resnet.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"gpuType":"T4","machine_shape":"hm","toc_visible":true},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","source":["import pandas as pd\n","import numpy as np\n","import tensorflow as tf\n"],"metadata":{"id":"aGKTYsQXIcYs"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(tf.__version__)"],"metadata":{"id":"MvlWG5PwI122","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1699458825260,"user_tz":-480,"elapsed":12,"user":{"displayName":"Farizz","userId":"04548903452047604671"}},"outputId":"a16a93c3-d744-40b2-eca4-2aca6df25820"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["2.14.0\n"]}]},{"cell_type":"markdown","source":["## Getting our data ready"],"metadata":{"id":"hbyWHMwuI8wD"}},{"cell_type":"code","source":["# Check number of data Images\n","import os\n","len(os.listdir(\"/content/drive/MyDrive/Indonesia AI Computer Vision/Face Recognition/Dataset/Images\"))"],"metadata":{"id":"3CN9FDN2JWG3","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1699458854011,"user_tz":-480,"elapsed":28758,"user":{"displayName":"Farizz","userId":"04548903452047604671"}},"outputId":"fa9d8bf7-09b5-4f0b-f998-4bde559384c7"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["5017"]},"metadata":{},"execution_count":5}]},{"cell_type":"code","source":["file_path = \"/content/drive/MyDrive/Indonesia AI Computer Vision/Face Recognition/Dataset/list_attribute.txt\"\n","df = pd.read_csv(file_path, skiprows = 1, sep = '\\s+')"],"metadata":{"id":"yzxFEyciJaD7"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df[\"Male\"]"],"metadata":{"id":"jphuvNPhJoG6","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1699458855515,"user_tz":-480,"elapsed":113,"user":{"displayName":"Farizz","userId":"04548903452047604671"}},"outputId":"faf0dc24-94f7-4eb5-cbfd-f29f1292b09a"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["000001.jpg   -1\n","000002.jpg   -1\n","000003.jpg    1\n","000004.jpg   -1\n","000005.jpg   -1\n","             ..\n","202595.jpg   -1\n","202596.jpg    1\n","202597.jpg    1\n","202598.jpg   -1\n","202599.jpg   -1\n","Name: Male, Length: 202599, dtype: int64"]},"metadata":{},"execution_count":7}]},{"cell_type":"code","source":["# Replace value -1 to 0\n","df[\"Male\"] = df[\"Male\"].replace(-1,0)"],"metadata":{"id":"RbTTrVxCJqTn"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Extract one column and create a new DataFrame\n","df = df[['Male']].copy()  # Replace 'A' with the name of the column you want\n","\n","# Print the new DataFrame\n","df"],"metadata":{"id":"8Lj0f95HJtgt","colab":{"base_uri":"https://localhost:8080/","height":423},"executionInfo":{"status":"ok","timestamp":1699458855517,"user_tz":-480,"elapsed":110,"user":{"displayName":"Farizz","userId":"04548903452047604671"}},"outputId":"77bed6da-339a-4abe-c342-50f898966bb8"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["            Male\n","000001.jpg     0\n","000002.jpg     0\n","000003.jpg     1\n","000004.jpg     0\n","000005.jpg     0\n","...          ...\n","202595.jpg     0\n","202596.jpg     1\n","202597.jpg     1\n","202598.jpg     0\n","202599.jpg     0\n","\n","[202599 rows x 1 columns]"],"text/html":["\n","  <div id=\"df-721bde86-a072-4342-9299-b3bd01fa9eef\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Male</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>000001.jpg</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>000002.jpg</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>000003.jpg</th>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>000004.jpg</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>000005.jpg</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>202595.jpg</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>202596.jpg</th>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>202597.jpg</th>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>202598.jpg</th>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>202599.jpg</th>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>202599 rows × 1 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-721bde86-a072-4342-9299-b3bd01fa9eef')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </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","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-721bde86-a072-4342-9299-b3bd01fa9eef button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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Recognition/Dataset/Images\")"],"metadata":{"id":"TghCdhcPKFAz"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# using index.isin() to match the images.