From 30bc300bedeb7bdd56eb06a5d9a8dde4af51c6bb Mon Sep 17 00:00:00 2001 From: ablejo1234 <114792430+ablejo1234@users.noreply.github.com> Date: Sat, 1 Oct 2022 13:16:18 +0900 Subject: [PATCH 1/2] HW 0: setup your environments (conda, git, colab) I added my name/department/message. Request Confirmation. --- COSE474-03.md | 1 + 1 file changed, 1 insertion(+) diff --git a/COSE474-03.md b/COSE474-03.md index 0d8daf6..7707c2a 100644 --- a/COSE474-03.md +++ b/COSE474-03.md @@ -3,6 +3,7 @@ | No | Name | Department/Major | Message | | ---- | :------------: | ---------------: | -----------: | | 1 | Hyunwoo J. Kim | CS | Hello World! | +| 2 | Sung-yoon Jo | STAT | Hello World! | 2021 Fall == From a3bbf1054759ea06f7d8b5f17c51133e060390ea Mon Sep 17 00:00:00 2001 From: ablejo1234 <114792430+ablejo1234@users.noreply.github.com> Date: Sun, 4 Dec 2022 13:57:14 +0900 Subject: [PATCH 2/2] =?UTF-8?q?Colaboratory=EB=A5=BC=20=ED=86=B5=ED=95=B4?= =?UTF-8?q?=20=EC=83=9D=EC=84=B1=EB=90=A8?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- dl_final_project_code_(COSE474).ipynb | 7708 +++++++++++++++++++++++++ 1 file changed, 7708 insertions(+) create mode 100644 dl_final_project_code_(COSE474).ipynb diff --git a/dl_final_project_code_(COSE474).ipynb b/dl_final_project_code_(COSE474).ipynb new file mode 100644 index 0000000..7dd124c --- /dev/null +++ b/dl_final_project_code_(COSE474).ipynb @@ -0,0 +1,7708 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU", + "gpuClass": "standard" + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Credit Card Fraud Detection Project Code\n", + "\n", + "\n" + ], + "metadata": { + "id": "xTMZ0sI-dcQA" + } + }, + { + "cell_type": "markdown", + "source": [ + "# 0. Import Necessary Modules" + ], + "metadata": { + "id": "mowP6MWjm1BP" + } + }, + { + "cell_type": "code", + "source": [ + "# Import Libraries\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n", + "import seaborn as sns\n", + "import tensorflow as tf\n", + "\n", + "import warnings\n", + "warnings.filterwarnings('ignore')" + ], + "metadata": { + "id": "KJ49-jP9dfY6" + }, + "execution_count": 1, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Set working path\n", + "from google.colab import drive\n", + "drive.mount('/content/drive')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "GK5tAX8LdsSl", + "outputId": "c841e1ed-843a-427a-c56b-711b4984af4d" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# 1. Data Loading" + ], + "metadata": { + "id": "xUHKiV_Am8Ff" + } + }, + { + "cell_type": "code", + "source": [ + "# read \"creditcard.csv\" file\n", + "df = pd.read_csv(\"/content/drive/My Drive/Colab Notebooks/DLProject/creditcard.csv\", delimiter=',', dtype=np.float32)\n", + "print(df.shape)\n", + "df.head()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 318 + }, + "id": "V6a1F8tPd6Fo", + "outputId": "5d82934d-d402-44bf-af88-fdc5c69b4aa6" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(284807, 31)\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Time V1 V2 V3 V4 V5 V6 V7 \\\n", + "0 0.0 -1.359807 -0.072781 2.536347 1.378155 -0.338321 0.462388 0.239599 \n", + "1 0.0 1.191857 0.266151 0.166480 0.448154 0.060018 -0.082361 -0.078803 \n", + "2 1.0 -1.358354 -1.340163 1.773209 0.379780 -0.503198 1.800499 0.791461 \n", + "3 1.0 -0.966272 -0.185226 1.792993 -0.863291 -0.010309 1.247203 0.237609 \n", + "4 2.0 -1.158233 0.877737 1.548718 0.403034 -0.407193 0.095921 0.592941 \n", + "\n", + " V8 V9 ... V21 V22 V23 V24 V25 \\\n", + "0 0.098698 0.363787 ... -0.018307 0.277838 -0.110474 0.066928 0.128539 \n", + "1 0.085102 -0.255425 ... -0.225775 -0.638672 0.101288 -0.339846 0.167170 \n", + "2 0.247676 -1.514654 ... 0.247998 0.771679 0.909412 -0.689281 -0.327642 \n", + "3 0.377436 -1.387024 ... -0.108300 0.005274 -0.190321 -1.175575 0.647376 \n", + "4 -0.270533 0.817739 ... -0.009431 0.798279 -0.137458 0.141267 -0.206010 \n", + "\n", + " V26 V27 V28 Amount Class \n", + "0 -0.189115 0.133558 -0.021053 149.619995 0.0 \n", + "1 0.125895 -0.008983 0.014724 2.690000 0.0 \n", + "2 -0.139097 -0.055353 -0.059752 378.660004 0.0 \n", + "3 -0.221929 0.062723 0.061458 123.500000 0.0 \n", + "4 0.502292 0.219422 0.215153 69.989998 0.0 \n", + "\n", + "[5 rows x 31 columns]" + ], + "text/html": [ + "\n", + "
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\n", + " " + ] + }, + "metadata": {}, + "execution_count": 3 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The dataset has 284807 observations and 31 variables." + ], + "metadata": { + "id": "zGZpnzoTUqS4" + } + }, + { + "cell_type": "markdown", + "source": [ + "# 2. Data Preprocessing" + ], + "metadata": { + "id": "fSkiSAvYd-Nm" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Missing Value Check" + ], + "metadata": { + "id": "Eruk5T2On6uE" + } + }, + { + "cell_type": "code", + "source": [ + "df.info()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Yc_nS27Rohiu", + "outputId": "e64b5b2e-1d9a-4536-de20-8d516a804fbb" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "RangeIndex: 284807 entries, 0 to 284806\n", + "Data columns (total 31 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Time 284807 non-null float32\n", + " 1 V1 284807 non-null float32\n", + " 2 V2 284807 non-null float32\n", + " 3 V3 284807 non-null float32\n", + " 4 V4 284807 non-null float32\n", + " 5 V5 284807 non-null float32\n", + " 6 V6 284807 non-null float32\n", + " 7 V7 284807 non-null float32\n", + " 8 V8 284807 non-null float32\n", + " 9 V9 284807 non-null float32\n", + " 10 V10 284807 non-null float32\n", + " 11 V11 284807 non-null float32\n", + " 12 V12 284807 non-null float32\n", + " 13 V13 284807 non-null float32\n", + " 14 V14 284807 non-null float32\n", + " 15 V15 284807 non-null float32\n", + " 16 V16 284807 non-null float32\n", + " 17 V17 284807 non-null float32\n", + " 18 V18 284807 non-null float32\n", + " 19 V19 284807 non-null float32\n", + " 20 V20 284807 non-null float32\n", + " 21 V21 284807 non-null float32\n", + " 22 V22 284807 non-null float32\n", + " 23 V23 284807 non-null float32\n", + " 24 V24 284807 non-null float32\n", + " 25 V25 284807 non-null float32\n", + " 26 V26 284807 non-null float32\n", + " 27 V27 284807 non-null float32\n", + " 28 V28 284807 non-null float32\n", + " 29 Amount 284807 non-null float32\n", + " 30 Class 284807 non-null float32\n", + "dtypes: float32(31)\n", + "memory usage: 33.7 MB\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Dataset with 284807 observations and 31 variables(features). There seem to be no missing values(NA) here." + ], + "metadata": { + "id": "4gCXKko_le6y" + } + }, + { + "cell_type": "code", + "source": [ + "df.isnull().sum() / df.shape[0]" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "nYi2Mk3Nokiw", + "outputId": "d3ec435e-62f7-486e-e124-e2f22cadf5fd" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Time 0.0\n", + "V1 0.0\n", + "V2 0.0\n", + "V3 0.0\n", + "V4 0.0\n", + "V5 0.0\n", + "V6 0.0\n", + "V7 0.0\n", + "V8 0.0\n", + "V9 0.0\n", + "V10 0.0\n", + "V11 0.0\n", + "V12 0.0\n", + "V13 0.0\n", + "V14 0.0\n", + "V15 0.0\n", + "V16 0.0\n", + "V17 0.0\n", + "V18 0.0\n", + "V19 0.0\n", + "V20 0.0\n", + "V21 0.0\n", + "V22 0.0\n", + "V23 0.0\n", + "V24 0.0\n", + "V25 0.0\n", + "V26 0.0\n", + "V27 0.0\n", + "V28 0.0\n", + "Amount 0.0\n", + "Class 0.0\n", + "dtype: float64" + ] + }, + "metadata": {}, + "execution_count": 5 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "0 missing values(NA)! No need for preprocessing here." + ], + "metadata": { + "id": "H1ZA39ucmcs0" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Correlation" + ], + "metadata": { + "id": "6YunCYQZnJ4B" + } + }, + { + "cell_type": "code", + "source": [ + "# check correlation\n", + "df_num = df.drop(\"Class\",axis=1)\n", + "corr = df_num.corr()\n", + "\n", + "# correlation plot\n", + "f, ax = plt.subplots(figsize = (25,15))\n", + "sns.heatmap(df_num.corr(), annot=True, linewidths=0.5, fmt=\"0.1f\", ax=ax, cmap=\"viridis\")\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 711 + }, + "id": "IharNKWEounn", + "outputId": "d0ca46b4-3924-4bbf-e627-25cddd9ff97e" + }, + "execution_count": 6, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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JjfQ9FqSlFz525j9eBOq5F1vwNSbIayquYjgpBc7vYoP83LP1uAmeY0JKvvkLxOvamRR6zcsoYp1HFFjnR90dq7iIcFIyC5y3BPnvDEWdG9hwLgXeY9WhM6+pQuctLq+76OgK7EsrcI4QfYZzhLBQWrW+nJUr3DmeQ9HHzrM673Tx+GrbOaetTTZ3iRTHhguT/3Mc5wSwFigDfO39/lqgJlAfaATMNsasAp4GqhX8TYwxA40xPxhjfhg/fnxAwqX0qN2wKpfWiOb7r9cEO6VU6JFwBY3j43hn6Y/BThG54ISUMTz915uZ+tkP7N2bXvIvEClBmRDDM49359MvVrA3NSPYOSIico5CQgxPP92TqVN/1DmCiGVyS8H/2cq1D7/5FY4BOI6Ta4w54Zx+P38unj4DJDuO0/pMv4njOOOBvCuSTm7K3/0WGBcNKfl+VmzKPoiN8dtvX6TUzCwurXj6X8riK4aTejjrDL/CPbdf25RebRsBkLwzlfh8973HFfiXFvC8SyL/v8jERYaTluHf9u53XUOXOz1LYtPqn4iuEnnqsehLI9mf4vsXrwbNa1G3SXXeXTycMmXLULFyOH/75I88cZv/PnDm99c3o2eHxgCs35ZCbL7b5WIrVWDfoeDMX56UrCwuzXdrdnyFotdUm5rVeahNC+6c/AnHc3JcabmtY1NuvtYzVuu2pxJXKf9YhZMWxLEq+NyLiwgn1c/r92zdltiUW67xPPfW7UglLirfOEWFs6/Ac29fehZx+f6lOi6q8PPTH2w8JuRJTc8iLn9PxcL7Ss3wbJOakUWZEEN4+VDSj2T7vaXnzc25sXszADZu2ENMvtuyY2IqsH9f0R8s9cijN7J790E+m7K8yMf9ITWzwPEgIpzUzCCt845Nubl9MceDqOAcD35/XdN8x/PUAsfz8KAdz2++8Uq6d24CwMbNe4nN946amMoV2Heg6K7HBnVm955DTPncvX9sSkvP8jkWxEYWPnamZXi2SUt397l3ayf71lSe1Iws4guc36UF6bmXx6bjJsBtHZpy8zXe+duRSnylCqzOa3Ppde1s5a3hPHEVi1jnmZ5tTo3Vxe6NFXiO5/l/9E5cRPD+zpCn0DgVcTwIpELnLfnOp4paU4XOW1xYdz17XsWN3bznCBv3EhNb4Bxhf3HnCF3Z/fMhPvvU/+cItyU25ZZrveedRRw7z+q808/HVxvPOW1ssrlL5GzYcGGyJBuBGGNMa8dxFntv7a7nOE5yoAI6tIUPP4MbO8HqdVAhDGIru7vPtXtSqFEpiqqREaQdzuLGRvV5bMr/3N1pMT5euJqPF3pOCdsl1KJ3+6Z8/eNGGteMJ+vocfZn+v5Mn/2ZRziSfZzGNeNZuyOF7i0b8N8Fq/za9OV73/Lle98Cng+/uWnAtSyYvoIrrqrJkcPZHErz/fTDryYt4qtJiwCIrVaJke8O9OtFSYBPZ6/i09meP2ebZrW47YYrmb14AwmXX0rW0WMB/dljRVm7J4WaUVFUqxhB6uEsujWozyOf+66phnExjO7SiXs+msrBX4661vLJ3NV8Mtezpto2qcXtnZoxa+lGGtW+lKxfjgfsZ8cVJWl3CtWjo6gaFUFaZhY3Nq3P4/8NznPvk/mr+WS+97nXuBa3d2jKzOUbaVTL+9wrME77M46QdfQ4jWrFk7Q9hW6tG/DRXP8+98DOY0Ke5F0p1IiJomqlCFIzsuhyZX2e/MB3/uYnbaNHi4as2bmX65vWZdmWXa60TJ/2I9OneS4EtWxVh5tv+R3z5q6jQcMqHDlyjIMHC6/zAfe2JywslBdf+MqVpjxr96RQo3KB15hPg7TOz3Q8OBqc48Gnc1bz6RxPU5tmtbjt+mbMXryRhDreY1SQjufTZqxk2oyVALT6XW16db+SbxZuoGH9SznyyzEOHircdW/fdoSFhfL8P78u9Jg/Jf+UQvX8z72r6jP0vWKeezv2cn2zuizb7M5zb8o3q5nyjXdNNa3FbXlrqk7w1lSepJ9TqJHvNaZrk/oM+Sg4z708Nh03AT6Zt5pP5nnnr3Etbu/YjJnLgntMyJO0yzt/3rHqemV9nig4Vsnb6PG7hqzeuZfrm7i3zvOs3ZNCzUpRVIuMIDUzi24J9Xl0anDXVKFxalafJyYHr8nnvKWR97zlh1953jLfv+ct06evYPr0FQC0bFmHm29u7jlHaHCGc4R7rvWcI/x9hl9b8hR53rlsI41qn+G8M/s4jWrHk7QthW6t/H/eaeM5p41NNneJnA3rL0w6jnPcGHMr8KoxpiKe5n8Afrsw+ehIWLYK0jMg8VYYNAC8n/9B757QvhUsXAKd74TyoTDmSX/tuXg5uQ6jZ8zl3/16ERJi+HRlMlv2HeCPHVqTtCeVeRu30ahKHK/1vomIi8vToX5tBnVozU2vv+9q16Lk7bRLqMnnIwaQffwkIz44/UEy/x3ah95jPZ8wPfajuYzsdwOh5cry3bodLEre4VrT8rnruLpjAhMWPUN29nFefuT0p1y/NnMIgzo/79q+i/P9qu20aVabKS/dS/bxEzz71sxTj70/ph/9n5oEwKD/u5Yb2lxB+YvK8fk/B/L5vLW889liV5pyHIeRs+cyoXcvyhjDlDXJbNl/gD9f05q1e1OZu2UbQzpcyyUXleOft3QDYE/mYR6Y8rkrPXm+W7Odtk1qMXXcPWQfP8moCafHavKIvvQZ8QEAf7ztGjq39IzVl3+/j+nfJvH2dP+PVU6uw3Ofz2X8Pb0IMYapPySzNe0Ag65rTfLPqcxbv41G1eJ4pa/nuZfYoDYPX9eanv9w+bm3djttG9Vk+nPe5967p597H/61D3eO9qz7cR/OZcTdN1D+orJ8l7SD75J2uNtl2TEhJ9dhzKdz+df9vSgTYpi2NJmtKQd4qEtr1u1KZX7yNqYuTWJMny58+dQAMn7JZsgkd07w81u6ZCstW17OpMkPkn3sBC/87ctTj731zr3c/4d/Ex1Tgb792rJz537efPteAKZP/YEZX60u7rc9Zz6vMaaE15jy5elQrzaDEltz0xvurvNTx4O/eY8H/853PBjZlz7D8x0PWnmPBy/ex/SF7hwPwHs8b1qLKS96mp4dn+94/lxf+g/zNA3qfc3p4/mr9/H5/CTXjucAS37YRqvf1ebD8fdx7NgJxr1y+i/977xyF3/483vEVA6n/x2t2bnrAG//4y4Apn61gq9mrfV7T06uw9gpc/nXQ57zlmlLvM+9G1uT/FMqC5K2MXVxEs/168IXfx1A5i/ZDHnX/efed6u306ZJLT57/h6yj51kdL419cGovvR9xrumbr+GG7xr6ouX7uPzhUm8Pc2915i3B3hfY35MZkvea8zuVOZt2EajqnG86n2N6dCgNoM6tabHK+4992w9bgJ8t3Y7bRvXYtoYz/Nv5MR8x4Rn+tJnlGf+/nTrNXRu4Zm/r56/j+mLkhj/uTvzN+azubw50HMuNXVZMltTD/BwZ8/8zU/exmdLkxh7Zxe+GhqYscpxHEZ9PZd37vQ0fbraczz/U/vWJO1NZe6mbTS+NI7Xbvcez+vW5o/tW9P9TZfX1NS5vHmfd5yW5xunXanMX7eNhMvieOWum6hwSXnaN6zNQze05pa/u/saA7AoyXveMtJ73jKpmPOW/85lZH/veUuyu3+XWbp0Ky1b1mHSBw+QnX2CF54//Y+Tb42/h/sHTiA6ugJ9+3rPEd66B/D8A+iMGf4/RwDveWfjYs47n+nDnaO8552T5zJiwA2UL+f+eadt55y2Ntncdb7LCfCHeZ1PTKA/CS1AnNyUesFu8BESv4krhr8c7AwfG0YO5sqH7Wpa+fpgALpW+1OQS0773+5XAWjV58Ugl/haMvlR6o61a/42Dx3M1fe8FOwMH8snPELCULvGKXmsZ503H2hP14/jPU02HhOaDLarac3Lg+mUOCbYGT6+mf8UV4ywa5w2jPCsqasH2HNMWD7xEQBa9bWnCWDJB4/Q/qYXgp3hY8EXj9P0T3atqdWvDqbF3XbN3bJ3PWuq4VP2jNW6MZ7nno3Hzt/9wa75++GdR2j8qF3jtPbFwdQfbVfTxr961lTjx+zpWvt373nLQ/Y0Aax8YzCdOo4NdoaPb+YOpfl9do3Tj2/b+/dQm7psbAJY+fpgE+wGG2XuqW79xbWIKj9ZOXc2fPiNiIiIiIiIiIiIXGCsv5VbRERERERERETEVrlY/4ZJa+kdkyIiIiIiIiIiIhJwujApIiIiIiIiIiIiAadbuUVERERERERERM5Rjm7lPmd6x6SIiIiIiIiIiIgEnC5MioiIiIiIiIiISMDpVm4REREREREREZFzpE/lPnfGcc7LwTsv/1AiIiIiIiIiIkFkgh1go317qlp/HSqmys9Wzt15+47JK4a/HOwEHxtGDiY3pV6wM3yExG+i2SC7xmnVa4MBrOqysQk8XWoqma1NYNeasrEJ7J0/NZXMxjVlYxPYO39qKpmNa8rGJrB3/tRUMhvXlI1NYO/8qalkNq6pvKaOncYFucTX3G+eDHaCnGf0MyZFREREREREREQk4M7bd0yKiIiIiIiIiIi4Lef8/DGJAaF3TIqIiIiIiIiIiEjA6cKkiIiIiIiIiIiIBJxu5RYRERERERERETlHucEOKMX0jkkREREREREREREJOF2YFBERERERERERkYDTrdwiIiIiIiIiIiLnKAd9Kve5suLCpDFmHjDOcZyZ+b73F6A+UAtoBSxyHKe7Ww3tLq/BsK6JhJgQpqxI4u1Fy30e/12Nqgzt0p76cTE8OmUGM9dtdisFgGHjYP5iqBQFX7xb+HHHgTGvwsKlUD4UxgyFhHquJp0y5NZE2iXUIvv4CZ6ZNIsNu9MKbdPgslhG9etMaLmyLErezvNT5rvW06ZBDYbcmkhISAhTv09i4mzfuStXtgzP9utMg+pxZBw5yhMTZrDnYKZrPbY22dqlprNn23PP5i7bmmxcUzY2gX1zZ3OXmtTkL7YeD8C+sbKxydb5s22cbO5Sk5r8adDD19GyZR2yj53g+ee/YvPmVJ/HQ0PLMvyZm6lSJYrc3FwWL97C2+8scLVJ5ExsuZX7P0DvAt/r7f3+C0A/N3ceYgzPdOvIfR9Mo/vr79GtcX3qxFTy2WZvxmGGTpvFl2s3uJlyys1dYfwLxT++cCns3A1fT4aRj8GolwKSRbuGNakeE0mPkRMZ/Z85DOvdscjtht3RiVEfzqbHyIlUj4mkbcOarvSEGMPQ2zvy8BvT6PXse3RpXp/a8b5zd0vrBDKPHqPHyIl8MG8Ff+7ZzpUWm5ts7VLT2bPtuWdzl21NNq4pG5vAvrmzuUtNavIXW48HYN9Y2dhk6/zZNk42d6lJTf7UskVtqlaLol//t3jppa/5y587F7ndx58s4+4BbzPw/okkNKpGixa1XWsSKYktFyanAN2MMRcBGGNqAlWAbx3H+QY47ObOm1SN56eD6ew+lMGJnFxmJG2k0xV1fLb5OT2TTan7cZzAvD336qYQWaH4x+cugp6dwRholgCZWZB2wP2uxCZ1+HLZegDW7kihwsWhREeE+WwTHRFGWPmLWLsjBYAvl62nQ5M6hX4vf2hUM55d+9P5+UAGJ3NymbliI4kF9pXYpA5fLF0HwJyVm2lRv7orLTY32dqlprNn23PP5i7bmmxcUzY25e3TprmzuUtNavIXW48Hefu1aaxsbLJ1/mwbJ5u71KQmf2rTti6zZyUBsH79HsLDQ6lUybfp2LGTrFr1EwAnT+ayeXMqMdFnuPggZyXHsf8/W1lxYdJxnIPAMqCr91u9gY+dAF0FjIsIZ2/G6WufKRlZxFUID8Suz1nqfoiPPf11fAyk7XN/v7GR4aQcOj1WqelZxEaGF9omNT3rjNv4radigZ5DWcRWDC92m5xch6yjx4gMK+9Kj61Ntnap6Vd0Wfbcs7nLtiYb15SNTWDf3NncpSY1+a3H0uMB2DdWNjbZOn+2jZPNXWpSkz9FR1cgbd/ppn37DhN9houOYWGhtG51OStW7nCtSaQkVlyY9Mp/O3febdxnzRgz0BjzgzHmh/Hjx/s9TkRERERERETkfBASYnj66R5MnfoDe/dmBDtHLmBWfPiN13TgZWPMVcGmxXsAACAASURBVMAljuP8+Gt+seM444G8K5LOS8NfPutfm5qZxaUVT/8rQnzFcFIPZ53hVwRfXDSk5Pu5uin7IDbGnX3dcW1TerVpBEDyzlTio06PVVxkOGnpvmOVlp5FXL5/BSpqG39Jy8jy7YkKJy0jq8ht0tKzKBNiCL84lPQj2a702Npka5eazszW556NXTY2ndqXRWvKxiZb587GLjWpyQ02HQ/AzrGysenUviyaP1vHycYuNanJn3r2vIpuNzYFYOPGvcTGnG6KianA/v1F/2S8Rx/pys+7D/HpZz/4tedClRvsgFLMmndMOo6TBcwDJvAr3y35W63dk0KNSlFUjYygXJkQbmxUn7kbtgUy4Vfr0Bamz/R8OveqZKgQBrGV3dnXRwtXc8e4ydwxbjLz1myle4sGADSuGU/W0ePszzzis/3+zCMcyT5O45rxAHRv0YD5a7a60pa8M4XqMVFUqRxB2TIhdL6qPgvW+M7dgrXbuKllQwCuu7IuyzftcqXF5iZbu9R0ZrY+92zssrEpj01rysYmW+fOxi41qckNNh0PwM6xsrEpj03zZ+s42dilJjX50/TpKxh4/0QG3j+RRd9t5vobPBdOGzSowpEjxzh48EihX3PPgGsICwvl9Tfm+LVF5FzY9I5J8FyQnEq+T+g2xnwLXAGEG2N2A/c6jjPTnzvNyXUYPWMu/+7Xi5AQw6crk9my7wB/7NCapD2pzNu4jUZV4nit901EXFyeDvVrM6hDa256/X1/Zvh4dCQsWwXpGZB4KwwaACdPeh7r3RPat4KFS6DznVA+FMY86VqKj2+Tt9MuoSZfDB9A9omTDP9g1qnHPnqyD3eMmwzAmI/nMqrvDYSWK8t363awaN0OV3pych3GfTyXfz3cixBjmL4kma0pB3iwW2vW/ZTKgrXbmPp9Es/178LnwweQeSSbJybOcKXF5iZbu9R09mx77tncZVuTjWvKxiawb+5s7lKTmvzF1uMB2DdWNjbZOn+2jZPNXWpSkz8tXbqVli1r88Gk+8nOPsHzL5x+vo9/awAD759IdHQF+vZty86d+3nrzQEATJv+IzNmrHGtS+RMTKA+ZTrAnCt+xa3cgbBh5GByU+oFO8NHSPwmmg2ya5xWvTYYwKouG5vA06WmktnaBHatKRubwN75U1PJbFxTNjaBvfOnppLZuKZsbAJ7509NJbNxTdnYBPbOn5pKZuOaymvq2GlckEt8zf3mSRPsBhtt3l3F+otrdavtsXLurLmVW0RERERERERERC4cujApIiIiIiIiIiIiAacLkyIiIiIiIiIiIhJwtn34jYiIiIiIiIiISKmRa/1PmLSX3jEpIiIiIiIiIiIiAacLkyIiIiIiIiIiIhJwupVbRERERERERETkHOVggp1QaukdkyIiIiIiIiIiIhJwxnHOy5/QeV7+oUREREREREREgkhvDSzCul1Vrb8O1fCyn62cO93KLSIiIiIiIiIico50K/e5O28vTF758MvBTvCx8vXBNBtkV9Oq1waTm1Iv2Bk+QuI3AVg1VqteGwzY1QSeLjWVzNYmsGtN2dgE9s6fmkpm45qysQnsnT81lczGNWVjE9g7f2oqmY1rysYmsHf+1FQyG9dUXtP1IbcFucTX7NxPgp0g5xn9jEkREREREREREREJuPP2HZMiIiIiIiIiIiJuy3V0K/e50jsmRUREREREREREJOB0YVJEREREREREREQCTrdyi4iIiIiIiIiInCN9Kve50zsmRUREREREREREJOB0YVJEREREREREREQCzopbuY0x84BxjuPMzPe9vwCdgUggAsgBnnMc5yO3OobclkjbhFpkHz/B8Emz2LArrdA2DS6LZWS/zoReVJbvkrfz/Cfz3crxNN2aSDtv0zOTZrFhd9FNo/p1JrRcWRYlb+f5Ke41DRsH8xdDpSj44t3CjzsOjHkVFi6F8qEwZigk1HMtB4A2DWow5NZEQkJCmPp9EhNnL/d5vFzZMjzbrzMNqseRceQoT0yYwZ6DmRdck81dtq1zNZX+LjWpyV9sPW6Cxqq0NoHm7tewbaxsbLJ1/mwbJ5u7bGvSmirdTQ+9MoAWXa/i2C/HeGHA62xZub3QNn+fO4JKl0Zx/OhxAJ7sPJr0fYE5rosUZMs7Jv8D9C7wvd7AWKC/4zgJQBfgH8aYSDcC2iXUpHpMJD1HTOTZD+fwVO+ORW73VO9OjP5wNj1HTKR6TCRtG9Z0I8fT1NDT1GPkREb/Zw7DimkadkcnRn04mx4j3W+6uSuMf6H4xxcuhZ274evJMPIxGPWSaykAhBjD0Ns78vAb0+j17Ht0aV6f2vGVfLa5pXUCmUeP0WPkRD6Yt4I/92x3wTXZ3GXjOldT6e5Sk5r8xdbjJmisSmsTaO5+DdvGysYmW+fPtnGyucu2Jq2p0t3UouuVVL38Uu6u90f+cf9b/OmN+4rddlzfV3jgqsd54KrHdVHSD3IIsf4/W9lSNgXoZoy5CMAYUxOoAnzrOM5mAMdx9gBpQIwbAe2b1OHLpesBWLsjhQoXhxIdEeazTXREGGHlL2LtjhQAvly6nsSmddzIASCxSR2+XPYrm5atp0MT95qubgqRFYp/fO4i6NkZjIFmCZCZBWkHXMuhUc14du1P5+cDGZzMyWXmio0kFvjzJzapwxdL1wEwZ+VmWtSv7l6QpU02d9m4ztVUurvUpCZ/sfW4mbdfjVXpa8rbp+bu7Ng2VjY22Tp/to2TzV22NWlNle6m1j2vZs6kBQCsX7qZ8MgwKsW78t4uEb+x4sKk4zgHgWVAV++3egMfO47j5G1jjGkBXARsdaMhtmI4KemHT32dmp5FbGS47zaR4aSlZ/luU9F3G782RYaTcqjkptSCTZHuNZUkdT/Ex57+Oj4G0va5t7/YigXG6FDhOcm/TU6uQ9bRY0SGlb+gmqzusnCdq6l0d6lJTX7rsfS4CRqr0toEmrtf1WbZWNnYZOv82TZONnfZ1qQ1VbqboqtUIm3X6XcG7d99gOiqlYrc9rEJD/Pmihfo8/TvXesRORtW/IxJr7zbuad7//+9eQ8YYy4FJgF3OY6TW9QvNsYMBAYCvPXWW67HioiIiIiIiIiUNmP7vsqBPQe5OLw8w6c8xnX9rmXOpIXBzirVch0T7IRSy6YLk9OBl40xVwGXOI7zI4AxJgL4ChjmOM6S4n6x4zjjgfF5X/7r4ZdL3OHt1zalV9tGACTvTCU+3z3KcQXeHQmQVuBfN+Iiw0nL8N3mt7rj2qb0apOvKarkpriCTen+bfo14qIhJd/P+03ZB7Gu3HzvkZaR5TtGUYXnJG+btPQsyoQYwi8OJf1I9gXVZFuXjetcTaW7S01qcoNNx03QWJXmJs3d2bNxrGxsOrUvi+bP1nGyscvGplP70poqdU09HurMjX+4DoCNP2wh9rLKJHsfi65Wmf0/Hyz0aw7s8XzvaFY2c/+ziCta1NWFSQkaK27lBnAcJwuYB0zA8+5JvD9zcirwvuM4U/y9z48Xrqb32Mn0HjuZeau30r1lAwAa14wn6+hx9mce8dl+f+YRjmQfp3HNeAC6t2zAgjX+vbP8o4WruWPcZO4YN5l5a7bSvcWvbGrRgPl+bvo1OrSF6TM9n869KhkqhEFsZff2l7wzheoxUVSpHEHZMiF0vqo+C9Zs89lmwdpt3NSyIQDXXVmX5Zt2uRdkaZNtXTauczWV7i41qckNNh03QWNVmps0d2fPxrGysSmPTfNn6zjZ2GVjUx6tqdLX9PkbM099iM1305ZzXb/2ADRoWZcjGb9wMCXdZ/uQMiFEVPZcUC1TtgwtuzVnR9JPfm0S+TVsesckeC5ITuX0J3TfDlwLVDbG3O393t2O46zy944XJW+nXUJNPh8xgOzjJxnxwaxTj/13aB96j50MwNiP5jKy3w2ElivLd+t2sCh5h79TTvnW2/TF8AFknzjJ8HxNHz3ZhzvGeZrGfDyXUX3zNa1zr+nRkbBsFaRnQOKtMGgAnDzpeax3T2jfChYugc53QvlQGPOkaymA52eajPt4Lv96uBchxjB9STJbUw7wYLfWrPsplQVrtzH1+ySe69+Fz4cPIPNINk9MnHHBNdncZeM6V1Pp7lKTmvzF1uMmaKxKaxNo7n4N28bKxiZb58+2cbK5y7YmranS3bRsxgpa3ngl723+J8d+Oc7f73n91GNvrniBB656nItCyzH266cpW64MIWVCWPnNWma8/Y1rTReKHHQr97ky+T5f5nziXHkWt3IH0srXB9NskF1Nq14bTG5KvWBn+AiJ3wRg1Vitem0wYFcTeLrUVDJbm8CuNWVjE9g7f2oqmY1rysYmsHf+1FQyG9eUjU1g7/ypqWQ2rikbm8De+VNTyWxcU3lN14fcFuQSX7NzP9EVuCJ8v7O29RfX2tTYZuXcWXMrt4iIiIiIiIiIiFw4bLuVW0REREREREREpNTIcfS+v3OlkRMREREREREREZGA04VJERERERERERERCTjdyi0iIiIiIiIiInKOcvW+v3OmkRMREREREREREZGA04VJERERERERERERCThdmBQREREREREREZGAM47jBLvBDeflH0pEREREREREJIhMsANsNHdHfeuvQ3WsudHKudM7JkVERERERERERCTgzttP5e5a7U/BTvDxv92v0mzQy8HO8LHqtcFWNgHkptQLcslpIfGbAKwcKzWVzNYmsGtN2dgE9s6fmkpm45qysQnsnT81lczGNWVjE9g7f2oqmY1rysYmsHf+1FQyG9dUXlOHzn8LcomveTOfCHaCnGfO2wuTIiIiIiIiIiIibstxdEPyudLIiYiIiIiIiIiISMDpwqSIiIiIiIiIiIgEnG7lFhEREREREREROUe5+rDyc6Z3TIqIiIiIiIiIiEjA6cKkiIiIiIiIiIiIBJxu5RYRERERERERETlHOXrf3zmz4sKkMWYeMM5xnJn5vvcXoKn3vxCgHPBPx3HedKvjgVG/5+qODTl29DgvDp7M1qTdxW47fMJ9xFevzIPXjXMrhzYNajDk1kRCQkKY+n0SE2cv93m8XNkyPNuvMw2qx5Fx5ChPTJjBnoOZrvXY2jRsHMxfDJWi4It3Cz/uODDmVVi4FMqHwpihkFDP1SQrxynPkFsTaZdQi+zjJ3hm0iw27E4rtE2Dy2IZ1a8zoeXKsih5O89Pme9aj61jpXEqnU15NH9q8jfb1pSa1HShdKlJTRdCl5rU5E9/fLATLVvUITv7BH97cQabt6T6PB4aWpYRw26mSpVIcnMdvl+yhbcnLHC1SeRMbLmk+x+gd4Hv9QYmAq0dx2kGtASeNMZUcSPg6o4NqVIrhnvbjebVJz5i0Njbi922TdcmHP3lmBsZp4QYw9DbO/LwG9Po9ex7dGlen9rxlXy2uaV1AplHj9Fj5EQ+mLeCP/dsd8E1AdzcFca/UPzjC5fCzt3w9WQY+RiMesndHlvHCaBdw5pUj4mkx8iJjP7PHIb17ljkdsPu6MSoD2fTY+REqsdE0rZhTVd6bB0rjVPpbMqj+VOTv9m2ptSkpgulS01quhC61KQmf2p5dW2qVq1E3wHjefGVmQz+4w1FbvfRp8u46w/vcN9DE2mUUJUWv6vtWpNISWy5MDkF6GaMuQjAGFMTqAJ86zhO3hXAUFzsbXVDY76ZsgyADSt2EB5xMVGxEYW2K3/JRfS6rwP/fWWWWykANKoZz6796fx8IIOTObnMXLGRxCZ1fLZJbFKHL5auA2DOys20qF/9gmsCuLopRFYo/vG5i6BnZzAGmiVAZhakHXCvx9Zxytvvl8vWA7B2RwoVLg4lOiLMZ5voiDDCyl/E2h0pAHy5bD0dCvT7i61jpXEqnU3596v5U5M/2bam1KSmC6VLTWq6ELrUpCZ/atu6LrPmJAGwfsMewsJCqVTJt+nYsZOsWv0TACdP5rJ5cyoxMWf4C7WclRwnxPr/bGVFmeM4B4FlQFfvt3oDHzuO4xhjLjPGrAF2AX9zHGePGw2V4yuyf0/6qa/3700nOr5ioe36P96Nz8bPI/vocTcyTomtGE7KocOnvk49lEVsxfBit8nJdcg6eozIsPIXVNPZSN0P8bGnv46PgbR97u3P5nGKjSzQlp5FbGR4oW1S07POuI3feiwdK41T6Ww6tV/Nn5r83WbZmlKTmi6ULjWp6ULoUpOa/Ck6Opy0fad/zM3+/YeJrlz8RcewsFBat7qcFSt3uNYkUhIrLkx65b+du7f3axzH2eU4ThPgcuAuY0xckPqo3bAql9aI5vuv1wQrQURERERERETkNwkJMfx1aA8+m/4je1Mygp0jFzArPvzGazrwsjHmKuASx3F+zP+g4zh7jDFJwDV4bv32YYwZCAwEeOutt85qh93vuoYud7YGYNPqn4iuEnnqsehLI9lf4MnZoHkt6japzruLh1OmbBkqVg7nb5/8kSdu++ev+GOenbSMLOKjTv/LRlxUOGkZWUVuk5aeRZkQQ/jFoaQfyfZ7i81NZyMuGlLy/QzilH0QG+Pe/mwbpzuubUqvNo0ASN6Z6tsWGU5aeoG29Czi8v0rXlHb+ItNY6VxKt1Nmj81+ZuNa0pNavI3G7vUpCZ/s7FLTWryp5tvupJuXZsCsGFTCrExEcDPAERHV2D/gcNF/rrH/tKFn38+yKdTf/Brz4Uq16r3/ZUu1oyc4zhZwDxgAt53SxpjqhljLvb+7yigHbCxmF8/3nGc3zmO87uBAwee1T6/fO9bBnV+nkGdn2fx12vodGsLAK64qiZHDmdzKM33kz6/mrSIvr/7K3e3Hsmjt/yDn7eluXJREiB5ZwrVY6KoUjmCsmVC6HxVfRas2eazzYK127ipZUMArruyLss37XKlxeams9GhLUyf6fl07lXJUCEMYiu7tz/bxumjhau5Y9xk7hg3mXlrttK9RQMAGteMJ+vocfZnHvHZfn/mEY5kH6dxzXgAurdowPw1W11ps2msNE6lu0nzpyZ/s3FNqUlN/mZjl5rU5G82dqlJTf407YuV3PfQu9z30Lt89/0mbrjOc+G0wRVVOPLLMQ4ePFLo19xz1zWEhYXy2pvf+LVF5FzY9I5J8FyQnMrpW7obAC8aYxzAAH93HGetGztePncdV3dMYMKiZ8jOPs7Lj0w+9dhrM4cwqPPzbuy2WDm5DuM+nsu/Hu5FiDFMX5LM1pQDPNitNet+SmXB2m1M/T6J5/p34fPhA8g8ks0TE2dccE0Aj46EZasgPQMSb4VBA+DkSc9jvXtC+1awcAl0vhPKh8KYJ93tsXWcAL5N3k67hJp8MXwA2SdOMvyD0x/i9NGTfbhjnGfdj/l4LqP63kBoubJ8t24Hi9btcKXH1rHSOJXOpjyaPzX5m21rSk1qulC61KSmC6FLTWrypyXLttHy6jp8MHEgx46d5G8vnj5XevuNu7nvoXeJjq5AvzvbsPOnA4x//W4Apn6+ghn6kXUSJMZxnGA3uMHpWu1PwW7w8b/dr9Js0MvBzvCx6rXBVjYB5KbUC3LJaSHxmwCsHCs1lczWJrBrTdnYBPbOn5pKZuOasrEJ7J0/NZXMxjVlYxPYO39qKpmNa8rGJrB3/tRUMhvXVF5Th85/C3KJr3kznzDBbrDR9G3NrL+41rP2KivnzrZ3TIqIiIiIiIiIiJQaOY6V1/xKBWt+xqSIiIiIiIiIiIhcOHRhUkRERERERERERAJOt3KLiIiIiIiIiIicoxy97++caeREREREREREREQuYMaYLsaYjcaYLcaYJ4t4vLoxZp4xZqUxZo0x5kZ/7FcXJkVERERERERERC5QxpgywOtAV6Ah8H/GmIYFNnsa+NhxnCuB3sAb/ti3buUWERERERERERE5R7lOqX/fXwtgi+M42wCMMf8FegLr8m3jABHe/10R2OOPHZf6kRMREREREREREZHiGWMGGmN+yPffwHwPVwV25ft6t/d7+Y0A+hpjdgMzgD/6pctxHH/8PrY5L/9QIiIiIiIiIiJBZIIdYKOPtlxt/XWoOy5fXuzcGWNuBbo4jvMH79f9gJaO4wzKt80jeK4jvmiMaQ38G2jkOE7ub+nSrdwiIiIiIiIiIiLn6Dz4VO6fgcvyfV3N+7387gW6ADiOs9gYUx6IBtJ+y47P2wuTrfq8GOwEH0smP0qzQS8HO8PHqtcGW9kEWNWV15SbUi/IJb5C4jdZNU5g75qysQnsXOc2NYG986emktm4pmxsAnvnT00ls3FN2dgE9s6fmkpm45qysQnsnT81lczGNZXX1PBpe5oA1j07ONgJ4o7lQF1jTC08FyR7A3cW2OYnoBPwrjGmAVAe2Pdbd1zqL+mKiIiIiIiIiIjIuXEc5yQwCJgJrMfz6dvJxphRxpge3s0eBe4zxqwG/gPc7fjh50Oet++YFBERERERERERcVuOU/p/9KbjODPwfKhN/u89k+9/rwPa+nu/esekiIiIiIiIiIiIBJwuTIqIiIiIiIiIiEjA6cKkiIiIiIiIiIiIBJx+xqSIiIiIiIiIiMg5ytX7/s6ZRk5EREREREREREQCThcmRUREREREREREJOCsuJXbGDMPGOc4zsx83/sLUN9xnAeNMRHAOmCa4ziD3Op4pH8HWjetxbHjJxn91tds3JFWaJsHbmtL12sSqBAWSsd7/+lWCgBtGtRgyK2JhISEMPX7JCbOXu7zeLmyZXi2X2caVI8j48hRnpgwgz0HM9VkQdOwcTB/MVSKgi/eLfy448CYV2HhUigfCmOGQkI9V5MAO8fKxiaAIbcm0i6hFtnHT/DMpFls2F34eNDgslhG9etMaLmyLErezvNT5rvWY+M42diUR/N3dmwbJxubbJ07W7s0f2o637vUVHqb8th2nLKxycb5U9PZaVe3BkNvTKRMSAhTfkzinYW+Tc1rVmXoje2pFxfDYx/PYFbyZld7LiQ5jt73d65sGbn/AL0LfK+39/sAo4GFbga0blqLy+KjuO3RCYz992yGDLiuyO2+XbmNe56Z7GYKACHGMPT2jjz8xjR6PfseXZrXp3Z8JZ9tbmmdQObRY/QYOZEP5q3gzz3bqcmCJoCbu8L4F4p/fOFS2Lkbvp4MIx+DUS+5nmTlWNnYBNCuYU2qx0TSY+RERv9nDsN6dyxyu2F3dGLUh7PpMXIi1WMiaduwpis9No6TjU15NH9nx7ZxsrHJ1rmztUvzp6bzvUtNpbcpj23HKRubbJw/NZ1909M3deT+96dx06vvcWPj+tSJ8W3am36Ypz6dxVdrNrjaIvJr2HJhcgrQzRhzEYAxpiZQBfjWGNMciANmuRlwbfM6zPh2HQDJW/YSfkkolSPDCm2XvGUvB9KPuJkCQKOa8ezan87PBzI4mZPLzBUbSWxSx2ebxCZ1+GKpp3nOys20qF9dTRY0AVzdFCIrFP/43EXQszMYA80SIDML0g6422TjWNnYlLfPL5etB2DtjhQqXBxKdITv8SA6Ioyw8hexdkcKAF8uW0+HAu3+YuM42diUf7+av5LZNk42Ntk6d7Z2af7UdL53qan0NuXfr03HKRubbJw/NZ2dxtXi+elAOrsPZXAiJ5f/rd1Ixwa+TXvSM9mUup9cx3G1ReTXsOLCpOM4B4FlQFfvt3oDHwMGeBF4zO2GmErhpB04fOrrtIOHiYkKd3u3xYqtGE7KodM9qYeyiK0YXuw2ObkOWUePERlWXk1BbjobqfshPvb01/ExkLbP3X3aOFY2NgHERhboSs8iNjK80Dap6Vln3MZvPRaOk41Np/ar+Tu7LsvGycYma+fO1i7Nn5rO8y41ld6mU/u17DhlY5ON86emsxMXEU5KxummlMwsYiOCd03jQpOLsf4/W1lxYdIr/+3cebdxPwTMcBxnd0m/2Bgz0BjzgzHmh/Hjx7uYKSIiIiIiIiIiIr+VFR9+4zUdeNkYcxVwieM4PxpjHgGuMcY8BIQDFxljshzHebLgL3YcZzyQd0XSmbDgxRJ3+Pvrm9GzQ2MA1m9LIbby6XtvYytVYN+hrOJ+qevSMrKIjzrdExcVTlpGVpHbpKVnUSbEEH5xKOlHstUU5KazERcNKfl+pnXKPoiNcXefNo6VTU13XNuUXm0aAZC8M9W3KzKctPQCXelZxOX7l+qitvEXm8bJ1ibN39mxcZxsbDq1L4vmztYuzZ+aLqQuNZXOJhuPUzY2ndqXZfOnprOXmplFfMXTTfER4aRlBu+ahsjZsuYdk47jZAHzgAl4P/TGcZw+juNUdxynJp7bud8v6qLkufp09ir6PzWJ/k9NYsEPW7jxmoYAJFx+KVlHjwXkZ0kWJ3lnCtVjoqhSOYKyZULofFV9FqzZ5rPNgrXbuKmlp/m6K+uyfNMuNVnQdDY6tIXpMz2fzr0qGSqEQWxld/dp41jZ1PTRwtXcMW4yd4ybzLw1W+neogEAjWvGk3X0OPszfY8H+zOPcCT7OI1rxgPQvUUD5q/Z6kqbTeNka5Pm7+zYOE42NuWxae5s7dL8qelC6lJT6Wyy8ThlY1Me2+ZPTWcv6ecUalSOompUBOXKhNC1cX3mbdhW8i8Uv8hxQqz/z1Y2vWMSPBckp1L4E7pd9/2q7bRpVpspL91L9vETPPvWzFOPvT+mH/2fmgTAoP+7lhvaXEH5i8rx+T8H8vm8tbzz2WK/9+TkOoz7eC7/ergXIcYwfUkyW1MO8GC31qz7KZUFa7cx9fsknuvfhc+HDyDzSDZPTJzh9w41nZtHR8KyVZCeAYm3wqABcPKk57HePaF9K1i4BDrfCeVDYYzfLrcXz8axsrEJ4Nvk7bRLqMkXwweQfeIkwz84/dlbHz3ZhzvGTQZgzMdzGdX3BkLLleW7dTtYtG6HKz02jpONTXk0f2fHtnGyscnWubO1S/OnpvO9S02ltymPbccpG5tsnD81nX3Tc1/O5e27ehESYpj6YzJb0g4wqFNrkn9OZd6GbTSqQk9dUAAAIABJREFUGserd95ExMXl6XBFbQZ1bE2Pf77vapdISYxzfn4ak9OqT8m3cgfSksmP0mzQy8HO8LHqtcFWNgFWdeU15abUC3KJr5D4TVaNE9i7pmxsAjvXuU1NYO/8qalkNq4pG5vA3vlTU8lsXFM2NoG986emktm4pmxsAnvnT00ls3FN5TU1fNqeJoB1zw6291NUguitje2tv7h2f/0FVs6dbe+YFBERERERERERKTVy7PlJiaWORk5EREREREREREQCThcmRUREREREREREJOB0K7eIiIiIiIiIiMg5ynWs/PGNpYLeMSkiIiIiIiIiIiIBpwuTIiIiIiIiIiIiEnC6MCkiIiIiIiIiIiIBp58xKSIiIiIiIiIico5y9L6/c2Ycxwl2gxvOyz+UiIiIiIiIiEgQ6VNeivDKhuusvw715yvmWDl35+07JuuOfTnYCT42Dx1Ms0F2Na16zc4mwKouG5vA05WbUi/YGT5C4jdZOU42NoFda8rGJrB3/tRUMhvXlI1NYO/8qalkNq4pG5vA3vlTU8lsXFM2NoG986emktm4pvKaul7+eJBLfP1vywvBTpDzzHl7YVJERERERERERMRtuY5u5T5XGjkREREREREREREJOF2YFBERERERERERkYDTrdwiIiIiIiIiIiLnKEefCXTO9I5JERERERERERERCThdmBQREREREREREZGA063cIiIiIiIiIiIi50ifyn3uNHIiIiIiIiIiIiIScFa8Y9IYMw8Y5zjOzHzf+wtQHxgIrPV++yfHcXq40XBN7Ro8fV0iZUJC+HhVEuOXLPd5fMDVV3F7s0aczM3l4C9HGfrVLPZkHnYj5ZQ2DWow5NZEQkJCmPp9EhNn+zaVK1uGZ/t1pkH1ODKOHOWJCTPYczDzgmsCGHJrIu0SapF9/ATPTJrFht1phbZpcFkso/p1JrRcWRYlb+f5KfNd67FxnIaNg/mLoVIUfPFu4ccdB8a8CguXQvlQGDMUEuq5mnSKbfNnY5ONayqPbWOlprNj65qybZxs7rKtycY1paZfR2vq7Ng2Tmo6O7auJ1u7bJs/G5tsnTuAB/7ak6sTr/h/9u49vKryzvv/+w6HoAmQADnUKo1SwRBO6ohyqAa0goWCUhQq6jPU1taR57HUjop0qqAirb/KTGsP0go6ilbKFEFlRCwnUQFPEQgoyklQk3BK4k4Jp9y/P5IdspNgNjFr57vh87ourk6yV1xv7++dXONirWwOHjjMb+56ji35nx732Hsf+1cyz+rIrd/5TUzaRGqzcsfks8DYWp8bW/X5A977PlV/ArkomeAc9105mB/OfZ6rZj7J8O7d+GbHDhHHbCws4prZz/Ddx59m8QcfceegbwWREtE06brB3PaH5xn1wJMMvbAb52RGNl3TL4fSAwcZMWU2Ty97l9tHDjzlmgAGds+ic1oKI6bM5v5nX2Xy2MH1Hjd5zOVMfWYJI6bMpnNaCgO6ZwXSY3Wdrr4KZj58/NdXroEdu+DlOTDl5zD1kcCTAHvzs9hkdU+BvbVSU3Ss7ilr62S5y1qTxT2lphOjPRUda+ukpuhY3U9Wu6zNz2KT1dkBXHTZeZyR1YmbL/8Vv/3FPCZMGXXcY/tf2YMDZYdi0nWyO4oz/8cqKxcm5wHDnHOtAZxzWcAZwGuxOHmvMzLZsb+YncUlHK6o4KVNH3J51y4Rx6z5ZBflR44AkPfZ52S2axtoU4+sTHbuKebTvSUcOVrB4nc/JLdXZFNury68sGYjAK++9xF9u3U+5ZrC53xx7SYA1m8voO1piXRqlxRxTKd2SSS1ac367QUAvLh2E4NqtTcVq+t0UW9I+ZJtu3QVjBwCzkGfHCgNQdHewLPMzc9ik9U9FT6vpbVSU3Ss7ilr62S5y1qTxT2lphOjPRUda+ukpuhY3U9Wu6zNz2KT1dkBXHJFDv+Y/w4AH+R9QnK7NqSm1f0PwTant2bUDy7lr394NSZdIsdj4sKk934fsBa4qupTY4G53nsPtHHOve2cW+2cuzqI82cmJ/N5jceyC74IkdE2+bjHj+7dg5VbtgWRUi29fTIF+481Fe4Pkd4++bjHHK3whA4cJCWpzSnVBJCeUqurOER6SnKdYwqLQ196TJP1GF2nhhTugcz0Yx9npkHR7uDPa21+Fpss7ylra6WmKHuM7ilr62S5y1qTxT2lphNs056KrsvYOqkpyh6r+8lql7H5WWyyOjuAjhnt2PN5cfXHewpK6JTRvs5xN00cwt8fX0n5gcOBN4l8GRMXJqvUfJw7/Bg3wDe89/8CXA/8p3Ou3r/ycM7dUnUB8+2ZM2cGFjki5zx6ZmbwlzXvBHYOEREREREREZEgnJN9Bl/r3JE3lmxo7hQRG29+U2UBMMM5dwFwuvf+HQDv/adV/7vVObccOB/YUvuLvfczgfAVSf/wQzOiPnFBKMTXajyandk2mcIvQnWO65/VmX/r35fr5/yNQ0ePRv3Pb4yikhCZqceaMlKTKSoJ1XtMUXGIFgmO5NMSKS4rPyWaxlzam1H9ewCQv6MwsislmaLiWl3FITJq/G1Zfcc0FUvrdCIyOkFBjd8fXbAb0tOCOZfF+Vlsqj6XsT1lca3UdGIs7Smr62Sxy2JT9bkM7Sk1RU97KjoW10lNJ8bSfrLaZXF+Fpuqz2VodgDDb+jP0OsuBmDz+p10+lpK9WudMtuzp7Ak4vjs87/BuT3O5Inlk2jRMoH2HZL51ZyfcNe4PwXSdyqo8Jbu+4svZlbOex8ClgGzqLpb0jmX6pxLrPq/OwEDgI1Nfe71nxWQlZrKme3b0SohgWHZ3fjHR1sjjumekcb9Qy/nx/MWsu+fB5o6oY78HQV0TkvljI7taNkigSEXdGPFusimFeu38t2LuwNwxfnn8tbmnadM03Mr32fM9DmMmT6HZeu2MLxvNgA9szIJHTjEntKyiOP3lJZRVn6InlmZAAzvm83ydXWubzcJS+t0IgYNgAWLK9+dOy8f2iZBesdgzmVxfhabwqztKYtrpaYTY2lPWV0ni10Wm8Is7Sk1RU97KjoW10lNJ8bSfrLaZXF+FpvCLM0O4MWn32DCiBlMGDGDN5ds4PJrLgTgvD6dKfuinP27v4g4/qVn3uSGAQ/wr7kPcceYP/Dp9j26KCnNxtIdk1B5QXI+xx7pzgYec85VUHkRdbr3vskvTB71nilLljJr7ChaOMe8dfl8vGcvt3+rH+s/L2Tpx1u5c9ClnN66Fb+7ZhgAn5V+wU/mLWzqlGNNFZ7pc5fyx9tGkeAcC1bns6VgL7cO68fGTwpZsX4r89/YwIM3DWXhveMpLSvnrtmLAuux2gTwWv42BuZk8cK94yk/fIR7n36l+rXn7h7HmOlzAJg2dylTb7iSxFYteX3jdlZt3B5Ij9V1umMKrM2D4hLIHQ0TxkPV+zkxdiRcdgmsXA1Droc2iTDt7sCTAHvzs9hkdU+BvbVSU3Ss7ilr62S5y1qTxT2lphOjPRUda+ukpuhY3U9Wu6zNz2KT1dkBvLX8Ay7KzWbW0rspP3CIGXfNrX7t0YUTmTAi+qdLRWLBVb6/zEnHn3sCj3LHwkeTJtJngq2mvEdtNgGmuiw2QWVXRUHX5s6IkJC52eQ6WWwCW3vKYhPYnZ+aGmZxT1lsArvzU1PDLO4pi01gd35qapjFPWWxCezOT00Ns7inwk1XffPfm7kk0v9+/LBr7gaLpm4YYf7i2i97LDQ5OzOPcouIiIiIiIiIiMipQxcmRUREREREREREJOas/Y5JERERERERERGRuFGByaek44LumBQREREREREREZGY04VJERERERERERERiTk9yi0iIiIiIiIiItJIR73u+2ssrZyIiIiIiIiIiIjEnC5MioiIiIiIiIiISMw5731zNwThpPyXEhERERERERFpRnr76XpMXjfK/HWoB3v93eTsTtrfMXnRDx5p7oQIb836GX0mzGjujAh5j0402QSY6rLYBHbnV1HQtbkzIiRkbja5TmBrT1lsArv7XE0Ns7inLDaB3fmpqWEW95TFJrA7PzU1zOKestgEduenpoZZ3FPhpi6P2Lq2seVnP2vuBDnJ6FFuERERERERERERiTldmBQREREREREREZGYO2kf5RYREREREREREQnaUd3312haOREREREREREREYk5XZgUERERERERERGRmNOj3CIiIiIiIiIiIo1U4V1zJ8Qt3TEpIiIiIiIiIiIiMacLkyIiIiIiIiIiIhJzJh7lds4tA6Z77xfX+NxPgW7AQ8BfgLMAD3zHe789iI47rh/EgJ5nU37oMFMeX8yHnxTVOebWUQMY1r87bU9P5LJ/ezSIjGr9s7/BnaNzSUhIYP4bG5i95K2I11u1bMEDNw4hu3MGJWUHuGvWIj7bVxpoE8Cdo3MZmFO5Tr986hU+2FV3nbLPSmfqjUNIbNWSVfnb+PW85YH1WFwni01hluY3eTosfxM6pMILT9R93XuY9ltYuQbaJMK0SZDTNZCUCBbnp6b47rLYBLZ+HoDddQKtVbSsrZOa4r9LTQ2z+PPAYlOYtflZbLI4P4tNYG92l2Zl8R+5ubRISOC59et57K3IdfrBBRdwXc+eHK2oYN+BA9y1eDGfffFFYD2nkgrd99doVlbuWWBsrc+Nrfr8fwMPe++zgb5A3e/0JtC/59l0zkhh1KRZTHvyVe6+6fJ6j3stbyv/5/5ngkiIkOAck64bzG1/eJ5RDzzJ0Au7cU5mh4hjrumXQ+mBg4yYMpunl73L7SMHBt41sHsWndNSGDFlNvc/+yqTxw6u97jJYy5n6jNLGDFlNp3TUhjQPSuQHovrZLEpzNr8rr4KZj58/NdXroEdu+DlOTDl5zD1kUAyIlicn5riu8tiE9j7eWB1nUBrFS1r66Sm+O9SU8Ms/jyw2BRmbX4WmyzOz2IT2JzdfYMH84P58xnyxBN897zz+GaHyHXauHs3V8+Zw7CnnuJ/N2/m7ksvDaRF5ERYuTA5DxjmnGsN4JzLAs4A9gItvfdLALz3Ie/9P4MIuOz8Lrz0xkYANmz9nLanJ9KxfVKd4zZs/Zy9JWVBJETokZXJzj3FfLq3hCNHK1j87ofk9uoScUxury68sKay+dX3PqJvt86Bd+X26sKLazcBsH57AW1PS6RTu8h16tQuiaQ2rVm/vQCAF9duYlCt9qZicZ0sNtU8r6X5XdQbUtoe//Wlq2DkEHAO+uRAaQiK9gaSUs3i/NQU310Wm8LntPTzwOo6hc+rtWqYtXVSU/x3qalhFn8eWGyqeV5L87PYZHF+FpvC57Q0u96ZmewoLmZnSQmHKyp48YMPuKJL5LlW79xJ+ZEjAOR9/jmZycmBtIicCBMXJr33+4C1wFVVnxoLzAXOBYqdc393zr3nnHvYOdciiIa01GQK9x27hbloX4j01Ob7Jk1vn0zB/mM9hftDpLdPPu4xRys8oQMHSUlqE2xXSq2u4hDpKcl1jiksDn3pMU3WY3CdLDZVn9fY/BpSuAcy0499nJkGRbuDPafF+akpvrssNoG9nwdW1wm0VlF3GVsnNcV/l5qi6DH488BiU/V5jc3PYpPF+VlsAnuzy0hO5vMaj2UXhEJktD3+XSDX9uzJiu3bA2k5FR31zvwfq0xcmKxS83Hu8GPcLYFvAT8HLgLOAf61vi92zt3inHvbOff2zJkzg68VEREREREREYkzI7Oz6ZmRwZ/ffru5U0RsvPlNlQXADOfcBcDp3vt3nHOtgDzv/VYA59zzwCXA47W/2Hs/EwhfkfR/Xt3wL6O7dnBvrr60JwAbtxWS0eHY3yakd0imaH/oeF8auKKSEJmpx3oyUpMpKgnVe0xRcYgWCY7k0xIpLitv8pYxl/ZmVP8eAOTvKIzsSkmmqLhWV3GIjBp/C1TfMU3F0jpZbbI8v4ZkdIKCGr9VtmA3pKcFe05r81NT/HdZarL888DSOoHWKloW10lN8d2lphNj6eeB1SaL87PYVH0uY/Oz1mR5doWhEF+rcYdkZnIyhfW8sU3/zp35t759uX7uXA4dPRpIi8iJMHPHpPc+BCwDZlF5tyTAW0CKcy58KWIwsLGpzvm3pe8z7r6nGXff0yx/72OG9e8OQI9zvkbon4di8rskjyd/RwGd01I5o2M7WrZIYMgF3VixbmvEMSvWb+W7F1c2X3H+uby1eWcgLc+tfJ8x0+cwZvoclq3bwvC+2QD0zMokdOAQe0oj12lPaRll5YfomZUJwPC+2SxftyWQNkvrZLXJ8vwaMmgALFhc+e7cefnQNgnSOwZ7TmvzU1P8d1lqsvzzwNI6gdYqWhbXSU3x3aWmE2Pp54HVJovzs9gUZm1+1posz25dQQFZKSmc2a4drRISGH7eefxja+Q6dU9L44ErruDHCxaw98CBQDpOVRXemf9jlaU7JqHyguR8qh7p9t4fdc79HPiHc84B7wB/DuLEr6/bxoBeZzN/+g8oP3SEqbMWV782574bGHff0wD832u/xZCLz6NN61a8+P/9iAWvbeDPC95s8p6jFZ7pc5fyx9tGkeAcC1bns6VgL7cO68fGTwpZsX4r89/YwIM3DWXhveMpLSvnrtmLmryjttfytzEwJ4sX7h1P+eEj3Pv0K9WvPXf3OMZMnwPAtLlLmXrDlSS2asnrG7ezauP2QHosrpPFpjBr87tjCqzNg+ISyB0NE8ZD1e9iZuxIuOwSWLkahlwPbRJh2t2BZESwOD81xXeXxSaw9/PA6jqB1ipa1tZJTfHfpaaGWfx5YLEpzNr8LDZZnJ/FJjA4O++ZsmwZT3zveyQ4x7wNG/ho715+2r8/6wsK+MfWrdx96aUktWrF74YPB+CzL77gxwsWBNIjEi3nvW/uhiD4i37Q8KPcsfTWrJ/RZ8KM5s6IkPfoRJNNgKkui01gd34VBV2bOyNCQuZmk+sEtvaUxSawu8/V1DCLe8piE9idn5oaZnFPWWwCu/NTU8Ms7imLTWB3fmpqmMU9FW7q8oitaxtbfvYzu7feNaPb3/u++Ytr/3X+syZnZ+2OSRERERERERERkbhR4c38psS4o5UTERERERERERGRmNOFSREREREREREREYk5XZgUERERERERERGRmNPvmBQREREREREREWmko5h8X5m4oDsmRUREREREREREJOZ0YVJERERERERERERiTo9yi4iIiIiIiIiINFKF16PcjeW8983dEIST8l9KRERERERERKQZ6QpcPX7yzo3mr0P96cKnTM7upL1jMmfSjOZOiJD/0ET6TLDVlPeozSbAVJfFJrA7P4tNFQVdmzsjQkLmZsDWntI+j56aomNxT1lsArvzU1PDLO4pi01gd35qapjFPWWxCezOT00Ns7inwk3n/u2BZi6J9NG1v2juBDnJnLQXJkVERERERERERIJW4fUWLo2llRMREREREREREZGY04VJERERERERERERiTk9yi0iIiIiIiIiItJIFXpPoEbTHZMiIiIiIiIiIiISc7owKSIiIiIiIiIiIjGnR7lFREREREREREQa6ajXo9yNpTsmRUREREREREREJOZM3DHpnFsGTPfeL67xuZ8CPwYO1jj0PGCs9/75pm4Y2PUb3D08lxYJCfzPWxv4y4q3Il6/MOvr3D38MrpmpvHvf13EKxs+auqEOvpnf4M7R+eSkJDA/Dc2MHtJZFOrli144MYhZHfOoKTsAHfNWsRn+0oD77pzdC4Dc86m/NBhfvnUK3ywq6jOMdlnpTP1xiEktmrJqvxt/Hre8lOuyWqXtSaL+3zydFj+JnRIhReeqPu69zDtt7ByDbRJhGmTIKdroEmAZhfvXZqfmpqa9pSampr2VHw2gWZ3IrRW0bG2ThabLM7uWxnn8Ivzh9DCOeZuzWPmh2/Ue9yQr5/Ho/1Hc82rj7Nh/+eBNok0xModk88CY2t9bizwY+99H+99H2Aw8E/glaY+eYJzTB4xmJ/Mfp4RM57kO7270SW9Q8Qxnxd/weR5r/DS+x809emP2zTpusHc9ofnGfXAkwy9sBvnZEY2XdMvh9IDBxkxZTZPL3uX20cODLxrYPcsOqelMGLKbO5/9lUmjx1c73GTx1zO1GeWMGLKbDqnpTCge9Yp1WS1y1qT1X1+9VUw8+Hjv75yDezYBS/PgSk/h6mPBJ6k2cV5l+anpqamPaWmpqY9FZ9NoNmdCK1VdKytk8Umi7NLwHHfBVfxw9ee5aqX/8Twzjl8s22nOscltWzN/zm3L3l7dwXaIxItKxcm5wHDnHOtAZxzWcAZwGs1jhkN/K/3/p9NffKeZ2Wyc28xu/aXcPhoBYve/5BB2V0ijvmsuJTNBXvw3jf16evVIyuTnXuK+XRvCUeOVrD43Q/J7RXZlNurCy+s2QjAq+99RN9unQPvyu3VhRfXbgJg/fYC2p6WSKd2SRHHdGqXRFKb1qzfXgDAi2s3MahW+8neZLXLWpPVfX5Rb0hpe/zXl66CkUPAOeiTA6UhKNobbJNmF99dmp+ampr2lJqamvZUfDaFz6nZRUdrFR1r62SxyeLsenU4gx2hfewsK+awr+Clnflc/vW6j3X9NOcyZn7wBgePHg2051RT4RPM/7HKRJn3fh+wFriq6lNjgbk+8irgWCrvrGxyGe2S+bzki+qPC0tDZLRPDuJUUUtvn0zB/hpN+0Ok12qqeczRCk/owEFSktoE25VSq6s4RHpKcp1jCotDX3rMyd5ktctak9V93pDCPZCZfuzjzDQo2h3sOTW7OO/S/NTU1G3aU2pq6jbtqbhsAs3uhNq0VtF1GVsni00WZ5d5Wls+/+exR8UL/vkFGadF3m3RPSWTr53ejuUFHwfWIXKiTFyYrFLzce6Ii5DOua8BPYHF9Xxd+JhbnHNvO+fenjlzZqChIiIiIiIiIiLxwgH39P42D73/anOniEQw8eY3VRYAM5xzFwCne+/fqfHadcB87/3h432x934mEL4i6f9r0oyoT1xYGuJr7Y/9TUJGu2QKS0Jf8hXBKyoJkZlaoyk1maJaTeFjiopDtEhwJJ+WSHFZeZO3jLm0N6P69wAgf0dhZFdKMkXFtbqKQ2TU+Jup+o45GZusdllsqj6XoX1+IjI6QUGN37VdsBvS05r+PJpdfHdpfmpqatpTampq2lPx26TZRU9rFR2L62SxqfpchmYXVnDgC752ervqjzNPb0vhgWN3dSa1TOTc9mk8nXsjAGltkvnTgOv4yetz9QY4TaDCu+ZOiFtm7pj03oeAZcAs6j6y/f16PtdkNuwqoHOnVL6e2o5WLRL4Tu9uLNu0NajTRSV/RwGd01I5o2M7WrZIYMgF3VixLrJpxfqtfPfi7gBccf65vLV5ZyAtz618nzHT5zBm+hyWrdvC8L7ZAPTMyiR04BB7Sssijt9TWkZZ+SF6ZmUCMLxvNsvXbTnpm6x2WWwKs7TPT8SgAbBgceW7c+flQ9skSO/Y9OfR7OK7S/NTU1PTnlJTU9Oeit8mzS56WqvoWFwni01hlmYXtn7/Z2Qld+DM01No5RIYdlYO//hsc/XroSMHuXjhIwxa9CiDFj1K3t5PdVFSTLB0xyRUXnycT4136K56I5yzgBVBnfRohefBhUuZ+YNRJDjH/Lfz2VK0lwlX9CP/00KWbdpKjzMz+K8bvku709qQm30Ot13Rj5H/+d9BJXG0wjN97lL+eFtl04LV+Wwp2Mutw/qx8ZNCVqzfyvw3NvDgTUNZeO94SsvKuWv2osB6wl7L38bAnCxeuHc85YePcO/Tx94k/bm7xzFm+hwAps1dytQbriSxVUte37idVRu3n1JNVrusNVnd53dMgbV5UFwCuaNhwng4cqTytbEj4bJLYOVqGHI9tEmEaXcHnqTZxXmX5qempqY9paampj0Vn02g2Z0IrVV0rK2TxSaLszvqPVPee5lZl36fFi6Bedvy+Lh0D7fnXMb6fZ+x9POPAj2/SGO5WL3LdIz5nBN4lDsW8h+aSJ8JtpryHrXZBJjqstgEdudnsamioO670TWnhMzKv7m0tFba59FTU3Qs7imLTWB3fmpqmMU9ZbEJ7M5PTQ2zuKcsNoHd+ampYRb3VLjp3L890MwlkT669hd6Zrke49b8yPzFtTkX/9nk7Mw8yi0iIiIiIiIiIiKnDl2YFBEREREREREROYU554Y65z50zn3snKv3F5Y5565zzm10zuU7555pivNa+x2TIiIiIiIiIiIicSPe35XbOdcC+D3wbWAX8JZzbqH3fmONY84FJgEDvPf7nXPpTXFu3TEpIiIiIiIiIiJy6uoLfOy93+q9PwT8FRhZ65gfAb/33u8H8N4XNcWJdWFSRERERERERETkJOacu8U593aNP7fUePnrwM4aH++q+lxNXYGuzrnXnXOrnXNDm6JLj3KLiIiIiIiIiIg0UoW3f9+f934mMPMr/CNaAucCucCZwErnXE/vffFX6bK/ciIiIiIiIiIiIhKUT4Gzanx8ZtXnatoFLPTeH/bebwM2U3mh8ivRhUkREREREREREZFT11vAuc65s51zrYGxwMJaxzxP5d2SOOc6Uflo99avemLnvf+q/wyLTsp/KRERERERERGRZhTfbz8dkGvfuNX8dai/9f/jl87OOfcd4D+BFsAs7/2DzrmpwNve+4XOOQf8BhgKHAUe9N7/9at26cKkiIiIiIiIiIhEQxcm63EyXJhsLiftm99ceMuM5k6I8M7MifSZYKsp71GbTYCpLotNYHd+ampYeE9VFHRt5pJjEjI3A9rn0VBTdCz+7LTYBHbnp6aGWdxTFpvA7vzU1DCLe8piE9idn5oaZnFPhZsuGfebZi6JtHrOHc2dICcZ/Y5JERERERERERERibmT9o5JERERERERERGRoFXoCfdG0x2TIiIiIiIiIiIiEnO6MCkiIiIiIiIiIiIxp0e5RUREREREREREGqnC61HuxtIdkyKXTpIuAAAgAElEQVQiIiIiIiIiIhJzujApIiIiIiIiIiIiMWfiUW7n3DJguvd+cY3P/RToBnwBDKPyIuoS4HbvvQ+i49/H5DKg59mUHzrMfU+8wgefFNU55rzO6UwZP4TEVi15ff02Hn5ueRAp1e4cncvAnMqmXz71Ch/sqtuUfVY6U2+sbFqVv41fzzu1mvpnf4M7R+eSkJDA/Dc2MHvJWxGvt2rZggduHEJ25wxKyg5w16xFfLavNLAeq01Wuyw2gb19Pnk6LH8TOqTCC0/Ufd17mPZbWLkG2iTCtEmQ0zWwHMDu7MDe/Cw2WZyfmuK7S03x2xSmn1PR0TpFR+sU313W5mexyersfnbTIPr1PpuDh45w/2Mv8+H2uuv0k2sHcNW3cmiblMjgm38XeNOpQI9yN56VOyafBcbW+tzYqs8PAHoBPYCLgMuCCBjQI4uzMlK4+hezeeCpV5k0bnC9x00adzn3//cSrv7FbM7KSKF/j6wgcgAY2D2LzmkpjJgym/uffZXJY+tvmjzmcqY+s4QRU2bTOS2FAd1PnaYE55h03WBu+8PzjHrgSYZe2I1zMjtEHHNNvxxKDxxkxJTZPL3sXW4fOTCQFstNVrssNoG9fQ5w9VUw8+Hjv75yDezYBS/PgSk/h6mPBJYC2J0d2JyftSaL81NTfHepKX6bwvRzKjpap+honeK7y9r8LDZZnV2/3mdzVmYq194xi4ceX8Kd46+o97jX3tvKD345J/AekWhYuTA5DxjmnGsN4JzLAs4ADgNtgNZAItAKKAwi4LI+XXjpzU0AbNhWQPJpiXRqnxRxTKf2SSSf1poN2woAeOnNTeT26RJEDgC5vbrw4trKpvXbC2h7WiKd2tVqapdEUpvWrN9e2fTi2k0M6nXqNPXIymTnnmI+3VvCkaMVLH73Q3JrnSu3VxdeWLMRgFff+4i+3ToH0mK5yWqXxabwOS3tc4CLekNK2+O/vnQVjBwCzkGfHCgNQdHewHLMzi58Xmvzs9ZkcX5qiu8uNcVvU83z6udUw7RO0dE6xXeXtflZbLI6u0sv7MKi1yrPmf/x5ySfnkjHlKQ6x+V//Dl7i8sC7xGJhokLk977fcBa4KqqT40F5nrv3wSWAZ9X/Vnsvd8UREN6SjKF+7+o/rhof4i0lOSIY9JSkincH6r+uHB/iPRaxzR1U0GNpsLiuudLT0mmsDj0pceczE3p7Wv17A+R3j75uMccrfCEDhwkJalNID1Wm6x2WWwCe/s8GoV7IDP92MeZaVC0O7jzWZ0d2JyftSaL81NTfHepKX6bqs+rn1PRdWmdouvSOsV3l7H5WWyyOru0DskU7a1xXWPfF6SlNt9/o5xKKrwz/8cqExcmq9R8nHss8Kxz7ptANnAm8HVgsHPuW/V9sXPuFufc2865t2fOnBmTYBEREREREREREWkcE29+U2UBMMM5dwFwuvf+HefcvwOrvfchAOfc/wL9gNdqf7H3fiYQviLpH3t7RoMnvDa3N9d8qwcAG7cXkpF67DnJ9NRkdtf42xaA3cUhMmr8bUNGajJFtY75qsZc2ptR/Sub8ncUklmjKSOl7vmKikNk1PhboPqOORmbqs9VEorsSU2mqCRU7zFFxSFaJDiST0ukuKw8kB6rTVa7LDVZ3ufRyOgEBTV+r3XBbkhPC+58lmYHNudnsan6XMbmp6b471JTfDbp51R0tE7R0TrFd5fF+Vlsqj6Xodl979t9GDmoJwCbthaQ3rHGdY0Obdm9v/n+G0UkGmbumKy6+LgMmEXl3ZMAnwCXOedaOudaUfnGN032KPfflr/P9ffP4fr757A8bwvD+mUD0OPsTEIHDrGnJPJ3LuwpKSN04BA9zs4EYFi/bFbkbWmqHACeW/k+Y6bPYcz0OSxbt4XhfSubemZVNZXWaioto6z8ED2zKpuG981m+bqTvyksf0cBndNSOaNjO1q2SGDIBd1YsW5rxDEr1m/luxd3B+CK88/lrc07A2mx3GS1y1KT5X0ejUEDYMHiynfnzsuHtkmQ3jG481maHdicn8WmMGvzU1P8d6kpPpv0cyo6WqfoaJ3iu8vi/Cw2hVma3f8syeOme57ipnueYsXbH/Odb1WeM+ebXyN04KB+l2SMNPdj2vH8KLelOyah8oLkfI490j0PGAysBzzwsvf+hSBOvGr9Ngb0yGLBg+MpP3SE+554pfq1Z/5jHNffX/mOVdOfWcp9/3olbVq35PUN23l9w/YgcgB4LX8bA3OyeOHe8ZQfPsK9Tx9reu7ucYyZXtk0be5Spt5wJYmtWvL6xu2s2njqNB2t8Eyfu5Q/3jaKBOdYsDqfLQV7uXVYPzZ+UsiK9VuZ/8YGHrxpKAvvHU9pWTl3zV4USIvlJqtdFpvA3j4HuGMKrM2D4hLIHQ0TxsORI5WvjR0Jl10CK1fDkOuhTSJMuzuwFMDu7MDm/Kw1WZyfmuK7S03x2xSmn1PR0TpFR+sU313W5mexyers3sjbRv8+5zDvkZspP3SYBx5bXP3af0+7kZvueQqACd+/lCv7n0eb1q1Y+LtbWLhsPX/5+5uB94nUx3nvm7shCP7CWxp+lDuW3pk5kT4TbDXlPWqzCTDVZbEJ7M5PTQ0L76mKgq7NXHJMQuZmQPs8GmqKjsWfnRabwO781NQwi3vKYhPYnZ+aGmZxT1lsArvzU1PDLO6pcNMl437TzCWRVs+5w+6td81o2Mr/Z/7i2kuX/tbk7Mw8yi0iIiIiIiIiIiKnDmuPcouIiIiIiIiIiMSNCkzejBgXdMekiIiIiIiIiIiIxJwuTIqIiIiIiIiIiEjM6VFuERERERERERGRRqrwepS7sXTHpIiIiIiIiIiIiMScLkyKiIiIiIiIiIhIzOlRbhERERERERERkUbSo9yN57z3zd0QhJPyX0pEREREREREpBnpClw9vr18ovnrUEtyZ5icnR7lFhERERERERERkZg7aR/lPv+2Gc2dEOG930+kzwRbTXmP2mwCTHVZbAK781NTwyzuqXBTRUHXZi6JlJC52dQ6gd09ZbEJbO5zS01gd35qapjFPWWxCezOT00Ns7inLDaB3fmpqWEW91S46cIf2WkCeOfPE5s7wSQ9yt14umNSREREREREREREYk4XJkVERERERERERCTmTtpHuUVERERERERERIKmR7kbT3dMioiIiIiIiIiISMzpwqSIiIiIiIiIiIjEnC5MioiIiIiIiIiISMzpd0yKiIiIiIiIiIg0ktfvmGw0ExcmnXPLgOne+8U1PvdToBtQCgyr+vT93vvnguq489pcBuScTfmhw9z71Ct8sLOozjHZZ6Uz5cYhJLZuyev52/j135YHlVPZNDqXgVVNv3zqFT7YVX/T1BuHkNiqJavyt/HreadWU//sb3Dn6FwSEhKY/8YGZi95K+L1Vi1b8MCNQ8junEFJ2QHumrWIz/aVBtZjtSlM81NTU5k8HZa/CR1S4YUn6r7uPUz7LaxcA20SYdokyOkaaBJgc63UFL9NYfrZGZ9NYG92Fpuszg7srZWaoqM9FT2La6Wm6FnbT2H/PjaXAT0ru+6b/QoffFK367zO6UwZX3VtY/02Hv5r8F0i9bHyKPezwNhanxsLFAAXAH2Ai4GfO+faBREwMCeLzmkpjLxvNg888yr3jB1c73H3jL2c+59Zwsj7ZtM5LYUB3bOCyKls6l7ZNGLKbO5/9lUmH6dp8pjLmfrMEkZMOfWaEpxj0nWDue0PzzPqgScZemE3zsnsEHHMNf1yKD1wkBFTZvP0sne5feTAQFosN4VpfmpqSldfBTMfPv7rK9fAjl3w8hyY8nOY+kjgSSbXSk3x2xSmn53x2QT2ZmexyerswN5aqSk62lPRs7hWaoqetf0UNqBHFmelp3D15Nk88NSrTBpXf9ekGy7n/qeWcPXk2ZyVnkL/HsF2iRyPlQuT84BhzrnWAM65LOAM4J/ASu/9Ee99GbAOGBpEwGW9uvDimk0ArN9eQNvTEunULinimE7tkkhq05r12wsAeHHNJnJ7dwkiB4DcXl14ce0JNq3dxKBep05Tj6xMdu4p5tO9JRw5WsHidz8kt9a5cnt14YU1GwF49b2P6NutcyAtlptqnlfzU1NTuag3pLQ9/utLV8HIIeAc9MmB0hAU7Q22yeJaqSl+m2qeVz87468pfE5Ls7PYZHV24fNaWis1RUd7KnoW10pN0bO2n8Iu69OFl1ZXdm3YWkDy6Yl0al+rq30SyW1as2FrZddLqzeR2yfYrpNdBc78H6tMXJj03u8D1gJXVX1qLDAXeB8Y6pw73TnXCRgEnBVEQ3r7ZAqKv6j+uLA4RHpKcuQxKckUFYcij2kfeUyTNqUkU7C/4abC2k0pp05TevtaPfvrzqTmMUcrPKEDB0lJahNIj9Wm6vNqfmqKocI9kJl+7OPMNCjaHew5La6VmuK3qfq8+tkZl01gb3YWm6zODuytlZqi7NGeir7H4Fqp6QS6jO2n6nOmJlO471hX0f4QabXOmZaSTOH+Gl37Q6SnBtslcjwmfsdklfDj3Auq/vdm7/07zrmLgDeA3cCbwNHmSxQREREREREREZGmYOnC5AJghnPuAuB07/07AN77B4EHAZxzzwCb6/ti59wtwC0Ajz32WFQnvO7S3owa0AOA/B2FZNZ4JjGj1t2RAEW1/nYjIyWZopLIY76qMZf2ZlT/Gk2pDTdl1G4qPvmbqs9VEorsSa07k/AxRcUhWiQ4kk9LpLisPJAei02an5qaS0YnKKjxe7YLdkN6WrDntLhWaorPJv3sjN8mi7Oz2FR9LkOzA5trpaYToz0VPWtrpaaGWd1P1+b25ppLK7s2bisko8OxrvTUZHbXOufu4hAZNe6QzEhNpmh/MPv8VFGhd+VuNBOPcgN470PAMmAWlXdP4pxr4ZzrWPV/9wJ6Aa8c5+tneu//xXv/L7fccktU55y78n3GPjSHsQ/NYdn7Wxh+cTYAPbMyCR04xJ7Ssojj95SWUVZ+iJ5ZmQAMvzibFeu2NOLf9vieW/k+Y6bPYcz0OSxbt4XhfU+wqW82y0+BprD8HQV0TkvljI7taNkigSEXdGPFuq0Rx6xYv5XvXtwdgCvOP5e3Nu8MpMVqk+anpuYyaAAsWFz57tx5+dA2CdI7BntOi2ulpvhs0s/O+G2yODuLTWGWZgc210pNJ0Z7KnrW1kpNDbO6n/62/H2unzqH66fOYXneFoZdUtnV45yqrpJaXSVlhMoP0eOcyq5hl2SzIi+YfS7SEEt3TELlBcn5HHuH7lbAa845gFLgBu/9kSBOvCp/GwNzslh433jKDx3hvqePXf/866RxjH1oDgAPPbeUKTdeSWKrlry+cTur8rcHkQPAa1VNL9w7nvLDR7i3RtNzd49jzPTKpmlzlzL1hhpNG0+dpqMVnulzl/LH20aR4BwLVuezpWAvtw7rx8ZPClmxfivz39jAgzcNZeG94yktK+eu2YsCabHcFKb5qakp3TEF1uZBcQnkjoYJ4+FI1U/osSPhsktg5WoYcj20SYRpdweeZHKt1BS/TWH62RmfTWBvdhabrM4O7K2VmqKjPRU9i2ulpuhZ209hq9ZvY0DPLBY8WHVt44ljXc/8chzXT63smj5nKfeNv5I2rVry+obtvL4h2C6R43He++ZuCII//7YZzd0Q4b3fT6TPBFtNeY/abAJMdVlsArvzU1PDLO6pcFNFQddmLomUkLnZ1DqB3T1lsQls7nNLTWB3fmpqmMU9ZbEJ7M5PTQ2zuKcsNoHd+ampYRb3VLjpwh/ZaQJ4588T9cxyPQYsucv8xbXXv/0rk7Mz8yi3iIiIiIiIiIiInDp0YVJERERERERERERiztrvmBQREREREREREYkbelfuxtMdkyIiIiIiIiIiIhJzujApIiIiIiIiIiIiMadHuUVERERERERERBrJ61HuRtMdkyIiIiIiIiIiIhJzujApIiIiIiIiIiIiMee8983dEIST8l9KRERERERERKQZ6ZnlelyyeJL561Crhzxkcnb6HZMiIiIiIiIiIiKNVKHfMdloJ+2FyV4TZzR3QoR1MybSZ4KtprxHbTYBprosNoHd+ampYRb3lMUmqOyqKOja3BkREjI3m1wni01ga09ZbAK781NTwyzuKYtNYHd+amqYxT1lsQnszk9NDbO4p8JNl4z7TTOXRFo9547mTpCTjH7HpIiIiIiIiIiIiMTcSXvHpIiIiIiIiIiISNBOzrdviQ3dMSkiIiIiIiIiIiIxpwuTIiIiIiIiIiIiEnN6lFtERERERERERKSRKtC7cjeW7pgUERERERERERGRmNOFSREREREREREREYk5PcotIiIiIiIiIiLSSN7rUe7GiumFSefcMmC6935xjc/9FOgGnA1cAqzy3g+v8frZwF+BjsA7wI3e+0NN3TbgvG9w1zW5JLgE/r5mA7P+8VbE661atODBcUPofmYGJf88wL8/uYjP9pc2dUYdd47OZWDO2ZQfOswvn3qFD3YV1Tkm+6x0pt44hMRWLVmVv41fz1uuJgNNVrusNfXP/gZ3js4lISGB+W9sYPaSWt97LVvwwI1DyO6cQUnZAe6atYjP9gX7vWexCTS7aEyeDsvfhA6p8MITdV/3Hqb9FlaugTaJMG0S5HQNNAmwuVYWm8DePrfaZXF+FptAs4vXpjDNLzrW1slik9XZWe2yNj+LTVZn97ObBtGv99kcPHSE+x97mQ+3112nn1w7gKu+lUPbpEQG3/y7wJtEvkysH+V+Fhhb63Njqz7/MHBjPV/zK2CG9/6bwH7g5qaOSnCOe743mFtnPs/Vv3qSq87vxjkZHSKOGXVJDqUHDjJ82myeWvEuP/3uwKbOqGNg9yw6p6UwYsps7n/2VSaPHVzvcZPHXM7UZ5YwYspsOqelMKB7lpqauclql7WmBOeYdN1gbvvD84x64EmGXtiNczIjv/eu6Vf5vTdiymyeXvYut48M9nvPYhNodtG6+iqY+fDxX1+5BnbsgpfnwJSfw9RHAk8yuVYWm8DePrfaZXF+FptAs4vXpjDNLzrW1slik9XZWe2yNj+LTVZn16/32ZyVmcq1d8zioceXcOf4K+o97rX3tvKDX84JvEckGrG+MDkPGOacaw3gnMsCzgBe897/A/ii5sHOOQcMrvo6gCeBq5s6qkfnTD7ZU8yne0s4crSCl9/7kEE9ukQck9ujCwvXbgRgyfsfcfG5nZs6o47cXl14ce0mANZvL6DtaYl0apcUcUyndkkktWnN+u0FALy4dhODenWp889SU2ybrHZZa+qRlcnOGt97i9/9kNxa58rt1YUX1lR+77363kf07Rbs957FpvA5NbuGXdQbUtoe//Wlq2DkEHAO+uRAaQiK9gbbZHGtLDaFz2lpn1vtsjg/i03hc2p28ddU87yaX8OsrZPFJquzs9plbX4Wm6zO7tILu7Dotcpz5n/8OcmnJ9IxJanOcfkff87e4rLAe04lFd6Z/2NVTC9Meu/3AWuBq6o+NRaY6733x/mSjkCx9/5I1ce7gK83dVdGSjKFxceuiRaWhEhvnxx5TPtjxxyt8ITKD5KS1KapUyKkpyRTsL9GV3GI9JTkOscUFoe+9Bg1xb7Jape1pvT2tXr21/3eq3nM0QpP6ECw33sWm0CzayqFeyAz/djHmWlQtDvYc1pcK4tNYG+fW+2yOD+LTaDZxWtT9Xk1v+i6jK2TxSazs7PaZWx+Fpuszi6tQzJFe491Fe37grTUYP//JJGvqjnelbvm49zhx7i/MufcLc65t51zb8+cObMp/pEiIiIiIiIiIiISkOZ4V+4FwAzn3AXA6d77d77k2L1AinOuZdVdk2cCn9Z3oPd+JhC+IukfnTgj6qDC4hAZNZ79y2ifTFFJKPKYkspjCktCtEhwJLdJpLisPOpzRGvMpb0Z1b8HAPk7CslMrdGVkkxRcWRXUXGIjBp/C1TfMWqKTZPVLotN1ecqCUX2pNb93gsfU1Rc9b13WjDfexabNLuml9EJCmr8/u+C3ZCeFuw5La6VpSar+9xqF9ian8UmzS6+mzS/6FhcJ4tN1ecyNDurXRbnZ7Gp+lyGZve9b/dh5KCeAGzaWkB6x2Nd6R3asnt/MGsg0lRifsek9z4ELANm0cDdklWPeC8DRld96v9QeWGzSeXvLOAbaal8vUM7WrZIYOj53VievzXimOUbtjKib3cAvt37XNZ+vLOpMwB4buX7jJk+hzHT57Bs3RaG980GoGdWJqEDh9hTGvl7IPaUllFWfoieWZkADO+bzfJ1W9TUDE1Wuyw2heXvKKBzWipndKz83htyQTdWrIv83luxfivfvbjye++K88/lrc3BfO9ZbNLsmt6gAbBgceW7c+flQ9skSO8Y7DktrpWlJqv73GoX2JqfxSbNLr6bNL/oWFwni01hlmZntcvi/Cw2hVma3f8syeOme57ipnueYsXbH/Odb1WeM+ebXyN04KB+l2SMeG//j1XNccckVF6QnE+Nd+h2zr0GnAckO+d2ATd77xcDdwF/dc49ALwHPN7UMUcrPNP+Zyl//PEoWiQ4nl+Tz5aCvfzb0H5s3FnI8vytzF+zgWnjhvLiPeMp+Wc5dz61qKkz6ngtfxsDc7J44d7xlB8+wr1Pv1L92nN3j2PM9Mp30Zo2dylTb7iSxFYteX3jdlZt3K6mZm6y2mWt6WiFZ/rcpfzxtlEkOMeC1ZXfe7cO68fGTwpZsX4r89/YwIM3DWXhveMpLSvnrtnBfu9ZbALNLlp3TIG1eVBcArmjYcJ4OFL1W4rHjoTLLoGVq2HI9dAmEabdHXiSybWy2AT29rnVLovzs9gEml28NoVpftGxtk4Wm6zOzmqXtflZbLI6uzfyttG/zznMe+Rmyg8d5oHHFle/9t/TbuSme54CYML3L+XK/ufRpnUrFv7uFhYuW89f/v5m4H0i9XHHf9+ZuOZ7ncCj3LGwbsZE+kyw1ZT3qM0mwFSXxSawOz81NczinrLYBJVdFQVdmzsjQkLmZpPrZLEJbO0pi01gd35qapjFPWWxCezOT00Ns7inLDaB3fmpqWEW91S46ZJxv2nmkkir59xh9+2dm1HvF//D/MW194ffb3J2zXXHpIiIiIiIiIiISNzz3uQ1v7jQHO/KLSIiIiIiIiIiIqc4XZgUERERERERERGRmNOj3CIiIiIiIiIiIo2kR7kbT3dMioiIiIiIiIiISMzpwqSIiIiIiIiIiIjEnB7lFhERERERERERaaQKPcrdaLpjUkRERERERERERGLOee+buyEIJ+W/lIiIiIiIiIhIM9KtgfXIWXCf+etQ+SPvMzm7k/ZR7stzpzV3QoR/LL+HPhNmNHdGhLxHJ5psAkx1WWwCu/NTU8Ms7imLTWB3fhUFXZs7I0JC5maT6wS29pTFJrC7z9XUMIt7ymIT2J2fmhpmcU9ZbAK781NTwyzuKYtNcKxLIp2c9/zFhh7lFhERERERERERkZjThUkRERERERERERGJuZP2UW4REREREREREZGgeb0rd6PpjkkRERERERERERGJOV2YFBERERERERERkZjThUkRERERERERERGJOf2OSRERERERERERkUbS75hsPN0xKSIiIiIiIiIiIjEX0zsmnXPLgOne+8U1PvdToBtwNnAJsMp7P7zG6xOAnwJdgDTv/Z6g+m77v9/m4ku6cLD8CL+e/gIffVQY8XpiYkt+ed8ozvh6KhVHK3jzzY/4y8zlQeXQP/sb3Dk6l4SEBOa/sYHZS96KeL1VyxY8cOMQsjtnUFJ2gLtmLeKzfaWB9YTdOTqXgTlnU37oML986hU+2FVU55jss9KZeuMQElu1ZFX+Nn49b3lgPRbXyWJTmOYXHWvrZLHJ6uysdU2eDsvfhA6p8MITdV/3Hqb9FlaugTaJMG0S5HQNLCeCtT1lscnafqpJaxUdrVN8NoVZm5/FJovzs9gUZm1+Fpuszk/rFJ9NIg2J9R2TzwJja31ubNXnHwZurOdrXgeuAHYEGdb34i6ceWYHbhr3Jx75zSJunzi03uP+9twaxt/0GD/+0eP06HEWffueE0hPgnNMum4wt/3heUY98CRDL+zGOZkdIo65pl8OpQcOMmLKbJ5e9i63jxwYSEtNA7tn0TkthRFTZnP/s68yeezgeo+bPOZypj6zhBFTZtM5LYUB3bMC6bG4ThabwjS/6FhbJ4tNVmdnsevqq2Dmw8d/feUa2LELXp4DU34OUx8JNKeatT1lscnifgrTWkVH6xSfTWHW5mexyeL8LDaFWZufxSar89M6xWfTqcTHwR+rYn1hch4wzDnXGsA5lwWcAbzmvf8H8EXtL/Dev+e93x502IABXXll8XoANm38jOTkNnTokBRxzMGDR8jLq7w+euRIBR9tLqBTWrtAenpkZbJzTzGf7i3hyNEKFr/7Ibm9ukQck9urCy+s2QjAq+99RN9unQNpqX3OF9duAmD99gLanpZIp3aR69SpXRJJbVqzfnsBAC+u3cSgWu1NxeI6WWyqeV7Nr2HW1slik9XZWey6qDektD3+60tXwcgh4Bz0yYHSEBTtDTQJsLenLDZZ3E81z6u1apjWKT6bap7X0vwsNlmcn8Wmmue1ND+LTVbnp3WKzyaRaMT0wqT3fh+wFriq6lNjgbne+2a/eNspLZndu4/dwrx79xd0Sjv+f0kmJSdySf9v8t672wPpSW+fTMH+Y9dpC/eHSG+ffNxjjlZ4QgcOkpLUJpCe6nOm1OoqDpGeklznmMLi0Jce02Q9BtfJYlP1eTW/6LqMrZPFJrOzM9r1ZQr3QGb6sY8z06Bod/DntbanLDZZ3k9aqyi7tE5x2VR9XmPzs9hkcX4Wm6rPa2x+Fpuszk/rFJ9NItFojnflDj/OvaDqf29uin+oc+4W4BaAxx57rCn+kceV0MLxi/+4mvl/f5vPPy8O9FwiIiIiIsXk/cIAACAASURBVCIiImKX3pW78ZrjwuQCYIZz7gLgdO/9O03xD/XezwRmhj987plpDX7NyKsv5DvD+wDw4QefkVbjsey0tLbs2V3nyXIAfnbHd9i1ax9/n/dWva83haKSEJmpx+7YzEhNpqgkVO8xRcUhWiQ4kk9LpLisvMlbxlzam1H9ewCQv6MwsislmaLiWl3FITJq/M1Ufcc0FUvrZLVJ84uOxXWy2FR9LkOzi4euL5PRCQpq/O72gt2QnhbMuSzuKYtN1ecytp+0VtHROsV3k8X5WWyqPpex+Vlssjg/i03V5zI0P61T/DeJRCPWv2MS730IWAbMovLuyWaz4Pl3+PEPH+fHP3yc11dt5sohPQHI7n4GZWUH2bevrM7XjL/5MpKSEvnDo0sCbcvfUUDntFTO6NiOli0SGHJBN1as2xpxzIr1W/nuxd0BuOL8c3lr885AWp5b+T5jps9hzPQ5LFu3heF9swHomZVJ6MAh9pRGrtOe0jLKyg/RMysTgOF9s1m+bksgbZbWyWqT5hcdi+tksSnM0uzioevLDBoACxZXvjt3Xj60TYL0jsGcy+KestgUZm0/aa2io3WK7yaL87PYFGZtfhabLM7PYlOYpflpneK/SSQazXHHJFRekJxPjXfods69BpwHJDvndgE3e+8XO+f+H3AnkAmsc84t8t7/sKmD1qzewsUXf5On5txK+cHDPPyrF6tfe+wvN/PjHz5Op7S23HDjAHbs2MOf/lz5BPqC+W+z6KX3mzqHoxWe6XOX8sfbRpHgHAtW57OlYC+3DuvHxk8KWbF+K/Pf2MCDNw1l4b3jKS0r567Zi5q8o7bX8rcxMCeLF+4dT/nhI9z79CvVrz139zjGTJ8DwLS5S5l6w5UktmrJ6xu3s2rj9kB6LK6TxaYwzS861tbJYpPV2VnsumMKrM2D4hLIHQ0TxsORI5WvjR0Jl10CK1fDkOuhTSJMuzvQnGrW9pTFJov7KUxrFR2tU3w2hVmbn8Umi/Oz2BRmbX4Wm6zOT+sUn02nlGZ/55T45Qy870wQ/OW5DT/KHUv/WH4PfSbMaO6MCHmPTjTZBJjqstgEduenpoZZ3FMWm8Du/CoKujZ3RoSEzM0m1wls7SmLTWB3n6upYRb3lMUmsDs/NTXM4p6y2AR256emhlncUxabAPIenahfpliPrvPuN39xbfPo//jS2TnnhgL/BbQA/uK9n36c474HzAMu8t6//VW7Yv4ot4iIiIiIiIiIiNjgnGsB/B64CugOfN85172e49oCtwNrmurcujApIiIiIiIiIiLSSN47838a0Bf42Hu/1Xt/CPgrMLKe4+4HfgU02bsm6cKkiIiIiIiIiIjIScw5d4tz7u0af26p8fLXgZrvhrSr6nM1v/4C4Czv/UtN2dVcb34jIiIiIiIiIiIiMeC9nwnMbMzXOucSgEeAf23KJtAdkyIiIiIiIiIiIqeyT4Gzanx8ZtXnwtoCPYDlzrntwCXAQufcv3zVE+uOSRERERERERERkUby5t+Tu0FvAec6586m8oLkWOD68Ive+xKgU/hj59xy4Od6V24RERERERERERFpNO/9EWACsBjYBMz13uc756Y650YEeW7dMSkiIiIiIiIiInIK894vAhbV+twvj3NsblOd1/mT4H7TepyU/1IiIiIiIiIiIs3INXeARV2ee9D8dagtYyabnN1Je8fkeffNaO6ECB/cN5E+E2w15T1qswkw1WWxCezOT00Ns7inLDaB3flZbKoo6NrcGRESMjcDtvaU9nn01BQdi3vKYhPYnZ+aGmZxT1lsArvzU1PDLO6pcNP5t9lpAnjv9xObO0FOMvodkyIiIiIiIiIiIhJzJ+0dkyIiIiIiIiIiIoHzJp+Sjgu6Y1JERERERERERERiThcmRUREREREREREJOb0KLeIiIiIiIiIiEgjefPvyW2X7pgUERERERER+f/Zu/fwKMs7/+PvO4BBCRAOOVRXCLISw9kTctoasG1QEZTSwqq4pQd3rWwVtSCyPymomNZd2XXZuqUt0Qq6smwBcVkR5FwVUEQgKCgIgppEkCQdJCDk/v2RTMgkIRlpZuY74fO6Lq5K5kmft/f3TuR6eJ6MiIhEnS5MioiIiIiIiIiISNTpUW4REREREREREZGzpUe5z1pUL0w651YDud775dU+di+QCXQB+gMbvPfDq70+H7gK+ArYBPy99/6rxm4b/NedmTosm4SEBBZu2cFvN2wOef2qzhcxZdi1ZKalcP/CZSzf+UFjJ9QyMKszk0ZXNC16fQd5K0KbWjRvxqPjcsjqlEbJ0WNMnruMT78ojXjXpNHZDO7RhbITX/Hwc6/y/sGiWsdkXZzKjHE5JLZozob8j/jVwjUR67G6TqC1CpfWSU3nQpe1fT41F9a8Ae3bwdJnar/uPcx8CtZthJaJMHMK9OgWsRzA7uzA3vwsrpWa4rcpyNo+V1P8Nmmfh8/qWmmd4rMpaNL3shlUOb9pz73K+wfqnt/0cTkkntecP+V/xK/+e01U2kRqivaj3C8AY2t8bGzlx58AxtXxOfOBy4BewPnAjxs7KsE5Hr5hKD+Zv5jh//EsN/bMpGtK+5BjPiv5M1MWv8rL299v7NOfsWnK94dy968XM+rRZxl2ZSaXpIc23TKgB6XHjjNieh7zVm/hnpGDI941uHsGnVKSGTE9j0deWMnUsUPrPG7qmOuY8fwKRkzPo1NKMoO6Z0Skx+o6gdYqXFonNZ0LXdb2OcDN18OcJ878+rqNsP8gvDIfpj8AM56MWApgd3Zgb34W10pN8dsUZG2fqyl+m7TPw2d1rbRO8dkUNLhHxfxG/iKPR59fyUNnmN9DY6/jkedXMPIXkf8+JVKfaF+YXAjc6Jw7D8A5lwFcCKz33r8G/LnmJ3jvl/lKVNwx+VeNHdX7onQ+/qKYg0dK+OpUOct27OK6zK4hx3xSXMruwkP4KL3VUs+MdA4cKuaTwyWcPFXO8i27yO4d2pTduytLN+4EYOU7H9Avs1PEu7J7d+XlTe8BsH1fAa3PT6Rjm1Yhx3Rs04pWLc9j+74CAF7e9B5DarQ3FqvrFDyv1qphWic1nQtd1vY5wNV9ILn1mV9ftQFG5oBz0LcHlAag6HDEcszOLnheS/OzuFZqit+m6ue1tM/VFL9N2ufhs7pWWqf4bAq6tndXXt74Nee38T2y+0Tu+5RIfaJ6YdJ7/wUVFxevr/zQWGCBD+Nqn3OuBRV3VL7S2F1pbZL4rPT0NdGC0gBpbZIa+zRfS2rbJAqOnG4qPBIgtW3SGY85Ve4JHDtOcquWke1KrtFVHCA1OanWMYXFgXqPabQeo+sEWquwu7ROajoXuozt83AUHoL01NO/T0+Bos8jdz6rswN787O4VmqK36aq8xrb52qK3ybt86/RY3SttE7x2RRy3uKG51dUc35tY/fnzqbAe2f+l1WxeFfu6o9zBx/jDsevgXXe+/V1veicu9M595Zz7q05c+Y0QqaIiIiIiIiIiIhESizelXsJMMs5dwVwgff+7YY+wTk3DUgB/v5Mx3jv5wDBK5L+yV/MCjuosDTAN9qcfp4tvU0ShaWBej4j8opKAqS3O92U1i6JopJAnccUFQdoluBIOj+R4qNljd4y5pt9GDWwJwD5+wtDu2r8TQtAUXGAtGp/I1PXMY3F0jqB1ipcWic1nQtdlvd5ONI6QkG1n5Ne8DmkpkTufJZmB7bnZ22t1BS/TRb3uZrit6nqXNrnYbO0Vlqn+G76/jf7MGpQtfklNzy/1JrzK4ndnzvl3Bb1Oya99wFgNTCXMO6WdM79GMgB/tZ7Xx6Jpu2fFtC5QzsuSm5Di2YJ3NAzk1W79kbiVGHL319Ap5R2XNihDc2bJZBzRSZrt4U2rd2+l5uu6Q7Aty6/lM27D0Sk5cV17zImdz5jcuezetsehvfLAqBXRjqBYyc4VHo05PhDpUc5WnaCXhnpAAzvl8WabXsi0mZpnUBrFS6tk5rOhS7L+zwcQwbBkuUV7869NR9at4LUDpE7n6XZge35WVsrNcVvk8V9rqb4bQrSPg+fpbXSOsV304J17zL28fmMfXw+q9/dw/Brvub8rslibQz/3Nkk+Dj4ZVQs7piEiguSi6j2Dt3OufVUvPt2knPuIPAj7/1y4D+B/cAbzjmAP3rvZzRmzKlyzyPLVvH7caNIcI7/eSefDz8/zD8OGcCOTwtZvWsvPS9MY/bYm2jTsiVDul3ChOwB3PTrPzRmRq2m3AWrePruiqYlb+azp+Awd904gJ0fF7J2+14Wvb6Dx+4YxkvTxlN6tIzJecsi1hO0Pv8jBvfIYOm08ZR9dZJp816teu3FB29jTO58AGYuWMWM279DYovm/GnnPjbs3BeRHqvrBFqrcGmd1HQudFnb5wD3T4dNW6G4BLJHw4TxcPJkxWtjR8K1/WHdm5BzK7RMhJkPRiwFsDs7sDc/i2ulpvhtCrK2z9UUv03a5+GzulZap/hsCtpQOb+XfjGeshMn+UW1+f3XlNsY+3jF/B5/cRXTx1WbX/6+qPSJ1OSi9S7TUeYv+xqPckfD+7+YSN8Jtpq2zrbZBJjqstgEduenpoZZ3FMWm8Du/Cw2lRd0i3VGiIT03YCtPaV9Hj41hcfinrLYBHbnp6aGWdxTFpvA7vzU1DCLeyrYdPnddpoA3vmPiXbfRSWGusx73PzFtY9un2JydrG6Y1JERERERERERCTuWX7Xa+ti8a7cIiIiIiIiIiIico7ThUkRERERERERERGJOj3KLSIiIiIiIiIicrbM/4RJu3THpIiIiIiIiIiIiESdLkyKiIiIiIiIiIhI1OlRbhERERERERERkbOmd+U+W7pjUkRERERERERERKLOed8kf0Jnk/yXEhERERERERGJId0aWIeMP/zS/HWofXdMNjm7Jvso99Xjn4x1QojNeffRd8KsWGeE2Dp7oskmwFSXxSawOz81NczinrLYBHbnp6aGBfdUeUG3GJeclpC+G9A+D4eawmPxe6fFJrA7PzU1zOKestgEduenpoZZ3FNVTf9opwlg679PjHWCTeYvS9qlR7lFREREREREREQk6nRhUkRERERERERERKJOFyZFREREREREREQk6prsz5gUERERERERERGJOP2MybOmOyZFREREREREREQk6nRhUkRERERERERERKJOj3KLiIiIiIiIiIicLe9iXRC3dMekiIiIiIiIiIiIRF1U75h0zq0Gcr33y6t97F4gE+gC9Ac2eO+HV3v998BVgAN2Az/w3gci0Xf/rUMY1LsLZSe+Yvrvl7Nrf1GtY+4aNYgbB3Wn9QWJXHvX7EhkhJg0OpvBPSqaHn7uVd4/WLsp6+JUZozLIbFFczbkf8SvFq45p5oGZnVm0uhsEhISWPT6DvJWbA55vUXzZjw6LoesTmmUHD3G5LnL+PSL0oj1VGdtrSw2WZyfmuK3KcjaPrfYZHF+U3NhzRvQvh0sfab2697DzKdg3UZomQgzp0CPbhFNMrlOlru0z+OzKUjzi88m0Oy+Dq2VmhqTyf303cp1eqOedbq4cp3yove1J3Im0b5j8gVgbI2Pja38+BPAuDo+Z6L3vo/3vjfwMTAhEmEDe3ehU1oyox6cy8xnVvLguOvqPG791r383YznI5FQy+DuGXRKSWbE9DweeWElU8cOrfO4qWOuY8bzKxgxPY9OKckM6p5xzjQlOMeU7w/l7l8vZtSjzzLsykwuSW8fcswtA3pQeuw4I6bnMW/1Fu4ZOTgiLTVZWyuLTRbnp6b4bQqyts8tNlmd383Xw5wnzvz6uo2w/yC8Mh+mPwAznoxsj9V1stqlfR6fTUGaX3w2gWb3dWit1NSYTO6n7w3l7qcXM+qxetbpy+OMmBHdr71zgff2f1kV7QuTC4EbnXPnATjnMoALgfXe+9eAP9f8BO99aeWxDjifCL0J+7WXd+V/X98JwI69n9H6gkQ6tG1V67gdez/jcMnRSCTUkt27Ky9veg+A7fsKaH1+Ih3bhDZ1bNOKVi3PY/u+AgBe3vQeQ3p3PWeaemakc+BQMZ8cLuHkqXKWb9lFdo1zZffuytKNFbNd+c4H9MvsFJGWmqytlcUmi/NTU/w2VT+vpX1uscnq/K7uA8mtz/z6qg0wMgecg749oDQARYcj12N1nax2aZ/HZ1P182p+8dcUPKdmFx6tlZoak7n91LnGOr29i+xeNdapV7V12voB/bpF52tPpD5RvTDpvf8C2ARcX/mhscAC7+u/duucywMKgMuAf49EW0pyEoVfnL4uWnQkQGq7pEicKmypyUkUHDndVFgcIDU5qdYxhcWBeo9pyk2pbWv0HAmQ2jbpjMecKvcEjh0nuVXLiPSEnNfYWllssjg/NcVvU9V5je1zi02W51efwkOQnnr69+kpUPR55M5ndZ3Mdmmfx2VT1Xk1v7hsAs3ua7VprdTUmF3W9lM4PW2TKCi29ec7kVi8+U31x7mDj3HXy3s/noo7K98DxtR1jHPuTufcW865t+bMmdNYrSIiIiIiIiIiImfm4+CXUVF985tKS4BZzrkrgAu892+H80ne+1POuf8CJgF5dbw+BwhekfS/faPhHzz1vaF9uPnaXgDs/KiQtPannx9LbZdE0ZGIvMdOvcZ8sw+jBvYEIH9/IentTjelJSdRVBzaVFQcIK3a34LUdUxTbKo6V0kgtKddEkUlgTqPKSoO0CzBkXR+IsVHyyLSY3GtLDZVncvY/NQUv00W97nFpqpzGZtfuNI6QkG1nylf8DmkpkTufFbXyVKX9nl8N2l+8duk2YVPa6WmxmR6PxUHGu4pCZCebOvPdyJRv2Oy8h21VwNzaeBuSVfhr4P/DIwA3m+slv9e9S63TZvHbdPmsWbLh9w4sDsAPS/5BoFjJ6L2sySre3Hdu4zJnc+Y3Pms3raH4f2yAOiVkU7g2AkOlYY2HSo9ytGyE/TKSAdgeL8s1mzb0+SbgvL3F9AppR0XdmhD82YJ5FyRydpte0OOWbt9LzddUzHbb11+KZt3H4hIC9hcK4tNQdbmp6b4bbK4zy02BVmbX7iGDIIlyyt+ePfWfGjdClI7RO58VtfJUpf2eXw3aX7x26TZhU9rpabGZHo/fVxjna7MZO32etapr40/34nE4o5JqLgguYhq79DtnFtPxc+QTHLOHQR+BKwAnnXOtQEc8C5wVySC/rTtIwb17sKiX/6QshMnmfH75VWvzZ9+O7dNmwfAP37vb8jpfxktz2vBy//yE5as28Fvl7wRiSTW53/E4B4ZLJ02nrKvTjJt3qtVr7344G2MyZ0PwMwFq5hx+3dIbNGcP+3cx4ad+yLSY7HpVLknd8Eqnr57FAnOseTNfPYUHOauGwew8+NC1m7fy6LXd/DYHcN4adp4So+WMTlvWURaarK2VhabLM5PTfHbFGRtn1tssjq/+6fDpq1QXALZo2HCeDh5suK1sSPh2v6w7k3IuRVaJsLMByPbY3WdrHZpn8dnU5DmF59NoNl9HVorNTUmk/vpv1fx9E9rrNMNleu0Yy+L3qhcp4fHU/pl9L72zgnexbogbrkG3ncmXvmrxzf8KHc0bc67j74TZsU6I8TW2RNNNgGmuiw2gd35qalhFveUxSawOz81NSy4p8oLusW45LSE9N2A9nk41BQei987LTaB3fmpqWEW95TFJrA7PzU1zOKeqmr6RztNAFv/faKuwNWh8++eMH9xbf+Pf25ydrF48xsRERERERERERE5x+nCpIiIiIiIiIiIiERdrH7GpIiIiIiIiIiISNxz5h/ktkt3TIqIiIiIiIiIiEjU6cKkiIiIiIiIiIiIRJ0e5RYRERERERERETlbepT7rOmOSREREREREREREYk6XZgUERERERERERGRqHPeN8n7TZvkv5SIiIiIiIiISAy5WAdYlPGbfzZ/HWrf3z9gcna6Y1JERERERERERESirsm++U3/25+MdUKIN+fdR98Js2KdEWLr7IkmmwBTXRabwO781NQwi3vKYhPYnZ+aGmZxTwWbygu6xbgkVEL6blPrBHb3lMUmsLnPLTWB3fmpqWEW95TFJrA7PzU1zOKeCjb1vs9OE8C2JyfGOkGamCZ7YVJERERERERERCTizD/IbZce5RYREREREREREZGo04VJERERERERERERiTo9yi0iIiIiIiIiInK29Cj3WdMdkyIiIiIiIiIiIhJ1ujApIiIiIiIiIiIiUadHuUVERERERERERM6WHuU+a1G9MOmcWw3keu+XV/vYvUAm0AXoD2zw3g+v43OfAn7ovU+KVN9944YwoG8Xjh//ikfmLGfXvqJax/zD9wZx/eDutG6VyNAfz45USpVJo7MZ3KMLZSe+4uHnXuX9g7Wbsi5OZca4HBJbNGdD/kf8auGaiPUMzOrMpNHZJCQksOj1HeSt2BzyeovmzXh0XA5ZndIoOXqMyXOX8ekXpRHrCdI6hc/aWllssjg/i01B1uZnscnq/LRODZuaC2vegPbtYOkztV/3HmY+Bes2QstEmDkFenSLaFIVa/NTU/w2WfzaC9JaqakpN1nu0tdefDYNuqwzk2+uaPrjmzuYu6pGU7NmPHZrDt0vrmj6+R+W8emR6Hw/FzmTaD/K/QIwtsbHxlZ+/AlgXF2f5Jy7CmgXybABfbpwcXoy37t/Lo//fiWTfnBdncet37KXH057PpIpVQZ3z6BTSjIjpufxyAsrmTp2aJ3HTR1zHTOeX8GI6Xl0SklmUPeMiPQkOMeU7w/l7l8vZtSjzzLsykwuSW8fcswtA3pQeuw4I6bnMW/1Fu4ZOTgiLdVpncJnba0sNlmcn8WmIGvzs9hkdX5ap/DcfD3MeeLMr6/bCPsPwivzYfoDMOPJiCcB9uanpvhtsvq1B1orNTXtJstd+tqL36aHRg3lrjmLufmXz3L9FZlckhbaNOqaiqbhM/N4bu0W7h0ene/nIvWJ9oXJhcCNzrnzAJxzGcCFwHrv/WvAn2t+gnOuGRUXLSdFMuybV3Zl2YadAOTv+YykVol0SG5V67j8PZ9xuPhoJFOqZPfuysub3gNg+74CWp+fSMc2oU0d27SiVcvz2L6vAICXN73HkN5dI9LTMyOdA4eK+eRwCSdPlbN8yy6ya5wru3dXlm6sWMeV73xAv8xOEWmpeU6tU3isrZXFJovzs9hU/byW5mexyer8tE7huboPJLc+8+urNsDIHHAO+vaA0gAUHY54lrn5qSl+m6x+7QXPq7VSU1Ntstylr704beqUzseHivnki4qmV97ZxZCeNZp6duWlzRVNK7Z9wDWXRuf7uUh9onph0nv/BbAJuL7yQ2OBBd77+p7GnwC85L3/LJJtKe2SKDp8+rpo0RcBUtpF7KnxsKQmJ1Fw5HRTYXGA1OSkWscUFgfqPabRetrW6DkSILVt0hmPOVXuCRw7TnKrlhHpqTqn1in8NmNrZbHJ4vwsNlWd19j8LDZZnZ/WqXEUHoL01NO/T0+Bos8jf15r81NT/DZZ/trTWqmpKTeZ7tLXXlw2pbVNorC4xtxqNFU/5lS5J1AW+z9LNRne2f9lVCzelbv649zBx7jr5Jy7EPge8O9R6BIREREREREREZEoicW7ci8BZjnnrgAu8N6/Xc+xlwN/DXzonAO4wDn3off+r2se6Jy7E7gT4De/+U1YId/9Vh9GDukFwHt7C0ntcPpZrdT2SXx+JHCmT42YMd/sw6iBPQHI319IervTTWnJSRQVhzYVFQdIq/Y3U3Ud01iKSgKhPe2SKCoJ1HlMUXGAZgmOpPMTKT5a1ugtWqfwWVwri01V5zI2P4tNFudnsanqXIbmp3VqfGkdoaDa+wEUfA6pKZE5l8X5qSl+m6rOZexrT2ulpnOlyVqXvvbiv6mwJEBaco251WgKHlNYUtnUMvZ/lhKJ+h2T3vsAsBqYSz13S1Ye+7/e+3TvfYb3PgP4sq6LkpXHzvHeX+W9v+rOO+8Mq+V/Vr7LHVPnccfUeax9+0NuGNwdgB5dv0HgyxNR+1mS1b247l3G5M5nTO58Vm/bw/B+WQD0ykgncOwEh0pDmw6VHuVo2Ql6ZaQDMLxfFmu27YlIW/7+AjqltOPCDm1o3iyBnCsyWbttb8gxa7fv5aZrKtbxW5dfyubdByLSonUKn8W1stgUZG1+Fpsszs9iU5Cl+WmdGt+QQbBkecW7c2/Nh9atILVDZM5lcX5qit+mIGtfe1orNZ0rTda69LXXBJoOFNA5pR0Xta9oGnZ5Jmt2hDatyd/LiKsrmr7d+1I2fRj7P0s1Fc7b/2VVLO6YhIoLkouo9g7dzrn1wGVAknPuIPAj7/3yaAW9vvUjBvbpwsJ/+SFlJ07y6JzTp/7DY7dzx9R5AEwY+zd8Z+BltDyvBS899RNeWrOD3/3xjYg0rc//iME9Mlg6bTxlX51k2rxXq1578cHbGJM7H4CZC1Yx4/bvkNiiOX/auY8NO/dFpOdUuSd3wSqevnsUCc6x5M189hQc5q4bB7Dz40LWbt/Lotd38Ngdw3hp2nhKj5YxOW9ZRFqq0zqFz9paWWyyOD+LTUHW5mexyer8tE7huX86bNoKxSWQPRomjIeTJyteGzsSru0P696EnFuhZSLMfDDiSYC9+akpfpusfu2B1kpNTbvJcpe+9uK3aeYfV/H0naNoluBYvCmfPYWH+emwAew8UMia/L0s2riDmbcO4+WHxlPyZRmT/hCd7+ci9XH1v+9M3PL9b38y1g0h3px3H30nzIp1RoitsyeabAJMdVlsArvzU1PDLO4pi01gd35qapjFPRVsKi/oFuOSUAnpu02tE9jdUxabwOY+t9QEduenpoZZ3FMWm8Du/NTUMIt7KtjU+z47TQDbnpxo911UYqjL7H8xf3Htown3m5xdrO6YFBERERERERERiX/mL0vaFYt35RYREREREREREZFznC5MioiISvEt+gAAIABJREFUiIiIiIiISNTpwqSIiIiIiIiIiIhEnS5MioiIiIiIiIiISNTpwqSIiIiIiIiIiIhEnd6VW0RERERERERE5Cw5vSv3WdMdkyIiIiIiIiIiIhJ1zvsmeVm3Sf5LiYiIiIiIiIjEkIt1gEWXPPUv5q9D7f3Z/SZnpzsmRUREREREREREJOqa7M+YvPamJ2KdEGLt0p/Td8KsWGeE2Dp7oskmwFSXxSawOz81NczinrLYBHbnp6aGWdxTFpugoqu8oFusM0IkpO82uU4Wm8DWnrLYBHbnp6aGWdxTFpvA7vzU1DCLe8piE5zukhq8yZsR44LumBQREREREREREZGo04VJERERERERERERibom+yi3iIiIiIiIiIhIxJl/6xu7dMekiIiIiIiIiIiIRJ0uTIqIiIiIiIiIiEjU6VFuERERERERERGRs6VHuc+a7pgUERERERERERGRqIvqHZPOudVArvd+ebWP3QtkAl2A/sAG7/3waq8/A1wLlFR+6Afe+62R6PvZnUO55spLOH78JI//2zI+2FMU8npiYnOmTx7Bhd9Iprzc8/qmPcx5dl0kUqpMGp3N4B5dKDvxFQ8/9yrvHyyqdUzWxanMGJdDYovmbMj/iF8tXKOmGDcNzOrMpNHZJCQksOj1HeSt2BzyeovmzXh0XA5ZndIoOXqMyXOX8ekXpRHrqU5rFR6tU3w2We7SnorPpiBL85uaC2vegPbtYOkztV/3HmY+Bes2QstEmDkFenSLSEotltYJ7O4pa+tktcvi/Cw2gWYXr01Bmp+aGpu1PSVSn2jfMfkCMLbGx8ZWfvwJYNwZPu/n3vu+lb8iclHymiu78FcXtuO2v/8d//wfy7nvrm/XedyLizZzx11z+fE9z9Ir6yKuubJLJHIAGNw9g04pyYyYnscjL6xk6tihdR43dcx1zHh+BSOm59EpJZlB3TPUFMOmBOeY8v2h3P3rxYx69FmGXZnJJentQ465ZUAPSo8dZ8T0POat3sI9IwdHpKUmrVV4tE7x2WS5S3sqPpuCrM3v5uthzhNnfn3dRth/EF6ZD9MfgBlPRiSjFmvrZHVPWVsnq10W52exCTS7eG0K0vzU1Nis7alzhfP2f1kV7QuTC4EbnXPnATjnMoALgfXe+9eAP0e5p8rg/peyfFU+ADt3fUZSq5a0b9cq5Jjjx0/yzvYDAJw8Wc7uPYWkdGgdsabs3l15edN7AGzfV0Dr8xPp2Ca0qWObVrRqeR7b9xUA8PKm9xjSu6uaYtjUMyOdA4eK+eRwCSdPlbN8yy6ya5wru3dXlm7cCcDKdz6gX2aniLTUpLUKj9YpPpssd2lPxWdT9fNamt/VfSC5nj9+rNoAI3PAOejbA0oDUHQ4IikhrK2T1T1lbZ2sdlmcn8Wm4Dk1u/hrqn5ezU9NjcnanhJpSFQvTHrvvwA2AddXfmgssMB739C128ecc9ucc7Occ4mRaOvYIYmiQ6evi35++M+kdEg64/FJrRIZ2K8rb7+7PxI5AKQmJ1Fw5HRTYXGA1OSkWscUFgfqPUZN0W1KbVuj50iA1LZJZzzmVLkncOw4ya1aRqQn5Lxaq/C6tE5x2WS6S3sqLpuqzmtsfg0pPATpqad/n54CRZ9H/rzW1snqnrK2Tla7LM7PYhNodvHaVHVezU9Njd1mbE+JNCQWb35T/XHu4GPc9ZkCXAZcDbQHJkcuLTzNEhwP/3w4/7N0C58VljT8CSIiIiIiIiIi0jT5OPhlVFTf/KbSEmCWc+4K4ALv/dv1Hey9/6zyH4875/KAB+o6zjl3J3AnwG9+85uwQm6+4XKG5/QGYNcHn5Ha8fRzUSkdWvP54UCdn/fAhBwOfnqEhS/Vm35WxnyzD6MG9gQgf38h6e1ON6UlJ1FUHNpUVBwgrdrfbNR1jJqi01R1rpJAaE+7JIpKAnUeU1QcoFmCI+n8RIqPlkWkR2sVHq1T/DdZ69Keiu8my/NrSFpHKKj2M+4LPofUlMicy/I6WdpTVtfJahfYmp/FJs0uvps0PzU1Nst7SqQhUb9j0nsfAFYDc2n4bkmcc9+o/F8H3AzsOMP/7xzv/VXe+6vuvPPOsFoWL3uHH9/zLD++51nWv/khOUN7ANA98xsc/fI4Xxw5WutzfnT7YFq1SuTff7sqrHN8XS+ue5cxufMZkzuf1dv2MLxfFgC9MtIJHDvBodLQpkOlRzladoJeGekADO+XxZpte9QUg6ag/P0FdEppx4Ud2tC8WQI5V2SydtvekGPWbt/LTdd0B+Bbl1/K5t0HItICWqtwaZ3iv8lal/ZUfDdZnl9DhgyCJcsr3p17az60bgWpHSJzLsvrZGlPWV0nq11ga34WmzS7+G7S/NTU2CzvKYkfzrlhzrldzrkPnXMP1vH6fc65nZU/avE151znxjhvLO6YhIoLkouo9g7dzrn1VDyyneScOwj8yHu/HJjvnEsBHLAV+IdIBL351l76X3UJz8/5CcePf0Xuv/1f1Wu/+7e/48f3PEtKhyTuGDOA/QcO89t//TsAFv3vFv731e2RSGJ9/kcM7pHB0mnjKfvqJNPmvVr12osP3saY3PkAzFywihm3f4fEFs350859bNi5LyI9agrPqXJP7oJVPH33KBKcY8mb+ewpOMxdNw5g58eFrN2+l0Wv7+CxO4bx0rTxlB4tY3Lesoi01KS1Co/WKT6bLHdpT8VnU5C1+d0/HTZtheISyB4NE8bDyZMVr40dCdf2h3VvQs6t0DIRZtb6Y2VkWFsnq3vK2jpZ7bI4P4tNoNnFa1OQ5qemxmZtT0l8cM41A/4D+DZwENjsnHvJe7+z2mHvAFd57790zt0F/AoY8xefu+H3nYlL/tqbnoh1Q4i1S39O3wmzYp0RYuvsiSabAFNdFpvA7vzU1DCLe8piE9idn5oaZnFPWWyCiq7ygm6xzgiRkL7b5DpZbAJbe8piE9idn5oaZnFPWWwCu/NTU8Ms7imLTQBbZ090sW6wqOs/P2n+4tqeB+474+yccwOAX3jvcyp/PwXAe//4GY6/HJjtvR/0l3bF4s1vREREREREREREJEqcc3c6596q9qv6z0G8CKj+8wYOVn7sTH4E/F89r4ctVo9yi4iIiIiIiIiISBR47+cAc/7S/x/n3O3AVcC1f3EUujApIiIiIiIiIiJy1pz5B7kb9AlwcbXf/1Xlx0I4574FTAWu9d4fb4wT61FuERERERERERGRc9dm4FLnXBfn3HlUvFn1S9UPqPy5kr8BRnjvixrrxLowKSIiIiIiIiIico7y3p8EJgDLgfeABd77fOfcDOfciMrDngCSgP92zm11zr10hv+7r0WPcouIiIiIiIiIiJwtH/9vVu69XwYsq/Gxh6v987cicV7dMSkiIiIiIiIiIiJRpwuTIiIiIiIiIiIiEnV6lFtERERERERERORsxf+7cseM875Jrl6T/JcSEREREREREYmh+P9hihHw17+cZf461IeTJ5qcXZO9Y7LPz2bFOiHEu09NpO8EW01bZ9tsAkx1WWwCu/NTU8Ms7imLTWB3fmpqmMU9ZbEJ7M6vvKBbrDNCJKTvNrlOYGtPWWwCu/tcTQ2zuKcsNoHd+ampYRb3lMUmON0l0lia7IVJERERERERERGRSHPm75e0S29+IyIiIiIiIiIiIlGnC5MiIiIiIiIiIiISdXqUW0RERERERERE5GzpUe6zpjsmRUREREREREREJOp0YVJERERERERERESiThcmRUREREREREREJOr0MyZFRERERERERETOktPPmDxrUb0w6ZxbDeR675dX+9i9QCbQBegPbPDeD6/2ugMeBb4HnAKe9t4/1dhtA7M6M3lUNgkJCSx6YwdzV24Oeb1F82Y8dnsOWRenUXL0GJOeWcanX5Q2dkYtk0ZnM7hHF8pOfMXDz73K+weLah2TdXEqM8blkNiiORvyP+JXC9dErGdgVmcmja5cp9d3kLei9jo9Oi6HrE4V6zR57rm5Tla7NL/wWFwnNcV3l5rCZ+37geUuS01Tc2HNG9C+HSx9pvbr3sPMp2DdRmiZCDOnQI9uEUkJoX0eHqvrZLFLTfHbFKSvv/hsAnuzs9pkuUukLtF+lPsFYGyNj42t/PgTwLg6PucHwMXAZd77LOC/GjsqwTke+t5Qfvqfi7ll5rMMuzKTS9LbhxxzS/8elH55nJseyWPemi3cO2JwY2fUMrh7Bp1SkhkxPY9HXljJ1LFD6zxu6pjrmPH8CkZMz6NTSjKDumdEpCfBOaZ8fyh3/3oxox49wzoN6EHpseOMmJ7HvNVbuGfkubdOVrs0v/BYXCc1xXeXmsJn7fuB5S5rTTdfD3OeOPPr6zbC/oPwynyY/gDMeDIiGSG0z8NjdZ0sdqkpfpuC9PUXn01gb3ZWmyx3iZxJtC9MLgRudM6dB+CcywAuBNZ7718D/lzH59wFzPDelwN472tf6v8L9eyczoHPi/nkcAknT5XzypZdZPfqGnLMkF5deWnTTgBWbP2Aft06NXZGLdm9u/LypvcA2L6vgNbnJ9KxTauQYzq2aUWrluexfV8BAC9veo8hvbvW+v9qDD0z0jlw6PQ6Ld+yi+wa58ru3ZWlGyvWaeU7H9Av89xbJ6tdml94LK6TmuK7S03hs/b9wHKXtaar+0By6zO/vmoDjMwB56BvDygNQNHhiKRU0T4Pj9V1stilpvhtqn5eff3FX1PwnJZmZ7XJcleT5+Pgl1FRvTDpvf8C2ARcX/mhscAC7319S9QVGOOce8s593/OuUsbuys1OYmC4tPXRIuKA6S1TQo9pu3pY06VewJlx0lu1bKxU2p3HTndVVgcIDU5qdYxhcWBeo9ptJ62NXqOBEita52OVFunY+feOlnt0vzC7DG4TmqK7y41fY0uY98PLHdZbKpP4SFITz39+/QUKPo8sufUPg+zx+o6GexSU/w2VZ1XX39x2QT2Zme1yXKXyJnE4l25qz/OHXyMuz6JQJn3/irgt8Dcug5yzt1ZefHyrTlz5jRarIiIiIiIiIiIiDS+WLwr9xJglnPuCuAC7/3bDRx/EPhj5T8vAvLqOsh7PwcIXpH0//GzWWEHFRUHSK/2/FFqchKFJYHQY0oqjikqDtAswZHUMpHio2VhnyNcY77Zh1EDewKQv7+Q9Hanu9KSkygqrtFVHCCt2t9s1HVMYykqCYT2tEuiqK51aldtnc4/t9bJahdofuGytE5qahpdaqqf1e8HFrssNoUrrSMUVPthPAWfQ2pKZM+pfR4eS+tkvUtN8dmkr7/4bbI4O4tNlrvOKYYflbYu6ndMeu8DwGoq7nxs6G5JgMXAkMp/vhbY3dhN+R8X0CmlHRe1b0PzZgkMuyKTtdv3hhyzZsdeRvTrDsC3+17Kpg8ONHYGAC+ue5cxufMZkzuf1dv2MLxfFgC9MtIJHDvBodKjIccfKj3K0bIT9MpIB2B4vyzWbNsTkbb8/RXrdGGHinXKuSKTtdtC12nt9r3cdE3FOn3r8kvZvPvcWierXaD5hcvSOqmpaXSpqX5Wvx9Y7LLYFK4hg2DJ8op3596aD61bQWqHyJ5T+zw8ltbJepea4rNJX3/x22RxdhabLHeJhCMWd0xCxQXJRVR7h27n3HrgMiDJOXcQ+JH3fjmQC8x3zk0EAsCPGzvmVLnn8YWrePqno0hIcCx+M589BYf56Q0DyP+4kLU79rLojR08Nm4YS//feEq/LGPSM8saO6OW9fkfMbhHBkunjafsq5NMm/dq1WsvPngbY3LnAzBzwSpm3P4dEls0508797Fh576I9Jwq9+QuWMXTd48iwTmWVK7TXTcOYOfHhazdvpdFr+/gsTuG8dK08ZQeLWNy3rm3Tla7NL/wWFwnNcV3l5rCZ+37geUua033T4dNW6G4BLJHw4TxcPJkxWtjR8K1/WHdm5BzK7RMhJkPRiQjhPZ5eKyuk8UuNcVvU5C+/uKzCezNzmqT5S6RM3H1v+9M3PJ9vsaj3NHw7lMT6TvBVtPW2TabAFNdFpvA7vzU1DCLe8piE9idn5oaZnFPWWwCu/MrL+gW64wQCem7Ta4T2NpTFpvA7j5XU8Ms7imLTWB3fmpqmMU9ZbEJYOvsiS7WDRZlPjLL/MW1Xf/P5uxi8eY3IiIiIiIiIiIico7ThUkRERERERERERGJOl2YFBERERERERERkajThUkRERERERERERGJOl2YFBERERERERERkajThUkRERERERERERGJuuaxDhAREREREREREYlbPtYB8Ut3TIqIiIiIiIiIiEjUOe+b5GXdJvkvJSIiIiIiIiISQy7WARZlzphl/jrUrocnmpxdk32Uu98Pnox1QohNz9xH3wmzYp0RYuvsiSabAFNdFpvA7vzU1DCLe8piE9idn5oaZnFPWWwCu/Oz2FRe0C3WGSES0ncDtvaU9nn41BQei3vKYhPYnZ+aGmZxTwWb+vzMThPAu09NjHWCSc78ZUm79Ci3iIiIiIiIiIiIRJ0uTIqIiIiIiIiIiEjUNdlHuUVERERERERERCJOj3KfNd0xKSIiIiIiIiIiIlGnC5MiIiIiIiIiIiISdXqUW0RERERERERE5GzpUe6zpjsmRUREREREREREJOp0YVJERERERERERESiLqqPcjvnVgO53vvl1T52L5AJdAH6Axu898Orvb4eaF3521Rgk/f+5kj03X/bEAb27kLZia+Y8bvl7NpfVOuYu747iBsGdqd1q0Sy/2F2JDJCTBqdzeAeFU0PP/cq7x+s3ZR1cSozxuWQ2KI5G/I/4lcL10SsZ2BWZyaNziYhIYFFr+8gb8XmkNdbNG/Go+NyyOqURsnRY0yeu4xPvyiNWE+Q1im+uzS/+GwKsjY/NYXH4p6y2BSk+cVn09RcWPMGtG8HS5+p/br3MPMpWLcRWibCzCnQo1tEk0yuk8Umy13Wvh+oKb67tM/Do3UKz8CszkweVblOb+xg7sra6/TY7TlkXVyxTpOeid6fpZo6p0e5z1q075h8ARhb42NjKz/+BDCu5id47//Ge9/Xe98XeAP4YyTCBvbuwsVpyXx38lwef2Ylk++4rs7j1m/dyw9mPB+JhFoGd8+gU0oyI6bn8cgLK5k6dmidx00dcx0znl/BiOl5dEpJZlD3jIj0JDjHlO8P5e5fL2bUo88y7MpMLklvH3LMLQN6UHrsOCOm5zFv9RbuGTk4Ii3VaZ3iu0vzi8+mIGvzU1N4LO4pi01Bml98NgHcfD3MeeLMr6/bCPsPwivzYfoDMOPJyPZYXCeLTZa7rH0/UFN8d2mfh0frFJ4E53joe0P56X8u5paZZ1in/j0o/fI4Nz2Sx7w1W7h3RHT+LCVSn2hfmFwI3OicOw/AOZcBXAis996/Bvz5TJ/onGsDDAUWRyLsm5d3ZdmfdgKwY89ntL4gkQ5tW9U6bseezzhccjQSCbVk9+7Ky5veA2D7vgJan59IxzahTR3btKJVy/PYvq8AgJc3vceQ3l0j0tMzI50Dh4r55HAJJ0+Vs3zLLrJrnCu7d1eWbqxYx5XvfEC/zE4Raal5Tq1T/HZpfvHZVP28luanpvBY3FMWm6qfV/OLvyaAq/tAcuszv75qA4zMAeegbw8oDUDR4cj1WFwni02Wu6x9P1BTfHdpn4dH6xSenp3TOfD56XV6ZcsusnuFnmtIr668tKlinVZs/YB+3aLzZymR+kT1wqT3/gtgE3B95YfGAgu89+Hc9Hoz8Jr3PiL3Gae2S6Lwi9PXRYuOBEhtlxSJU4UtNTmJgiOnmwqLA6QmJ9U6prA4UO8xjdbTtkbPkQCpbZPOeMypck/g2HGSW7WMSE/VObVO8d2l+cVlU9V5jc1PTWH2GNxTFpuqzqv5xWVTOAoPQXrq6d+np0DR55E7n8V1sthkusvY9wM1xXeX9nmYPVqn8HuKq13TKA6QVtc6FVdbp7LY/7e4yfBx8MuoWLz5TfXHuYOPcYfjb+s71jl3p3PuLefcW3PmzPkLE0VERERERERERCSSovrmN5WWALOcc1cAF3jv327oE5xzHYF+wC1nOsZ7PwcIXpH0v3u94R8SNPq6Ptx8bS8Adn5USFr708/6pLZLouhI4EyfGjFjvtmHUQN7ApC/v5D0dqeb0pKTKCoObSoqDpBW7W9c6jqmsRSVBEJ72iVRVBKo85ii4gDNEhxJ5ydSfLSs0Vu0TvHdpfnFd5PF+anp67G2pyw2aX7x3xSOtI5QUO19Cgo+h9SUyJ3P4jpZbLLWZfH7gZrivwu0z8OldQpPUXGA9Go/vyQ1OYnCutYpudo6tYz9f4tFon7HpPc+AKwG5hL+3ZKjgZe99436FbPwtXe5/eF53P7wPNZu+ZAbBnUHoGfXbxA4diJqP0uyuhfXvcuY3PmMyZ3P6m17GN4vC4BeGekEjp3gUGlo06HSoxwtO0GvjHQAhvfLYs22PRFpy99fQKeUdlzYoQ3NmyWQc0Uma7ftDTlm7fa93HRNxTp+6/JL2bz7QERatE7x3aX5xXeTxfmp6euxtqcsNml+8d8UjiGDYMnyinfn3poPrVtBaofInc/iOllsstZl8fuBmuK/C7TPw6V1Ck/+xxXrdFH7inUadkUma7eHrtOaHXsZ0a9inb7d91I2fRD7/xaLxOKOSai4ILmIau/Q7ZxbD1wGJDnnDgI/8t4vr3x5LJAbyaA/vfsRA3t34Y+/+iFlx0/yyO+XV702b8bt3P7wPAD+8ft/w3f6X0bL81qw9Mmf8NK6Hfx28RsRaVqf/xGDe2SwdNp4yr46ybR5r1a99uKDtzEmdz4AMxesYsbt3yGxRXP+tHMfG3bui0jPqXJP7oJVPH33KBKcY8mb+ewpOMxdNw5g58eFrN2+l0Wv7+CxO4bx0rTxlB4tY3Lesoi0VKd1iu8uzS8+m4KszU9N4bG4pyw2BWl+8dkEcP902LQViksgezRMGA8nT1a8NnYkXNsf1r0JObdCy0SY+WBkeyyuk8Umy13Wvh+oKb67tM/Do3UKz6lyz+MLV/H0T0eRkOBYXLlOP71hAPkfF7J2x14WvbGDx8YNY+n/G0/pl2VMeiY6f5Y6FzjDP8PROhfe+87EHd/vBw0/yh1Nm565j74TZsU6I8TW2RNNNgGmuiw2gd35qalhFveUxSawOz81NczinrLYBHbnZ7GpvKBbrDNCJKTvBmztKe3z8KkpPBb3lMUmsDs/NTXM4p4KNvX5mZ0mgHefmuhi3WBR93+aZf7i2s5Hbc4uFm9+IyIiIiIiIiIiIue4WD3KLSIiIiIiIiIiEv/M3y9pl+6YFBERERERERERkajThUkRERERERERERGJOj3KLSIiIiIiIiIicrb0KPdZ0x2TIiIiIiIiIiIiEnW6MCkiIiIiIiIiIiJRp0e5RUREREREREREzpLTo9xnzXnfJFevSf5LiYiIiIiIiIjEkIt1gEU9pswyfx0q//GJJmfXZO+Y7P7QrFgnhNg5cyJ9J9hq2jrbZhNgqstiE9idn5oaZnFPWWwCu/NTU8Ms7imLTWB3fmpqWHBPlRd0i3HJaQnpuwHt83CoKTwWv3dabAK781NTwyzuqWBT7/vsNAFse3JirBOkiWmyFyZFREREREREREQizvz9knbpzW9EREREREREREQk6nRhUkRERERERERERKJOFyZFREREREREREQk6vQzJkVERERERERERM6WfsbkWdMdkyIiIiIiIiIiIhJ1ujApIiIiIiIiIiIiUadHuUVERERERERERM6S06PcZy2qFyadc6uBXO/98mofuxfIBLoA/YEN3vvh1V6/DniCirs7A8APvPcfNnbb4Es7M2V4Ns0SEli4eQe/W7c55PUrMy5iyo3X0i09hQdeXMarOz5o7IQ6TRqdzeAeXSg78RUPP/cq7x8sqnVM1sWpzBiXQ2KL5mzI/4hfLVyjphg3DczqzKTR2SQkJLDo9R3krQjdTy2aN+PRcTlkdUqj5OgxJs9dxqdflEasx3qX5hefTUGan5qaclOQtX2upvBY3FNTc2HNG9C+HSx9pvbr3sPMp2DdRmiZCDOnQI9uEU2qYm1+Fpss7imwt05Wm6x2WWuyuM8tNoG92Q26rDOTb65Ypz++uYO5q2qsU7NmPHZrDt0vrlinn/9hGZ8eic6fpUTOJNqPcr8AjK3xsbGVH38CGFfH5zwN3Oa97ws8D/xTY0clOMc/jRjK3z+zmJv+9Vlu6JNJ19T2Icd8VvxnHvqfV/nfd99v7NOf0eDuGXRKSWbE9DweeWElU8cOrfO4qWOuY8bzKxgxPY9OKckM6p6hphg2JTjHlO8P5e5fL2bUo88y7MpMLkkP3U+3DOhB6bHjjJiex7zVW7hn5OCItMRDl+YXn01Bmp+amnJTkLV9rqbwWN1TN18Pc5448+vrNsL+g/DKfJj+AMx4MuJJgL35WWyyuqesrZPVJqtd1pos7nOLTWBzdg+NGspdcxZz8y+f5forMrkkLXSdRl1TsU7DZ+bx3Not3Ds8On+WEqlPtC9MLgRudM6dB+CcywAuBNZ7718D/lzH53igTeU/twU+beyoXn+VzseHizl4pISvTpXzf9t2MTSra8gxnxaXsrvgEOU+evfnZvfuysub3gNg+74CWp+fSMc2rUKO6dimFa1ansf2fQUAvLzpPYb07lrr/0tN0WvqmZHOgUPFfHK4hJOnylm+ZRfZNc6V3bsrSzfuBGDlOx/QL7NTRFrioUvzi8+m6ufV/NTUVJuqn9fSPldTeKzuqav7QHLrM7++agOMzAHnoG8PKA1A0eGIZ5mbn8Umq3vK2jpZbbLaZa3J4j632BQ8p6nZdUrn40PFfPJFxTq98s4uhvSssU49u/LS5op1WrHtA665NDp/ljon+Dj4ZVRUL0x6778ANgHXV35oLLDA+3qv9v0YWOacO0jFHZW5jd2V1jaJgpLT10QLSgKktklq7NNekfBxAAAgAElEQVR8banJSRQcOd1VWBwgNTmp1jGFxYF6j1FTdJtS29boORIgtW3SGY85Ve4JHDtOcquWEekx36X5xWVT1Xk1PzU14aaq8xrb52oKs8fwnqpP4SFITz39+/QUKPo88ue1Nj+LTVb3lLV1stpktctak8V9brEJ7M0urW0ShcU1emqsU/VjTpV7AmWx/++eSCzelbv649zBx7jrMxG4wXv/V0AeUOcDLc65O51zbznn3pozZ06jxYqIiIiIiIiIiEjji8W7ci8BZjnnrgAu8N6/faYDnXMpQB/v/cbKD70IvFLXsd77OUDwiqT/14dmhR1UWBIgve3pZ2rS2yZRVBqo5zMiZ8w3+zBqYE8A8vcXkt7udFdachJFxaFdRcUB0qr9jUtdx6gpOk1V5yoJhPa0S6KoJFDnMUXFAZolOJLOT6T4aFlEeix2aX7x3aT5qelcaLK4z9X09VjbU+FK6wgF1d47oeBzSE2JzLkszs9iU9W5DO0pi+tksclql8WmqnMZ2ucWmyzPrrAkQFpyjZ4a6xQ8prCkcp1axv6/e02F3pX77EX9jknvfQBYDcyl4bsljwBtnXPB9yL8NvBeYzft+KSAzh3bcVG7NrRolsD1vTNZ/d7exj5NWF5c9y5jcuczJnc+q7ftYXi/LAB6ZaQTOHaCQ6VHQ44/VHqUo2Un6JWRDsDwflms2bZHTTFoCsrfX0CnlHZc2KENzZslkHNFJmu3he6ntdv3ctM13QH41uWXsnn3gYi0WO3S/OK7SfNT07nQZHGfq+nrsbanwjVkECxZXvHu3FvzoXUrSO0QmXNZnJ/FpiBLe8riOllsstplsSnI0j632GR6dgcK6JzSjovaV6zTsMszWbMjdJ3W5O9lxNUV6/Tt3pey6cPY/3dPJBZ3TELFBclFVHuHbufceuAyIKny50n+yHu/3Dn3E+B/nHPlVFyo/GFjx5wq9zz20ip+O34UCc6x6O18Piw6zIRvDSD/YCGr399Lz4vSeOr2m2hzfkuGZF3ChOsGMOLf/tDYKSHW53/E4B4ZLJ02nrKvTjJt3qtVr7344G2MyZ0PwMwFq5hx+3dIbNGcP+3cx4ad+9QUw6ZT5Z7cBat4+u6K/bTkzXz2FBzmrhsHsPPjQtZu38ui13fw2B3DeGnaeEqPljE5b1lEWuKhS/OLz6YgzU9NTbkpyNo+V1N4rO6p+6fDpq1QXALZo2HCeDh5suK1sSPh2v6w7k3IuRVaJsLMByOeBNibn8Umq3vK2jpZbbLaZa3J4j632AQ2Zzfzj6t4+s5RNEtwLN6Uz57Cw/x02AB2HihkTf5eFm3cwcxbh/HyQ+Mp+bKMSX+Izp+lROrj6n/fmbjlu3+NR7mjYefMifSdYKtp62ybTYCpLotNYHd+amqYxT1lsQnszk9NDbO4pyw2gd35qalhwT1VXtCtgSOjJyF9N6B9Hg41hcfi906LTWB3fmpqmMU9FWzqfZ+dJoBtT050sW6wqNf9s8xfXNv+LzZnF4s3vxEREREREREREZFznC5MioiIiIiIiIiISNTpwqSIiIiIiIiIiIhEXaze/EZERERERERERCT+mf8Jk3bpjkkRERERERERERGJOl2YFBERERERERERkajTo9wiIiIiIiIiIiJnycU6II7pjkkRERERERERERGJOud9k/wJnU3yX0pEREREREREJIZ0c2Adet83y/x1qG1PTjQ5Oz3KLSIiIiIiIiIicrbMX5a0q8lemOw9cVasE0JsmzWRvhNsNW2dbbMJMNVlsQnszk9NDbO4pyw2gd35qalhFveUxSawOz81Nczingo2lRd0i3FJqIT03abWCezuKYtNYHOfW2oCu/NTU8Ms7qlg01U/ejLGJaHe+v19sU6QJkY/Y1JERERERERERESirsneMSkiIiIiIiIiIhJpTo9ynzXdMSkiIiIiIiIiIiJRpwuTIiIiIiIiIiIiEnV6lFtERERERERERORs6VHus6Y7JkVERERERERERCTqdGFSREREREREREREoi6qj3I751YDud775dU+di+QCXQB+gMbvPfDq70+FPhn4DzgbeBH3vuTjd026LLOTL4lmwSXwB837mDua5tDXm/RrBmP3ZZD979Ko+TLY/z82WV8eqS0sTNCDMzqzKTR2SQkJLDo9R3krajR1LwZj47LIatTGiVHjzF57jI+/eLcawKYNDqbwT26UHbiKx5+7lXeP1hU65isi1OZMS6HxBbN2ZD/Eb9auCZiPVbXCeytlZrit8nqPrfYZbEJ7O0pi01WZwdaq3htAnuzm5oLa96A9u1g6TO1X/ceZj4F6zZCy0SYOQV6dItYThWL81NT/DYFWfv6U1N4LO4pi00AD/ztEAb1qpjdL+YuZ9fHtWf301sGccPA7rS5IJFv3j074k3nBD3KfdaifcfkC8DYGh8bW/nxJ4Bx1V9wziUAzwJjvfc9gf3A3zV2VIJzPPTdodw1ZzE3//JZrr88k0vS2occM6p/D0qPHWf4zDyeW7uFe28a3NgZtZqmfH8od/96MaMefZZhV2ZySXpo0y0DKppGTM9j3uot3DPy3GsCGNw9g04pyYyYnscjL6xk6tihdR43dcx1zHh+BSOm59EpJZlB3TMi0mN1ncDeWqkpfpus7nOLXRabwN6esthkdXagtYrXJrA3O4Cbr4c5T5z59XUbYf9BeGU+TH8AZjwZsZQqFuenpvhtCrL49aemhlncUxabAAb16sLFacnc8tBcHvvDSqaMu67O49a9u5e/e/T5iPeIhCPaFyYXAjc6584DcM5lABcC6733rwF/rnF8B+CE93535e9XAN9t7KiendL5+FAxnxwu4eSpcl55ZxdDenYNOSa7Z1de2rSzIuLdD7jm0k6NnRHalJHOgWpNy7fsIrt3jabeXVm6saJp5Tsf0C/z3GsKnvPlTe8BsH1fAa3PT6Rjm1Yhx3Rs04pWLc9j+74CAF7e9N7/Z+/e46Oq7v3/v1a4BCVAAuSirRilghBuWsVyqQa0BquC8qVCxRvWY08rPS3qUZFvRVAxx7bSX+u3tpwWqoAW9BTxwhGx3EQRRI1AUEFuippELkmcSLhl/f7IJGSSYIaYmflMeD8fjzxKZu9kv1hrJX242Xs2Q2q1NxWr41R1XEtjpab4bbK6zi12WWyqOqalNWWxyercVR1XYxV/TVXHtDR3AOf3heR2x96+dBWMyAHnoF8WlAagaE/EcgCb86em+G2qeVxrP39qapjFNWWxCeCifl1Z9EblMTdu+5x2JyfSqUPbOvtt3PY5e0rKIt4jEo6onpj03u8F1gKXBV8aA8z33h/rotfdQEvn3HnBz0cBpzV1V3pyEoXFR8+JFpYESOuQFLpPh6P7HKnwBMoPkNy2TVOnVEvrkETBvhpN++o21dznSIUnsP/EawJIS67VVRwgLTmpzj6FxYGv3afJeoyOE9gbKzXFb5PVdW6xy2IT2FtTFpuszh1orOK1CezNXTgKd0NG2tHPM1Kh6IvIHtPi/Kkpfpuqj2vw509NYfQYXFMWmwBSU5Io2FurK4b//yESjlg8/Kbm7dxVt3HXK3jCcgww3Tm3lsorKo/Ut69z7lbn3Drn3LoZM2Y0cbKIiIiIiIiIiEhdztv/aPDv4Nww59yHzrmPnHP31LM90Tk3L7h9TfAu6G8sqg+/CVpI5YnGc4GTvfdvf93O3vvVwPcBnHOXAvW+1bb3fgZQdUbSPzZhethBhcUB0mvcv5LeIYmikkDoPiWV+xSWBGiR4Ehqk0hxWXnYxzheRSUBMlJqNKXUbarap6g42HTSidM0+sK+jBzYC4D8nYWhXclJFBXX6ioOkF7jX4rq26epWBonsDlWaorfpupjGVvnlrssNVlcUxabqo9laO5AYxXPTZbnLhzpnaGgxrMTCr6AtNTIHtPS/Kkpvpss/vyp6fhYW1PWmn40pC9XXdgbgE07Csno2I73anbF8P8/JH4451oA/w/4AbALeMs597z3flON3X4C7PPef8c5Nwb4L2D0Nz121K+Y9N4HgGXATL7maskqzrm04P8mAncDf27qpvxPCjg9NYVvdWxPyxYJDDunO8vzt4Xss3zjNob37wnAD/qexdqPPmnqjNCmnQV0SU3h1E6VTTnndmfF+tCmFRu2ceUFlU2XnHMWb20+cZrmrXyP0blzGZ07l2Xrt3JF/x4A9M7MILD/ILtLQ98vY3dpGWXlB+mdmQHAFf17sHz91oi0WRonsDlWaorfpirW1rnlLktNFteUxaYqluYONFbx3GR57sIxZBAsXFz5dO68fGjXFtI6RfaYluZPTfHdZPHnT03Hx9qastb0zLL3GDtlDmOnzGH5ux/xw4GVx+x15ikEvjqo95KUcPUHPvLeb/PeHwT+AYyotc8IKh9QDZXPkLnYOee+6YFjccUkVJ6QXECNJ3Q7514DzgaSnHO7gJ947xcD/+mcu4LKk6iPe++XNnXMkQrPtP9ZyuM/HUmLBMdza/LZWrCHnw8bwKZPClmev40FazYybewwXrx3HCVflXPX7EVNnVGnKXf+Uh6/bSQJzrHwzcqmn10+gE0fF7JiwzYWvLGRh24YxvOTx1FaVs7ds068JoDX8rczOCuTFyaPo/zQYSbPeaV627x7xjI6dy4A0+YvZep1l5LYqiWvb9rBqk07ItJjdZzA3lipKX6brK5zi10Wm8DemrLYZHXuQGMVr01gb+4A7pgCa/OguASyR8H4cXD4cOW2MSPgou/Byjch51pokwjT6tzc1fQszp+a4repisWfPzU1zOKastgE8Pr67QzqfQbPPXwz5QcPM2Xm4uptcydfx9gpcwD4j1HfJ+eCs2nTuhUv/ebfWPjaRmY8vzrifc1aGLdKx5pz7lbg1hovzQjefQzwLaDm2fNdwAW1vkX1Pt77w865EiofWr37G3Ud+7kzcc33OY5buaNh/fQJ9BtvqynvMZtNgKkui01gd/7U1DCLa8piE9idPzU1zOKastgEdudPTQ2zuKaqmioK6n33o5hJyNhsapzA7pqy2AQ217mlJrA7f2pqmMU1VdV03k8ejXFJqHV/u/0bXyHXHPUbP938ybW8xyYcc+6cc6OAYd77W4KfXw9c4L0fX2OfjcF9dgU/3xrc5xudmIzFw29ERERERERERETEhk+B02p8/u3ga/Xu45xrCXQA9nzTA+vEpIiIiIiIiIiISCPF+onbTfBU7reAs5xzZzjnWlP51ovP19rneeDG4J9HAUt9E9yGHav3mBQREREREREREZEYC75n5HhgMdACmOm9z3fOTQXWee+fB/4GzHbOfQTspcZzY74JnZgUERERERERERE5gXnvFwGLar12X40/lwM/aurj6sSkiIiIiIiIiIhIY5l/9I1deo9JERERERERERERiTqdmBQREREREREREZGo063cIiIiIiIiIiIijRTGU6/lGFwTPNnbomb5lxIRERERERERiSEX6wCLzv3ZdPPnod55fILJudOt3CIiIiIiIiIiIhJ1zfZW7vNueTTWCSHW/fV2+o2fHuuMEHmPTTDZBJjqstgEdudPTQ2zuKYsNoHd+VNTwyyuKYtNYHf+1NQwi2vKYhNUdlUUdIt1RoiEjM0mx8liE9haUxabwO78qalhFtdUVVPmk/8V45JQO264O9YJ0sw02xOTIiIiIiIiIiIiEWf+Rm67dCu3iIiIiIiIiIiIRJ1OTIqIiIiIiIiIiEjU6VZuERERERERERGRxtKt3I2mKyZFREREREREREQk6nRiUkRERERERERERKJOt3KLiIiIiIiIiIg0ktOt3I0W1ROTzrllQK73fnGN134F5ADJQHvgCPCQ935ecPsZwD+ATsDbwPXe+4OR6Lvzx0MY1PsMyg8e4v6Zi/nw46I6+/z86kH8cEBP2p+cyIXjH4tERoi7RmUzOKuy6b7Zr/DBrrpNPU5LY+r1OSS2asmq/O088uxyNcW4aWCP07lrVDYJCQkseGMjs5a8FbK9VcsWPHh9Dj26pFNStp+7Zy7is72lEeux3qX5i8+mKtbmT03x26R1Hj6LY6Wm8Gk9NWxSLixfDR1T4IW/193uPUz7A6xcA20SYdpEyOoW0aRq1ubPYpPFNWWxyXKX1lR4rI3TRaeewX3nX0wLl8C8j97j8Y1rQraP7daP67ufS4WvoOzwISaufpmPSvZErEckHNG+lftpYEyt18YADwM3eO+zgGHA751zycHt/wVM995/B9gH/CQSYYN6n8Fpaclcfe9MHnryVSZed3G9+618bxs3PvRUJBLqGNwzky6pyQyfMosHnn6VSWOG1rvfpNEXM/WpJQyfMosuqckM6pmpphg2JTjHxGuGctufnmPkg08w7LvdOTOjY8g+Vw/IonT/AYZPmcWcZe/wyxGDI9ISD12av/hsqmJt/tQUv01a5+GzOFZqCp/WU3iuugxm/ObY21eugZ274OW5MOVOmPpoxJMAe/NnscnimrLYZLlLayo8Fsdp6gU/4KZ/PcMPnv8rwzN78p0OnUL2Wbh9E8NemMkPX/w7f9m4hl+fV3+zSDRF+8Tks8DlzrnWAM65TOBU4DXv/RYA7/1nQBGQ6pxzwNDg1wE8AVwVibCL+nVl0epNAGzc9jntTk6kU4e2dfbbuO1z9pSURSKhjuw+XXlx7fsAbNhRQLuTEuncPrSpc/u2tG3Tmg07CgB4ce37DOnTVU0xbOqVmcEnu4v5dE8Jh49UsPidD8mudazsPl15YU3lenv13S30794lIi3x0KX5i8+mmse1NH9qit8mrfPwWRwrNYVP6yk85/eF5HbH3r50FYzIAeegXxaUBqAoChf9WJs/i00W15TFJstdWlPhsTZO/Tqdws4vi/kkUMKhigpe2PE+l552Vsg+gUNHbz49uWUrPUi6Kfk4+DAqqicmvfd7gbXAZcGXxgDzvffVQ+Sc6w+0BrZSeft2sff+cHDzLuBbkWhLTU6iYO+X1Z8X7guQlpwUiUOFLS05iYJ9NZqK6zalJSdRWBz42n3UFN2mtA61evYFSOuQdMx9jlR4AvsPkNy2TUR6zHdp/uKyqfq4xuZPTfHbpHV+HD0Gx0pNx9Gl9dQkCndDRtrRzzNSoeiLyB/X2vxZbLK4piw2me7Smgqvy9g4pZ/cjs/Kjt6+/vlXX5J+ct1jXd/9HFZcfSv3fDeb+9e+GpEWkeMRi6dy17yde0zwcwCcc6cAs4Fx3vuKGLSJiIiIiIiIiDRLsz98l4sWzCD3neX8os+AWOeIxOSp3AuB6c65c4GTvfdvAzjn2gMvAZO8928G990DJDvnWgavmvw28Gl939Q5dytwK8Bf/vKXsEJ+NKQvV32/NwCbdhSS0bEd7wW3packUVTjXzaiZfSFfRk5sBcA+TsLyUg5eg9LenLdpqLiAOk1/sWlvn3UFJ2m6mOVBEJ7UpIoKgnUu09RcYAWCY6kkxIpLiuPSI/FLs1ffDdZnD81xW9T9bG0zsNmbazU1DCtp6aX3hkKajxjouALSEuNzLEszp/FpupjGVxTFpusdWlNhcfyOBV+9SWntm1f/fkpJ7ej8KtjH+uF7e/z4AU5wKKI9JxonDd8r7RxUb9i0nsfAJYBMwleLRl8z8kFwJPe+2dr7OuD+44KvnQjlSc26/u+M7z353nvz7v11lvDanlm2XuMnTqHsVPnsPzdj/jhgJ4A9DrzFAL7D0btvSRrmrfyPUbnzmV07lyWrd/KFf17ANA7M4PA/oPsLg1t2l1aRln5QXpnZgBwRf8eLF+/VU0xaKqSv7OALqkpnNqpPS1bJJBzbndWrN8Wss+KDdu48oLK9XbJOWfx1uZPItJitUvzF99NFudPTfHbVEXrPHzWxkpNDdN6anpDBsHCxZVP587Lh3ZtIa1Tw1/XGBbnz2JTFYtrymKTtS6tqfBYHqf39nxOZrsUvp3UgVYJCVyZ2YMln3wUsk9mu5TqPw/9dld2lO6NSIvI8YjFFZNQeUJyAUdv6b4GuBDo5Jy7KfjaTd77POBu4B/OuQeBd4G/RSLo9Q3bGdT7DJ6bdjPlBw8zZdbi6m1z77uOsVPnAPAfo75PTv+zadO6FS898m8sXLWRGc+vjkQSr+VvZ3BWJi9MHkf5ocNMnvNK9bZ594xldO5cAKbNX8rU6y4lsVVLXt+0g1WbdkSkR03hOVLhyZ2/lMdvG0mCcyx8M5+tBXv42eUD2PRxISs2bGPBGxt56IZhPD95HKVl5dw9K/L/SmW1S/MXn01VrM2fmuK3Ses8fBbHSk3h03oKzx1TYG0eFJdA9igYPw4OB991fswIuOh7sPJNyLkW2iTCtHsingTYmz+LTRbXlMUmy11aU+ExN07ec9/aJTx5yTW0cI75H21gS8luJvQdzIY9Bby66yNuPPtcBp2SyeGKI5QcLOeO13W1pMSe883zclN/3i2PxrohxLq/3k6/8dNjnREi77EJJpsAU10Wm8Du/KmpYRbXlMUmsDt/amqYxTVlsQnszp+aGmZxTVlsgsquioJusc4IkZCx2eQ4WWwCW2vKYhPYnT81Nczimqpqynzyv2JcEmrHDXe7WDdYdN5PHjV/cm3d3243OXexePiNiIiIiIiIiIiInOB0YlJERERERERERESiTicmRUREREREREREJOpi9fAbERERERERERGRuOfMv8OkXbpiUkRERERERERERKJOJyZFREREREREREQk6nQrt4iIiIiIiIiISGPpVu5G0xWTIiIiIiIiIiIiEnXO+2Z5WrdZ/qVERERERERERGLIxTrAovPHPWr+PNRbs243OXe6lVtERERERERERKSR9FTuxmu2JyZ73zE91gkhNvxuAv3G22rKe8xmE2Cqy2IT2J0/NTXM4pqy2AR2509NDbO4piw2gd35U1PDLK4pi01gd/4qCrrFOiNEQsZmk+MEttaUxSawu87V1DCLa6qqqcev7TQBvP/AhFgnSDOj95gUERERERERERGRqGu2V0yKiIiIiIiIiIhEnG7