Ionosphere_Assignment_Solved_updated.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.3"
    },
    "colab": {
      "name": "(Copy) Ionosphere Assignment Solved.ipynb",
      "provenance": [],
      "collapsed_sections": []
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HJAWyOiTZ_zh"
      },
      "source": [
        "# Assignment: Ionosphere Data Problem\n",
        "\n",
        "### Dataset Description: \n",
        "\n",
        "This radar data was collected by a system in Goose Bay, Labrador. This system consists of a phased array of 16 high-frequency antennas with a total transmitted power on the order of 6.4 kilowatts. See the paper for more details. The targets were free electrons in the ionosphere. \"Good\" radar returns are those showing evidence of some type of structure in the ionosphere. \"Bad\" returns are those that do not; their signals pass through the ionosphere.\n",
        "\n",
        "Received signals were processed using an autocorrelation function whose arguments are the time of a pulse and the pulse number. There were 17 pulse numbers for the Goose Bay system. Instances in this databse are described by 2 attributes per pulse number, corresponding to the complex values returned by the function resulting from the complex electromagnetic signal.\n",
        "\n",
        "### Attribute Information:\n",
        "\n",
        "- All 34 are continuous\n",
        "- The 35th attribute is either \"good\" or \"bad\" according to the definition summarized above. This is a binary classification task.\n",
        "\n",
        " <br><br>\n",
        "\n",
        "<table border=\"1\"  cellpadding=\"6\">\n",
        "\t<tbody>\n",
        "        <tr>\n",
        "\t\t<td bgcolor=\"#DDEEFF\"><p class=\"normal\"><b>Data Set Characteristics:&nbsp;&nbsp;</b></p></td>\n",
        "\t\t<td><p class=\"normal\">Multivariate</p></td>\n",
        "\t\t<td bgcolor=\"#DDEEFF\"><p class=\"normal\"><b>Number of Instances:</b></p></td>\n",
        "\t\t<td><p class=\"normal\">351</p></td>\n",
        "\t\t<td bgcolor=\"#DDEEFF\"><p class=\"normal\"><b>Area:</b></p></td>\n",
        "\t\t<td><p class=\"normal\">Physical</p></td>\n",
        "        </tr>\n",
        "     </tbody>\n",
        "    </table>\n",
        "<table border=\"1\" cellpadding=\"6\">\n",
        "    <tbody>\n",
        "        <tr>\n",
        "            <td bgcolor=\"#DDEEFF\"><p class=\"normal\"><b>Attribute Characteristics:</b></p></td>\n",
        "            <td><p class=\"normal\">Integer,Real</p></td>\n",
        "            <td bgcolor=\"#DDEEFF\"><p class=\"normal\"><b>Number of Attributes:</b></p></td>\n",
        "            <td><p class=\"normal\">34</p></td>\n",
        "            <td bgcolor=\"#DDEEFF\"><p class=\"normal\"><b>Date Donated</b></p></td>\n",
        "            <td><p class=\"normal\">N/A</p></td>\n",
        "        </tr>\n",
        "     </tbody>\n",
        "    </table>\n",
        "<table border=\"1\" cellpadding=\"6\">\t\n",
        "    <tbody>\n",
        "    <tr>\n",
        "\t\t<td bgcolor=\"#DDEEFF\"><p class=\"normal\"><b>Associated Tasks:</b></p></td>\n",
        "\t\t<td><p class=\"normal\">Classification</p></td>\n",
        "\t\t<td bgcolor=\"#DDEEFF\"><p class=\"normal\"><b>Missing Values?</b></p></td>\n",
        "\t\t<td><p class=\"normal\">N/A</p></td>\n",
        "\t\t<td bgcolor=\"#DDEEFF\"><p class=\"normal\"><b>Number of Web Hits:</b></p></td>\n",
        "\t\t<td><p class=\"normal\">N/A</p></td>\n",
        "\t</tr>\n",
        "    </tbody>\n",
        "    </table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cp-u2JbpZ_zl"
      },
      "source": [
        "### WORKFLOW :\n",
        "- Load Data\n",
        "- Check Missing Values ( If Exist ; Fill each record with mean of its feature ) or any usless column.\n",
        "- Shuffle the data if needed.\n",
        "- Standardized the Input Variables. **Hint**: Centeralized the data\n",
        "- Split into 60 and 40 ratio.\n",
        "- Encode labels.\n",
        "- Model : 1 hidden layers including 16 unit.\n",
        "- Compilation Step (Note : Its a Binary problem , select loss , metrics according to it)\n",
        "- Train the Model with Epochs (100).\n",
        "- If the model gets overfit tune your model by changing the units , No. of layers , epochs , add dropout layer or add Regularizer according to the need .\n",
        "- Prediction should be > **92%**\n",
        "- Evaluation Step\n",
        "- Prediction\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z9IxuEY4bS52"
      },
      "source": [
        "## **Loading Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "id": "oiVK6_H8fhHi",
        "outputId": "c3e89165-ab50-4106-c4db-ed0ad42dd50f"
      },
      "source": [
        "from google.colab import files \n",
        "\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-01504a7a-1a5b-47c0-a0ab-1e15334209c7\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-01504a7a-1a5b-47c0-a0ab-1e15334209c7\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving ionosphere_data.csv to ionosphere_data.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lS-HyveCfnB8"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "dataframe = pd.read_csv('ionosphere_data.csv')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jjZF_i1AbWj7"
      },
      "source": [
        "## **Checking for Missing Values**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cSyaDnhxgn7s",
        "outputId": "9bf051c8-55bb-4338-c6b3-24739aefe0ea"
      },
      "source": [
        "dataframe.isnull().any().any()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2XXaxJYhbbho"
      },
      "source": [
        "## **Shuffling our Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MKRcqUJhjj1z"
      },
      "source": [
        "dataframe_shuffled = pd.DataFrame(np.random.permutation(dataframe) , columns = dataframe.columns)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "ue3-7k2ikdCP",
        "outputId": "ba8e78b0-727d-40a9-9e3f-1c360bed1e6c"
      },
      "source": [
        "dataframe_shuffled.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>feature1</th>\n",
              "      <th>feature2</th>\n",
              "      <th>feature3</th>\n",
              "      <th>feature4</th>\n",
              "      <th>feature5</th>\n",
              "      <th>feature6</th>\n",
              "      <th>feature7</th>\n",
              "      <th>feature8</th>\n",
              "      <th>feature9</th>\n",
              "      <th>feature10</th>\n",
              "      <th>feature11</th>\n",
              "      <th>feature12</th>\n",
              "      <th>feature13</th>\n",
              "      <th>feature14</th>\n",
              "      <th>feature15</th>\n",
              "      <th>feature16</th>\n",
              "      <th>feature17</th>\n",
              "      <th>feature18</th>\n",
              "      <th>feature19</th>\n",
              "      <th>feature20</th>\n",
              "      <th>feature21</th>\n",
              "      <th>feature22</th>\n",
              "      <th>feature23</th>\n",
              "      <th>feature24</th>\n",
              "      <th>feature25</th>\n",
              "      <th>feature26</th>\n",
              "      <th>feature27</th>\n",
              "      <th>feature28</th>\n",
              "      <th>feature29</th>\n",
              "      <th>feature30</th>\n",
              "      <th>feature31</th>\n",
              "      <th>feature32</th>\n",
              "      <th>feature33</th>\n",
              "      <th>feature34</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.94531</td>\n",
              "      <td>-0.03516</td>\n",
              "      <td>-1</td>\n",
              "      <td>-0.33203</td>\n",
              "      <td>-1</td>\n",
              "      <td>-0.01563</td>\n",
              "      <td>0.97266</td>\n",
              "      <td>0.01172</td>\n",
              "      <td>0.93359</td>\n",
              "      <td>-0.01953</td>\n",
              "      <td>-1</td>\n",
              "      <td>0.16406</td>\n",
              "      <td>-1</td>\n",
              "      <td>-0.00391</td>\n",
              "      <td>0.95313</td>\n",
              "      <td>-0.03516</td>\n",
              "      <td>0.92188</td>\n",
              "      <td>-0.02734</td>\n",
              "      <td>-0.99219</td>\n",
              "      <td>0.11719</td>\n",
              "      <td>-0.93359</td>\n",
              "      <td>0.34766</td>\n",
              "      <td>0.95703</td>\n",
              "      <td>-0.00391</td>\n",
              "      <td>0.82041</td>\n",
              "      <td>0.13758</td>\n",
              "      <td>0.90234</td>\n",
              "      <td>-0.06641</td>\n",
              "      <td>-1</td>\n",
              "      <td>-0.1875</td>\n",
              "      <td>-1</td>\n",
              "      <td>-0.34375</td>\n",
              "      <td>b</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.62745</td>\n",
              "      <td>-0.07843</td>\n",
              "      <td>0.72549</td>\n",
              "      <td>0</td>\n",
              "      <td>0.60784</td>\n",
              "      <td>-0.07843</td>\n",
              "      <td>0.62745</td>\n",
              "      <td>-0.11765</td>\n",
              "      <td>0.68627</td>\n",
              "      <td>-0.11765</td>\n",
              "      <td>0.66667</td>\n",
              "      <td>-0.13725</td>\n",
              "      <td>0.64706</td>\n",
              "      <td>-0.09804</td>\n",
              "      <td>0.54902</td>\n",
              "      <td>-0.11765</td>\n",
              "      <td>0.54902</td>\n",
              "      <td>-0.21569</td>\n",
              "      <td>0.58824</td>\n",
              "      <td>-0.19608</td>\n",
              "      <td>0.66667</td>\n",
              "      <td>-0.23529</td>\n",
              "      <td>0.45098</td>\n",
              "      <td>-0.2549</td>\n",
              "      <td>0.52409</td>\n",
              "      <td>-0.24668</td>\n",
              "      <td>0.56863</td>\n",
              "      <td>-0.31373</td>\n",
              "      <td>0.43137</td>\n",
              "      <td>-0.21569</td>\n",
              "      <td>0.47059</td>\n",
              "      <td>-0.27451</td>\n",
              "      <td>b</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>b</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.51724</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.10991</td>\n",
              "      <td>-1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>-1</td>\n",
              "      <td>-0.22414</td>\n",
              "      <td>-0.55711</td>\n",
              "      <td>-0.83297</td>\n",
              "      <td>0.7694</td>\n",
              "      <td>0.63147</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.53448</td>\n",
              "      <td>0.35668</td>\n",
              "      <td>-0.90302</td>\n",
              "      <td>0.44828</td>\n",
              "      <td>1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>0.81573</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>b</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.41932</td>\n",
              "      <td>0.12482</td>\n",
              "      <td>0.35</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.23182</td>\n",
              "      <td>0.27955</td>\n",
              "      <td>-0.03636</td>\n",
              "      <td>0.44318</td>\n",
              "      <td>0.04517</td>\n",
              "      <td>0.36194</td>\n",
              "      <td>-0.19091</td>\n",
              "      <td>0.33636</td>\n",
              "      <td>-0.1335</td>\n",
              "      <td>0.27322</td>\n",
              "      <td>0.02727</td>\n",
              "      <td>0.40455</td>\n",
