20221105_code.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ZzangJay/team_project/blob/main/20221105_code.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "xCBwZEG88iTZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "07633a01-6414-4619-e5a7-5e2a7d9e9bca"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:7: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
            "  import sys\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "#import tensorflow as tf \n",
        "import os\n",
        "import sys\n",
        "from datetime import datetime, timedelta\n",
        "from tqdm.autonotebook import tqdm\n",
        "import csv\n",
        "import re\n",
        "import gzip\n",
        "import shutil"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dKfa-fAw8qIn",
        "outputId": "da8630b5-b5ea-43bc-b8f6-8065afb98d85"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive/\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['CHANGELOG.txt',\n",
              " 'LICENSE.txt',\n",
              " 'SHA256SUMS.txt',\n",
              " 'index.html',\n",
              " 'icu',\n",
              " 'hosp',\n",
              " 'drop_2_chartevents.csv']"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "from google.colab import drive  # for colab\n",
        "drive.mount('/content/gdrive/')  # for colab\n",
        "cloud_directory = '/content/gdrive/MyDrive/mimiciv_unzip/'\n",
        "os.listdir(cloud_directory) # for colab, check cloud directory mount"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "gicdKWPb8xfP"
      },
      "outputs": [],
      "source": [
        "admission = pd.read_csv('/content/gdrive/MyDrive/mimiciv_unzip/hosp/admissions.csv', low_memory=False, dtype='str', keep_default_na=False)\n",
        "patients = pd.read_csv('/content/gdrive/MyDrive/mimiciv_unzip/hosp/patients.csv', low_memory=False, dtype='str', keep_default_na=False)\n",
        "diagnoses_icd = pd.read_csv('/content/gdrive/MyDrive/mimiciv_unzip/hosp/diagnoses_icd.csv', low_memory=False, dtype='str', keep_default_na=False)\n",
        "icustays = pd.read_csv('/content/gdrive/MyDrive/mimiciv_unzip/icu/icustays.csv', low_memory=False, dtype='str', keep_default_na=False)\n",
        "sepsis = pd.read_csv('/content/gdrive/MyDrive/mimiciv_der/sepsis3.csv', low_memory=False, dtype='str', keep_default_na=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "M4GF5YNSEXLE"
      },
      "outputs": [],
      "source": [
        "admission = pd.merge(left = admission, right = sepsis[['subject_id','sepsis3']], how='inner', left_on='subject_id', right_on='subject_id')\n",
        "patients = pd.merge(left = patients, right = sepsis[['subject_id','sepsis3']], how='inner', left_on='subject_id', right_on='subject_id')\n",
        "diagnoses_icd = pd.merge(left = diagnoses_icd, right = sepsis[['subject_id','sepsis3']], how='inner', left_on='subject_id', right_on='subject_id')\n",
        "sepsis = pd.merge(left = sepsis, right = icustays[['hadm_id','stay_id']], how='left', left_on='stay_id', right_on='stay_id')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Total_HADMIDS = set(sepsis['hadm_id'])"
      ],
      "metadata": {
        "id": "ga1QlWtYrpgE"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "xD5AA-mHrl4B"
      },
      "outputs": [],
      "source": [
        "feature_keys = ['FiO2', 'HR', 'PaO2', 'SBP', 'Temperature','Creatinine','Glucose', 'Hb', 'Lactate', 'Plt','Sodium' ,'WBC']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "Yt6qYxMBwAtn"
      },
      "outputs": [],
      "source": [
        "EXTRACT_FEATURE_CODES_VS = dict({\n",
        "    '226754': 'FiO2', '223835': 'FiO2', '227010': 'FiO2', '229280':'FiO2', '229841': 'FiO2',\n",
        "    '220045': 'HR', '220046': 'HR', '226764': 'HR', '227018': 'HR',\n",
        "    '220224': 'PaO2', '227039': 'PaO2', '227516': 'PaO2',\n",
        "    '220050': 'SBP' ,'220179': 'SBP', '224167': 'SBP', '227243': 'SBP', '228152': 'SBP',\n",
        "    '223762': 'Temperature', '226329': 'Temperature', '227632': 'Temperature', '227634': 'Temperature',\n",
        "    })\n",
        "EXTRACT_FEATURE_CODES_LAB = dict({\n",
        "    '220615': 'Creatinine', '229761': 'Creatinine',\n",
        "    '220621': 'Glucose', '225664': 'Glucose', '226537': 'Glucose',\n",
        "    '220228': 'Hb',\n",
        "    '225668': 'Lactate',\n",
        "    '227457': 'Plt',\n",
        "    '220645': 'Sodium', '226534': 'Sodium',\n",
        "    '220546': 'WBC'}) \n",
        "EXTRACT_FEATURE_CODES_NORMAL = dict({\n",
        "        'FiO2': [10, 100],\n",
        "        'HR': [30, 200],\n",
        "        'PaO2': [50, 200],\n",
        "        'SBP': [40, 250],\n",
        "        'Temperature': [30, 45],\n",
        "        'Creatinine': [0.2, 100],\n",
        "        'Glucose': [30, 1000],\n",
        "        'Hb': [5, 20],\n",
        "        'Lactate': [0.5, 2.5],\n",
        "        'Plt': [10, 500],\n",
        "        'Sodium': [90, 170],\n",
        "        'WBC': [1, 30]\n",
        "})\n",
        "NORMAL_VALUE = dict({\n",
        "        'FiO2': 40,\n",
        "        'HR': 75, #(50+100)/2\n",
        "        'PaO2': 87.5, #(75+100)/2\n",
        "        'SBP': 115, #(90+140)/2 = 115\n",
        "        'Temperature': 36.65, #(36.1+37.2)/2\n",
        "        'Creatinine': 1.0, #(0.7+1.0)/2\n",
        "        'Glucose': 100, #(60+140)/2\n",
        "        'Hb': 15.0, #(13.8+17.2)/2\n",
        "        'Lactate': 1.35, #(0.5+2.2)/2 = 1.35\n",
        "        'Plt': 270, #(140+400)/2\n",
        "        'Sodium': 140, #(135+145)/2\n",
        "        'WBC': 7.5 #(5+10)/2\n",
        "})\n",
        "\n",
        "# EXTRACT_FEATURE_CODES_anti = ('225798','225837','225838','225840','225842','225843','225844','225845','225847','225848','225850','225851','225853','225855','225857','225859','225860','225862','225863','225865','225866','225868','225869','225871','225873','225875','225876','225877','225879','225881','225882','225883','225884','225885','225886','225888','225889','225890','225892','225893','225895','225896','225897','225898','225899','225900','225902','225903','225905','227691','228003','229059','229061','229064','229587')\n",
        "# EXTRACT_FEATURE_CODES_ANTI_2 = dict({\n",
        "#    '225850': 'Cephalosporin', '225851': 'Cephalosporin', '225853': 'Cephalosporin', '225855': 'Cephalosporin', '229587':'Cephalosporin',\n",
        "#    '225798': 'Glycopetide',\n",
        "#    '225842': 'Penicillin', '225843': 'Penicillin', '225888': 'Penicillin', '225889': 'Penicillin', '225890': 'Penicillin', '225892': 'Penicillin', '225893': 'Penicillin',\n",
        "#    '225859': 'Quninolone', '225859': 'Quninolone', '225879': 'Quninolone', '225886': 'Quninolone',\n",
        "#    '225845': 'Macrolide', '225865': 'Macrolide', '225866': 'Macrolide'\n",
        "# })"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "ZwMLOPrMI-6d"
      },
      "outputs": [],
      "source": [
        "## Define some util functions\n",
        "NOT_CONVERTED = 'NOT_CONVERTED'\n",
        "## 문자로 기록된 날짜 값들을 파이썬의 datetime 데이터로 바꿔주는 함수 작성\n",
        "def str2datetime(s):\n",
        "    def _convert(_s, _dformat):\n",
        "        try:\n",
        "            converted_dt = datetime.strptime(_s, _dformat)\n",
        "        except Exception:\n",
        "            return NOT_CONVERTED\n",
        "\n",
        "        return converted_dt\n",
        "\n",
        "    if isinstance(s, datetime):\n",
        "        return s\n",
        "\n",
        "    dformats = [\n",
        "        '%Y-%m-%d %p %I:%M:%S',\n",
        "        '%Y-%m-%d %H:%M:%S.%f',\n",
        "        '%Y-%m-%d %H:%M:%S',\n",
        "        '%Y-%m-%d %H:%M',\n",
        "        '%y-%m-%d %H:%M',\n",
        "        '%Y-%m-%d',\n",
        "        '%Y%m%d',\n",
        "        '%Y-%m-%d +%H:%M',\n",
        "        '%Y%m%d %H%M',\n",
        "        '%Y%m%d%H%M%S',\n",
        "        ]\n",
        "\n",
        "    s = s.split('.')[0]\n",
        "    s = s.replace('오전', 'am').replace('오후', 'pm')\n",
        "    for dformat in dformats:\n",
        "        dt = _convert(s, dformat)\n",
        "        if dt != NOT_CONVERTED:\n",
        "            return dt\n",
        "\n",
        "    return NOT_CONVERTED"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "l4eGWeCbGRQR"
      },
      "outputs": [],
      "source": [
        "def extract_features():\n",
        "  features_hadm_id = dict()\n",
        "  chartevents = csv.DictReader(open('/content/gdrive/MyDrive/mimiciv_unzip/icu/chartevents.csv', 'r', encoding='utf-8'))\n",
        "  labevents = csv.DictReader(open('/content/gdrive/MyDrive/mimiciv_unzip/hosp/labevents.csv', 'r', encoding='utf-8'))\n",
        "  for row in tqdm(chartevents, desc='Extract_VS', total=329822285, ncols=700):\n",
        "    if row['hadm_id'] in Total_HADMIDS:\n",
        "      hadm_id = row['hadm_id']\n",
        "      features_hadm_id[hadm_id] = features_hadm_id.get(hadm_id, dict()) #get 없으면 2번째로 return\n",
        "\n",
        "      item_id = row['itemid']\n",
        "      if item_id not in EXTRACT_FEATURE_CODES_VS:\n",
        "        continue\n",
        "      item_name = EXTRACT_FEATURE_CODES_VS[item_id]\n",
        "      try:\n",
        "        #정규식을 이용해서 기록된 값에서 숫자 형태의 값만 가져오기\n",
        "        #소수점 둘째자리 까지만 값을 사용하기\n",
        "        item_value = round(float(re.findall(\"\\d+\\.\\d+|\\d+\", row['valuenum'])[0]), 2)\n",
        "      except Exception:\n",
        "        continue\n",
        "      \n",
        "      item_time = str2datetime(row['charttime'])\n",
        "      if item_time == NOT_CONVERTED:\n",
        "        continue\n",
        "      #GCP total score도 시간대에 총합으로 사용함\n",
        "      if item_name == 'TEMP_F':\n",
        "        item_value = round((item_value - 32.0) / 1.8, 2)\n",
        "        item_name = 'TEMP'\n",
        "\n",
        "    #   if item_value > FEATURE_NORMAL_RANGE[item_name][1]:\n",
        "    #     item_value = FEATURE_NORMAL_RANGE[item_name][1]\n",
        "    #   elif item_value < FEATURE_NORMAL_RANGE[item_name][0]:\n",
        "    #     item_value = FEATURE_NORMAL_RANGE[item_name][0]\n",
        "\n",
        "      features_hadm_id[hadm_id][item_name] = features_hadm_id[hadm_id].get(item_name, list()) #같은 환자의 여러개 feature가 있으면 list로 반환하도록\n",
        "      features_hadm_id[hadm_id][item_name].append((item_time, item_value))\n",
        "      \n",
        "    else:\n",
        "      continue\n",
        "\n",
        "  for row in tqdm(labevents, desc='Extract_LAB', total=124342638, ncols=700):\n",
        "    if row['hadm_id'] in Total_HADMIDS:\n",
        "      hadm_id = row['hadm_id']\n",
        "      features_hadm_id[hadm_id] = features_hadm_id.get(hadm_id, dict())\n",
        "\n",
        "      item_id = row['itemid']\n",
        "      if item_id not in EXTRACT_FEATURE_CODES_LAB:\n",
        "        continue\n",
        "      item_name = EXTRACT_FEATURE_CODES_LAB[item_id]\n",
        "      try:\n",
        "        #정규식을 이용해서 기록된 값에서 숫자 형태의 값만 가져오기\n",
        "        #소수점 둘째자리 까지만 값을 사용하기\n",
        "        item_value = round(float(re.findall(\"\\d+\\.\\d+|\\d+\", row['valuenum'])[0]), 2)\n",
        "      except Exception:\n",
        "        continue\n",
        "      \n",
        "      item_time = str2datetime(row['charttime'])\n",
        "      if item_time == NOT_CONVERTED:\n",
        "        continue\n",
        "\n",
        "      #if item_value > FEATURE_NORMAL_RANGE[item_name][1]:\n",
        "      #  item_value = FEATURE_NORMAL_RANGE[item_name][1]\n",
        "      #elif item_value < FEATURE_NORMAL_RANGE[item_name][0]:\n",
        "      #  item_value = FEATURE_NORMAL_RANGE[item_name][0]\n",
        "        \n",
        "      features_hadm_id[hadm_id][item_name] = features_hadm_id[hadm_id].get(item_name, list())\n",
        "      features_hadm_id[hadm_id][item_name].append((item_time, item_value))\n",
        "      \n",
        "    else:\n",
        "      continue\n",
        "      \n",
        "  return features_hadm_id"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8IuFnUqELI10"
      },
      "outputs": [],
      "source": [
        "# extracted_features = extract_features()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "import gzip"
      ],
      "metadata": {
        "id": "eIuZed5Vt0eL"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# with gzip.open('/content/gdrive/MyDrive/sepsis_model_weight/extracted_features.pickle', 'wb') as f:\n",
        "#     pickle.dump(extracted_features, f)"
      ],
      "metadata": {
        "id": "saRvt6bLqzTj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with gzip.open('/content/gdrive/MyDrive/sepsis_model_weight/extracted_features.pickle','rb') as f:\n",
        "    extracted_features = pickle.load(f)"
      ],
      "metadata": {
        "id": "5lYSignRQ_M-"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "GUHXlnCqNTjo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "b26b4829c84b450d9affcc98add68e5c",
            "18ea08ff19f2434690fef9b677cd823a",
            "3f128a217a6c41e2b98095fd490eb3cf",
            "75d612adc1ce4b85b467ed3f26225dd0",
            "cffa9a618579475d981596db06f58f7b",
            "347d28a2f41e42109a9fd82dfa0a721e",
            "e9ca4ebc52b84481b5c80fe61175ab54",
            "249f4afa5ead46be89e319b66163be95",
            "4e7ac9f037a24299b99b22aea3851718",
            "beb99f843c9b42ea9296d0f001a917c9",
            "e4ec9ec935654034bf5635ddd724562b"
          ]
        },
        "outputId": "94440e9c-15a1-4d55-a520-ca762f851d41"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Get Death Label: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b26b4829c84b450d9affcc98add68e5c"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 사망 레이블 하기\n",
        "death_labels = dict()\n",
        "admissions = csv.DictReader(open('/content/gdrive/MyDrive/mimiciv_der/sepsis3_admissions.csv', 'r', encoding='utf-8'))\n",
        "for row in tqdm(admissions, desc='Get Death Label'):\n",
        "  hadm_id = row['hadm_id']\n",
        "  death_yn = int(row['hospital_expire_flag'])\n",
        "  if death_yn:\n",
        "    event_time = str2datetime(row['deathtime'])\n",
        "  else:\n",
        "    event_time = str2datetime(row['dischtime'])\n",
        "  death_labels[hadm_id] = dict({'DEATH': death_yn, 'EVENT_TIME': event_time})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "PtXfAzo0i7Hq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "e8d1bc1954dd44b9851cb724234a2994",
            "f341b1472caa4594ad26c3309a016fbd",
            "72c596bc75d246418e9b6e3922789e72",
            "f9a34bd0778f4d57863a681557113dc7",
            "1a23dc9a6092403a8f940b9bf9e11667",
            "cfda1c32aae24a07b808f1f5e433ffc9",
            "bf07e886a077438e8a3244a046aded5d",
            "3468d7756083401d93b7cdc3583a18f6",
            "205ad099f86a408d9c10a8e1b882fcfa",
            "327614df89f1484f8ccd0ea7f2eabbcd",
            "18f755280ec744879067c8ea22782e2c"
          ]
        },
        "outputId": "4405f62c-4363-46b1-b4cf-a815fa1a1717"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Get loss Label: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e8d1bc1954dd44b9851cb724234a2994"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# 재실 기간 레이블 하기\n",
        "los_labels = dict()\n",
        "admissions = csv.DictReader(open('/content/gdrive/MyDrive/mimiciv_der/sepsis3_admissions.csv', 'r', encoding='utf-8'))\n",
        "for row in tqdm(admissions, desc='Get loss Label'):\n",
        "  hadm_id = row['hadm_id']\n",
        "  admin_time = str2datetime(row['admittime'])\n",
        "  disc_time = str2datetime(row['dischtime'])\n",
        "  los = (disc_time - admin_time).total_seconds() / (60*60)\n",
        "  los_labels[hadm_id] = int(los)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "Zm7qGtNPOH3p"
      },
      "outputs": [],
      "source": [
        "## 환자의 입실 퇴실 시간 정보 가져오기\n",
        "Extracted_HADMIDS = list(extracted_features.keys())\n",
        "admin_disc_time = dict()\n",
        "\n",
        "admissions = csv.DictReader(open('/content/gdrive/MyDrive/mimiciv_der/sepsis3_admissions.csv', 'r', encoding='utf-8'))\n",
        "for row in admissions:\n",
        "  hadm_id = row['hadm_id']\n",
        "  admin_time = str2datetime(row['admittime'])\n",
        "  disc_time = str2datetime(row['dischtime'])\n",
        "\n",
        "  if (admin_time != NOT_CONVERTED) and (disc_time != NOT_CONVERTED):\n",
        "    admin_disc_time[hadm_id] = dict({'ADMIN': admin_time, 'DISC': disc_time})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "Auy7XQu0N5cN"
      },
      "outputs": [],
      "source": [
        "## 입실 시점으로부터 1시간 간격으로 데이터를 읽어오는 함수 정의\n",
        "def quantize_features(feature_list, admin_time, disc_time, missing_feature=False):\n",
        "  total_hours = int((disc_time - admin_time).total_seconds() / (60*60))\n",
        "  #입실 이후 최대 24시간 동안의 데이터만 가져오기\n",
        "  if total_hours > 24:\n",
        "    total_hours = 24\n",
        "  \n",
        "  quantized_features = list()\n",
        "  if missing_feature:\n",
        "    for i in range(total_hours):\n",
        "      quantized_time = admin_time + timedelta(hours=i)\n",
        "      quantized_value = np.nan #지금은 dic 형태라, pandas 쓰면 NAN이랑 동일, carry forward 도 가능\n",
        "      quantized_features.append((quantized_time, quantized_value))\n",
        "  else:\n",
        "    feature_list = list(sorted(feature_list, key=lambda x: x[0]))\n",
        "    for i in range(total_hours):\n",
        "      quantized_time = admin_time + timedelta(hours=i)\n",
        "      quantized_time_start = quantized_time - timedelta(hours=1)\n",
        "      quantized_values = list(filter(lambda x: quantized_time_start < x[0] <= quantized_time, feature_list)) #filter함수 True만 필터\n",
        "      if len(quantized_values) > 0:\n",
        "        quantized_value = quantized_values[-1][1]\n",
        "      else:\n",
        "        quantized_value = np.nan \n",
        "\n",
        "      quantized_features.append((quantized_time, quantized_value))\n",
        "  \n",
        "  return quantized_features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "SxMcge9WRy-m",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "fa66a869081343d29a59d4d7245efdfb",
            "ce2813c497b244a8ac51c3725ac2df98",
            "c347202047fa4a3f811bc9c4b79fc7f1",
            "9e7d4cc143fa48ac98d96c25acde330d",
            "e9e42e3c4d08478a8d4cc7b95c817109",
            "6e65e75fd5324907bc50bdb928698726",
            "606805e6e8d64cc8b0de7bfd3b275022",
            "0da00d672fba4542bc44d49ff2ff6f40",
            "4fefa2248b4a4846b7335a18e0462619",
            "94d77ad5564e4250b17169e9edc822b7",
            "0cb26fa93ae14e139a2bfbb3c0512734"
          ]
        },
        "outputId": "6beedad9-7ad5-4e33-8fdf-f8f1c277893a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Doing Quantization:   0%|          | 0/32812 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fa66a869081343d29a59d4d7245efdfb"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "## 전체 데이터에 대해서 데이터 변환하기\n",
        "quantized_features = dict()\n",
        "for hadm_id in tqdm(Extracted_HADMIDS, desc='Doing Quantization'):\n",
        "    admin_time = admin_disc_time[hadm_id]['ADMIN']\n",
        "    disc_time = admin_disc_time[hadm_id]['DISC']\n",
        "    if (disc_time - admin_time).total_seconds() / (60*60) < 1.0:\n",
        "      continue\n",
        "    quantized_features[hadm_id] = dict()\n",
        "    for kk in feature_keys:\n",
        "      if kk in extracted_features[hadm_id]:\n",
        "        feature_list = extracted_features[hadm_id][kk]\n",
        "        quantized_features[hadm_id][kk] = quantize_features(feature_list, admin_time, disc_time)\n",
        "      else:\n",
        "        quantized_features[hadm_id][kk] = quantize_features(None, admin_time, disc_time, missing_feature=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "th7CkR4pSJE8"
      },
      "outputs": [],
      "source": [
        "def carry_forward(feature_name, feature_list):\n",
        "  imputed_features = list()\n",
        "  for i, (feature_t, feature_v) in enumerate(feature_list):\n",
        "    if np.isnan(feature_v):\n",
        "      last_observations = list(filter(lambda x: (x[0] <= feature_t) and (not np.isnan(x[1])), feature_list))\n",
        "      if len(last_observations) > 0:\n",
        "        imputed_features.append((feature_t, last_observations[-1][1]))\n",
        "      else:\n",
        "        imputed_features.append((feature_t, NORMAL_VALUE[feature_name]))\n",
        "    else:\n",
        "      imputed_features.append((feature_t, feature_v))\n",
        "\n",
        "  return imputed_features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "uhmbOBT_Shgz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "802582b4180148929b8fcefde8b07262",
            "500e70514a72456cade52edeee44cc65",
            "594985e7e1734403b6698b8fac399dad",
            "43c90e6e44ea49eaa51e38460692ad8c",
            "4d3edd9095284ed7bb03568d6854e38a",
            "c69869f8194a4491bc312a670c010a8a",
            "4c0890c4360a4bf69dc56d01ac863c2b",
            "cbb7aa58bb9c46209bec28f7661c074b",
            "57bbb10a32da47ea8fbea350fed472f0",
            "c950bc1251484fddace4741cdad18933",
            "1eeafaafa8a846998a916f6dbe0dfb0b"
          ]
        },
        "outputId": "5d3301e7-26c8-430a-f8c3-8a352f3301ce"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Carry-Forward:   0%|          | 0/32785 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "802582b4180148929b8fcefde8b07262"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "Quantized_HADMIDS = list(quantized_features.keys())\n",
        "carry_forwarded_features = dict()\n",
        "for hadm_id in tqdm(Quantized_HADMIDS, desc='Carry-Forward'):\n",
        "  carry_forwarded_features[hadm_id] = dict()\n",
        "\n",
        "  for kk in feature_keys:\n",
        "    feature_list = quantized_features[hadm_id][kk]\n",
        "    carry_forwarded_features[hadm_id][kk] = carry_forward(kk, feature_list)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "uOusHtWwTGAV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "5ff2588180a54900a56ba8762d971fd2",
            "2fd836e07f7b4d5abb0fe34bcaa5fc45",
            "d4ad7366ec83400da6a535ed62a09759",
            "78c25a377d434fd7a821040891e221fb",