\n","matched_images = df.index.isin(all_images)\n","matched_images"],"metadata":{"id":"JT_U_89YKN13","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1699458855519,"user_tz":-480,"elapsed":102,"user":{"displayName":"Farizz","userId":"04548903452047604671"}},"outputId":"0eae28e7-5074-4d52-f7ce-050cc55de470"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([False, False, False, ..., False, False, False])"]},"metadata":{},"execution_count":12}]},{"cell_type":"code","source":["# using \"boolean indexing\" to return the orignal data frame which only \"True\"\n","df = df[matched_images]"],"metadata":{"id":"O7GP7ksSKQpU"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df"],"metadata":{"id":"0FXlgzjeKSQZ","colab":{"base_uri":"https://localhost:8080/","height":423},"executionInfo":{"status":"ok","timestamp":1699458855520,"user_tz":-480,"elapsed":96,"user":{"displayName":"Farizz","userId":"04548903452047604671"}},"outputId":"2d28b6cf-69a0-4c63-b587-b6a742cfd9dc"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["            Male\n","000051.jpg     1\n","000052.jpg     1\n","000065.jpg     1\n","000166.jpg     1\n","000198.jpg     0\n","...          ...\n","202320.jpg     0\n","202340.jpg     0\n","202347.jpg     0\n","202357.jpg     0\n","202566.jpg     1\n","\n","[5000 rows x 1 columns]"],"text/html":["\n","  <div id=\"df-4fec43e7-96f9-4781-80f8-4c7c24f7f8f5\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        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0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-4fec43e7-96f9-4781-80f8-4c7c24f7f8f5 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-4fec43e7-96f9-4781-80f8-4c7c24f7f8f5');\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","\n","<div id=\"df-b8fb0413-764f-4ce5-b7d2-ebb417715978\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b8fb0413-764f-4ce5-b7d2-ebb417715978')\"\n","            title=\"Suggest charts\"\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","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </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-b8fb0413-764f-4ce5-b7d2-ebb417715978 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":14}]},{"cell_type":"code","source":["# Create a list from data frame index\n","id_column = [item for item in df.index]\n","# Create new column \"id\" in data frame from list \"id_column\"\n","df[\"id\"] = id_column\n","df"],"metadata":{"id":"v287ivWFKT1W","colab":{"base_uri":"https://localhost:8080/","height":527},"executionInfo":{"status":"ok","timestamp":1699458855521,"user_tz":-480,"elapsed":93,"user":{"displayName":"Farizz","userId":"04548903452047604671"}},"outputId":"7f44aaa3-db44-4c60-856f-65a86aabce8e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-15-3ac37e4c53e9>:4: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\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","  df[\"id\"] = id_column\n"]},{"output_type":"execute_result","data":{"text/plain":["            Male          id\n","000051.jpg     1  000051.jpg\n","000052.jpg     1  000052.jpg\n","000065.jpg     1  000065.jpg\n","000166.jpg     1  000166.jpg\n","000198.jpg     0  000198.jpg\n","...          ...         ...\n","202320.jpg     0  202320.jpg\n","202340.jpg     0  202340.jpg\n","202347.jpg     0  202347.jpg\n","202357.jpg     0  202357.jpg\n","202566.jpg     1  202566.jpg\n","\n","[5000 rows x 2 columns]"],"text/html":["\n","  <div id=\"df-ba07ae6d-be5f-47c4-83c5-d3c7557689d9\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    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'block' : 'none';\n","\n","      async function convertToInteractive(key) {\n","        const element = document.querySelector('#df-ba07ae6d-be5f-47c4-83c5-d3c7557689d9');\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","\n","<div id=\"df-acbfce12-a58b-4a50-a26f-075e2b8725af\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-acbfce12-a58b-4a50-a26f-075e2b8725af')\"\n","            title=\"Suggest charts\"\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","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </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-acbfce12-a58b-4a50-a26f-075e2b8725af button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":15}]},{"cell_type":"markdown","source":["### Reset index"],"metadata":{"id":"lo8A877MKXo2"}},{"cell_type":"code","source":["# Reset index start from 0 to len(df)\n","df.index = range(len(df))\n","df"],"metadata":{"id":"2-0GP4ZqKcEy","colab":{"base_uri":"https://localhost:8080/","height":423},"executionInfo":{"status":"ok","timestamp":1699458855521,"user_tz":-480,"elapsed":84,"user":{"displayName":"Farizz","userId":"04548903452047604671"}},"outputId":"a7d8dd1c-eafb-4ff5-8c30-85169cbd8c0b"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["      Male          id\n","0        1  000051.jpg\n","1        1  000052.jpg\n","2        1  000065.jpg\n","3        1  000166.jpg\n","4        0  000198.jpg\n","...    ...         ...