index.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wO3sNOt0Hhhn"
      },
      "source": [
        "## Mount drive"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VRIQO7QKG1SK",
        "outputId": "fc617d95-0d10-421e-eeb3-e5abf509533d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AV-deo-LHlga"
      },
      "source": [
        "## Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "VCCE_uaoHANx"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import random\n",
        "import csv\n",
        "from datetime import datetime\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import KFold\n",
        "from tensorflow.keras import Model\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Input, Conv1D, MaxPool1D, Flatten, Dense, DepthwiseConv1D, BatchNormalization, Activation, MaxPooling1D, GlobalAveragePooling1D, AveragePooling1D, Add, ReLU, Dropout, Multiply, Concatenate\n",
        "from sklearn.metrics import accuracy_score, f1_score\n",
        "from scipy.fft import fft\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import classification_report\n",
        "import seaborn as sns\n",
        "from keras.optimizers import Adam"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TXogow0KHqMt"
      },
      "source": [
        "## Data Loading"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "x-Tqx5q8HFCz"
      },
      "outputs": [],
      "source": [
        "project_dataset_dir = '/content/drive/MyDrive/EMG/EMG-data.csv'\n",
        "df = pd.read_csv(project_dataset_dir)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o0-RxymOHtpH"
      },
      "source": [
        "## Feature extraction and engineering"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qbvxt26_HcS_",
        "outputId": "542ba6fe-46cc-47a3-c3fb-cfaa7980d287"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4237907 entries, 0 to 4237906\n",
            "Data columns (total 11 columns):\n",
            " #   Column    Dtype  \n",
            "---  ------    -----  \n",
            " 0   time      int64  \n",
            " 1   channel1  float64\n",
            " 2   channel2  float64\n",
            " 3   channel3  float64\n",
            " 4   channel4  float64\n",
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            " 6   channel6  float64\n",
            " 7   channel7  float64\n",
            " 8   channel8  float64\n",
            " 9   class     int64  \n",
            " 10  label     int64  \n",
            "dtypes: float64(8), int64(3)\n",
            "memory usage: 355.7 MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
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        "id": "gLYgGt4gHdgk",
        "outputId": "f6252e28-0a8f-4226-a1f3-70ca16f62d10"
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            "text/plain": [
              "   channel1  channel2  channel3  channel4  channel5  channel6  channel7  \\\n",
              "0   0.00001  -0.00002  -0.00001  -0.00003   0.00000  -0.00001   0.00000   \n",
              "1   0.00001  -0.00002  -0.00001  -0.00003   0.00000  -0.00001   0.00000   \n",
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              "4  -0.00001   0.00001   0.00002   0.00000   0.00001  -0.00002  -0.00001   \n",
              "\n",
              "   channel8  class  label  \n",
              "0  -0.00001      0      1  \n",
              "1  -0.00001      0      1  \n",
              "2   0.00001      0      1  \n",
              "3   0.00001      0      1  \n",
              "4   0.00001      0      1  "
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          "execution_count": 5,
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        "# Drop the time column, as it is not necessary\n",
        "df.drop('time', inplace=True, axis=1)\n",
        "df.head()"
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            "RangeIndex: 4237907 entries, 0 to 4237906\n",
            "Data columns (total 10 columns):\n",
            " #   Column    Dtype  \n",
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            " 8   class     int64  \n",
            " 9   label     int64  \n",
            "dtypes: float64(8), int64(2)\n",
            "memory usage: 323.3 MB\n"
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        "df.info()\n",
        "len(df)"
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>channel7</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>channel8</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>class</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>label</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ],
            "text/plain": [
              "channel1    0\n",
              "channel2    0\n",
              "channel3    0\n",
              "channel4    0\n",
              "channel5    0\n",
              "channel6    0\n",
              "channel7    0\n",
              "channel8    0\n",
              "class       0\n",
              "label       0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Check for null dataset\n",
        "df.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qbwNBgtZH1jn",
        "outputId": "0b4d5d9c-fef0-432e-c736-26ab5df0e3a3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "class\n",
            "0    2725157\n",
            "6     253009\n",
            "5     251733\n",
            "4     251570\n",
            "1     250055\n",
            "3     249494\n",
