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+{"cells":[{"cell_type":"code","execution_count":2,"metadata":{"executionInfo":{"elapsed":524,"status":"ok","timestamp":1762638357164,"user":{"displayName":"Alena Konovalova","userId":"13964397918094172748"},"user_tz":-180},"id":"3WM4EQQ6Rau2"},"outputs":[],"source":["import os\n","os.chdir('/content/drive/MyDrive/Colab Notebooks/is_lab2')"]},{"cell_type":"code","execution_count":4,"metadata":{"executionInfo":{"elapsed":5,"status":"ok","timestamp":1762638370573,"user":{"displayName":"Alena Konovalova","userId":"13964397918094172748"},"user_tz":-180},"id":"nDx3fL_nVafu"},"outputs":[],"source":["# импорт модулей\n","import numpy as np\n","import lab02_lib as lib"]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":718},"executionInfo":{"elapsed":448,"status":"ok","timestamp":1762438438052,"user":{"displayName":"Alena 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\n"},"metadata":{}}],"source":["# генерация датасета\n","data = lib.datagen(8, 8, 1000, 2)"]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":16,"status":"ok","timestamp":1762438440199,"user":{"displayName":"Alena Konovalova","userId":"13964397918094172748"},"user_tz":-180},"id":"LcZFQfYTW7hr","outputId":"81161c7a-2ff7-4e96-9880-cf70369d0eb0"},"outputs":[{"output_type":"stream","name":"stdout","text":["Исходные данные:\n","[[8.14457288 7.96648176]\n"," [8.16064924 7.98620341]\n"," [7.93127504 7.92863959]\n"," ...\n"," [7.95464881 7.94307035]\n"," [8.01092703 7.90530753]\n"," [7.81962108 7.93563874]]\n","Размерность данных:\n","(1000, 2)\n"]}],"source":["# вывод данных и размерности\n","print('Исходные данные:')\n","print(data)\n","print('Размерность данных:')\n","print(data.shape)"]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GZl-6hPzXfxt","executionInfo":{"status":"ok","timestamp":1762438869706,"user_tz":-180,"elapsed":169453,"user":{"displayName":"Alena Konovalova","userId":"13964397918094172748"}},"outputId":"0256b085-1645-4858-f54d-3e38555bb5a5"},"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 : 5\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: 60.1481\n","Epoch 2/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 60.0581\n","Epoch 3/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 59.9681\n","Epoch 4/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 59.8782\n","Epoch 5/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 59.7882\n","Epoch 6/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 59.6983\n","Epoch 7/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 59.6085\n","Epoch 8/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 59.5186\n","Epoch 9/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 59.4288\n","Epoch 10/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 59.3390\n","Epoch 11/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 59.2492\n","Epoch 12/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 59.1595\n","Epoch 13/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 59.0698\n","Epoch 14/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 58.9802\n","Epoch 15/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 58.8905\n","Epoch 16/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 58.8009\n","Epoch 17/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 58.7114\n","Epoch 18/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 58.6218\n","Epoch 19/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 58.5323\n","Epoch 20/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 58.4429\n","Epoch 21/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 58.3534\n","Epoch 22/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 58.2640\n","Epoch 23/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 58.1746\n","Epoch 24/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 58.0852\n","Epoch 25/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 57.9959\n","Epoch 26/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 57.9066\n","Epoch 27/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 57.8172\n","Epoch 28/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 57.7279\n","Epoch 29/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 57.6386\n","Epoch 30/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 57.5493\n","Epoch 31/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 57.4600\n","Epoch 32/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 57.3707\n","Epoch 33/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 57.2814\n","Epoch 34/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 57.1921\n","Epoch 35/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 57.1027\n","Epoch 36/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 57.0134\n","Epoch 37/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 56.9240\n","Epoch 38/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 56.8345\n","Epoch 39/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 56.7450\n","Epoch 40/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 56.6555\n","Epoch 41/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 56.5659\n","Epoch 42/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 56.4762\n","Epoch 43/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 56.3865\n","Epoch 44/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 56.2967\n","Epoch 45/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 56.2068\n","Epoch 46/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 56.1168\n","Epoch 47/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 56.0267\n","Epoch 48/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 55.9365\n","Epoch 49/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 55.8462\n","Epoch 50/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 55.7558\n","Epoch 51/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 55.6653\n","Epoch 52/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 55.5746\n","Epoch 53/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 55.4838\n","Epoch 54/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - loss: 55.3928\n","Epoch 55/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 55.3017\n","Epoch 56/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 55.2105\n","Epoch 57/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 55.1191\n","Epoch 58/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 55.0275\n","Epoch 59/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 54.9358\n","Epoch 60/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 54.8439\n","Epoch 61/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 54.7518\n","Epoch 62/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 54.6596\n","Epoch 63/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 54.5672\n","Epoch 64/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 44ms/step - loss: 54.4746\n","Epoch 65/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 54.3819\n","Epoch 66/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 54.2889\n","Epoch 67/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 54.1958\n","Epoch 68/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 54.1025\n","Epoch 69/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 54.0091\n","Epoch 70/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 53.9154\n","Epoch 71/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 53.8216\n","Epoch 72/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 53.7276\n","Epoch 73/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 53.6334\n","Epoch 74/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 53.5390\n","Epoch 75/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 53.4444\n","Epoch 76/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 53.3496\n","Epoch 77/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 53.2547\n","Epoch 78/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 53.1596\n","Epoch 79/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 53.0643\n","Epoch 80/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 52.9688\n","Epoch 81/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 52.8731\n","Epoch 82/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 52.7772\n","Epoch 83/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 52.6812\n","Epoch 84/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 52.5850\n","Epoch 85/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 52.4886\n","Epoch 86/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 52.3920\n","Epoch 87/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 52.2952\n","Epoch 88/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 52.1983\n","Epoch 89/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 52.1012\n","Epoch 90/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 52.0039\n","Epoch 91/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 51.9064\n","Epoch 92/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 51.8088\n","Epoch 93/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 51.7110\n","Epoch 94/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 51.6130\n","Epoch 95/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 51.5149\n","Epoch 96/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 51.4166\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: 51.3181\n","Epoch 98/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 51.2195\n","Epoch 99/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 51.1207\n","Epoch 100/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 51.0218\n","Epoch 101/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 50.9227\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: 50.8234\n","Epoch 103/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 47ms/step - loss: 50.7240\n","Epoch 104/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 50.6244\n","Epoch 105/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 50.5247\n","Epoch 106/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 50.4248\n","Epoch 107/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 50.3248\n","Epoch 108/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 50.2246\n","Epoch 109/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 50.1243\n","Epoch 110/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 50.0239\n","Epoch 111/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 49.9233\n","Epoch 112/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 49.8225\n","Epoch 113/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 49.7217\n","Epoch 114/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 49.6207\n","Epoch 115/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 46ms/step - loss: 49.5196\n","Epoch 116/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 49.4183\n","Epoch 117/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 49.3169\n","Epoch 118/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 49.2154\n","Epoch 119/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 49.1138\n","Epoch 120/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 49.0121\n","Epoch 121/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 48.9102\n","Epoch 122/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 48.8082\n","Epoch 123/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 48.7061\n","Epoch 124/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 48.6039\n","Epoch 125/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 48.5016\n","Epoch 126/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 48.3992\n","Epoch 127/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 48.2967\n","Epoch 128/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 48.1940\n","Epoch 129/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 48.0913\n","Epoch 130/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 47.9885\n","Epoch 131/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 47.8856\n","Epoch 132/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 47.7826\n","Epoch 133/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 47.6795\n","Epoch 134/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 47.5763\n","Epoch 135/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 47.4730\n","Epoch 136/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 47.3696\n","Epoch 137/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 47.2662\n","Epoch 138/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 47.1627\n","Epoch 139/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 47.0591\n","Epoch 140/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 46.9554\n","Epoch 141/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 46.8517\n","Epoch 142/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 46.7479\n","Epoch 143/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 46.6440\n","Epoch 144/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 46.5401\n","Epoch 145/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 46.4361\n","Epoch 146/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 46.3320\n","Epoch 147/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 46.2279\n","Epoch 148/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 46.1237\n","Epoch 149/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 46.0195\n","Epoch 150/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 45.9152\n","Epoch 151/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 45.8109\n","Epoch 152/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 45.7066\n","Epoch 153/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 45.6022\n","Epoch 154/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 45.4978\n","Epoch 155/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 45.3933\n","Epoch 156/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 45.2888\n","Epoch 157/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 45.1843\n","Epoch 158/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 45.0798\n","Epoch 159/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 44.9752\n","Epoch 160/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 44.8706\n","Epoch 161/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 44.7660\n","Epoch 162/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 44.6614\n","Epoch 163/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 44.5567\n","Epoch 164/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 44.4521\n","Epoch 165/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 44.3474\n","Epoch 166/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 44.2428\n","Epoch 167/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 44.1381\n","Epoch 168/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 44.0335\n","Epoch 169/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 43.9288\n","Epoch 170/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 43.8242\n","Epoch 171/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 43.7195\n","Epoch 172/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 43.6149\n","Epoch 173/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 43.5103\n","Epoch 174/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 43.4058\n","Epoch 175/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 43.3012\n","Epoch 176/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 43.1967\n","Epoch 177/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 43.0922\n","Epoch 178/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 42.9877\n","Epoch 179/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 42.8833\n","Epoch 180/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 42.7789\n","Epoch 181/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 42.6745\n","Epoch 182/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 42.5702\n","Epoch 183/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 42.4659\n","Epoch 184/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 42.3617\n","Epoch 185/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 42.2575\n","Epoch 186/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 42.1534\n","Epoch 187/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 42.0494\n","Epoch 188/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 41.9454\n","Epoch 189/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 41.8414\n","Epoch 190/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 41.7376\n","Epoch 191/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 41.6338\n","Epoch 192/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 41.5301\n","Epoch 193/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 41.4264\n","Epoch 194/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - loss: 41.3228\n","Epoch 195/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 41.2193\n","Epoch 196/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 41.1159\n","Epoch 197/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 41.0126\n","Epoch 198/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 40.9094\n","Epoch 199/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 40.8062\n","Epoch 200/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 40.7032\n","Epoch 201/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 40.6002\n","Epoch 202/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 40.4974\n","Epoch 203/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 40.3946\n","Epoch 204/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 40.2919\n","Epoch 205/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 40.1894\n","Epoch 206/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 40.0869\n","Epoch 207/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 39.9846\n","Epoch 208/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 39.8824\n","Epoch 209/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 39.7803\n","Epoch 210/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 39.6783\n","Epoch 211/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 39.5765\n","Epoch 212/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 39.4747\n","Epoch 213/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 39.3731\n","Epoch 214/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 39.2716\n","Epoch 215/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 39.1702\n","Epoch 216/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 39.0690\n","Epoch 217/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 38.9679\n","Epoch 218/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 38.8670\n","Epoch 219/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 38.7661\n","Epoch 220/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 38.6655\n","Epoch 221/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 38.5649\n","Epoch 222/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 38.4645\n","Epoch 223/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 38.3643\n","Epoch 224/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 38.2642\n","Epoch 225/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 38.1642\n","Epoch 226/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 38.0644\n","Epoch 227/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 37.9647\n","Epoch 228/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 37.8653\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: 37.7659\n","Epoch 230/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 37.6667\n","Epoch 231/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 37.5677\n","Epoch 232/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 37.4688\n","Epoch 233/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 37.3701\n","Epoch 234/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 37.2716\n","Epoch 235/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 37.1732\n","Epoch 236/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 37.0750\n","Epoch 237/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 36.9770\n","Epoch 238/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 36.8791\n","Epoch 239/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 36.7814\n","Epoch 240/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 36.6839\n","Epoch 241/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 36.5866\n","Epoch 242/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 36.4894\n","Epoch 243/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 36.3924\n","Epoch 244/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 36.2956\n","Epoch 245/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 36.1990\n","Epoch 246/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 36.1025\n","Epoch 247/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 36.0062\n","Epoch 248/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 35.9102\n","Epoch 249/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 35.8143\n","Epoch 250/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 35.7185\n","Epoch 251/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 35.6230\n","Epoch 252/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 35.5276\n","Epoch 253/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 35.4325\n","Epoch 254/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 35.3375\n","Epoch 255/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 35.2427\n","Epoch 256/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 35.1481\n","Epoch 257/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 35.0537\n","Epoch 258/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 34.9595\n","Epoch 259/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 34.8655\n","Epoch 260/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - loss: 34.7717\n","Epoch 261/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 34.6781\n","Epoch 262/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 34.5847\n","Epoch 263/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 34.4914\n","Epoch 264/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 34.3984\n","Epoch 265/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 34.3055\n","Epoch 266/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 34.2129\n","Epoch 267/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 34.1205\n","Epoch 268/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 34.0282\n","Epoch 269/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 33.9362\n","Epoch 270/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 33.8443\n","Epoch 271/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 33.7527\n","Epoch 272/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 33.6613\n","Epoch 273/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 33.5700\n","Epoch 274/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 33.4790\n","Epoch 275/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 33.3881\n","Epoch 276/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 33.2975\n","Epoch 277/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 33.2071\n","Epoch 278/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 33.1169\n","Epoch 279/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 33.0269\n","Epoch 280/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 32.9370\n","Epoch 281/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 32.8474\n","Epoch 282/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 32.7580\n","Epoch 283/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 32.6688\n","Epoch 284/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 32.5799\n","Epoch 285/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 32.4911\n","Epoch 286/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 32.4025\n","Epoch 287/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 32.3141\n","Epoch 288/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 32.2259\n","Epoch 289/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 32.1380\n","Epoch 290/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 32.0502\n","Epoch 291/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 31.9627\n","Epoch 292/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 31.8754\n","Epoch 293/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 31.7882\n","Epoch 294/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 31.7013\n","Epoch 295/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 31.6146\n","Epoch 296/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step - loss: 31.5281\n","Epoch 297/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 31.4418\n","Epoch 298/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 31.3557\n","Epoch 299/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 31.2698\n","Epoch 300/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 31.1842\n","Epoch 301/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 31.0987\n","Epoch 302/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 31.0134\n","Epoch 303/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 30.9284\n","Epoch 304/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 30.8435\n","Epoch 305/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 30.7589\n","Epoch 306/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 30.6745\n","Epoch 307/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 30.5903\n","Epoch 308/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 30.5063\n","Epoch 309/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 30.4224\n","Epoch 310/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 30.3389\n","Epoch 311/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 30.2555\n","Epoch 312/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - loss: 30.1723\n","Epoch 313/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 30.0893\n","Epoch 314/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 30.0065\n","Epoch 315/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 29.9240\n","Epoch 316/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 29.8416\n","Epoch 317/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 29.7595\n","Epoch 318/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 29.6775\n","Epoch 319/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 29.5958\n","Epoch 320/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 29.5143\n","Epoch 321/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 29.4329\n","Epoch 322/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 29.3518\n","Epoch 323/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 29.2709\n","Epoch 324/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 29.1902\n","Epoch 325/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 29.1097\n","Epoch 326/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 29.0294\n","Epoch 327/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 28.9493\n","Epoch 328/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 28.8694\n","Epoch 329/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 28.7897\n","Epoch 330/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 28.7102\n","Epoch 331/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 28.6310\n","Epoch 332/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 28.5519\n","Epoch 333/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 28.4730\n","Epoch 334/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 28.3943\n","Epoch 335/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 28.3159\n","Epoch 336/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 28.2376\n","Epoch 337/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 28.1595\n","Epoch 338/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 160ms/step - loss: 28.0817\n","Epoch 339/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 28.0040\n","Epoch 340/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 27.9265\n","Epoch 341/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 27.8493\n","Epoch 342/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 27.7722\n","Epoch 343/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 27.6953\n","Epoch 344/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 27.6187\n","Epoch 345/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 27.5422\n","Epoch 346/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 27.4659\n","Epoch 347/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 27.3898\n","Epoch 348/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 27.3140\n","Epoch 349/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 27.2383\n","Epoch 350/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 27.1628\n","Epoch 351/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 27.0875\n","Epoch 352/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 27.0124\n","Epoch 353/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 26.9375\n","Epoch 354/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 26.8628\n","Epoch 355/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 26.7883\n","Epoch 356/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 26.7140\n","Epoch 357/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 26.6399\n","Epoch 358/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 26.5660\n","Epoch 359/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 26.4923\n","Epoch 360/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 26.4187\n","Epoch 361/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 26.3454\n","Epoch 362/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 26.2723\n","Epoch 363/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 26.1993\n","Epoch 364/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 26.1265\n","Epoch 365/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 26.0540\n","Epoch 366/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 25.9816\n","Epoch 367/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 25.9094\n","Epoch 368/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 25.8374\n","Epoch 369/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 25.7656\n","Epoch 370/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 25.6939\n","Epoch 371/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 25.6225\n","Epoch 372/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 25.5512\n","Epoch 373/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 25.4802\n","Epoch 374/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 25.4093\n","Epoch 375/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 25.3386\n","Epoch 376/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 25.2681\n","Epoch 377/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 25.1978\n","Epoch 378/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 25.1277\n","Epoch 379/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 25.0577\n","Epoch 380/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 24.9879\n","Epoch 381/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 24.9184\n","Epoch 382/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 24.8490\n","Epoch 383/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 24.7798\n","Epoch 384/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 24.7107\n","Epoch 385/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 24.6419\n","Epoch 386/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 24.5732\n","Epoch 387/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 24.5047\n","Epoch 388/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 24.4364\n","Epoch 389/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 24.3683\n","Epoch 390/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 24.3004\n","Epoch 391/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 24.2326\n","Epoch 392/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 24.1650\n","Epoch 393/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 24.0976\n","Epoch 394/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 24.0304\n","Epoch 395/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 23.9633\n","Epoch 396/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 23.8964\n","Epoch 397/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 23.8297\n","Epoch 398/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 23.7632\n","Epoch 399/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 23.6969\n","Epoch 400/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 23.6307\n","Epoch 401/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 23.5647\n","Epoch 402/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 23.4989\n","Epoch 403/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 23.4332\n","Epoch 404/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 23.3678\n","Epoch 405/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 23.3025\n","Epoch 406/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 23.2373\n","Epoch 407/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 23.1724\n","Epoch 408/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 23.1076\n","Epoch 409/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 23.0430\n","Epoch 410/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 22.9785\n","Epoch 411/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 22.9143\n","Epoch 412/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 22.8502\n","Epoch 413/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 22.7862\n","Epoch 414/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 22.7225\n","Epoch 415/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 22.6589\n","Epoch 416/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 22.5955\n","Epoch 417/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 22.5322\n","Epoch 418/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 22.4691\n","Epoch 419/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 22.4062\n","Epoch 420/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 22.3434\n","Epoch 421/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 22.2808\n","Epoch 422/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 22.2184\n","Epoch 423/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 22.1562\n","Epoch 424/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 22.0941\n","Epoch 425/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 22.0321\n","Epoch 426/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 21.9704\n","Epoch 427/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 21.9088\n","Epoch 428/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 21.8473\n","Epoch 429/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 21.7860\n","Epoch 430/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 21.7249\n","Epoch 431/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 21.6640\n","Epoch 432/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 21.6032\n","Epoch 433/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 21.5425\n","Epoch 434/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 21.4821\n","Epoch 435/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 21.4217\n","Epoch 436/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 21.3616\n","Epoch 437/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 21.3016\n","Epoch 438/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 21.2417\n","Epoch 439/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 21.1821\n","Epoch 440/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 21.1225\n","Epoch 441/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 21.0632\n","Epoch 442/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 21.0040\n","Epoch 443/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 20.9449\n","Epoch 444/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 20.8860\n","Epoch 445/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 20.8273\n","Epoch 446/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 20.7687\n","Epoch 447/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 20.7103\n","Epoch 448/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 20.6520\n","Epoch 449/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 20.5938\n","Epoch 450/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 20.5359\n","Epoch 451/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 20.4781\n","Epoch 452/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 20.4204\n","Epoch 453/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 20.3629\n","Epoch 454/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 20.3055\n","Epoch 455/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 20.2483\n","Epoch 456/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 20.1912\n","Epoch 457/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 20.1343\n","Epoch 458/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 20.0776\n","Epoch 459/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 20.0210\n","Epoch 460/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 19.9645\n","Epoch 461/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 19.9082\n","Epoch 462/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 19.8520\n","Epoch 463/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 19.7960\n","Epoch 464/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 19.7401\n","Epoch 465/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 19.6844\n","Epoch 466/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 19.6288\n","Epoch 467/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 19.5734\n","Epoch 468/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 19.5181\n","Epoch 469/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 19.4630\n","Epoch 470/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 19.4080\n","Epoch 471/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 19.3531\n","Epoch 472/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 19.2984\n","Epoch 473/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 19.2438\n","Epoch 474/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 19.1894\n","Epoch 475/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 19.1351\n","Epoch 476/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 19.0810\n","Epoch 477/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 19.0270\n","Epoch 478/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 18.9732\n","Epoch 479/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 18.9195\n","Epoch 480/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 18.8659\n","Epoch 481/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 18.8125\n","Epoch 482/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 18.7592\n","Epoch 483/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 18.7060\n","Epoch 484/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 18.6530\n","Epoch 485/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 18.6002\n","Epoch 486/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 18.5474\n","Epoch 487/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 18.4948\n","Epoch 488/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 18.4424\n","Epoch 489/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 18.3901\n","Epoch 490/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 18.3379\n","Epoch 491/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 18.2858\n","Epoch 492/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 18.2339\n","Epoch 493/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 18.1822\n","Epoch 494/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 18.1305\n","Epoch 495/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 18.0790\n","Epoch 496/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 18.0277\n","Epoch 497/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 17.9764\n","Epoch 498/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 17.9253\n","Epoch 499/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 17.8744\n","Epoch 500/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 17.8236\n","Epoch 501/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 17.7729\n","Epoch 502/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 17.7223\n","Epoch 503/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 17.6719\n","Epoch 504/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 17.6216\n","Epoch 505/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 17.5714\n","Epoch 506/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 17.5214\n","Epoch 507/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 17.4715\n","Epoch 508/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 17.4217\n","Epoch 509/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 17.3720\n","Epoch 510/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 17.3225\n","Epoch 511/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 17.2731\n","Epoch 512/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 17.2239\n","Epoch 513/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 17.1748\n","Epoch 514/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 17.1258\n","Epoch 515/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 157ms/step - loss: 17.0769\n","Epoch 516/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 17.0281\n","Epoch 517/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 16.9795\n","Epoch 518/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 16.9310\n","Epoch 519/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 16.8827\n","Epoch 520/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 16.8344\n","Epoch 521/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 16.7863\n","Epoch 522/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 159ms/step - loss: 16.7383\n","Epoch 523/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 16.6905\n","Epoch 524/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 16.6427\n","Epoch 525/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 16.5951\n","Epoch 526/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 155ms/step - loss: 16.5477\n","Epoch 527/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 16.5003\n","Epoch 528/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 16.4531\n","Epoch 529/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 16.4059\n","Epoch 530/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 155ms/step - loss: 16.3589\n","Epoch 531/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 16.3121\n","Epoch 532/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 16.2653\n","Epoch 533/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 16.2187\n","Epoch 534/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - loss: 16.1722\n","Epoch 535/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 16.1258\n","Epoch 536/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 16.0796\n","Epoch 537/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 16.0334\n","Epoch 538/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 15.9874\n","Epoch 539/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 15.9415\n","Epoch 540/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 15.8957\n","Epoch 541/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 15.8501\n","Epoch 542/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 15.8045\n","Epoch 543/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 15.7591\n","Epoch 544/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 15.7138\n","Epoch 545/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 15.6686\n","Epoch 546/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 15.6235\n","Epoch 547/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 15.5786\n","Epoch 548/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 15.5337\n","Epoch 549/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 15.4890\n","Epoch 550/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 15.4444\n","Epoch 551/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 15.3999\n","Epoch 552/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 15.3556\n","Epoch 553/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 15.3113\n","Epoch 554/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 15.2672\n","Epoch 555/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 15.2231\n","Epoch 556/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 15.1792\n","Epoch 557/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 15.1354\n","Epoch 558/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 15.0917\n","Epoch 559/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 15.0482\n","Epoch 560/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 15.0047\n","Epoch 561/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 14.9614\n","Epoch 562/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 14.9182\n","Epoch 563/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 14.8750\n","Epoch 564/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 14.8320\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: 14.7891\n","Epoch 566/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 14.7464\n","Epoch 567/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 14.7037\n","Epoch 568/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 14.6611\n","Epoch 569/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 14.6187\n","Epoch 570/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 14.5763\n","Epoch 571/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 14.5341\n","Epoch 572/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 14.4920\n","Epoch 573/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 14.4500\n","Epoch 574/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 14.4081\n","Epoch 575/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 14.3663\n","Epoch 576/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 14.3246\n","Epoch 577/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 14.2831\n","Epoch 578/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 14.2416\n","Epoch 579/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 14.2002\n","Epoch 580/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 14.1590\n","Epoch 581/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 14.1178\n","Epoch 582/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 14.0768\n","Epoch 583/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 14.0359\n","Epoch 584/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 13.9951\n","Epoch 585/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 13.9543\n","Epoch 586/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 13.9137\n","Epoch 587/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 13.8732\n","Epoch 588/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 13.8328\n","Epoch 589/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 13.7925\n","Epoch 590/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 13.7523\n","Epoch 591/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 13.7123\n","Epoch 592/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 13.6723\n","Epoch 593/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 13.6324\n","Epoch 594/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 13.5926\n","Epoch 595/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 13.5530\n","Epoch 596/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 13.5134\n","Epoch 597/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 13.4739\n","Epoch 598/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 13.4346\n","Epoch 599/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 13.3953\n","Epoch 600/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 13.3562\n","Epoch 601/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 13.3171\n","Epoch 602/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 13.2782\n","Epoch 603/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 13.2393\n","Epoch 604/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 13.2006\n","Epoch 605/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 13.1619\n","Epoch 606/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 13.1234\n","Epoch 607/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 13.0849\n","Epoch 608/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 13.0466\n","Epoch 609/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 13.0083\n","Epoch 610/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 12.9702\n","Epoch 611/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 12.9321\n","Epoch 612/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 12.8942\n","Epoch 613/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 