U_Net_binary.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/heejjj/Sleeping_Project/blob/main/U_Net_binary.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OyAn-okDuu-m",
        "outputId": "182a5786-3c8f-424b-cef8-f9c026e3468b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "WdfRZR0XvU3e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# weekday binary "
      ],
      "metadata": {
        "id": "knLWLH4wwvQ5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "weekday = pd.read_csv('/content/drive/MyDrive/KDT_project/Hackathon/CNNsegmentation/data/weekday.csv')\n",
        "weekday = weekday.drop('Unnamed: 0', axis=1)\n",
        "print(weekday.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "waWaS7RVxead",
        "outputId": "5f613a07-b769-45d1-dac9-ce528706ac52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1434, 7)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "weekday[\"Duration (hr)\"] = weekday[\"Duration (hr)\"].astype('float') * 1000\n",
        "weekday['Value']= weekday['Value'].replace('Core',1)\n",
        "weekday['Value']= weekday['Value'].replace('REM',0)\n",
        "weekday['Value']= weekday['Value'].replace('Deep',0)\n",
        "weekday['Value']= weekday['Value'].replace('Awake',0)\n",
        "tmp = weekday[[\"Duration (hr)\", \"Value\"]]\n",
        "tmp = np.array(tmp)\n",
        "\n",
        "x_list = []\n",
        "for x in tmp:\n",
        "  if x[1] != 0:\n",
        "    timeseries = np.ones([int(x[0])]) * x[1]\n",
        "    x_list.append(timeseries)\n",
        "  else:\n",
        "    timeseries = np.zeros([int(x[0])])\n",
        "    x_list.append(timeseries)\n",
        "\n",
        "ts = np.concatenate(x_list)\n",
        "print(ts.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FBlPDVumxnbF",
        "outputId": "8b4bc255-19ea-41b7-9f63-6293d2cb796a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(378383,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = ts\n",
        "x = np.arange(len(y))\n",
        "\n",
        "plt.plot(x, y, color='black',linewidth=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "OURM72cmyGG4",
        "outputId": "fa8789f1-8332-4f31-e469-8f88dc72956d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f6819c4ec20>]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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L4/EcMX/IkCHatGmTqqqqQuOiiy7S+eefr6qqKj5+AQAA0V0ZkaSSkhIVFRVp9OjRysvL0+LFi7Vv3z7NnDlTkjRjxgxlZ2fL5/MpKSlJw4YNC1s/NTVVko5YDgAA4lPUYaSwsFC7du3SwoUL5ff7NWrUKJWVlYVuaq2pqVFCAn/YFQAARMZhjDG2iziWYDColJQUBQIBJScnt9l2ExMTQ/ewAAAQ79o6EkR6/uYSBgAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwKq4DiMOh8N2CQAAxL24DiMAAMA+wggAALAqrsOIMcZ2CQAAxL24DiMAAMA+wggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAqrgOI/xQHgAA9sV1GAEAAPYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFgV12GEH8oDAMC+uA4jAADAPsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArIrrMMJv0wAAYF9chxEAAGAfYQQAAFhFGAEAAFYRRgAAgFWEEQAAYNVxhZElS5aob9++SkpKUn5+vtavX9/q3Icfflhjx45Vjx491KNHD3m93qPOBwAA8SXqMLJixQqVlJRo0aJFevfddzVy5EgVFBRo586dLc5fu3atpk6dqjVr1qiiokI5OTkaP368PvvssxMuHgAAdAAmSnl5eaa4uDj0uKmpyWRlZRmfzxfR+ocOHTLdu3c3jz32WMT7DAQCRpIJBALRlntUnTt3NpIYDAaDwWAo6khwTJGev6O6MtLY2KjKykp5vd7QsoSEBHm9XlVUVES0jf379+vgwYPq2bNnq3MaGhoUDAbDBgAA6JiiCiO7d+9WU1OT3G532HK32y2/3x/RNubMmaOsrKywQPO/fD6fUlJSQiMnJyeaMgEAQAw5qd+mufvuu1VaWqqVK1cqKSmp1Xlz585VIBAIjdra2pNYJQAAOJk6RTM5LS1NiYmJqqurC1teV1enjIyMo65777336u6779arr76qESNGHHWuy+WSy+WKpjQAABCjoroy4nQ6lZubq/Ly8tCy5uZmlZeXy+PxtLrePffcozvuuENlZWUaPXr08VfbxvihPAAA7IvqyogklZSUqKioSKNHj1ZeXp4WL16sffv2aebMmZKkGTNmKDs7Wz6fT5L0+9//XgsXLtQTTzyhvn37hu4t6datm7p169aGrQAAgFgUdRgpLCzUrl27tHDhQvn9fo0aNUplZWWhm1pramqUkPD/F1yWLl2qxsZG/fKXvwzbzqJFi3TrrbeeWPUAACDmOYwxxnYRxxIMBpWSkqJAIKDk5OQ2267L5VJjY2ObbQ8AgFjW1pEg0vM3v00DAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArIrrMMIP5QEAYF9chxEAAGAfYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVcR1G+KE8AADsi+swAgAA7COMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsCquwwg/lAcAgH1xHUYAAIB9hBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWxXUY4YfyAACwL67DCAAAsI8wAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrjiuMLFmyRH379lVSUpLy8/O1fv36o85/+umnNWTIECUlJWn48OFavXr1cRULAAA6nqjDyIoVK1RSUqJFixbp3Xff1ciRI1VQUKCdO3e2OP9f//qXpk6dqssuu0wbNmzQlClTNGXKFG3evPmEiwcAAB2AiVJeXp4pLi4OPW5qajJZWVnG5/O1OP+SSy4xkyZNCluWn59vrrzyyoj3GQgEjCQTCASiLfeounbtaiQxGAwGg8FQ1JHgmCI9f0d1ZaSxsVGVlZXyer2hZQkJCfJ6vaqoqGhxnYqKirD5klRQUNDqfElqaGhQMBgMGwAAoGOKKozs3r1bTU1NcrvdYcvdbrf8fn+L6/j9/qjmS5LP51NKSkpo5OTkRFNmxC6++OJ22S4AALFm6NCh1vZ9Sn6bZu7cuQoEAqFRW1vbLvv585//rMmTJ7f5dv83fJ2IrKysNtvW8RozZoz69OmjtLS0Y87t37//MeecccYZR+2rX79+Ou+886Kq8USNHTtWkpSWlqZBgwadtP3+9Kc/1fjx45WZmakhQ4ZoyJAhrc4dPXq0ZsyY0ab7b+149ejRI/TvlJQUpaamttk+v/e972nAgAHHvb7T6VRqaqr69evXZjUdy8iRIyOeO27cOPXt21ddunSJaH6vXr2OtyxlZmYqIyOjxedaW96SgQMHHncN7elk/rd4PPLz83XWWWcddc7YsWM1ZswYpaWlKSHh6Kfc7OzstiyvVWPGjNG4cePUuXPnsOVlZWUnZf8t6RTN5LS0NCUmJqquri5seV1d3VH/g4hmviS5XC65XK5oSjsuaWlpev7559t9P0Bbeuyxx2yXAABtKqorI06nU7m5uSovLw8ta25uVnl5uTweT4vreDyesPmS9Morr7Q6HwAAxJeoroxIUklJiYqKijR69Gjl5eVp8eLF2rdvn2bOnClJmjFjhrKzs+Xz+SRJ1157rcaNG6f77rtPkyZNUmlpqd555x099NBDbdsJAACISVGHkcLCQu3atUsLFy6U3+/XqFGjVFZWFrpPoqamJuxzsTFjxuiJJ57Q/PnzNW/ePA0aNEirVq3SsGHD2q4LAAAQsxzGGGO7iGMJBoNKSUlRIBBQcnKy7XIAAEAEIj1/n5LfpgEAAPGDMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwKuo/B2/D4T8SGwwGLVcCAAAidfi8faw/9h4TYaS+vl6SlJOTY7kSAAAQrfr6eqWkpLT6fEz8Nk1zc7M+//xzde/eXQ6Ho822GwwGlZOTo9ra2rj4zZt46jeeepXotyOLp16l+Oo3Hno1xqi+vl5ZWVlhP6L7v2LiykhCQoJ69erVbttPTk7usG+ElsRTv/HUq0S/HVk89SrFV78dvdejXRE5jBtYAQCAVYQRAABgVVyHEZfLpUWLFsnlctku5aSIp37jqVeJfjuyeOpViq9+46nXY4mJG1gBAEDHFddXRgAAgH2EEQAAYBVhBAAAWEUYAQAAVsV1GFmyZIn69u2rpKQk5efna/369bZLCnPrrbfK4XCEjSFDhoSe/+abb1RcXKzvf//76tatm37xi1+orq4ubBs1NTWaNGmSunbtqvT0dN144406dOhQ2Jy1a9fq7LPPlsvl0sCBA7V8+fIjammP1+qNN97Q5MmTlZWVJYfDoVWrVoU9b4zRwoULlZmZqS5dusjr9erjjz8Om/PVV19p2rRpSk5OVmpqqi677DLt3bs3bM7GjRs1duxYJSUlKScnR/fcc88RtTz99NMaMmSIkpKSNHz4cK1evTrqWk6k11//+tdHHOsJEybEZK8+n08/+MEP1L17d6Wnp2vKlCmqrq4Om3MqvXcjqeVE+/3Rj350xPG96qqrYrLfpUuXasSIEaE/1OXxePTSSy9Ftf2O0mtHOq7WmThVWlpqnE6neeSRR8z7779vZs2aZVJTU01dXZ3t0kIWLVpkzjzzTPPFF1+Exq5du0LPX3XVVSYnJ8eUl5ebd955x/zwhz80Y8aMCT1/6NAhM2zYMOP1es2GDRvM6tWrTVpampk7d25ozvbt203Xrl1NSUmJ2bJli3nggQdMYmKiKSsrC81pr9dq9erV5pZbbjHPPvuskWRWrlwZ9vzdd99tUlJSzKpVq8x7771nLrroItOvXz9z4MCB0JwJEyaYkSNHmrfeesv885//NAMHDjRTp04NPR8IBIzb7TbTpk0zmzdvNk8++aTp0qWL+ctf/hKa8+abb5rExERzzz33mC1btpj58+ebzp07m02bNkVVy4n0WlRUZCZMmBB2rL/66quwObHSa0FBgXn00UfN5s2bTVVVlbnwwgtN7969zd69e0NzTqX37rFqaYt+x40bZ2bNmhV2fAOBQEz2+/zzz5sXX3zRfPTRR6a6utrMmzfPdO7c2WzevDmi7XekXjvScbUtbsNIXl6eKS4uDj1uamoyWVlZxufzWawq3KJFi8zIkSNbfG7Pnj2mc+fO5umnnw4t++CDD4wkU1FRYYz59gSYkJBg/H5/aM7SpUtNcnKyaWhoMMYYc9NNN5kzzzwzbNuFhYWmoKAg9PhkvFb/e4Jubm42GRkZ5g9/+ENo2Z49e4zL5TJPPvmkMcaYLVu2GEnm3//+d2jOSy+9ZBwOh/nss8+MMcY8+OCDpkePHqF+jTFmzpw5ZvDgwaHHl1xyiZk0aVJYPfn5+ebKK6+MuJYT6dWYb8PIxRdf3Oo6sdqrMcbs3LnTSDKvv/56aHunyns3klpOtF9jvj1pXXvtta2uE8v9GmNMjx49zF//+tcOf2y/26sxHf+4nkxx+TFNY2OjKisr5fV6Q8sSEhLk9XpVUVFhsbIjffzxx8rKylL//v01bdo01dTUSJIqKyt18ODBsB6GDBmi3r17h3qoqKjQ8OHD5Xa7Q3MKCgoUDAb1/vvvh+Z8dxuH5xzehq3XaseOHfL7/WH7TUlJUX5+flh/qampGj16dGiO1+tVQkKC3n777dCc8847T06nM6y/6upqff3116E5R3sNIqmlLaxdu1bp6ekaPHiwrr76an355Zeh52K510AgIEnq2bOnpFPrvRtJLSfa72GPP/640tLSNGzYMM2dO1f79+8PPRer/TY1Nam0tFT79u2Tx+Pp0Mf2f3s9rCMeVxti4ofy2tru3bvV1NQU9gaRJLfbrQ8//NBSVUfKz8/X8uXLNXjwYH3xxRe67bbbNHbsWG3evFl+v19Op1Opqalh67jdbvn9fkmS3+9vscfDzx1tTjAY1IEDB/T1119bea0O19fSfr9be3p6etjznTp1Us+ePcPm9OvX74htHH6uR48erb4G393GsWo5URMmTNDPf/5z9evXT9u2bdO8efM0ceJEVVRUKDExMWZ7bW5u1nXXXadzzjlHw4YNC+3jVHnvRlLLifYrSZdeeqn69OmjrKwsbdy4UXPmzFF1dbWeffbZmOx306ZN8ng8+uabb9StWzetXLlSQ4cOVVVVVYc7tq31KnW842pTXIaRWDFx4sTQv0eMGKH8/Hz16dNHTz31lLp06WKxMrS1X/3qV6F/Dx8+XCNGjNCAAQO0du1aXXDBBRYrOzHFxcXavHmz1q1bZ7uUk6K1fq+44orQv4cPH67MzExdcMEF2rZtmwYMGHCyyzxhgwcPVlVVlQKBgJ555hkVFRXp9ddft11Wu2it16FDh3a442pTXH5Mk5aWpsTExCPuNK6rq1NGRoalqo4tNTVVp59+urZu3aqMjAw1NjZqz549YXO+20NGRkaLPR5+7mhzkpOT1aVLF2uv1eFtH22/GRkZ2rlzZ9jzhw4d0ldffdUmr8F3nz9WLW2tf//+SktL09atW0M1xFqv11xzjV544QWtWbNGvXr1Ci0/ld67kdRyov22JD8/X5LCjm8s9et0OjVw4EDl5ubK5/Np5MiR+tOf/tQhj21rvbYk1o+rTXEZRpxOp3Jzc1VeXh5a1tzcrPLy8rDPAk81e/fu1bZt25SZmanc3Fx17tw5rIfq6mrV1NSEevB4PNq0aVPYSeyVV15RcnJy6DKjx+MJ28bhOYe3Yeu16tevnzIyMsL2GwwG9fbbb4f1t2fPHlVWVobmvPbaa2pubg79T8Hj8eiNN97QwYMHw/obPHiwevToEZpztNcgklra2n/+8x99+eWXyszMjLlejTG65pprtHLlSr322mtHfHR0Kr13I6nlRPttSVVVlSSFHd9Y6bclzc3Namho6HDH9mi9tqSjHdeTyvYdtLaUlpYal8tlli9fbrZs2WKuuOIKk5qaGnbXs23XX3+9Wbt2rdmxY4d58803jdfrNWlpaWbnzp3GmG+/ytW7d2/z2muvmXfeecd4PB7j8XhC6x/+Wtn48eNNVVWVKSsrM6eddlqLXyu78cYbzQcffGCWLFnS4tfK2uO1qq+vNxs2bDAbNmwwksz9999vNmzYYD799FNjzLdfMU1NTTXPPfec2bhxo7n44otb/GrvWWedZd5++22zbt06M2jQoLCvu+7Zs8e43W4zffp0s3nzZlNaWmq6du16xNddO3XqZO69917zwQcfmEWLFrX4dddj1XK8vdbX15sbbrjBVFRUmB07dphXX33VnH322WbQoEHmm2++ibler776apOSkmLWrl0b9pXH/fv3h+acSu/dY9Vyov1u3brV3H777eadd94xO3bsMM8995zp37+/Oe+882Ky35tvvtm8/vrrZseOHWbjxo3m5ptvNg6Hw/zjH/+IaPsdpdeOdlxti9swYowxDzzwgOndu7dxOp0mLy/PvPXWW7ZLClNYWGgyMzON0+k02dnZprCw0GzdujX0/IEDB8xvf/tb06NHD9O1a1fzs5/9zHzxxRdh2/jkk0/MxIkTTZcuXUxaWpq5/vrrzcGDB8PmrFmzxowaNco4nU7Tv39/8+ijjx5RS3u8VmvWrDGSjhhFRUXGmG+/ZrpgwQLjdruNy+UyF1xwgamurg7bxpdffmmmTp1qunXrZpKTk83MmTNNfX192Jz33nvPnHvuucblcpns7Gxz9913H1HLU089ZU4//XTjdDrNmWeeaV588cWw5yOp5Xh73b9/vxk/frw57bTTTOfOnU2fPn3MrFmzjgh7sdJrS31KCntfnUrv3UhqOZF+a2pqzHnnnWd69uxpXC6XGThwoLnxxhvD/h5FLPX7m9/8xvTp08c4nU5z2mmnmQsuuCAURCLdfkfotaMdV9scxhhz8q7DAAAAhIvLe0YAAMCpgzACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAqv8DDfKFZCtQHBcAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JhSlKIR3yNTO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "AB_X2yJLxACM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## 해당 경로에 있는 .csv 파일명 리스트 가져오기\n",
        "\n",
        "import os\n",
        "\n",
        "path = '/content/drive/MyDrive/KDT_project/HACKATHON/data/day_split_weekday'\n",
        "file_list = os.listdir(path)\n",
        "file_name = [file for file in file_list if file.endswith('.csv')] ## 파일명 끝이 .csv인 경우"
      ],
      "metadata": {
        "id": "97xM9JnJyJPN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cd /content/drive/MyDrive/KDT_project/HACKATHON/data/day_split_weekday"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mQyd01JNzdjP",
        "outputId": "4ebbf5c8-8f82-4656-a746-667462fd0147"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/KDT_project/HACKATHON/data/day_split_weekday\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ZnEbI_Wc3rg4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len_list = []\n",
        "for file in file_name:\n",
        "  df = pd.read_csv(file)\n",
        "  df = df.drop('Unnamed: 0', axis=1)\n",
        "\n",
        "  tmp = df\n",
        "  tmp[\"Duration (hr)\"] = tmp[\"Duration (hr)\"].astype('float') * 1000\n",
        "  tmp['Value']= tmp['Value'].replace('Core',2)\n",
        "  tmp['Value']= tmp['Value'].replace('REM',3)\n",
        "  tmp['Value']= tmp['Value'].replace('Deep',1)\n",
        "  tmp['Value']= tmp['Value'].replace('Awake',4)\n",
        "  tmp = tmp[[\"Duration (hr)\", \"Value\"]]\n",
        "  tmp = np.array(tmp)\n",
        "\n",
        "  x_list = []\n",
        "  for x in tmp:\n",
        "    if x[1] != 0:\n",
        "      timeseries = np.ones([int(x[0])]) * x[1]\n",
        "      x_list.append(timeseries)\n",
        "\n",
        "  ts = np.concatenate(x_list)\n",
        "\n",
        "  \n",
        "  len_list.append(len(ts))"
      ],
      "metadata": {
        "id": "LZQgp79fzih-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sum(len_list) / len(len_list)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dr3udAI7zk3t",
        "outputId": "348502f3-3f51-4484-83a5-d70b2a9814de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7567.66"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "3aU1ZKH7zqi0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "OR0yzPBUwWYV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "\n",
        "# 데이터 전처리를 위한 라이브러리\n",
        "import json\n",
        "from scipy.signal import butter, lfilter, medfilt\n",
        "\n",
        "# 모델 학습을 위한 라이브러리\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.layers import Input, Conv1D, BatchNormalization, MaxPooling1D\n",
        "from tensorflow.keras.layers import Conv1DTranspose, concatenate, Activation, SpatialDropout1D\n",
        "\n",
        "# 성능평가 함수를 위한 라이브러리\n",
        "from tensorflow.keras import backend as K\n",
        "\n",
        "# 시각화를 위한 라이브러리\n",
        "from tqdm import tqdm\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "h_PltWEZuyMx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(action='ignore')\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "weekday = pd.read_csv('/content/drive/MyDrive/KDT_project/Hackathon/CNNsegmentation/data/weekday.csv')\n",
        "weekend = pd.read_csv('/content/drive/MyDrive/KDT_project/Hackathon/CNNsegmentation/data/weekend.csv')\n",
        "weekday = weekday.drop('Unnamed: 0', axis=1)\n",
        "weekend = weekend.drop('Unnamed: 0', axis=1)\n",
        "print(weekday.shape, weekend.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R6KFmSvFu3h4",
        "outputId": "2d13b25f-cdd1-4bc4-cfd1-8daa1bf36ab3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1434, 7) (575, 7)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "img_weekday = weekday.iloc[:,[2,3]]\n",
        "img_weekday[\"Duration (hr)\"] = img_weekday[\"Duration (hr)\"].astype('float') * 1000\n",
        "img_weekday['Value']= img_weekday['Value'].replace('Core',1)\n",
        "img_weekday['Value']= img_weekday['Value'].replace('REM',0)\n",
        "img_weekday['Value']= img_weekday['Value'].replace('Deep',0)\n",
        "img_weekday['Value']= img_weekday['Value'].replace('Awake',0)\n",
        "img_weekday = img_weekday[[\"Duration (hr)\", \"Value\"]]\n",
        "img_weekday = np.array(img_weekday)\n",
        "\n",
        "x_list = []   # 라벨을 duration 만큼 반복한 array를 list에 append\n",
        "for x in img_weekday:\n",
        "  if x[1] != 0:\n",
        "    timeseries = np.ones([int(x[0])]) * x[1]\n",
        "    x_list.append(timeseries)\n",
        "  else:\n",
        "    timeseries = np.zeros([int(x[0])]) * x[1]\n",
        "    x_list.append(timeseries)\n",
        "\n",
        "ts = np.concatenate(x_list)[:-3383]   # 7567로 나누어떨어지기 위해 데이터 버림.\n",
        "print(ts.shape)\n",
        "ts\n",
        "tmp = ts.reshape(50, 7500, -1)\n",
        "print(tmp.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6X8V8vgeu41V",
        "outputId": "3ff9df42-c0ac-471a-8ebd-95e20fb71fbe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(375000,)\n",
            "(50, 7500, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 변수 정의하기\n",
        "# variables\n",
        "FREQUENCY_OF_DATASET = 500   # 심박수 한 사이클. 1/Hz = 주기. 일단 해보고 바꾼다면 2000\n",
        "\n",
        "num_classes = 2 # Core, Not-Core"
      ],
      "metadata": {
        "id": "8mRAtbzgwDvF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 데이터 증강"
      ],
      "metadata": {
        "id": "9Nq-EwS10uiw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def augmentation(dataset, ratio = 0.8):\n",
        "  train_idx = int(50 * ratio)  # 80%를 트레인세트로 분류\n",
        "  idx = np.arange(50)        # 0 ~ 49\n",
        "  np.random.shuffle(idx)\n",
        "\n",
        "  x = dataset[:][idx]\n",
        "  y = dataset[:][idx]\n",
        "\n",
        "  # 2. Split into 8:2\n",
        "  tmp_train_x = x[:train_idx]\n",
        "  tmp_valid_x = x[train_idx:]\n",
        "  tmp_train_y = y[:train_idx]\n",
        "  tmp_valid_y = y[train_idx:]\n",
        "\n",
        "  # 3. Augmentation on Train and Valid dataset\n",
        "  # step 설명 : 250 (0.5초) 이동하며 4초 신호를 augmentation한다.\n",
        "  step = 250\n",
        "\n",
        "  N_train = 1500  # 여유있게 범위를 줌\n",
        "  N_valid = 400\n",
        "\n",
        "  train_x, train_y = np.zeros((N_train, 2000, 1), dtype=np.float32), np.zeros((N_train, 2000, 2), dtype=np.float32) \n",
        "  valid_x, valid_y = np.zeros((N_valid, 2000, 1), dtype=np.float32), np.zeros((N_valid, 2000, 2), dtype=np.float32) \n",
        "  # print(N_train, N_valid)\n",
        "  print(\"Start Augmentation\")\n",
        "\n",
        "  # 4.1. Train\n",
        "  t_cnt, v_cnt = 0, 0\n",
        "  for patient in range(train_idx):\n",
        "    tmp = tmp_train_y[patient]\n",
        "\n",
        "    # tmp = np.argmax(tmp[:, 1:], axis=-1) \n",
        "    tmp = np.nonzero(tmp)[0]            # 0이 아닌 값들의 index 들을 반환해 주는 함수\n",
        "    start_idx, end_idx = tmp[0], tmp[-1]\n",
        "    length = end_idx - start_idx + 1\n",
        "\n",
        "    tmp_x = tmp_train_x[patient, start_idx:end_idx + 1]\n",
        "    tmp_y = tmp_train_y[patient, start_idx:end_idx + 1]\n",
        "    for idx in range(0, length-2000, step):        # 원본 15초에서 6초만큼씩 잘라, step 만큼 이동하여 데이터 증강\n",
        "      train_x[t_cnt] = tmp_x[idx:idx + 2000, :]\n",
        "      train_y[t_cnt] = tmp_y[idx:idx + 2000, :]\n",
        "      t_cnt += 1\n",
        "  print(f\"Number of Train set : {t_cnt}\")\n",
        "\n",
        "  # # 4.2. Valid\n",
        "  for patient in range(50-train_idx):\n",
        "    tmp = tmp_valid_y[patient]\n",
        "    \n",
        "    tmp = np.nonzero(tmp)[0]  \n",
        "    start_idx, end_idx = tmp[0], tmp[-1]\n",
        "    length = end_idx - start_idx + 1\n",
        "\n",
        "    tmp_x = tmp_valid_x[patient, start_idx:end_idx + 1]\n",
        "    tmp_y = tmp_valid_y[patient, start_idx:end_idx + 1]\n",
        "    for idx in range(0, length-2000, step):\n",
        "      valid_x[v_cnt] = tmp_x[idx:idx + 2000, :]\n",
        "      valid_y[v_cnt] = tmp_y[idx:idx + 2000, :]\n",
        "      v_cnt += 1\n",
