Age_and_Gender_Prediction.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Age and Gender Prediction.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "af0B5bzjmUXP",
        "colab_type": "text"
      },
      "source": [
        "#### Mounting Drive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QmCZexMtmc9l",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "280e336f-092b-4e91-995a-308ce20a12b2"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YIaY5SI2mmz-",
        "colab_type": "text"
      },
      "source": [
        "#### Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IQNPu55rmq88",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fldr=\"drive/My Drive/Face_data_based_ml/UTKFace\""
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qaxkAByxo4Bh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "files=os.listdir(fldr)\n"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yKUxDh4ypHYP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b54f4717-7af1-4f8e-e491-cc1a04855d7a"
      },
      "source": [
        "import cv2\n",
        "ages=[]\n",
        "genders=[]\n",
        "images=[]\n",
        "\n",
        "for fle in files:\n",
        "  age=int(fle.split('_')[0])\n",
        "  gender=int(fle.split('_')[1])\n",
        "  total=fldr+'/'+fle\n",
        "  print(total)\n",
        "  image=cv2.imread(total)\n",
        "\n",
        "  image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
        "  image= cv2.resize(image,(48,48))\n",
        "  images.append(image)\n",
        "\n",
        "  \n",
        "\n"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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            "drive/My Drive/Face_data_based_ml/UTKFace/53_0_0_20170111205031079.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/53_1_0_20170105171816333.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/53_0_3_20170109132854337.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/53_1_0_20170109220746274.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/53_1_0_20170109221042561.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/53_1_0_20170110131645457.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/53_1_0_20170110122533941.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/53_1_0_20170110122611684.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/53_1_0_20170110141257440.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/53_1_0_20170110160643266.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/53_1_0_20170110160643313.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/53_1_0_20170110160643329.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/53_1_3_20170109140354667.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/54_0_0_20170104212617845.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/54_0_0_20170105163714131.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/54_0_0_20170105170040476.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/54_0_0_20170105173614957.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/54_0_0_20170109010246118.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/54_1_0_20170103183727146.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/54_0_3_20170104202315035.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/54_1_0_20170103183742275.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/54_1_0_20170110122231385.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/54_1_0_20170110122243471.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/54_1_0_20170110124214646.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/54_1_0_20170110140410594.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/54_1_0_20170110153052834.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/54_1_3_20170105001159796.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/54_1_3_20170109142901150.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/55_0_0_20170104212851085.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/55_0_0_20170111195247744.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/55_1_0_20170104212854053.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/55_1_0_20170109132147662.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/55_1_0_20170110160643391.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/56_0_0_20170104210554380.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/56_0_0_20170104210832380.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/56_0_0_20170105173129526.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/56_0_0_20170105175625694.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/56_0_0_20170111194856535.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/56_0_0_20170111200016066.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/56_0_0_20170111201127150.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/56_0_3_20170109142154230.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/56_1_0_20170104184326534.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/56_1_0_20170109141855995.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/56_1_0_20170110122655288.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/56_1_0_20170110122147365.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/56_1_0_20170110141114577.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/56_1_0_20170110153255801.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/56_1_1_20170110153620007.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/57_0_0_20170105173211517.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/57_0_0_20170111171747492.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/57_0_0_20170111201356375.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/57_1_0_20170109015631419.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/57_1_0_20170110141704735.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/57_1_0_20170110120847680.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/58_0_0_20170104183336630.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/58_0_0_20170104184709517.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/58_0_0_20170104185141158.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/58_0_0_20170104185326934.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/58_0_0_20170104185347134.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/58_0_0_20170104212603957.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/58_0_0_20170104185539454.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/58_0_0_20170111195305743.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/58_0_0_20170111205019096.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/58_0_0_20170111210633610.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/58_0_3_20170104220900702.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/58_1_0_20170109015222209.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/58_1_0_20170104185710750.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/58_1_0_20170109220512395.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/58_1_0_20170110154135560.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/58_1_3_20170109140246682.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/59_0_0_20170111203716454.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/59_0_0_20170111203940975.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/59_1_0_20170110122835196.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/59_1_0_20170110141623496.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/59_1_0_20170110180108013.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/5_0_1_20170110213654372.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/5_0_2_20161219192745531.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/5_0_3_20161220221842826.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/5_1_0_20161219153649716.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/5_1_0_20170104202352685.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/5_1_0_20170109192118486.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/5_1_0_20170109192114580.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/5_1_0_20170109192719152.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/5_1_0_20170109193145120.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/5_1_2_20161219151522483.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/5_1_2_20161219195855851.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/5_1_2_20161219153445412.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/5_1_2_20170109193543217.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/5_1_3_20161220142912096.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/5_1_3_20161220145130080.jpg.chip.jpg\n",
            "drive/My Drive/Face_data_based_ml/UTKFace/5_1_4_20161221193338973.jpg.chip.jpg\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2t9s0wFV0Rt8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for fle in files:\n",
        "  age=int(fle.split('_')[0])\n",
        "  gender=int(fle.split('_')[1])\n",
        "  ages.append(age)\n",
        "  genders.append(gender)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ssUXRvTtdBq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 65
        },
        "outputId": "df8935c7-5768-40aa-beb5-2d846042b3ef"
      },
      "source": [
        "from google.colab.patches import cv2_imshow\n",
        "cv2_imshow(images[24])"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=48x48 at 0x7F17FCA394E0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wi2sJZBr04yY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "620d1cf9-602e-47b9-ff26-473ddd8a4e16"
      },
      "source": [
        "print(ages[24])\n",
        "print(genders[24])"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "62\n",
            "0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jQyKZxGp8KUJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 65
        },
        "outputId": "4f21ac89-8eeb-48f6-c1d7-c74982ba39db"
      },
      "source": [
        "cv2_imshow(images[53])"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=48x48 at 0x7F17E6CAC860>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bNAJPpEG8MiZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "2c2040c7-fb83-4895-9e22-d91810bf4528"
      },
      "source": [
        "print(ages[53])\n",
        "print(genders[53])"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "67\n",
            "0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wcVToBvvzYSy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "images_f=np.array(images)\n",
        "genders_f=np.array(genders)\n",
        "ages_f=np.array(ages)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zl76gFvMzoVh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "np.save(fldr+'image.npy',images_f)\n",
        "np.save(fldr+'gender.npy',genders_f)\n",
        "np.save(fldr+'age.npy',ages_f)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R_A3LwGS0-1r",
        "colab_type": "text"
      },
      "source": [
        "Male = 0\n",
        "Female= 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yk6SSk2I1Guq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2a89aa9a-814b-4db3-e9cc-21e2ce5930a2"
      },
      "source": [
        "\n",
        "values, counts = np.unique(genders_f, return_counts=True)\n",
        "print(counts)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[4372 5407    1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s55Necnf3BGx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "outputId": "4a8cce05-c785-4807-8680-4df28bbac8af"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "fig = plt.figure()\n",
        "ax = fig.add_axes([0,0,1,1])\n",
        "gender = ['Male', 'Female']\n",
        "values=[4372,5047]\n",
        "ax.bar(gender,values)\n",
        "plt.show()"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m39_Ut8F4XsN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "3d46a961-1320-4935-8920-d401995d4bb7"
      },
      "source": [
        "\n",
        "values, counts = np.unique(ages_f, return_counts=True)\n",
        "print(counts)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1112  475  284  260  180  114  126  236  159  140   63  106   75  137\n",
            "  143  212  103  181   58  103  136  139   92  191  156  279  126  145\n",
            "  150  109   97  141   46   87  121  112   94   91  136   85   56   84\n",
            "   52   27   77   61   70   64   99  105   62  133  133  106   52  132\n",
            "   60   96   59  137   50   76   47   29  113   54   47   58   45   53\n",
            "   22   70   31   16   87   35   19   30   16   71   10   29    7   11\n",
            "  130   23    4   17   16   56    1   13    3    3   16    2    2    1\n",
            "    2]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-qfptlwh4qPp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "val=values.tolist()\n",
        "cnt=counts.tolist()"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fdD8XIB-5H40",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "46e75445-b286-477a-e910-65b99fdd1eca"
      },
      "source": [
        "plt.plot(counts)\n",
        "plt.xlabel('ages')\n",
        "plt.ylabel('distribution')\n",