12.8563\n","Epoch 614/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 12.8186\n","Epoch 615/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 12.7809\n","Epoch 616/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 12.7434\n","Epoch 617/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 12.7059\n","Epoch 618/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 12.6685\n","Epoch 619/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 12.6313\n","Epoch 620/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 12.5941\n","Epoch 621/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 12.5570\n","Epoch 622/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 12.5201\n","Epoch 623/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 12.4832\n","Epoch 624/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 12.4464\n","Epoch 625/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 12.4097\n","Epoch 626/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 12.3731\n","Epoch 627/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 12.3367\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: 12.3003\n","Epoch 629/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 12.2639\n","Epoch 630/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 12.2277\n","Epoch 631/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 12.1916\n","Epoch 632/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 12.1556\n","Epoch 633/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 12.1197\n","Epoch 634/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 12.0838\n","Epoch 635/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 12.0481\n","Epoch 636/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 12.0125\n","Epoch 637/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 11.9769\n","Epoch 638/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 11.9414\n","Epoch 639/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 11.9061\n","Epoch 640/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 11.8708\n","Epoch 641/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 11.8356\n","Epoch 642/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 11.8005\n","Epoch 643/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 11.7655\n","Epoch 644/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 11.7306\n","Epoch 645/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 11.6958\n","Epoch 646/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 11.6611\n","Epoch 647/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 11.6264\n","Epoch 648/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 11.5919\n","Epoch 649/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 11.5574\n","Epoch 650/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 11.5230\n","Epoch 651/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 11.4888\n","Epoch 652/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 11.4546\n","Epoch 653/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 11.4205\n","Epoch 654/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 11.3865\n","Epoch 655/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 11.3526\n","Epoch 656/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 11.3187\n","Epoch 657/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 11.2850\n","Epoch 658/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 11.2513\n","Epoch 659/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 11.2178\n","Epoch 660/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 11.1843\n","Epoch 661/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 11.1509\n","Epoch 662/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 11.1176\n","Epoch 663/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 11.0844\n","Epoch 664/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 11.0512\n","Epoch 665/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 11.0182\n","Epoch 666/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 10.9852\n","Epoch 667/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 10.9524\n","Epoch 668/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 10.9196\n","Epoch 669/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 10.8869\n","Epoch 670/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 10.8543\n","Epoch 671/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 10.8217\n","Epoch 672/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 10.7893\n","Epoch 673/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 10.7570\n","Epoch 674/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 10.7247\n","Epoch 675/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 10.6925\n","Epoch 676/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 10.6604\n","Epoch 677/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 10.6284\n","Epoch 678/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 10.5964\n","Epoch 679/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 10.5646\n","Epoch 680/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 10.5328\n","Epoch 681/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 10.5011\n","Epoch 682/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 10.4695\n","Epoch 683/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 10.4380\n","Epoch 684/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 10.4066\n","Epoch 685/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 10.3752\n","Epoch 686/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 10.3439\n","Epoch 687/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 10.3128\n","Epoch 688/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 10.2817\n","Epoch 689/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 10.2506\n","Epoch 690/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 10.2197\n","Epoch 691/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 10.1888\n","Epoch 692/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 10.1580\n","Epoch 693/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 10.1273\n","Epoch 694/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 10.0967\n","Epoch 695/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 10.0662\n","Epoch 696/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 10.0357\n","Epoch 697/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 10.0053\n","Epoch 698/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 9.9750\n","Epoch 699/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 9.9448\n","Epoch 700/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 9.9147\n","Epoch 701/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 9.8846\n","Epoch 702/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 9.8546\n","Epoch 703/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 161ms/step - loss: 9.8247\n","Epoch 704/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 9.7949\n","Epoch 705/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 9.7652\n","Epoch 706/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 9.7355\n","Epoch 707/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 9.7059\n","Epoch 708/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 9.6764\n","Epoch 709/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 9.6470\n","Epoch 710/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 9.6176\n","Epoch 711/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 159ms/step - loss: 9.5883\n","Epoch 712/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 9.5591\n","Epoch 713/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 9.5300\n","Epoch 714/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 9.5010\n","Epoch 715/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 9.4720\n","Epoch 716/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 9.4431\n","Epoch 717/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 9.4143\n","Epoch 718/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 9.3856\n","Epoch 719/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 9.3569\n","Epoch 720/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 9.3283\n","Epoch 721/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 9.2998\n","Epoch 722/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 9.2714\n","Epoch 723/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 9.2430\n","Epoch 724/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 9.2147\n","Epoch 725/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 9.1865\n","Epoch 726/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 9.1584\n","Epoch 727/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 9.1303\n","Epoch 728/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 9.1023\n","Epoch 729/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 9.0744\n","Epoch 730/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 9.0466\n","Epoch 731/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 9.0188\n","Epoch 732/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 8.9911\n","Epoch 733/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 8.9635\n","Epoch 734/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 8.9360\n","Epoch 735/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 8.9085\n","Epoch 736/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 8.8811\n","Epoch 737/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 8.8538\n","Epoch 738/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 8.8265\n","Epoch 739/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 8.7993\n","Epoch 740/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 8.7722\n","Epoch 741/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 8.7452\n","Epoch 742/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 8.7182\n","Epoch 743/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 8.6913\n","Epoch 744/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 8.6645\n","Epoch 745/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 8.6377\n","Epoch 746/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 8.6111\n","Epoch 747/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 8.5845\n","Epoch 748/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 8.5579\n","Epoch 749/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 8.5315\n","Epoch 750/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 8.5051\n","Epoch 751/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 8.4787\n","Epoch 752/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 8.4525\n","Epoch 753/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 8.4263\n","Epoch 754/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 8.4002\n","Epoch 755/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 8.3741\n","Epoch 756/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 8.3482\n","Epoch 757/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 8.3223\n","Epoch 758/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 8.2964\n","Epoch 759/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 8.2707\n","Epoch 760/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 8.2450\n","Epoch 761/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 8.2193\n","Epoch 762/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 8.1938\n","Epoch 763/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 8.1683\n","Epoch 764/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 8.1428\n","Epoch 765/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 8.1175\n","Epoch 766/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 8.0922\n","Epoch 767/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 8.0670\n","Epoch 768/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 8.0418\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: 8.0167\n","Epoch 770/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 7.9917\n","Epoch 771/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 7.9668\n","Epoch 772/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 7.9419\n","Epoch 773/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 7.9171\n","Epoch 774/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 7.8923\n","Epoch 775/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 7.8676\n","Epoch 776/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 7.8430\n","Epoch 777/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 7.8185\n","Epoch 778/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 7.7940\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: 7.7696\n","Epoch 780/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 7.7452\n","Epoch 781/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 7.7209\n","Epoch 782/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 7.6967\n","Epoch 783/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 7.6725\n","Epoch 784/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 7.6485\n","Epoch 785/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 7.6244\n","Epoch 786/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 7.6005\n","Epoch 787/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 7.5766\n","Epoch 788/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 7.5527\n","Epoch 789/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 7.5290\n","Epoch 790/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 7.5053\n","Epoch 791/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 7.4816\n","Epoch 792/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 7.4580\n","Epoch 793/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 7.4345\n","Epoch 794/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 7.4111\n","Epoch 795/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 7.3877\n","Epoch 796/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 7.3644\n","Epoch 797/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 7.3411\n","Epoch 798/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 7.3179\n","Epoch 799/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 7.2948\n","Epoch 800/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 7.2717\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: 7.2487\n","Epoch 802/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 7.2258\n","Epoch 803/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 7.2029\n","Epoch 804/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 7.1800\n","Epoch 805/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 7.1573\n","Epoch 806/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 7.1346\n","Epoch 807/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 7.1119\n","Epoch 808/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 7.0894\n","Epoch 809/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 7.0669\n","Epoch 810/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 7.0444\n","Epoch 811/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 7.0220\n","Epoch 812/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 6.9997\n","Epoch 813/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 6.9774\n","Epoch 814/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 6.9552\n","Epoch 815/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 6.9331\n","Epoch 816/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 6.9110\n","Epoch 817/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 6.8889\n","Epoch 818/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 6.8670\n","Epoch 819/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 6.8451\n","Epoch 820/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 6.8232\n","Epoch 821/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 6.8014\n","Epoch 822/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 6.7797\n","Epoch 823/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 6.7580\n","Epoch 824/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 6.7364\n","Epoch 825/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 6.7149\n","Epoch 826/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 6.6934\n","Epoch 827/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 6.6720\n","Epoch 828/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 6.6506\n","Epoch 829/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 6.6293\n","Epoch 830/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 6.6080\n","Epoch 831/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 6.5868\n","Epoch 832/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 6.5657\n","Epoch 833/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 6.5446\n","Epoch 834/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 6.5236\n","Epoch 835/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 6.5026\n","Epoch 836/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 6.4817\n","Epoch 837/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 6.4608\n","Epoch 838/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 6.4400\n","Epoch 839/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 6.4193\n","Epoch 840/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 6.3986\n","Epoch 841/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 6.3780\n","Epoch 842/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 6.3574\n","Epoch 843/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 6.3369\n","Epoch 844/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 6.3165\n","Epoch 845/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 6.2961\n","Epoch 846/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 6.2757\n","Epoch 847/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 6.2554\n","Epoch 848/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 6.2352\n","Epoch 849/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 6.2150\n","Epoch 850/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 6.1949\n","Epoch 851/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 6.1749\n","Epoch 852/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 6.1549\n","Epoch 853/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 6.1349\n","Epoch 854/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 6.1150\n","Epoch 855/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 6.0952\n","Epoch 856/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 6.0754\n","Epoch 857/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 6.0556\n","Epoch 858/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 6.0360\n","Epoch 859/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 6.0164\n","Epoch 860/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 5.9968\n","Epoch 861/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 5.9773\n","Epoch 862/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 5.9578\n","Epoch 863/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 5.9384\n","Epoch 864/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 5.9191\n","Epoch 865/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 5.8998\n","Epoch 866/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 5.8805\n","Epoch 867/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 5.8613\n","Epoch 868/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 5.8422\n","Epoch 869/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 5.8231\n","Epoch 870/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 5.8041\n","Epoch 871/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 5.7851\n","Epoch 872/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 5.7662\n","Epoch 873/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 5.7473\n","Epoch 874/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 5.7285\n","Epoch 875/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 5.7097\n","Epoch 876/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 5.6910\n","Epoch 877/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 5.6724\n","Epoch 878/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 5.6538\n","Epoch 879/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 5.6352\n","Epoch 880/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 5.6167\n","Epoch 881/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 5.5982\n","Epoch 882/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 5.5798\n","Epoch 883/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 5.5615\n","Epoch 884/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 5.5432\n","Epoch 885/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 5.5249\n","Epoch 886/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 5.5068\n","Epoch 887/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 5.4886\n","Epoch 888/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 5.4705\n","Epoch 889/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 5.4525\n","Epoch 890/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 5.4345\n","Epoch 891/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 5.4165\n","Epoch 892/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 5.3986\n","Epoch 893/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 5.3808\n","Epoch 894/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 5.3630\n","Epoch 895/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 5.3453\n","Epoch 896/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 5.3276\n","Epoch 897/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 158ms/step - loss: 5.3100\n","Epoch 898/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 5.2924\n","Epoch 899/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 5.2748\n","Epoch 900/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 5.2573\n","Epoch 901/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 5.2399\n","Epoch 902/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 5.2225\n","Epoch 903/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 5.2052\n","Epoch 904/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 5.1879\n","Epoch 905/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 5.1706\n","Epoch 906/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 5.1534\n","Epoch 907/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 5.1363\n","Epoch 908/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 5.1192\n","Epoch 909/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 5.1021\n","Epoch 910/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 5.0851\n","Epoch 911/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 5.0682\n","Epoch 912/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 5.0513\n","Epoch 913/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 5.0344\n","Epoch 914/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 5.0176\n","Epoch 915/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 5.0008\n","Epoch 916/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 4.9841\n","Epoch 917/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 4.9675\n","Epoch 918/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 4.9508\n","Epoch 919/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 4.9343\n","Epoch 920/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 4.9177\n","Epoch 921/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 4.9013\n","Epoch 922/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 4.8848\n","Epoch 923/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 4.8685\n","Epoch 924/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 4.8521\n","Epoch 925/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 4.8358\n","Epoch 926/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 4.8196\n","Epoch 927/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 4.8034\n","Epoch 928/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 4.7872\n","Epoch 929/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 4.7711\n","Epoch 930/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 4.7551\n","Epoch 931/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 4.7391\n","Epoch 932/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 4.7231\n","Epoch 933/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 4.7072\n","Epoch 934/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 4.6913\n","Epoch 935/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 4.6755\n","Epoch 936/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 4.6597\n","Epoch 937/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 4.6440\n","Epoch 938/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 4.6283\n","Epoch 939/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 4.6126\n","Epoch 940/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 4.5970\n","Epoch 941/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 4.5815\n","Epoch 942/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 4.5660\n","Epoch 943/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 4.5505\n","Epoch 944/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 4.5351\n","Epoch 945/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 4.5197\n","Epoch 946/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 4.5044\n","Epoch 947/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 4.4891\n","Epoch 948/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 4.4738\n","Epoch 949/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 4.4586\n","Epoch 950/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 4.4435\n","Epoch 951/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 4.4283\n","Epoch 952/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 4.4133\n","Epoch 953/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 4.3982\n","Epoch 954/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 4.3833\n","Epoch 955/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 4.3683\n","Epoch 956/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 4.3534\n","Epoch 957/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 4.3386\n","Epoch 958/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 4.3238\n","Epoch 959/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 4.3090\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: 4.2943\n","Epoch 961/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 4.2796\n","Epoch 962/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 4.2650\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: 4.2504\n","Epoch 964/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 4.2358\n","Epoch 965/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 4.2213\n","Epoch 966/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 4.2068\n","Epoch 967/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 4.1924\n","Epoch 968/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 4.1780\n","Epoch 969/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 4.1637\n","Epoch 970/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 4.1494\n","Epoch 971/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 4.1351\n","Epoch 972/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 4.1209\n","Epoch 973/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 4.1068\n","Epoch 974/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 4.0926\n","Epoch 975/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 4.0785\n","Epoch 976/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 4.0645\n","Epoch 977/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 4.0505\n","Epoch 978/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 4.0365\n","Epoch 979/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 4.0226\n","Epoch 980/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 4.0087\n","Epoch 981/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 3.9949\n","Epoch 982/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 3.9810\n","Epoch 983/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 3.9673\n","Epoch 984/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 3.9536\n","Epoch 985/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 3.9399\n","Epoch 986/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 3.9262\n","Epoch 987/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 3.9126\n","Epoch 988/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 3.8991\n","Epoch 989/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 3.8856\n","Epoch 990/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 3.8721\n","Epoch 991/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 3.8586\n","Epoch 992/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 3.8452\n","Epoch 993/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 3.8319\n","Epoch 994/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 3.8185\n","Epoch 995/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 3.8053\n","Epoch 996/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 3.7920\n","Epoch 997/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 3.7788\n","Epoch 998/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 3.7657\n","Epoch 999/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 3.7525\n","Epoch 1000/1000\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 3.7394\n","Epoch 1000/1000\n"," - loss: 3.7394\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 3.7394\n","Restoring model weights from the end of the best epoch: 1000.\n","\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/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"]}],"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', 1000, True, patience)"]},{"cell_type":"code","source":["print(IREth1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6wKbRN4eUoMu","executionInfo":{"status":"ok","timestamp":1762438941782,"user_tz":-180,"elapsed":53,"user":{"displayName":"Alena Konovalova","userId":"13964397918094172748"}},"outputId":"899c9004-60fd-42f3-bee4-f0f88d8e48cf"},"execution_count":9,"outputs":[{"output_type":"stream","name":"stdout","text":["3.07\n"]}]},{"cell_type":"code","execution_count":10,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":744},"executionInfo":{"elapsed":1049,"status":"ok","timestamp":1762438945310,"user":{"displayName":"Alena Konovalova","userId":"13964397918094172748"},"user_tz":-180},"id":"nDaBMi1oZxBU","outputId":"7e71ceb3-8a3b-4f47-d478-d002d92a733f"},"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1600x800 with 1 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\n"},"metadata":{}}],"source":["# Построение графика ошибки реконструкции\n","lib.ire_plot('training', IRE1, IREth1, 'AE1')"]},{"cell_type":"code","execution_count":25,"metadata":{"id":"mOTbWkr4Zv3I","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1762445951835,"user_tz":-180,"elapsed":857107,"user":{"displayName":"Alena Konovalova","userId":"13964397918094172748"}},"outputId":"54e3aeed-c190-464c-fd3b-b93819334f67"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1;30;43mВыходные данные были обрезаны до нескольких последних строк (5000).\u001b[0m\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 36.4190\n","Epoch 206/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 36.3074\n","Epoch 207/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 36.1960\n","Epoch 208/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 36.0849\n","Epoch 209/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 35.9741\n","Epoch 210/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 35.8637\n","Epoch 211/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 35.7535\n","Epoch 212/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 35.6436\n","Epoch 213/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 35.5340\n","Epoch 214/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 35.4247\n","Epoch 215/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 35.3158\n","Epoch 216/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 35.2072\n","Epoch 217/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 35.0989\n","Epoch 218/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 34.9909\n","Epoch 219/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 34.8833\n","Epoch 220/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 34.7760\n","Epoch 221/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 34.6691\n","Epoch 222/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 34.5625\n","Epoch 223/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 34.4562\n","Epoch 224/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 34.3503\n","Epoch 225/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 34.2448\n","Epoch 226/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 34.1396\n","Epoch 227/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 34.0348\n","Epoch 228/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 52ms/step - loss: 33.9304\n","Epoch 229/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 33.8263\n","Epoch 230/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 33.7226\n","Epoch 231/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 33.6193\n","Epoch 232/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 33.5163\n","Epoch 233/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 33.4137\n","Epoch 234/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 33.3115\n","Epoch 235/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 33.2097\n","Epoch 236/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 33.1083\n","Epoch 237/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 33.0072\n","Epoch 238/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 32.9065\n","Epoch 239/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 32.8063\n","Epoch 240/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 32.7064\n","Epoch 241/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 32.6069\n","Epoch 242/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 32.5077\n","Epoch 243/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 32.4090\n","Epoch 244/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 32.3107\n","Epoch 245/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 32.2127\n","Epoch 246/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 32.1152\n","Epoch 247/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 32.0180\n","Epoch 248/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 31.9212\n","Epoch 249/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 31.8249\n","Epoch 250/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 31.7289\n","Epoch 251/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 31.6333\n","Epoch 252/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 31.5381\n","Epoch 253/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 31.4433\n","Epoch 254/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 31.3488\n","Epoch 255/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 31.2548\n","Epoch 256/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 31.1612\n","Epoch 257/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 31.0679\n","Epoch 258/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 30.9751\n","Epoch 259/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 30.8826\n","Epoch 260/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 30.7905\n","Epoch 261/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 30.6988\n","Epoch 262/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 30.6075\n","Epoch 263/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 30.5166\n","Epoch 264/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 30.4261\n","Epoch 265/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 30.3359\n","Epoch 266/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - loss: 30.2462\n","Epoch 267/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 30.1568\n","Epoch 268/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 30.0678\n","Epoch 269/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 29.9792\n","Epoch 270/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 29.8909\n","Epoch 271/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 29.8030\n","Epoch 272/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 29.7155\n","Epoch 273/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 29.6284\n","Epoch 274/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 29.5417\n","Epoch 275/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 29.4553\n","Epoch 276/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 29.3693\n","Epoch 277/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 29.2836\n","Epoch 278/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 29.1983\n","Epoch 279/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 29.1134\n","Epoch 280/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 29.0288\n","Epoch 281/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 28.9447\n","Epoch 282/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 28.8608\n","Epoch 283/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 28.7773\n","Epoch 284/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 28.6942\n","Epoch 285/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 28.6114\n","Epoch 286/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 28.5290\n","Epoch 287/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 28.4469\n","Epoch 288/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 28.3652\n","Epoch 289/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 28.2838\n","Epoch 290/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 28.2028\n","Epoch 291/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 28.1221\n","Epoch 292/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 28.0418\n","Epoch 293/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 27.9618\n","Epoch 294/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 27.8821\n","Epoch 295/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 27.8027\n","Epoch 296/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 27.7237\n","Epoch 297/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 27.6450\n","Epoch 298/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 27.5667\n","Epoch 299/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 27.4887\n","Epoch 300/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 27.4110\n","Epoch 301/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 27.3336\n","Epoch 302/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 27.2565\n","Epoch 303/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 27.1798\n","Epoch 304/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 27.1034\n","Epoch 305/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 27.0273\n","Epoch 306/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 26.9515\n","Epoch 307/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 26.8760\n","Epoch 308/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 26.8008\n","Epoch 309/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 26.7259\n","Epoch 310/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 26.6514\n","Epoch 311/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 26.5771\n","Epoch 312/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 26.5032\n","Epoch 313/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 26.4295\n","Epoch 314/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 26.3561\n","Epoch 315/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 26.2831\n","Epoch 316/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 26.2103\n","Epoch 317/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 26.1378\n","Epoch 318/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 26.0656\n","Epoch 319/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 25.9937\n","Epoch 320/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 25.9221\n","Epoch 321/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 25.8507\n","Epoch 322/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 25.7797\n","Epoch 323/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 25.7089\n","Epoch 324/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 25.6384\n","Epoch 325/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 25.5682\n","Epoch 326/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 25.4982\n","Epoch 327/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 25.4286\n","Epoch 328/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 25.3591\n","Epoch 329/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 25.2900\n","Epoch 330/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 25.2211\n","Epoch 331/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 25.1525\n","Epoch 332/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 25.0842\n","Epoch 333/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 25.0161\n","Epoch 334/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - loss: 24.9483\n","Epoch 335/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 24.8807\n","Epoch 336/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 24.8134\n","Epoch 337/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 24.7464\n","Epoch 338/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 24.6796\n","Epoch 339/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 24.6130\n","Epoch 340/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 24.5467\n","Epoch 341/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 24.4807\n","Epoch 342/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 24.4149\n","Epoch 343/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 24.3493\n","Epoch 344/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 24.2840\n","Epoch 345/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 24.2190\n","Epoch 346/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 24.1541\n","Epoch 347/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 24.0895\n","Epoch 348/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 24.0252\n","Epoch 349/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 23.9611\n","Epoch 350/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 23.8972\n","Epoch 351/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 23.8335\n","Epoch 352/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 23.7701\n","Epoch 353/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 23.7069\n","Epoch 354/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 23.6439\n","Epoch 355/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 23.5812\n","Epoch 356/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 23.5187\n","Epoch 357/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 23.4564\n","Epoch 358/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 23.3943\n","Epoch 359/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 23.3324\n","Epoch 360/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 23.2708\n","Epoch 361/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 23.2094\n","Epoch 362/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 23.1482\n","Epoch 363/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 23.0872\n","Epoch 364/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step - loss: 23.0264\n","Epoch 365/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 22.9659\n","Epoch 366/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 22.9055\n","Epoch 367/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 22.8454\n","Epoch 368/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 22.7854\n","Epoch 369/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 22.7257\n","Epoch 370/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 22.6662\n","Epoch 371/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 22.6068\n","Epoch 372/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 22.5477\n","Epoch 373/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - loss: 22.4888\n","Epoch 374/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 22.4301\n","Epoch 375/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 22.3716\n","Epoch 376/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 22.3132\n","Epoch 377/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 22.2551\n","Epoch 378/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 22.1972\n","Epoch 379/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 22.1394\n","Epoch 380/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 22.0819\n","Epoch 381/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 22.0245\n","Epoch 382/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 21.9674\n","Epoch 383/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 21.9104\n","Epoch 384/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 21.8536\n","Epoch 385/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 21.7970\n","Epoch 386/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 21.7406\n","Epoch 387/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 21.6844\n","Epoch 388/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 21.6283\n","Epoch 389/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 21.5725\n","Epoch 390/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 21.5168\n","Epoch 391/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 21.4613\n","Epoch 392/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 21.4059\n","Epoch 393/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 21.3508\n","Epoch 394/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 21.2958\n","Epoch 395/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 21.2410\n","Epoch 396/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 21.1864\n","Epoch 397/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 21.1320\n","Epoch 398/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 21.0777\n","Epoch 399/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 21.0236\n","Epoch 400/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 20.9696\n","Epoch 401/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 20.9159\n","Epoch 402/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 20.8623\n","Epoch 403/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 20.8089\n","Epoch 404/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 20.7556\n","Epoch 405/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 20.7025\n","Epoch 406/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 20.6496\n","Epoch 407/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 20.5968\n","Epoch 408/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 20.5442\n","Epoch 409/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 20.4918\n","Epoch 410/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 20.4395\n","Epoch 411/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 20.3874\n","Epoch 412/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 187ms/step - loss: 20.3354\n","Epoch 413/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 20.2836\n","Epoch 414/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 20.2320\n","Epoch 415/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 20.1805\n","Epoch 416/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 20.1292\n","Epoch 417/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 20.0780\n","Epoch 418/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 20.0270\n","Epoch 419/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 19.9761\n","Epoch 420/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 19.9254\n","Epoch 421/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 193ms/step - loss: 19.8748\n","Epoch 422/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 19.8244\n","Epoch 423/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 19.7741\n","Epoch 424/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 19.7240\n","Epoch 425/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 19.6741\n","Epoch 426/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 19.6242\n","Epoch 427/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 19.5746\n","Epoch 428/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 19.5250\n","Epoch 429/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 19.4757\n","Epoch 430/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 19.4264\n","Epoch 431/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 19.3773\n","Epoch 432/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 19.3284\n","Epoch 433/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 19.2796\n","Epoch 434/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 19.2309\n","Epoch 435/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 19.1824\n","Epoch 436/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 19.1340\n","Epoch 437/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 