            "316efa90942e407b8a39afe1e7b67730",
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            "284fe5aa0ef14b29aac02167f05a59d6",
            "0f69af43940b4be99a2bafd88c706f52",
            "cced94d1c6a3475984b85bd59ab8d3e6",
            "87732f99bf8948d897bcc1a2aa020188"
          ]
        },
        "outputId": "c0a6c18e-db88-4166-f566-c938e058c0eb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Min-Max Normalization:   0%|                                                                                  …"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5ff2588180a54900a56ba8762d971fd2"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "#Min-Max Normalization\n",
        "min_max_normalized = dict()\n",
        "for hadm_id in tqdm(Quantized_HADMIDS, desc='Min-Max Normalization', ncols=700):\n",
        "  min_max_normalized[hadm_id] = dict()\n",
        "  for kk in feature_keys:\n",
        "    features = np.array(carry_forwarded_features[hadm_id][kk])\n",
        "\n",
        "    min = EXTRACT_FEATURE_CODES_NORMAL[kk][0]\n",
        "    max = EXTRACT_FEATURE_CODES_NORMAL[kk][1]\n",
        "\n",
        "    features[:, 1] = (features[:, 1] - min) / (max - min)\n",
        "    min_max_normalized[hadm_id][kk] = features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "Tg6sx0ytTLzE"
      },
      "outputs": [],
      "source": [
        "total_death_included_hadm_ids = list(death_labels.keys())\n",
        "total_los_included_hadm_ids = list(los_labels.keys())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "GLGBtCnTk0XN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "f4be96d9d55148b28c45b8e8243b771a",
            "4fe551833d2f4250b2d482efd4ede791",
            "4acce2395f444496a02af40697de612f",
            "a1b522f00de24bf2918f1ed0bd2694f0",
            "2372720c2a0a4842bc3e24effe16ef61",
            "1e0018abaae24c379662b63fea7cd3af",
            "6f2287e2b1364e48b85e61d52e55c47b",
            "ce353413bfb94800acd76e11bffd1cd4",
            "6025cff11a624d33a97d85240a0438f9",
            "e65223766abf4474b58167d328766edc",
            "34d71330c66a40e798c188544fd87936"
          ]
        },
        "outputId": "f83f90fd-ef7b-422f-f4ca-0fdfaf8d52dc"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Make Death Dataset:   0%|                                                                                     …"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f4be96d9d55148b28c45b8e8243b771a"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "## 사망 예측용 데이터\n",
        "death_dataset = dict({\n",
        "    'HADM_IDS': list(),\n",
        "    'LABEL': list(),\n",
        "    'DATA': list()\n",
        "})\n",
        "\n",
        "for hadm_id in tqdm(total_death_included_hadm_ids, desc='Make Death Dataset', ncols=700):\n",
        "  if hadm_id not in min_max_normalized:\n",
        "    continue\n",
        "  if len(min_max_normalized[hadm_id][feature_keys[0]]) >= 24:\n",
        "    time_len = 24\n",
        "  else:\n",
        "    time_len = len(min_max_normalized[hadm_id][feature_keys[0]])\n",
        "  \n",
        "  try:\n",
        "    data = np.array(min_max_normalized[hadm_id][feature_keys[0]])[:time_len, 1].reshape(time_len, 1)\n",
        "  except:\n",
        "    continue\n",
        "\n",
        "  for kk in feature_keys[1:]:\n",
        "    data = np.concatenate((data, np.array(min_max_normalized[hadm_id][kk])[:time_len, 1].reshape(time_len, 1)), axis=1)\n",
        "  \n",
        "  death_dataset['HADM_IDS'].append(hadm_id)\n",
        "  death_dataset['LABEL'].append(death_labels[hadm_id]['DEATH'])\n",
        "  death_dataset['DATA'].append(data)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "id_cepha = pd.read_csv('/content/gdrive/MyDrive/mimiciv_der/temp/id_cepha.csv', low_memory=False, dtype='str', keep_default_na=False)\n",
        "id_glyco = pd.read_csv('/content/gdrive/MyDrive/mimiciv_der/temp/id_glyco.csv', low_memory=False, dtype='str', keep_default_na=False)\n",
        "id_peni = pd.read_csv('/content/gdrive/MyDrive/mimiciv_der/temp/id_peni.csv', low_memory=False, dtype='str', keep_default_na=False)\n",
        "id_fluoro = pd.read_csv('/content/gdrive/MyDrive/mimiciv_der/temp/id_fluoro.csv', low_memory=False, dtype='str', keep_default_na=False)\n",
        "id_macro = pd.read_csv('/content/gdrive/MyDrive/mimiciv_der/temp/id_macro.csv', low_memory=False, dtype='str', keep_default_na=False)"
      ],
      "metadata": {
        "id": "5veRjKeQJo7W"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(death_dataset['HADM_IDS'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4rRS-BXqImP3",
        "outputId": "085846b1-61c1-4b12-915d-53ec05da705e"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "32785"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cepha_dataset = dict({\n",
        "    'HADM_IDS': list(),\n",
        "    'LABEL': list(),\n",
        "    'DATA': list()\n",
        "})\n",
        "\n",
        "glyco_dataset = dict({\n",
        "    'HADM_IDS': list(),\n",
        "    'LABEL': list(),\n",
        "    'DATA': list()\n",
        "})\n",
        "\n",
        "peni_dataset = dict({\n",
        "    'HADM_IDS': list(),\n",
        "    'LABEL': list(),\n",
        "    'DATA': list()\n",
        "})\n",
        "\n",
        "fluoro_dataset = dict({\n",
        "    'HADM_IDS': list(),\n",
        "    'LABEL': list(),\n",
        "    'DATA': list()\n",
        "})\n",
        "\n",
        "macro_dataset = dict({\n",
        "    'HADM_IDS': list(),\n",
        "    'LABEL': list(),\n",
        "    'DATA': list()\n",
        "})"
      ],
      "metadata": {
        "id": "6C6WRpfKJX3R"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "list_glyco=[]\n",
        "for i in id_glyco['hadm_id']:\n",
        "  list_glyco.append(i)\n",
        "\n",
        "list_cepha=[]\n",
        "for i in id_cepha['hadm_id']:\n",
        "  list_cepha.append(i)\n",
        "\n",
        "list_peni=[]\n",
        "for i in id_peni['hadm_id']:\n",
        "  list_peni.append(i)\n",
        "\n",
        "list_fluoro=[]\n",
        "for i in id_fluoro['hadm_id']:\n",
        "  list_fluoro.append(i)\n",
        "\n",
        "list_macro=[]\n",
        "for i in id_macro['hadm_id']:\n",
        "  list_macro.append(i)\n"
      ],
      "metadata": {
        "id": "79NTdKqTTEf3"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in tqdm(range(len(death_dataset['HADM_IDS'])), desc='Make Glycopeptides Dataset', ncols = 700):\n",
        "  if (death_dataset['HADM_IDS'][i] in list_glyco) == True:\n",
        "      glyco_dataset['HADM_IDS'].append(death_dataset['HADM_IDS'][i])\n",
        "      glyco_dataset['LABEL'].append(death_dataset['LABEL'][i])\n",
        "      glyco_dataset['DATA'].append(death_dataset['DATA'][i])\n",
        "\n",
        "for i in tqdm(range(len(death_dataset['HADM_IDS'])), desc='Make Cephalosporins Dataset', ncols = 700):\n",
        "  if (death_dataset['HADM_IDS'][i] in list_cepha) == True:\n",
        "      cepha_dataset['HADM_IDS'].append(death_dataset['HADM_IDS'][i])\n",
        "      cepha_dataset['LABEL'].append(death_dataset['LABEL'][i])\n",
        "      cepha_dataset['DATA'].append(death_dataset['DATA'][i])\n",
        "\n",
        "for i in tqdm(range(len(death_dataset['HADM_IDS'])), desc='Make Penicillins Dataset', ncols = 700):\n",
        "  if (death_dataset['HADM_IDS'][i] in list_peni) == True:\n",
        "      peni_dataset['HADM_IDS'].append(death_dataset['HADM_IDS'][i])\n",
        "      peni_dataset['LABEL'].append(death_dataset['LABEL'][i])\n",
        "      peni_dataset['DATA'].append(death_dataset['DATA'][i])\n",
        "\n",
        "for i in tqdm(range(len(death_dataset['HADM_IDS'])), desc='Make Fluoroquinolones Dataset', ncols = 700):\n",
        "  if (death_dataset['HADM_IDS'][i] in list_fluoro) == True:\n",
        "      fluoro_dataset['HADM_IDS'].append(death_dataset['HADM_IDS'][i])\n",
        "      fluoro_dataset['LABEL'].append(death_dataset['LABEL'][i])\n",
        "      fluoro_dataset['DATA'].append(death_dataset['DATA'][i])\n",
        "\n",
        "for i in tqdm(range(len(death_dataset['HADM_IDS'])), desc='Make Macrolides Dataset', ncols = 700):\n",
        "  if (death_dataset['HADM_IDS'][i] in list_macro) == True:\n",
        "      macro_dataset['HADM_IDS'].append(death_dataset['HADM_IDS'][i])\n",
        "      macro_dataset['LABEL'].append(death_dataset['LABEL'][i])\n",
        "      macro_dataset['DATA'].append(death_dataset['DATA'][i])\n",
        "\n",
        "print(len(glyco_dataset['HADM_IDS']))\n",
        "print(len(cepha_dataset['HADM_IDS']))\n",
        "print(len(peni_dataset['HADM_IDS']))\n",
        "print(len(fluoro_dataset['HADM_IDS']))\n",
        "print(len(macro_dataset['HADM_IDS']))"
      ],
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        "colab": {
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            "858df8ca8dd840cbb6bda202abaaf563",
            "145814dd92f14c0fbdf361d8464fa73e",
            "546d64f2ed3d482c9de19686a8b52e88",
            "bb34fdcb39cf46039da7b347a8f5ae64",
            "c3a98712398343788932760f7495b37a",
            "31805622989a4908bfb528f25a424f47",
            "c74f2f151dba4298b2ae25f27b9af197",
            "bac2360af68745cba7d378d7128176a3",
            "f34a6dbd215b4a54b23c850b4dcbfb3a",
            "bd944f68a84f442ea7f7e6ba906268c3",
            "c8000313b427498498f48f8694972869",
            "bf59572737374479b327142519bc3a52"
          ]
        },
        "id": "UFDwDJW3KpgR",
        "outputId": "9cc98683-5568-4516-9f55-bc3f380de5c9"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Make Glycopeptides Dataset:   0%|                                                                             …"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d0aeb3b1e927427c91607e62977c5617"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Make Cephalosporins Dataset:   0%|                                                                            …"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "34e9a3367ff448cda06e6b3877d99835"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Make Penicillins Dataset:   0%|                                                                               …"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a1e425bcc40e4fb3ab829240991c39eb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Make Fluoroquinolones Dataset:   0%|                                                                          …"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f81cc34af63b43d6847153cd294460b8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Make Macrolides Dataset:   0%|                                                                                …"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "145814dd92f14c0fbdf361d8464fa73e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "11754\n",
            "10954\n",
            "6062\n",
            "3653\n",
            "1919\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "jQUuy4y9qEjl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "1d1e85070e9b4468ac771efcf7f3518b",
            "07c9c52ce0444bd797ef5ef143a965dd",
            "5fae4b42406644e4988b0a6fbcfb80d8",
            "45eb303902644fb7aa3f7e22bd867568",
            "5a9bcffbfff64e1395decf1e77b58e70",
            "92b20b8cfcff40d3ac26a0a8df498000",
            "c133606c5dbd415596096b16d9d2489c",
            "9bb9076589fd41389436ad9ac003b443",
            "9cbe78ee2699409ca7e255bc2c1c84ce",
            "9ad2628b4bd847b09a400d0b9f918848",
            "06a9b58e08ce4337b71e00515b56dbff"
          ]
        },
        "outputId": "f4fefe46-7000-4c42-9193-99dda7449d6e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Make LOS Dataset:   0%|                                                                                       …"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1d1e85070e9b4468ac771efcf7f3518b"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "## 재실기간 예측용 데이터\n",
        "los_dataset = dict({\n",
        "    'HADM_IDS': list(),\n",
        "    'LABEL': list(),\n",
        "    'DATA': list()\n",
        "})\n",
        "\n",
        "for hadm_id in tqdm(total_los_included_hadm_ids, desc='Make LOS Dataset', ncols=700):\n",
        "  time_len = 12\n",
        "  \n",
        "  try:\n",
        "    data = np.array(min_max_normalized[hadm_id][feature_keys[0]])[:time_len, 1].reshape(time_len, 1)\n",
        "  except:\n",
        "    continue\n",
        "\n",
        "  for kk in feature_keys[1:]:\n",
        "    data = np.concatenate((data, np.array(min_max_normalized[hadm_id][kk])[:time_len, 1].reshape(time_len, 1)), axis=1)\n",
        "  \n",
        "  los_dataset['HADM_IDS'].append(hadm_id)\n",
        "  los_dataset['LABEL'].append(los_labels[hadm_id])\n",
        "  los_dataset['DATA'].append(data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "78GmjAzpqJWt"
      },
      "source": [
        "## train-test 분할"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "o-6aOPFhqFQ_"
      },
      "outputs": [],
      "source": [
        "## 학습용 검증용 데이터 나누기 (8:2)\n",
        "def split_train_test(dataset, train_ratio=0.8, pos_neg=True):\n",
        "  #레이블이 양성, 음성인 경우, 학습 데이터, 테스트 데이터를 나눌 때 \n",
        "  #양성, 음성의 비율을 각각 유지할 수 있도록 구현\n",
        "  if pos_neg:\n",
        "    pos_idx = np.where(np.array(dataset['LABEL']) == 1)[0]\n",
        "    neg_idx = np.where(np.array(dataset['LABEL']) == 0)[0]\n",
        "\n",
        "    # suffle 넣어줘서 index 섞어줘야함. np.random, random,suffle\n",
        "    np.random.shuffle(pos_idx)\n",
        "    np.random.shuffle(neg_idx)\n",
        "\n",
        "    pos_num = len(pos_idx)\n",
        "    neg_num = len(neg_idx)\n",
        "\n",
        "    train_pos_idx = list(pos_idx[:int(pos_num * train_ratio)])\n",
        "    test_pos_idx = list(pos_idx[int(pos_num * train_ratio):])\n",
        "\n",
        "    train_neg_idx = list(neg_idx[:int(neg_num * train_ratio)])\n",
        "    test_neg_idx = list(neg_idx[int(neg_num * train_ratio):])\n",
        "\n",
        "    train_idx = train_pos_idx + train_neg_idx\n",
        "    test_idx = test_pos_idx + test_neg_idx\n",
        "  \n",
        "  #레이블이 양성, 음성이 아닌경우 단순하게 전체 데이터에서 학습, 테스트 비율만큼 나누기\n",
        "  else:\n",
        "    idx = np.arange(len(dataset['LABEL']))\n",
        "    np.random.shuffle(idx)\n",
        "    train_idx = idx[:int(len(idx) * train_ratio)]\n",
        "    test_idx = idx[int(len(idx) * train_ratio):]\n",
        "\n",
        "  train_set = dict({\n",
        "      'HADM_IDS': np.array(dataset['HADM_IDS'])[train_idx],\n",
        "      'LABEL': np.array(dataset['LABEL'])[train_idx],\n",
        "      'DATA': np.array(dataset['DATA'])[train_idx]\n",
        "  })\n",
        "\n",
        "  test_set = dict({\n",
        "      'HADM_IDS': np.array(dataset['HADM_IDS'])[test_idx],\n",
        "      'LABEL': np.array(dataset['LABEL'])[test_idx],\n",
        "      'DATA': np.array(dataset['DATA'])[test_idx]\n",
        "  })\n",
        "  return train_set, test_set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "GI5h60FIqQXK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0f12dc40-37f2-4183-b29f-5321bc150b88"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:35: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:41: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n"
          ]
        }
      ],
      "source": [
        "death_train, death_test = split_train_test(death_dataset)\n",
        "los_train, los_test = split_train_test(los_dataset, pos_neg=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "QgYq2iiPqSZj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "24fd636d-2b48-413e-9aad-9d87a276ad65"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "26227\n",
            "6558\n",
            "26072\n",
            "6519\n"
          ]
        }
      ],
      "source": [
        "print(len(death_train['HADM_IDS']))\n",
        "print(len(death_test['HADM_IDS']))\n",
        "print(len(los_train['HADM_IDS']))\n",
        "print(len(los_test['HADM_IDS']))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cepha_train, cepha_test = split_train_test(cepha_dataset)\n",
        "glyco_train, glyco_test = split_train_test(glyco_dataset)\n",
        "peni_train, peni_test = split_train_test(peni_dataset)\n",
        "fluoro_train, fluoro_test = split_train_test(fluoro_dataset)\n",
        "macro_train, macro_test = split_train_test(macro_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j_xBm2WGntvp",
        "outputId": "02dd966b-8ef5-4df5-fcd3-2786accc748a"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:35: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:41: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(glyco_train['HADM_IDS']))\n",
        "print(len(glyco_test['HADM_IDS']))\n",
        "print(len(cepha_train['HADM_IDS']))\n",
        "print(len(cepha_test['HADM_IDS']))\n",
        "print(len(peni_train['HADM_IDS']))\n",
        "print(len(peni_test['HADM_IDS']))\n",
        "print(len(fluoro_train['HADM_IDS']))\n",
        "print(len(fluoro_test['HADM_IDS']))\n",
        "print(len(macro_train['HADM_IDS']))\n",
        "print(len(macro_test['HADM_IDS']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BonHHRm6pR_g",
        "outputId": "819b79ce-f144-4529-e536-3965ff3c1bd5"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9402\n",
            "2352\n",
            "8762\n",
            "2192\n",
            "4849\n",
            "1213\n",
            "2922\n",
            "731\n",
            "1534\n",
            "385\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VZvM3xUVqF5A"
      },
      "source": [
        "## 모델 학습 \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "b38QBa6_TYQm"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "import math\n",
        "from tensorflow.keras.utils import Sequence\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "jtelKrDCpoOL"
      },
      "outputs": [],
      "source": [
        "## 사망예측 모델용 데이터 로더\n",
        "class Death_Dataloader(Sequence):\n",
        "    def __init__(self, x_set, y_set, hadm_id_set, batch_size, shuffle=False):\n",
        "        self.x, self.y, self.hadm_id = x_set, y_set, hadm_id_set\n",
        "        self.batch_size = batch_size\n",
        "        self.shuffle = shuffle\n",
        "        self.on_epoch_end()\n",
        "\n",
        "    def __len__(self):\n",
        "        return math.ceil(len(self.x) / self.batch_size)\n",
        "\n",
        "\t\t# batch 단위로 직접 묶어줘야 함\n",
        "    def __getitem__(self, idx):\n",
        "\t\t# sampler의 역할(index를 batch_size만큼 sampling해줌)\n",
        "        indices = self.indices[idx*self.batch_size:(idx+1)*self.batch_size]\n",
        "\n",
        "        batch_x = [self.x[i] for i in indices]\n",
        "        ## 입력 데이터들의 크기를 최대 24시간으로 통일하고 24시간 보다 짧은 데이터들의 경우 끝에 '0' padding 붙이기\n",
        "        padded_batch_x = np.zeros((len(batch_x), 24, len(feature_keys)))\n",
        "        for i in range(len(batch_x)):\n",
        "          valid_length = len(batch_x[i])\n",
        "          padded_batch_x[i][:valid_length, :] = batch_x[i]\n",
        "        batch_y = [self.y[i] for i in indices]\n",
        "        batch_hadm_id = [self.hadm_id[i] for i in indices]\n",
        "\n",
        "        return np.array(padded_batch_x).astype('float32'), np.array(batch_y).astype('float32')\n",
        "\n",
        "    # epoch이 끝날때마다 실행\n",
        "    def on_epoch_end(self):\n",
        "        self.indices = np.arange(len(self.x))\n",
        "        if self.shuffle == True:\n",
        "            np.random.shuffle(self.indices)\n",
        "\n",
        "# batch 2의 64,128\n",
        "# resampled_ds = tf.data.experimental.sample_from_datasets([pos_ds, neg_ds], weights=[0.5, 0.5])\n",
        "# resampled_ds = resampled_ds.batch(BATCH_SIZE).prefetch(2)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#항생제 별로 trainloader 만들기\n",
        "death_train_loader = Death_Dataloader(death_train['DATA'], death_train['LABEL'], death_train['HADM_IDS'], 125, True)\n",
        "death_test_loader = Death_Dataloader(death_test['DATA'], death_test['LABEL'], death_test['HADM_IDS'], 125, False)"
      ],
      "metadata": {
        "id": "B5fBhj-Dpy9F"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "glyco_train_loader = Death_Dataloader(glyco_train['DATA'], glyco_train['LABEL'], glyco_train['HADM_IDS'], 125, True)\n",
        "glyco_test_loader = Death_Dataloader(glyco_test['DATA'], glyco_test['LABEL'], glyco_test['HADM_IDS'], 125, False)\n",
        "\n",
        "cepha_train_loader = Death_Dataloader(cepha_train['DATA'], cepha_train['LABEL'], cepha_train['HADM_IDS'], 125, True)\n",
        "cepha_test_loader = Death_Dataloader(cepha_test['DATA'], cepha_test['LABEL'], cepha_test['HADM_IDS'], 125, False)\n",
        "\n",
        "peni_train_loader = Death_Dataloader(peni_train['DATA'], peni_train['LABEL'], peni_train['HADM_IDS'], 125, True)\n",
        "peni_test_loader = Death_Dataloader(peni_test['DATA'], peni_test['LABEL'], peni_test['HADM_IDS'], 125, False)\n",
        "\n",
        "fluoro_train_loader = Death_Dataloader(fluoro_train['DATA'], fluoro_train['LABEL'], fluoro_train['HADM_IDS'], 125, True)\n",
        "fluoro_test_loader = Death_Dataloader(fluoro_test['DATA'], fluoro_test['LABEL'], fluoro_test['HADM_IDS'], 125, False)\n",
        "\n",
        "macro_train_loader = Death_Dataloader(macro_train['DATA'], macro_train['LABEL'], macro_train['HADM_IDS'], 125, True)\n",
        "macro_test_loader = Death_Dataloader(macro_test['DATA'], macro_test['LABEL'], macro_test['HADM_IDS'], 125, False)"
      ],
      "metadata": {
        "id": "eXLOky9WJr5a"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(1-(sum(glyco_train['LABEL'])/len(glyco_train['LABEL']))) \n",
        "print(1-(sum(cepha_train['LABEL'])/len(cepha_train['LABEL'])))\n",
        "print(1-(sum(peni_train['LABEL'])/len(peni_train['LABEL'])))\n",
        "print(1-(sum(fluoro_train['LABEL'])/len(fluoro_train['LABEL'])))\n",
        "print(1-(sum(macro_train['LABEL'])/len(macro_train['LABEL'])))\n",
        "#print(len(glyco_train['HADM_IDS']))\n",
        "#print(len(glyco_test['HADM_IDS']))\n",
        "#print(len(cepha_train['HADM_IDS']))\n",
        "#print(len(cepha_test['HADM_IDS']))\n",
        "#print(len(peni_train['HADM_IDS']))\n",
        "#print(len(peni_test['HADM_IDS']))\n",
        "#print(len(fluoro_train['HADM_IDS']))\n",