            "2     243193\n",
            "7      13696\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# Count the size of each class\n",
        "print(df['class'].value_counts())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "2w8XHP2PH26y"
      },
      "outputs": [],
      "source": [
        "# drop the 0th class, as it is unmarked data\n",
        "df.drop(df[df['class'] == 0].index, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "yVMqirMCIPeS",
        "outputId": "bd182e58-b331-48d3-a584-3e9e8923a469"
      },
      "outputs": [
        {
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              "\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-b64cb398-5bc4-413b-85dc-ea3e0d7383e7');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
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              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
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              "    border-radius: 50%;\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-face822d-2ad3-446a-9665-4cebcf5ab63f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "      channel1  channel2  channel3  channel4  channel5  channel6  channel7  \\\n",
              "2287  -0.00001   0.00000  -0.00001   0.00000   0.00000  -0.00001  -0.00001   \n",
              "2288  -0.00001  -0.00002   0.00000  -0.00001  -0.00001  -0.00001  -0.00003   \n",
              "2289  -0.00001  -0.00002   0.00000  -0.00001  -0.00001  -0.00001  -0.00003   \n",
              "2290  -0.00001  -0.00002   0.00000  -0.00001  -0.00001  -0.00001  -0.00003   \n",
              "2291  -0.00001  -0.00002   0.00000  -0.00001  -0.00001  -0.00001  -0.00003   \n",
              "\n",
              "      channel8  class  label  \n",
              "2287   0.00001      1      1  \n",
              "2288  -0.00002      1      1  \n",
              "2289  -0.00002      1      1  \n",
              "2290  -0.00002      1      1  \n",
              "2291  -0.00002      1      1  "
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "ZyE5jOA7IUsA",
        "outputId": "b635a0f0-4720-4c43-a340-d2be3caabcda"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>class</th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>True</td>\n",
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              "      <th>6</th>\n",
              "      <td>True</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> bool</label>"
            ],
            "text/plain": [
              "class\n",
              "1     True\n",
              "2     True\n",
              "3     True\n",
              "4     True\n",
              "5     True\n",
              "6     True\n",
              "7    False\n",
              "Name: label, dtype: bool"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Check if all the classes have 36 unique labels\n",
        "all_labels_present = df.groupby('class')['label'].nunique() == 36\n",
        "all_labels_present"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "id": "anyOaGdvI6FO",
        "outputId": "b4ce8202-a3ed-41d0-8987-882917f1eb73"
      },
      "outputs": [
        {
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              "summary": "{\n  \"name\": \"class_7_label_counts\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 11,\n        \"max\": 30,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          30,\n          11\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 363,\n        \"min\": 6591,\n        \"max\": 7105,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          6591,\n          7105\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>label</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>11</td>\n",
              "      <td>7105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>30</td>\n",
              "      <td>6591</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2917df68-73c2-4b9f-8119-c47cc2920f6d')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2917df68-73c2-4b9f-8119-c47cc2920f6d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2917df68-73c2-4b9f-8119-c47cc2920f6d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-65170709-5f60-418e-85c4-9cee3b0e8fe0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_4ebca5a5-8e71-4294-9fbe-83b4cdc39ce2\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('class_7_label_counts')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_4ebca5a5-8e71-4294-9fbe-83b4cdc39ce2 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('class_7_label_counts');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "   label  count\n",
              "0     11   7105\n",
              "1     30   6591"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Check how many label in class 7\n",
        "class_7_data = df[df['class'] == 7]\n",
        "class_7_label_counts = class_7_data.groupby('label').size().reset_index(name='count')\n",
        "class_7_label_counts"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "hnBcpXTuI9Q2"
      },
      "outputs": [],
      "source": [
        "# Drop the 7th class\n",
        "data = df[df['class'] != 7]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "74SrMYMiJA0V"
      },
      "outputs": [],
      "source": [
        "# data['class'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "-yRCKpucJET3"
      },
      "outputs": [],
      "source": [