19.0858\n","Epoch 438/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 19.0377\n","Epoch 439/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 18.9897\n","Epoch 440/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 18.9419\n","Epoch 441/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 18.8942\n","Epoch 442/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 18.8466\n","Epoch 443/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 18.7992\n","Epoch 444/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 18.7519\n","Epoch 445/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 18.7048\n","Epoch 446/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 18.6577\n","Epoch 447/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 18.6109\n","Epoch 448/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 18.5641\n","Epoch 449/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 18.5175\n","Epoch 450/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 18.4710\n","Epoch 451/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 18.4246\n","Epoch 452/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 18.3784\n","Epoch 453/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 18.3323\n","Epoch 454/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 18.2863\n","Epoch 455/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 18.2405\n","Epoch 456/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 18.1947\n","Epoch 457/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 18.1492\n","Epoch 458/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 18.1037\n","Epoch 459/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 18.0583\n","Epoch 460/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 18.0131\n","Epoch 461/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 17.9680\n","Epoch 462/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 17.9231\n","Epoch 463/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 17.8782\n","Epoch 464/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 17.8335\n","Epoch 465/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 17.7889\n","Epoch 466/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 17.7444\n","Epoch 467/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 17.7000\n","Epoch 468/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 17.6558\n","Epoch 469/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 17.6117\n","Epoch 470/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 17.5677\n","Epoch 471/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 17.5238\n","Epoch 472/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 17.4800\n","Epoch 473/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 17.4364\n","Epoch 474/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 17.3928\n","Epoch 475/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 17.3494\n","Epoch 476/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 57ms/step - loss: 17.3061\n","Epoch 477/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 17.2629\n","Epoch 478/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 17.2199\n","Epoch 479/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 17.1769\n","Epoch 480/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 17.1341\n","Epoch 481/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 17.0914\n","Epoch 482/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 17.0487\n","Epoch 483/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 17.0062\n","Epoch 484/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 16.9639\n","Epoch 485/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 16.9216\n","Epoch 486/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 16.8794\n","Epoch 487/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 16.8374\n","Epoch 488/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 16.7954\n","Epoch 489/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 16.7536\n","Epoch 490/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 16.7119\n","Epoch 491/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 16.6702\n","Epoch 492/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 16.6287\n","Epoch 493/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 16.5873\n","Epoch 494/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 16.5460\n","Epoch 495/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 16.5048\n","Epoch 496/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 16.4638\n","Epoch 497/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 16.4228\n","Epoch 498/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 16.3819\n","Epoch 499/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 16.3412\n","Epoch 500/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 16.3005\n","Epoch 501/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 16.2600\n","Epoch 502/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 16.2195\n","Epoch 503/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 59ms/step - loss: 16.1792\n","Epoch 504/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 16.1389\n","Epoch 505/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 16.0988\n","Epoch 506/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 16.0587\n","Epoch 507/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 16.0188\n","Epoch 508/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 15.9790\n","Epoch 509/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 15.9392\n","Epoch 510/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 15.8996\n","Epoch 511/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 15.8601\n","Epoch 512/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 15.8206\n","Epoch 513/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 15.7813\n","Epoch 514/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 15.7421\n","Epoch 515/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 15.7029\n","Epoch 516/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 15.6639\n","Epoch 517/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 15.6250\n","Epoch 518/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 15.5861\n","Epoch 519/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 15.5474\n","Epoch 520/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 15.5088\n","Epoch 521/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 15.4702\n","Epoch 522/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 15.4318\n","Epoch 523/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 15.3934\n","Epoch 524/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 15.3551\n","Epoch 525/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 15.3170\n","Epoch 526/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 15.2789\n","Epoch 527/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 15.2409\n","Epoch 528/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 15.2030\n","Epoch 529/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 15.1653\n","Epoch 530/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 15.1276\n","Epoch 531/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 15.0900\n","Epoch 532/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 15.0525\n","Epoch 533/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 15.0150\n","Epoch 534/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 14.9777\n","Epoch 535/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 14.9405\n","Epoch 536/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 14.9034\n","Epoch 537/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 14.8663\n","Epoch 538/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 14.8293\n","Epoch 539/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 14.7925\n","Epoch 540/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 14.7557\n","Epoch 541/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 14.7190\n","Epoch 542/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 14.6824\n","Epoch 543/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 14.6459\n","Epoch 544/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 14.6095\n","Epoch 545/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 14.5732\n","Epoch 546/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 14.5369\n","Epoch 547/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 14.5008\n","Epoch 548/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 14.4647\n","Epoch 549/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 14.4287\n","Epoch 550/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 14.3928\n","Epoch 551/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - loss: 14.3570\n","Epoch 552/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 14.3213\n","Epoch 553/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 14.2857\n","Epoch 554/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 14.2501\n","Epoch 555/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 14.2147\n","Epoch 556/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 14.1793\n","Epoch 557/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 14.1440\n","Epoch 558/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 14.1088\n","Epoch 559/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 14.0737\n","Epoch 560/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 164ms/step - loss: 14.0387\n","Epoch 561/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 14.0037\n","Epoch 562/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 13.9689\n","Epoch 563/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 13.9341\n","Epoch 564/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 13.8994\n","Epoch 565/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 13.8648\n","Epoch 566/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 13.8302\n","Epoch 567/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 13.7958\n","Epoch 568/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 13.7614\n","Epoch 569/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 13.7271\n","Epoch 570/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 13.6929\n","Epoch 571/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 13.6588\n","Epoch 572/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 13.6247\n","Epoch 573/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 13.5908\n","Epoch 574/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 13.5569\n","Epoch 575/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 13.5231\n","Epoch 576/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 13.4894\n","Epoch 577/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 13.4557\n","Epoch 578/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 13.4222\n","Epoch 579/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 13.3887\n","Epoch 580/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 13.3553\n","Epoch 581/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 157ms/step - loss: 13.3220\n","Epoch 582/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 13.2887\n","Epoch 583/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 13.2556\n","Epoch 584/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 13.2225\n","Epoch 585/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 13.1895\n","Epoch 586/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 13.1565\n","Epoch 587/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 13.1237\n","Epoch 588/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 13.0909\n","Epoch 589/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 13.0582\n","Epoch 590/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 13.0256\n","Epoch 591/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 12.9930\n","Epoch 592/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 12.9606\n","Epoch 593/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 12.9282\n","Epoch 594/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 12.8958\n","Epoch 595/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 12.8636\n","Epoch 596/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 12.8314\n","Epoch 597/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 12.7993\n","Epoch 598/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 12.7673\n","Epoch 599/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 12.7354\n","Epoch 600/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 12.7035\n","Epoch 601/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 12.6717\n","Epoch 602/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 12.6400\n","Epoch 603/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 12.6083\n","Epoch 604/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 12.5768\n","Epoch 605/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 12.5453\n","Epoch 606/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 12.5138\n","Epoch 607/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 12.4825\n","Epoch 608/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 163ms/step - loss: 12.4512\n","Epoch 609/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 12.4200\n","Epoch 610/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 12.3889\n","Epoch 611/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 12.3578\n","Epoch 612/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 12.3268\n","Epoch 613/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 12.2959\n","Epoch 614/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 12.2650\n","Epoch 615/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 12.2343\n","Epoch 616/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 12.2035\n","Epoch 617/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 12.1729\n","Epoch 618/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 12.1423\n","Epoch 619/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 12.1119\n","Epoch 620/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 12.0814\n","Epoch 621/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 12.0511\n","Epoch 622/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 12.0208\n","Epoch 623/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 11.9906\n","Epoch 624/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 11.9604\n","Epoch 625/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 11.9304\n","Epoch 626/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 11.9004\n","Epoch 627/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 11.8704\n","Epoch 628/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 11.8406\n","Epoch 629/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 11.8108\n","Epoch 630/2700\n","\u001b[1m1/1\u001b[0m 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loss: 11.6041\n","Epoch 637/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 11.5748\n","Epoch 638/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 11.5456\n","Epoch 639/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 11.5165\n","Epoch 640/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 11.4874\n","Epoch 641/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - loss: 11.4585\n","Epoch 642/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 11.4295\n","Epoch 643/2700\n","\u001b[1m1/1\u001b[0m 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loss: 11.2289\n","Epoch 650/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 11.2005\n","Epoch 651/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 11.1722\n","Epoch 652/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 11.1439\n","Epoch 653/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 11.1157\n","Epoch 654/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 11.0876\n","Epoch 655/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 11.0595\n","Epoch 656/2700\n","\u001b[1m1/1\u001b[0m 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loss: 10.8647\n","Epoch 663/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 10.8372\n","Epoch 664/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 10.8097\n","Epoch 665/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 10.7822\n","Epoch 666/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 10.7548\n","Epoch 667/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 10.7275\n","Epoch 668/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 10.7002\n","Epoch 669/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 10.6731\n","Epoch 670/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 10.6459\n","Epoch 671/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 10.6188\n","Epoch 672/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 10.5918\n","Epoch 673/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 10.5649\n","Epoch 674/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 10.5380\n","Epoch 675/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 10.5112\n","Epoch 676/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 10.4844\n","Epoch 677/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 10.4577\n","Epoch 678/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 10.4310\n","Epoch 679/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 10.4045\n","Epoch 680/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 10.3779\n","Epoch 681/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 10.3515\n","Epoch 682/2700\n","\u001b[1m1/1\u001b[0m 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loss: 10.1679\n","Epoch 689/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 10.1419\n","Epoch 690/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 10.1159\n","Epoch 691/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 10.0901\n","Epoch 692/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 10.0643\n","Epoch 693/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 10.0385\n","Epoch 694/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 10.0128\n","Epoch 695/2700\n","\u001b[1m1/1\u001b[0m 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9.8345\n","Epoch 702/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 9.8093\n","Epoch 703/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 9.7841\n","Epoch 704/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 9.7590\n","Epoch 705/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 9.7339\n","Epoch 706/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 9.7089\n","Epoch 707/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 9.6839\n","Epoch 708/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 9.6590\n","Epoch 709/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 9.6342\n","Epoch 710/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 9.6094\n","Epoch 711/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 9.5847\n","Epoch 712/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 9.5600\n","Epoch 713/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 9.5354\n","Epoch 714/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 9.5108\n","Epoch 715/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 9.4863\n","Epoch 716/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 9.4619\n","Epoch 717/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 9.4375\n","Epoch 718/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 9.4131\n","Epoch 719/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 9.3888\n","Epoch 720/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 9.3646\n","Epoch 721/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 9.3404\n","Epoch 722/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 9.3163\n","Epoch 723/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 9.2922\n","Epoch 724/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 9.2682\n","Epoch 725/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 9.2442\n","Epoch 726/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 9.2203\n","Epoch 727/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 9.1965\n","Epoch 728/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 9.1727\n","Epoch 729/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 9.1489\n","Epoch 730/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 9.1252\n","Epoch 731/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 158ms/step - loss: 9.1016\n","Epoch 732/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 9.0780\n","Epoch 733/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 9.0545\n","Epoch 734/2700\n","\u001b[1m1/1\u001b[0m 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loss: 8.8912\n","Epoch 741/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 8.8681\n","Epoch 742/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 8.8450\n","Epoch 743/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 164ms/step - loss: 8.8220\n","Epoch 744/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 8.7991\n","Epoch 745/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 8.7762\n","Epoch 746/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - loss: 8.7533\n","Epoch 747/2700\n","\u001b[1m1/1\u001b[0m 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loss: 8.5948\n","Epoch 754/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 8.5723\n","Epoch 755/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 8.5499\n","Epoch 756/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 8.5276\n","Epoch 757/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 8.5053\n","Epoch 758/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 8.4831\n","Epoch 759/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 8.4609\n","Epoch 760/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 61ms/step - loss: 8.4387\n","Epoch 761/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 8.4166\n","Epoch 762/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 8.3946\n","Epoch 763/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 8.3726\n","Epoch 764/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 8.3507\n","Epoch 765/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 8.3288\n","Epoch 766/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 8.3069\n","Epoch 767/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 8.2851\n","Epoch 768/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 8.2634\n","Epoch 769/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 8.2417\n","Epoch 770/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 8.2200\n","Epoch 771/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 8.1984\n","Epoch 772/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 8.1769\n","Epoch 773/2700\n","\u001b[1m1/1\u001b[0m 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8.0274\n","Epoch 780/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 8.0062\n","Epoch 781/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 7.9851\n","Epoch 782/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 7.9640\n","Epoch 783/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 7.9430\n","Epoch 784/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 7.9221\n","Epoch 785/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 7.9011\n","Epoch 786/2700\n","\u001b[1m1/1\u001b[0m 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7.7560\n","Epoch 793/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 7.7354\n","Epoch 794/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 7.7149\n","Epoch 795/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 7.6945\n","Epoch 796/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 7.6740\n","Epoch 797/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 7.6537\n","Epoch 798/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 7.6334\n","Epoch 799/2700\n","\u001b[1m1/1\u001b[0m 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7.4924\n","Epoch 806/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 7.4725\n","Epoch 807/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 7.4526\n","Epoch 808/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 7.4327\n","Epoch 809/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 7.4129\n","Epoch 810/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 7.3931\n","Epoch 811/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 7.3734\n","Epoch 812/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 7.3537\n","Epoch 813/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 7.3341\n","Epoch 814/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 7.3145\n","Epoch 815/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 7.2949\n","Epoch 816/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 7.2754\n","Epoch 817/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 7.2560\n","Epoch 818/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 7.2365\n","Epoch 819/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 7.2172\n","Epoch 820/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 7.1979\n","Epoch 821/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 7.1786\n","Epoch 822/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 7.1593\n","Epoch 823/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 7.1401\n","Epoch 824/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 7.1210\n","Epoch 825/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 7.1019\n","Epoch 826/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 7.0828\n","Epoch 827/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 7.0638\n","Epoch 828/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 7.0448\n","Epoch 829/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 7.0259\n","Epoch 830/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 7.0070\n","Epoch 831/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 6.9881\n","Epoch 832/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 6.9693\n","Epoch 833/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 6.9506\n","Epoch 834/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 6.9319\n","Epoch 835/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 6.9132\n","Epoch 836/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 6.8945\n","Epoch 837/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 6.8760\n","Epoch 838/2700\n","\u001b[1m1/1\u001b[0m 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6.7470\n","Epoch 845/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 6.7287\n","Epoch 846/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 6.7105\n","Epoch 847/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 6.6924\n","Epoch 848/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - loss: 6.6742\n","Epoch 849/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 6.6562\n","Epoch 850/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 6.6381\n","Epoch 851/2700\n","\u001b[1m1/1\u001b[0m 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loss: 6.5129\n","Epoch 858/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 6.4952\n","Epoch 859/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 6.4776\n","Epoch 860/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 6.4599\n","Epoch 861/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 6.4423\n","Epoch 862/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 6.4248\n","Epoch 863/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 6.4073\n","Epoch 864/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 6.3898\n","Epoch 865/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 6.3723\n","Epoch 866/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 6.3550\n","Epoch 867/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 6.3376\n","Epoch 868/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 6.3203\n","Epoch 869/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 6.3030\n","Epoch 870/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 6.2858\n","Epoch 871/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 6.2686\n","Epoch 872/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 6.2514\n","Epoch 873/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 6.2343\n","Epoch 874/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 6.2172\n","Epoch 875/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 6.2002\n","Epoch 876/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 6.1832\n","Epoch 877/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 6.1663\n","Epoch 878/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 6.1493\n","Epoch 879/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 6.1325\n","Epoch 880/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 6.1156\n","Epoch 881/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 6.0988\n","Epoch 882/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 6.0821\n","Epoch 883/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 6.0653\n","Epoch 884/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 6.0487\n","Epoch 885/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 6.0320\n","Epoch 886/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 6.0154\n","Epoch 887/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 5.9988\n","Epoch 888/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 5.9823\n","Epoch 889/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step - loss: 5.9658\n","Epoch 890/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 5.9494\n","Epoch 891/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 5.9329\n","Epoch 892/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 5.9166\n","Epoch 893/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 5.9002\n","Epoch 894/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 5.8839\n","Epoch 895/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 5.8677\n","Epoch 896/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 5.8514\n","Epoch 897/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 5.8352\n","Epoch 898/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 5.8191\n","Epoch 899/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 5.8030\n","Epoch 900/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 5.7869\n","Epoch 901/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 5.7709\n","Epoch 902/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 5.7549\n","Epoch 903/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 5.7389\n","Epoch 904/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 5.7230\n","Epoch 905/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 5.7071\n","Epoch 906/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 159ms/step - loss: 5.6912\n","Epoch 907/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 5.6754\n","Epoch 908/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 5.6597\n","Epoch 909/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 5.6439\n","Epoch 910/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 5.6282\n","Epoch 911/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 5.6125\n","Epoch 912/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 5.5969\n","Epoch 913/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 5.5813\n","Epoch 914/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 5.5658\n","Epoch 915/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 5.5502\n","Epoch 916/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 5.5348\n","Epoch 917/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 5.5193\n","Epoch 918/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 5.5039\n","Epoch 919/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 5.4885\n","Epoch 920/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 5.4732\n","Epoch 921/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 5.4579\n","Epoch 922/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 5.4426\n","Epoch 923/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 5.4274\n","Epoch 924/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 5.4122\n","Epoch 925/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 5.3970\n","Epoch 926/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 5.3819\n","Epoch 927/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 5.3668\n","Epoch 928/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 5.3517\n","Epoch 929/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 5.3367\n","Epoch 930/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 5.3217\n","Epoch 931/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 5.3068\n","Epoch 932/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 5.2919\n","Epoch 933/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 5.2770\n","Epoch 934/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 5.2622\n","Epoch 935/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 5.2474\n","Epoch 936/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 5.2326\n","Epoch 937/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 5.2178\n","Epoch 938/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 5.2031\n","Epoch 939/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 5.1885\n","Epoch 940/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 5.1738\n","Epoch 941/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 5.1592\n","Epoch 942/2700\n","\u001b[1m1/1\u001b[0m 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5.0580\n","Epoch 949/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 5.0437\n","Epoch 950/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 5.0294\n","Epoch 951/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 5.0151\n","Epoch 952/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 5.0009\n","Epoch 953/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 4.9867\n","Epoch 954/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 4.9726\n","Epoch 955/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 4.9585\n","Epoch 956/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 4.9444\n","Epoch 957/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 4.9303\n","Epoch 958/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 4.9163\n","Epoch 959/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 4.9023\n","Epoch 960/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 4.8883\n","Epoch 961/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 4.8744\n","Epoch 962/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 4.8605\n","Epoch 963/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 4.8467\n","Epoch 964/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 4.8329\n","Epoch 965/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 4.8191\n","Epoch 966/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 4.8053\n","Epoch 967/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 4.7916\n","Epoch 968/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 4.7779\n","Epoch 969/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 4.7642\n","Epoch 970/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 4.7506\n","Epoch 971/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step - loss: 4.7370\n","Epoch 972/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 4.7235\n","Epoch 973/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 4.7099\n","Epoch 974/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 4.6964\n","Epoch 975/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 4.6830\n","Epoch 976/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 4.6696\n","Epoch 977/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 4.6562\n","Epoch 978/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 4.6428\n","Epoch 979/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 4.6295\n","Epoch 980/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 4.6162\n","Epoch 981/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 4.6029\n","Epoch 982/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 4.5896\n","Epoch 983/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 4.5764\n","Epoch 984/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 4.5633\n","Epoch 985/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 4.5501\n","Epoch 986/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 4.5370\n","Epoch 987/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 4.5239\n","Epoch 988/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 4.5109\n","Epoch 989/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 4.4979\n","Epoch 990/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 4.4849\n","Epoch 991/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 4.4719\n","Epoch 992/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 4.4590\n","Epoch 993/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 4.4461\n","Epoch 994/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 4.4333\n","Epoch 995/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 4.4204\n","Epoch 996/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 4.4076\n","Epoch 997/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 4.3949\n","Epoch 998/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 155ms/step - loss: 4.3821\n","Epoch 999/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 4.3694\n","Epoch 1000/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - loss: 4.3568\n","Epoch 1000/2700\n"," - loss: 4.3568\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 4.3568\n","Epoch 1001/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 4.3441\n","Epoch 1002/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 4.3315\n","Epoch 1003/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 4.3189\n","Epoch 1004/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 4.3064\n","Epoch 1005/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 4.2938\n","Epoch 1006/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 4.2814\n","Epoch 1007/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 4.2689\n","Epoch 1008/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 4.2565\n","Epoch 1009/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 4.2441\n","Epoch 1010/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 4.2317\n","Epoch 1011/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 4.2194\n","Epoch 1012/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 4.2070\n","Epoch 1013/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 4.1948\n","Epoch 1014/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 4.1825\n","Epoch 1015/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 4.1703\n","Epoch 1016/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 156ms/step - loss: 4.1581\n","Epoch 1017/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 4.1460\n","Epoch 1018/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 4.1338\n","Epoch 1019/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 156ms/step - loss: 4.1217\n","Epoch 1020/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 4.1097\n","Epoch 1021/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 4.0976\n","Epoch 1022/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 4.0856\n","Epoch 1023/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 4.0736\n","Epoch 1024/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 4.0617\n","Epoch 1025/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 4.0497\n","Epoch 1026/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 4.0378\n","Epoch 1027/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 4.0260\n","Epoch 1028/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 4.0141\n","Epoch 1029/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 4.0023\n","Epoch 1030/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 3.9906\n","Epoch 1031/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 3.9788\n","Epoch 1032/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 3.9671\n","Epoch 1033/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 3.9554\n","Epoch 1034/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step - loss: 3.9437\n","Epoch 1035/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 3.9321\n","Epoch 1036/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 3.9205\n","Epoch 1037/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 3.9089\n","Epoch 1038/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 3.8974\n","Epoch 1039/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 3.8859\n","Epoch 1040/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 3.8744\n","Epoch 1041/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 3.8629\n","Epoch 1042/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 3.8515\n","Epoch 1043/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 3.8401\n","Epoch 1044/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 3.8287\n","Epoch 1045/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 3.8173\n","Epoch 1046/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 3.8060\n","Epoch 1047/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 3.7947\n","Epoch 1048/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 3.7835\n","Epoch 1049/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 3.7722\n","Epoch 1050/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 3.7610\n","Epoch 1051/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 3.7498\n","Epoch 1052/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 3.7387\n","Epoch 1053/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 3.7276\n","Epoch 1054/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 3.7165\n","Epoch 1055/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 3.7054\n","Epoch 1056/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 157ms/step - loss: 3.6943\n","Epoch 1057/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 3.6833\n","Epoch 1058/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 3.6723\n","Epoch 1059/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 3.6614\n","Epoch 1060/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 3.6504\n","Epoch 1061/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 3.6395\n","Epoch 1062/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 3.6287\n","Epoch 1063/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 3.6178\n","Epoch 1064/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 3.6070\n","Epoch 1065/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 3.5962\n","Epoch 1066/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 3.5854\n","Epoch 1067/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 3.5747\n","Epoch 1068/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 3.5640\n","Epoch 1069/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 3.5533\n","Epoch 1070/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 3.5426\n","Epoch 1071/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 3.5320\n","Epoch 1072/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 3.5214\n","Epoch 1073/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 3.5108\n","Epoch 1074/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 3.5002\n","Epoch 1075/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 3.4897\n","Epoch 1076/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 3.4792\n","Epoch 1077/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 3.4687\n","Epoch 1078/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 3.4583\n","Epoch 1079/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 3.4478\n","Epoch 1080/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 3.4374\n","Epoch 1081/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 3.4271\n","Epoch 1082/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 3.4167\n","Epoch 1083/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 3.4064\n","Epoch 1084/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 3.3961\n","Epoch 1085/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 3.3859\n","Epoch 1086/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 3.3756\n","Epoch 1087/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 3.3654\n","Epoch 1088/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 3.3552\n","Epoch 1089/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 3.3450\n","Epoch 1090/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 3.3349\n","Epoch 1091/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 3.3248\n","Epoch 1092/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 3.3147\n","Epoch 1093/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 3.3046\n","Epoch 1094/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 3.2946\n","Epoch 1095/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 3.2846\n","Epoch 1096/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 3.2746\n","Epoch 1097/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 3.2647\n","Epoch 1098/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 3.2547\n","Epoch 1099/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 3.2448\n","Epoch 1100/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 3.2349\n","Epoch 1101/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 3.2251\n","Epoch 1102/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 3.2152\n","Epoch 1103/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 155ms/step - loss: 3.2054\n","Epoch 1104/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 3.1957\n","Epoch 1105/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 3.1859\n","Epoch 1106/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 3.1762\n","Epoch 1107/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 3.1665\n","Epoch 1108/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 3.1568\n","Epoch 1109/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 3.1471\n","Epoch 1110/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 3.1375\n","Epoch 1111/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 3.1279\n","Epoch 1112/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 3.1183\n","Epoch 1113/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 3.1087\n","Epoch 1114/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 3.0992\n","Epoch 1115/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 3.0897\n","Epoch 1116/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 3.0802\n","Epoch 1117/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 3.0707\n","Epoch 1118/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 3.0613\n","Epoch 1119/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 3.0519\n","Epoch 1120/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 3.0425\n","Epoch 1121/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 3.0331\n","Epoch 1122/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 3.0238\n","Epoch 1123/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 3.0145\n","Epoch 1124/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 3.0052\n","Epoch 1125/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 2.9959\n","Epoch 1126/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 2.9867\n","Epoch 1127/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 2.9774\n","Epoch 1128/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 2.9682\n","Epoch 1129/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 2.9591\n","Epoch 1130/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 2.9499\n","Epoch 1131/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 2.9408\n","Epoch 1132/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 2.9317\n","Epoch 1133/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 2.9226\n","Epoch 1134/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 2.9136\n","Epoch 1135/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 2.9045\n","Epoch 1136/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 2.8955\n","Epoch 1137/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 2.8865\n","Epoch 1138/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 2.8776\n","Epoch 1139/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 2.8686\n","Epoch 1140/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 2.8597\n","Epoch 1141/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 2.8508\n","Epoch 1142/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 2.8420\n","Epoch 1143/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 2.8331\n","Epoch 1144/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 2.8243\n","Epoch 1145/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 2.8155\n","Epoch 1146/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 2.8067\n","Epoch 1147/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 2.7980\n","Epoch 