        "#print(len(fluoro_test['HADM_IDS']))\n",
        "#print(len(macro_train['HADM_IDS']))\n",
        "#print(len(macro_test['HADM_IDS']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XYbf2NjxVUsc",
        "outputId": "2163f2f1-95f0-4ab8-e458-031f31aa8f49"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7841948521591151\n",
            "0.8519744350604885\n",
            "0.7937719117343782\n",
            "0.8497604380561259\n",
            "0.8409387222946545\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## 사망예측 모델\n",
        "#death_lstm = keras.Sequential([\n",
        "#    layers.LSTM(25, input_shape=(13,16)),\n",
        "#    layers.Dense(125, activation='relu'),\n",
        "#    layers.Dense(1, activation='sigmoid')\n",
        "#])\n",
        "#death_lstm.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9XPIB81hIQRU",
        "outputId": "f9478ca0-4938-4395-c5b1-9246b344eb5d"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm (LSTM)                 (None, 25)                4200      \n",
            "                                                                 \n",
            " dense (Dense)               (None, 125)               3250      \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 126       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 7,576\n",
            "Trainable params: 7,576\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## 사망예측 모델\n",
        "#death_lstm = keras.Sequential([\n",
        "#    layers.LSTM(25, input_shape=(13,16)),\n",
        "#    layers.Dense(125),\n",
        "#    layers.BatchNormalization(),\n",
        "#    layers.Activation('relu'),\n",
        "#    layers.Dropout(0.2),\n",
        "#    #batch normalization, dropout,optimizer deat\n",
        "#    layers.Dense(1, activation='sigmoid')\n",
        "#])\n",
        "#death_lstm.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6wKJee0FU76t",
        "outputId": "daa68d49-91c4-40dc-b0ef-2cd343e13112"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm (LSTM)                 (None, 25)                4200      \n",
            "                                                                 \n",
            " dense (Dense)               (None, 125)               3250      \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 125)              500       \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " activation (Activation)     (None, 125)               0         \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 125)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 126       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 8,076\n",
            "Trainable params: 7,826\n",
            "Non-trainable params: 250\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## 사망예측 모델\n",
        "death_lstm_re = keras.Sequential([\n",
        "    layers.LSTM(256, input_shape=(13,16), return_sequences=True),\n",
        "    layers.LSTM(256),\n",
        "    layers.Dense(128),\n",
        "    layers.BatchNormalization(),\n",
        "    layers.Activation('relu'),\n",
        "    layers.Dropout(0.2),\n",
        "    #batch normalization, dropout,optimizer deat\n",
        "    layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "death_lstm_re.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2n1Cj63FWsNK",
        "outputId": "e1124718-854d-46a5-aa5a-603b4eecb931"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm_1 (LSTM)               (None, 13, 256)           279552    \n",
            "                                                                 \n",
            " lstm_2 (LSTM)               (None, 256)               525312    \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 128)               32896     \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 128)              512       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " activation_1 (Activation)   (None, 128)               0         \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 1)                 129       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 838,401\n",
            "Trainable params: 838,145\n",
            "Non-trainable params: 256\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## 사망예측 모델 학습\n",
        "#death_lstm.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "#keras.optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)\n",
        "adam = keras.optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=1e-6, amsgrad=False)\n",
        "#adam = keras.optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=1e-5, amsgrad=False)\n",
        "#adam = keras.optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=1e-4, amsgrad=False)\n",
        "\n",
        "death_lstm_re.compile(loss='binary_crossentropy', optimizer=adam, metrics=['AUC'])\n",
        "num_epochs = 100\n",
        "history = death_lstm_re.fit(death_train_loader, validation_data=death_test_loader, epochs=num_epochs, verbose=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S7CYdcPRY96f",
        "outputId": "ea95734f-19c8-4e44-883b-721f763cb290"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "210/210 - 6s - loss: 0.4374 - auc: 0.5990 - val_loss: 0.4735 - val_auc: 0.6074 - 6s/epoch - 29ms/step\n",
            "Epoch 2/100\n",
            "210/210 - 2s - loss: 0.4145 - auc: 0.6248 - val_loss: 0.4652 - val_auc: 0.6178 - 2s/epoch - 10ms/step\n",
            "Epoch 3/100\n",
            "210/210 - 2s - loss: 0.4142 - auc: 0.6261 - val_loss: 0.5097 - val_auc: 0.6237 - 2s/epoch - 10ms/step\n",
            "Epoch 4/100\n",
            "210/210 - 2s - loss: 0.4084 - auc: 0.6357 - val_loss: 0.4149 - val_auc: 0.6340 - 2s/epoch - 10ms/step\n",
            "Epoch 5/100\n",
            "210/210 - 2s - loss: 0.4055 - auc: 0.6414 - val_loss: 0.6010 - val_auc: 0.6418 - 2s/epoch - 10ms/step\n",
            "Epoch 6/100\n",
            "210/210 - 2s - loss: 0.4068 - auc: 0.6347 - val_loss: 0.4059 - val_auc: 0.6443 - 2s/epoch - 9ms/step\n",
            "Epoch 7/100\n",
            "210/210 - 2s - loss: 0.4037 - auc: 0.6435 - val_loss: 0.4837 - val_auc: 0.6453 - 2s/epoch - 10ms/step\n",
            "Epoch 8/100\n",
            "210/210 - 2s - loss: 0.4033 - auc: 0.6456 - val_loss: 0.4393 - val_auc: 0.6563 - 2s/epoch - 9ms/step\n",
            "Epoch 9/100\n",
            "210/210 - 2s - loss: 0.4010 - auc: 0.6570 - val_loss: 0.4517 - val_auc: 0.6520 - 2s/epoch - 9ms/step\n",
            "Epoch 10/100\n",
            "210/210 - 2s - loss: 0.4014 - auc: 0.6546 - val_loss: 0.4282 - val_auc: 0.5957 - 2s/epoch - 9ms/step\n",
            "Epoch 11/100\n",
            "210/210 - 2s - loss: 0.4017 - auc: 0.6473 - val_loss: 0.4690 - val_auc: 0.6387 - 2s/epoch - 10ms/step\n",
            "Epoch 12/100\n",
            "210/210 - 2s - loss: 0.3994 - auc: 0.6599 - val_loss: 0.4105 - val_auc: 0.6386 - 2s/epoch - 10ms/step\n",
            "Epoch 13/100\n",
            "210/210 - 2s - loss: 0.3981 - auc: 0.6583 - val_loss: 0.4450 - val_auc: 0.5668 - 2s/epoch - 10ms/step\n",
            "Epoch 14/100\n",
            "210/210 - 2s - loss: 0.3981 - auc: 0.6595 - val_loss: 0.4159 - val_auc: 0.6559 - 2s/epoch - 9ms/step\n",
            "Epoch 15/100\n",
            "210/210 - 2s - loss: 0.3969 - auc: 0.6670 - val_loss: 0.4013 - val_auc: 0.6682 - 2s/epoch - 10ms/step\n",
            "Epoch 16/100\n",
            "210/210 - 2s - loss: 0.3972 - auc: 0.6638 - val_loss: 0.4069 - val_auc: 0.6428 - 2s/epoch - 9ms/step\n",
            "Epoch 17/100\n",
            "210/210 - 2s - loss: 0.3962 - auc: 0.6678 - val_loss: 0.4034 - val_auc: 0.6518 - 2s/epoch - 9ms/step\n",
            "Epoch 18/100\n",
            "210/210 - 2s - loss: 0.3952 - auc: 0.6672 - val_loss: 0.4524 - val_auc: 0.5984 - 2s/epoch - 10ms/step\n",
            "Epoch 19/100\n",
            "210/210 - 2s - loss: 0.3955 - auc: 0.6686 - val_loss: 0.4231 - val_auc: 0.6570 - 2s/epoch - 10ms/step\n",
            "Epoch 20/100\n",
            "210/210 - 2s - loss: 0.3944 - auc: 0.6749 - val_loss: 0.4677 - val_auc: 0.6615 - 2s/epoch - 10ms/step\n",
            "Epoch 21/100\n",
            "210/210 - 2s - loss: 0.3943 - auc: 0.6724 - val_loss: 0.4132 - val_auc: 0.6551 - 2s/epoch - 10ms/step\n",
            "Epoch 22/100\n",
            "210/210 - 2s - loss: 0.3940 - auc: 0.6723 - val_loss: 0.4164 - val_auc: 0.6581 - 2s/epoch - 10ms/step\n",
            "Epoch 23/100\n",
            "210/210 - 2s - loss: 0.3931 - auc: 0.6787 - val_loss: 0.4350 - val_auc: 0.6290 - 2s/epoch - 10ms/step\n",
            "Epoch 24/100\n",
            "210/210 - 2s - loss: 0.3929 - auc: 0.6782 - val_loss: 0.4092 - val_auc: 0.6490 - 2s/epoch - 10ms/step\n",
            "Epoch 25/100\n",
            "210/210 - 2s - loss: 0.3930 - auc: 0.6777 - val_loss: 0.4143 - val_auc: 0.6130 - 2s/epoch - 10ms/step\n",
            "Epoch 26/100\n",
            "210/210 - 2s - loss: 0.3904 - auc: 0.6823 - val_loss: 0.4083 - val_auc: 0.6605 - 2s/epoch - 10ms/step\n",
            "Epoch 27/100\n",
            "210/210 - 2s - loss: 0.3902 - auc: 0.6846 - val_loss: 0.5001 - val_auc: 0.6233 - 2s/epoch - 10ms/step\n",
            "Epoch 28/100\n",
            "210/210 - 2s - loss: 0.3911 - auc: 0.6817 - val_loss: 0.4407 - val_auc: 0.6318 - 2s/epoch - 10ms/step\n",
            "Epoch 29/100\n",
            "210/210 - 2s - loss: 0.3906 - auc: 0.6832 - val_loss: 0.4021 - val_auc: 0.6428 - 2s/epoch - 9ms/step\n",
            "Epoch 30/100\n",
            "210/210 - 2s - loss: 0.3895 - auc: 0.6871 - val_loss: 0.4119 - val_auc: 0.6452 - 2s/epoch - 10ms/step\n",
            "Epoch 31/100\n",
            "210/210 - 2s - loss: 0.3891 - auc: 0.6891 - val_loss: 0.4086 - val_auc: 0.6664 - 2s/epoch - 10ms/step\n",
            "Epoch 32/100\n",
            "210/210 - 2s - loss: 0.3892 - auc: 0.6884 - val_loss: 0.4237 - val_auc: 0.6724 - 2s/epoch - 10ms/step\n",
            "Epoch 33/100\n",
            "210/210 - 2s - loss: 0.3883 - auc: 0.6922 - val_loss: 0.4204 - val_auc: 0.6623 - 2s/epoch - 10ms/step\n",
            "Epoch 34/100\n",
            "210/210 - 2s - loss: 0.3870 - auc: 0.6957 - val_loss: 0.4946 - val_auc: 0.6738 - 2s/epoch - 10ms/step\n",
            "Epoch 35/100\n",
            "210/210 - 2s - loss: 0.3860 - auc: 0.6987 - val_loss: 0.4210 - val_auc: 0.6634 - 2s/epoch - 9ms/step\n",
            "Epoch 36/100\n",
            "210/210 - 2s - loss: 0.3859 - auc: 0.7011 - val_loss: 0.4260 - val_auc: 0.6611 - 2s/epoch - 9ms/step\n",
            "Epoch 37/100\n",
            "210/210 - 2s - loss: 0.3852 - auc: 0.6990 - val_loss: 0.4141 - val_auc: 0.6675 - 2s/epoch - 10ms/step\n",
            "Epoch 38/100\n",
            "210/210 - 2s - loss: 0.3841 - auc: 0.7045 - val_loss: 0.3990 - val_auc: 0.6684 - 2s/epoch - 10ms/step\n",
            "Epoch 39/100\n",
            "210/210 - 2s - loss: 0.3850 - auc: 0.7042 - val_loss: 0.3929 - val_auc: 0.7016 - 2s/epoch - 10ms/step\n",
            "Epoch 40/100\n",
            "210/210 - 2s - loss: 0.3842 - auc: 0.7023 - val_loss: 0.3990 - val_auc: 0.6734 - 2s/epoch - 10ms/step\n",
            "Epoch 41/100\n",
            "210/210 - 2s - loss: 0.3826 - auc: 0.7068 - val_loss: 0.4013 - val_auc: 0.6676 - 2s/epoch - 10ms/step\n",
            "Epoch 42/100\n",
            "210/210 - 2s - loss: 0.3817 - auc: 0.7091 - val_loss: 0.3963 - val_auc: 0.6900 - 2s/epoch - 9ms/step\n",
            "Epoch 43/100\n",
            "210/210 - 2s - loss: 0.3823 - auc: 0.7073 - val_loss: 0.4117 - val_auc: 0.6619 - 2s/epoch - 9ms/step\n",
            "Epoch 44/100\n",
            "210/210 - 2s - loss: 0.3821 - auc: 0.7061 - val_loss: 0.4085 - val_auc: 0.6528 - 2s/epoch - 9ms/step\n",
            "Epoch 45/100\n",
            "210/210 - 2s - loss: 0.3801 - auc: 0.7129 - val_loss: 0.4048 - val_auc: 0.6607 - 2s/epoch - 9ms/step\n",
            "Epoch 46/100\n",
            "210/210 - 2s - loss: 0.3797 - auc: 0.7129 - val_loss: 0.3923 - val_auc: 0.6851 - 2s/epoch - 10ms/step\n",
            "Epoch 47/100\n",
            "210/210 - 2s - loss: 0.3774 - auc: 0.7184 - val_loss: 0.3940 - val_auc: 0.6846 - 2s/epoch - 9ms/step\n",
            "Epoch 48/100\n",
            "210/210 - 2s - loss: 0.3773 - auc: 0.7197 - val_loss: 0.3935 - val_auc: 0.7002 - 2s/epoch - 9ms/step\n",
            "Epoch 49/100\n",
            "210/210 - 2s - loss: 0.3762 - auc: 0.7191 - val_loss: 0.4024 - val_auc: 0.6732 - 2s/epoch - 10ms/step\n",
            "Epoch 50/100\n",
            "210/210 - 2s - loss: 0.3751 - auc: 0.7250 - val_loss: 0.3941 - val_auc: 0.7041 - 2s/epoch - 10ms/step\n",
            "Epoch 51/100\n",
            "210/210 - 2s - loss: 0.3755 - auc: 0.7214 - val_loss: 0.3946 - val_auc: 0.6951 - 2s/epoch - 9ms/step\n",
            "Epoch 52/100\n",
            "210/210 - 2s - loss: 0.3740 - auc: 0.7263 - val_loss: 0.3974 - val_auc: 0.6923 - 2s/epoch - 9ms/step\n",
            "Epoch 53/100\n",
            "210/210 - 2s - loss: 0.3740 - auc: 0.7246 - val_loss: 0.3918 - val_auc: 0.7048 - 2s/epoch - 9ms/step\n",
            "Epoch 54/100\n",
            "210/210 - 2s - loss: 0.3718 - auc: 0.7306 - val_loss: 0.3985 - val_auc: 0.6844 - 2s/epoch - 9ms/step\n",
            "Epoch 55/100\n",
            "210/210 - 2s - loss: 0.3685 - auc: 0.7377 - val_loss: 0.3931 - val_auc: 0.6983 - 2s/epoch - 10ms/step\n",
            "Epoch 56/100\n",
            "210/210 - 2s - loss: 0.3684 - auc: 0.7353 - val_loss: 0.3962 - val_auc: 0.6985 - 2s/epoch - 10ms/step\n",
            "Epoch 57/100\n",
            "210/210 - 2s - loss: 0.3685 - auc: 0.7385 - val_loss: 0.4033 - val_auc: 0.7094 - 2s/epoch - 9ms/step\n",
            "Epoch 58/100\n",
            "210/210 - 2s - loss: 0.3678 - auc: 0.7390 - val_loss: 0.3997 - val_auc: 0.6849 - 2s/epoch - 10ms/step\n",
            "Epoch 59/100\n",
            "210/210 - 2s - loss: 0.3630 - auc: 0.7447 - val_loss: 0.3949 - val_auc: 0.7008 - 2s/epoch - 10ms/step\n",
            "Epoch 60/100\n",
            "210/210 - 2s - loss: 0.3628 - auc: 0.7465 - val_loss: 0.3998 - val_auc: 0.6863 - 2s/epoch - 10ms/step\n",
            "Epoch 61/100\n",
            "210/210 - 2s - loss: 0.3596 - auc: 0.7530 - val_loss: 0.4057 - val_auc: 0.6861 - 2s/epoch - 10ms/step\n",
            "Epoch 62/100\n",
            "210/210 - 2s - loss: 0.3575 - auc: 0.7553 - val_loss: 0.4177 - val_auc: 0.6841 - 2s/epoch - 9ms/step\n",
            "Epoch 63/100\n",
            "210/210 - 2s - loss: 0.3574 - auc: 0.7553 - val_loss: 0.4138 - val_auc: 0.6914 - 2s/epoch - 9ms/step\n",
            "Epoch 64/100\n",
            "210/210 - 2s - loss: 0.3555 - auc: 0.7590 - val_loss: 0.3992 - val_auc: 0.6996 - 2s/epoch - 10ms/step\n",
            "Epoch 65/100\n",
            "210/210 - 2s - loss: 0.3510 - auc: 0.7656 - val_loss: 0.4089 - val_auc: 0.6964 - 2s/epoch - 10ms/step\n",
            "Epoch 66/100\n",
            "210/210 - 2s - loss: 0.3478 - auc: 0.7702 - val_loss: 0.4264 - val_auc: 0.6856 - 2s/epoch - 10ms/step\n",
            "Epoch 67/100\n",
            "210/210 - 2s - loss: 0.3466 - auc: 0.7716 - val_loss: 0.4117 - val_auc: 0.6869 - 2s/epoch - 10ms/step\n",
            "Epoch 68/100\n",
            "210/210 - 2s - loss: 0.3410 - auc: 0.7805 - val_loss: 0.4197 - val_auc: 0.6899 - 2s/epoch - 10ms/step\n",
            "Epoch 69/100\n",
            "210/210 - 2s - loss: 0.3401 - auc: 0.7808 - val_loss: 0.4267 - val_auc: 0.6581 - 2s/epoch - 10ms/step\n",
            "Epoch 70/100\n",
            "210/210 - 2s - loss: 0.3346 - auc: 0.7905 - val_loss: 0.4404 - val_auc: 0.6826 - 2s/epoch - 9ms/step\n",
            "Epoch 71/100\n",
            "210/210 - 2s - loss: 0.3285 - auc: 0.7948 - val_loss: 0.4510 - val_auc: 0.6771 - 2s/epoch - 9ms/step\n",
            "Epoch 72/100\n",
            "210/210 - 2s - loss: 0.3270 - auc: 0.7999 - val_loss: 0.4484 - val_auc: 0.6867 - 2s/epoch - 9ms/step\n",
            "Epoch 73/100\n",
            "210/210 - 2s - loss: 0.3208 - auc: 0.8067 - val_loss: 0.4537 - val_auc: 0.6824 - 2s/epoch - 10ms/step\n",
            "Epoch 74/100\n",
            "210/210 - 2s - loss: 0.3144 - auc: 0.8138 - val_loss: 0.4567 - val_auc: 0.6789 - 2s/epoch - 10ms/step\n",
            "Epoch 75/100\n",
            "210/210 - 2s - loss: 0.3101 - auc: 0.8195 - val_loss: 0.4656 - val_auc: 0.6614 - 2s/epoch - 10ms/step\n",
            "Epoch 76/100\n",
            "210/210 - 2s - loss: 0.3023 - auc: 0.8300 - val_loss: 0.4863 - val_auc: 0.6671 - 2s/epoch - 10ms/step\n",
            "Epoch 77/100\n",
            "210/210 - 2s - loss: 0.2995 - auc: 0.8348 - val_loss: 0.4711 - val_auc: 0.6686 - 2s/epoch - 10ms/step\n",
            "Epoch 78/100\n",
            "210/210 - 2s - loss: 0.2922 - auc: 0.8437 - val_loss: 0.5066 - val_auc: 0.6546 - 2s/epoch - 10ms/step\n",
            "Epoch 79/100\n",
            "210/210 - 2s - loss: 0.2890 - auc: 0.8453 - val_loss: 0.5070 - val_auc: 0.6639 - 2s/epoch - 10ms/step\n",
            "Epoch 80/100\n",
            "210/210 - 2s - loss: 0.2775 - auc: 0.8561 - val_loss: 0.5313 - val_auc: 0.6533 - 2s/epoch - 10ms/step\n",
            "Epoch 81/100\n",
            "210/210 - 2s - loss: 0.2771 - auc: 0.8596 - val_loss: 0.5199 - val_auc: 0.6485 - 2s/epoch - 9ms/step\n",
            "Epoch 82/100\n",
            "210/210 - 2s - loss: 0.2702 - auc: 0.8650 - val_loss: 0.5388 - val_auc: 0.6457 - 2s/epoch - 10ms/step\n",
            "Epoch 83/100\n",
            "210/210 - 2s - loss: 0.2625 - auc: 0.8760 - val_loss: 0.5397 - val_auc: 0.6518 - 2s/epoch - 9ms/step\n",
            "Epoch 84/100\n",
            "210/210 - 2s - loss: 0.2570 - auc: 0.8804 - val_loss: 0.5945 - val_auc: 0.6462 - 2s/epoch - 9ms/step\n",
            "Epoch 85/100\n",
            "210/210 - 2s - loss: 0.2513 - auc: 0.8830 - val_loss: 0.5957 - val_auc: 0.6452 - 2s/epoch - 10ms/step\n",
            "Epoch 86/100\n",
            "210/210 - 2s - loss: 0.2420 - auc: 0.8939 - val_loss: 0.6137 - val_auc: 0.6470 - 2s/epoch - 10ms/step\n",
            "Epoch 87/100\n",
            "210/210 - 2s - loss: 0.2376 - auc: 0.8993 - val_loss: 0.6153 - val_auc: 0.6538 - 2s/epoch - 9ms/step\n",
            "Epoch 88/100\n",
            "210/210 - 2s - loss: 0.2307 - auc: 0.9033 - val_loss: 0.6497 - val_auc: 0.6380 - 2s/epoch - 9ms/step\n",
            "Epoch 89/100\n",
            "210/210 - 2s - loss: 0.2225 - auc: 0.9106 - val_loss: 0.6619 - val_auc: 0.6306 - 2s/epoch - 9ms/step\n",
            "Epoch 90/100\n",
            "210/210 - 2s - loss: 0.2257 - auc: 0.9082 - val_loss: 0.6336 - val_auc: 0.6378 - 2s/epoch - 9ms/step\n",
            "Epoch 91/100\n",
            "210/210 - 2s - loss: 0.2185 - auc: 0.9146 - val_loss: 0.6817 - val_auc: 0.6329 - 2s/epoch - 10ms/step\n",
            "Epoch 92/100\n",
            "210/210 - 2s - loss: 0.2158 - auc: 0.9176 - val_loss: 0.6703 - val_auc: 0.6378 - 2s/epoch - 9ms/step\n",
            "Epoch 93/100\n",
            "210/210 - 2s - loss: 0.2040 - auc: 0.9246 - val_loss: 0.7041 - val_auc: 0.6259 - 2s/epoch - 9ms/step\n",
            "Epoch 94/100\n",
            "210/210 - 2s - loss: 0.1953 - auc: 0.9317 - val_loss: 0.7557 - val_auc: 0.6302 - 2s/epoch - 10ms/step\n",
            "Epoch 95/100\n",
            "210/210 - 2s - loss: 0.1969 - auc: 0.9306 - val_loss: 0.7518 - val_auc: 0.6195 - 2s/epoch - 10ms/step\n",
            "Epoch 96/100\n",
            "210/210 - 2s - loss: 0.1863 - auc: 0.9376 - val_loss: 0.7850 - val_auc: 0.6235 - 2s/epoch - 10ms/step\n",
            "Epoch 97/100\n",
            "210/210 - 2s - loss: 0.1885 - auc: 0.9378 - val_loss: 0.7991 - val_auc: 0.6180 - 2s/epoch - 9ms/step\n",
            "Epoch 98/100\n",
            "210/210 - 2s - loss: 0.1817 - auc: 0.9410 - val_loss: 0.8335 - val_auc: 0.6151 - 2s/epoch - 9ms/step\n",
            "Epoch 99/100\n",
            "210/210 - 2s - loss: 0.1798 - auc: 0.9419 - val_loss: 0.7840 - val_auc: 0.6206 - 2s/epoch - 9ms/step\n",
            "Epoch 100/100\n",
            "210/210 - 2s - loss: 0.1719 - auc: 0.9478 - val_loss: 0.8286 - val_auc: 0.6162 - 2s/epoch - 10ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib import pyplot as plt\n",
        "plt.plot(history.history['auc'])\n",
        "plt.plot(history.history['val_auc'])\n",
        "plt.title('model auc')\n",
        "plt.ylabel('auc')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "6w-7i8CdccJt",
        "outputId": "313cba9f-54d7-4d75-8b26-d9126dbc6b97"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Trainset, Testset에 대한 ROC 커브 그리고 Confusion Matrix 구해보기\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import roc_curve, precision_recall_curve, roc_auc_score, average_precision_score, confusion_matrix\n",
        "\n",
        "%matplotlib inline\n",
        "lstm_train_predicts, lstm_test_predicts = list(), list()\n",
        "lstm_train_labels, lstm_test_labels = list(), list()\n",
        "\n",
        "for tr_x, tr_y in death_train_loader:\n",
        "  batch_predict = death_lstm.predict(tr_x)\n",
        "  lstm_train_predicts.append(batch_predict)\n",
        "  lstm_train_labels.append(tr_y)\n",
        "\n",
        "  batch_predict = death_trans.predict(tr_x)\n",
        "  trans_train_predicts.append(batch_predict)\n",
        "  trans_train_labels.append(tr_y)\n",
        "\n",
        "for test_x, test_y in death_test_loader:\n",
        "  batch_predict = death_lstm.predict(test_x)\n",
        "  lstm_test_predicts.append(batch_predict)\n",
        "  lstm_test_labels.append(test_y)\n",
        "\n",
        "  batch_predict = death_trans.predict(test_x)\n",
        "  trans_test_predicts.append(batch_predict)\n",
        "  trans_test_labels.append(test_y)\n",
        "\n",
        "lstm_train_predicts = np.concatenate(lstm_train_predicts).reshape(-1, 1)\n",
        "lstm_train_labels = np.concatenate(lstm_train_labels).astype('int')\n",
        "lstm_test_predicts = np.concatenate(lstm_test_predicts).reshape(-1, 1)\n",
        "lstm_test_labels = np.concatenate(lstm_test_labels).astype('int')\n",
        "\n",
        "trans_train_predicts = np.concatenate(trans_train_predicts).reshape(-1, 1)\n",
        "trans_train_labels = np.concatenate(trans_train_labels).astype('int')\n",
        "trans_test_predicts = np.concatenate(trans_test_predicts).reshape(-1, 1)\n",
        "trans_test_labels = np.concatenate(trans_test_labels).astype('int')\n",
        "\n",
        "\n",
        "trans_train_roc_x, trans_train_roc_y, _ = roc_curve(trans_train_labels, trans_train_predicts)\n",
        "trans_train_auroc = roc_auc_score(trans_train_labels, trans_train_predicts)\n",
        "trans_test_roc_x, trans_test_roc_y, _ = roc_curve(trans_test_labels, trans_test_predicts)\n",
        "trans_test_auroc = roc_auc_score(trans_test_labels, trans_test_predicts)\n",