        "  print(f\"Number of Valid set : {v_cnt}\")\n",
        "\n",
        "  train_x, train_y = train_x[:t_cnt], train_y[:t_cnt]\n",
        "  valid_x, valid_y = valid_x[:v_cnt], valid_y[:v_cnt]\n",
        "\n",
        "  # 5. Shuffle and Save\n",
        "  print(\"\\nStart saving the dataset...\")\n",
        "  idx = np.arange(t_cnt)\n",
        "  np.random.shuffle(idx)\n",
        "  train_x = train_x[idx]\n",
        "  train_y = train_y[idx]\n",
        "\n",
        "  np.save(f\"train_x\", train_x)\n",
        "  np.save(f\"train_y\", train_y)\n",
        "\n",
        "  np.save(f\"valid_x\", valid_x)\n",
        "  np.save(f\"valid_y\", valid_y)\n",
        "  print(\"Complete saving\\n\")"
      ],
      "metadata": {
        "id": "urovyKx30vvh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 모델 설계"
      ],
      "metadata": {
        "id": "AtMtuRDc1lKc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def UNet1D():\n",
        "    num_filter = 16\n",
        "\n",
        "    inputs = Input((2000, 1))  # length of 4 sec signal is 2,000\n",
        "\n",
        "    # Encoder\n",
        "    x = Conv1D(num_filter, 9, kernel_initializer='he_normal', padding='same')(inputs)\n",
        "    # kernel_initializer : kernel가중치 행렬의 초기화 프로그램입니다( 참조 keras.initializers). 기본값은 'glorot_uniform'입니다.\n",
        "    x = BatchNormalization()(x)\n",
        "    x = SpatialDropout1D(0.3)(x)\n",
        "    x = Activation('relu')(x)\n",
        "    x = Conv1D(num_filter, 9, kernel_initializer='he_normal', padding='same')(x)\n",
        "    x = BatchNormalization()(x)\n",
        "    x = SpatialDropout1D(0.3)(x)\n",
        "    o1 = Activation('relu')(x)\n",
        "    x = MaxPooling1D(2)(o1)\n",
        "\n",
        "    x = Conv1D(num_filter*2, 9, kernel_initializer='he_normal', padding='same')(x)\n",
        "    x = BatchNormalization()(x)\n",
        "    x = SpatialDropout1D(0.3)(x)\n",
        "    x = Activation('relu')(x)\n",
        "    x = Conv1D(num_filter*2, 9, kernel_initializer='he_normal', padding='same')(x)\n",
        "    x = BatchNormalization()(x)\n",
        "    x = SpatialDropout1D(0.3)(x)\n",
        "    o2 = Activation('relu')(x)\n",
        "    x = MaxPooling1D(2)(o2)\n",
        "\n",
        "    x = Conv1D(num_filter*4, 9, kernel_initializer='he_normal', padding='same')(x)\n",
        "    x = BatchNormalization()(x)\n",
        "    x = SpatialDropout1D(0.3)(x)\n",
        "    x = Activation('relu')(x)\n",
        "    x = Conv1D(num_filter*4, 9, kernel_initializer='he_normal', padding='same')(x)\n",
        "    x = BatchNormalization()(x)\n",
        "    x = SpatialDropout1D(0.3)(x)\n",
        "    o3 = Activation('relu')(x)\n",
        "    x = MaxPooling1D(2)(o3)\n",
        "\n",
        "    x = Conv1D(num_filter*8, 9, kernel_initializer='he_normal', padding='same')(x)\n",
        "    x = BatchNormalization()(x)\n",
        "    x = SpatialDropout1D(0.3)(x)\n",
        "    x = Activation('relu')(x)\n",
        "    x = Conv1D(num_filter*8, 9, kernel_initializer='he_normal', padding='same')(x)\n",
        "    x = BatchNormalization()(x)\n",
        "    x = SpatialDropout1D(0.3)(x)\n",
        "    o4 = Activation('relu')(x)\n",
        "    x = MaxPooling1D(2)(o4)\n",
        "\n",
        "    x = Conv1D(num_filter*16, 9, kernel_initializer='he_normal', padding='same')(x)\n",
        "    x = BatchNormalization()(x)\n",
        "    x = SpatialDropout1D(0.3)(x)\n",
        "    x = Activation('relu')(x)\n",
        "    x = Conv1D(num_filter*16, 9, kernel_initializer='he_normal', padding='same')(x)\n",
        "    x = BatchNormalization()(x)\n",
        "    x = SpatialDropout1D(0.3)(x)\n",
        "    x = Activation('relu')(x)  \n",
        "\n",
        "    # Decoder\n",
        "    x = Conv1DTranspose(num_filter*8, 8, strides=2, padding='same')(x)\n",
        "    print(x.shape, o4.shape)\n",
        "    x = concatenate([x, o4])\n",
        "    x = Conv1D(num_filter*8, 9, kernel_initializer='he_normal', padding='same')(x)\n",
        "    x = BatchNormalization()(x)\n",
        "    x = SpatialDropout1D(0.3)(x)\n",
        "    x = Activation('relu')(x)\n",
        "    x = Conv1D(num_filter*8, 9, kernel_initializer='he_normal', padding='same')(x)\n",
        "    x = BatchNormalization()(x)\n",
        "    x = SpatialDropout1D(0.3)(x)\n",
        "    x = Activation('relu')(x)\n",
        "\n",
        "    x = Conv1DTranspose(num_filter*4, 8, strides=2, padding='same')(x)\n",
        "    print(x.shape, o3.shape)\n",
        "    x = concatenate([x, o3])\n",
        "    x = Conv1D(num_filter*4, 9, kernel_initializer='he_normal', padding='same')(x)\n",
        "    x = BatchNormalization()(x)\n",
        "    x = SpatialDropout1D(0.3)(x)\n",
        "    x = Activation('relu')(x)\n",
        "    x = Conv1D(num_filter*4, 9, kernel_initializer='he_normal', padding='same')(x)\n",
        "    x = BatchNormalization()(x)\n",
        "    x = SpatialDropout1D(0.3)(x)\n",
        "    x = Activation('relu')(x)\n",
        "\n",
        "    x = Conv1DTranspose(num_filter*2, 8, strides=2, padding='same')(x)\n",
        "    print(x.shape, o2.shape)\n",
        "    x = concatenate([x, o2])\n",
        "    x = Conv1D(num_filter*2, 9, kernel_initializer='he_normal', padding='same')(x)\n",
        "    x = BatchNormalization()(x)\n",
        "    x = SpatialDropout1D(0.3)(x)\n",
        "    x = Activation('relu')(x)\n",
        "    x = Conv1D(num_filter*2, 9, kernel_initializer='he_normal', padding='same')(x)\n",
        "    x = BatchNormalization()(x)\n",
        "    x = SpatialDropout1D(0.3)(x)\n",
        "    x = Activation('relu')(x)\n",
        "\n",
        "    x = Conv1DTranspose(num_filter, 8, strides=2, padding='same')(x)\n",
        "    print(x.shape, o1.shape)\n",
        "    x = concatenate([x, o1])\n",
        "    x = Conv1D(num_filter, 9, kernel_initializer='he_normal', padding='same')(x)\n",
        "    x = BatchNormalization()(x)\n",
        "    x = SpatialDropout1D(0.3)(x)\n",
        "    x = Activation('relu')(x)\n",
        "    x = Conv1D(num_filter, 9, kernel_initializer='he_normal', padding='same')(x)\n",
        "    x = BatchNormalization()(x)\n",
        "    x = SpatialDropout1D(0.3)(x)\n",
        "    x = Activation('relu')(x)\n",
        "\n",
        "    outputs = Conv1D(num_classes, 1, activation='softmax', kernel_initializer='he_normal')(x)\n",
        "\n",
        "    model = Model(inputs=inputs, outputs=outputs)\n",
        "    return model"
      ],
      "metadata": {
        "id": "Bjw1p69s1lv3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 성능평가 함수 정의"
      ],
      "metadata": {
        "id": "YMOUbR892Pp5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. F1-Score = Dice score\n",
        "def f1_score(y_true, y_pred):\n",
        "    smooth = 0.0001\n",
        "    intersection = K.sum(y_true * y_pred, axis=[1, 2])\n",
        "    union = K.sum(y_true, axis=[1, 2]) + K.sum(y_pred, axis=[1, 2])\n",
        "    return K.mean( (2. * intersection + smooth) / (union + smooth), axis=0)\n",
        "\n",
        "class f1_loss(tf.losses.Loss):\n",
        "    def __init__(self):\n",
        "        super(f1_loss, self).__init__(reduction='auto', name=\"f1_loss\")\n",
        "\n",
        "    def call(self, y_true, y_pred):\n",
        "        return 1 - f1_score(y_true, y_pred)\n",
        "\n",
        "\n",
        "# 2. Jaccard = IoU\n",
        "def Jaccard(y_true, y_pred):\n",
        "    smooth = 0.0001\n",
        "    intersection = K.sum(y_true * y_pred, axis=[1, 2])\n",
        "    union = K.sum(y_true, axis=[1, 2]) + K.sum(y_pred, axis=[1, 2])\n",
        "    return K.mean((intersection + smooth) / (union - intersection + smooth), axis=0)\n",
        "\n",
        "class JaccardLoss(tf.losses.Loss):\n",
        "    def __init__(self):\n",
        "        super(JaccardLoss, self).__init__(reduction='auto', name=\"JaccardLoss\")\n",
        "\n",
        "    def call(self, y_true, y_pred):\n",
        "        return 1 - Jaccard(y_true, y_pred)"
      ],
      "metadata": {
        "id": "PyZMEtWV1S71"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 예측함수 정의"
      ],
      "metadata": {
        "id": "aexAlcN82SZS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def Predict(test, n_sample=5):\n",
        "    '''\n",
        "    Input\n",
        "        - test: [valid_x, valid_y]\n",
        "        - n_sample: 보여줄 샘플 갯수\n",
        "    테스트셋에 대해서 예측한 결과와 실제 labeling 데이터를 시각화한다.    \n",
        "    '''\n",
        "    model = UNet1D()\n",
        "    model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.005),\n",
        "                  loss='binary_crossentropy',\n",
        "                  metrics=[Jaccard, f1_score])\n",
        "    model.load_weights(\"weights.h5\")\n",
        "\n",
        "    for i, test_ecg in enumerate(test[0]): # valid_x\n",
        "        pred = model.predict(np.reshape(test_ecg, (1, 2000, 1)))\n",
        "        true = np.argmax(test[1][i], axis=-1)\n",
        "        pred = np.argmax(pred[0], axis=-1)\n",
        "\n",
        "        fig, ax = plt.subplots(3, 1, figsize=(20, 12))\n",
        "\n",
        "        ax[0].plot(test_ecg[:, 1])\n",
        "        ax[0].set_title(f\"ECG_{i}'s lead II\", fontsize=10)\n",
        "\n",
        "        ax[1].plot(true)\n",
        "        ax[1].set_title(f\"ECG_{i}'s Groud Truth\", fontsize=10)\n",
        "\n",
        "        ax[2].plot(pred)\n",
        "        ax[2].set_title(f\"ECG_{i}'s Prediction\", fontsize=10)\n",
        "\n",
        "        plt.show()\n",
        "        \n",
        "        # 20개의 샘플만 보여준 후 멈춤\n",
        "        if i == n_sample - 1:\n",
        "            break"
      ],
      "metadata": {
        "id": "xvmkoZj42TGJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "PkBrNYdNWi6d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LTLxOBqZWiuL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 데이터 증강"
      ],
      "metadata": {
        "id": "9mvtu7d-WjKS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Augmentation\n",
        "augmentation(tmp)\n",
        "del tmp"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "74IsH33D2uy3",
        "outputId": "88ef3eeb-746f-4f54-d35c-17a6e58ae482"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Start Augmentation\n",
            "Number of Train set : 867\n",
            "Number of Valid set : 220\n",
            "\n",
            "Start saving the dataset...\n",
            "Complete saving\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터 로드\n",
        "train = [np.load(\"train_x.npy\"), np.load(\"train_y.npy\")]\n",
        "valid = [np.load(\"valid_x.npy\"), np.load(\"valid_y.npy\")]"
      ],
      "metadata": {
        "id": "tvCqOrmU2xJ9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 모델 학습"
      ],
      "metadata": {
        "id": "e3KbbruK2zzF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 4. Model\n",
        "print(\"Start Training\")\n",
        "batch_size = 256\n",
        "lr = 0.005\n",
        "epochs = 200\n",
        "\n",
        "model = UNet1D()\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iN52RXUk200Y",
        "outputId": "757df992-159e-4138-a40f-e5bf9ee37af2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Start Training\n",
            "(None, 250, 128) (None, 250, 128)\n",
            "(None, 500, 64) (None, 500, 64)\n",
            "(None, 1000, 32) (None, 1000, 32)\n",
            "(None, 2000, 16) (None, 2000, 16)\n",
            "Model: \"model_3\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_4 (InputLayer)           [(None, 2000, 1)]    0           []                               \n",
            "                                                                                                  \n",
            " conv1d_57 (Conv1D)             (None, 2000, 16)     160         ['input_4[0][0]']                \n",
            "                                                                                                  \n",
            " batch_normalization_54 (BatchN  (None, 2000, 16)    64          ['conv1d_57[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " spatial_dropout1d_54 (SpatialD  (None, 2000, 16)    0           ['batch_normalization_54[0][0]'] \n",
            " ropout1D)                                                                                        \n",
            "                                                                                                  \n",
            " activation_54 (Activation)     (None, 2000, 16)     0           ['spatial_dropout1d_54[0][0]']   \n",
            "                                                                                                  \n",
            " conv1d_58 (Conv1D)             (None, 2000, 16)     2320        ['activation_54[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_55 (BatchN  (None, 2000, 16)    64          ['conv1d_58[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " spatial_dropout1d_55 (SpatialD  (None, 2000, 16)    0           ['batch_normalization_55[0][0]'] \n",
            " ropout1D)                                                                                        \n",
            "                                                                                                  \n",
            " activation_55 (Activation)     (None, 2000, 16)     0           ['spatial_dropout1d_55[0][0]']   \n",
            "                                                                                                  \n",
            " max_pooling1d_12 (MaxPooling1D  (None, 1000, 16)    0           ['activation_55[0][0]']          \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " conv1d_59 (Conv1D)             (None, 1000, 32)     4640        ['max_pooling1d_12[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_56 (BatchN  (None, 1000, 32)    128         ['conv1d_59[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " spatial_dropout1d_56 (SpatialD  (None, 1000, 32)    0           ['batch_normalization_56[0][0]'] \n",
            " ropout1D)                                                                                        \n",
            "                                                                                                  \n",
            " activation_56 (Activation)     (None, 1000, 32)     0           ['spatial_dropout1d_56[0][0]']   \n",
            "                                                                                                  \n",
            " conv1d_60 (Conv1D)             (None, 1000, 32)     9248        ['activation_56[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_57 (BatchN  (None, 1000, 32)    128         ['conv1d_60[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " spatial_dropout1d_57 (SpatialD  (None, 1000, 32)    0           ['batch_normalization_57[0][0]'] \n",
            " ropout1D)                                                                                        \n",
            "                                                                                                  \n",
            " activation_57 (Activation)     (None, 1000, 32)     0           ['spatial_dropout1d_57[0][0]']   \n",
            "                                                                                                  \n",
            " max_pooling1d_13 (MaxPooling1D  (None, 500, 32)     0           ['activation_57[0][0]']          \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " conv1d_61 (Conv1D)             (None, 500, 64)      18496       ['max_pooling1d_13[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_58 (BatchN  (None, 500, 64)     256         ['conv1d_61[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " spatial_dropout1d_58 (SpatialD  (None, 500, 64)     0           ['batch_normalization_58[0][0]'] \n",
            " ropout1D)                                                                                        \n",
            "                                                                                                  \n",
            " activation_58 (Activation)     (None, 500, 64)      0           ['spatial_dropout1d_58[0][0]']   \n",
            "                                                                                                  \n",
            " conv1d_62 (Conv1D)             (None, 500, 64)      36928       ['activation_58[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_59 (BatchN  (None, 500, 64)     256         ['conv1d_62[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " spatial_dropout1d_59 (SpatialD  (None, 500, 64)     0           ['batch_normalization_59[0][0]'] \n",
            " ropout1D)                                                                                        \n",
            "                                                                                                  \n",
            " activation_59 (Activation)     (None, 500, 64)      0           ['spatial_dropout1d_59[0][0]']   \n",
            "                                                                                                  \n",
            " max_pooling1d_14 (MaxPooling1D  (None, 250, 64)     0           ['activation_59[0][0]']          \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " conv1d_63 (Conv1D)             (None, 250, 128)     73856       ['max_pooling1d_14[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_60 (BatchN  (None, 250, 128)    512         ['conv1d_63[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " spatial_dropout1d_60 (SpatialD  (None, 250, 128)    0           ['batch_normalization_60[0][0]'] \n",
