Deep_Learning/Face Verification/Models/Siamese_Neural_Network.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uqFHriVOlt-3",
        "outputId": "deb41366-912b-4c74-c195-52249625ff14"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting MTCNN\n",
            "  Downloading mtcnn-0.1.1-py3-none-any.whl (2.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m10.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: keras>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from MTCNN) (2.15.0)\n",
            "Requirement already satisfied: opencv-python>=4.1.0 in /usr/local/lib/python3.10/dist-packages (from MTCNN) (4.8.0.76)\n",
            "Requirement already satisfied: numpy>=1.21.2 in /usr/local/lib/python3.10/dist-packages (from opencv-python>=4.1.0->MTCNN) (1.25.2)\n",
            "Installing collected packages: MTCNN\n",
            "Successfully installed MTCNN-0.1.1\n"
          ]
        }
      ],
      "source": [
        "!pip install MTCNN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "gJz2UP6yLOFz"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import zipfile\n",
        "import cv2\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from tqdm.notebook import tqdm_notebook\n",
        "from mtcnn.mtcnn import MTCNN\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.applications import InceptionResNetV2 , MobileNetV2 , VGG19\n",
        "from tensorflow.keras.layers import Input , MaxPooling2D , Conv2D , Dense , Flatten , GlobalAveragePooling2D , Dropout , BatchNormalization"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ws2xg4MurEQa"
      },
      "source": [
        "# Importing the LFW dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cxTuKclI2sk-",
        "outputId": "fca2d059-acdb-4d46-d708-251226236fff"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "po2H0yNuZdQs"
      },
      "outputs": [],
      "source": [
        "# To Download the dataset and extract all the files\n",
        "!pip install -q kaggle\n",
        "\n",
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "!chmod 600 ~/.kaggle/kaggle.json\n",
        "\n",
        "!kaggle datasets download -d jessicali9530/lfw-dataset -p /content/drive/MyDrive/Face_Verification_Model\n",
        "root = \"/content/drive/MyDrive/Face_Verification_Model\"\n",
        "\n",
        "with zipfile.ZipFile(os.path.join(root, 'lfw-dataset.zip'), 'r') as zipper:\n",
        "    zipper.extractall(root)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r1X8xXb8AVjZ"
      },
      "source": [
        "# Data Generation and Preprocessing 🔍\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yNIigVpZtTTS"
      },
      "outputs": [],
      "source": [
        "face_cropper = MTCNN()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "k0MKebKqBWhp"
      },
      "outputs": [],
      "source": [
        "people = pd.read_csv(\"/content/drive/MyDrive/Face_Verification_Model/people.csv\")\n",
        "positives = people[people.images >= 2] # Positive Image pair folders\n",
        "negatives = people[people.images == 1] # Negative Images"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3opxqX2KfafI"
      },
      "outputs": [],
      "source": [
        "sample_path = \"/content/drive/MyDrive/Face_Verification_Model/lfw-deepfunneled/lfw-deepfunneled/AJ_Lamas/AJ_Lamas_0001.jpg\"\n",
        "\n",
        "def load_image(base_path = None , name = None, id = None, direct_path = None):\n",
        "    try:\n",
        "        if direct_path == None:\n",
        "          img_path = os.path.join(base_path, name, f\"{name}_{id:04d}.jpg\")\n",
        "        else:\n",
        "          img_path = direct_path\n",
        "        img = cv2.imread(img_path)\n",
        "        img = cv2.cvtColor(img , cv2.COLOR_BGR2GRAY)\n",
        "        img = cv2.cvtColor(img , cv2.COLOR_GRAY2BGR)\n",
        "        results = face_cropper.detect_faces(img)\n",
        "        x1, y1, width, height = results[0]['box']\n",
        "        x1, y1 = abs(x1), abs(y1)\n",
        "        x2, y2 = (x1 + width), (y1 + height)\n",
        "\n",
        "        img = img[y1:y2, x1:x2]\n",
        "        img = cv2.resize(img, (100, 100))\n",
        "        img = img / 255.0\n",
        "        return img\n",
        "    except Exception as e:\n",
        "        print(f\"Error loading or preprocessing image: {img_path}, {e}\")\n",
        "\n",
        "\n",
        "def _triplet_generator(positives, negatives, root, train_size):\n",
        "    triplets = []\n",
        "    for (index_pos, row_pos), (index_neg, row_neg) in tqdm_notebook(zip(positives[:train_size].iterrows(), negatives[:train_size].iterrows())):\n",
        "        num_of_imgs = row_pos['images']\n",
        "\n",
        "        choice = [1, 2]\n",
        "        if num_of_imgs != 2:\n",
        "            for i in range(2):\n",
