CNN_Model_Age_Gender_Ethnicity.ipynb

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  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "CNN_Model_Age_Gender_Ethnicity.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
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      "name": "python3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/hargurjeet/DeepLearning/blob/main/CNN_Model_Age_Gender_Ethnicity.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eYJT0u4XBaUr"
      },
      "source": [
        "# **Ethnicity Predicition Using TensorFlow- Kaggle Age, Gender and Ethnicity Dataset**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w169jScgC-y-"
      },
      "source": [
        "It is an open source artificial intelligence library, using data flow graphs to build models. It allows developers to create large-scale neural networks with many layers. TensorFlow is mainly used for: Classification, Perception, Understanding, Discovering, Prediction and Creation.\n",
        "\n",
        "Today I build a classifer using TensorFlow that predicts the ethinicy of a given image."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "32VbKWesC-qV"
      },
      "source": [
        "# **Table Of Contents**<a name=\"top\"></a>\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "  1. [About the Dataset](#1)\n",
        "  2. [Loading the dataset Preprocessing](#2)\n",
        "  3. [Building Neural network using Tensorflow](#3)\n",
        "  4. [Analysing Results](#4)\n",
        "  5. [Hyper Parameterization](#5)\n",
        "  6. [Making Predicition on the best parameters](#6)\n",
        "  7. [Summary](#7)\n",
        "  8. [Future Work](#8)\n",
        "  9. [References](#9)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SZN6uxzkFSXd"
      },
      "source": [
        "# <font color=\"white\" size=+2.0><b>1: AboutDataset</b></font> <a name=\"1\"></a>\n",
        "\n",
        "\n",
        "---\n",
        "<a href=\"#top\" class=\"btn btn-primary btn-sm\" role=\"button\" aria-pressed=\"true\" style=\"color:white\" data-toggle=\"popover\">Go to TOC</a> "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j6o0XOnwKBGu"
      },
      "source": [
        "This dataset includes a CSV of facial images that are labeled on the basis of age, gender, and ethnicity.\n",
        "The dataset includes 27305 rows and 5 columns.\n",
        "- Age - Age of the person in the image\n",
        "- Gender - Gender of the person\n",
        "- Ethnicity - Specifies the ethnicity of the person\n",
        "- Img name - Name of the Image\n",
        "- pixels - Array to String of the image pixels"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j-w47jWoFjFR"
      },
      "source": [
        "# <font color=\"white\" size=+2.0><b>2: Loading and Preprocessing the datasets</b></font> <a name=\"2\"></a>\n",
        "\n",
        "\n",
        "---\n",
        "<a href=\"#top\" class=\"btn btn-primary btn-sm\" role=\"button\" aria-pressed=\"true\" style=\"color:white\" data-toggle=\"popover\">Go to TOC</a> "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mYJXnoTpKr88"
      },
      "source": [
        "Importing all the required libraries to get us started working on this dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fzy4VOnHDqNP",
        "outputId": "b3b807ed-d6a9-41c7-d636-609433705d56"
      },
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import datasets, layers, models\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import numpy as np\n",
        "import pandas as pd"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.5.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
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        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "id": "V6_v-TGWD7zL",
        "outputId": "7d80d3f0-d215-462b-c670-0f68acca3499"
      },
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-d938ec92-3af3-4a44-ad4b-a5514ef654fb\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-d938ec92-3af3-4a44-ad4b-a5514ef654fb\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"hargurjeet\",\"key\":\"c3882bdbb49388021171402c7018655e\"}'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dx67i5XgEovQ",
        "outputId": "d0bfef6a-76f0-4fdb-ecc5-209fca426aa6"
      },
      "source": [
        "! pip install opendatasets --upgrade\n",
        "import opendatasets as od\n",
        "\n",
        "dataset_url = 'https://www.kaggle.com/nipunarora8/age-gender-and-ethnicity-face-data-csv'\n",
        "od.download('https://www.kaggle.com/nipunarora8/age-gender-and-ethnicity-face-data-csv')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting opendatasets\n",
            "  Downloading https://files.pythonhosted.org/packages/b6/3f/cdd30cbd950efdb1fa5c766ffb2c38d1da5314292b6cd226e3871171a776/opendatasets-0.1.20-py3-none-any.whl\n",
