Classification_ECG.ipynb

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      "name": "Klasifikace_ECG.ipynb",
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    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "<a href=\"https://colab.research.google.com/github/KristynaPijackova/Tutorials_NNs_and_signals/blob/main/Classification_ECG.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "source": [
        "# Classification of ECG signals\n",
        "\n",
        "The aim of this notebook is to introduce you how you can use recurrent layers and 1d convolutional layer for classification tasks with signals. It will also show you how you can prepare a dataset for training. \n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "We are going to use ECG5000 dataset, which has 5000 samples of ECG signals with 5 classes: \n",
        "\n",
        "1 - Normal\n",
        "\n",
        "2 - R-on-T Premature Ventricular Contraction\n",
        "\n",
        "3 - Premature Ventricular Contraction \n",
        "\n",
        "4 - Supraventricular Premature beat\n",
        "\n",
        "5 - Unclassified Beat\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "Lu7FunAaX2N_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Few jupyter notebook shortcuts you can use to make your life easier**\n",
        "\n",
        "Run cell: `shift + enter`\n",
        "\n",
        "Undo last action (inside a cell): `ctrl + m + z`\n",
        "\n",
        "Find and replace: `ctrl + h`\n",
        "\n",
        "Insert code cell above: `ctrl + a`\n",
        "\n",
        "Insert code cell below: `ctrl + b`\n",
        "\n",
        "Delete cell: `ctrl + m + d`"
      ],
      "metadata": {
        "id": "SXn1TcZXqQ2s"
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    {
      "cell_type": "markdown",
      "source": [
        "## Download data\n",
        "\n",
        "For this we use data from following page: \n",
        "\n",
        "http://www.timeseriesclassification.com/description.php?Dataset=ECG5000\n",
        "\n",
        "Options you have to download the data:\n",
        "\n",
        "\n",
        "1.   Go to the website, download it localy or if you have  and upload back into your Colab workspace. However, uploading data in here isn't the quickest and you lose the data and have to re-do the process everytime you switch workspace (**not recommended**).\n",
        "\n",
        "2.   Use command !wget along with the link which lets you download the data. Command !unzip along with the name with the zip file then unzips the content of the file.\n",
        "```\n",
        "! wget http://www.timeseriesclassification.com/Downloads/ECG5000.zip\n",
        "! unzip ECG5000.zip\n",
        "```\n",
        "\n",
        "3. Another option is to store your data in gdrive and use another command to download the data into Colab.\n",
        "```\n",
        "!gdown --id 1jo255jnoJniagZZd3IKbixb1i0kUIQCr\n",
        "!unzip /content/data.zip\n",
        "```\n",
        "To get the file id go the the sharable link for your data and copy the higlihted part (between d/ and /view?...)\n",
        "\n",
        "    https:// drive.google.com/file/d/__1jo255jnoJniagZZd3IKbixb1i0kUIQCr__/view?usp=sharing\n",
        "\n",
        "Mind that you lose the data with the later two options as well after you switch workspace, but the process of uploading the data back this way is much faster.\n"
      ],
      "metadata": {
        "id": "VbYkDGBAX_4x"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "### Download the ECG dataset \n",
        "\n",
        "Here we use the direct link to the website along with !wget and unzip the file - if you don't know the name of the file you can see the name of the downloaded file in the left panel if you click on the folder icon 📁"
      ],
      "metadata": {
        "id": "EeUv5JthcxuJ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "RWqSe_Ge6DJQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b3816825-8a65-4844-c359-f529a022299e"
      },
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "--2021-12-21 21:56:05--  http://www.timeseriesclassification.com/Downloads/ECG5000.zip\n",
            "Resolving www.timeseriesclassification.com (www.timeseriesclassification.com)... 109.123.71.232\n",
            "Connecting to www.timeseriesclassification.com (www.timeseriesclassification.com)|109.123.71.232|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 10614407 (10M) [application/zip]\n",
            "Saving to: ‘ECG5000.zip’\n",
            "\n",
            "ECG5000.zip         100%[===================>]  10.12M  13.0MB/s    in 0.8s    \n",
            "\n",
            "2021-12-21 21:56:06 (13.0 MB/s) - ‘ECG5000.zip’ saved [10614407/10614407]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "! wget http://www.timeseriesclassification.com/Downloads/ECG5000.zip"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Or you can use another unix command !ls /content/ (this is how the folder where you work with in here is named). \n",
        "\n",
        "This shows you the file stored in here. "
      ],
      "metadata": {
        "id": "j4yHVe0pdUU6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!ls /content/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uTa-aoQZdXXM",
        "outputId": "56778016-b361-4dc7-b918-4a2808036e28"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ECG5000.zip  sample_data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we know how our file is named we can unzip it"
      ],
      "metadata": {
        "id": "UQAIJMiSdsjX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "! unzip ECG5000.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BLM0hRB2dylJ",
        "outputId": "edeccfd8-91ee-4f75-a95b-8ea849ea57ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  ECG5000.zip\n",
            "  inflating: ECG5000.txt             \n",
            "  inflating: ECG5000_TEST.arff       \n",
            "  inflating: ECG5000_TEST.txt        \n",
            "  inflating: ECG5000_TRAIN.arff      \n",
            "  inflating: ECG5000_TRAIN.txt       \n",
            "  inflating: ECG5000_TEST.ts         \n",
            "  inflating: ECG5000_TRAIN.ts        \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We see there are quite few files - we are going to use the text files `ECG5000_TEST.txt` and `ECG5000_TRAIN.txt`\n",
        "\n",
        "And we can once again use another unix command !head to get a peak at how are data looks like."
      ],
      "metadata": {
        "id": "kUN3U4M2dpf_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "! head /content/ECG5000_TRAIN.txt"
      ],
      "metadata": {
        "id": "qMiaPz-aXMZy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Import libraries\n",
        "\n",
        "Now that we have a basic idea what we are going to work with, we can import libraries we are about to use."
      ],
      "metadata": {
        "id": "RLaBEwMUfHiO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.models import Model\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn import preprocessing"
      ],
      "metadata": {
        "id": "4nF9o-trWOXB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Let's prepare our data\n",
        "\n",
        "We start by creating a dataframe - basicly we just get content of the textfiles into pandas dataframe so it is easier for us to work with it."
      ],
      "metadata": {
        "id": "yZuoZ51Ef1rz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_train = pd.read_csv('/content/ECG5000_TEST.txt', sep='  ', header=None)\n",
        "df_test = pd.read_csv('/content/ECG5000_TRAIN.txt', sep='  ', header=None)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UYt3gvQbWTxe",
        "outputId": "81317d3d-a97f-4c8c-e0ff-5b1ffca2740c"
      },
      "execution_count": null,
      "outputs": [
        {
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          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n",
            "  \n"
          ]
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      "cell_type": "code",
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        "df_train"
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      "metadata": {
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          "height": 487
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        "id": "CRe357VuXYfW",
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              "      <td>-0.111385</td>\n",
              "      <td>-0.128527</td>\n",
              "      <td>-0.047618</td>\n",
              "      <td>-0.016406</td>\n",
              "      <td>0.108356</td>\n",
              "      <td>0.293390</td>\n",
              "      <td>0.937984</td>\n",
              "      <td>1.570938</td>\n",
              "      <td>1.591394</td>\n",
              "      <td>1.549193</td>\n",
              "      <td>1.193077</td>\n",
              "      <td>0.515134</td>\n",
              "      <td>0.126274</td>\n",
              "      <td>0.267532</td>\n",
              "      <td>1.071148</td>\n",
              "      <td>-1.164009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1.024295</td>\n",
              "      <td>-0.590314</td>\n",
              "      <td>-1.916949</td>\n",
              "      <td>-2.806989</td>\n",
              "      <td>-3.527905</td>\n",
              "      <td>-3.638675</td>\n",
              "      <td>-2.779767</td>\n",
              "      <td>-2.019031</td>\n",
              "      <td>-1.980754</td>\n",
              "      <td>-1.440680</td>\n",
              "      <td>-0.842542</td>\n",
              "      <td>-0.829726</td>\n",
              "      <td>-0.790482</td>\n",
              "      <td>-0.785441</td>\n",
              "      <td>-0.835461</td>\n",
              "      <td>-0.796361</td>\n",
              "      <td>-0.771146</td>\n",
              "      <td>-0.815385</td>\n",
              "      <td>-0.735402</td>\n",
              "      <td>-0.798965</td>\n",
              "      <td>-0.679510</td>\n",
              "      <td>-0.683254</td>\n",
              "      <td>-0.739599</td>\n",
              "      <td>-0.815913</td>\n",
              "      <td>-0.902384</td>\n",
              "      <td>-0.813008</td>\n",
              "      <td>-0.855560</td>\n",
              "      <td>-0.793842</td>\n",
              "      <td>-0.853332</td>\n",
              "      <td>-0.843024</td>\n",
              "      <td>-0.844967</td>\n",
              "      <td>-0.917276</td>\n",
              "      <td>-0.979700</td>\n",
              "      <td>-1.031643</td>\n",
              "      <td>-0.958200</td>\n",
              "      <td>-1.008464</td>\n",
              "      <td>-1.043407</td>\n",
              "      <td>-1.009558</td>\n",
              "      <td>-1.001219</td>\n",
              "      <td>...</td>\n",
              "      <td>0.443230</td>\n",
              "      <td>0.663832</td>\n",
              "      <td>0.939063</td>\n",
              "      <td>1.196396</td>\n",
              "      <td>1.238014</td>\n",
              "      <td>1.310015</td>\n",
              "      <td>1.594162</td>\n",
              "      <td>1.836205</td>\n",
              "      <td>1.873225</td>\n",
              "      <td>1.960083</td>\n",
              "      <td>1.803756</td>\n",
              "      <td>1.718426</td>\n",
              "      <td>1.592971</td>\n",
              "      <td>1.344949</td>\n",
              "      <td>1.166471</td>\n",
              "      <td>0.695750</td>\n",
              "      <td>0.279590</td>\n",
              "      <td>0.180811</td>\n",
              "      <td>0.201507</td>\n",
              "      <td>0.191033</td>\n",
              "      <td>0.153371</td>\n",
              "      <td>0.178460</td>\n",
              "      <td>0.263660</td>\n",
              "      <td>0.271427</td>\n",
              "      <td>0.271596</td>\n",
              "      <td>0.356748</td>\n",
              "      <td>0.257862</td>\n",
              "      <td>0.277773</td>\n",
              "      <td>0.331739</td>\n",
              "      <td>0.384731</td>\n",
              "      <td>0.368820</td>\n",
              "      <td>0.443502</td>\n",
              "      <td>0.827582</td>\n",
              "      <td>1.237007</td>\n",
              "      <td>1.235121</td>\n",
              "      <td>1.738103</td>\n",
              "      <td>1.800767</td>\n",
              "      <td>1.816301</td>\n",
              "      <td>1.473963</td>\n",
              "      <td>1.389767</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.545657</td>\n",
              "      <td>-1.014383</td>\n",
              "      <td>-2.316698</td>\n",
              "      <td>-3.634040</td>\n",
              "      <td>-4.196857</td>\n",
              "      <td>-3.758093</td>\n",
              "      <td>-3.194444</td>\n",
              "      <td>-2.221764</td>\n",
              "      <td>-1.588554</td>\n",
              "      <td>-1.202146</td>\n",
              "      <td>-0.589493</td>\n",
              "      <td>-0.455109</td>\n",
              "      <td>-0.201613</td>\n",
              "      <td>0.055583</td>\n",
              "      <td>-0.095133</td>\n",
              "      <td>0.023464</td>\n",
              "      <td>-0.291556</td>\n",
              "      <td>-0.405063</td>\n",
              "      <td>-0.269354</td>\n",
              "      <td>-0.197477</td>\n",
              "      <td>-0.032429</td>\n",
              "      <td>-0.501257</td>\n",
              "      <td>-0.399656</td>\n",
              "      <td>-0.283003</td>\n",
              "      <td>-0.202945</td>\n",
              "      <td>-0.570158</td>\n",
              "      <td>-0.777244</td>\n",
              "      <td>-0.419418</td>\n",
              "      <td>-0.375513</td>\n",
              "      <td>-0.638091</td>\n",
              "      <td>-0.969082</td>\n",
              "      <td>-0.780202</td>\n",
              "      <td>-0.617075</td>\n",
              "      <td>-0.898172</td>\n",
              "      <td>-0.970493</td>\n",
              "      <td>-0.774366</td>\n",
              "      <td>-0.650623</td>\n",
              "      <td>-0.680303</td>\n",
              "      <td>-1.015144</td>\n",
              "      <td>...</td>\n",
              "      <td>1.602372</td>\n",
              "      <td>1.800425</td>\n",
              "      <td>2.146134</td>\n",
              "      <td>1.995252</td>\n",
              "      <td>1.892413</td>\n",
              "      <td>2.119649</td>\n",
              "      <td>1.480067</td>\n",
              "      <td>1.071041</td>\n",
              "      <td>1.186209</td>\n",
              "      <td>0.863298</td>\n",
              "      <td>0.301564</td>\n",
              "      <td>-0.126937</td>\n",
              "      <td>-0.301484</td>\n",
              "      <td>-0.436105</td>\n",
              "      <td>-0.284822</td>\n",
              "      <td>-0.224644</td>\n",
              "      <td>-0.308797</td>\n",
              "      <td>-0.301286</td>\n",
              "      <td>-0.131489</td>\n",
              "      <td>-0.388988</td>\n",
              "      <td>-0.400571</td>\n",
              "      <td>-0.340024</td>\n",
              "      <td>-0.413135</td>\n",
              "      <td>-0.391857</td>\n",
              "      <td>-0.360207</td>\n",
              "      <td>-0.280891</td>\n",
              "      <td>-0.314775</td>\n",
              "      <td>0.061143</td>\n",
              "      <td>0.294456</td>\n",
              "      <td>0.324627</td>\n",
              "      <td>0.894638</td>\n",
              "      <td>0.777530</td>\n",
              "      <td>1.119240</td>\n",
              "      <td>0.902984</td>\n",
              "      <td>0.554098</td>\n",
              "      <td>0.497053</td>\n",
              "      <td>0.418116</td>\n",
              "      <td>0.703108</td>\n",
              "      <td>1.064602</td>\n",
              "      <td>-0.044853</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.661133</td>\n",
              "      <td>-1.552471</td>\n",
              "      <td>-3.124641</td>\n",
              "      <td>-4.313351</td>\n",
              "      <td>-4.017042</td>\n",
              "      <td>-3.005993</td>\n",
              "      <td>-1.832411</td>\n",
              "      <td>-1.503886</td>\n",
              "      <td>-1.071705</td>\n",
              "      <td>-0.521316</td>\n",
              "      <td>-0.386373</td>\n",
              "      <td>-0.469063</td>\n",
              "      <td>-0.471558</td>\n",
              "      <td>-0.437022</td>\n",
              "      <td>-0.431340</td>\n",
              "      <td>-0.311658</td>\n",
              "      <td>-0.341819</td>\n",
              "      <td>-0.397136</td>\n",
              "      <td>-0.476920</td>\n",
              "      <td>-0.459986</td>\n",
              "      <td>-0.390030</td>\n",
              "      <td>-0.519005</td>\n",
              "      <td>-0.773094</td>\n",
              "      <td>-0.672552</td>\n",
              "      <td>-0.488372</td>\n",
              "      <td>-0.606983</td>\n",
              "      <td>-0.806713</td>\n",
              "      <td>-0.904297</td>\n",
              "      <td>-0.756202</td>\n",
              "      <td>-0.720340</td>\n",
              "      <td>-0.967101</td>\n",
              "      <td>-0.865493</td>\n",
              "      <td>-0.888156</td>\n",
              "      <td>-1.003868</td>\n",
              "      <td>-0.966961</td>\n",
              "      <td>-1.058695</td>\n",
              "      <td>-1.150677</td>\n",
              "      <td>-1.154902</td>\n",
              "      <td>-0.868811</td>\n",
              "      <td>...</td>\n",
              "      <td>1.372542</td>\n",
              "      <td>1.683346</td>\n",
              "      <td>1.834551</td>\n",
              "      <td>1.902015</td>\n",
              "      <td>1.861268</td>\n",
              "      <td>1.680313</td>\n",
              "      <td>1.607964</td>\n",
              "      <td>1.364396</td>\n",
              "      <td>1.180712</td>\n",
              "      <td>0.933070</td>\n",
              "      <td>0.622765</td>\n",
              "      <td>0.419651</td>\n",
              "      <td>0.196120</td>\n",
              "      <td>0.136137</td>\n",
              "      <td>0.187574</td>\n",
              "      <td>0.235852</td>\n",
              "      <td>0.154591</td>\n",
              "      <td>0.154583</td>\n",
              "      <td>0.210870</td>\n",
              "      <td>0.316320</td>\n",
              "      <td>0.332887</td>\n",
              "      <td>0.334439</td>\n",
              "      <td>0.273814</td>\n",
              "      <td>0.216870</td>\n",
              "      <td>0.255356</td>\n",
              "      <td>0.275036</td>\n",
              "      <td>0.361964</td>\n",
              "      <td>0.406368</td>\n",
              "      <td>0.920488</td>\n",
              "      <td>1.481026</td>\n",
              "      <td>1.416582</td>\n",
              "      <td>1.280823</td>\n",
              "      <td>1.494316</td>\n",
              "      <td>1.618764</td>\n",
              "      <td>1.447449</td>\n",
              "      <td>1.238577</td>\n",
              "      <td>1.749692</td>\n",
              "      <td>1.986803</td>\n",
              "      <td>1.422756</td>\n",
              "      <td>-0.357784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4495</th>\n",
              "      <td>4.0</td>\n",
              "      <td>-1.122969</td>\n",
              "      <td>-2.252925</td>\n",
              "      <td>-2.867628</td>\n",
              "      <td>-3.358605</td>\n",
              "      <td>-3.167849</td>\n",
              "      <td>-2.638360</td>\n",
              "      <td>-1.664162</td>\n",
              "      <td>-0.935655</td>\n",
              "      <td>-0.866953</td>\n",
              "      <td>-0.645363</td>\n",
              "      <td>-0.093342</td>\n",
              "      <td>0.099746</td>\n",
              "      <td>0.121585</td>\n",
              "      <td>0.125760</td>\n",
              "      <td>0.071543</td>\n",
              "      <td>0.050503</td>\n",
              "      <td>0.021200</td>\n",
              "      <td>0.026780</td>\n",
              "      <td>0.158262</td>\n",
              "      <td>0.099955</td>\n",
              "      <td>-0.009558</td>\n",
              "      <td>0.078789</td>\n",
              "      <td>0.086691</td>\n",
              "      <td>0.046382</td>\n",
              "      <td>0.068272</td>\n",
              "      <td>0.102911</td>\n",
              "      <td>0.106083</td>\n",
              "      <td>0.057099</td>\n",
              "      <td>0.028555</td>\n",
              "      <td>0.046705</td>\n",
              "      <td>0.046524</td>\n",
              "      <td>0.095233</td>\n",
              "      <td>0.059824</td>\n",
              "      <td>-0.021798</td>\n",
              "      <td>-0.022737</td>\n",
              "      <td>-0.083263</td>\n",
              "      <td>-0.125486</td>\n",
              "      <td>-0.082084</td>\n",
              "      <td>-0.023000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.643776</td>\n",
              "      <td>0.597168</td>\n",
              "      <td>0.625141</td>\n",
              "      <td>0.648181</td>\n",
              "      <td>0.594234</td>\n",
              "      <td>0.630589</td>\n",
              "      <td>0.654510</td>\n",
              "      <td>0.602251</td>\n",
              "      <td>0.656946</td>\n",
              "      <td>0.603251</td>\n",
              "      <td>0.599274</td>\n",
              "      <td>0.561487</td>\n",
              "      <td>0.547185</td>\n",
              "      <td>0.537524</td>\n",
              "      <td>0.603885</td>\n",
              "      <td>0.541677</td>\n",
              "      <td>0.580715</td>\n",
              "      <td>0.689346</td>\n",
              "      <td>0.746181</td>\n",
              "      <td>0.867390</td>\n",
              "      <td>0.783529</td>\n",
              "      <td>0.698720</td>\n",
              "      <td>0.798727</td>\n",
              "      <td>0.875711</td>\n",
              "      <td>0.985118</td>\n",
              "      <td>0.799260</td>\n",
              "      <td>0.577579</td>\n",
              "      <td>0.394182</td>\n",
              "      <td>0.458721</td>\n",
              "      <td>0.411458</td>\n",
              "      <td>0.205543</td>\n",
              "      <td>-0.472419</td>\n",
              "      <td>-1.310147</td>\n",
              "      <td>-2.029521</td>\n",
              "      <td>-3.221294</td>\n",
              "      <td>-4.176790</td>\n",
              "      <td>-4.009720</td>\n",
              "      <td>-2.874136</td>\n",
              "      <td>-2.008369</td>\n",
              "      <td>-1.808334</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4496</th>\n",
              "      <td>2.0</td>\n",
              "      <td>-0.547705</td>\n",
              "      <td>-1.889545</td>\n",
              "      <td>-2.839779</td>\n",
              "      <td>-3.457912</td>\n",
              "      <td>-3.929149</td>\n",
              "      <td>-3.966026</td>\n",
              "      <td>-3.492560</td>\n",
              "      <td>-2.695269</td>\n",
              "      <td>-1.849691</td>\n",
              "      <td>-1.374321</td>\n",
              "      <td>-1.288459</td>\n",
              "      <td>-1.117578</td>\n",
              "      <td>-0.536271</td>\n",
              "      <td>-0.251050</td>\n",
              "      <td>-0.215496</td>\n",
              "      <td>-0.072500</td>\n",
              "      <td>-0.170556</td>\n",
              "      <td>-0.182279</td>\n",
              "      <td>-0.271306</td>\n",
              "      <td>-0.242693</td>\n",
              "      <td>-0.225397</td>\n",
              "      <td>-0.187956</td>\n",
              "      <td>-0.215918</td>\n",
              "      <td>-0.173543</td>\n",
              "      <td>-0.233401</td>\n",
              "      <td>-0.190231</td>\n",
              "      <td>-0.160668</td>\n",
              "      <td>-0.241456</td>\n",
              "      <td>-0.204146</td>\n",
              "      <td>-0.228155</td>\n",
              "      <td>-0.084758</td>\n",
              "      <td>-0.142782</td>\n",
              "      <td>-0.153214</td>\n",
              "      <td>-0.314872</td>\n",
              "      <td>-0.335928</td>\n",
              "      <td>-0.266875</td>\n",
              "      <td>-0.313519</td>\n",
              "      <td>-0.304439</td>\n",
              "      <td>-0.294795</td>\n",
              "      <td>...</td>\n",
              "      <td>0.427870</td>\n",
              "      <td>0.471064</td>\n",
              "      <td>0.532288</td>\n",
              "      <td>0.541352</td>\n",
              "      <td>0.588983</td>\n",
              "      <td>0.753851</td>\n",
              "      <td>0.650792</td>\n",
              "      <td>0.720441</td>\n",
              "      <td>0.752308</td>\n",
              "      <td>0.813847</td>\n",
              "      <td>0.690120</td>\n",
              "      <td>0.685244</td>\n",
              "      <td>0.632990</td>\n",
              "      <td>0.688632</td>\n",
              "      <td>0.676820</td>\n",
              "      <td>0.639733</td>\n",
              "      <td>0.708132</td>\n",
              "      <td>0.675063</td>\n",
              "      <td>0.646640</td>\n",
              "      <td>0.635341</td>\n",
              "      <td>0.670459</td>\n",
              "      <td>0.732434</td>\n",
              "      <td>0.866642</td>\n",
              "      <td>0.845284</td>\n",
              "      <td>1.045767</td>\n",
              "      <td>1.429693</td>\n",
              "      <td>1.783791</td>\n",
              "      <td>1.697750</td>\n",
              "      <td>1.492639</td>\n",
              "      <td>1.378843</td>\n",
              "      <td>1.218185</td>\n",
              "      <td>1.258419</td>\n",
              "      <td>1.907529</td>\n",
              "      <td>2.280888</td>\n",
              "      <td>1.895242</td>\n",
              "      <td>1.437702</td>\n",
              "      <td>1.193433</td>\n",
              "      <td>1.261335</td>\n",
              "      <td>1.150449</td>\n",
              "      <td>0.804932</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4497</th>\n",
              "      <td>2.0</td>\n",
              "      <td>-1.351779</td>\n",
              "      <td>-2.209006</td>\n",
              "      <td>-2.520225</td>\n",
              "      <td>-3.061475</td>\n",
              "      <td>-3.065141</td>\n",
              "      <td>-3.030739</td>\n",
              "      <td>-2.622720</td>\n",
              "      <td>-2.044092</td>\n",
              "      <td>-1.295874</td>\n",
              "      <td>-0.733839</td>\n",
              "      <td>-0.562769</td>\n",
              "      <td>-0.461068</td>\n",
              "      <td>-0.310064</td>\n",
              "      <td>-0.004490</td>\n",
              "      <td>0.184161</td>\n",
              "      <td>0.252896</td>\n",
              "      <td>0.302077</td>\n",
              "      <td>0.237223</td>\n",
              "      <td>0.304171</td>\n",
              "      <td>0.276120</td>\n",
              "      <td>0.330877</td>\n",
              "      <td>0.356674</td>\n",
              "      <td>0.323301</td>\n",
              "      <td>0.413409</td>\n",
              "      <td>0.366606</td>\n",
              "      <td>0.328516</td>\n",
              "      <td>0.358460</td>\n",
              "      <td>0.353359</td>\n",
              "      <td>0.451590</td>\n",
              "      <td>0.419947</td>\n",
              "      <td>0.330580</td>\n",
              "      <td>0.317684</td>\n",
              "      <td>0.270813</td>\n",
              "      <td>0.258002</td>\n",
              "      <td>0.317464</td>\n",
              "      <td>0.358579</td>\n",
              "      <td>0.295808</td>\n",
              "      <td>0.276734</td>\n",
              "      <td>0.230999</td>\n",
              "      <td>...</td>\n",
              "      <td>0.716374</td>\n",
              "      <td>0.802243</td>\n",
              "      <td>0.803918</td>\n",
              "      <td>0.826138</td>\n",
              "      <td>0.774642</td>\n",
              "      <td>0.637822</td>\n",
              "      <td>0.747995</td>\n",
              "      <td>0.762681</td>\n",
              "      <td>0.807652</td>\n",
              "      <td>0.816406</td>\n",
              "      <td>0.818481</td>\n",
              "      <td>0.900709</td>\n",
              "      <td>0.876553</td>\n",
              "      <td>0.861227</td>\n",
              "      <td>0.764280</td>\n",
              "      <td>0.628381</td>\n",
              "      <td>0.485624</td>\n",
              "      <td>0.462129</td>\n",
              "      <td>0.442520</td>\n",
              "      <td>0.402201</td>\n",
              "      <td>0.380095</td>\n",
              "      <td>0.289096</td>\n",
              "      <td>0.214385</td>\n",
              "      <td>0.106227</td>\n",
              "      <td>-0.009595</td>\n",
              "      <td>-0.025391</td>\n",
              "      <td>0.035677</td>\n",
              "      <td>-0.030661</td>\n",
              "      <td>-0.203568</td>\n",
              "      <td>-0.445023</td>\n",
              "      <td>-0.896575</td>\n",
              "      <td>-1.512234</td>\n",
              "      <td>-2.076075</td>\n",
              "      <td>-2.586042</td>\n",
              "      <td>-3.322799</td>\n",
              "      <td>-3.627311</td>\n",
              "      <td>-3.437038</td>\n",
              "      <td>-2.260023</td>\n",
              "      <td>-1.577823</td>\n",
              "      <td>-0.684531</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4498</th>\n",
              "      <td>2.0</td>\n",
              "      <td>-1.124432</td>\n",
              "      <td>-1.905039</td>\n",
              "      <td>-2.192707</td>\n",
              "      <td>-2.904320</td>\n",
              "      <td>-2.900722</td>\n",
              "      <td>-2.761252</td>\n",
              "      <td>-2.569705</td>\n",
              "      <td>-2.043893</td>\n",
              "      <td>-1.490538</td>\n",
              "      <td>-0.938473</td>\n",
              "      <td>-0.485372</td>\n",
              "      <td>-0.374298</td>\n",
              "      <td>-0.389414</td>\n",
              "      <td>-0.146131</td>\n",
              "      <td>0.212746</td>\n",
              "      <td>0.429797</td>\n",
              "      <td>0.499480</td>\n",
              "      <td>0.536705</td>\n",
              "      <td>0.558162</td>\n",
              "      <td>0.543721</td>\n",
              "      <td>0.550670</td>\n",
              "      <td>0.560516</td>\n",
              "      <td>0.537170</td>\n",
              "      <td>0.537986</td>\n",
              "      <td>0.549989</td>\n",
              "      <td>0.519434</td>\n",
              "      <td>0.508304</td>\n",
              "      <td>0.540967</td>\n",
              "      <td>0.570611</td>\n",
              "      <td>0.587472</td>\n",
              "      <td>0.556779</td>\n",
              "      <td>0.519021</td>\n",
              "      <td>0.507215</td>\n",
              "      <td>0.464078</td>\n",
              "      <td>0.450806</td>\n",
              "      <td>0.475988</td>\n",
              "      <td>0.485856</td>\n",
              "      <td>0.466050</td>\n",
              "      <td>0.392451</td>\n",
              "      <td>...</td>\n",
              "      <td>0.762903</td>\n",
              "      <td>0.757111</td>\n",
              "      <td>0.704867</td>\n",
              "      <td>0.726405</td>\n",
              "      <td>0.720765</td>\n",
              "      <td>0.769695</td>\n",
              "      <td>0.809712</td>\n",
              "      <td>0.703485</td>\n",
              "      <td>0.610350</td>\n",
              "      <td>0.683131</td>\n",
              "      <td>0.746369</td>\n",
              "      <td>0.605945</td>\n",
              "      <td>0.437233</td>\n",
              "      <td>0.395451</td>\n",
              "      <td>0.396267</td>\n",
              "      <td>0.396361</td>\n",
              "      <td>0.354488</td>\n",
              "      <td>0.279633</td>\n",
              "      <td>0.227358</td>\n",
              "      <td>0.123992</td>\n",
              "      <td>-0.031222</td>\n",
              "      <td>-0.155341</td>\n",
              "      <td>-0.219141</td>\n",
              "      <td>-0.239644</td>\n",
              "      <td>-0.333443</td>\n",
              "      <td>-0.557080</td>\n",
              "      <td>-0.811259</td>\n",
              "      <td>-1.072168</td>\n",
              "      <td>-1.497530</td>\n",
              "      <td>-2.047796</td>\n",
              "      <td>-2.495989</td>\n",
              "      <td>-2.821782</td>\n",
              "      <td>-3.268355</td>\n",
              "      <td>-3.634981</td>\n",
              "      <td>-3.168765</td>\n",
              "      <td>-2.245878</td>\n",
              "      <td>-1.262259</td>\n",
              "      <td>-0.443307</td>\n",
              "      <td>-0.559769</td>\n",
              "      <td>0.108568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4499</th>\n",
              "      <td>2.0</td>\n",
              "      <td>0.728813</td>\n",
              "      <td>0.192597</td>\n",
              "      <td>-0.733884</td>\n",
              "      <td>-1.779456</td>\n",
              "      <td>-2.345908</td>\n",
              "      <td>-2.977565</td>\n",
              "      <td>-3.380053</td>\n",
              "      <td>-3.417164</td>\n",
              "      <td>-3.030925</td>\n",
              "      <td>-2.313867</td>\n",
              "      <td>-1.671269</td>\n",
              "      <td>-1.255303</td>\n",
              "      <td>-1.146982</td>\n",
              "      <td>-1.049088</td>\n",
              "      <td>-0.513524</td>\n",
              "      <td>-0.100892</td>\n",
              "      <td>-0.087888</td>\n",
              "      <td>-0.179308</td>\n",
              "      <td>-0.226885</td>\n",
              "      <td>-0.156335</td>\n",
              "      <td>-0.141478</td>\n",
              "      <td>-0.024932</td>\n",
              "      <td>-0.089615</td>\n",
              "      <td>-0.165480</td>\n",
              "      <td>-0.043243</td>\n",
              "      <td>-0.132201</td>\n",
              "      <td>-0.193009</td>\n",
              "      <td>-0.177407</td>\n",
              "      <td>-0.276008</td>\n",
              "      <td>-0.202072</td>\n",
              "      <td>-0.110621</td>\n",
              "      <td>-0.176676</td>\n",
              "      <td>-0.177095</td>\n",
              "      <td>-0.277154</td>\n",
              "      <td>-0.054093</td>\n",
              "      <td>-0.188261</td>\n",
              "      <td>-0.283490</td>\n",
              "      <td>-0.250398</td>\n",
              "      <td>-0.280567</td>\n",
              "      <td>...</td>\n",
              "      <td>0.343283</td>\n",
              "      <td>0.351328</td>\n",
              "      <td>0.350081</td>\n",
              "      <td>0.487850</td>\n",
              "      <td>0.514130</td>\n",
              "      <td>0.655713</td>\n",
              "      <td>0.659875</td>\n",
              "      <td>0.574770</td>\n",
              "      <td>0.675136</td>\n",
              "      <td>0.624451</td>\n",
              "      <td>0.697013</td>\n",
              "      <td>0.749857</td>\n",
              "      <td>0.829295</td>\n",
              "      <td>0.871171</td>\n",
              "      <td>0.863445</td>\n",
              "      <td>0.916639</td>\n",
              "      <td>0.841814</td>\n",
              "      <td>0.961598</td>\n",
              "      <td>0.877710</td>\n",
              "      <td>0.831334</td>\n",
              "      <td>0.774570</td>\n",
              "      <td>0.894903</td>\n",
              "      <td>1.087536</td>\n",
              "      <td>1.021885</td>\n",
              "      <td>1.208764</td>\n",
              "      <td>1.623936</td>\n",
              "      <td>1.851147</td>\n",
              "      <td>1.818792</td>\n",
              "      <td>1.672569</td>\n",
              "      <td>1.389086</td>\n",
              "      <td>1.219499</td>\n",
              "      <td>1.267275</td>\n",
              "      <td>1.678989</td>\n",
              "      <td>2.483389</td>\n",
              "      <td>2.569073</td>\n",
              "      <td>2.122891</td>\n",
              "      <td>1.753963</td>\n",
              "      <td>1.538975</td>\n",
              "      <td>1.713781</td>\n",
              "      <td>1.309382</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4500 rows × 141 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8abec6fa-47fe-47ea-9fd8-44e3abbecd51')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  <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",
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              "    }\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",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8abec6fa-47fe-47ea-9fd8-44e3abbecd51');\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",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      0         1         2         3    ...       137       138       139       140\n",
              "0     1.0  3.690844  0.711414 -2.114092  ...  0.665527  1.035997  1.492287 -1.905073\n",
              "1     1.0 -1.348132 -3.996038 -4.226750  ...  0.126274  0.267532  1.071148 -1.164009\n",
              "2     1.0  1.024295 -0.590314 -1.916949  ...  1.800767  1.816301  1.473963  1.389767\n",
              "3     1.0  0.545657 -1.014383 -2.316698  ...  0.418116  0.703108  1.064602 -0.044853\n",
              "4     1.0  0.661133 -1.552471 -3.124641  ...  1.749692  1.986803  1.422756 -0.357784\n",
              "...   ...       ...       ...       ...  ...       ...       ...       ...       ...\n",
              "4495  4.0 -1.122969 -2.252925 -2.867628  ... -4.009720 -2.874136 -2.008369 -1.808334\n",
              "4496  2.0 -0.547705 -1.889545 -2.839779  ...  1.193433  1.261335  1.150449  0.804932\n",
              "4497  2.0 -1.351779 -2.209006 -2.520225  ... -3.437038 -2.260023 -1.577823 -0.684531\n",
              "4498  2.0 -1.124432 -1.905039 -2.192707  ... -1.262259 -0.443307 -0.559769  0.108568\n",
              "4499  2.0  0.728813  0.192597 -0.733884  ...  1.753963  1.538975  1.713781  1.309382\n",
              "\n",
              "[4500 rows x 141 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "See? It's basicaly the same thing as wee saw just few moments ago 😉\n",
        "\n",
        "However, since pandas df doesn't like just numerical columns, we add a prefix to it, so we can work with them."