lbjRdMSkiIiIiIiIiIiJRpxOTIiIiIiIiIiIiEnW6lVtERERERERERKSR9FTuxtMVkyIiIiIiIiIiIhJ1OjEpIiIiIiIiIiIiUacTkyIiIiIiIiIiIhJ1UX2PSefcMiDXe7+4xmu/AnKAZKA9cAR4yHs/L7h9PPAroCuQ6r3fHYm2Qd1P5+6rsmmRkMA/12zkb0vfCtneqkULpl2bQ89vp1Nctp//nL2Iz/aVRiIlxF2jshmcdQblBw9x3+xX+GBXUZ19epyWxtTrc0hs1ZJV+dt55Nnlaopx08Aep3PXqGwSEhJY8MZGZi2ptZ5atuDB63Po0SWdkrL93D1zEZ/tjfx6AntjZbHJ6vxZGyerXZq/+G2yOHcWm6x2WWwCrfN4bapiaf4m5cLy1dAxBV74e93t3sO0P8DKNdAmEaZNhKxuEUmpw9I4WW6y2qWm+Gyy+Ltz8HdO597Ls0lwCTz79kb++lpo03mnf4uJP7yIbump3PHMIl7J3xLRnhOK3mOy0aJ9xeTTwJhar40BHgZu8N5nAcOA3zvnkoPbXwcuAXZGKirBOSaNHMrP//s5RjzyBJed050z0zuG7DPygixKvzrA5Q/PYvbKd5hwxeBI5VQb3DOTLqnJDJ8yiweefpVJY4bWu9+k0Rcz9aklDJ8yiy6pyQzqmammGDYlOMfEa4Zy25+eY+SDTzDsu905MyN0PV09IIvS/QcYPmUWc5a9wy9HRH49gb2xsthkdf6sjZPVLs1f/DZZnDuLTVa7LDaB1nm8NlWxNn9XXQYzfnPs7SvXwM5d8PJcmHInTH00Ihl1WBsnq01Wu9QUn00Wf3cmOMevrxzKrU8+x5V/fILL+3Sna2po02clXzLxn6/w0oYPItoicjyifWLyWeBy51xrAOdcJnAq8Jr3fguA9/4zoAhIDX7+rvd+RySjenfJ4OM9xezaW8LhIxX877sfMiSra8g+Q3p15fl1mwBYsn4LF5zVJZJJAGT36cqLa98HYMOOAtqdlEjn9m1D9uncvi1t27Rmw44CAF5c+z5D+nSt873UFL2mXpkZfLK7mE/3VK6nxe98SHatY2X36coLayrX06vvbqF/98ivp6rjWhori01W58/aOFnt0vzFb5PFubPYZLXLYlPVMbXO46+p5nEtzd/5fSG53bG3L10FI3LAOeiXBaUBKNoTkZQQ1sbJapPVLjXFZ5PF3519vh08r7GvhENHKli04UOG9ght+qy4lM2Fu6mo0OV9YkdUT0x67/cCa4HLgi+NAeZ776t/Kpxz/YHWwNZodaV1SKKg+MvqzwtLAqR3SArdp/3RfY5UeAL7D5Dctk1ku5KTKNhXo6s4QFpyUp19CosDX7uPmqLblNahVs++AGm111ONfaK1nsDeWFlssjp/1sbJapfmL36bLM6dxSarXRabQOs8Xpuqj2ts/hpSuBsy0o5+npEKRV9E/rgWx8lik9UuNcVnk8XfnWntkygoqXVeo11sfh+eiJy3/2FVLB5+U/N27jHBzwFwzp0CzAbGee8rjuebOududc6tc86tmzFjRpPFioiIiIiIiIiISNOL6sNvghYC051z5wIne+/fBnDOtQdeAiZ579883m/qvZ8BVJ2R9H+8Y3rYX1tUEiCjxn0Z6R2SKCwJhO5TWrlPYUmAFgmOpJMSKS4rP97MBo2+sC8jB/YCIH9nIRkpNbqSkygqrtVVHCC9xr8C1bePmqLTVH2skkBoT0oSRbXXU3CfouLIriewOVYWm6qPZWj+rI6T1S7Q/MVjU/WxDM2d5SarXZaatM7ju8ny/DUkvTMU1HgWR8EXkJYamWNZHCeLTVa71BS/TdXHMva7E4LnLDrUOq/xZWx+H4ocj6hfMem9DwDLgJkEr5YMvufkAuBJ7/2z0W7a+EkBp3dO4Vsd29OyRQKXndOd5fnbQvZZnr+N4VnJgewAACAASURBVOf1BOAHfc5i7ZZPItIyb+V7jM6dy+jcuSxbv5Ur+vcAoHdmBoH9B9ldWhay/+7SMsrKD9I7MwOAK/r3YPn6pr0LXk3HJ39nAV1SUzi1U+V6yjm3OyvWh66nFRu2ceUFlevpknPO4q3NkVlPYHOsLDZVsTR/VsfJahdo/uKxqYqlubPcZLXLUpPWeXw3WZ6/hgwZBAsXVz6dOy8f2rWFtE6ROZbFcbLYZLVLTfHbVMXa706ADZ8WcHqnFL6V3J5WLRL4Ye/uLPtgW8NfKE3De/sfRsXiikmoPCG5gKO3dF8DXAh0cs7dFHztJu99nnPuP4C7gAxgvXNukff+lqaMOVLhmfbPpfz51pG0cI4Fa/PZWriH23IGkL+rkOX52/jnmo08fO0wXpo4jpKvyrlr9qKmTKjXa/nbGZyVyQuTx1F+6DCT57xSvW3ePWMZnTsXgGnzlzL1uktJbNWS1zftYNWmHWqKYdORCk/u/KU8fttIEpxj4Zv5bC3Yw88uH8CmjwtZsWEbC97YyEM3DOP5yeMoLSvn7lmRX09gb6wsNlmdP2vjZLVL8xe/TRbnzmKT1S6LTaB1Hq9NVazN3x1TYG0eFJdA9igYPw4OH67cNmYEXPQ9WPkm5FwLbRJh2j0RyajD2jhZbbLapab4bLL4u/NIhefBF5fy1xtHkpDg+Oc7+XxUtIdfDB3Axs8KWfbBNnp9K50//vhK2p/UhiFnn8kvhg7gyj8+GdEukYY4b/is6Tfgex/HrdzRsOF3E+g33lZT3mM2mwBTXRabwO78qalhFteUxSawO39qapjFNWWxCezOn5oaZnFNWWwCu/NXUdAt1hkhEjI2mxwnsLWmLDaB3XWupoZZXFNVTT1+bacJ4P0HJrhYN1j0vbG/M39y7c25d5icu1hdMSkiIiIiIiIiIhL3LD/12rpYPJVbRERERERERERETnA6MSkiIiIiIiIiIiJRp1u5RUREREREREREGku3cjearpgUERERERERERGRqNOJSREREREREREREYk6nZgUERERERERERGRqNN7TIqIiIiIiIiIiDSSq4h1Qfxy3jfLd+hsln8pEREREREREZEYcrEOsGjg6N+ZPw/1xrw7TM5ds71isvsD02OdEOLDX0+g33hbTXmP2WwCTHVZbAK786emhllcUxabwO78qalhFteUxSawO39qapjFNWWxCezOn8WmioJusc4IkZCxGbC1prTOw6em8FhcU1VN59/8aIxLQr018/ZYJ0gz02xPTIqIiIiIiIiIiESc+esl7dLDb0RERERERERERCTqdGJSREREREREREREok63couIiIiIiIiIiDSS063cjaYrJkVERERERERERCTqdGJSREREREREREREok63couIiIiIiIiIiDSW173cjaUrJkVERERERERERCTqonrFpHNuGZDrvV9c47VfATlAMtAeOAI85L2fF9w+FzgPOASsBX7qvT/U1G3f73o6k3KySXAJPPPuRv77jbdCtp/X5Vvce+lFdE9P5fZ/LmLx+1uaOqGOgT1O565R2SQkJLDgjY3MWhLa1KplCx68PoceXdIpKdvP3TMX8dne0hOuCeCuUdkMzjqD8oOHuG/2K3ywq6jOPj1OS2Pq9TkktmrJqvztPPLs8oj1WB0n0FiFS+MUPmtjZbHJ6vxZGyeLTVbnDjRW4bI2ThabrM4d2Bsri02TcmH5auiYAi/8ve5272HaH2DlGmiTCNMmQla3iOUANteUxSarXRabwN7PntWmO64dwqDelU1T/raYDz+u2/SzkYO4fGBP2p2cyEU/fyyiPSINifYVk08DY2q9NgZ4GLjBe58FDAN+75xLDm6fC5wN9AZOAm5p6qgE57hv2FBueeo5Ln/8Ca7o1Z2unTuG7PN5yZdMfP4VXtz4QVMf/phNE68Zym1/eo6RDz7BsO9258yM0KarB2RRuv8Aw6fMYs6yd/jliMEnXBPA4J6ZdElNZviUWTzw9KtMGjO03v0mjb6YqU8tYfiUWXRJTWZQz8yI9FgdJ9BYhUvjFD5rY2Wxyer8WRsni01W5w40VuGyNk4Wm6zOHdgbK6tNV10GM35z7O0r18DOXfDyXJhyJ0x9NGIpgM01ZbHJapfFJrD5s2exaWDvM+iSnszIiTOZ9sSr3HPDxfXu91reNm584KmIdZyInLf/YVW0T0w+C1zunGsN4JzLBE4FXvPebwHw3n8GFAGpwc8X+SAqr5j8dlNH9Tk1g537itlVXMKhigpeyv+Qi7t3Ddnn05JSPizaTUWU3jegV2YGn+wu5tM9JRw+UsHidz4ku09oU3afrrywZhMAr767hf7du5xwTVXHfHHt+wBs2FFAu5MS6dy+bcg+ndu3pW2b1mzYUQDAi2vfZ0it9qZidZyqjquxapjGKXzWxspik9X5szZOFpuszl3VcTVWDbM2ThabrM5d1XEtjZXVpvP7QnK7Y29fugpG5IBz0C8LSgNQtCdiOSbXlMUmq10Wm6qOae1nz2LTRed05aU3Kudm47bPaXdyIp06tK2z38Ztn7OnpCxiHSLHI6onJr33e6k8uXhZ8KUxwPzgSUcAnHP9gdbA1ppf65xrBVwPvNzUXentkygo/bL688LSAOntkpr6MMclrUMSBftqNO0LkNYh6Zj7HKnwBPYfILltmxOqCSAtuVZXcYC05KQ6+xQWB752nybrMTpOoLEKu0vjFH6bsbGy2GR1/qyNk8Umq3MHGquwu4yNk8Umq3MH9sbKalNDCndDRtrRzzNSoeiLyB3P4pqy2GS1y2IT2PzZs9iUmpJE4d6jTUV7A6SlxPbchkhDYvHwm5q3c48Jfg6Ac+4UYDYwzntfUevr/gSs9N6/Vt83dc7d6pxb55xbN2PGjAhki4iIiIiIiIiI1OLj4MOoqD78JmghMN05dy5wsvf+bQDnXHvgJWCS9/7Nml/gnJtM5a3dPz3WN/XezwCqzkj63z0wPeygwtIAGe2P3v+Q3j6Jwi8DX/MVkVdUEiAjpUZTShJFJYF69ykqDtAiwZF0UiLFZeUnRNPoC/sycmAvAPJ3FoZ2JSdRVFyrqzhAeo1/mapvn6ZiaZxAYxUujVP4LI6VxabqYxmaP4vjZLGp+liG5g40VuGyOE4Wm6qPZWjuwOZYWWw6HumdoaDGsy8KvoC01Mgdz9qastpktctSk8WfPYtNPxral6su7A3Apu2FpHc82pTWMYmifbE9tyHSkKhfMem9DwDLgJkEr5YMvufkAuBJ7/2zNfd3zt1C5VO7f1zPVZRNYsNnBWR2TOHbye1plZDA5VndWbp5WyQOFbb8nQV0SU3h1E7tadkigZxzu7NifWjTig3buPKCngBccs5ZvLX5kxOmad7K9xidO5fRuXNZtn4rV/TvAUDvzAwC+w+yuzT0/TJ2l5ZRVn6Q3pkZAFzRvwfL12+t832bgqVxAo1VuDRO4bM4VhabqliaP4vjZLGpiqW5A41VuCyOk8WmKpbmDmyOlcWm4zFkECxcXPl07rx8aNcW0jpF7njW1pTVJqtdlpos/uxZbHpm6XuMvX8OY++fw/J3P+LygZVz0+vMUwh8dVDvJSnmxeKKSag8IbmAo7d0XwNcCHRyzt0UfO0m730e8GdgJ7DaOQfwT+/91KaMOeI9U19eyl+vHUkL5/if9/L56Is9/MdFA9j4eSFLN2+j9ynpPHbNlbRv04YhZ53JLy4awBV/frIpM0KbKjy585fy+G0jSXCOhW/ms7VgDz+7fACbPi5kxYZtLHhjIw/dMIznJ4+jtKycu2ctiliP1SaA1/K3Mzgrkxcmj6P80GEmz3mletu8e8YyOncuANPmL2XqdZeS2Kolr2/awapNOyLSY3WcQGMVLo1T+KyNlcUmq/NnbZwsNlmdO9BYhcvaOFlssjp3YG+srDbdMQXW5kFxCWSPgvHj4PDhym1jRsBF34OVb0LOtdAmEabdE7EUwOaasthktctiE9j82bPY9Pr67QzqcwYLcm+m/OBhps5cXL1t7v3XMfb+OQD84kffJ+eCs2nTuhUv/vbfWPjaRv574eqIdYl8Heej9JTpKPPdj+NW7mj48NcT6DfeVlPeYzabAFNdFpvA7vypqWEW15TFJrA7f2pqmMU1ZbEJ7M6fmhpmcU1ZbAK782exqaKgW6wzQiRkbAZsrSmt8/CpKTwW11RV0/k3PxrjklBvzbzdxbrBou9f/VvzJ9deW3CnybmLxcNvRERERERERERE5ASnE5MiIiIiIiIiIiISdbF6j0kREREREREREZH41zzfJjEqdMWkiIiIiIiIiIiIRJ1OTIqIiIiIiIiIiEjU6VZuERERERERERGRRnK6k7vRdMWkiIiIiIiIiIiIRJ1OTIqIiIiIiIiIiEjUOd88nxzULP9SIiIiIiIiIiIx5GIdYNGFw39j/jzUyuf/0+TcNdv3mOx95/RYJ4TY8NsJ9BtvqynvMZtNgKkui01gd/7U1DCLa8piE9idPzU1zOKastgEdudPTQ2zuKYsNoHd+VNTw6rWVEVBtxiXHJWQsRnQOg+HmsJj8XdnVdOAa38X45JQq5+6I9YJ0szoVm4RERERERERERGJumZ7xaSIiIiIiIiIiEik6ancjacrJkVERERERERERCTqdGJSREREREREREREok4nJkVERERERERERCTq9B6TIiIiIiIiIiIijVWhN5lsLF0xKSIiIiIiIiIiIlGnE5MiIiIiIiIiIiISdVG9lds5twzI9d4vrvHar4AcIBloDxwBHvLezwtu/xtwHuCAzcBN3vtAU7cN6n46d4/IpkVCAv9cs5G/LXsrZPt3z/wWdw2/iG6npHLX3EUsWb+lqRPqddeobAZnnUH5wUPcN/sVPthVVGefHqelMfX6HBJbtWRV/nYeeXa5mmLcNLDH6dw1KpuEhAQWvLGRWUtC11Orli148PocenRJp6RsP3fPXMRne0sj1lOTxio+m0Bzdzw0Vmpqzk1Wuyw2gb3fBxabrM6d1S7NX8Mm5cLy1dAxBV74e93t3sO0P8DKNdAmEaZNhKxuEU0yOU5VrK0pNcV304QbhjCw3xmUHzzMA39+mc076jb99JpBXPb9LNq1TeTim/8Y0Z4Thu7kbrRoXzH5NDCm1mtjgIeBG7z3WcAw4PfOueTg9gne+77e+z7Ax8D4po5KcI5JVw/l5399jhG/eYLLzunOmekdQ/b5fN+X/HreKyx694OmPvwxDe6ZSZfUZIZPmcUDT7/KpDFD691v0uiLmfrUEoZPmUWX1GQG9cxUUwybEpxj4jVDue1PzzHywScY9t3unJkRup6uHpBF6f4DDJ8yiznL3uGXIwZHpKU2jVV8NoHm7nhorNTUnJusdllsAnu/Dyw2WZ07q12av/BcdRnM+M2xt69cAzt3wctzYcqdMPXRyPZYHSewt6bUFN9NA/qdwWkZKfzo9pnk/nUJd918Sb37rXpnGz/59dyIdUjz45zr6Jxb4pzbEvzflHr26eecW+2cy3fOrXfOjQ7ne0f7xOSzwOXOudYAzrlM4FTgNe/9FgDv/WdAEZAa/Lw0uK8DTiIC56F7d8ng4z3F7NpbwuEjFfxv3ocMyeoass9n+0rZ/PluvI/eafDsPl15ce37AGzYUUC7kxLp3L5tyD6d27elbZvWbNhRAMCLa99nSJ+udb6XmqLX1Cszg092F/Ppnsr1tPidD8mudazsPl15Yc0mAF59dwv9u3eJSEttGqv4bKo6puYuPBorNTXnJqtdFpuqjmnp94HFJqtzZ7VL8xee8/tCcrtjb1+6CkbkgHPQLwtKA1C0J3I9Vsep6riW1pSa4rvpwu925X9fq1zH+R99TtLJiXRKbltnv/yPPmdPcVnEOqRZugf4l/f+LOBfwc9r+4pjX3R4TFE9Mem93wusBS4LvjQGmO9rnO1zzvUHWgNba7w2CygAzgaa/DrjtA5JFBR/Wf15YXGA9A5JTX2Y45aWnETBvtCutOSkOvsUFge+dh81RbcprUOtnn0B0mqtp5r7HKnwBPYfILltm4j0hBxXYxWXTaC5O642jZWamnGT1S6LTWDv94HFJrNzZ7VL89ckCndDRtrRzzNSoeiLyB3P8jhZW1Nqiu+m1JQkCvcebfpi75ekpsT+3MaJwHn7H9/QCOCJ4J+fAK6qvYP3fvOxLjr8OrF4+E3N27nHBD8HwDl3CjAbGOe9r6h63Xs/jsorK98H6r0U1Dl3q3NunXNu3YwZMyLVLiIiIiIiIiIiEldqnjcLftx6HF+e7r3/PPjnAiC9gWPVuejwWKL68JughcB059y5wMne+7cBnHPtgZeASd77N2t/kff+iHPuH8BdwKx6ts8Aqs5I+j/eOT3soKKSABk17jVIT06isKTJn68TltEX9mXkwF4A5O8sJCMltKuoOLSrqDhAeo1/calvHzVFp6n6WCWB0J6UJIpqraeqfYqKA7RIcCSdlEhxWXlEejRW8dukuQufxkpNJ0qT1S5LTRZ/H1hsqj6Wobmz2qX5a3rpnaGgxvM4Cr6AtAavqWk8a+NkcU2pKX6b/s8P+jF8SG8A3t9WQHrHo02pHdvxxb7YnNsQe2qdN6vDOfcqkFHPpkm1vo937tjXYNa46PDGmhcdHkvUr5gMPlF7GTCT4NWSwfecXAA86b1/tmpfV+k7VX8GhgNN/vSZjZ8UcHrnFL7VsT0tWyRwWb/uLM/f1tSHCcu8le8xOncuo3Pnsmz9Vq7o3wOA3pkZBPYfZHdp6PtA7C4to6z8IL0zK9fOFf17sHx9gyek1RSBpir5OwvokprCqZ0q11POud1ZsT50Pa3YsI0rL+gJwCXnnMVbmz+JSAtorOK5SXMXPo2Vmk6UJqtdlpos/j6w2FTF0txZ7dL8Nb0hg2Dh4sqnc+flQ7u2kNYpcsezNk4W15Sa4rfpf5bkceO9s7nx3tmsXPcRl32/ch1nfecUyvYf0HtJRov39j8a/Cv4S7z3ver5WAgUBk84Vp14rPu4dxq+6LA+sbhiEipPSC7g6C3d1wAXAp2cczcFX7sJWA88EfyLOeA94GdNHXOkwjNtwVL+/G8jaeEcC97KZ2vhHm7LGUD+J4Us37SNrNPS+f9uvJJ2J7fhop5n8vNLB3D1b59s6pQQr+VvZ3BWJi9MHkf5ocNMnvNK9bZ594xldG7lU7SmzV/K1OsuJbFVS17ftINVm3aoKYZNRyo8ufOX8vhtI0lwjoVv5rO1YA8/u3wAmz4uZMWGbSx4YyMP3TCM5yePo7SsnLtnLYpIS20aq/hsAs3d8dBYqak5N1ntstgE9n4fWGyyOndWuzR/4bljCqzNg+ISyB4F48fB4cOV28aMgIu+ByvfhJxroU0iTKvvEQpNyOo4gb01pab4bnojbzsD+53JM9N/woEDh3jwL4urtz0x7XpuvHc2ALf9+EIuHXg2bVq3YuEfb+X55Rv42/+sjliXNAvPAzcCucH/XVh7h2NddNgQF82nTEeR730ct3JHw4bfTqDfeFtNeY/ZbAJMdVlsArvzp6aGWVxTFpvA7vypqWEW15TFJrA7f2pqmMU1ZbEJ7M6fmhpWtaYqCrrFuOSohIzNgNZ5ONQUHou/O6uaBlz7uxiXhFr91B0u1g0WZQ/7L/Mn15a/fHej58451wmYD3QBdgLXeO/3OufOA/7de3+Lc+46Kt96Mb/Gl97kvc/7uu8dqysmRURERERERERE4l4TPPXaNO/9HuDiel5fB9wS/PMcYM7xfu9YPJVbRERERERERERETnA6MSkiIiIiIiIiIiJRp1u5RUREREREREREGquZ38odSbpiUkRERERERERERKJOJyZFREREREREREQk6nRiUkRERERERERERKJO7zEpIiIiIiIiIiLSSM7rTSYby/nmOXjN8i8lIiIiIiIiIhJDLtYBFg39Qa7581BLl9xjcu6a7RWT5/x8eqwTQrz7pwn0G2+rKe8xm02AqS6LTWB3/tTUMItrymIT2J0/NTXM4pqy2AR2509NDbO4piw2gd35U1PDLK6pqqaKgm4xLgmVkLHZ1DiB3TVlsQlsrvN+v7DTBJD3xwmxTpBmptmemBQREREREREREYm4ilgHxC89/EZERERERERERESiTicmRUREREREREREJOp0K7eIiIiIiIiIiEgj6ancjacrJkVERERERERERCTqdGJSREREREREREREok63couIiIiIiIiIiDSW7uRuNF0xKSIiIiIiIiIiIlEX1SsmnXPLgFzv/eIar/0KyAGSgfbAEeAh7/28Wl/7B+Bm731SpPru+lE2g7LOoPzQISY/+QoffFJUZ58ep6Ux5YYcElu15PX87TzyzPJI5VQ2jcpmcNYZlB88xH2zX+GDXfU3Tb2+smlV/nYeeVZNFposdg3scTp3jcomISGBBW9sZNaSt0K2t2rZggevz6FHl3RKyvZz98xFfLa3NGI9VayNk8Umq3MHGqtwWRsnNYXH6nqy2mVt/tQU311a5+GxOE4WmyblwvLV0DEFXvh73e3ew7Q/wMo10CYRpk2ErG4RTQJsjhXYW+dqCs/AHqdz1/8JrqfVX7OeTguup1nR+28GkWOJ9hWTTwNjar02BngYuMF7nwUMA37vnEuu2sE5dx6QEsmwwVmZdElLZsT9s3hw7qvcO2Zovfvd++OLeWDuEkbcP4suackM6pkZuaaemXRJTWb4lFk88PSrTDpG06TRFzP1qSUMnzKLLqlqstBksSvBOSZeM5Tb/vQcIx98gmHf7c6ZGR1D9rl6QBal+w8wfMos5ix7h1+OGByRlpqsjZPFJqtzBxqrcFkbJzWFx+p6stplbf7UFN9dWufhsThOFpsArroMZvzm2NtXroGdu+DluTDlTpj6aMSTzI6VtXWupvAkOMfEHw3ltsefY+RDX7OevjrA8KnR/W+GE4L39j+MivaJyWeBy51zrQGcc5nAqcBr3vstAN77z4AiIDW4TwvgN8BdkQy7qE9XXlzzPgAbdhTQ7uREOrdvG7JP5/ZtadumNRt2FADw4pr3ye7bNWJN2X268uLaGk0nhdG09n2G9FFTrJssdvXKzOCT3cV8uqeEw0cqWPzOh2TXOlZ2n668sGYTAK++u4X+3btEpKX2MS2Nk8Umq3NXdVyNVcOsjZOawmN1PVntsjZ/aorvLq3z8FgcJ4tNAOf3heR2x96+dBWMyAHnoF8WlAagaE9km6yOlbV1rqbw9Dq91np6+0Oye9daT71rrKe8LfTvFp3/ZhD5OlE9Mem93wusBS4LvjQGmO/90VO3zrn+QGtga/Cl8cDz3vvPI9mWlpxEwb4vqz8v3BcgLTmpzj5FxYGv3SeiTcX1NxXWbKpnHzVFv8liV1qHetZ4h6Rj7nOkwhPYf4Dktm0i0lN9TGPjZLHJ6tyBxirsLmPjpKYwe6yuJ6tdxuZPTfHdpXUeZo/BcbLYFI7C3ZCRdvTzjFQo+iKyx7Q6VtbWuZqasKdDEgXFtn72RGLx8Juat3OPCX4OgHPuFGA2MM57X+GcOxX4EfDHhr6pc+5W59w659y6GTNmRCBbREREREREREREmkpUH34TtBCY7pw7FzjZe/82gHOuPfASMMl7/2Zw33OA7wAfOecATnbOfeS9/07tb+q9nwFUnZH0j/98eoMh11zYl5GDegGQv7OQjJSj1/anp4ReHQlQVOtfHOrb55safWFfRg48RlNy/U3pNZvq2UdN0Wmy3AVQVBKou8ZLAvXuU1QcoEWCI+mkRIrLypu8xeI4WWyqPpahuQONVbgsjpOajo+l9WS1y+L8qSn+u0DrPFyWxslyUzjSO0NBjeeWFHwBaamRPaalsbK4ztV0fIqKAw33lATISLb1s9dcOLtv4Whe1K+Y9N4HgGXATIJXSwbfc3IB8KT3/tka+77kvc/w3md67zOBr+o7KdlY81e+x5iH5zLm4bksW7+VKy7oAUDvzAwC+w+yu7QsZP/dpWWUlR+kd2YGAFdc0IMV67fW+b7fxLyV7zE6dy6jc4NN/Y+zqX8PlqspJk2WuwDydxbQJTWFUzu1p2WLBHLO7c6K9dtC9lmxYRtXXtATgEvOOYu3Nn8SkRaL42SxqYqluQONVbgsjpOajo+l9WS1y+L8qSn+u0DrPFyWxslyUziGDIKFiyufD5GXD+3aQlqnyB7T0lhZXOdqOj75H9daT9/tzooNX7Oe+tn42ROJxRWTUHlCcgFHb+m+BrgQ6OScuyn42k3e+7xoBa3auJ3BWZk8P2Uc5QcPc//sV6q3/WPiWMY8PBeAh/+xlCk3XEpiq5a8nr+DVfk7Itb0Wn5l0wuTx1F+6DCT5xxtmnfPWEbnVjZNm7+UqdcFmzbtYNUmNcW6yWLXkQpP7vylPH7bSBKcY+Gb+Wwt2MPPLh/Apo8LWbFhGwve2MhDNwzj+cnjKC0r5+5ZiyLSUpO1cbLYZHXuQGMVLmvjpKbwWF1PVruszZ+a4rtL6zw8FsfJYhPAHVNgbR4Ul0D2KBg/Dg4frtw2ZgRc9D1Y+SbkXAttEmHaPRFPMjtW1ta5msJzpMKT+8xSHv95rfX0w+B62riNBauD6+m+cZR+Fb3/ZhD5Os4bfmT4N+DPCeNW7mh6908T6DfeVlPeYzabAFNdFpvA7vypqWEW15TFJrA7f2pqmMU1ZbEJ7M6fmhpmcU1ZbAK786emhllcU1VNFQXdYlwSKiFjs6lxArtrymIT2Fzn/X5hpwkg748TXKwbLLrkwofMn1x7deUkk3MXi4ffiIiIiIiIiIiIyAlOJyZFREREREREREQk6mL1HpMiIiIiIiIiIiJxz1XEuiB+6YpJERERERERERERiTqdmBQREREREREREZGo063cIiIiIiIiIiIijeXNP5TbLF0xKSIiIiIiIiIiIlGnE5MiIiIiIiIiIiISdc43z8tNm+VfSkREREREREQkhlysd3v+ZgAAIABJREFUAyz6wcAHzZ+HWvLG/zU5d7piUkRERERERERERKKu2T785uKhD8c6IcS/lk6k3/jpsc4IkffYBJNNgKkui01gd/7U1DCLa8piE9idPzU1zOKastgEdudPTQ2zuKYsNoHd+VNTwyyuKYtNUNlVUdAt1hkhEjI2mxwni01ga01VNZ13y6MxLgm17q+3xzpBmhldMSkiIiIiIiIiIiJR12yvmBQREREREREREYk01zyf3xIVumJSREREREREREREok4nJkVERERERERERCTqdCu3iIiIiIiIiIhIY+lW7kbTFZMiIiIiIiIiIiISdToxKSIiIiIiIiIiIlEX1Vu5nXPLgFzv/eIar/0KyAGSgfbAEeAh7/284Pa/AxcBJcEvucl7nxeJvtvG/4ALLujKgfJDPPLIi2zZUhiyPTGxJfdNvppTT02hoqKC1as/4q//vTwSKdXuGpXN4KwzKD94iPtmv8IHu4rq7NPjtDSmXp9DYquWrMrfziPPqslCk8WugT1O565R2SQkJLDgjY3MWvJWyPZWLVvw4PU59OiSTknZfu6euYjP9pZGrKeKxklNJ0KX1rmamprWVHw2gb25s9ql+QuPxik8FsdpUi4sXw0dU+CFv9fd7j1M+wOsXANtEmHaRMjqFtEkwOZYWWwCe+u8yp0/HsKg3pVd989czIcf1+06+/Q07h83jMTWLXl9w3Z++/SyiHc1axWxDohf0b5i8mlgTK3XxgAPAzd477OAYcDvnXPJNfb5T+99v+BHRE5K9r+gK9/+Vgo3XP9nHn30f/nlr4bVu98z89cw7qYZ/PTWmfTK+jb9+58ZiRwABvfMpEtqMsOnzOKBp19l0pih9e43afTFTH1qCcOnzKJLajKDemaqKcZNFrsSnGPiNUO57U/PMfLBJxj23e6cmdExZJ+rB2RRuv8Aw6fMYs6yd/jliMERaalJ46SmE6FL61xNTU1rKj6bwN7cWe3S/IVH4xQeq+N01WUw4zfH3r5yDezcBS/PhSl3wtRHI55kcqwsNoG9dV5lUO8zOC0tmavvnclDT77KxOsurne/idddwoNPLuHqe2dyWloyA3tFtkvkWKJ9YvJZ4HLnXGsA51wmcCrwmvd+C4D3/jOgCEiNZtiggWfxypKNALz//mckJSXSsWPbkH0OHDhMXt7HABw+XMGWLQV0Tm0XsabsPl15ce37AGzYUUC7kxLp3D60qXP7trRt05oNOwoAeHHt+wzp01VNMW6y2NUrM4NPdhfz6Z4SDh+pYPE7H5Jd61jZfbrywppNALz67hb6d+8SkZbax9Q4qam5d2mdq6mpaU3FZ1PVMS3NndUuzV94NE7hsTpO5/eF5K/5z8mlq2BEDjgH/bKgNABFeyLbZHGsLDZVHdPSOq9yUb+uLFpdORYbt31Ou5MT6dQhtKtTh8qujds+B2DR6k1kn/OdiHaJHEtUT0x67/cCa4HLgi+NAeZ7f/TxRc65/kBrYGuNL33IObfeOTfdOZcYibbOndvxRdHRS72/+OJLOnc+9v9LtG2byPcGfId339kZiRwA0pKTKNj3ZfXnhcUB0pKT6uxTWBz42n3UFP0mi11pHWr17AuQ1iHpmPscqfAE9h8guW2biPRUH1PjpKYToUvrXE1N3aY1FZdNYG/urHZp/sLs0TiF12N0nBpSuBsy0o5+npEKRV9E9pgWx8piE9hb51VSk5Mo2FtrvOrrqjWmqRHuau6c9+Y/rIrFw29q3s49Jvg5AM65U4DZwDjvfdUd+hOBs4HzgY7A3fV9U+fcrc65dc65dTNmzIhUOwAJCY7/+39HsGDB23z+eXFEjyUiIiIiIiIiItIcRfXhN0ELgenOuXOBk733bwM459oDLwGTvPdvVu3svf88+McDzrlZwJ31fVPv/Qyg6oykn/ePhxsMGTHiXH54eT8APvzwc1LT2ldvS01tx+7dX9b7dbffcRm7Pt3HP//nrXq3fxOjL+zLyIG9AMjfWUhGytGrNtOTkyiq8a8tAEXFAdJr/MtGffuoKTpNlrsAikoCoT0pSRSVBOrdp6g4QIsER9JJiRSXlTd5i8ZJTSdCl9a5mpqa1lT8NlmdO6tdoPkLl8YpPJbG6Xikd4aCGs8sKfgC0iL8hmcWx8pSk9V1/qMhfbnq+70B2LSjkIyO7Xiv6pgpx+iqNaZfROjnT6QhUb9i0nsfAJYBMwleLRl8z8kFwJPe+2dr7h+8ihLnnAOuAjY2VcvChe/w01tn8tNbZ/L6qs1c+oPKXzA9epxKWdkB9u4tq/M1426+kLZtE/nT/1vSVBkh5q18j9G5cxmdO5dl67dyRf8eAPTOzCCw/yC7S0ObdpeWUVZ+kN6ZGQBc0b8Hy9dvrfN91RT5JstdAPk7C+iSmsKpndrTskUCOed2Z8X6bSH7rNiwjSsv6AnAJeecxVubP4lIi8ZJTSdCl9a5mpqa1lT8NlmdO6tdoPkLl8YpPJbG6XgMGQQLF1c+nTsvH9q1hbROkT2mxbGy1GR1nT+z7D3GTp3D2KlzWP7uR/xwQOVY9DrzFAL7D7KnJLRrT0llV68zTwHghwN6siIvMj9/Jwzv7X8YFYsrJqHyhOQCjt7SfQ1wIdDJOXdT8LWbgk/gnuucSwUckAf8eySC1qzZygUXdGX2nH+nvPwQv3nkpeptf5lxMz+9dSadO7fjuusGsXPnbv78l5sBWPjc2yxa9N6xvu038lr+dgZnZfLC5HGUHzrM5DmvVG+bd89YRufOBWDa/KVMve5SElu15PVNO1i1aUdEetQU311HKjy585fy+G0jSXCOhW/ms7VgDz+7fACbPi5kxYZtLHhjIw/dMIznJ4+jtKycu2ctikhLTRonNZ0IXVrnampqWlPx2QT25s5ql+YvPBqn8FgdpzumwNo8KC6B7FEwfhwcPly5bcwIuOh7sPJNyLkW2iTCtHsinmRyrCw2gb11XuX1DdsZ1PsMnpt2M+UHDzNl1uLqbXPvu46xU+cAkDvnX9x/cw6JrVryxsYdvL5he0S7RI7FecNnTb8Bf/HQhm/ljqZ/LZ1Iv/HTY50RIu+xCSabAFNdFpvA7vypqWEW15TFJrA7f2pqmMU1ZbEJ7M6fmhpmcU1ZbAK786emhllcUxaboLKroqBbrDNCJGRsNjlOFpvA1pqqajrvlkdjXBJq3V9vd7FusCjnvPvNn1xbvO5+k3MXqysmRURERERERERE4l/zvOgvKmLxVG4RERERERERERE5wenEpIiIiIiIiIiIiESdTkyKiIiIiIiIiIhI1Ok9JkVERERERERERBqrItYB8UtXTIqIiIiIiIiIiEjU6cSkiIiIiIiIiIiIRJ1u5RYREREREREREWkk532sE+KW881z8JrlX0pEREREREREJIZcrAMsGtbvPvPnoV7Om2py7nQrt4iIiIiIiIiIiERds72V+7v/Nj3WCSHe/u8J9BtvqynvMZtNgKkui01gd/7U1DCLa8piE9idPzU1zOKastgEdudPTQ2zuKYsNoHd+VNTwyyuKYtNYHf+Kgq6xTojRELGZpPjBLbWlMUmONoltTTPu5GjQldMioiIiIiIiIiISNTpxKSIiIiIiIiIiIhEXbO9lVtERERERERERCTidCt3o+mKSREREREREREREYk6nZgUERERERERERGRqNOt3CIiIiIiIiIiIo2lW7kb7f9v777jo6ry/4+/TihBCRBKig2iiEoHCwi4ELAAFkBWBUVU3BXXtit21FVABFa/q/tTd1V0CYroigWxIArSLAgqUhJQuoKQUBMMEhByfn/cCZmEhAyQO/cE3s/HYx5Mucm8+Zwz596cuUV7TIqIiIiIiIiIiEjURXWPSWPMDGCUtfaTsOfuBLoC8UBNYC/wuLX2zdDrBhgOXBl67Xlr7TN+5Lu3byodmp9M3u7fGZL2KT/8vHG/Zc6on8jQAV2JrVqZLxev5sn/zfQjyj73XZHKeU29TI+M+5Qf1u2fqfFJiQzr35XYKpX5ImM1T7ztX6b2jRtw3xWpxMTEMPGrdNKmflPk9SqVKzG8f1ca108iZ8dO7h8zmfVbt/uWp4DqFDnVKjKu1cnFTK62HahWkVKdIqdaVcxMoLY7GK7VSpki42qfcq1OruZyrf0eGgUz50Cd2vDB2P1ftxZGPAOz50K1WBgxGJqe5lucfVyrUwHX+pPruURKEu09Jt8A+hZ7ri8wErjOWtsU6Ab8yxgTH3r9BuAk4AxrbWPgf34E69AshZMS4+n1UBrDx01jcL8uJS43+NrzeWzcVHo9lMZJifG0b5biRxwAzmuSQv2EeHoMTeOxN6bxUN+SMz3U53yGvT6VHkPTqJ8QT4cm/mSKMYbBV3Xhtv+8R+/hr9DtrNM5JblOkWUub9eU7Tt30WNoGq/NmM/fep7nS5ZwqlPkVKvIuFYnFzO52nagWkVKdYqcalUxM4Ha7mC4Vitlioyrfcq1Ormay8X269UdRj9Z+uuz58JP62DKeBh6Dwx7ytc4gJt1Avf6k+u5REoT7YnJt4FLjDFVAYwxKcDxwOfW2uUA1tr1wEYgIfQztwDDrLX5odf3n+ovB51aNeSjr5cCkL4qk7hjY6lXq3qRZerVqk5ctaqkr8oE4KOvl5LaqqEfcQBIbdGQD+d5mRavyaTGMbHUq1ksU83qVK9WlcVrvEwfzltK5xb+ZGqWkszazdn8siWHPXvz+WT+j6QWe6/UFg35YO4SAKZ9v5w2p9f3JUvx91SdIqNaRca1OrmYydW2K3hf1apsqlPkVKuKmangPdV2kXGtVsoUGVf7lGt1cjWXi+13TkuIr1H669O/gJ5dwRho1RS258LGLb5GcrJOBe/pUn9yPdcRL78C3BwV1YlJa+1WYB7QPfRUX2CCtYVnCTXGtAGqAitDTzUE+hhjvjXGfGyMaeRHtsTacWRt/XXf443bckmIjyuyTEJ8HFnbcvc9ztqWS2LtosuUa6b4ODK3FWbKys4lsVimxPg4srJzD7hMueWpVSzPtlwSa8WVuszefEvuzl3EV6/mS55976k6RZ5NtYosl2N1cjGTq20HqlXEuVSnyLOpVhUyE6jtDiqbY7VSpgjzONqnXKuTq7lcbb8DydoMyYmFj5MTYOMmf9/T1Tq51p9czyVSmiAufhN+OHff0GMAjDHHAeOAAQV7SAKxQJ619mzgJWBMFLOKiIiIiIiIiIiID6J68ZuQScDTxpgzgWOttd8BGGNqAh8BD1lrvw5bfh3wbuj+RCCtpF9qjBkIDAR48cUXIwpyZWpLLu/YDIAlq7NIqlO4z3pi7Tg2hX2DALApO5eksD0kk2rHsXFb0WUOV5+OLend3suU8VMWybULMyXFx7GxWKaN2bkkhX2zUdIy5WVjTm7RPLXj2JiTW+IyG7NzqRRjiDsmluwdeeWeRXWKnGoVGRfr5GKmfe/lUNuBahUp1SlyqlXFzaS2i5yLtVKmg+NSn3K1Tq7mArfaL1JJ9SAz7ORqmZsgMaH05cuDS3VytT+5mutoYgoPBJaDFPU9Jq21ucAMvD0f3wAInXNyIvCqtfbtYj/yHtA5dL8TsKyU3zvaWnu2tfbsgQMHRpTlrZkLuWbYeK4ZNp6ZC1ZyybmNAWh2SjK5O3ezOWdHkeU35+wgN283zU5JBuCScxsza8HK/X7v4Xhz9kL6jBpPn1HjmbFoJZe28TI1Twll2l4s0/Yd7MjbTfMUL9OlbRozc1H5ZiqQ8VMm9RNqc3zdmlSuFEPXM09n1qJVRZaZtXgVl7VtAsAFrRvxzbK1vmRRnSKnWkXGxTq5mKmAS20HqlWkVKfIqVYVN5PaLnIu1kqZDo5LfcrVOrmaC9xqv0h17gCTPvGuzr0gA2pUh8S6/r6nS3VytT+5mkskEkHsMQnehORECg/pvgroCNQ1xtwQeu4Ga+0CYBQw3hgzCMgF/uxHoC8Wr6ZD8xQmPT6AvN17GDL2032vvf5IP64ZNh6AUeOnM2TARVSrUpkv09fwZfoaP+IA8HnGas5rmsIHjw4g7/c9PPpaYaY3H+hHn1FephETpjPs2ouIrVKZL5es4Ysl/mTam28ZNWE6z9/WmxhjmPR1Biszt3DLJe1Y8nMWsxavYuJX6Tx+XTfef3QA23fkcX/aZF+yhFOdIqdaRca1OrmYydW2A9UqUqpT5FSripkJ1HYHw7VaKVNkXO1TrtXJ1Vwutt/dQ2HeAsjOgdQr4PYBsGeP91rfntDpXJj9NXS9BqrFwogHfI0DuFkncK8/uZ5LpDTGHpm7m9qzbno66AxFfPfSIFrd7lamBc+5mQlwKpeLmcDd9lOmsrnYp1zMBO62nzKVzcU+5WImcLf9lKlsLvYpFzOBu+2nTGVzsU+5mAncbb/8zNOCjlFETPIyJ+sEbvUpFzMBLHhukAk6g4u6Nx7s/OTax0tHOtl2QVz8RkRERERERERERI5ympgUERERERERERGRqAvqHJMiIiIiIiIiIiIVX77zR3I7S3tMioiIiIiIiIiISNRpYlJERERERERERESiTodyi4iIiIiIiIiIHCqrQ7kPlfaYFBERERERERERkajTxKSIiIiIiIiIiIhEnQ7lFhEREREREREROVQ6lPuQGXtkFu+I/E+JiIiIiIiIiATIBB3ARd0b3ef8PNTHy59wsu2O2D0mW9/2dNARivj+34NodbtbmRY852YmwKlcLmYCd9tPmcrmYp9yMRO4237KVDYX+5SLmcDd9lOmsrnYp1zMBO62nzKVzcU+5WImcLf9XMyUn3la0DGKiEleBrjVpwr6eYtB7mQCWPT0oKAjyBFG55gUERERERERERGRqDti95gUERERERERERHx3ZF5msSo0B6TIiIiIiIiIiIiEnWamBQREREREREREZGo06HcIiIiIiIiIiIihypfh3IfKu0xKSIiIiIiIiIiIlGniUkRERERERERERGJOk1MioiIiIiIiIiIHCqb7/7tMBhj6hhjphpjlof+rX2AZWsaY9YZY56L5HdH9RyTxpgZwChr7Sdhz90JdAXigZrAXuBxa+2bodc/B2qEFk8E5llre/mR774rU+nQ9GTydv/Oo+M+5Ye1G/dbpvFJiQzt35XYqpX5MmM1T7w1048ohZmuSOW8UKZHxn3KD+tKzjSsf1diq1Tmi4zVPPG2MgWdqX3jBtx3RSoxMTFM/CqdtKnfFHm9SuVKDO/flcb1k8jZsZP7x0xm/dbtvuUJp1pFxrU6uZjJ1bYD92rlYiYX20+ZKn4u9XNlOhpyudbPlali53Kxn7uY6aFRMHMO1KkNH4zd/3VrYcQzMHsuVIuFEYOh6Wm+RnKyTh3OaMD9l6cSY2J4d246Yz4rlqlSJR7v15UmJyaR89tO7n1lMuu3RWf7XCq8B4DPrLWjjDEPhB7fX8qyjwGzI/3F0d5j8g2gb7Hn+gIjgeustU2BbsC/jDHxANbaP1hrW1lrWwFzgHf9CHZe0xTqJ8TTc0gaw1+fxoN9u5S43IN9z+ex16fSc0ga9RPi6dAkxY84XqYmXqYeQ9N47I1pPFRKpof6nM+w16fSY6gyuZApxhgGX9WF2/7zHr2Hv0K3s07nlOQ6RZa5vF1Ttu/cRY+habw2Yz5/63meL1mKU60i41qdXMzkatuBe7VyMZOL7adMFT+X+rkyHQ25XOvnylSxc7nYz13MBNCrO4x+svTXZ8+Fn9bBlPEw9B4Y9pS/eVysU4wxPPjHLtwy+j16/eMVurc+nVOSimbqfa6X6dIRaYybNZ87L4vO9rkcEXoCr4TuvwKUuMOgMeYsIAn4NNJfHO2JybeBS4wxVQGMMSnA8cDn1trlANba9cBGICH8B40xNYEuwHt+BOvUoiEfzl0KwOI1mdQ4JpZ6NasXWaZezepUr1aVxWsyAfhw7lJSWzb0Iw4AqS0a8uG8g8w0bymdWyhTkJmapSSzdnM2v2zJYc/efD6Z/yOpxd4rtUVDPpi7BIBp3y+nzen1fclSnGoVGdfq5GImV9uu4H1dqpWLmVxsP2Wq+LnUz5XpaMjlWj9Xpoqdy8V+7mImgHNaQnyN0l+f/gX07ArGQKumsD0XNm7xL4+LdWpWP5mfwzJN+f5HOjcrlqlZQ96f52WaunA5bRtFZ/v8qGCt8zdjzEBjzLdht4EH8T9MstZuCN3PxJt8LMIYEwP8E7jnYEoX1YlJa+1WYB7QPfRUX2CCtXbfddWNMW2AqsDKYj/eC2+3UV/2M06sFUdm9q/7Hmdl55IYH1d0mfg4NmbnFl2mVtFlyjVTfByZ28rOlFU8U7wyBZkpsVaxPNv27yfhy+zNt+Tu3EV89Wq+5CnyvqpVZLkcq5OLmVxtO3CvVi5mcrH9lOkIyKV+rkxHQy7H+rkyVexcLvZzFzNFImszJCcWPk5OgI2b/Hs/F+uUFB9HVvicRs7+mZJqFS6zN9+Smxd820n0WGtHW2vPDruNDn/dGDPNGJNewq1nsd9jAcv+bgUmW2vXHUyuqJ5jMqTgcO5JoX//VPCCMeY4YBxwvbX7nZnzauDl0n5paKZ3IMCLL75YzpFFRERERERERESOTNbaC0p7zRiTZYw5zlq7ITR3t/8JeqEd8AdjzK1AHFDVGJNrrX3gQO8bxMTkJOBpY8yZwLHW2u9g36HaHwEPWWu/Dv8BY0w9oA1weWm/NDTTWzDba5+/7ekyg1zVsSW9OzQDIOOnLJLD9g1PKrZ3JMDGYt+WJcXHsTGn6DKHq0/HlvRuH5apdtmZkopnylamIDLte6+c3KJ5au/fTwqW2ZidS6UYQ9wxsWTvyPMlj2oVGRfr5GKmfe/lUNuBm7VyMdO+93Ks/ZSp4uZSP1emoyGXi/1cmSp+LnCrn7ucKRJJ9SAzbJokcxMkJpS+/OFysU5Z2bkkhc9p1No/U1aOt0xWTihTteDb7oiRX9IOhEeU94HrgVGhfycVX8Ba26/gvjHmBuDssiYlIfrnmMRamwvMAMbg7T1J6JyTE4FXrbVvl/BjVwAfWmvL9RMzYfZC+o4cT9+R45mxcCWXtm0MQPOUZHJ37mbz9h1Flt+8fQc78nbTPCUZgEvbNmbWouJHnB+eN2cvpM+o8fQZNZ4Zi1ZyaZuDzNSmMTOVKZBMBTJ+yqR+Qm2Or1uTypVi6Hrm6cxatKrIMrMWr+Kytk0AuKB1I75ZttaXLKBaRcrFOrmYqYBLbQdu1srFTAVcaz9lqri51M+V6WjI5WI/V6aKnwvc6ucuZ4pE5w4w6RPvVHoLMqBGdUis69/7uVinjLWZNEiozQl1vEzdWp/OzIyimWamr6JHGy/ThS0bMW9F8G0nFcYo4EJjzHLggtBjjDFnG2NKPbo5EkHsMQnehORECq/QfRXQEagbmlUFuMFauyB0vy+h/7RfvshYzXlNU3h/yADydu9hyGuFFxD63+B+9B05HoCRb05naP+LiK1SmS+XrOGLjDW+Zfo8lOmDRweQ9/seHg3L9OYD/egzyss0YsJ0hl0blmmJMgWZaW++ZdSE6Tx/W29ijGHS1xmszNzCLZe0Y8nPWcxavIqJX6Xz+HXdeP/RAWzfkcf9aZN9yVKcahUZ1+rkYiZX2w7cq5WLmVxsP2Wq+LnUz5XpaMjlWj9Xpoqdy8V+7mImgLuHwrwFkJ0DqVfA7QNgzx7vtb49odO5MPtr6HoNVIuFEWXuo3V4XKzT3nzLiHem8/zNvakUY3hvrpfp1m7tWLI2i5kZq5g4N50R/brx4YMDyPktj/vGRWf7XCo+a+0W4PwSnv8W+HMJz48Fxkbyu03YdWeOJLZ1BIdyR9P3/x5Eq9vdyrTgOTczAU7lcjETuNt+ylQ2F/uUi5nA3fZTprK52KdczATutp8ylc3FPuViJnC3/ZSpbC72KRczgbvt52Km/MzTgo5RREzyMsCtPlXQz1sMcicTwKKnB5mgM7ioe/07nZ9c+/jnfznZdkHtMSkiIiIiIiIiIlLxHZk7/UVF1M8xKSIiIiIiIiIiIqKJSREREREREREREYk6HcotIiIiIiIiIiJyqHQo9yHTHpMiIiIiIiIiIiISdZqYFBERERERERERkajTodwiIiIiIiIiIiKHSodyHzLtMSkiIiIiIiIiIiJRZ+yROat7RP6nREREREREREQCZIIO4KLuJ9zh/DzUx78862TbHbGHcre+7emgIxTx/b8H0ep2tzIteM7NTIBTuVzMBO62nzKVzcU+5WImcLf9lKlsLvYpFzOBu+2nTGVzsU+5mAncbT9lKpuLfcrFTOBu+ylT2Qr6VH7maQEnKRSTvAyAln91q1YLnxkUdAQ35ecHnaDC0qHcIiIiIiIiIiIiEnWamBQREREREREREZGoO2IP5RYREREREREREfHdkXn9lqjQHpMiIiIiIiIiIiISdZqYFBERERERERERkajTxKSIiIiIiIiIiIhEnc4xKSIiIiIiIiIicqh0jslDpj0mRUREREREREREJOqiusekMWYGMMpa+0nYc3cCXYF4oCawF3jcWvtm6PXzgSfxJlFzgRustSv8yHfflal0aHoyebt/59Fxn/LD2o37LdP4pESG9u9KbNXKfJmxmifemulHlMJMV6RyXijTI+M+5Yd1JWca1r8rsVUq80XGap54W5mCztS+cQPuuyKVmJgYJn6VTtrUb4q8XqVyJYb370rj+knk7NjJ/WMms37rdt/yuJ5L7VcxMxVQ+ylTeVJ/qti5XGs/ZYqMq/0JVKtIuVYnVzO5mkuZyubiZ++hUTBzDtSpDR+M3f91a2HEMzB7LlSLhRGDoelpvkaifeMG3N87VKc56YyZtn+dHr+2K41P8up039jojecipYn2HpNvAH2LPdcXGAlcZ61tCnQD/mWMiQ+9/jzQz1rbCngdeNiPYOc1TaF+Qjw9h6Qx/PVpPNi3S4nLPdj3fB57fSo9h6RRPyGeDk1S/IjjZWriZeoxNI3H3pjGQ6VkeqhDVauEAAAgAElEQVTP+Qx7fSo9hiqTC5lijGHwVV247T/v0Xv4K3Q763ROSa5TZJnL2zVl+85d9Biaxmsz5vO3nuf5kqUi5FL7VcxMBdR+ylSe1J8qdi7X2k+ZIuNqfwLVKlKu1cnVTK7mUqayufrZ69UdRj9Z+uuz58JP62DKeBh6Dwx7yt88Mcbw4JVduPWF97h8RCl1Orcp23/bxWWPpfHazPnc2SM64/lRId+6f3NUtCcm3wYuMcZUBTDGpADHA59ba5cDWGvXAxuBhNDPWLw9KQFqAev9CNapRUM+nLsUgMVrMqlxTCz1alYvsky9mtWpXq0qi9dkAvDh3KWktmzoRxwAUls05MN5B5lp3lI6t1CmIDM1S0lm7eZsftmSw569+Xwy/0dSi71XaouGfDB3CQDTvl9Om9Pr+5KlIuRS+1XMTOHvq/ZTpvKi/lSxc7nWfsoUGVf7U8H7qlZlc61OrmZyNZcylc3Vz945LSG+RumvT/8CenYFY6BVU9ieCxu3+JenWYNk1m4qrNOU+T+S2rxonTo3b8j787w6TV2wnDanRWc8FzmQqE5MWmu3AvOA7qGn+gITrC08S6gxpg1QFVgZeurPwGRjzDqgPzDKj2yJteLIzP513+Os7FwS4+OKLhMfx8bs3KLL1Cq6TLlmio8jc1vZmbKKZ4pXpiAzJdYqlmfb/v0kfJm9+ZbcnbuIr17NlzzO51L7VchM+95X7adM5ZlL/ali53Ks/ZQpwjyO9idQrSLO5VidXM3kai5liiCPo5+9smRthuTEwsfJCbBxk3/vlxhfdE5jY3YuSSXVKTusTnnB10kkiIvfhB/O3Tf0GABjzHHAOGCAtTY/9PQg4GJr7YlAGlDiDtDGmIHGmG+NMd+OHj3at/AiIiIiIiIiIiIFrM13/uaqqF78JmQS8LQx5kzgWGvtdwDGmJrAR8BD1tqvQ88lAC2ttXNDP/smMKWkX2qtHQ0UzEja5297uswgV3VsSe8OzQDI+CmL5LD9sJOK7R0J3jcO4d8CJcXHsTGn6DKHq0/HlvRuH5apdtmZkopnylamIDLte6+c3KJ5au/fTwqW2ZidS6UYQ9wxsWTvyPMlj4u51H4VO5PaT5nKk/pTxc7lYvsp08FxqT+BahUpF+vkYiZXcynTwXHps3cwkupBZtg1gzI3QWJC6csfro3ZuUXmNBLj48gqqU7xYXWqFnydRKK+x6S1NheYAYwhtLdk6JyTE4FXrbVvhy2+DahljCm4dtWFwNLyyjJh9kL6jhxP35HjmbFwJZe2bQxA85RkcnfuZvP2HUWW37x9BzvydtM8JRmAS9s2Ztailfv93sPx5uyF9Bk1nj6jxjNj0UoubXOQmdo0ZqYyBZKpQMZPmdRPqM3xdWtSuVIMXc88nVmLVhVZZtbiVVzWtgkAF7RuxDfL1vqSxdVcar+KnUntp0zlSf2pYudysf2U6eC41J9AtYqUi3VyMZOruZTp4Lj02TsYnTvApE+8q3MvyIAa1SGxrn/vl/GzV6cT6nh16nbm6cxaXLROM9NX0aONV6cLWzVi3vLg6yQSxB6T4E1ITqTwkO6rgI5AXWPMDaHnbrDWLjDG3AS8Y4zJx5uovNGPQF9krOa8pim8P2QAebv3MOS1T/e99r/B/eg7cjwAI9+cztD+FxFbpTJfLlnDFxlr/IgDwOehTB88OoC83/fwaFimNx/oR59RXqYRE6Yz7NqwTEuUKchMe/MtoyZM5/nbehNjDJO+zmBl5hZuuaQdS37OYtbiVUz8Kp3Hr+vG+48OYPuOPO5Pm+xLloqQS+1XMTMVUPspU3lSf6rYuVxrP2WKjKv9CVSrSLlWJ1czuZpLmcrm6mfv7qEwbwFk50DqFXD7ANizx3utb0/odC7M/hq6XgPVYmHEA/7m2ZtvGfn2dJ6/tTcxMYb3QnW69eJ2ZPycxaz0VUyck87j/bvxwd8HsP23PO4bG53x/Kjg8FWvXWfCrjtzJLGtIziUO5q+//cgWt3uVqYFz7mZCXAql4uZwN32U6ayudinXMwE7rafMpXNxT7lYiZwt/2UqWwu9ikXM4G77adMZXOxT7mYCdxtP2UqW0Gfys88rYwloycmeRkALf/qVq0WPjPIBJ3BRd3q3OT85NqUrS852XZBXPxGREREREREREREjnJBHcotIiIiIiIiIiJS8R2ZRyNHhfaYFBERERERERERkajTxKSIiIiIiIiIiIhEnQ7lFhEREREREREROVT5+UEnqLC0x6SIiIiIiIiIiIhEnSYmRUREREREREREJOo0MSkiIiIiIiIiIiJRZ+yReUnzI/I/JSIiIiIiIiISIBN0ABd1qznA+XmoKdvTnGy7I/biN61vezroCEV8/+9BdDl/VNAxipj+2QNcGHNl0DGKmJr/FgCdu/4j4CSFZnxyPwBNHnarTy0ZPojup94bdIwiPl7xJA2feiroGEWsvOsuGr01POgYRSy/8mEAzu33z4CTFPp6/N0AnHWTW/38u5cGOVUn8GrV6na36rTguUFOrvcAWt3hTq4Fz3qZWtzlTiaARU8NcrJPuZip5V/dyrTwGff61KKnvExn/8mt9fG3/72LlFfd2b4DWHPd/TT+uzttB7D0sUGcc6NbbffNmLsAaHeNO+vjOa972y0urWPAW8+c/We32u/bl+9ycjxvMcitTIue9sZOl9YzBeuY/MzTAk5SVEzysqAjyBFGh3KLiIiIiIiIiIhI1B2xe0yKiIiIiIiIiIj4zebnBx2hwtIekyIiIiIiIiIiIhJ1mpgUERERERERERGRqNOh3CIiIiIiIiIiIofKOn9Rbmdpj0kRERERERERERGJOk1MioiIiIiIiIiISNTpUG4REREREREREZFDla9DuQ9VRBOTxphewESgsbX2B38jlZrhTmC0tfY3v97jvitT6dD0ZPJ2/86j4z7lh7Ub91um8UmJDO3fldiqlfkyYzVPvDXTrzgA3H7bBbRt25C8Xb/zxBMfsXx5VpHXY2Mr8+gjvTj++Nrk5+czZ84KXnp5lq+Zbv1/A2jT/Ux2/baLJwf8mxXfr95vmf+bPoQ6x9Vm987dADzQ9TGyN233LdMdt5xP2zYNycv7nX/8czLLV+xfpyEP9eL44+PJz7d89fUKXhrjX53Oa9SAwRenUikmhre/S+fl2d8Uef2slBMYfHEnTktK4J4Jk/k0Y7lvWYr7y997ck7qGeza+Tv/vP9NVmb8Uuqyj754A8kn1eWWi//pW56OKSn8PdWr1ZuLF/PiN0VrdeOZZ3JV8+bszc9n686d3P/JJ6z/9Vff8gD8IekUHm7dlUrGMGHVAkb/+FWJy3U94Qyea38Fl0/7L+nbNviaCeCu6zrTruXJ7Nq9h8denMKPa/Yfo/5yZQe6/6EpNarH0uVPz/qeCeDevql0aO6NnUPSPuWHn/fPdUb9RIYOCI2di1fz5P9m+prJtVq1b9yA+65IJSYmholfpZM2tWg/r1K5EsP7d6Vx/SRyduzk/jGTWb/VvzGzgGvrvfaNG3DfH0N1mnOAOp0UqlOa/3XqcEYD7u/lZXr363TGTC+WqVIlHr+mK01Cme59dTLrt/nfdgD3XZHKeaH2e2Tcp/ywruT2G9a/K7FVKvNFxmqeeHvmUZWpfeMG3N+7sE+NmbZ/n3r82sI+dd/Yo7dP3XN158KxfMwn/FjCWH7r5R24uH0Tah4bS8fbnvM9U6fjT+aRc86nkonhzRULeT59bpHX+53Wiv6nn0m+zWfHnt8ZPGcKK3K2+JrpvFMb8OAlqcSY0Dbe50Xb7+wGhdt4d78VvW28u68pbL+h/y25/W7p3YFL2jehxrGxdLrV//YbdF1n2rc6mbzde3jshSksK2FdfPNVhevi82+MwrrYsXVMgUg+f2c0SGTIgG77tqX+740ZvmZybTzvcEYD7r/c++y9OzedMZ+VMHb260qTE5PI+W0n977i/9jp4jrmoVEwcw7UqQ0fjN3/dWthxDMwey5Ui4URg6Hpab5GEilTpIdyXw18Efo3KHcCx/r1y89rmkL9hHh6Dklj+OvTeLBvlxKXe7Dv+Tz2+lR6DkmjfkI8HZqk+BWJtm1O4YQTa9P/uhd56qkp3Pm3riUuN+Gtedww4CUG3pxG02Yn0qbNKb5latO9NSecehw3nHYH/7r5Rf76n5tKXXbUtf+Pv5x5L385815fJyXbnnMKJ5xQh2sHjOaf/+8TBt1xUYnLvfnOPK7/88vcdGsazZqeQJuz/alTjDE8fFkXbn71PS575hUubn46DRPqFFlmQ/avPPjOp3y0KLrz/Od0OoPjU+rxp/P/wTMPv83tQ3uXumz7i5qxc8duX/PEGMOQLl24ceJEuo4dy2VnnMGpdYrWasmmTfQaP55Lxo3j42XLeKBjR38zYRhyZnf+/PkbdJ/yApfWb8qpNertt1z1ylW5vlEbFmxZ52ueAu1ansxJybW58u4xjPzvVO4bcEGJy33+/SpufGR8VDIBdGiWwkmJ8fR6KI3h46YxuF/JY+fga8/nsXFT6fVQGiclxtO+WYpvmVyrVYwxDL6qC7f95z16D3+FbmedzinJRfv55e2asn3nLnoMTeO1GfP5W8/zfM/l2novxhgGX9mF255/j96PH6BOv+2ix7Do1CnGGB7s3YVbRr9Hr3+8QvczT+eUpKKZerf12u7SEWmMmzWfOy/1v+0AzmvitV+PoWk89sY0Hiql/R7qcz7DXp9Kj6H+b7e4linGGB68sgu3vvAel48opU+d6/Wpyx5L47WZ87mzx9HZpzo0P5mTkuK5/MExPP7qNAb3P7/E5WYvXMX1w1/3PQ94tRrW9kJu+OwtLnz/ZXqkNOHUWnWLLDNp9RK6fTCGiz8cy4vpc/n72SX3ufLM9PfLujDw1fe47NlXuKTF/tt463N+ZfC7n/LR4uht47VvfjL1k+LpPXgMI16ZxgPXldx+ny9YxfWPRaf92rUKrYvvGsOol6dy340lr4u/mL+KP/09Sutix9YxBTo0P5mTEsM+f9eW3H6Dr72A4a9O5fIHx/i+LeXkeP7HsLGzdQlj57nFxs7LojCeO7aOAejVHUY/Wfrrs+fCT+tgyngYeg8Me8r3SCJlKnNi0hgTB5wH/AnoG3ou1RgzyxgzyRizyhgzyhjTzxgzzxiz2BjTMLRcijFmujFmkTHmM2NM/dDzY40xV4S9R27Y751pjHnbGPODMWa88fwVOB6YYYzx5auhTi0a8uHcpQAsXpNJjWNiqVezepFl6tWsTvVqVVm8JhOAD+cuJbVlQz/iANC+QyOmfpoOwNKl64mLi6VOnaKZdu3aw4IFPwOwZ08+y5dnkVCvhm+Z2vU8h2njvD0Nl85dTlx8deokx/v2fpHo0K4Rn04L1emH9VSvXkqdFharU4I/dWp+YjI/b8lm3bYcft+bz8eLf6RL46L9ZH32dpZlbSY/ylfuOveCpnw28TsAfljwM3E1q1G7hDpUO7YqvW/syP/+M83XPC2Tk/kpO5u1OTn8np/Phz/8wAUNi9bq67VryduzB4AFGzaQHBfna6YWdY7np9ytrN2Rze82n4/WZnD+Cft/jXhn006M/uErdu3d62ueAh3Pasjkz5cAkLFiA3HHxlI3vvp+y2Ws2MCW7B1RyQTQqVVDPvraGzvTV2USd2ws9WoVGztrVSeuWlXSV3lj50dfLyW1lX9jp2u1apaSzNrN2fyyJYc9e/P5ZP6PpLYo+v9PbdGQD+Z6mad9v5w2p9f3PZdr671mDYrV6bsfSW1erE7Nw+q0YDltTvO3Ts3qJ/Pz5mx+2eplmvL9j3RuVixTs4a8/42Xaeqi5bRt5H/bgddnPpx3kO03bymdW/j32XMtU7MGyazdVNinpszfv091bt6Q9+eF2u8o7lOdWjVk8lfee6av2kCNY2OpW2v/cTN91Qa25ERnHdOq7nH89Gs2a3O9bYQP1izlopMaFVkm9/fCL1CPrVwFv7eqWhTbxpt8oG28KB7S16l1Qz5yrP06ntWQj11aFzu4jinQqVVDJs85cPvVreWNnemrvCN0Js9ZQmrrU33L5Nx4XjB2bilj7CwYzxf6P3a6uI4BOKclxB/gz9zpX0DPrmAMtGoK23Nho787mh89bL77N0dFssdkT2CKtXYZsMUYc1bo+ZbAX4DGQH/gNGttG+Bl4I7QMs8Cr1hrWwDjgWcieL/WeHtHNgFOATpYa58B1gOdrbWdI/qfHaTEWnFkZhceHpqVnUtifNEJkMT4ODZm5xZdppZ/kyT16tVg46bCTJs2/Uq9A0w6Vq8eS7tzT2X+92v8y3R8HTauLRy5Nq/bQr0T6pS47D1jbuOF+U/S7+E/+pYHoF69ODaG7ZG5efOv1KsbXJ2SasaRmVPYbpnbc0ms6e9kWqTqJtVk84bsfY83Z+ZQL6nWfstdN6gr7/53Nnk7f/c1T1JcHBvCDsvOzM0lqUbpbXdl8+bMWrPG10zJx9Rgw2+F/Snzt19JOqZopibxyRx3bE1mZq7wNUu4hDpxbNxSWKuNW38loXbw/SqxdhxZW8NybcslodjYmRAfR9a2sLFzWy6JPmZ3rVaJteLI3Ba2ftm2/7ojfJm9+ZbcnbuIr17N/1wOrfcS44vVqaQ8YZmjUaekWnFkFa9Rsf9/+DJ78y25ef63HURYr/g4soq3X7x/nwXXMiXGF+3jG7NzSSrpsxfF9nO1TyXUjiNza7Fxyse+EomkY2uwfkfh+njDb7+SdOz+mfqf3ppZlw/kgbNSGTLP3y9UE4tt42Xl5JJUI/h1cULxdfFWf9ezkSieaVPQ62IH1zEFEuLL/vwlxseRVWxbovj2VnlybTxPii82duYEP3a6uI6JRNZmSE4sfJycABs3BZdHBCKbmLwa+F/o/v8oPJz7G2vtBmvtLmAl8Gno+cVASuh+O6DgeIFxeHtelmWetXadtTYfWBD2uw7IGDPQGPOtMebb0aNHR/IjR5SYGMPDD/dg4sRv2bAhJ+g4jLz2GQa2vJtBHf9O8/Mac0F/fw+/jVRMjOHvg3vw7qTv2JAZfJ1cdErj4zmufl2+mpoedJQiejZuTPOkJF769ttAcxjgwZYXMnKhv3/8iIiISNnG/fg9nSaOZtT8mdzRol3QcUREROQgHfDiN8aYOkAXoLkxxgKVAAt8BOwKWzQ/7HF+Wb8X2ENoUtQYEwNUDXst/PfujeB3AWCtHQ0UzEja5297usyfuapjS3p3aAZAxk9ZJIft85xUbC8R8L4FCf8WKCk+jo05RZc5XD17nsklF7cE4McfN5AYdphtQkINNm8u+aIfd9/VnV/WbeOdd8t/0qbHrV25+M/eeWF+/HYFiSfVJSP0Wr0T67L5l637/cyW9d5zO3PzmP7GF5zRphHTxs0ut0y9LmvNJd29Ov2wLJPEhJqAdxGXevVqsHlLyXW6585u/PLLVt6Z6N/kVtb2XJJrFbZbcs04Nm4v335yMC69tj3drmoLwLLFa6l3XOGh9/WSa7E5q+gEbePWDWjU7ETGzhxMpcox1KoTxz/G/4X7+71Q7tmycnM5LmwPyeS4OLJKuLBN+/r1ubVNG66ZMIHdPh86nbnzV447tmZhpmNrkLWzMFP1yrE0qpXAa6n9AUioFscLHa7iL19OKPcL4Pzxwlb07NwcgKWrMkkM2xM4sU4NNm0Lpl9dmdqSyzt6Y+eS1Vkk1QnLVTuOTcXGzk3ZuSSF7SWRVDuOjeWc3dVaAWzMySW5dtj6pfb+646CZTZm51IpxhB3TCzZO/LKPYuL673w9ypSp5Ly5OSSHO9/nQpk5eSSVLxGxf7/Bctk5YQyVfMvU5+OLendPqz9yqpXdi5Jxdsvu3zbz8VM4e8V3scT4+PIKumzF96nfGw/cKtPXdm5Jb06euPmkjVZJNepwcKCXLX9a5dIZf32K8dXL1wfH3dsDbJ+Kz3TB6uXMrxtV2Cyb5k2FtvGS6oVR9avAa2Lu4S1X/F1cZ3yX89G4o8XtqJH2Lo4PFNC0Otix9YxV3ZuSa8/RP7525idS1KxbYni21uHy+XxPCu72NhZK9j1Mbi5jolEUj3IDLuOUeYmSEwILo8IlL3H5BXAOGttA2ttirX2JGA18IcIf/9XhM5LCfQDPg/dXwMUHBLeA6gSwe/6FSjXkwJOmL2QviPH03fkeGYsXMmlbRsD0Dwlmdydu9m8vej5TjZv38GOvN00T0kG4NK2jZm1aGV5RmLSpPkMvDmNgTen8cWXy7nwIm/l0Ljx8ezYsYutW/c/B8uNA/5A9eqx/Nun8wG+/59P9l3E5sv3vuGC/p28TG0bsSPnN7ZmZhdZPqZSDDVDkwKVKlei7SVnsSb953LN9N4H33PTrWO56daxfPnVMi66IFSnM45nx2+l1Ol6r07PvfBZuWYpLv2XTBrUrc0JtWtSpVIM3ZufzowfVvn6ngfy4WtfcXuPp7m9x9PMmZrO+Zd7H70zWtVnx695bNtUdCLwo9fncG2H4dyQOpK7+/yHX9Zs9mVSEmBRZiYp8fGcWLMmVWJiuPSMM/hsVdFaNUlIYPgFF3DzpEls2bnTlxzhFm9bT0pcHU48Np4qJoZLTmrKZ+uX7Xs9d88u2r7/FJ0nP0fnyc+xYMsvvkxKArwzdQHXPTiO6x4cx6xvV3DxH5oA0PTU48jduSuq55IM99bMhVwzbDzXDBvPzAUrueRcb+xsdkpo7Cx2/qrNOTvIzdtNs1O8sfOScxsza0H5jp2u1gog46dM6ifU5vi6NalcKYauZ57OrEVF+/msxau4rK2X+YLWjfhm2Vpfsri43iuQ8XOxOp11OrMWH6BOrfyr075MazNpkFCbE+p4mbq1Pp2Z6UUzzcxYRY9zvEwXtmjEvBX+ZXpz9kL6jBpPn1HjmbFoJZe2Ocj2a9OYmeXcfi5mKlDQp/a135n796mZ6avo0SbUfq0aMW/50dOn3pqxkH5DX6Pf0NeY+f0KLm7vvWezU44j97fdUTsXYWkWbtlASo3anBhXiyoxMVyW0pipa4ueQiWlRu1997uc2JA12/f/srw8LS7Yxov3tvEuDnAb763pC+k35DX6DfHa7xIH2u+dqQu4/sFxXP/gOGZ/u4LuYeviHUGvix1bx7w1YyH9hr1Gv2Ghz1+7sPbbuX/7bcnxxs5mpxwHwMXtmpT7tpTT43lJY2fGAcbzlv6uj8HNdUwkOneASZ94V+dekAE1qkNi3bJ/Tspm863zN1eVtTfi1cA/ij33DnAL3uHbZbkDSDPG3AtsAgaEnn8JmGSMWQhMASJZS40Gphhj1vtxnskvMlZzXtMU3h8ygLzdexjy2qf7Xvvf4H70HeldLW7km9MZ2v8iYqtU5ssla/giY015R9ln7tyVtG17Cq+Nu5m8vN954snCb4BHvziAgTenUa9eDa69tgM//bSZF1/wyvvepO+YPHmRL5nmTZ5P24tb88ryZ9n1227+78Z/73vthflP8pcz76VqbBVGTnmYylUqEVMphu8/W8zkl/ybDPx63irantOQ19IGsmvXHv7xz8I6vfSfG7jp1rHUq1eD/te056eftzD63zcAMPH9+UyeUv512ptvefzD6bx0fW9iYgwTv8tgxcYt3H5+OzJ+yWLGD6todkISz1xzGTWPqUbnM07h9i7t6PHsq+WepbhvZv7AOamNGTP9AfJ27ubp+yfse+259wdxe4+y9zQuT3utZeiMGYz94x+JMYa309NZvmULd7Zvz+LMTD5btYoHOnakepUqPHvppQCs//VXbp40yd9M309hTMerqWRieHv1AlZs38zfmnZi8db1TN+w3Lf3PpCvFqymfatTePupP5G3+3eGv/jJvtdeHdGf6x4cB8DtV3fkovZnUK1qFd5/diDvz1jMy+/O8S3XF4tX06F5CpMeD42dYwvHztcf6cc1w7yxc9T46QwZcBHVqlTmy/Q1fJm+xrdMrtVqb75l1ITpPH9bb2KMYdLXGazM3MItl7Rjyc9ZzFq8iolfpfP4dd14/9EBbN+Rx/1p/u3xU8C19d7efMuot6bz/K3F6nRxqE7pq5g4J1SnRwaw/Tf/67Q33zLi3ek8P7A3lWIM783LYGXWFm7t1o4la7OYmbGKiXPTGXFNNz58cAA5v+Vx36v+tx3A56H2++DRAeT9vodHw9rvzQf60WeU134jJkxn2LVh7bdkzVGTaW++ZeTboT4VY3gv1KduvbgdGeF9qn83Pvi716fuG3t09qkvF62mQ/OTeW/kjeTt3sPQMYXj5vhHr6Xf0NcA+OsVf6BrW2/c/OjJm5j0eTqj3/dnHbPXWh6ZN5VXL7iKSsYwYcViludsZlDL81i8JZNp61Zw/Rln0uG4FPbk7yVndx53f+l/+w3/cDovh7bx3p3vbePd0aUd6esLt/GevbpwG++OLu24zOdtvC8XraZDi5OZOMprv2Hh7TfkWvoN8drvjisL2+/D//Pa76VJ/rRfwbr4raf/xK5dRdfFr4zoz/WhdfFtYeviSc8O5P2Zi/nvOz6tix1bxxT4cnHo8zci9PlLC2u/R66l3zCv/Ua99hlDbuxKbJXKfJW+hi8Xr/Ytk4vj+Yh3pvP8zaGxc25oPC8+dvYLGzvH+T8euLaOAbh7KMxbANk5kHoF3D4AQtcRpW9P6HQuzP4aul4D1WJhxAO+RxIpk7FRvipwlNjWERzKHU3f/3sQXc4fFXSMIqZ/9gAXxlwZdIwipua/BUDnrsXnw4Mz45P7AWjysFt9asnwQXQ/9d6gYxTx8YonafjUU0HHKGLlXXfR6K3hQccoYvmVDwNwbr9/Bpyk0Nfj7wbgrJvc6uffvTTIqTqBV6tWt7tVpwXPDcLF9R5AqzvcybXgWS9Ti7vcyQSw6KlBTvYpFzO1/KtbmRY+416fWvSUl+nsP7m1Pv72v3eR8qo723cAa667n8Z/d6ftAJY+NohzbnSr7b4ZcxcA7a5xZ30850AF7Z4AAA1tSURBVHVvu8WldQx465mz/+xW+3378l1OjuctBrmVadHT3tjp0nqmYB2Tn3lawEmKikleZoLO4KKLql7j/OTap7tfd7LtIjp/o4iIiIiIiIiIiJTA5gedoMKK5KrcIiIiIiIiIiIiIuVKE5MiIiIiIiIiIiISdTqUW0RERERERERE5BC5fNVr12mPSREREREREREREYk6TUyKiIiIiIiIiIhI1OlQbhERERERERERkUOlq3IfMu0xKSIiIiIiIiIiIlFnrD0iT9B5RP6nREREREREREQCZIIO4KILY650fh5qav5bTrbdkToxWW6MMQOttaODzhFOmSLnYi5liowyRc7FXMoUGWWKnIu5lCkyyhQ5F3MpU2SUKXIu5lKmyChT5FzM5WImEdCh3JEYGHSAEihT5FzMpUyRUabIuZhLmSKjTJFzMZcyRUaZIudiLmWKjDJFzsVcyhQZZYqci7lczCSiiUkRERERERERERGJPk1MioiIiIiIiIiISNRpYrJsLp6DQZki52IuZYqMMkXOxVzKFBllipyLuZQpMsoUORdzKVNklClyLuZSpsgoU+RczOViJhFd/EZERERERERERESiT3tMioiIiIiIiIiISNRpYlJERERERERERESi7qibmDTG1DXGLAjdMo0xv4Tu5xpj/hN0vnDGmBnGmK7FnrvTGPO8MWaKMSbbGPOhI5k+NsbMMcZkGGMWGWP6OJApzRgzP9S+GcaYvziQ6fnQ/ZrGmHXGmOdcyGSM2Rv2uXg/WpkiyFXfGPOpMWapMWaJMSYl4ExLw+q0wBiTZ4zpFXCm540xT4T6+FJjzDPGGONApn8YY9JDN1/Hg0MZK40xJxtj5hpjVhhj3jTGVHUg0+2hPNYYU6888xxmrvHGmB9DbTnGGFPFgUz/NcYsDK1v3jbGxAWdKWy5Z4wxueWZ51AzGWPGGmNWh41ZrRzIZIwxjxtjloXGrL+WZ6bDyPV5WJ3WG2PecyDT+aZwO+YLY8ypDmTqEsqUbox5xRhTOUqZSt3ODHA8P1CmIMfzA+UKajw/UKagxvMy/3YJYDw/UJ2CGs8PlCnI8fxAuYIazw+U6bDGc2NMr9B4ckZ5/T8OVuj/eGxQ7y9HOGvtUXsDhgD3BJ3jAPkGAmnFnvsa6AicD1wGfOhQpkahx8cDG4B4BzLFhh7HAWuA44POFLr//4DXgeccabvcaPajg8g1E7gwrA2PDTpT2OM6wFYXMgFfApVCtzlAasCZHgWmApWB6sA3QM2AalPiWAlMAPqG7r8A3OJAptZASmisqudQrS4GTOj2hiO1qhl2/ynggaAzhZY5Gxjnx5h6iHUaC1zhR186jEwDgFeBmNDjRBdyFVv2HeC6oDMBy4DGofu3AmODzIS3Q8Na4LTQ42HAn6KYqcTtTIIdz0vLFPR4XlquIMfz0jIFOZ6X+rcLwY3npdVpLMGN56VlCno8L/NvT6I/npdWq8Maz4E3gc+BoX71gQgy+Dae6aZb4AEC/c+HTUwCqYQ2vkLPvxL68P8E9AaeABYDU4AqoeXOAmYB3wGfAMeVc746wEagauhxCvAzhRct2pc5ijU7YKaw5RYWDMwuZALqhp6L1sRkqZlC/eZ/wA1Ed2LyQJmCnJgsLVdT4AvHMoX3qYHAeAcytQuNQccAxwLfEtrwCTDTvcDfw5b7L3BVUO1VfKwM9fnNQOXQ43bAJ0FmKvaza/DvD9nDWq8Ag4DHXckUasvngfuDzoT3xcAM4Dj8+UP2UDKNxd8/ZA8l0zzgVL8ylUOfqglso5y/TDnEWv0ItA3dHwyMCDITkACsDHv8B2ByNDOFLbcQaIQD43nxTMWeW0NA4/mBcoWej/p4XkatAhnPS8pEwON5KZnGEuB4XkqmwMfzMvpUION5KbU65PEcb+eMX4DTgB9Dz6XizUNMAlYBo4B+oTZZDDQMyzYdWAR8BtQvqT8V9PPQ750JvA38AIwPfTb/CuwO/e4Zfra5bkfn7ag7lPsgNAS6AD2A1/A+gM2BncAloUMfnsX7QJ8FjAEeL88A1tqteINL99BTfYEJ1lpbnu9T3pmMMW2AqsDKoDMZY04yxizC+4b/H9ba9UFmwhvY/wncE40ckWQKtV01Y8y3xpivTZQOTS4rF94fHNnGmHeNMd8bY540xlQKMlOxz15fvL0NouIAmebgbTxvCN0+sdYuDTIT3oZYN2PMsaFD2DoDJ0U7xwHGyrpAtrV2T+jxOuCEgDNFxeHkCq33+uN9QRd4JmNMGpAJnIG3Pg460+3A+9baDeWZ5TAzATweOqTsaWNMrAOZGgJ9Quucj40xjcoz02HkKtAL+Mxau92BTH8GJhtj1uF99kYFnGkzUNkYc3bo8RWU89h+CNuZTozn0d72PdxcQY7nJWUKejwvIVPg43kpbRfoeF5CJifG8wN8/gIbz0vIdDjjeU9girV2GbDFGHNW6PmWwF+AxqHfeZq1tg3wMnBHaJlngVestS3wJhmfieD9WgN3Ak2AU4AO1tpngPVAZ2tt54PILhIRTUyW7mNr7e943wpUonDFvRjvm4fTgWbAVGPMAuBh4EQfcryBN9hBlCdCDqDUTMaY4/AOexhgrc0POpO1dm1oID4VuN4YkxRwplvx9i5YF8UcZWUCaGCtPRu4BviXMaahA7kq4+2NcQ9wDt6K8YaAMwH7+nlzvD2lo2m/TKFz1DTGG39OALoYY/4QZCZr7afAZOCr0OtzgL3RzuHz+0XCxUxw6Ln+A8y21n7uQiZr7QC8Q6WWAn6cxzTiTMaY44ErKec/qA8nU8hgvD/0z8Hbw+N+BzLFAnmhdc5LeF/s+uFQ+/nVB7HswTrYTIOAi621JwJpeIe5BpYp9Ed3X+BpY8w84Ff8GdsrzHZmwJkOJ1cg43lpmYIcz4tncmE8L6VOgY7npWQKfDwvo58HMp6XkulwxvOr8Y62I/Tv1aH731hrN1hrd+FNgH4aer5gvgK8vchfD90fB5wXwfvNs9auC2VfEPa7RPxTfBfKo+nGgQ/lvidsudziP4M3KTEnChnj8HYVPxNYVuy1fZmjXLcSM+HtLj8fHw8zOJQ6hS0zJprZSsqE903Vz3iH9mwGtgOjHKvT2Gi3YSm1OheYFbZMf+DfLtQK+BswOpo1OkCdih82/Qhwnwt1ClvmdbyNsaDaq8hYSRQO/TvYTMVeW4OP5/A5lFx45w19j9B5pFzIFPZ6xwO9HqU+dQne3j5rQrd8YIVjdTrg69HKhHdo2Mmh+wbIcaVPAfWALUC1oDOx/2HT9YElLtQp7PWL8PYSikomStjOJODxvKRMxX5uDQGM5wfKRUDjeVm1Ci0T1fG8lD4V6HgeYZ0O+NmMViYCHs/L6OeBjOel9KlDHs/xJqF/wzu93Bq8owB/xjsaKXydMRM4u3j/wBsfC05DVwXYHLr/MqHTLOHtrLa7pL4FPAfcELq/Bp1jUjefbtpj8tD9CCQYY9qBd0iEMaZpeb+JtTYX7zDNMTiyt01JmYx3BcSJwKvW2rcdyXSiMeaY0P3aeN8Q/RhkJmttP2ttfWttCt4E96vW2geCzGSMqV1wOEjosNsOwJJoZSotF94FU+KNMQmhx12imauMz56f38AebKafgU7GmMqhQ7M64e1xEFgmY0wlY0zd0P0WQAsKv8WNWo4DLGtDy14Reup6vHP0BJYpmg42lzHmz0BX4Grr095AB5MpdBXQUwvu451y5YcgM1lrP7LWJltrU0Lj+2/W2nK9gvLBZoJ9e20U1KkXkB50JrwJkYLDwDrhXRCg3B3i5+8KvD/I8hzItA2oZYw5LfT4QnwY2w+hTyWG/o3F22PrhWhkKm07M8jxPOht30PJFdR4XlqmIMfzA/SpwMbzMtoukPG8jH4e2Hgewecv6uP5ATIdznh+BTDOWtsg1CdPAlbjHVUWia8o3LOzH941NMCbZCw4JLwH3qRlWX4FakT4viIHJ+iZ0SBvHMYek6H7rYDZeOdTywBu8ilnL8ACZ4Q99zmwCe+cl+uArlGuXZFMwLXA73i7exfcWgWc6UK8E/0uDP07MIA+tl/bhb12A1G8+M0B6tQeb5f/haF/y/UKm4dTq7A2XIy3J2dVBzKl4J2A2pc9DQ6h/SoBL+Jt4CwBnnIgU7VQliV4VymMylhwMGMl3qkB5gErgLeAWAcy/TX0eA/eeXxedqRWe/AOESoY2x8JMhPeN/tfhsaFdLw90X256vvB1KnYz/l2QbGDbLvpYXV6DYhzIFM88FEo1xygpQu1Cr02E+jmV55DqNXlFK6fZwKnOJDpSbz1zY/AndGqEwfYziSg8byMTIGN52XkCmQ8Ly0TAY7nB6pTsZ+L2nheRtsFMp6XkSmw8bys9iOA8byMWh3SeI43+dmt2HN/xRuHI9ljsgElX/wmCW8bfSHwD4pe/Ka0PSbvwBv7Z/hZV92OzlvBVfdEREREREREREREokaHcouIiIiIiIiIiEjUaWJSREREREREREREok4TkyIiIiIiIiIiIhJ1mpgUERERERERERGRqNPEpIiIiIiIiIiIiESdJiZFREREREREREQk6jQxKSIiIiIiIiIiIlH3/wECHeTSsG2ABAAAAABJRU5ErkJggg==\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "We look for independence between features.\n", + "Because variables V1~V28 were already transformed by PCA beforehand, there seem to be very low correlation in-between features.\n", + "No need to address multicollinearity problem." + ], + "metadata": { + "id": "nQns-hijnR3m" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Examining the Dataset" + ], + "metadata": { + "id": "slM7mWbKmv_V" + } + }, + { + "cell_type": "code", + "source": [ + "print(df[\"Class\"].value_counts()) # number of normal and fradulent transactions\n", + "print(df[\"Class\"].value_counts(normalize=True)) # proportion of normal and fradulent transactions\n", + "\n", + "# barplot showing distribution of normal and fradulent data\n", + "labels = [\"Normal\", \"Fraud\"]\n", + "sns.countplot(x=\"Class\",data=df)\n", + "plt.xticks(range(2), labels)\n", + "plt.title(\"Transaction Class Distribution\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 425 + }, + "id": "m8Vs5HzHeID_", + "outputId": "7f9271b2-ce24-4291-d530-dea29261a404" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "0.0 284315\n", + "1.0 492\n", + "Name: Class, dtype: int64\n", + "0.0 0.998273\n", + "1.0 0.001727\n", + "Name: Class, dtype: float64\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Transaction Class Distribution')" + ] + }, + "metadata": {}, + "execution_count": 7 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Our main goal of this project is to differentiate effectively between the Target Class: Normal Transactions vs Fradulent Transactions." + ], + "metadata": { + "id": "ewIkrAeLolnT" + } + }, + { + "cell_type": "markdown", + "source": [ + "We use binary coding and set \"Class\" as categorical variable:\n", + "\n", + "0.0 : normal transactions\n", + "1.0 : fradulent transactions" + ], + "metadata": { + "id": "oJOtPLMVaMmM" + } + }, + { + "cell_type": "markdown", + "source": [ + "There are 284315 normal transactions,\n", + "and 492 fradulent transactions." + ], + "metadata": { + "id": "IKqFZF1ZadTD" + } + }, + { + "cell_type": "code", + "source": [ + "# proportion of fradulent data\n", + "df[\"Class\"].value_counts(normalize=True)[1]" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ZSOEkrrvamDH", + "outputId": "345905c7-d834-4f70-a2a1-c60297584396" + }, + "execution_count": 8, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "0.001727485630620034" + ] + }, + "metadata": {}, + "execution_count": 8 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Observing the visualized plot, we see there is a huge imbalance of data. \\\\\n", + "Only 0.0017% of the dataset are fradulent entries. Due to imbalance of data, the Accuracy metric cannot be used to assess performance. \\\\\n", + "For imbalanced data, we consider using other metrics such as Precision, Recall, or F1 Score. \\\\\n", + "Or we can also consider resampling to make a balanced dataset and use the AUC ROC metric. " + ], + "metadata": { + "id": "5_5sF0kuak-P" + } + }, + { + "cell_type": "code", + "source": [ + "# Delete unnecessary variables\n", + "df.drop(columns = \"Time\", inplace=True)\n", + "df.head()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 300 + }, + "id": "NDLtC19dfy-6", + "outputId": "7838ba7e-ff7e-466f-9746-f0d8a2269210" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " V1 V2 V3 V4 V5 V6 V7 \\\n", + "0 -1.359807 -0.072781 2.536347 1.378155 -0.338321 0.462388 0.239599 \n", + "1 1.191857 0.266151 0.166480 0.448154 0.060018 -0.082361 -0.078803 \n", + "2 -1.358354 -1.340163 1.773209 0.379780 -0.503198 1.800499 0.791461 \n", + "3 -0.966272 -0.185226 1.792993 -0.863291 -0.010309 1.247203 0.237609 \n", + "4 -1.158233 0.877737 1.548718 0.403034 -0.407193 0.095921 0.592941 \n", + "\n", + " V8 V9 V10 ... V21 V22 V23 V24 \\\n", + "0 0.098698 0.363787 0.090794 ... -0.018307 0.277838 -0.110474 0.066928 \n", + "1 0.085102 -0.255425 -0.166974 ... -0.225775 -0.638672 0.101288 -0.339846 \n", + "2 0.247676 -1.514654 0.207643 ... 0.247998 0.771679 0.909412 -0.689281 \n", + "3 0.377436 -1.387024 -0.054952 ... -0.108300 0.005274 -0.190321 -1.175575 \n", + "4 -0.270533 0.817739 0.753074 ... -0.009431 0.798279 -0.137458 0.141267 \n", + "\n", + " V25 V26 V27 V28 Amount Class \n", + "0 0.128539 -0.189115 0.133558 -0.021053 149.619995 0.0 \n", + "1 0.167170 0.125895 -0.008983 0.014724 2.690000 0.0 \n", + "2 -0.327642 -0.139097 -0.055353 -0.059752 378.660004 0.0 \n", + "3 0.647376 -0.221929 0.062723 0.061458 123.500000 0.0 \n", + "4 -0.206010 0.502292 0.219422 0.215153 69.989998 0.0 \n", + "\n", + "[5 rows x 30 columns]" + ], + "text/html": [ + "\n", + "
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V1V2V3V4V5V6V7V8V9V10...V21V22V23V24V25V26V27V28AmountClass
0-1.359807-0.0727812.5363471.378155-0.3383210.4623880.2395990.0986980.3637870.090794...-0.0183070.277838-0.1104740.0669280.128539-0.1891150.133558-0.021053149.6199950.0
11.1918570.2661510.1664800.4481540.060018-0.082361-0.0788030.085102-0.255425-0.166974...-0.225775-0.6386720.101288-0.3398460.1671700.125895-0.0089830.0147242.6900000.0
2-1.358354-1.3401631.7732090.379780-0.5031981.8004990.7914610.247676-1.5146540.207643...0.2479980.7716790.909412-0.689281-0.327642-0.139097-0.055353-0.059752378.6600040.0
3-0.966272-0.1852261.792993-0.863291-0.0103091.2472030.2376090.377436-1.387024-0.054952...-0.1083000.005274-0.190321-1.1755750.647376-0.2219290.0627230.061458123.5000000.0
4-1.1582330.8777371.5487180.403034-0.4071930.0959210.592941-0.2705330.8177390.753074...-0.0094310.798279-0.1374580.141267-0.2060100.5022920.2194220.21515369.9899980.0
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\n", + " " + ] + }, + "metadata": {}, + "execution_count": 9 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The feature \"Time\" is just a measurement of when the credit card transaction took place. \\\\\n", + "It is not necessary for our analysis. Thus, we eliminated the variable." + ], + "metadata": { + "id": "Uq0IU56-prxU" + } + }, + { + "cell_type": "markdown", + "source": [ + "To deal with the imbalance of data issue, we use the SMOTE(Synthetic Minority Oversampling Technique)." + ], + "metadata": { + "id": "eHm16iX_KdNn" + } + }, + { + "cell_type": "code", + "source": [ + "# Classify the variables\n", + "X = df.loc[:,df.columns!=\"Class\"]\n", + "y = df.loc[:,df.columns==\"Class\"]" + ], + "metadata": { + "id": "hg140yo6KqhG" + }, + "execution_count": 14, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "We split between the X data of features and y data of labels(0 or 1)." + ], + "metadata": { + "id": "0NqiFFm1Me0S" + } + }, + { + "cell_type": "code", + "source": [ + "# import additional models\n", + "from sklearn.datasets import make_classification\n", + "from imblearn.over_sampling import SMOTE\n", + "\n", + "# Oversampling\n", + "oversample = SMOTE()\n", + "X_overSamp, y_overSamp = oversample.fit_resample(X, y)" + ], + "metadata": { + "id": "oScrP-ijLFvm" + }, + "execution_count": 15, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# achieve balance of data\n", + "sns.countplot(x=\"Class\",data=y_overSamp)\n", + "print(y_overSamp[\"Class\"].value_counts())" + ], + "metadata": { + "id": "QRbh4c_xLhHA", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 335 + }, + "outputId": "9e5a92c2-02c0-42aa-f5ef-123cd76662d9" + }, + "execution_count": 16, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "0.0 284315\n", + "1.0 284315\n", + "Name: Class, dtype: int64\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "By looking at the visualization, we can see that now the dataset has been balanced between normal and fradulent cases. \\\\\n", + "We consider using the AUC ROC metric for assessing performance later on." + ], + "metadata": { + "id": "0NMJyD43MCSH" + } + }, + { + "cell_type": "code", + "source": [ + "# transform to numpy arrays\n", + "x_data = df.iloc[:,0:-1].values\n", + "y_data = df.iloc[:,[-1]].values\n", + "\n", + "x_data = np.array(x_data, dtype=np.float32)\n", + "y_data = np.array(y_data, dtype=np.float32)\n", + "\n", + "print(x_data.shape, y_data.shape)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3fkRl_rif8W2", + "outputId": "17b062b6-bb7e-4798-d08d-5b562685adf0" + }, + "execution_count": 17, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(284807, 29) (284807, 1)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "For calculation purposes we store them as the same float type data." + ], + "metadata": { + "id": "7z4dn9o4p_Q2" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Normalization" + ], + "metadata": { + "id": "ail51ekZKVSH" + } + }, + { + "cell_type": "code", + "source": [ + "scaler = MinMaxScaler()\n", + "x_data = scaler.fit_transform(x_data)\n", + "print(x_data)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "LXOEDZ45gfcU", + "outputId": "ce308b49-30b2-4d97-9cac-ea759d4f52c1" + }, + "execution_count": 18, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[[9.3519241e-01 7.6649040e-01 8.8136494e-01 ... 4.1897613e-01\n", + " 3.1269664e-01 5.8237929e-03]\n", + " [9.7854203e-01 7.7006662e-01 8.4029853e-01 ... 4.1634512e-01\n", + " 3.1342265e-01 1.0470528e-04]\n", + " [9.3521708e-01 7.5311762e-01 8.6814088e-01 ... 4.1548926e-01\n", + " 3.1191131e-01 1.4738923e-02]\n", + " ...\n", + " [9.9090487e-01 7.6407969e-01 7.8110206e-01 ... 4.1659316e-01\n", + " 3.1258485e-01 2.6421540e-03]\n", + " [9.5420909e-01 7.7285570e-01 8.4958714e-01 ... 4.1851953e-01\n", + " 3.1524515e-01 3.8923896e-04]\n", + " [9.4923186e-01 7.6525640e-01 8.4960151e-01 ... 4.1646636e-01\n", + " 3.1340083e-01 8.4464857e-03]]\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "To prevent data distortion I used MinMaxScaler so that the values will be between 0 and 1." + ], + "metadata": { + "id": "Mx5FK5uhE4io" + } + }, + { + "cell_type": "markdown", + "source": [ + "# 3. Deep Neural Networks Model (DNN)" + ], + "metadata": { + "id": "-5ZNj5vMq1aC" + } + }, + { + "cell_type": "code", + "source": [ + "# import modules for DNN design\n", + "from tensorflow.keras.models import Sequential\n", + "from tensorflow.keras.layers import Dense, BatchNormalization, Dropout\n", + "from sklearn.model_selection import train_test_split\n", + "from keras import optimizers, metrics, callbacks" + ], + "metadata": { + "id": "GlsINqtvq29J" + }, + "execution_count": 19, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Splitting the dataset" + ], + "metadata": { + "id": "ihiTiB6ByO6e" + } + }, + { + "cell_type": "code", + "source": [ + "x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size = 0.2, random_state=22)" + ], + "metadata": { + "id": "cBC4OooHFOvA" + }, + "execution_count": 20, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Split the training set for learning and test set for performance measuring into ratio of 8:2" + ], + "metadata": { + "id": "h_EbAGrYFPQU" + } + }, + { + "cell_type": "code", + "source": [ + "x_train, x_validate, y_train, y_validate = train_test_split(x_train, y_train, test_size = 0.2, random_state=22)" + ], + "metadata": { + "id": "btGlIHLKw-7c" + }, + "execution_count": 21, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Split the training set again to make a validation set with ratio of 8:2. This is necessary to implement EarlyStopping(=callbacks) in the future." + ], + "metadata": { + "id": "B-aLR5sNx3e7" + } + }, + { + "cell_type": "code", + "source": [ + "print(x_train.shape, y_train.shape) # train data\n", + "print(x_validate.shape, y_validate.shape) # validation data\n", + "print(x_test.shape, y_test.shape) # test data" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "CUlqQyvIsivA", + "outputId": "c9548fb6-f5ff-425f-b77d-770c1d07dd47" + }, + "execution_count": 22, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(182276, 29) (182276, 1)\n", + "(45569, 29) (45569, 1)\n", + "(56962, 29) (56962, 1)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Neural Network Design" + ], + "metadata": { + "id": "XA2H3HkQyTny" + } + }, + { + "cell_type": "code", + "source": [ + "# model design\n", + "model = Sequential([\n", + " Dense(256, activation='relu', input_shape=(x_train.shape[-1],)), # input data dimenisions = number of features (29)\n", + " BatchNormalization(),\n", + " Dropout(0.3),\n", + " Dense(128, activation='relu'),\n", + " BatchNormalization(),\n", + " Dropout(0.3),\n", + " Dense(64, activation='relu'),\n", + " BatchNormalization(),\n", + " Dropout(0.3),\n", + " Dense(32, activation='relu'),\n", + " BatchNormalization(),\n", + " Dropout(0.3),\n", + " Dense(16, activation='relu'),\n", + " BatchNormalization(),\n", + " Dropout(0.3),\n", + " Dense(10, activation='softmax'),\n", + " BatchNormalization(),\n", + " Dropout(0.3),\n", + " Dense(1, activation='sigmoid'), # activation function must be sigmoid!\n", + "])\n", + "\n", + "model.summary()" + ], + "metadata": { + "id": "4OdrSuQ334hS", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "234eee6d-e80d-402f-e048-c15688256d3d" + }, + "execution_count": 23, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Model: \"sequential\"\n", + "_________________________________________________________________\n", + " Layer (type) Output Shape Param # \n", + "=================================================================\n", + " dense (Dense) (None, 256) 7680 \n", + " \n", + " batch_normalization (BatchN (None, 256) 1024 \n", + " ormalization) \n", + " \n", + " dropout (Dropout) (None, 256) 0 \n", + " \n", + " dense_1 (Dense) (None, 128) 32896 \n", + " \n", + " batch_normalization_1 (Batc (None, 128) 512 \n", + " hNormalization) \n", + " \n", + " dropout_1 (Dropout) (None, 128) 0 \n", + " \n", + " dense_2 (Dense) (None, 64) 8256 \n", + " \n", + " batch_normalization_2 (Batc (None, 64) 256 \n", + " hNormalization) \n", + " \n", + " dropout_2 (Dropout) (None, 64) 0 \n", + " \n", + " dense_3 (Dense) (None, 32) 2080 \n", + " \n", + " batch_normalization_3 (Batc (None, 32) 128 \n", + " hNormalization) \n", + " \n", + " dropout_3 (Dropout) (None, 32) 0 \n", + " \n", + " dense_4 (Dense) (None, 16) 528 \n", + " \n", + " batch_normalization_4 (Batc (None, 16) 64 \n", + " hNormalization) \n", + " \n", + " dropout_4 (Dropout) (None, 16) 0 \n", + " \n", + " dense_5 (Dense) (None, 10) 170 \n", + " \n", + " batch_normalization_5 (Batc (None, 10) 40 \n", + " hNormalization) \n", + " \n", + " dropout_5 (Dropout) (None, 10) 0 \n", + " \n", + " dense_6 (Dense) (None, 1) 11 \n", + " \n", + "=================================================================\n", + "Total params: 53,645\n", + "Trainable params: 52,633\n", + "Non-trainable params: 1,012\n", + "_________________________________________________________________\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Deep neural network with multiple hidden layers. Output layer should have dimension of 1. \\\\\n", + "I used the Sequential model. \\\\\n", + "Between hidden layers we use ReLU activation function. \\\\\n", + "This is a binary classification problem so we use the output layer activation function as sigmoid. \\\\\n", + "\n", + "I used Dropout in between each hidden layer to prevent overfitting problem. \\\\\n", + "Batch Normalization was also applied to ensure quick and stable training process. \\\\\n", + "\n", + "The number of neurons in each hidden layer decreases as we go down the network. \\\\\n", + "More information can be stored and thus more features can be learned. \\\\\n", + "I used softmax activation function for the previous hidden layer of the Ouput Layer." + ], + "metadata": { + "id": "zQ0JZkO-yYIY" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Model compiling" + ], + "metadata": { + "id": "Kdv8zLMXz6cS" + } + }, + { + "cell_type": "code", + "source": [ + "model.compile(optimizer=optimizers.Adam(1e-4),\n", + " loss = \"binary_crossentropy\",\n", + " metrics = [metrics.AUC(name=\"AUC_ROC\"),\n", + " metrics.Recall(name=\"recall\"),\n", + " metrics.Precision(name=\"precision\")])" + ], + "metadata": { + "id": "4QPOc2oCs9Fy" + }, + "execution_count": 24, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "callbacks = [tf.keras.callbacks.ModelCheckpoint('epcoh.h5')]" + ], + "metadata": { + "id": "hRa5piR3yXNC" + }, + "execution_count": 25, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Setting Hyperparameters: \\\n", + "\\\n", + "Adam is the best optimizer in terms of performance. \\\n", + "\\\n", + "I switched the default learning rate to 1e-4.\n", + "After learning, the original learning rate was too large so I had to decrease it. \\\n", + "\\\n", + "As for the loss function I used the binary crossentropy suitable for binary classification. \\\n", + "\\\n", + "For performance metric, I included AUC_ROC(for comparison with SOTA), and Precision & Recall. \\\n", + "\\\n", + "Early Stopping method is also applied. (callbacks)" + ], + "metadata": { + "id": "lKHTLu810EDG" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Model Training" + ], + "metadata": { + "id": "PuSIuUHC1c2m" + } + }, + { + "cell_type": "code", + "source": [ + "history = model.fit(x_train, y_train,\n", + " validation_data = (x_validate, y_validate),\n", + " batch_size = 65536,\n", + " epochs = 10000,\n", + " callbacks = callbacks)" + ], + "metadata": { + "id": "LCX0zglQ0Y3l", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "12abe7ee-d278-4196-f1c1-94b8021a2167" + }, + "execution_count": 26, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[1;30;43m스트리밍 출력 내용이 길어서 마지막 5000줄이 삭제되었습니다.\u001b[0m\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0021 - AUC_ROC: 0.9728 - recall: 0.8019 - precision: 0.9307 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7502/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0020 - AUC_ROC: 0.9712 - recall: 0.8270 - precision: 0.9293 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7503/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0021 - AUC_ROC: 0.9727 - recall: 0.8208 - precision: 0.9062 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7504/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0020 - AUC_ROC: 0.9744 - recall: 0.8208 - precision: 0.9190 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7505/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0020 - AUC_ROC: 0.9743 - recall: 0.8365 - precision: 0.9236 - val_loss: 0.0046 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7506/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0022 - AUC_ROC: 0.9633 - recall: 0.7956 - precision: 0.9200 - val_loss: 0.0046 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7507/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0022 - AUC_ROC: 0.9569 - recall: 0.8208 - precision: 0.9288 - val_loss: 0.0046 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7508/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0022 - AUC_ROC: 0.9696 - recall: 0.8145 - precision: 0.9317 - val_loss: 0.0046 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7509/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0020 - AUC_ROC: 0.9664 - recall: 0.8302 - precision: 0.9263 - val_loss: 0.0046 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7510/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0022 - AUC_ROC: 0.9633 - recall: 0.8239 - precision: 0.9391 - val_loss: 0.0046 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7511/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0023 - AUC_ROC: 0.9696 - recall: 0.8176 - precision: 0.8966 - val_loss: 0.0046 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7512/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0021 - AUC_ROC: 0.9743 - recall: 0.8302 - precision: 0.9167 - val_loss: 0.0045 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7513/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0021 - AUC_ROC: 0.9696 - recall: 0.8208 - precision: 0.9255 - val_loss: 0.0044 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7514/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0022 - AUC_ROC: 0.9664 - recall: 0.8113 - precision: 0.9117 - val_loss: 0.0044 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7515/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0020 - AUC_ROC: 0.9759 - recall: 0.8428 - precision: 0.9024 - val_loss: 0.0045 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7516/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0019 - AUC_ROC: 0.9697 - recall: 0.8270 - precision: 0.9460 - val_loss: 0.0044 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7517/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0021 - AUC_ROC: 0.9712 - recall: 0.8333 - precision: 0.9170 - val_loss: 0.0044 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7518/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0022 - AUC_ROC: 0.9554 - recall: 0.7925 - precision: 0.9403 - val_loss: 0.0044 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7519/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0021 - AUC_ROC: 0.9648 - recall: 0.8239 - precision: 0.9291 - val_loss: 0.0044 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7520/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0021 - AUC_ROC: 0.9617 - recall: 0.7925 - precision: 0.9333 - val_loss: 0.0045 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7521/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0019 - AUC_ROC: 0.9681 - recall: 0.8113 - precision: 0.9451 - val_loss: 0.0044 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7522/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0018 - AUC_ROC: 0.9776 - recall: 0.8270 - precision: 0.9261 - val_loss: 0.0044 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7523/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0023 - AUC_ROC: 0.9648 - recall: 0.7830 - precision: 0.9188 - val_loss: 0.0044 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7524/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0021 - AUC_ROC: 0.9680 - recall: 0.8208 - precision: 0.9223 - val_loss: 0.0043 - val_AUC_ROC: 0.8949 - val_recall: 0.7654 - val_precision: 0.9538\n", + "Epoch 7525/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0021 - AUC_ROC: 0.9633 - recall: 0.8176 - precision: 0.9386 - val_loss: 0.0043 - val_AUC_ROC: 0.9011 - val_recall: 0.7654 - val_precision: 0.9538\n", + "Epoch 7526/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0023 - AUC_ROC: 0.9616 - recall: 0.8082 - precision: 0.9414 - val_loss: 0.0043 - val_AUC_ROC: 0.9011 - val_recall: 0.7654 - val_precision: 0.9538\n", + "Epoch 7527/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0022 - AUC_ROC: 0.9710 - recall: 0.8082 - precision: 0.8924 - val_loss: 0.0043 - val_AUC_ROC: 0.9011 - val_recall: 0.7654 - val_precision: 0.9538\n", + "Epoch 7528/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0022 - AUC_ROC: 0.9728 - recall: 0.8082 - precision: 0.9049 - val_loss: 0.0043 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9538\n", + "Epoch 7529/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0023 - AUC_ROC: 0.9601 - recall: 0.8145 - precision: 0.9384 - val_loss: 0.0043 - val_AUC_ROC: 0.8888 - val_recall: 0.7654 - val_precision: 0.9538\n", + "Epoch 7530/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0021 - AUC_ROC: 0.9712 - recall: 0.7987 - precision: 0.9270 - val_loss: 0.0044 - val_AUC_ROC: 0.8888 - val_recall: 0.7654 - val_precision: 0.9538\n", + "Epoch 7531/10000\n", + "3/3 [==============================] - 0s 98ms/step - loss: 0.0023 - AUC_ROC: 0.9680 - recall: 0.7925 - precision: 0.9333 - val_loss: 0.0043 - val_AUC_ROC: 0.8949 - val_recall: 0.7654 - val_precision: 0.9538\n", + "Epoch 7532/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0021 - AUC_ROC: 0.9633 - recall: 0.8396 - precision: 0.9239 - val_loss: 0.0044 - val_AUC_ROC: 0.8949 - val_recall: 0.7654 - val_precision: 0.9538\n", + "Epoch 7533/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0022 - AUC_ROC: 0.9648 - recall: 0.7956 - precision: 0.9134 - val_loss: 0.0044 - val_AUC_ROC: 0.8949 - val_recall: 0.7531 - val_precision: 0.9531\n", + "Epoch 7534/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0024 - AUC_ROC: 0.9584 - recall: 0.7956 - precision: 0.9267 - val_loss: 0.0044 - val_AUC_ROC: 0.8949 - val_recall: 0.7531 - val_precision: 0.9531\n", + "Epoch 7535/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0019 - AUC_ROC: 0.9697 - recall: 0.8239 - precision: 0.9357 - val_loss: 0.0044 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7536/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0021 - AUC_ROC: 0.9680 - recall: 0.8145 - precision: 0.9317 - val_loss: 0.0044 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7537/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0020 - AUC_ROC: 0.9743 - recall: 0.8113 - precision: 0.8990 - val_loss: 0.0044 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7538/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0021 - AUC_ROC: 0.9602 - recall: 0.8365 - precision: 0.9333 - val_loss: 0.0044 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7539/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0020 - AUC_ROC: 0.9696 - recall: 0.8270 - precision: 0.8976 - val_loss: 0.0045 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7540/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0021 - AUC_ROC: 0.9728 - recall: 0.8050 - precision: 0.9309 - val_loss: 0.0045 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7541/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0021 - AUC_ROC: 0.9617 - recall: 0.7925 - precision: 0.9368 - val_loss: 0.0045 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7542/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0022 - AUC_ROC: 0.9665 - recall: 0.8145 - precision: 0.9250 - val_loss: 0.0046 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7543/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0021 - AUC_ROC: 0.9601 - recall: 0.8050 - precision: 0.9446 - val_loss: 0.0046 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7544/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0020 - AUC_ROC: 0.9617 - recall: 0.8302 - precision: 0.9429 - val_loss: 0.0046 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7545/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0021 - AUC_ROC: 0.9601 - recall: 0.8050 - precision: 0.9517 - val_loss: 0.0046 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7546/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0020 - AUC_ROC: 0.9743 - recall: 0.8239 - precision: 0.9161 - val_loss: 0.0046 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7547/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0021 - AUC_ROC: 0.9696 - recall: 0.8019 - precision: 0.9515 - val_loss: 0.0046 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7548/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0018 - AUC_ROC: 0.9728 - recall: 0.8302 - precision: 0.9362 - val_loss: 0.0045 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7549/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0022 - AUC_ROC: 0.9758 - recall: 0.8050 - precision: 0.9176 - val_loss: 0.0045 - val_AUC_ROC: 0.8888 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7550/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0019 - AUC_ROC: 0.9696 - recall: 0.8365 - precision: 0.9534 - val_loss: 0.0045 - val_AUC_ROC: 0.8888 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7551/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0020 - AUC_ROC: 0.9758 - recall: 0.8239 - precision: 0.9003 - val_loss: 0.0045 - val_AUC_ROC: 0.8888 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7552/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0021 - AUC_ROC: 0.9649 - recall: 0.8145 - precision: 0.9184 - val_loss: 0.0045 - val_AUC_ROC: 0.8888 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7553/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0019 - AUC_ROC: 0.9697 - recall: 0.8270 - precision: 0.9359 - val_loss: 0.0045 - val_AUC_ROC: 0.8888 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7554/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0021 - AUC_ROC: 0.9696 - recall: 0.8082 - precision: 0.9113 - val_loss: 0.0045 - val_AUC_ROC: 0.8888 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7555/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0021 - AUC_ROC: 0.9680 - recall: 0.8208 - precision: 0.9190 - val_loss: 0.0045 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7556/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0021 - AUC_ROC: 0.9649 - recall: 0.8176 - precision: 0.9489 - val_loss: 0.0045 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7557/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0021 - AUC_ROC: 0.9681 - recall: 0.8302 - precision: 0.9329 - val_loss: 0.0045 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7558/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0019 - AUC_ROC: 0.9743 - recall: 0.8239 - precision: 0.9193 - val_loss: 0.0045 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7559/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0021 - AUC_ROC: 0.9665 - recall: 0.7987 - precision: 0.9442 - val_loss: 0.0046 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7560/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0020 - AUC_ROC: 0.9680 - recall: 0.8176 - precision: 0.9123 - val_loss: 0.0045 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7561/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0019 - AUC_ROC: 0.9759 - recall: 0.8239 - precision: 0.9258 - val_loss: 0.0046 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7562/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0019 - AUC_ROC: 0.9696 - recall: 0.8113 - precision: 0.9314 - val_loss: 0.0046 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7563/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0021 - AUC_ROC: 0.9632 - recall: 0.8208 - precision: 0.9321 - val_loss: 0.0045 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7564/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0020 - AUC_ROC: 0.9696 - recall: 0.8176 - precision: 0.9155 - val_loss: 0.0045 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7565/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0020 - AUC_ROC: 0.9712 - recall: 0.8270 - precision: 0.9164 - val_loss: 0.0044 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7566/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0019 - AUC_ROC: 0.9774 - recall: 0.8270 - precision: 0.9427 - val_loss: 0.0044 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7567/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0021 - AUC_ROC: 0.9680 - recall: 0.8208 - precision: 0.9388 - val_loss: 0.0044 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7568/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0021 - AUC_ROC: 0.9728 - recall: 0.8113 - precision: 0.9021 - val_loss: 0.0044 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7569/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0022 - AUC_ROC: 0.9585 - recall: 0.8145 - precision: 0.9317 - val_loss: 0.0045 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7570/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0021 - AUC_ROC: 0.9696 - recall: 0.8082 - precision: 0.9380 - val_loss: 0.0045 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7571/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0022 - AUC_ROC: 0.9680 - recall: 0.8145 - precision: 0.9184 - val_loss: 0.0045 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7572/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0021 - AUC_ROC: 0.9617 - recall: 0.8176 - precision: 0.9220 - val_loss: 0.0045 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7573/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0020 - AUC_ROC: 0.9680 - recall: 0.8239 - precision: 0.9324 - val_loss: 0.0045 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7574/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0021 - AUC_ROC: 0.9726 - recall: 0.8176 - precision: 0.8874 - val_loss: 0.0045 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7575/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0021 - AUC_ROC: 0.9759 - recall: 0.8208 - precision: 0.9288 - val_loss: 0.0046 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7576/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0020 - AUC_ROC: 0.9665 - recall: 0.8302 - precision: 0.9531 - val_loss: 0.0046 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7577/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0019 - AUC_ROC: 0.9759 - recall: 0.8145 - precision: 0.9384 - val_loss: 0.0046 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7578/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0021 - AUC_ROC: 0.9743 - recall: 0.8239 - precision: 0.9003 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7579/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0021 - AUC_ROC: 0.9680 - recall: 0.7987 - precision: 0.9373 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7580/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0021 - AUC_ROC: 0.9649 - recall: 0.8050 - precision: 0.9517 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7581/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0018 - AUC_ROC: 0.9807 - recall: 0.8176 - precision: 0.9386 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7582/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0019 - AUC_ROC: 0.9790 - recall: 0.8208 - precision: 0.9288 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7583/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0021 - AUC_ROC: 0.9743 - recall: 0.8365 - precision: 0.9048 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7584/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0021 - AUC_ROC: 0.9617 - recall: 0.8270 - precision: 0.9393 - val_loss: 0.0047 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7585/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0020 - AUC_ROC: 0.9681 - recall: 0.8145 - precision: 0.9283 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7586/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0021 - AUC_ROC: 0.9618 - recall: 0.8113 - precision: 0.9416 - val_loss: 0.0047 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7587/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0021 - AUC_ROC: 0.9632 - recall: 0.8050 - precision: 0.9275 - val_loss: 0.0047 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7588/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0022 - AUC_ROC: 0.9617 - recall: 0.8176 - precision: 0.9286 - val_loss: 0.0046 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7589/10000\n", + "3/3 [==============================] - 0s 114ms/step - loss: 0.0021 - AUC_ROC: 0.9648 - recall: 0.8176 - precision: 0.9353 - val_loss: 0.0046 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7590/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0021 - AUC_ROC: 0.9712 - recall: 0.8333 - precision: 0.8923 - val_loss: 0.0047 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7591/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0020 - AUC_ROC: 0.9664 - recall: 0.8082 - precision: 0.9345 - val_loss: 0.0047 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7592/10000\n", + "3/3 [==============================] - 0s 123ms/step - loss: 0.0021 - AUC_ROC: 0.9695 - recall: 0.8113 - precision: 0.9021 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7593/10000\n", + "3/3 [==============================] - 0s 140ms/step - loss: 0.0019 - AUC_ROC: 0.9681 - recall: 0.8270 - precision: 0.9359 - val_loss: 0.0047 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7594/10000\n", + "3/3 [==============================] - 0s 132ms/step - loss: 0.0019 - AUC_ROC: 0.9728 - recall: 0.8333 - precision: 0.9266 - val_loss: 0.0047 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7595/10000\n", + "3/3 [==============================] - 0s 120ms/step - loss: 0.0018 - AUC_ROC: 0.9650 - recall: 0.8396 - precision: 0.9536 - val_loss: 0.0047 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7596/10000\n", + "3/3 [==============================] - 0s 148ms/step - loss: 0.0021 - AUC_ROC: 0.9617 - recall: 0.8050 - precision: 0.9143 - val_loss: 0.0046 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7597/10000\n", + "3/3 [==============================] - 0s 129ms/step - loss: 0.0020 - AUC_ROC: 0.9696 - recall: 0.8270 - precision: 0.9529 - val_loss: 0.0046 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7598/10000\n", + "3/3 [==============================] - 0s 155ms/step - loss: 0.0022 - AUC_ROC: 0.9665 - recall: 0.8050 - precision: 0.9209 - val_loss: 0.0046 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7599/10000\n", + "3/3 [==============================] - 0s 138ms/step - loss: 0.0021 - AUC_ROC: 0.9726 - recall: 0.8208 - precision: 0.8969 - val_loss: 0.0046 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7600/10000\n", + "3/3 [==============================] - 0s 138ms/step - loss: 0.0020 - AUC_ROC: 0.9649 - recall: 0.8333 - precision: 0.9266 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7601/10000\n", + "3/3 [==============================] - 0s 123ms/step - loss: 0.0022 - AUC_ROC: 0.9648 - recall: 0.8050 - precision: 0.9275 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7602/10000\n", + "3/3 [==============================] - 0s 129ms/step - loss: 0.0018 - AUC_ROC: 0.9727 - recall: 0.8365 - precision: 0.9568 - val_loss: 0.0046 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7603/10000\n", + "3/3 [==============================] - 0s 142ms/step - loss: 0.0019 - AUC_ROC: 0.9697 - recall: 0.8019 - precision: 0.9551 - val_loss: 0.0046 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7604/10000\n", + "3/3 [==============================] - 0s 140ms/step - loss: 0.0022 - AUC_ROC: 0.9649 - recall: 0.7893 - precision: 0.9127 - val_loss: 0.0046 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7605/10000\n", + "3/3 [==============================] - 0s 146ms/step - loss: 0.0021 - AUC_ROC: 0.9680 - recall: 0.8113 - precision: 0.9085 - val_loss: 0.0046 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7606/10000\n", + "3/3 [==============================] - 0s 130ms/step - loss: 0.0020 - AUC_ROC: 0.9744 - recall: 0.8396 - precision: 0.9303 - val_loss: 0.0045 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7607/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0019 - AUC_ROC: 0.9712 - recall: 0.8616 - precision: 0.9384 - val_loss: 0.0046 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7608/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0020 - AUC_ROC: 0.9680 - recall: 0.8270 - precision: 0.9393 - val_loss: 0.0046 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7609/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0022 - AUC_ROC: 0.9728 - recall: 0.8365 - precision: 0.8779 - val_loss: 0.0047 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7610/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0022 - AUC_ROC: 0.9616 - recall: 0.8019 - precision: 0.9107 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7611/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0021 - AUC_ROC: 0.9728 - recall: 0.8208 - precision: 0.9190 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7612/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0020 - AUC_ROC: 0.9696 - recall: 0.8176 - precision: 0.9420 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7613/10000\n", + "3/3 [==============================] - 0s 114ms/step - loss: 0.0019 - AUC_ROC: 0.9697 - recall: 0.8302 - precision: 0.9231 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7614/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0022 - AUC_ROC: 0.9680 - recall: 0.7862 - precision: 0.9158 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7615/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0022 - AUC_ROC: 0.9680 - recall: 0.7987 - precision: 0.9039 - val_loss: 0.0048 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7616/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0021 - AUC_ROC: 0.9665 - recall: 0.8302 - precision: 0.9496 - val_loss: 0.0047 - val_AUC_ROC: 0.8764 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7617/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0022 - AUC_ROC: 0.9648 - recall: 0.7893 - precision: 0.9194 - val_loss: 0.0047 - val_AUC_ROC: 0.8764 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7618/10000\n", + "3/3 [==============================] - 0s 114ms/step - loss: 0.0021 - AUC_ROC: 0.9665 - recall: 0.8113 - precision: 0.9314 - val_loss: 0.0047 - val_AUC_ROC: 0.8764 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7619/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0020 - AUC_ROC: 0.9712 - recall: 0.8113 - precision: 0.9314 - val_loss: 0.0047 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7620/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0022 - AUC_ROC: 0.9632 - recall: 0.8050 - precision: 0.9143 - val_loss: 0.0047 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7621/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0020 - AUC_ROC: 0.9680 - recall: 0.8333 - precision: 0.9201 - val_loss: 0.0047 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7622/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0021 - AUC_ROC: 0.9696 - recall: 0.8270 - precision: 0.9164 - val_loss: 0.0047 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7623/10000\n", + "3/3 [==============================] - 0s 116ms/step - loss: 0.0020 - AUC_ROC: 0.9665 - recall: 0.8176 - precision: 0.9455 - val_loss: 0.0047 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7624/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0023 - AUC_ROC: 0.9695 - recall: 0.8176 - precision: 0.8935 - val_loss: 0.0046 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7625/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0023 - AUC_ROC: 0.9695 - recall: 0.8019 - precision: 0.9273 - val_loss: 0.0046 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7626/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0020 - AUC_ROC: 0.9697 - recall: 0.8082 - precision: 0.9345 - val_loss: 0.0045 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7627/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0022 - AUC_ROC: 0.9680 - recall: 0.8050 - precision: 0.9078 - val_loss: 0.0044 - val_AUC_ROC: 0.9011 - val_recall: 0.7778 - val_precision: 0.9692\n", + "Epoch 7628/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0020 - AUC_ROC: 0.9711 - recall: 0.8239 - precision: 0.9424 - val_loss: 0.0044 - val_AUC_ROC: 0.9011 - val_recall: 0.7778 - val_precision: 0.9692\n", + "Epoch 7629/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0020 - AUC_ROC: 0.9665 - recall: 0.8145 - precision: 0.9418 - val_loss: 0.0044 - val_AUC_ROC: 0.9011 - val_recall: 0.7778 - val_precision: 0.9692\n", + "Epoch 7630/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0020 - AUC_ROC: 0.9727 - recall: 0.8428 - precision: 0.9504 - val_loss: 0.0044 - val_AUC_ROC: 0.9011 - val_recall: 0.7778 - val_precision: 0.9692\n", + "Epoch 7631/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0021 - AUC_ROC: 0.9696 - recall: 0.8459 - precision: 0.9088 - val_loss: 0.0044 - val_AUC_ROC: 0.9011 - val_recall: 0.7778 - val_precision: 0.9692\n", + "Epoch 7632/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0020 - AUC_ROC: 0.9696 - recall: 0.8396 - precision: 0.9144 - val_loss: 0.0044 - val_AUC_ROC: 0.8950 - val_recall: 0.7778 - val_precision: 0.9692\n", + "Epoch 7633/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0021 - AUC_ROC: 0.9711 - recall: 0.8113 - precision: 0.9214 - val_loss: 0.0045 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7634/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0022 - AUC_ROC: 0.9665 - recall: 0.7893 - precision: 0.9194 - val_loss: 0.0045 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7635/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0021 - AUC_ROC: 0.9680 - recall: 0.8176 - precision: 0.9420 - val_loss: 0.0046 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7636/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0020 - AUC_ROC: 0.9790 - recall: 0.8082 - precision: 0.9081 - val_loss: 0.0046 - val_AUC_ROC: 0.8888 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7637/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0022 - AUC_ROC: 0.9617 - recall: 0.7956 - precision: 0.9267 - val_loss: 0.0046 - val_AUC_ROC: 0.8888 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7638/10000\n", + "3/3 [==============================] - 0s 120ms/step - loss: 0.0022 - AUC_ROC: 0.9617 - recall: 0.8050 - precision: 0.9209 - val_loss: 0.0046 - val_AUC_ROC: 0.8888 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7639/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0021 - AUC_ROC: 0.9713 - recall: 0.8145 - precision: 0.9317 - val_loss: 0.0046 - val_AUC_ROC: 0.8888 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7640/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0020 - AUC_ROC: 0.9774 - recall: 0.8239 - precision: 0.9193 - val_loss: 0.0046 - val_AUC_ROC: 0.8888 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7641/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0023 - AUC_ROC: 0.9727 - recall: 0.8270 - precision: 0.8738 - val_loss: 0.0046 - val_AUC_ROC: 0.8888 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7642/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0021 - AUC_ROC: 0.9711 - recall: 0.8019 - precision: 0.9173 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7643/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0021 - AUC_ROC: 0.9665 - recall: 0.7987 - precision: 0.9373 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7644/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0020 - AUC_ROC: 0.9650 - recall: 0.8239 - precision: 0.9458 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7645/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0020 - AUC_ROC: 0.9727 - recall: 0.8239 - precision: 0.9258 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7646/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0022 - AUC_ROC: 0.9617 - recall: 0.8145 - precision: 0.9350 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7647/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0018 - AUC_ROC: 0.9681 - recall: 0.8239 - precision: 0.9704 - val_loss: 0.0046 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7648/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0020 - AUC_ROC: 0.9759 - recall: 0.7987 - precision: 0.9304 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7649/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0021 - AUC_ROC: 0.9665 - recall: 0.8082 - precision: 0.9278 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7650/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0019 - AUC_ROC: 0.9649 - recall: 0.8239 - precision: 0.9668 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7651/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0020 - AUC_ROC: 0.9649 - recall: 0.8050 - precision: 0.9242 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7652/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0020 - AUC_ROC: 0.9648 - recall: 0.8145 - precision: 0.9487 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7653/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0021 - AUC_ROC: 0.9696 - recall: 0.8082 - precision: 0.9211 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7654/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0019 - AUC_ROC: 0.9760 - recall: 0.8145 - precision: 0.9384 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7655/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0019 - AUC_ROC: 0.9775 - recall: 0.8145 - precision: 0.9283 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7656/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0020 - AUC_ROC: 0.9696 - recall: 0.8176 - precision: 0.9286 - val_loss: 0.0048 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7657/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0019 - AUC_ROC: 0.9758 - recall: 0.8428 - precision: 0.9306 - val_loss: 0.0048 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7658/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0020 - AUC_ROC: 0.9697 - recall: 0.8239 - precision: 0.9324 - val_loss: 0.0048 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7659/10000\n", + "3/3 [==============================] - 0s 117ms/step - loss: 0.0020 - AUC_ROC: 0.9712 - recall: 0.8082 - precision: 0.9146 - val_loss: 0.0048 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7660/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0021 - AUC_ROC: 0.9680 - recall: 0.8208 - precision: 0.9255 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7661/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0023 - AUC_ROC: 0.9680 - recall: 0.8270 - precision: 0.9100 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7662/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0021 - AUC_ROC: 0.9727 - recall: 0.8145 - precision: 0.9283 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7663/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0020 - AUC_ROC: 0.9712 - recall: 0.8113 - precision: 0.9214 - val_loss: 0.0046 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7664/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0022 - AUC_ROC: 0.9633 - recall: 0.8019 - precision: 0.9551 - val_loss: 0.0046 - val_AUC_ROC: 0.8888 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7665/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0019 - AUC_ROC: 0.9743 - recall: 0.8270 - precision: 0.9293 - val_loss: 0.0045 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7666/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0021 - AUC_ROC: 0.9633 - recall: 0.8333 - precision: 0.9138 - val_loss: 0.0045 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7667/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0020 - AUC_ROC: 0.9649 - recall: 0.8050 - precision: 0.9412 - val_loss: 0.0045 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7668/10000\n", + "3/3 [==============================] - 0s 114ms/step - loss: 0.0021 - AUC_ROC: 0.9633 - recall: 0.7893 - precision: 0.9366 - val_loss: 0.0044 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7669/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0019 - AUC_ROC: 0.9712 - recall: 0.8208 - precision: 0.9190 - val_loss: 0.0044 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7670/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0019 - AUC_ROC: 0.9712 - recall: 0.8176 - precision: 0.9455 - val_loss: 0.0044 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7671/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0022 - AUC_ROC: 0.9649 - recall: 0.8050 - precision: 0.9078 - val_loss: 0.0045 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7672/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0022 - AUC_ROC: 0.9649 - recall: 0.8176 - precision: 0.9253 - val_loss: 0.0045 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7673/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0020 - AUC_ROC: 0.9633 - recall: 0.8270 - precision: 0.9359 - val_loss: 0.0046 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7674/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0022 - AUC_ROC: 0.9617 - recall: 0.8019 - precision: 0.8947 - val_loss: 0.0046 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7675/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0022 - AUC_ROC: 0.9617 - recall: 0.8113 - precision: 0.9181 - val_loss: 0.0046 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7676/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0022 - AUC_ROC: 0.9680 - recall: 0.7925 - precision: 0.9032 - val_loss: 0.0047 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7677/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0022 - AUC_ROC: 0.9680 - recall: 0.7799 - precision: 0.9219 - val_loss: 0.0047 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7678/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0020 - AUC_ROC: 0.9727 - recall: 0.8019 - precision: 0.9273 - val_loss: 0.0047 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7679/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0021 - AUC_ROC: 0.9711 - recall: 0.8208 - precision: 0.9355 - val_loss: 0.0047 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7680/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0021 - AUC_ROC: 0.9696 - recall: 0.8145 - precision: 0.9487 - val_loss: 0.0047 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7681/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0020 - AUC_ROC: 0.9791 - recall: 0.7925 - precision: 0.9509 - val_loss: 0.0047 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7682/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0021 - AUC_ROC: 0.9695 - recall: 0.8365 - precision: 0.9236 - val_loss: 0.0046 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7683/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0020 - AUC_ROC: 0.9712 - recall: 0.8113 - precision: 0.9348 - val_loss: 0.0046 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7684/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0020 - AUC_ROC: 0.9712 - recall: 0.8082 - precision: 0.9380 - val_loss: 0.0045 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7685/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0020 - AUC_ROC: 0.9712 - recall: 0.8145 - precision: 0.9152 - val_loss: 0.0045 - val_AUC_ROC: 0.8826 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7686/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0022 - AUC_ROC: 0.9680 - recall: 0.8050 - precision: 0.9275 - val_loss: 0.0045 - val_AUC_ROC: 0.8826 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7687/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0022 - AUC_ROC: 0.9679 - recall: 0.8145 - precision: 0.9088 - val_loss: 0.0046 - val_AUC_ROC: 0.8826 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7688/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0019 - AUC_ROC: 0.9680 - recall: 0.8208 - precision: 0.9321 - val_loss: 0.0046 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7689/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0021 - AUC_ROC: 0.9647 - recall: 0.8208 - precision: 0.9321 - val_loss: 0.0047 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7690/10000\n", + "3/3 [==============================] - 0s 123ms/step - loss: 0.0023 - AUC_ROC: 0.9647 - recall: 0.8208 - precision: 0.8847 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7691/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0022 - AUC_ROC: 0.9649 - recall: 0.7956 - precision: 0.9301 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7692/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0019 - AUC_ROC: 0.9681 - recall: 0.8459 - precision: 0.9244 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7693/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0021 - AUC_ROC: 0.9680 - recall: 0.8270 - precision: 0.9196 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7694/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0021 - AUC_ROC: 0.9680 - recall: 0.7987 - precision: 0.9071 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7695/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0019 - AUC_ROC: 0.9712 - recall: 0.8270 - precision: 0.9164 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7696/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0021 - AUC_ROC: 0.9680 - recall: 0.8019 - precision: 0.9551 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7697/10000\n", + "3/3 [==============================] - 0s 116ms/step - loss: 0.0021 - AUC_ROC: 0.9696 - recall: 0.8050 - precision: 0.9209 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7698/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0021 - AUC_ROC: 0.9633 - recall: 0.8145 - precision: 0.9522 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7699/10000\n", + "3/3 [==============================] - 0s 118ms/step - loss: 0.0021 - AUC_ROC: 0.9696 - recall: 0.8145 - precision: 0.9120 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7700/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0019 - AUC_ROC: 0.9712 - recall: 0.8270 - precision: 0.9326 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7701/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0022 - AUC_ROC: 0.9696 - recall: 0.8208 - precision: 0.9000 - val_loss: 0.0046 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7702/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0022 - AUC_ROC: 0.9681 - recall: 0.8145 - precision: 0.9152 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7703/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0018 - AUC_ROC: 0.9697 - recall: 0.8365 - precision: 0.9333 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7704/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0018 - AUC_ROC: 0.9792 - recall: 0.8333 - precision: 0.9170 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7705/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0020 - AUC_ROC: 0.9649 - recall: 0.8459 - precision: 0.9276 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7706/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0019 - AUC_ROC: 0.9664 - recall: 0.8491 - precision: 0.9408 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7707/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0021 - AUC_ROC: 0.9602 - recall: 0.8019 - precision: 0.9480 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7708/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0018 - AUC_ROC: 0.9696 - recall: 0.8239 - precision: 0.9493 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7709/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0020 - AUC_ROC: 0.9712 - recall: 0.8082 - precision: 0.9312 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7710/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0021 - AUC_ROC: 0.9712 - recall: 0.8239 - precision: 0.9225 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7711/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0019 - AUC_ROC: 0.9775 - recall: 0.8239 - precision: 0.9324 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7712/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0021 - AUC_ROC: 0.9664 - recall: 0.7956 - precision: 0.9234 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7713/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0017 - AUC_ROC: 0.9775 - recall: 0.8428 - precision: 0.9404 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7714/10000\n", + "3/3 [==============================] - 0s 115ms/step - loss: 0.0021 - AUC_ROC: 0.9696 - recall: 0.8302 - precision: 0.9103 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7715/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0022 - AUC_ROC: 0.9711 - recall: 0.8176 - precision: 0.9059 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7716/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0021 - AUC_ROC: 0.9680 - recall: 0.8239 - precision: 0.9097 - val_loss: 0.0048 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7717/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0018 - AUC_ROC: 0.9712 - recall: 0.8459 - precision: 0.9373 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7718/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0020 - AUC_ROC: 0.9650 - recall: 0.8302 - precision: 0.9329 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7719/10000\n", + "3/3 [==============================] - 0s 98ms/step - loss: 0.0020 - AUC_ROC: 0.9697 - recall: 0.8113 - precision: 0.9520 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7720/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0019 - AUC_ROC: 0.9791 - recall: 0.8333 - precision: 0.9397 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7721/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0021 - AUC_ROC: 0.9665 - recall: 0.8050 - precision: 0.9242 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7722/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0020 - AUC_ROC: 0.9697 - recall: 0.8176 - precision: 0.9253 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7723/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0021 - AUC_ROC: 0.9649 - recall: 0.8113 - precision: 0.9348 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7724/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0021 - AUC_ROC: 0.9774 - recall: 0.7987 - precision: 0.9304 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7725/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0021 - AUC_ROC: 0.9728 - recall: 0.7956 - precision: 0.9267 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7726/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0021 - AUC_ROC: 0.9665 - recall: 0.8176 - precision: 0.9319 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7727/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0020 - AUC_ROC: 0.9696 - recall: 0.8302 - precision: 0.9362 - val_loss: 0.0048 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7728/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0020 - AUC_ROC: 0.9712 - recall: 0.8113 - precision: 0.9085 - val_loss: 0.0048 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7729/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0020 - AUC_ROC: 0.9649 - recall: 0.8333 - precision: 0.9567 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7730/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0021 - AUC_ROC: 0.9633 - recall: 0.7987 - precision: 0.9270 - val_loss: 0.0046 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7731/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0019 - AUC_ROC: 0.9680 - recall: 0.8113 - precision: 0.9214 - val_loss: 0.0045 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7732/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0021 - AUC_ROC: 0.9633 - recall: 0.8113 - precision: 0.9348 - val_loss: 0.0045 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7733/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0021 - AUC_ROC: 0.9648 - recall: 0.8208 - precision: 0.9158 - val_loss: 0.0045 - val_AUC_ROC: 0.8888 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7734/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0020 - AUC_ROC: 0.9744 - recall: 0.8208 - precision: 0.9062 - val_loss: 0.0045 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7735/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0020 - AUC_ROC: 0.9649 - recall: 0.8208 - precision: 0.9321 - val_loss: 0.0046 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7736/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0022 - AUC_ROC: 0.9696 - recall: 0.8145 - precision: 0.9250 - val_loss: 0.0046 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7737/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0020 - AUC_ROC: 0.9743 - recall: 0.8365 - precision: 0.9236 - val_loss: 0.0047 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7738/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0020 - AUC_ROC: 0.9743 - recall: 0.8428 - precision: 0.9085 - val_loss: 0.0047 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7739/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0019 - AUC_ROC: 0.9697 - recall: 0.8239 - precision: 0.9291 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7740/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0021 - AUC_ROC: 0.9633 - recall: 0.8302 - precision: 0.9103 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7741/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0021 - AUC_ROC: 0.9728 - recall: 0.8239 - precision: 0.9258 - val_loss: 0.0047 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7742/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0021 - AUC_ROC: 0.9649 - recall: 0.7956 - precision: 0.9440 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7743/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0020 - AUC_ROC: 0.9681 - recall: 0.8050 - precision: 0.9242 - val_loss: 0.0046 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7744/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0018 - AUC_ROC: 0.9775 - recall: 0.8553 - precision: 0.9220 - val_loss: 0.0046 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7745/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0020 - AUC_ROC: 0.9775 - recall: 0.8333 - precision: 0.8983 - val_loss: 0.0046 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7746/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0022 - AUC_ROC: 0.9664 - recall: 0.7893 - precision: 0.9262 - val_loss: 0.0046 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7747/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0018 - AUC_ROC: 0.9775 - recall: 0.8208 - precision: 0.9255 - val_loss: 0.0046 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7748/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0021 - AUC_ROC: 0.9632 - recall: 0.8208 - precision: 0.9190 - val_loss: 0.0046 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7749/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0022 - AUC_ROC: 0.9696 - recall: 0.7987 - precision: 0.9304 - val_loss: 0.0046 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7750/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0021 - AUC_ROC: 0.9633 - recall: 0.8082 - precision: 0.9554 - val_loss: 0.0045 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7751/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0020 - AUC_ROC: 0.9665 - recall: 0.7956 - precision: 0.9547 - val_loss: 0.0045 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7752/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0019 - AUC_ROC: 0.9759 - recall: 0.8302 - precision: 0.9362 - val_loss: 0.0044 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7753/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0018 - AUC_ROC: 0.9633 - recall: 0.8365 - precision: 0.9366 - val_loss: 0.0044 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7754/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0022 - AUC_ROC: 0.9790 - recall: 0.8082 - precision: 0.8955 - val_loss: 0.0044 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7755/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0020 - AUC_ROC: 0.9712 - recall: 0.8302 - precision: 0.8919 - val_loss: 0.0044 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7756/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0021 - AUC_ROC: 0.9680 - recall: 0.8145 - precision: 0.9317 - val_loss: 0.0044 - val_AUC_ROC: 0.8888 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7757/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0019 - AUC_ROC: 0.9744 - recall: 0.8082 - precision: 0.9146 - val_loss: 0.0044 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7758/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0017 - AUC_ROC: 0.9760 - recall: 0.8302 - precision: 0.9362 - val_loss: 0.0045 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7759/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0020 - AUC_ROC: 0.9680 - recall: 0.8145 - precision: 0.9593 - val_loss: 0.0045 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7760/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0020 - AUC_ROC: 0.9649 - recall: 0.8302 - precision: 0.9199 - val_loss: 0.0045 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7761/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0019 - AUC_ROC: 0.9696 - recall: 0.8145 - precision: 0.9217 - val_loss: 0.0045 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7762/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0022 - AUC_ROC: 0.9695 - recall: 0.7956 - precision: 0.9405 - val_loss: 0.0045 - val_AUC_ROC: 0.8888 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7763/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0021 - AUC_ROC: 0.9665 - recall: 0.7925 - precision: 0.9403 - val_loss: 0.0046 - val_AUC_ROC: 0.8888 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7764/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0023 - AUC_ROC: 0.9602 - recall: 0.8050 - precision: 0.9242 - val_loss: 0.0046 - val_AUC_ROC: 0.8888 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7765/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0020 - AUC_ROC: 0.9697 - recall: 0.8208 - precision: 0.9223 - val_loss: 0.0046 - val_AUC_ROC: 0.8888 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7766/10000\n", + "3/3 [==============================] - 0s 120ms/step - loss: 0.0021 - AUC_ROC: 0.9695 - recall: 0.8333 - precision: 0.9138 - val_loss: 0.0046 - val_AUC_ROC: 0.8888 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7767/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0022 - AUC_ROC: 0.9648 - recall: 0.8239 - precision: 0.9097 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7768/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0019 - AUC_ROC: 0.9744 - recall: 0.8239 - precision: 0.9258 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7769/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0021 - AUC_ROC: 0.9712 - recall: 0.8396 - precision: 0.8960 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7770/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0020 - AUC_ROC: 0.9696 - recall: 0.7893 - precision: 0.9331 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7771/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0020 - AUC_ROC: 0.9634 - recall: 0.8113 - precision: 0.9591 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7772/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0020 - AUC_ROC: 0.9649 - recall: 0.8113 - precision: 0.9247 - val_loss: 0.0046 - val_AUC_ROC: 0.8888 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7773/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0023 - AUC_ROC: 0.9648 - recall: 0.7956 - precision: 0.8972 - val_loss: 0.0046 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7774/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0022 - AUC_ROC: 0.9649 - recall: 0.7893 - precision: 0.9366 - val_loss: 0.0046 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7775/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0020 - AUC_ROC: 0.9681 - recall: 0.7956 - precision: 0.9370 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7776/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0020 - AUC_ROC: 0.9760 - recall: 0.8145 - precision: 0.9283 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7777/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0022 - AUC_ROC: 0.9681 - recall: 0.7987 - precision: 0.9203 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7778/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0020 - AUC_ROC: 0.9712 - recall: 0.8302 - precision: 0.9296 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7779/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0021 - AUC_ROC: 0.9680 - recall: 0.8208 - precision: 0.9126 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7780/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0022 - AUC_ROC: 0.9664 - recall: 0.7987 - precision: 0.9104 - val_loss: 0.0048 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7781/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0020 - AUC_ROC: 0.9649 - recall: 0.8239 - precision: 0.9493 - val_loss: 0.0048 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7782/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0018 - AUC_ROC: 0.9697 - recall: 0.8396 - precision: 0.9502 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7783/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0018 - AUC_ROC: 0.9807 - recall: 0.8365 - precision: 0.9110 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7784/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0020 - AUC_ROC: 0.9712 - recall: 0.8208 - precision: 0.9190 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7785/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0021 - AUC_ROC: 0.9681 - recall: 0.8428 - precision: 0.9404 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7786/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0024 - AUC_ROC: 0.9569 - recall: 0.7925 - precision: 0.9097 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7787/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0020 - AUC_ROC: 0.9618 - recall: 0.8302 - precision: 0.9429 - val_loss: 0.0046 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7788/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0018 - AUC_ROC: 0.9713 - recall: 0.8365 - precision: 0.9500 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7789/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0021 - AUC_ROC: 0.9726 - recall: 0.8113 - precision: 0.9117 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7790/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0021 - AUC_ROC: 0.9649 - recall: 0.7956 - precision: 0.9200 - val_loss: 0.0046 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7791/10000\n", + "3/3 [==============================] - 0s 119ms/step - loss: 0.0019 - AUC_ROC: 0.9744 - recall: 0.8270 - precision: 0.9359 - val_loss: 0.0046 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7792/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0020 - AUC_ROC: 0.9696 - recall: 0.8270 - precision: 0.9326 - val_loss: 0.0046 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7793/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0020 - AUC_ROC: 0.9664 - recall: 0.8176 - precision: 0.9187 - val_loss: 0.0046 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7794/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0019 - AUC_ROC: 0.9680 - recall: 0.8428 - precision: 0.9470 - val_loss: 0.0046 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7795/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0021 - AUC_ROC: 0.9696 - recall: 0.8113 - precision: 0.9214 - val_loss: 0.0046 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7796/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0022 - AUC_ROC: 0.9649 - recall: 0.7956 - precision: 0.9234 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7797/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0019 - AUC_ROC: 0.9760 - recall: 0.8491 - precision: 0.9122 - val_loss: 0.0048 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7798/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0022 - AUC_ROC: 0.9695 - recall: 0.8208 - precision: 0.9031 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7799/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0021 - AUC_ROC: 0.9697 - recall: 0.8176 - precision: 0.9286 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7800/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0019 - AUC_ROC: 0.9696 - recall: 0.8365 - precision: 0.9268 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7801/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0019 - AUC_ROC: 0.9697 - recall: 0.8428 - precision: 0.9273 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7802/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0019 - AUC_ROC: 0.9696 - recall: 0.8145 - precision: 0.9557 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7803/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0019 - AUC_ROC: 0.9744 - recall: 0.8270 - precision: 0.9228 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7804/10000\n", + "3/3 [==============================] - 0s 116ms/step - loss: 0.0020 - AUC_ROC: 0.9728 - recall: 0.8270 - precision: 0.9164 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7805/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0021 - AUC_ROC: 0.9711 - recall: 0.8145 - precision: 0.9024 - val_loss: 0.0051 - val_AUC_ROC: 0.8703 - val_recall: 0.7160 - val_precision: 0.9667\n", + "Epoch 7806/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0022 - AUC_ROC: 0.9727 - recall: 0.8113 - precision: 0.9117 - val_loss: 0.0051 - val_AUC_ROC: 0.8703 - val_recall: 0.7160 - val_precision: 0.9667\n", + "Epoch 7807/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0020 - AUC_ROC: 0.9665 - recall: 0.8050 - precision: 0.9377 - val_loss: 0.0051 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7808/10000\n", + "3/3 [==============================] - 0s 114ms/step - loss: 0.0022 - AUC_ROC: 0.9649 - recall: 0.7956 - precision: 0.9301 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7809/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0019 - AUC_ROC: 0.9744 - recall: 0.8302 - precision: 0.9429 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7810/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0021 - AUC_ROC: 0.9633 - recall: 0.8082 - precision: 0.9590 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7811/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0019 - AUC_ROC: 0.9697 - recall: 0.8208 - precision: 0.9457 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7812/10000\n", + "3/3 [==============================] - 0s 119ms/step - loss: 0.0020 - AUC_ROC: 0.9712 - recall: 0.7862 - precision: 0.9398 - val_loss: 0.0047 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7813/10000\n", + "3/3 [==============================] - 0s 117ms/step - loss: 0.0019 - AUC_ROC: 0.9712 - recall: 0.8365 - precision: 0.9017 - val_loss: 0.0046 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7814/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0022 - AUC_ROC: 0.9664 - recall: 0.8145 - precision: 0.8962 - val_loss: 0.0045 - val_AUC_ROC: 0.8888 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7815/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0020 - AUC_ROC: 0.9681 - recall: 0.8208 - precision: 0.9126 - val_loss: 0.0045 - val_AUC_ROC: 0.8888 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7816/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0021 - AUC_ROC: 0.9696 - recall: 0.8302 - precision: 0.9072 - val_loss: 0.0045 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7817/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0018 - AUC_ROC: 0.9713 - recall: 0.8333 - precision: 0.9397 - val_loss: 0.0045 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7818/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0020 - AUC_ROC: 0.9617 - recall: 0.8082 - precision: 0.9554 - val_loss: 0.0045 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7819/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0019 - AUC_ROC: 0.9634 - recall: 0.8113 - precision: 0.9627 - val_loss: 0.0045 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7820/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0019 - AUC_ROC: 0.9744 - recall: 0.8365 - precision: 0.9466 - val_loss: 0.0045 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7821/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0019 - AUC_ROC: 0.9728 - recall: 0.8050 - precision: 0.9517 - val_loss: 0.0045 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7822/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0020 - AUC_ROC: 0.9665 - recall: 0.7893 - precision: 0.9472 - val_loss: 0.0045 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7823/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0019 - AUC_ROC: 0.9712 - recall: 0.8145 - precision: 0.9283 - val_loss: 0.0045 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7824/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0021 - AUC_ROC: 0.9633 - recall: 0.8050 - precision: 0.9046 - val_loss: 0.0045 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7825/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0022 - AUC_ROC: 0.9726 - recall: 0.8302 - precision: 0.9167 - val_loss: 0.0046 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7826/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0019 - AUC_ROC: 0.9759 - recall: 0.8113 - precision: 0.9149 - val_loss: 0.0046 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7827/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0019 - AUC_ROC: 0.9697 - recall: 0.8365 - precision: 0.9366 - val_loss: 0.0047 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7828/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0022 - AUC_ROC: 0.9696 - recall: 0.8082 - precision: 0.9146 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7829/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0021 - AUC_ROC: 0.9728 - recall: 0.8176 - precision: 0.9353 - val_loss: 0.0047 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7830/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0019 - AUC_ROC: 0.9760 - recall: 0.8428 - precision: 0.9273 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7831/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0019 - AUC_ROC: 0.9680 - recall: 0.8270 - precision: 0.9132 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7832/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0021 - AUC_ROC: 0.9743 - recall: 0.8208 - precision: 0.9223 - val_loss: 0.0048 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7833/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0021 - AUC_ROC: 0.9759 - recall: 0.7925 - precision: 0.9299 - val_loss: 0.0048 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7834/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0020 - AUC_ROC: 0.9728 - recall: 0.8113 - precision: 0.9247 - val_loss: 0.0048 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7835/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0018 - AUC_ROC: 0.9681 - recall: 0.8270 - precision: 0.9599 - val_loss: 0.0048 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7836/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0018 - AUC_ROC: 0.9680 - recall: 0.8679 - precision: 0.9293 - val_loss: 0.0048 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7837/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0019 - AUC_ROC: 0.9713 - recall: 0.8365 - precision: 0.9204 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7838/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0020 - AUC_ROC: 0.9775 - recall: 0.8208 - precision: 0.9321 - val_loss: 0.0047 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7839/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0020 - AUC_ROC: 0.9696 - recall: 0.8082 - precision: 0.9380 - val_loss: 0.0047 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7840/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0020 - AUC_ROC: 0.9665 - recall: 0.8019 - precision: 0.9140 - val_loss: 0.0048 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7841/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0020 - AUC_ROC: 0.9775 - recall: 0.8050 - precision: 0.9377 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7842/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0019 - AUC_ROC: 0.9728 - recall: 0.8365 - precision: 0.9433 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7843/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0021 - AUC_ROC: 0.9698 - recall: 0.8208 - precision: 0.9255 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7844/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0020 - AUC_ROC: 0.9681 - recall: 0.8082 - precision: 0.9414 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7845/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0019 - AUC_ROC: 0.9697 - recall: 0.8428 - precision: 0.9338 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7846/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0019 - AUC_ROC: 0.9791 - recall: 0.8396 - precision: 0.9082 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7847/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0019 - AUC_ROC: 0.9713 - recall: 0.8270 - precision: 0.9293 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7848/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0020 - AUC_ROC: 0.9697 - recall: 0.8333 - precision: 0.9233 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7849/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0018 - AUC_ROC: 0.9776 - recall: 0.8113 - precision: 0.9520 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7850/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0020 - AUC_ROC: 0.9743 - recall: 0.8239 - precision: 0.9424 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7851/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0019 - AUC_ROC: 0.9649 - recall: 0.8302 - precision: 0.9531 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7852/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0018 - AUC_ROC: 0.9759 - recall: 0.8270 - precision: 0.9261 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7853/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0021 - AUC_ROC: 0.9634 - recall: 0.8365 - precision: 0.9568 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7854/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0021 - AUC_ROC: 0.9664 - recall: 0.8302 - precision: 0.9329 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7855/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0020 - AUC_ROC: 0.9665 - recall: 0.8176 - precision: 0.9319 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7856/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0021 - AUC_ROC: 0.9712 - recall: 0.8239 - precision: 0.9161 - val_loss: 0.0048 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7857/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0019 - AUC_ROC: 0.9681 - recall: 0.8302 - precision: 0.9296 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7858/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0019 - AUC_ROC: 0.9776 - recall: 0.8428 - precision: 0.9338 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7859/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0019 - AUC_ROC: 0.9728 - recall: 0.8145 - precision: 0.9350 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7860/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0020 - AUC_ROC: 0.9712 - recall: 0.8365 - precision: 0.9366 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7861/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0021 - AUC_ROC: 0.9633 - recall: 0.8145 - precision: 0.9384 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7862/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0018 - AUC_ROC: 0.9681 - recall: 0.8585 - precision: 0.9161 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7863/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0020 - AUC_ROC: 0.9728 - recall: 0.8019 - precision: 0.9239 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7864/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0022 - AUC_ROC: 0.9617 - recall: 0.7987 - precision: 0.9338 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7865/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0018 - AUC_ROC: 0.9697 - recall: 0.8585 - precision: 0.9381 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7866/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0020 - AUC_ROC: 0.9728 - recall: 0.8333 - precision: 0.9201 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7867/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0021 - AUC_ROC: 0.9696 - recall: 0.8333 - precision: 0.9431 - val_loss: 0.0048 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7868/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0018 - AUC_ROC: 0.9760 - recall: 0.8145 - precision: 0.9250 - val_loss: 0.0048 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7869/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0018 - AUC_ROC: 0.9713 - recall: 0.8333 - precision: 0.9567 - val_loss: 0.0048 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7870/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0021 - AUC_ROC: 0.9697 - recall: 0.8050 - precision: 0.9046 - val_loss: 0.0048 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7871/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0021 - AUC_ROC: 0.9649 - recall: 0.7987 - precision: 0.9338 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7872/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0020 - AUC_ROC: 0.9727 - recall: 0.8113 - precision: 0.9247 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7873/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0020 - AUC_ROC: 0.9697 - recall: 0.7987 - precision: 0.9373 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7874/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0019 - AUC_ROC: 0.9744 - recall: 0.8270 - precision: 0.9326 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7875/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0020 - AUC_ROC: 0.9712 - recall: 0.8082 - precision: 0.9113 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7876/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0020 - AUC_ROC: 0.9665 - recall: 0.8302 - precision: 0.9199 - val_loss: 0.0048 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7877/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0020 - AUC_ROC: 0.9650 - recall: 0.8333 - precision: 0.9364 - val_loss: 0.0049 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7878/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0019 - AUC_ROC: 0.9697 - recall: 0.8176 - precision: 0.9386 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7879/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0020 - AUC_ROC: 0.9712 - recall: 0.8239 - precision: 0.8973 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7880/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0021 - AUC_ROC: 0.9743 - recall: 0.7893 - precision: 0.9366 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7881/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0019 - AUC_ROC: 0.9666 - recall: 0.7987 - precision: 0.9373 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7882/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0019 - AUC_ROC: 0.9712 - recall: 0.8176 - precision: 0.9386 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7883/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0021 - AUC_ROC: 0.9727 - recall: 0.8082 - precision: 0.9345 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7884/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0019 - AUC_ROC: 0.9712 - recall: 0.8208 - precision: 0.9422 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7885/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0019 - AUC_ROC: 0.9728 - recall: 0.8145 - precision: 0.9487 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7886/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0020 - AUC_ROC: 0.9697 - recall: 0.8491 - precision: 0.9343 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7887/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0017 - AUC_ROC: 0.9745 - recall: 0.8396 - precision: 0.9604 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7888/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0020 - AUC_ROC: 0.9680 - recall: 0.8365 - precision: 0.9236 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7889/10000\n", + "3/3 [==============================] - 0s 114ms/step - loss: 0.0021 - AUC_ROC: 0.9681 - recall: 0.8333 - precision: 0.9170 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7890/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0020 - AUC_ROC: 0.9743 - recall: 0.8396 - precision: 0.9175 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7891/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0019 - AUC_ROC: 0.9681 - recall: 0.8176 - precision: 0.9489 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7892/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0021 - AUC_ROC: 0.9681 - recall: 0.8270 - precision: 0.9164 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7893/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0018 - AUC_ROC: 0.9761 - recall: 0.8396 - precision: 0.9336 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7894/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0020 - AUC_ROC: 0.9697 - recall: 0.8208 - precision: 0.9223 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7895/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0019 - AUC_ROC: 0.9744 - recall: 0.8176 - precision: 0.9420 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7896/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0020 - AUC_ROC: 0.9680 - recall: 0.8239 - precision: 0.9458 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7897/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0020 - AUC_ROC: 0.9650 - recall: 0.8176 - precision: 0.9319 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7898/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0017 - AUC_ROC: 0.9776 - recall: 0.8365 - precision: 0.9204 - val_loss: 0.0046 - val_AUC_ROC: 0.8888 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7899/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0021 - AUC_ROC: 0.9775 - recall: 0.8333 - precision: 0.8660 - val_loss: 0.0046 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7900/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0020 - AUC_ROC: 0.9728 - recall: 0.8082 - precision: 0.9312 - val_loss: 0.0046 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7901/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0019 - AUC_ROC: 0.9681 - recall: 0.8082 - precision: 0.9483 - val_loss: 0.0046 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7902/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0020 - AUC_ROC: 0.9727 - recall: 0.8176 - precision: 0.9187 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7903/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0021 - AUC_ROC: 0.9649 - recall: 0.8428 - precision: 0.9338 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7904/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0019 - AUC_ROC: 0.9712 - recall: 0.8082 - precision: 0.9414 - val_loss: 0.0048 - val_AUC_ROC: 0.8827 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7905/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0020 - AUC_ROC: 0.9664 - recall: 0.8050 - precision: 0.9446 - val_loss: 0.0048 - val_AUC_ROC: 0.8827 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7906/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0019 - AUC_ROC: 0.9728 - recall: 0.8239 - precision: 0.9324 - val_loss: 0.0048 - val_AUC_ROC: 0.8827 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7907/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0020 - AUC_ROC: 0.9665 - recall: 0.8145 - precision: 0.9384 - val_loss: 0.0048 - val_AUC_ROC: 0.8827 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7908/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0020 - AUC_ROC: 0.9680 - recall: 0.8145 - precision: 0.9350 - val_loss: 0.0048 - val_AUC_ROC: 0.8827 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7909/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0020 - AUC_ROC: 0.9650 - recall: 0.8239 - precision: 0.9424 - val_loss: 0.0048 - val_AUC_ROC: 0.8827 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7910/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0021 - AUC_ROC: 0.9743 - recall: 0.8145 - precision: 0.8840 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7911/10000\n", + "3/3 [==============================] - 0s 121ms/step - loss: 0.0021 - AUC_ROC: 0.9759 - recall: 0.8270 - precision: 0.9069 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7912/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0018 - AUC_ROC: 0.9744 - recall: 0.8239 - precision: 0.9527 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7913/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0019 - AUC_ROC: 0.9681 - recall: 0.8333 - precision: 0.9464 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7914/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0020 - AUC_ROC: 0.9649 - recall: 0.8113 - precision: 0.9485 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7915/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0019 - AUC_ROC: 0.9728 - recall: 0.8145 - precision: 0.9522 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7916/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0020 - AUC_ROC: 0.9681 - recall: 0.8176 - precision: 0.9455 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7917/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0020 - AUC_ROC: 0.9713 - recall: 0.8270 - precision: 0.9069 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7918/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0018 - AUC_ROC: 0.9712 - recall: 0.8333 - precision: 0.9464 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7919/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0019 - AUC_ROC: 0.9728 - recall: 0.8208 - precision: 0.9288 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7920/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0021 - AUC_ROC: 0.9681 - recall: 0.8239 - precision: 0.9324 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7921/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0021 - AUC_ROC: 0.9697 - recall: 0.8333 - precision: 0.9170 - val_loss: 0.0046 - val_AUC_ROC: 0.8888 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7922/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0021 - AUC_ROC: 0.9728 - recall: 0.8459 - precision: 0.9119 - val_loss: 0.0046 - val_AUC_ROC: 0.8888 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7923/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0020 - AUC_ROC: 0.9680 - recall: 0.8270 - precision: 0.9100 - val_loss: 0.0046 - val_AUC_ROC: 0.8826 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7924/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0021 - AUC_ROC: 0.9617 - recall: 0.8270 - precision: 0.9228 - val_loss: 0.0046 - val_AUC_ROC: 0.8826 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7925/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0021 - AUC_ROC: 0.9728 - recall: 0.8208 - precision: 0.9062 - val_loss: 0.0046 - val_AUC_ROC: 0.8826 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7926/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0020 - AUC_ROC: 0.9681 - recall: 0.8333 - precision: 0.9464 - val_loss: 0.0046 - val_AUC_ROC: 0.9011 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7927/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0018 - AUC_ROC: 0.9885 - recall: 0.8333 - precision: 0.9138 - val_loss: 0.0045 - val_AUC_ROC: 0.9011 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7928/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0019 - AUC_ROC: 0.9728 - recall: 0.8333 - precision: 0.9138 - val_loss: 0.0045 - val_AUC_ROC: 0.9011 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7929/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0020 - AUC_ROC: 0.9712 - recall: 0.8145 - precision: 0.9453 - val_loss: 0.0046 - val_AUC_ROC: 0.8888 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7930/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0017 - AUC_ROC: 0.9744 - recall: 0.8208 - precision: 0.9526 - val_loss: 0.0046 - val_AUC_ROC: 0.8826 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 7931/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0019 - AUC_ROC: 0.9681 - recall: 0.8428 - precision: 0.9338 - val_loss: 0.0046 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7932/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0019 - AUC_ROC: 0.9696 - recall: 0.8365 - precision: 0.9268 - val_loss: 0.0047 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7933/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0019 - AUC_ROC: 0.9713 - recall: 0.8239 - precision: 0.9668 - val_loss: 0.0047 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7934/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0019 - AUC_ROC: 0.9681 - recall: 0.8239 - precision: 0.9324 - val_loss: 0.0047 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7935/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0020 - AUC_ROC: 0.9665 - recall: 0.8239 - precision: 0.9391 - val_loss: 0.0047 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7936/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0019 - AUC_ROC: 0.9744 - recall: 0.8176 - precision: 0.9386 - val_loss: 0.0048 - val_AUC_ROC: 0.8764 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7937/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0020 - AUC_ROC: 0.9744 - recall: 0.8239 - precision: 0.9258 - val_loss: 0.0049 - val_AUC_ROC: 0.8764 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7938/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0021 - AUC_ROC: 0.9711 - recall: 0.8176 - precision: 0.9091 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7939/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0018 - AUC_ROC: 0.9760 - recall: 0.8302 - precision: 0.9395 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7940/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0019 - AUC_ROC: 0.9760 - recall: 0.8113 - precision: 0.9314 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7941/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0020 - AUC_ROC: 0.9680 - recall: 0.8270 - precision: 0.9393 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7942/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0021 - AUC_ROC: 0.9712 - recall: 0.8176 - precision: 0.9319 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7943/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0019 - AUC_ROC: 0.9713 - recall: 0.8270 - precision: 0.9293 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7944/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0020 - AUC_ROC: 0.9713 - recall: 0.8113 - precision: 0.9281 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7945/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0019 - AUC_ROC: 0.9681 - recall: 0.8176 - precision: 0.9524 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7946/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0019 - AUC_ROC: 0.9713 - recall: 0.8145 - precision: 0.9487 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7947/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0020 - AUC_ROC: 0.9697 - recall: 0.8019 - precision: 0.9480 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7948/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0020 - AUC_ROC: 0.9665 - recall: 0.8113 - precision: 0.9247 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7949/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0021 - AUC_ROC: 0.9696 - recall: 0.8459 - precision: 0.9181 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7950/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0020 - AUC_ROC: 0.9759 - recall: 0.8302 - precision: 0.9199 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7951/10000\n", + "3/3 [==============================] - 0s 114ms/step - loss: 0.0020 - AUC_ROC: 0.9728 - recall: 0.8082 - precision: 0.9519 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7952/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0020 - AUC_ROC: 0.9743 - recall: 0.8239 - precision: 0.9193 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7953/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0019 - AUC_ROC: 0.9696 - recall: 0.8585 - precision: 0.9381 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7954/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0021 - AUC_ROC: 0.9727 - recall: 0.8050 - precision: 0.9242 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7955/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0019 - AUC_ROC: 0.9744 - recall: 0.8459 - precision: 0.9119 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7956/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0020 - AUC_ROC: 0.9696 - recall: 0.8302 - precision: 0.9231 - val_loss: 0.0048 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7957/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0022 - AUC_ROC: 0.9665 - recall: 0.7925 - precision: 0.9474 - val_loss: 0.0048 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7958/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0020 - AUC_ROC: 0.9713 - recall: 0.7987 - precision: 0.9236 - val_loss: 0.0048 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7959/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0018 - AUC_ROC: 0.9745 - recall: 0.8176 - precision: 0.9594 - val_loss: 0.0048 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7960/10000\n", + "3/3 [==============================] - 0s 98ms/step - loss: 0.0021 - AUC_ROC: 0.9602 - recall: 0.7987 - precision: 0.9513 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7961/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0018 - AUC_ROC: 0.9760 - recall: 0.8302 - precision: 0.9231 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7962/10000\n", + "3/3 [==============================] - 0s 117ms/step - loss: 0.0020 - AUC_ROC: 0.9633 - recall: 0.8585 - precision: 0.9317 - val_loss: 0.0047 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7963/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0020 - AUC_ROC: 0.9680 - recall: 0.8082 - precision: 0.9179 - val_loss: 0.0048 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 7964/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0019 - AUC_ROC: 0.9760 - recall: 0.8176 - precision: 0.9220 - val_loss: 0.0048 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7965/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0018 - AUC_ROC: 0.9713 - recall: 0.8208 - precision: 0.9457 - val_loss: 0.0048 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7966/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0020 - AUC_ROC: 0.9743 - recall: 0.7987 - precision: 0.9170 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7967/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0019 - AUC_ROC: 0.9760 - recall: 0.8145 - precision: 0.9522 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7968/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0019 - AUC_ROC: 0.9713 - recall: 0.8553 - precision: 0.9128 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7969/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0020 - AUC_ROC: 0.9665 - recall: 0.8082 - precision: 0.9146 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7970/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0019 - AUC_ROC: 0.9839 - recall: 0.7987 - precision: 0.9170 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7971/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0020 - AUC_ROC: 0.9618 - recall: 0.8082 - precision: 0.9554 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7972/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0019 - AUC_ROC: 0.9744 - recall: 0.8239 - precision: 0.9291 - val_loss: 0.0049 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7973/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0019 - AUC_ROC: 0.9743 - recall: 0.8522 - precision: 0.9186 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7974/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0021 - AUC_ROC: 0.9759 - recall: 0.7956 - precision: 0.9267 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 7975/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0019 - AUC_ROC: 0.9760 - recall: 0.8270 - precision: 0.9359 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7976/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0018 - AUC_ROC: 0.9760 - recall: 0.8239 - precision: 0.9493 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7977/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0019 - AUC_ROC: 0.9680 - recall: 0.8270 - precision: 0.9427 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7978/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0019 - AUC_ROC: 0.9744 - recall: 0.8082 - precision: 0.9345 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7979/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0018 - AUC_ROC: 0.9776 - recall: 0.8239 - precision: 0.9324 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7980/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0021 - AUC_ROC: 0.9697 - recall: 0.7987 - precision: 0.9236 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7981/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0018 - AUC_ROC: 0.9791 - recall: 0.8302 - precision: 0.9103 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7982/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0019 - AUC_ROC: 0.9728 - recall: 0.8082 - precision: 0.9414 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7983/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0017 - AUC_ROC: 0.9808 - recall: 0.8396 - precision: 0.9271 - val_loss: 0.0050 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7984/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0020 - AUC_ROC: 0.9713 - recall: 0.8270 - precision: 0.9261 - val_loss: 0.0050 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7985/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0019 - AUC_ROC: 0.9697 - recall: 0.8082 - precision: 0.9312 - val_loss: 0.0050 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7986/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0019 - AUC_ROC: 0.9727 - recall: 0.8365 - precision: 0.9500 - val_loss: 0.0051 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7987/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0020 - AUC_ROC: 0.9744 - recall: 0.7925 - precision: 0.9032 - val_loss: 0.0051 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7988/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0019 - AUC_ROC: 0.9682 - recall: 0.8333 - precision: 0.9397 - val_loss: 0.0050 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7989/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0021 - AUC_ROC: 0.9728 - recall: 0.8113 - precision: 0.9117 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7990/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0020 - AUC_ROC: 0.9680 - recall: 0.8145 - precision: 0.9024 - val_loss: 0.0050 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7991/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0020 - AUC_ROC: 0.9759 - recall: 0.8396 - precision: 0.9239 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7992/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0018 - AUC_ROC: 0.9744 - recall: 0.8396 - precision: 0.9207 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7993/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0017 - AUC_ROC: 0.9697 - recall: 0.8459 - precision: 0.9276 - val_loss: 0.0051 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7994/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0020 - AUC_ROC: 0.9760 - recall: 0.8208 - precision: 0.9158 - val_loss: 0.0052 - val_AUC_ROC: 0.8703 - val_recall: 0.7160 - val_precision: 0.9667\n", + "Epoch 7995/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0020 - AUC_ROC: 0.9696 - recall: 0.8050 - precision: 0.9552 - val_loss: 0.0052 - val_AUC_ROC: 0.8641 - val_recall: 0.7160 - val_precision: 0.9667\n", + "Epoch 7996/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0020 - AUC_ROC: 0.9680 - recall: 0.8270 - precision: 0.9359 - val_loss: 0.0052 - val_AUC_ROC: 0.8641 - val_recall: 0.7160 - val_precision: 0.9667\n", + "Epoch 7997/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0019 - AUC_ROC: 0.9854 - recall: 0.8019 - precision: 0.9341 - val_loss: 0.0051 - val_AUC_ROC: 0.8641 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7998/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0019 - AUC_ROC: 0.9791 - recall: 0.8365 - precision: 0.9268 - val_loss: 0.0051 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 7999/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0020 - AUC_ROC: 0.9696 - recall: 0.8113 - precision: 0.9181 - val_loss: 0.0051 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8000/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0020 - AUC_ROC: 0.9744 - recall: 0.8333 - precision: 0.9364 - val_loss: 0.0051 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8001/10000\n", + "3/3 [==============================] - 0s 121ms/step - loss: 0.0021 - AUC_ROC: 0.9682 - recall: 0.8113 - precision: 0.9485 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8002/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0020 - AUC_ROC: 0.9743 - recall: 0.8208 - precision: 0.9094 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8003/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0020 - AUC_ROC: 0.9664 - recall: 0.8113 - precision: 0.9520 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8004/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0020 - AUC_ROC: 0.9744 - recall: 0.8302 - precision: 0.9199 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8005/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0018 - AUC_ROC: 0.9682 - recall: 0.8208 - precision: 0.9388 - val_loss: 0.0051 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8006/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0020 - AUC_ROC: 0.9696 - recall: 0.8113 - precision: 0.9416 - val_loss: 0.0050 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8007/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0018 - AUC_ROC: 0.9745 - recall: 0.8396 - precision: 0.9336 - val_loss: 0.0050 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8008/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0018 - AUC_ROC: 0.9728 - recall: 0.8365 - precision: 0.9433 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8009/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0017 - AUC_ROC: 0.9729 - recall: 0.8365 - precision: 0.9366 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8010/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0020 - AUC_ROC: 0.9744 - recall: 0.8208 - precision: 0.8758 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8011/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0019 - AUC_ROC: 0.9713 - recall: 0.8082 - precision: 0.9483 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8012/10000\n", + "3/3 [==============================] - 0s 98ms/step - loss: 0.0022 - AUC_ROC: 0.9711 - recall: 0.8176 - precision: 0.9187 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8013/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0021 - AUC_ROC: 0.9728 - recall: 0.8082 - precision: 0.9345 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8014/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0018 - AUC_ROC: 0.9744 - recall: 0.8176 - precision: 0.9319 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8015/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0019 - AUC_ROC: 0.9728 - recall: 0.8333 - precision: 0.9266 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8016/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0020 - AUC_ROC: 0.9697 - recall: 0.8176 - precision: 0.9559 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8017/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0020 - AUC_ROC: 0.9728 - recall: 0.8365 - precision: 0.9301 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8018/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0018 - AUC_ROC: 0.9729 - recall: 0.8208 - precision: 0.9526 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8019/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0020 - AUC_ROC: 0.9649 - recall: 0.8333 - precision: 0.9464 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8020/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0019 - AUC_ROC: 0.9744 - recall: 0.8145 - precision: 0.9317 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8021/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0020 - AUC_ROC: 0.9681 - recall: 0.8113 - precision: 0.9382 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8022/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0018 - AUC_ROC: 0.9759 - recall: 0.8239 - precision: 0.9357 - val_loss: 0.0048 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8023/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0017 - AUC_ROC: 0.9791 - recall: 0.8648 - precision: 0.9076 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8024/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0019 - AUC_ROC: 0.9697 - recall: 0.8270 - precision: 0.9495 - val_loss: 0.0048 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8025/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0016 - AUC_ROC: 0.9824 - recall: 0.8302 - precision: 0.9362 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8026/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0021 - AUC_ROC: 0.9775 - recall: 0.8019 - precision: 0.8979 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8027/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0021 - AUC_ROC: 0.9729 - recall: 0.8208 - precision: 0.9255 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8028/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0020 - AUC_ROC: 0.9712 - recall: 0.8239 - precision: 0.9324 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8029/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0021 - AUC_ROC: 0.9713 - recall: 0.8050 - precision: 0.9176 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8030/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0019 - AUC_ROC: 0.9745 - recall: 0.8176 - precision: 0.9455 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8031/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0021 - AUC_ROC: 0.9743 - recall: 0.8302 - precision: 0.9135 - val_loss: 0.0048 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8032/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0018 - AUC_ROC: 0.9760 - recall: 0.8428 - precision: 0.9273 - val_loss: 0.0048 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8033/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0020 - AUC_ROC: 0.9775 - recall: 0.8365 - precision: 0.8896 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8034/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0019 - AUC_ROC: 0.9823 - recall: 0.8302 - precision: 0.9362 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8035/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0018 - AUC_ROC: 0.9666 - recall: 0.8050 - precision: 0.9412 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8036/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0018 - AUC_ROC: 0.9745 - recall: 0.8396 - precision: 0.9239 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8037/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0016 - AUC_ROC: 0.9808 - recall: 0.8302 - precision: 0.9531 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8038/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0019 - AUC_ROC: 0.9759 - recall: 0.8302 - precision: 0.9329 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8039/10000\n", + "3/3 [==============================] - 0s 115ms/step - loss: 0.0018 - AUC_ROC: 0.9713 - recall: 0.8176 - precision: 0.9489 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8040/10000\n", + "3/3 [==============================] - 0s 98ms/step - loss: 0.0021 - AUC_ROC: 0.9649 - recall: 0.8239 - precision: 0.9193 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8041/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0021 - AUC_ROC: 0.9649 - recall: 0.8082 - precision: 0.9113 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8042/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0019 - AUC_ROC: 0.9760 - recall: 0.8333 - precision: 0.9498 - val_loss: 0.0048 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8043/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0018 - AUC_ROC: 0.9760 - recall: 0.8428 - precision: 0.9024 - val_loss: 0.0048 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8044/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0020 - AUC_ROC: 0.9696 - recall: 0.8428 - precision: 0.9437 - val_loss: 0.0048 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8045/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0020 - AUC_ROC: 0.9634 - recall: 0.8365 - precision: 0.9301 - val_loss: 0.0048 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8046/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0022 - AUC_ROC: 0.9697 - recall: 0.8113 - precision: 0.9181 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8047/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0018 - AUC_ROC: 0.9792 - recall: 0.8270 - precision: 0.9326 - val_loss: 0.0046 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8048/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0019 - AUC_ROC: 0.9728 - recall: 0.8302 - precision: 0.9429 - val_loss: 0.0045 - val_AUC_ROC: 0.9012 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8049/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0018 - AUC_ROC: 0.9729 - recall: 0.8459 - precision: 0.9439 - val_loss: 0.0045 - val_AUC_ROC: 0.9012 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8050/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0017 - AUC_ROC: 0.9760 - recall: 0.8365 - precision: 0.9236 - val_loss: 0.0045 - val_AUC_ROC: 0.9012 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8051/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0020 - AUC_ROC: 0.9728 - recall: 0.8176 - precision: 0.9319 - val_loss: 0.0045 - val_AUC_ROC: 0.9012 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8052/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0020 - AUC_ROC: 0.9728 - recall: 0.8459 - precision: 0.9539 - val_loss: 0.0046 - val_AUC_ROC: 0.9012 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8053/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0019 - AUC_ROC: 0.9682 - recall: 0.8428 - precision: 0.9504 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8054/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0018 - AUC_ROC: 0.9807 - recall: 0.8585 - precision: 0.9317 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8055/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8428 - precision: 0.9404 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8056/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0018 - AUC_ROC: 0.9792 - recall: 0.8302 - precision: 0.9263 - val_loss: 0.0048 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8057/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0020 - AUC_ROC: 0.9712 - recall: 0.7925 - precision: 0.9438 - val_loss: 0.0048 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8058/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0021 - AUC_ROC: 0.9665 - recall: 0.7925 - precision: 0.9403 - val_loss: 0.0047 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8059/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0019 - AUC_ROC: 0.9697 - recall: 0.8176 - precision: 0.9420 - val_loss: 0.0046 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8060/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0021 - AUC_ROC: 0.9665 - recall: 0.8050 - precision: 0.9377 - val_loss: 0.0045 - val_AUC_ROC: 0.9073 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8061/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0019 - AUC_ROC: 0.9760 - recall: 0.8145 - precision: 0.9418 - val_loss: 0.0045 - val_AUC_ROC: 0.9073 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8062/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0020 - AUC_ROC: 0.9665 - recall: 0.8239 - precision: 0.9391 - val_loss: 0.0045 - val_AUC_ROC: 0.9073 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8063/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0019 - AUC_ROC: 0.9712 - recall: 0.8270 - precision: 0.9261 - val_loss: 0.0045 - val_AUC_ROC: 0.9073 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8064/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0021 - AUC_ROC: 0.9649 - recall: 0.8145 - precision: 0.9453 - val_loss: 0.0045 - val_AUC_ROC: 0.9011 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8065/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0019 - AUC_ROC: 0.9776 - recall: 0.8333 - precision: 0.9298 - val_loss: 0.0046 - val_AUC_ROC: 0.9011 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8066/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0018 - AUC_ROC: 0.9744 - recall: 0.8459 - precision: 0.9212 - val_loss: 0.0046 - val_AUC_ROC: 0.9011 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8067/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0020 - AUC_ROC: 0.9744 - recall: 0.8208 - precision: 0.9190 - val_loss: 0.0046 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8068/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0018 - AUC_ROC: 0.9775 - recall: 0.8365 - precision: 0.9433 - val_loss: 0.0046 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8069/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0019 - AUC_ROC: 0.9697 - recall: 0.8365 - precision: 0.9301 - val_loss: 0.0046 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8070/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0017 - AUC_ROC: 0.9807 - recall: 0.8239 - precision: 0.9527 - val_loss: 0.0046 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8071/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0018 - AUC_ROC: 0.9760 - recall: 0.8428 - precision: 0.9437 - val_loss: 0.0046 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8072/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0020 - AUC_ROC: 0.9697 - recall: 0.8208 - precision: 0.9422 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8073/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0019 - AUC_ROC: 0.9792 - recall: 0.7987 - precision: 0.9513 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8074/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0019 - AUC_ROC: 0.9665 - recall: 0.8333 - precision: 0.9298 - val_loss: 0.0047 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8075/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0019 - AUC_ROC: 0.9713 - recall: 0.8270 - precision: 0.9359 - val_loss: 0.0047 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8076/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0021 - AUC_ROC: 0.9712 - recall: 0.8208 - precision: 0.9062 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8077/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0020 - AUC_ROC: 0.9712 - recall: 0.8365 - precision: 0.9366 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8078/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0019 - AUC_ROC: 0.9712 - recall: 0.8270 - precision: 0.9293 - val_loss: 0.0046 - val_AUC_ROC: 0.9011 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8079/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0021 - AUC_ROC: 0.9712 - recall: 0.8113 - precision: 0.9117 - val_loss: 0.0046 - val_AUC_ROC: 0.9011 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8080/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0019 - AUC_ROC: 0.9712 - recall: 0.8208 - precision: 0.9062 - val_loss: 0.0046 - val_AUC_ROC: 0.9011 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8081/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0018 - AUC_ROC: 0.9728 - recall: 0.8270 - precision: 0.9393 - val_loss: 0.0046 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8082/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0023 - AUC_ROC: 0.9648 - recall: 0.8302 - precision: 0.9010 - val_loss: 0.0046 - val_AUC_ROC: 0.9011 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8083/10000\n", + "3/3 [==============================] - 0s 96ms/step - loss: 0.0018 - AUC_ROC: 0.9792 - recall: 0.8302 - precision: 0.9231 - val_loss: 0.0046 - val_AUC_ROC: 0.9011 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8084/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0019 - AUC_ROC: 0.9759 - recall: 0.8270 - precision: 0.8976 - val_loss: 0.0046 - val_AUC_ROC: 0.9011 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8085/10000\n", + "3/3 [==============================] - 0s 115ms/step - loss: 0.0021 - AUC_ROC: 0.9696 - recall: 0.8176 - precision: 0.9353 - val_loss: 0.0047 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8086/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0019 - AUC_ROC: 0.9728 - recall: 0.8145 - precision: 0.9487 - val_loss: 0.0047 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8087/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0017 - AUC_ROC: 0.9745 - recall: 0.8270 - precision: 0.9634 - val_loss: 0.0047 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8088/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0019 - AUC_ROC: 0.9681 - recall: 0.8082 - precision: 0.9519 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8089/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0018 - AUC_ROC: 0.9775 - recall: 0.8302 - precision: 0.9362 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8090/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0020 - AUC_ROC: 0.9680 - recall: 0.8302 - precision: 0.9199 - val_loss: 0.0048 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8091/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0021 - AUC_ROC: 0.9712 - recall: 0.8176 - precision: 0.8997 - val_loss: 0.0048 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8092/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0019 - AUC_ROC: 0.9650 - recall: 0.8333 - precision: 0.9464 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8093/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0019 - AUC_ROC: 0.9713 - recall: 0.8145 - precision: 0.9350 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8094/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0019 - AUC_ROC: 0.9744 - recall: 0.8270 - precision: 0.9427 - val_loss: 0.0050 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8095/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0019 - AUC_ROC: 0.9728 - recall: 0.8208 - precision: 0.9288 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8096/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0021 - AUC_ROC: 0.9728 - recall: 0.8239 - precision: 0.9193 - val_loss: 0.0050 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8097/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0018 - AUC_ROC: 0.9792 - recall: 0.8302 - precision: 0.9103 - val_loss: 0.0049 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8098/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0021 - AUC_ROC: 0.9712 - recall: 0.8239 - precision: 0.8912 - val_loss: 0.0049 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8099/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0019 - AUC_ROC: 0.9712 - recall: 0.8365 - precision: 0.9301 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8100/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0020 - AUC_ROC: 0.9744 - recall: 0.8333 - precision: 0.9075 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8101/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0019 - AUC_ROC: 0.9807 - recall: 0.8270 - precision: 0.9196 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8102/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0018 - AUC_ROC: 0.9792 - recall: 0.8208 - precision: 0.9190 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8103/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0021 - AUC_ROC: 0.9617 - recall: 0.7987 - precision: 0.9270 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8104/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0018 - AUC_ROC: 0.9776 - recall: 0.8302 - precision: 0.9296 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8105/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0019 - AUC_ROC: 0.9806 - recall: 0.8239 - precision: 0.9458 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8106/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0018 - AUC_ROC: 0.9650 - recall: 0.8208 - precision: 0.9631 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8107/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0019 - AUC_ROC: 0.9697 - recall: 0.8270 - precision: 0.9359 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8108/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0018 - AUC_ROC: 0.9713 - recall: 0.7956 - precision: 0.9301 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8109/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0019 - AUC_ROC: 0.9696 - recall: 0.8396 - precision: 0.9401 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8110/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0020 - AUC_ROC: 0.9665 - recall: 0.8333 - precision: 0.9397 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8111/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0019 - AUC_ROC: 0.9713 - recall: 0.8239 - precision: 0.9424 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8112/10000\n", + "3/3 [==============================] - 0s 116ms/step - loss: 0.0018 - AUC_ROC: 0.9760 - recall: 0.8428 - precision: 0.9306 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8113/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0018 - AUC_ROC: 0.9744 - recall: 0.8365 - precision: 0.9333 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8114/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0019 - AUC_ROC: 0.9650 - recall: 0.8428 - precision: 0.9178 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8115/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0018 - AUC_ROC: 0.9760 - recall: 0.8239 - precision: 0.9324 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8116/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0018 - AUC_ROC: 0.9761 - recall: 0.8365 - precision: 0.9399 - val_loss: 0.0048 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8117/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8302 - precision: 0.9565 - val_loss: 0.0048 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8118/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0018 - AUC_ROC: 0.9728 - recall: 0.8176 - precision: 0.9253 - val_loss: 0.0048 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8119/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0018 - AUC_ROC: 0.9791 - recall: 0.8208 - precision: 0.9223 - val_loss: 0.0048 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8120/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0019 - AUC_ROC: 0.9681 - recall: 0.8270 - precision: 0.9460 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8121/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0021 - AUC_ROC: 0.9649 - recall: 0.8239 - precision: 0.9291 - val_loss: 0.0048 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8122/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0019 - AUC_ROC: 0.9744 - recall: 0.8491 - precision: 0.9215 - val_loss: 0.0048 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8123/10000\n", + "3/3 [==============================] - 0s 117ms/step - loss: 0.0019 - AUC_ROC: 0.9759 - recall: 0.8145 - precision: 0.9152 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8124/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0021 - AUC_ROC: 0.9712 - recall: 0.8239 - precision: 0.8912 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8125/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0020 - AUC_ROC: 0.9745 - recall: 0.8019 - precision: 0.9551 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8126/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0018 - AUC_ROC: 0.9744 - recall: 0.8553 - precision: 0.9544 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8127/10000\n", + "3/3 [==============================] - 0s 121ms/step - loss: 0.0017 - AUC_ROC: 0.9729 - recall: 0.8239 - precision: 0.9291 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8128/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0022 - AUC_ROC: 0.9775 - recall: 0.8239 - precision: 0.9225 - val_loss: 0.0048 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8129/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0020 - AUC_ROC: 0.9744 - recall: 0.8365 - precision: 0.9236 - val_loss: 0.0048 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8130/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0020 - AUC_ROC: 0.9650 - recall: 0.8270 - precision: 0.9293 - val_loss: 0.0048 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8131/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0018 - AUC_ROC: 0.9776 - recall: 0.8333 - precision: 0.9266 - val_loss: 0.0048 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8132/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0017 - AUC_ROC: 0.9745 - recall: 0.8459 - precision: 0.9373 - val_loss: 0.0048 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8133/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0018 - AUC_ROC: 0.9650 - recall: 0.8239 - precision: 0.9527 - val_loss: 0.0048 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8134/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0020 - AUC_ROC: 0.9697 - recall: 0.7925 - precision: 0.9333 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8135/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0018 - AUC_ROC: 0.9744 - recall: 0.8302 - precision: 0.9429 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8136/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0019 - AUC_ROC: 0.9807 - recall: 0.8396 - precision: 0.9051 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8137/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0018 - AUC_ROC: 0.9728 - recall: 0.8270 - precision: 0.9599 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8138/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0017 - AUC_ROC: 0.9744 - recall: 0.8396 - precision: 0.9468 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8139/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0019 - AUC_ROC: 0.9791 - recall: 0.8113 - precision: 0.9214 - val_loss: 0.0050 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8140/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0019 - AUC_ROC: 0.9745 - recall: 0.8239 - precision: 0.9066 - val_loss: 0.0050 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8141/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0019 - AUC_ROC: 0.9728 - recall: 0.8176 - precision: 0.9187 - val_loss: 0.0050 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8142/10000\n", + "3/3 [==============================] - 0s 121ms/step - loss: 0.0019 - AUC_ROC: 0.9697 - recall: 0.8208 - precision: 0.9388 - val_loss: 0.0050 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8143/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0018 - AUC_ROC: 0.9697 - recall: 0.8302 - precision: 0.9199 - val_loss: 0.0049 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8144/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0017 - AUC_ROC: 0.9728 - recall: 0.8491 - precision: 0.9247 - val_loss: 0.0049 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8145/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0019 - AUC_ROC: 0.9713 - recall: 0.8019 - precision: 0.9515 - val_loss: 0.0049 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8146/10000\n", + "3/3 [==============================] - 0s 118ms/step - loss: 0.0017 - AUC_ROC: 0.9792 - recall: 0.8333 - precision: 0.9464 - val_loss: 0.0048 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8147/10000\n", + "3/3 [==============================] - 0s 139ms/step - loss: 0.0018 - AUC_ROC: 0.9807 - recall: 0.8239 - precision: 0.9161 - val_loss: 0.0048 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8148/10000\n", + "3/3 [==============================] - 0s 121ms/step - loss: 0.0019 - AUC_ROC: 0.9665 - recall: 0.8239 - precision: 0.9324 - val_loss: 0.0048 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8149/10000\n", + "3/3 [==============================] - 0s 135ms/step - loss: 0.0017 - AUC_ROC: 0.9713 - recall: 0.8459 - precision: 0.9406 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8150/10000\n", + "3/3 [==============================] - 0s 139ms/step - loss: 0.0018 - AUC_ROC: 0.9776 - recall: 0.8522 - precision: 0.9094 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8151/10000\n", + "3/3 [==============================] - 0s 129ms/step - loss: 0.0019 - AUC_ROC: 0.9713 - recall: 0.8239 - precision: 0.9066 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8152/10000\n", + "3/3 [==============================] - 0s 145ms/step - loss: 0.0018 - AUC_ROC: 0.9792 - recall: 0.8239 - precision: 0.9562 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8153/10000\n", + "3/3 [==============================] - 0s 132ms/step - loss: 0.0019 - AUC_ROC: 0.9728 - recall: 0.8113 - precision: 0.9520 - val_loss: 0.0049 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8154/10000\n", + "3/3 [==============================] - 0s 125ms/step - loss: 0.0019 - AUC_ROC: 0.9635 - recall: 0.8396 - precision: 0.9271 - val_loss: 0.0049 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8155/10000\n", + "3/3 [==============================] - 0s 143ms/step - loss: 0.0018 - AUC_ROC: 0.9760 - recall: 0.8208 - precision: 0.9321 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8156/10000\n", + "3/3 [==============================] - 0s 138ms/step - loss: 0.0018 - AUC_ROC: 0.9697 - recall: 0.8365 - precision: 0.9603 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8157/10000\n", + "3/3 [==============================] - 0s 155ms/step - loss: 0.0022 - AUC_ROC: 0.9759 - recall: 0.8050 - precision: 0.9046 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8158/10000\n", + "3/3 [==============================] - 0s 145ms/step - loss: 0.0019 - AUC_ROC: 0.9744 - recall: 0.8176 - precision: 0.9155 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8159/10000\n", + "3/3 [==============================] - 0s 123ms/step - loss: 0.0019 - AUC_ROC: 0.9776 - recall: 0.8176 - precision: 0.9319 - val_loss: 0.0049 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8160/10000\n", + "3/3 [==============================] - 0s 146ms/step - loss: 0.0019 - AUC_ROC: 0.9697 - recall: 0.8396 - precision: 0.9051 - val_loss: 0.0049 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8161/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0021 - AUC_ROC: 0.9681 - recall: 0.8208 - precision: 0.9190 - val_loss: 0.0048 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8162/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0020 - AUC_ROC: 0.9760 - recall: 0.8491 - precision: 0.8940 - val_loss: 0.0048 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8163/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0019 - AUC_ROC: 0.9744 - recall: 0.8270 - precision: 0.9293 - val_loss: 0.0048 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8164/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0019 - AUC_ROC: 0.9744 - recall: 0.7987 - precision: 0.9203 - val_loss: 0.0048 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8165/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0018 - AUC_ROC: 0.9760 - recall: 0.8365 - precision: 0.9333 - val_loss: 0.0048 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8166/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0017 - AUC_ROC: 0.9760 - recall: 0.8208 - precision: 0.9223 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8167/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0022 - AUC_ROC: 0.9712 - recall: 0.8302 - precision: 0.9041 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8168/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0020 - AUC_ROC: 0.9712 - recall: 0.8302 - precision: 0.9362 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8169/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0020 - AUC_ROC: 0.9618 - recall: 0.8113 - precision: 0.9451 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8170/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0018 - AUC_ROC: 0.9729 - recall: 0.8396 - precision: 0.9239 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8171/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0020 - AUC_ROC: 0.9697 - recall: 0.8176 - precision: 0.9386 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8172/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0017 - AUC_ROC: 0.9776 - recall: 0.8396 - precision: 0.9368 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8173/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0018 - AUC_ROC: 0.9791 - recall: 0.8176 - precision: 0.9319 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8174/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0019 - AUC_ROC: 0.9760 - recall: 0.8396 - precision: 0.9271 - val_loss: 0.0047 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8175/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0018 - AUC_ROC: 0.9775 - recall: 0.8270 - precision: 0.9495 - val_loss: 0.0047 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8176/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0018 - AUC_ROC: 0.9729 - recall: 0.8428 - precision: 0.9338 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8177/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0019 - AUC_ROC: 0.9744 - recall: 0.8333 - precision: 0.9464 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8178/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0018 - AUC_ROC: 0.9760 - recall: 0.8176 - precision: 0.9286 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8179/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0018 - AUC_ROC: 0.9729 - recall: 0.8774 - precision: 0.9300 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8180/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0020 - AUC_ROC: 0.9728 - recall: 0.8396 - precision: 0.9175 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8181/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0018 - AUC_ROC: 0.9713 - recall: 0.8239 - precision: 0.9161 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8182/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0020 - AUC_ROC: 0.9729 - recall: 0.8113 - precision: 0.9348 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8183/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0019 - AUC_ROC: 0.9712 - recall: 0.7987 - precision: 0.9270 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8184/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0019 - AUC_ROC: 0.9712 - recall: 0.8459 - precision: 0.9027 - val_loss: 0.0047 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8185/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0017 - AUC_ROC: 0.9791 - recall: 0.8145 - precision: 0.9628 - val_loss: 0.0046 - val_AUC_ROC: 0.9012 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8186/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0019 - AUC_ROC: 0.9745 - recall: 0.8365 - precision: 0.9366 - val_loss: 0.0046 - val_AUC_ROC: 0.9012 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8187/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0020 - AUC_ROC: 0.9713 - recall: 0.8145 - precision: 0.9350 - val_loss: 0.0046 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8188/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0019 - AUC_ROC: 0.9744 - recall: 0.8082 - precision: 0.9483 - val_loss: 0.0046 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8189/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0016 - AUC_ROC: 0.9840 - recall: 0.8428 - precision: 0.9054 - val_loss: 0.0046 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8190/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0018 - AUC_ROC: 0.9729 - recall: 0.8302 - precision: 0.9296 - val_loss: 0.0046 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8191/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0019 - AUC_ROC: 0.9666 - recall: 0.8239 - precision: 0.9391 - val_loss: 0.0046 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8192/10000\n", + "3/3 [==============================] - 0s 116ms/step - loss: 0.0020 - AUC_ROC: 0.9728 - recall: 0.8082 - precision: 0.9449 - val_loss: 0.0046 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8193/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0020 - AUC_ROC: 0.9713 - recall: 0.8082 - precision: 0.9211 - val_loss: 0.0045 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8194/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0017 - AUC_ROC: 0.9745 - recall: 0.8616 - precision: 0.9164 - val_loss: 0.0045 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8195/10000\n", + "3/3 [==============================] - 0s 116ms/step - loss: 0.0017 - AUC_ROC: 0.9839 - recall: 0.8365 - precision: 0.9301 - val_loss: 0.0046 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8196/10000\n", + "3/3 [==============================] - 0s 116ms/step - loss: 0.0019 - AUC_ROC: 0.9696 - recall: 0.8396 - precision: 0.9113 - val_loss: 0.0045 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8197/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0018 - AUC_ROC: 0.9745 - recall: 0.8459 - precision: 0.9308 - val_loss: 0.0045 - val_AUC_ROC: 0.9012 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8198/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0019 - AUC_ROC: 0.9729 - recall: 0.8208 - precision: 0.9491 - val_loss: 0.0044 - val_AUC_ROC: 0.9012 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8199/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0019 - AUC_ROC: 0.9728 - recall: 0.8428 - precision: 0.9404 - val_loss: 0.0045 - val_AUC_ROC: 0.9012 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8200/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0018 - AUC_ROC: 0.9681 - recall: 0.8491 - precision: 0.9343 - val_loss: 0.0045 - val_AUC_ROC: 0.9011 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8201/10000\n", + "3/3 [==============================] - 0s 121ms/step - loss: 0.0019 - AUC_ROC: 0.9712 - recall: 0.8365 - precision: 0.9366 - val_loss: 0.0046 - val_AUC_ROC: 0.9011 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8202/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0020 - AUC_ROC: 0.9650 - recall: 0.8333 - precision: 0.9266 - val_loss: 0.0047 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8203/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0018 - AUC_ROC: 0.9697 - recall: 0.8302 - precision: 0.9362 - val_loss: 0.0047 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8204/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0018 - AUC_ROC: 0.9713 - recall: 0.8302 - precision: 0.9362 - val_loss: 0.0047 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8205/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0016 - AUC_ROC: 0.9823 - recall: 0.8616 - precision: 0.9384 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8206/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0017 - AUC_ROC: 0.9776 - recall: 0.8585 - precision: 0.9286 - val_loss: 0.0048 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8207/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0019 - AUC_ROC: 0.9744 - recall: 0.8333 - precision: 0.9201 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8208/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0018 - AUC_ROC: 0.9745 - recall: 0.8459 - precision: 0.9308 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8209/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0020 - AUC_ROC: 0.9791 - recall: 0.8553 - precision: 0.8947 - val_loss: 0.0049 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8210/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0015 - AUC_ROC: 0.9903 - recall: 0.8208 - precision: 0.9388 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8211/10000\n", + "3/3 [==============================] - 0s 117ms/step - loss: 0.0020 - AUC_ROC: 0.9713 - recall: 0.8082 - precision: 0.9049 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8212/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0019 - AUC_ROC: 0.9650 - recall: 0.8396 - precision: 0.9368 - val_loss: 0.0049 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8213/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0019 - AUC_ROC: 0.9729 - recall: 0.8176 - precision: 0.9353 - val_loss: 0.0048 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8214/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0021 - AUC_ROC: 0.9618 - recall: 0.8208 - precision: 0.9457 - val_loss: 0.0049 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8215/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0018 - AUC_ROC: 0.9665 - recall: 0.8270 - precision: 0.9529 - val_loss: 0.0049 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8216/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0019 - AUC_ROC: 0.9713 - recall: 0.8270 - precision: 0.9261 - val_loss: 0.0049 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8217/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0018 - AUC_ROC: 0.9713 - recall: 0.8208 - precision: 0.9388 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8218/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0018 - AUC_ROC: 0.9744 - recall: 0.8396 - precision: 0.9271 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8219/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0019 - AUC_ROC: 0.9713 - recall: 0.8239 - precision: 0.9258 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8220/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0017 - AUC_ROC: 0.9713 - recall: 0.8428 - precision: 0.9504 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8221/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0020 - AUC_ROC: 0.9744 - recall: 0.8333 - precision: 0.9107 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8222/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0018 - AUC_ROC: 0.9713 - recall: 0.8145 - precision: 0.9453 - val_loss: 0.0051 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8223/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0016 - AUC_ROC: 0.9792 - recall: 0.8302 - precision: 0.9565 - val_loss: 0.0052 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8224/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0017 - AUC_ROC: 0.9760 - recall: 0.8428 - precision: 0.9606 - val_loss: 0.0051 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8225/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0018 - AUC_ROC: 0.9745 - recall: 0.8019 - precision: 0.9341 - val_loss: 0.0051 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8226/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0017 - AUC_ROC: 0.9760 - recall: 0.8428 - precision: 0.9404 - val_loss: 0.0051 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8227/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0019 - AUC_ROC: 0.9728 - recall: 0.8050 - precision: 0.9377 - val_loss: 0.0051 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8228/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0017 - AUC_ROC: 0.9729 - recall: 0.8365 - precision: 0.9433 - val_loss: 0.0051 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8229/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0020 - AUC_ROC: 0.9713 - recall: 0.8333 - precision: 0.9266 - val_loss: 0.0051 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8230/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0017 - AUC_ROC: 0.9823 - recall: 0.8145 - precision: 0.9217 - val_loss: 0.0051 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8231/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0019 - AUC_ROC: 0.9728 - recall: 0.8270 - precision: 0.9164 - val_loss: 0.0052 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8232/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0018 - AUC_ROC: 0.9791 - recall: 0.8333 - precision: 0.9201 - val_loss: 0.0052 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8233/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0019 - AUC_ROC: 0.9728 - recall: 0.8396 - precision: 0.9051 - val_loss: 0.0052 - val_AUC_ROC: 0.8703 - val_recall: 0.7160 - val_precision: 0.9667\n", + "Epoch 8234/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0018 - AUC_ROC: 0.9729 - recall: 0.8176 - precision: 0.9559 - val_loss: 0.0053 - val_AUC_ROC: 0.8703 - val_recall: 0.7160 - val_precision: 0.9667\n", + "Epoch 8235/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0019 - AUC_ROC: 0.9729 - recall: 0.8239 - precision: 0.9324 - val_loss: 0.0053 - val_AUC_ROC: 0.8703 - val_recall: 0.7160 - val_precision: 0.9667\n", + "Epoch 8236/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0017 - AUC_ROC: 0.9730 - recall: 0.8333 - precision: 0.9397 - val_loss: 0.0053 - val_AUC_ROC: 0.8703 - val_recall: 0.7160 - val_precision: 0.9667\n", + "Epoch 8237/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0021 - AUC_ROC: 0.9603 - recall: 0.7830 - precision: 0.9577 - val_loss: 0.0053 - val_AUC_ROC: 0.8703 - val_recall: 0.7160 - val_precision: 0.9667\n", + "Epoch 8238/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0018 - AUC_ROC: 0.9760 - recall: 0.8522 - precision: 0.9094 - val_loss: 0.0053 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8239/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0017 - AUC_ROC: 0.9729 - recall: 0.8270 - precision: 0.9564 - val_loss: 0.0052 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8240/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0019 - AUC_ROC: 0.9744 - recall: 0.8176 - precision: 0.9286 - val_loss: 0.0052 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8241/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0017 - AUC_ROC: 0.9729 - recall: 0.8553 - precision: 0.9315 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8242/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0018 - AUC_ROC: 0.9808 - recall: 0.8396 - precision: 0.9144 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8243/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0019 - AUC_ROC: 0.9775 - recall: 0.8302 - precision: 0.9329 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8244/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0019 - AUC_ROC: 0.9760 - recall: 0.8176 - precision: 0.9286 - val_loss: 0.0051 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8245/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0019 - AUC_ROC: 0.9697 - recall: 0.8239 - precision: 0.9424 - val_loss: 0.0051 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8246/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0018 - AUC_ROC: 0.9745 - recall: 0.8270 - precision: 0.9460 - val_loss: 0.0051 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8247/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0018 - AUC_ROC: 0.9775 - recall: 0.8113 - precision: 0.9485 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8248/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0020 - AUC_ROC: 0.9728 - recall: 0.8459 - precision: 0.9276 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8249/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0019 - AUC_ROC: 0.9744 - recall: 0.8270 - precision: 0.9228 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8250/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0017 - AUC_ROC: 0.9776 - recall: 0.8396 - precision: 0.9303 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8251/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0018 - AUC_ROC: 0.9777 - recall: 0.8396 - precision: 0.9336 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8252/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0020 - AUC_ROC: 0.9712 - recall: 0.8270 - precision: 0.9196 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8253/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0019 - AUC_ROC: 0.9760 - recall: 0.8616 - precision: 0.8867 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8254/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0019 - AUC_ROC: 0.9776 - recall: 0.8176 - precision: 0.9155 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8255/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0020 - AUC_ROC: 0.9775 - recall: 0.8396 - precision: 0.8990 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8256/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0019 - AUC_ROC: 0.9666 - recall: 0.8050 - precision: 0.9242 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8257/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0019 - AUC_ROC: 0.9666 - recall: 0.8396 - precision: 0.9336 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8258/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0017 - AUC_ROC: 0.9777 - recall: 0.8333 - precision: 0.9431 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8259/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0018 - AUC_ROC: 0.9729 - recall: 0.8522 - precision: 0.9281 - val_loss: 0.0048 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8260/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0020 - AUC_ROC: 0.9744 - recall: 0.7925 - precision: 0.9403 - val_loss: 0.0048 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8261/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0019 - AUC_ROC: 0.9760 - recall: 0.8333 - precision: 0.9014 - val_loss: 0.0048 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8262/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0018 - AUC_ROC: 0.9760 - recall: 0.7987 - precision: 0.9338 - val_loss: 0.0048 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8263/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0018 - AUC_ROC: 0.9792 - recall: 0.8270 - precision: 0.9164 - val_loss: 0.0048 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8264/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0019 - AUC_ROC: 0.9744 - recall: 0.8176 - precision: 0.9386 - val_loss: 0.0049 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8265/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0019 - AUC_ROC: 0.9761 - recall: 0.7862 - precision: 0.9542 - val_loss: 0.0049 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8266/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0018 - AUC_ROC: 0.9745 - recall: 0.8302 - precision: 0.9296 - val_loss: 0.0049 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8267/10000\n", + "3/3 [==============================] - 0s 114ms/step - loss: 0.0019 - AUC_ROC: 0.9681 - recall: 0.8270 - precision: 0.9293 - val_loss: 0.0050 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8268/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0018 - AUC_ROC: 0.9776 - recall: 0.8239 - precision: 0.9097 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8269/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0019 - AUC_ROC: 0.9712 - recall: 0.8459 - precision: 0.9373 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8270/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0019 - AUC_ROC: 0.9792 - recall: 0.8113 - precision: 0.9181 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8271/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0017 - AUC_ROC: 0.9808 - recall: 0.8365 - precision: 0.9433 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8272/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0018 - AUC_ROC: 0.9744 - recall: 0.8239 - precision: 0.9324 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8273/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0019 - AUC_ROC: 0.9714 - recall: 0.8396 - precision: 0.9239 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8274/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0019 - AUC_ROC: 0.9775 - recall: 0.8365 - precision: 0.9268 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8275/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0019 - AUC_ROC: 0.9776 - recall: 0.8050 - precision: 0.9517 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8276/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0019 - AUC_ROC: 0.9681 - recall: 0.8270 - precision: 0.9228 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8277/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0020 - AUC_ROC: 0.9728 - recall: 0.8333 - precision: 0.9170 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8278/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0019 - AUC_ROC: 0.9776 - recall: 0.8145 - precision: 0.9350 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8279/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0019 - AUC_ROC: 0.9760 - recall: 0.8239 - precision: 0.9161 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8280/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0018 - AUC_ROC: 0.9761 - recall: 0.8365 - precision: 0.9141 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8281/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0018 - AUC_ROC: 0.9792 - recall: 0.8113 - precision: 0.9149 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8282/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0018 - AUC_ROC: 0.9792 - recall: 0.8333 - precision: 0.9397 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8283/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0018 - AUC_ROC: 0.9666 - recall: 0.8176 - precision: 0.9524 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8284/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0018 - AUC_ROC: 0.9760 - recall: 0.8491 - precision: 0.9441 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8285/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0019 - AUC_ROC: 0.9775 - recall: 0.8522 - precision: 0.9218 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8286/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0018 - AUC_ROC: 0.9776 - recall: 0.8208 - precision: 0.9321 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8287/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0022 - AUC_ROC: 0.9680 - recall: 0.8145 - precision: 0.9152 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8288/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0018 - AUC_ROC: 0.9729 - recall: 0.8396 - precision: 0.9113 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8289/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0019 - AUC_ROC: 0.9728 - recall: 0.8428 - precision: 0.9210 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8290/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0020 - AUC_ROC: 0.9697 - recall: 0.8113 - precision: 0.9281 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8291/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0017 - AUC_ROC: 0.9839 - recall: 0.8333 - precision: 0.9266 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8292/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0019 - AUC_ROC: 0.9713 - recall: 0.7987 - precision: 0.9478 - val_loss: 0.0050 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8293/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0019 - AUC_ROC: 0.9760 - recall: 0.8333 - precision: 0.9201 - val_loss: 0.0050 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8294/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0019 - AUC_ROC: 0.9760 - recall: 0.8050 - precision: 0.9309 - val_loss: 0.0051 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8295/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0017 - AUC_ROC: 0.9729 - recall: 0.8302 - precision: 0.9462 - val_loss: 0.0051 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8296/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8270 - precision: 0.9599 - val_loss: 0.0051 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8297/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0017 - AUC_ROC: 0.9777 - recall: 0.8333 - precision: 0.9464 - val_loss: 0.0050 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8298/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0020 - AUC_ROC: 0.9743 - recall: 0.8302 - precision: 0.9199 - val_loss: 0.0050 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8299/10000\n", + "3/3 [==============================] - 0s 116ms/step - loss: 0.0018 - AUC_ROC: 0.9729 - recall: 0.8145 - precision: 0.9317 - val_loss: 0.0050 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8300/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0017 - AUC_ROC: 0.9792 - recall: 0.8491 - precision: 0.9375 - val_loss: 0.0050 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8301/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0018 - AUC_ROC: 0.9728 - recall: 0.8270 - precision: 0.9293 - val_loss: 0.0050 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8302/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0018 - AUC_ROC: 0.9823 - recall: 0.8522 - precision: 0.8914 - val_loss: 0.0049 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8303/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0018 - AUC_ROC: 0.9824 - recall: 0.8491 - precision: 0.9091 - val_loss: 0.0049 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8304/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8365 - precision: 0.9500 - val_loss: 0.0049 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8305/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0019 - AUC_ROC: 0.9745 - recall: 0.8176 - precision: 0.9220 - val_loss: 0.0049 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8306/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0019 - AUC_ROC: 0.9697 - recall: 0.8050 - precision: 0.9309 - val_loss: 0.0049 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8307/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0018 - AUC_ROC: 0.9681 - recall: 0.8491 - precision: 0.9310 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8308/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0018 - AUC_ROC: 0.9792 - recall: 0.8302 - precision: 0.9462 - val_loss: 0.0048 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8309/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0018 - AUC_ROC: 0.9776 - recall: 0.8553 - precision: 0.8803 - val_loss: 0.0049 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8310/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0020 - AUC_ROC: 0.9743 - recall: 0.8302 - precision: 0.9199 - val_loss: 0.0050 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8311/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0016 - AUC_ROC: 0.9809 - recall: 0.8333 - precision: 0.9601 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8312/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0019 - AUC_ROC: 0.9759 - recall: 0.8208 - precision: 0.9094 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8313/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0017 - AUC_ROC: 0.9745 - recall: 0.8365 - precision: 0.9333 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8314/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8459 - precision: 0.9439 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8315/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0017 - AUC_ROC: 0.9760 - recall: 0.8553 - precision: 0.9283 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8316/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0020 - AUC_ROC: 0.9760 - recall: 0.8333 - precision: 0.8717 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8317/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0017 - AUC_ROC: 0.9729 - recall: 0.8459 - precision: 0.9119 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8318/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0017 - AUC_ROC: 0.9729 - recall: 0.8491 - precision: 0.9215 - val_loss: 0.0052 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8319/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0019 - AUC_ROC: 0.9729 - recall: 0.8428 - precision: 0.9241 - val_loss: 0.0052 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8320/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0019 - AUC_ROC: 0.9728 - recall: 0.8082 - precision: 0.9519 - val_loss: 0.0052 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8321/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0017 - AUC_ROC: 0.9808 - recall: 0.8208 - precision: 0.9388 - val_loss: 0.0052 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8322/10000\n", + "3/3 [==============================] - 0s 114ms/step - loss: 0.0019 - AUC_ROC: 0.9760 - recall: 0.8050 - precision: 0.9176 - val_loss: 0.0052 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8323/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0018 - AUC_ROC: 0.9698 - recall: 0.8176 - precision: 0.9386 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8324/10000\n", + "3/3 [==============================] - 0s 98ms/step - loss: 0.0017 - AUC_ROC: 0.9775 - recall: 0.8522 - precision: 0.9345 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8325/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0021 - AUC_ROC: 0.9712 - recall: 0.8333 - precision: 0.9170 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8326/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0019 - AUC_ROC: 0.9745 - recall: 0.8239 - precision: 0.9357 - val_loss: 0.0050 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8327/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0018 - AUC_ROC: 0.9729 - recall: 0.8585 - precision: 0.9317 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8328/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0018 - AUC_ROC: 0.9776 - recall: 0.8333 - precision: 0.9364 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8329/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0018 - AUC_ROC: 0.9808 - recall: 0.8396 - precision: 0.9239 - val_loss: 0.0048 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8330/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0017 - AUC_ROC: 0.9776 - recall: 0.8522 - precision: 0.9313 - val_loss: 0.0048 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8331/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0017 - AUC_ROC: 0.9760 - recall: 0.8585 - precision: 0.9317 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8332/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0016 - AUC_ROC: 0.9777 - recall: 0.8428 - precision: 0.9537 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8333/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0019 - AUC_ROC: 0.9729 - recall: 0.8208 - precision: 0.9255 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8334/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0020 - AUC_ROC: 0.9681 - recall: 0.8019 - precision: 0.9375 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8335/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0016 - AUC_ROC: 0.9761 - recall: 0.8333 - precision: 0.9331 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8336/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0019 - AUC_ROC: 0.9759 - recall: 0.8302 - precision: 0.9296 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8337/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0016 - AUC_ROC: 0.9824 - recall: 0.8270 - precision: 0.9564 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8338/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0019 - AUC_ROC: 0.9839 - recall: 0.8333 - precision: 0.9138 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8339/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0019 - AUC_ROC: 0.9649 - recall: 0.8239 - precision: 0.9357 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8340/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0018 - AUC_ROC: 0.9713 - recall: 0.8302 - precision: 0.9531 - val_loss: 0.0052 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8341/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0019 - AUC_ROC: 0.9729 - recall: 0.8302 - precision: 0.9429 - val_loss: 0.0052 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8342/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0017 - AUC_ROC: 0.9682 - recall: 0.8459 - precision: 0.9539 - val_loss: 0.0052 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8343/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0018 - AUC_ROC: 0.9761 - recall: 0.8648 - precision: 0.9076 - val_loss: 0.0052 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8344/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0017 - AUC_ROC: 0.9792 - recall: 0.8333 - precision: 0.9364 - val_loss: 0.0052 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8345/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0016 - AUC_ROC: 0.9808 - recall: 0.8553 - precision: 0.9379 - val_loss: 0.0051 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8346/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0018 - AUC_ROC: 0.9776 - recall: 0.8459 - precision: 0.9119 - val_loss: 0.0051 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8347/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8365 - precision: 0.9673 - val_loss: 0.0051 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8348/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0018 - AUC_ROC: 0.9745 - recall: 0.8302 - precision: 0.9395 - val_loss: 0.0050 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8349/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0018 - AUC_ROC: 0.9713 - recall: 0.8333 - precision: 0.9298 - val_loss: 0.0049 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8350/10000\n", + "3/3 [==============================] - 0s 121ms/step - loss: 0.0017 - AUC_ROC: 0.9745 - recall: 0.8522 - precision: 0.9281 - val_loss: 0.0049 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8351/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0018 - AUC_ROC: 0.9760 - recall: 0.8113 - precision: 0.9520 - val_loss: 0.0049 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8352/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0017 - AUC_ROC: 0.9823 - recall: 0.8113 - precision: 0.9485 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8353/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0019 - AUC_ROC: 0.9760 - recall: 0.8428 - precision: 0.9371 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8354/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0015 - AUC_ROC: 0.9871 - recall: 0.8585 - precision: 0.8980 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8355/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0019 - AUC_ROC: 0.9729 - recall: 0.8270 - precision: 0.9460 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8356/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0018 - AUC_ROC: 0.9792 - recall: 0.8333 - precision: 0.9107 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8357/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8616 - precision: 0.9416 - val_loss: 0.0050 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8358/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0018 - AUC_ROC: 0.9713 - recall: 0.8019 - precision: 0.9696 - val_loss: 0.0050 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8359/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0017 - AUC_ROC: 0.9713 - recall: 0.8522 - precision: 0.9443 - val_loss: 0.0050 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8360/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0017 - AUC_ROC: 0.9777 - recall: 0.8491 - precision: 0.9278 - val_loss: 0.0050 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8361/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0017 - AUC_ROC: 0.9760 - recall: 0.8428 - precision: 0.9210 - val_loss: 0.0049 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8362/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0018 - AUC_ROC: 0.9792 - recall: 0.8553 - precision: 0.9220 - val_loss: 0.0049 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8363/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0017 - AUC_ROC: 0.9745 - recall: 0.8396 - precision: 0.9303 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8364/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0017 - AUC_ROC: 0.9759 - recall: 0.8302 - precision: 0.9462 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8365/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0019 - AUC_ROC: 0.9744 - recall: 0.8302 - precision: 0.9072 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8366/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0017 - AUC_ROC: 0.9760 - recall: 0.8208 - precision: 0.9422 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8367/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0017 - AUC_ROC: 0.9808 - recall: 0.8396 - precision: 0.9435 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8368/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0017 - AUC_ROC: 0.9808 - recall: 0.8239 - precision: 0.9161 - val_loss: 0.0049 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8369/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0017 - AUC_ROC: 0.9792 - recall: 0.8428 - precision: 0.9241 - val_loss: 0.0049 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8370/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0018 - AUC_ROC: 0.9807 - recall: 0.8302 - precision: 0.9231 - val_loss: 0.0049 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8371/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0017 - AUC_ROC: 0.9776 - recall: 0.8365 - precision: 0.9172 - val_loss: 0.0049 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8372/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0018 - AUC_ROC: 0.9777 - recall: 0.8270 - precision: 0.9393 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8373/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0017 - AUC_ROC: 0.9792 - recall: 0.8396 - precision: 0.9368 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8374/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0017 - AUC_ROC: 0.9792 - recall: 0.8459 - precision: 0.9276 - val_loss: 0.0049 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8375/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0018 - AUC_ROC: 0.9760 - recall: 0.8585 - precision: 0.9254 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8376/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0017 - AUC_ROC: 0.9808 - recall: 0.8585 - precision: 0.9254 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8377/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0016 - AUC_ROC: 0.9792 - recall: 0.8176 - precision: 0.9386 - val_loss: 0.0048 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8378/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0018 - AUC_ROC: 0.9729 - recall: 0.8333 - precision: 0.9331 - val_loss: 0.0048 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8379/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0017 - AUC_ROC: 0.9744 - recall: 0.8176 - precision: 0.9489 - val_loss: 0.0048 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8380/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0017 - AUC_ROC: 0.9792 - recall: 0.8208 - precision: 0.9491 - val_loss: 0.0048 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8381/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0017 - AUC_ROC: 0.9729 - recall: 0.8679 - precision: 0.9420 - val_loss: 0.0048 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8382/10000\n", + "3/3 [==============================] - 0s 116ms/step - loss: 0.0019 - AUC_ROC: 0.9761 - recall: 0.8396 - precision: 0.9368 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8383/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0018 - AUC_ROC: 0.9745 - recall: 0.8302 - precision: 0.9296 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8384/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0019 - AUC_ROC: 0.9729 - recall: 0.8396 - precision: 0.9271 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8385/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8553 - precision: 0.9315 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8386/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0017 - AUC_ROC: 0.9792 - recall: 0.8553 - precision: 0.9220 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8387/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0017 - AUC_ROC: 0.9714 - recall: 0.8616 - precision: 0.9481 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8388/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0017 - AUC_ROC: 0.9745 - recall: 0.8491 - precision: 0.9310 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8389/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8365 - precision: 0.9236 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8390/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0017 - AUC_ROC: 0.9744 - recall: 0.8459 - precision: 0.9472 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8391/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0018 - AUC_ROC: 0.9729 - recall: 0.8145 - precision: 0.9317 - val_loss: 0.0047 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8392/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0019 - AUC_ROC: 0.9791 - recall: 0.8711 - precision: 0.9203 - val_loss: 0.0047 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8393/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0021 - AUC_ROC: 0.9665 - recall: 0.8176 - precision: 0.9559 - val_loss: 0.0047 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8394/10000\n", + "3/3 [==============================] - 0s 114ms/step - loss: 0.0018 - AUC_ROC: 0.9729 - recall: 0.8270 - precision: 0.9007 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8395/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8522 - precision: 0.9576 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8396/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0017 - AUC_ROC: 0.9808 - recall: 0.8585 - precision: 0.9317 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8397/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0019 - AUC_ROC: 0.9791 - recall: 0.8113 - precision: 0.9314 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8398/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0017 - AUC_ROC: 0.9729 - recall: 0.8522 - precision: 0.9443 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8399/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0018 - AUC_ROC: 0.9729 - recall: 0.8333 - precision: 0.9567 - val_loss: 0.0047 - val_AUC_ROC: 0.9012 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8400/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0016 - AUC_ROC: 0.9824 - recall: 0.8522 - precision: 0.9281 - val_loss: 0.0047 - val_AUC_ROC: 0.9012 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8401/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0019 - AUC_ROC: 0.9744 - recall: 0.8491 - precision: 0.9060 - val_loss: 0.0047 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8402/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0017 - AUC_ROC: 0.9745 - recall: 0.8239 - precision: 0.9424 - val_loss: 0.0047 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8403/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0018 - AUC_ROC: 0.9729 - recall: 0.8428 - precision: 0.9437 - val_loss: 0.0047 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8404/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0018 - AUC_ROC: 0.9666 - recall: 0.8302 - precision: 0.9329 - val_loss: 0.0046 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8405/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0018 - AUC_ROC: 0.9792 - recall: 0.8302 - precision: 0.9429 - val_loss: 0.0046 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8406/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0017 - AUC_ROC: 0.9714 - recall: 0.8396 - precision: 0.9435 - val_loss: 0.0045 - val_AUC_ROC: 0.9012 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8407/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8365 - precision: 0.9466 - val_loss: 0.0045 - val_AUC_ROC: 0.9012 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8408/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0018 - AUC_ROC: 0.9839 - recall: 0.8459 - precision: 0.8878 - val_loss: 0.0045 - val_AUC_ROC: 0.9012 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8409/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0019 - AUC_ROC: 0.9744 - recall: 0.8239 - precision: 0.9225 - val_loss: 0.0046 - val_AUC_ROC: 0.9012 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8410/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0017 - AUC_ROC: 0.9808 - recall: 0.8365 - precision: 0.9172 - val_loss: 0.0047 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8411/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0019 - AUC_ROC: 0.9760 - recall: 0.8365 - precision: 0.9268 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8412/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0018 - AUC_ROC: 0.9682 - recall: 0.8396 - precision: 0.9536 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8413/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0019 - AUC_ROC: 0.9776 - recall: 0.8113 - precision: 0.9382 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8414/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0019 - AUC_ROC: 0.9713 - recall: 0.8113 - precision: 0.9348 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8415/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0017 - AUC_ROC: 0.9824 - recall: 0.8113 - precision: 0.9556 - val_loss: 0.0047 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8416/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0015 - AUC_ROC: 0.9824 - recall: 0.8176 - precision: 0.9489 - val_loss: 0.0046 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8417/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0018 - AUC_ROC: 0.9713 - recall: 0.8365 - precision: 0.9268 - val_loss: 0.0046 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8418/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0018 - AUC_ROC: 0.9760 - recall: 0.8239 - precision: 0.8973 - val_loss: 0.0047 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8419/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0018 - AUC_ROC: 0.9760 - recall: 0.8679 - precision: 0.9139 - val_loss: 0.0047 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8420/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0019 - AUC_ROC: 0.9744 - recall: 0.8396 - precision: 0.9051 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8421/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0019 - AUC_ROC: 0.9729 - recall: 0.8113 - precision: 0.9348 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8422/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0017 - AUC_ROC: 0.9760 - recall: 0.8396 - precision: 0.9468 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8423/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0019 - AUC_ROC: 0.9697 - recall: 0.8145 - precision: 0.9250 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8424/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0017 - AUC_ROC: 0.9792 - recall: 0.8365 - precision: 0.9500 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8425/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0017 - AUC_ROC: 0.9792 - recall: 0.8365 - precision: 0.9301 - val_loss: 0.0047 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8426/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0019 - AUC_ROC: 0.9729 - recall: 0.8270 - precision: 0.9293 - val_loss: 0.0047 - val_AUC_ROC: 0.9012 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8427/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0017 - AUC_ROC: 0.9729 - recall: 0.8333 - precision: 0.9431 - val_loss: 0.0047 - val_AUC_ROC: 0.9012 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8428/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0018 - AUC_ROC: 0.9634 - recall: 0.8491 - precision: 0.9215 - val_loss: 0.0046 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8429/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0020 - AUC_ROC: 0.9682 - recall: 0.8082 - precision: 0.9278 - val_loss: 0.0047 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8430/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8553 - precision: 0.9252 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8431/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0018 - AUC_ROC: 0.9682 - recall: 0.8365 - precision: 0.9366 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8432/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0020 - AUC_ROC: 0.9697 - recall: 0.8365 - precision: 0.9172 - val_loss: 0.0046 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8433/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0017 - AUC_ROC: 0.9839 - recall: 0.8365 - precision: 0.9333 - val_loss: 0.0046 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8434/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0019 - AUC_ROC: 0.9760 - recall: 0.7925 - precision: 0.9231 - val_loss: 0.0045 - val_AUC_ROC: 0.9012 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8435/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0016 - AUC_ROC: 0.9824 - recall: 0.8239 - precision: 0.9357 - val_loss: 0.0045 - val_AUC_ROC: 0.9012 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8436/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0019 - AUC_ROC: 0.9745 - recall: 0.8396 - precision: 0.9082 - val_loss: 0.0045 - val_AUC_ROC: 0.9073 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8437/10000\n", + "3/3 [==============================] - 0s 120ms/step - loss: 0.0019 - AUC_ROC: 0.9744 - recall: 0.8396 - precision: 0.9336 - val_loss: 0.0045 - val_AUC_ROC: 0.9073 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8438/10000\n", + "3/3 [==============================] - 0s 120ms/step - loss: 0.0016 - AUC_ROC: 0.9792 - recall: 0.8585 - precision: 0.9446 - val_loss: 0.0045 - val_AUC_ROC: 0.9073 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8439/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0018 - AUC_ROC: 0.9823 - recall: 0.8333 - precision: 0.9138 - val_loss: 0.0045 - val_AUC_ROC: 0.9073 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8440/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0019 - AUC_ROC: 0.9697 - recall: 0.8208 - precision: 0.9255 - val_loss: 0.0046 - val_AUC_ROC: 0.9073 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8441/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0015 - AUC_ROC: 0.9808 - recall: 0.8553 - precision: 0.9412 - val_loss: 0.0046 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8442/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0018 - AUC_ROC: 0.9729 - recall: 0.8428 - precision: 0.9371 - val_loss: 0.0047 - val_AUC_ROC: 0.9012 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8443/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0017 - AUC_ROC: 0.9808 - recall: 0.8396 - precision: 0.9401 - val_loss: 0.0047 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8444/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0017 - AUC_ROC: 0.9730 - recall: 0.8365 - precision: 0.9399 - val_loss: 0.0047 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8445/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0017 - AUC_ROC: 0.9792 - recall: 0.8522 - precision: 0.9218 - val_loss: 0.0047 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8446/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0017 - AUC_ROC: 0.9839 - recall: 0.8208 - precision: 0.9000 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8447/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0017 - AUC_ROC: 0.9792 - recall: 0.8396 - precision: 0.9401 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8448/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0018 - AUC_ROC: 0.9729 - recall: 0.8428 - precision: 0.9306 - val_loss: 0.0047 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8449/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0020 - AUC_ROC: 0.9729 - recall: 0.8270 - precision: 0.9132 - val_loss: 0.0047 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8450/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0016 - AUC_ROC: 0.9824 - recall: 0.8333 - precision: 0.9532 - val_loss: 0.0047 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8451/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0018 - AUC_ROC: 0.9760 - recall: 0.8491 - precision: 0.9343 - val_loss: 0.0047 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8452/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0018 - AUC_ROC: 0.9713 - recall: 0.8333 - precision: 0.9331 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8453/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0017 - AUC_ROC: 0.9840 - recall: 0.8239 - precision: 0.9291 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8454/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0017 - AUC_ROC: 0.9744 - recall: 0.8491 - precision: 0.9343 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8455/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0017 - AUC_ROC: 0.9745 - recall: 0.8365 - precision: 0.9333 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8456/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0018 - AUC_ROC: 0.9791 - recall: 0.8302 - precision: 0.9496 - val_loss: 0.0048 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8457/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0019 - AUC_ROC: 0.9712 - recall: 0.8270 - precision: 0.9293 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8458/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0019 - AUC_ROC: 0.9682 - recall: 0.8333 - precision: 0.9364 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8459/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0019 - AUC_ROC: 0.9760 - recall: 0.8239 - precision: 0.9357 - val_loss: 0.0047 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8460/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0020 - AUC_ROC: 0.9713 - recall: 0.8239 - precision: 0.9225 - val_loss: 0.0046 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8461/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0018 - AUC_ROC: 0.9823 - recall: 0.8396 - precision: 0.9336 - val_loss: 0.0045 - val_AUC_ROC: 0.9011 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8462/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0018 - AUC_ROC: 0.9745 - recall: 0.8270 - precision: 0.9495 - val_loss: 0.0045 - val_AUC_ROC: 0.9011 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8463/10000\n", + "3/3 [==============================] - 0s 120ms/step - loss: 0.0018 - AUC_ROC: 0.9776 - recall: 0.8302 - precision: 0.9296 - val_loss: 0.0045 - val_AUC_ROC: 0.9011 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8464/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0017 - AUC_ROC: 0.9745 - recall: 0.8333 - precision: 0.9464 - val_loss: 0.0045 - val_AUC_ROC: 0.9011 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8465/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0017 - AUC_ROC: 0.9839 - recall: 0.8459 - precision: 0.9057 - val_loss: 0.0045 - val_AUC_ROC: 0.9011 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8466/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0018 - AUC_ROC: 0.9729 - recall: 0.8396 - precision: 0.9401 - val_loss: 0.0045 - val_AUC_ROC: 0.9011 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8467/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0018 - AUC_ROC: 0.9744 - recall: 0.8302 - precision: 0.9395 - val_loss: 0.0045 - val_AUC_ROC: 0.9011 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8468/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0017 - AUC_ROC: 0.9792 - recall: 0.8302 - precision: 0.9496 - val_loss: 0.0045 - val_AUC_ROC: 0.9011 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8469/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0017 - AUC_ROC: 0.9744 - recall: 0.8365 - precision: 0.9433 - val_loss: 0.0045 - val_AUC_ROC: 0.9012 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8470/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0019 - AUC_ROC: 0.9744 - recall: 0.8050 - precision: 0.9275 - val_loss: 0.0045 - val_AUC_ROC: 0.9012 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8471/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0018 - AUC_ROC: 0.9761 - recall: 0.8365 - precision: 0.9333 - val_loss: 0.0046 - val_AUC_ROC: 0.9011 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8472/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0016 - AUC_ROC: 0.9792 - recall: 0.8459 - precision: 0.9373 - val_loss: 0.0046 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8473/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0018 - AUC_ROC: 0.9666 - recall: 0.8648 - precision: 0.9076 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8474/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0017 - AUC_ROC: 0.9792 - recall: 0.8396 - precision: 0.9207 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8475/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0017 - AUC_ROC: 0.9807 - recall: 0.8553 - precision: 0.9252 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8476/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0017 - AUC_ROC: 0.9839 - recall: 0.8145 - precision: 0.9384 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8477/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0018 - AUC_ROC: 0.9745 - recall: 0.8302 - precision: 0.9496 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8478/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0016 - AUC_ROC: 0.9777 - recall: 0.8270 - precision: 0.9495 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8479/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0019 - AUC_ROC: 0.9760 - recall: 0.8428 - precision: 0.9054 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8480/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8333 - precision: 0.9707 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8481/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0019 - AUC_ROC: 0.9807 - recall: 0.8145 - precision: 0.9152 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8482/10000\n", + "3/3 [==============================] - 0s 117ms/step - loss: 0.0018 - AUC_ROC: 0.9745 - recall: 0.8270 - precision: 0.9634 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8483/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0016 - AUC_ROC: 0.9745 - recall: 0.8428 - precision: 0.9371 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8484/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0015 - AUC_ROC: 0.9777 - recall: 0.8711 - precision: 0.9358 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8485/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0018 - AUC_ROC: 0.9744 - recall: 0.8396 - precision: 0.9401 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8486/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0018 - AUC_ROC: 0.9776 - recall: 0.8333 - precision: 0.9266 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8487/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0016 - AUC_ROC: 0.9808 - recall: 0.8491 - precision: 0.9184 - val_loss: 0.0046 - val_AUC_ROC: 0.9012 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8488/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0016 - AUC_ROC: 0.9777 - recall: 0.8428 - precision: 0.9504 - val_loss: 0.0046 - val_AUC_ROC: 0.9012 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8489/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0017 - AUC_ROC: 0.9729 - recall: 0.8553 - precision: 0.9283 - val_loss: 0.0046 - val_AUC_ROC: 0.9012 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8490/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0016 - AUC_ROC: 0.9730 - recall: 0.8491 - precision: 0.9408 - val_loss: 0.0046 - val_AUC_ROC: 0.9012 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8491/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0017 - AUC_ROC: 0.9729 - recall: 0.8459 - precision: 0.9406 - val_loss: 0.0046 - val_AUC_ROC: 0.9012 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8492/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8491 - precision: 0.9310 - val_loss: 0.0045 - val_AUC_ROC: 0.9012 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8493/10000\n", + "3/3 [==============================] - 0s 115ms/step - loss: 0.0017 - AUC_ROC: 0.9760 - recall: 0.8428 - precision: 0.9273 - val_loss: 0.0045 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8494/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0017 - AUC_ROC: 0.9745 - recall: 0.8396 - precision: 0.9303 - val_loss: 0.0045 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8495/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0016 - AUC_ROC: 0.9808 - recall: 0.8459 - precision: 0.9406 - val_loss: 0.0045 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8496/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0015 - AUC_ROC: 0.9746 - recall: 0.8585 - precision: 0.9715 - val_loss: 0.0045 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8497/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0016 - AUC_ROC: 0.9714 - recall: 0.8333 - precision: 0.9532 - val_loss: 0.0044 - val_AUC_ROC: 0.9012 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8498/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0016 - AUC_ROC: 0.9730 - recall: 0.8365 - precision: 0.9603 - val_loss: 0.0044 - val_AUC_ROC: 0.9012 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8499/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0019 - AUC_ROC: 0.9744 - recall: 0.8208 - precision: 0.9223 - val_loss: 0.0043 - val_AUC_ROC: 0.9073 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8500/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0018 - AUC_ROC: 0.9745 - recall: 0.8113 - precision: 0.9281 - val_loss: 0.0043 - val_AUC_ROC: 0.9073 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8501/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0018 - AUC_ROC: 0.9729 - recall: 0.8239 - precision: 0.9225 - val_loss: 0.0044 - val_AUC_ROC: 0.9073 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8502/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0017 - AUC_ROC: 0.9714 - recall: 0.8302 - precision: 0.9296 - val_loss: 0.0044 - val_AUC_ROC: 0.9073 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8503/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0015 - AUC_ROC: 0.9824 - recall: 0.8553 - precision: 0.9347 - val_loss: 0.0045 - val_AUC_ROC: 0.9073 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8504/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0017 - AUC_ROC: 0.9776 - recall: 0.8428 - precision: 0.9210 - val_loss: 0.0046 - val_AUC_ROC: 0.9012 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8505/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0018 - AUC_ROC: 0.9714 - recall: 0.8365 - precision: 0.9301 - val_loss: 0.0047 - val_AUC_ROC: 0.9012 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8506/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0019 - AUC_ROC: 0.9744 - recall: 0.8491 - precision: 0.9215 - val_loss: 0.0047 - val_AUC_ROC: 0.9012 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8507/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0017 - AUC_ROC: 0.9760 - recall: 0.8176 - precision: 0.9420 - val_loss: 0.0047 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8508/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0017 - AUC_ROC: 0.9777 - recall: 0.8333 - precision: 0.9567 - val_loss: 0.0047 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8509/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0016 - AUC_ROC: 0.9824 - recall: 0.8491 - precision: 0.9408 - val_loss: 0.0047 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8510/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8491 - precision: 0.9247 - val_loss: 0.0048 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8511/10000\n", + "3/3 [==============================] - 0s 98ms/step - loss: 0.0014 - AUC_ROC: 0.9824 - recall: 0.8711 - precision: 0.9552 - val_loss: 0.0048 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8512/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0016 - AUC_ROC: 0.9808 - recall: 0.8553 - precision: 0.9220 - val_loss: 0.0049 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8513/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0020 - AUC_ROC: 0.9666 - recall: 0.8082 - precision: 0.9590 - val_loss: 0.0049 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8514/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0017 - AUC_ROC: 0.9667 - recall: 0.8302 - precision: 0.9565 - val_loss: 0.0049 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8515/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0019 - AUC_ROC: 0.9697 - recall: 0.7987 - precision: 0.9236 - val_loss: 0.0048 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8516/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0018 - AUC_ROC: 0.9760 - recall: 0.8365 - precision: 0.9236 - val_loss: 0.0047 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8517/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0019 - AUC_ROC: 0.9760 - recall: 0.8365 - precision: 0.9333 - val_loss: 0.0047 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8518/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0016 - AUC_ROC: 0.9792 - recall: 0.8616 - precision: 0.9384 - val_loss: 0.0047 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8519/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0015 - AUC_ROC: 0.9823 - recall: 0.8616 - precision: 0.9448 - val_loss: 0.0047 - val_AUC_ROC: 0.9011 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8520/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0017 - AUC_ROC: 0.9808 - recall: 0.8302 - precision: 0.9462 - val_loss: 0.0047 - val_AUC_ROC: 0.9011 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8521/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0019 - AUC_ROC: 0.9713 - recall: 0.8302 - precision: 0.9329 - val_loss: 0.0048 - val_AUC_ROC: 0.9011 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8522/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8459 - precision: 0.9573 - val_loss: 0.0048 - val_AUC_ROC: 0.9011 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8523/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0017 - AUC_ROC: 0.9698 - recall: 0.8270 - precision: 0.9564 - val_loss: 0.0049 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8524/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0017 - AUC_ROC: 0.9745 - recall: 0.8396 - precision: 0.9271 - val_loss: 0.0049 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8525/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0020 - AUC_ROC: 0.9791 - recall: 0.8459 - precision: 0.8937 - val_loss: 0.0049 - val_AUC_ROC: 0.9011 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8526/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0018 - AUC_ROC: 0.9761 - recall: 0.8459 - precision: 0.9181 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8527/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8333 - precision: 0.9397 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8528/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0016 - AUC_ROC: 0.9761 - recall: 0.8333 - precision: 0.9464 - val_loss: 0.0049 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8529/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0019 - AUC_ROC: 0.9744 - recall: 0.8396 - precision: 0.9113 - val_loss: 0.0049 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8530/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0017 - AUC_ROC: 0.9792 - recall: 0.8302 - precision: 0.9395 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8531/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0016 - AUC_ROC: 0.9777 - recall: 0.8616 - precision: 0.9514 - val_loss: 0.0050 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8532/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0016 - AUC_ROC: 0.9713 - recall: 0.8553 - precision: 0.9412 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8533/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0017 - AUC_ROC: 0.9839 - recall: 0.8176 - precision: 0.9319 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8534/10000\n", + "3/3 [==============================] - 0s 97ms/step - loss: 0.0017 - AUC_ROC: 0.9729 - recall: 0.8585 - precision: 0.9349 - val_loss: 0.0050 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8535/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0016 - AUC_ROC: 0.9745 - recall: 0.8428 - precision: 0.9306 - val_loss: 0.0050 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8536/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0017 - AUC_ROC: 0.9744 - recall: 0.8208 - precision: 0.9491 - val_loss: 0.0051 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8537/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0016 - AUC_ROC: 0.9808 - recall: 0.8522 - precision: 0.9610 - val_loss: 0.0051 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8538/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0019 - AUC_ROC: 0.9713 - recall: 0.8208 - precision: 0.9491 - val_loss: 0.0051 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8539/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0016 - AUC_ROC: 0.9714 - recall: 0.8302 - precision: 0.9706 - val_loss: 0.0050 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8540/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0018 - AUC_ROC: 0.9682 - recall: 0.8176 - precision: 0.9559 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8541/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0016 - AUC_ROC: 0.9809 - recall: 0.8208 - precision: 0.9288 - val_loss: 0.0048 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8542/10000\n", + "3/3 [==============================] - 0s 98ms/step - loss: 0.0017 - AUC_ROC: 0.9823 - recall: 0.8239 - precision: 0.9357 - val_loss: 0.0048 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8543/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0018 - AUC_ROC: 0.9760 - recall: 0.8365 - precision: 0.9268 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8544/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0015 - AUC_ROC: 0.9840 - recall: 0.8491 - precision: 0.9310 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8545/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0016 - AUC_ROC: 0.9792 - recall: 0.8585 - precision: 0.9446 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8546/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0017 - AUC_ROC: 0.9698 - recall: 0.8365 - precision: 0.9500 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8547/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0016 - AUC_ROC: 0.9808 - recall: 0.8396 - precision: 0.9113 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8548/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0017 - AUC_ROC: 0.9729 - recall: 0.8585 - precision: 0.9414 - val_loss: 0.0050 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8549/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0017 - AUC_ROC: 0.9713 - recall: 0.8459 - precision: 0.9373 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8550/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0016 - AUC_ROC: 0.9745 - recall: 0.8522 - precision: 0.9509 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8551/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0016 - AUC_ROC: 0.9809 - recall: 0.8679 - precision: 0.9293 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8552/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0016 - AUC_ROC: 0.9792 - recall: 0.8648 - precision: 0.9197 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8553/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0018 - AUC_ROC: 0.9760 - recall: 0.8459 - precision: 0.9340 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8554/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0019 - AUC_ROC: 0.9634 - recall: 0.8365 - precision: 0.9433 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8555/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0017 - AUC_ROC: 0.9792 - recall: 0.8428 - precision: 0.9241 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8556/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0016 - AUC_ROC: 0.9761 - recall: 0.8585 - precision: 0.9479 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8557/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0018 - AUC_ROC: 0.9776 - recall: 0.8239 - precision: 0.9225 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8558/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0017 - AUC_ROC: 0.9776 - recall: 0.8459 - precision: 0.9244 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8559/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0017 - AUC_ROC: 0.9793 - recall: 0.8522 - precision: 0.9281 - val_loss: 0.0048 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8560/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0018 - AUC_ROC: 0.9745 - recall: 0.8553 - precision: 0.9252 - val_loss: 0.0048 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8561/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0018 - AUC_ROC: 0.9682 - recall: 0.8491 - precision: 0.9247 - val_loss: 0.0049 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8562/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0019 - AUC_ROC: 0.9729 - recall: 0.8459 - precision: 0.9308 - val_loss: 0.0048 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8563/10000\n", + "3/3 [==============================] - 0s 97ms/step - loss: 0.0017 - AUC_ROC: 0.9823 - recall: 0.8459 - precision: 0.9181 - val_loss: 0.0048 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8564/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0017 - AUC_ROC: 0.9730 - recall: 0.8145 - precision: 0.9522 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8565/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0016 - AUC_ROC: 0.9761 - recall: 0.8176 - precision: 0.9386 - val_loss: 0.0047 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8566/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0018 - AUC_ROC: 0.9792 - recall: 0.8396 - precision: 0.9435 - val_loss: 0.0047 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8567/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0015 - AUC_ROC: 0.9824 - recall: 0.8428 - precision: 0.9537 - val_loss: 0.0047 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8568/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0018 - AUC_ROC: 0.9713 - recall: 0.8302 - precision: 0.9362 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8569/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0018 - AUC_ROC: 0.9713 - recall: 0.8396 - precision: 0.9303 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8570/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0018 - AUC_ROC: 0.9745 - recall: 0.8082 - precision: 0.9380 - val_loss: 0.0048 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8571/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0017 - AUC_ROC: 0.9823 - recall: 0.8459 - precision: 0.9212 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8572/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0018 - AUC_ROC: 0.9745 - recall: 0.8145 - precision: 0.9487 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8573/10000\n", + "3/3 [==============================] - 0s 97ms/step - loss: 0.0018 - AUC_ROC: 0.9792 - recall: 0.8459 - precision: 0.9276 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8574/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0017 - AUC_ROC: 0.9839 - recall: 0.8239 - precision: 0.9225 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8575/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0017 - AUC_ROC: 0.9823 - recall: 0.8396 - precision: 0.9271 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8576/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0016 - AUC_ROC: 0.9840 - recall: 0.8648 - precision: 0.9136 - val_loss: 0.0049 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8577/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0016 - AUC_ROC: 0.9808 - recall: 0.8239 - precision: 0.9493 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8578/10000\n", + "3/3 [==============================] - 0s 116ms/step - loss: 0.0018 - AUC_ROC: 0.9729 - recall: 0.8333 - precision: 0.9431 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8579/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0016 - AUC_ROC: 0.9792 - recall: 0.8459 - precision: 0.9607 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8580/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0019 - AUC_ROC: 0.9697 - recall: 0.8050 - precision: 0.9517 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8581/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8365 - precision: 0.9399 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8582/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0016 - AUC_ROC: 0.9840 - recall: 0.8333 - precision: 0.9107 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9531\n", + "Epoch 8583/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0019 - AUC_ROC: 0.9681 - recall: 0.8522 - precision: 0.9313 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9531\n", + "Epoch 8584/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0018 - AUC_ROC: 0.9761 - recall: 0.8491 - precision: 0.9000 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 8585/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0018 - AUC_ROC: 0.9792 - recall: 0.8145 - precision: 0.9184 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 8586/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0016 - AUC_ROC: 0.9792 - recall: 0.8302 - precision: 0.9429 - val_loss: 0.0050 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 8587/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0018 - AUC_ROC: 0.9698 - recall: 0.8459 - precision: 0.9244 - val_loss: 0.0050 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 8588/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0017 - AUC_ROC: 0.9840 - recall: 0.8459 - precision: 0.9212 - val_loss: 0.0050 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 8589/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0015 - AUC_ROC: 0.9792 - recall: 0.8333 - precision: 0.9397 - val_loss: 0.0050 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 8590/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0018 - AUC_ROC: 0.9807 - recall: 0.8522 - precision: 0.8856 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 8591/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0016 - AUC_ROC: 0.9761 - recall: 0.8742 - precision: 0.9456 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 8592/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0017 - AUC_ROC: 0.9824 - recall: 0.8396 - precision: 0.9336 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 8593/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0017 - AUC_ROC: 0.9744 - recall: 0.8208 - precision: 0.9457 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 8594/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0018 - AUC_ROC: 0.9745 - recall: 0.8333 - precision: 0.9138 - val_loss: 0.0047 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9538\n", + "Epoch 8595/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0016 - AUC_ROC: 0.9745 - recall: 0.8208 - precision: 0.9526 - val_loss: 0.0047 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9538\n", + "Epoch 8596/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0015 - AUC_ROC: 0.9792 - recall: 0.8585 - precision: 0.9381 - val_loss: 0.0047 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9538\n", + "Epoch 8597/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0015 - AUC_ROC: 0.9824 - recall: 0.8396 - precision: 0.9368 - val_loss: 0.0047 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9538\n", + "Epoch 8598/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0016 - AUC_ROC: 0.9776 - recall: 0.8616 - precision: 0.9288 - val_loss: 0.0048 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9531\n", + "Epoch 8599/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0018 - AUC_ROC: 0.9729 - recall: 0.8616 - precision: 0.9013 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8600/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0019 - AUC_ROC: 0.9713 - recall: 0.8522 - precision: 0.9094 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8601/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8396 - precision: 0.9536 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8602/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0017 - AUC_ROC: 0.9666 - recall: 0.8459 - precision: 0.9505 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8603/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0020 - AUC_ROC: 0.9682 - recall: 0.8365 - precision: 0.9433 - val_loss: 0.0048 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8604/10000\n", + "3/3 [==============================] - 0s 114ms/step - loss: 0.0017 - AUC_ROC: 0.9714 - recall: 0.8428 - precision: 0.9470 - val_loss: 0.0048 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8605/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0017 - AUC_ROC: 0.9776 - recall: 0.8176 - precision: 0.9455 - val_loss: 0.0048 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8606/10000\n", + "3/3 [==============================] - 0s 120ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8208 - precision: 0.9631 - val_loss: 0.0048 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8607/10000\n", + "3/3 [==============================] - 0s 118ms/step - loss: 0.0017 - AUC_ROC: 0.9729 - recall: 0.8365 - precision: 0.9534 - val_loss: 0.0048 - val_AUC_ROC: 0.9012 - val_recall: 0.7531 - val_precision: 0.9531\n", + "Epoch 8608/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0016 - AUC_ROC: 0.9808 - recall: 0.8522 - precision: 0.9410 - val_loss: 0.0048 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 8609/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0019 - AUC_ROC: 0.9855 - recall: 0.8491 - precision: 0.8970 - val_loss: 0.0049 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8610/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8711 - precision: 0.9552 - val_loss: 0.0050 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8611/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0017 - AUC_ROC: 0.9745 - recall: 0.8333 - precision: 0.9397 - val_loss: 0.0051 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8612/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0018 - AUC_ROC: 0.9730 - recall: 0.8270 - precision: 0.9326 - val_loss: 0.0051 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8613/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0018 - AUC_ROC: 0.9728 - recall: 0.8522 - precision: 0.9186 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8614/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0016 - AUC_ROC: 0.9761 - recall: 0.8491 - precision: 0.9541 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8615/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0018 - AUC_ROC: 0.9729 - recall: 0.8491 - precision: 0.9343 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8616/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0016 - AUC_ROC: 0.9808 - recall: 0.8270 - precision: 0.9393 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 8617/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0019 - AUC_ROC: 0.9744 - recall: 0.8270 - precision: 0.9393 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 8618/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0017 - AUC_ROC: 0.9824 - recall: 0.8522 - precision: 0.9218 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 8619/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0015 - AUC_ROC: 0.9840 - recall: 0.8522 - precision: 0.9249 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 8620/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8522 - precision: 0.9476 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8621/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0019 - AUC_ROC: 0.9792 - recall: 0.8365 - precision: 0.9268 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8622/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0018 - AUC_ROC: 0.9729 - recall: 0.8302 - precision: 0.9362 - val_loss: 0.0050 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8623/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8459 - precision: 0.9212 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8624/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8396 - precision: 0.9144 - val_loss: 0.0050 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8625/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8396 - precision: 0.9502 - val_loss: 0.0050 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8626/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0017 - AUC_ROC: 0.9792 - recall: 0.8396 - precision: 0.9207 - val_loss: 0.0050 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8627/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0016 - AUC_ROC: 0.9761 - recall: 0.8553 - precision: 0.9444 - val_loss: 0.0051 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8628/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0018 - AUC_ROC: 0.9792 - recall: 0.8333 - precision: 0.9298 - val_loss: 0.0051 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8629/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8428 - precision: 0.9371 - val_loss: 0.0051 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8630/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0016 - AUC_ROC: 0.9792 - recall: 0.8365 - precision: 0.9433 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8631/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8522 - precision: 0.9377 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8632/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0018 - AUC_ROC: 0.9713 - recall: 0.8365 - precision: 0.9301 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8633/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0017 - AUC_ROC: 0.9745 - recall: 0.8396 - precision: 0.9271 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8634/10000\n", + "3/3 [==============================] - 0s 98ms/step - loss: 0.0015 - AUC_ROC: 0.9824 - recall: 0.8428 - precision: 0.9371 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8635/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0017 - AUC_ROC: 0.9745 - recall: 0.8396 - precision: 0.9435 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8636/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0017 - AUC_ROC: 0.9760 - recall: 0.8459 - precision: 0.9276 - val_loss: 0.0052 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8637/10000\n", + "3/3 [==============================] - 0s 98ms/step - loss: 0.0017 - AUC_ROC: 0.9824 - recall: 0.8365 - precision: 0.9236 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8638/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0015 - AUC_ROC: 0.9840 - recall: 0.8428 - precision: 0.9640 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8639/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8774 - precision: 0.9208 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8640/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0017 - AUC_ROC: 0.9824 - recall: 0.8302 - precision: 0.9362 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8641/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0015 - AUC_ROC: 0.9825 - recall: 0.8428 - precision: 0.9404 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8642/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0015 - AUC_ROC: 0.9808 - recall: 0.8553 - precision: 0.9477 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8643/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0016 - AUC_ROC: 0.9761 - recall: 0.8585 - precision: 0.9254 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8644/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0016 - AUC_ROC: 0.9713 - recall: 0.8428 - precision: 0.9437 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8645/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0016 - AUC_ROC: 0.9777 - recall: 0.8428 - precision: 0.9470 - val_loss: 0.0052 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8646/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0016 - AUC_ROC: 0.9761 - recall: 0.8553 - precision: 0.9379 - val_loss: 0.0052 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8647/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0017 - AUC_ROC: 0.9808 - recall: 0.8459 - precision: 0.9276 - val_loss: 0.0052 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8648/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0017 - AUC_ROC: 0.9839 - recall: 0.8333 - precision: 0.9298 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8649/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0016 - AUC_ROC: 0.9823 - recall: 0.8459 - precision: 0.9244 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8650/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0015 - AUC_ROC: 0.9839 - recall: 0.8428 - precision: 0.9338 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8651/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0018 - AUC_ROC: 0.9729 - recall: 0.8270 - precision: 0.9196 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8652/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0017 - AUC_ROC: 0.9745 - recall: 0.8365 - precision: 0.9301 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8653/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0015 - AUC_ROC: 0.9824 - recall: 0.8585 - precision: 0.9579 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8654/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0015 - AUC_ROC: 0.9777 - recall: 0.8459 - precision: 0.9439 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8655/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0017 - AUC_ROC: 0.9744 - recall: 0.8491 - precision: 0.9408 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8656/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0018 - AUC_ROC: 0.9760 - recall: 0.8459 - precision: 0.8967 - val_loss: 0.0054 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8657/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0017 - AUC_ROC: 0.9808 - recall: 0.8208 - precision: 0.9288 - val_loss: 0.0054 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8658/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8208 - precision: 0.9457 - val_loss: 0.0054 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8659/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0017 - AUC_ROC: 0.9776 - recall: 0.8522 - precision: 0.9125 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8660/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0016 - AUC_ROC: 0.9792 - recall: 0.8428 - precision: 0.9437 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8661/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0018 - AUC_ROC: 0.9713 - recall: 0.8302 - precision: 0.9263 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8662/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0018 - AUC_ROC: 0.9760 - recall: 0.8585 - precision: 0.9192 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8663/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8365 - precision: 0.9568 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8664/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0018 - AUC_ROC: 0.9745 - recall: 0.8396 - precision: 0.9336 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8665/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0016 - AUC_ROC: 0.9808 - recall: 0.8459 - precision: 0.9505 - val_loss: 0.0052 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8666/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8836 - precision: 0.9305 - val_loss: 0.0052 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8667/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0015 - AUC_ROC: 0.9840 - recall: 0.8585 - precision: 0.9349 - val_loss: 0.0052 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8668/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0019 - AUC_ROC: 0.9776 - recall: 0.8302 - precision: 0.9231 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7160 - val_precision: 0.9667\n", + "Epoch 8669/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0016 - AUC_ROC: 0.9760 - recall: 0.8648 - precision: 0.9106 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7160 - val_precision: 0.9667\n", + "Epoch 8670/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0017 - AUC_ROC: 0.9729 - recall: 0.8333 - precision: 0.9498 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7160 - val_precision: 0.9667\n", + "Epoch 8671/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0017 - AUC_ROC: 0.9839 - recall: 0.8270 - precision: 0.9495 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7160 - val_precision: 0.9667\n", + "Epoch 8672/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8648 - precision: 0.9322 - val_loss: 0.0050 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8673/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8711 - precision: 0.9422 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8674/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8616 - precision: 0.9226 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8675/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0018 - AUC_ROC: 0.9745 - recall: 0.8239 - precision: 0.9597 - val_loss: 0.0050 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8676/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0017 - AUC_ROC: 0.9776 - recall: 0.8208 - precision: 0.9457 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8677/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0016 - AUC_ROC: 0.9871 - recall: 0.8585 - precision: 0.9070 - val_loss: 0.0051 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8678/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8491 - precision: 0.9609 - val_loss: 0.0052 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8679/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8333 - precision: 0.9464 - val_loss: 0.0052 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8680/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0018 - AUC_ROC: 0.9713 - recall: 0.8459 - precision: 0.9439 - val_loss: 0.0053 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8681/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0016 - AUC_ROC: 0.9777 - recall: 0.8711 - precision: 0.9295 - val_loss: 0.0053 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8682/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0017 - AUC_ROC: 0.9713 - recall: 0.8396 - precision: 0.9239 - val_loss: 0.0053 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8683/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0017 - AUC_ROC: 0.9714 - recall: 0.8553 - precision: 0.9252 - val_loss: 0.0053 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8684/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0017 - AUC_ROC: 0.9744 - recall: 0.8333 - precision: 0.9498 - val_loss: 0.0053 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8685/10000\n", + "3/3 [==============================] - 0s 129ms/step - loss: 0.0016 - AUC_ROC: 0.9824 - recall: 0.8553 - precision: 0.9220 - val_loss: 0.0053 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8686/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0017 - AUC_ROC: 0.9824 - recall: 0.8365 - precision: 0.9204 - val_loss: 0.0053 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8687/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0018 - AUC_ROC: 0.9730 - recall: 0.8365 - precision: 0.9500 - val_loss: 0.0053 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8688/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8365 - precision: 0.9301 - val_loss: 0.0053 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8689/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0017 - AUC_ROC: 0.9793 - recall: 0.8522 - precision: 0.9218 - val_loss: 0.0053 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8690/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0017 - AUC_ROC: 0.9745 - recall: 0.8396 - precision: 0.9239 - val_loss: 0.0052 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8691/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8428 - precision: 0.9437 - val_loss: 0.0052 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8692/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0018 - AUC_ROC: 0.9808 - recall: 0.8553 - precision: 0.9128 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8693/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0017 - AUC_ROC: 0.9808 - recall: 0.8522 - precision: 0.9377 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8694/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0017 - AUC_ROC: 0.9730 - recall: 0.8365 - precision: 0.9333 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8695/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0015 - AUC_ROC: 0.9808 - recall: 0.8459 - precision: 0.9373 - val_loss: 0.0050 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8696/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0017 - AUC_ROC: 0.9839 - recall: 0.8396 - precision: 0.9144 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8697/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0013 - AUC_ROC: 0.9872 - recall: 0.8459 - precision: 0.9439 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8698/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0018 - AUC_ROC: 0.9698 - recall: 0.8302 - precision: 0.9362 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8699/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0016 - AUC_ROC: 0.9792 - recall: 0.8522 - precision: 0.9186 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8700/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0017 - AUC_ROC: 0.9777 - recall: 0.8239 - precision: 0.9668 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8701/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0018 - AUC_ROC: 0.9713 - recall: 0.8270 - precision: 0.9228 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8702/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0015 - AUC_ROC: 0.9761 - recall: 0.8333 - precision: 0.9707 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8703/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0016 - AUC_ROC: 0.9777 - recall: 0.8270 - precision: 0.9427 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8704/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0017 - AUC_ROC: 0.9730 - recall: 0.8491 - precision: 0.9408 - val_loss: 0.0049 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8705/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0016 - AUC_ROC: 0.9761 - recall: 0.8648 - precision: 0.9322 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8706/10000\n", + "3/3 [==============================] - 0s 138ms/step - loss: 0.0016 - AUC_ROC: 0.9808 - recall: 0.8522 - precision: 0.9377 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8707/10000\n", + "3/3 [==============================] - 0s 145ms/step - loss: 0.0016 - AUC_ROC: 0.9745 - recall: 0.8585 - precision: 0.9317 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8708/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0017 - AUC_ROC: 0.9745 - recall: 0.8711 - precision: 0.9203 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8709/10000\n", + "3/3 [==============================] - 0s 133ms/step - loss: 0.0017 - AUC_ROC: 0.9776 - recall: 0.8428 - precision: 0.9470 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8710/10000\n", + "3/3 [==============================] - 0s 124ms/step - loss: 0.0015 - AUC_ROC: 0.9777 - recall: 0.8491 - precision: 0.9574 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8711/10000\n", + "3/3 [==============================] - 0s 133ms/step - loss: 0.0016 - AUC_ROC: 0.9792 - recall: 0.8616 - precision: 0.9416 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8712/10000\n", + "3/3 [==============================] - 0s 128ms/step - loss: 0.0015 - AUC_ROC: 0.9808 - recall: 0.8459 - precision: 0.9373 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8713/10000\n", + "3/3 [==============================] - 0s 146ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8396 - precision: 0.9368 - val_loss: 0.0048 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8714/10000\n", + "3/3 [==============================] - 0s 132ms/step - loss: 0.0016 - AUC_ROC: 0.9777 - recall: 0.8333 - precision: 0.9201 - val_loss: 0.0048 - val_AUC_ROC: 0.8826 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8715/10000\n", + "3/3 [==============================] - 0s 129ms/step - loss: 0.0017 - AUC_ROC: 0.9824 - recall: 0.8459 - precision: 0.9373 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8716/10000\n", + "3/3 [==============================] - 0s 148ms/step - loss: 0.0016 - AUC_ROC: 0.9809 - recall: 0.8428 - precision: 0.9470 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8717/10000\n", + "3/3 [==============================] - 0s 136ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8616 - precision: 0.9580 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8718/10000\n", + "3/3 [==============================] - 0s 126ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8585 - precision: 0.9381 - val_loss: 0.0050 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8719/10000\n", + "3/3 [==============================] - 0s 131ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8553 - precision: 0.9315 - val_loss: 0.0050 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8720/10000\n", + "3/3 [==============================] - 0s 122ms/step - loss: 0.0018 - AUC_ROC: 0.9777 - recall: 0.8616 - precision: 0.9195 - val_loss: 0.0050 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8721/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0016 - AUC_ROC: 0.9777 - recall: 0.8333 - precision: 0.9364 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8722/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0018 - AUC_ROC: 0.9729 - recall: 0.8333 - precision: 0.9364 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8723/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0018 - AUC_ROC: 0.9760 - recall: 0.8333 - precision: 0.9298 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8724/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0017 - AUC_ROC: 0.9745 - recall: 0.8522 - precision: 0.9249 - val_loss: 0.0051 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8725/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8270 - precision: 0.9634 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8726/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0016 - AUC_ROC: 0.9730 - recall: 0.8522 - precision: 0.9644 - val_loss: 0.0051 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8727/10000\n", + "3/3 [==============================] - 0s 114ms/step - loss: 0.0017 - AUC_ROC: 0.9666 - recall: 0.8553 - precision: 0.9412 - val_loss: 0.0051 - val_AUC_ROC: 0.8827 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 8728/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0019 - AUC_ROC: 0.9713 - recall: 0.8333 - precision: 0.9298 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8729/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0016 - AUC_ROC: 0.9809 - recall: 0.8365 - precision: 0.9366 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8730/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0017 - AUC_ROC: 0.9714 - recall: 0.8428 - precision: 0.9273 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8731/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0019 - AUC_ROC: 0.9729 - recall: 0.8459 - precision: 0.9373 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8732/10000\n", + "3/3 [==============================] - 0s 117ms/step - loss: 0.0018 - AUC_ROC: 0.9698 - recall: 0.8428 - precision: 0.9504 - val_loss: 0.0050 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8733/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0018 - AUC_ROC: 0.9823 - recall: 0.8491 - precision: 0.9153 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8734/10000\n", + "3/3 [==============================] - 0s 124ms/step - loss: 0.0014 - AUC_ROC: 0.9856 - recall: 0.8553 - precision: 0.9444 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8735/10000\n", + "3/3 [==============================] - 0s 98ms/step - loss: 0.0017 - AUC_ROC: 0.9776 - recall: 0.8239 - precision: 0.9391 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8736/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0015 - AUC_ROC: 0.9824 - recall: 0.8522 - precision: 0.9377 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8737/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0017 - AUC_ROC: 0.9777 - recall: 0.8459 - precision: 0.9340 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8738/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0018 - AUC_ROC: 0.9839 - recall: 0.8176 - precision: 0.9123 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8739/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0015 - AUC_ROC: 0.9840 - recall: 0.8333 - precision: 0.9364 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8740/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0015 - AUC_ROC: 0.9777 - recall: 0.8585 - precision: 0.9545 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8741/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0015 - AUC_ROC: 0.9808 - recall: 0.8553 - precision: 0.9477 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8742/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8428 - precision: 0.9437 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8743/10000\n", + "3/3 [==============================] - 0s 117ms/step - loss: 0.0017 - AUC_ROC: 0.9856 - recall: 0.8616 - precision: 0.9073 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8744/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0018 - AUC_ROC: 0.9713 - recall: 0.8491 - precision: 0.9343 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8745/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0018 - AUC_ROC: 0.9729 - recall: 0.8585 - precision: 0.9192 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8746/10000\n", + "3/3 [==============================] - 0s 114ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8648 - precision: 0.9582 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8747/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0016 - AUC_ROC: 0.9730 - recall: 0.8522 - precision: 0.9377 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8748/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0018 - AUC_ROC: 0.9698 - recall: 0.8270 - precision: 0.9196 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8749/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0016 - AUC_ROC: 0.9761 - recall: 0.8428 - precision: 0.9371 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8750/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0018 - AUC_ROC: 0.9792 - recall: 0.8365 - precision: 0.9236 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8751/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0017 - AUC_ROC: 0.9824 - recall: 0.8208 - precision: 0.9223 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8752/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8491 - precision: 0.9215 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8753/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0016 - AUC_ROC: 0.9824 - recall: 0.8239 - precision: 0.9493 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8754/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0015 - AUC_ROC: 0.9792 - recall: 0.8270 - precision: 0.9529 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8755/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0017 - AUC_ROC: 0.9682 - recall: 0.8553 - precision: 0.9379 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8756/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0014 - AUC_ROC: 0.9840 - recall: 0.8679 - precision: 0.9485 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8757/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0018 - AUC_ROC: 0.9776 - recall: 0.8459 - precision: 0.8937 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8758/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0016 - AUC_ROC: 0.9777 - recall: 0.8396 - precision: 0.9468 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8759/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0017 - AUC_ROC: 0.9839 - recall: 0.8428 - precision: 0.9210 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8760/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0017 - AUC_ROC: 0.9808 - recall: 0.8365 - precision: 0.9301 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8761/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0016 - AUC_ROC: 0.9746 - recall: 0.8459 - precision: 0.9472 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8762/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0017 - AUC_ROC: 0.9714 - recall: 0.8365 - precision: 0.9534 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8763/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0017 - AUC_ROC: 0.9776 - recall: 0.8428 - precision: 0.9437 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8764/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0016 - AUC_ROC: 0.9761 - recall: 0.8491 - precision: 0.9474 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8765/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8491 - precision: 0.9441 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8766/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0016 - AUC_ROC: 0.9761 - recall: 0.8302 - precision: 0.9462 - val_loss: 0.0050 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8767/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0016 - AUC_ROC: 0.9746 - recall: 0.8428 - precision: 0.9504 - val_loss: 0.0050 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8768/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0016 - AUC_ROC: 0.9776 - recall: 0.8333 - precision: 0.9567 - val_loss: 0.0049 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8769/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0018 - AUC_ROC: 0.9682 - recall: 0.8365 - precision: 0.9301 - val_loss: 0.0049 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8770/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0016 - AUC_ROC: 0.9840 - recall: 0.8648 - precision: 0.9354 - val_loss: 0.0050 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8771/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0015 - AUC_ROC: 0.9856 - recall: 0.8742 - precision: 0.9205 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8772/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0018 - AUC_ROC: 0.9791 - recall: 0.8491 - precision: 0.9310 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8773/10000\n", + "3/3 [==============================] - 0s 116ms/step - loss: 0.0016 - AUC_ROC: 0.9777 - recall: 0.8365 - precision: 0.9534 - val_loss: 0.0053 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8774/10000\n", + "3/3 [==============================] - 0s 116ms/step - loss: 0.0016 - AUC_ROC: 0.9776 - recall: 0.8428 - precision: 0.9306 - val_loss: 0.0053 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8775/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0017 - AUC_ROC: 0.9745 - recall: 0.8145 - precision: 0.9593 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7160 - val_precision: 0.9667\n", + "Epoch 8776/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0018 - AUC_ROC: 0.9791 - recall: 0.8208 - precision: 0.9321 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8777/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8333 - precision: 0.9464 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8778/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0017 - AUC_ROC: 0.9744 - recall: 0.8302 - precision: 0.9296 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8779/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0017 - AUC_ROC: 0.9666 - recall: 0.8522 - precision: 0.9576 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8780/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0018 - AUC_ROC: 0.9776 - recall: 0.8396 - precision: 0.9401 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8781/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0017 - AUC_ROC: 0.9713 - recall: 0.8585 - precision: 0.9381 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7160 - val_precision: 0.9667\n", + "Epoch 8782/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8396 - precision: 0.9303 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7160 - val_precision: 0.9667\n", + "Epoch 8783/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0016 - AUC_ROC: 0.9792 - recall: 0.8553 - precision: 0.9412 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7160 - val_precision: 0.9667\n", + "Epoch 8784/10000\n", + "3/3 [==============================] - 0s 119ms/step - loss: 0.0015 - AUC_ROC: 0.9808 - recall: 0.8396 - precision: 0.9639 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8785/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0016 - AUC_ROC: 0.9776 - recall: 0.8459 - precision: 0.9406 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8786/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0016 - AUC_ROC: 0.9777 - recall: 0.8365 - precision: 0.9399 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8787/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8522 - precision: 0.9218 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8788/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0018 - AUC_ROC: 0.9729 - recall: 0.8333 - precision: 0.9170 - val_loss: 0.0054 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8789/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0017 - AUC_ROC: 0.9840 - recall: 0.8208 - precision: 0.9094 - val_loss: 0.0054 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8790/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0019 - AUC_ROC: 0.9745 - recall: 0.8333 - precision: 0.9364 - val_loss: 0.0054 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8791/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8522 - precision: 0.9094 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8792/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0015 - AUC_ROC: 0.9824 - recall: 0.8553 - precision: 0.9544 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8793/10000\n", + "3/3 [==============================] - 0s 115ms/step - loss: 0.0017 - AUC_ROC: 0.9729 - recall: 0.8522 - precision: 0.9410 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8794/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0014 - AUC_ROC: 0.9762 - recall: 0.8459 - precision: 0.9746 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8795/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8302 - precision: 0.9199 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8796/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0017 - AUC_ROC: 0.9730 - recall: 0.8208 - precision: 0.9560 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8797/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0016 - AUC_ROC: 0.9777 - recall: 0.8396 - precision: 0.9368 - val_loss: 0.0054 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8798/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0017 - AUC_ROC: 0.9824 - recall: 0.8648 - precision: 0.9076 - val_loss: 0.0053 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8799/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0017 - AUC_ROC: 0.9776 - recall: 0.8396 - precision: 0.9368 - val_loss: 0.0052 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8800/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0020 - AUC_ROC: 0.9697 - recall: 0.8302 - precision: 0.9263 - val_loss: 0.0051 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8801/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0016 - AUC_ROC: 0.9730 - recall: 0.8522 - precision: 0.9576 - val_loss: 0.0051 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8802/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0017 - AUC_ROC: 0.9745 - recall: 0.8585 - precision: 0.9286 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8803/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0015 - AUC_ROC: 0.9825 - recall: 0.8302 - precision: 0.9531 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8804/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0015 - AUC_ROC: 0.9777 - recall: 0.8459 - precision: 0.9308 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8805/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0016 - AUC_ROC: 0.9808 - recall: 0.8585 - precision: 0.9192 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8806/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0016 - AUC_ROC: 0.9777 - recall: 0.8585 - precision: 0.9381 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8807/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0015 - AUC_ROC: 0.9792 - recall: 0.8553 - precision: 0.9379 - val_loss: 0.0050 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8808/10000\n", + "3/3 [==============================] - 0s 126ms/step - loss: 0.0018 - AUC_ROC: 0.9761 - recall: 0.8208 - precision: 0.9491 - val_loss: 0.0049 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8809/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0016 - AUC_ROC: 0.9856 - recall: 0.8585 - precision: 0.9161 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8810/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0016 - AUC_ROC: 0.9761 - recall: 0.8491 - precision: 0.9541 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8811/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0016 - AUC_ROC: 0.9777 - recall: 0.8585 - precision: 0.9446 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8812/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0016 - AUC_ROC: 0.9777 - recall: 0.8648 - precision: 0.9197 - val_loss: 0.0051 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8813/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0018 - AUC_ROC: 0.9776 - recall: 0.8428 - precision: 0.9116 - val_loss: 0.0052 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8814/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0016 - AUC_ROC: 0.9777 - recall: 0.8522 - precision: 0.9610 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8815/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0017 - AUC_ROC: 0.9730 - recall: 0.8491 - precision: 0.9474 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8816/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0018 - AUC_ROC: 0.9698 - recall: 0.8365 - precision: 0.9366 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8817/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0015 - AUC_ROC: 0.9824 - recall: 0.8365 - precision: 0.9568 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8818/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0018 - AUC_ROC: 0.9729 - recall: 0.8270 - precision: 0.9228 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8819/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8491 - precision: 0.9278 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8820/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0020 - AUC_ROC: 0.9682 - recall: 0.8491 - precision: 0.9247 - val_loss: 0.0050 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8821/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0018 - AUC_ROC: 0.9744 - recall: 0.8428 - precision: 0.9371 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8822/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0017 - AUC_ROC: 0.9682 - recall: 0.8396 - precision: 0.9271 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8823/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0015 - AUC_ROC: 0.9808 - recall: 0.8616 - precision: 0.9384 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8824/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0018 - AUC_ROC: 0.9745 - recall: 0.8585 - precision: 0.9381 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8825/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8585 - precision: 0.9512 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8826/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0016 - AUC_ROC: 0.9777 - recall: 0.8491 - precision: 0.9507 - val_loss: 0.0051 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8827/10000\n", + "3/3 [==============================] - 0s 132ms/step - loss: 0.0017 - AUC_ROC: 0.9714 - recall: 0.8270 - precision: 0.9669 - val_loss: 0.0050 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8828/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8396 - precision: 0.9207 - val_loss: 0.0048 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8829/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0017 - AUC_ROC: 0.9776 - recall: 0.8459 - precision: 0.9607 - val_loss: 0.0048 - val_AUC_ROC: 0.9012 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8830/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0018 - AUC_ROC: 0.9776 - recall: 0.8428 - precision: 0.9147 - val_loss: 0.0048 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8831/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0017 - AUC_ROC: 0.9746 - recall: 0.8365 - precision: 0.9568 - val_loss: 0.0048 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8832/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0016 - AUC_ROC: 0.9856 - recall: 0.8491 - precision: 0.9153 - val_loss: 0.0048 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8833/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0016 - AUC_ROC: 0.9840 - recall: 0.8522 - precision: 0.9155 - val_loss: 0.0049 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8834/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0017 - AUC_ROC: 0.9777 - recall: 0.8239 - precision: 0.9458 - val_loss: 0.0049 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8835/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8459 - precision: 0.9340 - val_loss: 0.0049 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8836/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0014 - AUC_ROC: 0.9871 - recall: 0.8585 - precision: 0.9381 - val_loss: 0.0050 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8837/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0016 - AUC_ROC: 0.9824 - recall: 0.8396 - precision: 0.9303 - val_loss: 0.0050 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8838/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0015 - AUC_ROC: 0.9855 - recall: 0.8270 - precision: 0.9564 - val_loss: 0.0050 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8839/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0017 - AUC_ROC: 0.9792 - recall: 0.8365 - precision: 0.9141 - val_loss: 0.0050 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8840/10000\n", + "3/3 [==============================] - 0s 115ms/step - loss: 0.0014 - AUC_ROC: 0.9856 - recall: 0.8774 - precision: 0.9088 - val_loss: 0.0050 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8841/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0016 - AUC_ROC: 0.9856 - recall: 0.8459 - precision: 0.9212 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8842/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0015 - AUC_ROC: 0.9777 - recall: 0.8742 - precision: 0.9267 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8843/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0016 - AUC_ROC: 0.9745 - recall: 0.8428 - precision: 0.9338 - val_loss: 0.0049 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8844/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8396 - precision: 0.9303 - val_loss: 0.0048 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8845/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0017 - AUC_ROC: 0.9777 - recall: 0.8270 - precision: 0.9196 - val_loss: 0.0048 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8846/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0017 - AUC_ROC: 0.9792 - recall: 0.8491 - precision: 0.9441 - val_loss: 0.0047 - val_AUC_ROC: 0.9012 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8847/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0015 - AUC_ROC: 0.9777 - recall: 0.8396 - precision: 0.9536 - val_loss: 0.0047 - val_AUC_ROC: 0.9011 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8848/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0018 - AUC_ROC: 0.9744 - recall: 0.8648 - precision: 0.9197 - val_loss: 0.0047 - val_AUC_ROC: 0.9011 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8849/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0015 - AUC_ROC: 0.9871 - recall: 0.8459 - precision: 0.9340 - val_loss: 0.0047 - val_AUC_ROC: 0.9011 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8850/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0019 - AUC_ROC: 0.9729 - recall: 0.8333 - precision: 0.9266 - val_loss: 0.0047 - val_AUC_ROC: 0.9011 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8851/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0016 - AUC_ROC: 0.9761 - recall: 0.8491 - precision: 0.9310 - val_loss: 0.0048 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8852/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0018 - AUC_ROC: 0.9792 - recall: 0.8428 - precision: 0.9241 - val_loss: 0.0048 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8853/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0015 - AUC_ROC: 0.9824 - recall: 0.8648 - precision: 0.9259 - val_loss: 0.0048 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8854/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0019 - AUC_ROC: 0.9714 - recall: 0.8302 - precision: 0.9362 - val_loss: 0.0048 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8855/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0016 - AUC_ROC: 0.9824 - recall: 0.8428 - precision: 0.9371 - val_loss: 0.0048 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8856/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0016 - AUC_ROC: 0.9729 - recall: 0.8459 - precision: 0.9439 - val_loss: 0.0049 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8857/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0017 - AUC_ROC: 0.9730 - recall: 0.8553 - precision: 0.9412 - val_loss: 0.0050 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8858/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8239 - precision: 0.9424 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8859/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0016 - AUC_ROC: 0.9746 - recall: 0.8491 - precision: 0.9247 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8860/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0017 - AUC_ROC: 0.9777 - recall: 0.8270 - precision: 0.9495 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8861/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0017 - AUC_ROC: 0.9792 - recall: 0.8302 - precision: 0.9167 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8862/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0019 - AUC_ROC: 0.9713 - recall: 0.8365 - precision: 0.9399 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8863/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0016 - AUC_ROC: 0.9808 - recall: 0.8333 - precision: 0.9498 - val_loss: 0.0050 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8864/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0015 - AUC_ROC: 0.9777 - recall: 0.8491 - precision: 0.9474 - val_loss: 0.0050 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8865/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8459 - precision: 0.9340 - val_loss: 0.0050 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8866/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8491 - precision: 0.9441 - val_loss: 0.0050 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8867/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0017 - AUC_ROC: 0.9777 - recall: 0.8616 - precision: 0.9481 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8868/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0017 - AUC_ROC: 0.9792 - recall: 0.8679 - precision: 0.9293 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8869/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0015 - AUC_ROC: 0.9824 - recall: 0.8302 - precision: 0.9329 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8870/10000\n", + "3/3 [==============================] - 0s 118ms/step - loss: 0.0017 - AUC_ROC: 0.9745 - recall: 0.8239 - precision: 0.9493 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8871/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8648 - precision: 0.9549 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8872/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0019 - AUC_ROC: 0.9744 - recall: 0.8333 - precision: 0.9170 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8873/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8270 - precision: 0.9599 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8874/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0018 - AUC_ROC: 0.9761 - recall: 0.8553 - precision: 0.9128 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8875/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8553 - precision: 0.9252 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8876/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0014 - AUC_ROC: 0.9872 - recall: 0.8396 - precision: 0.9604 - val_loss: 0.0052 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8877/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0016 - AUC_ROC: 0.9824 - recall: 0.8553 - precision: 0.9283 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8878/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8742 - precision: 0.9488 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8879/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0017 - AUC_ROC: 0.9777 - recall: 0.8491 - precision: 0.9247 - val_loss: 0.0051 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8880/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0014 - AUC_ROC: 0.9856 - recall: 0.8428 - precision: 0.9537 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8881/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0016 - AUC_ROC: 0.9776 - recall: 0.8616 - precision: 0.9320 - val_loss: 0.0052 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8882/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8585 - precision: 0.9579 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8883/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0015 - AUC_ROC: 0.9824 - recall: 0.8491 - precision: 0.9343 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8884/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0015 - AUC_ROC: 0.9777 - recall: 0.8585 - precision: 0.9479 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8885/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0014 - AUC_ROC: 