        "# subjects_data = data.groupby(['label','class'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pp9AxJ3EJF20",
        "outputId": "a9be5a3e-b901-4b83-c3fd-9d366e93ffde"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Class distribution:\n",
            "   class   count\n",
            "0      1  250055\n",
            "1      2  243193\n",
            "2      3  249494\n",
            "3      4  251570\n",
            "4      5  251733\n",
            "5      6  253009\n"
          ]
        }
      ],
      "source": [
        "# Check if the classes are\n",
        "class_counts = data.groupby('class').size().reset_index(name='count')\n",
        "\n",
        "print('Class distribution:')\n",
        "print(class_counts)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j5RO971mJgsj",
        "outputId": "54999175-1463-4fda-b5a0-c07a46e8e63f"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-17-2c4f8e338965>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  data.drop('label', inplace=True, axis=1)\n"
          ]
        }
      ],
      "source": [
        "# Drop the label column\n",
        "data.drop('label', inplace=True, axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v-8xukbiJnhk",
        "outputId": "c837323d-2252-49f5-9fb5-74a3a66a98c7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 1499054 entries, 2287 to 4237211\n",
            "Data columns (total 9 columns):\n",
            " #   Column    Non-Null Count    Dtype  \n",
            "---  ------    --------------    -----  \n",
            " 0   channel1  1499054 non-null  float64\n",
            " 1   channel2  1499054 non-null  float64\n",
            " 2   channel3  1499054 non-null  float64\n",
            " 3   channel4  1499054 non-null  float64\n",
            " 4   channel5  1499054 non-null  float64\n",
            " 5   channel6  1499054 non-null  float64\n",
            " 6   channel7  1499054 non-null  float64\n",
            " 7   channel8  1499054 non-null  float64\n",
            " 8   class     1499054 non-null  int64  \n",
            "dtypes: float64(8), int64(1)\n",
            "memory usage: 114.4 MB\n"
          ]
        }
      ],
      "source": [
        "data.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "ik-UDN8LJo7c"
      },
      "outputs": [],
      "source": [
        "# Normalize the data\n",
        "def normalized(data):\n",
        "    min_value = np.min(data)\n",
        "    max_value = np.max(data)\n",
        "    normalized_data = (data - min_value) / (max_value - min_value)\n",
        "    return normalized_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "-6TSOx8CJu7O"
      },
      "outputs": [],
      "source": [
        "# Sliding window approach to reduce and extract data features\n",
        "def sliding_window(data, window_size, stride):\n",
        "    windowed_data = []\n",
        "    labels = []\n",
        "\n",
        "    for i in range(0, len(data) - window_size + 1, stride):\n",
        "        windowed = data.iloc[i:i+window_size, :-1].values\n",
        "        label = data.iloc[i+window_size-1, -1]\n",
        "        windowed_data.append(windowed)\n",
        "        labels.append(label)\n",
        "\n",
        "    return np.array(windowed_data), np.array(labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S9HCNkh4J2Ct"
      },
      "outputs": [],
      "source": [
        "# Use the sliding window on the data\n",
        "window_size = 150 # hyperparameter\n",
        "stride = 30 # hyperparameter\n",
        "X, Y = sliding_window(data, window_size, stride)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GZTDUL2oJ4v1"
      },
      "outputs": [],
      "source": [
        "# Normalize the X data\n",
        "X = normalized(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ztAGYvyuJ9G3"
      },
      "outputs": [],
      "source": [
        "# Make the class start from 0, instead of 1, as Keras accepts [1,6)\n",
        "Y = Y - 1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ImWIGBLAL--s"
      },
      "source": [
        "## Apply Fourier Transform to the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aJ7k0XgsLylN"
      },
      "outputs": [],
      "source": [
        "def fourier_transform(X):\n",
        "    transformed_data = []\n",
        "\n",
        "    for item in X:\n",
        "        transformed_item = []\n",
        "        for channel in item.T:  # Transpose the data and apply Fourier transform to each channel separately\n",
        "            fft_values = fft(channel) # This gives amplitude\n",
        "            transformed_item.append(np.abs(fft_values))  # Only use amplitude information\n",
        "\n",
        "        transformed_data.append(np.array(transformed_item))\n",
        "\n",
        "    return np.array(transformed_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hJitzbFtL0eq"
      },
      "outputs": [],
      "source": [
        "# Apply fourier transform to the data\n",
        "transformed_X = fourier_transform(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "spxfA_2zMOMe"
      },
      "outputs": [],
      "source": [
        "transposed_X = np.transpose(transformed_X, (0, 2, 1))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yr_mdN9gOvDx"
      },
      "source": [
        "## Do not apply Fourier Transform"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oy2eobQfOt38"
      },
      "outputs": [],
      "source": [