\n","4995     0  202320.jpg\n","4996     0  202340.jpg\n","4997     0  202347.jpg\n","4998     0  202357.jpg\n","4999     1  202566.jpg\n","\n","[5000 rows x 2 columns]"],"text/html":["\n","  <div id=\"df-1f21a3c3-d2c1-4a3b-9efb-3976442c0885\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Male</th>\n","      <th>id</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>000051.jpg</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>000052.jpg</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1</td>\n","      <td>000065.jpg</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>1</td>\n","      <td>000166.jpg</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0</td>\n","      <td>000198.jpg</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>4995</th>\n","      <td>0</td>\n","      <td>202320.jpg</td>\n","    </tr>\n","    <tr>\n","      <th>4996</th>\n","      <td>0</td>\n","      <td>202340.jpg</td>\n","    </tr>\n","    <tr>\n","      <th>4997</th>\n","      <td>0</td>\n","      <td>202347.jpg</td>\n","    </tr>\n","    <tr>\n","      <th>4998</th>\n","      <td>0</td>\n","      <td>202357.jpg</td>\n","    </tr>\n","    <tr>\n","      <th>4999</th>\n","      <td>1</td>\n","      <td>202566.jpg</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5000 rows × 2 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1f21a3c3-d2c1-4a3b-9efb-3976442c0885')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </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","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-1f21a3c3-d2c1-4a3b-9efb-3976442c0885 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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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","\n","<div id=\"df-72321a19-57bb-4c0f-808b-848e925b6189\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-72321a19-57bb-4c0f-808b-848e925b6189')\"\n","            title=\"Suggest charts\"\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","    <g>\n","        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n","    </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-72321a19-57bb-4c0f-808b-848e925b6189 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":16}]},{"cell_type":"code","source":["# Swap the order of columns id and Male\n","df = df[['id', 'Male']]  # Specify the columns in the desired order\n","df"],"metadata":{"id":"VCSna_dtKdm1","colab":{"base_uri":"https://localhost:8080/","height":423},"executionInfo":{"status":"ok","timestamp":1699458855522,"user_tz":-480,"elapsed":82,"user":{"displayName":"Farizz","userId":"04548903452047604671"}},"outputId":"5347aef8-8535-4425-9da3-aa78402ed991"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["              id  Male\n","0     000051.jpg     1\n","1     000052.jpg     1\n","2     000065.jpg     1\n","3     000166.jpg     1\n","4     000198.jpg     0\n","...          ...   ...\n","4995  202320.jpg     0\n","4996  202340.jpg     0\n","4997  202347.jpg     0\n","4998  202357.jpg     0\n","4999  202566.jpg     1\n","\n","[5000 rows x 2 columns]"],"text/html":["\n","  <div id=\"df-02adeaf3-3d73-4f8f-a83b-ad6a0f0b459d\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>id</th>\n","      <th>Male</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>000051.jpg</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>000052.jpg</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>000065.jpg</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>000166.jpg</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>000198.jpg</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>4995</th>\n","      <td>202320.jpg</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4996</th>\n","      <td>202340.jpg</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4997</th>\n","      <td>202347.jpg</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4998</th>\n","      <td>202357.jpg</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4999</th>\n","      <td>202566.jpg</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5000 rows × 2 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-02adeaf3-3d73-4f8f-a83b-ad6a0f0b459d')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </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","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    .colab-df-buttons div {\n","      margin-bottom: 4px;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","    <script>\n","      const buttonEl =\n","        document.querySelector('#df-02adeaf3-3d73-4f8f-a83b-ad6a0f0b459d button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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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","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":18}]},{"cell_type":"markdown","source":["### Data Ready in data frame\n","\n","We have 5000 images to use for building our deep learning model."],"metadata":{"id":"8DJGKViBKi-I"}},{"cell_type":"code","source":["# How many images are there of each Male or Female?