            " ropout1D)                                                                                        \n",
            "                                                                                                  \n",
            " activation_60 (Activation)     (None, 250, 128)     0           ['spatial_dropout1d_60[0][0]']   \n",
            "                                                                                                  \n",
            " conv1d_64 (Conv1D)             (None, 250, 128)     147584      ['activation_60[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_61 (BatchN  (None, 250, 128)    512         ['conv1d_64[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " spatial_dropout1d_61 (SpatialD  (None, 250, 128)    0           ['batch_normalization_61[0][0]'] \n",
            " ropout1D)                                                                                        \n",
            "                                                                                                  \n",
            " activation_61 (Activation)     (None, 250, 128)     0           ['spatial_dropout1d_61[0][0]']   \n",
            "                                                                                                  \n",
            " max_pooling1d_15 (MaxPooling1D  (None, 125, 128)    0           ['activation_61[0][0]']          \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " conv1d_65 (Conv1D)             (None, 125, 256)     295168      ['max_pooling1d_15[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_62 (BatchN  (None, 125, 256)    1024        ['conv1d_65[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " spatial_dropout1d_62 (SpatialD  (None, 125, 256)    0           ['batch_normalization_62[0][0]'] \n",
            " ropout1D)                                                                                        \n",
            "                                                                                                  \n",
            " activation_62 (Activation)     (None, 125, 256)     0           ['spatial_dropout1d_62[0][0]']   \n",
            "                                                                                                  \n",
            " conv1d_66 (Conv1D)             (None, 125, 256)     590080      ['activation_62[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_63 (BatchN  (None, 125, 256)    1024        ['conv1d_66[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " spatial_dropout1d_63 (SpatialD  (None, 125, 256)    0           ['batch_normalization_63[0][0]'] \n",
            " ropout1D)                                                                                        \n",
            "                                                                                                  \n",
            " activation_63 (Activation)     (None, 125, 256)     0           ['spatial_dropout1d_63[0][0]']   \n",
            "                                                                                                  \n",
            " conv1d_transpose_12 (Conv1DTra  (None, 250, 128)    262272      ['activation_63[0][0]']          \n",
            " nspose)                                                                                          \n",
            "                                                                                                  \n",
            " concatenate_12 (Concatenate)   (None, 250, 256)     0           ['conv1d_transpose_12[0][0]',    \n",
            "                                                                  'activation_61[0][0]']          \n",
            "                                                                                                  \n",
            " conv1d_67 (Conv1D)             (None, 250, 128)     295040      ['concatenate_12[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_64 (BatchN  (None, 250, 128)    512         ['conv1d_67[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " spatial_dropout1d_64 (SpatialD  (None, 250, 128)    0           ['batch_normalization_64[0][0]'] \n",
            " ropout1D)                                                                                        \n",
            "                                                                                                  \n",
            " activation_64 (Activation)     (None, 250, 128)     0           ['spatial_dropout1d_64[0][0]']   \n",
            "                                                                                                  \n",
            " conv1d_68 (Conv1D)             (None, 250, 128)     147584      ['activation_64[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_65 (BatchN  (None, 250, 128)    512         ['conv1d_68[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " spatial_dropout1d_65 (SpatialD  (None, 250, 128)    0           ['batch_normalization_65[0][0]'] \n",
            " ropout1D)                                                                                        \n",
            "                                                                                                  \n",
            " activation_65 (Activation)     (None, 250, 128)     0           ['spatial_dropout1d_65[0][0]']   \n",
            "                                                                                                  \n",
            " conv1d_transpose_13 (Conv1DTra  (None, 500, 64)     65600       ['activation_65[0][0]']          \n",
            " nspose)                                                                                          \n",
            "                                                                                                  \n",
            " concatenate_13 (Concatenate)   (None, 500, 128)     0           ['conv1d_transpose_13[0][0]',    \n",
            "                                                                  'activation_59[0][0]']          \n",
            "                                                                                                  \n",
            " conv1d_69 (Conv1D)             (None, 500, 64)      73792       ['concatenate_13[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_66 (BatchN  (None, 500, 64)     256         ['conv1d_69[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " spatial_dropout1d_66 (SpatialD  (None, 500, 64)     0           ['batch_normalization_66[0][0]'] \n",
            " ropout1D)                                                                                        \n",
            "                                                                                                  \n",
            " activation_66 (Activation)     (None, 500, 64)      0           ['spatial_dropout1d_66[0][0]']   \n",
            "                                                                                                  \n",
            " conv1d_70 (Conv1D)             (None, 500, 64)      36928       ['activation_66[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_67 (BatchN  (None, 500, 64)     256         ['conv1d_70[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " spatial_dropout1d_67 (SpatialD  (None, 500, 64)     0           ['batch_normalization_67[0][0]'] \n",
            " ropout1D)                                                                                        \n",
            "                                                                                                  \n",
            " activation_67 (Activation)     (None, 500, 64)      0           ['spatial_dropout1d_67[0][0]']   \n",
            "                                                                                                  \n",
            " conv1d_transpose_14 (Conv1DTra  (None, 1000, 32)    16416       ['activation_67[0][0]']          \n",
            " nspose)                                                                                          \n",
            "                                                                                                  \n",
            " concatenate_14 (Concatenate)   (None, 1000, 64)     0           ['conv1d_transpose_14[0][0]',    \n",
            "                                                                  'activation_57[0][0]']          \n",
            "                                                                                                  \n",
            " conv1d_71 (Conv1D)             (None, 1000, 32)     18464       ['concatenate_14[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_68 (BatchN  (None, 1000, 32)    128         ['conv1d_71[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " spatial_dropout1d_68 (SpatialD  (None, 1000, 32)    0           ['batch_normalization_68[0][0]'] \n",
            " ropout1D)                                                                                        \n",
            "                                                                                                  \n",
            " activation_68 (Activation)     (None, 1000, 32)     0           ['spatial_dropout1d_68[0][0]']   \n",
            "                                                                                                  \n",
            " conv1d_72 (Conv1D)             (None, 1000, 32)     9248        ['activation_68[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_69 (BatchN  (None, 1000, 32)    128         ['conv1d_72[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " spatial_dropout1d_69 (SpatialD  (None, 1000, 32)    0           ['batch_normalization_69[0][0]'] \n",
            " ropout1D)                                                                                        \n",
            "                                                                                                  \n",
            " activation_69 (Activation)     (None, 1000, 32)     0           ['spatial_dropout1d_69[0][0]']   \n",
            "                                                                                                  \n",
            " conv1d_transpose_15 (Conv1DTra  (None, 2000, 16)    4112        ['activation_69[0][0]']          \n",
            " nspose)                                                                                          \n",
            "                                                                                                  \n",
            " concatenate_15 (Concatenate)   (None, 2000, 32)     0           ['conv1d_transpose_15[0][0]',    \n",
            "                                                                  'activation_55[0][0]']          \n",
            "                                                                                                  \n",
            " conv1d_73 (Conv1D)             (None, 2000, 16)     4624        ['concatenate_15[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_70 (BatchN  (None, 2000, 16)    64          ['conv1d_73[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " spatial_dropout1d_70 (SpatialD  (None, 2000, 16)    0           ['batch_normalization_70[0][0]'] \n",
            " ropout1D)                                                                                        \n",
            "                                                                                                  \n",
            " activation_70 (Activation)     (None, 2000, 16)     0           ['spatial_dropout1d_70[0][0]']   \n",
            "                                                                                                  \n",
            " conv1d_74 (Conv1D)             (None, 2000, 16)     2320        ['activation_70[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_71 (BatchN  (None, 2000, 16)    64          ['conv1d_74[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " spatial_dropout1d_71 (SpatialD  (None, 2000, 16)    0           ['batch_normalization_71[0][0]'] \n",
            " ropout1D)                                                                                        \n",
            "                                                                                                  \n",
            " activation_71 (Activation)     (None, 2000, 16)     0           ['spatial_dropout1d_71[0][0]']   \n",
            "                                                                                                  \n",
            " conv1d_75 (Conv1D)             (None, 2000, 2)      34          ['activation_71[0][0]']          \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 2,120,802\n",
            "Trainable params: 2,117,858\n",
            "Non-trainable params: 2,944\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 5. Train\n",
        "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=lr),\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['accuracy', f1_score])\n",
        "\n",
        "callbacks_list = [\n",
        "    tf.keras.callbacks.ModelCheckpoint(\n",
        "        filepath=\"weights.h5\",\n",
        "        monitor=\"val_f1_score\",\n",
        "        mode=\"max\",\n",
        "        save_best_only=True,\n",
        "        save_weights_only=True,\n",
        "        verbose=1),\n",
        "\n",
        "    tf.keras.callbacks.EarlyStopping(\n",
        "        monitor=\"val_f1_score\",\n",
        "        min_delta=0.005,\n",
        "        mode=\"max\",\n",
        "        patience=10,\n",
        "        verbose=1),\n",
        "\n",
        "    tf.keras.callbacks.ReduceLROnPlateau(\n",
        "        monitor='val_f1_score',\n",
        "        factor=0.5,\n",
        "        patience=5,\n",
        "        mode='max',\n",
        "        min_delta=0.01,\n",
        "        cooldown=0,\n",
        "        min_lr=0.0001,\n",
        "        verbose=1)\n",
        "]\n",
        "\n",
        "history = model.fit(train[0], train[1],\n",
        "                    validation_data=(valid[0], valid[1]),\n",
        "                    callbacks=callbacks_list,\n",
        "                    epochs=epochs,\n",
        "                    batch_size=batch_size,\n",
        "                    shuffle=True,\n",
        "                    )\n",
        "\n",
        "\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "accuracy = history.history[\"accuracy\"]\n",
        "val_acc = history.history[\"val_accuracy\"]\n",
        "# f1 = history.history[\"f1_score\"]\n",
        "# val_f1 = history.history[\"val_f1_score\"]\n",
        "\n",
        "epochs_range = range(len(loss))\n",
        "fig, ax = plt.subplots(1, 3, figsize = (20, 12))\n",
        "ax[0].plot(epochs_range, loss, label='Training Loss')\n",
        "ax[0].plot(epochs_range, val_loss, label='Validation Loss')\n",
        "\n",
        "ax[1].plot(epochs_range, accuracy, label='Training Accuracy')\n",
        "ax[1].plot(epochs_range, val_acc, label='Validation Accuracy')\n",
        "\n",
        "# ax[2].plot(epochs_range, f1, label='Training f1-score')\n",
        "# ax[2].plot(epochs_range, val_f1, label='Validation f1-score')\n",
        "plt.show()\n",
        "\n",
        "del model, history, train\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ByXO3dtj21Bu",
        "outputId": "2406f939-ade9-4767-88d1-545baef301ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "4/4 [==============================] - ETA: 0s - loss: 0.6361 - accuracy: 0.4095 - f1_score: 0.5571\n",
            "Epoch 1: val_f1_score improved from -inf to 0.56120, saving model to weights.h5\n",
            "4/4 [==============================] - 25s 2s/step - loss: 0.6361 - accuracy: 0.4095 - f1_score: 0.5571 - val_loss: 3844.3999 - val_accuracy: 0.0010 - val_f1_score: 0.5612 - lr: 0.0050\n",
            "Epoch 2/200\n",
            "4/4 [==============================] - ETA: 0s - loss: 0.3606 - accuracy: 0.4639 - f1_score: 0.5589\n",
            "Epoch 2: val_f1_score did not improve from 0.56120\n",
            "4/4 [==============================] - 2s 389ms/step - loss: 0.3606 - accuracy: 0.4639 - f1_score: 0.5589 - val_loss: 67584.7891 - val_accuracy: 0.2470 - val_f1_score: 0.5612 - lr: 0.0050\n",
            "Epoch 3/200\n",
            "4/4 [==============================] - ETA: 0s - loss: 0.3029 - accuracy: 0.4937 - f1_score: 0.5579\n",
            "Epoch 3: val_f1_score did not improve from 0.56120\n",
            "4/4 [==============================] - 2s 378ms/step - loss: 0.3029 - accuracy: 0.4937 - f1_score: 0.5579 - val_loss: 277275.9375 - val_accuracy: 0.0000e+00 - val_f1_score: 0.5612 - lr: 0.0050\n",
            "Epoch 4/200\n",
            "4/4 [==============================] - ETA: 0s - loss: 0.2620 - accuracy: 0.4841 - f1_score: 0.5583\n",
            "Epoch 4: val_f1_score did not improve from 0.56120\n",
            "4/4 [==============================] - 2s 376ms/step - loss: 0.2620 - accuracy: 0.4841 - f1_score: 0.5583 - val_loss: 326039.2500 - val_accuracy: 0.0000e+00 - val_f1_score: 0.5612 - lr: 0.0050\n",
            "Epoch 5/200\n",
            "4/4 [==============================] - ETA: 0s - loss: 0.2278 - accuracy: 0.4793 - f1_score: 0.5583\n",
            "Epoch 5: val_f1_score did not improve from 0.56120\n",
            "4/4 [==============================] - 2s 376ms/step - loss: 0.2278 - accuracy: 0.4793 - f1_score: 0.5583 - val_loss: 113059.7734 - val_accuracy: 5.0000e-04 - val_f1_score: 0.5612 - lr: 0.0050\n",
            "Epoch 6/200\n",
            "4/4 [==============================] - ETA: 0s - loss: 0.2030 - accuracy: 0.5094 - f1_score: 0.5573\n",
            "Epoch 6: val_f1_score did not improve from 0.56120\n",
            "\n",