      ],
      "metadata": {
        "id": "CU02ZJu2gTNe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_train = df_train.add_prefix('c')\n",
        "df_test = df_test.add_prefix('c')\n",
        "df_test.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v12I1mXZgs-b",
        "outputId": "82d11963-c3a1-4398-8000-95ccff13f04a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['c0', 'c1', 'c2', 'c3', 'c4', 'c5', 'c6', 'c7', 'c8', 'c9',\n",
              "       ...\n",
              "       'c131', 'c132', 'c133', 'c134', 'c135', 'c136', 'c137', 'c138', 'c139',\n",
              "       'c140'],\n",
              "      dtype='object', length=141)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Analyze the dataset\n",
        "\n",
        "In machine and deep learning it is important to know what data you are working with. Not just what is the data meant to be, but what is the structure, what values can I expect, what is the classes distribution, how does the data even look like, etc...\n",
        "\n",
        "So the following part is supposed to get us eve more familiar with the data we are working with."
      ],
      "metadata": {
        "id": "_DnlnzMgiFNv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here we can see basic info about the data - we have 141 columns c0 to c140. \n",
        "The data is stored as float64 and the memory usage is 4.8 MB. "
      ],
      "metadata": {
        "id": "AfKvPsVOiw2p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.info(memory_usage='deep')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xl8cBLtQhNpd",
        "outputId": "be258ffe-edc0-46af-b058-0553897b9bdb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4500 entries, 0 to 4499\n",
            "Columns: 141 entries, c0 to c140\n",
            "dtypes: float64(141)\n",
            "memory usage: 4.8 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next we can use `.describe()` which gets us a bit of a statistical view on the data. \n",
        "\n",
        "What we can see is that the max.min value are +/-7, the mean value is more or less in range between (-1,1) so we might be okay without normalization. "
      ],
      "metadata": {
        "id": "xBMMFcpejBo0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.describe() "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "id": "cseuV3FrfwQQ",
        "outputId": "49868e92-a177-49eb-e417-5d6d27cab4a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-0c0a9798-5602-44f9-a00d-437a51f03159\">\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>c0</th>\n",
              "      <th>c1</th>\n",
              "      <th>c2</th>\n",
              "      <th>c3</th>\n",
              "      <th>c4</th>\n",
              "      <th>c5</th>\n",
              "      <th>c6</th>\n",
              "      <th>c7</th>\n",
              "      <th>c8</th>\n",
              "      <th>c9</th>\n",
              "      <th>c10</th>\n",
              "      <th>c11</th>\n",
              "      <th>c12</th>\n",
              "      <th>c13</th>\n",
              "      <th>c14</th>\n",
              "      <th>c15</th>\n",
              "      <th>c16</th>\n",
              "      <th>c17</th>\n",
              "      <th>c18</th>\n",
              "      <th>c19</th>\n",
              "      <th>c20</th>\n",
              "      <th>c21</th>\n",
              "      <th>c22</th>\n",
              "      <th>c23</th>\n",
              "      <th>c24</th>\n",
              "      <th>c25</th>\n",
              "      <th>c26</th>\n",
              "      <th>c27</th>\n",
              "      <th>c28</th>\n",
              "      <th>c29</th>\n",
              "      <th>c30</th>\n",
              "      <th>c31</th>\n",
              "      <th>c32</th>\n",
              "      <th>c33</th>\n",
              "      <th>c34</th>\n",
              "      <th>c35</th>\n",
              "      <th>c36</th>\n",
              "      <th>c37</th>\n",
              "      <th>c38</th>\n",
              "      <th>c39</th>\n",
              "      <th>...</th>\n",
              "      <th>c101</th>\n",
              "      <th>c102</th>\n",
              "      <th>c103</th>\n",
              "      <th>c104</th>\n",
              "      <th>c105</th>\n",
              "      <th>c106</th>\n",
              "      <th>c107</th>\n",
              "      <th>c108</th>\n",
              "      <th>c109</th>\n",
              "      <th>c110</th>\n",
              "      <th>c111</th>\n",
              "      <th>c112</th>\n",
              "      <th>c113</th>\n",
              "      <th>c114</th>\n",
              "      <th>c115</th>\n",
              "      <th>c116</th>\n",
              "      <th>c117</th>\n",
              "      <th>c118</th>\n",
              "      <th>c119</th>\n",
              "      <th>c120</th>\n",
              "      <th>c121</th>\n",
              "      <th>c122</th>\n",
              "      <th>c123</th>\n",
              "      <th>c124</th>\n",
              "      <th>c125</th>\n",
              "      <th>c126</th>\n",
              "      <th>c127</th>\n",
              "      <th>c128</th>\n",
              "      <th>c129</th>\n",
              "      <th>c130</th>\n",
              "      <th>c131</th>\n",
              "      <th>c132</th>\n",
              "      <th>c133</th>\n",
              "      <th>c134</th>\n",
              "      <th>c135</th>\n",
              "      <th>c136</th>\n",
              "      <th>c137</th>\n",
              "      <th>c138</th>\n",
              "      <th>c139</th>\n",
              "      <th>c140</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "      <td>4500.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.527778</td>\n",
              "      <td>-0.273962</td>\n",
              "      <td>-1.660351</td>\n",
              "      <td>-2.497708</td>\n",
              "      <td>-3.121793</td>\n",
              "      <td>-3.166346</td>\n",
              "      <td>-2.862646</td>\n",
              "      <td>-2.271681</td>\n",
              "      <td>-1.797901</td>\n",
              "      <td>-1.407947</td>\n",
              "      <td>-0.934711</td>\n",
              "      <td>-0.585628</td>\n",
              "      <td>-0.434045</td>\n",
              "      <td>-0.337988</td>\n",
              "      <td>-0.258481</td>\n",
              "      <td>-0.184982</td>\n",
              "      <td>-0.121027</td>\n",
              "      <td>-0.071500</td>\n",
              "      <td>-0.042928</td>\n",
              "      <td>-0.038844</td>\n",
              "      <td>-0.042692</td>\n",
              "      <td>-0.050602</td>\n",
              "      <td>-0.062233</td>\n",
              "      <td>-0.072729</td>\n",
              "      <td>-0.087381</td>\n",
              "      <td>-0.103486</td>\n",
              "      <td>-0.118381</td>\n",
              "      <td>-0.136664</td>\n",
              "      <td>-0.158051</td>\n",
              "      <td>-0.180108</td>\n",
              "      <td>-0.203567</td>\n",
              "      <td>-0.226785</td>\n",
              "      <td>-0.248739</td>\n",
              "      <td>-0.273314</td>\n",
              "      <td>-0.292404</td>\n",
              "      <td>-0.305779</td>\n",
              "      <td>-0.316080</td>\n",
              "      <td>-0.316963</td>\n",
              "      <td>-0.313462</td>\n",
              "      <td>-0.304287</td>\n",
              "      <td>...</td>\n",
              "      <td>0.947228</td>\n",
              "      <td>1.052929</td>\n",
              "      <td>1.140186</td>\n",
              "      <td>1.189278</td>\n",
              "      <td>1.204343</td>\n",
              "      <td>1.193629</td>\n",
              "      <td>1.157774</td>\n",
              "      <td>1.089035</td>\n",
              "      <td>0.988832</td>\n",
              "      <td>0.865974</td>\n",
              "      <td>0.721445</td>\n",
              "      <td>0.569751</td>\n",
              "      <td>0.427732</td>\n",
              "      <td>0.318375</td>\n",
              "      <td>0.240838</td>\n",
              "      <td>0.179119</td>\n",
              "      <td>0.132642</td>\n",
              "      <td>0.108074</td>\n",
              "      <td>0.091474</td>\n",
              "      <td>0.079809</td>\n",
              "      <td>0.065038</td>\n",
              "      <td>0.046405</td>\n",
              "      <td>0.024178</td>\n",
              "      <td>0.003514</td>\n",
              "      <td>-0.010001</td>\n",
              "      <td>-0.019177</td>\n",
              "      <td>-0.026246</td>\n",
              "      <td>-0.029239</td>\n",
              "      <td>-0.000080</td>\n",
              "      <td>0.085341</td>\n",
              "      <td>0.146259</td>\n",
              "      <td>0.073410</td>\n",
              "      <td>-0.060409</td>\n",
              "      <td>-0.273551</td>\n",
              "      <td>-0.502636</td>\n",
              "      <td>-0.753850</td>\n",
              "      <td>-0.824197</td>\n",
              "      <td>-0.630282</td>\n",
              "      <td>-0.474437</td>\n",
              "      <td>-0.694958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.761301</td>\n",
              "      <td>1.153257</td>\n",
              "      <td>1.448446</td>\n",
              "      <td>1.384922</td>\n",
              "      <td>1.300375</td>\n",
              "      <td>1.101844</td>\n",
              "      <td>0.906330</td>\n",
              "      <td>0.735420</td>\n",
              "      <td>0.628203</td>\n",
              "      <td>0.642411</td>\n",
              "      <td>0.660208</td>\n",
              "      <td>0.592378</td>\n",
              "      <td>0.494081</td>\n",
              "      <td>0.426750</td>\n",
              "      <td>0.394789</td>\n",
              "      <td>0.384017</td>\n",
              "      <td>0.387249</td>\n",
              "      <td>0.401786</td>\n",
              "      <td>0.416191</td>\n",
              "      <td>0.424459</td>\n",
              "      <td>0.430667</td>\n",
              "      <td>0.436936</td>\n",
              "      <td>0.443310</td>\n",
              "      <td>0.449166</td>\n",
              "      <td>0.456154</td>\n",
              "      <td>0.463019</td>\n",
              "      <td>0.473821</td>\n",
              "      <td>0.484526</td>\n",
              "      <td>0.492577</td>\n",
              "      <td>0.504355</td>\n",
              "      <td>0.516666</td>\n",
              "      <td>0.530513</td>\n",
              "      <td>0.543503</td>\n",
              "      <td>0.552457</td>\n",
              "      <td>0.560785</td>\n",
              "      <td>0.563777</td>\n",
              "      <td>0.565087</td>\n",
              "      <td>0.558887</td>\n",
              "      <td>0.546928</td>\n",
              "      <td>0.534422</td>\n",
              "      <td>...</td>\n",
              "      <td>0.517638</td>\n",
              "      <td>0.572328</td>\n",
              "      <td>0.612933</td>\n",
              "      <td>0.620934</td>\n",
              "      <td>0.606668</td>\n",
              "      <td>0.582097</td>\n",
              "      <td>0.554094</td>\n",
              "      <td>0.531218</td>\n",
              "      <td>0.506708</td>\n",
              "      <td>0.484677</td>\n",
              "      <td>0.482879</td>\n",
              "      <td>0.510357</td>\n",
              "      <td>0.544097</td>\n",
              "      <td>0.563080</td>\n",
              "      <td>0.568491</td>\n",
              "      <td>0.569741</td>\n",
              "      <td>0.581223</td>\n",
              "      <td>0.586771</td>\n",
              "      <td>0.590115</td>\n",
              "      <td>0.596039</td>\n",
              "      <td>0.602675</td>\n",
              "      <td>0.608703</td>\n",
              "      <td>0.618635</td>\n",
              "      <td>0.631698</td>\n",
              "      <td>0.647564</td>\n",
              "      <td>0.665918</td>\n",
              "      <td>0.679359</td>\n",
              "      <td>0.694586</td>\n",
              "      <td>0.754245</td>\n",
              "      <td>0.924010</td>\n",
              "      <td>1.175640</td>\n",
              "      <td>1.410561</td>\n",
              "      <td>1.652410</td>\n",
              "      <td>1.899489</td>\n",
              "      <td>2.063698</td>\n",
              "      <td>2.087378</td>\n",
              "      <td>2.050563</td>\n",
              "      <td>1.848085</td>\n",
              "      <td>1.817460</td>\n",
              "      <td>1.411511</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-6.729499</td>\n",
              "      <td>-7.090374</td>\n",
              "      <td>-5.132459</td>\n",
              "      <td>-5.363241</td>\n",
              "      <td>-5.259295</td>\n",
              "      <td>-5.330194</td>\n",
              "      <td>-4.782240</td>\n",
              "      <td>-4.311288</td>\n",
              "      <td>-4.071361</td>\n",
              "      <td>-4.142476</td>\n",
              "      <td>-4.040526</td>\n",
              "      <td>-3.724538</td>\n",
              "      <td>-3.264576</td>\n",
              "      <td>-3.222345</td>\n",
              "      <td>-3.263484</td>\n",
              "      <td>-3.219212</td>\n",
              "      <td>-3.202187</td>\n",
              "      <td>-3.147504</td>\n",
              "      <td>-3.459917</td>\n",
              "      <td>-4.102102</td>\n",
              "      <td>-4.311241</td>\n",
              "      <td>-4.099888</td>\n",
              "      <td>-3.614973</td>\n",
              "      <td>-3.822298</td>\n",
              "      <td>-3.795567</td>\n",
              "      <td>-3.690817</td>\n",
              "      <td>-3.542485</td>\n",
              "      <td>-3.631665</td>\n",
              "      <td>-3.587112</td>\n",
              "      <td>-3.452215</td>\n",
              "      <td>-4.074752</td>\n",
              "      <td>-4.137454</td>\n",
              "      <td>-4.059662</td>\n",
              "      <td>-3.626646</td>\n",
              "      <td>-2.533600</td>\n",
              "      <td>-2.644038</td>\n",
              "      <td>-2.450031</td>\n",
              "      <td>-2.473158</td>\n",
              "      <td>-2.743753</td>\n",
              "      <td>...</td>\n",
              "      <td>-2.628040</td>\n",
              "      <td>-2.242705</td>\n",
              "      <td>-1.853174</td>\n",
              "      <td>-1.582690</td>\n",
              "      <td>-1.898351</td>\n",
              "      <td>-2.155814</td>\n",
              "      <td>-2.431403</td>\n",
              "      <td>-2.681604</td>\n",
              "      <td>-2.864706</td>\n",
              "      <td>-3.415656</td>\n",
              "      <td>-3.497624</td>\n",
              "      <td>-3.091282</td>\n",
              "      <td>-2.608510</td>\n",
              "      <td>-2.383204</td>\n",
              "      <td>-2.052557</td>\n",
              "      <td>-2.570546</td>\n",
              "      <td>-3.059078</td>\n",
              "      <td>-3.311559</td>\n",
              "      <td>-3.501555</td>\n",
              "      <td>-4.007212</td>\n",
              "      <td>-4.199563</td>\n",
              "      <td>-3.335678</td>\n",
              "      <td>-3.127603</td>\n",
              "      <td>-3.042532</td>\n",
              "      <td>-3.116177</td>\n",
              "      <td>-3.972320</td>\n",
              "      <td>-4.754057</td>\n",
              "      <td>-4.288919</td>\n",
              "      <td>-3.802469</td>\n",
              "      <td>-4.014642</td>\n",
              "      <td>-3.971715</td>\n",
              "      <td>-4.167040</td>\n",
              "      <td>-4.557576</td>\n",
              "      <td>-4.530488</td>\n",
              "      <td>-4.563270</td>\n",
              "      <td>-4.911417</td>\n",
              "      <td>-4.880460</td>\n",
              "      <td>-5.496107</td>\n",
              "      <td>-5.880890</td>\n",
              "      <td>-6.092227</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.018202</td>\n",
              "      <td>-2.710514</td>\n",
              "      <td>-3.675967</td>\n",
              "      <td>-4.221921</td>\n",
              "      <td>-4.006748</td>\n",
              "      <td>-3.472986</td>\n",
              "      <td>-2.779941</td>\n",
              "      <td>-2.164166</td>\n",
              "      <td>-1.773173</td>\n",
              "      <td>-1.360472</td>\n",
              "      <td>-0.929389</td>\n",
              "      <td>-0.702857</td>\n",
              "      <td>-0.558051</td>\n",
              "      <td>-0.455224</td>\n",
              "      <td>-0.392508</td>\n",
              "      <td>-0.348699</td>\n",
              "      <td>-0.331491</td>\n",
              "      <td>-0.333724</td>\n",
              "      <td>-0.345982</td>\n",
              "      <td>-0.360311</td>\n",
              "      <td>-0.375028</td>\n",
              "      <td>-0.394337</td>\n",
              "      <td>-0.416998</td>\n",
              "      <td>-0.442244</td>\n",
              "      <td>-0.473245</td>\n",
              "      <td>-0.503750</td>\n",
              "      <td>-0.541208</td>\n",
              "      <td>-0.571953</td>\n",
              "      <td>-0.618612</td>\n",
              "      <td>-0.651927</td>\n",
              "      <td>-0.697186</td>\n",
              "      <td>-0.737693</td>\n",
              "      <td>-0.771678</td>\n",
              "      <td>-0.800814</td>\n",
              "      <td>-0.813141</td>\n",
              "      <td>-0.827872</td>\n",
              "      <td>-0.816216</td>\n",
              "      <td>-0.795940</td>\n",
              "      <td>-0.763048</td>\n",
              "      <td>...</td>\n",
              "      <td>0.598982</td>\n",
              "      <td>0.638249</td>\n",
              "      <td>0.665747</td>\n",
              "      <td>0.689660</td>\n",
              "      <td>0.701634</td>\n",
              "      <td>0.715209</td>\n",
              "      <td>0.728687</td>\n",
              "      <td>0.719819</td>\n",
              "      <td>0.689630</td>\n",
              "      <td>0.631570</td>\n",
              "      <td>0.493726</td>\n",
              "      <td>0.259797</td>\n",
              "      <td>0.020941</td>\n",
              "      <td>-0.127936</td>\n",
              "      <td>-0.189702</td>\n",
              "      <td>-0.221528</td>\n",
              "      <td>-0.254920</td>\n",
              "      <td>-0.268765</td>\n",
              "      <td>-0.279460</td>\n",
              "      <td>-0.279514</td>\n",
              "      <td>-0.275251</td>\n",
              "      <td>-0.274988</td>\n",
              "      <td>-0.282309</td>\n",
              "      <td>-0.293451</td>\n",
              "      <td>-0.295058</td>\n",
              "      <td>-0.303435</td>\n",
              "      <td>-0.314400</td>\n",
              "      <td>-0.318691</td>\n",
              "      <td>-0.339960</td>\n",
              "      <td>-0.409038</td>\n",
              "      <td>-0.667551</td>\n",
              "      <td>-1.104062</td>\n",
              "      <td>-1.598281</td>\n",
              "      <td>-2.157220</td>\n",
              "      <td>-2.743237</td>\n",
              "      <td>-3.014821</td>\n",
              "      <td>-2.856324</td>\n",
              "      <td>-2.131719</td>\n",
              "      <td>-1.635179</td>\n",
              "      <td>-1.577515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.303240</td>\n",
              "      <td>-1.672456</td>\n",
              "      <td>-2.592071</td>\n",
              "      <td>-3.397653</td>\n",
              "      <td>-3.460329</td>\n",
              "      <td>-2.938500</td>\n",
              "      <td>-2.282163</td>\n",
              "      <td>-1.748535</td>\n",
              "      <td>-1.420713</td>\n",
              "      <td>-0.909183</td>\n",
              "      <td>-0.500556</td>\n",
              "      <td>-0.411444</td>\n",
              "      <td>-0.355088</td>\n",
              "      <td>-0.297975</td>\n",
              "      <td>-0.238953</td>\n",
              "      <td>-0.184412</td>\n",
              "      <td>-0.144184</td>\n",
              "      <td>-0.125619</td>\n",
              "      <td>-0.133373</td>\n",
              "      <td>-0.139483</td>\n",
              "      <td>-0.148190</td>\n",
              "      <td>-0.161350</td>\n",
              "      <td>-0.179135</td>\n",
              "      <td>-0.198481</td>\n",
              "      <td>-0.213106</td>\n",
              "      <td>-0.232047</td>\n",
              "      <td>-0.246432</td>\n",
              "      <td>-0.271932</td>\n",
              "      <td>-0.279797</td>\n",
              "      <td>-0.305549</td>\n",
              "      <td>-0.312811</td>\n",
              "      <td>-0.322147</td>\n",
              "      <td>-0.335867</td>\n",
              "      <td>-0.346792</td>\n",
              "      <td>-0.361730</td>\n",
              "      <td>-0.372444</td>\n",
              "      <td>-0.364674</td>\n",
              "      <td>-0.364847</td>\n",
              "      <td>-0.344104</td>\n",
              "      <td>...</td>\n",