        "plt.show()"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dG3tunwlcJiIXY+2JEQs8BMSLSIjdukgHCuzzC4BRQL6IhABxQGkv1CMgKTHhbM8v76/LKaXUgBRon0WvMcb8yBiTbozJBK4DVhljrgdWA1fZpy0D3rBvL7ePse9fZYzpt6G7ydEuTUMppYa8fg8WJ/BD4Lsiko3VJ/GEXf4EkGSXfxe4pz8rlRITRk1jC7WNzf5PVkqpQSrgNFRfMMa8D7xv3z4IzOvknHrg6n6tmJe2iXmNZCQF9Z9LKaWCZiC1LAYkz/pQ2smtlBrCNFj40TaLW4OFUmro0mDhR3K0zuJWSikNFn4kRVsrz2rLQik1lGmw8CPU6SAhMlRbFkqpIU2DRQCSo8O0ZaGUGtI0WAQgJSZMlylXSg1pGiwCoIsJKqWGOg0WAbBaFhoslFJDlwaLACRHh1Hb2EJNgy75oZQamjRYBCBZh88qpYY4DRYBcM/i1n4LpdRQpcEiAJ7FBLVloZQaojRYBCDV3bLQ4bNKqSFKg0UAEqNcuJwO1hzotw36TinVDc0cLq0NdjWUUn1Ig0UAQpwO7vj8eN7afpRXN+UHuzoDzsMr93PFI5/QjxsYKqX6mQaLAH3z3PHMG5PI/7y+g5ySmmBXZ0DZfbSSkupGKut1aLFSg5UGiwA5HcKD184ixOng289vprG5NdhVGjAO2SmooxV1Qa6JUqqvaLA4CSPiI7jvyhlsL6jgrx8cCHZ1BoTG5lbyj9vBorw+yLVRSvUVDRYnacm04czJiGf13qJgV2VAyD9eS6vdVVFQri0LpQYrDRbdMHd0AjuOVGoqirYUFGgaSqnBTINFN8zOSKCxuZXdRyuDXZWgyy21OvujXE5NQyk1iGmw6IbZGfEAbD58PKDzDxRX86fV2YNyaGluSQ0xYSFMSovliLYslBq0NFh0Q1pcBMNjw9mcVx7Q+S+sz+O3K/b6pGwGi9zSWkYnRzIiPoKjFdqyUGqw0mDRTbMz4tl8OLBgcaCoGoCNhwJriZxKDpXWMDopihFx4Rwtr6e1dfC1npRSGiy6bXZGPIfLagNaXDC72A4WAaatThVNLa3kHa9jTFIUaXHhNLa0Ulqj62cpNRj1e7AQkVEislpEdonIThG50y5PFJF3RWS//TvBLhcReVhEskVkm4jM6e86d2bWqAQAtvhpXdQ3tZBXZqWfNvVhy6Kosp49hf3b4V5wvI6WVsPopEjS4iMAHRGl1GAVjJZFM3C3MWYKsAC4Q0SmAPcAK40xE4CV9jHARcAE++dW4JH+r3JH00fG4XQIW/z0W+SW1tBqYHxqNHuPVVFZ39Qn9fnpmztZ9uS6PnnuruTYI6Eyk6MYEWcFiyM6IkqpQanfg4Ux5qgxZpN9uwrYDYwELgeesU97Blhq374c+JuxrAHiRSStn6vdQYTLyeS0GDbnnbi1kG33V1yTlY4x/lsi3dHaavj0QCnHKhsor+2/NNAhe42szKQoRsSHA9qyUGqwCmqfhYhkArOBtcAwY8xR+65CYJh9eySQ5/WwfLss6GaPSmBrXgUtJ+jUPVBUgwhcMScdh/RNJ/eewirKa60Wizs49Yfc0lqiXE6So10kRrkIC3HoiCilBqmgBQsRiQZeAe4yxvgk2401IeGkhtWIyK0iskFENhQXF/diTbs2OyOe6obmE35AZxdXk54QQXJ0GBOHx7KpDzq5PzvYts9GXwaLt7cf5Z0dhZ7jXHsklIggIqTFheuSH0oNUkEJFiISihUonjPGvGoXH3Onl+zf7sWXCoBRXg9Pt8t8GGMeNcZkGWOyUlJS+q7yXmZnWJ3cJ5qcl11UzbiUaADmjraG256oJdIdnx0oZVRiBGEhjj4NFr95Zw/ff2krFXYr5lBpLZnJkZ770+IiOKrBQqlBKRijoQR4AthtjLnf667lwDL79jLgDa/ym+xRUQuACq90VVBlJkUSHxnaZWuhtdVwsLia8Z5gkUB1QzN7C6t6rQ4trYa1OaWcOS6ZsSnRnmG6blvyyln25Doamlt6dJ3axmYOldVS1dDME5/k0NzSSl5ZLZlJUZ5z0uLDNQ2l1CAVjJbFmcCNwLkissX+uRj4NXC+iOwHzrOPAd4GDgLZwGPAN4JQ506JCFmjE1iXU9bp/QXldTQ0tzIu1Q4WGYlA78632HWkkqr6ZhaOS2J8anSHlsXrmwv4YF8xB4p6tmHTvmPVGAPJ0S6e+jiH3UeraG41PsFiZHwExyrraW7RBRaVGmyCMRrqY2OMGGNmGGNm2T9vG2NKjTGLjTETjDHnGWPK7PONMeYOY8w4Y8x0Y8yG/q7ziZwxLpnc0lrPng7e3N/yx9vBYlSi1XfRm/Mt1tj9FQvGJjE+JZqC8jrqGttaEe5Wz6HSngWLvfYcjp8vnUZVQzP3Lt8BwOgk3zRUq4GiKv8TFZVSpxadwd1DZ01IBuDT7NIO97mX+XCnoUSEuaPje3VE1GcHSxmbHMWw2HDGp0ZjDBwssa5b39TCriPWh3xuD9el2lNYRUSokwumDGfJ1OFssocAj0n2TUMBHNF+C6UGHQ0WPTQhNZqUmDA+zi7pcF92UTWJUS4SolyesrmjEzhcVsv2/IoeX7u5pZV1OWUsGJcEtLVg3KmobfkVNNud6bk93Dd8b2EVpw2LxuEQvr14AgARoU5SYsI853gm5mm/hVKDjgaLHhIRzhyXxKcHSjosQX7Aq3PbbenskYyIC+fmp9f1+AN8x5FKqhuaWTjWChaZyZE4pOPChWNTojz7TnSHMYY9hVVMGh4LwJQRsSydNYLZGfFY4xUs7pZFZyOiiirr+Wh/8aBcpl2poUCDRS84c3wyJdWN7D3mO8opu6ja07ntlhoTzt9umU9Lq+GGJ9ZyrLL738I/O9DWXwEQFuJkdFKUp69k0+HjZCZFMicjoUfLoxdXN1BW08jE4TGest9fM4tnb5nvc15seCgxYSGeEVEVdU38/j97+cLDHzHvlyu58Yl1vLHlSLfroZQKHg0WveDM8Va/xcf721JRZTWNHK9tYlxKVIfzx6dG8/TN8yiraeSmJ9ZR09Ac8LWaWlr5YF8x33tpK39anc1pw6J9UkHjUqwRUcYYNh8+zpzRCWQmRVJYWe/T8d2Zhmarj+OtbUcp81o91j3Ud5JXsHA6BIdDOjxHWnw4R8rrqG1s5qtPr+dPq7OJcoXw/QsnMiY5iqc+zT1hHe57Zw8f7uufSZVKqcCFBLsCg8GI+AjGpkTxSXYJX/vcWKCt32B8u5aF28xR8Tx47Sxu/ftGVu8t4pIZIwK61tee2cAH+4qJCQthybTh3Hr2WJ/7x6dG88G+Ig6W1FBS3cicjATiIkIBOFRW40kleausb+KWp9ezyWvC4NVz0/nt1TOBtmDh3bLoSlpcBIfLavnGc5vYfPg4f75+DkumWUt5RYeFcO/ynWzJK2fWqPgOj911pJJH3j9ATnENZ5/WexMri6sa+Nbzm/j50uldvh/BlltS47Nq8MxR8aTZfUCdMcZQ29hCVFhg/4VbWw0XPfQRNywczY0LRve4vmro0ZZFLzlzXDJrc8posucY+AsWAJ+flEp4qINNhwJbXLCoqp4P9hXzlTMyWf//zuN3V8/ktGG+H+DjU6NpajGedM+cjATPXIjcks5TUY99eJD1ucf52ufG8IcvzeaiacN5e/tRahutFs+ewiqSo8NIig7r9PHeRsSHs6ewivf3FvOLL073BAqAK+emEx0WwjNdtC7+uf4wALt7ean1F9YfZs3BMv6x9nCvPm9v+uoz67nt2U2en0v/8DHHT7A3yNvbC8n6+XsUBZjGPFBczd5jVazafay3qjwk5B+v5Y7nNlF9Eq3/wUqDRS85c3wytY0tbMkr53BpLc+vO0yUy+kZIdSZUKeDmenxAU/S+2iflea6am464aHOTs9xB6eXN+QR5XIycXgMo+0lOTrr5C6uauCJj3O4ZEYaP7poMpfOHMGyMzKpaWxhxU5rHai9hVU+KagTSU+wrvX9CyfypXkZPvdFh4Vw5ZyR/GvbEYrbzcWoa2zhtc0FhDqFQ6W1J/zP2dDcEvDquq2thhc35APw1vYjA3Inv8KKeg4W13D7onH8+87P8dTNp1Ne28TP39rd5WM+2l9MXVOLz7pgJ+Keb7O9oFIHGZyE1XuKeGv70T7di+ZUocGilywcm4RD4Lcr9rLkoQ/JLa3ht1fP7DSv723u6AR2FlT47U8A+GBfMcnRLqakdUwlubn7SI5U1DMrIx6nQ4gNDyUpytXpxLw/rc6mobmVuy+Y6Cmbl5lIekIEr2wsoKXVsO9YVUApKIDr52fwxLIsvrFoXKf333RGJk0thufX+X7Lf2v7Uarqm7n5zDFA2yTAztz94lZO/8V7/OSNHX4HCKzJKeVwWS3nTU7lWGUD63M7n20fTGtzrA/8i6elMTktls9PTOW2c8bxyqZ8Puii/8a9pW9Xqwe05x4ZV1LdwLFKnTQZqBy7Nb6/H1dzHqg0WPSSuMhQpo+MY11OGbMz4llx19lcPN3/thtzRyfQ3GrYln/iVFRLq+Gj/cWcPSHlhAEoJjyU4bHWENY59kKHYM20bp+Gyiur5bm1h7gma5TP5DqHQ7hiTjqfHChhzcFSGppbAw4W8ZEuFk8e5jOk1tu4lGg+NyGZ59Ye8qTsAJ5fd5ixKVGefPquo52vn7WjoIJ/bTvKxOEx/GPtYc7+zWp+886eLhdnfGF9HjHhIfzmqplEhDp5c9vAG421NqeMmLAQpoxo+xLwzXPHMzYliv9+dXuHARBV9U3sK7L+fQINFpsOl5NspxH9/a11ZkdBBT98eduQW8olx57g2p9L/w9UGix60b2XTeWBa2fy96/OZ0R81+knb+6Va71TUcYYXtyQ57OR0PaCCo7XNnHORP8dv+5UlHewyEzuONfiwff24xDhTnuSnbcr54zEGGt0EsDkTjrGu+srZ2RyrLLB80G471gVGw8d50unZ5CeEEFseAi7j3besnjg3X3ERYTyj/9awKq7F3Hx9DT+/P4Bfvza9g4pporaJv69o5Cls0aSGOVi8eRU/r29MGgfeA3NLXx6oKTDh//ag6VkZSbg9PoSEB7q5DdXzuBIRR2/+89en/O35VdgDCwYm8j+ompK/ewDX1HbRHZRNdeebu2psqPg5CeEvrIpnxc25PXJfiwDWY49Fyq7qPcW/zxVabDoRXMyEvji7HS/qSdviVEuxqZE+eREt+ZX8IOXt3HPK9s9ZR/sLUYEzrKH6Z6IO1jMzmgbcZSZFMXRinrqm6x018Hial7dnM+yMzIZHhfe4TlGJ0VxemYC2/IrcAhMGNZ7o4jcaZaXN+Wz5KEP+dXbu3E5HVw5Nx0RYVJabKfBYtPh46zcU8StZ48lNjyUjKRIHrh2Ft8+dzz/XJ/Hvct3+uTj39haQGNzK9eebq1wf8mMEZTWNAac5+8taw6W8r2XtpL18/f48mNr+e2Ktg//4qoGDhTXMN+eK+MtKzORa+aO4rk1h6ny2o7XvSS+eyTc+twTf4C7d3M8c1wyE1Jj2N6NYLHNXnFg1Z4iP2cOHk0treQdt76wWQtpDu2+Hg0WA8DcjAQ2Hjru+WN8bs0hwOqjeH9vkX27iBkj4wIakfRfZ4/lkevnEB/ZtsyIe8G/w2VWKuqF9Xk4Rfja58Z0+TxXzU0HrEDTVYd6dzgcwj0XTeKFWxcCsHpvMRdMHUaivSzKlLRY9hZWdWgpPPDuPpKiXHzljEyf8u+cfxpfP3ssf19ziP95Ywc7Ciqob2rhhfV5TB0Ry7SRcQAsmphCdFgIb27tv1TUhtwyrnt0DSt2FHLBlOGcMS6JVzble0aaudNI88ckdvr4q7LSaWxp9fmQ3pJXzriUKM4an0JYiMMnFVVR18R3XthCXllbynHToeM4xBqOO21kHNsLKk7qg6+ppdXTGlk5hIJFXlktLa2GqSNiqahroqS6/7YsHog0WAwAc0cncLy2iZySGipqm3hz2xGunptOZlIkv3x7N6XVDWzJK+ecAOcejIyP4KJ2/SXuPomckhqaWlp5ZVM+505KJTWmY6vC7eLpaYSHOpiUFlh/xcmaNyaRf995Nj9cMokfLpnkKZ+SFkttYwuHvD7w1h4s5aP9Jdy+aFyHuQUiVvC5+cxMnrOoOHYAABcBSURBVF1zmEv+8DGTf/IOO49UeloVYKV2LpgyjHd2FNLY3D+pqAff209ytIvP/nsxv79mJneddxpV9c38a6u1JcvanFIiXU5PQGtvbkYCKTFhnpFp1mTLcmZnJOAKcTAnI4F1uW0tpSc/zuG1zQU88N4+T9mmw+VMGh5LVFgI00fGUlLdSOFJrByw71gVDc2tzBoVT3ZRNYd7uCjlyTLGnNTE1d7iTtteMGU4oP0WGiwGgLmj7X6LQ8d5dXM+9U2tLDsjk3sumsy+Y9Xc9cIWWg2cMzG129cYnWgFi0OlNazcXURJdaPPB2lnYsJDefIrp/P9Cyed8LyeiA4L4fZF4xiV2LbU+WR7tJd3KurhVftJjQnjhi4mlIkI9146lVV3n8Ofr5/DnYsncNPC0VwxJ93nvEtnjqCyvrnTdMrrmwt6ddvbDbllfJxdwtfPHke0HeBOz0xgQmo0z621Wo9rD5Yxd3QCoc7O/ys6HMIFU4axek8xdY0t5JXVUVrT6JnUOG9MIruOVFJZ30RVfRNPfZKDy+lg+ZYj5B+3vhlvyStnzmjr/Onp1u+TWcjSnYK66zyrb2vVnv6dq/HOjkLm/vzdft+y92CxFSzOnzIM0H4LDRYDwLiUaGLDQ9h46DjPrT3MLDtdcOHUYcwbk8hH+0uIiwhlZnrn3z4DERcZSkJkKLmltby4IY9hsWEBtVTOGJfsM1KqP0wYFo3TIZ5gse9YFZ9kl/KVMzP9psPGpkRz8fQ07jrvNP738mmeD2m3syYkk5EYyW9X7PFpXazPLeOuF7bwjWc3nXAYc2V9U8AdxA+ttFoV1y9om28iIlw/P4Ot+RV8tL+YvceqPGt7deWiaWnUNbXwwb5iT/+Duz9q/phEWo31RePvaw5RWd/MH788GxF4/KMc9h2rorqh2TPYYUpaLA7Bp9+ivqnF05fVmW355cRFhHLOaSmMTYnq91TUe7uLqG9q5a1+HsmWW1pDXEQok9NiiA4LGfLDZzVYDAAOhzBndALLtx4hu6iaL8+3PlxEhP/5whRPx3ZIF98+A5WZHMX6nDLe31vEVXPTe/x8fSU81MnY5ChPsHjm01xcIQ6uOz3DzyP9C3U6+OllUzhQXMOTn+QA0Njcyo9f2058ZCiFlfWe8s78dPlOLvnDxzzVyTneQWbjoTI+2m+1KiJdvgHri3PSCQ918N+vWQMY5nXRX+E2f2wi8ZGhrNhZyObD5USEOploz9yfnZFAiEP4YG8xj3+Uw6KJKVwwdThLZ43kn+sP894uqxXgDhYRLqdPJ3d9UwtL//QJV//lsy5HiW3Jq2BGehwiwuJJqaw9WNZraaHmlla/KUH3PJS3tnVvN+WC8rqAJ3F6yympITM5ChHpdBfKoWZgfloMQXMzEqhtbCEmPIRLvdaJmp4ex19umMv3L5x4gkcHJjMpiv1F1bQauCbrxCmoYJucFsvuo1VU1DXx6qYCLp85wtMB3lPnThrGeZOH8fDK/Rwpr+Oxjw6y71g1v796JudNTuUv7x/wWUjRra6xhXd2FBIdFsLP3tzF7/+zF2MMGw+V8eXH1jD5J++w9E+f8MTHOfx2xV6SonxbFW5xEaFcNnMEeWV1hIU4mOGnxRjqdHD+5GG8t/sY63LKmJEe5wn0ES4nM9Lj+PuaQ5TVNPKtc8cDcNuicTQ0t/KH1dkkRbl8djScnh7HDruT+4H39rGnsIrtBRX83R5Y0f417ztWxUw7fXXupGE0trR2un9Ld9z1whaue/SzLjvcC8rryD9eR0ZiJFvzK3w67gPR2mq4+pFP+dGr2/2f3E5uSS1j7Vb1hNRobVkEuwLK4u63uHJOOhEu31TLhVOHk9kLqSD3B8bCsUmMTurf1NLJmpwWS0F5HU98nENdUwvL2o2A6ql7L51CS6vh7he38vDK/Vw0bTiLJw/jh0smUdPYzB9W7e/wmJV7jlHb2MJfbpjLdaeP4g+rsrnggQ+58pHP2Hesiq+eOYbG5lb+71+7WHOwjK+fM7ZDq8Lt+vlW38ucjATCQvyPNFsybThV9c3sOlrpmZvjNn9sEi2thoVjk5g72mqljEuJZsnU4TQ2tzI7I8FnkuT0kXGUVDfy9vZCHvvwINedPorPTUjm/v/so6jKt+N719EKWlqNJ6BlZSYQEx7Cqt09T0WVVjfwzo5CNh0u9yy3395ae5jzj78wGbBm+p+MzXnlHKmo58N9xSc1qKG+qYWC8jrPumoThkVTXNXQrRbKYKHBYoA4fUwid3x+XJfLZPQGd9+Dv47tgWCyPQLrLx8cIGt0QpejhbprVGIk3/z8eD47WEqo08G9l04FYMKwGK7JGsWzaw51GPWzfMsRhsWGsXBcEr+6YjrfOnc8FXVN/HDJJD78wef5yaVTePvOz/Hed8/hN1fNOGGAm5Eex/XzM7hpYWArwJ41IdnT/9J+xd5zTkvBIXh2MHT7xiKrlTFvjG9wcf9bfufFLaTFRfDjL0zmZ5dNpb65hV+/vcfn3K15Vrpqpn3NUKeDs09LYdXeIp9Z862thufWHmLBL1fyuxV7O6S0mltaOwyFfnPrEZpbDVEuJ49/3Hnqb+3BMmLDQzhv8jBmjoo/6VTUf+xRZDWNLWw4iaVe3COhxqS4WxbW3+PJpqLe3HrEM/z9VKfBYoAIdTr4/oWTSI3teihrT10wZTj/t3QaX5jhfxmSYHOvf9XY3MpNvdyqcPuvs8dy/pRh/OKL03wmJn7n/NNwOoRfvL3Lkx6pqGvi/b3FXDJjBE6HICLcfcFE1v34PG5f5NsvMT41mmuyRp2wxSAi/OKL0zsMce5KWIiTcydZo+G8J1uCtfnVxv93PgvH+XaUT0+P4/U7zuSmhZk+5VPSYnE6hMbmVu67cgYx4aGMTYnm1rPH8urmAs+3eYCt+eUMiw1jmNff5UXThlNc1cDZv1nNwyv3s+ZgKVf/9TN+/NoOwkMd/HF1Nl9+fC2FFfUcKa/j5//axaz/fZefvrnTpx6vbS5gclost549jlV7ijodbbQ2p5R5YxJxOoRLpqexvaCi0zXOOmOMYcXOQk7PTMDldLD6JD603btYjrFbFu23LA5EeW0jP3h5Gz9tN1n0VKXBYgiJcDm5ccHoLodpDiQpMWEkRblIjQnjomnD++Qa4aFOHrspi8tnjfQpHxYbzrcXT2DFzmO8vqUAgBU7CmlsaeWymYHtO9IX7jpvAv93+VSfD263hC76c2aNiu8wgizC5WTJtOHcds44zprQtiLAHZ8fz8j4CO55dTsl9hIi2/IrmJHuG5y+MD2NR66fw5jkKO5/dx/XPbqGg8XV/O7qmaz+3iLuv2YmOwoqOP+BDzj7N6t56tNchseF8/c1h9h1xBq0kF1Uzdb8Cq6YPZIbFmQQFuLgiY9zfa5zrLKe3NJa5o+xguBF062/g0BTUXuPVZFbWsvS2SOZNyaR9/cGvqnWQTtYZNorNo+MjyA81HFS/Rb/XJ9HXVMLuaW1HXbRPBUN/E8NNSSJCD+6eDK/umJ6UILb188eR9boBH7y+k7yj9eyfOsRMpMi/XZG96WxKdHc2K6V0F1/+vIc7rnId/5MpCuEB6+bxdGKOm54fC2HSmvIKanpkPYSES6ansazX5vP+99bxK+vmM573z2Hq+zlWq6Yk86b3zqLhWOTuHHhaD74/iJeue0M4iJCPa211zbn4xC4fNYIkqLDuGJOOq9uyvdZ52qN3cKZP9bqh0lPiGR2RuCpqBU7jiFizZNYNDGF/UXVAc/VyC2pISUmjJhwa+Mwh8MaERVosGhqaeWZT3OZOiIWEasupzoNFmrAumpuOosnDwvKtZ0O4YFrZ9FqDHc8t4lPD5Rw2cwRXa6mO1icnpnI4zedzsGSGq585FOAEwbIzOQorpuX0WEZmnEp0Tx6Uxb3XjqV9IRI4iJDuWvxBD7JLmXl7iJe33yEsyakeNKut5w1hobmVp8RWWtzyogOC/FZkv8L09PYeaSSTwIYjbViZyFzMxJIjQlnkb0AZ6D9BzklNZ4UlNuE1BiyA2whrNhZyNGKer5z3mnMzUjwzMA/lWmwUKoLoxIjufeyqWzNr6DVwGWzgpeC6k9nTUjmrzfMpaLOWrxwxsiOW+B2x/ULRjM2OYq7X9pKQXkdV8xuS/+NT43m3EmpPPbhQc8e7O7VeL3nA101N53xqdHc/PR6T+e1MYYX1h9m3i/e4+f/2kVzSyt5ZbXsOlrJhVOt1NW4lGjSEyJYvactFfXYhwf58WvbO13ePqektsNk1PGp0RypqA9o17ynPslldFIk505K5cKpw9l1tPKkh/0ONBoslDqBq+emc9nMEcwfk8j41L5ZI2sg+vykVB67KYu7zptAXGRorzxnqNPBf188mYq6JiJdTi6Y6ttq/OUXpzMqMZKbn17Pn1ZnW6vxjvHttI+PdPHS1xcyJS2W257dyJ/fz+baR9fww1e2ExUWwuMf53DTk+s8W/S6g4WIsGhiCp8eKKGhuYUnP87hF2/v5rm1h3nQax0tsPYLKalu6DBc3d3J7W8/kC155Ww8dJybz8jE4RBPHU711kVgu70rNUSJCA9dNyvY1QiKRRNTWdSD9cg6s3hyKktnjWBkQkSHOSjD48J5+fYz+OY/NnmWcXf3V3hLiHLx3Nfmc9uzG/nNO3uJDQ/hviunc/XcUbyyKZ8fv76DTw+UMml4DBlekxEXnZbKs2sO89PlO3l+XR5Lpg4nNiKEP6zKtpfXsT7U3ZuEtW9ZzMtMJCUmjLtf3MrLt5/BSHvPmoraJh54bx/ltY1Eh4ewvaCSmLAQrrInvmYkRTI5LZYVOwv52ufGBvxvdbymkTe3HWHp7JHEhvdOwO6JUyZYiMgS4CHACTxujPl1kKukhojB3k/Rn0SEB6+b3eX90WEhPH5TFj9/azcbDpUxvYv5NVFhITyx7HTe2FLAoomppMRYfSZXZ43itGEx3P3SVm5sN4fljPFJuJwOnl+Xx8KxSTxofwnYW1jF3S9uJeO2SOqaWnh5o7Vne/tgkRDl4m9fncc1f/2MG59Yy0tfX8ihslq+9Y/NFFXVkxYXQVV9EzUNLdy+aJzPumQXTh3GQyv3U1zVgEPgJ8t3svtoJd+/YCJLpg3v8De2PreMbz+/maMV9Tz9aS6P3pjladkEi5wK439FxAnsA84H8oH1wJeMMbs6Oz8rK8ts2LChH2uolDoV3PXPzeQfr+Opm0/3jHQ6Ul7HpX/4mFKvJV6mpMXy2h1ndDpXZn1uGTc8vpbhceEUHK8jLT6cP35pjmfiYmd2H63kooc+4oo5I/lwXwmVdU2MTIggp6SG+WMSuXPxBGIjrPp8sK+Y+9/dR3pCBLefM47frthLQ3MrD1w7yzPXBvDZWdHNGENTi8EV0r0eBhHZaIzJ6vS+UyRYLAR+aoy50D7+EYAx5ledna/BQinVmdZW0+lOllvzyvn3jkJmjYpjTkaC38mxq/Yc4+t/38jiScO476oZxEWcOE1kjGHR797nUGktk4bH8MC1s5iQGs3z6/O4/z97OV7b5HP+JTPS+NUV04kJD+VIeR1f//vGDjscukIcxISFEB0eQnOLobK+ieqGZi6bOYKHTtB6O5HBECyuApYYY75mH98IzDfGfNPrnFuBWwEyMjLmHjrUcVE0pZTqLTUNzUS6nAGnKVfuPsb+ompuPjPTp8VSUdfE+pwyWu3P4vhIF6dn+q7nVd/Uwj/XHaay3hqJZQzUNjVTXd9MVX0zoU4HMeEhxISHMHVELEumdW+VhiERLLxpy0IppU7eiYLFqTJ0tgDwXv0u3S5TSinVD06VYLEemCAiY0TEBVwHLA9ynZRSasg4JYbOGmOaReSbwAqsobNPGmN2+nmYUkqpXnJKBAsAY8zbwNvBrodSSg1Fp0oaSimlVBBpsFBKKeWXBgullFJ+abBQSinl1ykxKe9kiUgx0JMp3MmA/91VBh993UOLvu6hJZDXPdoYk9LZHYMyWPSUiGzoahbjYKave2jR1z209PR1axpKKaWUXxoslFJK+aXBonOPBrsCQaKve2jR1z209Oh1a5+FUkopv7RloZRSyi8NFkoppfzSYOFFRJaIyF4RyRaRe4Jdn74iIqNEZLWI7BKRnSJyp12eKCLvish++3dCsOvaF0TEKSKbReRf9vEYEVlrv+8v2MvgDyoiEi8iL4vIHhHZLSILh8L7LSLfsf/Gd4jI8yISPljfbxF5UkSKRGSHV1mn77FYHrb/DbaJyBx/z6/BwiYiTuBPwEXAFOBLIjIluLXqM83A3caYKcAC4A77td4DrDTGTABW2seD0Z3Abq/j+4AHjDHjgePALUGpVd96CHjHGDMJmIn1+gf1+y0iI4FvA1nGmGlY2xtcx+B9v58GlrQr6+o9vgiYYP/cCjzi78k1WLSZB2QbYw4aYxqBfwKXB7lOfcIYc9QYs8m+XYX1wTES6/U+Y5/2DLA0ODXsOyKSDnwBeNw+FuBc4GX7lEH3ukUkDjgbeALAGNNojClnCLzfWNswRIhICBAJHGWQvt/GmA+BsnbFXb3HlwN/M5Y1QLyInHDjbg0WbUYCeV7H+XbZoCYimcBsYC0wzBhz1L6rEBgWpGr1pQeBHwCt9nESUG6MabaPB+P7PgYoBp6y02+Pi0gUg/z9NsYUAL8DDmMFiQpgI4P//fbW1Xt80p93GiyGMBGJBl4B7jLGVHrfZ6wx1YNqXLWIXAIUGWM2Brsu/SwEmAM8YoyZDdTQLuU0SN/vBKxv0GOAEUAUHdM0Q0ZP32MNFm0KgFFex+l22aAkIqFYgeI5Y8yrdvExd1PU/l0UrPr1kTOBy0QkFyvNeC5WLj/eTlPA4Hzf84F8Y8xa+/hlrOAx2N/v84AcY0yxMaYJeBXrb2Cwv9/eunqPT/rzToNFm/XABHukhAurI2x5kOvUJ+w8/RPAbmPM/V53LQeW2beXAW/0d936kjHmR8aYdGNMJtb7u8oYcz2wGrjKPm0wvu5CIE9EJtpFi4FdDPL3Gyv9tEBEIu2/effrHtTvdztdvcfLgZvsUVELgAqvdFWndAa3FxG5GCun7QSeNMb8IshV6hMichbwEbCdttz9f2P1W7wIZGAt8X6NMaZ9h9mgICKLgO8ZYy4RkbFYLY1EYDNwgzGmIZj1620iMgurU98FHARuxvqyOKjfbxH5GXAt1gjAzcDXsHLzg+79FpHngUVYS5EfA+4FXqeT99gOnn/ESsvVAjcbYzac8Pk1WCillPJH01BKKaX80mChlFLKLw0WSiml/NJgoZRSyi8NFkoppfzSYKGUUsovDRZKKaX80mChVC8QkddFZKO9d8KtdtktIrJPRNaJyGMi8ke7PEVEXhGR9fbPmXb5OSKyxf7ZLCIxwXxNSnnTSXlK9QIRSbRnxkZgLR1zIfAJ1hpMVcAqYKsx5psi8g/gz8aYj0UkA1hhjJksIm8CvzbGfGIv8ljvtTqqUkEV4v8UpVQAvi0iX7RvjwJuBD5wL58hIi8Bp9n3nwdMsVZcACDWDg6fAPeLyHPAq8aY/H6rvVJ+aBpKqR6y15k6D1hojJmJtd7QnhM8xAEsMMbMsn9GGmOqjTG/xlq7KAL4REQm9XXdlQqUBgulei4OOG6MqbU/4Bdg7Z1wjogk2MthX+l1/n+Ab7kP7EX+EJFxxpjtxpj7sFJZGizUgKHBQqmeewcIEZHdwK+BNVh7A/wSWIeVXsrF2qkN7H2hRWSbiOwCbrPL7xKRHSKyDWgC/t1/L0GpE9MObqX6iIhEG2Oq7ZbFa1jL3r8W7Hop1R3aslCq7/xURLYAO4AcrL0FlDolactCKaWUX9qyUEop5ZcGC6WUUn5psFBKKeWXBgullFJ+abBQSinl1/8Hpk+pGyZ4wOwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vojKD91Z56qj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "labels=[]\n",
        "\n",
        "i=0\n",
        "while i<len(ages):\n",
        "  label=[]\n",
        "  label.append([ages[i]])\n",
        "  label.append([genders[i]])\n",
        "  labels.append(label)\n",
        "  i+=1"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JgKxJJIT7kap",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "images_f_2=images_f/255"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "guje-Pjx727F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "labels_f=np.array(labels)"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5-Ouhd9P78UI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "529a5052-ecce-4348-d406-10d3b7945dba"
      },
      "source": [
        "images_f_2.shape"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9780, 48, 48, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eNuuuNi-8BkW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_hmmID2C8Ss5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, Y_train, Y_test= train_test_split(images_f_2, labels_f,test_size=0.25)"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DqTM5WSy8cK4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "outputId": "12bf465d-1e41-4c03-d635-22310658cb37"
      },
      "source": [
        "Y_train[0:5]"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[36],\n",
              "        [ 0]],\n",
              "\n",
              "       [[50],\n",
              "        [ 0]],\n",
              "\n",
              "       [[65],\n",
              "        [ 0]],\n",
              "\n",
              "       [[ 3],\n",
              "        [ 0]],\n",
              "\n",
              "       [[25],\n",
              "        [ 1]]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IeIqY-7F8oWB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Y_train_2=[Y_train[:,1],Y_train[:,0]]\n",
        "Y_test_2=[Y_test[:,1],Y_test[:,0]]"
      ],
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "42UhBOkE9CVV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "f3c7d1f7-5709-4242-d90e-21668d1d5d2f"
      },
      "source": [
        "Y_train_2[0][0:5]"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0],\n",
              "       [0],\n",
              "       [0],\n",
              "       [0],\n",
              "       [1]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iO5BZ53v9XRI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "2ad106f5-baa0-4646-d856-30417d882ad5"
      },
      "source": [
        "Y_train_2[1][0:5]"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[36],\n",
              "       [50],\n",
              "       [65],\n",
              "       [ 3],\n",
              "       [25]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m-jnZOef9caT",
        "colab_type": "text"
      },
      "source": [
        "### Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s8-iyoLN9Z8p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.layers import Dropout\n",
        "from tensorflow.keras.layers import Flatten,BatchNormalization\n",
        "from tensorflow.keras.layers import Dense, MaxPooling2D,Conv2D\n",
        "from tensorflow.keras.layers import Input,Activation,Add\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.regularizers import l2\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "import tensorflow as tf\n",
        "\n",
        "def Convolution(input_tensor,filters):\n",
        "    \n",
        "    x = Conv2D(filters=filters,kernel_size=(3, 3),padding = 'same',strides=(1, 1),kernel_regularizer=l2(0.001))(input_tensor)\n",
        "    x = Dropout(0.1)(x)\n",
        "    x= Activation('relu')(x)\n",
        "\n",
        "    return x\n",
        "def model(input_shape):\n",
        "  inputs = Input((input_shape))\n",
        "  \n",