        "transposed_X = X"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N7wNUOaV11Yo"
      },
      "source": [
        "# RUN THIS"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ocZMgYxQ5J4G"
      },
      "source": [
        "## Dataset loading and preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5hIF4khh2Ty3"
      },
      "outputs": [],
      "source": [
        "num_classes = len(np.unique(Y))   # set class size"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dNsFU2DG5HqJ"
      },
      "source": [
        "## Model Definitions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sGoBq1V62Lp7"
      },
      "outputs": [],
      "source": [
        "# CNN\n",
        "def CNN_model0(input_shape, num_classes):\n",
        "    model = Sequential()\n",
        "    # Convolutional layers 1\n",
        "    model.add(Conv1D(32, kernel_size=3, activation='relu', input_shape=input_shape))\n",
        "    model.add(MaxPool1D(pool_size=2))\n",
        "    # Convolutional layers 2\n",
        "    model.add(Conv1D(64, kernel_size=3, activation='relu'))\n",
        "    model.add(MaxPool1D(pool_size=2))\n",
        "    # Convolutional layers 3\n",
        "    model.add(Conv1D(128, kernel_size=3, activation='relu'))\n",
        "    model.add(MaxPool1D(pool_size=2))\n",
        "    # Flattening layers\n",
        "    model.add(Flatten())\n",
        "    # Full connected layers\n",
        "    model.add(Dense(128, activation='relu'))\n",
        "    model.add(Dense(num_classes, activation='softmax'))\n",
        "    # compile the model\n",
        "    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "    print('---------------------------------3-LAYER-CNN---------------------------------')\n",
        "    model.summary()\n",
        "    return model\n",
        "\n",
        "# VGG\n",
        "def VGG_model(input_shape, num_classes):\n",
        "    model = Sequential()\n",
        "    model.add(Conv1D(filters=64, kernel_size=3, padding=\"same\", activation=\"relu\", input_shape=input_shape))\n",
        "    model.add(Conv1D(filters=64, kernel_size=3, padding=\"same\", activation=\"relu\"))\n",
        "    model.add(MaxPool1D(pool_size=2, strides=2))\n",
        "    model.add(Conv1D(filters=128, kernel_size=3, padding=\"same\", activation=\"relu\"))\n",
        "    model.add(Conv1D(filters=128, kernel_size=3, padding=\"same\", activation=\"relu\"))\n",
        "    model.add(MaxPool1D(pool_size=2, strides=2))\n",
        "    model.add(Conv1D(filters=256, kernel_size=3, padding=\"same\", activation=\"relu\"))\n",
        "    model.add(Conv1D(filters=256, kernel_size=3, padding=\"same\", activation=\"relu\"))\n",
        "    model.add(Conv1D(filters=256, kernel_size=3, padding=\"same\", activation=\"relu\"))\n",
        "    model.add(MaxPool1D(pool_size=2, strides=2))\n",
        "    model.add(Conv1D(filters=512, kernel_size=3, padding=\"same\", activation=\"relu\"))\n",
        "    model.add(Conv1D(filters=512, kernel_size=3, padding=\"same\", activation=\"relu\"))\n",
        "    model.add(Conv1D(filters=512, kernel_size=3, padding=\"same\", activation=\"relu\"))\n",
        "    model.add(MaxPool1D(pool_size=2, strides=2))\n",
        "    model.add(Conv1D(filters=512, kernel_size=3, padding=\"same\", activation=\"relu\"))\n",
        "    model.add(Conv1D(filters=512, kernel_size=3, padding=\"same\", activation=\"relu\"))\n",
        "    model.add(Conv1D(filters=512, kernel_size=3, padding=\"same\", activation=\"relu\"))\n",
        "    model.add(MaxPool1D(pool_size=2, strides=2))\n",
        "    model.add(Flatten())\n",
        "    model.add(Dense(units=4096,activation=\"relu\"))\n",
        "    model.add(Dense(units=4096,activation=\"relu\"))\n",
        "    model.add(Dense(num_classes, activation=\"softmax\"))\n",
        "    # Compile the model\n",
        "    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "    print('---------------------------------VGGNET---------------------------------')\n",
        "    model.summary()\n",
        "    return model\n",
        "\n",
        "# ZF\n",
        "def ZF_model(input_shape, num_classes):\n",
        "    model = Sequential()\n",
        "    model.add(Conv1D(filters=96, kernel_size=7, strides=2, padding=\"valid\", activation=\"relu\", input_shape=input_shape))\n",
        "    model.add(MaxPool1D(pool_size=3, strides=2))\n",
        "    model.add(Conv1D(filters=256, kernel_size=3, strides=2, padding=\"valid\", activation=\"relu\"))\n",
        "    model.add(MaxPool1D(pool_size=3, strides=2))\n",
        "    model.add(Conv1D(filters=384, kernel_size=3, strides=1, padding=\"same\", activation=\"relu\"))\n",
        "    model.add(Conv1D(filters=384, kernel_size=3, strides=1, padding=\"same\", activation=\"relu\"))\n",
        "    model.add(Conv1D(filters=256, kernel_size=3, strides=1, padding=\"same\", activation=\"relu\"))\n",
        "    model.add(MaxPool1D(pool_size=3, strides=2))\n",
        "    model.add(Flatten())\n",
        "    model.add(Dense(units=4096,activation=\"relu\"))\n",
        "    model.add(Dense(units=4096,activation=\"relu\"))\n",
        "    model.add(Dense(num_classes, activation=\"softmax\"))\n",
        "    # Compile the model\n",
        "    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "    print('---------------------------------ZFNET---------------------------------')\n",
        "    model.summary()\n",