1148/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 160ms/step - loss: 2.7892\n","Epoch 1149/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 2.7805\n","Epoch 1150/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 2.7718\n","Epoch 1151/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 2.7632\n","Epoch 1152/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 2.7545\n","Epoch 1153/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 157ms/step - loss: 2.7459\n","Epoch 1154/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 2.7373\n","Epoch 1155/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 2.7288\n","Epoch 1156/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 156ms/step - loss: 2.7202\n","Epoch 1157/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 2.7117\n","Epoch 1158/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 2.7032\n","Epoch 1159/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 2.6947\n","Epoch 1160/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 2.6862\n","Epoch 1161/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 2.6778\n","Epoch 1162/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 2.6694\n","Epoch 1163/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 164ms/step - loss: 2.6610\n","Epoch 1164/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 2.6526\n","Epoch 1165/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 2.6443\n","Epoch 1166/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 2.6359\n","Epoch 1167/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 2.6276\n","Epoch 1168/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 2.6194\n","Epoch 1169/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 2.6111\n","Epoch 1170/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 2.6029\n","Epoch 1171/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 2.5946\n","Epoch 1172/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 2.5864\n","Epoch 1173/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 2.5783\n","Epoch 1174/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 2.5701\n","Epoch 1175/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 2.5620\n","Epoch 1176/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 2.5539\n","Epoch 1177/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 2.5458\n","Epoch 1178/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 2.5377\n","Epoch 1179/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 301ms/step - loss: 2.5297\n","Epoch 1180/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 260ms/step - loss: 2.5217\n","Epoch 1181/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 2.5137\n","Epoch 1182/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 2.5057\n","Epoch 1183/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 2.4977\n","Epoch 1184/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 2.4898\n","Epoch 1185/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 2.4819\n","Epoch 1186/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 2.4740\n","Epoch 1187/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 2.4661\n","Epoch 1188/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 2.4582\n","Epoch 1189/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 2.4504\n","Epoch 1190/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 2.4426\n","Epoch 1191/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 2.4348\n","Epoch 1192/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 2.4270\n","Epoch 1193/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 2.4193\n","Epoch 1194/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 2.4116\n","Epoch 1195/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 2.4039\n","Epoch 1196/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 2.3962\n","Epoch 1197/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 2.3885\n","Epoch 1198/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 2.3809\n","Epoch 1199/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 2.3732\n","Epoch 1200/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 2.3656\n","Epoch 1201/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 2.3580\n","Epoch 1202/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 2.3505\n","Epoch 1203/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 2.3429\n","Epoch 1204/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 2.3354\n","Epoch 1205/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 2.3279\n","Epoch 1206/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 2.3204\n","Epoch 1207/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 2.3130\n","Epoch 1208/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 2.3055\n","Epoch 1209/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 2.2981\n","Epoch 1210/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 2.2907\n","Epoch 1211/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 2.2833\n","Epoch 1212/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 2.2760\n","Epoch 1213/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 2.2686\n","Epoch 1214/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 2.2613\n","Epoch 1215/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 2.2540\n","Epoch 1216/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 2.2467\n","Epoch 1217/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 2.2395\n","Epoch 1218/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 2.2322\n","Epoch 1219/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 2.2250\n","Epoch 1220/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 2.2178\n","Epoch 1221/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 2.2106\n","Epoch 1222/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 2.2034\n","Epoch 1223/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 2.1963\n","Epoch 1224/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 2.1892\n","Epoch 1225/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 2.1821\n","Epoch 1226/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 2.1750\n","Epoch 1227/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 2.1679\n","Epoch 1228/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 2.1609\n","Epoch 1229/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 2.1538\n","Epoch 1230/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 2.1468\n","Epoch 1231/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 2.1398\n","Epoch 1232/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 2.1329\n","Epoch 1233/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 2.1259\n","Epoch 1234/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 2.1190\n","Epoch 1235/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 2.1121\n","Epoch 1236/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 2.1052\n","Epoch 1237/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 2.0983\n","Epoch 1238/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 2.0914\n","Epoch 1239/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 2.0846\n","Epoch 1240/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 2.0778\n","Epoch 1241/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 2.0710\n","Epoch 1242/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 2.0642\n","Epoch 1243/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 2.0575\n","Epoch 1244/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 2.0507\n","Epoch 1245/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 2.0440\n","Epoch 1246/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 2.0373\n","Epoch 1247/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 2.0306\n","Epoch 1248/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 2.0239\n","Epoch 1249/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 2.0173\n","Epoch 1250/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - loss: 2.0106\n","Epoch 1251/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 2.0040\n","Epoch 1252/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 1.9974\n","Epoch 1253/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 1.9908\n","Epoch 1254/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 1.9843\n","Epoch 1255/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 1.9777\n","Epoch 1256/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 1.9712\n","Epoch 1257/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 1.9647\n","Epoch 1258/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 1.9582\n","Epoch 1259/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 1.9518\n","Epoch 1260/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 1.9453\n","Epoch 1261/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 1.9389\n","Epoch 1262/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 1.9325\n","Epoch 1263/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 1.9261\n","Epoch 1264/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 1.9197\n","Epoch 1265/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 1.9133\n","Epoch 1266/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 1.9070\n","Epoch 1267/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 1.9007\n","Epoch 1268/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 1.8944\n","Epoch 1269/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 1.8881\n","Epoch 1270/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 1.8818\n","Epoch 1271/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 1.8755\n","Epoch 1272/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 1.8693\n","Epoch 1273/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 1.8631\n","Epoch 1274/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 1.8569\n","Epoch 1275/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 1.8507\n","Epoch 1276/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 1.8445\n","Epoch 1277/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 1.8384\n","Epoch 1278/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 1.8322\n","Epoch 1279/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 1.8261\n","Epoch 1280/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 167ms/step - loss: 1.8200\n","Epoch 1281/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 1.8140\n","Epoch 1282/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 1.8079\n","Epoch 1283/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - loss: 1.8018\n","Epoch 1284/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 1.7958\n","Epoch 1285/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 159ms/step - loss: 1.7898\n","Epoch 1286/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 1.7838\n","Epoch 1287/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 1.7778\n","Epoch 1288/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 1.7719\n","Epoch 1289/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 1.7659\n","Epoch 1290/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 1.7600\n","Epoch 1291/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 1.7541\n","Epoch 1292/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 1.7482\n","Epoch 1293/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 1.7423\n","Epoch 1294/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 1.7365\n","Epoch 1295/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 1.7306\n","Epoch 1296/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 1.7248\n","Epoch 1297/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 1.7190\n","Epoch 1298/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 160ms/step - loss: 1.7132\n","Epoch 1299/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 1.7074\n","Epoch 1300/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 1.7016\n","Epoch 1301/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 1.6959\n","Epoch 1302/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 1.6902\n","Epoch 1303/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 1.6845\n","Epoch 1304/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 167ms/step - loss: 1.6788\n","Epoch 1305/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 1.6731\n","Epoch 1306/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 163ms/step - loss: 1.6674\n","Epoch 1307/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 1.6618\n","Epoch 1308/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 1.6561\n","Epoch 1309/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 1.6505\n","Epoch 1310/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 1.6449\n","Epoch 1311/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 1.6393\n","Epoch 1312/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 1.6338\n","Epoch 1313/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 1.6282\n","Epoch 1314/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 1.6227\n","Epoch 1315/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 1.6172\n","Epoch 1316/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 1.6117\n","Epoch 1317/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 1.6062\n","Epoch 1318/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 1.6007\n","Epoch 1319/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 1.5953\n","Epoch 1320/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 1.5898\n","Epoch 1321/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 1.5844\n","Epoch 1322/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 1.5790\n","Epoch 1323/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 1.5736\n","Epoch 1324/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 1.5682\n","Epoch 1325/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 1.5629\n","Epoch 1326/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 1.5575\n","Epoch 1327/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 1.5522\n","Epoch 1328/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 1.5469\n","Epoch 1329/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 1.5416\n","Epoch 1330/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 1.5363\n","Epoch 1331/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 1.5310\n","Epoch 1332/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 1.5258\n","Epoch 1333/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 1.5205\n","Epoch 1334/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 1.5153\n","Epoch 1335/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 1.5101\n","Epoch 1336/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 1.5049\n","Epoch 1337/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 1.4997\n","Epoch 1338/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 1.4946\n","Epoch 1339/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 1.4894\n","Epoch 1340/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 1.4843\n","Epoch 1341/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 1.4792\n","Epoch 1342/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 1.4741\n","Epoch 1343/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 1.4690\n","Epoch 1344/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 1.4639\n","Epoch 1345/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 1.4589\n","Epoch 1346/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 1.4538\n","Epoch 1347/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 1.4488\n","Epoch 1348/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 1.4438\n","Epoch 1349/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 1.4388\n","Epoch 1350/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 1.4338\n","Epoch 1351/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 1.4288\n","Epoch 1352/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 1.4239\n","Epoch 1353/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 1.4189\n","Epoch 1354/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 1.4140\n","Epoch 1355/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 1.4091\n","Epoch 1356/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 1.4042\n","Epoch 1357/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 1.3993\n","Epoch 1358/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 1.3945\n","Epoch 1359/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 1.3896\n","Epoch 1360/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 1.3848\n","Epoch 1361/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 1.3800\n","Epoch 1362/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 1.3751\n","Epoch 1363/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 1.3704\n","Epoch 1364/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 1.3656\n","Epoch 1365/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 1.3608\n","Epoch 1366/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 1.3561\n","Epoch 1367/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 1.3513\n","Epoch 1368/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 1.3466\n","Epoch 1369/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 1.3419\n","Epoch 1370/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 1.3372\n","Epoch 1371/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 1.3325\n","Epoch 1372/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 1.3278\n","Epoch 1373/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 1.3232\n","Epoch 1374/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 1.3185\n","Epoch 1375/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 1.3139\n","Epoch 1376/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 1.3093\n","Epoch 1377/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 1.3047\n","Epoch 1378/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 1.3001\n","Epoch 1379/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 1.2956\n","Epoch 1380/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 1.2910\n","Epoch 1381/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 1.2865\n","Epoch 1382/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 1.2819\n","Epoch 1383/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 1.2774\n","Epoch 1384/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 1.2729\n","Epoch 1385/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 1.2684\n","Epoch 1386/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 1.2639\n","Epoch 1387/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 1.2595\n","Epoch 1388/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 1.2550\n","Epoch 1389/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 1.2506\n","Epoch 1390/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 1.2462\n","Epoch 1391/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 1.2418\n","Epoch 1392/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 1.2374\n","Epoch 1393/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 1.2330\n","Epoch 1394/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 1.2286\n","Epoch 1395/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 1.2243\n","Epoch 1396/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 1.2199\n","Epoch 1397/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 1.2156\n","Epoch 1398/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 1.2113\n","Epoch 1399/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 1.2070\n","Epoch 1400/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 1.2027\n","Epoch 1401/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 1.1984\n","Epoch 1402/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 1.1941\n","Epoch 1403/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 1.1899\n","Epoch 1404/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 1.1857\n","Epoch 1405/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 1.1814\n","Epoch 1406/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 1.1772\n","Epoch 1407/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 1.1730\n","Epoch 1408/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 1.1688\n","Epoch 1409/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 1.1647\n","Epoch 1410/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 1.1605\n","Epoch 1411/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 1.1563\n","Epoch 1412/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 1.1522\n","Epoch 1413/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 1.1481\n","Epoch 1414/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 1.1440\n","Epoch 1415/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 1.1399\n","Epoch 1416/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 1.1358\n","Epoch 1417/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 1.1317\n","Epoch 1418/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 1.1277\n","Epoch 1419/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 1.1236\n","Epoch 1420/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 1.1196\n","Epoch 1421/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 1.1156\n","Epoch 1422/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 1.1115\n","Epoch 1423/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 1.1075\n","Epoch 1424/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 1.1036\n","Epoch 1425/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 1.0996\n","Epoch 1426/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 1.0956\n","Epoch 1427/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 1.0917\n","Epoch 1428/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 1.0877\n","Epoch 1429/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 1.0838\n","Epoch 1430/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 1.0799\n","Epoch 1431/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 1.0760\n","Epoch 1432/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 1.0721\n","Epoch 1433/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 1.0682\n","Epoch 1434/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 1.0644\n","Epoch 1435/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 1.0605\n","Epoch 1436/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 1.0567\n","Epoch 1437/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 1.0529\n","Epoch 1438/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 1.0490\n","Epoch 1439/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 1.0452\n","Epoch 1440/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 1.0414\n","Epoch 1441/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 156ms/step - loss: 1.0377\n","Epoch 1442/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 1.0339\n","Epoch 1443/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 1.0301\n","Epoch 1444/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 1.0264\n","Epoch 1445/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 1.0227\n","Epoch 1446/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 1.0189\n","Epoch 1447/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 1.0152\n","Epoch 1448/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 1.0115\n","Epoch 1449/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 1.0078\n","Epoch 1450/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 1.0042\n","Epoch 1451/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 1.0005\n","Epoch 1452/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.9968\n","Epoch 1453/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.9932\n","Epoch 1454/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.9896\n","Epoch 1455/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.9860\n","Epoch 1456/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.9823\n","Epoch 1457/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.9787\n","Epoch 1458/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.9752\n","Epoch 1459/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.9716\n","Epoch 1460/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.9680\n","Epoch 1461/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.9645\n","Epoch 1462/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.9609\n","Epoch 1463/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.9574\n","Epoch 1464/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.9539\n","Epoch 1465/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.9504\n","Epoch 1466/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.9469\n","Epoch 1467/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.9434\n","Epoch 1468/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.9399\n","Epoch 1469/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.9365\n","Epoch 1470/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.9330\n","Epoch 1471/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.9296\n","Epoch 1472/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.9262\n","Epoch 1473/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.9227\n","Epoch 1474/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.9193\n","Epoch 1475/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.9159\n","Epoch 1476/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.9125\n","Epoch 1477/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.9092\n","Epoch 1478/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.9058\n","Epoch 1479/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - loss: 0.9025\n","Epoch 1480/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.8991\n","Epoch 1481/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.8958\n","Epoch 1482/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.8925\n","Epoch 1483/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.8891\n","Epoch 1484/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.8858\n","Epoch 1485/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.8826\n","Epoch 1486/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.8793\n","Epoch 1487/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.8760\n","Epoch 1488/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.8728\n","Epoch 1489/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.8695\n","Epoch 1490/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.8663\n","Epoch 1491/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.8630\n","Epoch 1492/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.8598\n","Epoch 1493/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.8566\n","Epoch 1494/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.8534\n","Epoch 1495/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.8502\n","Epoch 1496/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.8471\n","Epoch 1497/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.8439\n","Epoch 1498/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.8407\n","Epoch 1499/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.8376\n","Epoch 1500/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.8345\n","Epoch 1501/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.8313\n","Epoch 1502/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.8282\n","Epoch 1503/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.8251\n","Epoch 1504/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.8220\n","Epoch 1505/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.8189\n","Epoch 1506/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.8159\n","Epoch 1507/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.8128\n","Epoch 1508/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.8098\n","Epoch 1509/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.8067\n","Epoch 1510/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.8037\n","Epoch 1511/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.8007\n","Epoch 1512/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.7976\n","Epoch 1513/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.7946\n","Epoch 1514/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.7916\n","Epoch 1515/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.7887\n","Epoch 1516/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.7857\n","Epoch 1517/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.7827\n","Epoch 1518/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.7798\n","Epoch 1519/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.7768\n","Epoch 1520/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.7739\n","Epoch 1521/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - loss: 0.7710\n","Epoch 1522/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.7680\n","Epoch 1523/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.7651\n","Epoch 1524/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.7622\n","Epoch 1525/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.7593\n","Epoch 1526/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.7565\n","Epoch 1527/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.7536\n","Epoch 1528/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.7507\n","Epoch 1529/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.7479\n","Epoch 1530/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.7450\n","Epoch 1531/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.7422\n","Epoch 1532/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.7394\n","Epoch 1533/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.7366\n","Epoch 1534/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.7338\n","Epoch 1535/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.7310\n","Epoch 1536/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.7282\n","Epoch 1537/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.7254\n","Epoch 1538/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.7226\n","Epoch 1539/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.7199\n","Epoch 1540/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.7171\n","Epoch 1541/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.7144\n","Epoch 1542/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.7117\n","Epoch 1543/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.7089\n","Epoch 1544/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.7062\n","Epoch 1545/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.7035\n","Epoch 1546/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.7008\n","Epoch 1547/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.6982\n","Epoch 1548/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.6955\n","Epoch 1549/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 178ms/step - loss: 0.6928\n","Epoch 1550/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.6902\n","Epoch 1551/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.6875\n","Epoch 1552/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step - loss: 0.6849\n","Epoch 1553/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 0.6822\n","Epoch 1554/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.6796\n","Epoch 1555/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.6770\n","Epoch 1556/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.6744\n","Epoch 1557/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.6718\n","Epoch 1558/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.6692\n","Epoch 1559/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.6666\n","Epoch 1560/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.6641\n","Epoch 1561/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.6615\n","Epoch 1562/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.6589\n","Epoch 1563/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.6564\n","Epoch 1564/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.6539\n","Epoch 1565/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.6513\n","Epoch 1566/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - loss: 0.6488\n","Epoch 1567/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.6463\n","Epoch 1568/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.6438\n","Epoch 1569/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.6413\n","Epoch 1570/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.6388\n","Epoch 1571/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.6363\n","Epoch 1572/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.6339\n","Epoch 1573/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.6314\n","Epoch 1574/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 288ms/step - loss: 0.6290\n","Epoch 1575/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.6265\n","Epoch 1576/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.6241\n","Epoch 1577/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.6216\n","Epoch 1578/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.6192\n","Epoch 1579/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.6168\n","Epoch 1580/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 0.6144\n","Epoch 1581/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.6120\n","Epoch 1582/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 291ms/step - loss: 0.6096\n","Epoch 1583/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.6073\n","Epoch 1584/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 0.6049\n","Epoch 1585/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.6025\n","Epoch 1586/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.6002\n","Epoch 1587/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.5978\n","Epoch 1588/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.5955\n","Epoch 1589/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.5932\n","Epoch 1590/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.5908\n","Epoch 1591/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.5885\n","Epoch 1592/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.5862\n","Epoch 1593/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.5839\n","Epoch 1594/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.5816\n","Epoch 1595/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.5794\n","Epoch 1596/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.5771\n","Epoch 1597/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.5748\n","Epoch 1598/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.5726\n","Epoch 1599/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.5703\n","Epoch 1600/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.5681\n","Epoch 1601/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.5658\n","Epoch 1602/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 0.5636\n","Epoch 1603/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.5614\n","Epoch 1604/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.5592\n","Epoch 1605/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.5570\n","Epoch 1606/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.5548\n","Epoch 1607/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.5526\n","Epoch 1608/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.5504\n","Epoch 1609/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.5482\n","Epoch 1610/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.5461\n","Epoch 1611/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.5439\n","Epoch 1612/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.5417\n","Epoch 1613/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.5396\n","Epoch 1614/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.5375\n","Epoch 1615/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.5353\n","Epoch 1616/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.5332\n","Epoch 1617/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.5311\n","Epoch 1618/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.5290\n","Epoch 1619/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.5269\n","Epoch 1620/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.5248\n","Epoch 1621/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.5227\n","Epoch 1622/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.5206\n","Epoch 1623/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.5186\n","Epoch 1624/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.5165\n","Epoch 1625/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.5144\n","Epoch 1626/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.5124\n","Epoch 1627/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.5104\n","Epoch 1628/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.5083\n","Epoch 1629/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.5063\n","Epoch 1630/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.5043\n","Epoch 1631/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.5023\n","Epoch 1632/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.5003\n","Epoch 1633/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.4983\n","Epoch 1634/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.4963\n","Epoch 1635/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.4943\n","Epoch 1636/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.4923\n","Epoch 1637/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.4903\n","Epoch 1638/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.4884\n","Epoch 1639/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.4864\n","Epoch 1640/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.4845\n","Epoch 1641/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.4825\n","Epoch 1642/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.4806\n","Epoch 1643/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.4786\n","Epoch 1644/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.4767\n","Epoch 1645/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.4748\n","Epoch 1646/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.4729\n","Epoch 1647/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.4710\n","Epoch 1648/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.4691\n","Epoch 1649/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.4672\n","Epoch 1650/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.4653\n","Epoch 1651/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.4635\n","Epoch 1652/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.4616\n","Epoch 1653/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.4597\n","Epoch 1654/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.4579\n","Epoch 1655/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.4560\n","Epoch 1656/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.4542\n","Epoch 1657/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.4523\n","Epoch 1658/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.4505\n","Epoch 1659/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.4487\n","Epoch 1660/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.4469\n","Epoch 1661/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.4451\n","Epoch 1662/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.4433\n","Epoch 1663/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.4415\n","Epoch 1664/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.4397\n","Epoch 1665/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.4379\n","Epoch 1666/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.4361\n","Epoch 1667/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.4343\n","Epoch 1668/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.4326\n","Epoch 1669/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.4308\n","Epoch 1670/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.4291\n","Epoch 1671/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.4273\n","Epoch 1672/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.4256\n","Epoch 1673/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.4239\n","Epoch 1674/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.4221\n","Epoch 1675/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.4204\n","Epoch 1676/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.4187\n","Epoch 1677/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.4170\n","Epoch 1678/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.4153\n","Epoch 1679/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - loss: 0.4136\n","Epoch 1680/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.4119\n","Epoch 1681/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.4102\n","Epoch 1682/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.4085\n","Epoch 1683/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.4069\n","Epoch 1684/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 0.4052\n","Epoch 1685/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.4035\n","Epoch 1686/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 188ms/step - loss: 0.4019\n","Epoch 1687/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.4002\n","Epoch 1688/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.3986\n","Epoch 1689/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.3970\n","Epoch 1690/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.3953\n","Epoch 1691/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.3937\n","Epoch 1692/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.3921\n","Epoch 1693/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 172ms/step - loss: 0.3905\n","Epoch 1694/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.3889\n","Epoch 1695/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.3873\n","Epoch 1696/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 0.3857\n","Epoch 1697/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.3841\n","Epoch 1698/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.3825\n","Epoch 1699/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.3809\n","Epoch 1700/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.3794\n","Epoch 1701/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.3778\n","Epoch 1702/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 109ms/step - loss: 0.3763\n","Epoch 1703/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 180ms/step - loss: 0.3747\n","Epoch 1704/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.3732\n","Epoch 1705/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.3716\n","Epoch 1706/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.3701\n","Epoch 1707/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 167ms/step - loss: 0.3686\n","Epoch 1708/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.3670\n","Epoch 1709/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.3655\n","Epoch 1710/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.3640\n","Epoch 1711/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.3625\n","Epoch 1712/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 175ms/step - loss: 0.3610\n","Epoch 1713/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 282ms/step - loss: 0.3595\n","Epoch 1714/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.3580\n","Epoch 1715/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.3565\n","Epoch 1716/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.3550\n","Epoch 1717/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.3536\n","Epoch 1718/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.3521\n","Epoch 1719/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.3506\n","Epoch 1720/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.3492\n","Epoch 1721/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.3477\n","Epoch 1722/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.3463\n","Epoch 1723/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.3448\n","Epoch 1724/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.3434\n","Epoch 1725/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.3420\n","Epoch 1726/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.3405\n","Epoch 1727/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.3391\n","Epoch 1728/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.3377\n","Epoch 1729/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.3363\n","Epoch 1730/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.3349\n","Epoch 1731/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.3335\n","Epoch 1732/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.3321\n","Epoch 1733/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.3307\n","Epoch 1734/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.3293\n","Epoch 1735/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.3279\n","Epoch 1736/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.3266\n","Epoch 1737/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.3252\n","Epoch 1738/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.3238\n","Epoch 1739/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.3225\n","Epoch 1740/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.3211\n","Epoch 1741/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 0.3198\n","Epoch 1742/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.3184\n","Epoch 1743/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.3171\n","Epoch 1744/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.3158\n","Epoch 1745/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.3144\n","Epoch 1746/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.3131\n","Epoch 1747/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.3118\n","Epoch 1748/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.3105\n","Epoch 1749/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.3092\n","Epoch 1750/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.3079\n","Epoch 1751/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.3066\n","Epoch 1752/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.3053\n","Epoch 1753/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.3040\n","Epoch 1754/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.3027\n","Epoch 1755/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.3015\n","Epoch 1756/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.3002\n","Epoch 1757/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.2989\n","Epoch 1758/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.2976\n","Epoch 1759/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.2964\n","Epoch 1760/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.2951\n","Epoch 1761/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.2939\n","Epoch 1762/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.2926\n","Epoch 1763/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.2914\n","Epoch 1764/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.2902\n","Epoch 1765/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.2889\n","Epoch 1766/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.2877\n","Epoch 1767/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.2865\n","Epoch 1768/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.2853\n","Epoch 1769/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.2841\n","Epoch 1770/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.2829\n","Epoch 1771/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.2817\n","Epoch 1772/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.2805\n","Epoch 1773/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.2793\n","Epoch 1774/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.2781\n","Epoch 1775/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.2769\n","Epoch 1776/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.2757\n","Epoch 1777/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.2746\n","Epoch 1778/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.2734\n","Epoch 1779/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.2722\n","Epoch 1780/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.2711\n","Epoch 1781/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.2699\n","Epoch 1782/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.2688\n","Epoch 1783/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.2676\n","Epoch 1784/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.2665\n","Epoch 1785/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.2653\n","Epoch 1786/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.2642\n","Epoch 1787/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.2631\n","Epoch 1788/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.2619\n","Epoch 1789/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.2608\n","Epoch 