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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/PacktPublishing/Hands-On-Computer-Vision-with-PyTorch/blob/master/Chapter05/age_gender_prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GO820-swkq7r"
      },
      "source": [
        "import torch\n",
        "import numpy as np, cv2, pandas as pd, glob, time\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch import optim\n",
        "import torch.nn.functional as F\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import torchvision\n",
        "from torchvision import transforms, models, datasets\n",
        "device = 'cuda' if torch.cuda.is_available() else 'cpu'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bZVyS3nCku1o"
      },
      "source": [
        "from pydrive.auth import GoogleAuth\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials\n",
        "\n",
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)\n",
        "\n",
        "def getFile_from_drive( file_id, name ):\n",
        "  downloaded = drive.CreateFile({'id': file_id})\n",
        "  downloaded.GetContentFile(name)\n",
        "\n",
        "getFile_from_drive('1Z1RqRo0_JiavaZw2yzZG6WETdZQ8qX86', 'fairface-img-margin025-trainval.zip')\n",
        "getFile_from_drive('1k5vvyREmHDW5TSM9QgB04Bvc8C8_7dl-', 'fairface-label-train.csv')\n",
        "getFile_from_drive('1_rtz1M1zhvS0d5vVoXUamnohB6cJ02iJ', 'fairface-label-val.csv')\n",
        "\n",
        "!unzip -qq fairface-img-margin025-trainval.zip"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NK77ld3gkyvk",
        "outputId": "fb5a9f83-6097-4773-e541-41c2a9c08492",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        }
      },
      "source": [
        "trn_df = pd.read_csv('fairface-label-train.csv')\n",
        "val_df = pd.read_csv('fairface-label-val.csv')\n",
        "trn_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>file</th>\n",
              "      <th>age</th>\n",
              "      <th>gender</th>\n",
              "      <th>race</th>\n",
              "      <th>service_test</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>train/1.jpg</td>\n",
              "      <td>59</td>\n",
              "      <td>Male</td>\n",
              "      <td>East Asian</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>train/2.jpg</td>\n",
              "      <td>39</td>\n",
              "      <td>Female</td>\n",
              "      <td>Indian</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>train/3.jpg</td>\n",
              "      <td>11</td>\n",
              "      <td>Female</td>\n",
              "      <td>Black</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>train/4.jpg</td>\n",
              "      <td>26</td>\n",
              "      <td>Female</td>\n",
              "      <td>Indian</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>train/5.jpg</td>\n",
              "      <td>26</td>\n",
              "      <td>Female</td>\n",
              "      <td>Indian</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          file  age  gender        race  service_test\n",
              "0  train/1.jpg   59    Male  East Asian          True\n",
              "1  train/2.jpg   39  Female      Indian         False\n",
              "2  train/3.jpg   11  Female       Black         False\n",
              "3  train/4.jpg   26  Female      Indian          True\n",
              "4  train/5.jpg   26  Female      Indian          True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XVOL0emTljbq"
      },
      "source": [
        "from torch.utils.data import Dataset, DataLoader\n",
        "import cv2\n",
        "IMAGE_SIZE = 224\n",
        "class GenderAgeClass(Dataset):\n",
        "    def __init__(self, df, tfms=None):\n",
        "        self.df = df\n",
        "        self.normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], \n",
        "                                              std=[0.229, 0.224, 0.225])\n",
        "    def __len__(self): return len(self.df)\n",
        "    def __getitem__(self, ix):\n",
        "        f = self.df.iloc[ix].squeeze()\n",
        "        file = f.file\n",
        "        gen = f.gender == 'Female'\n",
        "        age = f.age\n",
        "        im = cv2.imread(file)\n",
        "        im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)\n",
        "        return im, age, gen\n",