        "    return model\n",
        "\n",
        "# ResNEt 18\n",
        "def residual_block_18(x, filters, kernel_size=3, strides=1):\n",
        "    # Shortcut\n",
        "    shortcut = x\n",
        "    # First convolution\n",
        "    x = Conv1D(filters=filters, kernel_size=kernel_size, strides=strides, padding='same')(x)\n",
        "    x = BatchNormalization()(x)\n",
        "    x = ReLU()(x)\n",
        "    # Second convolution\n",
        "    x = Conv1D(filters=filters, kernel_size=kernel_size, strides=1, padding='same')(x)\n",
        "    x = BatchNormalization()(x)\n",
        "    # Adjusting the shortcut connection if needed\n",
        "    if strides != 1 or shortcut.shape[-1] != filters:\n",
        "        shortcut = Conv1D(filters=filters, kernel_size=1, strides=strides, padding='same')(shortcut)\n",
        "        shortcut = BatchNormalization()(shortcut)\n",
        "    # Adding shortcut\n",
        "    x = Add()([x, shortcut])\n",
        "    x = ReLU()(x)\n",
        "    return x\n",
        "\n",
        "def ResNet18(input_shape, num_classes=6):\n",
        "    inputs = Input(shape=input_shape)\n",
        "    x = Conv1D(filters=64, kernel_size=7, strides=2, padding='same')(inputs)\n",
        "    x = BatchNormalization()(x)\n",
        "    x = ReLU()(x)\n",
        "    x = MaxPooling1D(pool_size=3, strides=2, padding='same')(x)\n",
        "    # Stage 1\n",
        "    x = residual_block_18(x, filters=64)\n",
        "    x = residual_block_18(x, filters=64)\n",
        "    # Stage 2\n",
        "    x = residual_block_18(x, filters=128, strides=2)\n",
        "    x = residual_block_18(x, filters=128)\n",
        "    # Stage 3\n",
        "    x = residual_block_18(x, filters=256, strides=2)\n",
        "    x = residual_block_18(x, filters=256)\n",
        "    # Stage 4\n",
        "    x = residual_block_18(x, filters=512, strides=2)\n",
        "    x = residual_block_18(x, filters=512)\n",
        "    # Global average pooling\n",
        "    x = GlobalAveragePooling1D()(x)\n",
        "    # Output layer\n",
        "    outputs = Dense(num_classes, activation='softmax')(x)\n",
        "    # Model definition\n",
        "    model = Model(inputs=inputs, outputs=outputs)\n",
        "    # Compile the model\n",
        "    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "    print('---------------------------------RESNET 18---------------------------------')\n",
        "    model.summary()\n",
        "    return model\n",
        "\n",
        "\n",
        "# Resnet 34\n",
        "def residual_block_34(x, filters, kernel_size=3, strides=1):\n",
        "    # Shortcut\n",
        "    shortcut = x\n",
        "    # First convolution\n",
        "    x = Conv1D(filters=filters, kernel_size=kernel_size, strides=strides, padding='same')(x)\n",
        "    x = BatchNormalization()(x)\n",
        "    x = ReLU()(x)\n",
        "    # Second convolution\n",
        "    x = Conv1D(filters=filters, kernel_size=kernel_size, strides=1, padding='same')(x)\n",
        "    x = BatchNormalization()(x)\n",
        "    # Adjusting the shortcut connection if needed\n",
        "    if strides != 1 or shortcut.shape[-1] != filters:\n",
        "        shortcut = Conv1D(filters=filters, kernel_size=1, strides=strides, padding='same')(shortcut)\n",
        "        shortcut = BatchNormalization()(shortcut)\n",
        "    # Adding shortcut\n",
        "    x = Add()([x, shortcut])\n",
        "    x = ReLU()(x)\n",
        "    return x\n",
        "\n",
        "def ResNet34(input_shape, num_classes=6):\n",
        "    inputs = Input(shape=input_shape)\n",
        "    x = Conv1D(filters=64, kernel_size=7, strides=2, padding='same')(inputs)\n",
        "    x = BatchNormalization()(x)\n",
        "    x = ReLU()(x)\n",
        "    x = MaxPooling1D(pool_size=3, strides=2, padding='same')(x)\n",
        "    # First stage (with one residual block)\n",
        "    x = residual_block_34(x, filters=64)\n",
        "    # Second stage (with two residual blocks)\n",
        "    x = residual_block_34(x, filters=64)\n",
        "    x = residual_block_34(x, filters=64)\n",
        "    # Third stage (with three residual blocks)\n",
        "    x = residual_block_34(x, filters=128, strides=2)\n",
        "    x = residual_block_34(x, filters=128)\n",
        "    x = residual_block_34(x, filters=128)\n",
        "    # Fourth stage (with five residual blocks)\n",
        "    x = residual_block_34(x, filters=256, strides=2)\n",
        "    x = residual_block_34(x, filters=256)\n",
        "    x = residual_block_34(x, filters=256)\n",
        "    x = residual_block_34(x, filters=256)\n",
        "    x = residual_block_34(x, filters=256)\n",
        "    # Fifth stage (with two residual blocks)\n",
        "    x = residual_block_34(x, filters=512, strides=2)\n",
        "    x = residual_block_34(x, filters=512)\n",
        "    # Global average pooling\n",
        "    x = GlobalAveragePooling1D()(x)\n",
        "    # Output layer\n",
        "    outputs = Dense(num_classes, activation='softmax')(x)\n",
        "    model = Model(inputs=inputs, outputs=outputs)\n",
        "    # Compile the model\n",
        "    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "    print('---------------------------------RESNET 34---------------------------------')\n",
        "    model.summary()\n",
        "    return model\n",
