model.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ### Import Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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\n",
    "# %pip install opencv-python matplotlib\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ### Set GPU Growth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(-58.379044, shape=(), dtype=float32)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verify install:\n",
    "import tensorflow as tf; \n",
    "print(tf.reduce_sum(tf.random.normal([1000, 1000])))\n",
    "gpus = tf.config.experimental.list_physical_devices('GPU')\n",
    "for gpu in gpus:\n",
    "    tf.config.experimental.set_memory_growth(gpu, True)\n",
    "\n",
    "gpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "\n",
    "tf.config.list_physical_devices(\"GPU\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ### Create Folder Structures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup paths - joins repos \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": 10,
   "metadata": {},
   "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)",
      "Cell \u001b[1;32mIn[10], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Make the directories\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m \u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmakedirs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mPOS_PATH\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      3\u001b[0m os\u001b[38;5;241m.\u001b[39mmakedirs(NEG_PATH)\n\u001b[0;32m      4\u001b[0m os\u001b[38;5;241m.\u001b[39mmakedirs(ANC_PATH)\n",
      "File \u001b[1;32md:\\ANACONDA\\envs\\tf\\lib\\os.py:223\u001b[0m, in \u001b[0;36mmakedirs\u001b[1;34m(name, mode, exist_ok)\u001b[0m\n\u001b[0;32m    221\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m    222\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 223\u001b[0m     \u001b[43mmkdir\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    224\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m:\n\u001b[0;32m    225\u001b[0m     \u001b[38;5;66;03m# Cannot rely on checking for EEXIST, since the operating system\u001b[39;00m\n\u001b[0;32m    226\u001b[0m     \u001b[38;5;66;03m# could give priority to other errors like EACCES or EROFS\u001b[39;00m\n\u001b[0;32m    227\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m exist_ok \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m path\u001b[38;5;241m.\u001b[39misdir(name):\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)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Collect Positives and Anchors"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ### untar labelled faces in the wild ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!tar -xf lfw.tgz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#move lfw images to the negative repo\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)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ### collecting positive & anchor classes - via access of webcam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing uuid library to generate unique image names \n",
    "import uuid # universally unique identifier library "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'data\\\\anchor\\\\4e3abec3-f03a-11ee-8ec4-a8934a2ae548.jpg'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.path.join(ANC_PATH, '{}.jpg'.format(uuid.uuid1()))\n",
    "# this provides a unique id to the image captured & getting placed over the specified repos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# accessing of the web-cam\n",
    "cap = cv2.VideoCapture(0)\n",
    "# loop thorough every frame of cam\n",
    "while cap.isOpened():\n",
    "    ret, frame = cap.read() # read the capture at the time - unpacking of the result\n",
    "    \n",
    "    #only capture 250x250px\n",
    "    frame = frame[120:120+250,200:200+250 ,:]\n",
    "\n",
    "    # collect anchors \n",
    "    if cv2.waitKey(1) & 0XFF == ord('a'):\n",
    "        # creatign a unique file name\n",
    "        imgname = os.path.join(ANC_PATH, '{}.jpg'.format(uuid.uuid1()))\n",
    "        # write out anchor image with the name\n",
    "        cv2.imwrite(imgname, frame)\n",
    "\n",
    "    # collect positives \n",
    "    if cv2.waitKey(1) & 0XFF == ord('p'):\n",
    "        # creatign a unique file name\n",
    "        imgname = os.path.join(POS_PATH, '{}.jpg'.format(uuid.uuid1()))\n",
    "        # write out anchor image with the name\n",
    "        cv2.imwrite(imgname, frame)\n",
    "\n",
    "    cv2.imshow('Image Collection', frame) # rendering the image back at the screen \n",
    "\n",
    "\n",
    "    if cv2.waitKey(1) & 0XFF == ord('q'): # breaking gracefully wait for 1ms. AND unpack what key is pressed ( to esc press 'q') \n",
    "        break\n",
    "# release our web-camq\n",
    "cap.release()\n",
    "# close the webcam window\n",
    "cv2.destroyAllWindows()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x23b24582bb0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# this is the result from the abv code\n",
    "plt.imshow(frame)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x23b305fb940>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the input frame size (not 250 x 250) which we need \n",
    "frame.shape # -> (480, 640, 3)\n",
    "\n",
    "#applying slicing over the image to make it 250x250 (the training ds size)\n",
    "frame[:250, :250 ,:].shape # -> (250, 250, 3)\n",
    "\n",
    "# plt.imshow(frame[:250, :250 ,:]) # we will get the sliced top-right corner \n",
    "plt.imshow(frame[120:120+250,200:200+250 ,:]) # this will center up the slice roughly\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BOOST UP THE MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import uuid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#running 9 copies of anchor image to increase threshold - NOT USED YET \n",
    "def data_aug(img):\n",
    "    data = []\n",
    "    for i in range(9):\n",
    "        img = tf.image.stateless_random_brightness(img, max_delta=0.02, seed=(1,2))\n",
    "        img = tf.image.stateless_random_contrast(img, lower=0.6,upper=1, seed=(1,3))\n",
    "        img = tf.image.stateless_random_flip_left_right(img, seed=(np.random.randint(100),np.random.randint(100)))\n",
    "        img = tf.image.stateless_random_jpeg_quality(img, min_jpeg_quality=90, max_jpeg_quality=100, seed=(np.random.randint(100),np.random.randint(100)))\n",
    "        img = tf.image.stateless_random_saturation(img, lower=0.9,upper=1, seed=(np.random.randint(100),np.random.randint(100)))\n",
    "        \n",
    "\n",
    "        data.append(img)\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# do the pumping of the data set if required - else the model is fit rn (works 80% of the cases)\n",
    "# did over one image sample \n",
    "img_path = os.path.join(ANC_PATH, '8a1cec0e-130f-11ee-bbe6-84a938bd3327.jpg')\n",
    "img = cv2.imread(img_path)\n",
    "augmented_image = data_aug(img)\n",
    "\n",
    "for image in augmented_image:\n",
    "    # Generate a random UUID for the image name\n",
    "    image_name = '{}.jpg'.format(uuid.uuid1())\n",
    "    # Save the image\n",