\n","count_male = (df[\"Male\"] == 1).sum()\n","count_female = (df[\"Male\"] == 0).sum()\n","print(f\"Male : {count_male}\")\n","print(f\"Female : {count_female}\")\n","df[\"Male\"].value_counts().plot.bar(figsize=(12, 4))"],"metadata":{"id":"Rrp8NBUHKmkx","colab":{"base_uri":"https://localhost:8080/","height":415},"executionInfo":{"status":"ok","timestamp":1699458855523,"user_tz":-480,"elapsed":74,"user":{"displayName":"Farizz","userId":"04548903452047604671"}},"outputId":"8b03204a-e2a6-4b65-fd5a-75ba56f0604c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Male : 2047\n","Female : 2953\n"]},{"output_type":"execute_result","data":{"text/plain":["<Axes: >"]},"metadata":{},"execution_count":19},{"output_type":"display_data","data":{"text/plain":["<Figure size 1200x400 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["## Prepare X and y"],"metadata":{"id":"WxapMHYiKvXb"}},{"cell_type":"code","source":["# Take filename from data frame\n","pathfname = [\"/content/drive/MyDrive/Indonesia AI Computer Vision/Face Recognition/Dataset/Images/\" + fname for fname in df[\"id\"]]\n","pathfname = np.array(pathfname)\n","print(type(pathfname))\n","print(len(pathfname))\n","pathfname[:5]"],"metadata":{"id":"YM4C8i0qRgQt","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1699458855523,"user_tz":-480,"elapsed":66,"user":{"displayName":"Farizz","userId":"04548903452047604671"}},"outputId":"da4908bb-6b00-4839-cae3-8406b69622d4"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<class 'numpy.ndarray'>\n","5000\n"]},{"output_type":"execute_result","data":{"text/plain":["array(['/content/drive/MyDrive/Indonesia AI Computer Vision/Face Recognition/Dataset/Images/000051.jpg',\n","       '/content/drive/MyDrive/Indonesia AI Computer Vision/Face Recognition/Dataset/Images/000052.jpg',\n","       '/content/drive/MyDrive/Indonesia AI Computer Vision/Face Recognition/Dataset/Images/000065.jpg',\n","       '/content/drive/MyDrive/Indonesia AI Computer Vision/Face Recognition/Dataset/Images/000166.jpg',\n","       '/content/drive/MyDrive/Indonesia AI Computer Vision/Face Recognition/Dataset/Images/000198.jpg'],\n","      dtype='<U94')"]},"metadata":{},"execution_count":20}]},{"cell_type":"code","source":["# Let's view an image\n","from IPython.display import Image\n","Image(pathfname[3])"],"metadata":{"id":"E3l-ckjiRtkN","colab":{"base_uri":"https://localhost:8080/","height":235},"executionInfo":{"status":"ok","timestamp":1699458855524,"user_tz":-480,"elapsed":62,"user":{"displayName":"Farizz","userId":"04548903452047604671"}},"outputId":"3dd8155d-1714-4a71-e19f-bee41f02f960"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"image/jpeg":"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\n","text/plain":["<IPython.core.display.Image object>"]},"metadata":{},"execution_count":21}]},{"cell_type":"markdown","source":["### Split Data into train, validation and test set"],"metadata":{"id":"eIvfCjaZbFX9"}},{"cell_type":"code","source":["from sklearn.model_selection import train_test_split\n","\n","# Split the data into train, validation, and test sets\n","\n","X = pathfname  # File paths\n","y = df['Male'].values  # Labels\n","\n","X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.3, random_state=42)\n","X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n","\n","# Now you have X_train, y_train, X_val, y_val, X_test, and y_test ready for use in your deep learning model.\n"],"metadata":{"id":"y1ScAWeERWTA"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(\"Training data - X_train shape:\", X_train.shape)\n","print(\"Training data - y_train shape:\", y_train.shape)\n","print(\"Validation data - X_val shape:\", X_val.shape)\n","print(\"Validation data - y_val shape:\", y_val.shape)\n","print(\"Test data - X_test shape:\", X_test.shape)\n","print(\"Test data - y_test shape:\", y_test.shape)\n"],"metadata":{"id":"HNK2tmfZUPDB","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1699458856674,"user_tz":-480,"elapsed":41,"user":{"displayName":"Farizz","userId":"04548903452047604671"}},"outputId":"e0f3add6-eabd-4caf-a100-62105c1d7fe3"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Training data - X_train shape: (3500,)\n","Training data - y_train shape: (3500,)\n","Validation data - X_val shape: (750,)\n","Validation data - y_val shape: (750,)\n","Test data - X_test shape: (750,)\n","Test data - y_test shape: (750,)\n"]}]},{"cell_type":"markdown","source":["#### Check Class Distribution after Split Data"],"metadata":{"id":"ZuXNpAKupYhC"}},{"cell_type":"code","source":["import seaborn as sns\n","from collections import Counter\n","\n","# Convert your y_train, y_val, and y_test to lists if they are pandas Series\n","y_train_list = y_train.tolist()\n","y_val_list = y_val.tolist()\n","y_test_list = y_test.tolist()\n","\n","# Count the class distribution\n","train_class_distribution = Counter(y_train_list)\n","val_class_distribution = Counter(y_val_list)\n","test_class_distribution = Counter(y_test_list)\n","\n","print(\"Training set class distribution:\", train_class_distribution)\n","print(\"Validation set class distribution:\", val_class_distribution)\n","print(\"Test set class distribution:\", test_class_distribution)"],"metadata":{"id":"ms280zo9U5_B","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1699458856675,"user_tz":-480,"elapsed":37,"user":{"displayName":"Farizz","userId":"04548903452047604671"}},"outputId":"7b4a9569-2e3b-4f88-a011-7d2ae23e3c39"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Training set class distribution: Counter({0: 2067, 1: 1433})\n","Validation set class distribution: Counter({0: 450, 1: 300})\n","Test set class distribution: Counter({0: 436, 1: 314})\n"]}]},{"cell_type":"markdown","source":["#### Image Preprocessing Functions:\n","\n","Some pre-trained models may require specific preprocessing functions.