WgcrG8PPm663Uur1kjhi2orweuuUY8Z/petkFLr21Tv78atJRjoHutW/YODKJ3Z9dFybQMc+74Hdc33CC+/3XX2a8PEOeSPvSNJCd5DGoPqdTlgoKWthO0KctxIh6BQUtK3ZAhImD35pvOc3/9BWyxhflubpSgpel9XoEhmwkG9ODHkiV2mZarVolsis03FzNQSqWl7guADALE2T0c8L8IhK2w+F00Xn1VfB85vofNBDB+gbpEwlyh8eIVBFuxQsyE1727/yzFftthEiZoaZNpafpuapdx/aJl+i3i6h7uRV//Dz+IbiU77GDXJSkoaFlVJRo0//639zao2+r126jr1r+/10Q8fl3M9YDxGWcA++4rlgkTtPTqzirZZFoGzR7ud76bAo02DS7TsenVHcbrPPA7xkyveWVOqfTvo+9fU/dwG17jBJmO8e++A5Ytw4HnnYdmvXqZu7oD0ca01IOWNuMwx51paTpmvCYhksI0LoJ+n0TC+4aXVyO1RYvkhm/UoOXIkeLafu+94m/Tbyg/K8zkPlVV7iBnUAar3/kjf6Ogm3Km8eNU6nluOr/jDFraZlqalluwwOl6XVAgyuM2bfzX5dc9PM5us6YAGpA8iRbgfePHq4GaSqalzQ0wnWmiC/06qR9jrVoBV15pfs2PnE0+1e7hXvSyQq8L6ufCRReJ+qw+pIq8Jh9/vDjmJk4MV6bIZW2zvIPaNKab8WvWiHF9N98c2HVXZ/xI9fOluIOWtoHD1avF/tWDQGGClttv7/5umcq0NN1IV28Ql5cnXxu8zuXJk5OfM30vm6Dlgw+Ka5dpbH51nXl5gAxwrV2bHMxTj5myMvN2626+ObmuoWbNx5Fp6cf2GArT22LxYu9YwXvvid+0Tx/Ra85vaKNEAvjnP0X7To4FnY6gJTMtATBoSan64w/g2WdFIX7kkc7zZ5zhfaLrjTev7uF+F/hUu0rrhcaCBXZBy0cfFdkUf/3ljIUGuMe0BJwKbJzdw4Hki4BaSIetsPgtf9xxYvueekqMWxPUYArqHl5Tk3xB8ft8r0rII4+ITKE5c+yHAVB5XUTjzrQ0XWR32815HLV7eLqClomE2L5Fi4AffxQX7aD3BHUPv/de0XXMj7qtXlmXaiNG/528Mi39ZqI0VRg+/1wE6lPtHq6u2ybTUi8Lw3QPN1XQonYP96q8eZ0HfgGdqLOaB82MvXZttMZJba27gi1/I9Px9t13yBsxAi0WLkTOb7+JbG+TOMa0tOmaHRS09PsdTJlRpmM/aOzGOIOWpmt2UKZlixbemZZhgyL/+Y84PocPF3/7BS1tGxaA/T7ymg1dJX/jsEFL/dhQjx1TQM0rIGfLdkzLoKDl++8723r++SI407ateV2VleJ89su0jHOmWa8brabP8Mq09DpH/a7TQUFL0zptgpb6duvBbP1z16wR496ashT9lJY6nxmWTb1e3xZ98jf1e1ZViaD46tXixqyqtlbUJ8eOFcdO//7hzoOwN02CxrRcuzb5ul5RAQweLNocX3/tBJFN1N8vjkBemN/c1J4J0z0cAM4+23mc6qSItvT9bSqb1eDe6tX23cNN9WibTEvTcXXhheJYMI3N75VpaSLb5aWlwT3CNt1U/L9qlTtJB3Dvt0xmWqrCTMhrmjBSWrBAtH1/+EHsLzkWtcm774qensuWiUnMgOAxLdXyu0MH73Wze3gSBi0p2erVIptwp52Csxa9xn0yFdZycOC//3a/L0qmZap3FPUKnm3Q0quBV1rqDuLIC1/c3cO9KhFXXulf+JnY7sOZM5MvPnqGWFCmpekC5VeZ8brL6pX5ZEv+LlVVQN++QM+eIitA3xd+E/HY3M1T19e/v+iKpWYc2nQPT3empfqZEya4P2/OnOD3qMe7HpArLwduvz14G9QKolejTz0Wgo5ZrwCIun1ewYhly1LvHq7+rkET8axfn3wOpDrTo19F8IknxEyfpnF8bIKW6vf1uy5EHWszSvdwGzU17qxg+fubzpPvvhPlXZCJE/0Dhqbvq38/20zL6mrggANE+arPEh2me3h1tXmbg8ZuDHMMBp0/pt8vKGgJxBO0NJXbcQYt9fV37Chu6Kpk49avjI4atBw/HujcWcxmXlPj/t3ksTdliljmsMOSy2ybHioqtbva+vWiS3CnTu7xMxOJ5KClvp/ULDk5iYlX0BIQ+1Dd1q5dnZ4PcQctTWP0mZ4HwncPTyXT0vRZfsNVAOKY0I8p/T1e11fTzOh+ZN0gXZmW6novuCC5N4f6+/iVkbW1yfskSvdw2wwvfX98+aU7E86ru/jXXzt/q/unUyf3sur3rqwUdbBOnYD//c9u+9TtPOAA5D3ySLj36cJkWgJiiC8pTKZlKt3Dbbp6q+1BU/dw/VxOJESg+aGHktdlE7RcvFjM0bDjjuZyef58YLvtgH32Eceg15iWJrINu+++wTcI/vEP57F6DU4k3Ps83ZmWtm1Vm2vYvHkiq3ffff2XU7N+/ep6altJfn6Y2cP96gJqpiVnDwfAoCWZ3HijOGFnzDCPe6Py6hJjsuuuzmN1cH/bTEu10Ey1C5BeWZs/37m45uZ6j6nkRQ9ayoIo3d3DEwlRoNrMAqsLE7RMNdPS9Hv5fb5XILtzZ+/32JC/ywMPiNk7f/xRzFgdJtNS3R4v8ljacksRwL/rLqBdO+f1dGZa2mRwAe7v+NZb7te8gvO23cOnTLFr9KsXbK9Gnz5LqR+vssOve7hUWpp60NIv01I/pk1lZ5hMSxO//XPmme7fVW3weFX29OC9bJz5VQ5NlTCb8jpd3cNra93Hqvz9Tds5Zw5y4hgfy6Zstc20/PBD4OOPza+H6R4eNdMyzqClX9c4r6BlRUU8QUvTRA+mQE+U7uFeY5gOGeJ+LkzQMmhMS33br71WZGONHy+y3NXlV64U33evvcQy77yTPJ5rUINPHxJFLfPr6oBbbxU3FY8+2nneNIGi/j3UoKXMmtSDlmpW5+rVTqMwN1cMTSTHzC0vDx+0DDPZmTyvTTdm1PJLHd88HRPxmK4dNmNa6sed/h6vcnr16nDlgMy0DNtTALDLtJRlxapV5sBQmKCl/nqYhIhUMy0BMWSF/M7qEFuSX1mhJ1eo275unZjYY9Ei4NRTw23ngw96X3PCWLfObtxyyXZyR10qmZZBWXGAczwDIjs5KGj588/Aiy8mr0dO2KbTz5N77wW++Ua0k885J3n5IUPEzefPPxc3xvRMS5vxDwcMCA62q2ORe03KCDjXl1RmD/fjVabr+9ImaDlsmHnWcJ3ak8/vHDSV43o93m8inpNP9l43u4cnYdCSkqldKIK64IaZYdM2aGmTaRkmaLlkCbDHHu6uH36ZlsXFdoW+yitome7u4SNGAFtvHW5bJdvK2XffBTeYqqvNlc0vvgB69BAzgevCZFrKQj/KxAuqr78Gdt4ZuOIK57nPPw8ftPSqJMl9IPet2thSKwDZ1j1cb1jdc4+4y6tn5dl2D7dtOKrb6nUDJMzNCq+sraDu4XLdjdk93BSkDTMRj0mYcrFVK6cybpNpCTj7Jx1By6VLRZZY797iZoLpmIqSRVVb6y7P/TItgXjGBbP5vrZBS32SCVUcQcuga3iYY2rpUqBfP5EZatq/pgZbUKbl2rXxTMSjl3Hl5eZ9m87u4bZBS5uJ5vwCWSNHurv+VlWJoW1U33+f/Ll+9CFRbMpK0/Gpn1+ym15ursiaBJxuiZIaxFy92tnWNm3E+2TwZtWqcEESQNwIffpp82v6uuSxERS0VMfk9DrP/c7/oIl4TDe3gs5jU6alvl6vsm/16nDXItnA9jtGvNYXpnu4V3ZpmKClfp579TIxkb+TfsN+m23MQSuvTPOqKrHN116b/LrffvfKBAZSqyt/9ln096r0a7Z6495EHffd69j517+Sn0tH0PL++8X2dujgLiuXLAnOzvS7AW/aVv26qA6tNnZs8vJqQPnPP5PHtLTJyuvfP/hcU9ssJ5wgel/+8ov5O6xYkVxueR2D//mP6Olmy6tcCpqo0OSTT+w+U72Z5ndNMZXj+vEhk5BuukmUDerERqeeKsbUNVHqLLXMtATAoCWZqJXToELNVFHyqsA3ZtBSvYM0aJCYQXniRKfrUlDQUh0DzUaLFu5sHlnIRc20lLOU60wXgTCznKr89qGa0fjdd8mfq++/6mrzhWzvvcVdR7XLmBQm01Luq1S7fh1zjLsLCiAu8mGDll77TmajmYKWeheToPWZnguqiJoaJl7U9Zv263ffiZmZvd5TXOw81gNytg0cm0xLddviyLT0664XNtPSa5IXIJ5MS5uGh1rueC1vKkuKipxggClgYZphvqrKO/glmbKrbII6FRUiS+yHH0TXHVOmpe2xraqpMf/+XhXRbAta+gUjsq17+N13i8kHPv5YzOoZtE1yu7xeA8R+0veV3L9q2RBUqdeP2d9+M5/vlZXiuJefqQfQTEzdw03kOv0aQfLGk36s6/vHL7Nu1Srgo4/cz+mBuV9+cf8dFLTUx6W2KSuDgpaJhBO03Hxz5+aCnmmp/r1qlXPMyufVTMuwQculS8UY7CZemZamslvdH+r2Rglahs20XLIk+PirqUn+PukKWspjNUzATQrTPdzrHAgTtNTLRK8ZhU1k2ahv86+/iu7BOvUcVsfAX79e9G6TWe9HHSW6sKqfYeI3+VYqgiZns6V3D3/3XXd9RafPW2By993Jz6XSO0Iv9+S4otdcI8oG/fq4eHHwRDxe5/a6de5jQJ3gR+U1PJlJWVly9/CgdnunTsC22wYvp88uPmOGuKlsOiZXrLDrHr5kibipFmT8eOexV1kRNOZ3XMJmWprKnF9/FTclfv0VmDvXeb5dOzF+pgm7hydh0JKSqd2AgioQpq5lXncq+/RxCmk1aOk1aUYqY1q+8AKwyy5itsBJk5zn5QUo7qClbabl0qVO5dKvMuV10YvSPdKL7UQ4v/ySfJHW73BXVSVXHIIaD1GClmEbJDZyc5O3JSho6XURk2PQycqAGtjLtkxL9TO99qs+kY58T2Ghu8LlF7Ts3dt7G9TPtcm0jDqmpU338DVrwmda6pWpVDMtKyvdv7u6H/fZx7wdalaPetzW1YmshP32E5mLuuJiJyCzYkXy9nmNqeoVXJYSCfHeGTOAPfcUA8eHzfpYsSI5w6CyMtr5r3fJ8useDoRr+P3jH+I6o9MDRia2QUu/7tthMi0//NA8iH+cE/Govvgi+bkomZZ//ZX8mjye1H0Y1DtCL3O9xi6trHTv1y5d/Nerbk8QecwFldHLlvlnWq5fH9xA0wMPQRlkNg2+2lpxXu+1F/D888HLBwUtV6xwzkd1Qh2/oOUvvzjr2GIL8b9s5OuT9KTKK2j5xx/+77MJWtpOxLNuXfKEOX49eLw0ZqalzfANXoEIm2uwXCaOoKVeLpiulV5k2ej1PX/6SdyAO+205Jucapfjdevcx8muuzo3YcLsw2wLWuqZlkG92GSWOeB9HLRrJ9p0++3nPBd3puX69f6Bx4UL3c+FCVqq10CvAK7fGPu6VauSu4f79c4ARFmbkxM+aCm35Y03kp+vrrbrHm5Th8vNdSd82GZafv89sP/+ovu813cLM8O4VFEBjBol6nr6EA7qby1/T1OZY0rcAUS7UP2uKnYPT8KgJSVTuzqEzbRU75zrSkvF4MGAGFNCntimTEt9oF8gXDfRk08WXYGHDnU/Lyu3cQYtCwrE8jZBSxlUqq/3b7RmOmipvlZfn9zA0yt6pqyzyZP9Pz9M9/B0By3DZlp6qapy7zv1YlRY6FREg2bt1LdBSteYll77VQ20qu/Rg5b676UPlP/CC97bIN/rFQwLc943ZqalaTbWVMe0BNyVaHnstWwJfPqpueKjNpDV/TN9uggsfPop0KtX8vvUTMv6+uT94pXpa9NArqgQY2hOnQrccos5gBVE7UIDiP1nO4ayemyaMi1N3W+lMA2/t98O7vLmxXYiniiZlnV1dt0sgXgzLVV+M4WbnvNqeOplpbpNYYKWfhlVMvgFiHNaPRc239x/vUC83cMBcY75jWmpN5pN9HMlaIxOm/O6ulpMljNliv9y8nwzHZ/qvlLrirZBS7WnhHyPep2y2Te2vLrfqtt94IHJ71O31+u3ts20NN1UiCtoaTsRT9igpfyN/YJJYXoT6UyZlscc41631BiZll7bfN11oqv16NHA9dd7By3Xr3efF4WFTnlmuw/161wq0pVpGRS0rKtzfg+/79K3r2hfyOEkUgla6j3VKiqCy0r999bPZb+6hbqtXkFL/VzzG4NxxYrk7uFBk3LJSVvDjGmpMk3sZApaRm2vNmvmTviwDVree68IaD/7LPDKK+b3RAlarlkDnH66iCkceWTya5I8p03Hj9cM5GVl4vvqY0YD7tnDmWkJgEFLMkmle3h1tXssCFXz5s44FnV1zrgcpqBl0GDFUbM/ZCGnBy3nzXMuNGGDlrKgMs0ebmpkyywKv8LTL5MvLqYLwf/+JyrhekUvaNZuU6blP/9p9/k//QQceihw223Oa40ZtDR1D/ebPdyPHJtIUi+8gDP5id6w8su0XLsWOPFEcfcw6PtHHdPSa736jPRq0NIvI1s9V5s395/Y6qCDRMMsjjEtq6rEeHp6hqg8nu67D3j9dfN7KyrCdw/XG7Xl5eK8lt2oTdsgqQ1P9Y62emzI7ZEVFlOFv2VL57dQ909QRmRxsbtxrVbe580T+1EXJmipjoesT/phw7T9to0p9XvpmZb19cmTu6h3scMELUtL3e8NI0qm5S23ODf+AO8GeZhGnB4Uufpq4PDDnS5MUa85pl4aUSbiMbEJWspy81//Esv7ZVpus43zuLLS3eho3z44ILp+vV3ZYRu0XLrUP9PS5hwMOv91NuusqjIHkXVy/wVlWqp1xa22ch77BS2/+cZ5bApaBtVVvJiOP69MSzVoaZooI9UxLYN6QejXSpshgqqrg7uH+03EE3emZX29+XWbz5G/lRqc0c9hyS8AZcq0DDMEUdD3fPll5/HIke5zrKzMeawHLYuK7MYllHUNIL4sy5oauxtqNvSgZUlJcFkq95FNAFa20/Rlx48XGXemiY1UX30FXH65+7mKinDjGQPiJk7//mIseMCcfQckj2np11VepQ9ppVq5Mrl7eND2y7FDo2RaAmIcTZ0paDllCnDwwYA6C73NdT4vz53wYds9XDVtWvDn2PJKxALc11kZCzCdP6bfsHlz53ww1SOV8pqZlgKDlpRMDUYEFWp6g6eiwlyg5eWJf/37O8+995743zR7uN/YV0D0oKVch9/d+LAT8ciAjHoBqqgQjSJTBX/ZsuBKQWNkWpr24amnmrs2BnUh9BrT0o/cB0cdJYJMI0Y4jRj9mJAVz2zpHu5l/XrvTEvAGSt05crgbs/yc6+/XlR+n302uMG4fr39PrLpHu7VaCsqsu8eXlDgH7ScPFkEY2wyLW0q5h9/DJx3nvu52lpR8VEnYNLF0T1cfo4pKKHvI7XhqXahVxuScj/K8sh0M6VFC6fCox63QWWFmmkJuBtUp5/unpBNXadt0DJVpnXYBi3VMaFM15Pycvf6lSBDjm2GYm6u2O9eXXuC2DQMq6udsre0VAxgP3Om0xvCax021wk5NIC6T7/4QpyLb73l9FKIeq017cco3cNNZNmo/ob6ZBiXXy7KzeefF1kOekNOPb71gIe6bMuW7gCDyfr1dtcJmzEtgeDu4ZkKWtrWP2yDluqEE2q2qx60VIOApkxL9XyPmmlpurYEBS1zcsTN2Y4d3cups4enOhGP6fyLK9PS5uYpIH7PuDMtAXMwwuZzTJmWXkHLsN3DwwjqHq5Te0ro3cPVfa9mWgaR3zXV8d4l9ZxMlT6ki6ltdcUVwIUXOn/LY9nmeiDrQ/qyRx0lMu70rDjdzTcnP7d2bXCmpe7TT8WcCZdeKq7X6rmtjolsm2mp0ydNU+mZls2aiYCtH5mMEDQZjlempamMNQUtn30W+OADUR+X55nNuaIHLW0zLVV6fUCKkmnpd06oN4zy84N7UarUdlFAPZKZlgKDlpRMzbT8808xs5U+aLukZ1pWVJgvnvKE239/50Lz/vvif1OmZVDQMupdxaoqUaj4VWTiyLSsqHAHSNSMu1SClnFnWqoFuF8lIShYUFEBDB8e7vPlGFvqsSWze2wm4vEaPD+sxuoeDrjHR1PHnfGbiMera7WJzPSz8fzzwNFHiwHgvRrR8+eLCuDnn7u3Sc+09Ose3rx5cOVszBjvTEs16BQ1gFJbKyp3fsd4lIl4TGXdqlXm89cv03KHHZzHxxwDnH++WF4ee7Kib2rItGjhlK9hMoT9gpZeYzJWVdll9axd62QVR2UKWtr+Puo1zNRt7pRT3I1VNShiq7RUVIzTmWkJONlEMjsiN9f/jj4QvJ9ycpyudeXlToBxxgxnmcmTzZMx2ZLr/OADYOBA0aiLMhGPiSnTcuFCUVbJ2UHV2bKnTEluiKo34nr2dB7rQctWreyClnFmWq5cGS1oqdZbbIdSkIKGCdC3wY/cf6aAkHp9VH8T9RzUeyh4jdsrszPj6B5uKsu9Zg+XQcvOncX5r2aJAuJ6J8uFL74Q11n9JpBtpqWprjt6tBhjTYoatJSTjvh9FpCeTEvAvM+jdA8vKgI228x5/T//ETfB6+rCdw8PI6h7uB/b7uFB5D6M2iZ6+21RPsvhnPyyysJKJJwyICdH1FP079W8ubkeYpNpKW+cq9eOVHoZAOKcCHvDR7V8ufvcVoePsR3TUufX/lq5Mrl7+MMPAzvv7P0eWZe45RYRuOzf310HBcS1xHbMTcActFTJhAub30fvHq52904kxPk9ZIj9eN8ff+wkx0QJWvrRh3MKc8NejRkE1COrw0zOtAFj0JKS5WqHxdix5jtWpkkZKirMBZe8ULVoISZnAMTFcfHi5EaRTdAyle7hatfsgw5KHlA+bNBSVpjVAuiPP4B33hGPu3QR2XLSsmXBd3cbI9Oyvt598QkzxmQc/CpHQd3Dc3OBJ54Q/1IVZfZwL0Hdw9VZ2dUsAb9MyzDHepjjY/x4ced/9939f/vx452u/unoHg4AW27pX1GUv3sqQcugGwVhG/leQcvycvP56zcRjz5Z0cMPi3JX7x5uKpfUBrJ6HAXtK717uKx8+WUahuke7jXWo80kNUBq41Sp9O7hgMjCULvuRQ1aAukPWsrfUR2qQQbRonYPLytzun4lEs62mGaLj0oeRwcfLAbu79/fPtPyzjuBI47wXrcpaAmIsqpv3+Tzsm1b72tuXp67y73ePTzOTEvbiXhWrPAf09LrxsFDDzmPw5ZnQZM36NvgR+5rU0BILZfU38/vGmHqppiT42RnqpmNUcbPBeyCluvWie8mG+AyWKlnhhYUuINS48a5h8upq/O/5gYFLQHg3HOdfRk1aAm4j9tMBy0TiXDdw2UdqnPn5ADLbbeJ4WCyKdNS5dc9XB3/PIht0NIrWPPPf4ryuV8/8fevv9p9ri15rhQXi3PWNmhpuoapPfUAc6alPjyEX31Gnt/Fxe6xaaMOMQGIY8ovaKluq94+8OLXw8qUabnddmL8xVtvNb9HBi032UTUhd57L/kaV1gYrm6zbp3/DU55jkTJtASAE04Qx/C4cWKohWefFZmtXtT21QEHiLrBoYfGG7RMJJKDlmGGVlDLLJ99ncjNRVVQHWQjwaAlJdODlgDw888iO0WOQwmY7/4ccwwwYULy8+qFSh1w3XR3P93dw9VCpazMHUgCwgctZUC3qMhJSVdnl+zf351ht2xZcDZAnEHLXXcVAxSbyMxGwP5OrVoZT8XixcmNgiuvFP/03/enn4Bhw8TkIoBTAQo7y7vJ3LnAiy+6n/v6a+Cuu8TjODMt1WNNHY/JtO/vvFNUJqIe62HI32G77YA99kh+vbzcfTMhaCIedZ/ZBC032yw4aPnOO9Gza01By5NPdk+yYTNWmypspuXll7vH9vHqHi59+GH4TEtZvp1/PnDDDf7bb8q0nDpVlBdewnQP9zpu4yo//OgZ5KZgmdowyeagpSQbGoB30PKZZ0TXfq9xpdX3q9lpXpMfpBK0NDVgbDMt9YntdF5BS0mtpwDiXPMKTnTt6u5eHCXTsqoq3u7hpkxLdf+o5+Cxx4ouiCNHus/dsL+dzdALYbuHB2Vaqt/Rr1wwdVPs3Nkp9w47zH09iuKkk4CnnnI/Z5qIxzR5kB60bN48+fuo13v19zdNwGATtFy/3gk02wYtTcedzWetXh1+mBz5mX4qK8W4gqecIq53tbV2QYXrrxddiuX51LmzOQB02WX+AahUg5apZFoGjWkZd/fwujqxnRddJLJQ6+vN10W/rsjS+eeLmZRtyGNT/j56nV0PWsobMnLb2rUTAdWePZPPT3VMS3nc6OO0+pW1svxp0cJ9vbG5geNFHx7IL2hpm2kZFLTUx7SULrnEPcu6pI9VDyT/LmGHvgm6EWd7IwNIzrSUVq8WPTckfYJYlddkRHEGLfUhpfSgpdqDw8Q207JtW/N1YiPEoCUlMwUtAeC559yTM5gCjr/8Yq5AqXcN1QaCaVmvTEubO8JB9EKlZUtz0DLMmJZy/LycHOcipG5fq1bJwQH9wqpTLwBqYRa2ctStm+j+e9xx5tfVCrjtPtUr6KlQg7uAGGT/jjvEHXLVk0+6KyxyP4f5ncK6/HJRoU5X0DIo0xIADjkketBSzT4JIu9Gt2snulKefXbyMnqlOs5My6qq4KDlYYf5r8OP3k3smmtEeTZvnlMehe0SFDbTEhBlheyCKz8vJ8dcudl8c7uJeEpK3N3D//Mfkampn1s6PdNy8WKRvaNOdKHTMy29uqxUVHgf06YAkKkSHZbaG0CdLd3m/AlzrkgyKBF1TMuw3RJNmZYVFU7jbskSMQ7lqFGiO6qfqir3bycb7nFnWuoNBNtMS6+uafJaWFXln6320kvuv1es8N7f3bu7G0j6mGZxdg+vqbHrQrZoUXIdyKt7+DXXiIDjVVfZN4JNbI7HuLuHm2ZeNTEFLbfc0nncvTtw1ll22+Zl2jRxY1QOWwOYMy3VRrNX0FLPtJTk8a9+b9Ps9LYZ87IOYTseqem4U8+hxpyIR372fvuJa/FBB4X7jAcfdB5vtpl31tprr3mvI67u4VGSCYLGtIw707K6Grj9duCBB0QW6rvvmrOxv/su+DM7dxZ148MPD15WHlPy9wnbPbykRNyEmjkzua2mBtrktUtvW/llvnkFLYPaZ37WrvUe03L9+mjdw/3O7/Jy93VaDVoWFopu/3L+CEm9AWp6n3xvmBuyQTdh5XEaNdMSEPvBtgdOKr+hH3U/6b+LHl/Yf3//uqXtmJam32sjxaAlJQuK6MuGSJiLvXqhUiugpjtI6e4ermda6mOv2WZalpWJC79a2JiCMy1bhg9aelXmw1aOZAHbubO4O1pW5u5ioQYtbQfyDpOVZOrWpd6h9eoiLscv8dIYQUtATEykjvMWJM7u4VLUMeWijCkot9cUmNa7L4UJWgZVzlav9s8+DtvVUadnWqpBCHl+2U7yInkFLSsq/IMScsw9+Z1athTlht7IVcec9ZuIR73JUlUlApY2iorckxdMn+4/S6Vcv1pR85pdcu1a877JyzMfCw8+CGy7rXsyjjCuvVZ87y23FFmrI0Y4r6nHrVdDMEqmpSzrgyr2XgH7sNkcasVV3e/yOqxmFgRlDVdUuK/DXkFLdd+FrTj/+CNw5pne65O8gpb6fsvLc8q0xYvdQQvdlCnuv/0yLbt3dx8X1dXpm4gHENd2WT54HY+mTFmvoKXaKLZpBKvLhxU209JUR1y3TtxQOf984PXXnee18q/2tddQXVqK+tNPdwcoJf17DBpkt21BfvrJeawHLf/6SwR8AHGzSQ5hYApams57OUmlWr+TY8uq1PNErRuovZTk+m6+OTmzWCWTELy6h8vrzL33JmeySWGDlrbdpisr3d816mQynTpFC9hnsnt4Y49pWVMDXHed8/crryQHXerqzBPw6WSbJ8wNO6+gZUGBf/dwv7aYGkDShwyQzjgj+SaWJM/vFi3cx08qAa8rrnAHyv0m4omje7j+uqn9rpdFpmu5KdMyzqCl3Ne2mZam5Klly+yHKisvFz1PLrnEbnlbeXli3Zdd5lwLJD2+sMkmwI47eq/Ltns4g5YNGLSkZF6ZlpK8SIYZu0GtnKuNJdMdpMbuHq4XFiUlwUHL7t1FZWfAAPfzpoqqKdPSK3Vd8gpahs18US/qDz4otvmaa5znomRahmngmxpIu+9u/vwwZGEfR/dwP2oDxkbUTMt0dAGPErSU+9UUtNSzf/26h+tBy+bN/X+r33/3v4MaNHt9EJugZVg1Nd5BS78KnHxNZlrKO7F6A1kdt86ve7ialRGm8VRUJCqvskIkJ1vyU13tZNjn5XnvO69MS6/GWNeuwOzZIljTrZvV5jd46inRFb5zZ/H+GTPc1xh1O7zG2Uwl0zKoYu8VJApblquZlmrQUh5HYW7gVFaau4frv5m6jVEqzk8+6f7bFMSy7R6+6abu8tSvMaIH/YIyLdV9pwctbTMtTb/n1VeLfX3CCc5zatDSK6BtGlfOK2ipXo9tAjemIJmtuDItTzgh+eaKtu2Jww/HO888g7rHHjN/Lz3LO64eIOqxoJfJzzzjfKehQ50MeZvu4YBT31GvD+rQQZJXl+1rrnGPxfvEE+76nIk8Z7yClpWVIhPLbyLFlSv9xwYERNBYlnXyXAg7e3jUsQTbt7cPAKlqajI3EU/QmJZxdw/Xv2ezZsntr99+s2sHyGPK69pn6oHhl2mpXiP17uF+wz6odUq5vB5wfO89cWzqN8Vra519XlISrXu4adv0G79xzB4eFBBU64qmbdLLItPNsnRnWoYJWnolToXJtAREGa332ktVTY2oe9x9d/KcClVV7vOsrMzdO1Vn2z2cQcsGDFoSACD3rruw0wMPIO+kk4LHfJAnZZigpXqhUi9oV1+dvGxjZ1rqCgqCg2EtWjjjV6pMF6FWrdwNi7CZlmrBFrZbvF745+S479anO9PStKwatAwae81LY2VahqVnWupBy3btnMqBIdOyJmpXU5NUgpamQItaMfLqHr52rcj8ePZZ5zX5G+mNCjWAFDRzrc3Mtn7SEbT0y7QMClrW1zuZljJgpjcK1eNDbm9Q0DKoYamSv0efPvbvqawUwUVAZD95XSvWrAkXtCwpEWVTbm74MSLV5XNykse6Va8VpqBlUVG0Lt623cOjzvp4xBHOurt0ETO8SmqQdcUKMQHLHXeEW7+pe7h+3KaSaWliCmLZZlp26GDfZVK3YkW4TEt9Ip6ggIhX9/AWLcRvqJYxFRXuDJ/x48X6jzjCOc9N47CVl4txK597zgk2lJa6t92mEWzqjqxq1crcHRuIbyKer792P1dcbG6oyhvopu+l193iClrm54sxrm+5xT/7/vTTvT/bq3u4rG+pwcPNNhONa5VX0LKoyB3k/PRT7+1T3wP4j2mpT0aps+l+XlzsHqYECD5e9CBq1JnfO3SIFrSsq4vekwVILdNSr9frv3Pc3cP1CfB+/z35d5VjxgeR+9rr2meq98v3dOzofl4GLeX5L48BeT2wDVp6ZVpKegKCei5EHdMyaFzP4mJ32RV19vAgajllE7Q00du8BQXh6kU2QcvHH0/OTjSRx8KVV7qfDxu0TJXpmlRfL4bg8aJmvZaVARdcAPToIa6pegDTsns4My0dDFoSACBn/Hhs/tFHyB07Njh4JSukUYOWaoXY1O3TJmgZ15iWpqDlmjV2QUvb51u2FAWSvEAtXRoctFQbWOoFJ2gAf53pAqZW8NKdaWlqSMSRael11zbT9ExLvSKtdnE0BS3jqsQAojEUll+mpRq09Ooefu21IvNDbYzIyrfagD3ySPH9gxrQUhxBS/0OqBQ03qaXVIKWMnAJOFlzW2/tXk52JQScAJOpXAozaL/+PsActHzrLfN7fvjBaZD26eMOkqqNrFWrzI1Br6Clep6kErSU1HJPPR9NwXh1IqMw5LkStL1hAsmqAw4QFeCFC8U4e+q2q5mWDz8sKsZBgQdVly7m7uF6ECHVTEtdmExLvSxs3z56xnVQ93C/TMuWLYMnefGaiEceV+o1XM20LCkRwcoVK0Tw0i/j9667xHi1p5ziNMD1cjo/P3hbTZl9qgMPFOXtf/6T/FrcE/FIQY1q0xhnet1tk02CewnZmD4dGDxY3FDXx4JTqdeusEFLvSfNqFHuLEOvoGVhYfK4fkFk+eQ1pqUeSDGxCVqqmVm2E/HoQeGoE6C0bx/9hkYqamtF+R6lB5R6PDdGpuXEie6/TUFLtbeF33EW1D3cL2ipD3HQvLk4v2UwU/ZCk8dkqt3D9c+X1HNB7x5uc7wDwUHL0lL3PoqaaRlEPY9MgTabzzEFLcPUK4OClq+8IsYdnjYteF3yd73tNncCRGMHLbfdNvx71HZSWZn49+OPou6y117uZS27hzPT0sGgJQnqRSaoYRAlaKlWJr3u4kvp7B4+frx7Vl1Z8VULjBUrggvrMEFL+X1lo3PJkuA7ymoFRA1mxBG0VLMt58xxggs2Qcu8vHCZQ3plPi8P2Gorp0Kf7d3DwwrqHg44lTN5AVYqvTVRA2gmcXcPv+wy57FX9/B77kl+n+lcGjBA/Ha2x1Jjdw+3qaxVV5vPx5dfFtmmXtascU/6IwMVelcTdQgJWYYEZVqG4Zdp6TWD+JdfOo/1oKW6T9WKm8qrImxbefNap049Nj/7zHmsZ70B0YOWcjuDtjdqJo8M1HTsmNwQUYNb//tfuPUWFYkAp6l7uN7wUBvjXl3rwzAFLb/8UmQQzpvnPGfKtOzdO3omllemZZcuwPbbJwctZVmRmyu2I2icb6/u4fK4Ur/L6tXuTEt1ubDDFJjK6aAGqt6QNr3evLkYm00PXNgGZ/78U2T+qpPaSKbsRZtMIP140IOWeXnm/fevfwWvW+U1/p0qJ8edMZZK0FJ+L/Va6DURT1GRuOlsG5xt1sypI/l1Dw8qo2zGfSwoCJ9pqXcHTyXT0tTzyW/5uFRVhc+0zM93XzdMQcu4My31gPD8+cnPvfmm8/igg7zXFZRp2aOH93u22sr9vCx7ZVnz999in8bVPVx/XfLLtLSRk2P+niq9rF2/3mnb5uREn8RPF9Q9fNNNxaRJeXnA00+b12HqHp6TY18fCwpavvGG3XoA9/VWTb7wC1oefXQ8N61OOkmcezvs4H8OeNGDloDYjwUFyddmy+7hiTjqXhsIBi1JUCtdQePKROkerlaKggIV6ewevnq1O4NJFio77OA816JFvJmWsnEo9/GKFc4F1CZrcbfdnMdeE3yMG2d+3quxJYOWNTXORd6me7hXZdxLWZm7Qdi2rbiwyM9XG6phNJXu4aYuS2qF+e+/XcdyTZQuTl6iVMzlfjWdoz/+6DwOmohHZfqN5GRQtsdS1LGupDBBy+XL7WbQ9Mq0nDHDvwGxZo07w1xmzfXt624cqpXAdAQtZYW5d2/38x07ikru9tsnv+err5zHffq4G+7qZBle3SrlcaNXLuMOWnqVC82aJQc20h20jHqX3C9QFmUMTkCUv0uWAPvuG757uD7uVxSmoOXHH4usvgcecJ4zBS318aPDqKkxn5M33yyOCbXBVlXl7Ac5DEyqQUu1jJkxwxlWQf+OjRG0LCnx70ort7VlSzFhzo03Oq/ZBmc++ii5a59kGq/T5jqgfy9TLxl1f5SWiuP69tuD162yuXnboYO7jmga09J0rnhlWgLJQSzT9hQViWNV72LrpVkzpyz0m4jHdnILP2qmpW3QUr8ZGTVoGbaMDQo2heE1NIRKz/APClqG6T1hG7TUe7UlEsndwWVbYMcdzZNfqdun/q/bY4/k5/wyLQH3DRK1jebXFtO7h9fVeR9DeptRPReiXNuaNQvOWpfDg0jquZafH1/7Jah7OCASdpYvB047zfy6KdMSsA+sBgUtw1C/gz4fhNeNs5NOEm3rsWNT++zDDhP7c8aMaPUsU9BSihi0ZKalg0FLAgAk1OBZUIAgSqalGtQIyrRMZ9BSJwuVZ54RhXRZmahsBwUtvRoGfgPGmxoY+l1HXWGhM9g74J1pqVcEJK8LmLot8ne0qax7Vca96EFO+bnye0fNQmrKmZbqBWjJEtexXBc2YOMnylh6cr9uuaW54il5jWlpIitmt94q/h8wwPn9bY8lNWAahR60VPeNKXDQo4e5e6TKK2gZZPVqc6al3C5TAz6d3cO3396dWSnHV3vppeT1qvuwTx/gscdEY6SszD2xhlempTy+1QzNzp3d50nYAKJp+ZYtRTdXXbNmyTOel5SkFvT1q9S3bu2esTUMv0CZ16ztQdRMU1P3cL9My4KC6ONzSraTXpi6h++9tzje4rL//sDJJ4vHOTnOsV5d7ewHWT+wCVqaAhfyuNpuO+e5a691Hu+5p3v5sL9rOoOWgPgd1G2KMnYf4L6BZgpa2lwHgjItAff+kL1Cwp7bNvUgPQPVNNGFqRxftAj473/dY1Gq2TiyfFy7VnQZf/99c33CFCwxlW1qpmV5uTsYJK1bFzw7sQ315lltrbse73X+6EHLKN3DvTJsvZx5pvc1I4rKyuAMZL3XS35+cjBLHwYgbNAyKGhkGopr6lTzsv37+583QRPx7LJL8m8eJmipTgrll2mpdw9fudK7Lqqf13r38N13D5et26xZ8FANpaXufaR2D1dvKKRK7bHmtb9ycvyPcVOmpfp/kFQmtNKpx44etPQ6zmX9JNUbqzIOkJsbrfu+X9BSv+5yTMvQGLQkQS0YgiptUYKW6p3cqN3D1YqBuo2pBK5kobLddiKAtGCBuIutr1Pf5lQyLVVBQctevdwFnVempdcMtV4XML3CpP7vxzQrpt8EEAUF5guQV5DVVjoyLR99NPV1BE3EA7gbcIsXu/Z7XZzfJ8rFWx5rOTmiW+2FF5qXM3UP95qURX6nESNEl6S333Zes820/OUXu+W8+GVaev1eN98sujf27es8t/POzuOoQcsVK9wNCL3BZWqMyzLEVNal2j08Lw/44gsx6+UvvzjB5V69RJlo6n7cqpVoOG+zjchs+Osv9zAWfpmWuv793Y2FODItAdHg1zVmpuU224ggQVDDxqtBn45MS/V72nQPV683hYXB1+4gNt1MAe/j/Kyz3OVHWGVlolExdy7w7rvufawGLWVZYQrwmgRlWvbr55SXsm6zySbApZe6l8+mTEtJ7zofhXrNU4e98PpMk7BBS3ld9Tu3TWVblKClHuzwClouXQqcfTYwZozznPo9ZFkyc6aY6OeQQ4BZs5zX5fVJ//wddhB1Vz0bWQ1aeqmsTH3MaCD5OqR2m27e3FwOxtE9PD/frlvoCSeI8vixx8z14qCsOS8VFcHnhT6+eFCmZfPm4buHB7XHTEFLr2B1UNAyqHv4JpskZ7PK9+hlnCloOWGC8zhM93C/sSj9Mi1btBDjF556qnsZvYxRj7O8vOBre3Gx95iWqWZabrGF81i9CRx0g82LV6alepz6HROm4ysq9TuoCVVLl3of53LbUu2tpn7HKG2oMJmWHNMyNAYtCQCQCDPzYpTu4WrQ0lTZVD39tPmOq1emZdD6/KjvVbOc9AL8H/9w/20btMzJcdZpGtRXX6+ud2/3hc0r09KrseN1ATMFLU0BGL0bkqkyrl48dYWF7sZcNgctt98+eVzBsIIm4gGSMy21oGX9MccEf45NIy/KrNjqRTQvTwReTEzdw70qwOpv1Lmzu+Knb6NXcMArIGpLD1qqn6s22NSxY3JygG7dRGZUbq44x+66y3m9piZa0HLpUnempZ4dYyrP5B3ynBzzHfFUMgUBsc4dd0yeDKhVK3PZ0r2701CXd7dbtHB+a69sfa+gZdAyfry+e25u8k0hPXMMSF/QskMHcT5tsQWw007iuXPPTV7Oq7Hs11iLmmmpnoum7uH6TTG1IVJQkHrQMkym5X77Oefj6687r/XqFf3zL7hAXIO6dk2+dsi/16939oM8F884Q9wYzMkRgVOdTfdwfRD+q69O/h0bI9OyuDh60DLqBIhBk8Klo3u4/D38zm3TTWObem1QsMJ0c9eL+j30IFAiAUye7Pwtyxr9HNhqK1Gv0Ot7NkHLdetSHzNabpseiFMDNC+/nBxc1Mc8jToRj41NNhFlrekaCoSf4EiqqAjOtNTr0X5BSzmWoG3dVrYJgo5br4QHk9697TItvYKWRUXJw87ISWv0AL/82+s6GKZ7eJigpT6mJSBuVJtmJDd9XrNm5vJX/wx1H/3vf8DPPzvrSqX9ss025n0TNBGbF/19pqBl27be22x7QzLstqhtzmXL0h+0VL9fuoOWNt3DbY6zjQiDliSEmRE61e7heXn+Fbo//hB3mXVq0FLN5IkSnJG80uX1gvn4491/2wYtZZo5kNw4b9YMOPRQ/+3r08ddeTAFLXNzvS/sqWZa6o0NU2Xcr5FUUOCu0MlGb6pBS3lhirN7eFkZMGyYewbFsGy6h/tkWtY3b466558HLrnE/3PUIQO8RLng+l1UVaagpdcA6H6/kX4sRW04BFGDlsXF7vNCPdf1oB0gZnH+/Xfgt9/cM8bqWbW2li3z7h4O+Gda6tsLhOtKprIdq8jUeDFldufmBgcnTBUzfbDzuIKWptdMmZZRu4cHBS3lOnNyRDe8n392T2YleTXW0pFpqVfI5bXJK9NSD1o2dvfwX34R597Agc5rUb87ICYj8CL3jXpuyvKptFRsx9y5ImNL5zUZh3pcqdf/bt2A885LXj6OTMugRltJif81Wy/zbW6aBgnKYosr09JUj/VrxJv2lc3YbEHfp77evvHsF7TUydf1uqS8burf1aYL6sqV8XTtVCfiAcQ5Iet++fliKIQ//3SCNkDyvo4jeOpFvSEQZ9By7drgTMuysuRhF/Ruw7IeKH/juDMtw5CTcXmxCVrqSRqDBpmXlceA1/4P0z3cL2jp1z1cloedO4vhAyQ9w039u2PH4Azf8nLvfZRq0LK0VFxHdFGDlno9XR6f6vYXFXknCaUr0xJwrnONEbRMNdNSvWEf1Fa2CVputlk8EwxtILgnSAgTyU+1ezgQrZEoKwYffQRMmyYeb7GFu6IfdpYtr8pyUKalV+aXXiipwQZ9Hbvumtz41y9i3bu7nzPdLZX78rjjkl/zuoDpFSbAHIDRgxCmTEu/BpCeaSk/N9WgpdwncWZayovxjjtGX4dN93C/MS2bNxcX7KAMXJv916JF8LiMOv239Do/5IQAUm2tOWiZl+cffNHXrwYFwwgKXtfVOeWVXum68UZnso0HHzS/v1s30ejRG2VRMo+qqtxdJG0yLf2ClkVFweWp6XiyrdyZ1u11vQhq+Ml1DR4s/j/mmORAQ2MHLbfcMrWgpVejRP0ezZuLzAhTeeW1z9IxpqX6+bm5ThDSa0xLNXsiju7hYYKWgNg+fUKI4mL7m1X6curYrTp5DKjfWS2fSktF+WTaB7/95g7G6OsExDFfWip+1/vuMx9z2d49PGrQMqiBH1fQ0rQev3HqooxZBpjPWTnhz3bbiePEdnKbKEFL/TiWYybaZlrut5/zeO5c/8/0O2dUeqblmjXOdU7eqN1sM1F3idqFNRXqudXY3cNLStw3fPQxLfVMS8C/bqse06agpV/gKujGU7Nmomzy+/yg7uHFxaI9kpsr/r33nnubnntO/N+2rbgpDHhfB20zLb/5BnjhBe9l1UzLRYuAZ591/lbLlltuEZn4gBjKQXXDDc5+efJJ8b/p5pO0apX3Pkp1TMuiInM7IOq5ZZNpKcd7NIlrngnTtsh28vLl3nWIdAQtU+nh1atX8rHr1z3c6zhJVyJHE8WgJQFIsXu4zXv1wZGjFHCyYnDTTc5z11/vLvjDBi29Lu56YdOypbtx7ZUKr1es1QpCXp77zlifPskNdP0i1rp1cKalfH3MGNG9QWWTaSl/C1MARh9A3DQRj9/dqIICd8Evv2/nzqllScptTkfQsqjIfkDu008HXnvN+dume7iaaWnoHg7AruEZpEULca58+WXwsl7r9WpM2mZaBgWD9GNHr4TZ3ukMWk7NtNQrXTvuKCaH+OOP4FlF9QxleRwGZY/r1DE6bTIt9QaPyqZ7+OTJzo0eyfaOvOkcixq0lOf/s88CU6Y4jRfTMrbCBi31gN/BB0cLWspzJSjTUmXal17jFfk1PgoKolXO9W1Sg5b19cHdwxsr0zKowW4b3NP3kd8+NX1m0LkYRN3fW24pJhT78UfgqKPMy+vHZlDmctSgpV/Q2y9oGaaLqapVK/8yOq6JeMKeE1EbuKZy7vLLxWzMX34pAjW2DV7TmJZe5PVHL7vlb2QTtBw2zF2H1rto6/xmkVbp16EZM5zutX36OM/n5XmPwR6FbVkQFLRMZ/fw4mL3zY6g7uGAfxmonuN60DInx7/c2HRT/551paViHX7nRtBEPHIC0Z9/FmOy6pnBJ50EfPedeF1+jt6FXrId0/LMM8UQBF7k/q2vF707Jk1yXlPLlpYtxXk8bRpwxRXudWy9tagn/vKLM9zH/fd7987Sf2f9tXQELePOtFTP6dzc1IZjs+WVaeknHd3DUwnE3nBD8nNRMi2j3kzZQDFoSUIqmZZlZWImTj/6XeeoQctEAvjqK/F3ly7Av/7lLmTy8sQso6nSC81WrYDx451g1pAh5vfJO3Tq+1QPPCD+z88HLr44uRFpCloGZTrIdeTnJ4+bFWZMS9ugpd6oCQpaqmTBnJcX3CDzI7c1zu7h8nvl5Jgbfqbx6Lp3d4/7WFXljGmSl2feN2qQwtA9HEBww1O/wJmOo+bNRSXDNlPC9LmpBi2DKmX6+vUxxmy6wQPBjd6aGu+gJSB+R5ssTz1DWW1ohOlK8uGHzuNUMy2Duoc3ayZ+1912czJvt9/evsw3BTi83htUwZL7r1kzMTu96e5yHLOHe72Wn59cJu+xR/jP3HRT4J//FI/9Gm460+/kFUAKypiI0k1a/55yX5SXm68tatAy0xPxqGwzTYcOdRpx48f7L2v6bUzlX5h9oO/vzp29xwkGxIQqqqAGmCn4YxO0vOUW78xH00zY0rhxycsfcYRzTXvgAfN1srbW/6ZOlDEtTe8JmzkZpYHbvLn5upSTIzLa5XXg8MODj5WcHPd1Y599/JdVj1H5WzRrBhx7rHhsE7Q88ED39w4KWvqNWa7Sg5bqTTI1aAm4b9ymatQo57Ecc7pz5+QbkEHdw4OuXV6BVttMS/VYSCSCg5Z+1yT1OF+zBvj4Y+d3LC31vzaUlPgHaOV5deihSPxfEkjdgw+K9gogjnF5HHplh8l20lZbmW8E5+SIMS/V7ZTjGOtSyRpVyf37/ffixpFKLzfathX1JT15oahItFvUYYTy8kS7y5To8Mgj/okxQfVjv9+xuDjeoKVXpqX6vXJyGidoqW+LTc+ydGRaHn64833DrHfLLYGjj05+3m/2cK96JDMtXRi0JKFlS9TbjpugZ1qWlYkGgT7uo0rPtLTtOqOqrhaZabJx1bOnqHirF85EQswuqs8Cl6qWLcWF6dtvxUVPr4RJ++6b/D7VEUeImXq/+06M+eJ1oZA22cS+ezhgHtPIxHYiHj1oGaV7uNfnpjI2mTx+4sy09BvL5JNPzONM5ue731de7gTvOnUyBx5atHD2mVempd8FMi8vubJoOxZlEP1zU+0eHjZoqVdOttsu+T2jRycHN4O+7+rVzgyLqVS69PNGHYcqalfDsJmWYbuHq6/dcIMYXuPzz+2ziU03F6JmWtp0XUp3pqXewPTLhjC56y5xDQga18s209Kr4RW0r6J0Edc/Xzakq6vdA8hLcU/EY3uzMihoaduA2GEHkekzdaq49vpJd6alja23FjONS0HHQNSJeLp3B2bPFo1qnV+m5e+/Jy/fqpWoF02eLLpK3n236A6qWr/ev4yO0j3ctG8aI2h51ll2dZcWLcSx99lnIpvbpKzMXQ7feSfw1lvAaaclLysnaJGOOkpkq8+a5dwstwlalpa6yyzTbO76NtrQu4erPTz0+nIcs+Hm5YkEBnXfXnKJOA6//jr5N9IDZLqgoOXuu5ufX7s2ONNS7x5eWRk8pqVN92xAfN8DDnDG+S8r8y+jWrTwv07L86yoCLXTp+PTW29F/ZlnAnfcIW62fvSRcxzGPdbehAnJWaB+14IwAR25f/WyCbCvL/sN36BmVm+zjTg2BwzwXl52w/fjd57E3T3ca/Zw9Xs1VtBS/w5e5adKbq/tWO1B6wFEWSnL8DPOsF9Hp07m+nWU2cMZtHRh0JKE3FxU23ZvXLXKPch2WZko9GXmiUmUIKXu77/d6f+ywFZn9H34YbE9hx2W+uepZAWiT5/kbAhVcbG7kDE1BPfc0x2M2XNP8f+QIckVFX0cPb9MSyC1oKXtRDz6BT5oIp7//U88LiwE/v1v57Wo47Ll5QHnny8ex5lpqVK/45ZbimC0VxBis82cSuSrrzq/uV8lWFZGFi92jSNXKy9cpiCkrCC+9lryBS6uoKXteurrkzMtTYPopxq0NFXKBg40zwBtK5VKV26u851SybSUCgqSK1mmGQfV8zhs93C9fNh//3DBp+Li5AZg1KBlUEYKkP6g5RFHOOfSSy8Fr0PXt687UyjV7uFex2NQ4yNKRoXp+iLNm5e8fNzdw20Flev6rK5eWrYUDUivgIPKNtMyzH6PMuzAqFHO5w4d6r1cTo75Gmo7tMg224gsY53fRDwmzZuLrp377SfOq8JC0R30lVfE682aAaecknqmpU35euSRTtkme7X4CVt/6NBBjIFsq1Mn0fPHq4zQz/38fFF3VScDkUyN8T32cGfu2kzEU1oaLlhru4/065Bf0DKOTMsddhAzUqvBgdxccRy2a5d8vARlWuo36HWmcwUQ3eCDhk0oKXFvz9q1Yrvl/qqocNpINpmWeXn+x5Tf9T0oaKmeix06YMX224ttzc8XwVH1mDUNgeAXqAtSWirOYZVfeRsmoCPrau+/n/yabd3Ntm6y887ObOlebDIt/YKWxcXmYzauTEv5XeXNfiBzQct+/YK/lzxf9ESmsPTfpHNnUYaHSZDxuqnl177yWj+Dli4MWlKDatvCaNUq96DP8n1+BXocQcvKSuCii5y/ZUCjVy9xV+uzz5yu4XFm4IWlVm78ZrOT3nxTBKIefjg52y8/P7jS6Be09Gr46l1TAPtMS/0zgjItTzpJ3N385hv3mKNRMi2HDxfrkZkFUS/Qo0eLdXlRv5OsxJmOKTmgumldfhcbWRlZudIV7KuRn6Xv0512EtkDH3wggi6NFbT0akyuXJmcaWk61sMGLfWuaKauaS1aJK83TIZNqpUuWSHSu3RF2eebbJJ8R1bfPr0Bon532Zjw289hg4Am+rEcNWgZlJEChN9evzLAFLTs1k2MWfX++07vgDDBJb1M9dr3pu+Rl+fOTtEztf0+RxdlfEF9W9Xf0dRNNO6JeGwFXfNsgt9AuO0NE1C2FeXc69ZN1GfGjROzzXsdX61amY99PSCln6tB2R1+mZYmXq8feywwcaK4bnXp4h+YtB2jOUhpqdh3b70FnHNO8PK2nnoKGDtWdHmOcg54neNex5fpu9o0xKNkWgaxDVoWFLiPJ3mOtmuXHKSMI9My6Dvo+9BvTMvS0uTjU1/GK2j59NP+2wGI49s0DqXcX+pYv7YT8XidD3EGLYPoQcvXXgNefNH+/SZ6mRRn0LKyUrQTdbZ1NzWA58fmumETtPSbo6GoyFx+xzWmpanMsg1a2h5Dt91mft50fupDn+nU7U2li7hXWR3mBpdXQo6+XWqZ4FXfY9DShUFLamAdtCwvtwta7ryz8/ipp9yv2Vzog6hdRHfZRQQsZQDAJmh56aX+r/ftK/4/8MBw23XrrU4BZzN7c5s2YvyLkhL3dstKVm6uf4GpFnZxZ1qaxrTUNWvm3dW0oEBsf//+yV19o2RaDhwoxsKR9HGe/Fx+ufi/fXsxk6vf+FHqOv3ufMvlrrgiueLjd7FRK/E//dTwsFquQ2/EtWwpJow58EDxnfVzTW8U6Nsis139dOuWXNk1VT4KC4FDD03OtIwStNS3U9/HpaXusaSKi8Xn6usN0whSy6Uo5LmjdukqLDQ3vGVGsHTQQe7fynQO6OWwnt2mlgWyy2CYbMMo4gpapiPT0q+bu2lMS0DcBDj4YOe9qQQtc3LMk1XYVH5btxaZVabvENT4OPlk8X+YAETYoKU6s3BBgTh3bIcVSEVcQcswmaG2mZaAKP9sRD33tt1WdP81lXWSV8PMdO1QjxH1fXEELb1+q5wcUd7J67VfQ9Zm0hrbhuhWW4lzKmrj3aRTJxGEtRn32KSpBy1tjmOvjH99KBcgnkzLoONBPw/8gpatWiWXa/o2dusmxoIGkseuD6IHLWWPKXn+qTeHbLqHBwUt/co92zEtbey+u/O9rr1WtGNSvbGll0lRu4ervboAMQzC11+brx1+N00uvFD8366d/flv05a2mT08qHt4OoOWct1Ruofbtu1OPNH8vCmAp2fg6rIpaOmVkKMPp6D+Vl6/G4OWLgxaUgPr7uGVle4AhVfQcscdxV2t8eOTZ8s85RRx5/rLL8UYj7fcEn6D/QbnDboY3HyzeXYv1UsviYy8sHcOt95ajIH48svhx9ZUt1st+P2+j1oQxh20LCtzX9BN2+EXOPQLQETJtDRVxmwvJtddJ4J3n3wSLhNX7l+vTEtAVPR23NH9mk2mJeAKWtZ4BS31iqDe6PjHP9x/6+fy4MHA66+LMbD0buu77iqGWPjww+TKgr5vr7tOnK9t2sQTtAxqrCYS7kaP/F56xcI2ENmvX+oZOPKYVruHFxUlNyKee07sL1VBgdPwkevQ6ce4X6alTVeyxsy0bNfOv9KcjqClH1OmpUmY8sBUoZ48GXjmGfdzXt9DvzHVqpUY41gfdiQo03LECDEL+xdf2Afn/IKWaoDSpLBQXNs+/RS45x7/ZU8+OflGpc6v/I8raBlmfK8wQctnn3VPAOIljhsGXvvC61jWrx3Nmzt1iaIid31Bv44UFCR/XtRMS51f/dK2u3+mhJmo0iSbgpYtWpgb9ZtvLro66xNZNm/un/EleQUtgyYjBMT2hB2PNGympfqdTUFLnanO9e67ok3w3HOWG6l8tvr5st4jv4OaNW9zTfeaLBKwy7T023dheowUF4vM5jFjxPUoDrbXbEBsq9d3vesudxvvrbfME/3I9Xi59VbRbvj8c/u2hs25apNp6VfuFBd79+aIwmt+hSjdw23bdl7f3/Sbm4bM8FpXOoKWYYLBUdq2Xr9bnJOWbQAYtKQGNWEuVurg3bIQ00/2ggJRAVLHEJPkjIe77irGdDzmmPAb7DerYdDF4MwzgytJ7duLcSajVFj32EN0PQx7AVG70asXYzXQoVPvENt2D1cvqkuWiMxZU/fw/Hx3ASx/Y1mxlXecvfa3X8UrSsFuumDaNppKSsRs83IMKL8hC/S7i4D5u6iVGH3cJttMy9mzGx42ZFrqF129YqZXVnr1cv9tmrRg4EBxXOr76+KLRdaxKVtMd+65IksNcB9rFRXm8VaDfpsddnAaszLz+corndf33dd9c0J+L3W9eXn2F/Z//zv1oSPkuaNO7lNY6D6nWrUSgRtTJql6t/7PP5PXr5drpiCEJH8Dv+8UR+BED3R7nbt5ef7ZCDbBpqDtDVOm2jaAgrLZg9bRuXPyDSqv76GW1zKYtMMOTuakFPQ9mzcXN/923NE/0KsG6S+4wP2aem2bOdP/89SyP+h6vf/+YjxGfVIFlddsvM2aBTc6HnrIeaz3ZjjpJPF/WZl5Ii8vpt/Lq4HWtq3/eJN+6wwrTMMOSL5WFBQ4ZbY+63Xr1u7yxTR8Q1xBS7/6pU1vFtuumengdazaChu0NNVNowYtTWNa5ucnL9u9u6jD/Otf7ufz84EHH0z+LNlzRdIn4pFM30W/XpeVAf/v/znbbDMGbVAZoR9vfvVkedNn//3F/9tvn3w9KCoS18EhQ8yT0/nx6olh2l823cNzc6NnWrZoIW7eymNSTygJk2kJiDJ28OD4bjaGybQEvOt+ZWXBgS7J79yS7QZTxrAX/dzSr+1A6kFLrwkY4+4erraFcnPtgpa22bZe399U9yktBR591Lx8QYH7/PYqG3bdNfo2xdE9HHCuxXp70fSdt9wyehB6AxVj/wlq6mrDXHRMQUv9/al0ubPhd6EJuhikcicmndTAj1opefll7yBtqpmWY8aILDxT1hcgCmD5e8v9Onas+CcnPIoStIzSPdxUoYoahPLL7jAFLeV4dGrjSf3sMEFLNdNAmeyi4caBfoHUK6H6uVZYKM5DOWyD36yO+v6KOsC0es4uWWL3WbrSUpHh+fXXTmPp2mtFA6p3b/F5atBS/v7q5223nX2FImyF3ESeO2oDv7DQfVzI885091oNAJoa4rvuKo4dORu7PsGR+l3l79zYmZZ++7t3b+CPP5y/8/JEJi4QT6ZlixbuMcD8hMnaKChoKBPKu3VDS6/MQ9trldf3UMt4vy6LYa6Jfvts6FARaGzbNjkbXG0UzZjh/xnqvgw6r2VG32efifNbHyYBEIGgn39Oft7mfD7ySOD550WZp++nu+8WQ3/ssUdqXeeB1MuLOCaKC9uI0s/VggLgiSfE9frww5PXfeGFwMiR4T/f9nVJ35fXXy+6Xe+wg39wW8pk0LKxMy0LC5PrGjbluKn+px8nsu5bXOyaBLDhOquvIz9fnG8vvyzOtS5dRFlx8slitnN1+0zf01TX1jMty8pEELR9e6BHD9HLado0z68JIPjc9ktK8Mq0/N//xLiMRxwBnHCCexl1/4cdv7q+3rwf/IKWQZmWqYxp2b69GB991iwRRHnjDef1OOpIqQgzpiXgf520rfvEPeSJvk333SeGX/niC+c5m+7hJSXiODe1zeLuHu41EU/Y7uH6DXw/YW/InXWWOLZvu030TpH0c8WrfX/jjaIte8UVzpAMav3U77Pj6B4OiHNt3DiRRKLSP/eBB0Q5RC7MtKQGdWGCjGpjQ56gpkCKLb+C1nRBOfpo//XZNKqykdqgVStd3boBxx1nfk+qQUvAfFGU2QWmTMsOHURDVGbnpbN7uFwuL8+ue7jt3VV5Vx0ALrnE/ZopaAkkf884Mi0VntnOQd3D8/PdwT3TTMDqsn5/6+QA9PrEDzZBS5syZdddgbPPdirhxcUiI1JmW5iClkp2Krbbzr6iFmfQUn9OzdyV26NXXgsK3Bl5t9+evK6cHGfmXSA5q03PMpXr9ZKOMS39qGPOAqJ7lRQ0jjBgF7S05TWmpYlSca1u0QL1XgO/2wYTvfa7WkFWb9zo2xYmaOl3PSsqEgEG06yuYTLI1N8lqMyQ29OjB3DeeeaglFcgSC9HTXJzRUbl4Ycn7+d27UR2qcxosBVlIp6bb/Z/PY7GcNiGnX6uytm9L7jAfOPzyiudcvHgg5NfDyo/ogYtO3US5bzXBCe6/v2dx2o2fqoOOSR4mVRvcocNWpqCUnF1D5fHpL4+eZ011Q9yckTPoWOOEdfrf/87eZ/oE/FIpuBht27u7y6v4aefLibcsLmeB10nwtwwl/WrzTYT50m3bsn7MmzQUmZ8A2JYDdvs2TjGtAwKWgLixs5ZZyXvi6YWtDSRWbs254zN9SYs/dhp2zY5M9km07Kmxvt3jrt7uE2mZU5O8FA0xcX2dc4wmZby8wcNCu7V4lVet24tyi51yLega7gUV6Zlly6ijNF7L6nH4ZAhTjlELsy0pAZ1YYKMX3/tPJaVnXRlWpaUuMd82WwzcRfCT9Bn+2WiZZL6PYPGNZSidA/3+62bNxdj8R17rPjbZmzNdGZajh4tui7272+uwOifffDBIiA5eTLw2GPe6+3YUcxu+v33yYN2ewUtCwqccQz1z9a7aPt1WTYMsJ0oK0PC6/cKyrTMzxcB5G+/FX///rv3Z/sFXk1efFFkNfndGdSzAb0+KwpT0FKdCKxHj8xkWqoKC81BSz1oUVAguhm98w7w44+i0WCyxx5ime+/Tx6DM2zQMo7ASZiJjvRGwMkni+BYTk5yppdJOoOWfg0g5cZNbUkJEv36uTMjpFSDlqrGyLT0OwfDZJCp3yfofNNvgpWUAMuXu5/zCpiGbUTq3y/qtT1KpuVFF4nywCYYH1XYTEv9uhN0HLZsKY7zsWPNXd6DfuuoQcswkyQBopH33nsiO8z2xqSNUaPEP79JE1MtQ8MGLQFxzqjXubiCll7r8wta2igoMH9PU7CupAR4+21xc660NPn3tAlUBW2XX0+afv3cf5uORXUb5IR36t96JqzuwQdFWbb33uI7mvaD6TvYzh7uN6Zl0HGl0o+DbAtahs1Wv+MO53jy24e33y56fhx/fLj12zBdu03fK6jsXLhQ1HeWLk1+rahIHKP6cdjYmZZ6pmJJiX3b3+u3Dar7mMZiVnkFLeV36t9fZFWvXy96C9qMxxpXpqWXTTYR8y1MnQoMGxb+/RuJLI3cUCbUhgkyfv+987gxgpaq224LzvyJI1iSCV7dwwHvSn4cmZaq7bcXFfhttxV/qwVw2OBkHJmWnTuLi4o+2YzXZzRrJu7Gmbok6g46CBg+PLmiFiXTsrjYGffGNCaSyhTQ9AviBo1pKbv5SWG6+wWdK127iuNBH1dVPWfVYHuYddvYfnunEiL37/XXO68PGmRfUQvbrcvEq0uXKWjp9d5DDhHHnV8XtkMOEd1Y9GNTPR79JomSbGbmDaKOd6oHr3V60KmkBDjtNBEQsWn8m/avmll19tnB65DCBC0VNSUlqPeaRC3V7uGqphS0VD+jqCj5t1TLH72sNh3nXp+tZ+oGietaHyVo2aKFOI9TnTHXj9f38wqU5uW5fyubelivXqJMNWV2BJWZUcveKPusf3/RKyLspC1+OnQQ9Ys4MtK9eK3bb9+mK9NS0hv2PXo471H51WPU4Sa8uod7/VZ77y26zd50U3LWkb4NpuMyqHz0G4pEHybDtI3qNuj73i9oKLVuLa7fcvx3dRIYeZN81qzk93l1D99nH+fxJZf4Z1r6ZQYHHVdx1JFSEfaaLbMqATHM1eWXO22lnBzva+POOwNXX+20c1J1xRXO43/+M/l1UztFH4sRcAdRjzzSP9PS9P3iHtNSHQf7gguSg5b6eVBSYn9N9jqHg76DKcvb73VJ7qucHDEc1bBh9j1C0x20BMQY/pdfHm3otI0EMy2pgW/3cNlolN0ZZYVAnXghlaClXyGlF4o2F9WmGrRUAwxxZFpGCVrqr5m6h+vSmWkZdAHT1+M1nmAYfpmWKv17jxsnZhFWuwaZmDLXUgla5ueLSvEzz4gxW4YM8V5X2ExLLzZBlTjOw1atxDhTn37qBEYuukj8Rr17i6CmOsaun8bOtNTF0TBW76rbZFrGEbQsKRFjE37wQfDNAL0LatihOEyVyAsuEOdH27Zi4jZbYbqHK2qKi0WgdsIEMQbRE084L9qWKza/tXrOpyto6fedW7QQ56jawO/QwZw5rW5f8+biZpI873JyRIZLmzYiwKA3BPVrWWGh97kYNtMyrmCTXlbZBCakkhJnjKy46dt12mmi3Bs82Ps97dqZJ/mKIj9f9Fro29f8um35kmqmZboVFponIoqDzaRcunQHLdXzedNNnUmawmRajhsnerPIyTZtu4cH0cvCiROBp54SCQtey+j8gpZy4jWZjWnqmaJnWprWEUbXrsD48aKX2kUXief07HP1s/Tz/qSTxA3a6mpRvzMFPAERVPK7HujHlb5sU8u0PPFEYNEi8XvpkwrJ9am9o6S4y5+rrxb7dpttkic8k9uhys8Xx1Dr1u7j4NprRVC9Uyfxv9f5I8uDggL3JKpxzR4ut3fQIGDZMufxb7+5l9MzwsNkWnpJd6al33u9xNU9nFISqVW/+v8O0DKbWaR8VFRU4KOPPgIAHHnkkSmti1Ln2z38ttvEQNy6rl2dk9k0e7itMJmWNpWgphq0VNkGLaNkWsrZI9W0fkm/IKSre7jt3aigyqm+nlSCldLZZzvdcs84w3k+KODXs6dovAcpLhaVQ2Ug/ITf/tAvwqYxLYHksV5MUpmIJ+z74joPBwxwj8nXqpU72zIbxrQ89VTR5Q1wZ52p4giwqOesTdAyrgksDjhA/AuSmysq3AsXisdhjwGvbB154+zHH6OvK0SmJQCRMbHnnu6gZZyZlo0xpmVQN8O2bcVvJfXqlRy0NF1zu3d3gpaJhNjX115r/hy9oVxS4t2wCDMUAZC+TMsWLewDE3Fm/um6dAG++ko8zs8X15egDFk1aCkbnKnYbz/gmmtEVpzOtnzRy950ZqfaGD7c/bdXGaqOpRmV17r99l1QcMnEVP9Tb6apfvjBebz//s6xHiZo2bWru1eHqRyKI2i51Vbic8IELY84wqmLmcZAvfde5wac6QaAX6YlEC3AfcQR7sk1LrwweagrObSGXh4VFbmHb/Dar4WF3pN2AsHB8GwLWgb9zrm5yeeyqqjIfEMp7vKntFSUkX7boZLnVdu27qBlixbuoSq8knTkfkp3pmVurrs+q8d99O8VJtPSS1CPnKhBS1M5bLutfvtVv/Gb6WvbBizS0d2qVSvk5ubi+++/x/Z6l0EACxcuxNVXX42cnBw89dRTnuuZN28eBg4ciNzcXNR6XVip0QR2DzfdmVLHm0tX93C9ANuQMy1VeqUkSvdwv/1aVGTu1qsX+KlkWobJoPUSNdMyFcOGAX//LdZ18snO83FlKQKica7O3ul3odM/N5Vxf+L6DrJR79fwaqzz0PY7xBFc8Mq0PP54MUPk+vXeXZjjDlo2VvfwsD76CLj/fjEubtjx4IJmxAzzfVINWgLJZWicY1qqDQDbG04mUbuHA8lBy549RUatdM45IsNC1707MGmS3fbp166SkuRz8eyz7W666NKVaRnmpnw6g5YjR4pr8MqVYkZjmy796nihpvHQovBqCDa1oGX79iJbVe1WCiQfR9dcA5SXu7t9RuV1fvqVZUFjD5qYMi1NWWa6ffd1v0cVpn5gqqNGmcTIpuwLKof32Qd46CFgwQKRBac75xwRzKquFt1w/dZv2vc2+zXIlVeKY+DBB53n5CSn+g0T227cVVXi3Bo/XgRtP/vM/bp+XOmf09SClmHXJzV2+eP1vTbd1D2xrX6+ef3O/7+9+w5zqkzcPn5nOgMMgwIz9GKhCwgCIyqiKCLrioC6SlfQVXgVsKIroKi4soiiWLDhLrouLKjr/tB1pFpGlBEQUSwrYqOo9OIwzJz3j+yEJJNkUk6SJ5nv57q4yCQnyXOSPKfc5ykVz7fr8/J+XrDj8HrvN321tJwyxVnP3C86BFLVPqWq7uH+tpWRzLYeaDtYp47nfjbc1q6oUthbAyvAznb37t2aP39+laFlMK+F2KlyIh5fG/mKMeakyFpaem84evSQ1qxx3m7e3HMMzXBCy0BXnE1y5ZXSSy85b7uPgSPZ2z1ccm7AfYWW3uOFuo9n17Ch79cKJ5wKNswIt6Wle4udUMetSUvz3Wqoqu7hocjP9+hqUamlZffu0ocfOm97j+lkQmhZMZPg7t3+l4nVhFf+1qFmTc9xYu0oj7/QMiVFuv32wM+NR/dwu1pahqJ1a+nxx8N7rq/9kPv326jRsds+Lpp6CDO09BgqJdxu28F81+77snh0D5cqz+xdUOAMnCXnrLf+vkf3C5ZV8d53ZWdXPvF44ongX89dJBeO3HlvF0M5eY9maHnyyZ4tfYPRoMGx2zt32lMOu0PLWHYPv+oq6bnnnLdffrnyRCxS5To0ZsyxoY8i5W9b4H2M5877ODeY36Ov0NJfi8Bhw6QFC5y33Vv/hTsRj+T7GNWOlpbhLjNunP/HUlIqB9f+Xj/UIU6C1aiRs6Vlo0bHJgO55BLfywYbWlYMr3HRRc4Wlx07Bve8CqaFlpFu3/3tG2M9PIW/9fK+COW9vv7qT0VjjWi1tPT3uXn/Dr33m9nZvvelN9/sbP1fcX5boX37ykMdVLVPCTSEmeT/ON/XOgU6f3YX6HeYk2PfxUEExJiWcDlaVWhZVUtL7w1FJC0t//lPZ2jUq5e0fr3nY5GGlnbMphsts2c7d1KnnFJ5MgM7J+KRKm/4zz3XefDk3ZXm3HOdV6oPHDg2o7g3O1o3+hNuS8vcXOcJyhtv+L7SHg67W1q6816Pv//d2R3vvPMqz7Rb1YFDIN5ljiR4zc2tHFq6t76M1QUpf7+Rrl2dM/LZyd9EPMGIR/fwRLsoWNXV8Lp1nQe+b71Vdb0Oc0zLcl8ztPv7259gfhN2hZbhdg+XpB07jt12OJwTLb38snOog0Cfbyihpa+WluG0wIom79+KKaFlONz3F/4mSQtVpKFlON2d7fKXvxzrNusrsJQqf/92jkvma/s8a5bUs6f/53h/XlVdoJGqbmnpvv2bOtW5rz7/fGfDAF/L+Po7EF/HqPEMLSMRi9Cyws03H+siPGSI72WCCS0ff9zzAr2v766q86d4T8Rjd0tLf99drHvjBeoe7uv+Ct7fx+9+5xwmoKJeeX9edo1pGew4vMG0tKxYJ1/b/Ndek+69V5o//9h9oYaW3rNt+zvH9/edv/mm8wJOoGEGAm0H4x30VyOElnAp81ehBw1y/u/rKmqgE5dQTtDdNwjp6c7WfhVXxr1nyQxnTEv3DXm0D3Yi0aCBNG+e78eCGdMylBNs9w1/Xp5nl0Dv1/c1lpW7cINgf+NquotkTMvLL3f+s4t70OhwVG6lFArvVhzeJ0mtWknPP+/7uSa0tJQqn6RkZjpPbiuCzKq+W7v4W4fWre0PLf2NaRmM6tI9PBLev22Ho/J27IorAk9EUiHclpbuv6dwu237+07atJE2b3bedm9BHcmYloECoKrKe+GFx8YJfeYZ52cWzHbTvZdFVbz3XXaGlu6v7d7CMFSRdA+/5BLpP/8J/73t1q+fM6iTAk/KFgq7W1rGUt260ty5gZdxr0NpafaGN766ngc6QZYqv38wE1RV1dLSfR1PPNE5cZ+3SLqH5+Q4t9eBJpQMhmmhZbQD9rQ0aebMwMv4GjvQ3bx5x2Ylr+BrH2R6S8swLzT6ZUpX3XBbWnp/Xy+95PkdhXmMU0mwLS29+Qot/Y2b7+s1TzjBeZ6zcOGxCYWq2qe47+dzc50XWt35Ox/1d7/3mPm+BPodup+DmjbBXJKJUd89JAKf3cOvvPJY97Boh5Yvvujc+FR0C/f3OsEcTHq3+nTfcZmyEwtVMKGl93oH29Iy0vFdwu12+8EHzhn/5s51dle6887KM+/FYyIef6ZNky64wDl8wcMPR3aS7H0yGcrBvZ2hZaQtLd3Vq+dZv2IVWnp/5xddJL3yin1dR9356x4ejOrSPTwSdh2ER/BatrS09PdeS5Y4LwQ+/bTn9iOSlpaBvv+qLij9v//nbEE/bZpzrL9gRdI9vGZN+1ov1a7tPOm55BLnDPfhiqR7+JgxzhmBQ5nZPpr69pXuvtsZ7Ac7jlhV/IWW4c4ebhr3OlS7tr09csIZOsn7eCCY+ubrAot7S8tg9lORtLRMSakc9idDaFnVtsrhcE5mFE3e3533eZCv8yLv7y4lpepj/XjXU7tbWoYzYVI0+Fsv79DSe329609V4WK457f+JuLx5e23nedt//mP7+7sobS0rOC+va3qmLVxY+n++515wccfV95WR6M3ZaDtYM2a0l//6jwGWbXK/veGi8FNzhBrlULLceOcA1lXyMqqPEuW+3iH3kI9Qb/ySuc/b75m0QtVMoSWwXQP9xZoh+/+/YTSqiTUMgTSrZv06qvO29df7/z/jTc8l4nHRDz+dO1auXzh6tTJOVj8/wZKt1q0CP4EMJLWENFsaVmvnufMw/Fqafmvfzn/d/+u7GotEe/Q0ldLy2TqHp6W5jkWaWlp+K8VSmjZrp2rxeGBxo2P3e99ABxpaNm2rbR4cdXLh7KfiqQON2vmHI4lVKG0KPDVPdzO8W5HjfKcWTcckbS0TE11XsT6+edjQXS8ZxD1N5N7uPyFll26RPZ8U7hvK+wuq/d2KJj9hfeJezDbA18tLc8889g4cpdeWvVrRBJaSs66vnfvsb/tDC3PO08qLHTeDvZ3F66qJuJxL8vKlc4hnSqORaPRnbyq7uG+Qkvvbdpxx1X9O7JrYrNwJWto6a9ehdrSsqrQMtzALpTjj3PPdf6TjrXor+CrpWVFmQP9ttyPB4I5Zq0YAzZWqtoODh/u/IeooqUlXCrNHu6rkrofiOfnBz4gidasnuFslJMhtPR3BTTQ5xFoXd3D50gDHTtPQENtHRXLlpZ2e+UV59W5YcNkVRwEhMPUlpaxauXn7zs/fPjY7WiGlvHqHl7xWQf6DhOtpaUUXNfvYITS1WzxYqlfP5VNn+4ZWnqzawKxqpYPZT8Vr3GaFy+WeveW/u//Ai/nayIe08L0SFpaVqhf3zmJUd++0ooV9pTLFN5BXp060sSJUv/+wT3f4XCOW9anj/1DdtjBvf7ZPUZpOC0tv/322O1gLxD4Ci3HjHG2oL70UudM9FWJ5IKo5Luuh8rftvPZZ51j+t1yi7OORVNVLS2ffto5tMbttzsnVMrNdc4C3rdv5Rm77RBOaOn93XkHZBVWrpTOOcfZWizeY/7bPRGPKaGl9+fqL7T0rsPe36v343Yd14b7OYcypmWwoWWkx6zR+A0n0nllEuNbgEullpa+ToRzc4/NRlnVmFbRCi3DkShjWgbiLxjs1cv/cwKtq3toGel3ZedOwtckSoHEsqWl3erVc3YXlSJrURZKHbGzpaWv0NL9dxqrlpb+AhD30NKu1jPxnojHV/dw722De2uXc86J/D1j7e67neMrSs7xjcMVygWQNm2kN99UeWmpcxKaSIXa0i6SMS3jdaI5aNCxMa8D8dXS0n0WeH8n07FkR2gpSTfc4PyXbLy3n2+8EXp3+JEj7Rtj027R2FdUCCe07NFDWrTIeTvYz8xXaJmWdmx8+GDY0dLSXTgBsL9tX9Om0uuvh/564ahqTMvmzStfrBk/3vkvGqoa09LX5xxsaNm7d2RDa9jJ7paW7sMjmMRf93BvVdUfu0LLcD9n7/1mrVr+Q8tAxzShtrQMpHPnyJ7vSzSGmkLIaGkJF8t7g+KrkrofkFQ1xo6poWWitrT01qKFs5XezTf7XybQjsj9CmSk3xUtLePLpO7h8Qgt/b2P+4moXV22TOwe7u3++51jDg0YYH830Vho1MjZpbFjR2eAGS47x8cMxnvvSd27S3/+c+XtUlUiaWnpbfFi51AWCxaE/xp28jWmZatWzt9mz57OmeDjLZLu4dWBd5AX69l3o61iEgjJjO7h117rbMn3u985t+fB8BVahirS0NJXXU9EsZw9PBhVje0fTPdwEy4OVcXufbYpLS29VdSr+vUDL1fVHA6mtbRs1sz/OUagYxr3C6+RnjcMGiRddZXzwo9dCC2NENHWwBHvZuSIrqq6h1cVWtoVZNlxcNy9+7GxExOx5ZEvb70lnXRS4GWC7R4e6WcczZaWVZ28ex8kE1oGlowT8fgLGJIxtCwocA4+LjkDH18aNDi2vUtUwc4QHkisQ8vTT688kVywwp2lXKq8/Q22BWSs+Asy7r47slDaTna1tExWhJbhC6elZa1aVQ+74C2SbUgFu0PLcD5LE4aOMC20jGb3cJPYPXu4r5aWvXtH9pp28Nc93FtVoaVdjYPsaml5wgnSpk2e91Wsa6BMwO7u4c8+63wduxopEVoaIaIj+A4dOvh9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\n"},"metadata":{}}]},{"cell_type":"code","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')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"collapsed":true,"id":"-x71HpLiXY1k","outputId":"9077e382-43c2-4f97-a13f-9c0d20fc1e36","executionInfo":{"status":"ok","timestamp":1763337965667,"user_tz":-180,"elapsed":1425940,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}}},"execution_count":51,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mВыходные данные были обрезаны до нескольких последних строк (5000).\u001b[0m\n","Epoch 507/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.7800\n","Epoch 508/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.7760\n","Epoch 509/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.7719\n","Epoch 510/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.7679\n","Epoch 511/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.7640\n","Epoch 512/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.7600\n","Epoch 513/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.7561\n","Epoch 514/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.7521\n","Epoch 515/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.7482\n","Epoch 516/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.7443\n","Epoch 517/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.7404\n","Epoch 518/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.7366\n","Epoch 519/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.7327\n","Epoch 520/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.7289\n","Epoch 521/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.7251\n","Epoch 522/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.7213\n","Epoch 523/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.7175\n","Epoch 524/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.7138\n","Epoch 525/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.7100\n","Epoch 526/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.7063\n","Epoch 527/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.7026\n","Epoch 528/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.6989\n","Epoch 529/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.6952\n","Epoch 530/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.6915\n","Epoch 531/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.6879\n","Epoch 532/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.6843\n","Epoch 533/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.6806\n","Epoch 534/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.6770\n","Epoch 535/3000\n","\u001b[1m1/1\u001b[0m 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0.6523\n","Epoch 542/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.6488\n","Epoch 543/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.6453\n","Epoch 544/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.6419\n","Epoch 545/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.6385\n","Epoch 546/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.6351\n","Epoch 547/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.6317\n","Epoch 548/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.6283\n","Epoch 549/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.6249\n","Epoch 550/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.6216\n","Epoch 551/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.6182\n","Epoch 552/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.6149\n","Epoch 553/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.6116\n","Epoch 554/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.6083\n","Epoch 555/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.6050\n","Epoch 556/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.6018\n","Epoch 557/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.5985\n","Epoch 558/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.5953\n","Epoch 559/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.5920\n","Epoch 560/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.5888\n","Epoch 561/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.5857\n","Epoch 562/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.5825\n","Epoch 563/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.5793\n","Epoch 564/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.5762\n","Epoch 565/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.5730\n","Epoch 566/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 0.5699\n","Epoch 567/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.5668\n","Epoch 568/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.5637\n","Epoch 569/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.5606\n","Epoch 570/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 0.5576\n","Epoch 571/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.5545\n","Epoch 572/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.5515\n","Epoch 573/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.5485\n","Epoch 574/3000\n","\u001b[1m1/1\u001b[0m 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0.5277\n","Epoch 581/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.5248\n","Epoch 582/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.5219\n","Epoch 583/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.5190\n","Epoch 584/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.5161\n","Epoch 585/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.5133\n","Epoch 586/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.5104\n","Epoch 587/3000\n","\u001b[1m1/1\u001b[0m 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0.4909\n","Epoch 594/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.4882\n","Epoch 595/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.4854\n","Epoch 596/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.4827\n","Epoch 597/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.4800\n","Epoch 598/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 161ms/step - loss: 0.4773\n","Epoch 599/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.4746\n","Epoch 600/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.4720\n","Epoch 601/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.4693\n","Epoch 602/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 167ms/step - loss: 0.4667\n","Epoch 603/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.4641\n","Epoch 604/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.4615\n","Epoch 605/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.4589\n","Epoch 606/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.4563\n","Epoch 607/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.4537\n","Epoch 608/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 0.4511\n","Epoch 609/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.4486\n","Epoch 610/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 0.4460\n","Epoch 611/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.4435\n","Epoch 612/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.4410\n","Epoch 613/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.4385\n","Epoch 614/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 158ms/step - loss: 0.4360\n","Epoch 615/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.4335\n","Epoch 616/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.4311\n","Epoch 617/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 0.4286\n","Epoch 618/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.4262\n","Epoch 619/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.4237\n","Epoch 620/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.4213\n","Epoch 621/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.4189\n","Epoch 622/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.4165\n","Epoch 623/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.4141\n","Epoch 624/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.4117\n","Epoch 625/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.4094\n","Epoch 626/3000\n","\u001b[1m1/1\u001b[0m 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0.3932\n","Epoch 633/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.3909\n","Epoch 