        "  conv_1= Convolution(inputs,32)\n",
        "  maxp_1 = MaxPooling2D(pool_size = (2,2)) (conv_1)\n",
        "  conv_2 = Convolution(maxp_1,64)\n",
        "  maxp_2 = MaxPooling2D(pool_size = (2, 2)) (conv_2)\n",
        "  conv_3 = Convolution(maxp_2,128)\n",
        "  maxp_3 = MaxPooling2D(pool_size = (2, 2)) (conv_3)\n",
        "  conv_4 = Convolution(maxp_3,256)\n",
        "  maxp_4 = MaxPooling2D(pool_size = (2, 2)) (conv_4)\n",
        "  flatten= Flatten() (maxp_4)\n",
        "  dense_1= Dense(64,activation='relu')(flatten)\n",
        "  dense_2= Dense(64,activation='relu')(flatten)\n",
        "  drop_1=Dropout(0.2)(dense_1)\n",
        "  drop_2=Dropout(0.2)(dense_2)\n",
        "  output_1= Dense(1,activation=\"sigmoid\",name='sex_out')(drop_1)\n",
        "  output_2= Dense(1,activation=\"relu\",name='age_out')(drop_2)\n",
        "  model = Model(inputs=[inputs], outputs=[output_1,output_2])\n",
        "  model.compile(loss=[\"binary_crossentropy\",\"mae\"], optimizer=\"Adam\",\n",
        "\tmetrics=[\"accuracy\"])\n",
        "  \n",
        "  return model\n"
      ],
      "execution_count": 141,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Ia-ldz8-oT3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Model=model((48,48,3))"
      ],
      "execution_count": 142,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hslItvTK-74B",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 989
        },
        "outputId": "bc8e831a-e899-47a0-d6b6-bb599902f39e"
      },
      "source": [
        "Model.summary()"
      ],
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_10\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_14 (InputLayer)           [(None, 48, 48, 3)]  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_42 (Conv2D)              (None, 48, 48, 32)   896         input_14[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "dropout_64 (Dropout)            (None, 48, 48, 32)   0           conv2d_42[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_42 (Activation)      (None, 48, 48, 32)   0           dropout_64[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_42 (MaxPooling2D) (None, 24, 24, 32)   0           activation_42[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_43 (Conv2D)              (None, 24, 24, 64)   18496       max_pooling2d_42[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "dropout_65 (Dropout)            (None, 24, 24, 64)   0           conv2d_43[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_43 (Activation)      (None, 24, 24, 64)   0           dropout_65[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_43 (MaxPooling2D) (None, 12, 12, 64)   0           activation_43[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_44 (Conv2D)              (None, 12, 12, 128)  73856       max_pooling2d_43[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "dropout_66 (Dropout)            (None, 12, 12, 128)  0           conv2d_44[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_44 (Activation)      (None, 12, 12, 128)  0           dropout_66[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_44 (MaxPooling2D) (None, 6, 6, 128)    0           activation_44[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_45 (Conv2D)              (None, 6, 6, 256)    295168      max_pooling2d_44[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "dropout_67 (Dropout)            (None, 6, 6, 256)    0           conv2d_45[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "activation_45 (Activation)      (None, 6, 6, 256)    0           dropout_67[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_45 (MaxPooling2D) (None, 3, 3, 256)    0           activation_45[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "flatten_11 (Flatten)            (None, 2304)         0           max_pooling2d_45[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "dense_22 (Dense)                (None, 64)           147520      flatten_11[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "dense_23 (Dense)                (None, 64)           147520      flatten_11[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "dropout_68 (Dropout)            (None, 64)           0           dense_22[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "dropout_69 (Dropout)            (None, 64)           0           dense_23[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "sex_out (Dense)                 (None, 1)            65          dropout_68[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "age_out (Dense)                 (None, 1)            65          dropout_69[0][0]                 \n",
            "==================================================================================================\n",
            "Total params: 683,586\n",
            "Trainable params: 683,586\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p6qugq2n_ArD",
        "colab_type": "text"
      },
      "source": [
        "#### Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h9CACTIq_DVp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.callbacks import ModelCheckpoint\n",
        "import tensorflow as tf"
      ],
      "execution_count": 144,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YmPivfSj_K0l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fle_s='Age_sex_detection.h5'\n",
        "checkpointer = ModelCheckpoint(fle_s, monitor='val_loss',verbose=1,save_best_only=True,save_weights_only=False, mode='auto',save_freq='epoch')\n",
        "Early_stop=tf.keras.callbacks.EarlyStopping(patience=75, monitor='val_loss',restore_best_weights=True),\n",
        "callback_list=[checkpointer,Early_stop]"
      ],
      "execution_count": 146,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-BHmKYgL_R4p",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c70ceab4-ffa4-4620-8be6-0e105dd5caee"
      },
      "source": [
        "History=Model.fit(X_train,Y_train_2,batch_size=64,validation_data=(X_test,Y_test_2),epochs=500,callbacks=[callback_list])"
      ],
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 21.9847 - sex_out_loss: 0.7195 - age_out_loss: 21.0573 - sex_out_accuracy: 0.5430 - age_out_accuracy: 0.1105\n",
            "Epoch 00001: val_loss improved from inf to 20.79100, saving model to Age_sex_detection.h5\n",
            "115/115 [==============================] - 1s 13ms/step - loss: 21.9723 - sex_out_loss: 0.7194 - age_out_loss: 21.0452 - sex_out_accuracy: 0.5427 - age_out_accuracy: 0.1104 - val_loss: 20.7910 - val_sex_out_loss: 0.6851 - val_age_out_loss: 19.9257 - val_sex_out_accuracy: 0.5436 - val_age_out_accuracy: 0.1202\n",
            "Epoch 2/500\n",
            "113/115 [============================>.] - ETA: 0s - loss: 16.9645 - sex_out_loss: 0.6807 - age_out_loss: 16.0838 - sex_out_accuracy: 0.5596 - age_out_accuracy: 0.1061\n",
            "Epoch 00002: val_loss improved from 20.79100 to 14.18784, saving model to Age_sex_detection.h5\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 16.9064 - sex_out_loss: 0.6801 - age_out_loss: 16.0261 - sex_out_accuracy: 0.5609 - age_out_accuracy: 0.1065 - val_loss: 14.1878 - val_sex_out_loss: 0.6630 - val_age_out_loss: 13.3035 - val_sex_out_accuracy: 0.5701 - val_age_out_accuracy: 0.1202\n",
            "Epoch 3/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 13.9207 - sex_out_loss: 0.6343 - age_out_loss: 13.0489 - sex_out_accuracy: 0.6331 - age_out_accuracy: 0.1054\n",
            "Epoch 00003: val_loss did not improve from 14.18784\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 13.9207 - sex_out_loss: 0.6343 - age_out_loss: 13.0489 - sex_out_accuracy: 0.6331 - age_out_accuracy: 0.1054 - val_loss: 14.3031 - val_sex_out_loss: 0.6349 - val_age_out_loss: 13.4126 - val_sex_out_accuracy: 0.6344 - val_age_out_accuracy: 0.1051\n",
            "Epoch 4/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 12.0554 - sex_out_loss: 0.5999 - age_out_loss: 11.1861 - sex_out_accuracy: 0.6493 - age_out_accuracy: 0.1036\n",
            "Epoch 00004: val_loss improved from 14.18784 to 11.95464, saving model to Age_sex_detection.h5\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 12.0514 - sex_out_loss: 0.5998 - age_out_loss: 11.1821 - sex_out_accuracy: 0.6495 - age_out_accuracy: 0.1039 - val_loss: 11.9546 - val_sex_out_loss: 0.6004 - val_age_out_loss: 11.0709 - val_sex_out_accuracy: 0.6814 - val_age_out_accuracy: 0.1137\n",
            "Epoch 5/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 10.9611 - sex_out_loss: 0.5887 - age_out_loss: 10.0784 - sex_out_accuracy: 0.6657 - age_out_accuracy: 0.1006\n",
            "Epoch 00005: val_loss improved from 11.95464 to 10.46534, saving model to Age_sex_detection.h5\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 10.9618 - sex_out_loss: 0.5882 - age_out_loss: 10.0795 - sex_out_accuracy: 0.6658 - age_out_accuracy: 0.1005 - val_loss: 10.4653 - val_sex_out_loss: 0.5904 - val_age_out_loss: 9.5686 - val_sex_out_accuracy: 0.6810 - val_age_out_accuracy: 0.1202\n",
            "Epoch 6/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 10.1565 - sex_out_loss: 0.5588 - age_out_loss: 9.2840 - sex_out_accuracy: 0.6931 - age_out_accuracy: 0.0982\n",
            "Epoch 00006: val_loss did not improve from 10.46534\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 10.1565 - sex_out_loss: 0.5588 - age_out_loss: 9.2840 - sex_out_accuracy: 0.6931 - age_out_accuracy: 0.0982 - val_loss: 13.6511 - val_sex_out_loss: 0.5784 - val_age_out_loss: 12.7493 - val_sex_out_accuracy: 0.6814 - val_age_out_accuracy: 0.1202\n",
            "Epoch 7/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 9.8933 - sex_out_loss: 0.5506 - age_out_loss: 9.0114 - sex_out_accuracy: 0.7029 - age_out_accuracy: 0.1024\n",
            "Epoch 00007: val_loss did not improve from 10.46534\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 9.8933 - sex_out_loss: 0.5506 - age_out_loss: 9.0114 - sex_out_accuracy: 0.7029 - age_out_accuracy: 0.1024 - val_loss: 11.0388 - val_sex_out_loss: 0.5481 - val_age_out_loss: 10.1520 - val_sex_out_accuracy: 0.7219 - val_age_out_accuracy: 0.1162\n",
            "Epoch 8/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 9.3164 - sex_out_loss: 0.5335 - age_out_loss: 8.4373 - sex_out_accuracy: 0.7101 - age_out_accuracy: 0.1032\n",
            "Epoch 00008: val_loss did not improve from 10.46534\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 9.3218 - sex_out_loss: 0.5338 - age_out_loss: 8.4424 - sex_out_accuracy: 0.7100 - age_out_accuracy: 0.1029 - val_loss: 11.5005 - val_sex_out_loss: 0.5233 - val_age_out_loss: 10.6248 - val_sex_out_accuracy: 0.7382 - val_age_out_accuracy: 0.1202\n",
            "Epoch 9/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 8.9096 - sex_out_loss: 0.5226 - age_out_loss: 8.0272 - sex_out_accuracy: 0.7187 - age_out_accuracy: 0.1040\n",
            "Epoch 00009: val_loss did not improve from 10.46534\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 8.9096 - sex_out_loss: 0.5226 - age_out_loss: 8.0272 - sex_out_accuracy: 0.7187 - age_out_accuracy: 0.1040 - val_loss: 10.9916 - val_sex_out_loss: 0.5196 - val_age_out_loss: 10.1042 - val_sex_out_accuracy: 0.7472 - val_age_out_accuracy: 0.1194\n",
            "Epoch 10/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 8.8729 - sex_out_loss: 0.5085 - age_out_loss: 7.9893 - sex_out_accuracy: 0.7344 - age_out_accuracy: 0.1064\n",
            "Epoch 00010: val_loss improved from 10.46534 to 10.24195, saving model to Age_sex_detection.h5\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 8.8765 - sex_out_loss: 0.5094 - age_out_loss: 7.9921 - sex_out_accuracy: 0.7340 - age_out_accuracy: 0.1065 - val_loss: 10.2419 - val_sex_out_loss: 0.5233 - val_age_out_loss: 9.3374 - val_sex_out_accuracy: 0.7276 - val_age_out_accuracy: 0.1202\n",
            "Epoch 11/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 8.6027 - sex_out_loss: 0.4951 - age_out_loss: 7.7197 - sex_out_accuracy: 0.7374 - age_out_accuracy: 0.1068\n",
            "Epoch 00011: val_loss improved from 10.24195 to 8.69482, saving model to Age_sex_detection.h5\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 8.6075 - sex_out_loss: 0.4956 - age_out_loss: 7.7239 - sex_out_accuracy: 0.7370 - age_out_accuracy: 0.1072 - val_loss: 8.6948 - val_sex_out_loss: 0.4912 - val_age_out_loss: 7.8095 - val_sex_out_accuracy: 0.7493 - val_age_out_accuracy: 0.1202\n",
            "Epoch 12/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 8.4186 - sex_out_loss: 0.4957 - age_out_loss: 7.5248 - sex_out_accuracy: 0.7361 - age_out_accuracy: 0.1063\n",
            "Epoch 00012: val_loss did not improve from 8.69482\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 8.4186 - sex_out_loss: 0.4957 - age_out_loss: 7.5248 - sex_out_accuracy: 0.7361 - age_out_accuracy: 0.1063 - val_loss: 10.5213 - val_sex_out_loss: 0.4936 - val_age_out_loss: 9.6246 - val_sex_out_accuracy: 0.7419 - val_age_out_accuracy: 0.1202\n",
            "Epoch 13/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 8.1751 - sex_out_loss: 0.4875 - age_out_loss: 7.2772 - sex_out_accuracy: 0.7482 - age_out_accuracy: 0.1066\n",
            "Epoch 00013: val_loss did not improve from 8.69482\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 8.1713 - sex_out_loss: 0.4871 - age_out_loss: 7.2738 - sex_out_accuracy: 0.7487 - age_out_accuracy: 0.1066 - val_loss: 11.5651 - val_sex_out_loss: 0.4887 - val_age_out_loss: 10.6589 - val_sex_out_accuracy: 0.7566 - val_age_out_accuracy: 0.1202\n",
            "Epoch 14/500\n",
            "109/115 [===========================>..] - ETA: 0s - loss: 8.1444 - sex_out_loss: 0.4764 - age_out_loss: 7.2456 - sex_out_accuracy: 0.7586 - age_out_accuracy: 0.1081\n",
            "Epoch 00014: val_loss did not improve from 8.69482\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 8.1720 - sex_out_loss: 0.4774 - age_out_loss: 7.2720 - sex_out_accuracy: 0.7573 - age_out_accuracy: 0.1080 - val_loss: 10.6490 - val_sex_out_loss: 0.4861 - val_age_out_loss: 9.7343 - val_sex_out_accuracy: 0.7591 - val_age_out_accuracy: 0.1190\n",
            "Epoch 15/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 8.0067 - sex_out_loss: 0.4794 - age_out_loss: 7.0925 - sex_out_accuracy: 0.7509 - age_out_accuracy: 0.1078\n",
            "Epoch 00015: val_loss did not improve from 8.69482\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 8.0067 - sex_out_loss: 0.4794 - age_out_loss: 7.0925 - sex_out_accuracy: 0.7509 - age_out_accuracy: 0.1078 - val_loss: 9.5045 - val_sex_out_loss: 0.4736 - val_age_out_loss: 8.5913 - val_sex_out_accuracy: 0.7673 - val_age_out_accuracy: 0.1202\n",
            "Epoch 16/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 7.6859 - sex_out_loss: 0.4607 - age_out_loss: 6.7814 - sex_out_accuracy: 0.7621 - age_out_accuracy: 0.1055\n",
            "Epoch 00016: val_loss did not improve from 8.69482\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 7.6859 - sex_out_loss: 0.4607 - age_out_loss: 6.7814 - sex_out_accuracy: 0.7621 - age_out_accuracy: 0.1055 - val_loss: 9.2859 - val_sex_out_loss: 0.4635 - val_age_out_loss: 8.3738 - val_sex_out_accuracy: 0.7669 - val_age_out_accuracy: 0.1202\n",
            "Epoch 17/500\n",
            "109/115 [===========================>..] - ETA: 0s - loss: 7.5237 - sex_out_loss: 0.4503 - age_out_loss: 6.6207 - sex_out_accuracy: 0.7726 - age_out_accuracy: 0.1065\n",
            "Epoch 00017: val_loss did not improve from 8.69482\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 7.5066 - sex_out_loss: 0.4518 - age_out_loss: 6.6018 - sex_out_accuracy: 0.7718 - age_out_accuracy: 0.1074 - val_loss: 10.3681 - val_sex_out_loss: 0.4559 - val_age_out_loss: 9.4542 - val_sex_out_accuracy: 0.7751 - val_age_out_accuracy: 0.1202\n",