            "Epoch 6: ReduceLROnPlateau reducing learning rate to 0.0024999999441206455.\n",
            "4/4 [==============================] - 2s 407ms/step - loss: 0.2030 - accuracy: 0.5094 - f1_score: 0.5573 - val_loss: 123751.9766 - val_accuracy: 5.0000e-04 - val_f1_score: 0.5612 - lr: 0.0050\n",
            "Epoch 7/200\n",
            "4/4 [==============================] - ETA: 0s - loss: 0.1784 - accuracy: 0.4977 - f1_score: 0.5587\n",
            "Epoch 7: val_f1_score did not improve from 0.56120\n",
            "4/4 [==============================] - 2s 407ms/step - loss: 0.1784 - accuracy: 0.4977 - f1_score: 0.5587 - val_loss: 57708.1992 - val_accuracy: 0.0000e+00 - val_f1_score: 0.5612 - lr: 0.0025\n",
            "Epoch 8/200\n",
            "4/4 [==============================] - ETA: 0s - loss: 0.1654 - accuracy: 0.4928 - f1_score: 0.5586\n",
            "Epoch 8: val_f1_score did not improve from 0.56120\n",
            "4/4 [==============================] - 2s 385ms/step - loss: 0.1654 - accuracy: 0.4928 - f1_score: 0.5586 - val_loss: 22595.4336 - val_accuracy: 0.0000e+00 - val_f1_score: 0.5612 - lr: 0.0025\n",
            "Epoch 9/200\n",
            "4/4 [==============================] - ETA: 0s - loss: 0.1559 - accuracy: 0.4988 - f1_score: 0.5586\n",
            "Epoch 9: val_f1_score did not improve from 0.56120\n",
            "4/4 [==============================] - 2s 389ms/step - loss: 0.1559 - accuracy: 0.4988 - f1_score: 0.5586 - val_loss: 6066.0674 - val_accuracy: 5.0000e-04 - val_f1_score: 0.5612 - lr: 0.0025\n",
            "Epoch 10/200\n",
            "4/4 [==============================] - ETA: 0s - loss: 0.1418 - accuracy: 0.5141 - f1_score: 0.5574\n",
            "Epoch 10: val_f1_score did not improve from 0.56120\n",
            "4/4 [==============================] - 2s 374ms/step - loss: 0.1418 - accuracy: 0.5141 - f1_score: 0.5574 - val_loss: 1318.4298 - val_accuracy: 0.0010 - val_f1_score: 0.5612 - lr: 0.0025\n",
            "Epoch 11/200\n",
            "4/4 [==============================] - ETA: 0s - loss: 0.1329 - accuracy: 0.5009 - f1_score: 0.5579\n",
            "Epoch 11: val_f1_score did not improve from 0.56120\n",
            "\n",
            "Epoch 11: ReduceLROnPlateau reducing learning rate to 0.0012499999720603228.\n",
            "4/4 [==============================] - 2s 375ms/step - loss: 0.1329 - accuracy: 0.5009 - f1_score: 0.5579 - val_loss: 573.5456 - val_accuracy: 0.0010 - val_f1_score: 0.5612 - lr: 0.0025\n",
            "Epoch 11: early stopping\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1200 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# valid = [np.load(\"valid_x.npy\"), np.load(\"valid_y.npy\")]\n",
        "# print(valid[0].shape)\n",
        "\n",
        "# Predict(test=valid)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "J9SA3boH2yOG",
        "outputId": "65ea9c1a-ba3a-449a-96f6-3f33ed9a1d81"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(217, 2000, 1)\n",
            "(None, 250, 128) (None, 250, 128)\n",
            "(None, 500, 64) (None, 500, 64)\n",
            "(None, 1000, 32) (None, 1000, 32)\n",
            "(None, 2000, 16) (None, 2000, 16)\n",
            "1/1 [==============================] - 1s 1s/step\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "IndexError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-22-074d3b6d7965>\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalid\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mPredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-17-d71e00bcca6e>\u001b[0m in \u001b[0;36mPredict\u001b[0;34m(test, n_sample)\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m12\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m         \u001b[0max\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_ecg\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"ECG_{i}'s lead II\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfontsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mIndexError\u001b[0m: index 1 is out of bounds for axis 1 with size 1"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1200 with 3 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "I23Af_tK-2nr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "r_v3a7lY2Q-G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "img_weekday = weekday.iloc[:,[2,3]]\n",
        "img_weekday[\"Duration (hr)\"] = img_weekday[\"Duration (hr)\"].astype('float') * 1000\n",
        "img_weekday['Value']= img_weekday['Value'].replace('Core',1)\n",
        "img_weekday['Value']= img_weekday['Value'].replace('REM',0)\n",
        "img_weekday['Value']= img_weekday['Value'].replace('Deep',0)\n",
        "img_weekday['Value']= img_weekday['Value'].replace('Awake',0)\n",
        "img_weekday = img_weekday[[\"Duration (hr)\", \"Value\"]]\n",
        "img_weekday = np.array(img_weekday)\n",
        "\n",
        "x_list = []   # 라벨을 duration 만큼 반복한 array를 list에 append\n",
        "for x in img_weekday:\n",
        "  if x[1] != 0:\n",
        "    timeseries = np.ones([int(x[0])]) * x[1]\n",
        "    x_list.append(timeseries)\n",
        "  else:\n",
        "    timeseries = np.zeros([int(x[0])]) * x[1]\n",
        "    x_list.append(timeseries)\n",
        "\n",
        "ts = np.concatenate(x_list)[:-3383]   # 7567로 나누어떨어지기 위해 데이터 버림.\n",
        "print(ts.shape)\n",
        "ts\n",
        "tmp = ts.reshape(50, 7500, -1)\n",
        "print(tmp.shape)"
      ],
      "metadata": {
        "id": "pMWmMe2W0uWn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "17b148c1-fee7-4002-ae71-03d9f6294454"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(375000,)\n",
            "(50, 7500, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Augmentation\n",
        "augmentation(tmp)\n",
        "del tmp"
      ],
      "metadata": {
        "id": "OFjpa4a_0s6U",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "789e073e-0f6c-44c4-9ed7-01d0b4c3accc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Start Augmentation\n",
            "Number of Train set : 870\n",
            "Number of Valid set : 217\n",
            "\n",
            "Start saving the dataset...\n",
            "Complete saving\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터 로드\n",
        "train = [np.load(\"train_x.npy\"), np.load(\"train_y.npy\")]\n",
        "valid = [np.load(\"valid_x.npy\"), np.load(\"valid_y.npy\")]"
      ],
      "metadata": {
        "id": "0eLBF7o3diLq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 4. Model\n",
        "print(\"Start Training\")\n",
        "batch_size = 256\n",
        "lr = 0.005\n",
        "epochs = 2000\n",
        "\n",
        "model = UNet1D()\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "67wztTIcdk3S",
        "outputId": "fd99c791-eb8c-4d37-fe1e-ab345636263a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Start Training\n",
            "(None, 250, 128) (None, 250, 128)\n",
            "(None, 500, 64) (None, 500, 64)\n",
            "(None, 1000, 32) (None, 1000, 32)\n",
            "(None, 2000, 16) (None, 2000, 16)\n",
            "Model: \"model_5\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_6 (InputLayer)           [(None, 2000, 1)]    0           []                               \n",
            "                                                                                                  \n",
            " conv1d_95 (Conv1D)             (None, 2000, 16)     160         ['input_6[0][0]']                \n",
            "                                                                                                  \n",
            " batch_normalization_90 (BatchN  (None, 2000, 16)    64          ['conv1d_95[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " spatial_dropout1d_90 (SpatialD  (None, 2000, 16)    0           ['batch_normalization_90[0][0]'] \n",
            " ropout1D)                                                                                        \n",
            "                                                                                                  \n",
            " activation_90 (Activation)     (None, 2000, 16)     0           ['spatial_dropout1d_90[0][0]']   \n",
            "                                                                                                  \n",
            " conv1d_96 (Conv1D)             (None, 2000, 16)     2320        ['activation_90[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_91 (BatchN  (None, 2000, 16)    64          ['conv1d_96[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " spatial_dropout1d_91 (SpatialD  (None, 2000, 16)    0           ['batch_normalization_91[0][0]'] \n",
            " ropout1D)                                                                                        \n",
            "                                                                                                  \n",
            " activation_91 (Activation)     (None, 2000, 16)     0           ['spatial_dropout1d_91[0][0]']   \n",
            "                                                                                                  \n",
            " max_pooling1d_20 (MaxPooling1D  (None, 1000, 16)    0           ['activation_91[0][0]']          \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " conv1d_97 (Conv1D)             (None, 1000, 32)     4640        ['max_pooling1d_20[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_92 (BatchN  (None, 1000, 32)    128         ['conv1d_97[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " spatial_dropout1d_92 (SpatialD  (None, 1000, 32)    0           ['batch_normalization_92[0][0]'] \n",
            " ropout1D)                                                                                        \n",
            "                                                                                                  \n",
            " activation_92 (Activation)     (None, 1000, 32)     0           ['spatial_dropout1d_92[0][0]']   \n",
            "                                                                                                  \n",
            " conv1d_98 (Conv1D)             (None, 1000, 32)     9248        ['activation_92[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_93 (BatchN  (None, 1000, 32)    128         ['conv1d_98[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " spatial_dropout1d_93 (SpatialD  (None, 1000, 32)    0           ['batch_normalization_93[0][0]'] \n",
            " ropout1D)                                                                                        \n",
            "                                                                                                  \n",
            " activation_93 (Activation)     (None, 1000, 32)     0           ['spatial_dropout1d_93[0][0]']   \n",
            "                                                                                                  \n",
            " max_pooling1d_21 (MaxPooling1D  (None, 500, 32)     0           ['activation_93[0][0]']          \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " conv1d_99 (Conv1D)             (None, 500, 64)      18496       ['max_pooling1d_21[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_94 (BatchN  (None, 500, 64)     256         ['conv1d_99[0][0]']              \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " spatial_dropout1d_94 (SpatialD  (None, 500, 64)     0           ['batch_normalization_94[0][0]'] \n",
            " ropout1D)                                                                                        \n",
            "                                                                                                  \n",
            " activation_94 (Activation)     (None, 500, 64)      0           ['spatial_dropout1d_94[0][0]']   \n",
            "                                                                                                  \n",
            " conv1d_100 (Conv1D)            (None, 500, 64)      36928       ['activation_94[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_95 (BatchN  (None, 500, 64)     256         ['conv1d_100[0][0]']             \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " spatial_dropout1d_95 (SpatialD  (None, 500, 64)     0           ['batch_normalization_95[0][0]'] \n",
            " ropout1D)                                                                                        \n",
            "                                                                                                  \n",
            " activation_95 (Activation)     (None, 500, 64)      0           ['spatial_dropout1d_95[0][0]']   \n",
            "                                                                                                  \n",
            " max_pooling1d_22 (MaxPooling1D  (None, 250, 64)     0           ['activation_95[0][0]']          \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " conv1d_101 (Conv1D)            (None, 250, 128)     73856       ['max_pooling1d_22[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_96 (BatchN  (None, 250, 128)    512         ['conv1d_101[0][0]']             \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " spatial_dropout1d_96 (SpatialD  (None, 250, 128)    0           ['batch_normalization_96[0][0]'] \n",
            " ropout1D)                                                                                        \n",
            "                                                                                                  \n",
            " activation_96 (Activation)     (None, 250, 128)     0           ['spatial_dropout1d_96[0][0]']   \n",
            "                                                                                                  \n",
            " conv1d_102 (Conv1D)            (None, 250, 128)     147584      ['activation_96[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_97 (BatchN  (None, 250, 128)    512         ['conv1d_102[0][0]']             \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " spatial_dropout1d_97 (SpatialD  (None, 250, 128)    0           ['batch_normalization_97[0][0]'] \n",
            " ropout1D)                                                                                        \n",
            "                                                                                                  \n",
            " activation_97 (Activation)     (None, 250, 128)     0           ['spatial_dropout1d_97[0][0]']   \n",
            "                                                                                                  \n",
            " max_pooling1d_23 (MaxPooling1D  (None, 125, 128)    0           ['activation_97[0][0]']          \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " conv1d_103 (Conv1D)            (None, 125, 256)     295168      ['max_pooling1d_23[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_98 (BatchN  (None, 125, 256)    1024        ['conv1d_103[0][0]']             \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " spatial_dropout1d_98 (SpatialD  (None, 125, 256)    0           ['batch_normalization_98[0][0]'] \n",
            " ropout1D)                                                                                        \n",
            "                                                                                                  \n",
            " activation_98 (Activation)     (None, 125, 256)     0           ['spatial_dropout1d_98[0][0]']   \n",
            "                                                                                                  \n",
            " conv1d_104 (Conv1D)            (None, 125, 256)     590080      ['activation_98[0][0]']          \n",
            "                                                                                                  \n",
            " batch_normalization_99 (BatchN  (None, 125, 256)    1024        ['conv1d_104[0][0]']             \n",
            " ormalization)                                                                                    \n",
            "                                                                                                  \n",
            " spatial_dropout1d_99 (SpatialD  (None, 125, 256)    0           ['batch_normalization_99[0][0]'] \n",
            " ropout1D)                                                                                        \n",
            "                                                                                                  \n",
            " activation_99 (Activation)     (None, 125, 256)     0           ['spatial_dropout1d_99[0][0]']   \n",
            "                                                                                                  \n",
            " conv1d_transpose_20 (Conv1DTra  (None, 250, 128)    262272      ['activation_99[0][0]']          \n",
            " nspose)                                                                                          \n",
            "                                                                                                  \n",
            " concatenate_20 (Concatenate)   (None, 250, 256)     0           ['conv1d_transpose_20[0][0]',    \n",
            "                                                                  'activation_97[0][0]']          \n",
            "                                                                                                  \n",
            " conv1d_105 (Conv1D)            (None, 250, 128)     295040      ['concatenate_20[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_100 (Batch  (None, 250, 128)    512         ['conv1d_105[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " spatial_dropout1d_100 (Spatial  (None, 250, 128)    0           ['batch_normalization_100[0][0]']\n",