              "      <td>-0.34773</td>\n",
              "      <td>0.12727</td>\n",
              "      <td>-0.20028</td>\n",
              "      <td>0.05078</td>\n",
              "      <td>-0.18636</td>\n",
              "      <td>0.36364</td>\n",
              "      <td>-0.14003</td>\n",
              "      <td>-0.04802</td>\n",
              "      <td>-0.09971</td>\n",
              "      <td>-0.07114</td>\n",
              "      <td>-1</td>\n",
              "      <td>-1</td>\n",
              "      <td>-0.02916</td>\n",
              "      <td>-0.07464</td>\n",
              "      <td>-0.00526</td>\n",
              "      <td>-0.06314</td>\n",
              "      <td>b</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  feature1 feature2 feature3 feature4  ... feature32 feature33 feature34 label\n",
              "0        1        0  0.94531 -0.03516  ...   -0.1875        -1  -0.34375     b\n",
              "1        1        0  0.62745 -0.07843  ...  -0.21569   0.47059  -0.27451     b\n",
              "2        0        0        1        1  ...         0         1        -1     b\n",
              "3        1        0        0        0  ...         0         0         0     b\n",
              "4        1        0  0.41932  0.12482  ...  -0.07464  -0.00526  -0.06314     b\n",
              "\n",
              "[5 rows x 35 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ybr80flGbgk_"
      },
      "source": [
        "## **Preprocessing Data**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3YtWQCiucQoM"
      },
      "source": [
        "### Removing unnecessary features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g8EFXyj1nVoR",
        "outputId": "fd619a6a-815d-4364-980c-5c6e00df6e08"
      },
      "source": [
        "data = dataframe_shuffled.drop(['label', 'feature2'], axis = 1)\n",
        "data.shape"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(351, 33)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q7OiZEZCbnNr"
      },
      "source": [
        "### Converting labels to numbers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "THBiV5JZkhnC",
        "outputId": "a2a481d6-0a70-4f9f-b348-47497d1b7519"
      },
      "source": [
        "target_labels = dataframe_shuffled['label'].map({'b' : 0 , 'g' : 1})\n",
        "target_labels[:20]"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     0\n",
              "1     0\n",
              "2     0\n",
              "3     0\n",
              "4     0\n",
              "5     0\n",
              "6     0\n",
              "7     0\n",
              "8     0\n",
              "9     1\n",
              "10    1\n",
              "11    1\n",
              "12    1\n",
              "13    0\n",
              "14    0\n",
              "15    1\n",
              "16    1\n",
              "17    1\n",
              "18    1\n",
              "19    1\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Jdzv7sFb1tA"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UlPec04jlZFG"
      },
      "source": [
        "target_labels = np.array(target_labels)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G9nUTgvImFtO",
        "outputId": "a0016a3d-fa3d-4488-8b19-a9e6cd4e7f31"
      },
      "source": [
        "target_labels.shape"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(351,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QkrtKIslchba"
      },
      "source": [
        "## **Splitting Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J6GuQp3wkzT4"
      },
      "source": [
        "train_len = len(dataframe)*6//10\n",
        "test_len = len(dataframe) - train_len"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F4DHf8H_m97_"
      },
      "source": [
        "train_labels = target_labels[0 : train_len].copy()\n",
        "test_labels = target_labels[train_len : train_len + test_len].copy()"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LSjMKldmnQxe",
        "outputId": "57ebdcd5-b4eb-4ede-8461-d97d81702ad1"
      },
      "source": [
        "train_labels.shape, test_labels.shape"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((210,), (141,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0VemrWKUp2BZ",
        "outputId": "fb156b01-ba18-49d3-be91-6dac80761447"
      },
      "source": [
        "train_labels.dtype"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('int64')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5-JrAs3LuSBS"
      },
      "source": [
        "train_labels = train_labels.astype('float32')"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zGjttr1ynogF"
      },
      "source": [
        "train_data = data[0 : train_len].copy()\n",
        "test_data = data[train_len : train_len + test_len].copy()"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hIg348QFovwB",
        "outputId": "21793416-8262-4267-f2b5-cd3d1b2e078a"
      },
      "source": [
        "train_data.shape, test_data.shape"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((210, 33), (141, 33))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lrDkkStdoxUU"
      },
      "source": [
        "train_data = np.array(train_data).astype('float32')\n",
        "test_data = np.array(test_data).astype('float32')"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xp0eTfEFcodf"
      },
      "source": [
        "## **Normalizing Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VMMf_IfG7U5q"
      },
      "source": [
        "mean = np.mean(train_data, axis=0)\n",
        "std = np.std(train_data,axis=0)\n"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nTBYO4J18Pz7"
      },
      "source": [
        "train_data -= mean\n",
        "train_data /= std"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ecpaMu2y7U-h"
      },
      "source": [
        "test_data -= mean\n",
        "test_data /= std"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7fVRj_1CtBMT",
        "outputId": "3ae62db8-6546-4b99-9815-57f070d7ff01"
      },
      "source": [
        "train_data.shape, test_data.shape , train_labels.shape, test_labels.shape"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((210, 33), (141, 33), (210,), (141,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f3L6LCmactEc"
      },
      "source": [
        "## **Building & Compiling Model in a function**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SR6JEs0QpjDQ"
      },
      "source": [
        "# Building a model + K-Fold Validation:\n",
        "\n",
        "from keras import models\n",
        "from keras import layers\n",
        "from keras import regularizers\n",
        "def build_model():\n",
        "  modelI = models.Sequential()\n",
        "  modelI.add(layers.Dense(64, activation='relu', kernel_regularizer = regularizers.l2(0.001) ,input_shape = (train_data.shape[1],) ) )  \n",
        "  modelI.add(layers.Dropout(0.3))\n",
        "  modelI.add(layers.Dense(16,  activation = 'relu', kernel_regularizer = regularizers.l2(0.001)))\n",
        "  modelI.add(layers.Dropout(0.3))\n",
        "  modelI.add(layers.Dense(1 , activation = 'sigmoid'))\n",
        "\n",
        "  modelI.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "  return modelI"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rBALtLsec9Mg"
      },
      "source": [
        "## **Validating our Approach & Training Model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xekcBSR1qLHJ",
        "outputId": "9e90cee1-96ec-4d91-8bb0-628c7454607c"
      },
      "source": [
        "k = 5\n",
        "number_of_validation_samples = len(train_data)//k\n",
        "\n",
        "number_of_epochs = 100\n",
        "all_scores = []\n",
        "\n",
        "for i in range(k):\n",
        "  print('processing fold #', i)\n",
        "  val_data = train_data[i*number_of_validation_samples : (i+1) * number_of_validation_samples]        # Prepares the validation data from partition #k\n",
        "  val_labels = train_labels[i*number_of_validation_samples : (i+1) * number_of_validation_samples]\n",
        "\n",
        "  partial_train_data = np.concatenate( ( train_data[ : i*number_of_validation_samples],              # Prepares the training data from all other partitions\n",
        "                                         train_data[(i+1)*number_of_validation_samples : ] ) , axis = 0 )\n",
        "  \n",
        "  partial_train_labels = np.concatenate( ( train_labels[ : i*number_of_validation_samples], \n",
        "                                         train_labels[(i+1)*number_of_validation_samples : ] ) , axis = 0 )\n",
        "  \n",
        "  modelI = build_model()  # builds keras model (already compiled) --> remember the function we made\n",
        "\n",
        "  historyI = modelI.fit(partial_train_data,\n",
        "             partial_train_labels,\n",
        "             epochs = number_of_epochs,\n",
        "             batch_size =  16, \n",
        "             verbose = 1,  # -->    verbose: 0, 1, or 2  --> 0 = silent, 1 = progress bar, 2 = one line per epoch. \n",
        "             validation_data = (val_data, val_labels) )\n"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "processing fold # 0\n",
            "Epoch 1/100\n",
            "11/11 [==============================] - 1s 21ms/step - loss: 0.7487 - accuracy: 0.5262 - val_loss: 0.6254 - val_accuracy: 0.6667\n",
            "Epoch 2/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.6725 - accuracy: 0.6649 - val_loss: 0.5949 - val_accuracy: 0.7857\n",
            "Epoch 3/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.5592 - accuracy: 0.8107 - val_loss: 0.5733 - val_accuracy: 0.7857\n",
            "Epoch 4/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.5600 - accuracy: 0.8021 - val_loss: 0.5588 - val_accuracy: 0.7857\n",
            "Epoch 5/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.5308 - accuracy: 0.7892 - val_loss: 0.5484 - val_accuracy: 0.8095\n",
            "Epoch 6/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.4093 - accuracy: 0.8761 - val_loss: 0.5308 - val_accuracy: 0.8333\n",
            "Epoch 7/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.3936 - accuracy: 0.8987 - val_loss: 0.5139 - val_accuracy: 0.8571\n",
            "Epoch 8/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.3919 - accuracy: 0.8744 - val_loss: 0.4922 - val_accuracy: 0.8333\n",
            "Epoch 9/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.3443 - accuracy: 0.9078 - val_loss: 0.4747 - val_accuracy: 0.8333\n",
            "Epoch 10/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.3190 - accuracy: 0.9031 - val_loss: 0.4526 - val_accuracy: 0.8095\n",
            "Epoch 11/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.3030 - accuracy: 0.9302 - val_loss: 0.4564 - val_accuracy: 0.8333\n",
            "Epoch 12/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.3222 - accuracy: 0.8899 - val_loss: 0.4476 - val_accuracy: 0.8095\n",
            "Epoch 13/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.3525 - accuracy: 0.8724 - val_loss: 0.4238 - val_accuracy: 0.7857\n",
            "Epoch 14/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.3211 - accuracy: 0.8993 - val_loss: 0.4209 - val_accuracy: 0.8095\n",
            "Epoch 15/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.2708 - accuracy: 0.9344 - val_loss: 0.4168 - val_accuracy: 0.8333\n",
            "Epoch 16/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.2403 - accuracy: 0.9525 - val_loss: 0.4115 - val_accuracy: 0.8333\n",
            "Epoch 17/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.2347 - accuracy: 0.9471 - val_loss: 0.4191 - val_accuracy: 0.8333\n",
            "Epoch 18/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.2543 - accuracy: 0.9438 - val_loss: 0.4123 - val_accuracy: 0.8333\n",