    "    cv2.imwrite(os.path.join(ANC_PATH, image_name), image.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[22], line 4\u001b[0m\n\u001b[0;32m      2\u001b[0m img_path \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(ANC_PATH, file_name)\n\u001b[0;32m      3\u001b[0m img \u001b[38;5;241m=\u001b[39m cv2\u001b[38;5;241m.\u001b[39mimread(img_path)\n\u001b[1;32m----> 4\u001b[0m augmented_image \u001b[38;5;241m=\u001b[39m \u001b[43mdata_aug\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m      6\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m image \u001b[38;5;129;01min\u001b[39;00m augmented_image:\n\u001b[0;32m      7\u001b[0m     \u001b[38;5;66;03m# Generate a random UUID for the image name\u001b[39;00m\n\u001b[0;32m      8\u001b[0m     image_name \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m.jpg\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mformat(uuid\u001b[38;5;241m.\u001b[39muuid1())\n",
      "Cell \u001b[1;32mIn[20], line 9\u001b[0m, in \u001b[0;36mdata_aug\u001b[1;34m(img)\u001b[0m\n\u001b[0;32m      7\u001b[0m     img \u001b[38;5;241m=\u001b[39m tf\u001b[38;5;241m.\u001b[39mimage\u001b[38;5;241m.\u001b[39mstateless_random_flip_left_right(img, seed\u001b[38;5;241m=\u001b[39m(np\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mrandint(\u001b[38;5;241m100\u001b[39m),np\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mrandint(\u001b[38;5;241m100\u001b[39m)))\n\u001b[0;32m      8\u001b[0m     img \u001b[38;5;241m=\u001b[39m tf\u001b[38;5;241m.\u001b[39mimage\u001b[38;5;241m.\u001b[39mstateless_random_jpeg_quality(img, min_jpeg_quality\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m90\u001b[39m, max_jpeg_quality\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m100\u001b[39m, seed\u001b[38;5;241m=\u001b[39m(np\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mrandint(\u001b[38;5;241m100\u001b[39m),np\u001b[38;5;241m.\u001b[39mrandom\u001b[38;5;241m.\u001b[39mrandint(\u001b[38;5;241m100\u001b[39m)))\n\u001b[1;32m----> 9\u001b[0m     img \u001b[38;5;241m=\u001b[39m \u001b[43mtf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstateless_random_saturation\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlower\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.9\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mupper\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mseed\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrandom\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrandint\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrandom\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrandint\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m100\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     12\u001b[0m     data\u001b[38;5;241m.\u001b[39mappend(img)\n\u001b[0;32m     13\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m data\n",
      "File \u001b[1;32md:\\ANACONDA\\envs\\tf\\lib\\site-packages\\tensorflow\\python\\util\\traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 150\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    152\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[1;32md:\\ANACONDA\\envs\\tf\\lib\\site-packages\\tensorflow\\python\\util\\dispatch.py:1176\u001b[0m, in \u001b[0;36madd_dispatch_support.<locals>.decorator.<locals>.op_dispatch_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   1174\u001b[0m \u001b[38;5;66;03m# Fallback dispatch system (dispatch v1):\u001b[39;00m\n\u001b[0;32m   1175\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1176\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdispatch_target\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1177\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mTypeError\u001b[39;00m, \u001b[38;5;167;01mValueError\u001b[39;00m):\n\u001b[0;32m   1178\u001b[0m   \u001b[38;5;66;03m# Note: convert_to_eager_tensor currently raises a ValueError, not a\u001b[39;00m\n\u001b[0;32m   1179\u001b[0m   \u001b[38;5;66;03m# TypeError, when given unexpected types.  So we need to catch both.\u001b[39;00m\n\u001b[0;32m   1180\u001b[0m   result \u001b[38;5;241m=\u001b[39m dispatch(op_dispatch_handler, args, kwargs)\n",
      "File \u001b[1;32md:\\ANACONDA\\envs\\tf\\lib\\site-packages\\tensorflow\\python\\ops\\image_ops_impl.py:3071\u001b[0m, in \u001b[0;36mstateless_random_saturation\u001b[1;34m(image, lower, upper, seed)\u001b[0m\n\u001b[0;32m   3068\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m lower \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m   3069\u001b[0m   \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlower must be non-negative.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m-> 3071\u001b[0m saturation_factor \u001b[38;5;241m=\u001b[39m \u001b[43mstateless_random_ops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstateless_random_uniform\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   3072\u001b[0m \u001b[43m    \u001b[49m\u001b[43mshape\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mminval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlower\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmaxval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mupper\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mseed\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mseed\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   3073\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m adjust_saturation(image, saturation_factor)\n",
      "File \u001b[1;32md:\\ANACONDA\\envs\\tf\\lib\\site-packages\\tensorflow\\python\\util\\traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 150\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    152\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[1;32md:\\ANACONDA\\envs\\tf\\lib\\site-packages\\tensorflow\\python\\util\\dispatch.py:1176\u001b[0m, in \u001b[0;36madd_dispatch_support.<locals>.decorator.<locals>.op_dispatch_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   1174\u001b[0m \u001b[38;5;66;03m# Fallback dispatch system (dispatch v1):\u001b[39;00m\n\u001b[0;32m   1175\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1176\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdispatch_target\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1177\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mTypeError\u001b[39;00m, \u001b[38;5;167;01mValueError\u001b[39;00m):\n\u001b[0;32m   1178\u001b[0m   \u001b[38;5;66;03m# Note: convert_to_eager_tensor currently raises a ValueError, not a\u001b[39;00m\n\u001b[0;32m   1179\u001b[0m   \u001b[38;5;66;03m# TypeError, when given unexpected types.  So we need to catch both.\u001b[39;00m\n\u001b[0;32m   1180\u001b[0m   result \u001b[38;5;241m=\u001b[39m dispatch(op_dispatch_handler, args, kwargs)\n",