            " Dropout1D)                                                                                       \n",
            "                                                                                                  \n",
            " activation_100 (Activation)    (None, 250, 128)     0           ['spatial_dropout1d_100[0][0]']  \n",
            "                                                                                                  \n",
            " conv1d_106 (Conv1D)            (None, 250, 128)     147584      ['activation_100[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_101 (Batch  (None, 250, 128)    512         ['conv1d_106[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " spatial_dropout1d_101 (Spatial  (None, 250, 128)    0           ['batch_normalization_101[0][0]']\n",
            " Dropout1D)                                                                                       \n",
            "                                                                                                  \n",
            " activation_101 (Activation)    (None, 250, 128)     0           ['spatial_dropout1d_101[0][0]']  \n",
            "                                                                                                  \n",
            " conv1d_transpose_21 (Conv1DTra  (None, 500, 64)     65600       ['activation_101[0][0]']         \n",
            " nspose)                                                                                          \n",
            "                                                                                                  \n",
            " concatenate_21 (Concatenate)   (None, 500, 128)     0           ['conv1d_transpose_21[0][0]',    \n",
            "                                                                  'activation_95[0][0]']          \n",
            "                                                                                                  \n",
            " conv1d_107 (Conv1D)            (None, 500, 64)      73792       ['concatenate_21[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_102 (Batch  (None, 500, 64)     256         ['conv1d_107[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " spatial_dropout1d_102 (Spatial  (None, 500, 64)     0           ['batch_normalization_102[0][0]']\n",
            " Dropout1D)                                                                                       \n",
            "                                                                                                  \n",
            " activation_102 (Activation)    (None, 500, 64)      0           ['spatial_dropout1d_102[0][0]']  \n",
            "                                                                                                  \n",
            " conv1d_108 (Conv1D)            (None, 500, 64)      36928       ['activation_102[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_103 (Batch  (None, 500, 64)     256         ['conv1d_108[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " spatial_dropout1d_103 (Spatial  (None, 500, 64)     0           ['batch_normalization_103[0][0]']\n",
            " Dropout1D)                                                                                       \n",
            "                                                                                                  \n",
            " activation_103 (Activation)    (None, 500, 64)      0           ['spatial_dropout1d_103[0][0]']  \n",
            "                                                                                                  \n",
            " conv1d_transpose_22 (Conv1DTra  (None, 1000, 32)    16416       ['activation_103[0][0]']         \n",
            " nspose)                                                                                          \n",
            "                                                                                                  \n",
            " concatenate_22 (Concatenate)   (None, 1000, 64)     0           ['conv1d_transpose_22[0][0]',    \n",
            "                                                                  'activation_93[0][0]']          \n",
            "                                                                                                  \n",
            " conv1d_109 (Conv1D)            (None, 1000, 32)     18464       ['concatenate_22[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_104 (Batch  (None, 1000, 32)    128         ['conv1d_109[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " spatial_dropout1d_104 (Spatial  (None, 1000, 32)    0           ['batch_normalization_104[0][0]']\n",
            " Dropout1D)                                                                                       \n",
            "                                                                                                  \n",
            " activation_104 (Activation)    (None, 1000, 32)     0           ['spatial_dropout1d_104[0][0]']  \n",
            "                                                                                                  \n",
            " conv1d_110 (Conv1D)            (None, 1000, 32)     9248        ['activation_104[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_105 (Batch  (None, 1000, 32)    128         ['conv1d_110[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " spatial_dropout1d_105 (Spatial  (None, 1000, 32)    0           ['batch_normalization_105[0][0]']\n",
            " Dropout1D)                                                                                       \n",
            "                                                                                                  \n",
            " activation_105 (Activation)    (None, 1000, 32)     0           ['spatial_dropout1d_105[0][0]']  \n",
            "                                                                                                  \n",
            " conv1d_transpose_23 (Conv1DTra  (None, 2000, 16)    4112        ['activation_105[0][0]']         \n",
            " nspose)                                                                                          \n",
            "                                                                                                  \n",
            " concatenate_23 (Concatenate)   (None, 2000, 32)     0           ['conv1d_transpose_23[0][0]',    \n",
            "                                                                  'activation_91[0][0]']          \n",
            "                                                                                                  \n",
            " conv1d_111 (Conv1D)            (None, 2000, 16)     4624        ['concatenate_23[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_106 (Batch  (None, 2000, 16)    64          ['conv1d_111[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " spatial_dropout1d_106 (Spatial  (None, 2000, 16)    0           ['batch_normalization_106[0][0]']\n",
            " Dropout1D)                                                                                       \n",
            "                                                                                                  \n",
            " activation_106 (Activation)    (None, 2000, 16)     0           ['spatial_dropout1d_106[0][0]']  \n",
            "                                                                                                  \n",
            " conv1d_112 (Conv1D)            (None, 2000, 16)     2320        ['activation_106[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_107 (Batch  (None, 2000, 16)    64          ['conv1d_112[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " spatial_dropout1d_107 (Spatial  (None, 2000, 16)    0           ['batch_normalization_107[0][0]']\n",
            " Dropout1D)                                                                                       \n",
            "                                                                                                  \n",
            " activation_107 (Activation)    (None, 2000, 16)     0           ['spatial_dropout1d_107[0][0]']  \n",
            "                                                                                                  \n",
            " conv1d_113 (Conv1D)            (None, 2000, 2)      34          ['activation_107[0][0]']         \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 2,120,802\n",
            "Trainable params: 2,117,858\n",
            "Non-trainable params: 2,944\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 5. Train\n",
        "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=lr),\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['accuracy', f1_score])\n",
        "\n",
        "callbacks_list = [\n",
        "    tf.keras.callbacks.ModelCheckpoint(\n",
        "        filepath=\"weights.h5\",\n",
        "        monitor=\"val_f1_score\",\n",
        "        mode=\"max\",\n",
        "        save_best_only=True,\n",
        "        save_weights_only=True,\n",
        "        verbose=1),\n",
        "\n",
        "    tf.keras.callbacks.EarlyStopping(\n",
        "        monitor=\"val_f1_score\",\n",
        "        min_delta=0.005,\n",
        "        mode=\"max\",\n",
        "        patience=10,\n",
        "        verbose=1),\n",
        "\n",
        "    tf.keras.callbacks.ReduceLROnPlateau(\n",
        "        monitor='val_f1_score',\n",
        "        factor=0.5,\n",
        "        patience=5,\n",
        "        mode='max',\n",
        "        min_delta=0.01,\n",
        "        cooldown=0,\n",
        "        min_lr=0.0001,\n",
        "        verbose=1)\n",
        "]\n",
        "\n",
        "history = model.fit(train[0], train[1],\n",
        "                    validation_data=(valid[0], valid[1]),\n",
        "                    callbacks=callbacks_list,\n",
        "                    epochs=epochs,\n",
        "                    batch_size=batch_size,\n",
        "                    shuffle=True,\n",
        "                    )\n",
        "\n",
        "\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "accuracy = history.history[\"accuracy\"]\n",
        "val_acc = history.history[\"val_accuracy\"]\n",
        "f1 = history.history[\"f1_score\"]\n",
        "val_f1 = history.history[\"val_f1_score\"]\n",
        "\n",
        "epochs_range = range(len(loss))\n",
        "fig, ax = plt.subplots(1, 3, figsize = (20, 12))\n",
        "ax[0].plot(epochs_range, loss, label='Training Loss')\n",
        "ax[0].plot(epochs_range, val_loss, label='Validation Loss')\n",
        "\n",
        "ax[1].plot(epochs_range, accuracy, label='Training Accuracy')\n",
        "ax[1].plot(epochs_range, val_acc, label='Validation Accuracy')\n",
        "\n",
        "ax[2].plot(epochs_range, f1, label='Training f1-score')\n",
        "ax[2].plot(epochs_range, val_f1, label='Validation f1-score')\n",
        "plt.show()\n",
        "\n",
        "del model, history, train\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "-j8yJo-tdoAJ",
        "outputId": "2f6413d6-fea7-445a-8d69-27921752c727"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/2000\n",
            "4/4 [==============================] - ETA: 0s - loss: 0.6338 - accuracy: 0.1556 - f1_score: 0.5555\n",
            "Epoch 1: val_f1_score improved from -inf to 0.56928, saving model to weights.h5\n",
            "4/4 [==============================] - 24s 1s/step - loss: 0.6338 - accuracy: 0.1556 - f1_score: 0.5555 - val_loss: 167.8443 - val_accuracy: 0.0000e+00 - val_f1_score: 0.5693 - lr: 0.0050\n",
            "Epoch 2/2000\n",
            "4/4 [==============================] - ETA: 0s - loss: 0.3382 - accuracy: 0.1360 - f1_score: 0.5563\n",
            "Epoch 2: val_f1_score did not improve from 0.56928\n",
            "4/4 [==============================] - 2s 395ms/step - loss: 0.3382 - accuracy: 0.1360 - f1_score: 0.5563 - val_loss: 2466.6714 - val_accuracy: 0.0010 - val_f1_score: 0.5693 - lr: 0.0050\n",
            "Epoch 3/2000\n",
            "4/4 [==============================] - ETA: 0s - loss: 0.2583 - accuracy: 0.1682 - f1_score: 0.5575\n",
            "Epoch 3: val_f1_score did not improve from 0.56928\n",
            "4/4 [==============================] - 2s 389ms/step - loss: 0.2583 - accuracy: 0.1682 - f1_score: 0.5575 - val_loss: 4149.8394 - val_accuracy: 0.0000e+00 - val_f1_score: 0.5693 - lr: 0.0050\n",
            "Epoch 4/2000\n",
            "4/4 [==============================] - ETA: 0s - loss: 0.2157 - accuracy: 0.1981 - f1_score: 0.5571\n",
            "Epoch 4: val_f1_score did not improve from 0.56928\n",
            "4/4 [==============================] - 2s 372ms/step - loss: 0.2157 - accuracy: 0.1981 - f1_score: 0.5571 - val_loss: 10650.2100 - val_accuracy: 5.0000e-04 - val_f1_score: 0.5693 - lr: 0.0050\n",
            "Epoch 5/2000\n",
            "4/4 [==============================] - ETA: 0s - loss: 0.1812 - accuracy: 0.2289 - f1_score: 0.5559\n",
            "Epoch 5: val_f1_score did not improve from 0.56928\n",
            "4/4 [==============================] - 2s 398ms/step - loss: 0.1812 - accuracy: 0.2289 - f1_score: 0.5559 - val_loss: 12953.6885 - val_accuracy: 5.0000e-04 - val_f1_score: 0.5693 - lr: 0.0050\n",
            "Epoch 6/2000\n",
            "4/4 [==============================] - ETA: 0s - loss: 0.1530 - accuracy: 0.2743 - f1_score: 0.5574\n",
            "Epoch 6: val_f1_score did not improve from 0.56928\n",
            "\n",
            "Epoch 6: ReduceLROnPlateau reducing learning rate to 0.0024999999441206455.\n",
            "4/4 [==============================] - 2s 392ms/step - loss: 0.1530 - accuracy: 0.2743 - f1_score: 0.5574 - val_loss: 4534.8213 - val_accuracy: 5.0000e-04 - val_f1_score: 0.5693 - lr: 0.0050\n",
            "Epoch 7/2000\n",
            "4/4 [==============================] - ETA: 0s - loss: 0.1354 - accuracy: 0.2602 - f1_score: 0.5566\n",
            "Epoch 7: val_f1_score did not improve from 0.56928\n",
            "4/4 [==============================] - 2s 378ms/step - loss: 0.1354 - accuracy: 0.2602 - f1_score: 0.5566 - val_loss: 1503.2445 - val_accuracy: 5.0000e-04 - val_f1_score: 0.5693 - lr: 0.0025\n",
            "Epoch 8/2000\n",
            "4/4 [==============================] - ETA: 0s - loss: 0.1248 - accuracy: 0.2521 - f1_score: 0.5557\n",
            "Epoch 8: val_f1_score did not improve from 0.56928\n",
            "4/4 [==============================] - 2s 407ms/step - loss: 0.1248 - accuracy: 0.2521 - f1_score: 0.5557 - val_loss: 784.7792 - val_accuracy: 5.0000e-04 - val_f1_score: 0.5693 - lr: 0.0025\n",
            "Epoch 9/2000\n",
            "4/4 [==============================] - ETA: 0s - loss: 0.1136 - accuracy: 0.2733 - f1_score: 0.5572\n",
            "Epoch 9: val_f1_score did not improve from 0.56928\n",
            "4/4 [==============================] - 2s 388ms/step - loss: 0.1136 - accuracy: 0.2733 - f1_score: 0.5572 - val_loss: 568.9807 - val_accuracy: 5.0000e-04 - val_f1_score: 0.5693 - lr: 0.0025\n",
            "Epoch 10/2000\n",
            "4/4 [==============================] - ETA: 0s - loss: 0.1103 - accuracy: 0.2665 - f1_score: 0.5578\n",
            "Epoch 10: val_f1_score did not improve from 0.56928\n",
            "4/4 [==============================] - 2s 405ms/step - loss: 0.1103 - accuracy: 0.2665 - f1_score: 0.5578 - val_loss: 392.9796 - val_accuracy: 0.0027 - val_f1_score: 0.5693 - lr: 0.0025\n",
            "Epoch 11/2000\n",
            "4/4 [==============================] - ETA: 0s - loss: 0.0998 - accuracy: 0.2742 - f1_score: 0.5564\n",
            "Epoch 11: val_f1_score did not improve from 0.56928\n",
            "\n",
            "Epoch 11: ReduceLROnPlateau reducing learning rate to 0.0012499999720603228.\n",
            "4/4 [==============================] - 2s 406ms/step - loss: 0.0998 - accuracy: 0.2742 - f1_score: 0.5564 - val_loss: 184.9017 - val_accuracy: 0.0205 - val_f1_score: 0.5693 - lr: 0.0025\n",
            "Epoch 11: early stopping\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1200 with 3 Axes>"
            ],