            "Epoch 19/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.2025 - accuracy: 0.9726 - val_loss: 0.4161 - val_accuracy: 0.8333\n",
            "Epoch 20/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.2792 - accuracy: 0.9020 - val_loss: 0.4147 - val_accuracy: 0.8333\n",
            "Epoch 21/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.1702 - accuracy: 0.9819 - val_loss: 0.4494 - val_accuracy: 0.8333\n",
            "Epoch 22/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.1965 - accuracy: 0.9538 - val_loss: 0.4367 - val_accuracy: 0.8333\n",
            "Epoch 23/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.2492 - accuracy: 0.9330 - val_loss: 0.4150 - val_accuracy: 0.8333\n",
            "Epoch 24/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1742 - accuracy: 0.9620 - val_loss: 0.4126 - val_accuracy: 0.8571\n",
            "Epoch 25/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1747 - accuracy: 0.9677 - val_loss: 0.4278 - val_accuracy: 0.8333\n",
            "Epoch 26/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2059 - accuracy: 0.9352 - val_loss: 0.4421 - val_accuracy: 0.8333\n",
            "Epoch 27/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.1528 - accuracy: 0.9936 - val_loss: 0.4570 - val_accuracy: 0.8333\n",
            "Epoch 28/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1602 - accuracy: 0.9716 - val_loss: 0.4221 - val_accuracy: 0.8333\n",
            "Epoch 29/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1717 - accuracy: 0.9465 - val_loss: 0.4194 - val_accuracy: 0.8571\n",
            "Epoch 30/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.1802 - accuracy: 0.9259 - val_loss: 0.4584 - val_accuracy: 0.8571\n",
            "Epoch 31/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2074 - accuracy: 0.9624 - val_loss: 0.4495 - val_accuracy: 0.8571\n",
            "Epoch 32/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.1519 - accuracy: 0.9834 - val_loss: 0.4622 - val_accuracy: 0.8571\n",
            "Epoch 33/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1719 - accuracy: 0.9675 - val_loss: 0.4501 - val_accuracy: 0.8571\n",
            "Epoch 34/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1620 - accuracy: 0.9534 - val_loss: 0.4834 - val_accuracy: 0.8571\n",
            "Epoch 35/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1451 - accuracy: 0.9834 - val_loss: 0.4500 - val_accuracy: 0.8571\n",
            "Epoch 36/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1408 - accuracy: 0.9811 - val_loss: 0.4454 - val_accuracy: 0.8571\n",
            "Epoch 37/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1474 - accuracy: 0.9620 - val_loss: 0.4507 - val_accuracy: 0.8571\n",
            "Epoch 38/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1393 - accuracy: 0.9645 - val_loss: 0.4377 - val_accuracy: 0.8571\n",
            "Epoch 39/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1274 - accuracy: 0.9836 - val_loss: 0.4747 - val_accuracy: 0.8571\n",
            "Epoch 40/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0992 - accuracy: 0.9898 - val_loss: 0.5128 - val_accuracy: 0.8571\n",
            "Epoch 41/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1376 - accuracy: 0.9695 - val_loss: 0.4878 - val_accuracy: 0.8571\n",
            "Epoch 42/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0999 - accuracy: 0.9965 - val_loss: 0.5088 - val_accuracy: 0.8571\n",
            "Epoch 43/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1054 - accuracy: 0.9965 - val_loss: 0.5050 - val_accuracy: 0.8571\n",
            "Epoch 44/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.1058 - accuracy: 0.9925 - val_loss: 0.5004 - val_accuracy: 0.8571\n",
            "Epoch 45/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1494 - accuracy: 0.9652 - val_loss: 0.4925 - val_accuracy: 0.8571\n",
            "Epoch 46/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1128 - accuracy: 0.9810 - val_loss: 0.5077 - val_accuracy: 0.8571\n",
            "Epoch 47/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0863 - accuracy: 1.0000 - val_loss: 0.4783 - val_accuracy: 0.8571\n",
            "Epoch 48/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1080 - accuracy: 0.9929 - val_loss: 0.4689 - val_accuracy: 0.8571\n",
            "Epoch 49/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0971 - accuracy: 0.9933 - val_loss: 0.5351 - val_accuracy: 0.8571\n",
            "Epoch 50/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0850 - accuracy: 1.0000 - val_loss: 0.4960 - val_accuracy: 0.8571\n",
            "Epoch 51/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1010 - accuracy: 0.9970 - val_loss: 0.4718 - val_accuracy: 0.8810\n",
            "Epoch 52/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.0786 - accuracy: 1.0000 - val_loss: 0.4909 - val_accuracy: 0.8571\n",
            "Epoch 53/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.0764 - accuracy: 1.0000 - val_loss: 0.4859 - val_accuracy: 0.8571\n",
            "Epoch 54/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0865 - accuracy: 1.0000 - val_loss: 0.5221 - val_accuracy: 0.8571\n",
            "Epoch 55/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.0870 - accuracy: 0.9946 - val_loss: 0.4995 - val_accuracy: 0.8571\n",
            "Epoch 56/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0808 - accuracy: 0.9901 - val_loss: 0.5062 - val_accuracy: 0.8571\n",
            "Epoch 57/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.0834 - accuracy: 1.0000 - val_loss: 0.5230 - val_accuracy: 0.8571\n",
            "Epoch 58/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0840 - accuracy: 0.9890 - val_loss: 0.5311 - val_accuracy: 0.8810\n",
            "Epoch 59/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0884 - accuracy: 1.0000 - val_loss: 0.5175 - val_accuracy: 0.8571\n",
            "Epoch 60/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0761 - accuracy: 1.0000 - val_loss: 0.5341 - val_accuracy: 0.8571\n",
            "Epoch 61/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.0844 - accuracy: 1.0000 - val_loss: 0.5589 - val_accuracy: 0.8571\n",
            "Epoch 62/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0668 - accuracy: 1.0000 - val_loss: 0.5721 - val_accuracy: 0.8095\n",
            "Epoch 63/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0736 - accuracy: 0.9973 - val_loss: 0.5410 - val_accuracy: 0.8571\n",
            "Epoch 64/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0763 - accuracy: 0.9980 - val_loss: 0.5338 - val_accuracy: 0.8571\n",
            "Epoch 65/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0671 - accuracy: 1.0000 - val_loss: 0.5426 - val_accuracy: 0.8571\n",
            "Epoch 66/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0715 - accuracy: 1.0000 - val_loss: 0.5892 - val_accuracy: 0.8571\n",
            "Epoch 67/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0729 - accuracy: 0.9916 - val_loss: 0.5352 - val_accuracy: 0.8571\n",
            "Epoch 68/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0636 - accuracy: 1.0000 - val_loss: 0.5774 - val_accuracy: 0.8571\n",
            "Epoch 69/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0649 - accuracy: 1.0000 - val_loss: 0.6441 - val_accuracy: 0.8333\n",
            "Epoch 70/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0679 - accuracy: 1.0000 - val_loss: 0.5435 - val_accuracy: 0.8571\n",
            "Epoch 71/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0678 - accuracy: 0.9985 - val_loss: 0.5389 - val_accuracy: 0.8571\n",
            "Epoch 72/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0802 - accuracy: 1.0000 - val_loss: 0.6006 - val_accuracy: 0.8571\n",
            "Epoch 73/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0653 - accuracy: 1.0000 - val_loss: 0.6917 - val_accuracy: 0.8333\n",
            "Epoch 74/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0623 - accuracy: 1.0000 - val_loss: 0.6133 - val_accuracy: 0.8571\n",
            "Epoch 75/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0596 - accuracy: 1.0000 - val_loss: 0.6402 - val_accuracy: 0.8571\n",
            "Epoch 76/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.0655 - accuracy: 0.9916 - val_loss: 0.6533 - val_accuracy: 0.8333\n",
            "Epoch 77/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0563 - accuracy: 1.0000 - val_loss: 0.6274 - val_accuracy: 0.8333\n",
            "Epoch 78/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0600 - accuracy: 1.0000 - val_loss: 0.5900 - val_accuracy: 0.8571\n",
            "Epoch 79/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0950 - accuracy: 0.9838 - val_loss: 0.5657 - val_accuracy: 0.8571\n",
            "Epoch 80/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0578 - accuracy: 0.9990 - val_loss: 0.5762 - val_accuracy: 0.8571\n",
            "Epoch 81/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.0660 - accuracy: 1.0000 - val_loss: 0.6601 - val_accuracy: 0.8333\n",
            "Epoch 82/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.0550 - accuracy: 1.0000 - val_loss: 0.6787 - val_accuracy: 0.8333\n",
            "Epoch 83/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0595 - accuracy: 0.9965 - val_loss: 0.5793 - val_accuracy: 0.8333\n",
            "Epoch 84/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0578 - accuracy: 0.9985 - val_loss: 0.5617 - val_accuracy: 0.8333\n",
            "Epoch 85/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0523 - accuracy: 1.0000 - val_loss: 0.5853 - val_accuracy: 0.8333\n",
            "Epoch 86/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0529 - accuracy: 1.0000 - val_loss: 0.6130 - val_accuracy: 0.8333\n",
            "Epoch 87/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0540 - accuracy: 1.0000 - val_loss: 0.6324 - val_accuracy: 0.8333\n",
            "Epoch 88/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0496 - accuracy: 1.0000 - val_loss: 0.6015 - val_accuracy: 0.8333\n",
            "Epoch 89/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0479 - accuracy: 0.9973 - val_loss: 0.6991 - val_accuracy: 0.8333\n",
            "Epoch 90/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0660 - accuracy: 0.9890 - val_loss: 0.6494 - val_accuracy: 0.8333\n",
            "Epoch 91/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0441 - accuracy: 1.0000 - val_loss: 0.6160 - val_accuracy: 0.8333\n",
            "Epoch 92/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0521 - accuracy: 1.0000 - val_loss: 0.5946 - val_accuracy: 0.8333\n",
            "Epoch 93/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0790 - accuracy: 0.9890 - val_loss: 0.5885 - val_accuracy: 0.8571\n",
            "Epoch 94/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0621 - accuracy: 0.9846 - val_loss: 0.6690 - val_accuracy: 0.8333\n",
            "Epoch 95/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0488 - accuracy: 1.0000 - val_loss: 0.6559 - val_accuracy: 0.8333\n",
            "Epoch 96/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0517 - accuracy: 1.0000 - val_loss: 0.5656 - val_accuracy: 0.8571\n",
            "Epoch 97/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0566 - accuracy: 1.0000 - val_loss: 0.6135 - val_accuracy: 0.8571\n",
            "Epoch 98/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.0479 - accuracy: 1.0000 - val_loss: 0.6806 - val_accuracy: 0.8571\n",
            "Epoch 99/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0432 - accuracy: 1.0000 - val_loss: 0.7004 - val_accuracy: 0.8333\n",
            "Epoch 100/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0506 - accuracy: 1.0000 - val_loss: 0.5950 - val_accuracy: 0.8571\n",