              "      <td>0.839880</td>\n",
              "      <td>0.923226</td>\n",
              "      <td>1.034162</td>\n",
              "      <td>1.152401</td>\n",
              "      <td>1.252273</td>\n",
              "      <td>1.280230</td>\n",
              "      <td>1.195190</td>\n",
              "      <td>1.054629</td>\n",
              "      <td>0.928051</td>\n",
              "      <td>0.820504</td>\n",
              "      <td>0.720650</td>\n",
              "      <td>0.624533</td>\n",
              "      <td>0.503366</td>\n",
              "      <td>0.363607</td>\n",
              "      <td>0.248997</td>\n",
              "      <td>0.105071</td>\n",
              "      <td>0.014752</td>\n",
              "      <td>-0.013967</td>\n",
              "      <td>-0.021404</td>\n",
              "      <td>-0.026485</td>\n",
              "      <td>-0.037064</td>\n",
              "      <td>-0.054821</td>\n",
              "      <td>-0.069866</td>\n",
              "      <td>-0.088045</td>\n",
              "      <td>-0.098861</td>\n",
              "      <td>-0.103869</td>\n",
              "      <td>-0.105087</td>\n",
              "      <td>-0.075297</td>\n",
              "      <td>0.016408</td>\n",
              "      <td>0.187261</td>\n",
              "      <td>0.481311</td>\n",
              "      <td>0.676028</td>\n",
              "      <td>0.732593</td>\n",
              "      <td>0.608052</td>\n",
              "      <td>0.335153</td>\n",
              "      <td>-0.025628</td>\n",
              "      <td>-0.180293</td>\n",
              "      <td>-0.191248</td>\n",
              "      <td>-0.144524</td>\n",
              "      <td>-0.551293</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.485655</td>\n",
              "      <td>-0.684776</td>\n",
              "      <td>-1.519119</td>\n",
              "      <td>-2.240458</td>\n",
              "      <td>-2.530796</td>\n",
              "      <td>-2.397532</td>\n",
              "      <td>-1.822741</td>\n",
              "      <td>-1.483616</td>\n",
              "      <td>-1.059521</td>\n",
              "      <td>-0.476321</td>\n",
              "      <td>-0.246247</td>\n",
              "      <td>-0.205060</td>\n",
              "      <td>-0.173922</td>\n",
              "      <td>-0.114549</td>\n",
              "      <td>-0.021291</td>\n",
              "      <td>0.078973</td>\n",
              "      <td>0.184304</td>\n",
              "      <td>0.257424</td>\n",
              "      <td>0.294403</td>\n",
              "      <td>0.308848</td>\n",
              "      <td>0.312671</td>\n",
              "      <td>0.309699</td>\n",
              "      <td>0.310192</td>\n",
              "      <td>0.310867</td>\n",
              "      <td>0.307134</td>\n",
              "      <td>0.310250</td>\n",
              "      <td>0.310788</td>\n",
              "      <td>0.306532</td>\n",
              "      <td>0.297557</td>\n",
              "      <td>0.292449</td>\n",
              "      <td>0.287783</td>\n",
              "      <td>0.278746</td>\n",
              "      <td>0.269232</td>\n",
              "      <td>0.267853</td>\n",
              "      <td>0.256570</td>\n",
              "      <td>0.240730</td>\n",
              "      <td>0.236808</td>\n",
              "      <td>0.224325</td>\n",
              "      <td>0.215798</td>\n",
              "      <td>...</td>\n",
              "      <td>1.314812</td>\n",
              "      <td>1.521455</td>\n",
              "      <td>1.707284</td>\n",
              "      <td>1.768508</td>\n",
              "      <td>1.752310</td>\n",
              "      <td>1.686677</td>\n",
              "      <td>1.591305</td>\n",
              "      <td>1.426363</td>\n",
              "      <td>1.243509</td>\n",
              "      <td>1.055337</td>\n",
              "      <td>0.929295</td>\n",
              "      <td>0.859573</td>\n",
              "      <td>0.807131</td>\n",
              "      <td>0.773696</td>\n",
              "      <td>0.711554</td>\n",
              "      <td>0.621827</td>\n",
              "      <td>0.567461</td>\n",
              "      <td>0.543696</td>\n",
              "      <td>0.515846</td>\n",
              "      <td>0.485605</td>\n",
              "      <td>0.435060</td>\n",
              "      <td>0.383520</td>\n",
              "      <td>0.313531</td>\n",
              "      <td>0.246606</td>\n",
              "      <td>0.194930</td>\n",
              "      <td>0.175732</td>\n",
              "      <td>0.171212</td>\n",
              "      <td>0.211631</td>\n",
              "      <td>0.349887</td>\n",
              "      <td>0.717532</td>\n",
              "      <td>1.064272</td>\n",
              "      <td>1.173172</td>\n",
              "      <td>1.239463</td>\n",
              "      <td>1.267172</td>\n",
              "      <td>1.237470</td>\n",
              "      <td>1.037941</td>\n",
              "      <td>0.947254</td>\n",
              "      <td>0.948825</td>\n",
              "      <td>1.002692</td>\n",
              "      <td>0.277078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.966414</td>\n",
              "      <td>3.479689</td>\n",
              "      <td>2.660597</td>\n",
              "      <td>1.899798</td>\n",
              "      <td>2.147015</td>\n",
              "      <td>1.614375</td>\n",
              "      <td>1.868728</td>\n",
              "      <td>1.804251</td>\n",
              "      <td>1.683730</td>\n",
              "      <td>1.898627</td>\n",
              "      <td>2.033007</td>\n",
              "      <td>2.159861</td>\n",
              "      <td>2.390867</td>\n",
              "      <td>2.433236</td>\n",
              "      <td>2.160356</td>\n",
              "      <td>2.125758</td>\n",
              "      <td>2.566762</td>\n",
              "      <td>2.099727</td>\n",
              "      <td>2.117594</td>\n",
              "      <td>2.076279</td>\n",
              "      <td>2.053132</td>\n",
              "      <td>2.091794</td>\n",
              "      <td>2.003258</td>\n",
              "      <td>1.916382</td>\n",
              "      <td>1.888331</td>\n",
              "      <td>1.919321</td>\n",
              "      <td>1.916649</td>\n",
              "      <td>1.785353</td>\n",
              "      <td>1.866897</td>\n",
              "      <td>1.870258</td>\n",
              "      <td>1.862162</td>\n",
              "      <td>2.063265</td>\n",
              "      <td>1.941110</td>\n",
              "      <td>1.931091</td>\n",
              "      <td>1.890201</td>\n",
              "      <td>1.834472</td>\n",
              "      <td>1.721037</td>\n",
              "      <td>1.619301</td>\n",
              "      <td>1.583824</td>\n",
              "      <td>...</td>\n",
              "      <td>2.942679</td>\n",
              "      <td>2.836767</td>\n",
              "      <td>2.820616</td>\n",
              "      <td>2.811662</td>\n",
              "      <td>2.685726</td>\n",
              "      <td>2.959324</td>\n",
              "      <td>2.895572</td>\n",
              "      <td>2.604823</td>\n",
              "      <td>2.478775</td>\n",
              "      <td>2.513328</td>\n",
              "      <td>2.599892</td>\n",
              "      <td>2.937152</td>\n",
              "      <td>2.950522</td>\n",
              "      <td>2.830852</td>\n",
              "      <td>2.861781</td>\n",
              "      <td>2.828775</td>\n",
              "      <td>2.702857</td>\n",
              "      <td>2.538437</td>\n",
              "      <td>2.680203</td>\n",
              "      <td>2.764017</td>\n",
              "      <td>2.839710</td>\n",
              "      <td>2.714548</td>\n",
              "      <td>2.869542</td>\n",
              "      <td>3.052377</td>\n",
              "      <td>3.141808</td>\n",
              "      <td>3.215135</td>\n",
              "      <td>3.198790</td>\n",
              "      <td>3.086137</td>\n",
              "      <td>3.157157</td>\n",
              "      <td>3.037078</td>\n",
              "      <td>2.880345</td>\n",
              "      <td>3.007606</td>\n",
              "      <td>2.854680</td>\n",
              "      <td>2.912134</td>\n",
              "      <td>2.937685</td>\n",
              "      <td>3.351437</td>\n",
              "      <td>3.432518</td>\n",
              "      <td>3.579448</td>\n",
              "      <td>4.425976</td>\n",
              "      <td>7.402103</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 141 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0c0a9798-5602-44f9-a00d-437a51f03159')\"\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",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </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-0c0a9798-5602-44f9-a00d-437a51f03159 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-0c0a9798-5602-44f9-a00d-437a51f03159');\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",
              "  "
            ],
            "text/plain": [
              "                c0           c1  ...         c139         c140\n",
              "count  4500.000000  4500.000000  ...  4500.000000  4500.000000\n",
              "mean      1.527778    -0.273962  ...    -0.474437    -0.694958\n",
              "std       0.761301     1.153257  ...     1.817460     1.411511\n",
              "min       1.000000    -6.729499  ...    -5.880890    -6.092227\n",
              "25%       1.000000    -1.018202  ...    -1.635179    -1.577515\n",
              "50%       1.000000    -0.303240  ...    -0.144524    -0.551293\n",
              "75%       2.000000     0.485655  ...     1.002692     0.277078\n",
              "max       5.000000     4.966414  ...     4.425976     7.402103\n",
              "\n",
              "[8 rows x 141 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We could also have a peak at how the classes are distributed in the dataset. \n",
        "\n",
        "This dataset stores the info about the classes in the first column, thus we index the df and use `.value_counts()` to see byt how many samples is each class represented.\n",
        "\n"
      ],
      "metadata": {
        "id": "5D8RO1ngX1J4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_train['c0'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nMZ0xAloXh4I",
        "outputId": "caebec8b-df2f-4960-865f-b81e65956db2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0    2627\n",
              "2.0    1590\n",
              "4.0     175\n",
              "3.0      86\n",
              "5.0      22\n",
              "Name: c0, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test['c0'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WSvriZrCkk7n",
        "outputId": "da7b2b20-17ae-4dd8-835f-d102f1dd8d37"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0    292\n",
              "2.0    177\n",
              "4.0     19\n",
              "3.0     10\n",
              "5.0      2\n",
              "Name: c0, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "And we can even visualize it... "
      ],
      "metadata": {
        "id": "7OGtQQ0AlDuy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_train['c0'].value_counts().plot(kind='bar')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "yQyOP9ia_AoY",
        "outputId": "8a419d36-fb19-4687-eb3a-29b66e75e9a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe2caae1790>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX0AAAD+CAYAAADWKtWTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAQF0lEQVR4nO3df6zddX3H8efLgmb+iJRw17C2WOK6LZjNirWQaBaMEQqYFZNpYIs0hFmTlQwz/6kuS/0REv6YupAos44qJCrBX6FqHeuI07gNaMsaoDDCHZTQprTX4UCH0RTf++N+m3u83tv7o7fne+vn+UhOzve8v9/vOe/zPT2v8+3n+z33pKqQJLXhZX03IEkaHkNfkhpi6EtSQwx9SWqIoS9JDTmj7wZO5JxzzqlVq1b13YYknVb27t37o6oamWreog79VatWsWfPnr7bkKTTSpKnp5vn8I4kNcTQl6SGGPqS1BBDX5IaYuhLUkMMfUlqiKEvSQ0x9CWpIYa+JDVkUX8jdyGs2vKdvlsA4MDNV/bdgiS5py9JLTH0Jakhhr4kNcTQl6SGGPqS1BBDX5IaMmPoJ1mZ5HtJHk2yP8mNXf2jSQ4l2dddrhhY58NJRpM8nuSygfr6rjaaZMupeUqSpOnM5jz9Y8CHqurBJK8B9ibZ1c37dFX93eDCSS4ArgbeAPwO8C9Jfq+b/RngncBBYHeSHVX16EI8EUnSzGYM/ao6DBzupn+S5DFg+QlW2QDcWVU/B55KMgqs6+aNVtWTAEnu7JY19CVpSOY0pp9kFfAm4P6udEOSh5JsT7K0qy0HnhlY7WBXm64++TE2JdmTZM/Y2Nhc2pMkzWDWoZ/k1cDXgQ9W1QvArcDrgTWM/0/gkwvRUFVtq6q1VbV2ZGTKH3OXJM3TrP72TpIzGQ/8L1XVNwCq6sjA/M8D3+5uHgJWDqy+oqtxgrokaQhmc/ZOgNuAx6rqUwP1cwcWezfwSDe9A7g6ySuSnA+sBh4AdgOrk5yf5OWMH+zdsTBPQ5I0G7PZ038r8D7g4ST7utpHgGuSrAEKOAB8AKCq9ie5i/EDtMeAzVX1EkCSG4B7gCXA9qrav4DPRZI0g9mcvfNDIFPM2nmCdW4CbpqivvNE60mSTi2/kStJDTH0Jakhhr4kNcTQl6SGGPqS1BBDX5IaYuhLUkMMfUlqiKEvSQ0x9CWpIYa+JDXE0Jekhhj6ktQQQ1+SGmLoS1JDDH1JaoihL0kNMfQlqSGGviQ1xNCXpIYY+pLUEENfkhpi6EtSQwx9SWqIoS9JDTH0Jakhhr4kNcTQl6SGGPqS1BBDX5IaMmPoJ1mZ5HtJHk2yP8mNXf3sJLuSPNFdL+3qSXJLktEkDyW5cOC+NnbLP5Fk46l7WpKkqcxmT/8Y8KGqugC4GNic5AJgC3BvVa0G7u1uA1wOrO4um4BbYfxDAtgKXASsA7Ye/6CQJA3HjKFfVYer6sFu+ifAY8ByYANwe7fY7cBV3fQG4I4adx9wVpJzgcuAXVX1XFX9GNgFrF/QZyNJOqE5jeknWQW8CbgfWFZVh7tZzwLLuunlwDMDqx3satPVJz/GpiR7kuwZGxubS3uSpBnMOvSTvBr4OvDBqnphcF5VFVAL0VBVbauqtVW1dmRkZCHuUpLUmVXoJzmT8cD/UlV9oysf6YZt6K6PdvVDwMqB1Vd0tenqkqQhmc3ZOwFuAx6rqk8NzNoBHD8DZyNw90D92u4snouB57thoHuAS5Ms7Q7gXtrVJElDcsYslnkr8D7g4ST7utpHgJuBu5JcDzwNvLebtxO4AhgFXgSuA6iq55J8AtjdLffxqnpuQZ6FJGlWZgz9qvohkGlmv2OK5QvYPM19bQe2z6VBSdLC8Ru5ktQQQ1+SGmLoS1JDDH1JaoihL0kNMfQlqSGGviQ1xNCXpIYY+pLUEENfkhpi6EtSQwx9SWqIoS9JDTH0Jakhhr4kNcTQl6SGGPqS1BBDX5IaYuhLUkMMfUlqiKEvSQ0x9CWpIYa+JDXE0Jekhhj6ktQQQ1+SGmLoS1JDzui7AQ3Pqi3f6bsFAA7cfGXfLUjNck9fkhpi6EtSQ2YM/STbkxxN8shA7aNJDiXZ112uGJj34SSjSR5PctlAfX1XG02yZeGfiiRpJrPZ0/8isH6K+qerak132QmQ5ALgauAN3TqfTbIkyRLgM8DlwAXANd2ykqQhmvFAblX9IMmqWd7fBuDOqvo58FSSUWBdN2+0qp4ESHJnt+yjc+5YkjRvJzOmf0OSh7rhn6VdbTnwzMAyB7vadHVJ0hDNN/RvBV4PrAEOA59cqIaSbEqyJ8mesbGxhbpbSRLzDP2qOlJVL1XVL4HPMzGEcwhYObDoiq42XX2q+95WVWurau3IyMh82pMkTWNeoZ/k3IGb7waOn9mzA7g6ySuSnA+sBh4AdgOrk5yf5OWMH+zdMf+2JUnzMeOB3CRfAS4BzklyENgKXJJkDVDAAeADAFW1P8ldjB+gPQZsrqqXuvu5AbgHWAJsr6r9C/5sJEknNJuzd66ZonzbCZa/CbhpivpOYOecupMkLSi/kStJDTH0Jakhhr4kNcTQl6SGGPqS1BBDX5IaYuhLUkMMfUlqiKEvSQ0x9CWpIYa+JDXE0Jekhhj6ktQQQ1+SGmLoS1JDDH1JaoihL0kNMfQlqSGGviQ1xNCXpIYY+pLUEENfkhpi6EtSQwx9SWqIoS9JDTH0Jakhhr4kNcTQl6SGGPqS1BBDX5IaYuhLUkNmDP0k25McTfLIQO3sJLuSPNFdL+3qSXJLktEkDyW5cGCdjd3yTyTZeGqejiTpRGazp/9FYP2k2hbg3qpaDdzb3Qa4HFjdXTYBt8L4hwSwFbgIWAdsPf5BIUkanhlDv6p+ADw3qbwBuL2bvh24aqB+R427DzgrybnAZcCuqnquqn4M7OLXP0gkSafYfMf0l1XV4W76WWBZN70ceGZguYNdbbr6r0myKcmeJHvGxsbm2Z4kaSonfSC3qgqoBejl+P1tq6q1VbV2ZGRkoe5WksT8Q/9IN2xDd320qx8CVg4st6KrTVeXJA3RfEN/B3D8DJyNwN0D9Wu7s3guBp7vhoHuAS5NsrQ7gHtpV5MkDdEZMy2Q5CvAJcA5SQ4yfhbOzcBdSa4Hngbe2y2+E7gCGAVeBK4DqKrnknwC2N0t9/GqmnxwWJJ0is0Y+lV1zTSz3jHFsgVsnuZ+tgPb59SdJGlB+Y1cSWqIoS9JDTH0Jakhhr4kNcTQl6SGGPqS1BBDX5IaYuhLUkMMfUlqiKEvSQ0x9CWpIYa+JDXE0Jekhhj6ktQQQ1+SGmLoS1JDDH1JaoihL0kNMfQlqSGGviQ1xNCXpIYY+pLUEENfkhpi6EtSQwx9SWqIoS9JDTH0Jakhhr4kNcTQl6SGGPqS1JCTCv0kB5I8nGRfkj1d7ewku5I80V0v7epJckuS0SQPJblwIZ6AJGn2FmJP/+1Vtaaq1na3twD3VtVq4N7uNsDlwOrusgm4dQEeW5I0B6dieGcDcHs3fTtw1UD9jhp3H3BWknNPweNLkqZxsqFfwD8n2ZtkU1dbVlWHu+lngWXd9HLgmYF1D3Y1SdKQnHGS67+tqg4l+W1gV5L/GpxZVZWk5nKH3YfHJoDzzjvvJNuTJA06qT39qjrUXR8FvgmsA44cH7bpro92ix8CVg6svqKrTb7PbVW1tqrWjoyMnEx7kqRJ5h36SV6V5DXHp4FLgUeAHcDGbrGNwN3d9A7g2u4snouB5weGgSRJQ3AywzvLgG8mOX4/X66qf0qyG7gryfXA08B7u+V3AlcAo8CLwHUn8diSpHmYd+hX1ZPAG6eo/w/wjinqBWye7+NJkk6e38iVpIYY+pLUEENfkhpi6EtSQwx9SWqIoS9JDTH0Jakhhr4kNcTQl6SGGPqS1BBDX5IaYuhLUkMMfUlqiKEvSQ0x9CWpIYa+JDXE0Jekhhj6ktQQQ1+SGmLoS1JD5v3D6NLpbNWW7/TdAgAHbr6y7xbUGPf0Jakhhr4kNcTQl6SGGPqS1BBDX5IaYuhLUkM8ZVNqnKevtsU9fUlqiKEvSQ0x9CWpIUMP/STrkzyeZDTJlmE/viS1bKgHcpMsAT4DvBM4COxOsqOqHh1mH5I0lRYOag97T38dMFpVT1bVL4A7gQ1D7kGSmpWqGt6DJX8KrK+qv+huvw+4qKpuGFhmE7Cpu/n7wONDa3B65wA/6ruJRcJtMcFtMcFtMWExbIvXVdXIVDMW3Xn6VbUN2NZ3H4OS7KmqtX33sRi4LSa4LSa4LSYs9m0x7OGdQ8DKgdsrupokaQiGHfq7gdVJzk/ycuBqYMeQe5CkZg11eKeqjiW5AbgHWAJsr6r9w+xhnhbVcFPP3BYT3BYT3BYTFvW2GOqBXElSv/xGriQ1xNCXpIYY+pLUEENfmoMkZyc5u+8+Fgu3x+nH0NeMkixLcmF3WdZ3P8OW5LwkdyYZA+4HHkhytKut6re74XN7/LrT6T3i2TvT6F645d3NQ1V1pM9++pBkDfAPwGuZ+BLdCuB/gb+sqgf76m2YkvwH8PfA16rqpa62BHgP8MGqurjP/obN7THhdHyPGPqTnI4v4qmSZB/wgaq6f1L9YuBzVfXGfjobriRPVNXquc77TeX2mHA6vkcW3d/eWQS+yPQv4heARfcinkKvmrwdAKrqviSv6qOhnuxN8lngduCZrrYS2Aj8Z29d9cftMeG0e4+4pz/JDHsxo1X1u8PuqS9JbgFeD9zBr765rwWeGvzrqL/Juj8Zcj3jfwb8+JDfQeBbwG1V9fO+euuD22PC6fgeMfQnOR1fxFMpyeX86pv7ELCjqnb215W0eJxu7xFDfwqn24uo/iR5V1V9u+8+Fgu3x+LnmP4Uquq7wHf77mMxS7Kp++2D1r0FMOQmuD06i/U94nn6c9D9qpfGpe8G+pTkDoCq2tp3L31Isi7JW7rpC5L8dZIrWt0e01iU7xH39OdmUb6Ip1KSP2B8mOv+qvrpwKyne2pp6JJM/s2HAG9PchZAVf3J8LvqT5KtwOXAGUl2ARcB3wO2JHlTVd3Ua4M9SvI2xn8L/JGq+lzf/UzFMf05SHJdVX2h7z6GJclfAZuBx4A1wI1VdXc378GqurDP/oYlyYPAo8A/AsV46H+F8R8Boqq+3193w5fkYcb/PbwCeBZYUVUvJPktxncO/qjXBocoyQNVta6bfj/j75dvApcC36qqm/vsbyoO78zNx/puYMjeD7y5qq4CLgH+NsmN3byW/tezFtgL/A3wfFX9K/Czqvp+a4HfOVZVL1XVi8B/V9ULAFX1M+CX/bY2dGcOTG8C3llVH2M89P+8n5ZOzOGdSZI8NN0sYFH/TY1T4GXHh3Sq6kCSS4CvJXkdDYV+Vf0S+HSSr3bXR2j7vfOLJK/sQv/Nx4tJXkt7of+yJEsZ34FOVY0BVNX/JTnWb2tTa/kf7nSWAZcBP55UD/Dvw2+nV0eSrKmqfQBV9dMk7wK2A3/Yb2vDV1UHgfckuRJ4oe9+evTHx7+A1X0gHncm49/KbclrGf9fYIBKcm5VHU7yahbpjpFj+pMkuQ34QlX9cIp5X66qP+uhrV4kWcH4f+WfnWLeW6vq33poS1r0krwSWFZVT/Xdy2SGviQ1xAO5ktQQQ1+SGmLoS1JDDH1Jasj/A52QqgD8aBNrAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_test['c0'].value_counts().plot(kind='bar')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "BEhVeV3VkodN",
        "outputId": "4c70c181-755d-4566-a5ea-8dc972615433"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe2cadd1e50>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Handling imbalanced datasets\n",
        "\n",
        "As we can see, out dataset quite imbalanced - **which is not good!**\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "So what can we actually do about it? \n",
        "\n",
        "*   Undersampling\n",
        "*   Oversampling\n",
        "*   Combine under- and oversampling\n",
        "*   Generate new data - not ideal, not always possible\n",
        "*   Generate new data with generative networks\n",
        "\n",
        "---\n",
        "\n",
        "Few blog posts about over- and undersampling\n",
        "\n",
        "*   [5 Techniques to Handle Imbalanced Data For a Classification Problem](https://www.analyticsvidhya.com/blog/2021/06/5-techniques-to-handle-imbalanced-data-for-a-classification-problem/)\n",
        "\n",
        "\n",
        "* [Stop using SMOTE to handle all your Imbalanced Data](https://towardsdatascience.com/stop-using-smote-to-handle-all-your-imbalanced-data-34403399d3be)\n",
        " \n",
        "\n",
        "---\n",
        "\n",
        "Library which helps us deal with imbalanced dataset\n",
        "\n",
        "[imbalanced-learn](https://imbalanced-learn.org/stable/auto_examples/index.html)\n",
        "\n",
        "SMOTE =  Synthetic Minority Oversampling TEchnique\n"
      ],
      "metadata": {
        "id": "1QsUJC3fHR90"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here is a simple function which will apply under/oversampling method to our data. It returns arrays of new samples and their labels."
      ],
      "metadata": {
        "id": "KCXPLIYtosem"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.combine import SMOTETomek\n",
        "def over_under_sampling(dataframe):\n",
        "    \"\"\"\n",
        "    Use SMOTETomek technique to oversample our dataset. \n",
        "    \n",
        "    This function is written to be applied to our datasets, \n",
        "    where the first column holds the labels, and the rest is the \n",
        "    time sequence. \n",
        "\n",
        "    It passes the under-represented data - classes 2-5 along\n",
        "    with the dominant class 1 into the SMOTETomek over- & undersampler\n",
        "    to balance the dataset. \n",
        "    \"\"\"\n",
        "    # lists to store the created values in\n",
        "    x_res = []\n",
        "    y_res = []\n",
        "\n",
        "    for i in range(2,6):\n",
        "\n",
        "        # create copy of the dataframe\n",
        "        df_copy = dataframe.copy()\n",
        "        # choose samples of i-th class\n",
        "        df = df_copy[df_copy['c0'] == i]\n",
        "        # add samples from 1st class\n",
        "        df = df.append(df_copy[df_copy['c0'] == 1])\n",
        "        # split the dataframe into x - data and y - labels\n",
        "        x = df.values[:,1:]\n",
        "        y = df.values[:,0]\n",
        "\n",
        "        # define the imbalance function\n",
        "        smtomek = SMOTETomek(random_state=42)\n",
        "        # fit it on our data\n",
        "        x_r, y_r = smtomek.fit_resample(x, y)\n",
        "        \n",
        "        # we want to skip the data we fit it on - only want the new data\n",
        "        skip = y.shape[0]\n",
        "        # append the data into our above lists\n",
        "        x_res.append(x_r[skip:,:])\n",
        "        y_res.append(y_r[skip:])\n",
        "\n",
        "    # return the data as concatenated arrays -> only one array of all samples\n",
        "    # instead of a list of arrays\n",
        "    return np.concatenate(x_res), np.concatenate(y_res)"
      ],
      "metadata": {
        "id": "pE-V6nKTeBqH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here we call the above function"
      ],
      "metadata": {
        "id": "BwO2OaYOo8vs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_os,y_os = over_under_sampling(df_train)"
      ],
      "metadata": {
