Facial Verification with a Siamese Network - Part 5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 Install Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Installing collected packages: urllib3, pyasn1, idna, charset-normalizer, certifi, typing-extensions, six, setuptools, rsa, requests, pyasn1-modules, oauthlib, cachetools, requests-oauthlib, google-auth, wheel, werkzeug, tensorboard-plugin-wit, tensorboard-data-server, protobuf, numpy, markdown, grpcio, google-auth-oauthlib, absl-py, wrapt, termcolor, tensorflow-estimator, tensorboard, pyparsing, pillow, opt-einsum, kiwisolver, keras-preprocessing, h5py, google-pasta, gast, flatbuffers, cycler, astunparse, tensorflow-gpu, tensorflow, opencv-python, matplotlib\n",
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     ]
    }
   ],
   "source": [
    "!pip install tensorflow==2.4.1 tensorflow-gpu==2.4.1 opencv-python matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.2 Import Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import standard dependencies\n",
    "import cv2\n",
    "import os\n",
    "import random\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import tensorflow dependencies - Functional API\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.layers import Layer, Conv2D, Dense, MaxPooling2D, Input, Flatten\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3 Set GPU Growth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Avoid OOM errors by setting GPU Memory Consumption Growth\n",
    "gpus = tf.config.experimental.list_physical_devices('GPU')\n",
    "for gpu in gpus: \n",
    "    tf.config.experimental.set_memory_growth(gpu, True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.4 Create Folder Structures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup paths\n",
    "POS_PATH = os.path.join('data', 'positive')\n",
    "NEG_PATH = os.path.join('data', 'negative')\n",
    "ANC_PATH = os.path.join('data', 'anchor')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "FileExistsError",
     "evalue": "[WinError 183] Cannot create a file when that file already exists: 'data\\\\positive'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileExistsError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_12212/2704642804.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# Make the directories\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmakedirs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mPOS_PATH\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmakedirs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mNEG_PATH\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmakedirs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mANC_PATH\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\os.py\u001b[0m in \u001b[0;36mmakedirs\u001b[1;34m(name, mode, exist_ok)\u001b[0m\n\u001b[0;32m    219\u001b[0m             \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    220\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 221\u001b[1;33m         \u001b[0mmkdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    222\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    223\u001b[0m         \u001b[1;31m# Cannot rely on checking for EEXIST, since the operating system\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileExistsError\u001b[0m: [WinError 183] Cannot create a file when that file already exists: 'data\\\\positive'"
     ]
    }
   ],
   "source": [
    "# Make the directories\n",
    "os.makedirs(POS_PATH)\n",
    "os.makedirs(NEG_PATH)\n",
    "os.makedirs(ANC_PATH)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Collect Positives and Anchors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1 Untar Labelled Faces in the Wild Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# http://vis-www.cs.umass.edu/lfw/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uncompress Tar GZ Labelled Faces in the Wild Dataset\n",
    "!tar -xf lfw.tgz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Move LFW Images to the following repository data/negative\n",
    "for directory in os.listdir('lfw'):\n",
    "    for file in os.listdir(os.path.join('lfw', directory)):\n",
    "        EX_PATH = os.path.join('lfw', directory, file)\n",
    "        NEW_PATH = os.path.join(NEG_PATH, file)\n",
    "        os.replace(EX_PATH, NEW_PATH)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 Collect Positive and Anchor Classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import uuid library to generate unique image names\n",
    "import uuid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'data\\\\anchor\\\\03301c4a-1c06-11ec-b73a-a0cec8d2d278.jpg'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.path.join(ANC_PATH, '{}.jpg'.format(uuid.uuid1()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Establish a connection to the webcam\n",
    "cap = cv2.VideoCapture(4)\n",
    "while cap.isOpened(): \n",
    "    ret, frame = cap.read()\n",
    "   \n",
    "    # Cut down frame to 250x250px\n",
    "    frame = frame[120:120+250,200:200+250, :]\n",
    "    \n",
    "    # Collect anchors \n",
    "    if cv2.waitKey(1) & 0XFF == ord('a'):\n",
    "        # Create the unique file path \n",
    "        imgname = os.path.join(ANC_PATH, '{}.jpg'.format(uuid.uuid1()))\n",
    "        # Write out anchor image\n",