            "Requirement already satisfied, skipping upgrade: kaggle in /usr/local/lib/python3.7/dist-packages (from opendatasets) (1.5.12)\n",
            "Requirement already satisfied, skipping upgrade: click in /usr/local/lib/python3.7/dist-packages (from opendatasets) (7.1.2)\n",
            "Requirement already satisfied, skipping upgrade: tqdm in /usr/local/lib/python3.7/dist-packages (from opendatasets) (4.41.1)\n",
            "Requirement already satisfied, skipping upgrade: six>=1.10 in /usr/local/lib/python3.7/dist-packages (from kaggle->opendatasets) (1.15.0)\n",
            "Requirement already satisfied, skipping upgrade: certifi in /usr/local/lib/python3.7/dist-packages (from kaggle->opendatasets) (2021.5.30)\n",
            "Requirement already satisfied, skipping upgrade: requests in /usr/local/lib/python3.7/dist-packages (from kaggle->opendatasets) (2.23.0)\n",
            "Requirement already satisfied, skipping upgrade: python-slugify in /usr/local/lib/python3.7/dist-packages (from kaggle->opendatasets) (5.0.2)\n",
            "Requirement already satisfied, skipping upgrade: urllib3 in /usr/local/lib/python3.7/dist-packages (from kaggle->opendatasets) (1.24.3)\n",
            "Requirement already satisfied, skipping upgrade: python-dateutil in /usr/local/lib/python3.7/dist-packages (from kaggle->opendatasets) (2.8.1)\n",
            "Requirement already satisfied, skipping upgrade: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle->opendatasets) (2.10)\n",
            "Requirement already satisfied, skipping upgrade: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle->opendatasets) (3.0.4)\n",
            "Requirement already satisfied, skipping upgrade: text-unidecode>=1.3 in /usr/local/lib/python3.7/dist-packages (from python-slugify->kaggle->opendatasets) (1.3)\n",
            "Installing collected packages: opendatasets\n",
            "Successfully installed opendatasets-0.1.20\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\r  0%|          | 0.00/63.2M [00:00<?, ?B/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Downloading age-gender-and-ethnicity-face-data-csv.zip to ./age-gender-and-ethnicity-face-data-csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 63.2M/63.2M [00:01<00:00, 33.7MB/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "zeA9RJuvEzh8",
        "outputId": "8de851f2-6716-4da9-c121-726d8df41187"
      },
      "source": [
        "df = pd.read_csv('/content/age-gender-and-ethnicity-face-data-csv/age_gender.csv')\n",
        "\n",
        "df.head(10)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>ethnicity</th>\n",
              "      <th>gender</th>\n",
              "      <th>img_name</th>\n",
              "      <th>pixels</th>\n",
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              "      <th>5</th>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age  ...                                             pixels\n",
              "0    1  ...  129 128 128 126 127 130 133 135 139 142 145 14...\n",
              "1    1  ...  164 74 111 168 169 171 175 182 184 188 193 199...\n",
              "2    1  ...  67 70 71 70 69 67 70 79 90 103 116 132 145 155...\n",
              "3    1  ...  193 197 198 200 199 200 202 203 204 205 208 21...\n",
              "4    1  ...  202 205 209 210 209 209 210 211 212 214 218 21...\n",
              "5    1  ...  195 198 200 200 198 198 199 199 198 197 197 19...\n",
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              "\n",
              "[10 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zB_RD28rFR_c"
      },
      "source": [
        "Performing null check"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 48
        },
        "id": "a8kUKm8cEzdT",
        "outputId": "fd74eb4a-8e87-43a6-9cb2-440301e39257"
      },
      "source": [
        "df[df.isnull().any(axis=1)]"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>ethnicity</th>\n",
              "      <th>gender</th>\n",
              "      <th>img_name</th>\n",
              "      <th>pixels</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [age, ethnicity, gender, img_name, pixels]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JokA1MzPEzav",
        "outputId": "70cbefb3-4e1f-4d65-bbde-5010b2fb1aaf"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(23705, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HujLdacXEviI"
      },
      "source": [
        "# <font color=\"white\" size=+2.0><b>3: Explainatory Data Analysis </b></font> <a name=\"3\"></a>\n",
        "\n",
        "\n",
        "---\n",
        "<a href=\"#top\" class=\"btn btn-primary btn-sm\" role=\"button\" aria-pressed=\"true\" style=\"color:white\" data-toggle=\"popover\">Go to TOC</a> "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k24L-l_jEp6y"
