LeviHessener_Retrained_Checker.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "LeviHessener_Retrained_Checker.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "2DcUS5sIA7Vu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import keras\n",
        "import os\n",
        "from keras.models import model_from_json\n",
        "import tensorflow as tf \n",
        "import pandas as pd"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HstQOePjt02o",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "57591e1e-aabf-4f70-b68b-0ba180e966ba"
      },
      "source": [
        "# load json and create model\n",
        "json_file = open('/content/drive/My Drive/age.json', 'r')\n",
        "loaded_model_json = json_file.read()\n",
        "json_file.close()\n",
        "age_model = tf.keras.models.model_from_json(loaded_model_json)\n",
        "# load weights into new model\n",
        "age_model.load_weights(\"/content/drive/My Drive/age.h5\")\n",
        "print(\"Loaded model from disk\")\n",
        " \n",
        "# evaluate loaded model on test data\n",
        "age_model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loaded model from disk\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ldzcNgAdHO-X",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "cc574e9a-e4e1-4bfd-9a68-19ffccd7c0b6"
      },
      "source": [
        "# load json and create model\n",
        "json_file = open('/content/drive/My Drive/gender.json', 'r')\n",
        "loaded_model_json = json_file.read()\n",
        "json_file.close()\n",
        "gender_model = tf.keras.models.model_from_json(loaded_model_json)\n",
        "# load weights into new model\n",
        "gender_model.load_weights(\"/content/drive/My Drive/gender.h5\")\n",
        "print(\"Loaded model from disk\")\n",
        " \n",
        "# evaluate loaded model on test data\n",
        "gender_model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loaded model from disk\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jp-PVpO4GN_H",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84
        },
        "outputId": "c4e40e96-4b3f-4003-86c5-dd8cd709cd2e"
      },
      "source": [
        "!git clone https://github.com/circulosmeos/gdown.pl.git"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'gdown.pl'...\n",
            "remote: Enumerating objects: 92, done.\u001b[K\n",
            "remote: Total 92 (delta 0), reused 0 (delta 0), pack-reused 92\u001b[K\n",
            "Unpacking objects: 100% (92/92), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ekCphoeCGbKX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "outputId": "222681d2-470d-46b3-d01c-7eb90adbdb30"
      },
      "source": [
        "!./gdown.pl/gdown.pl https://drive.google.com/file/d/1Z1RqRo0_JiavaZw2yzZG6WETdZQ8qX86/edit dataset.zip"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cannot open cookies file ‘gdown.cookie.temp’: No such file or directory\n",
            "--2020-07-09 15:18:53--  https://docs.google.com/uc?id=1Z1RqRo0_JiavaZw2yzZG6WETdZQ8qX86&export=download\n",
            "Resolving docs.google.com (docs.google.com)... 64.233.189.138, 64.233.189.102, 64.233.189.100, ...\n",
            "Connecting to docs.google.com (docs.google.com)|64.233.189.138|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: unspecified [text/html]\n",
            "Saving to: ‘dataset.zip’\n",
            "\n",
            "     0K                                     30.1M=0s\n",
            "\n",
            "2020-07-09 15:18:53 (30.1 MB/s) - ‘dataset.zip’ saved [3271]\n",
            "\n",
            "--2020-07-09 15:18:53--  https://docs.google.com/uc?export=download&confirm=GTXf&id=1Z1RqRo0_JiavaZw2yzZG6WETdZQ8qX86\n",
            "Resolving docs.google.com (docs.google.com)... 74.125.204.139, 74.125.204.100, 74.125.204.101, ...\n",
            "Connecting to docs.google.com (docs.google.com)|74.125.204.139|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Moved Temporarily\n",
            "Location: https://doc-08-bc-docs.googleusercontent.com/docs/securesc/b7c1d845uv0o6ia17edik201q3adt36j/hdmsuv6g8ue6ng1f59gsklf32qa4pqev/1594307925000/03161587497725415728/05157863291812723554Z/1Z1RqRo0_JiavaZw2yzZG6WETdZQ8qX86?e=download [following]\n",