        "id": "7w0BGDxjf_nQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Split the dataframe into data-points and labels\n",
        "\n",
        "The first column of our dataframe 'c0' holds the labels from 1 to 5 which represent the classes we want to classify. \n",
        "\n",
        "At this point we will separate the data from the labels and create `x_train, x_test, y_train, y_test` arrays."
      ],
      "metadata": {
        "id": "1crZzRXaPPJO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = df_train.values[:,1:]  # [all rows, column 1 to end]\n",
        "y_train = df_train.values[:,0]   # [all rows, column 0]\n",
        "\n",
        "x_test = df_test.values[:,1:]    # [all rows, column 1 to end]\n",
        "y_test = df_test.values[:,0]     # [all rows, column 0]"
      ],
      "metadata": {
        "id": "RMmAoNs2P2yo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "And now we combine it with our original data."
      ],
      "metadata": {
        "id": "McjNz311pCFE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = np.concatenate((x_train, x_os))\n",
        "y_train = np.concatenate((y_train, y_os))"
      ],
      "metadata": {
        "id": "Q7XfRotYiV2J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's see how the before imbalanced data looks now..."
      ],
      "metadata": {
        "id": "7rzQXxWqpID7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lbl, count = np.unique(y_train, return_counts=True)\n",
        "\n",
        "plt.bar(lbl, count)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "vj4n6QtLi9w0",
        "outputId": "17cb5ef1-9003-4ac0-cbdf-bf2bc83020b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BarContainer object of 5 artists>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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K8jPgWeCHVfWjEfe0YN6yKUkd8Upfkjpi6EtSRwx9SeqIoS9JHTH0Jakjhr4kdcTQl6SO/A9zVaSUeZOsAwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "That's better, isn't it? But we could maybe check how the new synthetic data looks like and if it is somewhat similar to the original data?"
      ],
      "metadata": {
        "id": "3g-YqR6YpRxR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Let's visualize the signals"
      ],
      "metadata": {
        "id": "4HwdTnQnHe5b"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's write a for loop where we get few indexes of our target class with the help of `np.where()` and then plot two subplots one with original data and one with the synthetical for comparisson."
      ],
      "metadata": {
        "id": "82cyezzMsvlO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "t = np.linspace(0,140, 140)\n",
        "\n",
        "for i in range(2,6):\n",
        "    id = np.where(y_train == i)[0][:3]\n",
        "    id_os = np.where(y_os == i)[0][:3]\n",
        "\n",
        "\n",
        "    plt.figure(figsize=(8,4))\n",
        "    plt.subplot(1,2 , 1) \n",
        "    plt.plot(t, x_train[id[0]], t, x_train[id[1]], t, x_train[id[2]], )\n",
        "    plt.title('Original Data - class ' + str(i))\n",
        "    \n",
        "    plt.subplot(1, 2, 2) \n",
        "    plt.plot(t, x_os[id_os[0]], t, x_os[id_os[1]], t, x_os[id_os[2]], )\n",
        "    plt.title('Synthetical Data - class ' + str(i))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "nJf5T91zxDmf",
        "outputId": "5c93995d-9c89-44bc-be43-358080ee147f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AiEuIx/T9YNprDgPQG5jo9QyNB7zG4BmR/B2TSv9mgYDSW/7fzv+RVpTGqtOr2BZ3grRTvXmnR3ceaxNSXbdVRUXlIhSVFQHgoHMAIHfbF7wa8y0uWgN96wzkl2ML2Re/keaLHofQDtB0CNi7V6fK14VqiFUujrkEabWQU2aHu1HAlqmgs+eLzK6czixkxvCW3N/AF5GXCL++B241oPljoNVBzw/PldV0MGH75vFY49GsPXia+Q6LsAbfi6bLRBxm9WCsTzST/3FgpMtu3o5/Cc3hVPBpgHXoQrZvWUuLPW9Q6NsCun+HzE2i4x8v8OWKycSHfUGwsQjtT32ppU3maIOxRJUEE3T6d0YXzEAWt8PXxYk/o5Lp19Sf9yLf4/fjv2ORFsY0eZYlkaVkG37GIWQan63KRW83mEERwdVzv28RhBC/AvcBXkKIM8A7UsofqlcrlbuR1MJURq4aSUFZAaMbjcYz/TgLji0iw2hgbo8fqOVZn9Xxf/N5TW9mH/kTcWQF7JsHI5aC44XDUbcy1bIecUREhFQTxVcg5aDy4Dj7VbcmChYzxTN7YUk5zMKyDtRxiqG1KQ4d8KL5RRxbDGJyR3vY8AEcXgrSCg/PgkYDLy4vLQa+boP1nqcoS4rGkBYFz2wCz9rwdRukwYVDbT+lwdKeaDzDoOGDEDHq35atlCAER7KOsDtlNw/sXYXhxCoe03zERLufMXGE54MCcTG6MqbZGAaE9EI79yFIPsCssM/532F3hvTcxe8nF9E/rD9NvJuQl96cqJWzedf4Df2D/HC0eFGYM4kNr3RGoxE37VZfCSHEHillRHXrcSnUuqxSlWxM2MjBjIM46BxYfGwxWaYswt3C2Z++HwBvqWVcu//Qv47yrll0bBHvbn+Xj9t/SC9pDwsehVqdYdj86ryMi3K5uqwa4ktRlAXHVitGQWdftbKlhKR9yjmcvGHm/eAZBs9sBu3Nd1L8fvx36nrUpYFnAwpKCnlifl+6Zp/Ar9iDP90LibS3JzQnnHezz1CXOKydJ+K0fQplVjOmliNwvudp8Kh5+ZMsfFwx2gAPfae4kAC2fQlrJoJLIJhy4bntSu/6PLJMWTy8/GHSi9Np59OST/atJUOamOnqyipnFwJcgvAwerAvbR8hLiEUlubTPi+Ht5KT+I+hJav9EhlY6zFaugwnJjmP7zfHssbxbULd9fyis/A/XRFF8aOZ+cgwOtfzKT+vlJJMUyZe9tXTwlYNscqdSlxeHCdzThLuHk6wczDxefEMWD6AEksJALXdajPRuREt0mJJjttMgYs/NR9fic7Bs1yGxWph6J9DyTJlsWrgKuw2fgSbPoFx+8E9tJqu7OKohvhaMZfCTw9A/HZwrQEPTIOAZpB1CgJbnFs2Jx5cg0FcZS/KaoV5D8PJv5XvGh3YGaC0AHp9Aq2frtpruQK7UnYxavUo7DR2PN/sRTbt2sBe7b7y/R4Gd3wcAjiSdYyA7EG0dl5EojmPRL2BFDstOo2eRf0WEeoaevkTSQlJeyEvGer3/Xd7YSb8OhiMbtB6DIR3O+ewkzknWXB0AZHJkSTmJzKy0Uh+iP6BVp6NOJl9nCJpplvN3oxvOR4Powd/nfqLBUcX4GpwZUPCBnyFnjyLCaPJgzPx/4dVKvGJjzay54MTD0GXiZTqnLgv5it0xU0ItX+JOaNalZ9/RtQMpu+fzrL+y658jTcA1RCr3InEZMYw7M9hmKUZR50jH3f8mJnRMzmRfYKF/RZitDPitfNH2PC+0jB3DlC8bq6BF8hadWoVEzZNYG6vuTQzeMHnjaH9OOg26aZf1+W4XF1Wx4gvxrp3FCPc6TU4tBTmPgR6R8VYPrMJ/Jsq5Q4ugcWjoOdH0GbM1ck+9JtihDtOALcQ2D0Lek6GDR8qrt5mQyE/FTTaK/cyr4TVCprLB8Z/e+BbvIyeNCmz8vnez0AL95vsuK/ja2RaihlcdzBZpiz6/d6PJI95rNU5UksbQhPPuvT2qMu8mHlM3TOV9zq8x8KjC4nJjOHd9u/iqHPEZDYRlxdHbbfaylShwJZwfj1y9IQn1wFK77PUUoJBa8BitfDFvi+YfXA2Bq2BOh51mHzvZLqHdsfH3of3d7yPo86Rn3r9Sh33OuXietfqTe9avQH4J+Eflp1YijV2A4/nF/FXhzAebB6Eq4OOwLjlcAKo3Q29azD1Dkwl3SGOrcczyDeV4WzUsStlF9P3T8cqrWxN2lothlhF5XZmT+oejmQdIcAxgPuC70MIgVVa+WDHB7gYXPi448e8s+0dxv49Fo3Q8H779wlyDoKEnbDhfUobDuLvupPIKjbzsKMfufkmtELg6WQoP0cb/zYIBNuTt9Os6bMUhd4PkbOZrRnK8Ha1cLZNgbyVUQ3x+STtg8hv4J4nofObSstq9VtQkg9H/4LtXzPJz58TaQcZeXwH3QDT9u8wtn7m3F6x1QoFKeDsDyV5sOM7OL1FGS/1bQz3vaEY2xbDlfLd3oHvu8C6SXBgvmL06/aGwT8r5aIWQdR8cPCCNs8qPXSrRZHnWRt0RopLLUxZc5S1B+KY4/I1NUUqPPGXYuwuwq8H1rEzZSeDsx15KyeGiY5NOO5t4Y2H5+DtGV5ezkHnwAstXiAuL46XWryEm9GtfJ+9nT3T9k1j0/xNmKW5fFvvmr35cOeHxOXF4W3vzaf3fUpzn+aXvO0phSlM3DKRw1mHmXrfVH46/BObzmxiYPhAxrUYh7vx30jIwfUGY7AzEOoSeo4RPp9OwZ3oFNwJnGfBipdo3jgfAlyUnev/Vu6lX1PQaKhn8CRK5GO2mtl6IoOejfyZsnsKAY4BWKWV7UnbebT+o5c8l4rK3UJsbiyf7PoEf0d/htUbRm332heUSchP4IPID9iatLV824SICYxoOIIlx5dwIP0A77d/n9b+rfmhxw/8GfsnvWv2VowwkL9yElaNKx329SJ/jzI+PGNTLEk5xWg1guc712Zs59oIIXAzulHfsz6RSZE82/RZFhW34HHzapavXY9Br2d0h0p2aG4Cd4UhLigtoNhcjLeD9znbEwsSKcs6RYhHXYSTN5QVU/jbOOwMnuytOQbf9AIC3Owx9vucUxmFZKSPx/7IUpbk+2CwanjJx412aRF8l7uef377Fh8Pd3zcXfBs2Bn+GA9R87HoXdCU5iOQ4NsItHro9ZFiXG1IKVmR4U8Tt9aE7JoJemdo8xxEfg17ZisG9y9bD9q0G6IXQmAEMj8JkXsG7Izg04CkUjeapRYxzC6ZkIx4pFaHWPAYqyNmsC+xkGaeFno6HIX6D5CSkciMXRMIoowX8k6yPOAlWrV6ig+aXej6ARjVaNRFtw9vMJxTuafwdvCmR2gP1sWt4/vo71l2chn+jv680eoN5h6ey/9t+j8W91tMdEY0vx3/jbHNxhLmFgYo4zxPrXmK1KJUnPXOPLnmSbRCy8TWExlcb/BFz/tg7Qev/gFoPAjWvgN750CN1sqcwxN/Q1iXco9BPZ9mlGRsIcgpng1HQgn2z+Jw5mHebP0mJ7JPsCJ2BWXWMnSaW791raJSlZRaSkktSiXIKYgSSwmvbHyFpIIkANbFrWN2r9nUclWmElqllV+P/MoXe79AK7S8GvEqfWr14b3t7zF1z1Ss0so3B76htV9r+oX1AyDAMYAHgwfhU5oJO2aQdOgfApK3MlU7kpH3NaRjHW+yCkt5b8VhHm4ZRE5RGVPWHMNslYzvpjTE2/q3Zc6hOcRnZzEnwZfHddDT5TQbj6ZdvSE25cHvY6Bur387SDeJu8IQj9swjj2pe+hfuz9vtn4Tg9bAD9Gz+HzvVAA6Fxbxib4m1qJCdJkxdPe+h8xdA7CanTEljKaFX30OJ+Xha2lDw8ANGCxafk3I4aMQfyJ9Yvk415snD72Jh9UKQPFyJ+ytBWxx6kFcjhmTvTeN73uEVu2V8c/k3GI27Ignz1SIyWEzew+HsSnGxD2iB/Pt96LtOVmZCpQcBSsngLRAnZ4w6CcoK4YtU0k/up0Ted4sL+vJc3UtBJaehqTjRBismLw9GZLfGx97A5/H/8bfJ7+gEHues5uJVRSxuGE3FmUfI8MAU5zvx/WxD+hvdLmue2u0M/Lhvf9OVwp3D0ev1RPqGkqnoE7Y29nT2Ksxw/8aTof5HcrLFZQWMKP7DECJlDydd5pPOn1CQ8+GTNk1haH1h9LGv8116XQBBieo1xdiVkDfqYpnoijjnLHqerV7Q8YW2vtEs/FYPRwCtmLUGulTqw87knew8NhCDmYcvGyvXkXldmbV6VW4G9xp7d8aUDoI//nnC1bGz6NMmugZ2pNMUyYnck7wTbdvCHIK4vFVj/Pw8odp7NWYbFMeZ/KTKJNFNHJvzdSu7+PnqMwEeb/D+4xZN4ZP93yKg50D/23/XzRCg7Ra2PjtODqm/gxCiVcyajzYaNeeJ8e9h7Pzv++lHg39yvV6dVEUn687TlGphfHdwmkb0JYfDv7A15GribV4Y3b1oYvxFF+fyqK41IK9/gpZ9MwlMH8YnN4MeYmqIa5qdqXsYmfKTlr6tuS347+hERr61erHtL2f06moGL3Ji7UeMNaSybu52Tzp1ZVM52N08nuAPRlbcA77meT8znjUjCNfF81Giz3P5uQQLrOZ1vR9JuVH8fOpVSx3d6ODazt2ZkcxKKkIh8IafJAzkkFt7dlyMoFTK/IYkhKFj4uRGZtOYiqzovdah8F7HVZTEK/1/Ix1Me50TPuBxWHd8QNkr4/RLB4JLR7nq6JurP12F32au+If1IcJm5sR7u1NvqmMTXGSgS2DmBZznKmPhjA5ahTFxmhigIF2NfgsYym1KCSKQD5w1xJTdIyawsxDjv3oMeDjKr3fOo2OMU3PHS9v7N2YaV2msT9tPzVda5JalMoXe79gZ/JOWvm3Ys7hOQQ6BdKtRjfsNHZ80eWLKtUJUKZWHfgFTqyDw8vB6ArhPcp31wzrjn77Gxi1x8gwH2L5yT/oVbMnLnoXWvm1QiM0/JPwj2qIVe5I1sevZ8I/EwBoF9COF5q/wIw9i9iQ8htl+Q1o6BXG6tN/otc40DfgBdr6t0erEfzc62cWHF3A5oTdxKYYsJQ2Q1cWRuSRBmS0tMfPUZHvrHfm514/szt1N0atkUCnQCgtInbGo3TOWM9y2YFDuqZ07dmfQYszeKdfA+5zvnjnQAjB5AGN0dsJZmyKZU9cNj+NboFBa2RN7GYiQh7DzqMN4fH7KDVb2R6bQZd6vpe/Adu+VIxwUCs4s0uZ0eLgUZW3+LLc8VHTo1aP4nTuaVYOWMm3B77lh4NKbgJvs5VP0l0RQ/9g0C/TsfdfjJQ6hKaUIXWH8FabtziUeYin1jxFfmk+LnoXuod2p53Omy4r3kCrsYMJJ8HejRPZJ3hzy5vEZMXgrHfGbJE8Uftdcoli3tHZADhovMk8ORyzyYcu9XwY2dGJl7aOQC99KJQJRPhFMCTsBV6adwwXvReu9jrisrJo36AUV004S/Ym4eG3m1K3JQghwezCG60n4GlXnxf+nI6lKIRWvu3xDZ/P1sStzOszjx0JMXy27x3aFBUwPaeU8c36siF1C+3SA2ng8QgvjnoCcbXR3lWIyWyi39J+5JbkEuYaxsHMg7ze6vUbOwZrKYMpdcC3ISTuhcYPK9HwFRg8tw1nzAXkCi3ehmAW9J+Fj4MylWns32M5knWENQPX3NQc1WrUtMqNIM9Uhr1OS0xyHikF6fx330gCnQLpEdKbGdHfUmQuBMCx5F46uj/Nwj2JCF0G0uIEViONA1358KHGNA5yZc2hFMb+spdaXk58N7wl7g56Wn24jsH3BPNu/0YAHEzM5emfdtPUG7qEu2NFS/vIpwkoOsrvPs8R2udVBs+IxGyV2Ou0RL7ZFVf7Kw8DLdufyLj5+3m0dQ22Ff6PtKJk5nRfTFDcDKYcnMGWvMfp3mBguR4XJTcRvopAhnVBtH0efuypxObU71cl9/osd23UdEFpAbtSdvFc0+cw2hl5vvnzYDZh2fMnj2UcpnTQbIJDPHm6+SD+inUH102MazWCB2r3AaChZ0M2DNpAQWkBLnoXdFrbg3FwpdKjsleClmq712Zen3lkm7Ips5YxeMVgvj76MkNJDGoAACAASURBVAADwwfS2r81H+38CM/wb+kd+hBHc6IYuzkag9bAoge/Y2/aXj6I/IBXUkagCQVz1khydcXowhazo8RCaXo3WjcN4HDpYpp6tEZbdA+pmr+YvOc/ABi8QCCw897P3/H7GddiHHXc61DHvQ46XREf7PyQfh7BxKdt5sUWL/JA6HA8HfXVYoRBcWfP6jGLL/d9ybGsY7ze6nWG1B1yY0+q1UHzR5WWL0CzYRcU6RlwL8til+NS2pBAzfPlRhiUMemXN77M9uTtdAjscMGxKiq3Cz9tP82k5YeQKLMKDT4r0Xvk87D/i8xaWUpawTh0LlEEGJqwePQAPB31PN6+JvY6Lb4uRtYfSePdFYfpP30LbWp5sut0Fg0CXPnpiVa4OuggJ4Evff7gnX09eKtPfU5nFDF0RiTddfuZeOYrDAklJEsPvEU682pOZtiIMWg1ghUvduDXHfGE+zpflREG6N8sUOkRb49D5xGE0TcKg2Mio1LXkuDkCE6LWZmYzX/lVOV9l3FCCar1VOJTMJfC789w1E4wwnycWUYHGuoc4NSmKjfEl+OO7hEfyTrCI388wpROU+ih84JFI5EFaZjLyljh9xwPPfv+9Qm2lAHiksk3kgqSiMqIwlXvStuAtgAkFyQzeedkNiRsINQllP61+9O1RldqutYs3x+ZHMlPh38ivzSf3JJcmng3wUnnxIaEDUgknYM782mnT9FpdZitZiKTI9mbupduId2YfXA2+9P3MyB8AKMbjy4PKpJSMufQHCJTIgl3C+elli+hEXfpWh9SQkGqEgHvFX7h/tJC+LgWW1x680LOUPZMvL88y1aZpYwui7rQ2r81UzpNuWkqqz1ilcqQbypjb3wOTgY7mge7sXB3Aq//Fs19db1pHOiKjyt8fmwE5vw65MYPIcjdnsfbhhKbUcj4buH4uhgvKjfPVMa3G0/yd0wavq5GvhzaXDGeVgv82BsSIlli6YBmwHf8cSCZtNOH+UO8hPRpQKmDH4b4fyh+6EccGvW9qPxrQUrJoaQ8diYeZGrMGIxaIxqhYXpmAYvsnVmpL2R807cZXacrTGumJA4KjFC8Y5knIG4r/43oz+LMfTzT5BmeP7heGSceu6PSulXkrk3osTZuLS9vfJkFfRfQYMOncHwNxwIe5KUjdZn0zDDuCb15YwBnySjOwMPocUljuD1pO0+vfRoHOweWPbgMJ50TI1eNJMwtjPc7vK9G7d5ofh1GUdxuGuR8xurxnajr51y+6+2tb7MhYQObBm+6ad4E1RCrXAlTmYVNx9LpEO6Fg/7czsH/LT7Awt1nAOhQ24sdpzJpG+bFzBER6O00fLnvS2ZEzeCX3r/iQCgBrvZXDmy6GKWFyvTOmOVweBkypAMibgsHZS3WW5rQNQgapv8F46PB2VcJOq3ijIVSSros6kJhWSHfdvuWFodWUrb5UyK826B1ymSHd2d0O2dAuxchPhIyjoHBiaKIUXSOm0+RuYgWPi2Yow+DLZ/Df9LPmd1SWe5a13RCfgIAwXZOEPMHtBjBG/EDKPYqJSKkelbouFKqxLYBbRnbbCzh7uHlEYeL+i2qNjfyXUf9vjgc/ZPG4hSRsY3OMcRNvJvw+4nfSchPoIbLhWk4VVRuNnvjs3nhl30k5hTTKNCFmSPuwc9V6cVmF5aybH8SDzQNoK6fM1PWHCXEw4EvhzZHb6fhl5hfmBE1gz61+tDYLezihrGsGA79bktw9LqS2aq0SDG4AS0Uz1LCDpj3iJIvweACrccgenxI3sq3Kd39Ny/aLYUUlPzxzragqapOG4wSxPVlly/Ra/VKfgGdG7pNnzDApGWxYzHHDsyhYfPH4P7/nnPcquO/UXRSMcJRGVEUh7fGXlqgMP2m5f+/o32UCfkJuBnccD7yF1hKSK39CHvisnmkZfAtbdjGNB1D1xpdy7/fyrrecdS+HxA84HiIyNjMc3Y19moMQFRGVDUopqJyLqVmK68uPADAxD71OZVeyLCZkeQUlQKwcHcCJWYrY23JL5aP7cDCZ9riaq9j0bFFTN45mc5B9/GeyQAfBio5CypSVgw/9Yelz8Len+DHXpC4R8km+PszMP0eZd+y55UFWkauhNfiyvMkuPT9AL/xG8hs/Ro4+kC7F274PWnk1ejfJD8etaDhAEYXbQMg2iMIurx9wTF7U/fiafRkdOPRmK1mDogyZUde0g3X9yx3dI/4TP4Zgp2D4fha8KrLqkxfIIOejW6RVY5Ubj2cvCGgOfdnR/HNqSyklOUNodputbG3syc6PZq+tSo/tqVy4ziTf4aPdn5EenE6P/X6Cb1Wf0GZbFM2pZZSfB2vMLXlPHKLypSgpMtQ8bmpSqSU7DyVxdaTmZzKKCQ2o5Afn7iHznV9aBLkxmMzd/D03D18PrgZs7aeolVNj3KvTuMgVwB2Ju/k3e3vcm/gvUzJKUYX/Tm4BMGKlxTPoSlXcTVbSiHzpLJIi2c4/DxAyf4HSh7n4hzY+rny/bElENr+An0D3Oyh15vQ842rz8dflTw8i/Q/v8OY9i1banRiiJP3BUUS8hMIdQ2lpW9LtELLTlMqbUAxxOevLXCDuKMNcUJ+Ak28m8DxVRAUwbqYVGp5O1LTy7G6VVO5lQm/n5B/PsFqyuR4WgF1fJUXmVajpaFnQ6LS1R5xtWG1QOIetokyjDp7Wvhe+KI8knWE0atHU2IpocRSwpLjSxhabyhmi5Wdp7I4npbLwbKZrItbhaPekZUDVuKidyEus5A/DiRRVGrh6Y61cHO40HifnS4TEeLOm33qY7TT8vOOOJoEuvJQi0AMdlrS8k08NH0bHWp78d/+DTHqqm6c8fvNsXy48ghCKLGHPRv60bmuEt3fqqYHnzzShPEL9tP103+wWCUzRzQ45/gySxkf7PiAIHsfPsu3oI9eBJ3fgrZjYelzkH1a6d06+UJRJnR4+d+V0l7cp7iptfp/E1541lZcuLXPXazlAqrLqycEvh0ep2DeWg5lH71okfj8eO4NvBdHnSMhLiGcKs1WduQn3zQ171hDXGYtI6UwhT4hPSAnnpJGg4ncl8mo9rd+3lGVaia8O+Kfj+ioiSYytk25IQYlOcncw3MpsS1OoXITyT4NC0dQlnyAV8JqU2gt44lGTzC8wXDyS/M5mn2UA2kH+CP2Dxx0DszvO5+JWyYyI2oGfWv244UF29gcdxA7x5PoPTfRLagP6878yeyDs+nmN5Jhs9aQazKBxZ0dp7KYO7rVOcFPZRYrn609RrCHPUk5xQyZEYlBq6GozMIvO+KJTszlg4ca88mqo6TkmViwO4GDSbl8+1hLgj0cLnlZJWYL7604jL1OS7CHA7V9nGgXdmEsicUqmbMtjlY1PZg18h60QmCwO3d0sX+zQErNVt78PZp3+zdSesFWCxxdidw5k88NJcSWJDI9JQ1j8R5o+7yyAI0QMGjO5e+/gwfcM/rcbTc5A9X14O9qxGgNJaN0LUVlRTjo/v0tCssKySjOKI/58Hf0J7kkC4T29nNNCyF6Al8AWmCmlPJ/VSG3MiQXJGORFoKFHpAcLvWjzCLpWv/a3FAqdyEBzZEGFzppT7DuZCYj2oaW72rg0QCz1czp3NPU9ahbfTrejWz7EtKPEeVdkwJrKY3d6zLr4CxmHZxVXsQg7Ijwb8Wbrd8i2DmY8S3HM3LVSHovHki2OROHGspat+S3YsfO+4loUsys6NnM2LUaEXQGH509L9Sdzn+WpNB5ykYGtbOjQY1iuoZ0ZeneVOIyi5g5IoIWIe6M+XkP2YWlzB7ViukbTrBgVwLhPk4s2nOGZzrVolWoB+MX7Kfvl1uY+XgE94R6sOV4Bp+uPUq3+r6M7lATo07L3O1x/BwZj95OQ6lZSZP72aCmDGgRREJWEZuOpzM4IpitJzNJzCnmjd71cDJc+tX9SMsgHvDPweAfpKRz/WMcZJ5gsn8wvyIYlJdPx6aj4d6XwbF61tm+mQghCHOpz1HWcCTryDlelLMBvTWcFUPs5+hHTFaMEqR1O/WIhRBaYDpwP3AG2CWEWC6lPFxZ2ZXhTL4Ssh9UpgQubM91x1GvpXkNt8sdpqICGi3CrzER6Wd480gaaXkmfGzzKc+uDpNYkKga4ptN0j4IimCrfw20yZv47uB20qSJde2ewtfOkbqRMwkrzEGvqw9LXwTPMJp3nMDEllOZHPl/1JYaXvJsRVJ4Z7rsXUd0xkcs31mbUt8a+DoKurs0YkXhMZYnf8THQ1/l091TmR13EOKg0aHmHD0wkGbBXnSt74MQggVPt0FK0GgE47uG89veM0z64zANA1x4oUs4TgY7VrzQgSd+3MVjM3cwsU99vtpwgsISC/vic8gpKuX5zuF8uf4E94Z78ePIe8gqKuXFX/fx+m/RFJaY+W5TLGeyi1kZnUx2YRnuDjrub3CZzkRaDCwbiyFxD9TtoxhiR09293iHX4/9yKO1HuC14D4Q2u7m/W63AG2CmnL0DOxKPnCOIY7Piwco7xEHOAWQZcrC5OKP8TbrEbcCTkgpYwGEEPOB/kC1GuKkQuUmBhbmAPBXshMRoW7otHd0oLhKVeHXhODE2VitFr7eeJJJDzQElIoKlK8+o3KTMJdCykFo/TTbSo7TxCxwdvDEWasjbNscKCsCv8bQ/Ank1i/I1AfiFruJgl0LSDEOY4fpOBqjG9ozRyHlBCTvx8MpgG4FOzCXhGCXfgakhTYO9rxsLePdrNHY6ezo4DaS9YfziZZLsbpK/jdwenkQlhCifOjTx8XIfx9oSGx6IS/dX6d8XDjE05HFz7bj2Z/38J9lh9BqBL8/144ftpzilx3xJOYUk2cq4/Ve9bDTavBxNvL1oy15YvYu/rPsEPY6Lc/dF8b3m2Nxd9DzVp8GGOwuMeZsyoNfhypLqDYfDvvmgoMX1uG/88nW1/B18GVc24kIu6qfOnSr0y60Jj+ecmX7mf080+zf7fH5iiEOdg4GFNc0QKqjJyGZp26aflVhiAOBhArfzwCtzy8khHgaeBqgRo0bPwcztSgVjdDglX0Gi3MQ0WllvNbi4uvyqqhcgH8TNOZinmkombEjngebB9Is2A13gzv2dvYkFiRWt4Z3F+kxYCkh26cOhw8s57mmo6Dp00pU76KRmBs9wmz6s+l0ATmuHYlKKWZkeAnjz7zEC6bvyHOqhcuLW2HpGDi8DJoMQffQtxC1ELvds6DRQ1CvL51/HcxKqx9Lm/WnTUAbmvs0Z0+TbMauLKDQZTUWuwSgYblay08u5+v9X/No/UcZ0nLIRRPueDjq+eWpNszbEYej3o4mQW6M6RTGsv1JrIxOYVzXcBoGuJ5Tfulz7Vh/JA1PJwPNgt14sWs4BjvN5SOxV74KOXHKNKKQttBoALgE8XfecQ5nHubDDh9ifxcaYYC6vs5YioM4kXtu/zA+Lx4vey8cdUoA79ncDckOzoScunmu6ZvWPZRSzpBSRkgpI7y9Lwwhr2pSC1PxMnqhyzxBln0IAG1q3fxMWiq3KX5NAHgqPB9fVwOPzdzBwcRchBAEOgVypuBMNSt4e2C2mknISyCvNK9yghL3AnDayROJpFHAPaB3JF5fm/ZFU2i0oRnvrzlFUk4x+WYN04e1YNLoAbg+uRRTUHtcHpsLegd44Cvo9TH0maIEKDUdDKNXK0kegu+B+97A//R2ni3T0dzOFQ4vp6X1IH93aIWHwZ0Pd35ImUWZZ7rq1CombplIsbmYj3d9zMPLH2Zf2r6Lqq/VCEa0DWVgS2Voo76/C4MjgunbxJ8Xu16YblUIQdf6vjQLVobSjDrt5Y3w/l8hagHc94ZihAHCuiC9wpkVPYsazjXoXbP39d792x4PRz06cw1yzSnkluSWb4/Pjy8fH4Z/e8TJOgOU5ivpcG8CVWGIE4HgCt+DbNuqlZTCFHwdfSDzBCelP456LY0CXa98oIoKgHdd0BpwzYlh4TNt0WkF32w8CUCQU5Dqmr5Kckpy6P17b1adWlU5QUn7wOhKhp3S4zyboW7a+uNkFJQwrFUIPz5xD+te7sSGV++jTxPlhSoCm2N8ciX42VbfMbpA62fA4HzR0xAxCsK7w1+vwVetYOFwmNMXxyVP8UZ6OlHpUby97W12pexi4taJNPdpzqqBq5jWeRollhLGbxhPiaXkqi7po4eb8NWwFmg1lZzak3sG/nwFQjrAva8AyqyRNza/wZNrnuRg5kEeb/j4TV017FZDCIG/vZLo41DGIUCZynUs+1h5vn8AXwdfBIJkre03ybs5veKqMMS7gHAhRE0hhB4YAiyvArmVIrUoFT+9G5QWsL9Ymeyujg+rXDVaHfjUh5Qo/F3t6d8skLUxqeQWlRHoHEhiQSLVkaf9duPsFC+T2VQ5QckHwL8ZmaYsQDHEcZmF/L4vkUdbh/B2vwbl82krhUYLA75X1qVtOgSeXA8jlsHwpfS06hmbk8+K2BWMWj0KN4uVz7KLsdca6VyjM5PaTSLLlMXK2JXniNydspueS3ry7YFvb8wzs+VzsJTAg1+X50b+aOdHrIhdQXx+PEFOQTwQ9kDVn/c2o65bfQAOZh4EYHvydvJL87kv+L7yMjqtDm97b5KlEuR7syKnKz1GLKU0CyGeB1ajTF+aJaU8VGnNKklqUSrtHBWXw/ZcdxrWu/gi0yoql8S/CcSsACkZ2CKI2dtO82d0MgFOARSWFZJbkoubUY3CvxxGrRJtfrW9xEuSewYCmpFRnIFA4GZw4/sNx9EKwZhOtapA0wrYu8Govy7c/tQGxvz2FG0TNvGPgwM9CgrxLDsJcdsgtD2t/VpTx70Oc2Pm0r92f1adWsWyk8vYmbITg9bA9P3TkUiebfps5XWUEhY8pgRmxW2HpkPBXRmC25u6lwVHF/BEwyd4qeVLgJomF6COtw9/n/BnXswvdA/pzqpTq3DWO9Mu4NwIcn8nf5LLbC7p28g1jZRypZSyjpQyTEr5QVXIrAwFpQUUlhXiazYDcKTMnwYBqiFWuUb8mkBxFuQl0ijQhXAfJ5buTyTQORBADdi6Cuw0dghE5QyxpQyKMsDJj0xTJu5Gd+w0duw4lUXTYNfyqWU3HAcPGLaQpg/+yIthA6g7YDY4eJWneRSZJxkZ1I3j2ccZsmIIr21+jaSCJAaGD2T1wNX0qtmLGQdmlM9drRRx2+DICji9FaxmZU6wjeUnl2NvZ8+YpmNskd2qEQao6e2IKXEoZouFx/56jLVxa+lWo9sF6U/9Hf1JKbFl17qdDPGtRkphCgC+xfmY7RxIxV01xCrXjn9T5W9yFEIIejbyY09cNq46JbLyTjXEQoieQoijQogTQojXKykLo52xcoa4MF356+RDRnEGnvaemMosHErKpWXITQ7A1Gihfl/oO1X52/oZOL4GNn0CM7vQd8M03mr1Jsezj9O1Rld+e+A3JraZiKvBlVdavoJWo+Wb/d9UXo8tnymNgPFRMGazssABiudhzek1dKvR7ZwMUipQ08sRa6kPT9b+hAjfCCSSAeEDLijn4+BDmm0I5GYZ4jsyxWVqUSoAfvnppOtroLfTEubtVM1aqdx2+DYEBKREQb3edKzjzZfrT5CQqox7xuXFVa9+N4AbkaBHr9VXzhDnKw1rnP3ISsvCy+jFgYQcyiyy2pYzLaft8xC3Fda/D0KDMOUyxKUOPR9YiqtLMELzb1/H19GXYfWH8ePBHzFZTExsMxEP43U0JGL+gBProMtEcAmg1NGLr/Z8xvr49ZitZvLL8tVFSS5CqKcyRamo0IvPO39+yXIueheKLSbKAF2p2iO+bs4aYt+cRGIJoK6vsxqopXLt6B2V9VaTlaXmmgW74WywY2dsETVda7I/fX81K3hDKE/QI6UsBc4m6LluDFpD5QxxQZry18m3vEe8O05xHbasbkOsd4ChC+DeV+HRxUqO4s2f4fZVa8TC4WA+97qfb/Y8zzV9jo0JG/lo50fXfr7EPbDkSQiMgDZjAZhzaA4/HvyRYOdg/B39aeHTglb+rari6u4oHA12+LkYOZZ6eePqYlC8p3l2BrVHXBlSC1MRCLyz41kq2tGgluqWVrlO/JpAfCQAOo2gQ5g7m45l0L1jS1afXk1sTiw/Hf6JJxs/WZ7+8jbnigl6rjU5j0FrqFzUdIHSI5aOPmSaMvGy92J3dBa1fZxwd7xwhaSbjs4IXf+j/B/aXhm71Tkqf7+KUJ6h5sOhTg/0Wj3PNnuWMmsZ30d/z4gGI2jo1fDy8gGyToGDJ3FLn2KFlzc12o+mraWIgtw0vo/+nq41ul62l6ei0DLEncjYzMsuU+mitxlioxOe6hjx9ZNcmIyn3hUdcLDEl0ZB6vxhlevEvwnknYGT6+GH7rydO5HEnGJqODYivyyfV/55hSXHlzDoj0EcyTpS3dreFK41OY9Ba6DUUnr9J7T1iAsMjpRYSvAwenDgTC7Ng2/BiPX6tmlC/b+Ch2cpcQaJe+DXwbDhw/JioxqNwt3gzuSdkymzlLHw6EJ2p+zGKq0UFmUQtfwZzCfXQ3YczH8UpjXD/Gk9JugK+dZe8OauyXRd1JV+S/shpeTViFer6YJvLzqEe5GaV8LJ9IJLljlriHMNjmqP+HqxSiuRyZE0sCjz9Y7IGrxQ3e4rlduXBv1h+9cw9yEA/IE6IgFNidJJPJFzgj61+rAydiV/x/9NPY961ahslVDlCXoMWgMmSyV6xPkpYO9Opm1KiV64klVYSn3/W9DT1XIkBDSHoAjle6OBStT3srGw+VPFlV1ahFPHCUy4ZwJvbnmTAcsf4vRF4g2G/bmacbkFHDIasYsYzl8Ze4kR+Xzc8WNqutZkQ/wGdFodPUJ63CnemBtOh9pKIpjNxzOo7XPxpC7lrmm9A5Rc2mBXJXecIY5KjyK5MJkX0jJY4/8MaUk1zllPVkXlmnAPhWe3weYpUKMNcvFoBus2EZ/WkQDHAFKKUnix+YvsStlFcsHNy017AylP0INigIcAwyojsPI94lRw8iOjOAOAoiIHwEI9v1uwXmt1/xrhitt6fQynNsO6Scq20kL69viAjQkbWRO3hueycwi2SOL8GmCXdYo4V29+cYV17t6kWU2Q+Q8I6F2zNz1DeyKEuBMafTedYA8HQj0dWLo/iZyiMvo19b/AILvqFQ9qnt4AJZVMzXqV3HGG+K/D8zBYJV3qPMTDp/vRvIa+8inkVO5uHD2h52QARJ2FDDy6hWcTMhneaTg5JTkEOAUocw9t0+ZuZ25Egh6jnZGCskr0LApSwUkZHwbIyNUDxdS9FQ3xpbB3g1GroCgTDsyHyOmIklw+yDrJ8JQUmjUaBjoH2D8PSgspfXgJp/ZMptBcyNTmL6LX6qnpWpMgpyB1XnAl6VjHm5+2x3EgIYevN55gyiNN6d8ssHz/v8FaeihSXdPXjMVqYXXcWjqWmJH3vs3RyD30uEhCdRWV66bRANyOrMCafICh9caWN/L8Hf05nFmtK39WGVLKlcDKKxa8SvRaPaWmSvaIa7Qt7xEnZerwdrbi6WSoIg1vEu4hyse3IZhNEL0Yo96BZvd/BC2fUBah6Po2FKShdw9hbu+5aMUVFntQuWZevr8O3Rv4UcvbkRGzdjJ3e9w5hthZrzTw8rR2UJJ1U3S6owxxdOpeMrHQLaA9+zK1WOUtML1B5c4isCUAYZZYTqYXlA97+Dv6sz5+PVZpRSPuyBjI68aoNV5/1LSUkG/rERdnohVaYtOst6Zb+mqxM8AD0xR3tUaruK7PorMvT1Vpp7mjXs+3DG4OejqEK2PFXer5MHvraUxllvI1pHUaHQ52DuRpNEoK0ZvAHfXG2Bi7Ejsp6eDfhj1x2QhB+TJiKipVglsIFoMrjcQpos78u5yan6MfpdZSskxXbkEXlpiZuTmWoyk3x+1V3VQqoYcpV1nQoDy9pQfHU4tub0N8Fp3xXCOsctNpGeJOqcXKwcTcc7a7GFzI06CmuLwe/knaRktTCS7uNdkTl01dX2ecjeqDrlKFCIHGvymNNKeJ///23j1Gsvy67/v87qvq1rOf0/PYmZ1d7iylBWUuyVmJlBWCtGhRSgKtbMNBgkCW4jhrJZDtADEMRwIkA4oCIU5ixEkQgVEESzaj2AhAkaAlU6Qkh1GsDTkMl0uK3OXOPmZnZ2an3/V+3Mcvf/zurap+97Cq69F1PsCgp27X1P11Td3+3u8553fOVqN3OJ1jepo88e7OJt0v/DJvf/NPzmyZ00TWHqLFZTr9pniRrdYWRWeBbhjz3otTWDEtzBxpZ7avvrWz53jJK1FBQ9CEKDzzdUylEL+52eDFN7Ye6d/crd3ldvM+H2u2iAqX+Prbu9y8LmFpYfSoy+/n+6y32aoNCHEhGSjeOLlyOmrs8J85n2O5cfvM1jhNDOWIt5L3aOlJNlubuJiK1nPhiIWJs1zI8ORKnq/d2RvJKnklqkTmwRjC01MpxL/5J2/yn/6zrz3Sv0mHPT/XanO7XaTeCSU/LJwNl57FI8Tdeq1/KHHEp9nCFAYmX2q5M1Zs9D0y1NCHze+arys32GpvEYUFbEvx1AXpHS+Mhg89vsjX7uzsmRVd8kpU48A8GEN4eiqFeCHnUmkFxPHph2inFZVryuUrD2IAPnRtzJNZhPkgmcq0VO1XSZe8Er7jn8oRhx0jxMod0/i+CePZHmEcEsXRo//jje9C6QraK7DV2qLVMvtA08IaQRiW914sstMM2G0GvWPlTJlqnFT6z6sQl32XWEOtc/rY/GZrEwdFqbDGS3crrBQyXF3yz3CVwtyy9CQhDkutfjckpdSp9xJHc+SIK82Ar7xufpF9T65487uwcoNqt0oQB2zXPL5vGjtqCTNLOpXprYGaj5JXopZ2g5tXIV7MmUbuu83T7z3cam+xpBVW8Qp3t5s8uZKX/XfC2WDZVDMXWQoe7AlnXcxfPJ0jDowg2c75F2IU/MlrpiL1kYVYa9h8+GPUuAAAIABJREFUDVaeZqtlakZ2qlm+TzrlCSPk+oqZ23xnq9k7VsqUaMVdAoATRiF++v+9wz/43FA9b6ZTiBdyptJ5MFRwEputTZajGEqXuF9pcWlhPsJ+wmRo5h/jCutU2/2ozVpujfXm+on/NuomQuydfyEuZR0ytvk5H1mIaw/ML8GVp3tdtXRYmK2OWsLUc3Uph1KmSDilN/jBsk50xLfe2uGPXjn5uj+O6Rbi1umFeKu1xUrQQRcu8bDa5lJZwtLC2RGWrnJVrbNR64vLhdwFNlubBPHxn9s4mVFrz0GOWCnFom8cxyMLca9Q6+leDYgOi9M57EGYWTKOzeWyz52tg0JctU8W4nYQkXGGk9KpFOKy/z2EppsbLIcB9cwqQaS5LI5YOEPUwnWWVJ2t7f42u7X8GhrdC6MeRZyEph1vPj6jqRA/cnetjUSIV9/L//nSdwB4cmmNKwtyky2MlusrOd7aF5oGTHetE4S4E8Zk3HMoxKkjrpzSEcc6Zruzw0oUsWktA4gjFs4Ub/UJAFrrb/SOreXWAHjYfHjsv40TQZoLIa69y3++aebwPvIEptf/EMpXiXMX+MqdOyht85mf+4tYMsRFGDHXl/OHO2LLOnEUYieMyDrDVfFPpRCX/UfLEVc6FUIdsRzFvBublpaXynPwS06YGPm19wAQbr7ZO9YT4sbxQqznyRFniuSTbSDVTvOEJw/QrsDrfwTPPM/d3RaBqlBwFyhmvTNaqDDPXF/Os9MMqCSaU86YxjEVzz9xFGI7OKeO2LUtChnn1EKchgJXooi7XVPIcVnCV8IZUrj4FACq0t/CdCF3AeDEgq00R+zMQbEWbg4v+TXzoPoIs11f/X2IuvDMT/Ht+1WUU2fFXzmjRQrzzuPLJn3yZuKKe47YPVmIO2FE5jw6YjDh6d3W6UJZm21TyLEcRbzV8sk4Fos56TEtnB1WfpkGWbza3d6xhcwCnuWdKMQ6NJ9rbw6KtVAK2zFdsB4OtAQ9kW9/FkqPwWM3+bP7VSynxuXi6hktUph3riwa4/ZupQUMjEL0siY6cwydICZ7Hh0xJN21HtERL8eKOzWLywu+7CEWzhalWLcvUmjdGzikuJC7wLvN45t6pELsZuYjapNNfqk9rD9CY4T7L8ETHwWlePn+OlZmnacXZba4cDasJrOtN+rm2nQsh7ybp+p4JwtxGJ9PR/wb3/wNNgv/AzunrJruCXF2kfvVtuSHhbFQ8dYodfe63wu5CyfvJU5C0152Pj6nvm/CfJv1UzrioA21+7BkCuK+vf1NUCHPXXzurJYozDlLeQ+lYHNgO2LJK1F1nBOF+NxuX9pt79Ky3jz1PuLN9iYuipK/xINd2UMsjIeuu0Ah3ps/WsuvnVispaI56qwF+FlTQLnVPOUUm923zdfF62zWO1R5FYXFBy584IxWKMw7jm2xmPPYrO8TYsuC1u6x/9Y44gkKsVLqryql/kwpFSulbg61kgEKXoGYLrvN0+073GqZ9pbkVliviSMWxkOQXaKk94Zb0+5ag60vDxB16WobrKm8Dx452ayZgrbTOmXV9M5b5uvide5uN7Fzb3A1/zQFTyYuCWfHSmGfEGdKVBWnCE1HQw8hGfY3wbeAvwx8ecjX2UOaKK90a8f/QkuodqosxjEdb4FYw5oIsTAGouwiPh3igW05F3IX6MZdKp2jL14VdQjU/BQTZnwjxBunDU0PCPG93Qq2f5f3r3zwbBYnCAkrhQyb9X461Mwkjo0QH6FDWmuzfWmSjlhr/R2t9atDreAQUiGOaVE/xQSmSrdCKQyp2+aCXyvOR8hPmDC+aR7TrGz0Di1kTBi20j3mLjrqEjA/QuxkF7G0phG02Kqfos3lzlvg5iC/yh/f/32UivjYtY+e+TqF+Wa5kNnz+SxnylR1CDqC7uFplW5kRu5mJuyIT41S6gWl1C2l1K2NjY1jn1twTQhK2a1T7SWudiqUwi4VZQR8rSSOWDh7rLyZd93c7RdnpTeR9SMuXAAr6hLgnO3ipgiVWySjNZ5q8eq7p6ic3nkLFq/TjQP+n81/TtR8nI9d+/CZr1OYb0xoep8jTmcSHxGe7oSJEJ+1I1ZKfUkp9a1D/jz/KCfSWn9Ka31Ta31zdfX4/YBFzN2FstqnanNZ7VQoxzHbWoRYGB9OwTSYaA044vQmsto9ugmAFXcJ5yg0TXahJ8SvnEaId+/A4nU+/8bnaURbZGo/gTfk9hBBOImVQoZ6J6QdRIAR4rYO6cLRQhyMxhGfeFuutf7EUGf4Hih+7bcBUHab2+t13nelfOzzK90qpSjmYVRAKXNnIwhnTaZkhLhb7QtxzxEHRztiFQXzJcT+Ap7W4AW88u4J3bW0No74iY/y9fWv41Ji1XvfWJYpzDe9vcS1DleXcnsmMK0cIcSpaJ/L7UvFvGkVWPK7fOk7x28FaYdtunFAKY550M2xnM/g2FP5YwnnjGzZfE7D+mbvWCrEtWOGidtxd66KtciWyWpNKROcHJpubpl83OJ1Xt99HTu8xIWiRLiEs2elaAxcWjmdTmCqHLOFKQ1NT7RqWin1l5RS7wAfAf6lUuoLQ60moVC4BMD3Xwj4v767QZAkxA8jDQGW4pi3OznWSlKoJYyHfNmkWKJGf+zhaYTY0l0iNX1Rm7PajkjWOGLfDXn1YY0oPmYnRNV0KtPFS7y++zpR5wKrUnwpjIHlvPmcpXniPROYjswRT4Ej1lp/Rmv9mNY6o7Ve01p/cqjVJBSKlwG4WupQa4d89c3tI5+bbhMpxTFvNrOSHxbGRrmQo6p9VKv/+cy7eRTqBEc8taHpM9mOiL9AVmscq0s7iI+vnK6bMP+7rkszbNJsrIgQC2NhpZgKceKITyHE7WBMxVqTwC5eJB/HFDN1PNviy69tHvncniOOYl6rZcQRC2OjkHXY0UWs9k7vmKUsCm7h2ByxHXeJrOlzxGe1HZHsAplYE5uyl+MLMBumAv12aN6/oHmhl7sThLNkOW+uyfRGMR2FWLVPdsSTbuhxNuRXKcYxrW71QLeT/VSTEVUlx2e9GUk+SRgbtqWoWkXczs6e4wWvcKwjdnRAbE2lIz4Vj7IVEYBMkayGCCPAxwpx3Qjx610TZYg6a+KIhbGQdW2KWedgaNr1oX18jvhcOmLyqxTimFq3QjHrUmsffeGmjrjgLaA1XBBHLIyRulXG6+69SItecWqFeBTbER9lK2JyUnzLIdDmOj62N0B9Hdwct2t3KblLEOdEiIWxsZT32G4YIe6PQvSP2b6U5ojPePvSRMguUIw19aBOIesc210rzRG72YsArIkjFsZI0ynjh/f3HCu4p3HEkwlNT2I7IkDWcunqUzjixjrkV3mj8gYrmWvcA4lyCWOj7Lu9z6dt2RTcAhW3c3JDj3M5j9iyKCqXWtiimHWotY8W4mq3itKayDOV1uKIhXHScRfIR3v3xpa80rE54kkK8aTI2B4djHs4dqpafR0KF3jQeICvzD5tccTCuBgUYkiiW7Z7YkOP85kjBgp2hlrUoZA5QYjbOxTjmC17DUBGIApjpestkNNNCPut8U7KEbsExPb0CfFZbUcE8N0CbR2h1EmOeIMwt8pWawsVlfEci1J2OgN3wvljvxAXvAJ12z5mH/EUbF86S4qOT02HSY74mNB0/V1Kccw9vYxnW73KN0EYB1HW9JumuXcv8UlCrO3pc3lntR0RIJtdoI2mlHGoNLtHP7G+zla+jEYTdUusFjIopUa1DEE4loXcPkfsFqlbaj63L4H5ZVZH89Pv/BIf6/zxkc+rtjbNHuJgmbVyBsuSi1YYI76ZtjRYVZluXzpqhKerQ5hCR3yWZHPLhEpxLVc/OjQdhdDcYt3LAdDu5CUsLYyV1BGn127BK1BTwBFjTTthhLv0ZX71K7801HmnWIhLRAqu7/wxH+EbdMPDu2tV27uU45hXWwsSlhbGjpXM2g0b/aYeJa9ErGOaYfPA87XWeARzKMSmuvpJ9+HRoenmFqBZ98x7U2/kuSBCLIyRsu8SxbpXIFxwC9R1BJ36oTOJO2GMk33ASxtfH+q8UyvEhaz5BVezLJaoHlk5XQ1qlCLNt+p5LpelulIYL07eOOJWtS/Ex7W5jMIQR8XoORNiv2B2NVxx14/evpQ081hPolo7VV8csTBWFnxzXaY3i0WvSC2dSRy2Dzy/HURYVow75HbEqRXiUs5UTNYtxaKqUT8iT1yN2pTsLPdqERfFEQtjxiuYHHG71s8RFzwzCvEwIe52W+YvznwJTDZpW3tBrVM9yhEnzTw2iLCVzU7NEyEWxkrJN4Ka3iwW3AL1OECDccX76IQxth3j2udUiAs5UwW9a3ssU6N6SFOPWMdU44CCWyCINJcXxBEL4yVbXAagWz/oiA/bwhR0TJc4NWeOOJM17QLLrB+dI26YLl3rcYel7DJgiRALY2UhZwQ1vVkseAVCYtpKwSE31p0gxrKi8+uIrz/14wB87fKHjCM+JDRd69YIFRQcE8a+KAMfhDGTKxshDhv9NpdF9+jQdM8Ru/MlML5jolX5aHNPMcweEke8HtQoueZ9lWYewjgpp444DU0n13LdUkc44ghlRTjWcFvsplaIryzd4FL+Ei+5MQXVptFoHHjOVtPcQXu2uWgvL0hoWhgvpVyWqvaJm/2q6eNyxGHH5JmsKdy+dJZkbSOoXri5pxhmD80tsFw2OtvkbBPyF0csjJNUiCsDjhhMrRLdg0LcDmIsdY5zxEopnrv4HC+HG2igW1s/8Jzt7dsARFbazEPunoXxUvIdquTRg9uXjskRB10jxGrOHHHWMdemCrexiA8v2GptQ26Jh811PEyUS4RYGCdpaHqwWAugblnHOuJzK8QAN9duUonbvO66RLWDU162t18DoKEv4jkWS9LMQxgzZd+lovNY+/YRw+E54jBIHPGcCnGXmGWqh29ham7T8hepdWtYkZl8s1KQa1oYH75r49pqT7EWJEJ8WI44jFHqnAvxcxefA+Cr2QxRfevA97erdwB4EFzhcjkrHXiEseO7NlXy2N3+hv+MncFRDs3g4D7iqGuKtaw5q5r2bZM2aluKomoeLsStHTZzpqgrCkqUfXfoqTaC8CgopSj73sHQtDo8R9wOIlDnOEcMcKVwhSWvxHc9F9U8RIhr91Fa8+3KKtdX8hNYoTDvKKVoWQW8oLrnWM7NHeqIo+58O+KWsijSPDw03dxiJ2uu43YnK808hIlQ9p1e1XS/WOvwHHEnjOG8O2KlFIvZJSq2jdU6RIhbmyxoeG0r5vqyCLEwGdpOiUy4N2xVcAs0goMFhmlo2nbnq54hk0QA2kpRUk2+86B68EnNbXaT9pb1Zkbyw8JEKPsuuy3TDz11xEfniGMgOr/7iFPK2SV2LAu3s33ge9udCos4NLsRT66KEAuToeMW8aO9Qpxzc4cKcRSYC3zehDgNTXeU4kcec/ntP32L2mBvAK2htU3FNTnh3borQixMhIVcPzSdd/MoFDXHPTRH3A4i4Jw7YoCFzALbtovX3Tnwva2wSckyF7g4YmFShF6ZjO5A2OkdSwc/7EcH5jm2N18i41gOtrJoWYp/5+kc1XbIP3vx7f4TOjWIQ3Ztk2vbrrqsFubrPRKmg7Lv9lInlrLIu3nqbga6B2+sO2GMVuEcCHF2gYpl43f3zYOMQrYJyGJCB09IjliYEFHGFBgNzizNu/nDi7USIXa8+XLESimydpa2UjzmBzx7dYE/euVh/wktE/HatRSWsmh1xBELk+GwmcQ12z08NB1ExITnu1gLoJwpU7XBD/c54uo9ti0LS5fwHEuaeQiTI3NwFGLezR/uiJPG8c6cFWsBZB2ftmVDu8KTK3nu7w400W8aIa6gyTtFwOJCaf7eI2HylH2XWjskipNRiG6Buu0cKNaKY02jG6HnITRd9soECjy9t7gj2H6Dqm3TChd5fCmHLXOIhQmhcqb5hG71bxYLXoHGIaGsOHHEbma+HDGYyum240G7wuUFn3erbcIoGW+aCPEuITnH7CFeLczfeyRMnrS7VnWgqYcp1tqbI660AqJYo/U8hKYTt2Gxw3q11Tu+s/UqAJvtJQlLCxPFTkYhBvW+EOecHI3woBDraD5D02D6TbdttyfEUaxZryV59TQ0HXXJWCbdJKFpYRLs765VcAvULHXAEW81OoAmIjz/VdOpEMdOh//7ay/3jm/vvA7AW7VFnlwtTGRtggDg5k1f5FZ17yjERtAg1nHv2L+4dZevvPau+TeZ+UulZOzMgBCbG5H7u8nNdRqajpo4WoRYmBz7Bz8UvAJ1xYEc8Va9C5jr21ETzBErpf6hUuoVpdTLSqnPKKUWhlrNIZSTQphdy+LNb/4p240uQRSzVTUVl51uiR96YmnUpxWEU5OOQuwMdH/LOyZK0wr7UZxPv3iHQrRLpBzKydSmeSLrZGnbNrSrXElqOu5XkjxxaxtQ7HbrEOdwLMWCP5zLEITvhf2OuOSVqBEd4oi7oCKAiTviLwLv01r/OeC7wH855OsdIHXEu7aNtf5NPvRffZH//ovf5V79PgAqWuQHRYiFCZItJaMQB2YS5z0jxPWBi7cVRHxfvoFdvAjW1AejRk7WydJWFrQrXEqFeNARZ8tUuhXiMMdqMYMldR/CBNg/ganklajpCL3fEQ8K8SRzxFrrP9Bap/PMXgQeG2o1h7CQNUK8nV/lw7n7XFnw+eK3H/JOZwdHK95/6Rr5zHBhAUEYBjMKMUfUHBDixBEPNvVoBRFL8RYUL459jdOAb/u0FNCuUMg4lLLOgBBv0ckt0QpbdLu+hKWFiVFKhbhpmu+UvBIR2lzLA3O0t+od1DQI8T7+OvD7R31TKfWCUuqWUurWxsbBSUpHUfZMaLpevsgP5x/wMx+5zp31Xe7GLQpBhj//ntWhFy4Iw1D2XXZ1vpfnhH5rvD1C3I0ph/MrxMYRA20zIOPygt8X4tY2uzlz091qZ6SZhzAxDjjijKnir9pqT1OPrXqXUs5Ebc58H7FS6ktKqW8d8uf5gef8IhACnz7qdbTWn9Ja39Ra31xdPb14urZL3s2bi3TnTT76eIZLaot3HAenW+TPP7Vy6tcShLOg5LvsUETt20cMe0chtoOIcrg530KMhqABUciVBZ976V7i5ja72aTBfssTRyxMjIxj47t2T4hTM1jZN/hhu9FlIWckdFhHfKKMa60/cdz3lVI/C/y7wI9qPeDbR8hCZoFKxvxie3r7X/OMv8M3XIfL1mXJDwsTp+y7fFvnWWv3ty+lQjzYXSsOWviqNr9CbGfpaBPKo1Pl8oLPrTvJe9baobJ8BZpQbYgQC5NlIddvc9lzxOngB3O/yGa9w0LOZpsJh6aVUj8O/D3gJ7XWB/v5jYhypsyul4XHnkN94Rf4sZU3qVsWn3jmgzKDWJg4rm3RsEu4QX8m8X5HHEQxSzoRneKlsa/xNJz1Loisk6WVlpS0d7m84FNpBdQ7oXHEySCMOMzJCERhogy2uSx5A0I8MPhhu9Gl5Bv9mXTV9P+EuT/4olLqJaXUrw/5eoeykFmg0qnCT/06hB1u7P5zAL7/yvvP4nSC8Mh03DLZY4S4FUSskQrx1DriM90FkbWzhDomAGhXuLZkRh7efbgNQYNdx/wy01GOS+X522ctTA8l3+3tI94jxAPdtbYaAzniSe4j1lo/pbW+qrV+Nvnzc0Ot5gjKXpndzi6sPAU/+y95Z+UJAB4rPX4WpxOERybwyvhxHWITei24plgrDU23uxFrarod8Vnvgsg6xvG2ldojxO8+NFsRK7Zt1hHleHqtOMpTC8IjseC7vRaXaS+Lim1B27RajmLNTrNLMTsdjngsrOZWedh8SBRH8NiHuPvhvwHAleKVCa9MEAxRdhEL3asI9mwPx3L2OuIpF+J9HLkL4nvdAeE7xuW2LQXtKteWjRBvbTwAYFdpbDJknQxXFsURC5NjMDTtOz6Oso0jTq7vnWYXraGQCvEUbV86M55aeIpO1OFe/R4A79TeYdVf7V3YgjBplG8GPzA4+MEt9LYvGSHeJbZcSJ87AUaxC+J73QGRXq8tZUFrh7LvmqKYrXUAdomwdJ73rBZkiIswUQaLtZRSlLziHiE27S0hPyIhnolOGDcWbwDw2s5rXCtd427tLo8VR947RBC+Z+y86a4VN7awlt8DmDxxT4i7ERfUDh1/DX+CBYaT3AXRE2LLhspdAK4t5WjuGiGuxAFR6EtYWpg4Zd+lFUR0woiMY1PKLFC17vWFuGGGleQ88/y5cMRPlp8E4LXd1wB4p/4OjxVEiIXpwU36Tbcqm71jgzOJ02KtMHdhIus7DWe9C6InxMU12H4TMELcrZke3VtBk27X56kLMsRFmCyHNfWouF5v5nijk3TUcpOhD2fd0GMayLk5His8xu3d23SjLg8bD7lavDrpZQlCD79sQrSNynrv2GBouh1ElFSDODvyuSij5Ex3QaRC3CxdhJ23ACPEKulIttmpS6GWMBWUE6tbHaicriaTwwA6oRFipYwQz0VoGuCpxae4vXObe/V7aLSEpoWpIr9ghLg94IiLXpGHzYeAaW+Zo4Pypnd2ttb6qbN8/Z4jLqzC6y8C8PhyjhpVYjfHTruCjh7jhjhiYcL0RiE2+5XTbw3kiNuBEWA1JdOXxsaNhRvcqd7hjcobACLEwlRRXlgh1opgYBRiOVOm0kkv3AhfdbCmWIjPml7VdG4Jmlumcnopz6Kqcz/I0Y7rXCwscTXZ1iQIk6KUNR611jG7+UpeiepAn/SeI7amb+jDmXJj8QahDvnyO18GkNC0MFUsFX2q5IgafSEueSWqXbPvsBVE5GljZUWIW34Snt95i6fXCixbDTa9Ekpp/sZH3icV08LEKSZCXG/3hbimYuIkR5w6YjBCPBc5YoAPrX0IS1l8/vXP4zs+y9n5G6wuTC9LeY8dXYBWf/BDySvRCBoEcWAcMR3szPyGXX03EeL0Pdh5i+VCho8+ZrFwxeytXpjuHLowJxQyxuHWBxyxBmodc2OdOuKpmEc8Ti7kLvCRSx+hG3e5UrgiPaaFqSLr2tRUEXtg8EPaLL7WrdFpd/BUhJOdYyFOHXEmCT3vmMppq7XTG+qykBEhFiZPYZ8jTrtrVZNe06kj1syZEAP85Ht+EpD8sDCdNO3insEPvR61nSphx2xjmmdH7FoujnJooU1Tk6RymtY2lUScRYiFaSDn2ii1N0cMUA0bEJv9xZ5jEWpTzDU3xVoAH7/2cRYzizy9+PSklyIIBwjcIm7Yn1fav4uuEiVCjDffhUi+49MKW7D4BGy/YXpzt3bZdcx2ERFiYRqwLEXBc3qOuOiZLXVpd61OEJNxLILYCPGwQx9mZvsSmIv4sz/12d5kG0GYJkK3iN9t9B6nd9GVTgXdSY7P+We3J8TLT8Hbf5rk1DW7ycCHcrY82QUKQkIh61DvGKEteMkQl1SIw4isaxNEAZaysC17qHPNlCMGWMwu4tnepJchCAeI3CI5PSDE6UDxbhWdCvS8O2LXpxW0YOWGaXO5+V0Adh0HW9kUXWnmIUwHhYzTK9bqjTVNhLgdxGRdizAOh84PwwwKsSBMK3GmiEcAoelD28srdavQE2JxxD1HDPDt3wWg4vmUM2UpwhSmhkLWoZaEptOxpnVL9RxxxrEJ4kCEWBCmiiQnnM4sLXvJHNNOBZXMJZbQ9D4h/rPfBa/ALnEvpy4I08Bhjrip9jriIA6G3kMMIsSCMDKUb4SkUze9k13bxXd844hTIZ730HRPiM2EKurvwtr7qHQqUqglTBXFbL9Yy7M9PMs94IglNC0IU4aTM0LcqG33jpW8EtVOFTtMhVgccTNsmvehlGxDvPgD7HR2RIiFqWLQEQMU3DwNy4L27h5HLEIsCFOEmzNC0q7tbepR7Q4IsYSmjSMGWEnC0xd/gM3WJiv+yuQWJgj7KGTcniMGyHvFXrHWnhzxkHuIQYRYEEaGlzeOuF3vt7kse2bwg52Kj4Sm+0K8fAOA4ML3s93eZtVfneDKBGEvhaxDvRsSx9o8dgs0HA/aVamaFoRpJVtYAiBo7O03Xe1WcaIWMQqSNo/zyh4hfuKjsPQk26WLAKzkxBEL00Mx46A1NAPTxjLv5qlbNoTtviOOpFhLEKYKv7QIQNQcaHOZhKa9uEloZcGa70vOd3zaYRutNTzzk/C3v85mYPr3rmRFiIXpYX+/6YJbMDnisCM5YkGYVgqpELf6Qlz2ylQ7Vby4TWDPtxsGI8QaTTtq945ttDYAWM1JaFqYHgqZRIiT7lp5L2+qpsMWnUCqpgVhKinmstS0D529jrgdtXFoE4oQk3WyAP3wNLDZ2gSQYi1hqkgdcdrUI+/kaSiMIw5jMuKIBWH6yHs2NXxo13rH0o48lt0icua7UAsgl7wH7fCgI5YZ48I0Uew54kSIvTx1NDps0w3jXtW05IgFYYpQStFUeexutXcsdYCO6hDPeaEWDMwkHnTEzU0WM4sj2QYiCKPisBxxV0Gna7YiSo5YEKaUppXHCfqOOGNnAHCsDtoVR3yoELc2WfbFDQvTRZojru1rc1kLzGc3rZqe+D5ipdSvKKVeVkq9pJT6A6XU5aFXJAgzTMfK4w3MJE6F2FJd9Jw384CjhVj2EAvTRjFjBLa+b/BDIyk0zLoWoQ6nIjT9D7XWf05r/SzweeCXhl6RIMwwXadAJuoLcTqy01Zd1Jy3t4TDhXijtSEV08LUkc+YGcNpjrgnxHEXGHDEkw5Na62rAw/zgB5uOYIw2wRuET/uzyTO2iZHbFmBCDF9IW4mLT+11hKaFqYSx7bwXXtPsRZAIzZjTkeZIx7aUyulfhX4a0AF+Pgxz3sBeAHg2rVrw55WEKaS2CuS030hTh0xKsDKFia0qtOhlPoV4HkgBtaBn9Va3x/lOXw3ccRJnq3arRLEgYSmhakknzk4k7gRm8djrZpWSn26bn1sAAAPeElEQVRJKfWtQ/48D6C1/kWt9VXg08DPH/U6WutPaa1vaq1vrq7KRSecT+JMGY8QApNHSnPESoXYmal3xGeealrMmKYnW+0tAB40HgCwllsb9akEYWgKGZvGvmKtBubxWB2x1voTp3ytTwO/B/zyUCsShFkmUwIgbu1iuRd7QhxY4Ey5Ix5Hqinn5ljILHC/boz2neodAB4vPT7qUwnC0OQzDs3uXkfc1CGgQXVpBA0Ws4tDn2fYqukbAw+fB14ZbjmCMNtYvhmF2EpmEmccI8RtpXCnXIjBpJqUUneB/5AzKr68lL/E/YYR4ru1uwBcLV49i1MJwlDkvf5M4p4jthQuEZXARHOulYZPtQ5bNf1rSZj6ZeDHgL8z9IoEYYax8ubuuFkxbRtTR9xVCjdXnNi6UkaRalJKvaCUuqWUurWxsfHIa7hcuMyDuvkldqd6h1V/lZzssRamkHzGptEx05d8x8dCUVcWGbpsdRMhLg4vxENlmbXWf2XoFQjCOcItmOrfds3kQNNirY5SqCRsPUlGkWrSWn8K+BTAzZs3Hzl8fSl/iX9z/9+gtebt6tvihoWpJZ9xuLNlKvyVUuQtj7plkSVgs21utkfx+ZXOWoIwQrx0JnHdhKbT7UtdpSAzeUd8HONKNV0uXKYVttjt7PJ27e2RhPYE4SwoZPqhaYCSnaVuKTJ0edi6x1J2iaI3/HU9fN21IAg9siXjiMOGEeK0orKtVK+Qa4r5NaXUezHbl+4AP3cWJ7mcNw34bu/eZrO1KYVawtRiirWi3uOi41O1LDIq4N3mOyMJS4MIsSCMlHzJOOKosQOYcJat7T2OuN6t81vf/i0+fvXjPLP8zMTWup9xpZouFS4B8JV3vwJIoZYwveQ9m0Y3RGuNUoqik6NqW2QIuNd4hw9f+qGRnEdC04IwQoo5n6r2obXbO+Zoi86AEFe6FX79G7/Oq9uvTmqZEyV1xC/efxEYTbGLIJwF+YyD1vRccdHNU7MsPNVkvflwZDeRIsSCMEKKWYeKLqA6g0Ks6Fh9IQ6TzjzzOvavnCnjOz7f2PgGnuVJaFqYWvLJBKa0qUcqxG7WRLxGdRMpQiwIIyTr2lRVHnufELeVgqRXbRAFACPpyDOLKKW4UrgCwK/+yK/K1iVhaklHIaYFW0WvSM2ysDKm982V4pWRnEdyxIIwYhpWgUK336TK1dBSDigFQBDPtxAD/K0P/C00mh+99qOTXoogHEnOMxOY0tB0ySvTsCyUayasrfgrIzmPCLEgjJiWVcIL7/Uee7GmNdAYXoQY/sK1vzDpJQjCiex3xKVMGYAwccRL2aWRnEdC04IwYtpOET+s9R57WtOx7N7jnhDPaY5YEGaFAzniRIjbbpWck+uN9RwWEWJBGDFdb4F8XAWtiWNNJo4PF+I5dsSCMAvk9+eIkwEPNac2MjcMIsSCMHJCr4RLCEGLVhCR1TEd1b/U0mKtUcwxFQTh7Cj0HHGyfSkR34rVZNlfHtl5RIgFYcTEGTOBifYujW6Ir2O6lup9XxyxIMwGuYyJZKWh6VIivrHS4ogFYapJRiHS2qHVjcjrkG5fh/v7iEWIBWGqyXuJI+6mQtwXXxFiQZhilG/ySGFjm0YnIrdPiMURC8JsYFsK37V7jjjv9gc8SGhaEKYYO2/ulNvVLVrdrnHE9KcFStW0IMwO+YxDPckR69jD0uZaFkcsCFNMOgqxXdui3ajha01A3Pu+OGJBmB3ymb4jrnci8smlLI5YEKYYt7xGrBXRzl26zSqe1nSJ0Mmd9Ly3uBSEWSLvOQNCHPaFOCtCLAhTS6FQ5I6+gL35CmFzl4zWaPpOWByxIMwOhYzTK9aqtQNyyXhiCU0LwhRTzDq8qq+R3XmFoFUjkzjhTtQBJEcsCLOECU0b9a22Q/zYVF6KIxaEKaaUdXlVXyVfu8O7998+IMTp9iVHSUMPQZh28pl+aLrWDsnGFraGUqY0snOIEAvCiFkueNzmGooY/c4tvEMcsaUs7IG2l4IgTCd5z+m1uKy3Q8qhw2VtY6nRyacIsSCMmJzncOnpDwHwTPCdQ0PTkh8WhNlgueCx1egSxZpaO+AvbuX5jU6h/4RuE4LWUOcQIRaEM+CT/9ZHaGuX56xXyCjjfLtRFzBV0yLEgjAbXF7wiWLNRq1DrR2idJZLyc4HAH7v78L/eHOoc4gQC8IZ8MHrK7ztXAelyPzw3wagHbYBccSCMEtcXsgCcL/SotYOiO0MatABBy1ws0OdQ6pFBOEMUEqx9tP/G424SyYHvPZPe444jEMRYkGYES6VzczhB7ttau2QyM5C0Ow/IWyDI0IsCFNJ+fr7AchsvAzsyxHL1iVBmAkup0JcaVHrhLSdMrR2QGtQyjjiIYV4JKFppdR/oZTSSqmVUbyeIJwnMnYGkByxIMwiJd8h59nc2zWh6cArQxxCt26eELbB9Yc6x9BCrJS6CvwY8PawryUI5xHP9gBoRyZHHOoQx5JglCDMAkopLi/4vdB04CVjTpvb5usIQtOjcMT/CPh7MDBeRhCEHlnbXKSz4oglwiUIe7lUzprQdDskyvbnjQMQtIcu1hpKiJVSzwP3tNbfOMVzX1BK3VJK3drY2BjmtIIwU5QzZSxlcb9xH5juqmmJcAnCQS6Xfd7ZabHT7EIyb5xW6ohb4AwXmj4xPqaU+hJw8ZBv/SLwC5iL9kS01p8CPgVw8+ZNcc/C3JBzc7x38b18ff3rgBHiKQ5NpxGuz056IYIwLVxayLLVMBGtZ95zHW7TD02PwBGf+NtAa/2Jw44rpX4AeAL4hlIK4DHg/1NK/aDW+t2hViUI54xnLzzL797+XcI4JIiDXt54mhiMcCXXtCAI9Cunv/9SiR9+3w34Av3Q9Dgc8VForb8JXEgfK6XeAm5qrTeHWpEgnEM+cOED/M4rv8OrO68SRAF5Nz+RdYwiwqWUegF4AeDatWsjXZ8gTCPvuWBaWv6dH72ByiXjDwdzxE5mqNef2viYIJwnPnDhAwC8tP7SRHPEo4hwSZpJmDc+eG2Bf/13P8b1leQG2isaIY5jiDpDb18amRBrra+P6rUE4bxxMX+Ri/mLExfio5AIlyAcjVKqL8IAuUWTI07a1k7D9iVBEE7BteI1HjYfmhaX0llLEGYXf9E44lSIJ93QQxCE05FzczSCxlQ64v1ora+LGxaEI/CXzPYlccSCMFvk3XxPiKd4+5IgCCfhJ6HpdArTtOSIBUE4nryTpxk0iYmn3hELgnAMuaW9oWmZviQIs0HqiB3LESEWhFnGX4L2LnSTcYjiiAVhNsi5Obpxl1DLPGJBmGn8RdAx1B+ax5IjFoTZIG3iEetYqqYFYZZJm3rUHpivIsSCMBsMdtMSRywIM0w6+KF6z3yd5PQlQRBOT87N9f4uQiwIM4yfOOKqmag2bK9pEWJBGBN5RxyxIJwLeo44EWJxxIIwG0hoWhDOCQdyxOKIBWEmGBRiaeghCDNMtgwoccSCMGvsyRFL1bQgzC6WbcQ4SPYRiyMWhNlAQtOCcI5Iw9OWA/ZwES4RYkEYEwW30Pu7CLEgzDhpwdaQe4hBhFgQxoZne73csAixIMw46RYmEWJBmC3S8LQIsSDMOKkjHrLPNIgQC8JYSfcSS7GWIMw4OXHEgjCTpJXT4ogFYcZJQ9NDbl0CEWJBGCsSmhaEc0KvWEtC04IwU6RCLA09BGHGyYkjFoSZRByxIJwT/AXzVRyxIMwWOUdyxIJwLuhtX8oM/VIixIIwRnqOWKqmBWG2ke1LgjCbSI5YEM4Jsn1JEGYT2b4kCOeETMn0mR6BIx7qtlwp9Q+A/wTYSA79gtb694ZdlCCcVz75+CeJ4oiSV5r0UgRBGAal4JP/NVz9waFfahTxsX+ktf5vR/A6gnDuuVq6yt98/9+c9DIORW6sBeER+aHRXMuSqBIEYRC5sRaEMTOKHPHPK6VeVkr9plJqcQSvJwiCIAhzw4lCrJT6klLqW4f8eR74X4D3AM8CD4D/7pjXeUEpdUspdWtjY+OopwmCMFlOvLGWa1kQRovSWo/mhZS6Dnxea/2+k5578+ZNfevWrZGcVxDOM0qpr2mtb47w9b4EXDzkW78IvAhsAhr4FeCS1vqvH/d6ci0Lwuk47loetmr6ktb6QfLwLwHfGub1BEE4W7TWnzjN85RS/yvw+TNejiAIDF+s9d8opZ7F3EG/BUxnOaggCCciN9aCMBmGEmKt9U+PaiGCIEwcubEWhAkg25cEQQDkxloQJsXIirUe6aRKbQB3TnjaCqZwZN6R98Ewr+/D41rr1Ukv4ihOeS3D/P7/7Ufeh/l9D468licixKdBKXVrlNWis4q8DwZ5H2Yb+f8zyPsg78FhyNAHQRAEQZggIsSCIAiCMEGmWYg/NekFTAnyPhjkfZht5P/PIO+DvAcHmNocsSAIgiDMA9PsiAVBEATh3CNCLAiCIAgTZCqFWCn140qpV5VSt5VSf3/S6xknSqm3lFLfVEq9pJS6lRxbUkp9USn1WvL13I2bTKb9rCulvjVw7NCfWxn+cfL5eFkp9cHJrVw4DrmW5VpOjsm1fAxTJ8RKKRv4n4GfAJ4B/gOl1DOTXdXY+bjW+tmBvXZ/H/hDrfUN4A+Tx+eNfwL8+L5jR/3cPwHcSP68gBnHKUwZci0Dci2nyLV8DFMnxMAPAre11m9orbvA/wE8P+E1TZrngd9K/v5bwE9NcC1ngtb6y8D2vsNH/dzPA7+tDS8CC0qpS+NZqfAIyLV8ELmW5Vo+wDQK8RXg7sDjd5Jj84IG/kAp9TWl1AvJsbWBqTjvAmuTWdrYOernnvfPyKww7/9Pci33kWv5GGTow/TxI1rre0qpC8AXlVKvDH5Ta62VUnO352xef25hppFr+RDm9ec+jml0xPeAqwOPH0uOzQVa63vJ13XgM5jw3sM0XJN8XZ/cCsfKUT/3XH9GZoi5/n+Sa3kPci0fwzQK8VeBG0qpJ5RSHvDvA5+b8JrGglIqr5Qqpn8HfgwznP1zwM8kT/sZ4LOTWeHYOern/hzw15KKyw8DlYGwlzA9yLWMXMsJci0fw9SFprXWoVLq54EvADbwm1rrP5vwssbFGvAZpRSY/5v/XWv9r5RSXwX+hVLqP8aMnPv3JrjGM0Ep9TvAx4AVpdQ7wC8Dv8bhP/fvAf82cBtoAv/R2BcsnIhcy3Ity7V8OqTFpSAIgiBMkGkMTQuCIAjC3CBCLAiCIAgTRIRYEARBECaICLEgCIIgTBARYkEQBEGYICLEgiAIgjBBRIgFQRAEYYL8/8cGqtEpoo7SAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## And now we can get to training\n",
        "\n",
        "Just few last things we need to do, so that our models can train on the data\n",
        "\n"
      ],
      "metadata": {
        "id": "Sa3WZB-EuBQ7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We normalize the data with the help of MinMaxScaler that was imported from sklearn.\n",
        "\n",
        "Here the scaler holds the MinMaxScaler and we fit it on our training data in the second row of the code, which will be applied on the data we want to scale just in a second with `data_scaler.transform(data_we_want_to_scale)`"
      ],
      "metadata": {
        "id": "-iUEr97UwROW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = preprocessing.MinMaxScaler()\n",
        "data_scaler = scaler.fit(x_train)"
      ],
      "metadata": {
        "id": "JUQDz6O9yRym"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = data_scaler.transform(x_train)\n",
        "x_test = data_scaler.transform(x_test)"
      ],
      "metadata": {
        "id": "fydKAe1EyUE_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        ""
      ],
      "metadata": {
        "id": "_WNBZ2mWKUVC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = np.expand_dims(x_train, axis=-1)\n",
        "x_test = np.expand_dims(x_test, axis=-1)"
      ],
      "metadata": {
        "id": "K8aavQSazklf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# we need to index classes from 0 to 4\n",
        "y_train = y_train - 1            \n",
        "y_test = y_test - 1"
      ],
      "metadata": {
        "id": "6mOZwMaDOX14"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_val, y_train, y_val = train_test_split(x_train,\n",
        "                                                  y_train,\n",
        "                                                  test_size=0.2,\n",
        "                                                  random_state=42) "
      ],
      "metadata": {
        "id": "ZFJYarB62YHk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Define functions to plot loss, accuracy and confusion matrix"
      ],
      "metadata": {
        "id": "6xEz-6Iq6QSi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_acc(history):\n",
        "    plt.plot(history.history['accuracy'])\n",
        "    plt.plot(history.history['val_accuracy'])\n",
        "    plt.title('model accuracy')\n",
        "    plt.ylabel('accuracy')\n",
        "    plt.xlabel('epoch')\n",
        "    plt.legend(['train', 'val'])\n",
        "    return plt.show()\n",
        "\n",
        "def plot_loss(history):\n",
        "    plt.plot(history.history['loss'])\n",
        "    plt.plot(history.history['val_loss'])\n",
        "    plt.title('model loss')\n",
        "    plt.ylabel('loss')\n",
        "    plt.xlabel('epoch')\n",
        "    plt.legend(['train', 'val'])\n",
        "    return plt.show()\n",
        "\n",
        "def cm_plot(model, x_test):\n",
        "    y_predict = model.predict(x_test)\n",
        "    y_pred = []\n",
        "\n",
        "    for i in range(len(y_predict)):\n",
        "        y_pred.append(np.argmax(y_predict[i,:]))\n",
        "\n",
        "    cm = confusion_matrix(y_test, y_pred)\n",
        "    # Normalise\n",
        "    cmn = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "    fig, ax = plt.subplots(figsize=(10,10))\n",
        "    sns.heatmap(cmn, cmap='Blues', annot=True, fmt='.2f')\n",
        "    sns.set(font_scale=1.3)\n",
        "    plt.title(\"Confusion Matrix\")\n",
        "\n",
        "    return plt.show()"
      ],
      "metadata": {
        "id": "cpBraKaD6Xfh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model with Conv1D"
      ],
      "metadata": {
        "id": "MIF3vd8EAUTU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Comment to the input shape: \n",
        "\n",
        "We have data with 140 timesteps and only one feature (array 140x1). However, if you happen to have data which have more features - I/Q representation with (tx2) shape where t are the timesteps you can simply change the 1 to match the number of your feature representations. "
      ],
      "metadata": {
        "id": "LxTdSX19Xn_B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "layer_in = layers.Input(shape=(140,1))\n",
        "layer = layers.Conv1D(filters=32, kernel_size=4, activation='relu')(layer_in)\n",
        "layer = layers.MaxPool1D(pool_size=2)(layer)\n",
        "layer = layers.Conv1D(filters=32, kernel_size=4, activation='relu')(layer)\n",
        "layer = layers.MaxPool1D(pool_size=2)(layer)\n",
        "layer = layers.Flatten()(layer)\n",
        "layer = layers.Dense(32, activation='relu')(layer)\n",
        "layer = layers.Dropout(0.2)(layer)\n",
        "layer_out = layers.Dense(5, activation='softmax')(layer)\n",
        "\n",
        "model = keras.models.Model(layer_in, layer_out)\n",
        "\n",
        "optimizer = keras.optimizers.Adam(learning_rate=0.001)\n",
        "\n",
        "callbacks = [\n",
        "             keras.callbacks.ModelCheckpoint('model.h5', \n",
        "                                             save_best_only=True, \n",
        "                                             monitor='val_loss'),\n",
        "             keras.callbacks.ReduceLROnPlateau(monitor='val_loss', \n",
        "                                               factor=0.1, \n",
        "                                               patience=3,\n",
        "                                               ),\n",
        "             keras.callbacks.EarlyStopping(monitor='val_loss', \n",
        "                                           patience=8,\n",
        "                                           verbose=1)\n",
        "             ]\n",
        "\n",
        "model.compile(loss='sparse_categorical_crossentropy',\n",
        "              optimizer=optimizer, \n",
        "              metrics=['accuracy'])\n"
      ],
      "metadata": {
        "id": "5-2rHd41x81D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "In `.summary()` you can see the output shape has None at the beginning - this is for the batchsize which can vary - you set the value in the `.fit()` function"
      ],
      "metadata": {
        "id": "6HRnggr7XyWm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "id": "Gk4nsMD20ylr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cb7eecc3-8841-4dc1-86ea-a61b3973300c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 140, 1)]          0         \n",