634/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.3887\n","Epoch 635/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.3864\n","Epoch 636/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.3842\n","Epoch 637/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.3819\n","Epoch 638/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.3797\n","Epoch 639/3000\n","\u001b[1m1/1\u001b[0m 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0.3645\n","Epoch 646/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.3624\n","Epoch 647/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.3603\n","Epoch 648/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.3582\n","Epoch 649/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.3561\n","Epoch 650/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.3540\n","Epoch 651/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.3519\n","Epoch 652/3000\n","\u001b[1m1/1\u001b[0m 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0.3377\n","Epoch 659/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.3357\n","Epoch 660/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.3337\n","Epoch 661/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.3318\n","Epoch 662/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.3298\n","Epoch 663/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.3279\n","Epoch 664/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.3259\n","Epoch 665/3000\n","\u001b[1m1/1\u001b[0m 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0.3126\n","Epoch 672/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.3107\n","Epoch 673/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.3089\n","Epoch 674/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.3071\n","Epoch 675/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.3052\n","Epoch 676/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.3034\n","Epoch 677/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.3016\n","Epoch 678/3000\n","\u001b[1m1/1\u001b[0m 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0.2891\n","Epoch 685/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.2874\n","Epoch 686/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.2857\n","Epoch 687/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.2840\n","Epoch 688/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.2822\n","Epoch 689/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.2805\n","Epoch 690/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.2789\n","Epoch 691/3000\n","\u001b[1m1/1\u001b[0m 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0.2672\n","Epoch 698/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.2656\n","Epoch 699/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.2640\n","Epoch 700/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.2624\n","Epoch 701/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.2608\n","Epoch 702/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.2592\n","Epoch 703/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.2576\n","Epoch 704/3000\n","\u001b[1m1/1\u001b[0m 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0.2468\n","Epoch 711/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.2453\n","Epoch 712/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.2438\n","Epoch 713/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.2423\n","Epoch 714/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.2408\n","Epoch 715/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.2393\n","Epoch 716/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.2379\n","Epoch 717/3000\n","\u001b[1m1/1\u001b[0m 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0.2278\n","Epoch 724/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.2264\n","Epoch 725/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.2250\n","Epoch 726/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.2236\n","Epoch 727/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.2222\n","Epoch 728/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.2208\n","Epoch 729/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.2195\n","Epoch 730/3000\n","\u001b[1m1/1\u001b[0m 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0.2101\n","Epoch 737/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.2088\n","Epoch 738/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.2075\n","Epoch 739/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.2062\n","Epoch 740/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.2049\n","Epoch 741/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.2036\n","Epoch 742/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.2024\n","Epoch 743/3000\n","\u001b[1m1/1\u001b[0m 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0.1937\n","Epoch 750/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.1924\n","Epoch 751/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.1912\n","Epoch 752/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.1900\n","Epoch 753/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.1888\n","Epoch 754/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.1876\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: 0.1865\n","Epoch 756/3000\n","\u001b[1m1/1\u001b[0m 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0.1784\n","Epoch 763/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.1773\n","Epoch 764/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.1761\n","Epoch 765/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.1750\n","Epoch 766/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.1739\n","Epoch 767/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.1728\n","Epoch 768/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.1717\n","Epoch 769/3000\n","\u001b[1m1/1\u001b[0m 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0.1642\n","Epoch 776/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 0.1632\n","Epoch 777/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.1621\n","Epoch 778/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.1611\n","Epoch 779/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.1601\n","Epoch 780/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.1591\n","Epoch 781/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.1580\n","Epoch 782/3000\n","\u001b[1m1/1\u001b[0m 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0.1511\n","Epoch 789/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.1501\n","Epoch 790/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.1492\n","Epoch 791/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.1482\n","Epoch 792/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.1473\n","Epoch 793/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.1463\n","Epoch 794/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.1454\n","Epoch 795/3000\n","\u001b[1m1/1\u001b[0m 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loss: 0.1390\n","Epoch 802/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.1381\n","Epoch 803/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.1372\n","Epoch 804/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.1363\n","Epoch 805/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.1354\n","Epoch 806/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.1346\n","Epoch 807/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.1337\n","Epoch 808/3000\n","\u001b[1m1/1\u001b[0m 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0.1278\n","Epoch 815/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.1269\n","Epoch 816/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.1261\n","Epoch 817/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.1253\n","Epoch 818/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.1245\n","Epoch 819/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.1237\n","Epoch 820/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.1229\n","Epoch 821/3000\n","\u001b[1m1/1\u001b[0m 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0.1174\n","Epoch 828/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.1166\n","Epoch 829/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.1159\n","Epoch 830/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.1151\n","Epoch 831/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.1144\n","Epoch 832/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.1136\n","Epoch 833/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.1129\n","Epoch 834/3000\n","\u001b[1m1/1\u001b[0m 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0.1079\n","Epoch 841/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.1072\n","Epoch 842/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.1065\n","Epoch 843/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.1058\n","Epoch 844/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.1051\n","Epoch 845/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.1044\n","Epoch 846/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 0.1037\n","Epoch 847/3000\n","\u001b[1m1/1\u001b[0m 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0.0991\n","Epoch 854/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0984\n","Epoch 855/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0978\n","Epoch 856/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0972\n","Epoch 857/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0965\n","Epoch 858/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0959\n","Epoch 859/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0953\n","Epoch 860/3000\n","\u001b[1m1/1\u001b[0m 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0.0910\n","Epoch 867/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0904\n","Epoch 868/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0898\n","Epoch 869/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0892\n","Epoch 870/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0886\n","Epoch 871/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0881\n","Epoch 872/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0875\n","Epoch 873/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0869\n","Epoch 874/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0863\n","Epoch 875/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0858\n","Epoch 876/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0852\n","Epoch 877/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0847\n","Epoch 878/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0841\n","Epoch 879/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0836\n","Epoch 880/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0830\n","Epoch 881/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0825\n","Epoch 882/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0819\n","Epoch 883/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0814\n","Epoch 884/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0809\n","Epoch 885/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0803\n","Epoch 886/3000\n","\u001b[1m1/1\u001b[0m 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0.0767\n","Epoch 893/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0762\n","Epoch 894/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0757\n","Epoch 895/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0752\n","Epoch 896/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0748\n","Epoch 897/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0743\n","Epoch 898/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0738\n","Epoch 899/3000\n","\u001b[1m1/1\u001b[0m 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0.0705\n","Epoch 906/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0700\n","Epoch 907/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0696\n","Epoch 908/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0691\n","Epoch 909/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0687\n","Epoch 910/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0682\n","Epoch 911/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0678\n","Epoch 912/3000\n","\u001b[1m1/1\u001b[0m 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0.0648\n","Epoch 919/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0643\n","Epoch 920/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0639\n","Epoch 921/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0635\n","Epoch 922/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0631\n","Epoch 923/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0627\n","Epoch 924/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0623\n","Epoch 925/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0619\n","Epoch 926/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0615\n","Epoch 927/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.0611\n","Epoch 928/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0607\n","Epoch 929/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0603\n","Epoch 930/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0599\n","Epoch 931/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0595\n","Epoch 932/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 0.0591\n","Epoch 933/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0588\n","Epoch 934/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0584\n","Epoch 935/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0580\n","Epoch 936/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0576\n","Epoch 937/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0573\n","Epoch 938/3000\n","\u001b[1m1/1\u001b[0m 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0.0547\n","Epoch 945/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0544\n","Epoch 946/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0540\n","Epoch 947/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0537\n","Epoch 948/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0533\n","Epoch 949/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0530\n","Epoch 950/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0527\n","Epoch 951/3000\n","\u001b[1m1/1\u001b[0m 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loss: 0.0504\n","Epoch 958/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0500\n","Epoch 959/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0497\n","Epoch 960/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0494\n","Epoch 961/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0491\n","Epoch 962/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0488\n","Epoch 963/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.0485\n","Epoch 964/3000\n","\u001b[1m1/1\u001b[0m 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loss: 0.0464\n","Epoch 971/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.0461\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: 0.0458\n","Epoch 973/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0455\n","Epoch 974/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0452\n","Epoch 975/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0450\n","Epoch 976/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0447\n","Epoch 977/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0444\n","Epoch 978/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0441\n","Epoch 979/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0438\n","Epoch 980/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0436\n","Epoch 981/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0433\n","Epoch 982/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0430\n","Epoch 983/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 0.0428\n","Epoch 984/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0425\n","Epoch 985/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0422\n","Epoch 986/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0420\n","Epoch 987/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0417\n","Epoch 988/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0415\n","Epoch 989/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0412\n","Epoch 990/3000\n","\u001b[1m1/1\u001b[0m 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0.0395\n","Epoch 997/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0392\n","Epoch 998/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0390\n","Epoch 999/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0387\n","Epoch 1000/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - loss: 0.0385\n","Epoch 1000/3000\n"," - loss: 0.0385\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0385\n","Epoch 1001/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0383\n","Epoch 1002/3000\n","\u001b[1m1/1\u001b[0m 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loss: 0.0367\n","Epoch 1009/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0365\n","Epoch 1010/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0362\n","Epoch 1011/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0360\n","Epoch 1012/3000\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 1013/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0356\n","Epoch 1014/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0354\n","Epoch 1015/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0352\n","Epoch 1016/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0350\n","Epoch 1017/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0348\n","Epoch 1018/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0346\n","Epoch 1019/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0344\n","Epoch 1020/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0342\n","Epoch 1021/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0339\n","Epoch 1022/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0338\n","Epoch 1023/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0336\n","Epoch 1024/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0334\n","Epoch 1025/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0332\n","Epoch 1026/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0330\n","Epoch 1027/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 155ms/step - loss: 0.0328\n","Epoch 1028/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0326\n","Epoch 1029/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0324\n","Epoch 1030/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0322\n","Epoch 1031/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0320\n","Epoch 1032/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0318\n","Epoch 1033/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0317\n","Epoch 1034/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0315\n","Epoch 1035/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0313\n","Epoch 1036/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 0.0311\n","Epoch 1037/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0309\n","Epoch 1038/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0308\n","Epoch 1039/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0306\n","Epoch 1040/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0304\n","Epoch 1041/3000\n","\u001b[1m1/1\u001b[0m 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loss: 0.0292\n","Epoch 1048/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0291\n","Epoch 1049/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0289\n","Epoch 1050/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0287\n","Epoch 1051/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0286\n","Epoch 1052/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0284\n","Epoch 1053/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0283\n","Epoch 1054/3000\n","\u001b[1m1/1\u001b[0m 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loss: 0.0272\n","Epoch 1061/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0270\n","Epoch 1062/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0269\n","Epoch 1063/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0268\n","Epoch 1064/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0266\n","Epoch 1065/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0265\n","Epoch 1066/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0263\n","Epoch 1067/3000\n","\u001b[1m1/1\u001b[0m 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loss: 0.0254\n","Epoch 1074/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0252\n","Epoch 1075/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0251\n","Epoch 1076/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0250\n","Epoch 1077/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0248\n","Epoch 1078/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0247\n","Epoch 1079/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0246\n","Epoch 1080/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0244\n","Epoch 1081/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0243\n","Epoch 1082/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step - loss: 0.0242\n","Epoch 1083/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - loss: 0.0241\n","Epoch 1084/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0239\n","Epoch 1085/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.0238\n","Epoch 1086/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0237\n","Epoch 1087/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0236\n","Epoch 1088/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 156ms/step - loss: 0.0235\n","Epoch 1089/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0233\n","Epoch 1090/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0232\n","Epoch 1091/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0231\n","Epoch 1092/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0230\n","Epoch 1093/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 167ms/step - loss: 0.0229\n","Epoch 1094/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0228\n","Epoch 1095/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0227\n","Epoch 1096/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0225\n","Epoch 1097/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0224\n","Epoch 1098/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0223\n","Epoch 1099/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0222\n","Epoch 1100/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.0221\n","Epoch 1101/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0220\n","Epoch 1102/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0219\n","Epoch 1103/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0218\n","Epoch 1104/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.0217\n","Epoch 1105/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0216\n","Epoch 1106/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0215\n","Epoch 1107/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0214\n","Epoch 1108/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0213\n","Epoch 1109/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0212\n","Epoch 1110/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0211\n","Epoch 1111/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0210\n","Epoch 1112/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0209\n","Epoch 1113/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0208\n","Epoch 1114/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0207\n","Epoch 1115/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0206\n","Epoch 1116/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0205\n","Epoch 1117/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0204\n","Epoch 1118/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0203\n","Epoch 1119/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0202\n","Epoch 1120/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0201\n","Epoch 1121/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0200\n","Epoch 1122/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0199\n","Epoch 1123/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0198\n","Epoch 1124/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0198\n","Epoch 1125/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0197\n","Epoch 1126/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0196\n","Epoch 1127/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0195\n","Epoch 1128/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0194\n","Epoch 1129/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0193\n","Epoch 1130/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0192\n","Epoch 1131/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0192\n","Epoch 1132/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0191\n","Epoch 1133/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0190\n","Epoch 1134/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0189\n","Epoch 1135/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0188\n","Epoch 1136/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0187\n","Epoch 1137/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0187\n","Epoch 1138/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0186\n","Epoch 1139/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0185\n","Epoch 1140/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0184\n","Epoch 1141/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0183\n","Epoch 1142/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0183\n","Epoch 1143/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0182\n","Epoch 1144/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0181\n","Epoch 1145/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0180\n","Epoch 1146/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0180\n","Epoch 1147/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0179\n","Epoch 1148/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0178\n","Epoch 1149/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0178\n","Epoch 1150/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0177\n","Epoch 1151/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0176\n","Epoch 1152/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0175\n","Epoch 1153/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0175\n","Epoch 1154/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0174\n","Epoch 1155/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0173\n","Epoch 1156/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.0173\n","Epoch 1157/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0172\n","Epoch 1158/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0171\n","Epoch 1159/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0171\n","Epoch 1160/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0170\n","Epoch 1161/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0169\n","Epoch 1162/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0169\n","Epoch 1163/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0168\n","Epoch 1164/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0167\n","Epoch 1165/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0167\n","Epoch 1166/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0166\n","Epoch 1167/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0166\n","Epoch 1168/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0165\n","Epoch 1169/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0164\n","Epoch 1170/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0164\n","Epoch 1171/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0163\n","Epoch 1172/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0163\n","Epoch 1173/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0162\n","Epoch 1174/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0161\n","Epoch 1175/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0161\n","Epoch 1176/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0160\n","Epoch 1177/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.0160\n","Epoch 1178/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0159\n","Epoch 1179/3000\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 1180/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0158\n","Epoch 1181/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0157\n","Epoch 1182/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0157\n","Epoch 1183/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0156\n","Epoch 1184/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0156\n","Epoch 1185/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0155\n","Epoch 1186/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0155\n","Epoch 1187/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0154\n","Epoch 1188/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0154\n","Epoch 1189/3000\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 1190/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0153\n","Epoch 1191/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0152\n","Epoch 1192/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0152\n","Epoch 1193/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0151\n","Epoch 1194/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0151\n","Epoch 1195/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0150\n","Epoch 1196/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0150\n","Epoch 1197/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0149\n","Epoch 1198/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0149\n","Epoch 1199/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0148\n","Epoch 1200/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0148\n","Epoch 1201/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0147\n","Epoch 1202/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0147\n","Epoch 1203/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0147\n","Epoch 1204/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0146\n","Epoch 1205/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0146\n","Epoch 1206/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0145\n","Epoch 1207/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0145\n","Epoch 1208/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0144\n","Epoch 1209/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0144\n","Epoch 1210/3000\n","\u001b[1m1/1\u001b[0m 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loss: 0.0141\n","Epoch 1217/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0141\n","Epoch 1218/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0140\n","Epoch 1219/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0140\n","Epoch 1220/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 173ms/step - loss: 0.0139\n","Epoch 1221/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0139\n","Epoch 1222/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0139\n","Epoch 1223/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0138\n","Epoch 1224/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0138\n","Epoch 1225/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0138\n","Epoch 1226/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0137\n","Epoch 1227/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0137\n","Epoch 1228/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0136\n","Epoch 1229/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0136\n","Epoch 1230/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0136\n","Epoch 1231/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0135\n","Epoch 1232/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 0.0135\n","Epoch 1233/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 174ms/step - loss: 0.0135\n","Epoch 1234/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0134\n","Epoch 1235/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0134\n","Epoch 1236/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0134\n","Epoch 1237/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0133\n","Epoch 1238/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0133\n","Epoch 1239/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0133\n","Epoch 1240/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0132\n","Epoch 1241/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 161ms/step - loss: 0.0132\n","Epoch 1242/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.0132\n","Epoch 1243/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0131\n","Epoch 1244/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0131\n","Epoch 1245/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0131\n","Epoch 1246/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0131\n","Epoch 1247/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.0130\n","Epoch 1248/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.0130\n","Epoch 1249/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0130\n","Epoch 1250/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0129\n","Epoch 1251/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - loss: 0.0129\n","Epoch 1252/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0129\n","Epoch 1253/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 161ms/step - loss: 0.0128\n","Epoch 1254/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0128\n","Epoch 1255/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0128\n","Epoch 1256/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0128\n","Epoch 1257/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 0.0127\n","Epoch 1258/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0127\n","Epoch 1259/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0127\n","Epoch 1260/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0127\n","Epoch 1261/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0126\n","Epoch 1262/3000\n","\u001b[1m1/1\u001b[0m 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loss: 0.0124\n","Epoch 1269/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0124\n","Epoch 1270/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0124\n","Epoch 1271/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0124\n","Epoch 1272/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0124\n","Epoch 1273/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0123\n","Epoch 1274/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0123\n","Epoch 1275/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0123\n","Epoch 1276/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0123\n","Epoch 1277/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 155ms/step - loss: 0.0122\n","Epoch 1278/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0122\n","Epoch 1279/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0122\n","Epoch 1280/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0122\n","Epoch 1281/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0121\n","Epoch 1282/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0121\n","Epoch 1283/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0121\n","Epoch 1284/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0121\n","Epoch 1285/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0121\n","Epoch 1286/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 158ms/step - loss: 0.0120\n","Epoch 1287/3000\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 1288/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0120\n","Epoch 1289/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0120\n","Epoch 1290/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0120\n","Epoch 1291/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0119\n","Epoch 1292/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0119\n","Epoch 1293/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0119\n","Epoch 1294/3000\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 1295/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0119\n","Epoch 1296/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0118\n","Epoch 1297/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0118\n","Epoch 1298/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0118\n","Epoch 1299/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0118\n","Epoch 1300/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0118\n","Epoch 1301/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0117\n","Epoch 1302/3000\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 1303/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0117\n","Epoch 1304/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0117\n","Epoch 1305/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0117\n","Epoch 1306/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0117\n","Epoch 1307/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0116\n","Epoch 1308/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0116\n","Epoch 1309/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0116\n","Epoch 1310/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0116\n","Epoch 1311/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0116\n","Epoch 1312/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0116\n","Epoch 1313/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0115\n","Epoch 1314/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0115\n","Epoch 1315/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0115\n","Epoch 1316/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0115\n","Epoch 1317/3000\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 1318/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0115\n","Epoch 1319/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0114\n","Epoch 1320/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0114\n","Epoch 1321/3000\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 1322/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0114\n","Epoch 1323/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0114\n","Epoch 1324/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0114\n","Epoch 1325/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0114\n","Epoch 1326/3000\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 1327/3000\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 1328/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0113\n","Epoch 1329/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0113\n","Epoch 1330/3000\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 1331/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0113\n","Epoch 1332/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0113\n","Epoch 1333/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0112\n","Epoch 1334/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0112\n","Epoch 1335/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0112\n","Epoch 1336/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0112\n","Epoch 1337/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0112\n","Epoch 1338/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0112\n","Epoch 1339/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0112\n","Epoch 1340/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0112\n","Epoch 1341/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0111\n","Epoch 1342/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0111\n","Epoch 1343/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0111\n","Epoch 1344/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0111\n","Epoch 1345/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0111\n","Epoch 1346/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0111\n","Epoch 1347/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0111\n","Epoch 1348/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0111\n","Epoch 1349/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0111\n","Epoch 1350/3000\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 1351/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0110\n","Epoch 1352/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0110\n","Epoch 1353/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0110\n","Epoch 1354/3000\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 1355/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0110\n","Epoch 1356/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0110\n","Epoch 1357/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0110\n","Epoch 1358/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0110\n","Epoch 1359/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 0.0109\n","Epoch 1360/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0109\n","Epoch 1361/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0109\n","Epoch 1362/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0109\n","Epoch 1363/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0109\n","Epoch 1364/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 168ms/step - loss: 0.0109\n","Epoch 1365/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.0109\n","Epoch 1366/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0109\n","Epoch 1367/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0109\n","Epoch 1368/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0109\n","Epoch 1369/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0108\n","Epoch 1370/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0108\n","Epoch 1371/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0108\n","Epoch 1372/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0108\n","Epoch 1373/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0108\n","Epoch 1374/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - loss: 0.0108\n","Epoch 1375/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.0108\n","Epoch 1376/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0108\n","Epoch 1377/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0108\n","Epoch 1378/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0108\n","Epoch 1379/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0108\n","Epoch 1380/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0108\n","Epoch 1381/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0107\n","Epoch 1382/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 266ms/step - loss: 0.0107\n","Epoch 1383/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0107\n","Epoch 1384/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 156ms/step - loss: 0.0107\n","Epoch 1385/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0107\n","Epoch 1386/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0107\n","Epoch 1387/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0107\n","Epoch 1388/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0107\n","Epoch 1389/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0107\n","Epoch 1390/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0107\n","Epoch 1391/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0107\n","Epoch 1392/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0107\n","Epoch 1393/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0106\n","Epoch 1394/3000\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 1395/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0106\n","Epoch 1396/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0106\n","Epoch 1397/3000\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 1398/3000\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 1399/3000\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 1400/3000\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 1401/3000\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 1402/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0106\n","Epoch 1403/3000\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 1404/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0106\n","Epoch 1405/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0106\n","Epoch 1406/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0106\n","Epoch 1407/3000\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 