Test_Contractive_Loss.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "72e07068",
   "metadata": {},
   "source": [
    "# UTILS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1223d1ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow.keras.backend as K\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def make_pairs(images, labels):\n",
    "    # initialize two empty lists to hold the (image, image) pairs and\n",
    "    # labels to indicate if a pair is positive or negative\n",
    "    pairImages = []\n",
    "    pairLabels = []\n",
    "    \n",
    "    # calculate the total number of classes present in the dataset\n",
    "    # and then build a list of indexes for each class label that\n",
    "    # provides the indexes for all examples with a given label\n",
    "    numClasses = len(np.unique(labels))\n",
    "    idx = [np.where(labels == i)[0] for i in range(0, numClasses)]\n",
    "    \n",
    "    # loop over all images\n",
    "    for idxA in range(len(images)):\n",
    "        # grab the current image and label belonging to the current\n",
    "        # iteration\n",
    "        currentImage = images[idxA]\n",
    "        label = labels[idxA]\n",
    "        \n",
    "        # randomly pick an image that belongs to the *same* class\n",
    "        # label\n",
    "        idxB = np.random.choice(idx[label])\n",
    "        posImage = images[idxB]\n",
    "        \n",
    "        # prepare a positive pair and update the images and labels\n",
    "        # lists, respectively\n",
    "        pairImages.append([currentImage, posImage])\n",
    "        pairLabels.append([1])\n",
    "        \n",
    "        # grab the indices for each of the class labels *not* equal to\n",
    "        # the current label and randomly pick an image corresponding\n",
    "        # to a label *not* equal to the current label\n",
    "        negIdx = np.where(labels != label)[0]\n",
    "        negImage = images[np.random.choice(negIdx)]\n",
    "        \n",
    "        # prepare a negative pair of images and update our lists\n",
    "        pairImages.append([currentImage, negImage])\n",
    "        pairLabels.append([0])\n",
    "        \n",
    "    # return a 2-tuple of our image pairs and labels\n",
    "    return (np.array(pairImages), np.array(pairLabels))\n",
    "\n",
    "def euclidean_distance(vectors):\n",
    "    # unpack the vectors into separate lists\n",
    "    (featsA, featsB) = vectors\n",
    "    \n",
    "    # compute the sum of squared distances between the vectors\n",
    "    sumSquared = K.sum(K.square(featsA - featsB), axis=1,\n",
    "        keepdims=True)\n",
    "    \n",
    "    # return the euclidean distance between the vectors\n",
    "    return K.sqrt(K.maximum(sumSquared, K.epsilon()))\n",
    "\n",
    "def plot_training(H, plotPath):\n",
    "    # construct a plot that plots and saves the training history\n",
    "    plt.style.use(\"ggplot\")\n",
    "    plt.figure()\n",
    "    plt.plot(H.history[\"loss\"], label=\"train_loss\")\n",
    "    plt.plot(H.history[\"val_loss\"], label=\"val_loss\")\n",
    "    # plt.plot(H.history[\"accuracy\"], label=\"train_acc\")\n",
    "    # plt.plot(H.history[\"val_accuracy\"], label=\"val_acc\")\n",
    "    plt.title(\"Training Loss\")\n",
    "    plt.xlabel(\"Epoch #\")\n",
    "    plt.ylabel(\"Loss\")\n",
    "    plt.legend(loc=\"lower left\")\n",
    "    plt.savefig(plotPath)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3ccee2b",
   "metadata": {},
   "source": [
    "# CONFIG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ce537cd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the necessary packages\n",
    "import os\n",
    "\n",
    "# specify the shape of the inputs for our network\n",
    "IMG_SHAPE = (28, 28, 1)\n",
    "\n",
    "# specify the batch size and number of epochs\n",
    "BATCH_SIZE = 128\n",
    "EPOCHS = 20\n",
    "\n",
    "# define the path to the base output directory\n",
    "BASE_OUTPUT = \"Contrastive_Loss_output\"\n",
    "# use the base output path to derive the path to the serialized\n",
    "# model along with training history plot\n",
    "MODEL_PATH = os.path.sep.join([BASE_OUTPUT, \"siamese_model\"])\n",
    "PLOT_PATH = os.path.sep.join([BASE_OUTPUT, \"plot.png\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b686c16",
   "metadata": {},
   "source": [
    "# TEST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "15cb2e4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the necessary packages\n",
    "from tensorflow.keras.models import load_model\n",
    "from imutils.paths import list_images\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2ae63651",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] loading test dataset...\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.datasets import mnist\n",
    "print(\"[INFO] loading test dataset...\")\n",
    "(trainX, trainY), (testX, testY) = mnist.load_data()\n",
    "testX = testX / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e7b3005c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20000, 2, 28, 28)\n",
      "(10, 2, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "(pairTest, labelTest) = make_pairs(testX, testY)\n",
    "print(pairTest.shape)\n",
    "x = pairTest[:10,:]\n",
    "# x = np.reshape(x, (x.shape, 2, 28, 28, 1))\n",
    "print(x.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "671fe0da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] loading siamese model...\n"
     ]
    },
    {
     "ename": "SystemError",
     "evalue": "unknown opcode",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mSystemError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_12592/606076525.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# load the model from disk\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"[INFO] loading siamese model...\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mload_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mMODEL_PATH\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mD:\\anaconda3\\envs\\do_an\\lib\\site-packages\\tensorflow\\python\\keras\\saving\\save.py\u001b[0m in \u001b[0;36mload_model\u001b[1;34m(filepath, custom_objects, compile, options)\u001b[0m\n\u001b[0;32m    185\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msix\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstring_types\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    186\u001b[0m       \u001b[0mloader_impl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparse_saved_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 187\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0msaved_model_load\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcompile\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptions\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    188\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    189\u001b[0m   raise IOError(\n",