    "        cv2.imwrite(imgname, frame)\n",
    "    \n",
    "    # Collect positives\n",
    "    if cv2.waitKey(1) & 0XFF == ord('p'):\n",
    "        # Create the unique file path \n",
    "        imgname = os.path.join(POS_PATH, '{}.jpg'.format(uuid.uuid1()))\n",
    "        # Write out positive image\n",
    "        cv2.imwrite(imgname, frame)\n",
    "    \n",
    "    # Show image back to screen\n",
    "    cv2.imshow('Image Collection', frame)\n",
    "    \n",
    "    # Breaking gracefully\n",
    "    if cv2.waitKey(1) & 0XFF == ord('q'):\n",
    "        break\n",
    "        \n",
    "# Release the webcam\n",
    "cap.release()\n",
    "# Close the image show frame\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x220a4a93940>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(frame[120:120+250,200:200+250, :])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Load and Preprocess Images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 Get Image Directories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "anchor = tf.data.Dataset.list_files(ANC_PATH+'\\*.jpg').take(300)\n",
    "positive = tf.data.Dataset.list_files(POS_PATH+'\\*.jpg').take(300)\n",
    "negative = tf.data.Dataset.list_files(NEG_PATH+'\\*.jpg').take(300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "dir_test = anchor.as_numpy_iterator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'data\\\\anchor\\\\9d100db6-135f-11ec-8d05-a0cec8d2d278.jpg'\n"
     ]
    }
   ],
   "source": [
    "print(dir_test.next())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 Preprocessing - Scale and Resize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(file_path):\n",
    "    \n",
    "    # Read in image from file path\n",
    "    byte_img = tf.io.read_file(file_path)\n",
    "    # Load in the image \n",
    "    img = tf.io.decode_jpeg(byte_img)\n",
    "    \n",
    "    # Preprocessing steps - resizing the image to be 100x100x3\n",
    "    img = tf.image.resize(img, (100,100))\n",
    "    # Scale image to be between 0 and 1 \n",
    "    img = img / 255.0\n",
    "    \n",
    "    # Return image\n",
    "    return img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "img = preprocess('data\\\\anchor\\\\a4e73462-135f-11ec-9e6e-a0cec8d2d278.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img.numpy().max() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x20495465c88>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'dataset' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_12212/2569123063.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdataset\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpreprocess\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'dataset' is not defined"
     ]
    }
   ],
   "source": [
    "dataset.map(preprocess)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3 Create Labelled Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# (anchor, positive) => 1,1,1,1,1\n",
    "# (anchor, negative) => 0,0,0,0,0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "positives = tf.data.Dataset.zip((anchor, positive, tf.data.Dataset.from_tensor_slices(tf.ones(len(anchor)))))\n",
    "negatives = tf.data.Dataset.zip((anchor, negative, tf.data.Dataset.from_tensor_slices(tf.zeros(len(anchor)))))\n",
    "data = positives.concatenate(negatives)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "samples = data.as_numpy_iterator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "exampple = samples.next()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(b'data\\\\anchor\\\\a278c0fe-135f-11ec-8134-a0cec8d2d278.jpg',\n",
       " b'data\\\\positive\\\\c413f5e2-135f-11ec-a60b-a0cec8d2d278.jpg',\n",
       " 1.0)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exampple"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.4 Build Train and Test Partition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_twin(input_img, validation_img, label):\n",
    "    return(preprocess(input_img), preprocess(validation_img), label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = preprocess_twin(*exampple)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x204a11b6ac8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(res[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build dataloader pipeline\n",
    "data = data.map(preprocess_twin)\n",
    "data = data.cache()\n",
    "data = data.shuffle(buffer_size=1024)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Training partition\n",
    "train_data = data.take(round(len(data)*.7))\n",
    "train_data = train_data.batch(16)\n",
    "train_data = train_data.prefetch(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Testing partition\n",
    "test_data = data.skip(round(len(data)*.7))\n",
    "test_data = test_data.take(round(len(data)*.3))\n",
    "test_data = test_data.batch(16)\n",
    "test_data = test_data.prefetch(8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Model Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.1 Build Embedding Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "inp = Input(shape=(100,100,3), name='input_image')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "c1 = Conv2D(64, (10,10), activation='relu')(inp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "m1 = MaxPooling2D(64, (2,2), padding='same')(c1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "c2 = Conv2D(128, (7,7), activation='relu')(m1)\n",