        "\n",
        "    def preprocess_image(self, im):\n",
        "        im = cv2.resize(im, (IMAGE_SIZE, IMAGE_SIZE))\n",
        "        im = torch.tensor(im).permute(2,0,1)\n",
        "        im = self.normalize(im/255.)\n",
        "        return im[None]\n",
        "\n",
        "    def collate_fn(self, batch):\n",
        "        'preprocess images, ages and genders'\n",
        "        ims, ages, genders = [], [], []\n",
        "        for im, age, gender in batch:\n",
        "            im = self.preprocess_image(im)\n",
        "            ims.append(im)\n",
        "\n",
        "            ages.append(float(int(age)/80))\n",
        "            genders.append(float(gender))\n",
        "\n",
        "        ages, genders = [torch.tensor(x).to(device).float() for x in [ages, genders]]\n",
        "        ims = torch.cat(ims).to(device)\n",
        "\n",
        "        return ims, ages, genders"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cfQ7WlW1mO8c"
      },
      "source": [
        "trn = GenderAgeClass(trn_df)\n",
        "val = GenderAgeClass(val_df)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eJEiuBhlnPd-",
        "outputId": "fd1dd131-c563-4d02-af65-819886175782",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "device='cuda'\n",
        "train_loader = DataLoader(trn, batch_size=32, shuffle=True, drop_last=True, collate_fn=trn.collate_fn)\n",
        "test_loader = DataLoader(val, batch_size=32, collate_fn=val.collate_fn)\n",
        "a,b,c, = next(iter(train_loader))\n",
        "print(a.shape, b.shape, c.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([32, 3, 224, 224]) torch.Size([32]) torch.Size([32])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nvj3Nb6_ngim",
        "outputId": "e4eab945-a346-4b24-9251-24abeecb96f6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84,
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            "da9aacf457e948a9a406b0e5f7028bb0"
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      },
      "source": [
        "def get_model():\n",
        "    model = models.vgg16(pretrained = True)\n",
        "    # Freeze parameters so we don't backprop through them\n",
        "    for param in model.parameters():\n",
        "        param.requires_grad = False\n",
        "    model.avgpool = nn.Sequential(\n",
        "        nn.Conv2d(512,512, kernel_size=3),\n",
        "        nn.MaxPool2d(2),\n",
        "        nn.ReLU(),\n",
        "        nn.Flatten()\n",
        "    )\n",
        "    class ageGenderClassifier(nn.Module):\n",
        "        def __init__(self):\n",
        "            super(ageGenderClassifier, self).__init__()\n",
        "            self.intermediate = nn.Sequential(\n",
        "                nn.Linear(2048,512),\n",
        "                nn.ReLU(),\n",
        "                nn.Dropout(0.4),\n",
        "                nn.Linear(512,128),\n",
        "                nn.ReLU(),\n",
        "                nn.Dropout(0.4),\n",
        "                nn.Linear(128,64),\n",
        "                nn.ReLU(),\n",
        "            )\n",
        "            self.age_classifier = nn.Sequential(\n",
        "                nn.Linear(64, 1),\n",
        "                nn.Sigmoid()\n",
        "            )\n",
        "            self.gender_classifier = nn.Sequential(\n",
        "                nn.Linear(64, 1),\n",
        "                nn.Sigmoid()\n",
        "            )\n",
        "        def forward(self, x):\n",
        "            x = self.intermediate(x)\n",
        "            age = self.age_classifier(x)\n",
        "            gender = self.gender_classifier(x)\n",
        "            return gender, age\n",
        "        \n",
        "    model.classifier = ageGenderClassifier()\n",
        "    \n",
        "    gender_criterion = nn.BCELoss()\n",
        "    age_criterion = nn.L1Loss()\n",
        "    loss_functions = gender_criterion, age_criterion\n",
        "    optimizer = torch.optim.Adam(model.parameters(), lr= 1e-4)\n",
        "    \n",
        "    return model.to(device), loss_functions, optimizer\n",
        "\n",
        "model, loss_functions, optimizer = get_model()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading: \"https://download.pytorch.org/models/vgg16-397923af.pth\" to /root/.cache/torch/hub/checkpoints/vgg16-397923af.pth\n"
          ],