      "\u001b[1;32mD:\\anaconda3\\envs\\do_an\\lib\\site-packages\\tensorflow\\python\\keras\\saving\\saved_model\\load.py\u001b[0m in \u001b[0;36mload\u001b[1;34m(path, compile, options)\u001b[0m\n\u001b[0;32m    118\u001b[0m   \u001b[1;31m# TODO(kathywu): Add code to load from objects that contain all endpoints\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    119\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 120\u001b[1;33m   model = tf_load.load_internal(\n\u001b[0m\u001b[0;32m    121\u001b[0m       path, options=options, loader_cls=KerasObjectLoader)\n\u001b[0;32m    122\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda3\\envs\\do_an\\lib\\site-packages\\tensorflow\\python\\saved_model\\load.py\u001b[0m in \u001b[0;36mload_internal\u001b[1;34m(export_dir, tags, options, loader_cls)\u001b[0m\n\u001b[0;32m    630\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minit_scope\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    631\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--> 632\u001b[1;33m         loader = loader_cls(object_graph_proto, saved_model_proto, export_dir,\n\u001b[0m\u001b[0;32m    633\u001b[0m                             ckpt_options)\n\u001b[0;32m    634\u001b[0m       \u001b[1;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mNotFoundError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda3\\envs\\do_an\\lib\\site-packages\\tensorflow\\python\\keras\\saving\\saved_model\\load.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    192\u001b[0m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_models_to_reconstruct\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    193\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 194\u001b[1;33m     \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mKerasObjectLoader\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\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    195\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    196\u001b[0m     \u001b[1;31m# Now that the node object has been fully loaded, and the checkpoint has\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda3\\envs\\do_an\\lib\\site-packages\\tensorflow\\python\\saved_model\\load.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, object_graph_proto, saved_model_proto, export_dir, ckpt_options)\u001b[0m\n\u001b[0;32m    128\u001b[0m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_concrete_functions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_WrapperFunction\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconcrete_function\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    129\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 130\u001b[1;33m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_load_all\u001b[0m\u001b[1;33m(\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    131\u001b[0m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_restore_checkpoint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    132\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda3\\envs\\do_an\\lib\\site-packages\\tensorflow\\python\\keras\\saving\\saved_model\\load.py\u001b[0m in \u001b[0;36m_load_all\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    219\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    220\u001b[0m     \u001b[1;31m# Finish setting up layers and models. See function docstring for more info.\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[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_finalize_objects\u001b[0m\u001b[1;33m(\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;33m\u001b[0m\u001b[0m\n\u001b[0;32m    223\u001b[0m   \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda3\\envs\\do_an\\lib\\site-packages\\tensorflow\\python\\keras\\saving\\saved_model\\load.py\u001b[0m in \u001b[0;36m_finalize_objects\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    528\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    529\u001b[0m     \u001b[1;31m# Initialize graph networks, now that layer dependencies have been resolved.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 530\u001b[1;33m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reconstruct_all_models\u001b[0m\u001b[1;33m(\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    531\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    532\u001b[0m   \u001b[1;32mdef\u001b[0m \u001b[0m_unblock_model_reconstruction\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlayer_id\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlayer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda3\\envs\\do_an\\lib\\site-packages\\tensorflow\\python\\keras\\saving\\saved_model\\load.py\u001b[0m in \u001b[0;36m_reconstruct_all_models\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    546\u001b[0m       \u001b[0mall_initialized_models\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel_id\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    547\u001b[0m       \u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlayers\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel_layer_dependencies\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmodel_id\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 548\u001b[1;33m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reconstruct_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel_id\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlayers\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    549\u001b[0m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_add_object_graph_edges\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_proto\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnodes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmodel_id\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodel_id\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    550\u001b[0m       \u001b[0m_finalize_config_layers\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda3\\envs\\do_an\\lib\\site-packages\\tensorflow\\python\\keras\\saving\\saved_model\\load.py\u001b[0m in \u001b[0;36m_reconstruct_model\u001b[1;34m(self, model_id, model, layers)\u001b[0m\n\u001b[0;32m    586\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    587\u001b[0m       (inputs, outputs,\n\u001b[1;32m--> 588\u001b[1;33m        \u001b[0mcreated_layers\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunctional_lib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreconstruct_from_config\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    