        "\n",
        "# AlexNet\n",
        "def AlexNet(input_shape, num_classes):\n",
        "    inputs = Input(shape=input_shape)\n",
        "    x = Conv1D(filters=96, kernel_size=11, strides=4, padding='same', activation='relu')(inputs)\n",
        "    x = MaxPooling1D(pool_size=3, strides=2, padding='same')(x)\n",
        "    x = Conv1D(filters=256, kernel_size=5, padding='same', activation='relu')(x)\n",
        "    x = MaxPooling1D(pool_size=3, strides=2, padding='same')(x)\n",
        "    x = Conv1D(filters=384, kernel_size=3, padding='same', activation='relu')(x)\n",
        "    x = Conv1D(filters=384, kernel_size=3, padding='same', activation='relu')(x)\n",
        "    x = Conv1D(filters=256, kernel_size=3, padding='same', activation='relu')(x)\n",
        "    x = MaxPooling1D(pool_size=3, strides=2, padding='same')(x)\n",
        "    x = Flatten()(x)\n",
        "    x = Dense(4096, activation='relu')(x)\n",
        "    x = Dropout(0.5)(x)\n",
        "    x = Dense(4096, activation='relu')(x)\n",
        "    x = Dropout(0.5)(x)\n",
        "    outputs = Dense(num_classes, activation='softmax')(x)\n",
        "    model = Model(inputs=inputs, outputs=outputs)\n",
        "    # Compile the model\n",
        "    model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "    print('---------------------------------ALEXNET---------------------------------')\n",
        "    model.summary()\n",
        "    return model\n",
        "\n",
        "# RCCGNET\n",
        "def conv_block(filters, x):\n",
        "    x = Conv1D(filters, 3, padding='same')(x)\n",
        "    x = BatchNormalization()(x)\n",
        "    x = Activation('relu')(x)\n",
        "    return x\n",
        "\n",
        "def residual_block(filters, x):\n",
        "    _, length, channels = x.shape\n",
        "    r0 = AveragePooling1D(pool_size=length)(x)\n",
        "    r0 = Conv1D(filters, 1, activation='sigmoid', padding='same')(r0)\n",
        "    r1 = Multiply()([x, r0])\n",
        "    return r1\n",
        "\n",
        "def scr_block(x, y):\n",
        "    channel = x.shape[-1]\n",
        "    concat1 = Concatenate()([x, y])\n",
        "    conv3 = conv_block(channel, concat1)\n",
        "    res_x = residual_block(channel, conv3)\n",
        "    return res_x\n",
        "\n",
        "def resnet_block(block_input, filters):\n",
        "    conv1 = conv_block(filters, block_input)\n",
        "    conv2 = scr_block(block_input, conv1)\n",
        "    sum_layer = Add()([conv2, block_input])\n",
        "    res_out = Activation('relu')(sum_layer)\n",
        "    return res_out\n",
        "\n",
        "def rccgnet(input_shape, num_classes):\n",
        "    input_layer = Input(shape=input_shape)\n",
        "    conv1 = conv_block(16, input_layer)\n",
        "    scr1 = resnet_block(conv1, 16)\n",
        "    scr2 = resnet_block(scr1, 16)\n",
        "    pool1 = AveragePooling1D(2)(scr2)\n",
        "    conv2 = conv_block(32, pool1)\n",
        "    #scr3 = resnet_block(conv2, 32)\n",
        "    #scr4 = resnet_block(scr3, 32)\n",
        "    #pool2 = MaxPooling1D(2)(scr4)\n",
        "    #conv3 = conv_block(64, pool2)\n",
        "    scr5 = resnet_block(conv2, 64)\n",
        "    scr6 = resnet_block(scr5, 64)\n",
        "    pool3 = GlobalAveragePooling1D()(scr6)\n",
        "    dense1 = Dense(128, activation='relu')(pool3)\n",
        "    dense2 = Dense(128, activation='relu')(dense1)\n",
        "    output = Dense(num_classes, activation='softmax')(dense2)\n",
        "    model = Model(inputs=input_layer, outputs=output)\n",
        "    print('---------------------------------RCCGNET---------------------------------')\n",
        "    model.summary()\n",
        "    return model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pmfO6lRGAVPR"
      },
      "source": [
        "## Declarations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WOZtqLmH2lzI"
      },
      "outputs": [],
      "source": [
        "# model_cnn = CNN_model0(input_shape, num_classes)\n",
        "# model_vgg = VGG_model(input_shape, num_classes)\n",
        "# model_zf = ZF_model(input_shape, num_classes)\n",
        "# model_resnet_18 = ResNet18(input_shape, num_classes)\n",
        "# model_resnet_34 = ResNet34(input_shape, num_classes)\n",
        "# model_alexnet = AlexNet(input_shape, num_classes)\n",
        "# model_rccgnet = rccgnet()\n",
        "\n",
        "fold_no = 1\n",
        "cv_scores_cnn = []  # To store accuracy of each fold\n",
        "cv_f1_scores_cnn = []  # To store F1 score of each fold\n",
        "tt_cnn = []\n",
        "\n",
        "cv_scores_vgg = []  # To store accuracy of each fold\n",
        "cv_f1_scores_vgg = []  # To store F1 score of each fold\n",
        "tt_vgg = []\n",
        "\n",
        "cv_scores_zf = []  # To store accuracy of each fold\n",
        "cv_f1_scores_zf = []  # To store F1 score of each fold\n",
        "tt_zf = []\n",
        "\n",
        "cv_scores_resnet_18 = []  # To store accuracy of each fold\n",
        "cv_f1_scores_resnet_18 = []  # To store F1 score of each fold\n",
        "tt_resnet_18 = []\n",
        "\n",
        "cv_scores_resnet_34 = []  # To store accuracy of each fold\n",
        "cv_f1_scores_resnet_34 = []  # To store F1 score of each fold\n",
        "tt_resnet_34 = []\n",
        "\n",
        "cv_scores_alexnet = []  # To store accuracy of each fold\n",