    "m2 = MaxPooling2D(64, (2,2), padding='same')(c2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "c3 = Conv2D(128, (4,4), activation='relu')(m2)\n",
    "m3 = MaxPooling2D(64, (2,2), padding='same')(c3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "c4 = Conv2D(256, (4,4), activation='relu')(m3)\n",
    "f1 = Flatten()(c4)\n",
    "d1 = Dense(4096, activation='sigmoid')(f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "mod = Model(inputs=[inp], outputs=[d1], name='embedding')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"embedding\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_image (InputLayer)     [(None, 100, 100, 3)]     0         \n",
      "_________________________________________________________________\n",
      "conv2d (Conv2D)              (None, 91, 91, 64)        19264     \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 46, 46, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 40, 40, 128)       401536    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 20, 20, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 17, 17, 128)       262272    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 9, 9, 128)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 6, 6, 256)         524544    \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 9216)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 4096)              37752832  \n",
      "=================================================================\n",
      "Total params: 38,960,448\n",
      "Trainable params: 38,960,448\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "mod.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_embedding(): \n",
    "    inp = Input(shape=(100,100,3), name='input_image')\n",
    "    \n",
    "    # First block\n",
    "    c1 = Conv2D(64, (10,10), activation='relu')(inp)\n",
    "    m1 = MaxPooling2D(64, (2,2), padding='same')(c1)\n",
    "    \n",
    "    # Second block\n",
    "    c2 = Conv2D(128, (7,7), activation='relu')(m1)\n",
    "    m2 = MaxPooling2D(64, (2,2), padding='same')(c2)\n",
    "    \n",
    "    # Third block \n",
    "    c3 = Conv2D(128, (4,4), activation='relu')(m2)\n",
    "    m3 = MaxPooling2D(64, (2,2), padding='same')(c3)\n",
    "    \n",
    "    # Final embedding block\n",
    "    c4 = Conv2D(256, (4,4), activation='relu')(m3)\n",
    "    f1 = Flatten()(c4)\n",
    "    d1 = Dense(4096, activation='sigmoid')(f1)\n",
    "    \n",
    "    \n",
    "    return Model(inputs=[inp], outputs=[d1], name='embedding')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding = make_embedding()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"embedding\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_image (InputLayer)     [(None, 100, 100, 3)]     0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 91, 91, 64)        19264     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 46, 46, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 40, 40, 128)       401536    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 20, 20, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_6 (Conv2D)            (None, 17, 17, 128)       262272    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_5 (MaxPooling2 (None, 9, 9, 128)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_7 (Conv2D)            (None, 6, 6, 256)         524544    \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 9216)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 4096)              37752832  \n",
      "=================================================================\n",
      "Total params: 38,960,448\n",
      "Trainable params: 38,960,448\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "embedding.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.2 Build Distance Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Siamese L1 Distance class\n",
    "class L1Dist(Layer):\n",
    "    \n",
    "    # Init method - inheritance\n",
    "    def __init__(self, **kwargs):\n",
    "        super().__init__()\n",
    "       \n",
    "    # Magic happens here - similarity calculation\n",
    "    def call(self, input_embedding, validation_embedding):\n",
    "        return tf.math.abs(input_embedding - validation_embedding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "l1 = L1Dist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'anchor_embedding' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_12212/3877395630.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ml1\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0manchor_embedding\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalidation_embedding\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'anchor_embedding' is not defined"
     ]
    }
   ],
   "source": [
    "l1(anchor_embedding, validation_embedding)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.3 Make Siamese Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_image = Input(name='input_img', shape=(100,100,3))\n",
    "validation_image = Input(name='validation_img', shape=(100,100,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "inp_embedding = embedding(input_image)\n",