      "File \u001b[1;32md:\\ANACONDA\\envs\\tf\\lib\\site-packages\\tensorflow\\python\\ops\\stateless_random_ops.py:486\u001b[0m, in \u001b[0;36mstateless_random_uniform\u001b[1;34m(shape, seed, minval, maxval, dtype, name, alg)\u001b[0m\n\u001b[0;32m    484\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    485\u001b[0m     key, counter, alg \u001b[38;5;241m=\u001b[39m _get_key_counter_alg(seed, alg)\n\u001b[1;32m--> 486\u001b[0m     rnd \u001b[38;5;241m=\u001b[39m \u001b[43mgen_stateless_random_ops_v2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstateless_random_uniform_v2\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    487\u001b[0m \u001b[43m        \u001b[49m\u001b[43mshape\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcounter\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcounter\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43malg\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43malg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    488\u001b[0m     result \u001b[38;5;241m=\u001b[39m math_ops\u001b[38;5;241m.\u001b[39madd(rnd \u001b[38;5;241m*\u001b[39m (maxval \u001b[38;5;241m-\u001b[39m minval), minval, name\u001b[38;5;241m=\u001b[39mname)\n\u001b[0;32m    489\u001b[0m tensor_util\u001b[38;5;241m.\u001b[39mmaybe_set_static_shape(result, shape)\n",
      "File \u001b[1;32md:\\ANACONDA\\envs\\tf\\lib\\site-packages\\tensorflow\\python\\ops\\gen_stateless_random_ops_v2.py:474\u001b[0m, in \u001b[0;36mstateless_random_uniform_v2\u001b[1;34m(shape, key, counter, alg, dtype, name)\u001b[0m\n\u001b[0;32m    472\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m tld\u001b[38;5;241m.\u001b[39mis_eager:\n\u001b[0;32m    473\u001b[0m   \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 474\u001b[0m     _result \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_FastPathExecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    475\u001b[0m \u001b[43m      \u001b[49m\u001b[43m_ctx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mStatelessRandomUniformV2\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mshape\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcounter\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43malg\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    476\u001b[0m \u001b[43m      \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdtype\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    477\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _result\n\u001b[0;32m    478\u001b[0m   \u001b[38;5;28;01mexcept\u001b[39;00m _core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for file_name in os.listdir(os.path.join(ANC_PATH)):\n",
    "    img_path = os.path.join(ANC_PATH, file_name)\n",
    "    img = cv2.imread(img_path)\n",
    "    augmented_image = data_aug(img)\n",
    "\n",
    "    for image in augmented_image:\n",
    "        # Generate a random UUID for the image name\n",
    "        image_name = '{}.jpg'.format(uuid.uuid1())\n",
    "        # Save the image\n",
    "        cv2.imwrite(os.path.join(ANC_PATH, image_name), image.numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for file_name in os.listdir(os.path.join(POS_PATH)):\n",
    "    img_path = os.path.join(POS_PATH, file_name)\n",
    "    img = cv2.imread(img_path)\n",
    "    augmented_image = data_aug(img)\n",
    "\n",
    "    for image in augmented_image:\n",
    "        # Generate a random UUID for the image name\n",
    "        image_name = '{}.jpg'.format(uuid.uuid1())\n",
    "        # Save the image\n",
    "        cv2.imwrite(os.path.join(POS_PATH, image_name), image.numpy())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PreProcessing - Preparing data for deep learning"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ### Get image directories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "anchor = tf.data.Dataset.list_files(ANC_PATH+'\\*.jpg').take(3000) # extract everything frome the anchor repo, which has a .jpg extension\n",
    "negative = tf.data.Dataset.list_files(NEG_PATH+'\\*.jpg').take(3000)\n",
    "positive = tf.data.Dataset.list_files(POS_PATH+'\\*.jpg').take(3000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "dir_test = anchor.as_numpy_iterator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'data\\\\anchor\\\\15cc91c2-18a3-11ee-a5d4-fd56a1c449f3.jpg'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir_test.next()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ### Preprocessing - Scale & Resize "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loads our image, resize it for us \n",
    "def preprocess (file_path): \n",
    "    #load up the byte map of the image from the file_path - readin the image\n",
    "    byte_img = tf.io.read_file(file_path)\n",
    "    # decodes the image from the byte map \n",
    "    img = tf.io.decode_jpeg(byte_img)\n",
    "    #resizing of the image (preprocessing )\n",
    "    img = tf.image.resize(img, (100,100))\n",
    "    # preforming the scalling over the image to be between (0,255) ( preprocessing )\n",
    "    img = img / 255.0\n",
    "    return img\n",
    "# this function will return a numpy equvalent mappingof the image "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "img = preprocess('data\\\\anchor\\\\86803bca-130f-11ee-955f-84a938bd3327.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x23b9e5e6850>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "- ### creating a labelled dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# (anchor , positive) => 1,1,1,1,1,1\n",
    "# (anchor , negative) => 0,0,0,0,0,0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# anchor_image ( the test image ) , postive_image , 1\n",
    "positives = tf.data.Dataset.zip((anchor,positive, tf.data.Dataset.from_tensor_slices(tf.ones(len(anchor)))))\n",
    "# anchor_image ( the test image ) , negative_image , 0\n",
    "\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": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ConcatenateDataset element_spec=(TensorSpec(shape=(), dtype=tf.string, name=None), TensorSpec(shape=(), dtype=tf.string, name=None), TensorSpec(shape=(), dtype=tf.float32, name=None))>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_labels = tf.data.Dataset.from_tensor_slices(tf.zeros(len(anchor)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "samples = data.as_numpy_iterator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(b'data\\\\anchor\\\\1b5e77ca-18a3-11ee-9a14-fd56a1c449f3.jpg',\n",
       " b'data\\\\positive\\\\72cdee1e-18a3-11ee-8d29-fd56a1c449f3.jpg',\n",
       " 1.0)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eg = samples.next()\n",
    "eg"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ### Building Train & Train Partition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = preprocess_twin(*eg) # the star destructures the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# building up dataloader pipeline -  tensorflow pipeline\n",
    "# running twin data preprocessing over the entire ds\n",
    "data = data.map(preprocess_twin)\n",
    "# caching our images\n",
    "data = data.cache()\n",
    "#shuffling our images \n",
    "data = data.shuffle(buffer_size=10000) #changed from 1024"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "samples = data.as_numpy_iterator()\n",
    "samp = samples.next()\n",
    "plt.imshow(samp[1]) # negative / positive image \n",