1408/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0105\n","Epoch 1409/3000\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 1410/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0105\n","Epoch 1411/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0105\n","Epoch 1412/3000\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 1413/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0105\n","Epoch 1414/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0105\n","Epoch 1415/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0105\n","Epoch 1416/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0105\n","Epoch 1417/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0105\n","Epoch 1418/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0105\n","Epoch 1419/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0105\n","Epoch 1420/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0105\n","Epoch 1421/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0105\n","Epoch 1422/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0105\n","Epoch 1423/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0105\n","Epoch 1424/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0105\n","Epoch 1425/3000\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 1426/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0104\n","Epoch 1427/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0104\n","Epoch 1428/3000\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 1429/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0104\n","Epoch 1430/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0104\n","Epoch 1431/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0104\n","Epoch 1432/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0104\n","Epoch 1433/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0104\n","Epoch 1434/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0104\n","Epoch 1435/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0104\n","Epoch 1436/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0104\n","Epoch 1437/3000\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 1438/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0104\n","Epoch 1439/3000\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 1440/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0104\n","Epoch 1441/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0104\n","Epoch 1442/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0104\n","Epoch 1443/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0104\n","Epoch 1444/3000\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 1445/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0104\n","Epoch 1446/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0104\n","Epoch 1447/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0104\n","Epoch 1448/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0103\n","Epoch 1449/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0103\n","Epoch 1450/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0103\n","Epoch 1451/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0103\n","Epoch 1452/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0103\n","Epoch 1453/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0103\n","Epoch 1454/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0103\n","Epoch 1455/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0103\n","Epoch 1456/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0103\n","Epoch 1457/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0103\n","Epoch 1458/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0103\n","Epoch 1459/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0103\n","Epoch 1460/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0103\n","Epoch 1461/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0103\n","Epoch 1462/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0103\n","Epoch 1463/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0103\n","Epoch 1464/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0103\n","Epoch 1465/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0103\n","Epoch 1466/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0103\n","Epoch 1467/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0103\n","Epoch 1468/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0103\n","Epoch 1469/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0103\n","Epoch 1470/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0103\n","Epoch 1471/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0103\n","Epoch 1472/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0103\n","Epoch 1473/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0103\n","Epoch 1474/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0103\n","Epoch 1475/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0103\n","Epoch 1476/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0103\n","Epoch 1477/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0102\n","Epoch 1478/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0102\n","Epoch 1479/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0102\n","Epoch 1480/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0102\n","Epoch 1481/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0102\n","Epoch 1482/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0102\n","Epoch 1483/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0102\n","Epoch 1484/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0102\n","Epoch 1485/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0102\n","Epoch 1486/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0102\n","Epoch 1487/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0102\n","Epoch 1488/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0102\n","Epoch 1489/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0102\n","Epoch 1490/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0102\n","Epoch 1491/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0102\n","Epoch 1492/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 177ms/step - loss: 0.0102\n","Epoch 1493/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0102\n","Epoch 1494/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0102\n","Epoch 1495/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.0102\n","Epoch 1496/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0102\n","Epoch 1497/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0102\n","Epoch 1498/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0102\n","Epoch 1499/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 163ms/step - loss: 0.0102\n","Epoch 1500/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0102\n","Epoch 1501/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 0.0102\n","Epoch 1502/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0102\n","Epoch 1503/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0102\n","Epoch 1504/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 0.0102\n","Epoch 1505/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0102\n","Epoch 1506/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 171ms/step - loss: 0.0102\n","Epoch 1507/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0102\n","Epoch 1508/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 160ms/step - loss: 0.0102\n","Epoch 1509/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0102\n","Epoch 1510/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0102\n","Epoch 1511/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.0102\n","Epoch 1512/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0102\n","Epoch 1513/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - loss: 0.0102\n","Epoch 1514/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0102\n","Epoch 1515/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0102\n","Epoch 1516/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0102\n","Epoch 1517/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0102\n","Epoch 1518/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0102\n","Epoch 1519/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 156ms/step - loss: 0.0101\n","Epoch 1520/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0101\n","Epoch 1521/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0101\n","Epoch 1522/3000\n","\u001b[1m1/1\u001b[0m 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- loss: 0.0101\n","Epoch 1529/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0101\n","Epoch 1530/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 0.0101\n","Epoch 1531/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0101\n","Epoch 1532/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0101\n","Epoch 1533/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0101\n","Epoch 1534/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.0101\n","Epoch 1535/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0101\n","Epoch 1536/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0101\n","Epoch 1537/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0101\n","Epoch 1538/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0101\n","Epoch 1539/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0101\n","Epoch 1540/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0101\n","Epoch 1541/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0101\n","Epoch 1542/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0101\n","Epoch 1543/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0101\n","Epoch 1544/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0101\n","Epoch 1545/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0101\n","Epoch 1546/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0101\n","Epoch 1547/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0101\n","Epoch 1548/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0101\n","Epoch 1549/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0101\n","Epoch 1550/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0101\n","Epoch 1551/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0101\n","Epoch 1552/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0101\n","Epoch 1553/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0101\n","Epoch 1554/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0101\n","Epoch 1555/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0101\n","Epoch 1556/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0101\n","Epoch 1557/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0101\n","Epoch 1558/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0101\n","Epoch 1559/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0101\n","Epoch 1560/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0101\n","Epoch 1561/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0101\n","Epoch 1562/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0101\n","Epoch 1563/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0101\n","Epoch 1564/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0101\n","Epoch 1565/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0101\n","Epoch 1566/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0101\n","Epoch 1567/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0101\n","Epoch 1568/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0101\n","Epoch 1569/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0101\n","Epoch 1570/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0101\n","Epoch 1571/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0101\n","Epoch 1572/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0101\n","Epoch 1573/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0101\n","Epoch 1574/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0101\n","Epoch 1575/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0101\n","Epoch 1576/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0101\n","Epoch 1577/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0101\n","Epoch 1578/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0101\n","Epoch 1579/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 0.0101\n","Epoch 1580/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0101\n","Epoch 1581/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0101\n","Epoch 1582/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0101\n","Epoch 1583/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0101\n","Epoch 1584/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0101\n","Epoch 1585/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0101\n","Epoch 1586/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0101\n","Epoch 1587/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0101\n","Epoch 1588/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0101\n","Epoch 1589/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0101\n","Epoch 1590/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0101\n","Epoch 1591/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0101\n","Epoch 1592/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0101\n","Epoch 1593/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0101\n","Epoch 1594/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0101\n","Epoch 1595/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0101\n","Epoch 1596/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0101\n","Epoch 1597/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0101\n","Epoch 1598/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0101\n","Epoch 1599/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0101\n","Epoch 1600/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0101\n","Epoch 1601/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0101\n","Epoch 1602/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0101\n","Epoch 1603/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0101\n","Epoch 1604/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0100\n","Epoch 1605/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0100\n","Epoch 1606/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0100\n","Epoch 1607/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0100\n","Epoch 1608/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0100\n","Epoch 1609/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0100\n","Epoch 1610/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0100\n","Epoch 1611/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0100\n","Epoch 1612/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0100\n","Epoch 1613/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 0.0100\n","Epoch 1614/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0100\n","Epoch 1615/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.0100\n","Epoch 1616/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 1617/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0100\n","Epoch 1618/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0100\n","Epoch 1619/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0100\n","Epoch 1620/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0100\n","Epoch 1621/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 1622/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0100\n","Epoch 1623/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0100\n","Epoch 1624/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0100\n","Epoch 1625/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0100\n","Epoch 1626/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0100\n","Epoch 1627/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.0100\n","Epoch 1628/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0100\n","Epoch 1629/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0100\n","Epoch 1630/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0100\n","Epoch 1631/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0100\n","Epoch 1632/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0100\n","Epoch 1633/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 155ms/step - loss: 0.0100\n","Epoch 1634/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0100\n","Epoch 1635/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0100\n","Epoch 1636/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 0.0100\n","Epoch 1637/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0100\n","Epoch 1638/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0100\n","Epoch 1639/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0100\n","Epoch 1640/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0100\n","Epoch 1641/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.0100\n","Epoch 1642/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 0.0100\n","Epoch 1643/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0100\n","Epoch 1644/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0100\n","Epoch 1645/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0100\n","Epoch 1646/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0100\n","Epoch 1647/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0100\n","Epoch 1648/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0100\n","Epoch 1649/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0100\n","Epoch 1650/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.0100\n","Epoch 1651/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.0100\n","Epoch 1652/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.0100\n","Epoch 1653/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0100\n","Epoch 1654/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0100\n","Epoch 1655/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0100\n","Epoch 1656/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0100\n","Epoch 1657/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0100\n","Epoch 1658/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0100\n","Epoch 1659/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.0100\n","Epoch 1660/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0100\n","Epoch 1661/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.0100\n","Epoch 1662/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 173ms/step - loss: 0.0100\n","Epoch 1663/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0100\n","Epoch 1664/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0100\n","Epoch 1665/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0100\n","Epoch 1666/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0100\n","Epoch 1667/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0100\n","Epoch 1668/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 1669/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0100\n","Epoch 1670/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 1671/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0100\n","Epoch 1672/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0100\n","Epoch 1673/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0100\n","Epoch 1674/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0100\n","Epoch 1675/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0100\n","Epoch 1676/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0100\n","Epoch 1677/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0100\n","Epoch 1678/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0100\n","Epoch 1679/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0100\n","Epoch 1680/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 1681/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0100\n","Epoch 1682/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0100\n","Epoch 1683/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0100\n","Epoch 1684/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0100\n","Epoch 1685/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0100\n","Epoch 1686/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 1687/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0100\n","Epoch 1688/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 1689/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0100\n","Epoch 1690/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 1691/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0100\n","Epoch 1692/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0100\n","Epoch 1693/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0100\n","Epoch 1694/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0100\n","Epoch 1695/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 1696/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 1697/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0100\n","Epoch 1698/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 1699/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0100\n","Epoch 1700/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 1701/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 1702/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0100\n","Epoch 1703/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0100\n","Epoch 1704/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0100\n","Epoch 1705/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0100\n","Epoch 1706/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 1707/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0100\n","Epoch 1708/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0100\n","Epoch 1709/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 1710/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0100\n","Epoch 1711/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0100\n","Epoch 1712/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 1713/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 1714/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0100\n","Epoch 1715/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 1716/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0100\n","Epoch 1717/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0100\n","Epoch 1718/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0100\n","Epoch 1719/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0100\n","Epoch 1720/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0100\n","Epoch 1721/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0100\n","Epoch 1722/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0100\n","Epoch 1723/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 1724/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0100\n","Epoch 1725/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0100\n","Epoch 1726/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0100\n","Epoch 1727/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0100\n","Epoch 1728/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0100\n","Epoch 1729/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0100\n","Epoch 1730/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 1731/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0100\n","Epoch 1732/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0100\n","Epoch 1733/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0100\n","Epoch 1734/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0100\n","Epoch 1735/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - loss: 0.0100\n","Epoch 1736/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0100\n","Epoch 1737/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0100\n","Epoch 1738/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0100\n","Epoch 1739/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0100\n","Epoch 1740/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 1741/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0100\n","Epoch 1742/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0100\n","Epoch 1743/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0100\n","Epoch 1744/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0100\n","Epoch 1745/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0100\n","Epoch 1746/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0100\n","Epoch 1747/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0100\n","Epoch 1748/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0100\n","Epoch 1749/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0100\n","Epoch 1750/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0100\n","Epoch 1751/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 1752/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0100\n","Epoch 1753/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0100\n","Epoch 1754/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0100\n","Epoch 1755/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0100\n","Epoch 1756/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0100\n","Epoch 1757/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0100\n","Epoch 1758/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0100\n","Epoch 1759/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0100\n","Epoch 1760/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0100\n","Epoch 1761/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0100\n","Epoch 1762/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.0100\n","Epoch 1763/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0100\n","Epoch 1764/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 165ms/step - loss: 0.0100\n","Epoch 1765/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 0.0100\n","Epoch 1766/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0100\n","Epoch 1767/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0100\n","Epoch 1768/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0100\n","Epoch 1769/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step - loss: 0.0100\n","Epoch 1770/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 0.0100\n","Epoch 1771/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0100\n","Epoch 1772/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0100\n","Epoch 1773/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0100\n","Epoch 1774/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 0.0100\n","Epoch 1775/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0100\n","Epoch 1776/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0100\n","Epoch 1777/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0100\n","Epoch 1778/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0100\n","Epoch 1779/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0100\n","Epoch 1780/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0100\n","Epoch 1781/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0100\n","Epoch 1782/3000\n","\u001b[1m1/1\u001b[0m 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loss: 0.0100\n","Epoch 1789/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0100\n","Epoch 1790/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0100\n","Epoch 1791/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0100\n","Epoch 1792/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.0100\n","Epoch 1793/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0100\n","Epoch 1794/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0100\n","Epoch 1795/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0100\n","Epoch 1796/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0100\n","Epoch 1797/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0100\n","Epoch 1798/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0100\n","Epoch 1799/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0100\n","Epoch 1800/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 0.0100\n","Epoch 1801/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0100\n","Epoch 1802/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0100\n","Epoch 1803/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0100\n","Epoch 1804/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0100\n","Epoch 1805/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0100\n","Epoch 1806/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0100\n","Epoch 1807/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0100\n","Epoch 1808/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0100\n","Epoch 1809/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0100\n","Epoch 1810/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0100\n","Epoch 1811/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0100\n","Epoch 1812/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.0100\n","Epoch 1813/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.0100\n","Epoch 1814/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0100\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: 0.0100\n","Epoch 1816/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 1817/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 1818/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0100\n","Epoch 1819/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 1820/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0100\n","Epoch 1821/3000\n","\u001b[1m1/1\u001b[0m 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- loss: 0.0100\n","Epoch 1828/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 1829/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0100\n","Epoch 1830/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0100\n","Epoch 1831/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0100\n","Epoch 1832/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 1833/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0100\n","Epoch 1834/3000\n","\u001b[1m1/1\u001b[0m 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loss: 0.0100\n","Epoch 1841/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0100\n","Epoch 1842/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 1843/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 1844/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 1845/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0100\n","Epoch 1846/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 1847/3000\n","\u001b[1m1/1\u001b[0m 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loss: 0.0100\n","Epoch 1854/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 1855/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0100\n","Epoch 1856/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 1857/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0100\n","Epoch 1858/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0100\n","Epoch 1859/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0100\n","Epoch 1860/3000\n","\u001b[1m1/1\u001b[0m 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loss: 0.0100\n","Epoch 1867/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0100\n","Epoch 1868/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 1869/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 1870/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0100\n","Epoch 1871/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 1872/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 1873/3000\n","\u001b[1m1/1\u001b[0m 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loss: 0.0100\n","Epoch 1880/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0100\n","Epoch 1881/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0100\n","Epoch 1882/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 1883/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0100\n","Epoch 1884/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 1885/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0100\n","Epoch 1886/3000\n","\u001b[1m1/1\u001b[0m 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loss: 0.0100\n","Epoch 1893/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.0100\n","Epoch 1894/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0100\n","Epoch 1895/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0100\n","Epoch 1896/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 1897/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0100\n","Epoch 1898/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0100\n","Epoch 1899/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0100\n","Epoch 1900/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0100\n","Epoch 1901/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0100\n","Epoch 1902/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.0100\n","Epoch 1903/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 159ms/step - loss: 0.0100\n","Epoch 1904/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0100\n","Epoch 1905/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.0100\n","Epoch 1906/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0100\n","Epoch 1907/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0100\n","Epoch 1908/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0100\n","Epoch 1909/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0100\n","Epoch 1910/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0100\n","Epoch 1911/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0100\n","Epoch 1912/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.0100\n","Epoch 1913/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0100\n","Epoch 1914/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0100\n","Epoch 1915/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 195ms/step - loss: 0.0100\n","Epoch 1916/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0100\n","Epoch 1917/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0100\n","Epoch 1918/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0100\n","Epoch 1919/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.0100\n","Epoch 1920/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.0100\n","Epoch 1921/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0100\n","Epoch 1922/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0100\n","Epoch 1923/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0100\n","Epoch 1924/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0100\n","Epoch 1925/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0100\n","Epoch 1926/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 312ms/step - loss: 0.0100\n","Epoch 1927/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 274ms/step - loss: 0.0100\n","Epoch 1928/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0100\n","Epoch 1929/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0100\n","Epoch 1930/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.0100\n","Epoch 1931/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 0.0100\n","Epoch 1932/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.0100\n","Epoch 1933/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.0100\n","Epoch 1934/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.0100\n","Epoch 1935/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.0100\n","Epoch 1936/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 1937/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0100\n","Epoch 1938/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0100\n","Epoch 1939/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 1940/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0100\n","Epoch 1941/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0100\n","Epoch 1942/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 1943/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0100\n","Epoch 1944/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0100\n","Epoch 1945/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0100\n","Epoch 1946/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0100\n","Epoch 1947/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0100\n","Epoch 1948/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0100\n","Epoch 1949/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0100\n","Epoch 1950/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0100\n","Epoch 1951/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 1952/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0100\n","Epoch 1953/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0100\n","Epoch 1954/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0100\n","Epoch 1955/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0100\n","Epoch 1956/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0100\n","Epoch 1957/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0100\n","Epoch 1958/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0100\n","Epoch 1959/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0100\n","Epoch 1960/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0100\n","Epoch 1961/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0100\n","Epoch 1962/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0100\n","Epoch 1963/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0100\n","Epoch 1964/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0100\n","Epoch 1965/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 1966/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0100\n","Epoch 1967/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0100\n","Epoch 1968/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0100\n","Epoch 1969/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 1970/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0100\n","Epoch 1971/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0100\n","Epoch 1972/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0100\n","Epoch 1973/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0100\n","Epoch 1974/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0100\n","Epoch 1975/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0100\n","Epoch 1976/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0100\n","Epoch 1977/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 159ms/step - loss: 0.0100\n","Epoch 1978/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0100\n","Epoch 1979/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0100\n","Epoch 1980/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0100\n","Epoch 1981/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0100\n","Epoch 1982/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0100\n","Epoch 1983/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.0100\n","Epoch 1984/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0100\n","Epoch 1985/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0100\n","Epoch 1986/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0100\n","Epoch 1987/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0100\n","Epoch 1988/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0100\n","Epoch 1989/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 1990/3000\n","\u001b[1m1/1\u001b[0m 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loss: 0.0100\n","Epoch 1997/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0100\n","Epoch 1998/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 1999/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0100\n","Epoch 2000/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0100\n","Epoch 2000/3000\n"," - loss: 0.0100\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 0.0100\n","Epoch 2001/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0100\n","Epoch 2002/3000\n","\u001b[1m1/1\u001b[0m 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loss: 0.0100\n","Epoch 2009/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0100\n","Epoch 2010/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0100\n","Epoch 2011/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0100\n","Epoch 2012/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0100\n","Epoch 2013/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0100\n","Epoch 2014/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0100\n","Epoch 2015/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0100\n","Epoch 2016/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0100\n","Epoch 2017/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 2018/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 2019/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0100\n","Epoch 2020/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0100\n","Epoch 2021/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0100\n","Epoch 2022/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0100\n","Epoch 2023/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0100\n","Epoch 2024/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0100\n","Epoch 2025/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 2026/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0100\n","Epoch 2027/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 2028/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0100\n","Epoch 2029/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0100\n","Epoch 2030/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0100\n","Epoch 2031/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0100\n","Epoch 2032/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0100\n","Epoch 2033/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0100\n","Epoch 2034/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 2035/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 161ms/step - loss: 0.0100\n","Epoch 2036/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.0100\n","Epoch 2037/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0100\n","Epoch 2038/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0100\n","Epoch 2039/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0100\n","Epoch 2040/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.0100\n","Epoch 2041/3000\n","\u001b[1m1/1\u001b[0m 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123ms/step - loss: 0.0100\n","Epoch 2048/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0100\n","Epoch 2049/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0100\n","Epoch 2050/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0100\n","Epoch 2051/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 0.0100\n","Epoch 2052/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0100\n","Epoch 2053/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0100\n","Epoch 2054/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0100\n","Epoch 2055/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0100\n","Epoch 2056/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0100\n","Epoch 2057/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0100\n","Epoch 2058/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 0.0100\n","Epoch 2059/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0100\n","Epoch 2060/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0100\n","Epoch 2061/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 165ms/step - loss: 0.0100\n","Epoch 2062/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0100\n","Epoch 2063/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0100\n","Epoch 2064/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.0100\n","Epoch 2065/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0100\n","Epoch 2066/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0100\n","Epoch 2067/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0100\n","Epoch 2068/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0100\n","Epoch 2069/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0100\n","Epoch 2070/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0100\n","Epoch 2071/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 183ms/step - loss: 0.0100\n","Epoch 2072/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0100\n","Epoch 2073/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0100\n","Epoch 2074/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 0.0100\n","Epoch 