          "name": "stderr"
        },
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          "text": [
            "\n"
          ],
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      ]
    },
    {
      "cell_type": "code",
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        "id": "XceUr1tWoJEL",
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        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!pip install torchsummary\n",
        "from torchsummary import summary\n",
        "summary(model, input_size=(3,224,224), device=device)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: torchsummary in /usr/local/lib/python3.6/dist-packages (1.5.1)\n",
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Conv2d-1         [-1, 64, 224, 224]           1,792\n",
            "              ReLU-2         [-1, 64, 224, 224]               0\n",
            "            Conv2d-3         [-1, 64, 224, 224]          36,928\n",
            "              ReLU-4         [-1, 64, 224, 224]               0\n",
            "         MaxPool2d-5         [-1, 64, 112, 112]               0\n",
            "            Conv2d-6        [-1, 128, 112, 112]          73,856\n",
            "              ReLU-7        [-1, 128, 112, 112]               0\n",
            "            Conv2d-8        [-1, 128, 112, 112]         147,584\n",
            "              ReLU-9        [-1, 128, 112, 112]               0\n",
            "        MaxPool2d-10          [-1, 128, 56, 56]               0\n",
            "           Conv2d-11          [-1, 256, 56, 56]         295,168\n",
            "             ReLU-12          [-1, 256, 56, 56]               0\n",
            "           Conv2d-13          [-1, 256, 56, 56]         590,080\n",
            "             ReLU-14          [-1, 256, 56, 56]               0\n",
            "           Conv2d-15          [-1, 256, 56, 56]         590,080\n",
            "             ReLU-16          [-1, 256, 56, 56]               0\n",
            "        MaxPool2d-17          [-1, 256, 28, 28]               0\n",
            "           Conv2d-18          [-1, 512, 28, 28]       1,180,160\n",
            "             ReLU-19          [-1, 512, 28, 28]               0\n",
            "           Conv2d-20          [-1, 512, 28, 28]       2,359,808\n",
            "             ReLU-21          [-1, 512, 28, 28]               0\n",
            "           Conv2d-22          [-1, 512, 28, 28]       2,359,808\n",
            "             ReLU-23          [-1, 512, 28, 28]               0\n",
            "        MaxPool2d-24          [-1, 512, 14, 14]               0\n",
            "           Conv2d-25          [-1, 512, 14, 14]       2,359,808\n",
            "             ReLU-26          [-1, 512, 14, 14]               0\n",
            "           Conv2d-27          [-1, 512, 14, 14]       2,359,808\n",
            "             ReLU-28          [-1, 512, 14, 14]               0\n",
            "           Conv2d-29          [-1, 512, 14, 14]       2,359,808\n",
            "             ReLU-30          [-1, 512, 14, 14]               0\n",
            "        MaxPool2d-31            [-1, 512, 7, 7]               0\n",
            "           Conv2d-32            [-1, 512, 5, 5]       2,359,808\n",
            "        MaxPool2d-33            [-1, 512, 2, 2]               0\n",
            "             ReLU-34            [-1, 512, 2, 2]               0\n",
            "          Flatten-35                 [-1, 2048]               0\n",
            "           Linear-36                  [-1, 512]       1,049,088\n",
            "             ReLU-37                  [-1, 512]               0\n",
            "          Dropout-38                  [-1, 512]               0\n",
            "           Linear-39                  [-1, 128]          65,664\n",
            "             ReLU-40                  [-1, 128]               0\n",
            "          Dropout-41                  [-1, 128]               0\n",
            "           Linear-42                   [-1, 64]           8,256\n",
            "             ReLU-43                   [-1, 64]               0\n",
            "           Linear-44                    [-1, 1]              65\n",
            "          Sigmoid-45                    [-1, 1]               0\n",
            "           Linear-46                    [-1, 1]              65\n",
