AgeEthnicityGender_CNN.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "paw0wAN-yUM5"
      },
      "source": [
        "Author: Marc Vivas Baiges\n",
        "<br>\n",
        "E-mail: vivasmarc001@gmail.com"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "st0jLIPVwGBg"
      },
      "source": [
        "# <h1> Image recognition - Age, gender and ethnicity classifier </h1>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cu9y0E-Uyj7j"
      },
      "source": [
        "## Introduction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9j0N-2Hbwq5v"
      },
      "source": [
        "I will be using a CSV of facial images that are labeled on the basis of age, gender, and ethnicity.\n",
        "\n",
        "\n",
        "<br> You can download the dataset in the following link:\n",
        "https://www.kaggle.com/datasets/nipunarora8/age-gender-and-ethnicity-face-data-csv"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yUiQ4hOA2vyu"
      },
      "source": [
        "## Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {
        "id": "O6PDN77c2iBS"
      },
      "outputs": [],
      "source": [
        "from google.colab import drive\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import layers, models\n",
        "import tensorflow.keras as keras\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from keras.utils.np_utils import to_categorical\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.metrics import precision_score\n",
        "from sklearn.metrics import recall_score\n",
        "from sklearn.metrics import accuracy_score\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5qURX9Wlxhh5"
      },
      "source": [
        "## <h1> Data loading </h1>\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BstzHY3q3sC3",
        "outputId": "35271731-f02e-4ad8-e0bb-9a55e505cde9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "Ej8HLMOr1pxL"
      },
      "outputs": [],
      "source": [
        "PATH_DATA_RAW = 'drive/MyDrive/Colab Notebooks/Datasets/age_gender.csv'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "UHnugJWev225"
      },
      "outputs": [],
      "source": [
        "df_raw = pd.read_csv(PATH_DATA_RAW)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mNIFhByNKhhF"
      },
      "source": [
        "## Functions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 139,
      "metadata": {
        "id": "_WPucvMSKlR0"
      },
      "outputs": [],
      "source": [
        "def classification_evaluation(model, x_test, y_test):\n",
        "  y_pred = model.predict(x_test)\n",
        "  if len(y_pred[0]) > 1:\n",
        "    y_pred = y_pred.argmax(axis=1)\n",
        "    \n",
        "  else:\n",
        "    for i in range(len(y_pred)):\n",
        "      y_pred[i][0] = round(y_pred[i][0])\n",
        "      if y_pred[i][0] <0:\n",
        "        y_pred[i][0] = 0\n",
        "      elif y_pred[i][0] >1:\n",
        "        y_pred[i][0] = 1\n",
        "    \n",
        "   \n",
        "  cm = confusion_matrix(y_test, y_pred)  \n",
        "  disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
        "  disp.plot(\n",
        "      cmap=plt.cm.Blues,\n",
        "  )  \n",
        "\n",
        "  print(\"\")\n",
        "  print(f' Accuracy: {accuracy_score(y_test, y_pred) * 100}%')\n",
        "  \n",
        "\n",
        "  plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def build_model(output_act_fn, output_num_neurons, loss, metrics):\n",
        "  model = keras.Sequential([\n",
        "        # Convolutional layers\n",
        "        \n",
        "\n",
        "        layers.Conv2D(filters=64, kernel_size=(3, 3), activation='relu'), \n",
        "        layers.MaxPooling2D(pool_size=(2, 2)),\n",
        "        layers.Dropout(.15),\n",
        "\n",
        "               \n",
        "        layers.Conv2D(filters=128, kernel_size=(3, 3), activation='relu'), \n",
        "        layers.MaxPooling2D(pool_size=(2, 2)),\n",
        "        layers.Dropout(.15),\n",
        "\n",
        "       \n",
        "      \n",
        "        layers.Flatten(), \n",
        "\n",
        "        # Fully connected layers\n",
        "       \n",
        "        layers.Dense(output_num_neurons, activation=output_act_fn),\n",
        "                                  \n",
        "  ])\n",
        "  model.compile(optimizer='adam',\n",
        "              loss=loss,\n",
        "              metrics=metrics\n",
        "              )\n",
        "  return model\n"
      ],
      "metadata": {