      },
      "source": [
        "Let us see the spread of age across the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-17sQ5BIEv1u",
        "outputId": "1bf642b2-b2ae-44d0-cab4-5dfb7d58d2a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        }
      },
      "source": [
        "import seaborn as sns\n",
        "\n",
        "sns.set_theme(style=\"whitegrid\")\n",
        "plt.rcParams[\"figure.figsize\"] = (16,6)\n",
        "sns.histplot(data=df, x=\"age\");"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1RqQj400GPgO"
      },
      "source": [
        "Let us see the spread of ethnicity across the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "95ydcvf-GSQu",
        "outputId": "8c405df4-2f04-42d0-d11b-8b707fa4ca1d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        }
      },
      "source": [
        "sns.set_theme(style=\"whitegrid\")\n",
        "ax = sns.countplot(x=\"ethnicity\", hue=\"gender\", data=df)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WmSY51YwGn8_"
      },
      "source": [
        "Ethinicity 0 seems to be predomnant across the dataset but the distrbution on male and female is equal"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SSzW5VzjHAGh",
        "outputId": "cbc90b7c-a11f-4c5f-c24a-f3ba4172217a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        }
      },
      "source": [
        "ax = sns.countplot(x=\"gender\", hue=\"gender\", data=df)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TC6DKItTG_k8"
      },
      "source": [
        "Gender distribution seems to be uniform"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "id": "qnbUw6oBOJI9",
        "outputId": "5135c9e0-23bd-4233-899b-521b1d7721e4"
      },
      "source": [
        "df['pixels']=df['pixels'].apply(lambda x:  np.array(x.split(), dtype=\"float32\"))\n",
        "\n",
        "plt.figure(figsize=(16,16))\n",
        "for i in range(2500,2510):\n",
        "    plt.subplot(5,5,(i%25)+1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.grid(False)\n",
        "    plt.imshow(df['pixels'].iloc[i].reshape(48,48))\n",
        "    plt.xlabel(\n",
        "        \"Age:\"+str(df['age'].iloc[i])+\n",
        "        \"  Ethnicity:\"+str(df['ethnicity'].iloc[i])+\n",
        "        \"  Gender:\"+ str(df['gender'].iloc[i])\n",
        "    )\n",
        "plt.show()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x1152 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Goei2U0QFi9R"
      },
      "source": [
        "# <font color=\"white\" size=+2.0><b>3: Building Neural Network using TensorFlow</b></font> <a name=\"3\"></a>\n",
        "\n",
        "\n",
        "---\n",
        "<a href=\"#top\" class=\"btn btn-primary btn-sm\" role=\"button\" aria-pressed=\"true\" style=\"color:white\" data-toggle=\"popover\">Go to TOC</a> "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aX4ejqJNKl2S"
      },
      "source": [
        "df.drop('img_name', axis = 1, inplace=True)\n",
        "\n",
        "X = df.pixels\n",
        "y_gender = df.gender\n",
        "y_age = df.age\n",
        "y_ethnicity = df.ethnicity"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AECQ3s3ZYwUn"
      },
      "source": [
        "X = np.stack(np.array(X), axis=0)\n",
        "pictures = np.reshape(X, (-1, 48, 48, 1))"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YYVWd5LwMrgf"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train_gender, X_test_gender, y_train_gender, y_test_gender = train_test_split(pictures, y_gender, test_size=0.2, random_state=1)\n",
        "\n",
        "X_train_gender, X_val_gender, y_train_gender, y_val_gender = train_test_split(X_train_gender, y_train_gender, test_size=0.25, random_state=1) # 0.25 x 0.8 = 0.2\n",
        "\n",
        "X_train_age, X_test_age, y_train_age, y_test_age = train_test_split(pictures, y_age, test_size=0.2, random_state=1)\n",
        "\n",
        "X_train_age, X_val_age, y_train_age, y_val_age = train_test_split(X_train_age, y_train_age, test_size=0.25, random_state=1) # 0.25 x 0.8 = 0.2\n",
        "\n",
        "X_train_ethnicity, X_test_ethnicity, y_train_ethnicity, y_test_ethnicity = train_test_split(pictures, y_ethnicity, test_size=0.2, random_state=1)\n",
        "\n",
        "X_train_ethnicity, X_val_ethnicity, y_train_ethnicity, y_val_ethnicity = train_test_split(X_train_ethnicity, y_train_ethnicity, test_size=0.25, random_state=1) # 0.25 x 0.8 = 0.2"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IkG6Xw3CQGro",
        "outputId": "b767d38a-f327-4a33-cd9d-ef7699f61a3b"
      },
      "source": [
        "# Model for Gender\n",
        "\n",
        "gender_model = tf.keras.models.Sequential([\n",
        "  tf.keras.layers.Conv2D(64, (3,3), activation='relu', input_shape=(48, 48, 1)),\n",
        "  tf.keras.layers.MaxPooling2D(2, 2),\n",