            "processing fold # 1\n",
            "Epoch 1/100\n",
            "11/11 [==============================] - 1s 20ms/step - loss: 0.7833 - accuracy: 0.5452 - val_loss: 0.6696 - val_accuracy: 0.7143\n",
            "Epoch 2/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.7097 - accuracy: 0.6532 - val_loss: 0.6303 - val_accuracy: 0.7857\n",
            "Epoch 3/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.5883 - accuracy: 0.7614 - val_loss: 0.6024 - val_accuracy: 0.8095\n",
            "Epoch 4/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.5398 - accuracy: 0.8161 - val_loss: 0.5714 - val_accuracy: 0.8333\n",
            "Epoch 5/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.5470 - accuracy: 0.8508 - val_loss: 0.5400 - val_accuracy: 0.8571\n",
            "Epoch 6/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.4883 - accuracy: 0.8245 - val_loss: 0.5181 - val_accuracy: 0.8571\n",
            "Epoch 7/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.4780 - accuracy: 0.8716 - val_loss: 0.4919 - val_accuracy: 0.8571\n",
            "Epoch 8/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.4882 - accuracy: 0.8640 - val_loss: 0.4705 - val_accuracy: 0.8571\n",
            "Epoch 9/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.4077 - accuracy: 0.8883 - val_loss: 0.4609 - val_accuracy: 0.8571\n",
            "Epoch 10/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.4127 - accuracy: 0.8878 - val_loss: 0.4475 - val_accuracy: 0.8571\n",
            "Epoch 11/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.3158 - accuracy: 0.9273 - val_loss: 0.4317 - val_accuracy: 0.8810\n",
            "Epoch 12/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.3986 - accuracy: 0.8683 - val_loss: 0.4214 - val_accuracy: 0.8810\n",
            "Epoch 13/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.3421 - accuracy: 0.9350 - val_loss: 0.4154 - val_accuracy: 0.8810\n",
            "Epoch 14/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.3246 - accuracy: 0.9186 - val_loss: 0.3971 - val_accuracy: 0.9048\n",
            "Epoch 15/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.3300 - accuracy: 0.8946 - val_loss: 0.3943 - val_accuracy: 0.9048\n",
            "Epoch 16/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.3513 - accuracy: 0.9192 - val_loss: 0.3894 - val_accuracy: 0.9048\n",
            "Epoch 17/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2434 - accuracy: 0.9222 - val_loss: 0.3737 - val_accuracy: 0.9048\n",
            "Epoch 18/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.3335 - accuracy: 0.8915 - val_loss: 0.3682 - val_accuracy: 0.9048\n",
            "Epoch 19/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.2661 - accuracy: 0.9305 - val_loss: 0.3587 - val_accuracy: 0.9048\n",
            "Epoch 20/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2667 - accuracy: 0.9377 - val_loss: 0.3407 - val_accuracy: 0.9048\n",
            "Epoch 21/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.2555 - accuracy: 0.9403 - val_loss: 0.3438 - val_accuracy: 0.9048\n",
            "Epoch 22/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2199 - accuracy: 0.9619 - val_loss: 0.3440 - val_accuracy: 0.9048\n",
            "Epoch 23/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2658 - accuracy: 0.9189 - val_loss: 0.3365 - val_accuracy: 0.9048\n",
            "Epoch 24/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2527 - accuracy: 0.9558 - val_loss: 0.3214 - val_accuracy: 0.9048\n",
            "Epoch 25/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1847 - accuracy: 0.9695 - val_loss: 0.3306 - val_accuracy: 0.9048\n",
            "Epoch 26/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2130 - accuracy: 0.9660 - val_loss: 0.3243 - val_accuracy: 0.9048\n",
            "Epoch 27/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2016 - accuracy: 0.9716 - val_loss: 0.3246 - val_accuracy: 0.9048\n",
            "Epoch 28/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.1913 - accuracy: 0.9609 - val_loss: 0.3305 - val_accuracy: 0.9048\n",
            "Epoch 29/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1896 - accuracy: 0.9403 - val_loss: 0.3180 - val_accuracy: 0.9048\n",
            "Epoch 30/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1745 - accuracy: 0.9752 - val_loss: 0.3228 - val_accuracy: 0.9048\n",
            "Epoch 31/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1651 - accuracy: 0.9699 - val_loss: 0.3186 - val_accuracy: 0.9048\n",
            "Epoch 32/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.2030 - accuracy: 0.9504 - val_loss: 0.3044 - val_accuracy: 0.9286\n",
            "Epoch 33/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1755 - accuracy: 0.9718 - val_loss: 0.3235 - val_accuracy: 0.9048\n",
            "Epoch 34/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1451 - accuracy: 0.9795 - val_loss: 0.3365 - val_accuracy: 0.9048\n",
            "Epoch 35/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1891 - accuracy: 0.9651 - val_loss: 0.3539 - val_accuracy: 0.9048\n",
            "Epoch 36/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1595 - accuracy: 0.9806 - val_loss: 0.3617 - val_accuracy: 0.9048\n",
            "Epoch 37/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.1567 - accuracy: 0.9765 - val_loss: 0.3583 - val_accuracy: 0.9048\n",
            "Epoch 38/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.1594 - accuracy: 0.9604 - val_loss: 0.3441 - val_accuracy: 0.9048\n",
            "Epoch 39/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2140 - accuracy: 0.9383 - val_loss: 0.3484 - val_accuracy: 0.9286\n",
            "Epoch 40/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1798 - accuracy: 0.9707 - val_loss: 0.3591 - val_accuracy: 0.9286\n",
            "Epoch 41/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1458 - accuracy: 0.9804 - val_loss: 0.3644 - val_accuracy: 0.9286\n",
            "Epoch 42/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1406 - accuracy: 0.9845 - val_loss: 0.3604 - val_accuracy: 0.9286\n",
            "Epoch 43/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1553 - accuracy: 0.9686 - val_loss: 0.3364 - val_accuracy: 0.9286\n",
            "Epoch 44/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1434 - accuracy: 0.9807 - val_loss: 0.3650 - val_accuracy: 0.9048\n",
            "Epoch 45/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1329 - accuracy: 0.9870 - val_loss: 0.3598 - val_accuracy: 0.9286\n",
            "Epoch 46/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1466 - accuracy: 0.9849 - val_loss: 0.3587 - val_accuracy: 0.9286\n",
            "Epoch 47/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.1423 - accuracy: 0.9762 - val_loss: 0.3856 - val_accuracy: 0.9048\n",
            "Epoch 48/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1540 - accuracy: 0.9846 - val_loss: 0.3833 - val_accuracy: 0.9286\n",
            "Epoch 49/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1341 - accuracy: 0.9784 - val_loss: 0.3688 - val_accuracy: 0.9286\n",
            "Epoch 50/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1075 - accuracy: 0.9898 - val_loss: 0.3731 - val_accuracy: 0.9286\n",
            "Epoch 51/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1368 - accuracy: 0.9713 - val_loss: 0.3638 - val_accuracy: 0.9286\n",
            "Epoch 52/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1275 - accuracy: 0.9738 - val_loss: 0.3824 - val_accuracy: 0.9286\n",
            "Epoch 53/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1172 - accuracy: 0.9700 - val_loss: 0.3801 - val_accuracy: 0.9048\n",
            "Epoch 54/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.1097 - accuracy: 0.9917 - val_loss: 0.3918 - val_accuracy: 0.9286\n",
            "Epoch 55/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1131 - accuracy: 0.9842 - val_loss: 0.3920 - val_accuracy: 0.9286\n",
            "Epoch 56/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1194 - accuracy: 0.9890 - val_loss: 0.4087 - val_accuracy: 0.9286\n",
            "Epoch 57/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1175 - accuracy: 0.9879 - val_loss: 0.4042 - val_accuracy: 0.9048\n",
            "Epoch 58/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.1227 - accuracy: 0.9711 - val_loss: 0.4176 - val_accuracy: 0.9048\n",
            "Epoch 59/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1124 - accuracy: 0.9925 - val_loss: 0.4400 - val_accuracy: 0.9286\n",
            "Epoch 60/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1250 - accuracy: 0.9748 - val_loss: 0.4320 - val_accuracy: 0.9286\n",
            "Epoch 61/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1429 - accuracy: 0.9823 - val_loss: 0.3928 - val_accuracy: 0.9048\n",
            "Epoch 62/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1132 - accuracy: 0.9740 - val_loss: 0.3894 - val_accuracy: 0.9286\n",
            "Epoch 63/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.1208 - accuracy: 0.9985 - val_loss: 0.3916 - val_accuracy: 0.9286\n",
            "Epoch 64/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0941 - accuracy: 0.9956 - val_loss: 0.3854 - val_accuracy: 0.9286\n",
            "Epoch 65/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1051 - accuracy: 0.9923 - val_loss: 0.4028 - val_accuracy: 0.9286\n",
            "Epoch 66/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.1197 - accuracy: 0.9784 - val_loss: 0.4204 - val_accuracy: 0.9286\n",
            "Epoch 67/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1394 - accuracy: 0.9513 - val_loss: 0.3925 - val_accuracy: 0.9286\n",
            "Epoch 68/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0931 - accuracy: 0.9941 - val_loss: 0.4248 - val_accuracy: 0.9286\n",
            "Epoch 69/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1298 - accuracy: 0.9748 - val_loss: 0.4017 - val_accuracy: 0.9286\n",
            "Epoch 70/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0973 - accuracy: 0.9925 - val_loss: 0.4105 - val_accuracy: 0.9286\n",
            "Epoch 71/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.0863 - accuracy: 0.9985 - val_loss: 0.4085 - val_accuracy: 0.9286\n",
            "Epoch 72/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0938 - accuracy: 0.9933 - val_loss: 0.4295 - val_accuracy: 0.9286\n",
            "Epoch 73/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0957 - accuracy: 0.9913 - val_loss: 0.4366 - val_accuracy: 0.9286\n",
            "Epoch 74/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0786 - accuracy: 0.9895 - val_loss: 0.4283 - val_accuracy: 0.9286\n",
            "Epoch 75/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0856 - accuracy: 0.9946 - val_loss: 0.4333 - val_accuracy: 0.9286\n",
            "Epoch 76/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0869 - accuracy: 0.9979 - val_loss: 0.4445 - val_accuracy: 0.9286\n",
            "Epoch 77/100\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 0.0906 - accuracy: 0.9916 - val_loss: 0.4625 - val_accuracy: 0.9286\n",
            "Epoch 78/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0819 - accuracy: 0.9979 - val_loss: 0.4549 - val_accuracy: 0.9286\n",
            "Epoch 79/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1241 - accuracy: 0.9862 - val_loss: 0.4345 - val_accuracy: 0.9286\n",
            "Epoch 80/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1023 - accuracy: 0.9862 - val_loss: 0.4196 - val_accuracy: 0.9286\n",
            "Epoch 81/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0988 - accuracy: 0.9849 - val_loss: 0.4304 - val_accuracy: 0.9286\n",
            "Epoch 82/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1007 - accuracy: 0.9831 - val_loss: 0.4589 - val_accuracy: 0.9286\n",