            "          Sigmoid-47                    [-1, 1]               0\n",
            "ageGenderClassifier-48         [[-1, 1], [-1, 1]]               0\n",
            "================================================================\n",
            "Total params: 18,197,634\n",
            "Trainable params: 3,482,946\n",
            "Non-trainable params: 14,714,688\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.57\n",
            "Forward/backward pass size (MB): 218.55\n",
            "Params size (MB): 69.42\n",
            "Estimated Total Size (MB): 288.55\n",
            "----------------------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aJoiV7T2oMRc"
      },
      "source": [
        "def train_batch(data, model, optimizer, criteria):\n",
        "    model.train()\n",
        "    ims, age, gender = data\n",
        "    optimizer.zero_grad()\n",
        "    pred_gender, pred_age = model(ims)    \n",
        "    gender_criterion, age_criterion = criteria\n",
        "    gender_loss = gender_criterion(pred_gender.squeeze(), gender)\n",
        "    age_loss = age_criterion(pred_age.squeeze(), age)\n",
        "    total_loss = gender_loss + age_loss\n",
        "    total_loss.backward()\n",
        "    optimizer.step()\n",
        "    return total_loss\n",
        "\n",
        "def validate_batch(data, model, criteria):\n",
        "    model.eval()\n",
        "    ims, age, gender = data\n",
        "    with torch.no_grad():\n",
        "      pred_gender, pred_age = model(ims)\n",
        "    gender_criterion, age_criterion = criteria\n",
        "    gender_loss = gender_criterion(pred_gender.squeeze(), gender)\n",
        "    age_loss = age_criterion(pred_age.squeeze(), age)\n",
        "    total_loss = gender_loss + age_loss\n",
        "    pred_gender = (pred_gender > 0.5).squeeze()\n",
        "    gender_acc = (pred_gender == gender).float().sum()\n",
        "    age_mae = torch.abs(age - pred_age).float().sum()\n",
        "    return total_loss, gender_acc, age_mae"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uhbO3pA0oTuc",
        "outputId": "e67a2fa1-5939-4ce6-8651-c0d83c65cda0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "model, criteria, optimizer = get_model()\n",
        "val_gender_accuracies = []\n",
        "val_age_maes = []\n",
        "train_losses = []\n",
        "val_losses = []\n",
        "\n",
        "n_epochs = 5\n",
        "best_test_loss = 1000\n",
        "start = time.time()\n",
        "\n",
        "for epoch in range(n_epochs):\n",
        "    epoch_train_loss, epoch_test_loss = 0, 0\n",
        "    val_age_mae, val_gender_acc, ctr = 0, 0, 0\n",
        "    _n = len(train_loader)\n",
        "    for ix, data in enumerate(train_loader):\n",
        "        # if ix == 100: break\n",
        "        loss = train_batch(data, model, optimizer, criteria)\n",
        "        epoch_train_loss += loss.item()\n",
        "\n",
        "    for ix, data in enumerate(test_loader):\n",
        "        # if ix == 10: break\n",
        "        loss, gender_acc, age_mae = validate_batch(data, model, criteria)\n",
        "        epoch_test_loss += loss.item()\n",
        "        val_age_mae += age_mae\n",
        "        val_gender_acc += gender_acc\n",
        "        ctr += len(data[0])\n",
        "\n",
        "    val_age_mae /= ctr\n",
        "    val_gender_acc /= ctr\n",
        "    epoch_train_loss /= len(train_loader)\n",
        "    epoch_test_loss /= len(test_loader)\n",
        "\n",
        "    elapsed = time.time()-start\n",
        "    best_test_loss = min(best_test_loss, epoch_test_loss)\n",
        "    print('{}/{} ({:.2f}s - {:.2f}s remaining)'.format(epoch+1, n_epochs, time.time()-start, (n_epochs-epoch)*(elapsed/(epoch+1))))\n",
        "    info = f'''Epoch: {epoch+1:03d}\\tTrain Loss: {epoch_train_loss:.3f}\\tTest: {epoch_test_loss:.3f}\\tBest Test Loss: {best_test_loss:.4f}'''\n",
        "    info += f'\\nGender Accuracy: {val_gender_acc*100:.2f}%\\tAge MAE: {val_age_mae:.2f}\\n'\n",
        "    print(info)\n",
        "\n",
        "    val_gender_accuracies.append(val_gender_acc)\n",
        "    val_age_maes.append(val_age_mae)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1/5 (844.58s - 4222.92s remaining)\n",
            "Epoch: 001\tTrain Loss: 0.548\tTest: 0.469\tBest Test Loss: 0.4695\n",
            "Gender Accuracy: 83.54%\tAge MAE: 6.34\n",