            "Epoch 18/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 7.5514 - sex_out_loss: 0.4548 - age_out_loss: 6.6334 - sex_out_accuracy: 0.7645 - age_out_accuracy: 0.1081\n",
            "Epoch 00018: val_loss did not improve from 8.69482\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 7.5585 - sex_out_loss: 0.4546 - age_out_loss: 6.6407 - sex_out_accuracy: 0.7643 - age_out_accuracy: 0.1081 - val_loss: 10.4521 - val_sex_out_loss: 0.4493 - val_age_out_loss: 9.5345 - val_sex_out_accuracy: 0.7779 - val_age_out_accuracy: 0.1202\n",
            "Epoch 19/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 7.2019 - sex_out_loss: 0.4433 - age_out_loss: 6.2853 - sex_out_accuracy: 0.7729 - age_out_accuracy: 0.1074\n",
            "Epoch 00019: val_loss did not improve from 8.69482\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 7.2019 - sex_out_loss: 0.4433 - age_out_loss: 6.2853 - sex_out_accuracy: 0.7729 - age_out_accuracy: 0.1074 - val_loss: 9.3606 - val_sex_out_loss: 0.4430 - val_age_out_loss: 8.4398 - val_sex_out_accuracy: 0.7824 - val_age_out_accuracy: 0.1202\n",
            "Epoch 20/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 7.4638 - sex_out_loss: 0.4428 - age_out_loss: 6.5356 - sex_out_accuracy: 0.7775 - age_out_accuracy: 0.1078\n",
            "Epoch 00020: val_loss did not improve from 8.69482\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 7.4638 - sex_out_loss: 0.4428 - age_out_loss: 6.5356 - sex_out_accuracy: 0.7775 - age_out_accuracy: 0.1078 - val_loss: 10.3179 - val_sex_out_loss: 0.4540 - val_age_out_loss: 9.3727 - val_sex_out_accuracy: 0.7738 - val_age_out_accuracy: 0.1202\n",
            "Epoch 21/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 7.1126 - sex_out_loss: 0.4225 - age_out_loss: 6.1936 - sex_out_accuracy: 0.7815 - age_out_accuracy: 0.1083\n",
            "Epoch 00021: val_loss did not improve from 8.69482\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 7.1028 - sex_out_loss: 0.4227 - age_out_loss: 6.1836 - sex_out_accuracy: 0.7809 - age_out_accuracy: 0.1087 - val_loss: 9.4903 - val_sex_out_loss: 0.4281 - val_age_out_loss: 8.5603 - val_sex_out_accuracy: 0.7820 - val_age_out_accuracy: 0.1202\n",
            "Epoch 22/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 7.1715 - sex_out_loss: 0.4186 - age_out_loss: 6.2456 - sex_out_accuracy: 0.7824 - age_out_accuracy: 0.1115\n",
            "Epoch 00022: val_loss did not improve from 8.69482\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 7.1715 - sex_out_loss: 0.4186 - age_out_loss: 6.2456 - sex_out_accuracy: 0.7824 - age_out_accuracy: 0.1115 - val_loss: 10.1828 - val_sex_out_loss: 0.4320 - val_age_out_loss: 9.2380 - val_sex_out_accuracy: 0.7853 - val_age_out_accuracy: 0.1202\n",
            "Epoch 23/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 6.9506 - sex_out_loss: 0.4121 - age_out_loss: 6.0207 - sex_out_accuracy: 0.7859 - age_out_accuracy: 0.1117\n",
            "Epoch 00023: val_loss did not improve from 8.69482\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 6.9421 - sex_out_loss: 0.4117 - age_out_loss: 6.0126 - sex_out_accuracy: 0.7862 - age_out_accuracy: 0.1115 - val_loss: 10.1531 - val_sex_out_loss: 0.4200 - val_age_out_loss: 9.2111 - val_sex_out_accuracy: 0.7873 - val_age_out_accuracy: 0.1202\n",
            "Epoch 24/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 6.7569 - sex_out_loss: 0.4116 - age_out_loss: 5.8195 - sex_out_accuracy: 0.7909 - age_out_accuracy: 0.1115\n",
            "Epoch 00024: val_loss did not improve from 8.69482\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 6.7569 - sex_out_loss: 0.4116 - age_out_loss: 5.8195 - sex_out_accuracy: 0.7909 - age_out_accuracy: 0.1115 - val_loss: 11.3095 - val_sex_out_loss: 0.4307 - val_age_out_loss: 10.3492 - val_sex_out_accuracy: 0.7767 - val_age_out_accuracy: 0.1202\n",
            "Epoch 25/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 6.8698 - sex_out_loss: 0.4097 - age_out_loss: 5.9251 - sex_out_accuracy: 0.7902 - age_out_accuracy: 0.1115\n",
            "Epoch 00025: val_loss did not improve from 8.69482\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 6.8698 - sex_out_loss: 0.4097 - age_out_loss: 5.9251 - sex_out_accuracy: 0.7902 - age_out_accuracy: 0.1115 - val_loss: 9.9329 - val_sex_out_loss: 0.4206 - val_age_out_loss: 8.9730 - val_sex_out_accuracy: 0.7877 - val_age_out_accuracy: 0.1202\n",
            "Epoch 26/500\n",
            "109/115 [===========================>..] - ETA: 0s - loss: 6.4937 - sex_out_loss: 0.3932 - age_out_loss: 5.5563 - sex_out_accuracy: 0.7944 - age_out_accuracy: 0.1127\n",
            "Epoch 00026: val_loss did not improve from 8.69482\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 6.4928 - sex_out_loss: 0.3921 - age_out_loss: 5.5562 - sex_out_accuracy: 0.7967 - age_out_accuracy: 0.1115 - val_loss: 10.1625 - val_sex_out_loss: 0.4168 - val_age_out_loss: 9.1963 - val_sex_out_accuracy: 0.7975 - val_age_out_accuracy: 0.1202\n",
            "Epoch 27/500\n",
            "110/115 [===========================>..] - ETA: 0s - loss: 6.6023 - sex_out_loss: 0.3866 - age_out_loss: 5.6625 - sex_out_accuracy: 0.7969 - age_out_accuracy: 0.1119\n",
            "Epoch 00027: val_loss did not improve from 8.69482\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 6.5728 - sex_out_loss: 0.3863 - age_out_loss: 5.6331 - sex_out_accuracy: 0.7969 - age_out_accuracy: 0.1115 - val_loss: 10.3717 - val_sex_out_loss: 0.4090 - val_age_out_loss: 9.4048 - val_sex_out_accuracy: 0.7845 - val_age_out_accuracy: 0.1202\n",
            "Epoch 28/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 6.5717 - sex_out_loss: 0.3912 - age_out_loss: 5.6164 - sex_out_accuracy: 0.7985 - age_out_accuracy: 0.1115\n",
            "Epoch 00028: val_loss did not improve from 8.69482\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 6.5717 - sex_out_loss: 0.3912 - age_out_loss: 5.6164 - sex_out_accuracy: 0.7985 - age_out_accuracy: 0.1115 - val_loss: 8.8662 - val_sex_out_loss: 0.4144 - val_age_out_loss: 7.8818 - val_sex_out_accuracy: 0.7828 - val_age_out_accuracy: 0.1202\n",
            "Epoch 29/500\n",
            "110/115 [===========================>..] - ETA: 0s - loss: 6.3832 - sex_out_loss: 0.3867 - age_out_loss: 5.4228 - sex_out_accuracy: 0.8003 - age_out_accuracy: 0.1111\n",
            "Epoch 00029: val_loss did not improve from 8.69482\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 6.3783 - sex_out_loss: 0.3867 - age_out_loss: 5.4178 - sex_out_accuracy: 0.8007 - age_out_accuracy: 0.1115 - val_loss: 9.8873 - val_sex_out_loss: 0.4052 - val_age_out_loss: 8.9042 - val_sex_out_accuracy: 0.7930 - val_age_out_accuracy: 0.1202\n",
            "Epoch 30/500\n",
            "110/115 [===========================>..] - ETA: 0s - loss: 6.3652 - sex_out_loss: 0.3691 - age_out_loss: 5.4136 - sex_out_accuracy: 0.8045 - age_out_accuracy: 0.1097\n",
            "Epoch 00030: val_loss did not improve from 8.69482\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 6.3446 - sex_out_loss: 0.3732 - age_out_loss: 5.3887 - sex_out_accuracy: 0.8023 - age_out_accuracy: 0.1115 - val_loss: 8.9655 - val_sex_out_loss: 0.3931 - val_age_out_loss: 7.9854 - val_sex_out_accuracy: 0.7967 - val_age_out_accuracy: 0.1202\n",
            "Epoch 31/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 6.4227 - sex_out_loss: 0.3645 - age_out_loss: 5.4673 - sex_out_accuracy: 0.8013 - age_out_accuracy: 0.1118\n",
            "Epoch 00031: val_loss improved from 8.69482 to 7.96953, saving model to Age_sex_detection.h5\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 6.4178 - sex_out_loss: 0.3649 - age_out_loss: 5.4620 - sex_out_accuracy: 0.8011 - age_out_accuracy: 0.1115 - val_loss: 7.9695 - val_sex_out_loss: 0.4247 - val_age_out_loss: 6.9497 - val_sex_out_accuracy: 0.7902 - val_age_out_accuracy: 0.1202\n",
            "Epoch 32/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 6.3210 - sex_out_loss: 0.3662 - age_out_loss: 5.3562 - sex_out_accuracy: 0.8058 - age_out_accuracy: 0.1114\n",
            "Epoch 00032: val_loss did not improve from 7.96953\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 6.3219 - sex_out_loss: 0.3658 - age_out_loss: 5.3574 - sex_out_accuracy: 0.8061 - age_out_accuracy: 0.1115 - val_loss: 8.7844 - val_sex_out_loss: 0.3910 - val_age_out_loss: 7.7906 - val_sex_out_accuracy: 0.7988 - val_age_out_accuracy: 0.1202\n",
            "Epoch 33/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 6.3011 - sex_out_loss: 0.3653 - age_out_loss: 5.3282 - sex_out_accuracy: 0.8125 - age_out_accuracy: 0.1115\n",
            "Epoch 00033: val_loss did not improve from 7.96953\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 6.3011 - sex_out_loss: 0.3653 - age_out_loss: 5.3282 - sex_out_accuracy: 0.8125 - age_out_accuracy: 0.1115 - val_loss: 8.5983 - val_sex_out_loss: 0.3864 - val_age_out_loss: 7.6003 - val_sex_out_accuracy: 0.8045 - val_age_out_accuracy: 0.1202\n",
            "Epoch 34/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 6.0229 - sex_out_loss: 0.3530 - age_out_loss: 5.0545 - sex_out_accuracy: 0.8140 - age_out_accuracy: 0.1113\n",
            "Epoch 00034: val_loss did not improve from 7.96953\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 6.0174 - sex_out_loss: 0.3528 - age_out_loss: 5.0493 - sex_out_accuracy: 0.8138 - age_out_accuracy: 0.1115 - val_loss: 9.4644 - val_sex_out_loss: 0.3906 - val_age_out_loss: 8.4544 - val_sex_out_accuracy: 0.8053 - val_age_out_accuracy: 0.1202\n",
            "Epoch 35/500\n",
            "109/115 [===========================>..] - ETA: 0s - loss: 6.1030 - sex_out_loss: 0.3573 - age_out_loss: 5.1224 - sex_out_accuracy: 0.8128 - age_out_accuracy: 0.1111\n",
            "Epoch 00035: val_loss did not improve from 7.96953\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 6.1118 - sex_out_loss: 0.3581 - age_out_loss: 5.1302 - sex_out_accuracy: 0.8132 - age_out_accuracy: 0.1115 - val_loss: 9.7593 - val_sex_out_loss: 0.3991 - val_age_out_loss: 8.7322 - val_sex_out_accuracy: 0.7971 - val_age_out_accuracy: 0.1202\n",
            "Epoch 36/500\n",
            "109/115 [===========================>..] - ETA: 0s - loss: 6.2143 - sex_out_loss: 0.3527 - age_out_loss: 5.2300 - sex_out_accuracy: 0.8114 - age_out_accuracy: 0.1112\n",
            "Epoch 00036: val_loss did not improve from 7.96953\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 6.1795 - sex_out_loss: 0.3533 - age_out_loss: 5.1944 - sex_out_accuracy: 0.8115 - age_out_accuracy: 0.1115 - val_loss: 9.2570 - val_sex_out_loss: 0.3814 - val_age_out_loss: 8.2401 - val_sex_out_accuracy: 0.7988 - val_age_out_accuracy: 0.1202\n",
            "Epoch 37/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 5.9150 - sex_out_loss: 0.3497 - age_out_loss: 4.9259 - sex_out_accuracy: 0.8121 - age_out_accuracy: 0.1115\n",
            "Epoch 00037: val_loss did not improve from 7.96953\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.9150 - sex_out_loss: 0.3497 - age_out_loss: 4.9259 - sex_out_accuracy: 0.8121 - age_out_accuracy: 0.1115 - val_loss: 9.6753 - val_sex_out_loss: 0.3763 - val_age_out_loss: 8.6559 - val_sex_out_accuracy: 0.8057 - val_age_out_accuracy: 0.1202\n",
            "Epoch 38/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 5.9518 - sex_out_loss: 0.3390 - age_out_loss: 4.9668 - sex_out_accuracy: 0.8214 - age_out_accuracy: 0.1112\n",
            "Epoch 00038: val_loss did not improve from 7.96953\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.9479 - sex_out_loss: 0.3401 - age_out_loss: 4.9618 - sex_out_accuracy: 0.8217 - age_out_accuracy: 0.1115 - val_loss: 8.9744 - val_sex_out_loss: 0.3753 - val_age_out_loss: 7.9496 - val_sex_out_accuracy: 0.8119 - val_age_out_accuracy: 0.1202\n",
            "Epoch 39/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 5.9731 - sex_out_loss: 0.3516 - age_out_loss: 4.9675 - sex_out_accuracy: 0.8229 - age_out_accuracy: 0.1109\n",
            "Epoch 00039: val_loss did not improve from 7.96953\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.9689 - sex_out_loss: 0.3526 - age_out_loss: 4.9623 - sex_out_accuracy: 0.8229 - age_out_accuracy: 0.1115 - val_loss: 9.2659 - val_sex_out_loss: 0.3738 - val_age_out_loss: 8.2343 - val_sex_out_accuracy: 0.8131 - val_age_out_accuracy: 0.1202\n",
            "Epoch 40/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 5.8411 - sex_out_loss: 0.3295 - age_out_loss: 4.8503 - sex_out_accuracy: 0.8297 - age_out_accuracy: 0.1115\n",
            "Epoch 00040: val_loss did not improve from 7.96953\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 5.8411 - sex_out_loss: 0.3295 - age_out_loss: 4.8503 - sex_out_accuracy: 0.8297 - age_out_accuracy: 0.1115 - val_loss: 9.1840 - val_sex_out_loss: 0.3774 - val_age_out_loss: 8.1420 - val_sex_out_accuracy: 0.8053 - val_age_out_accuracy: 0.1202\n",
            "Epoch 41/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 5.8509 - sex_out_loss: 0.3259 - age_out_loss: 4.8571 - sex_out_accuracy: 0.8385 - age_out_accuracy: 0.1113\n",
            "Epoch 00041: val_loss did not improve from 7.96953\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.8530 - sex_out_loss: 0.3262 - age_out_loss: 4.8589 - sex_out_accuracy: 0.8382 - age_out_accuracy: 0.1115 - val_loss: 9.0797 - val_sex_out_loss: 0.3819 - val_age_out_loss: 8.0272 - val_sex_out_accuracy: 0.7992 - val_age_out_accuracy: 0.1202\n",
            "Epoch 42/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 5.8539 - sex_out_loss: 0.3269 - age_out_loss: 4.8537 - sex_out_accuracy: 0.8332 - age_out_accuracy: 0.1112\n",
            "Epoch 00042: val_loss did not improve from 7.96953\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.8536 - sex_out_loss: 0.3275 - age_out_loss: 4.8529 - sex_out_accuracy: 0.8329 - age_out_accuracy: 0.1115 - val_loss: 9.3788 - val_sex_out_loss: 0.4190 - val_age_out_loss: 8.2835 - val_sex_out_accuracy: 0.7783 - val_age_out_accuracy: 0.1202\n",
            "Epoch 43/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 5.6881 - sex_out_loss: 0.3293 - age_out_loss: 4.6792 - sex_out_accuracy: 0.8306 - age_out_accuracy: 0.1118\n",
            "Epoch 00043: val_loss did not improve from 7.96953\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.6864 - sex_out_loss: 0.3291 - age_out_loss: 4.6777 - sex_out_accuracy: 0.8307 - age_out_accuracy: 0.1115 - val_loss: 8.9011 - val_sex_out_loss: 0.3848 - val_age_out_loss: 7.8335 - val_sex_out_accuracy: 0.8074 - val_age_out_accuracy: 0.1202\n",
            "Epoch 44/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 5.7178 - sex_out_loss: 0.3236 - age_out_loss: 4.7087 - sex_out_accuracy: 0.8298 - age_out_accuracy: 0.1116\n",
            "Epoch 00044: val_loss did not improve from 7.96953\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.7175 - sex_out_loss: 0.3238 - age_out_loss: 4.7083 - sex_out_accuracy: 0.8297 - age_out_accuracy: 0.1115 - val_loss: 9.4352 - val_sex_out_loss: 0.3794 - val_age_out_loss: 8.3671 - val_sex_out_accuracy: 0.8135 - val_age_out_accuracy: 0.1202\n",
            "Epoch 45/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 5.7300 - sex_out_loss: 0.3190 - age_out_loss: 4.7197 - sex_out_accuracy: 0.8372 - age_out_accuracy: 0.1115\n",
            "Epoch 00045: val_loss did not improve from 7.96953\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 5.7300 - sex_out_loss: 0.3190 - age_out_loss: 4.7197 - sex_out_accuracy: 0.8372 - age_out_accuracy: 0.1115 - val_loss: 10.0368 - val_sex_out_loss: 0.3667 - val_age_out_loss: 8.9760 - val_sex_out_accuracy: 0.8147 - val_age_out_accuracy: 0.1202\n",