        "  tf.keras.layers.Conv2D(64, (3,3), activation='relu'),\n",
        "  tf.keras.layers.MaxPooling2D(2,2),\n",
        "  tf.keras.layers.Flatten(),\n",
        "  tf.keras.layers.Dense(128, activation='relu'),\n",
        "  tf.keras.layers.Dense(1, activation='sigmoid')\n",
        "])\n",
        "gender_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "gender_model.summary()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 46, 46, 64)        640       \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 23, 23, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 21, 21, 64)        36928     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 10, 10, 64)        0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 6400)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 128)               819328    \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 129       \n",
            "=================================================================\n",
            "Total params: 857,025\n",
            "Trainable params: 857,025\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ihhq4d16Nt67"
      },
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "train_datagen = ImageDataGenerator(\n",
        "    rescale=1./255,\n",
        "    featurewise_center=True,\n",
        "    featurewise_std_normalization=True,\n",
        "    rotation_range=40,\n",
        "    width_shift_range=0.2,\n",
        "    height_shift_range=0.2,\n",
        "    horizontal_flip=True,\n",
        "    fill_mode='nearest')"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XMJMPS-mO43q"
      },
      "source": [
        " train_generator = train_datagen.flow(\n",
        "        X_train_gender, y_train_gender, # This is the source directory for training images\n",
        "        batch_size=128)\n",
        " \n",
        " validation_generator = train_datagen.flow(\n",
        "        X_val_gender, y_val_gender,\n",
        "        batch_size=64)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 872
        },
        "id": "bv8ZoazLQC4v",
        "outputId": "fd1c1400-559a-4876-bc20-323c4f2f4201"
      },
      "source": [
        "history = gender_model.fit(\n",
        "      train_generator,\n",
        "      steps_per_epoch=len(X_train_gender)/ 128,  \n",
        "      epochs=15,\n",
        "      validation_data=validation_generator,  \n",
        "      verbose=2)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras_preprocessing/image/image_data_generator.py:720: UserWarning: This ImageDataGenerator specifies `featurewise_center`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n",
            "  warnings.warn('This ImageDataGenerator specifies '\n",
            "/usr/local/lib/python3.7/dist-packages/keras_preprocessing/image/image_data_generator.py:728: UserWarning: This ImageDataGenerator specifies `featurewise_std_normalization`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n",
            "  warnings.warn('This ImageDataGenerator specifies '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "111/111 - 38s - loss: 0.6498 - accuracy: 0.6192 - val_loss: 0.6347 - val_accuracy: 0.6404\n",
            "Epoch 2/15\n",
            "111/111 - 9s - loss: 0.6162 - accuracy: 0.6615 - val_loss: 0.6174 - val_accuracy: 0.6619\n",
            "Epoch 3/15\n",
            "111/111 - 9s - loss: 0.6013 - accuracy: 0.6788 - val_loss: 0.6106 - val_accuracy: 0.6686\n",
            "Epoch 4/15\n",
            "111/111 - 9s - loss: 0.6010 - accuracy: 0.6767 - val_loss: 0.6076 - val_accuracy: 0.6672\n",
            "Epoch 5/15\n",
            "111/111 - 9s - loss: 0.5880 - accuracy: 0.6906 - val_loss: 0.5999 - val_accuracy: 0.6807\n",
            "Epoch 6/15\n",
            "111/111 - 9s - loss: 0.5758 - accuracy: 0.7001 - val_loss: 0.5868 - val_accuracy: 0.6916\n",
            "Epoch 7/15\n",
            "111/111 - 9s - loss: 0.5684 - accuracy: 0.7045 - val_loss: 0.5843 - val_accuracy: 0.6883\n",
            "Epoch 8/15\n",
            "111/111 - 9s - loss: 0.5588 - accuracy: 0.7138 - val_loss: 0.5697 - val_accuracy: 0.7041\n",
            "Epoch 9/15\n",
            "111/111 - 9s - loss: 0.5430 - accuracy: 0.7288 - val_loss: 0.5645 - val_accuracy: 0.7087\n",
            "Epoch 10/15\n",
            "111/111 - 9s - loss: 0.5327 - accuracy: 0.7328 - val_loss: 0.5428 - val_accuracy: 0.7226\n",
            "Epoch 11/15\n",
            "111/111 - 9s - loss: 0.5247 - accuracy: 0.7398 - val_loss: 0.5372 - val_accuracy: 0.7300\n",
            "Epoch 12/15\n",
            "111/111 - 9s - loss: 0.5128 - accuracy: 0.7455 - val_loss: 0.5170 - val_accuracy: 0.7437\n",
            "Epoch 13/15\n",
            "111/111 - 9s - loss: 0.5045 - accuracy: 0.7541 - val_loss: 0.5146 - val_accuracy: 0.7469\n",