        "# trans_train_roc_x, trans_train_roc_y, _ = roc_curve(trans_train_labels, trans_train_predicts)\n",
        "# trans_train_auroc = roc_auc_score(trans_train_labels, trans_train_predicts)\n",
        "# trans_test_roc_x, trans_test_roc_y, _ = roc_curve(trans_test_labels, trans_test_predicts)\n",
        "# trans_test_auroc = roc_auc_score(trans_test_labels, trans_test_predicts)\n",
        "\n",
        "lstm_train_roc_x, lstm_train_roc_y, _ = roc_curve(lstm_train_labels, lstm_train_predicts)\n",
        "lstm_train_auroc = roc_auc_score(lstm_train_labels, lstm_train_predicts)\n",
        "lstm_test_roc_x, lstm_test_roc_y, _ = roc_curve(lstm_test_labels, lstm_test_predicts)\n",
        "lstm_test_auroc = roc_auc_score(lstm_test_labels, lstm_test_predicts)\n",
        "# lstm_train_roc_x, lstm_train_roc_y, _ = roc_curve(lstm_train_labels, lstm_train_predicts)\n",
        "# lstm_train_auroc = roc_auc_score(lstm_train_labels, lstm_train_predicts)\n",
        "# lstm_test_roc_x, lstm_test_roc_y, _ = roc_curve(lstm_test_labels, lstm_test_predicts)\n",
        "# lstm_test_auroc = roc_auc_score(lstm_test_labels, lstm_test_predicts)\n",
        "\n",
        "plt.figure()\n",
        "plt.xlabel('False Positive Rate (1 - Specificity)')\n",
        "plt.ylabel('True Positive Rate (Sensitivity)')\n",
        "plt.title('ROC Curve')\n",
        "\n",
        "\n",
        "plt.plot(lstm_train_roc_x, lstm_train_roc_y, linewidth=1.5, label='{} (AUROC={:.3f})'.format('LSTM Train', lstm_train_auroc))\n",
        "plt.plot(lstm_test_roc_x, lstm_test_roc_y, linewidth=1.5, label='{} (AUROC={:.3f})'.format('LSTM Test', lstm_test_auroc))\n",
        "\n",
        "plt.legend(fancybox=True)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "BvtrN0bDaeuX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras import backend as k\n",
        "k.clear_session()"
      ],
      "metadata": {
        "id": "JQC8OpbhZmLQ"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "glyco_lstm = keras.Sequential([\n",
        "    layers.LSTM(25, input_shape=(13,16)),\n",
        "    layers.Dense(125, activation='relu'),\n",
        "    layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "cepha_lstm = keras.Sequential([\n",
        "    layers.LSTM(25, input_shape=(13,16)),\n",
        "    layers.Dense(125, activation='relu'),\n",
        "    layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "peni_lstm = keras.Sequential([\n",
        "    layers.LSTM(25, input_shape=(13,16)),\n",
        "    layers.Dense(125, activation='relu'),\n",
        "    layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "fluoro_lstm = keras.Sequential([\n",
        "    layers.LSTM(25, input_shape=(13,16)),\n",
        "    layers.Dense(125, activation='relu'),\n",
        "    layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "\n",
        "macro_lstm = keras.Sequential([\n",
        "    layers.LSTM(25, input_shape=(13,16)),\n",
        "    layers.Dense(125, activation='relu'),\n",
        "    layers.Dense(1, activation='sigmoid')\n",
        "])"
      ],
      "metadata": {
        "id": "tbVJEpm5JBkW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "glyco_lstm.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "num_epochs = 100\n",
        "history = glyco_lstm.fit(glyco_train_loader, validation_data=glyco_test_loader, epochs=num_epochs, verbose=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tHU1S7S5Jmat",
        "outputId": "95d45b15-fae7-42df-e9b8-cb8f5d46a2a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "76/76 - 2s - loss: 0.5486 - accuracy: 0.7706 - val_loss: 0.5077 - val_accuracy: 0.7840 - 2s/epoch - 30ms/step\n",
            "Epoch 2/100\n",
            "76/76 - 0s - loss: 0.5083 - accuracy: 0.7847 - val_loss: 0.4964 - val_accuracy: 0.7993 - 424ms/epoch - 6ms/step\n",
            "Epoch 3/100\n",
            "76/76 - 0s - loss: 0.4993 - accuracy: 0.7977 - val_loss: 0.4851 - val_accuracy: 0.8048 - 451ms/epoch - 6ms/step\n",
            "Epoch 4/100\n",
            "76/76 - 0s - loss: 0.4923 - accuracy: 0.7994 - val_loss: 0.4867 - val_accuracy: 0.8044 - 434ms/epoch - 6ms/step\n",
            "Epoch 5/100\n",
            "76/76 - 0s - loss: 0.4913 - accuracy: 0.8003 - val_loss: 0.4797 - val_accuracy: 0.8048 - 421ms/epoch - 6ms/step\n",
            "Epoch 6/100\n",
            "76/76 - 1s - loss: 0.4881 - accuracy: 0.7998 - val_loss: 0.4787 - val_accuracy: 0.8040 - 586ms/epoch - 8ms/step\n",
            "Epoch 7/100\n",
            "76/76 - 1s - loss: 0.4877 - accuracy: 0.8009 - val_loss: 0.4763 - val_accuracy: 0.8053 - 605ms/epoch - 8ms/step\n",
            "Epoch 8/100\n",
            "76/76 - 1s - loss: 0.4875 - accuracy: 0.8021 - val_loss: 0.4786 - val_accuracy: 0.8036 - 606ms/epoch - 8ms/step\n",
            "Epoch 9/100\n",
            "76/76 - 1s - loss: 0.4861 - accuracy: 0.8020 - val_loss: 0.4776 - val_accuracy: 0.8070 - 580ms/epoch - 8ms/step\n",
            "Epoch 10/100\n",
            "76/76 - 1s - loss: 0.4865 - accuracy: 0.8020 - val_loss: 0.4783 - val_accuracy: 0.8053 - 547ms/epoch - 7ms/step\n",
            "Epoch 11/100\n",
            "76/76 - 1s - loss: 0.4856 - accuracy: 0.8003 - val_loss: 0.4760 - val_accuracy: 0.8087 - 597ms/epoch - 8ms/step\n",
            "Epoch 12/100\n",
            "76/76 - 0s - loss: 0.4864 - accuracy: 0.8027 - val_loss: 0.4737 - val_accuracy: 0.8074 - 410ms/epoch - 5ms/step\n",
            "Epoch 13/100\n",
            "76/76 - 0s - loss: 0.4849 - accuracy: 0.8024 - val_loss: 0.4817 - val_accuracy: 0.8048 - 424ms/epoch - 6ms/step\n",
            "Epoch 14/100\n",
            "76/76 - 0s - loss: 0.4861 - accuracy: 0.8006 - val_loss: 0.4760 - val_accuracy: 0.8074 - 457ms/epoch - 6ms/step\n",
            "Epoch 15/100\n",
            "76/76 - 0s - loss: 0.4835 - accuracy: 0.8030 - val_loss: 0.4735 - val_accuracy: 0.8048 - 429ms/epoch - 6ms/step\n",
            "Epoch 16/100\n",
            "76/76 - 0s - loss: 0.4841 - accuracy: 0.8020 - val_loss: 0.4740 - val_accuracy: 0.8061 - 448ms/epoch - 6ms/step\n",
            "Epoch 17/100\n",
            "76/76 - 0s - loss: 0.4852 - accuracy: 0.8019 - val_loss: 0.4757 - val_accuracy: 0.8040 - 430ms/epoch - 6ms/step\n",
            "Epoch 18/100\n",
            "76/76 - 0s - loss: 0.4839 - accuracy: 0.8031 - val_loss: 0.4797 - val_accuracy: 0.8061 - 450ms/epoch - 6ms/step\n",
            "Epoch 19/100\n",
            "76/76 - 0s - loss: 0.4848 - accuracy: 0.8036 - val_loss: 0.4753 - val_accuracy: 0.8040 - 430ms/epoch - 6ms/step\n",
            "Epoch 20/100\n",
            "76/76 - 0s - loss: 0.4825 - accuracy: 0.8031 - val_loss: 0.4735 - val_accuracy: 0.8074 - 462ms/epoch - 6ms/step\n",
            "Epoch 21/100\n",
            "76/76 - 0s - loss: 0.4828 - accuracy: 0.8039 - val_loss: 0.4732 - val_accuracy: 0.8078 - 424ms/epoch - 6ms/step\n",
            "Epoch 22/100\n",
            "76/76 - 0s - loss: 0.4833 - accuracy: 0.8030 - val_loss: 0.4739 - val_accuracy: 0.8074 - 442ms/epoch - 6ms/step\n",
            "Epoch 23/100\n",
            "76/76 - 0s - loss: 0.4834 - accuracy: 0.8029 - val_loss: 0.4739 - val_accuracy: 0.8074 - 423ms/epoch - 6ms/step\n",
            "Epoch 24/100\n",
            "76/76 - 0s - loss: 0.4825 - accuracy: 0.8043 - val_loss: 0.4837 - val_accuracy: 0.8078 - 430ms/epoch - 6ms/step\n",
            "Epoch 25/100\n",
            "76/76 - 0s - loss: 0.4829 - accuracy: 0.8043 - val_loss: 0.4761 - val_accuracy: 0.8074 - 428ms/epoch - 6ms/step\n",
            "Epoch 26/100\n",
            "76/76 - 0s - loss: 0.4821 - accuracy: 0.8025 - val_loss: 0.4748 - val_accuracy: 0.8057 - 424ms/epoch - 6ms/step\n",
            "Epoch 27/100\n",
            "76/76 - 0s - loss: 0.4820 - accuracy: 0.8024 - val_loss: 0.4732 - val_accuracy: 0.8095 - 439ms/epoch - 6ms/step\n",
            "Epoch 28/100\n",
            "76/76 - 0s - loss: 0.4822 - accuracy: 0.8025 - val_loss: 0.4802 - val_accuracy: 0.8065 - 412ms/epoch - 5ms/step\n",
            "Epoch 29/100\n",
            "76/76 - 0s - loss: 0.4841 - accuracy: 0.8040 - val_loss: 0.4725 - val_accuracy: 0.8074 - 449ms/epoch - 6ms/step\n",
            "Epoch 30/100\n",
            "76/76 - 0s - loss: 0.4816 - accuracy: 0.8043 - val_loss: 0.4750 - val_accuracy: 0.8087 - 436ms/epoch - 6ms/step\n",
            "Epoch 31/100\n",
            "76/76 - 0s - loss: 0.4856 - accuracy: 0.8030 - val_loss: 0.4749 - val_accuracy: 0.8065 - 427ms/epoch - 6ms/step\n",
            "Epoch 32/100\n",
            "76/76 - 0s - loss: 0.4816 - accuracy: 0.8027 - val_loss: 0.4739 - val_accuracy: 0.8065 - 423ms/epoch - 6ms/step\n",
            "Epoch 33/100\n",
            "76/76 - 0s - loss: 0.4806 - accuracy: 0.8053 - val_loss: 0.4741 - val_accuracy: 0.8040 - 437ms/epoch - 6ms/step\n",
            "Epoch 34/100\n",
            "76/76 - 0s - loss: 0.4813 - accuracy: 0.8033 - val_loss: 0.4736 - val_accuracy: 0.8091 - 438ms/epoch - 6ms/step\n",
            "Epoch 35/100\n",
            "76/76 - 0s - loss: 0.4829 - accuracy: 0.8047 - val_loss: 0.4781 - val_accuracy: 0.8074 - 422ms/epoch - 6ms/step\n",
            "Epoch 36/100\n",
            "76/76 - 0s - loss: 0.4816 - accuracy: 0.8036 - val_loss: 0.4767 - val_accuracy: 0.8087 - 424ms/epoch - 6ms/step\n",
            "Epoch 37/100\n",
            "76/76 - 0s - loss: 0.4815 - accuracy: 0.8033 - val_loss: 0.4760 - val_accuracy: 0.8061 - 422ms/epoch - 6ms/step\n",
            "Epoch 38/100\n",
            "76/76 - 0s - loss: 0.4806 - accuracy: 0.8044 - val_loss: 0.4764 - val_accuracy: 0.8070 - 425ms/epoch - 6ms/step\n",
            "Epoch 39/100\n",
            "76/76 - 0s - loss: 0.4817 - accuracy: 0.8045 - val_loss: 0.4717 - val_accuracy: 0.8095 - 422ms/epoch - 6ms/step\n",
            "Epoch 40/100\n",
            "76/76 - 0s - loss: 0.4810 - accuracy: 0.8028 - val_loss: 0.4747 - val_accuracy: 0.8082 - 450ms/epoch - 6ms/step\n",
            "Epoch 41/100\n",
            "76/76 - 0s - loss: 0.4809 - accuracy: 0.8048 - val_loss: 0.4723 - val_accuracy: 0.8087 - 418ms/epoch - 5ms/step\n",
            "Epoch 42/100\n",
            "76/76 - 0s - loss: 0.4801 - accuracy: 0.8039 - val_loss: 0.4734 - val_accuracy: 0.8112 - 427ms/epoch - 6ms/step\n",
            "Epoch 43/100\n",
            "76/76 - 0s - loss: 0.4802 - accuracy: 0.8068 - val_loss: 0.4804 - val_accuracy: 0.8065 - 438ms/epoch - 6ms/step\n",
            "Epoch 44/100\n",
            "76/76 - 0s - loss: 0.4828 - accuracy: 0.8038 - val_loss: 0.4720 - val_accuracy: 0.8091 - 438ms/epoch - 6ms/step\n",
            "Epoch 45/100\n",
            "76/76 - 0s - loss: 0.4817 - accuracy: 0.8055 - val_loss: 0.4734 - val_accuracy: 0.8078 - 433ms/epoch - 6ms/step\n",
            "Epoch 46/100\n",
            "76/76 - 0s - loss: 0.4789 - accuracy: 0.8056 - val_loss: 0.4731 - val_accuracy: 0.8082 - 442ms/epoch - 6ms/step\n",
            "Epoch 47/100\n",
            "76/76 - 0s - loss: 0.4791 - accuracy: 0.8049 - val_loss: 0.4740 - val_accuracy: 0.8104 - 442ms/epoch - 6ms/step\n",
            "Epoch 48/100\n",
            "76/76 - 0s - loss: 0.4788 - accuracy: 0.8067 - val_loss: 0.4729 - val_accuracy: 0.8082 - 441ms/epoch - 6ms/step\n",
            "Epoch 49/100\n",
            "76/76 - 0s - loss: 0.4792 - accuracy: 0.8058 - val_loss: 0.4747 - val_accuracy: 0.8074 - 464ms/epoch - 6ms/step\n",
            "Epoch 50/100\n",
            "76/76 - 0s - loss: 0.4797 - accuracy: 0.8066 - val_loss: 0.4780 - val_accuracy: 0.8070 - 458ms/epoch - 6ms/step\n",
            "Epoch 51/100\n",
            "76/76 - 0s - loss: 0.4800 - accuracy: 0.8071 - val_loss: 0.4730 - val_accuracy: 0.8104 - 457ms/epoch - 6ms/step\n",
            "Epoch 52/100\n",
            "76/76 - 0s - loss: 0.4795 - accuracy: 0.8049 - val_loss: 0.4722 - val_accuracy: 0.8061 - 438ms/epoch - 6ms/step\n",
            "Epoch 53/100\n",
            "76/76 - 0s - loss: 0.4786 - accuracy: 0.8055 - val_loss: 0.4752 - val_accuracy: 0.8099 - 447ms/epoch - 6ms/step\n",
            "Epoch 54/100\n",
            "76/76 - 0s - loss: 0.4789 - accuracy: 0.8064 - val_loss: 0.4730 - val_accuracy: 0.8070 - 435ms/epoch - 6ms/step\n",
            "Epoch 55/100\n",
            "76/76 - 0s - loss: 0.4782 - accuracy: 0.8060 - val_loss: 0.4731 - val_accuracy: 0.8074 - 437ms/epoch - 6ms/step\n",
            "Epoch 56/100\n",
            "76/76 - 0s - loss: 0.4792 - accuracy: 0.8053 - val_loss: 0.4737 - val_accuracy: 0.8087 - 425ms/epoch - 6ms/step\n",
            "Epoch 57/100\n",
            "76/76 - 0s - loss: 0.4781 - accuracy: 0.8064 - val_loss: 0.4725 - val_accuracy: 0.8070 - 422ms/epoch - 6ms/step\n",
            "Epoch 58/100\n",
            "76/76 - 0s - loss: 0.4808 - accuracy: 0.8046 - val_loss: 0.4747 - val_accuracy: 0.8082 - 425ms/epoch - 6ms/step\n",
            "Epoch 59/100\n",
            "76/76 - 0s - loss: 0.4779 - accuracy: 0.8070 - val_loss: 0.4746 - val_accuracy: 0.8057 - 442ms/epoch - 6ms/step\n",
            "Epoch 60/100\n",
            "76/76 - 0s - loss: 0.4775 - accuracy: 0.8068 - val_loss: 0.4720 - val_accuracy: 0.8082 - 438ms/epoch - 6ms/step\n",
            "Epoch 61/100\n",
            "76/76 - 0s - loss: 0.4776 - accuracy: 0.8051 - val_loss: 0.4751 - val_accuracy: 0.8070 - 439ms/epoch - 6ms/step\n",
            "Epoch 62/100\n",
            "76/76 - 0s - loss: 0.4769 - accuracy: 0.8081 - val_loss: 0.4719 - val_accuracy: 0.8082 - 428ms/epoch - 6ms/step\n",
            "Epoch 63/100\n",
            "76/76 - 0s - loss: 0.4773 - accuracy: 0.8060 - val_loss: 0.4717 - val_accuracy: 0.8091 - 449ms/epoch - 6ms/step\n",
            "Epoch 64/100\n",
            "76/76 - 0s - loss: 0.4772 - accuracy: 0.8074 - val_loss: 0.4760 - val_accuracy: 0.8061 - 437ms/epoch - 6ms/step\n",
            "Epoch 65/100\n",
            "76/76 - 0s - loss: 0.4792 - accuracy: 0.8062 - val_loss: 0.4743 - val_accuracy: 0.8087 - 434ms/epoch - 6ms/step\n",
            "Epoch 66/100\n",
            "76/76 - 0s - loss: 0.4775 - accuracy: 0.8057 - val_loss: 0.4709 - val_accuracy: 0.8087 - 428ms/epoch - 6ms/step\n",
            "Epoch 67/100\n",
            "76/76 - 0s - loss: 0.4761 - accuracy: 0.8089 - val_loss: 0.4730 - val_accuracy: 0.8091 - 427ms/epoch - 6ms/step\n",
            "Epoch 68/100\n",
            "76/76 - 0s - loss: 0.4770 - accuracy: 0.8078 - val_loss: 0.4749 - val_accuracy: 0.8087 - 429ms/epoch - 6ms/step\n",
            "Epoch 69/100\n",
            "76/76 - 0s - loss: 0.4798 - accuracy: 0.8043 - val_loss: 0.4752 - val_accuracy: 0.8061 - 434ms/epoch - 6ms/step\n",
            "Epoch 70/100\n",
            "76/76 - 0s - loss: 0.4759 - accuracy: 0.8065 - val_loss: 0.4732 - val_accuracy: 0.8070 - 429ms/epoch - 6ms/step\n",
            "Epoch 71/100\n",
            "76/76 - 0s - loss: 0.4775 - accuracy: 0.8067 - val_loss: 0.4725 - val_accuracy: 0.8078 - 442ms/epoch - 6ms/step\n",
            "Epoch 72/100\n",
            "76/76 - 0s - loss: 0.4787 - accuracy: 0.8059 - val_loss: 0.4757 - val_accuracy: 0.8091 - 442ms/epoch - 6ms/step\n",
            "Epoch 73/100\n",
            "76/76 - 0s - loss: 0.4787 - accuracy: 0.8056 - val_loss: 0.4740 - val_accuracy: 0.8074 - 428ms/epoch - 6ms/step\n",
            "Epoch 74/100\n",
            "76/76 - 0s - loss: 0.4758 - accuracy: 0.8081 - val_loss: 0.4746 - val_accuracy: 0.8061 - 434ms/epoch - 6ms/step\n",
            "Epoch 75/100\n",
            "76/76 - 0s - loss: 0.4748 - accuracy: 0.8093 - val_loss: 0.4734 - val_accuracy: 0.8048 - 422ms/epoch - 6ms/step\n",
            "Epoch 76/100\n",
            "76/76 - 0s - loss: 0.4757 - accuracy: 0.8083 - val_loss: 0.4720 - val_accuracy: 0.8070 - 437ms/epoch - 6ms/step\n",
            "Epoch 77/100\n",
            "76/76 - 0s - loss: 0.4752 - accuracy: 0.8088 - val_loss: 0.4749 - val_accuracy: 0.8087 - 449ms/epoch - 6ms/step\n",
            "Epoch 78/100\n",
            "76/76 - 0s - loss: 0.4763 - accuracy: 0.8073 - val_loss: 0.4729 - val_accuracy: 0.8057 - 457ms/epoch - 6ms/step\n",
            "Epoch 79/100\n",
            "76/76 - 0s - loss: 0.4747 - accuracy: 0.8071 - val_loss: 0.4723 - val_accuracy: 0.8087 - 448ms/epoch - 6ms/step\n",
            "Epoch 80/100\n",
            "76/76 - 0s - loss: 0.4751 - accuracy: 0.8066 - val_loss: 0.4718 - val_accuracy: 0.8078 - 425ms/epoch - 6ms/step\n",
            "Epoch 81/100\n",
            "76/76 - 0s - loss: 0.4743 - accuracy: 0.8076 - val_loss: 0.4721 - val_accuracy: 0.8108 - 450ms/epoch - 6ms/step\n",
            "Epoch 82/100\n",
            "76/76 - 0s - loss: 0.4744 - accuracy: 0.8075 - val_loss: 0.4727 - val_accuracy: 0.8057 - 429ms/epoch - 6ms/step\n",
            "Epoch 83/100\n",
            "76/76 - 0s - loss: 0.4746 - accuracy: 0.8089 - val_loss: 0.4726 - val_accuracy: 0.8099 - 423ms/epoch - 6ms/step\n",
            "Epoch 84/100\n",
            "76/76 - 0s - loss: 0.4748 - accuracy: 0.8092 - val_loss: 0.4743 - val_accuracy: 0.8095 - 456ms/epoch - 6ms/step\n",
            "Epoch 85/100\n",
            "76/76 - 0s - loss: 0.4734 - accuracy: 0.8091 - val_loss: 0.4763 - val_accuracy: 0.8057 - 448ms/epoch - 6ms/step\n",
            "Epoch 86/100\n",
            "76/76 - 0s - loss: 0.4733 - accuracy: 0.8090 - val_loss: 0.4758 - val_accuracy: 0.8044 - 425ms/epoch - 6ms/step\n",
            "Epoch 87/100\n",
            "76/76 - 0s - loss: 0.4730 - accuracy: 0.8101 - val_loss: 0.4748 - val_accuracy: 0.8082 - 449ms/epoch - 6ms/step\n",
            "Epoch 88/100\n",
            "76/76 - 0s - loss: 0.4763 - accuracy: 0.8075 - val_loss: 0.4742 - val_accuracy: 0.8057 - 445ms/epoch - 6ms/step\n",
            "Epoch 89/100\n",
            "76/76 - 0s - loss: 0.4752 - accuracy: 0.8073 - val_loss: 0.4724 - val_accuracy: 0.8099 - 436ms/epoch - 6ms/step\n",
            "Epoch 90/100\n",
            "76/76 - 0s - loss: 0.4727 - accuracy: 0.8081 - val_loss: 0.4732 - val_accuracy: 0.8057 - 436ms/epoch - 6ms/step\n",
            "Epoch 91/100\n",
            "76/76 - 0s - loss: 0.4742 - accuracy: 0.8093 - val_loss: 0.4774 - val_accuracy: 0.8057 - 431ms/epoch - 6ms/step\n",
            "Epoch 92/100\n",
            "76/76 - 0s - loss: 0.4729 - accuracy: 0.8075 - val_loss: 0.4733 - val_accuracy: 0.8040 - 438ms/epoch - 6ms/step\n",
            "Epoch 93/100\n",
            "76/76 - 0s - loss: 0.4727 - accuracy: 0.8089 - val_loss: 0.4734 - val_accuracy: 0.8078 - 435ms/epoch - 6ms/step\n",
            "Epoch 94/100\n",
            "76/76 - 0s - loss: 0.4723 - accuracy: 0.8087 - val_loss: 0.4756 - val_accuracy: 0.8078 - 441ms/epoch - 6ms/step\n",
            "Epoch 95/100\n",
            "76/76 - 0s - loss: 0.4717 - accuracy: 0.8084 - val_loss: 0.4743 - val_accuracy: 0.8048 - 430ms/epoch - 6ms/step\n",
            "Epoch 96/100\n",
            "76/76 - 0s - loss: 0.4723 - accuracy: 0.8090 - val_loss: 0.4756 - val_accuracy: 0.8048 - 445ms/epoch - 6ms/step\n",
            "Epoch 97/100\n",
            "76/76 - 0s - loss: 0.4721 - accuracy: 0.8092 - val_loss: 0.4728 - val_accuracy: 0.8061 - 467ms/epoch - 6ms/step\n",
            "Epoch 98/100\n",
            "76/76 - 0s - loss: 0.4719 - accuracy: 0.8076 - val_loss: 0.4755 - val_accuracy: 0.8065 - 441ms/epoch - 6ms/step\n",
            "Epoch 99/100\n",
            "76/76 - 0s - loss: 0.4710 - accuracy: 0.8097 - val_loss: 0.4738 - val_accuracy: 0.8040 - 442ms/epoch - 6ms/step\n",
            "Epoch 100/100\n",
            "76/76 - 0s - loss: 0.4721 - accuracy: 0.8088 - val_loss: 0.4723 - val_accuracy: 0.8099 - 442ms/epoch - 6ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cepha_lstm.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "num_epochs = 100\n",
        "history = cepha_lstm.fit(cepha_train_loader, validation_data=cepha_test_loader, epochs=num_epochs, verbose=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lf5GnrM9LLhj",
        "outputId": "304de5f0-bd8a-4add-f359-8724b6b9c9a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "71/71 - 2s - loss: 0.4697 - accuracy: 0.8347 - val_loss: 0.4096 - val_accuracy: 0.8517 - 2s/epoch - 33ms/step\n",
            "Epoch 2/100\n",
            "71/71 - 0s - loss: 0.4071 - accuracy: 0.8520 - val_loss: 0.4047 - val_accuracy: 0.8517 - 408ms/epoch - 6ms/step\n",
            "Epoch 3/100\n",
            "71/71 - 0s - loss: 0.4021 - accuracy: 0.8579 - val_loss: 0.3988 - val_accuracy: 0.8627 - 402ms/epoch - 6ms/step\n",
            "Epoch 4/100\n",
            "71/71 - 0s - loss: 0.3959 - accuracy: 0.8626 - val_loss: 0.3943 - val_accuracy: 0.8627 - 402ms/epoch - 6ms/step\n",
            "Epoch 5/100\n",
            "71/71 - 0s - loss: 0.3919 - accuracy: 0.8625 - val_loss: 0.3873 - val_accuracy: 0.8631 - 400ms/epoch - 6ms/step\n",
            "Epoch 6/100\n",
            "71/71 - 0s - loss: 0.3880 - accuracy: 0.8618 - val_loss: 0.3830 - val_accuracy: 0.8622 - 404ms/epoch - 6ms/step\n",
            "Epoch 7/100\n",
            "71/71 - 0s - loss: 0.3864 - accuracy: 0.8641 - val_loss: 0.3829 - val_accuracy: 0.8636 - 411ms/epoch - 6ms/step\n",
            "Epoch 8/100\n",
            "71/71 - 0s - loss: 0.3841 - accuracy: 0.8627 - val_loss: 0.3814 - val_accuracy: 0.8636 - 397ms/epoch - 6ms/step\n",
            "Epoch 9/100\n",
            "71/71 - 0s - loss: 0.3833 - accuracy: 0.8633 - val_loss: 0.3800 - val_accuracy: 0.8631 - 404ms/epoch - 6ms/step\n",
            "Epoch 10/100\n",
            "71/71 - 0s - loss: 0.3824 - accuracy: 0.8635 - val_loss: 0.3784 - val_accuracy: 0.8636 - 423ms/epoch - 6ms/step\n",
            "Epoch 11/100\n",
            "71/71 - 0s - loss: 0.3846 - accuracy: 0.8641 - val_loss: 0.3775 - val_accuracy: 0.8654 - 434ms/epoch - 6ms/step\n",
            "Epoch 12/100\n",
            "71/71 - 0s - loss: 0.3811 - accuracy: 0.8637 - val_loss: 0.3760 - val_accuracy: 0.8663 - 411ms/epoch - 6ms/step\n",
            "Epoch 13/100\n",
            "71/71 - 0s - loss: 0.3808 - accuracy: 0.8625 - val_loss: 0.3770 - val_accuracy: 0.8641 - 403ms/epoch - 6ms/step\n",
            "Epoch 14/100\n",
            "71/71 - 0s - loss: 0.3825 - accuracy: 0.8629 - val_loss: 0.3760 - val_accuracy: 0.8645 - 390ms/epoch - 5ms/step\n",
            "Epoch 15/100\n",
            "71/71 - 0s - loss: 0.3812 - accuracy: 0.8642 - val_loss: 0.3778 - val_accuracy: 0.8650 - 394ms/epoch - 6ms/step\n",
            "Epoch 16/100\n",
            "71/71 - 0s - loss: 0.3830 - accuracy: 0.8630 - val_loss: 0.3792 - val_accuracy: 0.8627 - 394ms/epoch - 6ms/step\n",
            "Epoch 17/100\n",
            "71/71 - 0s - loss: 0.3803 - accuracy: 0.8644 - val_loss: 0.3751 - val_accuracy: 0.8654 - 409ms/epoch - 6ms/step\n",
            "Epoch 18/100\n",
            "71/71 - 0s - loss: 0.3802 - accuracy: 0.8635 - val_loss: 0.3762 - val_accuracy: 0.8645 - 409ms/epoch - 6ms/step\n",
            "Epoch 19/100\n",
            "71/71 - 0s - loss: 0.3815 - accuracy: 0.8626 - val_loss: 0.3821 - val_accuracy: 0.8636 - 411ms/epoch - 6ms/step\n",