\n","\n"," For example, if you're using a ResNet model from a deep learning framework like PyTorch or TensorFlow, there are built-in functions to preprocess images as required by the model."],"metadata":{"id":"ugZ3ElSxhM-4"}},{"cell_type":"code","source":["from PIL import Image\n","\n","image = Image.open(X[1])\n","image\n"],"metadata":{"id":"DRyW8qqTh_2H","colab":{"base_uri":"https://localhost:8080/","height":235},"executionInfo":{"status":"ok","timestamp":1699458856675,"user_tz":-480,"elapsed":31,"user":{"displayName":"Farizz","userId":"04548903452047604671"}},"outputId":"32a8c99d-1906-4607-b376-4d1e8afe5ff6"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=178x218>"],"image/png":"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\n"},"metadata":{},"execution_count":25}]},{"cell_type":"code","source":["# import torch\n","# import torchvision.transforms as transforms\n","\n","# # Define the transformations for preprocessing\n","# preprocess = transforms.Compose([\n","#     transforms.Resize((224, 224)),  # Resize to the model's input size\n","#     transforms.ToTensor(),           # Convert to PyTorch tensor\n","#     transforms.Normalize(\n","#         mean=[0.485, 0.456, 0.406],  # Mean values for ImageNet\n","#         std=[0.229, 0.224, 0.225]    # Standard deviation for ImageNet\n","#     )\n","# ])"],"metadata":{"id":"jPHWU6ukguxs"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# # Preprocess an image using the defined transformation\n","# def preprocess_image(image_path):\n","#     image = Image.open(image_path)\n","#     image = preprocess(image)\n","#     return image"],"metadata":{"id":"ueNxrXXsiY2k"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# # Preprocess the training, validation, and test data\n","# X_train_preprocessed = [preprocess_image(image_path) for image_path in X_train]\n","# X_val_preprocessed = [preprocess_image(image_path) for image_path in X_val]\n","# X_test_preprocessed = [preprocess_image(image_path) for image_path in X_test]"],"metadata":{"id":"iGZjw5Kkib25"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["##### Save the Preprocessed Data"],"metadata":{"id":"ipGIJj_OmSfs"}},{"cell_type":"code","source":["# # Define the directory path in your Google Drive\n","# google_drive_path = \"/content/drive/MyDrive/Indonesia AI Computer Vision/Face Recognition/preprocessed data/\"\n","\n","# # Save the preprocessed data to your Google Drive\n","# np.save(google_drive_path + \"X_train_preprocessed.npy\", np.array(X_train_preprocessed))\n","# np.save(google_drive_path + \"X_val_preprocessed.npy\", np.array(X_val_preprocessed))\n","# np.save(google_drive_path + \"X_test_preprocessed.npy\", np.array(X_test_preprocessed))\n","# np.save(google_drive_path + \"y_train.npy\", y_train)\n","# np.save(google_drive_path + \"y_val.npy\", y_val)\n","# np.save(google_drive_path + \"y_test.npy\", y_test)\n"],"metadata":{"id":"_DaSaRsdlU4B"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["##### Loading the Preprocessed Data\n","\n","By setting allow_pickle=True, you allow NumPy to load object arrays that may have been saved with custom or complex data types. This should resolve the issue you're facing when loading your data."],"metadata":{"id":"uBaSYa6MW-5i"}},{"cell_type":"code","source":["# Define the directory path in your Google Drive\n","google_drive_path = \"/content/drive/MyDrive/Indonesia AI Computer Vision/Face Recognition/preprocessed data/\"\n","\n","# Load the preprocessed data\n","X_train_preprocessed = np.load(google_drive_path + \"X_train_preprocessed.npy\", allow_pickle=True)\n","X_val_preprocessed = np.load(google_drive_path + \"X_val_preprocessed.npy\", allow_pickle=True)\n","X_test_preprocessed = np.load(google_drive_path + \"X_test_preprocessed.npy\", allow_pickle=True)\n","y_train = np.load(google_drive_path + \"y_train.npy\", allow_pickle=True)\n","y_val = np.load(google_drive_path + \"y_val.npy\", allow_pickle=True)\n","y_test = np.load(google_drive_path + \"y_test.npy\", allow_pickle=True)\n"],"metadata":{"id":"1lfP6LfYmito"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["#### Loading a Pre-trained ResNet Model"],"metadata":{"id":"c0G6166UrYaQ"}},{"cell_type":"code","source":["import torch\n","import torchvision.models as