Gender_Classification👨👩.ipynb

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  "metadata": {
    "colab": {
      "name": "Gender Classification👨👩.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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      "name": "python"
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        "id": "ZTpashJ4kixF",
        "outputId": "49031ac1-0ba6-44fa-c181-7ea0a6bad40d"
      },
      "source": [
        "!pip install wandb\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "import numpy as np\n",
        "from tensorflow.keras.layers import Dense,MaxPool2D,Flatten,Conv2D\n",
        "import os\n",
        "from tensorflow.keras import layers\n",
        "from wandb.keras import WandbCallback\n",
        "import wandb\n",
        "from sklearn.metrics import classification_report, confusion_matrix"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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    },
    {
      "cell_type": "code",
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "N8OZoSfqyAoH",
        "outputId": "9cd7b2ba-b892-4a70-af0f-d04697239cae"
      },
      "source": [
        "!pip install kaggle"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.7/dist-packages (1.5.12)\n",
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            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from kaggle) (4.62.3)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.7/dist-packages (from kaggle) (5.0.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from kaggle) (2.23.0)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.7/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->kaggle) (3.0.4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "epMv1yU2yA5Q"
      },
      "source": [
        "!mkdir ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WSV5g6yXyBSw",
        "outputId": "145f7fa3-f267-446c-d6d5-ce10c660de0a"
      },
      "source": [
        "!kaggle datasets download -d ashishjangra27/gender-recognition-200k-images-celeba"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /root/.kaggle/kaggle.json'\n",
            "Downloading gender-recognition-200k-images-celeba.zip to /content\n",
            " 99% 1.31G/1.32G [00:11<00:00, 144MB/s]\n",
            "100% 1.32G/1.32G [00:11<00:00, 124MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MUlfJ07wzJyd"
      },
      "source": [
        "!unzip -qq gender-recognition-200k-images-celeba.zip"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XnCGieenwVZs"
      },
      "source": [
        "dataset_train='/content/Dataset/Train'\n",
        "dataset_test='/content/Dataset/Test'\n",
        "dataset_val='/content/Dataset/Validation'\n",
        "width=height=64\n",
        "batch_size=256"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a5y83PXiwYVc"
      },
      "source": [
        "data_generator=ImageDataGenerator(\n",
        "rescale=1/255,\n",
        "horizontal_flip=True,\n",
        ")\n"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6-dmcwLHwYf2",
        "outputId": "711a7606-9a9b-481a-b007-3cbe74fe92d0"
      },
      "source": [
        "train_data=data_generator.flow_from_directory(\n",
        "    dataset_train,\n",
        "    target_size=(width,height),\n",
        "    class_mode='categorical',\n",
        "    batch_size=batch_size,\n",
        "    shuffle=True,\n",
        "    subset='training'\n",
        ")\n",
        "val_data = data_generator.flow_from_directory(dataset_val,\n",
        "                                                    target_size = (width, height),\n",
        "                                                    class_mode = 'categorical',\n",
        "                                                    batch_size = batch_size,\n",
        "                                                    shuffle = False\n",
        "                                                    )\n",
        "test_data=data_generator.flow_from_directory(\n",
        "    dataset_test,\n",
        "    target_size=(width,height),\n",
        "    class_mode='categorical',\n",
        "    batch_size=batch_size,\n",
        "    shuffle=False,\n",
        "  \n",
        ")"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 160000 images belonging to 2 classes.\n",
            "Found 22598 images belonging to 2 classes.\n",
            "Found 20001 images belonging to 2 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lPfC1zCrwdBf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "205b8543-9068-474d-c3e3-f5f29539ff2b"
      },
      "source": [
        "base_model = tf.keras.applications.VGG16(\n",
        "    input_shape=(width, height, 3),\n",
        "    include_top = False,\n",
        "    weights = 'imagenet',\n",
        "    pooling = 'max'\n",
        "    )"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "58892288/58889256 [==============================] - 1s 0us/step\n",
            "58900480/58889256 [==============================] - 1s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cZotXSjiwfsT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ffe4d923-f4b0-42a6-b744-fbf039c5fb78"
      },
      "source": [
        "base_model.summary()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"vgg16\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         [(None, 64, 64, 3)]       0         \n",
            "_________________________________________________________________\n",