            "Epoch 14/15\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-25-881c04877aa3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m       \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m       \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidation_generator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m       verbose=2)\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1223\u001b[0m               \u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1224\u001b[0m               \u001b[0mreturn_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1225\u001b[0;31m               _use_cached_eval_dataset=True)\n\u001b[0m\u001b[1;32m   1226\u001b[0m           \u001b[0mval_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'val_'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mval\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mval_logs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1227\u001b[0m           \u001b[0mepoch_logs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval_logs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(self, x, y, batch_size, verbose, sample_weight, steps, callbacks, max_queue_size, workers, use_multiprocessing, return_dict, **kwargs)\u001b[0m\n\u001b[1;32m   1487\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mtrace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTrace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'test'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep_num\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_r\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1488\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_test_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1489\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1490\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1491\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    887\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    888\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 889\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    890\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    891\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    922\u001b[0m       \u001b[0;31m# In this case we have not created variables on the first call. So we can\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    923\u001b[0m       \u001b[0;31m# run the first trace but we should fail if variables are created.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 924\u001b[0;31m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    925\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_created_variables\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    926\u001b[0m         raise ValueError(\"Creating variables on a non-first call to a function\"\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   3022\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[1;32m   3023\u001b[0m     return graph_function._call_flat(\n\u001b[0;32m-> 3024\u001b[0;31m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[0m\u001b[1;32m   3025\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3026\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1959\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1960\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1961\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1962\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1963\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    594\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    595\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 596\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    597\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    598\u001b[0m           outputs = execute.execute_with_cancellation(\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 60\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     61\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nuAiC2SZFi1H"
      },
      "source": [
        "# <font color=\"white\" size=+2.0><b>4: Analysing Results</b></font> <a name=\"4\"></a>\n",
        "\n",
        "\n",
        "---\n",
        "<a href=\"#top\" class=\"btn btn-primary btn-sm\" role=\"button\" aria-pressed=\"true\" style=\"color:white\" data-toggle=\"popover\">Go to TOC</a> "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CPlEu4tZQC2l"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "def plot_graphs(history, string):\n",
        "  plt.plot(history.history[string])\n",
        "  plt.plot(history.history['val_'+string])\n",
        "  plt.xlabel(\"Epochs\")\n",
        "  plt.ylabel(string)\n",
        "  plt.legend([string, 'val_'+string])\n",
        "  plt.show()\n",
        "  \n",
        "plot_graphs(history, \"accuracy\")\n",
        "plot_graphs(history, \"loss\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RTOmXa24QCzz"
      },