            "Epoch 20/100\n",
            "71/71 - 0s - loss: 0.3795 - accuracy: 0.8641 - val_loss: 0.3890 - val_accuracy: 0.8672 - 413ms/epoch - 6ms/step\n",
            "Epoch 21/100\n",
            "71/71 - 0s - loss: 0.3794 - accuracy: 0.8630 - val_loss: 0.3769 - val_accuracy: 0.8645 - 414ms/epoch - 6ms/step\n",
            "Epoch 22/100\n",
            "71/71 - 0s - loss: 0.3782 - accuracy: 0.8640 - val_loss: 0.3837 - val_accuracy: 0.8641 - 394ms/epoch - 6ms/step\n",
            "Epoch 23/100\n",
            "71/71 - 0s - loss: 0.3777 - accuracy: 0.8637 - val_loss: 0.3751 - val_accuracy: 0.8672 - 402ms/epoch - 6ms/step\n",
            "Epoch 24/100\n",
            "71/71 - 0s - loss: 0.3777 - accuracy: 0.8646 - val_loss: 0.3755 - val_accuracy: 0.8645 - 396ms/epoch - 6ms/step\n",
            "Epoch 25/100\n",
            "71/71 - 0s - loss: 0.3870 - accuracy: 0.8621 - val_loss: 0.3814 - val_accuracy: 0.8627 - 408ms/epoch - 6ms/step\n",
            "Epoch 26/100\n",
            "71/71 - 0s - loss: 0.3821 - accuracy: 0.8629 - val_loss: 0.3805 - val_accuracy: 0.8641 - 407ms/epoch - 6ms/step\n",
            "Epoch 27/100\n",
            "71/71 - 0s - loss: 0.3799 - accuracy: 0.8644 - val_loss: 0.3758 - val_accuracy: 0.8645 - 399ms/epoch - 6ms/step\n",
            "Epoch 28/100\n",
            "71/71 - 0s - loss: 0.3789 - accuracy: 0.8641 - val_loss: 0.3754 - val_accuracy: 0.8636 - 421ms/epoch - 6ms/step\n",
            "Epoch 29/100\n",
            "71/71 - 0s - loss: 0.3819 - accuracy: 0.8635 - val_loss: 0.3751 - val_accuracy: 0.8645 - 408ms/epoch - 6ms/step\n",
            "Epoch 30/100\n",
            "71/71 - 0s - loss: 0.3787 - accuracy: 0.8632 - val_loss: 0.3749 - val_accuracy: 0.8641 - 413ms/epoch - 6ms/step\n",
            "Epoch 31/100\n",
            "71/71 - 0s - loss: 0.3789 - accuracy: 0.8638 - val_loss: 0.3736 - val_accuracy: 0.8641 - 411ms/epoch - 6ms/step\n",
            "Epoch 32/100\n",
            "71/71 - 0s - loss: 0.3766 - accuracy: 0.8641 - val_loss: 0.3736 - val_accuracy: 0.8641 - 406ms/epoch - 6ms/step\n",
            "Epoch 33/100\n",
            "71/71 - 0s - loss: 0.3784 - accuracy: 0.8634 - val_loss: 0.3797 - val_accuracy: 0.8636 - 432ms/epoch - 6ms/step\n",
            "Epoch 34/100\n",
            "71/71 - 0s - loss: 0.3768 - accuracy: 0.8630 - val_loss: 0.3725 - val_accuracy: 0.8668 - 413ms/epoch - 6ms/step\n",
            "Epoch 35/100\n",
            "71/71 - 0s - loss: 0.3757 - accuracy: 0.8634 - val_loss: 0.3729 - val_accuracy: 0.8645 - 401ms/epoch - 6ms/step\n",
            "Epoch 36/100\n",
            "71/71 - 0s - loss: 0.3760 - accuracy: 0.8629 - val_loss: 0.3769 - val_accuracy: 0.8663 - 400ms/epoch - 6ms/step\n",
            "Epoch 37/100\n",
            "71/71 - 0s - loss: 0.3745 - accuracy: 0.8643 - val_loss: 0.3725 - val_accuracy: 0.8641 - 419ms/epoch - 6ms/step\n",
            "Epoch 38/100\n",
            "71/71 - 0s - loss: 0.3802 - accuracy: 0.8637 - val_loss: 0.3757 - val_accuracy: 0.8654 - 407ms/epoch - 6ms/step\n",
            "Epoch 39/100\n",
            "71/71 - 0s - loss: 0.3751 - accuracy: 0.8635 - val_loss: 0.3721 - val_accuracy: 0.8650 - 411ms/epoch - 6ms/step\n",
            "Epoch 40/100\n",
            "71/71 - 0s - loss: 0.3739 - accuracy: 0.8641 - val_loss: 0.3712 - val_accuracy: 0.8641 - 433ms/epoch - 6ms/step\n",
            "Epoch 41/100\n",
            "71/71 - 0s - loss: 0.3735 - accuracy: 0.8640 - val_loss: 0.3754 - val_accuracy: 0.8645 - 397ms/epoch - 6ms/step\n",
            "Epoch 42/100\n",
            "71/71 - 0s - loss: 0.3748 - accuracy: 0.8645 - val_loss: 0.3754 - val_accuracy: 0.8627 - 400ms/epoch - 6ms/step\n",
            "Epoch 43/100\n",
            "71/71 - 0s - loss: 0.3772 - accuracy: 0.8638 - val_loss: 0.3715 - val_accuracy: 0.8631 - 423ms/epoch - 6ms/step\n",
            "Epoch 44/100\n",
            "71/71 - 0s - loss: 0.3724 - accuracy: 0.8641 - val_loss: 0.3743 - val_accuracy: 0.8636 - 404ms/epoch - 6ms/step\n",
            "Epoch 45/100\n",
            "71/71 - 0s - loss: 0.3732 - accuracy: 0.8641 - val_loss: 0.3754 - val_accuracy: 0.8641 - 401ms/epoch - 6ms/step\n",
            "Epoch 46/100\n",
            "71/71 - 0s - loss: 0.3717 - accuracy: 0.8629 - val_loss: 0.3724 - val_accuracy: 0.8650 - 409ms/epoch - 6ms/step\n",
            "Epoch 47/100\n",
            "71/71 - 0s - loss: 0.3715 - accuracy: 0.8641 - val_loss: 0.3720 - val_accuracy: 0.8636 - 387ms/epoch - 5ms/step\n",
            "Epoch 48/100\n",
            "71/71 - 0s - loss: 0.3712 - accuracy: 0.8646 - val_loss: 0.3720 - val_accuracy: 0.8659 - 420ms/epoch - 6ms/step\n",
            "Epoch 49/100\n",
            "71/71 - 0s - loss: 0.3728 - accuracy: 0.8646 - val_loss: 0.3784 - val_accuracy: 0.8641 - 436ms/epoch - 6ms/step\n",
            "Epoch 50/100\n",
            "71/71 - 0s - loss: 0.3714 - accuracy: 0.8651 - val_loss: 0.3754 - val_accuracy: 0.8631 - 406ms/epoch - 6ms/step\n",
            "Epoch 51/100\n",
            "71/71 - 0s - loss: 0.3709 - accuracy: 0.8651 - val_loss: 0.3734 - val_accuracy: 0.8636 - 400ms/epoch - 6ms/step\n",
            "Epoch 52/100\n",
            "71/71 - 0s - loss: 0.3713 - accuracy: 0.8646 - val_loss: 0.3734 - val_accuracy: 0.8636 - 391ms/epoch - 6ms/step\n",
            "Epoch 53/100\n",
            "71/71 - 0s - loss: 0.3702 - accuracy: 0.8656 - val_loss: 0.3737 - val_accuracy: 0.8659 - 426ms/epoch - 6ms/step\n",
            "Epoch 54/100\n",
            "71/71 - 0s - loss: 0.3705 - accuracy: 0.8661 - val_loss: 0.3760 - val_accuracy: 0.8645 - 414ms/epoch - 6ms/step\n",
            "Epoch 55/100\n",
            "71/71 - 0s - loss: 0.3740 - accuracy: 0.8650 - val_loss: 0.3737 - val_accuracy: 0.8641 - 397ms/epoch - 6ms/step\n",
            "Epoch 56/100\n",
            "71/71 - 0s - loss: 0.3684 - accuracy: 0.8634 - val_loss: 0.3757 - val_accuracy: 0.8631 - 402ms/epoch - 6ms/step\n",
            "Epoch 57/100\n",
            "71/71 - 0s - loss: 0.3693 - accuracy: 0.8646 - val_loss: 0.3725 - val_accuracy: 0.8645 - 395ms/epoch - 6ms/step\n",
            "Epoch 58/100\n",
            "71/71 - 0s - loss: 0.3674 - accuracy: 0.8657 - val_loss: 0.3814 - val_accuracy: 0.8645 - 414ms/epoch - 6ms/step\n",
            "Epoch 59/100\n",
            "71/71 - 0s - loss: 0.3689 - accuracy: 0.8666 - val_loss: 0.3731 - val_accuracy: 0.8663 - 401ms/epoch - 6ms/step\n",
            "Epoch 60/100\n",
            "71/71 - 0s - loss: 0.3704 - accuracy: 0.8651 - val_loss: 0.3760 - val_accuracy: 0.8659 - 416ms/epoch - 6ms/step\n",
            "Epoch 61/100\n",
            "71/71 - 0s - loss: 0.3687 - accuracy: 0.8645 - val_loss: 0.3747 - val_accuracy: 0.8650 - 405ms/epoch - 6ms/step\n",
            "Epoch 62/100\n",
            "71/71 - 0s - loss: 0.3666 - accuracy: 0.8656 - val_loss: 0.3733 - val_accuracy: 0.8636 - 438ms/epoch - 6ms/step\n",
            "Epoch 63/100\n",
            "71/71 - 0s - loss: 0.3683 - accuracy: 0.8651 - val_loss: 0.3718 - val_accuracy: 0.8613 - 424ms/epoch - 6ms/step\n",
            "Epoch 64/100\n",
            "71/71 - 0s - loss: 0.3667 - accuracy: 0.8662 - val_loss: 0.3731 - val_accuracy: 0.8641 - 413ms/epoch - 6ms/step\n",
            "Epoch 65/100\n",
            "71/71 - 0s - loss: 0.3646 - accuracy: 0.8683 - val_loss: 0.3738 - val_accuracy: 0.8636 - 421ms/epoch - 6ms/step\n",
            "Epoch 66/100\n",
            "71/71 - 0s - loss: 0.3654 - accuracy: 0.8660 - val_loss: 0.3745 - val_accuracy: 0.8654 - 414ms/epoch - 6ms/step\n",
            "Epoch 67/100\n",
            "71/71 - 0s - loss: 0.3660 - accuracy: 0.8667 - val_loss: 0.3774 - val_accuracy: 0.8641 - 422ms/epoch - 6ms/step\n",
            "Epoch 68/100\n",
            "71/71 - 0s - loss: 0.3662 - accuracy: 0.8680 - val_loss: 0.3718 - val_accuracy: 0.8627 - 420ms/epoch - 6ms/step\n",
            "Epoch 69/100\n",
            "71/71 - 0s - loss: 0.3638 - accuracy: 0.8672 - val_loss: 0.3744 - val_accuracy: 0.8627 - 418ms/epoch - 6ms/step\n",
            "Epoch 70/100\n",
            "71/71 - 0s - loss: 0.3651 - accuracy: 0.8669 - val_loss: 0.3752 - val_accuracy: 0.8663 - 409ms/epoch - 6ms/step\n",
            "Epoch 71/100\n",
            "71/71 - 0s - loss: 0.3652 - accuracy: 0.8654 - val_loss: 0.3751 - val_accuracy: 0.8627 - 426ms/epoch - 6ms/step\n",
            "Epoch 72/100\n",
            "71/71 - 0s - loss: 0.3624 - accuracy: 0.8669 - val_loss: 0.3774 - val_accuracy: 0.8636 - 419ms/epoch - 6ms/step\n",
            "Epoch 73/100\n",
            "71/71 - 0s - loss: 0.3616 - accuracy: 0.8682 - val_loss: 0.3707 - val_accuracy: 0.8645 - 415ms/epoch - 6ms/step\n",
            "Epoch 74/100\n",
            "71/71 - 0s - loss: 0.3628 - accuracy: 0.8673 - val_loss: 0.3723 - val_accuracy: 0.8618 - 410ms/epoch - 6ms/step\n",
            "Epoch 75/100\n",
            "71/71 - 0s - loss: 0.3657 - accuracy: 0.8668 - val_loss: 0.3745 - val_accuracy: 0.8636 - 409ms/epoch - 6ms/step\n",
            "Epoch 76/100\n",
            "71/71 - 0s - loss: 0.3620 - accuracy: 0.8676 - val_loss: 0.3715 - val_accuracy: 0.8631 - 423ms/epoch - 6ms/step\n",
            "Epoch 77/100\n",
            "71/71 - 0s - loss: 0.3651 - accuracy: 0.8668 - val_loss: 0.3751 - val_accuracy: 0.8618 - 410ms/epoch - 6ms/step\n",
            "Epoch 78/100\n",
            "71/71 - 0s - loss: 0.3633 - accuracy: 0.8673 - val_loss: 0.3715 - val_accuracy: 0.8636 - 402ms/epoch - 6ms/step\n",
            "Epoch 79/100\n",
            "71/71 - 0s - loss: 0.3603 - accuracy: 0.8686 - val_loss: 0.3710 - val_accuracy: 0.8641 - 408ms/epoch - 6ms/step\n",
            "Epoch 80/100\n",
            "71/71 - 0s - loss: 0.3617 - accuracy: 0.8678 - val_loss: 0.3707 - val_accuracy: 0.8641 - 414ms/epoch - 6ms/step\n",
            "Epoch 81/100\n",
            "71/71 - 0s - loss: 0.3610 - accuracy: 0.8664 - val_loss: 0.3737 - val_accuracy: 0.8618 - 404ms/epoch - 6ms/step\n",
            "Epoch 82/100\n",
            "71/71 - 0s - loss: 0.3614 - accuracy: 0.8674 - val_loss: 0.3743 - val_accuracy: 0.8627 - 408ms/epoch - 6ms/step\n",
            "Epoch 83/100\n",
            "71/71 - 0s - loss: 0.3608 - accuracy: 0.8667 - val_loss: 0.3751 - val_accuracy: 0.8636 - 418ms/epoch - 6ms/step\n",
            "Epoch 84/100\n",
            "71/71 - 0s - loss: 0.3654 - accuracy: 0.8650 - val_loss: 0.3774 - val_accuracy: 0.8631 - 418ms/epoch - 6ms/step\n",
            "Epoch 85/100\n",
            "71/71 - 0s - loss: 0.3602 - accuracy: 0.8666 - val_loss: 0.3717 - val_accuracy: 0.8613 - 448ms/epoch - 6ms/step\n",
            "Epoch 86/100\n",
            "71/71 - 0s - loss: 0.3587 - accuracy: 0.8669 - val_loss: 0.3740 - val_accuracy: 0.8654 - 427ms/epoch - 6ms/step\n",
            "Epoch 87/100\n",
            "71/71 - 0s - loss: 0.3607 - accuracy: 0.8664 - val_loss: 0.3742 - val_accuracy: 0.8618 - 402ms/epoch - 6ms/step\n",
            "Epoch 88/100\n",
            "71/71 - 0s - loss: 0.3574 - accuracy: 0.8669 - val_loss: 0.3716 - val_accuracy: 0.8636 - 429ms/epoch - 6ms/step\n",
            "Epoch 89/100\n",
            "71/71 - 0s - loss: 0.3600 - accuracy: 0.8660 - val_loss: 0.3791 - val_accuracy: 0.8641 - 406ms/epoch - 6ms/step\n",
            "Epoch 90/100\n",
            "71/71 - 0s - loss: 0.3588 - accuracy: 0.8669 - val_loss: 0.3721 - val_accuracy: 0.8631 - 413ms/epoch - 6ms/step\n",
            "Epoch 91/100\n",
            "71/71 - 0s - loss: 0.3601 - accuracy: 0.8682 - val_loss: 0.3715 - val_accuracy: 0.8659 - 400ms/epoch - 6ms/step\n",
            "Epoch 92/100\n",
            "71/71 - 0s - loss: 0.3592 - accuracy: 0.8672 - val_loss: 0.3741 - val_accuracy: 0.8613 - 407ms/epoch - 6ms/step\n",
            "Epoch 93/100\n",
            "71/71 - 0s - loss: 0.3575 - accuracy: 0.8678 - val_loss: 0.3740 - val_accuracy: 0.8636 - 413ms/epoch - 6ms/step\n",
            "Epoch 94/100\n",
            "71/71 - 0s - loss: 0.3585 - accuracy: 0.8682 - val_loss: 0.3735 - val_accuracy: 0.8622 - 406ms/epoch - 6ms/step\n",
            "Epoch 95/100\n",
            "71/71 - 0s - loss: 0.3567 - accuracy: 0.8684 - val_loss: 0.3733 - val_accuracy: 0.8618 - 412ms/epoch - 6ms/step\n",
            "Epoch 96/100\n",
            "71/71 - 0s - loss: 0.3571 - accuracy: 0.8684 - val_loss: 0.3744 - val_accuracy: 0.8627 - 405ms/epoch - 6ms/step\n",
            "Epoch 97/100\n",
            "71/71 - 0s - loss: 0.3568 - accuracy: 0.8683 - val_loss: 0.3772 - val_accuracy: 0.8618 - 415ms/epoch - 6ms/step\n",
            "Epoch 98/100\n",
            "71/71 - 0s - loss: 0.3565 - accuracy: 0.8677 - val_loss: 0.3766 - val_accuracy: 0.8631 - 412ms/epoch - 6ms/step\n",
            "Epoch 99/100\n",
            "71/71 - 0s - loss: 0.3553 - accuracy: 0.8681 - val_loss: 0.3743 - val_accuracy: 0.8636 - 404ms/epoch - 6ms/step\n",
            "Epoch 100/100\n",
            "71/71 - 0s - loss: 0.3540 - accuracy: 0.8696 - val_loss: 0.3708 - val_accuracy: 0.8618 - 416ms/epoch - 6ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "peni_lstm.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "num_epochs = 100\n",
        "history = peni_lstm.fit(peni_train_loader, validation_data=peni_test_loader, epochs=num_epochs, verbose=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xgqu-A0_LXrI",
        "outputId": "887aaf9d-7e2f-428f-bb09-42e3019facda"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "39/39 - 2s - loss: 0.5374 - accuracy: 0.7938 - val_loss: 0.4994 - val_accuracy: 0.7939 - 2s/epoch - 56ms/step\n",
            "Epoch 2/100\n",
            "39/39 - 0s - loss: 0.4911 - accuracy: 0.7938 - val_loss: 0.4923 - val_accuracy: 0.7939 - 256ms/epoch - 7ms/step\n",
            "Epoch 3/100\n",
            "39/39 - 0s - loss: 0.4846 - accuracy: 0.8043 - val_loss: 0.4821 - val_accuracy: 0.8096 - 300ms/epoch - 8ms/step\n",
            "Epoch 4/100\n",
            "39/39 - 0s - loss: 0.4744 - accuracy: 0.8140 - val_loss: 0.4706 - val_accuracy: 0.8145 - 237ms/epoch - 6ms/step\n",
            "Epoch 5/100\n",
            "39/39 - 0s - loss: 0.4656 - accuracy: 0.8142 - val_loss: 0.4633 - val_accuracy: 0.8170 - 259ms/epoch - 7ms/step\n",
            "Epoch 6/100\n",
            "39/39 - 0s - loss: 0.4676 - accuracy: 0.8138 - val_loss: 0.4632 - val_accuracy: 0.8195 - 278ms/epoch - 7ms/step\n",
            "Epoch 7/100\n",
            "39/39 - 0s - loss: 0.4615 - accuracy: 0.8136 - val_loss: 0.4584 - val_accuracy: 0.8195 - 245ms/epoch - 6ms/step\n",
            "Epoch 8/100\n",
            "39/39 - 0s - loss: 0.4619 - accuracy: 0.8156 - val_loss: 0.4613 - val_accuracy: 0.8186 - 270ms/epoch - 7ms/step\n",
            "Epoch 9/100\n",
            "39/39 - 0s - loss: 0.4611 - accuracy: 0.8134 - val_loss: 0.4600 - val_accuracy: 0.8145 - 236ms/epoch - 6ms/step\n",
            "Epoch 10/100\n",
            "39/39 - 0s - loss: 0.4617 - accuracy: 0.8121 - val_loss: 0.4586 - val_accuracy: 0.8162 - 242ms/epoch - 6ms/step\n",
            "Epoch 11/100\n",
            "39/39 - 0s - loss: 0.4623 - accuracy: 0.8136 - val_loss: 0.4580 - val_accuracy: 0.8203 - 264ms/epoch - 7ms/step\n",
            "Epoch 12/100\n",
            "39/39 - 0s - loss: 0.4613 - accuracy: 0.8140 - val_loss: 0.4577 - val_accuracy: 0.8186 - 293ms/epoch - 8ms/step\n",
            "Epoch 13/100\n",
            "39/39 - 0s - loss: 0.4593 - accuracy: 0.8132 - val_loss: 0.4579 - val_accuracy: 0.8153 - 245ms/epoch - 6ms/step\n",
            "Epoch 14/100\n",
            "39/39 - 0s - loss: 0.4589 - accuracy: 0.8134 - val_loss: 0.4561 - val_accuracy: 0.8162 - 260ms/epoch - 7ms/step\n",
            "Epoch 15/100\n",
            "39/39 - 0s - loss: 0.4614 - accuracy: 0.8150 - val_loss: 0.4564 - val_accuracy: 0.8195 - 259ms/epoch - 7ms/step\n",
            "Epoch 16/100\n",
            "39/39 - 0s - loss: 0.4580 - accuracy: 0.8142 - val_loss: 0.4545 - val_accuracy: 0.8186 - 259ms/epoch - 7ms/step\n",
            "Epoch 17/100\n",
            "39/39 - 0s - loss: 0.4586 - accuracy: 0.8119 - val_loss: 0.4569 - val_accuracy: 0.8170 - 281ms/epoch - 7ms/step\n",
            "Epoch 18/100\n",
            "39/39 - 0s - loss: 0.4599 - accuracy: 0.8132 - val_loss: 0.4642 - val_accuracy: 0.8129 - 294ms/epoch - 8ms/step\n",
            "Epoch 19/100\n",
            "39/39 - 0s - loss: 0.4591 - accuracy: 0.8150 - val_loss: 0.4565 - val_accuracy: 0.8162 - 255ms/epoch - 7ms/step\n",
            "Epoch 20/100\n",
            "39/39 - 0s - loss: 0.4576 - accuracy: 0.8148 - val_loss: 0.4558 - val_accuracy: 0.8170 - 278ms/epoch - 7ms/step\n",
            "Epoch 21/100\n",
            "39/39 - 0s - loss: 0.4580 - accuracy: 0.8127 - val_loss: 0.4561 - val_accuracy: 0.8153 - 272ms/epoch - 7ms/step\n",
            "Epoch 22/100\n",
            "39/39 - 0s - loss: 0.4585 - accuracy: 0.8152 - val_loss: 0.4614 - val_accuracy: 0.8153 - 249ms/epoch - 6ms/step\n",
            "Epoch 23/100\n",
            "39/39 - 0s - loss: 0.4601 - accuracy: 0.8148 - val_loss: 0.4552 - val_accuracy: 0.8186 - 250ms/epoch - 6ms/step\n",
            "Epoch 24/100\n",
            "39/39 - 0s - loss: 0.4607 - accuracy: 0.8158 - val_loss: 0.4611 - val_accuracy: 0.8153 - 256ms/epoch - 7ms/step\n",
            "Epoch 25/100\n",
            "39/39 - 0s - loss: 0.4575 - accuracy: 0.8142 - val_loss: 0.4628 - val_accuracy: 0.8162 - 263ms/epoch - 7ms/step\n",
            "Epoch 26/100\n",
            "39/39 - 0s - loss: 0.4579 - accuracy: 0.8150 - val_loss: 0.4574 - val_accuracy: 0.8162 - 252ms/epoch - 6ms/step\n",
            "Epoch 27/100\n",
            "39/39 - 0s - loss: 0.4555 - accuracy: 0.8136 - val_loss: 0.4564 - val_accuracy: 0.8195 - 303ms/epoch - 8ms/step\n",
            "Epoch 28/100\n",
            "39/39 - 0s - loss: 0.4564 - accuracy: 0.8146 - val_loss: 0.4612 - val_accuracy: 0.8153 - 299ms/epoch - 8ms/step\n",
            "Epoch 29/100\n",
            "39/39 - 0s - loss: 0.4571 - accuracy: 0.8156 - val_loss: 0.4594 - val_accuracy: 0.8170 - 272ms/epoch - 7ms/step\n",
            "Epoch 30/100\n",
            "39/39 - 0s - loss: 0.4602 - accuracy: 0.8130 - val_loss: 0.4537 - val_accuracy: 0.8178 - 257ms/epoch - 7ms/step\n",
            "Epoch 31/100\n",
            "39/39 - 0s - loss: 0.4562 - accuracy: 0.8140 - val_loss: 0.4542 - val_accuracy: 0.8170 - 239ms/epoch - 6ms/step\n",
            "Epoch 32/100\n",
            "39/39 - 0s - loss: 0.4565 - accuracy: 0.8140 - val_loss: 0.4616 - val_accuracy: 0.8162 - 251ms/epoch - 6ms/step\n",
            "Epoch 33/100\n",
            "39/39 - 0s - loss: 0.4600 - accuracy: 0.8171 - val_loss: 0.4526 - val_accuracy: 0.8186 - 254ms/epoch - 7ms/step\n",
            "Epoch 34/100\n",
            "39/39 - 0s - loss: 0.4556 - accuracy: 0.8146 - val_loss: 0.4537 - val_accuracy: 0.8153 - 282ms/epoch - 7ms/step\n",
            "Epoch 35/100\n",
            "39/39 - 0s - loss: 0.4566 - accuracy: 0.8136 - val_loss: 0.4519 - val_accuracy: 0.8186 - 293ms/epoch - 8ms/step\n",
            "Epoch 36/100\n",
            "39/39 - 0s - loss: 0.4571 - accuracy: 0.8142 - val_loss: 0.4535 - val_accuracy: 0.8203 - 256ms/epoch - 7ms/step\n",
            "Epoch 37/100\n",
            "39/39 - 0s - loss: 0.4618 - accuracy: 0.8148 - val_loss: 0.4535 - val_accuracy: 0.8195 - 243ms/epoch - 6ms/step\n",
            "Epoch 38/100\n",
            "39/39 - 0s - loss: 0.4558 - accuracy: 0.8150 - val_loss: 0.4528 - val_accuracy: 0.8186 - 232ms/epoch - 6ms/step\n",
            "Epoch 39/100\n",
            "39/39 - 0s - loss: 0.4564 - accuracy: 0.8163 - val_loss: 0.4537 - val_accuracy: 0.8178 - 247ms/epoch - 6ms/step\n",
            "Epoch 40/100\n",
            "39/39 - 0s - loss: 0.4546 - accuracy: 0.8140 - val_loss: 0.4538 - val_accuracy: 0.8153 - 246ms/epoch - 6ms/step\n",
            "Epoch 41/100\n",
            "39/39 - 0s - loss: 0.4548 - accuracy: 0.8150 - val_loss: 0.4520 - val_accuracy: 0.8186 - 258ms/epoch - 7ms/step\n",
            "Epoch 42/100\n",
            "39/39 - 0s - loss: 0.4548 - accuracy: 0.8142 - val_loss: 0.4518 - val_accuracy: 0.8186 - 242ms/epoch - 6ms/step\n",
            "Epoch 43/100\n",
            "39/39 - 0s - loss: 0.4524 - accuracy: 0.8163 - val_loss: 0.4563 - val_accuracy: 0.8228 - 248ms/epoch - 6ms/step\n",
            "Epoch 44/100\n",
            "39/39 - 0s - loss: 0.4584 - accuracy: 0.8156 - val_loss: 0.4552 - val_accuracy: 0.8186 - 248ms/epoch - 6ms/step\n",
            "Epoch 45/100\n",
            "39/39 - 0s - loss: 0.4540 - accuracy: 0.8150 - val_loss: 0.4537 - val_accuracy: 0.8186 - 261ms/epoch - 7ms/step\n",
            "Epoch 46/100\n",
            "39/39 - 0s - loss: 0.4537 - accuracy: 0.8167 - val_loss: 0.4514 - val_accuracy: 0.8203 - 299ms/epoch - 8ms/step\n",
            "Epoch 47/100\n",
            "39/39 - 0s - loss: 0.4546 - accuracy: 0.8154 - val_loss: 0.4508 - val_accuracy: 0.8195 - 302ms/epoch - 8ms/step\n",
            "Epoch 48/100\n",
            "39/39 - 0s - loss: 0.4571 - accuracy: 0.8144 - val_loss: 0.4545 - val_accuracy: 0.8170 - 357ms/epoch - 9ms/step\n",
            "Epoch 49/100\n",
            "39/39 - 0s - loss: 0.4540 - accuracy: 0.8165 - val_loss: 0.4533 - val_accuracy: 0.8162 - 287ms/epoch - 7ms/step\n",
            "Epoch 50/100\n",
            "39/39 - 0s - loss: 0.4538 - accuracy: 0.8169 - val_loss: 0.4514 - val_accuracy: 0.8186 - 286ms/epoch - 7ms/step\n",
            "Epoch 51/100\n",
            "39/39 - 0s - loss: 0.4547 - accuracy: 0.8160 - val_loss: 0.4542 - val_accuracy: 0.8211 - 265ms/epoch - 7ms/step\n",
            "Epoch 52/100\n",
            "39/39 - 0s - loss: 0.4541 - accuracy: 0.8156 - val_loss: 0.4505 - val_accuracy: 0.8178 - 266ms/epoch - 7ms/step\n",
            "Epoch 53/100\n",
            "39/39 - 0s - loss: 0.4540 - accuracy: 0.8171 - val_loss: 0.4621 - val_accuracy: 0.8153 - 251ms/epoch - 6ms/step\n",
            "Epoch 54/100\n",
            "39/39 - 0s - loss: 0.4554 - accuracy: 0.8163 - val_loss: 0.4579 - val_accuracy: 0.8162 - 303ms/epoch - 8ms/step\n",
            "Epoch 55/100\n",
            "39/39 - 0s - loss: 0.4544 - accuracy: 0.8160 - val_loss: 0.4628 - val_accuracy: 0.8153 - 272ms/epoch - 7ms/step\n",
            "Epoch 56/100\n",
            "39/39 - 0s - loss: 0.4547 - accuracy: 0.8214 - val_loss: 0.4529 - val_accuracy: 0.8137 - 273ms/epoch - 7ms/step\n",
            "Epoch 57/100\n",
            "39/39 - 0s - loss: 0.4521 - accuracy: 0.8169 - val_loss: 0.4529 - val_accuracy: 0.8203 - 285ms/epoch - 7ms/step\n",
            "Epoch 58/100\n",
            "39/39 - 0s - loss: 0.4512 - accuracy: 0.8189 - val_loss: 0.4515 - val_accuracy: 0.8195 - 266ms/epoch - 7ms/step\n",
            "Epoch 59/100\n",
            "39/39 - 0s - loss: 0.4512 - accuracy: 0.8171 - val_loss: 0.4508 - val_accuracy: 0.8195 - 306ms/epoch - 8ms/step\n",
            "Epoch 60/100\n",