    "samp[2] # labelled 0/1 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x23b9e64edc0>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MAXDbGhoQ+XIFeewkknsikdwoOU91KbdnrIMOQkt5OW61Y0R691ly9iwfzaQstYaWlMQvJKX6wvcn1DkPrDb9fhXLmF0bEz1i2Eap1LuCpccc5WLXNMP9oPZSZrALYokw+p8lxC7KGfcHo06aZddmli1TQ9E9Ua9r0E+M3XaaY8Xr3q8BpV9PLEHvfs93+P+/hD650mkiSbwEjiv33daW15Koe2pPFbivEhsuastyr+l9xqkDfYg3oEAgEAiMgngABQKBQGAUxAMoEAgEAqNg12pADufn6nB2UyFDacFeFN5SgxqEbp46yFOvUIsW8OwT3617N7q4gxyWIKuGnKo/7mrV7ZuWOazv9+2jHU3/xCZap1jGYdE/B4RKUxLJLbpIUdA6yLdJr0dqhYT45cTuSK47rWxgU9JorDOtRRK9SPuGupRhGb0hc5mGrpWZWZP163fTSb/Wx/1uzPb4JtCaR5arFfof7Ve7mjQMg7Y3eq5+21Rf6beYaWHTU+Kz+cbe9e9zaD4rxDF4C5rhiAvqPNqvO8VL672VXGfcS17/4nwoajMec4mAyTP/XdDiuK1Y8SRDL+f9LG3gPCBYg7HjXEyKu452Xog3oEAgEAiMgngABQKBQGAU7FoKTt2w86zfEoTQ1/kSqYFJSWFyUNk0hwNxnfAt5zzm2T9gUR7z8zlowE1PJSWlvuqGjSTDCVI2F4uu3JKUG5cnkqrJxNB8Nvx/ibp/J9XF6Cctwc1AB7Wk9oSeqEBD0S5oknn6xdNWdLj2bXQ2JThOmbj9KqVLR26WwvZTlXQrZsl2Ja7JFUrzJxM/ZmzARmkCmpmO3Zo+uiqGy+039nZ0F8cPy3W3tzuLqSlSTKfJlIb++3BS+vbWpMnd7/7+psO1czIn9V350uQW/eTGE75zaK+jpcgJVczUZf0us4GkXkvPpx34zqHjdSOfXSGhdsV7VKi/CrZbpGUzyg0y/lwqwIBDuCLegAKBQCAwCuIBFAgEAoFREA+gQCAQCIyCXasBNW221nuKuntOzqB7MGJhJZxrbShVTCxyqEloeS70o5xaQdd1Nco/raZVurQP/Cu1jrqi/bk0gaWW0KmyiegVtHqfQzORctcC5Z/UIFxWwNk/dL8lfLLfsqq7a9Dy/x3qLTIci4z90l8me3Z7bQdiKqg1CWe/JOcNrcZZ8STnijbQb0f48hVK6Kco1deuoe1NU/fbxhSUU9BPGdpYSZtmG75kmxqEnm+LctwVSrhzufANSp63ESvQJFZJ0r6acSQD150lwkmZvNgbQXdqKmoZSCqWc29a6sEoX5f7ZbWC7U3Wn1pcow8z6FCLhR8zjYzrRcXj9H+X5cZxyykaXfsT7XuFsVewrFxtfOTcdph20LUtEAgEAoEREA+gQCAQCIyCeAAFAoFAYBTsWg0oy0SjkcdkTfOXZHpOvzc656zQft6pCMsVtvXQduRJrDPm9giPXVW+/VxOUxR0HspwLPLQOtr4KArw36z9py5VllPZFvON0KeFcMScQ5BGpEufMlqa17LfET+ZZcJtde7CBJz2BFY8Os8maQO5dfSji4zAuc8xV6YU/Y59OikRw6E2MYhjyKiDMF5CtE32P+cMuc9hW2pyGuPO8UO9LpnJNzC/pYCoqONiiXuUYz4Xvbiq/La0KGpgMVOK/RHjrmnrUzfSpxwDub/OldxLNa4dtabplPOaunOoC87zwziQ7yDed7TL0vOpKs5B47b8DlL7nfacvw8h3oACgUAgMAriARQIBAKBUbBrKTiFJo62oD3oXqzJh0xenCR2G3gtl1fthB0CXOIfKIc2Z0mnlmn6Y7KclRSEWrjQzoX2KEp1JHQRqZrs3K/Oj6x0i+lruJSrgyKhlY2WdTI5tmUprFJWKMPOSMmxRZo+CspnBruajT2e2nBtSByJtZyVNI2/HvO5d4gupc1cN4O9jpbYMkGU132I6uC5k1oysa9pbbi0fThVl6XJHcWbpOTu8H9uMUAzV7y31IYIfci+UCqcNla8D7Ma1KVLF+6n9sy8+3oLSjdH/+tUiSS514DEtbpr0575XreO3wVbW501Ug26t277vxtob5Ql92E/Baf36E7u7+vtzmurQCAQCAQuMuIBFAgEAoFREA+gQCAQCIyC3asBZe1aBxgq6SM/q5QrUzVpEUKm2qWpskTVWLbZ7WvV0Koe9hvSDvLqbGPKY3fbL2FpkqSGqjZjHjnKQ5WjHeLZz36WEQVSBlzx3Gmv4z6IViGxVo7D0ulUn4CepGWnLKmdIc5AxkxSng5dZ0PSbGm1My39fifQj1zCLux0qgwWQHKd6XxUrYZ5eI9hXUfLpbmfBp9lHyvSxFo5BsZ4gxLoc+ysa69xSgPL/GWMow1V5a+l086wjhY/FcvKZeTynh38PsK6tuqPvJgjboGxKFxWnYdtotXTYEk0pL3MaXBYl3y2v+xfNbemYtTEuRFvQIFAIBAYBfEACgQCgcAoiAdQIBAIBEbB7tWArLFvkJWqx9CmhPND9Jma2TB3y5p3FZA454O29u3ARKFmYK4S44pTjr4/SjvPERWAJmgcdgHdhnz/SmIIklkbOLdsIJaX8wQ4j2aqFu04ECOtVRPiGkYf5BNcO5mrxEszhwak2sDBvXuwzu9X7Wk4H6rEnA9eW9feRNPCnK4BXSGdh9UhmatDKxv2WzLPST/KOIZ+zeEcn9ZP+jU76Hk+aprRAZgDJXPqeK+k7RftKe/XcM3MppgrpvNbVtRIk+gD0e+SuXmJMZS0169J5zhCy2zEhghzGHnPNtJ+amXUXvPehVT7Ziy9fuc4HTCseAKBQCCwmxEPoEAgEAiMgngABQKBQGAU7FoNqCxyKx/h33Ph7BcrzNsAZ6wcLC39E+420SC6ZXLP9Fqqdd4G9kINSOfnUCdINB9Q7e4cGN9dU+OSNjXkx/1+ZzrfJYk1h50+T1COQwf/MslJkIgFrJrQur5UHcfvuMb5TKGL1KJp7dnw8ytmmG+xf9++rnng9+eILt+QzzI+ndw6dR09A8Yk0MesES2N1zXVCkRHMK7zoOaTSzt24ul1fRo5Au1PNZSMc54QH00t1osH/jiI3VaNkV52SVSL9EaNmHnqj1nDeSvqa4ZV6GUXBcJ5NPwukL6hDyS9KpeMWJB90y+wYRR71q8BVYyLke82at2cBzQUraFjhPdvH+INKBAIBAKjIB5AgUAgEBgFu5aCqxuz/JG31UZeVbMG1MzA63ydJDOihBCvoivZ12rlaQO+WjuzG/JQrd+vvh0nBZFJaTjLZiWKguXQoBG03HtFBw1QEO58WDqKklS+3ueuXBoUD/pCLWhIkGxub7tljSjgqz6TGmkJMhNqs1rWWOe3XUl0AOmtxYqlsELFcOyxnDUH9SQ0FanVhC4atNfhpjItgXTpDnZHPq2UZcvYl6Sn7lTGrCO7RsxGymHB4kd2Xdc8jt/UWeTgPuP93UoZ9oR0PD5bD0Ts7kQ/avtb8MwVL7xGHyRtgjUV7q1avgtITWYJ/Sv33Yp2QL5JajFFCx0mHC8b2B0VQiWrWnCerzbxBhQIBAKBURAPoEAgEAiMggt6AB07dsye/exn2/79++2KK66wl770pXb//fe7bba3t+3o0aN2+eWX2759++ymm26ykydPXtRGBwKBQOCxjwvSgD7ykY/Y0aNH7dnPfrZVVWU/93M/Z//kn/wT+9znPmd7956NiH3Na15jv//7v2/vfe977eDBg3brrbfay172MvvjP/7jC2pY07TrqAVlIbMMVilJybPY6YB7XtWI4WXkgmxfG3jTAR642KHcWz9Zr1g6jW1Lah8d78vjsI2Omh6I9ibIHxP8rPLLjPpuwHkXrtQapciIrZjNumWWaJN33yel1GZmUzkH2s/M5v44zqaI5emJbU+3XJDYTmxu/Godq7RkaXDddSgOxy0Ml9imNjf9pclp1DT0F+oX53mcJAYliRhB9IFbP2whpaXX1B/bFvf3QOw8xwjjsQu53yn58NxVa0502QE9Ncd3WRp3zXJpjVhIBhs+K23g9xHyPlr5zuG5VjtEaWTluXVOTpvowwU9gD74wQ+65d/8zd+0K664wu677z77B//gH9hDDz1kv/Ebv2Hvfve77fu+7/vMzOyee+6xpz71qfaxj33Mnvvc5yb7XCwWtlh0gv+pU6cupEmBQCAQeIzim9KAHnroITMzu+yyy8zM7L777rPVamU33njjeptrr73WrrnmGrv33nvPuY9jx47ZwYMH1z9XX331N9OkQCAQCDxG8KgfQE3T2G233WbPf/7z7elPf7qZmZ04ccKm06ldcsklbttDhw7ZiRMnzrmfO+64wx566KH1zwMPPPBomxQIBAKBxxAe9Tygo0eP2mc/+1n76Ec/+k01YDabJVYpZmf1gck3asyHInD5B+FGc86p4WcZnyu/J/YnmDM0EyuYZEYEtA0XW13Q7h98OS1/ZLGmzX1iva/1/P1zLczMStGTEj6fPDZtS7S9jEyG5qBzGfbON9w62pRMRWuiLcnePXO3vDH3Ws1M9p1hWKfRFP3jiVYkanlSoE287vys2xaXI4m8EIuWJBI90Zr67XRSdylGmTvjGLeubagjyJwV6kWJ1YrOoaNd1rAt1NC6hlEIfq1fajiXR6yEcPc3sAtKLLKkL9im1AKrfzyxnwqxwGKfLha+3xgXr3ME+X3E+YMaRVHD0oe6VCGnA+nVIFMl0eWVzDEq5TuR93YfHtUb0K233mof+MAH7A//8A/tiU984vrvhw8ftuVyaQ8++KDb/uTJk3b48OFHc6hAIBAIPE5xQQ+gtm3t1ltvtfe973324Q9/2I4cOeLWP+tZz7LJZGLHjx9f/+3++++3L3/5y3bDDTdcnBYHAoFA4HGBC6Lgjh49au9+97vtd3/3d23//v1rXefgwYO2sbFhBw8etB//8R+322+/3S677DI7cOCA/fRP/7TdcMMN56yAG0JWV5Z9w5Zj6JWd5YhSmpxYgiTUC8tDJVEU1FjL13vdFk2olyhdVEsWUGwNHHGZtKqlmjVqJNM2dfuiK3WyrdAXdWLh65cnUzhPV91nWaY5xTu8WujUK9p4+G33SAn0fAoajf3C8mJ1As98m9oW+0oSImW/ybIeF6W8O7ir63EqlgGjDUqZMLQ0oVoLLZvtL7M+92eFJqFr9YrWNkrT9vfZ2aN2Y2+Ca0drpCSRU9pYVb5NtKvRYU0Knft14wvtb2G5hNkRrl9TuyMkE6szPu6lLOc41XPFvTNl4rHf19ays65imTP3pawtKecGaarqrs7LXGT+3s9wfmqHVK26/SbWWT24oAfQW9/6VjMz+0f/6B+5v99zzz32oz/6o2Zm9qu/+quW57nddNNNtlgs7EUvepH9+q//+oUcJhAIBALfBrigB9D55HzP53O7++677e67737UjQoEAoHA4x/hBRcIBAKBUbBr4xjOahRn+dN2wMK8GbDfoNUOy4lZMqyWNKhcTPl+4UKTElX/UVcsOi3A8yIqgLz2QhoyKX25OiMjtGsypnfC4kc1iKSUN0lb9H08l5LVIqMu4o+zZ8+ebh36lOmjaoOTISk2L6i3UOPqOOci97x1yWun9kYomU+Kq6VvaJ+ToU8pVarDR2obQ0t/0RxYNosBpZd2J1aCJcSq5bANNbQBPU6S9ppY5HT9zyblO9jr+GVqB/3bppZFHCPdufK7oKJOO6CdpZph/7apnQ6+Y0Qr4xSGdJl6mCbUQu+a+Dau3D3LiAhEs/hcU/Poj3wxM9O7Z+H07EhEDQQCgcAuRjyAAoFAIDAK4gEUCAQCgVGwizWgzL7BPyqvSluSyUBMAu1PaL/RDsQV8zjk8JUXzhj9jeM0wqOS103mG8HqQuMlyPdPJl6/0MhoWo9MoD3N5521jWoVZ9sIEn9Cm5Ju/QZslOZY1tgEXivOn5oIj03Njdz6FHOT/DwVWrRwHodcuwEbezOvdVAXzJNpKLD0d9cO870470TsatimpuFy1bstx0gSQyDjLZmT1lBXk7litHbixXMCH+aCQXOgJueQePEkmdzy6/C5LkUH4RjgfCPaLKUxFo8OjI/Reyedw0XLJcynEqseF4VtlnyT6/kxpiKZ0yVt5PydqqHehTaKhjqX3zmnrA/xBhQIBAKBURAPoEAgEAiMgl1LwbVt1pU6y2sfbWNINbm3cnAkLagZpiLqG3wBx1gwKM4Rt2aZ8gT0nZSA0soi56s/rHkyVwYMKsk3ybMXoG2UcjMzm290VNmllx50686c2XLL25t+WVNOSQPu2djjlrW0mvY5LC+eiFNwA7ffxLmc6Z3qfEwLX8AlZZJiQK/q2oaU7cpfd5bCNlI2S7qLrlBaYsuS4aQ816WPojw6H6C3zLsZt6QmOU2hp32PfBptFOqbjui006GTtrpu09CaibuOg+P93e8MnpSjY4ik5exCm+N8OBYrWZ/SarBCyrrrNZv5e4Xu13TdLoXiSlzneYIDJ5+4WMnloJVQWfDaYWyqQ7dLYY0y7EAgEAjsYsQDKBAIBAKjIB5AgUAgEBgFu1YDsjxbiwTKS+5QuXj2c4+AqYHkcrMkTVJsSnighLcWLhSEcsXYBMem016D9i7gvBdq3ZF4nOA4sqolR8/0UbHTQbnnbOpLqffMPVetega5Z9qumERiNBn7m2W0cr0SMcAvLpf+2k6kVJx0fsE0TOkLjpHprMS23fVCsKdl4MdZstp4Utyto53+RMZXMi7RpxO9dtAqk7RO32SXWskxXsHaydv2DGtAmVglTait1jh3lEBr+iinPzRIAXYSBcZeCe2vlvJvfk0kGgpiXZbSj8tEA+J3g1rx9EdNnIVYRmEKRpJMPKBLcZDXA3oYx5rhftfjUOecUlPMeG31XpIVSfzLuRFvQIFAIBAYBfEACgQCgcAoiAdQIBAIBEbBrtWAsiaz7BFuOHdWPJ43JT+u+gvtc0rY53MOjmoD3K/VA9rNQF29Ge3OoZmAYy0RaT2diU0GjkPOONeGtNgP5gFNROcpMZek3OM1IFr1TMv+uSac8+GQ2MRwtdjecN5MEi3NmO1uuUws/JPJJetfOQZ4XOXpc1wb6jrsFepwign2RcsWtx8s+7jo4Qhu6gi1cPycB5eXsFzS+TnUYqhzSp+uaMPSUKtBNEjZbwWT4TrrtWsSKyHeo/3RE+zVJN2+Zz9owiN/EBslrKpqP7dHu7E2jgHYZ02o74kevEPEhWqxZQlLn6p/zNCyq05Ed0R4iL6nc/Oo9/Yh3oACgUAgMAriARQIBAKBURAPoEAgEAiMgl2rAeV5vtY4VBtIbPrBhSo3St84a8mP+9Ve19mBW1etKee2iN3WGN6h2v5zQXn6hOPm+chcEtTz79u73y2Xwvfv37fXrasWtM/HfBfh6Xnuab/J5zAfoR2I/6UVPefcEK4r2CT0m3Li1NE4N0O3bdAPK3jBZYzwcNol4+F9G2tS7T374XGSOPUkxsLvq5R7gJ9lX7j9GH3uaNvf9QXPNfF+wxhhPyrq5C/dvpiUnc6bEc883GcrdHg9cBsORVqc3aD7cFUx1pyDsTujOomp8JpPgRiUIfDaTafdZxcL6FCIPddrm/rpYQ5XzclwOp76x2Uf4g0oEAgEAqMgHkCBQCAQGAW7loKrLVuXEjp2JeXN3KIm9DFDoaaFPFIetdybjE9CbchxEioGH56KXcpiuenW7USh5I5u8bRB8mot6xM7FBxHqaUaUZ+kCVegFaqqOy7bO0HJp+5rirRUcmXKkNKGnxY/tHtXC5olaAJaCymVxlJe0mgrXc8xQav6gmW1YtGSWDthUUtYdxinSWWsgMmenLawqrqSel67hJPWVaRmGtJSErtB2yGM0yS1Vc8dJ0cKS+8HjlPGWKjFT5NYXnm6i52q91p6jxrQH7eS2EKV3Vjkdwyva5JYq/vF9ajwHaRTJ5KUUyyXMq2C46Ulzc8h4xacF4+dD+INKBAIBAKjIB5AgUAgEBgF8QAKBAKBwCjYtRqQQnnINqNeAZ1H9Axy9LRGH+KXp+DzW9rcyyL1ibqGZiJ8KPczhd1GxcgIOd+ioI4DzUH2vX+vj1CgNjN1n0U0M6x2GqzXcmpqDsvG8/3a54slIqyht2yIVkPbG2o19ENx1h/QAhbwMMolgoFl/RXaqP3WZrDwn8DCCPtSbTCxvQGHX0rZeQOivRnQI1Mdh6XgfnWu1kJJyTatkmSZGgmmGjh9IrmvqENBu9TYh4HpDmeXJVJ8h+8CjU2ooaMtUQKd05pHr88O3xt6Ty+2vW1VEh0v7c8tqSP323KMO5sxWFNhfO3d6KZWNNB4afGzqgdiUDh9AP2kEfb6ySjDDgQCgcCuRjyAAoFAIDAK4gEUCAQCgVGwazWgVVOvbSsKZ/cwwFObt3eh9Util09Lc90PuOfVErz1gNV7ncGOQ7UAEPqMJ86gv+i8lAk0B87FKMV+Y2PDz31Ra46z++p4YFqLbC89j11TZ5Pz5fwDWgBpm2ta1TSccyN6lwG8ztDSVrI+B8edof3lwFySEvrLSq5PS9uhZED1W+Qn8RIFdUOxNxqYC2ZmljmdB/O90CbOfzE352M4ikLHHufY8HqoPMZx2bSwfqGmovtKpAPa9nT3YRIVwDT4esgCCzEV1Hhle8654a50Xk2Nc6+qoe8gf+8kFj+AXh9ej4Ix29I3+/Z6q62Hz5x2y3l7bh3n7DKuJcaIalz63Usdsw/xBhQIBAKBURAPoEAgEAiMgl1LwWVZsU4ibF2RJygHvjQKF0AHXHpfkHLIJUW0htVLNkVSoLxaN3hzrmu+HsvrPEp5LUeJcBKn2r3eM2lyue1tfQ5I6XWZb7h1+/b5suxCymhpP1OUtDhBMmOrNh9IEEXJrac5QSEy3VYoCLYJle0J9boQuyCWQ5Nqmky646xWvv3Tme83b4Xk+3+C9hegHVyQJi1OEnsXGXscl0V/yXkNmnkCi5mWJfYyxhucO8uwlXEssF+WIheZ3A8Y4xXSeeuWber21cAmJgdV6Sx06h0sZmSMlygbZx8v4O49GaBPU3ZJrl2Lr1T0ce5oTb8pk0tp8eWGE+2O4HCt42KJ7w1KBm1PKbVZatOllkuES6NOYmPPjXgDCgQCgcAoiAdQIBAIBEZBPIACgUAgMAp2rwbUNmse0fGmiebDD+a961qWZSfWI5X8DisY2qPIcdJyUNqu9B8zKS/G+ammQisellYXRXc55/M5DgPNREqtydae2jzjlx96CJ/VOAamjdJ2peOx28yXstOGyFvXQDPBtWM5qCbhPrzptTEOmdm0O25ZoA1Lv/F8o+vHrPXtZ2nvfOb73JVWU4tByW2ea4lt4r1j+EPvqsQuHxeX9keKClrBSnQeamUr6C1aQpxMYYCWwfulln0tMd2h4T0qF5NjermkUCjtG4jvMDNbnSN79RugtkRNcbGt3xvnHyeRRKRQa0qmnIguhekbjHZQmyJaH3Hc6lJSjs4S9CRHpLvutPg5H8QbUCAQCARGQTyAAoFAIDAK4gEUCAQCgVGwezWgrF3rJcqfTwtYTiR85kCULiN7QaArBbsCn0xLk0biflvM3YF84a3SaRlPW37OPxIuN8ecoQl0nkLmEZSl14coU6m1+0MPP+zW/c3Xv+6WVytvzePay9iEAesRctFnWq/VqG3PfOr5ZMZJFBgHC9Evtrdx7RrP4W9udcfN0N+XHLjULeu/aC2u3RSywda2P79S5hsVRv0OfLl0VAltL1GAMv2dY5g6J8Z8v9RhTQ1tQ3TCChOxuBvV7BirzTZxWTUiakDJ/S3ns1z5bRP9SPUM2udAl2pgs1Tq+OK8soF4Bmoz1FT0enBc8loNxavzvku0GdGLJvwuaHHuy64dSRw8o9hhkqX3pWrSOSc59SDegAKBQCAwCuIBFAgEAoFRsGspOMus4x7UqgOv9yyv1PJQlhvyrZCuN4VYzjCNsEV9q9pmsNSyzEFQ6Ks1tmUZbYNX6VZe05uGxAecd+X32cy7YbPfTp8+tf79a1/7W7cuKSsHXMktKIcF6DpHF+H1neezWmytf1+CcuP50LZkvtFZ6GzswbnDHqVawNlcQLud5XK7a8PEU55M+iRloumSTO9My1mlvej/yYZ3MyaFpdjp/lD6iG7wDcr8TSyXiqnv0xalyfq/bOrmTcqKbV7JtsNfSdtyPVg2jlvLWTvxZqflz+bmllvWcmmWXbMsW2lculJzW713eK2YRgp3IPfZbOX7dI7rUwrFS8uoBWhydXFfgb5uM//ZsuwvK380iDegQCAQCIyCeAAFAoFAYBTEAygQCAQCo2DXakBtk1n7SKlzLVoHy1mTMsFcLdixbaJtsCy7PwEyhy15JvrSFJbxTExUa5IsLar1xwGnqm1O+FZoKBvzTgeh1Uvb9FvXF9jvNLGJZ0m39CN0j405SohFDyAfTrhzRZkpwfLcTbHfmc9gGY9SWFcVn4wJWiFJuT2485qXEreTTgmgDrLd9JcbU9/Ksm23XCqnj7pqXsumgj2N3B8LaGG0zFnK9VotoGXg+jAw1e0H5dHb2/582gGNd4X2q8ZbYdtlord058qpBCtolwbtRmMtqtq3n8ml2YDGy211mdc5jejwnbpYdOfAz3IKQC6l1ZM5NVG36I6bD93r54A/n+6zbTMwILSd57VVIBAIBAIXGfEACgQCgcAoiAdQIBAIBEbBrtWAamstf4SP17kMpG7LvL8unZxqMWW0NObRyM5pQ875Oho5S3uTjDHCMoeIkbfUgDinSHlgtkGtXszMilLmLtCCBdqA7ncO3aaErfpk4ternrGBCOukz3XOEOZWVYhUVo57E5EKy8TSH1HHMkZoyz/D+aidS1b6fpqQ35frwTlCq+Vw1LH2U53YqkCrqVUHwX5r3xd7JSKC9kBlokf65dWqayPjlpc4n8at9/vh9bABC6ytrX7Nh8uMnaduqGNmxUhuaH11q3MCfWtpw8V+0l1NSsyxmSDiXecEog1V029DxDGc2Osw7kPmty0Xw+eu0S0NNLiCsQn6WYw9zsuaIXJkMunapLpUyw7vQbwBBQKBQGAUxAMoEAgEAqMgHkCBQCAQGAW7VgPSOIZWzN/J3SIp2KbiiVRg2wqxyKmnlhyn4fwKaAPia8Z6ffpM6VM+0WbAldb0d3P78vutwVvr3KUpdJ3TiFxwtmxoU5bRs83328Z8jxzT73aKiGudl0JtrGl9GzfEz+3ggQNuHa33Tz38oFuuK9XKoHeh/W51Iu5hfMk4yNnfvJbQRdSLjDZrVcvYhG5bHqfCfLZKxwz3i3kzJWIrdMhzDh3n1WSiW1Xbfh4N4xnarDsO+4VI57pppDW8+HDda9HZtpL5RP44OoeIbcowT2UKnz8VjegZyc+2oqHMEKUxYR+rBsToDMbF4HroZZ9MoaMN6ZFJNAJiH6RvakbHl/5+Hpq7VMk8uYp+cz2IN6BAIBAIjIJ4AAUCgUBgFOxaCi631vJHXgddKTJes4vklbB79WtoB8EPG607uu1paWJ4NXVRAihn5Wf1NZVJmLQ0qSuUsAo9wXVMCd2Qfde0jDePmZRWM1LBQAFNS6aTdstDJbUEy05Jayo92dIKCaWj00svd8sr5WLRBFrBeHsUlL2X/XRFi/7XsnGzlFrSrdkv3FZLthnrMAWto2OeVjtJebF5rMRWJmlT2U8BFYxUgA+RXtuMMRWggBoj/SjbFuxDRhbIFIA9KAnGOF3J9ZmAikzHXn/8B+10kn7T1GL2P+lI6cfFwlOI/C5YYDqB0vE53h32IB1ZGbAKND+pV988jh9eK47j7nf9/gwrnkAgEAjsasQDKBAIBAKj4Jt6AL35zW+2LMvstttuW/9te3vbjh49apdffrnt27fPbrrpJjt58uQ3285AIBAIPM7wqDWgT37yk/af/tN/su/+7u92f3/Na15jv//7v2/vfe977eDBg3brrbfay172MvvjP/7jC9r/bFau9RLVPli6SCuSSrYtGHUACw3y2srDU8og57qqOi59An1iCft2jSg4cMDHK0/B+26e8bYrZd5x0acf8pxxDuuXfXtk37DmKNEXlSzPoCXl4MuTeADpHDK9LCtfSlktqrCtwb8/Ey3hJnUOHYTXfTbVMeLPfQ5+vJESYsa2U5txTvsldZvh/9/qAe2PVk8am5zaxLAct98in+XG1N10qgEj0blb3Rd1kBpl2E4HxbXJjZoJNCFpcgsN6LJLL/WfLfqnP3BMzKdPWP++MfWWUdQ6OGa0LJ4RKpyWoHJSEmef2H1pDMpwjEuJKQ163FXZHx1v5rW0AnMAltANdRizT0uWYfM7VHRDLadv2uHolfWxz2sr4PTp03bzzTfbO97xDrtUBshDDz1kv/Ebv2G/8iu/Yt/3fd9nz3rWs+yee+6xP/mTP7GPfexj59zXYrGwU6dOuZ9AIBAIPP7xqB5AR48etRe/+MV24403ur/fd999tlqt3N+vvfZau+aaa+zee+89576OHTtmBw8eXP9cffXVj6ZJgUAgEHiM4YIfQO95z3vsT//0T+3YsWPJuhMnTth0OrVLLrnE/f3QoUN24sSJc+7vjjvusIceemj988ADD1xokwKBQCDwGMQFaUAPPPCAvfrVr7YPfehDCa/+aDGbzVzN/RptK6So2KEgqZlTe7ROndXuifVFUqMvvCn42RXmkuTSkJoxA0n8tbbXb7t/z363vAd6klrZb5/2djpJ3K+0iesa6BUaV0zyPzMuQyuQOSAZbVUoJOT9XHqWRAUoNw27EBDkE2gqOhAmU3/ujGOudd4GIxQ4dUzaz0Rxan9JFIXsu0zm/WC+S6PaAK8VluX3xDolmcdBbUPaBD6f8RI6xnPceNUKWoHOAaFmQu2PGopoQiWiPwz6o/7LnETJT7zOs2+js4ziXKQpdM4l5sIN3d+8zm5+DmO18Z2jOjPHwGqHyHq18cmhtebtpHdbtrfEGFH9sWphJ0VhinMGZTxlosFlifZ4blzQG9B9991nX/nKV+x7v/d7rSxLK8vSPvKRj9hb3vIWK8vSDh06ZMvl0h588EH3uZMnT9rhw4cv5FCBQCAQeJzjgt6AXvjCF9qf//mfu7+94hWvsGuvvdZ+9md/1q6++mqbTCZ2/Phxu+mmm8zM7P7777cvf/nLdsMNN1y8VgcCgUDgMY8LegDt37/fnv70p7u/7d271y6//PL133/8x3/cbr/9drvsssvswIED9tM//dN2ww032HOf+9wLathKbCj0JZAUA+mjvNXyQ9AcLP+k6/BK98XXbv/ZvXu71/sG5Z/bC19KXcy6bqZlhtGRG6/Wpbwezzf8ZxMqQNpxesu3gf22kuOSFswzluf65ZXSRaAyVnDBdaWj2fBw00TOJAGVNAgtWoTOYHkxrWwmk64dpMb2bXgaZzqVlMck8RS0GvtYqT+MEZakqxMyS7RZ7a3UMctk62rYOmUudDeTY2ll452OQc2QVpMyZrqRkwtnWb+OERaYJ1ZPrlwa/Y02bi+7/i/h8F4XnL7B0mpN8oVjfTIVRNbhOrew61cXcaaY0kGaUwT0bGm1ldMqadVfbs/vzFqmlBTkxND+JsdxpZ/KorvP6Ozdh4vuBferv/qrlue53XTTTbZYLOxFL3qR/fqv//rFPkwgEAgEHuP4ph9Af/RHf+SW5/O53X333Xb33Xd/s7sOBAKBwOMY4QUXCAQCgVGwa+MYLM878ls44prRB8by1n7ukeWfLA9tVROi7ThI+y2xUs9yv+1s6svK98467eYJl17m1jEpk8mlmWhCl8OWZAt27jMpC6blegOO25VpJ6GgKA9ded5XOeWtLZQ44zjO4gRiBjnvWg5zZtNrWIaogPnc93ElbSJ33mxuueXldrd8CZJXt5D8uSHXcoIS4SnKsGuWYcvvbTWsaem+kvbTVkl2TB2B21IHXW13fD/1lhV0N9VqMlr6U6OTQZRh7LXwXKoxTUE1rWVSitwfHbBZ+zEyVC5NiyiWcDMFWPU9TjmhhqVTNNp6WJvR8u4V7hXGWFBo03HBa1cywmOjG6s1kmNz2H+Vy357o737UNq+z08bUX1Vx96y9NNW+hBvQIFAIBAYBfEACgQCgcAoiAdQIBAIBEbBrtWA2rZdc7iqSTC2lrEDymG2LcWN4ehpZyuR6EOMnhZdCvz+HHrFvn1dTAJjtAvw1hNoTXp689xrELS90XOHLJXU5Ttbe9qs4HzoE7+S+RXUcTaht6xqnTNE6yB/PtvCpescDjOzatuf62nGVkjkBS1NJhNw3jLfhfOWaMu/lPWr5RnfJsaEMzJC5nxwPg51BLXxp70R5xc1ome0NcclrtUKmlAucd7QRGm5pJrQNuaVtYmeKm2mhsVt+2WdHaPkHz7TaaTU3CpEAOi1ZHx6onfluC9lrM5hFcZrp1HUnEeTRq93n93Ys8+tG46ON6dL54gGyfC9Ucp3WznBWMMY35h3Og+v1WzqPzuf+TGvbWzkO6Zp0PYexBtQIBAIBEZBPIACgUAgMAp2LQV3tvwyfVfPYHWRFf1l2Cs60TLltOkvTV6inJItUdoqz5CIinRCTTZcbNP+BCWqKAddLbp2bGG/k7mnfOpGkj7Bc5CqUWdq0oJ74DT9MErDlVaoQJlUdM4u+stZZzNf4nnowFXr30+f9lTe1x78ult+8JRfbuRaq7WImdl86lNo9210y5ciOuQAykxLoZbqpW/T1hboxoVfdvQLy6HJQ6lzNsrgc1ipzGTMlBNQtqxiJussY741Pxar2tNUxcAYWSb3ltAvaEJbDJTmmy+RnmIKw2Tmj1tudOu/8jd/4/cDx5mNWWeXtReu87TtOQ2neSX/mCBKF3TdFrdZas2j577tac1J6e/nVUUHfh0X5NhxfcQGinRjYiUkvxf4PpqAVq6Youv2q9ZN3wI37EAgEAgELhbiARQIBAKBURAPoEAgEAiMgl2rARVZYcUjvKbqFQXro1GoqXwyNR+WGLLMUUtYmaBImw+X7olybu43F/6fTkFpmSaTJrt9TaZeQyFnnMvlrGvY5+N/jUK0poacMBMTwXkrp7x3r9dXMpTJazpmDt6apcmaQnv5ZQd32BblxmLvsqf1/VIiMmLvnk57mjNZFZy92pY06IcNXLvtbV+mrTEW1C6T4NhMbW8wnjjktWQb14rRGkQSY6HrWNYsegWnNDRoo0aScPw0iRDlF/fs6cbQBnTBctr/FfUQtMmvf/0hfxiNDoDulEOTK1FqrSmte/fssSG0zioM8QUsg9eyZdoooZ8mGLca01FgLCbJxE23nhoc6+D1uk8nvh+m5XDydabxN1omzkiOHsQbUCAQCARGQTyAAoFAIDAK4gEUCAQCgVGwazWgSTGzaTHdeUNAdR5GGZPjHvxswXV+ORO+tpzgQODHS9E2SmgklLQKHFhjxFtYjVBrcnHMyTQT1OWLXVBOyw+AGpAelxrEfsYtuz5HLHWB+VMyL6KYe23pkoOYnwNhZClzWJawXdm/Hxbycj7ZgM2QmdfsGoO2h8teIq5hpfM8Bub9mJm11l3bdJji+mjMQGIRRZ0NGqksVqv+WAQzrysssG2dWPHoPCzGsrtFZ0djZlZJNHgLnXMPxoHaHV12yWm37uFTXoPT+zkJacHFm8/6Ixc2oAHxv/ZNiQ6hztzWSdaJtAlzbnDvzDEnykesDF/3DdFyeJ3N/LKLdecwhfaXGZfVgqnbL++rPsQbUCAQCARGQTyAAoFAIDAK4gEUCAQCgVGwazWgum7X3kLKTZNjJQ+v2GFKRBJnoHEM5K0TzlWWaelfQOvQeTacx8R6+SEOn75MDX3xZNsKvmVkwVWKamGdvuR8kKWfU6RtrOEzlXSbHLaEz1SBOU+t/D90ZtPP8aDehV3ZZNrNH9mA5xwt/tX2fg7dhsy16hk11hYZeXiOTWlkxnlkHjrvDLu1nPHwcp0ZPUHOnlR8PTCHqMU4rmrVmvx+GGev0Qgtzi7PqZ1RM+3aRN/BFrHVev+XONcDe328welN0YQ456aFzxq1WZ2vQy81xpNLG+lHx3PV+XeMS58k8xIxnlzUjD+O4XtEI1Wo4TL2oZHrTu2bMRYzzJfSITQT773sPB8t8QYUCAQCgVEQD6BAIBAIjIJdS8G1TdbZfQhVQ5sPWovkA+ta2LekbhFiq04rG1ro6B9IWcG+/W+/9tX175ce8GWlBG3idc9DZbJmZial1qS3Egcj7ZukUhTJpUh4LaRss63w6p9yl93ndig5z3W/yas+rV9IicrvTOREuXfrSvV9G6YlLE6EJlmC7mJSKaMQlKYl1ZrlvPXEbopWTjktWSSOofT9xItJ+3xtRZbQ2SihF3ujFvRjQ1sl2TPTd2eYTsFyYx3HSUorxsxE0j0X2z6e5MA+Xy69f393r3FMLFBuz3tgIuOAVk7cl1rmLEFXsy8KOdfZhMmqsOVCo3Q9q/oXGJvnuOG7/SbfG93OMozTAjftqvL9Vgr1vVhty++e5utty3ltFQgEAoHARUY8gAKBQCAwCuIBFAgEAoFRsGs1oMY6lcLF2ELHIUdZu8hhALRoUsbsFmiV4vemZacZCNkaYoCWltbgiFnOusJ6xylDi5mC/9dmlIhqoNmOxlaQz7fMt38yoX2Q2InAhogSUOGiy4ct/dV2qNrBUoZ2+i1JcW0DNZWB/aZWSLItrZ0GjmlmVogdCvs/R4RyKedTQHPIoWEVcm2phVGDK8D3l1KSW2/2l/GbeW2mhTbJWvFMzrBEHxbQFPfMvFZTqd7Kc0/iPTqUc98vFSxmZvOu/8uSOo7vpy3oSVU1oGXy/ETLmWKqRGKVpBZejF7B+KJUo1EPOww9t56l+gU1ONmWMTQl7n3Ge7fyYf3uHdKCFfEGFAgEAoFREA+gQCAQCIyCeAAFAoFAYBTsWg0om2aWTc9ypMpR1rDUWK7652YwZmCK+OUKc3107k+RMfIWnLfWzieZyf65PtvoauVXAxqJWTrnoJ50H1iA+K3A7Wocc1XTat96UUChUAsWM7Ma/L/qCitEEGdov85DyXCcpqEGIRoQ13EuDzUi1SugaSXzHuQ4ZTIHCvEMq27eA5xfEg2oyAfamEwk8/20qrZ7t81LXA8Z8xm1sIH5UWaMl4A9TQELqVZtiBixgPtOIqDr5Nr5VmxqTIV5DYVzhDjvr9Y2NX5cUlOpZa5Phnt9a9vPU6HN0lB0C5NN3DqMicSmS6NMcK4UdvKcmqlEclPXhGady3cSY8KrgTYtsW05g13QFBEwJv2q+hH9pHoQb0CBQCAQGAXxAAoEAoHAKNi1FFzVrCx7pEzXJRs2LP/0aOUVnbYeMIFNymr1VbWlAzGe1YUcOUlBxOv71lZHBfzt177m1n3H5U9wy6kViVCK2Q6ljeqGXdOW1y/OBqxGsgKlvBgmSsFldGOmu7e0qciGh1vd6nWGLUnO/faX5ya2Kizt1V2hn9oktVVsoEBNDlqxm3doos0KoZZASTl0wbJmOZ98gFu1dCxqkixth1ZwPi6FnyzpJk0jeTk/nilTNGlVpZ8lhdWifl3vwwkpz5LH6cYmaWWWZdPBXsF7cghtcvIoa5ZrydJ80r/5gJUYbcaSKSZyvZgezCPruM4SKnJ4fLmrrZvu9LH004FAIBAI/N0hHkCBQCAQGAXxAAoEAoHAKNi1GlCzWlhzTs0jqZvFWuUzWcboP8qkTN3VcumtOZKyTKnFrFbgj5lKOe0sQR5C4uP+/fvRxoRcX/86ge0NS5Vb1bDYXLQpE4sTlnuWOe3y/b7UGmk6hYU/+lyjA6jjUOuY5XNdaUNgaalazKTXqj9VM4f/CRNetSOZZsvkzySiIDu/dWaw3mcZNlM1ZbnGubI8N9EUW9UUOU3BJ8m69tdMT/V6ke4rOTeMrxXuF7WuKpHembUsNxbtlaXHsJ/SaRjUfCpY8WR1f3wAx1qqCUlCLWII9m4wVqQ/+TaDzsZCZh1/lCNrRrGKRsTxM8X9XGXyvVFxGoL/jpwXG25Zh59Oyajq89PN4g0oEAgEAqMgHkCBQCAQGAXxAAoEAoHAKNi1GlCeN5bnj/CILm/Zb5fE+6qdC6MP8Fna1bRi7cGYB9p86K4mG5633jrt+fGGWc0DSLhqaTTjutOY6u73HHM8DPMcKjdPALoBYyoQm6CWRrR65zwtpZ/JaSe2MXLc5NxAcZe06Zc+TiIWcH6tRrzj2jAGwtnnZ+wn36YkczzT+S0D/i2GaHBGijf91inclpHKlAac/Q41UQgLudMrqEP1x1Qnc1QwfuA+BUsjdmqSodKtYXQ8HZdkPW23kqhy5qLoutxrS7Qh0iYWjEjBcXTYtvj/n3OIqDXpKdAaqcKyng3Hf6KJyrWb7fE6YIE4deqGTvPVaPWw4gkEAoHAbkY8gAKBQCAwCnYtBTcpyrWFir7ep/Y6vuxxouW4eKume3SDZXXDbmBb3eL9XpMPYZRtRUnH2G4DvvqzWJF2L3oKE7xK0xlc28RzZdIhkzJdG0BHFCj/VudpUobkEfRykTZgeaiW6yYWMiyXrvvpSNJQTdtPEzIZkyXErpQ/2RbUHi2Zmn5KMXXQEbqLbRgosU0oN5agY1+1UroYPxn+H9V907aHdcC1UpWgbRIeEP2o92ziK5/0uVo7JUZcflvZWwnbnuTa8SbuOaaZ2STv/9pMp1GArpP+J+VJt/iyoHt/R+2T/mXCrjaZfbiCi7j2+gJ2TPv3ekouoXz1dxlPWSSiBgKBQGA3Ix5AgUAgEBgF8QAKBAKBwCjYtRpQ3VaWPyLitANVmhNYd6zESmVra8vvM7GHoP258LPkMJEi2FbKw3uOeAk7lz2zrjSTHP32Nix/YIWhugK1mMnMl3zW0kZy9hl4YNVbkopJljHT9l748wL9z24rhaymzUqeePyIVlYNl67zumtp7I52NKIn6XU820Z+VpqHc1tW7FPqXzJG/EddnIcZNSy/bQsNxZWrY7/U74hctEyOxZonKIvUmjKcq469FhY5U2ii05zxGKJ/MQWUKqlrcn/UgRlsezAGaDc1FKmSpu8Oxgt7UI9UG6iclkTDx2krWYaslkHwVvsg2kutsKw6aBIlg/TaKb5zNH7FTW/YwUrrG4g3oEAgEAiMgngABQKBQGAUxAMoEAgEAqNg12pAbduu+UhlE1vworTTUf2FUyKIJPpYjpSRy2VUrXDt1EHKSb+dCNvEunvOMWhF28hbzK+ATfxkIJKA8340IrpNYqk9yI/PS8wNEOSwo3Ht2IEWVi1gQs0B14oakVrZJFOcGEUhYyjVbWg10i8CMWagZly8tjmZC8P5O3I+HANGSEQEfG0a6JwFdYRWNUVY2WC+lA3oIIwYz+pumWMt57wZjAPV7HiuHAe6Pv3vuX+eVjJfDdsy1kLjMyhVXghoidUMWEatrce+0SbIXxPRLmnvlbpCybXFd9li28+d1PPjvd5AbKLFjp5CJfOL6pZzjc6NeAMKBAKBwCiIB1AgEAgERkE8gAKBQCAwCnatBpRZ3nlTCT/LmO3U+ko9tcBx05b8HOx691lwneTAS9F1uFfy8MI/Z+TsOf8go317d5wl9KISbco1PrqkXT7aKH9gtAHju/l/impGPE6NfnNzS9phXUc7ku3lf0plwfPrn8vAiU5+7g8jIciBd5gU9EODdpZzdbee2h5PXeeAcJQ21Cd0rk8SaTEsfOr1qCqvBaS+eDqPxnP6Ofpftb96NexDyNyElVyPxHMOGoRqom2iLVHP08/CDzBJeYBGJ/cp79k0RqTfq7KtqGGJjsNz422XcV5Wd5ycmmjL7xH1/PPXOcd8Qo1ETyJFGBGRuFdqBIOd8/chxBtQIBAIBEZBPIACgUAgMAp2LQXXNu06KbFtlQqgZQY/qZYmpHhYjotPyvYtYxNAQdSOb2FpImzVZ135NC1kksiIJJGzQz4BjQMqQ6kaEhk5qEu1gtEYBzOfwmp2jnJ1pWZSAhLbaikvtkwunpS2J7XUjA7gtZSy8sTKhrRgR0nk9CFKuDHlFUDPMbEyiUjtUMHKibSn0lSkf0v0hbPiSdI6Ye+CcnWlZsiSNDVTNRtZNxyD4uyZmFZLy6uEnml715FqVWqnACXKknpNNa4qpiPjeiRxBjLGcT1Ygq70aUqpG9DPTfGzFZeFZkvuO9LMbf89yu8NvT9qjIHFyn8WKTWWl90ffNTHcALw+vPntVUgEAgEAhcZ8QAKBAKBwCi44AfQX/7lX9qP/MiP2OWXX24bGxv2jGc8wz71qU+t17dta69//evtyiuvtI2NDbvxxhvtC1/4wkVtdCAQCAQe+7ggDejrX/+6Pf/5z7d//I//sf2P//E/7Du+4zvsC1/4gl166aXrbX75l3/Z3vKWt9g73/lOO3LkiN155532ohe9yD73uc/ZfN5v4UI0TbPmRMntuhMo+0uryYc3tJunJiSLtELPUWNbSSQ0y0wZ+6DtZ3kiLfATKwxpR8lYbegVqd2ItqFfx6GdS9UwCgFah5ZeopSX/L/2Y4GTT6qlpU1D0QZm6X9OjakGRBEIZcGTTjehDVGGaGYdQoxFIMifa5tnSXkxI7pFK0PMQ8ZSd9EcGCVNvQ50vy0W3fXiuSdx0lKey3Oj2rWUjmLUxAwaY5GU0Mv5sPSY2mWhlku0/DEsa8wD1uF+LiY+ZuDCoFEgiNFuBsYMIxUY/c1pI9JtW9AUqTurxsV+on7nxzhjHTilBPewxtvL+GkH9FDFBT2AfumXfsmuvvpqu+eee9Z/O3LkSHfQtrW77rrLfv7nf95e8pKXmJnZb/3Wb9mhQ4fs/e9/v7385S9P9rlYLJwf2qlTpy6kSYFAIBB4jOKCKLjf+73fs+uuu85+6Id+yK644gp75jOfae94xzvW67/0pS/ZiRMn7MYbb1z/7eDBg3b99dfbvffee859Hjt2zA4ePLj+ufrqqx/lqQQCgUDgsYQLegB98YtftLe+9a32lKc8xf7gD/7AfvInf9Je9apX2Tvf+U4zMztx4oSZmR06dMh97tChQ+t1xB133GEPPfTQ+ueBBx54NOcRCAQCgccYLoiCa5rGrrvuOnvTm95kZmbPfOYz7bOf/ay97W1vs1tuueVRNWA2m9lslvKveZ6JbiFRzbCVaOh/InwyOWFG0abakmoQtNqnlYesx7aMZ9Ca+BKccIYI7tmEWkGHyQxxDOT71dae3HOSu93xtasV+hSaD+OLHWfPuTA4H9WEWuM8DcwXEd441TIwTwOno3MQGvDPPK5qaTnmf6Q6oUYfYBUo+rzmHDX1FuJY6x+3tENJNEUZI4lmyGVoNzrmOT9qOjDnhmMtmVczEIle4HxWlbeUUvssznlqdsrwcE30x12I1sH7mXOTEv3Lh8C4Nel8o+64Sdx11T8m0lh2zs9BPLl893FeHyMvXEQ3LaKgG7rj4HujRL9NoEeq5rUp+mIyB7MHF/QGdOWVV9p3fdd3ub899alPtS9/+ctmZnb48GEzMzt58qTb5uTJk+t1gUAgEAiYXeAD6PnPf77df//97m+f//zn7UlPepKZnS1IOHz4sB0/fny9/tSpU/bxj3/cbrjhhovQ3EAgEAg8XnBBFNxrXvMae97znmdvetOb7Id/+IftE5/4hL397W+3t7/97WZ29rXrtttus1/8xV+0pzzlKesy7Kuuuspe+tKXXlDD8qKw/JFX21pKDmnBkroBy++oc2RaJx1lXdkmS5wnoL+E5pmihHN721MMy2X3Wj7FK+y++V7fBpzfYmt7/TtJtOnUH7eUcmq+OqdJh2LVQdoJjtykgBrhnpL0S9AIub7egzej7Yq3GmIZfGIV7JamQiuwvSx1z5ztjadtcoyZohD7E9jaLJdMEAWFoiWqaH5mtGtSqpLXCv0mnyXdSHqIx1UqqkFZPC1airIbX/XSj+kWiZy1lLpnGGvLyo95Wj/50mrf3mIyNLUA7uqJ47ikv3K6BunSgX1zOkdCjcn61Cqsn4qqan/UHWkrGbdtcn/wuN3ycuWvHe