models"],"metadata":{"id":"lChB42VxEVxA"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["\n","# # Load a pre-trained ResNet model (e.g., ResNet-50)\n","# model = models.resnet50(pretrained=True)\n","\n","# # Modify the final fully connected layer for binary classification\n","# num_features = model.fc.in_features\n","# model.fc = torch.nn.Linear(num_features, 2)  # Assuming 2 classes for binary classification\n","\n","# # Print the modified model architecture\n","# print(model)\n"],"metadata":{"id":"kUy4c7MroV_q"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# import torchvision.transforms as transforms\n","# from torch.utils.data import DataLoader, Dataset\n","# import torch.nn as nn\n","# import torch.optim as optim\n","\n","# # Define loss function, and optimizer\n","\n","# loss_function = nn.BCEWithLogitsLoss()\n","# optimizer = optim.Adam(model.parameters(), lr=0.001)"],"metadata":{"id":"VAc4Z2TjxHJb"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["##### Fine-tuning the Model\n","\n","Fine-tuning involves modifying the pre-trained ResNet model to adapt it for your specific binary classification task and then training it on your preprocessed data."],"metadata":{"id":"y4GSzWfko3Eu"}},{"cell_type":"code","source":["import torch\n","import torchvision.transforms as transforms\n","from torch.utils.data import DataLoader, Dataset\n","import torch.nn as nn\n","import torch.optim as optim\n","\n","# Define a custom dataset class\n","# a custom dataset class to load and preprocess your data, allowing you to feed it to the model during training.\n","class CustomDataset(Dataset):\n","    def __init__(self, data, labels, transform=None):\n","        self.data = data\n","        self.labels = labels\n","        self.transform = transform\n","\n","    def __len__(self):\n","        return len(self.data)\n","\n","    def __getitem__(self, idx):\n","        image = self.data[idx]\n","        label = self.labels[idx]\n","        if self.transform:\n","            image = self.transform(image)\n","        return image, label"],"metadata":{"id":"EwnlOyyGs1jf"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# Set up data loaders to efficiently load and batch your training and validation data.\n","train_dataset = CustomDataset(X_train_preprocessed, y_train)\n","train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n","\n","val_dataset = CustomDataset(X_val_preprocessed, y_val)\n","val_loader = DataLoader(val_dataset, batch_size=32)"],"metadata":{"id":"PovHaSiAwlCD"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# import matplotlib.pyplot as plt\n","\n","# # Lists to store accuracy and loss values\n","# train_accuracy_values = []\n","# val_accuracy_values = []\n","# train_loss_values = []\n","# val_loss_values = []\n","\n","# # Training loop\n","# num_epochs = 30\n","# for epoch in range(num_epochs):\n","#     model.train()\n","#     for inputs, labels in train_loader:\n","#         optimizer.zero_grad()\n","#         outputs = model(inputs)\n","\n","#         # Create one-hot encoded labels\n","#         one_hot_labels = torch.zeros(labels.size(0), 2)\n","#         one_hot_labels[range(labels.size(0)), labels] = 1  # Assuming labels are 0 (not male) and 1 (male)\n","\n","#         loss = loss_function(outputs, one_hot_labels)\n","#         loss.backward()\n","#         optimizer.step()\n","\n","#     # Validation loop\n","#     model.eval()\n","#     total_correct = 0\n","#     total_samples = 0\n","#     total_loss = 0.0\n","#     with torch.no_grad():\n","#         for inputs, labels in val_loader:\n","#             outputs = model(inputs)\n","#             _, predicted = torch.max(outputs, 1)\n","#             total_samples += labels.size(0)\n","#             total_correct += (predicted == labels).sum().item()\n","\n","#             # Calculate validation loss\n","#             one_hot_labels = torch.zeros(labels.size(0), 2)\n","#             one_hot_labels[range(labels.size(0)), labels] = 1\n","#             val_loss = loss_function(outputs, one_hot_labels)\n","#             total_loss += val_loss.item()\n","\n","#     # Calculate accuracy and loss values\n","#     accuracy = total_correct / total_samples\n","#     avg_val_loss = total_loss / len(val_loader)\n","\n","#     train_accuracy_values.append(accuracy)\n","#     val_accuracy_values.append(accuracy)\n","#     train_loss_values.append(loss.item())\n","#     val_loss_values.append(avg_val_loss)\n","\n","#     print(f\"Epoch {epoch+1}/{num_epochs}, Loss: {loss.item():.4f}, Validation Accuracy: {accuracy * 100:.2f}%\")\n"],"metadata":{"id":"g8ek2_Cd0xQT"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["# # Plot accuracy and loss curves\n","# plt.figure(figsize=(12, 4))\n","# plt.subplot(1, 2, 1)\n","# plt.plot(train_accuracy_values, label='Training Accuracy')\n","# plt.plot(val_accuracy_values, label='Validation Accuracy')\n","# plt.xlabel('Epochs')\n","# plt.ylabel('Accuracy')\n","# plt.legend()\n","\n","# plt.subplot(1, 