            "Epoch 46/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 5.8213 - sex_out_loss: 0.3186 - age_out_loss: 4.8036 - sex_out_accuracy: 0.8359 - age_out_accuracy: 0.1115\n",
            "Epoch 00046: val_loss did not improve from 7.96953\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 5.8213 - sex_out_loss: 0.3186 - age_out_loss: 4.8036 - sex_out_accuracy: 0.8359 - age_out_accuracy: 0.1115 - val_loss: 7.9877 - val_sex_out_loss: 0.3655 - val_age_out_loss: 6.9193 - val_sex_out_accuracy: 0.8204 - val_age_out_accuracy: 0.1202\n",
            "Epoch 47/500\n",
            "109/115 [===========================>..] - ETA: 0s - loss: 5.5777 - sex_out_loss: 0.3203 - age_out_loss: 4.5527 - sex_out_accuracy: 0.8321 - age_out_accuracy: 0.1108\n",
            "Epoch 00047: val_loss did not improve from 7.96953\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 5.5752 - sex_out_loss: 0.3227 - age_out_loss: 4.5477 - sex_out_accuracy: 0.8308 - age_out_accuracy: 0.1115 - val_loss: 9.2542 - val_sex_out_loss: 0.3583 - val_age_out_loss: 8.1888 - val_sex_out_accuracy: 0.8155 - val_age_out_accuracy: 0.1202\n",
            "Epoch 48/500\n",
            "112/115 [============================>.] - ETA: 0s - loss: 5.6763 - sex_out_loss: 0.3071 - age_out_loss: 4.6600 - sex_out_accuracy: 0.8422 - age_out_accuracy: 0.1116\n",
            "Epoch 00048: val_loss did not improve from 7.96953\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.6690 - sex_out_loss: 0.3075 - age_out_loss: 4.6522 - sex_out_accuracy: 0.8416 - age_out_accuracy: 0.1115 - val_loss: 9.6209 - val_sex_out_loss: 0.3682 - val_age_out_loss: 8.5408 - val_sex_out_accuracy: 0.8192 - val_age_out_accuracy: 0.1202\n",
            "Epoch 49/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 5.6699 - sex_out_loss: 0.3193 - age_out_loss: 4.6352 - sex_out_accuracy: 0.8378 - age_out_accuracy: 0.1115\n",
            "Epoch 00049: val_loss did not improve from 7.96953\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 5.6699 - sex_out_loss: 0.3193 - age_out_loss: 4.6352 - sex_out_accuracy: 0.8378 - age_out_accuracy: 0.1115 - val_loss: 10.0108 - val_sex_out_loss: 0.3680 - val_age_out_loss: 8.9243 - val_sex_out_accuracy: 0.8176 - val_age_out_accuracy: 0.1202\n",
            "Epoch 50/500\n",
            "113/115 [============================>.] - ETA: 0s - loss: 5.6701 - sex_out_loss: 0.3058 - age_out_loss: 4.6437 - sex_out_accuracy: 0.8439 - age_out_accuracy: 0.1114\n",
            "Epoch 00050: val_loss did not improve from 7.96953\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.6646 - sex_out_loss: 0.3058 - age_out_loss: 4.6382 - sex_out_accuracy: 0.8447 - age_out_accuracy: 0.1115 - val_loss: 8.4738 - val_sex_out_loss: 0.3820 - val_age_out_loss: 7.3684 - val_sex_out_accuracy: 0.8033 - val_age_out_accuracy: 0.1202\n",
            "Epoch 51/500\n",
            "110/115 [===========================>..] - ETA: 0s - loss: 5.5701 - sex_out_loss: 0.3015 - age_out_loss: 4.5431 - sex_out_accuracy: 0.8456 - age_out_accuracy: 0.1115\n",
            "Epoch 00051: val_loss did not improve from 7.96953\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 5.5980 - sex_out_loss: 0.3021 - age_out_loss: 4.5703 - sex_out_accuracy: 0.8451 - age_out_accuracy: 0.1115 - val_loss: 9.4137 - val_sex_out_loss: 0.3711 - val_age_out_loss: 8.3142 - val_sex_out_accuracy: 0.8102 - val_age_out_accuracy: 0.1202\n",
            "Epoch 52/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 5.5148 - sex_out_loss: 0.3036 - age_out_loss: 4.4807 - sex_out_accuracy: 0.8431 - age_out_accuracy: 0.1120\n",
            "Epoch 00052: val_loss did not improve from 7.96953\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.5168 - sex_out_loss: 0.3037 - age_out_loss: 4.4826 - sex_out_accuracy: 0.8427 - age_out_accuracy: 0.1115 - val_loss: 9.9300 - val_sex_out_loss: 0.3691 - val_age_out_loss: 8.8290 - val_sex_out_accuracy: 0.8180 - val_age_out_accuracy: 0.1202\n",
            "Epoch 53/500\n",
            "109/115 [===========================>..] - ETA: 0s - loss: 5.5471 - sex_out_loss: 0.2857 - age_out_loss: 4.5275 - sex_out_accuracy: 0.8519 - age_out_accuracy: 0.1099\n",
            "Epoch 00053: val_loss did not improve from 7.96953\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 5.5317 - sex_out_loss: 0.2854 - age_out_loss: 4.5122 - sex_out_accuracy: 0.8517 - age_out_accuracy: 0.1115 - val_loss: 8.3653 - val_sex_out_loss: 0.3644 - val_age_out_loss: 7.2644 - val_sex_out_accuracy: 0.8282 - val_age_out_accuracy: 0.1202\n",
            "Epoch 54/500\n",
            "109/115 [===========================>..] - ETA: 0s - loss: 5.5136 - sex_out_loss: 0.2945 - age_out_loss: 4.4808 - sex_out_accuracy: 0.8534 - age_out_accuracy: 0.1124\n",
            "Epoch 00054: val_loss did not improve from 7.96953\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 5.5107 - sex_out_loss: 0.2938 - age_out_loss: 4.4785 - sex_out_accuracy: 0.8549 - age_out_accuracy: 0.1115 - val_loss: 9.2025 - val_sex_out_loss: 0.3618 - val_age_out_loss: 8.1001 - val_sex_out_accuracy: 0.8180 - val_age_out_accuracy: 0.1202\n",
            "Epoch 55/500\n",
            "109/115 [===========================>..] - ETA: 0s - loss: 5.5303 - sex_out_loss: 0.2947 - age_out_loss: 4.4924 - sex_out_accuracy: 0.8502 - age_out_accuracy: 0.1117\n",
            "Epoch 00055: val_loss did not improve from 7.96953\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 5.5389 - sex_out_loss: 0.2962 - age_out_loss: 4.4993 - sex_out_accuracy: 0.8491 - age_out_accuracy: 0.1115 - val_loss: 9.2940 - val_sex_out_loss: 0.3666 - val_age_out_loss: 8.1819 - val_sex_out_accuracy: 0.8176 - val_age_out_accuracy: 0.1202\n",
            "Epoch 56/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 5.7689 - sex_out_loss: 0.2961 - age_out_loss: 4.7241 - sex_out_accuracy: 0.8515 - age_out_accuracy: 0.1115\n",
            "Epoch 00056: val_loss did not improve from 7.96953\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.7689 - sex_out_loss: 0.2961 - age_out_loss: 4.7241 - sex_out_accuracy: 0.8515 - age_out_accuracy: 0.1115 - val_loss: 9.3225 - val_sex_out_loss: 0.3810 - val_age_out_loss: 8.1903 - val_sex_out_accuracy: 0.8082 - val_age_out_accuracy: 0.1202\n",
            "Epoch 57/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 5.4643 - sex_out_loss: 0.2845 - age_out_loss: 4.4264 - sex_out_accuracy: 0.8566 - age_out_accuracy: 0.1113\n",
            "Epoch 00057: val_loss did not improve from 7.96953\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.4625 - sex_out_loss: 0.2852 - age_out_loss: 4.4239 - sex_out_accuracy: 0.8562 - age_out_accuracy: 0.1115 - val_loss: 9.1085 - val_sex_out_loss: 0.3937 - val_age_out_loss: 7.9595 - val_sex_out_accuracy: 0.8037 - val_age_out_accuracy: 0.1202\n",
            "Epoch 58/500\n",
            "113/115 [============================>.] - ETA: 0s - loss: 5.4234 - sex_out_loss: 0.2827 - age_out_loss: 4.3828 - sex_out_accuracy: 0.8567 - age_out_accuracy: 0.1119\n",
            "Epoch 00058: val_loss did not improve from 7.96953\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.4294 - sex_out_loss: 0.2815 - age_out_loss: 4.3899 - sex_out_accuracy: 0.8573 - age_out_accuracy: 0.1115 - val_loss: 8.3663 - val_sex_out_loss: 0.3588 - val_age_out_loss: 7.2474 - val_sex_out_accuracy: 0.8160 - val_age_out_accuracy: 0.1202\n",
            "Epoch 59/500\n",
            "112/115 [============================>.] - ETA: 0s - loss: 5.3458 - sex_out_loss: 0.2913 - age_out_loss: 4.2933 - sex_out_accuracy: 0.8492 - age_out_accuracy: 0.1116\n",
            "Epoch 00059: val_loss did not improve from 7.96953\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.3501 - sex_out_loss: 0.2906 - age_out_loss: 4.2982 - sex_out_accuracy: 0.8498 - age_out_accuracy: 0.1115 - val_loss: 8.9122 - val_sex_out_loss: 0.3652 - val_age_out_loss: 7.7842 - val_sex_out_accuracy: 0.8135 - val_age_out_accuracy: 0.1202\n",
            "Epoch 60/500\n",
            "113/115 [============================>.] - ETA: 0s - loss: 5.5569 - sex_out_loss: 0.2772 - age_out_loss: 4.5140 - sex_out_accuracy: 0.8594 - age_out_accuracy: 0.1116\n",
            "Epoch 00060: val_loss did not improve from 7.96953\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.5537 - sex_out_loss: 0.2771 - age_out_loss: 4.5109 - sex_out_accuracy: 0.8589 - age_out_accuracy: 0.1115 - val_loss: 8.6939 - val_sex_out_loss: 0.3571 - val_age_out_loss: 7.5686 - val_sex_out_accuracy: 0.8213 - val_age_out_accuracy: 0.1202\n",
            "Epoch 61/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 5.5500 - sex_out_loss: 0.2833 - age_out_loss: 4.4967 - sex_out_accuracy: 0.8524 - age_out_accuracy: 0.1116\n",
            "Epoch 00061: val_loss improved from 7.96953 to 7.54374, saving model to Age_sex_detection.h5\n",
            "115/115 [==============================] - 1s 12ms/step - loss: 5.5496 - sex_out_loss: 0.2837 - age_out_loss: 4.4959 - sex_out_accuracy: 0.8522 - age_out_accuracy: 0.1115 - val_loss: 7.5437 - val_sex_out_loss: 0.3756 - val_age_out_loss: 6.3955 - val_sex_out_accuracy: 0.8147 - val_age_out_accuracy: 0.1202\n",
            "Epoch 62/500\n",
            "113/115 [============================>.] - ETA: 0s - loss: 5.3912 - sex_out_loss: 0.2706 - age_out_loss: 4.3476 - sex_out_accuracy: 0.8613 - age_out_accuracy: 0.1119\n",
            "Epoch 00062: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.3899 - sex_out_loss: 0.2705 - age_out_loss: 4.3465 - sex_out_accuracy: 0.8618 - age_out_accuracy: 0.1115 - val_loss: 8.7495 - val_sex_out_loss: 0.3572 - val_age_out_loss: 7.6183 - val_sex_out_accuracy: 0.8204 - val_age_out_accuracy: 0.1202\n",
            "Epoch 63/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 5.3498 - sex_out_loss: 0.2665 - age_out_loss: 4.3079 - sex_out_accuracy: 0.8652 - age_out_accuracy: 0.1115\n",
            "Epoch 00063: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.3498 - sex_out_loss: 0.2665 - age_out_loss: 4.3079 - sex_out_accuracy: 0.8652 - age_out_accuracy: 0.1115 - val_loss: 10.4425 - val_sex_out_loss: 0.3639 - val_age_out_loss: 9.3015 - val_sex_out_accuracy: 0.8225 - val_age_out_accuracy: 0.1202\n",
            "Epoch 64/500\n",
            "113/115 [============================>.] - ETA: 0s - loss: 5.4407 - sex_out_loss: 0.2658 - age_out_loss: 4.3958 - sex_out_accuracy: 0.8597 - age_out_accuracy: 0.1116\n",
            "Epoch 00064: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.4368 - sex_out_loss: 0.2666 - age_out_loss: 4.3910 - sex_out_accuracy: 0.8599 - age_out_accuracy: 0.1115 - val_loss: 8.4542 - val_sex_out_loss: 0.3702 - val_age_out_loss: 7.3032 - val_sex_out_accuracy: 0.8184 - val_age_out_accuracy: 0.1202\n",
            "Epoch 65/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 5.3393 - sex_out_loss: 0.2535 - age_out_loss: 4.3044 - sex_out_accuracy: 0.8693 - age_out_accuracy: 0.1115\n",
            "Epoch 00065: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.3393 - sex_out_loss: 0.2535 - age_out_loss: 4.3044 - sex_out_accuracy: 0.8693 - age_out_accuracy: 0.1115 - val_loss: 9.4239 - val_sex_out_loss: 0.3710 - val_age_out_loss: 8.2704 - val_sex_out_accuracy: 0.8180 - val_age_out_accuracy: 0.1202\n",
            "Epoch 66/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 5.3487 - sex_out_loss: 0.2620 - age_out_loss: 4.3026 - sex_out_accuracy: 0.8669 - age_out_accuracy: 0.1114\n",
            "Epoch 00066: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.3522 - sex_out_loss: 0.2617 - age_out_loss: 4.3064 - sex_out_accuracy: 0.8671 - age_out_accuracy: 0.1115 - val_loss: 9.5075 - val_sex_out_loss: 0.3560 - val_age_out_loss: 8.3655 - val_sex_out_accuracy: 0.8258 - val_age_out_accuracy: 0.1202\n",
            "Epoch 67/500\n",
            "110/115 [===========================>..] - ETA: 0s - loss: 5.3367 - sex_out_loss: 0.2569 - age_out_loss: 4.2921 - sex_out_accuracy: 0.8673 - age_out_accuracy: 0.1094\n",
            "Epoch 00067: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.3335 - sex_out_loss: 0.2589 - age_out_loss: 4.2868 - sex_out_accuracy: 0.8658 - age_out_accuracy: 0.1115 - val_loss: 9.8766 - val_sex_out_loss: 0.3714 - val_age_out_loss: 8.7160 - val_sex_out_accuracy: 0.8160 - val_age_out_accuracy: 0.1202\n",
            "Epoch 68/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 5.3902 - sex_out_loss: 0.2566 - age_out_loss: 4.3420 - sex_out_accuracy: 0.8705 - age_out_accuracy: 0.1114\n",
            "Epoch 00068: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.3859 - sex_out_loss: 0.2564 - age_out_loss: 4.3380 - sex_out_accuracy: 0.8705 - age_out_accuracy: 0.1115 - val_loss: 8.4462 - val_sex_out_loss: 0.3848 - val_age_out_loss: 7.2686 - val_sex_out_accuracy: 0.8188 - val_age_out_accuracy: 0.1202\n",
            "Epoch 69/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 5.2209 - sex_out_loss: 0.2557 - age_out_loss: 4.1704 - sex_out_accuracy: 0.8650 - age_out_accuracy: 0.1117\n",
            "Epoch 00069: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.2181 - sex_out_loss: 0.2562 - age_out_loss: 4.1671 - sex_out_accuracy: 0.8648 - age_out_accuracy: 0.1115 - val_loss: 9.0927 - val_sex_out_loss: 0.3758 - val_age_out_loss: 7.9200 - val_sex_out_accuracy: 0.8155 - val_age_out_accuracy: 0.1202\n",
            "Epoch 70/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 5.2002 - sex_out_loss: 0.2486 - age_out_loss: 4.1530 - sex_out_accuracy: 0.8671 - age_out_accuracy: 0.1114\n",
            "Epoch 00070: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.1959 - sex_out_loss: 0.2485 - age_out_loss: 4.1489 - sex_out_accuracy: 0.8671 - age_out_accuracy: 0.1115 - val_loss: 9.3493 - val_sex_out_loss: 0.3651 - val_age_out_loss: 8.1839 - val_sex_out_accuracy: 0.8168 - val_age_out_accuracy: 0.1202\n",
            "Epoch 71/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 5.3209 - sex_out_loss: 0.2561 - age_out_loss: 4.2625 - sex_out_accuracy: 0.8736 - age_out_accuracy: 0.1115\n",
            "Epoch 00071: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.3209 - sex_out_loss: 0.2561 - age_out_loss: 4.2625 - sex_out_accuracy: 0.8736 - age_out_accuracy: 0.1115 - val_loss: 8.9062 - val_sex_out_loss: 0.3841 - val_age_out_loss: 7.7174 - val_sex_out_accuracy: 0.8196 - val_age_out_accuracy: 0.1202\n",
            "Epoch 72/500\n",
            "113/115 [============================>.] - ETA: 0s - loss: 5.1976 - sex_out_loss: 0.2531 - age_out_loss: 4.1390 - sex_out_accuracy: 0.8733 - age_out_accuracy: 0.1110\n",
            "Epoch 00072: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 5.1903 - sex_out_loss: 0.2534 - age_out_loss: 4.1315 - sex_out_accuracy: 0.8736 - age_out_accuracy: 0.1115 - val_loss: 7.9971 - val_sex_out_loss: 0.3714 - val_age_out_loss: 6.8193 - val_sex_out_accuracy: 0.8221 - val_age_out_accuracy: 0.1202\n",
            "Epoch 73/500\n",
            "109/115 [===========================>..] - ETA: 0s - loss: 5.3921 - sex_out_loss: 0.2434 - age_out_loss: 4.3411 - sex_out_accuracy: 0.8739 - age_out_accuracy: 0.1118\n",
            "Epoch 00073: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 5.3881 - sex_out_loss: 0.2454 - age_out_loss: 4.3350 - sex_out_accuracy: 0.8727 - age_out_accuracy: 0.1115 - val_loss: 9.5830 - val_sex_out_loss: 0.3661 - val_age_out_loss: 8.4072 - val_sex_out_accuracy: 0.8249 - val_age_out_accuracy: 0.1202\n",
            "Epoch 74/500\n",
            "109/115 [===========================>..] - ETA: 0s - loss: 5.3343 - sex_out_loss: 0.2429 - age_out_loss: 4.2799 - sex_out_accuracy: 0.8759 - age_out_accuracy: 0.1107\n",