8PpQVXuK5kJkmJFmLFo0MxYTR7cEEPoGc/+9n2vve9z+644w57wxveYEeOHLG77rrLbr755vU2r33ta+3MmTP2yle+0h588EF7wQteYB/84AcvaA5QIBAIBB7/uGAz0h/8wR+0H/zBH+xdn2WZveENb7A3vOEN31TDAoFAIPD4RnjBBQKBQGAU7No4hsyKNU+uvHUJK4iq6reNqegdnh7EwXPR/aXHZ5dlN7Ti4XGkFHaBVNPikgO+SWiylsbyXCfQpbSNSZvA7eZqv8MS8/7q9LOfTUq6O7CMVvuUV4MJnPr/EDUf2rcw3iCNb9B1A2XkOE4LHUftjTa3yY9D61gxcqErUU3TeZnmKTEi1CoTyyItpTaAFi20NNJEUfpCQQ+TdkymfnrAgrqCXA8GlfLKp2Xaol0OJK2aUadlurA/ajk0pWGKkvNEu9GOxXcOLYxEU6FlUT3wFbSAJQ6jWmjrowIZr11tvvx7KRZZi9W2W8cx3g7Zl6HN25Xf13SqOme339WK38vnRrwBBQKBQGAUxAMoEAgEAqMgHkCBQCAQGAW7VwPKsjV/rTb4TYtobERlq66QgSDPMQenBK/tbDIm4M5pRy+c9+bWGb8tuN3prCtBJ5/fYi7AfO51ndOrrfXv5JNXtT/3edYdh7EP5N11bkzWYI4Edaiyf05OEq+MuQwaJ8E0g5TD77bluS5xnek0pDb+bFMxMCmhKHx/1w2ipuW6U3dKtA3MnyoyGV9YxzlqKlc2GBOM2WhEz0vm/eCzBh1B52q04OmrGnN9KtUUoclBR5hLP2Y59UffpNUSOoMMg3IOHQoanc69yvP+2BMzr81wOk6DP+T4X1zHUI2BS43UxRDwOH7RKrl2jDZYYq5VMfXXUiNg+EXS1L5Rs+mGNNCvo12QzgtqsiEtLJ1P1egcIrl3+N3Uh3gDCgQCgcAoiAdQIBAIBEbBrqXgqqpap2mq826dJAz221nQ1Tkp2R5wvljg1TNxX5bX2BL0Vg7Xan1lZ+kx6TuWOGubi9Lvl7Y9tfTTJImH9Iua4kp7oxIJqVlCT6jrNl2FQW3Ukojqm5D0hR6nKOkMjOPgfBoZyjnGxDSxJRKqbwdX5JWjRfpTZc3SMtrclWHDNqboL4XleCIFp+7kbENb+vNh+f100u17O/cltUkJt/xOCrpB+/Xc68SRG7QO023l/DgW2W96vydlyrjfXcJuwXJ0WvH0p/UmPvIY80MGOryWmmK8WA5MjTBP85uZ1fr9RRsxUn/yPblc+utck6Z1+/LrWH7PvnDjTynn8wtEjTegQCAQCIyDeAAFAoFAYBTEAygQCAQCo2DXakDbi+213qNl2Cskoia2HmpXkZHf98sLxCZoGWeewZ4G7XMaCktUUapYCv/M9j506pRbXi48X6tayJweJ9BFKi2rpY09SjG17JRaEu1dyE2rTVHDsvIk6VNTZv2qhjqbcPi0z6mxbTlQVsv2Uz9Szpt6BMlr5c+ZiMprSZVLrUnUlscs5dY1EoP7TWI3ZD0TKlcJ3w+dUI4zM79fThHwtj1+XZ5D56xEW9qmtuQ/XC0xfUAspWawl8qh8xTSSJZ7Z7zf1XJpYN25PquLSaRCw7Eo14AaLoeetHkC2yEeZolyad31Com0OcaMThng/UtNbmu7+07lMfdAd96zd59vpJzPtuilictWD+INKBAIBAKjIB5AgUAgEBgF8QAKBAKBwCjYtRpQXdedJiPkJ2MS9u7d45ZVA9rc3HTrlrDIYfG8xj5k4HkL8KgziRWmxkCLH6WqW8ZbY14T46Mncr6cD0LrkblGHXPeDLQObSMt5Dk/h1YqagWfWHUksQPd77TeYRtVJ+G8E16rJCZ5YDYGNRW1iqc8lPST9HkD/ZHaALVAN4eFcctYnIr2kcTBQ1NU7SCDcDPFXKRtnE8mbea8uAuJJOD10fk7jFSok5gKRmfL3JgFIgpm0NW0TbiXqKtpJDfnDw5FcJv5/8zLFjZW/KycO/VUzs9xkSmc8zQwp5FtnOCrOyto1yTfZYn+hflHorPNSp9cvbF3P7aF1uTGiLbh/ESgeAMKBAKBwCiIB1AgEAgERkE8gAKBQCAwCnatBrRnY8+a01UutAW3SBt1tbKnD9OSsdo8qHDKae28Z341Wnu+seHWVfQ4W8mRGtrwg1HG3KSJ6CKrJSO5Pee9Et8v8vCUINSSPQdRndhvAe56JFHl4PvFH4pRxoRqEIwcTo8Dzr5UfzrqbAN8NFOpG9rI62f9xhu47lnm+3xZSeQF9trCo01t8BnXTd2wFd2nHoikN0vnlujcpEnp5wHRpn+Ixi8n/rN6PuXE67IZjPsYS6//B9eMYm943eX+5hwo9oUclvPVGtz9OcZX3ahW45HM/6pVU2T/93vQ0Z9uC3EMQ+N2J+1S71HO7dnEPK1MojSS+WmJ1yMiPOS4qiUnGm4P4g0oEAgEAqMgHkCBQCAQGAW7loKbzqY2faSsWF+7WTLMUsVGKJTVgmWm/WWNPA5f2RlJUAm9srnlX2mZvNrKa2td+TLTGqmU+YTnI8cEXUdKqxTbnppUTMPS3q6NifUO+5TloLo9qLBJSYt8sfwpeG6Iy5BOJ7VK632SE0oVkIJjabtPgMSOBuIZNNnWLC3DJjWj5a+kZnjuufQjE1HrrH+cEqvK0zi0MCqEdmO/ZJji4ErbWcYMqjVXCx3yfkAJa6GVXI8p3Y0GbKL4XZCjXN1513AMM2KElJb7zhlO99TYBF5X9rG5knMk0HKMkBbUdN6W4wcWUo52Y0ouElLl/FrQpQXTkjm9Q9qk6cFtQg2fG/EGFAgEAoFREA+gQCAQCIyCeAAFAoFAYBTsWg2oqet1nLDqFXU1UBtqZoWU707n3kp8i5wrSxfl90RrIhctvGoOz/UGUbqFaB81RIcGfHkFTaWV0t4JbfmhNak9TUvuNikHlf1M+rULs1QTcjxwEgUM3jpXXQ328ogvmMi2BSOUE02LbZT98rPQVHR9kRTj41rKfjkm5nN/PSpYzjhdYdiJx2lAE1jgl4go0O5n6XSBMUEfpamMxRb9tI1tK9k39S22qZQpAbT4QcW51bA0UsuiZPrDgN7FsTahTUzTXx7N+5kiUK7jdoeKYq8dQ2sqGE+u9yh3PBxH7rVYv66GvY4upxpWv81VhYtVrby+ncSXyLjVMcKojD7EG1AgEAgERkE8gAKBQCAwCuIBFAgEAoFRsGs1oLZt1ryt0zZ24nIFtLrYh+gGRtM6LnfA2oLbpjYxvh2eq8Z+doipVsuQM4w6hlazMZc4A1C1ZUl+We3a/baJnQj7Qnhtzo9qMD9kpfpLRs6ex3UH9cekr32C/uuR2O0ID18U/f3CfVHb2NracsurJaI19Dgl5p3kOD+Zw7KRkd/vn2/Ec21WHD/+OEuxe9ncPOPWMaLe2zX5Ns3mfl5WJTrDZML5UsOxD+xz91lOyBNtZoo5Z0ksio4ZRCqk0RPQair9zunXoc6ul98xTjlXT22vaLm0hNVWGjGilmScr+a3rFb9Ee85NGrVeFeYp1hhnCaxFRKxom2ISO5AIBAI7GrEAygQCAQCo2D3UnBZ5+ihVAZfhtMUSi0p9O+BU5SO5jPSImKHQpsY0C9KTzBpla+8bl8ZXmnxPwBTEZWCWOF8tgt/XJeIitf3GqXiZSZ9in9DdqK7tJ9oR5NYnui2fC1PLFu0/7mGPCEsT6rueiTEBSke6UeyKxOUAdeyvFqRSvLL26CwdBzQ6mWFku1KqLGtLb+f/fsO+jbKOE7SbHmqtH6SMtttULpbm/64Oq5JvWxtYbqASf8j+XZjjtTigl87Wn6P+7kgJdr9TnqRCam6L/Y/LYoS1lbPdzAp1tyYJ13Nc9XvkcQCC31cD7lhg5rc3qSTdj8lnSQES5sw/M9BufnzmU47utXT+jtR5o/s77y2CgQCgUDgIiMeQIFAIBAYBfEACgQCgcAo2LUaUFGUawt+l5RZUyNBiapYkeSJpQyP0W9zT96UGpDyqEzGrKHjbG9tdm3wTUjKyCewUtE2Vs1wOavafNCmJEN5tNZAU1ej3pLwwLJc1+xjcPi6d6xL7I7kMEkJakaLFnzWaQW4drAXqcWKhJw27U90mbQ2S2yXsDypl53GMm99aTJTZzWBszXf3oceftgtT0Xro51ROWFsCHxwpN+WiCthPIDqJEUSe4KSYdkvbqtEqykhyJSSyJlhHW2h9H6ghjKd9Guv7O8pzwe3h0Z28B7NILStaG0jqCreHxKDMjAGzgXt4woWTGx/IxppYjY1EHmcRtT079fMjy+9V6pqB/+ibxzvvLYKBAKBQOAiIx5AgUAgEBgF8QAKBAKBwCjYtRrQclGt55j4+nJEEOBzOveHXG2b2JL4ORLKf9LmnnXtGoWQ1O+39J/vOO7lAnY6O8x3URuiEvX75OWVruV+aFWv+sssm/SuO7sz8PJuvgX1Iv9RbWKec+4Fow9Em4GQwAjiJDah7dcGOFcpl+jpmtuyT+WEyiliwTGXh7qUnvwW5uPMEe9dTPvbXyKewU0rwwDa3qb1vl+/WnXtYMR7MfHntxJNqIGWNJn6+1C1jdncn9t8Tmse6CIyNKn5UGtSWyJa8aT6Rb+dDufRFAO6SBIJMTDFhbpsgTb5OYHDEfXpFDuZVwaLnLJkJEz3O7/ntvDdlst3wwwRI5wXl1hVSTM0jqRe9c9hcsc+r60CgUAgELjIiAdQIBAIBEbBrqXg2ixflwv6MkiWyfrPKfXUwgajbZAeCdsJxXTqaY+kZFgOzFfn6QSl1HN5Pa5QPskD83Vf6K48oQlAZQy0d4U2TqRkNaGOaOPDGs+qvzS5xb6GqUrSaFI6ijLOjHYiCd3SX8JKOtWV26Nf6GitLs8ZbJS2F6A2tr07di39yDZszXzJ80TGDGmOSeE/q2OTNCwduiuMt4nQbAus2zwDd29pM1Nms6U/rjrNL3Dt5okze3/qaWI/g7kTK20zxlqS1ik0G+nrZkW6C3Sq7Gvo3j+7rZRHY9uCbRReLS17981fVaR4dZoIrKjafjo4ofJw7xRlR7vloOOnU99vpLP1ftnY0+2nKAd4SkG8AQUCgUBgFMQDKBAIBAKjIB5AgUAgEBgFu1YDyixPbEbMLPGVyKHzqF5RNP0ag1mqX6i9DnWdEhY5qiMk63LPx05UT9rw+93a8mXZ5Jc9TwwdZOb5Wi23nOZIrARHPJloybkvdc1Y7t32l3DT2n2IL69AcjP2wV1LWoCgPJdWH5Xoe7zOtOlXbYOWRTVKq7VP2wyJqIhfeBgJo5pwSYWqAP8/m3b8+QQaYjWhjtDtjeX11HxoQ6Tl7Ittr2GdPnPaH0cuT9my7Npfu4dOnVr/fvDAAbduWdPGypf6OouZmtfOesF7dFkjFkXayPayn4bSA5LpAgMxCTt9VuMwqOPQNqll2rAcd8WpEjZwb/HkaIkl+6VOmwDnM0Hp/oUi3oACgUAgMAriARQIBAKBURAPoEAgEAiMgl2rAbVNu67rV6sIWo8ktvBCGsMQJ7H54JwV5eyT+UV5vwbE2v8WfOxSLctRR18UmG/UUKfq/kdYgjOeYF9q2UJtgHOI1NYjKyZY5xYtB+87E0uadN4G/qcRLnpSkC+mPYpqS5OBLc2W0I80bmIGTa7KvN5SigaxWPhrl2f+eqxUM1l5jWEbOkJN+yOxNUnilxEv4ebc8NwwkhuxoGnK4f8hF5irpPY77NPphtdmtiQmvMJ9x/E0kXtrCdshzneZwsZHowXm0HQ5jnWOGsfpZOAerWvOh+K2jDo5937OAveSanK8HriWrfRTw4h3zuVJbJa6flxBV0vm48kfqFFvU/OV4yS6VAPtL+f3iMxrEr10hTHQh3gDCgQCgcAoiAdQIBAIBEZBPIACgUAgMAp2rQZUlsWau1Sr9LYl/0pNSDhi2P8nGbhGPaY/kptQrjTxWsK26mjOKSoNbNWTJGqNWAbRu4T2tC0cLLlztmkifToHR8xeyifJX7rfmHOO5Ubm/lSN54Xp7+b45MrrBpx7sVh6HnspegxjN6olPc5EB2kwjwkRyguJ1a7hF0bvNCYqr5Zdm0poWgU0iMlMtCfOWeFcHtVxQLUv0U/U6HT8MWaAx9H7jt57K1j8r+TkNzd57aCJMupE7tPF0vcLfRV1jDBCIc8xvmTUl4gVKKHVcD6baslNcvdgXpAsJ1OEkmwQOW5DPQX6L79Y3LqhFpm18t3Ae6VOojW6sbdnzwb22w4u6zfLXDTE5HuhB/EGFAgEAoFREA+gQCAQCIyCXUvBTSfl+vVb32L5xKR9votuSGg0lLcOJRJmpIe4LG1iNAC2LeT1n3YbDZNWSTnIvhObG8QkLGT9FJYmTEit5bU8hz1+UsJdszSz+30w8dGQZtuQLu1fXtI+B9d5C8myW4uODssL3/+nzzzslpUGmeQoPd70NI5ap2QTWNEzEZVpmNKPSagmlguhW1ginIM+Uq4vib/AthOkhmpkAcujk4gLGast6SEwLEPxBV/7+tfd8p49KIuXNtPaJUdHTYv+ryxGqGxoEuuU1LD/LNl6pQ1JVSa0ZqsUHO87xEDIcUlNZg2/nwzL/REwZP625f5mIipLrSeO5kecBKjj5Px6rHuKeiDrRj9/XlsFAoFAIHCREQ+gQCAQCIyCC3oA1XVtd955px05csQ2NjbsO7/zO+0XfuEXfMJk29rrX/96u/LKK21jY8NuvPFG+8IXvnDRGx4IBAKBxzYuSAP6pV/6JXvrW99q73znO+1pT3uafepTn7JXvOIVdvDgQXvVq15lZma//Mu/bG95y1vsne98px05csTuvPNOe9GLXmSf+9znbK6c7A5ommbNN9KYREEOsmnV7nynY/SXAVtGDrNfE2prlrrCIl8+22K/RU7LmcRTQ9qL40xQWjrvOPCGUQdD+hdtYgbsNsy8zX0LMj0veD26NjOmN4fNx2kXJw3OnuWguLja5rpl9DojI7o2nUaENXl4bcdygfJh9NuUeotcO5YM817YI9duwngPlqCLvc5yCR0H57p37stq9VpSb9lEXyyqTmerFrSbQuS7CIMcL4Naq5m1QzEoE6/rmNpyJZ5RuGflXssz36cb6JcFdRK3n+Hz0dLkljZKGS2x5N5BmTUj6pP4aylfb9GGxYLWPN366XyGbREF3vRfO84t4DhOqrK/sZ8dvnu/gQt6AP3Jn/yJveQlL7EXv/jFZmb25Cc/2X77t3/bPvGJT5xtS9vaXXfdZT//8z9vL3nJS8zM7Ld+67fs0KFD9v73v99