            "image/png": 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19bRl+nBVlkKcDj00ursevryHwkNPvSRwfpckdU6OVVF5laZ/u9N3YW3K58WVCRMmaObMmZo1a5ZiY2OVnZ2t7Oxs7xyU+Ph43XLLLZo0aZIWLlyoFStWaPz48UpLS9PAgQMlSRdccIG6du2qG264QatXr9bnn3+uv/zlL5owYYIiIqyq2m9+8xtt375df/zjH7Vp0ya98MILeuedd3TPPff4+inZQ0GWlLtBkoPiCnA6IuOlxI7Wem+62SwAAAAAAADASUrPzNOlz32rNXvy1Sg6TG/ccqZuGJhy2td1On/qXnn12x3KL6087Wvamc+LKy+++KLy8/N17rnnqlmzZt6P2bNne8/517/+pYsvvlhjxozRkCFDlJycrP/+97/ex0NCQvTJJ58oJCREaWlpGjdunG688UY9+OCD3nPatGmjuXPn6osvvlCvXr305JNP6uWXX9aIESN8/ZTsoaZrpXkfKTrBbBbA7tgaDAAAAAAAADb07oo9unbacu0vLFenpFh9NPEsDWqX6LPrj+yWrA5NG6iwrEozlu702XXtKNTXF/R4PL94TmRkpJ5//nk9//zzxzwnJSVFn3766XGvc+6552rlypUnnTEgZVRvkdZ+uNkcQCBo0U9a/RbFFQAAAAAAANhClcutRz7bpJe/2SFJuqBrkp66prcaRPi2BOB0OnTnsA66662VeuWbHRo/OFWxkWE+vYdd1NlAe9Qjt0vavtBatx9mNgsQCFr0tY57V0gnUDAGAAAAAMCXcgvL9OGqvSoqrzIdBYAN5JdUavxrP3gLK3cN66Cp4/r5vLBSY1SPZmrbJEb5pZV6fdmuOrmHHVBcCQRZK6XSPCkiXmrR33QawP6Suksh4VLpISlvp+k0AAAAAIAgUuVy68ZXvtfv3l6lsx/9Sv/5eptKK1ymYwHwUxm5hRr9wrdasvWAosJC9MLYvpp0fkc5nY46u2eI06E7h7aXJL28ZLuKg7QQTHElENRsCdZ2iBRSN9VIIKiERkjJPaw1W4MBAAAAAOrRW99nalN2oSQpr6RSD3+6SUMeX6gZS3eqvIoiSyDyeDxauu0AnUo4aQs35ery55dqx4FitWgYpXfvSNNFPZrVy70v6dlcqY2jlVdSqZnLg7N7heJKINhWXVxpx5ZggM94h9qnm80BAAAAAAgaecUVemL+FknS3y7pqseu7KkWDaO0v7Bcf/tovc57fJHe/j5TlS634aTwpTkr9uj6l77TNdOWqaSCAgt+mcfj0YuLtulXM35QYXmVzkxN0IcTB6tb8/h6yxAa4tSE86zulf98vT0oO+worthd6WFpz4/WmnkrgO94iyt0rgAAAAAA6seTX2xWfmmlOifH6oaBKbq6fyst/MO5emh0dyXFRSgrv0x/+u9anf/UYn2wcq9cbuaE2p3H49HLS7ZLktZnFegPc1bLze8rjqOs0qW7Z6/So/M2yeORrjuztWbeOkCJDSLqPcvoPi3UKiFKB4sr9OZ3wde9QnHF7nYsljwuKbGj1LC16TRA4KgpruxbLbkqzWYBAAAAAAS89Vn5mvVdpiTpgUu7KTTEetkuPNSpGwamaPG95+kvo7qocUy4dh4s0d2zV2nk01/rs7X7eDHexpZuO6gtOUWKDHMqLMShT9dm69mvtpqOBT+VnV+mq6ct04ershTidOihy7rp4cu7KzzUzMv8YSFOTTjX6l6Z9vV2lVUGV/cKxRW7y2BLMKBOJLSTIuKlqlIpd6PpNAAAAACAAObxePT3jzbI7ZEu7tlMA9s2/tk5kWEhuvXstvr6j+fp3hGdFBcZqq25RbrjzXRd8tw3+mpTjjweiix28+q3OyRJV/dvpX+Otua/Pv3lVs1ds89kLPih9Mw8XfLcN1qzJ18No8P0xi1n6oa0VDkcdTe4/kRc0beld/vCt7/PNJqlvlFcsTOP56fiCluCAb7ldEot+lhrtgYDAAAAANShj1Zn6fudhxQZ5tT/XdTluOfGRIRqwnntteS+obprWAfFhIdofVaBfvXaj7rixaX6NuMARRab2HWwWAs25UqSbhqUqqvPaKVbz2ojSfr9nFVatzffZDz4kXdX7NG105Zrf2G5OiXF6qMJZ2lQu0TTsSRZ3XV3nNtOkvTi4m1B1b1CccXODmyRCvZIIRFSymDTaYDAw9wVAAAAAEAdKy6v0pRPN0mSJpzbXs0bRp3Q18VHhWnS+R215L6h+vU5bRUZ5tTKzMMa+/J3uu6l5fph56G6jA0feG3pTnk80rmdmqhdkwaSpMkXddE5HZuorNKt217/UbkFZYZTwqQql1v/+GSD/jBntSpcbl3QNUnv/XaQWjeONh2tlqv6t1RyXKRyCso1Z8Ue03HqDcUVO6vpWklJk8L9638oICB4iyvpZnMAAAAAAALWC4sylF1QplYJUbptSNuT/vqEmHBNvrCLvv7jebp5UKrCQ5xavv2Qrpq6TDdN/15r9hz2fWictsKySs350XoR+uZBqd7Phzgd+vf1fdSuSYz25Zfp9jdWBFUnAH6SX1Kp8a/9oJe/sbaOu2toe00d108NIkINJ/u5iNCQn7pXFmaoosptOFH9oLhiZ9uYtwLUqZriyv6NUnmR2SwAAAAAgICz62CxXvraeuH0r6O6KjIs5JSv1TQ2Ug9c2k2L7j1X153ZWqFOhxZv2a9Ln/tWt7/+ozZlF/gqNnzg3RV7VFRepbZNYjSkQ5Naj8VFhumVm85QfFSYVu0+rMn/XctWb0EmI7dQo1/4Vku2HlBUWIiev76vJl3QSU6n2fkqx3PNGa3UNDZCWfllei89OLpXKK7YVWWZtPNba91+uNksQKCKTZbiWkget7Rvtek0AAAAAIAA89AnG1XhcuvsDok6v2uST67ZvGGUplzRQwt+f46u6NNCDoc0f0OOLnxmie58a6W27efNg6a53R7NWLpTkjR+UOpRXzBPTYzRi2P7KsTp0Psr92rq4u31nBKmLNyUq8ufX6odB4rVomGU3r0jTaN6NjMd6xdFhoXo1+dY3SvPL8xQpSvwu1corthV5lKpqlSKbS41Pf6gMwCnoUVf68jcFQAAAACADy3anKsvN+Yo1OnQ3y7pKofDt+9IT2kco6eu6a35dw/RqB7N5PFIH6/O0vlPLdYf5qzW7kMlPr0fTtzCzbnaebBEsZGhuqJvy2OeN6h9oh64tJsk6bHPN+mLDTn1FREGeDweTV28Tb+a8YMKy6t0RmojfThxsLo1jzcd7YRdf2ZrJTYI1568Ur2/cq/pOHWO4opd1cxbaTdU8vEPXwBHYKg9AAAAAMDHKqrcevCTDZKseRvtm8bW2b06JMXq+bF9NfeuszS8S1O5PdaWVOc9sUh/fn+t9uWX1tm9cXSvVXetXHtGK8X8wvyMGwam6IaBKfJ4pLvfXsn2bgGqrNKle2av0iOfbZLHI113Ziu9eetAJTaIMB3tpESFh+j26tlRzy/MUFWAd69QXLGrmuJK+6FmcwCBjqH2AAAAAAAfm7F0p7bvL1Zig3DdNbxDvdyzW/N4vXzTGXr/t4N0dodEVbk9evO7TJ3z+CI99MkGHSgqr5ccwW5rTqGWbD0gp0O6MS31hL7m/ku6alC7xiqucOnWGT/qIL9XASU7v0xXT1umD1ZlKcTp0IOXddPDl/dQeKg9X7ofOyBFCTHh2nWwRB+tzjIdp07Z83co2OXvtQZsyyG1Pc90GiCwNestySHlZ0pFuabTAAAAAABsLrewTM8s2CpJ+uPIzoqLDKvX+/dp3Uhv3DJAb98+UGekNlJFlVuvfLNDZz+6UI/N26TDJRX1mifYvFrdtXJ+1yS1Sog+oa8JC3HqhbF9ldI4WnvySnXHzHRVVAV2R0CwSM/M0yXPfaM1e/LVMDpMb/zqTN2YlurzbQLrU0xEqG49u40k6bmvMuRyewwnqjsUV+xo21fWsUU/KTrBbBYg0EXGSU06WWu6VwAAAAAAp+nRzzarqLxKvVrG68rjzNuoawPbNtY7v07T6786U71axqu00qUXFm3T2Y8u1DNfblVhWaWxbIHqcEmF/pu+R5I0fnCbk/rahtHheuWm/oqNCNX3Ow/prx+sk8cTuC9aB4N3V+zRtdOWa39huTolxeqjCWdpUPtE07F84sa0VDWMDtP2A8X6ZE3gdq9QXLGjbTVbgg0zmwMIFsxdAQAAAAD4QHpmnt6rfnH9gUu7yek0++50h8OhIR2b6IMJg/WfG/qpc3KsCsur9K8vt+jsxxZq6uJtKqmoMpoxkLz9w26VVbrVOTlWA9qc/Bum2zeN1bPX95HTIc3+cbde/Xan70OiXjw6b5P+MGe1Klxund81Se/9dpBaNz6xTiY7aBARqluqC4j//ipD7gDtXqG4Yjdul7RtobVuR3EFqBct+lpHiisAAAAAgFPkdnv0wEfrJUlX9mupPq0bGU70E4fDoQu6JevTu87Wv6/ro7ZNYnS4pFKPfLZJQx5bpFe/3aGySpfpmLZW5XLr9eotwX41uM0pb/t0Xqem+r+LukiS/jF3gxZv2e+riKgn6Zl5enHRNknSnUPba9q4fmoQEWo4le/dNDhVsZGhysgt0mfrsk3HqRMUV+xmb7pUdliKiP/p3fQA6taRnSu03AIAAAAATsG7K/ZozZ58xUaE6r6RnU3HOSqn06FLejXX/LuH6ImreqlVQpQOFJXr7x9v0HlPLNKs7zJV6WLWx6mYvyFHWfllSogJ16W9m5/WtW45q42u7t9Sbo80cVa6MnKLfJQS9WFqdWFlTN+W+v0FnYx3sNWVuMgw/crbvbI1ILtXKK7YTc2WYG3PkUICr6IJ+KWm3aSQCKuweWi76TQAAAAAAJvJL63Uo/M2SZJ+N7yDmsRGGE50fKEhTl3Zr6UWTDpX/7y8u5LjIrUvv0z/9/5aDXtysd5bsSegh1TXhVe/3SFJuv7M1ooMCzmtazkcDj00urvOSG2kwrIq3TrjBx0uqfBFTNSxjNxCzd+QI0m649y2htPUvV8NbqMGEaHalP3T8w4kFFfsJoN5K0C9Cw2XmvW01gy1BwAAAACcpGcXbNXB4gq1axKjG9NSTcc5YeGhTo0dkKJF956r+y/uqsQG4co8VKLfz1mtC/61WJ+syQrId6P72rq9+fphZ55CnQ7dkJbik2tGhIboxXH91KJhlHYeLNGEWel0FdnA1MXWm3Yv6Jqk9k1jDaepe/HRYbp5UKok689BT4DtCENxxU5K86S9P1pr5q0A9atma7AsiisAAAAAgBO3NadQM6pnbfztkm4KD7Xfy3GRYSH61Vlt9PUfz9N9IzsrPipM2/YXa+KslZr0zirT8fxezeD5i3o0U1JcpM+um9ggQi/f1F/R4SH6NuOg/vHJBp9dG76XdbhUH6zcK0n6zbntDKepP7ec1UbR4SHasK9ACzbmmo7jU/b70zyYbV8sedxSYiepYSvTaYDg0pyh9gAAAACAk+PxePT3jzeoyu3R+V2TNKRjE9ORTkt0eKjuOLedltx3nu4e3kEhToc+WJWl73ccMh3Nb+0vLNfHq7MkSeMHp/r8+l2axenpa3rL4ZBmLNulmct3+fwe8I2XlmxXldujgW0T1Ld1I9Nx6k2jmHBvx96zXwVW9wrFFTvJ+NI6siUYUP+8nSsrpZz1ZrMAAAAAAGxh/oYcfZNxQOGhTv11VFfTcXwmLjJMdw/vqKv7W2/+nfLZxoB6wdSXZn2XqQqXW71bNVSfOnpB/YJuyfrDBZ0kSQ98tF5Ltx2ok/vg1B0qrtDb3++WJN1xbnvDaerfrWe3UVRYiNbsydeiLftNx/EZiit24fFI276y1mwJBtS/xu2k1LMlV4U080opf4/pRAAAAAAAP1ZW6dJD1ds03X52W7VuHG04ke/dM7yDosJCtDLzsD5fn206jt+pqHJr5ndWJ0lddK0c6bfnttNlvZuryu3Rb99M166DxXV6P5ycGUt3qrTSpW7N4zSkQ6LpOPUusUGExg1sLUl65svA6V6huGIX+zdLBXulkAgpZZDpNEDwcTikq1+XEjtKhVlWgaX0sOlUAAAAAAA/9Z+vt2tPXqmS4yL12/MCc75C07hI3Xp2G0nSY/M2M1D9f8xdm6X9heVqGhuhC7s3q9N7ORwOPTqmp3q1aqjDJZW6ZcaPKiyrrNN74sQUl1dpxrKdkqQ7zm0nh8NhNpAhtw1pq4hQp1btPqxvMgKju4riil1sW2AdUwZJ4YH3TgfAFqITpHHvSQ2Spf0bpbfHSlXlplMBAAAAAPzM3sOlemFRhiTp/0Z1UXR4qOFEdef2IW2VEBOu7QeKNfuH3abj+A2Px+MdZH/DwBSFh9b9y7CRYSF66YZ+So6LVEZuke56a6Vc7sDoELCzt3/YrcMllUppHF3nRTZ/1jQ2UtcPCKzuFYordpFRXVxpP9xsDiDYNWwtjXtXCo+Vdn0jvf9ryc07cwAAAAAAP3n4040qq3TrzNQEXdIzsF9MjY0M011DrRkST3+5VcXlVYYT+Yf0zDyt2ZOv8FCn9wXl+tA0LlIv3dhfkWFOLdy8X4/O21Rv98bPVVS59fKS7ZKkXw9ppxBncHat1PjNOe0UHurUj7vytGz7QdNxThvFFTuoLJV2fWutGWYPmJfcQ7p2puQMk9a/L83/i+lEAAAAAAA/sWzbQc1ds09Oh/TApd2CYgug6wekqHVCtA4UleuVb3aYjuMXarpWLuvVXI0bRNTrvXu0jNcTV/WSZG1PN+dHOopM+WDVXu3LL1OT2Ahd0beF6TjGJcVF6tozWkmSnl2w1XCa00dxxQ52fStVlUmxzaUmnU2nASBJbc+VRr9grZc/Ly19zmgcAAAAAIB5VS63/v7xeknS2AEp6to8znCi+hEe6tQfRnSSJE1bvE0HioJ7C+19+aX6bF22JGn84DZGMlzcs7nuGtZBkvTn99fpx52HjOQIZm63R1MXb5Mk3XJWG0WGhRhO5B9+c047hYU4tHz7IX1n8+4Viit2kPGVdWw/1BqqDcA/9LxaOv9Baz3/z9Lad83mAQAAAAAYNev7TG3KLlTD6DBNOr+j6Tj16uIezdSjRbyKK1z6dwC8I/10vLFsl1xujwa0STBaYLt7WAdd2D1ZFS63fjNzhfbklRjLEozmb8jR9v3Fio0M1dh63BrO3zVvGKWr+lvdK//+KsNwmtNDccUOaobZt2NLMMDvDLpLOvPX1vqDO6QdX5vNAwAAAAAw4lBxhZ6cv0WS9PsLOqlRTLjhRPXL6XRo8oXWjitvfpepnQeKDScyo6zSpbe+z5RkrmulhtPp0JNX91LXZnE6UFSh215fwUyceuLxePRiddfKjWkpio0MM5zIv9xxTjuFOh36JuOAVuyyb1cVxRV/l79H2r9JcjitbYgA+BeHQxo5RepyqeSqkN4eK+WsN50KAAAAAFDPnpy/WfmllerSLE7Xnxmc71If1D5R53Rsoiq3R4/P32w6jhEfrNyrvJJKtWgYpfO7JpmOo+jwUL10U38lNojQxn0FmvTOKrndHtOxAt6y7Qe1evdhRYQ6dfMgs0U2f9QqIVpj+raUJD27wL7dKxRX/N226i3BWvSTohPMZgFwdM4Q6YqXpNaDpPICaeaVVmEUAAAAABAU1u3N16zqboUHLumqEGfwbut+38jOcjikuWv2afXuw6bj1CuPx+MdZH/ToBS/+T5o0TBK027op/AQpz5fn6N/fbnFdKSA9+Iiq2vl6v6t1CQ2wnAa//Tb89opxOnQ4i37tcqmf1ZQXPF3GWwJBthCWKR03SypSWepMMsqsJTmmU4FAAAAAKhjHo9Hf/94vTwe6ZJezTWgbWPTkYzq2jxOl/duIUma8tlGeTzB0yWxbNtBbc4pVFRYiK7p71/dS/1SGmnKFT0kWXMuPly113CiwLV2T76WbD2gEKdDtw9pazqO30ppHKPR1X9W2HVOE8UVf+aqkrYvtNbtKa4Afi+qkTT2XSm2mbR/o7VFWGWZ6VQAAAAAgDr00eos/bAzT1FhId6ZI8Fu0gUdFR7i1PLth7Roy37TcerN9OqulTH9Wig+2v9mbIzp11K/Psd6sf+P764Jus6i+jK1etbKxT2bqVVCtOE0/m3Cee3kdEgLNuVq3d5803FOGsUVf5aVLpXlS5HxUvO+ptMAOBENW1kFlog4ade30vu/ltxu06kAAAAAAHWguLxKD3+6UZL1ImHzhlGGE/mHlo2iddOgFEnSo59tkisIZnxkHizRgk05kuTXMzb+OKKzhnVuqvIqt257/Udl5/OmUF/acaBYn67bJ0m649x2htP4v7ZNGujSXs0lSc/asHuF4oo/q9kSrO25Ukio0SgATkJyd+mamZIzTNrwgfT5/0lB1AYNAAAAAMHi+YUZyikoV6uEKN16Ntv/HGnCee0VFxmqTdmFen9l4G9BNWPZTnk80pCOTdS+aQPTcY4pxOnQ09f2VsekBsotLNftb/yoskqX6VgB4z9fb5PHIw3t3FSdk+NMx7GFiUPby+GQ5m/I0YasAtNxTgrFFX+2jXkrgG21PUe6fKq1/u5FadlzZvMAAAAAAHxq54FivbxkhyTpr6O6KjIsxHAi/9IwOly/Pa+9JOmp+ZsD+gX8ovIqvfPDbknS+MGpZsOcgNjIML184xlqFB2mNXvyde+7a4JqNk5dySko03srrEIiXSsnrn3TWI3q0UyS9NxCe3WvUFzxV6V50t4V1pp5K4A99bhSOv8haz3/L9Lad83mAQAAAAD4zD/mblCFy60hHZvo/K5JpuP4pZsHpapZfKSy8ss0Y+lO03HqzHsr9qiwvEptE2N0TocmpuOckNaNo/XiuH4KdTr08eosPb8ww3Qk25v+zQ5VuNzqn9JIZ6QmmI5jK3cO7SBJ+nRttjZnFxpOc+Iorvir7Yskj1tq0lmKb2k6DYBTNehOacAd1vr930jbF5vNAwAAAAA4bQs35+rLjbkKdTp0/8Vd5XA4TEfyS5FhIZp0fkdJ1hZq+SWVhhP5ntvt0WvVhaObBqXK6bTP98LAto310OjukqQn5m/RvHXZhhPZV35JpWYu3yWJrpVT0Sk5Vhd2T5YkPWejQh/FFX+V8aV1ZEswwN4cDmnEw1LX0ZK7Upo9TspeZzoVAAAAAOAUVVS59dDHGyRZW0D583wNf3BF35bqlBSrgrIqvbDIPi+anqjFW/Zrx4FixUaEakw/+71B+rozW+vmQamSpHtmr9L6rHyzgWzqjeU7VVzhUqekWJ3XqanpOLY0cai1jeAna7KUkVtkOM2JobjijzweKeMra91+qNksAE6f0yldPk1KGSyVF0hvXikd3m06FQAAAADgFLy2dIe2HyhWYoMI3TWsg+k4fi/E6dB9F3aSJL26dKf2Hi41nMi3pn9rzd25+oxWahARajjNqfnLqC46u0OiSitdum3Gj9pfWG46kq2UVrj06rc7JVldK3bqXvIn3ZrH6/yuSfJ4ZJtt6iiu+KP9m6TCLCk00noxFoD9hUVK175pbfVXuM8qsJTmmU4FAAAAADgJuQVleuZLa+DyfSM7KTYyzHAiezivU1MNaJOgiiq3npq/xXQcn8nILdSSrQfkcEg3paWajnPKQkOceu66vmqbGKOs/DL9ZuYKlVe5TMeyjTkrdutgcYVaNorSxT2bmY5ja3dVz175cNVe7ThQbDjNL6O44o8yFljHlMFSWJTZLAB8J6qRNO49Kba5VUR963qpssx0KgAAAADACXp03mYVV7jUq1VDjelrvy2gTHE4HJp8URdJ0n9X7tHGfQWGE/lGzayV4V2S1LpxtNkwpyk+Okwv39RfcZGhWrErT39+f508Ho/pWH6v0uXWtMXbJUm3D2mr0BBebj8dPVrGa2jnpnLbpHuF321/tK26uNKeeStAwIlvKY17V4qIkzKXSu/fLrndplMBAAAAAH7Bil15ei99jyTp75d2Y+ufk9S7VUON6tFMHo/06LxNpuOctvySSr23Yq8ka/ZOIGjbpIGeH9tXIU6H3l2xRy8v2WE6kt+bu2af9h4uVeOYcF3Vr5XpOAHhzurZK++v3KvMgyWG0xwfxRV/U1kq7VpqrRlmDwSmpG7WFmEh4dKGD6XPJ1uzlgAAAAAAfsnt9uiBj9ZLkq7q11K9WzU0G8im7h3RSaFOhxZt3q+l2w6YjnNaZv+YqdJKlzonxyqtbWPTcXzm7A5N9NdRVpfRw59t1MJNuYYT+S+326MXF22TZBXYosJDDCcKDH1aN9KQjk3kcnv0wiL/7l6huOJvdn4rVZVJcS2kJp1MpwFQV9oMkUa/aK2/myot/bfZPAAAAACAY5qzYrfW7s1XbESo/jiys+k4tpWaGKPrB7SWJD362SbbbjtV5XJrxtJdkqSbB6XK4QisLqabBqXqujNby+OR7nxrpbbmFJqO5JcWbs7V5pxCxYSH6IaBqabjBJTfDbO6V95dsUd78vy3e4Xiir+p2RKs3VApwP5gBvA/elwpXfBPa/3FX6U1c8zmAQAAAAD8TH5ppR6bt1mS9LvhHdQkNsJwInu7c2gHxYSHaPWefM1du890nFPy5cYc7T1cqkbRYRrdp4XpOD7ncDj090u7aUCbBBWVV+mWGT8qr7jCdCy/U9O1Mm5giuKjwwynCSz9UhI0uH1jVR3RHeSPKK74mwzmrQBBZdBEaeAEa/3BHdL2RUbjAAAAAABqe+bLrTpYXKF2TWJ0Y1qq6Ti21yQ2QrcNaStJevzzzaqost8c0unf7pQkXXdma0WGBeZWUOGhTr04rp9aJUQp81CJ7nhzhSpd9vu9qis/7DykH3flKTzEqV+d1cZ0nIB019AOkqQ5P+7RvvxSw2mOjuKKP8nfIx3YLDmcUttzTacBUF8u+IfU7XLJXSnNvkHKXms6EQAAAABA0tacQs1YtlOS9MCl3RQeyktpvnDb2W2V2CBCuw6W6K3vM03HOSnrs/L1/Y5DCnE6dENaiuk4dSohJlyv3HSGYsJDtHz7If3to/W23crN12q6Kcb0a6GkuEjDaQLTgLaNNaBNgipcbk1bvN10nKPiJ4I/qelaadFfimpkNguA+uN0SqOnSilnSeUF0swrpcP2+sslAAAAAAQaj8ejBz5eL5fbowu6JunsDk1MRwoYMRGh+t1w613pzy7YqsKySsOJTtxr1V0rF3ZPVrP4KLNh6kHHpFg9e10fORzSrO8y9cbyXaYjGbdxX4G+2pQrh0O6fUg703EC2u+GWX9OzPo+U7kFZYbT/BzFFX+yjS3BgKAVFild+6bUpItUlG0VWEoOmU4FAAAAAEHr8/U5+jbjoMJDnfrLqK6m4wSca89opbaJMTpYXKGXluwwHeeEHCwq14ersyRJ4wcHz1ZQw7ok6U8jO0uS/v7xBi3fftBwIrOmLba6Vi7q3kxtEmMMpwlsae0aq39KI1VUuTXta//rXqG44i9cVdK2Rda6HcUVIChFNZTGvSvFNre2CHz7eqnS/6ryAAAAABDoyipd+sfcDZKkXw9pq9aNow0nCjxhIU7dO6KTJOnlJduVW+j///6d9V2mKqrc6tUyXn1bNzQdp17dPqStLu/TQi63RxNnpSs73/9/v+rC7kMl+njNPknSHefStVLXHA6H7qruXnnzu13aX1huOFFtFFf8xd4VUnm+FNlQatHXdBoApsS3tAosEfFS5jLpv7dJbpfpVAAAAAAQVP7z9XbtyStVs/hIXkCtQyO7J6t3q4YqqXDpmS+3mo5zXBVVbu+WWOMHt5HD4TCcqH45HA49fHkPdU6O1YGiCv32zRWqqAq+Aff/+Xq7XG6Pzu6QqO4t4k3HCQpnd0hUn9YNVVbp1stL/Kt7heKKv6jZEqztuZIzxGgUAIYldbO2CAsJlzZ+JM2bLDEwDgAAAADqxZ68Er2wKEOS9H8XdVF0eKjhRIHL4XBo8oXWdlNv/7Bb2/YXGU50bJ+t26fcwnI1iY3QRT2amY5jRFR4iKbd0E9xkaFKzzysf1Z3dwWL/YXleufH3ZLoWqlPR3avvL5slw4W+U/3CsUVf1EzzL79cLM5APiHNmdLl0+z1t9Pk5Y+azYPAAAAAASJKZ9uUlmlW2e2SdDFPYPzRfT6NKBtYw3r3FQut0ePz9tsOs4xTa8eZD9uQIrCQ4P3JdWUxjF6+trekqQZy3bp/ZV7zAaqR68t3aHyKrd6tWqotLaNTccJKud2bKKeLeNVWunSK9/4z4ym4P2TwJ+UHJKy0q11u6FmswDwH92vkEY8bK2/uF9a847ZPAAAAMCR3C7pnZukzZ+ZTgL4zNJtBzR37T45HdIDl3QLuq2fTLnvws5yOqR567O1Ylee6Tg/k56Zp9W7Dys8xKnrB7Q2Hce4oZ2TvJ0Ek/+7VhuyCgwnqnuFZZV6fZm1Ldwd57Tjz4Z65nA4dNdQ63tuxtKdOlxSYTiRheKKP9i+UPK4pSZdpPgWptMA8CdpE6S0idb6g99K2xaazQMAAADUWPyYtOED6d1bpOIDptMAp63K5dbfP7K2ORo7IEVdm8cZThQ8OibF6sp+LSVJj362SR4/2xr71equlUt6NVeT2AizYfzE3cM66NxOTVRW6dZvZq5Qfkml6Uh1atZ3mSosq1LbJjG6oGuS6ThBaViXpuraLE7FFS5N95PuFYor/iDjK+vYfpjZHAD80/kPSd2ukNyV0uwbpH1rTCcCAABAsMtYIC1+1Fpf/C8pJtFsHsAH3vwuU5tzCtUwOkyTzu9oOk7Quef8jooIder7nYe0YGOu6The2fll+mztPknS+MGpZsP4EafToaev6a2WjaKUeahE97yzSm63fxXFfKWs0qWXq1/M/8057eR00rViwpGzV179dqfyS80X9CiumObx/DTMni3BAByN0yldPlVKPVuqKJTevEo6nGk6FQAAAIJV/l7pv7dJ8kh9b5J6XWM6EXDaDhVX6Mn51ryPP1zQSY1iwg0nCj7N4qP0q7PaSJIenbdJVS634USWmct3qcrt0ZmpCereIt50HL/SMDpcU8f1U0SoU19tytVzCzNMR6oT/03fq/2F5WoWH6nRvdl1yKQLuiapc3KsCsur9Fp1R5lJFFdMy90oFe6TQiOllEGm0wDwV6ER0jUzpaZdpaJsaeYYa14TAAAAUJ9cldK746WSg1JyD+nCx0wnAnziifmbVVBWpS7N4nTdmczUMOU357RTw+gwbc0t0nvp5gell1W6NOt7682NdK0cXfcW8frn5T0kSf/6cosWbvafriNfcLk9mvb1NknSrWe3VXgoL6eb5HQ6dGf17JVXvtmuwjKz3St8N5hW07WSepYUFmU2CwD/FtVQGvuuFNdCOrBFeus6qbLUdCoAAAAEky8fkHZ/J0XESVe/LoVFmk4EnLZ1e/P1VvUL6H+/tJtC2PLHmPioME08r70k6akvtqi0wmU0z0ersnSouEItGkbpfOZsHNOV/Vpq7IDW8niku99epd2HSkxH8pnP1u3TroMlahgdpmvPaGU6DiRd2D1ZHZo2UEFZlV5ftstoFoorpmXUbAnGvBUAJyC+hTTuPSkyXtq93NqOwW32L5sAAAAIEhs/kZY9Z60ve15KaGs2D+ADHo9HD3y0Xh6PdGmv5jqzTYLpSEHvhrQUtWgYpZyCck3/1tzQao/H473/DWkpCg3hZdTjuf+SrurdqqHySyv16zdWqKzS/q9VeDwevbjI6lq5KS1VMRGhhhNBsrpXJg61irAvLdmuovIqc1mM3RlSRYm0a6m1Zpg9gBPVtIt07VtSSLi08WPps/us+U0AAABAXTm0Q/rgt9Z64ASp66Vm8wA+8tHqLP24K09RYSGafFFn03EgKSI0RH8Y0VGSNHXRNuUVVxjJsXz7IW3KLlRkmJOOhRMQERqiF8f1VeOYcG3YV6A/v79OHpu/VrFk6wGtzypQVFiIbh6UajoOjnBxz+ZqmxijwyWVmrncXPcKxRWTdn0rucqluJZSYkfTaQDYSepg6Yr/SHJIP7wkffu06UQAAAAIVJVl0js3SuX5UsszpfP/bjoR4BPF5VV6+NONkqSJQ9urWTzbtfuLy3q1UNdmcSosrzI2JP3V6q6VK/q2VMPocCMZ7KZZfJT+fV0fOR3Se+l7vPNq7OqFRdb33nVntlajGL4H/EnIkd0rX29XSYWZ7hWKKybVbAnWfqjkYD9PACep2+XSyCnW+ssHpNWzjcYBAABAgJr3Jyl7jRSVIF31qhQSZjoR4BPPLcxQTkG5WidE65az2piOgyM4nQ796UKrk+iNZbvqfYbH7kMl+mJjjiRpPB0LJ2VQ+0TdN9L6vXvgo/VamZlnONGpSc/M0/LthxTqdOjWs/nzwR9d2qu5UhpH62BxhWZ9Z6aQR3HFpG3MWwFwmgbeIQ2601p/+Ftp21dm8wAAACCwrHlHWvGqJIc05iUpvqXpRIBP7DhQrFeWWJ0Jf724qyLDQgwnwv8a0rGJzmqfqAqXW0/O31yv93592U55PNLZHRLVISm2Xu8dCG4f0lYjuyWr0uXRb99M14GictORTtrU6lkro/u0UPOGdLX5o9AQpyacZ3WvTF283cicH4orphzeLR3YIjlCpLbnmk4DwM6GPyh1HyO5q6TZN0j7VptOBAAAgECwf7P08d3Wesi9UvvhRuMAvvSPTzaowuXWkI5NNLxLU9NxcAw1HRAfrMrSur359XLP4vIqvf3DbknS+MGp9XLPQONwOPT4VT3VrkmM9uWX6c5ZK1XlcpuOdcIycgs1f4PVufSbc9oaToPjubxPC3VKitUVfVuowsD3GMUVU2q6Vlr2l6IaGo0CwOacTmn0i1Lq2VJFkfTmVdLad6XyItPJAAAAYFcVxdaclcpiqc0Q6dw/mU4E+MzCTblasClXoU6H7r+4qxxs1e63erSM16W9mkuSHp23qV7u+d/0PSosq1Jq42id25HC26mKjQzTtBv6KSY8RMu2H9Tj9dx9dDqmLt4uSbqga5LaN6VzyZ+FhTj16e/O1v9d1EVxkfW/bSnFFVMyvrSObAkGwBdCI6Rr35SadpOKcqT3bpEeb2/9g3j9+1JF/e5PCwAAABvzeKRP7pH2b5IaJEtjXpGcbJmEwFBW6dJDn2yQZHUltG/awHAi/JJ7R3RSWIhDS7Ye0JKt++v0Xm63R68u3SlJumlQqpxOCm+no33TWD12ZS9J0rTF2/XZ2n2GE/2yrMOl+mDlXknSHee2M5wGJyLE4P+nFFdMcFVJ27+21u0prgDwkch46eZPpLP/ICW0lapKpQ0fSnNulh5vJ80ZL238WKosNZ0UAAAA/mzFa9Ka2dY21ldOlxrwzm0EBo/Hoz/MWa3tB4qV2CBCdw3rYDoSTkCrhGiNG5giSXrks01yuz11dq+vt+7X9v3FahARqiv7MWPKF0b1bKbbh1hba/1hzmpl5Pr3LhsvLdmuKrdHaW0bq0/rRqbjwM9RXDFh749Seb4U1Uhq3sd0GgCBJDpBGvZX6c506ddfS2fdIzVMkSpLpPX/lWaPszpa3rtV2vSpVGW/oXIAAACoQ1mrpM/us9bD/iqlDjYaB/Cl577K0Cdr9inU6dC/r+ujWANbyODU3Dm0g2IjQrU+q0Afr8mqs/u8+u1OSdJV/Vvy/eFDfxzRSQPbJqi4wqXfzFyhovIq05GO6lBxhd7+3pq3Q9cKTgTFFRMyquettD2X1moAdcPhkJr1koY/IP1utXTbQmnQnVJ8K2suy9o50tvXWYWW938jbflcqqownRoAAAAmlR6W5twkucqljiOlQb8znQjwmXnr9unJL7ZIkh68rLvS2jU2nAgnIyEmXL+pfrH78c83q7zK5fN7ZOQWafGW/XI4pJsHpfr8+sEsNMSpf1/XV8lxkcrILdIf310tj6fuOpBO1YylO1Va6VK35nE6u0Oi6TiwAYorJtQMs28/3GwOAMHB4ZBa9JUu+Id091rp1gXSwAlSbHOpvEBa/ZY062rpifbSBxOkrV9KrkrTqQEAAFCfPB7pwwlS3k4pvrU0+kXJyUsGCAzr9ubrntmrJVkvml8/oLXhRDgV4wenqmlshPbklWrm8kyfX//1ZTslScM6N1VK4xifXz/YNYmN0PNj+yosxKFP12br5SU7TEeqpbi8SjOqvwfuOLedHA7m7eCX8Tel+lZySNqbbq3bDTWbBUDwcTiklv2lkQ9L96yXfvW5NOA31qDSsnxp1UzpzTHSEx2kj+6Uti205kQBAAAgsC17Xtr0iRQSLl39mrXdLBAAcgvLdPvrP6q00qWzOyTqL6O6mI6EUxQdHqp7zu8oSXruq60qKPPdmwLzSyv17oo9kqTxg9v47LqorV9KI91/cVdJ0iPzNmnZtoOGE/3k7R9263BJpVIaR+vC7s1Mx4FNUFypb9u+kuSRmnaV4pqbTgMgmDmdUuuB0oWPSpM2SDd/Kp1xqxTTRCrNk9Jfl94YLT3ZSfrkHmnH15Lb963XAAAAMCzzO+nLv1nrEQ9LLfqZzQP4SFmlS79+Y4Wy8svUNjFGz13XV6EhvBRmZ1f1a6l2TWKUV1KpaYu3+ey6c37crZIKlzolxWoQW8bVqXEDU3RFnxZyuT2686107csvNR1JFVVuvbxkuyTp10PaKcRJ1wpODD9R6tu2r6wjXSsA/IkzxBpWOupJ6febpRs/kvqNl6IbSyUHpB+nSzMukZ7sLM39g7TzW8ntNp0aAAAAp6v4gDTnZsldJXUfY73ZBggAHo9H//f+Wq3MPKy4yFC9fFN/xUczoNzuQkOcum9kZ0nSK9/sUHZ+2Wlf0+X26LWlOyVJNw9OZTuoOuZwOPTPy3uoS7M4HSiq0G/fTFdFldnXFz5YtVf78svUNDZCY/q1MJoF9kJxpT55PD8VV9oPM5sFAI7FGSK1PUe65Gnp91ukG96X+t4oRTWSinOlH16SXrtIeqqL9Nl91jsdKbQAAADYj9st/fd2qTBLatxeuuQZaxtZIAD85+vt+m/6XoU4HXp+bF+1bdLAdCT4yPldk9Q/pZHKKt16+sstp329LzfmaE9eqRpGh2l0b15Yrw9R4SGaOq6v4iJDtTLzsB76ZIOxLG63R1Oru6BuOauNIkJDjGWB/VBcqU+5G6TCfVJolNR6kOk0APDLQkKtTrtL/y39Yas09j2p91gpIl4qypa+mypNv0B6urs07/+kPT9ahWQAAAD4vyVPSNsWWP9Gvfp1KSLWdCLAJ77ckKNH5m2SJN1/cVed3aGJ4UTwJYfDockXWd0r7/y4W1tzCk/req9+aw1Wv/aM1ooK54X1+pLSOEbPXNtHkvTG8l16r3rmTX2bvyFH2/cXKzYyVNcPaG0kA+yL4kp9ylhgHVPPksIizWYBgJMVEiZ1GC6NfkG6N0O6/h2p57VSeKxUsFda/rz08jDp6Z7S/L9Ke9MptAAAAPir7YukhQ9b61FPSkndjMYBfGVzdqF+9/ZKeTzS9QNa68a0FNORUAf6pSRoRLckuT3So/M2n/J1Nu4r0PLthxTidPC9YsB5nZvqd8M6SJL+7/21Wp+VX6/393g8erG6a+XGtBTFRrJ1IE4OxZX6tK26uMKWYADsLjRc6jhCumKaVWi5dpbU4yopvIGUnyktfVZ66Tzp2d7Slw9I+1ZTaAEAAPAXBfuk926V5JH6jJP6jDWdCPCJg0XlumXGDyqucGlg2wT9/dJuzM8IYPeO6KwQp0NfbszRDzsPndI1arpWRnZLVvOGUb6MhxP0u2EddG6nJiqvcuuOmenKL6mst3sv235Qq3cfVkSoUzcPalNv90XgoLhSXyqKpV1LrXU7iisAAkhYpNR5lDTmZavQcvUbUrfLpbBoKW+n9M2/pGlDpH/3kxY8JOWsp9ACAABgiqtKevdXUvF+Kam7dNETphMBPlFR5dYdb6ZrT16pWidE68Wx/RQWwstegax90wa6un8rSdKUTzfKc5L/zjxUXKEPVmVJksYPTvV1PJwgp9Ohp6/prVYJUco8VKK7Z6+U210/rxm8uMjqWrm6fys1iY2ol3sisPBTpr7s/FZyVUjxraTEDqbTAEDdCIuSul4qXfWaVWi58lWpy6VSaKR0aJu1r/eLg6Tn/7+9O4+rssz/P/4+h+WAKIgiIAqKSu5boLiVlRTZamNmZWVWWo1NmlONLbZMpZlTP9MatylbLfPblGXTYmg6TmouWZqKe6KFO6CoLOfcvz9uRU1cDtyHmwOv5+NxPc4597nvmw8Xyv3h/pzrujpL3z4rbVskFRfaHTUAAED1Me/v0vbvzaldb3rHzN8AP2cYhp6avUY/bN2vmq5AvTEwRZFhwXaHhQrwUFqSQoMCtHJ7jr7+ZZdXx37ww3YVFnvUtkGEkhtF+ihCnI/aNYI1aUCyXIFOzc/cownzNvr8a67ekav/btyrAKdDQy5u4vOvh6qJ4kpFOT4lWNPLJIakAqgOgsOkNn+S+r8rPbJZ6vuG1OIaKcAl7d0gLXpFeutq6aVEacbN0tKp0r7NjGoBAADwlfX/kf73qvn8+tekuk3tjQewyPT/bdOHy7LkcEgTb+mopJhadoeEChIdHqJ7LjKnc3rp6/UqdnvO67git0fvLv5Vkjlqhenj7NemQYRG39BWkvRqxkbNX7/bp19v8rG1Vq5tV1/xdWr49Guh6qK4UlGOL2bfLM3eOADADq6aUtsbpZvfN0e0/Gma1PYmqUaUVHhI2vCl9OUj0sQLpVfbSZ8Pl9Z+Jh2t2MXsAAAAqqwD26RP7zOfp94nte5jZzSAZRZs2KPnv1grSXq8d0td2iLa5ohQ0YZc3ER1woK1ZU++Zi7POq9jvlyTrey8o4qq6dLV7er7OEKcr77JDXVblwQZhjTswx+1fd9hn3ydrXvz9Z81v0uS7ruEDxqg7CiuVISc7dK+jZIjQGrS0+5oAMBeIeFSu5ukvtOkhzdK9y6U0p6RGl8kOYPM35krpksf3S6NTZTeuEL6bqyUtUzyuO2OHgAAwP8UF0iz7jQ/uNIgRbr8ObsjAiyxafchPTBjpTyG1C+5YckIBlQvtUKC9OBlzSRJ47/dqMOFxec85vhC9gNSE+QKDPBpfPDOqGtaqUN8beUdLdZ9763QkULr7wNMXbhZhiFd1iJaLWLDLT8/qg+KKxXh+KiVhp2kkAh7YwGAysTplOq3l3o8JN05Rxr5q3TrR+anKesmSYZbyloqfTdaeiNNeqmJ9NEd0oq3pdwddkcPAADgH75+QvrtRyk00lwbL5C1KKz2+uuvq3HjxgoJCVFqaqp++OGHM+771ltvyeFwnNJCQkJO2efOO+88bZ8rr7zytHN98cUXSk1NVWhoqCIjI9WnTx+rv7VKK+dwoe55e5kOHi1WSqNIPX9DG6Z2qsZuTW2khDo1tOdggf71361n3XdVVo5+3J6joACHBnRJqKAIcb5cgQGadNuFqhsWrLW/5+mJT1fLsHD68F15R/Xxip2SpPsZtYJyCrQ7gGph07fmY7Ne9sYBAJVdcJh0QbrZJHMUy+Z5ZtvynXQ0R1o722ySFHWB1LSXuZ5V4+7m8QAAADhhzcfSsmnm8xumSLXj7Y2nCpo5c6ZGjBihyZMnKzU1VePHj1d6eroyMzMVHV36FFXh4eHKzMwseV1aUeDKK6/U9OnTS167XK5T3v/44481ePBgjR49WpdddpmKi4u1Zs0ai76ryq3I7dHQGSu1bd9hNagdqsm3JzP6oJoLDnTq4fTmevCDHzVlwWYNSE1Q3ZquUvc9Pmrl2nZxiq4VUuo+sFf9iFBNvLWjbvvXUv175U51TIjU7V0aWXLuNxdtVaHbo5RGkerUuI4l50T1RXHF19xF0taF5vOmFFcAwCu1E6TkO83mLjY/cbk5wyy27Fgm7d1gtqWTpIBgKaHLiWJLTBtzZAwAAEB1tXej9NmD5vMeI058gAWWeuWVVzR48GANGjRIkjR58mR98cUXevPNNzVy5MhSj3E4HIqNjT3reV0u1xn3KS4u1rBhwzRu3DjdfffdJdtbtWp11nMWFBSooKCg5HVeXt5Z96+snpuzVv/btE81ggM07Y4URZ3hJjqql2va1te0hVu0emeuJs7bpGeua33aPrvyjuqLn821NgZ1Zxq5yqxb0yj97coWGvPlev3981/UOi5cFyZEluucuYeL9N6SXyVJf76UUSsoP+46+dqO5VJBnjn8Oq6D3dEAgP8KCJTiO0mXjJTu/kZ6dKt007tm4SUiQXIXmsXsb5+Wplwkvdxc+vcQ6aeZ0qHddkcPAABQsQoPm9OpFh6SGvWQLn3C7oiqpMLCQq1YsUJpaWkl25xOp9LS0rR48eIzHnfo0CE1atRI8fHxuv766/XLL7+cts93332n6OhoNW/eXPfff7/27dtX8t7KlSu1c+dOOZ1OdezYUfXr11fv3r3POXJlzJgxioiIKGnx8f43kundJb/qncXmzdH/17+DWsWxXgJMTqdDj/VuIUl6f+mv+nVf/mn7vL/kVxV7DKU0ilTbhkzdX9kNubiJereJVZHb0J/fW6m9hwrOfdBZvLtkm/IL3WoeU0uXNi99ZCHgDYorvrb52HorTS+TnAxRBQDLhNaWWl0nXfuqNPxn6YEVUu9x0gVXSkFhUv5u6eeZ0idDpH8kSZN7SHOfkrYsMBd1BQAAqKoMQ/rir9LutVJYtHTjG+YHVWC5vXv3yu12KyYm5pTtMTExys7OLvWY5s2b680339Ts2bP13nvvyePxqFu3btqx48SagldeeaXeeecdZWRkaOzYsVqwYIF69+4tt9tc2HnLli2SpGeeeUZPPvmk5syZo8jISF1yySXav3//GeN97LHHlJubW9KysrLK2wUV6vtNe/XMZ2Yh6pH05kpvffbRP6h+ujWLUs8L6qnIbWjc15mnvHe0yK33l26XxKgVf+FwODSuX3s1rRem7LyjemDGShW7PWU615FCt6b/b5skc60V1miCFciufO34YvZMCQYAvuNwSFHNzJY6RCoulLKWHluvJUP6/Scpe7XZ/veqFFRDatzDLHw37SVFJZnnAAAAqAp+fFf6aYbkcEo3vinV4gZ0ZdK1a1d17dq15HW3bt3UsmVLTZkyRc8995wk6eabby55v23btmrXrp2aNm2q7777Tr169ZLHY95cfOKJJ9S3b19J0vTp09WwYUPNmjVL9957b6lf2+VynbZ2i7/Ytjdf97+/Um6Poes7xOnPLESNMxjZu4UWbtyjOT//rsEX5ah9fG1J0uc//aZ9+YWqHxGi9NYxZz8JKo2arkBNuT1Z17/2Py3Zsl/jvs7UY1e19Po8s1ZkaV9+oRpGhuqadvV9ECmqI0au+FL+PnN9AMm8gQcAqBiBwVLiRVLa09K9C6VHNkt/+pfU/lapZqxUdFja+I301Ujp9U7S+LbSZ3+RfvlEOnLA7ugBAADKLnu19J9HzOeXPmHmRPCZqKgoBQQEaNeuXads37Vr1znXVDkuKChIHTt21KZNm864T5MmTRQVFVWyT/365o3Bk9dYcblcatKkibZv3+7tt1Hp5R0t0j3vLFfukSK1j6+tsX3b8alznFHL+uG6oWMDSdKLX66XYRgyDKNk1MLtXRspMIBbov6kWXQtjevXXpI0ZeEW/Wf1714dX+T2aMoCc8TfkIub8POHZfiX5Etb5ksypOjWUjgVUQCwTViU1K6fdMMk6a/rpfu/ly5/TmpyqRTgknKzpJXvSLPulF5qIk3rJc0fLW1fKnnKNuQYAACgwh3NNddZKT4qJV1hLmIPnwoODlZycrIyMjJKtnk8HmVkZJwyOuVs3G63Vq9eXVIwKc2OHTu0b9++kn2Sk5PlcrmUmXli2qOioiJt27ZNjRo1KuN3Uzm5PYb+MuNHbdp9SLHhIZp2e7JCgph2HWc34vILFBzg1OIt+7Rgwx79sHW/1v6ep5Agp27plGB3eCiDq9rW15CLm0iSHpn1kzbtPnjex37x8+/amXNEdcOC1S/Z/9aaQuXFtGC+dHxKsGaMWgGASsPhkGJam637g+Zir9u/lzbPN39v71kn7VxutgVjpZoxUotrpFbXS426M185AAConAxDmv2AtH+LFBEv3TBFcvJ5yoowYsQIDRw4UCkpKercubPGjx+v/Px8DRo0SJJ0xx13qEGDBhozZowk6e9//7u6dOmiZs2aKScnR+PGjdOvv/6qe+65R5K52P2zzz6rvn37KjY2Vps3b9ajjz6qZs2aKT09XZIUHh6u++67T08//bTi4+PVqFEjjRs3TpLUr18/G3rBd8b8Z50WbNijkCCnpt2RoujwELtDgh9oGFlDA7s10rT/btWLX65XQp0akqQbOjZQZFiwzdGhrB5Nb66fd+RoyZb9uvfdFZr9QA/VdJ39b3TDMDTpu82SpLt6JCo0mOIsrMMdIl8xDHOuf4n1VgCgMguuITVLM1v6C1Leb8fWapknbfxWOrRLWv6G2WrUlZpfJbXqIyVebE4/BgAAUBksnSKt+0xyBkn93pJq1LE7omqjf//+2rNnj5566illZ2erQ4cO+uqrr0oWud++fbucJxW6Dhw4oMGDBys7O1uRkZFKTk7W999/XzLFV0BAgH7++We9/fbbysnJUVxcnK644go999xzp6yXMm7cOAUGBur222/XkSNHlJqaqnnz5ikyrfMeBwAAGz1JREFUMrJiO8CHPlqWpX8t2ipJerlfB7VtGGFzRPAnQy9tppnLsrQ++6DWZ5ujHO7sxkL2/iwwwKnXbr1Q10xYpM178vXIrJ/0zwEXnnWawHnrdytz10HVdAXqti5Va2Qf7OcwDMOwOwi75OXlKSIiQrm5uQoPD7f25NlrpMndzUWTH90qBfHJCgDwO8WF0taF0rrZ0ro50pH9J94LiTALLS2vM9fVqiK/5316bcRp6G8AgCV2LJfevFLyFElXjpW63Gd3ROXC9bHiVOa+/mHrfg341xIVuQ0N65Wkhy6/wO6Q4IcmL9isF79cL0nq3qyu3r+ni80RwQortx9Q/ymLVeQ29PhVLTTk4qZn3PfGSd9r+a8HdO/FTfTYVS0rMEr4s/O9PjJG2Fc2H5sSrHGPKnPDDQCqncBgKSlNum6i9PBG6Y7PpE73mFOFHc2VfvpA+vAWaVxT6f/ukn75VCrMtztqAABQnRzeL3000CystLxOSr3X7oiAcsvaf1j3vbdCRW5DV7WN1bBeSXaHBD91Z7fGiosw78vd3YNRK1XFhQmReura1pKkF79cr+837y11v2Xb9mv5rwcUHODUXfz84QNMC+Yrm741H5kSDACqhoBAqUlPs/UeJ2UtNafeWPuZlLdDWvOx2QJDzYJMy+ulC9KlkMr1CUAAAFCFeDzSv4eYuUidJtL1r5nrywF+7FBBsQa/s1z78wvVOi5c/+jXXk4n/65RNiFBAfpgSBdt2ZOvS1tE2x0OLHRbaoJ+3H5A/165U3+Z8aPmPNhD9SNCT9nn+ForfZMbKIb1muADFFd8oTBf2r7EfN6M4goAVDlOp9Soq9nSR0s7V5pTh62dLR3YJq373GwBweaUYS2vk5r3Zu5zAABgrUWvSJvmSoEh0k3vmNOWAn7M4zE0/MNVWp99UFE1XZp2R4pqBHPrCuXTqG6YGtUNszsMWMzhcGj0DW21/veDWvt7nu5/b6Vm3ttFrkBzwfp1v+dp3vrdcjqke88ybRhQHkwL5gvbFknuQikiQarbzO5oAAC+5HBIDZOly/8uPbhKuve/0sWPSFEXmNeCDV9Js/8s/SNJevcGafl06dAeu6MGAAD+butCaf4L5vOrxkmxbe2NB7DAP77J1Lfrdik40KlpdyQrrnbouQ8CUG2FBAVo8m3JCg8J1KqsHD03Z23Je1MWmKNWeretr8ZRFNfgGxRXfGHTsfVWml3GkGwAqE4cDql+O+myJ6UHlkl/Xipd+oQU00byFEub50lzhksvXyC9dY20dKqU95vdUQMAAH9zMFv6v7slwyO1v1XqeLvdEQHl9smPO/TPY1P4jO3bVh0TIm2OCIA/SKhbQ6/e3FEOh/Teku36vxU7lLX/sD7/+XdJ0v09GbUC32FspS8cX8y+WZq9cQAA7BXdwmw9H5X2bT62Rsts6bcfpW3/NduXj0gNO0utrpdaXSfVTrA7agAAUJm5i83CSv5uKbqVdPXLfKgPfm/l9gP628erJUn3X9JUN3RsaHNEAPzJpS2iNaxXksZ/u1FPfLJaqU3qyu0xdFFSlNo0YMpM+A7FFasd2Cbt2yQ5AqTEi+2OBgBQWdRtKvV4yGw52801WdbOlrKWSjt+MNs3T0j1OxwrtFxvHgMAAHCy+S9Ivy6Sgmua66wE17A7IqBcfss5oiHvrFBhsUdpLaP1yBXN7Q4JgB968LIk/ZSVo/mZe7RwgzkV9/2X8Dc1fItpwax2fEqw+M4sJggAKF3tBKnrUOnub6QR66Te46TGF0kOp/T7KinjWWnihdI/u0nfjZV2r5MMw+6oAQCA3TZ8Yy5iL0nXTZCikuyNByinw4XFGvzOcu09VKAWsbU0/uaOcjoZiQXAe06nQ+P7d1RCHfNDB+3ja6trk7o2R4WqjpErVts8z3xs2sveOAAA/iE8TkodYrZDe6T1c8zpw7YulHb/YrbvRkt1k05MHRbbjuk/AACobnKypE+GmM87DZba9LU3HqCcPB5DD8/6Sb/8lqc6YcGadkeKarq4TQWg7CJqBOnNO1M0IWOThlzcRA7+boaPcdWykrtI2rLAfN7sMntjAQD4n5r1pJRBZju8X8r80iy0bJ4n7dso/fcfZotsLLW8ziy2NEim0AIAQFVXXCjNulM6ckCK6yilv2B3REC5vZqxUf9Zna2gAIcm35as+DpMcQeg/JpF19KEWzraHQaqCYorVtqxTCo8KNWoK9XnPzEAoBxq1JE6DjDb0Txpw9fSutnSxm/N9b2+n2C28AZmkeWK5yVngN1RAwAAX5g7Stq53Jx6ut9bUqDL7oiAcpnz8296NWOjJOn5Pm3UObGOzREBAOA9iitWOr7eSpNLJSfL2QAALBISLrXrZ7bCfGnjXHNEy4avpbydUtZSCisAAFRVv3wiLZ1sPr9hijmCFfBjq3fk6uFZP0mS7u6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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8Mo7Ged8d0dE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "img_weekday = weekday.iloc[:,[2,3]]\n",
        "img_weekday[\"Duration (hr)\"] = img_weekday[\"Duration (hr)\"].astype('float') * 1000\n",
        "img_weekday['Value']= img_weekday['Value'].replace('Core',1)\n",
        "img_weekday['Value']= img_weekday['Value'].replace('REM',0)\n",
        "img_weekday['Value']= img_weekday['Value'].replace('Deep',0)\n",
        "img_weekday['Value']= img_weekday['Value'].replace('Awake',0)\n",
        "img_weekday = img_weekday[[\"Duration (hr)\", \"Value\"]]\n",
        "img_weekday = np.array(img_weekday)\n",
        "\n",
        "x_list = []   # 라벨을 duration 만큼 반복한 array를 list에 append\n",
        "for x in img_weekday:\n",
        "  if x[1] != 0:\n",
        "    timeseries = np.ones([int(x[0])]) * x[1]\n",
        "    x_list.append(timeseries)\n",
        "  else:\n",
        "    timeseries = np.zeros([int(x[0])]) * x[1]\n",
        "    x_list.append(timeseries)\n",
        "\n",
        "ts = np.concatenate(x_list)[:-3383]   # 7567로 나누어떨어지기 위해 데이터 버림.\n",
        "print(ts.shape)\n",
        "ts\n",
        "tmp = ts.reshape(50, 7500, -1)\n",
        "print(tmp.shape)\n",
        "\n",
        "# Augmentation\n",
        "augmentation(tmp)\n",
        "del tmp\n",
        "\n",
        "# 데이터 로드\n",
        "train = [np.load(\"train_x.npy\"), np.load(\"train_y.npy\")]\n",
        "valid = [np.load(\"valid_x.npy\"), np.load(\"valid_y.npy\")]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hgr4FQakeb_L",
        "outputId": "aeb99ece-5036-4986-8c18-1a3407754dec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(375000,)\n",
            "(50, 7500, 1)\n",
            "Start Augmentation\n",
            "Number of Train set : 870\n",
            "Number of Valid set : 217\n",
            "\n",
            "Start saving the dataset...\n",
            "Complete saving\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 4. Model\n",
        "print(\"Start Training\")\n",
        "batch_size = 256\n",
        "lr = 0.005\n",
        "epochs = 2000\n",
        "\n",
        "model = UNet1D()\n",
        "model.summary()\n",
        "\n",
        "# 5. Train\n",
        "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=lr),\n",
        "              loss='binary_crossentropy',\n",
        "              metrics=['accuracy', f1_score])\n",
        "\n",
        "callbacks_list = [\n",
        "    tf.keras.callbacks.ModelCheckpoint(\n",
        "        filepath=\"weights.h5\",\n",
        "        monitor=\"val_f1_score\",\n",
        "        mode=\"max\",\n",
        "        save_best_only=True,\n",
        "        save_weights_only=True,\n",
        "        verbose=1),\n",
        "\n",
        "    tf.keras.callbacks.EarlyStopping(\n",
        "        monitor=\"val_f1_score\",\n",
        "        min_delta=0.005,\n",
        "        mode=\"max\",\n",
        "        patience=10,\n",
        "        verbose=1),\n",
        "\n",
        "    tf.keras.callbacks.ReduceLROnPlateau(\n",
        "        monitor='val_f1_score',\n",
        "        factor=0.5,\n",
        "        patience=5,\n",
        "        mode='max',\n",
        "        min_delta=0.01,\n",
        "        cooldown=0,\n",
        "        min_lr=0.0001,\n",
        "        verbose=1)\n",
        "]\n",
        "\n",
        "history = model.fit(train[0], train[1],\n",
        "                    validation_data=(valid[0], valid[1]),\n",
        "                    callbacks=callbacks_list,\n",
        "                    epochs=epochs,\n",
        "                    batch_size=batch_size,\n",
        "                    shuffle=True,\n",
        "                    )\n",
        "\n",
        "\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "accuracy = history.history[\"accuracy\"]\n",
        "val_acc = history.history[\"val_accuracy\"]\n",
        "f1 = history.history[\"f1_score\"]\n",
        "val_f1 = history.history[\"val_f1_score\"]\n",
        "\n",
        "epochs_range = range(len(loss))\n",
        "fig, ax = plt.subplots(1, 3, figsize = (20, 12))\n",
        "ax[0].plot(epochs_range, loss, label='Training Loss')\n",
        "ax[0].plot(epochs_range, val_loss, label='Validation Loss')\n",
        "\n",
        "ax[1].plot(epochs_range, accuracy, label='Training Accuracy')\n",
        "ax[1].plot(epochs_range, val_acc, label='Validation Accuracy')\n",
        "\n",
        "ax[2].plot(epochs_range, f1, label='Training f1-score')\n",
        "ax[2].plot(epochs_range, val_f1, label='Validation f1-score')\n",
        "plt.show()\n",
        "\n",
        "del model, history, train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "swHBoZ65ekEn",
        "outputId": "c2cc9268-c4e0-424c-d99a-73643a86d64e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Start Training\n",
            "(None, 250, 128) (None, 250, 128)\n",
            "(None, 500, 64) (None, 500, 64)\n",
            "(None, 1000, 32) (None, 1000, 32)\n",
            "(None, 2000, 16) (None, 2000, 16)\n",
            "Model: \"model_6\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_7 (InputLayer)           [(None, 2000, 1)]    0           []                               \n",
            "                                                                                                  \n",
            " conv1d_114 (Conv1D)            (None, 2000, 16)     160         ['input_7[0][0]']                \n",
            "                                                                                                  \n",
            " batch_normalization_108 (Batch  (None, 2000, 16)    64          ['conv1d_114[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " spatial_dropout1d_108 (Spatial  (None, 2000, 16)    0           ['batch_normalization_108[0][0]']\n",
            " Dropout1D)                                                                                       \n",
            "                                                                                                  \n",
            " activation_108 (Activation)    (None, 2000, 16)     0           ['spatial_dropout1d_108[0][0]']  \n",
            "                                                                                                  \n",
            " conv1d_115 (Conv1D)            (None, 2000, 16)     2320        ['activation_108[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_109 (Batch  (None, 2000, 16)    64          ['conv1d_115[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " spatial_dropout1d_109 (Spatial  (None, 2000, 16)    0           ['batch_normalization_109[0][0]']\n",
            " Dropout1D)                                                                                       \n",
            "                                                                                                  \n",
            " activation_109 (Activation)    (None, 2000, 16)     0           ['spatial_dropout1d_109[0][0]']  \n",
            "                                                                                                  \n",
            " max_pooling1d_24 (MaxPooling1D  (None, 1000, 16)    0           ['activation_109[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " conv1d_116 (Conv1D)            (None, 1000, 32)     4640        ['max_pooling1d_24[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_110 (Batch  (None, 1000, 32)    128         ['conv1d_116[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " spatial_dropout1d_110 (Spatial  (None, 1000, 32)    0           ['batch_normalization_110[0][0]']\n",
            " Dropout1D)                                                                                       \n",
            "                                                                                                  \n",
            " activation_110 (Activation)    (None, 1000, 32)     0           ['spatial_dropout1d_110[0][0]']  \n",
            "                                                                                                  \n",
            " conv1d_117 (Conv1D)            (None, 1000, 32)     9248        ['activation_110[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_111 (Batch  (None, 1000, 32)    128         ['conv1d_117[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " spatial_dropout1d_111 (Spatial  (None, 1000, 32)    0           ['batch_normalization_111[0][0]']\n",
            " Dropout1D)                                                                                       \n",
            "                                                                                                  \n",
            " activation_111 (Activation)    (None, 1000, 32)     0           ['spatial_dropout1d_111[0][0]']  \n",
            "                                                                                                  \n",
            " max_pooling1d_25 (MaxPooling1D  (None, 500, 32)     0           ['activation_111[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " conv1d_118 (Conv1D)            (None, 500, 64)      18496       ['max_pooling1d_25[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_112 (Batch  (None, 500, 64)     256         ['conv1d_118[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " spatial_dropout1d_112 (Spatial  (None, 500, 64)     0           ['batch_normalization_112[0][0]']\n",
            " Dropout1D)                                                                                       \n",
            "                                                                                                  \n",
            " activation_112 (Activation)    (None, 500, 64)      0           ['spatial_dropout1d_112[0][0]']  \n",
            "                                                                                                  \n",
            " conv1d_119 (Conv1D)            (None, 500, 64)      36928       ['activation_112[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_113 (Batch  (None, 500, 64)     256         ['conv1d_119[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " spatial_dropout1d_113 (Spatial  (None, 500, 64)     0           ['batch_normalization_113[0][0]']\n",
            " Dropout1D)                                                                                       \n",
            "                                                                                                  \n",
            " activation_113 (Activation)    (None, 500, 64)      0           ['spatial_dropout1d_113[0][0]']  \n",
            "                                                                                                  \n",
            " max_pooling1d_26 (MaxPooling1D  (None, 250, 64)     0           ['activation_113[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " conv1d_120 (Conv1D)            (None, 250, 128)     73856       ['max_pooling1d_26[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_114 (Batch  (None, 250, 128)    512         ['conv1d_120[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " spatial_dropout1d_114 (Spatial  (None, 250, 128)    0           ['batch_normalization_114[0][0]']\n",
            " Dropout1D)                                                                                       \n",
            "                                                                                                  \n",
            " activation_114 (Activation)    (None, 250, 128)     0           ['spatial_dropout1d_114[0][0]']  \n",
            "                                                                                                  \n",