            "--2020-07-09 15:18:53--  https://doc-08-bc-docs.googleusercontent.com/docs/securesc/b7c1d845uv0o6ia17edik201q3adt36j/hdmsuv6g8ue6ng1f59gsklf32qa4pqev/1594307925000/03161587497725415728/05157863291812723554Z/1Z1RqRo0_JiavaZw2yzZG6WETdZQ8qX86?e=download\n",
            "Resolving doc-08-bc-docs.googleusercontent.com (doc-08-bc-docs.googleusercontent.com)... 64.233.189.132, 2404:6800:4008:c07::84\n",
            "Connecting to doc-08-bc-docs.googleusercontent.com (doc-08-bc-docs.googleusercontent.com)|64.233.189.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://docs.google.com/nonceSigner?nonce=bkad2scsdam8i&continue=https://doc-08-bc-docs.googleusercontent.com/docs/securesc/b7c1d845uv0o6ia17edik201q3adt36j/hdmsuv6g8ue6ng1f59gsklf32qa4pqev/1594307925000/03161587497725415728/05157863291812723554Z/1Z1RqRo0_JiavaZw2yzZG6WETdZQ8qX86?e%3Ddownload&hash=eritv784h8nn1o3kraplf5bej77ikpic [following]\n",
            "--2020-07-09 15:18:54--  https://docs.google.com/nonceSigner?nonce=bkad2scsdam8i&continue=https://doc-08-bc-docs.googleusercontent.com/docs/securesc/b7c1d845uv0o6ia17edik201q3adt36j/hdmsuv6g8ue6ng1f59gsklf32qa4pqev/1594307925000/03161587497725415728/05157863291812723554Z/1Z1RqRo0_JiavaZw2yzZG6WETdZQ8qX86?e%3Ddownload&hash=eritv784h8nn1o3kraplf5bej77ikpic\n",
            "Connecting to docs.google.com (docs.google.com)|74.125.204.139|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://doc-08-bc-docs.googleusercontent.com/docs/securesc/b7c1d845uv0o6ia17edik201q3adt36j/hdmsuv6g8ue6ng1f59gsklf32qa4pqev/1594307925000/03161587497725415728/05157863291812723554Z/1Z1RqRo0_JiavaZw2yzZG6WETdZQ8qX86?e=download&nonce=bkad2scsdam8i&user=05157863291812723554Z&hash=31cndov18bqp2ndi3br81s9qk058njd6 [following]\n",
            "--2020-07-09 15:18:54--  https://doc-08-bc-docs.googleusercontent.com/docs/securesc/b7c1d845uv0o6ia17edik201q3adt36j/hdmsuv6g8ue6ng1f59gsklf32qa4pqev/1594307925000/03161587497725415728/05157863291812723554Z/1Z1RqRo0_JiavaZw2yzZG6WETdZQ8qX86?e=download&nonce=bkad2scsdam8i&user=05157863291812723554Z&hash=31cndov18bqp2ndi3br81s9qk058njd6\n",
            "Connecting to doc-08-bc-docs.googleusercontent.com (doc-08-bc-docs.googleusercontent.com)|64.233.189.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: unspecified [application/zip]\n",
            "Saving to: ‘dataset.zip’\n",
            "\n",
            "     0K ........ ........ ........ ........ 94.8M\n",
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            "557056K .......                              236M=4.1s\n",
            "\n",
            "2020-07-09 15:18:58 (133 MB/s) - ‘dataset.zip’ saved [578232678]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ql48XYA_Ghj1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "outputId": "feb68319-6782-4e54-e6d9-ed36078d4e09"
      },
      "source": [
        "!./gdown.pl/gdown.pl https://drive.google.com/file/d/1i1L3Yqwaio7YSOCj7ftgk8ZZchPG7dmH/edit train.csv"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cannot open cookies file ‘gdown.cookie.temp’: No such file or directory\n",
            "--2020-07-09 15:19:01--  https://docs.google.com/uc?id=1i1L3Yqwaio7YSOCj7ftgk8ZZchPG7dmH&export=download\n",
            "Resolving docs.google.com (docs.google.com)... 64.233.189.138, 64.233.189.101, 64.233.189.139, ...\n",
            "Connecting to docs.google.com (docs.google.com)|64.233.189.138|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Moved Temporarily\n",
            "Location: https://doc-04-64-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/un1l2ikgod3ur03k1ccklsopt63bnhie/1594307925000/03161587497725415728/*/1i1L3Yqwaio7YSOCj7ftgk8ZZchPG7dmH?e=download [following]\n",
            "Warning: wildcards not supported in HTTP.\n",
            "--2020-07-09 15:19:02--  https://doc-04-64-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/un1l2ikgod3ur03k1ccklsopt63bnhie/1594307925000/03161587497725415728/*/1i1L3Yqwaio7YSOCj7ftgk8ZZchPG7dmH?e=download\n",
            "Resolving doc-04-64-docs.googleusercontent.com (doc-04-64-docs.googleusercontent.com)... 64.233.189.132, 2404:6800:4008:c07::84\n",
            "Connecting to doc-04-64-docs.googleusercontent.com (doc-04-64-docs.googleusercontent.com)|64.233.189.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: unspecified [text/csv]\n",
