AI_in_Materials_Science_Final_Project_-checkpoint.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CPECYqerElUi"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "import os\n",
        "import requests\n",
        "import subprocess\n",
        "import shutil\n",
        "from logging import getLogger, StreamHandler, INFO\n",
        "\n",
        "\n",
        "logger = getLogger(__name__)\n",
        "logger.addHandler(StreamHandler())\n",
        "logger.setLevel(INFO)\n",
        "\n",
        "\n",
        "def install(\n",
        "        chunk_size=4096,\n",
        "        file_name=\"Miniconda3-latest-Linux-x86_64.sh\",\n",
        "        url_base=\"https://repo.continuum.io/miniconda/\",\n",
        "        conda_path=os.path.expanduser(os.path.join(\"~\", \"miniconda\")),\n",
        "        rdkit_version=None,\n",
        "        add_python_path=True,\n",
        "        force=False):\n",
        "    \"\"\"install rdkit from miniconda\n",
        "    ```\n",
        "    import rdkit_installer\n",
        "    rdkit_installer.install()\n",
        "    ```\n",
        "    \"\"\"\n",
        "\n",
        "    python_path = os.path.join(\n",
        "        conda_path,\n",
        "        \"lib\",\n",
        "        \"python{0}.{1}\".format(*sys.version_info),\n",
        "        \"site-packages\",\n",
        "    )\n",
        "\n",
        "    if add_python_path and python_path not in sys.path:\n",
        "        logger.info(\"add {} to PYTHONPATH\".format(python_path))\n",
        "        sys.path.append(python_path)\n",
        "\n",
        "    if os.path.isdir(os.path.join(python_path, \"rdkit\")):\n",
        "        logger.info(\"rdkit is already installed\")\n",
        "        if not force:\n",
        "            return\n",
        "\n",
        "        logger.info(\"force re-install\")\n",
        "\n",
        "    url = url_base + file_name\n",
        "    python_version = \"{0}.{1}.{2}\".format(*sys.version_info)\n",
        "\n",
        "    logger.info(\"python version: {}\".format(python_version))\n",
        "\n",
        "    if os.path.isdir(conda_path):\n",
        "        logger.warning(\"remove current miniconda\")\n",
        "        shutil.rmtree(conda_path)\n",
        "    elif os.path.isfile(conda_path):\n",
        "        logger.warning(\"remove {}\".format(conda_path))\n",
        "        os.remove(conda_path)\n",
        "\n",
        "    logger.info('fetching installer from {}'.format(url))\n",
        "    res = requests.get(url, stream=True)\n",
        "    res.raise_for_status()\n",
        "    with open(file_name, 'wb') as f:\n",
        "        for chunk in res.iter_content(chunk_size):\n",
        "            f.write(chunk)\n",
        "    logger.info('done')\n",
        "\n",
        "    logger.info('installing miniconda to {}'.format(conda_path))\n",
        "    subprocess.check_call([\"bash\", file_name, \"-b\", \"-p\", conda_path])\n",
        "    logger.info('done')\n",
        "\n",
        "    logger.info(\"installing rdkit\")\n",
        "    subprocess.check_call([\n",
        "        os.path.join(conda_path, \"bin\", \"conda\"),\n",
        "        \"install\",\n",
        "        \"--yes\",\n",
        "        \"-c\", \"rdkit\",\n",
        "        \"python=={}\".format(python_version),\n",
        "        \"rdkit\" if rdkit_version is None else \"rdkit=={}\".format(rdkit_version)])\n",
        "    logger.info(\"done\")\n",
        "\n",
        "    import rdkit\n",
        "    logger.info(\"rdkit-{} installation finished!\".format(rdkit.__version__))\n",
        "\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    install()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install rdkit-pypi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UjXr4gW5S2ZI",
        "outputId": "af3c1162-23b9-437e-c31f-e260d391ffce"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting rdkit-pypi\n",
            "  Downloading rdkit_pypi-2022.3.5-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (36.8 MB)\n",
            "\u001b[K     |████████████████████████████████| 36.8 MB 23 kB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from rdkit-pypi) (1.21.6)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from rdkit-pypi) (7.1.2)\n",
            "Installing collected packages: rdkit-pypi\n",
            "Successfully installed rdkit-pypi-2022.3.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DljZ8gdvQvyB",
        "outputId": "f9289351-228c-4170-f82c-14d8bb534be0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.7/dist-packages (5.5.0)\n",
            "Collecting plotly\n",
            "  Downloading plotly-5.10.0-py2.py3-none-any.whl (15.2 MB)\n",
            "\u001b[K     |████████████████████████████████| 15.2 MB 27.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: tenacity>=6.2.0 in /usr/local/lib/python3.7/dist-packages (from plotly) (8.0.1)\n",
            "Installing collected packages: plotly\n",
            "  Attempting uninstall: plotly\n",
            "    Found existing installation: plotly 5.5.0\n",
            "    Uninstalling plotly-5.5.0:\n",
            "      Successfully uninstalled plotly-5.5.0\n",
            "Successfully installed plotly-5.10.0\n"
          ]
        }
      ],
      "source": [
        "!pip install plotly --upgrade"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jw5op3c6O1RK"
      },
      "source": [
        "#Kindly Run this Notebook On Google Colab to install Rdkit and to view interactive plotly plots. Do not open it in Jupyter Notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "0qmHjs3qPouC"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sb\n",
        "import rdkit\n",
        "import plotly.graph_objects as go\n",
        "import plotly.express as px\n",
        "from matplotlib.colors import ColorConverter\n",
        "from skimage import data\n",
        "from skimage.color import rgb2gray\n",
        "import numpy\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from matplotlib.colors import ColorConverter\n",
        "from skimage import data\n",
        "from skimage.color import rgb2gray\n",
        "from sklearn.model_selection import train_test_split\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense,MaxPool2D,Conv2D,Flatten\n",
        "from keras import optimizers\n",
        "from keras.layers import Dropout\n",
        "from rdkit.Chem import AllChem\n",
        "from rdkit import Chem\n",
        "from rdkit.Chem import Draw\n",
        "from rdkit.Chem import DataStructs\n",
        "from rdkit.Chem import Descriptors \n",
        "from rdkit import Chem\n",
        "from rdkit.Chem import rdMolDescriptors\n",
        "from rdkit.Chem.Draw import IPythonConsole\n",
        "from rdkit.Chem import Draw\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r5b5UnFvPUeM"
      },
      "source": [
        "## Reading and Cleaning Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "WsEUC0SyyON6",
        "outputId": "79b19a2d-3f79-4938-8403-20b6abfe6d2b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Nomenclature Name          Molecular Structure     Tg\n",
              "0         Poly(4-biphenyl acrylate)  C=CC(=O)Oc2ccc(c1ccccc1)cc2  383.0\n",
              "1              Poly(butyl acrylate)                CCCCOC(=O)C=C  219.0\n",
              "2          Poly(sec-butyl acrylate)              CC(OC(=O)C=C)CC  250.0\n",
              "3  Poly(2-tertbutylphenyl acrylate)    C=CC(=O)Oc1ccccc1C(C)(C)C  345.0\n",
              "4  Poly(4-tertbutylphenyl acrylate)  C=CC(=O)Oc1ccc(C(C)(C)C)cc1  344.0"
            ],
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              "      <div>\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>Nomenclature Name</th>\n",
              "      <th>Molecular Structure</th>\n",
              "      <th>Tg</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Poly(4-biphenyl acrylate)</td>\n",
              "      <td>C=CC(=O)Oc2ccc(c1ccccc1)cc2</td>\n",
              "      <td>383.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Poly(butyl acrylate)</td>\n",
              "      <td>CCCCOC(=O)C=C</td>\n",
              "      <td>219.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Poly(sec-butyl acrylate)</td>\n",
              "      <td>CC(OC(=O)C=C)CC</td>\n",
              "      <td>250.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Poly(2-tertbutylphenyl acrylate)</td>\n",
              "      <td>C=CC(=O)Oc1ccccc1C(C)(C)C</td>\n",
              "      <td>345.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Poly(4-tertbutylphenyl acrylate)</td>\n",
              "      <td>C=CC(=O)Oc1ccc(C(C)(C)C)cc1</td>\n",
              "      <td>344.0</td>\n",
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          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "df=pd.read_csv(\"molecules.csv\",encoding= 'unicode_escape')\n",
        "df=df.dropna(how=\"all\")\n",
        "df=df.iloc[:,:3]\n",
        "df[\"Nomenclature Name\"]=df.iloc[:,2]\n",
        "df[\"Molecular Structure\"]=df.iloc[:,1]\n",
        "df[\"Tg\"]=df.iloc[:,0]\n",
        "df=df[[\"Nomenclature Name\",\"Molecular Structure\",\"Tg\"]]\n",
        "df.head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MGH87xm8PYaF"
      },
      "source": [
        "##Exploratory Data Analysis\n",
        "### Classifying Polymers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CImGgCGbfyQ8",
        "outputId": "61b77278-3b04-46a6-9fb8-15a3a1a3d900"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Acrylates     149\n",
              "Others        101\n",
              "Styrenes       69\n",
              "Amides         15\n",
              "Ether           9\n",
              "Carbonates      8\n",
              "Name: Class_of_Polymer, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "def assign_groups(x):\n",
        "  if'acrylate'in x:\n",
        "    return 'Acrylates'\n",
        "  if'styrene' in x:\n",
        "    return 'Styrenes'\n",
        "  if 'amide' in x:\n",
        "    return 'Amides' \n",
        "  if 'ether' in x:\n",
        "    return 'Ether'\n",
        "  if 'carbonate' in x:\n",
        "    return 'Carbonates'\n",
        "  else:\n",
        "    return 'Others' \n",
        "df[\"Class_of_Polymer\"]=df[\"Nomenclature Name\"].apply(assign_groups)\n",
        "df[\"Class_of_Polymer\"].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "G5_VMbAmh5q2",
        "outputId": "6e1ae4c0-1c4b-42d4-e3f6-28e8276571a0"
      },
      "outputs": [
        {
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            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "labels=df.Class_of_Polymer.unique()\n",
        "count=df.Class_of_Polymer.value_counts().tolist()\n",
        "labels = labels\n",
        "values = count\n",
        "\n",
        "colors = ['gold', 'mediumturquoise', 'darkorange']\n",
        "\n",
        "fig = go.Figure(data=[go.Pie(labels=labels, values=values,pull=[0,0,0.1])])\n",
        "\n",
        "fig.update_layout( font=dict(\n",
        "        family=\"arial\",\n",
        "        size=24,\n",
        "        \n",
        "))\n",
        "\n",
        "\n",
        "fig.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "ZnyCCxT5f0Fj",
        "outputId": "dee5f8e0-fcdd-44ae-9dee-47a576eadd35"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "            observer.disconnect();\n",
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig = px.box(df, y=\"Tg\",color=\"Class_of_Polymer\",)\n",
        "fig.update_layout(\n",
        "    \n",
        "    yaxis_title=\"Tg\",\n",
        "    \n",
        "    xaxis_title=\"Class of Polymer\",\n",
        "    paper_bgcolor='rgb(243, 243, 243)',\n",
        "    plot_bgcolor='rgb(243, 243, 243)',\n",
        "    \n",
        "    \n",
        "    \n",
        "    font=dict(\n",
        "        family=\"arial\",\n",
        "        size=22,\n",
        "        color=\"black\"\n",
        "    )\n",
        "    )\n",
        "\n",
        "fig.update_traces(boxpoints='all', jitter=0)\n",
        "fig.update_layout(showlegend=True)\n",
        "\n",
        "\n",
        "     \n",
        "fig.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wv-KF-kASjSQ"
      },
      "source": [
        "### Distribution of Charge in Monomer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 481
        },
        "id": "aPaKHmsz2sef",
        "outputId": "55ab6d6a-2edc-44f1-ab93-4a012b8b1232"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 180x180 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "from rdkit.Chem.Draw import SimilarityMaps\n",
        "mol = Chem.MolFromSmiles('C=CC(=O)Oc1ccc(C(C)(C)C)cc1\t')\n",
        "AllChem.ComputeGasteigerCharges(mol)\n",
        "contribs = [mol.GetAtomWithIdx(i).GetDoubleProp('_GasteigerCharge') for i in range(mol.GetNumAtoms())]\n",
        "fig = SimilarityMaps.GetSimilarityMapFromWeights(mol, contribs, colorMap='jet', contourLines=10)\n",
        "AllChem.ComputeGasteigerCharges(mol)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2OspHDGEVSYK"
      },
      "source": [
        "## Visualization of Functional Groups"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "CmtIupA53ucv",
        "outputId": "836d1595-1273-456e-b643-fa3c83d00660"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=300x300 at 0x7F7765BC85D0>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "mol = Chem.MolFromSmiles('CC(OC(=O)C=C)CC')\n",
        "image=rdkit.Chem.Draw.MolToImage(mol, highlightAtoms=[1,2], highlightColor=ColorConverter().to_rgb(\"gray\"))\n",
        "image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "29OlI177THWi",
        "outputId": "9484e2bf-b783-4362-9994-07a3da740967"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=300x300 at 0x7F7765BDD950>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "mol=Chem.MolFromSmiles(\"C=CC(=O)Oc2ccc(c1ccccc1)cc2\")\n",
        "rdkit.Chem.Draw.MolToImage(mol)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xFCxepUqS-F6"
      },
      "source": [
        "##Model 1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SpxfBDgVTT9u"
      },
      "source": [
        "##Encoding SMILES TO Image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "nrDCqYj5XS81"
      },
      "outputs": [],
      "source": [
        "def encoding(df):\n",
        "  encoded=[]\n",
        "  elements=[['c'], ['n'], ['o'], ['C'], ['N'], ['F'], ['='], ['O'], \n",
        "            ['('], [')'], ['1'],['2'],['#'],['Cl'],['/'],['S'],['Br']]\n",
        "  enc = OneHotEncoder(handle_unknown='ignore')\n",
        "  enc.fit(elements)\n",
        "  enc.categories_\n",
        "  df1=df[\"Molecular Structure\"].apply(lambda x: pd.Series(list(x)))\n",
        "  for i in range(df1.shape[0]):\n",
        "    x=enc.transform(pd.DataFrame(df1.iloc[i,:]).dropna(how=\"all\").values).toarray()\n",
        "    y=np.zeros(((df1.shape[1]-x.shape[0]),len(elements)))\n",
        "    encoded.append(np.vstack((x,y)))\n",
        "  return encoded\n",
        "\n",
        "def encoded_generate_images(df):\n",
        "  listt=encoding(df)\n",
        "  plt.figure(figsize=(20,100))\n",
        "  for i in range(len(listt)):\n",
        "    plt.subplot(len(listt),5,i+1)\n",
        "    plt.imshow(listt[i])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "QM3twHvq8Y40",
        "outputId": "076a00c6-0a12-4339-de26-0b497c49ea94"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Nomenclature Name          Molecular Structure     Tg  \\\n",
              "0         Poly(4-biphenyl acrylate)  C=CC(=O)Oc2ccc(c1ccccc1)cc2  383.0   \n",
              "1              Poly(butyl acrylate)                CCCCOC(=O)C=C  219.0   \n",
              "2          Poly(sec-butyl acrylate)              CC(OC(=O)C=C)CC  250.0   \n",
              "3  Poly(2-tertbutylphenyl acrylate)    C=CC(=O)Oc1ccccc1C(C)(C)C  345.0   \n",
              "4  Poly(4-tertbutylphenyl acrylate)  C=CC(=O)Oc1ccc(C(C)(C)C)cc1  344.0   \n",
              "\n",
              "  Class_of_Polymer  \n",
              "0        Acrylates  \n",
              "1        Acrylates  \n",
              "2        Acrylates  \n",
              "3        Acrylates  \n",
              "4        Acrylates  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8f3b9c36-155e-4210-ba3c-6ac35c9f1b05\">\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",
              "    .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>Nomenclature Name</th>\n",
              "      <th>Molecular Structure</th>\n",
              "      <th>Tg</th>\n",
              "      <th>Class_of_Polymer</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Poly(4-biphenyl acrylate)</td>\n",
              "      <td>C=CC(=O)Oc2ccc(c1ccccc1)cc2</td>\n",
              "      <td>383.0</td>\n",
              "      <td>Acrylates</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Poly(butyl acrylate)</td>\n",
              "      <td>CCCCOC(=O)C=C</td>\n",
              "      <td>219.0</td>\n",
              "      <td>Acrylates</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Poly(sec-butyl acrylate)</td>\n",
              "      <td>CC(OC(=O)C=C)CC</td>\n",
              "      <td>250.0</td>\n",
              "      <td>Acrylates</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Poly(2-tertbutylphenyl acrylate)</td>\n",
              "      <td>C=CC(=O)Oc1ccccc1C(C)(C)C</td>\n",
              "      <td>345.0</td>\n",
              "      <td>Acrylates</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Poly(4-tertbutylphenyl acrylate)</td>\n",
              "      <td>C=CC(=O)Oc1ccc(C(C)(C)C)cc1</td>\n",
              "      <td>344.0</td>\n",
              "      <td>Acrylates</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8f3b9c36-155e-4210-ba3c-6ac35c9f1b05')\"\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",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      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",
              "    }\n",
              "\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",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8f3b9c36-155e-4210-ba3c-6ac35c9f1b05 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-8f3b9c36-155e-4210-ba3c-6ac35c9f1b05');\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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x7200 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "encoded_generate_images(df.head(10))\n",
        "df.head(5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q2eixWljTf_i"
      },
      "source": [
        "## CNN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "id": "lmNFyfcqtW6R"
      },
      "outputs": [],
      "source": [
        "X=encoding(df)\n",
        "X=np.array(X)\n",
        "X=X.reshape(351,65,17,1)\n",
        "X.shape\n",
        "Y=df[\"Tg\"].values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rcX41hDvxcQE",
        "outputId": "3902af31-d142-4916-a462-6c46c4ffd548"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/180\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/adam.py:105: UserWarning:\n",
            "\n",
            "The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5/5 [==============================] - 1s 47ms/step - loss: 402.3169 - val_loss: 227.9659\n",
            "Epoch 2/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 274.5190 - val_loss: 305.5340\n",
            "Epoch 3/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 298.1152 - val_loss: 305.8198\n",
            "Epoch 4/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 298.2872 - val_loss: 305.8651\n",
            "Epoch 5/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 298.2886 - val_loss: 305.7322\n",
            "Epoch 6/180\n",
            "5/5 [==============================] - 0s 12ms/step - loss: 298.0751 - val_loss: 305.3402\n",
            "Epoch 7/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 297.6104 - val_loss: 304.7692\n",
            "Epoch 8/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 297.0110 - val_loss: 304.0763\n",
            "Epoch 9/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 296.2631 - val_loss: 303.2732\n",
            "Epoch 10/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 295.4360 - val_loss: 302.3851\n",