            "block1_conv1 (Conv2D)        (None, 64, 64, 64)        1792      \n",
            "_________________________________________________________________\n",
            "block1_conv2 (Conv2D)        (None, 64, 64, 64)        36928     \n",
            "_________________________________________________________________\n",
            "block1_pool (MaxPooling2D)   (None, 32, 32, 64)        0         \n",
            "_________________________________________________________________\n",
            "block2_conv1 (Conv2D)        (None, 32, 32, 128)       73856     \n",
            "_________________________________________________________________\n",
            "block2_conv2 (Conv2D)        (None, 32, 32, 128)       147584    \n",
            "_________________________________________________________________\n",
            "block2_pool (MaxPooling2D)   (None, 16, 16, 128)       0         \n",
            "_________________________________________________________________\n",
            "block3_conv1 (Conv2D)        (None, 16, 16, 256)       295168    \n",
            "_________________________________________________________________\n",
            "block3_conv2 (Conv2D)        (None, 16, 16, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_conv3 (Conv2D)        (None, 16, 16, 256)       590080    \n",
            "_________________________________________________________________\n",
            "block3_pool (MaxPooling2D)   (None, 8, 8, 256)         0         \n",
            "_________________________________________________________________\n",
            "block4_conv1 (Conv2D)        (None, 8, 8, 512)         1180160   \n",
            "_________________________________________________________________\n",
            "block4_conv2 (Conv2D)        (None, 8, 8, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "block4_conv3 (Conv2D)        (None, 8, 8, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "block4_pool (MaxPooling2D)   (None, 4, 4, 512)         0         \n",
            "_________________________________________________________________\n",
            "block5_conv1 (Conv2D)        (None, 4, 4, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv2 (Conv2D)        (None, 4, 4, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "block5_conv3 (Conv2D)        (None, 4, 4, 512)         2359808   \n",
            "_________________________________________________________________\n",
            "block5_pool (MaxPooling2D)   (None, 2, 2, 512)         0         \n",
            "_________________________________________________________________\n",
            "global_max_pooling2d (Global (None, 512)               0         \n",
            "=================================================================\n",
            "Total params: 14,714,688\n",
            "Trainable params: 14,714,688\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D2HzNuWUwm42"
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      "source": [
        "model = tf.keras.Sequential([\n",
        "        base_model,\n",
        "        Flatten(),\n",
        "        layers.Dense(128, activation='relu'),\n",
        "        layers.Dense(32, activation='relu'),\n",
        "        layers.BatchNormalization(),\n",
        "        layers.Dense(2, activation='sigmoid'),              \n",
        "])"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
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        "colab": {
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          "height": 130
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        "outputId": "26234af8-a1a4-4125-ce08-15917a2eb1da"
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      "source": [
        "wandb.init(project=\"Gender_classification\")"
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      "execution_count": 12,
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            "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n"
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            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
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        "config = wandb.config\n",
        "config.learning_rate = 0.001\n",
        "model.compile(\n",
        "    loss=tf.keras.losses.categorical_crossentropy,optimizer=tf.keras.optimizers.Adam(learning_rate=config.learning_rate),\n",
        "    metrics=['accuracy']\n",
        ")"
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      "execution_count": 13,
      "outputs": []
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    {
      "cell_type": "code",
      "metadata": {
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9615896e-0884-4536-87b0-78470440a472"
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      "source": [
        "model.fit(train_data,\n",
        "          steps_per_epoch = train_data.samples / batch_size,\n",
        "          validation_data = val_data,\n",
        "          validation_steps = val_data.samples / batch_size,\n",
        "          epochs = 5\n",
        ")\n",
        "\n"
      ],
      "execution_count": 14,
      "outputs": [
        {
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          "text": [
            "Epoch 1/5\n",
            "625/625 [==============================] - 404s 578ms/step - loss: 0.1658 - accuracy: 0.9344 - val_loss: 0.1203 - val_accuracy: 0.9544\n",
            "Epoch 2/5\n",