            "Saving to: ‘train.csv’\n",
            "\n",
            "     0K ...                                  110M=0.03s\n",
            "\n",
            "2020-07-09 15:19:03 (110 MB/s) - ‘train.csv’ saved [3793020]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gUDE1c97Gjll",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d64538a1-8ce9-402c-90b9-ffce07c5f372"
      },
      "source": [
        "!unzip dataset.zip"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b-m1QthKGmNE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "outputId": "7e47a03e-32f3-451f-bee4-a0ca10ae6855"
      },
      "source": [
        "!./gdown.pl/gdown.pl https://drive.google.com/file/d/1wOdja-ezstMEp81tX1a-EYkFebev4h7D/edit val.csv"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cannot open cookies file ‘gdown.cookie.temp’: No such file or directory\n",
            "--2020-07-09 15:19:31--  https://docs.google.com/uc?id=1wOdja-ezstMEp81tX1a-EYkFebev4h7D&export=download\n",
            "Resolving docs.google.com (docs.google.com)... 64.233.189.102, 64.233.189.101, 64.233.189.139, ...\n",
            "Connecting to docs.google.com (docs.google.com)|64.233.189.102|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Moved Temporarily\n",
            "Location: https://doc-08-64-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/j31p2hfm3630d05tli42667dpqhkarsq/1594307925000/03161587497725415728/*/1wOdja-ezstMEp81tX1a-EYkFebev4h7D?e=download [following]\n",
            "Warning: wildcards not supported in HTTP.\n",
            "--2020-07-09 15:19:31--  https://doc-08-64-docs.googleusercontent.com/docs/securesc/ha0ro937gcuc7l7deffksulhg5h7mbp1/j31p2hfm3630d05tli42667dpqhkarsq/1594307925000/03161587497725415728/*/1wOdja-ezstMEp81tX1a-EYkFebev4h7D?e=download\n",
            "Resolving doc-08-64-docs.googleusercontent.com (doc-08-64-docs.googleusercontent.com)... 64.233.189.132, 2404:6800:4008:c07::84\n",
            "Connecting to doc-08-64-docs.googleusercontent.com (doc-08-64-docs.googleusercontent.com)|64.233.189.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 447631 (437K) [text/csv]\n",
            "Saving to: ‘val.csv’\n",
            "\n",
            "     0K                                    100% 59.2M=0.007s\n",
            "\n",
            "2020-07-09 15:19:32 (59.2 MB/s) - ‘val.csv’ saved [447631/447631]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "papM80RzGxvV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from PIL import Image\n",
        "import numpy as np\n",
        "def preprocess(path):\n",
        "  image = Image.open(path)\n",
        "  image = image.resize((227, 227))\n",
        "  return np.array(image)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "at9v2LllH7gx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset_train = pd.read_csv('train.csv')\n",
        "dataset_test = pd.read_csv('val.csv')"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3koloQuGMeoi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def age_predict(path):\n",
        "  l = []\n",
        "  l.append(preprocess(path))\n",
        "  l = np.asarray(l)\n",
        "  prediction = age_model.predict(l)[0]\n",
        "  return np.argmax(prediction)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lIHvBVJnMe_3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def gender_predict(path):\n",
        "  l = []\n",
        "  l.append(preprocess(path))\n",
        "  l = np.asarray(l)\n",
        "  prediction = gender_model.predict(l)[0]\n",
        "  return np.argmax(prediction)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9WK4au6uKA_Z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "72d851c1-0b3a-4f90-f4e6-c9d33b26c067"
      },
      "source": [
        "rows, cols = (9, 4)\n",
        "age_vs_gender = [[0 for i in range(cols)] for j in range(rows)]         # TM, FF, TF, FM\n",
        "rows, cols = (9, 9) \n",
        "age_vs_age = [[0 for i in range(cols)] for j in range(rows)] \n",
        "for ind in dataset_test.index: \n",
        "  if(dataset_test['race'][ind] != 'Indian'):\n",
        "    continue\n",
        "  print(ind)\n",
        "  path = dataset_test['file'][ind]\n",
        "  actual_gender = dataset_test['gender'][ind]\n",
        "  actual_age = dataset_test['age'][ind]\n",