      "source": [
        "gender_model.evaluate(X_test_gender, y_test_gender)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tTMWLCVUKFZI"
      },
      "source": [
        "## Model for predicting ethnicity"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jTIfV410aG7t"
      },
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "train_datagen = ImageDataGenerator(\n",
        "    rescale=1./255,\n",
        "    rotation_range=40,\n",
        "    brightness_range=[0.8,1.2],\n",
        "    zoom_range=0.2,\n",
        "    shear_range = 0.2,\n",
        "    width_shift_range=0.2,\n",
        "    height_shift_range=0.2,\n",
        "    horizontal_flip=True,\n",
        "    fill_mode='nearest')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TZXGtDw3LfXE"
      },
      "source": [
        "# Model for Ethnicity\n",
        "\n",
        "ethnicity_model = tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Conv2D(64, (3,3), activation='relu', input_shape=(48,48,1)),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    tf.keras.layers.Conv2D(128, (3,3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    tf.keras.layers.Conv2D(128, (3,3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    tf.keras.layers.Conv2D(64, (3,3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(512, activation='relu'),\n",
        "    tf.keras.layers.Dropout(0.5),\n",
        "    # tf.keras.layers.Dense(8, activation='relu'),\n",
        "    # tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.Dense(5, activation='softmax'),\n",
        "])\n",
        "ethnicity_model.compile(optimizer='rmsprop', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "ethnicity_model.summary()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RG-6nBtLQCu3"
      },
      "source": [
        "train_generator = train_datagen.flow(\n",
        "        X_train_ethnicity, y_train_ethnicity, \n",
        "        batch_size=128)\n",
        " \n",
        "validation_generator = train_datagen.flow(\n",
        "        X_val_ethnicity, y_val_ethnicity,\n",
        "        batch_size=64)\n",
        " \n",
        "history = ethnicity_model.fit(\n",
        "      train_generator,\n",
        "      steps_per_epoch=len(X_train_ethnicity)/128,  \n",
        "      epochs=15,\n",
        "      validation_data=validation_generator,  \n",
        "      verbose=2)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ofTFmxOgQCsJ"
      },
      "source": [
        "def plot_graphs(history, string):\n",
        "  plt.plot(history.history[string])\n",
        "  plt.plot(history.history['val_'+string])\n",
        "  plt.xlabel(\"Epochs\")\n",
        "  plt.ylabel(string)\n",
        "  plt.legend([string, 'val_'+string])\n",
        "  plt.show()\n",
        "  \n",
        "plot_graphs(history, \"accuracy\")\n",
        "plot_graphs(history, \"loss\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "azd5GSILMVA6"
      },
      "source": [
        "ethnicity_model.evaluate(X_test_ethnicity, y_test_ethnicity)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-c33-vbOOCUR"
      },
      "source": [
        "Predicting the Age"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yxubAjDSOFEW"
      },
      "source": [
        "# Model for Age\n",
        "\n",
        "age_model = tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(48,48,1)),\n",
        "    tf.keras.layers.BatchNormalization(),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    tf.keras.layers.Conv2D(64, (3,3), activation='relu'),\n",
        "    tf.keras.layers.BatchNormalization(),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(64, activation='relu'),\n",
        "    tf.keras.layers.Dropout(0.5),\n",
        "    tf.keras.layers.Dense(1),\n",
        "])\n",
        "age_model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy'])\n",
        "\n",
        "age_model.summary()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VAgdgTQQjsmq"
      },
      "source": [
        "age_model = tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Conv2D(16, (3,3), activation='relu', input_shape=(48,48,1)),\n",
        "    tf.keras.layers.Conv2D(32, (3,3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    tf.keras.layers.Dropout(0.3),\n",
        "    tf.keras.layers.Conv2D(64, (3,3), activation='relu'),\n",
        "    tf.keras.layers.Conv2D(96, (3,3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    tf.keras.layers.Dropout(0.3),\n",
        "    tf.keras.layers.Conv2D(128, (3,3), activation='relu'),\n",
        "    tf.keras.layers.Conv2D(160, (3,3), activation='relu'),\n",
        "    tf.keras.layers.BatchNormalization(),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(512, activation='relu'),\n",
        "    # tf.keras.layers.Dense(128, activation='relu'),\n",