            "Epoch 83/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0843 - accuracy: 1.0000 - val_loss: 0.4593 - val_accuracy: 0.9286\n",
            "Epoch 84/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0835 - accuracy: 0.9901 - val_loss: 0.4385 - val_accuracy: 0.9286\n",
            "Epoch 85/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0804 - accuracy: 0.9931 - val_loss: 0.4519 - val_accuracy: 0.9286\n",
            "Epoch 86/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0763 - accuracy: 1.0000 - val_loss: 0.4771 - val_accuracy: 0.9286\n",
            "Epoch 87/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0762 - accuracy: 0.9916 - val_loss: 0.4356 - val_accuracy: 0.9524\n",
            "Epoch 88/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0948 - accuracy: 0.9912 - val_loss: 0.4350 - val_accuracy: 0.9286\n",
            "Epoch 89/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0985 - accuracy: 0.9916 - val_loss: 0.4384 - val_accuracy: 0.9286\n",
            "Epoch 90/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0781 - accuracy: 0.9933 - val_loss: 0.4460 - val_accuracy: 0.9286\n",
            "Epoch 91/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0721 - accuracy: 0.9933 - val_loss: 0.4596 - val_accuracy: 0.9286\n",
            "Epoch 92/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0679 - accuracy: 0.9985 - val_loss: 0.4768 - val_accuracy: 0.9286\n",
            "Epoch 93/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1063 - accuracy: 0.9753 - val_loss: 0.4429 - val_accuracy: 0.9524\n",
            "Epoch 94/100\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 0.0764 - accuracy: 0.9946 - val_loss: 0.4568 - val_accuracy: 0.9286\n",
            "Epoch 95/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0800 - accuracy: 0.9946 - val_loss: 0.4704 - val_accuracy: 0.9286\n",
            "Epoch 96/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0735 - accuracy: 0.9973 - val_loss: 0.4753 - val_accuracy: 0.9286\n",
            "Epoch 97/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.1131 - accuracy: 0.9771 - val_loss: 0.4813 - val_accuracy: 0.9286\n",
            "Epoch 98/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0942 - accuracy: 0.9847 - val_loss: 0.4625 - val_accuracy: 0.9286\n",
            "Epoch 99/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0851 - accuracy: 0.9838 - val_loss: 0.4717 - val_accuracy: 0.9286\n",
            "Epoch 100/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0686 - accuracy: 0.9933 - val_loss: 0.4471 - val_accuracy: 0.9524\n",
            "processing fold # 2\n",
            "Epoch 1/100\n",
            "11/11 [==============================] - 1s 20ms/step - loss: 0.8278 - accuracy: 0.5662 - val_loss: 0.6642 - val_accuracy: 0.8333\n",
            "Epoch 2/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.6608 - accuracy: 0.6958 - val_loss: 0.5982 - val_accuracy: 0.8333\n",
            "Epoch 3/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.6175 - accuracy: 0.7730 - val_loss: 0.5499 - val_accuracy: 0.8810\n",
            "Epoch 4/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.6288 - accuracy: 0.7230 - val_loss: 0.5058 - val_accuracy: 0.9286\n",
            "Epoch 5/100\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 0.5932 - accuracy: 0.7703 - val_loss: 0.4572 - val_accuracy: 0.9048\n",
            "Epoch 6/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.5047 - accuracy: 0.8153 - val_loss: 0.4165 - val_accuracy: 0.9286\n",
            "Epoch 7/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.5070 - accuracy: 0.7988 - val_loss: 0.3815 - val_accuracy: 0.9286\n",
            "Epoch 8/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.4702 - accuracy: 0.8696 - val_loss: 0.3492 - val_accuracy: 0.9286\n",
            "Epoch 9/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.4178 - accuracy: 0.8636 - val_loss: 0.3298 - val_accuracy: 0.9286\n",
            "Epoch 10/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.4294 - accuracy: 0.8652 - val_loss: 0.3164 - val_accuracy: 0.9286\n",
            "Epoch 11/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.3686 - accuracy: 0.8971 - val_loss: 0.2998 - val_accuracy: 0.9286\n",
            "Epoch 12/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.3908 - accuracy: 0.8765 - val_loss: 0.2873 - val_accuracy: 0.9286\n",
            "Epoch 13/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.3282 - accuracy: 0.9051 - val_loss: 0.2744 - val_accuracy: 0.9286\n",
            "Epoch 14/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2891 - accuracy: 0.9330 - val_loss: 0.2634 - val_accuracy: 0.9286\n",
            "Epoch 15/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.2911 - accuracy: 0.9274 - val_loss: 0.2559 - val_accuracy: 0.9286\n",
            "Epoch 16/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.2953 - accuracy: 0.9270 - val_loss: 0.2521 - val_accuracy: 0.9286\n",
            "Epoch 17/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2834 - accuracy: 0.9195 - val_loss: 0.2413 - val_accuracy: 0.9286\n",
            "Epoch 18/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.3137 - accuracy: 0.9188 - val_loss: 0.2339 - val_accuracy: 0.9524\n",
            "Epoch 19/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.2965 - accuracy: 0.9267 - val_loss: 0.2284 - val_accuracy: 0.9286\n",
            "Epoch 20/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2351 - accuracy: 0.9469 - val_loss: 0.2237 - val_accuracy: 0.9524\n",
            "Epoch 21/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2453 - accuracy: 0.9398 - val_loss: 0.2193 - val_accuracy: 0.9524\n",
            "Epoch 22/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2491 - accuracy: 0.9318 - val_loss: 0.2128 - val_accuracy: 0.9524\n",
            "Epoch 23/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2387 - accuracy: 0.9421 - val_loss: 0.2179 - val_accuracy: 0.9524\n",
            "Epoch 24/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2145 - accuracy: 0.9703 - val_loss: 0.2235 - val_accuracy: 0.9524\n",
            "Epoch 25/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2095 - accuracy: 0.9588 - val_loss: 0.2096 - val_accuracy: 0.9524\n",
            "Epoch 26/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.1927 - accuracy: 0.9461 - val_loss: 0.2103 - val_accuracy: 0.9524\n",
            "Epoch 27/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.2266 - accuracy: 0.9400 - val_loss: 0.1973 - val_accuracy: 0.9524\n",
            "Epoch 28/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1886 - accuracy: 0.9869 - val_loss: 0.2113 - val_accuracy: 0.9524\n",
            "Epoch 29/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1652 - accuracy: 0.9906 - val_loss: 0.1925 - val_accuracy: 0.9524\n",
            "Epoch 30/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.1825 - accuracy: 0.9716 - val_loss: 0.1845 - val_accuracy: 0.9524\n",
            "Epoch 31/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2294 - accuracy: 0.9495 - val_loss: 0.1841 - val_accuracy: 0.9524\n",
            "Epoch 32/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1759 - accuracy: 0.9583 - val_loss: 0.1824 - val_accuracy: 0.9524\n",
            "Epoch 33/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.1621 - accuracy: 0.9710 - val_loss: 0.1875 - val_accuracy: 0.9524\n",
            "Epoch 34/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1839 - accuracy: 0.9668 - val_loss: 0.1973 - val_accuracy: 0.9524\n",
            "Epoch 35/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.1705 - accuracy: 0.9702 - val_loss: 0.2000 - val_accuracy: 0.9524\n",
            "Epoch 36/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1693 - accuracy: 0.9701 - val_loss: 0.1946 - val_accuracy: 0.9524\n",
            "Epoch 37/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1600 - accuracy: 0.9601 - val_loss: 0.1809 - val_accuracy: 0.9524\n",
            "Epoch 38/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.1456 - accuracy: 0.9640 - val_loss: 0.1965 - val_accuracy: 0.9524\n",
            "Epoch 39/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1625 - accuracy: 0.9687 - val_loss: 0.1985 - val_accuracy: 0.9524\n",
            "Epoch 40/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1362 - accuracy: 0.9818 - val_loss: 0.1989 - val_accuracy: 0.9524\n",
            "Epoch 41/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1286 - accuracy: 0.9952 - val_loss: 0.1727 - val_accuracy: 0.9524\n",
            "Epoch 42/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1326 - accuracy: 0.9873 - val_loss: 0.1808 - val_accuracy: 0.9762\n",
            "Epoch 43/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1644 - accuracy: 0.9701 - val_loss: 0.1706 - val_accuracy: 0.9286\n",
            "Epoch 44/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1571 - accuracy: 0.9621 - val_loss: 0.1867 - val_accuracy: 0.9762\n",
            "Epoch 45/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1250 - accuracy: 0.9714 - val_loss: 0.1876 - val_accuracy: 0.9762\n",
            "Epoch 46/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.1089 - accuracy: 0.9950 - val_loss: 0.1605 - val_accuracy: 0.9762\n",
            "Epoch 47/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1531 - accuracy: 0.9616 - val_loss: 0.1590 - val_accuracy: 0.9762\n",
            "Epoch 48/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.1117 - accuracy: 0.9898 - val_loss: 0.1742 - val_accuracy: 0.9762\n",
            "Epoch 49/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1147 - accuracy: 0.9875 - val_loss: 0.1736 - val_accuracy: 0.9762\n",
            "Epoch 50/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1739 - accuracy: 0.9675 - val_loss: 0.1599 - val_accuracy: 0.9762\n",
            "Epoch 51/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1407 - accuracy: 0.9804 - val_loss: 0.1869 - val_accuracy: 0.9762\n",
            "Epoch 52/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0920 - accuracy: 0.9930 - val_loss: 0.1946 - val_accuracy: 0.9762\n",
            "Epoch 53/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1249 - accuracy: 0.9805 - val_loss: 0.1872 - val_accuracy: 0.9762\n",
            "Epoch 54/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1660 - accuracy: 0.9755 - val_loss: 0.1809 - val_accuracy: 0.9762\n",
            "Epoch 55/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1179 - accuracy: 0.9881 - val_loss: 0.1675 - val_accuracy: 0.9762\n",
            "Epoch 56/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1169 - accuracy: 0.9940 - val_loss: 0.1554 - val_accuracy: 0.9762\n",
            "Epoch 57/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1456 - accuracy: 0.9803 - val_loss: 0.1712 - val_accuracy: 0.9762\n",
            "Epoch 58/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1138 - accuracy: 0.9838 - val_loss: 0.1427 - val_accuracy: 0.9762\n",
            "Epoch 59/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1616 - accuracy: 0.9610 - val_loss: 0.1499 - val_accuracy: 0.9762\n",
            "Epoch 60/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0971 - accuracy: 0.9947 - val_loss: 0.1342 - val_accuracy: 0.9524\n",
            "Epoch 61/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0979 - accuracy: 0.9946 - val_loss: 0.1449 - val_accuracy: 0.9762\n",
            "Epoch 62/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.1291 - accuracy: 0.9743 - val_loss: 0.1427 - val_accuracy: 0.9762\n",
            "Epoch 63/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0903 - accuracy: 0.9973 - val_loss: 0.1268 - val_accuracy: 0.9762\n",