            "625/625 [==============================] - 358s 572ms/step - loss: 0.0793 - accuracy: 0.9701 - val_loss: 2.5162 - val_accuracy: 0.6154\n",
            "Epoch 3/5\n",
            "625/625 [==============================] - 358s 572ms/step - loss: 0.0651 - accuracy: 0.9761 - val_loss: 0.1311 - val_accuracy: 0.9602\n",
            "Epoch 4/5\n",
            "625/625 [==============================] - 358s 572ms/step - loss: 0.0582 - accuracy: 0.9786 - val_loss: 0.0838 - val_accuracy: 0.9711\n",
            "Epoch 5/5\n",
            "625/625 [==============================] - 357s 571ms/step - loss: 0.0543 - accuracy: 0.9806 - val_loss: 0.0743 - val_accuracy: 0.9739\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f7f80151f10>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xWviNMhLwYqT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "27e7d21a-496b-492a-bd2b-18ca449abfcf"
      },
      "source": [
        "Y_pred = model.predict(test_data)\n",
        "y_pred = np.argmax(Y_pred, axis= 1)\n",
        "\n",
        "print(confusion_matrix(test_data.classes, y_pred))\n",
        "targets = set(test_data.class_indices.keys())\n",
        "print('classification_report:')\n",
        "print(classification_report(test_data.classes, y_pred, target_names= targets))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[11463    79]\n",
            " [  320  8139]]\n",
            "classification_report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "        Male       0.97      0.99      0.98     11542\n",
            "      Female       0.99      0.96      0.98      8459\n",
            "\n",
            "    accuracy                           0.98     20001\n",
            "   macro avg       0.98      0.98      0.98     20001\n",
            "weighted avg       0.98      0.98      0.98     20001\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FRGaSYOLJdIw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "92f76c28-ac56-4ee2-ecb5-11290afa0b69"
      },
      "source": [
        "!pip install retina-face\n",
        "from retinaface import RetinaFace\n",
        "import cv2\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting retina-face\n",
            "  Downloading retina_face-0.0.5-py3-none-any.whl (14 kB)\n",
            "Requirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.7/dist-packages (from retina-face) (1.19.5)\n",
            "Collecting gdown>=3.10.1\n",
            "  Downloading gdown-4.1.1.tar.gz (13 kB)\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: Pillow>=5.2.0 in /usr/local/lib/python3.7/dist-packages (from retina-face) (7.1.2)\n",
            "Requirement already satisfied: opencv-python>=3.4.4 in /usr/local/lib/python3.7/dist-packages (from retina-face) (4.1.2.30)\n",
            "Requirement already satisfied: tensorflow>=1.9.0 in /usr/local/lib/python3.7/dist-packages (from retina-face) (2.6.0)\n",
            "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.7/dist-packages (from gdown>=3.10.1->retina-face) (2.23.0)\n",
            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.7/dist-packages (from gdown>=3.10.1->retina-face) (4.6.3)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from gdown>=3.10.1->retina-face) (1.15.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from gdown>=3.10.1->retina-face) (3.3.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from gdown>=3.10.1->retina-face) (4.62.3)\n",
            "Requirement already satisfied: clang~=5.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=1.9.0->retina-face) (5.0)\n",
            "Requirement already satisfied: flatbuffers~=1.12.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=1.9.0->retina-face) (1.12)\n",
            "Requirement already satisfied: opt-einsum~=3.3.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=1.9.0->retina-face) (3.3.0)\n",
            "Requirement already satisfied: astunparse~=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=1.9.0->retina-face) (1.6.3)\n",
            "Requirement already satisfied: keras~=2.6 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=1.9.0->retina-face) (2.6.0)\n",
            "Requirement already satisfied: gast==0.4.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=1.9.0->retina-face) (0.4.0)\n",
            "Requirement already satisfied: h5py~=3.1.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=1.9.0->retina-face) (3.1.0)\n",
            "Requirement already satisfied: protobuf>=3.9.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=1.9.0->retina-face) (3.17.3)\n",
            "Requirement already satisfied: wheel~=0.35 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=1.9.0->retina-face) (0.37.0)\n",
            "Requirement already satisfied: wrapt~=1.12.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=1.9.0->retina-face) (1.12.1)\n",
            "Requirement already satisfied: grpcio<2.0,>=1.37.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=1.9.0->retina-face) (1.41.0)\n",
            "Requirement already satisfied: tensorboard~=2.6 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=1.9.0->retina-face) (2.6.0)\n",
            "Requirement already satisfied: google-pasta~=0.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=1.9.0->retina-face) (0.2.0)\n",
            "Requirement already satisfied: termcolor~=1.1.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=1.9.0->retina-face) (1.1.0)\n",
            "Requirement already satisfied: typing-extensions~=3.7.4 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=1.9.0->retina-face) (3.7.4.3)\n",