    "val_embedding = embedding(validation_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "siamese_layer = L1Dist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "distances = siamese_layer(inp_embedding, val_embedding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier = Dense(1, activation='sigmoid')(distances)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<KerasTensor: shape=(None, 1) dtype=float32 (created by layer 'dense_2')>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "siamese_network = Model(inputs=[input_image, validation_image], outputs=classifier, name='SiameseNetwork')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"SiameseNetwork\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_img (InputLayer)          [(None, 100, 100, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "validation_img (InputLayer)     [(None, 100, 100, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding (Functional)          (None, 4096)         38960448    input_img[0][0]                  \n",
      "                                                                 validation_img[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "l1_dist_1 (L1Dist)              (None, 4096)         0           embedding[0][0]                  \n",
      "                                                                 embedding[1][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 1)            4097        l1_dist_1[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 38,964,545\n",
      "Trainable params: 38,964,545\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "siamese_network.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_siamese_model(): \n",
    "    \n",
    "    # Anchor image input in the network\n",
    "    input_image = Input(name='input_img', shape=(100,100,3))\n",
    "    \n",
    "    # Validation image in the network \n",
    "    validation_image = Input(name='validation_img', shape=(100,100,3))\n",
    "    \n",
    "    # Combine siamese distance components\n",
    "    siamese_layer = L1Dist()\n",
    "    siamese_layer._name = 'distance'\n",
    "    distances = siamese_layer(embedding(input_image), embedding(validation_image))\n",
    "    \n",
    "    # Classification layer \n",
    "    classifier = Dense(1, activation='sigmoid')(distances)\n",
    "    \n",
    "    return Model(inputs=[input_image, validation_image], outputs=classifier, name='SiameseNetwork')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "siamese_model = make_siamese_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"SiameseNetwork\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_img (InputLayer)          [(None, 100, 100, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "validation_img (InputLayer)     [(None, 100, 100, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "embedding (Functional)          (None, 4096)         38960448    input_img[0][0]                  \n",
      "                                                                 validation_img[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "distance (L1Dist)               (None, 4096)         0           embedding[2][0]                  \n",
      "                                                                 embedding[3][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dense_3 (Dense)                 (None, 1)            4097        distance[0][0]                   \n",
      "==================================================================================================\n",
      "Total params: 38,964,545\n",
      "Trainable params: 38,964,545\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "siamese_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.1 Setup Loss and Optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "binary_cross_loss = tf.losses.BinaryCrossentropy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt = tf.keras.optimizers.Adam(1e-4) # 0.0001"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.2 Establish Checkpoints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoint_dir = './training_checkpoints'\n",
    "checkpoint_prefix = os.path.join(checkpoint_dir, 'ckpt')\n",
    "checkpoint = tf.train.Checkpoint(opt=opt, siamese_model=siamese_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.3 Build Train Step Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_batch = train_data.as_numpy_iterator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_1 = test_batch.next()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = batch_1[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = batch_1[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.losses.BinaryCrossentropy??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "@tf.function\n",
    "def train_step(batch):\n",
    "    \n",
    "    # Record all of our operations \n",
    "    with tf.GradientTape() as tape:     \n",
    "        # Get anchor and positive/negative image\n",
    "        X = batch[:2]\n",
    "        # Get label\n",
    "        y = batch[2]\n",
    "        \n",
    "        # Forward pass\n",
    "        yhat = siamese_model(X, training=True)\n",
    "        # Calculate loss\n",
    "        loss = binary_cross_loss(y, yhat)\n",
    "    print(loss)\n",
    "        \n",
    "    # Calculate gradients\n",
    "    grad = tape.gradient(loss, siamese_model.trainable_variables)\n",
    "    \n",
    "    # Calculate updated weights and apply to siamese model\n",