            "39/39 - 0s - loss: 0.4515 - accuracy: 0.8175 - val_loss: 0.4573 - val_accuracy: 0.8170 - 273ms/epoch - 7ms/step\n",
            "Epoch 61/100\n",
            "39/39 - 0s - loss: 0.4518 - accuracy: 0.8179 - val_loss: 0.4512 - val_accuracy: 0.8195 - 256ms/epoch - 7ms/step\n",
            "Epoch 62/100\n",
            "39/39 - 0s - loss: 0.4517 - accuracy: 0.8189 - val_loss: 0.4497 - val_accuracy: 0.8195 - 290ms/epoch - 7ms/step\n",
            "Epoch 63/100\n",
            "39/39 - 0s - loss: 0.4502 - accuracy: 0.8175 - val_loss: 0.4500 - val_accuracy: 0.8203 - 302ms/epoch - 8ms/step\n",
            "Epoch 64/100\n",
            "39/39 - 0s - loss: 0.4528 - accuracy: 0.8212 - val_loss: 0.4505 - val_accuracy: 0.8203 - 294ms/epoch - 8ms/step\n",
            "Epoch 65/100\n",
            "39/39 - 0s - loss: 0.4499 - accuracy: 0.8179 - val_loss: 0.4503 - val_accuracy: 0.8203 - 292ms/epoch - 7ms/step\n",
            "Epoch 66/100\n",
            "39/39 - 0s - loss: 0.4515 - accuracy: 0.8179 - val_loss: 0.4525 - val_accuracy: 0.8203 - 282ms/epoch - 7ms/step\n",
            "Epoch 67/100\n",
            "39/39 - 0s - loss: 0.4522 - accuracy: 0.8167 - val_loss: 0.4496 - val_accuracy: 0.8203 - 301ms/epoch - 8ms/step\n",
            "Epoch 68/100\n",
            "39/39 - 0s - loss: 0.4492 - accuracy: 0.8165 - val_loss: 0.4526 - val_accuracy: 0.8170 - 307ms/epoch - 8ms/step\n",
            "Epoch 69/100\n",
            "39/39 - 0s - loss: 0.4504 - accuracy: 0.8179 - val_loss: 0.4507 - val_accuracy: 0.8203 - 270ms/epoch - 7ms/step\n",
            "Epoch 70/100\n",
            "39/39 - 0s - loss: 0.4500 - accuracy: 0.8187 - val_loss: 0.4493 - val_accuracy: 0.8186 - 285ms/epoch - 7ms/step\n",
            "Epoch 71/100\n",
            "39/39 - 0s - loss: 0.4522 - accuracy: 0.8206 - val_loss: 0.4503 - val_accuracy: 0.8228 - 280ms/epoch - 7ms/step\n",
            "Epoch 72/100\n",
            "39/39 - 0s - loss: 0.4493 - accuracy: 0.8177 - val_loss: 0.4499 - val_accuracy: 0.8203 - 260ms/epoch - 7ms/step\n",
            "Epoch 73/100\n",
            "39/39 - 0s - loss: 0.4522 - accuracy: 0.8163 - val_loss: 0.4517 - val_accuracy: 0.8170 - 252ms/epoch - 6ms/step\n",
            "Epoch 74/100\n",
            "39/39 - 0s - loss: 0.4480 - accuracy: 0.8179 - val_loss: 0.4506 - val_accuracy: 0.8211 - 257ms/epoch - 7ms/step\n",
            "Epoch 75/100\n",
            "39/39 - 0s - loss: 0.4484 - accuracy: 0.8210 - val_loss: 0.4486 - val_accuracy: 0.8236 - 271ms/epoch - 7ms/step\n",
            "Epoch 76/100\n",
            "39/39 - 0s - loss: 0.4483 - accuracy: 0.8231 - val_loss: 0.4520 - val_accuracy: 0.8244 - 249ms/epoch - 6ms/step\n",
            "Epoch 77/100\n",
            "39/39 - 0s - loss: 0.4496 - accuracy: 0.8198 - val_loss: 0.4505 - val_accuracy: 0.8170 - 244ms/epoch - 6ms/step\n",
            "Epoch 78/100\n",
            "39/39 - 0s - loss: 0.4478 - accuracy: 0.8175 - val_loss: 0.4528 - val_accuracy: 0.8186 - 268ms/epoch - 7ms/step\n",
            "Epoch 79/100\n",
            "39/39 - 0s - loss: 0.4485 - accuracy: 0.8169 - val_loss: 0.4507 - val_accuracy: 0.8203 - 311ms/epoch - 8ms/step\n",
            "Epoch 80/100\n",
            "39/39 - 0s - loss: 0.4477 - accuracy: 0.8189 - val_loss: 0.4525 - val_accuracy: 0.8195 - 265ms/epoch - 7ms/step\n",
            "Epoch 81/100\n",
            "39/39 - 0s - loss: 0.4480 - accuracy: 0.8185 - val_loss: 0.4764 - val_accuracy: 0.8046 - 285ms/epoch - 7ms/step\n",
            "Epoch 82/100\n",
            "39/39 - 0s - loss: 0.4532 - accuracy: 0.8163 - val_loss: 0.4544 - val_accuracy: 0.8170 - 288ms/epoch - 7ms/step\n",
            "Epoch 83/100\n",
            "39/39 - 0s - loss: 0.4460 - accuracy: 0.8210 - val_loss: 0.4500 - val_accuracy: 0.8203 - 239ms/epoch - 6ms/step\n",
            "Epoch 84/100\n",
            "39/39 - 0s - loss: 0.4452 - accuracy: 0.8198 - val_loss: 0.4520 - val_accuracy: 0.8186 - 242ms/epoch - 6ms/step\n",
            "Epoch 85/100\n",
            "39/39 - 0s - loss: 0.4490 - accuracy: 0.8198 - val_loss: 0.4556 - val_accuracy: 0.8186 - 238ms/epoch - 6ms/step\n",
            "Epoch 86/100\n",
            "39/39 - 0s - loss: 0.4477 - accuracy: 0.8165 - val_loss: 0.4506 - val_accuracy: 0.8203 - 269ms/epoch - 7ms/step\n",
            "Epoch 87/100\n",
            "39/39 - 0s - loss: 0.4492 - accuracy: 0.8214 - val_loss: 0.4612 - val_accuracy: 0.8178 - 304ms/epoch - 8ms/step\n",
            "Epoch 88/100\n",
            "39/39 - 0s - loss: 0.4493 - accuracy: 0.8179 - val_loss: 0.4506 - val_accuracy: 0.8203 - 284ms/epoch - 7ms/step\n",
            "Epoch 89/100\n",
            "39/39 - 0s - loss: 0.4448 - accuracy: 0.8212 - val_loss: 0.4511 - val_accuracy: 0.8228 - 263ms/epoch - 7ms/step\n",
            "Epoch 90/100\n",
            "39/39 - 0s - loss: 0.4433 - accuracy: 0.8196 - val_loss: 0.4565 - val_accuracy: 0.8153 - 260ms/epoch - 7ms/step\n",
            "Epoch 91/100\n",
            "39/39 - 0s - loss: 0.4443 - accuracy: 0.8200 - val_loss: 0.4496 - val_accuracy: 0.8211 - 248ms/epoch - 6ms/step\n",
            "Epoch 92/100\n",
            "39/39 - 0s - loss: 0.4438 - accuracy: 0.8214 - val_loss: 0.4527 - val_accuracy: 0.8211 - 239ms/epoch - 6ms/step\n",
            "Epoch 93/100\n",
            "39/39 - 0s - loss: 0.4451 - accuracy: 0.8196 - val_loss: 0.4503 - val_accuracy: 0.8186 - 292ms/epoch - 7ms/step\n",
            "Epoch 94/100\n",
            "39/39 - 0s - loss: 0.4435 - accuracy: 0.8202 - val_loss: 0.4505 - val_accuracy: 0.8186 - 304ms/epoch - 8ms/step\n",
            "Epoch 95/100\n",
            "39/39 - 0s - loss: 0.4419 - accuracy: 0.8220 - val_loss: 0.4533 - val_accuracy: 0.8236 - 242ms/epoch - 6ms/step\n",
            "Epoch 96/100\n",
            "39/39 - 0s - loss: 0.4450 - accuracy: 0.8210 - val_loss: 0.4497 - val_accuracy: 0.8228 - 238ms/epoch - 6ms/step\n",
            "Epoch 97/100\n",
            "39/39 - 0s - loss: 0.4426 - accuracy: 0.8216 - val_loss: 0.4548 - val_accuracy: 0.8112 - 247ms/epoch - 6ms/step\n",
            "Epoch 98/100\n",
            "39/39 - 0s - loss: 0.4447 - accuracy: 0.8216 - val_loss: 0.4588 - val_accuracy: 0.8178 - 257ms/epoch - 7ms/step\n",
            "Epoch 99/100\n",
            "39/39 - 0s - loss: 0.4465 - accuracy: 0.8218 - val_loss: 0.4514 - val_accuracy: 0.8211 - 245ms/epoch - 6ms/step\n",
            "Epoch 100/100\n",
            "39/39 - 0s - loss: 0.4420 - accuracy: 0.8231 - val_loss: 0.4510 - val_accuracy: 0.8145 - 275ms/epoch - 7ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fluoro_lstm.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "num_epochs = 100\n",
        "history = fluoro_lstm.fit(fluoro_train_loader, validation_data=fluoro_test_loader, epochs=num_epochs, verbose=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fo1N3_WoLez6",
        "outputId": "4f5e0a79-6860-4c53-d971-32ee65179aee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "24/24 - 4s - loss: 0.5660 - accuracy: 0.8066 - val_loss: 0.4326 - val_accuracy: 0.8495 - 4s/epoch - 165ms/step\n",
            "Epoch 2/100\n",
            "24/24 - 0s - loss: 0.4186 - accuracy: 0.8498 - val_loss: 0.4141 - val_accuracy: 0.8495 - 163ms/epoch - 7ms/step\n",
            "Epoch 3/100\n",
            "24/24 - 0s - loss: 0.4122 - accuracy: 0.8498 - val_loss: 0.4135 - val_accuracy: 0.8495 - 157ms/epoch - 7ms/step\n",
            "Epoch 4/100\n",
            "24/24 - 0s - loss: 0.4099 - accuracy: 0.8498 - val_loss: 0.4122 - val_accuracy: 0.8495 - 161ms/epoch - 7ms/step\n",
            "Epoch 5/100\n",
            "24/24 - 0s - loss: 0.4076 - accuracy: 0.8522 - val_loss: 0.4102 - val_accuracy: 0.8577 - 168ms/epoch - 7ms/step\n",
            "Epoch 6/100\n",
            "24/24 - 0s - loss: 0.4046 - accuracy: 0.8569 - val_loss: 0.4048 - val_accuracy: 0.8591 - 157ms/epoch - 7ms/step\n",
            "Epoch 7/100\n",
            "24/24 - 0s - loss: 0.3980 - accuracy: 0.8576 - val_loss: 0.4044 - val_accuracy: 0.8605 - 157ms/epoch - 7ms/step\n",
            "Epoch 8/100\n",
            "24/24 - 0s - loss: 0.3973 - accuracy: 0.8593 - val_loss: 0.4033 - val_accuracy: 0.8605 - 166ms/epoch - 7ms/step\n",
            "Epoch 9/100\n",
            "24/24 - 0s - loss: 0.3943 - accuracy: 0.8583 - val_loss: 0.4003 - val_accuracy: 0.8605 - 172ms/epoch - 7ms/step\n",
            "Epoch 10/100\n",
            "24/24 - 0s - loss: 0.3947 - accuracy: 0.8580 - val_loss: 0.3982 - val_accuracy: 0.8605 - 155ms/epoch - 6ms/step\n",
            "Epoch 11/100\n",
            "24/24 - 0s - loss: 0.3932 - accuracy: 0.8590 - val_loss: 0.3971 - val_accuracy: 0.8605 - 153ms/epoch - 6ms/step\n",
            "Epoch 12/100\n",
            "24/24 - 0s - loss: 0.3936 - accuracy: 0.8597 - val_loss: 0.3986 - val_accuracy: 0.8591 - 157ms/epoch - 7ms/step\n",
            "Epoch 13/100\n",
            "24/24 - 0s - loss: 0.3932 - accuracy: 0.8583 - val_loss: 0.3970 - val_accuracy: 0.8605 - 160ms/epoch - 7ms/step\n",
            "Epoch 14/100\n",
            "24/24 - 0s - loss: 0.3947 - accuracy: 0.8583 - val_loss: 0.3987 - val_accuracy: 0.8605 - 162ms/epoch - 7ms/step\n",
            "Epoch 15/100\n",
            "24/24 - 0s - loss: 0.3931 - accuracy: 0.8593 - val_loss: 0.3991 - val_accuracy: 0.8605 - 165ms/epoch - 7ms/step\n",
            "Epoch 16/100\n",
            "24/24 - 0s - loss: 0.3931 - accuracy: 0.8580 - val_loss: 0.3993 - val_accuracy: 0.8605 - 158ms/epoch - 7ms/step\n",
            "Epoch 17/100\n",
            "24/24 - 0s - loss: 0.3902 - accuracy: 0.8593 - val_loss: 0.3966 - val_accuracy: 0.8577 - 157ms/epoch - 7ms/step\n",
            "Epoch 18/100\n",
            "24/24 - 0s - loss: 0.3905 - accuracy: 0.8583 - val_loss: 0.4018 - val_accuracy: 0.8550 - 152ms/epoch - 6ms/step\n",
            "Epoch 19/100\n",
            "24/24 - 0s - loss: 0.3893 - accuracy: 0.8573 - val_loss: 0.3959 - val_accuracy: 0.8605 - 157ms/epoch - 7ms/step\n",
            "Epoch 20/100\n",
            "24/24 - 0s - loss: 0.3912 - accuracy: 0.8590 - val_loss: 0.3945 - val_accuracy: 0.8605 - 166ms/epoch - 7ms/step\n",
            "Epoch 21/100\n",
            "24/24 - 0s - loss: 0.3898 - accuracy: 0.8593 - val_loss: 0.4017 - val_accuracy: 0.8605 - 152ms/epoch - 6ms/step\n",
            "Epoch 22/100\n",
            "24/24 - 0s - loss: 0.3916 - accuracy: 0.8587 - val_loss: 0.3945 - val_accuracy: 0.8605 - 164ms/epoch - 7ms/step\n",
            "Epoch 23/100\n",
            "24/24 - 0s - loss: 0.3885 - accuracy: 0.8587 - val_loss: 0.3937 - val_accuracy: 0.8577 - 159ms/epoch - 7ms/step\n",
            "Epoch 24/100\n",
            "24/24 - 0s - loss: 0.3886 - accuracy: 0.8600 - val_loss: 0.3938 - val_accuracy: 0.8577 - 155ms/epoch - 6ms/step\n",
            "Epoch 25/100\n",
            "24/24 - 0s - loss: 0.3908 - accuracy: 0.8611 - val_loss: 0.3957 - val_accuracy: 0.8605 - 158ms/epoch - 7ms/step\n",
            "Epoch 26/100\n",
            "24/24 - 0s - loss: 0.3905 - accuracy: 0.8593 - val_loss: 0.3977 - val_accuracy: 0.8605 - 160ms/epoch - 7ms/step\n",
            "Epoch 27/100\n",
            "24/24 - 0s - loss: 0.3912 - accuracy: 0.8590 - val_loss: 0.4009 - val_accuracy: 0.8605 - 159ms/epoch - 7ms/step\n",
            "Epoch 28/100\n",
            "24/24 - 0s - loss: 0.3948 - accuracy: 0.8600 - val_loss: 0.4091 - val_accuracy: 0.8605 - 158ms/epoch - 7ms/step\n",
            "Epoch 29/100\n",
            "24/24 - 0s - loss: 0.3893 - accuracy: 0.8611 - val_loss: 0.3946 - val_accuracy: 0.8605 - 166ms/epoch - 7ms/step\n",
            "Epoch 30/100\n",
            "24/24 - 0s - loss: 0.3863 - accuracy: 0.8611 - val_loss: 0.3953 - val_accuracy: 0.8591 - 159ms/epoch - 7ms/step\n",
            "Epoch 31/100\n",
            "24/24 - 0s - loss: 0.3860 - accuracy: 0.8617 - val_loss: 0.3941 - val_accuracy: 0.8591 - 165ms/epoch - 7ms/step\n",
            "Epoch 32/100\n",
            "24/24 - 0s - loss: 0.3857 - accuracy: 0.8583 - val_loss: 0.3982 - val_accuracy: 0.8605 - 161ms/epoch - 7ms/step\n",
            "Epoch 33/100\n",
            "24/24 - 0s - loss: 0.3914 - accuracy: 0.8597 - val_loss: 0.4037 - val_accuracy: 0.8605 - 163ms/epoch - 7ms/step\n",
            "Epoch 34/100\n",
            "24/24 - 0s - loss: 0.3874 - accuracy: 0.8604 - val_loss: 0.3994 - val_accuracy: 0.8605 - 170ms/epoch - 7ms/step\n",
            "Epoch 35/100\n",
            "24/24 - 0s - loss: 0.3876 - accuracy: 0.8611 - val_loss: 0.3972 - val_accuracy: 0.8605 - 164ms/epoch - 7ms/step\n",
            "Epoch 36/100\n",
            "24/24 - 0s - loss: 0.3882 - accuracy: 0.8607 - val_loss: 0.3978 - val_accuracy: 0.8605 - 165ms/epoch - 7ms/step\n",
            "Epoch 37/100\n",
            "24/24 - 0s - loss: 0.3841 - accuracy: 0.8624 - val_loss: 0.3957 - val_accuracy: 0.8591 - 173ms/epoch - 7ms/step\n",
            "Epoch 38/100\n",
            "24/24 - 0s - loss: 0.3864 - accuracy: 0.8631 - val_loss: 0.3987 - val_accuracy: 0.8605 - 160ms/epoch - 7ms/step\n",
            "Epoch 39/100\n",
            "24/24 - 0s - loss: 0.3867 - accuracy: 0.8611 - val_loss: 0.3943 - val_accuracy: 0.8605 - 161ms/epoch - 7ms/step\n",
            "Epoch 40/100\n",
            "24/24 - 0s - loss: 0.3856 - accuracy: 0.8624 - val_loss: 0.3984 - val_accuracy: 0.8605 - 164ms/epoch - 7ms/step\n",
            "Epoch 41/100\n",
            "24/24 - 0s - loss: 0.3857 - accuracy: 0.8628 - val_loss: 0.3920 - val_accuracy: 0.8591 - 157ms/epoch - 7ms/step\n",
            "Epoch 42/100\n",
            "24/24 - 0s - loss: 0.3852 - accuracy: 0.8628 - val_loss: 0.4011 - val_accuracy: 0.8605 - 158ms/epoch - 7ms/step\n",
            "Epoch 43/100\n",
            "24/24 - 0s - loss: 0.3888 - accuracy: 0.8624 - val_loss: 0.3945 - val_accuracy: 0.8591 - 165ms/epoch - 7ms/step\n",
            "Epoch 44/100\n",
            "24/24 - 0s - loss: 0.3853 - accuracy: 0.8631 - val_loss: 0.3962 - val_accuracy: 0.8591 - 166ms/epoch - 7ms/step\n",
            "Epoch 45/100\n",
            "24/24 - 0s - loss: 0.3843 - accuracy: 0.8621 - val_loss: 0.3925 - val_accuracy: 0.8591 - 159ms/epoch - 7ms/step\n",
            "Epoch 46/100\n",
            "24/24 - 0s - loss: 0.3836 - accuracy: 0.8628 - val_loss: 0.3931 - val_accuracy: 0.8605 - 158ms/epoch - 7ms/step\n",
            "Epoch 47/100\n",
            "24/24 - 0s - loss: 0.3855 - accuracy: 0.8628 - val_loss: 0.3946 - val_accuracy: 0.8605 - 161ms/epoch - 7ms/step\n",
            "Epoch 48/100\n",
            "24/24 - 0s - loss: 0.3845 - accuracy: 0.8611 - val_loss: 0.3921 - val_accuracy: 0.8605 - 160ms/epoch - 7ms/step\n",
            "Epoch 49/100\n",
            "24/24 - 0s - loss: 0.3841 - accuracy: 0.8631 - val_loss: 0.3931 - val_accuracy: 0.8605 - 164ms/epoch - 7ms/step\n",
            "Epoch 50/100\n",
            "24/24 - 0s - loss: 0.3842 - accuracy: 0.8628 - val_loss: 0.3932 - val_accuracy: 0.8591 - 162ms/epoch - 7ms/step\n",
            "Epoch 51/100\n",
            "24/24 - 0s - loss: 0.3862 - accuracy: 0.8617 - val_loss: 0.3968 - val_accuracy: 0.8605 - 154ms/epoch - 6ms/step\n",
            "Epoch 52/100\n",
            "24/24 - 0s - loss: 0.3854 - accuracy: 0.8634 - val_loss: 0.3948 - val_accuracy: 0.8591 - 160ms/epoch - 7ms/step\n",
            "Epoch 53/100\n",
            "24/24 - 0s - loss: 0.3831 - accuracy: 0.8631 - val_loss: 0.3963 - val_accuracy: 0.8591 - 158ms/epoch - 7ms/step\n",
            "Epoch 54/100\n",
            "24/24 - 0s - loss: 0.3836 - accuracy: 0.8628 - val_loss: 0.3940 - val_accuracy: 0.8605 - 160ms/epoch - 7ms/step\n",
            "Epoch 55/100\n",
            "24/24 - 0s - loss: 0.3835 - accuracy: 0.8624 - val_loss: 0.3943 - val_accuracy: 0.8605 - 155ms/epoch - 6ms/step\n",
            "Epoch 56/100\n",
            "24/24 - 0s - loss: 0.3837 - accuracy: 0.8624 - val_loss: 0.3958 - val_accuracy: 0.8605 - 152ms/epoch - 6ms/step\n",
            "Epoch 57/100\n",
            "24/24 - 0s - loss: 0.3824 - accuracy: 0.8624 - val_loss: 0.3946 - val_accuracy: 0.8605 - 159ms/epoch - 7ms/step\n",
            "Epoch 58/100\n",
            "24/24 - 0s - loss: 0.3822 - accuracy: 0.8645 - val_loss: 0.3957 - val_accuracy: 0.8605 - 164ms/epoch - 7ms/step\n",
            "Epoch 59/100\n",
            "24/24 - 0s - loss: 0.3836 - accuracy: 0.8631 - val_loss: 0.3942 - val_accuracy: 0.8605 - 155ms/epoch - 6ms/step\n",
            "Epoch 60/100\n",
            "24/24 - 0s - loss: 0.3821 - accuracy: 0.8614 - val_loss: 0.3940 - val_accuracy: 0.8605 - 165ms/epoch - 7ms/step\n",
            "Epoch 61/100\n",
            "24/24 - 0s - loss: 0.3835 - accuracy: 0.8621 - val_loss: 0.3932 - val_accuracy: 0.8605 - 159ms/epoch - 7ms/step\n",
            "Epoch 62/100\n",
            "24/24 - 0s - loss: 0.3818 - accuracy: 0.8645 - val_loss: 0.3979 - val_accuracy: 0.8605 - 158ms/epoch - 7ms/step\n",
            "Epoch 63/100\n",
            "24/24 - 0s - loss: 0.3832 - accuracy: 0.8634 - val_loss: 0.3949 - val_accuracy: 0.8605 - 162ms/epoch - 7ms/step\n",
            "Epoch 64/100\n",
            "24/24 - 0s - loss: 0.3826 - accuracy: 0.8631 - val_loss: 0.4018 - val_accuracy: 0.8605 - 168ms/epoch - 7ms/step\n",
            "Epoch 65/100\n",
            "24/24 - 0s - loss: 0.3856 - accuracy: 0.8628 - val_loss: 0.3979 - val_accuracy: 0.8605 - 158ms/epoch - 7ms/step\n",
            "Epoch 66/100\n",
            "24/24 - 0s - loss: 0.3855 - accuracy: 0.8617 - val_loss: 0.3963 - val_accuracy: 0.8605 - 173ms/epoch - 7ms/step\n",
            "Epoch 67/100\n",
            "24/24 - 0s - loss: 0.3846 - accuracy: 0.8614 - val_loss: 0.3947 - val_accuracy: 0.8605 - 159ms/epoch - 7ms/step\n",
            "Epoch 68/100\n",
            "24/24 - 0s - loss: 0.3859 - accuracy: 0.8614 - val_loss: 0.3998 - val_accuracy: 0.8605 - 171ms/epoch - 7ms/step\n",
            "Epoch 69/100\n",
            "24/24 - 0s - loss: 0.3862 - accuracy: 0.8628 - val_loss: 0.3994 - val_accuracy: 0.8605 - 190ms/epoch - 8ms/step\n",
            "Epoch 70/100\n",
            "24/24 - 0s - loss: 0.3834 - accuracy: 0.8617 - val_loss: 0.3931 - val_accuracy: 0.8605 - 162ms/epoch - 7ms/step\n",
            "Epoch 71/100\n",
            "24/24 - 0s - loss: 0.3828 - accuracy: 0.8634 - val_loss: 0.3940 - val_accuracy: 0.8605 - 164ms/epoch - 7ms/step\n",
            "Epoch 72/100\n",
            "24/24 - 0s - loss: 0.3821 - accuracy: 0.8634 - val_loss: 0.3944 - val_accuracy: 0.8591 - 157ms/epoch - 7ms/step\n",
            "Epoch 73/100\n",
            "24/24 - 0s - loss: 0.3820 - accuracy: 0.8628 - val_loss: 0.3928 - val_accuracy: 0.8605 - 167ms/epoch - 7ms/step\n",
            "Epoch 74/100\n",
            "24/24 - 0s - loss: 0.3804 - accuracy: 0.8628 - val_loss: 0.3973 - val_accuracy: 0.8605 - 165ms/epoch - 7ms/step\n",
            "Epoch 75/100\n",
            "24/24 - 0s - loss: 0.3819 - accuracy: 0.8634 - val_loss: 0.3943 - val_accuracy: 0.8605 - 170ms/epoch - 7ms/step\n",
            "Epoch 76/100\n",
            "24/24 - 0s - loss: 0.3812 - accuracy: 0.8631 - val_loss: 0.3936 - val_accuracy: 0.8618 - 173ms/epoch - 7ms/step\n",
            "Epoch 77/100\n",
            "24/24 - 0s - loss: 0.3819 - accuracy: 0.8631 - val_loss: 0.3980 - val_accuracy: 0.8605 - 176ms/epoch - 7ms/step\n",
            "Epoch 78/100\n",
            "24/24 - 0s - loss: 0.3820 - accuracy: 0.8614 - val_loss: 0.3955 - val_accuracy: 0.8605 - 159ms/epoch - 7ms/step\n",
            "Epoch 79/100\n",
            "24/24 - 0s - loss: 0.3812 - accuracy: 0.8648 - val_loss: 0.3937 - val_accuracy: 0.8591 - 161ms/epoch - 7ms/step\n",
            "Epoch 80/100\n",
            "24/24 - 0s - loss: 0.3808 - accuracy: 0.8628 - val_loss: 0.3974 - val_accuracy: 0.8605 - 179ms/epoch - 7ms/step\n",
            "Epoch 81/100\n",
            "24/24 - 0s - loss: 0.3818 - accuracy: 0.8634 - val_loss: 0.3942 - val_accuracy: 0.8605 - 177ms/epoch - 7ms/step\n",
            "Epoch 82/100\n",
            "24/24 - 0s - loss: 0.3807 - accuracy: 0.8652 - val_loss: 0.3975 - val_accuracy: 0.8605 - 157ms/epoch - 7ms/step\n",
            "Epoch 83/100\n",
            "24/24 - 0s - loss: 0.3830 - accuracy: 0.8628 - val_loss: 0.3930 - val_accuracy: 0.8605 - 166ms/epoch - 7ms/step\n",
            "Epoch 84/100\n",
            "24/24 - 0s - loss: 0.3812 - accuracy: 0.8611 - val_loss: 0.3956 - val_accuracy: 0.8605 - 158ms/epoch - 7ms/step\n",
            "Epoch 85/100\n",
            "24/24 - 0s - loss: 0.3806 - accuracy: 0.8621 - val_loss: 0.3947 - val_accuracy: 0.8605 - 157ms/epoch - 7ms/step\n",
            "Epoch 86/100\n",
            "24/24 - 0s - loss: 0.3816 - accuracy: 0.8628 - val_loss: 0.3936 - val_accuracy: 0.8591 - 162ms/epoch - 7ms/step\n",
            "Epoch 87/100\n",
            "24/24 - 0s - loss: 0.3863 - accuracy: 0.8621 - val_loss: 0.3951 - val_accuracy: 0.8605 - 162ms/epoch - 7ms/step\n",
            "Epoch 88/100\n",
            "24/24 - 0s - loss: 0.3822 - accuracy: 0.8641 - val_loss: 0.3956 - val_accuracy: 0.8605 - 161ms/epoch - 7ms/step\n",
            "Epoch 89/100\n",
            "24/24 - 0s - loss: 0.3806 - accuracy: 0.8641 - val_loss: 0.3941 - val_accuracy: 0.8591 - 155ms/epoch - 6ms/step\n",
            "Epoch 90/100\n",
            "24/24 - 0s - loss: 0.3805 - accuracy: 0.8638 - val_loss: 0.3969 - val_accuracy: 0.8605 - 162ms/epoch - 7ms/step\n",
            "Epoch 91/100\n",
            "24/24 - 0s - loss: 0.3812 - accuracy: 0.8638 - val_loss: 0.3974 - val_accuracy: 0.8605 - 159ms/epoch - 7ms/step\n",
            "Epoch 92/100\n",
            "24/24 - 0s - loss: 0.3832 - accuracy: 0.8631 - val_loss: 0.3975 - val_accuracy: 0.8605 - 163ms/epoch - 7ms/step\n",
            "Epoch 93/100\n",
            "24/24 - 0s - loss: 0.3824 - accuracy: 0.8641 - val_loss: 0.3946 - val_accuracy: 0.8605 - 159ms/epoch - 7ms/step\n",
            "Epoch 94/100\n",
            "24/24 - 0s - loss: 0.3801 - accuracy: 0.8628 - val_loss: 0.3943 - val_accuracy: 0.8632 - 162ms/epoch - 7ms/step\n",
            "Epoch 95/100\n",
            "24/24 - 0s - loss: 0.3810 - accuracy: 0.8641 - val_loss: 0.3964 - val_accuracy: 0.8605 - 171ms/epoch - 7ms/step\n",
            "Epoch 96/100\n",
            "24/24 - 0s - loss: 0.3811 - accuracy: 0.8645 - val_loss: 0.3973 - val_accuracy: 0.8605 - 162ms/epoch - 7ms/step\n",
            "Epoch 97/100\n",
            "24/24 - 0s - loss: 0.3805 - accuracy: 0.8634 - val_loss: 0.3973 - val_accuracy: 0.8605 - 160ms/epoch - 7ms/step\n",
            "Epoch 98/100\n",
            "24/24 - 0s - loss: 0.3802 - accuracy: 0.8628 - val_loss: 0.3939 - val_accuracy: 0.8618 - 162ms/epoch - 7ms/step\n",
            "Epoch 99/100\n",
            "24/24 - 0s - loss: 0.3800 - accuracy: 0.8645 - val_loss: 0.3945 - val_accuracy: 0.8605 - 164ms/epoch - 7ms/step\n",
            "Epoch 100/100\n",