            "                                                                 \n",
            " conv1d (Conv1D)             (None, 137, 16)           80        \n",
            "                                                                 \n",
            " max_pooling1d (MaxPooling1D  (None, 68, 16)           0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv1d_1 (Conv1D)           (None, 65, 16)            1040      \n",
            "                                                                 \n",
            " max_pooling1d_1 (MaxPooling  (None, 32, 16)           0         \n",
            " 1D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 512)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 32)                16416     \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 32)                0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 5)                 165       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 17,701\n",
            "Trainable params: 17,701\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(x_train, y_train,\n",
        "                    batch_size=128, epochs=50, verbose=2,\n",
        "                    validation_data=(x_val, y_val),\n",
        "                    shuffle=True, callbacks=callbacks)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C_5Fb2opzz8o",
        "outputId": "bf84425f-36d1-4649-c0fa-e7e245416c91"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "83/83 - 14s - loss: 1.5090 - accuracy: 0.3774 - val_loss: 1.1741 - val_accuracy: 0.6353 - lr: 0.0010 - 14s/epoch - 168ms/step\n",
            "Epoch 2/50\n",
            "83/83 - 1s - loss: 0.9996 - accuracy: 0.6184 - val_loss: 0.8081 - val_accuracy: 0.7240 - lr: 0.0010 - 934ms/epoch - 11ms/step\n",
            "Epoch 3/50\n",
            "83/83 - 1s - loss: 0.8307 - accuracy: 0.6873 - val_loss: 0.7138 - val_accuracy: 0.7472 - lr: 0.0010 - 943ms/epoch - 11ms/step\n",
            "Epoch 4/50\n",
            "83/83 - 1s - loss: 0.7545 - accuracy: 0.7161 - val_loss: 0.6608 - val_accuracy: 0.7628 - lr: 0.0010 - 866ms/epoch - 10ms/step\n",
            "Epoch 5/50\n",
            "83/83 - 1s - loss: 0.7098 - accuracy: 0.7332 - val_loss: 0.6099 - val_accuracy: 0.7701 - lr: 0.0010 - 813ms/epoch - 10ms/step\n",
            "Epoch 6/50\n",
            "83/83 - 1s - loss: 0.6731 - accuracy: 0.7425 - val_loss: 0.5746 - val_accuracy: 0.7754 - lr: 0.0010 - 903ms/epoch - 11ms/step\n",
            "Epoch 7/50\n",
            "83/83 - 1s - loss: 0.6322 - accuracy: 0.7519 - val_loss: 0.5406 - val_accuracy: 0.7864 - lr: 0.0010 - 839ms/epoch - 10ms/step\n",
            "Epoch 8/50\n",
            "83/83 - 1s - loss: 0.5970 - accuracy: 0.7635 - val_loss: 0.5079 - val_accuracy: 0.7952 - lr: 0.0010 - 924ms/epoch - 11ms/step\n",
            "Epoch 9/50\n",
            "83/83 - 1s - loss: 0.5738 - accuracy: 0.7759 - val_loss: 0.5046 - val_accuracy: 0.7941 - lr: 0.0010 - 833ms/epoch - 10ms/step\n",
            "Epoch 10/50\n",
            "83/83 - 1s - loss: 0.5434 - accuracy: 0.7864 - val_loss: 0.4616 - val_accuracy: 0.8161 - lr: 0.0010 - 1s/epoch - 15ms/step\n",
            "Epoch 11/50\n",
            "83/83 - 1s - loss: 0.5275 - accuracy: 0.7913 - val_loss: 0.4468 - val_accuracy: 0.8333 - lr: 0.0010 - 858ms/epoch - 10ms/step\n",
            "Epoch 12/50\n",
            "83/83 - 1s - loss: 0.5073 - accuracy: 0.7988 - val_loss: 0.4262 - val_accuracy: 0.8340 - lr: 0.0010 - 786ms/epoch - 9ms/step\n",
            "Epoch 13/50\n",
            "83/83 - 1s - loss: 0.4892 - accuracy: 0.8068 - val_loss: 0.4203 - val_accuracy: 0.8333 - lr: 0.0010 - 831ms/epoch - 10ms/step\n",
            "Epoch 14/50\n",
            "83/83 - 1s - loss: 0.4771 - accuracy: 0.8101 - val_loss: 0.4024 - val_accuracy: 0.8378 - lr: 0.0010 - 837ms/epoch - 10ms/step\n",
            "Epoch 15/50\n",
            "83/83 - 1s - loss: 0.4539 - accuracy: 0.8199 - val_loss: 0.4207 - val_accuracy: 0.8249 - lr: 0.0010 - 840ms/epoch - 10ms/step\n",
            "Epoch 16/50\n",
            "83/83 - 1s - loss: 0.4453 - accuracy: 0.8192 - val_loss: 0.3878 - val_accuracy: 0.8477 - lr: 0.0010 - 887ms/epoch - 11ms/step\n",
            "Epoch 17/50\n",
            "83/83 - 1s - loss: 0.4384 - accuracy: 0.8212 - val_loss: 0.3621 - val_accuracy: 0.8607 - lr: 0.0010 - 820ms/epoch - 10ms/step\n",
            "Epoch 18/50\n",
            "83/83 - 1s - loss: 0.4183 - accuracy: 0.8285 - val_loss: 0.3654 - val_accuracy: 0.8584 - lr: 0.0010 - 737ms/epoch - 9ms/step\n",
            "Epoch 19/50\n",
            "83/83 - 1s - loss: 0.4194 - accuracy: 0.8332 - val_loss: 0.3484 - val_accuracy: 0.8645 - lr: 0.0010 - 777ms/epoch - 9ms/step\n",
            "Epoch 20/50\n",
            "83/83 - 1s - loss: 0.4014 - accuracy: 0.8389 - val_loss: 0.3441 - val_accuracy: 0.8664 - lr: 0.0010 - 893ms/epoch - 11ms/step\n",
            "Epoch 21/50\n",
            "83/83 - 1s - loss: 0.3952 - accuracy: 0.8391 - val_loss: 0.3433 - val_accuracy: 0.8599 - lr: 0.0010 - 860ms/epoch - 10ms/step\n",
            "Epoch 22/50\n",
            "83/83 - 1s - loss: 0.3909 - accuracy: 0.8413 - val_loss: 0.3160 - val_accuracy: 0.8744 - lr: 0.0010 - 888ms/epoch - 11ms/step\n",
            "Epoch 23/50\n",
            "83/83 - 1s - loss: 0.3826 - accuracy: 0.8442 - val_loss: 0.3176 - val_accuracy: 0.8812 - lr: 0.0010 - 803ms/epoch - 10ms/step\n",
            "Epoch 24/50\n",
            "83/83 - 1s - loss: 0.3666 - accuracy: 0.8485 - val_loss: 0.3157 - val_accuracy: 0.8679 - lr: 0.0010 - 863ms/epoch - 10ms/step\n",
            "Epoch 25/50\n",
            "83/83 - 1s - loss: 0.3612 - accuracy: 0.8490 - val_loss: 0.2998 - val_accuracy: 0.8862 - lr: 0.0010 - 872ms/epoch - 11ms/step\n",
            "Epoch 26/50\n",
            "83/83 - 1s - loss: 0.3611 - accuracy: 0.8527 - val_loss: 0.2937 - val_accuracy: 0.8793 - lr: 0.0010 - 606ms/epoch - 7ms/step\n",
            "Epoch 27/50\n",
            "83/83 - 0s - loss: 0.3505 - accuracy: 0.8568 - val_loss: 0.3202 - val_accuracy: 0.8641 - lr: 0.0010 - 497ms/epoch - 6ms/step\n",
            "Epoch 28/50\n",
            "83/83 - 1s - loss: 0.3430 - accuracy: 0.8587 - val_loss: 0.2872 - val_accuracy: 0.8839 - lr: 0.0010 - 603ms/epoch - 7ms/step\n",
            "Epoch 29/50\n",
            "83/83 - 1s - loss: 0.3421 - accuracy: 0.8587 - val_loss: 0.2877 - val_accuracy: 0.8801 - lr: 0.0010 - 501ms/epoch - 6ms/step\n",
            "Epoch 30/50\n",
            "83/83 - 1s - loss: 0.3320 - accuracy: 0.8619 - val_loss: 0.2803 - val_accuracy: 0.8862 - lr: 0.0010 - 605ms/epoch - 7ms/step\n",
            "Epoch 31/50\n",
            "83/83 - 1s - loss: 0.3278 - accuracy: 0.8648 - val_loss: 0.2891 - val_accuracy: 0.8904 - lr: 0.0010 - 582ms/epoch - 7ms/step\n",
            "Epoch 32/50\n",
            "83/83 - 1s - loss: 0.3298 - accuracy: 0.8634 - val_loss: 0.2692 - val_accuracy: 0.8904 - lr: 0.0010 - 616ms/epoch - 7ms/step\n",
            "Epoch 33/50\n",
            "83/83 - 1s - loss: 0.3157 - accuracy: 0.8656 - val_loss: 0.2625 - val_accuracy: 0.8904 - lr: 0.0010 - 619ms/epoch - 7ms/step\n",
            "Epoch 34/50\n",
            "83/83 - 1s - loss: 0.3210 - accuracy: 0.8622 - val_loss: 0.2723 - val_accuracy: 0.8839 - lr: 0.0010 - 505ms/epoch - 6ms/step\n",
            "Epoch 35/50\n",
            "83/83 - 0s - loss: 0.3165 - accuracy: 0.8667 - val_loss: 0.2647 - val_accuracy: 0.8923 - lr: 0.0010 - 490ms/epoch - 6ms/step\n",
            "Epoch 36/50\n",
            "83/83 - 1s - loss: 0.3136 - accuracy: 0.8670 - val_loss: 0.2686 - val_accuracy: 0.8873 - lr: 0.0010 - 513ms/epoch - 6ms/step\n",
            "Epoch 37/50\n",
            "83/83 - 1s - loss: 0.2981 - accuracy: 0.8772 - val_loss: 0.2504 - val_accuracy: 0.8995 - lr: 1.0000e-04 - 540ms/epoch - 7ms/step\n",
            "Epoch 38/50\n",
            "83/83 - 1s - loss: 0.2988 - accuracy: 0.8725 - val_loss: 0.2482 - val_accuracy: 0.9022 - lr: 1.0000e-04 - 604ms/epoch - 7ms/step\n",
            "Epoch 39/50\n",
            "83/83 - 1s - loss: 0.2896 - accuracy: 0.8799 - val_loss: 0.2469 - val_accuracy: 0.8995 - lr: 1.0000e-04 - 604ms/epoch - 7ms/step\n",
            "Epoch 40/50\n",
            "83/83 - 1s - loss: 0.2909 - accuracy: 0.8779 - val_loss: 0.2478 - val_accuracy: 0.9006 - lr: 1.0000e-04 - 515ms/epoch - 6ms/step\n",
            "Epoch 41/50\n",
            "83/83 - 1s - loss: 0.2898 - accuracy: 0.8771 - val_loss: 0.2472 - val_accuracy: 0.9018 - lr: 1.0000e-04 - 573ms/epoch - 7ms/step\n",
            "Epoch 42/50\n",
            "83/83 - 1s - loss: 0.2953 - accuracy: 0.8780 - val_loss: 0.2473 - val_accuracy: 0.9010 - lr: 1.0000e-04 - 577ms/epoch - 7ms/step\n",
            "Epoch 43/50\n",
            "83/83 - 1s - loss: 0.2896 - accuracy: 0.8780 - val_loss: 0.2468 - val_accuracy: 0.9006 - lr: 1.0000e-05 - 629ms/epoch - 8ms/step\n",
            "Epoch 44/50\n",
            "83/83 - 1s - loss: 0.2907 - accuracy: 0.8757 - val_loss: 0.2465 - val_accuracy: 0.9006 - lr: 1.0000e-05 - 534ms/epoch - 6ms/step\n",
            "Epoch 45/50\n",
            "83/83 - 1s - loss: 0.2878 - accuracy: 0.8782 - val_loss: 0.2463 - val_accuracy: 0.9006 - lr: 1.0000e-05 - 614ms/epoch - 7ms/step\n",
            "Epoch 46/50\n",
            "83/83 - 1s - loss: 0.2871 - accuracy: 0.8804 - val_loss: 0.2464 - val_accuracy: 0.9006 - lr: 1.0000e-05 - 505ms/epoch - 6ms/step\n",
            "Epoch 47/50\n",
            "83/83 - 0s - loss: 0.2913 - accuracy: 0.8777 - val_loss: 0.2464 - val_accuracy: 0.9006 - lr: 1.0000e-05 - 499ms/epoch - 6ms/step\n",
            "Epoch 48/50\n",
            "83/83 - 1s - loss: 0.2840 - accuracy: 0.8812 - val_loss: 0.2461 - val_accuracy: 0.9010 - lr: 1.0000e-05 - 611ms/epoch - 7ms/step\n",
            "Epoch 49/50\n",
            "83/83 - 1s - loss: 0.2872 - accuracy: 0.8781 - val_loss: 0.2462 - val_accuracy: 0.9006 - lr: 1.0000e-05 - 578ms/epoch - 7ms/step\n",
            "Epoch 50/50\n",
            "83/83 - 0s - loss: 0.2874 - accuracy: 0.8785 - val_loss: 0.2461 - val_accuracy: 0.9018 - lr: 1.0000e-05 - 491ms/epoch - 6ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_loss, test_acc = model.evaluate(x_test, y_test)\n",
        "print(\"Test accuracy\", test_acc)\n",
        "print(\"Test loss\", test_loss)\n",
        "\n",
        "plot_acc(history)\n",
        "plot_loss(history)\n",
        "cm_plot(model, x_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "S-azHsBL80Vc",
        "outputId": "afc13cde-a0e7-4c74-c568-9c645d964f16"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "16/16 [==============================] - 0s 8ms/step - loss: 0.2719 - accuracy: 0.9360\n",
            "Test accuracy 0.9359999895095825\n",
            "Test loss 0.2719200849533081\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## LSTM"
      ],
      "metadata": {
        "id": "cJ6WXaKaP51-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Comment to the input shape: \n",
        "\n",
        "We have data with 140 timesteps and only one feature (array 140x1). However, if you happen to have data which have more features - I/Q representation with (tx2) shape where t are the timesteps you can simply change the 1 to match the number of your feature representations. \n",
        "\n",
        "However unlike when using conv1d only, you could set the timesteps value to None. This allows you to train and test your data on a variable sequence length. \n",
        "\n",
        "The only thing you have to do when training the model is to have batches of the same size. "
      ],
      "metadata": {
        "id": "pRWLOM1WYbsI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "layer_in = layers.Input(shape=(140,1)) \n",
        "layer = layers.LSTM(16, return_sequences=True)(layer_in)\n",
        "layer = layers.Dropout(0.4)(layer)\n",
        "layer = layers.GRU(16, return_sequences=False)(layer)\n",
        "layer = layers.Dropout(0.4)(layer)\n",
        "layer = layers.Dense(32, activation='relu')(layer)\n",
        "layer = layers.Dropout(0.2)(layer)\n",
        "layer_out = layers.Dense(5, activation='softmax')(layer)\n",
        "\n",
        "model1 = keras.models.Model(layer_in, layer_out)\n",
        "\n",
        "optimizer = keras.optimizers.Adam(learning_rate=0.001)\n",
        "\n",
        "callbacks = [\n",
        "             keras.callbacks.ModelCheckpoint('model.h5', \n",
        "                                             save_best_only=True, \n",
        "                                             monitor='val_loss'),\n",
        "             keras.callbacks.ReduceLROnPlateau(monitor='val_loss', \n",
        "                                               factor=0.1, \n",
        "                                               patience=3,\n",
        "                                               ),\n",
        "             keras.callbacks.EarlyStopping(monitor='val_loss', \n",
        "                                           patience=8,\n",
        "                                           verbose=1)\n",
        "             ]\n",
        "\n",
        "model1.compile(loss='sparse_categorical_crossentropy',\n",
        "              optimizer=optimizer, \n",
        "              metrics=['accuracy'])\n"
      ],
      "metadata": {
        "id": "cEOawcl3P9Is"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model1.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BuR6e71kQ-cJ",
        "outputId": "9dde71e2-7b82-48a8-e16d-c0237ca0c214"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_4 (InputLayer)        [(None, 140, 1)]          0         \n",
            "                                                                 \n",
            " lstm_1 (LSTM)               (None, 140, 16)           1152      \n",
            "                                                                 \n",
            " dropout_4 (Dropout)         (None, 140, 16)           0         \n",
            "                                                                 \n",
            " gru_1 (GRU)                 (None, 16)                1632      \n",
            "                                                                 \n",
            " dropout_5 (Dropout)         (None, 16)                0         \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 32)                544       \n",
            "                                                                 \n",
            " dropout_6 (Dropout)         (None, 32)                0         \n",
            "                                                                 \n",
            " dense_6 (Dense)             (None, 5)                 165       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 3,493\n",
            "Trainable params: 3,493\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history1 = model1.fit(x_train, y_train,\n",
        "                      batch_size=128, epochs=50, verbose=2,\n",
        "                      validation_data=(x_val, y_val),\n",
        "                      shuffle=True, callbacks=callbacks)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t8hsc0HvRAlB",
        "outputId": "5cef56ca-c922-4771-9a77-808b3110ae81"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "83/83 - 13s - loss: 1.5955 - accuracy: 0.2453 - val_loss: 1.5379 - val_accuracy: 0.4134 - lr: 0.0010 - 13s/epoch - 159ms/step\n",
            "Epoch 2/50\n",
            "83/83 - 4s - loss: 1.3271 - accuracy: 0.4218 - val_loss: 1.1653 - val_accuracy: 0.5752 - lr: 0.0010 - 4s/epoch - 52ms/step\n",
            "Epoch 3/50\n",
            "83/83 - 4s - loss: 1.1577 - accuracy: 0.5131 - val_loss: 1.0209 - val_accuracy: 0.5752 - lr: 0.0010 - 4s/epoch - 52ms/step\n",
            "Epoch 4/50\n",
            "83/83 - 4s - loss: 1.0657 - accuracy: 0.5762 - val_loss: 0.9337 - val_accuracy: 0.6281 - lr: 0.0010 - 4s/epoch - 51ms/step\n",
            "Epoch 5/50\n",
            "83/83 - 4s - loss: 1.0109 - accuracy: 0.6083 - val_loss: 0.8710 - val_accuracy: 0.6802 - lr: 0.0010 - 4s/epoch - 52ms/step\n",
            "Epoch 6/50\n",
            "83/83 - 4s - loss: 0.9544 - accuracy: 0.6425 - val_loss: 0.8098 - val_accuracy: 0.7073 - lr: 0.0010 - 4s/epoch - 52ms/step\n",
            "Epoch 7/50\n",
            "83/83 - 4s - loss: 0.8959 - accuracy: 0.6676 - val_loss: 0.8084 - val_accuracy: 0.6978 - lr: 0.0010 - 4s/epoch - 52ms/step\n",
            "Epoch 8/50\n",
            "83/83 - 4s - loss: 0.9117 - accuracy: 0.6608 - val_loss: 0.7486 - val_accuracy: 0.7324 - lr: 0.0010 - 4s/epoch - 52ms/step\n",
            "Epoch 9/50\n",
            "83/83 - 4s - loss: 0.8548 - accuracy: 0.6842 - val_loss: 0.7309 - val_accuracy: 0.7354 - lr: 0.0010 - 4s/epoch - 51ms/step\n",
            "Epoch 10/50\n",
            "83/83 - 4s - loss: 0.8357 - accuracy: 0.6929 - val_loss: 0.7030 - val_accuracy: 0.7400 - lr: 0.0010 - 4s/epoch - 51ms/step\n",
            "Epoch 11/50\n",
            "83/83 - 4s - loss: 0.8095 - accuracy: 0.6945 - val_loss: 0.6878 - val_accuracy: 0.7469 - lr: 0.0010 - 4s/epoch - 52ms/step\n",
            "Epoch 12/50\n",
            "83/83 - 4s - loss: 0.7917 - accuracy: 0.7043 - val_loss: 0.7084 - val_accuracy: 0.7320 - lr: 0.0010 - 4s/epoch - 51ms/step\n",
            "Epoch 13/50\n",
            "83/83 - 4s - loss: 0.7899 - accuracy: 0.7052 - val_loss: 0.6703 - val_accuracy: 0.7472 - lr: 0.0010 - 4s/epoch - 51ms/step\n",
            "Epoch 14/50\n",
            "83/83 - 4s - loss: 0.7712 - accuracy: 0.7192 - val_loss: 0.6368 - val_accuracy: 0.7541 - lr: 0.0010 - 4s/epoch - 51ms/step\n",
            "Epoch 15/50\n",
            "83/83 - 4s - loss: 0.7297 - accuracy: 0.7277 - val_loss: 0.6046 - val_accuracy: 0.7632 - lr: 0.0010 - 4s/epoch - 51ms/step\n",
            "Epoch 16/50\n",
            "83/83 - 4s - loss: 0.7261 - accuracy: 0.7299 - val_loss: 0.5996 - val_accuracy: 0.7655 - lr: 0.0010 - 4s/epoch - 51ms/step\n",
            "Epoch 17/50\n",
            "83/83 - 4s - loss: 0.7085 - accuracy: 0.7352 - val_loss: 0.6335 - val_accuracy: 0.7636 - lr: 0.0010 - 4s/epoch - 52ms/step\n",
            "Epoch 18/50\n",
            "83/83 - 4s - loss: 0.7532 - accuracy: 0.7202 - val_loss: 0.6176 - val_accuracy: 0.7590 - lr: 0.0010 - 4s/epoch - 51ms/step\n",
            "Epoch 19/50\n",
            "83/83 - 4s - loss: 0.6953 - accuracy: 0.7425 - val_loss: 0.5850 - val_accuracy: 0.7724 - lr: 0.0010 - 4s/epoch - 52ms/step\n",
            "Epoch 20/50\n",
            "83/83 - 4s - loss: 0.6901 - accuracy: 0.7417 - val_loss: 0.5580 - val_accuracy: 0.7754 - lr: 0.0010 - 4s/epoch - 51ms/step\n",
            "Epoch 21/50\n",
            "83/83 - 4s - loss: 0.6808 - accuracy: 0.7470 - val_loss: 0.5751 - val_accuracy: 0.7667 - lr: 0.0010 - 4s/epoch - 54ms/step\n",
            "Epoch 22/50\n",
            "83/83 - 4s - loss: 0.6668 - accuracy: 0.7491 - val_loss: 0.5633 - val_accuracy: 0.7701 - lr: 0.0010 - 4s/epoch - 54ms/step\n",
            "Epoch 23/50\n",
            "83/83 - 4s - loss: 0.6663 - accuracy: 0.7512 - val_loss: 0.5523 - val_accuracy: 0.7720 - lr: 0.0010 - 4s/epoch - 51ms/step\n",
            "Epoch 24/50\n",
            "83/83 - 4s - loss: 0.6766 - accuracy: 0.7441 - val_loss: 0.5439 - val_accuracy: 0.7750 - lr: 0.0010 - 4s/epoch - 51ms/step\n",
            "Epoch 25/50\n",
            "83/83 - 4s - loss: 0.6563 - accuracy: 0.7526 - val_loss: 0.5413 - val_accuracy: 0.7781 - lr: 0.0010 - 4s/epoch - 53ms/step\n",
            "Epoch 26/50\n",
            "83/83 - 4s - loss: 0.6446 - accuracy: 0.7582 - val_loss: 0.5512 - val_accuracy: 0.7750 - lr: 0.0010 - 4s/epoch - 51ms/step\n",
            "Epoch 27/50\n",
            "83/83 - 5s - loss: 0.6485 - accuracy: 0.7556 - val_loss: 0.5408 - val_accuracy: 0.7796 - lr: 0.0010 - 5s/epoch - 54ms/step\n",
            "Epoch 28/50\n",
            "83/83 - 4s - loss: 0.6691 - accuracy: 0.7511 - val_loss: 0.5361 - val_accuracy: 0.7819 - lr: 0.0010 - 4s/epoch - 51ms/step\n",
            "Epoch 29/50\n",
            "83/83 - 4s - loss: 0.6202 - accuracy: 0.7615 - val_loss: 0.5281 - val_accuracy: 0.7830 - lr: 0.0010 - 4s/epoch - 52ms/step\n",
            "Epoch 30/50\n",
            "83/83 - 4s - loss: 0.6263 - accuracy: 0.7636 - val_loss: 0.5186 - val_accuracy: 0.7891 - lr: 0.0010 - 4s/epoch - 52ms/step\n",
            "Epoch 31/50\n",
            "83/83 - 4s - loss: 0.6245 - accuracy: 0.7614 - val_loss: 0.5370 - val_accuracy: 0.7788 - lr: 0.0010 - 4s/epoch - 51ms/step\n",
            "Epoch 32/50\n",
            "83/83 - 4s - loss: 0.6187 - accuracy: 0.7656 - val_loss: 0.5270 - val_accuracy: 0.7830 - lr: 0.0010 - 4s/epoch - 51ms/step\n",
            "Epoch 33/50\n",
            "83/83 - 4s - loss: 0.6198 - accuracy: 0.7638 - val_loss: 0.5212 - val_accuracy: 0.7895 - lr: 0.0010 - 4s/epoch - 51ms/step\n",
            "Epoch 34/50\n",
            "83/83 - 4s - loss: 0.5866 - accuracy: 0.7753 - val_loss: 0.5100 - val_accuracy: 0.7914 - lr: 1.0000e-04 - 4s/epoch - 51ms/step\n",
            "Epoch 35/50\n",
            "83/83 - 4s - loss: 0.5978 - accuracy: 0.7703 - val_loss: 0.5134 - val_accuracy: 0.7910 - lr: 1.0000e-04 - 4s/epoch - 51ms/step\n",
            "Epoch 36/50\n",
            "83/83 - 4s - loss: 0.5944 - accuracy: 0.7765 - val_loss: 0.5102 - val_accuracy: 0.7891 - lr: 1.0000e-04 - 4s/epoch - 51ms/step\n",
            "Epoch 37/50\n",
            "83/83 - 4s - loss: 0.5997 - accuracy: 0.7726 - val_loss: 0.5084 - val_accuracy: 0.7895 - lr: 1.0000e-04 - 4s/epoch - 51ms/step\n",
            "Epoch 38/50\n",