0.9855 - recall: 0.8679 - precision: 0.9420 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8886/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0015 - AUC_ROC: 0.9777 - recall: 0.8459 - precision: 0.9607 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8887/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8428 - precision: 0.9470 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8888/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0016 - AUC_ROC: 0.9730 - recall: 0.8428 - precision: 0.9710 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8889/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0018 - AUC_ROC: 0.9682 - recall: 0.8428 - precision: 0.9338 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8890/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0016 - AUC_ROC: 0.9729 - recall: 0.8428 - precision: 0.9640 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8891/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0018 - AUC_ROC: 0.9729 - recall: 0.8145 - precision: 0.9487 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8892/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0016 - AUC_ROC: 0.9745 - recall: 0.8491 - precision: 0.9278 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8893/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0016 - AUC_ROC: 0.9761 - recall: 0.8396 - precision: 0.9368 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8894/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0015 - AUC_ROC: 0.9856 - recall: 0.8522 - precision: 0.9218 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8895/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0015 - AUC_ROC: 0.9824 - recall: 0.8742 - precision: 0.9145 - val_loss: 0.0054 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8896/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0017 - AUC_ROC: 0.9745 - recall: 0.8428 - precision: 0.9437 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8897/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0018 - AUC_ROC: 0.9761 - recall: 0.8208 - precision: 0.9321 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8898/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0015 - AUC_ROC: 0.9856 - recall: 0.8742 - precision: 0.9145 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8899/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0015 - AUC_ROC: 0.9824 - recall: 0.8616 - precision: 0.9320 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8900/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0018 - AUC_ROC: 0.9698 - recall: 0.8050 - precision: 0.9446 - val_loss: 0.0054 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8901/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0016 - AUC_ROC: 0.9840 - recall: 0.8428 - precision: 0.9306 - val_loss: 0.0053 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8902/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0017 - AUC_ROC: 0.9698 - recall: 0.8333 - precision: 0.9498 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8903/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0016 - AUC_ROC: 0.9777 - recall: 0.8616 - precision: 0.9195 - val_loss: 0.0050 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8904/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0016 - AUC_ROC: 0.9746 - recall: 0.8491 - precision: 0.9408 - val_loss: 0.0050 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8905/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0018 - AUC_ROC: 0.9745 - recall: 0.8333 - precision: 0.9331 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8906/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0018 - AUC_ROC: 0.9745 - recall: 0.8365 - precision: 0.9141 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8907/10000\n", + "3/3 [==============================] - 0s 97ms/step - loss: 0.0017 - AUC_ROC: 0.9776 - recall: 0.8302 - precision: 0.9429 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8908/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8868 - precision: 0.9369 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8909/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8648 - precision: 0.9322 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8910/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0015 - AUC_ROC: 0.9745 - recall: 0.8491 - precision: 0.9507 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8911/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0015 - AUC_ROC: 0.9840 - recall: 0.8679 - precision: 0.9356 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8912/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0016 - AUC_ROC: 0.9776 - recall: 0.8522 - precision: 0.9410 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8913/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8270 - precision: 0.9427 - val_loss: 0.0049 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8914/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0015 - AUC_ROC: 0.9745 - recall: 0.8553 - precision: 0.9577 - val_loss: 0.0048 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8915/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8585 - precision: 0.9446 - val_loss: 0.0047 - val_AUC_ROC: 0.9011 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8916/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0016 - AUC_ROC: 0.9808 - recall: 0.8428 - precision: 0.9210 - val_loss: 0.0047 - val_AUC_ROC: 0.9011 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8917/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0016 - AUC_ROC: 0.9730 - recall: 0.8302 - precision: 0.9496 - val_loss: 0.0047 - val_AUC_ROC: 0.9012 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8918/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8648 - precision: 0.9386 - val_loss: 0.0047 - val_AUC_ROC: 0.9012 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8919/10000\n", + "3/3 [==============================] - 0s 115ms/step - loss: 0.0016 - AUC_ROC: 0.9808 - recall: 0.8396 - precision: 0.9468 - val_loss: 0.0048 - val_AUC_ROC: 0.9012 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8920/10000\n", + "3/3 [==============================] - 0s 114ms/step - loss: 0.0017 - AUC_ROC: 0.9777 - recall: 0.8648 - precision: 0.9418 - val_loss: 0.0048 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8921/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0016 - AUC_ROC: 0.9824 - recall: 0.8428 - precision: 0.9241 - val_loss: 0.0049 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8922/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0015 - AUC_ROC: 0.9856 - recall: 0.8585 - precision: 0.9317 - val_loss: 0.0049 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8923/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0015 - AUC_ROC: 0.9729 - recall: 0.8585 - precision: 0.9579 - val_loss: 0.0050 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8924/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0014 - AUC_ROC: 0.9824 - recall: 0.8522 - precision: 0.9576 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8925/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0014 - AUC_ROC: 0.9856 - recall: 0.8459 - precision: 0.9373 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8926/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0016 - AUC_ROC: 0.9714 - recall: 0.8491 - precision: 0.9278 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8927/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0016 - AUC_ROC: 0.9776 - recall: 0.8553 - precision: 0.9347 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8928/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0018 - AUC_ROC: 0.9745 - recall: 0.8270 - precision: 0.9164 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8929/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0016 - AUC_ROC: 0.9777 - recall: 0.8553 - precision: 0.9379 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8930/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0018 - AUC_ROC: 0.9777 - recall: 0.8333 - precision: 0.9331 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8931/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0016 - AUC_ROC: 0.9824 - recall: 0.8491 - precision: 0.9215 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8932/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0015 - AUC_ROC: 0.9823 - recall: 0.8774 - precision: 0.9458 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8933/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0016 - AUC_ROC: 0.9840 - recall: 0.8239 - precision: 0.9458 - val_loss: 0.0053 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8934/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0015 - AUC_ROC: 0.9808 - recall: 0.8208 - precision: 0.9526 - val_loss: 0.0054 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8935/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0017 - AUC_ROC: 0.9698 - recall: 0.8396 - precision: 0.9536 - val_loss: 0.0054 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8936/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0016 - AUC_ROC: 0.9840 - recall: 0.8302 - precision: 0.9296 - val_loss: 0.0054 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8937/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0018 - AUC_ROC: 0.9777 - recall: 0.8522 - precision: 0.9186 - val_loss: 0.0054 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8938/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0017 - AUC_ROC: 0.9777 - recall: 0.8428 - precision: 0.9338 - val_loss: 0.0054 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8939/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8585 - precision: 0.9381 - val_loss: 0.0053 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8940/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0018 - AUC_ROC: 0.9698 - recall: 0.8522 - precision: 0.9679 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8941/10000\n", + "3/3 [==============================] - 0s 114ms/step - loss: 0.0017 - AUC_ROC: 0.9824 - recall: 0.8365 - precision: 0.9204 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8942/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8491 - precision: 0.9677 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8943/10000\n", + "3/3 [==============================] - 0s 125ms/step - loss: 0.0015 - AUC_ROC: 0.9840 - recall: 0.8553 - precision: 0.9252 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8944/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0016 - AUC_ROC: 0.9809 - recall: 0.8239 - precision: 0.9357 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8945/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0016 - AUC_ROC: 0.9760 - recall: 0.8239 - precision: 0.9391 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8946/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0015 - AUC_ROC: 0.9825 - recall: 0.8616 - precision: 0.9481 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8947/10000\n", + "3/3 [==============================] - 0s 98ms/step - loss: 0.0017 - AUC_ROC: 0.9792 - recall: 0.8491 - precision: 0.9184 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8948/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0015 - AUC_ROC: 0.9840 - recall: 0.8585 - precision: 0.9349 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8949/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0016 - AUC_ROC: 0.9777 - recall: 0.8553 - precision: 0.9477 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8950/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8742 - precision: 0.9424 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8951/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8585 - precision: 0.9479 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8952/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0014 - AUC_ROC: 0.9761 - recall: 0.8585 - precision: 0.9681 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8953/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8459 - precision: 0.9406 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8954/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0017 - AUC_ROC: 0.9682 - recall: 0.8333 - precision: 0.9636 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8955/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0016 - AUC_ROC: 0.9840 - recall: 0.8396 - precision: 0.9368 - val_loss: 0.0053 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8956/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0014 - AUC_ROC: 0.9793 - recall: 0.8805 - precision: 0.9180 - val_loss: 0.0052 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8957/10000\n", + "3/3 [==============================] - 0s 122ms/step - loss: 0.0018 - AUC_ROC: 0.9776 - recall: 0.8365 - precision: 0.8926 - val_loss: 0.0052 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8958/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0016 - AUC_ROC: 0.9824 - recall: 0.8522 - precision: 0.9249 - val_loss: 0.0052 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8959/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0017 - AUC_ROC: 0.9714 - recall: 0.8648 - precision: 0.9354 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8960/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0016 - AUC_ROC: 0.9761 - recall: 0.8522 - precision: 0.9610 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8961/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0015 - AUC_ROC: 0.9746 - recall: 0.8522 - precision: 0.9542 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8962/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8522 - precision: 0.9542 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8963/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0016 - AUC_ROC: 0.9808 - recall: 0.8333 - precision: 0.9331 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8964/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0017 - AUC_ROC: 0.9855 - recall: 0.8648 - precision: 0.9167 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8965/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0015 - AUC_ROC: 0.9856 - recall: 0.8270 - precision: 0.9564 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8966/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0016 - AUC_ROC: 0.9840 - recall: 0.8428 - precision: 0.9470 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8967/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0016 - AUC_ROC: 0.9762 - recall: 0.8333 - precision: 0.9364 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8968/10000\n", + "3/3 [==============================] - 0s 98ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8459 - precision: 0.9027 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8969/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0015 - AUC_ROC: 0.9824 - recall: 0.8459 - precision: 0.9276 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8970/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0015 - AUC_ROC: 0.9777 - recall: 0.8302 - precision: 0.9429 - val_loss: 0.0053 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8971/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0014 - AUC_ROC: 0.9778 - recall: 0.8679 - precision: 0.9684 - val_loss: 0.0054 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8972/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0016 - AUC_ROC: 0.9840 - recall: 0.8459 - precision: 0.9276 - val_loss: 0.0054 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8973/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0017 - AUC_ROC: 0.9793 - recall: 0.8302 - precision: 0.9462 - val_loss: 0.0054 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8974/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8333 - precision: 0.9364 - val_loss: 0.0054 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8975/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0017 - AUC_ROC: 0.9745 - recall: 0.8365 - precision: 0.9568 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8976/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0014 - AUC_ROC: 0.9856 - recall: 0.8648 - precision: 0.9483 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8977/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0016 - AUC_ROC: 0.9745 - recall: 0.8585 - precision: 0.9381 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8978/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0015 - AUC_ROC: 0.9776 - recall: 0.8522 - precision: 0.9476 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8979/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0016 - AUC_ROC: 0.9840 - recall: 0.8616 - precision: 0.9226 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8980/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8648 - precision: 0.9197 - val_loss: 0.0052 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8981/10000\n", + "3/3 [==============================] - 0s 118ms/step - loss: 0.0015 - AUC_ROC: 0.9761 - recall: 0.8711 - precision: 0.9233 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8982/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0014 - AUC_ROC: 0.9762 - recall: 0.8491 - precision: 0.9474 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8983/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0017 - AUC_ROC: 0.9808 - recall: 0.8365 - precision: 0.9466 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8984/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8742 - precision: 0.9586 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8985/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0016 - AUC_ROC: 0.9792 - recall: 0.8333 - precision: 0.9567 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8986/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0016 - AUC_ROC: 0.9777 - recall: 0.8648 - precision: 0.9197 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8987/10000\n", + "3/3 [==============================] - 0s 117ms/step - loss: 0.0013 - AUC_ROC: 0.9904 - recall: 0.8553 - precision: 0.9283 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8988/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8522 - precision: 0.9410 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8989/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8522 - precision: 0.9410 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8990/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0017 - AUC_ROC: 0.9840 - recall: 0.8365 - precision: 0.8956 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8991/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0017 - AUC_ROC: 0.9777 - recall: 0.8396 - precision: 0.9368 - val_loss: 0.0052 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8992/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0017 - AUC_ROC: 0.9745 - recall: 0.8522 - precision: 0.9377 - val_loss: 0.0052 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8993/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0016 - AUC_ROC: 0.9745 - recall: 0.8648 - precision: 0.9483 - val_loss: 0.0050 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8994/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0016 - AUC_ROC: 0.9840 - recall: 0.8333 - precision: 0.9464 - val_loss: 0.0049 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8995/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0015 - AUC_ROC: 0.9840 - recall: 0.8553 - precision: 0.9510 - val_loss: 0.0048 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8996/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0015 - AUC_ROC: 0.9840 - recall: 0.8522 - precision: 0.9281 - val_loss: 0.0048 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 8997/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0016 - AUC_ROC: 0.9808 - recall: 0.8616 - precision: 0.9352 - val_loss: 0.0049 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8998/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0015 - AUC_ROC: 0.9855 - recall: 0.8459 - precision: 0.9150 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 8999/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0018 - AUC_ROC: 0.9777 - recall: 0.8270 - precision: 0.9100 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9000/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0016 - AUC_ROC: 0.9809 - recall: 0.8333 - precision: 0.9431 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9001/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8333 - precision: 0.9397 - val_loss: 0.0051 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9002/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8302 - precision: 0.9296 - val_loss: 0.0051 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9003/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0015 - AUC_ROC: 0.9824 - recall: 0.8491 - precision: 0.9375 - val_loss: 0.0050 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9004/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0017 - AUC_ROC: 0.9667 - recall: 0.8208 - precision: 0.9491 - val_loss: 0.0051 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9005/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8459 - precision: 0.9642 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9006/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0015 - AUC_ROC: 0.9777 - recall: 0.8648 - precision: 0.9615 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9007/10000\n", + "3/3 [==============================] - 0s 115ms/step - loss: 0.0014 - AUC_ROC: 0.9840 - recall: 0.8711 - precision: 0.9327 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 9008/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0013 - AUC_ROC: 0.9903 - recall: 0.8616 - precision: 0.9448 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 9009/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0017 - AUC_ROC: 0.9729 - recall: 0.8270 - precision: 0.9359 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 9010/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8491 - precision: 0.9310 - val_loss: 0.0053 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9011/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8396 - precision: 0.9468 - val_loss: 0.0054 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9012/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0015 - AUC_ROC: 0.9808 - recall: 0.8585 - precision: 0.9317 - val_loss: 0.0054 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9013/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0017 - AUC_ROC: 0.9745 - recall: 0.8553 - precision: 0.9189 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9014/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0017 - AUC_ROC: 0.9777 - recall: 0.8491 - precision: 0.9153 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9015/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0018 - AUC_ROC: 0.9714 - recall: 0.8459 - precision: 0.9212 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9016/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0015 - AUC_ROC: 0.9824 - recall: 0.8648 - precision: 0.9106 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9017/10000\n", + "3/3 [==============================] - 0s 115ms/step - loss: 0.0013 - AUC_ROC: 0.9825 - recall: 0.8648 - precision: 0.9386 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9018/10000\n", + "3/3 [==============================] - 0s 114ms/step - loss: 0.0015 - AUC_ROC: 0.9824 - recall: 0.8616 - precision: 0.9514 - val_loss: 0.0057 - val_AUC_ROC: 0.8641 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9019/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0017 - AUC_ROC: 0.9840 - recall: 0.8428 - precision: 0.9210 - val_loss: 0.0056 - val_AUC_ROC: 0.8641 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9020/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0017 - AUC_ROC: 0.9746 - recall: 0.8302 - precision: 0.9462 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9021/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0016 - AUC_ROC: 0.9808 - recall: 0.8679 - precision: 0.9485 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9022/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0016 - AUC_ROC: 0.9761 - recall: 0.8333 - precision: 0.9567 - val_loss: 0.0055 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9023/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8585 - precision: 0.9381 - val_loss: 0.0055 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9024/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0016 - AUC_ROC: 0.9714 - recall: 0.8585 - precision: 0.9317 - val_loss: 0.0054 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9025/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0017 - AUC_ROC: 0.9698 - recall: 0.8428 - precision: 0.9504 - val_loss: 0.0054 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9026/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0016 - AUC_ROC: 0.9792 - recall: 0.8522 - precision: 0.9509 - val_loss: 0.0053 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9027/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0015 - AUC_ROC: 0.9824 - recall: 0.8459 - precision: 0.9439 - val_loss: 0.0054 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9028/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8491 - precision: 0.9184 - val_loss: 0.0054 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9029/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0018 - AUC_ROC: 0.9761 - recall: 0.8176 - precision: 0.9253 - val_loss: 0.0054 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9030/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0014 - AUC_ROC: 0.9793 - recall: 0.8459 - precision: 0.9607 - val_loss: 0.0054 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9031/10000\n", + "3/3 [==============================] - 0s 115ms/step - loss: 0.0016 - AUC_ROC: 0.9824 - recall: 0.8459 - precision: 0.9244 - val_loss: 0.0053 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9032/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0014 - AUC_ROC: 0.9856 - recall: 0.8459 - precision: 0.9505 - val_loss: 0.0052 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9033/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8585 - precision: 0.9223 - val_loss: 0.0051 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9034/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0016 - AUC_ROC: 0.9730 - recall: 0.8585 - precision: 0.9545 - val_loss: 0.0050 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9035/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0018 - AUC_ROC: 0.9792 - recall: 0.8553 - precision: 0.9189 - val_loss: 0.0050 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9036/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0016 - AUC_ROC: 0.9762 - recall: 0.8459 - precision: 0.9472 - val_loss: 0.0050 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9037/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8616 - precision: 0.9384 - val_loss: 0.0051 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9038/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8459 - precision: 0.9119 - val_loss: 0.0052 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9039/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8459 - precision: 0.9244 - val_loss: 0.0053 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9040/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0016 - AUC_ROC: 0.9855 - recall: 0.8491 - precision: 0.9184 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9041/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0016 - AUC_ROC: 0.9777 - recall: 0.8774 - precision: 0.9394 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9042/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0015 - AUC_ROC: 0.9840 - recall: 0.8522 - precision: 0.9094 - val_loss: 0.0053 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9043/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0015 - AUC_ROC: 0.9746 - recall: 0.8365 - precision: 0.9708 - val_loss: 0.0053 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9044/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0016 - AUC_ROC: 0.9792 - recall: 0.8428 - precision: 0.9371 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9045/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0015 - AUC_ROC: 0.9777 - recall: 0.8302 - precision: 0.9635 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9046/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0014 - AUC_ROC: 0.9840 - recall: 0.8679 - precision: 0.9420 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9047/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0016 - AUC_ROC: 0.9840 - recall: 0.8302 - precision: 0.9429 - val_loss: 0.0051 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9048/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0016 - AUC_ROC: 0.9776 - recall: 0.8396 - precision: 0.9468 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9049/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0015 - AUC_ROC: 0.9808 - recall: 0.8711 - precision: 0.9422 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9050/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8648 - precision: 0.9516 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9051/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0016 - AUC_ROC: 0.9808 - recall: 0.8616 - precision: 0.9448 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9052/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0015 - AUC_ROC: 0.9840 - recall: 0.8931 - precision: 0.9342 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9053/10000\n", + "3/3 [==============================] - 0s 119ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8459 - precision: 0.9406 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9054/10000\n", + "3/3 [==============================] - 0s 117ms/step - loss: 0.0015 - AUC_ROC: 0.9872 - recall: 0.8333 - precision: 0.9464 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9055/10000\n", + "3/3 [==============================] - 0s 115ms/step - loss: 0.0017 - AUC_ROC: 0.9730 - recall: 0.8522 - precision: 0.9476 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9056/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8333 - precision: 0.9233 - val_loss: 0.0050 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9057/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8239 - precision: 0.9357 - val_loss: 0.0050 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9058/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0015 - AUC_ROC: 0.9824 - recall: 0.8585 - precision: 0.9545 - val_loss: 0.0050 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9059/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0015 - AUC_ROC: 0.9777 - recall: 0.8585 - precision: 0.9130 - val_loss: 0.0050 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9060/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8711 - precision: 0.9618 - val_loss: 0.0051 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9061/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8491 - precision: 0.9278 - val_loss: 0.0052 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9062/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0014 - AUC_ROC: 0.9793 - recall: 0.8742 - precision: 0.9424 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9063/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0014 - AUC_ROC: 0.9793 - recall: 0.8553 - precision: 0.9544 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9064/10000\n", + "3/3 [==============================] - 0s 119ms/step - loss: 0.0016 - AUC_ROC: 0.9777 - recall: 0.8648 - precision: 0.9354 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9065/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0015 - AUC_ROC: 0.9840 - recall: 0.8396 - precision: 0.9271 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9066/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0016 - AUC_ROC: 0.9746 - recall: 0.8396 - precision: 0.9536 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9067/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0015 - AUC_ROC: 0.9840 - recall: 0.8428 - precision: 0.9437 - val_loss: 0.0053 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9068/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0018 - AUC_ROC: 0.9714 - recall: 0.8491 - precision: 0.9310 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9069/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0016 - AUC_ROC: 0.9809 - recall: 0.8302 - precision: 0.9429 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9070/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0016 - AUC_ROC: 0.9761 - recall: 0.8270 - precision: 0.9359 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9071/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0014 - AUC_ROC: 0.9840 - recall: 0.8679 - precision: 0.9517 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9072/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0015 - AUC_ROC: 0.9777 - recall: 0.8648 - precision: 0.9354 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9073/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0017 - AUC_ROC: 0.9745 - recall: 0.8491 - precision: 0.9408 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9074/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0017 - AUC_ROC: 0.9825 - recall: 0.8491 - precision: 0.9408 - val_loss: 0.0052 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9075/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8679 - precision: 0.9583 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9076/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0015 - AUC_ROC: 0.9855 - recall: 0.8585 - precision: 0.9317 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9077/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0016 - AUC_ROC: 0.9714 - recall: 0.8365 - precision: 0.9744 - val_loss: 0.0050 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9078/10000\n", + "3/3 [==============================] - 0s 98ms/step - loss: 0.0016 - AUC_ROC: 0.9777 - recall: 0.8396 - precision: 0.9271 - val_loss: 0.0049 - val_AUC_ROC: 0.9012 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9079/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8648 - precision: 0.9483 - val_loss: 0.0048 - val_AUC_ROC: 0.9012 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9080/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8365 - precision: 0.9172 - val_loss: 0.0049 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9081/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0014 - AUC_ROC: 0.9840 - recall: 0.8585 - precision: 0.9317 - val_loss: 0.0050 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9082/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0019 - AUC_ROC: 0.9792 - recall: 0.8365 - precision: 0.9236 - val_loss: 0.0050 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9083/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8522 - precision: 0.9443 - val_loss: 0.0050 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9084/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0015 - AUC_ROC: 0.9777 - recall: 0.8491 - precision: 0.9541 - val_loss: 0.0050 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9085/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8553 - precision: 0.9510 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9086/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0014 - AUC_ROC: 0.9793 - recall: 0.8648 - precision: 0.9615 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9087/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0014 - AUC_ROC: 0.9871 - recall: 0.8648 - precision: 0.9549 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9088/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8585 - precision: 0.9381 - val_loss: 0.0052 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9089/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0016 - AUC_ROC: 0.9762 - recall: 0.8365 - precision: 0.9534 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9090/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0016 - AUC_ROC: 0.9792 - recall: 0.8585 - precision: 0.9414 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9091/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8522 - precision: 0.9377 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9092/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8553 - precision: 0.9444 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9093/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0016 - AUC_ROC: 0.9745 - recall: 0.8679 - precision: 0.9262 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9094/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0016 - AUC_ROC: 0.9855 - recall: 0.8365 - precision: 0.9301 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9095/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0015 - AUC_ROC: 0.9808 - recall: 0.8491 - precision: 0.9747 - val_loss: 0.0052 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9096/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8585 - precision: 0.9545 - val_loss: 0.0052 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9097/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0015 - AUC_ROC: 0.9840 - recall: 0.8585 - precision: 0.9254 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9098/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0015 - AUC_ROC: 0.9856 - recall: 0.8616 - precision: 0.9320 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9099/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8459 - precision: 0.9539 - val_loss: 0.0054 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9100/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0017 - AUC_ROC: 0.9745 - recall: 0.8428 - precision: 0.9306 - val_loss: 0.0054 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9101/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0014 - AUC_ROC: 0.9840 - recall: 0.8648 - precision: 0.9549 - val_loss: 0.0054 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9102/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0017 - AUC_ROC: 0.9777 - recall: 0.8270 - precision: 0.9228 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9103/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0017 - AUC_ROC: 0.9793 - recall: 0.8333 - precision: 0.9201 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9104/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8553 - precision: 0.9379 - val_loss: 0.0052 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9105/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0017 - AUC_ROC: 0.9823 - recall: 0.8239 - precision: 0.8973 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9106/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0016 - AUC_ROC: 0.9809 - recall: 0.8459 - precision: 0.9244 - val_loss: 0.0050 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9107/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8522 - precision: 0.9410 - val_loss: 0.0050 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9108/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0016 - AUC_ROC: 0.9809 - recall: 0.8333 - precision: 0.9397 - val_loss: 0.0050 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9109/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0016 - AUC_ROC: 0.9809 - recall: 0.8270 - precision: 0.9359 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9110/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8459 - precision: 0.9276 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9111/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0017 - AUC_ROC: 0.9808 - recall: 0.8302 - precision: 0.9429 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9112/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0015 - AUC_ROC: 0.9840 - recall: 0.8270 - precision: 0.9427 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9113/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0015 - AUC_ROC: 0.9824 - recall: 0.8679 - precision: 0.9231 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9114/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0016 - AUC_ROC: 0.9809 - recall: 0.8491 - precision: 0.9310 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9115/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0016 - AUC_ROC: 0.9761 - recall: 0.8396 - precision: 0.9468 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9116/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0016 - AUC_ROC: 0.9809 - recall: 0.8396 - precision: 0.9468 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9117/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8333 - precision: 0.9567 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9118/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8679 - precision: 0.9583 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9119/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0016 - AUC_ROC: 0.9762 - recall: 0.8396 - precision: 0.9536 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9120/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0017 - AUC_ROC: 0.9729 - recall: 0.8365 - precision: 0.9399 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9121/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0016 - AUC_ROC: 0.9792 - recall: 0.8553 - precision: 0.9412 - val_loss: 0.0052 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9122/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8491 - precision: 0.9541 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9123/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8491 - precision: 0.9408 - val_loss: 0.0050 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9124/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8711 - precision: 0.9685 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9125/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0015 - AUC_ROC: 0.9825 - recall: 0.8522 - precision: 0.9377 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9126/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8553 - precision: 0.9315 - val_loss: 0.0049 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9127/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0016 - AUC_ROC: 0.9761 - recall: 0.8491 - precision: 0.9408 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9128/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0016 - AUC_ROC: 0.9777 - recall: 0.8365 - precision: 0.9399 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9129/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0016 - AUC_ROC: 0.9809 - recall: 0.8616 - precision: 0.9133 - val_loss: 0.0050 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9130/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0016 - AUC_ROC: 0.9777 - recall: 0.8711 - precision: 0.9264 - val_loss: 0.0050 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9131/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0013 - AUC_ROC: 0.9778 - recall: 0.8679 - precision: 0.9550 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9132/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0017 - AUC_ROC: 0.9793 - recall: 0.8459 - precision: 0.9119 - val_loss: 0.0050 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9133/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0014 - AUC_ROC: 0.9793 - recall: 0.8774 - precision: 0.9522 - val_loss: 0.0050 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9134/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0016 - AUC_ROC: 0.9839 - recall: 0.8648 - precision: 0.9322 - val_loss: 0.0050 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9135/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0015 - AUC_ROC: 0.9856 - recall: 0.8742 - precision: 0.9026 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9136/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0014 - AUC_ROC: 0.9762 - recall: 0.8679 - precision: 0.9485 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9137/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0016 - AUC_ROC: 0.9825 - recall: 0.8333 - precision: 0.9298 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9138/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0016 - AUC_ROC: 0.9809 - recall: 0.8553 - precision: 0.9315 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9139/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0016 - AUC_ROC: 0.9777 - recall: 0.8491 - precision: 0.9375 - val_loss: 0.0051 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9140/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0016 - AUC_ROC: 0.9746 - recall: 0.8239 - precision: 0.9527 - val_loss: 0.0051 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9141/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0016 - AUC_ROC: 0.9777 - recall: 0.8459 - precision: 0.9406 - val_loss: 0.0050 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9142/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0016 - AUC_ROC: 0.9761 - recall: 0.8302 - precision: 0.9429 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9143/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8805 - precision: 0.9211 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9144/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0013 - AUC_ROC: 0.9857 - recall: 0.8868 - precision: 0.9431 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9145/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0013 - AUC_ROC: 0.9872 - recall: 0.8711 - precision: 0.9685 - val_loss: 0.0050 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9146/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8208 - precision: 0.9457 - val_loss: 0.0050 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9147/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0015 - AUC_ROC: 0.9746 - recall: 0.8522 - precision: 0.9509 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9148/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0015 - AUC_ROC: 0.9745 - recall: 0.8522 - precision: 0.9610 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9149/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0014 - AUC_ROC: 0.9841 - recall: 0.8553 - precision: 0.9477 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9150/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0015 - AUC_ROC: 0.9888 - recall: 0.8616 - precision: 0.9226 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9151/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0015 - AUC_ROC: 0.9872 - recall: 0.8459 - precision: 0.9373 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9152/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0015 - AUC_ROC: 0.9777 - recall: 0.8396 - precision: 0.9271 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9153/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8459 - precision: 0.9212 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9154/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0016 - AUC_ROC: 0.9745 - recall: 0.8585 - precision: 0.9479 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9155/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0016 - AUC_ROC: 0.9841 - recall: 0.8648 - precision: 0.9016 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9156/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0015 - AUC_ROC: 0.9825 - recall: 0.8711 - precision: 0.9422 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9157/10000\n", + "3/3 [==============================] - 0s 98ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8522 - precision: 0.9410 - val_loss: 0.0050 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9158/10000\n", + "3/3 [==============================] - 0s 98ms/step - loss: 0.0016 - AUC_ROC: 0.9840 - recall: 0.8396 - precision: 0.9082 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9159/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0014 - AUC_ROC: 0.9777 - recall: 0.8553 - precision: 0.9444 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9160/10000\n", + "3/3 [==============================] - 0s 115ms/step - loss: 0.0015 - AUC_ROC: 0.9824 - recall: 0.8302 - precision: 0.9296 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9161/10000\n", + "3/3 [==============================] - 0s 98ms/step - loss: 0.0017 - AUC_ROC: 0.9714 - recall: 0.8491 - precision: 0.9343 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9162/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0017 - AUC_ROC: 0.9745 - recall: 0.8522 - precision: 0.9281 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9163/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0013 - AUC_ROC: 0.9840 - recall: 0.8711 - precision: 0.9486 - val_loss: 0.0051 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9164/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8365 - precision: 0.9333 - val_loss: 0.0052 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9165/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0015 - AUC_ROC: 0.9794 - recall: 0.8648 - precision: 0.9386 - val_loss: 0.0053 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9166/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0016 - AUC_ROC: 0.9777 - recall: 0.8239 - precision: 0.9424 - val_loss: 0.0054 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9167/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0015 - AUC_ROC: 0.9777 - recall: 0.8805 - precision: 0.9365 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9168/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0015 - AUC_ROC: 0.9840 - recall: 0.8711 - precision: 0.9203 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9169/10000\n", + "3/3 [==============================] - 0s 98ms/step - loss: 0.0014 - AUC_ROC: 0.9793 - recall: 0.8742 - precision: 0.9653 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9170/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0016 - AUC_ROC: 0.9887 - recall: 0.8365 - precision: 0.9399 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9171/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8585 - precision: 0.9414 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9172/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0016 - AUC_ROC: 0.9824 - recall: 0.8774 - precision: 0.9178 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9173/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0014 - AUC_ROC: 0.9856 - recall: 0.8585 - precision: 0.9192 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9174/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0013 - AUC_ROC: 0.9793 - recall: 0.8742 - precision: 0.9521 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9175/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0015 - AUC_ROC: 0.9840 - recall: 0.8648 - precision: 0.9418 - val_loss: 0.0052 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9176/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0016 - AUC_ROC: 0.9761 - recall: 0.8522 - precision: 0.9377 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9177/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8302 - precision: 0.9462 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9178/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0018 - AUC_ROC: 0.9729 - recall: 0.8428 - precision: 0.9178 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9179/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0015 - AUC_ROC: 0.9761 - recall: 0.8648 - precision: 0.9549 - val_loss: 0.0050 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9180/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0017 - AUC_ROC: 0.9745 - recall: 0.8491 - precision: 0.9153 - val_loss: 0.0049 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9181/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0016 - AUC_ROC: 0.9824 - recall: 0.8333 - precision: 0.9266 - val_loss: 0.0049 - val_AUC_ROC: 0.9012 - val_recall: 0.7531 - val_precision: 0.9683\n", + "Epoch 9182/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0016 - AUC_ROC: 0.9840 - recall: 0.8585 - precision: 0.9070 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9183/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0015 - AUC_ROC: 0.9824 - recall: 0.8522 - precision: 0.9443 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9184/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0017 - AUC_ROC: 0.9745 - recall: 0.8302 - precision: 0.9565 - val_loss: 0.0052 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9185/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0015 - AUC_ROC: 0.9840 - recall: 0.8302 - precision: 0.9395 - val_loss: 0.0053 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9186/10000\n", + "3/3 [==============================] - 0s 98ms/step - loss: 0.0016 - AUC_ROC: 0.9730 - recall: 0.8585 - precision: 0.9446 - val_loss: 0.0053 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9187/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0016 - AUC_ROC: 0.9730 - recall: 0.8428 - precision: 0.9537 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9188/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0018 - AUC_ROC: 0.9776 - recall: 0.8396 - precision: 0.9604 - val_loss: 0.0050 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9189/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0017 - AUC_ROC: 0.9729 - recall: 0.8616 - precision: 0.9352 - val_loss: 0.0049 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9190/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0017 - AUC_ROC: 0.9792 - recall: 0.8428 - precision: 0.9273 - val_loss: 0.0049 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9191/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0016 - AUC_ROC: 0.9824 - recall: 0.8428 - precision: 0.9210 - val_loss: 0.0050 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9192/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0017 - AUC_ROC: 0.9745 - recall: 0.8270 - precision: 0.9529 - val_loss: 0.0050 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9193/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8648 - precision: 0.9228 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9194/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0016 - AUC_ROC: 0.9809 - recall: 0.8239 - precision: 0.9632 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9195/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8553 - precision: 0.9510 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9196/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0015 - AUC_ROC: 0.9856 - recall: 0.8742 - precision: 0.9329 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9197/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0014 - AUC_ROC: 0.9824 - recall: 0.8711 - precision: 0.9486 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9198/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0015 - AUC_ROC: 0.9872 - recall: 0.8648 - precision: 0.9291 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9199/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8711 - precision: 0.9422 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9200/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0014 - AUC_ROC: 0.9777 - recall: 0.8553 - precision: 0.9645 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9201/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8522 - precision: 0.9345 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9202/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0016 - AUC_ROC: 0.9840 - recall: 0.8648 - precision: 0.9418 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9203/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8428 - precision: 0.9404 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9204/10000\n", + "3/3 [==============================] - 0s 116ms/step - loss: 0.0017 - AUC_ROC: 0.9730 - recall: 0.8522 - precision: 0.9313 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9205/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0015 - AUC_ROC: 0.9840 - recall: 0.8396 - precision: 0.9368 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9206/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0015 - AUC_ROC: 0.9840 - recall: 0.8459 - precision: 0.9539 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9207/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0016 - AUC_ROC: 0.9809 - recall: 0.8396 - precision: 0.9435 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9208/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0014 - AUC_ROC: 0.9840 - recall: 0.8365 - precision: 0.9433 - val_loss: 0.0054 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9209/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0017 - AUC_ROC: 0.9729 - recall: 0.8553 - precision: 0.9510 - val_loss: 0.0054 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9210/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0018 - AUC_ROC: 0.9777 - recall: 0.8396 - precision: 0.9020 - val_loss: 0.0053 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9211/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0016 - AUC_ROC: 0.9776 - recall: 0.8396 - precision: 0.9468 - val_loss: 0.0053 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9212/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0013 - AUC_ROC: 0.9825 - recall: 0.8648 - precision: 0.9752 - val_loss: 0.0053 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9213/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0018 - AUC_ROC: 0.9746 - recall: 0.8239 - precision: 0.9291 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9214/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0016 - AUC_ROC: 0.9777 - recall: 0.8459 - precision: 0.9340 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9215/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0014 - AUC_ROC: 0.9903 - recall: 0.8553 - precision: 0.9444 - val_loss: 0.0051 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9216/10000\n", + "3/3 [==============================] - 0s 97ms/step - loss: 0.0017 - AUC_ROC: 0.9777 - recall: 0.8522 - precision: 0.9155 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9217/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0016 - AUC_ROC: 0.9762 - recall: 0.8333 - precision: 0.9464 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9218/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8491 - precision: 0.9375 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9219/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8553 - precision: 0.9510 - val_loss: 0.0054 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9220/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0014 - AUC_ROC: 0.9872 - recall: 0.8522 - precision: 0.9377 - val_loss: 0.0054 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9221/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0016 - AUC_ROC: 0.9808 - recall: 0.8522 - precision: 0.9377 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9222/10000\n", + "3/3 [==============================] - 0s 97ms/step - loss: 0.0017 - AUC_ROC: 0.9808 - recall: 0.8270 - precision: 0.9495 - val_loss: 0.0054 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9223/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0017 - AUC_ROC: 0.9840 - recall: 0.8585 - precision: 0.9010 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9224/10000\n", + "3/3 [==============================] - 0s 98ms/step - loss: 0.0014 - AUC_ROC: 0.9824 - recall: 0.8491 - precision: 0.9474 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9225/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0016 - AUC_ROC: 0.9745 - recall: 0.8585 - precision: 0.9286 - val_loss: 0.0056 - val_AUC_ROC: 0.8641 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9226/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0015 - AUC_ROC: 0.9872 - recall: 0.8491 - precision: 0.9375 - val_loss: 0.0056 - val_AUC_ROC: 0.8641 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9227/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0014 - AUC_ROC: 0.9887 - recall: 0.8553 - precision: 0.9379 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9228/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0015 - AUC_ROC: 0.9825 - recall: 0.8585 - precision: 0.9317 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9229/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8302 - precision: 0.9429 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9230/10000\n", + "3/3 [==============================] - 0s 117ms/step - loss: 0.0015 - AUC_ROC: 0.9825 - recall: 0.8585 - precision: 0.9579 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9231/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8270 - precision: 0.9427 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9232/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8616 - precision: 0.9580 - val_loss: 0.0054 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9233/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8522 - precision: 0.9476 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9234/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0017 - AUC_ROC: 0.9808 - recall: 0.8522 - precision: 0.9313 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9235/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0017 - AUC_ROC: 0.9793 - recall: 0.8711 - precision: 0.9112 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9236/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0017 - AUC_ROC: 0.9729 - recall: 0.8585 - precision: 0.9381 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9237/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0015 - AUC_ROC: 0.9762 - recall: 0.8522 - precision: 0.9644 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9238/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0015 - AUC_ROC: 0.9840 - recall: 0.8365 - precision: 0.9399 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9239/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0016 - AUC_ROC: 0.9730 - recall: 0.8522 - precision: 0.9509 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9240/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0014 - AUC_ROC: 0.9793 - recall: 0.8616 - precision: 0.9384 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9241/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8648 - precision: 0.9386 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9242/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0015 - AUC_ROC: 0.9824 - recall: 0.8616 - precision: 0.9320 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9243/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0015 - AUC_ROC: 0.9777 - recall: 0.8428 - precision: 0.9470 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9244/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0016 - AUC_ROC: 0.9776 - recall: 0.8553 - precision: 0.9189 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9245/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0012 - AUC_ROC: 0.9888 - recall: 0.8648 - precision: 0.9549 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9246/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8679 - precision: 0.9550 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9247/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0014 - AUC_ROC: 0.9762 - recall: 0.8585 - precision: 0.9681 - val_loss: 0.0056 - val_AUC_ROC: 0.8641 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9248/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0016 - AUC_ROC: 0.9714 - recall: 0.8616 - precision: 0.9648 - val_loss: 0.0056 - val_AUC_ROC: 0.8641 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9249/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0015 - AUC_ROC: 0.9825 - recall: 0.8742 - precision: 0.9205 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9250/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0015 - AUC_ROC: 0.9808 - recall: 0.8585 - precision: 0.9223 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9251/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0013 - AUC_ROC: 0.9856 - recall: 0.8805 - precision: 0.9396 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9252/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0017 - AUC_ROC: 0.9698 - recall: 0.8208 - precision: 0.9526 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9253/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0014 - AUC_ROC: 0.9856 - recall: 0.8648 - precision: 0.9386 - val_loss: 0.0056 - val_AUC_ROC: 0.8641 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9254/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0014 - AUC_ROC: 0.9871 - recall: 0.8616 - precision: 0.9320 - val_loss: 0.0056 - val_AUC_ROC: 0.8641 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9255/10000\n", + "3/3 [==============================] - 0s 115ms/step - loss: 0.0015 - AUC_ROC: 0.9761 - recall: 0.8616 - precision: 0.9614 - val_loss: 0.0056 - val_AUC_ROC: 0.8641 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9256/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0014 - AUC_ROC: 0.9856 - recall: 0.8585 - precision: 0.9286 - val_loss: 0.0056 - val_AUC_ROC: 0.8641 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9257/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8459 - precision: 0.9308 - val_loss: 0.0056 - val_AUC_ROC: 0.8641 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9258/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0018 - AUC_ROC: 0.9762 - recall: 0.7925 - precision: 0.9545 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9259/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8522 - precision: 0.9576 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9260/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8365 - precision: 0.9366 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9261/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0016 - AUC_ROC: 0.9856 - recall: 0.8648 - precision: 0.9259 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9262/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0016 - AUC_ROC: 0.9824 - recall: 0.8491 - precision: 0.9278 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9263/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0014 - AUC_ROC: 0.9872 - recall: 0.8522 - precision: 0.9249 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9264/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0016 - AUC_ROC: 0.9824 - recall: 0.8679 - precision: 0.9262 - val_loss: 0.0052 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9265/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8459 - precision: 0.9406 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9266/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0015 - AUC_ROC: 0.9824 - recall: 0.8585 - precision: 0.9349 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9267/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0015 - AUC_ROC: 0.9761 - recall: 0.8553 - precision: 0.9645 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9268/10000\n", + "3/3 [==============================] - 0s 124ms/step - loss: 0.0017 - AUC_ROC: 0.9777 - recall: 0.8679 - precision: 0.9200 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9269/10000\n", + "3/3 [==============================] - 0s 133ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8459 - precision: 0.9439 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9270/10000\n", + "3/3 [==============================] - 0s 131ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8648 - precision: 0.9483 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9271/10000\n", + "3/3 [==============================] - 0s 126ms/step - loss: 0.0016 - AUC_ROC: 0.9808 - recall: 0.8428 - precision: 0.9371 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9272/10000\n", + "3/3 [==============================] - 0s 134ms/step - loss: 0.0016 - AUC_ROC: 0.9824 - recall: 0.8459 - precision: 0.9181 - val_loss: 0.0051 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9273/10000\n", + "3/3 [==============================] - 0s 120ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8648 - precision: 0.9197 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9274/10000\n", + "3/3 [==============================] - 0s 122ms/step - loss: 0.0015 - AUC_ROC: 0.9840 - recall: 0.8553 - precision: 0.9158 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9275/10000\n", + "3/3 [==============================] - 0s 145ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8491 - precision: 0.9408 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9276/10000\n", + "3/3 [==============================] - 0s 134ms/step - loss: 0.0016 - AUC_ROC: 0.9856 - recall: 0.8553 - precision: 0.9510 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9277/10000\n", + "3/3 [==============================] - 0s 152ms/step - loss: 0.0014 - AUC_ROC: 0.9872 - recall: 0.8648 - precision: 0.9549 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9278/10000\n", + "3/3 [==============================] - 0s 138ms/step - loss: 0.0018 - AUC_ROC: 0.9793 - recall: 0.8365 - precision: 0.9236 - val_loss: 0.0054 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9279/10000\n", + "3/3 [==============================] - 0s 120ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8522 - precision: 0.9410 - val_loss: 0.0054 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9280/10000\n", + "3/3 [==============================] - 0s 136ms/step - loss: 0.0015 - AUC_ROC: 0.9761 - recall: 0.8711 - precision: 0.9233 - val_loss: 0.0054 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9281/10000\n", + "3/3 [==============================] - 0s 125ms/step - loss: 0.0015 - AUC_ROC: 0.9777 - recall: 0.8491 - precision: 0.9408 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9282/10000\n", + "3/3 [==============================] - 0s 140ms/step - loss: 0.0015 - AUC_ROC: 0.9824 - recall: 0.8491 - precision: 0.9474 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9283/10000\n", + "3/3 [==============================] - 0s 121ms/step - loss: 0.0013 - AUC_ROC: 0.9824 - recall: 0.8396 - precision: 0.9468 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9284/10000\n", + "3/3 [==============================] - 0s 115ms/step - loss: 0.0015 - AUC_ROC: 0.9824 - recall: 0.8428 - precision: 0.9504 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9285/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0015 - AUC_ROC: 0.9762 - recall: 0.8522 - precision: 0.9377 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9286/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0015 - AUC_ROC: 0.9887 - recall: 0.8585 - precision: 0.9100 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9287/10000\n", + "3/3 [==============================] - 0s 128ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8522 - precision: 0.9443 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9288/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0014 - AUC_ROC: 0.9777 - recall: 0.8522 - precision: 0.9509 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9289/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0013 - AUC_ROC: 0.9888 - recall: 0.8522 - precision: 0.9377 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9290/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0014 - AUC_ROC: 0.9903 - recall: 0.8711 - precision: 0.9233 - val_loss: 0.0053 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9291/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0015 - AUC_ROC: 0.9777 - recall: 0.8459 - precision: 0.9244 - val_loss: 0.0054 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9292/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0014 - AUC_ROC: 0.9777 - recall: 0.8522 - precision: 0.9644 - val_loss: 0.0054 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9293/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8742 - precision: 0.9360 - val_loss: 0.0054 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9294/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0017 - AUC_ROC: 0.9855 - recall: 0.8270 - precision: 0.9293 - val_loss: 0.0053 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9295/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0017 - AUC_ROC: 0.9745 - recall: 0.8553 - precision: 0.9283 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9296/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8585 - precision: 0.9286 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9297/10000\n", + "3/3 [==============================] - 0s 97ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8553 - precision: 0.9347 - val_loss: 0.0054 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9298/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0014 - AUC_ROC: 0.9840 - recall: 0.8616 - precision: 0.9481 - val_loss: 0.0055 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9299/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0014 - AUC_ROC: 0.9872 - recall: 0.8616 - precision: 0.9133 - val_loss: 0.0055 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9300/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8553 - precision: 0.9412 - val_loss: 0.0055 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9301/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0016 - AUC_ROC: 0.9761 - recall: 0.8239 - precision: 0.9704 - val_loss: 0.0055 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9302/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0016 - AUC_ROC: 0.9809 - recall: 0.8270 - precision: 0.9564 - val_loss: 0.0055 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9303/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0017 - AUC_ROC: 0.9746 - recall: 0.8365 - precision: 0.9466 - val_loss: 0.0055 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9304/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0015 - AUC_ROC: 0.9777 - recall: 0.8491 - precision: 0.9507 - val_loss: 0.0055 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9305/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0017 - AUC_ROC: 0.9792 - recall: 0.8491 - precision: 0.9278 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9306/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0013 - AUC_ROC: 0.9841 - recall: 0.8679 - precision: 0.9617 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9307/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0015 - AUC_ROC: 0.9762 - recall: 0.8585 - precision: 0.9613 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9308/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0014 - AUC_ROC: 0.9856 - recall: 0.8428 - precision: 0.9470 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9309/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0016 - AUC_ROC: 0.9777 - recall: 0.8396 - precision: 0.9570 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9310/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0018 - AUC_ROC: 0.9824 - recall: 0.8491 - precision: 0.8824 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9311/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0018 - AUC_ROC: 0.9730 - recall: 0.8333 - precision: 0.9331 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9312/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0015 - AUC_ROC: 0.9856 - recall: 0.8648 - precision: 0.9016 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9313/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8711 - precision: 0.9358 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9314/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8679 - precision: 0.9293 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9315/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8239 - precision: 0.9597 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9316/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0016 - AUC_ROC: 0.9824 - recall: 0.8522 - precision: 0.9281 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9317/10000\n", + "3/3 [==============================] - 0s 115ms/step - loss: 0.0014 - AUC_ROC: 0.9793 - recall: 0.8616 - precision: 0.9448 - val_loss: 0.0054 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9318/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8553 - precision: 0.9510 - val_loss: 0.0054 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9319/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8616 - precision: 0.9195 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9320/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0016 - AUC_ROC: 0.9777 - recall: 0.8553 - precision: 0.9444 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9321/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8522 - precision: 0.9713 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9322/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8585 - precision: 0.9545 - val_loss: 0.0051 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9323/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0014 - AUC_ROC: 0.9840 - recall: 0.8679 - precision: 0.9388 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9324/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0014 - AUC_ROC: 0.9824 - recall: 0.8679 - precision: 0.9452 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9325/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0013 - AUC_ROC: 0.9888 - recall: 0.8711 - precision: 0.9295 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9326/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8616 - precision: 0.9320 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9327/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8302 - precision: 0.9296 - val_loss: 0.0054 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9328/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0014 - AUC_ROC: 0.9841 - recall: 0.8711 - precision: 0.9233 - val_loss: 0.0054 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9329/10000\n", + "3/3 [==============================] - 0s 114ms/step - loss: 0.0016 - AUC_ROC: 0.9762 - recall: 0.8396 - precision: 0.9368 - val_loss: 0.0055 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9330/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0016 - AUC_ROC: 0.9808 - recall: 0.8396 - precision: 0.9468 - val_loss: 0.0055 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9331/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0015 - AUC_ROC: 0.9745 - recall: 0.8711 - precision: 0.9390 - val_loss: 0.0054 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9332/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0014 - AUC_ROC: 0.9872 - recall: 0.8711 - precision: 0.9203 - val_loss: 0.0055 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9333/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0014 - AUC_ROC: 0.9840 - recall: 0.8679 - precision: 0.9452 - val_loss: 0.0055 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9334/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0016 - AUC_ROC: 0.9856 - recall: 0.8333 - precision: 0.9266 - val_loss: 0.0055 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9335/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8585 - precision: 0.9512 - val_loss: 0.0055 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9336/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0015 - AUC_ROC: 0.9903 - recall: 0.8585 - precision: 0.9317 - val_loss: 0.0054 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9337/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8962 - precision: 0.9406 - val_loss: 0.0054 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9338/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0014 - AUC_ROC: 0.9841 - recall: 0.8899 - precision: 0.9248 - val_loss: 0.0054 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9339/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0016 - AUC_ROC: 0.9746 - recall: 0.8459 - precision: 0.9539 - val_loss: 0.0054 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9340/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8302 - precision: 0.9565 - val_loss: 0.0053 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9341/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8648 - precision: 0.9483 - val_loss: 0.0054 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9342/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0016 - AUC_ROC: 0.9808 - recall: 0.8616 - precision: 0.9352 - val_loss: 0.0054 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9343/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0016 - AUC_ROC: 0.9777 - recall: 0.8396 - precision: 0.9051 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9344/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8491 - precision: 0.9375 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9345/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0016 - AUC_ROC: 0.9761 - recall: 0.8396 - precision: 0.9368 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9346/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8553 - precision: 0.9283 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9347/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0015 - AUC_ROC: 0.9872 - recall: 0.8396 - precision: 0.9271 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9348/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8333 - precision: 0.9601 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9349/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0013 - AUC_ROC: 0.9841 - recall: 0.8836 - precision: 0.9398 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9350/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0014 - AUC_ROC: 0.9841 - recall: 0.8585 - precision: 0.9381 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9351/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0016 - AUC_ROC: 0.9730 - recall: 0.8396 - precision: 0.9536 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9352/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8585 - precision: 0.9446 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9353/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0017 - AUC_ROC: 0.9778 - recall: 0.8365 - precision: 0.9204 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9354/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0015 - AUC_ROC: 0.9856 - recall: 0.8522 - precision: 0.9542 - val_loss: 0.0052 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9355/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0015 - AUC_ROC: 0.9840 - recall: 0.8616 - precision: 0.9257 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9356/10000\n", + "3/3 [==============================] - 0s 114ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8868 - precision: 0.9126 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9357/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0016 - AUC_ROC: 0.9872 - recall: 0.8774 - precision: 0.9148 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9358/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0013 - AUC_ROC: 0.9841 - recall: 0.8711 - precision: 0.9585 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9359/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0016 - AUC_ROC: 0.9777 - recall: 0.8585 - precision: 0.9446 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9360/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0015 - AUC_ROC: 0.9840 - recall: 0.8553 - precision: 0.9283 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9361/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8428 - precision: 0.9640 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9362/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0015 - AUC_ROC: 0.9714 - recall: 0.8805 - precision: 0.9428 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9363/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0015 - AUC_ROC: 0.9872 - recall: 0.8396 - precision: 0.9502 - val_loss: 0.0054 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9364/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8805 - precision: 0.9241 - val_loss: 0.0053 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9365/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8774 - precision: 0.9300 - val_loss: 0.0053 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9366/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0015 - AUC_ROC: 0.9824 - recall: 0.8585 - precision: 0.9130 - val_loss: 0.0053 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9367/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8648 - precision: 0.9106 - val_loss: 0.0053 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9368/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0014 - AUC_ROC: 0.9840 - recall: 0.8648 - precision: 0.9386 - val_loss: 0.0053 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9369/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0016 - AUC_ROC: 0.9824 - recall: 0.8553 - precision: 0.9128 - val_loss: 0.0053 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9370/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0012 - AUC_ROC: 0.9825 - recall: 0.8868 - precision: 0.9724 - val_loss: 0.0053 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 9371/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0013 - AUC_ROC: 0.9856 - recall: 0.8585 - precision: 0.9579 - val_loss: 0.0052 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 9372/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8711 - precision: 0.9552 - val_loss: 0.0052 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 9373/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0015 - AUC_ROC: 0.9746 - recall: 0.8459 - precision: 0.9406 - val_loss: 0.0052 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 9374/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0014 - AUC_ROC: 0.9856 - recall: 0.8522 - precision: 0.9345 - val_loss: 0.0052 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 9375/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0015 - AUC_ROC: 0.9777 - recall: 0.8679 - precision: 0.9293 - val_loss: 0.0052 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 9376/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0013 - AUC_ROC: 0.9809 - recall: 0.8553 - precision: 0.9611 - val_loss: 0.0052 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 9377/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0013 - AUC_ROC: 0.9825 - recall: 0.8742 - precision: 0.9360 - val_loss: 0.0052 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9378/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0015 - AUC_ROC: 0.9903 - recall: 0.8616 - precision: 0.9195 - val_loss: 0.0052 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9379/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0013 - AUC_ROC: 0.9935 - recall: 0.8553 - precision: 0.9444 - val_loss: 0.0053 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9380/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0013 - AUC_ROC: 0.9809 - recall: 0.8774 - precision: 0.9522 - val_loss: 0.0054 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9381/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0018 - AUC_ROC: 0.9761 - recall: 0.8302 - precision: 0.9429 - val_loss: 0.0054 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9382/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0016 - AUC_ROC: 0.9777 - recall: 0.8616 - precision: 0.9416 - val_loss: 0.0053 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9383/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0015 - AUC_ROC: 0.9746 - recall: 0.8679 - precision: 0.9550 - val_loss: 0.0052 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 9384/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0016 - AUC_ROC: 0.9856 - recall: 0.8459 - precision: 0.9406 - val_loss: 0.0051 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 9385/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0015 - AUC_ROC: 0.9761 - recall: 0.8522 - precision: 0.9377 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9386/10000\n", + "3/3 [==============================] - 0s 116ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8585 - precision: 0.9254 - val_loss: 0.0048 - val_AUC_ROC: 0.8888 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 9387/10000\n", + "3/3 [==============================] - 0s 115ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8585 - precision: 0.9613 - val_loss: 0.0048 - val_AUC_ROC: 0.9012 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 9388/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0013 - AUC_ROC: 0.9793 - recall: 0.8648 - precision: 0.9786 - val_loss: 0.0047 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 9389/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0014 - AUC_ROC: 0.9840 - recall: 0.8585 - precision: 0.9381 - val_loss: 0.0047 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 9390/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0016 - AUC_ROC: 0.9855 - recall: 0.8176 - precision: 0.9286 - val_loss: 0.0048 - val_AUC_ROC: 0.8950 - val_recall: 0.7654 - val_precision: 0.9688\n", + "Epoch 9391/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0013 - AUC_ROC: 0.9809 - recall: 0.8585 - precision: 0.9579 - val_loss: 0.0048 - val_AUC_ROC: 0.8950 - val_recall: 0.7531 - val_precision: 0.9531\n", + "Epoch 9392/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0015 - AUC_ROC: 0.9746 - recall: 0.8899 - precision: 0.9497 - val_loss: 0.0049 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 9393/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8522 - precision: 0.9377 - val_loss: 0.0050 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9394/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0016 - AUC_ROC: 0.9840 - recall: 0.8302 - precision: 0.9395 - val_loss: 0.0050 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9395/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0016 - AUC_ROC: 0.9746 - recall: 0.8553 - precision: 0.9544 - val_loss: 0.0050 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9396/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8711 - precision: 0.9203 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9397/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0016 - AUC_ROC: 0.9824 - recall: 0.8428 - precision: 0.9470 - val_loss: 0.0051 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 9398/10000\n", + "3/3 [==============================] - 0s 117ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8648 - precision: 0.9197 - val_loss: 0.0052 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 9399/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0015 - AUC_ROC: 0.9777 - recall: 0.8365 - precision: 0.9638 - val_loss: 0.0053 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9400/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0014 - AUC_ROC: 0.9778 - recall: 0.8491 - precision: 0.9541 - val_loss: 0.0053 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9401/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8522 - precision: 0.9155 - val_loss: 0.0053 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9402/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0013 - AUC_ROC: 0.9841 - recall: 0.8836 - precision: 0.9243 - val_loss: 0.0053 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9403/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0017 - AUC_ROC: 0.9683 - recall: 0.8648 - precision: 0.9322 - val_loss: 0.0053 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9404/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0015 - AUC_ROC: 0.9825 - recall: 0.8491 - precision: 0.9408 - val_loss: 0.0053 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9405/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0015 - AUC_ROC: 0.9824 - recall: 0.8648 - precision: 0.9418 - val_loss: 0.0053 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9406/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0014 - AUC_ROC: 0.9746 - recall: 0.8742 - precision: 0.9720 - val_loss: 0.0052 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 9407/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8459 - precision: 0.9539 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 9408/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0013 - AUC_ROC: 0.9888 - recall: 0.8711 - precision: 0.9327 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 9409/10000\n", + "3/3 [==============================] - 0s 97ms/step - loss: 0.0014 - AUC_ROC: 0.9777 - recall: 0.8522 - precision: 0.9377 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 9410/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0015 - AUC_ROC: 0.9762 - recall: 0.8742 - precision: 0.9456 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 9411/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0016 - AUC_ROC: 0.9761 - recall: 0.8522 - precision: 0.9410 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 9412/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0015 - AUC_ROC: 0.9825 - recall: 0.8459 - precision: 0.9505 - val_loss: 0.0053 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9413/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0015 - AUC_ROC: 0.9777 - recall: 0.8836 - precision: 0.9398 - val_loss: 0.0054 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9414/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0015 - AUC_ROC: 0.9762 - recall: 0.8522 - precision: 0.9476 - val_loss: 0.0054 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9415/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0014 - AUC_ROC: 0.9793 - recall: 0.8711 - precision: 0.9486 - val_loss: 0.0055 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9416/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0015 - AUC_ROC: 0.9872 - recall: 0.8648 - precision: 0.9291 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9417/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0015 - AUC_ROC: 0.9825 - recall: 0.8679 - precision: 0.9356 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9418/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0014 - AUC_ROC: 0.9856 - recall: 0.8491 - precision: 0.9408 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9419/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0015 - AUC_ROC: 0.9824 - recall: 0.8868 - precision: 0.9156 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9420/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0014 - AUC_ROC: 0.9778 - recall: 0.8648 - precision: 0.9354 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9421/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8459 - precision: 0.9539 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9422/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8616 - precision: 0.9580 - val_loss: 0.0053 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9423/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0014 - AUC_ROC: 0.9824 - recall: 0.8491 - precision: 0.9507 - val_loss: 0.0053 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9424/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0015 - AUC_ROC: 0.9761 - recall: 0.8491 - precision: 0.9408 - val_loss: 0.0053 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9425/10000\n", + "3/3 [==============================] - 0s 114ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8648 - precision: 0.9516 - val_loss: 0.0053 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9426/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0014 - AUC_ROC: 0.9904 - recall: 0.8553 - precision: 0.9283 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9427/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0014 - AUC_ROC: 0.9872 - recall: 0.8679 - precision: 0.9231 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9428/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8616 - precision: 0.9320 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9429/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0017 - AUC_ROC: 0.9730 - recall: 0.8742 - precision: 0.9236 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9430/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0013 - AUC_ROC: 0.9825 - recall: 0.8679 - precision: 0.9583 - val_loss: 0.0053 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9431/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0014 - AUC_ROC: 0.9872 - recall: 0.8648 - precision: 0.9418 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9432/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8522 - precision: 0.9345 - val_loss: 0.0053 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9433/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8679 - precision: 0.9517 - val_loss: 0.0053 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9434/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0016 - AUC_ROC: 0.9840 - recall: 0.8396 - precision: 0.9336 - val_loss: 0.0053 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9435/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0014 - AUC_ROC: 0.9841 - recall: 0.8836 - precision: 0.9213 - val_loss: 0.0053 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9436/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0014 - AUC_ROC: 0.9872 - recall: 0.8616 - precision: 0.9195 - val_loss: 0.0054 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9437/10000\n", + "3/3 [==============================] - 0s 125ms/step - loss: 0.0015 - AUC_ROC: 0.9777 - recall: 0.8522 - precision: 0.9576 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9438/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0016 - AUC_ROC: 0.9730 - recall: 0.8522 - precision: 0.9476 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9439/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0014 - AUC_ROC: 0.9873 - recall: 0.8836 - precision: 0.9398 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9440/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0014 - AUC_ROC: 0.9872 - recall: 0.8585 - precision: 0.9479 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9441/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0016 - AUC_ROC: 0.9777 - recall: 0.8239 - precision: 0.9291 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9442/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0013 - AUC_ROC: 0.9809 - recall: 0.8931 - precision: 0.9498 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9443/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8711 - precision: 0.9082 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9444/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8333 - precision: 0.9532 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9445/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0013 - AUC_ROC: 0.9888 - recall: 0.8553 - precision: 0.9577 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9446/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0014 - AUC_ROC: 0.9856 - recall: 0.8679 - precision: 0.9200 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9447/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0014 - AUC_ROC: 0.9856 - recall: 0.8522 - precision: 0.9218 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9448/10000\n", + "3/3 [==============================] - 0s 114ms/step - loss: 0.0014 - AUC_ROC: 0.9793 - recall: 0.8585 - precision: 0.9545 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9449/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0016 - AUC_ROC: 0.9825 - recall: 0.8522 - precision: 0.9281 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9450/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0014 - AUC_ROC: 0.9856 - recall: 0.8428 - precision: 0.9306 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9451/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8239 - precision: 0.9632 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9452/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0013 - AUC_ROC: 0.9840 - recall: 0.8742 - precision: 0.9521 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9453/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0015 - AUC_ROC: 0.9840 - recall: 0.8616 - precision: 0.9226 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9454/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0016 - AUC_ROC: 0.9825 - recall: 0.8428 - precision: 0.9178 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9455/10000\n", + "3/3 [==============================] - 0s 116ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8585 - precision: 0.9613 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9456/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0013 - AUC_ROC: 0.9856 - recall: 0.8711 - precision: 0.9422 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9457/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0014 - AUC_ROC: 0.9841 - recall: 0.8553 - precision: 0.9412 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9458/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0013 - AUC_ROC: 0.9888 - recall: 0.8648 - precision: 0.9483 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9459/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0014 - AUC_ROC: 0.9872 - recall: 0.8585 - precision: 0.9349 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9460/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0013 - AUC_ROC: 0.9841 - recall: 0.8742 - precision: 0.9553 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9461/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0013 - AUC_ROC: 0.9825 - recall: 0.8742 - precision: 0.9521 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9462/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0016 - AUC_ROC: 0.9856 - recall: 0.8491 - precision: 0.9184 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9463/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0016 - AUC_ROC: 0.9839 - recall: 0.8585 - precision: 0.9100 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9464/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0015 - AUC_ROC: 0.9887 - recall: 0.8742 - precision: 0.9115 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9465/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8428 - precision: 0.9537 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9466/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0015 - AUC_ROC: 0.9746 - recall: 0.8553 - precision: 0.9680 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9467/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8459 - precision: 0.9505 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9468/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8428 - precision: 0.9537 - val_loss: 0.0055 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9469/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8522 - precision: 0.9345 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9470/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0017 - AUC_ROC: 0.9792 - recall: 0.8428 - precision: 0.9404 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9471/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0013 - AUC_ROC: 0.9857 - recall: 0.8742 - precision: 0.9360 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9472/10000\n", + "3/3 [==============================] - 0s 97ms/step - loss: 0.0014 - AUC_ROC: 0.9856 - recall: 0.8553 - precision: 0.9412 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9473/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8616 - precision: 0.9320 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9474/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0013 - AUC_ROC: 0.9856 - recall: 0.8742 - precision: 0.9267 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9475/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0017 - AUC_ROC: 0.9777 - recall: 0.8585 - precision: 0.9223 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9476/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8522 - precision: 0.9186 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9477/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0015 - AUC_ROC: 0.9730 - recall: 0.8522 - precision: 0.9509 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9478/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0014 - AUC_ROC: 0.9840 - recall: 0.8553 - precision: 0.9347 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9479/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8679 - precision: 0.9485 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9480/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0015 - AUC_ROC: 0.9840 - recall: 0.8742 - precision: 0.9329 - val_loss: 0.0055 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9481/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0014 - AUC_ROC: 0.9841 - recall: 0.8491 - precision: 0.9441 - val_loss: 0.0054 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9482/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0013 - AUC_ROC: 0.9856 - recall: 0.8742 - precision: 0.9392 - val_loss: 0.0054 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9483/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0014 - AUC_ROC: 0.9762 - recall: 0.8459 - precision: 0.9406 - val_loss: 0.0053 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9484/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0013 - AUC_ROC: 0.9856 - recall: 0.8491 - precision: 0.9677 - val_loss: 0.0053 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9485/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8679 - precision: 0.9388 - val_loss: 0.0054 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9486/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8459 - precision: 0.9439 - val_loss: 0.0054 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9487/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8491 - precision: 0.9343 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9488/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8616 - precision: 0.9320 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9489/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0013 - AUC_ROC: 0.9825 - recall: 0.8553 - precision: 0.9347 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9490/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0014 - AUC_ROC: 0.9856 - recall: 0.8711 - precision: 0.9142 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9491/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0012 - AUC_ROC: 0.9872 - recall: 0.8553 - precision: 0.9645 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9492/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8491 - precision: 0.9474 - val_loss: 0.0055 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9493/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8616 - precision: 0.9481 - val_loss: 0.0055 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9494/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0013 - AUC_ROC: 0.9794 - recall: 0.8711 - precision: 0.9585 - val_loss: 0.0055 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9495/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0013 - AUC_ROC: 0.9825 - recall: 0.8616 - precision: 0.9580 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9496/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8679 - precision: 0.9517 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9497/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8679 - precision: 0.9388 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9498/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0014 - AUC_ROC: 0.9856 - recall: 0.8742 - precision: 0.9456 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9499/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0014 - AUC_ROC: 0.9872 - recall: 0.8585 - precision: 0.9414 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9500/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0014 - AUC_ROC: 0.9856 - recall: 0.8270 - precision: 0.9634 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9501/10000\n", + "3/3 [==============================] - 0s 119ms/step - loss: 0.0015 - AUC_ROC: 0.9777 - recall: 0.8616 - precision: 0.9384 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9502/10000\n", + "3/3 [==============================] - 0s 115ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8648 - precision: 0.9291 - val_loss: 0.0053 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9503/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0014 - AUC_ROC: 0.9840 - recall: 0.8805 - precision: 0.9333 - val_loss: 0.0052 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9504/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8491 - precision: 0.9343 - val_loss: 0.0052 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9505/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0015 - AUC_ROC: 0.9825 - recall: 0.8365 - precision: 0.9568 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9506/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0012 - AUC_ROC: 0.9920 - recall: 0.8836 - precision: 0.9336 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9507/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0015 - AUC_ROC: 0.9777 - recall: 0.8239 - precision: 0.9458 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9508/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0014 - AUC_ROC: 0.9856 - recall: 0.8931 - precision: 0.9221 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9509/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0015 - AUC_ROC: 0.9856 - recall: 0.8553 - precision: 0.9128 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9510/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0015 - AUC_ROC: 0.9856 - recall: 0.8805 - precision: 0.9180 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9511/10000\n", + "3/3 [==============================] - 0s 98ms/step - loss: 0.0014 - AUC_ROC: 0.9840 - recall: 0.8491 - precision: 0.9441 - val_loss: 0.0054 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9512/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8365 - precision: 0.9366 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9513/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0015 - AUC_ROC: 0.9777 - recall: 0.8522 - precision: 0.9313 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9514/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0015 - AUC_ROC: 0.9794 - recall: 0.8553 - precision: 0.9544 - val_loss: 0.0055 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9515/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0016 - AUC_ROC: 0.9777 - recall: 0.8428 - precision: 0.9571 - val_loss: 0.0054 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9516/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0013 - AUC_ROC: 0.9872 - recall: 0.8836 - precision: 0.9461 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9517/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0013 - AUC_ROC: 0.9840 - recall: 0.8774 - precision: 0.9555 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 9518/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0014 - AUC_ROC: 0.9872 - recall: 0.8868 - precision: 0.9246 - val_loss: 0.0053 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 9519/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8711 - precision: 0.9454 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 9520/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0016 - AUC_ROC: 0.9903 - recall: 0.8711 - precision: 0.8964 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 9521/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0013 - AUC_ROC: 0.9888 - recall: 0.8491 - precision: 0.9677 - val_loss: 0.0053 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 9522/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0016 - AUC_ROC: 0.9840 - recall: 0.8396 - precision: 0.9113 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 9523/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8553 - precision: 0.9379 - val_loss: 0.0052 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 9524/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0014 - AUC_ROC: 0.9762 - recall: 0.8868 - precision: 0.9431 - val_loss: 0.0051 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 9525/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0014 - AUC_ROC: 0.9856 - recall: 0.8679 - precision: 0.9452 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9526/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0015 - AUC_ROC: 0.9840 - recall: 0.8711 - precision: 0.9327 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9527/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0014 - AUC_ROC: 0.9872 - recall: 0.8679 - precision: 0.9388 - val_loss: 0.0053 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9528/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8774 - precision: 0.9426 - val_loss: 0.0054 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9529/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0016 - AUC_ROC: 0.9746 - recall: 0.8365 - precision: 0.9366 - val_loss: 0.0054 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9530/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8365 - precision: 0.9568 - val_loss: 0.0054 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9531/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8553 - precision: 0.9379 - val_loss: 0.0054 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9532/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8459 - precision: 0.9439 - val_loss: 0.0054 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9533/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0014 - AUC_ROC: 0.9793 - recall: 0.8648 - precision: 0.9322 - val_loss: 0.0053 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9534/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0015 - AUC_ROC: 0.9825 - recall: 0.8396 - precision: 0.9401 - val_loss: 0.0053 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9535/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0017 - AUC_ROC: 0.9762 - recall: 0.8491 - precision: 0.9310 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9536/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8553 - precision: 0.9645 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9537/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0016 - AUC_ROC: 0.9777 - recall: 0.8459 - precision: 0.9505 - val_loss: 0.0052 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9538/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0015 - AUC_ROC: 0.9824 - recall: 0.8585 - precision: 0.9161 - val_loss: 0.0052 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9539/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0013 - AUC_ROC: 0.9809 - recall: 0.8522 - precision: 0.9476 - val_loss: 0.0052 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9540/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0013 - AUC_ROC: 0.9841 - recall: 0.8742 - precision: 0.9553 - val_loss: 0.0051 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9541/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0013 - AUC_ROC: 0.9872 - recall: 0.8459 - precision: 0.9406 - val_loss: 0.0051 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9677\n", + "Epoch 9542/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0016 - AUC_ROC: 0.9761 - recall: 0.8491 - precision: 0.9310 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 9543/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8805 - precision: 0.9241 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 9544/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0014 - AUC_ROC: 0.9762 - recall: 0.8616 - precision: 0.9682 - val_loss: 0.0053 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9545/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0014 - AUC_ROC: 0.9793 - recall: 0.8616 - precision: 0.9514 - val_loss: 0.0054 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9546/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0015 - AUC_ROC: 0.9825 - recall: 0.8711 - precision: 0.9264 - val_loss: 0.0054 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9547/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0013 - AUC_ROC: 0.9857 - recall: 0.8679 - precision: 0.9617 - val_loss: 0.0055 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9548/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0015 - AUC_ROC: 0.9871 - recall: 0.8679 - precision: 0.9388 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9549/10000\n", + "3/3 [==============================] - 0s 114ms/step - loss: 0.0012 - AUC_ROC: 0.9920 - recall: 0.8836 - precision: 0.9493 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9550/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8459 - precision: 0.9472 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9551/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0018 - AUC_ROC: 0.9777 - recall: 0.8491 - precision: 0.9030 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9552/10000\n", + "3/3 [==============================] - 0s 98ms/step - loss: 0.0015 - AUC_ROC: 0.9825 - recall: 0.8805 - precision: 0.9272 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9553/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8365 - precision: 0.9366 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9554/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0014 - AUC_ROC: 0.9794 - recall: 0.8553 - precision: 0.9577 - val_loss: 0.0054 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9555/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8648 - precision: 0.9450 - val_loss: 0.0054 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9556/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0016 - AUC_ROC: 0.9777 - recall: 0.8333 - precision: 0.9397 - val_loss: 0.0054 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9557/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0014 - AUC_ROC: 0.9840 - recall: 0.8616 - precision: 0.9384 - val_loss: 0.0055 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9558/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0015 - AUC_ROC: 0.9825 - recall: 0.8459 - precision: 0.9308 - val_loss: 0.0056 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9559/10000\n", + "3/3 [==============================] - 0s 121ms/step - loss: 0.0013 - AUC_ROC: 0.9841 - recall: 0.8616 - precision: 0.9580 - val_loss: 0.0056 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9560/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0016 - AUC_ROC: 0.9777 - recall: 0.8616 - precision: 0.9614 - val_loss: 0.0056 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9561/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0013 - AUC_ROC: 0.9888 - recall: 0.8616 - precision: 0.9288 - val_loss: 0.0056 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9562/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0015 - AUC_ROC: 0.9761 - recall: 0.8585 - precision: 0.9446 - val_loss: 0.0056 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9563/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0013 - AUC_ROC: 0.9809 - recall: 0.8491 - precision: 0.9677 - val_loss: 0.0056 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9564/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8491 - precision: 0.9507 - val_loss: 0.0057 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9565/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8774 - precision: 0.8971 - val_loss: 0.0057 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9566/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8522 - precision: 0.9576 - val_loss: 0.0057 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9567/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0016 - AUC_ROC: 0.9824 - recall: 0.8679 - precision: 0.9356 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9568/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8648 - precision: 0.9228 - val_loss: 0.0057 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9569/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0013 - AUC_ROC: 0.9841 - recall: 0.8774 - precision: 0.9394 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9570/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0014 - AUC_ROC: 0.9841 - recall: 0.8553 - precision: 0.9315 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9571/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8553 - precision: 0.9379 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9572/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0012 - AUC_ROC: 0.9873 - recall: 0.8679 - precision: 0.9420 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9573/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0013 - AUC_ROC: 0.9841 - recall: 0.8742 - precision: 0.9329 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9574/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0014 - AUC_ROC: 0.9841 - recall: 0.8899 - precision: 0.9340 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9575/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0014 - AUC_ROC: 0.9841 - recall: 0.8679 - precision: 0.9262 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9576/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8616 - precision: 0.9448 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9577/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0012 - AUC_ROC: 0.9793 - recall: 0.8868 - precision: 0.9559 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9578/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0012 - AUC_ROC: 0.9825 - recall: 0.8868 - precision: 0.9691 - val_loss: 0.0054 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9579/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0013 - AUC_ROC: 0.9841 - recall: 0.8742 - precision: 0.9329 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9580/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0014 - AUC_ROC: 0.9840 - recall: 0.8648 - precision: 0.9354 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9581/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0013 - AUC_ROC: 0.9794 - recall: 0.8616 - precision: 0.9416 - val_loss: 0.0054 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9582/10000\n", + "3/3 [==============================] - 0s 120ms/step - loss: 0.0014 - AUC_ROC: 0.9824 - recall: 0.8742 - precision: 0.9298 - val_loss: 0.0054 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9583/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8553 - precision: 0.9477 - val_loss: 0.0055 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9584/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8553 - precision: 0.9444 - val_loss: 0.0055 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9585/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8616 - precision: 0.9547 - val_loss: 0.0055 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9586/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0013 - AUC_ROC: 0.9888 - recall: 0.8679 - precision: 0.9356 - val_loss: 0.0054 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9587/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0013 - AUC_ROC: 0.9840 - recall: 0.8805 - precision: 0.9396 - val_loss: 0.0054 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9588/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0014 - AUC_ROC: 0.9841 - recall: 0.8679 - precision: 0.9200 - val_loss: 0.0054 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9589/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0013 - AUC_ROC: 0.9888 - recall: 0.8742 - precision: 0.9392 - val_loss: 0.0054 - val_AUC_ROC: 0.8827 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 9590/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0017 - AUC_ROC: 0.9809 - recall: 0.8396 - precision: 0.9207 - val_loss: 0.0054 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 9591/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0013 - AUC_ROC: 0.9825 - recall: 0.8648 - precision: 0.9582 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 9592/10000\n", + "3/3 [==============================] - 0s 98ms/step - loss: 0.0015 - AUC_ROC: 0.9840 - recall: 0.8616 - precision: 0.9073 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 9593/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0014 - AUC_ROC: 0.9904 - recall: 0.8648 - precision: 0.9228 - val_loss: 0.0053 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9594/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0014 - AUC_ROC: 0.9778 - recall: 0.8396 - precision: 0.9674 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9595/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0014 - AUC_ROC: 0.9778 - recall: 0.8774 - precision: 0.9458 - val_loss: 0.0052 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9596/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0014 - AUC_ROC: 0.9793 - recall: 0.8742 - precision: 0.9653 - val_loss: 0.0052 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9597/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0014 - AUC_ROC: 0.9856 - recall: 0.8553 - precision: 0.9315 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9598/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0014 - AUC_ROC: 0.9872 - recall: 0.8711 - precision: 0.9422 - val_loss: 0.0055 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9599/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0015 - AUC_ROC: 0.9840 - recall: 0.8585 - precision: 0.9414 - val_loss: 0.0056 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9600/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0015 - AUC_ROC: 0.9872 - recall: 0.8805 - precision: 0.9211 - val_loss: 0.0056 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9601/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0012 - AUC_ROC: 0.9872 - recall: 0.8648 - precision: 0.9483 - val_loss: 0.0056 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9602/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8459 - precision: 0.9373 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9603/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0014 - AUC_ROC: 0.9761 - recall: 0.8616 - precision: 0.9514 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9604/10000\n", + "3/3 [==============================] - 0s 96ms/step - loss: 0.0013 - AUC_ROC: 0.9856 - recall: 0.8553 - precision: 0.9379 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9605/10000\n", + "3/3 [==============================] - 0s 115ms/step - loss: 0.0014 - AUC_ROC: 0.9841 - recall: 0.8742 - precision: 0.9586 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9606/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0013 - AUC_ROC: 0.9841 - recall: 0.8805 - precision: 0.9396 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9607/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0012 - AUC_ROC: 0.9778 - recall: 0.8742 - precision: 0.9653 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9608/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0012 - AUC_ROC: 0.9825 - recall: 0.8805 - precision: 0.9492 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9609/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8459 - precision: 0.9573 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9610/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0015 - AUC_ROC: 0.9730 - recall: 0.8522 - precision: 0.9509 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9611/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0016 - AUC_ROC: 0.9809 - recall: 0.8522 - precision: 0.9218 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9612/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0013 - AUC_ROC: 0.9825 - recall: 0.8585 - precision: 0.9545 - val_loss: 0.0053 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9613/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0013 - AUC_ROC: 0.9841 - recall: 0.8491 - precision: 0.9541 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9614/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0013 - AUC_ROC: 0.9888 - recall: 0.8774 - precision: 0.9208 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9615/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0013 - AUC_ROC: 0.9825 - recall: 0.8931 - precision: 0.9251 - val_loss: 0.0052 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9616/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8648 - precision: 0.9386 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9617/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0014 - AUC_ROC: 0.9856 - recall: 0.8333 - precision: 0.9532 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9618/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0015 - AUC_ROC: 0.9762 - recall: 0.8428 - precision: 0.9640 - val_loss: 0.0053 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9619/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0015 - AUC_ROC: 0.9825 - recall: 0.8396 - precision: 0.9468 - val_loss: 0.0053 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9620/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0015 - AUC_ROC: 0.9746 - recall: 0.8585 - precision: 0.9349 - val_loss: 0.0053 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9621/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0013 - AUC_ROC: 0.9857 - recall: 0.8679 - precision: 0.9139 - val_loss: 0.0053 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9622/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8585 - precision: 0.9479 - val_loss: 0.0053 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9623/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0016 - AUC_ROC: 0.9746 - recall: 0.8270 - precision: 0.9326 - val_loss: 0.0054 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9624/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0014 - AUC_ROC: 0.9841 - recall: 0.8774 - precision: 0.9238 - val_loss: 0.0055 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9625/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0015 - AUC_ROC: 0.9841 - recall: 0.8491 - precision: 0.9541 - val_loss: 0.0056 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9626/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0014 - AUC_ROC: 0.9778 - recall: 0.8805 - precision: 0.9272 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9627/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0014 - AUC_ROC: 0.9762 - recall: 0.8585 - precision: 0.9414 - val_loss: 0.0056 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9628/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0015 - AUC_ROC: 0.9856 - recall: 0.8522 - precision: 0.9476 - val_loss: 0.0055 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9629/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0013 - AUC_ROC: 0.9857 - recall: 0.8679 - precision: 0.9452 - val_loss: 0.0054 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9630/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0015 - AUC_ROC: 0.9856 - recall: 0.8522 - precision: 0.9377 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9631/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0014 - AUC_ROC: 0.9840 - recall: 0.8679 - precision: 0.9485 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 9632/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0013 - AUC_ROC: 0.9888 - recall: 0.8711 - precision: 0.9454 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 9633/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8679 - precision: 0.9452 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 9634/10000\n", + "3/3 [==============================] - 0s 114ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8428 - precision: 0.9470 - val_loss: 0.0051 - val_AUC_ROC: 0.8888 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 9635/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8333 - precision: 0.9532 - val_loss: 0.0051 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 9636/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8365 - precision: 0.9366 - val_loss: 0.0052 - val_AUC_ROC: 0.8826 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 9637/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0016 - AUC_ROC: 0.9777 - recall: 0.8711 - precision: 0.9358 - val_loss: 0.0053 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9638/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8522 - precision: 0.9542 - val_loss: 0.0054 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9639/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0014 - AUC_ROC: 0.9856 - recall: 0.8836 - precision: 0.9153 - val_loss: 0.0054 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9640/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0015 - AUC_ROC: 0.9825 - recall: 0.8428 - precision: 0.9371 - val_loss: 0.0055 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9641/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8333 - precision: 0.9498 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9642/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8616 - precision: 0.9481 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9643/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0014 - AUC_ROC: 0.9872 - recall: 0.8616 - precision: 0.9133 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9644/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8679 - precision: 0.9452 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9645/10000\n", + "3/3 [==============================] - 0s 98ms/step - loss: 0.0013 - AUC_ROC: 0.9888 - recall: 0.8648 - precision: 0.9418 - val_loss: 0.0056 - val_AUC_ROC: 0.8641 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9646/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0014 - AUC_ROC: 0.9793 - recall: 0.8585 - precision: 0.9479 - val_loss: 0.0056 - val_AUC_ROC: 0.8641 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9647/10000\n", + "3/3 [==============================] - 0s 116ms/step - loss: 0.0013 - AUC_ROC: 0.9857 - recall: 0.8648 - precision: 0.9418 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9648/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0014 - AUC_ROC: 0.9794 - recall: 0.8553 - precision: 0.9252 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9649/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0015 - AUC_ROC: 0.9857 - recall: 0.8585 - precision: 0.9161 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9650/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0015 - AUC_ROC: 0.9904 - recall: 0.8553 - precision: 0.9347 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9651/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0014 - AUC_ROC: 0.9888 - recall: 0.8742 - precision: 0.9145 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9652/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0013 - AUC_ROC: 0.9856 - recall: 0.8836 - precision: 0.9243 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9653/10000\n", + "3/3 [==============================] - 0s 122ms/step - loss: 0.0014 - AUC_ROC: 0.9793 - recall: 0.8648 - precision: 0.9386 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9654/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0014 - AUC_ROC: 0.9793 - recall: 0.8711 - precision: 0.9422 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9655/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0015 - AUC_ROC: 0.9824 - recall: 0.8459 - precision: 0.9406 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9656/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0013 - AUC_ROC: 0.9825 - recall: 0.8679 - precision: 0.9583 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9657/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0015 - AUC_ROC: 0.9841 - recall: 0.8522 - precision: 0.9313 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9658/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0015 - AUC_ROC: 0.9887 - recall: 0.8585 - precision: 0.9130 - val_loss: 0.0054 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9659/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0013 - AUC_ROC: 0.9888 - recall: 0.8711 - precision: 0.9422 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9660/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0013 - AUC_ROC: 0.9778 - recall: 0.8774 - precision: 0.9426 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9661/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0013 - AUC_ROC: 0.9841 - recall: 0.8774 - precision: 0.9588 - val_loss: 0.0053 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9662/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0017 - AUC_ROC: 0.9777 - recall: 0.8585 - precision: 0.9381 - val_loss: 0.0054 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9663/10000\n", + "3/3 [==============================] - 0s 98ms/step - loss: 0.0013 - AUC_ROC: 0.9809 - recall: 0.8711 - precision: 0.9719 - val_loss: 0.0055 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9664/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8396 - precision: 0.9502 - val_loss: 0.0056 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9665/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0014 - AUC_ROC: 0.9840 - recall: 0.8459 - precision: 0.9472 - val_loss: 0.0056 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9666/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8491 - precision: 0.9474 - val_loss: 0.0056 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9667/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0014 - AUC_ROC: 0.9872 - recall: 0.8679 - precision: 0.9231 - val_loss: 0.0056 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9668/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0015 - AUC_ROC: 0.9762 - recall: 0.8459 - precision: 0.9573 - val_loss: 0.0055 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9669/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0015 - AUC_ROC: 0.9872 - recall: 0.8962 - precision: 0.9076 - val_loss: 0.0056 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9670/10000\n", + "3/3 [==============================] - 0s 98ms/step - loss: 0.0014 - AUC_ROC: 0.9872 - recall: 0.8931 - precision: 0.9281 - val_loss: 0.0056 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9671/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0014 - AUC_ROC: 0.9857 - recall: 0.8522 - precision: 0.9443 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9672/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0016 - AUC_ROC: 0.9746 - recall: 0.8553 - precision: 0.9158 - val_loss: 0.0055 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9673/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0015 - AUC_ROC: 0.9857 - recall: 0.8774 - precision: 0.9300 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9674/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0012 - AUC_ROC: 0.9857 - recall: 0.8774 - precision: 0.9362 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9675/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8522 - precision: 0.9542 - val_loss: 0.0054 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9676/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0014 - AUC_ROC: 0.9793 - recall: 0.8459 - precision: 0.9642 - val_loss: 0.0053 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9677/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0014 - AUC_ROC: 0.9762 - recall: 0.8585 - precision: 0.9349 - val_loss: 0.0052 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9678/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0014 - AUC_ROC: 0.9841 - recall: 0.8491 - precision: 0.9215 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9679/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8805 - precision: 0.9180 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9680/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0014 - AUC_ROC: 0.9872 - recall: 0.8868 - precision: 0.9369 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9681/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0014 - AUC_ROC: 0.9888 - recall: 0.8679 - precision: 0.9169 - val_loss: 0.0053 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9682/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8585 - precision: 0.9545 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9683/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0013 - AUC_ROC: 0.9809 - recall: 0.8616 - precision: 0.9580 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9684/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0013 - AUC_ROC: 0.9841 - recall: 0.8553 - precision: 0.9714 - val_loss: 0.0053 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9685/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0013 - AUC_ROC: 0.9825 - recall: 0.8616 - precision: 0.9481 - val_loss: 0.0054 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9686/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0013 - AUC_ROC: 0.9872 - recall: 0.8836 - precision: 0.9525 - val_loss: 0.0054 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9687/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0016 - AUC_ROC: 0.9761 - recall: 0.8491 - precision: 0.9441 - val_loss: 0.0054 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9688/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0012 - AUC_ROC: 0.9825 - recall: 0.8805 - precision: 0.9556 - val_loss: 0.0055 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9689/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0014 - AUC_ROC: 0.9872 - recall: 0.8459 - precision: 0.9406 - val_loss: 0.0054 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9690/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0016 - AUC_ROC: 0.9825 - recall: 0.8616 - precision: 0.9384 - val_loss: 0.0054 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9691/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0015 - AUC_ROC: 0.9841 - recall: 0.8428 - precision: 0.9306 - val_loss: 0.0053 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9692/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0013 - AUC_ROC: 0.9825 - recall: 0.8742 - precision: 0.9521 - val_loss: 0.0053 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9693/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0017 - AUC_ROC: 0.9777 - recall: 0.8553 - precision: 0.9252 - val_loss: 0.0053 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9694/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0012 - AUC_ROC: 0.9888 - recall: 0.8711 - precision: 0.9486 - val_loss: 0.0053 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9695/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0015 - AUC_ROC: 0.9872 - recall: 0.8428 - precision: 0.9273 - val_loss: 0.0054 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9696/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0013 - AUC_ROC: 0.9841 - recall: 0.8679 - precision: 0.9517 - val_loss: 0.0054 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9697/10000\n", + "3/3 [==============================] - 0s 116ms/step - loss: 0.0017 - AUC_ROC: 0.9761 - recall: 0.8459 - precision: 0.9276 - val_loss: 0.0054 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9698/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0015 - AUC_ROC: 0.9762 - recall: 0.8491 - precision: 0.9441 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9699/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0013 - AUC_ROC: 0.9825 - recall: 0.8742 - precision: 0.9392 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9700/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0014 - AUC_ROC: 0.9872 - recall: 0.8774 - precision: 0.9426 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9701/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8522 - precision: 0.9576 - val_loss: 0.0054 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9702/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0015 - AUC_ROC: 0.9840 - recall: 0.8711 - precision: 0.9203 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9703/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0014 - AUC_ROC: 0.9841 - recall: 0.8805 - precision: 0.9459 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9704/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0013 - AUC_ROC: 0.9857 - recall: 0.8805 - precision: 0.9365 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9705/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8491 - precision: 0.9609 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9706/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0012 - AUC_ROC: 0.9888 - recall: 0.8648 - precision: 0.9549 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9707/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0014 - AUC_ROC: 0.9840 - recall: 0.8616 - precision: 0.9384 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9708/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0015 - AUC_ROC: 0.9856 - recall: 0.8711 - precision: 0.9203 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9709/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8679 - precision: 0.9324 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9710/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0013 - AUC_ROC: 0.9841 - recall: 0.8396 - precision: 0.9639 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9711/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0014 - AUC_ROC: 0.9841 - recall: 0.8491 - precision: 0.9474 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9712/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0013 - AUC_ROC: 0.9794 - recall: 0.8742 - precision: 0.9586 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9713/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0015 - AUC_ROC: 0.9872 - recall: 0.8553 - precision: 0.9128 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9714/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0013 - AUC_ROC: 0.9872 - recall: 0.8711 - precision: 0.9390 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9715/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0014 - AUC_ROC: 0.9841 - recall: 0.8396 - precision: 0.9368 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9716/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0014 - AUC_ROC: 0.9872 - recall: 0.8396 - precision: 0.9435 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9717/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8679 - precision: 0.9356 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9718/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0015 - AUC_ROC: 0.9825 - recall: 0.8679 - precision: 0.9293 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9719/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0014 - AUC_ROC: 0.9856 - recall: 0.8459 - precision: 0.9642 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9720/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0014 - AUC_ROC: 0.9840 - recall: 0.8616 - precision: 0.9481 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9721/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0013 - AUC_ROC: 0.9825 - recall: 0.8585 - precision: 0.9479 - val_loss: 0.0055 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9722/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8553 - precision: 0.9412 - val_loss: 0.0055 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9723/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0015 - AUC_ROC: 0.9856 - recall: 0.8365 - precision: 0.9268 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9724/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0015 - AUC_ROC: 0.9825 - recall: 0.8805 - precision: 0.9180 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9725/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0016 - AUC_ROC: 0.9793 - recall: 0.8491 - precision: 0.9153 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9726/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0013 - AUC_ROC: 0.9825 - recall: 0.8805 - precision: 0.9333 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9727/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0014 - AUC_ROC: 0.9856 - recall: 0.8836 - precision: 0.9461 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9728/10000\n", + "3/3 [==============================] - 0s 116ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8553 - precision: 0.9444 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9729/10000\n", + "3/3 [==============================] - 0s 114ms/step - loss: 0.0015 - AUC_ROC: 0.9730 - recall: 0.8648 - precision: 0.9549 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9730/10000\n", + "3/3 [==============================] - 0s 114ms/step - loss: 0.0015 - AUC_ROC: 0.9824 - recall: 0.8585 - precision: 0.9381 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9731/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0014 - AUC_ROC: 0.9841 - recall: 0.8522 - precision: 0.9443 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9732/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0014 - AUC_ROC: 0.9778 - recall: 0.8805 - precision: 0.9556 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9733/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0012 - AUC_ROC: 0.9856 - recall: 0.8836 - precision: 0.9525 - val_loss: 0.0055 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9734/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0015 - AUC_ROC: 0.9825 - recall: 0.8648 - precision: 0.9167 - val_loss: 0.0056 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9735/10000\n", + "3/3 [==============================] - 0s 126ms/step - loss: 0.0015 - AUC_ROC: 0.9761 - recall: 0.8679 - precision: 0.9356 - val_loss: 0.0056 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9736/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8711 - precision: 0.9358 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9737/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0012 - AUC_ROC: 0.9872 - recall: 0.8899 - precision: 0.9561 - val_loss: 0.0057 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9738/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0013 - AUC_ROC: 0.9872 - recall: 0.8679 - precision: 0.9231 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9739/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0015 - AUC_ROC: 0.9794 - recall: 0.8396 - precision: 0.9502 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9740/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0012 - AUC_ROC: 0.9872 - recall: 0.8491 - precision: 0.9507 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9741/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0012 - AUC_ROC: 0.9841 - recall: 0.8774 - precision: 0.9458 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9742/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0014 - AUC_ROC: 0.9856 - recall: 0.8805 - precision: 0.9428 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9743/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8522 - precision: 0.9476 - val_loss: 0.0055 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9744/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0014 - AUC_ROC: 0.9840 - recall: 0.8616 - precision: 0.9448 - val_loss: 0.0055 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9745/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8270 - precision: 0.9564 - val_loss: 0.0055 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9746/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0013 - AUC_ROC: 0.9841 - recall: 0.8868 - precision: 0.9156 - val_loss: 0.0055 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9747/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0013 - AUC_ROC: 0.9904 - recall: 0.8774 - precision: 0.9394 - val_loss: 0.0055 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9748/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0012 - AUC_ROC: 0.9841 - recall: 0.8899 - precision: 0.9159 - val_loss: 0.0055 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9749/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8742 - precision: 0.9236 - val_loss: 0.0055 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9750/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0013 - AUC_ROC: 0.9809 - recall: 0.8742 - precision: 0.9553 - val_loss: 0.0056 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9751/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0013 - AUC_ROC: 0.9793 - recall: 0.8899 - precision: 0.9626 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9752/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0014 - AUC_ROC: 0.9856 - recall: 0.8302 - precision: 0.9429 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9753/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0013 - AUC_ROC: 0.9841 - recall: 0.8333 - precision: 0.9431 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9754/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0013 - AUC_ROC: 0.9841 - recall: 0.8711 - precision: 0.9552 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9755/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0013 - AUC_ROC: 0.9856 - recall: 0.8585 - precision: 0.9647 - val_loss: 0.0057 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9756/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0015 - AUC_ROC: 0.9888 - recall: 0.8553 - precision: 0.9067 - val_loss: 0.0057 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9757/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0013 - AUC_ROC: 0.9825 - recall: 0.8774 - precision: 0.9394 - val_loss: 0.0057 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9758/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0012 - AUC_ROC: 0.9841 - recall: 0.8585 - precision: 0.9579 - val_loss: 0.0058 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9759/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8396 - precision: 0.9468 - val_loss: 0.0058 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9760/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0014 - AUC_ROC: 0.9778 - recall: 0.8585 - precision: 0.9414 - val_loss: 0.0058 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9761/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0013 - AUC_ROC: 0.9904 - recall: 0.8931 - precision: 0.9281 - val_loss: 0.0058 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9762/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0014 - AUC_ROC: 0.9762 - recall: 0.8616 - precision: 0.9320 - val_loss: 0.0058 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9763/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8679 - precision: 0.9517 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9764/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8428 - precision: 0.9504 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9765/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8648 - precision: 0.9483 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9766/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8365 - precision: 0.9301 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9767/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8491 - precision: 0.9247 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9768/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8491 - precision: 0.9441 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9769/10000\n", + "3/3 [==============================] - 0s 98ms/step - loss: 0.0014 - AUC_ROC: 0.9840 - recall: 0.8679 - precision: 0.9420 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9770/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0013 - AUC_ROC: 0.9888 - recall: 0.8679 - precision: 0.9452 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9771/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0014 - AUC_ROC: 0.9857 - recall: 0.8899 - precision: 0.9159 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9772/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0015 - AUC_ROC: 0.9841 - recall: 0.8805 - precision: 0.9302 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9773/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8553 - precision: 0.9611 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9774/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0016 - AUC_ROC: 0.9762 - recall: 0.8459 - precision: 0.9607 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9775/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0016 - AUC_ROC: 0.9731 - recall: 0.8679 - precision: 0.9420 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9776/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0013 - AUC_ROC: 0.9841 - recall: 0.8302 - precision: 0.9395 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9777/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0014 - AUC_ROC: 