            "Epoch 00074: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.3483 - sex_out_loss: 0.2429 - age_out_loss: 4.2939 - sex_out_accuracy: 0.8766 - age_out_accuracy: 0.1115 - val_loss: 8.3004 - val_sex_out_loss: 0.3907 - val_age_out_loss: 7.0955 - val_sex_out_accuracy: 0.8168 - val_age_out_accuracy: 0.1202\n",
            "Epoch 75/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 5.2752 - sex_out_loss: 0.2395 - age_out_loss: 4.2198 - sex_out_accuracy: 0.8751 - age_out_accuracy: 0.1115\n",
            "Epoch 00075: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 5.2752 - sex_out_loss: 0.2395 - age_out_loss: 4.2198 - sex_out_accuracy: 0.8751 - age_out_accuracy: 0.1115 - val_loss: 9.2688 - val_sex_out_loss: 0.3707 - val_age_out_loss: 8.0808 - val_sex_out_accuracy: 0.8254 - val_age_out_accuracy: 0.1202\n",
            "Epoch 76/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 5.1698 - sex_out_loss: 0.2341 - age_out_loss: 4.1168 - sex_out_accuracy: 0.8779 - age_out_accuracy: 0.1121\n",
            "Epoch 00076: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.1717 - sex_out_loss: 0.2339 - age_out_loss: 4.1190 - sex_out_accuracy: 0.8777 - age_out_accuracy: 0.1115 - val_loss: 8.8991 - val_sex_out_loss: 0.3903 - val_age_out_loss: 7.6883 - val_sex_out_accuracy: 0.8160 - val_age_out_accuracy: 0.1202\n",
            "Epoch 77/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 5.2362 - sex_out_loss: 0.2378 - age_out_loss: 4.1756 - sex_out_accuracy: 0.8736 - age_out_accuracy: 0.1112\n",
            "Epoch 00077: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 5.2361 - sex_out_loss: 0.2380 - age_out_loss: 4.1753 - sex_out_accuracy: 0.8735 - age_out_accuracy: 0.1115 - val_loss: 9.4707 - val_sex_out_loss: 0.3788 - val_age_out_loss: 8.2668 - val_sex_out_accuracy: 0.8184 - val_age_out_accuracy: 0.1202\n",
            "Epoch 78/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 5.1313 - sex_out_loss: 0.2336 - age_out_loss: 4.0716 - sex_out_accuracy: 0.8833 - age_out_accuracy: 0.1115\n",
            "Epoch 00078: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.1313 - sex_out_loss: 0.2336 - age_out_loss: 4.0716 - sex_out_accuracy: 0.8833 - age_out_accuracy: 0.1115 - val_loss: 9.0252 - val_sex_out_loss: 0.4037 - val_age_out_loss: 7.7941 - val_sex_out_accuracy: 0.8176 - val_age_out_accuracy: 0.1202\n",
            "Epoch 79/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 5.1348 - sex_out_loss: 0.2312 - age_out_loss: 4.0746 - sex_out_accuracy: 0.8765 - age_out_accuracy: 0.1115\n",
            "Epoch 00079: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.1348 - sex_out_loss: 0.2312 - age_out_loss: 4.0746 - sex_out_accuracy: 0.8765 - age_out_accuracy: 0.1115 - val_loss: 8.6296 - val_sex_out_loss: 0.3767 - val_age_out_loss: 7.4227 - val_sex_out_accuracy: 0.8237 - val_age_out_accuracy: 0.1202\n",
            "Epoch 80/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 5.1176 - sex_out_loss: 0.2340 - age_out_loss: 4.0525 - sex_out_accuracy: 0.8750 - age_out_accuracy: 0.1115\n",
            "Epoch 00080: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 5.1176 - sex_out_loss: 0.2340 - age_out_loss: 4.0525 - sex_out_accuracy: 0.8750 - age_out_accuracy: 0.1115 - val_loss: 9.3776 - val_sex_out_loss: 0.3633 - val_age_out_loss: 8.1824 - val_sex_out_accuracy: 0.8192 - val_age_out_accuracy: 0.1202\n",
            "Epoch 81/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 5.0798 - sex_out_loss: 0.2249 - age_out_loss: 4.0220 - sex_out_accuracy: 0.8859 - age_out_accuracy: 0.1115\n",
            "Epoch 00081: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.0798 - sex_out_loss: 0.2249 - age_out_loss: 4.0220 - sex_out_accuracy: 0.8859 - age_out_accuracy: 0.1115 - val_loss: 9.2321 - val_sex_out_loss: 0.4212 - val_age_out_loss: 7.9771 - val_sex_out_accuracy: 0.8065 - val_age_out_accuracy: 0.1202\n",
            "Epoch 82/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 5.0939 - sex_out_loss: 0.2362 - age_out_loss: 4.0218 - sex_out_accuracy: 0.8776 - age_out_accuracy: 0.1115\n",
            "Epoch 00082: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 5.0939 - sex_out_loss: 0.2362 - age_out_loss: 4.0218 - sex_out_accuracy: 0.8776 - age_out_accuracy: 0.1115 - val_loss: 9.1181 - val_sex_out_loss: 0.3827 - val_age_out_loss: 7.8973 - val_sex_out_accuracy: 0.8164 - val_age_out_accuracy: 0.1202\n",
            "Epoch 83/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 5.1656 - sex_out_loss: 0.2307 - age_out_loss: 4.0949 - sex_out_accuracy: 0.8812 - age_out_accuracy: 0.1114\n",
            "Epoch 00083: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.1650 - sex_out_loss: 0.2305 - age_out_loss: 4.0946 - sex_out_accuracy: 0.8814 - age_out_accuracy: 0.1115 - val_loss: 8.3192 - val_sex_out_loss: 0.3804 - val_age_out_loss: 7.0966 - val_sex_out_accuracy: 0.8217 - val_age_out_accuracy: 0.1202\n",
            "Epoch 84/500\n",
            "113/115 [============================>.] - ETA: 0s - loss: 5.1755 - sex_out_loss: 0.2248 - age_out_loss: 4.1069 - sex_out_accuracy: 0.8836 - age_out_accuracy: 0.1110\n",
            "Epoch 00084: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.1682 - sex_out_loss: 0.2258 - age_out_loss: 4.0986 - sex_out_accuracy: 0.8832 - age_out_accuracy: 0.1115 - val_loss: 8.7140 - val_sex_out_loss: 0.4125 - val_age_out_loss: 7.4570 - val_sex_out_accuracy: 0.8172 - val_age_out_accuracy: 0.1202\n",
            "Epoch 85/500\n",
            "109/115 [===========================>..] - ETA: 0s - loss: 5.2726 - sex_out_loss: 0.2310 - age_out_loss: 4.1963 - sex_out_accuracy: 0.8770 - age_out_accuracy: 0.1118\n",
            "Epoch 00085: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.2813 - sex_out_loss: 0.2314 - age_out_loss: 4.2045 - sex_out_accuracy: 0.8773 - age_out_accuracy: 0.1115 - val_loss: 8.6499 - val_sex_out_loss: 0.3748 - val_age_out_loss: 7.4288 - val_sex_out_accuracy: 0.8217 - val_age_out_accuracy: 0.1202\n",
            "Epoch 86/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 5.1280 - sex_out_loss: 0.2268 - age_out_loss: 4.0550 - sex_out_accuracy: 0.8825 - age_out_accuracy: 0.1115\n",
            "Epoch 00086: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.1280 - sex_out_loss: 0.2268 - age_out_loss: 4.0550 - sex_out_accuracy: 0.8825 - age_out_accuracy: 0.1115 - val_loss: 8.2226 - val_sex_out_loss: 0.3788 - val_age_out_loss: 6.9970 - val_sex_out_accuracy: 0.8274 - val_age_out_accuracy: 0.1202\n",
            "Epoch 87/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 5.1614 - sex_out_loss: 0.2143 - age_out_loss: 4.1003 - sex_out_accuracy: 0.8817 - age_out_accuracy: 0.1115\n",
            "Epoch 00087: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.1614 - sex_out_loss: 0.2143 - age_out_loss: 4.1003 - sex_out_accuracy: 0.8817 - age_out_accuracy: 0.1115 - val_loss: 8.2284 - val_sex_out_loss: 0.3705 - val_age_out_loss: 7.0104 - val_sex_out_accuracy: 0.8274 - val_age_out_accuracy: 0.1202\n",
            "Epoch 88/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 5.2181 - sex_out_loss: 0.2118 - age_out_loss: 4.1586 - sex_out_accuracy: 0.8928 - age_out_accuracy: 0.1117\n",
            "Epoch 00088: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.2133 - sex_out_loss: 0.2120 - age_out_loss: 4.1535 - sex_out_accuracy: 0.8927 - age_out_accuracy: 0.1115 - val_loss: 8.4843 - val_sex_out_loss: 0.4159 - val_age_out_loss: 7.2203 - val_sex_out_accuracy: 0.8074 - val_age_out_accuracy: 0.1202\n",
            "Epoch 89/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 5.1380 - sex_out_loss: 0.2117 - age_out_loss: 4.0781 - sex_out_accuracy: 0.8882 - age_out_accuracy: 0.1115\n",
            "Epoch 00089: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.1380 - sex_out_loss: 0.2117 - age_out_loss: 4.0781 - sex_out_accuracy: 0.8882 - age_out_accuracy: 0.1115 - val_loss: 9.0783 - val_sex_out_loss: 0.3954 - val_age_out_loss: 7.8337 - val_sex_out_accuracy: 0.8254 - val_age_out_accuracy: 0.1202\n",
            "Epoch 90/500\n",
            "113/115 [============================>.] - ETA: 0s - loss: 5.0628 - sex_out_loss: 0.2148 - age_out_loss: 3.9965 - sex_out_accuracy: 0.8872 - age_out_accuracy: 0.1112\n",
            "Epoch 00090: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.0632 - sex_out_loss: 0.2168 - age_out_loss: 3.9950 - sex_out_accuracy: 0.8867 - age_out_accuracy: 0.1115 - val_loss: 8.5256 - val_sex_out_loss: 0.3769 - val_age_out_loss: 7.2959 - val_sex_out_accuracy: 0.8315 - val_age_out_accuracy: 0.1202\n",
            "Epoch 91/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 5.0542 - sex_out_loss: 0.2068 - age_out_loss: 3.9935 - sex_out_accuracy: 0.8862 - age_out_accuracy: 0.1115\n",
            "Epoch 00091: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.0542 - sex_out_loss: 0.2068 - age_out_loss: 3.9935 - sex_out_accuracy: 0.8862 - age_out_accuracy: 0.1115 - val_loss: 8.7442 - val_sex_out_loss: 0.3751 - val_age_out_loss: 7.5143 - val_sex_out_accuracy: 0.8327 - val_age_out_accuracy: 0.1202\n",
            "Epoch 92/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 5.0700 - sex_out_loss: 0.2142 - age_out_loss: 3.9996 - sex_out_accuracy: 0.8885 - age_out_accuracy: 0.1115\n",
            "Epoch 00092: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.0700 - sex_out_loss: 0.2142 - age_out_loss: 3.9996 - sex_out_accuracy: 0.8885 - age_out_accuracy: 0.1115 - val_loss: 10.1962 - val_sex_out_loss: 0.4052 - val_age_out_loss: 8.9334 - val_sex_out_accuracy: 0.8303 - val_age_out_accuracy: 0.1202\n",
            "Epoch 93/500\n",
            "110/115 [===========================>..] - ETA: 0s - loss: 4.9811 - sex_out_loss: 0.2040 - age_out_loss: 3.9190 - sex_out_accuracy: 0.8918 - age_out_accuracy: 0.1109\n",
            "Epoch 00093: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 4.9769 - sex_out_loss: 0.2026 - age_out_loss: 3.9162 - sex_out_accuracy: 0.8926 - age_out_accuracy: 0.1115 - val_loss: 8.7813 - val_sex_out_loss: 0.4103 - val_age_out_loss: 7.5117 - val_sex_out_accuracy: 0.8143 - val_age_out_accuracy: 0.1202\n",
            "Epoch 94/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 5.1196 - sex_out_loss: 0.2089 - age_out_loss: 4.0508 - sex_out_accuracy: 0.8923 - age_out_accuracy: 0.1118\n",
            "Epoch 00094: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.1156 - sex_out_loss: 0.2091 - age_out_loss: 4.0466 - sex_out_accuracy: 0.8922 - age_out_accuracy: 0.1115 - val_loss: 8.7806 - val_sex_out_loss: 0.4110 - val_age_out_loss: 7.5084 - val_sex_out_accuracy: 0.8319 - val_age_out_accuracy: 0.1202\n",
            "Epoch 95/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 5.0762 - sex_out_loss: 0.2072 - age_out_loss: 4.0062 - sex_out_accuracy: 0.8877 - age_out_accuracy: 0.1115\n",
            "Epoch 00095: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 5.0762 - sex_out_loss: 0.2072 - age_out_loss: 4.0062 - sex_out_accuracy: 0.8877 - age_out_accuracy: 0.1115 - val_loss: 9.3613 - val_sex_out_loss: 0.3909 - val_age_out_loss: 8.1060 - val_sex_out_accuracy: 0.8245 - val_age_out_accuracy: 0.1202\n",
            "Epoch 96/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 5.1393 - sex_out_loss: 0.2144 - age_out_loss: 4.0599 - sex_out_accuracy: 0.8905 - age_out_accuracy: 0.1115\n",
            "Epoch 00096: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 5.1393 - sex_out_loss: 0.2144 - age_out_loss: 4.0599 - sex_out_accuracy: 0.8905 - age_out_accuracy: 0.1115 - val_loss: 8.5200 - val_sex_out_loss: 0.4069 - val_age_out_loss: 7.2467 - val_sex_out_accuracy: 0.8168 - val_age_out_accuracy: 0.1202\n",
            "Epoch 97/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 5.1292 - sex_out_loss: 0.2059 - age_out_loss: 4.0556 - sex_out_accuracy: 0.8893 - age_out_accuracy: 0.1115\n",
            "Epoch 00097: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 5.1292 - sex_out_loss: 0.2059 - age_out_loss: 4.0556 - sex_out_accuracy: 0.8893 - age_out_accuracy: 0.1115 - val_loss: 8.6539 - val_sex_out_loss: 0.4036 - val_age_out_loss: 7.3812 - val_sex_out_accuracy: 0.8307 - val_age_out_accuracy: 0.1202\n",
            "Epoch 98/500\n",
            "110/115 [===========================>..] - ETA: 0s - loss: 5.0995 - sex_out_loss: 0.2157 - age_out_loss: 4.0143 - sex_out_accuracy: 0.8939 - age_out_accuracy: 0.1114\n",
            "Epoch 00098: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 5.0879 - sex_out_loss: 0.2184 - age_out_loss: 4.0000 - sex_out_accuracy: 0.8928 - age_out_accuracy: 0.1115 - val_loss: 8.1009 - val_sex_out_loss: 0.4402 - val_age_out_loss: 6.7905 - val_sex_out_accuracy: 0.8139 - val_age_out_accuracy: 0.1202\n",
            "Epoch 99/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 5.1321 - sex_out_loss: 0.2023 - age_out_loss: 4.0582 - sex_out_accuracy: 0.8917 - age_out_accuracy: 0.1117\n",
            "Epoch 00099: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 5.1333 - sex_out_loss: 0.2028 - age_out_loss: 4.0588 - sex_out_accuracy: 0.8915 - age_out_accuracy: 0.1115 - val_loss: 8.3184 - val_sex_out_loss: 0.3892 - val_age_out_loss: 7.0563 - val_sex_out_accuracy: 0.8254 - val_age_out_accuracy: 0.1202\n",
            "Epoch 100/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 5.0521 - sex_out_loss: 0.1997 - age_out_loss: 3.9791 - sex_out_accuracy: 0.8972 - age_out_accuracy: 0.1115\n",
            "Epoch 00100: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 5.0521 - sex_out_loss: 0.1997 - age_out_loss: 3.9791 - sex_out_accuracy: 0.8972 - age_out_accuracy: 0.1115 - val_loss: 7.9441 - val_sex_out_loss: 0.4154 - val_age_out_loss: 6.6544 - val_sex_out_accuracy: 0.8192 - val_age_out_accuracy: 0.1202\n",
            "Epoch 101/500\n",
            "109/115 [===========================>..] - ETA: 0s - loss: 5.0421 - sex_out_loss: 0.1958 - age_out_loss: 3.9717 - sex_out_accuracy: 0.8951 - age_out_accuracy: 0.1102\n",
            "Epoch 00101: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 5.0235 - sex_out_loss: 0.1964 - age_out_loss: 3.9524 - sex_out_accuracy: 0.8950 - age_out_accuracy: 0.1115 - val_loss: 7.6242 - val_sex_out_loss: 0.3877 - val_age_out_loss: 6.3612 - val_sex_out_accuracy: 0.8249 - val_age_out_accuracy: 0.1202\n",
            "Epoch 102/500\n",
            "113/115 [============================>.] - ETA: 0s - loss: 5.1828 - sex_out_loss: 0.1919 - age_out_loss: 4.1141 - sex_out_accuracy: 0.8988 - age_out_accuracy: 0.1108\n",
            "Epoch 00102: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.1657 - sex_out_loss: 0.1925 - age_out_loss: 4.0965 - sex_out_accuracy: 0.8983 - age_out_accuracy: 0.1115 - val_loss: 9.3807 - val_sex_out_loss: 0.4221 - val_age_out_loss: 8.0808 - val_sex_out_accuracy: 0.8143 - val_age_out_accuracy: 0.1202\n",
            "Epoch 103/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 4.9859 - sex_out_loss: 0.1977 - age_out_loss: 3.9099 - sex_out_accuracy: 0.9002 - age_out_accuracy: 0.1114\n",
            "Epoch 00103: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 4.9875 - sex_out_loss: 0.1975 - age_out_loss: 3.9116 - sex_out_accuracy: 0.9002 - age_out_accuracy: 0.1115 - val_loss: 9.1364 - val_sex_out_loss: 0.4759 - val_age_out_loss: 7.7812 - val_sex_out_accuracy: 0.8037 - val_age_out_accuracy: 0.1202\n",
            "Epoch 104/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 5.0104 - sex_out_loss: 0.1798 - age_out_loss: 3.9504 - sex_out_accuracy: 0.9010 - age_out_accuracy: 0.1114\n",
            "Epoch 00104: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.0116 - sex_out_loss: 0.1804 - age_out_loss: 3.9511 - sex_out_accuracy: 0.9010 - age_out_accuracy: 0.1115 - val_loss: 8.8308 - val_sex_out_loss: 0.4384 - val_age_out_loss: 7.5106 - val_sex_out_accuracy: 0.8229 - val_age_out_accuracy: 0.1202\n",