2075/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0100\n","Epoch 2076/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0100\n","Epoch 2077/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0100\n","Epoch 2078/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0100\n","Epoch 2079/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0100\n","Epoch 2080/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 2081/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0100\n","Epoch 2082/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 2083/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 2084/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0100\n","Epoch 2085/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0100\n","Epoch 2086/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\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: 0.0100\n","Epoch 2088/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0100\n","Epoch 2089/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0100\n","Epoch 2090/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0100\n","Epoch 2091/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.0100\n","Epoch 2092/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 2093/3000\n","\u001b[1m1/1\u001b[0m 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loss: 0.0100\n","Epoch 2100/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0100\n","Epoch 2101/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0100\n","Epoch 2102/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0100\n","Epoch 2103/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0100\n","Epoch 2104/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0100\n","Epoch 2105/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0100\n","Epoch 2106/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0100\n","Epoch 2107/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 2108/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 2109/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0100\n","Epoch 2110/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0100\n","Epoch 2111/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0100\n","Epoch 2112/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0100\n","Epoch 2113/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 0.0100\n","Epoch 2114/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 2115/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0100\n","Epoch 2116/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0100\n","Epoch 2117/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 2118/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0100\n","Epoch 2119/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0100\n","Epoch 2120/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0100\n","Epoch 2121/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 2122/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0100\n","Epoch 2123/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0100\n","Epoch 2124/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0100\n","Epoch 2125/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0100\n","Epoch 2126/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0100\n","Epoch 2127/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 2128/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0100\n","Epoch 2129/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 2130/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0100\n","Epoch 2131/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0100\n","Epoch 2132/3000\n","\u001b[1m1/1\u001b[0m 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loss: 0.0100\n","Epoch 2139/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 2140/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0100\n","Epoch 2141/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0100\n","Epoch 2142/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0100\n","Epoch 2143/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.0100\n","Epoch 2144/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 2145/3000\n","\u001b[1m1/1\u001b[0m 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loss: 0.0100\n","Epoch 2152/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0100\n","Epoch 2153/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0100\n","Epoch 2154/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0100\n","Epoch 2155/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 2156/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0100\n","Epoch 2157/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0100\n","Epoch 2158/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 2159/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 2160/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0100\n","Epoch 2161/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0100\n","Epoch 2162/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0100\n","Epoch 2163/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step - loss: 0.0100\n","Epoch 2164/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0100\n","Epoch 2165/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0100\n","Epoch 2166/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 0.0100\n","Epoch 2167/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 2168/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 2169/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0100\n","Epoch 2170/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0100\n","Epoch 2171/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0100\n","Epoch 2172/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0100\n","Epoch 2173/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 2174/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0100\n","Epoch 2175/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0100\n","Epoch 2176/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 2177/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0100\n","Epoch 2178/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0100\n","Epoch 2179/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - loss: 0.0100\n","Epoch 2180/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0100\n","Epoch 2181/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.0100\n","Epoch 2182/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0100\n","Epoch 2183/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0100\n","Epoch 2184/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0100\n","Epoch 2185/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0100\n","Epoch 2186/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0100\n","Epoch 2187/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 171ms/step - loss: 0.0100\n","Epoch 2188/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0100\n","Epoch 2189/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0100\n","Epoch 2190/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0100\n","Epoch 2191/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.0100\n","Epoch 2192/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.0100\n","Epoch 2193/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0100\n","Epoch 2194/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0100\n","Epoch 2195/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0100\n","Epoch 2196/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 167ms/step - loss: 0.0100\n","Epoch 2197/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0100\n","Epoch 2198/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0100\n","Epoch 2199/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0100\n","Epoch 2200/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0100\n","Epoch 2201/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 0.0100\n","Epoch 2202/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 0.0100\n","Epoch 2203/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 167ms/step - loss: 0.0100\n","Epoch 2204/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0100\n","Epoch 2205/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0100\n","Epoch 2206/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.0100\n","Epoch 2207/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0100\n","Epoch 2208/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0100\n","Epoch 2209/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.0100\n","Epoch 2210/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0100\n","Epoch 2211/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0100\n","Epoch 2212/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - loss: 0.0100\n","Epoch 2213/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0100\n","Epoch 2214/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - loss: 0.0100\n","Epoch 2215/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.0100\n","Epoch 2216/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0100\n","Epoch 2217/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0100\n","Epoch 2218/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0100\n","Epoch 2219/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 2220/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0100\n","Epoch 2221/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0100\n","Epoch 2222/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0100\n","Epoch 2223/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0100\n","Epoch 2224/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 2225/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0100\n","Epoch 2226/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0100\n","Epoch 2227/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0100\n","Epoch 2228/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 2229/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 2230/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0100\n","Epoch 2231/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0100\n","Epoch 2232/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0100\n","Epoch 2233/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 2234/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0100\n","Epoch 2235/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0100\n","Epoch 2236/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0100\n","Epoch 2237/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0100\n","Epoch 2238/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0100\n","Epoch 2239/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 2240/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0100\n","Epoch 2241/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0100\n","Epoch 2242/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0100\n","Epoch 2243/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0100\n","Epoch 2244/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 2245/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0100\n","Epoch 2246/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0100\n","Epoch 2247/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 2248/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0100\n","Epoch 2249/3000\n","\u001b[1m1/1\u001b[0m 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- loss: 0.0100\n","Epoch 2256/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0100\n","Epoch 2257/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0100\n","Epoch 2258/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - loss: 0.0100\n","Epoch 2259/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0100\n","Epoch 2260/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0100\n","Epoch 2261/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 2262/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0100\n","Epoch 2263/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 2264/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0100\n","Epoch 2265/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0100\n","Epoch 2266/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0100\n","Epoch 2267/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0100\n","Epoch 2268/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 2269/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 2270/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 2271/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0100\n","Epoch 2272/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.0100\n","Epoch 2273/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0100\n","Epoch 2274/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0100\n","Epoch 2275/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0100\n","Epoch 2276/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 2277/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0100\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: 0.0100\n","Epoch 2279/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0100\n","Epoch 2280/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0100\n","Epoch 2281/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0100\n","Epoch 2282/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0100\n","Epoch 2283/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0100\n","Epoch 2284/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0100\n","Epoch 2285/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 2286/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.0100\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: 0.0100\n","Epoch 2288/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0100\n","Epoch 2289/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 2290/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0100\n","Epoch 2291/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0100\n","Epoch 2292/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0100\n","Epoch 2293/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0100\n","Epoch 2294/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0100\n","Epoch 2295/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0100\n","Epoch 2296/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0100\n","Epoch 2297/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0100\n","Epoch 2298/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 2299/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0100\n","Epoch 2300/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0100\n","Epoch 2301/3000\n","\u001b[1m1/1\u001b[0m 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- loss: 0.0100\n","Epoch 2308/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0100\n","Epoch 2309/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0100\n","Epoch 2310/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0100\n","Epoch 2311/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0100\n","Epoch 2312/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0100\n","Epoch 2313/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 176ms/step - loss: 0.0100\n","Epoch 2314/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 159ms/step - loss: 0.0100\n","Epoch 2315/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0100\n","Epoch 2316/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0100\n","Epoch 2317/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 158ms/step - loss: 0.0100\n","Epoch 2318/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.0100\n","Epoch 2319/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 0.0100\n","Epoch 2320/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.0100\n","Epoch 2321/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0100\n","Epoch 2322/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.0100\n","Epoch 2323/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0100\n","Epoch 2324/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0100\n","Epoch 2325/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.0100\n","Epoch 2326/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0100\n","Epoch 2327/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - loss: 0.0100\n","Epoch 2328/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0100\n","Epoch 2329/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0100\n","Epoch 2330/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0100\n","Epoch 2331/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0100\n","Epoch 2332/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0100\n","Epoch 2333/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0100\n","Epoch 2334/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0100\n","Epoch 2335/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 160ms/step - loss: 0.0100\n","Epoch 2336/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0100\n","Epoch 2337/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 163ms/step - loss: 0.0100\n","Epoch 2338/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0100\n","Epoch 2339/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0100\n","Epoch 2340/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 170ms/step - loss: 0.0100\n","Epoch 2341/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.0100\n","Epoch 2342/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.0100\n","Epoch 2343/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0100\n","Epoch 2344/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 2345/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 173ms/step - loss: 0.0100\n","Epoch 2346/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - loss: 0.0100\n","Epoch 2347/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0100\n","Epoch 2348/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0100\n","Epoch 2349/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0100\n","Epoch 2350/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0100\n","Epoch 2351/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0100\n","Epoch 2352/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0100\n","Epoch 2353/3000\n","\u001b[1m1/1\u001b[0m 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loss: 0.0100\n","Epoch 2360/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0100\n","Epoch 2361/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0100\n","Epoch 2362/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 2363/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0100\n","Epoch 2364/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0100\n","Epoch 2365/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0100\n","Epoch 2366/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0100\n","Epoch 2367/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 2368/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 160ms/step - loss: 0.0100\n","Epoch 2369/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.0100\n","Epoch 2370/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 2371/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0100\n","Epoch 2372/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0100\n","Epoch 2373/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0100\n","Epoch 2374/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 2375/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0100\n","Epoch 2376/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0100\n","Epoch 2377/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0100\n","Epoch 2378/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 2379/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0100\n","Epoch 2380/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0100\n","Epoch 2381/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 2382/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 2383/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0100\n","Epoch 2384/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0100\n","Epoch 2385/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0100\n","Epoch 2386/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0100\n","Epoch 2387/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0100\n","Epoch 2388/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0100\n","Epoch 2389/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0100\n","Epoch 2390/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 2391/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0100\n","Epoch 2392/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0100\n","Epoch 2393/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0100\n","Epoch 2394/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0100\n","Epoch 2395/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0100\n","Epoch 2396/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0100\n","Epoch 2397/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0100\n","Epoch 2398/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0100\n","Epoch 2399/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 2400/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 2401/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0100\n","Epoch 2402/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0100\n","Epoch 2403/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0100\n","Epoch 2404/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0100\n","Epoch 2405/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0100\n","Epoch 2406/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0100\n","Epoch 2407/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0100\n","Epoch 2408/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0100\n","Epoch 2409/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0100\n","Epoch 2410/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 0.0100\n","Epoch 2411/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0100\n","Epoch 2412/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0100\n","Epoch 2413/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0100\n","Epoch 2414/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0100\n","Epoch 2415/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0100\n","Epoch 2416/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0100\n","Epoch 2417/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0100\n","Epoch 2418/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0100\n","Epoch 2419/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0100\n","Epoch 2420/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 2421/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0100\n","Epoch 2422/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0100\n","Epoch 2423/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 2424/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0100\n","Epoch 2425/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0100\n","Epoch 2426/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0100\n","Epoch 2427/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0100\n","Epoch 2428/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 2429/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0100\n","Epoch 2430/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 2431/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0100\n","Epoch 2432/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0100\n","Epoch 2433/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 2434/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0100\n","Epoch 2435/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0100\n","Epoch 2436/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 0.0100\n","Epoch 2437/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0100\n","Epoch 2438/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 2439/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0100\n","Epoch 2440/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 2441/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0100\n","Epoch 2442/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0100\n","Epoch 2443/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0100\n","Epoch 2444/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0100\n","Epoch 2445/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 2446/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0100\n","Epoch 2447/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0100\n","Epoch 2448/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0100\n","Epoch 2449/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0100\n","Epoch 2450/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0100\n","Epoch 2451/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0100\n","Epoch 2452/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 2453/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0100\n","Epoch 2454/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0100\n","Epoch 2455/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0100\n","Epoch 2456/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0100\n","Epoch 2457/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 158ms/step - loss: 0.0100\n","Epoch 2458/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0100\n","Epoch 2459/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0100\n","Epoch 2460/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0100\n","Epoch 2461/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0100\n","Epoch 2462/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0100\n","Epoch 2463/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0100\n","Epoch 2464/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0100\n","Epoch 2465/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0100\n","Epoch 2466/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0100\n","Epoch 2467/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.0100\n","Epoch 2468/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0100\n","Epoch 2469/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 167ms/step - loss: 0.0100\n","Epoch 2470/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0100\n","Epoch 2471/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step - loss: 0.0100\n","Epoch 2472/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0100\n","Epoch 2473/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0100\n","Epoch 2474/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0100\n","Epoch 2475/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0100\n","Epoch 2476/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.0100\n","Epoch 2477/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0100\n","Epoch 2478/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0100\n","Epoch 2479/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0100\n","Epoch 2480/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0100\n","Epoch 2481/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0100\n","Epoch 2482/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0100\n","Epoch 2483/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0100\n","Epoch 2484/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step - loss: 0.0100\n","Epoch 2485/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 310ms/step - loss: 0.0100\n","Epoch 2486/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0100\n","Epoch 2487/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step - loss: 0.0100\n","Epoch 2488/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 2489/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 158ms/step - loss: 0.0100\n","Epoch 2490/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0100\n","Epoch 2491/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0100\n","Epoch 2492/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0100\n","Epoch 2493/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0100\n","Epoch 2494/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0100\n","Epoch 2495/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0100\n","Epoch 2496/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0100\n","Epoch 2497/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0100\n","Epoch 2498/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0100\n","Epoch 2499/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0100\n","Epoch 2500/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0100\n","Epoch 2501/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0100\n","Epoch 2502/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0100\n","Epoch 2503/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0100\n","Epoch 2504/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0100\n","Epoch 2505/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0100\n","Epoch 2506/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0100\n","Epoch 2507/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0100\n","Epoch 2508/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0100\n","Epoch 2509/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 2510/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0100\n","Epoch 2511/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 2512/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0100\n","Epoch 2513/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0100\n","Epoch 2514/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 2515/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 2516/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.0100\n","Epoch 2517/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0100\n","Epoch 2518/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0100\n","Epoch 2519/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0100\n","Epoch 2520/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0100\n","Epoch 2521/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0100\n","Epoch 2522/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0100\n","Epoch 2523/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0100\n","Epoch 2524/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0100\n","Epoch 2525/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0100\n","Epoch 2526/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0100\n","Epoch 2527/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 2528/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 2529/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0100\n","Epoch 2530/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 2531/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 2532/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 2533/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0100\n","Epoch 2534/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 2535/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0100\n","Epoch 2536/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0100\n","Epoch 2537/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0100\n","Epoch 2538/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0100\n","Epoch 2539/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0100\n","Epoch 2540/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0100\n","Epoch 2541/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 2542/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0100\n","Epoch 2543/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0100\n","Epoch 2544/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 2545/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0100\n","Epoch 2546/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0100\n","Epoch 2547/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0100\n","Epoch 2548/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0100\n","Epoch 2549/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 2550/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0100\n","Epoch 2551/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0100\n","Epoch 2552/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 2553/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0100\n","Epoch 2554/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0100\n","Epoch 2555/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0100\n","Epoch 2556/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0100\n","Epoch 2557/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 2558/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 0.0100\n","Epoch 2559/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0100\n","Epoch 2560/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0100\n","Epoch 2561/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0100\n","Epoch 2562/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0100\n","Epoch 2563/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0100\n","Epoch 2564/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0100\n","Epoch 2565/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0100\n","Epoch 2566/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 2567/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0100\n","Epoch 2568/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0100\n","Epoch 2569/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0100\n","Epoch 2570/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0100\n","Epoch 2571/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0100\n","Epoch 2572/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0100\n","Epoch 2573/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0100\n","Epoch 2574/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0100\n","Epoch 2575/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0100\n","Epoch 2576/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0100\n","Epoch 2577/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0100\n","Epoch 2578/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0100\n","Epoch 2579/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0100\n","Epoch 2580/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0100\n","Epoch 2581/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0100\n","Epoch 2582/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0100\n","Epoch 2583/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 2584/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 2585/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0100\n","Epoch 2586/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 2587/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 2588/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0100\n","Epoch 2589/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0100\n","Epoch 2590/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 2591/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 2592/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 2593/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0100\n","Epoch 2594/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0100\n","Epoch 2595/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0100\n","Epoch 2596/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.0100\n","Epoch 2597/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0100\n","Epoch 2598/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0100\n","Epoch 2599/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.0100\n","Epoch 2600/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0100\n","Epoch 2601/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0100\n","Epoch 2602/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 0.0100\n","Epoch 2603/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 292ms/step - loss: 0.0100\n","Epoch 2604/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0100\n","Epoch 2605/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 164ms/step - loss: 0.0100\n","Epoch 2606/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0100\n","Epoch 2607/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.0100\n","Epoch 2608/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.0100\n","Epoch 2609/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 157ms/step - loss: 0.0100\n","Epoch 2610/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.0100\n","Epoch 2611/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0100\n","Epoch 2612/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0100\n","Epoch 2613/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0100\n","Epoch 2614/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0100\n","Epoch 2615/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0100\n","Epoch 2616/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 189ms/step - loss: 0.0100\n","Epoch 2617/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 277ms/step - loss: 0.0100\n","Epoch 2618/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0100\n","Epoch 2619/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0100\n","Epoch 2620/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0100\n","Epoch 2621/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0100\n","Epoch 2622/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0100\n","Epoch 2623/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0100\n","Epoch 2624/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0100\n","Epoch 2625/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0100\n","Epoch 2626/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0100\n","Epoch 2627/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0100\n","Epoch 2628/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0100\n","Epoch 2629/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0100\n","Epoch 2630/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0100\n","Epoch 2631/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0100\n","Epoch 2632/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0100\n","Epoch 2633/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0100\n","Epoch 2634/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0100\n","Epoch 2635/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0100\n","Epoch 2636/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 2637/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0100\n","Epoch 2638/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0100\n","Epoch 2639/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 2640/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0100\n","Epoch 2641/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 2642/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 0.0100\n","Epoch 2643/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0100\n","Epoch 2644/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0100\n","Epoch 2645/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0100\n","Epoch 2646/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0100\n","Epoch 2647/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0100\n","Epoch 2648/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0100\n","Epoch 2649/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0100\n","Epoch 2650/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0100\n","Epoch 2651/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0100\n","Epoch 2652/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 2653/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0100\n","Epoch 2654/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0100\n","Epoch 2655/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0100\n","Epoch 2656/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0100\n","Epoch 2657/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0100\n","Epoch 2658/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0100\n","Epoch 2659/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0100\n","Epoch 2660/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0100\n","Epoch 2661/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 176ms/step - loss: 0.0100\n","Epoch 2662/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0100\n","Epoch 2663/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0100\n","Epoch 2664/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0100\n","Epoch 2665/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0100\n","Epoch 2666/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0100\n","Epoch 2667/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0100\n","Epoch 2668/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0100\n","Epoch 2669/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0100\n","Epoch 2670/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 2671/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 2672/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0100\n","Epoch 2673/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0100\n","Epoch 2674/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0100\n","Epoch 2675/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0100\n","Epoch 2676/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0100\n","Epoch 2677/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0100\n","Epoch 2678/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.0100\n","Epoch 2679/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0100\n","Epoch 2680/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0100\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: 0.0100\n","Epoch 2682/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0100\n","Epoch 2683/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0100\n","Epoch 2684/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 2685/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 2686/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 2687/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0100\n","Epoch 2688/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0100\n","Epoch 2689/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0100\n","Epoch 2690/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0100\n","Epoch 2691/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0100\n","Epoch 2692/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 0.0100\n","Epoch 2693/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 2694/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 2695/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 2696/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0100\n","Epoch 2697/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0100\n","Epoch 2698/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0100\n","Epoch 2699/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0100\n","Epoch 2700/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0100\n","Epoch 2701/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0100\n","Epoch 2702/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0100\n","Epoch 2703/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 2704/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0100\n","Epoch 2705/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0100\n","Epoch 2706/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0100\n","Epoch 2707/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0100\n","Epoch 2708/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 2709/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0100\n","Epoch 2710/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 2711/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 2712/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0100\n","Epoch 2713/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 2714/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0100\n","Epoch 2715/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 2716/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0100\n","Epoch 2717/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0100\n","Epoch 2718/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0100\n","Epoch 2719/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0100\n","Epoch 2720/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 2721/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0100\n","Epoch 2722/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0100\n","Epoch 