            "Requirement already satisfied: keras-preprocessing~=1.1.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=1.9.0->retina-face) (1.1.2)\n",
            "Requirement already satisfied: absl-py~=0.10 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=1.9.0->retina-face) (0.12.0)\n",
            "Requirement already satisfied: tensorflow-estimator~=2.6 in /usr/local/lib/python3.7/dist-packages (from tensorflow>=1.9.0->retina-face) (2.6.0)\n",
            "Requirement already satisfied: cached-property in /usr/local/lib/python3.7/dist-packages (from h5py~=3.1.0->tensorflow>=1.9.0->retina-face) (1.5.2)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.6->tensorflow>=1.9.0->retina-face) (57.4.0)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.6->tensorflow>=1.9.0->retina-face) (1.0.1)\n",
            "Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.6->tensorflow>=1.9.0->retina-face) (1.35.0)\n",
            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.6->tensorflow>=1.9.0->retina-face) (1.8.0)\n",
            "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.6->tensorflow>=1.9.0->retina-face) (0.6.1)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.6->tensorflow>=1.9.0->retina-face) (3.3.4)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard~=2.6->tensorflow>=1.9.0->retina-face) (0.4.6)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard~=2.6->tensorflow>=1.9.0->retina-face) (0.2.8)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard~=2.6->tensorflow>=1.9.0->retina-face) (4.2.4)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard~=2.6->tensorflow>=1.9.0->retina-face) (4.7.2)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.6->tensorflow>=1.9.0->retina-face) (1.3.0)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard~=2.6->tensorflow>=1.9.0->retina-face) (4.8.1)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard~=2.6->tensorflow>=1.9.0->retina-face) (0.4.8)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests[socks]->gdown>=3.10.1->retina-face) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests[socks]->gdown>=3.10.1->retina-face) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests[socks]->gdown>=3.10.1->retina-face) (2021.5.30)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests[socks]->gdown>=3.10.1->retina-face) (1.24.3)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.6->tensorflow>=1.9.0->retina-face) (3.1.1)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->markdown>=2.6.8->tensorboard~=2.6->tensorflow>=1.9.0->retina-face) (3.6.0)\n",
            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.7/dist-packages (from requests[socks]->gdown>=3.10.1->retina-face) (1.7.1)\n",
            "Building wheels for collected packages: gdown\n",
            "  Building wheel for gdown (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gdown: filename=gdown-4.1.1-py3-none-any.whl size=14211 sha256=7d45fa492d7450b4412e65322005f0879dc3ad105baf6772d7e5e8b80d7822b8\n",
            "  Stored in directory: /root/.cache/pip/wheels/0c/45/5c/0c6bf549923e217b4a0fd24a90ce36e0d183ca50b7a1a7306d\n",
            "Successfully built gdown\n",
            "Installing collected packages: gdown, retina-face\n",
            "  Attempting uninstall: gdown\n",
            "    Found existing installation: gdown 3.6.4\n",
            "    Uninstalling gdown-3.6.4:\n",
            "      Successfully uninstalled gdown-3.6.4\n",
            "Successfully installed gdown-4.1.1 retina-face-0.0.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5fgxnJS2HvcC"
      },
      "source": [
        "def predict(image_path):\n",
        "    faces = RetinaFace.extract_faces(img_path= image_path, align= True)\n",
        "    image = faces[0]\n",
        "    image = cv2.resize(image, (width, height))\n",
        "    plt.imshow(image)\n",
        "    image = image / 255.0\n",
        "    image = np.expand_dims(image, axis= 0)\n",
        "    res = np.argmax(model.predict(image))\n",
        "    if res == 0:\n",
        "        print('Female\\n')\n",
        "    elif res==1:\n",
        "        print('Male\\n')\n",
        "\n"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W-UAWjxBHvvu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "outputId": "a1ddb5e2-2465-4ee5-a846-88ff9774ecd7"
      },
      "source": [
        "predict('2.jpg')"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Directory  /root /.deepface created\n",
            "Directory  /root /.deepface/weights created\n",
            "retinaface.h5 will be downloaded from the url https://github.com/serengil/deepface_models/releases/download/v1.0/retinaface.h5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading...\n",
            "From: https://github.com/serengil/deepface_models/releases/download/v1.0/retinaface.h5\n",
            "To: /root/.deepface/weights/retinaface.h5\n",
            "100%|██████████| 119M/119M [00:02<00:00, 39.8MB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Male\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1XZJTPNdMRJO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "a6fdeba6-df6a-4ce1-d067-3d726425cc01"
      },
      "source": [
        "predict('3.jpg')"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Female\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RayYe8nNMRPB"
      },
      "source": [
        ""
      ],
      "execution_count": 19,
      "outputs": []
    }
  ]
}