    "    opt.apply_gradients(zip(grad, siamese_model.trainable_variables))\n",
    "    \n",
    "    # Return loss\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.4 Build Training Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(data, EPOCHS):\n",
    "    # Loop through epochs\n",
    "    for epoch in range(1, EPOCHS+1):\n",
    "        print('\\n Epoch {}/{}'.format(epoch, EPOCHS))\n",
    "        progbar = tf.keras.utils.Progbar(len(data))\n",
    "        \n",
    "        # Loop through each batch\n",
    "        for idx, batch in enumerate(data):\n",
    "            # Run train step here\n",
    "            train_step(batch)\n",
    "            progbar.update(idx+1)\n",
    "        \n",
    "        # Save checkpoints\n",
    "        if epoch % 10 == 0: \n",
    "            checkpoint.save(file_prefix=checkpoint_prefix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.5 Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "EPOCHS = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Epoch 1/50\n",
      "Tensor(\"binary_crossentropy/weighted_loss/value:0\", shape=(), dtype=float32)\n",
      "Tensor(\"binary_crossentropy/weighted_loss/value:0\", shape=(), dtype=float32)\n",
      "26/27 [===========================>..] - ETA: 0sTensor(\"binary_crossentropy/weighted_loss/value:0\", shape=(), dtype=float32)\n",
      "27/27 [==============================] - 8s 143ms/step\n",
      "\n",
      " Epoch 2/50\n",
      "27/27 [==============================] - 3s 128ms/step\n",
      "\n",
      " Epoch 3/50\n",
      "27/27 [==============================] - 3s 127ms/step\n",
      "\n",
      " Epoch 4/50\n",
      "27/27 [==============================] - 3s 124ms/step\n",
      "\n",
      " Epoch 5/50\n",
      "27/27 [==============================] - 3s 125ms/step\n",
      "\n",
      " Epoch 6/50\n",
      "27/27 [==============================] - 3s 123ms/step\n",
      "\n",
      " Epoch 7/50\n",
      "27/27 [==============================] - 3s 121ms/step\n",
      "\n",
      " Epoch 8/50\n",
      "27/27 [==============================] - 3s 122ms/step\n",
      "\n",
      " Epoch 9/50\n",
      "27/27 [==============================] - 3s 124ms/step\n",
      "\n",
      " Epoch 10/50\n",
      "27/27 [==============================] - 3s 124ms/step\n",
      "\n",
      " Epoch 11/50\n",
      "27/27 [==============================] - 3s 125ms/step\n",
      "\n",
      " Epoch 12/50\n",
      "27/27 [==============================] - 3s 124ms/step\n",
      "\n",
      " Epoch 13/50\n",
      "27/27 [==============================] - 3s 124ms/step\n",
      "\n",
      " Epoch 14/50\n",
      "27/27 [==============================] - 3s 126ms/step\n",
      "\n",
      " Epoch 15/50\n",
      "27/27 [==============================] - 3s 125ms/step\n",
      "\n",
      " Epoch 16/50\n",
      "27/27 [==============================] - 3s 126ms/step\n",
      "\n",
      " Epoch 17/50\n",
      "27/27 [==============================] - 3s 125ms/step\n",
      "\n",
      " Epoch 18/50\n",
      "27/27 [==============================] - 3s 125ms/step\n",
      "\n",
      " Epoch 19/50\n",
      "27/27 [==============================] - 3s 124ms/step\n",
      "\n",
      " Epoch 20/50\n",
      "27/27 [==============================] - 3s 125ms/step\n",
      "\n",
      " Epoch 21/50\n",
      "27/27 [==============================] - 3s 128ms/step\n",
      "\n",
      " Epoch 22/50\n",
      "27/27 [==============================] - 3s 126ms/step\n",
      "\n",
      " Epoch 23/50\n",
      "27/27 [==============================] - 3s 126ms/step\n",
      "\n",
      " Epoch 24/50\n",
      "27/27 [==============================] - 3s 126ms/step\n",
      "\n",
      " Epoch 25/50\n",
      "27/27 [==============================] - 3s 125ms/step\n",
      "\n",
      " Epoch 26/50\n",
      "27/27 [==============================] - 3s 124ms/step\n",
      "\n",
      " Epoch 27/50\n",
      "27/27 [==============================] - 3s 125ms/step\n",
      "\n",
      " Epoch 28/50\n",
      "27/27 [==============================] - 3s 125ms/step\n",
      "\n",
      " Epoch 29/50\n",
      "27/27 [==============================] - 3s 125ms/step\n",
      "\n",
      " Epoch 30/50\n",
      "27/27 [==============================] - 3s 126ms/step\n",
      "\n",
      " Epoch 31/50\n",
      "27/27 [==============================] - 3s 126ms/step\n",
      "\n",
      " Epoch 32/50\n",
      "27/27 [==============================] - 3s 125ms/step\n",
      "\n",
      " Epoch 33/50\n",
      "27/27 [==============================] - 3s 125ms/step\n",
      "\n",
      " Epoch 34/50\n",
      "27/27 [==============================] - 3s 128ms/step\n",
      "\n",
      " Epoch 35/50\n",
      "27/27 [==============================] - 3s 126ms/step\n",
      "\n",
      " Epoch 36/50\n",
      "27/27 [==============================] - 3s 126ms/step\n",
      "\n",
      " Epoch 37/50\n",
      "18/27 [===================>..........] - ETA: 1s"
     ]
    }
   ],
   "source": [
    "train(train_data, EPOCHS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Evaluate Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Save Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8. Real Time Test"
   ]
  }
 ],
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