        "    tf.keras.layers.Dense(1),\n",
        "])\n",
        "\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "\n",
        "age_model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy'])\n",
        "\n",
        "age_model.summary()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vDxKKC9DBW4o"
      },
      "source": [
        "history = age_model.fit(\n",
        "      X_train_age, y_train_age, \n",
        "      epochs=7,\n",
        "      validation_split=0.2,\n",
        "      batch_size=64)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ofbWKfbCinZ"
      },
      "source": [
        "def plot_graphs(history, string):\n",
        "  plt.plot(history.history[string])\n",
        "  plt.plot(history.history['val_'+string])\n",
        "  plt.xlabel(\"Epochs\")\n",
        "  plt.ylabel(string)\n",
        "  plt.legend([string, 'val_'+string])\n",
        "  plt.show()\n",
        "  \n",
        "plot_graphs(history, \"accuracy\")\n",
        "plot_graphs(history, \"loss\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_v5ue_brXHa-"
      },
      "source": [
        "age_model.evaluate(X_test_age, y_test_age)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YgIaobT3-BKg"
      },
      "source": [
        "print ('Actual Values:', y_test_age[:10])\n",
        "print()\n",
        "y_age_pred = age_model.predict(X_test_age[:10])\n",
        "print ('Predicted Values:', y_age_pred)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wyChZgr2FmbC"
      },
      "source": [
        "# <font color=\"white\" size=+2.0><b>5: Hyper Parameterization</b></font> <a name=\"5\"></a>\n",
        "\n",
        "\n",
        "---\n",
        "<a href=\"#top\" class=\"btn btn-primary btn-sm\" role=\"button\" aria-pressed=\"true\" style=\"color:white\" data-toggle=\"popover\">Go to TOC</a> "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GxHeyuBcGyfw"
      },
      "source": [
        "## Optimizing the best parameters\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorboard.plugins.hparams import api as hp\n",
        "\n",
        "HP_NUM_UNITS = hp.HParam('num_units', hp.Discrete([16, 32]))\n",
        "HP_DROPOUT = hp.HParam('dropout', hp.RealInterval(0.1, 0.2))\n",
        "HP_OPTIMIZER = hp.HParam('optimizer', hp.Discrete(['adam', 'sgd', 'rmsprop']))\n",
        "\n",
        "METRIC_ACCURACY = 'accuracy'\n",
        "\n",
        "# hparams=[HP_NUM_UNITS, HP_DROPOUT, HP_OPTIMIZER]\n",
        "# metrics=[hp.Metric(METRIC_ACCURACY, display_name='Accuracy')]\n",
        "\n",
        "with tf.summary.create_file_writer('logs/hparam_tuning').as_default():\n",
        "\n",
        "  hp.hparams_config(\n",
        "      hparams=[HP_NUM_UNITS, HP_DROPOUT, HP_OPTIMIZER],\n",
        "      metrics=[hp.Metric(METRIC_ACCURACY, display_name='Accuracy')],\n",
        "  )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9SDVk1drGydC"
      },
      "source": [
        "# Model for Ethnicity\n",
        "def train_test_model(hparams):\n",
        "  ethnicity_model = tf.keras.models.Sequential([\n",
        "      tf.keras.layers.Conv2D(64, (3,3), activation='relu', input_shape=(48,48,1)),\n",
        "      tf.keras.layers.MaxPooling2D(2,2),\n",
        "      tf.keras.layers.Conv2D(128, (3,3), activation='relu'),\n",
        "      tf.keras.layers.MaxPooling2D(2,2),\n",
        "      tf.keras.layers.Conv2D(128, (3,3), activation='relu'),\n",
        "      tf.keras.layers.MaxPooling2D(2,2),\n",
        "      tf.keras.layers.Conv2D(64, (3,3), activation='relu'),\n",
        "      tf.keras.layers.MaxPooling2D(2,2),\n",
        "      tf.keras.layers.Flatten(),\n",
        "      tf.keras.layers.Dense(hparams[HP_NUM_UNITS], activation='relu'),\n",
        "      tf.keras.layers.Dropout(hparams[HP_DROPOUT]),\n",
        "      # tf.keras.layers.Dense(8, activation='relu'),\n",
        "      # tf.keras.layers.Dropout(0.2),\n",
        "      tf.keras.layers.Dense(5, activation='softmax'),\n",
        "  ])\n",
        "  ethnicity_model.compile(optimizer=hparams[HP_OPTIMIZER], loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "  \n",
        "  ethnicity_model.fit(train_generator,\n",
        "                      steps_per_epoch=len(X_train_ethnicity)/128,  \n",
        "                      epochs=15,\n",
        "                      validation_data=validation_generator,  \n",
        "                      verbose=2)\n",
        "  \n",
        "  _, accuracy = ethnicity_model.evaluate(X_test_ethnicity, y_test_ethnicity)\n",
        "\n",
        "  return accuracy\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-F9jNWo-GyaO"
      },
      "source": [
        "def run(run_dir, hparams):\n",
        "  with tf.summary.create_file_writer(run_dir).as_default():\n",
        "    hp.hparams(hparams)  # record the values used in this trial\n",