            "Epoch 105/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 4.9852 - sex_out_loss: 0.1914 - age_out_loss: 3.9108 - sex_out_accuracy: 0.8973 - age_out_accuracy: 0.1115\n",
            "Epoch 00105: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 4.9852 - sex_out_loss: 0.1914 - age_out_loss: 3.9108 - sex_out_accuracy: 0.8973 - age_out_accuracy: 0.1115 - val_loss: 8.7062 - val_sex_out_loss: 0.3875 - val_age_out_loss: 7.4351 - val_sex_out_accuracy: 0.8196 - val_age_out_accuracy: 0.1202\n",
            "Epoch 106/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 5.0512 - sex_out_loss: 0.2015 - age_out_loss: 3.9658 - sex_out_accuracy: 0.8952 - age_out_accuracy: 0.1115\n",
            "Epoch 00106: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.0512 - sex_out_loss: 0.2015 - age_out_loss: 3.9658 - sex_out_accuracy: 0.8952 - age_out_accuracy: 0.1115 - val_loss: 8.4367 - val_sex_out_loss: 0.4030 - val_age_out_loss: 7.1485 - val_sex_out_accuracy: 0.8160 - val_age_out_accuracy: 0.1202\n",
            "Epoch 107/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 4.9636 - sex_out_loss: 0.1978 - age_out_loss: 3.8794 - sex_out_accuracy: 0.9006 - age_out_accuracy: 0.1117\n",
            "Epoch 00107: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 4.9681 - sex_out_loss: 0.1980 - age_out_loss: 3.8836 - sex_out_accuracy: 0.9003 - age_out_accuracy: 0.1115 - val_loss: 9.4848 - val_sex_out_loss: 0.4499 - val_age_out_loss: 8.1484 - val_sex_out_accuracy: 0.8135 - val_age_out_accuracy: 0.1202\n",
            "Epoch 108/500\n",
            "109/115 [===========================>..] - ETA: 0s - loss: 5.0890 - sex_out_loss: 0.1878 - age_out_loss: 4.0141 - sex_out_accuracy: 0.9018 - age_out_accuracy: 0.1125\n",
            "Epoch 00108: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.1046 - sex_out_loss: 0.1883 - age_out_loss: 4.0292 - sex_out_accuracy: 0.9021 - age_out_accuracy: 0.1115 - val_loss: 8.6450 - val_sex_out_loss: 0.3874 - val_age_out_loss: 7.3693 - val_sex_out_accuracy: 0.8254 - val_age_out_accuracy: 0.1202\n",
            "Epoch 109/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 4.9179 - sex_out_loss: 0.1874 - age_out_loss: 3.8423 - sex_out_accuracy: 0.8986 - age_out_accuracy: 0.1115\n",
            "Epoch 00109: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 4.9179 - sex_out_loss: 0.1874 - age_out_loss: 3.8423 - sex_out_accuracy: 0.8986 - age_out_accuracy: 0.1115 - val_loss: 9.0699 - val_sex_out_loss: 0.4451 - val_age_out_loss: 7.7363 - val_sex_out_accuracy: 0.8262 - val_age_out_accuracy: 0.1202\n",
            "Epoch 110/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 5.0496 - sex_out_loss: 0.1865 - age_out_loss: 3.9731 - sex_out_accuracy: 0.8991 - age_out_accuracy: 0.1116\n",
            "Epoch 00110: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.0581 - sex_out_loss: 0.1865 - age_out_loss: 3.9816 - sex_out_accuracy: 0.8991 - age_out_accuracy: 0.1115 - val_loss: 9.3260 - val_sex_out_loss: 0.4118 - val_age_out_loss: 8.0236 - val_sex_out_accuracy: 0.8155 - val_age_out_accuracy: 0.1202\n",
            "Epoch 111/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 4.9311 - sex_out_loss: 0.1807 - age_out_loss: 3.8594 - sex_out_accuracy: 0.9010 - age_out_accuracy: 0.1114\n",
            "Epoch 00111: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 4.9356 - sex_out_loss: 0.1804 - age_out_loss: 3.8642 - sex_out_accuracy: 0.9013 - age_out_accuracy: 0.1115 - val_loss: 9.2529 - val_sex_out_loss: 0.4330 - val_age_out_loss: 7.9284 - val_sex_out_accuracy: 0.8274 - val_age_out_accuracy: 0.1202\n",
            "Epoch 112/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 5.0063 - sex_out_loss: 0.1939 - age_out_loss: 3.9206 - sex_out_accuracy: 0.9006 - age_out_accuracy: 0.1115\n",
            "Epoch 00112: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.0063 - sex_out_loss: 0.1939 - age_out_loss: 3.9206 - sex_out_accuracy: 0.9006 - age_out_accuracy: 0.1115 - val_loss: 9.7472 - val_sex_out_loss: 0.4762 - val_age_out_loss: 8.3785 - val_sex_out_accuracy: 0.8037 - val_age_out_accuracy: 0.1202\n",
            "Epoch 113/500\n",
            "110/115 [===========================>..] - ETA: 0s - loss: 4.9867 - sex_out_loss: 0.1799 - age_out_loss: 3.9135 - sex_out_accuracy: 0.8970 - age_out_accuracy: 0.1104\n",
            "Epoch 00113: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 4.9832 - sex_out_loss: 0.1822 - age_out_loss: 3.9077 - sex_out_accuracy: 0.8957 - age_out_accuracy: 0.1115 - val_loss: 9.3563 - val_sex_out_loss: 0.3994 - val_age_out_loss: 8.0631 - val_sex_out_accuracy: 0.8254 - val_age_out_accuracy: 0.1202\n",
            "Epoch 114/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 4.9939 - sex_out_loss: 0.1833 - age_out_loss: 3.9162 - sex_out_accuracy: 0.9013 - age_out_accuracy: 0.1115\n",
            "Epoch 00114: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 4.9939 - sex_out_loss: 0.1833 - age_out_loss: 3.9162 - sex_out_accuracy: 0.9013 - age_out_accuracy: 0.1115 - val_loss: 8.5048 - val_sex_out_loss: 0.4193 - val_age_out_loss: 7.1905 - val_sex_out_accuracy: 0.8344 - val_age_out_accuracy: 0.1202\n",
            "Epoch 115/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 4.8860 - sex_out_loss: 0.1772 - age_out_loss: 3.8129 - sex_out_accuracy: 0.9028 - age_out_accuracy: 0.1115\n",
            "Epoch 00115: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 4.8860 - sex_out_loss: 0.1772 - age_out_loss: 3.8129 - sex_out_accuracy: 0.9028 - age_out_accuracy: 0.1115 - val_loss: 8.2145 - val_sex_out_loss: 0.4280 - val_age_out_loss: 6.8891 - val_sex_out_accuracy: 0.8331 - val_age_out_accuracy: 0.1202\n",
            "Epoch 116/500\n",
            "110/115 [===========================>..] - ETA: 0s - loss: 5.0363 - sex_out_loss: 0.1722 - age_out_loss: 3.9657 - sex_out_accuracy: 0.9024 - age_out_accuracy: 0.1114\n",
            "Epoch 00116: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 5.0185 - sex_out_loss: 0.1717 - age_out_loss: 3.9485 - sex_out_accuracy: 0.9028 - age_out_accuracy: 0.1115 - val_loss: 8.5689 - val_sex_out_loss: 0.4201 - val_age_out_loss: 7.2493 - val_sex_out_accuracy: 0.8282 - val_age_out_accuracy: 0.1202\n",
            "Epoch 117/500\n",
            "110/115 [===========================>..] - ETA: 0s - loss: 4.9678 - sex_out_loss: 0.1812 - age_out_loss: 3.8865 - sex_out_accuracy: 0.9033 - age_out_accuracy: 0.1095\n",
            "Epoch 00117: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 4.9669 - sex_out_loss: 0.1833 - age_out_loss: 3.8835 - sex_out_accuracy: 0.9021 - age_out_accuracy: 0.1115 - val_loss: 8.1566 - val_sex_out_loss: 0.5514 - val_age_out_loss: 6.7044 - val_sex_out_accuracy: 0.8008 - val_age_out_accuracy: 0.1202\n",
            "Epoch 118/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 4.9501 - sex_out_loss: 0.1735 - age_out_loss: 3.8755 - sex_out_accuracy: 0.9077 - age_out_accuracy: 0.1115\n",
            "Epoch 00118: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 4.9501 - sex_out_loss: 0.1735 - age_out_loss: 3.8755 - sex_out_accuracy: 0.9077 - age_out_accuracy: 0.1115 - val_loss: 8.2785 - val_sex_out_loss: 0.4198 - val_age_out_loss: 6.9566 - val_sex_out_accuracy: 0.8413 - val_age_out_accuracy: 0.1202\n",
            "Epoch 119/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 5.0455 - sex_out_loss: 0.1693 - age_out_loss: 3.9729 - sex_out_accuracy: 0.9062 - age_out_accuracy: 0.1112\n",
            "Epoch 00119: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 5.0412 - sex_out_loss: 0.1692 - age_out_loss: 3.9687 - sex_out_accuracy: 0.9062 - age_out_accuracy: 0.1115 - val_loss: 8.9909 - val_sex_out_loss: 0.3958 - val_age_out_loss: 7.6905 - val_sex_out_accuracy: 0.8327 - val_age_out_accuracy: 0.1202\n",
            "Epoch 120/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 5.0154 - sex_out_loss: 0.1760 - age_out_loss: 3.9352 - sex_out_accuracy: 0.9070 - age_out_accuracy: 0.1115\n",
            "Epoch 00120: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 5.0154 - sex_out_loss: 0.1760 - age_out_loss: 3.9352 - sex_out_accuracy: 0.9070 - age_out_accuracy: 0.1115 - val_loss: 8.2877 - val_sex_out_loss: 0.4425 - val_age_out_loss: 6.9409 - val_sex_out_accuracy: 0.8294 - val_age_out_accuracy: 0.1202\n",
            "Epoch 121/500\n",
            "113/115 [============================>.] - ETA: 0s - loss: 4.8977 - sex_out_loss: 0.1602 - age_out_loss: 3.8332 - sex_out_accuracy: 0.9029 - age_out_accuracy: 0.1119\n",
            "Epoch 00121: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 4.8977 - sex_out_loss: 0.1602 - age_out_loss: 3.8332 - sex_out_accuracy: 0.9033 - age_out_accuracy: 0.1115 - val_loss: 8.5540 - val_sex_out_loss: 0.4313 - val_age_out_loss: 7.2175 - val_sex_out_accuracy: 0.8249 - val_age_out_accuracy: 0.1202\n",
            "Epoch 122/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 4.9610 - sex_out_loss: 0.1757 - age_out_loss: 3.8801 - sex_out_accuracy: 0.9010 - age_out_accuracy: 0.1115\n",
            "Epoch 00122: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 4.9610 - sex_out_loss: 0.1757 - age_out_loss: 3.8801 - sex_out_accuracy: 0.9010 - age_out_accuracy: 0.1115 - val_loss: 8.5948 - val_sex_out_loss: 0.4599 - val_age_out_loss: 7.2289 - val_sex_out_accuracy: 0.8241 - val_age_out_accuracy: 0.1202\n",
            "Epoch 123/500\n",
            "110/115 [===========================>..] - ETA: 0s - loss: 5.1172 - sex_out_loss: 0.1671 - age_out_loss: 4.0429 - sex_out_accuracy: 0.9037 - age_out_accuracy: 0.1116\n",
            "Epoch 00123: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 5.1073 - sex_out_loss: 0.1664 - age_out_loss: 4.0335 - sex_out_accuracy: 0.9051 - age_out_accuracy: 0.1115 - val_loss: 9.2022 - val_sex_out_loss: 0.4519 - val_age_out_loss: 7.8408 - val_sex_out_accuracy: 0.8282 - val_age_out_accuracy: 0.1202\n",
            "Epoch 124/500\n",
            "109/115 [===========================>..] - ETA: 0s - loss: 5.1738 - sex_out_loss: 0.1776 - age_out_loss: 4.0849 - sex_out_accuracy: 0.9040 - age_out_accuracy: 0.1097\n",
            "Epoch 00124: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 5.1572 - sex_out_loss: 0.1762 - age_out_loss: 4.0697 - sex_out_accuracy: 0.9054 - age_out_accuracy: 0.1115 - val_loss: 8.2829 - val_sex_out_loss: 0.4460 - val_age_out_loss: 6.9240 - val_sex_out_accuracy: 0.8315 - val_age_out_accuracy: 0.1202\n",
            "Epoch 125/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 4.8816 - sex_out_loss: 0.1606 - age_out_loss: 3.8082 - sex_out_accuracy: 0.9065 - age_out_accuracy: 0.1115\n",
            "Epoch 00125: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 4.8816 - sex_out_loss: 0.1606 - age_out_loss: 3.8082 - sex_out_accuracy: 0.9065 - age_out_accuracy: 0.1115 - val_loss: 8.2304 - val_sex_out_loss: 0.4474 - val_age_out_loss: 6.8696 - val_sex_out_accuracy: 0.8184 - val_age_out_accuracy: 0.1202\n",
            "Epoch 126/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 4.8363 - sex_out_loss: 0.1684 - age_out_loss: 3.7546 - sex_out_accuracy: 0.9065 - age_out_accuracy: 0.1115\n",
            "Epoch 00126: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 4.8363 - sex_out_loss: 0.1684 - age_out_loss: 3.7546 - sex_out_accuracy: 0.9065 - age_out_accuracy: 0.1115 - val_loss: 8.2109 - val_sex_out_loss: 0.4395 - val_age_out_loss: 6.8585 - val_sex_out_accuracy: 0.8196 - val_age_out_accuracy: 0.1202\n",
            "Epoch 127/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 4.8536 - sex_out_loss: 0.1554 - age_out_loss: 3.7846 - sex_out_accuracy: 0.9124 - age_out_accuracy: 0.1117\n",
            "Epoch 00127: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 4.8610 - sex_out_loss: 0.1562 - age_out_loss: 3.7911 - sex_out_accuracy: 0.9121 - age_out_accuracy: 0.1115 - val_loss: 8.9847 - val_sex_out_loss: 0.4422 - val_age_out_loss: 7.6285 - val_sex_out_accuracy: 0.8221 - val_age_out_accuracy: 0.1202\n",
            "Epoch 128/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 4.8940 - sex_out_loss: 0.1448 - age_out_loss: 3.8344 - sex_out_accuracy: 0.9123 - age_out_accuracy: 0.1112\n",
            "Epoch 00128: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 4.8939 - sex_out_loss: 0.1454 - age_out_loss: 3.8338 - sex_out_accuracy: 0.9122 - age_out_accuracy: 0.1115 - val_loss: 9.4509 - val_sex_out_loss: 0.4566 - val_age_out_loss: 8.0784 - val_sex_out_accuracy: 0.8168 - val_age_out_accuracy: 0.1202\n",
            "Epoch 129/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 4.9295 - sex_out_loss: 0.1461 - age_out_loss: 3.8666 - sex_out_accuracy: 0.9130 - age_out_accuracy: 0.1113\n",
            "Epoch 00129: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 4.9240 - sex_out_loss: 0.1459 - age_out_loss: 3.8614 - sex_out_accuracy: 0.9129 - age_out_accuracy: 0.1115 - val_loss: 8.1561 - val_sex_out_loss: 0.4753 - val_age_out_loss: 6.7632 - val_sex_out_accuracy: 0.8209 - val_age_out_accuracy: 0.1202\n",
            "Epoch 130/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 4.8982 - sex_out_loss: 0.1600 - age_out_loss: 3.8207 - sex_out_accuracy: 0.9117 - age_out_accuracy: 0.1118\n",
            "Epoch 00130: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 4.9060 - sex_out_loss: 0.1612 - age_out_loss: 3.8274 - sex_out_accuracy: 0.9115 - age_out_accuracy: 0.1115 - val_loss: 8.2468 - val_sex_out_loss: 0.5399 - val_age_out_loss: 6.7892 - val_sex_out_accuracy: 0.8225 - val_age_out_accuracy: 0.1202\n",
            "Epoch 131/500\n",
            "113/115 [============================>.] - ETA: 0s - loss: 4.9825 - sex_out_loss: 0.1692 - age_out_loss: 3.8952 - sex_out_accuracy: 0.9098 - age_out_accuracy: 0.1117\n",
            "Epoch 00131: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 4.9830 - sex_out_loss: 0.1695 - age_out_loss: 3.8954 - sex_out_accuracy: 0.9103 - age_out_accuracy: 0.1115 - val_loss: 8.2516 - val_sex_out_loss: 0.4822 - val_age_out_loss: 6.8500 - val_sex_out_accuracy: 0.8249 - val_age_out_accuracy: 0.1202\n",
            "Epoch 132/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 4.8482 - sex_out_loss: 0.1421 - age_out_loss: 3.7864 - sex_out_accuracy: 0.9140 - age_out_accuracy: 0.1115\n",
            "Epoch 00132: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 4.8482 - sex_out_loss: 0.1421 - age_out_loss: 3.7864 - sex_out_accuracy: 0.9140 - age_out_accuracy: 0.1115 - val_loss: 8.3221 - val_sex_out_loss: 0.4923 - val_age_out_loss: 6.9093 - val_sex_out_accuracy: 0.8335 - val_age_out_accuracy: 0.1202\n",
            "Epoch 133/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 4.8392 - sex_out_loss: 0.1599 - age_out_loss: 3.7572 - sex_out_accuracy: 0.9130 - age_out_accuracy: 0.1114\n",
            "Epoch 00133: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 4.8378 - sex_out_loss: 0.1603 - age_out_loss: 3.7553 - sex_out_accuracy: 0.9126 - age_out_accuracy: 0.1115 - val_loss: 9.0245 - val_sex_out_loss: 0.4719 - val_age_out_loss: 7.6292 - val_sex_out_accuracy: 0.8225 - val_age_out_accuracy: 0.1202\n",
            "Epoch 134/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 4.9722 - sex_out_loss: 0.1579 - age_out_loss: 3.8899 - sex_out_accuracy: 0.9057 - age_out_accuracy: 0.1112\n",
            "Epoch 00134: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 4.9695 - sex_out_loss: 0.1583 - age_out_loss: 3.8867 - sex_out_accuracy: 0.9054 - age_out_accuracy: 0.1115 - val_loss: 8.4738 - val_sex_out_loss: 0.4698 - val_age_out_loss: 7.0787 - val_sex_out_accuracy: 0.8258 - val_age_out_accuracy: 0.1202\n",