589\u001b[0m            config, created_layers={layer.name: layer for layer in layers})\n\u001b[0;32m    590\u001b[0m       \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'name'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda3\\envs\\do_an\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\functional.py\u001b[0m in \u001b[0;36mreconstruct_from_config\u001b[1;34m(config, custom_objects, created_layers)\u001b[0m\n\u001b[0;32m   1212\u001b[0m       \u001b[1;32mif\u001b[0m \u001b[0mlayer\u001b[0m \u001b[1;32min\u001b[0m \u001b[0munprocessed_nodes\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1213\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mnode_data\u001b[0m \u001b[1;32min\u001b[0m \u001b[0munprocessed_nodes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlayer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1214\u001b[1;33m           \u001b[0mprocess_node\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlayer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnode_data\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   1215\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1216\u001b[0m   \u001b[0minput_tensors\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda3\\envs\\do_an\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\functional.py\u001b[0m in \u001b[0;36mprocess_node\u001b[1;34m(layer, node_data)\u001b[0m\n\u001b[0;32m   1160\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0minput_tensors\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1161\u001b[0m       \u001b[0minput_tensors\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbase_layer_utils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnest_if_single_tensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_tensors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1162\u001b[1;33m       \u001b[0moutput_tensors\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlayer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_tensors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\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   1163\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1164\u001b[0m       \u001b[1;31m# Update node index map.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda3\\envs\\do_an\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\base_layer.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    923\u001b[0m     \u001b[1;31m# >> model = tf.keras.Model(inputs, outputs)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    924\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0m_in_functional_construction_mode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 925\u001b[1;33m       return self._functional_construction_call(inputs, args, kwargs,\n\u001b[0m\u001b[0;32m    926\u001b[0m                                                 input_list)\n\u001b[0;32m    927\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda3\\envs\\do_an\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\base_layer.py\u001b[0m in \u001b[0;36m_functional_construction_call\u001b[1;34m(self, inputs, args, kwargs, input_list)\u001b[0m\n\u001b[0;32m   1115\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[0;32m   1116\u001b[0m             \u001b[1;32mwith\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0menable_auto_cast_variables\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_compute_dtype_object\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1117\u001b[1;33m               \u001b[0moutputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcall_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcast_inputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\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   1118\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1119\u001b[0m           \u001b[1;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOperatorNotAllowedInGraphError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda3\\envs\\do_an\\lib\\site-packages\\tensorflow\\python\\keras\\layers\\core.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, inputs, mask, training)\u001b[0m\n\u001b[0;32m    901\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mbackprop\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mGradientTape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwatch_accessed_variables\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mtape\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    902\u001b[0m         \u001b[0mvariable_scope\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvariable_creator_scope\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_variable_creator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 903\u001b[1;33m       \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\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    904\u001b[0m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check_variables\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcreated_variables\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtape\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwatched_variables\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    905\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda3\\envs\\do_an\\lib\\site-packages\\tensorflow\\python\\keras\\layers\\core.py\u001b[0m in \u001b[0;36meuclidean_distance\u001b[1;34m(vectors)\u001b[0m\n",
      "\u001b[1;31mSystemError\u001b[0m: unknown opcode"
     ]
    }
   ],
   "source": [
    "# load the model from disk\n",
    "print(\"[INFO] loading siamese model...\")\n",
    "model = load_model(MODEL_PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e392a05",
   "metadata": {},
   "outputs": [],
   "source": [
    "rd = np.arange(20000)\n",
    "for i in range (0,2):\n",
    "  a = np.random.choice(rd)\n",
    "  origA = pairTest[a,0,:]\n",
    "  origB = pairTest[a,1,:]\n",
    "  x1 = np.reshape(pairTest[a,0,:],(1,28,28))\n",
    "  x2 = np.reshape(pairTest[a,1,:],(1,28,28))\n",
    "  preds = model.predict([x1, x2])\n",
    "\n",
    "  proba = preds[0][0] \n",
    "  # initialize the figure\n",
    "  fig = plt.figure(\"Pair #{}\".format(i + 1), figsize=(4, 2))\n",
    "  plt.suptitle(\"Distance: {:.2f}\".format(proba))\n",
    "  # show first image\n",
    "  ax = fig.add_subplot(1, 2, 1)\n",
    "  plt.imshow(origA, cmap=plt.cm.gray)\n",
    "  plt.axis(\"off\")\n",
    "  # show the second image\n",
    "  ax = fig.add_subplot(1, 2, 2)\n",
    "  plt.imshow(origB, cmap=plt.cm.gray)\n",
    "  plt.axis(\"off\")\n",
    "  # show the plot\n",
    "  plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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