        "id": "CgeyWQ0K89uI"
      },
      "execution_count": 163,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zfsi-Nrb2IyD"
      },
      "source": [
        "## Data understanding"
      ]
    },
    {
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      "execution_count": 6,
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        "colab": {
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        "outputId": "79897fcb-962c-4b3d-cdf0-42cf6d7e7f08"
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      "outputs": [
        {
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          "data": {
            "text/plain": [
              "   age  ethnicity  gender                        img_name  \\\n",
              "0    1          2       0  20161219203650636.jpg.chip.jpg   \n",
              "1    1          2       0  20161219222752047.jpg.chip.jpg   \n",
              "2    1          2       0  20161219222832191.jpg.chip.jpg   \n",
              "3    1          2       0  20161220144911423.jpg.chip.jpg   \n",
              "4    1          2       0  20161220144914327.jpg.chip.jpg   \n",
              "\n",
              "                                              pixels  \n",
              "0  129 128 128 126 127 130 133 135 139 142 145 14...  \n",
              "1  164 74 111 168 169 171 175 182 184 188 193 199...  \n",
              "2  67 70 71 70 69 67 70 79 90 103 116 132 145 155...  \n",
              "3  193 197 198 200 199 200 202 203 204 205 208 21...  \n",
              "4  202 205 209 210 209 209 210 211 212 214 218 21...  "
            ],
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 23705 entries, 0 to 23704\n",
            "Data columns (total 5 columns):\n",
            " #   Column     Non-Null Count  Dtype \n",
            "---  ------     --------------  ----- \n",
            " 0   age        23705 non-null  int64 \n",
            " 1   ethnicity  23705 non-null  int64 \n",
            " 2   gender     23705 non-null  int64 \n",
            " 3   img_name   23705 non-null  object\n",
            " 4   pixels     23705 non-null  object\n",
            "dtypes: int64(3), object(2)\n",
            "memory usage: 926.1+ KB\n"
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          "data": {
            "text/plain": [
              "                age     ethnicity        gender\n",
              "count  23705.000000  23705.000000  23705.000000\n",
              "mean      33.300907      1.269226      0.477283\n",
              "std       19.885708      1.345638      0.499494\n",
              "min        1.000000      0.000000      0.000000\n",
              "25%       23.000000      0.000000      0.000000\n",
              "50%       29.000000      1.000000      0.000000\n",
              "75%       45.000000      2.000000      1.000000\n",
              "max      116.000000      4.000000      1.000000"
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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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f751ac9f-491e-44ad-921c-98cb1b8e529c 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-f751ac9f-491e-44ad-921c-98cb1b8e529c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "df_raw.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D6YeumLa29tG"
      },
      "source": [
        "### Columns study "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9eBlCAxn3FGu"
      },
      "source": [
        "Brief definition of each column value and an expected value which can end up providing to the study."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a7dOeLfl321T"
      },
      "source": [
        "1. age: Age of the person in the photo, it is one of the objectives attributes.\n",
        "2. ethnicity: Ethnicity of the person in the photo, it is one of the objectives attributes.\n",
        "> There is a total of 5 categories in the ethnicity column encoded as:\n",
        "> <br> 0 --> White\n",
        "> <br> 1 --> Black \n",
        "> <br> 2 --> Asian\n",
        "> <br> 3 --> Indian\n",
        "> <br> 4 --> Hispanic\n",
        "\n",
        "3. gender: Gender of the person in the photo, it is one of the objectives attributes.\n",
        "> There are 2 possible genders encoded as:\n",
        "> <br> 0 --> Male\n",
        "> <br> 1 --> Female\n",
        "\n",
        "4. img_name: Image file name. This column won't be used because it doesn't provide useful information for the future models.\n",