            "\n",
            "2/5 (1682.39s - 3364.79s remaining)\n",
            "Epoch: 002\tTrain Loss: 0.400\tTest: 0.440\tBest Test Loss: 0.4399\n",
            "Gender Accuracy: 84.88%\tAge MAE: 6.23\n",
            "\n",
            "3/5 (2526.27s - 2526.27s remaining)\n",
            "Epoch: 003\tTrain Loss: 0.286\tTest: 0.494\tBest Test Loss: 0.4399\n",
            "Gender Accuracy: 84.67%\tAge MAE: 6.27\n",
            "\n",
            "4/5 (3370.63s - 1685.32s remaining)\n",
            "Epoch: 004\tTrain Loss: 0.199\tTest: 0.613\tBest Test Loss: 0.4399\n",
            "Gender Accuracy: 83.80%\tAge MAE: 6.41\n",
            "\n",
            "5/5 (4208.70s - 841.74s remaining)\n",
            "Epoch: 005\tTrain Loss: 0.159\tTest: 0.710\tBest Test Loss: 0.4399\n",
            "Gender Accuracy: 83.30%\tAge MAE: 6.29\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "akzO0Z3Br2GP",
        "outputId": "557b3b50-82c3-42df-dacf-2ee4f54c34c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "source": [
        "epochs = np.arange(1,len(val_gender_accuracies)+1)\n",
        "fig,ax = plt.subplots(1,2,figsize=(10,5))\n",
        "ax = ax.flat\n",
        "ax[0].plot(epochs, val_gender_accuracies, 'bo')\n",
        "ax[1].plot(epochs, val_age_maes, 'r')\n",
        "ax[0].set_xlabel('Epochs')\n",
        "ax[1].set_xlabel('Epochs')\n",
        "ax[0].set_ylabel('Accuracy')\n",
        "ax[1].set_ylabel('MAE')\n",
        "ax[0].set_title('Validation Gender Accuracy')\n",
        "ax[0].set_title('Validation Age Mean-Absolute-Error')\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U3MVHGy2tQgn",
        "outputId": "322370b2-2b44-471c-800f-2637206d469d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 652
        }
      },
      "source": [
        "!wget https://www.dropbox.com/s/6kzr8l68e9kpjkf/5_9.JPG\n",
        "im = cv2.imread('/content/5_9.JPG')\n",
        "im = trn.preprocess_image(im).to(device)\n",
        "gender, age = model(im)\n",
        "pred_gender = gender.to('cpu').detach().numpy()\n",
        "pred_age = age.to('cpu').detach().numpy()\n",
        "im = cv2.imread('/content/5_9.JPG')\n",
        "im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)\n",
        "plt.imshow(im)\n",
        "print('predicted gender:',np.where(pred_gender[0][0]<0.5,'Male','Female'), '; Predicted age', int(pred_age[0][0]*80))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-11-09 14:08:08--  https://www.dropbox.com/s/6kzr8l68e9kpjkf/5_9.JPG\n",
            "Resolving www.dropbox.com (www.dropbox.com)... 162.125.5.1, 2620:100:601f:1::a27d:901\n",
            "Connecting to www.dropbox.com (www.dropbox.com)|162.125.5.1|:443... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: /s/raw/6kzr8l68e9kpjkf/5_9.JPG [following]\n",
            "--2020-11-09 14:08:08--  https://www.dropbox.com/s/raw/6kzr8l68e9kpjkf/5_9.JPG\n",
            "Reusing existing connection to www.dropbox.com:443.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://uca373aa04bc3c60dd027c22b5aa.dl.dropboxusercontent.com/cd/0/inline/BC3TH3a9c3lP0QNxxm8x3r7gmpJ4kF79o3OdKPnoPKKZckshL_T1F5dD_lg7QKQdBUIUXjUwZ_Ljau6bhBMpll1ZeIuk42O44KyGUGEJyV3VAzJzHvtn7gN00jGfqvtrTeU/file# [following]\n",
            "--2020-11-09 14:08:09--  https://uca373aa04bc3c60dd027c22b5aa.dl.dropboxusercontent.com/cd/0/inline/BC3TH3a9c3lP0QNxxm8x3r7gmpJ4kF79o3OdKPnoPKKZckshL_T1F5dD_lg7QKQdBUIUXjUwZ_Ljau6bhBMpll1ZeIuk42O44KyGUGEJyV3VAzJzHvtn7gN00jGfqvtrTeU/file\n",
            "Resolving uca373aa04bc3c60dd027c22b5aa.dl.dropboxusercontent.com (uca373aa04bc3c60dd027c22b5aa.dl.dropboxusercontent.com)... 162.125.9.15, 2620:100:601d:15::a27d:50f\n",
            "Connecting to uca373aa04bc3c60dd027c22b5aa.dl.dropboxusercontent.com (uca373aa04bc3c60dd027c22b5aa.dl.dropboxusercontent.com)|162.125.9.15|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 46983 (46K) [image/jpeg]\n",
            "Saving to: ‘5_9.JPG.1’\n",
            "\n",
            "5_9.JPG.1           100%[===================>]  45.88K  --.-KB/s    in 0.02s   \n",
            "\n",
            "2020-11-09 14:08:09 (2.44 MB/s) - ‘5_9.JPG.1’ saved [46983/46983]\n",
            "\n",
            "predicted gender: Female ; Predicted age 24\n"
          ],
          "name": "stdout"
        },
        {
          "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": "kst7Qp5lwlhJ"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}