            "83/83 - 4s - loss: 0.5911 - accuracy: 0.7730 - val_loss: 0.5089 - val_accuracy: 0.7933 - lr: 1.0000e-04 - 4s/epoch - 51ms/step\n",
            "Epoch 39/50\n",
            "83/83 - 4s - loss: 0.5872 - accuracy: 0.7782 - val_loss: 0.5097 - val_accuracy: 0.7891 - lr: 1.0000e-04 - 4s/epoch - 51ms/step\n",
            "Epoch 40/50\n",
            "83/83 - 4s - loss: 0.5922 - accuracy: 0.7736 - val_loss: 0.5078 - val_accuracy: 0.7914 - lr: 1.0000e-04 - 4s/epoch - 51ms/step\n",
            "Epoch 41/50\n",
            "83/83 - 4s - loss: 0.5902 - accuracy: 0.7755 - val_loss: 0.5100 - val_accuracy: 0.7887 - lr: 1.0000e-04 - 4s/epoch - 51ms/step\n",
            "Epoch 42/50\n",
            "83/83 - 4s - loss: 0.5872 - accuracy: 0.7768 - val_loss: 0.5050 - val_accuracy: 0.7906 - lr: 1.0000e-04 - 4s/epoch - 54ms/step\n",
            "Epoch 43/50\n",
            "83/83 - 4s - loss: 0.5906 - accuracy: 0.7772 - val_loss: 0.5050 - val_accuracy: 0.7906 - lr: 1.0000e-04 - 4s/epoch - 52ms/step\n",
            "Epoch 44/50\n",
            "83/83 - 4s - loss: 0.5915 - accuracy: 0.7759 - val_loss: 0.5149 - val_accuracy: 0.7910 - lr: 1.0000e-04 - 4s/epoch - 51ms/step\n",
            "Epoch 45/50\n",
            "83/83 - 4s - loss: 0.5882 - accuracy: 0.7758 - val_loss: 0.5057 - val_accuracy: 0.7922 - lr: 1.0000e-04 - 4s/epoch - 51ms/step\n",
            "Epoch 46/50\n",
            "83/83 - 4s - loss: 0.5915 - accuracy: 0.7760 - val_loss: 0.5054 - val_accuracy: 0.7918 - lr: 1.0000e-05 - 4s/epoch - 51ms/step\n",
            "Epoch 47/50\n",
            "83/83 - 4s - loss: 0.5831 - accuracy: 0.7792 - val_loss: 0.5053 - val_accuracy: 0.7929 - lr: 1.0000e-05 - 4s/epoch - 51ms/step\n",
            "Epoch 48/50\n",
            "83/83 - 4s - loss: 0.5867 - accuracy: 0.7778 - val_loss: 0.5055 - val_accuracy: 0.7933 - lr: 1.0000e-05 - 4s/epoch - 51ms/step\n",
            "Epoch 49/50\n",
            "83/83 - 4s - loss: 0.5852 - accuracy: 0.7746 - val_loss: 0.5055 - val_accuracy: 0.7929 - lr: 1.0000e-06 - 4s/epoch - 51ms/step\n",
            "Epoch 50/50\n",
            "83/83 - 4s - loss: 0.5961 - accuracy: 0.7744 - val_loss: 0.5055 - val_accuracy: 0.7929 - lr: 1.0000e-06 - 4s/epoch - 53ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_loss, test_acc = model1.evaluate(x_test, y_test)\n",
        "print(\"Test accuracy\", test_acc)\n",
        "print(\"Test loss\", test_loss)\n",
        "\n",
        "plot_acc(history1)\n",
        "plot_loss(history1)\n",
        "cm_plot(model1, x_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "3fOewe47SfoR",
        "outputId": "e9d3277d-b48c-4d5c-c310-a59a4996f729"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "16/16 [==============================] - 0s 21ms/step - loss: 0.4247 - accuracy: 0.9020\n",
            "Test accuracy 0.9020000100135803\n",
            "Test loss 0.42470425367355347\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JsWSlxJGVHEtibBRLPynj+899Sr2/jZtPz+frp03psz7e529jU3kDs8alEBc9hEcCm276SwThnmLCDHeBDvjkaadqpmaHUz/d1wjMTnGjICUXco9x6s0nndq9br/NDy9/F1Y/DHmnwKV/gaRel1rdX3YBXP5XpyTx8VNOP/Sxc2DM7OAT0BBT19TGrpomSmqbKalp3vu8p6mV7JQ4xqfFk5uWwPjR8YxPSyAzOZYNu328v72aD7bvYfWOmj7rzwESYiJpau3giJxRPHbZccwYk9LnsQDJcdEcMzFtoC/ThJGVCMyhUYVNL8Pr9zgNqmmTnPrzvb1gJjmjQJOyoalqX118Z718TRHsfA9a3O6TY45wEsL44+CtXzlVOCffBgvucgYnedC2ygbufqGQNzd1H2wfFx1BTmo86Ymx7K73U1rb3GdDZ35WEvMnjd77SIiOosLnp7y+Ze9zeb2fiekJXHv8xKCrZ8zwYyUCM7B2vAOv/wh2ve90hbz0ISi4pHudfVdxKb33W+9oh7KPYNsyp4vlBw/Cit85w/gXPQ4zzgvpZQy0QED5cFctL39axjtbqklPimFKZhKTMxOZkpnElMwkslNiD9iY2Nzawe+WbeaPb24nNiqCW8/MZ3p2Mjlp8eSkxjM6MabbOToCyu56P8V7mthV00x5vZ8pmYkcmzea9KT9G7VHJUSTn22DyMw+ViIwwVF1Bie98TPY8qrT5/3U78LcawZu+H5rE5Sscvrlp4wbmHOGWEdAWbVjDy99upuXP93N7no/0ZHCsXmjaWhpZ1tlIw1dqmUSYyI5anwqJ05J58SpGRyZM2rvr3BV5V+F5dzzQiEltc18bm4Od5w7g6zkg5xHyJheWGOxOXT+OmeOm9WPQPknTv3+yf/hTHYW08dEXsOMv62DktpmSmubyUmNZ3Lmgfvf1zW38fA7O/jbe0VUNbQQGxXBqdMyOeeIMZw+I5tR8U5yVFUqfC1srWhga1UjW8p9fLCjhvVlziRvSbFRHDdpNCdMSeftLVW8sbGS6dnJ3HPRLI6bHJoJxow3WdWQOTiqzvw0qx92GoHbmpw6/PPuhSMuc6p6hhGfv22/htbimmaK3ddVDd2nDz5pajrXHj+RM2dm71dnXtfcxl/e2c6f396Oz9/OGTOyuOToHBZMzyIxdv//nUSE7JQ4slPiOHHqvgbx6oYW3tu2h3e2VrFiazWvb6ggKTaK759fwBdOmNjvCFpjBpqVCEayxiqnC2bxB3Dmj5wpdQ/EtxsWf8Gp/49OdKYLPuaLznQFw6CLZWc9/auF5by9pZKd1U3U+7v3mImJchpb9z7cuvexqXF8uLOWx94rorTOz5iUOK6cP4Er548nNiqSP7+znb+84ySAswuyufmMfGbnDMwo4bK6ZhKiow56WgZjgmVVQ14SCMCON51f8+tfdCYci4x1phtY9ET/ffErN8Kjl0JTNZz1Izhq0ZCZmbKtI8C2ykYSYiJJjI0iMTaSmMgIRAR/Wwfvbq3i1UJnjppKXwtREcK8vDSmZSd3u9nnpMWTkRjb73wuHQHl3xsq+Nt7Rby5qZKoCCE2KoLG1g4+O8tJALPGhXiaCGMGmFUNeUFNkVONs/oRZ3qDuFSYfz0cfZ3TmPvYpfDIBXDJ/TD7c/t/vmgFPLHIOfaLL0LO0YN/DX1obQ+w6MEVrNlZ2217VISQGBtFS3sH/rYAiTGRnDY9i7MKslkwPeuQf11HRghnFWRzVkE2O6oaefyDnexpbOXLJ02iYNzwqhYzJhiWCIarpj3OlMads1vWbHe2TzwJFtwJMy/svmrVV16DJ6+Cp7/kDPw6+bZ9VT2Fz8Mz1zurUV39tDPNwhDyk38WsmZnLd9ZOJ2MpFgaW9qdR2sHjS3tREYIp07L5IQp6cRGDexI17yMRO48d+aAntOYocYSwXDQ2ggVG5wZLssLnWX9yj4CFGKSYdIpztw6U8/sux0gMR2+8Dw8f6MzBqBmu9P4u/JP8PL3IPdYuPJJ57gQ6qzDX120h7MKxjApo//Rvks+KuWRFUV89eRJfPO0qSGNzRivskQwVK35mzNyt3yd8wsety0nOsFpuF1wpzM9w7ijgx95Gx0Hn/ujM/L3rf+B7W85CWHG+fD5P0F0/AFPcSj8bR28s2VfHX5nL53fv7GVP35hHsfm9T6x2+ZyH3c88zHH5qXx3XNmhCQ2Y4wlgqGpeDUsucld43UuHHWlM4dO9ixIzet7BG8wIiLgjO871T8v3gbzv+YsbzgAC7u0dQQoq/VTXNPErpomdu1pZmO5j7c3V9Hc1kFSbBSnTs/k7IJspmQmcfMTH3L1n97n3suP4vwjuw8ga2hp5+uPriYhJpLfXXW0dac0JoQsEQxFy3/mzLj5jXdD12tn7jUw+9Lu7QiHoKLezwNvbuPlT3dTVtdM1ylvIgRy0xL43NE5nD1rDMdPHt2tDv+Zb5zIDX9bxU2Pf0hJTTM3fGYyIoKqcsczH7O9qpFHv3oc2Sk2staYULJEMNSUrIbN/4LTvx/6rpuHkQTK6pp5YPk2Hv9gJx0B5cyZWXz+6Bxy0xLIdWfBHDMqrt9f8mmJMfztK8dx+98/4qcvbWBXTRN3XzCLR98r4sWPy/jPz07nxCmHueKYMeaALBEMNW/83CkNzL8h3JH0qqS2mT+8sYXFK4sJqPL5o3P55oIpTEw/tCme46Ij+b9Fc8lNjeeBN7exqbyBD3fWcMaMLL5xahAD4Iwxh80SwVBSsgY2vwKn3zXkpnFo7wjwy39t5KG3nW6ql80bzzdOncL40Yc/31BEhPC9c2eSmxbPD5esIyctnnsvnxOyRbyNMd1ZIhhKlv/CHQj2tXBH0k1NYys3PbGGd7ZUc/m8XG49cxrjUge+h9G1J+Qxd0Ia6UkxNtWCMYPIEsFQUfohbHoJFvy/IVUa2Ljbx/V/XcXuOj+/vPRILps3PqTfN1Bz9xhjgmeJYKhY/gtniufjhk5p4OVPd/Mfi9eSFBvFk187nqMn2PKExoxE1jl7MDTtcQaItTT0vr/sI9i4FI6/0UkGYRYIKL9+bRNff3Q1+dnJvPCtky0JGDOChbREICILgd8AkcCfVPVnPfb/L7DAfZsAZKlqaihjGnSq8PxNsPGfzvq+C+6Eudd2Hw28/BfO8oyDWBpobQ/wyrrdlNf78fnbaWhpp8F93lXTxMfFdXz+6Fx+csls4qIHdv4eY8zQErJEICKRwH3AWUAxsFJElqhqYecxqnpbl+O/BcwNVTxhs36JkwTmfdmZJ+jFW+H9++Gs/4L8s2D3J7DhRTjtexAf+hyoqry+voKfLF3P9qrGvdsTYyJJiosiKTaKpLhofnThLL5wwsQDrq9rjBn+QlkimA9sUdVtACLyJHARUNjH8VcCPwxhPIOvuRaW/qezutc5v3SmcVj/Arz2Q3j8Mpj0GafEEDsKjvt6yMPZsLueH7+4nre3VDElM5E/XzePYyeNJjEmikjrqmmMZ4UyEeQAu7q8LwZ6XRVFRCYCk4B/97H/BuAGgAkTJgxslKH02g+hsRKuempfVVDBhTBtIaz+i7MQfPMeZxH4EJYGqhtauPfVTTzxwU6S46K5+4ICrj7elkM0xjiGSq+hRcDTqtrR205VfRB4EJwVygYzsEO24x1nlbATbnImjusqKsZpDzhqEWxYCrMuPuyvq2po4b1t1VT5WqhqaKW6sYVKn/O8abePlvYA152Yxy1n5JOaEHPY32eMGTlCmQhKgK6dznPdbb1ZBNwYwlgGV5sfXrjFmT10wZ19Hxc3CuZceVhftaexlQeWb+WRFTvwtwUAZ7K30YmxZCTFkJEUywVHjeOrp0xmalbSYX2XMWZkCmUiWAnki8gknASwCLiq50EiMgNIA1aEMJbB9davoHozXPMPiDm0OXgOpLaplT++tY2H39lBU1sHFx01ji+dNInctHjSEmJsegZjTNBClghUtV1EbgJewek++pCqrhORe4BVqrrEPXQR8KSqDo8qnwOpWA9v/y8ceQVMPWPAT1/vb+PPb23nobe309DaznlHjOXWM/OZmjU0Fpk3xgw/IW0jUNWlwNIe237Q4/3doYxhUAUCsORmZ4qIz/50wE/f3NrBpX94l03lDZwzewy3nJnPjDFDZzoKY8zwNFQai0eGNQ9D8QdwyYMhWfv3Ry+sY3NFA3/54rEsmJE14Oc3xniT9R8cSCsfctYQPvLyAT/1ko9KeXLlLr552hRLAsaYAWWJYKBUb4XyT+CIy2CAR+MWVTdy5z8+4ZiJadx65rQBPbcxxlgiGCiFzznPBRcO6Glb2wN864kPiRD4zaI5NgjMGDPg7K4yUAqfh9xjYVTuQX2swuenvw5Tv3xlAx8X1/GLS48kN+3wVwMzxpieLBEMhD3bnamkCy46qI+9vr6c4/77dU7/1XIeWL6VqoaWbvv/vaGcP761nS+cMJGFs8cOZMTGGLOX9RoaCIXPO88HkQj2NLby3Wc+YXJGIulJsfz0pQ38z782cvasMVw1fwKTMxP59t8/ZsaYZO48d2aIAjfGGEsEA6PwOae3UGpwE+KpKnc99wl1za387SvzmTk2hS0VPp78YBdPrynmnx+XERMVQaQIv7vqaFsPwBgTUpYIDldNkbPe8Fn3BP2RJR+VsvST3Xxn4XRmjnUGhE3NSuau8wv49men88q63Ty/tpRLj8m1+YGMMSFnieBwHWS10O46P99/7lOOmZjG1z4zZb/9cdGRXDQnh4vm5AxklMYY0ydrLD5chc/B2DmQlnfAQ1WV/3z6I9o6lF9ddpQtBmOMGRIsERyO2p1Qsjro0sCj7+/krc1V3HneTPIyQjMrqTHGHCxLBIej0J1ANYhEsKOqkf/+53pOyc/gmuOG0SprxpgRzxLB4Sh83lmPOH3/uv6uOgLK7X//iOhI4ReXHmkLwhtjhhRLBIeqrtiZabTgwMtMPv5+EauLarjnotmMHRU/CMEZY0zwLBEcqvUvOM8HSAT+tg5+t2wL8/NGc9GccYMQmDHGHBxLBIdq3XOQPRsypvZ72BMf7KS8voVbz8q3KiFjzJBkieBQ1JfCrvcO2Ejsb+vg929s5bhJozlxSsYgBWeMMQfHEsGhCLJa6LH3d1Lpa+G2s2wNAWPM0GWJ4FAUPg+ZMyGz7xt8c2sHf3hjKydMTuf4yQO/bKUxxgwUSwQHy1cORe8esFrosfeLqGqw0oAxZuizRHCwNrwIaL8rkTW1tnP/8q2cNDWd+ZNGD15sxhhzCGzSuYO1fgmkT4Wsgj4PefS9IqoaWrnf1hc2xgwDViI4GE17YPtbMPPCPheob2pt54Hl2zglP4N5eVYaMMYMfZYIDsaGf4J29Fst9NcVRVQ3tnKrlQaMMcOEJYKDsX6JswrZ2Dm97m5saefBN7fxmWmZHDMxbZCDM8aYQ2OJIFjNtbB1Wb/VQg+/u4M9ja3cdmb+IAdnjDGHLqSJQEQWishGEdkiInf0cczlIlIoIutE5PFQxnNYNr0CgbY+u43WNrVy//KtnD4ji7kTrDRgjBk+QtZrSEQigfuAs4BiYKWILFHVwi7H5APfA05S1RoRyQpVPIdt/RJIHgc583rd/Yc3ttLQ0s53Fk4f5MCMMebwhLJEMB/YoqrbVLUVeBLo+XP6euA+Va0BUNWKEMZz6FoaYMtrMPMCiNj/n6y0tpm/vLuDS+bmMGNMShgCNMaYQxfKRJAD7Oryvtjd1tU0YJqIvCMi74nIwt5OJCI3iMgqEVlVWVkZonD7sflf0O7vs7fQr1/bBAr/YaOIjTHDULgbi6OAfOA04ErgjyKS2vMgVX1QVeep6rzMzMxBDhGnWigxEyacsN+uzeU+nl5dzLUnTCQ3LWHwYzPGmMMUykRQAozv8j7X3dZVMbBEVdtUdTuwCScxDB1tzbDpXzDjfIiI3G/3L17ZSGJMFDcu6H9dAmOMGapCmQhWAvkiMklEYoBFwJIexzyHUxpARDJwqoq2hTCmg7fldWhr7LVaaHXRHl4tLOdrp05mdGJMGIIzxpjDF7JEoKrtwE3AK8B6YLGqrhORe0Sk8676ClAtIoXAMuA/VbU6VDEdkvVLID4N8k7ptllV+dlLG8hMjuXLJ08KU3DGGHP4QnrHyu8AABbRSURBVDrpnKouBZb22PaDLq8V+A/3MfS0t8DGl53eQpHR3Xb9e0MFK3fU8F8XzyYhxubuM8YMX+FuLB7ati2Hlrr9qoU6AsovXt5IXnoCi44d38eHjTFmeLBE0J/1z0NsCkw+rdvmZz8sYWO5j29/djrRkfZPaIwZ3qxOw7cbnrwaWhv337dnmzOlRFRst833L9/K7JwUzp09dpCCNMaY0AkqEYjIP4A/Ay+paiC0IQ2y4pVQsgomL4DY5O77smbASTd321RS28yWigbuOm8mERG9Tz5njDHDSbAlgt8DXwJ+KyJ/B/6iqhtDF9Ygqi9znj/3ICQdeKqjtzc7I5s/My0MA9uMMSYEgqrgVtXXVPVq4GhgB/CaiLwrIl8Skej+Pz3E+UohIhoSMoI6/M3NVWSnxJKflRTiwIwxZnAE3dIpIunAF4GvAh8Cv8FJDK+GJLLBUl8GyWN6nUyup46A8s6WKk7Jz0T6WJPAGGOGm2DbCJ4FpgN/Ay5QVbc+hadEZFWoghsU9SWQHFyj76clddQ2tXFKfnClB2OMGQ6CbSP4raou622HqvY+Qf9w4SuD7NlBHfqW2z5w0lRLBMaYkSPYqqGCrrOCikiaiHwzRDENHlWnaihlXFCHv7m5ilnjUshIij3wwcYYM0wEmwiuV9XazjfuQjLXhyakQdRS70woF0TVUENLO2uKajgl33oLGWNGlmATQaR0aR11l6Ec/tNt1pc6z0GUCN7fVk17QPmMtQ8YY0aYYNsIXsZpGH7Aff81d9vwdhCJ4K3NVcRFR3BMni1Mb4wZWYJNBN/Fufl/w33/KvCnkEQ0mHxu56cgqobe3FzJ8ZPTiY3af3EaY4wZzoJKBO60En9wHyNHfXCJoLimiW2VjVw1f8IgBGWMMYMr2HEE+cBPgQIgrnO7qk4OUVyDo74E4kdDdFy/h729uQqwaSWMMSNTsI3Ff8EpDbQDC4C/Ao+GKqhB4yuDlJwDHvaWTSthjBnBgk0E8ar6OiCqWqSqdwPnhS6sQVJfCin9Vwt1BJR3ttq0EsaYkSvYxuIWEYkANovITUAJMPx/HvvKYNzcfg+xaSWMMSNdsCWCW4AE4GbgGOAa4LpQBTUo2lugsfKAXUc7p5U42aaVMMaMUAcsEbiDx65Q1W8DDTjrEgx/vt3O8wESQee0Euk2rYQxZoQ6YIlAVTuAkwchlsG1dwxB34nAppUwxnhBsG0EH4rIEuDvwN7FfVX1HyGJajDsHVXcd2Pxe1ttWgljzMgXbCKIA6qB07tsU2D4J4J+BpO9tbnSppUwxox4wY4sHhntAl35yiAqHuL7vsm/taXKppUwxox4wY4s/gtOCaAbVf3ygEc0WDrHEPQxNqCqoYVtlY0sOnb8IAdmjDGDK9iqoRe7vI4DLgFKBz6cQeQr67eheF1pPQCzc0YNVkTGGBMWQY0jUNVnujweAy4HDrhEpYgsFJGNIrJFRO7oZf8XRaRSRNa6j68e/CUcovqSfhuKC91EUDA2ZbAiMsaYsAi2RNBTPpDV3wHu+IP7gLOAYmCliCxR1cIehz6lqjcdYhyHRtUZR9DPGILCsnpyUuNJTRj+6+8YY0x/gm0j8NG9jWA3zhoF/ZkPbFHVbe45ngQuAnomgsHXVA0drf1WDRWW1lEwzkoDxpiRL9iqoWRVTenymKaqzxzgYznAri7vi91tPX1eRD4WkadFpNeWWRG5QURWiciqysrKYELu3wHGEDS1trOtqtGqhYwxnhBUIhCRS0RkVJf3qSJy8QB8/wtAnqoeibPq2SO9HaSqD6rqPFWdl5k5AKN8944h6L1EsGG3D1WsRGCM8YRgJ537oarWdb5R1Vrghwf4TAnQ9Rd+rrttL1WtVtUW9+2fcCa0Cz1f/2sVdzYUz7JEYIzxgGATQW/HHah9YSWQLyKTRCQGWAQs6XqAiHStm7kQWB9kPIenvgwkApKye91dWFZPSlwUOanxgxKOMcaEU7C9hlaJyL04vYAAbgRW9/cBVW131y54BYgEHlLVdSJyD7BKVZcAN4vIhTgrn+0BvngI13DwfKWQmAWRvV9+YWk9BeNSbCEaY4wnBJsIvgV8H3gKp/fQqzjJoF+quhRY2mPbD7q8/h7wvWCDHTD1ZX02FHcElA2767n6uImDHJQxxoRHsHMNNQL7DQgbtupLIX1Kr7u2VzXgbwtYjyFjjGcE22voVRFJ7fI+TUReCV1YIeYr7XPW0c6pJazHkDHGK4JtLM5wewoBoKo1HGBk8ZDV2gT+uj6rhgrL6omJjGBq1vBfktkYY4IRbCIIiMiEzjcikkcvs5EOCwdYmaywtJ5pY5KIjgz2n8YYY4a3YBuL/x/wtogsBwQ4BbghZFGFUr07lKGXMQSqSmFpPWfMHJ6FHWOMORTBNha/LCLzcG7+HwLPAc2hDCxk6t0SQS+JoMLXQnVjqzUUG2M8JdhJ574K3IIzOngtcDywgu5LVw4Pvr6XqNw79fQ4W4PAGOMdwVaE3wIcCxSp6gJgLlDb/0eGqPoyiE2B2P0bgwvLnEQwc2zyYEdljDFhE2wi8KuqH0BEYlV1AzA9dGGFUH1Jn3MMrSutY2J6Aslx0YMclDHGhE+wjcXF7jiC54BXRaQGKApdWCHkK+tzDEFhab21DxhjPCfYxuJL3Jd3i8gyYBTwcsiiCqX6MpgyY7/NDS3t7Khu4tJjcsMQlDHGhM9BL1WpqstDEcig6GiHhvJeSwQbymxEsTHGm7w1aqqxArSj1zaCvVNLjLUeQ8YYb/FWIuhnDEFhaT2jE2PITokd5KCMMSa8vJUI+htDUFbPLFuDwBjjQd5KBH2UCNo6Amws91mPIWOMJ3ksEZRARDQkZHTbvLWygdb2gDUUG2M8yVuJoHMMQUT3y947tYSVCIwxHuStRFBf2us6BIWl9cRFRzA509YgMMZ4j7cSQR+jigvL6pk+JoXICGsoNsZ4j3cSgapbIsjpsVkpLLOpJYwx3uWdROCvg7am/aqGSuv81Da1Mcsaio0xHuWdRLB3icruiWBrRQMA+bZGsTHGo7yTCOrdwWQ9xhCU1/sBGDMqbrAjMsaYIcF7iaBHiaDC1wJAVrIlAmOMN3knETRWOM89EkGlr4Xk2CjiYyLDEJQxxoSfdxLBKbfD94ohuvsv/wqfn0ybaM4Y42EhTQQislBENorIFhG5o5/jPi8iKiLzQhkPsfuvRVxR30JWsiUCY4x3hSwRiEgkcB9wDlAAXCkiBb0clwzcArwfqlj6U+FrsfYBY4ynhbJEMB/YoqrbVLUVeBK4qJfj/gv4OeAPYSy9UlUqfH4rERhjPC2UiSAH2NXlfbG7bS8RORoYr6r/7O9EInKDiKwSkVWVlZUDFqCvpR1/W4AsayMwxnhY2BqLRSQCuBe4/UDHquqDqjpPVedlZmYOWAwV9dZ11BhjQpkISoDxXd7nuts6JQOzgTdEZAdwPLAk5A3GXVT4nNooqxoyxnhZKBPBSiBfRCaJSAywCFjSuVNV61Q1Q1XzVDUPeA+4UFVXhTCmbio7B5NZ1ZAxxsNClghUtR24CXgFWA8sVtV1InKPiFwYqu89GJ1VQ5lWNWSM8bCoUJ5cVZcCS3ts+0Efx54Wylh6U+HzExsVQUpcSP8ZjDFmSPPOyOJeVPhayEqJRcQWpDHGeJe3E0G9DSYzxhhvJwIbTGaMMV5PBDbPkDHGeDYR+Ns68PnbyUqxqiFjjLd5NhF0jiHItBKBMcbjPJsIbFSxMcY4vJsIbJ4hY4wBvJwIbHoJY4wBPJ0I/ERFCKMTYsIdijHGhJV3E0F9CxlJsURE2KhiY4y3eTcRuNNLGGOM13k7EViPIWOM8W4iqPT5bfppY4zBo4mgvSNAdWOrlQiMMQaPJoKqhlZUreuoMcaARxPBvlHFVjVkjDHeTAR7RxVbicAYY7yZCGxUsTHG7OXRROBHBDKSLBEYY4xHE0ELoxNiiI705OUbY0w3nrwTVtS32DoExhjjigp3AOFQ6fPbymTGeExbWxvFxcX4/f5whxJScXFx5ObmEh0dHfRnPJkIKnwt5GcnhzsMY8wgKi4uJjk5mby8PERG5mSTqkp1dTXFxcVMmjQp6M95rmooEFAqbZ4hYzzH7/eTnp4+YpMAgIiQnp5+0KUezyWCmqZW2gNqicAYDxrJSaDToVyj5xLBvjEE1kZgjDEQ4kQgIgtFZKOIbBGRO3rZ/3UR+URE1orI2yJSEMp4oEsisBKBMWYQ1dbW8vvf//6gP3fuuedSW1sbgoj2CVkiEJFI4D7gHKAAuLKXG/3jqnqEqs4BfgHcG6p4OlXU2zxDxpjB11ciaG9v7/dzS5cuJTU1NVRhAaHtNTQf2KKq2wBE5EngIqCw8wBVre9yfCKgIYwHsOkljDHwoxfWUVhaf+ADD0LBuBR+eMGsPvffcccdbN26lTlz5hAdHU1cXBxpaWls2LCBTZs2cfHFF7Nr1y78fj+33HILN9xwAwB5eXmsWrWKhoYGzjnnHE4++WTeffddcnJyeP7554mPjz/s2ENZNZQD7Oryvtjd1o2I3CgiW3FKBDf3diIRuUFEVonIqsrKysMKqtLXQnJcFHHRkYd1HmOMORg/+9nPmDJlCmvXruWXv/wla9as4Te/+Q2bNm0C4KGHHmL16tWsWrWK3/72t1RXV+93js2bN3PjjTeybt06UlNTeeaZZwYktrCPI1DV+4D7ROQq4C7gul6OeRB4EGDevHmHVWqo8PmtfcAYj+vvl/tgmT9/fre+/r/97W959tlnAdi1axebN28mPT2922cmTZrEnDlzADjmmGPYsWPHgMQSykRQAozv8j7X3daXJ4E/hDAewKaXMMYMDYmJiXtfv/HGG7z22musWLGChIQETjvttF7HAsTG7rt3RUZG0tzcPCCxhLJqaCWQLyKTRCQGWAQs6XqAiOR3eXsesDmE8QCdi9ZbQ7ExZnAlJyfj8/l63VdXV0daWhoJCQls2LCB9957b1BjC1mJQFXbReQm4BUgEnhIVdeJyD3AKlVdAtwkImcCbUANvVQLDXBMVjVkjAmL9PR0TjrpJGbPnk18fDzZ2dl79y1cuJD777+fmTNnMn36dI4//vhBjS2kbQSquhRY2mPbD7q8viWU39+Tr6Udf1vAegwZY8Li8ccf73V7bGwsL730Uq/7OtsBMjIy+PTTT/du//a3vz1gcXlqZPG+JSqtasgYYzp5KxHsXbTeSgTGGNPJU4mg0gaTGWPMfjyVCDqrhjKtasgYY/byViLw+YmNiiAlLuzj6IwxZsjwWCJoISsl1hNzkhtjTLC8lQjqbTCZMWZ4SEpKGrTv8lYisMFkxhizH09Vllf4Wjh5aka4wzDGhNtLd8DuTwb2nGOOgHN+1ufuO+64g/Hjx3PjjTcCcPfddxMVFcWyZcuoqamhra2NH//4x1x00UUDG1cQPFMi8Ld14PO32xKVxpiwuOKKK1i8ePHe94sXL+a6667j2WefZc2aNSxbtozbb78d1ZAvy7Ifz5QI9nUdtaohYzyvn1/uoTJ37lwqKiooLS2lsrKStLQ0xowZw2233cabb75JREQEJSUllJeXM2bMmEGNzTuJwEYVG2PC7LLLLuPpp59m9+7dXHHFFTz22GNUVlayevVqoqOjycvL63X66VDzUCKweYaMMeF1xRVXcP3111NVVcXy5ctZvHgxWVlZREdHs2zZMoqKisISl3cSQeei9Ta9hDEmTGbNmoXP5yMnJ4exY8dy9dVXc8EFF3DEEUcwb948ZsyYEZa4PJMIxqXGc3ZBNqMTYsIdijHGwz75ZF9vpYyMDFasWNHrcQ0NDYMVkncSwdmzxnD2rMFtgDHGmOHAM91HjTHG9M4SgTHGM8LRR3+wHco1WiIwxnhCXFwc1dXVIzoZqCrV1dXExR1c70jPtBEYY7wtNzeX4uJiKisrwx1KSMXFxZGbm3tQn7FEYIzxhOjoaCZNmhTuMIYkqxoyxhiPs0RgjDEeZ4nAGGM8ToZbC7qIVAKHOiFHBlA1gOEMF169bvDutdt1e0sw1z1RVTN72zHsEsHhEJFVqjov3HEMNq9eN3j32u26veVwr9uqhowxxuMsERhjjMd5LRE8GO4AwsSr1w3evXa7bm85rOv2VBuBMcaY/XmtRGCMMaYHSwTGGONxnkkEIrJQRDaKyBYRuSPc8YSKiDwkIhUi8mmXbaNF5FUR2ew+p4UzxlAQkfEiskxECkVknYjc4m4f0dcuInEi8oGIfORe94/c7ZNE5H337/0pERmRS/OJSKSIfCgiL7rvR/x1i8gOEflERNaKyCp322H9nXsiEYhIJHAfcA5QAFwpIgXhjSpkHgYW9th2B/C6quYDr7vvR5p24HZVLQCOB250/xuP9GtvAU5X1aOAOcBCETke+Dnwv6o6FagBvhLGGEPpFmB9l/deue4Fqjqny9iBw/o790QiAOYDW1R1m6q2Ak8CF4U5ppBQ1TeBPT02XwQ84r5+BLh4UIMaBKpapqpr3Nc+nJtDDiP82tXRubhttPtQ4HTgaXf7iLtuABHJBc4D/uS+Fzxw3X04rL9zrySCHGBXl/fF7javyFbVMvf1biA7nMGEmojkAXOB9/HAtbvVI2uBCuBVYCtQq6rt7iEj9e/918B3gID7Ph1vXLcC/xKR1SJyg7vtsP7ObT0Cj1FVFZER22dYRJKAZ4BbVbXe+ZHoGKnXrqodwBwRSQWeBWaEOaSQE5HzgQpVXS0ip4U7nkF2sqqWiEgW8KqIbOi681D+zr1SIigBxnd5n+tu84pyERkL4D5XhDmekBCRaJwk8Jiq/sPd7IlrB1DVWmAZcAKQKiKdP/RG4t/7ScCFIrIDp6r3dOA3jPzrRlVL3OcKnMQ/n8P8O/dKIlgJ5Ls9CmKARcCSMMc0mJYA17mvrwOeD2MsIeHWD/8ZWK+q93bZNaKvXUQy3ZIAIhIPnIXTPrIMuNQ9bMRdt6p+T1VzVTUP5//nf6vq1Yzw6xaRRBFJ7nwNnA18ymH+nXtmZLGInItTpxgJPKSqPwlzSCEhIk8Ap+FMS1sO/BB4DlgMTMCZwvtyVe3ZoDysicjJwFvAJ+yrM74Tp51gxF67iByJ0zgYifPDbrGq3iMik3F+KY8GPgSuUdWW8EUaOm7V0LdV9fyRft3u9T3rvo0CHlfVn4hIOofxd+6ZRGCMMaZ3XqkaMsYY0wdLBMYY43GWCIwxxuMsERhjjMdZIjDGGI+zRGDMIBKR0zpnyjRmqLBEYIwxHmeJwJheiMg17jz/a0XkAXditwYR+V933v/XRSTTPXaOiLwnIh+LyLOdc8GLyFQRec1dK2CNiExxT58kIk+LyAYReUy6TohkTBhYIjCmBxGZCVwBnKSqc4AO4GogEVilqrOA5TijtgH+CnxXVY/EGdncuf0x4D53rYATgc7ZIecCt+KsjTEZZ94cY8LGZh81Zn9nAMcAK90f6/E4k3gFgKfcYx4F/iEio4BUVV3ubn8E+Ls7H0yOqj4LoKp+APd8H6hqsft+LZAHvB36yzKmd5YIjNmfAI+o6ve6bRT5fo/jDnV+lq5z33Rg/x+aMLOqIWP29zpwqTvfe+d6sBNx/n/pnNnyKuBtVa0DakTkFHf7tcByd5W0YhG52D1HrIgkDOpVGBMk+yViTA+qWigid+GsAhUBtAE3Ao3AfHdfBU47AjjT/t7v3ui3AV9yt18LPCAi97jnuGwQL8OYoNnso8YESUQaVDUp3HEYM9CsasgYYzzOSgTGGONxViIwxhiPs0RgjDEeZ4nAGGM8zhKBMcZ4nCUCY4zxuP8P+feKlfXIhV8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## GRU"
      ],
      "metadata": {
        "id": "0qNNwgX0Pqiq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "layer_in = layers.Input(shape=(140,1))\n",
        "layer = layers.Bidirectional(layers.GRU(16, return_sequences=True,))(layer_in)\n",
        "layer = layers.Dropout(0.4)(layer)\n",
        "layer = layers.Bidirectional(layers.GRU(32, return_sequences=False,))(layer)\n",
        "layer = layers.Dropout(0.4)(layer)\n",
        "layer = layers.Dense(32, activation='relu')(layer)\n",
        "layer = layers.Dropout(0.2)(layer)\n",
        "layer_out = layers.Dense(5, activation='softmax')(layer)\n",
        "\n",
        "model2 = keras.models.Model(layer_in, layer_out)\n",
        "\n",
        "optimizer = keras.optimizers.Adam(learning_rate=0.001)\n",
        "\n",
        "callbacks = [\n",
        "             keras.callbacks.ModelCheckpoint('model.h5', \n",
        "                                             save_best_only=True, \n",
        "                                             monitor='val_loss'),\n",
        "             keras.callbacks.ReduceLROnPlateau(monitor='val_loss', \n",
        "                                               factor=0.1, \n",
        "                                               patience=3,\n",
        "                                               ),\n",
        "             keras.callbacks.EarlyStopping(monitor='val_loss', \n",
        "                                           patience=8,\n",
        "                                           verbose=1)\n",
        "             ]\n",
        "\n",
        "model2.compile(loss='sparse_categorical_crossentropy',\n",
        "              optimizer=optimizer, \n",
        "              metrics=['accuracy'])\n"
      ],
      "metadata": {
        "id": "ObH9NarB8H7H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model2.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6i2MZHOGBOzM",
        "outputId": "182f1e7c-f605-4946-f553-36fe5ab0c8c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_11\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_12 (InputLayer)       [(None, 140, 1)]          0         \n",
            "                                                                 \n",
            " bidirectional_16 (Bidirecti  (None, 140, 32)          1824      \n",
            " onal)                                                           \n",
            "                                                                 \n",
            " dropout_30 (Dropout)        (None, 140, 32)           0         \n",
            "                                                                 \n",
            " bidirectional_17 (Bidirecti  (None, 64)               12672     \n",
            " onal)                                                           \n",
            "                                                                 \n",
            " dropout_31 (Dropout)        (None, 64)                0         \n",
            "                                                                 \n",
            " dense_22 (Dense)            (None, 32)                2080      \n",
            "                                                                 \n",
            " dropout_32 (Dropout)        (None, 32)                0         \n",
            "                                                                 \n",
            " dense_23 (Dense)            (None, 5)                 165       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 16,741\n",
            "Trainable params: 16,741\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history2 = model2.fit(x_train, y_train,\n",
        "                      batch_size=128, epochs=50, verbose=2,\n",
        "                      validation_data=(x_val, y_val),\n",
        "                      shuffle=True, callbacks=callbacks)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fbY5XLF7BiPL",
        "outputId": "5272f84a-7677-4b36-9527-f2bed6dc36b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "83/83 - 42s - loss: 1.5029 - accuracy: 0.3251 - val_loss: 1.3024 - val_accuracy: 0.4960 - lr: 0.0010 - 42s/epoch - 502ms/step\n",
            "Epoch 2/50\n",
            "83/83 - 10s - loss: 1.1876 - accuracy: 0.5071 - val_loss: 0.9118 - val_accuracy: 0.6426 - lr: 0.0010 - 10s/epoch - 117ms/step\n",
            "Epoch 3/50\n",
            "83/83 - 10s - loss: 0.9458 - accuracy: 0.6168 - val_loss: 0.7705 - val_accuracy: 0.6791 - lr: 0.0010 - 10s/epoch - 116ms/step\n",
            "Epoch 4/50\n",
            "83/83 - 10s - loss: 0.8403 - accuracy: 0.6604 - val_loss: 0.7136 - val_accuracy: 0.7175 - lr: 0.0010 - 10s/epoch - 117ms/step\n",