        "                choice[i] = np.random.randint(1, num_of_imgs + 1)\n",
        "\n",
        "        # Anchor image\n",
        "        anchor_img = load_image(root, row_pos['name'], choice[0])\n",
        "\n",
        "        # Positive image\n",
        "        positive_img = load_image(root, row_pos['name'], choice[1])\n",
        "\n",
        "        # Negative image\n",
        "        negative_img = load_image(root, row_neg['name'], 1)\n",
        "\n",
        "        if anchor_img is not None and positive_img is not None and negative_img is not None:\n",
        "          triplets.append([anchor_img, positive_img, negative_img])\n",
        "\n",
        "    return np.array(triplets)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iPsi6CarfaUv"
      },
      "outputs": [],
      "source": [
        "base_path = \"/content/drive/MyDrive/Face_Verification_Model/lfw-deepfunneled/lfw-deepfunneled\"\n",
        "\n",
        "TRAIN_DATA_SIZE = 1500 # Maximum Number of pairs you can get is 1500\n",
        "\n",
        "triplets = _triplet_generator(positives , negatives , base_path , TRAIN_DATA_SIZE)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        },
        "id": "YGGk4ndWK-v2",
        "outputId": "6e13b06f-df43-4780-8d79-b15479b34f2a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Visualizing Triplets\n",
        "def show_triplet(images):\n",
        "    plt.subplot(1 , 3 , 1)\n",
        "    plt.title(\"Anchor\")\n",
        "    plt.imshow(images[0])\n",
        "\n",
        "    plt.subplot(1 , 3 , 2)\n",
        "    plt.title(\"Positive\")\n",
        "    plt.imshow(images[1])\n",
        "\n",
        "    plt.subplot(1 , 3 , 3)\n",
        "    plt.title(\"Negative\")\n",
        "    plt.imshow(images[2])\n",
        "\n",
        "    plt.savefig(\"Triplet_data.png\")\n",
        "\n",
        "show_triplet(triplets[280])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RB-7Hd3bLPC3"
      },
      "outputs": [],
      "source": [
        "# Necessary Step inorder to avoid wastage of time for loading the data\n",
        "# Instead save the computed numpy array\n",
        "np.save('/content/drive/MyDrive/Face_Verification_Model/TripletDataset.npy' , triplets)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "DjI2RHtOPsSG"
      },
      "outputs": [],
      "source": [
        "# Loading ...\n",
        "triplets = np.load('/content/drive/MyDrive/Face_Verification_Model/TripletDataset.npy')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p2y2vJO0OP1B"
      },
      "source": [
        "# Training Triplet Loss function 📊"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "dAAxaOWHLOF0"
      },
      "outputs": [],
      "source": [
        "# Custom Implementation\n",
        "def Dense_Block(input_shape):\n",
        "    model = tf.keras.Sequential([\n",
        "          Input(shape = input_shape),\n",
        "          Flatten(),\n",
        "          Dense(units=1024, activation=\"relu\"),\n",
        "          Dropout(0.2),\n",
        "          BatchNormalization(),\n",
        "          Dense(units=512, activation=\"relu\"),\n",
        "          Dropout(0.2),\n",
        "          BatchNormalization(),\n",
        "          Dense(units=256, activation=\"relu\"),\n",
        "          Dropout(0.2),\n",
        "          Dense(units=128)\n",
        "    ])\n",
        "    return model\n",
        "\n",
        "def Siamese_Network(base_model):\n",
        "    input = Input(shape = (3 , 100 , 100 , 3))\n",
        "    base_model.trainable = False\n",
        "\n",
        "    output1 = base_model(input[: , 0])\n",
        "    output2 = base_model(input[: , 1])\n",
        "    output3 = base_model(input[: , 2])\n",
        "\n",
        "    dense_block = Dense_Block(output1.shape[1:])\n",
        "\n",
        "    output1 = dense_block(output1)\n",
        "    output2 = dense_block(output2)\n",
        "    output3 = dense_block(output3)\n",
        "\n",
        "    output = [output1 , output2 , output3]\n",
        "    return tf.keras.Model(inputs = input , outputs = output)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "HJbZg9Gmw3t2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7d113cb5-46ff-4e23-fb1d-be49aa759601"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n"
          ]
        }
      ],
      "source": [
        "@tf.function\n",
        "def Triplet_loss(y_pred , alpha):\n",
        "    x1 = y_pred[0][:]\n",
        "    x2 = y_pred[1][:]\n",
        "    x3 = y_pred[2][:]\n",
        "\n",
        "    norm1 = tf.reduce_sum(tf.square(tf.subtract(x1 , x2)) , axis = -1)\n",
        "    norm2 = tf.reduce_sum(tf.square(tf.subtract(x1 , x3)) , axis = -1)\n",
        "\n",
        "    loss = tf.reduce_sum(tf.maximum(tf.subtract(norm1 , norm2) + alpha, 0.0))\n",
        "    return loss\n",
        "\n",