        "5. pixels: Array to String of the image pixels. This is the image, obviously is going to be used."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bx_d4OKq-9hU"
      },
      "source": [
        "## Data preparation (1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dCaSouGu_DFr"
      },
      "source": [
        "Drop img_name column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 145,
      "metadata": {
        "id": "lbGCWaBt-3IL"
      },
      "outputs": [],
      "source": [
        "df = df_raw.drop(['img_name'], axis=1)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 146,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hsM8AKWP5rqY",
        "outputId": "2369f872-a4f2-45ef-a52f-3ff4a4ef5630"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "num_pixels = 2304\n",
            "img_height = 48\n",
            "img_width = 48\n"
          ]
        }
      ],
      "source": [
        "num_pixels = len(df['pixels'][0].split(\" \"))\n",
        "img_height = int(np.sqrt(len(df['pixels'][0].split(\" \"))))\n",
        "img_width = int(np.sqrt(len(df['pixels'][0].split(\" \"))))\n",
        "print(f'num_pixels = {num_pixels}\\nimg_height = {img_height}\\nimg_width = {img_width}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 147,
      "metadata": {
        "id": "SUGrrrRWBvKe"
      },
      "outputs": [],
      "source": [
        "# String of pixels to array of pixels\n",
        "df['pixels'] = df['pixels'].apply(lambda x: np.array(x.split(), dtype='float'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 168,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 690
        },
        "id": "b_tqPZnQ3e9G",
        "outputId": "ecf3df35-0175-43d1-8aee-c45452509721"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1440 with 15 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(20, 20))\n",
        "\n",
        "for i in range(15):  \n",
        "    index = np.random.randint(0, len(df))\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])   \n",
        "    plt.grid(False)\n",
        "    plt.subplot(5, 5, i+1)\n",
        "    plt.imshow(df['pixels'].iloc[index].reshape(48, 48),\"gray\")\n",
        "    plt.title(' Age: {}\\n Ethnicity: {}\\n gender: {}'.format(df['age'].iloc[index], {0:\"White\", 1:\"Black\", 2:\"Asian\", 3:\"Indian\", 4:\"Hispanic\"}[df['ethnicity'].iloc[index]], {0:\"Male\", 1:\"Female\"}[df['gender'].iloc[index]]),loc=\"left\",color='red',fontsize = 8)\n",
        "\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 148,
      "metadata": {
        "id": "KP_VT6Ff8qK_"
      },
      "outputs": [],
      "source": [
        "x = np.array(df['pixels'].tolist())\n",
        "x = np.reshape(x, (-1, img_height, img_width, 1))\n",
        "x = x/255 # Normalizing the images\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 149,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BRyeyVfR9FcQ",
        "outputId": "9cfa8283-b31f-4220-8625-43db6f67c9a0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(23705, 48, 48, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 149
        }
      ],
      "source": [
        "\"\"\"\n",
        "23705 48x48 images with only 1 channel (no color, just brightness intensity)\n",
        "\"\"\"\n",
        "x.shape "
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## First model - Gender prediction"
      ],
      "metadata": {
        "id": "yPFPqYY_AZwe"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 158,
      "metadata": {
        "id": "Dx4WCakvEgEw"
      },
      "outputs": [],
      "source": [
        "y = np.array(df['gender'])\n",
        "\n",
        "x_train_gender, x_test_gender, y_train_gender, y_test_gender = train_test_split(x, y, test_size=0.2, random_state=222)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 161,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "usTK42gdDh56",
        "outputId": "13ccc5cb-23e7-4109-f55b-bf9c68da1ce9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/40\n",
            "593/593 [==============================] - 6s 9ms/step - loss: 0.4638 - accuracy: 0.7781\n",
            "Epoch 2/40\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 0.3525 - accuracy: 0.8480\n",
            "Epoch 3/40\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 0.3222 - accuracy: 0.8627\n",
            "Epoch 4/40\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 0.3010 - accuracy: 0.8713\n",
            "Epoch 5/40\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 0.2846 - accuracy: 0.8804\n",
            "Epoch 6/40\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 0.2726 - accuracy: 0.8832\n",