        "    accuracy = train_test_model(hparams)\n",
        "    tf.summary.scalar(METRIC_ACCURACY, accuracy, step=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AzgNeG7FGyX6"
      },
      "source": [
        "session_num = 0\n",
        "\n",
        "for num_units in HP_NUM_UNITS.domain.values:\n",
        "  for dropout_rate in (HP_DROPOUT.domain.min_value, HP_DROPOUT.domain.max_value):\n",
        "    for optimizer in HP_OPTIMIZER.domain.values:\n",
        "      hparams = {\n",
        "          HP_NUM_UNITS: num_units,\n",
        "          HP_DROPOUT: dropout_rate,\n",
        "          HP_OPTIMIZER: optimizer,\n",
        "      }\n",
        "      run_name = \"run-%d\" % session_num\n",
        "      print('--- Starting trial: %s' % run_name)\n",
        "      print({h.name: hparams[h] for h in hparams})\n",
        "      run('logs/hparam_tuning/' + run_name, hparams)\n",
        "      session_num += 1"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WAOTbmSgFmSp"
      },
      "source": [
        "# <font color=\"white\" size=+2.0><b>6: Making Prediction on the best parameters</b></font> <a name=\"6\"></a>\n",
        "\n",
        "\n",
        "---\n",
        "<a href=\"#top\" class=\"btn btn-primary btn-sm\" role=\"button\" aria-pressed=\"true\" style=\"color:white\" data-toggle=\"popover\">Go to TOC</a> "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5r6nSd1xGx9f"
      },
      "source": [
        "# Model for Ethnicity\n",
        "\n",
        "ethnicity_model = tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Conv2D(64, (3,3), activation='relu', input_shape=(48,48,1)),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    tf.keras.layers.Conv2D(128, (3,3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    tf.keras.layers.Conv2D(128, (3,3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    tf.keras.layers.Conv2D(64, (3,3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(32, activation='relu'),\n",
        "    tf.keras.layers.Dropout(0.2),\n",
        "    # tf.keras.layers.Dense(8, activation='relu'),\n",
        "    # tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.Dense(5, activation='softmax'),\n",
        "])\n",
        "ethnicity_model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "ethnicity_model.summary()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "juouOE7kAHxK"
      },
      "source": [
        "train_generator = train_datagen.flow(\n",
        "        X_train_ethnicity, y_train_ethnicity, \n",
        "        batch_size=128)\n",
        " \n",
        "validation_generator = train_datagen.flow(\n",
        "        X_val_ethnicity, y_val_ethnicity,\n",
        "        batch_size=64)\n",
        " \n",
        "history = ethnicity_model.fit(\n",
        "      train_generator,\n",
        "      steps_per_epoch=len(X_train_ethnicity)/128,  \n",
        "      epochs=15,\n",
        "      validation_data=validation_generator,  \n",
        "      verbose=2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dobbRu36AV2I"
      },
      "source": [
        "ethnicity_model.evaluate(X_test_ethnicity, y_test_ethnicity)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uq8pv6WEAPIQ"
      },
      "source": [
        "def plot_graphs(history, string):\n",
        "  plt.plot(history.history[string])\n",
        "  plt.plot(history.history['val_'+string])\n",
        "  plt.xlabel(\"Epochs\")\n",
        "  plt.ylabel(string)\n",
        "  plt.legend([string, 'val_'+string])\n",
        "  plt.show()\n",
        "  \n",
        "plot_graphs(history, \"accuracy\")\n",
        "plot_graphs(history, \"loss\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ukGlo75-FmLj"
      },
      "source": [
        "# <font color=\"white\" size=+2.0><b>7: Summary</b></font> <a name=\"7\"></a>\n",
        "\n",
        "\n",
        "---\n",
        "<a href=\"#top\" class=\"btn btn-primary btn-sm\" role=\"button\" aria-pressed=\"true\" style=\"color:white\" data-toggle=\"popover\">Go to TOC</a> "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qaaJnYP9FmEG"
      },
      "source": [
        "# <font color=\"white\" size=+2.0><b>8: Future Work</b></font> <a name=\"8\"></a>\n",
        "\n",
        "\n",
        "---\n",
        "<a href=\"#top\" class=\"btn btn-primary btn-sm\" role=\"button\" aria-pressed=\"true\" style=\"color:white\" data-toggle=\"popover\">Go to TOC</a> "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZOOxtWccGU5X"
      },
      "source": [
        "# <font color=\"white\" size=+2.0><b>9: Reference</b></font> <a name=\"9\"></a>\n",
        "\n",
        "\n",
        "---\n",
        "<a href=\"#top\" class=\"btn btn-primary btn-sm\" role=\"button\" aria-pressed=\"true\" style=\"color:white\" data-toggle=\"popover\">Go to TOC</a> "
      ]
    }
  ]
}