            "Epoch 11/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 294.5269 - val_loss: 301.4326\n",
            "Epoch 12/180\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 293.5418 - val_loss: 300.4059\n",
            "Epoch 13/180\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 292.5072 - val_loss: 299.2985\n",
            "Epoch 14/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 291.3549 - val_loss: 298.0976\n",
            "Epoch 15/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 290.1375 - val_loss: 296.7844\n",
            "Epoch 16/180\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 288.7670 - val_loss: 295.3333\n",
            "Epoch 17/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 287.3160 - val_loss: 293.7112\n",
            "Epoch 18/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 285.5165 - val_loss: 291.8722\n",
            "Epoch 19/180\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 283.6439 - val_loss: 289.7530\n",
            "Epoch 20/180\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 281.4232 - val_loss: 287.2702\n",
            "Epoch 21/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 278.8483 - val_loss: 284.3292\n",
            "Epoch 22/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 275.8496 - val_loss: 280.8011\n",
            "Epoch 23/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 271.8809 - val_loss: 276.4831\n",
            "Epoch 24/180\n",
            "5/5 [==============================] - 0s 12ms/step - loss: 267.3942 - val_loss: 271.1062\n",
            "Epoch 25/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 261.4873 - val_loss: 264.3194\n",
            "Epoch 26/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 253.7964 - val_loss: 255.5975\n",
            "Epoch 27/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 244.6611 - val_loss: 244.2737\n",
            "Epoch 28/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 232.6152 - val_loss: 229.2708\n",
            "Epoch 29/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 216.2116 - val_loss: 209.1829\n",
            "Epoch 30/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 192.4410 - val_loss: 181.7685\n",
            "Epoch 31/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 163.6119 - val_loss: 143.8535\n",
            "Epoch 32/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 120.9021 - val_loss: 97.4031\n",
            "Epoch 33/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 79.8297 - val_loss: 71.9200\n",
            "Epoch 34/180\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 64.9024 - val_loss: 64.0510\n",
            "Epoch 35/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 72.3269 - val_loss: 63.9986\n",
            "Epoch 36/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 75.1586 - val_loss: 63.9489\n",
            "Epoch 37/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 66.9695 - val_loss: 66.2076\n",
            "Epoch 38/180\n",
            "5/5 [==============================] - 0s 12ms/step - loss: 64.9702 - val_loss: 70.0493\n",
            "Epoch 39/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 65.1729 - val_loss: 71.0208\n",
            "Epoch 40/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 67.3686 - val_loss: 70.2090\n",
            "Epoch 41/180\n",
            "5/5 [==============================] - 0s 15ms/step - loss: 65.4656 - val_loss: 68.5725\n",
            "Epoch 42/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 62.7157 - val_loss: 67.1899\n",
            "Epoch 43/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 65.5089 - val_loss: 66.9088\n",
            "Epoch 44/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 66.6060 - val_loss: 67.0461\n",
            "Epoch 45/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 67.4109 - val_loss: 66.8847\n",
            "Epoch 46/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 64.8659 - val_loss: 66.7065\n",
            "Epoch 47/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 65.6366 - val_loss: 66.2863\n",
            "Epoch 48/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 68.7702 - val_loss: 66.6007\n",
            "Epoch 49/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 68.0892 - val_loss: 68.2461\n",
            "Epoch 50/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 67.2701 - val_loss: 68.0551\n",
            "Epoch 51/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 66.0056 - val_loss: 66.9473\n",
            "Epoch 52/180\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 67.3793 - val_loss: 65.7955\n",
            "Epoch 53/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 66.8075 - val_loss: 66.3076\n",
            "Epoch 54/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 65.8928 - val_loss: 68.3310\n",
            "Epoch 55/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 65.0995 - val_loss: 70.6071\n",
            "Epoch 56/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 68.9751 - val_loss: 70.7056\n",
            "Epoch 57/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 63.9809 - val_loss: 69.3029\n",
            "Epoch 58/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 65.7241 - val_loss: 67.1111\n",
            "Epoch 59/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 64.0365 - val_loss: 65.5385\n",
            "Epoch 60/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 64.0723 - val_loss: 64.4838\n",
            "Epoch 61/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 68.5802 - val_loss: 66.2211\n",
            "Epoch 62/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 64.2222 - val_loss: 67.6984\n",
            "Epoch 63/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 63.3862 - val_loss: 68.2950\n",
            "Epoch 64/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 65.4485 - val_loss: 67.9848\n",
            "Epoch 65/180\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 64.1639 - val_loss: 66.9936\n",
            "Epoch 66/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 61.3849 - val_loss: 66.2181\n",
            "Epoch 67/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 64.2489 - val_loss: 64.8155\n",
            "Epoch 68/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 64.3154 - val_loss: 66.2974\n",
            "Epoch 69/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 63.5195 - val_loss: 66.2219\n",
            "Epoch 70/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 65.1238 - val_loss: 66.1205\n",
            "Epoch 71/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 65.3484 - val_loss: 66.2522\n",
            "Epoch 72/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 67.1227 - val_loss: 67.5321\n",
            "Epoch 73/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 62.5350 - val_loss: 67.9472\n",
            "Epoch 74/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 65.0433 - val_loss: 66.0681\n",
            "Epoch 75/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 65.5134 - val_loss: 65.0227\n",
            "Epoch 76/180\n",
            "5/5 [==============================] - 0s 12ms/step - loss: 68.4395 - val_loss: 65.6100\n",
            "Epoch 77/180\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 68.0637 - val_loss: 67.0672\n",
            "Epoch 78/180\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 63.3051 - val_loss: 67.4856\n",
            "Epoch 79/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 65.9269 - val_loss: 67.2726\n",
            "Epoch 80/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 65.7142 - val_loss: 67.6395\n",
            "Epoch 81/180\n",
            "5/5 [==============================] - 0s 12ms/step - loss: 62.8901 - val_loss: 67.7111\n",
            "Epoch 82/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 65.3176 - val_loss: 66.5250\n",
            "Epoch 83/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 66.2542 - val_loss: 65.6110\n",
            "Epoch 84/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 65.7005 - val_loss: 65.1293\n",
            "Epoch 85/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 66.0665 - val_loss: 67.0121\n",
            "Epoch 86/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 65.9396 - val_loss: 66.7627\n",
            "Epoch 87/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 61.4925 - val_loss: 65.9572\n",
            "Epoch 88/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 62.5655 - val_loss: 65.9780\n",
            "Epoch 89/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 63.8427 - val_loss: 66.9992\n",
            "Epoch 90/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 63.3805 - val_loss: 66.6655\n",
            "Epoch 91/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 67.0479 - val_loss: 66.0289\n",
            "Epoch 92/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 63.5397 - val_loss: 66.8995\n",
            "Epoch 93/180\n",
            "5/5 [==============================] - 0s 12ms/step - loss: 63.7983 - val_loss: 68.3704\n",
            "Epoch 94/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 64.7181 - val_loss: 66.6952\n",
            "Epoch 95/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 65.4897 - val_loss: 64.4722\n",
            "Epoch 96/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 64.9646 - val_loss: 65.3812\n",
            "Epoch 97/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 64.8526 - val_loss: 66.2325\n",
            "Epoch 98/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 64.0105 - val_loss: 66.1439\n",
            "Epoch 99/180\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 62.9038 - val_loss: 65.1189\n",
            "Epoch 100/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 64.5452 - val_loss: 64.7562\n",
            "Epoch 101/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 66.4597 - val_loss: 66.1061\n",
            "Epoch 102/180\n",
            "5/5 [==============================] - 0s 12ms/step - loss: 64.3406 - val_loss: 69.4393\n",
            "Epoch 103/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 64.7783 - val_loss: 71.1879\n",
            "Epoch 104/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 66.2592 - val_loss: 69.8542\n",
            "Epoch 105/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 61.7692 - val_loss: 66.4532\n",
            "Epoch 106/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 63.4307 - val_loss: 64.4263\n",
            "Epoch 107/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 62.9112 - val_loss: 65.8888\n",
            "Epoch 108/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 63.9112 - val_loss: 66.7116\n",
            "Epoch 109/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 64.7672 - val_loss: 66.1410\n",
            "Epoch 110/180\n",
            "5/5 [==============================] - 0s 12ms/step - loss: 63.9686 - val_loss: 65.2602\n",
            "Epoch 111/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 61.6079 - val_loss: 67.2708\n",
            "Epoch 112/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 62.8101 - val_loss: 67.7608\n",
            "Epoch 113/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 63.8802 - val_loss: 66.2822\n",
            "Epoch 114/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 62.7681 - val_loss: 64.5387\n",
            "Epoch 115/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 69.2544 - val_loss: 65.8038\n",
            "Epoch 116/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 64.9431 - val_loss: 68.6526\n",
            "Epoch 117/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 65.5057 - val_loss: 68.9910\n",
            "Epoch 118/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 62.4237 - val_loss: 69.1098\n",
            "Epoch 119/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 64.8386 - val_loss: 65.3730\n",
            "Epoch 120/180\n",
            "5/5 [==============================] - 0s 12ms/step - loss: 64.4970 - val_loss: 63.3493\n",
            "Epoch 121/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 67.7160 - val_loss: 64.2367\n",
            "Epoch 122/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 62.6439 - val_loss: 67.2706\n",
            "Epoch 123/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 64.9263 - val_loss: 69.9123\n",
            "Epoch 124/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 65.9371 - val_loss: 69.8985\n",
            "Epoch 125/180\n",
            "5/5 [==============================] - 0s 12ms/step - loss: 63.8455 - val_loss: 66.4962\n",
            "Epoch 126/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 64.2545 - val_loss: 64.9687\n",
            "Epoch 127/180\n",
            "5/5 [==============================] - 0s 12ms/step - loss: 62.8681 - val_loss: 65.1993\n",
            "Epoch 128/180\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 62.5357 - val_loss: 65.9525\n",
            "Epoch 129/180\n",
            "5/5 [==============================] - 0s 14ms/step - loss: 63.3478 - val_loss: 68.3386\n",
            "Epoch 130/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 65.3985 - val_loss: 70.0959\n",
            "Epoch 131/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 63.6263 - val_loss: 68.4671\n",
            "Epoch 132/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 66.3113 - val_loss: 65.6103\n",
            "Epoch 133/180\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 62.9038 - val_loss: 65.6230\n",
            "Epoch 134/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 62.4033 - val_loss: 64.3299\n",
            "Epoch 135/180\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 60.9456 - val_loss: 66.9474\n",
            "Epoch 136/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 66.0616 - val_loss: 69.4082\n",
            "Epoch 137/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 64.9028 - val_loss: 70.1798\n",
            "Epoch 138/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 65.8639 - val_loss: 66.7711\n",
            "Epoch 139/180\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 62.5380 - val_loss: 65.1148\n",
            "Epoch 140/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 67.0315 - val_loss: 64.2245\n",
            "Epoch 141/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 65.1148 - val_loss: 66.7498\n",
            "Epoch 142/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 63.3279 - val_loss: 67.3309\n",
            "Epoch 143/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 64.7789 - val_loss: 67.2572\n",
            "Epoch 144/180\n",
            "5/5 [==============================] - 0s 17ms/step - loss: 65.4702 - val_loss: 66.2377\n",
            "Epoch 145/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 62.8144 - val_loss: 64.9534\n",
            "Epoch 146/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 63.1001 - val_loss: 64.7227\n",
            "Epoch 147/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 62.5752 - val_loss: 65.8844\n",
            "Epoch 148/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 63.6839 - val_loss: 66.7300\n",
            "Epoch 149/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 60.2911 - val_loss: 65.6358\n",
            "Epoch 150/180\n",
            "5/5 [==============================] - 0s 14ms/step - loss: 64.5758 - val_loss: 65.3535\n",
            "Epoch 151/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 63.4599 - val_loss: 65.7163\n",
            "Epoch 152/180\n",
            "5/5 [==============================] - 0s 12ms/step - loss: 62.4484 - val_loss: 66.3568\n",
            "Epoch 153/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 63.9968 - val_loss: 66.0645\n",
            "Epoch 154/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 64.1832 - val_loss: 66.6465\n",
            "Epoch 155/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 61.8326 - val_loss: 68.5393\n",
            "Epoch 156/180\n",
            "5/5 [==============================] - 0s 12ms/step - loss: 63.3771 - val_loss: 70.0895\n",
            "Epoch 157/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 63.7096 - val_loss: 69.0101\n",
            "Epoch 158/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 65.2320 - val_loss: 66.4220\n",
            "Epoch 159/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 63.1433 - val_loss: 66.1460\n",
            "Epoch 160/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 62.8553 - val_loss: 67.4066\n",
            "Epoch 161/180\n",
            "5/5 [==============================] - 0s 12ms/step - loss: 65.4556 - val_loss: 68.1182\n",
            "Epoch 162/180\n",
            "5/5 [==============================] - 0s 12ms/step - loss: 63.3783 - val_loss: 68.4933\n",
            "Epoch 163/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 64.7674 - val_loss: 67.8027\n",
            "Epoch 164/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 62.5983 - val_loss: 65.5546\n",
            "Epoch 165/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 62.7750 - val_loss: 64.3212\n",
            "Epoch 166/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 61.3399 - val_loss: 65.0723\n",
            "Epoch 167/180\n",
            "5/5 [==============================] - 0s 12ms/step - loss: 63.8076 - val_loss: 67.2148\n",
            "Epoch 168/180\n",
            "5/5 [==============================] - 0s 12ms/step - loss: 62.3890 - val_loss: 68.5706\n",
            "Epoch 169/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 64.7539 - val_loss: 66.5308\n",
            "Epoch 170/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 64.0458 - val_loss: 64.8282\n",
            "Epoch 171/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 63.6288 - val_loss: 65.8786\n",
            "Epoch 172/180\n",
            "5/5 [==============================] - 0s 10ms/step - loss: 63.5915 - val_loss: 65.6392\n",
            "Epoch 173/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 61.5297 - val_loss: 64.8936\n",
            "Epoch 174/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 60.7134 - val_loss: 63.7930\n",
            "Epoch 175/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 63.2493 - val_loss: 65.2170\n",
            "Epoch 176/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 60.9147 - val_loss: 65.9199\n",
            "Epoch 177/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 63.5449 - val_loss: 67.7224\n",
            "Epoch 178/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 60.8778 - val_loss: 66.7061\n",
            "Epoch 179/180\n",
            "5/5 [==============================] - 0s 11ms/step - loss: 61.8178 - val_loss: 65.3926\n",
            "Epoch 180/180\n",
            "5/5 [==============================] - 0s 12ms/step - loss: 63.0161 - val_loss: 65.8536\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.15, random_state=0)\n",
        "from keras import optimizers\n",
        "model=Sequential()\n",
        "model.add(Conv2D(64,(5,5), activation=\"relu\",kernel_regularizer=\"l2\",input_shape=(65,17,1)))\n",
        "model.add(Conv2D(32,(3,3),kernel_regularizer=\"l2\", activation=\"relu\"))\n",
        "model.add(MaxPool2D(pool_size=(3,3)))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(32,activation=\"relu\",kernel_regularizer=\"l2\"))\n",
        "model.add(Dropout(0.1))\n",
        "model.add(Dense(10))\n",
        "model.add(Dense(1))\n",
        "optimizer=tf.keras.optimizers.Adam(lr=0.03)\n",
        "  \n",
        "model.compile(optimizer=optimizer,loss=\"mean_absolute_error\",)\n",
        "Model_rms_1=model.fit(x=X_train,y=y_train,epochs=180,batch_size=64,validation_split=0.1)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "anBQTj5Y62oP",
        "outputId": "6f43d7a2-c992-41fd-f57b-12b68cf6cf60"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Absolute Error on Training Set =  60.88095871074088\n",
            "Mean Absolute Error on Test Set =  58.567690867298055\n"
          ]
        }
      ],
      "source": [
        "y_predtrain=model.predict(X_train)\n",
        "y_predtest=model.predict(X_test)\n",
        "MAE_test=abs(y_predtest.reshape(y_test.shape)-y_test).sum()/y_test.shape\n",