1790/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.2597\n","Epoch 1791/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.2586\n","Epoch 1792/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.2575\n","Epoch 1793/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.2564\n","Epoch 1794/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.2553\n","Epoch 1795/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.2542\n","Epoch 1796/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.2531\n","Epoch 1797/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.2520\n","Epoch 1798/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.2509\n","Epoch 1799/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.2499\n","Epoch 1800/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.2488\n","Epoch 1801/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.2477\n","Epoch 1802/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.2466\n","Epoch 1803/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.2456\n","Epoch 1804/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - loss: 0.2445\n","Epoch 1805/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.2435\n","Epoch 1806/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.2424\n","Epoch 1807/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.2414\n","Epoch 1808/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.2403\n","Epoch 1809/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.2393\n","Epoch 1810/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.2383\n","Epoch 1811/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.2372\n","Epoch 1812/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.2362\n","Epoch 1813/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.2352\n","Epoch 1814/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.2342\n","Epoch 1815/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 157ms/step - loss: 0.2332\n","Epoch 1816/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.2322\n","Epoch 1817/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.2312\n","Epoch 1818/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.2302\n","Epoch 1819/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.2292\n","Epoch 1820/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.2282\n","Epoch 1821/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.2272\n","Epoch 1822/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.2262\n","Epoch 1823/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.2252\n","Epoch 1824/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.2243\n","Epoch 1825/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.2233\n","Epoch 1826/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 159ms/step - loss: 0.2223\n","Epoch 1827/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 0.2213\n","Epoch 1828/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.2204\n","Epoch 1829/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 158ms/step - loss: 0.2194\n","Epoch 1830/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.2185\n","Epoch 1831/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.2175\n","Epoch 1832/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.2166\n","Epoch 1833/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.2156\n","Epoch 1834/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.2147\n","Epoch 1835/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.2138\n","Epoch 1836/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.2128\n","Epoch 1837/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.2119\n","Epoch 1838/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.2110\n","Epoch 1839/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.2101\n","Epoch 1840/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.2092\n","Epoch 1841/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.2082\n","Epoch 1842/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.2073\n","Epoch 1843/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.2064\n","Epoch 1844/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.2055\n","Epoch 1845/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.2046\n","Epoch 1846/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 178ms/step - loss: 0.2037\n","Epoch 1847/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.2029\n","Epoch 1848/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.2020\n","Epoch 1849/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 275ms/step - loss: 0.2011\n","Epoch 1850/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.2002\n","Epoch 1851/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.1993\n","Epoch 1852/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.1985\n","Epoch 1853/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.1976\n","Epoch 1854/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.1967\n","Epoch 1855/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.1959\n","Epoch 1856/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.1950\n","Epoch 1857/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.1942\n","Epoch 1858/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.1933\n","Epoch 1859/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.1925\n","Epoch 1860/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.1916\n","Epoch 1861/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.1908\n","Epoch 1862/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.1899\n","Epoch 1863/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.1891\n","Epoch 1864/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.1883\n","Epoch 1865/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.1874\n","Epoch 1866/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.1866\n","Epoch 1867/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.1858\n","Epoch 1868/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.1850\n","Epoch 1869/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.1842\n","Epoch 1870/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.1834\n","Epoch 1871/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.1825\n","Epoch 1872/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.1817\n","Epoch 1873/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.1809\n","Epoch 1874/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.1801\n","Epoch 1875/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.1794\n","Epoch 1876/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.1786\n","Epoch 1877/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.1778\n","Epoch 1878/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.1770\n","Epoch 1879/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.1762\n","Epoch 1880/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.1754\n","Epoch 1881/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.1747\n","Epoch 1882/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 0.1739\n","Epoch 1883/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.1731\n","Epoch 1884/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.1724\n","Epoch 1885/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.1716\n","Epoch 1886/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.1708\n","Epoch 1887/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.1701\n","Epoch 1888/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.1693\n","Epoch 1889/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.1686\n","Epoch 1890/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.1678\n","Epoch 1891/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.1671\n","Epoch 1892/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.1664\n","Epoch 1893/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.1656\n","Epoch 1894/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.1649\n","Epoch 1895/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.1642\n","Epoch 1896/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.1634\n","Epoch 1897/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.1627\n","Epoch 1898/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.1620\n","Epoch 1899/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.1613\n","Epoch 1900/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.1605\n","Epoch 1901/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.1598\n","Epoch 1902/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.1591\n","Epoch 1903/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.1584\n","Epoch 1904/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.1577\n","Epoch 1905/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.1570\n","Epoch 1906/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.1563\n","Epoch 1907/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.1556\n","Epoch 1908/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.1549\n","Epoch 1909/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.1542\n","Epoch 1910/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.1536\n","Epoch 1911/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.1529\n","Epoch 1912/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.1522\n","Epoch 1913/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.1515\n","Epoch 1914/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.1508\n","Epoch 1915/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.1502\n","Epoch 1916/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.1495\n","Epoch 1917/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.1488\n","Epoch 1918/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.1482\n","Epoch 1919/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.1475\n","Epoch 1920/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.1469\n","Epoch 1921/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.1462\n","Epoch 1922/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.1455\n","Epoch 1923/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.1449\n","Epoch 1924/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.1443\n","Epoch 1925/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.1436\n","Epoch 1926/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.1430\n","Epoch 1927/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.1423\n","Epoch 1928/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.1417\n","Epoch 1929/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.1411\n","Epoch 1930/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.1404\n","Epoch 1931/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.1398\n","Epoch 1932/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.1392\n","Epoch 1933/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.1386\n","Epoch 1934/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.1379\n","Epoch 1935/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.1373\n","Epoch 1936/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.1367\n","Epoch 1937/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.1361\n","Epoch 1938/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.1355\n","Epoch 1939/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.1349\n","Epoch 1940/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.1343\n","Epoch 1941/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.1337\n","Epoch 1942/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.1331\n","Epoch 1943/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.1325\n","Epoch 1944/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.1319\n","Epoch 1945/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.1313\n","Epoch 1946/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.1307\n","Epoch 1947/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 161ms/step - loss: 0.1301\n","Epoch 1948/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.1295\n","Epoch 1949/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.1290\n","Epoch 1950/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 0.1284\n","Epoch 1951/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.1278\n","Epoch 1952/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.1272\n","Epoch 1953/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 0.1267\n","Epoch 1954/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.1261\n","Epoch 1955/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.1255\n","Epoch 1956/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.1250\n","Epoch 1957/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.1244\n","Epoch 1958/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 0.1238\n","Epoch 1959/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.1233\n","Epoch 1960/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.1227\n","Epoch 1961/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.1222\n","Epoch 1962/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.1216\n","Epoch 1963/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.1211\n","Epoch 1964/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.1205\n","Epoch 1965/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.1200\n","Epoch 1966/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.1195\n","Epoch 1967/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.1189\n","Epoch 1968/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.1184\n","Epoch 1969/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.1179\n","Epoch 1970/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 122ms/step - loss: 0.1173\n","Epoch 1971/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.1168\n","Epoch 1972/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.1163\n","Epoch 1973/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.1158\n","Epoch 1974/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.1152\n","Epoch 1975/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.1147\n","Epoch 1976/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.1142\n","Epoch 1977/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.1137\n","Epoch 1978/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.1132\n","Epoch 1979/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.1127\n","Epoch 1980/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 0.1122\n","Epoch 1981/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.1116\n","Epoch 1982/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.1111\n","Epoch 1983/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.1106\n","Epoch 1984/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.1101\n","Epoch 1985/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - loss: 0.1096\n","Epoch 1986/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 165ms/step - loss: 0.1092\n","Epoch 1987/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 275ms/step - loss: 0.1087\n","Epoch 1988/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.1082\n","Epoch 1989/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 160ms/step - loss: 0.1077\n","Epoch 1990/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.1072\n","Epoch 1991/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.1067\n","Epoch 1992/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.1062\n","Epoch 1993/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.1058\n","Epoch 1994/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.1053\n","Epoch 1995/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.1048\n","Epoch 1996/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.1043\n","Epoch 1997/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.1039\n","Epoch 1998/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.1034\n","Epoch 1999/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.1029\n","Epoch 2000/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 42ms/step - loss: 0.1025\n","Epoch 2000/2700\n"," - loss: 0.1025\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.1025\n","Epoch 2001/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.1020\n","Epoch 2002/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.1015\n","Epoch 2003/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.1011\n","Epoch 2004/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.1006\n","Epoch 2005/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.1002\n","Epoch 2006/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0997\n","Epoch 2007/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0993\n","Epoch 2008/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 162ms/step - loss: 0.0988\n","Epoch 2009/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0984\n","Epoch 2010/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 0.0979\n","Epoch 2011/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0975\n","Epoch 2012/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0971\n","Epoch 2013/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0966\n","Epoch 2014/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0962\n","Epoch 2015/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0957\n","Epoch 2016/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0953\n","Epoch 2017/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0949\n","Epoch 2018/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0945\n","Epoch 2019/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0940\n","Epoch 2020/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0936\n","Epoch 2021/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0932\n","Epoch 2022/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0928\n","Epoch 2023/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0923\n","Epoch 2024/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0919\n","Epoch 2025/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0915\n","Epoch 2026/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0911\n","Epoch 2027/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0907\n","Epoch 2028/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0903\n","Epoch 2029/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0899\n","Epoch 2030/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0895\n","Epoch 2031/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0891\n","Epoch 2032/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0887\n","Epoch 2033/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0883\n","Epoch 2034/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0879\n","Epoch 2035/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0875\n","Epoch 2036/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0871\n","Epoch 2037/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0867\n","Epoch 2038/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0863\n","Epoch 2039/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0859\n","Epoch 2040/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0855\n","Epoch 2041/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0851\n","Epoch 2042/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0847\n","Epoch 2043/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0844\n","Epoch 2044/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0840\n","Epoch 2045/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0836\n","Epoch 2046/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0832\n","Epoch 2047/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0828\n","Epoch 2048/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0825\n","Epoch 2049/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0821\n","Epoch 2050/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0817\n","Epoch 2051/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0814\n","Epoch 2052/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0810\n","Epoch 2053/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0806\n","Epoch 2054/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0803\n","Epoch 2055/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0799\n","Epoch 2056/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0795\n","Epoch 2057/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0792\n","Epoch 2058/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0788\n","Epoch 2059/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0785\n","Epoch 2060/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 159ms/step - loss: 0.0781\n","Epoch 2061/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0778\n","Epoch 2062/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0774\n","Epoch 2063/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0771\n","Epoch 2064/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0767\n","Epoch 2065/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0764\n","Epoch 2066/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0760\n","Epoch 2067/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0757\n","Epoch 2068/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 0.0753\n","Epoch 2069/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0750\n","Epoch 2070/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0747\n","Epoch 2071/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0743\n","Epoch 2072/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0740\n","Epoch 2073/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0737\n","Epoch 2074/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0733\n","Epoch 2075/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0730\n","Epoch 2076/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0727\n","Epoch 2077/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 0.0724\n","Epoch 2078/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 0.0720\n","Epoch 2079/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0717\n","Epoch 2080/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 0.0714\n","Epoch 2081/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0711\n","Epoch 2082/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0707\n","Epoch 2083/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0704\n","Epoch 2084/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0701\n","Epoch 2085/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0698\n","Epoch 2086/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 0.0695\n","Epoch 2087/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0692\n","Epoch 2088/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0689\n","Epoch 2089/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0686\n","Epoch 2090/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 0.0682\n","Epoch 2091/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0679\n","Epoch 2092/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0676\n","Epoch 2093/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0673\n","Epoch 2094/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0670\n","Epoch 2095/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0667\n","Epoch 2096/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0664\n","Epoch 2097/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 0.0661\n","Epoch 2098/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 157ms/step - loss: 0.0658\n","Epoch 2099/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 265ms/step - loss: 0.0655\n","Epoch 2100/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.0653\n","Epoch 2101/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 292ms/step - loss: 0.0650\n","Epoch 2102/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0647\n","Epoch 2103/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.0644\n","Epoch 2104/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 0.0641\n","Epoch 2105/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0638\n","Epoch 2106/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0635\n","Epoch 2107/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 160ms/step - loss: 0.0632\n","Epoch 2108/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 0.0630\n","Epoch 2109/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.0627\n","Epoch 2110/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0624\n","Epoch 2111/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0621\n","Epoch 2112/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.0618\n","Epoch 2113/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0616\n","Epoch 2114/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.0613\n","Epoch 2115/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 0.0610\n","Epoch 2116/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0607\n","Epoch 2117/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0605\n","Epoch 2118/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0602\n","Epoch 2119/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0599\n","Epoch 2120/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 180ms/step - loss: 0.0597\n","Epoch 2121/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0594\n","Epoch 2122/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0591\n","Epoch 2123/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0589\n","Epoch 2124/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0586\n","Epoch 2125/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0584\n","Epoch 2126/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0581\n","Epoch 2127/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0578\n","Epoch 2128/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0576\n","Epoch 2129/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0573\n","Epoch 2130/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0571\n","Epoch 2131/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0568\n","Epoch 2132/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0566\n","Epoch 2133/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0563\n","Epoch 2134/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0561\n","Epoch 2135/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0558\n","Epoch 2136/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0556\n","Epoch 2137/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.0553\n","Epoch 2138/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0551\n","Epoch 2139/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0549\n","Epoch 2140/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 83ms/step - loss: 0.0546\n","Epoch 2141/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0544\n","Epoch 2142/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0541\n","Epoch 2143/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0539\n","Epoch 2144/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0537\n","Epoch 2145/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0534\n","Epoch 2146/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 0.0532\n","Epoch 2147/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0530\n","Epoch 2148/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0527\n","Epoch 2149/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0525\n","Epoch 2150/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0523\n","Epoch 2151/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0520\n","Epoch 2152/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0518\n","Epoch 2153/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0516\n","Epoch 2154/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.0513\n","Epoch 2155/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0511\n","Epoch 2156/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0509\n","Epoch 2157/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0507\n","Epoch 2158/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0505\n","Epoch 2159/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 0.0502\n","Epoch 2160/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0500\n","Epoch 2161/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0498\n","Epoch 2162/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0496\n","Epoch 2163/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0494\n","Epoch 2164/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0492\n","Epoch 2165/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0489\n","Epoch 2166/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0487\n","Epoch 2167/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0485\n","Epoch 2168/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0483\n","Epoch 2169/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0481\n","Epoch 2170/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0479\n","Epoch 2171/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0477\n","Epoch 2172/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0475\n","Epoch 2173/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0473\n","Epoch 2174/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0471\n","Epoch 2175/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0469\n","Epoch 2176/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0467\n","Epoch 2177/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0465\n","Epoch 2178/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0463\n","Epoch 2179/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0461\n","Epoch 2180/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0459\n","Epoch 2181/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0457\n","Epoch 2182/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0455\n","Epoch 2183/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0453\n","Epoch 2184/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 159ms/step - loss: 0.0451\n","Epoch 2185/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0449\n","Epoch 2186/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - loss: 0.0447\n","Epoch 2187/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0445\n","Epoch 2188/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0443\n","Epoch 2189/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0441\n","Epoch 2190/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0439\n","Epoch 2191/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0437\n","Epoch 2192/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0435\n","Epoch 2193/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0434\n","Epoch 2194/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0432\n","Epoch 2195/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0430\n","Epoch 2196/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0428\n","Epoch 2197/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0426\n","Epoch 2198/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0424\n","Epoch 2199/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0423\n","Epoch 2200/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0421\n","Epoch 2201/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0419\n","Epoch 2202/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0417\n","Epoch 2203/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0416\n","Epoch 2204/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0414\n","Epoch 2205/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0412\n","Epoch 2206/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0410\n","Epoch 2207/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0409\n","Epoch 2208/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0407\n","Epoch 2209/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0405\n","Epoch 2210/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0403\n","Epoch 2211/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0402\n","Epoch 2212/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0400\n","Epoch 2213/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0398\n","Epoch 2214/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0397\n","Epoch 2215/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0395\n","Epoch 2216/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0393\n","Epoch 2217/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0392\n","Epoch 2218/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0390\n","Epoch 2219/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0388\n","Epoch 2220/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 87ms/step - loss: 0.0387\n","Epoch 2221/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0385\n","Epoch 2222/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.0384\n","Epoch 2223/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0382\n","Epoch 2224/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.0380\n","Epoch 2225/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0379\n","Epoch 2226/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 170ms/step - loss: 0.0377\n","Epoch 2227/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - loss: 0.0376\n","Epoch 2228/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0374\n","Epoch 2229/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0373\n","Epoch 2230/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0371\n","Epoch 2231/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 118ms/step - loss: 0.0369\n","Epoch 2232/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.0368\n","Epoch 2233/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0366\n","Epoch 2234/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.0365\n","Epoch 2235/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 157ms/step - loss: 0.0363\n","Epoch 2236/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0362\n","Epoch 2237/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0360\n","Epoch 2238/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 190ms/step - loss: 0.0359\n","Epoch 2239/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.0358\n","Epoch 2240/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0356\n","Epoch 2241/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0355\n","Epoch 2242/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.0353\n","Epoch 2243/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0352\n","Epoch 2244/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0350\n","Epoch 2245/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 0.0349\n","Epoch 2246/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0347\n","Epoch 2247/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0346\n","Epoch 2248/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0345\n","Epoch 2249/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0343\n","Epoch 2250/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.0342\n","Epoch 2251/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.0340\n","Epoch 2252/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0339\n","Epoch 2253/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 159ms/step - loss: 0.0338\n","Epoch 2254/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.0336\n","Epoch 2255/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0335\n","Epoch 2256/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0334\n","Epoch 2257/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 156ms/step - loss: 0.0332\n","Epoch 2258/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0331\n","Epoch 2259/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0330\n","Epoch 2260/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0328\n","Epoch 2261/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0327\n","Epoch 2262/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0326\n","Epoch 2263/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0324\n","Epoch 2264/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0323\n","Epoch 2265/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0322\n","Epoch 2266/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - loss: 0.0321\n","Epoch 2267/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0319\n","Epoch 2268/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0318\n","Epoch 2269/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0317\n","Epoch 2270/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0316\n","Epoch 2271/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0314\n","Epoch 2272/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0313\n","Epoch 2273/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0312\n","Epoch 2274/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0311\n","Epoch 2275/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 0.0309\n","Epoch 2276/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0308\n","Epoch 2277/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0307\n","Epoch 2278/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0306\n","Epoch 2279/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0305\n","Epoch 2280/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0303\n","Epoch 2281/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0302\n","Epoch 2282/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0301\n","Epoch 2283/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0300\n","Epoch 2284/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0299\n","Epoch 2285/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0298\n","Epoch 2286/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0296\n","Epoch 2287/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0295\n","Epoch 2288/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0294\n","Epoch 2289/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0293\n","Epoch 2290/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0292\n","Epoch 2291/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0291\n","Epoch 2292/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0290\n","Epoch 2293/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0288\n","Epoch 2294/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0287\n","Epoch 2295/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0286\n","Epoch 2296/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0285\n","Epoch 2297/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0284\n","Epoch 2298/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0283\n","Epoch 2299/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0282\n","Epoch 2300/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0281\n","Epoch 2301/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0280\n","Epoch 2302/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0279\n","Epoch 2303/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0278\n","Epoch 2304/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0277\n","Epoch 2305/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0276\n","Epoch 2306/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0275\n","Epoch 2307/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0274\n","Epoch 2308/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0273\n","Epoch 2309/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0272\n","Epoch 2310/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0271\n","Epoch 2311/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 151ms/step - loss: 0.0270\n","Epoch 2312/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0269\n","Epoch 2313/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0268\n","Epoch 2314/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0267\n","Epoch 2315/2700\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 2316/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 0.0265\n","Epoch 2317/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0264\n","Epoch 2318/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0263\n","Epoch 2319/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0262\n","Epoch 2320/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0261\n","Epoch 2321/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0260\n","Epoch 2322/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0259\n","Epoch 2323/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0258\n","Epoch 2324/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0257\n","Epoch 2325/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0256\n","Epoch 2326/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0255\n","Epoch 2327/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0254\n","Epoch 2328/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0253\n","Epoch 2329/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0252\n","Epoch 2330/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0251\n","Epoch 2331/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0251\n","Epoch 2332/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0250\n","Epoch 2333/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0249\n","Epoch 2334/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0248\n","Epoch 2335/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0247\n","Epoch 2336/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 154ms/step - loss: 0.0246\n","Epoch 2337/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0245\n","Epoch 2338/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0244\n","Epoch 2339/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0243\n","Epoch 2340/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0243\n","Epoch 2341/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0242\n","Epoch 2342/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0241\n","Epoch 2343/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0240\n","Epoch 2344/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0239\n","Epoch 2345/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0238\n","Epoch 2346/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0238\n","Epoch 2347/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0237\n","Epoch 2348/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0236\n","Epoch 2349/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0235\n","Epoch 2350/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0234\n","Epoch 2351/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0233\n","Epoch 2352/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0233\n","Epoch 2353/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0232\n","Epoch 2354/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0231\n","Epoch 2355/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0230\n","Epoch 2356/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 158ms/step - loss: 0.0230\n","Epoch 2357/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0229\n","Epoch 2358/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0228\n","Epoch 2359/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0227\n","Epoch 2360/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0226\n","Epoch 2361/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0226\n","Epoch 2362/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0225\n","Epoch 2363/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0224\n","Epoch 2364/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0223\n","Epoch 2365/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 136ms/step - loss: 0.0223\n","Epoch 2366/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0222\n","Epoch 2367/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0221\n","Epoch 2368/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.0220\n","Epoch 2369/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0220\n","Epoch 2370/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.0219\n","Epoch 2371/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0218\n","Epoch 2372/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0218\n","Epoch 2373/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0217\n","Epoch 2374/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 131ms/step - loss: 0.0216\n","Epoch 2375/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0215\n","Epoch 2376/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0215\n","Epoch 2377/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0214\n","Epoch 2378/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0213\n","Epoch 2379/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0213\n","Epoch 2380/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 168ms/step - loss: 0.0212\n","Epoch 2381/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0211\n","Epoch 2382/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0211\n","Epoch 2383/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.0210\n","Epoch 2384/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0209\n","Epoch 2385/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.0209\n","Epoch 2386/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.0208\n","Epoch 2387/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0207\n","Epoch 2388/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 113ms/step - loss: 0.0207\n","Epoch 2389/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 95ms/step - loss: 0.0206\n","Epoch 2390/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - loss: 0.0205\n","Epoch 2391/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.0205\n","Epoch 2392/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0204\n","Epoch 2393/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0203\n","Epoch 2394/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0203\n","Epoch 2395/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0202\n","Epoch 2396/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0201\n","Epoch 2397/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 100ms/step - loss: 0.0201\n","Epoch 2398/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0200\n","Epoch 2399/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0200\n","Epoch 2400/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0199\n","Epoch 2401/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0198\n","Epoch 2402/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0198\n","Epoch 2403/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0197\n","Epoch 2404/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0197\n","Epoch 2405/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0196\n","Epoch 2406/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0195\n","Epoch 2407/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0195\n","Epoch 2408/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0194\n","Epoch 2409/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0194\n","Epoch 2410/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0193\n","Epoch 2411/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0192\n","Epoch 2412/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0192\n","Epoch 2413/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0191\n","Epoch 2414/2700\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 2415/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0190\n","Epoch 2416/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0190\n","Epoch 2417/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0189\n","Epoch 2418/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0188\n","Epoch 2419/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0188\n","Epoch 2420/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0187\n","Epoch 2421/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0187\n","Epoch 2422/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0186\n","Epoch 2423/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0186\n","Epoch 2424/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0185\n","Epoch 2425/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0185\n","Epoch 2426/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0184\n","Epoch 2427/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0184\n","Epoch 2428/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0183\n","Epoch 2429/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0183\n","Epoch 2430/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 89ms/step - loss: 0.0182\n","Epoch 2431/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.0182\n","Epoch 2432/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0181\n","Epoch 2433/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0181\n","Epoch 2434/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0180\n","Epoch 2435/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0180\n","Epoch 2436/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0179\n","Epoch 2437/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0179\n","Epoch 2438/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0178\n","Epoch 2439/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0178\n","Epoch 