0.9872 - recall: 0.8522 - precision: 0.9377 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9778/10000\n", + "3/3 [==============================] - 0s 115ms/step - loss: 0.0015 - AUC_ROC: 0.9857 - recall: 0.8742 - precision: 0.9236 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9779/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0015 - AUC_ROC: 0.9840 - recall: 0.8553 - precision: 0.9315 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9780/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8648 - precision: 0.9450 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9781/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0013 - AUC_ROC: 0.9856 - recall: 0.8742 - precision: 0.9392 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9782/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0013 - AUC_ROC: 0.9841 - recall: 0.8679 - precision: 0.9452 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9783/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0014 - AUC_ROC: 0.9840 - recall: 0.8836 - precision: 0.9274 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9784/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0013 - AUC_ROC: 0.9872 - recall: 0.8836 - precision: 0.9065 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9785/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0014 - AUC_ROC: 0.9872 - recall: 0.8679 - precision: 0.9324 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9786/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0015 - AUC_ROC: 0.9777 - recall: 0.8302 - precision: 0.9395 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9787/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0014 - AUC_ROC: 0.9841 - recall: 0.8836 - precision: 0.9623 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9788/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0014 - AUC_ROC: 0.9857 - recall: 0.8616 - precision: 0.9320 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9789/10000\n", + "3/3 [==============================] - 0s 117ms/step - loss: 0.0015 - AUC_ROC: 0.9840 - recall: 0.8585 - precision: 0.9286 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9790/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0013 - AUC_ROC: 0.9857 - recall: 0.8742 - precision: 0.9360 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9791/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0013 - AUC_ROC: 0.9841 - recall: 0.8962 - precision: 0.9344 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9792/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0013 - AUC_ROC: 0.9825 - recall: 0.8648 - precision: 0.9450 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9793/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0015 - AUC_ROC: 0.9825 - recall: 0.8365 - precision: 0.9500 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9794/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8616 - precision: 0.9547 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9795/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0013 - AUC_ROC: 0.9841 - recall: 0.8836 - precision: 0.9430 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9796/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0014 - AUC_ROC: 0.9824 - recall: 0.8648 - precision: 0.9450 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9797/10000\n", + "3/3 [==============================] - 0s 122ms/step - loss: 0.0013 - AUC_ROC: 0.9872 - recall: 0.8648 - precision: 0.9450 - val_loss: 0.0055 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9798/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0013 - AUC_ROC: 0.9794 - recall: 0.8553 - precision: 0.9544 - val_loss: 0.0055 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9799/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0013 - AUC_ROC: 0.9872 - recall: 0.8742 - precision: 0.9360 - val_loss: 0.0056 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9800/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0012 - AUC_ROC: 0.9920 - recall: 0.8931 - precision: 0.9342 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9801/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8648 - precision: 0.9516 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9802/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0013 - AUC_ROC: 0.9872 - recall: 0.8679 - precision: 0.9617 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9803/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0013 - AUC_ROC: 0.9857 - recall: 0.8742 - precision: 0.9236 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9804/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0013 - AUC_ROC: 0.9856 - recall: 0.8459 - precision: 0.9472 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9805/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0013 - AUC_ROC: 0.9888 - recall: 0.8553 - precision: 0.9252 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9806/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0012 - AUC_ROC: 0.9920 - recall: 0.8711 - precision: 0.9264 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9807/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0013 - AUC_ROC: 0.9872 - recall: 0.8491 - precision: 0.9441 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9808/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0013 - AUC_ROC: 0.9888 - recall: 0.8491 - precision: 0.9408 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9809/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8774 - precision: 0.9269 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9810/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0012 - AUC_ROC: 0.9825 - recall: 0.8899 - precision: 0.9561 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9811/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0014 - AUC_ROC: 0.9841 - recall: 0.8931 - precision: 0.9342 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9812/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0013 - AUC_ROC: 0.9825 - recall: 0.8491 - precision: 0.9541 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9813/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0014 - AUC_ROC: 0.9840 - recall: 0.8868 - precision: 0.9186 - val_loss: 0.0058 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9814/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0014 - AUC_ROC: 0.9794 - recall: 0.8648 - precision: 0.9418 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9815/10000\n", + "3/3 [==============================] - 0s 114ms/step - loss: 0.0014 - AUC_ROC: 0.9856 - recall: 0.8428 - precision: 0.9306 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9816/10000\n", + "3/3 [==============================] - 0s 114ms/step - loss: 0.0013 - AUC_ROC: 0.9825 - recall: 0.8522 - precision: 0.9610 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9817/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8805 - precision: 0.9333 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9818/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0012 - AUC_ROC: 0.9841 - recall: 0.8931 - precision: 0.9498 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9819/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0013 - AUC_ROC: 0.9825 - recall: 0.8711 - precision: 0.9519 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9820/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8553 - precision: 0.9412 - val_loss: 0.0056 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9821/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0013 - AUC_ROC: 0.9888 - recall: 0.8742 - precision: 0.9392 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9822/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0014 - AUC_ROC: 0.9840 - recall: 0.8679 - precision: 0.9356 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9823/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0012 - AUC_ROC: 0.9935 - recall: 0.8774 - precision: 0.9269 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9824/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0014 - AUC_ROC: 0.9841 - recall: 0.8396 - precision: 0.9536 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9825/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0013 - AUC_ROC: 0.9872 - recall: 0.8428 - precision: 0.9404 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9826/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0013 - AUC_ROC: 0.9857 - recall: 0.8774 - precision: 0.9394 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9827/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0014 - AUC_ROC: 0.9778 - recall: 0.8428 - precision: 0.9470 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9828/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0015 - AUC_ROC: 0.9825 - recall: 0.8585 - precision: 0.9381 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9829/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0013 - AUC_ROC: 0.9841 - recall: 0.8742 - precision: 0.9653 - val_loss: 0.0057 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9830/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8836 - precision: 0.9525 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9831/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8648 - precision: 0.9291 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9832/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0011 - AUC_ROC: 0.9873 - recall: 0.8962 - precision: 0.9500 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9833/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0013 - AUC_ROC: 0.9841 - recall: 0.8742 - precision: 0.9360 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9834/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0014 - AUC_ROC: 0.9746 - recall: 0.8805 - precision: 0.9524 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9835/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0013 - AUC_ROC: 0.9825 - recall: 0.8711 - precision: 0.9422 - val_loss: 0.0058 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9836/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0015 - AUC_ROC: 0.9840 - recall: 0.8428 - precision: 0.9338 - val_loss: 0.0058 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9837/10000\n", + "3/3 [==============================] - 0s 117ms/step - loss: 0.0013 - AUC_ROC: 0.9841 - recall: 0.8805 - precision: 0.9589 - val_loss: 0.0058 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9838/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0013 - AUC_ROC: 0.9841 - recall: 0.8805 - precision: 0.9396 - val_loss: 0.0058 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9839/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0015 - AUC_ROC: 0.9730 - recall: 0.8553 - precision: 0.9611 - val_loss: 0.0058 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9840/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0013 - AUC_ROC: 0.9856 - recall: 0.8899 - precision: 0.9497 - val_loss: 0.0058 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9841/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0012 - AUC_ROC: 0.9873 - recall: 0.8868 - precision: 0.9369 - val_loss: 0.0058 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9842/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0014 - AUC_ROC: 0.9731 - recall: 0.8553 - precision: 0.9283 - val_loss: 0.0058 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9843/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0012 - AUC_ROC: 0.9856 - recall: 0.8774 - precision: 0.9522 - val_loss: 0.0058 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9844/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0015 - AUC_ROC: 0.9762 - recall: 0.8585 - precision: 0.9414 - val_loss: 0.0058 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9845/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0013 - AUC_ROC: 0.9762 - recall: 0.8679 - precision: 0.9718 - val_loss: 0.0058 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9846/10000\n", + "3/3 [==============================] - 0s 114ms/step - loss: 0.0013 - AUC_ROC: 0.9825 - recall: 0.8585 - precision: 0.9613 - val_loss: 0.0058 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9847/10000\n", + "3/3 [==============================] - 0s 127ms/step - loss: 0.0014 - AUC_ROC: 0.9841 - recall: 0.8553 - precision: 0.9379 - val_loss: 0.0058 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9848/10000\n", + "3/3 [==============================] - 0s 135ms/step - loss: 0.0015 - AUC_ROC: 0.9840 - recall: 0.8742 - precision: 0.9145 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9849/10000\n", + "3/3 [==============================] - 0s 133ms/step - loss: 0.0013 - AUC_ROC: 0.9888 - recall: 0.8774 - precision: 0.9300 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9850/10000\n", + "3/3 [==============================] - 0s 120ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8585 - precision: 0.9446 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9851/10000\n", + "3/3 [==============================] - 0s 147ms/step - loss: 0.0011 - AUC_ROC: 0.9935 - recall: 0.8679 - precision: 0.9550 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9852/10000\n", + "3/3 [==============================] - 0s 146ms/step - loss: 0.0012 - AUC_ROC: 0.9888 - recall: 0.8711 - precision: 0.9454 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9853/10000\n", + "3/3 [==============================] - 0s 142ms/step - loss: 0.0014 - AUC_ROC: 0.9856 - recall: 0.8585 - precision: 0.9479 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9854/10000\n", + "3/3 [==============================] - 0s 150ms/step - loss: 0.0014 - AUC_ROC: 0.9903 - recall: 0.8899 - precision: 0.8927 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9855/10000\n", + "3/3 [==============================] - 0s 143ms/step - loss: 0.0012 - AUC_ROC: 0.9888 - recall: 0.8899 - precision: 0.9465 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9856/10000\n", + "3/3 [==============================] - 0s 129ms/step - loss: 0.0015 - AUC_ROC: 0.9778 - recall: 0.8459 - precision: 0.9439 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9857/10000\n", + "3/3 [==============================] - 0s 135ms/step - loss: 0.0013 - AUC_ROC: 0.9825 - recall: 0.8428 - precision: 0.9571 - val_loss: 0.0055 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9858/10000\n", + "3/3 [==============================] - 0s 138ms/step - loss: 0.0015 - AUC_ROC: 0.9872 - recall: 0.8396 - precision: 0.9303 - val_loss: 0.0056 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9859/10000\n", + "3/3 [==============================] - 0s 140ms/step - loss: 0.0015 - AUC_ROC: 0.9872 - recall: 0.8616 - precision: 0.8726 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9860/10000\n", + "3/3 [==============================] - 0s 149ms/step - loss: 0.0014 - AUC_ROC: 0.9871 - recall: 0.8491 - precision: 0.9310 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9861/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0014 - AUC_ROC: 0.9856 - recall: 0.8585 - precision: 0.9414 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9862/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0013 - AUC_ROC: 0.9841 - recall: 0.8616 - precision: 0.9514 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9863/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0014 - AUC_ROC: 0.9872 - recall: 0.8553 - precision: 0.9252 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9864/10000\n", + "3/3 [==============================] - 0s 121ms/step - loss: 0.0015 - AUC_ROC: 0.9825 - recall: 0.8648 - precision: 0.9259 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9865/10000\n", + "3/3 [==============================] - 0s 118ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8553 - precision: 0.9444 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9866/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0014 - AUC_ROC: 0.9777 - recall: 0.8553 - precision: 0.9544 - val_loss: 0.0055 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9867/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0013 - AUC_ROC: 0.9825 - recall: 0.8648 - precision: 0.9516 - val_loss: 0.0055 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9868/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0015 - AUC_ROC: 0.9746 - recall: 0.8711 - precision: 0.9486 - val_loss: 0.0055 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9869/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0015 - AUC_ROC: 0.9840 - recall: 0.8553 - precision: 0.9315 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9870/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0015 - AUC_ROC: 0.9872 - recall: 0.8270 - precision: 0.9132 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9871/10000\n", + "3/3 [==============================] - 0s 116ms/step - loss: 0.0013 - AUC_ROC: 0.9857 - recall: 0.8616 - precision: 0.9580 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9872/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8805 - precision: 0.9365 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9873/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8679 - precision: 0.9485 - val_loss: 0.0057 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9874/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0015 - AUC_ROC: 0.9840 - recall: 0.8365 - precision: 0.9301 - val_loss: 0.0057 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9875/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0014 - AUC_ROC: 0.9872 - recall: 0.8585 - precision: 0.9512 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9876/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8774 - precision: 0.9522 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9877/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0015 - AUC_ROC: 0.9825 - recall: 0.8805 - precision: 0.9396 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9878/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8365 - precision: 0.9534 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9879/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0013 - AUC_ROC: 0.9904 - recall: 0.8648 - precision: 0.9322 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9880/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0013 - AUC_ROC: 0.9872 - recall: 0.8648 - precision: 0.9516 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9881/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0015 - AUC_ROC: 0.9856 - recall: 0.8365 - precision: 0.9399 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9882/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0013 - AUC_ROC: 0.9857 - recall: 0.8648 - precision: 0.9450 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9883/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0014 - AUC_ROC: 0.9841 - recall: 0.8553 - precision: 0.9252 - val_loss: 0.0055 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9884/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0014 - AUC_ROC: 0.9856 - recall: 0.8553 - precision: 0.9189 - val_loss: 0.0055 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9885/10000\n", + "3/3 [==============================] - 0s 118ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8774 - precision: 0.9148 - val_loss: 0.0055 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9886/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0013 - AUC_ROC: 0.9825 - recall: 0.8742 - precision: 0.9488 - val_loss: 0.0054 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9887/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0016 - AUC_ROC: 0.9809 - recall: 0.8805 - precision: 0.9333 - val_loss: 0.0054 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9888/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8648 - precision: 0.9483 - val_loss: 0.0054 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9889/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0015 - AUC_ROC: 0.9794 - recall: 0.8522 - precision: 0.9377 - val_loss: 0.0053 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9890/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0013 - AUC_ROC: 0.9809 - recall: 0.8805 - precision: 0.9396 - val_loss: 0.0053 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9891/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0013 - AUC_ROC: 0.9888 - recall: 0.8648 - precision: 0.9197 - val_loss: 0.0053 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9892/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8428 - precision: 0.9537 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9893/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0011 - AUC_ROC: 0.9888 - recall: 0.8962 - precision: 0.9375 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9894/10000\n", + "3/3 [==============================] - 0s 113ms/step - loss: 0.0012 - AUC_ROC: 0.9841 - recall: 0.8679 - precision: 0.9485 - val_loss: 0.0052 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9895/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0012 - AUC_ROC: 0.9873 - recall: 0.8742 - precision: 0.9456 - val_loss: 0.0053 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9896/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0013 - AUC_ROC: 0.9841 - recall: 0.8742 - precision: 0.9360 - val_loss: 0.0054 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9897/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0016 - AUC_ROC: 0.9809 - recall: 0.8616 - precision: 0.9416 - val_loss: 0.0055 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9898/10000\n", + "3/3 [==============================] - 0s 114ms/step - loss: 0.0015 - AUC_ROC: 0.9761 - recall: 0.8491 - precision: 0.9609 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9899/10000\n", + "3/3 [==============================] - 0s 117ms/step - loss: 0.0013 - AUC_ROC: 0.9825 - recall: 0.8648 - precision: 0.9649 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9900/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0012 - AUC_ROC: 0.9888 - recall: 0.8931 - precision: 0.9660 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9901/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0012 - AUC_ROC: 0.9872 - recall: 0.8868 - precision: 0.9186 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9902/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0013 - AUC_ROC: 0.9825 - recall: 0.8836 - precision: 0.9558 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9903/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0014 - AUC_ROC: 0.9778 - recall: 0.8553 - precision: 0.9477 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9904/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0014 - AUC_ROC: 0.9872 - recall: 0.8459 - precision: 0.9539 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9905/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0015 - AUC_ROC: 0.9856 - recall: 0.8648 - precision: 0.9106 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9906/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8585 - precision: 0.9349 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9907/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0012 - AUC_ROC: 0.9857 - recall: 0.8742 - precision: 0.9619 - val_loss: 0.0058 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9908/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0015 - AUC_ROC: 0.9762 - recall: 0.8522 - precision: 0.9281 - val_loss: 0.0058 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9909/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8805 - precision: 0.9524 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9910/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0014 - AUC_ROC: 0.9840 - recall: 0.8679 - precision: 0.9517 - val_loss: 0.0057 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9911/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0014 - AUC_ROC: 0.9793 - recall: 0.8616 - precision: 0.9514 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9912/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0013 - AUC_ROC: 0.9888 - recall: 0.8711 - precision: 0.9327 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9913/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0013 - AUC_ROC: 0.9857 - recall: 0.8648 - precision: 0.9386 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9914/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0015 - AUC_ROC: 0.9762 - recall: 0.8553 - precision: 0.9283 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9915/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0014 - AUC_ROC: 0.9761 - recall: 0.8742 - precision: 0.9456 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9916/10000\n", + "3/3 [==============================] - 0s 114ms/step - loss: 0.0014 - AUC_ROC: 0.9841 - recall: 0.8648 - precision: 0.9354 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9917/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0014 - AUC_ROC: 0.9856 - recall: 0.8742 - precision: 0.9488 - val_loss: 0.0054 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9918/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0013 - AUC_ROC: 0.9857 - recall: 0.8836 - precision: 0.9461 - val_loss: 0.0054 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9919/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8836 - precision: 0.9305 - val_loss: 0.0055 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9920/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0013 - AUC_ROC: 0.9857 - recall: 0.8805 - precision: 0.9428 - val_loss: 0.0055 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9921/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0013 - AUC_ROC: 0.9857 - recall: 0.8931 - precision: 0.9435 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9922/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0013 - AUC_ROC: 0.9872 - recall: 0.8805 - precision: 0.9396 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9923/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0014 - AUC_ROC: 0.9794 - recall: 0.8553 - precision: 0.9510 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9924/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8711 - precision: 0.9390 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9925/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0014 - AUC_ROC: 0.9793 - recall: 0.8742 - precision: 0.9392 - val_loss: 0.0055 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9926/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0014 - AUC_ROC: 0.9793 - recall: 0.8679 - precision: 0.9356 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9927/10000\n", + "3/3 [==============================] - 0s 116ms/step - loss: 0.0015 - AUC_ROC: 0.9762 - recall: 0.8459 - precision: 0.9539 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9928/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0015 - AUC_ROC: 0.9746 - recall: 0.8553 - precision: 0.9611 - val_loss: 0.0055 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9929/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8774 - precision: 0.9522 - val_loss: 0.0055 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9930/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0014 - AUC_ROC: 0.9856 - recall: 0.8616 - precision: 0.9320 - val_loss: 0.0053 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9931/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0013 - AUC_ROC: 0.9856 - recall: 0.8836 - precision: 0.9430 - val_loss: 0.0052 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9932/10000\n", + "3/3 [==============================] - 0s 99ms/step - loss: 0.0013 - AUC_ROC: 0.9809 - recall: 0.8931 - precision: 0.9530 - val_loss: 0.0052 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 9933/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0012 - AUC_ROC: 0.9809 - recall: 0.8899 - precision: 0.9593 - val_loss: 0.0052 - val_AUC_ROC: 0.8950 - val_recall: 0.7407 - val_precision: 0.9524\n", + "Epoch 9934/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0013 - AUC_ROC: 0.9888 - recall: 0.8868 - precision: 0.9400 - val_loss: 0.0052 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9935/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8648 - precision: 0.9516 - val_loss: 0.0053 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9936/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0015 - AUC_ROC: 0.9746 - recall: 0.8679 - precision: 0.9485 - val_loss: 0.0053 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9937/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0014 - AUC_ROC: 0.9872 - recall: 0.8553 - precision: 0.9477 - val_loss: 0.0054 - val_AUC_ROC: 0.8888 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9938/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0013 - AUC_ROC: 0.9857 - recall: 0.8522 - precision: 0.9509 - val_loss: 0.0054 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9939/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0011 - AUC_ROC: 0.9920 - recall: 0.8679 - precision: 0.9550 - val_loss: 0.0055 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9940/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0012 - AUC_ROC: 0.9904 - recall: 0.8899 - precision: 0.9340 - val_loss: 0.0055 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9941/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0018 - AUC_ROC: 0.9745 - recall: 0.8145 - precision: 0.9317 - val_loss: 0.0055 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9942/10000\n", + "3/3 [==============================] - 0s 98ms/step - loss: 0.0017 - AUC_ROC: 0.9825 - recall: 0.8302 - precision: 0.9429 - val_loss: 0.0054 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9943/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0015 - AUC_ROC: 0.9856 - recall: 0.8585 - precision: 0.9100 - val_loss: 0.0054 - val_AUC_ROC: 0.8950 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9944/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0015 - AUC_ROC: 0.9746 - recall: 0.8585 - precision: 0.9381 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9945/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0013 - AUC_ROC: 0.9841 - recall: 0.8711 - precision: 0.9454 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7160 - val_precision: 0.9508\n", + "Epoch 9946/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0014 - AUC_ROC: 0.9794 - recall: 0.8868 - precision: 0.9338 - val_loss: 0.0058 - val_AUC_ROC: 0.8703 - val_recall: 0.7160 - val_precision: 0.9508\n", + "Epoch 9947/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0015 - AUC_ROC: 0.9762 - recall: 0.8459 - precision: 0.9406 - val_loss: 0.0058 - val_AUC_ROC: 0.8703 - val_recall: 0.7160 - val_precision: 0.9508\n", + "Epoch 9948/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0013 - AUC_ROC: 0.9857 - recall: 0.8836 - precision: 0.9274 - val_loss: 0.0059 - val_AUC_ROC: 0.8703 - val_recall: 0.7160 - val_precision: 0.9508\n", + "Epoch 9949/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0013 - AUC_ROC: 0.9825 - recall: 0.8742 - precision: 0.9392 - val_loss: 0.0059 - val_AUC_ROC: 0.8703 - val_recall: 0.7160 - val_precision: 0.9508\n", + "Epoch 9950/10000\n", + "3/3 [==============================] - 0s 111ms/step - loss: 0.0012 - AUC_ROC: 0.9888 - recall: 0.8774 - precision: 0.9588 - val_loss: 0.0058 - val_AUC_ROC: 0.8703 - val_recall: 0.7160 - val_precision: 0.9508\n", + "Epoch 9951/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0014 - AUC_ROC: 0.9841 - recall: 0.8616 - precision: 0.9481 - val_loss: 0.0058 - val_AUC_ROC: 0.8703 - val_recall: 0.7160 - val_precision: 0.9667\n", + "Epoch 9952/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8616 - precision: 0.9416 - val_loss: 0.0057 - val_AUC_ROC: 0.8765 - val_recall: 0.7160 - val_precision: 0.9667\n", + "Epoch 9953/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8522 - precision: 0.9410 - val_loss: 0.0057 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9954/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8711 - precision: 0.9358 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9955/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0012 - AUC_ROC: 0.9825 - recall: 0.8962 - precision: 0.9500 - val_loss: 0.0056 - val_AUC_ROC: 0.8827 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9956/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0014 - AUC_ROC: 0.9856 - recall: 0.8459 - precision: 0.9439 - val_loss: 0.0055 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9957/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0014 - AUC_ROC: 0.9872 - recall: 0.8616 - precision: 0.9416 - val_loss: 0.0055 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9958/10000\n", + "3/3 [==============================] - 0s 98ms/step - loss: 0.0013 - AUC_ROC: 0.9872 - recall: 0.8679 - precision: 0.9324 - val_loss: 0.0055 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9959/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0013 - AUC_ROC: 0.9888 - recall: 0.8522 - precision: 0.9542 - val_loss: 0.0056 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9960/10000\n", + "3/3 [==============================] - 0s 110ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8553 - precision: 0.9544 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9961/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8585 - precision: 0.9479 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9962/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8553 - precision: 0.9379 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9963/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8805 - precision: 0.9396 - val_loss: 0.0058 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9964/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0016 - AUC_ROC: 0.9825 - recall: 0.8553 - precision: 0.9347 - val_loss: 0.0058 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9965/10000\n", + "3/3 [==============================] - 0s 120ms/step - loss: 0.0013 - AUC_ROC: 0.9825 - recall: 0.8774 - precision: 0.9490 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9966/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0012 - AUC_ROC: 0.9904 - recall: 0.8805 - precision: 0.9524 - val_loss: 0.0057 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9967/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0015 - AUC_ROC: 0.9841 - recall: 0.8648 - precision: 0.9418 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9968/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0014 - AUC_ROC: 0.9841 - recall: 0.8774 - precision: 0.9269 - val_loss: 0.0056 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9969/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0016 - AUC_ROC: 0.9777 - recall: 0.8553 - precision: 0.9315 - val_loss: 0.0056 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9970/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0012 - AUC_ROC: 0.9857 - recall: 0.8836 - precision: 0.9525 - val_loss: 0.0056 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9971/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0013 - AUC_ROC: 0.9856 - recall: 0.8711 - precision: 0.9203 - val_loss: 0.0056 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9972/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8522 - precision: 0.9542 - val_loss: 0.0056 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9973/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0013 - AUC_ROC: 0.9888 - recall: 0.8742 - precision: 0.9392 - val_loss: 0.0056 - val_AUC_ROC: 0.8826 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9974/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0013 - AUC_ROC: 0.9778 - recall: 0.8805 - precision: 0.9459 - val_loss: 0.0056 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9975/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0013 - AUC_ROC: 0.9841 - recall: 0.8679 - precision: 0.9293 - val_loss: 0.0056 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9976/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0013 - AUC_ROC: 0.9857 - recall: 0.8711 - precision: 0.9422 - val_loss: 0.0056 - val_AUC_ROC: 0.8765 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9977/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0015 - AUC_ROC: 0.9715 - recall: 0.8491 - precision: 0.9609 - val_loss: 0.0056 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9978/10000\n", + "3/3 [==============================] - 0s 100ms/step - loss: 0.0013 - AUC_ROC: 0.9825 - recall: 0.8742 - precision: 0.9553 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9979/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0013 - AUC_ROC: 0.9857 - recall: 0.8679 - precision: 0.9517 - val_loss: 0.0058 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9980/10000\n", + "3/3 [==============================] - 0s 112ms/step - loss: 0.0013 - AUC_ROC: 0.9919 - recall: 0.8868 - precision: 0.9369 - val_loss: 0.0058 - val_AUC_ROC: 0.8641 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9981/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0013 - AUC_ROC: 0.9872 - recall: 0.9057 - precision: 0.9260 - val_loss: 0.0058 - val_AUC_ROC: 0.8641 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9982/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0013 - AUC_ROC: 0.9903 - recall: 0.8868 - precision: 0.9156 - val_loss: 0.0058 - val_AUC_ROC: 0.8641 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9983/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0013 - AUC_ROC: 0.9856 - recall: 0.8711 - precision: 0.9618 - val_loss: 0.0058 - val_AUC_ROC: 0.8641 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9984/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0014 - AUC_ROC: 0.9841 - recall: 0.8491 - precision: 0.9343 - val_loss: 0.0058 - val_AUC_ROC: 0.8641 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9985/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0014 - AUC_ROC: 0.9888 - recall: 0.8648 - precision: 0.9291 - val_loss: 0.0058 - val_AUC_ROC: 0.8641 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9986/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0014 - AUC_ROC: 0.9841 - recall: 0.8459 - precision: 0.9244 - val_loss: 0.0058 - val_AUC_ROC: 0.8641 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9987/10000\n", + "3/3 [==============================] - 0s 105ms/step - loss: 0.0014 - AUC_ROC: 0.9809 - recall: 0.8585 - precision: 0.9579 - val_loss: 0.0057 - val_AUC_ROC: 0.8641 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9988/10000\n", + "3/3 [==============================] - 0s 109ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8428 - precision: 0.9371 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9989/10000\n", + "3/3 [==============================] - 0s 106ms/step - loss: 0.0012 - AUC_ROC: 0.9920 - recall: 0.8742 - precision: 0.9488 - val_loss: 0.0057 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9990/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0012 - AUC_ROC: 0.9857 - recall: 0.8962 - precision: 0.9437 - val_loss: 0.0057 - val_AUC_ROC: 0.8764 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9991/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0012 - AUC_ROC: 0.9810 - recall: 0.8742 - precision: 0.9586 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9516\n", + "Epoch 9992/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0012 - AUC_ROC: 0.9936 - recall: 0.8742 - precision: 0.9115 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9993/10000\n", + "3/3 [==============================] - 0s 108ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8648 - precision: 0.9386 - val_loss: 0.0058 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9994/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8585 - precision: 0.9317 - val_loss: 0.0058 - val_AUC_ROC: 0.8641 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9995/10000\n", + "3/3 [==============================] - 0s 107ms/step - loss: 0.0015 - AUC_ROC: 0.9824 - recall: 0.8711 - precision: 0.9203 - val_loss: 0.0058 - val_AUC_ROC: 0.8641 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9996/10000\n", + "3/3 [==============================] - 0s 101ms/step - loss: 0.0015 - AUC_ROC: 0.9793 - recall: 0.8805 - precision: 0.9556 - val_loss: 0.0058 - val_AUC_ROC: 0.8641 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9997/10000\n", + "3/3 [==============================] - 0s 104ms/step - loss: 0.0015 - AUC_ROC: 0.9809 - recall: 0.8774 - precision: 0.9269 - val_loss: 0.0058 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9998/10000\n", + "3/3 [==============================] - 0s 102ms/step - loss: 0.0016 - AUC_ROC: 0.9746 - recall: 0.8302 - precision: 0.9429 - val_loss: 0.0058 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 9999/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0014 - AUC_ROC: 0.9904 - recall: 0.8679 - precision: 0.9452 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n", + "Epoch 10000/10000\n", + "3/3 [==============================] - 0s 103ms/step - loss: 0.0014 - AUC_ROC: 0.9825 - recall: 0.8868 - precision: 0.9369 - val_loss: 0.0057 - val_AUC_ROC: 0.8703 - val_recall: 0.7284 - val_precision: 0.9672\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Model Performance Check on Test Data: Loss, AUC_ROC, Precision, Recall\n", + "score = model.evaluate(x_test, y_test)\n", + "print(score)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "7dmMkfS94eBS", + "outputId": "eff9c139-fc80-4c94-87cb-8c36b4e4835c" + }, + "execution_count": 27, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1781/1781 [==============================] - 7s 4ms/step - loss: 0.0031 - AUC_ROC: 0.9301 - recall: 0.8065 - precision: 0.9494\n", + "[0.0031251548789441586, 0.9300675988197327, 0.8064516186714172, 0.949367105960846]\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Training Visualization" + ], + "metadata": { + "id": "hpnfOBS41-Ov" + } + }, + { + "cell_type": "code", + "source": [ + "plt.figure(figsize = (30,15))\n", + "\n", + "plt.subplot(3,1,1)\n", + "plt.plot(history.history[\"loss\"], label = \"Loss\")\n", + "plt.plot(history.history[\"val_loss\"], label = \"Val_Loss\")\n", + "plt.title(\"Training Loss & Validation Loss\")\n", + "plt.xlabel('epoch')\n", + "plt.ylabel('loss')\n", + "plt.legend()\n", + "\n", + "plt.subplot(3,1,2)\n", + "plt.plot(history.history[\"precision\"], label = \"Precision\")\n", + "plt.plot(history.history[\"val_precision\"], label = \"Val_Precision\")\n", + "plt.title(\"Training Precision & Validate Precision\")\n", + "plt.legend()\n", + "\n", + "plt.subplot(3,1,3)\n", + "plt.plot(history.history[\"recall\"], label = \"Recall\")\n", + "plt.plot(history.history[\"val_recall\"], label = \"Val_Recall\")\n", + "plt.title(\"Training Recall & Validate Recall\")\n", + "plt.legend()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 573 + }, + "id": "PPLmH3e30rY0", + "outputId": "f8296800-23fc-40cd-e23b-cbce4454c878" + }, + "execution_count": 28, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 28 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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+ }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## DNN Performance" + ], + "metadata": { + "id": "--Tyu5WkELB9" + } + }, + { + "cell_type": "code", + "source": [ + "from sklearn.metrics import accuracy_score, classification_report, f1_score\n", + "# Threshold sent to 0.5\n", + "Dnn_predict = tf.cast( model.predict(x_test) > 0.5, dtype = tf.float32)\n", + "print(\"Test Report = \\n\", classification_report(Dnn_predict, y_test))" + ], + "metadata": { + "id": "znb1PvSzEUGn", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "6732a0ee-85bc-43f2-ba5f-384d369fc888" + }, + "execution_count": 30, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1781/1781 [==============================] - 4s 2ms/step\n", + "Test Report = \n", + " precision recall f1-score support\n", + "\n", + " 0.0 1.00 1.00 1.00 56883\n", + " 1.0 0.81 0.95 0.87 79\n", + "\n", + " accuracy 1.00 56962\n", + " macro avg 0.90 0.97 0.94 56962\n", + "weighted avg 1.00 1.00 1.00 56962\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Comparison with other Machine Learning Methods" + ], + "metadata": { + "id": "DYLKSPxjEwEK" + } + }, + { + "cell_type": "code", + "source": [ + "x_train, x_test, y_train, y_test = train_test_split(x_data, y_data, test_size = 0.2, random_state=22)" + ], + "metadata": { + "id": "5kcxo62OGYqe" + }, + "execution_count": 31, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "For traditional machine learining methods we did not use EarlyStopping by making the validation set." + ], + "metadata": { + "id": "1mCByrmDcC6g" + } + }, + { + "cell_type": "markdown", + "source": [ + "### XGboost" + ], + "metadata": { + "id": "Wg597O9jE8rr" + } + }, + { + "cell_type": "code", + "source": [ + "from xgboost import XGBClassifier\n", + "\n", + "Xgboost_model = XGBClassifier()\n", + "Xgboost_model.fit(x_train, y_train, eval_metric='aucpr')\n", + "\n", + "Xg_predict = tf.cast( Xgboost_model.predict(x_test) > 0.5, dtype = tf.float32)\n", + "print(\"Test Report = \\n\", classification_report(Xg_predict, y_test))" + ], + "metadata": { + "id": "Wz6NxBF9E0lo", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "3ed19d60-f9a9-44c6-aa6e-4cd0a589191f" + }, + "execution_count": 32, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Test Report = \n", + " precision recall f1-score support\n", + "\n", + " 0.0 1.00 1.00 1.00 56879\n", + " 1.0 0.84 0.94 0.89 83\n", + "\n", + " accuracy 1.00 56962\n", + " macro avg 0.92 0.97 0.94 56962\n", + "weighted avg 1.00 1.00 1.00 56962\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Random Forest" + ], + "metadata": { + "id": "alEMVZ3PE5Jz" + } + }, + { + "cell_type": "code", + "source": [ + "from sklearn.ensemble import RandomForestClassifier\n", + "\n", + "RandomForest_model = RandomForestClassifier(n_estimators=100, oob_score=False)\n", + "RandomForest_model.fit(x_train, y_train)\n", + "\n", + "RF_predict = tf.cast( RandomForest_model.predict(x_test) > 0.5, dtype = tf.float32)\n", + "print(\"Test Report = \\n\", classification_report(RF_predict, y_test))" + ], + "metadata": { + "id": "0Hg7v1luFFlH", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "f92560db-5f5b-4c13-a004-0fa053e22c46" + }, + "execution_count": 33, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Test Report = \n", + " precision recall f1-score support\n", + "\n", + " 0.0 1.00 1.00 1.00 56883\n", + " 1.0 0.82 0.96 0.88 79\n", + "\n", + " accuracy 1.00 56962\n", + " macro avg 0.91 0.98 0.94 56962\n", + "weighted avg 1.00 1.00 1.00 56962\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Light Gradient Boosting" + ], + "metadata": { + "id": "NB9WVakOFJeB" + } + }, + { + "cell_type": "code", + "source": [ + "from lightgbm import LGBMClassifier\n", + "\n", + "LGBM_model = LGBMClassifier()\n", + "LGBM_model.fit(x_train, y_train, eval_metric='aucpr')\n", + "\n", + "LGBM_predict = tf.cast( LGBM_model.predict(x_test) > 0.5, dtype = tf.float32)\n", + "print(\"Test Report = \\n\", classification_report(LGBM_predict, y_test))" + ], + "metadata": { + "id": "qqBRv4V6FMju", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "a16d9ed7-2bfb-4cbc-aa10-75d335252694" + }, + "execution_count": 34, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Test Report = \n", + " precision recall f1-score support\n", + "\n", + " 0.0 1.00 1.00 1.00 56838\n", + " 1.0 0.10 0.07 0.08 124\n", + "\n", + " accuracy 1.00 56962\n", + " macro avg 0.55 0.54 0.54 56962\n", + "weighted avg 1.00 1.00 1.00 56962\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Support Vector Machine" + ], + "metadata": { + "id": "S2cS9psyFPJC" + } + }, + { + "cell_type": "code", + "source": [ + "from sklearn.svm import SVC\n", + "\n", + "SVC_model = SVC(kernel=\"rbf\")\n", + "SVC_model.fit(x_train, y_train)\n", + "\n", + "SVC_predict = tf.cast( SVC_model.predict(x_test) > 0.5, dtype = tf.float32)\n", + "print(\"Test Report = \\n\", classification_report(SVC_predict, y_test))" + ], + "metadata": { + "id": "nJZTFQsdFRr5", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "b6a5621d-b752-4228-edc6-3ea4cc673626" + }, + "execution_count": 35, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Test Report = \n", + " precision recall f1-score support\n", + "\n", + " 0.0 1.00 1.00 1.00 56872\n", + " 1.0 0.82 0.84 0.83 90\n", + "\n", + " accuracy 1.00 56962\n", + " macro avg 0.91 0.92 0.92 56962\n", + "weighted avg 1.00 1.00 1.00 56962\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Precision Comparison" + ], + "metadata": { + "id": "HyMsxCEjFVDq" + } + }, + { + "cell_type": "code", + "source": [ + "from sklearn.metrics import precision_score\n", + "\n", + "Precision_dict={}\n", + "Precision_dict[\"DNN\"] = {\"Test\" : precision_score(Dnn_predict, y_test)}\n", + "Precision_dict[\"XGboost\"] = {\"Test\" : precision_score(Xg_predict, y_test)}\n", + "Precision_dict[\"RandomForest\"] = {\"Test\" : precision_score(RF_predict, y_test)}\n", + "Precision_dict[\"LGBM\"] = {\"Test\" : precision_score(LGBM_predict, y_test)}\n", + "Precision_dict[\"SVM\"] = {\"Test\" : precision_score(SVC_predict, y_test)}" + ], + "metadata": { + "id": "nHeWU7ktFcw9" + }, + "execution_count": 36, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "Precision_df = pd.DataFrame(Precision_dict)\n", + "Precision_df.plot(kind='barh', figsize=(15, 8))" + ], + "metadata": { + "id": "4jOaD6p1FkNw", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 502 + }, + "outputId": "d1635e99-67fd-4dad-c589-f6f8d6a0e22d" + }, + "execution_count": 37, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 37 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Recall Comparison" + ], + "metadata": { + "id": "uBsjGoOmFfPF" + } + }, + { + "cell_type": "code", + "source": [ + "from sklearn.metrics import recall_score\n", + "\n", + "Recall_dict={}\n", + "Recall_dict[\"DNN\"] = {\"Test\" : recall_score(Dnn_predict, y_test)}\n", + "Recall_dict[\"XGboost\"] = {\"Test\" : recall_score(Xg_predict, y_test)}\n", + "Recall_dict[\"RandomForest\"] = {\"Test\" : recall_score(RF_predict, y_test)}\n", + "Recall_dict[\"LGBM\"] = {\"Test\" : recall_score(LGBM_predict, y_test)}\n", + "Recall_dict[\"SVM\"] = {\"Test\" : recall_score(SVC_predict, y_test)}" + ], + "metadata": { + "id": "dOrn50eUFh6W" + }, + "execution_count": 38, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "Recall_df = pd.DataFrame(Recall_dict)\n", + "Recall_df.plot(kind='barh', figsize=(15, 8))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 502 + }, + "id": "cMV_U1cvd107", + "outputId": "0b230b39-8b46-40e8-e6c1-cbb0b14f9ab2" + }, + "execution_count": 39, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 39 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### F1 Score Comparison" + ], + "metadata": { + "id": "YyylmG0HGRti" + } + }, + { + "cell_type": "code", + "source": [ + "F1_dict={}\n", + "F1_dict[\"DNN\"] = {\"Test\" : f1_score(Dnn_predict, y_test)}\n", + "F1_dict[\"XGboost\"] = {\"Test\" : f1_score(Xg_predict, y_test)}\n", + "F1_dict[\"RandomForest\"] = {\"Test\" : f1_score(RF_predict, y_test)}\n", + "F1_dict[\"LGBM\"] = {\"Test\" : f1_score(LGBM_predict, y_test)}\n", + "F1_dict[\"SVM\"] = {\"Test\" : f1_score(SVC_predict, y_test)}" + ], + "metadata": { + "id": "ZAgC4rpqGSYe" + }, + "execution_count": 43, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "F1_df = pd.DataFrame(F1_dict)\n", + "F1_df.plot(kind='barh', figsize=(15, 8))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 502 + }, + "id": "eAnGfdGueUFl", + "outputId": "62b53dc5-1a98-4d8a-8a67-106d38cfe854" + }, + "execution_count": 44, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 44 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# 4. Autoencoder" + ], + "metadata": { + "id": "CWwxdSx-ddpP" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Data Preparation" + ], + "metadata": { + "id": "Pj1o8WDfClkA" + } + }, + { + "cell_type": "code", + "source": [ + "from sklearn.preprocessing import StandardScaler\n", + "\n", + "df['Amount'] = StandardScaler().fit_transform(df['Amount'].values.reshape(-1, 1))\n", + "\n", + "X_train, X_test = train_test_split(df, test_size=0.2, random_state=22)\n", + "X_train = X_train[X_train.Class == 0]\n", + "X_train = X_train.drop(['Class'], axis=1)\n", + "\n", + "y_test = X_test['Class']\n", + "X_test = X_test.drop(['Class'], axis=1)\n", + "\n", + "X_train = X_train.values\n", + "X_test = X_test.values" + ], + "metadata": { + "id": "FpgIadm2Coek" + }, + "execution_count": 45, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "For autoencoders, it is important to use for the training data only normal transactions, NOT FRADULENT ONES!" + ], + "metadata": { + "id": "BW32fqR9Co6K" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Model Design" + ], + "metadata": { + "id": "EX4ILmKur6nw" + } + }, + { + "cell_type": "code", + "source": [ + "from keras import Input, regularizers, Model\n", + "input_dim = X_train.shape[1]\n", + "\n", + "# Bottleneck Design\n", + "input_layer = Input(shape=(input_dim, ))\n", + "encoder = Dense(14, activation=\"tanh\", name='encoder', activity_regularizer=regularizers.l1(10e-5))(input_layer)\n", + "latent = Dense(7, activation=\"relu\",name='latent')(encoder)\n", + "decoder = Dense(7, activation='tanh',name='decoder')(latent)\n", + "output_layer = Dense(input_dim,name='output_layer')(decoder)\n", + "\n", + "autoencoder = Model(inputs=input_layer, outputs=output_layer)" + ], + "metadata": { + "id": "q_kPwASUsDML" + }, + "execution_count": 46, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "The autoencoder is a fully connected design as following: input-encoder-latent-decoder-output\n", + "Each layer has 29,14,7,7,29 neurons, respectively. \\\\\n", + "I also applied L1 regularization." + ], + "metadata": { + "id": "fafZk8d2WBDn" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Model Compile" + ], + "metadata": { + "id": "NjFHMM8sDQxq" + } + }, + { + "cell_type": "code", + "source": [ + "from keras.callbacks import ModelCheckpoint, EarlyStopping\n", + "early_stop = EarlyStopping(monitor='val_loss',mode='min', verbose=1, patience=50)\n", + "autoencoder.compile(optimizer='adam', \n", + " loss='mse', \n", + " metrics=['accuracy'])" + ], + "metadata": { + "id": "38cl1R_McRPj" + }, + "execution_count": 47, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Model Training" + ], + "metadata": { + "id": "IewbcDLDcRu6" + } + }, + { + "cell_type": "code", + "source": [ + "history = autoencoder.fit(X_train, X_train,\n", + " epochs=30,\n", + " batch_size=256,\n", + " validation_data=(X_test, X_test),\n", + " callbacks=[early_stop]).history" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Ev3OnSxHDPzi", + "outputId": "93bebc9e-9f0f-45c1-b239-ab34abb88b6a" + }, + "execution_count": 48, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1/30\n", + "889/889 [==============================] - 4s 4ms/step - loss: 0.7983 - accuracy: 0.3799 - val_loss: 0.6832 - val_accuracy: 0.5351\n", + "Epoch 2/30\n", + "889/889 [==============================] - 5s 6ms/step - loss: 0.6164 - accuracy: 0.5647 - val_loss: 0.6042 - val_accuracy: 0.5888\n", + "Epoch 3/30\n", + "889/889 [==============================] - 3s 4ms/step - loss: 0.5728 - accuracy: 0.5917 - val_loss: 0.5857 - val_accuracy: 0.5907\n", + "Epoch 4/30\n", + "889/889 [==============================] - 3s 4ms/step - loss: 0.5583 - accuracy: 0.5905 - val_loss: 0.5737 - val_accuracy: 0.5904\n", + "Epoch 5/30\n", + "889/889 [==============================] - 3s 4ms/step - loss: 0.5473 - accuracy: 0.5975 - val_loss: 0.5656 - val_accuracy: 0.6009\n", + "Epoch 6/30\n", + "889/889 [==============================] - 3s 4ms/step - loss: 0.5379 - accuracy: 0.6142 - val_loss: 0.5546 - val_accuracy: 0.6202\n", + "Epoch 7/30\n", + "889/889 [==============================] - 3s 4ms/step - loss: 0.5296 - accuracy: 0.6242 - val_loss: 0.5477 - val_accuracy: 0.6260\n", + "Epoch 8/30\n", + "889/889 [==============================] - 3s 4ms/step - loss: 0.5243 - accuracy: 0.6274 - val_loss: 0.5436 - val_accuracy: 0.6246\n", + "Epoch 9/30\n", + "889/889 [==============================] - 3s 4ms/step - loss: 0.5206 - accuracy: 0.6281 - val_loss: 0.5400 - val_accuracy: 0.6299\n", + "Epoch 10/30\n", + "889/889 [==============================] - 3s 4ms/step - loss: 0.5180 - accuracy: 0.6289 - val_loss: 0.5378 - val_accuracy: 0.6303\n", + "Epoch 11/30\n", + "889/889 [==============================] - 3s 4ms/step - loss: 0.5150 - accuracy: 0.6306 - val_loss: 0.5362 - val_accuracy: 0.6255\n", + "Epoch 12/30\n", + "889/889 [==============================] - 4s 4ms/step - loss: 0.5131 - accuracy: 0.6290 - val_loss: 0.5336 - val_accuracy: 0.6280\n", + "Epoch 13/30\n", + "889/889 [==============================] - 3s 4ms/step - loss: 0.5112 - accuracy: 0.6300 - val_loss: 0.5321 - val_accuracy: 0.6281\n", + "Epoch 14/30\n", + "889/889 [==============================] - 4s 4ms/step - loss: 0.5095 - accuracy: 0.6307 - val_loss: 0.5305 - val_accuracy: 0.6334\n", + "Epoch 15/30\n", + "889/889 [==============================] - 3s 4ms/step - loss: 0.5077 - accuracy: 0.6326 - val_loss: 0.5287 - val_accuracy: 0.6311\n", + "Epoch 16/30\n", + "889/889 [==============================] - 4s 4ms/step - loss: 0.5063 - accuracy: 0.6341 - val_loss: 0.5267 - val_accuracy: 0.6370\n", + "Epoch 17/30\n", + "889/889 [==============================] - 3s 4ms/step - loss: 0.5050 - accuracy: 0.6368 - val_loss: 0.5247 - val_accuracy: 0.6374\n", + "Epoch 18/30\n", + "889/889 [==============================] - 3s 3ms/step - loss: 0.5037 - accuracy: 0.6387 - val_loss: 0.5265 - val_accuracy: 0.6358\n", + "Epoch 19/30\n", + "889/889 [==============================] - 3s 4ms/step - loss: 0.5025 - accuracy: 0.6411 - val_loss: 0.5242 - val_accuracy: 0.6400\n", + "Epoch 20/30\n", + "889/889 [==============================] - 3s 4ms/step - loss: 0.5015 - accuracy: 0.6425 - val_loss: 0.5224 - val_accuracy: 0.6438\n", + "Epoch 21/30\n", + "889/889 [==============================] - 3s 4ms/step - loss: 0.5004 - accuracy: 0.6443 - val_loss: 0.5206 - val_accuracy: 0.6453\n", + "Epoch 22/30\n", + "889/889 [==============================] - 3s 4ms/step - loss: 0.5002 - accuracy: 0.6446 - val_loss: 0.5202 - val_accuracy: 0.6450\n", + "Epoch 23/30\n", + "889/889 [==============================] - 3s 4ms/step - loss: 0.4993 - accuracy: 0.6463 - val_loss: 0.5199 - val_accuracy: 0.6447\n", + "Epoch 24/30\n", + "889/889 [==============================] - 3s 3ms/step - loss: 0.4979 - accuracy: 0.6474 - val_loss: 0.5216 - val_accuracy: 0.6465\n", + "Epoch 25/30\n", + "889/889 [==============================] - 3s 4ms/step - loss: 0.4983 - accuracy: 0.6470 - val_loss: 0.5217 - val_accuracy: 0.6431\n", + "Epoch 26/30\n", + "889/889 [==============================] - 3s 3ms/step - loss: 0.4981 - accuracy: 0.6474 - val_loss: 0.5175 - val_accuracy: 0.6464\n", + "Epoch 27/30\n", + "889/889 [==============================] - 3s 3ms/step - loss: 0.4965 - accuracy: 0.6488 - val_loss: 0.5174 - val_accuracy: 0.6481\n", + "Epoch 28/30\n", + "889/889 [==============================] - 3s 4ms/step - loss: 0.4959 - accuracy: 0.6488 - val_loss: 0.5192 - val_accuracy: 0.6439\n", + "Epoch 29/30\n", + "889/889 [==============================] - 3s 4ms/step - loss: 0.4956 - accuracy: 0.6491 - val_loss: 0.5161 - val_accuracy: 0.6483\n", + "Epoch 30/30\n", + "889/889 [==============================] - 3s 4ms/step - loss: 0.4962 - accuracy: 0.6485 - val_loss: 0.5159 - val_accuracy: 0.6470\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Now we visualize the performance." + ], + "metadata": { + "id": "npwqktaAXcQJ" + } + }, + { + "cell_type": "code", + "source": [ + "plt.plot(history['loss'])\n", + "plt.plot(history['val_loss'])\n", + "plt.title('model loss')\n", + "plt.ylabel('reconstruction error')\n", + "plt.xlabel('epoch')\n", + "plt.legend(['train', 'test'], loc='upper right')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 314 + }, + "id": "gSFufKBwFlBe", + "outputId": "07a25528-fb7e-4556-9a9b-d6084bec93c1" + }, + "execution_count": 49, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 49 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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3piv6dVcDwA3yZ9VNxpjk1mdJQlVDInKpqt4BrOrPhb0EcxcwDygDlonIYlVdG3H9b0cc/w1glrdcANwMzMb1yXjHO7e6PzEcjMKsAG9uspKEMSa5xdIm8XcR+bWInCQix3RMMZw3F9igqptUtRVYBJzTx/GXAo95y6cDS1W1yksMS4H5MXzmgCnKSqN6bxttofBgfqwxxgwpsbRJzPTmt0RsU+CU/ZxXCmyLWC8DjuvtQBE5BJgE/K2Pc0t7OW8BsABgwoQJ+wmnfzr6SlQ3tjIqJzig1zbGmOGizyThVRkt9qqb4ukS4AlV7ddTU6p6H3AfwOzZs3UgA+oYmqOiocWShDEmae1vFNgQrhroQJQD4yPWx3nbenMJXVVN/T03LmxoDmOMiW+bxDJgiohMEpEALhEs7nmQiBwJ5ANvRGx+DjhNRPJFJB84zds2aAptaA5jjIlfm4SqtovItbgvdz+wUFXXiMgtwHJV7UgYlwCLVFUjzq0SkVtxiQbgFlWtiiHWARM5EqwxxiSrWEaB/eyBXlxVlwBLemz7YY/1H0U5dyGw8EA/+2BlpaWQluKz6iZjTFLbb3WTiIwWkQdF5FlvfZqIfDX+oSWWiNjQHMaYpBdLm8RDuCqjEm/9I+Bb8QpoKHFDc1hJwhiTvGJJEkWq+jgQBtfWQBIM8AcdQ3NYScIYk7xiSRKNIlKIa6xGRI4HauMa1RBRaIP8GWOSXCxPN30H9+jqZBH5O1AMXBjXqAZTayOsXwpjZkDh5G67Ogb5C4cVn08SFKAxxiTOfksSqroC+AzwSeAqYLqq9muwvyGtrQn+dAV88Mw+uwqz0mgPK3XNI/ZNrcYY06dYShId7RBr4hxLYmQWQe4E2P7uPrsi+0rkZQQGOzJjjEm4WNokRr6Smb0miWKv13VFvT3hZIxJTpYkAEpmQfVmaOr+uorOoTkarfHaGJOcYqpuEpFS3GtLO49X1VfjFdSgK5nl5ttXwuSuDuad1U31liSMMclpv0lCRP4buBhYS1f/CAVGUJLwhqfa/m63JJGfEcAnUNlo1U3GmOQUS0niXNx7rUfuz+n0fCg4dJ92CZ9PKMi0oTmMMckrljaJTUBqvANJuJJZrrqph6KsgDVcG2OSViwlib3AShF5Eej8Sa2q18UtqkQomQWrn4TGPe6xWE9RVpo1XBtjklYsSWIxvbwsaMTpbLx+F6bM69xclBVg68eNCQrKGGMSK5b3STzsvVnucG/Th6o68rogjzkakF6SRBp7rLrJGJOkYnm66WTgYWALIMB4EbliRD0CCxDMgaIp+zReF2al0dQWYm9rOxmBmJ4YNsaYESOWb72fA6ep6ocAInI48BhwbDwDS4iSWbC5e+7r6ivRyoRCSxLGmOQSy9NNqR0JAkBVP2KkPu1UMgvqd0Ddjs5NRdmu1/Uea7w2xiShWH4aLxeRB4BHvPUvAMvjF1ICdTRe71gJOWMBKMr0koT1ujbGJKFYShJfx/W2vs6b1nrbRp4xM0B83dolirI7RoK1xmtjTPKJ5emmFuB2bxrZAplQPLVbkij0ShL2GlNjTDKKmiRE5HFVvUhE3sd7dWkkVT06rpElSsksWP8cqIIIgRQfOcEUG5rDGJOU+ipJfNObf24wAhkySmbCykegtgzyxgOu8dqqm4wxyShqm4Sqdjzi86+qujVyAv41louLyHwR+VBENojIjVGOuUhE1orIGhF5NGJ7SERWetPg9fguOcbNI9slbJA/Y0ySiqXhel4v287Y30ki4gfu8o6dBlwqItN6HDMFuAk4UVWnA9+K2N2kqjO96ewY4hwYo6eDL2WfxmtLEsaYZBQ1SYjI1732iCNFZFXEtBl4P4ZrzwU2qOomVW0FFgHn9DjmSuAuVa0GUNXdB3YbAyg1CKOmdU8SWVbdZIxJTn2VJB4FPg/8rzfvmI5V1S/EcO1SYFvEepm3LdLhwOEi8ncReVNE5kfsC4rIcm/7ub19gIgs8I5ZXlFREUNIMSqZ5ZKEuvb6wsw0apvaaG0PD9xnGGPMMNBXm0Stqm4BfgFURbRHtIvIcQP0+SnAFOBk4FLgfhHJ8/YdoqqzgcuAO0Vkci8x3qeqs1V1dnFx8QCFhEsSzTVQvQXo6itRZW+oM8YkmVjaJO4GGiLWG7xt+1MOjI9YH+dti1QGLFbVNlXdDHyESxqoark33wS8DMyK4TMHRuSw4bjqJsDaJYwxSSeWJCGq2tlPQlXDxDacxzJgiohM8oYav4R930vxZ1wpAhEpwlU/bRKRfBFJi9h+Iq6n9+AYNQ38aRFJoqPXtSUJY0xyien1pSJynYiketM3ca807ZOqtgPXAs8B64DHVXWNiNwiIh1PKz0HVIrIWuAl4AZVrQSm4saMes/bfpuqDl6SSAnAmKN6KUlYdZMxJrnEUiK4Gvgl8H1cz+sXgQWxXFxVlwBLemz7YcSyAt/xpshj/gHMiOUz4qZkFqx6HMJhCrNsaA5jTHLab0lCVXer6iWqOkpVR6vqZUPiUdV4K5kFLXVQtZHMgJ9gqs+qm4wxSSeWN9P9lt7HbvpKXCIaKiIar6VoivWVMMYkpVjaJP4KPONNLwI5dH/aaWQqOgJS0jvbJQqzbGgOY0zyiWWo8Ccj10XkMeD1uEU0VPhTYOzRnUmiOCtAeU1zgoMyxpjBFUtJoqcpwKiBDmRIKpkFO96DcIiirDRruDbGJJ39JgkRqReRuo4J+AvwvfiHNgSUzIK2vbDnIwqzAlQ2thIO79M8Y4wxI1af1U0iIsB0Vf14kOIZWiIar4uyTiAUVmqa2ijIDCQ2LmOMGSR9liS8fgzPDFIsQ0/hFAhkwfZ3ra+EMSYpxdImsUJE5sQ9kqHI54OxM72ShCs9VFiSMMYkkViSxHHAGyKy0XufxPsisiregQ0ZJTNh5/sUp7v/VNZXwhiTTGIZluP0uEcxlJXMgvZmRjVvBqy6yRiTXGIpSfxHL++4/o94BzZkeI3X2VWr8fvEOtQZY5JKLElieuSK9+7qY+MTzhBUcCik5eLb8S6FmQEqrbrJGJNE+nrH9U0iUg8cHdFPoh7YjXulaXIQce0S3hNOVpIwxiSTvl5f+l+qmg38VFVzvClbVQtV9aZBjDHxSmbBrjWMyYQKK0kYY5JITAP8iUgmgIhcLiK3i8ghcY5raCmZBeE2ZqSUs6feShLGmOQR6zuu94rIJ4DvAhuB38U1qqGm9BgA5qZtpbymiY0VI38QXGOMgdiSRLvX8/oc4NeqeheQHd+whpjc8ZBRyLGpW/AJPL2iPNERGWPMoIglSdSLyE3A5cAzIuIDUuMb1hAjAiWzSK9YxUlTinn63XIb6M8YkxRiSRIXAy3AV1V1JzAO+GlcoxqKSmbB7nX889EFlNc08ebmykRHZIwxcRfLO653qurtqvqat/6xqiZXmwS4JKEh5hVWkJ2WwlNW5WSMSQKxvE/ifBFZLyK1HX0lvPdKJBev53Xa7lWcOWMsz76/g72t7QkOyhhj4iuW6qb/Ac5W1dyIvhI58Q5syMkeC1mjYfu7nH9MKY2tIZ5bszPRURljTFzFkiR2qeq6uEcy1HmN13z8BnPGZzG+IN2qnIwxI14sSWK5iPxRRC71qp7OF5HzY7m4iMwXkQ9FZIOI3BjlmItEZK2IrBGRRyO2X+FVc60XkStivJ/4+sQlUL0F3zPf4byZpby+YQ87apsSHZUxxsRNLEOF5wB7gdMitinwVF8neQMB3gXMA8qAZSKyWFXXRhwzBbgJOFFVq0VklLe9ALgZmO191jveudUx31k8TD8Pdq+DV/6bLx83ll/qLP787na+fvLkhIZljDHxst8koapfPsBrzwU2qOomABFZhOuQtzbimCuBuzq+/FV1t7f9dGCpqlZ55y4F5gOPHWAsA+fkm6B6K/lv/ZRvj7qBp1ZkcfVnDsW9DtwYY0aWWJ5uGiciT4vIbm96UkTGxXDtUmBbxHqZty3S4cDhIvJ3EXlTROb341xEZIGILBeR5RUVFTGENABE4OxfwcSTuLb+Dgr3vM3q8uR72MsYkxxiaZP4LbAYKPGmv3jbBkIKMAU4GbgUuF9E8mI9WVXvU9XZqjq7uLh4gEKKQUoALn4ECiZzb+odvPL31wfvs40xZhDFkiSKVfW3qtruTQ8BsXwjlwPjI9bHedsilQGLVbVNVTcDH+GSRiznJlZ6Hv7L/4SkBDh/3TdprdmR6IiMMWbAxZIkKr0hwv3edDkQy5gUy4ApIjJJRALAJbgSSaQ/40oRiEgRrvppE/AccJqI5ItIPq7R/LmY7mgw5R/Ch6c+QJ7W0fTwhdDamOiIjDFmQMWSJL4CXATsBHYAFwL7bcxW1XbgWtyX+zrgcVVdIyK3iMjZ3mHP4ZLQWuAl4AZVrfQarG/FJZplwC0djdhDzczjTuHf/d8mu3otPPk1CIcSHZIxxgwYcaOAD3+zZ8/W5cuXJ+Szb/3rWkJv3ceP/L+F466GM/47IXEYY0x/icg7qjo72v5Ynm56OLIx2asCWjhQAY4E5x9TykNt81g78V/grXvgjd8kOiRjjBkQsVQ3Ha2qNR0rXp+GWfELafiZNjaHI8dk8+8NF8HUs+G5f4N1f0l0WMYYc9BiSRI+r/EY6OwNHUtP7aQhIpx/TCnvltWx6dO3w7jZrn1iw4uJDs0YYw5KLEni58AbInKriNwK/AM3MqyJcO7MUnwCT62qgksXQcGh8MgF8OKtELIhxY0xw1MsLx36HXA+sMubzlfV38c7sOFmVE6w69Wm6YXwtRdh1hfgtZ/Bw5+Huu2JDtEYY/otlpIEQAHQqKq/BipEZFIcYxq2zj+mtOvVpoEMOOcuOO8+2PEe3PMpWP9CokM0xph+ieXpppuB7+FGawVIBR6JZ1DD1enTx+z7atNPXAwLXoasMfCHC2DpzRBqS1SIxhjTL7GUJM4DzgYaAVR1O5Adz6CGq2Cqv/dXmxYfDle+CMd+Cf5+Jzx0FtSWJSxOY4yJVSxJolVdjzsFEJHM+IY0vEV9tWlqOnz+F3DBg7Brjat++vD/EhOkMcbEKJYk8biI3AvkiciVwAvA/fENa/iaM7Gg71ebzrgQrnoVcsfDYxfDc/8O7a2DG6QxxsSozyQh7k06fwSeAJ4EjgB+qKq/GoTYhiWfTzhv1jhe37CHnbXNvR9UOBm+uhTmXAlv/BoeOAU+eh5GyBApxpiRo88k4VUzLVHVpap6g6per6pLBym2YeuCY0oR4Ft/fJe65iiN1KlBOOtncNHvobkWHv1neOCf3BNQliyMMUNELNVNK0RkTtwjGUEOKczkjotn8s7Wai6+901210UpUQBMOxuufce1VzTsck9APXia661tycIYk2D7HQVWRD4ADgO24p5wElwh4+j4hxe7RI4CG82rH1Vw9SPvUJAZ4PdfPY5JRftp829vhZWPwKs/h7oyGH+ce6f2oSe716YaY8wA298osLEkiUN6266qWw8ytgE1FJMEwHvbavjyQ8sQ4LdfnsPR42J4O2t7C7z7e3jtdqgrhwknwMk3wqTPWLIwxgyog04Sw8VQTRIAmyoa+JeFb1PV2Mo9lx/Lpw+P8X3c7S2w4nfw2s+hfgccciLMvw3GDqlCnDFmGDvo90mYg3docRZPff2THFKYyVceWsb/rozxdd0paTD3SrhuJZzxU9izHu4/BV79mQ0aaIwZFJYkBsmonCB/vOp4Zk/M55uLVvLg65tjPzk1CMctgGvegqmfg7/dCr89Ayo3xi9gY4zBksSgygmm8tCX53LmjDHc+te13PbsB/Srui+jAC78reu1vedD12t72YP2FJQxJm4sSQyyYKqfX116DJcfP4F7XtnI9X9aRVsoHPsFRFyv7X99EyYcD898B/5wIdTtiF/QxpikZUkiAfw+4dZzjuI78w7nyRVlfOWhZX33pehNTglc/hSc+TPY8nf4zfGw+sn4BGyMSVqWJBJERLju1Cn89wUzeHtzFafd+Sp/ea+fLyYScQ3bV78OhYfBE1+BJ74Ke6viE7QxJulYkkiwi+dMYMk3T2JiYSbfeOxdrnl0BVWN/Rzwr+gw+MpzcMr3Ye2f4e5PwgdLrK3CGHPQ4pokRGS+iHwoIhtE5MZe9n9JRCpEZLtMPr8AABX1SURBVKU3fS1iXyhi++J4xplok4uzeOLqE7jh9CN4fs1OTrvjVV5Yu6t/F/GnwKdvcK9NDebCokvhrrnw9v3QUh+fwI0xI17cOtOJiB/4CJgHlAHLgEtVdW3EMV8CZqvqtb2c36CqWbF+3lDuTNcf63bU8Z3H32PdjjouPHYcP/z8NHKCqf27SHsLrHka3roXtq+AtByYeRnMXeBGoDXGGE8iO9PNBTao6iZVbQUWAefE8fNGhKljc/jfa07kG6ccxtPvljP/jld5ff2e/l0kJQ0+cQkseMmVLA4/3T0q+6tj4JELYf1SCPfjiSpjTNKKZ5IoBbZFrJd523q6QERWicgTIjI+YntQRJaLyJsicm5vHyAiC7xjlldUVAxg6IkVSPHx3dOO4Mmvf5L0gJ/LH3yLH/x5dfdXosZq3Gy44AH49ho3WODOVe6R2V/PhjfvdsOUG2NMFPGsbroQmK+qX/PWvwgcF1m1JCKFQIOqtojIVcDFqnqKt69UVctF5FDgb8Cpqhq1i/FIqW7qqbktxM+e+5AH/76ZCQUZ/OTcGXxqStGBX7C9FdYthrfugbJlkJoJkz8Lkz4NE0+CUVNtEEFjkkjCBvgTkROAH6nq6d76TQCq+l9RjvcDVaqa28u+h4C/quoT0T5vpCaJDm9tquT/PbmKrZV7mT99DP9+1lTGF2Qc3EXLV8CKh2HjS1DjDeqbWeySxaST3KizBYda0jBmBEtkkkjBNVyfCpTjGq4vU9U1EceMVdUd3vJ5wPdU9XgRyQf2eiWMIuAN4JzIRu+eRnqSAFeqePD1zfz6bxsIq3L1Zybz9ZMnE0z1H/zFq7fCltdg86tuqvd6cOeUdpUyJn4K8iZY0jBmBEnoUOEiciZwJ+AHFqrqT0TkFmC5qi4Wkf8CzgbagSrg66r6gYh8ErgXCOPaTe5U1Qf7+qxkSBIdttc08Z9L1vHXVTsozUvnB5+byunTxyAD9eWt6gYP3PyKSxhbXoO9lW5f1mgYNwfGz4Vxc6FkJqSmD8znGmMGnb1PYgR7c1MlP1q8hg921vOpw4q4+fPTmDI6e+A/KByG3Wvh4zdg29uuLaPaG8XWlwJjjo5IHHOstGHMMGJJYoRrD4X5w1sf8/PnP2Rva4grPjmRb/7TlP73reivhgqXLMrehm3LXH+Mtr1uX9ZoKDocCiZB/qTu8+A+TU7GmASyJJEkKhta+NnzH7Fo2ccUZga44fQjuOCYcaT4B2nklVA77F7jShrlK6BygyttNPZ4NDm9oHvSyJsAWWMge7SbZxaBbwDaWIwxMbEkkWTeL6vl5sWrWfFxDYcWZfKteYfzuRlj8fkSVP3TUg/VW6BqE1RtdomjY15bBtqjU5/43RNWHUmjcz7G9RYvPtKVVKw6y5gBYUkiCakqz6/dxe3Pf8SHu+o5ckw235l3OPOmjR64xu2B0N4K9duhYTfU74SGXd58J9Tv6po3VgAR/06DeS5ZFB/RfZ5TYsnDmH6yJJHEwmHlL6u2c+cL69m8p5FPjMvlu6cdwUlTioZWstifULtLIJXroeJDqPjAzXevg6aIYdED2S5hFBwKWaO8abQrmWSNdlNGgVVnGRPBkoShPRTmqXfL+cUL6ymvaWLuxAKuP/0I5k4qSHRoB69xj5c0PuhKINVbXMN6e9O+x4vPJY1ML4lkj3VVWdlj3HLOWDfPHOVG1jVmhLMkYTq1tId4fNk2fvW3Deyub+GkKUVcf9oRfGJ8XqJDG3iq0NrgqrIadnlzb7lxd/cqroZd+7aNIF4S8ZJH9ljILXWdCzunEggcZK93YxLMkoTZR1NriEfe3Mrdr2ykqrGVGaW5nDljLGccNYaJRZmJDm/whUOu3aN+h0scvc3rtnd1KIyUXuASRq6XNHJKIXc85I138+yxViIxQ5olCRNVQ0s7i97+mL+s2sF722oAmDY2hzNnjOGMGWOZXBzz6zySQ1sz1JV703b3dFbncjnUlUFTdfdzxO8SR0fS6DafALnjIDU4MPGFQ65E5I9zHxkzoliSMDEpq97L/63eybOrd/LOVvdFd8TobM6YMYYzZ4xlyqis4dXYnSite13yqP0YarZB7bbu8/rt+1ZtZY1x/UXyJrjk0bl8iJdE0l0CaNjlJaOeiWq7W6/f6a6XNx4KJrtHhgsmu4b8wsnumpZATA+WJEy/7axt5v9W72DJ6p0s21KFKkwuzmTetDHMmZjPMRPyyc8MJDrM4SnU5qqvarZBzcde8tjqlms+dl/64R7vDUnPh+Y60FD37SnpEdVc49wyuD4plRvdvKWu63hfiksUHYkjb4I7J3e8S0aZo8Bnr71PNpYkzEHZXdfMc2t2suR9lzDaw+7fy+TiTGYfUsCxE/OZfUg+k4oyraQxEMIhVyLoSBo1H7vSR3p+92SQU+q29fXfXNU9/VW10UsaG7uSR9Um17AfyZfqPiN3vJc8xrkpZ5x76iuWzzTDjiUJM2Ca20K8t62G5VurecebapvaACjIDHDMhHxme0njiDHZZMd7/Chz4FShucZVX9WWufaU2o6pvKu9ZZ/SS9B7VLjUSxxeY33HE2CBTPf63JQ0d6w/4M1TDz65tNRD3Y6IBwq2u3mdN2+pd/FquMek3dfFByXHwKEnu6loSlInPksSJm7CYWVjRQPvbK3uTByb9zR27h+TE+SwUVkcNiqLyaOymOItF2YGrNQxHHSUauq2uy/kjraPuh0R7SA7INQa2/VSguD3Eog/4Kq2fCmucd+X4jo5+vzdt0HXk2c9Sz4AaTld/VyCud55fpcIuk3Stdze4kY0rt7irpFd0pUwDv2Mu9ZAU3VPx9WVuxJb0ZQh0z5kScIMqj0NLbz7cQ0f7apn4+4GNlQ0sHF3A42tXb9I8zJSOazYJYzDR2czrSSHaSU58R+51gy8zi8/79d82173JRxqcfP2Zm8eua3FJZZwyP3yD7e75X3W2931M4u6d3TsnMZA2kE8gVe12b0zZdPLsOmVrt77xVO7kkbn63y9HzWdP24i18XF3bDLS57bux4siEysoZauz/anuWuPPdoNtT9mBoyeDmlxGOp/PyxJmIRTVXbUNrNhd4ObKho6l6sau36FTijIYNrYHKaX5DC9NIfpJbmMyk6zUoeJv3AYdr3vJYyXYes/XII7UP5A96q4juWcEmhrcp+1YxXsXNX9semCQ7uSxqhpLgn6UiJKWindJ/FKY6npLpkeAEsSZkjbXd/M2u11rNle581r2VK5t3N/YWags6QxsTCTkrx0SvOClOSlkxGwTmomTtqa3btSarbRObikavRlETc2WEcyyCiMrZ1D1ZU2dr7vEsbOVW65oyosVqWz4coX+3eOx5KEGXbqm9v4YGc9a8prWeMlkPW762kLdf+3mp+RSkleupc43OTWg5TmpVOUlZa4IdKNORjNtbBnvSvNhNu7V8H1tp5RCEeedUAftb8kYT/FzJCTHUxlzsQC5kzsGoCwPRRmV30L22ua2F7TRHlNE+XVbvnjyr28sbGShpbu/QtS/cKY3CBjc10CGZsb7EwiJXnpjM1NJyeYYtVZZugJ5sK4qN/bg8qShBkWUvy+ztJCNHXNbZ2JY3ttM9trmthR08T2mmbe3lzFrrrmzn4eHTID/s5E4ubBzvnoHLc9PyPVEolJWpYkzIiRE0wlZ2wqU8fm9Lo/FFYq6lsor2liR61LJjtqm9lZ28yO2mb+vmEPu+qa6ZFHCKT4XPLI8ZJHbpCxOUHG5KZ3JpWirDT8VrVlRiBLEiZp+H2u+mlMbhDI7/WY9lCYPQ2t7Kht6kweO+vcfFdtM+98XM2u2hZaQ+F9rj06O43RuUGKs9Ioyk6jKDPg5llpFEYsWxWXGU4sSRgTIcXvi0g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+ }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "code", + "source": [ + "predictions = autoencoder.predict(X_test)\n", + "mse = np.mean(np.power(X_test - predictions, 2), axis=1)\n", + "error_df = pd.DataFrame({'reconstruction_error': mse,\n", + " 'true_class': y_test})\n", + "error_df.describe()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 319 + }, + "id": "TKLF2BVdGVRF", + "outputId": "2d85d881-0784-4212-c6a2-a46484e15fc0" + }, + "execution_count": 50, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1781/1781 [==============================] - 3s 2ms/step\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " reconstruction_error true_class\n", + "count 56962.000000 56962.000000\n", + "mean 0.515257 0.001633\n", + "std 2.210901 0.040374\n", + "min 0.064416 0.000000\n", + "25% 0.200646 0.000000\n", + "50% 0.306612 0.000000\n", + "75% 0.493023 0.000000\n", + "max 224.676743 1.000000" + ], + "text/html": [ + "\n", + "
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count56962.00000056962.000000
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\n", + " " + ] + }, + "metadata": {}, + "execution_count": 50 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Now we construct the AUC_ROC Curve" + ], + "metadata": { + "id": "rsK4aBYRX30Q" + } + }, + { + "cell_type": "code", + "source": [ + "from sklearn.metrics import (confusion_matrix, precision_recall_curve, auc,\n", + " roc_curve, recall_score, classification_report, f1_score,\n", + " precision_recall_fscore_support)\n", + "\n", + "fpr, tpr, thresholds = roc_curve(error_df.true_class, error_df.reconstruction_error)\n", + "roc_auc = auc(fpr, tpr)\n", + "\n", + "plt.title('Receiver Operating Characteristic')\n", + "plt.plot(fpr, tpr, label='AUC = %0.4f'% roc_auc)\n", + "plt.legend(loc='lower right')\n", + "plt.plot([0,1],[0,1],'r--')\n", + "plt.xlim([-0.001, 1])\n", + "plt.ylim([0, 1.001])\n", + "plt.ylabel('True Positive Rate')\n", + "plt.xlabel('False Positive Rate')\n", + "plt.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 295 + }, + "id": "CCZ4MSFOGeAh", + "outputId": "78c2bc33-a312-4153-b8ce-28477ac9e538" + }, + "execution_count": 51, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# 5. Model Comparison" + ], + "metadata": { + "id": "g1pTUzOhRk-A" + } + }, + { + "cell_type": "code", + "source": [ + "sota_dict={}\n", + "sota_dict[\"DNN\"] = {\"Test\" : score[1]}\n", + "sota_dict[\"Autoencoder\"] = {\"Test\" : 0.97}\n", + "sota_dict[\"DevNet\"] = {\"Test\" : 0.98}\n", + "sota_dict[\"DIF\"] = {\"Test\" : 0.953}" + ], + "metadata": { + "id": "JS1GdeOzBYDf" + }, + "execution_count": 52, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "sota_df = pd.DataFrame(sota_dict)\n", + "sota_df.plot(kind='barh', figsize=(15, 8))" + ], + "metadata": { + "id": "DLhNESg37z3-", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 502 + }, + "outputId": "203cfff2-4279-4036-9b3e-de504920618b" + }, + "execution_count": 53, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 53 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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MtAEAABQm2gAAAAoTbQAAAIWJNgAAgMJEGwAAQGGiDQAAoDDRBgAAUJhoAwAAKEy0AQAAFCbaAAAAChNtAAAAhYk2AACAwkQbAABAYaINAACgMNEGAABQmGgDAAAoTLQBAAAUJtoAAAAKE20AAACFiTYAAIDCRBsAAEBhog0AAKAw0QYAAFCYaAMAAChMtAEAABQm2gAAAAoTbQAAAIWJNgAAgMJEGwAAQGGiDQAAoDDRBgAAUJhoAwAAKEy0AQAAFCbaAAAAChNtAAAAhYk2AACAwkQbAABAYaINAACgMNEGAABQmGgDAAAoTLQBAAAUJtoAAAAKE20AAACFiTYAAIDCWtd1/Z4hSTLh6And7131e/0eAwAAOEit/bO1/R5hj1prq7quG3r18860AQAAFCbaAAAAChNtAAAAhYk2AACAwkQbAABAYaINAACgMNEGAABQmGgDAAAoTLQBAAAUJtoAAAAKE20AAACFiTYAAIDCRBsAAEBhog0AAKAw0QYAAFCYaAMAAChMtAEAABQm2gAAAAoTbQAAAIWJNgAAgMJEGwAAQGGiDQAAoDDRBgAAUJhoAwAAKEy0AQAAFCbaAAAAChNtAAAAhYk2AACAwkQbAABAYaINAACgMNEGAABQmGgDAAAoTLQBAAAUJtoAAAAKE20AAACFiTYAAIDCRBsAAEBhog0AAKAw0QYAAFCYaAMAAChMtAEAABQm2gAAAAoTbQAAAIWJNgAAgMJEGwAAQGGiDQAAoDDRBgAAUJhoAwAAKEy0AQAAFCbaAAAAChvY08bW2uFJ7tn18F8n2Z5k067HJ3Zd99IbvH5ukpe6rvs/b3JOAACAMWmP0dZ13eYkg0nSWrsqyfNd131mL44/N8nzSUQbAADAPtjryyNba8e31u5rra1qrX2rtXbkrucvbq2ta62taa3d1lqbmmRhkk+01la31v7t/h0dAADg4LfHM2270ZLcmGRe13WbWmvzk1yb5MNJ/jLJ0V3Xvdha+42u637SWvtC9nB2rrX20SQfTZLfmdiy9gf/vM+fCAAAMMZd9Uy/Jzgg9jba3pJkRpK7WmtJMi7JU7u2rUny5dbaHUnuGM3Buq67OcnNSTJ01LhuL2cBAAA46O3LmbZHuq6bs5ttZyc5Ocm5SRa31ma+2eEAAADGur19T9uLSSa11uYkSWvtkNba9NbaryT57a7rlie5PMnEJL+e5Lkkh+3PgQEAAMaSvY22HUnem+Q/t9b+b5LVSf5Ndl4m+T9ba2uT/GOSG7qu+0mSO5O8x41IAAAA9s2oL4/suu6qX3h48m52ecduXvNokll7PxYAAADJPtzyHwAAgN4RbQAAAIWJNgAAgMJEGwAAQGGiDQAAoDDRBgAAUJhoAwAAKEy0AQAAFCbaAAAAChNtAAAAhYk2AACAwkQbAABAYaINAACgMNEGAABQmGgDAAAoTLQBAAAUJtoAAAAKE20AAACFiTYAAIDCRBsAAEBhog0AAKAw0QYAAFCYaAMAAChMtAEAABQm2gAAAAoTbQAAAIWJNgAAgMJEGwAAQGGiDQAAoDDRBgAAUJhoAwAAKEy0AQAAFCbaAAAAChNtAAAAhYk2AACAwkQbAABAYaINAACgMNEGAABQmGgDAAAoTLQBAAAUJtoAAAAKE20AAACFiTYAAIDCRBsAAEBhog0AAKAw0QYAAFCYaAMAAChMtAEAABQm2gAAAAob6PcAP7e2OyZTX7i+32MAAAD74IlPnd3vEQ5azrQBAAAUJtoAAAAKE20AAACFiTYAAIDCRBsAAEBhog0AAKAw0QYAAFCYaAMAAChMtAEAABQm2gAAAAoTbQAAAIWJNgAAgMJEGwAAQGGiDQAAoDDRBgAAUJhoAwAAKEy0AQAAFCbaAAAAChNtAAAAhYk2AACAwkQbAABAYaINAACgMNEGAABQmGgDAAAoTLQBAAAUJtoAAAAKE20AAACFiTYAAIDCRBsAAEBhog0AAKAw0QYAAFCYaAMAAChMtAEAABQm2gAAAAoTbQAAAIWJNgAAgMJEGwAAQGGiDQAAoDDRBgAAUJhoAwAAKEy0AQAAFCbaAAAAChNtAAAAhYk2AACAwkQbAABAYaINAACgMNEGAABQmGgDAAAoTLQBAAAUJtoAAAAKE20AAACFiTYAAIDCRBsAAEBhA/0e4Odm/tbEDH/q7H6PAQAAUIozbQAAAIWJNgAAgMJEGwAAQGGiDQAAoDDRBgAAUJhoAwAAKEy0AQAAFCbaAAAAChNtAAAAhYk2AACAwkQbAABAYaINAACgMNEGAABQmGgDAAAoTLQBAAAUJtoAAAAKE20AAACFiTYAAIDCRBsAAEBhog0AAKAw0QYAAFCYaAMAAChMtAEAABQm2gAAAAoTbQAAAIWJNgAAgMJEGwAAQGGiDQAAoDDRBgAAUJhoAwAAKEy0AQAAFCbaAAAAChNtAAAAhYk2AACAwkQbAABAYaINAACgMNEGAABQmGgDAAAoTLQBAAAUJtoAAAAKE20AAACFiTYAAIDCRBsAAEBhog0AAKAw0QYAAFCYaAMAAChMtAEAABQm2gAAAAoTbQAAAIWJNgAAgMJEGwAAQGGiDQAAoDDRBgAAUJhoAwAAKEy0AQAAFCbaAAAAChNtAAAAhYk2AACAwkQbAABAYaINAACgMNEGAABQmGgDAAAoTLQBAAAUJtoAAAAKE20AAACFiTYAAIDCRBsAAEBhog0AAKAw0QYAAFCYaAMAAChMtAEAABQm2gAAAAoTbQAAAIWJNgAAgMJEGwAAQGGiDQAAoDDRBgAAUJhoAwAAKEy0AQAAFCbaAAAAChNtAAAAhYk2AACAwkQbAABAYaINAACgMNEGAABQmGgDAAAoTLQBAAAUJtoAAAAKE20AAACFiTYAAIDCRBsAAEBhog0AAKAw0QYAAFCYaAMAAChMtAEAABQm2gAAAAoTbQAAAIWJNgAAgMJEGwAAQGGiDQAAoDDRBgAAUJhoAwAAKEy0AQAAFCbaAAAAChNtAAAAhYk2AACAwkQbAABAYaINAACgsNZ1Xb9nSJK01p5L8v1+z8GYd0SSp/s9BMRapA5rkQqsQ6o40Gvxd7uum/TqJwcO4AfcW9/vum6o30MwtrXWhq1DKrAWqcJapALrkCr6tRZdHgkAAFCYaAMAACisUrTd3O8BINYhdViLVGEtUoF1SBV9WYtlbkQCAADAa1U60wYAAMCr9DTaWmtntta+31p7rLX2l7vZ/pbW2u27tv9Da21qL+dj7BjFWry0tbautbamtXZPa+13+zEnB783Wou/sN95rbWutebuaex3o1mHrbXzd/1cfKS19r96PSNjwyj+fv6d1try1to/7vo7+l39mJODW2vtr1prG1tr332d7a21dsOudbqmtXbcgZ6pZ9HWWhuX5HNJzkry1iTva6299VW7/XmSLV3X/V6S/5rkP/dqPsaOUa7Ff0wy1HXdrCTLkny6t1MyFoxyLaa1dliSS5L8Q28nZCwYzTpsrf1+kv+Y5KSu66Yn+XjPB+WgN8qfiVck+UrXdW9LckGS/9bbKRkjliQ5cw/bz0ry+7v++2iSzx/ogXp5pu3EJI91Xfd413UvJbktybxX7TMvyV/v+vOyJH/cWms9nJGx4Q3XYtd1y7uu+9muhw8nmdLjGRkbRvNzMUk+mZ3/iPVCL4djzBjNOvz3ST7Xdd2WJOm6bmOPZ2RsGM1a7JL8q11/npjkyR7OxxjRdd39SX68h13mJfnv3U4PJ/mN1tqRB3KmXkbbbyX50S883rDrud3u03Xdy0meSXJ4T6ZjLBnNWvxFf57kmwd0IsaqN1yLuy65+O2u677Ry8EYU0bzM/HYJMe21h5srT3cWtvTv0DDvhrNWrwqyQdaaxuS/O8kH+vNaPAKe/v/km/awIE8OPyya619IMlQklP6PQtjT2vtV5J8NslFfR4FBrLzMqC52Xnlwf2ttZld1/2kr1MxFr0vyZKu665rrc1J8j9aazO6rtvR78HgQOrlmbb/l+S3f+HxlF3P7Xaf1tpAdp723tyT6RhLRrMW01r7d0kWJ3l313Uv9mg2xpY3WouHJZmRZEVr7Ykkb0/ydTcjYT8bzc/EDUm+3nXdtq7rfpDk0eyMONifRrMW/zzJV5Kk67qHkoxPckRPpoN/Mar/l9yfehltK5P8fmvt6Nbaodn55tGvv2qfryf5s11/fm+Sezu/SI797w3XYmvtbUm+mJ3B5r0bHCh7XItd1z3Tdd0RXddN7bpuana+v/LdXdcN92dcDlKj+fv5juw8y5bW2hHZebnk470ckjFhNGvxn5P8cZK01qZlZ7Rt6umUsHNdfnDXXSTfnuSZruueOpAfsGeXR3Zd93JrbVGSbyUZl+Svuq57pLX2n5IMd1339SS3ZOdp7sey881/F/RqPsaOUa7F/5Lk15N8dde9cP6567p3921oDkqjXItwQI1yHX4ryemttXVJtie5rOs6V8KwX41yLf6HJF9qrX0iO29KcpF/4Gd/a60tzc5/qDpi1/snr0xySJJ0XfeF7Hw/5buSPJbkZ0k+dMBnss4BAADq6ukv1wYAAGDviDYAAIDCRBsAAEBhog0AAKAw0QYAAFCYaAMAAChMtAEAABQm2gAAAAr7/8/l9bqrhb48AAAAAElFTkSuQmCC\n" 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