        "  if (actual_age == '0-2'):\n",
        "    actual_age = 0\n",
        "  elif (actual_age == '3-9'):\n",
        "    actual_age = 1\n",
        "  elif (actual_age == '10-19'):\n",
        "    actual_age = 2\n",
        "  elif (actual_age == '20-29'):\n",
        "    actual_age = 3    \n",
        "  elif (actual_age == '30-39'):\n",
        "    actual_age = 4    \n",
        "  elif (actual_age == '40-49'):\n",
        "    actual_age = 5    \n",
        "  elif (actual_age == '50-59'):\n",
        "    actual_age = 6\n",
        "  elif (actual_age == '60-69'):\n",
        "    actual_age = 7\n",
        "  elif (actual_age == 'more than 70'):\n",
        "    actual_age = 8\n",
        "  predicted_age = age_predict(path)\n",
        "  actual_gender = (0, 1) [actual_gender == 'Male']\n",
        "  predicted_gender = gender_predict(path)\n",
        "  if(predicted_gender == 0 and actual_gender==0):\n",
        "    age_vs_gender[actual_age][0]+=1\n",
        "  elif(predicted_gender == 0 and actual_gender==1):\n",
        "    age_vs_gender[actual_age][3]+=1\n",
        "  elif(predicted_gender == 1 and actual_gender==1):\n",
        "    age_vs_gender[actual_age][2]+=1\n",
        "  elif(predicted_gender == 1 and actual_gender==0):\n",
        "    age_vs_gender[actual_age][1]+=1\n",
        "  age_vs_age[actual_age][predicted_age]+=1"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
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          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pmaacBxpMwmn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "377c01e1-7d3a-4f32-9bf2-d741e7c7e9be"
      },
      "source": [
        "for ind in dataset_train.index: \n",
        "  if(dataset_train['race'][ind] != 'Indian'):\n",
        "    continue\n",
        "  print(ind)\n",
        "  path = dataset_train['file'][ind]\n",
        "  actual_gender = dataset_train['gender'][ind]\n",
        "  actual_age = dataset_train['age'][ind]\n",
        "  if (actual_age == '0-2'):\n",
        "    actual_age = 0\n",
        "  elif (actual_age == '3-9'):\n",
        "    actual_age = 1\n",
        "  elif (actual_age == '10-19'):\n",
        "    actual_age = 2\n",
        "  elif (actual_age == '20-29'):\n",
        "    actual_age = 3    \n",
        "  elif (actual_age == '30-39'):\n",
        "    actual_age = 4    \n",
        "  elif (actual_age == '40-49'):\n",
        "    actual_age = 5    \n",
        "  elif (actual_age == '50-59'):\n",
        "    actual_age = 6\n",
        "  elif (actual_age == '60-69'):\n",
        "    actual_age = 7\n",
        "  elif (actual_age == 'more than 70'):\n",
        "    actual_age = 8\n",
        "  predicted_age = age_predict(path)\n",
        "  actual_gender = (0, 1) [actual_gender == 'Male']\n",
        "  predicted_gender = gender_predict(path)\n",
        "  if(predicted_gender == 0 and actual_gender==0):\n",
        "    age_vs_gender[actual_age][0]+=1\n",
        "  elif(predicted_gender == 0 and actual_gender==1):\n",
        "    age_vs_gender[actual_age][3]+=1\n",
        "  elif(predicted_gender == 1 and actual_gender==1):\n",
        "    age_vs_gender[actual_age][2]+=1\n",
        "  elif(predicted_gender == 1 and actual_gender==0):\n",
        "    age_vs_gender[actual_age][1]+=1\n",
        "  age_vs_age[actual_age][predicted_age]+=1"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Tapy2pHN6-5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "67339d87-f147-44f7-d39e-7768cdd42bd4"
      },
      "source": [
        "print(age_vs_age)\n",
        "print(age_vs_gender)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[175, 8, 1, 3, 0, 0, 0, 0, 0], [267, 1187, 78, 106, 24, 14, 36, 1, 0], [62, 418, 651, 414, 64, 50, 55, 1, 0], [53, 65, 93, 2539, 338, 264, 107, 1, 0], [38, 38, 42, 942, 1081, 722, 253, 3, 0], [17, 13, 8, 174, 212, 941, 468, 1, 0], [20, 3, 2, 69, 37, 190, 787, 5, 1], [6, 2, 2, 15, 10, 38, 347, 70, 11], [12, 0, 1, 4, 2, 9, 110, 19, 35]]\n",
            "[[55, 32, 88, 12], [561, 221, 793, 138], [784, 216, 645, 70], [1701, 222, 1484, 53], [1157, 166, 1752, 44], [641, 105, 1062, 26], [382, 65, 645, 22], [190, 56, 249, 6], [90, 28, 69, 5]]\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}