        "MAE_train=abs(y_predtrain.reshape(y_train.shape)-y_train).sum()/y_train.shape\n",
        "print(\"Mean Absolute Error on Training Set = \",MAE_train.item())\n",
        "print(\"Mean Absolute Error on Test Set = \",MAE_test.item())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "8XU9IvIREViR",
        "outputId": "bc5fcf6c-c255-49c1-c60b-a759ae3dacf1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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      ],
      "source": [
        "fig = go.Figure()\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "    x=y_predtest.reshape(y_test.shape), y=y_test.reshape(y_test.shape),\n",
        "    name='sin',\n",
        "    mode='markers',\n",
        "    marker_color='rgba(100, 10, 90, .8)'))\n",
        "\n",
        "fig.update_traces(mode='markers', marker_line_width=2, marker_size=13)\n",
        "fig.update_layout(\n",
        "    \n",
        "    yaxis_title=\"Exprimental Tg\",\n",
        "    xaxis_title=\"Predicted Tg\",\n",
        "    title=\"Test Set\",\n",
        "    \n",
        "    font=dict(\n",
        "        family=\"arial\",\n",
        "        size=24,\n",
        "        color=\"black\"\n",
        "    )\n",
        "    )\n",
        "                  \n",
        "fig.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
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          "height": 542
        },
        "id": "xeLimd_m6DR1",
        "outputId": "33d8e4aa-9e93-47f2-dbcc-0f0ec65d8aa0"
      },
      "outputs": [
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              "                            \n",
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              "            observer.disconnect();\n",
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              "\n",
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              "</body>\n",
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            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig = go.Figure()\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "    x=y_predtrain.reshape(y_train.shape), y=y_train.reshape(y_train.shape),\n",
        "    name='sin',\n",
        "    mode='markers',\n",
        "    marker_color='rgba(100, 10, 90, .8)'))\n",
        "\n",
        "fig.update_traces(mode='markers', marker_line_width=2, marker_size=13)\n",
        "fig.update_layout(\n",
        "    \n",
        "    yaxis_title=\"Exprimental Tg\",\n",
        "    xaxis_title=\"Predicted Tg\",\n",
        "    title=\"Train Set\",\n",
        "    \n",
        "    font=dict(\n",
        "        family=\"arial\",\n",
        "        size=24,\n",
        "        color=\"black\"\n",
        "    )\n",
        "    )\n",
        "                  \n",
        "fig.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bXUMZCWZjMOV",
        "outputId": "b47fd9ea-58fc-4d71-d55e-66bc7a55c927"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Relative Train Error in % =  [21.37471029]\n"
          ]
        }
      ],
      "source": [
        "y_p=y_predtrain.reshape(y_train.shape)\n",
        "y_a=y_train.reshape(y_train.shape)\n",
        "print(\"Relative Train Error in % = \",(((abs(y_p-y_a)/y_a)*100).sum())/y_train.shape)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GL7pLz4djZ4Z",
        "outputId": "919bb023-daec-47ca-a555-80f042de04b0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AVG % Unseen Test Error= [19.50640394]\n"
          ]
        }
      ],
      "source": [
        "y_p=y_predtest.reshape(y_test.shape)\n",
        "y_a=y_test.reshape(y_test.shape)\n",
        "print(\"AVG % Unseen Test Error=\",(((abs(y_p-y_a)/y_a)*100).sum())/y_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "prcPx9tWF3PX",
        "outputId": "c5093af8-7e0b-4715-e0ea-803c6475ff65"
      },
      "outputs": [
        {
          "output_type": "display_data",
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      ],
      "source": [
        "\n",
        "\n",
        "\n",
        "\n",
        "fig = go.Figure()\n",
        "\n",
        "fig.add_trace(go.Scatter(\n",
        "    x=np.arange(1,201), y=Model_rms_1.history[\"loss\"],\n",
        "    name='sin',\n",
        "    mode='markers',\n",
        "    marker_color='rgba(100, 10, 90, .8)'))\n",
        "\n",
        "fig.update_traces(mode='markers', marker_line_width=2, marker_size=13)\n",
        "fig.update_layout(\n",
        "    \n",
        "    yaxis_title=\"Train Error\",\n",
        "    xaxis_title=\"Number of Epochs\",\n",
        "    \n",
        "    font=dict(\n",
        "        family=\"arial\",\n",
        "        size=24,\n",
        "        color=\"black\"\n",
        "    )\n",
        "    )\n",
        "                  \n",
        "fig.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        },
        "id": "iGY3gAAVIjhv",
        "outputId": "c6e3e2db-32d3-4f8e-94b8-a3aff101abfb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x504 with 1 Axes>"
            ],
            "image/png": 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AkCFD2LlzJ3PmzGHdunUkJSUxceJEiupZsJk4cSLLli1jwIABLF68mLfffrtezwmIi4sDIDo6mpKSkgY9KykpiY8++og33niDhQsXsnTpUv785z+zYsUK1qxZw/Lly5k9ezaffPJJg0KmKpctnSqXIiIiItKCvPvuu2RmZob8ee3NN1ldWEhepTCVV1LCqsJCXnvzzWr7VjclNiAxMZERI0Zwww03lFct9+/fT5s2bWjfvj27d+/mtddeq/EZ55xzDsuWLaOwsJADBw6wfPmRt/QOHDhA586dOXz4MM8++2z59bZt23LgwIEqz+rTpw+ZmZls27YNgL/+9a+ce+65Nf/hHcWwYcP45z//SU5ODqWlpTz33HOce+655OTkUFZWxlVXXcVvfvMbNm7cSFlZGV999RUjRozgkUceIS8vj/z8/AZ9viqXLZ0W9BERERGRCJGens5td97JrLlzOT8xka6tWvHl4cO8lZ/P7XfeSXp6eoOeP27cOK644ory6bEDBgxg0KBBnHzyyZx44omceeaZNfYfPHgw1157LQMGDOCYY45h6NCh5fcefPBBTjvtNDp16sRpp51WHiivu+46Jk2axPz588sX8gGIj4/n6aef5pprrqGkpIShQ4dy66231un7rFy5skLF9oUXXuDhhx9mxIgROOe4+OKLueyyy/joo4/42c9+RllZGQAPPfQQpaWlXH/99eTl5eGcY+rUqfVeETfAnHMNesAPTVpamgvsdxMWsrLgxBN9v//Hf0DQ/yUREREREQkHn332Gaecckqt22dkZPDkggXs3LaN7iedxOQpUxocLKV+Qv3dmdkG51xa5baqXLZ0qlyKiIiISIRJT09XmGyB9M5lS6cFfUREREREJAwoXLZ0qlyKiIiISAug1/Fanrr+nSlctnQxMb4fULgUERERkbAUHx9Pbm6uAmYL4pwjNzeX+Gr2HQ1F71xGgtat4cABTYsVERERkbDUpUsXsrKyyM7O9nooUgfx8fFH3T80mMJlJIiP94VLVS5FREREJAy1atWK7t27ez0MaWKaFhsJWrf2HVW5FBERERERjyhcRoLAPGhVLkVERERExCMKl5EgEC5VuRQREREREY8oXEaCwLRYVS5FRERERMQjCpeRIFC5LC6GsjJvxyIiIiIiIj9ICpeRIFC5BFUvRURERETEEwqXkSB4Y1OFSxERERER8YDCZSQIDpda1EdERERERDygcBkJNC1WREREREQ8pnAZCVS5FBERERERjylcRgJVLkVERERExGMKl5FAC/qIiIiIiIjHFC4jQXDlUtNiRURERETEAwqXkUCVSxERERER8ZjCZSTQgj4iIiIiIuIxhctIoAV9RERERETEYwqXkUCVSxERERER8ZjCZSRQ5VJERERERDymcBkJtKCPiIiIiIh4TOEyEmgrEhERERER8ZjCZSRQ5VJERERERDymcBkJtKCPiIiIiIh4TOEyEmhBHxERERER8ZjCZSTQtFgREREREfGYwmUk0II+IiIiIiLisbAIl2b2iJmtNLOvzKzQzPaY2YdmNtPMkiu1TTUzV8PPkho+Z4KZfWBm+WaWZ2Zvm9mYpv+GTUyVSxERERER8ViM1wPwmw5sBN4EvgfaAOnALOBmM0t3zn1Vqc9HwLIQz9oc6gPMbA5wF5AFLAJigeuA5WY2xTn3RCN8D29oQR8REREREfFYuITLds65KiU3M5sN3AfcC/xnpdubnHOzavNwMzsDX7DcDgx1zu31X38M2ADMMbNXnHOZ9f4GXoqNBTNwTpVLERERERHxRFhMiw0VLP2W+o+9GvgRt/qPswPB0v+5mcDvgTjgZw38DO+YHaleqnIpIiIiIiIeCItwWYNL/MePQ9w73sxuMbP7/Mf+NTznPP/x9RD3XqvUpmUKLOqjyqWIiIiIiHggXKbFAmBmM4BEoD2QBpyFL1g+HKL5Bf6f4P5vAxOcc18GXWsDnADkO+e+DfGcrf5j7xrGdTNwM0DXrl1r+W2aWaByqXApIiIiIiIeCKtwCcwAjg06fx2Y6JzLDrpWADyIbzGfHf5r/fEt/jMCWGlmA51zB/332vuPedV8ZuB6h+oG5Zz7I/BHgLS0NFerb9LcApVLTYsVEREREREPhNW0WOfccc45A44DrgR6AB+a2eCgNt87537pnNvonNvn/1kDjALeB04CbvJi/J5S5VJERERERDwUVuEywDm32zn3Er7AmAz8Ty36lABP+U/PCboVqEy2J7TA9X31GGr40II+IiIiIiLiobAMlwHOuV3Av4FTzSylFl0C02fbBD3jIPA1kGhmnUP0CaxE+0VDxuq54AV9XHjO3BURERERkcgV1uHS73j/sbQWbdP9xx2Vrq/yH0eH6HNhpTYtU6By6RwUF3s7FhERERER+cHxPFyaWW8zqzJl1cyizGw2cAywNrA/pZkNNrMq4zazkcB0/+kzlW4v9B/vN7OkoD6pwG3AIeDpBn4VbwUql6D3LkVEREREpNmFw2qxFwEPmdm7wE4gF9+KsefiW9DnO2BSUPu5QC8zWwtk+a/158g+lQ8459YGf4Bzbq2ZzQXuBD42sxeBWOBaoCMwxTmX2QTfrfkEKpfgC5ftq3vFVEREREREpPGFQ7h8C98Kr2cBg/BtCXIQ3zuQfwXmO+f2BLX/K3AFMBTflNZWwG5gKfCEc+6dUB/inLvLzD7BV6m8GSgDNgKPOedeaYLv1byCK5da1EdERERERJqZ5+HSObcZuL0O7f8E/Kmen7UYWFyfvmGvcuVSRERERESkGXn+zqU0kuBwqcqliIiIiIg0M4XLSKEFfURERERExEMKl5FClUsREREREfGQwmWkUOVSREREREQ8pHAZKbSgj4iIiIiIeEjhMlJoWqyIiIiIiHhI4TJSaFqsiIiIiIh4SOEyUqhyKSIiIiIiHlK4jBSqXIqIiIiIiIcULiOFFvQREREREREPKVxGiuDKpabFioiIiIhIM1O4jBSqXIqIiIiIiIcULiOFFvQREREREREPKVxGCi3oIyIiIiIiHlK4jBSqXIqIiIiIiIcULiOFKpciIiIiIuIhhctIoQV9RERERETEQwqXkULTYkVERERExEMKl5EiKgpiY32/q3IpIiIiIiLNTOEykgSql6pcioiIiIhIM1O4jCSBRX1UuRQRERERkWamcBlJVLkUERERERGPKFxGElUuRURERETEIwqXkSRQuVS4FBERERGRZqZwGUk0LVZERERERDyicBlJAtNiS0p8PyIiIiIiIs1E4TKSBCqXoKmxIiIiIiLSrBQuI0mgcgkKlyIiIiIi0qwULiNJcOVS712KiIiIiEgzUriMJKpcioiIiIiIRxQuI4neuRQREREREY/EeD0AaUTx8WQAT5qx47rr6DFwIGePGME7q1ezY+tWevTqVeV88pQpADy5YEGFa+np6d5+FxERERERaVHMOef1GFqUtLQ0t379eq+HEdKD557L7997j5HJyXSLj+e1PXvYXljImJQUUuPiqpzvOnyYFXv2EGXGhR060C02ll2HD/NaXh49evakdXx8tYFU4VNERERE5IfJzDY459IqX1flMkJkZGTw+w8+YGbPnrSPiWFrQQFfFRXxSDXnAHEFBbxeWspvevQov7YtOxsrLqbX7t10j4/ntc8+46Xnn2dMSgpnx8WxKyuLy5cv59KxYzlUWKjAKSIiIiIigMJlxHhywQJGtm9fHhJX79vHjzt2rPY8cG100LWtBQWs2ru3PGyGCqR9gQPFxSxZvJhLOnXi7NhYdmVlcdGyZRWqnQqbIiIiIiI/LGGxoI+ZPWJmK83sKzMrNLM9Zvahmc00s+Rq+pxhZq/62xaa2cdmdoeZRdfwOWPM7G0zyzOzfDN738wmNN03az47tm6lW1xc+XlOcTHdghb4qXwe6lptAunWggLW5uXxSM+eXNyxI30TEzlcUlJe7Tx7925KVq/mopEjSevfn7OHDWPC+PFkZGQ01VcXEREREZEwEBbhEpgOtAHeBOYBzwIlwCzgYzM7MbixmV0GrAHOAV4CngBigceBJaE+wMxuB5bjK749AywCjgcWm9mcRv9GzaxHr17sKi4uP0+JjWVX0Iqxlc9DXatNIK0cOIOrnZekpChsioiIiIj8QIVLuGznnEt3zt3gnPt/zrkpzrmhwG/xBcB7Aw3NrB2+YFgKDHfO3eicuxsYCPwLuNrMrgt+uJmlAnOAPUCac+4259x0oD+wHbjLzE5v8m/ZhCZPmcLK/fvJKykBYESHDryxZ0+154Frrwddq00gPVq1s7Zh8/JRo7j5ppuYMH68AqeIiIiISAQIi3DpnKtuU8al/mOvoGtXA52AJc658mVb/c/4hf90cqXn3ADEAU845zKD+uzFF2ABbq3X4MNEeno6t11yCfdt28bLOTkUlZVxYnw8P9++nVdyc6ucf5KfzxeHDlESHc39X37Jir176RoXx6tHCaQpsbFk1lDtrE3YvDgpiWGxsSxZvJiSVatU3RQRERERiQDhvqDPJf7jx0HXzvMfXw/Rfg1QAJxhZnHOuUO16PNapTYt1gMTJ7L++ed5KTubGDN+fNFFjO7blxeXvMCbOTl0SDmWK84+g3+9s9Z/nsLvnvg1AI/Mfph9OTm0SmzHfbt2Mbp9B1Lj4+gSF8fPt29nTHIyqfHxtDLjldxczu7QgfYxMeXVzb6JiYAvbJ7Rrl35mI723mZ1q9RqkSARERERkZYlrMKlmc0AEoH2QBpwFr5g+XBQsz7+4xeV+zvnSsxsJ3Aq0AP4rBZ9vjWzg0AXM0twzhU0xnfxRHw8CUCxcxQ7R2qvHzF//rMUFk7FuYHk5D/OtszXgJ8Dg9hTsInJk/8fZq0oKbkL5wbie231Pf4v+3hirJASV0qx28f/ZZ/gP0+G6C+4N3MXF3boQNe4OF7OyeWsWoZNqLm6qbApIiIiItIyhVW4BGYAxwadvw5MdM5lB11r7z/mVfOMwPUOdezTxt+uSrg0s5uBmwG6du1a3di917o1+4NOFy58lsLCDfj+SDOAT4DNBP6Iy8oSKStrDayv1GYLxe5Yil0GMBbY7j/3P7jkXop5MiiAWnl182hhE45e3axt2Lx8+XJuu/NOHpg1q7H/JEVEREREpI7CKlw6544DMLNjgTPwVSw/NLMxzrmNHo7rj8AfAdLS0txRmnsnPr5CuCwsnMaRrL4ImErF7L4IqKlNqD4Z+Bbz3RIUOGdTXDrPHzaLagybcOS9zdpOpQ0VNvsCZ7Vty/2PPcb7//oXeXv3qpopIiIiIuKhsFjQpzLn3G7n3EvAKCAZ+J+g24HqY/sqHSte31ePPtVVNluGoHAZbVHAoKCbmfgW1KWGa0c7h6qBMwP4b+ATit2HFJR9RrG7nfzSjvxf9vH8LTuJwrIUfr59O8tzcvgkP7/8vc3qVqk92iJB5df37sUVF9N20yYtCiQiIiIi4rGwDJcBzrldwL+BU80sxX95i//Yu3J7M4sBuuPbI3NH0K2a+nTGNyU2q0W/bwkVpsXGRUURFbUp6GYqsKlSh8rXjnYOVQNn3cLm/KwE1uxL5ZDFc2/mLpbn5NI1Lo4VudVviRJqv81ANfO3tdjy5EFNmxURERERaXJhHS79jvcfS/3HVf7j6BBtzwESgLVBK8Uerc+Fldq0XEGVy+PiYomPnw/s9l+ZBASfB67Nq6FNqD6pwIdB55nUPWxupLhkCnnF8SGrm0cLm1D7LU9+2qEDj/32tww+9VRVMkVEREREmpDn4dLMeptZlemqZhZlZrOBY/CFxb3+Wy8COcB1ZpYW1D4e+I3/9MlKj3saOATcbmapQX2SgPv8pwsb/m285YLC5bGxscyYcQsJUX2J4hHgANAPOBWzR4E3iYp6l5iYQlq1GkBU1JwQbar2MUsAHuVI4EylYnUzk5rDJlR8b3PjUafSVg6bULups8uys/nzt99ycVISo/PzVckUEREREWlC5py369OY2R3AQ8C7wE4gF18SORffdiLfASOdc/8O6nM5vpBZBCwB9gCX4tty5EVgrKv0xcxsCr4yXC7wPFAMXA10AX7nnJtRm/GmpaW59evX1/frNqnC/ftJaO/L6T/u2JHXs7PJaNOGeUXG1t8YdhIAACAASURBVPh29Lr8YkaOPJ2VK//F1q276NWrG9OmTQJg3rxF5dcqt6l8npAQy5Ilr1BUNI2yslh8f32b8P213Qicgm/hX4CRwL3A+UEjrdwmsCrtOo6E0NnAPGLtBP8iQfuIjcou32/zxe+zSWvXlktSfLOlf5uZyaX+iiX4KpkLsrJ4MGgRIIBNBw6w8LvvOKlXL/oNHKgFgERERERE6sjMNjjn0qpcD4Nw2Re4Fd+ell3wbSFyEN+elCuA+c65PSH6nQncD5wOxAPbgD/725dWbu/vcwm+RDMYX9X238ATzrm/1Ha84Rwud+/ezXHHHQfA1cnJvPD++3DSSb6b114LS5Y02mdlZGSUB9Kiov1s3/41xcXTaxE2oWrgrHvYLC4rIz56O4/27En7mBj++M03nBAby8X+sFn5HHyVzFV793JBx46+7UwOH2Zlfr62MxERERERqYPqwqXnW5E45zYDt9ej33vARXXssxxYXtfPaikOHDhQ/ns7gI8/PnKzX79G/az09PQKFb+KYfNEtm8f7A+bA/CFzZ9wJCim4ntvMxAuM4FxQU+v6b3NI/ttFpaezd3b/sWYlJQqW55U3t4k8E7mgyG2M5k1dy4XjB6tCqaIiIiISAN4Hi6l8ezff2SXy3bOwSefHLnZv3+TfnZdwqZZAs49CvwUX4BMxVfprG3YBMiglF0cLHuZl7KfJca2UeKO4e5tWxmT0o4YM3YG7aVZ3XYm7WNiGBgby4Rx4zimUyftlSkiIiIiUk+eL+gjjadCuCwrq1i5bOJwWVl6ejrPPfcn1q9/i82bP2D16r8xduxnDBnyCNdeW8ANN1xNQkKafyGhAcDvqP0iQXAkcF5EsXuWgrL3KXbXcrCsHS9ln8DnBSm8kru3fBGgUNuZgG+q7Lq8PAYXFGj7EhERERGRBlDlMoJUCJelpUcql+3aQdeuHo3Kp3JlE2DSpAm1nEqbSsXKJlStbgamzn5MsfNVOEtKZ3H3tscYk5JYpZIJR6bKztZUWRERERGRBlO4jCAVwmVhIWzd6jvp1w/MPBpV9Rr3vc2qU2dLacXBskReyj4BYw/bi77hHP87meCbKjsqxFTZ74uLaV9ayjWXXMJ5o0ZpmqyIiIiISC0oXEaQCuEyaE/Ixl7Mp6k07L3NTOpSyeweH8+2gsIKi/7AkRVlR3XsSGp8PLtWr+by5cu1oqyIiIiIyFEoXEaQCuEy+EYzv2/ZWKoPm6/799u8miVL0vz7bcYDG6ltJTPGCilzbdgRNFVWK8qKiIiIiNSfwmUECQ6XbYNvtJDK5dHU9N7mhx9+z7ZtcyktnUBtKpm+7UwyWJF7Aed2KKF9TEy1K8pqmqyIiIiIyNFptdgIUmWfy4AICZehBFal/fzzddx//5SgFWgDlcyAUNuZpFNYOoy7t23n5ZxcviwqqrKi7LLsbBZkZZGWmMhPEhK0mqyIiIiISDUULiNIyGmx3bpB+/aejKe5/epX97Ny5QuMHfsZffp8T3T0XI5sb5JJ1e1MMihlq3+vzJF8dyiRHUVF5XeDp8mOSUmhb2IiFyclMbNzZ56YO5eMjIzm+WIiIiIiIi2AwmUECRkuI7hqGUrtK5lQea/MQrecFbn7y/fGrG6abPuYGM5PTOTJBQua4RuJiIiIiLQMCpcRJOQ7ly10MZ/GUHMlE0JVMwtL47h72ze8nLM35DTZgK6tWrFz27amGrqIiIiISIujcBlBAuGyNdAqcPEHVrmsrPpK5ptAIbAhqPUiSvkFB8ve5KXsEVWmyQZsLSjgb9nZ7Nyxgwnjx2t6rIiIiIgICpcRJRAuI2EbkqYQXMkcMuQRzj8/mfj4BVR9LzM95DRZ8C3wMz8riyFt22qBHxERERGRINqKJIIEwmUBxhDa0dtKmLZnD9o044jK25nMnDmbOXOq2yvzyDTZMSltibUy/rFnDw/17Kl9MEVEREREKlHlMoLk5uYCcIDj2ciLLHUPMPLH/8HMmbM9Hln4qvm9zIrTZF/8vpQLk5O1wI+IiIiISAgKlxHivffe4/Dhw/6zXsD5lHEPBQXrmDNnod4LrEH172V+TPA0Waw73bXAj4iIiIhISAqXEWLu3CeDzoLfujyWoqJpzJu3qLmH1CIFVzKTknII3r6k1PViR9HhkP025uez+/vvOXvYMC3yIyIiIiI/SAqXEeKLL3YGnVVY0oeysgFs3bqreQfUggUqma+++hwJCU8QmCZb7KawIndvhQV+AJbs3s3avDzSCgs5e/duLfIjIiIiIj9ICpcRokuXY4POKobLqKiP6N27W/MOKAKkp6czY8YtQdNkP6CwNKZ8H8xP8vP587ffsnrfPh7t2ZOLk5Ppm5jIxUlJzOzcmSfmzlUFU0RERER+MBQuI8QVV4wOOgsOl7uJj5/H1KmTmntIEaHiNNnHKeWB8gV+5mcdx3v72jFGi/yIiIiIiGgrkkjRtWvX8t/NPsK5N4mK+oj4+HnMmHGrtshogMD2JUOGjGTv3sH4FvhJp9hB66hhdI/fHbJf11ateE+L/IiIiIjID4TCZYQI7HEJMHjwXuARevXqxrRpLyhYNpLevVPZtGkTZWVH9sH0LfLzJX0TE6u033XoEN1POqk5hygiIiIi4hlNi40QweFy2rT/ZP36t3juuT8pWDaiadMmER8/nyP7YEKxG8GK3Nwqi/zklZSwPCeXs0eMaOZRioiIiIh4Q+EyQgSHy7Zt23o4kshVdYGfN4E/U1g6nLu3fcvLOXv4JD+fl3NyuHPrNg6XJTHr3vu0NYmIiIiI/CAoXEaIAwcOlP/erl27GlpKQwQv8DNkyCMkJe2mlHs4WPYPXso+j/lZZbyUnU1MlHFFJ+OnCa21NYmIiIiI/CDoncsIEVy5VLhsWoEFfgDGjbuRpUs3UVY2w7/IzxoSo0fyaM8e5SvI9gXOatuWWXPncsHo0ZqqLCIiIiIRSZXLCKFw6Y3K72HG2n8zJjlFW5OIiIiIyA+OwmWEULj0RsX3MB8j2jbQPT42ZNuurVqxU1uTiIiIiEiEUriMEAqX3jnyHubnJLTJZkdRUch2O4uKaNehQzOPTkRERESkeShcRohAuIyOjqZ169Yej+aHJz09neee+xODTjuDFbn7Q25N8krufg6Vha5qioiIiIi0dFrQJ0IEwmW7du0wM49H88O1Z08BhaXXcPe2ZYxJaUP3+Fh2FhXxSs4eisrGsnfv7qM/RERERESkBVK4jBCBcKk9Lr3Vu3cqmzadwsGy/+Sl7AXE2BuUuD0UO4dZMb17d/N6iCIiIiIiTULTYiNEYJ9LvW/prSOrx3an2D1LQdlbFDsHQCv+j882/YsJ48eTkZHh7UBFRERERBqZwmUEcM5VmBYr3qm8eixkE00KidHRXNkphQsPHqRk9WouHzWKB2fN8nq4IiIiIiKNRuEyAhQWFlJaWgooXIaD4NVj+3S+jcTovTzasyeXpKTQNzGRi5OSmNm5M0/MnasKpoiIiIhEDIXLCKBtSMJPYPXY04ancVFyMu1jKr7e3D4mhpEJCTy5YIFHIxQRERERaVyeh0szSzazm8zsJTPbZmaFZpZnZu+a2Y1mFlWpfaqZuRp+ltTwWRPM7AMzy/d/xttmNqbpv2XTUrgMX5989BGp8fEh73WLi2Pzpk3NPCIRERERkaYRDqvFXgM8CXwLrAa+BI4FrgSeAi40s2uc86+KcsRHwLIQz9sc6kPMbA5wF5AFLAJigeuA5WY2xTn3RCN8F08oXIavAwWH2FF0iL6JiVXu7Sw6xP6oVh6MSkRERESk8YVDuPwCuBRY4ZwrC1w0s/uAD4Cr8AXNv1Xqt8k5N6s2H2BmZ+ALltuBoc65vf7rjwEbgDlm9opzLrNhX8UbCpfhKzquAyu++pZzO7SvMDU2r6SEV3IO0KXXiR6OTkRERESk8Xg+LdY5t8o5tzw4WPqvfwcs9J8Ob+DH3Oo/zg4ES/9nZAK/B+KAnzXwMzwTHC61z2V4GTRoAEVlZ3L3tm95OWcPn+Tn83JODndv28khdyaDB/f3eogiIiIiIo3C83B5FIf9x5IQ9443s1vM7D7/saZ/pZ/nP74e4t5rldq0OKpchq9p0yYR2/pzDpYt4aXs85iftY+XsrM5WHaYmLiPmTp1ktdDFBERERFpFGEbLs0sBvip/zRUKLwAX2Vztv/4kZmtNrOulZ7TBjgByHfOfRviOVv9x96NMnAPHDhwoPx3hcvwcmTfy1sosYEUlP2MYv/rw4N6Hkt6errHIxQRERERaRxhGy6Bh4G+wKvOuTeCrhcADwJDgCT/z7n4FgMaDqz0B8qA9v5jXjWfE7jeobqBmNnNZrbezNZnZ2fX9Xs0OVUuw1vwvpcDuq0u/48uc9sWSkpCFeVFRERERFqeOodLM0sysx+ZWVyl6z8zs7+b2f+a2bCGDMrMpuJbgOdz4CfB95xz3zvnfumc2+ic2+f/WQOMAt4HTgJuasjnV+ac+6NzLs05l9apU6fGfHSjULgMf4F9Lzdt38g1bXz/7yO3uJhObdvTq/tJPPXUUx6PUERERESkYepTufwtvhBX3tfMpuDbNuQSfNt7vG1mP6rPgMzsdmAe8G9ghHNuT236OedK/GMAOCfoVqAy2Z7QAtf31XGoYUPhsgWJjubz9kkkRkdzZadOTD4mhSEF+Uy/dTLnDR/p9ehEREREROqtPuHyTGClc64w6NoM4Gt8oW6s/9qddX2wmd0BLMC3V+UI/4qxdRGYs1o+LdY5d9A/tkQz6xyiTy//8Ys6flbYULhsOZ566im27/6OR3v25JKUFPomJnJpSjKP9uzBunfXqIIpIiIiIi1WfcLlCcDOwIm/QnkisMA5965z7kVgORWrh0dlZvcAjwOb8AXL7+sxtsDqKDsqXV/lP44O0efCSm1aHIXLluOR2Q8zJjm5wp6XAO1jYrg4OZlHZj/s0chERERERBqmPuGyNVAUdH4m4IC3gq5txxdCa8XMHsC3gM8GYKRzLqeGtoPNrMq4zWwkMN1/+kyl24H9Mu83s6SgPqnAbcAh4OnajjfcaJ/LlmNfTg7d4+NC3useH8e+nNxmHpGIiIiISOOIOXqTKr4GTg46/zGwH/go6FoSEDxttlpmNgH4NVAKvANMNbPKzTKdc4v9v88FepnZWiDLf60/R/apfMA5tza4s3NurZnNxTdV92MzexGIBa4FOgJTnHOZtRlvOAqEy4SEBKKjoz0ejdSkQ0oKOwry6ZuYWOXezqJDJKUkezAqEREREZGGq0+4XA1M8C+8UwRcCvzNOVcW1KYn8FUtn9fdf4wG7qimzT+Bxf7f/wpcAQzFN6W1FbAbWAo84Zx7J9QDnHN3mdkn+CqVNwNlwEbgMefcK7Uca1gK7HOpKbHh7577/x/Tb53MuR3aV5gam1dSworcXB6f/RsPRyciIiIiUn/m/Bu617qDWXdgPb59IQ3IB4Y657b477cDvgOeds7d1rjD9V5aWppbv36918Oo4LjjjmP37t307t2bLVu2eD0cOYrzho9k3btruDg5me7xcewsKuKVnFyGnXk2q9as9np4IiIiIiI1MrMNzrm0ytfr/M6lc24ncCowDZgK9A0ES7+TgP/mSKVRmlhgWqwqly3DqrdX8vjCJ1kfn8D8rCxeys6mbVkpq+67x+uhiYiIiIjUW30W9ME5951z7gn/z5eV7m10zk13zq1rnCFKTQ4fPkxhoe/1VoXLluOmm25i264dpPfrR7FzfAfs+uMfvR6WiIiIiEi91StchmJmyWZ2hZn92My0qkwzCbxvCQqXLdHo664r//2NV1+FwlqtgyUiIiIiEnbqHC7NbLKZvW9mHYOuDQE+B14EXgXWmlmbxhumVEd7XLZsP77oovLfXz90CFas8HA0IiIiIiL1V5/K5bWAc87tCbr2GL7tR57GFy6HArc2fHhyNAqXLVu/fv3onOzbfuQfwLV33MuQISMZN+5GMjIyvB2ciIiIiEgd1Cdc9gI+DpyYWQpwLvAn59xNzrlLgHXAfzTOEKUmweGybdu2Ho5E6sPMGH3JJQAcBJZ+fT4bN97L0qWnMHLkWGbOnO3tAEVEREREaqk+4TIZ+D7o/Ez/8aWga+8A3eo7KKk9vXPZ8vXo2TPorCNwPmVlMygoWMecOQtVwRQRERGRFqE+4XIPkBJ0fi5QBqwNuuaA+AaMS2pJ02Jbvo0bP8e3ZSzAG0F3jqWoaBrz5i3yYFQiIiIiInVTn3D5GXCJf3XYDsB1wDrn3P6gNqnAd40wPjkKhcuWb9eub4GTAYi1jbSOGkysjQcyKCsbwNatuzwdn4iIiIhIbdQnXM4DOgNZwFfAscAfKrVJBz5q2NCkNj755BNizWgdFcXiRYs0hbIF6t07lRg7RGJ0NFd26sQdXXK5otNqEqNHEWOP0bu3ZpiLiIiISPirc7h0zr2MbyXYT4EtwAzn3DOB+2Y2HEik4vw+aQIPzprFM4sW+QNJF4794gsuHzWKB2fN8npoUgcjR55OfNQuHu3Zk0tSUuibmMilKUk82rMz8VGrOe+8070eooiIiIjIUZlzzusxtChpaWlu/fr1Xg+DjIwMLh81ipmdO9M+Jqb8el5JCbO+/Za//+MfpKenezhCqa0J48dzeOVKxvi3JAn2Sm4urUaO5C/PPuvByEREREREqjKzDc65tMrX6zMtVsLAkwsWMLJNmwrBEqB9TAznJyby5IIFHo1M6mrH1q2kxsWFvNctLo6d27Y184hEREREROqu3uHSzNLN7Ckz22Bm281so5ktMrMzGnOAEtqOrVvpFhsb8l7XVq0USFqQHr16sevw4ZD3vjx8mO4nndTMIxIRERERqbt6hUsz+w3wHnADMAjoDgwEbgTeMbPfNtoIJSQFksgxecoUVubnk1dSUuF6XkkJb+XnM3nKFI9GJiIiIiJSe3UOl2Z2DXAf8CVwE9ADaO0/3uS/fo+ZjW3EcUolCiSRIz09ndvuvJOZ33zDyzk5fJKfz/KcHGbt2MHtd96pd2dFREREpEWo84I+ZrYG6AX0c87lhLifAmwGtjjnzm2UUYaRcFnQB3yrxT4xdy7nJybStVUrvjx8mLfy87n9zjt5QCvGtjgZGRlcNGoUhw4eBOd40znOyM2Fjh29HpqIiIiISLnGXNBnAPBiqGAJ4L/+Ar5pstKEHpg1i7//4x/EjBjBe8cdR8yIEfz9H/9QsGyh0tPT+fHFF1NQVkaBcyQB/POfXg9LRERERKRWYo7eJGSfgqO0Kajns6WO0tPTNW0yggwbNowlS5YAsA44ZdUquOIKbwclIiIiIlIL9alcbgfGmFnIvv7rF/nbiUgdDB06tPz3DwBWr/ZsLCIiIiIidVGfcPm/wCnA382sV/ANM+sJvAj8yN9OROpg0KBBREdHA77KJZ9+Crt3ezomEREREZHaqE+4nAusAS4GPjOzL83sfTPbBWwBLse3TcncxhumyA9DmzZtOPXUUwH4EBhLPENOH824cTeSkZHh7eBERERERGpQ53DpnCsGLgDuB3YCXYChwIn+8/uBkf52IlJHw4YNA+Aw8AKT2LjzMZYuPYWRI8cyc+ZsbwcnIiIiIlKN+lQucc4dds495JzrBbTDFyzbOed6OeceAqLNrF1jDlTkhyI5OTno7GTgfMrKZlBQsI45cxaqgikiIiIiYale4TKYcy7fOfe1cy4/6PKTwJ6GPlvkh2jTps+Dzj4I+v1YioqmMW/eouYekoiIiIjIUTU4XNbAmvDZIhHr++/zgDj/2boK98rKBrB1665mH5OIiIiIyNE0ZbgUkXro06cHcJz/7DPgQPm9qKiP6N27mxfDEhERERGpkcKlSJiZNm0SMTG5/jMHbPD/vpv4+HlMnTrJo5GJiIiIiFRP4VIkzKSnp3PJJRcCEGtG66hribVBxMX1Y8aMW0lPT/d4hCIiIiIiVcV4PQARqapbl+NIjI5mTHIy3ePj2Vn0DW8dLCDGDns9NBERERGRkBQuRcJMRkYG/7t4MY/27En7GN9/on0TEzmnQwdmzZ3LBaNHq3opIiIiImGnVtNizay0Lj/AT5t43CIR68kFCzi/TZvyYBnQPiaG8xMTeXLBAo9GJiIiIiJSvdq+c2n1+BGRetixdSvdYmND3uvaqhU7t21r5hGJiIiIiBxdrcKlcy6qHj/RTT14kUjUo1cvdh0O/W7ll4cP0/2kk5p5RCIiIiIiR6fVYkXCzOQpU1iZn09eSUmF63klJbyVn8/kKVM8GpmIiIiISPW0oI9ImElPT+e2O+9k5u9+x3kJCXSPjyezqIhVOTncfvfdWsxHRERERMKS55VLM0s2s5vM7CUz22ZmhWaWZ2bvmtmNZhZyjGZ2hpm9amZ7/H0+NrM7zKza6bhmNsbM3vY/P9/M3jezCU337UTq54FZs3j5zTdZsX8/87OyeCM7m7+XlfHAmDFeD01EREREJCTPwyVwDbAIOA14H/gv4G9AX+ApYKmZVVggyMwuA9YA5wAvAU8AscDjwJJQH2JmtwPL/c99xv+ZxwOLzWxOo38rkQZKT09nwJAhFJSVsc85BgJ88onXwxIRERERCSkcwuUXwKVAF+fceOfcvc65G4CTga+Aq4ArA43NrB2+YFgKDHfO3eicuxsYCPwLuNrMrgv+ADNLBeYAe4A059xtzrnpQH9gO3CXmZ3etF9TpO769OkDgAO2AXz8sZfDERERERGplufh0jm3yjm33DlXVun6d8BC/+nwoFtXA52AJc659UHti4Bf+E8nV/qYG4A44AnnXGZQn73Ab/2ntzbsm4g0vkC4BNgCqlyKiIiISNjyPFweRWA/huBlM8/zH18P0X4NUACcYWZxtezzWqU2ImGjSrjcvBmc82w8IiIiIiLVCdtwaWYxwE/9p8GhMPCv7S8q93HOlQA78a2C26OWfb4FDgJdzCyhmrHcbGbrzWx9dnZ2nb6HSENUCZd5efDVV56NR0RERESkOmEbLoGH8S2+86pz7o2g6+39x7xq+gWud6hHn/ahbjrn/uicS3POpXXq1KnmUYs0op49exIT49sx6PPARb13KSIiIiJhKCzDpZlNBe7C9+/pn3g8HBHPtGrVih49fEX4LfgW9tF7lyIiIiISjsIuXPq3DJkH/BsY4ZzbU6lJjVXGoOv76tGnusqmiGcCU2PzgO9B4VJEREREwlJYhUszuwNYAGzGFyy/C9Fsi//YO0T/GKA7vgWAdtSyT2egDZDlnCuo/+hFmkbwe5f/QSxDXlrNuHE3kpGR4eGoREREREQqCptwaWb3AI8Dm/AFy++rabrKfxwd4t45