            "Epoch 7/40\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 0.2598 - accuracy: 0.8918\n",
            "Epoch 8/40\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 0.2504 - accuracy: 0.8942\n",
            "Epoch 9/40\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 0.2374 - accuracy: 0.9015\n",
            "Epoch 10/40\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 0.2360 - accuracy: 0.9027\n",
            "Epoch 11/40\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 0.2247 - accuracy: 0.9073\n",
            "Epoch 12/40\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 0.2199 - accuracy: 0.9099\n",
            "Epoch 13/40\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 0.2109 - accuracy: 0.9129\n",
            "Epoch 14/40\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 0.2079 - accuracy: 0.9145\n",
            "Epoch 15/40\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 0.2034 - accuracy: 0.9177\n",
            "Epoch 16/40\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 0.1967 - accuracy: 0.9215\n",
            "Epoch 17/40\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 0.1937 - accuracy: 0.9229\n",
            "Epoch 18/40\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 0.1864 - accuracy: 0.9249\n",
            "Epoch 19/40\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 0.1817 - accuracy: 0.9271\n",
            "Epoch 20/40\n",
            "593/593 [==============================] - 4s 7ms/step - loss: 0.1805 - accuracy: 0.9296\n",
            "Epoch 21/40\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 0.1745 - accuracy: 0.9299\n",
            "Epoch 22/40\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 0.1734 - accuracy: 0.9313\n",
            "Epoch 23/40\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 0.1698 - accuracy: 0.9338\n",
            "Epoch 24/40\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 0.1624 - accuracy: 0.9372\n",
            "Epoch 25/40\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 0.1629 - accuracy: 0.9368\n",
            "Epoch 26/40\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 0.1612 - accuracy: 0.9366\n",
            "Epoch 27/40\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 0.1595 - accuracy: 0.9370\n",
            "Epoch 28/40\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 0.1530 - accuracy: 0.9397\n",
            "Epoch 29/40\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 0.1500 - accuracy: 0.9418\n",
            "Epoch 30/40\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 0.1500 - accuracy: 0.9422\n",
            "Epoch 31/40\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 0.1471 - accuracy: 0.9432\n",
            "Epoch 32/40\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 0.1460 - accuracy: 0.9442\n",
            "Epoch 33/40\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 0.1405 - accuracy: 0.9472\n",
            "Epoch 34/40\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 0.1374 - accuracy: 0.9463\n",
            "Epoch 35/40\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 0.1347 - accuracy: 0.9478\n",
            "Epoch 36/40\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 0.1357 - accuracy: 0.9474\n",
            "Epoch 37/40\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 0.1341 - accuracy: 0.9470\n",
            "Epoch 38/40\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 0.1340 - accuracy: 0.9493\n",
            "Epoch 39/40\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 0.1296 - accuracy: 0.9515\n",
            "Epoch 40/40\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 0.1255 - accuracy: 0.9502\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fea179e4390>"
            ]
          },
          "metadata": {},
          "execution_count": 161
        }
      ],
      "source": [
        "model_gender = build_model(output_act_fn='sigmoid', output_num_neurons=1, loss='binary_crossentropy', metrics=['accuracy'] ) \n",
        "model_gender.fit(x_train_gender, y_train_gender, epochs=40)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classification_evaluation(model_gender, x_test_gender, y_test_gender)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "id": "nTXizSxnArI1",
        "outputId": "3888a864-79a9-4384-cb4e-1c5933dd2b5c"
      },
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Accuracy: 90.12866483864164%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Second model - Ethnicity prediction"
      ],
      "metadata": {