            "Epoch 5/50\n",
            "83/83 - 10s - loss: 0.7817 - accuracy: 0.6925 - val_loss: 0.6266 - val_accuracy: 0.7526 - lr: 0.0010 - 10s/epoch - 116ms/step\n",
            "Epoch 6/50\n",
            "83/83 - 10s - loss: 0.7144 - accuracy: 0.7211 - val_loss: 0.5968 - val_accuracy: 0.7625 - lr: 0.0010 - 10s/epoch - 117ms/step\n",
            "Epoch 7/50\n",
            "83/83 - 10s - loss: 0.6885 - accuracy: 0.7295 - val_loss: 0.5697 - val_accuracy: 0.7754 - lr: 0.0010 - 10s/epoch - 116ms/step\n",
            "Epoch 8/50\n",
            "83/83 - 10s - loss: 0.6716 - accuracy: 0.7400 - val_loss: 0.5772 - val_accuracy: 0.7613 - lr: 0.0010 - 10s/epoch - 117ms/step\n",
            "Epoch 9/50\n",
            "83/83 - 10s - loss: 0.6514 - accuracy: 0.7490 - val_loss: 0.5600 - val_accuracy: 0.7743 - lr: 0.0010 - 10s/epoch - 117ms/step\n",
            "Epoch 10/50\n",
            "83/83 - 10s - loss: 0.6193 - accuracy: 0.7562 - val_loss: 0.5270 - val_accuracy: 0.7743 - lr: 0.0010 - 10s/epoch - 117ms/step\n",
            "Epoch 11/50\n",
            "83/83 - 10s - loss: 0.6137 - accuracy: 0.7570 - val_loss: 0.5191 - val_accuracy: 0.7872 - lr: 0.0010 - 10s/epoch - 117ms/step\n",
            "Epoch 12/50\n",
            "83/83 - 10s - loss: 0.5980 - accuracy: 0.7633 - val_loss: 0.5240 - val_accuracy: 0.7788 - lr: 0.0010 - 10s/epoch - 116ms/step\n",
            "Epoch 13/50\n",
            "83/83 - 10s - loss: 0.6000 - accuracy: 0.7666 - val_loss: 0.4946 - val_accuracy: 0.8002 - lr: 0.0010 - 10s/epoch - 117ms/step\n",
            "Epoch 14/50\n",
            "83/83 - 10s - loss: 0.5832 - accuracy: 0.7695 - val_loss: 0.5291 - val_accuracy: 0.7788 - lr: 0.0010 - 10s/epoch - 116ms/step\n",
            "Epoch 15/50\n",
            "83/83 - 10s - loss: 0.5729 - accuracy: 0.7727 - val_loss: 0.4794 - val_accuracy: 0.7967 - lr: 0.0010 - 10s/epoch - 117ms/step\n",
            "Epoch 16/50\n",
            "83/83 - 10s - loss: 0.5605 - accuracy: 0.7777 - val_loss: 0.4791 - val_accuracy: 0.8070 - lr: 0.0010 - 10s/epoch - 117ms/step\n",
            "Epoch 17/50\n",
            "83/83 - 10s - loss: 0.5518 - accuracy: 0.7816 - val_loss: 0.4625 - val_accuracy: 0.8040 - lr: 0.0010 - 10s/epoch - 117ms/step\n",
            "Epoch 18/50\n",
            "83/83 - 10s - loss: 0.5422 - accuracy: 0.7856 - val_loss: 0.4639 - val_accuracy: 0.8032 - lr: 0.0010 - 10s/epoch - 117ms/step\n",
            "Epoch 19/50\n",
            "83/83 - 10s - loss: 0.5316 - accuracy: 0.7854 - val_loss: 0.4696 - val_accuracy: 0.8005 - lr: 0.0010 - 10s/epoch - 116ms/step\n",
            "Epoch 20/50\n",
            "83/83 - 10s - loss: 0.5266 - accuracy: 0.7862 - val_loss: 0.4493 - val_accuracy: 0.8043 - lr: 0.0010 - 10s/epoch - 117ms/step\n",
            "Epoch 21/50\n",
            "83/83 - 10s - loss: 0.5320 - accuracy: 0.7835 - val_loss: 0.4422 - val_accuracy: 0.8104 - lr: 0.0010 - 10s/epoch - 117ms/step\n",
            "Epoch 22/50\n",
            "83/83 - 10s - loss: 0.5223 - accuracy: 0.7934 - val_loss: 0.4471 - val_accuracy: 0.8203 - lr: 0.0010 - 10s/epoch - 116ms/step\n",
            "Epoch 23/50\n",
            "83/83 - 10s - loss: 0.5109 - accuracy: 0.7965 - val_loss: 0.4659 - val_accuracy: 0.8051 - lr: 0.0010 - 10s/epoch - 115ms/step\n",
            "Epoch 24/50\n",
            "83/83 - 10s - loss: 0.5213 - accuracy: 0.7901 - val_loss: 0.4267 - val_accuracy: 0.8203 - lr: 0.0010 - 10s/epoch - 115ms/step\n",
            "Epoch 25/50\n",
            "83/83 - 10s - loss: 0.5000 - accuracy: 0.7979 - val_loss: 0.4292 - val_accuracy: 0.8173 - lr: 0.0010 - 10s/epoch - 115ms/step\n",
            "Epoch 26/50\n",
            "83/83 - 9s - loss: 0.4920 - accuracy: 0.8073 - val_loss: 0.4347 - val_accuracy: 0.8253 - lr: 0.0010 - 9s/epoch - 114ms/step\n",
            "Epoch 27/50\n",
            "83/83 - 10s - loss: 0.4904 - accuracy: 0.8050 - val_loss: 0.4164 - val_accuracy: 0.8279 - lr: 0.0010 - 10s/epoch - 116ms/step\n",
            "Epoch 28/50\n",
            "83/83 - 10s - loss: 0.4868 - accuracy: 0.8056 - val_loss: 0.4489 - val_accuracy: 0.8154 - lr: 0.0010 - 10s/epoch - 115ms/step\n",
            "Epoch 29/50\n",
            "83/83 - 10s - loss: 0.4883 - accuracy: 0.8036 - val_loss: 0.4257 - val_accuracy: 0.8241 - lr: 0.0010 - 10s/epoch - 115ms/step\n",
            "Epoch 30/50\n",
            "83/83 - 10s - loss: 0.4787 - accuracy: 0.8109 - val_loss: 0.4280 - val_accuracy: 0.8211 - lr: 0.0010 - 10s/epoch - 115ms/step\n",
            "Epoch 31/50\n",
            "83/83 - 10s - loss: 0.4678 - accuracy: 0.8139 - val_loss: 0.3905 - val_accuracy: 0.8352 - lr: 1.0000e-04 - 10s/epoch - 116ms/step\n",
            "Epoch 32/50\n",
            "83/83 - 10s - loss: 0.4549 - accuracy: 0.8174 - val_loss: 0.3890 - val_accuracy: 0.8352 - lr: 1.0000e-04 - 10s/epoch - 116ms/step\n",
            "Epoch 33/50\n",
            "83/83 - 10s - loss: 0.4549 - accuracy: 0.8209 - val_loss: 0.3873 - val_accuracy: 0.8382 - lr: 1.0000e-04 - 10s/epoch - 116ms/step\n",
            "Epoch 34/50\n",
            "83/83 - 10s - loss: 0.4536 - accuracy: 0.8176 - val_loss: 0.3912 - val_accuracy: 0.8337 - lr: 1.0000e-04 - 10s/epoch - 116ms/step\n",
            "Epoch 35/50\n",
            "83/83 - 10s - loss: 0.4498 - accuracy: 0.8209 - val_loss: 0.3898 - val_accuracy: 0.8340 - lr: 1.0000e-04 - 10s/epoch - 116ms/step\n",
            "Epoch 36/50\n",
            "83/83 - 10s - loss: 0.4516 - accuracy: 0.8215 - val_loss: 0.3848 - val_accuracy: 0.8394 - lr: 1.0000e-04 - 10s/epoch - 117ms/step\n",
            "Epoch 37/50\n",
            "83/83 - 10s - loss: 0.4509 - accuracy: 0.8209 - val_loss: 0.3881 - val_accuracy: 0.8363 - lr: 1.0000e-04 - 10s/epoch - 117ms/step\n",
            "Epoch 38/50\n",
            "83/83 - 10s - loss: 0.4473 - accuracy: 0.8172 - val_loss: 0.3889 - val_accuracy: 0.8382 - lr: 1.0000e-04 - 10s/epoch - 117ms/step\n",
            "Epoch 39/50\n",
            "83/83 - 10s - loss: 0.4483 - accuracy: 0.8163 - val_loss: 0.3851 - val_accuracy: 0.8405 - lr: 1.0000e-04 - 10s/epoch - 117ms/step\n",
            "Epoch 40/50\n",
            "83/83 - 10s - loss: 0.4473 - accuracy: 0.8215 - val_loss: 0.3826 - val_accuracy: 0.8413 - lr: 1.0000e-05 - 10s/epoch - 117ms/step\n",
            "Epoch 41/50\n",
            "83/83 - 10s - loss: 0.4439 - accuracy: 0.8193 - val_loss: 0.3824 - val_accuracy: 0.8420 - lr: 1.0000e-05 - 10s/epoch - 117ms/step\n",
            "Epoch 42/50\n",
            "83/83 - 10s - loss: 0.4445 - accuracy: 0.8211 - val_loss: 0.3827 - val_accuracy: 0.8409 - lr: 1.0000e-05 - 10s/epoch - 116ms/step\n",
            "Epoch 43/50\n",
            "83/83 - 10s - loss: 0.4457 - accuracy: 0.8225 - val_loss: 0.3823 - val_accuracy: 0.8397 - lr: 1.0000e-05 - 10s/epoch - 117ms/step\n",
            "Epoch 44/50\n",
            "83/83 - 10s - loss: 0.4495 - accuracy: 0.8166 - val_loss: 0.3818 - val_accuracy: 0.8413 - lr: 1.0000e-05 - 10s/epoch - 117ms/step\n",
            "Epoch 45/50\n",
            "83/83 - 10s - loss: 0.4481 - accuracy: 0.8211 - val_loss: 0.3817 - val_accuracy: 0.8416 - lr: 1.0000e-05 - 10s/epoch - 117ms/step\n",
            "Epoch 46/50\n",
            "83/83 - 10s - loss: 0.4474 - accuracy: 0.8200 - val_loss: 0.3808 - val_accuracy: 0.8420 - lr: 1.0000e-05 - 10s/epoch - 117ms/step\n",
            "Epoch 47/50\n",
            "83/83 - 10s - loss: 0.4472 - accuracy: 0.8206 - val_loss: 0.3813 - val_accuracy: 0.8420 - lr: 1.0000e-05 - 10s/epoch - 116ms/step\n",
            "Epoch 48/50\n",
            "83/83 - 10s - loss: 0.4437 - accuracy: 0.8228 - val_loss: 0.3813 - val_accuracy: 0.8424 - lr: 1.0000e-05 - 10s/epoch - 117ms/step\n",
            "Epoch 49/50\n",
            "83/83 - 10s - loss: 0.4454 - accuracy: 0.8204 - val_loss: 0.3807 - val_accuracy: 0.8416 - lr: 1.0000e-05 - 10s/epoch - 117ms/step\n",
            "Epoch 50/50\n",
            "83/83 - 10s - loss: 0.4388 - accuracy: 0.8240 - val_loss: 0.3810 - val_accuracy: 0.8413 - lr: 1.0000e-05 - 10s/epoch - 116ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_loss, test_acc = model2.evaluate(x_test, y_test)\n",
        "print(\"Test accuracy\", test_acc)\n",
        "print(\"Test loss\", test_loss)\n",
        "\n",
        "plot_acc(history2)\n",
        "plot_loss(history2)\n",
        "cm_plot(model2, x_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "fBylcEtX1kMh",
        "outputId": "bdd63429-fed9-435e-bde3-ddb8b884459d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "16/16 [==============================] - 1s 46ms/step - loss: 0.3055 - accuracy: 0.8980\n",
            "Test accuracy 0.8980000019073486\n",
            "Test loss 0.3054575026035309\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Conv1d + LSTM"
      ],
      "metadata": {
        "id": "PhshmzcgPhc2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "layer_in = layers.Input(shape=(140,1))\n",
        "layer = layers.Conv1D(filters=32, kernel_size=8, activation='leaky_relu')(layer_in)\n",
        "layer = layers.MaxPool1D(pool_size=4)(layer)\n",
        "layer = layers.Bidirectional(layers.LSTM(16, return_sequences=False,))(layer)\n",
        "layer = layers.Dropout(0.2)(layer)\n",
        "layer = layers.Dense(32, activation='leaky_relu')(layer)\n",
        "layer = layers.Dropout(0.2)(layer)\n",
        "layer_out = layers.Dense(5, activation='softmax')(layer)\n",
        "\n",
        "model3 = keras.models.Model(layer_in, layer_out)\n",
        "\n",
        "optimizer = keras.optimizers.Adam(learning_rate=0.001)\n",
        "\n",
        "callbacks = [\n",
        "             keras.callbacks.ModelCheckpoint('model.h5', \n",
        "                                             save_best_only=True, \n",
        "                                             monitor='val_loss'),\n",
        "             keras.callbacks.ReduceLROnPlateau(monitor='val_loss', \n",
        "                                               factor=0.1, \n",
        "                                               patience=3,\n",
        "                                               ),\n",
        "             keras.callbacks.EarlyStopping(monitor='val_loss', \n",
        "                                           patience=8,\n",
        "                                           verbose=1)\n",
        "             ]\n",
        "\n",
        "model3.compile(loss='sparse_categorical_crossentropy',\n",
        "              optimizer=optimizer, \n",
        "              metrics=['accuracy'])\n"
      ],
      "metadata": {
        "id": "g91pjjffBi8N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model3.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yZrW_hNqP3Ip",
        "outputId": "68dc1ba9-bc34-44af-eead-3f2f62c5a228"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_3 (InputLayer)        [(None, 140, 1)]          0         \n",
            "                                                                 \n",
            " conv1d_2 (Conv1D)           (None, 133, 32)           288       \n",
            "                                                                 \n",
            " max_pooling1d_2 (MaxPooling  (None, 33, 32)           0         \n",
            " 1D)                                                             \n",
            "                                                                 \n",
            " bidirectional_1 (Bidirectio  (None, 32)               6272      \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " dropout_4 (Dropout)         (None, 32)                0         \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 32)                1056      \n",
            "                                                                 \n",
            " dropout_5 (Dropout)         (None, 32)                0         \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 5)                 165       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 7,781\n",
            "Trainable params: 7,781\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history3 = model3.fit(x_train, y_train,\n",
        "                      batch_size=128, epochs=50, verbose=2,\n",
        "                      validation_data=(x_val, y_val),\n",
        "                      shuffle=True, callbacks=callbacks)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "51gFnWnQP43V",
        "outputId": "2b364b1a-469a-46eb-f9bf-793c004e25b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "83/83 - 7s - loss: 1.5928 - accuracy: 0.2540 - val_loss: 1.5373 - val_accuracy: 0.3316 - lr: 0.0010 - 7s/epoch - 85ms/step\n",
            "Epoch 2/50\n",
            "83/83 - 1s - loss: 1.3709 - accuracy: 0.4225 - val_loss: 1.1013 - val_accuracy: 0.5866 - lr: 0.0010 - 1s/epoch - 18ms/step\n",
            "Epoch 3/50\n",
            "83/83 - 1s - loss: 1.0719 - accuracy: 0.5744 - val_loss: 0.9217 - val_accuracy: 0.6502 - lr: 0.0010 - 1s/epoch - 18ms/step\n",
            "Epoch 4/50\n",
            "83/83 - 1s - loss: 0.9331 - accuracy: 0.6404 - val_loss: 0.8085 - val_accuracy: 0.7004 - lr: 0.0010 - 1s/epoch - 18ms/step\n",
            "Epoch 5/50\n",
            "83/83 - 1s - loss: 0.8306 - accuracy: 0.6846 - val_loss: 0.7151 - val_accuracy: 0.7214 - lr: 0.0010 - 1s/epoch - 18ms/step\n",
            "Epoch 6/50\n",
            "83/83 - 1s - loss: 0.7760 - accuracy: 0.7023 - val_loss: 0.6689 - val_accuracy: 0.7431 - lr: 0.0010 - 1s/epoch - 18ms/step\n",
            "Epoch 7/50\n",
            "83/83 - 2s - loss: 0.7253 - accuracy: 0.7167 - val_loss: 0.6123 - val_accuracy: 0.7575 - lr: 0.0010 - 2s/epoch - 20ms/step\n",
            "Epoch 8/50\n",
            "83/83 - 1s - loss: 0.7013 - accuracy: 0.7281 - val_loss: 0.5916 - val_accuracy: 0.7708 - lr: 0.0010 - 1s/epoch - 18ms/step\n",
            "Epoch 9/50\n",
            "83/83 - 1s - loss: 0.6527 - accuracy: 0.7435 - val_loss: 0.5636 - val_accuracy: 0.7857 - lr: 0.0010 - 1s/epoch - 18ms/step\n",
            "Epoch 10/50\n",
            "83/83 - 1s - loss: 0.6334 - accuracy: 0.7497 - val_loss: 0.5329 - val_accuracy: 0.7952 - lr: 0.0010 - 1s/epoch - 18ms/step\n",
            "Epoch 11/50\n",
            "83/83 - 1s - loss: 0.6099 - accuracy: 0.7623 - val_loss: 0.5393 - val_accuracy: 0.7933 - lr: 0.0010 - 1s/epoch - 17ms/step\n",
            "Epoch 12/50\n",
            "83/83 - 1s - loss: 0.6044 - accuracy: 0.7607 - val_loss: 0.4999 - val_accuracy: 0.8032 - lr: 0.0010 - 1s/epoch - 18ms/step\n",
            "Epoch 13/50\n",
            "83/83 - 1s - loss: 0.5754 - accuracy: 0.7732 - val_loss: 0.4835 - val_accuracy: 0.8154 - lr: 0.0010 - 1s/epoch - 18ms/step\n",
            "Epoch 14/50\n",
            "83/83 - 1s - loss: 0.5637 - accuracy: 0.7813 - val_loss: 0.4860 - val_accuracy: 0.8051 - lr: 0.0010 - 1s/epoch - 17ms/step\n",
            "Epoch 15/50\n",
            "83/83 - 1s - loss: 0.5498 - accuracy: 0.7852 - val_loss: 0.4773 - val_accuracy: 0.8139 - lr: 0.0010 - 1s/epoch - 17ms/step\n",
            "Epoch 16/50\n",
            "83/83 - 1s - loss: 0.5476 - accuracy: 0.7824 - val_loss: 0.4607 - val_accuracy: 0.8131 - lr: 0.0010 - 1s/epoch - 18ms/step\n",
            "Epoch 17/50\n",
            "83/83 - 1s - loss: 0.5295 - accuracy: 0.7870 - val_loss: 0.4609 - val_accuracy: 0.8127 - lr: 0.0010 - 1s/epoch - 17ms/step\n",
            "Epoch 18/50\n",
            "83/83 - 1s - loss: 0.5186 - accuracy: 0.7912 - val_loss: 0.4459 - val_accuracy: 0.8352 - lr: 0.0010 - 1s/epoch - 18ms/step\n",
            "Epoch 19/50\n",
            "83/83 - 2s - loss: 0.5064 - accuracy: 0.7990 - val_loss: 0.4162 - val_accuracy: 0.8264 - lr: 0.0010 - 2s/epoch - 20ms/step\n",
            "Epoch 20/50\n",
            "83/83 - 1s - loss: 0.4943 - accuracy: 0.8014 - val_loss: 0.4279 - val_accuracy: 0.8306 - lr: 0.0010 - 1s/epoch - 17ms/step\n",
            "Epoch 21/50\n",
            "83/83 - 1s - loss: 0.4974 - accuracy: 0.8029 - val_loss: 0.4087 - val_accuracy: 0.8344 - lr: 0.0010 - 1s/epoch - 18ms/step\n",
            "Epoch 22/50\n",
            "83/83 - 2s - loss: 0.4811 - accuracy: 0.8045 - val_loss: 0.4206 - val_accuracy: 0.8310 - lr: 0.0010 - 2s/epoch - 24ms/step\n",
            "Epoch 23/50\n",
            "83/83 - 1s - loss: 0.4774 - accuracy: 0.8081 - val_loss: 0.3927 - val_accuracy: 0.8390 - lr: 0.0010 - 1s/epoch - 18ms/step\n",
            "Epoch 24/50\n",
            "83/83 - 1s - loss: 0.4692 - accuracy: 0.8111 - val_loss: 0.4090 - val_accuracy: 0.8295 - lr: 0.0010 - 1s/epoch - 17ms/step\n",
            "Epoch 25/50\n",
            "83/83 - 1s - loss: 0.4546 - accuracy: 0.8113 - val_loss: 0.3754 - val_accuracy: 0.8462 - lr: 0.0010 - 1s/epoch - 18ms/step\n",
            "Epoch 26/50\n",
            "83/83 - 1s - loss: 0.4526 - accuracy: 0.8176 - val_loss: 0.3784 - val_accuracy: 0.8394 - lr: 0.0010 - 1s/epoch - 17ms/step\n",
            "Epoch 27/50\n",
            "83/83 - 1s - loss: 0.4555 - accuracy: 0.8193 - val_loss: 0.4082 - val_accuracy: 0.8287 - lr: 0.0010 - 1s/epoch - 17ms/step\n",
            "Epoch 28/50\n",
            "83/83 - 1s - loss: 0.4532 - accuracy: 0.8130 - val_loss: 0.3672 - val_accuracy: 0.8477 - lr: 0.0010 - 1s/epoch - 18ms/step\n",
            "Epoch 29/50\n",
            "83/83 - 2s - loss: 0.4345 - accuracy: 0.8208 - val_loss: 0.3783 - val_accuracy: 0.8344 - lr: 0.0010 - 2s/epoch - 19ms/step\n",
            "Epoch 30/50\n",
            "83/83 - 1s - loss: 0.4354 - accuracy: 0.8254 - val_loss: 0.3715 - val_accuracy: 0.8382 - lr: 0.0010 - 1s/epoch - 17ms/step\n",
            "Epoch 31/50\n",
            "83/83 - 2s - loss: 0.4216 - accuracy: 0.8291 - val_loss: 0.3647 - val_accuracy: 0.8432 - lr: 0.0010 - 2s/epoch - 20ms/step\n",
            "Epoch 32/50\n",
            "83/83 - 1s - loss: 0.4374 - accuracy: 0.8227 - val_loss: 0.3670 - val_accuracy: 0.8435 - lr: 0.0010 - 1s/epoch - 17ms/step\n",
            "Epoch 33/50\n",
            "83/83 - 1s - loss: 0.4240 - accuracy: 0.8252 - val_loss: 0.3500 - val_accuracy: 0.8508 - lr: 0.0010 - 1s/epoch - 18ms/step\n",
            "Epoch 34/50\n",
            "83/83 - 1s - loss: 0.4305 - accuracy: 0.8250 - val_loss: 0.3538 - val_accuracy: 0.8481 - lr: 0.0010 - 1s/epoch - 17ms/step\n",
            "Epoch 35/50\n",
            "83/83 - 2s - loss: 0.4137 - accuracy: 0.8330 - val_loss: 0.3422 - val_accuracy: 0.8413 - lr: 0.0010 - 2s/epoch - 20ms/step\n",
            "Epoch 36/50\n",
            "83/83 - 1s - loss: 0.3992 - accuracy: 0.8370 - val_loss: 0.3457 - val_accuracy: 0.8462 - lr: 0.0010 - 1s/epoch - 17ms/step\n",
            "Epoch 37/50\n",
            "83/83 - 1s - loss: 0.4246 - accuracy: 0.8276 - val_loss: 0.3420 - val_accuracy: 0.8576 - lr: 0.0010 - 1s/epoch - 18ms/step\n",
            "Epoch 38/50\n",
            "83/83 - 1s - loss: 0.3956 - accuracy: 0.8350 - val_loss: 0.3409 - val_accuracy: 0.8523 - lr: 0.0010 - 1s/epoch - 18ms/step\n",
            "Epoch 39/50\n",
            "83/83 - 2s - loss: 0.3935 - accuracy: 0.8382 - val_loss: 0.3502 - val_accuracy: 0.8550 - lr: 0.0010 - 2s/epoch - 19ms/step\n",
            "Epoch 40/50\n",
            "83/83 - 2s - loss: 0.4001 - accuracy: 0.8341 - val_loss: 0.3612 - val_accuracy: 0.8500 - lr: 0.0010 - 2s/epoch - 19ms/step\n",
            "Epoch 41/50\n",
            "83/83 - 1s - loss: 0.3889 - accuracy: 0.8407 - val_loss: 0.3304 - val_accuracy: 0.8565 - lr: 0.0010 - 1s/epoch - 18ms/step\n",
            "Epoch 42/50\n",
            "83/83 - 1s - loss: 0.3979 - accuracy: 0.8355 - val_loss: 0.3416 - val_accuracy: 0.8458 - lr: 0.0010 - 1s/epoch - 17ms/step\n",
            "Epoch 43/50\n",
            "83/83 - 1s - loss: 0.3848 - accuracy: 0.8399 - val_loss: 0.3236 - val_accuracy: 0.8671 - lr: 0.0010 - 1s/epoch - 18ms/step\n",
            "Epoch 44/50\n",
            "83/83 - 1s - loss: 0.3790 - accuracy: 0.8479 - val_loss: 0.3162 - val_accuracy: 0.8573 - lr: 0.0010 - 1s/epoch - 18ms/step\n",
            "Epoch 45/50\n",
            "83/83 - 1s - loss: 0.3882 - accuracy: 0.8409 - val_loss: 0.3234 - val_accuracy: 0.8618 - lr: 0.0010 - 1s/epoch - 17ms/step\n",
            "Epoch 46/50\n",
            "83/83 - 1s - loss: 0.3783 - accuracy: 0.8420 - val_loss: 0.3154 - val_accuracy: 0.8668 - lr: 0.0010 - 1s/epoch - 18ms/step\n",
            "Epoch 47/50\n",
            "83/83 - 1s - loss: 0.3793 - accuracy: 0.8404 - val_loss: 0.3091 - val_accuracy: 0.8671 - lr: 0.0010 - 1s/epoch - 18ms/step\n",
            "Epoch 48/50\n",
            "83/83 - 1s - loss: 0.3702 - accuracy: 0.8460 - val_loss: 0.3136 - val_accuracy: 0.8675 - lr: 0.0010 - 1s/epoch - 17ms/step\n",
            "Epoch 49/50\n",
            "83/83 - 1s - loss: 0.3659 - accuracy: 0.8513 - val_loss: 0.3091 - val_accuracy: 0.8683 - lr: 0.0010 - 1s/epoch - 17ms/step\n",
            "Epoch 50/50\n",
            "83/83 - 2s - loss: 0.3629 - accuracy: 0.8520 - val_loss: 0.3032 - val_accuracy: 0.8725 - lr: 0.0010 - 2s/epoch - 24ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_loss, test_acc = model3.evaluate(x_test, y_test)\n",
        "print(\"Test accuracy\", test_acc)\n",
        "print(\"Test loss\", test_loss)\n",
        "\n",
        "plot_acc(history3)\n",
        "plot_loss(history3)\n",
        "cm_plot(model3, x_test)"
      ],
      "metadata": {
        "id": "w3PaJXp8-ygX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c9706f35-f55f-43f4-af2c-91d7dae107d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "16/16 [==============================] - 0s 11ms/step - loss: 0.3103 - accuracy: 0.9120\n",
            "Test accuracy 0.9120000004768372\n",
            "Test loss 0.3103255331516266\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}