2723/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0100\n","Epoch 2724/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.0100\n","Epoch 2725/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 2726/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 2727/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0100\n","Epoch 2728/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0100\n","Epoch 2729/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0100\n","Epoch 2730/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 2731/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 184ms/step - loss: 0.0100\n","Epoch 2732/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0100\n","Epoch 2733/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0100\n","Epoch 2734/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 0.0100\n","Epoch 2735/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0100\n","Epoch 2736/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0100\n","Epoch 2737/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0100\n","Epoch 2738/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 176ms/step - loss: 0.0100\n","Epoch 2739/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0100\n","Epoch 2740/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0100\n","Epoch 2741/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 174ms/step - loss: 0.0100\n","Epoch 2742/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0100\n","Epoch 2743/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0100\n","Epoch 2744/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0100\n","Epoch 2745/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0100\n","Epoch 2746/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0100\n","Epoch 2747/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 0.0100\n","Epoch 2748/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.0100\n","Epoch 2749/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.0100\n","Epoch 2750/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0100\n","Epoch 2751/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0100\n","Epoch 2752/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - loss: 0.0100\n","Epoch 2753/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.0100\n","Epoch 2754/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 2755/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 191ms/step - loss: 0.0100\n","Epoch 2756/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - loss: 0.0100\n","Epoch 2757/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0100\n","Epoch 2758/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 192ms/step - loss: 0.0100\n","Epoch 2759/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 286ms/step - loss: 0.0100\n","Epoch 2760/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0100\n","Epoch 2761/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0100\n","Epoch 2762/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.0100\n","Epoch 2763/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0100\n","Epoch 2764/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 2765/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0100\n","Epoch 2766/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0100\n","Epoch 2767/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0100\n","Epoch 2768/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 2769/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0100\n","Epoch 2770/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0100\n","Epoch 2771/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0100\n","Epoch 2772/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0100\n","Epoch 2773/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 0.0100\n","Epoch 2774/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.0100\n","Epoch 2775/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0100\n","Epoch 2776/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0100\n","Epoch 2777/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0100\n","Epoch 2778/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 2779/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 2780/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0100\n","Epoch 2781/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0100\n","Epoch 2782/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0100\n","Epoch 2783/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0100\n","Epoch 2784/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0100\n","Epoch 2785/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 2786/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.0100\n","Epoch 2787/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 2788/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 2789/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0100\n","Epoch 2790/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0100\n","Epoch 2791/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0100\n","Epoch 2792/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0100\n","Epoch 2793/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 2794/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 2795/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0100\n","Epoch 2796/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0100\n","Epoch 2797/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 164ms/step - loss: 0.0100\n","Epoch 2798/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0100\n","Epoch 2799/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0100\n","Epoch 2800/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0100\n","Epoch 2801/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0100\n","Epoch 2802/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0100\n","Epoch 2803/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0100\n","Epoch 2804/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0100\n","Epoch 2805/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0100\n","Epoch 2806/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0100\n","Epoch 2807/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 0.0100\n","Epoch 2808/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0100\n","Epoch 2809/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0100\n","Epoch 2810/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0100\n","Epoch 2811/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0100\n","Epoch 2812/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0100\n","Epoch 2813/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0100\n","Epoch 2814/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 2815/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0100\n","Epoch 2816/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0100\n","Epoch 2817/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0100\n","Epoch 2818/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0100\n","Epoch 2819/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0100\n","Epoch 2820/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0100\n","Epoch 2821/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0100\n","Epoch 2822/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0100\n","Epoch 2823/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0100\n","Epoch 2824/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0100\n","Epoch 2825/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0100\n","Epoch 2826/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0100\n","Epoch 2827/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0100\n","Epoch 2828/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0100\n","Epoch 2829/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0100\n","Epoch 2830/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 2831/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0100\n","Epoch 2832/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0100\n","Epoch 2833/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 2834/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0100\n","Epoch 2835/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0100\n","Epoch 2836/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 2837/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0100\n","Epoch 2838/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0100\n","Epoch 2839/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0100\n","Epoch 2840/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 2841/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0100\n","Epoch 2842/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0100\n","Epoch 2843/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 2844/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0100\n","Epoch 2845/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0100\n","Epoch 2846/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 2847/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0100\n","Epoch 2848/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 2849/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0100\n","Epoch 2850/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0100\n","Epoch 2851/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 2852/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0100\n","Epoch 2853/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 2854/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0100\n","Epoch 2855/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0100\n","Epoch 2856/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0100\n","Epoch 2857/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0100\n","Epoch 2858/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0100\n","Epoch 2859/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0100\n","Epoch 2860/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0100\n","Epoch 2861/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0100\n","Epoch 2862/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0100\n","Epoch 2863/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.0100\n","Epoch 2864/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0100\n","Epoch 2865/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.0100\n","Epoch 2866/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0100\n","Epoch 2867/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.0100\n","Epoch 2868/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0100\n","Epoch 2869/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0100\n","Epoch 2870/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0100\n","Epoch 2871/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0100\n","Epoch 2872/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.0100\n","Epoch 2873/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - loss: 0.0100\n","Epoch 2874/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.0100\n","Epoch 2875/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0100\n","Epoch 2876/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 176ms/step - loss: 0.0100\n","Epoch 2877/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0100\n","Epoch 2878/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0100\n","Epoch 2879/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0100\n","Epoch 2880/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.0100\n","Epoch 2881/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0100\n","Epoch 2882/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0100\n","Epoch 2883/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0100\n","Epoch 2884/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 0.0100\n","Epoch 2885/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.0100\n","Epoch 2886/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0100\n","Epoch 2887/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0100\n","Epoch 2888/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0100\n","Epoch 2889/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.0100\n","Epoch 2890/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0100\n","Epoch 2891/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - loss: 0.0100\n","Epoch 2892/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0100\n","Epoch 2893/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.0100\n","Epoch 2894/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0100\n","Epoch 2895/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 163ms/step - loss: 0.0100\n","Epoch 2896/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0100\n","Epoch 2897/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 248ms/step - loss: 0.0100\n","Epoch 2898/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 2899/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0100\n","Epoch 2900/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0100\n","Epoch 2901/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0100\n","Epoch 2902/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0100\n","Epoch 2903/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0100\n","Epoch 2904/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 0.0100\n","Epoch 2905/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 0.0100\n","Epoch 2906/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.0100\n","Epoch 2907/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0100\n","Epoch 2908/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0100\n","Epoch 2909/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0100\n","Epoch 2910/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0100\n","Epoch 2911/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0100\n","Epoch 2912/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0100\n","Epoch 2913/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 2914/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 2915/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0100\n","Epoch 2916/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0100\n","Epoch 2917/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.0100\n","Epoch 2918/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0100\n","Epoch 2919/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0100\n","Epoch 2920/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0100\n","Epoch 2921/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0100\n","Epoch 2922/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0100\n","Epoch 2923/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0100\n","Epoch 2924/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 2925/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 2926/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 2927/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0100\n","Epoch 2928/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0100\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: 0.0100\n","Epoch 2930/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 0.0100\n","Epoch 2931/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 2932/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0100\n","Epoch 2933/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0100\n","Epoch 2934/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0100\n","Epoch 2935/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 0.0100\n","Epoch 2936/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0100\n","Epoch 2937/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0100\n","Epoch 2938/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0100\n","Epoch 2939/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 2940/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0100\n","Epoch 2941/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0100\n","Epoch 2942/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 2943/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0100\n","Epoch 2944/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 2945/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0100\n","Epoch 2946/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 2947/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 2948/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0100\n","Epoch 2949/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0100\n","Epoch 2950/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0100\n","Epoch 2951/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0100\n","Epoch 2952/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0100\n","Epoch 2953/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 2954/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0100\n","Epoch 2955/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0100\n","Epoch 2956/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 2957/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0100\n","Epoch 2958/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0100\n","Epoch 2959/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0100\n","Epoch 2960/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0100\n","Epoch 2961/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0100\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: 0.0100\n","Epoch 2963/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0100\n","Epoch 2964/3000\n","\u001b[1m1/1\u001b[0m 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loss: 0.0100\n","Epoch 2971/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0100\n","Epoch 2972/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0100\n","Epoch 2973/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0100\n","Epoch 2974/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 2975/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0100\n","Epoch 2976/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0100\n","Epoch 2977/3000\n","\u001b[1m1/1\u001b[0m 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- loss: 0.0100\n","Epoch 2984/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0100\n","Epoch 2985/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0100\n","Epoch 2986/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0100\n","Epoch 2987/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0100\n","Epoch 2988/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0100\n","Epoch 2989/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0100\n","Epoch 2990/3000\n","\u001b[1m1/1\u001b[0m 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- loss: 0.0100\n","Epoch 2997/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0100\n","Epoch 2998/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0100\n","Epoch 2999/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 165ms/step - loss: 0.0100\n","Epoch 3000/3000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0100\n","Epoch 3000/3000\n"," - loss: 0.0100\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - loss: 0.0100\n","Restoring model weights from the end of the best epoch: 1405.\n","\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/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"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","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)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"collapsed":true,"id":"QmVJYJW_X3pn","executionInfo":{"status":"ok","timestamp":1763318035007,"user_tz":-180,"elapsed":5975,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"ba8e34fb-5c79-4637-f303-e876f93ff7d8"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["amount: 21\n","amount_ae: 226\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["
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R2qU5e9S3sEnwr4/iZQanqpTyjv4hKP/xvV/7Tr5/T27tFr3vHpIP//5zzV9+nRHakih6bFCUSeS+qxw67Ek+qzeiJSft9Qwr04oavj3e/v1G6RxMx9STPy4oNco+uvLqji9T7HDpypu+HXtHlNdekw1pUc7PaYzFYXvy1PxUY8fL0n158tUlv+uBg1O1OXjbm5zj75g1OhKeeF7ulDxiaT+Shp/qwZdlhT0Gp7KXFWc3qe70lK0JG1U0M8v0WeZVsOpHqtb3c1vf/tbff/739f8+fN1/PhxDR8+vNcLiIqK0sSJvn8gzpgxQwcPHtSTTz6pp59+us2xI0eObPNbT2fOnNHIkSM7PH90dLSio3s/ZOmJOWMv19LrnNvU3ldwVjvzyrR4cooe/sLntGDTe/JcbNTeb94YtHuQPfjmEZ0oq9HiySkB3cNq7Z4cHSzM69Y9r7pboysHTpfrlhf3aVbqMG1bmq646EFBr9Ge5jXIw+ZmHsfLapSYMlMjJ33FkRqSVHRio5LG3eRYU0GNntUJ5GurJ9eHZF8jU0cm9Pp7/IHT5XrwzaOanpLYdH34ZWTl9ercCD/0Wd2TNjxO01MSHTm3/20JF145wrEaku86j4QaoapDDXNq5Jaf15bsIg0ZOtGxn+v+t42MhP4kEmpUlx5T3sEnHP83bkZWnqPf39E3hWOPJdFn9VYk/Lylhpl1nK7h3+8dN/MhTZj5XUdqVBRlqeL0PiWNn68JM9vegy/v0FMqOrGxV/fo++vun8hT8VGHNbriv0df/IipHd6jr7c1unKxrkb7N98oSUpJ+5qumrcu6DUkqfjkq6o4vU9TRybSZ6FX+gd64IIFC/SjH/1I//mf/6lXXnklKM1RexobG1u8J3lz6enp2rlzZ4vP7dixo8P3Re8rvBcbtGDTe/qwpEo77p0TtMFSd63dk9P021lODXC64h8sXZsc32bjPFTIw2ZCHkB7uD5gGvosAACA4KPHAhCunHhFXyDyDj2l3ANP9GrQ11v+Qd+QYZM6HPQ57WJdjY6+uUz1FyokSdGxvX9nJ8BpAb+yr6GhQX/5y180evTooBVfuXKlbrvtNo0dO1bV1dXavHmzMjMztX37dknSsmXLlJqaqnXrfFPzhx9+WDfeeKOeeOIJ3X777XrppZeUlZWl//mf/wnamsLRC0cLVOm9yGDJkI1z8vAxJQ+gNa4PmIg+CwAAIPjosQAgcAz6fPyDvtpzJ5U0br7K8raFfA1ATwQ87NuxY0fQi5eUlGjZsmUqKipSQkKCpkyZou3bt+uWW26RJBUUFKh/f/vFh1/84he1efNmPfLII/rXf/1XXXnllXrttdd07bXXBn1t4aCuoVGSVFLr1Z5v3MBgyeWNc/KwmZQH0BzXB0xFnwUAABB89FgAEBgGfT7NB33T7tiowr++HPI1AD3VuzsS99Kzzz7b6Z9nZma2+dxXv/pVffWrX3VoReGj2luvrSeLJUnLp41jsOTyxjl52EzJ482TZ7o+EH0K1wf6GvosAACA4KPHAhBpGPT5tB70xY+YxrAPYSXge/bBHNXeei3Y9J7OeeokSaPjY1xZBxvnPuRhMymPs5fyACSuDwAAAAAAgNYY9Pm0N+gDwg3DvjDjH2R8WFKlRZPduzEoG+c+5GEzL4+RIa8PM3F9AAAAAAAAtFRRuJ9Bnxj0IXIw7AsjzQcZO+6doxFDol1ZBxvnPuRhIw+YiusDAAAAAACgrYrT+xj0MehDBGHYFyZaDzK4J5xZgyXyIA+Yh+sDAAAAAACgperSY5KkxNQ5DPoMGPSV5m53pS4iz0C3F4CumTLIYOPchzxs5AFTZeaVamduWZ+/PgAAAAAAAPzyDj2lmtKjkqTEUX/jyhoY9NnyDj2lsk/fdqU2Ig/DPsOZMshg49yHPGzkAZNxfQAAAAAAANjyDj2l3ANPKHb41KaBX6gx6LP580j63G0M/BAUvI2nwUwaZLBxTh7NkQdMd/OEpD59fQAAAAAAAPj5B0sTZn9fccOvc2UNDPpszfMYPuHLrqwBkYdhn6FMGWRkFVZIYuOcPGzkgXAwb/xwV+qacH0AAAAAAAD4NR8scY8+swZ9buWByMSwz0CmDDLW7snRwUvDpb68cU4eNvIAOmbC9QEAAAAAAOBnwmCJQZ/NhDwQuRj2GcaUQcbaPTl6dFe2Zo1KdKW+ZMbGOXnYyAPomAnXBwAAAAAAgJ8JgyVvTRGDvktMyAORjWGfQUwZZPgHS4/dlKaZLg2XTNg4Jw8beQAdM+H6AAAAAAAA8DNlsFScs4VBn8zJA5GNYZ8hTBlkNB8scU848pDIA+iMCdcHAAAAAACAnwmDpfrzZZKkqMuSGPQZkAf6BoZ9BjBlkMFgyYc8bOQBdOyzKo/r1wcAAAAAAICfCYOlqjNHVJb/riRpxKT/w6CPQR9ChGGfy0wZZJgwWDJh45w8bOQBdG7DkXwGfQAAAAAAwAgmDJaqzhzRka33aNDgRElS/wFRIV8Dgz70VQz7XGTKIMOEwZLk/sY5ebREHkD7ztR6JUnJQ6IZ9AEAAAAAANeZMFjyD/qGDJuky8fd7MoaGPShL2PY5xJTBhkmDJZM2DgnDxt5AB07cLpcb+QUSZLumzqWQR8AAAAAAHCVCYOl5oO+qbf/r/r1D/1+CYM+9HUM+1xgyiDDhMGSCRvn5GEjD6Bj/ntYDovxvQVF9MABLq8IAAAAAAD0ZSYMlloP+rhHH4M+uINhX4iZMsgwZbDk9sY5edjIA+iY//q4Njled0wa6fZyAAAAAABAH2fCYMmEQV9jQx2DPkAM+0LKlEGGSYMlNzfOycNGHkDHml8f25amK2oAPzoBAAAAAIB7TBgsmTDok6QzJ3/PoA8Qw76QMWWQYdpgya2Nc/KwkQfQsdbXB/foAwAAAAAAbjJhsGTCoM9qrJck1Z0vY9AHyOVh37p16zRr1izFxcUpOTlZS5YsUU5OTqePqa+v109/+lNdccUVGjx4sKZOnapt27aFaMU9Y8ogw8TBUl++Jxx5+JiSB9CaCdcH0FN9pccCAAAINfosAG4yYbBkwqDvYl2NzubvlCSNnHwXgz5ALg/7du/erRUrVmj//v3asWOH6uvrdeutt6q2trbDxzzyyCN6+umn9f/+3//TiRMndP/99+srX/mKPvjggxCuPHCmDDIYLPl4LzaQxyXkAXTMhOsD6I2+0GMBAAC4gT4LgFtMGCyZMug7+uYy1V+okCRFx6aEfA2SGXkAzQ10s3jr32LasGGDkpOTdejQId1www3tPubFF1/UT37yEy1cuFCS9MADD+jdd9/VE088oY0bNzq+5u7w1JsxyGCwZHvhaIEqvRfJgzyADplwfXjqG0JeE5El0nssAAAAt9BnAXCDCYMlkwZ9tedOKmncfJXlufMqaRPyAFpzddjXWmVlpSRp2LBhHR7j9Xo1ePDgFp+LiYnR3r17Ozze6/U2fVxVVRWElQbmx+8eV03dRS2fNk7vfFKidz4pCer5sworJEmZeaUdHpOZV6qduWW6eUKSJN+gKdg1uvJZlUcbjuQreUi0vjQ+SU++/2nQa3Tl/c/KJUmF1Rf07enjyaMP5HHo0vOoKMoK6nmb85+7tvxjx2p4qk5Rowd1evr129X1ITl/jXgvNigjK1eStG/fPkdq+JWXlzt6fpjDiR7L/xi3+qzs0mrHzp1bfp4ahtWhhlk1Cqs9ksK/d6BG4Pznzi0/r8NFFY7UCMXXrv/c2dnZjtWQJI/H4+j5YRb6rO6JlJ+F1DCrRqjqhKJGaa3vuq8stvezSnO3q+zTt5X0uds0OG6Mik++2qsankrfnkNF4X7lBrht5q0pUnHOFkVdlqTEUV/Uqb881+nx1aXHul2jK40NdTpz8veqO1+mkZPvUnXZ8aDXaK2951FRuF8Vp/cpMXWOLEvKzXqqVzVqzvr6ksJqD31WAOizOtbPsizL7UVIUmNjoxYvXqyKiopOm527775bR48e1WuvvaYrrrhCO3fu1J133qmGhoYWTZDf6tWrtWbNmjafX7BggQYNcubVGvn5+frLX/7iyLkBAAimuXPnauhQ515Ze+DAAc2ePdux89fX12vbtm2qrKxUfHy8Y3XCmVM9luROn1VeXt7p8wAAwATz589XTEyMozXos9xHnwUAQOg53WeFa49lzLDvgQce0Ntvv629e/dq9OjRHR5XWlqq73znO3rjjTfUr18/XXHFFZo/f76ee+65dqe67f0m1JgxYxxtVjdt2qR77rlHs1ISNTM10ZEakrQ7r0wnymo0a1SiZo5qWSersEIHCyva/bNg1ejKmVqv3sgp0rCYKN0xaaSiBrR/i8je1OhKXUOjtp4sVkltnSzJkRp+5NE1N/KYPSpRMxzKY09emY6X1Sjl6nuUmDKzdwvuQEVRlopObNT4Wd9XTPwYagRYp7vZBnp9SM5dI/7r45ynTqnxMcqr8OiutFFakubMe8/vKzirjKw8bdy4UUuXLnWkhiQtXrxYr7/+umPnr6qqUkJCAptQnXCqx5Lc6bMOHz6sGTNm6LGb0jRh6GWO1PBfH9Qwpw41zKzxwMzxGhXnzD+yM3NLtTOvLOz7k0ip4ak6pbyDT+iBmeM1Z+zljtQI5dfuxq/MUNrwOEdqZJdW655XD+nQoUOaPn26IzX86LPcR5/VfZH2s5AaZtQIVZ1Q1rgrLUXDh0Q7UsO/r5GYOkcxCRM6Pbb+fJnK8t/VoMGJunzczerXP7BfNqgofF+eio8UO3yq4oZf16v1Wo31Opu/U/UXKpQ0br4GXZYU9BodaV5DkmpKjwa9nqcyVxWn9+mutBQtSRsVtPM2R5/VPeHaYxnxNp4PPfSQtm7dqj179nTaHEnS8OHD9dprr+nChQs6e/asRo0apR//+Mf63Oc+1+7x0dHRio525htjVx5Ov0JLr3NuU/vBN4/oRFmNFk9OaXHvt7V7cnSwMC8o94TrqEZX/Pe8mpU6rMt7XvW0RleqvfVasOk9eS426itpKXoluyjoNZojj865lccih/M4XlajxJSZGjnpK71dcoeKTmxU0ribHGtcIqmGv053vra6c31Izlwjza+Pvd+8URuO5CsjK09TRyY4+nMkIyvPsXPDDE72WJK7fdbCK0doekqiY+fPyMqjhmF1qGFejW9PH+9ojZ15ZRHRn0RCjerSY8o7+ITmjL3c8d4kFF+7acPjHP++iMhHn9VzkfSzkBrm1AhVnVDVWHn9ZMdq+Pc1Uq76aqf7Wf579MWPmNrte/T9dfdP5Kn4SEnj52vCzJ7f085/j77GBq9mfOX3ih8xLeg1OuOvMTAqVhWn9zlyj77ik6+q4vQ+TR2ZSJ+FXun4pQshYFmWHnroIb366qv64x//qAkTOv9NguYGDx6s1NRUXbx4Ub///e915513OrjS8LF2T44e3ZUdlEFGT/k3zq9Njg9o49wJ/o3zD0uqtOPeORrh0G/CdIU8fMgDpjLx+pid6txbaqLvoMcCAABwBn0WgL7AP+gbMmxStwd9weIf9NWeO6lpd2xsMegLNacGfUAwuTrsW7FihTZu3KjNmzcrLi5OxcXFKi4ubvEWBsuWLdPKlSubPn7//ff1yiuv6NNPP9Wf/vQnLViwQI2NjfrhD3/oxlMwigmDDDbObeThQx4wFdcHIhk9FgAAgDPoswBEOgZ9turSY5KkxNQ5DPpgPFffxjMjI0OSNG/evBaff/7557V8+XJJUkFBgfr3t2eSFy5c0COPPKJPP/1UsbGxWrhwoV588UUlJiaGaNVmMmGQwca5jTx8yAOm4vpApKPHAgAAcAZ9FoBIxqDPlnfoKdWUHpUkJY76G1fWAHSHq8M+y7K6PCYzM7PFxzfeeKNOnDjh0IrCU2ZeqXbmljFYMmTjnDx8yAOm4vpAX0CPBQAA4Az6LACRikGfLe/QU8o98IRih09tGvgBpnP1bTwRHG4PMtg4b4k8yAPm4vqwvZZd6EpdAAAAAABgFgZ9Nv+gb8Ls7ytu+HWurAHoCYZ9YSyrsEKSdPOEJAZLBmyck4cPecBUXB+2tXtytCW7yJXaAAAAAADAHAz6bM0HfdyjD+GGYV+YWrsnRwcvDTPmjR/uyhrYOLeRhw95wFRcHzb/PSzvSktxpT4AAAAAADADgz4bgz6EO4Z9Yci/UTtrVKJra2Dj3EYePuQBU3F92PzXx2M3pWlJ2ihX1gAAAAAAANznqcpn0HcJgz5EAoZ9Yab5Ru1Ml4YZbJzbyMOHPGAqrg9b8+uDt7YFAAAAAKBvKzicwaBPDPoQORj2hRETNmrZOLeRhw95wFRcHzauDwAAAAAA0FxU7EgGfQYM+jxV+a7UReRh2BcmTNioZePcRh4+5AFTfVbl4fq4hOsDAAAAAAC0Nm7a/Qz6XB70VZ05ooLDGa7URuRh2BcGTNioZePcRh4+5AGTbTiSz/Uhrg8AAAAAANC+/gMHh7wmgz5b1ZkjOrL1HkXFjnSlPiIPwz7DmbJRy8a5D3n4kAdMlzwkmuuD6wMAAAAAABiCQZ/NP+gbMmySxk2735U1IPIw7DOYCRu1Z2q9ktg4l8jDjzxs/jxgnvumjuX6YNAHAAAAAAAMwKDP1nzQN/X2/3XlFZaITAz7DGXCRu2B0+V6I6dIEhvn5OFDHjZfHsWu1EbXogcOCHlNrg8AAAAAAICWGPTZWg/63LhnIiIXwz4DmbBRe+B0uW55cZ+GxURJYuOcPMijOX8el1/KA+D6AAAAAAAAaKmxoY5B3yUM+uA0hn2GMWGj1j/IuDY5XndMcucGoWyc28jDZloet08a4coaYBauDwAAAAAAgLbOnPw9gz4x6ENoMOwziAkbtc0HGduWpitqQOi/RNg4t5GHjTxgIq4PAAAAAACAlqzGeklS3fkyBn0M+hAi7FQbwoSN2taDDO4JRx7kYTMhD5jFe7GB6wMAAAAAAKCZi3U1Opu/U5I0cvJdDPoY9CFEBrq9AJixUWvCIIONcxt52MgDpnrhaIEqvRf7/PUBAAAAAAAg+QZ9R99cpvoLFZKk6NgUV9bBoA99Ea/sc5kJG7WmDDJeOFrAYEnk0Rx5wER1DY2SpJJab5+/PgAAAAAAACR70Fd77qSSxs13bR0M+tBXMexzkQkbtSYMMtg4t5GHjTxgompvvbaeLJYkLZ82rk9fHwAAAAAAAFLLQd+0OzZq0GVJrqyDQR/6MoZ9LjFho9aEQQYb5zbysJEHTOS/h+U5T50kaXR8jCvrMOH6AAAAAAAAkNoO+rhHH4M+uMPVYd+6des0a9YsxcXFKTk5WUuWLFFOTk6Xj/uP//gPTZ48WTExMRozZoy+973v6cKFCyFYcXCYsFFrwiCDjXMbedjIAybyXx8fllRp0WR33m9eMuP6QHjoqz0WAACA0+izAMDGoM/GoA9uc3XYt3v3bq1YsUL79+/Xjh07VF9fr1tvvVW1tbUdPmbz5s368Y9/rFWrVik7O1vPPvusfve73+lf//VfQ7jynjNho9aEQQYb5zbysJEHTNT8+thx7xyNGBLtyjpMuD4QPvpijwUAABAK9FkA4GPKoK+icD+DPkDSQDeLb9u2rcXHGzZsUHJysg4dOqQbbrih3cf8+c9/1pw5c3T33XdLksaPH6+vf/3rev/99x1fb2+ZsFFrwiCj9cb5hiP5IV+DRB5+5GEzIQ+YpfX1MTt1qCvXiAnXB8JLX+uxAAAAQoU+CwDMGfRJUsXpfQz6ALk87GutsrJSkjRs2LAOj/niF7+ojRs36sCBA5o9e7Y+/fRTvfXWW7r33nvbPd7r9crr9TZ9XFVVJUn63e9+p8suuyyIq7ft27dPkvRadpFyy89LkjLzSrUzt0w3T/DdnHTtnq7f4qErWYUVTecOxGdVHm04kq/kIdH60vgkPfn+p0Gv0RXvxQa9cLRAJbVeLZ82Tu98UhL0Gu1pXYM8fMjD1pM8Dl2q4ak6perSYz1daqc8VackSbXlHzty/lDXKMvf5WidiqIsScH5+m3v+nDjGnHi+pCko8WVQTkPwoMTPZbUcZ915MgRxcY68w+c7Oxs3/+WVjtyfklN/Rs1zKlDjb5Xo7DaIylyeqBwr+E/d275eR0uqnCkRqR87Tr9PRfmoc/qnki51qlhVo1Q1YmUGqW1vu8tpbnvNPUR3dHYUKczJ3+vuvNlGjn5Lp09tUdnT+1pcYx/n6yicL9ys3q/5vaU5b0rSYqOGyvLknKzngp6Df/zqDmbreKTr7b5c09VvgoOZygqdqRSJn9VZXk7ul2jstj3F1RY7aHP6gJ9Vuf6WZZlub0ISWpsbNTixYtVUVGhvXv3dnrsU089pR/84AeyLEsXL17U/fffr4yMjHaPXb16tdasWePEkgEACHtTpkzRuHHjHDu/f0PDKfX19dq2bZsqKysVHx/vWJ1w5lSPJdFnAQDQkblz52ro0KGO1qDPch99FgAAoed0nxWuPZYxw74HHnhAb7/9tvbu3avRo0d3eFxmZqa+9rWvae3atfrCF76gjz/+WA8//LC+853v6NFHH21zfHu/CTVmzBh96/NjddOE4Y48l+cP52tnXplmjUqUJB0srNCsUYmaeenjYNmdV6YTZTVdnvtMrVdv5BRpWEyU7pg0UlEDAr9VY6A1ulLX0KitJ4t1zlOnRZNTWtzzKlg1OuOvkRo7WKdrLpAHeTTpTR578sp0vKxGKVffo8SUmUFYdVsVRVkqOrFR42d9XzHxY8K+xuxRiZrRZR7FujwmSrdPGtGtPCQ7k958TXV2fUihvUYkOVbnZFmNduaVaePGjVq6dGnQz++3ePFivf76646dv6qqSgkJCWxCdcKpHkvquM/6nzumdXqt98ZbH51pelvbCUMdepeGgrPKyMqjhkF1qNH3aryWXagt2UUR0wM5WcNTdUp5B5/QXWkpmjoy0ZEaR4srtCW7KOy/rkJRI7f8vB7dla1Dhw5p+vTpjtTwo89yH31W90XKtU4Ns2qEqk6k1PDvXSemzlFMwoSAH2c11uts/k7VX6hQ0rj5GnRZUofHVhS+L0/FR4odPlVxw68LxrKbVJceU03pUQ0cfLkuXjjrSA0/T2WuKk7v06yURM1MTXSkhn9/Jty/riKpzwrXHsuIt/F86KGHtHXrVu3Zs6fT5kiSHn30Ud1777369re/LUm67rrrVFtbq7//+7/XT37yE/Xv33JjODo6WtHR0W3O84XRw7T0Omf+sbWv4Kx25pUpPnqgduaWOXaPpQffPKITZTVaPDmlw/P770E2K3VYj+5BFkiNrvjveeW52Ki937xRs1NbTt2DUaMr/hqnay6QB3k0CUYex8tqlJgyUyMnfSUYy25X0YmNShp3k2ONSyhrLAooj6E9vmeiP5Oefv12dX34azh9jcz+baYk6eYJSXp32VxHamw6dko788ocOTfM4WSPJXXcZ01OitX0lMSgPIfWsst8b9ux8MoRjtWQpIysPGoYVocafatGbvl5bckuipgeyMka1aXHlHfwCS1JG+XYv3E3HTulLdlFYf91FYoah4sq9OiubEfODbPQZ/VcJFzr1DCvRqjqREIN/951ylVfDXg/y3+PvsYGr2Z85fdd3qPvr7t/Ik/FR0oaP18TZgbvXnp5h55S0YmNmjD7+7pQc8bXZwW5RnPFJ19Vxel9WnyVc3tA/v2ZcP+6CkUN+qzOde8lE0FmWZYeeughvfrqq/rjH/+oCRO6/k2C8+fPt2mCBgwY0HQ+kzg56AuEf+P82uT4Hm+c95Z/4/zDkirtuHdOuxvnoeC//9XNE5LIgzwkmZEHbCbkYcr1sXZPjg5eukbmjXfmFeiIfJHeYwEAALiFPgtAX+Mf9NWeO6lpd2zsctDnlLxDTyn3wBOaMPv7Gj/DmeEeEM5cfWXfihUrtHnzZv3hD39QXFyciouLJUkJCQmKiYmRJC1btkypqalat26dJGnRokX69a9/rc9//vNNb33w6KOPatGiRU2NktsYZPiwcW4jDxt5oDUT8jDp+nh0V7Zm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качества AE1\n","IDEAL = 0. Excess: 9.761904761904763\n","IDEAL = 0. Deficit: 0.0\n","IDEAL = 1. Coating: 1.0\n","summa: 1.0\n","IDEAL = 1. Extrapolation precision (Approx): 0.09292035398230088\n","\n","\n"]}]},{"cell_type":"code","source":["numb_square = 20\n","xx, yy, Z2 = lib.square_calc(numb_square, data, ae2_trained, IREth2, '2', True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"collapsed":true,"id":"XaPeEWS6eTLa","executionInfo":{"status":"ok","timestamp":1763318043880,"user_tz":-180,"elapsed":2860,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"e03acdb5-5207-41bd-d9ac-1184e9524550"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m225/225\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["amount: 21\n","amount_ae: 26\n"]},{"output_type":"display_data","data":{"text/plain":["
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качества AE2\n","IDEAL = 0. Excess: 0.23809523809523808\n","IDEAL = 0. Deficit: 0.0\n","IDEAL = 1. Coating: 1.0\n","summa: 1.0\n","IDEAL = 1. Extrapolation precision (Approx): 0.8076923076923076\n","\n","\n"]}]},{"cell_type":"code","source":["# сравнение характеристик качества обучения и областей аппроксимации\n","lib.plot2in1(data, xx, yy, Z1, Z2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"collapsed":true,"id":"Xa9xKSDse02C","executionInfo":{"status":"ok","timestamp":1763318051268,"user_tz":-180,"elapsed":409,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"55c7b09a-f2ed-4192-dd2d-d61f0b61cfa2"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["# загрузка тестового набора\n","data_test = np.loadtxt('data_test.txt', dtype=float)"],"metadata":{"collapsed":true,"id":"90w92Kqqe5FB"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(data_test)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"id":"5pVLRFSthZwU","executionInfo":{"status":"ok","timestamp":1763320115174,"user_tz":-180,"elapsed":43,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"a2a2532b-629a-4ff7-8cf6-fb86e67e7017"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["[[2.72 2.72]\n"," [3.28 2.71]\n"," [2.7 3.29]\n"," [3.3 3.27]]\n"]}]},{"cell_type":"code","source":["# тестирование АE1\n","predicted_labels1, ire1 = lib.predict_ae(ae1_trained, data_test, IREth1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"11SIwVrMoSpW","executionInfo":{"status":"ok","timestamp":1763320117297,"user_tz":-180,"elapsed":75,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"dea30fea-9465-447d-d789-5c4d8807b5e0"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step\n"]}]},{"cell_type":"code","source":["# тестирование АE1\n","lib.anomaly_detection_ae(predicted_labels1, ire1, IREth1)\n","lib.ire_plot('test', ire1, IREth1, 'AE1')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":733},"collapsed":true,"id":"y_DpUuG70rFT","executionInfo":{"status":"ok","timestamp":1763320119012,"user_tz":-180,"elapsed":255,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"b04e54a9-01c3-4622-bf8f-c1bf8c87ca4e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","i Labels IRE IREth \n","0 [0.] [1.33] 2.0 \n","1 [0.] [1.59] 2.0 \n","2 [0.] [1.87] 2.0 \n","3 [1.] [2.07] 2.0 \n","Обнаружено 1.0 аномалий\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["# тестирование АE2\n","predicted_labels2, ire2 = lib.predict_ae(ae2_trained, data_test, IREth2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MpEE4xHR8soP","executionInfo":{"status":"ok","timestamp":1763320123133,"user_tz":-180,"elapsed":53,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"3acc37a4-baba-48b6-be01-ed20cc1d6f45"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n"]}]},{"cell_type":"code","source":["# тестирование АE2\n","lib.anomaly_detection_ae(predicted_labels2, ire2, IREth2)\n","lib.ire_plot('test', ire2, IREth2, 'AE2')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":733},"collapsed":true,"id":"thv9cjm58wge","executionInfo":{"status":"ok","timestamp":1763320124805,"user_tz":-180,"elapsed":426,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"9be60cc2-2ea8-49ec-e43e-a5604d88137d"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","i Labels IRE IREth \n","0 [1.] [0.39] 0.38 \n","1 [1.] [0.4] 0.38 \n","2 [1.] [0.42] 0.38 \n","3 [1.] [0.41] 0.38 \n","Обнаружено 4.0 аномалий\n"]},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["# построение областей аппроксимации и точек тестового набора\n","lib.plot2in1_anomaly(data, xx, yy, Z1, Z2, data_test)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"collapsed":true,"id":"XGx5pH2m8yat","executionInfo":{"status":"ok","timestamp":1763320128501,"user_tz":-180,"elapsed":595,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"e7f44c9e-9484-4944-a1d4-7d6607021605"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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xQ0RoaKsDIufWoBbBiUjUR2OEf37I/P+Kdj7zTcAgI/eew/333UXBkb1Qu9gFWpraqRtMBF5BW/NDhY3RH6uuLAQ40amYd/u3Xh+xUp8c+QINnz5JUaOGoVFC+YDAK5evYIxd9+N3y98RuLWEpG38Obs4FRwIj/37PzfQxAEbNv3P4SEhpqOJw8ZgukzZwEAHnvy9wCA/Xv2SNFEIvJC3pwd7Lkh8kI6nWfep/ryZez66ivMmvtbi3Ayiuje3TMNISKXYHYYsLgh8iJnfgRGDQ9AfGgQRg0PwJkf3ft+hWfPQhRFDEhOdu8bEZFbMTsssbgh8iKzfx2Asz8ZFno4+5OA2b9275VjUeQaNERywOywxDE3RF5CpwN+zFOY3RfwY54AnQ5QKt3znv0GDIAgCDiTn++eNyAit2N22GLPDZGXUCqB6wfpoVSK1+6L1+677z0je/TA6Lvuwvur3sEVjcbmcU75JvJ+zA5bLG6IvMiaf7eg/0BDQPUfKGLNv1vc/p4r3ngTOp0O995+G7Zs3IhzZ37Cj3mn8d4/38KEUXcCMKxlcTInBwVnzwIATp88iZM5Oai+fNnt7SOi9jE7LPGyFJEXGXA9sOd4i1u7k60lXncdvjrwHd58ZSWWPfMMKsouomdUFFJuugmv/P0fAIAP330Xry9/yfScKWPTAQBvrH4XGZmZnmkoETnE7LAkiN4+KsjF6urqEBERgRdy8hEcHi51c0gmQnTNGHG1Dn0S+yJQFSx1c7xCs7YRF4rO40g3Na4oLXc8bqyvx1+GJaO2thZqtVqiFnYMs4PcgdlhyVW5wctSREREJCssboiIiEhWWNwQERGRrLC4ISIiIllhcUPkAiIE4w9kJBr/EKRtB5EXY3ZYcVFusLghcoEmhQJ6UUTz1atSN8VrNF+9Cr0ooknBmCFypEUQIIoidM3NUjfFK7gqN7jODZEL6AQFSgJUCLxUCQAI7NYNftthIRoCqupSJUoCVNAJLG6IHGkWFKhSBCCkqgqKgAAICj8NDhfnBosbIhcpCg4DGhvQXFEBheCnAXWNXhRREqAy/J0QkWOCgLPdwhGmqcHV4mKpWyMpV+YGixsiVxEEFHULR4kYiiC9HoKfXkQXIaBJoWCPDZGTmhRKHA7rgWC9jrnhotxgcUPkYjpBgatK/o+diJwnCgKuKvm/ZFdhAhMREZGssLghIiIiWWFxQ0RERLLC4oaIiIhkhcUNERERyQqLGyIiIpIVFjdEREQkKyxuiIiISFZY3BAREZGseE1xs3LlSgiCgAULFrR53qeffopBgwYhODgYN954I7Zu3eqZBhKRV2J2EJE1ryhuDh06hFWrViElJaXN8/bv34/p06dj9uzZOHbsGCZPnozJkyfj5MmTHmopEXkTZgcR2SN5cdPQ0IAZM2bg3XffRWRkZJvnvvnmm7jnnnvwpz/9CYMHD8aLL76I1NRUvPXWWx5qLRF5C2YHETkieXEzb948jB8/HmPHjm333AMHDticN27cOBw4cMDhc7RaLerq6ixuROT7mB1E5IikW5BmZ2fj6NGjOHTokFPnl5WVISYmxuJYTEwMysrKHD5nxYoVWLZsWZfaSUTehdlBRG2RrOemuLgY8+fPR1ZWFoKDg932PosWLUJtba3pVlxc7Lb3IiL3Y3YQUXsk67k5cuQIKioqkJqaajqm0+nw7bff4q233oJWq4VSqbR4TmxsLMrLyy2OlZeXIzY21uH7qFQqqFQq1zaeiCTD7CCi9kjWc5Oeno7c3FwcP37cdPvZz36GGTNm4Pjx4zbhBABpaWnYuXOnxbEdO3YgLS3NU80mIokxO4ioPZL13ISHh+OGG26wOBYaGoqePXuajmdmZqJPnz5YsWIFAGD+/PkYNWoUXn/9dYwfPx7Z2dk4fPgwVq9e7fH2E5E0mB1E1B7JZ0u15fz587h48aLp/siRI7FhwwasXr0aw4YNw2effYZNmzbZBB0R+TdmB5F/E0RRFKVuhCfV1dUhIiICL+TkIzg8XOrmEPmlxvp6/GVYMmpra6FWq6VujlOYHUTS6khueHXPDREREVFHsbghIiIiWWFxQ0RERLLC4oaIiIhkhcUNERERyQqLGyIiIpIVFjdEREQkKyxuiIiISFZY3BAREZGssLghIiIiWWFxQ0RERLLC4oaIiIhkhcUNERERyQqLGyIiIpIVFjdEREQkKyxuiIiISFZY3BAREZGssLghIiIiWWFxQ0RERLLC4oaIiIhkhcUNERERyQqLGyIiIpIVFjdEREQkKyxuiIiISFZY3BAREZGssLghIiIiWWFxQ0RERLLC4oaIiIhkhcUNERERyQqLGyIiIpIVFjdEREQkKyxuiIiISFZY3BAREZGssLghIiIiWWFxQ0RERLLC4oaIiIhkhcUNERERyQqLGyIiIpIVFjdEREQkKyxuiIiISFZY3BAREZGssLghIiIiWWFxQ0RERLLC4oaIiIhkhcUNERERyQqLGyIiIpIVFjdEREQkKyxuiIiISFZY3BAREZGssLghIiIiWWFxQ0RERLLC4oaIiIhkhcUNERERyQqLGyIiIpIVFjdEREQkKyxuiIiISFZY3BAREZGssLghIiIiWWFxQ0RERLIiaXHz9ttvIyUlBWq1Gmq1Gmlpadi2bZvD899//30IgmBxCw4O9mCLiUhqzA0iak+AlG8eHx+PlStXYuDAgRBFER988AEmTZqEY8eOYejQoXafo1arkZ+fb7ovCIKnmktEXoC5QUTtkbS4mThxosX95cuX4+2338Z3333nMKQEQUBsbKwnmkdEXoi5QUTt8ZoxNzqdDtnZ2dBoNEhLS3N4XkNDAxITE5GQkIBJkybh1KlTHmwlEXkT5gYR2SNpzw0A5ObmIi0tDY2NjQgLC8PGjRsxZMgQu+cmJydj7dq1SElJQW1tLV577TWMHDkSp06dQnx8vN3naLVaaLVa0/26ujq3fA4i8hx35wbA7CDyZZL33CQnJ+P48eP4/vvv8bvf/Q4zZ87EDz/8YPfctLQ0ZGZmYvjw4Rg1ahS++OILREVFYdWqVQ5ff8WKFYiIiDDdEhIS3PVRiMhD3J0bALODyJcJoiiKUjfC3NixY9G/f/92g8do6tSpCAgIwMcff2z3cXvfvhISEvBCTj6Cw8Nd0mZqX2FhoVteNykpyS2vS+7VWF+PvwxLRm1tLdRqdZdfz9W5ATA7iLxNR3JD8stS1vR6vUWgtEWn0yE3Nxe//OUvHZ6jUqmgUqlc1TzqhMLCQmhKqvDLCxdc+ro7r08Gklz6kuSjXJ0bALODyJdJWtwsWrQI9957L/r27Yv6+nps2LABu3fvxvbt2wEAmZmZ6NOnD1asWAEAeOGFF3DrrbdiwIABqKmpwauvvoqioiI8+uijUn4MakdLtRbj8vIQqS126es2RkWhsLCQvTd+hrlBRO2RtLipqKhAZmYmLl68iIiICKSkpGD79u246667AADnz5+HQtE6LKi6uhpz5sxBWVkZIiMjMWLECOzfv9/hQEKSXmFhIbSaBjRVfY/mniEufe0xP53Ggcge7L3xM8wNImqP1425cbe6ujpERETwurmHnDmWj7SD/0NT3XHsTU9x6Wtn7gpC1i1DMfT2ES59XXI/V4+58QRmB5G0fHrMDXmnzg4I1moa0KchAB+mpyBVneHSNhXoF2P0qRYcCA1DQKTzYyN4GYuISN5Y3JBTWqq1mFJd0+HnVZ87g7yGA0hV/9XlbdqbnoLMXQFI/zEf6qgYp56zMbI7CsFxOkREcsbihtp15lg+Rh85jJjmjnfFK6/osHXiCKS6oV2p6gzkNSzE7ZfvBS6XO/Wc36Icb97YB2BxQ0QkWyxuqF1aTQP6aICLofs7/NwS/RWkqpe7oVUGByeOgGrn14iPdG6wcmXVFYhN0znLiohIxljcUJsKCwshNjUhp2YfDt7RuYG77ui1Mb22OgN7050//46dJzjLiohI5ljcUJuDhTUlVXjo2E/4cOIIlw8IdpWOtGtvOpC5KwBaTUObn5u9OkTkSvbyhjnjPixu/FxhYWGbg4XdOSBYCqnqDNMsq9imoXbP4aBjInIleznLnHEvFjd+rqVai/Qf85HQrY/dx0M0QH1IIFo83C53Ms6yimuw/8//9w0NeFXTwEHHROQSmpIq/OkyYP6/3PTKfG4h40YsbvycVtOA2LJ6HG78wO7jdY3NaJkhj14bI+Msq9LThx2eI97MQcdE1HXGcYuHT9tu0sotZNyHxY0fsxgsPNHxYGF3DgiWSlufl4OOichVWqq1GPPTaWgVsFilnTnjXixu/IS9wWy+MFjYXdr6vHvTgZ9/eQhazTAOAiQip9nLi8bqy6gvO4SWGX+1+KLY3uQG5kzXsLjxE/YGDcttsLCrpKozoAo5gdGncmwGHXMQIBHZ42hyRtlPp9FkZ9xiW5MbNkZ2Z29OF7G48QPGnbkTrAbQynGwsKtYDzrW6QGlgoMAicg+e5MzdHpAadxbz85zHE1uSK/Mx04AA25Kdm+jZYzFjR9oqdZiXF4eSrXFFsfzaopkN1jYVYyDjotzWrDsP4/gfFVv9O15EW9MboC2T4PUzSMiL6PVNGDgZaBUvwPFl6NNudE7chhmprYA6mab5xhzBsWlFscHKoZgq4Y50xUKqRtA7lVYWIjG6su4dGEfCq6UWtxUIYFSN8+rHZw4As9tykTJ5WgAQMnlaDz+2UiITU2d3iWdiOTnzLF8jMvLw6GGr1FwpdQiN8pq+2DdsjiHzz04cYRNNu+r2Mac6SL23MiccaR+U0ggNFPct8eTHA0PzcD66gTTfb2oRFltX0w/ko3soCCug0NEAAy9Nr1qddianoLhoRm4+HZrboh6BcrPq6DXAQql7XNT1RnQTLGc4HC07hPM2XsOa5kzncbiRoaMA9uAa5tetnHNl9oW01eLypIg6PUCFAoRUfFN+OnKfohNA3DmWL7N+bxGTuQfzH//xaYm0+QMvc5+btgrbBxJVWcgp2YhxKbrTO8TEKniRIYOYHEjQ8aBbUO79UFVQR7y9IfdujO33FQUB2LdsjiUn1ehZ+8mRMY0o+piEKLim/DwklKoDgdidu459OwXZPG8V3uAC3IR+QmtpuHaqsNAVcE57NYm4l+PJjrMjY5ShVjmDFdN7xgWNzJjHGMTeekHlOIH5NUU4eDEEey16YB1y+JQWWIIlOryQETFN+G1bT+avnlpEpYjJ2shBhVbDtAeV5GA3aE/40wqIpkzjrExTtLIqynCv/Z/0mZudNTe9BRov9xnyhkxbBS/PHUABxTLjHGMTbOqBs2qGqhCAv1ugb6u0OtguD6uFwz39QLKz6tszlOFBJr+jo23pqrv0Vh9mYMAiWROq2lAHw1Mv/uBwSqncqMjDOtttebM7Nxz0JRUuaL5foE9NzJjGmMzZoDpGHttAKW2ESlbshFRVoza2AScmDANOlWwzXkKpXPXy82XUTficupE8hPQeBXDsj5C9+Ii1CQk4j+3j2rdtuaO1m1cYrZ1bZyNPeY58/Mv90Fsuo69N05icePDrHsIWqq1GH0qBwX64xxjY0apbUTGU9MRdfY0RIUSgl6HwTs345O/fWy3wHl4SalpzI2j6+X2esP2jBYw61vb5dQZRES+KaDxKh6cOglRP/wAUamAoNPjur5JKJnwKFZZbVsT70RuONLWTCojVQi/PHUEixsfdeZYPtJ/tJ2tE8mZUTZStmQj6uxpKEQR0BnWY446exopW7Jx7IFZNudHJzTjmfeKHAaOtdYByM9hV2QxntRuQf/+FwCAqxkT+bBhWR8h6ocfoBD1QIseAND3fAGKv9+A1EmfW5zb0dwALCcvxPTV4uElpYhOsF3sDwA0U5ZDm7UQjdWD2XvjBBY3Psq4GuYl/Q8Wx/MairhXlJWIsmKICqWpsAEAUaFERFlxG89yLqD0OssByGW1ffD3T8fjw0dfBwA0RkU5DCLr7u6cGb9BS3A35z8YEblV9+IiiEqFqbABAFEQENl41eFznC1srLOjsiQI65bF4Zn3ihw+RxUSiDE/ncbhkFCM2Lma2dEGFjc+qLCwEGJTE07UfY2oniGm4yXVVzgzyo7a2AQIep3FMUGvQ21sgoNntM/8G5c5Ua9AWW1fNAbWQqkQHXYj2+vuHrrpM2z4dDNDishL1CQkQtDpLY4p9HrkjBjY6dd0lB3GQcht9fzsTU/BrB0iRi96HH3PFzI72sDZUj5IU1KF2bnn8PVdKfg4dYDptjc9hTOj7DgxYRoq+w+GXhCgUwZALwio7D8YJyZMa/e55jWR+c/m37gA8doNUChERMRX4t8/64+PUwegT4NhDI418+5uZUsLFKIeUT/8gGFZH3XhkxKRK+XM+A0qhwyBXlCgRRkAURBwqVd3VP3qlXafa8wLq+9VbWZHTF9tmz0/qeoMBB35BInnC5gd7WDPjY8wH6BqHKnPy0/O0amC8cnfPnZqtpSR9UJ+AFB1MQgxfbWY+Xyp1bcuwfSTYSBhA6KvFZkF+sUYfaoFB0LDEBBpeE5SUpL97m6lAt2LHXdJE5FntQR3w4ZPN2NY1kfQf38YCVdFvPJ4Km50MjuUAXroWhSm8TS94prbyY72ByFfjQqFKAgQRNF0jNlhi8WNj2ip1mJKdQ0AoOzaXlEtbT+FzOhUwXYHDzti/u2q6mLrBqOVJUH44MU49OzddO24MZxExPRtsrlevjc9BZm7AjAm70d0j4nGxsjuKESh3e5uQadHTUJiZz4eEblJS3A3fJ5+N24XQ3H6wj7cGPVQm+ebZ4euxZAP5uNpnM0OwP4sqqC+v4Cg/97iGLPDFi9L+QDjqsMx+eWIyS9Hfdkhu+uskGtYL+RnCCHLxbkMX5oEs2cZjrc0tb4GAMTXPoTM9dMxffFjmPfMnbj+q7NoqdZadHfrAgKgFxSoGDIUOTN+45kPSURO05RUoVetDgcnjmjzPPvZYTmexlF26HWtuVFRHIhXHk3E0/dej1ceTURFcesXrBMTpqGqV3eIgoAWJbPDEfbc+ADjGJuLobkAwFWH3cy4kF/5+SBYhpCBoNDjclmQnefpsXDC9RZd0S3NAi7XG37NCi9H4B+fTsLo276z6O7mjAci72WcwGHcGLMtbWWHQqnHikeS7GYHADwzcYBFblSXGwoa61lUOlUwNqzbg5uWzUCjOgG1yQNR/OR8ZocVFjdezviLZb0aJmdEudfM50vx1zn97D4m6gUYBgGah5d47bhlV3TrNzhALypRdLkX7sw9ge9DwzDgpmQcmf2Ymz4BEbmCcUsbZ4cC/GZxKV6ba5sdep1gdonbPD8MPzvMDTuzqHSqYJy+eRCC1MNx4Oe3YQALGxssbryc+S8We2s8JzaxuY3eG9veHEBA6/i+1q5oA0N4KRQiwuMuIeGKEt9yFWMir2ccElBfdggtM5ybwBHbt/la723r5WyD9n62zA1BECGKbW8Bk7nL/mxMYnHj1Trzi0VdV1EciLVL41BRrIKg0Jt6ZFpZ99o4Oi5CGSBC12IY2hYZ04w5yxqQ9+0BjMtTIrLJsP/XxsjuXMWYyAt1pNfGPDfCI5tQX23dS2P89mMvOyzPUygNxY2uRYCgEHHf3Eqbs1PVGchrWAhgaMc/mB/ggGIvZvzFUoUEtn8yuURFcSBenZuIimLDdM3WS1DmbMMpMqbJzvHWrmaFQkRAoIjohGYcnDgCvWp1SGgIQEKD7V5UROQdjBsRtzeBwzo3Wgsba7bZoVDqrR4ToNcZboAhg/6zKqoTrfdvLG68mLO/WOQ665bFmXpaDKy7lo1Ei5/nvnwBygA9HBVC5tfNDd+4DuDw6Q9w+PQHGH0qBy3VWpd+DiLqmjPH8q9tRHy43SEBbeeG9Z+W2bFwdRHMF/Mz/imKttlBzuNlKS915lg+xuXlXfvF4g7fnmCcxmnLdvCf9TXz1Yv7WIWbJevr5uZTSo3XzTuy4R4RuZfxy2V7GxG3nxvWf1pmx3t/ibM5BrQ/5sb0/nr7x/0de268lFbTgF61OvbaeJBxGqcgWPa+KBTm9+1fL79cZn59HaafDb05tquPpqozTLc9F4rw9R9uxbMDe+O1u3uh4hwrHCIpWU7/brvXxpQbCvu9tgpl22NtLl0wFkatPTfKAD2iEwyLZjlaubiiOBAvb/o7Ppo1gblhB3tuvMiZY/mmn8fl5Tm1rgK5jl4HPLykFK/OTTSNlQEM3cLRCVoIgu00zVbWXc9Az97NmPPSBfSKa26zR+atw8tQX9UDAFBZEIAPfxuJp7+65IJPRESddc+5c6h3cvr3zOdL8cGLxs0wLXt6o/o0A2grO4xai6FZS0ox9JYrbfbmrlsWh8o6w//CK88xN6yx58aLaDUN+H2x4dZHAw4k9hDz1UDffc7e5SUBFcUq/ObPpYiKv7YEsc3YGmsiqi4aFt+qKHH831GvA2pLoqAXDe8p6gRUnAnk9XUiH2DMjr/O6YdLpcbfc9vVh2c+b54dbREhCMCa5+Oxcnai2WtaMq2ELBoqH1HP3LDG4sZLGMfYlBbvQGnxDuTU7INmCsfaeILtPlLWhYuhm/i13xoW5uoe1ezEqxoCrvx8EP46p5/NEupGxi5thWBIJUEhInpA2z09ROQd7O0jZX1pGgA+eDEODy8pRUS72SGYvlxVFKvw18fayY1rl8IEhR6RiVeYG2ZY3HgJ4xibgiulKLhSyl4bD2lrHylzxuAqPx+EmspAu+fYZ7lxnr33f3hJKaISDJ3f6ph6pD93soOfgog8zdE+UvayobIkCGuWxKG2smO5rtcp7OYGAIveoN6RVbjrj7kdem2545gbL2A+eI2L9XmW8RtQ6/Vwewv0OVpZ1HnWS6hXFAdi3TLDNfqYvlo8vKQUiQefRXD3FBwIvw1AbOc+EBF5hDE7KkqC7Cz0adQ6nbuyxN6MqvYZN+QNuLYllXV2LHy3APH7nsKR7o8C6Nup95Aj9txIqLCwEIWFhaaNMdvbcZbc4+Elzl4PN2pvvI3j5zwzcQBOHwqx6M429ur8764b0MdsUT/jjYg8q6VaC62u/bWnHl5SajWbsj2dyQ5g4YTrsXxmkqmwMc+OD16037Pj79hzI5Ezx/KR/qNhdtTAy8C+mn2cGSUBvQ6ITmjGn1YV4el7r4dzPTPtnWNvXRwDXYuAtUssF/wy9uoMD81AgX4xHjmiRNmFcADAzuuTuTUDkQc5s+2NsQe2V1xzm+tb2WorO+xNGW/Nj6qLrT02pnYYe4T17KewxuJGIlpNAwZeBi7pf8CJBsMYG2emHJJr2Lss1CO2CZfLglzw6o43xTNuyRCdoMWlC4ZLYeaLdO1NT4H2y30YFJAIAGiMikJhYSE31iTykLb2k7KXGzF9tSgvDgLEzl2ybtXWEhOGn8vPq+xnh4Ir+VljuScB4xibE3Vfo1lVA60CXKzPw+xdFmpxZhJUh5gvp265QNcjS1svhZkv0pWqzoAqJBDNqho0q2ow5qfT3JqByIPa2vbGXm7YW2Cv60SrPw0/x/TVOswOssSeGwloSqrw0LGf8OFdrb887a2CSa5jvVy6sWvX9YTWP65llDJAxCPLShGd0Ixn3iuyu0iXeagat2YgIvdra9sbR7mxZkmcC3ptrNm+Xs/ezXh4SdvZQa1Y3HiY5bLeHGMjBedmSLnQteB7+YsfERxm2xZr5oVugX4xRp9qwYHQMAy4Kdl9bSQiAEAfDfCunf2kHOWGsSfHPQzZsXLzjwjqZtseI1VIIEafymFOmOFlKQ8zXs/lOjbSMa0tY5oh5cbCBoDxUtSf778eK2fbX5TLkb3pKaYZVEQkLcuVhh2va+MqgmDIjmcntZ0dzAlbLG48yHwUPlcf9jzzbRaM18p7xDahs9MznSeYFgGsKFbh1bnOFzip6gzkNRyA2NTEaeFEEjHfZgGAh3IDEEU4lR3MCVssbjyIvTbSsjcYUHB3p42J+XRwx6uO2nNw4gg8dOwnaEqq3NEwImqHdXYIgmH8nPtZrpjeVnYwJyyxuPGgtkbhk3tZL5VuHAxYdTEI7r8sZav8vAplRey9IfJ29rKj6mJQB9e3cR3jSufWmBOWWNx4iHEUvmEgMWdGeZr1RnMKhWFaZUxfLTzRvWxvM86OrCyqCgk0TQvnysVEniNtdpgvI2G4rwzQO5wlpQoJxD3nzrm5Tb6BxY2HGDfG5BYL0jEfRGxcH2Lm86XwTM+N7X5Vjr6B2aOZshz1ZYcw+shhTDxXjonnylngEHmIdNlhvZGvYddwZ3PDn3EquAdw+rd3cLQ+hDJAf23QnvtnTZm/hzJAj0ulgYhOcG71QFVIIPpogPD8cgCAJigA4MrFRG7neG0Ze1smuFdHc8NfOd1zU1rKVRA7iwOJvYt5OOl1uHbt3PO9N3q90KGBxXvTU5DX9D0uBu7HibqvfeLaem15mdRNIHIZ8+woORMI254V9+tobvgrp4uboUOHYsOGDe5siyxx+rf3On0oBM9MHCDZ+4v6jl2aSlVnYG96Cj5OHQCtAj6xNcP/jRuDY5u/kLoZRC5TURyI5TOT8H+P95Pk/Y250dLU/rn+zOniZvny5Zg7dy6mTp2Ky5cvu7NNsmAc9MleG++k1+Ha7tyenyllvW9MR9e9SVVnmMbgNFZf9urem3F/fAafP/cMPpr3GK7UVEvdHKIue/e5Pqi6KGWeGwYZL5xwPV55tGOLgvoTp4ubxx9/HCdOnEBVVRWGDBmCL7/80p3t8mnGombiuXKk/5jP6d9exHwhP89djrJmubKpcc2djjKfQeWtRv5mFv6wdSeu1FTjtbtH44edX0ndJKJOqSgOxMrZiZItH9Gq9VJYZ7PDH3RoQHG/fv3wzTff4K233sL999+PwYMHIyDA8iWOHj3q0gb6Ik1JFf50GQACUFVWjzw7m7CRNMwX4/IWerNu5oAONG1veopPbKzZI6Ev5mZ9iv99uBYf/u5RRPcfCOHa6ol33HEHlEolc4O83rplcbh0QR7Z4Q86PBW8qKgIX3zxBSIjIzFp0iSbW0e8/fbbSElJgVqthlqtRlpaGrZt29bmcz799FMMGjQIwcHBuPHGG7F169aOfgS3Ms6MOnz6Axw+/QFyavax18ZLWC/GJR3Ly1KCwrB2RUe7mVPVGSjQH8boUzk4cyzfTW11jeoLJTi5fRu6RURg6F3jMGhMOgBg/PjxzA3yet6THYD52jfW2VFWzV4cow713Lz77rv44x//iLFjx+LUqVOIiorq0pvHx8dj5cqVGDhwIERRxAcffIBJkybh2LFjGDp0qM35+/fvx/Tp07FixQpMmDABGzZswOTJk3H06FHccMMNXWqLqxjH2GgVMBU1XLRPOuZTN9vfDdzNu4Ob3sOSQiFCr7PsZn7mvSKnXs0Xem++z87ClpeXYeDIO/DH/+5GWM+eaKyvx663/4Fnn30WarW6Q68nx9wg7+NcdngiM4yM7yVAodRDrxNssuOd7U/j9d994qH2eDdBFEWnlli85557cPDgQbzxxhvIzMx0W4N69OiBV199FbNnz7Z5LCMjAxqNBlu2bDEdu/XWWzF8+HC88847Tr1+XV0dIiIi8EJOPoLDw13WbqNT+45gxven8OGYJhY1EqooDsS6ZXEoP69CTF8tHl5SiuiEZovjUlMoRPTq04SKYtu2vLbtR4erkFoLyFqIw3dkIuy63kjysnVv3pv1IIpzjuO+55dhxP1TTccb6+vxl2HJqK2t7XBxY4+7cwNwf3aQ9M4cy8cdm8/iiW/uQ21JlE9mx/pnX8f+u0d6XRa4Qkdyw+nLUjqdDidOnHBbYaPT6ZCdnQ2NRoO0tDS75xw4cABjx461ODZu3DgcOHDA4etqtVrU1dVZ3NzlzLF8jD6VgwL9YRY2ErO3SSbQuhhXdELrcuqe2X7Bll4voKJYZdEW49LuzhY2gGHDvNm557xywzxRp8NTW7+2KGxcyV25AXg2O8h7PLdlEupKewJwJjuk4Sg7ekcWS942b+F0cbNjxw7Ex8e7vAG5ubkICwuDSqXCb3/7W2zcuBFDhgyxe25ZWRliYmIsjsXExKCszPFCYStWrEBERITplpCQ4NL2m+PGmN7B0SaZ5uvJTPptJQSF+eqingoE871iDNfLH1lqu7R7R6SqM5BTs88rF/Wb89En6N7b9eMA3J0bgGezg7yDXgcUXe4FUW/4X6Oj7IBgud+TZ7SfHb8d95qH2uL9JN9+ITk5GcePH0dtbS0+++wzzJw5E3v27HEYVB21aNEi/OEPfzDdr6urc0tIcYsF72F9fVyhEBEV32TRG/LFW9FWa9y4/7p5VLwWlSXm3cgCdC0CesU5WtrdecZp4QciewBJrmitd3N3bgCeyw7yHgolkNjjEs7X9ICoV9hkR0VxINYuiYNeZ94v4P5xetEJ1peg7GdH6MZSAPw3CnjBxplBQUEYMGAARowYgRUrVmDYsGF488037Z4bGxuL8vJyi2Pl5eWIjY11+Poqlco0q8J4cwdNSRUeOvYTN8b0EvY2ujPS6+DxtSoUSj1mLytFz95NMP/21bN3k8Wgxc7am56C+rJD0GoavK73xh3cnRuA57KDvMtLEzZDHWe4xGudHWuXxl1bH8tzYvo24ZGl7ssOuZK858aaXq+HVmt/UbK0tDTs3LkTCxYsMB3bsWOHw2vtnsJeG+/jeKM7z+se3YTfrrjg1o3uUtUZUIWcwOhTOTgQeptf9N6Y88XcIO/UN7IaE994B8NDM2z2obM3gNd9RDy9qhBx/bhBZmdIWtwsWrQI9957L/r27Yv6+nps2LABu3fvxvbt2wEAmZmZ6NOnD1asWAEAmD9/PkaNGoXXX38d48ePR3Z2Ng4fPozVq1dL+TFM07+bQgLRImlLyJq9wkahBHr2brq2hLr7e29qKoLw6txEzFpSeq3HyEhA1cUglxVgvjAt3BXkkhvk3ax/J42XuyuKgyCKntlo97W5/dA9qhmPvVzi1uyQI0kvS1VUVCAzMxPJyclIT0/HoUOHsH37dtx1110AgPPnz+PixYum80eOHIkNGzZg9erVGDZsGD777DNs2rRJ0rUquDGmb5r9wgXYL2zcMzhQ1yLg/WVxhplQVrO0OrK3VFtS1RnIaziAcXl5sr40JYfcIN9kfrnbU2oqA/DBi4bsEMxmQikD9LhUyn2lHJG052bNmjVtPr57926bY1OnTsXUqe6ZVtoZ7LXxTbGJzYZvYSVBEK/NqlIG6BEcooOmzh2BYRgA2KQVEBnTbPEtrKML97Xl4MQRyNylM0wLl+E6F4A8coN8U3RCM55dU2RYSdwsO9y7mJ9hxlaP2CYoFCJ0xlmgOsFluSFHkg8o9nWc/u27Hl5Sih4xrdezdS0KNxU2rWorA6EMMJ+Cbn+6aWcZe2+8cVo4kVxYZ4cnLm9XlwdaDGYWRdflhhyxuOkCLtrn21oH+Hpu0Svj4lvml6c6s3BfW3xht3AiXyZFdhjH+bgrN+SGxU0XsNfGt3l2SnhrIEUnaC2u3ffq0/GF+9qimbIc9WWH0Fh9mb03RG4gRXYIgmH6t/kSFzOft8wN7ZVm/Pe66zzQJu/H4qaTLKd/s9eG2mO8BGWYTrpuWRzum1t5bfaF4b4rBhUbsfeGSC4M2SGKAlqaDZfEFr5bAAD465x+hvE/xYE4WvcJBoWlQQgKkuW+Uh3F4qaTNCVVmJ17DqoQjlb3VQqlYRCxFHtLlZ8Pwpq/2N//yhX2pqegT4P8p4UTSUGq7Ki9ZFhWYs1f+thkx8+/PIL1Nw1EaHxPj7bJW7G46QRjr01OzT5O//ZBxgF4eh2uDdDz3ErFre8lQK9TtLn/VVekqjNQoD+M0adycOZYvmtelMjPOZcd7i14dC0Kwxo3VtkxMGQke23MeN0Kxb6A0799U0VxINYti0P5ecOA3oeXlCKmrxbl59296qjjaaKCIEIU7e9/1VX+sqgfkbt1LDtc+WXJueyIiShBmboZqtAwF763b2PPTSdwILFvWrfM9jLQw0tKr3UvG5nvvOsOrQv4dY9qRnRC53cDbw+nhRN13IVQ4I6dJyyOeUd2GAgKw8Bi8+x4ZNQK7B46DANuSnb7+/sK9tx0UOv07+NIVfOSlK/Q62DxLcvYldsrrhndo1rMtmJwxyUq293HlQEipi4ox+Cbr7h1CfWDE0dgzt5zWBsUJNtF/YhcZcBNydiuacCM73X4sO4TpKoz2syO8MgW1FSab+Pi6vywfb0eMc2Y85JhrzpjdgRklaLExe/s69hz00HstfFNxn1hrNeIADw9pdPw/qJewH9WRZna5i6p6gzk1Oxj7w2Rk1ShYbgQ1mLqvWkrO2oqzbPD1ZejbO8rFCICAkXTOjtc48YxFjcdwOnfvs18bRnjZaBLpYFQKPXtPNNVWnuGXD2AuC2cFk7kvIBIFXYNHAztldYViO1lR0WJJ2fKej43fB0vS3WApqQKDx37CR9OHIFUqRtDHRad0Ixn3iuyuAz0yqOJHtrh18g4QFBEz97NHvnmtTc9BT//8hC0mmEoLCzkbAqiNiQlJeFMtRbhsTejaeNiaKYsd5gd7ttTyvFrKpSGDTNbV0kme9hz4yT22siHMZyM19JbN7/zhNYu7JrKAJcu3OdIqjoDqpBAjD6Vw94bIicERKqgUtrOhLLODs8uI2F8bwVenZvokezwZSxunGSc/s1F++TD+lq6p+laFC5duK8tXNSPyHU8mx227+HJ7PBVLG6cUFhYiMbqy6gvO8RF+2RErzNcS+/Vx3AtXRmghyA4E1ZtndOxsPPU9XPjtPBxeXkcWEzURfayw33s9w5x7E3bOObGCVy0T17MF+RSBuiha1Egpq8WE+ZUYt3SOIg6R13Nzlxfd76bWlCIiHbxwn1tOThxBDJ36aApqeK0cKJOsJcd0QlaNDUKVlPC3aE1fzydHb6IPTdO4PRveTFfkEvXYgiL8vNBWPN8PPS6tn4luhpchl4d47e8aDcs3NcWLupH1DX2sqOiWGU1Jdw9evZuRs/ehp4iT2eHL2LPTTu4aJ+8WC/IZbtGhWh13x7zcwU4O2NCoRTx8NILGHqLexfua4txWviByB5Akuffn8hX/Pe66/CzvftM9x1nh5G7Zk4BDzxZhtsm1pnawR6b9rHnph3stZEX24GA1mNkOrNKsXPn63UCtrzr/oX72qKZshz1ZYfQWH2ZvTdEDiQlJUEICsKgsDQcrfsEgDODiN3VcyNi09vRpnvW2XG07hMMCktz03v7LhY3beD0b3kyX5BLGWBd5DizP0xnZ0h4xwJcXNSPqH3WKxUDltlhnQOGy83OZkdHMkSArkWBlib7j/78yyNYf9NAhMb37MBryh+LmzZoSqowO/ccDk4cIXVTyIWMC3K9tu1HvLr1jOk6tvM6u9y6Ydl2qbuUOS2cqH0DbkrG7qHD0E/xM1PvjXl2PLum0GKWlHEMTts6kx0ilAF6BATZPmLstRGCgrg4pxUWNw4Ye21yavax10amFErD9WvLvaVctXmm7Teznr2bvWIQYKo6AwX6wxh9KgdnjuVL3Rwir2Wv9wYwZEevuGboWsz/F+rqjXeNExBEPLLMfm7csfMELoS1QBUa5sL3lQcWNw5w0T7/YH8MjqOuY3uXsBw9Zhly0QlaLP6g0GuWTGfvDVH77O0zZdSx7LA+5sxlKQE9Y5vw6tYzGHzzFZtHj9Z9gn6Kn2H30GEYcFOyE6/nX1jcOMCBxP7D/Dq6Ybpls+nnsO72ihF7XcttdzdXFEs/1sYcp4UTtc84sNgRR9nRPaoZCqW9yQr2fnasqiwIZUX2v2Cz16ZtnApuB6d/+xd7m+JZ/2y8v+KRJFRdNC7WZdj8UhkgorIkqI0NOEUoA0TJx9pYOzhxBObsPYe1QUFc1I+oE9rLDuMg4Ffm2OZGQKCI8vPtrY8jYs1f+mDxB4U2j2ivNON/IwYjLNJ2DyxicWOXsdfmw/QU7v7tR8yLD/OfL5VarkraSjAFlmG1Ukdr3gjQtQhetz5FqjoDOTULITZdx93CibrAXnZYr2bcSkB1RQDUPVrQ/ppZAqouBtlkR+jGxRjU/XacjOzB31sHeFnKCqd/kzXLVUkVsNe9LOoF9OzdhOiEjs68khanhRO5R1u5odcprm3XYNhKwXgJy9n80F5pxpobr0MAe20cYnFjRVNShYeO/cTp3wSgdVVSvb7ta+R6veEbVkWxCjF9tege1QTzwYU9e3vnPjB701NQX3YIWk0Dx94QuYhzudH6xUivM+xR9cjS0mtLUzjODk7/dg6LGzPstfE/7Q3ytZ4RISisF/mznSlRURwEZQAQ09fwLSymbxPmvHTBpe12lVR1BlQhgRh9Koe9N0QOmK9UDLgqNyxnVl26EIS1S+Mw56ULbWbHHTtP4FKEkgOJ28ExN2a4+7f8KbWNSNmSjaCfLuC/R2/AqzVPIKKvAg8vKXU4TfvhJaWma+fR8U24b24lNr8ThYpiFZQB4rWxNq3f0ETR0Iuz8N0CRMc3e2WPjbm96SnI3MVp4UT2hMb3xCcNffHYypehVJ9xSW707N0MUQQul7UOKNbrBVQUq7BuWRweXlKKXnG22XG07hNkKn6GrEGDMJTTv9skiKLY2bXkfVJdXR0iIiLwQk4+gsPDTccLCwvRcO4ifrb3Q7TM+KuELSR3UWobkfHUdESdPY0WMQBK6HAcw3CnsA/hCUo8815Rm883Duqz9+ercxOtZj6IiOnb1O5reouArIXo1ed27Bt7m0e6uhvr6/GXYcmora2FWq12+/u5gqPsIHkLaLyKKffehcSiAjSj87lh/Blovf/Ko4moLAm6dvnKMJhYoRARFW8/O0I3LkaQejgO/Pw2v1zbpiO5wctS13DRPvlL2ZKNqLOnoRBFBKEZSugxHDl4TFzl1J5Pl0oD8cqjiXj63uvxyqOJuFRq+LeiUAIzny+F9UDjtl5TqW3ETZ+/j9H/fBE3ff4+lNpGl3zGzjo4cQR61eqgKamStB1E3mZY1kfoe74AAjqXGwqlYeaUMTtenZuIimJDdjy8pBS9+hgHEbf24Dh6Xe2VZuxLvA53fb0R6UsXY8Sa1QhovOrCTysfLG6u4aJ98hdRVgzRqp9XByX646xTez6Zz36oLAnCumVxpsdiE5strrErFI73kTL2IN25eiVStmTjztUrkfHUdEkLHC7qR2Rf9+IiiMrO5wbgODuiE5rx7Joip7IjdONidO85HM+/ugT3v/MWhm34CKNffgEPTp3EAscOFjcwXJIal5eHAv1hDiSWsdrYBAhWX4eU0KGie2K7ez5Zz36w9+3KfLXSqPgmh69p3oOk1LVAIYqIOnsaKVuyu/Dpuo7Twols1SQkQtDpLY4poUNpRIxTe8W5Kju0V5qhLClCQuEZKEQ9lC0tUIh6RP3wA4ZlfdSFTyhPHFBMfuPEhGkYvHMzos6ehqhQQtDrcOm6Qej1xjjoVG3v+WSc/WC8Pm68Lm7+7creaqX2mHqQdK3D1kWFEhFlxV39iF2imbIc2qyFaKwezEX9iK7JmfEbDN30GaJ+OAW9QgGlTof6mO7of98x7I4AotH2F2JXZMfRuk+QGZYG4cI+Qy9Si1l2KBXoXuwbY/s8iT035Dd0qmB88reP8e1jz+LEhGn49rFn8ckbH0OnCnbq+c72zLTXTW2vB0nQ61Abm+BUO9yJvTdEllqCu2HDp5ux+89LsHvMvbgw5lc4kjENAQqlzW7hjnQ1O37+5RGsv2kgagZeZ9OLJOj0qElIdP4D+Qn23JBf0amCceyBWZ16rrM9M+2x14NU2X8wTkyY1vkXdRFOCyey1RLcDUdmP4bC9LuhKanCny4DsQBKfzqFD+s+aXc4Q1eyw9hrcyQoCMVPzkflt98g6ocfICoVEHR6VAwZipwZv+n8h5MpFjdEHdTVdWuMPUgpW7IRUVaM2tgEnJgwzekeJHdKVWegQL8Yo0+14EBomF9ONyVyJCkpCWeqtfh7qOF+2sUW3LHzBDRTnBur2ZnsuGPnCVxQD4cqNMzUizQs6yN0Ly5CTUIicmb8Bi3B3Tr+wjLH4oZIAl3pQXK3vekpmLNXhQNSN4TICxkL/sLCQuwaOBg/23vIbe9lWrRv6FDTon3GXiRqG4sbGPaT6lWrw1buAk5ERE4w9uKEx96Mpo2LTcc1U5Z3+bVDr73eHYCp14Y6xu8HFHM/KSIi6oyASBV2DRyMfiFx6BcSB+2VZos9qDrjaN0n0F5pNr3mroGDuft3J/h9zw33kyIios5ISkrCqZIqxCXcBQBQteRBtfN7p8fg2HPHzhMY1P129EwYBAAIjgzjsgyd4NfFTWFhIRqrL6O+7BD3kyIiog5ThYahuKkGAFAdCmhr2l4zqy3GMTZlseG4EtaCjZHd2WvTSX5d3LDXhsg+bYuO08GJnDDgpmR8eW3LkpbI7kjTadG0cXGnxt4YZ0YduD4ZAZEqBADstekkvy5ujPtJfciBxEQmhn2mFkJsGsiViomcYPwdKUTrDKrOjL35+ZVm/G/EYIRFqvh710V+W9ycO/ETxuXlXRtIzEtSROaMKxUfiOwBJEndGiLfYByDM6z77VDv+L7Dzxdjb0ZwZA8WNi7gt8WN9orGMP174gj22hBZ4T5TRJ0TGt8TazEIf7o8qMPPfbUHEMoxNi7ht8VN6/Rv9toQ2cPeG6KOM65/Yxxk3BGq0O78IuEiflvc3Hk2H0oOJCZyiPtMEXVOQKQKX0bGdPx5bmiLv/Lbv8ukRhU2jOVAYiJHjPtMjctTYjf3mSJyGntfpOf3KxQTkWN701PQq5bTwonIt7C4ISKHDNPCD0BsakLhtbU8iIi8HYsbImqTKiQQs3PPoaVaK3VTiIicwuKGiNqkmbIcOTX70Fh9mb03ROQT/La4ya09IHUTiHyGcVo4e2+IyBf4bXGjCglEqrrzO7cS+ZO96Sno08Bp4UTkG/y2uNk/aqjUTSDyGYZp4Ycx+lQOzhzLl7o5RERt8tviZnj4A1I3gcinsPeGiHyF3xY3RNQxnBZORL5C0uJmxYoVuPnmmxEeHo7o6GhMnjwZ+fltd3m///77EATB4hYcHOyhFhP5t4MTR0g+LZy5QUTtkbS42bNnD+bNm4fvvvsOO3bsQHNzM+6++25oNJo2n6dWq3Hx4kXTraioyEMtJiKpMTeIqD2S7i313//+1+L++++/j+joaBw5cgR33nmnw+cJgoDY2Fh3N4+IvBBzg4ja41VjbmprawEAPXr0aPO8hoYGJCYmIiEhAZMmTcKpU6ccnqvValFXV2dxIyL5cEduAMwOIl/mNcWNXq/HggULcNttt+GGG25weF5ycjLWrl2LzZs3Y/369dDr9Rg5ciRKSkrsnr9ixQpERESYbgkJCe76CETkYe7KDYDZQeTLBFEURakbAQC/+93vsG3bNuzbtw/x8fFOP6+5uRmDBw/G9OnT8eKLL9o8rtVqodW2Dn6sq6tDQkICXt54BMGhYS5pO5G/OFr3Cebs7YasO3+GATcld/p1Guvr8ZdhyaitrYVare7067grNwDH2fFCTj6Cw8M73WYi6pyO5IakY26MnnjiCWzZsgXffvtthwIKAAIDA3HTTTfhzJkzdh9XqVRQqVSuaCaR30tVZyCnZiEaq69DYWEhkpKSJGuLO3MDYHYQ+TJJL0uJoognnngCGzduxDfffIN+/fp1+DV0Oh1yc3PRu3dvN7SQiKxJvc8Uc4OI2iNpcTNv3jysX78eGzZsQHh4OMrKylBWVoarV6+azsnMzMSiRYtM91944QV89dVXOHfuHI4ePYqHHnoIRUVFePTRR6X4CER+R+qVipkbRNQeSS9Lvf322wCA0aNHWxxft24dZs2aBQA4f/48FIrWGqy6uhpz5sxBWVkZIiMjMWLECOzfvx9DhgzxVLOJ/Jphn6nFGH2qBQdCw7o09qYzmBtE1B6vGVDsKXV1dYiIiOCAYqIuMA4sXjtiEIbePqLDz3fVgGJPMmYHBxQTSaMjueE1U8GJyHcYBhbvk7oZRER2sbghIiIiWWFxQ0RERLLC4oaIiIhkhcUNERERyQqLGyLqNLGpCYWFhVI3g4jIAosbIuoUqVcqJiJyhMUNEXWKZspy1JcdQmP1ZfbeEJFXYXFDRJ3G3hsi8kYsboio06TeZ4qIyB4WN0TUaanqDOQ1HJC6GUREFljcEBERkaywuCEiIiJZYXFDREREssLihoiIiGSFxQ0Rddm4vDyudUNEXoPFDRF1ycGJI9CrVgdNSZXUTSEiAsDihoi6yDgdnPtMEZG3YHFDRF3GlYqJyJuwuCGiLtubnoL6skPQahrYe0NEkmNxQ0RdlqrOgCokEOPy8qRuChERixsiIiKSFxY3REREJCssboiIiEhWWNwQERGRrLC4ISKX0eq0nA5ORJJjcUNELrE3PQV9GgKg1TRI3RQi8nMsbojIJbhSMRF5CxY3ROQyqpBAzM49x32miEhSLG6IyGU0U5Yjp2Yfe2+ISFIsbojIpbjPFBFJjcUNEbnU3vQUXNeokroZROTHWNwQERGRrLC4ISIiIllhcUNERESywuKGiIiIZIXFDREREckKixsiIiKSFRY3REREJCssboiIiEhWWNwQERGRrLC4ISKX07booNU0SN0MIvJTLG6IyKVS1RnIazjAzTOJSDIsbojI5Q5OHIHZueegKamSuilE5IdY3BCRy6WqM5BTs4+9N0QkCRY3ROQWqpBAjPnpNFqqtVI3hYj8DIsbInKLvekp6NMQwIHFRORxLG6IyC2MA4uJiDyNxQ0RERHJCosbIiIikhUWN0RERCQrLG6IiIhIVljcEJHbqEICMS4vD2eO5UvdFCLyIyxuiMht9qanoFct95kiIs9icUNEbsN9pohICixuiMituFIxEXkaixsicivNlOWoLzuExurL7L0hIo9gcUNEbqcKCcQ9585J3Qwi8hMsboiIiEhWJC1uVqxYgZtvvhnh4eGIjo7G5MmTkZ/f/pTRTz/9FIMGDUJwcDBuvPFGbN261QOtJSJvwNwgovZIWtzs2bMH8+bNw3fffYcdO3agubkZd999NzQajcPn7N+/H9OnT8fs2bNx7NgxTJ48GZMnT8bJkyc92HIikgpzg4jaI4iiKErdCKPKykpER0djz549uPPOO+2ek5GRAY1Ggy1btpiO3XrrrRg+fDjeeeeddt+jrq4OEREReHnjEQSHhrms7UTkWOjGxQiPvAX7xt6GpKQkNNbX4y/DklFbWwu1Wt2l1/ZEbgCt2fFCTj6Cw8O71GYi6riO5IZXjbmpra0FAPTo0cPhOQcOHMDYsWMtjo0bNw4HDhxwa9uIyDsxN4jIWoDUDTDS6/VYsGABbrvtNtxwww0OzysrK0NMTIzFsZiYGJSVldk9X6vVQqttXV+jrq7ONQ0mIsm5KzcAZgeRL/Oanpt58+bh5MmTyM7OdunrrlixAhEREaZbQkKCS1+fiKTjrtwAmB1EvswripsnnngCW7Zswa5duxAfH9/mubGxsSgvL7c4Vl5ejtjYWLvnL1q0CLW1taZbcXGxy9pNRNJxZ24AzA4iXyZpcSOKIp544gls3LgR33zzDfr169fuc9LS0rBz506LYzt27EBaWprd81UqFdRqtcWNiHyXJ3IDYHYQ+TJJx9zMmzcPGzZswObNmxEeHm66/h0REYFu3boBADIzM9GnTx+sWLECADB//nyMGjUKr7/+OsaPH4/s7GwcPnwYq1evluxzEJHnMDeIqD2S9ty8/fbbqK2txejRo9G7d2/T7ZNPPjGdc/78eVy8eNF0f+TIkdiwYQNWr16NYcOG4bPPPsOmTZvaHExIRPLB3CCi9kjac+PMEju7d++2OTZ16lRMnTrVDS0iIm/H3CCi9njFgGIiIiIiV2FxQ0RERLLC4oaIiIhkhcUNEbnd3vQU9NEAmpIqqZtCRH6AxQ0RuV2qOgM5NfsgNjWhsLBQ6uYQkcyxuCEij1CFBGLMT6fRUq1t/2Qioi5gcUNEHrE3PQX1ZYeg1TTg/PnzUjeHiGSMxQ0ReUSqOgOqkECMy8tDSw17b4jIfVjcEJHHGAcWExG5E4sbIiIikhUWN0RERCQrLG6IiIhIVljcEBERkaywuCEiIiJZYXFDREREssLihoiIiGSFxQ0RERHJCosbIiIikhUWN0RERCQrLG6IiIhIVljcEBERkaywuCEiIiJZYXFDREREssLihoiIiGSFxQ0RERHJCosbIiIikhUWN0RERCQrLG6IiIhIVljcEBERkaywuCEiIiJZYXFDREREssLihoiIiGSFxQ0RERHJSoDUDfA0URQBAI1XGiRuCZH/abrSCI1WQNPVKwBafx99gSk7GpgdRFIw/u45kxuC6Evp4gIlJSVISEiQuhlEBKC4uBjx8fFSN8MpzA4i7+BMbvhdcaPX61FaWorw8HAIguDS166rq0NCQgKKi4uhVqtd+treyh8/M+Cfn9uVn1kURdTX1yMuLg4KhW9cHXdXdvjjvyXAPz+3P35mwHWfuyO54XeXpRQKhdu/KarVar/6hwv452cG/PNzu+ozR0REuKA1nuPu7PDHf0uAf35uf/zMgGs+t7O54RtfmYiIiIicxOKGiIiIZIXFjQupVCosWbIEKpVK6qZ4jD9+ZsA/P7c/fmZP8Ne/V3/83P74mQFpPrffDSgmIiIieWPPDREREckKixsiIiKSFRY3REREJCssboiIiEhWWNy4wLfffouJEyciLi4OgiBg06ZNUjfJ7VasWIGbb74Z4eHhiI6OxuTJk5Gfny91s9zq7bffRkpKimkhqrS0NGzbtk3qZnncypUrIQgCFixYIHVTfBpzwz9yA2B2AJ7PDRY3LqDRaDBs2DD885//lLopHrNnzx7MmzcP3333HXbs2IHm5mbcfffd0Gg0UjfNbeLj47Fy5UocOXIEhw8fxi9+8QtMmjQJp06dkrppHnPo0CGsWrUKKSkpUjfF5zE3/CM3AGaHJLkhkksBEDdu3Ch1MzyuoqJCBCDu2bNH6qZ4VGRkpPjee+9J3QyPqK+vFwcOHCju2LFDHDVqlDh//nypmyQbzA3/yg1R9J/skCo32HNDLlFbWwsA6NGjh8Qt8QydTofs7GxoNBqkpaVJ3RyPmDdvHsaPH4+xY8dK3RSSCX/LDcD/skOq3PC7jTPJ9fR6PRYsWIDbbrsNN9xwg9TNcavc3FykpaWhsbERYWFh2LhxI4YMGSJ1s9wuOzsbR48exaFDh6RuCsmEP+UG4J/ZIWVusLihLps3bx5OnjyJffv2Sd0Ut0tOTsbx48dRW1uLzz77DDNnzsSePXtkHVLFxcWYP38+duzYgeDgYKmbQzLhT7kB+F92SJ0b3H7BxQRBwMaNGzF58mSpm+IRTzzxBDZv3oxvv/0W/fr1k7o5Hjd27Fj0798fq1atkropbrNp0yZMmTIFSqXSdEyn00EQBCgUCmi1WovHqOOYG/5H7tkhdW6w54Y6RRRFPPnkk9i4cSN2797ttwGl1+uh1WqlboZbpaenIzc31+LYww8/jEGDBuGZZ55hYUNOY260knt2SJ0bLG5coKGhAWfOnDHdLygowPHjx9GjRw/07dtXwpa5z7x587BhwwZs3rwZ4eHhKCsrAwBERESgW7duErfOPRYtWoR7770Xffv2RX19PTZs2IDdu3dj+/btUjfNrcLDw23GRISGhqJnz55+MVbCXZgb/pEbgH9mh+S54ZE5WTK3a9cuEYDNbebMmVI3zW3sfV4A4rp166Rumts88sgjYmJiohgUFCRGRUWJ6enp4ldffSV1syTBqeBdx9zwj9wQRWaHkSdzg2NuiIiISFa4zg0RERHJCosbIiIikhUWN0RERCQrLG6IiIhIVljcEBERkaywuCEiIiJZYXFDREREssLihoiIiGSFxQ35BJ1Oh5EjR+L++++3OF5bW4uEhAQsXrxYopYRkbdibvgvrlBMPuPHH3/E8OHD8e6772LGjBkAgMzMTOTk5ODQoUMICgqSuIVE5G2YG/6JxQ35lL///e9YunQpTp06hYMHD2Lq1Kk4dOgQhg0bJnXTiMhLMTf8D4sb8imiKOIXv/gFlEolcnNz8eSTT+K5556TullE5MWYG/6HxQ35nLy8PAwePBg33ngjjh49ioCAAKmbRERejrnhXzigmHzO2rVrERISgoKCApSUlEjdHCLyAcwN/8KeG/Ip+/fvx6hRo/DVV1/hpZdeAgB8/fXXEARB4pYRkbdibvgf9tyQz7hy5QpmzZqF3/3udxgzZgzWrFmDgwcP4p133pG6aUTkpZgb/ok9N+Qz5s+fj61btyInJwchISEAgFWrVuHpp59Gbm4ukpKSpG0gEXkd5oZ/YnFDPmHPnj1IT0/H7t27cfvtt1s8Nm7cOLS0tLCbmYgsMDf8F4sbIiIikhWOuSEiIiJZYXFDREREssLihoiIiGSFxQ0RERHJCosbIiIikhUWN0RERCQrLG6IiIhIVljcEBERkaywuCEiIiJZYXFDREREssLihoiIiGSFxQ0RERHJyv8DmYj902oMm/gAAAAASUVORK5CYII=\n"},"metadata":{}}]},{"cell_type":"code","source":["# задание 2"],"metadata":{"id":"wyM0ZsTS85nI"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# загрузка обчуающей выборки\n","train = np.loadtxt('cardio_train.txt', dtype=float)"],"metadata":{"id":"m1GP2hhaFnmM","executionInfo":{"status":"ok","timestamp":1763327784142,"user_tz":-180,"elapsed":373,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}}},"execution_count":9,"outputs":[]},{"cell_type":"code","source":["print('train:\\n', train)\n","print('train.shape:', np.shape(train))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"id":"r2g3UBF9FoX2","executionInfo":{"status":"ok","timestamp":1763327785456,"user_tz":-180,"elapsed":8,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"2029ae9d-7a32-40f6-ecd8-8fde731bdeac"},"execution_count":10,"outputs":[{"output_type":"stream","name":"stdout","text":["train:\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","train.shape: (1654, 21)\n"]}]},{"cell_type":"code","source":["# **kwargs\n","# verbose_every_n_epochs - отображать прогресс каждые N эпох (по умолчанию - 1000)\n","# early_stopping_delta - дельта для ранней остановки (по умолчанию - 0.001)\n","# early_stopping_value = значение для ранней остановки (по умолчанию - 0.0001)\n","\n","from time import time\n","\n","patience = 4000\n","start = time()\n","ae3_v1_trained, IRE3_v1, IREth3_v1 = lib.create_fit_save_ae(train,'out/AE3_V1.h5','out/AE3_v1_ire_th.txt',\n","100000, False, patience, verbose_every_n_epochs = 1000, early_stopping_delta = 0.001)\n","print(\"Время на обучение: \", time() - start)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fyf_3nxCF2t-","outputId":"7f541b8f-e2ca-450d-8708-fa36154d9957","collapsed":true,"executionInfo":{"status":"ok","timestamp":1763328841089,"user_tz":-180,"elapsed":786744,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}}},"execution_count":14,"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) : 7\n","Задайте архитектуру скрытых слоёв автокодировщика, например, в виде 3 1 3 : 48 36 24 12 24 36 48\n","\n","Epoch 1000/100000\n"," - loss: 0.0303\n","\n","Epoch 2000/100000\n"," - loss: 0.0162\n","\n","Epoch 3000/100000\n"," - loss: 0.0127\n","\n","Epoch 4000/100000\n"," - loss: 0.0116\n","\n","Epoch 5000/100000\n"," - loss: 0.0101\n","\n","Epoch 6000/100000\n"," - loss: 0.0094\n","\n","Epoch 7000/100000\n"," - loss: 0.0090\n","\n","Epoch 8000/100000\n"," - loss: 0.0099\n","\n","Epoch 9000/100000\n"," - loss: 0.0083\n","\n","Epoch 10000/100000\n"," - loss: 0.0082\n","\n","Epoch 11000/100000\n"," - loss: 0.0079\n","\n","Epoch 12000/100000\n"," - loss: 0.0078\n","\n","Epoch 13000/100000\n"," - loss: 0.0073\n","\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/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: 786.7256836891174\n"]}]},{"cell_type":"code","source":["# Построение графика ошибки реконструкции\n","lib.ire_plot('training', IRE3_v1, IREth3_v1, 'AE3_v1')"],"metadata":{"id":"grKMKaYIGDN1","colab":{"base_uri":"https://localhost:8080/","height":612},"executionInfo":{"status":"ok","timestamp":1763329139373,"user_tz":-180,"elapsed":912,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"28774bf6-d3e5-4164-8faa-28b92486db41","collapsed":true},"execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["#загрузка тестовой выборки\n","test = np.loadtxt('cardio_test.txt', dtype=float)\n","print('\\n test:\\n', test)\n","print('test.shape:', np.shape(test))"],"metadata":{"id":"zJLF8jc9giAd","colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"executionInfo":{"status":"ok","timestamp":1763329887734,"user_tz":-180,"elapsed":359,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"76c014db-cd42-4f64-aa30-d4d73ba1be73"},"execution_count":29,"outputs":[{"output_type":"stream","name":"stdout","text":["\n"," test:\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","test.shape: (109, 21)\n"]}]},{"cell_type":"code","source":["# тестирование АE3\n","predicted_labels3_v1, ire3_v1 = lib.predict_ae(ae3_v1_trained, test, IREth3_v1)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"q5zDhjM1p9fu","executionInfo":{"status":"ok","timestamp":1763330150859,"user_tz":-180,"elapsed":116,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"bc9264f2-6066-4f33-bf98-ce1725d70e36"},"execution_count":38,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step \n"]}]},{"cell_type":"code","source":["# Построение графика ошибки реконструкции\n","lib.ire_plot('test', ire3_v1, IREth3_v1, 'AE3_v1')\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":614},"collapsed":true,"id":"kWfJdFmIqtCB","executionInfo":{"status":"ok","timestamp":1763330161719,"user_tz":-180,"elapsed":743,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"180f77e7-1628-4f10-8166-09e38c0122ba"},"execution_count":39,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["# тестирование АE2\n","lib.anomaly_detection_ae(predicted_labels3_v1, IRE3_v1, IREth3_v1)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"id":"pq4HCuXqqvhu","executionInfo":{"status":"ok","timestamp":1763330180712,"user_tz":-180,"elapsed":7,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"d5fc5f17-19c4-4433-f25d-5123409ad3fb"},"execution_count":40,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","i Labels IRE IREth \n","0 [1.] 0.4 1.84 \n","1 [1.] 0.35 1.84 \n","2 [1.] 0.38 1.84 \n","3 [1.] 0.35 1.84 \n","4 [0.] 0.92 1.84 \n","5 [1.] 0.32 1.84 \n","6 [1.] 0.91 1.84 \n","7 [1.] 0.59 1.84 \n","8 [1.] 0.57 1.84 \n","9 [1.] 0.5 1.84 \n","10 [1.] 0.37 1.84 \n","11 [0.] 0.63 1.84 \n","12 [1.] 0.59 1.84 \n","13 [1.] 0.63 1.84 \n","14 [1.] 0.64 1.84 \n","15 [1.] 1.35 1.84 \n","16 [1.] 0.56 1.84 \n","17 [1.] 0.46 1.84 \n","18 [1.] 0.53 1.84 \n","19 [1.] 0.59 1.84 \n","20 [1.] 0.37 1.84 \n","21 [1.] 0.19 1.84 \n","22 [0.] 0.41 1.84 \n","23 [1.] 0.74 1.84 \n","24 [1.] 0.37 1.84 \n","25 [1.] 0.38 1.84 \n","26 [1.] 0.28 1.84 \n","27 [0.] 0.6 1.84 \n","28 [0.] 0.63 1.84 \n","29 [0.] 0.47 1.84 \n","30 [1.] 0.38 1.84 \n","31 [1.] 0.66 1.84 \n","32 [1.] 0.21 1.84 \n","33 [1.] 0.28 1.84 \n","34 [1.] 0.8 1.84 \n","35 [1.] 0.37 1.84 \n","36 [1.] 0.15 1.84 \n","37 [1.] 0.35 1.84 \n","38 [0.] 0.27 1.84 \n","39 [0.] 0.35 1.84 \n","40 [0.] 0.23 1.84 \n","41 [0.] 0.46 1.84 \n","42 [0.] 0.71 1.84 \n","43 [0.] 0.15 1.84 \n","44 [0.] 0.82 1.84 \n","45 [0.] 0.24 1.84 \n","46 [0.] 0.24 1.84 \n","47 [0.] 0.49 1.84 \n","48 [0.] 0.31 1.84 \n","49 [0.] 0.27 1.84 \n","50 [0.] 0.25 1.84 \n","51 [0.] 0.39 1.84 \n","52 [0.] 0.24 1.84 \n","53 [0.] 0.24 1.84 \n","54 [0.] 0.16 1.84 \n","55 [1.] 0.68 1.84 \n","56 [1.] 0.26 1.84 \n","57 [1.] 0.6 1.84 \n","58 [1.] 0.57 1.84 \n","59 [1.] 0.37 1.84 \n","60 [0.] 0.33 1.84 \n","61 [0.] 0.2 1.84 \n","62 [0.] 0.45 1.84 \n","63 [0.] 0.65 1.84 \n","64 [0.] 0.64 1.84 \n","65 [0.] 0.32 1.84 \n","66 [1.] 0.48 1.84 \n","67 [1.] 0.7 1.84 \n","68 [1.] 0.37 1.84 \n","69 [1.] 0.17 1.84 \n","70 [1.] 0.14 1.84 \n","71 [1.] 0.19 1.84 \n","72 [0.] 0.51 1.84 \n","73 [1.] 0.4 1.84 \n","74 [0.] 0.26 1.84 \n","75 [0.] 0.79 1.84 \n","76 [0.] 0.28 1.84 \n","77 [0.] 1.23 1.84 \n","78 [0.] 0.52 1.84 \n","79 [1.] 0.85 1.84 \n","80 [1.] 0.5 1.84 \n","81 [1.] 0.8 1.84 \n","82 [1.] 0.8 1.84 \n","83 [1.] 0.25 1.84 \n","84 [1.] 0.33 1.84 \n","85 [1.] 0.26 1.84 \n","86 [1.] 0.66 1.84 \n","87 [1.] 0.65 1.84 \n","88 [1.] 0.41 1.84 \n","89 [1.] 0.64 1.84 \n","90 [1.] 0.52 1.84 \n","91 [1.] 0.24 1.84 \n","92 [1.] 0.51 1.84 \n","93 [0.] 0.39 1.84 \n","94 [0.] 0.22 1.84 \n","95 [0.] 0.34 1.84 \n","96 [0.] 0.34 1.84 \n","97 [1.] 0.41 1.84 \n","98 [1.] 0.51 1.84 \n","99 [1.] 0.62 1.84 \n","100 [1.] 0.5 1.84 \n","101 [1.] 0.45 1.84 \n","102 [1.] 0.42 1.84 \n","103 [1.] 0.45 1.84 \n","104 [1.] 0.33 1.84 \n","105 [1.] 0.28 1.84 \n","106 [1.] 0.14 1.84 \n","107 [0.] 0.17 1.84 \n","108 [1.] 0.6 1.84 \n","Обнаружено 69.0 аномалий\n"]}]},{"cell_type":"code","source":["# **kwargs\n","# verbose_every_n_epochs - отображать прогресс каждые N эпох (по умолчанию - 1000)\n","# early_stopping_delta - дельта для ранней остановки (по умолчанию - 0.01)\n","# early_stopping_value = значение для ранней остановки (по умолчанию - 0.0001)\n","\n","from time import time\n","\n","patience = 4000\n","start = time()\n","ae3_v2_trained, IRE3_v2, IREth3_v2 = lib.create_fit_save_ae(train,'out/AE3_V2.h5','out/AE3_v2_ire_th.txt',\n","100000, False, patience, early_stopping_delta = 0.001, verbose_every_n_epochs = 1000)\n","print(\"Время на обучение: \", time() - start)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"id":"YfxMr7cQq0WA","executionInfo":{"status":"ok","timestamp":1763331222910,"user_tz":-180,"elapsed":985402,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"bf8d4d1c-74d7-4985-8d91-89490e63670a"},"execution_count":41,"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) : 11\n","Задайте архитектуру скрытых слоёв автокодировщика, например, в виде 3 1 3 : 48 40 36 30 24 12 24 30 36 40 48\n","\n","Epoch 1000/100000\n"," - loss: 0.0293\n","\n","Epoch 2000/100000\n"," - loss: 0.0177\n","\n","Epoch 3000/100000\n"," - loss: 0.0139\n","\n","Epoch 4000/100000\n"," - loss: 0.0120\n","\n","Epoch 5000/100000\n"," - loss: 0.0102\n","\n","Epoch 6000/100000\n"," - loss: 0.0092\n","\n","Epoch 7000/100000\n"," - loss: 0.0086\n","\n","Epoch 8000/100000\n"," - loss: 0.0082\n","\n","Epoch 9000/100000\n"," - loss: 0.0078\n","\n","Epoch 10000/100000\n"," - loss: 0.0073\n","\n","Epoch 11000/100000\n"," - loss: 0.0073\n","\n","Epoch 12000/100000\n"," - loss: 0.0068\n","\n","Epoch 13000/100000\n"," - loss: 0.0067\n","\n","Epoch 14000/100000\n"," - loss: 0.0065\n","\u001b[1m52/52\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/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: 985.3792371749878\n"]}]},{"cell_type":"code","source":["# Построение графика ошибки реконструкции\n","lib.ire_plot('training', IRE3_v2, IREth3_v2, 'AE3_v2')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":608},"collapsed":true,"id":"5ufAXUELrCNn","executionInfo":{"status":"ok","timestamp":1763331254854,"user_tz":-180,"elapsed":661,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"f5b24d2e-faa9-43bd-ba24-3977ef06b7c3"},"execution_count":42,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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vffeBuvbfffdNXbsWB133HHxbFwnfPzxx5KidW3UqFENjmFMkyZNdMYZZ+iMM85I+/qIESM0b948SdHM4ZdffjntxFwtW7bUww8/rK1bt2r06NFauXKlHnnkEf3lL39xaI/cs23bNr344ovxv1OzCPfcc0/16NFDH3zwgaRo1ubw4cMNr/+7777Ttddea3j5Sy65RN27d8+53L777hvPMk2nXbt2eu6553TiiSca3rZZxx9/vDp27KjFixdr9erVeu+999SrV6+MyycOyXD22WerefPmlrf98MMPxzP0S0pKdNVVV1leFwAADXgdWQUAoNg4kbFZX18fWb16deTVV1+NHHnkkUmZfi+//LKpMpj950TmltGMzQ0bNiRlx7366qtZ11tfXx/p3LlzfPmbbrop7XKJ+3PBBRfY2ZW07GZsNmnSJGPmnh2JmXpffPFF2mXMZmxKirzyyitZtzt+/Pj4sl27ds24XOI6M0nMIjzrrLOybjccDkcqKiqy7vNbb72VlJm2YcOGrOtMzHwz8/lmUl5eHpEU2WWXXSyvo6amJrL77rvHs0lXr16d8z3Lly+PhEKhiKTIIYccknYZv2VsjhkzJl6eXXfdNVJTU9NgmcT627Zt20htbW3WdSaeq2b/5co2j9lnn30yruOyyy6zlQ1pxs033xzf7m9/+9uMy4XD4UiHDh3iy77zzjuWtzl37tykjPsrrrjC8roAAEiHjE0AAAIgcQy8TPbff38NHz485yQpQfLJJ5+ourpaktSmTRudddZZWZcvKSnRpZdeqj/96U+SpA8//DDnNsxkt+bLqaeeql122cX0+3788UdNmzZN8+bN04YNG7Rly5b47NFSdDzHmDlz5uiQQw6xXdYmTZrk/FwOP/zw+GMnZ0ROHF8xnVAopC5dusQnnFq0aFGDfU6cZCc2yUo2J5xwgvbaay8tWbLEUplTdejQQT/88IM2bNigF154QRdeeKHpdcycOTM+C/jJJ5+s3XffPed72rdvrwMOOEDz5s3T3LlztWnTpgaZ0H4zevTo+OM+ffqkHQP3vPPO0zXXXKOtW7dq9erVevfddz2/Jl5yySXxCYK2b9+uFStWaPr06Vq/fr2eeOIJjR8/Xv/4xz9sj2OZy29/+1v97W9/kxSddGvr1q1pJ1mbPHlyfNKyiooK9ezZ09L2Nm7cqN69e8cz7vfbbz+NGDHCYukBAEiPwCYAAAWgbdu2evrpp9NOUpPLsGHDdOuttzpfKAfMnj07/vjII49s0I04nWOPPTbp/ZFIJGlG3lS/+MUv7BXSBWbL9PXXX+svf/mL3n77bcMzDpsZfiCb/fffP+ckS4mzsFdWVjqyXUmGArO5tj1nzpz4YyPdiqVoXXQqsHnBBRfo73//u6ToBEax4OZJJ51kKEApSVOnTo0/XrZsmeHu1Bs3bpQkRSIRLVu2zNeBzRUrVui9996L/50pCLjTTjupd+/eGjNmjKRoMNRoYPPEE09MO5u8XYlDHcTU1NTomWee0Z/+9CetXLlS/fr106JFi/TXv/7V8e3HHHjggeratatmzZqlqqoqjR07VhdddFGD5RK7offt29fSkBXbt2/Xb37zGy1YsEBSdAiEl19+OeNQCwAAWEVgEwCAAOjdu3fS7MPr1q3T999/Hx/rb/Xq1Tr++OP1yiuvxMcULASxLCdJ6tixo6H3dOrUKf64pqZGmzdvVsuWLTMuv9tuu1kun1vMlOndd9/Vb37zm3hmq1Gx2a7tMhIMSwx8xmb/9mLbtbW1DV5PrGNGZ2vec889DS1nxC233KJJkyZp2rRpikQieu211/Taa69Jima4HX/88Tr55JN11llnpZ2VXIoG/WK++OILffHFF6bLsWHDBms7kCf//e9/40H7/fbbL2sQ+pJLLokHNt944w1t2LDBUga0mxo1aqTLLrtMhxxyiI477jjV1tZq2LBh+uUvf5n044zTfvvb32rWrFmSorOepwY2q6ur4+PhxpY3q66uThdeeKEmT54sKZrV/cYbb+jQQw+1UXIAANJrOKI4AADwnUGDBumhhx6K/3vuuec0Y8YMff755+rSpYukaBCvb9++WSepCJpYF0ZJhievSF0uVwCvadOm5gvmMqNlWrNmjS688MJ4ULNjx466++67NWXKFK1YsUJbt25VOBxWJBJRJBJJyhwLh8OOlDVbNqzbnNh2Yh1L1y03HSezzpo3b66PPvpI//znP5OC8lJ0Mpsnn3xSF198sSoqKnTjjTdq27ZtDdaxadMm2+VwMuDshsRu6LmCbaeccooqKiokRQN1L7zwgqtls+PII49Uv379JEUzZ++77z5Xt5eYgTlhwoSkwL4kjRs3Lp7Je9BBB6lr166m1h8OhzVgwAC98cYbkqSysjK99NJLrk6MBAAobgQ2AQAIsEMPPVQTJkyIZ3NWVVXp8ssv97hUzkkMIG3ZssXQe1KXy5TlVggee+yxeFCrS5cu+uKLL3TTTTfp2GOPVbt27dS0adOk4J9TWZqFJLGObd261dB7jNZFoxo1aqQbbrhBP/zwg+bMmaMHH3xQffr0ScrS3rp1q/75z3/qpJNOahDcTAzm//GPf4wHss3869Gjh6P75KSZM2fq66+/jv89bNgwhUKhjP/Kysri46pKyUFRPzrllFPij//3v/+5uq3EMTPr6uoaBH2fffbZ+GMr2ZpXXnllfB0lJSV6+umnC6oXAQDAfwhsAgAQcLvvvrv+9a9/xf+eNGmSxo0b52GJnJPYJdvomIaJk9M0atSooAOb77//fvzxLbfckrXLvSQtXrzY7SIFTps2beKPly1bZug9RpczKzbZ0R/+8Ac999xzWrZsmWbNmqWBAwfGl5k+fbpGjhyZ9L62bdvGHycG9AqF3cDktGnT9O233zpUGucldpNft26d69tLDFgmjqe5cePG+HdHKBTSxRdfbGq9119/vR577LH43//5z3/Ut29fm6UFACA7xtgEAKAAnH322Tr22GPj2T633HKL5zMBOyFxNu0ZM2aovr4+50QWn3zySdL7vegqna9tJo6tmGsinfr6etezwYLosMMOiweIp0+fbug9M2bMcLNISQ4//HA9+eSTKi0t1eOPPy4pOm7kDTfcEF8mcbzJTz75JOeEWUZ5OcxATE1NjZ577rn43/vvv3/OmetjvvvuO61fv15SNDh61113uVFE21auXBl/3Lp1a9e3d/bZZ6t58+basmWLpk+fru+//1777LOPXn755fiwFieccIL22msvw+v8v//7P91///3xv++7776C6j0AAPAvMjYBACgQiTObz5kzJz7GWZAdc8wxaty4saToeJK5MlHD4bCeeuqp+N+//OUvXS1fJk2aNIk/TjdhjVNKSnY05XJ1ox47dmxBZvPZldgFe9y4cTnHq5wyZYonma+//vWv449Xr16d9Nqxxx4bD/YtW7ZMb775piPbzFc9zmbcuHHxLMaysjJNnjxZ06ZNM/Tv5ptvjq/nmWeecWxcWae99dZb8ccHHnig69tr3ry5evfuHf87lrWZmL1pphv6XXfdpb/97W/xv2+//XZdd911tssJAIARBDYBACgQPXv21DHHHBP/+8477/SwNM7YeeeddeGFF8b//vOf/5x1nMiHHnpIX375paRo0O93v/ud62VMZ9ddd40/Xr58uWvb+dnPfhZ/nC2QvWbNGl1//fWulSPITjvtNLVv315SdIzaxGBYqpqamqRMSbuqq6uTJi/KZunSpfHHu+++e9JrjRs3TgokXX311abqXWqgNCZf9TibxG7op5xySoN9z6Zv377x4P/SpUv14YcfOl6+VHV1daYmcxo/frxeeeWV+N/nnnuuG8Vq4JJLLok/fvbZZ7V06dKkWczPP/98Q+t54IEHdMstt8T/vvHGG/XXv/7V2cICAJAFgU0AAArI0KFD448//fRTvfPOOx6WxhlDhw6NT/Dy7bffqlevXvrhhx+SlgmHw3rggQc0ePDg+HPXXHNNg1mm8+Xggw+OP54wYYIjs1anc9ZZZ8Uf33333UkZVzGzZs3SiSeeqKVLlxqeWb6YlJWVJWU7P/zww/rLX/6impqapOXWrFmjc889V9OnT49nEdu1cuVKdejQQTfccINmzpyZcbmJEycmzWh/+umnN1jmT3/6kzp37iwpGoTs1q2bXnrppYxZimvXrtWjjz6qrl276p///GfaZRLr8UsvvWRon0aNGpU0kU/imLdmrV27VuPHj4//bXbMx/bt2+ukk06K/52PSYSqqqq09957a+jQoZo/f37G5TZs2KA777xT55xzjiKRiCRp33331RVXXOF6GaXoD2GxmeO/++47XX/99fFy/OpXv1KrVq1yruPJJ59M+sHkmmuu0T/+8Q93CgwAQAaMsQkAQAHp1auXunfvHh8r8I477tBpp52W9T3jx4/X2rVrDW+jWbNmuueee2yV04x99tlHjz/+uC6++GLV19dr6tSp2n///XX88cdrn332UVVVlT7++OOkjLKjjjoqr2VMdeSRR6pDhw5aunSpVq5cqQMOOECnnnqq2rRpEx+38IgjjkjKRrWif//+Gj58uL799ltVV1frkksu0d/+9jd16dJFTZo00dy5c+MBsy5duqhXr16eHhe/uvzyyzVu3Di9/vrrkqR77rlHTzzxhHr06KHWrVtr2bJl+vDDD7V9+3b97Gc/029+8xvdd999kpKHA7Bi48aNGj58uIYPH67WrVvr8MMP1x577KEmTZroxx9/1BdffJEUyP/5z3+uQYMGNVjPTjvtpDfeeEM9e/bUwoULtWrVKl1wwQVq06aNjjrqKFVUVCgSiWj9+vX6+uuv9d1338WDnpmGbDj33HP1n//8R1I04PvZZ5+pa9euatasWXyZq666Svvss4+tY5DJmDFj4l3gU7tPG3XxxRfHx1B99dVX9fDDD8d/KEn13Xff6dprrzW1/iFDhiTNXi9Fg5Z33HGH7rjjDrVv316HHnqodt99dzVr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axZEYBNWFMus6ECMXwOGqYHNQh9j0+/lA4KKbGgA6dBmRjZ+bR/DXX4LbAIisAm3+fECR1d0mEXGpj8Q2ATcQWATQAyzorun0NoxhdzuLNa6H5QxNhMV2nkFS2jJwrxCHGOTyYOQjZUxNteulbp2lbp3lzZvdqdcTmds+h3BF8AdnFsAYmj/uqfQ2mn5CmR5cdwK7bMyyshn6rfAZrF+VkhCSxbmWQls+umCQ8YmzLKSsfnnP0cHs54xQ7rlFnfKlTp5kJMNCj/WeW62AHcQ2AQAmEXGZuEJYsYmIAKbcJsfL3BGszMJoiDGSsbmnDk7Hn/+uaPFiXN6tna/13mCL4A7OLcApFOswR23FNrx9ON9HuyxE9j06j7C7/cvyAtasjAv9WI2YoTxZf3AaFd0M6+jsFnJ2Cwr2/G4rs7Z8sSkNijtNjDdrOdONDpouADuILAJIIbvWhhVyIHNYr33sxPYBDxESxb2/elPmV/z46zoRgWxzHCHlcBkPgKbqQ0LJxsafqz/fiwTUAhKS70uAQA/IoDhrEI7nl4ENvPVFiy0z8ooO2NsenXMuD+ACGzCimKZPAiIqa5O/tsvGZup5Sj0MTYBuIOMTaB4bdmS/HchzYq+ZUt0osXUyRbhDDI2Cw8ZmwgoWrJwlx+/8IxejAupYQd7rAQ2y8t3PM5XV/RCH2OT8xBwB4FNoDj9/vdSq1bSv/614zm/twWMeuopaaedpMaNpQ4dpOXLvS5R4fHjfZ5TirXNGcTAZqFcs2ALLVmYV4izojN5ELLZvj3572LI2PSjQt8/wCsENoHi9Oij0XHE//hHr0vivEsv3fH4xx8Lcx+95vQP7PAenyECipYs3OXHiyNd0WGW213Ra2ulNWvMl8vNMTb9qND3D/AKP+QBSKeQvneXLvW6BIV1PKWGgc3aWm/K4YZC+6yMYoxNBBSBTZhXiGNspsNFEjFuZmzW1UmHHCK1by+NH2+uXE7Pip7Ij+cuGZuAO/x4vgPwRqG2fwt1v7xEYLPwBLErut/KA0+U5V4EMCFT11g/NSbI2IRZqRmbRhgNbL75pjR/fvTxmWfa++Gg0Otxoe8f4BXOLQCFzk/3IoUiNbDp1tBLXijW78UgBjYBkbEJK7JdzIIQaPFjmeBvbnZF37bNWpnSlcPJjEY/3gCQsQm4g+9FoPgUWwDDD+2aQjqekjdjbPrhcyxkQbwuUCcgAptwWlADm0YnFEJxcrMrup1JO9xsUAbl3AVgH+cWUHwynfeJ7d9CujYwSZrzvAhs5qtOFlLdNyOIY2wCIrAJK7JdtDJ9wfnpQkdXdJhlN2Mz25hDpaXWypSuHHYzGhPX58fsSD+WCSgEfAcCxSfTd2qh/rBfqPvlpUKeFb2Q9sUMMjYRUAQ2YZ6Zruh+DESQsQmzrGRslpfveJwtY9NOYNPpBqXfA5t+a0gBhYJzCyg+xXbe0653npuTWGaSr8+x2M6PGDuBzWI9ZvAFAptwVhB+ufNjmeBvbo6xaadrlNM/JCS+n8AmUDw4t4DiE8TMLDv8ENgspOMpBeO+z6pC2hczgnhd8MO5Dc8R2IR5ViYP8tMFh4xNmJWasWmE0cCmk13R7TY0/B7Y9GOZgELgt5sUAO4zMsZmISnU/fKSFxmbcJfdwCbtCXiEwCacFYRf7hhjE2ZZydhMDFjma/IgP2dsOnGOcZ4C7uDcAopPEDOz7PBDYLOQjqfUsK34zjvSypXelMVphfZZGWVn8qBcr7nFD+c2PEdgE+YVyxibQIzdWdGznQdOdkX38xibTpxjfryeAIWA70Cg+BiZPKiQrg0EP5yXWocGDJCOOCL7D/pBUUh13wwyNhFQBDbhLCMZm15f8OiKDrPsjrGZjZ9mRU98v9PnKRmbgH9xbgHFp9i6otv5IRnppWt3Ll8uffll/svitGL9XnQySQLII67wMM/KGJt+usgZLUuhNuxgnt2MzWz8NCu637uik7EJuMNP39EA8qPYznva9c4r5HZZsZ0fMWRsIqAIbMJZRgItXjcsyNiEWakZm0aUlxtbzk+zovu9KzqNJcAdnFtA8WGMTdhlZDgDp/E5uiuIY2wCIrAJK6yMsemnLyE/do+Hv1nJ2DSaiemnMTaZFR0oTnwHAsWn2Lqi+2G/Cu1aW8jtskL7rIwKYsamH85teI7AJpxVSLOic5FEjJUxNvNRf5gVHYATOLeA4mM3MytonG6XLVsmPfCAtGiR8ff4MRA4e7Y0cqS0aZP593qRsZkvhVT3zQhiYBOQZHAQOCBBtgtWIU0eBMRYCWwarVN26l6QMjYJbAL+xbkFFB8jGZuFdG1wOth2/vnStGnS/fdLCxcae4/fjufWrVLXrtHHn34qjRpl7v1+DNQ6xW+fVb4EMbBZCIF02EbGJpzldKDFDX4sE/zNSlf0IAY2/TbGptMZqQDS43sRKD50Rbdn2rTo/0HO2Jw/f8fj0aPNv7+QMzaLFWNsIqAIbMI8MxmbfvsCl9KXf9w4af365OcK7Ut5wgTp7ruljRvtr+uTT6Q77pBWr7a/Lr+rq7M2xEI+AptudkV3umFiZX319fbXAQDprF8v3XWX9OGHXpeksG3YIP3tb9Jrr0l//7s0frzXJXLe9OnS7bdLK1d6XRJziu071cyY5itXRj/T6dONLb9tm7Hl/HbMnWw3Fhq/fVb5QsYmAirQgc2RI0eqU6dOatKkibp3764ZM2ZkXLZHjx4KhUIN/p155pnxZQYMGNDg9dNOOy0fuxIsZi5mfhy7MlP5r7oqv+XIp5UrpV69pJtvlgYPtreu7dulY4+Vhg6VfvtbZ8rnZ6nZmkYZ/WK30ygs9K7oqYHNQm5AA14KQm8Lp115pXTLLdIvf9nwh0045w9/kP7v/6RzzpGGDJHOPFNavNjrUjmntlY66ihp2LBo1+QgYYzNzM4/P/qZHnVU9DPOZd06Y+v1WzsmtZ1lViFnbBZS3TcjiIFNQAEObL7wwgsaPHiwhg0bplmzZqlLly7q1auXfvzxx7TLv/rqq1q5cmX839y5c1VaWqrzUxohp512WtJyzz33XD52p3AEdfIgSXrxxeS/C+FLOWbq1B2Pn3rK3rpWrdrx+L337K0rCOrqGj7nZL12sit6oU0eRMYmkB+p55bdm90geOmlHY+/+sq7chS6Z59t+FwhZclWVu54/L//eVcOK+iKnlniZ7l5c+7l1641tl6/tWMIbGbmt88qXwhsIqACG9gcMWKErrjiCg0cOFAHHXSQHnnkETVr1kxPPvlk2uVbt26tioqK+L+JEyeqWbNmDQKbjRs3Tlpul112ycfuBIvdjE2v+bFMbnNyn8105SkE6Rp9fu2K7ucxNq0IwtAWQCEoxsAmvMO13B+KrT1sNdhm5H2xwGZtbXTYp+HD09dzv9V9uqJnVmznRwxjbCKgAjkrek1NjT777DMNGTIk/lxJSYl69uypqYmZaVk88cQT6tOnj5o3b570/KRJk7T77rtrl1120S9/+Uvdeeed2nXXXdOuo7q6WtUJsyVX/vSrbW1trWqNdFsImNg+hSORBhHx+P5WV6s84fm6mhpFamtVGg7H3xOJRFTn5fGpqUkqY6LEz62kvl6lGV4LmlBtbdLJbmtf6uqSjp/fjkusPI6VK6VOS1J9XZ3COdZfUldnqP7Y+WxS3xuuq1O9jf0uC4cVa75HwmHb52nStaC2VhGz69u+PWkdRo57kDheVwGLGnzfbd8e/xGrUOtp0vWprs789QmGpGtvxdqGTvOkrvq8TZRVSns4VvayUCjeFgjX19tqV3gpte6FIxHD+5J0XOrq0nZHT7qGrFqlSG2tSkaOVOnNN0ef2203Rfr2TXqP3+4tQtXVtu4PyurrlS7sW1tba6wLv0FJbcFw2LW2YNJ26usLqs1pVKiurkGAqEG9yHYvXVPj6GefCZ9VcTBzTQpkYHPt2rWqr69X27Ztk55v27atvvnmm5zvnzFjhubOnasnnngi6fnTTjtN55xzjvbee299//33uvnmm3X66adr6tSpKi0tbbCeu+++W7fddluD5ydMmKBmzZqZ3Kvg+HHVKrVPeW78uHFSKKSmq1fr1ITnZ376qVZLOnzZMu3103O1tbV628PB43davlwnZ3htfEK5Dvj+e+2f4bWgaT9rlo5I+NvOvjRZs0a9HFqXmyZOnOjIehpv2KDUkXbnfvmlFuXY7wMWLDBUf3afNUtHG1gunfazZyd9rsuWLtVsG59Hr23b1OSnxzXbt+sdm5/tbxIez549WytSfkjKpbyyUmck/P3N119rgU/rmx1O1VXAqs4//KB9E/6e8PbbqmvaNGmZQqunidenqVOnan1il2I45jdpnvtqzpyc36F25LOulm/enPQ95dc2UTqN169Pat/Eyt6rpibeFli+fLlmBWifEqXWvdU//qgZBvcl8b0TJkxQ3U47ZV3m68mTtbBFC/1y+HC1+Om5dQ88oGmtWiW959BFi7R3wt9e15ddv/xSxyX8bbY8J2/erIZHRvr444+1eckSW2VLlHisFy5cqK9cOm6J2/l2/nx9G9C6b8den3+uw1OeS72m7rRsWcZ76Qnvvpv2fHFa0mf13XdF+VkVg61btxpeNpCBTbueeOIJHXLIITryyCOTnu/Tp0/88SGHHKJDDz1U++yzjyZNmqSTT254+g4ZMkSDEyZiqaysVIcOHXTqqaeqZcuW7u2AR2prazVx4kTtnhJQlqQzTjtNKi2Vfvgh6fluv/iFImecodJXXok/V15erjPOOCN1FfmTJfidWK6SlJkQ813m0IcfquTppxW+5hpFunWzt64tW5L+trUvixY5ty4rfvhBpXffrXDPnopceGGDl2P19JRTTlF5eabfE01YvrzBUwd37qyDcux3SUr2eKbjFErp4mTmeIZSbsT3bN9e7Wx8HmUJx6tRWZmjn+3hXbroMLPrSxkz+YD999fPvbx2OMzxugrbQs8/r5L331f9kCHSz37mdXHypuSDD5L+PvXkk6Wdd5ZUHPX06KOPVuS443IvCEccfNBBOb9DrfCkrqZMPOVp+9aslPZNrOxljRvHn9ujfXtVBGmfsmhbUWHp8zn1lFOkHEOTdW7bVgeecYbKEhJbdttttwbbK3nrraS/835vMX26Sh55ROFLL1Xk+OMVatIk6XWz5SnLkMhz/AknSJ07Wy5nNnv/7GfqmIfj9vP99tO+BVL3zQglzqXwkwbX1HnzMr7fyPnitJ///OdF+VkVg0oTPzoHMrDZpk0blZaWavXq1UnPr169WhUVFVnfu2XLFj3//PO6/fbbc27nZz/7mdq0aaMFCxakDWw2btxYjRO+/GPKy8sLtvEvpR+YtbysTIr9S1BWWiqVlyeNyxgKhbw9PmWZq31SuVKWy3uZe0XzIkuefdb+eCVO7kvKGJt5Py7nnSfNnauS0aOl00+XMgwV4dh5mCZbu7S0VKW51m30OKWs31SZU95bEgqpxM4+J9SzUCTi6GcbvxaYkXIMS0tKch/3ACr074zAWL9e6tdPklTy6afFNaFMyg8s5SUlDc7XQq6nZWVl5q9PsKxUcvVante62qhRg20HhoG2YUlJib12hVfStJtLSkst7Uu5gfZL6caN0TqdcC1Ne+xSr7X5PrbHHy8p4d7Cbps+w3iM5eXlrl1T89UWLNQ2Z05pxpRtcE3Ndi/twfdpaVlZcX5WRcDMNSmQs4A0atRIv/jFL/T+++/HnwuHw3r//fd19NFHZ3mn9NJLL6m6ulq//e1vc25n2bJlWrdundq1a2e7zAUv1oAI8qzoVpcLAif3xetJJebO3fF46VL3t+fU5EGZBuNmVvTMUo99IQ9SD++tXLnj8ddfe1cOLzB5EPKprs7rEjgnyG1FJydC9Jt05bY6+aWRY2C0TvutHcOs6JkFte7bxazoCKhABjYlafDgwXrsscc0evRozZs3T1dddZW2bNmigQMHSpL69euXNLlQzBNPPKHevXs3mBCoqqpKf/7znzVt2jQtWrRI77//vn7zm99o3333Va9evRqsp6ilu2DFnnM60OKGbBfc3r13ZOkU0oXZyc/BTze8+bg5shrYTJVp8GM/zYru98BmIZ2T8J9i/rWfwCbyKV9tw0hEGjJE+u1vpXXr3NtGUGUqe2JQyo/teCPSXcOsBtuMHIPYMrnqg9/qi1uzovttP2FcEAObhRBIh22B7IouSRdeeKHWrFmjoUOHatWqVTrssMP0zjvvxCcUWrJkiUpSfpmbP3++pkyZogkTJjRYX2lpqb744guNHj1aGzduVPv27XXqqafqjjvuSNvdHCliX2xGAi1ef9ll2/7rr0vTpkmrVnlfTicVUsZmonzMgJeu0WblS7+2Vkp3LSmWjE0rUsvghzKhcBHY3MFP13kUnnzVr9dfl/7+9+jjSER69lnntxHk7yUjZQ9qWzjdvlkNfhg5Bka357f64lbGZlDrTaJC2AcrghjYBBTgwKYkXXvttbr22mvTvjZp0qQGz+2///6KZDjZmjZtqnfffdfJ4hUuMxmbfry45SpTbOxWP5bdD/x0w+vnjM18dEV3OmMz8f1kbKLYFPMv/kH47kbhyFc74n//2/F4zBh3AptBPleMZGwGdf/I2DSGjM3MCmEfrLD7g0exHjd4LrBd0eEzjLHpb4WasRmkwGam42YnK9Fo8HTePOnkk6Vbb82+Pr90RX/ySem446T33kt+3m+ZDm5asUI64wzpqqsK61rkZ366tuVbEIaRgT+89FL0+vzmm9bX4da5Vl2tXwwfrtILLpA2b87PjxVBvj4Xcld0P2Rs2lkuXwo1Y/PVV6PXqbFjra/D633wChmbCKhAZ2zCI+kuWJl+qXSyYeEUApv2+GnQf6+6ohthNWOzvt74APdGs6xOO01askT64APp/POlzp3TL+eXwOZll0X/T8y2MbuOoLviCuntt6OPe/aUzj3X2/IUA7/dcOYTGZsw6oILov//+tfW64lLgc2S++7Tnh9/HP1j6NAGM5a7IsjnSiFPHuRVxmYufjuehRrYjLWZ/vc/78sSNAQ2EVBkbMK8bF3Rydj0JzI2rXM7YzN1OTP7ZDTbc8mSHY8XLza2Pj90RU9VTIGn8eN3PJ41y7tyFJNiql+pCGwin1z67g5NnrzjjzTj6bsiyOdKIQc2nUyssBrYDMIYm0Hsip6vBJmg1n27ghjY9DppCr5AYBPOMDPGptdfFAQ27fFTYDMfGZt+Dmw6HYxIfL/T9d+J9RXSOQn/8dO1Ld/oio58qqnJz3bycbMb5HMlU9kLoSt6uuu50d4wqU4+WaqszL5MUMfYDGLGZr6Ood8+q3xhjE0EFIFNmEfGZvAQ2LTOamAzldHGn53AppHGSLYbPT/Mik5jqaFi3e98C+oNvBPI2EQ+VVe7v41QiDE2cyFj05gFC6Sbbza/vRzLRfyQZWY3e5rAZuEJYsYmIAKbcIqZMTa9RmDTHj/NVJ2Prujp6rCbkweZCRw7/UNC4voiEWc/WyfOOz9eT/KhkK5Fflas9UsKxnc3CkchZWwG+fpsZPKgoO6f0z/Cf/JJ9teDOsamW4FNN79D8vX95LfPKl+c7P2VL8X6WSEJgU04w82Mzdtvl/bYQ3r5ZfvrkoIR2PRjN+CY1Mailxmcfs7YtDp5kNsZm9m6YrmZteXEeVesDReCTPmReq4XynEPh6XevaX99pPmzk2/DBmbyCe3MjZT6y2BzeyMBDaDeh1MV26rXdGl3J+z0QxRO/XlgQekdu2kxx+3vo5UZGwiVRAzNqkTEIFNOCXXGJt2LjjDhkkrVkRnc3ZCEAObdhuWbgY2vZwl3avJg4zw6xibmW700r3XyRsaMjato8GWH6n1K19ZZW4bO1Z6/fVod8pf/zr9MgQ2kU/5CGzSFT23Qu6K7uSs6OlkaqdbCYAadd110qpV0hVXWF9HKgKb3m/Hb4I4xmaxflZIQmAT5lkZYzO1sWlnW6nq63NfYBMbOEEIbNrpnpyOm4HNfGRNZpKPbbvdFd3ocukYnRXdyrrsri+V0TqYbZvF2nAp1v3Ot9S6t327N+Vw2qJFOx4vXJh+GbqiI58K5UcDKdjnit0Ahp+ZHWPTyL1Etr/pip7Mzf2kK7q7yNhEQBHYhDNiXzKZAi2JFxwzF59cy377rdSxo/SLX6S/Cd26VTrsMKlTJ+n7781v3yt2gl1uS20EeZmxSVd08+XK1LD3S2CTjM2GgnDNKgSp9SsfE5zkg5XrFXUObsrXuUXGZnaF3BXdTMbm119LHTpI3bsbD7pbDWwmLmenfjpV74IY2CRj011G2gMENuFDBDZhXrYLnNNd0XO976KLpOXLpdmzo2PPpLr3XumLL6Rly6R+/cyVxU8Zm3YbHoWaselVV3QnMzbtfNZOZllZzUw1ijE2rSvW/c631HO0UDI2rWRlBTWYYRXnWH7lqyt6PgS57hRyV3QzY2yed560cqX06afSf/6Tfplc18h8jLGZaMsWZ9ZDYNP77fiNkfaA39rqxfpZIQmBTTjDTFd0K+vN5IsvdjxesqTh64sX73gcmzQhiIFNuqKnF6Su6EYbf27Pip6pYe/2GJtbthgrX7Zt+j3Y4tSNRioabPlRjBmbkUj6eltsda7Y9tdrbnVFT63LZGxmZyRj0w/7V1Vl/j1muqLPm7fj8fLlxtp9ToyxaWdork2bjL83GwKbSJVyfNPWUr8FNv1+f4C8ILAJ86xkbFrlxoUqCIFNO92T03HyOPpp8qB8jNNlNWPTyHrSrcvtjE2vuqIPHix165Y7cOu3xpJRV14ptWolPfig8+v2834XkkIdYzPbOfeb30itW0vPP5/8fLHdJHCO5ZcbPxo884xKZsxIfi4fgc0gnyuZyu6nruhPPintsku0h5YZdhICjLT78p2xmVomJwKbkUgwA5uMsekuI0kTfhs2qlg/KyQhsAlnmBlj04xc77Pyq3IQAptOZ2y6Gdj0MmPTq8CmEUYzMfM9xmYmbgc2JWnWLOnVV7Mv47fGklH/+U/0Mx40yJn1+enGsliknqOFkrGZqf4sWiS9+Wb0OpoaxC22mwTOsfxy47s7NtxQDF3RcwtCV/TLLou2i557zlwwz+zkQYmMtPucGGPTjNS2th8Cm179EE1XdHcxxiYCisAmzLM7xqaTs6IXamDT6YxNJycfImPT3a7oZo6nlVnRzWRsunEO5GqM+62x5BW/dQUsBoWasZmp/mQL3KZ7z8qVhVsXC22//P5ZMXmQPwQhsJnIzDXZTrs3XTvMaMZmruNl9XimBjZXrbK2nkThsL02fLY2J4FN71n9HghiV/Sgf1ZwBIFNOMPMGJtmLj65AjWFGth0OmPTzcAmY2ymZzRj085n7WTGpttjbMZkGuPTyDb92nBxo1wENvOvUMfYNNLdNMd7DnzmGZV37Cj97ncOFsxHCilj8557pPbtpQsu8LokmRHY9AcjY2z66dww0+az82OtkR+0jfx4na7+WR1jM3XfzznH/I9v6fYhiIFNP9VJv/rb36LfA336mH+vkbpNYBM+RGAT5mULgBjJ2LS7rUSFGtgM0hibxdgV3S8Zm1a7QqWTr4zNXA35IHZFJ7BZGAq1K3qm+pPtR4aU9/z8lVeiDx5/3KFC+UwhnWN/+Uv0/5df9rYc2ViZDMasUIjAZi52u5zmm5k2n53x0e2MsZmvjE1Jev99c+tItw92Eh/I2PSv//u/6P8vvmj+vSmfKxmbCAoCmzAvW8PHyzE2ja7L7cDm2rXR9H87/JyxmRp4K/Su6MuXN3zOyYzN1OW++cZYuSRrs6JnWiYIgU2/NlzcCLgS2My/Qu2KnunaYyJjs+DV1Ulff11455rb+2N1/evWuV82NwOb27ZJ334bfRzkc8XuJCH5ZqbN53TGZq51Ga0HTgY2d9rJ3DrSfd5uZWy6eV4Q2DTH7H7Y7Q3mxnGrr8/+HV0onxVsIbAJZ2QKGtq90Jj5Yky3LTszElop+7JlUocO0X9ffmn+/TGp++3nMTbznbFZVrbjsduBza+/js7mbYXVjM3f/U4aPdrZbRhZJh+TB0mFmbHp5PkVQ2Az/4qtK7qJjM2C17ev1Lmz9Mc/el0SZzn9ORr9wS6Xmpr8ZG26oa5O6tJF2n9/6amngn2uZGqz+/X7x0x706uMzVyc6oou5R7aJ9u2Jbqi5+Knum+H2eu03UxuN47bOedEv6Nvuil/20TgENiEeWYyNr3uim7n12crZb7xxmiWT3291L+/+fdn2rafZ0XPd8ZmPgObmcaTs1I3jGZsStKAAcbWaeWHBDOBTTcaCoU4xiYZm4WhUAObVrqi+/VHBLdUVkb/f+ghb8vhNKc/RyeHP1m71l5ZjHAjY/ODD6Tvvos+vvTSYF+fM32efh1j027GptF9cSpj005yRap0gU27mXRMHpR9vUE+txOZ/YxT9tsXXdHfeCP6/z335G+bCBwCm3BG7MtxypTk5+0GNn/4IfvrZm7+Y8u6GdhMzEDINftzNukyNr/4wvosiEHK2Kytlf73v8wN2PLyHY+nTJE2bHB2+4liN7upnOyK7uS4mHYyNu1kN5hRiBmbbk+y5Nf9LjRe/2jjFiuTB3GTUBjcztgsxsDm6tXJfwf5XAnaGJteZmx+882O+l5dLX38cfLrRofesnrOOBHYLJSMTbfGMfVTXbdjzZrkv81+xkbuLfyWhGDlvKqri95vFsqwQyCwCQsyNXwGDZLuuiv5eTtjbC5YIHXtmn0Zv00e5FSWVep733gj2vVpn32sBfLcDGw6ffM/YIB03HHSxRenfz0xY3PRIunQQ90LQJSWpn/eSmDTaFd0M5zM2ExXR/zWFd2vjU66ohcGp4cA8Qsr5zF1rjD4tSu6FB1n001ujbGZWu4g//AUtDE2zWTR2+mFkunaf/PN0f8vuEDq3Tv39tJxMmPTbN0rlsCm1XOyEDI2t2+X9t47+blC6Iqei5VtXnNN9H4z9VxGYBHYhDMiEenhh9M/n/i/GUbGNvTb5EGJWVZ2LuypX8pDh0b/37rVWle5IGVsjhkT/T/TjK6JGZtSdFzTqVOdLUNMpq6aTmZsOllPjDTmMm0vCF3R/XoDSVf0wlBsgU2r2dHUx+CgK7rz61y/PvnvIJ8PmQIYic/76XvXy1nRJekf/4j+H+sWm8iLMTbtZmwGtSt6rmPtVMZmEM/tDz6QtmxJfs6Nruh+ayNY2eajj0b/f/ddZ8sCzxDYhHlmfrmxc3Hbts3c8onbWrNGGjcufSPIzQuuU+MSzZ2b+TUrQUo3x9jM9+RBqYHN1OcWLVKbzz93Zp+dDGwGMWPTb13RvWpkbt0qvfVW5uElCGwWhkLtim7l+zmIQ0KgIaevHVYnTEknH4FNNyT2mikpCfb1OWhd0fOVselUO9vvY2zmM2Nzy5ZoOyrdEE9r1kjjx6e/Z1uwIBqsy7buXNs2qhACm+nqnM3AZsFmbKLglOVeBDAgV9DGygXHSIM53c1/JCKdeKI0b172MuXiVVf0RYukXr2MbcOoIHVFz6UszWWrcePo/5s3q6xrVx1bVaW6iorMk/8YlakruhH5yNi0kj1DV3Tzrrgimkl88snSe+81fN3tQA8NtvxI/Rzz/aONW5zO2AyH7V0bkT9+zthMHQfOaW5ka0rJXdFbtw729dlIxqaf9s9MYNONjM1srIyxWUwZm5deKr34YvT+5p13kpeL3bP9+c/Jk8OsWxcdhmvr1uzrNlOubAohsJkuIcPuGJteBzatJJOgKJGxCfPMXODsjLFpNbC5cWPmoKaZsngV2PzrX629LxsnA5upX5D5vvlPF9hs1Cj6/wsvKPTTBE5lv/+9/W3loyu6k5MHFXrGpleZYrHhEd5/P/3rbo+xifygK7qx19yo73CH2zeYduqC2V45VrjdFb0YApt+ytC2Oyu6E4FNu205JzM2nR5j0+z5ku07MnU/X3wx+n9qt981a3bcs/3zn8mvPfVUw6CmlL+u6EGU2g1dsj3GZijdcclnV3QCmzCIwCackaurm5ULjtUGc65MEr8HNlPHb8q2DaPc7Iqe75v/bF+wTn+xBW3yIDsZm0EYY9Pthsvy5dIzz2Tucp4JXdGDad066emnpR9/jP5dDIHNxPMvW7194QXp66/Tvxb0wOaWLdHzvBi4fYPp5A9zbshHYNNPgT+zMv04WqgZm0bHIc92jctUBitjbOayZIn03/9Gu2/nY1Z0s5n42T4PJ+61Mv34UYxd0SOR6I/rmX5gj/kpuSNJ0LuiE9iEQQQ2YZ6ZX27y2RU9dZuZuBnYdGLyoFyznvutK3q+MzbzGdi0k7GZKh9d0e18+QehK7rbN5DHHiv16ydddpm597lRLqMBKFh37rlS//7SGWdE//b6Rxu3ZApsZjvXHn1U6tw5/XhoQQ9s3nhj9DwvBk5fO5wcY9PteuTWrOiJgc2ddw72DXWmQLVfA5t+yNjMVAYrY2zm+iG3e3fpkkukK6/MT1f0XD88p3IisJlNpmNdjF3Rp0yRevaM/ps8OfNyLgQ2PZ88yM7EqCgqBDbhDD9kbMa24VQXBbsZm1a/WN3I2HQzsLl9u3PrNiLd5xIrk9M3cX6fPMjKTWaQu6K73XBZvDj6/yuvmHuf213RabC546OPov9/9ln0/0LN2EysP2Z/fPvww4bPBT2w+fDDXpcgf/zcFT2oGZuJWWSFOnmQX7ui52uMzWzX/lwZm2aSK3IN+bFqVfTxc8+lL5PZzybd520nYzNboNmJey0zQWQr287FT+f2jTemf5wqXWDT7HWaMTYRUAQ2YZ6ZC5zb3ZTS3fznuoDnK7BpVa7AphVuBjbTjX/jli+/jE6ulMpKxuaCBdIDD+xoOKaTjzE2852x6UZgc+1a6cEHs49tG5MrI8CrjE0nA8xOILCZf8UweZATvQq8DmxGItLLL0svvWR+H5Ytc6dMfuXnyYP8FDAzI7H+h8PBvj7b7XKab36YFd1qsC3dcmbaO/nI2HSyK7rR42HlWOcrY9OIb7+Ntn9jQ9q4xeh3dyF2RSdjEwYxKzrMM3OBcztjM93Nv18yNq1eZIM2xmY+JgCQog2cQw9N/5qVjM0jjohONPX889LUqemXMdstJ5EXgU2nx9g0ejz795fGj48er9ra7MctV/31aoxNv2UeEdjMv2Loim62XqU7X70OSL39tnT++dHH48btGErAiGnT3CmTX7mdOePnwKZbXdFzBTYjkeBM/mYkY9NP3z9muqK7NSu6mTE2c3VFz5WxmSgfY2yabfM6kbFp5Vg7NeRYrvcZWc+hh0bL+dpr6Xs4OMVoj8Bi7Ypu9/skSNdtZETGJpzhhzE2/RbYtHqRzRVg8VtX9HxlbGbLrLSSsblxY/T/bDe6+Zg8qBBmRR8/fse6c914+HWMTTvnCF3RC0OhdkUvtIzN22/f8fiOO8y9N9cY1oXG6zE2zQRu3ODGjWridSESsfY97BdGJg/y+oeMRH7I2LTbFd1oxqYXgU2z54sTY2xa6fbvp8BmrIyTJlnbplFGx113oiu6kfuXQuuK7qfrHCwjsAnz/Jqx+eyz0ujRuTPj3AxsOnHT6IZCCGwaaQA6fczd6Ir+xRfSrbfu6FKf74zNxPe88470979HJwhxaoxNu3Utn42lRHaCWGRsFgYCm+kZzT4PgiCX3Qq3bzDtDP0T1DE2c2VsBukGOWgZm/kaY9PNrugJZQjZ7Ypud4zNcDh5Xzdtiv5wlC2RIJGdjM1ly6TbbpOmT8+8zObN6Z+P7ffq1dEftz75JP3rZpkNbObzXDfaJnQiY9PIjzV+y9i0u00/XedgGV3R4Qw3AptWvjAGDJBGjcq+jJ+7olvtGpiLk1++qV+Q+QpsZtsHt2ZFdyNjs0uX6P/PPCN9/72zgU0zGZtLlkinn77jcZ8+mZc1I9e4hHZ+aS/WruhBulEOMiMZMkGUeN5YCWym1r8gBwcLJVhtlNdd0Y18b7vFra7oiXWIwGZ+2Z0V3YlxH810RTeyXKYusPnK2EzdzrBh0S7VRrpV28nYPPtsaebM7Mts2pR93RddJH3wgTR0aHRYrCZN0m/baDdjAptp15/2yJGxCR8iYxPmmcnYtBNwMpuxGfPYY9mXdTOwaff9RiYOsjLuY1AyNrM1KrJ96fhxVvRUqcfthx+sryvGzqzoid1m/v1v5zI2cwWD/HojbPQcMfvLtVWJdS/IgaQgKYaMTStjbBZSYDPIZbfC667oRr63gyax3JGItR8Y/cJIYNNPN/xB6IqeKNcYm9nK5FVgUzLerdpOYDNXUFPKHdj84IMdz61du+Ox1eEhzB7PfF7DPAxsFmTGppX7J/gegU04I9cYm06uM1G6RkOuWf2sfsEZeZ/druhLluReppDH2Ew9Zv/5T+bXEgUhY9PJyYM++0waNEj6/HPz64odq9SGjp2bgES5GlBBz9hMVwa3x9gMagAgaIohsGl0nK5EqfXP6/qYeA6a/T70uuz55nVX9Gyv5yNj043vjNSu6EG+QTZSdj8FavM1eVC2a3+uruhms/wyBY3uuSf5uXzMip7Oo49GMyK3bGn4mhOTB2VTWZn++XT1NLG9bvb+LRKRRo6MDhNl5n35/D4x+t2d7nOyO8ammYSmXK9ZYeWea/ly6U9/2jEHQKrUeu+n6xwsoys6zDNzgbPTFd1qxmZZjmpt9eJlpCuD3e6jS5eaf48RQQlspq77qqukgw+WjjvOm67oTs6KbnbyoPr6zIHVbt3MrStduVK/1P2SsWkkgO0Go0GscLhhvXC7K3qxBWO8UmyBTatd0YMUuElVKJ+pUU5/VkHqii7lJ7BJV3R3lZTsOKZBy9hMx0jQ6NlnpX/+M/k5N8bYzJSxGTN5svT730cf19REx2RPZDVj0+jnkCmwme792b7bcm3v3Xela681tp1EfgxsujDGZtqxYPMZ2LSSsXn++dLUqdKIEdHJYlu1Sn7dSHIHAoeMTTjDTGDT6s1UOm4GNgspY9PJC7abgc105Xzrrej/2Y6n2cmDjC7nxuRBRtdlZXw/I42P2DE2koVlpd7YzdjMZ/eWREYbqHbG7TKDwGb+FeoYm3YCm6GQ/zI27Yyb6HXZ883tzJliC2ymaxfSFd1d5eU7Hgdh8iAnMjaffLLhc25lbGbb1zff3PH4H/9o+LrVjE2j5c70HZyrrWu2K/pLLxnfTiI/dkVP1wYvxq7oU6fueLxsWcPlUz+7IF23kRGBTRgXiejnL7ygktTZ5356LS23x9hMJ1Ng02z2qJVf4Y1+8Xz+udSvX3RW6kR+7YpeVSVdd510550N17Vtm711J8rWEDVyg2T1F/NM3Jg8yOi6rARVli6V+vffEQzOVo7Uhk5qXcxWtmxi5c71Y0cm+crY/Prr6GRjb7wR/dtOYJOu6MG3das0ZEjyc6++Ks2f7015nJR4TpkNbE6ZotL+/ZOf87o+2rkB8brs+eb2DWbq8RwzRrr00h1jSBsdY3Pu3Oh317hxzpRTcqcrerosHzI23dWo0Y7HdicPynfGpp0xNlPlc4zNmGy9lrZula65JvPrTrTlMpUtVzvMqR8bpk+P3qtlGnPUj4FNJ9qoKesP/ORB6c5DMjYLEl3RYVjohRd04HPPpX8xV9Am8YJjNDAXxDE2jS5/1FHS9u3RmbETl1u+3Nw2jLL75XvbbdIDD0QfpzZ03M7YjB2fbPtgNmPT6BeY0xmbZrpzWM0We/rp6L9wOH22VabA5oMPGi9bNrH15vqxI5N8NZZOPDE62Pzo0dGbJTI2i9tdd6V//rTTpIUL81sWp9mZPOgf/2j4C7if6qPZH/roim5PtiDe2rXSxRdHH3/6qfTll8bH2DzmGGnz5uTvLjfKa1fq93K6jM0g3SAHIbDpZMam3YBatjI42RU9Xf33W2Bz+PDs23IisGkmISB1iAijZcnmm2+i/1Lv1dJt021Gu6KnK1MxZGyave4S2CxIZGzCsJJMqfqSucCmUV6PsWmlsWo0G2b79vTLGfk12ouu6I88knldbo6xKRkLbLqVsel0YNNMBoGZzIR0Mh2TTIHNbOswIygZm4kzaFZXE9gsdo8/nv75RYvyWgxX2B1jM1WQ62OQy25FPruiJ44RPnduw9dTJb62eXP65+1y+tqcGlxKN3mQ14FAMzKV3U9d0a1MeJZp2Xx0Rc/Fan1xY4zNXF3RswU203WXT123ldeMLJcr0GY2McXqDyn5PDfsZGzaHGPTVFJGrtessJuxma4eM3lQQSKwCeMyDeIsOTvjc4zTY2zGlnUzY9PK5EGJF1erwdxc7N7MZdtmIWdsGmlU3Xqr1LevtGpV7nWZCYqZGVso0/a++Ub69a/Tv99IQ8fItlIzPe1mbLrxK/CPP0oXXSTdckvmBprRhp/ZX66tSmyIFVuWmV3btklXXCFdfbXx88ipDDE/Sqyf2WaOtbK+fJs1S5oxw/r7iy2wmc+MzXTnkJXghlOfkRtd0dMFNoOWsfn441Lv3tJXXwUjY9PqpJxujbGZK2PT7I+3RvfJbxmbLVtm35YTgS+rGZtOdUU3s0235TOwmdoV3evAppUxNhOl+27K1JsNgUZXdBgW2rQp84uZLpp2xth0OrAZ42bGptluflK0kRTramN1n3MJSmAzW0M02xez2XqWrtFj9qZMkj78MNpNX5K2bNkxXmO6bTiVsWm0i3o4LJ16anIWTez5WHlyyXU8Fy+WBg1KXz43MjatNpauuio6XqIknXBC9LikrtfojzOMsel/d9+9IwNzv/2k669vuEwxHVM7XdHT8fLYHX20vfcX248Ebt9g5qoLZrtM5nqPWXRFT7Z+ffRHH0maMkX697+TXzcbwMg3M2WxmrGZ64dOu2NsGsmGMzImYKb3ZuOXwKbd5IJcSQJuZFFv3y41bZr8nB+7orswxqbnXdHtZmymQ8ZmQSJjE8YldhVKlakRYOfXX6vZi7kmfMlXxqbR7SQGsNxqENud/S1bYHPbNufK7VXGptnMkdh2pk/f8Vzi7JHpypIpYzNTmTMFMM0ENlODmrHnJWe6oqeb7CpXYNPOGJtW61ksqClFx35LVV9v/Cabruj+l/h5jx+ffpnUzyzbDVzQJZ5TQe+KbneIjmI7l/KZsWl2+25nbEreZGz6+QZ5zZodj9ety/x5krGZ+TW7XdGt1pd0bTaz53e69ozVwGaLFtm3la0OGS23md6AbmdspkviCEpg027GpoFlDL9mhZF7J7MZm4yxWZAKuCUPx2XL2Mz15ZN4wdm0SXr33dzbs3qRsRM4ybacXwKbToyx6fSXTuKYoXbYHWPT6mebK+M41/uNLJMuY3PQoMyTltjN2MwVWHSiK3q67OhcXdG9yNhMlDgRQYwfA5uJii0YY1diPcl0vUw9poUc2CykjE27glx2K9zOnMnVFd3o5EFGnjcrX13Rjf5Q6kdBGGMzUT4yNiV3Z0W32h53ImMz3X1NtragUxmb6drDRvipK7rXgc18dUVfuzY6iVsirzM27QY206ErekEq4JY8HJdtjM1c45ilXnBOOy339oz82mp05sBs6zC6nJGLnpVsmMRGUr66ojuZsSk51x3dbsam1e4tmb70zQTsMy2TuK7U9aWbiTzGiYzNbM+7Fdi02xXdSoaPGY0aNXzObmDTjcZt4jqLLRhjl5HAZjFlbCbuq9WJOBL5qT6a/T50YgiOIHE7sOlGV3Qn65fTN6tB74qeKlNwoNgzNrO1s3JlbJr98dZOsNVuV/RcGZvZrq9mMjZTt+tUOzbTc2aDx0a+R4Ic2DRTzsGDGz5ndoiKfHRFtxvYpCt6QSrgljycFsrWBSxfY2x6Gdg0m7FptAFmNmPTyrF0+5cpp77cvRpjMx8Zm5m6omeSqR4b/eU1V9md6IqebtgHu5MHeZGxGQ4bD27nK2MzcZ1+CiQFgZWMTSYPMi7I9dGJ616QOL0vZrMT6YruL6nXuSAENhPZzdg0ej6sX5/5tUw9lKz+sJ5un4IwxqadwKbRXl5+z9jM53dFvjI2X3+94aa9DmwaOZftHpNC+t4vYgQ24Qw3ZkW3mrGZ7QL+9NPSJZcY276VX+Hz0RXdysXX7a5STt2IWMnuSHzN7xmbZo67E5MHZXveTubSiy9KnTtLo0dnLp8bXdEnT5Yuvthe/Q1KV/TEdc6ZE/2H6Od0/vnSMcdIy5alX4bAZrLEfXWiK3qQbwDI2HR2fW4ENlOf//e/o983r72Wu3yJmBU9NyOBTa/3x4uMzbVrM7+2bVv65412RXcyY9PuGJt2ApvNm2ffVrY6lKk7f673JS6T7UdnN4bf8lPGptl7JTOBzXQJC2a7ojt9zbCbsUlgs2gQ2IQxuS6KZiYPMspIAMFsxmb//sa3bzdj063AphcZm7lu+p36QvDbGJt2Gi1uZWwaDWzmanzb6Yp+4YXS11+n70pvN2Mz1+tjxkiffZZ9mWzsBjZzZQo4JXW755/v/DaC6KmnpJdflqZO3TG7byq6oidLrJ9BnzzILiNlL6QbHLdvMPORsXn11dHvm3POyV2+VE4HNtN1XwxSxmausprNzMo3M/XZSK+vTLIFNnNlbJodbsdoQNSNMTZzdUXP9r2Y677AamAztmy2zzoSafjjf7aMzVz1Jghd0fM1eZDRwKbXGZt2A5t2h2iDLxVwSx6O2rJFkT32yPy6G4FNq7+2Gg38ZGO1sWolsJn4Be/WTZfbgU03MzaNdJ22Oyu6G13RU+U7Y3PjxvTPmwlsWqlrbmZsxlRVmStTIrOBTT90RZekBQuc30YQff75jscTJ6ZfhozNZE5nbPopsGn2c3NibOEgcTtjM1PdSve60decvJ7mI2PT7R4xbsr0w52fuqJbvWbZydhcty7za9mG5bIyTJGdjE27XdHTZRwnyhbYNNOWS102W2DTSA+scDj9uZhpe4WQsWn0R0kjQTyj2/lJ2m/ZXIFnJxnJGDVbHjI2CxKBTRjTqpXqpk3L/Hq+xti0m7FplNm0+xgrWT/56Iru9uRB+Qhs+ilj00rjNd8Zmxs2pH/eiYzNbOyOcWtkm3YaIJkmDzIa3PYqsImoTNmHZhn5LikUiXWbjM3cyxTSuef0vmS7HjrVXfFPf5L23FMaN858+RL5tSv65ZdLHTtKU6Y4WzYjrIyx6afzwQ8Zm9naOFba8EbLlC1j8+WXpfbtpWHDzG07VzvQSLs7k2x1KNsYm0YCm5FIw88hsayF3hXdbrfrbIolY9PNyYOGDZP22EN69VXn1glDCGzCuHSZTjH56oqeuh63ApvpGqtmMzYzSV0PY2xmX4+RwKZbY2zmytg088We74zNTIFNIxmwqcua4ebkQTF26lu6YJjTY2w60UDy082knyR+Tuka4FLy8c8U/CzWjE0Cm7mX8TpDzUluZ85kG9cu03O5Xnv+eWn5culXv7Jffqevo0ZmRc9W5i+/lJ54QlqyRDr+eGfLZkWmz9NPgU2r2aNujbFpJrCZ7nvFansh2xib558vrVwp3X67uQBPrvZktuulmR+pzXRFNxrYNNMVPVdZg9AV3egYmy4ENtNOHpTPjE277W4jx8Sp61w4HD0PV6yQzj3XmXXCMAKbMC5dplOMlcCmlS8EI4FNMxfwbNsx+yt8pvKkyvYFn6+MzULriu5UxmbqfjiZsWk2sJlp205lbLqVuWS3K7pb50C292YbZ8pIYNONsXpSt9Okif115oNb442mq7dlZemXpyt6Mqcn9wtyYNPuEBxB2/d8dkU3G9g0ciztHG8/ZmyuWeNseewykrHpdaDfapDVasZmJJI9sJmtK3qurt1S+uB4KqMTwNjNpMvVnjTS7pakTp2yv+50YDNXV/R8ZWzmM+ifLXCbyIWu6J5nbBrZvt1j4mRgE54hsAnj7GRsppOtcZCJna7oZi60TmVsPvVUw2VSy+dFV3S/BjbtZmwa/YzTZWyOGCG1bCnddVfm5TKVzchr+e6K7sQYm37N2HQ6sOn0GJtuBDZ33dX+Ot0UDks9e0oVFdL06c6td/166cADpf32k1atSq63RjI2M0k9vsUyeZATAQs/NdzNBqTtZGxeeqnUurX52bm95PRnlY+MzUR2f6j2W2DT6x9QcrVr/dgV3cmMTSP7smVL9sCbmYzNdOXN1QX2hRekDz/M/b5M6zdzztnpip647QsvzP66la7oubbtZMamEV5nbNoJbBZiV3SnMzadKrPX18siV8AteTguW2AzV7ZTugtGtoZDJkYuPG5NHmQlY/PSSxsuk3oxDUJg0+z6rcr2C7vbY2z+6U/Rhsstt+x43suMzUwNEaP1e/Pm9M+7HdjMx+RBduqb2cCmla7oTpxfqeto3dr+Ot00bpz0/vvRTJeTTnJuvUOGSN9+K/3wgzRokPmu6GRsZr4hslpPg5a1mMhqpvqqVdEfKisrrc3O7RW3bzBzBTbtjNGX6/1GuB3YjETM3WD7KftRMlZ2P5U5HxmbmzZlf91Mxma6MuTK2OzTJ/2607X97P6YkKsdaDRjM1cwzI2u6PmePGjbtobPeRXYNJsJbyawGYTJg9Ldo/upKzo8Q2ATxpWUKJwpq8VKV/R8Z2yauXE1mrGZOjuzkW34IbBp9ksn1345dSHPlrFppIFltBy5GnfpgqmJmZxWAptbt+Y3sJlpObe7oufK2HQisGk3YzP1vE03eVCsHEZu/PLRFd3vgc3ErnvpbgCs+vbbHY/nz899MyUZC2wa+S5xUmqdy6fEup3t5s8oPwU26+qyZ/+kWz6XdMclXbZOELidsWmnK7rbGZt+6Iq+ZYv3gcFEub7P3M7YrKkx3/Z3MmPTiR9O7WRsRiLWAyrpjpvdjE2nxtjMd2AzXVf0Qp88KNePSFL6gJ9krpxByNg0+4NSPjM2/XS9L0IENmFKJNOYZlYCm05kbLo5K3quwOb990utWkm//3328qRKLR9jbO5gNWPTbFf0dBmbiWKBmVh5SkulX/868/uNeP55aeFC48vb7Yqea0gGrzI2c9U9t86BmPvui563idJlbGYKAHvVFT21zH6TKchoV+JxCIXcy9h0syv6H/8Y/fzuvde9bWTjdFd0PwU2p06VOnSIZvQaUWyzorudOWOnK7qRzyL1e8ps3XP6szQT2Jw8WWrbVurWzT/njJeBzZUrpb32iv5bscL4+/KdsZlrG7kyNrNtL1d38mztMicCm0bKY/R1rzM2i23yICNd0ROfT7xfd6MrepAyNo0Ee8nYLAgENmFK2MnAplsZm05MHmRk3KTrr48+9+ij5tadLWPTrZuuoHRFtzrGptMZm5WVydssKUmua9nqdeoyiW680Vj50pUpxqmMTa/G2PQ6Y3PWrIbvTxfYzJSx6VVXdL+PAelW+RLrQ0mJscBmIj90Rf/Xv6Kf55//7N42ssmUyVIIgU0pmi18zTXGlrU6xmZQhyrwuiu63YzN1M8rU7vR6veNWWZmRe/RI5qxOWuW9OqrzpbDKi8Dm9dfL61eHf03aJDx91ktix8zNtO9N3GZlSvNbTdXl+xc5cu1L25lbBoZY5Ou6Jm3ZSRxIHH4ODdmRfcyY9Nor8pM75foil6gfH6nBL8JZ7qRzHTRzHaCu5WxuWyZsffm2o6VL0YrXavy0RXd7gXcyYzNVavMZfMZyTA0Mth4unXGpK47NsZSYsZmusCmmW1I2RutqewGNnOdk37N2HTixiORkVlos2VsWglsGj1u4bCarF+f8bWc2/UTMxmbkYjxc8GJjM1028t3V3QvFXLGZkym7/1UVmdFD0r3slw/yLq5fquBzWyfSbb2Uq7t5Ksreqab7sRtZ5rML99y1Q83A5uLF+94bPR8TS2Lmc/T6nmc6xphZ4zNXIHNJUvMbTccbhhwMxPYzNWu9jJj087kQfnoRZO6TcmZxJpMjIyxmfh8o0Y7Hhfa5EHpuqKb/REt9Zg4NdyM39vqBY7AJkzJ2BU91yQrTnVFz9cFw0jGZrrXrDSagjB5kFOBzYcektq1k8480/x6nMzYNBrYzJSxmWk9uV7beWdj5UtXpph8ZmxaqWv5mDzIaLkmTJB2393Y+uwENi2eX6Vnn61el16qkn/+M32ZEvk9sGImY/OSS6T27aXbbsu9bGrGZmK9zfR9lBoMPffc6PYSj3MxTR6UeMycCGz6seHerJmx5Qq9K7obmUrZ1pfrZjvXd3o4LB1xROZljAY2vcrYzNRW7N/f2e06Jdf3Srq2rBuZTGaut/nuip6rbeRmxqbZwGYk0jAg41TGZrZJFWPbjvHDGJtuZ2wa+eGmSxf3fvgzm7GZGNg0U6Z0bTm/dUV3ImMz9ZgcdZSUKdHAjCC3HwoAgU2YkrEreq7x/Jzqim4kY9MJuTI203VlTV0mEz+MsWn2Syfdce7WLfP6M/nDH6L/v/12+pknszVEjQQ9E5aJZKsbmT6/mNTAZmqjzWrGppnApt0xNnNlbFrtkml0u1YzNp08B3r1MrZcusyETMfJqYzN6mqVvP22JKn0//4v9zr93lgyk7H57LPR/2+9NfeyqdmXZruih8PSa69FHycOBWEmY9PvQeVcCr0ruiQ1b25sObeue37h9nXD6YzNyZOlOXMyL2MnY1Pypit6XZ30zDP5KY9Zub6r0rXZnSpz6o9UVt5npizpznUj50Oua4SdMTZzBTazBVacCGwaKU9MtnZR6mM/dEV3O2MzXb1Ife7rr6X337e/rVzbNxvYdGNWdL9lbNrtii5Jt99uvmxGtoW8IbAJUxwdYzMxoBeJRL8QcjUo7HQftDsremI3mtQZbnMFc9Itm7iu+fN3bDcXO8GmGLsX3nbtpJ497a3PSCNBih739euzZ1ilC0Jlazib7Yru1BibfsjYNNK1P3VZM/KRsel0YCVTV/QFC6QNGxo+n8piYDNJ4mQK6W6W/d5Y8tMYm4nvST0PYt32zNQhPwbyzCiGrui5Mja3b5e+/ZaMTbuyBU6tBDZj41lnYmeMzVDI+QCHkYzN5cvtbcNNRgObqZzOeHMqYzMcjt47GM0oc6J94XTGZq5zKNt7IxF3u6KnHovEMuQKVGf7rlm9Ovt205U1dd2p1wI7GZtG6qPRjHSnujRn276RruiJY2xmO96pCnHyIKOBTTI2Ay/Qgc2RI0eqU6dOatKkibp3764ZM2ZkXHbUqFEKhUJJ/5o0aZK0TCQS0dChQ9WuXTs1bdpUPXv21Hfffef2bgRKJNONZKaGQLYu2ok39n/5i9S5s3TOOTkKYCNj08yFNl1jtWdP6cMPo49Tsw3NZGymXkzvuUc64ADpgQeC0xV9wIDkLz8rN7pGvyjffz86i+bSpcnPJwbZ043JYyZjM1dX9ExjbBrtphPTsmXm5VO53RXdyHqs1DW/Tx6UTroG/KuvSvvtJx13XO5tWzm/Uo////6343G6Y+B1pk8ubgU2U2+GzQY2U8+jQw9N/3mn/n3AAZlfC5piyNjMFtgMh6WuXaX995emT8+9LrM3cX7i9g8iZrui5wqy5Oq5k3qdzPW9liq1vFdemX17uRgJbCb+CO43uYIBmX60daIeJa7DqYzNiy+O3jvEegMlcmvyIDfH2Mx2nDONsWmnK7rZMTYzDZ1lNmPzsceybzfdexKl64puZ1Z0I4wmY7jVVjMyxmbiMokZm++8Y3xc26BMHuRGxqYTgtJWKFCBDWy+8MILGjx4sIYNG6ZZs2apS5cu6tWrl3788ceM72nZsqVWrlwZ/7c4pfFxzz336MEHH9Qjjzyi6dOnq3nz5urVq5e2Z0uZLzKuzYoeG/vsjTcyL5/uebcCm+l+DZKk00+P/p8a2Iztv5ELWqZjdd11wQlslpa6E9jMtJ4tW6QHH2xYhhi3MzatBDbt8kNg042MzXx2RTcqXaBr8OD0yxoJehg5bqk3KYsWZV5fpuf8xOiNqtn9sJuxmZoZu2CB9NFHuW/wrI5Pla0sXin2jM0ZM6R584yvy2hmjh+50QUz2/pyZRHlGkIm181l6vlrtyv6o49m314uqeVN1yXSz4HNXEMVWP3eNrttq4HN1HI8/3z0/5Ejs28v3boysTPGZq6AmtOBTbNd0Y0EWmPMZGyaDWxmE+TJg9xkdvKg9u2TX5s0ydh2/Dh5kBsZm259dn5vqxe4wAY2R4wYoSuuuEIDBw7UQQcdpEceeUTNmjXTk08+mfE9oVBIFRUV8X9t27aNvxaJRHT//ffrlltu0W9+8xsdeuihevrpp7VixQqNHTs2D3sUDJYDm+kYGTy6QQFsXDDMvDfTsrEyp3afsjPGZrr1WClbNukCm5WV0tSp1r6A3Apsmtm3fGZsZuqKnq0BnG7/zPxCmKmeGF1HrjE2jYxxa6VubNwYrVe5JhSL+eab5BtBr7qipx6vTD9oWe2K/tVXyVnHqcc/sTtZEAObqY3hTz819+NFJnYzNlO76UnRcyvbDZtkfXyqVH4IiGWaPMhqnfJjXcwW2ExXB7Kx+t00Z46U5cd119TWRjO+a2rc74puNrBpNusq1bffJv/t9eRB6dpSqdtI/JHKb4x0RXcrYzlxvfmYPMirjM1EU6dGh5v65JPM9T2fgc1cP+onspuxabSLfbrt5npPJJL/yYP8lLFZV5f7PG3eXLr66h1/G53Xwo+TB6W7bpkJXhu9H3MrAI68yRCl8reamhp99tlnGjJkSPy5kpIS9ezZU1OnTs34vqqqKnXs2FHhcFhdu3bV3/72N3Xu3FmStHDhQq1atUo9E8YNbNWqlbp3766pU6eqT58+DdZXXV2t6oTgXOVPwa7a2lrVGs2qCpDa2tqMXdHDNTVpo+Th+nrV19aqLBxuMPhw3dativx0nBJGAokeu+3bk56Lv1ZTk/RlVqYMgxqnEUlThkxqa2pUGg6n3afa2lqF1q1LOnlqt22TamtVUl+v1COUWhdC27dnPPGMlLG+rk5hk/WrrK4uab2127er7KijFJo3T/W33aZwwrmU9v2hUNL7639qlMb2ta66Ov5ZZpP0Oad8lpIUqq42fFGKlJXFyxQ7JqV1dTs+s1Ao83lYU5NUlrqUz6R+40aFE+ptpLRUdXV18feEw2HV19aqpLY26fNO3F66+pPpPEmnvqYm7edcsn17gzqWTri6Ou22YseqrKYmZ12rq6lJ+7mmOzfjXnxRevFFhQcMSL/92tr4foVmzFDZccdFj++8eVKnTgrV1uasA3W1tabrW9b11dQoVFOTdFzDv/iFSiZPTrts6rZLUt5bW12dVLdDH32kslNOUaRRI9XNny/tsYe0ZUtS+eo3b97xeVdXNyh77FrqV6FwOPlzO/JI1T38sCKXX568YMq1Pdd3ZeJ3RzgUil5nY3+XlKQ9JmWRSPw9ka1bG373/NQFNrG8kZRrZLisLL6d2u3bpaZNs5Yzo5RrTW1NTd5nYC+rr99xPMJh1cXOPwPnWjqZrk35kOmcrm/SJGOZQlu3mtrP1PNXUoNzssH3+ssvq+yiixRp1Up1CxdKO+1kYov2lPbvr5LnnlP4nHNU/+STyd9tGa7hVoVSzpuk63nKa7W1tQ2eSxQJh1WfpT0kSUppe/8/e+cdN0Vx//HP3T2NIr0rXYooiKJgRwVB0SiR2GLFHjXWqNGoWH8ae0zsBks0ttgVC6JgAVFQBCwoSlHpvfOU298fw97Nzs3Mzmy5At/36/W8nrvbNrs7O+Wz38KPGz0o2kzHYExmQ3LzZs/+nHQadbW13rZk7lxpm+PU1CAhrJvvuULO/aut9ZxPbU0NHKHNArY8EyHDjYhtuWl/5mnPHSfTfgGSuQOHZzzobs+1fzz8ftJcHyPDqalRjp1qNm/2lBcrVgDbbQcAqLvsMqSPPTb32nJjYXFM6UddbS2cNWu8dUrWfm0h5/5rxpM1mzYhJVyLmg0bMvtO1dVlltUiV1DwzFUkz6eKzHxCs42s7eDbupy2SDLf4Emm077XPV1bm1NnZe1bbW1tpG2ui2weB9HYiLtmaQDp3XfPlK9uwwajfjuVTObU/wRyn6+k0HbwmI7RTcm5n5s354yt3HmZi18/KLblsn0EwmesQNhjcw1LUthctmwZ6urqPBaXANC6dWt8//330m169OiB0aNHo0+fPli9ejXuvPNO7LPPPvjmm2+www47YNGiRZl9iPt0l4nceuutuOGGG3J+f++991DfL5B9ibKvwmLzt3nz0F7y+4LffsPUMWNw4Jo1aCwsm/7FF/hlS4d/FPf7mLfeQmrTJhwh2d+H48ZhY6tWme8D165FE8OyO9zkzo8P3n8fe6xYgeaSZWPGjMH2H3+MPfj1x47FpmbN0HvOHHSRrM/TfOZMCFH7Mmxavx5+0+f5c+ZgurBPLY6Do4Q3SF8+9xwGbHHNS40ahTd33VW7i0M2bQJfo2fNng0kEui15fvUzz/HIoNBL3+fx40di81Nm3qWt/niCwzw3QujOp1G5ZbPc2fPxswxY9Dv11+xw5bf6tLpnGvvst28eTiY+z79yy+xO/d9/qxZmD5mDIZsuR+bNm/GpxMmwH3t8dtvv+HLMWOwy48/oiu3HX+8/ZYvz6k/SxYuRBvD85s3ezZmSMrf/ZtvsJPB9osXLEBbye+/zJuHr8eMwSGrVsGvlfrqyy+xQCLoHCVZVyT5xBPS33/44Qf8sOW8Dvrzn9EIQKKuDktOOw1TrrgCbaZM8a0D30yfjrkGz4BJOQHgyy++QMMFCzL1GQCWrF8vvVeTPv0UK4SEQjt++y125r6/P3YsqhtnW7xB556LhmCDswVnnolpf/4ztps/31MH5333XeZ+pzZvzmn/li9bhok2z32eafXll9hb+K3svPPwmuAOJbbtY956Syv0DVy1KtPGr1ixAsmaGjTb8n3V2rX4WHJNhm7aBDeC9vplyyBKTJ9/8QWcRAL7cr9tWrfO0/auWLsWLbZ8fv/dd1FtEx+XI1ldjd9x399+/XU45aZTvGg4nJsUbNqwAe9tuWYmz5qMH777LvMM5xvVMz3711/xvaJMradMwV4Wxxj/4YfYIIwFG82di4O472LfctQf/wgASKxeje+uvBJzDj/c4ojhOOrZZwEAyZdfxphjjvE8X59PnoylppY6BjT75hvsz33/buZM/OTWJ6H/HjNmDFp//rny2ldv3IjvvvoKfS2O/9lHH2H58uU5v5etWwfxiq9cvhxr5s5FZ+F31bjAhF4//ohu3Pe62lrMnDHDcw4bFixAA2G7GTNmYP6YMWj59dfYJ6KyBCHn/n37LXbhvk/89FOsXLIk5zl79+23USfkJbDloDVr4Laiy5YvxyTDcz+cE4M3rl+Psdx2nrmDsL/+kjHQujVr8IHkuPx+1qxYoZ1XJDQWWeM/+AADa2pQIVmWuusufNq6NQ4Qfp8wfjzWb8nnsON333nGEn7M+u47bFy6FP243z4cNw4bW7aUrr/9l1965i4/fv89ekrXZPOaPr/95rmG48eOxYY2bGTUd948dNzy+4xvvsFuwvbff/cdZm+51uKYW8fEjz/GyqVL0fCXXzBIsc6G9esx+8svwc9cZkybhvlbzrvFjBme/n38hx9ig0IfAIA+8+fntBMiSxYuxGSh7nSYNi3nvL/88kssrKxE1By4apVnHv3Om28iXeGtaZWrVuHQLZ8XL12K3779NnO/v502DT8bPHN7r1yJVuKPjoOxY8d6fur5ww/oodjH19Om4dfG4qw/OC2mT/fcz7HvvYfy9etxCPfb8qVLPWNk/pme8vnnENMndfvmG8+YH8jO7cJQb+lSDOG+57uN3xrZYJGQqySFzSDsvffe2Hvv7LRrn332wU477YSHH34YN910U6B9XnXVVbiUi8O2Zs0atG/fHkOGDEGjgJOgYqampgbrRo2SLttemAS4tGvTBq2HDUPZ3/6Ws6xPz57oPWxYzu/Dhg5lMRUlHHTggUCnTpnvZddf71tuF2nwYwUHH3QQUo89Jl02bNgwJIWslwcfcADQoQOS774rXd9TDs3gsMqgM+zQvj12kFw3KatWIeEmPOLo1727towiZYJQ36OXtzvot9tucEzLtIVBBx2UEwMmoQtPIFBRv34mJECnjh3RYdgwpP7zn8zyVHm5+rxmzPB87bOzdyjZcYcdsMOwYSjbIkBUNWyIgQcemFm+fdu2aDNsGJLvvefZjj9e6vbbcw7bShBydXTcYQe0l5Q/+cUXRtu3UcT56rDDDth+2DCjxn+3vn3R1/K++tG9bVt0cxw4+++PMq6+t23cGMOGDUPCYBK+S69e6KUq18qVSEycCGeQakicy+677oqEUMdV92rvAQPg7L+/57fkN994vg8eNAjgXsCUceJw+8pKtBs2DPjqK882nVq1yt7vtWtzjtu8aVPf57SQJBQvNnLKLITxGDZ4MKBp98quuy7zuVnLlp5r06R5c+k14etVA0m5+vfvn+NyVCW8tGvWpg0LHwBg8IEHAm04mfunn5D45Rc4Bxzgb8UkDMgOGzyYuYjlEd4qoaqyMnPNTJ41Gd27dsWORVYXd+zWDV0UZUpYWkwceMABQNeu3h+nTfN81T2LO3fvjp0KdH2GDhni+d5/jz3gDB0a2f4TW15Gu+zUowd6uPVJ6L+HDRuGhMTdz0kkkHAcVJSVoXdPlawiZ6/dd5e37cLLJgBo2rQpmrTPfeUeph1NCnHqUokEegvjofoVubJW7969scuwYUgIbV2+2/SEYEm8Uw+vNLHPXnvBGZD7umPoIYdkLA+DUsZ5BrVo1cr43FNcG1uvqkq5nfh7SpKgpmFVFQ4vK4Oz887Mc0JC4xDt84EHHJAZN8rYZ889c34beMABLLEZgKQwNvWjR/fuQIsWnt8OGjjQM0/iSQjPSbeOHaXrAcDBAwciJYRiO3CffTKJ9VKvvJL5vXffvjnb9+zRA93dezJ9uvI4IvsMGABn330z/a+M+vXqYReh7vbeeWfs4rZFwgv5AwcOBHbcUbm/5Ntv+5arVcuWufO5hQtz1tt9992t50MmlAmedYcOHpzrGcCVp3XbtmjJPcu9unZFT4NypR54IPdHx8EhhxyCcq5uJz/7TLmPXfv0QR/Vsb77Donly9k9NvReEdvNIZ065dSP5vx4UBjf7SG5J0lhDA4A22+/PdqEvXdCKJJiHreXCmvE8H8aSlLYbNGiBVKpFBYv9urvixcvRps2ZvZQ5eXl2G233TB79mwAyGy3ePFitG2bfT+1ePFi9JU02ABQWVmJSsmErLy83PPwb02oYmwmFW8wk4kEkoprUVZXB0iWlSeTythp5amUdxsL1xgbYbNcFbsN7P5i3Trvb8mk9Fwy6/NoyqF7E+ySAhPtjDj1VEDSYZcJExDb+poSBu5liYTy/FVIr5nN/eTWTTkOuyZCDCfleQn3t0y4J0nHYfV2y/1IpFIo5845U6+F++V3HZMWsfpSdXXy+2wYvyWxWHw/uaUMACu7wRuwMk29Dkrq7ruBu+8GDjkE4ET+ZHU1K5fm2cvsA5pn4MgjWebjc84xLlOZZHCVVIgh0msibJ/TTjVtCsyZw/a7ejU7T7HObd6cbSsl1yBTJ4sVVZvt84yXK/qBDNx1SiaTnvqfLCuTXxNum4QkvmJZeXlOfCVRgElyfbunrVq5kmXY3rgRePpplpVXh3Bdyh0n8mfKFy42V8JxsvfE4FmTYdUH5YlUIqEuk2XcrPKyMt++SdfWa8sSM+XCGK1MbIvCItQZz7nKrpGkbU2UlwPV1Uik00hZxqAtS6fl56NoM2XHDzU+F/rfRDrtEd4A5oEgkrkPwrHzPlcQyiqWvSyVynVvheKZsEVoy437M749T6eV18xkrJ2YPRtlRxwBtG4NzJ/vjaXMHSMourkDkDvWzGzjlt0yTEkqmczJV5Az/vBsIDy/mnMtF/pbQN1/lUnmwqlkMts2WPQ1mfmEZpuE4+S0HWVAtmxiW+RXfw3mHsl02qjORt7mugh1R3ovuGuWLCtDknthr5xTiMja7C3jBs8zpqmrymvw669s/FRXB7z1FmAq+olztv1y/R49Y2RhPCcdt0ueRat2ybCsW6selE9srmFJJg+qqKhAv379MG7cuMxv6XQa48aN81hl6qirq8OMGTMyImbnzp3Rpk0bzz7XrFmDyZMnG+9zW0AVY9M3UYhsYqGyzpNl4hP3F4QosqK7CMJmphENm3ExyozQa9ZIRU0AueW3pawsfPKgsFnq4syKLu5PlTzIL2ujiE0SkrBZ0VW4yQ5MTPvjzOg8dqzXUs9tD0yOqXsGJk9m/x9+2LwssiD5flnlxe15xHPgrT9diwnRWo6/H0GzuRYS03ZJvFZ+VtpiVnS/hAXiNrLEMWISIiD3/vMDKX7dxx7L7vOkk9Tllm0LhH9+bXEcdTbVUsuKbpv11MX2modN3lXIZ9Uv63XU+/fL1CuzCnafrXTaPrGTTfIgv3FcEGRtvU1ClkK344VMHhQ0K3rQNktX5sWLgU8+kS8L077Jkknx+CUPsq0fW+JF5/ymW5/HL3mQrp8U+2aROLOiyxIxxZ08yHTOko/kQQBLvCkiPmOyMbbtcYDosqLffnt2/0eZBouCWf3R1TfZ9hLPqEig5EEFpSSFTQC49NJL8eijj+LJJ5/Ed999hz/96U9Yv349Ro4cCQA45ZRTPMmFbrzxRrz33nv4+eef8eWXX+Kkk07CvHnzcOaWxAaJRAIXX3wxbr75Zrz++uuYMWMGTjnlFLRr1w7Dhw8vxCkWJXnJil5Xp24Y8tVg+A1ORGFQlxXdZtAbpbCpcROwFjbFN3NRZEUXz2PxYkARl1EKP5CKOiu6u9z9n0rlPyt6nMLm5s1mg6+4J2GmwuZRRwGnn5797t6X1auBJ5/MWEMGbh9kA3jVdTaZ9MUhbBb7YCmosKnKPi/br0yQlGEibPq1Aaqs6LaJf8TjRBjvMNDxw0yiVfvMF0GzsNpmtQ8r7BRK+AWKLyu67AWaK2zW1UUnbKpeBoU9/88/B157TS22yMaKtvUtn/jVj2IUNqPIii6bu6ja8jD3z698YbKiy5DdryiFTXG56kWG7CVj2PtmmxVd95LFrx0w6ddNs6LHhXgsmTAvGnfwY+zHH1eGecPSpWwsvXhxvMImf52jflnIr7PFG1d7rGXLgh3Hj2Ifq2/llKQrOgAcd9xxWLp0Ka677josWrQIffv2xTvvvJNJ/jN//nzmtraFlStX4qyzzsKiRYvQtGlT9OvXDxMnTkQvLjbOFVdcgfXr1+Pss8/GqlWrsN9+++Gdd95BVciA2VsT1sKm+4DHZbEZV3bZKC0202lvp58vYXPiRPUyVedmSirlPdcgnbu4zWGH5cQd1MIPjEUhEtDXDb+JiDiJES02xfVMKQZh09RaE4i/g+bFI7c9kB2zYUNWP0aPZt/d637JJWyw1qUL8OOPwQeZ6bS5sGkiOorfmzXLfl6xgv3fVoVNsV7bWmzy26uOyd9LVZsc1GLTFrGM+bbY1FkTl5rFpp8FjwqL2M3KfdmccyGf1VIQNt12P0qLTVWbGeb858wB3Bh1Tz0FnHyy/CWoeAxbT458UkiLTSFUUKDtbMQyUXgzHX/FabEpK0NYYdPGStumP7Kx2Ixb2EwkcuuBzlLVth00qY+m47J8WWxOmqQvTzLpHWP/9htwzTXAPffkbnfSScB77wEHHyw9J+nVCfKiUTZvM8HGYrO2FthtN/kyHpmwGQWy59HmRQ4RipIVNgHgggsuwAUXXCBdNl4I8H3PPffgHtnDzJFIJHDjjTfixhtvjKqIWx1KV3Q/i01bYdPUYjMuYTOoxaZqQsRfN7+BhEnZTFi6VL3MVtgUr3NZmbccUVhs2oiagLyDjNNikycfFpuqemI7ERRJp82FzXxabLqDVNkxk8nc+51OM1ETAH7+mVlvSjK4GyEbwNtMoP1c0flkBG67t7W5opuWz9YVXWexqWp3/J4zPwvdvn3VwmapWWzKLMxcSk3YtJmw8/hZBYuEFXaKSdiMuiw6Ec/WYjNKYVNlURTm/O+/P/v5lFOYsClr622s4Ar9gspPBMuXsBmFxaZfmUSLzTCuuKYUwmLTRti0sS6WWWwGFTZt+hrZfKqy0tuOp9N6i804XvAUm8Xm8uW564hivhj79N575cKmmwj1gw+AfffNXW47JjWx2LTBRthcuNAsPIPs+kWBeKy6OhI28whdacIKR/VwqjrHV15RD5TeeYfFKxMHG1ubxaZuYCASpcWmTkCdOtVsHy4yV3SZK7gN/HlIssn74ueKbhNjUzVAso2xyWcij8sVPWx81GIVNnWu6GLog3QamDXLu86yZcFdyGxibL75Zu5vfhML2TO7rVps2rqii5NhE2HTzyqytlZ/j8aN87ouhpm8FDrGplivw0yiXUpN2LQVz8I+f4V8Vm1dMMPuP4jFZhhX9DvuYAko/MoF+I/jvvgCePBBdZ/KP6tu/x80xqa7faFfUJlYbMp4661oj82Pp2bPZmLLggXy7YIKm/xylbeZjFISNt2XvKr9ydb3K4+Ln8WmKKKJRGmxKXpO+llsxtEOmlry58ti06+fEi02VYjlXbQoZ5VYLDZtsHFFlx3DLeucOUzc/fXX/Flsxjlechzg+eeBl18ufN9SJJS0xSaRf5TCpu7BnTRJ/sB9/TVw1lm5jUtcyYNsCGqxKdvGz+VRPK4fptdAN2BxE6yYIhM2eYJM5NzrMm0acOih9ttHabGpGiC5+zONsXnwwcBPP3nX4Sk1YTPuCbpM2JQdU7TYrKvLdcNZtgxo1SpYOWwsNv/1L+DUU4E99sj+5jeIlrVnJGwybC02+XYtqMVmba2+z6pfXx0+JKzFZqFd0UvZYjOoK3oUFps251xMMTbjttjUiQkqi8wwrujff8/GCzNn6svl7l9Vx1evBvr3Z59nzAAeeCB3Hf5Z5cVY2XF4itli00/IVInBp5/OLNlFF8+gx3b7c8cBBg5koubYscCYMfrtbEIgbSsxNm0sNm3qql+MTT8L3LDCJn+sxo2BVau8+y5Wi824nnGT+ywKm5Js9TmIbfDcubnrqCziVUQtbNrMjXXC5uGHA999x4TArUHYfPVV4Pjj2ecPPgAOOii+Y5UIZLFJWGFtsQmwgaiuAeSSPAGwc0WPqwOxtdjMp7AZhcVmWKLMiv7QQ8HKECZ5kJ+FhcwV3cRi8+ef1ceQHUdHMQibtoMZW/jJhp/Fpihku0l4XJYtCz6A8HNNFrn3Xu93EjbzZ7HJXzeVtYRfPZBZbPIkk+HbN5dic0UP6h6o22e+CGIhAmxbFptxTOh1+/MTNnUWm45j3hfxfPNN7m+2Fptffpn9/OCD8nVkwqasXxafh6ACfD7w66t0YvDDD0d3bHc8NX9+1lLz7bft9mFjsakKoyUjrLCpe+Zk4wqbcwJyx6I2LzNK1WLz978HOnf27lvsS8MImybtpKnFZlz9YxCLTRNhc/Vq/XGAwgubNhabKrHZcZioCQAffwysWROsLH7kU9i8+urs59tui+84JQQJm4QdQYRN20GCjcVmXBacthabOld0G8GkWIXNOLKiu9vYWtPIyiQKkQASS5cC//d/3re84voutsmDVBabPGGFzffeY3FSR48GzjsPmDCB/R5W2LSZTN50EyDEK450YsbXIb8Ym6Irungtw7iif/ZZrsWOToB65hlvfB6/AafsmlGMTYatxaafsGnS7un6GIDVN5UreqlbbEYhbD74ILO0D8qCBcANN7CM0zYEFYyiEDZt+jhVWZYuBW6+mU2q4iLfrui6GJt1dXphEwifyFB1bEAtbDqOmWuyqcVmmLrxxRfm20aBiSt6nONqF3cu8emn+m10Y34bi00bQSWMEHHPPblCEY9oyAHYC4D882NrsfnKK97vfsJmmBibtoItf1xxm8pK4Ntvs89tOp1b9jCu6CblM3328yVs+lmLqlzRxWzquvq6hchc0fMRY1M2D0inzeZOcYQsiFPY5Mc2lOgaALmiE5YEstisrbVrLGwsNuMagPnt1zZ5EE+xWWyaDPhkwqbNAFOGex5BhU0/i00A+NvfgB9+AJ54wvu7eH9lAyRx0GabFT2ssAkAu+wCLFnCPj/+OBPv8mmxOWcOc21YuRJo0oT9ViwWm+K1D2Ox+cwzub/5PT/XXpsVQ20tNmXWBhs2sN8TiW3LYtMmK7qJsGnyjPm5ootCepSu6IW22IzCFR1gLqlBtx8xgr1MuP56dm1NralsLJF48p08SFW3Ro7Mxipct86bVCwq4vZq0bVtthabQHTCpuqeqdpSkzqXD2Gzf3+2fb6SS/jVD52wGbbvlwmbfmGRbCyEVcdTjd9UhBEi/vtf/XJZUs8gwqbbj6TTueem2sf33wPvv+/9zc8VXSem5dNiM5lkwk3HjizcU9Su6FuLxaYo5sssNvffn7XLbrJNA2HT+mV7ISw23TKqhM24XM9V5XCJU9jkxzYkbAIgi03CkrRtVnSAdT62wubWZLFZjMmDeIJkko7CYjNKYdPdl6wcTz6pPraLzBVd97bfxGJThu36rqgJsOu0eHF+hU2XGTOyn+MSNvmBuogsxqbMYjPKAYTf88O7L9oKm+m0XOByn4VtSdj0e/7FyY1OSAHM2j0/V/REIjpXdBuL/TjQJQ8qlBXwZ59lP9sIvUGFzWJxRecTsPCJ5qJEZ6EbBVG4ovNWRGH7M9WxAbVIV1cX3GLTxBVdVz5ZefLZJviNoeO02JTFZPQTVXTlNe13RY8bP/IdaiNfFpuy+KX5yooehbDJ/5e9HC6ExaZsuzChDGyOb+KKrkoetHhx9rOJsCkjiMVmPmJsqlzp8yVsmoSiigp+/BxkLr8VQsImYUXeLDZVDUG+LDbffttO2LSJsVlsruj16/uvIw4Ky8rCZ0Wvq2PZ9157zX5bwMxiU4V4n2Su6Py+TGNs6o4hO44t69YVRtj0c5MOimyyobLYFF3RxWu/fHm0A0pTwcVxgH/8w/ub3xvbujr5/t3rXIqu6Kb1QrxHthab/HGCWmzq+phEQi9sksWmd99//ztw++3h4ttGsW7cFptRuKLbrmOCX19WWwvceitzgY9igmVjsfnLL8Drr+fuI1+u6KoX1Ol0tK7oNvfbpG2fPBm46CLmehs1Jq7oKh55hLlaB203+GO77ajfOFUnpPuJ+O664otRsQwicQlTKmwFQL7u2gibMpFLd/3vuYd5PPHkM8amzLjAvWcyV/QwFptB2+xis9gU74kqxiZfFwxiTSaiirFZSFf0rdFikx8/k8UmAHJFJ2wptCu6bpATJVdeCey4o3xZdXVuh2oTY9M2LqNIISw2/WJsBrkP6TSLHRkU2fFNOxCxvDJXdPHNZ75jbMoolLDJTz6jFNhk10MVY1O00BW3XbAg2gGE6b16883c34JabG7YADRvXpoWm0FjbNomD+K3DxpjU2ex6dYzfvIYZZbcrSHGpstjjwF//Sv73LIlc7W2JSrBUHcuxWKxGXR/NvsR6+rbb2dDbTRpAlxwQbTH0wmbqv6d77vjdEXXWWyaWA5F6YquC1cklnGvvdj/0aOBtWv9922DibCpe5YuvRTYfnvg2GPtjy2+pAL821YbC2HxvrrXPJ+u6EEIY7GZTufWZRthU3f933hDv34hLDbd+yiz2Ixb2Cy2GJsmFpsqL0t+PROLzaiEzXwkDwojbEYxvyFX9IJCFpuEFUqLTd2DW4oWmwCzJpQhG4hHZbFpQtTCJj9IUhFX8iAxkLkN26LF5rJl6jp/yCFm+3Ac+4k+nygnymdOVkdVFpvi/Rav/Rdf5NfSonFj9v/553OXBRU2de74xS5sBnVFt00e5GexaSpsqp5dt12LKyv61iRs8uEYHn882D62RotNk3Wjakf9JlF8/ODLLgt/PBuh6cMP5fvg2/JCuKKrLDlFTF3RTfodncWmqu5GdW10x5KNqf2ujRiz3BSZK7qtxSb/3U/o4S02bYTNYrfYDOqKbmuxKaNQMTbd/fOu6PlOHmRqsRlX/TERzGRisAx+23y6ogclrCt6Ou2dx8RJPoVNHnJFB0DCJmGJUtjUdY5BsqL7WWyuWMGsAWbOtNu3bTlkyAab7jmaxFspNmEziGtAWVl0MTaDIouxabpPW4tNmbC5eLE+o2ccwqZKbAeAd98FRo2SL5syJfs5iMUm/6YzSoFNVkdl+zex2Fy2DPjDH6Irmx/bb8/+uwInj99EQyVsutdja3ZFtxU2RSsf3cQWMHP11gmbbj0jV3R/wiRVku3DD77sxx8PTJ0qXwYAq1YB558P3Hlnfiw2Zf3R9OnAKafIY9tF9Tz7eYS0bp39HEXds3FFV8E/W7q+aOzY4OVyy6MSqU3KGqUrus5iM58vraKIsenXZquI2hXdT0Bwl9tabOYb2zY5qLApM2IIEydeJlTzxGmxGbUrusl1L5TF5saN7KVUEItNFfx9NMmKbjsmVd3voNcmXxabURCHsLl8OdM87r1XvY4q7MA2BrmiE1YUTYzNSy4BnnrKfJ9BsBE2S9li0+TexGGxGaWwGbXFZjqdG99HvAZ+bvT5FjYTCXW81IYNs5/DCpuFstg0CX0Q54sOkbZt2X83WzxPUItNnbC5tVps2iQP8tsXYNbu1dWpJ7oyV/QwwmbU7b8tuuRBYetUFMJmUFf0ZNLb3onncsUVwKOPBiuTSiTTkUrlvmA78EBg5UrgP/8xFx9s8ZtEifHTVq+Wv4wxJQph09Qd0ca1TjXxVrWlJvUuSld0txxB6laU+FmymVizBk34KBNdbF3Rde2X6lmwtdjMNzqxVoYobJqG6JIJm7b9UT5jbNq6oheLxWbUwuY//gHcfbdZeYJYbBrE2LRut1TXO+j4x8ZiUyVslrLF5mWXZRPh9u8P7LNP7jrF3MblEbLYJKzImyu6n8Vm3KImoLZukFl/2MTYLDZhM8igOipXdBvEN1V8I+6eQ1QxNsU6KA7aHAd4+WX9MfItbALqjk2MyWRbjnxabJrE2Kyry7+7mIhbHhI2GUFjbNpa7PDIrklYV3SZxWYYAW9bsdgMGjsrqIu3KFaI1zmoqCnbl3hsGTKLzZUrs7/pJuFhEPcjtovieMX2pZbf8eIUNnUJfkzEHF2MTVuLTbcsYYXNQrftfpNuE4vNKIVN/hqbJBPk9yGW3dZis1i8IPhrYCtsit5FpvuQHduEoDE2ba61W7/E9h7wCpv5Th5kaq0dtbCpCvMSlbBpEPIiaWuxqVoWdNwehcVmHKE9ZMQhbLqiJgCMHy9fp9BzoiKBhE3CCqWwqRNcorTYLIaBiEzYLGWLzSDxwKJwRT/zTLv1jzkG2Gmn7PdCWmyadOi2wbZN4GPayVA9n/wE0dRahSeuGJsykcckxmaQc4iadJpZiF5xRe6yX38FjjsOuOkm9j0KYbMY2j4dqnZp2jTgqKOyL6P8XGd1+/UTjAFzV3RVef1c0W3xe4kSN3HG2Awi+MriGZsivmzi24Qon48gVnWy/ohHnFRFJWbZPk9h2818WmzqhE1dvebLo/o9qCu67PqaXNNicUWPQtgM6orO7/fxx4HTT2chI1xMxEedFeANN3i/+1lsFsvLQl4oNimTmBXd1GIzaHxYnnzE2HRfvshEOj7Gpu5lURzCpqmIGbXApIqd6FeeCIXNhEosVBG1sGljsakSoMO+1DMlDmFTt/+4jlOikCs6YYWjyrCmw0/YlMVN83NFLySyt9W6GJvFLmwG6dBlQpMtc+farS9a7cmOH9RiM2jyIBmOUziXANVAxi8mkx9xCZuy+LOysonZHYNYnUZNOg0cfLB82amnAkuXAi+8wNaxFTaDxnErJKry7b8/Gzi//jqLgWobc1gnxMXpir61xtgstLAp9p82z7E4aQvbB6kw6cfFdt6vLOLkMar2y3YSVQzCpuk40k/YFF/YiURpsRmVK7qfsBn3Cyy/l7omx4/CYhPItUTzi9Mofhev+z33ACNGAPvu6z2eOF5VladQ8EJxEFd0U4tNWZuTL4vNsMKmmDyork5vsRmHK3qhYmyqhE2/caJpjE0Ti03ZsfJpsRnWFd1xzITNKNrfQgmbhZ4TFQlksUlYobTY1FFTo28sxH2auKIXEluLzWJPHhRU2IzKoskU8a17lBabsuRB/H0rK/MeWxeTRlcX4sZE2Axi7WjrKhVkvwATfUxc0YvBYrOujomXMvjfv/pK7jK3rQib/KB5zRr7Fz22FptRuaLzoolqMmdCscXYBPRW5UH3bSpsiv1nmBibcQmbJhabsj5RV5a4hE1bi82wx9VZh+XTYtNEkFMJm6b9R76ETX77uPu1QrqiB2lvdJ4vsmv51VfZz6VisVlIYdP2XuYjxqbOYtNtE2pr8588yNRiM1/Cpt9LEt0LpCgsNoMIm0HHPzb3R2VdurVYbKqs/0nYBEDCJmFJIGHT72ETBxzFbrEpEzbPPFMt4EY9sTUdHNoImzNnskQHiQRwxBFZYWbBAuCww4DZs73b2Aibo0YBgwYB339vVh4VYictm9SaNux+HUI6nTup4evp/Pn++y5mYdP2OdINGMMginubN5u5ohdDjE2bayibeOiETVu3n2LApF7IBEXb5BE8hXBFt63/xeaKDsQnbNbVASNHsj5ElYFUnEhH5Yoet8Wmn3hQLBabcbui6yZtqnswaJD3e5Su6CtWsFAXJ52Uu46qr4vaFd1kX3/7G/NS8RPNS0HY/O03YMqU8McWMbUKO/FEYPhwdu9FZHF3VTE2C9Wnnnsu6v7xj+x3W1f0oDE2ZXVXNp/RoeoLZSJakJcegF7YdM89nc7tS8gV3dxi0zbGpu3L9mKz2Eyn7et6UPJlsSmeT6HnREUCuaITVgQWNm0tNlXrF4PFpuoN5z33lGaMzXQaGDIEWLiQfX/rLeCii4D//he44ALgnXdytzGNsTl9OnDjjezzUUeZlUeFLrNl1DE2ReFMnGAtWKDedyGFTdX1CRtjMy5hU2axaeqKXgwWm0HXDSJsFkPbp8PUlSuMxabJgNHUYlMlmrj1TNW+2da7YnNFB1iZksnwdUp00f/Pf4AnnmDfL74YePrp3G3CDMZ1cY/jttiUPcM8fFlMYmxuja7oqn03bOj9HqXF5q23sjAXMorJFR1g4uupp+b+nk9h08/q3UTYBIDf/x745Zdwx5YtF0M8yLZ59ln2X/byRObJoxo7FmoMUVEBp6oq+93WYlOMsZlPYTOfFpuyeJEVFdnf1q/3bretuaKHETYtXdEThXZFN2mTiiXGpolnURjceyx6DhZ6TlQkkMUmYUXehM1Ss9gEgDfeKC5h07QDqavLipour7zi/S9iarH544/Zzz/8YFYeFbK37m7dCWuxKXNF11ls6o4TRtjs1s1+G564kgcFiaNmgnjdTS02xVABhcDmOshEEV28q1K02DQpX1iLTZlltYiJcKgTNmSu6GHqf9xv7/2I02JTFDY//zz7/fnn5dvEZbEZpfBvYrEpltvWYjMqy91Cu6KbPBuisBlVjE0AeO899To2ruh+L0nccwtjofXpp/51K+52PgqLTYAlyAt7bJN1dGX59NPc30rBYrO83CvQhbHYLKSwyd8bv/ioNtfaLZPOYhPIFaoKYbFpasUZBl4EtymPe80++CB3PVuLzVJKHhTGFb2UYmzyuQ+Aws+JigQSNgkrYhE2RXTCZjFYLaksNpctM+vk8iFsPvoosHJl7u9RDe5Mhc0oG1qx7jlOtgxTpgDnnuvfgUycCOy6K3D11fpyihaborCpI4ywyceHCkJcyYPyabFpEmNT10bki7DCpqwdyJeweccdwC676EUBW0wtoMRr8fjjwIABwM8/++/XVoxQYRJjk2/f+Psh2+6SS4C+fYEvv8xdZjvI/b//A3r3lk9GgqBzIbN4lmeccQYcvh0BvNc6mfROwFRt/7YQY1O2vY3F5kUXAbvvDkybpi2mtBx+fe5++wETJgD9+gF/+pP//kWiEDajtNhs1069TjptbrGps6B39+VuKzuOKX5iRKFd0VXXLI5jy5AJrTbILKdVFptHHgmcc07+5xXl5d628sILgW+/ZZ+DuKLr4t7yRDEeD2KxOXo0Cx9gii55EC8I85auQPFYbEYtMIn9ru7YMmHzoINYu6/aNp8Wm+L47KmngJ13Bp58Ul8AG4vNrT3Gprt/0WKdhE0AJGwSlsQibMoCQKs6mWKwWlK94Vy2rHiSB519tvz3ykqz/bmDQJ0FoMzlRyROYdMth8vDD+uT+gAsW+b06cB333l/94uxKSYP0uFeiyCD5SDPl8n2/MCeLDajwfQaurEHeYIIm1FNvmprgSuuAL75Bhg6NJp9Amblq66WX7fPPweOO85/v7IXECJxJw8S6/+sWcC99wJff507eZCVUVdv161jsfhmzsyNSxgUXV2yeJYdWfsnxtiU9S8iW0tWdHEdv/7QVNj86ivgvvvY/8GD7cvq1y6tWcPiaX/5JfDQQ8Ann/gfw/R4+RQ23ePqhE2dxaaf1wYgf6Ehu29BxXnZb4UWNuMU+Uz2HbY8sjZBZbEJAI88Arz5pt0xwlJRkdtW/uEP7L9JO8aLe2EtNm2xibHpLj/jDLtjmMTYtCmb7LtI0ORBYdsDE1RjGhtX9EMP9a7nlttxgltsRuGKfuqpTNQ/7TR9AWwsNgvtil4oi81CG3sUCSRsElbEkjxIZoVTihaby5cXlyu6DFNh00U1iBAtNlX7iDJRhmzgVL9+NPu2dUXXEeb+JBL2E00AeOkl9l/3fPJu+8Vqsbl5s7xsxRhjM4yVTiFd0eOK8WhSvupqdX+gSkahm/TLrFXicEXXWWzyL1NkL71sBrlxBLePyhVd1raIwqbKZY4nKovNQicP0rmiy+q4qbDJu/iKExcZtq7oInPm2K2vsw6L2hXdJEZcq1bqdVR9XdQWmzZ12M8aOO6X91HF2AxC1K7oMlQWm7q69MYbdscIS3l57ljcfdluco2aN89+zrewGTTGpgnufCOosBm3K7rsOZfNB6Mem6rGNH4v4Pg6f9ll8vV0YzIOqcVmPl3R82WxGcW9i1vYdPslstiUQsImYUXeXNFL0WJTF6iepxSETXdwyL8Z5imUK7oYVF6cMKm4/nr9cpPkQTbC5ksvZeOLmm7nrrvvvsAee5hvAwBHH83+657PqCw2C+GKXoxZ0UU3KB2ylzeFckWP67qZCpu2dU9nsSk7blQWm/wEit+nuF2DBvpj2QxyxbaiXTvgxRf1+/cjIld0qcUmT1CLTZv6IE7aiil5kK2wGdVLP7Ecxx4bbns/8umKrquf7nF19VI1Jjv00Nz2u7oaWL0a2HNPFq5myRL5cx9W2MyXxeYDDwBt2wIPPqg/fhhhs2VLdeKmoOTbYhMAPvvM7hhhqaiwi5so0rJl9nMhhU0/i81//UueLEuFmyTHNHmQCG9Vfc453mVxxdiUCZtRj7FEYdO9BjYWm1VVwA03ZL+714rvl7bbTlmEyCw2g/Z7NhabsrJu3mzWtkYxjohb2HTrHAmbUkjYJKxwTN+082wrMTYB+Zu1fAubuuWywZTsWruDQFOLTdX9itpiUxycmgqbN9ygvy4mFpumpNNZtyLAzr3cPT+dC57J9jJ4i81Sc0WXuZ0W2mLTwH0ng0wUsbXYjKrti/KZ5IlL2OT3a+L2ZWKxWVurfkHlJ2zaDlrF9XWDT3FfCxfaC1V++wQCWWz6vtRMJs3ubZis6DqLzXwnDxK/q2KyuogZfKOahIRtB23b8yCu6KL4b9onNmzIYoHKcK+f7jqqRLpffwX++U/vb9XVLN7blCksXM1JJ3nbEvfcSsUV/fzzgUWLgPPO0x9fPB8bYXPZMhavOUryGWPTZdYsu2OERWax6WLyPPJWyjKr5EJYbKqe6aeeMttvhw5ZD6ywFpujR+cui8tiUzaOiNtis0MH9t+vLRHn6zIvFH4cK4b14K51stAWm2GFTdPxerFabPLPl1vnSNiUQsImYUVkFptHHKFeX2exWQzCps5dUGbFle8Ym7r9R+WKXlbm7TRVYkKUDa04MLWx2AT0gocsxmbY5EEuthab7vGCYOKKXorJg2ShDwrdiYcVNgtlsRmXsBkmxqbpfqOy2KyuVmf1deuZqcWmrbCpWz9snTZNZBDEYtOv708kzKyYo8qKXgzJg6qr2Xigrs5bFlkdNHVFF/sLWbgFXblsyYfFZoMGzN336KOBSZPMhM2nn2bb/e9/wAkn5C7XWVDy5VFduwkTvN+rq5mVpsvYsebHi1LYjNsryU8Es3VFt+kHTSiExWa+qaiAoxI2Tc5XtNjMZ/IgfhztHjeRCBYf/tVXmQh57LEskaFO2JQlDxJx645MqM6nxWacwuZjj2Wvk5/YKN4TmTEK/8Jt++2963OGMIm4YmyaUFdnFwPVJMa17lhhCSNsuus6jjqsgMpis9DGHkUCCZuEFYGEzZqa3EZp6FCgUyf5+jqLzWJwRddZbMomdvm22NQJeLJBgc4VXWU5KApNzz8PjBiRu16UIkoiEdwVHdDfN1kCq7DJg4Kgeyt9443m2+uWFbMrui7G5rZusVnswqZJ+WRZ0W32ayIqmJzff/4DvPuu/nhBLTbFOE42QmiYiefLLwNNm+YmaogqxqaJK7rYxt53X+56YWJs8n1MGGHTz/PEL3YZALzzDrOcql+f9Q98rMowMTZFdtwR2GknYNUq87LaYNueBxE269dnL7NfegnYay+zGJtuFuVOnYD//jd3eRiLTRnV1VlLKBm6SbPNPciXxaaKqGNsBvHi0iETWm0IYrGZb9EzrMVmixbZz/l2RX/kEZZ4jD+OODY35aijgJEj2fyhR4/wFpvu/ZaNAaISNsX9yAxd4nRFP+UU71iep64OOP747HcTYZPvlzTCptRiM2phU7bN668DzZoBl16q3k7cPozFZqFc0R2HJVBq1IiJ/X36sL7PHVeQxaYxJGwSVkRmsZlIqEWzYndF11lsFoMruk7YlLmi6/anGrSKboAAm1h/9ZX3t7gb2qiETb+s6Pm22JQ9GybnaiJs6iyiVRTaFd0vxmafPtGVyRSb5zgKi81t1RXdz2IziCs6oG4P3OsT1GJTTPhi44oepr0cMQJYu5YNinnLs6hc0RMJpC+8UL1CMpn7Yu+ii3LXC5oV/ZtvgJUrs9/DJA/yE2NMLDYfe4zFZJQhOydR8DY97zlzmAXSddfJl+fbFT1I8iAxyV+QcaSIicWmrbCp2xcfv0+1zAS/uhWHsKm7R1G6fkdBlOUxtdgshLBZqjE2AWDYsOyxAfP4yn647cTGjbnnZeOKHmT8ZFrPxPXybbHJe8uJ9/nVV73fxXZW5orOW2y2aeNdn7unkSUP0o0/Zcc46ihvgkYdpeyKPns28OSTbJxwxhnAzJnMq8h9US2z2BTHHyRsAiBhk7AksqzoyaRe2FQ1mFuDxaZt43PVVV4rnDDCpm3yIJVroeiK7iI2tFGLKGEsNnVukmGSB4kxpuJyRfdLUgLoJ4zu/dK9OFBRaFd0P4vNl1/2xjV1KRb3M9lAR2aBtbVbbOZD2Ax7fu4xggqb/Fv0devsLDajmhDxfZRuQmLpip7+29/UK5i6oge12BRjVIZJHhSFxSYvsorEUU9/+kn+e74tNoPE2IxD2HTHObrxlCz+oG5/fvsC4ndFj0PY5J85v0m3X+gDkajLG4UruuOwlzz8b8UkbFZUmFlsHnMMcO213uX163vHgvmOsQkAixd7j5NMsnN67TVgwIDg+3XbCcdh7avMFdfEFT0ui03+GADr3/MZY7Oiwuv2L5b5t9+8300sNvmxQoMG3meBd0XPh8Vm2LGbqbDZuLF6H4USNlUJzL7+mv3n751b58TzJGETAAmbhCVWwqbbQMosNv2ETVuLzc6dzcsVFtsYm2EnNAMGMOsQtzGOWtjUIVqZuIiu6CriFDaB+FzRbSw2W7TwxgALM9EMa7GpG6BHJWyqzi9IwiPxeVa5outibCaTQNeu8oDxxYJ4vYcPlwsjOvfKYhc2TWNshhl8RWmxqcLPYtNv0OoKmy+9BDRvnhuiI84YmzKickVPJlkbpLLalLmiywgaY1N82SO+7JB5haiIwmJTZa0JyJ+xsC84VfeqFFzR3WzHLlG4L++1l/+LEluLTZPEXqUQY1MXHsPPgtzWFT1qESesKzoA7Lcfc+l0kXkY8ejG9HGgstj885+95//oo0D37t51WrTIfcFvKmxGfa94i00AOPJIVuag8C9AmjdnVvoucVtsmj537nrPPMNcpF2RlycuV3RX1FUJm+JxTYRNfs5aWemdZ/Cu6LZj0kIKm7J6/skn2c9Nmqj3UShhc+JE+e/uOfH7dMdQMoMcgoRNwg6rrOiu0BHEFd3WYjORAHr1Mi9bGGwtNsMmD3KvHZ/8RYetK7ruuGGFzbAigw7H0b+9FbF1RReTB6mQuUmHRXa8sK7o7vMW1hVdVf+CZnLnMbXY5MVZ97iya1YMoSuA3Doxf758PbLY1FMKFpuu4PWHP5iFJpEdWyRMPY4qeZA7eVW1MTJXdBlBs6LLJmk6V/Qwwqaf+ATo3eNM6mmpZkUPGmOTx+aFpI6nnoo2xubWYrEptjv8OC7qGJvFZrE5fnyuSJBOh7fKjNKqU2Wx+a9/5bpfi+1e06a5wmY+kwfxiHMTINx14sVowPuyWpbUT8S1WMyHxeZJJ6nHGnFabALqfk88rokrOj8vqqoCttvO+30LJW+xybPTTuplUYyz/dpYGT/+qF/Ot+nuGCquMUWJQ8ImYYeNxabbiK5alWvdkEyqJxdBLDYTCeCtt8zLFoawMTZV2XhViIMHv7dk8+apl5tabCYS+kQfKld0sWE1seCxQRzQ2WAjbNbVmScPChPrTUUcwqZ7v2pr43FFD5rJncc0xiYvPJsMeAuN6fWmGJt6TEQF1fndfLPZMWwtNhcuNDu+S5AYmyohXAVfV3QTEou2KuOtoeq3ZRabFRW51yOoxabMGiUuYVP2rInltBU2bWKt2hC2vymEsNm/f+46QeIkL1umrz82btUmFpsy6zh3mSlxCpsbNzJPAJ2waRJjs5SFTVmIl1Wrggtu55wDXHklc7OOCtPkQTJhc7vtci3VVRabixZ5r2eUwse6ddnj8Nc2jLC5ww7qZSau6N99B6xYEa+wabJePoVN/lzF+yv2cyYWm02b5q4DRfKgqGNs5kvYPP109bJCWWyqjIDc7Oj8PlQWmyRsAiBhk7AkHUTY/PlnucWm6mEX4+eJy2QkEiyD2B//aFa2HXc0W0+GTiDzEzavvjr4cU2EzRNOAA45RL3cxhVdZa3plkU2ORSvTdTCZhjCWmzqhE3RTTosMutHk3tnGmMzjuRBcQqbYn3j2wj3WkURty0uohA2i91i09QVPWqLTfG6qAaIf/6z3TFMLTb5MBSqMvIEsdjs1EmdPMaPiGJsZto/nbApWmxWVzPrCJm1ga58MsR6W2iLTR1hLItV5Vbdq7ATMdvJUBQxNvfZJ3edoGOjfFpsqq61zT3wC3MQ1BV75UqW1b1tW2DyZO+ybUnYlNXBlSuDC26DBwO33QZ07Bhsexnl5eo2iG8XZOPshg1zYwvLhM0nnwTatQMOOMBc8LHhl1+it9js0EG9zMQVHQA+/TTe5EEm9T1uYZOvEwMGqF2wbWNsisImF5dS6operBabuut/0UX5d0U3GTuozt1xcpepLDbJFR0ACZuEJVYxNnWuqcmk3oxf1RDoLDbd/ZoQRgTRWUT6xdi89Vb749lYbD7/vH5fNhabOmGTLw+POGmNW9i0GfTalCVKi8102r6+yQZviYR/AiFTV/Q4LDajckVXxdhUZUV3B2vFkihIxrYgbBa7K7pp/fQTNv3O0+/6Bo2xedNN+v3y8M9CRDE2A1lsAizpzQsvZL8HtdgUr2ucyYNMYmzqCBNjMwohx4agoXFkxzcVNps1yxWKgo7J/GJsml6fjRv9LTajEDb9LD7FfZmW//bbmQXr5s0shjOPrbBpQ9RWQmFjbMqu14oVwccHfu1eEHRWh3z76Cbl4RGFTZXF5mmnsWUTJwKzZrHfo7xXixdHb7FpImzKrh0/r/npp8JbbOYrxibAkst8/jn7LOsjefgxkMxis6rKK2xyCbi2Glf0Dh30z18U4+woLTZly9z7QhabUkjYJKwIFGNTRiKhfgh1A0iVa5Ht4CNM5+u+xerSJXeZSYzNoLjnOH8+sHx5sOPYxNhUCZuyLG0ucVtshrlvNmWxSR7kJ2w6jv2kTfbsJJPMEuOWW/QJPFSEcUUHsudVKItN/hrOm5dN0hKFoBo3msFSHT/I0gmbQDTu6Fu7sKkaIJr2DX6u6H7lj8MV3RY/19Ygwqbfy0NdVnS+LwkaY1M2aYsreRB//dy2xqbehnFFt51YhZ2I2cbBjsIVHchNKBS0HfezsjSt4+vX++9LN2Y1xdYV3fT+8uGexGeM/x53jM05c7wZiG2Jw2Jz8+bwwmaU4wzdWIm/V8lkbtxJU2GTR5eUUIZ4rq++mrvO+vXRW2y2b69eprPY5MU41QuKOLKih1nHBp2wCWSFLnHOprPYrK1l80hX9AbsLTaLNXmQn7CpM/AJe+++/z7Yy1udxaY4rlq7Vj6WJmETAAmbhC2ySQ0fbJjHT9gMarEp287PTU4kCrfVPffM/c0kNpctMovNTp2ApUu965lMUMJabHbpko2FJbvWOotN2wHPRRfpl0cZY1NEnMTYWGzKJiZRWGwmk8DOOzOXPdWb7Shc0VX7cM+rUMmD+Po2dy6zwgCitaSIC00bQMKmBWFibNrWz6DCZhyu6Lbw9yKi5EG+Lw91WdH5tjMqi81USm+xGUWMzYkTga5dWb+n89QQkV1zU+uKMNb0QciHsCnrz0SrmSBjMl1II8BOpFu3LrjFpo247CdsBnFjBPTtW75c0d94gz0r3boFzzQeVthU3aOgY/58W2zy1y2RABo39i5v2DD3Rbpf8iD+pbYJvLgFAIMG5a4TR4xNncWmLpY6PwfdsCE/WdF15FvYdJeLLxR0wub8+exZfeCB7G9VVcya3mXdusz9jMxiM84Ym+690dXz9u31z1+Ye3f77Sz0zsUX2+9TJ2zK+ujly8liUwEJm4QVUld0lVgWxhVdlzxIJ2yaDl6iGKRccYXZemE7uQMPZP/5c1u3Dnj4Ye96JhlpTS02VZNUvoGVWTHqLDZtr7ms/ohvqs87z3x/YVzRw8TYDGKxqbNKBoBjjsl+vv12+Toipq7ouqReqrLx+w+DafKgqI8bNzphk28//YTNKNxkCh1jM8zgK6gr+imnhHu5UAyu6DboXFuBcBabNjE2+WUuUcXYdO+DKkSL2F7z5Ta12Lz1Vla+tWuBF180Kycg72vE8ttm1FXdq3xbbAaJsSlDFCh69cp+PvNMs304Tn4tNuNyRY/CYjMqYdPmmonbH3kk+79oEfDMM971TPcZ1hVddQ+DCm7udrrre+edwNCh7HOrVv773FL3V/TokbtMdEWXCZu2Fpu2wiY/pgTk575uXfQWm82bq5fpXNFFYTMfWdF1RCkw8cKW22YGFTb5+3j33bkJ8ESLTe6YJWWxqbtHXbvqDXzC9KdXXin/PWzyINkymRcJxdgEQMImYYlU2FSJZXHE2Eyn5Q95WItNm0kLAOy+O/ubNg3o3Fm/bpDG5vzzgdGjgSlTsp29WGZxYmAyQZG5hKmQ3R8xsLlorbtqlTd4Nz/RtRU2ZeuLg6addmKihQlhLDZdYVNWp02yotuKKjKBgN9Hhw7sOj/1lNey1TQruq7zVj23v/4KzJgRryv6zJnq66cTlosZN6uhAl+LTXESE5ZSstgUz9fWFX3cOOChh4B//MN+wrW1WGzqkgd9/73xLn1d0ZNJf4vNb74BZs/2Lgvqiu62ke6+/eoffz9NLTaDvjRZvz73N7H8M2bIt7UdL4QVNtevBz75xOzFKBDMYlOG2F9stx27Jo88wibdJsycySyPVNTWmrd3fhabCxd6Y8XyTJ9udoxEwj9+a6GFzaiSB4njUdN6HdZiU2UpGkeMzSOPZEl6LryQjcUeeICFC3LDNanY0ud/LhNCRC+nKFzRTSzZjjwSePllZnX7u995l6mEzagtNhMJ4Omn5ct0ruj8NdqwQd6WRZU8KF8Wm199xRI0TZ+eLZsseRCQPV+x39FZbMoQY2wCmesdeYxN3jLUJezYtK6OzYtEwdale3egRYvwrujTprEYszbl8oMsNiODhE3CisiEzaAxNqOy2BTX+8MfgIMOMtsWAIYMYf933RU491z9ukE6uQYNgJEjgX79sr+JZW7b1vtd1pmLwqMY10qFKlSA2MCK+xs1CthvP+Dss9l38e2zDX6TSrfjdO+FH2FibLplkQ2qRGFTdt2iEDbFAeM++wAnn+x9g23qiq6rkyqBsls3Fobg7bfly6OwnHz/ffmE3y277PyK3WLT53r7Wmzy97eYLTbjEDZNYgiJx+XPr08f4Jxz9FkwVcRlsVksMTYnTwb++1/jXWbiawe12PzwQ2CXXdgLEp6gFptue66y2BTX59sJvzbD3RdvRWiDLM6bWJ4JE5iru0i+XdH/+U9g//2BE04wWz8qYVO0vCorY/XjrLPU4Y1EnnlGLRC7TJ1qti8/YXP9ev+xnh/JZOFd0f1eFsWVFT1fwqZJOAwbdDE2Bw1iL9bLy5ml5p/+xMJEueGaVGzpWzY3awZHtMh0y59ImLmimwib7jXV1e969YDf/x444gh2DB5Zmx+HxSbAjBVk2FhsysJolVKMzddeY4YzHToAfftmf4/SFV2GzmIzamGzXbvcZWHHpitWsPnnU0/Jl++7L/sfxhX95ZeB3XZjIikf11hH2ORBsnGVzGKThE0AJGwSlkiFTZVYprPgCuqKrrLYtI2DIzsPm0aBH+T4Waq5+w0Sz0z3m3hcWeMn3htTYVMlIIu/qTqIxx9n//lBpm1nb2KxCZgLWzbCpviWzL3WJsKmrn6aIiuryT5M4sr5xdj0u55XXSX/PQqLTYBZDIjonu9it9hMp8MlD+Kv67YubMq2Fds9/vkT26fevc2PXcoWm/wxVPHGTjzRbp8mXhE6YfP3v5cvi0rY5PtX2TP3xBPZz7fcoj9WFM8Zl1EWgLxfOOec3N/ybbHp8sorZuvp4vnZlF3sL6LqP4LiJ2xGgYmwmW+LTZnrdxTCpjgWKaTF5g03xGOxqRNIdPDbie2CW36VkCdabMrauiDJg3bf3Vu+nj3Z57PPll87lcVm2PwFqnpsY7EZp7CZj6zoo0bJf7cVNsU6a2Kx+bvfZYXtu+/OWmxG4YqeTmf7bZn3YFxjU5dddmH/w1hsjhjB/q9ZAzz2mNlxTeqDrcXmsmW5+yVXdAAkbBKWSLOiqzr3MBabcScPknXUQYVNv8GN29jYNDqywYHsLeyiRcCYMazhkzV+ojWtqSt6TY2ZxabfufMCnW0sL1OLTdN7bpuhnS+vjcWmnxu5CUH3YRpj86ef/NezJU7LyVK22Fy7Vuvm6WuxWSrCpskEtKZG3w6+/74+PqRsW3EiK8bG5Rkzxr+MLny74u4znQbGjtVvVwwxNk1c0WWTPw2+ruiOo7eWUmVKDuuKLlpsqu7RoYey0AQTJ3qtYAA2gePjFqfTzKLyu+/MyiZDtOaQ9X9uAjSefFts8rzxBrBypX6dqGJsimMHEjYZQYVN3fXTCZs//uj9HldMS9N6GjbGpqxdu/LKeCw2TZNx6rZTueyr2lmZK7pf8iA/YfOFF7wJTxIJ1lY+9xxw113ybeKy2FSN522SB5W6xabKC1InbH77LTBpkvd38Zz9xsqVlcxCeMoUZpl4wQXRWmx+8kn2d9ESGZCPnYK+PJDRoIH/Pm36MNMxlG5MuGkT8Oab8rEAYBdjkyw2AZCwSVgitdgMktQjjMVmHK7o/D5MsLHYdM/FRtiTde5i3JDNm5k78uGHAzfeGK3FZnV1OItNF1sxkcfPYtM2DpptWfjr6WexyZc1CovNOIRNvowqkQEILhRGNTF1Bx887nnJzq/YLTZVb9+3kJZZBqpc0bf2GJuHHMJiC7uYTBDEwaVO2Nx+e//9uSQS2e3dfY4ezQaUOorBFd0keZCtSO7nFZFO6y02wyZeMXVFf/ppJmKKVFYCBx8M7L13bjtywAHAsGHZ7y+9xJL2mVoxyhD7a9PnrlAWmwCLs3fUUfp14oqxWegXVPkQNhOJwlhs8rH3/PZX6hab4jjviCPYs1+sFpuqcZONsBnWYvOYY3LPpV074Ljjct3SXeKIsQn4W2zKrvnWZrEpvnhzUQmbP//M3KNFxLmIicUmAPTowTwsysujs9icNQsYODD73VTYNJ2zmuCeX9gYmy6mcal1ff9ll+XGtOWxsdgkYRMACZuEJXkTNnUWm7rkQWGyot9/v9m2QDCLTRthU3Ye/MQLYB3FnDns8y235MdiU+ys/N5YhxE2/bKi69aLoiw2wqafxaat+Ba3sKmj0MKmrN7p4vsVekIckrTMMnBbjrHpxucFzAZqOmFTrC+JhN2z6NYtd59nneW/TZyu6EESHKgsNi3rkm9W9Lo6dR+nm+yGFTbF5EGnnirfni+32E5WVnp/87PKNSFqYTOurOgiH3+sX24rbJqGLil0O54vi02/5EFxxNjkn0uTJCpxxNg0vbZBhV0X0YLfHaMGdZHWZUWPwGKzTswe76Iqb/368cTYtGX9+ngsNoO4ovMvwzdskI/1o0oelI+s6Ko+1q034vL//lfe94rzCJMYmyJRWWzecIP3e+PGubEwZX1klP2CK5IWk7D5wAP6bR1Hfpy1a8liUwEJm4QVkQmbquyQAMsy+PPP8mVPPCHPRuZ2pqadk6zMffuybIatW/tvH7fFpqx8jzzC3j67iAO4qGNsmgiB+bbY5LF1RTfthFyefDL72cYVPS6LTZMBo4kruh+FdkXXJV8qRYtNP2SJp0rRFd1U2DQZfK1eDfznP7kZtGWIwqbb3qZS8mfGRoAXLTZNiNMVPYg7p2yb118Hli4129cWHJ3lEqA/b13bFbUrukkZxPJUVYWfkIuIwqZp/19IV3QXG0sc9/jffceSXri89BLw3nvMm0SGOHYIG5svLJ98wpIRxUmcrui6ftDGqjaIsDlpEnOn5BGfJ9PnPKwrujgudutZHBabQYVNru47RxyRa7TAH1dGHDE2bSkmi83y8qzRxoYN8rY2nxabtnMNEdV9cgVcsW6owoeI18FvjC5zgXeFTbFM1dXy5Hcu4vUW54KNGrE437y1omwMEWX/5p6frq20GUdEIWyaIKvPGzZQjE0FJGwSVkRqsali+XIW20XGJ58Ap5+u3p9pA6I6fp8+3kzkKoIkDworbFZVeeOAiTG8ZI1sUItNwCx+SL4tNsOsZxlTTiou5UvYlF3XfFlsBrW8FLcLKjjKBqClHGPTD8fJxi4uZVd0k7Jt3mw2+DrvPJZt9uCD/dfVCZsy4hY2/SaPYVzRg1j9yfZ5+eVm++FwdC8XAH3/FofFptgmhEnOJ1psRkEpuqK76CZsMmuwdety3ScHDmShJVTtc6FjahaCIMKmab3Wrce3AX71JZ22e5bSaRYSSedOKZZBR9TJg6ISNmX1OKgrOr+vRMLroiseV/Z71K7oQch3jE2dxWYqlZ3byCzZgPzG2IxL2HTPUawbsoRZsnKEsNhMiOd9zz36fYnXW6wXjRuz8+DHeHELmyaGPX7H469RPoRNlZeqLJwYWWwCIGGTsCRSi82gzJ+v3p9pA6I7vknZonBFv/de9TYm11QMNixr/IJabALeuEwqdOduavWpIuqs6CbnoyLfyYP++c9g+5BdH9cFxFRoDCpIivchqDWDDF2oiVK32HSc7HPkPsNbqyt6TY3Z4Ou//2X/dbFgXYpN2Kyp0U+iwlhsBrF6imqw6+eKrhvkxyFs3nQT+29isSmKLmI7kg+LTVkdEjOnA8UhbKomyoA8Ucknn+SOP/z6K1tR6Nln7dYvRkyEzaCu6KYvTKK22FRRqBibIm57VUwWm2JZZM8C//w8+CD736EDE5H9hE2VJVeYvoA3qgDyb7HpXiNZ/11Wlp3biAYfLvm02LRNlCpiK2yalsNvrKyyhoVE2PzrX/X78rveboxNWYx5nijFOlVSJp6gwqauXoQZc9fWysdW4vjCXZcgYZOwoyiETd3+wlpsmhLWFX233YAzz1Rvoyof3zEtX+5dNnly7vpiQ27SsLuEtdisrQ036TLNim4qbJqIJCpskgdFIWz27p3rnhTEYvOLL4Brr2Wfo7KAVT274rWJMpuh22FvhTE2E+l09rl0XwRsra7o48YBn30W7XHDCpvvvqsepAcVNnXru+WrqWHhLt59N7ssKotN/l5EdL99Y2wGFTZl57xpE/DooywzuQt/Hg8/DHTsyD77CZtduwL/+5/3t0JYbMomu2vX5l432xibJoKRGMvMD90LSZkruqxO+E2ibS02jz9eHa+zVIjTFd30hUlUsQb9COqKHrWwqSqPKW67kEjkthFRjXH8PHTOOQf46itg+nT23PDLZBa24vMbhcXmM8+wOYabqCeIxaaJEKwaz7ljJNk15y024xY2lywB7rtPv87KlcBHH5ntT0ZcwqZurFxRId+v64oexJKbRxyHFELYNDHs8asHKmFTN/4JMwZLp+VzcdlLURI2AZCwSVgiFTabN5evrGtEq6qiFT5shc2wVl5hLTb33FM/sA9isXnzzbnrh8koZ2LhqItHGsZaM5k0t9g0vZdhLDbdeyWr0yau6EEG1V27er8HETb32MPf0krEb8KpGpyK20Vpsek+11uhxWbCcbLXyh0claIruunEQGZxH4awwmbr1vKMovy6tq7oOosNt3z//jdw2mksg/f06dlt/fZtAi8ORHW/w7ii21qw3nEHSyJ14IHAL7+w3/jz6NQp+1lMHiSyzz65fXQhYmyqroH4gjIOi81mzez2qbPYlLmiy541v/4qiCv6TjvZbxMXQeqLSfKgOITNOGNsqihUjE3T8gTZTqznUY1x/ITNRIKFenDFID+LTfH5jULYLC8H+vcHmjRh39eutbfY7N/f/ziqeaM7l5HNt8rKsqKfqr5EJeifeipw0UX+6w0cCPz6q9k+RVT3yb0GqrFN27bedlW8Vrqxssm43sYSVbye4nx1a7PY1M13VWMw0zons86U/SZrC7ZBSNgkrHDEhrFDB2YNwE80XFQD1+7dgUGDgLfeis5KopgtNmUxNisqglm06iw2ZYgN+c47A7vs4r8dYGaxecst3oyEPGGEzffeKy6LzbDJg3Sd5QUXsP9iNl9ZRmc/8pEVXTU4ELeL8sWFTtjcGiw23cGS+8zw9SVuV/SoJo5R7ccWcRJnK2yWl/vHAYzSYtPtD847L/vbv//tXabbtwkxWGzWqTKyutjEZfTsWNI2Xndd9vPrr7P//Hnw99DPYlPWZovtiGgFFQUqyyERlTBvisn6Np4agJ3FJiC/dlG7ogPFZZHSsKH9NonEtuOKriuDjmK12ATiG+P4uaKL2AqbUWZF51/C2lps/v73bK6YSAAvvyxfR9W/uG1Y587ybfzyB0Rlsblwodl6gDeZmg1BLTbbtWNzp2QSaNGCWfry6OYAqj7CNKZk+/be7+L1XrbM+91E2PSzEj33XPUyGbyhjzv+ErWLoMJmEItNU6FYZp0p+w2gBEIgYZOwJMdi8/vvmbD13XfAY495l6kmi9OnswZ28GBg3rxoChZV8iBTbITN1auB229nWUJdVGb/Lt9/73/cIMJmMglMmcKyzp99tn5b14pIR9u26reSQYXNs89mwrfs+ogDOiA/MTZtkgfJOjhdvbzvPuCHH4DHH/f+Lp5/2ORBUbmir1pltl0cwmYQl8ewPPooszLcuBGYOxcYOjTa/ftZbEbhil5Xx+J0jR6dWxejGggV6k2xKAy51840aYnuJVMQYfO554AFC9TL8xFj84svgOuvZ/U2ImGzxp1UqZ43EytVGaaZ4P2ETdUESHZvxXYykYjfYlNFTQ3rm26/Hbj0UuCbb/Trr1gB/N//sbAOgNlzZ2tZZhNjE5Df3zgsNuMSNv/9b+CII+y2Ub3Q1aFyRX/44ex1tbHYXLyYxZqdONH8Gcunxea77wK33srGwIUSNmXimw38dvm02DTNAyATNkeP9n6PMnmQO66rqbG32KxXj80V585lIqcMP4vNZDI3PwFvsakiKmFTpHVr4Kyz5Mtsx1b/+x9w111q0cpP2GzenHk5zJ8PzJmT+/LFzxVdBi8G6uZ0s2Z557h+wqY7N+X7gU2bWFKiJ59k3/2u3wMPsLmT6Zicnw//619sW1G38Dsmf53GjMn287pr88wzrM6LmCa0NbXYBIrr5V+BKG1TFyLv5AibbqNXVQX07OldpmpE+U58hx3Ymx7X3SwoUSYPMsHGFf3ll3PfTvpto7p2tiKO7DwrK9lbT7+B3ocfmh2jSROgaVMWV4YnqLDpvvmTla9Nm9zPxWaxKRM2/TIGd+sm3y9P0ORBqv2pCGoBKW7Xrh3w00/B9iXithmFsNhs0iRbJzt2jNyyy2OxGZcr+pNPqt9QRyVIFouwGcRi07UeUK1rKw6efrp6mVs+/l66z63foNq0HFdeyf6//nquJUVAfIVNncWCqZusDLdeBbXYNBE2Vb+FwdRis6aGTa7ce6bCrS9//nM2udbSpfEIm7YWm0HiSheTsNmjR27oFz+CWGyqhM0JE5ioccwxdsLmqacy8fC66/Tusfw+TVxyoxA2lywBzjiDff7xR+D88822KzZXdJ3FZlTCpmw+oCuvGGPTrw2IUtjk+0T3s6nFZlkZmyt26KBfRwYvSskEu0IJmz17qsOx2Vzv6dNzEzSJ+AmbLVqw/9tvL1+umwOorg9/3XUvvOrV895Xfn/pdO78UJYM6u9/zxrs9O7tH3rEnTuZeiTwIq27rRgaySbGJsDC5tx0k3+G9MMOY6I+TxhhU9VHk8UmWWwSdkhjbLrssw+LoVJWxt7cyDqoO+7I/e3VV8MXrJhd0WXohM3ttgMuvtj/uH7ssguw++7q5VFO5GTl0nWCOvhg7SL33stE1MaN2ds6IL8xNk2SB8lEzCBWU1FbbMYtbIrX5vrrgZYtg+2LZ7fd2FtowD/G6nPPhT+eiPisRi2AOA4cPnmQ40SfFf2WW7KfxTfHJGwC++/P4h6XlXlfQrnr1tbaTbA//1y9zNRlNMhyka++isRi00kmUefW0SAxNsMIm6YWm6r6J4szLWtHorbY/O03s/Wqq+1eALmiJsAmwyYTGds23SbGJiCfZBXCFb1RIzhNm9rvN5Wyd9eP0mITAP7xD/bfRtjkE4/pjAOitNgcPFj9Iojnyy+znx9/vHAWm7YxxkV0FptxJg/Snbdosel3jdxr7/aV3bqxsFTl5cA779iV1T3n6mq5xabuuTeZK8nuU3m593dZ7MjtttPv10bY7NPHfOxaVsaSD/JGFy42IhNv7ajCVNhUoXsG2raV/25qsQnIPeoA1p/w17d/f6BfP/aZrxO8F+Jbb5lfP9MXDLI2XpYMz+ZYbm4Lv2sj88KUCZuysH42RjlksUnCJmGHVthMJIBJk4BFi4Cjj84dTL//PvCXv+Rut/vuuW8ybCnm5EEydB38L7+o31SZlvuJJ9jAUlc22b3cb79scHAbZBMn07dRIjphs1075vr+22/ZrLimkzZTCxoZYWNs2gTddilWV3QV4rVp1kydKObtt832+cEHzK3WPS9Zp82X97jj5C6FPXsGnyDZBGAPgCd5EMDasKhd0XWDnagEyULF2AwrbLphQT77jLl28i5y/LpRDRhlA2c39t6NN+q3DSJSRuGK3qiRv0Agc7VysY2xKdtWJWz6JQ+SWfLkw2JTdz14xOfdBtNkAbZtlq3FZhBhM2qLza5dgQULUDtnjv1+k0l767sgwibgn9wkaIxNk7i+Jvu76ip9OISdd2bjryFD9PsRreoKHWMzSH0DvOPQuCw2ZfvRXS8/V3SR665jFmXuy/0mTYCvv2b9nW1oHfc68i9gbSw2/Ugmc/chvpwSx2RlZf5iu6mw2aQJmzuZJl51jz13bu4zYSNsmlwbP68JP2FTdwyVEQx/HfyMVVTCJj9G22cfphO4dUb1XLZvb95mqOa5opBpImxWVwN33ql+plTPvJ/FJsAs7A8/nM3PAfkcWeY54GeUs/vu7Hn+5ht/gX8bgIRNwg6/wWoymTXLFxtRXQZtlSm/KW6DatoQFrPFpq6DNhWd2rTxT4igymQqC87th6xcQS0kdcImwDp3flKRj+QxYV3RVZMOnftbsSYPMt0ulVIPAkzdY5s185Zbdh3F48retOvaHj9itthMpNPeAdemTd4JA3/+QUVI3aR3a4uxGcRiE2DXWswe7ZexMwiq633//f7bFkrY5PukIMJ+mBibpq7oqomrqbAZtcXmkiVm61VXB38GTYXNKC02ZddZtr7f9QzyQlg3Tkyl2LjAzyVVRjJpb7EZxBVd5zYcJMYmT1TCJgCccop6WePG7DrbTqBLVdjMh8WmbD+668WXyaQNmDOHucu617JhQ3YuQayb+evojnVNY2wGfbkuPpuyl82NGun3aZoVvWXLXC8sHW5ZKytzhUWbdt2kfsZpsel6RYlw1z7hJ96ZCJvt23vLrzrv6mrzNkM1zxDFadl1k9WLyy9Xh2JTldck9NpTT7G4nCNHMiEyiLApayt69GBWxr16xZ9zoAQgYZOID7Fz0nVqYcWpfCcP4huPqIVN0+Oa7F+3vsqtVzZgr6piv7/3nnxfQYXNTp1yRSc/YVMk7ob8jDP0bxdNhE0ZlZXAK6+ol5e6K7rszbtLs2ZmxxG3l8UO2nHH3OOKhHneTYXN/fcPtn/H8R5j82ZvAhxxEhOEfFhslqqwqWuL82mxeckl/tsGKUNUFpsuQZ6lKFzRVVbMoiu6WL5CWWyaUlNjNgGWPfuOY7Ztvi0269f377+DjJtOOIGFJpERZgypewmnKqfrMWJDXZ1/OxlU2NS1DTYxNv1wX3L4XW9R7DZtu6KOsWmbZFIkH8mDZM+KqcWmSYxNwBuOJogo78L3l3FYbMrWy6fFpnsuQcoqlssmz4CNxWbUwuY++6jje0ZtsSn2x6r2dePG6IVNGap6ofI2E9tn18PRdN7nMm6cXNhs0iQbm9iFd0WXeVSSmOmBhE0iPmQWXCqCvk11KebkQTKCCpumna0u2YqLbEKkEjY//pglKjjkEPNymcQFmT07twOxFTZ116RPH+Y6FZQbbvBmzVOJwX6u6DKWLGEBslWUevIg3fEqKvwHYUDuOe+xR+46oqAouwZRCpuy8/rpJ/YWNgA5rui//MJiIwK5wmYcFpvburCp63visNhUDdZNBKoCWWw6YS0243RFd5+PpUvZhEA8lkzYjLqNCIOpxWY6Dbzxhve3uFzRw8bYNInBGKQelZcDU6ey4332Wfj9uehc0VVjNTFZpgkmIpS4/MQTWdZzP3QTa1uLTR3uSw6/6y3WCdNxUTFbbPJjnEQiOm8hmQAWpSu6SBhhU3Ydo7bYFOuWicVm1MJmkPGy2IbYxEY0uTZ8ZngZQYTN884DPvlE/XyI3kSAekwRpbC5aZO5xauqjTax3lfVC1V7JZbJ9fKxTZZbUSEXNsvL2ZzTTfYJeI2EZMJmPrwWSwgSNon4sBE6wj6YboPql0XNRTeJMRmw+rmi+51P3BabbgerW1/WaSST8thR5eV6V62gFpuplNoiznSiqbvWQV3TXEzcrUySB4W04/YAAHi1SURBVMnwi9EVxGIzjrflpsgsNnXrmoSfEM9HJqzvtZf/cd39BHmhYWKx2aBB4Jcla3fYwfts/f3v2c8bNuRaZwTB1JonDIWKsSm2M7y1qwyxnvoJ8C7V1dGIX+l07rWSZb2UUQwWm/l2RTdNHgQAo0fnbm9qsVmobKKmMTarq4Ejj8z9vVgtNv0I+nI5kWDjG7FvDjOG1Lmiq8ZqPXrYH0dnsalyRZ8+Hdh3X/996+5ZlMKmKyDZCpt8YhAdUQubrmtn1BabFRXRha+QuVHbCJu21ygqi01ZeXTXxFRcLqTFpm2yqTiFTfE3t11VXWO/MbXsGNttp79nMotNVf6ErcliUzVuEtsnt32wtdisqJC/QHSvB9+/8WNcWfgIEjY9kLBJWFN33XXsrcGjj+pXLITF5u23swFnr1769XVlue46llFch64zA/zPJ26LTXf/uom4bICrstj0Ox/Z9YwrxqaI7pr4CbJh9u0S1BXdJF6tzfpAYS02bQSj8nKzQYd4PkcfzYJvu1xxRe6AVudmGkSYMrHYTKUCT3K+PfVUbxvywgveFaJwRd+aLTbXrvV+t7XY1N030VohqgGkeK1ME60FESltLQlkxClsRpU8SIVMZKuoAC6+mE0Snn+e/RbFdTKFL7OpK7rKirIYYmyK185kfBNWFLLpb/zQWWyqxj7du9sfR/ZSw0UlbJqiu2fFIGwuWmS2/yhd0fv1Ay69lH2OOnlQVG7oACvniBHe33T3qdgsNuN2RRfH8LJnvxgsNsV2z0bYNDGQ8Xth5Jf4VXZOfvdEZrGZD2GTjzXvRxwWm6qxllgm9x7bjh/Ky9UWm/x/wN9ik1zRPZCwSViTvuYa9vb1zDP1K9oMPE0Eh48+Am6+Wb7MbVBbtmQZ1mfO1O9L1/k2asTekqfTzMVUhp8relBh02+wb2uxGZUrut/55DN5kIifgBa3sJlM2ruimwhhQZIH6QZvcQubNq7z5eXm11Y8xptvsrpbV+e1bnTRuZny+/vTn1j2Sz9MLDZFcVuGRMitffNNVDdqBEdXR01d0d94g2WElwU935pjbK5enf18993ZFwumwqYOfmC8YUN0A0jxfsja4pdfzv3t/PNZnMHrrjM/lij8BsDjih5XjM10GrjsMqB/f+9y91qZWGzKUE1u7rkHWLYMOPZY9t0vfliU8M+7qSu6bOIUpSs6L9Rs2gRMmwYcdRTw5JO5xxQRr10hhM2wMTZtLTZNEq+cdpr3u4nFZtB2VFd/44ixaStsLl5stv+oLDZ32AH44otsv2tSP2QeOjqLzahIJID//S83tp7fNoB5jE2eYndFj8pis1u37GfT5EHF5IquEjZV99tP3A0ibPLXPgphU7yXOotN05c8YSw2VYjzuPnzgT/8AZg82fu7e49tLTarq+XX0W3H+euydGn2M7mi+0JXgwiGyeTGxmIzkWAPss4ipV49dcNt2rG6+JXfzx3BT9gM6orut52txaauM99ahE0/i80w7qNxWWwGEfXCusHG7YouPiu65z2VMhOYVHVAdy10FpsHHgiMHcs+9+xpNng1ETbLyvzra1VV7uSzvJwJNzrrD1NXdNdN9a23cp973QCx1IXN6mo24P7mGyaMucQhbEY1gBTvh+z+yISWH35gfwBwwAHA4MH+x4pA2PRM+KOOsen2+RMnMmFaxG1PoxY2xW3zabHJtwWmruiy8plae5rcs5YtgV9/ZZ83bmSxi9etA15/nSWW0E2qt2aLzTDWQGLdNBGhVPfTcfTXrNhc0cW+Lt/CppjAyqTdb9Yst71UxdiM0mLTxeYlfCKRdUPPp7Apex6ittiMKsYmv12xW2zK+gDxerl1WHW//UJnmYinInxW9GK12AwjbLZpI/9d1CLOPht4993c9davZ/fDVthUudq7YYlU10X2Qo0sNj2QxSYRHzbCpslyx1ELm7aij+n6qvX4c0sm7TLAA8GFTfEaHXFEboxBINso2roAJpOFFzZt4yHqrllFRbjYaSaD4SAxNk06oiDCZhQWm1EE2Zd95zENvB9k8qsTNv/9b6BvX2DIEGaxGUTYDOqKLht8uddA1R5cfbWZK3rQZw0o/RibALPafOUV72/FbLEpXnPZANdvkitaDqgwjd+pg78OQZ4ZXR1zhYQlS+TL4xQ2eYrdYlNWvpqa6FzR+X5/0ybvpJz/bOKKHofoI6Kx2Pz4llvg7LijeYKfIMJmVRXw2mv67OjJJPDQQ9nvQWJsuvhNnIvdFd20j4pK2BTbT5NnQGYNFbXFpq7/sdmnO94IImz6xXfXEcZiM98xNvlnulSFzRdfZG3Mbbd5rXRl+PWFFRXya6ejWGJs6hKt6l4+3Xgj0KkTa6tl7L478z7t3h246abs7+I8TiZqAuw8N260fzG6caN8rugnbMoSRJHFpgcSNon4sBU2TTCx2DQpS5TCJpA7KPY7V1VDbCsgtmwJTJqU+9YpqLAZpcXmfffpt1ERtcVmkIQbJvt2ictiM0hWdN0At5hc0YFwFps6dMmD2rdnGcfffZcdPyqLTRNXdJ2wqRKxLrzQu9+PP2aJJMRn65df9MfWsc8+wPvvA/PmAUOHApdcEmwymW+LTf7erV6dK/QVs7Bp4oruJw7x98jkfnXoAAwY4L+eDL4sJu2QWJ9HjVKv64YSUHlryDKxii8WdZgKm0EtNlUTHh389TG1ulRZbEblis73+6JI5lfX/v1v7/cCW2yu2Hln1H77LfDgg2b7CuKKnkwyK/m5c9X7TSaBc84BdtuNfd+8mVnAyvj6a+ZRcMst8uV+cXhNXdGjEjb9xglifTV9cRBVjE1Tl1ceP2EzCotNvxfxprjlUsVtbdVKvW2xW2z6xdg0tdjMt7ApYiJs1tUBp58OXHCBfN9/+ANrY668Mvt7mJfI4nWysNhM/fWvwF13AQ88IF83zqzo++2nDsGjeharqoBrrwXmzJEn3nN59FFg1ixvkjabeOZ77mkW1opnwwb5MfyEzZYtc38ji00PJGwS8WFrxWgy0LURNvkGtEMH7zLThiCMsKmbcEVlsekeV3WtbV1QoxQ2gxJ1jM24hc1Ewj7G5rbuig6YTTSCnLMuxqaIrIziulG5ouuETdXArLLSu9/zzmMuuxdd5K1n8+frj63j119ZtvmTTgLeew+4917ggw/s95NvYbNZs+zn1atZXGQeVf3jJ69t2+qPIQqbUWRFB8xc0W0mzibXvrzcTOQTY1wC3omlbbxGP/yETfdFER97yib0jGmcrSDJYIBgMZxFYdOkj1JZbEbliq4TNm1jNJrc/3zE2DQVioJYbJruFzC7/tXVwIQJagEwjLDJ16+wlvXu82Trim4qbMZlsWky3pDNL1QWm0E9W6ISNv0sNmWZ1l22xhibDRuq50eAvh7x19Atf5CyivfBRNh88kng8cf9982TT2FTvPZ/+Qvw1FPydVXx4KOw2CwrU5+3bvxsA18WG2Hzu++AZ56xO5bKYnOPPXLLwiMTNsli0wMJm0R8xGGxqeqsZZ3phAnsTcq113qDSKvWl2EqbIodbSKhP1+3wRUFBFsBUSVsFoMruh+ffCL/3VbY9EtSE6WwaRLzMSpX9EIlD7Jh4EBmjfLQQ/ZCbCFc0UVk10R8lk1c0ZNJsxibAo6fxWZFhbrs/EQxjLDpwj+P335rv32+hU0+1tDq1cDKld7lqvp11lnMcnHHHYF//EN/DFHYjMptPwph0zT2qoupsHnZZcCwYeqymLQjNpN+U2Hzt9/Y/3btvMv9zt3UYnP4cJad2JYgwiY/WQyTPCioK/of/8jabh5+bCW6DfP3xuR4xRJjUyyHKuFlMqm+j7K6/MQT3u+ffMLqzs475+6X/x8GP2FTR1Su6Pff7x9/3iWoxWZUwqYoyAR1RefrKX9OfolaJkyQtye6ttGm3SwmYZOv37q6bnrcIDE2k0lg1129v5sKm7W12eXuNkEsNsX7YBJ+4dNPzfbNE+YZFuuFTVZ0P/j7ws+DooixmUqpz1vV39gKm6ryx8HGjd5+tX594PDDmdECoL4u/At9F7LY9FDSwub999+PTp06oaqqCgMGDMDnn3+uXPfRRx/F/vvvj6ZNm6Jp06YYPHhwzvqnnXYaEomE5+/QQw+N+zS2XmyFzagtNvfYA/j8cxZjQ2wk4nZF9xM23c7ioINYYH7VfkXEfboNcVTCZiolj78TtbB50UVes3+eKCcDYYVN04GmrSu67T5l320x7fxsrtcRRzAXjHPOicdiM25hU1ZvxXL5tR2uqOl332WDLPca6d44q8oelcWmjCB1Ld8xNnlh8/DDc4UxVf3r2RP47DPgxx+9ba8MUdgM05bwmMTYtJnkRilsNmwIvPCC5yfHVti0GWivWcPKr7q2mzaxCeLy5ey76H3hNwExFTYTCXurC0De9/sRxBVdRlCLzWeeAcaP9/7Gj61ESyP+2Spli81HH5WHRbCx2Bw/Hjj1VO9v++4LTJkCXHNN7n6BaCaeUQibl13GkssF5bzzsp/9zkmM7ZtvV/QgFpt+Ywf+5ZlMBOU54ABWJ0SidkVXCZu6RDJRu6KbWmyaxvYMYrEJ5M4pTIVNXrB2600UwubKlcAJJ+j7Z92yYrTY1ME/c889x4TmyZODCZvvv+8VfYNYbNq+dNRZbEYtHooWm+PGAW++mb0+qusi00DIYtNDyQqbzz//PC699FKMGjUKX375JXbddVcMHToUSxQB6MePH48TTjgBH374ISZNmoT27dtjyJAh+M21AtjCoYceioULF2b+nn322XycztZJ1MJm27bBkweJZdlpp+znXXZRbxeXsMmvr4odZnPcIK7ohRQ2TbJa77hj9rcePez275IPV3TAe6+XLg1+PB7xGplMBHk3BfHNrKlYZTPJ5vcpZuvzGxDFZbEp20a1H9kzevTR+m3Fbdzz8IvRp3FFd1QDM11IC15IXbRIf2xbos58HQd8fZOJW1EMRPlBuCqLZRBMYmyaCnJAtMJmVVVunbeNsWkjyjoOEy51Fpt8DNn27XOX67CZmNm6l6ZS/vHgZIjJg4LWqyhjbPL9RVhh8+CD/dfp1Sv7ec89/dcXEeuhicWmbDt3W1NhU3ctVXGmC22xWVfHYijffXf4crjYtq86YZN/HgppsSlLBMXfu1Wrsp/9hE0XcRz2+9+r1w3iip5OF5fFpm7MZnpc8V6JcxLVM7nPPt7fTYVNvg8JY7EpO8ZzzwH/+596e914Ow6LzRAxNn0R29Dp04H99w8mbALA5ZdnP+uEzTgsNsXxSJiEWzJEi03xHGyETbLY9FCywubdd9+Ns846CyNHjkSvXr3w0EMPoX79+hg9erR0/WeeeQbnnXce+vbti549e+Kxxx5DOp3GuHHjPOtVVlaiTZs2mb+m4mSdMCcqV/QhQ4A77wR22MHOFZ1HbCR23x24/nrmcvfqq+rtwrii6zoMvsHlJzV+EyvTGJvuekFc0cUBYTIZjVsxj+5+ucfq1Qu4+Wbg0EPVAff9yJewyV+fMBMQniCu6K1bsxiJhxzCEs3wmE4QbIRNvkxHHslcHHfZhbmsyQSFgw5ibyUBb103cfE3JYwrelUV8Pe/2+3f3UcIYVM7oVFdG17Ms4kHZEKQ617IGJsyohY247TYFO/f/ff7T5z559nkmTUVNisq9MKmyXW17Q9Wr9YnD+KFTVuLTZu6bCtsyhKQ2QqbYS02TYXN0aOZ4KhyfayszLbZorBp48q8555ql2+e/v1ZmKBhw4Dnn/df3w/TGJuqUCKqex9G2HS/R9EWmVo8yqitZQk0oqTYhc0gFpu9egGDB3t/UwmbpnPDjz7KCv3HHKMfXwSNsSm7Rjphs3lz8+OI5MNiU6xbu++uL4P77IshxwptsemiC+0TxGIzn8JmUItNl5qa4MImj84VPQ6LTXFcEeZlgAzRYtPPU8zFLwQGgZK0X62ursbUqVNx1VVXZX5LJpMYPHgwJk2aZLSPDRs2oKamBs2EydH48ePRqlUrNG3aFAcffDBuvvlmNFd0Aps3b8Zm7k3Pmi2uFzU1NaiJeqJZBLjnZHpuiUTCU8Fq6uq0DXJZIgGxS3R23hm1rhCy5biyxz3tOKjTlCuVTHpU/Np0Gs7VV3OFU2xbVyc9Xo3jeLZJVVR49u8kk0AqlXM+me1Tqcz2qerqzLZOWRlqddfXcTzlqUulkK6pQRl3LKesDLVbJiLJ6mrw3TN/71K1tTlvNtKJBOqSSc8xnPJyfZmQe339qAOQ5vbJH682nYbjLrviCvbHCq/cn6prrEulkKypUd4HP2qBbFkApNLpnPOsqalBIp22akwdwPeaJhzH+/yYtinnnZd1GeO2SdbUwGSolubqox916XT2PpaXe2OPSZ7XGjeDcE2Nt844DpxUCglhUlNTW2st2iUdJ+c804C8fUinvXXv2WfhbLedt8zCdknAs38nlUJtTQ2S69Zpr29aaCMAVr8AIL1woXSbmpqanONllq1bJ21DAKDmu+9QNnw4nA4dUPf668rnQ0Wt48CZMwdlRxwBp1kz1I0Z4xkkJiZOROqUU+AceCDqHnuMlaGuLq9vStM+x0snEto+wYRERUXmGaxbuxbJ2lokAKT79UNy6tTgO+7SxfM1vWlT5lzqLrsM6bPOAjZs0N63urq67LO3ebPvPU6XlcGpqvJtA2qTSThCv1e7ZfJTs0VI8zuWo+n7ZNQsW4bkpk3SsqU3bUJ6zpzsfdh+e0/fUbZ5s/ZYVmOxRMLqWXHKy1ErXA+nqsr33Pm2oG7TJiQkfbEJdZs2sbbUZ72amhqWHOykk9wfAHjb5joAyYYNkdi4EZgxw7s9Z12SrKvTHq/mscfUVmQi117LF9J/fQG+/O7z7hmnCvezpqZG2j+4Y1Pp2DKV8o4dHcczJuAR+2x3nJNKJEK3jbVr1gD/+Q9SV16J9LnnIn3VVcZ11amtRd3PP4ee8PHPkqpfUpZh40blc+FUVmaW1VVXe57vRE2Nb7nrLrsMqbvu8v5WWenZTzKR8G/7HAe48UaUvf9+5reaujr589KokWf/Snr1At55x/ubWE+3kEgmjcd87nzJ2fKside2rkED5fnWNGoU+GVoMpnMHV/xfW1trbReOvXqodawXUgJz1zNnnvmWI17nut0mi1v29Z7jyoqMmWtq6lBuroaqdNPR2LCBNQ9+SSc/fdnC9ety2yXLi9HncUzW5dIZOqBaozt6avFc9W0/e7YMmcbydjHd+64heR223nKKM5xcjdIGrUzNTU1ynXT69ZlyltTXp5T98qSSSR86kVdIpHT17nPRyKVkrYRdWVlZs8oR6YebN6MupqazFg3sWCB1X78SK9fDzRqlL0uW87PRdRPXGrq18+5xnWbN1ufZ6lhM44rSWFz2bJlqKurQ+vWrT2/t27dGt9//73RPq688kq0a9cOg7m3c4ceeiiOPvpodO7cGT/99BOuvvpqHHbYYZg0aRJSkrc3t956K2644Yac39977z3Ut3EjKzHGjh1rtF77b78F/55tzNtva9c/vLY2p0J+s+ee+GnMGM9vR0m2XbxkCT4X1uPZbckS8HYekyZPxgqDjHVlGzficMnv733wAWq5Nzj7r18PXiJfv2EDyuvqoDKEf//jj1G95Y3qPosWwXUgXrNxI8ZrziO1eTOO4L5/+9NP+HnMGBywfj3c98d1ySTGbNlHkw0b4KYIWLzbbviM2/eAxYvRRtj/vN9+w8+ff45B3G91iURmfyr6LV2KHbRrePn555/xLbdP/p5Omz4dv5m6+Ui2nzdoEDpuscT+okEDNO7bFzsFSYYC4LOpU7Gce5u7+4IFEBwhMWbMGLT56isMsNhvdXU13vG5pu1nzvQ+Pz7r+9F91izs5L8afp03Dx38VwMAfPv99/jZp1z8veHPYdeFC9GJW5aWTDw+mDABm/ws8wR6zpkDMXDBb4sW4UtJOcvXrgWfJuWLqVOxRFNmAOg1bx54u4DadBpjxoxBj+nT0VNTrkUrV0JIe4KPJ00CWrbEF7W1kEWcHTNmDHaeOxc7SpZ9Mm4c1myxwun3yy+e52/9EUegyc8/I/HDD5j217966pEJM775Bm0ffRRtvv0WCQDfn38+fhwxIrP8qOHDAQCJp57C+D32wNoOHbD/ihWwu1Ph+L68HL00yxctW4YvQj4zTb//Hgds+Tx35kx03TL4XrVuXaTnum7ZMrj2NT/Nm4fvxoxBoqYGR2q2+eGHH/DDlvMrW7dO2k/xLF+zBit++y3n2RD5ePJkrF2wwHPsqTNnArvuirFjx6Lhr796+gcZazZuhI1dwaR330WLmTOl93PNkiVY+NFHmbZryoIFWMTd12Hr12snXjbtptge+FEDYMLHH+MQ7rcN6TT87JJ+W7Ei04/M/eEHNFuxAob2Xx5mzZyJylWr0FWzzvrWrfG+4hrw7dxP8+Zh+2RSWvZJEyZg5ZaXL7vOnetpt0UmTJyI9VFbByrgy//b0qWeNn7s2LGoWLUKh3HrjBkzBjv++COEFD8Y+8EHSJeXe8ZWLguXL8f23PdPJk3CakW4mdZffom9uO8/z5mDb8eMwT4rV2bGeEH5auJE7HnnnQCA1KhReLNPH+lYWEbd5s34cdw4o/5fB/8sdf/5Z6v91a1bp5xwbnCcTL37+YcfPGPDFjNmSPtGnh9//TWn752zYAG+4fbT4bvvsJvPfqZ+/TXWL1oEPpDC2PffZ0IgvPXt24ULfcc+JvDzqQ7ffuspo67tGrZFiF+3di3qScTfWevXK/vHMR9+GLS46PLjj+gt/LZ8xQpMdMtaVyetl9UVFb5jXpf9Vq+Ga060vlUrvC/Jn8EfY/wnn2DDjz8CjuOdCyxcCPcV4jczZ2LN7bdjvy1xlDecdho+/Oc/AQDb/fJL5p7/snQppo0Zg/7LlqGtQVln/fQTftxyXi0rKrCPZJ0ff/wRsxTnvsevv3raF55V69fjI8l2u/3yS84Yfc6QIZhhcH17LlniGQNMmzkTv+nmnBs3SttFEbeu/i6VQlIwUFj53XeZ+/neZ5+hVnhxdkQqhZSPsPnDzz+jbMMGz9jbPSY/TuP59uefrZ7RquXLMXTL50Xz5+OLMWMw7I9/RCIqLzyOlb/9hg3V1ZlxwPhPP8WGn37KLG/99deevsRlzPjxOc/X7Fmz8H0EbVExs8HiHpSksBmW2267Dc899xzGjx+PKs4K5fjjj8987t27N/r06YOuXbti/PjxGDQodyh/1VVX4dJLL818X7NmTSZ2ZyOdG0CJUlNTg7Fjx+KQQw5BuYH5eMLNdrqFYWKmVYFUebnHJaDuvPPQ48470UMwla99+20kH3oIyddey/zWuk0b7f5Tgrv53vvuC0eMxyJDIX4OGTbMY5qeuuceYNaszPcG7jLhGrgMPuKIzPYpLu5Ro6ZN9ddJMI/v1bcveg4bhtSttwKzZ7P9VVZ69lFXW4vEjBloduutGNY221WnHnggZ/cdOnXCDocc4vktVa+e/70Tkk340WXHHdFJsc++u+2GXX2OJ1L71ltIPvoo0hdeiJbdumHeaaehXf/+6DdqFLBpE5xXXmGWKBKc3r2REDpal7322w/O3ntnvqeeey5nnWHDhuVYGvpRUVHhe00TK1bkHCcMyWnTjNbboa3JcI7Ra+ed0dOiXPw5JF3rTfd7RUWOq+/Bw4dbu4AkJ0/O+W37HXZAG1k5hedzzwED4Awd6vlNvO5JwcW/bMvzkeSzikto0749S1rDsf9BB+G9mTOx28UXo27lSk9b4B47OWGCdH/7DxgAZ0tsutTTT3uWNfn558znXQOEU+ndp49nnz0dB90U9/mA3XaDM2AAUv/3f9bHCUP344+H8+GHSAhxsl3atGsX+pnB9tkpR+cWLTKfm7RogdoHHkCZZFwQhO0417quPXqg87Bhvu7J3bt2xY7u+QlthYzmbdqgmZi1WcJ+Bx+c49LXb9998e66dazvnzvXdx/bNWsG+KznNG6cGSPs06uXcvLQuLIS23Xq5CmLM2RI5nuZ5jrVvvMOhpnEe3QxeNnJU16vHg4U6kD9pk2BxYu1223fpQtzUQXQqW1bJH/91eq4Lj26dFHWf5eKDz7AMNFFU0LXbt2QnDVLWvZ99twTzpbEHCkft/GBhxySGy4gD7Tr3x9thg3zjlOFrMTDhg1DUmL8cMjQoUDjxqh96imkrr0WiXnzMsvaduzocd/fd+DA3OzLW0gIbrjuOCf1r3+FOTUAwG47el9vDbNoe1IAeti4lCrw9N+KMZOKMk0s3PotWwJb8iN0ad/eMzZMGJS7W58+OeEMOvfqhY78fgzayH4DBsDp3t3z2yFDh0rdznfaZx+rsY+IbD6VcBOkbUHXf5Vt2aZh/foe9+/0vvvC2W03dBs5UpkMLUy/KGurmrdsmd2nQqCqaNbM+LipW27JfK7XqZPvdgcOGiRtczp2755JlrVzr16efrLRL79k9/vVV5nfd9hxR7QbNgypJ580KmuPXr2yY6PDDkNdXR1SN9/sWafbjjuiq+IcUk89pdx34+bNpeeeEmJ2pkeORPs77kB7A90hOWuWJzlg3z328J1vbX74YVSec452HbeciaqqnGzwzbaMR52uXTFEkrAxWVnpa0HcvVevbAJB4ZhQzFl67bab3TPK5Whps+XalyvGJU6bNkgEiG3vbLFObVqvHppyY8oDDznEEz88IQkRUDt2LIYNHJjz+46dOqFL2LFukbNGTEanoSSFzRYtWiCVSmGxMABbvHgx2rQRbdC83Hnnnbjtttvw/vvvo0+fPtp1u3TpghYtWmD27NlSYbOyshKVktgO5eXlRsJfqWJ8fkJ8C99t+EHhyJFI3X+/3JXi0EPZX6dOLCA6mHtEUrd/4T6VVVSYxfZQxO4or1fPu71wrgmf5EHlDRtmt+fEnITftRXihaXq12eCMLdNIpn07uOvf2WbivuSDEBS5eVICUKSb5kAu9hA7nEU+ywTzseIYcOAYcOQBHOrmPbnP6PdsGGs3BUVwL//zeI/Skj8979Ab/Ed9JayVFV5yyKJ11ZeXm4dpDoBg+dBuKah2xTDWHNJi/g9uvsow3MOwvnldOSpFMqbNLFPICQpTzKVkrcPwnMraxdyrrvwPZFKsXV8Yv0lJVb8ZVuOX15ZyVzpVq1isfD4YytiIJXV1WXLorlnqQBBz8vOOsvzPQko29eyRo1YOdwYVokEi437t79ZH9eqjBUVLF6Z614rkKyo0PcJJnDxjJKc6JUsL0fy4INZLFveg2GvvXLEaxMS3KQ/VVHBnimf2Fep2trss2cQDyxZUWHUTpXXr5/bXzZowNz1ystRbhC3yuS6J5o2zbxYKFu/XlmHE9XVHmuOnDZZ9dw1aYIy4SWFL5aeNgnJ9TARYpLcM5l69FH7WGDutum0Prbvf/+Lcj5Jj25flZXKTMpljpO95j6xhMvr17fvvyMg1bmzpy8qLy9HudD2ZcYDAuWVlazMJ5/MXIf32COzLCmOY8X6xyM8N5n+MYKstWXnnusth4UFS6K21lcAN0HXf4chwdW7VHW1d0xh0LaJY1aA9Xue/Rg8Y2VVVTnPQKZuiOs2bx5JPffMp2zmTFvGRQkg23b27Zt5wZr68UftMQMjuY6e+ZcilmWifn3z43IvnJNNm/r2JznzsS2k+H41mfSG2Nhuu2x5uJdjqXr1WL0xrN+pykpvPbvpJjanu+02TzmU42RN7M9kebl/X9q8OZKjR5uHuhBE+pz+VELNyJH4YvZs7HnHHcp1PHVYEDbd8U1iv/3kdcCgXqQksb8z+1L02zltgB9cf5Gsq9Ne+0TjxoGSdibq1QPWr0dy0yZPvStv0MB7HcRxxO9/jzIx/u8WUo5jd54liE2bVZLJgyoqKtCvXz9P4h83EdDenHWVyO23346bbroJ77zzDvbgBi4qfv31VyxfvhxtLSyYCI4IBnNa+A7WL3GH+FCYiiVxJA8Sl/ET81NPtSuPLHmQ6bmpsqKLAxeTDj7K5EFBg8Xr0J2DTvAT640qo6WfaNi/v365jKgz3ZkKlpYx6QIjXluxDjVqFGz/NsmDxGOaiL+qBBGHHZa7Lo9MVFIlAzMpE28Bo7Pui6IemTyTbv1KJgMLNVYkk3ohKurkQfwbY7femNw/E/h76e7br+7z25hmRTcZHLZpk3Nshz8vk7beZB1+guWXPMgmwZ5LkH4kX8mDxHriN35RISZmELFpP1MptXU8f2/8BLVCTbDai0FiYJcVXfYZyL1XhcqKLsInsvGjthaIQNj0EOX4hBcTRctOk+dY9jJB/M2kXpaVyZNnuhzOBfwQE9pEgc04mk8exPe9LnFlSvZ7plRtjk3iPdvs8/y58kZIfCxrxwE4TxbPsqiSB7mI7ajCcw9AsORB/DNhO0a2TR60hVrTMZ1uDKSaB5k+m6prpXp5b2ulbvLC1CWV8k9gKcMdU9omD+LrlFi/okpquZVQksImAFx66aV49NFH8eSTT+K7777Dn/70J6xfvx4jR44EAJxyyime5EJ///vfce2112L06NHo1KkTFi1ahEWLFmHdFiuMdevW4fLLL8dnn32GuXPnYty4cTjqqKOw4447YqjtW3+CEUbsMpmo8Y2WX8bIIAKGbj2/we+W5EFSqqq853rWWSyI/nXXAT7m/jmdmDvICDKIkV3jVCr3XEysWHT3WtYhxjHQ1xEk6zSQe14jRngTHrj4nY+lq77RPm2RTRB+/3vgL3/x/ha1sDlhAsuSy7n7AMi9tmIdDpr9T1Ym0+fYXe+jj4AzzgBkCWLEfbnnMXSo0noQgD4ruovsWTHJiq4TNsOIzy5r17L/Mlfd1au9mVmTSY8Ld2ykUsUnbOoG/0cdBZx+unyZTNj0o7oaWLqUXX/TrOi6fR98MPDqq0xk0GVFN5ngmExU+EnBmjXqdmfzZu+yOIVN2zY3qLAZwIpayrp1eqHRJpN2VMJmhJZ8Vsjc32VtgCorumq5ODkOImzGITLZCJuAx80SZ5wR/vhRjk/4eue2hW5fE1TYFJ9D0xcy4nb8eT70EHDuuczFO45wCzZzJrdc6bS37w2yLxtk7a/YX7z7Lhvz8eETbF7e2Gaf58/1P/9h9+jJJ4EduMjjjuPdL18evv91+zrTZ1bWb9kIm7q+20TYtH0OoxQ2L7ggd5ysEzZbKiINBxE2+TrXToxevwVF4mclfDn85kGpFMAZ12WoqACuvx743e/k27ltFZeQL7OdqiyAt06JYa9I2PRQssLmcccdhzvvvBPXXXcd+vbti2nTpuGdd97JJBSaP38+FnKZZh988EFUV1fjD3/4A9q2bZv5u9MNxp1KYfr06TjyyCPRvXt3nHHGGejXrx8+/vhjqbs5YUDcFps2wqbYSIQVNnUTP3e5qmMU1y0rA268EbjhBvtrFrXFpszaKqywKRM5ohBbbAhqsSmeVyIRTNjs2FHpCh+oXEGQTRAaNwbuuMM74NDEwsrBpIwHHAA8+ijQt6/3dxOLzSDYWGyqJqL77w889pjcKkNlsZlI6N2vTYRNUwsjwHufdAMbm/up4vXX2fk1bZr77B5wADBgQHaQlkzmJ8ZeMql/I58PYVO8f7qxQq9erE7J7id/j0zL/a9/Aa1bsz/VwJ7Hz2LzkUeY+Ark3mO+TzCxgpCdw5VXer8Htdg07SMtQmoEptDC5v33A5MmqZe7LyRMKCsrbYtNWZtj+pKL/03lFeNSihabQDZ26q67snYoQOxlD34WTTbwFpubNgG33sr6/wsvzL/Fpvj88nVohx2ABx+0H8uZEqXFZj6FTbF+DxnCxny7camQbIRNvm6ZvODmn7u2bdk9OuUU771zHK/AyL+kDWOxOX167m/inEknbOrGZ6p7yPdtYYVNw/Oskz1jhx6aO07W9X+qeynWKdULKZWgqxp3cTEsjbC12Ozblxk08NSvD4waxcbMMtzruGFDcIvNXXf1njMJmx5KVtgEgAsuuADz5s3D5s2bMXnyZAwYkM1LPH78eDzxxBOZ73PnzoXjODl/119/PQCgXr16ePfdd7FkyRJUV1dj7ty5eOSRR3IyrxMWhLHYtB3M2FpsmpbNtNOQuaLrLDajIoywKbMOTaVyr43JRFZ3PWWT7lK12AT8LT5MMLlH+XBFd4/Biw5CLC8tYQRq8dqKz3BQi00bYVP83eQ+iuvw90lXz2QvP2S/+R3PxdRi0yLoti+qAdQXX2TjVyUSZsKmynrRlHy4ovNxnXiRSGWxqbv/brxYmXjET6xs+k3HMReu/YRN3bNsa7EpOwexrvMWmzphM58Wm34ceqj3e1Bh0zKWpxbd823z7KdSasG1WC02+Qm1X3/hxt+3tdgU76eNsOl+LwaLTRd3ss9fL5N+77jjvN+Dhk+QIVpsXn01e37/+U977y2XIBabsuc5n2PVoMKmzGJTVeeExDbWyJ5vVd/B35eg9cXEFV0X9sulrs7bh/PCZhiLTS7pcAax79EJm7r21ETYLKQrumwso3u5qzJWMDE8Ki8H/vCH7PdrrlEfx8VW2OTDxPlZbLr3WOzL+es0YkTudiqLTRthE/DeNxI2PZRk8iCiRIgy7qKMMDE2TTMtm5YpjMVmGNxBRpC3s3/8I3ORfuON7G+yMoe12JQl9ComYdMmxqZqfduJi8mkOx8Wm+4x/vxn1jk2asSstsaPB95+Gzj/fPZ/+XI22RCJMsam6OYcpSt62Ji6unX4um8jbLZrJ7cIFlGV3TTGpiQLcKwkk/JnnufWW1lMUi5RUqDjxC1suuLzpk3egPgqYVMnurkD2oYN9YJTnJY2un3rnhG+rzURFg0SdeVYbKqendraYK76UVtsXnUVcNFFLJajOyEptMWmHzYWm1G5oscV20/Gs88yS2NJpt0MX3zBxjlnnsm+b4sxNnlkwmZZmdpC6eabWft3ySXe323CHPjB1ztxLC8kiZViImyatFuy/RS7sMn3/TqLzdGjgV9/BS69NHj5APl1VL1c4+9BUM8RE8ti1TPJ92lr13rHwOvWse+JhL3F5ogRgJvP45BDcpeL47Gohc1icUWX9RdRWWyKwmL9+sw76N//BhYuNKvHtq7obllqa4H589n3sjK5cOj+phM2H3wQeOkl73K3jampyda7VCr3PorXRHffSNj0QMImER9xW2zyDYitxWbUwqY4KUsm1R1OsVhsJpPAZZd5hU1ZmeMQNovJFd3WYlOGeN0CZkf2EPUEUfZMuceoqPBabQ4cyP4A4Oyz2X+ZsBlm0C9eW7Fztg387WJjsRlkPZVVDmAnbMqSXcjqokoEM3VFf+UV9bI40MUXBoA+fYC//hWYPTv8ceIWNgHWXm/a5BXeVcKmrr1w779f36Pah2qAbYqfxaau7tu+jAtisamzzOGvfSEsNvv2Bf7v/7LHDytsBm3bbLG11o5C2Mwn3bsDW8JJKdljD0+Wc2m7wLe7fsKm7hkv5hibLjJhU9euHHMMu84icVlsivudN89/e9kzt3y597vJWK7Q8eCDxNjkRTS+Hov72pJ/IjSyMYrqBYqN8YkK2+RBPHxZRXGxro6Noaqq7C02r7kmN7SSuG+eoMKmqgwFsNiskz0bshd0urFCGFd0d6xn4+kTxDOivJzpCcuWsVAvDRrI7597j3XCZsuWLDzVLbfIy+T2zyaZ4kVrVxI2lZS0KzpR5MRtsRkmxqapsGlKoSw2ZcKmDTqRxiWssCkL51BMFpu6AYzqvNzy9+7t/e6y117mZVORD4tNk6QjOsII1H6TPVmiGhPiFjaDuqKLg0DTmHC//irfn6krer5xrw8XGkZK2Fh8fsmDorJ8dNtY/noHsdh0B7x+fY/quQhr5VdeDhx0UPb7eed5l+ueZVv34ihd0QG5qOxHUIvNnj1zf+PvCf+s1atXeFd0HaedZre+ylXQVNjMlyVqGPza+Chd0d26WkwWm64VE3+vdc+Kqk2L0mKzqip77UQRwUTYlL0oOPBA7/dt3WIzKmSWvaoXKLyYet995se4+OLsZ/cFu8h++2U/m7iiy54X1wvDVtj08yY6/HDvd90LpnxbbIp9j4XFpsMnY2rYUJ4/Ia4Ym359pk390sHfqwsuUI+tTSw2gdx5F9/GuG2dbHzlZ7HJi6Wu8QkBgCw2iTgJM3G1jbFp64oe9cTCRtiMMri+LCu6jdiUD2GzVavc34rJYlNnPaM6rxkzWAbhk09m38XBRZcu+vKYnH8+hE1Z4HMbwpTRT9jUveXWEUbYDHJfTIVN8TkIK2yauqLnG/ccXnrJm5VUXB524pUvi01d0icbYTOsxWbYF2Ll5Sz8wfvvA19/zbKZPvBAdrmu7tu217JzkCWlc60fV69WZ0wFvGEA4rbYfPNN4LnngHvuyVp98efPP3ctWvhb+MmIW9gcNIhNdvxeLoioYpK5wmZtrfdeiEyebHe8QuDXLkTpiu7eZ9t+8sorgb//Xb9O0P7RvcemL/dVfVqUwmZ5ObvOGzYAS5d6l40Z47+9OIY7+GCgW7fcY/ghE2XyOVa1mRv4CZtxhYSQuZSrLDb79gVefhn47TfgrLPMj3H99Uw069dPHdbmhRdY5vNDDzVL8ioTNtetY0K/zBVdNz7xEzY7d2b1dtgw9n39+qzbu0i+Y2yK61t4pdW+9BLKn3+e9QMjRniTfrmo+j/di2iTkEx+feZ55zH3cT8LfltUBhZRCJs2FptinTvjDHadGjViyU6JDCRsEvFRzMmDohaNbJIHRenC43YiQQdfJoHSwwqbskFAMVls6oRN1UCzVy/25yJeR5mgw2NSv/Phiv7TT+H2GWbQ71cHglps2iTgCbKezv1YV89Ey2VTYXPhQvn+ilXYdK/h9tsDPXoAs2bJ1wv7gicfWdEB+WRXJmzyged1+/GzalPtI2xSFvd6DxrE/sT2IMoJvGxfMoGoUSMmHq5Zo7fY5CfOpvUmqMVm167MfeyDD9gfoI4PJxM2TcoXpdeGjJNPBo491m4bx/EXNg87LPtbKuVtd04/Hdh5Z7tjFgK/Nl5cHsZi0x072bRFl10mD1MiEtYV3VTYzIfFZnk5u84bNgBLlniXLVrkv734PLkvnHlM5iOy/iSfwmZYV/R8CJsyi01dLN/f/97+GI0bA3/5i36dtm1ZSBsdJsImYG+xqbJs5znsMOYh8eGHrG3duFE+l9I9R3FYbJoeQ8ZuuwH9++vXUVlsNm6sfpZMkgf5zUNTKeDUU6MXNlWYuKIDemHTfZaCCJtlZWSpqYBc0Yn4iMsVwoVvIPwmMVFaSe62W+5vMotN1fnbBPT3wz0u33gWwmJTNwiQDaCLSdjUuU4EjbEZRRzVqK+RLDbNjTeG22ecruh+FisqZNctyuRBvAstoE88wR9fDGRuGmPz7rvl++QnGMUUY4e/hrJ76MYqjMJiU1eHopqQ6iw2+Xa3QYNoLDbjsvQX2yTx+kR1vRo3lsfjlQmb7mA9Dlf0sDE2TWJ4t2iRe91M3LHLy+VxC6Mi6EshVbKFmhrW3rz/fvY3MalHKbihA7nPl2gRVmiLzXr1zNYPKmy6/VdYYfOKK4IdX3WMMGK/uK1szGpjsTl0KPuvsyKPA/7FwJ/+pF/XbXf4vl90RXfjU/7ud5EUDwAwZEjub1HOaaLEVNg0SR7ktm9t25r3QfwzJmuTN2wIZrHJx8O/4w6zstgeIyiq51hn5So+m//8Z+46JvPQHXfM3ie/5ycs7nMnvgyxETZdgriiE0rIYpOIj3wmD4q6LCoeeIBljRYRG3PdpDvopEOGrEG0maCK10VWZpNEB7rrKxtAF4srejKpH/Ca1htxUu43MTE5/6jfuHfvzt4eL1jABmfz5wMnnBBun3G5oh93XHZyYUvcMTbF+KkmYSCaNs2tZzKLTRnDhwOnnAI89ZT392K32ASAI49kLumVlezlU21t1urLZKL5v/+x9S6/HPjhB/VxZESVFVtnsclPSho2jDd5UFiLTb+BcRRt8lFHMTfCXXYBXnvN21fK+sggwmbcFpsuJqFuZEKgibVdeTnwzjusTY7jpUTQMYbKYnP8eJaRlqdxY5Zggf9eCojtxqRJ3u9xCJs2fXlVVbzCpnufwrqi77knEzdvvz1YOXjKysK9DBbLKBNBTMZy7n165hngxRflGa/jpH59FiZk8mT/sZnbXqvckhMJ4Isv2MuIY46JrowtWrDyjRihDpNTLPDXQxZ2ycZic/Ro5l0gE3ZV8M/YO+8Af/iDt27+8ot+e1Wd3Xtv5uq+fn0wi1ieqPufKITN4cPZ2OG117K/mQibVVXAxImsfh5/vP/6NtSr533B6Y65xfMNImwGsdgklJCwScRHPpMH+RGVxabqLZCNK3qUwqbMYtMGE4tNk0G57l7LLDnyLWyqqKqKJis6P8ED/CcmJvcrDqtWMaB+WOKy2Bw5Mvi+43ZF796dxRZyrRRMtmnRInc9U1f0VIpdD1HYLNbkQeLk6uij5euZtMkjRrD/f/5z7jK/dimqrNg6i02+LW/YUC/OmSYPKmVh8/zzs9lixbZG5tLrlqm2Vp9gIYjFZlhMLTZFTF5YlJezOGwXXQTcdVew8unQxcHUoRI233yT/fGILqkm7pnFgNhudOqkXx6FK3ocFpsrV5rvk8dW2NS108OHRyNsplLRWmyaigcqmjcHzj03eHnC0KcP+/PDL8YmwCzYdtwxurK59O8PXHhhtFa7ceDXp7ljOBOLzVat7MN78M/YqacC//43MGFC9rf58/Xb6/o6PixIGHQWo0HQuaKrEJ/NigrgxBPthU3A/Pmx5fDD2bk9/TT77j534vn6CZuy8yCLzUghV3QiPsJMQKK22AwzaXNdz3XuHOJASidsymLUBMVtEKNyRZcNLk0G2X4Wm2ec4f1N95YwKkGCp2VLNpEU8atDppYWBx6Y7bDuvjsaUTLf7vpBiEvYDOPWGLfFZiLhtdr88Uf/bWTCpsza65xzstf0P//J/r7XXrkxOnkrg2JyRTdtl2VtRv/+2XvvuqwDcos593oeeST7P2iQd3lUFps2wqZukuDuZ8899cdTtaVhX87ZCptuHXevrwl82U0s3/gyiS+HeHQWm2HDaagwieHtunrymAqbQDyxNlMp1o4EQZYMQoX4gnZrmXj51VvdWCcqi02TftVNbGVLlMJmVPU3lQpnsWniip6vFyL5wm2f+LYpn2NGm7aiUJgKmyYWm0HqjziO/egjrxDtJ2yqXjSFZdQo9r9ly+yLyKhQtQmyDOouYhtTVmb2TOcTx/HWAXfMHZfFpljf4o7LvRVRAjNnomTJZ/IgP1TB/014913g2We9QoOIOEjUCZtREraxE8som5yEtdhs2BC4917vb/kWYpJJ5k4nxiu0qUM6GjUCpkwBXnkFuOCCaKyf8lF/whKXK3oxC5uAV9hcvNi7rGPH3PVlsfhkdaRtW2DaNGYdxbuiVVUBn34KXHtt9je3TVu5Up2gpxCYxiUT7/+zzzL3qq++Yu7rl12WXSYTltx79cQTbNvnnvMuj9MV3S27KGzqLOXc/fzxj/rjqZ6LfFtsvv46u66ipbAOvh8QnyWdKzqgFzZVxwBYvDEx7m0UmAibMitFv8RxgL2wKbNYFtlpJ+Ctt5j7qW4SqUKVtVeFGFdvaxU2xedf10eohE2b8aepxaaYPdwUW2FTVyfCtkkuyWS4sazYJpjE2HzqKeDhh4Mfs9DIRK98CpulYKHt156tXs3+mwibQYR32TPGt5t+wua++9of04SrrmJhfiZNil4wU12nffZRbyM+m4mEmRV2Plm/3lsnTF3RxXGoqbBZCoYtRQpdOSI+4hZmbN7ghMlE3rIli9ehG7jLhM18vCGWJbEIY7EZNIO5n7ApXp9CWJh16JAbdyWKJD8uO+3EXLPKy6OJsVkKHVupWGxGmTwIYDGOVMgGoy1amGeR7dOHub2I16drV68o5lp+H3642X7zhamVgXhPjj+eWbF268bc1/lJs6z9dtuQpk3ZtqJVbD5c0Xkh09RiM5XKjVco27dI2IG930RUvB+6fk/1nPADdFuLTXeS6VdGcb8VFSwmb9Tw11tVl2TX1GRc4l4nU2FIFc6Bp7oaGDZMntwwDsT6WCrCpl+74CfI6/oSlbC5YIFZ2QDzGJs8551nVp5UKlsmU2FTh58o0q6d2X7CWmyK98RE2Dz5ZLnFdalAwqY/smd1992zn90+x8QVPYgAKHvG+H7OT9gU47lHRWUlC/PTtWs8+5ZhI2wCdi+U8sH69XKLTT9XdL7ciYR58iAiMCUwcyZKFtuGyNZi8+ijs5Yafm9eo3T/liF2YMmk903NdttlXTeefDLcsdzYP3vvLe+4oxY2TQRqW5FKF4suTsRyup3Ms8+y//XrZ4Nxh4lH6XcPChVjM2rClFHc1rVG7N493IBLdu1NJ02m5zNoULaM11zjXXbbbbl1fv/92Xm5llS33WZ2HBF+0OhaGYjJLwpNHO5TYnvRsqW8jtj2ISbI6o6bSZm32GzQwCtei3WA349uQq0SNs84w06MsBWewiSdc+HbV7+JoWixaYLKLfZvf4v+RaJKSL7+eva/SxegVy/22U0o4b44O/VU/b5NLDbd8C39+qnbw8MOy9azm2/WH9MP93k5/3yz9UeP9n4vFWHTjzCChkrY/O03832YWmzyNGsGDBiQ+7t4Txo3zj7nUQibfhNy02cybIxNEVNX9FLwilEhC2WTz9j1gwdn+8F77snfcW2QXQ/emt2N6yyz2BTrS1QWm7ywqfNSOPzw0nD3F5G9zOjRQx/3UtavF8r1ms84z7NuXTBX9Msuy57f66/bxf8dOJD9HzlSX2bCw1YWdIQoKmw7Wdv1GzRgme5mz9ZbwADhXNFNkFls8i6qO+/MXCXnzwf22y/csf7xDya+8QPZqJIHyd7Cmgz+dEKlbEBZqJiA4oTB7YSOO45ls23fngUJnzBB/4bR9jhBKIVBd5QWm9ddx57jvn3Dnbvs2ptOuk3vW0UF8PHHwMyZwMEHe5e1b8/cqefOZYPYFi3YeSWTwNSpbBt3wGKLKGzm8zlq1Eif4MUlrrhQLnvtlc20LpJMZl2E4rTYdK1yRVd0vs3fbjuvRSe/H52wqar7zZuzujN7dvZF3bBh6v00bOgVWv2eAZs2q7xc/rKQL7uf5VsqZW/1oxJKtt8e+PZbFlvyww/t9qlCJWxecw27/336ZMvz8svAJ59k+/aHHtK/wDSx2Lz/fpbNeM891ZPql19mmYkXLYrObfGuu9ixdUyalCuklYqw6dcuhHFBNRE2u3ZlITe++UZuiVtVZZ9gsqwMePttVh/OPDP7e5MmwIoV2e/8PcqHxaZpXGCZsNm4MbDHHsC4cfblMrHYBIoniWUQCm2xWVUFfPkl8P33wcczceMnbD7wAIs3mU+LTX4MpfJSGDMmPjf0uBHnTa++yoxwdGN6vk12n90ovelsuPlmZriQTLJ5oRvLWOWK7mex2aYN8N13TMTu3x94/vncY6rGAW+8wbK8F+vzVaSQsEnEh604wXcCpp3I9tubxZMSzcGjRiZs8oPZ7bdnsfdk8fdsqajIWoi4xOmKbtLB2GZhLRZh0528JhLegcTQodEeR4Rc0eXB2QcPDlceID/CJsDiYbZtK1/WrRv7E2ndOjcJkA38AGjz5nAhNmw5+WR/wQOI38rg8MPVLo58fYwrxuZ22wG9e7PP9eplJyfNmgG//JJdr1UrJji5mAqbKvEumcztQ3bemQkkMho08Mbhy4fFpq6f9XNFN0EnlHTrxsS2uIXNVCq3nWrQwNtn+PWZJhablZX6fmiHHdhxos58XFnJ6ti8efLlu+8ud5EsFWHTDz9BXofKI4R/Xo4/nlnvq+pyEIvNsjIWkkMMSyKz2HTJh7BparGZTOY+M/vsw14IRiVsyspSKM+hKCi0sAnox0DFgJ+wuW4dcMMN+Y2xyYuZKmEzqoznhaBHD+/33/3Ov17yL4Td+lQoi82yMuCQQ9jnww7LZkGvV8/bhrhzbrGcsnJ37Zr1ujB96QKwsWbYueg2SAnMnImSpXlz5kYFsM7DD7cBKS8Hbrkl2rKce27WZevtt6PdNyAXNvlJrWmsoXyjEjb/9z/2v2lTJmj4oRI2jz02+5kfZBSLK3pcbwW3FYvNuJIHhUE2mI1D2CwEfH3dvDl+S3SXAw5gLqqpFLtGV1+tXtcme/eIEey/jauNLtYkf//iyoq+007ZAe4zz7D/9euzgPz33svqXzIJPPigdzv+3unqo0oMkNVr04zFFRX+bV3UrugisthTtmKYn1AS5fMbp8VInFnRo0B3HcUEfC6lImz6WWwmElkLmcsvD2ex6d5f122/cWPgkkvYZz47Mk+QGJvuccXn44gjvN/5cZoujrVbbr9EZ6KlkTjOtbHYFGNQn3BC8EzIMguoyspsbOyrrmL/4w5RFSfFIGwWO37CJgDcfrv3BbFK2IwjxqZJXOlSI5kELryQfXY9lfxYuDD7udDCJs8tt2TbsAcflI9v/FzRRWxc0YlAkMUmER+JBHPX/OabrMCpo39/5mZXr170QmCzZsDPPzOz8p12inbfgFzY5Ilb2AxqsSkOhF3XwBEjgBkzWCdj8mZfJWy6YjXgdV8sNovNqNlWYmzGlTwoDLLrZuryWuzXnB80bdyYH4vNli2Bd99lx/7hB1Z3Z85Ur28T6/DZZ5nbvkn/4KIbOOYjeRA/oTz4YOZm1LQpu04tWwI//cSugSgc8NcliMWm7Hx0rsx8uU1Ep6gtNnXlAaK32ASi9caIMwurbfIgGVHVbxmy6zhtGiuvavwUJuFbPjG5bu++y8Ic9etnJ36JfZp7HY84go2DW7TIxkZUvXgJYrHpioLic3nMMcziceJE9v3HH73HUfHUU0CnTt5EKzLEZ/qbb5jrpfvCzSbG5pdfZr83bQqcdBLw2GNm24uo2oEPPmDjWre/KWVhUxZjs9jHL/lGVg9kc7HPPmP/+cSfURhByNrEq69m3nxnnpkVNuvX1yceLDXuvJO9FNl1V7P1eWHTjduaj+S7fnToAMyZw+5Nt24sRqaInyu6CCUPip0iqDnEVk29eixOjilxZGlzadWK/cWB+GZZ7FC7dInnuDLCuKLznckuu5jvR/V2jZ+IlpVlB5I6i80439SpYmzGfZxC7SNuokweFBVhXNGLPeZWWRn7q61lomY+LDb32y/7nLjt2Pffq9d3E7qZUF7OXmjZoJuQ58MVXbSU6dnT+71zZ/Z/1Srv73zZ3MG7DJXgLxNkTC02oxY227b1uo+52AibcVhs8mEQwgqTYTMmp1Jqq7woLDZlwkYY+Oslu499+ujrSLG3nS4mwmZlJYtt6n42Rfeyzk005aJ6VoJYbLptjdgeNGwInHZaVtjk22adNWRZmVm7LD6PTZp4y25jsdmoUTZ7/MUXs/oU1GJTRVVV9r4CpS1syiw2S+UZzBey66Fr1/lnXXyWg4hPsvZg3jzgiivYS2I33maPHuwF79ZCebk8mZmKJk2AJUvYZzeEU7F4rPEWvrLxB1lsFh0lMHMmiBJAHIg2bcqSBQFMrB0+PN7jR5U8KCgXXug/EeQbb9Fi86WX2CCkc2cWkyUuVDGwosZvgGkyAC2Wjl1HGKuhYnRFLwXcgVO+LDZlE3txgDdiBLvubdqYha4Ig27gGEdWdJ3Fpg5d/R4xgllEybCxVNANiHlh10QEs5kUP/WU3CpVd87iecVhsXn++cxqNpkE3nrLbt8ihx2WnWS5fbkNqmuRTGbHCzaTZT6+bVkZuwdhefddVpY2bbzhIGR1Qfbbeeex/35Z4EuZMC+KdXTt6hXZXOrVs2+7XMsv8Rlr2BA48cTsy5YHHsgua9oUOOgg9vnvf/duZ3PORx3F/l9zTe62NsLmHXewbdu1Ay66iP2uEzbdLMt/+xv7f8457P/pp5sdEyhtYVMWw7sUXobnE1k91nmg8WMLlfW1Df36qfv5F17I9tFNm2bDdl18sf1xSp1HHmHXu3lzFv4DYO2AGzri3nsLVjQPsvbd1mJTNk+2MQYgfKFWkCDioGNHllFtzhzmthm3qXlUyYOC0qwZexP55z+r1+EH3aKwefTRLIv0t9/G+/aqWCw2txZX9DAhBfLpim4qoMTp3hkVrhifL4tNE2Fz+HD2/M+eHb9LqqnFZlT30s9iU4Wf9eJ338nb6qhc0V0LCMAsYZVNv9G7N3On+/VX7++6cxaXxSFsNmzI+tz587OiTVDKypjb6ty5rC+3RdW+8edgYw143nksnM7Spey6m7r56RgyhD23P/3kb7Ep41//YpZHjz8eviz5Is423qZPSySATz/NFTKqqvxjkP/pT17rZNdiUyZs1q/PxqBz5uTW47FjWZt9xRXm5RZ5+WXm4n7jjew7f31tkgcdcQQr46xZ2XZBJ2y+8go77k03se8PPsjqoo37eikLmy1a5CZqKYUxYz6xFTZ1FptBqKhg4RncnAU8fHb0xo1ZWJ4fflDHMN6aGTiQ9UNz5zKRF2D37qOPWPvkvugoNFFYbMpeMkeRVJjIQK0gQcRBhw7sf6dO8SYhCEuUcUwaNfIOtMTsyDphE2DXLO5rlS+LTdl+W7bMft5hB/99lILFZhhhM4qsrDJkZdqahM0oLDZ1rtAiJsJmZSXQvn1+4uyZxtiMK3lQFBabADsPWYgSm+deJ/TxruImIVhsLVJatcq1PrAR6uJwRQdYHRQTRATFzRAeBNdKzm//tvts0cJMqDZlhx1yBaT27c22TSSY5Ri5wDJshaXy8tyQP/Xq+QtuHToA++6b/e6KAaq4gPXryy3HUil5+CcbF/BkEthxx2wd4Ntd05fUbr/RsaN3XKArh3jcIHWRH6OWSoxYHr4OAKUxZswnUVpsBqV+faBvX/06jRuz+rwtt6Xbb597b8rK4g1PZwsvSrpltbXYbNw4t265egERCSRsEkQcmE4MoiKoxWZFBfD737PP990XvhxnnMEG0JWVudnnda7o+UIXUzRKdtqJvYVMpbKZk6+7jokBDRt6kyqpKIW372Hu4157sTg8qRTw4ovRlUnMrgqYC+alIGwGsdgUXV1sAtWbCJv5fHmTb4tN8fxNYxuaPL+yyZONxaaurefFERMhO+iEavRodq5Dhtj1e6kUawuPPNJ8m2JIKGDKc88xsaR1a68AwT+zxZo04OGHvaLPww8XrixRE6RdcC2GTCwbTziB/b/zTrN9i+JdVVWusCm2r+Xl7J40asReLrhWiyI2z/Qbb7D99u0LHHKI+XYiosXm7bdnv8tcLvfaS+6SD+Remz32YG3NwQerXXxtOO009rKgqoqFZSg1zjgjO64uK2OeT0QWlbCpcm3m+/oo+5ouXfQxJ7emUElbMyNHsntZWQmMGcN+s7XYTCRyX47nWy/YyimhUSJBlBBxZ0GPkpdeYtniTS2RdLiZk9evz7Xm4QcKfq5WcdK4cTYmlS4JShgSCeDDD4EVK7JCSLNmzEWyujrXmlXG1i5sJhIsqcHKldEmwli/Pvi2pSBsugOnVauAs88222bBAu+Ai3eD8sPUYjNf5NtiM6gruonFh2zyZDOhMm1Ho3ZF5xk5ksVFtn2G3Xv16qvM/bS6mlme6awXCvVCLAi9e7PnrrKSXaNPP81dJ5/PjQ1durCyJ5PsJUgUY4NS5t57WQxJk+vwzDPsJbHpNRNf1JSV5b6wWrgwa5UJMDGrQwf2u+NEY214xBHAokVZ67Gg8AmzystZzLzTTmPtaHm593yfeorFAFW1PaKwOXIk8M470cWkq6pibc+GDaUpLu2zD7PM/+UX9vIqruSopYqsXlVUsBcVY8YA773nXRaHxaZbjokTWfiZb77JDQlRinVvW8RtL9auzbbHQV7yt2jh9aghi81IKYGZM0GUIG3b5vd4/Buf7t3ttpW9QQpDebk8QDLvph6Vq2AQ+EROUbr0iSQSuZP9ykozURMoPmFTJriEFRqSyeiz+9pYI4oUq9DAw08Mf/zRbJugVoeybYHCCpv8BF+Ez1AeVRsjTq75kBI6TJ7fOIVNvp6YuHOFaW9atLAXRt2JYyLB7lufPrmWC/36eb+HebYLQaNG7Nlo1Ei+XGWxWQwupW58xq1N1Aya7d70OtiOp2Tu1qLFplhm10qvfv1oXaibNQtf92QxNlu2ZOMecdLvWmCqEK+NO6aK0l23vLy0haWmTVnbSaJmLrzI7uLWHVmfGHWMTZ5kEth5Z7n3RNA2icg/ZWX6MaipsMmTb71gK6fIZs4EUcI8+yybqIwYkf+4IDfcwFxzWrcGHn00v8c25ZFHWKfeoYPadSof3HUXi+XUsmW4oPlxU2yxdj74IHeCXowWVKL4wbvCyXj+eTYYOeaYwgruppi6fTdrxgZhl12Wu+zyy+WufLLnshhc0V96iR3jqKNyEybwPPkke667dAGuuiqaYw8alI2FeeCBXvFURyLBMvVWVKhdeWWihGpCJbMm1j1/r77KhIGBA4GhQ32LG0t7c/31rK7ccEPuMtV5Xn452+aOO1hCEJ5SEzZdVMKJ7Nnabjtm7U/Ew6GHstAAxXKdTYRNEZ3o8pe/ZJ+fQsBP+mWi5fPPs3r/+9+zkD06xPax2MZERHEjxiC/8srsZ1mbzL9oiuvlkuy4FEJg68FkLMzXs6ZNi+NF5lYEuaITRFQcfzyzBixEsqAmTVj2uHQ63qziYejQgbliJ5OFbcibN2fZZWtqittCr9gG8fvvz9wneEuwUhA2L79cv/6xx7I4f8Wc5IvHNOHV1VcD558vP6+2bZm151/+AvzjH9nfr7mGCby8eFcMFptHH81CFvjdox13ZBmjU6no2phGjdi1WryYvZixeS4feoi5sarKLZvk2MTY1FlsDhnCQoxUVhauLRk1ik0mZeevstS6/XaWYdnd5sQTs3GKt3Zh85xz9PWFCE8yCXz8MXP3LobrHETY1MVWvuMO9oKqUOfWti1rdwBg2bLc5Tb9rZhMpNjGRERxIz4nt92W/Syzos9HKCKxLzjpJMqKvTVh0q7Nnp39bPqinDCGLDYJIkoKOVBOpYpX1HQpLy+Ot1PJZHGLmsWKWL+LUdjkM87vvbfZNsUwwTXFVNisV099XvXqMQFNJqKJk8lisNi0OUZFRTxuZG3bBptY68ptI2zKwgf4PX9VVeZljks0UJ2/7h7x2/Dx9MLEzy0kKmFTdEUvKyuttqhUSSSK5zrLhE0+Rrts4itLkMdTyHPj3SoXLZKvY1o+se8hYZOwQfcCQOb+zbuux5UHQOwLyA1568KkbeOTpQ0bFl9ZtlFI2CQIgiCCUYzC5vnns9h8nTtnLb22Jkwnhbr13GUyCwUTa8xCxtjcmvATNj/6iMVOGzFCnlVVNfn63//Mjv/cc8wV6tJL85+h2zSmJy/8lEJyLxmmFpvF8NKPyC8yYfO005i7fPv2wMsv5y4vZstlXpRduDDcvkQhk4RNwgbRFZ1HloCKTzgY19hWtBTd2mIYb+uYjM9vuIGFverfn429iEghV3SCIIhipBQG8cUobNarB3zxBRNBii0BUxTYWGz6LYtK2CwW66dSQ+YOx4tb++/PxAFVPZYJm8OHMyHUhOOOY6EHCvGcmIp4MuGn1LCx2CS2LWTtdHk58MknTGiRPZvFbLnMW6CtXRvtvkthTEQUDzqLTZmgmA+LTbHfI2Fz68LkJX/PntmwbETk0FUlCIIoRop1Qt+7d/azLAFNMZBIbL2DBrLY3HowcUXX1WPZiwXe6sSEQj0nQSw2SxXTrOjFHkqGiB5d/eafkYMOyn7Od3JKG/bYI/u5f/9o903CJmEDnwxSfGZkgmI+LDZFSNjcujAdC2+t85MigK4sQRBEMdKoEfC3vzGX6nfeKXRpsrz0ErDLLswy7JhjCl2abQ9Ti02da7FuHyRs5g9R2LzhBjt3ZJlVia2wWShMz1OWOb7UUJ2DOLnZZZf4y0IUFw0asKRtnTsDb72lXu/JJ4Fdd2VJwc44I3/ls+Xoo1mCoJ49gccei3bfJGwSNhx+OPCHPwC9egFvvOFd5mexyX+OE1nsbKK0eOUVZuRx000kWBYB5PdCEARRrNx8M/srJrp1A2bMKHQptl1MLTZXrfLfB1lsFhZR2LzuOrvtZVYl+ZqQhWVbckU3PYd99423HERxctNN7E9H+/bAtGl5KU4oEgng+efj2bdftniC4EkkgBdflC+TCYr8S8G4XNFFZEmMiNJi+HD2RxQFJC0TBEEQRKlg6iLFu2GJyIRN1xomiLBJsQGDEVa0Gzw497dSETZNLRv4rNA9esRTlrhp1Sr7WWd1VqyhPQiiGCjm2KJEaSF7Qcz3nd26ZT/vtlu0x+bHVC1bRrtvgtjGIWGTIAiCIEqFpUv1y/feGzjzTGDgQPU67sBaJmyKgpOJsEkEI2xMxdtvzxU3S0XYNLXYHDAAuOQS9t8023ux0aIFcMstQL9+LNM9z+jRQJ8+wAsvkKstQeggYZOIE77vPOQQ4OyzmRX9Cy9Ee5wPPgB2351ZafMvvQiCCA3NTgiCIAiiVNAJm+3aARMn+u/DFdRkwqYorpCwGR+6OKgmNG8OjB0L9O0LfP01+61UYmzaxKK6++74ypEvrr6a/YmMHMn+CILQQ8ImESd835lIAA8/HM9x9tkHmDo1nn0TxDYOWWwSBEEQRKnAZ8cV0YlavGC53Xbs/957Z3879lj5diRsxseOO2Y/8y7Xtgwblv08aFDw/eQTmyRJBEFsm/DtBAmbRJSIoWB+97vClIMgiMig2QlBEARBlAp/+hMwZQqztrz8cubS5KJzQ540Cfjb31gme3dAf/zxwKefAosWAffdJ99OJmxS5sdo6NYNuPVW4MMPgXvvDb6fa64B5s1jIsBll0VWvFghYZMgCD8aNADWrGGfSdgkouSjj4BLL2X/jzvOPnkfQRBFBwmbBEEQBFEqVFUBzzyT/X7iidnvOovNAQOA99/3/pZMAvffrz+eTNikWIDR8de/sr8w1K/vrROlAInjBEH4QcImERf9+gETJhS6FARBRAiNLAmCIAiiVOEt3/iYmVEhEzYJIixksUkQhB+nnZb9PGRIwYpBEARBFD9ksUkQBEEQpQpv+RZHRmyVsPnaa8BTTzF3eIKwhax+CYLw49prgZUrmacCL3ISBEEQhAAJmwRBEARRqvDCZhwZsVXC5pFHsj+CIAiCiIN69YAHHyx0KQiCIIgSgFzRCYIgCKJU4YVNckUnihkxCy1BEARBEARBRAAJmwRBEARRqsTtil5eHv0+iW2Tzz4DzjwzN4kVQRAEQRAEQYSAXNEJgiAIolSJ2xWdYiESUdG7N/Doo4UuBUEQBEEQBLGVQRabBEEQBFGqHHdc9vPf/hbNPtu0iWY/BEEQBEEQBEEQMUMWmwRBEARRqhx4IPDYY8DixcAll0Szz/Hj2T550ZQgCIIgCIIgCKIIIWGTIAiCIEqZM86Idn89egB33BHtPgmCIAiCIAiCIGKAXNEJgiAIgiAIgiAIgiAIgig5SNgkCIIgCIIgCIIgCIIgCKLkIGGTIAiCIAiCIAiCIAiCIIiSg4RNgiAIgiAIgiAIgiAIgiBKDhI2CYIgCIIgCIIgCIIgCIIoOUjYJAiCIAiCIAiCIAiCIAii5CBhkyAIgiAIgiAIgiAIgiCIkoOETYIgCIIgCIIgCIIgCIIgSg4SNgmCIAiCIAiCIAiCIAiCKDlI2CQIgiAIgiAIgiAIgiAIouQgYZMgCIIgCIIgCIIgCIIgiJKDhE2CIAiCIAiCIAiCIAiCIEoOEjYJgiAIgiAIgiAIgiAIgig5SNgkCIIgCIIgCIIgCIIgCKLkIGGTIAiCIAiCIAiCIAiCIIiSg4RNgiAIgiAIgiAIgiAIgiBKDhI2CYIgCIIgCIIgCIIgCIIoOUpa2Lz//vvRqVMnVFVVYcCAAfj888+167/44ovo2bMnqqqq0Lt3b4wZM8az3HEcXHfddWjbti3q1auHwYMH48cff4zzFAiCIAiCIAiCIAiCIAiCCEDJCpvPP/88Lr30UowaNQpffvkldt11VwwdOhRLliyRrj9x4kSccMIJOOOMM/DVV19h+PDhGD58OGbOnJlZ5/bbb8d9992Hhx56CJMnT0aDBg0wdOhQbNq0KV+nRRAEQRAEQRAEQRAEQRCEASUrbN59990466yzMHLkSPTq1QsPPfQQ6tevj9GjR0vX/8c//oFDDz0Ul19+OXbaaSfcdNNN2H333fGvf/0LALPWvPfee3HNNdfgqKOOQp8+ffDUU09hwYIFePXVV/N4ZgRBEARBEARBEARBEARB+FFW6AIEobq6GlOnTsVVV12V+S2ZTGLw4MGYNGmSdJtJkybh0ksv9fw2dOjQjGg5Z84cLFq0CIMHD84sb9y4MQYMGIBJkybh+OOPz9nn5s2bsXnz5sz31atXAwBWrFiBmpqawOdXrNTU1GDDhg1Yvnw5ysvLC10cgpBC9ZQoFaiuEqUA1VOiVKC6SpQCVE+JUoHqKlFo1q5dC4AZIfpRksLmsmXLUFdXh9atW3t+b926Nb7//nvpNosWLZKuv2jRosxy9zfVOiK33norbrjhhpzfO3fubHYiBEEQBEEQBEEQBEEQBEHksHbtWjRu3Fi7TkkKm8XCVVdd5bECTafTWLFiBZo3b45EIlHAksXDmjVr0L59e/zyyy9o1KhRoYtDEFKonhKlAtVVohSgekqUClRXiVKA6ilRKlBdJQqN4zhYu3Yt2rVr57tuSQqbLVq0QCqVwuLFiz2/L168GG3atJFu06ZNG+367v/Fixejbdu2nnX69u0r3WdlZSUqKys9vzVp0sTmVEqSRo0aUeNGFD1UT4lSgeoqUQpQPSVKBaqrRClA9ZQoFaiuEoXEz1LTpSSTB1VUVKBfv34YN25c5rd0Oo1x48Zh7733lm6z9957e9YHgLFjx2bW79y5M9q0aeNZZ82aNZg8ebJynwRBEARBEARBEARBEARBFIaStNgEgEsvvRSnnnoq9thjD/Tv3x/33nsv1q9fj5EjRwIATjnlFGy//fa49dZbAQAXXXQRBg4ciLvuuguHH344nnvuOUyZMgWPPPIIACCRSODiiy/GzTffjG7duqFz58649tpr0a5dOwwfPrxQp0kQBEEQBEEQBEEQBEEQhISSFTaPO+44LF26FNdddx0WLVqEvn374p133skk/5k/fz6SyaxB6j777IP//ve/uOaaa3D11VejW7duePXVV7HLLrtk1rniiiuwfv16nH322Vi1ahX2228/vPPOO6iqqsr7+RUjlZWVGDVqVI77PUEUE1RPiVKB6ipRClA9JUoFqqtEKUD1lCgVqK4SpUTCMcmdThAEQRAEQRAEQRAEQRAEUUSUZIxNgiAIgiAIgiAIgiAIgiC2bUjYJAiCIAiCIAiCIAiCIAii5CBhkyAIgiAIgiAIgiAIgiCIkoOETYIgCIIgCIIgCIIgCIIgSg4SNgkj7r//fnTq1AlVVVUYMGAAPv/880IXidiGuPXWW7Hnnntiu+22Q6tWrTB8+HDMmjXLs86BBx6IRCLh+Tv33HM968yfPx+HH3446tevj1atWuHyyy9HbW1tPk+F2Mq5/vrrc+phz549M8s3bdqE888/H82bN0fDhg0xYsQILF682LMPqqdE3HTq1CmnniYSCZx//vkAqD0lCsdHH32E3/3ud2jXrh0SiQReffVVz3LHcXDdddehbdu2qFevHgYPHowff/zRs86KFStw4oknolGjRmjSpAnOOOMMrFu3zrPO9OnTsf/++6Oqqgrt27fH7bffHvepEVsRunpaU1ODK6+8Er1790aDBg3Qrl07nHLKKViwYIFnH7J2+LbbbvOsQ/WUCItfm3raaafl1MNDDz3Usw61qUQpQMIm4cvzzz+PSy+9FKNGjcKXX36JXXfdFUOHDsWSJUsKXTRiG2HChAk4//zz8dlnn2Hs2LGoqanBkCFDsH79es96Z511FhYuXJj54zvVuro6HH744aiursbEiRPx5JNP4oknnsB1112X79MhtnJ23nlnTz385JNPMssuueQSvPHGG3jxxRcxYcIELFiwAEcffXRmOdVTIh988cUXnjo6duxYAMAxxxyTWYfaU6IQrF+/Hrvuuivuv/9+6fLbb78d9913Hx566CFMnjwZDRo0wNChQ7Fp06bMOieeeCK++eYbjB07Fm+++SY++ugjnH322Znla9aswZAhQ9CxY0dMnToVd9xxB66//no88sgjsZ8fsXWgq6cbNmzAl19+iWuvvRZffvklXn75ZcyaNQtHHnlkzro33nijp53985//nFlG9ZSIAr82FQAOPfRQTz189tlnPcupTSVKAocgfOjfv79z/vnnZ77X1dU57dq1c2699dYClorYllmyZIkDwJkwYULmt4EDBzoXXXSRcpsxY8Y4yWTSWbRoUea3Bx980GnUqJGzefPmOItLbEOMGjXK2XXXXaXLVq1a5ZSXlzsvvvhi5rfvvvvOAeBMmjTJcRyqp0RhuOiii5yuXbs66XTacRxqT4niAIDzyiuvZL6n02mnTZs2zh133JH5bdWqVU5lZaXz7LPPOo7jON9++60DwPniiy8y67z99ttOIpFwfvvtN8dxHOeBBx5wmjZt6qmrV155pdOjR4+Yz4jYGhHrqYzPP//cAeDMmzcv81vHjh2de+65R7kN1VMiamR19dRTT3WOOuoo5TbUphKlAllsElqqq6sxdepUDB48OPNbMpnE4MGDMWnSpAKWjNiWWb16NQCgWbNmnt+feeYZtGjRArvssguuuuoqbNiwIbNs0qRJ6N27N1q3bp35bejQoVizZg2++eab/BSc2Cb48ccf0a5dO3Tp0gUnnngi5s+fDwCYOnUqampqPO1pz5490aFDh0x7SvWUyDfV1dV4+umncfrppyORSGR+p/aUKDbmzJmDRYsWedrQxo0bY8CAAZ42tEmTJthjjz0y6wwePBjJZBKTJ0/OrHPAAQegoqIis87QoUMxa9YsrFy5Mk9nQ2xLrF69GolEAk2aNPH8ftttt6F58+bYbbfdcMcdd3jCeVA9JfLF+PHj0apVK/To0QN/+tOfsHz58swyalOJUqGs0AUgiptly5ahrq7OM3kBgNatW+P7778vUKmIbZl0Oo2LL74Y++67L3bZZZfM73/84x/RsWNHtGvXDtOnT8eVV16JWbNm4eWXXwYALFq0SFqP3WUEEQUDBgzAE088gR49emDhwoW44YYbsP/++2PmzJlYtGgRKioqciY2rVu3ztRBqqdEvnn11VexatUqnHbaaZnfqD0lihG3bsnqHt+GtmrVyrO8rKwMzZo186zTuXPnnH24y5o2bRpL+Yltk02bNuHKK6/ECSecgEaNGmV+v/DCC7H77rujWbNmmDhxIq666iosXLgQd999NwCqp0R+OPTQQ3H00Uejc+fO+Omnn3D11VfjsMMOw6RJk5BKpahNJUoGEjYJgigpzj//fMycOdMTtxCAJ9ZL79690bZtWwwaNAg//fQTunbtmu9iEtsohx12WOZznz59MGDAAHTs2BEvvPAC6tWrV8CSEYScf//73zjssMPQrl27zG/UnhIEQYSnpqYGxx57LBzHwYMPPuhZdumll2Y+9+nTBxUVFTjnnHNw6623orKyMt9FJbZRjj/++Mzn3r17o0+fPujatSvGjx+PQYMGFbBkBGEHuaITWlq0aIFUKpWTtXfx4sVo06ZNgUpFbKtccMEFePPNN/Hhhx9ihx120K47YMAAAMDs2bMBAG3atJHWY3cZQcRBkyZN0L17d8yePRtt2rRBdXU1Vq1a5VmHb0+pnhL5ZN68eXj//fdx5plnatej9pQoBty6pRuTtmnTJie5ZW1tLVasWEHtLJFXXFFz3rx5GDt2rMdaU8aAAQNQW1uLuXPnAqB6ShSGLl26oEWLFp7+ntpUohQgYZPQUlFRgX79+mHcuHGZ39LpNMaNG4e99967gCUjtiUcx8EFF1yAV155BR988EGOu4OMadOmAQDatm0LANh7770xY8YMT+fsDjR79eoVS7kJYt26dfjpp5/Qtm1b9OvXD+Xl5Z72dNasWZg/f36mPaV6SuSTxx9/HK1atcLhhx+uXY/aU6IY6Ny5M9q0aeNpQ9esWYPJkyd72tBVq1Zh6tSpmXU++OADpNPpjEC/995746OPPkJNTU1mnbFjx6JHjx7kMklEgitq/vjjj3j//ffRvHlz322mTZuGZDKZcfulekoUgl9//RXLly/39PfUphIlQaGzFxHFz3PPPedUVlY6TzzxhPPtt986Z599ttOkSRNPNlSCiJM//elPTuPGjZ3x48c7CxcuzPxt2LDBcRzHmT17tnPjjTc6U6ZMcebMmeO89tprTpcuXZwDDjggs4/a2lpnl112cYYMGeJMmzbNeeedd5yWLVs6V111VaFOi9gKueyyy5zx48c7c+bMcT799FNn8ODBTosWLZwlS5Y4juM45557rtOhQwfngw8+cKZMmeLsvffezt57753ZnuopkS/q6uqcDh06OFdeeaXnd2pPiUKydu1a56uvvnK++uorB4Bz9913O1999VUmm/Rtt93mNGnSxHnttdec6dOnO0cddZTTuXNnZ+PGjZl9HHrooc5uu+3mTJ482fnkk0+cbt26OSeccEJm+apVq5zWrVs7J598sjNz5kznueeec+rXr+88/PDDeT9fojTR1dPq6mrnyCOPdHbYYQdn2rRpnnGrmzV64sSJzj333ONMmzbN+emnn5ynn37aadmypXPKKadkjkH1lIgCXV1du3at85e//MWZNGmSM2fOHOf99993dt99d6dbt27Opk2bMvugNpUoBUjYJIz45z//6XTo0MGpqKhw+vfv73z22WeFLhKxDQFA+vf44487juM48+fPdw444ACnWbNmTmVlpbPjjjs6l19+ubN69WrPfubOnescdthhTr169ZwWLVo4l112mVNTU1OAMyK2Vo477jinbdu2TkVFhbP99ts7xx13nDN79uzM8o0bNzrnnXee07RpU6d+/frO73//e2fhwoWefVA9JfLBu+++6wBwZs2a5fmd2lOikHz44YfS/v7UU091HMdx0um0c+211zqtW7d2KisrnUGDBuXU4eXLlzsnnHCC07BhQ6dRo0bOyJEjnbVr13rW+frrr5399tvPqaysdLbffnvntttuy9cpElsBuno6Z84c5bj1ww8/dBzHcaZOneoMGDDAady4sVNVVeXstNNOzv/93/95xCTHoXpKhEdXVzds2OAMGTLEadmypVNeXu507NjROeuss3KMl6hNJUqBhOM4Th4MQwmCIAiCIAiCIAiCIAiCICKDYmwSBEEQBEEQBEEQBEEQBFFykLBJEARBEARBEARBEARBEETJQcImQRAEQRAEQRAEQRAEQRAlBwmbBEEQBEEQBEEQBEEQBEGUHCRsEgRBEARBEARBEARBEARRcpCwSRAEQRAEQRAEQRAEQRBEyUHCJkEQBEEQBEEQBEEQBEEQJQcJmwRBEARBEARBEARBEARBlBwkbBIEQRAEQRDEVkqnTp2QSCRw2mmnFbooBEEQBEEQkUPCJkEQBEEQRAycc845SCQSSCQS+OCDD6y2fe+99zLbXnTRRTGVkCAIgiAIgiBKGxI2CYIgCIIgYuCUU07JfH766aettv3Pf/4j3U+hGD9+fEZoHT9+fKGLQxAEQRAEQRAASNgkCIIgCIKIhX333Rddu3YFALz00kvYuHGj0Xbr16/HK6+8AgDYeeed0a9fv9jKSBAEQRAEQRClDAmbBEEQBEEQMXHyyScDANasWYPXXnvNaJuXX34Z69ev92xPEARBEARBEEQuJGwSBEEQBEHExMknn4xEIgHA3B3ddUNPJpM46aSTYisbQRAEQRAEQZQ6JGwSBEEQBEHERJcuXbDvvvsCAN59910sWbJEu/6CBQswbtw4AMDBBx+M7bffPmedV199Fccccww6dOiAqqoqNGnSBHvssQduuOEGrFy50qhcY8aMwUknnYQuXbqgQYMGqKqqQufOnTFixAg88cQT2LBhAwBg7ty5SCQSOOiggzLbHnTQQZl4m+7fE088kXOM6upqPPDAAzjooIPQsmVLVFRUoE2bNhg2bBiefvpppNNpZflOO+00JBIJdOrUCQCwcOFCXHnlldh5552x3XbbWcf6lMUIfeGFFzBo0CC0bNkS9erVQ48ePXDFFVdgxYoVyv0ceOCBSCQSOPDAA7XHu/766zPHk+Euu/766wEAH374IYYPH4527dqhXr162GmnnXDTTTdlLHddxowZg2HDhmXW69WrF2699VZUV1cbX4svvvgCJ5xwAtq3b4+qqiq0b98eI0eOxPfff2+0/ezZs3HJJZegd+/eaNy4MerVq4cuXbrgtNNOw5QpU5TbifcgnU5j9OjROOigg9C6dWskk0nK3E4QBEEQhD0OQRAEQRAEERuPPPKIA8AB4PzjH//QrnvHHXdk1n3qqac8y1asWOEcfPDBmeWyv1atWjmTJk1S7n/ZsmXOoEGDtPsA4Dz++OOO4zjOnDlzfNfl13eZM2eO07NnT+02++23n7N8+XJpOU899VQHgNOxY0dn0qRJTosWLXK2//DDD32vvcuHH36Y2W7cuHHOSSedpCzXjjvu6CxcuFC6n4EDBzoAnIEDB2qPN2rUqMz+ZLjLRo0a5dx6661OIpGQlmWfffZx1q1b56TTaefCCy9UlvnQQw91amtrpcfq2LGjA8A59dRTnX//+99OWVmZdB+VlZXOCy+8oD2vO+64wykvL1eWI5FIONdee610W/4evP32287gwYNztj/11FO1xycIgiAIghAhi02CIAiCIIgYOfbYY1FVVQXAm+1chru8YcOGOProozO/b968GYMHD8YHH3yAVCqFk08+Gc8++yw+++wzfPzxx7jlllvQvHlzLFmyBMOGDcO8efNy9r1hwwYcdNBBGYvQfv364eGHH8ann36KKVOm4JVXXsEll1yCdu3aZbbZfvvtMWPGDIwePTrz2+jRozFjxgzP3/DhwzPL161bh0GDBmUsAIcPH47XX38dU6ZMwYsvvoiBAwcCAD755BP87ne/Q11dnfJ6rFu3DiNGjMCmTZvwt7/9DePHj8fnn3+Of//732jbtq32Wqq49tpr8fTTT2P48OF4+eWXMXXqVIwZMwaHH344gKxFYj54++23cdVVV2GvvfbCf//7X0yZMgXvvPMODjvsMADAxIkTceutt+Kee+7Bfffdh8MOOwwvvfQSpk6ditdeew177bUXAOCdd97Bo48+qj3WtGnTcO6556JVq1b45z//icmTJ2PChAm48sorUVlZic2bN+PEE09UWl3ecccduPzyy1FTU4M+ffrgwQcfxPvvv48pU6bgmWeewd577w3HcXDTTTfhvvvu05blyiuvxPvvv48jjzzScw/c8yYIgiAIgjCm0MoqQRAEQRDE1s6xxx6bsUr7/vvvpet8/fXXmXVOOeUUz7Krr77aAeA0adLEmTJlinT7uXPnOm3btnUAOH/84x9zll9yySWZ/Z9//vlOOp2W7mfz5s3OokWLPL/x1nZ+lpJ/+ctfMutec801OcvT6bRz4oknZtZ54IEHctZxLTYBOA0bNnSmTZumPaYffPkBODfffLO0XEOGDHEAOGVlZc6SJUty1onaYhOAM2LEiBxry9raWmevvfZyADjbbbedU1VV5Vx88cU5+1m/fn3GIrNPnz7SY7nLscUCVmaN+sEHH2QsOffcc8+c5d98803GUnPUqFHSulNXV5exhG3YsKGzYsUKz3LxHsjqBkEQBEEQhC1ksUkQBEEQBBEzp5xySuazymqT/51ff926dbj//vsBADfddBP69esn3b5jx4649tprAQAvvviiJz7jqlWr8PDDDwNglpr/+Mc/lPEfKyoq0Lp1a5PTymHz5s147LHHAAA777xzJoYkTyKRwAMPPIDmzZsDAP71r39p93nFFVdg1113DVQeGf369cPVV18tLdell14KAKitrcWkSZMiO6aK+vXr45FHHkEqlfL8nkqlcPbZZwMA1q5di5YtW+L222+Xbn/qqacCAKZPn47Vq1drj3fXXXehTZs2Ob8fdNBBOOusswCwGJyi1eZdd92Fmpoa7LHHHhg1apS07iSTSfzzn/9EZWUl1q1bh//973/KcnTv3l1aNwiCIAiCIGwhYZMgCIIgCCJmhg4dmhELn3nmGTiO41meTqfx3//+FwCwww47eJL1TJgwISNY/eEPf9Ae54ADDgAA1NTUYOrUqZnfP/jgg0xCoAsvvDBHSIuKqVOnYtWqVQBYAiDVcRo1aoRjjz0WAPDtt99i4cKFyn2eeOKJkZbxj3/8o1LU5UXjn3/+OdLjyjjkkEPQrFkz6TJezD366KNRXl7uu96cOXOUx2ratCmOOuoo5fLTTz898/n999/3LHvjjTcAACNGjFBeOwBo0qQJevfuDQBaYfi4446LrQ4SBEEQBLFtQcImQRAEQRBEzJSVleGPf/wjAJZp/JNPPvEsHzduHBYsWACACXnJZHaIxlvPtW3bNicjOf+3yy67ZNZdtGhR5vNXX32V+bz//vtHe3IcM2fOzHweMGCAdl1+Ob8dT8OGDdGlS5doCreFnj17KpfxIuPatWsjPa6M7t27K5c1adLEej1dmXfbbTeUlZUpl/ft2xcVFRUAgBkzZmR+nzdvHpYuXQoAuOqqq7T1L5FIZOorX/9E+vTpo1xGEARBEARhAwmbBEEQBEEQeUDnjq5yQweAJUuWBDqea6EJAMuWLct8Dpp0x4QVK1ZkPrf6//buLSTKbw/j+LNxDqZMUWmiSQeJBEmyxA4YSCFhRUR5wIjMwAOWhAZdGCSV3SlYahARiaQElQYFBpnhgTIDM5m0ENICQwTRQqQ8ZP8L8d1jOv5NzJq9v5+r11lrzazxfa+eWb+1VqyYsa9jSbTjOEeOod188fDwcNrmGCjPdKjRQs9lPub8b/fDZDIZwa7j/ZiP5+9nS5cundN7AgAA/Mz5z7YAAACYNyEhIQoODpbdbtfdu3eN/QgHBwdVUVEhabwUOigoaNI4x7Dq1atXTkuSf+bv7z9/k5+DmUqWZ4ty5fkz1/vh+PxlZ2crNjZ2VuM8PT2dtnFfAQDAfCHYBAAAWCAJCQk6c+aMPn/+rIcPHyomJkb37983Dvr5ebWmJOOQHUny9vaeU2Dp5eVlXHd3d2vt2rVzmP2/cyzl7unpmbGE2rFU2dk+k3+bidWRY2NjM/ZzPLjpb9HT0zNj++joqLFS0/F+OD5/ZrN50nYHAAAAfxql6AAAAAvkyJEjxmq10tJSSf8tQzebzTp8+PCUMZs2bTKunz17NqfP3bx5s3FdV1f3y+Nnu9rPMfRqbGycse/Lly+nHfc3s9lskqT+/v4Z+7W3ty/EdH7J69evNTo66rS9paVFw8PDkibfj4CAAC1ZskTS3J8/AACA34VgEwAAYIH4+voqMjJSklRZWak3b96ourpakhQVFSVvb+8pYyIjI409FgsKCqacqD4bO3fuNEqDCwsLf3n/SHd3d+N6aGjIab/Q0FBjX8ySkhKnKxsHBgZ0584dSVJQUNBv3fdzPk2sdG1vb3d6UE9vb6+qqqoWclqz0tfXZ5xuPp2bN28a1xPPqDReNr53715J0uPHj/X27dvfN0kAAIBfRLAJAACwgCbKzUdGRhQfH2+EjNOVoUvjB+ikp6dLkp4/f67MzMwZS6F7enp048aNKe+RmpoqSWpqalJGRobTgHRkZGTKgTGOweP79++dfrbValVSUpKk8ZPOc3JypvT58eOH0tPTjQONJr6bK4iIiJAkDQ8Pq7CwcEr7yMiIkpKS9PXr14We2qycPn162pL02tpaXb9+XdJ4OB0WFjapPSsrS25ubhobG1NMTIy6urqcfsb3799VVlY2Yx8AAID5wh6bAAAAC+jgwYOy2WwaGBhQa2urpPFTovfv3+90zMWLF1VbW6vGxkZduXJFNTU1Sk5OVkhIiDw9PdXf36/W1lY9efJEjx49UnBwsBEwTsjJyVFVVZXsdruKiorU0NCg1NRUBQcHy2KxqKurS/X19bp9+7YuXbqkxMREY+yqVavk7++vrq4u5eXlyd/fX4GBgUZZvY+Pj1GmnZ2drYqKCnV0dOj8+fOy2+06fvy4fH191dnZqaKiItXU1EiStm/frpSUlHn87/5e+/bt0+rVq/Xx40edO3dOvb29OnTokNzd3dXa2qqCggI1Nzdr27ZtevHixZ+e7iQbN25UW1ubQkNDlZWVpS1btmhoaEiVlZXKz8/X6OioTCaTrl69OmVscHCw8vLylJmZqba2Nm3YsEEpKSnatWuXfHx89O3bN3348EENDQ26d++euru7Zbfb//gBVgAA4H8fwSYAAMACWrRokWJiYlRcXGy8FhcXJ6vV6nSM1WpVVVWVEhMTVVFRoZaWlhlXOi5evHjKax4eHnr69Kmio6NVV1enpqamXwoVz549qxMnTqizs1MHDhyY1FZcXGwEoTabTdXV1dqzZ4/evXun8vJylZeXT3m/8PBwPXjwwKVOyLZYLCotLVVUVJQGBweVn5+v/Px8o93NzU2XL19WX1/fXxdshoSEKD09XWlpadM+OxaLRSUlJdq6deu04zMyMuTp6amMjAx9+fJFubm5ys3NnbavxWKZtH0BAADA70IpOgAAwAI7duzYpL+dlaE7stlsKi8vV319vZKSkhQYGCibzSaTyaRly5YpLCxMJ0+eVGVlpdM9Hr28vFRbW6uKigrFxMTI399fVqtV7u7uCggIUGxsrMrKyqY9xCgtLU3l5eXavXu3VqxYIZPJ+e/ja9asUUtLi4qKihQREaHly5fLbDbLx8dHUVFRunXrlurq6lzmNHRHO3bsUFNTk44ePSo/Pz+ZzWb5+voagfGpU6f+9BSdSkpKUn19veLi4uTn5yeLxaKVK1cqISFBzc3Nio+Pn3F8cnKyOjo6dOHCBYWHh8vLy0smk0menp5av369oqOjde3aNX369Enr1q1boG8FAAD+n/3nx1x2oAcAAAAAAACAP4gVmwAAAAAAAABcDsEmAAAAAAAAAJdDsAkAAAAAAADA5RBsAgAAAAAAAHA5BJsAAAAAAAAAXA7BJgAAAAAAAACXQ7AJAAAAAAAAwOUQbAIAAAAAAABwOQSbAAAAAAAAAFwOwSYAAAAAAAAAl0OwCQAAAAAAAMDlEGwCAAAAAAAAcDkEmwAAAAAAAABcDsEmAAAAAAAAAJfzDySdcrZGfVXBAAAAAElFTkSuQmCC\n"},"metadata":{}}]},{"cell_type":"code","source":["# тестирование АE3\n","predicted_labels3_v2, ire3_v2 = lib.predict_ae(ae3_v2_trained, test, IREth3_v2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SFt1gFoau6bH","executionInfo":{"status":"ok","timestamp":1763331303251,"user_tz":-180,"elapsed":110,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"9004caf1-25b6-4513-d391-b579ffa1f2ee"},"execution_count":43,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step \n"]}]},{"cell_type":"code","source":["# Построение графика ошибки реконструкции\n","lib.ire_plot('test', ire3_v2, IREth3_v2, 'AE3_v2')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":614},"collapsed":true,"id":"AN11aPvBvGYD","executionInfo":{"status":"ok","timestamp":1763331312443,"user_tz":-180,"elapsed":486,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"45563481-eeaa-42f9-cf62-21ec8db003c4"},"execution_count":44,"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{}}]},{"cell_type":"code","source":["# тестирование АE2\n","lib.anomaly_detection_ae(predicted_labels3_v2, IRE3_v2, IREth3_v2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"collapsed":true,"id":"9frRIdvBvIhY","executionInfo":{"status":"ok","timestamp":1763331332350,"user_tz":-180,"elapsed":15,"user":{"displayName":"Александр Ли","userId":"11169366018002229978"}},"outputId":"0ae5d205-e404-45ef-8962-44e6a504ff12"},"execution_count":45,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","i Labels IRE IREth \n","0 [1.] 0.56 1.4 \n","1 [1.] 0.59 1.4 \n","2 [1.] 0.48 1.4 \n","3 [1.] 0.29 1.4 \n","4 [1.] 1.4 1.4 \n","5 [1.] 0.32 1.4 \n","6 [1.] 0.84 1.4 \n","7 [1.] 0.37 1.4 \n","8 [1.] 0.43 1.4 \n","9 [1.] 0.4 1.4 \n","10 [1.] 0.32 1.4 \n","11 [1.] 0.42 1.4 \n","12 [1.] 0.65 1.4 \n","13 [1.] 0.31 1.4 \n","14 [1.] 0.56 1.4 \n","15 [1.] 0.95 1.4 \n","16 [1.] 0.47 1.4 \n","17 [1.] 0.47 1.4 \n","18 [1.] 0.32 1.4 \n","19 [1.] 0.18 1.4 \n","20 [1.] 0.37 1.4 \n","21 [1.] 0.32 1.4 \n","22 [1.] 0.31 1.4 \n","23 [1.] 1.03 1.4 \n","24 [1.] 0.32 1.4 \n","25 [1.] 0.57 1.4 \n","26 [1.] 0.48 1.4 \n","27 [1.] 0.41 1.4 \n","28 [1.] 0.4 1.4 \n","29 [0.] 0.47 1.4 \n","30 [1.] 0.32 1.4 \n","31 [1.] 0.39 1.4 \n","32 [1.] 0.36 1.4 \n","33 [1.] 0.31 1.4 \n","34 [1.] 0.51 1.4 \n","35 [1.] 0.31 1.4 \n","36 [1.] 0.18 1.4 \n","37 [1.] 0.33 1.4 \n","38 [0.] 0.3 1.4 \n","39 [0.] 0.25 1.4 \n","40 [0.] 0.27 1.4 \n","41 [0.] 0.5 1.4 \n","42 [0.] 0.65 1.4 \n","43 [0.] 0.26 1.4 \n","44 [0.] 0.7 1.4 \n","45 [1.] 0.29 1.4 \n","46 [1.] 0.29 1.4 \n","47 [0.] 0.33 1.4 \n","48 [0.] 0.29 1.4 \n","49 [1.] 0.29 1.4 \n","50 [1.] 0.25 1.4 \n","51 [1.] 0.33 1.4 \n","52 [0.] 0.44 1.4 \n","53 [0.] 0.25 1.4 \n","54 [0.] 0.19 1.4 \n","55 [1.] 0.69 1.4 \n","56 [1.] 0.39 1.4 \n","57 [1.] 0.54 1.4 \n","58 [1.] 0.61 1.4 \n","59 [1.] 0.38 1.4 \n","60 [1.] 0.52 1.4 \n","61 [0.] 0.29 1.4 \n","62 [0.] 0.37 1.4 \n","63 [0.] 0.64 1.4 \n","64 [0.] 0.56 1.4 \n","65 [0.] 0.27 1.4 \n","66 [1.] 0.36 1.4 \n","67 [1.] 0.69 1.4 \n","68 [1.] 0.37 1.4 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аномалий\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"2HPJTt52vNgF"},"execution_count":null,"outputs":[]}]} \ No newline at end of file