        "id": "pvJE06DlBwfi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_eth = np.array(df['ethnicity'])\n",
        "\n",
        "x_train_eth, x_test_eth, y_train_eth, y_test_eth = train_test_split(x, y_eth, test_size=0.2, random_state=222)"
      ],
      "metadata": {
        "id": "8sLDsnqeB22D"
      },
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_eth = build_model(output_act_fn='softmax', output_num_neurons=5, loss='sparse_categorical_crossentropy', metrics=['accuracy'] ) \n",
        "model_eth.fit(x_train_eth, y_train_eth, epochs=26)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DW5vWekwCJkm",
        "outputId": "bc49bd88-1ae4-4208-f4e5-49802e553716"
      },
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/26\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 1.0158 - accuracy: 0.6242\n",
            "Epoch 2/26\n",
            "593/593 [==============================] - 3s 6ms/step - loss: 0.7846 - accuracy: 0.7259\n",
            "Epoch 3/26\n",
            "593/593 [==============================] - 3s 6ms/step - loss: 0.7141 - accuracy: 0.7549\n",
            "Epoch 4/26\n",
            "593/593 [==============================] - 3s 6ms/step - loss: 0.6731 - accuracy: 0.7667\n",
            "Epoch 5/26\n",
            "593/593 [==============================] - 3s 6ms/step - loss: 0.6362 - accuracy: 0.7818\n",
            "Epoch 6/26\n",
            "593/593 [==============================] - 3s 6ms/step - loss: 0.6037 - accuracy: 0.7902\n",
            "Epoch 7/26\n",
            "593/593 [==============================] - 3s 6ms/step - loss: 0.5782 - accuracy: 0.7991\n",
            "Epoch 8/26\n",
            "593/593 [==============================] - 3s 6ms/step - loss: 0.5547 - accuracy: 0.8082\n",
            "Epoch 9/26\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 0.5281 - accuracy: 0.8163\n",
            "Epoch 10/26\n",
            "593/593 [==============================] - 3s 6ms/step - loss: 0.5071 - accuracy: 0.8228\n",
            "Epoch 11/26\n",
            "593/593 [==============================] - 3s 6ms/step - loss: 0.4898 - accuracy: 0.8295\n",
            "Epoch 12/26\n",
            "593/593 [==============================] - 3s 6ms/step - loss: 0.4728 - accuracy: 0.8353\n",
            "Epoch 13/26\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 0.4539 - accuracy: 0.8413\n",
            "Epoch 14/26\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 0.4407 - accuracy: 0.8467\n",
            "Epoch 15/26\n",
            "593/593 [==============================] - 3s 6ms/step - loss: 0.4254 - accuracy: 0.8506\n",
            "Epoch 16/26\n",
            "593/593 [==============================] - 3s 6ms/step - loss: 0.3982 - accuracy: 0.8584\n",
            "Epoch 17/26\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 0.3899 - accuracy: 0.8613\n",
            "Epoch 18/26\n",
            "593/593 [==============================] - 3s 6ms/step - loss: 0.3857 - accuracy: 0.8664\n",
            "Epoch 19/26\n",
            "593/593 [==============================] - 3s 6ms/step - loss: 0.3689 - accuracy: 0.8718\n",
            "Epoch 20/26\n",
            "593/593 [==============================] - 3s 6ms/step - loss: 0.3596 - accuracy: 0.8744\n",
            "Epoch 21/26\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 0.3520 - accuracy: 0.8755\n",
            "Epoch 22/26\n",
            "593/593 [==============================] - 3s 6ms/step - loss: 0.3332 - accuracy: 0.8837\n",
            "Epoch 23/26\n",
            "593/593 [==============================] - 3s 6ms/step - loss: 0.3246 - accuracy: 0.8863\n",
            "Epoch 24/26\n",
            "593/593 [==============================] - 3s 6ms/step - loss: 0.3225 - accuracy: 0.8859\n",
            "Epoch 25/26\n",
            "593/593 [==============================] - 3s 6ms/step - loss: 0.3098 - accuracy: 0.8902\n",
            "Epoch 26/26\n",
            "593/593 [==============================] - 3s 6ms/step - loss: 0.3074 - accuracy: 0.8902\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fea161e3fd0>"
            ]
          },
          "metadata": {},
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "classification_evaluation(model_eth, x_test_eth, y_test_eth)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "outputId": "ae8bcfae-85ba-48ca-f874-1556607dc262",
        "id": "1J25v4dICRjH"
      },
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            " Accuracy: 78.9706812908669%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Third model - Age prediction"