        "base_model = MobileNetV2(input_shape = (100 , 100 , 3) , weights = 'imagenet' , include_top = False) # Replace with Different Models"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "v8aZ1fBSwrIj"
      },
      "outputs": [],
      "source": [
        "Siamese_model = Siamese_Network(base_model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "id": "48Bp9VZtz_MU",
        "outputId": "5b3cbde2-fc75-4693-8c80-e676b6910d50"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "tf.keras.utils.plot_model(Siamese_model , to_file = \"Siamese_model.png\", show_trainable=True , show_shapes=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r1pIJuiS0DyM",
        "outputId": "c51ae2b5-0322-4f4e-9722-3c2c3a5258fe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_20\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " input_31 (InputLayer)       [(None, 3, 100, 100, 3)]     0         []                            \n",
            "                                                                                                  \n",
            " tf.__operators__.getitem_4  (None, 100, 100, 3)          0         ['input_31[0][0]']            \n",
            " 7 (SlicingOpLambda)                                                                              \n",
            "                                                                                                  \n",
            " tf.__operators__.getitem_4  (None, 100, 100, 3)          0         ['input_31[0][0]']            \n",
            " 8 (SlicingOpLambda)                                                                              \n",
            "                                                                                                  \n",
            " tf.__operators__.getitem_4  (None, 100, 100, 3)          0         ['input_31[0][0]']            \n",
            " 9 (SlicingOpLambda)                                                                              \n",
            "                                                                                                  \n",
            " mobilenetv2_1.00_224 (Func  (None, 4, 4, 1280)           2257984   ['tf.__operators__.getitem_47[\n",
            " tional)                                                            0][0]',                       \n",
            "                                                                     'tf.__operators__.getitem_48[\n",
            "                                                                    0][0]',                       \n",
            "                                                                     'tf.__operators__.getitem_49[\n",
            "                                                                    0][0]']                       \n",
            "                                                                                                  \n",
            " sequential_8 (Sequential)   (None, 128)                  2166771   ['mobilenetv2_1.00_224[0][0]',\n",
            "                                                          2          'mobilenetv2_1.00_224[1][0]',\n",
            "                                                                     'mobilenetv2_1.00_224[2][0]']\n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 23925696 (91.27 MB)\n",
            "Trainable params: 21664640 (82.64 MB)\n",
            "Non-trainable params: 2261056 (8.63 MB)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "Siamese_model.summary()"
      ]
    },
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      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n"
          ]
        },
        {
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              "  0%|          | 0/19 [00:00<?, ?it/s]"
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          "output_type": "stream",
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          "text": [
            "Epoch 1 Loss: 289.9024353027344\n",
            "Epoch 2/20\n"
          ]
        },
        {
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          "data": {
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              "  0%|          | 0/19 [00:00<?, ?it/s]"
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          "text": [
            "Epoch 2 Loss: 247.69374084472656\n",
            "Epoch 3/20\n"
          ]
        },
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              "  0%|          | 0/19 [00:00<?, ?it/s]"
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          "text": [
            "Epoch 3 Loss: 203.0453643798828\n",
            "Epoch 4/20\n"
          ]
        },
        {
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          "data": {
            "text/plain": [
              "  0%|          | 0/19 [00:00<?, ?it/s]"
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          "text": [
            "Epoch 4 Loss: 154.06776428222656\n",