QAKw1jl3qJZ9LqzURiSsnHzyyeW/r2IMG4v+ytKlJzNy5Fhmzpzt4chERERERI4Ii3BpZg/gW8BnAzDSOZdTQ/MXgRzgOjNLC3pGPPAb/+mTlfo8DRwCbjez1KA+ScB9/tOFiIShkpLgnXhSgfMpK7ubgoJ1zJmzUBVMEREREQkLMV4PwMwmAL8GSoF3gKlmVrlZpnNuMYBzbr+ZTcIXMt82syXAHuBSfFuOvAg8H9zZObfTzO4G5gPrzex5oBi4GugC/M4596+m+YYiDfP66/8MOtsS9PuxFBVNY968RaSnpzf3sEREREREKvA8XOJ7RxIgGrijmjb/BBYHTpxzy8zsXOB+4CogHtgG3AnMd67qLvPOuQVmlgnMwLd/ZhS+RYN+4Zz7S6N8E5EmsGvXd0Bb4AAVwyWUlQ1g69bXQ3UTEREREWlWnodL59wsYFY9+r0HXFTHPsuB5XX9LBEv9enTnQ8/3IEvXO7EN8M7DoCoqI/o3bubh6MTEREREfEJi3cuRaR606ZNIjo6239WCmz3/76b+Ph5TJ06yaORiYiIiIgcoXApEubS09M599wzg648TxSPkJCQxowZt+p9SxEREREJC55PixWRo7v99smsWvUmACfwCGcTxbQXXiT9ojrNDBcRERERaTIKlyItQJ8+fcp/v4BDPA3Qrp1n4xERERERqUzTYkVagJ49exIdHQ3A54GLW7d6Nh4RERERkcoULkVagLi4ODp37kysGZ9GRfH/2bv3+LjqOv/jr8/k2qT3NNDaC70DgiLSlYCCllYWoSDrIhZQcEEQfwooF90VsUUuKxfZLQVBWIVdYeUqdxCkLcIuDVKQ25ZLL5SSXtKkTdIkk8ll5vv7Y85MJ5Mzk0maZDLJ+/l45DGZM+fyPd9zvt/z/XzP7SwzKletynayRERERETiFFyK5ICrli6lcccOvlZezg+mTKGjvJyT77+fq5YuzXbSREREREQAMOdcttOQU+bNm+fWrFmT7WTIMFJZWcnJxx7LkkmTGJO/5zbpho4Olm7bxmPPPacnxoqIiIjIgDGz15xz85KH68ylyCB32/LlLCgt7RRYAozJz2fhyJHctnx5llImIiIiIrKHgkuRQW7junXsV1jo+9u0ggI+XL9+gFMkIiIiItKVgkuRQW7mnDl81N7u+9vmtjZmzJ49wCkSEREREelKwaXIIPe9Cy5gRVMTDR0dnYY3dHTwfEMD37vggiylTERERERkj/zuRxGRbKqoqOD7F1/M0ptu4pgRI5heXMymUIiVtbX84PDD9TAfERERERkUFFyK5IArli7ly8cdx4lf+QrB2lpwjhXOUVFenu2kiYiIiIgAuixWJGdUVFRw2OGHE4xECDrHJwHWrct2skREREREAAWXIjll6tSp8f+rADZsgHA4a+kREREREYlRcCmSQ6ZMmRL/vwqgrQ02b85aekREREREYhRciuSQLmcuQZfGioiIiMigoOBSJIcknrn8OPbPBx9kJS0iIiIiIokUXIrkkC6XxYLOXIqIiIjIoKDgUiSHJF4WGz9zqeBSRERERAYBBZciOWTUqFGMHj0agKq8vOhAXRYrIiIiIoOAgkuRHBM7exm/LHbTJmhvz1ZyREREREQABZciOSd2m9kzDwAAIABJREFU32VDOMwpFHNYeCSn/cPpVFZWZjllIiIiIjKcKbgUyTGJD/V5mP/H6zzEA09/jgULTmXJkmuymDIRERERGc4UXIrkGDNL+HYcsJCIu4xg8FVuvPF2ncEUERERkaxQcCmSY958872Ebx8n/L8vodBFLFt250AnSUREREREwaVIrqmra0z4VtXpt0jkENat+2hgEyQiIiIigoJLkZwzZ87MhG8fd/otEHiTuXP3G9gEiYiIiIig4FIk51xyyfcTviWeuaymuHgZF1547kAnSUREREREwaVIrlmwYAFFRUXetzeAPxPgOkpK5nHppedTUVGRzeSJiIiIyDCVn+0EiEjPzZo1i7Vr11JgO/i0+zpzaOOiBx6k4oQTsp00ERERERmmFFyK5KApU6awdu1a2l2EVTQwCmDSpGwnS0RERESGMV0WK5KDpk6dGv8/ftfltm1ZSYuIiIiICCi4FMlJU6ZMif+v4FJEREREBoNBEVya2SlmttzMXjKz3WbmzOyeFONO935P9XdfmuWcZWZ/NbMmM2swsxfMbFH/rZlI/1BwKSIiIiKDzWC55/JnwCFAE9G28gEZTPMm8KjP8Hf8RjazG4FLvPnfCRQCi4EnzOwC59wtvUi3SFYkXhYbf9OlgksRERERyaLBElz+iGjQtx74IrAqg2necM4tzWTmZnYk0cByA/B3zrk6b/gNwGvAjWb2pHNuU8+TLjLwfM9cbt2albSIiIiIiMAguSzWObfKObfOOef6aRHne5/XxAJLb7mbgFuBIuCf+mnZIn2u05lLs+g/OnMpIiIiIlk0KILLXvqEmX3XzH7qfX46zbjHeJ9/8vntmaRxRAa90aNHM2rUKACq8r0LEBRcioiIiEgWDZbLYnvjy95fnJm9AJzlnNucMKwUmAw0Oef8Wt/rvM+5/ZROkX4xZcoU3n33XaoikeiA7dvBOYidyRQRERERGUC5eOYyCFwFHAaM8/5i92l+CVjhBZQxY7zPhhTziw0fm2qBZnaema0xszU1NTV7kXSRvhO7NLY+HKYJoL0ddu7MappEREREZPjKueDSObfDOfdz59zrzrl67+9F4FjgFWA28J0+XuYdzrl5zrl55eXlfTlrkV7TQ31EREREZDDJueAyFedcB/Af3tejE36KnZkcg7/Y8Pr+SJdIfyo0Y0QgwEVmVILuuxQRERGRrBkywaUnds1q/LJY51wzsAUYaWaTfKaZ431+0M9pE+kzVy1dyqN/+ANfKy/nh1OmUFZezsl5eVx1223ZTpqIiIiIDFNDLbis8D43Jg1f6X0e5zPNV5LGERnUKisrufWmm7h66lROnDCBg0eOZNGECSyZNYtbnn2WysrKbCdRRERERIahnAsuzeyzZtYl3Wa2APiR9/WepJ9v9z4vN7NxCdNMB74PtAJ39XliRfrBbcuXs6C0lDH5nR/2PCY/n4VjxnDb8uVZSpmIiIiIDGeD4lUkZnYycLL3daL3eYSZ3e39X+ucu9T7/yZgjpm9zJ7nmHyaPe+pvMI593Li/J1zL5vZTcDFwFtm9hBQCHwDGA9c4Jzb1LdrJdI/Nq5bx1GFhb6/TSsq4n/Xrx/gFImIiIiIDJLgEvgMcFbSsJneH8BHQCy4/D3wD8DfEb2ktQCoBh4AbnHOveS3AOfcJWb2NtEzlecBEeB14Abn3JN9tyoi/WvmnDl8VFXFwT6/bQ6FmDF79oCnSURERETEnHPZTkNOmTdvnluzZk22kyHDWGVlJScfeyxLJk3qdGlsQ0cHSzdu5LGXXqKioiLNHEREREREes/MXnPOzUsePljOXIpIhioqKvj+xRez9KabOGbECKYXF/NhKMSq2lp+EAhQcfjh2U6iiIiIiAxDOnPZQzpzKYNFZWUli085hZpt2+hwjied48sAdXUwdmy2kyciIiIiQ1SqM5c597RYEYmqqKhgwd//PcFIhDbnmBb7Ydu2bCZLRERERIYpBZciOWzfffeN/18d+2fr1qykRURERESGNwWXIjnMN7jUmUsRERERyQIFlyI5TMGliIiIiAwWCi5FcpiCSxEREREZLBRciuSwxOBye+wfBZciIiIikgUKLkVy2MSJE+P/64E+IiIiIpJNCi5Fcti4ceMoKCgAoDo/PzpQZy5FREREJAsUXIrkMDNjn332AaDaLDpQwaWIiIiIZIGCS5EcF7vvsjocxgE0NUX/REREREQGkIJLkRwXCy5DkQiNsYE6eykiIiIiA0zBpUiO830diR7qIyIiIiIDTMGlSI5LfGKsXkciIiIiItmi4FIkx/meuVRwKSIiIiIDTMGlSI5LDC4rgdMYwWHX3c5pp51DZWVl9hImIiIiIsOKgkuRHJcYXC5jBPezlNerb+WBBw5kwYJTWbLkmiymTkRERESGCwWXIjkuMbjs4BQcPwYWEolcSjD4KjfeeLvOYIqIiIhIv1NwKZLjEoNL2J38K6HQRSxbdudAJklEREREhiEFlyI5bvz48YB537Z3+T0SOYR16z4a0DSJiIiIyPCj4FIkxwUCAUaMGOF9q/b5/U3mzt1vYBMlIiIiIsOOgkuRIWDq1Cnef9WAS/ilmuLiZVx44blZSJWIiIiIDCcKLkWGgJkzZ3r/tWBcBfyZgN1ASck8Lr30fCoqKrKZPBEREREZBvKznQAR2XuJD/U5nmvZTjFzKuZx0U0PKrAUERERkQGh4FJkCEgMLv+FVj5PK5xyPCiwFBEREZEBostiRYaAxOAy/kifrVuzkhYRERERGZ4UXIoMARMnToz/H38ZiYJLERERERlACi5FhgDfM5dbtmQlLSIiIiIyPCm4FBkCOgWXxcXRf3TmUkREREQGkIJLkSGgU3BZVBT9Z+tWcC7FFCIiIiIifUvBpcgQUFZWRl5eHgDVZtGBwSDs3p3FVImIiIjIcKLgUmQICAQClJeXA1AdDu/5QZfGioiIiMgAUXApMkTEnhi7PRTaM1AP9RERERGRAaLgUmSIiN13GWxvpyk2UGcuRURERGSAKLgUGSJ8X0ei4FJEREREBkjWg0szO8XMlpvZS2a228ycmd3TzTRHmtnTZrbLzFrM7C0z+6GZ5aWZZpGZvWBmDWbWZGavmNlZfb9GItmh4FJEREREsik/2wkAfgYcAjQBVcAB6UY2s68CDwMh4H5gF3Ai8G/A54Gv+0zzA2A5sBO4B2gDTgHuNrNPOecu7auVEckWBZciIiIikk1ZP3MJ/AiYC4wGvpduRDMbDdwJhIEvOefOcc5dBnwGWA2cYmaLk6aZDtxINAid55z7vnPuR8CngQ3AJWZ2RJ+ukUgWdAouY68j0QN9RERERGSAZD24dM6tcs6tcy6jt72fApQD9znn1iTMI0T0DCh0DVDPBoqAW5xzmxKmqQOu9b6e38vkiwwasafFAmwfOTL6j85cioiIiMgAyXpw2UPHeJ9/8vntRSAIHGlmRRlO80zSOCI5K3bmstCMu1pbOcuMyi1bIBLJcspEREREZDjIteByf+/zg+QfnHMdwIdE7yOdmeE024BmYIqZlaRaqJmdZ2ZrzGxNTU1Nb9Mu0q/u/a//YmReHl8rL+eMiRPpKC/nZOCqn/wk20kTERERkWEg14LLMd5nQ4rfY8PH9mKaMSl+xzl3h3NunnNuXnl5eUYJFRlIlZWV3PWb33D9rFmcOGECB48cyQkTJrBk1ixuue02Kisrs51EERERERnici24FBEfty1fzsLSUsbkd34A9Jj8fBaOGsVty5dnKWUiIiIiMlzkWnDZ3VnG2PD6XkyT6symyKC3cd069iss9P1tWmEhH65fP8ApEhEREZHhJteCy/e9z7nJP5hZPjAD6AA2ZjjNJKAUqHLOBfs2qSIDZ+acOXzU3u772+bWVmbMnj3AKRIRERGR4SbXgsuV3udxPr8dDZQALzvnWjOc5itJ44jkpO9dcAErmppo6OjoNLyho4Pn6+v53gUXZCllIiIiIjJc5Fpw+RBQCyw2s3mxgWZWDFztfb0taZq7gFbgB2Y2PWGaccBPva+391N6RQZERUUF37/4Yq6oquLx2lrebmriydpalq5fzw/224+KiopsJ1FEREREhrj87kfpX2Z2MnCy9zX2FvgjzOxu7/9a59ylAM653WZ2LtEg8wUzuw/YBZxE9JUjDwH3J87fOfehmV0G3AysMbP7gTbgFGAK8Cvn3Or+Wj+RgXLF0qUcfMghLP7HfyTfjEnO8ZhzVIwale2kiYiIiMgwYM657CbAbCmwJM0oHznnpidN83ngcuAIoBhYD/wOuNk5F06xnBOBS4HPEj1juxa4xTn3nz1J77x589yaNWt6MonIgIlEIowaNYpgMMhBBQW8094OEyfCtm3ZTpqIiIiIDBFm9ppzbl6X4dkOLnONgksZ7ObNm8drr71GgRnNzlFgBm1tkJ/1CxVEREREZAhIFVzm2j2XItKNT37ykwC0O8cGAOegujqraRIRERGRoU/BpcgQc9BBB8X/Xxv7Z+vWrKRFRERERIYPBZciQ0zszCXA/8X+UXApIiIiIv1MwaXIEJMYXMbPXG7ZkpW0iIiIiMjwoeBSZIiZPn06xcXFgC6LFREREZGBo+BSZIjJy8vjwAMPBOB9oAMUXIqIiIhIv1NwKTIExS6NbQU2goJLEREREel3Ci5FhqDE+y7PoZDD/vImp512DpWVlVlMlYiIiIgMZQouRYagxODyf5jP66Hf88ADB7JgwaksWXJNFlMmIiIiIkOVgkuRIai9vT3h2wRgIZHIpQSDr3LjjbfrDKaIiIiI9DkFlyJD0EMPPQ3ke9/WJvyyL6HQRSxbdmcWUiUiIiIiQ5mCS5EhaP36zcA079u7QDj+WyRyCOvWfZRy2srKSk477RwOO2yB7tMUERERkYwpuBQZgubOnQ6M9r6FgE3x3wKBN5k7dz/f6ZYsuYYFC07l/vsP5PXX/0X3aYqIiIhIxhRcigxBF110LgUFGxOGxC6Nraa4eBkXXnhul2kqKyu58cbfEAy+inOXovs0RURERKQnFFyKDEEVFRWcfPIJCUMeI8B1lBR9lksvPZ+Kigqg8yWw3/jGd2hpuQDYN2luuk9TRERERLqn4FJkiPrFL34OQKEZowJ3M9N+xrIvfYorr7wc6HoJ7ObN4NyhvvPq7j5NEZHeqKys5KwzzuCoz32Os844Q1dIiIjkuPzuRxGRXHTfvfcyMi+PRWVlzCgu5qNQiJ+tXMm2pUv58nHHxS+B3XOm8nDgb8DCLvNKd59mZWUlty1fzsZ165g5Zw7fu+ACgC7DYmdLhzrlh0hmrlq6lFtvuokFpaUcVVjIR1VVnPzEE3z/4ou5YunSbCdPRER6wZxz2U5DTpk3b55bs2ZNtpMhklZlZSUnH3ssSyZNYkz+nj6kho4Olm7dyqcrvsSKFV/07q2MTwWcCiQGnADVlJTMY8WKB6moqOgUPAVDITZv3Mixo0axX2EhH7W389SuXQTM+MrYsfFhK5qahkWDMbGxnC4/nmloYOasWYwoLlawKcNKrP54429/Y/OGDVw7fXqXOuryqiqO/MIXaKiry6nyUVlZybJld/LBB5uYO3c6F110blbSna0OLr/l9sf6Jy/nqPnzeWnVKnXe7YWB2nYytJjZa865eV2GK7jsGQWXkgvOOuMMOlau5ITx47v89tSuXaxog+pdv6frWcprgFuBi4DPEuB1ivNu4tLLL+TKKy/vFDzlO8fjO3dy7cyZ8cbhumCQm6uquDphGMAbjY3cvn07s+fM4VOf+cyAHbiSG3sLFhzR542QdI1lv/x4tKaGFXV1HDt+fPSMci+D7+4askO9sTBYGvKD1WDc/on1x9rGRg4oLeXECRM6jfNoTQ1/rqvjuL0sHwNtyZJruPHG39DSciHOfYZA4A2Ki29m8eLj6Qg1pgyE+jowyrSDqy/ytLuOxv7oREtev2d27WJDSwuLJkxgelFRzuwv2ZRcNxSPGMFjDzzQaZ/pj22Xrc6HwVD3DVUKLvuIgkvJBUd97nMcVV3NwSNHdvnt7aYmfh8MsW3XpUQil3X53ewCpk5ZSfm2auZ0BLnIQlSsXUtlfX2ns6F3bN3K5MJCTkhoHPoNe7SmhpV1dXy5n4OpZMmNvXy7nuLAyvhlwh+GWnm6oZ7iEaVEOsKMnTCBn1z+z3znO9/JeLndNZaT82NdMMjyqiquSgq+Gzo6WLptG48991xGB0G/hmxBwQ18cu4kSosLfRt6K5qaOOnUU2ltacn6QXdvzzykWv/Zs2dQVFTq25HgtwxgSJ4B8Qswst3gTr6a4tpNmzhpwoROdVRflY/epq+3Z/sqKytZsODUpNsMII9LGZG3jJPKJ/gGQr0NjFJ1mmXawQV7n6fddTT2VSda8non7kP9ub8M1bOjyXXDa83NrK6v57pZs/Z626XLs2x1PgyGui9mKAa9Ci77iIJLyQVnnXEGHatWccK4cV1+e6q2loZDDmHVqrcIdrxJyktgKyvhRz+iElg2bX9WNTUwPz8SD578GofJw1Id/JPPZPoduN955x2uu+aX1NfWQn4+9U1hAuEZ5FmQsCvB5W/i4AOmUVpc2OVANmbcOJ7/yxu0tt5Fod2L8TcKAuu5ftaMlAfQjaFWHq+tpay8nOnTpjJzzhzyi0dx331Pdwpi8vKuZUxpHu2tLUTaW7nOW7dM8sMv+I7l0707dtBSUsIxxx6b9qDj15DNYykj8n7ForISCs26NPQA7quu5oX6ek4sL9+rg3tPD5B+veR/vO8+FpaWMqO4mKd37mRjKJRxA9u/IX8NsIxCm0yetRB2DRQGajgxTSM++WyO3zjJ+XPU/PmsWLE6bQdHXzRKu5uHXzqAtJeb9ubqgb5oDMUCoVXPPd2p/si0cwp6Vj56k/a9vZz9tNPO4f77D+xym8HIvGO5fpZ/INTbwMi/02wVJ04o492mpm47uPYmTxPzNl1HY7p1u2LLFg49Yj67dgUz6iRM3I47amqYFwrFr8jZm3VLd1VLciCUqhOgLzrr+qqMpZtHuqtr7ti6lU8UFrKom23XXf2R7oxyAfRb50Piuo8aN45X//d/+cXkyYPiyqlEfnXMULg9RsFlH1FwKbkg7T2X69fz2M9+xjNXX8+NkRJC/JgInyUQeJPi4mVceun50SfKhkIsKZ/MdU0dOJtJgb3PhVPK0wZKycMyOZMZOwidkHBG8YldOzHnWFQ2gRnFRTwVC0Di33exMdQSPwvZdR4hHqvdRZ7ls6hsLO81dz6r6HcATT7YfRgK8eTOXQTDXyffjDxbR9htpjBQy6KyCbzX3Lkhl0l++AWgsfw4dvx4pnvr/8zuBmbPmeN70NnTkP0ChbbcC5w3cP2s6SnPKGeyvpkc7DI5QCb3Vm9av55jR41iRnExrzY2Url7N9d7veQ96XyIBVO1tdV8/PFZOHcUhbacPHuDsFtPYSCfRWWjKTTHY7U7+ddZqRvxyWdzMt0fHqvdScTtQ8BGE3YlWOEWzjjjHwgG2/ngg02E2+r4eMN78fWNBc6J+7bftgVSXl6Y3LDd1NrKozW1ndJB3geU5of5ytixvmfQ/a4eeG73bibPmEte4bguAWpfnWlYsuQarrvuZlzbZK/+SN/xlEn58GuE9uYyv9h+2heXsz/6xEu8/vq/ACO9fXIdztVwcnkLJ04o860LehMYde1Y6RzAZtLBlWme+onl84pnn+WovLyUHY2p1g3gd9u28b/1hlm5bydh4vom1zf/VV3NmfvuG19OpvXp881NXHjppfF1SxegJwdCqeqoWGddYtlOXk6q/OvNfpqqo6m7eSSWY7+6IZNt51d/JC4jOahLzrOedD705H7r5P3joZoaDhs1qtu6r7v6o6/uUU4X1Gd6/O2LdPSnVMGlnhYrMgRVVFTw/YsvZulNN7Fw5EimFRREg6XaWsojxvevupm5zljGLlYc/EfWFf2ZOXP246KLHtzTkHnjDa5vaaMgDxaVbee95gAfhkLxg9D8sWNZXlXFF8aOjVeY88eO5eaEYbVtbRw5enQ8XeuCQVbW1XXqvf84FOp0SU5RIMBzu+DqhABkS2trp4BkS2uo03ffeQR2cfXMaYzJz+ftpt3MKC6Op2NVfT1/P358p8bkyrq6To3Jg0eOpDkcZlX9AywqK/eCllr+1VvO202NneaZSX5MKCzko4Q8TM4PgPUtLeR1dDCnujp60PGeoBnrJX/+yacoYDyFeUtZVDbKC5z3LDM5zzNd3/U1NVhbW6flHv/ooykbEH7TPPPuuzxy//2cUFbGTLNO+QXw8u7dnFhWFv+enC7Y0xg4Yfx4ZjQ18eHzz3PhffcnBFPVBNxKRuRdxaKyUm+7RPjXWVPiDZkTysanXcaq+nqOG596HN/8aWlhRMA4dnyYGcUtbAzt5qmdu7n7d3eTb5/E2ElBYCvXJwS1Va2tnfZLv2173MMPx8+QzUy6vNBv305Ox18ba3lld4hrpkXHeXN3533dbx87GJicn8+ta9/GMZl3/lbHHx9aSEleB8eNGeNtuz0Beib7R/IVBzt3VNPc0kphoIBF5dt4r9k61R9zSko4Ztw4frZxYzwQaI1EOo2TLu03XHstjzz4IJaXF288H1VYyGsbN3a5zC857U+vXcsj99/PogkTCDQ1cVxCmfXbP3zrh4R0lJSMpNC+RWGgkUVlo5hRXMh/ba9mRvGeq0KSy6VfOe0SGD2/ghMfe4yvLV7M7mbHs8+uJBj8AbEz9oW2nEVlpfE0JdcvfsPS5el1V1/Nv93wK8r22ZdTFn+drZs3+wYxRc3NzNhnn5TL8Fu32Pq93tjIyeXjmVHcwlM7t7Ax1ML+1du67FPtHR1dGuSzd+9mU8JyMlq3kSOZWlTIdddcwyMPPsgnpkzxrmp5i1iAXhyojJfbO7Zu5fg0dUNsOS83NHTax5KXk3xFTmKQl+l+mlifHpW03x5VVNTtPJKvYkmuGzLZdr7HqKR0PvTOOywcNSplniXP0y9PAVbV1eHa2hj1xht8OkUdk65T6PHa2ozrvlT1R3KdHBvmV9ele8ZBYnkJNDZ2qmMyPf76pSNXnqat4FJkiLrCe+XIbcuX86e//Y21H39Mm3O0WzHVtg/vUMLDbh0HuxZKA47CQAjYU0k+89TT5NMav5T0gJJilldVcbRXScYah5dv3Mjfx3uJQwTDxmXrN7NowjjyrXODMvmg0puGf2/m0d0BNF0DIhbERoOWspTz9GssR/ODeH5MKyri8dqd8WAzoyAXaGxr47677+aEsjKOLy3msZaP4w3/5MDZr4HZ3fpmcrB78O13WDh6VMppkgOh5PzyS0dGDZkuwZTxyu4/J22X1A0Zv4Zud+Nk1vkAwXAbq+rrWVS2zQvyexawFgWD/Ckcjg/rrmHrN4/kgD15+6cL4E+aMJ4ZxY6/Nm7mld0hrt3PP08zbQwtfOCB+BUHhaNKeaw1FA/6k+sPgJPLy5leXMytVdvAptAWKWZ7e0N8nO46Hwp37uwSBL+8ezeLEvLDbz9NDPrfamzs0uDOpH7olA4zHmuq6dSRMrskfSCUaWB09NgxXHLXXYTdbPKslkJ7ljY3nkJbRYE9wYziPbc+ZNLBlW5dTvTOwj1Vs51bbriBRV5Q89qGDaxOCKa2hEKd0p68XL86KLZ+1yRsh8ROwuR96r3m5i5Bf2w5R3nDk5ebdt1inVVvvElBx27auZx8ayGPZ1lUNqJH9Ucmy3nm8cfjgeBMul4Wmsl+mlif+nVWdTeP5PrEb7t0t+0yqYOSg7rkPMuk8yE232vT1DGxYDtVp1BP6r5U9UdyneyXjuRgs0vHQVJ5SQ7qe3NsgGhb4AujRrH0ppv48nHHDaozmMkC2U6AiPSfiooK/vPee7ntd78jQh4j8/L4WvlofjglxAEl6ynKC7F/TTVHVVfTsWoVx33pSxw/fz4dK1cyuaOdRWXj4hVbYvD0RG0tbzc1ETCjJVzAH2v25eaqiTxSM4qQO4/myCoeqZnPe8FCntxZR0NHBxA9qOyXdBDaz6dhl26c3sxj/tixPLtrVzwdsYNQunn49b6mmydEG8vnTJrE4zV13Fw1kkdqPkPIjaM58jCP1Mzn4Zr9aImUcdn6DTxeu5PNoVCneXbXS37ihAl81NraqeGfvC5+6ZpQWMimNOub7mB3onfJVFHA0h4gu8svv7Qmf88kHR3OdQqmkpfT3TIyGae7/ImlLdb5cOKE8bQ716WB1d08kjtButu3/eZR29bG9DT7ZfI8EoOYPXkaTpunmWyXuUVFFIGXH2Vd9lO/+uPx2lp+vaWOVnc5wciHdPBZWsKnctn6bTxeW9elfCSnPXkZfvnR3X6ayf7RXR5G09G5I2X+2LE8l7AdkrdL8vd0Z3MKLcDXyuv54ZTxHFDyCiPzzudr5SuYMyJ6pjfGL4/fDbbEO7gyydOiQCB+pUhs27ZGIp2CmOS0J3Y0PlFby7Si6K0LiXXQqvp6js2w4+TECRO6lKfE5fx040Yer60lFIkwuaiIH2/wr0/99vUTJ5Qxf2wpJXl387XylUwqavbtnEv1PZP9oSgQiAeGi8qiZeH4bsptbzpRu5tHJsesOSUlHDlmTDwPo9tuZ8r6wy8d3eVZd8dfv/km7w/Jeeq3f/S07vOrP9JdtRDbh9o7OuLB5szNm9n47rv8YvJkThg/3re89Ob4kpyOmDH5+SwcOZLbli9nMFNwKTIMXHHFNRQH8uINhmgDItSpATG3qIi8cJhrpk3jhPFdG8sQDZ4unDKFd5ubWV61nUdqJhNyK2lzbxGMvEKbuwd4HJhBm7uXVncoLeHF8cZi7ExmTG8a/r0JHpIbXckH0EwaEN3NM9pY3smvt9TT6n5KMPJ/tLlnib7W5Wza3CEEI2fQ5ozmyOM8UrOA7a0RNvZxkJvc0Hu7qYl8M55Is769aUB0Fwj55Wl3DezeBFPdNWT8GlTzx47lT2nFMSX8AAAgAElEQVTG6U3nQ08D1kzyLJN5dLdfJpe53uRpXwTK4F9/NEf+TJil3hjTCfNpmiPP8UjNfLa3juxUPnrTgdHdfprJ/tFdJ4hfOvwDoeJ4I75zYFTbJTCCPQ3bf53lF/iVcXJ5OX/utoPLCLlfxTv8trcWp83TTPaPxH3syXhHYx4t4UL+WLMvD9eU0RIJcNn6Kh6vrePtpibWB1s6zaO7fcqv/oit32GjxvBoTQk3V4V4LziXpvDtvvVpJp1CU4uLe1x/JHfW9UVHW286UbubRybHrCdqa1lV30AwfCqP1Czwtl1efNsl1x9+6eguzzLpfOhuf8hk/4gt51+8MtVd3ZdJndxd0JtJx0Fvji9+w2KmFRTw4fr1vr8NFgouRYaBt19f0+ksZE8uJU02p6SEA0pH0+bG0+auBxIvzagAvgscClwPFBPm4Hhj8b1gOU8mBDm9afj3JniAhEZX7S4erhlBS6Q0fgbR72CX3IDo/kzlLh6pOYjmyHMJjWWAy4GfMm7cbYwb92/Aj4DjaXP30uKO5amdu/s8yA1YIN7Qu7lqIi/Vj6clfFg8yE9e30wOdt0dIDM5g9q5l3xXlzMPmTRkultOckMmeRl+Z3OSx+kuf/zS1t1+mUknSG/m0d1++V4wkvbqgUzS0ReBckz6+uNc4GZinVMt7olO5aM3HRjd7aepz/ZFuGz9R77lJdN17RoIzYkHQjdXjeS9IDSFr+eRmoVdAiPovoHt38G1K6mD63igDajw8vQLafM0k/0jtm4XTpnCq7ubuLmqhkdqJhFyK7yOxgra3DU0R1bwSM18bq6ayM728Z3Wr7t9ym+/huhDXyob2mh1fyAYOdjbh77jW59m0imUWf1R3Kn+SO6s64uOtt50onY3j/R1wy5urgrwxxpHU/g8Ivw3be7eLtvuvSCd6o9Mgjq/OjfxmPRwTYiWyFQuW78l3vnQGnFp6/5M94/548bRGinkkZrPdEl7Jtulpx1rvenwy+T4kqpOAfgwFGL02LG+vw0WeUsH+U2hg80dd9yx9Lzzzst2MkR65N9vvJ4vjShmn8JCAJ7duZPDR4+Of/cbNjovj/+urubIMWMoDuzph2ro6ODWql2E3JFAMXBk0tKOxmwT06Y9xsSJbdTXP4tz/0KYMwnzNmFXxqq693BEGJUXoLqtjQdqanAYbZEI61raeKcpyPN1u3AYo/Ly4uMAjEz4nvx753mEWFG/k0AgQGs4zJrmZh6qr2fG3E+y75TJfOHoI/n7E45lxfvreauujmB7mBV1zTgcbZEw29s6WFlfx9Fjx1IcCFBWUEBrJMJ/bNtG2DnaIhFe2b2be6sbaXU/oc1NJcwRwKld8j8QeImTTx5FOGxs23YqMNP7ZSZhdx+r6nbjiDA+P48nd+7iC2P35Pm7wSBh55hbUpJyuxxQWsonCgu5Z3strzS2s7b5UFrdfxLmGtrduYT5HI67aHd3sy7YyPvBd2lzE1lRtx2H85YbvRe0OBDg3WCQSMIygfj637F1K2FgfH5+p7Qmp8s/v5p5dlcDLZEAHwRn8mrjDna0z6IlciXrgsbO9g/ZENrN0d48/dKRyXIawo53mjpYG5zKq40F7GhvoCVyLeuCxbza2Mba5um0unra3R9YF2zk1cYP2NE+I56O94O7aXN1rKhrSsif9Nsllo7feulI3k+j+bUnj2Prcm91NZ9PsS7RebTzQM2OhHl0TkdZQQENHR3cuW0bjgBtkTCv7G7y9ssf0840wm58vMztam8jPxBg/x7kafJy/bZL8rDe1R9TgCDwT0AEqCbsXmBVXaNv2v2WkZwfyduu+/IzibXNebS602h3N7EuuKNLeckkHbF1vWtbAy3uMdrdaYT5T+BGwpxJu3udMF8HLiHMP9LBX/ko9C5Hjx0Vn0dynexXbx9QWsqcESN4qb6eP+3azf81zyTk7sPxTW+MfYFLgdOBkcBvCbsKVtWt8c3TTMpcTL4Zf6xpJhj5d8I8A/zYW8bNwNeBownzj7S7c+ng83wUuiO+fsnzTF5ucnmK1h+N3Fq1hVDk+zjOBl4Ewgn7UOf61G9fT87DxOVEnKO1S/3RwI726fG64dXGNjaFZtEW2caquqaM8rAv9tPezKP7Y9ajhPkT0U6d2DGp87YLs6FT/eFXFwJMKiri6Z1B3g1+jlcbQ0l17tusbT4k4Zi0jjDH0+5+xrrgDl5tbGNnewcbQ3UcPXa07/6Q+f6xgzb3Ezr4bZe0Z1Juk+vk5OUm7z+ZlpfEOubd4BTaHKyo25LxsSGmoaODW7fsYNL0OZx55ulk25VXXrlt6dKldyQPV3DZQwouJRetXPE8zVVVaRsQmVXeu6MNw8gl3sE9sdESU01JycU88cQ9/PKXvyAcbuXVV88jHI7g3EocS2h358QPKjvax9ASaWFdcBqvNhprm8dBYTsLjlvI23X1vLyznrzyffj2d88lWFbG6l27eL+mgWDHnmBhzzymJsyjjdO+dQZMncxbbW2UHXYY/3brrVx97VWcd96ZnHLKV1m4cCEX/vBCfnL5T+mIBHhp9eu82/gJXm00NoUmEA7U82JjPQCt4TBNwPttbXxsebzWHOJDBy3hAiLuGJybTPRs7be65MeIEefw61//kvXrN7J2bRjnYo2hKTjCtLvXWBecxPvBXbS50ayoq00IcttYWV/fTZDbGA8m/IPcWKP9csL8A+0uQphTaHdXeI3njbS5kfHlpm9ANPNu8HDeD26nzZXGp/HrKGgIR3inKcja4EhebRzF2uZxuMJWzjzrNGbvvw/vb/iYUPutwCleQ+aLhN39aYPtxAZVrOOgCVjf0YGbOpWX6xp4rfYTtLoHvIbM9wnjgFsIcxLt7gxcoJS8vJfIy3uCsPtH2iJVhLkuno7oNAHa3eusC36C94P5tLkAK+urAGiLRKhO2i6Q0ICoruGV3WPY0V5IS6SddcHpvtt2XUsL7zSFeL6uEQe0RcJenoV9G7bvByO0ubGsqNuEI0xbJMJfd+/m2bomWsJn8kFwKq82fsDa5k8kBBj74rgt3rGwsz3EhtCWeACfnKexALUhHPYa2NN4P9jUaVv7bZfuAuXM64/9KSr6Nccc46ire4xg6JL4fprc+ZCqLDy7q5GWSDQ/kjsK/PbTzuXnQcI8RrTBHQuOOjeGd7a/z4ZQc9p0rGlq4pZOgVCsDJ4LtAOrgG+SqqOpLRLm7aZmSvLy0tbbse23s8PxTvPhtLvN7AnyYmV/B3AOZgZU4fhcwrrsYkNoe3w/ziSIiXZgNHJrVR2hyMU4LvbW7TygA9gCFAGf71QHhV0Hq+qfAiIZdZx0DfqPJOQOwfGgty7JdW7n+nRney0bQsFO+3pyp1BsOXNGjOBPO+t4vq6Ztc0zEuqPo5I6Bc4lzLdwNNLu1rAuOI2d7TWdlpNZR1vyfhqhzbXFA47kTlO/TtRXdu/m2V1BWiLf8uaxsVM59atP1jZPJuTuT+h8SA7Qk793rj+SO1qi+8Jubq2qpc1dRge/Tapzv+odbxYA34jPM1r2fxzP0whPEXZHxDs9etcpFCLkrsTxE9+0Z1J/rGsJ8U5TS7xOTk5HbzoOYuVlTx1zCWH+SLu7l3XBpi7H31THhld2N3v159cYO66O8847k2xLFVzqPZc9pPdcSi6qrKzk+PnzuWbatPiT57p7919M7J2Dc+bMweUVsPaDrbS3/5hI5BDg34FXMPsxzh3a9V2ZCctftuxOnn12JXV13yPa+OmUQuBixo/fwbHHfrHbF2tH31N2O6HQRUQihxAIvElBwfXMnj2T4uKR3mtV0s8jVT4tW3Yn69Z9FJ8HRN8z9eH69cyYPdv3EeSxaUKh3WzYsIW2th/F05WYH13fUxfzNHl5ZzJlyr5UVe0kHP4dhXYv+baeDjeWsPsfigMBFk0YxYziAj4MtfNYTT0EwsyYNplNVTtoaX8AON7Ly1OB5GVUU1T0KY4++gg2b97K+vUfEw6/6Y2zANjznr58e4MOt54Cy2fRhDHxZT5Zu4tQJEDErsS5IuBfgd8mpLWVNvcRhTadfAvR4Ubg8j9k7tz9fbfLaaedwwMPHEgkkvjy+WuAZRTaZG8e9RQGalhUVsb04mI2hVr5c3MTX1u8mNaWli7bpbs8njNnNp/5zEHxbduT/TL2ovXYMotHjODh++7jy6UjmV5cxKZQK8817mbqrAPILxrPnDn7sWDBEaxYsZrHH3+OYPCuhDxeT4fbSZv7CnBGQr5/RJvrbls+zYiCUzlwzn5M8nm1QtdprgFuAy4APku+3UBxYGWnPH2mvp6WcB5E9iffWuhwjbS5Rswux7nipG39PB0uQIE1smjC6IR9soY8cyyaEH0f7YehVh6vraWsvJwZ+02jqaW1R/XHYYct8N4fuTC+PfI4juJAOKEstPJYTSMRZpFvI+hwY2lzbwBvJa1/bJ+qp8NV0+auptBeI9+eosMdRpu7hj2X6J4DHEi0ARzbDxLz9Bzy+JjiQCWLJoxMWP8GHGMoLwtzzLELyS8exX33PeVbT23dWu2zz3Xe99siEYrzNnT7XtiGjg4uW7+N5shzwArgdm9b78nTxYtPIBhs529/eyup7EMeCygOvMiiCWXMKC5KeD/rnu34ZG0DLZEzyLcWn/20cxnbU4/tWUZUtA5a+MVDeeP1N6jZOZk29/8otFUp9qlWnqwNEopc3OlWA7MLmDp1FeXlE33r3MQ83l03m+LAK/Ht9GpjM6/sru/0lNp4Hm7YTHP4e8AfgDdSlp/kPH322ZVdluOXh+n309g+dnuK+jR5v43VH8cBtyTtp7F5JG+nWD0fK09++3Z39ceLwBeBo33qseSHzETrz9GjN9Pc3JG0P3TOU1gCfBX4ojffrvvD0977rffkacjbPy7x9o9zgAOAyxLScA17ykMxeVxOcYCE+iPkbZfZCdvlb8DdXh4+RYcbT4HtYNGE0RRahMeTnjD7aE0Nz9fVJTw1v7vykly/JB9//Y4N6+lws2lzFxAI/A/f+Ma7/Pd//5ZsS/WeS5xz+uvB32GHHeZEctEvlixxZaWlbvE++7p/njbNHVJa6kbm5bnT9o1+P33SJDe6qMiNKy52p0+aFB+2z6hR7hdLlsTns3r1ard48dnusMMWuMWLz3Z33nlnp++rV69OmYbVq1e7kpKpDrY7cAl/211JyZS00/rNK9PlDqTu0vXzn1/tSkqmuEDgBgfPuUDgBldSMsX9/OdXp/w9P7/M5eWNc4V2qCsJHOAK7VBXVFSecho43kGZM7vOdxldpznewXVJ2+RqB+Wu0D7TaZlnn/3d+PoddNDfueLiT3RKa1FRuTvooMP3cn94yuXllbkDDjg8vo+defrp7qjPfc6defrp3W7r7vI483R0v1+uXr06o7QtXny2l57E+a92kLzcqx1MdvDLXm5L/2mSt4tfnqYr25239TIHEx085QrtdFcS+JwrtNMdPOUKCsa6aZOmuAkjx7rZ02e5O++8s0t+ZVp/+OdZ4n453RXaCAdPJY3zz976X++7/p3XxW/f7267pF7/5P0lVX2Q6b5/3jnnpK23v7HPPq40MNLlsSTlPDKpgwoKxroJYye48SUjXWFghINfJazbsQ7K0+6nmdRjib93Xf9YnsfydIwrtGO84S7p7zl32GELepDHnbdTHqe7kXn57vSJE+PHubLSUnfIwYf2ql7rupxPukIrTcjD2Pd0++nxDq73WdfVDo5w48bN6pIusx84mNCD+sNvX49Ns6/3W/r6Y//957m8vH2TlrnawRSffXlP/em3PyTm6cKFi1xx8eQ0+8M+rtAOdXBnwrY8xNsvr0uTH53Lw0EH/Z0rLJyQcCyd7GB0p3XNzy9zBQX7JtUPqxP2yxmuNFDqvrHPpHgZLLYRrtA+5aVroldHJOfxFG8bz3Pw54TfznaQybGhd22l/gSscT6xUtaDtVz7U3ApuSy5IZyqgdmThnxP9bThPxR1F4D6/d7TaTIJ+mPT+DcYum+kZrIu3emv/aGn6erv/TJ1MJE+EOrptuxNp09P1iFdx0Jfl+NMArBU6UjsBElXXlLv+z0JUHu3/pnuc+nq7UM/dYgrKirvcTrSlY9UHVx7GtyZdST1tKMts0DIuUDgBnfaaWfvVR6fffZ30x7n9rb+SA7QUnUSxfbTkpIpSQFH4t+eYDo5XWeffX7aoC2xLki9r293RUXl7stfPimj+iPT/SN5P9y7/SHWoZP+GOWXH92lw29dU9cPsUBwdcogF45w0eA+eTtGOwoKC2PjdhdIdq2DBltbScGlgkuRQWOwnnUczrIZ9A+W/aG/05GuoTsY1r+nBmK7ZbJf9lcHR6YB6t6s/2CZRybzHIjlZBIIDdarXLoLWnp+lj6zYLon69dX9Xx/7R+ZXz2RvjOmL7d35zzrPsjteha287575513+nSa+V8JMNiPDamCS91z2UO651JEhiq/e057et+qpKc87rmByDNtl8HH7956v3v6h4LU94pXU1IyjxUrHuyz/TGX9/Vspb0nz1aA7vfd/npuxEBLdc+lgsseUnApIiIi0v9yORDqqeEUTOe6TPbL7sYZCvv2kAsuzWwTsF+Kn6udcxN9pjkS+BnRR8KNANYBvwOWO+fCmSxXwaWIiIiI9LWhEHDI8JEquMz3GzmHNBB9lnmypuQBZvZV4GEgBNwP7AJOBP6N6MuYvt5/yRQRERERSa2iokLBpOS8XA8u651zS7sbycxGA3cSfTPsl5xza7zhVwArgVPMbLFz7r7+TKyIiIiIiMhQFch2AgbIKUA5cF8ssARwzoWIXiYL8L1sJExERERERGQoyPUzl0Vm9k1gGtAMvAW86HP/5DHe55985vEiEASONLMi51xrv6VWRERERERkiMr14HIi8PukYR+a2T855/6SMGx/7/OD5Bk45zrM7EPgIGAm8G6/pFRERERERGQIy+XLYu8CFhANMEuBTwG/AaYDz5jZIQnjjvE+G1LMKzZ8rN+PZnaema0xszU1NTV7m24REREREZEhJ2eDS+fclc65lc65audc0Dn3jnPufOAmoq8ZWdqHy7rDOTfPOTevvLy8r2YrIiIiIiIyZORscJnG7d7n0QnDYmcmx+AvNry+X1IkIiIiIiIyxA3F4DJ23WppwrD3vc+5ySObWT4wA+gANvZv0kRERERERIamoRhcxt4+mxgorvQ+j/MZ/2igBHhZT4oVERERERHpnZwMLs3sQDMr9Rk+HbjF+3pPwk8PAbXAYjOblzB+MXC19/W2fkmsiIiIiIjIMJCrryL5BnCJmb0IfAQ0ArOAE4Bi4GngxtjIzrndZnYu0SDzBTO7D9gFnET0NSUPAfcP6BqIiIiIiIgMIeacy3YaeszMvgicDxzKnleR1ANvEH3v5e+dz4qZ2eeBy4EjiAah64HfATc758IZLruGaEA72EwgenZWBjdtp9yg7ZQ7tK1yg7ZTbtB2yg3aTrljKG+r/ZxzXV6jkZPBpXRlZmucc/O6H1OySdspN2g75Q5tq9yg7ZQbtJ1yg7ZT7hiO2yon77kUERERERGRwUXBpYiIiIiIiOw1BZdDxx3ZToBkRNspN2g75Q5tq9yg7ZQbtJ1yg7ZT7hh220r3XIqIiIiIiMhe05lLERERERER2WsKLkVERERERGSvKbgUERERERGRvabgMoeZ2RQz+52ZbTWzVjPbZGb/bmbjsp224cTMyszsO2b2iJmtN7MWM2sws/8xs3PMLJA0/nQzc2n+7svWugx1XhlJle/bU0xzpJk9bWa7vG37lpn90MzyBjr9w4GZfbub8uHMLJwwvspTPzOzU8xsuZm9ZGa7vXy9p5tpelxuzGyRmb3g1Z9NZvaKmZ3V92s0NPVkO5nZHDP7iZmtNLOPzazNzKrN7DEzm59imu7K5vn9u4ZDQw+3U6/rNzM7y8z+6pWlBq9sLeq/NRt6erit7s7g2LUiaZohWabys50A6R