            "Epoch 64/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1421 - accuracy: 0.9712 - val_loss: 0.1234 - val_accuracy: 0.9524\n",
            "Epoch 65/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0978 - accuracy: 0.9985 - val_loss: 0.1229 - val_accuracy: 0.9524\n",
            "Epoch 66/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.1052 - accuracy: 0.9753 - val_loss: 0.1398 - val_accuracy: 0.9762\n",
            "Epoch 67/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0952 - accuracy: 0.9933 - val_loss: 0.1327 - val_accuracy: 0.9762\n",
            "Epoch 68/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0943 - accuracy: 0.9862 - val_loss: 0.1195 - val_accuracy: 0.9762\n",
            "Epoch 69/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0874 - accuracy: 0.9906 - val_loss: 0.1067 - val_accuracy: 1.0000\n",
            "Epoch 70/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.1043 - accuracy: 0.9956 - val_loss: 0.1069 - val_accuracy: 1.0000\n",
            "Epoch 71/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0818 - accuracy: 0.9955 - val_loss: 0.1194 - val_accuracy: 0.9762\n",
            "Epoch 72/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1070 - accuracy: 0.9741 - val_loss: 0.1185 - val_accuracy: 0.9762\n",
            "Epoch 73/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0940 - accuracy: 0.9825 - val_loss: 0.1267 - val_accuracy: 0.9762\n",
            "Epoch 74/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1039 - accuracy: 0.9874 - val_loss: 0.1243 - val_accuracy: 0.9762\n",
            "Epoch 75/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.0883 - accuracy: 0.9916 - val_loss: 0.1179 - val_accuracy: 0.9762\n",
            "Epoch 76/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0806 - accuracy: 0.9973 - val_loss: 0.1160 - val_accuracy: 0.9762\n",
            "Epoch 77/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0877 - accuracy: 0.9890 - val_loss: 0.1220 - val_accuracy: 0.9762\n",
            "Epoch 78/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0775 - accuracy: 0.9964 - val_loss: 0.1200 - val_accuracy: 0.9762\n",
            "Epoch 79/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0771 - accuracy: 0.9890 - val_loss: 0.1320 - val_accuracy: 0.9524\n",
            "Epoch 80/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0888 - accuracy: 0.9872 - val_loss: 0.1269 - val_accuracy: 0.9762\n",
            "Epoch 81/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0840 - accuracy: 0.9985 - val_loss: 0.1182 - val_accuracy: 0.9524\n",
            "Epoch 82/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0776 - accuracy: 0.9965 - val_loss: 0.1106 - val_accuracy: 0.9762\n",
            "Epoch 83/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0951 - accuracy: 0.9956 - val_loss: 0.1214 - val_accuracy: 0.9524\n",
            "Epoch 84/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.1017 - accuracy: 0.9817 - val_loss: 0.1170 - val_accuracy: 0.9524\n",
            "Epoch 85/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0733 - accuracy: 0.9944 - val_loss: 0.1176 - val_accuracy: 0.9524\n",
            "Epoch 86/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0953 - accuracy: 0.9890 - val_loss: 0.1269 - val_accuracy: 0.9524\n",
            "Epoch 87/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1076 - accuracy: 0.9890 - val_loss: 0.1528 - val_accuracy: 0.9762\n",
            "Epoch 88/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0778 - accuracy: 0.9916 - val_loss: 0.1186 - val_accuracy: 0.9524\n",
            "Epoch 89/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0746 - accuracy: 0.9985 - val_loss: 0.1196 - val_accuracy: 0.9524\n",
            "Epoch 90/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0852 - accuracy: 0.9946 - val_loss: 0.1123 - val_accuracy: 0.9524\n",
            "Epoch 91/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0868 - accuracy: 0.9985 - val_loss: 0.1325 - val_accuracy: 0.9524\n",
            "Epoch 92/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0867 - accuracy: 0.9849 - val_loss: 0.1298 - val_accuracy: 0.9286\n",
            "Epoch 93/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0681 - accuracy: 0.9956 - val_loss: 0.1388 - val_accuracy: 0.9286\n",
            "Epoch 94/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0761 - accuracy: 0.9956 - val_loss: 0.1256 - val_accuracy: 0.9524\n",
            "Epoch 95/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0662 - accuracy: 0.9979 - val_loss: 0.1134 - val_accuracy: 0.9524\n",
            "Epoch 96/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0743 - accuracy: 0.9794 - val_loss: 0.1139 - val_accuracy: 0.9524\n",
            "Epoch 97/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0737 - accuracy: 0.9973 - val_loss: 0.1104 - val_accuracy: 0.9524\n",
            "Epoch 98/100\n",
            "11/11 [==============================] - 0s 5ms/step - loss: 0.0641 - accuracy: 0.9985 - val_loss: 0.1118 - val_accuracy: 0.9524\n",
            "Epoch 99/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0749 - accuracy: 0.9957 - val_loss: 0.1080 - val_accuracy: 0.9524\n",
            "Epoch 100/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0802 - accuracy: 0.9916 - val_loss: 0.1116 - val_accuracy: 0.9524\n",
            "processing fold # 3\n",
            "Epoch 1/100\n",
            "11/11 [==============================] - 1s 20ms/step - loss: 0.7963 - accuracy: 0.4705 - val_loss: 0.7081 - val_accuracy: 0.7381\n",
            "Epoch 2/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.7398 - accuracy: 0.5463 - val_loss: 0.6606 - val_accuracy: 0.8095\n",
            "Epoch 3/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.6596 - accuracy: 0.7021 - val_loss: 0.6119 - val_accuracy: 0.8333\n",
            "Epoch 4/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.6108 - accuracy: 0.7748 - val_loss: 0.5673 - val_accuracy: 0.8333\n",
            "Epoch 5/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.5813 - accuracy: 0.8257 - val_loss: 0.5285 - val_accuracy: 0.8571\n",
            "Epoch 6/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.5427 - accuracy: 0.7849 - val_loss: 0.4903 - val_accuracy: 0.8095\n",
            "Epoch 7/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.4809 - accuracy: 0.8453 - val_loss: 0.4558 - val_accuracy: 0.8571\n",
            "Epoch 8/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.4588 - accuracy: 0.8485 - val_loss: 0.4317 - val_accuracy: 0.8571\n",
            "Epoch 9/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.4705 - accuracy: 0.8331 - val_loss: 0.4125 - val_accuracy: 0.8810\n",
            "Epoch 10/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.4896 - accuracy: 0.8588 - val_loss: 0.3941 - val_accuracy: 0.8810\n",
            "Epoch 11/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.3966 - accuracy: 0.8588 - val_loss: 0.3770 - val_accuracy: 0.9048\n",
            "Epoch 12/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.4140 - accuracy: 0.8450 - val_loss: 0.3573 - val_accuracy: 0.9048\n",
            "Epoch 13/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.3691 - accuracy: 0.8833 - val_loss: 0.3402 - val_accuracy: 0.9048\n",
            "Epoch 14/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.3483 - accuracy: 0.9060 - val_loss: 0.3266 - val_accuracy: 0.9048\n",
            "Epoch 15/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.3707 - accuracy: 0.8801 - val_loss: 0.3166 - val_accuracy: 0.9048\n",
            "Epoch 16/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.3687 - accuracy: 0.8550 - val_loss: 0.3025 - val_accuracy: 0.9048\n",
            "Epoch 17/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.3247 - accuracy: 0.8913 - val_loss: 0.2885 - val_accuracy: 0.9048\n",
            "Epoch 18/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.3507 - accuracy: 0.8972 - val_loss: 0.2841 - val_accuracy: 0.9048\n",
            "Epoch 19/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.3062 - accuracy: 0.9097 - val_loss: 0.2759 - val_accuracy: 0.9048\n",
            "Epoch 20/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.3259 - accuracy: 0.8661 - val_loss: 0.2702 - val_accuracy: 0.9048\n",
            "Epoch 21/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2962 - accuracy: 0.8935 - val_loss: 0.2699 - val_accuracy: 0.9048\n",
            "Epoch 22/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.3145 - accuracy: 0.8907 - val_loss: 0.2703 - val_accuracy: 0.9048\n",
            "Epoch 23/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2589 - accuracy: 0.9237 - val_loss: 0.2614 - val_accuracy: 0.9048\n",
            "Epoch 24/100\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 0.2782 - accuracy: 0.9054 - val_loss: 0.2563 - val_accuracy: 0.9048\n",
            "Epoch 25/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2356 - accuracy: 0.9371 - val_loss: 0.2408 - val_accuracy: 0.9048\n",
            "Epoch 26/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2349 - accuracy: 0.9292 - val_loss: 0.2264 - val_accuracy: 0.9048\n",
            "Epoch 27/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.2012 - accuracy: 0.9590 - val_loss: 0.2220 - val_accuracy: 0.9286\n",
            "Epoch 28/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2162 - accuracy: 0.9416 - val_loss: 0.2107 - val_accuracy: 0.9286\n",
            "Epoch 29/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1931 - accuracy: 0.9709 - val_loss: 0.2107 - val_accuracy: 0.9286\n",
            "Epoch 30/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2220 - accuracy: 0.9434 - val_loss: 0.2063 - val_accuracy: 0.9286\n",
            "Epoch 31/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2009 - accuracy: 0.9350 - val_loss: 0.1981 - val_accuracy: 0.9286\n",
            "Epoch 32/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2116 - accuracy: 0.9526 - val_loss: 0.1895 - val_accuracy: 0.9286\n",
            "Epoch 33/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1852 - accuracy: 0.9527 - val_loss: 0.1902 - val_accuracy: 0.9286\n",
            "Epoch 34/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2072 - accuracy: 0.9445 - val_loss: 0.1794 - val_accuracy: 0.9524\n",
            "Epoch 35/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2463 - accuracy: 0.9485 - val_loss: 0.1686 - val_accuracy: 0.9524\n",
            "Epoch 36/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1699 - accuracy: 0.9797 - val_loss: 0.1536 - val_accuracy: 0.9524\n",
            "Epoch 37/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1568 - accuracy: 0.9610 - val_loss: 0.1518 - val_accuracy: 0.9524\n",
            "Epoch 38/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.1591 - accuracy: 0.9754 - val_loss: 0.1546 - val_accuracy: 0.9524\n",
            "Epoch 39/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1531 - accuracy: 0.9701 - val_loss: 0.1439 - val_accuracy: 0.9762\n",
            "Epoch 40/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1433 - accuracy: 0.9556 - val_loss: 0.1393 - val_accuracy: 0.9762\n",
            "Epoch 41/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1673 - accuracy: 0.9749 - val_loss: 0.1369 - val_accuracy: 0.9762\n",
            "Epoch 42/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1590 - accuracy: 0.9676 - val_loss: 0.1398 - val_accuracy: 0.9762\n",
            "Epoch 43/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1857 - accuracy: 0.9669 - val_loss: 0.1316 - val_accuracy: 0.9762\n",
            "Epoch 44/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.2131 - accuracy: 0.9292 - val_loss: 0.1317 - val_accuracy: 0.9762\n",