            "Epoch 5/20\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/19 [00:00<?, ?it/s]"
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          "text": [
            "Epoch 5 Loss: 171.08523559570312\n",
            "Epoch 6/20\n"
          ]
        },
        {
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          "data": {
            "text/plain": [
              "  0%|          | 0/19 [00:00<?, ?it/s]"
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          "name": "stdout",
          "text": [
            "Epoch 6 Loss: 87.68141174316406\n",
            "Epoch 7/20\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/19 [00:00<?, ?it/s]"
            ],
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        {
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          "text": [
            "Epoch 7 Loss: 191.58999633789062\n",
            "Epoch 8/20\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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        {
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          "text": [
            "Epoch 8 Loss: 140.55160522460938\n",
            "Epoch 9/20\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "8a8696f3bbfe41f69ddf0b1a3220512c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 9 Loss: 301.2049560546875\n",
            "Epoch 10/20\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5f46accae7024f41a59712e31d7e8ef4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 10 Loss: 174.65684509277344\n",
            "Epoch 11/20\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/19 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e898d65c5da0439e8a43ae54cf95ee70"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 11 Loss: 59.22861862182617\n",
            "Epoch 12/20\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/19 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "755ab8a880034e219ec1fea7725698fb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 12 Loss: 36.098297119140625\n",
            "Epoch 13/20\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/19 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3efa27374308475f8a7f7cb5d8be4745"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 13 Loss: 88.0936508178711\n",
            "Epoch 14/20\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/19 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "937342beeaf348648149af1ae2a1878b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 14 Loss: 99.01046752929688\n",
            "Epoch 15/20\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/19 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3106e5a6264a4137a3fbd5426c5edc85"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 15 Loss: 85.12237548828125\n",
            "Epoch 16/20\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/19 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3662d9eab965414a947df5751f6476d1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 16 Loss: 162.51698303222656\n",
            "Epoch 17/20\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/19 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f00a08e0629442f3852ece8ad6dedd64"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 17 Loss: 136.7094268798828\n",
            "Epoch 18/20\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/19 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d4f7088883044f6ea0ec6958bfbc5971"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 18 Loss: 107.46011352539062\n",
            "Epoch 19/20\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/19 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "73f1e3e251b74d8fb5eed4fe125480ef"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 19 Loss: 117.4250259399414\n",
            "Epoch 20/20\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/19 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "91e57d4f9eb04fe6b8c055696711253a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 20 Loss: 66.99921417236328\n"
          ]
        }
      ],
      "source": [
        "def batch_generator(triplets, batch_size):\n",
        "  idx = np.random.randint(len(triplets), size=batch_size)\n",
        "  return triplets[idx]\n",
        "\n",
        "# Custom Training Loop\n",
        "NUMBER_OF_EPOCHS = 20\n",
        "BATCH_SIZE = 50\n",
        "n_steps = len(triplets) // BATCH_SIZE\n",
        "\n",
        "optimizer = tf.keras.optimizers.Adam()\n",
        "loss_fn = Triplet_loss\n",
        "mean_loss = keras.metrics.Mean()\n",
        "\n",
        "for epoch in range(1, NUMBER_OF_EPOCHS + 1):\n",
        "  print(f\"Epoch {epoch}/{NUMBER_OF_EPOCHS}\")\n",
        "\n",
        "  for step in tqdm_notebook(range(1, n_steps + 1)):\n",
        "      x_train = batch_generator(triplets, BATCH_SIZE)\n",
        "\n",