0zmwW8DOwDPAa8B3wOuAg4zsw+75zbmcUkDidfB24DtgGrgM3AvsDXgP8AvmJmX3ddn571JvCoz/ze6ce0CjQA/+4zvCl5gJl9FXgYCAH3A7uAE4F/Az5PdNtL33oDuDLFb0cBxwDP+Pym8tR/fgYcQrSMVAEHpBu5N+XGzH4ALAd2AvcAbcApwN1m9inn3KV9tTJDWE+201XAN4C1wNNEt9H+wEnASWZ2kXPu5hTTPka0nCZb08t0Dzc9Kk+eHtVvZnYjcIk3/zuBQmAx8ISZXeCcu6UX6R6OerKtHgU2pfjtW8BM/I9dMNTKlHNOfzn4BzwLOOCCpOE3ecNvz3Yah8sf0cbuiUAgafhEooGmA/4xYfh0b9jd2U77cPsjWvFvynDc0cAOoBWYlzC8mGjHjgMWZ3udhtMfsNrL95MShqk89X++zwfmAAZ8ycvve1KM2+Ny423DENHAcnrC8HHAem+aI7KdD4P9r4fb6dvAoT7Dv0g0sG8FJvlM44BvZ3tdc/mvh9upx/UbcKQ3zXpgXNK8dnplbXq28yEX/nqyrdLMYywQ9MrUhKTfhmSZ0mWxOcg7a3ks0YbyrUk/LwGagW+ZWekAJ21Ycs6tdM494ZyLJA3fDtzuff3SgCdM9tYpQDlwn3Mu3nvonAsR7c0E+F42EjYcmdmngApgC/BUlpMzrDjnVjnn1jmvNdSN3pSbs4Ei4Bbn3KaEaeqAa72vOXl52EDqyXZyzt3tnPubz/C/AC8QPdN1ZN+nUnpYnnojVlau8cpQbLmbiLYZi4B/6qdlDyl9tK2+BYwA/uicq+2jpA1quiw2N8Xuh3jOJ6BpNLP/JRp8VgArkieWAdXufXb4/PYJM/suUEa0N3G1c+6tAUvZ8FVkZt8EphHtiHkLeNE5F04a7xjv808+83iRaE/kkWZW5Jxr7bfUSsx53udvfbYVqDwNFr0pN+mmeSZpHOl/6Y5bAJ8xsx8SPRu9BVjlnKsakJQNXz2p37orT1d44yzp81SKn3O9zzvSjDOkypSCy9y0v/f5QYrf1xENLuei4DJrzCwfONP76lfJf9n7S5zmBeAs59zm/k3dsDYR+H3SsA/N7J+8XvuYlOXMOddhZh8CBxG9j+LdfkmpAGBmI4BvAmGi9zH7UXkaHHpTbtJNs83MmoEpZlbinAv2Q5rFY2b7AQuIdgK8mGK0i5K+h83sP4Afemeope9lVL95V6xNBpqcc9t85rPO+5zbT+mUBGZ2BPAp4APn3Ko0ow6pMqXLYnPTGO+zIcXvseFjByAtktovgYOBp51zzyYMDxJ9mMJhRO8pGkf0PpdVRC+fXaFLmvvNXUQbThOBUqKV/m+I3ovyjJkdkjCuytngcSrRfP6Tc+7jpN9UngaX3pSbTKcZk+J36QNmVgTcS/SyyaWJl1R6PgQuINoZUAp8gmjZ3AR8F/jdgCV2+Ohp/abj1uASu+LmzhS/D8kypeBSpB+Y2YVEn9T2HtHr7eOcczuccz93zr3unKv3/l4kerb5FWA28J0BT/Qw4Jy70rtHtto5F3TOveOcO5/og7BGAEuzm0JJIXaA/k3yDypPInvPe0XM74k+zfd+4MbkcZxzf3HO3eKc+8CrP7c55x4keqtOHXBaUged7CXVb7nLzMYQDRTbgLv9xhmqZUrBZW7qrhc3Nrx+ANIiSbxH6i8j+oj3+c65XZlM55zrYM8lf0f3U/LEX+zBS4n5rnI2CJjZQUQfLFJF9JUJGVF5yprelJtMp0l1Nkb2ghdY3kP0FTEPAN/syQNMvKsJYmVTZW0ApKnfdNwaPL4JlNCLB/nkeplScJmb3vc+U10zP8f7THVPpvQT74bs5UTfPTXfe2JsT9R4n7qMb2D55XvKcubdTzuD6AMvNvZv0oa97h7kk47K08DrTblJN80kotuvSvdb9j0zKwD+QPQdiP8NnO4FLj2lsjbwuuS5c66Z6ANhRnplJ5nahwMn9iCfLlfcZChny5SCy9wUuyn4WDPrtA3NbBTRy1qCQOVAJ2w4M7OfEH1J+BtEA8sdvZhNhfepgGVg+eX7Su/zOJ/xjybaI/mynhTbf8ysmOhl5WHgt72YhcrTwOtNuUk3zVeSxpE+YmaFwINEz1j+F/CtXnTgxBzufaqsDZxU9ZvKU5aZ2eHAIUQf5PNCL2eTs2VKwWUOcs5tAJ4j+hCS7yf9fCXRXo7fez1YMgDM7AqiD/B5DViQ7hIIM/tscqeAN3wB8CPv6z39ktBhzMwO9Huwi5lNB27xvibm+0NALbDYzOYljF8MXO19va1fEisxXyf6AItnfB7kA6g8DUK9KTd3EX3B+A+88hibZhzwU+/r7Uif8R7e8wjwVaIdN/+U/Gozn2nm+QwLmNm/AEcQ3e5+T0aXXupl/RYrK5d7ZSg2zXSibcZWomVO+k/sipt0rx8ZsmXK+u8drtKfzGwW8DKwD/AY0Ue6H070JuAPgCOdczuzl8Lhw8zOInqzdpjoJbF+9wVtcs7d7Y3/AtFLU14meh8ZwKfZ826qK5xzVyfPQPaOmS0l+pClF4GPgEZgFnAC0XdLPQ38g3OuLWGak4k2lkPAfcAu4CSiT3Z7CDi1H1+EPeyZ2UvAF4CTnHNPpBjnBVSe+pVXDk72vk4E/p5ob/pL3rBa59ylSeP3qNyY2QXAzUTf4Xc/0YdgnAJMAX6VOH/x15PtZGZ3Ad8m2nj9NeBXj72QeNbFzBzRWz7eJHrp5RiiV0odTPRqqX9wzj3Xpys1BPVwO71AL+o3M/sVcLE3zUNAIfANou/JvMA5d0vyNNJVT+s+b5rRwFair3uc0s3JhqFZppxz+svRP2Aq0d6nbUQPxB8B/w6My3bahtMf0SeMum7+XkgY/xzgSaKPmm4i2ou4mWiD6qhsr89Q/SP6+PY/EH2Cbz3RF4XXAH8m+j5SSzHd54kGnnVAC/A20R7jvGyv01D+Aw70ys7H6fJa5WlAtkV3dWmLAsYAAAnbSURBVNwmn2l6XG6AE4G/EO34aQZeJfoev6znQS789WQ7AS9kcNxamjT/G7zts5Vox0HQq09vAWZme/1z5a+H26nX9RvRzoNXvbLU6G27Rdle/1z662Xd9z3vtz9kMP8hWaZ05lJERERERET2mu65FBERERERkb2m4FJERERERET2moJLERERERER2WsKLkVERERERGSvKbgUERERERGRvabgUkRERERERPaagksRERERERHZawouRURk2DOzF8xsSL342czmmNkjZrbdzJyZ1Wc7Tf3NzJZ66/qlbKdFRGQ4ys92AkREZGhICM42A/s750I+42wC9gMKnHMdA5i8YcXM8oBHgdnA74EqoMv28JkukwB7vnPuhb1KoIiIDEkKLkVEpK9NA34I/DLbCRnGZgCfBO50zp3Xi+mvTPPbpl6lSEREhjwFlyIi0pfqAAf8s5n9h3OuNtsJGqY+4X1u7c3EzrmlfZcUEREZLnTPpYiI9KUgcBUwBliSyQRm9iXvPrmlKX7f5F1Omzjs29403zazL5vZS2bWZGY1ZnaXmY31xjvUzJ40szrv98fNbHqatBSZ2dVm9qGZtZrZBjNbYmaFKcY/wMzuNrOPzazNzKrN7L/NbH+fce/20jzTzC4ws7fMrMXMXsgwnw4zs4fNbIeXto/M7NdmNilpPAf8xfu6xFtmyvzdG4n3OJrZWWb2N2+ddpjZ78xsYorp5pjZf5nZFi/ftnrf56QYP8/Mzjez/zWzBm8Z683sP9JMc4qZ/dXMgma2y8zuM7PJPuPNNLM7vPm1eOO+bWa3m1nZ3uWQiMjwojOXIiLS126F/9/e/cd6VddxHH++KH8tC+3ndNJYtkQtEBXpx6VgidESyEbeWSiFE8zVRquWaO5edct0OoNVo5nz1xJBCEGzWmKQOCgzXSmo2VIZ65dalCUI+vaPz+d4D+eec7n3fu/3ssnrsX139v2czzmf8znfu929z+d83h++DMyXtDgi/tTGtmYApwN3AUuADwNfAEZLWgisBe4Drgc+AEwH3iNpbES8UnO+5cAEYAWwC5gJdAMnS5oREa/NSZQ0DfgJcABwJ/AkcBTwGeBTkqZExO9r2lgETAJ+CtwNvLy3Tko6HVgJKF/b08BJwJeAmZI6IuIvufqlwGhgDinIXJfL19E+XwVOA5YBPwc6gC8CkyVNjIh/lvoyAbgHeDOwBtgMjAFm576cGhEPlOofSPp9pwJbgVuB/5D6eAawAaj+jV1A+ttYQ7oHE4FOYJykEyJiZz73EcADwFtIv8VK4GDSa8VnA98Dnmv57piZ7SccXJqZ2ZCKiF2SLgRuB64kBVvtMgP4eESsB5A0AvgFcCopWJgXET8uKku6HphLCjJX15zvWOD4iPhXrn8x8CtSADublBwHSYcDS0kjtR+NiM2lNt4PbAJ+BJxY08aJwPhSMNgnSYcCN5H+Z0+OiPtK+75Jmtv6Q1JwR0R052ypc4B1g3nFtY9Rzh0RUTeX9pPAxIh4qHSOa+mZe3tuLhNwMymYm135bTqB24BbJB1XCv67SYHlncBni8AwH3NQPlfVNGBCRPyxVPdW4CzSA4PluXgW8FZgQUQsqtyDNwF1DyDMzKyBX4s1M7MhFxErgI3AGZI62tjU0iKwzO2+Qg4AgUfKwUt2c96e0HC+y4vAMp9vB7Awf51bqncOcBjQVQ4s8zGPANcB4yUdV9PGVf0NLLOZpABoWTmwzK4hJdiZKundAzjn3nQ1fC5sqH9LObDMuoHtwOdyEAhpZHkMsLH620TEMtIo5DGkkc8i6+0FwIvA+eXAMh+zszwqWrK4HFhm1+XtKTX1X6wWRMT/IqJXuZmZNXNwaWZm7fK1vL26jW38rqasSGLzYM2+bXl7VMP51teUbSC9ujq+VPahvB2X5x3u8QHel/cfW3O+3za03aQY/by3uiMv5/Lr/HV8df9gRYQaPoc1HNLrvkXEduBh0mumxX1o7EulvOjLGNL83T9ExECSE9X9XWzN28NLZWuAF4Dv5/ms8yQdn0dYzcxsgPxarJmZtUVEbJS0ApglqTOPTA217TVlu/ux74CG8/29WhARuyU9C7yzVFwkejlvL9d3aE3Z3/ZyTNXIvP1rw/6ivCnwGw697ltW9HVkZdvfvhTbbTV1+/LvmrLit39DURART0s6hTTKOo2eV7i3Sro6IhYPsF0zs/2aRy7NzKydFpIS41zRlHGVnnltTQ88hzNoele1QNIbgbeTksgUisB1XB+jfIqIm2raiJqyvhRt1WZeBY6o1NsXet23rLjm7ZVtf/tSBIm9srwOlYjYEhGdpAcGJ5Ne/R0BLJJ0brvaNTN7PXJwaWZmbRMRTwI/IGXf/EpDtWKO46jqDknvpWe0azh8rKasgzTaVZ5TuClvJ7X9inranVzdkQPf4hrqMtMOl173TdJI0tzWHcCWXNzYl2xK3hZ9eYwUYI6VdGT9IUMjInZHxIMRcSUp8Q/Ap9vZppnZ642DSzMza7fLSAHCxdS/JvoYaVRwpqTXXj2VdAgw3K8lXpIzwRbXcDBwRf56Q6neDaQ+deXXKvcgaUTO2DoU7gCeB86S9MHKvgWkwP2eiHhmiNobjLMlVed8dpMeDCwtJeK5H3gc6JA0q1w5f58EPEGa50pEvEx6OHEIsKSUGKg45kBJ7xjsRee1Q+seXhQjsf8f7LnNzPZHnnNpZmZtFRHPS/o2cFXD/l2SFgGXAA9JWkX6/zSVlJxnIIlcWrUFeDTPFS3WuTyatCZlkYWWiHguB0OrgE2S1gKPkl55HUVK+PM2UjKblkTEC5LmkpZ2WS/pduAZ0jqXp5HmNc5vtZ2yPpYiAbgjIh6ulP0MuF/SctK8yY78eYpShtmICElzgF8CyyStJj1cOIY0Svhf4JzKGqSXktapnA48IemuXG8Uqf/fAG4cVEfTWpbzJW0A/kwaRT86t7UT+O4gz2tmtl9ycGlmZsNhMWlJidEN+7tIo0TnAfNIAdNtpNGvzQ3HtMOZpCD388CRpEQy3cB3ImKPuZIRsVbSWODrwCdIo24vkYLhe4GVQ3VREbFa0keAi3JbI0n3aAlp+ZShDsC7+tj3FCkLbNm1pEB7AdBJysB6I3BRRPyjXDEifiNpAvAt0nqk04FnSeuGXh4Rj1fqvyRpGnA+aQmYOYBI93kVeZRzkJYCB5GWSDmJNEK6jfS3d01eVsbMzPpJlf+VZmZmZv2SRzi7gCkRsW7fXo2Zme1rnnNpZmZmZmZmLXNwaWZmZmZmZi1zcGlmZmZmZmYt85xLMzMzMzMza5lHLs3MzMzMzKxlDi7NzMzMzMysZQ4uzczMzMzMrGUOLs3MzMzMzKxlDi7NzMzMzMysZa8CN2QCGduI470AAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(15,7))\n",
        "plt.plot(Model_rms_1.history[\"loss\"],color='r', linewidth=2.75, linestyle='-', marker='.', markersize=15,markerfacecolor='b',\n",
        "         markeredgecolor='black',\n",
        "         markeredgewidth=1,label=\"Training Loss\")\n",
        "plt.plot(Model_rms_1.history['val_loss'],color='black', linewidth=2.75, linestyle='-', marker='.', markersize=15,markerfacecolor='brown',\n",
        "         markeredgecolor='black',\n",
        "         markeredgewidth=1,label=\"Validation Loss\")\n",
        "plt.xlabel(\"Number of Epochs\",size=20,color=\"black\")\n",
        "plt.ylabel(\"Loss\",size=20,color=\"black\")\n",
        "plt.xticks(size=20)\n",
        "plt.yticks(size=20)\n",
        "plt.legend()\n",
        "\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cMY9ZxxiUgnh"
      },
      "source": [
        "##Model 2-ANN"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PnZrusg1MIDp"
      },
      "source": [
        "### Neural Network using Bond Features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "kTXE-qJp3HMa"
      },
      "outputs": [],
      "source": [
        "mw={\"C\":12,\"c\":12,\"O\":16,\"o\":16,\"N\":14,\"F\":19}\n",
        "def Molecular_Weight(text):\n",
        "  Mass=[]\n",
        "  for elements in text:\n",
        "    if elements==\"C\":\n",
        "      Mass.append(mw[elements])\n",
        "    elif elements==\"c\":\n",
        "      Mass.append(mw[elements])\n",
        "    elif elements==\"o\":\n",
        "      Mass.append(mw[elements])\n",
        "    elif elements==\"O\":\n",
        "      Mass.append(mw[elements])\n",
        "    elif elements==\"F\":\n",
        "      Mass.append(mw[elements])\n",
        "    elif elements==\"N\":\n",
        "      Mass.append(mw[elements])  \n",
        "  return sum(Mass)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "id": "cVXRamFQIvB4",
        "outputId": "97c6f37b-8171-4725-f10d-85484aa40836"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Nomenclature Name          Molecular Structure     Tg  \\\n",
              "0         Poly(4-biphenyl acrylate)  C=CC(=O)Oc2ccc(c1ccccc1)cc2  383.0   \n",
              "1              Poly(butyl acrylate)                CCCCOC(=O)C=C  219.0   \n",
              "2          Poly(sec-butyl acrylate)              CC(OC(=O)C=C)CC  250.0   \n",
              "3  Poly(2-tertbutylphenyl acrylate)    C=CC(=O)Oc1ccccc1C(C)(C)C  345.0   \n",
              "4  Poly(4-tertbutylphenyl acrylate)  C=CC(=O)Oc1ccc(C(C)(C)C)cc1  344.0   \n",
              "\n",
              "  Class_of_Polymer  C_Atoms_Ring  C_Atoms_Chain  O_Atoms_Chain  O_Atoms_Ring  \\\n",
              "0        Acrylates            12              3              2             0   \n",
              "1        Acrylates             0              7              2             0   \n",
              "2        Acrylates             0              7              2             0   \n",
              "3        Acrylates             6              7              2             0   \n",
              "4        Acrylates             6              7              2             0   \n",
              "\n",
              "   N_Double Bonds_Ring  N_Double Bonds_Chain  ...  No_of_Branched_C  \\\n",
              "0                  6.0                     2  ...                 0   \n",
              "1                  0.0                     2  ...                 0   \n",
              "2                  0.0                     2  ...                 0   \n",
              "3                  3.0                     2  ...                 2   \n",
              "4                  3.0                     2  ...                 2   \n",
              "\n",
              "   No_of_Branched_F  No_of_Branched_Cl  No_of_Branched_Br  No_of_Branched_=O  \\\n",
              "0                 0                  0                  0                  1   \n",
              "1                 0                  0                  0                  1   \n",
              "2                 0                  0                  0                  1   \n",
              "3                 0                  0                  0                  1   \n",
              "4                 0                  0                  0                  1   \n",
              "\n",
              "   Number_of_C=C  Number_of_C-C  Benze_Ring  Benze_Ring_var  Benze_Ring_varr  \n",
              "0              5              1           1               1                1  \n",
              "1              1              2           0               0                0  \n",
              "2              1              2           0               0                0  \n",
              "3              3              1           1               1                1  \n",
              "4              3              1           0               1                1  \n",
              "\n",
              "[5 rows x 32 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d2583018-be8f-4e98-99f4-72e0419c650b\">\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",
              "    .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>Nomenclature Name</th>\n",
              "      <th>Molecular Structure</th>\n",
              "      <th>Tg</th>\n",
              "      <th>Class_of_Polymer</th>\n",
              "      <th>C_Atoms_Ring</th>\n",
              "      <th>C_Atoms_Chain</th>\n",
              "      <th>O_Atoms_Chain</th>\n",
              "      <th>O_Atoms_Ring</th>\n",
              "      <th>N_Double Bonds_Ring</th>\n",
              "      <th>N_Double Bonds_Chain</th>\n",
              "      <th>...</th>\n",
              "      <th>No_of_Branched_C</th>\n",
              "      <th>No_of_Branched_F</th>\n",
              "      <th>No_of_Branched_Cl</th>\n",
              "      <th>No_of_Branched_Br</th>\n",
              "      <th>No_of_Branched_=O</th>\n",
              "      <th>Number_of_C=C</th>\n",
              "      <th>Number_of_C-C</th>\n",
              "      <th>Benze_Ring</th>\n",
              "      <th>Benze_Ring_var</th>\n",
              "      <th>Benze_Ring_varr</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Poly(4-biphenyl acrylate)</td>\n",
              "      <td>C=CC(=O)Oc2ccc(c1ccccc1)cc2</td>\n",
              "      <td>383.0</td>\n",
              "      <td>Acrylates</td>\n",
              "      <td>12</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Poly(butyl acrylate)</td>\n",
              "      <td>CCCCOC(=O)C=C</td>\n",
              "      <td>219.0</td>\n",
              "      <td>Acrylates</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Poly(sec-butyl acrylate)</td>\n",
              "      <td>CC(OC(=O)C=C)CC</td>\n",
              "      <td>250.0</td>\n",
              "      <td>Acrylates</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Poly(2-tertbutylphenyl acrylate)</td>\n",
              "      <td>C=CC(=O)Oc1ccccc1C(C)(C)C</td>\n",
              "      <td>345.0</td>\n",
              "      <td>Acrylates</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Poly(4-tertbutylphenyl acrylate)</td>\n",
              "      <td>C=CC(=O)Oc1ccc(C(C)(C)C)cc1</td>\n",
              "      <td>344.0</td>\n",
              "      <td>Acrylates</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 32 columns</p>\n",
              "</div>\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wJPrDt6HbY2T"
      },
      "source": [
        "### Feature Engineering"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "PBNC8xSYeoYe"
      },
      "outputs": [],
      "source": [
        "df[\"C_Atoms_Ring\"]=df[\"Molecular Structure\"].apply(lambda x: x.count('c'))\n",
        "df[\"C_Atoms_Chain\"]=df[\"Molecular Structure\"].apply(lambda x: x.count('C'))\n",
        "df[\"O_Atoms_Chain\"]=df[\"Molecular Structure\"].apply(lambda x: x.count('O'))\n",
        "df[\"O_Atoms_Ring\"]=df[\"Molecular Structure\"].apply(lambda x: x.count('o'))\n",
        "df[\"N_Double Bonds_Ring\"]=df[\"Molecular Structure\"].apply(lambda x: x.count('c')/2)\n",
        "df[\"N_Double Bonds_Chain\"]=df[\"Molecular Structure\"].apply(lambda x: x.count('='))\n",
        "df[\"Nitrogen_Atoms\"]=df[\"Molecular Structure\"].apply(lambda x: x.count('N'))\n",
        "df[\"F_Atoms\"]=df[\"Molecular Structure\"].apply(lambda x: x.count('F'))\n",
        "df[\"MW\"]=df[\"Molecular Structure\"].apply(lambda x: Molecular_Weight(x))\n",
        "df[\"Total Number of Atoms\"]=df[\"Molecular Structure\"].apply(lambda x: len(x))\n",
        "df[\"Number of Double Bonds\"]=df[\"Molecular Structure\"].apply(lambda x:len(x)-x.count('=')-1)\n",
        "df[\"Number of Triple Bonds\"]=df[\"Molecular Structure\"].apply(lambda x: x.count(\"#\"))\n",
        "df[\"Branching\"]=df[\"Molecular Structure\"].apply(lambda x: (x.count(\"(\")+x.count(\")\"))/2)\n",
        "df[\"Chlorine_Atoms\"]=df[\"Molecular Structure\"].apply(lambda x: x.count('Cl'))\n",
        "df[\"Bromine_Atoms\"]=df[\"Molecular Structure\"].apply(lambda x: x.count('Br'))\n",
        "df[\"Sulphur_Atoms\"]=df[\"Molecular Structure\"].apply(lambda x: x.count('S'))\n",
        "df[\"Two_Structures\"]=df[\"Molecular Structure\"].apply(lambda x: x.count('.'))\n",
        "df[\"If_Aromatic\"]=df[\"Molecular Structure\"].apply(lambda x: 1 if 'c' in x else 0)\n",
        "df[\"No_of_Branched_C\"]=df[\"Molecular Structure\"].apply(lambda x: x.count('(C)'))\n",
        "df[\"No_of_Branched_F\"]=df[\"Molecular Structure\"].apply(lambda x: x.count('(F)'))\n",
        "df[\"No_of_Branched_Cl\"]=df[\"Molecular Structure\"].apply(lambda x: x.count('(Cl)'))\n",
        "df[\"No_of_Branched_Br\"]=df[\"Molecular Structure\"].apply(lambda x: x.count('(Br)'))\n",
        "df[\"No_of_Branched_=O\"]=df[\"Molecular Structure\"].apply(lambda x: x.count('(=O)'))\n",
        "df[\"Number_of_C=C\"]=df[\"Molecular Structure\"].apply(lambda x: x.count('C=C')+x.count('cc'))\n",
        "df[\"Number_of_C-C\"]=df[\"Molecular Structure\"].apply(lambda x: x.count('CC'))\n",
        "df[\"Benze_Ring\"]=df[\"Molecular Structure\"].apply(lambda x: x.count('c1ccccc1'))\n",
        "df[\"Benze_Ring_var\"]=df[\"Molecular Structure\"].apply(lambda x: x.count('c1cc'))\n",
        "df[\"Benze_Ring_varr\"]=df[\"Molecular Structure\"].apply(lambda x: x.count('cc1'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 456
        },
        "id": "MB1HrSAwbyXx",
        "outputId": "30e0253c-bbe9-4f91-e46d-444ccb17d383"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Nomenclature Name          Molecular Structure     Tg  \\\n",
              "0         Poly(4-biphenyl acrylate)  C=CC(=O)Oc2ccc(c1ccccc1)cc2  383.0   \n",
              "1              Poly(butyl acrylate)                CCCCOC(=O)C=C  219.0   \n",
              "2          Poly(sec-butyl acrylate)              CC(OC(=O)C=C)CC  250.0   \n",
              "3  Poly(2-tertbutylphenyl acrylate)    C=CC(=O)Oc1ccccc1C(C)(C)C  345.0   \n",
              "4  Poly(4-tertbutylphenyl acrylate)  C=CC(=O)Oc1ccc(C(C)(C)C)cc1  344.0   \n",
              "\n",
              "  Class_of_Polymer  C_Atoms_Ring  C_Atoms_Chain  O_Atoms_Chain  O_Atoms_Ring  \\\n",
              "0        Acrylates            12              3              2             0   \n",
              "1        Acrylates             0              7              2             0   \n",
              "2        Acrylates             0              7              2             0   \n",
              "3        Acrylates             6              7              2             0   \n",
              "4        Acrylates             6              7              2             0   \n",
              "\n",
              "   N_Double Bonds_Ring  N_Double Bonds_Chain  ...  No_of_Branched_C  \\\n",
              "0                  6.0                     2  ...                 0   \n",
              "1                  0.0                     2  ...                 0   \n",
              "2                  0.0                     2  ...                 0   \n",
              "3                  3.0                     2  ...                 2   \n",
              "4                  3.0                     2  ...                 2   \n",
              "\n",
              "   No_of_Branched_F  No_of_Branched_Cl  No_of_Branched_Br  No_of_Branched_=O  \\\n",
              "0                 0                  0                  0                  1   \n",
              "1                 0                  0                  0                  1   \n",
              "2                 0                  0                  0                  1   \n",
              "3                 0                  0                  0                  1   \n",
              "4                 0                  0                  0                  1   \n",
              "\n",
              "   Number_of_C=C  Number_of_C-C  Benze_Ring  Benze_Ring_var  Benze_Ring_varr  \n",
              "0              5              1           1               1                1  \n",
              "1              1              2           0               0                0  \n",
              "2              1              2           0               0                0  \n",
              "3              3              1           1               1                1  \n",
              "4              3              1           0               1                1  \n",
              "\n",
              "[5 rows x 32 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7d595f74-a801-489d-8086-f031f7195ab8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Nomenclature Name</th>\n",
              "      <th>Molecular Structure</th>\n",
              "      <th>Tg</th>\n",
              "      <th>Class_of_Polymer</th>\n",
              "      <th>C_Atoms_Ring</th>\n",
              "      <th>C_Atoms_Chain</th>\n",
              "      <th>O_Atoms_Chain</th>\n",
              "      <th>O_Atoms_Ring</th>\n",