        "cv_f1_scores_alexnet = []  # To store F1 score of each fold\n",
        "tt_alexnet = []\n",
        "\n",
        "cv_scores_rccgnet = []  # To store accuracy of each fold\n",
        "cv_f1_scores_rccgnet = []  # To store F1 score of each fold\n",
        "tt_rccgnet = []"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Eh-LlMxoAa45"
      },
      "source": [
        "## 5-fold cross-validation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SOAXPrEzhysN"
      },
      "outputs": [],
      "source": [
        "kf = KFold(n_splits=5, shuffle=True, random_state=42)\n",
        "# Loop over each fold\n",
        "for train_index, val_index in kf.split(X_train):\n",
        "    # Split data into training and validation sets for the current fold\n",
        "    X_train_fold, X_val_fold = X_train[train_index], X_train[val_index]\n",
        "    y_train_fold, y_val_fold = y_train[train_index], y_val[val_index]\n",
        "\n",
        "    # Reshape the data as required by the model\n",
        "    X_train_fold_reshaped = X_train_fold.reshape((-1,X_train_fold.shape[1], X_train_fold.shape[2]))\n",
        "    X_val_fold_reshaped = X_val_fold.reshape((-1, X_val_fold.shape[1],X_val_fold.shape[2]))\n",
        "\n",
        "    # Initialize a new model for each fold\n",
        "    model_cnn = CNN_model0(input_data.shape[1:], num_classes)\n",
        "    model_vgg = VGG_model(input_data.shape[1:], num_classes)\n",
        "    model_zf = ZF_model(input_data.shape[1:], num_classes)\n",
        "    model_resnet_18 = ResNet18(input_data.shape[1:], num_classes)\n",
        "    model_resnet_34 = ResNet34(input_data.shape[1:], num_classes)\n",
        "    model_alexnet = AlexNet(input_data.shape[1:], num_classes)\n",
        "    model_rccgnet = rccgnet(input_data.shape[1:], num_classes)\n",
        "\n",
        "    print(f'Training fold {fold_no}...')\n",
        "\n",
        "    # Train the model\n",
        "    st=datetime.now()\n",
        "    history_cnn = model_cnn.fit(X_train_fold_reshaped, y_train_fold, epochs=65, validation_data=(X_val_fold_reshaped, y_val_fold))\n",
        "    tt_cnn.append(datetime.now()-st)\n",
        "\n",
        "    st=datetime.now()\n",
        "    history_vgg = model_vgg.fit(X_train_fold_reshaped, y_train_fold, epochs=65, validation_data=(X_val_fold_reshaped, y_val_fold))\n",
        "    tt_vgg.append(datetime.now()-st)\n",
        "\n",
        "    st=datetime.now()\n",
        "    history_zf = model_zf.fit(X_train_fold_reshaped, y_train_fold, epochs=65, validation_data=(X_val_fold_reshaped, y_val_fold))\n",
        "    tt_zf.append(datetime.now()-st)\n",
        "\n",
        "    st=datetime.now()\n",
        "    history_resnet_18 = model_resnet_18.fit(X_train_fold_reshaped, y_train_fold, epochs=65, validation_data=(X_val_fold_reshaped, y_val_fold))\n",
        "    tt_resnet_18.append(datetime.now()-st)\n",
        "\n",
        "    st=datetime.now()\n",
        "    history_resnet_34 = model_resnet_34.fit(X_train_fold_reshaped, y_train_fold, epochs=65, validation_data=(X_val_fold_reshaped, y_val_fold))\n",
        "    tt_resnet_34.append(datetime.now()-st)\n",
        "\n",
        "    st=datetime.now()\n",
        "    history_alexnet = model_alexnet.fit(X_train_fold_reshaped, y_train_fold, epochs=65, validation_data=(X_val_fold_reshaped, y_val_fold))\n",
        "    tt_alexnet.append(datetime.now()-st)\n",
        "\n",
        "    st=datetime.now()\n",
        "    history_rccgnet = model_rccgnet.fit(X_train_fold_reshaped, y_train_fold, epochs=65, validation_data=(X_val_fold_reshaped, y_val_fold))\n",
        "    tt_rccgnet.append(datetime.now()-st)\n",
        "\n",
        "    # Evaluate the model on the validation data for this fold\n",
        "    y_val_true = np.argmax(y_val_fold, axis=1)\n",
        "\n",
        "    # CNN\n",
        "    scores_1 = model_cnn.evaluate(X_val_fold_reshaped, y_val_fold, verbose=1)\n",
        "    print(f'Score for fold {fold_no}: {model_cnn.metrics_names[1]} of {scores_1[1]}; loss of {scores_1[0]}')\n",
        "    cv_scores_cnn.append(scores_1[1])\n",
        "    y_val_pred_cnn = model_cnn.predict(X_val_fold_reshaped)\n",
        "    y_val_pred_cnn = np.argmax(y_val_pred_cnn, axis=1)\n",
        "    f1 = f1_score(y_val_true, y_val_pred_cnn, average='weighted')\n",
        "    cv_f1_scores_cnn.append(f1)\n",
        "\n",
        "    # VGG\n",
        "    scores_2 = model_vgg.evaluate(X_val_fold_reshaped, y_val_fold, verbose=1)\n",
        "    print(f'Score for fold {fold_no}: {model_vgg.metrics_names[1]} of {scores_2[1]}; loss of {scores_2[0]}')\n",
        "    cv_scores_vgg.append(scores_2[1])\n",
        "    y_val_pred_vgg = model_vgg.predict(X_val_fold_reshaped)\n",
        "    y_val_pred_vgg = np.argmax(y_val_pred_vgg, axis=1)\n",
        "    f1 = f1_score(y_val_true, y_val_pred_vgg, average='weighted')\n",
        "    print(f\"F1 score for fold {fold_no}: {f1}\")\n",
        "    cv_f1_scores_vgg.append(f1)\n",
        "\n",
        "    # ZF\n",
        "    scores_3 = model_zf.evaluate(X_val_fold_reshaped, y_val_fold, verbose=1)\n",
        "    print(f'Score for fold {fold_no}: {model_zf.metrics_names[1]} of {scores_3[1]}; loss of {scores_3[0]}')\n",
        "    cv_scores_zf.append(scores_3[1])\n",
        "    y_val_pred_zf = model_zf.predict(X_val_fold_reshaped)\n",
        "    y_val_pred_zf = np.argmax(y_val_pred_zf, axis=1)\n",