2, 2)\n","# plt.plot(train_loss_values, label='Training Loss')\n","# plt.plot(val_loss_values, label='Validation Loss')\n","# plt.xlabel('Epochs')\n","# plt.ylabel('Loss')\n","# plt.legend()\n","\n","# plt.show()"],"metadata":{"id":"XY5dTqSb2A1W"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["##### Save the Trained model"],"metadata":{"id":"a7PxtIrNBUMr"}},{"cell_type":"code","source":["# # Save the trained model to a specific directory in your Google Drive\n","# save_path = '/content/drive/MyDrive/Indonesia AI Computer Vision/Face Recognition/saved model/trained_model.pth'\n","# torch.save(model.state_dict(), save_path)\n"],"metadata":{"id":"_Pe2WzC40x_P"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["##### Loading the Trained Model"],"metadata":{"id":"8W_tpK9fBqvy"}},{"cell_type":"code","source":["# Specify the path to the saved model in your Google Drive\n","load_path = '/content/drive/MyDrive/Indonesia AI Computer Vision/Face Recognition/saved model/trained_model.pth'\n","\n","# Load the saved model\n","model = models.resnet50(pretrained=True)\n","num_features = model.fc.in_features\n","model.fc = nn.Linear(num_features, 2)\n","\n","# Ensure the same architecture\n","\n","# Load the saved model's state dictionary from the specified path\n","model.load_state_dict(torch.load(load_path))\n","\n","# Set the model in evaluation mode\n","model.eval()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Nskvj1chBz_I","executionInfo":{"status":"ok","timestamp":1699459614866,"user_tz":-480,"elapsed":378,"user":{"displayName":"Farizz","userId":"04548903452047604671"}},"outputId":"34097bc9-862a-4a37-99a3-fdb37b521932"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.\n","  warnings.warn(msg)\n"]},{"output_type":"execute_result","data":{"text/plain":["ResNet(\n","  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n","  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (relu): ReLU(inplace=True)\n","  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n","  (layer1): Sequential(\n","    (0): Bottleneck(\n","      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","      (downsample): Sequential(\n","        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (1): Bottleneck(\n","      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","    )\n","    (2): Bottleneck(\n","      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","    )\n","  )\n","  (layer2): Sequential(\n","    (0): Bottleneck(\n","      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","      (downsample): Sequential(\n","        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n","        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (1): Bottleneck(\n","      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","    )\n","    (2): Bottleneck(\n","      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","    )\n","    (3): Bottleneck(\n","      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","    )\n","  )\n","  (layer3): Sequential(\n","    (0): Bottleneck(\n","      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","      (downsample): Sequential(\n","        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n","        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (1): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","    )\n","    (2): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","    )\n","    (3): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","    )\n","    (4): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","    )\n","    (5): Bottleneck(\n","      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","    )\n","  )\n","  (layer4): Sequential(\n","    (0): Bottleneck(\n","      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","      (downsample): Sequential(\n","        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n","        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      )\n","    )\n","    (1): Bottleneck(\n","      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","    )\n","    (2): Bottleneck(\n","      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","      (relu): ReLU(inplace=True)\n","    )\n","  )\n","  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n","  (fc): Linear(in_features=2048, out_features=2, bias=True)\n",")"]},"metadata":{},"execution_count":53}]},{"cell_type":"markdown","source":["##### Classification Report on Validation set"],"metadata":{"id":"cVYkdhEO5_oQ"}},{"cell_type":"code","source":["from sklearn.metrics import classification_report\n","\n","# Lists to store true and predicted labels\n","true_labels = []\n","predicted_labels = []\n","\n","# Validation loop with recording true and predicted labels\n","model.eval()\n","with torch.no_grad():\n","    for inputs, labels in val_loader:\n","        outputs = model(inputs)\n","        _, predicted = torch.max(outputs, 1)\n","        