              "      <th>N_Double Bonds_Ring</th>\n",
              "      <th>N_Double Bonds_Chain</th>\n",
              "      <th>...</th>\n",
              "      <th>No_of_Branched_C</th>\n",
              "      <th>No_of_Branched_F</th>\n",
              "      <th>No_of_Branched_Cl</th>\n",
              "      <th>No_of_Branched_Br</th>\n",
              "      <th>No_of_Branched_=O</th>\n",
              "      <th>Number_of_C=C</th>\n",
              "      <th>Number_of_C-C</th>\n",
              "      <th>Benze_Ring</th>\n",
              "      <th>Benze_Ring_var</th>\n",
              "      <th>Benze_Ring_varr</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Poly(4-biphenyl acrylate)</td>\n",
              "      <td>C=CC(=O)Oc2ccc(c1ccccc1)cc2</td>\n",
              "      <td>383.0</td>\n",
              "      <td>Acrylates</td>\n",
              "      <td>12</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Poly(butyl acrylate)</td>\n",
              "      <td>CCCCOC(=O)C=C</td>\n",
              "      <td>219.0</td>\n",
              "      <td>Acrylates</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Poly(sec-butyl acrylate)</td>\n",
              "      <td>CC(OC(=O)C=C)CC</td>\n",
              "      <td>250.0</td>\n",
              "      <td>Acrylates</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Poly(2-tertbutylphenyl acrylate)</td>\n",
              "      <td>C=CC(=O)Oc1ccccc1C(C)(C)C</td>\n",
              "      <td>345.0</td>\n",
              "      <td>Acrylates</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Poly(4-tertbutylphenyl acrylate)</td>\n",
              "      <td>C=CC(=O)Oc1ccc(C(C)(C)C)cc1</td>\n",
              "      <td>344.0</td>\n",
              "      <td>Acrylates</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 32 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7d595f74-a801-489d-8086-f031f7195ab8')\"\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",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      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",
              "    }\n",
              "\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",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7d595f74-a801-489d-8086-f031f7195ab8 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-7d595f74-a801-489d-8086-f031f7195ab8');\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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7nG9Ur3rUsRv"
      },
      "source": [
        "## Standardization of Features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "jBi-i8M-3bz9"
      },
      "outputs": [],
      "source": [
        "X=df.iloc[:,4:]\n",
        "Y=df[[\"Tg\"]]\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X = sc.fit_transform(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nA9zZFJb4TNH",
        "outputId": "789dc9a7-ae8d-48e1-c94f-7fa3c6cfa1eb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/180\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/adam.py:105: UserWarning:\n",
            "\n",
            "The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3/3 [==============================] - 4s 450ms/step - loss: 294.9202 - val_loss: 288.3083\n",
            "Epoch 2/180\n",
            "3/3 [==============================] - 0s 87ms/step - loss: 256.5818 - val_loss: 179.9936\n",
            "Epoch 3/180\n",
            "3/3 [==============================] - 0s 48ms/step - loss: 141.3594 - val_loss: 117.1732\n",
            "Epoch 4/180\n",
            "3/3 [==============================] - 0s 38ms/step - loss: 94.1392 - val_loss: 75.2134\n",
            "Epoch 5/180\n",
            "3/3 [==============================] - 0s 48ms/step - loss: 77.1005 - val_loss: 66.3682\n",
            "Epoch 6/180\n",
            "3/3 [==============================] - 0s 46ms/step - loss: 64.2468 - val_loss: 65.8426\n",
            "Epoch 7/180\n",
            "3/3 [==============================] - 0s 30ms/step - loss: 58.0685 - val_loss: 53.6545\n",
            "Epoch 8/180\n",
            "3/3 [==============================] - 0s 35ms/step - loss: 49.0097 - val_loss: 50.0558\n",
            "Epoch 9/180\n",
            "3/3 [==============================] - 0s 41ms/step - loss: 43.5976 - val_loss: 37.7242\n",
            "Epoch 10/180\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 38.8088 - val_loss: 35.8187\n",
            "Epoch 11/180\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 37.1262 - val_loss: 33.9111\n",
            "Epoch 12/180\n",
            "3/3 [==============================] - 0s 32ms/step - loss: 32.1874 - val_loss: 27.7161\n",
            "Epoch 13/180\n",
            "3/3 [==============================] - 0s 37ms/step - loss: 30.7078 - val_loss: 29.0627\n",
            "Epoch 14/180\n",
            "3/3 [==============================] - 0s 47ms/step - loss: 28.3753 - val_loss: 28.0318\n",
            "Epoch 15/180\n",
            "3/3 [==============================] - 0s 46ms/step - loss: 26.2096 - val_loss: 24.8438\n",
            "Epoch 16/180\n",
            "3/3 [==============================] - 0s 34ms/step - loss: 24.2089 - val_loss: 30.7108\n",
            "Epoch 17/180\n",
            "3/3 [==============================] - 0s 44ms/step - loss: 26.5224 - val_loss: 24.4921\n",
            "Epoch 18/180\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 25.6844 - val_loss: 21.8637\n",
            "Epoch 19/180\n",
            "3/3 [==============================] - 0s 17ms/step - loss: 27.0769 - val_loss: 23.2478\n",
            "Epoch 20/180\n",
            "3/3 [==============================] - 0s 41ms/step - loss: 27.1898 - val_loss: 30.6887\n",
            "Epoch 21/180\n",
            "3/3 [==============================] - 0s 36ms/step - loss: 27.7655 - val_loss: 28.5276\n",
            "Epoch 22/180\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 26.0855 - val_loss: 28.6722\n",
            "Epoch 23/180\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 27.4524 - val_loss: 23.2702\n",
            "Epoch 24/180\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 26.0048 - val_loss: 23.2280\n",
            "Epoch 25/180\n",
            "3/3 [==============================] - 0s 17ms/step - loss: 23.6371 - val_loss: 25.1743\n",
            "Epoch 26/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 23.6320 - val_loss: 19.0022\n",
            "Epoch 27/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 22.0355 - val_loss: 20.0416\n",
            "Epoch 28/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 20.4110 - val_loss: 21.8754\n",
            "Epoch 29/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 23.4475 - val_loss: 21.5289\n",
            "Epoch 30/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 22.7167 - val_loss: 22.9066\n",
            "Epoch 31/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 22.8717 - val_loss: 22.0028\n",
            "Epoch 32/180\n",
            "3/3 [==============================] - 0s 11ms/step - loss: 24.7192 - val_loss: 21.6917\n",
            "Epoch 33/180\n",
            "3/3 [==============================] - 0s 14ms/step - loss: 23.2943 - val_loss: 26.9978\n",
            "Epoch 34/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 27.3673 - val_loss: 32.6379\n",
            "Epoch 35/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 28.5678 - val_loss: 31.0384\n",
            "Epoch 36/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 25.4545 - val_loss: 24.9182\n",
            "Epoch 37/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 23.3296 - val_loss: 20.8664\n",
            "Epoch 38/180\n",
            "3/3 [==============================] - 0s 11ms/step - loss: 24.7249 - val_loss: 18.7138\n",
            "Epoch 39/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 22.8377 - val_loss: 21.5220\n",
            "Epoch 40/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 22.8050 - val_loss: 27.8086\n",
            "Epoch 41/180\n",
            "3/3 [==============================] - 0s 11ms/step - loss: 25.3110 - val_loss: 34.2928\n",
            "Epoch 42/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 29.0744 - val_loss: 31.5849\n",
            "Epoch 43/180\n",
            "3/3 [==============================] - 0s 14ms/step - loss: 25.8521 - val_loss: 24.6895\n",
            "Epoch 44/180\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 27.0429 - val_loss: 24.9564\n",
            "Epoch 45/180\n",
            "3/3 [==============================] - 0s 11ms/step - loss: 24.3807 - val_loss: 22.5538\n",
            "Epoch 46/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 25.1016 - val_loss: 21.6486\n",
            "Epoch 47/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 24.1269 - val_loss: 22.1950\n",
            "Epoch 48/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 22.8464 - val_loss: 23.3707\n",
            "Epoch 49/180\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 22.4682 - val_loss: 24.9970\n",
            "Epoch 50/180\n",
            "3/3 [==============================] - 0s 14ms/step - loss: 23.5210 - val_loss: 28.3459\n",
            "Epoch 51/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 25.5378 - val_loss: 25.7884\n",
            "Epoch 52/180\n",
            "3/3 [==============================] - 0s 11ms/step - loss: 23.8767 - val_loss: 22.6665\n",
            "Epoch 53/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 23.2043 - val_loss: 23.0812\n",
            "Epoch 54/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 24.7675 - val_loss: 22.3974\n",
            "Epoch 55/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 23.0588 - val_loss: 23.3439\n",
            "Epoch 56/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 21.6793 - val_loss: 22.7999\n",
            "Epoch 57/180\n",
            "3/3 [==============================] - 0s 11ms/step - loss: 21.6435 - val_loss: 24.0050\n",
            "Epoch 58/180\n",
            "3/3 [==============================] - 0s 11ms/step - loss: 22.7600 - val_loss: 25.6653\n",
            "Epoch 59/180\n",
            "3/3 [==============================] - 0s 11ms/step - loss: 23.5128 - val_loss: 23.4018\n",
            "Epoch 60/180\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 20.9804 - val_loss: 26.1428\n",
            "Epoch 61/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 23.8936 - val_loss: 23.0040\n",
            "Epoch 62/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 22.8146 - val_loss: 20.8033\n",
            "Epoch 63/180\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 20.8721 - val_loss: 21.4791\n",
            "Epoch 64/180\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 20.8851 - val_loss: 24.1200\n",
            "Epoch 65/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 20.7855 - val_loss: 27.2614\n",
            "Epoch 66/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 21.8207 - val_loss: 23.1369\n",
            "Epoch 67/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 21.4928 - val_loss: 23.9233\n",
            "Epoch 68/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 22.2458 - val_loss: 21.3000\n",
            "Epoch 69/180\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 19.6502 - val_loss: 21.1884\n",
            "Epoch 70/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 20.9167 - val_loss: 20.5412\n",
            "Epoch 71/180\n",
            "3/3 [==============================] - 0s 11ms/step - loss: 20.2726 - val_loss: 21.1063\n",
            "Epoch 72/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 19.9955 - val_loss: 23.0553\n",
            "Epoch 73/180\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 20.5166 - val_loss: 19.4595\n",
            "Epoch 74/180\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 17.9060 - val_loss: 19.3804\n",
            "Epoch 75/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 19.0314 - val_loss: 23.3895\n",
            "Epoch 76/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 21.1380 - val_loss: 22.1513\n",
            "Epoch 77/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 21.8315 - val_loss: 17.8907\n",
            "Epoch 78/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 19.9272 - val_loss: 20.5329\n",
            "Epoch 79/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 19.9286 - val_loss: 22.7096\n",
            "Epoch 80/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 20.2981 - val_loss: 19.5752\n",
            "Epoch 81/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 17.6904 - val_loss: 22.0021\n",
            "Epoch 82/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 19.7736 - val_loss: 17.6430\n",
            "Epoch 83/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 18.3604 - val_loss: 20.5474\n",
            "Epoch 84/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 20.3982 - val_loss: 22.6948\n",
            "Epoch 85/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 20.2408 - val_loss: 19.6496\n",
            "Epoch 86/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 19.6148 - val_loss: 21.7979\n",
            "Epoch 87/180\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 19.0907 - val_loss: 19.1634\n",
            "Epoch 88/180\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 18.4983 - val_loss: 25.1548\n",
            "Epoch 89/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 19.1445 - val_loss: 19.4162\n",
            "Epoch 90/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 17.8381 - val_loss: 18.1458\n",
            "Epoch 91/180\n",
            "3/3 [==============================] - 0s 14ms/step - loss: 19.6311 - val_loss: 20.1074\n",
            "Epoch 92/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 19.2958 - val_loss: 20.4466\n",
            "Epoch 93/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 18.8021 - val_loss: 18.7544\n",
            "Epoch 94/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 18.8467 - val_loss: 20.6860\n",
            "Epoch 95/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 18.2831 - val_loss: 23.0287\n",
            "Epoch 96/180\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 21.7314 - val_loss: 23.2260\n",
            "Epoch 97/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 20.1258 - val_loss: 19.9615\n",
            "Epoch 98/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 20.6196 - val_loss: 20.7966\n",
            "Epoch 99/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 18.8698 - val_loss: 22.2926\n",
            "Epoch 100/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 20.3185 - val_loss: 18.2858\n",
            "Epoch 101/180\n",
            "3/3 [==============================] - 0s 14ms/step - loss: 20.3323 - val_loss: 23.2750\n",
            "Epoch 102/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 21.5471 - val_loss: 22.1659\n",
            "Epoch 103/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 18.4304 - val_loss: 19.9459\n",
            "Epoch 104/180\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 19.8248 - val_loss: 18.0280\n",
            "Epoch 105/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 21.6930 - val_loss: 22.1982\n",
            "Epoch 106/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 22.6021 - val_loss: 25.4424\n",
            "Epoch 107/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 27.1401 - val_loss: 25.9313\n",
            "Epoch 108/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 23.2597 - val_loss: 25.4262\n",
            "Epoch 109/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 22.5592 - val_loss: 24.7261\n",
            "Epoch 110/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 23.3428 - val_loss: 28.2730\n",
            "Epoch 111/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 21.7911 - val_loss: 20.4390\n",
            "Epoch 112/180\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 19.2218 - val_loss: 18.9910\n",
            "Epoch 113/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 19.5496 - val_loss: 18.5567\n",
            "Epoch 114/180\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 19.1684 - val_loss: 19.9541\n",
            "Epoch 115/180\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 19.4678 - val_loss: 20.1222\n",
            "Epoch 116/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 21.1156 - val_loss: 20.9415\n",
            "Epoch 117/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 20.4956 - val_loss: 21.0887\n",
            "Epoch 118/180\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 19.6325 - val_loss: 18.0715\n",
            "Epoch 119/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 19.6181 - val_loss: 22.5550\n",
            "Epoch 120/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 19.4519 - val_loss: 20.0045\n",
            "Epoch 121/180\n",
            "3/3 [==============================] - 0s 14ms/step - loss: 19.7183 - val_loss: 23.0846\n",
            "Epoch 122/180\n",
            "3/3 [==============================] - 0s 18ms/step - loss: 19.0700 - val_loss: 20.2010\n",
            "Epoch 123/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 18.5870 - val_loss: 18.5036\n",
            "Epoch 124/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 17.3660 - val_loss: 20.9810\n",
            "Epoch 125/180\n",
            "3/3 [==============================] - 0s 14ms/step - loss: 17.7314 - val_loss: 21.7440\n",
            "Epoch 126/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 18.5511 - val_loss: 23.0939\n",
            "Epoch 127/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 21.4021 - val_loss: 18.3562\n",
            "Epoch 128/180\n",
            "3/3 [==============================] - 0s 14ms/step - loss: 18.8966 - val_loss: 19.0800\n",
            "Epoch 129/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 18.7403 - val_loss: 19.8771\n",
            "Epoch 130/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 17.3819 - val_loss: 23.2193\n",
            "Epoch 131/180\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 20.4537 - val_loss: 21.7556\n",
            "Epoch 132/180\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 18.6961 - val_loss: 19.6428\n",
            "Epoch 133/180\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 17.7983 - val_loss: 19.3834\n",
            "Epoch 134/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 18.1174 - val_loss: 21.9881\n",
            "Epoch 135/180\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 18.6883 - val_loss: 19.8530\n",
            "Epoch 136/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 17.6942 - val_loss: 18.9871\n",
            "Epoch 137/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 19.1037 - val_loss: 21.6247\n",
            "Epoch 138/180\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 19.3015 - val_loss: 23.5010\n",
            "Epoch 139/180\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 20.2205 - val_loss: 19.6829\n",
            "Epoch 140/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 19.9980 - val_loss: 21.5101\n",
            "Epoch 141/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 18.7450 - val_loss: 18.2276\n",
            "Epoch 142/180\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 18.5531 - val_loss: 20.4171\n",
            "Epoch 143/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 17.7755 - val_loss: 21.9595\n",
            "Epoch 144/180\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 18.4163 - val_loss: 20.4650\n",
            "Epoch 145/180\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 17.9845 - val_loss: 18.8932\n",
            "Epoch 146/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 17.5853 - val_loss: 20.3576\n",
            "Epoch 147/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 19.1227 - val_loss: 20.4099\n",
            "Epoch 148/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 18.5110 - val_loss: 22.2790\n",
            "Epoch 149/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 19.1085 - val_loss: 20.9505\n",
            "Epoch 150/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 20.2928 - val_loss: 20.8439\n",
            "Epoch 151/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 19.9169 - val_loss: 21.5710\n",