            "Epoch 45/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1438 - accuracy: 0.9705 - val_loss: 0.1304 - val_accuracy: 0.9762\n",
            "Epoch 46/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1613 - accuracy: 0.9679 - val_loss: 0.1348 - val_accuracy: 0.9524\n",
            "Epoch 47/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1269 - accuracy: 0.9773 - val_loss: 0.1285 - val_accuracy: 0.9762\n",
            "Epoch 48/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1435 - accuracy: 0.9902 - val_loss: 0.1239 - val_accuracy: 0.9762\n",
            "Epoch 49/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1545 - accuracy: 0.9769 - val_loss: 0.1180 - val_accuracy: 0.9762\n",
            "Epoch 50/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1338 - accuracy: 0.9844 - val_loss: 0.1217 - val_accuracy: 0.9762\n",
            "Epoch 51/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.1491 - accuracy: 0.9650 - val_loss: 0.1150 - val_accuracy: 0.9762\n",
            "Epoch 52/100\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 0.1333 - accuracy: 0.9723 - val_loss: 0.1104 - val_accuracy: 0.9762\n",
            "Epoch 53/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1342 - accuracy: 0.9701 - val_loss: 0.1111 - val_accuracy: 0.9762\n",
            "Epoch 54/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1551 - accuracy: 0.9693 - val_loss: 0.1138 - val_accuracy: 0.9762\n",
            "Epoch 55/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1310 - accuracy: 0.9589 - val_loss: 0.1083 - val_accuracy: 0.9762\n",
            "Epoch 56/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1178 - accuracy: 0.9779 - val_loss: 0.1056 - val_accuracy: 0.9762\n",
            "Epoch 57/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1168 - accuracy: 0.9859 - val_loss: 0.1056 - val_accuracy: 0.9762\n",
            "Epoch 58/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1204 - accuracy: 0.9787 - val_loss: 0.1030 - val_accuracy: 0.9762\n",
            "Epoch 59/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1071 - accuracy: 0.9973 - val_loss: 0.1027 - val_accuracy: 0.9762\n",
            "Epoch 60/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1197 - accuracy: 0.9908 - val_loss: 0.1018 - val_accuracy: 0.9762\n",
            "Epoch 61/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1259 - accuracy: 0.9709 - val_loss: 0.1000 - val_accuracy: 0.9762\n",
            "Epoch 62/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1068 - accuracy: 0.9784 - val_loss: 0.0981 - val_accuracy: 0.9762\n",
            "Epoch 63/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1064 - accuracy: 0.9895 - val_loss: 0.0989 - val_accuracy: 0.9762\n",
            "Epoch 64/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0995 - accuracy: 0.9890 - val_loss: 0.0950 - val_accuracy: 1.0000\n",
            "Epoch 65/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1201 - accuracy: 0.9783 - val_loss: 0.0943 - val_accuracy: 0.9762\n",
            "Epoch 66/100\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 0.1515 - accuracy: 0.9723 - val_loss: 0.0947 - val_accuracy: 0.9762\n",
            "Epoch 67/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1099 - accuracy: 0.9732 - val_loss: 0.0919 - val_accuracy: 0.9762\n",
            "Epoch 68/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1039 - accuracy: 0.9931 - val_loss: 0.0932 - val_accuracy: 0.9762\n",
            "Epoch 69/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1325 - accuracy: 0.9817 - val_loss: 0.0936 - val_accuracy: 0.9762\n",
            "Epoch 70/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0928 - accuracy: 0.9956 - val_loss: 0.0954 - val_accuracy: 0.9762\n",
            "Epoch 71/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0919 - accuracy: 0.9950 - val_loss: 0.0911 - val_accuracy: 0.9762\n",
            "Epoch 72/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0905 - accuracy: 0.9890 - val_loss: 0.0864 - val_accuracy: 0.9762\n",
            "Epoch 73/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1176 - accuracy: 0.9826 - val_loss: 0.0955 - val_accuracy: 0.9762\n",
            "Epoch 74/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0826 - accuracy: 0.9979 - val_loss: 0.0938 - val_accuracy: 0.9762\n",
            "Epoch 75/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1208 - accuracy: 0.9802 - val_loss: 0.0914 - val_accuracy: 0.9762\n",
            "Epoch 76/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0910 - accuracy: 0.9956 - val_loss: 0.0848 - val_accuracy: 0.9762\n",
            "Epoch 77/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.1095 - accuracy: 0.9727 - val_loss: 0.0868 - val_accuracy: 0.9762\n",
            "Epoch 78/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1025 - accuracy: 0.9821 - val_loss: 0.0908 - val_accuracy: 0.9762\n",
            "Epoch 79/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0868 - accuracy: 0.9887 - val_loss: 0.0904 - val_accuracy: 0.9762\n",
            "Epoch 80/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0900 - accuracy: 0.9973 - val_loss: 0.0936 - val_accuracy: 0.9762\n",
            "Epoch 81/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0870 - accuracy: 0.9874 - val_loss: 0.0862 - val_accuracy: 0.9762\n",
            "Epoch 82/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0980 - accuracy: 0.9782 - val_loss: 0.0875 - val_accuracy: 0.9762\n",
            "Epoch 83/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1054 - accuracy: 0.9838 - val_loss: 0.0842 - val_accuracy: 0.9762\n",
            "Epoch 84/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0805 - accuracy: 0.9965 - val_loss: 0.0811 - val_accuracy: 0.9762\n",
            "Epoch 85/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0899 - accuracy: 0.9807 - val_loss: 0.0840 - val_accuracy: 0.9762\n",
            "Epoch 86/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0864 - accuracy: 0.9933 - val_loss: 0.0867 - val_accuracy: 0.9762\n",
            "Epoch 87/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0958 - accuracy: 0.9807 - val_loss: 0.0867 - val_accuracy: 0.9762\n",
            "Epoch 88/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0819 - accuracy: 0.9885 - val_loss: 0.0845 - val_accuracy: 0.9762\n",
            "Epoch 89/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0686 - accuracy: 0.9956 - val_loss: 0.0807 - val_accuracy: 0.9762\n",
            "Epoch 90/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0658 - accuracy: 0.9990 - val_loss: 0.0819 - val_accuracy: 0.9762\n",
            "Epoch 91/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1038 - accuracy: 0.9732 - val_loss: 0.0896 - val_accuracy: 0.9762\n",
            "Epoch 92/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.1114 - accuracy: 0.9803 - val_loss: 0.0875 - val_accuracy: 0.9762\n",
            "Epoch 93/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0887 - accuracy: 0.9938 - val_loss: 0.0847 - val_accuracy: 0.9762\n",
            "Epoch 94/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0823 - accuracy: 0.9933 - val_loss: 0.0926 - val_accuracy: 0.9762\n",
            "Epoch 95/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0918 - accuracy: 0.9855 - val_loss: 0.0870 - val_accuracy: 0.9762\n",
            "Epoch 96/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0701 - accuracy: 0.9973 - val_loss: 0.0788 - val_accuracy: 0.9762\n",
            "Epoch 97/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0697 - accuracy: 0.9979 - val_loss: 0.0833 - val_accuracy: 0.9762\n",
            "Epoch 98/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0664 - accuracy: 1.0000 - val_loss: 0.0847 - val_accuracy: 0.9762\n",
            "Epoch 99/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0811 - accuracy: 0.9862 - val_loss: 0.0830 - val_accuracy: 0.9762\n",
            "Epoch 100/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0784 - accuracy: 0.9946 - val_loss: 0.0796 - val_accuracy: 0.9762\n",
            "processing fold # 4\n",
            "Epoch 1/100\n",
            "11/11 [==============================] - 1s 21ms/step - loss: 0.9076 - accuracy: 0.5317 - val_loss: 0.7584 - val_accuracy: 0.6667\n",
            "Epoch 2/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.6952 - accuracy: 0.7217 - val_loss: 0.6541 - val_accuracy: 0.8095\n",
            "Epoch 3/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.6501 - accuracy: 0.6935 - val_loss: 0.5875 - val_accuracy: 0.8571\n",
            "Epoch 4/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.5488 - accuracy: 0.8135 - val_loss: 0.5297 - val_accuracy: 0.8333\n",
            "Epoch 5/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.5397 - accuracy: 0.7715 - val_loss: 0.4774 - val_accuracy: 0.8810\n",
            "Epoch 6/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.5246 - accuracy: 0.8221 - val_loss: 0.4361 - val_accuracy: 0.8810\n",
            "Epoch 7/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.4674 - accuracy: 0.7859 - val_loss: 0.4039 - val_accuracy: 0.8810\n",
            "Epoch 8/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.4275 - accuracy: 0.8301 - val_loss: 0.3785 - val_accuracy: 0.8810\n",
            "Epoch 9/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.4232 - accuracy: 0.8861 - val_loss: 0.3565 - val_accuracy: 0.8810\n",
            "Epoch 10/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.4329 - accuracy: 0.8647 - val_loss: 0.3362 - val_accuracy: 0.8810\n",
            "Epoch 11/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.3861 - accuracy: 0.8880 - val_loss: 0.3209 - val_accuracy: 0.8810\n",
            "Epoch 12/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.3663 - accuracy: 0.9199 - val_loss: 0.3038 - val_accuracy: 0.8810\n",
            "Epoch 13/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.3888 - accuracy: 0.8764 - val_loss: 0.2925 - val_accuracy: 0.8810\n",
            "Epoch 14/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.3581 - accuracy: 0.8913 - val_loss: 0.2845 - val_accuracy: 0.8810\n",
            "Epoch 15/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2767 - accuracy: 0.9266 - val_loss: 0.2724 - val_accuracy: 0.8810\n",
            "Epoch 16/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.2638 - accuracy: 0.9527 - val_loss: 0.2648 - val_accuracy: 0.8810\n",
            "Epoch 17/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.3645 - accuracy: 0.8802 - val_loss: 0.2620 - val_accuracy: 0.8810\n",
            "Epoch 18/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2987 - accuracy: 0.9105 - val_loss: 0.2568 - val_accuracy: 0.8810\n",
            "Epoch 19/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.3448 - accuracy: 0.8872 - val_loss: 0.2482 - val_accuracy: 0.9048\n",
            "Epoch 20/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2983 - accuracy: 0.9100 - val_loss: 0.2455 - val_accuracy: 0.9048\n",
            "Epoch 21/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.3142 - accuracy: 0.9120 - val_loss: 0.2379 - val_accuracy: 0.9048\n",
            "Epoch 22/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2586 - accuracy: 0.9210 - val_loss: 0.2327 - val_accuracy: 0.9048\n",
            "Epoch 23/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.2513 - accuracy: 0.9110 - val_loss: 0.2315 - val_accuracy: 0.9048\n",
            "Epoch 24/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2784 - accuracy: 0.9095 - val_loss: 0.2329 - val_accuracy: 0.9048\n",
            "Epoch 25/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.2417 - accuracy: 0.9399 - val_loss: 0.2267 - val_accuracy: 0.9048\n",
            "Epoch 26/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2157 - accuracy: 0.9443 - val_loss: 0.2222 - val_accuracy: 0.9048\n",