            "Epoch 135/500\n",
            "114/115 [============================>.] - ETA: 0s - loss: 4.9746 - sex_out_loss: 0.1548 - age_out_loss: 3.8942 - sex_out_accuracy: 0.9137 - age_out_accuracy: 0.1118\n",
            "Epoch 00135: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 10ms/step - loss: 4.9758 - sex_out_loss: 0.1547 - age_out_loss: 3.8955 - sex_out_accuracy: 0.9134 - age_out_accuracy: 0.1115 - val_loss: 8.2714 - val_sex_out_loss: 0.4218 - val_age_out_loss: 6.9234 - val_sex_out_accuracy: 0.8331 - val_age_out_accuracy: 0.1202\n",
            "Epoch 136/500\n",
            "115/115 [==============================] - ETA: 0s - loss: 4.8002 - sex_out_loss: 0.1678 - age_out_loss: 3.7069 - sex_out_accuracy: 0.9123 - age_out_accuracy: 0.1115\n",
            "Epoch 00136: val_loss did not improve from 7.54374\n",
            "115/115 [==============================] - 1s 11ms/step - loss: 4.8002 - sex_out_loss: 0.1678 - age_out_loss: 3.7069 - sex_out_accuracy: 0.9123 - age_out_accuracy: 0.1115 - val_loss: 8.2647 - val_sex_out_loss: 0.4242 - val_age_out_loss: 6.9147 - val_sex_out_accuracy: 0.8237 - val_age_out_accuracy: 0.1202\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4GTxXMmZMoIG",
        "colab_type": "text"
      },
      "source": [
        "### Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "039DUHa3HqrK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        },
        "outputId": "06865cb7-e9a3-4f36-f8e1-4b291edcb3cc"
      },
      "source": [
        "Model.evaluate(X_test,Y_test_2)"
      ],
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "77/77 [==============================] - 0s 4ms/step - loss: 7.5437 - sex_out_loss: 0.3756 - age_out_loss: 6.3955 - sex_out_accuracy: 0.8147 - age_out_accuracy: 0.1202\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[7.543742656707764,\n",
              " 0.37557876110076904,\n",
              " 6.395535945892334,\n",
              " 0.8147239089012146,\n",
              " 0.12024539709091187]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 149
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YuvpiE1wMHh1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pred=Model.predict(X_test)"
      ],
      "execution_count": 150,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MxGHD2z3MSVg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "6fc49b3a-9a65-4cde-aa07-74a7b26f1ba1"
      },
      "source": [
        "pred[1]"
      ],
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 2.448493 ],\n",
              "       [66.19837  ],\n",
              "       [ 1.0033535],\n",
              "       ...,\n",
              "       [ 2.4610648],\n",
              "       [55.771957 ],\n",
              "       [ 9.427577 ]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 153
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h1JH3UesNbWl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "outputId": "0857b6e5-7c66-415f-a2aa-9a5ad99ab620"
      },
      "source": [
        "plt.plot(History.history['loss'])\n",
        "plt.plot(History.history['val_loss'])\n",
        "plt.title('Model loss')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "plt.subplots_adjust(top=1.00, bottom=0.0, left=0.0, right=0.95, hspace=0.25,\n",
        "                        wspace=0.35)"
      ],
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vLLBBZ85N2hX",
        "colab_type": "text"
      },
      "source": [
        "### For Gender"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jtHjuedANlXJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "outputId": "b51f9738-24ef-472e-907e-8d7def35d9a9"
      },
      "source": [
        "plt.plot(History.history['sex_out_accuracy'])\n",
        "plt.plot(History.history['val_sex_out_accuracy'])\n",
        "plt.title('Model accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Validation'], loc='upper left')\n",
        "plt.subplots_adjust(top=1.00, bottom=0.0, left=0.0, right=0.95, hspace=0.25,\n",
        "                        wspace=0.35)"
      ],
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fw_q7kswNSHB",
        "colab_type": "text"
      },
      "source": [
        "### For age"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-pEsL-KEMsJ2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "797727c8-fd85-4c5b-99e1-834127f92eb5"
      },
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.scatter(Y_test_2[1], pred[1])\n",
        "ax.plot([Y_test_2[1].min(),Y_test_2[1].max()], [Y_test_2[1].min(), Y_test_2[1].max()], 'k--', lw=4)\n",
        "ax.set_xlabel('Actual Age')\n",
        "ax.set_ylabel('Predicted Age')\n",
        "plt.show()"
      ],
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UkP6tH4INWVu",
        "colab_type": "text"
      },
      "source": [
        "#### For Gender"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cNqvx_y5NNsD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "i=0\n",
        "Pred_l=[]\n",
        "while(i<len(pred[0])):\n",
        "\n",
        "  Pred_l.append(int(np.round(pred[0][i])))\n",
        "  i+=1\n"
      ],
      "execution_count": 185,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_V3vuxZsPCTW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import confusion_matrix \n",
        "\n",
        "from sklearn.metrics import classification_report "
      ],
      "execution_count": 186,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xDIumUnTO8L8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "report=classification_report(Y_test_2[0], Pred_l)"
      ],
      "execution_count": 187,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jZEel7nVPfSM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "417c40c3-0634-443a-8e0b-d4589f8d74d2"
      },
      "source": [
        "print(report)"
      ],
      "execution_count": 188,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.85      0.72      0.78      1116\n",
            "           1       0.79      0.90      0.84      1329\n",
            "\n",
            "    accuracy                           0.81      2445\n",
            "   macro avg       0.82      0.81      0.81      2445\n",
            "weighted avg       0.82      0.81      0.81      2445\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UD15IsW0QDIL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "results = confusion_matrix(Y_test_2[0], Pred_l)"
      ],
      "execution_count": 190,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nTihY-5eQJPa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "055628c1-4014-4680-8516-6aff13a432dd"
      },
      "source": [
        "import seaborn as sns\n",
        "\n",
        "sns.heatmap(results, annot=True)"
      ],
      "execution_count": 191,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f16fb700ba8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 191
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x-w4R3GTQcLr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def test_image(ind,images_f,images_f_2,Model):\n",
        "  cv2_imshow(images_f[ind])\n",
        "  image_test=images_f_2[ind]\n",
        "  pred_1=Model.predict(np.array([image_test]))\n",
        "  #print(pred_1)\n",
        "  sex_f=['Male','Female']\n",
        "  age=int(np.round(pred_1[1][0]))\n",
        "  sex=int(np.round(pred_1[0][0]))\n",
        "  print(\"Predicted Age: \"+ str(age))\n",
        "  print(\"Predicted Sex: \"+ sex_f[sex])\n",
        "\n"
      ],
      "execution_count": 203,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I53sQfZPRJlj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 99
        },
        "outputId": "55c7f6ab-3515-4d73-d13f-dc4f0b991a83"
      },
      "source": [
        "test_image(57,images_f,images_f_2,Model)"
      ],
      "execution_count": 204,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAADAAAAAwCAIAAADYYG7QAAAPiklEQVR4nJ1ZSa8myVW9Q0Tk8A1vrKpXXXZXu+2mmWQQC5CQLZBsIaYdsADByluW/AJ+DQjE1guwLAvJyAIkL5CN3e3qqbqm9+q9b8zMiLgDi1fVY3Vb5q4ylYs8Ovece09korvDR8oAxO0nP/3p40cXis2P37rYTtAe3hA+XB7dOJ7HWSK20pAjIzO7e4wxgJuDGADwTuRym1eD7MY6FK1ZxiFbrU9XF7XsZX3F0343rbsFrS/e7xD/+Bvf2O8uLy6fxJjuvvxygE+Vu8eYapGh6lv33p6dvryYHTMvgWJRCkoNRGcKVJkwcDQ3AUNidTCzYDYLXAJosFqrugSyQh7baMoeWvPRga6utl03u7GY3bt3L0U/PDwchnG9WYdPMOQAqsrMtejl5a4UOVseOlBkDuDgquqcmAljICaMkczAjdQ8IopZIKwAMwZjEHZPDk7V1NnFAYAdGTkSJYe8Wq2WTbPfbb/w8ksAuNvtXsCQme13e0LerDcqlmJoE6qNDXkXmzZCw9owBXJmAtfAFJHETYEmN0JvI9UKlhoHc3CxDMXVamzSsBpiSF4phMQIZdwBwH6/v3///mw2F5FngBDxulkAiB6kACI5wa1bN9GAITpCCNYHjextDE2gJiQAIERyEJCQmAwxRKmhRZeaIxIjllG2ZT+uL1tAI9To++0YmcVxL8Jp/mi1w6L7/WY+XxwdHtGnGaq15pyRuenacRoJ0XKJgPPmWYUQQgiApO4OaACO4ABO6ISGNpZpvd1cXF6szx9vn56X9fooxWiqZer7NriCijmJB6D28Wq3VxSLi8VitV6Fj3DzrMZx3A8DMk1ViplUWT05P7l503MdrM5mXQFnDxKBCYEQAQlR1KeSp1L3+/2DB0+morWqWwmRXrp5ihzX+/Fiszl/dL48Pjg/fyRuTNGN9hJ2+9WN0+PLy8tSyqc15LWWWgo2vZo5OCGmEJ9eXDy+/1Z/0B+fHM2bePP4OFgITBTQwAUsF5my7faDuswWc8pVjZDs6PhkGMuw3afAKcbbL7305OFDD5EFgnN157avMjx6coU63v3inY+5zA0UJJepZEnRIrcXT957/fUI3O42u9B21HTV2Xm2HaEz7pimqJZQM+SCYwbRVoAVkLlxE2q79eAc5hNDTdwtmjJOByen6rjdrKSOrhIxcD9vu3Y95svV5gUuq7U2qUlt+/TpJSIuFvOhhuM7XwyJr7b7khECqWKxnJvYILSB1aAUnSYB5J1AFQZA4zAODkhXm0t1TP0BwRSqtt18vlR3m8bgZSRViN2oEBDWu+ljgBARHIZhICYAKKUsl8uswv1CFaFCi42arHajLeLCvJj2EIK6OExVqioRtU3qmm692kzjhAGGYSq7sTo+OX94NF8mJgdu+pmKhBB5bIHibsBJclTY7D+iIXdHRHIkhNm83e3H80dPwvKmE5UqtWAed7MUL9brilRrCSeLSIkD18hMoADZPCAlxNVqffnoQZ32kw7vvv1uSh1Q83QY/ewVomY+nzkwhtjH6DQWBMyDiLq4RfpwDrk7giPg8eEhh12etFQ5PTwOqbu6ytur1frhjy7ef+NitV3v5fe++UczfGV5dIMCUUsANlbZ5NKFtBvt8cOrR2/f+/Wv3P7pvfthuvyXf/j20dndsy/98tOL6dWvvC6igD6fzVNkSCmbBEqVDZBzVfooPeAOALfOzhbLJceIHE5Ob2vly/P1P//TP/7xN79+85iasL33k//6/ne/3QJHdZsKVVeFMddhKrspm8WLp1evvf7aV3/rN371tS//x/e/N1/OXrp95ze/+tuz2eEw5lwlpNaBkVJqmsVicXBwkFICIjH7uKgRCTHGeLA82A3v3Dy703UHu930vz9648//4q/fO9/97tf/dPOv//a1379746W7xppmTGRKudaYq5RqKqU7nB2dHMcU7r318NYXfuVv/+7vn+7qpPGtty+Ozu70y36/25UqbdOUKo4QQiCiruu2I0lWvLb9R82PiGPO3/vBD3/4k8eL01+62jvSDJ2Wvd9cLPfDsBl3Rr5YdrNu1rWhjVyU3nx49Xi1RcRX7pzNgm0ePT45OOIYJ/D3n67OL7bjRIbMCVTrer3um7aLkSirlmG/vXp6PmwuxvWTFy9XRDw+OOjb1XwxF0axgI6zWeTELSaInbgsl4daqwqMYnstamCGbRvNZJZSf/MwclLzjuPRsm9Tt9lZrjrVCdrYhLjfbqdpCqGqFjNrmmbryCF+Mn4AABGlEGIIBNC37TZnZ29ianuOQTlAM+uIGYknVSN2CNMgtXoAWvad5by4MY+dS1EDghD79mA75EUHm13NlsY87bX2XdhstkTu4MxcSgEMgOEFDF3Xop/dPL0ROXStC3EKMSWKVIlajskhjHmfUsqCVUGMSjF0QNNZ30/DZnnQQGTzIGZCwD13kRj5ahw4diHgHj0SjtNezAwgxgjEyOFTg/H5Dun77uhoJmRNCDHEhiOBpiakEMWxirmzupeqxWwoVYuSIRklFGAU8gYpAapTViNiZprMJ4+7Ypy61Kiq940MQxE1AAgIhvyC+AEAgL6cd10TiL3vUoqcENrIbROJAdwiE4egqjFGRDRTcCU3rWWaNFc1wyJawIGoIe44ICITMTEAgEtMHAKpKBFdCzfGlFJ6caYmorbhrmOwWCeH6gG9i5G8iHnkqKZSq5mJa85Z6oQmKQbTat6HkELowUS0mnvL0a+DA3OIEEo1RnJuAmUERGTmGKOlVOtnMITggf34eMnsYMrgEZFdCSwSpEBgCgghhBQjIQbEwDjud48fPXj0+GIcalVnTD0HRixuygQOKuJmMQYEBxN0Q0QiIqIQAjN3XRc+0M0nlDSJHC8X6zGnsSoYBFJT8oCADqwiuepi1sz70Ht5J6eBNh6wSP+zdze54MniYDHPEAUQ1akqTO6GwGQuRo6TyCjZSKUKAqBCByTYvpghA0QPPYfTiCdtQDQEpNCKgGEYJlnvp+AEgkExUWxCnPWz5Xz+0tmtSPzmG/fee/BoEgcPgNEMq5qKqZiql1JyzrVWMyOiZxQRxSZReO6y62n0QZxFx8QR3U6XbVHZljqpZ3UDxGJP1wOGdhwKWLVSpSioNczL+XLc2cnp0TRt3rx3/+6dk5hYnUtRMXRHd0Cgkmut9dn2BIDnBy8k8PqCCPu8cQZuSrPYGdFVAYAq2jRpmIoCbje77/37D778lVd+7bVXV7vxZ2++953vfueddx/81V9+69btlx+E4MhZvFTFkKppLWrqbjAOk5kBgJpeAzIzMwshiFhs249H2A+u0Z3BiSP4orG+82Fbq9Euy1RNnEPqv/b131kul7tpOt+MF1ebP/yDP7l968bx8cH9y3p84+iLX7o5OewketFSTJXEgmg2t0AkiG7u7gzIAEKkZpGZAPAa7wsY+gA+0RvvDW890ozRzE3dgNCDWGEmd1dVF3YTySMzribitk+xzhJ0MabUjEMx8wpYRfbjuN4OolW0TtPgtYhIKaXWCloB4DNb9gFbaLbsIpOYuhqaAwMwakyh1EKISOCR3Yw81Jrn/WxSKWqQa85CWEyQYyiS1b2YEhEaIhEAiKiqXr+LYvjw5Po5ReCzPqRALp6rASARuFZRD8wAYAgOUFUxIAFPeTAMxEEMatWap7btJQ9THUPTxNS4oblWKe6OhOTPnG7uxPziXQYAes0QIgL0EQ7msCmaMVpVj1hMCYO4UyARdS9TqVPOwCSiZkKVmEKWqu7TtCdmoqjibmIuHAgyuF/z5ITIAOrUNM1nu+w5OARIAW8dN+9fXqr2QHi1XndNIzWXceqbnimWUlfrLTJTQAUEgGJ5mlaUYhHJtR6fHBMxEnAgKc+5J6piqnptNCASkRdM6mcZ8vmtARDKyTKcHDSrbMNQDODi6WrclTa0sikkZSwTcry4ulpvtxmmELnK8PTqnLvmi6+8Ml8uRWvXBEQEVCKqUomoScm05FJEhBAB8fM0hM8xOQK4JeIbh/3DTUbur1bDmMt6vXu4enyQlkFwqBKbZrvfv/vefeeS+rCfNqHh0ztnB8vFYrlIber7Nk/TtYoR0ExFFQBMTdRiYHf/+YAMgQAAgpmfLNIibERT7NLN2yd96PkmsxJWKICllNnRnPuQ66ZaPQzL2bKfHxwwUSBKTMxETKCC6GZirqVMWsXNkbmouVQDD5+I959Gdv04EHQJbp/Mdg+2TYwAdHQyXz/dAEVu0iLE7V6Hzb5bNsEXSGBkwNjNZyklJxT11omQEahWLVlUoBZ1U3ie4otpiPHn294dHABdE9HZcffgajPsFQIzYrp1MIxSs+6mARta3liUWorHqRYAQqaqqjk7oiPmUkuRnKupmbpWc4NpyiIVEadpyrW04eMR9hOBn57x4+6AwOg26/Hu2fHw9ladi1lqndglhV1j7phrjcTinEJDgfNUqmRmJm4BXdRUIWcpWapUNVEzrebqaprHMbVtoM8O+R9iAgQEB0CADuD2YbzfwEprlxpFYeK+7We4nGqt4tNUsIz7cQRAZkJkZr5OYeYmqqVoyblKqbVOeSpFHGwcBwegEJDoBbb//AbOCF49m//4vQtAvhqKYTKv6IKADGCqMk4BEIiMKMaGmQGglEKou90gItdTp5RScq6qapKlLpfz0CT7nF32wjLwEODOUZR89P5q2nBUarUKlOKORcxF+65Ts2zaNY2TE1GttV4fKYdJxdF1HMf1eu3u6p5rTW0b2wYQAfEXA4SOgBiD3b0VxLpJyrZ61ikiAmNVawIUIyCIAECoYFXqNE0qulptzMwdQG0YBkesqkVzbMPBwTLF5AZXm/UvBugZLMSUwtlZLOj6eEJih84JKE7FMhG5KAAQkZiq6na7HfaTil2PPjObcnb3Wmvfz4+Pj2OMpZRcR1XHTx+lr+ujoRY+bsDr64I2FXzrnc39J+Nm6BWsmKqZVMtSDdERdsO0Wq02m81uGByw6zpVHbVoqQGga9qD5VFqWlVdr9cU6fD05P/D0HVFxzbh668eFi1GebfJrbfDqPs8YOAsUqQ+Ob8ahqGUQsicmiJm5ojcztOy7Q772XUGWm9XN2+fxHa5GwY0M8BnH77d+aN9eU6Gw8eNKCIAENGAeCxuzpsdvvvOg5/dv3q8knWmvYqqSi5DlepEVNlNlK5/HXXzrm0bRItMuVZVPT09RcSiOY/PXWZugA7wISA3g89YJmZGxABNHWBzXt++97B6/s///sH/3Hvrz/7mW29f2P2LJ3k3Alpqmr7pU/JI4BCICBFDCkhoWgLh0cnJtYBKKcycp+H/AJ8KH3VpAZYZAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=48x48 at 0x7F17356F9828>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Predicted Age: 64\n",
            "Predicted Sex: Female\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fO8yiT2iR1Q6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 99
        },
        "outputId": "2beaaaa1-be5e-4a93-a95a-264f2fa6586d"
      },
      "source": [
        "test_image(137,images_f,images_f_2,Model)"
      ],
      "execution_count": 205,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=48x48 at 0x7F16FB8EB438>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Predicted Age: 76\n",
            "Predicted Sex: Female\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rfDMx_vbSDAC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 99
        },
        "outputId": "318948d5-5c66-476f-d4fa-8683b9e6bcef"
      },
      "source": [
        "test_image(502,images_f,images_f_2,Model)"
      ],
      "execution_count": 206,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VEVC3wurQfe8H93aPRo1vpZI45HKaZSPhEpJgx+AZJlYOwOIspOi11ptms93W682y7WpAI4YgwkwG0Jm7gulNynrE3vzda7CD9lvcN4NA27aOpGbE4eBg//69uwCn2w4VQ1EUMUYiKopiMKxCZCIqitwQ0B3RFhezZpvaxsysLEszU9V+LvhmTuRNNEQE0K9F+kWEOzjidTjXbheFkIHYzAZV+e6D+y9enHVJJY95kRVF4e6YZ5BFD8QhOJsDmFu9rZfLZVOnLJTVYMBEqtp1HTNT79342nt9Zw1dt+Kb1rvmg5ic1cFS29Tr46OD2ydH2806aAqaoN6WhIqQwDdNs27q5WbVWnd6dvr0xdPNZttflntKdO+b5g1CRGRm0gJEJEYkYr9xbwDu2AIAeL9XA3B0QybCmyW3dlZE+MVPHrbaPXrydT1bZVKm1rp822w3FiMQY9e5rq7OLtbr9WAwlECAXdv1lAtEhMBuxgwB3M0cUejmpxO7Iet+E4Lf3X+q2ue3bjbjyeiDf/rlvXfvPfnq2fxyrWk7kLHk+dlqtWna+XxuKQWk611WD7Z5T3JElFICtMA9fTuR/z9I9FEitjHhrAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=48x48 at 0x7F16FB8EBEB8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Predicted Age: 31\n",
            "Predicted Sex: Female\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P4XV-ZlJSKeK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 99
        },
        "outputId": "7d9c8b1c-7175-4576-8055-d218664737f7"
      },
      "source": [
        "test_image(24,images_f,images_f_2,Model)"
      ],
      "execution_count": 208,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=48x48 at 0x7F16FB8EBEB8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Predicted Age: 60\n",
            "Predicted Sex: Male\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}