            "Epoch 152/180\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 19.4544 - val_loss: 21.9207\n",
            "Epoch 153/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 20.3517 - val_loss: 22.8451\n",
            "Epoch 154/180\n",
            "3/3 [==============================] - 0s 14ms/step - loss: 20.5000 - val_loss: 19.6355\n",
            "Epoch 155/180\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 19.5980 - val_loss: 22.7599\n",
            "Epoch 156/180\n",
            "3/3 [==============================] - 0s 17ms/step - loss: 18.7236 - val_loss: 20.5027\n",
            "Epoch 157/180\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 18.9422 - val_loss: 21.7334\n",
            "Epoch 158/180\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 19.9869 - val_loss: 20.7515\n",
            "Epoch 159/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 18.6293 - val_loss: 19.8268\n",
            "Epoch 160/180\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 17.9165 - val_loss: 18.6889\n",
            "Epoch 161/180\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 17.7361 - val_loss: 18.4813\n",
            "Epoch 162/180\n",
            "3/3 [==============================] - 0s 14ms/step - loss: 18.0467 - val_loss: 23.1475\n",
            "Epoch 163/180\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 21.0999 - val_loss: 19.2634\n",
            "Epoch 164/180\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 19.5779 - val_loss: 19.1625\n",
            "Epoch 165/180\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 18.7586 - val_loss: 20.0740\n",
            "Epoch 166/180\n",
            "3/3 [==============================] - 0s 14ms/step - loss: 18.0579 - val_loss: 20.2118\n",
            "Epoch 167/180\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 19.3455 - val_loss: 20.3581\n",
            "Epoch 168/180\n",
            "3/3 [==============================] - 0s 14ms/step - loss: 17.9420 - val_loss: 21.0450\n",
            "Epoch 169/180\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 17.9469 - val_loss: 17.3362\n",
            "Epoch 170/180\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 18.6806 - val_loss: 19.8711\n",
            "Epoch 171/180\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 20.4223 - val_loss: 21.7201\n",
            "Epoch 172/180\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 20.6750 - val_loss: 25.7871\n",
            "Epoch 173/180\n",
            "3/3 [==============================] - 0s 14ms/step - loss: 22.7721 - val_loss: 24.6753\n",
            "Epoch 174/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 21.6003 - val_loss: 24.2403\n",
            "Epoch 175/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 22.0091 - val_loss: 29.0339\n",
            "Epoch 176/180\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 23.5685 - val_loss: 23.8033\n",
            "Epoch 177/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 20.7534 - val_loss: 22.4382\n",
            "Epoch 178/180\n",
            "3/3 [==============================] - 0s 13ms/step - loss: 19.7513 - val_loss: 21.8737\n",
            "Epoch 179/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 18.5104 - val_loss: 19.6769\n",
            "Epoch 180/180\n",
            "3/3 [==============================] - 0s 12ms/step - loss: 17.2948 - val_loss: 17.8836\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.15, random_state=0)\n",
        "model = Sequential()\n",
        "model.add(Dense(30, input_dim=X_train.shape[1],activation=\"relu\",kernel_regularizer=\"l2\"))\n",
        "model.add(Dense(10,activation=\"relu\",kernel_regularizer=\"l2\"))\n",
        "model.add(Dense(30,activation=\"relu\",kernel_regularizer=\"l2\"))\n",
        "model.add(Dense(1))\n",
        "optimizer=tf.keras.optimizers.Adam(lr=0.05)\n",
        "model.compile(optimizer=optimizer, loss='mean_absolute_error')\n",
        "Model=model.fit(x=X_train,y=y_train,epochs=180,batch_size=100,validation_split=0.1)\n",
        "  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6Wvt2Dfi5Xxs"
      },
      "outputs": [],
      "source": [
        "y_predtrain=model.predict(X_train)\n",
        "y_predtest=model.predict(X_test)\n",
        "y_p=y_predtrain.reshape(y_train.shape)\n",
        "y_a=y_train.values.reshape(y_train.shape)\n",
        "print(\"Relative Train Error in % = \",(((abs(y_p-y_a)/y_a)*100).sum())/y_train.shape)\n",
        "y_p=y_predtest.reshape(y_test.shape)\n",
        "y_a=y_test.values.reshape(y_test.shape)\n",
        "print(\"AVG % Unseen Test Error=\",(((abs(y_p-y_a)/y_a)*100).sum())/y_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "DfmMX4KNoBrL",
        "outputId": "1c12804b-bc5e-402a-be52-26d6abec2a2f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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Tg\"}},\"xaxis\":{\"title\":{\"text\":\"Predicted Tg\"}}},                        {\"responsive\": true}                    ).then(function(){\n",
              "                            \n",
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig = go.Figure(data=go.Scattergl(\n",
        "    x = y_predtrain.reshape(y_train.shape[0]),\n",
        "    y = y_train.values.reshape(y_train.shape[0]),\n",
        "    mode='markers',\n",
        "    marker=dict(\n",
        "        color=np.random.randn(y_train.shape[0]),\n",
        "        colorscale='Viridis',\n",
        "        line_width=2\n",
        "    )))\n",
        "\n",
        "\n",
        "fig.update_traces(mode='markers', marker_line_width=2, marker_size=13)\n",
        "fig.update_layout(\n",
        "    \n",
        "    yaxis_title=\"Experimental Tg\",\n",
        "    xaxis_title=\"Predicted Tg\",\n",
        "    \n",
        "    font=dict(\n",
        "        family=\"arial\",\n",
        "        size=24,\n",
        "        color=\"black\"\n",
        "    )\n",
        "    )\n",
        "\n",
        "fig.show()\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "TajQ1AF4-A5E",
        "outputId": "971378a7-09d2-4c69-975d-308d1ee45a6b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "                            \n",
              "var gd = document.getElementById('523b20e5-9de7-4155-a5e7-fd948e7d8e1d');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "\n",
        "fig = go.Figure(data=go.Scattergl(\n",
        "    x = y_predtest.reshape(y_test.shape[0]),\n",
        "    y = y_test.values.reshape(y_test.shape[0]),\n",
        "    mode='markers',\n",
        "    marker=dict(\n",
        "        color=np.random.randn(y_test.shape[0]),\n",
        "        colorscale='Viridis',\n",
        "        line_width=2\n",
        "    )))\n",
        "\n",
        "\n",
        "fig.update_traces(mode='markers', marker_line_width=2, marker_size=13)\n",
        "fig.update_layout(\n",
        "    \n",
        "    yaxis_title=\"Experimental Tg\",\n",
        "    xaxis_title=\"Predicted Tg\",\n",
        "    \n",
        "    font=dict(\n",
        "        family=\"arial\",\n",
        "        size=24,\n",
        "        color=\"black\"\n",
        "    )\n",
        "    )\n",
        "\n",
        "fig.show()\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 481
        },
        "id": "_CF7uHnFe2K-",
        "outputId": "12b7942a-4182-4edf-8416-ae5d82883d39"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(15,7))\n",
        "plt.plot(Model.history[\"loss\"],color='r', linewidth=2.75, linestyle='-', marker='.', markersize=15,markerfacecolor='b',\n",
        "         markeredgecolor='black',\n",
        "         markeredgewidth=1,label=\"Training Loss\")\n",
        "plt.plot(Model.history['val_loss'],color='green', linewidth=2.75, linestyle='-', marker='.', markersize=15,markerfacecolor='orange',\n",
        "         markeredgecolor='black',\n",
        "         markeredgewidth=1,label=\"Validation Loss\")\n",
        "plt.xlabel(\"Number of Epochs\",size=20,color=\"black\")\n",
        "plt.ylabel(\"Loss\",size=20,color=\"black\")\n",
        "\n",
        "plt.xticks(size=20)\n",
        "plt.yticks(size=20)\n",
        "plt.title(\"Mean Absolute Error vs Number of Epochs\",size=20)\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2VOhCHusVFt0",
        "outputId": "567713af-55b4-45f7-c91f-aa966a44b9d7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Relative Train Error in % =  [   5.34238383 1592.03038282]\n"
          ]
        }
      ],
      "source": [
        "y_p=y_predtrain.reshape(y_train.shape)\n",
        "y_a=y_train.values.reshape(y_train.shape)\n",
        "print(\"Relative Train Error in % = \",(((abs(y_p-y_a)/y_a)*100).sum())/y_train.shape)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HsOLIv11Vk0Z",
        "outputId": "92b14fc5-ab61-4cd3-bb0b-06f8a0e2559b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AVG % Unseen Test Error= [  6.71984734 356.15190906]\n"
          ]
        }
      ],
      "source": [
        "y_p=y_predtest.reshape(y_test.shape)\n",
        "y_a=y_test.values.reshape(y_test.shape)\n",
        "print(\"AVG % Unseen Test Error=\",(((abs(y_p-y_a)/y_a)*100).sum())/y_test.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FU4Ukm1pTtsD"
      },
      "source": [
        "### Future Work\n",
        "#### Chemical Fingerprints Approach \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "z6aZYg4Fmj4C"
      },
      "outputs": [],
      "source": [
        "from rdkit import Chem\n",
        "from rdkit.Chem import rdMolDescriptors\n",
        "from rdkit.Chem.Draw import IPythonConsole\n",
        "from rdkit.Chem import Draw"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 730
        },
        "id": "e-coP-_eoTzY",
        "outputId": "8fd3d8f5-adbe-4fd0-dbea-c40159e4bb6a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    Nomenclature Name          Molecular Structure     Tg  \\\n",
              "0           Poly(4-biphenyl acrylate)  C=CC(=O)Oc2ccc(c1ccccc1)cc2  383.0   \n",
              "1                Poly(butyl acrylate)                CCCCOC(=O)C=C  219.0   \n",
              "2            Poly(sec-butyl acrylate)              CC(OC(=O)C=C)CC  250.0   \n",
              "3    Poly(2-tertbutylphenyl acrylate)    C=CC(=O)Oc1ccccc1C(C)(C)C  345.0   \n",
              "4    Poly(4-tertbutylphenyl acrylate)  C=CC(=O)Oc1ccc(C(C)(C)C)cc1  344.0   \n",
              "..                                ...                          ...    ...   \n",
              "385             Poly(4-chlorostyrene)              C=Cc1ccc(cc1)Cl  398.0   \n",
              "386             Poly(2-chlorostyrene)                C=Cc1ccccc1Cl  395.0   \n",
              "387              Poly(4-bromostyrene)              C=Cc1ccc(cc1)Br  411.0   \n",
              "389              Poly(vinyl chloride)                        ClC=C  350.0   \n",
              "390         Poly(vinylidene chloride)                    ClC(=C)Cl  270.0   \n",
              "\n",
              "    Class_of_Polymer  C_Atoms_Ring  C_Atoms_Chain  O_Atoms_Chain  \\\n",
              "0          Acrylates            12              3              2   \n",
              "1          Acrylates             0              7              2   \n",
              "2          Acrylates             0              7              2   \n",
              "3          Acrylates             6              7              2   \n",
              "4          Acrylates             6              7              2   \n",
              "..               ...           ...            ...            ...   \n",
              "385         Styrenes             6              3              0   \n",
              "386         Styrenes             6              3              0   \n",
              "387         Styrenes             6              2              0   \n",
              "389           Others             0              3              0   \n",
              "390           Others             0              4              0   \n",
              "\n",
              "     O_Atoms_Ring  N_Double Bonds_Ring  N_Double Bonds_Chain  ...  \\\n",
              "0               0                  6.0                     2  ...   \n",
              "1               0                  0.0                     2  ...   \n",
              "2               0                  0.0                     2  ...   \n",
              "3               0                  3.0                     2  ...   \n",
              "4               0                  3.0                     2  ...   \n",
              "..            ...                  ...                   ...  ...   \n",
              "385             0                  3.0                     1  ...   \n",
              "386             0                  3.0                     1  ...   \n",
              "387             0                  3.0                     1  ...   \n",
              "389             0                  0.0                     1  ...   \n",
              "390             0                  0.0                     1  ...   \n",
              "\n",
              "     No_of_Branched_C  No_of_Branched_F  No_of_Branched_Cl  No_of_Branched_Br  \\\n",
              "0                   0                 0                  0                  0   \n",
              "1                   0                 0                  0                  0   \n",
              "2                   0                 0                  0                  0   \n",
              "3                   2                 0                  0                  0   \n",
              "4                   2                 0                  0                  0   \n",
              "..                ...               ...                ...                ...   \n",
              "385                 0                 0                  0                  0   \n",
              "386                 0                 0                  0                  0   \n",
              "387                 0                 0                  0                  0   \n",
              "389                 0                 0                  0                  0   \n",
              "390                 0                 0                  0                  0   \n",
              "\n",
              "     No_of_Branched_=O  Number_of_C=C  Number_of_C-C  Benze_Ring  \\\n",
              "0                    1              5              1           1   \n",
              "1                    1              1              2           0   \n",
              "2                    1              1              2           0   \n",
              "3                    1              3              1           1   \n",
              "4                    1              3              1           0   \n",
              "..                 ...            ...            ...         ...   \n",
              "385                  0              3              0           0   \n",
              "386                  0              3              0           1   \n",
              "387                  0              3              0           0   \n",
              "389                  0              1              0           0   \n",
              "390                  0              0              0           0   \n",
              "\n",
              "     Benze_Ring_var  Benze_Ring_varr  \n",
              "0                 1                1  \n",
              "1                 0                0  \n",
              "2                 0                0  \n",
              "3                 1                1  \n",
              "4                 1                1  \n",
              "..              ...              ...  \n",
              "385               1                1  \n",
              "386               1                1  \n",
              "387               1                1  \n",
              "389               0                0  \n",
              "390               0                0  \n",
              "\n",
              "[351 rows x 32 columns]"
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              "      <th>0</th>\n",
              "      <td>Poly(4-biphenyl acrylate)</td>\n",
              "      <td>C=CC(=O)Oc2ccc(c1ccccc1)cc2</td>\n",
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              "      <td>Poly(butyl acrylate)</td>\n",
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              "      <td>219.0</td>\n",
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              "      <th>2</th>\n",
              "      <td>Poly(sec-butyl acrylate)</td>\n",
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              "      <td>250.0</td>\n",
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              "      <th>3</th>\n",
              "      <td>Poly(2-tertbutylphenyl acrylate)</td>\n",
              "      <td>C=CC(=O)Oc1ccccc1C(C)(C)C</td>\n",
              "      <td>345.0</td>\n",
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              "      <th>4</th>\n",
              "      <td>Poly(4-tertbutylphenyl acrylate)</td>\n",
              "      <td>C=CC(=O)Oc1ccc(C(C)(C)C)cc1</td>\n",
              "      <td>344.0</td>\n",
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              "      <th>387</th>\n",
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              "      <th>389</th>\n",
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              "      <td>350.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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"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\n"
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "\n",
        "mol=Chem.MolFromSmiles(\"C=CC(=O)Oc2ccc(c1ccccc1)cc2\")\n",
        "mol2=Chem.MolFromSmiles(\"C=CC(=O)Oc1ccc(C(C)(C)C)cc1\")\n",
        "Descriptors.MolWt(mol)\n",
        "rdkit.Chem.Draw.MolToImage(mol)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1OixcFRaUAwK",
        "outputId": "9f579e0d-926d-4685-c7e2-a0162c2c4624"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([   9,  111,  139,  141,  172,  182,  231,  288,  301,  419,  541,\n",
              "         586,  632,  744,  808,  869,  871,  898,  913,  996, 1014, 1017,\n",
              "        1071, 1098, 1102, 1193, 1222, 1297, 1346, 1456, 1473, 1500, 1574,\n",
              "        1583, 1633, 1696, 1702, 1703, 1820, 1861, 1869, 1870, 1972, 2004,\n",
              "        2016, 2034, 2050]),)"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ],
      "source": [
        "bi={}\n",
        "fp=AllChem.GetMorganFingerprintAsBitVect(mol,6,2056,bitInfo=bi)\n",
        "fp_array=np.zeros((1,))\n",
        "DataStructs.ConvertToNumpyArray(fp,fp_array)\n",
        "np.nonzero(fp_array)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "p_Fv8pBPZz_0",
        "outputId": "2eb508f4-7072-4165-ff14-2e540a7e38f5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=600x1800 at 0x7F77341EE790>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 71
        }
      ],
      "source": [
        "prints=[(mol2,x,bi) for x in fp.GetOnBits()]\n",
        "prints\n",
        "\n",
        "\n",
        "Draw.DrawMorganBits(prints,molsPerRow=4,legends=[str(x) for x in fp.GetOnBits()])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "id": "DDAcfpyGnhWG"
      },
      "outputs": [],
      "source": [
        "from ipywidgets import interact,fixed,IntSlider\n",
        "def renderFpBit(mol,bitIdx,bitInfo,fn):\n",
        "    bid = bitIdx\n",
        "    return(display(fn(mol,bid,bitInfo)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217,
          "referenced_widgets": [
            "92f993625fdf40b3a56ae76df0db631e",
            "a0c2721f2dd443668ea3f5211fd0c2fe",
            "66b1860a2c494685a4ff5ee2b8dd237f",
            "b7ef44fd95b04ba8953c05d62f8cdea0",
            "2517eb66263c45c7adb08b6eb7376593",
            "e6044ebaaaab40f2bd7a144cb8bffdf1",
            "689f99d775694783a120b75e19b0e87f"
          ]
        },
        "id": "gqs7rIwfoGOn",