            "24/24 - 0s - loss: 0.3781 - accuracy: 0.8641 - val_loss: 0.3949 - val_accuracy: 0.8564 - 178ms/epoch - 7ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "macro_lstm.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "num_epochs = 100\n",
        "history = macro_lstm.fit(macro_train_loader, validation_data=macro_test_loader, epochs=num_epochs, verbose=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UHpTJj_lLwsC",
        "outputId": "c6de627a-f163-41b9-862d-fa5cfcdf4159"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "13/13 - 2s - loss: 0.6112 - accuracy: 0.8409 - val_loss: 0.5292 - val_accuracy: 0.8390 - 2s/epoch - 159ms/step\n",
            "Epoch 2/100\n",
            "13/13 - 0s - loss: 0.4691 - accuracy: 0.8409 - val_loss: 0.4327 - val_accuracy: 0.8390 - 103ms/epoch - 8ms/step\n",
            "Epoch 3/100\n",
            "13/13 - 0s - loss: 0.4451 - accuracy: 0.8409 - val_loss: 0.4327 - val_accuracy: 0.8390 - 105ms/epoch - 8ms/step\n",
            "Epoch 4/100\n",
            "13/13 - 0s - loss: 0.4360 - accuracy: 0.8409 - val_loss: 0.4335 - val_accuracy: 0.8390 - 112ms/epoch - 9ms/step\n",
            "Epoch 5/100\n",
            "13/13 - 0s - loss: 0.4328 - accuracy: 0.8409 - val_loss: 0.4311 - val_accuracy: 0.8390 - 108ms/epoch - 8ms/step\n",
            "Epoch 6/100\n",
            "13/13 - 0s - loss: 0.4327 - accuracy: 0.8409 - val_loss: 0.4302 - val_accuracy: 0.8390 - 109ms/epoch - 8ms/step\n",
            "Epoch 7/100\n",
            "13/13 - 0s - loss: 0.4311 - accuracy: 0.8409 - val_loss: 0.4291 - val_accuracy: 0.8390 - 107ms/epoch - 8ms/step\n",
            "Epoch 8/100\n",
            "13/13 - 0s - loss: 0.4301 - accuracy: 0.8409 - val_loss: 0.4275 - val_accuracy: 0.8390 - 103ms/epoch - 8ms/step\n",
            "Epoch 9/100\n",
            "13/13 - 0s - loss: 0.4292 - accuracy: 0.8409 - val_loss: 0.4253 - val_accuracy: 0.8390 - 105ms/epoch - 8ms/step\n",
            "Epoch 10/100\n",
            "13/13 - 0s - loss: 0.4268 - accuracy: 0.8409 - val_loss: 0.4231 - val_accuracy: 0.8390 - 102ms/epoch - 8ms/step\n",
            "Epoch 11/100\n",
            "13/13 - 0s - loss: 0.4233 - accuracy: 0.8409 - val_loss: 0.4188 - val_accuracy: 0.8390 - 104ms/epoch - 8ms/step\n",
            "Epoch 12/100\n",
            "13/13 - 0s - loss: 0.4213 - accuracy: 0.8409 - val_loss: 0.4171 - val_accuracy: 0.8390 - 116ms/epoch - 9ms/step\n",
            "Epoch 13/100\n",
            "13/13 - 0s - loss: 0.4253 - accuracy: 0.8409 - val_loss: 0.4232 - val_accuracy: 0.8390 - 124ms/epoch - 10ms/step\n",
            "Epoch 14/100\n",
            "13/13 - 0s - loss: 0.4230 - accuracy: 0.8409 - val_loss: 0.4179 - val_accuracy: 0.8390 - 111ms/epoch - 9ms/step\n",
            "Epoch 15/100\n",
            "13/13 - 0s - loss: 0.4226 - accuracy: 0.8409 - val_loss: 0.4149 - val_accuracy: 0.8390 - 111ms/epoch - 9ms/step\n",
            "Epoch 16/100\n",
            "13/13 - 0s - loss: 0.4196 - accuracy: 0.8409 - val_loss: 0.4152 - val_accuracy: 0.8390 - 103ms/epoch - 8ms/step\n",
            "Epoch 17/100\n",
            "13/13 - 0s - loss: 0.4224 - accuracy: 0.8409 - val_loss: 0.4161 - val_accuracy: 0.8390 - 103ms/epoch - 8ms/step\n",
            "Epoch 18/100\n",
            "13/13 - 0s - loss: 0.4254 - accuracy: 0.8409 - val_loss: 0.4143 - val_accuracy: 0.8390 - 105ms/epoch - 8ms/step\n",
            "Epoch 19/100\n",
            "13/13 - 0s - loss: 0.4196 - accuracy: 0.8409 - val_loss: 0.4143 - val_accuracy: 0.8390 - 103ms/epoch - 8ms/step\n",
            "Epoch 20/100\n",
            "13/13 - 0s - loss: 0.4251 - accuracy: 0.8409 - val_loss: 0.4136 - val_accuracy: 0.8390 - 103ms/epoch - 8ms/step\n",
            "Epoch 21/100\n",
            "13/13 - 0s - loss: 0.4186 - accuracy: 0.8409 - val_loss: 0.4134 - val_accuracy: 0.8390 - 111ms/epoch - 9ms/step\n",
            "Epoch 22/100\n",
            "13/13 - 0s - loss: 0.4194 - accuracy: 0.8409 - val_loss: 0.4111 - val_accuracy: 0.8416 - 103ms/epoch - 8ms/step\n",
            "Epoch 23/100\n",
            "13/13 - 0s - loss: 0.4210 - accuracy: 0.8409 - val_loss: 0.4118 - val_accuracy: 0.8416 - 100ms/epoch - 8ms/step\n",
            "Epoch 24/100\n",
            "13/13 - 0s - loss: 0.4198 - accuracy: 0.8409 - val_loss: 0.4131 - val_accuracy: 0.8390 - 101ms/epoch - 8ms/step\n",
            "Epoch 25/100\n",
            "13/13 - 0s - loss: 0.4178 - accuracy: 0.8409 - val_loss: 0.4108 - val_accuracy: 0.8390 - 101ms/epoch - 8ms/step\n",
            "Epoch 26/100\n",
            "13/13 - 0s - loss: 0.4169 - accuracy: 0.8409 - val_loss: 0.4086 - val_accuracy: 0.8416 - 98ms/epoch - 8ms/step\n",
            "Epoch 27/100\n",
            "13/13 - 0s - loss: 0.4170 - accuracy: 0.8416 - val_loss: 0.4134 - val_accuracy: 0.8416 - 97ms/epoch - 7ms/step\n",
            "Epoch 28/100\n",
            "13/13 - 0s - loss: 0.4200 - accuracy: 0.8409 - val_loss: 0.4096 - val_accuracy: 0.8416 - 106ms/epoch - 8ms/step\n",
            "Epoch 29/100\n",
            "13/13 - 0s - loss: 0.4183 - accuracy: 0.8409 - val_loss: 0.4097 - val_accuracy: 0.8416 - 102ms/epoch - 8ms/step\n",
            "Epoch 30/100\n",
            "13/13 - 0s - loss: 0.4157 - accuracy: 0.8416 - val_loss: 0.4076 - val_accuracy: 0.8416 - 105ms/epoch - 8ms/step\n",
            "Epoch 31/100\n",
            "13/13 - 0s - loss: 0.4147 - accuracy: 0.8435 - val_loss: 0.4059 - val_accuracy: 0.8442 - 102ms/epoch - 8ms/step\n",
            "Epoch 32/100\n",
            "13/13 - 0s - loss: 0.4159 - accuracy: 0.8416 - val_loss: 0.4060 - val_accuracy: 0.8442 - 112ms/epoch - 9ms/step\n",
            "Epoch 33/100\n",
            "13/13 - 0s - loss: 0.4154 - accuracy: 0.8416 - val_loss: 0.4076 - val_accuracy: 0.8416 - 106ms/epoch - 8ms/step\n",
            "Epoch 34/100\n",
            "13/13 - 0s - loss: 0.4145 - accuracy: 0.8416 - val_loss: 0.4057 - val_accuracy: 0.8442 - 109ms/epoch - 8ms/step\n",
            "Epoch 35/100\n",
            "13/13 - 0s - loss: 0.4147 - accuracy: 0.8435 - val_loss: 0.4069 - val_accuracy: 0.8416 - 109ms/epoch - 8ms/step\n",
            "Epoch 36/100\n",
            "13/13 - 0s - loss: 0.4142 - accuracy: 0.8455 - val_loss: 0.4070 - val_accuracy: 0.8416 - 106ms/epoch - 8ms/step\n",
            "Epoch 37/100\n",
            "13/13 - 0s - loss: 0.4126 - accuracy: 0.8462 - val_loss: 0.4059 - val_accuracy: 0.8519 - 104ms/epoch - 8ms/step\n",
            "Epoch 38/100\n",
            "13/13 - 0s - loss: 0.4126 - accuracy: 0.8475 - val_loss: 0.4109 - val_accuracy: 0.8494 - 104ms/epoch - 8ms/step\n",
            "Epoch 39/100\n",
            "13/13 - 0s - loss: 0.4136 - accuracy: 0.8475 - val_loss: 0.4050 - val_accuracy: 0.8519 - 104ms/epoch - 8ms/step\n",
            "Epoch 40/100\n",
            "13/13 - 0s - loss: 0.4129 - accuracy: 0.8475 - val_loss: 0.4074 - val_accuracy: 0.8519 - 105ms/epoch - 8ms/step\n",
            "Epoch 41/100\n",
            "13/13 - 0s - loss: 0.4108 - accuracy: 0.8449 - val_loss: 0.4039 - val_accuracy: 0.8519 - 100ms/epoch - 8ms/step\n",
            "Epoch 42/100\n",
            "13/13 - 0s - loss: 0.4132 - accuracy: 0.8462 - val_loss: 0.4065 - val_accuracy: 0.8519 - 100ms/epoch - 8ms/step\n",
            "Epoch 43/100\n",
            "13/13 - 0s - loss: 0.4109 - accuracy: 0.8475 - val_loss: 0.4072 - val_accuracy: 0.8545 - 101ms/epoch - 8ms/step\n",
            "Epoch 44/100\n",
            "13/13 - 0s - loss: 0.4096 - accuracy: 0.8462 - val_loss: 0.4061 - val_accuracy: 0.8494 - 107ms/epoch - 8ms/step\n",
            "Epoch 45/100\n",
            "13/13 - 0s - loss: 0.4116 - accuracy: 0.8442 - val_loss: 0.4055 - val_accuracy: 0.8494 - 107ms/epoch - 8ms/step\n",
            "Epoch 46/100\n",
            "13/13 - 0s - loss: 0.4130 - accuracy: 0.8481 - val_loss: 0.4095 - val_accuracy: 0.8468 - 103ms/epoch - 8ms/step\n",
            "Epoch 47/100\n",
            "13/13 - 0s - loss: 0.4091 - accuracy: 0.8488 - val_loss: 0.4039 - val_accuracy: 0.8519 - 100ms/epoch - 8ms/step\n",
            "Epoch 48/100\n",
            "13/13 - 0s - loss: 0.4143 - accuracy: 0.8494 - val_loss: 0.4075 - val_accuracy: 0.8494 - 98ms/epoch - 8ms/step\n",
            "Epoch 49/100\n",
            "13/13 - 0s - loss: 0.4085 - accuracy: 0.8494 - val_loss: 0.4095 - val_accuracy: 0.8494 - 101ms/epoch - 8ms/step\n",
            "Epoch 50/100\n",
            "13/13 - 0s - loss: 0.4126 - accuracy: 0.8462 - val_loss: 0.4046 - val_accuracy: 0.8494 - 101ms/epoch - 8ms/step\n",
            "Epoch 51/100\n",
            "13/13 - 0s - loss: 0.4107 - accuracy: 0.8468 - val_loss: 0.4105 - val_accuracy: 0.8494 - 103ms/epoch - 8ms/step\n",
            "Epoch 52/100\n",
            "13/13 - 0s - loss: 0.4077 - accuracy: 0.8488 - val_loss: 0.4047 - val_accuracy: 0.8494 - 106ms/epoch - 8ms/step\n",
            "Epoch 53/100\n",
            "13/13 - 0s - loss: 0.4100 - accuracy: 0.8507 - val_loss: 0.4065 - val_accuracy: 0.8468 - 104ms/epoch - 8ms/step\n",
            "Epoch 54/100\n",
            "13/13 - 0s - loss: 0.4061 - accuracy: 0.8514 - val_loss: 0.4061 - val_accuracy: 0.8468 - 106ms/epoch - 8ms/step\n",
            "Epoch 55/100\n",
            "13/13 - 0s - loss: 0.4066 - accuracy: 0.8520 - val_loss: 0.4053 - val_accuracy: 0.8494 - 101ms/epoch - 8ms/step\n",
            "Epoch 56/100\n",
            "13/13 - 0s - loss: 0.4081 - accuracy: 0.8507 - val_loss: 0.4072 - val_accuracy: 0.8468 - 103ms/epoch - 8ms/step\n",
            "Epoch 57/100\n",
            "13/13 - 0s - loss: 0.4074 - accuracy: 0.8514 - val_loss: 0.4030 - val_accuracy: 0.8494 - 99ms/epoch - 8ms/step\n",
            "Epoch 58/100\n",
            "13/13 - 0s - loss: 0.4065 - accuracy: 0.8507 - val_loss: 0.4097 - val_accuracy: 0.8416 - 104ms/epoch - 8ms/step\n",
            "Epoch 59/100\n",
            "13/13 - 0s - loss: 0.4094 - accuracy: 0.8494 - val_loss: 0.4071 - val_accuracy: 0.8468 - 106ms/epoch - 8ms/step\n",
            "Epoch 60/100\n",
            "13/13 - 0s - loss: 0.4053 - accuracy: 0.8527 - val_loss: 0.4049 - val_accuracy: 0.8442 - 103ms/epoch - 8ms/step\n",
            "Epoch 61/100\n",
            "13/13 - 0s - loss: 0.4032 - accuracy: 0.8546 - val_loss: 0.4096 - val_accuracy: 0.8442 - 102ms/epoch - 8ms/step\n",
            "Epoch 62/100\n",
            "13/13 - 0s - loss: 0.4073 - accuracy: 0.8533 - val_loss: 0.4045 - val_accuracy: 0.8442 - 105ms/epoch - 8ms/step\n",
            "Epoch 63/100\n",
            "13/13 - 0s - loss: 0.4066 - accuracy: 0.8546 - val_loss: 0.4064 - val_accuracy: 0.8390 - 110ms/epoch - 8ms/step\n",
            "Epoch 64/100\n",
            "13/13 - 0s - loss: 0.4045 - accuracy: 0.8540 - val_loss: 0.4080 - val_accuracy: 0.8416 - 101ms/epoch - 8ms/step\n",
            "Epoch 65/100\n",
            "13/13 - 0s - loss: 0.4041 - accuracy: 0.8546 - val_loss: 0.4140 - val_accuracy: 0.8390 - 108ms/epoch - 8ms/step\n",
            "Epoch 66/100\n",
            "13/13 - 0s - loss: 0.4086 - accuracy: 0.8559 - val_loss: 0.4127 - val_accuracy: 0.8442 - 101ms/epoch - 8ms/step\n",
            "Epoch 67/100\n",
            "13/13 - 0s - loss: 0.4044 - accuracy: 0.8501 - val_loss: 0.4057 - val_accuracy: 0.8442 - 108ms/epoch - 8ms/step\n",
            "Epoch 68/100\n",
            "13/13 - 0s - loss: 0.4013 - accuracy: 0.8540 - val_loss: 0.4131 - val_accuracy: 0.8442 - 100ms/epoch - 8ms/step\n",
            "Epoch 69/100\n",
            "13/13 - 0s - loss: 0.4014 - accuracy: 0.8559 - val_loss: 0.4069 - val_accuracy: 0.8545 - 106ms/epoch - 8ms/step\n",
            "Epoch 70/100\n",
            "13/13 - 0s - loss: 0.4008 - accuracy: 0.8527 - val_loss: 0.4226 - val_accuracy: 0.8468 - 105ms/epoch - 8ms/step\n",
            "Epoch 71/100\n",
            "13/13 - 0s - loss: 0.4055 - accuracy: 0.8533 - val_loss: 0.4053 - val_accuracy: 0.8442 - 123ms/epoch - 9ms/step\n",
            "Epoch 72/100\n",
            "13/13 - 0s - loss: 0.4037 - accuracy: 0.8559 - val_loss: 0.4110 - val_accuracy: 0.8442 - 123ms/epoch - 9ms/step\n",
            "Epoch 73/100\n",
            "13/13 - 0s - loss: 0.4007 - accuracy: 0.8546 - val_loss: 0.4099 - val_accuracy: 0.8442 - 108ms/epoch - 8ms/step\n",
            "Epoch 74/100\n",
            "13/13 - 0s - loss: 0.4000 - accuracy: 0.8566 - val_loss: 0.4081 - val_accuracy: 0.8390 - 106ms/epoch - 8ms/step\n",
            "Epoch 75/100\n",
            "13/13 - 0s - loss: 0.4000 - accuracy: 0.8546 - val_loss: 0.4103 - val_accuracy: 0.8468 - 108ms/epoch - 8ms/step\n",
            "Epoch 76/100\n",
            "13/13 - 0s - loss: 0.3969 - accuracy: 0.8572 - val_loss: 0.4086 - val_accuracy: 0.8416 - 117ms/epoch - 9ms/step\n",
            "Epoch 77/100\n",
            "13/13 - 0s - loss: 0.3992 - accuracy: 0.8585 - val_loss: 0.4140 - val_accuracy: 0.8390 - 104ms/epoch - 8ms/step\n",
            "Epoch 78/100\n",
            "13/13 - 0s - loss: 0.3973 - accuracy: 0.8585 - val_loss: 0.4150 - val_accuracy: 0.8416 - 108ms/epoch - 8ms/step\n",
            "Epoch 79/100\n",
            "13/13 - 0s - loss: 0.3952 - accuracy: 0.8585 - val_loss: 0.4152 - val_accuracy: 0.8468 - 108ms/epoch - 8ms/step\n",
            "Epoch 80/100\n",
            "13/13 - 0s - loss: 0.4000 - accuracy: 0.8592 - val_loss: 0.4239 - val_accuracy: 0.8442 - 108ms/epoch - 8ms/step\n",
            "Epoch 81/100\n",
            "13/13 - 0s - loss: 0.4040 - accuracy: 0.8579 - val_loss: 0.4146 - val_accuracy: 0.8364 - 107ms/epoch - 8ms/step\n",
            "Epoch 82/100\n",
            "13/13 - 0s - loss: 0.3986 - accuracy: 0.8598 - val_loss: 0.4199 - val_accuracy: 0.8468 - 104ms/epoch - 8ms/step\n",
            "Epoch 83/100\n",
            "13/13 - 0s - loss: 0.3997 - accuracy: 0.8559 - val_loss: 0.4161 - val_accuracy: 0.8390 - 106ms/epoch - 8ms/step\n",
            "Epoch 84/100\n",
            "13/13 - 0s - loss: 0.3932 - accuracy: 0.8605 - val_loss: 0.4220 - val_accuracy: 0.8416 - 103ms/epoch - 8ms/step\n",
            "Epoch 85/100\n",
            "13/13 - 0s - loss: 0.4025 - accuracy: 0.8559 - val_loss: 0.4297 - val_accuracy: 0.8468 - 103ms/epoch - 8ms/step\n",
            "Epoch 86/100\n",
            "13/13 - 0s - loss: 0.4030 - accuracy: 0.8559 - val_loss: 0.4141 - val_accuracy: 0.8416 - 107ms/epoch - 8ms/step\n",
            "Epoch 87/100\n",
            "13/13 - 0s - loss: 0.3964 - accuracy: 0.8540 - val_loss: 0.4160 - val_accuracy: 0.8442 - 108ms/epoch - 8ms/step\n",
            "Epoch 88/100\n",
            "13/13 - 0s - loss: 0.3935 - accuracy: 0.8605 - val_loss: 0.4173 - val_accuracy: 0.8494 - 111ms/epoch - 9ms/step\n",
            "Epoch 89/100\n",
            "13/13 - 0s - loss: 0.3957 - accuracy: 0.8572 - val_loss: 0.4171 - val_accuracy: 0.8442 - 116ms/epoch - 9ms/step\n",
            "Epoch 90/100\n",
            "13/13 - 0s - loss: 0.3963 - accuracy: 0.8585 - val_loss: 0.4206 - val_accuracy: 0.8442 - 108ms/epoch - 8ms/step\n",
            "Epoch 91/100\n",
            "13/13 - 0s - loss: 0.3911 - accuracy: 0.8611 - val_loss: 0.4195 - val_accuracy: 0.8390 - 107ms/epoch - 8ms/step\n",
            "Epoch 92/100\n",
            "13/13 - 0s - loss: 0.3908 - accuracy: 0.8598 - val_loss: 0.4208 - val_accuracy: 0.8442 - 123ms/epoch - 9ms/step\n",
            "Epoch 93/100\n",
            "13/13 - 0s - loss: 0.3947 - accuracy: 0.8579 - val_loss: 0.4218 - val_accuracy: 0.8416 - 114ms/epoch - 9ms/step\n",
            "Epoch 94/100\n",
            "13/13 - 0s - loss: 0.3965 - accuracy: 0.8585 - val_loss: 0.4170 - val_accuracy: 0.8416 - 102ms/epoch - 8ms/step\n",
            "Epoch 95/100\n",
            "13/13 - 0s - loss: 0.3909 - accuracy: 0.8572 - val_loss: 0.4191 - val_accuracy: 0.8364 - 105ms/epoch - 8ms/step\n",
            "Epoch 96/100\n",
            "13/13 - 0s - loss: 0.3902 - accuracy: 0.8598 - val_loss: 0.4169 - val_accuracy: 0.8442 - 105ms/epoch - 8ms/step\n",
            "Epoch 97/100\n",
            "13/13 - 0s - loss: 0.3904 - accuracy: 0.8592 - val_loss: 0.4208 - val_accuracy: 0.8364 - 105ms/epoch - 8ms/step\n",
            "Epoch 98/100\n",
            "13/13 - 0s - loss: 0.3889 - accuracy: 0.8585 - val_loss: 0.4245 - val_accuracy: 0.8442 - 112ms/epoch - 9ms/step\n",
            "Epoch 99/100\n",
            "13/13 - 0s - loss: 0.3873 - accuracy: 0.8605 - val_loss: 0.4267 - val_accuracy: 0.8286 - 104ms/epoch - 8ms/step\n",
            "Epoch 100/100\n",
            "13/13 - 0s - loss: 0.3883 - accuracy: 0.8611 - val_loss: 0.4253 - val_accuracy: 0.8468 - 100ms/epoch - 8ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "glyco_lstm.save('/content/gdrive/MyDrive/sepsis_model_weight/model_glyco.h5')\n",
        "cepha_lstm.save('/content/gdrive/MyDrive/sepsis_model_weight/model_cepha.h5')\n",
        "peni_lstm.save('/content/gdrive/MyDrive/sepsis_model_weight/model_peni.h5')\n",
        "fluoro_lstm.save('/content/gdrive/MyDrive/sepsis_model_weight/model_fluoro.h5')\n",
        "macro_lstm.save('/content/gdrive/MyDrive/sepsis_model_weight/model_macro.h5')"
      ],
      "metadata": {
        "id": "ruT8Z7iSN4Dg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "glyco_label=pd.DataFrame(glyco_test['LABEL'])\n",
        "\n",
        "death_predict=pd.DataFrame(death_lstm.predict(glyco_test_loader))\n",
        "glyco_predict=pd.DataFrame(glyco_lstm.predict(glyco_test_loader))\n",
        "cepha_predict=pd.DataFrame(cepha_lstm.predict(glyco_test_loader))\n",
        "peni_predict=pd.DataFrame(peni_lstm.predict(glyco_test_loader))\n",
        "fluoro_predict=pd.DataFrame(fluoro_lstm.predict(glyco_test_loader))\n",
        "macro_predict=pd.DataFrame(macro_lstm.predict(glyco_test_loader))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_UMxzcetOvVi",
        "outputId": "425023c3-bf63-4de3-a03b-1f2f303a69dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "19/19 [==============================] - 0s 4ms/step\n",
            "19/19 [==============================] - 0s 4ms/step\n",
            "19/19 [==============================] - 0s 4ms/step\n",
            "19/19 [==============================] - 0s 4ms/step\n",
            "19/19 [==============================] - 0s 3ms/step\n",
            "19/19 [==============================] - 0s 4ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "glyco_total=pd.concat([glyco_label,death_predict,glyco_predict,cepha_predict,peni_predict,fluoro_predict,macro_predict],axis=1)\n",
        "glyco_total.columns=['label','death_model','glyco_model','cepha_model','peni_model','fluoro_model','macro_model']"
      ],
      "metadata": {
        "id": "LF8Mrk2rVCmU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "glyco_total"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "7O0m9XqOPFNS",
        "outputId": "92fbe5ee-85ed-4002-f8eb-14cab220803f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      label  death_model  glyco_model  cepha_model  peni_model  fluoro_model  \\\n",
              "0         1     0.014108     0.223974     0.092540    0.349622      0.311748   \n",
              "1         1     0.485061     0.470382     0.522545    0.205113      0.142648   \n",
              "2         1     0.356203     0.426065     0.678691    0.231644      0.217998   \n",
              "3         1     0.195743     0.163080     0.122726    0.144958      0.110240   \n",
              "4         1     0.193229     0.137318     0.205912    0.104218      0.109623   \n",
              "...     ...          ...          ...          ...         ...           ...   \n",
              "2347      0     0.157966     0.136068     0.127145    0.076348      0.094608   \n",
              "2348      0     0.152380     0.152679     0.114211    0.131388      0.103832   \n",
              "2349      0     0.262742     0.167690     0.180576    0.158227      0.145779   \n",
              "2350      0     0.186726     0.192592     0.222549    0.164990      0.206763   \n",
              "2351      0     0.195743     0.163080     0.122726    0.144958      0.110240   \n",
              "\n",
              "      macro_model  \n",
              "0        0.180778  \n",
              "1        0.128929  \n",
              "2        0.113417  \n",
              "3        0.103486  \n",
              "4        0.064443  \n",
              "...           ...  \n",
              "2347     0.062250  \n",
              "2348     0.099097  \n",
              "2349     0.119568  \n",
              "2350     0.104039  \n",
              "2351     0.103486  \n",
              "\n",
              "[2352 rows x 7 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8fc051e9-8a5a-4f5e-8bb8-d186cdf92a25\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>death_model</th>\n",
              "      <th>glyco_model</th>\n",
              "      <th>cepha_model</th>\n",
              "      <th>peni_model</th>\n",
              "      <th>fluoro_model</th>\n",
              "      <th>macro_model</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0.014108</td>\n",
              "      <td>0.223974</td>\n",
              "      <td>0.092540</td>\n",
              "      <td>0.349622</td>\n",
              "      <td>0.311748</td>\n",
              "      <td>0.180778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0.485061</td>\n",
              "      <td>0.470382</td>\n",
              "      <td>0.522545</td>\n",
              "      <td>0.205113</td>\n",
              "      <td>0.142648</td>\n",
              "      <td>0.128929</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>0.356203</td>\n",
              "      <td>0.426065</td>\n",
              "      <td>0.678691</td>\n",
              "      <td>0.231644</td>\n",
              "      <td>0.217998</td>\n",
              "      <td>0.113417</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0.195743</td>\n",
              "      <td>0.163080</td>\n",
              "      <td>0.122726</td>\n",
              "      <td>0.144958</td>\n",
              "      <td>0.110240</td>\n",
              "      <td>0.103486</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0.193229</td>\n",
              "      <td>0.137318</td>\n",
              "      <td>0.205912</td>\n",
              "      <td>0.104218</td>\n",
              "      <td>0.109623</td>\n",
              "      <td>0.064443</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2347</th>\n",
              "      <td>0</td>\n",
              "      <td>0.157966</td>\n",
              "      <td>0.136068</td>\n",
              "      <td>0.127145</td>\n",
              "      <td>0.076348</td>\n",
              "      <td>0.094608</td>\n",
              "      <td>0.062250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2348</th>\n",
              "      <td>0</td>\n",
              "      <td>0.152380</td>\n",
              "      <td>0.152679</td>\n",
              "      <td>0.114211</td>\n",
              "      <td>0.131388</td>\n",