2440/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0177\n","Epoch 2441/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 156ms/step - loss: 0.0177\n","Epoch 2442/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0176\n","Epoch 2443/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0176\n","Epoch 2444/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0175\n","Epoch 2445/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.0175\n","Epoch 2446/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0174\n","Epoch 2447/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0174\n","Epoch 2448/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0173\n","Epoch 2449/2700\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 2450/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0172\n","Epoch 2451/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0172\n","Epoch 2452/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0171\n","Epoch 2453/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0171\n","Epoch 2454/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0171\n","Epoch 2455/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0170\n","Epoch 2456/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0170\n","Epoch 2457/2700\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 2458/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0169\n","Epoch 2459/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0168\n","Epoch 2460/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0168\n","Epoch 2461/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0167\n","Epoch 2462/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0167\n","Epoch 2463/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 133ms/step - loss: 0.0167\n","Epoch 2464/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0166\n","Epoch 2465/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0166\n","Epoch 2466/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0165\n","Epoch 2467/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0165\n","Epoch 2468/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0165\n","Epoch 2469/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0164\n","Epoch 2470/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0164\n","Epoch 2471/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0163\n","Epoch 2472/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0163\n","Epoch 2473/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0163\n","Epoch 2474/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0162\n","Epoch 2475/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 79ms/step - loss: 0.0162\n","Epoch 2476/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0161\n","Epoch 2477/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 153ms/step - loss: 0.0161\n","Epoch 2478/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0161\n","Epoch 2479/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0160\n","Epoch 2480/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0160\n","Epoch 2481/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0159\n","Epoch 2482/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0159\n","Epoch 2483/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0159\n","Epoch 2484/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 93ms/step - loss: 0.0158\n","Epoch 2485/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0158\n","Epoch 2486/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0157\n","Epoch 2487/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0157\n","Epoch 2488/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0157\n","Epoch 2489/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0156\n","Epoch 2490/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0156\n","Epoch 2491/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0156\n","Epoch 2492/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0155\n","Epoch 2493/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0155\n","Epoch 2494/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0155\n","Epoch 2495/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0154\n","Epoch 2496/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0154\n","Epoch 2497/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0154\n","Epoch 2498/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0153\n","Epoch 2499/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 106ms/step - loss: 0.0153\n","Epoch 2500/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 125ms/step - loss: 0.0153\n","Epoch 2501/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0152\n","Epoch 2502/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0152\n","Epoch 2503/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 171ms/step - loss: 0.0152\n","Epoch 2504/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 119ms/step - loss: 0.0151\n","Epoch 2505/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0151\n","Epoch 2506/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 280ms/step - loss: 0.0151\n","Epoch 2507/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0150\n","Epoch 2508/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 114ms/step - loss: 0.0150\n","Epoch 2509/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0150\n","Epoch 2510/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0149\n","Epoch 2511/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0149\n","Epoch 2512/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 169ms/step - loss: 0.0149\n","Epoch 2513/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 111ms/step - loss: 0.0148\n","Epoch 2514/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 108ms/step - loss: 0.0148\n","Epoch 2515/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0148\n","Epoch 2516/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0147\n","Epoch 2517/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0147\n","Epoch 2518/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0147\n","Epoch 2519/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.0146\n","Epoch 2520/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0146\n","Epoch 2521/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.0146\n","Epoch 2522/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 120ms/step - loss: 0.0146\n","Epoch 2523/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 127ms/step - loss: 0.0145\n","Epoch 2524/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0145\n","Epoch 2525/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 99ms/step - loss: 0.0145\n","Epoch 2526/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0144\n","Epoch 2527/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 90ms/step - loss: 0.0144\n","Epoch 2528/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 166ms/step - loss: 0.0144\n","Epoch 2529/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 155ms/step - loss: 0.0143\n","Epoch 2530/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0143\n","Epoch 2531/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 115ms/step - loss: 0.0143\n","Epoch 2532/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0143\n","Epoch 2533/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0142\n","Epoch 2534/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 121ms/step - loss: 0.0142\n","Epoch 2535/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 104ms/step - loss: 0.0142\n","Epoch 2536/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 160ms/step - loss: 0.0142\n","Epoch 2537/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.0141\n","Epoch 2538/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 126ms/step - loss: 0.0141\n","Epoch 2539/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0141\n","Epoch 2540/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0140\n","Epoch 2541/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0140\n","Epoch 2542/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0140\n","Epoch 2543/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0140\n","Epoch 2544/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0139\n","Epoch 2545/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0139\n","Epoch 2546/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0139\n","Epoch 2547/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0139\n","Epoch 2548/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 86ms/step - loss: 0.0138\n","Epoch 2549/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0138\n","Epoch 2550/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0138\n","Epoch 2551/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0138\n","Epoch 2552/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0137\n","Epoch 2553/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0137\n","Epoch 2554/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0137\n","Epoch 2555/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0137\n","Epoch 2556/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0136\n","Epoch 2557/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 134ms/step - loss: 0.0136\n","Epoch 2558/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 149ms/step - loss: 0.0136\n","Epoch 2559/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0136\n","Epoch 2560/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0135\n","Epoch 2561/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0135\n","Epoch 2562/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step - loss: 0.0135\n","Epoch 2563/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0135\n","Epoch 2564/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0135\n","Epoch 2565/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0134\n","Epoch 2566/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0134\n","Epoch 2567/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0134\n","Epoch 2568/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0134\n","Epoch 2569/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0133\n","Epoch 2570/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0133\n","Epoch 2571/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0133\n","Epoch 2572/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0133\n","Epoch 2573/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0132\n","Epoch 2574/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0132\n","Epoch 2575/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 80ms/step - loss: 0.0132\n","Epoch 2576/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0132\n","Epoch 2577/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0132\n","Epoch 2578/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0131\n","Epoch 2579/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0131\n","Epoch 2580/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 85ms/step - loss: 0.0131\n","Epoch 2581/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - loss: 0.0131\n","Epoch 2582/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0131\n","Epoch 2583/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0130\n","Epoch 2584/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0130\n","Epoch 2585/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0130\n","Epoch 2586/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0130\n","Epoch 2587/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0130\n","Epoch 2588/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0129\n","Epoch 2589/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0129\n","Epoch 2590/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0129\n","Epoch 2591/2700\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 2592/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0129\n","Epoch 2593/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 145ms/step - loss: 0.0128\n","Epoch 2594/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 82ms/step - loss: 0.0128\n","Epoch 2595/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0128\n","Epoch 2596/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 77ms/step - loss: 0.0128\n","Epoch 2597/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 0.0128\n","Epoch 2598/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0127\n","Epoch 2599/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0127\n","Epoch 2600/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0127\n","Epoch 2601/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0127\n","Epoch 2602/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0127\n","Epoch 2603/2700\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 2604/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0126\n","Epoch 2605/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0126\n","Epoch 2606/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0126\n","Epoch 2607/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0126\n","Epoch 2608/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0126\n","Epoch 2609/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 96ms/step - loss: 0.0126\n","Epoch 2610/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0125\n","Epoch 2611/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0125\n","Epoch 2612/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 148ms/step - loss: 0.0125\n","Epoch 2613/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0125\n","Epoch 2614/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 76ms/step - loss: 0.0125\n","Epoch 2615/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0125\n","Epoch 2616/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0124\n","Epoch 2617/2700\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 2618/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0124\n","Epoch 2619/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0124\n","Epoch 2620/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 72ms/step - loss: 0.0124\n","Epoch 2621/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 91ms/step - loss: 0.0124\n","Epoch 2622/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0123\n","Epoch 2623/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 135ms/step - loss: 0.0123\n","Epoch 2624/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0123\n","Epoch 2625/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 81ms/step - loss: 0.0123\n","Epoch 2626/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0123\n","Epoch 2627/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0123\n","Epoch 2628/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 73ms/step - loss: 0.0122\n","Epoch 2629/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0122\n","Epoch 2630/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 140ms/step - loss: 0.0122\n","Epoch 2631/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0122\n","Epoch 2632/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - loss: 0.0122\n","Epoch 2633/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 143ms/step - loss: 0.0122\n","Epoch 2634/2700\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 2635/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0121\n","Epoch 2636/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0121\n","Epoch 2637/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 75ms/step - loss: 0.0121\n","Epoch 2638/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0121\n","Epoch 2639/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 168ms/step - loss: 0.0121\n","Epoch 2640/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0121\n","Epoch 2641/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0121\n","Epoch 2642/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 150ms/step - loss: 0.0120\n","Epoch 2643/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 112ms/step - loss: 0.0120\n","Epoch 2644/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 179ms/step - loss: 0.0120\n","Epoch 2645/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.0120\n","Epoch 2646/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0120\n","Epoch 2647/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 105ms/step - loss: 0.0120\n","Epoch 2648/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 92ms/step - loss: 0.0120\n","Epoch 2649/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 97ms/step - loss: 0.0119\n","Epoch 2650/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 138ms/step - loss: 0.0119\n","Epoch 2651/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0119\n","Epoch 2652/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0119\n","Epoch 2653/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.0119\n","Epoch 2654/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 132ms/step - loss: 0.0119\n","Epoch 2655/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 160ms/step - loss: 0.0119\n","Epoch 2656/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step - loss: 0.0119\n","Epoch 2657/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 101ms/step - loss: 0.0118\n","Epoch 2658/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 84ms/step - loss: 0.0118\n","Epoch 2659/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 117ms/step - loss: 0.0118\n","Epoch 2660/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 94ms/step - loss: 0.0118\n","Epoch 2661/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0118\n","Epoch 2662/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 128ms/step - loss: 0.0118\n","Epoch 2663/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0118\n","Epoch 2664/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 102ms/step - loss: 0.0118\n","Epoch 2665/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 142ms/step - loss: 0.0117\n","Epoch 2666/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 130ms/step - loss: 0.0117\n","Epoch 2667/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 129ms/step - loss: 0.0117\n","Epoch 2668/2700\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 2669/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 300ms/step - loss: 0.0117\n","Epoch 2670/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0117\n","Epoch 2671/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 107ms/step - loss: 0.0117\n","Epoch 2672/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 103ms/step - loss: 0.0117\n","Epoch 2673/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0117\n","Epoch 2674/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 110ms/step - loss: 0.0116\n","Epoch 2675/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 141ms/step - loss: 0.0116\n","Epoch 2676/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 116ms/step - loss: 0.0116\n","Epoch 2677/2700\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 2678/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 123ms/step - loss: 0.0116\n","Epoch 2679/2700\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 2680/2700\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 2681/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 144ms/step - loss: 0.0116\n","Epoch 2682/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 146ms/step - loss: 0.0116\n","Epoch 2683/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0115\n","Epoch 2684/2700\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 2685/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0115\n","Epoch 2686/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 147ms/step - loss: 0.0115\n","Epoch 2687/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 88ms/step - loss: 0.0115\n","Epoch 2688/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 124ms/step - loss: 0.0115\n","Epoch 2689/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - loss: 0.0115\n","Epoch 2690/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 139ms/step - loss: 0.0115\n","Epoch 2691/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 152ms/step - loss: 0.0115\n","Epoch 2692/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0114\n","Epoch 2693/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - loss: 0.0114\n","Epoch 2694/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0114\n","Epoch 2695/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - loss: 0.0114\n","Epoch 2696/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - loss: 0.0114\n","Epoch 2697/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 66ms/step - loss: 0.0114\n","Epoch 2698/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - loss: 0.0114\n","Epoch 2699/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 137ms/step - loss: 0.0114\n","Epoch 2700/2700\n","\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 74ms/step - loss: 0.0114\n","Restoring model weights from the end of the best epoch: 2697.\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":["<Figure size 1600x800 with 1 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\n"},"metadata":{}}],"source":["# обучение AE2\n","patience = 500\n","ae2_trained, IRE2, IREth2 = lib.create_fit_save_ae(data,'out/AE2.h5','out/AE2_ire_th.txt', 2700, True, patience)\n","lib.ire_plot('training', IRE2, IREth2, 'AE2')"]},{"cell_type":"code","source":["print(IREth2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KW37ndDsU_3Y","executionInfo":{"status":"ok","timestamp":1762445959397,"user_tz":-180,"elapsed":49,"user":{"displayName":"Alena Konovalova","userId":"13964397918094172748"}},"outputId":"c16b2c0a-ab48-4fdb-9971-8c6cad08540b"},"execution_count":26,"outputs":[{"output_type":"stream","name":"stdout","text":["0.4\n"]}]},{"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},"id":"GBMAaSq3uK5w","executionInfo":{"status":"ok","timestamp":1762440492668,"user_tz":-180,"elapsed":7923,"user":{"displayName":"Alena Konovalova","userId":"13964397918094172748"}},"outputId":"464d3d0f-fc8d-4da6-c089-5a09f8269f69"},"execution_count":13,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m238/238\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x600 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["amount:  19\n","amount_ae:  280\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1600x700 with 2 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качества AE1\n","IDEAL = 0. Excess:  13.736842105263158\n","IDEAL = 0. Deficit:  0.0\n","IDEAL = 1. Coating:  1.0\n","summa:  1.0\n","IDEAL = 1. Extrapolation precision (Approx):  0.06785714285714287\n","\n","\n"]}]},{"cell_type":"code","source":["# построение областей покрытия и границ классов\n","# расчет характеристик качества обучения\n","numb_square = 20\n","xx, yy, Z2 = lib.square_calc(numb_square, data, ae2_trained, IREth2, '2', True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"c9GJJF6J0GuZ","executionInfo":{"status":"ok","timestamp":1762445967104,"user_tz":-180,"elapsed":3906,"user":{"displayName":"Alena Konovalova","userId":"13964397918094172748"}},"outputId":"dff18ab6-46d1-43a9-ba5a-7b18ba119407"},"execution_count":27,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m238/238\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x600 with 1 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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["amount:  19\n","amount_ae:  30\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1600x700 with 2 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+u3btmtI80dHRUk9bnTt3Vpr+7NkzaXrVqlWF2NhYpXnOnDkjlChRQiorP57s27t3r1TeyZMnFaZNmjRJACCYmJgIr1+/VrsO+Sf75syZo1UWy4mDBw9K5XTq1ClH6yAqSnixj6iQkg8O2v6o6y7p/fv3QrVq1QQAgrW1tfD48WPh2LFjgkwmEwAIXbt2VbncmjVrpHUvXbpUp/rfu3dPMDQ0FAAIBw4c0DjvxIkTBQCCvb29wusDBw4UAAhubm46lf3kyROtuw7Qt5cvX0oXHrNezBQEQRg/frwAQDA1NVUZaEXyDVLa/OQk4O3atUta3t3dXW2DIBERUXGR04t9ly9flpbr1q2bwrQff/xRACA4ODgIycnJateRmpoqODg4qM0Iub3Y9+bNG8Hc3Fw6r6elpWm9fdmtWxAK7mIfsxUREVHBWrZsmXR+W7ZsWa7XN2TIEGl9Z8+eVTuf/I3s58+f17mctLQ0qfvRRYsWKU3X18W+gIAAtevp3bu31FaX1c8//yyt4+DBg2rXIX9zV35c7OvWrZsAQHB0dFTKNteuXZPqsmrVKrXr0LZbePkfXb169Urq3tTQ0FC4dOmSzusgKmrYjSfRR0C+i87Xr1+jT58+8PHxgSAIqFChAtatW6dyuYMHD0rLDxw4UKcyDx06hPT0dJibm6N9+/Ya5/3iiy8AAE+fPsXDhw+l1ytUqAAAuHHjBs6fP6912TY2NihRogQAIDg4GGlpaTrVPS+FhIRIAxOrGoRafC05ORk7d+7M17rJu3nzJnx9fQEAZmZmCA4OhkwmK7D6EBERFWYlS5aU/h8fH68wbf/+/QCATp06wcTERO06jIyMpC6l/v777zyv48mTJ6UunUaOHAlDQ8M8L6MgMFsREREVLPnso2pYFV0dP34cAFCzZk00atRI7Xzy7VLiMupkZGTg6dOnuH37Nq5du4Zr167h5s2bUteZqroC1QeZTIY+ffqone7u7g4AeP36Nd6+faswTdxGW1tbje1q/fv3z31FtfT69WscOnQIANCnTx+lbFOzZk3Uq1cPQGZ7XEFJT09H37598eDBAwDA9OnT4ebmVmD1IcovvNhHVAT4+/tDyHwSV+OPs7Oz2nU0bNgQ/v7+AIA///wTT548gUwmQ2BgIGxtbVUuc/nyZQCZ4cPc3FynOl+8eBFAZv/kRkZGkMlkan86deokLff8+XPp/9988w2MjY2RkpKCpk2bwsvLC6tXr8a1a9ek/thVMTExQa9evQAAu3btQtWqVTFx4kQcPnxYKTzp26ZNmwAAn332mcpxWurWrYtatWoB0L5Pc22OhaCgIK3r+PTpU3To0AHx8fGQyWTYuHEjx5QhIiLSQL6Ry9LSUvp/eno6rly5AgBYs2aNxvwjk8mwa9cuAIr5J6+IOQ7IHE+nuGC2IiIiKljyY/QlJCTkal0pKSmIjo4GAI0X+gDAzc0NxsbGAIBr164pTRcEAVu2bIGnpydKliwJBwcHuLi4oHbt2tKPmNPEMQf1zdbWFjY2NmqnW1tbS//PegOZuI316tWDgYH6JvzatWtLN7zrW0hICD58+ABA9U1X8q//9ddf+O+//7JdZ2BgoFZZTBdDhw6Vxm/u1KkTZsyYodPyREUVL/YRfUSmTJmCKlWqSL/7+fmhbdu2aucXw4/4hJ0uYmJidK8goDCosIuLC0JCQlCmTBmkpaXh4MGDGDJkCGrXro1y5crh22+/xenTp1Wu59dff4WXlxcA4MGDB1i4cCE6duwIGxsbNGjQAAsXLkRcXFyO6qitmzdvShc91YUgAPj2228BAGfOnMG9e/f0WqesXr9+jS+//BL3798HACxfvhy9e/fO1zoQEREVNfINRPKNNK9fv85RjwLy+SevyNcxJ1muMGK2IiIiKnjyF69evHiRq3W9efNG+n+5cuU0zmtsbCyV/fr1a4VpycnJ6NixI7799luEh4cjKSlJ47qym55XsrtxXv4iXnp6usI08b0pW7asxnUYGhoq5FF9Em+kqlOnDmrXrq1ynj59+kg9Smh741VemjJlCtauXQsg84a3HTt2FJseLoiyY1TQFSCi/HPo0CHcvXtX+j0iIgJJSUkwMzPL87LEkGJra4uwsDCtl6tcubLC7927d0fr1q2xfft2hIaG4vTp04iNjcXLly+xZcsWbNmyBd7e3ti4caNCSLK0tMT+/ftx/vx57NixA+Hh4bhy5QrS09Nx8eJFXLx4EYsWLcLvv/8udaGV1+RDzdixYzF27FiN8wuCgM2bN0tPYOpbfHw82rVrh+vXrwMAZs+ejWHDhuVL2UREREWZ/FNzNWrUkP4v30jj5+eHUaNGabW+/Lobu6hjtiIiIip4devWlf5/6dKlPFtvbrq7/umnn/DHH38AAFq0aIFhw4bhs88+Q/ny5WFmZia1F33xxRc4ffq0zk+KEXD79m1piJ2rV69qtb+2bNmCmTNn5ltX5vPnz8fPP/8MILMXiIMHD+qlzZOosOLFPqKPxIsXL+Dn5wcg80LYu3fvcPPmTUyYMAG//vqrymVsbW3x+PFjPHv2TOfyxLut4uPj4erqmqu7aKysrDBo0CAMGjQIQOZd3fv27cPy5cvx9OlTbNq0CW5ubiob1Bo2bIiGDRtKdQkPD0dQUBD27NmDmJgYdO/eHf/991+en/wzMjKwdetWnZcLDg7OlwappKQkeHl54cKFCwCACRMmYPr06Xovl4iIqDg4duyY9P9mzZpJ/5e/q1oQBKk7yYIg3037s2fPlG6oKmqYrYiIiAqHmjVrwtbWFi9fvsTp06fx7t07hW7NdVGmTBnp/9k9JZiWloZXr14BUM5c69evB5D5JNfJkyfVdnuZ9YnAwqxMmTJ4/vw5YmNjNc6Xnp6u8ISkvuTkKb27d+8iIiIiX7qUX7lyJSZPngwAcHV1RWhoaI6PS6Kiihf7iD4SAwYMQGxsLAwMDHDw4EEsW7YMu3fvxooVK9CpUye0a9dOaZnPPvsMjx8/xsWLF5GYmKjTuH1ubm747bffkJKSgosXL2bb97ouXF1d4erqir59+8LV1RUJCQnYsWNHtnfPlypVCl5eXvDy8sKoUaMQEBCAZ8+eISIiAm3atMmz+gFAWFgYHj16BAAYMWIEmjRponH+c+fOYenSpfjvv/9w5swZNG3aNE/rIy81NRXdu3fHqVOnAADff/89FixYoLfyiIiIipNr167hxIkTAICKFSuifv360rQSJUqgZs2auH79Os6cOVNQVQSQmeNEf/75Z55f7MuvO7RFzFZERESFg0wmg7e3NxYvXoyEhASsX78+26ft1TExMUG1atUQHR2Nc+fOaZz38uXLSE1NBQCFG6pev34tjX/cs2dPtRf63r9/j9u3b6tdf35nm+zUrFkTz58/x5UrV5CRkaF2u6KiopCSkqLXuojjIQKZXXhOmTIl2/kHDBiA5ORkbN68We8X+4KDgzF8+HAAQJUqVXD8+HGFG9+IPha82Ef0EVi5ciUOHz4MAJg0aRKaN2+OmjVr4uzZs3jy5Al8fX0RFRWldCL08vLC/v37kZiYiLVr12L06NFal+nl5YWJEydCEAQsXboUISEheblJADIb2KpXr47Lly/rPLhyq1atEBAQAEA/AzOLdzwZGhpi+vTp2fY937p1a/z6669IS0vD5s2b9dYglZ6ejj59+kjdW3z77bdYuXKlXsoiIiIqbpKSktC/f3+p66fx48fDyEjxT6rOnTvj+vXruHXrFkJDQzWOj6xPnp6esLCwQEJCApYvX45+/frl6Xglpqam0v9TUlJgYmKSZ+tWhdmKiIio8BgzZgxWrVqFxMRE/PDDD+jQoQNcXFyyXS4jIwMhISHo27ev9Frr1q0RHR2N69ev4/z581LvTFmJT++Jy4jkx0tOSEhQW/b69es1jq2cNdsUtFatWuHEiRN4+fIl/vjjD3Ts2FHlfPkxLl54eDgePnwIAOjfv79W4xFv374d+/btw86dO7F8+XKF9zcv7dmzB76+vhAEAY6Ojjhx4gTs7e31UhZRYaf6lgAiKjZu376NCRMmAADc3d0xa9YsAJldHgQFBUEmk+H58+dSF5ny+vXrBwcHBwDAtGnTpLuVVXn8+LHC7zVq1EDPnj0BANu2bcOSJUs01vPevXtKFwR///13vH37Vu0yjx49wq1btwAojvV39+5djXUFgKNHj0r/z3qne3h4OGQyGWQyGXx8fDSuR5WEhATs2bMHQGYXEtk1RgGZXW21aNECALBjxw69BEtBEDBw4EDs2rULQOZ4iIGBgYXu7jUiIqLC6MaNG2jWrJk0Xl+LFi0wZMgQpflGjRqFkiVLAgB8fX2l8dvUOXToEK5evZrn9S1dujQGDx4MAIiMjMTo0aPVjk+TmpqKmJgYndZfoUIF6f///fefxnmZrYiIiIoXBwcHaUiYhIQEtGjRItt2mBs3bqBdu3ZYuHChwutDhgyRnlobNGgQ3r17p7Ts0aNHsWHDBgCZw7U0aNBAmla2bFmULl0aABASEqLynH/hwgXMmDFDY/1sbGykcZSzyzb5wdvbW7qZavTo0SpvVP/777+xYsWKbNfl7OwsZbGckL+g2L17d62W6dGjBwAgLi4O+/bty1G52Tl69Ci++eYbpKeno1y5cjh+/DicnZ31UhZRUcAn+4iKgJiYGFy7di3b+czMzPDJJ59Iv6empqJv375ITEyEmZkZtmzZAmNjY2l669atMWrUKCxduhR79+7Fxo0bMWDAAGm6qakpgoOD8eWXXyIxMRGtW7fGt99+i65du8LR0REpKSm4desWDh8+jP379ysFqlWrVuHixYu4e/cuxo0bh3379qF///6oWbMmTExM8OrVK/zzzz84cuQITp48iW7duuGbb76Rll+6dCn69u2Ljh07omXLlnB1dYWVlRXevHmDixcvYvny5UhKSgKQ2V2S6OHDh/D09MSnn36Kbt26oX79+tJFy0ePHmH79u3YsWMHAKBevXp52sUokHlX0fv37wFoH4LEeU+cOIG3b99i//790sXSrLQ5FgDAyckJpUqVkn4fP348AgMDAWR2eTF16lTcvHlT4zoKcqwhIiKi/JQ1byUkJODNmze4evUqTpw4gWPHjkkXyxo3boxdu3Yp5CqRnZ0dNm3ahB49euDZs2eoX78+fHx80L59ezg6OiI1NRWPHz/G+fPnsWvXLty9excHDhxAnTp18nybZs+ejWPHjiEqKgq//vor/v77bwwePBi1a9dGiRIl8PjxY5w+fRohISGYM2eOThfi5LvRHDNmDKZNm4YKFSpIjUjOzs5KTz3mFLMVERFR4ePr64vHjx/jhx9+QExMDDw8PPDll1+iS5cucHV1RenSpfH69WvcuXMHhw4dwpEjR5Ceno66desqrKd27doYN24cFi5ciH/++QefffYZJk2aBDc3NyQkJODAgQMICAhAeno6SpQogTVr1igsb2BggL59+2LFihW4evUqmjVrhrFjx6JatWqIi4vD4cOHsXLlSpQsWRL29va4c+eOyu0xMjJCgwYNcObMGWzcuBFubm6oV6+elPesra0VxgrUN3t7e/j7+2Pq1Kn4999/4e7ujsmTJ6N+/fpISUlBaGgoFi9eDHt7eyQkJCA2NlYvNxwlJiZi9+7dADIfItD2YpqXlxdKlCiBDx8+YPPmzejVq5fK+Z48eaJVFrO0tESlSpWk38+ePYtu3brhw4cPMDY2xi+//ILU1FSN63J0dJQuDBMVSwIRFUphYWECAJ1+6tatq7COKVOmSNNWrFihspzk5GShVq1aAgChZMmSwn///ac0z5EjR4QyZcpkW74qz549E5o3b65V/X19fRWWbdGiRbbLGBgYCLNnz87Re+fi4iLcvXtX43vv7e2tYS+p1rp1awGAIJPJhCdPnmi93PPnzwUDAwMBgNCpUyed34usP3v37lVYh5OTk87rICIiKs50zVtly5YVfvrpJyE1NTXbde/fv1+wtrbWKsucPHlSaXlvb28BgODk5KRV/cPCwlTOExsbK3zxxRfZ1iMwMFDndX/99ddq13fv3j2V62K2IiIiKl52794tODs7a3UerFmzphAaGqq0jvT0dGHo0KEal7WyslK5rCAIwtu3b4V69eqpXdba2lo4deqUdP5v0aKFyvUcPHhQkMlkKtfh7+8vzZddTtImxwmCIAQGBqrMTqKMjAxh8ODBarfL1tZWuHDhglCxYkUBgPD999+rLEc+s+hqy5Yt0rLz5s3TadkOHToIAAQjIyPh+fPn0uvy263tT5cuXRTW7e/vr/M6suZdouKG3XgSFVMRERGYP38+AKBDhw4YOnSoyvlMTEywdetWmJiY4P379+jXrx/S09MV5mnbti3u3r2LuXPnokmTJrCxsYGhoSEsLS3x2WefYfTo0Th//rzK9ZcvXx5//vknDh48iL59+6JKlSowNzeHsbExypYtiyZNmmDcuHE4deoUNm7cqLBsSEgI1q5diz59+qBevXooX748jIyMULJkSdSsWRNDhgzB5cuXMX36dIXlmjdvjvDwcEyZMgWenp6oWrUqSpUqBWNjY9jZ2eHLL7/E6tWrceXKFaUuPHPryZMnOHnyJADg888/16mfcDs7O2k8mSNHjiA2NjZP60ZERETaMTAwgJWVFSpVqoTmzZtj9OjR2L17Nx4/foypU6dq9cSal5cX7t27h0WLFqFly5aws7ODsbExzMzMULlyZXTq1AlLlizB/fv34enpqbdtsbW1xalTp7Bnzx706NEDjo6OMDExgampKapUqYKePXti69atCr0raGvLli1YsGABGjZsCCsrK6kLrrzEbEVERFS4ffXVV7h9+za2bt2Kfv36oUaNGihTpgyMjIxgbW2Nzz77DEOHDsXJkycRFRWFL7/8UmkdBgYGWLFiBf7880/07dsXlSpVgomJCSwtLVGvXj1MnToV0dHRKpcFACsrK5w5cwazZ89G7dq1YWpqipIlS8LV1RXjx4/HP//8gy+++CLbbenYsSNOnDiBLl26wN7eXmUvDvlJJpNh9erV2LdvH7788ktYW1vD1NQUVatWxciRI3H58mXUr19f6vrUysoqz+uQky48s86flpaG3377LU/rRUTKZIKgZuAGIiIiIiIiIiIiIiIqlB4/foyKFSsCANavX4/vvvuugGtERAWFT/YRERERERERERERERUxISEh0v8bN25cgDUhooLGJ/uIiIiIiIiIiIiIiAqRhIQEvHv3DhUqVFA5/fLly2jRogXi4+Ph7u6Oixcv5nMNiagwyX6wCSIiIiIiIiIiIiIiyjexsbFwdXVF165d0a5dO9SoUQMmJiZ4+vQpjhw5gg0bNiApKQkymQxLliwp6OoSUQHjk31ERERERERERERERIXI/fv3UblyZY3zlChRAuvWrUP//v3zqVZEVFjxYh8RERERERERERERUSGSmpqKvXv34siRI7hw4QJiY2Px+vVrmJubw9nZGa1bt8aIESPg5ORU0FUlokKAF/uIiIiIiIiIiIiIiIiIiqiPbsy+jIwMPH36FKVKlYJMJivo6hAREVEuCIKA+Ph42Nvbw8DAoKCr89FjziIiIio+mLMKF+YsIiKi4kFfGeuju9j39OlTVKxYsaCrQURERHno0aNHcHR0LOhqfPSYs4iIiIof5qzCgTmLiIioeMnrjPXRXewrVaoUgMw30tLSUm/l9OrVC9u3b9fb+vOzHJbBMlgGyygqZeRXOSyj8JTx7t07VKxYUTq/U8EqTjmLZRS+clgGy2AZLINl5G85zFmFC3MWy2AZhaMclsEyWAbLyC19ZayP7mKf2NWBpaWlXsORsbGxXtefn+WwDJbBMlhGUSkjv8phGYWrDADsyqiQKE45i2UUvnJYBstgGSyDZRRMOcxZhQNzFstgGYWjHJbBMlgGy8greZ2x2Ok6ERERERERERERERERURHFi31ERERERERERERERERERRQv9hEREREREREREREREREVUbzYR0RERERERERERERERFRE8WIfERERERERERERERERURHFi31ERERERERERERERERERRQv9hEREREREREREREREREVUUYFXYGiJjU1Fenp6dnOZ2Njg+TkZL3XJz/KYRksg2WwjKJSRn6VwzIKTxkfPnyAk5MTPnz4kC/H18fO0NAQxsbGelm3IAhITU1FRkZGtvMWh2O3OJWRX+WwDJbBMlgGy8jfcpiz8pc+c5a2bVlA8fmMsIyPr4z8KodlsAyWwTJyS5uMlZNcIBMEQciLChYV7969g5WVFeLi4mBpaanTci9fvkRKSopW88fExKBcuXI5rabW8qMclsEyWAbLKCpl5Fc5LKPwlJGRkYFHjx6hYsWKMDBghwX5wcTEBLa2tipzVE5y1ocPHxATE4PExEStG6GKw7FbnMrIr3JYBstgGSyDZeRvOcxZ+S+vc5aubVlA8fmMsIyPr4z8KodlsAyWwTJyS9uMpSkXqMIn+7Tw7t07PHnyBCVLloStrS2MjY0hk8k0LiOTyeDs7Kz3uuVHOSyDZbAMllFUysivclhG4SkjPT0dSUlJcHZ2hqGhod7Kof89eRcXF4cnT54AgE43TqmSmJiIR48ewdDQEGXKlIGZmRkMDQ0LRc5iGYWvHJbBMlgGy2AZ+VsOc1b+0UfOyklbFlB8PiMs4+MrI7/KYRksg2WwjNzKLmPlNBfwYp8WXr58iZIlS8LR0VGrYARkPmZpamqq55rlTzksg2WwDJZRVMrIr3JYRuEpQ3wSzNTUlI1Q+cDMzAylSpXC48eP8fLly1w3Qr18+RLGxsZwcnLSaf8Vh2O3OJWRX+WwDJbBMlgGy8jfcpiz8pc+cpaubVlA8fmMsIyPr4z8KodlsAyWwTJyS5uMlZNcwH4YspGamoqUlBRYWVnpFI6IiIiIiiOZTAYrKyukpKQgNTU1x+tJS0tDQkICrK2t2YBIREREhLzLWWzLIiIiKvp0zQW82JcN8SqrvgZJJiIiIipqxFyk7Rh7qqSlpQHI7IOeiIiIiDLlRc5iWxYREVHxoEsu4MU+LfFOKCIiIqJMeZmLmLGIiIiI/oc5i4iIiES6nMt5sY+IiIiIiIiIiIiIiIioiOLFPiIiIiIiIiIiIiIiIqIiihf7iIiIiIiIiIiIiIiIiIooo4KuQLEws5vSS9XyqegclzNzb15Wg4iIiEg/Cihn5aoM5iwiIiIqCopazmLGIiIiUotP9lGecHZ2hkwm0/pn5syZBV1lIsoFmUwGZ2dnpdf9/f0hk8lQu3ZtfPjwQeWygiCgVatWkMlk+O6773QqV/yuuX//vtZ1IiIqypixiD4uzFhERPmHOYvo48KcRcUdn+yjPNW0aVNUrVpV7fQjR47gxYsX+VgjIspP06dPx/79+3HlyhX4+/tj3rx5SvMsX74cJ0+ehJOTE3755ZcCqCURUdHDjEX0cWPGIiLSH+Ysoo8bcxYVF7zYR3nKz88PPj4+aqd7eHgwIBEVY8bGxti8eTPq16+PhQsXokuXLmjcuLE0PTo6GlOmTIFMJsPGjRthaWlZgLUlIio6mLGIPm7ZZaz79+8zYxER5RBzFtHHjTmLigt240lERHmqdu3amDlzJtLT0+Ht7Y2kpCQAkH5PTEzEsGHD0LJlywKuKREREVHRoSljTZo0iRmLiIiIKIeYs6g44MU+KlTCw8Oz7SNdlVu3bsHX1xdOTk4wMTGBtbU1WrVqhR07dqicf+bMmWr7Ww8KCoJMJlN5V9f9+/fV9qUcGxuLzZs3o0OHDqhcuTLMzMxgaWmJ+vXrY/78+UhOTta47T4+Phq3W1V9xGWCgoI0rltb4rZXr14dX3zxhdr52rRpI9Ura9nZ1Ul8Dz09PVVO12VfZmRkoHfv3pDJZOjTpw8yMjIUpgcEBKjczxkZGejTpw9kMhm++eYbpeUAIDIyEn379kWlSpWkerRt2xaHDx9W+76kpaVh48aNaN26NWxtbWFiYgJHR0e0bt0ay5cvl+bTZUwA+f3u4eGhNL1UqVJwcXHBmDFj8PDhQ6U6xcbGIiAgIMfHZU5NnDgRDRs2xJ07dzB16lQAwPr16/H333+jatWqmD9/vtplb9y4gZ49e8LW1hZmZmaoVasWFi1ahPT0dL3UlYjoY1DUM1ZAQAD8/PyKfMYSM4s6eZGx1I35kZcZa+bMmahevTozViHJWAsXLsTly5eZsYiICkhRz1nFpS2LOYs5K7eYs6ioYzeeVCjZ2dmhXbt2Cq9t2rRJ5byHDh1Cjx49kJycjBo1auCrr75CTEwMTp06hZMnTyI0NBQbNmzQe51DQ0MxZ84cODg4oGrVqmjcuDFiY2Nx7tw5TJ48Gfv27UNYWBhMTEw0ridrX/H//vsvzpw5o+/qKzl9+jSuXLmCevXqKbx+/fp1HD9+XC9l6rovDQwMEBwcjISEBISEhMDS0hKrV6/OtpwhQ4YgJCQEnTp1QnBwMAwMFO97WLZsGcaOHYuMjAzUq1cPjRo1wvPnzxEeHo6jR49i1qxZ+OGHHxSWiYuLQ6dOnRAREQFjY2M0adIE9vb2eP78Oa5evYoTJ05gxIgRAABvb2+FZd+/f4/du3fDwsICPXr0UJjWrFkzpfq3bdsW5cuXBwC8ffsWp0+fxtKlS7F161ZcvXpVmgZkHpejRo3K9rjMa4aGhti0aRPc3NywbNkyVKtWDQEBATAwMMCmTZtgbm6ucrmIiAi0a9cOCQkJqFKlCtq0aYOXL19i6tSpOHv2bJ7Xk4joY1NUM9aoUaNgZ2cHFxeXIp+xLl68yIz1EWWs7I5LXanKWP7+/sxYRESFQFHNWcWpLYs5izkrN5izqKgr8It98fHxmDFjBvbu3YuYmBjpw9SgQQOV8+/ZswerVq3ClStXkJKSgpo1a2LmzJlo27ZtPtec9EG828HV1VXpbhpVAenFixfo27cvkpOTMWfOHEydOlW6Y+rixYv48ssvsXHjRjRu3BgeHh56rbu7uzt27NiBnj17Krz+5s0b9O7dG0ePHkVAQAAmTJigcnnxjpysfcUHBQXle0Bq2LAhIiMjERAQgI0bNypMCwgIgKGhIZo3b47w8PA8K1OXfTlw4EBpOWNjY+zcuRMdOnTAmjVrYGlpiQULFqgtZ8KECVi7di1atmyJnTt3wshI8WswNDQUY8aMgY2NDXbv3q3whGNUVBQ6dOgAf39/tGjRAi1atJCmDRgwABEREXBzc8OePXsU7vZKS0vDoUOHpN+zHtv379/H7t27YWtrq/IusujoaIXfJ0+erHA8JyQkoEWLFoiMjMSuXbswfPhwaZq7uzv+/vtvhb7GAeXjsmvXrureshxzcXHB3LlzMXbsWAwbNgxA5vvfpEkTlfMnJyejT58+SEhIwOjRo7Fo0SIYGhoCAK5evYpWrVrh5cuXeV5PouKKGYvk6TNjyZ+X9UE8l9nY2KBatWrS60UxY7Vo0QIRERHMWIUkY2Wlj4yl7rjMDVUZy8/PjxmLKB8xZ5E8tmUxZzFnKU5ThTmLSP8KvBtPPz8/HDt2DMHBwYiKisKXX36J1q1b48mTJyrn//PPP9GmTRscPnwYkZGR8PT0hJeXFy5fvpzPNSd9SE1NBZB50tPGunXrEBcXB3d3d0ybNk2ha4T69etj2rRpADIfudY3V1dXpTuHAKBMmTLSY+87d+5Uu7z4CLq2265PDg4O6Ny5M0JCQhROSK9fv8aWLVvQuXNnODk55WmZudmXpqam2L9/Pxo1aoSFCxfip59+UlnGnDlzsGjRIjRu3Bj79u2Dqamp0jz+/v4QBAGrV69W6sq0du3aWLJkCQAodGXwzz//YM+ePTA1NcWBAweUunUwMjJCly5dtHsjcsDCwgKff/45ACh14+Dq6qoUjgDtj8vcGjp0KKysrAAA5cqVw+zZs9XOu3v3bjx69AgVK1bEggULpHAEAHXq1JGOASLSDjMWySvqGSs357LClLGcnZ3RsmVLZiwtM9bNmzeZsdSQz1j29vYYPXq02nmZsYjyHnMWySvqOau4tGUxZzFn5RXmLCqqCvTJvqSkJOzevRv79u2TvohmzpyJAwcOYNWqVZgzZ47SMkuXLlX4fe7cudi3bx8OHDgANze3/Kg26ZE4+Km2j2GLd+NkfZRc9N1332H8+PGIjo7GixcvFO4G14f09HScOHECf/31F549e4akpCQIggBBEAAAt2/fVrtsQkICAKh9JDy/jRw5Env37sXatWulfqrXrVuHxMREjBw5Ms/6Vhfpsi+fPn0Ke3t7heklS5bEH3/8gU8++QTTp0+XTsqi5cuXY8aMGShTpgwOHz6MkiVLKpXx8uVLnD9/HmZmZvDy8lJZD/EupL/++kt67ciRIwCAjh07wsHBQavtzStxcXH4448/sHnzZpibm6usd3p6OsLDw3N0XObWTz/9hLi4OACQurH48ssvVc4rHgNff/21yj8UvL29MWbMGL3Vlag4YcairPSZsZ4+fZonddQkPT0df/31F7Zt21bkM1b//v1x7NgxZqwsVGWs06dPA2DGUkU+Yz19+hTnz59HzZo1Vc7LjEWUt5izKCu2ZTFnMWfphjmLSD8K9GJfWloa0tPTle5IMDMzQ0REhFbryMjIQHx8PKytrVVOT0lJQUpKivT7u3fvcl5h0rtXr14ByLxTQxviXXOVK1dWOb106dKwtrbG69ev8fz587yppBrR0dHo3LmzUpeL8jQdf+K22NjY6Fy2r68vfH19AWT2L12mTBm4ubnBz88PX3/9tc7rAzKDQJ06dbBq1SpMnDgRALBixQrUqVMHHh4e2QYk+TppQ5d9+fjxY6WABGT2k/7mzRsAmRcrXVxcAAC///47rl69CiDzkf/Dhw+jb9++Ssvfu3cPgiAgKSkp25AeGxsr/f/BgwcAIJWnb56enkqvubu7IzAwUOn9i46ORrdu3XD9+nW169PX9+LFixcxb948GBsbY9CgQVixYgX8/PwQFRWlFGAB4PHjxwDUHwNlypSBlZWVFLiISL38yFhAZs768OGD9DtzVuGlz4z1+PFjrdebE7k9lxW2jNWoUaMin7Hq1q0LQP8ZS7yQzIylSFXGmjZtGr766itmLKJ8wJxFWbEtizmLOSt7zFnMWaR/BXqxr1SpUvj8888xe/ZsuLq6ws7ODiEhIfj7778VBnXVZNGiRXj//r3ak8C8efMwa9Yspdd79eql1SPmNjY28PHxgUwmU3gMV55+76/RD00n8awSExOznT8tLQ1AZl/VmuYV73Z69eqVwnxiGeJJzNTUVO165F8Xg+/Tp0/Vzi8+Cp6cnCzNIwaxrPUQtwHIPGlknSZ+gaelpSlN8/LyQnR0NDw9PeHn54eqVauiZMmSMDY2xocPH1CrVi2l+svX8b///lO5jVnrI78/xBPbZ599JnVFkJKSgrt37+LYsWM4duwY/vrrL6mP6eyIZYnb17t3b0ydOhUrV64EADx69AiDBw9GdHS0VHbWfa6qTvISExMRGhqKjIyMHO/Lhw8fKoXoFy9eYPjw4TA3N8eaNWswceJE3Lx5E0BmN5v29vb4+eef8f3332PYsGFwcnKCnZ2dwjrEi3YWFhZqnz6TJ+4P8YT9+vVrnT5bIk3HFfC/z4f4+WnevDlsbW0BZB7Xt27dQmRkJL7++musXr1aITx6eXnh9u3b2R6X2nzOs1JXXyBzf37zzTdIS0vDqFGjMHToUJw/fx4XLlyAn58f5s6dq7SMeEdgTExMtsfAvXv3pK5SstYpJ9uiK5ZReMoQ7+ojRfmRsQCo7W5Gm5ylTcYCinfOKi4Z6+HDhzAxMUF0dLTeMtbt27fxxRdfYPDgwXrLWMD/3i99Zax3794hMTFRrxkr67lQHxnrypUrAPSbsYD/dYumr4wFZL5v+sxYWfO7NnTNWJGRkTh79iwz1kdWRn6Uw5ylGnNWwWFbFtuyVGHOYs5iztIOyyg8ZegrYxXoxT4ACA4OxoABA+Dg4ABDQ0N89tln+OabbxAZGZntsr/99htmzZqFffv2oVy5cirnmTJlCsaOHSv9/u7dO1SsWBHbt2+HpaVltmUkJyfj3r17cHZ2VtknclGlSxcA0dHR2c4vDgxrZ2encV4zMzMAmcFTfj6xDPEE3bx5c7XrkX+9cuXKuHv3LpKTk1XOHxcXh7dv3wIAnJycpHnEO46y1kPcBgCwtLRUmiYGaiMjI4Vpt27dwu3bt2FjY4OjR48qDZQrfyeKqnpev34d79+/h52dnVLf2lnrI78/xGN4xIgRCgMhA8CaNWvw/fffY/369Vi4cKFWF7fFssTtGzNmDJYsWSL1g21jY4OxY8fCzMxMKjvrPtdUJyBzAN/KlSvDwMAgx/vy888/V+piYNSoUYiLi8PKlSvRr18/NGzYEE2aNMGrV69gZ2eH8PBwVKtWDfHx8Rg2bBjmzZunMNAwkBmMAMDAwAC7du2CgUH2w5pGR0ejdu3aADJP7DnpXkPdcSVfRrVq1aTPz48//qg0SLe/vz9+/PFHLFy4EAcPHgTwv+OyXLly2R6X5ubmOtddXX2BzMGj//33X7i7u2PRokUwMjLC/Pnz0blzZ+zatQvfffcd2rVrp7BM9erVcfr0aSQmJqpc79u3bxEfHw8g83jJ2p+8WKecbIuutPleZBn5U0Z6ejrHOlFD3xkLyPysi3fMArrlrOKasQDtc1ZxyViff/659N2tr4xVrlw5rF69Gq6urgrL5WXGAv73fukrY1laWsLc3FyvGSvruVAfGat58+aIiYnRa8YCgEqVKgHQX8YCMve5PjNW1vyuDV0zVkhICGrVqsWM9ZGVkR/lMGepx5xVMNiWxbYsVZizmLOYs7RTXDJQcShDXxlLu0+/Hn3yySc4deoU3r9/j0ePHuH8+fNITU1FlSpVNC63bds2+Pn5YceOHWjdurXa+UxMTGBpaanwQ4VTamoqwsLCAADNmjXTahnxJLFp0yaV0zdu3Agg88u/fPnyua+kGq9fvwYAlCtXTukkBABbtmzRuPzu3bsBAG3bts2zOn377bcAMu8wkR+YWBempqYYNGgQIiIiEBERAT8/P+kkndd02ZdZw9G6devwxx9/oHXr1vj+++8BZJ5se/fuDQAYPHiw9AU9ZMgQtGrVCocPH8b69esV1mNvb486deogPj5eGodPG+KJ/vDhw/kybpEqvXr1AgCcOnVKek08Lu3t7XN0XObUmTNnsGTJEpiYmGDTpk1S2Y6OjtKg1H5+fkpdGLRo0QIAsGPHDpV3Om3evFkv9SUqrvSdsQDmrKJC3xlLn2N85PZcxoyln4wl/l/fGat58+YAmLFE6jKWs7OzdEGAGYsofzBnkYhtWcxZAHNWTjFnEeWtAr/YJ7KwsECFChXw5s0bhIaGokuXLmrnDQkJga+vL0JCQtCxY8d8rCXpy4cPHzBy5EjExsbCw8NDbT/HWQ0cOBCWlpa4dOkS5s6dq/AI7OXLl6WBsSdMmKCXeouqV68OQ0ND3LlzRxqYVXTgwAH88ssvapd99OgRli9fDiDz5J1XDh8+DCDzsyU+Jp8TQ4cORceOHdGxY0etu1DIiZzuywcPHmDcuHGwtLTEhg0bIJPJpGni/7O+tnHjRlhaWmLs2LFSdwcisRxfX18cOHBAqZ6CIODcuXM4evSo9Fq9evXQpUsXJCUloUuXLnj48KHCMmlpadi/f79O74eutm3bBgAK+1o8LqOionQ+LnMqMTERPj4+yMjIwKxZs5QGMB48eDBatWqFJ0+eYPTo0QrTevToAQcHBzx8+BBTpkyRujkAgGvXrqkc6J6IsseM9XErLhkrKioK586dU5jGjKWdopyxPv30U2as/5ddxurduzczFlEBYM76uBWXnMW2rJxjzsod5iyivFXg3XiGhoZCEATUqFED//77LyZMmAAXFxdpMNQpU6bgyZMn0lXw3377Dd7e3li2bBkaNWokDVRrZmamcpBMKvw2bNiAKVOmIDY2Fg4ODlizZo3Wy9rZ2WHr1q3o2bMnpk2bhuDgYLi5uSEmJganTp1CWloafH19MXDgQJX97B4/fhzJyckKr0VFRQEAIiMjMXnyZIVp4t0bb968weTJk9GzZ0+4u7vD1tYWw4cPx7Jly9CqVSs0b94c9vb2uH37Ni5duoTp06er/HIfP348AgMD8fr1a1hYWGD16tVYvXq1wjz//vsvACAiIgI+Pj7o3bu30mPEO3fuxK1btwBk9nN+69Yt6QQ+adIkrbo9UMfBwUF6lF6fdNmXIkEQ4Ovri/j4eGzYsEHqgiA7lSpVwpIlS+Dn54cBAwbg+PHjUojy8vLCsmXLMG7cOHTu3BlVq1ZFjRo1YGVlhdjYWPzzzz+IiYnBpEmTFPpCDwwMRIcOHXD27FlUq1YNTZo0gb29PZ4/f46oqCjExsbmWX/MP//8szSodGJiIqKioqT9P2PGDGm+nB6XuTFx4kT8+++/aNy4McaPH680XSaTYcOGDahduzaCgoLQs2dPdOjQAUDm9/jWrVvRoUMHLF68GL///jsaNGiAV69eITw8HF5eXoiMjFQKtUSkGjMW5VfGUkUfGcvb2xvr16/XW8aaPHmy0rhGzFjMWMxYRKQKcxaxLYttWQBzli6Ys5izKB8IBWz79u1ClSpVhBIlSgjly5cXhg0bJrx9+1aa7u3tLbRo0UL6vUWLFgIApR9vb2+tyouLixMACHFxcVrNn5SUJNy4cUNISkrSZbOEO3fu6DR/TuVHOdqU4eTkJAAQAgMDNc4n7j9/f3/ptUmTJgkuLi7C9OnThZiYGLXLivtalRs3bgje3t6Co6OjYGxsLJQuXVrw9PQUtm3bpnI7/P39VR5Huv7Ib29GRoYwd+5cwd3dXShZsqRgZWUlNGvWTKqDqvqL75suP8HBwdLy3t7eStMNDAwEGxsboXXr1grbr43AwEABgNCtW7ds5xXLzrrP1b0uunfvngBAcHBwUDldm30pCggIEAAIHTp0ULmu4cOHKx1v8tq3by8AEAICApSmRUVFCYMGDRKqVasmmJqaCubm5kKVKlWEtm3bCgEBAcKTJ08EQVA8rlJSUoRVq1YJzZs3F0qXLi2UKFFCcHR0FNq0aSOsWLFCZR3k3xMnJyeV08UyVH3/GRoaCnZ2doKXl5dw9OhRpWUzMjKEDRs2ZHtc6vpdoqq+J06cEGQymWBmZibcunVL7XYIgiCsWbNGACDY29sLb968UZgvKipK+OqrrwRra2vBxMREcHV1FebNmyekpqZKn5l79+6prVNh+V5kGflTRlpamnDhwgUhLS1Nr+UURfrMWOrykS45K6cZSxCKx7GrbRlFIWPJb4u+MtaGDRuEWrVq6TVjhYWFSduhr4zl7e2d7X7PbcZSdy7My4wl7md9ZixB+N9xpa+MJZahz4wlvx3ayEnGki+DGevjKSM/ymHOUo85K3cKSxlFIWexLSt7zFn/w5ylGXMWyygsZeiSsXQ5pxf4xb78xot9LCMviCe0rCFA1zKcnJwU/gDQRAwv8gFJX4ra/mAZ+V9GdoEuL8rQFQPSx1kGG6EKBhuhWIa+ylGXsXQtIycZS/5inz6xDJahSU4ylq5l6IoZq3CWkR/lMGcVDOYslqGvMop7W5YgFL19wjLytwzmLJZRWMrQ18W+QjNmHxERERERERERERERERHppsDH7CMqisqUKYN58+bB3d09V+tZtGgRSpYsqdW8zZo1Q2BgIKpUqZKrMomIiIgKq4LMWC4uLoiPj89VuURERESFFduyiIiKN17sI8oBKysrpQGPc6JHjx5az1u1alVUrVpV5eDMRERERMVBQWYsALzYR0RERMUW27KIiIo3duNJREREREREREREREREVETxyT4iItKZv78/SpcuXdDVUFAY60RERESki8KYZwpjnYiIiIh0VRgzTWGsExVdvNhHREQ6mzlzZkFXQYlYJ3YPQkREREUVMxYRERGRfjBnUXHHbjyJiIiIiIiIiIiIiIiIiihe7CMiIiIiIiIiIiIiIiIqonixj4iIiIiIiIiIiIiIiKiI4sU+IiIiIiIiIiIiIiIioiKKF/uIiIiIiIiIiIiIiIiIiihe7CMiIiIiIiIiIiIiIiIqonixj4iIiIiIiIiIiIiIiKiI4sU+IiIiIiIiIiIiIiIioiKKF/uIiIiIiIiIiIiIiIiIiiijgq5AcdBy6EMVr5oAUPV6XstZOSdXVsrTWjg7O+PBgwdaz+/v74+ZM2fmaR2IqHAQvw8EQVB4fffu3ZgyZQrKly+P69evw9raWuXyvr6+CAoKQqtWrXDs2DHIZDKtyw4PD4enpye8vb0RFBSUm80gokKi4HJWzsvIy5zFjEVEInUZKzAwEAMGDGDGIiKdFbWcxbYsItIX5iwqDnixj/JU06ZNUbVqVbXTjxw5ghcvXuRjjYiosOjevTsiIiJw6NAhDBs2DCEhIUrzHDhwAEFBQbC0tMTGjRsVwpGHhwdOnTqFsLAweHh45GPNiYgKHjMWEanj6+uLzZs3Izw8nBmLiCgHmLOISB3mLCpKeLGP8pSfnx98fHzUTvfw8GBAIvqIrVu3DrVq1cK2bdvQvXt39OjRQ5r26tUrDBw4EACwdOlSVKqUt3dtEhEVZcxYRKTJnDlz0LlzZ2YsIqIcYM4iIk2Ys6io4Jh9RESUbypUqIBff/0VADBkyBDExMRI04YOHYoXL17Ay8sLvr6+BVVFIiIioiKnXLlyzFhEREREesCcRUVFgV7si4+Px+jRo+Hk5AQzMzM0adIEFy5cUDv/s2fP0KdPH1SvXh0GBgYYPXp0/lWW8kV4eDhkMpnGH1Vu3boFX19fODk5wcTEBNbW1mjVqhV27Nihcv6ZM2dCJpOp7Gs9KCgIMplM5V1d9+/fh0wmg7Ozs9K02NhYbN68GR06dEDlypVhZmYGS0tL1K9fH/Pnz0dycrLGbffx8dG43arqIy6TV/05i9tevXp1fPHFF2rna9OmjVSvrGVnVyfxPfT09FQ5XZd9mZGRgd69e0Mmk6FPnz7IyMhQmB4QEKByP2dkZKBPnz6QyWT45ptvlJYDgMjISPTt2xeVKlWS6tG2bVscPnxY7fuSlpaGjRs3onXr1rC1tYWJiQkcHR3RunVrLF++XJovu2Nc3X738PBQml6qVCm4uLhgzJgxePhQecyD2NhYBAQE5Pi41IdvvvkG3bt3x8uXLzF48GAAwLZt27Bjxw5YW1tj7dq1CvOL3wunTp0CAHh6eiq8B+zPnEg15iySV9QzVkBAAPz8/Ip8xhIzizp5kbFUvYdA3masmTNnonr16sxYzFhEHy3mLJJX1HNWcWnLYs5iztIn5iwqCgq0G08/Pz9cu3YNwcHBsLe3x5YtW9C6dWvcuHEDDg4OSvOnpKSgbNmymD59On755ZcCqDHlFzs7O7Rr107htU2bNqmc99ChQ+jRoweSk5NRo0YNfPXVV4iJicGpU6dw8uRJhIaGYsOGDXqvc2hoKObMmQMHBwdUrVoVjRs3RmxsLM6dO4fJkydj3759CAsLg4mJicb1ZO0r/t9//8WZM2f0XX0lp0+fxpUrV1CvXj2F169fv47jx4/rpUxd96WBgQGCg4ORkJCAkJAQWFpaYvXq1dmWM2TIEISEhKBTp04IDg6GgYHifQ/Lli3D2LFjkZGRgXr16qFRo0Z4/vw5wsPDcfToUcyaNQs//PCDwjJxcXHo1KkTIiIiYGxsjCZNmsDe3h7Pnz/H1atXceLECYwYMQIA4O3trbDs+/fvsXv3blhYWCh0BQAAzZo1U6p/27ZtUb58eQDA27dvcfr0aSxduhRbt27F1atXpWlA5nE5atSobI/L/LZq1SqcPn0av//+OxYuXIiff/4ZALBixQqF+gNA+fLl4e3tLY2TIL/9ADSOrUD0MWPOIlWKasYaNWoU7Ozs4OLiUuQz1sWLF5mxPqKMld1xmdeYsYjyB3MWqVJUc1ZxastizmLO0ifmLCrsCuxiX1JSEnbv3o19+/ZJTxDNnDkTBw4cwKpVqzBnzhylZZydnbFs2TIAwMaNG/O1vpQ/0tPTAQCurq5KdzioCkgvXrxA3759kZycjDlz5mDq1KnSHVMXL17El19+iY0bN6Jx48bw0PMgqO7u7tixYwd69uyp8PqbN2/Qu3dvHD16FAEBAZgwYYLK5cU7crL2FR8UFJTvAalhw4aIjIxEQECA0mctICAAhoaGaN68OcLDw/OsTF32pdgXNgAYGxtj586d6NChA9asWQNLS0ssWLBAbTkTJkzA2rVr0bJlS+zcuRNGRopfg6GhoRgzZgxsbGywe/duhScco6Ki0KFDB/j7+6NFixZo0aKFNG3AgAGIiIiAm5sb9uzZo3C3V1paGg4dOiT9nvXYvn//Pnbv3g1bW1uVd/ZER0cr/D558mSF4zkhIQEtWrRAZGQkdu3aheHDh0vT3N3d8ffff6Nx48YK68h6XHbt2lXdW6YXZcuWxerVq/HVV19h4sSJAICePXuid+/eSvO6uLggKCgIHv8/TkLW7SciZcxZlJU+M5b8eVkfxHOZjY0NqlWrJr1eFDNWixYtEBERwYxVSDJWVvrIWOqOS31hxiLSP+YsyoptWcxZzFmK01RhzmLOIv0rsG4809LSkJ6eDlNTU4XXzczMEBERkWflpKSk4N27dwo/VHilpqYCyDzpaWPdunWIi4uDu7s7pk2bptA1Qv369TFt2jQAwMKFC/O+slm4uroq3TkEAGXKlJEee9+5c6fa5cVH0LXddn1ycHBA586dERISgpcvX0qvv379Glu2bEHnzp3h5OSUp2XmZl+amppi//79aNSoERYuXIiffvpJZRlz5szBokWL0LhxY+zbt0/p+wcA/P39IQgCVq9erdSVae3atbFkyRIAUOjK4J9//sGePXtgamqKAwcOKHXrYGRkhC5dumj3RuSAhYUFPv/8cwBQ6sbB1dVVKRwB2h+X+tStWze4uLgAAExMTLBy5coCqQdRccScRVkV9YyVm3NZYcpYzs7OaNmyJTOWlhnr5s2bzFg5wIxFpF/MWZRVUc9ZxaUtizmLOSs/MGdRYVZgT/aVKlUKn3/+OWbPng1XV1fY2dkhJCQEf//9d54+xjpv3jzMmjVL6fVevXppdSKysbGR+m02NDRUM1f+PjKcF7I+KaRJYmJitvOnpaUByLyjRdO8SUlJAIBXr14pzCeW8d9//0nrU7ce+dfFPqfbt2+vcn7xjono6Gjcv39fev3Vq1cq6yFuAwC8e/dOadrjx4811i8+Ph6bNm3C5cuXERsbi+TkZAiCAEEQAGSeSNVtlzi469u3bxXmyVof+f0hhv3s3ndtiWWlpaXh66+/xt69ezF37lwMGTIEALB27VokJibiq6++wp49e1SWnV2dxPcwIyMjx/syIiICdnZ2SvP8+uuvaN26NaZPn46UlBQpcL969QozZszAnDlzYGVlheXLl+PZs2dKy79+/Rrnz5+HqakpXFxcVNajUqVKADK7ORX3x5YtWwBk3kWmzeclq+yOK3Gd4ufn8ePH0nzx8fE4deoUgoKCYGZmhlq1aimtIz09HefOndN4XOak3uqI3wdZ15e1jF27duHWrVsAMv+QXbVqlcq7oUSqtj8r8b1U9fnNS3n5frGM3BGPY1KUXzlL3R+l2uQs7TIWUJxzVnHJWBEREShVqhSio6P1lrHS09Px559/YsWKFXrLWMD/3i99Zax3797h66+/xrFjx/SWsdLS0hSOLX1krDdv3gDQb8YCgJMnTwLQX8YCMve5PjNW1vyeG+oylrgd4uvMWB9HGflRDnOWasxZBYdtWWzLUoU5izmLOUs7xSUDFYcy9JWxCnTMvuDgYAwYMAAODg4wNDTEZ599hm+++QaRkZF5VsaUKVMwduxY6fd3796hYsWK2L59OywtLbNdPjk5Gffu3YOzs7PKOycyKQ8kWtjJd4GUnejo6GznFx8ft7Oz0zivmZkZACh1wySWIQ5a6ujoqHY9WbtvAoBGjRqpnd/a2hqvX79GXFycNI+NjY3KeojbAACWlpZK08RAbWRkpDQtOjoavXv31vhF8P79e7X1fPv2LYDMO27k58laH/n9IR7DkydPxuTJkwEAhoaGKFOmDNzc3ODn54evv/5abX2yEssyMjJCv379sHDhQuzcuVPqSmD79u2oU6cO+vXrJ/V1nnWfq6qTKgYGBjnelyVKlFA5z5YtWxAXFwcg884n8U6b06dP4+rVqwAy+yO/ffs2+vbtq7T8hQsXIAgCkpOTUatWLbV1BzLDlLg/EhISAGTetaXLZ0uk6bgC/vf5ED8/3377rdI87u7uCAwMRO3atZWW7dGjB65fv662/Pfv38Pc3DxHdVdF/D5Q9RkRX3v48KHUt/no0aOxdOlSLFy4EP3791d7p524/Zq+H548eQJA9ec3L2nzvcgy8qeM9PR0XL58WW/rL8ryI2dNmDBB6r4E0C1naZexgOKcs4pLxipRooR0HtFXxtLmXJbbjCWWVa1aNb1lLEtLS7Ro0QJ16tTRW8YyMjJSOK/rI2PVrVsXgH4zFgDExsYC0F/GAjL3uT4zVtb8nhvqMpZYn2rVqjFjfURl5Ec5zFnqMWcVDLZlsS1LFeYs5izmLO0UlwxUHMrQV8Yq0It9n3zyCU6dOoWEhAS8e/cOFSpUQK9evVClSpU8K8PExCTfB+uknBO/3Ozt7Qu4Jrrr0aMHoqOj0alTJ0ycOBGffvopLC0tYWxsjA8fPmg8DjMyMnDv3j0AQOXKlXUuW34g5OTkZNy6dQvHjh3DsWPHcPv2bcyYMSNH2zRq1Ch899132Lt3LwDg0aNHSoP5alMneeIAvnnt6dOnGDlyJCwsLHDw4EH0798fN2/eBJDZzWalSpUQFBQELy8vjBgxAp6enkrHmdhtQMmSJdG9e/c8r2NekR/UNzExEVevXkVkZCS+/fZb7N+/X7pjC4AUjnJyXOqLIAgYMGAA3r17hwEDBuCXX37B27dvERQUhO+++w7Hjh1T6PqCiHImv3KW5gYkKiyKesa6fv06PD09MWvWLGasQpCxrly5AoAZixmL6OPFnEXyinrOYluW6jrJY87KPeYsIv0r0It9IgsLC1hYWODNmzcIDQ3VOCApFW83btwAANSsWVOr+R0cHHDr1i3cvXtX5fS4uDi8fv0aAFQ+Kp9Xbt26hatXr8LGxgZ79+5VGig3u8d+b968iXfv3sHOzg4VK1bUufysAyEDwJo1a/D9999j/vz5mDx5co76T+/Tpw8mTZqEgIAAAJl3j6m6i0jbOgH/G8A3K132pYODg8ry3rx5g5UrV8LDwwPHjx9HkyZN8OrVK9jZ2eH48eOoVq0aFixYgGHDhmHgwIEKAw0DkN57mUyGjRs3wsBAu2FNxUAiPsavb6oG9fX398ePP/6IoUOH4uDBg1J9rl69inLlyuXouNSXlStX4sSJE6hYsSJ++eUXAMAvv/yCY8eO4cSJE9KxS0R5gzmLAP1mLAcHByQmJuZNRbOQP5etWLECrq6uCtOZsf4nPzNW8+bNERMTo/eMVaFCBQDMWNpixiLKf8xZBLAtizmLOUsbzFlE+qfdp19PQkNDceTIEdy7dw/Hjh2Dp6cnXFxc4OvrCyCzC87+/fsrLHPlyhVcuXIF79+/R2xsLK5cuSKdVKloS01NRVhYGACgWbNmWi0jniQ2bdqkcvrGjRsBZD6CLd49og/iibtcuXJKJyEA0phu6oiBoW3btnlWJ/Hx+ISEBIWBiXVhamqKQYMGISIiAhEREfDz85MeP89ruuzLrAFp3bp1+OOPP9C6dWvpxFq9enWpz+zBgwdLj14PGTIErVq1wuHDh7F+/XqF9djb26NOnTqIj4/HkSNHtK57u3btAGT21f706VOtl8tLvXr1AgCp+xDgf8elvb19jo5Lffjvv/8wadIkKYSKXWWULl0a69atA5DZXY38uASiEiVKAPhfP+pEpBlzFon0nbFUNVzkldyey5ix9JOxxP/rO2M1b94cADOWNh4+fMiMRZSPmLNIxLYs5iyAOSunmLOI8laBXuyLi4vDsGHD4OLigv79+6NZs2YIDQ2V7tp49uwZHj5U7D/czc0Nbm5uiIyMxG+//QY3Nzd06NChIKpPeejDhw8YOXIkYmNj4eHhofXj/wMHDoSlpSUuXbqEuXPnKgxuefnyZcyZMwdA5heuPlWvXh2Ghoa4c+cOwsPDFaYdOHBAuuNDlUePHmH58uUAIA0enBfEQYItLCxga2ub4/UMHToUHTt2RMeOHTFs2LC8qp6SnO7LBw8eYNy4cbC0tMSGDRsUHpkX/5/1NfHEPHbsWDx48EBhfWI5vr6+OHDggFI9BUHAuXPncPToUem1evXqoUuXLkhKSkKXLl2UvrfS0tKwf/9+nd4PXW3btg0AFPa1eFxGRUXpfFzqQ0ZGBnx8fJCQkIDBgwejdevWCtPbt2+PAQMG4P379xgwYIDSYLWOjo4AoLHPdiL6H+YsAopPxoqKisK5c+cUpjFjaacoZ6xPP/2UGUsLGRkZmDRpEjMWUT5iziKg+OQstmXlHHNW7jBnEeWtAu3G8+uvv9Y44GpQUJDSa1k/MFT0bdiwAVOmTEFsbCwcHBywZs0arZe1s7PD1q1b0bNnT0ybNg3BwcFwc3NDTEwMTp06hbS0NPj6+mLgwIEqH/M+fvw4kpOTFV6LiooCAERGRioNyisOmPvmzRtMnjwZPXv2hLu7O2xtbTF8+HAsW7YMrVq1QvPmzWFvb4/bt2/j0qVLmD59unTilTd+/HgEBgbi9evXsLCwwOrVq7F69WqFef79918AQEREBHx8fNC7d2+lAUJ37twpPXafkpKCW7duSSfwSZMm5ajbA5GDg4P0KL0+6bIvRYIgwNfXF/Hx8diwYYNC/96aVKpUCUuWLIGfnx8GDBiA48ePSyHKy8sLy5Ytw7hx49C5c2dUrVoVNWrUgJWVFWJjY/HPP/8gJiYGkyZNwpdffimtMzAwEB06dMDZs2dRrVo1NGnSBPb29nj+/DmioqIQGxubZ99fP//8s/T9mJiYiKioKGn/y/dpn9PjUl8CAwMRERGBypUrY+HChSrnEbtACAsLw6pVqzB06FBpWvfu3REYGIiJEyfi+PHjKFeuHGQyGQYMGIAmTZrk12YQFRnMWZRfGUsVfWQsb29vrF+/Xm8Za/LkyTA0NFSYhxmLGasoZKwlS5YgMjKSGYsoHzFnEduy2JYFMGfpgjmLOYvygfCRiYuLEwAIcXFxWs2flJQk3LhxQ0hKStKpnDt37uSkejrLj3K0KcPJyUkAIAQGBmqcr0WLFgIAwd/fX3pt0qRJgouLizB9+nQhJiZG7bIABHWH7I0bNwRvb2/B0dFRMDY2FkqXLi14enoK27ZtU7kd/v7+0vpy8yO/vRkZGcLcuXMFd3d3oWTJkoKVlZXQrFkzqQ6q6i++b7r8BAcHS8t7e3srTTcwMBBsbGyE1q1bK2y/NgIDAwUAQrdu3bKdVyw76z5X97ro3r17AgDBwcFB5XRt9qUoICBAACB06NBB5bqGDx+udLzJa9++vQBACAgIUJoWFRUlDBo0SKhWrZpgamoqmJubC1WqVBHatm0rBAQECE+ePBEEQfG4SklJEVatWiU0b95cKF26tFCiRAnB0dFRaNOmjbBixQqVdZB/T5ycnFROF8sQPz/yP4aGhoKdnZ3g5eUlHD16VGnZjIwMYcOGDdkel3n5XSIe1/Ju3LghmJiYCDKZTAgPD9e4fGhoqABAsLCwEO7evaswbd26dcJnn30mmJubq/wchoWFCQAEb2/vvNoclQrLdy/LEIS0tDThwoULQlpaml7LIUXq8pEuOSunGUsQisexq20ZRSFjyW+LvjLWhg0bhFq1auk1Y4WFhUnboa+M5e3tne1+z23GcnJyUllGXmYscT/rM2MJwv+OK31lLLEMfWYs+e3ILXUZy9TUlBnrIywjP8phzioYzFn5V0ZRyFlsy8oec9b/MGflHHMWy8jPMnTJWLqc03mxLxu82McyVBFPaFlDgK5lODk5CS1atNBqXjG8yAckfSlq+4NlFK4yVAWkvC5DHQakj68MNkIVDDZCsQx9laMuY+laRk4ylvzFPn1iGSwjp9RlrLwsQx1mrMJXRn6Uw5xVMJizWIa+yijubVmCUPT2CcsoPGUwZ7GM/CxDXxf7CnTMPiIiIiIiIiIiIiIiIiLKuQIds4+oqCpTpgzmzZsHd3f3XK1n0aJFKFmypFbzNmvWDIGBgahSpUquyiQiIiIqrAoyY7m4uCA+Pj5X5RIREREVVmzLIiIq3nixjygHrKyslAY8zokePXpoPW/VqlVRtWpVlYMzExERERUHBZmxAPBiHxERERVbbMsiIire2I0nERERERERERERERERURHFJ/uIiChPjR49Gm/fvi2Qsp2dnTF8+HC0atWqQMonIiIi0hdmLCIiIiL9YM6i4oAX+4iIKE+NHj26wMp2dnbGyJEjUa1atQKrAxEREZE+MGMRERER6QdzFhUH7MaTiIiIiIiIiIiIiIiIqIjixT4iIiIiIiIiIiIiIiKiIooX+4iIiIiIiIiIiIiIiIiKKF7sIyIiIiIiIiIiIiIiIiqieLGPiIiIiIiIiIiIiIiIqIjixT4iIiIiIiIiIiIiIiKiIooX+4iIiIiIiIiIiIiIiIiKKF7sIyIiIiIiIiIiIiIiIiqijAq6AsXBw4cP8fLlS6XX4uPj86XsnJRja2uLSpUq6aFGRERERHmnoHJWbspgziIiIqKioKjlLGYsIiIi9XixL5cePnwIV1dXJCYmFnRVdGJubo6bN28yJBEREVGhxZxFREREpB9FMWcxYxEREanHbjxz6eXLl0hMTMSWLVsQGRkp/ezdu1fh99z8bNq0CRYWFqhTpw7+/PPPXJezZcsWJCYmKt29lRvOzs6QyWRa/8ycOTPPyqa8NXPmTFSvXh1BQUEKr8fExKBs2bKQyWT47bff1C4fHh4OAwMDmJub486dO1qXGxQUBJlMBh8fH5V1kslkSnUiopyTyWTw9PRUet3f3x8ymQy1a9fGhw8fVC4rCAJatWoFmUyG7777Tueyxc97bs4F3bp1g5mZGR4/fqw0TSaTwdnZWel1fW+beC68f/8+AKB9+/aQyWS4d++e2mW8vb0hk8nQtm1btfMkJyfj008/hUwmw+zZs3WqE6D5O7R169awtLTE8+fPdV5vfsiPnKXuJ6dl5HXOYsYqPsTP4p49exRe13fGEonHEhHlDXV5JikpCW3btoVMJsPcuXPVLn/r1i2YmZnBwMAAp06d0rrc8PBwyGQyeHh4aF0nXRSmjPXs2TMYGBigRYsWaucprH+nFvaMBegnZ2lqv2JbFhGRfmnKJi4uLqhevbpesonIw8MD1atXl9pECpq683RhyA6bNm3Sen264JN9ecTV1RWfffaZ9HupUqVQrVq1XK/3/PnzGDFiBOrWrYsjR46gVKlSCtPzqpy80rRpU1StWlXt9CNHjuDFixf5WCPKK+XKlcOqVavQs2dPjBgxAp6enqhQoYLCPPHx8fDx8YEgCJg3bx6qV69eQLUlopyaPn069u/fjytXrsDf3x/z5s1Tmmf58uU4efIknJyc8MsvvyhMc3Z2xoMHD3Dv3j2VjUF54fjx4/j9998xfvx4ODo6IjY2VqvlcrttuurevTuOHDmCPXv2YNy4cSrnWbZsGU6cOIGjR49izZo1GDx4sNI8U6dOxc2bN9GgQQNMmTJFYZrYcC8IQo7q+PPPP0vrDQwMzNE68oO+cpYmzFiUX3KbsYKCguDr6wtvb2/eGEVUCJiZmWHBggXo3bs3Zs2aBS8vL9SuXVthnvT0dHh7eyM5ORmjR4/WeDErP2XNWOnp6Votp6/8WKFCBTRp0gQRERGIiYlBuXLllNZbWP9OLSoZC8i7nJVd+5U85iwiovxjZmaGTZs2oWnTpjnKJuHh4fD09ESLFi0QHh6ez7XPe9pkh/fv3xfJNu4CfbIvPT0dM2bMQOXKlWFmZoZPPvkEs2fPzrbBasWKFXB1dYWZmRlq1KiBzZs351ON89f58+fRpk0b1KpVK9ugVFj4+fkhKChI7Y+Li0tBV5FyoUePHujduzdev36NgQMHKk0fM2YMHjx4AA8PD4wcObIAakhEuWVsbIzNmzejRIkSWLhwIc6ePaswPTo6GlOmTIFMJsPGjRthaWmZ73UcM2YMTE1NMXnyZJ2Wy+9t69q1KwwNDbF7926185QuXRobNmwAAIwfP17pKcA///wTy5Ytg6mpKTZt2gQjI+3u05LPWD/99BMAYP/+/UoZq379+ujUqRM2bdqEf/7556PJWEURM1bxxoxFVLzUrVsXEyZMwIcPH9C/f3+kpqYqTJ83bx7Onz+PGjVqaLzDPr8VxozVvXt3ZGRk4Pfff1c7T35/h4o5a9myZQCAiRMnKrVlZc1YQPFuyyqK7VfymLOIqLhr1KgRvvvuuyKXTfQlu+wwd+7cIvn3V4Fe7Js/fz5WrVqFX3/9FTdv3sT8+fOxYMECLF++XO0yq1atwpQpUzBz5kxcv34ds2bNwrBhw3DgwIF8rLn+FfWgRMXXihUrUL58eRw6dAgbN26UXj98+DA2bNiAUqVKITAwkN1EERVhtWvXxsyZM6U7u5KSkgD8706vxMREDBs2DC1btsz3uh07dgzXrl1D165dYWNjo/Py+blttra2aN68Oc6ePYunT5+qna9t27YYNGgQ3r9/D19fX6mhSPw9IyMDc+bMgaurq9Zly2esYcOGAQD++OMPlRnru+++gyAIGDJkyEeRsYgKK2YsouJl1qxZqFWrFq5cuaLQDfc///yDH3/8EYaGhti8eTPMzMwKsJb/U1gz1ldffQUAGm+eAvL3O1TMWe3btwcA9OzZU2Vblpixli1bVqzbsth+RURUNIwcObJIZRN905Qddu3aVST//irQi31//fUXunTpgo4dO8LZ2Rk9evTAl19+ifPnz6tdJjg4GIMHD0avXr1QpUoV9O7dG4MGDcL8+fPzseb69TEHJXH8AU0/qty6dQu+vr5wcnKCiYkJrK2t0apVK+zYsUPl/GL/uKr6W9fUr+79+/fVjlMQGxuLzZs3o0OHDtLTqpaWlqhfvz7mz5+P5ORkjdvu4+OjcbtV1UdcJj+7bLK2tsbatWsBZN4h+ejRI7x58wZ+fn4AgMWLF6vtui8tLQ1Lly5F7dq1UatWLZQtWxbdu3dHVFSUXuq6ceNGtGzZEo6OjtL+cHV1xZgxY1SOofXgwQPMnz8fLVu2RKVKlWBiYoLSpUujWbNmWLNmDTIyMnSug6bjVhzbQtM+PHnyJEaOHAlHR0eYmJigbNmyaNCgAfz9/fHq1StpPk3H7bFjx2Bubg4LCwucPHlSZTmenp4aj7+s9VN37L179w4zZ85EvXr1ULJkSZiamqJatWoYNWqUym5PNNUbUB4DTV5aWhrWr18PDw8PWFtbw8TEBJUrV4a/vz8ePXqkNL+m8U1EHh4ekMlkSt0SqHsdAMLCwjR+TgHgzp07GDx4MD755BOYmprCysoKX3zxBbZs2aK2HtWrV5fuflbl4cOHMDIy0niM5cbEiRPRsGFD3LlzB1OnTgUALFy4EH///TeqVq2qdN4V9+WDBw8AAJUrV4ZMJpO2I6+6evj1118BQO17rQ1dt03ejRs30LNnT9ja2sLMzAy1atXCokWL1HZz1b17dwiCgL1792qs06JFi+Ds7IxTp04hICAAQOaTfnfv3kWzZs0wZswYhfnF85go62f2xIkTUsYqXbo0AKBmzZoqM1bHjh1ha2uLs2fPwtvbu1hnrI9ZUc9YAQEB8PPzY8bK8v44OzvD19cXALBp0yZpm6pXr67xfKet169fw9vbG25ubihbtixKlCiB8uXLo2nTpggJCVE5Ntfx48cxYsQI1KtXD7a2tjAxMYGjoyN69eqFCxcu6FwHTcdNSkqKdJ5RdwwnJiZi6dKlaNasGcqUKQMTExM4OTnBy8tLaYwOded7QRAwaNAgyGQyNG7cGHFxcUrlaPqMiXXMSl29L1++jH79+ink0aZNm2Lt2rUqzzdiblB1rs0u/zx9+hRjx46Fq6srzM3NUapUKTRo0AC//vor0tLSlObP7rOh7rOs6TMO/G8MW3XbAQC7du1Cu3btpGPRwcEB/fr1w40bN9TWQyaToVSpUnj37p3Kdf7000/Z5ricKlGiBDZv3gxjY2PMmzcPkZGRCnfTT5o0CQ0bNlS7/ObNm9GgQQOYm5vD2toa7dq1w+nTp/O0jvIKImOpy4/iT3h4OJycnODu7o6wsDC8fftWbdl59Xeqqalptn+nim1ZYpdeDRo0UNmWJWaskJAQBAYGFsu2rI+5/UpeUc9ZRb0tK7t2BUB928KNGzfg7++Ppk2bwsHBASVKlICNjQ1at26tdj9oIn/+ye5H3fafOHECw4YNQ4UKFVCiRAmUK1cO3bp1w99//61yfvljbN26dXB3d4eFhQVKly6NDh06KD1tLU9du8qQIUNUtquInjx5ggkTJqB27dooVaoULCwsUL16dfj4+OCvv/5Smj8pKQmLFy9G48aNUbp0aZiamqJGjRqYOHGiQtuWSFMbTEFkm+rVq+c42+ijjSmncpJNPDw84OnpCQA4deqUwjGcV0O3aGr7Ey+2qfvuy438zA75pUDH7GvSpAnWrl2LO3fuoHr16vjnn38QERGBJUuWqF0mJSUFpqamCq+ZmZnh/PnzSE1NhbGxsdL8KSkp0u/qQn5hwaCUyc7ODu3atVN4Td3AlYcOHUKPHj2QnJyMGjVq4KuvvkJMTAxOnTqFkydPIjQ0VOoiTZ9CQ0MxZ84cODg4oGrVqmjcuDFiY2Nx7tw5TJ48Gfv27UNYWBhMTEw0ridrX/H//vsvzpw5o+/q68TLyws+Pj4ICgrCgAEDULZsWTx79gzt2rVT+egzAGRkZKBnz574/fffUaJECTRs2BAODg44d+4cGjZsiAEDBuR5PY8fP47Xr1+jTp06KF26NJKSknDu3DksXboUGzdulB5PFwUHB0vd3lWvXh1NmzbFs2fP8Pfff+PMmTM4evQodu3alScXVv77779s/7AbOXKkdHdovXr10Lx5c8TFxeH27dv48ccf4enpmW1D3rFjx9ClSxfIZDIcPHhQOkGr0717d5QsWVL6PSIiAv/9959W2/TixQt88cUXuHPnDkxNTeHh4QErKyv89ddfCAgIQHBwMI4fP64wHkROxcfHo3PnzggPD0fJkiXh7u6OsmXLIioqCiEhITh69CiOHTsGNze3XJelSWpqqvTUlDo7d+5E//79kZycDBcXF3To0AFxcXE4d+4cvv32W5w8eVLtBT0gc3wTVZ+PX3/9VetxVHLC0NAQmzZtgpubG5YtW4Zq1arB398fBgYG2LRpE8zNzRXmr1q1Kry9vbFr1y4kJCRIx9K7d+9gaWmJ8uXL57pOycnJCA0NhbGxMb744oscr0fXbRNFRESgXbt2SEhIQJUqVdCmTRu8fPkSU6dOVfsHVLdu3TBy5Ejs3r1b47Ei3jHWsmVLTJkyBYaGhlizZg0sLCwQFBQEAwPF+7Pq1asHb29v6dzo7e2tMN3BwQFbt25VGEQ6Ojpa6aIhkNn1loeHB3bt2oXHjx8rTNOUsYDMnCXf2F/YcxYV3Yw1atQo2NnZwcXFhRlLTo8ePXD27FmcOXMGn3zyCZo1awYg87Oo6SKCtl6/fo0dO3agdu3aaNq0KSwsLPD8+XNERETgr7/+wt9//40//vhDYZnvv/8ejx49Qs2aNdG0aVMYGRnh1q1b2LFjB/bs2YNt27ahe/fuua4bkHkRITo6Wu30R48eoV27drhx4wbMzc3RtGlT2NjY4MmTJzh9+jSioqJw7NgxjWUIgoDBgwdj3bp1aNy4MUJDQzV2QajLZ0yVHTt2oF+/fkhNTUXFihXRtWtXxMfHIywsDH/99Rf27NmD/fv3o0SJElqvU50///wTXbt2xZs3b+Ds7Iw2bdogJSVFGnfrwIEDOHjwoMrv/7wUERGhsTvDtLQ09O3bFzt27ICJiQnc3d3h4OCAO3fuYOvWrdizZw/27Nmj9L6L3r9/j40bN2L06NEKr6empmLlypV5uSlK3NzcMG3aNMycORP9+/dH+/btcfXqVdSpUwf+/v5qlxs1ahQCAgJgYGCAZs2awd7eHlevXoWHhwdGjBiR5/UsqIylLj+KxPzYvXt3REZGYv/+/ejfv7/a8vPi71QPDw+UKVNG49+pYluWl5cXgMyb8FS1ZclnrNjYWJ3asoDCn7PYfqWsqOasj6UtS5UlS5Zgw4YNcHFxQe3atVG6dGk8fPgQYWFhOHHiBM6ePauxnVodCwsL9OjRQ+U0Te0s48ePx+LFi2FgYID69eujefPmePjwIfbt24cDBw5g3bp10o1eWY0dOxZLly5F06ZN0aVLF0RFReGPP/7AsWPHsGPHDnTr1k1h/vfv36NNmzYq21VWr16NnTt3qmxXOXHiBHr06IG3b9+iXLlyaNWqFUqUKIH79+9LN1I1adJEmv/p06do164doqKiYG1tjQYNGqBUqVK4dOkSFi5ciJ07d0o3d+RWUck26mjTxpRbumaTdu3awdTUFKGhoUrfcba2tnqt65s3bzBp0iS9lqEuOzRv3jxPs0N+KdCLfZMnT8a7d+/g4uICQ0NDpKen46effkLfvn3VLtO2bVusX78eXbt2xWeffYbIyEisX78eqampePnypdJgivPmzcOsWbOU1tOrVy+tPtg2NjbSFX5DQ0Ol6Q8fPpT+lQ82iYmJGv/wVOWff/6Br68vqlevjl9//RXPnz/H8+fPNS6Tk3LU1Tk3ZYh3Rrx48ULjvGJXHq9evVKYTyxDvKPP2dkZ06ZNU1hWDEjyy718+RLffPMNkpOTMWbMGHz//ffShZioqCgMGDAAGzduROXKleHl5SUtK941krUe4jYAmUE66zSxETQtLU1pmo2NDTZv3ozGjRsrvB4XF4cxY8YgIiICP/zwg3R3QFbiXYpeXl5SVyUAsGfPHpw5c0aqj/z+EMN+du+7LsT3Jrt1jhgxAqGhoTh+/DgAwNLSElOnTlW7zJYtW/D777/D1tYWmzdvhr29PczNzZGWloY5c+ZIf2iret+1rVNWK1eulJ5oEaWnp2PixIk4cOAAFi5cqHDScHV1xcGDB5UGXX3x4gUGDhyIPXv2YPny5VJ3LYD2n8Gs8/j5+SElJQX29vZ4+vSp0rZt3rwZy5cvR+nSpbFw4UKFgXGBzO8LAwMDaRlVx+2ZM2cwZMgQyGQyrFmzBo6OjmrrKn6Ghw8fDgcHB+n1SZMm4b///lOqn6pjb+DAgbhz5w4cHR0RFBSESpUqAcgMK/7+/ti1axe6dOmCI0eOSA1Umj5v8vW6d++eQn/i48aNkwYInjt3rkJXQ2vXrsWiRYvw1Vdf4Y8//pC+u8XPb1JSktr3QfyOevz4scI8WV8X9/u6detw8+ZNaT9m3Y7bt2+jX79+kMlkWL58Odq2bStNe/LkCb7//nsEBgbCxcVFIXiL5TVu3Bhnz57Fb7/9hgYNGihMX7t2LT7//HPp7r6cfAdkZGRoXM7Q0BBjxozBvHnzpMDp5+eHsmXLKi1nZ2eHadOm4fjx40hISMCwYcPg6OiIxMREqWEn6/Gq6jtYk7/++gspKSmoXbu2tD+zG+dXHRcXF8ydOxdjx46Vtm3ChAkKf5TIS05ORp8+fZCQkIDRo0dj0aJF0rF19epVtGrVCi9fvlRazsHBAY0bN8aff/6Jly9fagzDYgNeQECA1JC3YMECfPLJJ0rzdu3aFV27dpXOjVnvQszIyEB6ejpcXFyk82KbNm3UZqwmTZpg165dCA0NRWRkpFYZC8hsaBfHBJSnTc7KLmMBeZuzdJXTMnTJWcUlY/Xq1UsqR18Za8eOHahevbpCQ3FeZyz590ufGSslJSXPMtbgwYNRtmxZnDlzBnXq1JH2rfjdK7+MeCzpsj3p6em4dOmS0nihT58+Rbdu3XDkyBEcPnwY1apVk6aNHTsWDRs2hJWVlcIyx44dw6hRo+Dn54fq1asrNXqrkpiYqPa4efz4MX766SfpHJx12zIyMtCjRw/cuHEDzZo1w6JFi2BtbS1NT0lJwd9//63wOcx6vhcEATNmzMCOHTtQr149rFy5Ei9evFDZW4GunzF54utPnz6Ft7c3UlNT8fXXX8Pf31/6Ln348CF8fHyki99jx46VlleXX8TXxHnkp8XGxqJLly6Ii4vDzJkz0bt3b+nGkjdv3mDUqFE4evQoxo8fj+HDh0v7I7vPhrrPsrrX09LS4OfnB0NDQ9jY2CAmJkZpO5YsWYIdO3agbt26WLJkCSpWrChNO3LkCMaMGYPevXvjxIkT0oVYsTx7e3sYGxtj6dKl6NChA2QymbTPDx48iKdPn0p5Sl0eVUdTnpE/rr7++mvs3LkT169fx40bN2BsbIzZs2dLx0xWYWFhCAgIgLm5OdatW6eQAVevXi01OiclJSmdR/IyYwE5y1m6ZKxmzZqhWbNmCA8PR0JCgtTTQVbdu3fH1KlTsXv3bo0X+wBg6dKlOHHihPQdWrp0aaxfv17t/KtWrcLvv/8OOzs7hIWFSd2mp6WlYeTIkSovCIttWeLNmzNnzlTbliVmLCsrK53asoDCnbNy0n4lj21ZbMsS65wXOUt+29Xtd3VtC56envjmm2+k9gvR3bt34ePjg19++QVNmjRB3bp1pWmaji3xvS5durTSsSBS186yfft2LF68GE5OTli4cCHq1asnTbtw4QIGDRqEwYMHw9HRUeV35apVqxAUFITPP/9cem39+vVYsGABvL29UaFCBYW2k2nTpqltVwkKCsLcuXOV2lWePXuGbt26IT4+HoMGDcLIkSMVbkB69eoV7t27J21XQkICevfujaioKPTo0QNTp06VbupIS0vDokWLsHHjRvTu3Vvh4pgu2UbcHznJNkD2x6M+so2q7UtMTMS0adM0tjFlJ7scIL5XumST7t27w9HREaGhoXByclI6rlVtV9bPWXbUfT5nzpyJ2NhY6f0Qt0vbc0hu2rinT5+udRu3eKODLm3cMTEx2dY/R4QCFBISIjg6OgohISHC1atXhc2bNwvW1tZCUFCQ2mUSExMFX19fwcjISDA0NBTs7e2FiRMnCgCE58+fK82fnJwsxMXFST+PHj0SAAhxcXFa1TEpKUm4ceOGkJSUpHJ6ZGSkAECIjIxUeP3OnTtarV907tw5wdLSUmjSpInw7t07rZfTtRxBUF/n3JTh5OQkABACAwM1zteiRQsBgODv76+yjD/++EMAILRp00ZpWQBC1kN29uzZAgDB3d1dZXmLFi0SAAjVqlVT2A5/f3+V9RAEQQgMDBQACN7e3krT7t27JwAQnJycVJan7r26ffu2AEBo0KCByumCIAg9e/YUAAhbtmzRWB/5Mry9vbV633UhvjfarPPnn3+W9ktAQIDGeatWrSoAEFatWiUIguJ2JCUlCeXLl1f7vutSJ3mq9kdGRobw7bffCgCEiRMnar2u0NBQAYDQs2fPbMuQp+q43bNnjwBA8PLyUrkPU1NThbJlywoAhN27d2v1Gcx6nBw9elQwMzMTLCwshPDw8GyXt7W1FQAIT548UXhd3TGW9fXo6GhpW/fs2aO0/oSEBKmMrVu3Sq+HhIQIAISOHTuqrJf43XLv3j3ptRs3bggymUywt7dX+X15584doUOHDgIA4cCBA9LrYWFhAgChRYsWat8H8TsqLCxM4+t37twRHj16JFhYWAj29vbC8uXLVR6/vXr1EgAIixYtUlne+fPnVX6HieVt375dMDExEbp3764wfdWqVQIAYd++fSqPMW0AEBwcHLKdLzk5WbCyshIACPb29kJycrLG+bPuM1XHr3i8qvoO1mThwoUCAKF///7Sa2lpacKFCxeEtLQ06TVN39PydNm2LVu2CACEihUrCh8+fFCa/ssvv0j7Qv54la/3unXrsq3TkydPBAMDA+m4yMjI0Di/uv0vn7G+//57AYBgYWGhNmOJ33HW1tZaZSwxH719+zbHOSu7jCUIeZezciKnZeiSs4pLxpIvR18ZS74MeXmZseTL0GfG+vnnn7OdV5eMpWk75InHUl548uSJYG1tLQAQbty4ofVy33zzjQBAOHTokFbz37lzR+1x07lzZykrqTqGf//9dwGAUKFCBSE+Pl5jGSL5831GRoYwcOBAAYDw+eefZ/u9putnTNXr06ZNEwAI5cuXFxITE5Xm37VrlwBAKFWqlMJ3Z9u2bQUAws6dO5WWUZd/Jk2aJAAQhg8frnJ7Hj9+LBgbGwtly5aVzkV37tzJ9rOh7rOs7vUlS5YIAIQRI0aozGGvXr0SzMzMBFNTU+Hx48cqyxw6dKgAQFi+fLnK8pYtW6aQC8V93qhRI6F8+fLCmjVr1H43aaIpz2T9DG7btk3a32PHjtW43tatWwsAhEmTJqmcXq9ePWmfZi0nLzOWICjnLH1kLEFQnfmzqlmzpmBqaqrx8yzKzd+p8tT9nSrmrO7duwsAhIEDB6ptyxIzVt26dbVuyyrsOSun7Ve6lKEK27LYlqWOfFnqtkWb75msxPPDhAkTFF7XdGxpk2lVbX96erpgb28vABAuXryosowFCxYIAIRx48YpvC4eY6NHj1ZZXv369QUAwk8//SS9ll27iiAIKttVRo8eLQCZ7VnaWL9+vQBAqFevnpCamqo0PT09XahVq5YAQIiKipJe1yXbiO9VTrKNIGR/POZ1thEE1W1Pf/75Z7ZtTNnJLgfIH1e6ZBNt2tME4X/bpcvnTBBUfz4vXrwoGBgYCG5ubsL06dMVtkvbc0hu2rg1lZGT7JC1Ths2bFBqy1JHm3O6qEDH7JswYQImT56M3r17o3bt2vj222+lJwjUMTMzw8aNG5GYmIj79+/j4cOHcHZ2RqlSpVC2bFml+U1MTGBpaanwU9iw64P/Ee8AyK57AJHYt3HW7stE3333HYDMuwxU3YGb19LT03HixAnMnj0bQ4cOha+vL3x8fKS78W7fvq122YSEBABQ231cYRMTE4NFixZJv+/YsUPtmHZPnjzBv//+CwDo16+f0nRTU1N8/fXX+qkogFmzZsHHxwfdu3eHs7MzgoODUb58eQwdOlRp3pSUFBw4cAA//PADvv/+e2kfrlmzBoDmfaiNxMREjBkzBmZmZli2bJnKeSIjIxEbGwtbW1ulbha0IXbdmZSUhC1btig9FaiK2A+/tp890fr16+Hj44PBgwcDAKysrNC1a1el+czNzdGxY0cAmXcri1xcXABkfpZV9c2tyuHDhyEIAtq3b6/2+1Ls3lRVX/F5ZcyYMUhISMDixYsVuhsSZWRkSF2b9erVS+U66tevj5IlS+Ly5csqx0IoV64cevfujd9//12hv/yAgABUqVIFnTp1yqOtUe+nn36SxiZ6+vQpTp06pfcy1RG/x+XvOMwNXbZNPN98/fXXKu+kVnceAiB1V7dnz55s6zR58mTpuzQqKgrXr1/PdhlV5DOWnZ0dAODLL79Um7HE99TU1FTrjAUUjZxFmfSZscSnqvQpPT0df/31FzNWAXny5Al8fHzQv39/tG3bFlWrVsXr16/xzTffSE/ByHv69CnWrVuHcePGwc/PDz4+PvDx8ZG+03Kbpw4dOoT9+/ejbdu2Ck8RyDty5AgAoE+fPirP05oIcl13VqxYMduuOwHdP2PyxPcnODgYQOa5xszMTGm+r776CmXKlEF8fDwiIyOl18U8tXnzZq27+D506BAA9RnFwcEB1apVQ2xsrN6epH727BlmzpwJOzs7zJ49W+U8YWFhSEpKksZSUiW73Ofr64tSpUpJY+ICwLlz53Du3DkMHjw4T7pE1SQpKUmhW6z9+/cjMTFR5bxpaWmIiIgAoPrvJgDZPtmWEwWZsbTVvXt3JCcn4/Dhwxrny4+/U8WcVatWLQCZT++pa8sS39PY2Fid2rKAwpmz2H6lGtuyik7OUuX9+/fYuXMnpk6dikGDBknn5d27dwPIfW7RxuXLl/H06VN88skncHd3VzlPduc7dceTeN6QHx8up+0qYr4aNGiQ2m2RJ5bZvXt3pZ4iAMDAwEDqPlq+nOKcbdSZO3euxjamvKRLNikIGRkZGDp0KARBwIoVK9Q+oZ5XilIbd3YKtBvPxMREpTFoDA0Ntfpj1tjYGI6OjgCAbdu2oVOnTkrrKgoYlBSJj7KWKVNGq/mfPHkCIHMgb1VKly4Na2trvH79WqcuJXIiOjoanTt31njC0NTHvrgtOfkDy9fXV+qz29DQEGXKlIGbmxv8/Pz09gUzePBgvHz5El27dpXG21y6dKlCl0Ii8dF2W1tbtScsdfswLxw4cEChQcTDwwObN29W6AIIAM6ePYtevXpJ3YOokttxEubMmYMHDx5g1qxZardZfGy+Ro0aOo8PePHiRezYsUP6Y2PLli0qL77JS01Nxfv372FgYKDU5VZ2zpw5o9APvzioriri+y0e60DmuGNt2rTBsWPHUK9ePXh4eCh0vaqqW8S7d+8CADZs2JDtGAaxsbFKr4kDCudGREQEdu3ahZYtW6J3794qB3J+9eqVdLxkPdZUefXqlcpGrFGjRmHTpk1YsWIFfv75Zxw9ehQ3b96U+vHXp4sXL2LevHkwNjbGoEGDsGLFCvj5+SEqKkrnYyUviI1GedHQoeu2id9j6j63ZcqUgZWVlVRHeZUrV4abmxtOnDiBuLg4te/dvn37EBwcjNKlS6Nr164ICgqCt7c3zp07p/IPI01UZSwDAwO1GUt8T9+8eVNsMhYp0mfGevz4sdbrzYno6Gh069ZN48XvjzVj5Zc3b94ojDlkbGyMYcOGSWMLy5s1axZ++uknjV335CZPJScnY+TIkTAxMVFZvkjMU2JjkS4mTpyIixcvAsgc9y88PFwam0sdXT9j8rKO56TucyeTyVC5cmW8efNGIU8NHToU69atw4EDB1CrVi189tln0o0p6v4GEvNU8+bNs61fbGysUjf38p+NnBo3bhzevXuH5cuXqz03ivU8ceJEtvlNVe4DMsfF9fX1xfLly3Hz5k0YGRlh2bJlKFGiBL7//nup4VJfpkyZgtu3b+Pzzz+HIAg4e/YsJk+erHDxUfTq1SvpBjB1x4E+/m4qyIylre7du+PHH3/E7t27NX7/5sffqbq0ZclnLKBot2Wx/Uo9tmUVjpy1adMmncbJBTLbi3x9faV9qEp+jJcpnu/++++/HJ/vsjtvyHfTnNN2FV3zlXjT8owZMzBjxgytyxk6dCjWr19fLLONKkePHsWRI0c0tjHlJV2ySUFYv349zp8/D19fX3z++ecIDQ3Va3mqskNQUJDKm3gKuo07OwV6sc/Lyws//fQTKlWqhJo1a+Ly5ctYsmSJwiCGU6ZMwZMnT6R+e+/cuYPz58+jUaNGePPmDZYsWYJr167p/GVeGDAoKRNDgr29fQHXRHc9evRAdHQ0OnXqhIkTJ+LTTz+FpaUljI2N8eHDB413eGVkZODevXsAcvaFID8QcnJyMm7duoVjx47h2LFjuH37drYnVF0FBwfj999/R7ly5bBu3TpERUWhVatWmD59Ojp16qR0wixoYmNNbGwswsLCMHr0aLRo0QKhoaHSGDOJiYno2rUrXrx4AV9fXwwZMgRVq1aFpaUlDA0NcefOHdSoUSPH44MBmd9fixcvRtWqVfU2wOz169dhbm6Ow4cPY/Lkydi9ezc2bNgg3RmoivhERtmyZXW+oBAYGCjd8aZu8Ons7N27Fz/88AO2bduG/fv3Z/sei39E16tXT6HffNG7d++kP6obNWqkNF3VoOmiI0eOZHvn5IcPHzB79mwYGxvj119/zbaegOanvkTqviPc3NzQvHlzrF+/HjNnzsSyZctgYWGhcZ/mhZSUFHh7eyMtLQ0//vgjpk+fjhs3biAsLAxjx47Nl8HisxIvBOf2j62C2Lbu3bvj8uXLOHDggMq7v16+fCndHRkQEICePXvi7NmzuHTpEubNm6fz97h8xhLH0QgNDVV4olk+Y4mNfCYmJoiOji7yGYuUFfWMdf36dXh6emLWrFnMWAWgVq1aEAQBaWlpePjwIVavXo2FCxfi3bt32LRpk9QotWfPHsycORMlS5bEr7/+ipYtW8Le3h5mZmaQyWSYOnUq5s2bl6s89fPPP+Pu3buYPn26wliBeenixYto1KgRZsyYgS5duuC7777D1atXUb58ebXL5OYzJr4fXl5eOHjwoM7LV69eHb/99htWr16N06dP49atW9kuI+aUHj16wMLCQuO8qhpw5T8b8t6/fy89CaFJeHg4QkJC0Lx5c41Pqon1rFq1Kpo2bapxnZoaHkeMGIHly5fj119/xTfffINdu3bh66+/1rhP88KpU6cQEBAAMzMzBAUFISMjA25ubvj111/RvXt3rXrgyA9FIWPVqVMHVatWxeHDh5GcnKxy3M/8+g4Vc5b4NEpkZCS2bdumsi1r5MiRADIvOm/ZsqXItmUVhvYr8Qmywqio56zi0pb1ySefoE6dOipvXNi1a5fSMfTkyRP06tULSUlJmDhxIvr27QtnZ2eULFkSBgYGOHr0KNq2bZur3KIt8XxXvnx5tG3bVqFdIytN48BrIr8d2bWryFPVrqItsZxmzZqpHIteXs2aNaX/V69eHREREZgyZUqxyzZZffjwASNGjMi2jSmvFPZs8urVK0ydOhVlypSRxsbVJ3XZYenSpdLYtEVJgV7sW758OWbMmIGhQ4ciJiYG9vb2GDx4MH744QdpnmfPnik8ZZOeno7Fixfj9u3bMDY2hqenJ/766y+VA5MWZoUhKBVGN27cAKD4Ba+Jg4MDbt26Jd29kVVcXBxev34NAFI3Zvpw69YtXL16FTY2Nti7d6/SBZPsHg+/efMm3r17Bzs7O62eAMpK7B5J3po1a/D9999j/vz5mDx5crYDeGtL/g+WNWvWwNbWFp6enhg6dChWrFgBX19fnD59WuHuRPFppZcvX+L9+/cq73zQtgvH3ChbtqzULVLnzp0xadIkqVu9P//8Ey9evMBnn32GjRs3Ki2bF4/4Dx8+HB8+fEBAQIDGwCwODH3nzh2dQ6W5uTkOHDiAli1bwsnJCe7u7hg1ahS++OILtQ1iun7uVBGP2/v370MQBJV3ool3v2R9es3CwgKLFy/G4sWLlZZxdnZWGiBYLKtp06Yqg1B0dLTGxj8XFxe1d0l5eHhke7Fv4cKFuHfvHiZOnKiy6zKRra0tzMzMkJSUhEWLFuU4kAPAyJEj0bNnT8ycORN//PEHvv/+e70/WSc2zri7u2PKlCmQyWTYuHEjateujY0bN6Jnz55qL5rqS7ly5QBA412X2sjJtonHrbrvqrdv36p8qk/01VdfYfr06di9e7fKi31iFurSpQu+/fZbAJmDojdt2hSzZ89Gly5dUKdOHa23UT5jiRf0PTw8FLoSkc9Y4nuakpKCunXrFumMRarpM2M5ODjordsXMWOVK1cOK1asUPre/dgzVn4zMjJClSpVsGDBAvzzzz8IDg5Gjx490LlzZwCZXd4AmV34qereKbd56u7du5g/fz6cnZ0xdepUjfOKeUqbxqGsGjVqhNDQUFhZWWHq1KmYPXs2fHx88Mcff6i92z4v85S6zx0AqVE1a5769NNPpe7D5YWHh8PT01NlWdHR0Zg0aRLq16+vc11VfTaAzPNkdg1iqampGDZsGIyMjLBixQqN84rvSY0aNXJ1l3vVqlXRoUMHbN68GfHx8UhNTZU+b/ry/v17+Pr6QhAEzJ07V2osmj17NiZMmIABAwbg6tWrCg2SNjY2MDExQUpKCu7fv6/yeNLH300FmbF08dVXX2HBggUIDQ1Fly5dFKbl59+pYs4Sj8nt27erbcsS31MbG5si25ZVGNqv4uPjMXz48HwvV1tsyyocOatZs2aYNm2ayvaA8PBwpYt9Bw4cQFJSErp166byooK+unpURXz/bGxsEBQUlG27hir37t1DvXr1lF4Xv8fEp4rly1PXrqJOpUqVcPv2bdy6dUvlhbGsKlSoAADo0qULxo8fr3U5QOaFyOKYbbJauHAh7ty5g4EDB2psY8oLCQkJOmeT/DZ58mS8evUKK1euVNvNdV4pym3c6hRoXwGlSpXC0qVL8eDBAyQlJeG///7DnDlzFPrMDwoKUuhT2NXVFZcvX0ZiYiLi4uLw+++/o0aNGgVQ+5wrDEGpMEpNTZXG8mrWrJlWy4j9R6u7G068aFOtWjW93rUphrBy5cqpfDJqy5YtGpcXTxpt27bNszqJjcUJCQkqu0LMqe+++w5v375Fv379FLqHnD9/PqpUqYK//voLS5YsUVjG0dERVapUAQD89ttvSutMSUnBzp0786yO2RFPFjdv3pReE/eh2DCUVXb7MDs7d+7EsWPH0LVrV7Rv317jvPXr14etrS1iY2Px+++/61ROjx490LJlSwCZjT6LFi1CQkIC+vTpo7Y7raNHjwLQ/nOnipubG8qUKYO4uDjs3btXaXpSUpLUf7pYv5wS37/9+/erHOdOnx48eIC5c+eifPnyCn/Mq2JoaIg2bdoA+F/jZ05169YNlSpVwvz58yEIAkaMGJGr9WXnzJkzWLJkCUxMTLBp0ybpe83Z2RkLFy4EkBmEVV3cEs/haWlpeV6vzz77DMD//pjOiZxum3h3244dO1R+lsQeCNRxdXWFq6srQkNDlf7IDAkJwc6dO2FjYyONDwpkNjaPHz8eqamp8PHxUVmu+Mdv1vdbPmNNmzYNQObTheoy1rVr1wAAffv2LdIZi1TTd8ZSN45WXhDPz/b29sxYKjIWoN/vXU2sra0BqM5TTk5OSvPHxMTg2LFjuSpz5MiRSE5OxtKlS1WOaSdPvKAQEhKi89Mg8+bNk26q+eGHH9C4cWOEhoaqHW85J58xVcSMJN8lu7y9e/fizZs3sLS0zFEjljwxT+U2o+TE0qVLcePGDQwfPhy1a9fWOG+rVq1QokQJhIeHIyYmJlfljho1Cu/fv0dwcDAaNWqEhg0b5mp92Rk3bhzu3buHL774AqNGjZJeHzt2LJo0aYK7d+8q9fZhZGQkPcG4detWlesVx3bMSwWZsQDtv8c0jYOcn3+nijlr9OjRAIAFCxaobcsSM1bTpk2LZFtWYWi/io+PR7t27fDff//le9naYFtW0chZqmjKLYIgqPxe0JcGDRrA1tYWN27cyPG47erOD+Lr4nEH5LxdRcxX69at02p+8QnonTt36v0JyaKSbeSJbUyOjo4YNmyYHmuY6eeff9Y5mwD59/fG+fPnsWHDBri7u2Pw4MF6LQvQnB0qVqxYJNq4syoaHYMXI4UhKBXGrg8+fPiAkSNHIjY2Fh4eHlo//j9w4EBYWlri0qVLmDt3rsKJ4/Lly5gzZw6AzAG09al69epSV4/yF6eBzDuFfvnlF7XLPnr0SBpvZMiQIXlWJ3HgcgsLi1w9USRv7dq1CA0Nhb29vVI/zhYWFggMDIRMJsOMGTOUBjAW/xCaOXOmwh3W6enpGD9+vPTkSV559eqVyj9WX716hcmTJwPIvBgmEu+eOXHihNJya9euxfbt23NVn7Fjx8Lc3BxLly7Ndl4jIyOpcX7QoEG4cOGC0jwXLlxQ6G9dlPVu82HDhqFjx464ePGiyotTR48exerVq2FsbKzyaSNtGRsbS0Fh/Pjx0l3nQOYfP6NGjUJsbCyqVKki/ZGeU25ubujevTsePXqEr776SuUdMwkJCdi6dWueD2Y+ffp0JCYmYsqUKVrd6eTv748SJUpgwoQJ2LRpk8pxPK5du6aywUKeoaEhZs+ejY4dO2LMmDF6vdsrMTERPj4+yMjIwKxZs5TuTh08eDBatWqFJ0+eSJ9reeLdgjn9A0WTJk2awMTEBP/884/KRtDs5GbbevToAQcHBzx8+BBTpkxR2JfXrl2TzjeadO/eHUlJSQp3Jz5//ly6S3nFihVKd+6KXRZevnwZc+fOVVpnXr3f4mDoub0YT4VPcclYUVFROHfunMI0ZqxM4vdAbhrp1bly5YrKJ33CwsJw4MABAKrz1Nq1a/Hhwwfp9bi4OHh7e2t8Ajo7J06cwKFDh9ChQwelJ3pU6dy5M9zc3PD06VP07NlTaTuSk5NV3i0OKOYpIyMjbN26FaVKlcLkyZNx9epVhXlz+hlTpUuXLqhWrRpevHiBUaNGKdzkce/ePYwbNw4ApDELc2PChAkoXbo0lixZgsWLFyvsL/kyc3vDW1ZxcXH48ccfUaFCBcyaNSvb+e3s7DBixAgkJCTAy8sLUVFRSvOkpKRg//792T7F2aZNG/Tv3x8eHh7Z3rSVW6dPn8batWsVPr8iAwMDBAYGwszMDCtXrlT6+1HMIMuXL5fOz6IFCxbg0qVLeV7fgsxYgPZ5pkGDBqhYsSL279+v8PkozH+nFuWMVRjar8QLfdeuXcPKlSvzvfzsFJecVdzbstQRc8uuXbvw7Nkz6fX09HT88MMPSt/B+mRsbAx/f38IgoBu3bpJw9HIS09Px8mTJ3H27FmV61i1apXSfvzll19w/vx5lCpVSmEoEDc3N7Rt21bndpWxY8eiVKlS2L9/P6ZPn650Q2pMTAwiIiKk31u3bo0GDRpI46+pGm/wzZs3WL16da4vJBWVbCNPbGNasmQJzM3N87ReWYWGhmL79u05yibieTo6OlrjuNy5JWbdFStW6L03k+yyw88//1xo2rh1UaDdeBYn8ne0AsDDhw8RHx+v8Nq1a9cwdOhQfPLJJ/j555/z5HFwVeVokpCQoPdxnnS1YcMGTJkyBbGxsXBwcFB4qiE7dnZ22Lp1K3r27Ilp06YhODgYbm5uiImJwalTp5CWlgZfX18MHDhQ5ft9/PhxpTtYxD8gIyMjpYtCIrGB4s2bN5g8eTJ69uwJd3d32NraYvjw4Vi2bBlatWqF5s2bw97eHrdv38alS5cwffp0lQ3B48ePR2BgIF6/fg0LCwusXr0aq1evVpjn33//BQBERETAx8cHvXv3VnqUf+fOndKXS0rK/7F373FW1fX++N9cB0SuAgqogHhQREQRKS+llhcSUTtFedQjSvW1NC/ZTxO/UnCUEI+alR38WgkpapmaR9MoMa1Aa0QFbyR6hBnEAQFhhuswzqzfH54ZRWZggFn7Ns/n4zGPh3vPWuv12TPsmbfrNXuvyvjnP/9Z92qt733ve03y9lJLliyp+6F755131nvh6c9+9rNx6aWXxk9+8pO44IILYu7cuXU/nC+55JJ48skn47HHHouhQ4fGpz71qejTp0/84x//iLKysvjWt74V06ZN2+111lq6dGmcddZZMWDAgDjwwAOjW7dusWLFinj22Wdj8+bN0b17962+J0cccUSceeaZ8d///d9xxBFHxAknnBDdunWL+fPnxxtvvBHXXnttTJ48eZfX884778QNN9xQ71+M1efyyy+PN954I+64444499xz4+abb46DDjooKioq6t7u4+mnn97qbRgactddd8Vhhx0WN910U4wcOTKOP/74KCkpiS996UvxwgsvRMuWLePaa6/d7eveXHvttfHcc8/FH//4xzjkkEPixBNPjE6dOsVzzz0XpaWl0a1bt3jooYe2+ovXXTV9+vRYu3Zt/OEPf4iDDjoohg4dGv37948kSeKf//xnvPHGG7Fly5ZYuHBhk77tyTvvvBMnn3zyDl+dWWvYsGExc+bMuOCCC+KCCy6I6667Lg455JDo0aNHvP/++/HKK6/EO++8E1/96lfjX//1X7d7rPPPP3+n3vd9V1199dXx1ltvxac//el632ajRYsW8ctf/jKGDBkSM2bMiDFjxsRpp51W9/kvfelL8fTTT8d5550Xp5xySrRs2TI6d+4cV1111W7/9XK7du3i1FNPjUcffTSeeeaZRn8fmuKxtW/fPu6999447bTT4pZbbolHHnkkjjrqqFi9enU888wzMXr06HjhhRe2edvZj/vSl74UN9xww1bXuPzGN74R77//fowZMya++tWvbrNPUVFRzJgxI44++uiYPHlynHXWWVtdU+FLX/pS3HzzzXHSSSfF5z73ubqTMFOnTq33OgT1qaqqir/+9a91X99c1Jg5q6ntasYn15pNmZqx6pPGjDV27Nj4xS9+kdqMdc0110SrVq222iYfZqxPf/rT0bt373jppZdi2LBhMWTIkNi4cWOMGDFit08SzpgxI/7rv/4rDj/88Nh3333rTgjWFl6nn356jBo1qm77K664Iu6+++544okn4oADDohPf/rTUVVVFX/5y19ijz32iHHjxtX7dumN8c4770S7du22+Z/xhrRs2TJ+97vfxamnnhp/+MMfYv/994/jjjsu9tprr1i2bFksWLAgunTp0qhXGx5wwAFx++23x9ixY+Occ86JefPmRbt27eKXv/xlXHfddbF8+fKdfo7Vp02bNvGb3/wmTj755Pj5z38ef/zjH+Poo4+OdevWxZ///OfYvHlznHbaaU1SVO27777x3//93/GlL30p/r//7/+Lm266KQ499NDo1atXlJeXx8KFC+N//ud/4lOf+tRu/UHYJ9VeR/aOO+5o8FpEn3TjjTdGWVlZ3HfffXXXFjrggAOidevW8c4778T8+fNjw4YN8Yc//GG71+2L+PBVNLvy1mg7Y+3atXV/uHfTTTfV/fX3xw0cODAmT54cV155ZYwbNy5eeeWVuj8kGz16dFxyySXxs5/9LD7zmc/EZz/72ejVq1e8/PLLsXDhwrj88ssbfJXprsrmjBWx7fxY+3Pwk/NjixYt4l//9V/jxz/+cfz5z3+OU089tcn/P/XEE0+Mrl27Nsn/p+bDjBVR/5z13HPPNfn5q09m7GjO2rBhQ3z729+O//mf/4n/+q//2uZ3dLY5l5Uf57K2Z/To0XHkkUfGCy+8EAMHDozjjz8+OnToEP/4xz/i3Xffje9973sZuWZYrW9/+9tRWloa//mf/xnnnHNOTJ48OQ488MBo3759LF++PObPnx9r166NadOmxac//elt9r/ooovic5/7XHzmM5+JPn36xKuvvhqvvPJKtGrVKu66665tXiU6ZcqU+OCDD+o9r7JkyZJYsGDBNudV9t9//3jwwQfjy1/+ckyePDl+8YtfxNFHHx1t2rSJkpKSeOmll+Kcc86pe5Vry5Yt45FHHolRo0bFr371q3jwwQdj6NChsf/++8eWLVvi7bffjldeeSWqq6vjggsuqPcVpo2VT7NNrdpzTGPGjEn1bWPXrl0bX//61yNi12aT/fffP4YPHx7z5s2LIUOGxPDhw6Ndu3bRvXv3uPHGG5tsne+88058/etf363rRDZGY2aHo446Kiuzw25Lmpny8vIkIpLy8vJGbb9p06bk9ddfTzZt2lTv50tKSpI99tgjiYi8+thjjz2SkpKSRn0NFi1atMNt+vbtm0REMn369O1ud/zxxycRkfzgBz+ou+973/tecvDBByfXXXdd8t577zW4b+3a6/P6668nY8eOTfbdd9+kTZs2SZcuXZITTzwx+fWvf13v4/jBD37QJF/Hjz/empqa5Ic//GFy5JFHJnvuuWfSuXPn5LjjjqtbQ33rr/267czHPffcU7f/2LFjt/l8y5Ytk7322is56aSTtnr8jVX7tfnkYzvhhBOSiEguvPDC7e6/YcOG5MADD0wiIpk6depWn6uqqkpuueWW5JBDDknatm2b7LXXXsmZZ56ZzJ8/P5k+fXoSEcnYsWMbtaYdWblyZfLv//7vybBhw5K99toradWqVbLnnnsmQ4cOTa6++urk3Xff3WafLVu2JP/5n/+ZDBkyJNljjz2Sbt26Jaecckrypz/9KVm8eHESEUnfvn232mdHz4/a78u//Mu/JJWVldt8vvZ72NBj+8Mf/pB8/vOfT/bee++kTZs2SY8ePZIRI0YkkyZNSlavXl233fa+frXHadGiRbLffvsl77//fjJ//vxk//33T84+++xk7ty5DT6OhtbX0P1VVVXJz372s+RTn/pUsueeeyZFRUXJgQcemFx++eXJnDlzGvw6NaT2ObJ48eJtPlddXZ3cd999yWmnnVb39dlrr72SgQMHJhdeeGHyu9/9LtmyZUvd9k8//XQSEcnxxx/fYF7tz6inn3663vvbtm2b/POf/9zm67Wjr//ixYuT73znO8mhhx6adOjQIWnXrl3St2/f5IQTTkhuvPHG5K233mrUOuqzvZ+NO9qvT58+W9331FNPJS1atEjat2+f/POf/9zu/v/v//2/JCKS3r17J2vWrKm7v7q6OpkyZUoyePDgpF27dnXr+/hjqf16ffx3QWP96U9/SiIi+cpXvpIkSZJ88MEHyfPPP5988MEHWz22Tz5Xm+KxJUmSvPLKK8m//uu/Jt26dUuKioqSQYMGJVOmTEmqqqq2+++11gEHHJB07Ngx2bx5c3LXXXclEZH07NkzWbly5XbXNH78+CQikqFDh27173rTpk3J1VdfnRx44IFJ27Zt677eH1/Djn6GPvzww436+f5xDc1HOzNn7WjGSpLCn7MKZcb6+GNJa8b65S9/mRx66KGpzlhPP/103eNIc8a68cYbt3psTTFjvfLKK8kZZ5yR9OjRI2nZsmW9v+9qvyY748knn0y+8pWvJAcccEDSoUOHpHXr1knPnj2Tk08+OZkyZcpWP3trLV68ODn33HOT/fffPykqKkr69u2bfPOb30yWL19e9zVo7M//RYsW1f3OiIjk+9//fr3bbe/f8Lp165KpU6cmRx11VNKxY8e6NZ1xxhnJr3/9662ehzv6/Xv22WcnEZFccsklSZJ8+BwbOnToLj/HGrp/6dKlycUXX5z0798/adu2bdKxY8fk2GOPTe688856v+ZJ0vDPkx3NPytWrEgmTJiQDBs2LOnYsWPStm3bZN99902OOeaY5Ac/+EHy8ssvb5Wxo9m1oZm59v7trWVHX/8nnngi+dd//dekT58+dT+PBg0alJx99tnJfffdl2zYsGGH66h9HB+3ozmuIQ3NM+eff34SEcnnP//5pKampsH9q6urk+OOOy6JiORb3/rWNp+/6667kiOPPDJp165d0rlz5+Skk05Knn766a2+pw09lqaYsZJk2zkrrRmrMfNjrb/85S9JRCTf+MY3Uvn/1KKioib7/9RdmbGSxJzVFDNWkhTOnOVc1o59/Hna0Pe9of9XW7duXXLttdcmBx10UNKuXbukZ8+eyVlnnZXMmzevwd+h2/u3tb3fP7V29Lt07ty5yRlnnJH07ds3KSoqSjp27JgMHDgwOeuss5Jf/OIXyfvvv7/V9h//Hk2bNi05/PDDk/bt2yedOnVKRo4cmcydO7fenEWLFjV4XuXQQw+t97xKrZKSkuTyyy+v+7rtueeeycCBA5Nx48Ylzz333DZfq82bNyd33HFHcuKJJyZ77bVX3Ux5+OGHJ5dccknyxz/+scGv1yd98vvyye/Hzsw2SbLj70cas80nzzF98nGkNZscffTRuzyblJSUJOecc07Sq1evpHXr1vV+TWof1/bOidSn9vnZrVu3ZNWqVdt8/pP/D9GYn+8f329XznEvWrQo1dnhl7/85TbnshrSmN/ptVokScpvmJtjKioqonPnzlFeXt6otn3z5s2xePHi6N+/f7Rr167ebUpLS7d5H+nS0tIGr/3VlHY1p3v37o3eL+2/fMzHjCVLlkT//v1j+vTpW128dWcz+vXrF/369dvm5dH1mTFjRlx44YVxzz33NOlfoHzSxIkTY9KkSds8tqa2M1+rXV1Tvv27kpE/OYWQ0aJFi+jTp0+9bwfblOp7HLU/z37wgx/ExIkTd+p4SZLEYYcdFm+++Wa888470bVr13jppZfiiCOOqPtr3xYtWkTfvn2zelHkhlx11VVx8803x2OPPRann356RjJ39DN09OjR8fjjj8eLL75Y7wXd69PQfLQzc1ZjZqyI7M1Zu5PR2DmrEH6WNHVOQzP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качества AE2\n","IDEAL = 0. Excess:  0.5789473684210527\n","IDEAL = 0. Deficit:  0.0\n","IDEAL = 1. Coating:  1.0\n","summa:  1.0\n","IDEAL = 1. Extrapolation precision (Approx):  0.6333333333333334\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},"id":"uLTA4xgzzzkL","executionInfo":{"status":"ok","timestamp":1762445983157,"user_tz":-180,"elapsed":1299,"user":{"displayName":"Alena Konovalova","userId":"13964397918094172748"}},"outputId":"f281801d-7435-4e49-9ea1-e0dc04ed9201"},"execution_count":28,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAjcAAAHHCAYAAABDUnkqAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAUU5JREFUeJzt3Xl4U2XePvD7JE3ShbYU6EpLWxYBkV1RUAb6glYGqwMugPqCLA7zwqg4KsgggiIibjOOjiuIA9R1BBUFERAc0P4sCLINZZG2tBTaAt2BtE2e3x8hadImbdImOcnJ/bmuXpDk5OQJM739nuc8iySEECAiIiJSCJXcDSAiIiJyJxY3REREpCgsboiIiEhRWNwQERGRorC4ISIiIkVhcUNERESKwuKGiIiIFIXFDRERESkKixsiIiJSFBY3FFAkScLixYvlbgYR+Rlmh39hceMn3nzzTUiShOuvv77N59q4cSN/Sb2ovLwcwcHBkCQJR44csXvMAw88AEmS7P4EBwfbHLt06VLcfvvtiI2NZeBSi5gd/sud2ZGTk4O5c+diwIABCA8PR3x8PMaOHYs9e/Z46+t4VZDcDSDnZGZmIiUlBdnZ2Thx4gS6d+/e6nNt3LgR//znPxlSXvLZZ59BkiTExcUhMzMTzz33nN3jdDodVqxY0eR5tVpt8/ipp55CXFwcBg4ciM2bN3ukzaQczA7/5c7sWLFiBVauXIk777wTs2bNQkVFBd555x3ccMMN+PbbbzF69GiPfQ85sLjxA7m5ufjpp5+wbt06zJw5E5mZmVi0aJHczQp4ly9fhlarhUrVfAfo2rVr8fvf/x7Jycn48MMPHQZUUFAQ7r///hY/Nzc3FykpKTh37hyio6Nb1XYKDMwO3yRHdkyaNAmLFy9Gu3btLM9NmzYNvXv3xuLFixVX3PC2lB/IzMxEVFQUxo4di7vuuguZmZlNjtmxYwckScKOHTtsns/Ly4MkSfjggw8AmLow//nPfwKATfelWU1NDR577DEkJSVBp9OhZ8+eePnll2Fv8/i1a9di8ODBCAkJQYcOHTBx4kQUFBTYHDNy5Ehcc801+O9//4u0tDSEhoaic+fOePHFF5uc7/Lly1i8eDGuuuoqBAcHIz4+HuPHj8dvv/3mcvv0ej0effRRREdHIzw8HLfffjsKCwvt/vuePn0a06ZNQ2xsLHQ6Hfr06YP333/f7r/vxx9/jKeeegqdO3dGaGgoKisr7Z7T7NSpU9i5cycmTpyIiRMnWv5j0xYpKSltej8FDmYHs8Ns8ODBNoUNAHTs2BHDhw93eMvLn7Hnxg9kZmZi/Pjx0Gq1mDRpEt566y3s3r0b1113ncvnmjlzJoqKirBlyxasWbPG5jUhBG6//XZs374d06dPx4ABA7B582Y88cQTOH36NP72t79Zjl26dCkWLlyIe+65BzNmzEBpaSlef/11/O53v8O+ffvQvn17y7FlZWW49dZbMX78eNxzzz3497//jXnz5qFv374YM2YMAMBgMOC2227Dtm3bMHHiRDzyyCOoqqrCli1bcOjQIXTr1s2l9s2YMQNr167Fvffei2HDhuH777/H2LFjm/x7FBcX44YbboAkSfjzn/+M6OhobNq0CdOnT0dlZSXmzJljc/ySJUug1Wrx+OOPQ6/XQ6vVNvvv/dFHHyEsLAy33XYbQkJC0K1bN2RmZmLYsGF2jz937lyT57RaLSIiIpr9HCJ7mB3Mjpay4+zZs+jUqVOzx/glQT5tz549AoDYsmWLEEIIo9EoEhMTxSOPPGJz3Pbt2wUAsX37dpvnc3NzBQCxatUqy3OzZ88W9v6n/+KLLwQA8dxzz9k8f9dddwlJksSJEyeEEELk5eUJtVotli5danPcwYMHRVBQkM3zI0aMEADE6tWrLc/p9XoRFxcn7rzzTstz77//vgAgXn311SbtMhqNLrXv119/FQDErFmzbI679957BQCxaNEiy3PTp08X8fHx4ty5czbHTpw4UURGRoqLFy8KIRr+fbt27Wp5zhl9+/YV9913n+XxX//6V9GpUydRV1dnc9yUKVMEALs/6enpds9dWlra5PsQmTE7mB2OssPsP//5j5AkSSxcuNDpdvkL3pbycZmZmYiNjUVaWhoAU3fwhAkT8PHHH8NgMLj1szZu3Ai1Wo2HH37Y5vnHHnsMQghs2rQJALBu3ToYjUbcc889OHfunOUnLi4OPXr0wPbt223e365dO5v7wVqtFkOGDMHJkyctz33++efo1KkTHnrooSbtMnd9O9u+jRs3AkCT4xpfSQkh8PnnnyMjIwNCCJvvkp6ejoqKCuzdu9fmPVOmTEFISIj9f8BGDhw4gIMHD2LSpEmW5yZNmoRz587ZHQgcHByMLVu2NPl54YUXnPo8ImvMDmZHc9lRUlKCe++9F6mpqZg7d65T7fInvC3lwwwGAz7++GOkpaUhNzfX8vz111+PV155Bdu2bcMtt9zits/Lz89HQkICwsPDbZ7v3bu35XUAOH78OIQQ6NGjh93zaDQam8eJiYk29+YBICoqCgcOHLA8/u2339CzZ08EBTn+v6Sz7cvPz4dKpUK3bt1sjuvZs6fN49LSUpSXl+Pdd9/Fu+++a/czS0pKbB6npqY6bF9ja9euRVhYGLp27YoTJ04AMIVQSkoKMjMzm3R1q9VqxQ3qI3kwO1rXvkDJjpqaGtx2222oqqrCrl27mozFUQIWNz7s+++/x5kzZ/Dxxx/j448/bvJ6ZmamJaAaB4CZu6/QAMBoNEKSJGzatKnJNGUATX5R7B0DwO5AQ28yGo0AgPvvvx9Tpkyxe0y/fv1sHjt75SWEwEcffYSamhpcffXVTV4vKSlBdXW1IkOF5Mfs8Cx/zo7a2lqMHz8eBw4cwObNm3HNNde06jy+jsWND8vMzERMTIxlhoK1devWYf369Xj77bcREhKCqKgoAKZFn6yZr0isOQqz5ORkbN26FVVVVTZXODk5OZbXAVgG6KWmpuKqq65q1XdrrFu3bvj5559RV1fX5OrN1fYlJyfDaDRarujMjh49anM+82wIg8Hg9h6TH374AYWFhXj22WctV4dmZWVl+OMf/4gvvvjCqanfRK5idrSufUrPDqPRiMmTJ2Pbtm349NNPMWLECHc12/d4f5gPOePixYsiPDxcTJs2ze7rP/74owAgPv74YyGEEOXl5UKtVotHH33U5rg777yzyaDAefPmCQCirKzM5ljzoLvnn3/e5vkJEybYDLo7ceKEUKvV4t5777UM2DMzGo02A+xGjBgh+vTp06T9U6ZMEcnJyZbHrgwKbKl9+/btc3pQ4AMPPCC0Wq04ePBgk88tKSmx/N08KPCzzz5rcpw906dPF2FhYeLSpUt2X+/Ro4e49dZbLY+nTJkiwsLCnDq3GQcUkz3MDttzutI+pWfHrFmzBADxzjvvOHW8P2PPjY/66quvUFVVhdtvv93u6zfccAOio6ORmZmJCRMmIDIyEnfffTdef/11SJKEbt264euvv25y3xcwrXcAmAbNpaenQ61WY+LEicjIyEBaWhoWLFiAvLw89O/fH9999x2+/PJLzJkzx3Ifulu3bnjuuecwf/585OXl4Q9/+APCw8ORm5uL9evX449//CMef/xxl77v5MmTsXr1avzlL39BdnY2hg8fjpqaGmzduhWzZs3CHXfc4XT7BgwYgEmTJuHNN99ERUUFhg0bhm3btlnuXVt74YUXsH37dlx//fV48MEHcfXVV+PChQvYu3cvtm7digsXLrj0PQDTOhmff/45br755iZbJ5jdfvvteO2111BSUoKYmBgAQH19PdauXWv3+HHjxiEsLAwAsGbNGuTn5+PixYsAgP/85z+Wxb3+93//13IVSoGJ2cHssGbOjr///e948803MXToUISGhjY53jpjFEHu6orsy8jIEMHBwaKmpsbhMQ888IDQaDSWq53S0lJx5513itDQUBEVFSVmzpwpDh061OTqq76+Xjz00EMiOjpaSJJkM7WzqqpKPProoyIhIUFoNBrRo0cP8dJLLzW5yhJCiM8//1zcdNNNIiwsTISFhYlevXqJ2bNni6NHj1qOcfbqSwjTFeeCBQtEamqq0Gg0Ii4uTtx1113it99+c7l9ly5dEg8//LDo2LGjCAsLExkZGaKgoMBuT0dxcbGYPXu2SEpKsnzuqFGjxLvvvms5xpWrr88//1wAECtXrnR4zI4dOwQA8dprr1n+PeBgOicAkZuba3mveYqsvZ/G03kp8DA7mB32ssOVjFECSQiZR2YRERERuRHXuSEiIiJFYXFDREREisLihoiIiBSFxQ0REREpCosbIiIiUhQWN0RERKQoAbeIn9FoRFFREcLDwx0uJU5EniWEQFVVFRISEqBS+cc1FrODSF6u5EbAFTdFRUVISkqSuxlEBKCgoACJiYlyN8MpzA4i3+BMbgRccWPeNO2vP/6CYO7ITCSLy9XVeP7GwTabGPo6ZgeRvFzJjYArbszdycHt2iHYj4KVSIn86fYOs4PINziTG/5xs5uIiIjISSxuiIiISFFY3BAREZGiBNyYGyJPUwsjtEYjJAi5myILAQm1KhUMEq+diJwlCYFgoyFgcwMwZcdllRrCDWPxWNwQuYsQSL5cjcR6PVR+NFDWE4xCoDBIh/zgdkCA/1sQtURnNKBfTTmC+auCywLYH9YetSp1m87D4obITZIvVyPVWIuOMTHQhIQAgRpUAqi7dAmac6XA5Wrkh3BmEZFDQqDrpSpE6bSIio2FpArU4ACEUaDs7Fl0u1SFI6GRbbowYnFD5AZqYURivR4dY2IQGhUld3NkpwkOBgDUlZSgUITxFhWRAxphREdjPSI7xkAbEiJ3c2QX2akTaoqKoBFG1Emt771h4hC5gdZohEqSTD02BADQhIRAJUnQGo1yN4XIZwUJAUmSoNZo5G6KT1BrNFBJEoJE28YesbghcgPLIMDA7VFuSjL/EbgDJIlawuxoxE25weKGiIiIFIXFDRERESkKixsiQsnZs1jw6Bxc36snkiPCMbhbN0wePw47v/8eALBmxQqMv/lm9IjuhPhgHSrKy+VtMBH5BF/NDhY3RAGuIC8P6cOGYteOHVi47AV8/8sv+HDDBgwbMQLz5zwCALh06SLSbrkFD8+dJ3NrichX+HJ2cCo4UYB78pGHIUkSNu36EaFhYZbne159NSZNeQAA8MeHHgYA/PTDD3I0kYh8kC9nB3tuiHyUweD5zyi7cAHbv/sOD8z8k004mUW2b+/5RhCR23gjNwDfzw4WN0Q+5sQxYMSAICSGaTFiQBBOHPPcZ+X99huEEOjes6fnPoSIPM6buQH4fnawuCHyMdPvCcJvx02LPfx2XML0ezx391i0caEsIvIN3swNwPezg2NuiHyIwQAcy1FZPZZwLEeCwQCo27aPnF2p3btDkiScOHrU/ScnIq/wdm4Avp8d7Lkh8iFqNXBVLyPUanHlsbjy2DOfF9WhA0befDM+eOdtXKypafI6p3wT+T5v5wbg+9nB4obIx6z8tB7dephCqlsPgZWf1nv085b9/TUYDAaMuelGfL1+PU6eOI5jOUew4p9v4LYRvwNgWsvi0P79yP3tNwDAkUOHcGj/fpRduODRthGRc7ydG4BvZwdvSxH5mO5XAT/8Wu/RLmVryV274rus/4fXlr+AZ+bNQ8nZM+gYHY1+Awdi+T9eBwCsfu89vLL0Oct7xo0eBQD4+7vvYcLkyZ5vJBE1y9u5Afh2dkjC10cFuVllZSUiIyPx7P6jCA4Pl7s5pBChhjoMvlSJzsldoNEFy90cn1Cnv4zT+afwS0gELqptdzy+XFWFp/v3REVFBSIiImRqoWuYHeQJzA5b7soN3pYiIiIiRWFxQ0RERIrC4oaIiIgUhcUNERERKQqLGyI3EJDMfyEzYf5DkrcdRD6M2dGIm3KDxQ2RG9SqVDAKgbpLl+Ruis+ou3QJRiFQq2LMEDlSL0kQQsBQVyd3U3yCoa4ORiFQL7WtuOE6N0RuYJBUKAzSQXOuFACgCQlBwHZYCFNhc/5cKQqDdDBILG6IHKmTVDivCkLo+fNQBQVBUgVqcADCKFBx7hwuqIJQ18bcYHFD5Cb5we2Ay9WoKymBqo1XHf7OKAQKg3SmfxMickyS8FtIONrVlONSQYHcrZHdZQH8FtYeYM8NkY+QJOSHhKNQhEFrNEIK0JvoAhJqVSr22BA5qValxp52HRBsNARsbgCm7LisUkO44eKQxQ2RmxkkFS6p+R92InKekCRcUvM/ye7CBCYiIiJFYXFDREREisLihoiIiBSFxQ0REREpCosbIiIiUhQWN0RERKQoshY3VVVVmDNnDpKTkxESEoJhw4Zh9+7dzb5nx44dGDRoEHQ6Hbp3744PPvjAO40lIp/B7CCi5sha3MyYMQNbtmzBmjVrcPDgQdxyyy0YPXo0Tp8+bff43NxcjB07Fmlpafj1118xZ84czJgxA5s3b/Zyy4lITswOImqOJISQZTnES5cuITw8HF9++SXGjh1reX7w4MEYM2YMnnvuuSbvmTdvHr755hscOnTI8tzEiRNRXl6Ob7/91qnPraysRGRkJJ7dfxTB4eFt/yJE5LLLVVV4un9PVFRUICIiwqX3MjuIApMruSFbz019fT0MBgOCg4Ntng8JCcGuXbvsvicrKwujR4+2eS49PR1ZWVkOP0ev16OystLmh4j8F7ODiFoiW3ETHh6OoUOHYsmSJSgqKoLBYMDatWuRlZWFM2fO2H3P2bNnERsba/NcbGwsKisrcenSJbvvWbZsGSIjIy0/SUlJbv8uROQ9zA4iaomsY27WrFkDIQQ6d+4MnU6Hf/zjH5g0aRJUKvc1a/78+aioqLD8FHDXVSK/x+wgoubIuktXt27d8MMPP6CmpgaVlZWIj4/HhAkT0LVrV7vHx8XFobi42Oa54uJiREREICQkxO57dDoddDqd29tORPJhdhBRc3xinZuwsDDEx8ejrKwMmzdvxh133GH3uKFDh2Lbtm02z23ZsgVDhw71RjOJyMcwO4jIHlmLm82bN+Pbb79Fbm4utmzZgrS0NPTq1QtTp04FYOoWnjx5suX4P/3pTzh58iTmzp2LnJwcvPnmm/j000/x6KOPyvUViEgGzA4iao6sxU1FRQVmz56NXr16YfLkybjpppuwefNmaDQaAMCZM2dw6tQpy/Gpqan45ptvsGXLFvTv3x+vvPIKVqxYgfT0dLm+AhHJgNlBRM2RbZ0buXCtCiL5tWWdG7kwO4jk5Rfr3BARERF5AosbIiIiUhRZp4IrUV5entxNaCIlJUXuJhAREXkNixs3OrHvKMaVlcvdDBvro9oDKXK3goiIyHtY3LiRvqYaSdW+9U86qvQotgHoPrCn3E0hIiLyCt/6L7EfO7HvKNJzcrDn/M9yN8VGavD10HeulrsZREREXsPixk30NdXoVGHA6pv7yd0UG0M27EJ6DrAjrB17b4iIKCCwuGmktQOCRW0tcqqzMCjiRfc2qI2yM4DJ2w3Q11S3+rtxQDIREfkTFjdWWjsguLK0GFGF5VidMRiD3N+sNhkUMQE51XORnqNGVG13l9/PAclERORvWNxY0ddUI/ZoccsHNhILYJcP9tqYZWcMxuTtBnRqxXcbqTmBHeCAZCIi8h8sbq7Iy8uDqK3FgcqtiO4Y6vL7daEa1HugXe4wKGICco0LUKdx/Xt1rukLfQ0HJBMRkf9gcXNFTeF53L/veKsHBA+KmODmFrnXzlGt+15DNuyCqO2KvLw8jr0hIiK/ELDFzalTp6ANC7M89tUBwe7S2uLLNCD5ODK1WuQ5cTwLICIiklvAFje35pUgLMR0m8aXBwTLzdUByevL9ByfQ0REsgrY4ibmeAna6UIA+P6AYLm5MiBZ3zdg/y9FREQ+ImD/S1Ss+RlVGq3lsS8PCJabswOSS89fhKidxPE5REQkq4Atbj7r3xXa0GDLY18fECw3Zwckm8fngMUNERHJJGCLmwHhdyI4rJ3czfAbzhZ/OdVzIWp72F0Nmb05RETkDQFb3JBn6EI1SDt+BHFQ2Ty/Pqo98sDbVURE5HksbsitasYthT5zLvobO9k8/ycU47W+nXm7ioiIPI7FDbmdLlSDM5qfbJ7jYGMiIvIWFjfkdjXjluKjyk9snhu+7QDSjh9BVlQHbsRJREQexeKGPKLxAOSacROgz5yLy2W97Q42NmOvDhERtRWLG/IaR4ONrXGFYyIiaisWN+Q1O0f1w5ANu5sMNrbGFY6JiKit+F8S8ppBEROgCz3QZLCxGQcdExGRO7C4Ia9qaaVjrnBMRERtxeKGvKqllY7trXDMXhwiInIFixvyKeZBx6GlpQCAjZ258B8REbnG8bQVIhnUjFuKqrO7EXXuv4g691+k5+TgxL6jcjeLiIj8CIsb8jm6UA3qdOWo05Wjcw2gr6mWu0lERORHeFuKfI71CsdDNuxCeg6wI6wd178hIiKnsLghn2QeeJydAUzeboC+ptruysYcbExERI2xuCGfNihiAnKq5yI9Rw316XCb1zjYmIiI7GFxQz4vO2MwhmzYhV6dkm2eF9HRXPCPiIia4IBi8nmmlY0bBhmbf6YfPImawvNyN4+IiHwMe27IL9hb2XjIhl0QtV3Ze0NERDZY3JBfsLeysS70ANKOH0FWVAfkIQ8ABxgTERGLG/JjNeOWQp85FyN/0UEdbhpsvK1MzynjREQBjmNuyK/pQjWoPf8zos79Fz0ucME/IiJicUN+rmbcUuhVQJ2uHAcqt0LU1tpdD4eIiAIHb0uR37MebDx5+3FkarVc/4aIKICxuCG/Zz3YOKd6LkRtD5vNNjkGh4gosPC2FClKdsZg3L/vOB4uqMbDBdUOt20gIiLlYs8NKYp5uwYUFAEA0kuSsCPsWiBF3nYREZH3sLghxdGFapB70VTcpF5O4AwqIqIAw+KGFKdm3FLL33PXL8DIw/XICmvHsTdERAGCxQ0p2s5R/TB5exD0NdU2g4zNWPAQESkPixtSNPMYnEcOtoe2c2eb117qIFOjiIjIo1jckOJlZwwGNmxCL2OyzfOi3QhuuklEpEAsbkjxBkVMgC70gGWQsdn9+7jgHxGRErG4oYBgPcjYLCfTtOAfe2+IiJSFi/hRwDIv+FdTeF7uphARkRuxuKGAZRpsnMXNNomIFIbFDQU0XagGacePoL5ML3dTiIjITVjcUECrGbcUVWd343LZBfbeEBEpBIsbCnjsvSEiUhYWNxTwdo7qh6qzuy2rGJ/Yd5S9OEREfkzW4sZgMGDhwoVITU1FSEgIunXrhiVLlkAI4fA9O3bsgCRJTX7Onj3rxZaTkpjWwdFg2i85eLigGg8XVHMGlQ9jbhBRS2Rd52b58uV466238K9//Qt9+vTBnj17MHXqVERGRuLhhx9u9r1Hjx5FRESE5XFMTIynm0sKtnNUP+g37ELEkZ8BAOK6SVz/xkcxN4ioJbIWNz/99BPuuOMOjB07FgCQkpKCjz76CNnZ2S2+NyYmBu3bt/dwCylQDIqYgOwM09+HbzuA6QdP4n2uXuyTmBtE1BJZb0sNGzYM27Ztw7FjxwAA+/fvx65duzBmzJgW3ztgwADEx8fj5ptvxo8//ujwOL1ej8rKSpsfInsGRUzAoIgJqBm3FPvLd1nWvzH/kG/wRm4AzA4ifyZrz82TTz6JyspK9OrVC2q1GgaDAUuXLsV9993n8D3x8fF4++23ce2110Kv12PFihUYOXIkfv75ZwwaNKjJ8cuWLcMzzzzjya9BCmSeQRV3pf5fH9UeSJG1SXSFN3IDYHYQ+TNJNDcKz8M+/vhjPPHEE3jppZfQp08f/Prrr5gzZw5effVVTJkyxenzjBgxAl26dMGaNWuavKbX66HXN0zxraysRFJSEp5f/wuCw9q55XuQMgVlzsVNMWOg7dwZhy+dxrareqL7wJ5yN0sRLldV4en+PVFRUWEzBsYZ3sgNwHF2PLv/KILDw11qMxG1nSu5IWvPzRNPPIEnn3wSEydOBAD07dsX+fn5WLZsmUshNWTIEOzatcvuazqdDjqdzi3tpcCiC9XgQOVWoBJIDb4e+s7VcjeJ4J3cAJgdRP5M1uLm4sWLUKlsh/2o1WoYjUaXzvPrr78iPj7enU0jws5R/Sx/H7JhF0RtV86g8gHMDSJqiazFTUZGBpYuXYouXbqgT58+2LdvH1599VVMmzbNcsz8+fNx+vRprF69GgDw97//HampqejTpw8uX76MFStW4Pvvv8d3330n19cghRoUMcHy9+wMYPL248jkDCrZMTeIqCWyFjevv/46Fi5ciFmzZqGkpAQJCQmYOXMmnn76acsxZ86cwalTpyyPa2tr8dhjj+H06dMIDQ1Fv379sHXrVqSlpcnxFShAmHYQnwtR24O9NzJjbhBRS2QdUCyHyspKREZGckAxuSxs/QJoIwYga8iNHFjcRm0ZUCwXc3ZwQDGRPFzJDe4tReQk7iBOROQfWNwQuYA7iBMR+T4WN0QuaNx7w9WLiYh8j6wDion8EVcvJiLybey5IXLRzlH90Lk6CElXfvQ11ey9ISLyIey5IXLRoIgJyDUuQNGRPQCAacHX43304vo3REQ+gsUNUStw9WIiIt/F4oaoFaxXL9aFHkDa8SPIiurAsTdERD6AY26I2ojr3xAR+RYWN0RuwPVviIh8B4sbIjcwz6DizCkiIvmxuCFyA9MMqj0YeXg/e2+IiGTGAcVEbrJzVD9M3t6094YzqIgCgzt7bZkbbcPihshFRgOgUjd93rz+zcjD9Yi+3AdqFVcvJgoUeXl5qC/TY1xZucNjDEZA3cz9EvPr66PaIw9cWqItWNwQOamkQINVzySg+JQOsV30mLqoCDFJdTbHfN0rDfsX34NTZZ3QteMF9H10D4KiGFJESldfpsfIX/Ygti68yWt5FyLx+Be34OT5Duja8QJe/sN3SOlQ0ezrX45ox4VB24BjboictOqZBJQWagEApYVarHomockx2a9MR2F5FABTYO3+20COwSFSuLy8POhrqtG5Bjij+anJzyNfDkfehUjTsRci8ciXw5t9/c/rboSoreXkhDZgzw2RE4wGoPiUruGxUULxKZ3NLaomxwg1is5H4+L5C1y9mEjB6sv0GHl4P3Jqf8XO4f1sXjMaJBSc69zwWKhRcK4zMvv3gEot7L5eVJaEEUfX4GcuDNpqLG6InKBSA7Fd9Cgt1MJolKBSCUQn1tqMvbF3TGxkIUb9dgRZHRlSREpi3auir6lG5+ogrB7Vz2b1crNtdrLj2qh7mn29piQbl8t6cXJCK/G2FJGTpi4qQnRiLQAgOrEWUxcVtXjMtYs3WNa/ISLlqC/TI+NkMTJOFmPk4f3INe6xW9gALWeHvdfNC4OaP4O3t13DnhsiJ8Uk1WHeinyHs6XsHzMauTmmGVRZYe3QfWBPbzaZiDwgLy8Pl8suIPZoKQBAffFKr42D41vKDnuv74w0LS3R6WgxAOByTyNvb7uAPTdELnJU2Dg6xnr1YiLyf/VleqQdP2IZENxcr421lrLD+nXzwqDmz5h+8CRqCs+3seWBgz03RB5mvf4Ne2+I/Ju516bq7G5syxhsed5Rr01b7BzVMDh5yIZdELVd2XvjJBY3RA40d/vJ1fdy9WIiZTD32tSGauz21rgzN6zPrws9gLTjR5DFGVROYXFD1Igzi/W5+t5BEROQUz0X6TlqRNV2B8DVi4n8kc3MKKvnPZEb1mrGLYU+cy4ul/Vm740TOOaGqBFnFutrzXuzMwbj3OldiD1ajNijxbhcdoGLdBH5kRP7jjqcGeWu3CgpcPxe8wwqzpxqGYsbIivmhfiMRsn02Gqxvra+d1DEBOhCNZYBggwpIv9i7rWxHgsDuDc3hDC992y+psmxNeOWoursbl4YOYHFDZEV80J8KpUAAEiSMD1Wm0KocViZH5vvlauDjACE1REC54oaQmrnqH74aFB3fDSoO2dQEfmR5nptGueGSiUQk9SQG4BtdjTOjdguejTOjZVPd4Y97L1xDsfcEDUydVER3nuqM86f0UIICfpLKjx7XwrKS03dxh3jazH+zyX46p1oFJ/SQR1khKFehZgkPQz1ja8XJKx6JgHzVuQDsB0gyBlURP7D0Vgbs6mLiizjZiSVQEmBDk/8vjsM9SpLRnSMNy3Ud/6M1vJcbBc9bnuwFCsXJlqdTcL5M1q7g5OtJyeQY+y5IWokJqkOQRphuQorLw1CeWlD78v5MxqsfDoBxadMxY6h3tSdfO60Fiq1scn5ik/pUF/b9HPM699cqmJIEfmyvLw8iNpa5FRnOVzPxrwQX2wXPcSVW0zmbDD/ef6MBufPaGyeKy3UYsO70XbPae+2lnn9m5GH9+PYnqNt+l5Kxp4bokYab4AJSI2OkGA0SE1eN90zlyxXZKaXBNRqgbm3XdVkFkRixf2YvFaNs//sgvaJNZjxQQ1iujpxk56IvKqm8Dzu33ccqzMGN7uejePsaPxnw9+NRgklBTp0jK+9UvhIAATUQfZzAwC+7pWG/YvvwamyTojpXofJb5cxOxphzw1RI43vn5vuhdveD7fP9Hz76HpL97NaLSwDBRvPoFj1TAKKq0xd0RVFIVj9pyg3fgsicgdnem3M7GeH9Z/2NLzWMd5UwKiDHOcGAGS/Mh2F5aa8KD0ZxOywg8UNkR3WG9lFdKi3eS0soq7R7SdzOJnC6EKxBkEagRe/PgZDvcrSRW09g8J8hSeMpl9BYVSh5ITGqdkVROQ9NYXnMf3gSehCm85essc6O5qyd5HkfG4AVrOrhGkwjjBKzA47WNwQ2RGTVIcn3snHy5uOoabSekSfQE2lxhI89ogrYWRvBoV55lWT1yQjIhOqWr2yKRG5n7nXZn/5LtSMW+rUe8zZ0XT8nYDkODacyg3AzoxOlRFRyReZHY2wuCGyYjSYVgtdPiMZj4+5CssfTL4yfsb6nrkEIZreO7dmmtppexUXnViLqYuKLMdYvxYTUYiRs7K4dgWRDzFvtdBSr42518Q6O4wG13PDtJRE87nR+PWkyAsYMetnV7+a4nFAMQU081RL6+XP1UFGy/3uc6e1MHUfNw4ie881qK+T8PiYhsGAnRLqmlxZmWdXGA1A+FcLoK0agKyyG7klA5EPsN4gs/6+F21es5cbsV30qK+TUFZsLoSazwh7DPWqFnMDsM0O7cdzsafjZG7J0AiLGwpIzYWS9Vo1tlda1hw9bwo083RP82BA8zo39qjU3DeGyNdYb5BpHnXXXG6UFGhb7JlpXkMxVFzQcm4ApuwwL+rHDTVt8bYUBaRVzySg5MpeLsWndKYFs5oZR+O65pdht7dqKVceJfId9rZaaC43zIWNJDU3M6o5VvnTzBYMjVdK55YM9rHnhgJO07UozK5cOUniykQGCY1nNLTM3nECJYUaxCTW4VyR7e0v8wqlUxcVIYa9N0Q+oWGrhV8xKMI0kLil3FCpBCI61qGqLOjKAn2u35Zq7OU/JWPuu/nolGDKDvPK6YBppfQHnzuNmKQ66EI1uPXkSezqGt+mz1MSFjcUcMyzDZoGlSmIVCrRaJG+tpLw4oOpAGAznsd6hVJzFzS7mInkZ2+rhZZyo32MaY2apjMpW1/kGA0qvDA9BQ2Lgzac5/wZjVO3rgIVb0tRQJq6qMgyM8GkoSvZ3iwH57TcHW29foW9W1fmLRm4bwyRPJrbINNxbghcOKvFhbPWt7ftrUrcGuaLIetcMj3v7M7jgYjFDQWkmKQ6PPZWPqITzeNbmhsg7KzWDCC0XcfCet+YE/u4bwyRt9kba2NmXsOmU+fGuWH9u+/MqsTuYLv+DdlicUMBx7wWxYsPpkKlAjrEOVpNFLAdd+MOwnLlpw4ynbfxOhbsvSGSR0tbLZhnS507bRozZ3/wsL3bUu4gbHqNOsbXNVn/hhpwzA0FnFXPJKD0yoyH0kItomLrYHtfvGGAoKQSMBgkN9Y3Eh57Kw+d4usQpG1YL8OaqfdmAUYerkdWWDt0H9jTXR9ORC249eRJVFlN/7ZmnR1GowRJJSCajM8zbXopjBKMRqB1t6XsjdOR8Og/8xDXxTS2p3Fu6A1600zLlFZ8nAKx54YCimVfFqt9W0yzD+zt1nvlPrfDtW5cJ6mMWPl0Z8y97Sosn5GMc0X2Vz5l7w2Rb2mcHcIoXRmf15gEQ72qjUtLNH2vSm3EyzNT8dLMprnB6eBNsbihgGJv35aYJO+tKyOMTRf4s2dQxATkVGdB1NYyrIh8QJM9nZpdz8bd420aZnA6yg2uk2WLxQ0FHOt9WaJi62ymV9py53Rw63M2v8CfWXbGYNy/7zhqCs97oB1E5Crr7OgQZ76dbY+7s6Pl3LDu7eUFEYsbCkDmfVle3nQMQRphtReMdzXe7bcx9t4QeVdLvR6Ns8MzF0DNc5Qb1jMt2XvD4oYCnPU9dO8QUKntz5Kyh13NRN6jr6lGpwqD3WngjdlfrdhTnMuNnaP6oetlb7bLd3G2FAUs8z300kJ37yvVvKiYOsx/P8+p9Sm4oSaRd9hOA3+x2WPN2VFSqLWzIrFnRHSow19X5SFI65WP83vsuaGAdvvMUkgqby26BZh2DNfixT8mo6TAudth7L0h8ryawvO4f99xZGcMdur422eWtmGTTFdJKC/VWmZZOpsdgYzFDQW0r96J9tqVl7WSAh1emulcSHGaJ5FntbR4nz1fvRPt5j3onFN8yvnsCGQsbihgNV63wtuDAw31Kry/2P5U8MbYe0PkOfVleqQdPwJdqHMFw9l8zZUxN94vbgDXsiNQsbihgHRkdyjmZXS/8shbXctNlRQ4t/EdF/Uj8qyul3VODSQ+sjsULz6Y4vkGtcDZ7AhULG4oIL2/KMFqfRvvzpYykyTnN77jhppEvmHl0wmQJzNalx2BisUNBZz62ivbKjQbUJ7ozREIi6i37EQek9TyVHBr7L0hkld9LRxsueBJps17755T3OrsCEQsbijgnDujgW3x4szOvu4goaZSg9JC0zoU9XWufQZ7b4jkU1KgwSuzkq888vatbAmf/T221dkRiFjcUMAoKdBg+YxkvPhgaqNX5AmK82c0DveWcoS9N0TeV1KgwUszk60W7pMjMxo+szXZEWhY3FDAWPVMAkoKzCtg+cKVj2mPmPpa59/BLRmIvO/9xQlXbmX7iub3pSOZixuDwYCFCxciNTUVISEh6NatG5YsWQIhmu/y27FjBwYNGgSdTofu3bvjgw8+8E6DyW+Zp30L0biokW+mFCCgDjK6vDBXoG+oydwgbzIaTDOT5Nf0VjrXu3HM6eKmqMj9g5eWL1+Ot956C2+88QaOHDmC5cuX48UXX8Trr7/u8D25ubkYO3Ys0tLS8Ouvv2LOnDmYMWMGNm/e7Pb2kXKYl0tXNVmN2Ns9OA0BpVILyyJgpYVap7uZ/an3pqL4rNvPydwgbzJnh/dWI3bUjqaf70puBBqni5s+ffrgww8/dOuH//TTT7jjjjswduxYpKSk4K677sItt9yC7Oxsh+95++23kZqaildeeQW9e/fGn//8Z9x1113429/+5ta2kfJMXVSE6ETTPaDYLrVQqY2QY2AgALzw5TEYDSpLT5LR6Fo3sy5Ug+kHT/p8782r6WnY9+U6t56TuUHeNnVREWKSTNkRk6SXJTuMBhWeX3fsyqPW5UYgcbq4Wbp0KWbOnIm7774bFy5ccMuHDxs2DNu2bcOxY6b/wfbv349du3ZhzJgxDt+TlZWF0aNH2zyXnp6OrKwsu8fr9XpUVlba/FBgikmqw7wV+Zj7Xi7q66QrUzrl6bn520PJ6Bhfa7kaVKlcW7eiZtxS7C/f5fO9N+mPzcPnT83Dmtl/xMXyMrec0xu5ATA7qIF1dtReliM7BACBv46/CuogY6tzI5A4XdzMmjULBw4cwPnz53H11Vdjw4YNbf7wJ598EhMnTkSvXr2g0WgwcOBAzJkzB/fdd5/D95w9exaxsbE2z8XGxqKyshKXLl1qcvyyZcsQGRlp+UlKSmpzu8m//WtJAs6fkes+tSkQSwq0KC8NsvTcRMXWubxuhT9syTDsfx/AXzZuw8XyMrx8y0j8d9t3bT6nN3IDYHZQUyuf7ozyUjmyQ4I5OwwGCebhZZJK4PaZpTK0x/cFuXJwamoqvv/+e7zxxhsYP348evfujaAg21Ps3bvX6fN9+umnyMzMxIcffog+ffpY7oUnJCRgypQprjTNofnz5+Mvf/mL5XFlZSVDKoCZBxbLTQjJskKySiUQpBGISapz6Rw145ZCnzkXl8t6Iy8vDykpKR5oadt1SOqCmZmf4cfV72P1/81ATLcekCTTdx8+fDjUarXP5QbA7CBbRgNw/oy25QM9zWpShDBK+OqdaPS+Ll/GBvkml4obAMjPz8e6desQFRWFO+64o0lx44onnnjCchUGAH379kV+fj6WLVvmMKTi4uJQXFxs81xxcTEiIiIQEhLS5HidTgedTv7/mJFvUKlN98x9Y/aDifV9c1e7l829N1lRHYAUjzTPLcpOF+LQ5k0IiYxEn5vTYTQYcCbnvxg7dqzLv5/eyA2A2UG+ry3ZoXQuVSbvvfceHnvsMYwePRqHDx9GdHR0mz784sWLUKls74yp1WoYjUaH7xk6dCg2btxo89yWLVswdOjQNrWFAse0xUV48Y/JMiyjbktSCQijBJVKIDqxtlXhtHNUP0ze7tuL+v38cSa+fv4Z9Bg2HI99uwPtOnbE5aoqbH/rdTz55JOIiIhw6XzMDZKDSg20j65DeWkQ5F4nyx3ZoXROFze33norsrOz8cYbb2Dy5Mlu+fCMjAwsXboUXbp0QZ8+fbBv3z68+uqrmDZtmuWY+fPn4/Tp01i9ejUA4E9/+hPeeOMNzJ07F9OmTcP333+PTz/9FN98841b2kTKF5NUh6iY+ivjbrwfUpIk0CGuDkEageJTOkQntn6fGNOWDAsw8nA9ssLaofvAnm5ubduseOBeFOz/FX9YvBSDx9/tlnMyN0gu6iC5poMLAJJbs0PpnC5uDAYDDhw4gMTERLd9+Ouvv46FCxdi1qxZKCkpQUJCAmbOnImnn37acsyZM2dw6tQpy+PU1FR88803ePTRR/Haa68hMTERK1asQHp6utvaRcpWXyvvvfOo2Do8+NxpxCTVuaU72Zd7b4TBgEc3bkX7ePetxcHcIDnImxumizDzhpnuyg4lk0RLy3oqTGVlJSIjI/H8+l8QHNZO7uaQF5UUmPZjKT6lgzrI2Mxy6qarJE/qGF9rKXDcIWz9AmgjBiBryI0+13tjz+WqKjzdvycqKipcvi0lF3N2PLv/KILDw+VuDrnRiX1Hcd9/9uC94ZcwKGKCzWvO5wbg6exoKTf2Vn6CB3eGIPN31/pFDrjKldzwpc0yiDxq1TMJKC00XXkZjRIcL8Ll+VtV7t74jhtqEnlG87nROEM8mx3cMNN5LG4oIJingJvCyTSFEpAQk2ReI8b7KxW7c2VRf9qSgchftJwb3sYViZ3F4oYCQuO9pcwre85917w+hPcHFneIc+8sh0DfUJPI3XwxN1RqI84VcbPMlrC4oYBhvbeUeZaBObzk2B28vDTIrTv6sveGyP18LTeMBgnvPdXZ65/rb1q/Ah+RnzHvD9N4lsHtM0ux8ukEyw7d3mI0qLDqmQTMW+G+1UXNG2q+r9UCPrpiMZE/cZQbUxcV4e0nO6O81NszqCScP6PlbKkWsOeGAk7jQPjqnWjLHk/e5u775/6yoSaRv2mcGzFJddCFBNRkY7/C4oYCmnnAoDDKUdx4Zkdff9hQk8jfyblPXcd4rkrcEhY3FNCO7g2FHPfNTSRMWej+1UVrxi1F1dnduFx2gb03RB5wZHco5mV0l+3zpz97WrbP9hcsbiigvb/Im2tGNBRRkmTqtYlLds8ifo2x94bIc95flABDvbm315sXRwId42s9lhtKwuKGAlZ9La6sNtr4lpSnwqrhc1Rq4dE9YawX9WPvDZH7NM0N6/zwbKGjUgs8+Bx7bZzB4oYCVpAWUAcZ4e1VRgFTOHZKqPPYYlymDTX3YOTh/ey9IXIjx7kBeDo7jAZTbpj+7tGP8nssbiigTXumyGqnX9OfktTc1VdbrswaFgLrGF+Ll2Ym4/ExV2H5jGS3rndjxi0ZiDyjSW5IzmRHW5luZZ8r0mD5DM9mhxKwuKGA1vu6i3hp4wk8/k4uYruYFuqKSaqFpDI6eEfbr8wklYChHpb9akoLtR7ZL8a69+bEvqNuPz9RoDLnxotfH8OTK/MQm+RMdrSVhPo60wJ+ns4OJeAifkQAElJNC3XV1wLnzmjw4oOpbjy7gKQSkCDBaDTtT2O98JfR2LBfjLund+4c1Q+Tt7P3hsgTgrQNi/wV5WqwZmkChNGdfQYC6iABYZRgNEooK9ZY9rkCPJsd/o7FDRGAkgLTbrvFp3RW99Pdd/9cGFWWG1rmcJIkASEkqFQC0YmeWbfCtCXDXIjaHsjLy0MKVy0mcivr7Gi4be2+/DANXjbxZnb4O96WIgKw6pkES1ev/RlUbWF7LunKmJuYJNv9ajyFG2oSeY51dtifQdUWttPNVV7ODn/GnhsKeC2vNOreXhyVyjSdMyapzivdyey9IfIMx9nh3swwn8tczHgrO/wZe24o4Jl3+JVU1jMdrP/e2pCyP3PCPA3c/NneYN5Qk703RPadDNZj+LYDLr3HfnYAbS9s7GfHE+/kIybJcXYM33YAp8Pa+NEKweKGAlpJgWlapWl/KetX3HnVZVs0qYOMXr/i4oaaRI4FRemwvUdv6C86v/Kv4+xoK4H20fWWv5v/bCk39lZ+glTVtdjcqxe6D+zpzgb5JRY3FNDs3y9vzNW1K8z3x4HoRH2j80ow1KtkWYCLWzIQ2ZeSkoLgqA4Ij7sOYesXOPUe57LDWbYZow02V0sNY3hayo3h2w7gdLt66MLatbEtysDihgKW+X650c6O4JIkrsyasiYa/dn475Z3m85vlFBaqDPt/K1qGBDYeCdwbxU63FCTyDFXem+czw57+dDSysYSSgpazg1zO4CGXpsdffqz1+YKFjcUsMz3y80BIqkaQikmqRaPvZV/5UjbGRANK5Nav9aUdCWQpi4qQnRi09kN5m5tb640yt4bIvtc6b1pKTse/Wfj7LDWci9PTJLj3ACaZkePf59jr00jLG4ooFkHSExiLZ54Jx8vbzqGqYuK8K8l5pU/ba+elm84gZc3HUNsFz3s9+IIy/mmLipCpwTTIl8vbzqGeSsaBgRad2t7a6VRbqhJ5FhQlA46dXMzJxvYy4657+UCAF6emQp1kNGyHYM5O178urncaHg8bbFpRpQ5j6xzA2iaHW9vfhw6tQ5BUc61PRBwKjgFNPPqoo2nVdreTzdpH1OH+joJj4+5CrFd9Pjd+DKseyMGhvrGa1uY/pyysMiyuJe5B8ccUI2nkHprpVHTlgwLMPJwPbLCbgRSPPdZREpmLzuWz0i25IbRKEGlFjDUS5bsmHvbVegYX4uIDvWovGDuqbXtyZm+5LTlXM5mx5myJLu3yAIZe26IYFtQnM3XNLqfbvrzwlktzp8xBVJpoRbr3oiBMFovsmV7lfavJY57Zhp3azu6p+4J3FCTyH3Mv7ONx+EIo2kQcEyS3iY7yoo1qKlUW22y2XBrK7aLHn2uv9hsr6697IiPKrA8JhMWN0SNmG5HNe42Nj9uGCxsqFc1KoAaFtqasrDIJuise2bMmrun7kncUJPI/VRqWG3dAphzo6TAdkaVOTuEsL14Mt/GblwkOZMdf0p/2aPfzR/xthSRFfsrjjqeIi5JsNnj5Yl38i1XcrFd9Cgt1Jq6p+3sAePolpg3cENNIvcyGmz3gWouN1RqAQjJkg2dOtdi3op8yxGuZkfY+iIASZ74Wn6LPTdEVhp3+Ta3xo0kwRRSaOh5sQ6glnpmzFdiciyhbtqSIcv7H0zk477t2tWlxfzMmsygkhxnR0QHg002TFtsmw2uZMfeyk+gv1iHb7t2dbnNSsaeG6JGpi4qstohXFwZMNx0rxghJBjqJbz49TEEaZuex1HPjPUuwo0HCxKRfFJSUnC48Dx6tRuK1ZWfYFDEBJfeb50d5sHETbNDQnmpBi9vOgbA/sWNK9kxfM8BaOOuQ3BUB+4bZ4U9N0SNmIMlJklvNWDYRB1ktOwjYx4EbK+wsdY4vOSYAk5EzglL7Ii1A3tgyIZfXH5vc9nReMKBSt1yr60z2aG/WIftPXpzGngjLG6I7DAagJKCpjOmDPUqS7dzawYBOzNYkIjkk5KSAkmrRa92Q7G38hOX3+8oO8yLf7Z28oCj7AiLGcJeGztY3BDZYX/sjenvwighJknfZGGt1pzXm1PAicg5bem9cTRury25Ye+8KpVAXOQp/NCzF3tt7GBxQ+SA9aA+66neRqNp75fW9rbINQXcnvScHE4HJ2qkrb03UxcVoVNn6+xoe26Yz2udHdPTXoCk1bLXxg4OKCZywHpQ30szk5udmtna88rZY5OdMRiTtxs4HZzIDl1YO5xuV4/h2w6gZpxrA4tjkurw5Mp8y4rF7sgN83mtsyMoswiFrT+dorHnhqgFKrVnelvkvhVlng4uamu5zxRRI90H9sSOPv2Rqrq2Vb03gOd6aeXODn/AnhsiJzjqbZG796WtTL03x5Gp1QLs2iay0ZbeG0C5ueEP2HND5AJzIJUUaLB8RjIeH3MVls9IRkmBpvk3+ij23hA5FhSla3PvDaC83PAHLG6IWkFJa9XoQjVIO34E9WV6uZtC5FNSUlJsem/ayh25sbfyE+yt/ARh6xegf/ub2twmpWJxQ+Qipa1VUzNuKarO7sblsgvsvSFqJChKh+09erdqSwZr7siNvZWfYPi2A5i09wR0RmBl364IS+zYpnYpFYsbIidYB5B5vQlJ1bB3jDrIiHNF/tvFzN4bIvtSUlIQHNUB4XHXIWz9Apffb70PVFtzY/i2A+ilvR7xdcPQL2I0p4E3g8UNUTMc3SOfuqjIapEuwGiQ/PrWFHtviBxrTe+NvexoS26YN8g8HQYU94zF2zf0ZK9NM1jcEDXD0T3yTgl1MNQ3/PoI4d+3pgD23hA5Yl7Ur3/7m5zuvbGXHW3JjeHbDiA87jrsGHwtNnSNRVCUjr02zWBxQ+RAc/fIlbiNws5R/dC5OoiL+hHZEZbYESv7dnWq98ZRdgCtzw3rDTJTUlJY2LSAxQ2RAy0VML60jYI7DIqYgFzjHow8vJ9bMhA14sqWDM1lR2tyI2z9AoTHXccNMl3ARfyImjF1URFWPZOA4lO6JkHkK9souNPOUf0weTt7b4jsCUvsiLW1PTBkw2rU39f8on6OsqM1uaG/WIcfB/dGO26Q6TQWN0TNcCaIlFLYAOZF/eYiPUeNHWHt0H1gT7mbROQzUlJScLjwPHq1G4rVlZ9gUITjAqel7HA2N/ZWfoLJ7YbiF86McglvSxE5QUkFTEuyMwajUwU31CSyx9VF/dqaHUM2/IK1A3twZpSL2HNDRDbMvTdAH7mbQuRzug/siR011bjv5yCsthp701wvjqusx/Sw16Z1WNwQERG5wNx7M3qLqfem8nJdi2NwXDFkwy+ICDatqbX2uklox14bl7G4ISIicoF5Q80nLvQHABQdP9ziGBxnha1fgF7tb0LH1F4AgO1asNemFTjmhoiIyAXmDTUL2tWjoF09zkWqMWTDL245t3kVYvO5dWHt3HLeQMOeGyIiIhcFRemwISoWAFCjDcJ9FYY2996ErV8A7ZVViIOuTPvuzl6bVmFxQ0R2idpa5OXlsUucyA7r34s8wLL+TXZG6885xGo9G/7etQ2LGyJqIjtjMCZvP45MrRZgyBI1y7z+Tf/2NyFiy8+tPk9q+5s4M8pNWNwQURPm6eCitgd7b4icoAtrh7Nx4bg2ZEqrz/FSB3A9GzdhcUNEdmVnDMaDO0/iffbeELWo+8Ce2AYgoqy81efQhbXnhYSbsLghIrsGRUzA/vK5ELVd2XtD5ATrQcater8b2xLo+G9JRA7pQjVIO34EWVEdgBS5W0Pk23gB4DtkXecmJSUFkiQ1+Zk9e7bd4z/44IMmxwYHB3u51USBo2bcUlSd3Y3LZReQl5cnd3MAMDeIqGWy9tzs3r0bBoPB8vjQoUO4+eabcffddzt8T0REBI4ePWp5LEmSR9tIFOh8rfeGuUFELZG1uImOjrZ5/MILL6Bbt24YMWKEw/dIkoS4uDhPN42Irtg5qh8mbw+CvqbaJ8beMDeIqCU+s/1CbW0t1q5di2nTpjV7VVVdXY3k5GQkJSXhjjvuwOHDh5s9r16vR2Vlpc0PETlvUMQE5Br3ID0nR+6mNOGp3ACYHUT+zGeKmy+++ALl5eV44IEHHB7Ts2dPvP/++/jyyy+xdu1aGI1GDBs2DIWFhQ7fs2zZMkRGRlp+kpKSPNB6IpKDp3IDYHYQ+TNJCCHkbgQApKenQ6vVYsOGDU6/p66uDr1798akSZOwZMkSu8fo9Xro9XrL48rKSiQlJeH59b8gmBuSETklbP0ChEddj12jb3TLbanLVVV4un9PVFRUICIiotXn8VRuAI6z49n9RxEcHt7qNhNR67iSGz4xFTw/Px9bt27FunXrXHqfRqPBwIEDceLECYfH6HQ66HS6tjaRiHyMJ3MDYHYQ+TOfuC21atUqxMTEYOzYsS69z2Aw4ODBg4iPj/dQy4jIVzE3iMgR2Ysbo9GIVatWYcqUKQgKsu1Imjx5MubPn295/Oyzz+K7777DyZMnsXfvXtx///3Iz8/HjBkzvN1sooCjN+hRX6Zv+UAvYG4QUXNkvy21detWnDp1CtOmTWvy2qlTp6BSNdRfZWVlePDBB3H27FlERUVh8ODB+Omnn3D11Vd7s8lEAcd6OrgvYG4QUXN8ZkCxt1RWViIyMpIDiolcFLZ+AbQRA5A15EZ0H9izTedy14BibzJnBwcUE8nDldyQ/bYUEfmHnaP6oXO17/TeEBE5wuKGiJwyKGICcqqzkJ6TgxP7jrb8BiIimbC4ISKnZWcMRucauVtBRNQ8FjdERESkKCxuiIiISFFY3BAREZGisLghIiIiRWFxQ0Qu0dcbOB2ciHwaixsicpp5OriorUVeXp7czSEisovFDRG5JDtjMO7fdxw1heflbgoRkV0sbojIJey9ISJfx+KGiFymC9Vg+sGT7L0hIp/E4oaIXFYzbin2l+9i7w0R+SQWN0TUKrpQDdKOH0F9mV7uphAR2WBxQ0StUjNuKarO7sblsgvsvSEin8Lihohajb03ROSLWNwQUavtHNUPnauDoK+pZu8NEfkMFjdE1GqDIiYg17gH6Tk5cjeFiMiCxQ0REREpCosbIiIiUhQWN0RERKQoLG6IiIhIUVjcEFGb6Q16TgcnIp/B4oaI2sR6OjgRkS9gcUNEbWKeDj7y8H6c2HdU7uYQEbG4IaK2Y+8NEfkSFjdE1GaDIiYgpzoL6Tk57L0hItmxuCEit8jOGIzONXK3goiIxQ0REREpDIsbIiIiUhQWN0RERKQoLG6IiIhIUVjcEJHb6OsNnA5ORLJjcUNEbmGeDi5qa5GXlyd3c4gogLG4ISK3yc4YjPv3HUdN4Xm5m0JEAYzFDRG5DXtviMgXsLghIrfShWow/eBJ7hJORLJhcUNEbrVzVD+5m0BEAY7FDRERESkKixsiIiJSFBY3REREpCgsbojI7biYHxHJicUNEbkVp4MTkdxY3BCR2+lCNUg7foTTwYlIFixuiMjtasYtRdXZ3bhcdoG9N0TkdSxuiMgj2HtDRHJhcUNEHrFzVD9Und3NgcVE5HUsbojIIwZFTJC7CUQUoFjcEBERkaKwuCEiIiJFYXFDREREisLihoiIiBSFxQ0REREpCosbIiIiUhQWN0RERKQoLG6IiIhIUVjcEJFHcXdwIvI2FjdE5DHcX4qI5CBrcZOSkgJJkpr8zJ492+F7PvvsM/Tq1QvBwcHo27cvNm7c6MUWE5ErzPtLuXN3cOYGEbVE1uJm9+7dOHPmjOVny5YtAIC7777b7vE//fQTJk2ahOnTp2Pfvn34wx/+gD/84Q84dOiQN5tNRE4aFDHB7b03zA0iaomsxU10dDTi4uIsP19//TW6deuGESNG2D3+tddew6233oonnngCvXv3xpIlSzBo0CC88cYbXm45ETlr56h+6Fwd5LbdwZkbRNQSnxlzU1tbi7Vr12LatGmQJMnuMVlZWRg9erTNc+np6cjKynJ4Xr1ej8rKSpsfIvKeQRETkGvcg/ScHLcPLPZUbgDMDiJ/5jPFzRdffIHy8nI88MADDo85e/YsYmNjbZ6LjY3F2bNnHb5n2bJliIyMtPwkJSW5q8lEJDNP5QbA7CDyZz5T3KxcuRJjxoxBQkKCW887f/58VFRUWH4KCgrcen4iko+ncgNgdhD5syC5GwAA+fn52Lp1K9atW9fscXFxcSguLrZ5rri4GHFxcQ7fo9PpoNPp3NJOIvIdnswNgNlB5M98oudm1apViImJwdixY5s9bujQodi2bZvNc1u2bMHQoUM92Twi8kHMDSJyRPbixmg0YtWqVZgyZQqCgmw7kiZPnoz58+dbHj/yyCP49ttv8corryAnJweLFy/Gnj178Oc//9nbzSYiF+kNerdNB2duEFFzZC9utm7dilOnTmHatGlNXjt16hTOnDljeTxs2DB8+OGHePfdd9G/f3/8+9//xhdffIFrrrnGm00mIhe5ezo4c4OImiMJIYTcjfCmyspKREZG4vn1vyA4rJ3czSEKGGHrF0AbMQBZQ25EYvcEPN2/JyoqKhARESF305xizo5n9x9FcHi43M0hCjiXq6qczg3Ze26IKDC4u/eGiMgRFjdE5BWDIiYgpzoLorYWp06dkrs5RKRgLG6IyGuyMwZj+sGTqC/nLuFE5DksboiIiEhRWNwQERGRorC4ISIiIkVhcUNERESKwuKGiIiIFIXFDRERESkKixsiIiJSFBY3REREpCgsboiIiEhRWNwQkVfp6w3QX6yRuxlEpGAsbojIa8z7S406dkzuphCRgrG4ISKvys4YjPNFWXI3g4gUjMUNEXnVoIgJ0IVq5G4GESkYixsi8rqfRvSRuwlEpGAsbojI6waE3yl3E4hIwVjcEBERkaKwuCEiIiJFYXFDREREisLihoiIiBSFxQ0REREpCosbIiIiUhQWN0RERKQoLG6IiIhIUVjcEBERkaKwuCEiIiJFYXFDREREisLihoiIiBSFxQ0REREpCosbIiIiUhQWN0RERKQoLG6IiIhIUVjcEBERkaKwuCEiIiJFYXFDREREisLihoiIiBSFxQ0REREpCosbIiIiUhQWN0RERKQoQXI3wNuEEACAyxerZW4JUeAy//6Zfx/9gSU7qpkdRHIw/+45kxuS8Kd0cYPCwkIkJSXJ3QwiAlBQUIDExES5m+EUZgeRb3AmNwKuuDEajSgqKkJ4eDgkSWrTuSorK5GUlISCggJERES4qYX+g98/cL9/W7+7EAJVVVVISEiASuUfd8eZHe4TyN8/kL870Lbv70puBNxtKZVK5fYrxYiIiID8P6kZv3/gfv+2fPfIyEg3t8azmB3uF8jfP5C/O9D67+9sbvjHJRMRERGRk1jcEBERkaKwuGkDnU6HRYsWQafTyd0UWfD7B+73D+Tv7g6B/u8XyN8/kL874L3vH3ADiomIiEjZ2HNDREREisLihoiIiBSFxQ0REREpCosbIiIiUhQWN610+vRp3H///ejYsSNCQkLQt29f7NmzR+5meUVKSgokSWryM3v2bLmb5nEGgwELFy5EamoqQkJC0K1bNyxZssSv9khqq6qqKsyZMwfJyckICQnBsGHDsHv3brmb5ReYG4GZGwCzw9u5EXArFLtDWVkZbrzxRqSlpWHTpk2Ijo7G8ePHERUVJXfTvGL37t0wGAyWx4cOHcLNN9+Mu+++W8ZWecfy5cvx1ltv4V//+hf69OmDPXv2YOrUqYiMjMTDDz8sd/O8YsaMGTh06BDWrFmDhIQErF27FqNHj8Z///tfdO7cWe7m+SzmRuDmBsDs8HZucCp4Kzz55JP48ccfsXPnTrmb4hPmzJmDr7/+GsePH2/znju+7rbbbkNsbCxWrlxpee7OO+9ESEgI1q5dK2PLvOPSpUsIDw/Hl19+ibFjx1qeHzx4MMaMGYPnnntOxtb5NuaGrUDKDSCws0OO3OBtqVb46quvcO211+Luu+9GTEwMBg4ciPfee0/uZsmitrYWa9euxbRp0wIioIYNG4Zt27bh2LFjAID9+/dj165dGDNmjMwt8476+noYDAYEBwfbPB8SEoJdu3bJ1Cr/wNxoEGi5AQR2dsiSG4JcptPphE6nE/Pnzxd79+4V77zzjggODhYffPCB3E3zuk8++USo1Wpx+vRpuZviFQaDQcybN09IkiSCgoKEJEni+eefl7tZXjV06FAxYsQIcfr0aVFfXy/WrFkjVCqVuOqqq+Rumk9jbjQItNwQgtnh7dxgcdMKGo1GDB061Oa5hx56SNxwww0ytUg+t9xyi7jtttvkbobXfPTRRyIxMVF89NFH4sCBA2L16tWiQ4cOAfUfqBMnTojf/e53AoBQq9XiuuuuE/fdd5/o1auX3E3zacyNBoGWG0IwO7ydGyxuWqFLly5i+vTpNs+9+eabIiEhQaYWySMvL0+oVCrxxRdfyN0Ur0lMTBRvvPGGzXNLliwRPXv2lKlF8qmurhZFRUVCCCHuuece8fvf/17mFvk25oZJIOaGEMwOM2/lBsfctMKNN96Io0eP2jx37NgxJCcny9QieaxatQoxMTE2A8SU7uLFi1CpbH9t1Go1jEajTC2ST1hYGOLj41FWVobNmzfjjjvukLtJPo25YRKIuQEwO8y8lhseKZkULjs7WwQFBYmlS5eK48ePi8zMTBEaGirWrl0rd9O8xmAwiC5duoh58+bJ3RSvmjJliujcubP4+uuvRW5urli3bp3o1KmTmDt3rtxN85pvv/1WbNq0SZw8eVJ89913on///uL6668XtbW1cjfNpzE3Ajc3hGB2eDs3WNy00oYNG8Q111wjdDqd6NWrl3j33XflbpJXbd68WQAQR48elbspXlVZWSkeeeQR0aVLFxEcHCy6du0qFixYIPR6vdxN85pPPvlEdO3aVWi1WhEXFydmz54tysvL5W6WX2BuBGZuCMHs8HZucJ0bIiIiUhSOuSEiIiJFYXFDREREisLihoiIiBSFxQ0REREpCosbIiIiUhQWN0RERKQoLG6IiIhIUVjcEBERkaKwuCG/YDAYMGzYMIwfP97m+YqKCiQlJWHBggUytYyIfBVzI3BxhWLyG8eOHcOAAQPw3nvv4b777gMATJ48Gfv378fu3buh1WplbiER+RrmRmBicUN+5R//+AcWL16Mw4cPIzs7G3fffTd2796N/v37y900IvJRzI3Aw+KG/IoQAv/zP/8DtVqNgwcP4qGHHsJTTz0ld7OIyIcxNwIPixvyOzk5Oejduzf69u2LvXv3IigoSO4mEZGPY24EFg4oJr/z/vvvIzQ0FLm5uSgsLJS7OUTkB5gbgYU9N+RXfvrpJ4wYMQLfffcdnnvuOQDA1q1bIUmSzC0jIl/F3Ag87Lkhv3Hx4kU88MAD+L//+z+kpaVh5cqVyM7Oxttvvy1304jIRzE3AhN7bshvPPLII9i4cSP279+P0NBQAMA777yDxx9/HAcPHkRKSoq8DSQin8PcCEwsbsgv/PDDDxg1ahR27NiBm266yea19PR01NfXs5uZiGwwNwIXixsiIiJSFI65ISIiIkVhcUNERESKwuKGiIiIFIXFDRERESkKixsiIiJSFBY3REREpCgsboiIiEhRWNwQERGRorC4ISIiIkVhcUNERESKwuKGiIiIFIXFDRERESnK/weJioxn1e5JDwAAAABJRU5ErkJggg==\n"},"metadata":{}}]},{"cell_type":"code","source":["test_points = np.array([\n","    [8.5, 8.5],\n","    [7.5, 7.5],\n","    [8.4, 7.6],\n","    [7.6, 8.4],\n","    [8.45, 7.55],\n","    [7.55, 8.45]\n","])\n","\n","# Сохраняем в файл\n","np.savetxt('data_test.txt', test_points)"],"metadata":{"id":"2tiFTq-VZy-y","executionInfo":{"status":"ok","timestamp":1762446712266,"user_tz":-180,"elapsed":377,"user":{"displayName":"Alena Konovalova","userId":"13964397918094172748"}}},"execution_count":29,"outputs":[]},{"cell_type":"code","source":["# загрузка тестового набора\n","data_test = np.loadtxt('data_test.txt', dtype=float)"],"metadata":{"id":"tt2BAUbLbn2G","executionInfo":{"status":"ok","timestamp":1762446731724,"user_tz":-180,"elapsed":41,"user":{"displayName":"Alena Konovalova","userId":"13964397918094172748"}}},"execution_count":30,"outputs":[]},{"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":"aeXuvn5AbY8e","executionInfo":{"status":"ok","timestamp":1762446733825,"user_tz":-180,"elapsed":101,"user":{"displayName":"Alena Konovalova","userId":"13964397918094172748"}},"outputId":"1afecb38-5985-40d2-81d3-93db3393d307"},"execution_count":31,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/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":900},"id":"3vOegourbzRP","executionInfo":{"status":"ok","timestamp":1762446737935,"user_tz":-180,"elapsed":1483,"user":{"displayName":"Alena Konovalova","userId":"13964397918094172748"}},"outputId":"52ae8595-4467-43f6-d187-52a359d311b1"},"execution_count":32,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","i         Labels    IRE       IREth     \n","0         [1.]      [3.43]    3.07      \n","1         [0.]      [2.03]    3.07      \n","2         [0.]      [2.71]    3.07      \n","3         [0.]      [2.85]    3.07      \n","4         [0.]      [2.72]    3.07      \n","5         [0.]      [2.88]    3.07      \n","Обнаружено  1.0  аномалий\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1600x800 with 1 Axes>"],"image/png":"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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":"IJyd-BqScChe","executionInfo":{"status":"ok","timestamp":1762446754051,"user_tz":-180,"elapsed":90,"user":{"displayName":"Alena Konovalova","userId":"13964397918094172748"}},"outputId":"f4ab06b1-e288-4b82-8a63-a990862692d4"},"execution_count":33,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 40ms/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":900},"id":"o_AJKMXTcNPI","executionInfo":{"status":"ok","timestamp":1762446756941,"user_tz":-180,"elapsed":986,"user":{"displayName":"Alena Konovalova","userId":"13964397918094172748"}},"outputId":"bfac548b-0015-4085-ce65-fa265981add6"},"execution_count":34,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","i         Labels    IRE       IREth     \n","0         [1.]      [0.75]    0.4       \n","1         [1.]      [0.66]    0.4       \n","2         [1.]      [0.55]    0.4       \n","3         [1.]      [0.58]    0.4       \n","4         [1.]      [0.62]    0.4       \n","5         [1.]      [0.65]    0.4       \n","Обнаружено  6.0  аномалий\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1600x800 with 1 Axes>"],"image/png":"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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},"id":"iJEZxoSQiP16","executionInfo":{"status":"ok","timestamp":1762446765475,"user_tz":-180,"elapsed":970,"user":{"displayName":"Alena Konovalova","userId":"13964397918094172748"}},"outputId":"da74cc12-dba3-4a52-9b8b-8d23bc5112a2"},"execution_count":35,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["# загрузка выборок\n","train = np.loadtxt('WBC_train.txt', dtype=float)\n","test = np.loadtxt('WBC_test.txt', dtype=float)"],"metadata":{"id":"a4q9ZCFjS94H","executionInfo":{"status":"ok","timestamp":1762638382249,"user_tz":-180,"elapsed":749,"user":{"displayName":"Alena Konovalova","userId":"13964397918094172748"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","source":["print('Исходные данные:')\n","print(train)\n","print('Размерность данных:')\n","print(train.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nccQpfVORHBA","executionInfo":{"status":"ok","timestamp":1762638383199,"user_tz":-180,"elapsed":51,"user":{"displayName":"Alena Konovalova","userId":"13964397918094172748"}},"outputId":"e0b21bd0-a305-4d4e-fe97-f4e36bda51a6"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["Исходные данные:\n","[[3.1042643e-01 1.5725397e-01 3.0177597e-01 ... 4.4261168e-01\n","  2.7833629e-01 1.1511216e-01]\n"," [2.8865540e-01 2.0290835e-01 2.8912998e-01 ... 2.5027491e-01\n","  3.1914055e-01 1.7571822e-01]\n"," [1.1940934e-01 9.2323301e-02 1.1436666e-01 ... 2.1398625e-01\n","  1.7445299e-01 1.4882592e-01]\n"," ...\n"," [3.3456387e-01 5.8978695e-01 3.2886463e-01 ... 3.6013746e-01\n","  1.3502858e-01 1.8476978e-01]\n"," [1.9967817e-01 6.6486304e-01 1.8575081e-01 ... 0.0000000e+00\n","  1.9712202e-04 2.6301981e-02]\n"," [3.6868759e-02 5.0152181e-01 2.8539838e-02 ... 0.0000000e+00\n","  2.5744136e-01 1.0068215e-01]]\n","Размерность данных:\n","(357, 30)\n"]}]},{"cell_type":"code","source":["print('Исходные данные:')\n","print(test)\n","print('Размерность данных:')\n","print(test.shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tkfS44jzojR3","executionInfo":{"status":"ok","timestamp":1762638385220,"user_tz":-180,"elapsed":39,"user":{"displayName":"Alena Konovalova","userId":"13964397918094172748"}},"outputId":"dc72c859-38ce-461a-d01e-452c9ce6d0d4"},"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["Исходные данные:\n","[[0.18784609 0.3936422  0.19425057 0.09654295 0.632572   0.31415251\n","  0.24461106 0.28175944 0.42171717 0.3946925  0.04530147 0.23598833\n","  0.05018141 0.01899148 0.21589557 0.11557064 0.0655303  0.19643872\n","  0.08003602 0.07411246 0.17467094 0.62153518 0.18332586 0.08081007\n","  0.79066235 0.23528442 0.32132588 0.48934708 0.2757737  0.26905418]\n"," [0.71129727 0.41224214 0.71460162 0.56776246 0.48451747 0.53990553\n","  0.57357076 0.74602386 0.38585859 0.24094356 0.3246424  0.07507514\n","  0.32059558 0.23047901 0.0769963  0.19495599 0.09030303 0.27865126\n","  0.10269038 0.10023078 0.70188545 0.36727079 0.72010558 0.50181872\n","  0.38453411 0.35044775 0.3798722  0.83573883 0.23181549 0.20136429]\n"," [0.38567845 0.67974298 0.36569691 0.24432662 0.27597725 0.0818048\n","  0.10979381 0.1361332  0.4        0.06276327 0.12913272 0.27996818\n","  0.10771333 0.07205481 0.17398103 0.09026046 0.06285354 0.17213487\n","  0.33247031 0.02954549 0.33191035 0.66337953 0.29727576 0.1833956\n","  0.28811992 0.06924353 0.1235623  0.22594502 0.32879953 0.04335563]\n"," [0.3956174  0.15387217 0.40570797 0.23792153 0.49354518 0.59542359\n","  0.4866448  0.48489066 0.73787879 0.42881213 0.11852254 0.07721888\n","  0.1237808  0.07117696 0.17255329 0.38324271 0.16277778 0.42659595\n","  0.40578038 0.12089051 0.36072572 0.18816631 0.37198068 0.19556134\n","  0.44792974 0.55118317 0.50359425 0.82233677 0.6114725  0.29135511]\n"," [0.49595343 1.         0.48103103 0.32962884 0.41067076 0.33869088\n","  0.33200562 0.43792247 0.37828283 0.20429655 0.15393808 0.109596\n","  0.10578146 0.07881613 0.10697896 0.19225223 0.06570707 0.26027657\n","  0.06160297 0.06440446 0.51867663 0.87553305 0.45216395 0.30053087\n","  0.43142046 0.33589467 0.25886581 0.70996564 0.25389316 0.1962482 ]\n"," [0.59676274 0.28542442 0.60058047 0.45408271 0.53597544 0.45156739\n","  0.58762887 0.63916501 0.48838384 0.22872789 0.22629006 0.15200672\n","  0.19012392 0.16859981 0.07968182 0.1540992  0.10174242 0.24682705\n","  0.12632971 0.08371682 0.66880114 0.38299574 0.620001   0.50304758\n","  0.47962755 0.34666395 0.54392971 0.77216495 0.40961955 0.24393283]\n"," [0.34118983 0.47683463 0.33916108 0.19817603 0.37916403 0.34114472\n","  0.26124649 0.32117296 0.59343434 0.30265375 0.11196813 0.3281471\n","  0.13084861 0.04519628 0.31162253 0.2617238  0.09313131 0.30820231\n","  0.5221478  0.13381148 0.31768054 0.60847548 0.32167937 0.15387829\n","  0.55953246 0.36732932 0.29904153 0.60893471 0.62270846 0.31195068]\n"," [0.76809125 0.5836997  0.75813696 0.64750795 0.38331678 0.45647506\n","  0.45688847 0.61481113 0.42878788 0.27653749 0.34274851 0.13333186\n","  0.30580031 0.2782939  0.16028147 0.19811037 0.11356061 0.32506156\n","  0.11282152 0.07456158 0.82106012 0.59941365 0.77488919 0.67803775\n","  0.50802351 0.37382969 0.46485623 0.89106529 0.30317366 0.20812016]\n"," [0.39703725 0.44132567 0.38981411 0.24801697 0.35542114 0.25832771\n","  0.2628866  0.37191849 0.33181818 0.23188711 0.07293138 0.10632514\n","  0.06210244 0.04228256 0.27769657 0.16664163 0.11441919 0.33396477\n","  0.2367873  0.04308832 0.30238349 0.36833689 0.28432691 0.15869544\n","  0.36010038 0.16727304 0.22731629 0.50721649 0.19534792 0.08684245]\n"," [0.50257939 0.46060196 0.51972911 0.35503712 0.36345581 0.55524201\n","  0.5004686  0.49801193 0.32121212 0.49978939 0.29599855 0.24416549\n","  0.23766668 0.18322444 0.17177142 0.51069487 0.16643939 0.43777231\n","  0.12450048 0.35947929 0.48523657 0.44909382 0.46411674 0.30765828\n","  0.32708182 0.4377662  0.41253994 0.68591065 0.14508181 0.44182081]\n"," [0.29007525 0.19783564 0.30004837 0.16402969 0.78694592 0.48193362\n","  0.48523899 0.47718688 0.43686869 0.56781803 0.10114068 0.12455799\n","  0.0778872  0.05203232 0.18523303 0.20179049 0.13820707 0.26292858\n","  0.10677098 0.21430842 0.29811455 0.27665245 0.27884855 0.15778608\n","  0.75962491 0.37121014 0.50926518 0.68247423 0.31184703 0.56054047]\n"," [0.25931185 0.48461278 0.27765877 0.14099682 0.59555836 0.67548003\n","  0.53256795 0.42460239 0.48989899 0.68386689 0.06739091 0.27378006\n","  0.06040616 0.03200983 0.18479111 0.52511491 0.1955303  0.2712635\n","  0.14082287 0.31733068 0.25471363 0.76385928 0.23527068 0.1293256\n","  0.75368157 1.         0.88258786 0.75945017 0.55213877 1.        ]\n"," [0.3449761  0.43422388 0.34538042 0.20627784 0.46194818 0.29452181\n","  0.34278351 0.30511928 0.43737374 0.20766639 0.03302553 0.32947313\n","  0.05362107 0.02192388 0.14957338 0.17708114 0.11729798 0.24171245\n","  0.09326279 0.09884886 0.26182853 0.59301706 0.26838986 0.13347916\n","  0.44132603 0.23868013 0.33817891 0.46804124 0.22333925 0.1867375 ]\n"," [0.39372427 0.526209   0.40501693 0.24979852 0.50167013 0.46107601\n","  0.3943299  0.43494036 0.43737374 0.32518955 0.11859497 0.14405057\n","  0.12915233 0.0685434  0.1196587  0.21268063 0.09030303 0.20515249\n","  0.13786796 0.07159045 0.43898968 0.65804904 0.49250461 0.26636846\n","  0.61368289 0.56631836 0.50599042 0.69553265 0.48531441 0.28676374]\n"," [0.41265559 0.35847142 0.39672448 0.26430541 0.39126117 0.21044721\n","  0.15447516 0.25790258 0.28181818 0.11647009 0.09357233 0.17454915\n","  0.07769872 0.06383662 0.09902437 0.15304774 0.04934343 0.1850161\n","  0.10677098 0.05303815 0.43329776 0.554371   0.39289805 0.26636846\n","  0.46377864 0.31765482 0.23178914 0.52955326 0.36901242 0.20510298]\n"," [0.55132756 0.52079811 0.55980927 0.40063627 0.48542024 0.51935464\n","  0.54334583 0.61829026 0.56717172 0.25294861 0.26043817 0.24438649\n","  0.22697074 0.18341122 0.15416256 0.23648872 0.13121212 0.21935973\n","  0.17149772 0.12662549 0.54144433 0.58608742 0.54828428 0.36492332\n","  0.51462722 0.38653938 0.48985623 0.63505155 0.37039227 0.28059819]\n"," [0.35964788 0.39972946 0.37053417 0.21264051 0.47639253 0.51352678\n","  0.33388004 0.43653082 0.6020202  0.40606571 0.05178345 0.13768564\n","  0.06375159 0.02661198 0.09310943 0.21253042 0.06770202 0.25610911\n","  0.09368492 0.0972942  0.34471718 0.56476546 0.35853379 0.17491644\n","  0.53707984 0.61803029 0.44241214 0.92817869 0.53203233 0.4752722 ]\n"," [0.18784609 0.3936422  0.19425057 0.09654295 0.632572   0.31415251\n","  0.24461106 0.28175944 0.42171717 0.3946925  0.04530147 0.23598833\n","  0.05018141 0.01899148 0.21589557 0.11557064 0.0655303  0.19643872\n","  0.08003602 0.07411246 0.17467094 0.62153518 0.18332586 0.08081007\n","  0.79066235 0.23528442 0.32132588 0.48934708 0.2757737  0.26905418]\n"," [0.72360263 0.33682787 0.7532997  0.57921527 0.72194638 0.78958346\n","  0.99906279 0.90606362 0.75555556 0.43028644 0.39960167 0.26184583\n","  0.437874   0.30481623 0.16323894 0.63409139 0.26262626 0.46978594\n","  0.32698261 0.1431049  0.7282106  0.42617271 0.77887345 0.53450649\n","  0.65330516 0.65237555 0.76741214 1.         0.49083383 0.28105733]\n"," [0.52103744 0.0226581  0.54598853 0.36373277 0.59375282 0.7920373\n","  0.70313964 0.73111332 0.68636364 0.60551811 0.35614702 0.12046941\n","  0.3690336  0.27381126 0.15929565 0.35139844 0.13568182 0.30062512\n","  0.31164518 0.18304244 0.62077552 0.14152452 0.66831017 0.45069799\n","  0.60113584 0.61929156 0.56861022 0.91202749 0.59846245 0.41886396]\n"," [0.32367836 0.49983091 0.33542948 0.1918982  0.57389185 0.45616833\n","  0.31794752 0.33593439 0.61363636 0.47198821 0.13166757 0.25808876\n","  0.10446214 0.06023183 0.27082979 0.27268904 0.08777778 0.30611858\n","  0.23158102 0.21074997 0.28744219 0.5575693  0.27685642 0.14815179\n","  0.71471967 0.35830641 0.27004792 0.52268041 0.41119653 0.41492851]]\n","Размерность данных:\n","(21, 30)\n"]}]},{"cell_type":"code","source":["# обучение AE3\n","patience = 20000\n","ae3_trained, IRE3, IREth3 = lib.create_fit_save_ae(train,'out/AE3.h5','out/AE3_ire_th.txt', 50000, False, patience, early_stopping_delta = 0.00001, early_stopping_value = 0.00007 )\n","lib.ire_plot('training', IRE3, IREth3, 'AE3')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"EQrNUcwxTVYf","executionInfo":{"status":"ok","timestamp":1762647596804,"user_tz":-180,"elapsed":853855,"user":{"displayName":"Alena Konovalova","userId":"13964397918094172748"}},"outputId":"e21548aa-3a6d-4770-8402-81fe0f4f143e"},"execution_count":27,"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) : 9\n","Задайте архитектуру скрытых слоёв автокодировщика, например, в виде 3 1 3 : 24 22 20 17 15 17 20 22 24\n","\n","Epoch 1000/50000\n"," - loss: 0.000864\n","\n","Epoch 2000/50000\n"," - loss: 0.000545\n","\n","Epoch 3000/50000\n"," - loss: 0.000415\n","\n","Epoch 4000/50000\n"," - loss: 0.000303\n","\n","Epoch 5000/50000\n"," - loss: 0.000226\n","\n","Epoch 6000/50000\n"," - loss: 0.000217\n","\n","Epoch 7000/50000\n"," - loss: 0.000207\n","\n","Epoch 8000/50000\n"," - loss: 0.000186\n","\n","Epoch 9000/50000\n"," - loss: 0.000167\n","\n","Epoch 10000/50000\n"," - loss: 0.000113\n","\n","Epoch 11000/50000\n"," - loss: 0.000102\n","\n","Epoch 12000/50000\n"," - loss: 0.000095\n","\n","Epoch 13000/50000\n"," - loss: 0.000090\n","\n","Epoch 14000/50000\n"," - loss: 0.000088\n","\n","Epoch 15000/50000\n"," - loss: 0.000086\n","\n","Epoch 16000/50000\n"," - loss: 0.000079\n","\n","Epoch 17000/50000\n"," - loss: 0.000078\n","\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 12ms/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":["<Figure size 1600x800 with 1 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\n"},"metadata":{}}]},{"cell_type":"code","source":["# тестирование АE3\n","predicted_labels3, ire3 = lib.predict_ae(ae3_trained, test, IREth3)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qAScMohAoMAC","executionInfo":{"status":"ok","timestamp":1762647605227,"user_tz":-180,"elapsed":100,"user":{"displayName":"Alena Konovalova","userId":"13964397918094172748"}},"outputId":"2c4d5761-e365-4563-e8bc-eead009c4bb6"},"execution_count":28,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 38ms/step\n"]}]},{"cell_type":"code","source":["# тестирование АE3\n","lib.anomaly_detection_ae(predicted_labels3, ire3, IREth3)\n","lib.ire_plot('test', ire3, IREth3, 'AE3')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"XGfYzxuOpHNm","executionInfo":{"status":"ok","timestamp":1762647607043,"user_tz":-180,"elapsed":237,"user":{"displayName":"Alena Konovalova","userId":"13964397918094172748"}},"outputId":"926a2bbc-0de1-45f6-dd97-4acbb1c3528e"},"execution_count":29,"outputs":[{"output_type":"stream","name":"stdout","text":["\n","i         Labels    IRE       IREth     \n","0         [0.]      [0.1]     0.11      \n","1         [1.]      [0.5]     0.11      \n","2         [0.]      [0.05]    0.11      \n","3         [1.]      [0.15]    0.11      \n","4         [1.]      [0.27]    0.11      \n","5         [1.]      [0.39]    0.11      \n","6         [1.]      [0.16]    0.11      \n","7         [1.]      [0.61]    0.11      \n","8         [0.]      [0.07]    0.11      \n","9         [1.]      [0.21]    0.11      \n","10        [1.]      [0.22]    0.11      \n","11        [1.]      [0.57]    0.11      \n","12        [0.]      [0.11]    0.11      \n","13        [1.]      [0.21]    0.11      \n","14        [1.]      [0.12]    0.11      \n","15        [1.]      [0.32]    0.11      \n","16        [1.]      [0.29]    0.11      \n","17        [0.]      [0.1]     0.11      \n","18        [1.]      [0.87]    0.11      \n","19        [1.]      [0.52]    0.11      \n","20        [0.]      [0.11]    0.11      \n","Обнаружено  15.0  аномалий\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1600x800 with 1 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\n"},"metadata":{}}]}],"metadata":{"colab":{"provenance":[],"mount_file_id":"18LkCpZh0St7o9ZxC55LTbYhv_HKlCqbh","authorship_tag":"ABX9TyO3UaY/bSyHbtabXnLp+wkG"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}
\ No newline at end of file
diff --git a/labworks/LW2/XtXd_1.png b/labworks/LW2/XtXd_1.png
new file mode 100644
index 0000000..62a2adf
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diff --git a/labworks/LW2/XtXd_1_metrics.png b/labworks/LW2/XtXd_1_metrics.png
new file mode 100644
index 0000000..fd07fda
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diff --git a/labworks/LW2/XtXd_2.png b/labworks/LW2/XtXd_2.png
new file mode 100644
index 0000000..3461a2b
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diff --git a/labworks/LW2/XtXd_2_metrics.png b/labworks/LW2/XtXd_2_metrics.png
new file mode 100644
index 0000000..c615c64
Binary files /dev/null and b/labworks/LW2/XtXd_2_metrics.png differ
diff --git a/labworks/LW2/lab02_lib.py b/labworks/LW2/lab02_lib.py
index ec90383..a0888ef 100644
--- a/labworks/LW2/lab02_lib.py
+++ b/labworks/LW2/lab02_lib.py
@@ -29,12 +29,14 @@ from pandas import DataFrame
 from sklearn.metrics import precision_score, recall_score, f1_score, confusion_matrix
 from tensorflow.keras.models import Sequential
 from tensorflow.keras.layers import Dense, Activation
+from tensorflow.keras.callbacks import Callback
 
 
 
 visual = True
 verbose_show = False
 
+
 # generate 2d classification dataset
 def datagen(x_c, y_c, n_samples, n_features):
 
@@ -91,8 +93,27 @@ class EarlyStoppingOnValue(tensorflow.keras.callbacks.Callback):
             )
         return monitor_value
 
+
+class VerboseEveryNEpochs(Callback):
+    def __init__(self, every_n_epochs=1000, verbose=1):
+        super().__init__()
+        self.every_n_epochs = every_n_epochs
+        self.verbose = verbose
+
+    def on_epoch_end(self, epoch, logs=None):
+        if (epoch + 1) % self.every_n_epochs == 0:
+            if self.verbose:
+                print(f"\nEpoch {epoch + 1}/{self.params['epochs']}")
+                if logs:
+                    log_str = ", ".join([f"{k}: {v:.4f}" for k, v in logs.items()])
+                    print(f" - {log_str}")
+
+
 #создание и обучение модели автокодировщика
-def create_fit_save_ae(cl_train, ae_file, irefile, epohs, verbose_show, patience):
+def create_fit_save_ae(cl_train, ae_file, irefile, epohs, verbose_show, patience, **kwargs):
+    verbose_every_n_epochs = kwargs.get('verbose_every_n_epochs', 1000)
+    early_stopping_delta = kwargs.get('early_stopping_delta', 0.001)
+    early_stopping_value = kwargs.get('early_stopping_value', 0.0001)
 
     size = cl_train.shape[1]
     #ans = '2'
@@ -140,22 +161,28 @@ def create_fit_save_ae(cl_train, ae_file, irefile, epohs, verbose_show, patience
 
     optimizer = tensorflow.keras.optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, amsgrad=False)
     ae.compile(loss='mean_squared_error', optimizer=optimizer)
-    error_stop = 0.0001
     epo = epohs
 
-    early_stopping_callback_on_error = EarlyStoppingOnValue(monitor='loss', baseline=error_stop)
+
+    verbose = 1 if verbose_show else 0
+
+    early_stopping_callback_on_error = EarlyStoppingOnValue(monitor='loss', baseline=early_stopping_value)
     early_stopping_callback_on_improving = tensorflow.keras.callbacks.EarlyStopping(monitor='loss',
-                                                                                           min_delta=0.0001, patience = patience,
-                                                                                           verbose=1, mode='auto',
+                                                                                           min_delta=early_stopping_delta, patience = patience,
+                                                                                           verbose=verbose, mode='min',
                                                                                            baseline=None,
-                                                                                           restore_best_weights=False)
+                                                                                           restore_best_weights=True)
     history_callback = tensorflow.keras.callbacks.History()
-    verbose = 1 if verbose_show else 0
+
     history_object = ae.fit(cl_train, cl_train,
                                 batch_size=cl_train.shape[0],
                                 epochs=epo,
-                                callbacks=[early_stopping_callback_on_error, history_callback,
-                                early_stopping_callback_on_improving],
+                                callbacks=[
+                                  early_stopping_callback_on_error, 
+                                  history_callback,
+                                  early_stopping_callback_on_improving,
+                                  VerboseEveryNEpochs(every_n_epochs=verbose_every_n_epochs),
+                                ],
                                 verbose=verbose)
     ae_trainned = ae
     ae_pred = ae_trainned.predict(cl_train)
@@ -538,4 +565,4 @@ def ire_plot(title, IRE_test, IREth, ae_name):
     plt.gcf().savefig('out/IRE_' + title + ae_name + '.png')
     plt.show()
 
-    return
\ No newline at end of file
+    return
diff --git a/labworks/LW2/report.md b/labworks/LW2/report.md
new file mode 100644
index 0000000..c43974a
--- /dev/null
+++ b/labworks/LW2/report.md
@@ -0,0 +1,675 @@
+# Отчет по ЛР2
+
+Коновалова Алёна, Ильинцева Любовь, А-01-22
+
+## Задание 1
+
+## 1. Импорт необходимых библиотек и модулей
+
+
+```py
+import os 
+os.chdir('/content/drive/MyDrive/Colab Notebooks/is_lab2')
+```
+
+```py
+# импорт модулей 
+import numpy as np 
+import lab02_lib as lib
+```
+
+## 2. Генерация индивидуального набора двумерных данных 
+
+Сгенерируем индивидуальный набор двумерных данных в пространстве признаков с координатами центра (k, k), где k – номер бригады, равный 8 в нашем случае.
+
+
+```py
+data = lib.datagen(8, 8, 1000, 2)
+```
+
+**Вывод:**
+![Training set](train_set.png)
+
+
+Выведем данные и размерность
+
+```py
+print('Исходные данные:')
+print(data)
+print('Размерность данных:')
+print(data.shape)
+```
+
+**Вывод:**
+```bash
+Исходные данные:
+[[8.14457288 7.96648176]
+ [8.16064924 7.98620341]
+ [7.93127504 7.92863959]
+ ...
+ [7.95464881 7.94307035]
+ [8.01092703 7.90530753]
+ [7.81962108 7.93563874]]
+Размерность данных:
+(1000, 2)
+```
+
+## 3. Создание и обучение автокодировщика АЕ1
+
+Создадим автокодировщик простой архитектуры. Обучим автокодировщик в течение 1000 эпох с параметром patience = 300. Добавим 1 скрытый слой с 5 нейронами, т.к. нам нужно добиться, чтобы MSE_stop была не меньше 1-10.
+
+```py
+patience = 300
+ae1_trained, IRE1, IREth1 = lib.create_fit_save_ae(data,'out/AE1.h5','out/AE1_ire_th.txt', 1000, True, patience)
+```
+
+**Вывод:**
+```bash
+...
+Epoch 1000/1000 - loss: 3.7394
+```
+
+Ошибка MSE_stop равна **3.7394**, что является удовлетворительным.
+
+Пороговое значение ошибки реконструкции IREth1 - **3.07**
+
+## 4. Построение графика ошибки реконструкции для AE1
+
+
+```py
+lib.ire_plot('training', IRE1, IREth1, 'AE1')
+```
+**Вывод:**
+
+![IRE for training set AE1](IRE_trainingAE1.png)
+
+Из графика видим, что нейросеть обучена оптимально и порог обнаружения аномалий адекватно описывает границу области генеральной совокупности исследуемых данных.
+
+
+## 5. Создание и обучение автокодировщика АЕ1
+
+Создадим автокодировщик с более сложной архитектурой. Будем обучать в течение 2700 эпох с параметром patience = 500. Добавим 5 скрытых слоев с архитектурой 4-3-2-3-4 нейронов на каждом слое. В случае с автокодировщиком АЕ2 нам нужно добиться ошибки MSE_stop не меньше 0.01.
+
+```py
+patience = 500
+ae2_trained, IRE2, IREth2 = lib.create_fit_save_ae(data,'out/AE2.h5','out/AE2_ire_th.txt', 2700, True, patience) 
+```
+
+**Вывод:**
+```bash
+...
+Epoch 2700/2700 - loss: 0.0114
+```
+Ошибка MSE_stop равна **0.0114**, мы сумели достичь результата, близкого к идеалу.
+
+Пороговое значение ошибки реконструкции IREth2 - **0.4**
+
+## 6. Построение графика ошибки реконструкции для AE2
+
+```py
+lib.ire_plot('training', IRE2, IREth2, 'AE2')
+```
+
+**Вывод:**
+
+![IRE for training set AE2](IRE_trainingAE2.png)
+
+Из графика также видим, что нейросеть обучена хорошо и порог обнаружения аномалий не завышен относительно средних значений ошибки.
+
+
+## 7. Расчет характеристик качества обучения EDCA
+
+Рассчитаем характеристики для АЕ1 и АЕ2. Визуализируем области пространства признаков, распознаваемые автокодировщиками АЕ1 и АЕ2.
+
+### 7.1. AE1
+
+```py
+numb_square = 20 
+xx, yy, Z1 = lib.square_calc(numb_square, data, ae1_trained, IREth1, '1', True)
+```
+
+**Вывод:**
+
+![Class boundary AE1](AE1_train_def.png)
+
+```bash
+amount:  19
+amount_ae:  280
+```
+
+![Xt Xd AE1](XtXd_1.png)
+
+![Xt Xd metrics AE1](XtXd_1_metrics.png)
+
+```bash
+Оценка качества AE1
+IDEAL = 0. Excess:  13.736842105263158
+IDEAL = 0. Deficit:  0.0
+IDEAL = 1. Coating:  1.0
+summa:  1.0
+IDEAL = 1. Extrapolation precision (Approx):  0.06785714285714287
+```
+
+### 7.2. AE2
+
+```py
+numb_square = 20 
+xx, yy, Z2 = lib.square_calc(numb_square, data, ae2_trained, IREth2, '2', True)
+```
+
+**Вывод:**
+
+![Class boundary AE2](AE2_train_def.png)
+
+```bash
+amount:  19
+amount_ae:  30
+```
+
+![Xt Xd AE2](XtXd_2.png)
+
+![Xt Xd metrics AE2](XtXd_2_metrics.png)
+
+```bash
+Оценка качества AE2
+IDEAL = 0. Excess:  0.5789473684210527
+IDEAL = 0. Deficit:  0.0
+IDEAL = 1. Coating:  1.0
+summa:  1.0
+IDEAL = 1. Extrapolation precision (Approx):  0.6333333333333334
+```
+
+
+### 7.3. Сравнение характеристик качества обучения и областей аппроксимации
+
+```py
+lib.plot2in1(data, xx, yy, Z1, Z2)
+```
+**Вывод:**
+
+![Class boundary AE2](AE1_AE2_train_def.png)
+
+По результатам подсчетов характеристик качества обучения EDCA, можно сделать вывод о непригодности автокодировщика АЕ1. Значение Excess = 13.74 значительно превышает идеальный показатель 0. АЕ1 считает нормой данные, которые находятся в 14 раз за пределами реального распределения обучающей выборки. Низкое значение Approx = 0.07 подтверждает плохую аппроксимацию исходных данных. Такой автокодировщик будет пропускать большинство аномалий и не может быть рекомендован для практического применения.
+
+Автокодировщик АЕ2 показывает более высокие результаты. Значение Excess = 0.58 близко к идеальному, что указывает на точное определение границ нормального класса. Approx = 0.63 демонстрирует хорошую точность аппроксимации исходных данных. Данный автокодировщик пригоден для решения практических задач обнаружения аномалий.
+
+## 8. Создание тестовой выборки
+
+Нужно создать тестовую выборку, состояющую, как минимум, из 4 элементов, не входящих в обучающую выборку. Элементы должны быть такими, чтобы AE1 распознавал их как норму, а AE2 детектировал как аномалии.
+
+Подберем 6 точек. Условие, чтобы точка не попала в обучающую выборку:
+
+```py
+(x < 7.7 or x > 8.3) or (y < 7.7 or y > 8.3)
+```
+
+Поскольку центр располагается в точке (8;8) и в функции для генерации датасета используется правило 3σ, где параметр cluster_std = 0.1, то точки за пределами 7.7 и 8.3 не входят в обучающую выборку с вероятностью 99.7%.
+
+
+Запишем точки в массив и сохраним в файл.
+
+```py
+test_points = np.array([
+    [8.5, 8.5],    
+    [7.5, 7.5],   
+    [8.4, 7.6],    
+    [7.6, 8.4],  
+    [8.45, 7.55],
+    [7.55, 8.45]    
+])
+
+np.savetxt('data_test.txt', test_points)
+
+```
+
+
+## 9. Тестирование автокодировщиков АЕ1 и АЕ2
+
+
+Загрузим тестовый набор.
+
+```py
+data_test = np.loadtxt('data_test.txt', dtype=float)
+```
+
+Проведем тестирование первого автокодировщика.
+
+```py
+predicted_labels1, ire1 = lib.predict_ae(ae1_trained, data_test, IREth1)
+
+lib.anomaly_detection_ae(predicted_labels1, ire1, IREth1) 
+lib.ire_plot('test', ire1, IREth1, 'AE1')
+```
+
+**Вывод:**
+
+
+```bash
+i         Labels    IRE       IREth
+0         [1.]      [3.43]    3.07
+1         [0.]      [2.03]    3.07
+2         [0.]      [2.71]    3.07
+3         [0.]      [2.85]    3.07
+4         [0.]      [2.72]    3.07
+5         [0.]      [2.88]    3.07
+Обнаружено  1.0  аномалий
+```
+
+![IRE for test set AE1](IRE_testAE1.png)
+
+Условие выполнено - 5 точек АЕ1 распознал как норму и лишь одну точку определил как аномалию. Данный автокодировщик плохо справляется с распознаванием аномалий.
+
+
+
+Проведем тестирование второго автокодировщика.
+
+```py
+predicted_labels2, ire2 = lib.predict_ae(ae2_trained, data_test, IREth2)
+
+lib.anomaly_detection_ae(predicted_labels2, ire2, IREth2) 
+lib.ire_plot('test', ire2, IREth2, 'AE2')
+```
+
+```bash
+i         Labels    IRE       IREth     
+0         [1.]      [0.75]    0.4       
+1         [1.]      [0.66]    0.4       
+2         [1.]      [0.55]    0.4       
+3         [1.]      [0.58]    0.4       
+4         [1.]      [0.62]    0.4       
+5         [1.]      [0.65]    0.4       
+Обнаружено  6.0  аномалий
+```
+
+![IRE for test set AE2](IRE_testAE2.png)
+
+Мы видим, что условие также выполнено - все точки являются аномальными.
+
+
+## 10. Визуализация элементов обучающей и тестовой выборки в областях пространства признаков
+
+Построим области аппроксимации и точки тестового набора
+
+```py
+lib.plot2in1_anomaly(data, xx, yy, Z1, Z2, data_test)
+```
+
+![](AE1_AE2_train_def_anomalies.png)
+
+
+## 11. Результаты исследования
+
+Занесем результаты исследования в таблицу:
+
+| Параметр | AE1 | AE2 |
+|----------|-----|-----|
+| **Количество скрытых слоев** | 1 | 5 |
+| **Количество нейронов в скрытых слоях** | 5 | 4-3-2-3-4 |
+| **Количество эпох обучения** | 1000 | 2700 |
+| **Ошибка MSE_stop** | 3.7394 | 0.0114 |
+| **Порог ошибки реконструкции** | 3.07 | 0.4 |
+| **Значение показателя Excess** | 13.7368 | 0.5789 |
+| **Значение показателя Approx** | 0.0679 | 0.6333 |
+| **Количество обнаруженных аномалий** | 1 | 6 |
+
+
+## 12. Общие выводы
+
+
+На основе проведенного исследования автокодировщиков AE1 и AE2 были определены ключевые требования для эффективного обнаружения аномалий:
+
+**1. Данные для обучения** должны быть репрезентативными и не содержать аномалий. Объем выборки должен быть достаточным для покрытия всей области нормального поведения объектов.
+
+**2. Архитектура автокодировщика** должна иметь не менее 3-5 скрытых слоев с симметричной структурой, обеспечивающей плавное сжатие и восстановление данных. Простые архитектуры, как у AE1 (1 слой), не способны качественно выявлять аномалии.
+
+**3. Количество эпох обучения** должно составлять не менее 2000-3000 для достижения удовлетворительного качества. Короткое обучение (1000 эпох у AE1) приводит к недообучению и низкой эффективности.
+
+**4. Ошибка MSE_stop** должна находиться в диапазоне 0.01-0.05. Высокие значения ошибки (3.74 у AE1) свидетельствуют о непригодности модели для обнаружения аномалий.
+
+**5. Порог обнаружения аномалий** должен быть строгим (0.3-0.5) для минимизации ложных пропусков. Завышенный порог (3.07 у AE1) приводит к некорректной классификации аномальных объектов как нормальных.
+
+**6. Характеристики EDCA** должны быть близки к идеальным значениям: Excess → 0, Approx → 1. Значения AE2 (Excess=0.58, Approx=0.63) демонстрируют удовлетворительное качество, в то время как показатели AE1 (Excess=13.74, Approx=0.07) указывают на полную непригодность для практического применения.
+
+Таким образом, для надежного обнаружения аномалий необходимо использовать сложные архитектуры автокодировщиков с продолжительным обучением и контролем качества через метрики EDCA.
+
+
+## Задание 2
+
+
+## 1. Описание набора реальных данных
+
+Номер бригады k = 8. Следовательно, наш набор реальных данных - WBC.
+
+```bash
+N = k mod 3
+```
+Он представляет из себя 378 примеров с 30 признаками, где из 378 примеров 357 являются нормальными и относятся к доброкачественному классу, 21 - аномалиями и относятся к злокачественному классу.
+
+
+## 2. Загрузка обучающей выборки
+
+```py
+train = np.loadtxt('WBC_train.txt', dtype=float)
+```
+
+## 3. Вывод полученных данных и их размерность
+
+```py
+print('Исходные данные:')
+print(train)
+print('Размерность данных:')
+print(train.shape)
+```
+
+**Вывод:**
+
+```bash
+Исходные данные:
+[[3.1042643e-01 1.5725397e-01 3.0177597e-01 ... 4.4261168e-01
+  2.7833629e-01 1.1511216e-01]
+ [2.8865540e-01 2.0290835e-01 2.8912998e-01 ... 2.5027491e-01
+  3.1914055e-01 1.7571822e-01]
+ [1.1940934e-01 9.2323301e-02 1.1436666e-01 ... 2.1398625e-01
+  1.7445299e-01 1.4882592e-01]
+ ...
+ [3.3456387e-01 5.8978695e-01 3.2886463e-01 ... 3.6013746e-01
+  1.3502858e-01 1.8476978e-01]
+ [1.9967817e-01 6.6486304e-01 1.8575081e-01 ... 0.0000000e+00
+  1.9712202e-04 2.6301981e-02]
+ [3.6868759e-02 5.0152181e-01 2.8539838e-02 ... 0.0000000e+00
+  2.5744136e-01 1.0068215e-01]]
+Размерность данных:
+(357, 30)
+```
+
+
+## 4. Создание и обучение автокодировщика АЕ3
+
+Для начала попробуем обучить автокодировщик при минимально возможных параметрах и посмотреть на порог ошибки реконструкции. То есть будем обучать в течение 50000 эпох с параметром patience = 5000, с 9 скрытыми слоями и архиектурой 15-13-11-9-7-9-11-13-15
+
+
+```py
+patience = 5000
+ae3_trained, IRE3, IREth3 = lib.create_fit_save_ae(train,'out/AE3.h5','out/AE3_ire_th.txt', 50000, False, patience)
+lib.ire_plot('training', IRE3, IREth3, 'AE3')
+```
+
+**Вывод:**
+
+```bash
+Задать архитектуру автокодировщиков или использовать архитектуру по умолчанию? (1/2): 1
+Задайте количество скрытых слоёв (нечетное число) : 9
+Задайте архитектуру скрытых слоёв автокодировщика, например, в виде 3 1 3 : 15 13 11 9 7 9 11 13 15
+
+Epoch 1000/50000
+ - loss: 0.0020
+
+Epoch 2000/50000
+ - loss: 0.0013
+
+Epoch 3000/50000
+ - loss: 0.0012
+
+Epoch 4000/50000
+ - loss: 0.0012
+
+Epoch 5000/50000
+ - loss: 0.0011
+
+Epoch 6000/50000
+ - loss: 0.0010
+```
+MSE_stop = **0.001**
+
+
+## 5. График ошибки реконструкции 
+
+Построим график ошибки реконструкции и выведем порог ошибки реконструкции.
+
+
+![IRE for training set. AE3](IRE_trainingAE3_min.png)
+
+
+IREth3 = **0.84**
+
+
+## 6. Вывод о пригодности обученного автокодировщика
+
+Обученный автокодировщик демонстрирует удовлетворительные результаты для обнаружения аномалий. Модель со архитектурой 15-13-11-9-7-9-11-13-15 успешно прошла обучение, достигнув MSE = 0.001 за 6000 эпох. Стабильное снижение функции потерь свидетельствует о корректной работе алгоритма.
+
+Установленный порог IREth = 0.84 является разумным для разделения нормальных и аномальных образцов. Узкое горлышко из 7 нейронов обеспечивает необходимое сжатие данных для выделения ключевых признаков.
+
+Модель можно считать пригодной для практического использования при условии, что тестирование подтвердит достижение целевых 70% обнаружения аномалий.
+
+
+## 7. Загрузка тестовой выборки
+
+
+```py
+test = np.loadtxt('WBC_test.txt', dtype=float)
+```
+
+```py
+print('Исходные данные:')
+print(test)
+print('Размерность данных:')
+print(test.shape)
+```
+
+
+**Вывод:**
+
+```bash
+Исходные данные:
+[[0.18784609 0.3936422  0.19425057 0.09654295 0.632572   0.31415251
+  0.24461106 0.28175944 0.42171717 0.3946925  0.04530147 0.23598833
+  0.05018141 0.01899148 0.21589557 0.11557064 0.0655303  0.19643872
+  0.08003602 0.07411246 0.17467094 0.62153518 0.18332586 0.08081007
+  0.79066235 0.23528442 0.32132588 0.48934708 0.2757737  0.26905418]
+ [0.71129727 0.41224214 0.71460162 0.56776246 0.48451747 0.53990553
+  0.57357076 0.74602386 0.38585859 0.24094356 0.3246424  0.07507514
+  0.32059558 0.23047901 0.0769963  0.19495599 0.09030303 0.27865126
+  0.10269038 0.10023078 0.70188545 0.36727079 0.72010558 0.50181872
+  0.38453411 0.35044775 0.3798722  0.83573883 0.23181549 0.20136429]
+ ..............
+ [0.32367836 0.49983091 0.33542948 0.1918982  0.57389185 0.45616833
+  0.31794752 0.33593439 0.61363636 0.47198821 0.13166757 0.25808876
+  0.10446214 0.06023183 0.27082979 0.27268904 0.08777778 0.30611858
+  0.23158102 0.21074997 0.28744219 0.5575693  0.27685642 0.14815179
+  0.71471967 0.35830641 0.27004792 0.52268041 0.41119653 0.41492851]]
+Размерность данных:
+(21, 30)
+```
+
+
+## 8. Тестирование обученного автокодировщика
+
+
+```py
+predicted_labels3, ire3 = lib.predict_ae(ae3_trained, test, IREth3)
+
+lib.anomaly_detection_ae(predicted_labels3, ire3, IREth3) 
+lib.ire_plot('test', ire3, IREth3, 'AE3')
+```
+
+**Вывод:**
+
+```bash
+i         Labels    IRE       IREth     
+0         [0.]      [0.27]    0.84      
+1         [0.]      [0.8]     0.84      
+2         [0.]      [0.32]    0.84      
+3         [0.]      [0.53]    0.84      
+4         [0.]      [0.55]    0.84      
+5         [0.]      [0.68]    0.84      
+6         [0.]      [0.53]    0.84      
+7         [1.]      [0.95]    0.84      
+8         [0.]      [0.31]    0.84      
+9         [0.]      [0.47]    0.84      
+10        [0.]      [0.44]    0.84      
+11        [1.]      [1.07]    0.84      
+12        [0.]      [0.25]    0.84      
+13        [0.]      [0.45]    0.84      
+14        [0.]      [0.27]    0.84      
+15        [0.]      [0.58]    0.84      
+16        [0.]      [0.53]    0.84      
+17        [0.]      [0.27]    0.84      
+18        [1.]      [1.29]    0.84      
+19        [1.]      [0.93]    0.84      
+20        [0.]      [0.24]    0.84      
+Обнаружено  4.0  аномалий
+```
+
+![IRE for test set. AE3](IRE_testAE3_min.png)
+
+При текущих параметрах было достигнуто лишь 19% выявленных аномалий, что свидетельствует о непригодности данного автокодировщика.
+
+
+## 9. Подбор подходящих параметров
+
+
+Мы провели несколько тестов и путем подбора пришли к архитектуре, удовлетворяющей условие пригодности автокодировщика - не менее 70% выявления аномалий.
+
+Для этого мы установили параметр early_stopping_delta = 0.00001 и увеличили параметр patience до 20000. Однако критерием останова в нашем случае являлся параметр early_stopping_value, который мы также изменили до 0.00007.
+Количество эпох мы оставили прежним - 50000.
+
+Помимо этого, мы установили оптимальную архитектуру - 9 скрытых слоев с 24-22-20-17-15-17-20-22-24 нейронами.
+
+
+```py
+patience = 20000
+ae3_trained, IRE3, IREth3 = lib.create_fit_save_ae(train,'out/AE3.h5','out/AE3_ire_th.txt', 50000, False, patience, early_stopping_delta = 0.00001, early_stopping_value = 0.00007)
+
+lib.ire_plot('training', IRE3, IREth3, 'AE3')
+```
+
+**Вывод:**
+
+```bash
+Задать архитектуру автокодировщиков или использовать архитектуру по умолчанию? (1/2): 1
+Задайте количество скрытых слоёв (нечетное число) : 9
+Задайте архитектуру скрытых слоёв автокодировщика, например, в виде 3 1 3 : 24 22 20 17 15 17 20 22 24
+
+Epoch 1000/50000
+ - loss: 0.000864
+
+Epoch 2000/50000
+ - loss: 0.000545
+
+Epoch 3000/50000
+ - loss: 0.000415
+
+Epoch 4000/50000
+ - loss: 0.000303
+
+Epoch 5000/50000
+ - loss: 0.000226
+
+Epoch 6000/50000
+ - loss: 0.000217
+
+Epoch 7000/50000
+ - loss: 0.000207
+
+Epoch 8000/50000
+ - loss: 0.000186
+
+Epoch 9000/50000
+ - loss: 0.000167
+
+Epoch 10000/50000
+ - loss: 0.000113
+
+Epoch 11000/50000
+ - loss: 0.000102
+
+Epoch 12000/50000
+ - loss: 0.000095
+
+Epoch 13000/50000
+ - loss: 0.000090
+
+Epoch 14000/50000
+ - loss: 0.000088
+
+Epoch 15000/50000
+ - loss: 0.000086
+
+Epoch 16000/50000
+ - loss: 0.000079
+
+Epoch 17000/50000
+ - loss: 0.000078
+```
+
+![](IRE_trainingAE3_ideal2.png)
+
+Пороговое значение IREth3 = 0.11 является оптимальным, т.к. позволяет обнаруживать даже слабые аномалии, при этом сам по себе не слишком строгий.
+
+```py
+predicted_labels3, ire3 = lib.predict_ae(ae3_trained, test, IREth3)
+
+lib.anomaly_detection_ae(predicted_labels3, ire3, IREth3) 
+lib.ire_plot('test', ire3, IREth3, 'AE3')
+
+i         Labels    IRE       IREth     
+0         [0.]      [0.1]     0.11      
+1         [1.]      [0.5]     0.11      
+2         [0.]      [0.05]    0.11      
+3         [1.]      [0.15]    0.11      
+4         [1.]      [0.27]    0.11      
+5         [1.]      [0.39]    0.11      
+6         [1.]      [0.16]    0.11      
+7         [1.]      [0.61]    0.11      
+8         [0.]      [0.07]    0.11      
+9         [1.]      [0.21]    0.11      
+10        [1.]      [0.22]    0.11      
+11        [1.]      [0.57]    0.11      
+12        [0.]      [0.11]    0.11      
+13        [1.]      [0.21]    0.11      
+14        [1.]      [0.12]    0.11      
+15        [1.]      [0.32]    0.11      
+16        [1.]      [0.29]    0.11      
+17        [0.]      [0.1]     0.11      
+18        [1.]      [0.87]    0.11      
+19        [1.]      [0.52]    0.11      
+20        [0.]      [0.11]    0.11      
+Обнаружено  15.0  аномалий
+```
+
+![](IRE_testAE3_ideal2.png)
+
+
+Тестирование модели продемонстрировало превосходные результаты - обнаружено 15 из 21 аномалий, что соответствует 71.4% точности и превышает целевую метрику в 70%.
+
+
+## 10. Результаты исследования
+
+Занесем результаты исследования в таблицу:
+
+
+| **Dataset name** | WBC |
+| **Количество скрытых слоев** | 9 |
+| **Количество нейронов в скрытых слоях** | 24-22-20-17-15-17-20-22-24 |
+| **Количество эпох обучения** | 50000 |
+| **Ошибка MSE_stop** | 0.000078 |
+| **Порог ошибки реконструкции** | 0.11 |
+| **% обнаруженных аномалий** | 71.4 |
+
+
+## 11. Общие выводы
+
+
+На основе проведенного исследования автокодировщиков AE1 и AE2 были определены ключевые требования для эффективного обнаружения аномалий:
+
+**1. Данные для обучения** должны быть тщательно отобраны и содержать только репрезентативные нормальные образцы. Для 30-мерного пространства признаков объем выборки должен составлять не менее 300-500 объектов для адекватного покрытия области нормального поведения.
+
+**2. Архитектура автокодировщика** должна иметь глубокую симметричную структуру с 7-9 скрытыми слоями. Оптимальная конфигурация 24-22-20-17-15-17-20-22-24 нейронов обеспечивает плавное сжатие 30-мерного пространства до 15 нейронов в горлышке с последующим восстановлением, что позволяет эффективно выделять существенные признаки.
+
+**3. Количество эпох обучения** должно составлять не менее 15000-20000 для достижения высокого качества реконструкции. Продолжительное обучение необходимо для сложных высокоразмерных данных.
+
+**4. Ошибка MSE_stop** должна достигать значений 0.00007-0.0001. Столь низкий порог обусловлен высокой размерностью данных и необходимостью точной реконструкции многочисленных признаков.
+
+**5. Порог обнаружения аномалий** должен быть строгим (0.1-0.15) для надежного выявления аномалий в сложном многомерном пространстве. Низкое значение IREth компенсирует высокую размерность данных и обеспечивает чувствительность к слабым отклонениям.
+
+Таким образом, для качественного обнаружения аномалий в высокоразмерных данных необходимы глубокие архитектуры автокодировщиков с продолжительным обучением до достижения экстремально низких значений ошибки реконструкции.
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