        "    f1 = f1_score(y_val_true, y_val_pred_zf, average='weighted')\n",
        "    print(f\"F1 score for fold {fold_no}: {f1}\")\n",
        "    cv_f1_scores_zf.append(f1)\n",
        "\n",
        "    # resnet 18\n",
        "    scores_4 = model_resnet_18.evaluate(X_val_fold_reshaped, y_val_fold, verbose=1)\n",
        "    print(f'Score for fold {fold_no}: {model_resnet_18.metrics_names[1]} of {scores_4[1]}; loss of {scores_4[0]}')\n",
        "    cv_scores_resnet_18.append(scores_4[1])\n",
        "    y_val_pred_resnet_18 = model_resnet_18.predict(X_val_fold_reshaped)\n",
        "    y_val_pred_resnet_18 = np.argmax(y_val_pred_resnet_18, axis=1)\n",
        "    f1 = f1_score(y_val_true, y_val_pred_resnet_18, average='weighted')\n",
        "    print(f\"F1 score for fold {fold_no}: {f1}\")\n",
        "    cv_f1_scores_resnet_18.append(f1)\n",
        "\n",
        "    # resnet 34\n",
        "    scores_5 = model_resnet_34.evaluate(X_val_fold_reshaped, y_val_fold, verbose=1)\n",
        "    print(f'Score for fold {fold_no}: {model_resnet_34.metrics_names[1]} of {scores_5[1]}; loss of {scores_5[0]}')\n",
        "    cv_scores_resnet_34.append(scores_5[1])\n",
        "    y_val_pred_resnet_34 = model_resnet_34.predict(X_val_fold_reshaped)\n",
        "    y_val_pred_resnet_34 = np.argmax(y_val_pred_resnet_34, axis=1)\n",
        "    f1 = f1_score(y_val_true, y_val_pred_resnet_34, average='weighted')\n",
        "    print(f\"F1 score for fold {fold_no}: {f1}\")\n",
        "    cv_f1_scores_resnet_34.append(f1)\n",
        "\n",
        "    # Alexnet\n",
        "    scores_6 = model_alexnet.evaluate(X_val_fold_reshaped, y_val_fold, verbose=1)\n",
        "    print(f'Score for fold {fold_no}: {model_alexnet.metrics_names[1]} of {scores_6[1]}; loss of {scores_6[0]}')\n",
        "    cv_scores_alexnet.append(scores_6[1])\n",
        "    y_val_pred_alexnet = model_alexnet.predict(X_val_fold_reshaped)\n",
        "    y_val_pred_alexnet = np.argmax(y_val_pred_alexnet, axis=1)\n",
        "    f1 = f1_score(y_val_true, y_val_pred_alexnet, average='weighted')\n",
        "    print(f\"F1 score for fold {fold_no}: {f1}\")\n",
        "    cv_f1_scores_alexnet.append(f1)\n",
        "\n",
        "    # RCCGNET\n",
        "    scores_7 = model_rccgnet.evaluate(X_val_fold_reshaped, y_val_fold, verbose=1)\n",
        "    print(f'Score for fold {fold_no}: {model_rccgnet.metrics_names[1]} of {scores_7[1]}; loss of {scores_7[0]}')\n",
        "    cv_scores_rccgnet.append(scores_7[1])\n",
        "    y_val_pred_rccgnet = model_rccgnet.predict(X_val_fold_reshaped)\n",
        "    y_val_pred_rccgnet = np.argmax(y_val_pred_rccgnet, axis=1)\n",
        "    f1 = f1_score(y_val_true, y_val_pred_rccgnet, average='weighted')\n",
        "    print(f\"F1 score for fold {fold_no}: {f1}\")\n",
        "    cv_f1_scores_rccgnet.append(f1)\n",
        "\n",
        "\n",
        "    fold_no += 1\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mnOBhbvCA46x"
      },
      "source": [
        "## Report"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FVJvWhGS_G9T"
      },
      "outputs": [],
      "source": [
        "print('::::::::::::::::::REPORT:::::::::::::::::::')\n",
        "\n",
        "# cnn\n",
        "print(f\"Average validation accuracy over 5 folds: {np.mean(cv_scores_cnn)}\")\n",
        "print(f\"Average validation F1 score over 5 folds: {np.mean(cv_f1_scores_cnn)}\")\n",
        "print(f\"Average training time over 5 folds: {np.mean(tt_cnn)}\")\n",
        "\n",
        "# vgg\n",
        "print(f\"Average validation accuracy over 5 folds: {np.mean(cv_scores_vgg)}\")\n",
        "print(f\"Average validation F1 score over 5 folds: {np.mean(cv_f1_scores_vgg)}\")\n",
        "print(f\"Average training time over 5 folds: {np.mean(tt_vgg)}\")\n",
        "\n",
        "# ZF\n",
        "print(f\"Average validation accuracy over 5 folds: {np.mean(cv_scores_zf)}\")\n",
        "print(f\"Average validation F1 score over 5 folds: {np.mean(cv_f1_scores_zf)}\")\n",
        "print(f\"Average training time over 5 folds: {np.mean(tt_zf)}\")\n",
        "\n",
        "# resnet-18\n",
        "print(f\"Average validation accuracy over 5 folds: {np.mean(cv_scores_resnet_18)}\")\n",
        "print(f\"Average validation F1 score over 5 folds: {np.mean(cv_f1_scores_resnet_18)}\")\n",
        "print(f\"Average training time over 5 folds: {np.mean(tt_resnet_18)}\")\n",
        "\n",
        "# resnet-34\n",
        "print(f\"Average validation accuracy over 5 folds: {np.mean(cv_scores_resnet_34)}\")\n",
        "print(f\"Average validation F1 score over 5 folds: {np.mean(cv_f1_scores_resnet_34)}\")\n",
        "print(f\"Average training time over 5 folds: {np.mean(tt_resnet_34)}\")\n",
        "\n",
        "# alexnet\n",
        "print(f\"Average validation accuracy over 5 folds: {np.mean(cv_scores_alexnet)}\")\n",
        "print(f\"Average validation F1 score over 5 folds: {np.mean(cv_f1_scores_alexnet)}\")\n",
        "print(f\"Average training time over 5 folds: {np.mean(tt_alexnet)}\")\n",
        "\n",
        "# rcc\n",
        "print(f\"Average validation accuracy over 5 folds: {np.mean(cv_scores_rccgnet)}\")\n",
        "print(f\"Average validation F1 score over 5 folds: {np.mean(cv_f1_scores_rccgnet)}\")\n",
        "print(f\"Average training time over 5 folds: {np.mean(tt_rccgnet)}\")"
      ]
    }
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