        "outputId": "8df94beb-78a1-4cb7-80b7-1c19ce24c4b7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "interactive(children=(Dropdown(description='bitIdx', options=(9, 111, 139, 141, 172, 182, 231, 288, 301, 419, …"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "92f993625fdf40b3a56ae76df0db631e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<function __main__.renderFpBit(mol, bitIdx, bitInfo, fn)>"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ],
      "source": [
        "interact(renderFpBit, bitIdx=list(bi.keys()),mol=fixed(mol2),\n",
        "         bitInfo=fixed(bi),fn=fixed(Draw.DrawMorganBit))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        },
        "id": "-6TNaYr6oK7j",
        "outputId": "64e96ed1-4822-4087-8209-f4595854742d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ],
      "source": [
        "Draw.MolsToGridImage([mol,mol2],4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EFgpw9NWptRG",
        "outputId": "a4fa9aac-a390-4cdd-a9ac-6105684ec7f1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[9, 139, 172, 229, 231, 322, 393, 394, 529, 541, 724, 869, 913, 1014, 1056, 1071, 1098, 1102, 1130, 1222, 1297, 1387, 1574, 1583, 1703, 1788, 1820, 1841, 1869, 1870, 1880, 1935, 1972, 1993, 2004, 2034, 2050]\n"
          ]
        }
      ],
      "source": [
        "fp2=AllChem.GetMorganFingerprintAsBitVect(mol2,4,2056,bitInfo=bi)\n",
        "fp2_array=np.zeros((1,))\n",
        "DataStructs.ConvertToNumpyArray(fp2,fp2_array)\n",
        "np.nonzero(fp2_array)\n",
        "print(list(fp2.GetOnBits()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1oKqmmM7qF3q",
        "outputId": "feb185a5-a943-49de-ce79-d1b7dadcf9cc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[9, 111, 139, 141, 172, 182, 231, 288, 301, 419, 541, 586, 632, 744, 808, 869, 871, 898, 913, 996, 1014, 1017, 1071, 1098, 1102, 1193, 1222, 1297, 1346, 1456, 1473, 1500, 1574, 1583, 1633, 1696, 1702, 1703, 1820, 1861, 1869, 1870, 1972, 2004, 2016, 2034, 2050]\n",
            "[9, 139, 172, 229, 231, 322, 393, 394, 529, 541, 724, 869, 913, 1014, 1056, 1071, 1098, 1102, 1130, 1222, 1297, 1387, 1574, 1583, 1703, 1788, 1820, 1841, 1869, 1870, 1880, 1935, 1972, 1993, 2004, 2034, 2050]\n",
            "{2050, 9, 139, 913, 1297, 1820, 541, 1574, 1703, 172, 1071, 1583, 1972, 1222, 1098, 1869, 1102, 1870, 2004, 869, 231, 2034, 1014}\n",
            "{2050, 9, 1297, 529, 1820, 541, 288, 1056, 1574, 808, 301, 1071, 1583, 1841, 1346, 322, 1861, 586, 1098, 1869, 1102, 1870, 1880, 1633, 869, 871, 1130, 1387, 111, 632, 898, 393, 394, 139, 141, 1935, 913, 1696, 419, 1702, 1703, 1193, 172, 1456, 1972, 182, 1473, 1222, 1993, 2004, 724, 1500, 2016, 996, 229, 231, 744, 2034, 1014, 1017, 1788}\n"
          ]
        }
      ],
      "source": [
        "common=set(fp.GetOnBits()) & set(fp2.GetOnBits())\n",
        "combined=set(fp.GetOnBits()) | set(fp2.GetOnBits())\n",
        "print(list(fp.GetOnBits()))\n",
        "print(list(fp2.GetOnBits()))\n",
        "print(common)\n",
        "print(combined)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eJiMF6zvsXfZ",
        "outputId": "cc0da733-1905-4f1c-b6b4-16d33a7562fd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5515413083678437"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ],
      "source": [
        "def cosine_simialrity(fp1,fp2):\n",
        "  cosine_similarity=(fp1*fp2).sum()/(np.sqrt(fp1.sum())*np.sqrt(fp2.sum()))\n",
        "  return cosine_similarity\n",
        "\n",
        "cosine_simialrity(fp_array,fp2_array)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "id": "LxKAiovZsk5E"
      },
      "outputs": [],
      "source": [
        "from numpy import dot\n",
        "from numpy.linalg import norm\n",
        "\n",
        "cos_sim = dot(fp_array, fp2_array)/(norm(fp_array)*norm(fp2_array))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LafsxilPtHuy",
        "outputId": "94db1239-83a8-4c8c-80a6-916a1025fce9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5515413083678437"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ],
      "source": [
        "cos_sim"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v5PFnTETthEq",
        "outputId": "3a434efb-8ebb-4de2-d113-d25530d6f7c4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[23, 33, 44, 49, 114, 203, 242, 248, 315, 317, 322, 342, 348, 356, 392, 393, 410, 422, 452, 500, 532, 621, 641, 650, 694, 695, 705, 718, 726, 807, 844, 849, 877, 893, 903, 959]\n"
          ]
        }
      ],
      "source": [
        "fp2=AllChem.GetMorganFingerprintAsBitVect(mol2,5,1024,bitInfo=bi)\n",
        "fp2_array=np.zeros((1,))\n",
        "DataStructs.ConvertToNumpyArray(fp2,fp2_array)\n",
        "np.nonzero(fp2_array)\n",
        "print(list(fp2.GetOnBits()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "id": "Kz5vKcqWxKrA"
      },
      "outputs": [],
      "source": [
        "listt=[]\n",
        "for i in range(df.shape[0]):\n",
        "  mol=Chem.MolFromSmiles(df.iloc[i,1])\n",
        "  fp=AllChem.GetMorganFingerprintAsBitVect(mol2,1,40,bitInfo=bi)\n",
        "  fp_array=np.zeros((1,))\n",
        "  DataStructs.ConvertToNumpyArray(fp,fp_array)\n",
        "  np.nonzero(fp2_array)\n",
        "  listt.append(fp_array)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {
        "id": "MjrfB-4mxou9"
      },
      "outputs": [],
      "source": [
        "df3=pd.DataFrame(listt)\n",
        "df4=pd.DataFrame(np.hstack((df.loc[:,\"C_Atoms_Ring\":].values,df3.values)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L39bAShAx9LZ",
        "outputId": "c175a317-5560-4282-8d0e-4848edf4db99"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(351, 68)"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ],
      "source": [
        "df4.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "metadata": {
        "id": "3l74HvPjynZl"
      },
      "outputs": [],
      "source": [
        "X=df4\n",
        "Y=df[\"Tg\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "3RpdeGiQ1_mb",
        "outputId": "52d42590-b524-4ab9-b96a-aa9d01e4d8c7"
      },
      "outputs": [
        {
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              "          document.querySelector('#df-c39db98c-e19f-43ed-8430-aefa844f24ee 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-c39db98c-e19f-43ed-8430-aefa844f24ee');\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",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X = sc.fit_transform(X)\n",
        "pd.DataFrame(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ShWc5Nm9x-Pj",
        "outputId": "60332c92-c26a-48e7-8b35-11cbaf80f224"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/adam.py:105: UserWarning:\n",
            "\n",
            "The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2/2 [==============================] - 1s 131ms/step - loss: 298.6016 - val_loss: 297.9397\n",
            "Epoch 2/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 292.2904 - val_loss: 280.7410\n",
            "Epoch 3/100\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 266.0872 - val_loss: 225.0226\n",
            "Epoch 4/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 190.3979 - val_loss: 104.9291\n",
            "Epoch 5/100\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 122.6354 - val_loss: 144.4134\n",
            "Epoch 6/100\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 145.3097 - val_loss: 93.6635\n",
            "Epoch 7/100\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 86.7010 - val_loss: 74.5869\n",
            "Epoch 8/100\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 97.8092 - val_loss: 90.5598\n",
            "Epoch 9/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 98.3636 - val_loss: 67.3735\n",
            "Epoch 10/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 64.1318 - val_loss: 84.6450\n",
            "Epoch 11/100\n",
            "2/2 [==============================] - 0s 27ms/step - loss: 74.9770 - val_loss: 75.2023\n",
            "Epoch 12/100\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 60.2559 - val_loss: 45.4174\n",
            "Epoch 13/100\n",
            "2/2 [==============================] - 0s 28ms/step - loss: 56.9492 - val_loss: 51.3089\n",
            "Epoch 14/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 56.4372 - val_loss: 38.5636\n",
            "Epoch 15/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 45.3156 - val_loss: 56.7443\n",
            "Epoch 16/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 49.2134 - val_loss: 40.0903\n",
            "Epoch 17/100\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 42.8627 - val_loss: 44.2620\n",
            "Epoch 18/100\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 47.8222 - val_loss: 35.5192\n",
            "Epoch 19/100\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 37.9649 - val_loss: 42.5262\n",
            "Epoch 20/100\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 38.7093 - val_loss: 34.5442\n",
            "Epoch 21/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 34.6035 - val_loss: 30.5538\n",
            "Epoch 22/100\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 32.0086 - val_loss: 31.1504\n",
            "Epoch 23/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 32.3542 - val_loss: 32.1208\n",
            "Epoch 24/100\n",
            "2/2 [==============================] - 0s 26ms/step - loss: 30.0632 - val_loss: 26.3542\n",
            "Epoch 25/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 30.5393 - val_loss: 26.9884\n",
            "Epoch 26/100\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 28.5546 - val_loss: 28.1241\n",
            "Epoch 27/100\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 27.7058 - val_loss: 24.1018\n",
            "Epoch 28/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 27.0770 - val_loss: 24.2215\n",
            "Epoch 29/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 27.6395 - val_loss: 26.8691\n",
            "Epoch 30/100\n",
            "2/2 [==============================] - 0s 33ms/step - loss: 25.5723 - val_loss: 22.9985\n",
            "Epoch 31/100\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 25.5077 - val_loss: 24.0408\n",
            "Epoch 32/100\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 25.8243 - val_loss: 21.0134\n",
            "Epoch 33/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 25.8360 - val_loss: 20.5926\n",
            "Epoch 34/100\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 24.2821 - val_loss: 27.4834\n",
            "Epoch 35/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 25.4036 - val_loss: 25.6373\n",
            "Epoch 36/100\n",
            "2/2 [==============================] - 0s 26ms/step - loss: 28.7678 - val_loss: 22.8387\n",
            "Epoch 37/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 25.1728 - val_loss: 26.5674\n",
            "Epoch 38/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 25.2443 - val_loss: 27.5463\n",
            "Epoch 39/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 27.8672 - val_loss: 24.0937\n",
            "Epoch 40/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 26.1931 - val_loss: 24.7488\n",
            "Epoch 41/100\n",
            "2/2 [==============================] - 0s 40ms/step - loss: 25.4428 - val_loss: 24.0561\n",
            "Epoch 42/100\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 22.4070 - val_loss: 28.9866\n",
            "Epoch 43/100\n",
            "2/2 [==============================] - 0s 31ms/step - loss: 24.8934 - val_loss: 24.1293\n",
            "Epoch 44/100\n",
            "2/2 [==============================] - 0s 25ms/step - loss: 25.2517 - val_loss: 21.7422\n",
            "Epoch 45/100\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 22.6312 - val_loss: 26.0410\n",
            "Epoch 46/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 22.4712 - val_loss: 21.9967\n",
            "Epoch 47/100\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 22.3132 - val_loss: 24.2522\n",
            "Epoch 48/100\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 22.5221 - val_loss: 21.7672\n",
            "Epoch 49/100\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 21.2328 - val_loss: 21.1747\n",
            "Epoch 50/100\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 20.4071 - val_loss: 22.0104\n",
            "Epoch 51/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 19.5609 - val_loss: 20.6400\n",
            "Epoch 52/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 20.0914 - val_loss: 21.2227\n",
            "Epoch 53/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 19.7595 - val_loss: 22.1094\n",
            "Epoch 54/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 19.3159 - val_loss: 20.5569\n",
            "Epoch 55/100\n",
            "2/2 [==============================] - 0s 25ms/step - loss: 19.1454 - val_loss: 21.4871\n",
            "Epoch 56/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 19.5912 - val_loss: 22.5676\n",
            "Epoch 57/100\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 20.2021 - val_loss: 20.4311\n",
            "Epoch 58/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 20.0102 - val_loss: 21.6008\n",
            "Epoch 59/100\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 20.0830 - val_loss: 19.9595\n",
            "Epoch 60/100\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 20.1293 - val_loss: 20.6790\n",
            "Epoch 61/100\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 19.9894 - val_loss: 21.9214\n",
            "Epoch 62/100\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 20.5933 - val_loss: 21.3827\n",
            "Epoch 63/100\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 19.2458 - val_loss: 21.2736\n",
            "Epoch 64/100\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 18.4919 - val_loss: 20.5981\n",
            "Epoch 65/100\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 18.9242 - val_loss: 20.5450\n",
            "Epoch 66/100\n",
            "2/2 [==============================] - 0s 27ms/step - loss: 18.6783 - val_loss: 20.9360\n",
            "Epoch 67/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 18.7289 - val_loss: 19.8608\n",
            "Epoch 68/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 18.0640 - val_loss: 19.4721\n",
            "Epoch 69/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 18.6933 - val_loss: 21.9795\n",
            "Epoch 70/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 18.5699 - val_loss: 21.3605\n",
            "Epoch 71/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 18.8606 - val_loss: 20.6201\n",
            "Epoch 72/100\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 18.9224 - val_loss: 24.1327\n",
            "Epoch 73/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 19.3573 - val_loss: 24.1942\n",
            "Epoch 74/100\n",
            "2/2 [==============================] - 0s 28ms/step - loss: 20.6129 - val_loss: 20.7176\n",
            "Epoch 75/100\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 19.9859 - val_loss: 23.5317\n",
            "Epoch 76/100\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 19.1425 - val_loss: 20.5789\n",
            "Epoch 77/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 20.0082 - val_loss: 21.7947\n",
            "Epoch 78/100\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 18.7216 - val_loss: 23.1543\n",
            "Epoch 79/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 19.2031 - val_loss: 20.9614\n",
            "Epoch 80/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 20.3777 - val_loss: 20.0614\n",
            "Epoch 81/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 18.7320 - val_loss: 20.5791\n",
            "Epoch 82/100\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 19.3025 - val_loss: 19.8211\n",
            "Epoch 83/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 18.3160 - val_loss: 20.4372\n",
            "Epoch 84/100\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 18.0626 - val_loss: 23.5274\n",
            "Epoch 85/100\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 19.8661 - val_loss: 22.3019\n",
            "Epoch 86/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 18.5605 - val_loss: 21.7794\n",
            "Epoch 87/100\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 18.9993 - val_loss: 22.6224\n",
            "Epoch 88/100\n",
            "2/2 [==============================] - 0s 24ms/step - loss: 19.3446 - val_loss: 22.2914\n",
            "Epoch 89/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 20.3242 - val_loss: 21.9598\n",
            "Epoch 90/100\n",
            "2/2 [==============================] - 0s 20ms/step - loss: 18.8567 - val_loss: 21.4314\n",
            "Epoch 91/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 17.9344 - val_loss: 21.5218\n",
            "Epoch 92/100\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 18.5745 - val_loss: 21.2910\n",
            "Epoch 93/100\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 17.7208 - val_loss: 22.3082\n",
            "Epoch 94/100\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 18.8973 - val_loss: 25.6250\n",
            "Epoch 95/100\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 20.4477 - val_loss: 21.6866\n",
            "Epoch 96/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 19.2510 - val_loss: 24.1827\n",
            "Epoch 97/100\n",
            "2/2 [==============================] - 0s 22ms/step - loss: 21.5262 - val_loss: 22.9600\n",
            "Epoch 98/100\n",
            "2/2 [==============================] - 0s 23ms/step - loss: 19.2891 - val_loss: 22.0030\n",
            "Epoch 99/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 18.9707 - val_loss: 21.5319\n",
            "Epoch 100/100\n",
            "2/2 [==============================] - 0s 21ms/step - loss: 17.8462 - val_loss: 21.3387\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=0)\n",
        "model = Sequential()\n",
        "model.add(Dense(30, input_dim=X_train.shape[1],activation=\"relu\",kernel_regularizer=\"l2\"))\n",
        "model.add(Dense(10,activation=\"relu\",kernel_regularizer=\"l2\"))\n",
        "model.add(Dense(30,activation=\"relu\",kernel_regularizer=\"l2\"))\n",
        "model.add(Dense(1))\n",
        "optimizer=tf.keras.optimizers.Adam(lr=0.05)\n",
        "model.compile(optimizer=optimizer, loss='mean_absolute_error')\n",
        "Model=model.fit(x=X_train,y=y_train,epochs=100,batch_size=128,validation_split=0.1) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LzWKG_E1y7Fu",
        "outputId": "c1186657-32b8-4fd2-b987-940987d2aa22"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_9\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_30 (Dense)            (None, 30)                2070      \n",
            "                                                                 \n",
            " dense_31 (Dense)            (None, 10)                310       \n",
            "                                                                 \n",
            " dense_32 (Dense)            (None, 30)                330       \n",
            "                                                                 \n",
            " dense_33 (Dense)            (None, 1)                 31        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2,741\n",
            "Trainable params: 2,741\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-gX0H7gpzB2B",
        "outputId": "fd770f9a-d9ed-41c9-aedc-82d326d06cbe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Relative Train Error in % =  [6.42517874]\n",
            "AVG % Unseen Test Error= [8.00115946]\n"
          ]
        }
      ],
      "source": [
        "y_predtrain=model.predict(X_train)\n",
        "y_predtest=model.predict(X_test)\n",
        "y_p=y_predtrain.reshape(y_train.shape)\n",
        "y_a=y_train.values.reshape(y_train.shape)\n",
        "print(\"Relative Train Error in % = \",(((abs(y_p-y_a)/y_a)*100).sum())/y_train.shape)\n",
        "y_p=y_predtest.reshape(y_test.shape)\n",
        "y_a=y_test.values.reshape(y_test.shape)\n",
        "print(\"AVG % Unseen Test Error=\",(((abs(y_p-y_a)/y_a)*100).sum())/y_test.shape)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "AI in Materials Science Final Project .ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.6"
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