      ],
      "metadata": {
        "id": "JczzWV2oNMys"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_age = np.array(df['age'])\n",
        "\n",
        "x_train_age, x_test_age, y_train_age, y_test_age = train_test_split(x, y_age, test_size=0.2, random_state=222)"
      ],
      "metadata": {
        "id": "LExYD107NMy0"
      },
      "execution_count": 151,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_age = build_model(output_act_fn=None, output_num_neurons=1, loss='mae', metrics=['mae'] ) \n",
        "model_age.fit(x_train_age, y_train_age, epochs=26)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "18ff8446-a4dc-4027-f521-b82bc1c0b748",
        "id": "DReD9WyqNMy0"
      },
      "execution_count": 154,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/26\n",
            "593/593 [==============================] - 5s 7ms/step - loss: 14.8326 - mae: 14.8326\n",
            "Epoch 2/26\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 12.2236 - mae: 12.2236\n",
            "Epoch 3/26\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 11.5578 - mae: 11.5578\n",
            "Epoch 4/26\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 11.0317 - mae: 11.0317\n",
            "Epoch 5/26\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 10.7124 - mae: 10.7124\n",
            "Epoch 6/26\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 10.3191 - mae: 10.3191\n",
            "Epoch 7/26\n",
            "593/593 [==============================] - 4s 7ms/step - loss: 9.9420 - mae: 9.9420\n",
            "Epoch 8/26\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 9.6264 - mae: 9.6264\n",
            "Epoch 9/26\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 9.3303 - mae: 9.3303\n",
            "Epoch 10/26\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 9.1162 - mae: 9.1162\n",
            "Epoch 11/26\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 8.9004 - mae: 8.9004\n",
            "Epoch 12/26\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 8.7566 - mae: 8.7566\n",
            "Epoch 13/26\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 8.5424 - mae: 8.5424\n",
            "Epoch 14/26\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 8.4396 - mae: 8.4396\n",
            "Epoch 15/26\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 8.2699 - mae: 8.2699\n",
            "Epoch 16/26\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 8.1497 - mae: 8.1497\n",
            "Epoch 17/26\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 7.9921 - mae: 7.9921\n",
            "Epoch 18/26\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 7.9396 - mae: 7.9396\n",
            "Epoch 19/26\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 7.8211 - mae: 7.8211\n",
            "Epoch 20/26\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 7.6769 - mae: 7.6769\n",
            "Epoch 21/26\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 7.6564 - mae: 7.6564\n",
            "Epoch 22/26\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 7.5685 - mae: 7.5685\n",
            "Epoch 23/26\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 7.4954 - mae: 7.4954\n",
            "Epoch 24/26\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 7.4303 - mae: 7.4303\n",
            "Epoch 25/26\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 7.3269 - mae: 7.3269\n",
            "Epoch 26/26\n",
            "593/593 [==============================] - 4s 6ms/step - loss: 7.3219 - mae: 7.3219\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fe97a72e050>"
            ]
          },
          "metadata": {},
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_age.evaluate(x_test_age, y_test_age)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0d72a70b-4201-4265-b445-c1e87fd47cd7",
        "id": "tcDzGgUvNMy0"
      },
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "149/149 [==============================] - 1s 5ms/step - loss: 7.6992 - mae: 7.6992\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[7.699179172515869, 7.699179172515869]"
            ]
          },
          "metadata": {},
          "execution_count": 157
        }
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
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      "name": "AgeEthnicityGender_CNN.ipynb",
      "provenance": [],
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    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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}