true_labels.extend(labels.cpu().numpy())\n","        predicted_labels.extend(predicted.cpu().numpy())\n","\n","# Calculate accuracy\n","accuracy = accuracy_score(true_labels, predicted_labels)\n","print(f\"Validation Accuracy: {accuracy * 100:.2f}%\")\n","\n","# Generate a classification report\n","report = classification_report(true_labels, predicted_labels)\n","print(\"Classification Report:\")\n","print(report)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Jx1TgCOG58aB","executionInfo":{"status":"ok","timestamp":1699459661550,"user_tz":-480,"elapsed":46688,"user":{"displayName":"Farizz","userId":"04548903452047604671"}},"outputId":"b635b87d-74d4-425e-ee1f-4e1f209b0687"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Validation Accuracy: 96.00%\n","Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.97      0.97      0.97       450\n","           1       0.95      0.95      0.95       300\n","\n","    accuracy                           0.96       750\n","   macro avg       0.96      0.96      0.96       750\n","weighted avg       0.96      0.96      0.96       750\n","\n"]}]},{"cell_type":"code","source":["from sklearn.metrics import confusion_matrix\n","import seaborn as sns\n","import matplotlib.pyplot as plt\n","\n","# Calculate the confusion matrix\n","cm = confusion_matrix(true_labels, predicted_labels)\n","\n","# Display the confusion matrix using a heatmap\n","plt.figure(figsize=(8, 6))\n","sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['Not Male', 'Male'], yticklabels=['Not Male', 'Male'])\n","plt.xlabel('Predicted')\n","plt.ylabel('True')\n","plt.title('Confusion Matrix')\n","plt.show()\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":564},"id":"sHZ9983Nu_QV","executionInfo":{"status":"ok","timestamp":1699459661551,"user_tz":-480,"elapsed":39,"user":{"displayName":"Farizz","userId":"04548903452047604671"}},"outputId":"dedcd2b7-d113-4b50-8f9d-4c4b2ac51704"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 800x600 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["##### Classification Report on Test set"],"metadata":{"id":"wFrxebxT5zKg"}},{"cell_type":"code","source":["import torch\n","import torch.nn as nn\n","from torchvision import transforms\n","from torch.utils.data import DataLoader\n","from sklearn.metrics import classification_report, accuracy_score\n","\n","# Assuming you have loaded and preprocessed your test data as X_test_preprocessed and y_test\n","# Just like Train and validation\n","\n","# Create a DataLoader for the test data\n","test_data = CustomDataset(X_test_preprocessed, y_test)\n","test_loader = DataLoader(test_data, batch_size=32, shuffle=False)\n","\n","# Set the model to evaluation mode\n","model.eval()\n","\n","true_labels = []\n","predicted_labels = []\n","\n","with torch.no_grad():\n","    for inputs, labels in test_loader:\n","        outputs = model(inputs)\n","        _, predicted = torch.max(outputs, 1)\n","        true_labels.extend(labels.cpu().numpy())\n","        predicted_labels.extend(predicted.cpu().numpy())\n","\n","# Calculate accuracy\n","accuracy = accuracy_score(true_labels, predicted_labels)\n","print(f\"Test Accuracy: {accuracy * 100:.2f}%\")\n","\n","# Generate a classification report\n","report = classification_report(true_labels, predicted_labels)\n","print(\"Classification Report:\")\n","print(report)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qjYhfKfyiBr2","executionInfo":{"status":"ok","timestamp":1699459708945,"user_tz":-480,"elapsed":47429,"user":{"displayName":"Farizz","userId":"04548903452047604671"}},"outputId":"437b3d06-ec71-4e24-f6a6-e547f71a8cc4"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Test Accuracy: 95.87%\n","Classification Report:\n","              precision    recall  f1-score   support\n","\n","           0       0.97      0.96      0.96       436\n","           1       0.95      0.95      0.95       314\n","\n","    accuracy                           0.96       750\n","   macro avg       0.96      0.96      0.96       750\n","weighted avg       0.96      0.96      0.96       750\n","\n"]}]},{"cell_type":"code","source":["# Calculate the confusion matrix\n","cm = confusion_matrix(true_labels, predicted_labels)\n","\n","# Display the confusion matrix using a heatmap\n","plt.figure(figsize=(8, 6))\n","sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['Not Male', 'Male'], yticklabels=['Not Male', 'Male'])\n","plt.xlabel('Predicted')\n","plt.ylabel('True')\n","plt.title('Confusion Matrix')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":564},"id":"fQ_89wa8jTpC","executionInfo":{"status":"ok","timestamp":1699459708946,"user_tz":-480,"elapsed":17,"user":{"displayName":"Farizz","userId":"04548903452047604671"}},"outputId":"81b84810-31af-4251-8e7b-0239b976de9d"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 800x600 with 2 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":[],"metadata":{"id":"Xsa1WShUjllP"},"execution_count":null,"outputs":[]}]}