        "      with tf.GradientTape() as tape:\n",
        "        prediction = Siamese_model(x_train)\n",
        "        loss = loss_fn(y_pred = prediction , alpha=0.2)\n",
        "\n",
        "      gradients = tape.gradient(loss, Siamese_model.trainable_variables)\n",
        "      optimizer.apply_gradients(zip(gradients, Siamese_model.trainable_variables))\n",
        "      mean_loss.update_state(loss)\n",
        "\n",
        "  print(f\"Epoch {epoch} Loss: {mean_loss.result()}\" , end = \"\\n\")\n",
        "  mean_loss.reset_states()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5jJjZyORFEg4",
        "outputId": "7e026d73-6139-43f3-a24c-10c6f225d53c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n",
            "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
          ]
        }
      ],
      "source": [
        "Siamese_model.save('/content/drive/MyDrive/Face_Verification_Model/Siamese_model_MobileNetV2.h5')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zePApAfQp87k"
      },
      "source": [
        "# Face Verification"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IhhGRPXSOcUs",
        "outputId": "095bf360-ccc0-4dbb-ad0e-273b3f96e3f0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
          ]
        }
      ],
      "source": [
        "model_A = tf.keras.models.load_model(\"/content/drive/MyDrive/Face_Verification_Model/Siamese_model_MobileNetV2.h5\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "3SYSA_62UUbG"
      },
      "outputs": [],
      "source": [
        "model_B = tf.keras.models.Sequential(model_A.layers[-2:])\n",
        "cloned_model = tf.keras.models.clone_model(model_B)\n",
        "cloned_model.set_weights(model_A.get_weights())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "RKiX53LdZO1d"
      },
      "outputs": [],
      "source": [
        "class DistanceLayer(keras.layers.Layer):\n",
        "  def __init__(self , threshold = 0.3 , **kwargs):\n",
        "    self.threshold = threshold\n",
        "    super().__init__(**kwargs)\n",
        "\n",
        "  def call(self , input):\n",
        "    encoding_a , encoding_b = input\n",
        "    # Cosine Similarity\n",
        "    distance = tf.reduce_sum(tf.multiply(encoding_a , encoding_b) , axis = -1) / (tf.linalg.norm(encoding_a , axis = -1) * tf.linalg.norm(encoding_b , axis = -1))\n",
        "    return tf.cast(distance >= self.threshold , dtype = 'float32')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "uUSIXNH3XJXr"
      },
      "outputs": [],
      "source": [
        "input = Input(shape = (2 , 100 , 100 , 3))\n",
        "\n",
        "encoding1 = cloned_model(input[: , 0])\n",
        "encoding2 = cloned_model(input[: , 1])\n",
        "\n",
        "output = DistanceLayer(threshold = 0.9)([encoding1 ,encoding2])\n",
        "Model = tf.keras.models.Model(inputs = input , outputs = output)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "BR1MWSHA0k51",
        "outputId": "3afadbeb-4437-43fe-e47f-5a91441f75de"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "tf.keras.utils.plot_model(Model , to_file = \"Face_verfication.png\" , show_shapes = True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r20kgl4djc69"
      },
      "source": [
        "# Testing the Model 🧪"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ENknKe2iUdRD",
        "outputId": "6ef27e8b-bf72-4037-d17a-c950e9212317"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(199, 2, 100, 100, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 173
        }
      ],
      "source": [
        "test_pairs = np.load('/content/drive/MyDrive/Face_Verification_Model/TestTriplets.npy')\n",
        "test_labels = np.load('/content/drive/MyDrive/Face_Verification_Model/TestTripletsLabels.npy')\n",
        "test_pairs.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WxfXkNiWjSCr",
        "outputId": "b2f3f9a9-6ba9-46dd-e94b-5bd9bb035861"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7/7 [==============================] - 2s 26ms/step\n"
          ]
        }
      ],
      "source": [
        "prediction = Model.predict(test_pairs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fbJXtdSXODvt",
        "outputId": "bc1fad1e-7106-4eaa-a502-bc205d74802b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy of the model is 0.5778894424438477\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[18, 83],\n",
              "       [ 1, 97]])"
            ]
          },
          "metadata": {},
          "execution_count": 175
        }
      ],
      "source": [
        "print(f\"Test Accuracy of the model is {tf.keras.metrics.Accuracy()(prediction , test_labels)}\")\n",
        "confusion_matrix(y_pred = prediction , y_true = test_labels)"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "30KqMnlYgvWd"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
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