            "Epoch 27/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2136 - accuracy: 0.9748 - val_loss: 0.2236 - val_accuracy: 0.9048\n",
            "Epoch 28/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2140 - accuracy: 0.9494 - val_loss: 0.2263 - val_accuracy: 0.9048\n",
            "Epoch 29/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.2109 - accuracy: 0.9552 - val_loss: 0.2226 - val_accuracy: 0.9048\n",
            "Epoch 30/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.2121 - accuracy: 0.9606 - val_loss: 0.2219 - val_accuracy: 0.9048\n",
            "Epoch 31/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.1976 - accuracy: 0.9620 - val_loss: 0.2210 - val_accuracy: 0.9048\n",
            "Epoch 32/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.2294 - accuracy: 0.9562 - val_loss: 0.2207 - val_accuracy: 0.9048\n",
            "Epoch 33/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2384 - accuracy: 0.9502 - val_loss: 0.2244 - val_accuracy: 0.9048\n",
            "Epoch 34/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2340 - accuracy: 0.9038 - val_loss: 0.2271 - val_accuracy: 0.9048\n",
            "Epoch 35/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2424 - accuracy: 0.9465 - val_loss: 0.2331 - val_accuracy: 0.9048\n",
            "Epoch 36/100\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 0.1936 - accuracy: 0.9436 - val_loss: 0.2282 - val_accuracy: 0.9048\n",
            "Epoch 37/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1741 - accuracy: 0.9762 - val_loss: 0.2275 - val_accuracy: 0.9048\n",
            "Epoch 38/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.1631 - accuracy: 0.9715 - val_loss: 0.2285 - val_accuracy: 0.9048\n",
            "Epoch 39/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1889 - accuracy: 0.9594 - val_loss: 0.2288 - val_accuracy: 0.9048\n",
            "Epoch 40/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.2008 - accuracy: 0.9421 - val_loss: 0.2238 - val_accuracy: 0.9048\n",
            "Epoch 41/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1452 - accuracy: 0.9809 - val_loss: 0.2249 - val_accuracy: 0.9048\n",
            "Epoch 42/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1344 - accuracy: 0.9842 - val_loss: 0.2342 - val_accuracy: 0.9048\n",
            "Epoch 43/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1750 - accuracy: 0.9575 - val_loss: 0.2324 - val_accuracy: 0.9048\n",
            "Epoch 44/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.1630 - accuracy: 0.9713 - val_loss: 0.2370 - val_accuracy: 0.9048\n",
            "Epoch 45/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1421 - accuracy: 0.9798 - val_loss: 0.2354 - val_accuracy: 0.9048\n",
            "Epoch 46/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1638 - accuracy: 0.9545 - val_loss: 0.2419 - val_accuracy: 0.9048\n",
            "Epoch 47/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.1772 - accuracy: 0.9583 - val_loss: 0.2373 - val_accuracy: 0.9048\n",
            "Epoch 48/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.1463 - accuracy: 0.9524 - val_loss: 0.2339 - val_accuracy: 0.9048\n",
            "Epoch 49/100\n",
            "11/11 [==============================] - 0s 31ms/step - loss: 0.1407 - accuracy: 0.9777 - val_loss: 0.2391 - val_accuracy: 0.9048\n",
            "Epoch 50/100\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 0.1541 - accuracy: 0.9683 - val_loss: 0.2522 - val_accuracy: 0.9048\n",
            "Epoch 51/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1402 - accuracy: 0.9782 - val_loss: 0.2640 - val_accuracy: 0.9048\n",
            "Epoch 52/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1439 - accuracy: 0.9623 - val_loss: 0.2569 - val_accuracy: 0.9048\n",
            "Epoch 53/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1187 - accuracy: 0.9862 - val_loss: 0.2633 - val_accuracy: 0.9048\n",
            "Epoch 54/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1364 - accuracy: 0.9714 - val_loss: 0.2652 - val_accuracy: 0.8810\n",
            "Epoch 55/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1215 - accuracy: 0.9756 - val_loss: 0.2766 - val_accuracy: 0.8810\n",
            "Epoch 56/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1144 - accuracy: 0.9849 - val_loss: 0.2663 - val_accuracy: 0.8810\n",
            "Epoch 57/100\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 0.1328 - accuracy: 0.9738 - val_loss: 0.2707 - val_accuracy: 0.8810\n",
            "Epoch 58/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.1111 - accuracy: 0.9852 - val_loss: 0.2613 - val_accuracy: 0.8810\n",
            "Epoch 59/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.1312 - accuracy: 0.9777 - val_loss: 0.2524 - val_accuracy: 0.8810\n",
            "Epoch 60/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.1046 - accuracy: 0.9902 - val_loss: 0.2466 - val_accuracy: 0.9048\n",
            "Epoch 61/100\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 0.1114 - accuracy: 0.9777 - val_loss: 0.2449 - val_accuracy: 0.9048\n",
            "Epoch 62/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.1084 - accuracy: 0.9946 - val_loss: 0.2521 - val_accuracy: 0.9048\n",
            "Epoch 63/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.1046 - accuracy: 0.9828 - val_loss: 0.2647 - val_accuracy: 0.8810\n",
            "Epoch 64/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0909 - accuracy: 0.9906 - val_loss: 0.2577 - val_accuracy: 0.9048\n",
            "Epoch 65/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1086 - accuracy: 0.9862 - val_loss: 0.2565 - val_accuracy: 0.9286\n",
            "Epoch 66/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0903 - accuracy: 0.9913 - val_loss: 0.2594 - val_accuracy: 0.8810\n",
            "Epoch 67/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0925 - accuracy: 0.9916 - val_loss: 0.2644 - val_accuracy: 0.8810\n",
            "Epoch 68/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0893 - accuracy: 0.9946 - val_loss: 0.2713 - val_accuracy: 0.8810\n",
            "Epoch 69/100\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 0.0989 - accuracy: 0.9979 - val_loss: 0.2769 - val_accuracy: 0.8810\n",
            "Epoch 70/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0993 - accuracy: 0.9800 - val_loss: 0.2856 - val_accuracy: 0.8810\n",
            "Epoch 71/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1014 - accuracy: 0.9912 - val_loss: 0.2733 - val_accuracy: 0.8810\n",
            "Epoch 72/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0808 - accuracy: 0.9931 - val_loss: 0.2849 - val_accuracy: 0.8810\n",
            "Epoch 73/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.1323 - accuracy: 0.9669 - val_loss: 0.2880 - val_accuracy: 0.8810\n",
            "Epoch 74/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0950 - accuracy: 0.9847 - val_loss: 0.2778 - val_accuracy: 0.8810\n",
            "Epoch 75/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0975 - accuracy: 0.9794 - val_loss: 0.2885 - val_accuracy: 0.8810\n",
            "Epoch 76/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1137 - accuracy: 0.9838 - val_loss: 0.2967 - val_accuracy: 0.8810\n",
            "Epoch 77/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1307 - accuracy: 0.9851 - val_loss: 0.2891 - val_accuracy: 0.8810\n",
            "Epoch 78/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1074 - accuracy: 0.9864 - val_loss: 0.2965 - val_accuracy: 0.8810\n",
            "Epoch 79/100\n",
            "11/11 [==============================] - 0s 8ms/step - loss: 0.1041 - accuracy: 0.9956 - val_loss: 0.2993 - val_accuracy: 0.8571\n",
            "Epoch 80/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0921 - accuracy: 0.9895 - val_loss: 0.2923 - val_accuracy: 0.8571\n",
            "Epoch 81/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0807 - accuracy: 0.9965 - val_loss: 0.2812 - val_accuracy: 0.8571\n",
            "Epoch 82/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0982 - accuracy: 0.9828 - val_loss: 0.2906 - val_accuracy: 0.8810\n",
            "Epoch 83/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0873 - accuracy: 0.9965 - val_loss: 0.2922 - val_accuracy: 0.8810\n",
            "Epoch 84/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.1055 - accuracy: 0.9654 - val_loss: 0.2942 - val_accuracy: 0.8810\n",
            "Epoch 85/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0939 - accuracy: 0.9880 - val_loss: 0.3036 - val_accuracy: 0.9048\n",
            "Epoch 86/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0713 - accuracy: 0.9969 - val_loss: 0.2857 - val_accuracy: 0.8810\n",
            "Epoch 87/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0806 - accuracy: 0.9933 - val_loss: 0.2999 - val_accuracy: 0.8810\n",
            "Epoch 88/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0814 - accuracy: 0.9890 - val_loss: 0.2926 - val_accuracy: 0.9048\n",
            "Epoch 89/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0876 - accuracy: 0.9898 - val_loss: 0.3021 - val_accuracy: 0.8810\n",
            "Epoch 90/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.1515 - accuracy: 0.9673 - val_loss: 0.2936 - val_accuracy: 0.9048\n",
            "Epoch 91/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0717 - accuracy: 0.9979 - val_loss: 0.3066 - val_accuracy: 0.8810\n",
            "Epoch 92/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0791 - accuracy: 0.9838 - val_loss: 0.3075 - val_accuracy: 0.8810\n",
            "Epoch 93/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0764 - accuracy: 0.9973 - val_loss: 0.3041 - val_accuracy: 0.9286\n",
            "Epoch 94/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0737 - accuracy: 0.9912 - val_loss: 0.3173 - val_accuracy: 0.9286\n",
            "Epoch 95/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0730 - accuracy: 0.9901 - val_loss: 0.3248 - val_accuracy: 0.8810\n",
            "Epoch 96/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0755 - accuracy: 0.9890 - val_loss: 0.3343 - val_accuracy: 0.8810\n",
            "Epoch 97/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0909 - accuracy: 0.9822 - val_loss: 0.3132 - val_accuracy: 0.8810\n",
            "Epoch 98/100\n",
            "11/11 [==============================] - 0s 7ms/step - loss: 0.0747 - accuracy: 0.9916 - val_loss: 0.3214 - val_accuracy: 0.9048\n",
            "Epoch 99/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.0781 - accuracy: 0.9912 - val_loss: 0.3207 - val_accuracy: 0.9048\n",
            "Epoch 100/100\n",
            "11/11 [==============================] - 0s 6ms/step - loss: 0.1079 - accuracy: 0.9838 - val_loss: 0.3324 - val_accuracy: 0.9048\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WtPZ3AYrdVpE"
      },
      "source": [
        "## **Finalizing Model & Evaluating on Test Data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nTnVDNDAurH7",
        "outputId": "11bd1696-912b-4db3-ce90-ed2b18c72a7d"
      },
      "source": [
        "model = build_model()\n",
        "model.fit(train_data, train_labels,\n",
        "          epochs = 40, batch_size = 16, verbose = 0)\n",
        "\n",
        "test_loss , test_accuracy = model.evaluate(test_data, test_labels)\n"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5/5 [==============================] - 0s 2ms/step - loss: 0.2356 - accuracy: 0.9220\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8SQLKvWB1Qph",
        "outputId": "332f1e4f-ba5a-4023-84ca-0378b59dff10"
      },
      "source": [
        "test_accuracy"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9219858050346375"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    }
  ]
}