            " conv1d_121 (Conv1D)            (None, 250, 128)     147584      ['activation_114[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_115 (Batch  (None, 250, 128)    512         ['conv1d_121[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " spatial_dropout1d_115 (Spatial  (None, 250, 128)    0           ['batch_normalization_115[0][0]']\n",
            " Dropout1D)                                                                                       \n",
            "                                                                                                  \n",
            " activation_115 (Activation)    (None, 250, 128)     0           ['spatial_dropout1d_115[0][0]']  \n",
            "                                                                                                  \n",
            " max_pooling1d_27 (MaxPooling1D  (None, 125, 128)    0           ['activation_115[0][0]']         \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " conv1d_122 (Conv1D)            (None, 125, 256)     295168      ['max_pooling1d_27[0][0]']       \n",
            "                                                                                                  \n",
            " batch_normalization_116 (Batch  (None, 125, 256)    1024        ['conv1d_122[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " spatial_dropout1d_116 (Spatial  (None, 125, 256)    0           ['batch_normalization_116[0][0]']\n",
            " Dropout1D)                                                                                       \n",
            "                                                                                                  \n",
            " activation_116 (Activation)    (None, 125, 256)     0           ['spatial_dropout1d_116[0][0]']  \n",
            "                                                                                                  \n",
            " conv1d_123 (Conv1D)            (None, 125, 256)     590080      ['activation_116[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_117 (Batch  (None, 125, 256)    1024        ['conv1d_123[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " spatial_dropout1d_117 (Spatial  (None, 125, 256)    0           ['batch_normalization_117[0][0]']\n",
            " Dropout1D)                                                                                       \n",
            "                                                                                                  \n",
            " activation_117 (Activation)    (None, 125, 256)     0           ['spatial_dropout1d_117[0][0]']  \n",
            "                                                                                                  \n",
            " conv1d_transpose_24 (Conv1DTra  (None, 250, 128)    262272      ['activation_117[0][0]']         \n",
            " nspose)                                                                                          \n",
            "                                                                                                  \n",
            " concatenate_24 (Concatenate)   (None, 250, 256)     0           ['conv1d_transpose_24[0][0]',    \n",
            "                                                                  'activation_115[0][0]']         \n",
            "                                                                                                  \n",
            " conv1d_124 (Conv1D)            (None, 250, 128)     295040      ['concatenate_24[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_118 (Batch  (None, 250, 128)    512         ['conv1d_124[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " spatial_dropout1d_118 (Spatial  (None, 250, 128)    0           ['batch_normalization_118[0][0]']\n",
            " Dropout1D)                                                                                       \n",
            "                                                                                                  \n",
            " activation_118 (Activation)    (None, 250, 128)     0           ['spatial_dropout1d_118[0][0]']  \n",
            "                                                                                                  \n",
            " conv1d_125 (Conv1D)            (None, 250, 128)     147584      ['activation_118[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_119 (Batch  (None, 250, 128)    512         ['conv1d_125[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " spatial_dropout1d_119 (Spatial  (None, 250, 128)    0           ['batch_normalization_119[0][0]']\n",
            " Dropout1D)                                                                                       \n",
            "                                                                                                  \n",
            " activation_119 (Activation)    (None, 250, 128)     0           ['spatial_dropout1d_119[0][0]']  \n",
            "                                                                                                  \n",
            " conv1d_transpose_25 (Conv1DTra  (None, 500, 64)     65600       ['activation_119[0][0]']         \n",
            " nspose)                                                                                          \n",
            "                                                                                                  \n",
            " concatenate_25 (Concatenate)   (None, 500, 128)     0           ['conv1d_transpose_25[0][0]',    \n",
            "                                                                  'activation_113[0][0]']         \n",
            "                                                                                                  \n",
            " conv1d_126 (Conv1D)            (None, 500, 64)      73792       ['concatenate_25[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_120 (Batch  (None, 500, 64)     256         ['conv1d_126[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " spatial_dropout1d_120 (Spatial  (None, 500, 64)     0           ['batch_normalization_120[0][0]']\n",
            " Dropout1D)                                                                                       \n",
            "                                                                                                  \n",
            " activation_120 (Activation)    (None, 500, 64)      0           ['spatial_dropout1d_120[0][0]']  \n",
            "                                                                                                  \n",
            " conv1d_127 (Conv1D)            (None, 500, 64)      36928       ['activation_120[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_121 (Batch  (None, 500, 64)     256         ['conv1d_127[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " spatial_dropout1d_121 (Spatial  (None, 500, 64)     0           ['batch_normalization_121[0][0]']\n",
            " Dropout1D)                                                                                       \n",
            "                                                                                                  \n",
            " activation_121 (Activation)    (None, 500, 64)      0           ['spatial_dropout1d_121[0][0]']  \n",
            "                                                                                                  \n",
            " conv1d_transpose_26 (Conv1DTra  (None, 1000, 32)    16416       ['activation_121[0][0]']         \n",
            " nspose)                                                                                          \n",
            "                                                                                                  \n",
            " concatenate_26 (Concatenate)   (None, 1000, 64)     0           ['conv1d_transpose_26[0][0]',    \n",
            "                                                                  'activation_111[0][0]']         \n",
            "                                                                                                  \n",
            " conv1d_128 (Conv1D)            (None, 1000, 32)     18464       ['concatenate_26[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_122 (Batch  (None, 1000, 32)    128         ['conv1d_128[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " spatial_dropout1d_122 (Spatial  (None, 1000, 32)    0           ['batch_normalization_122[0][0]']\n",
            " Dropout1D)                                                                                       \n",
            "                                                                                                  \n",
            " activation_122 (Activation)    (None, 1000, 32)     0           ['spatial_dropout1d_122[0][0]']  \n",
            "                                                                                                  \n",
            " conv1d_129 (Conv1D)            (None, 1000, 32)     9248        ['activation_122[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_123 (Batch  (None, 1000, 32)    128         ['conv1d_129[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " spatial_dropout1d_123 (Spatial  (None, 1000, 32)    0           ['batch_normalization_123[0][0]']\n",
            " Dropout1D)                                                                                       \n",
            "                                                                                                  \n",
            " activation_123 (Activation)    (None, 1000, 32)     0           ['spatial_dropout1d_123[0][0]']  \n",
            "                                                                                                  \n",
            " conv1d_transpose_27 (Conv1DTra  (None, 2000, 16)    4112        ['activation_123[0][0]']         \n",
            " nspose)                                                                                          \n",
            "                                                                                                  \n",
            " concatenate_27 (Concatenate)   (None, 2000, 32)     0           ['conv1d_transpose_27[0][0]',    \n",
            "                                                                  'activation_109[0][0]']         \n",
            "                                                                                                  \n",
            " conv1d_130 (Conv1D)            (None, 2000, 16)     4624        ['concatenate_27[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_124 (Batch  (None, 2000, 16)    64          ['conv1d_130[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " spatial_dropout1d_124 (Spatial  (None, 2000, 16)    0           ['batch_normalization_124[0][0]']\n",
            " Dropout1D)                                                                                       \n",
            "                                                                                                  \n",
            " activation_124 (Activation)    (None, 2000, 16)     0           ['spatial_dropout1d_124[0][0]']  \n",
            "                                                                                                  \n",
            " conv1d_131 (Conv1D)            (None, 2000, 16)     2320        ['activation_124[0][0]']         \n",
            "                                                                                                  \n",
            " batch_normalization_125 (Batch  (None, 2000, 16)    64          ['conv1d_131[0][0]']             \n",
            " Normalization)                                                                                   \n",
            "                                                                                                  \n",
            " spatial_dropout1d_125 (Spatial  (None, 2000, 16)    0           ['batch_normalization_125[0][0]']\n",
            " Dropout1D)                                                                                       \n",
            "                                                                                                  \n",
            " activation_125 (Activation)    (None, 2000, 16)     0           ['spatial_dropout1d_125[0][0]']  \n",
            "                                                                                                  \n",
            " conv1d_132 (Conv1D)            (None, 2000, 2)      34          ['activation_125[0][0]']         \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 2,120,802\n",
            "Trainable params: 2,117,858\n",
            "Non-trainable params: 2,944\n",
            "__________________________________________________________________________________________________\n",
            "Epoch 1/2000\n",
            "4/4 [==============================] - ETA: 0s - loss: 0.7529 - accuracy: 0.6068 - f1_score: 0.5581\n",
            "Epoch 1: val_f1_score improved from -inf to 0.56326, saving model to weights.h5\n",
            "4/4 [==============================] - 22s 924ms/step - loss: 0.7529 - accuracy: 0.6068 - f1_score: 0.5581 - val_loss: 471.5576 - val_accuracy: 0.6120 - val_f1_score: 0.5633 - lr: 0.0050\n",
            "Epoch 2/2000\n",
            "4/4 [==============================] - ETA: 0s - loss: 0.4443 - accuracy: 0.6004 - f1_score: 0.5577\n",
            "Epoch 2: val_f1_score did not improve from 0.56326\n",
            "4/4 [==============================] - 2s 370ms/step - loss: 0.4443 - accuracy: 0.6004 - f1_score: 0.5577 - val_loss: 2985.2676 - val_accuracy: 0.4995 - val_f1_score: 0.5633 - lr: 0.0050\n",
            "Epoch 3/2000\n",
            "4/4 [==============================] - ETA: 0s - loss: 0.3775 - accuracy: 0.6216 - f1_score: 0.5572\n",
            "Epoch 3: val_f1_score did not improve from 0.56326\n",
            "4/4 [==============================] - 2s 393ms/step - loss: 0.3775 - accuracy: 0.6216 - f1_score: 0.5572 - val_loss: 12527.5439 - val_accuracy: 0.9975 - val_f1_score: 0.5633 - lr: 0.0050\n",
            "Epoch 4/2000\n",
            "4/4 [==============================] - ETA: 0s - loss: 0.3388 - accuracy: 0.6586 - f1_score: 0.5577\n",
            "Epoch 4: val_f1_score did not improve from 0.56326\n",
            "4/4 [==============================] - 2s 374ms/step - loss: 0.3388 - accuracy: 0.6586 - f1_score: 0.5577 - val_loss: 21976.1953 - val_accuracy: 0.9995 - val_f1_score: 0.5633 - lr: 0.0050\n",
            "Epoch 5/2000\n",
            "4/4 [==============================] - ETA: 0s - loss: 0.3019 - accuracy: 0.6642 - f1_score: 0.5569\n",
            "Epoch 5: val_f1_score did not improve from 0.56326\n",
            "4/4 [==============================] - 2s 395ms/step - loss: 0.3019 - accuracy: 0.6642 - f1_score: 0.5569 - val_loss: 34005.0000 - val_accuracy: 1.0000 - val_f1_score: 0.5633 - lr: 0.0050\n",
            "Epoch 6/2000\n",
            "4/4 [==============================] - ETA: 0s - loss: 0.2761 - accuracy: 0.6737 - f1_score: 0.5583\n",
            "Epoch 6: val_f1_score did not improve from 0.56326\n",
            "\n",
            "Epoch 6: ReduceLROnPlateau reducing learning rate to 0.0024999999441206455.\n",
            "4/4 [==============================] - 2s 398ms/step - loss: 0.2761 - accuracy: 0.6737 - f1_score: 0.5583 - val_loss: 28126.0527 - val_accuracy: 1.0000 - val_f1_score: 0.5633 - lr: 0.0050\n",
            "Epoch 7/2000\n",
            "4/4 [==============================] - ETA: 0s - loss: 0.2524 - accuracy: 0.6560 - f1_score: 0.5582\n",
            "Epoch 7: val_f1_score did not improve from 0.56326\n",
            "4/4 [==============================] - 2s 381ms/step - loss: 0.2524 - accuracy: 0.6560 - f1_score: 0.5582 - val_loss: 9792.6309 - val_accuracy: 1.0000 - val_f1_score: 0.5633 - lr: 0.0025\n",
            "Epoch 8/2000\n",
            "4/4 [==============================] - ETA: 0s - loss: 0.2314 - accuracy: 0.7025 - f1_score: 0.5567\n",
            "Epoch 8: val_f1_score did not improve from 0.56326\n",
            "4/4 [==============================] - 2s 397ms/step - loss: 0.2314 - accuracy: 0.7025 - f1_score: 0.5567 - val_loss: 4547.4165 - val_accuracy: 1.0000 - val_f1_score: 0.5633 - lr: 0.0025\n",
            "Epoch 9/2000\n",
            "4/4 [==============================] - ETA: 0s - loss: 0.2267 - accuracy: 0.6756 - f1_score: 0.5586\n",
            "Epoch 9: val_f1_score did not improve from 0.56326\n",
            "4/4 [==============================] - 2s 383ms/step - loss: 0.2267 - accuracy: 0.6756 - f1_score: 0.5586 - val_loss: 1607.1245 - val_accuracy: 1.0000 - val_f1_score: 0.5633 - lr: 0.0025\n",
            "Epoch 10/2000\n",
            "4/4 [==============================] - ETA: 0s - loss: 0.2100 - accuracy: 0.6511 - f1_score: 0.5572\n",
            "Epoch 10: val_f1_score did not improve from 0.56326\n",
            "4/4 [==============================] - 2s 381ms/step - loss: 0.2100 - accuracy: 0.6511 - f1_score: 0.5572 - val_loss: 668.1489 - val_accuracy: 1.0000 - val_f1_score: 0.5633 - lr: 0.0025\n",
            "Epoch 11/2000\n",
            "4/4 [==============================] - ETA: 0s - loss: 0.1954 - accuracy: 0.6911 - f1_score: 0.5593\n",
            "Epoch 11: val_f1_score did not improve from 0.56326\n",
            "\n",
            "Epoch 11: ReduceLROnPlateau reducing learning rate to 0.0012499999720603228.\n",
            "4/4 [==============================] - 2s 383ms/step - loss: 0.1954 - accuracy: 0.6911 - f1_score: 0.5593 - val_loss: 264.4163 - val_accuracy: 1.0000 - val_f1_score: 0.5633 - lr: 0.0025\n",
            "Epoch 11: early stopping\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1200 with 3 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cSjqPEv3e7Mj"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}