              "      <td>0.103832</td>\n",
              "      <td>0.099097</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2349</th>\n",
              "      <td>0</td>\n",
              "      <td>0.262742</td>\n",
              "      <td>0.167690</td>\n",
              "      <td>0.180576</td>\n",
              "      <td>0.158227</td>\n",
              "      <td>0.145779</td>\n",
              "      <td>0.119568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2350</th>\n",
              "      <td>0</td>\n",
              "      <td>0.186726</td>\n",
              "      <td>0.192592</td>\n",
              "      <td>0.222549</td>\n",
              "      <td>0.164990</td>\n",
              "      <td>0.206763</td>\n",
              "      <td>0.104039</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2351</th>\n",
              "      <td>0</td>\n",
              "      <td>0.195743</td>\n",
              "      <td>0.163080</td>\n",
              "      <td>0.122726</td>\n",
              "      <td>0.144958</td>\n",
              "      <td>0.110240</td>\n",
              "      <td>0.103486</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2352 rows × 7 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8fc051e9-8a5a-4f5e-8bb8-d186cdf92a25')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-8fc051e9-8a5a-4f5e-8bb8-d186cdf92a25 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8fc051e9-8a5a-4f5e-8bb8-d186cdf92a25');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "glyco_total.to_csv('glyco_total.csv')"
      ],
      "metadata": {
        "id": "HjfziTwyQv4L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "glyco_total.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "UEbvszS1c_AG",
        "outputId": "3a3bd87d-bfc6-467d-8ca1-72a9c50faea7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             label  death_model  glyco_model  cepha_model   peni_model  \\\n",
              "count  2352.000000  2352.000000  2352.000000  2352.000000  2352.000000   \n",
              "mean      0.215986     0.196350     0.214887     0.186830     0.198750   \n",
              "std       0.411592     0.170452     0.158309     0.153455     0.152484   \n",
              "min       0.000000     0.001662     0.015854     0.000040     0.003022   \n",
              "25%       0.000000     0.106631     0.149175     0.122726     0.139193   \n",
              "50%       0.000000     0.164574     0.163080     0.135749     0.144958   \n",
              "75%       0.000000     0.195743     0.181829     0.198761     0.177399   \n",
              "max       1.000000     0.999955     0.999038     0.994983     0.988274   \n",
              "\n",
              "       fluoro_model  macro_model  \n",
              "count   2352.000000  2352.000000  \n",
              "mean       0.176921     0.142387  \n",
              "std        0.155931     0.126896  \n",
              "min        0.043336     0.002842  \n",
              "25%        0.108697     0.096551  \n",
              "50%        0.112443     0.103486  \n",
              "75%        0.180022     0.125479  \n",
              "max        0.999859     0.991618  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1b54843d-56f8-48b1-9f93-faee84152d07\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>death_model</th>\n",
              "      <th>glyco_model</th>\n",
              "      <th>cepha_model</th>\n",
              "      <th>peni_model</th>\n",
              "      <th>fluoro_model</th>\n",
              "      <th>macro_model</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2352.000000</td>\n",
              "      <td>2352.000000</td>\n",
              "      <td>2352.000000</td>\n",
              "      <td>2352.000000</td>\n",
              "      <td>2352.000000</td>\n",
              "      <td>2352.000000</td>\n",
              "      <td>2352.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.215986</td>\n",
              "      <td>0.196350</td>\n",
              "      <td>0.214887</td>\n",
              "      <td>0.186830</td>\n",
              "      <td>0.198750</td>\n",
              "      <td>0.176921</td>\n",
              "      <td>0.142387</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.411592</td>\n",
              "      <td>0.170452</td>\n",
              "      <td>0.158309</td>\n",
              "      <td>0.153455</td>\n",
              "      <td>0.152484</td>\n",
              "      <td>0.155931</td>\n",
              "      <td>0.126896</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.001662</td>\n",
              "      <td>0.015854</td>\n",
              "      <td>0.000040</td>\n",
              "      <td>0.003022</td>\n",
              "      <td>0.043336</td>\n",
              "      <td>0.002842</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.106631</td>\n",
              "      <td>0.149175</td>\n",
              "      <td>0.122726</td>\n",
              "      <td>0.139193</td>\n",
              "      <td>0.108697</td>\n",
              "      <td>0.096551</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.164574</td>\n",
              "      <td>0.163080</td>\n",
              "      <td>0.135749</td>\n",
              "      <td>0.144958</td>\n",
              "      <td>0.112443</td>\n",
              "      <td>0.103486</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.195743</td>\n",
              "      <td>0.181829</td>\n",
              "      <td>0.198761</td>\n",
              "      <td>0.177399</td>\n",
              "      <td>0.180022</td>\n",
              "      <td>0.125479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.999955</td>\n",
              "      <td>0.999038</td>\n",
              "      <td>0.994983</td>\n",
              "      <td>0.988274</td>\n",
              "      <td>0.999859</td>\n",
              "      <td>0.991618</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1b54843d-56f8-48b1-9f93-faee84152d07')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "      <script>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1b54843d-56f8-48b1-9f93-faee84152d07');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cepha_label=pd.DataFrame(cepha_test['LABEL'])\n",
        "\n",
        "death_predict=pd.DataFrame(death_lstm.predict(cepha_test_loader))\n",
        "glyco_predict=pd.DataFrame(glyco_lstm.predict(cepha_test_loader))\n",
        "cepha_predict=pd.DataFrame(cepha_lstm.predict(cepha_test_loader))\n",
        "peni_predict=pd.DataFrame(peni_lstm.predict(cepha_test_loader))\n",
        "fluoro_predict=pd.DataFrame(fluoro_lstm.predict(cepha_test_loader))\n",
        "macro_predict=pd.DataFrame(macro_lstm.predict(cepha_test_loader))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ao_qiRBFcAHR",
        "outputId": "b814daec-a1f4-407b-bf2c-4ff3dea3d03a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "18/18 [==============================] - 0s 4ms/step\n",
            "18/18 [==============================] - 0s 3ms/step\n",
            "18/18 [==============================] - 0s 3ms/step\n",
            "18/18 [==============================] - 0s 4ms/step\n",
            "18/18 [==============================] - 0s 3ms/step\n",
            "18/18 [==============================] - 0s 4ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cepha_total=pd.concat([cepha_label,death_predict,glyco_predict,cepha_predict,peni_predict,fluoro_predict,macro_predict],axis=1)\n",
        "cepha_total.columns=['label','death_model','glyco_model','cepha_model','peni_model','fluoro_model','macro_model']"
      ],
      "metadata": {
        "id": "82y7lKLXcJqt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cepha_total"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "5ewxY3jEcdBt",
        "outputId": "a2ef83d7-f2c1-442b-f27e-9794d6ed3dae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      label  death_model  glyco_model  cepha_model  peni_model  fluoro_model  \\\n",
              "0         1     0.236570     0.148248     0.279651    0.132168      0.122248   \n",
              "1         1     0.410458     0.297525     0.233613    0.239752      0.316624   \n",
              "2         1     0.999856     0.895030     0.984093    0.980664      0.999827   \n",
              "3         1     0.195743     0.163080     0.122726    0.144958      0.110240   \n",
              "4         1     0.237204     0.201668     0.251227    0.167174      0.294863   \n",
              "...     ...          ...          ...          ...         ...           ...   \n",
              "2187      0     0.077487     0.146791     0.097477    0.153996      0.095492   \n",
              "2188      0     0.263328     0.408858     0.108774    0.162515      0.205582   \n",
              "2189      0     0.195743     0.163080     0.122726    0.144958      0.110240   \n",
              "2190      0     0.195743     0.163080     0.122726    0.144958      0.110240   \n",
              "2191      0     0.015855     0.146331     0.036363    0.114590      0.101359   \n",
              "\n",
              "      macro_model  \n",
              "0        0.086393  \n",
              "1        0.117778  \n",
              "2        0.656816  \n",
              "3        0.103486  \n",
              "4        0.145911  \n",
              "...           ...  \n",
              "2187     0.111025  \n",
              "2188     0.099497  \n",
              "2189     0.103486  \n",
              "2190     0.103486  \n",
              "2191     0.072932  \n",
              "\n",
              "[2192 rows x 7 columns]"
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              "      <td>0.297525</td>\n",
              "      <td>0.233613</td>\n",
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              "      <td>0.117778</td>\n",
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              "      <th>2</th>\n",
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              "      <td>0.984093</td>\n",
              "      <td>0.980664</td>\n",
              "      <td>0.999827</td>\n",
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              "      <td>0.110240</td>\n",
              "      <td>0.103486</td>\n",
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              "      <td>0.201668</td>\n",
              "      <td>0.251227</td>\n",
              "      <td>0.167174</td>\n",
              "      <td>0.294863</td>\n",
              "      <td>0.145911</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "      <th>2187</th>\n",
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              "      <td>0.077487</td>\n",
              "      <td>0.146791</td>\n",
              "      <td>0.097477</td>\n",
              "      <td>0.153996</td>\n",
              "      <td>0.095492</td>\n",
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              "      <th>2188</th>\n",
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              "      <td>0.263328</td>\n",
              "      <td>0.408858</td>\n",
              "      <td>0.108774</td>\n",
              "      <td>0.162515</td>\n",
              "      <td>0.205582</td>\n",
              "      <td>0.099497</td>\n",
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              "    <tr>\n",
              "      <th>2189</th>\n",
              "      <td>0</td>\n",
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              "      <td>0.110240</td>\n",
              "      <td>0.103486</td>\n",
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              "    <tr>\n",
              "      <th>2190</th>\n",
              "      <td>0</td>\n",
              "      <td>0.195743</td>\n",
              "      <td>0.163080</td>\n",
              "      <td>0.122726</td>\n",
              "      <td>0.144958</td>\n",
              "      <td>0.110240</td>\n",
              "      <td>0.103486</td>\n",
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              "    <tr>\n",
              "      <th>2191</th>\n",
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              "      <td>0.036363</td>\n",
              "      <td>0.114590</td>\n",
              "      <td>0.101359</td>\n",
              "      <td>0.072932</td>\n",
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              "<p>2192 rows × 7 columns</p>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-95d59a96-4be6-47ec-a6b0-b4f8b1c5158e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cepha_total.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "GbdMUJGjdRz_",
        "outputId": "6a9b93e6-32e8-4365-d4b6-ea0136b1b3f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             label  death_model  glyco_model  cepha_model   peni_model  \\\n",
              "count  2192.000000  2192.000000  2192.000000  2192.000000  2192.000000   \n",
              "mean      0.148266     0.153331     0.193897     0.146789     0.186245   \n",
              "std       0.355445     0.141966     0.128466     0.128426     0.124085   \n",
              "min       0.000000     0.004505     0.014203     0.000041     0.003430   \n",
              "25%       0.000000     0.071000     0.145172     0.085396     0.136709   \n",
              "50%       0.000000     0.128333     0.163080     0.122726     0.144958   \n",
              "75%       0.000000     0.195743     0.174878     0.166112     0.178013   \n",
              "max       1.000000     0.999952     0.998940     0.993771     0.988274   \n",
              "\n",
              "       fluoro_model  macro_model  \n",
              "count   2192.000000  2192.000000  \n",
              "mean       0.165484     0.136834  \n",
              "std        0.127099     0.117513  \n",
              "min        0.041863     0.002022  \n",
              "25%        0.108039     0.091566  \n",
              "50%        0.115422     0.103486  \n",
              "75%        0.180804     0.127038  \n",
              "max        0.999858     0.989579  "
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              "      <th>label</th>\n",
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              "      <td>2192.000000</td>\n",
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              "      <td>2192.000000</td>\n",
              "      <td>2192.000000</td>\n",
              "      <td>2192.000000</td>\n",
              "      <td>2192.000000</td>\n",
              "      <td>2192.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.148266</td>\n",
              "      <td>0.153331</td>\n",
              "      <td>0.193897</td>\n",
              "      <td>0.146789</td>\n",
              "      <td>0.186245</td>\n",
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              "      <th>std</th>\n",
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              "      <td>0.141966</td>\n",
              "      <td>0.128466</td>\n",
              "      <td>0.128426</td>\n",
              "      <td>0.124085</td>\n",
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              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.014203</td>\n",
              "      <td>0.000041</td>\n",
              "      <td>0.003430</td>\n",
              "      <td>0.041863</td>\n",
              "      <td>0.002022</td>\n",
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              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.071000</td>\n",
              "      <td>0.145172</td>\n",
              "      <td>0.085396</td>\n",
              "      <td>0.136709</td>\n",
              "      <td>0.108039</td>\n",
              "      <td>0.091566</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.128333</td>\n",
              "      <td>0.163080</td>\n",
              "      <td>0.122726</td>\n",
              "      <td>0.144958</td>\n",
              "      <td>0.115422</td>\n",
              "      <td>0.103486</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.195743</td>\n",
              "      <td>0.174878</td>\n",
              "      <td>0.166112</td>\n",
              "      <td>0.178013</td>\n",
              "      <td>0.180804</td>\n",
              "      <td>0.127038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.999952</td>\n",
              "      <td>0.998940</td>\n",
              "      <td>0.993771</td>\n",
              "      <td>0.988274</td>\n",
              "      <td>0.999858</td>\n",
              "      <td>0.989579</td>\n",
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              "          const element = document.querySelector('#df-d3c83ad7-de8e-4262-93bc-a0f93a3c24a1');\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "peni_label=pd.DataFrame(peni_test['LABEL'])\n",
        "\n",
        "death_predict=pd.DataFrame(death_lstm.predict(peni_test_loader))\n",
        "glyco_predict=pd.DataFrame(glyco_lstm.predict(peni_test_loader))\n",
        "cepha_predict=pd.DataFrame(cepha_lstm.predict(peni_test_loader))\n",
        "peni_predict=pd.DataFrame(peni_lstm.predict(peni_test_loader))\n",
        "fluoro_predict=pd.DataFrame(fluoro_lstm.predict(peni_test_loader))\n",
        "macro_predict=pd.DataFrame(macro_lstm.predict(peni_test_loader))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pP5bZHfSdhZj",
        "outputId": "4c04cbce-5519-4a37-83e5-5202c827119f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10/10 [==============================] - 0s 3ms/step\n",
            "10/10 [==============================] - 0s 3ms/step\n",
            "10/10 [==============================] - 0s 4ms/step\n",
            "10/10 [==============================] - 0s 4ms/step\n",
            "10/10 [==============================] - 0s 4ms/step\n",
            "10/10 [==============================] - 0s 4ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "peni_total=pd.concat([peni_label,death_predict,glyco_predict,cepha_predict,peni_predict,fluoro_predict,macro_predict],axis=1)\n",
        "peni_total.columns=['label','death_model','glyco_model','cepha_model','peni_model','fluoro_model','macro_model']"
      ],
      "metadata": {
        "id": "fGo7HZm5ds_U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "peni_total.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "7d5igRVOd5NJ",
        "outputId": "565d1383-8f81-4527-8b6c-cb4d874358e4"
      },
      "execution_count": null,
      "outputs": [
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              "             label  death_model  glyco_model  cepha_model   peni_model  \\\n",
              "count  1213.000000  1213.000000  1213.000000  1213.000000  1213.000000   \n",
              "mean      0.206101     0.185862     0.207560     0.182194     0.188175   \n",
              "std       0.404670     0.161580     0.148215     0.141028     0.136850   \n",
              "min       0.000000     0.006523     0.013199     0.000043     0.000992   \n",
              "25%       0.000000     0.100963     0.147211     0.122726     0.134231   \n",
              "50%       0.000000     0.141116     0.163080     0.138982     0.144958   \n",
              "75%       0.000000     0.195743     0.182099     0.195352     0.174253   \n",
              "max       1.000000     0.999936     0.998780     0.994983     0.988192   \n",
              "\n",
              "       fluoro_model  macro_model  \n",
              "count   1213.000000  1213.000000  \n",
              "mean       0.174800     0.137230  \n",
              "std        0.147643     0.122632  \n",
              "min        0.032825     0.001846  \n",
              "25%        0.107627     0.092442  \n",
              "50%        0.117527     0.103486  \n",
              "75%        0.183134     0.127393  \n",
              "max        0.999842     0.994304  "
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              "      <th>label</th>\n",
              "      <th>death_model</th>\n",
              "      <th>glyco_model</th>\n",
              "      <th>cepha_model</th>\n",
              "      <th>peni_model</th>\n",
              "      <th>fluoro_model</th>\n",
              "      <th>macro_model</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>count</th>\n",
              "      <td>1213.000000</td>\n",
              "      <td>1213.000000</td>\n",
              "      <td>1213.000000</td>\n",
              "      <td>1213.000000</td>\n",
              "      <td>1213.000000</td>\n",
              "      <td>1213.000000</td>\n",
              "      <td>1213.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
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              "      <td>0.207560</td>\n",
              "      <td>0.182194</td>\n",
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              "      <th>std</th>\n",
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              "      <th>min</th>\n",
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              "      <td>0.000992</td>\n",
              "      <td>0.032825</td>\n",
              "      <td>0.001846</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.100963</td>\n",
              "      <td>0.147211</td>\n",
              "      <td>0.122726</td>\n",
              "      <td>0.134231</td>\n",
              "      <td>0.107627</td>\n",
              "      <td>0.092442</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.141116</td>\n",
              "      <td>0.163080</td>\n",
              "      <td>0.138982</td>\n",
              "      <td>0.144958</td>\n",
              "      <td>0.117527</td>\n",
              "      <td>0.103486</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.195743</td>\n",
              "      <td>0.182099</td>\n",
              "      <td>0.195352</td>\n",
              "      <td>0.174253</td>\n",
              "      <td>0.183134</td>\n",
              "      <td>0.127393</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.999936</td>\n",
              "      <td>0.998780</td>\n",
              "      <td>0.994983</td>\n",
              "      <td>0.988192</td>\n",
              "      <td>0.999842</td>\n",
              "      <td>0.994304</td>\n",
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        "diagnoses_icd = pd.merge(left = diagnoses_icd, right = sepsis[['subject_id','sepsis3']], how='inner', left_on='subject_id', right_on='subject_id')"
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        "test data로 test( 5개 모델간 차이가 있는지)"
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