e/vKXJ/tcLBauQ05JrkggEAgEHr+4IAruec97nh0/ftw+//nPm5nZZz7zGfvoRz9q3//9329mZl/60pfsxIkTduONN64/c/DgQbv++uvt3nvvPec+jx07ZgcPHlz/XH311Y/2XAKBQCDwGMIFvQG97nWvs1OnTtm1115rRVFYXdf2xje+0W6++WYzMztx4oSZmR06dMh97tChQ+t1xB133GG33377evnUqVPxEAoEAoFvA1zQA+h3fud37F3vepe9+93vtqc97Wn26U9/2m677Ta76qqr7JZbbnlUDZjNZjabzZK/1/XK1qnLyo021ID851xBRB9B+QiGYngT842En+3/7E42PgpapSf7kt85p4N8rUZrz1C/r5HVZmalcNUVrOrLCnOTprBhWSmHjLkvsDuqlR9Hm7aW/rjbom0wmXkbWgfjpNUyZzIZ1hzUFme+H/HQ1AV1DhrtjZJIcVwfbQPmxkyn/rj79naaBOdhFdA2lLNPYpwx9krO6ZI5XytY4pzZ3HTL32GXr39P4sbxWQX3yzjs1DZG7ZoYU+3ne6nGxXtna8uPka3N7rP79u71x0wiuvsjrhkHT9QDERdLRHaohRG3pZVTDX1V21xTm8Fcvlq01wrXg5qQjutkTiOOMzTe9HpwrlEfLugB9DM/8zP2ute9bq3lPOMZz7D/+3//rx07dsxuueUWO3z4sJmZnTx50q688sr1506ePGnf8z3fcyGHCgQCgcDjHBekAW1ubiZPyKIo1v9hHjlyxA4fPmzHjx9frz916pR9/OMftxtuuOEiNDcQCAQCjxdc0BvQP/2n/9Te+MY32jXXXGNPe9rT7M/+7M/sV37lV+zHfuzHzOwsJXTbbbfZL/7iL9pTnvKUdRn2VVddZS996UsvqGF1XVte5+v9fgN5Uo4LukUrqcmxkdfBq7WWDDPxMaXVtJzSr2GIY9vz+7lQJORfh6phaTjsOKT9TCSkbU8rr8t03V2tPO0xQ/m0CdXUMpEWqZTKUm2j1HVz2x/n1JmOAuKlIo22DSseR22ABqS90cbGnvXvWev7dGPqy6PV3kUTZ88FliY7CylaLrFkWDpqz549blVStiw75hAnRcJSa6X3SJXNZ0iDFfpl3/4Dvg3+sM5h+cyZ034/cNlWaszM03k5/ide1Sz37q5zUdGGy2NL6Tva6WC/k5nv81LHfHLr0yG69zDJ9VjKfcgkZVJwpCNb1zewxME96lh1TI3IQV0WYm9EmrlmUmzLMa7J0Eql9lO0igt6AP3ar/2a3XnnnfZTP/VT9pWvfMWuuuoq+zf/5t/Y61//+vU2r33ta+3MmTP2yle+0h588EF7wQteYB/84AcvaA5QIBAIBB7/uKAH0P79++2uu+6yu+66q3ebLMvsDW94g73hDW/4ZtsWCAQCgccxwgsuEAgEAqNg98YxFKVNHuEmVctJSjxRCjsbKGvOyuHogMbpJP12G0SB/a7AfzoNK0m79LoIeWB/HJQBQyfJhVMmx10mtvxyTGN5Mc4H/6dMpB0TWnMAroIela60KalET6IGlKTXQtdR/Wux8uW4E5T5a3nrng1PDSc2MsKfb6IUnPZGZizllTYgRoCVvRvT7vost72GYrg+ateUlIZzugB0kjOiu1FfYcyAJq+ukBSbJ0mf2qfe5mZzExY/GF97RfNi2TJjIBoZi1U1rDNorAXDkNNiKqarar8OlxS77yToqRxPOmZKjImHGRvC8eVuICT3LqD5yr01h51RXvj7QXXQ1aJ/DJul1mEaVaH3YDOQ5uracl5bBQKBQCBwkREPoEAgEAiMgngABQKBQGAU7FoNyLKOP9Va+q2tzb5PmJmft5HEQ4PnpdahNh/UUJJIZeF2CzzHhyx+uF+S0zV0BKcfoRHJYXQ9ueiEshf7kBpzJMB515VfXuncBnLrtN6vlCMetj9R248lrHZ47ahpqZ3TdDocB6zcewbRrZwhCkHEqIxzL5I4Yiw3/fN1SuiGNebKDK1TS5aGNivQCXkPrMT+qIJdSkLby3gqMs6j4Xw8iXjHyJwgNuTAPm+Lo+0oKt8vVe2vh86NoRXPEjEDagfD+TjbKz++lhgHe0UbTOxpqK8OzK1aVf0xCTXm0NWp8OmPK7pPhTl1yaWT75Es8/dKiTGi84BqjAlqYxxPqqdmboycnxVPvAEFAoFAYBTEAygQCAQCo2DXUnBZ1tEWWm6Z2pL45a1tSQ1kSSpLhunSO1ADzaRPLdOk4zDDVDXhMhtKJjUzawd8fJi8ylJxbSJdiEANFFIqy/ZPprDTYUKqprTSHRfnU2v5bjFs+WONlnH6/eagYooJ6VOhKmEHRDuaTDp1BSqDzIGmv2Yom6VLNakNLUuls3GDQVJKHyf2UqBqqlr3C1rQ/LkS6uCdw1me1jBK+fC+U/fxs/vq2rhDBX1SOj6X+2OF8mI6wOu+z5zxDt0sa1a6e7X0fVjkpHhxbwntuQGLooRi16kGFZ3A/aZqS7SEG3xi94UPb297inHos9pvpCqnvHfUxZoUOu2lMsoa3e9KizfnmQgQb0CBQCAQGAXxAAoEAoHAKIgHUCAQCARGwa7VgNq26ay/lXcEB8kSVZ/Kx1JRctwgaNWBnfbtKBnOc+FyV+BfoTmoVT1jHhKrDugtpWobSUQE9bBu/Qp61mwgTZX9Qh2qYbm0ltVmLPuFZYvoMStoGU1Syqtl5GgSzrVkWbboMRtwXmfpu5aLtiVzH2DTL/ti6TcbyTJtLYluUG7PcmnVBofiF8y8/VQGvYtBlElCqly7RHPAsqa4cpwWFEJkHGQ7tJ+6VSNl/jl1NdxLGt0wQ6rsBFrHQsqyFyjRrmrqULi20pFLRH9MJv0aacuohgGrKpZss4+po5QyxWSFMvIF9CFNvi1w361Q6q7xDLx/qUMl11LGiPYDx3Af4g0oEAgEAqMgHkCBQCAQGAXxAAoEAoHAKNi1GpDJPCC1yEm4aOZfO3i+krEJhM5Z4RyPhNOUXXNuDzUTJ5PQrQXR2bOpn3NQlp0GQasU8rU6j6MAd9uAx96YyTwBzJ/Y3PL2+TPYuat20+bD9jQ6p4V9OsF+p6uOq27RUSW4dGpae/d29i4Jl45rp9oBdRvOa3K8NuZhcW5YEn896Z+Tk8ytkjbzY5OJv85LaWMydQTLjCPXeUDVDhpEUfXP7eHcMbVVok6QaB2J3YvMqYOOk0ErU+2POtrDD/s4g72i39GeiedKuyAdX5yHxfNTm58KY6TJMIdL9K5k3mEybv1qvf9bjJ/p3EdgTCYz+R3zmJJr17WR/ULtlRpdLvEM2i+JvtiDeAMKBAKBwCiIB1AgEAgERkE8gAKBQCAwCnatBtQ2zZq7X4pf105zJJTDzKAbkMOvMIcoz7ruSLWM/uOkFuXwg3IMOjhU6EfUY/Q45Je3FvB3c3ESvr1zaAGbEs08Y2Q15s0szOtUE9GPGAGdt+SQu31Nwe/v3bMP7e/2O8W8DfLYBw8c8Ot1jgQjLwDVDujVxXgD7YtiThMw6pH980MYP07otUtiRJil4cYm5pFx04L/Y3YbMDqjwJyiuuzOvaoRM8Ax3qMFmJnN4KXG8aX9VFAbQ7dN5SurqqDbTPu1zIJz2+ihh3E7Ea2Dc3kY7bCUMc5EhQrzylYS657GxfDe9/tygzXRH/FVLuvne/e4Vcn3lVwPnhsHVMFsc07YW/85NKBAIBAI7GLEAygQCAQCo2D3UnB5Fy+g1iMFSi9XsDRXsHSUlAPpL10itVSCGnCvsaTnkvJoseJhUmnyns02Sgk6/l1gyapapzDJkLSIWrDTGoVtIGWifV7C2p2N9JY/oOtYWi1UIMs9C5wrbVd0mXb/aWlpPw1lE9JqEhHR9KdbmqXXQ/+/41jj+em/gqRhSZkUGvNQsaSZqb+0zNGIBdjyg05pNSahxvjfgr2L7De1l8KYT5JkZduClDSpS/kuIDVWIN7AZvI7SsNpz8QIDNmc44n10a0sc6oEKS0nEZTD9yj3lesgSeyZELkwk+kbLe/n/jLyjBRbMvUDsSKamiv9xP7uQ7wBBQKBQGAUxAMoEAgEAqMgHkCBQCAQGAW7VgNq2nxtYV8Jz1hDX6F6oWWcLcqsG8YZ5ANawZDmY577TKxf8FwvBkrDCVYvLtXegvY6LOPU3qA2AOt61WpACSdaQEm9olWbfvYhuWn5WH/6hZl5W37at/A6s4S+qFXTGi7D1rLfZc0+9FD9K2sZ/eE9cxYLX64+VcsflMky/rqQbuMYr1c8e9GwKJHknGrAuAn5fVh+dNEH1AH5v2tTd+XFnMLQ1MNah5vSgLgVjhm1r6mN5wpdR8deEhNi2BZ6kvTbcstPCeA9monuVi99GzjFYQXrLbcOevbmtt9WtahJCXsdWObUMo2hoi6FfiplX5zusKoQA059r1FNUW2G7LwQb0CBQCAQGAXxAAoEAoHAKIgHUCAQCARGwa7VgFrrmG7VTchFJ9HAjodkFHA//2pmlsv8C8ZUJxbywsOz4r1O5uBI5PBkWFuidUdp3XIDLSCJiNC+gRYDScUKF/Xt103Qfs7jqEQ0yhBHnte0a9e5Acyi4HyErv2renjODfUW5c9piZPGInTHZbR3onU0arXvO3E5MAfNzOz06S4egPMi8pK2Pd35rXCd28p/diqWM6sVophp0494D/2fcydbK0WigSZzx0R7bSj2cR5W72GYiG4V70PpR94rnHPTikaUzF/B9WC0yWqlx6E1Es5H9k3NaguWUv6YjG6AvQ4sjJxWxu/BZO+ilS2GI9FrsTpb5T62xbIdHhFyMdXqjFpYH+INKBAIBAKjIB5AgUAgEBgFu5aCc5DHZPr621/vRzudpFy67acVmCg6RBvQZqWAg2yZqYOvpwlIkTCZMa2V1eMOlKHi3FagGJSWIj1EamMCew51rU5ozoql792+lkyAHEh0XZEmSK6l39Wk6PqRdAppKb+O7uQ4n6QAvAOteSqUvp/Z7tyYSelyHGxt91OISTKmXtuGZb9ztzx87qRa+5dpM8Rt3Xii7dMOtlCufBfjlmnDWh7N+5e2RDpVIi3DxrnCbqfQcUv3bmy7GkgUZe9rm5Zws2dteEsLLEkMnk49rczpENrHnNKQlMEXSunCDRuyBen4Pkq3robp6fTTgUAgEAj8HSIeQIFAIBAYBfEACgQCgcAo2LUaUJZlaz5V+WcWNQ5Z7VMooN1GhnLRzDSV0h+HaZGuDJKCBMp1tTQz4Z6T+IV+vpyaVkGbeIkOIHfO42q8wRJ8Lbn12YbXqVpXDopkycRqSNoPvaWFvqL6S4Z13C01OdV90m2hf0kZM3UcajWlxn/A5oYlqlULKx5Jjl0mA9cfZ7537/r3Fcqut8/40lgt+82wnwZlv/MNrwmp9lQi2qTE+bTu2vpz534Vg/ekDWtN1IDS1FnVHFBCj2upZfI85rzwGkqB+66WKQET+h0xckQsmRJ7LPTxYik6DrS+ihopPLJ0+9lkOMVY7zX2S1myNF+swnCdJyU1H4rhGkUhvxf92qki3oACgUAgMAriARQIBAKBURAPoEAgEAiMgt2rAVnHTFbLqnc7zg9x0QdJpALq+XeYB+GOw7hiXd5hvpFqWDvNp2DMgFrzc7+s/XfpAJxHg3knamUzwVykZes1oa1Fv4XRZAceW+dpNZxTQ85b7IPING9ubfk/wPOkFC0kr6j9oU1b2g5oPsm0K40+wHhivDq0glIilzeTOR9eg2iFh19Ak1uSw3dzMRC3jLG4xLyO1lk0DcdSq05YlLxXoPPIvqg/ti3vQ4xNGfN14899sWQsh1gWcc4ZdRC18Kpw72f+s7MB+6x0/mD/nLSaETC02nJzcoajvnPGY0ubltv+fqAerNHme+Ybbh2tePR0CtpW1bDmSaC2VqKhhwYUCAQCgd2MeAAFAoFAYBTsWgquaep1maXSUkNpimaeusnwWk17nQS6nvQQoKXISZkvXofVeTd5qyaVQZaq0dJq0I0o8VSLnFXjy3FpT6P2Ig1SGrPCt2kBKw9Nedxewaoj8eXVfkKJKrpY1y+XoP3QT0scVyuXSUewSr6S851N+6mLs5+V5ZZjDdQexoEm7u7ds9+toz3Kdq2UKEp3VzgBoW1Zxk86OEmHlfNjeTEpxlLsXmagZljCrWXAya0DOxfSUgspHee6bYxNve+2t5hAizTPZUdTkepO7iXzn9X7peZNWfTT5oslSuYxTcHZTZFCpO0QnfHlHmhA9U1B7c/leiVu9uaxWsm14xQStIn9pvdHLfdHXe/wXfuNtp3XVoFAIBAIXGTEAygQCAQCoyAeQIFAIBAYBbtXA7JqzYkqfUvNh3C2N+TkB8oPz35WONYW5dDgawvhRhMens910SRos1JAc6B+MZHa6hV0j8ZY3ioW7ChJZXlrK6XtWY7kRex3gYTOrz/cffbggQN+v+DL1Zongz6UakDdcJxOB7QYO4fOo8dMEl2hbQhfzjZkuCV02wrl6dQUpzNof2L1tI3PNigD1riAAv10YGOvW3ZltCy73vbaX4a6ch1PJeylpoUfB4VcjwwaSQsbnNYl7KJsuaJO6FFKKf8SCsVk6kuIXQE6BFWOEb3VJjjX1MYKbZIxU62gxXB8ZV3fVBn0rdaXS1OLdfuF9sdxoCXek9Jb8cw3BjQs6DrbGCOVmyoxPNWj4MWTCIyy1KiM/qkzingDCgQCgcAoiAdQIBAIBEZBPIACgUAgMAp2rQaUtR0/rxoLXFWS6ABn7Q6rkYK2JZzHIRwrpwyVJSJw5aMlI5Shv6guNRRHfC4olcrP1jU1LeFgwTXTgn0ufPN8w/PsnKuUxFgI13769Bm3bg8ioQvpqCynNoA5EmKXQt6aczyo66iGQh1qM9H+xF4HnP0cx9FebDPOgTCAscjKg3NOGudLde1IbW489NzTCGg2qv/+KAvGOvfrq0nMwED/c95MvUomt7nFlVgPMTq+opYpy2xDhbljK50rgwgFWs7QOUa/G3jfcf6OXqDt7U2/CmMml/lTHP/cL8e86jG8HozS1njyBSI6KnxvaCQ3bYb4irKELVru1ml7IpI7EAgEArsY8QAKBAKBwCiIB1AgEAgERsGu1YDKoljbe7fC9TaMTGBKspsHNDyXJJm9I/umzxot5p3hFSMhwC+vWvXJ8tuSnyW3W5jOxcCcjo3++G5qVgW46Im08cB+P5dnzz4/72QJPld92k49+KBbd2blOfBCuXdcO1rkq3SQob0V2nDg4EG/XiMLaupffpif3uraWCQKi4f6cVFHY3x3m/jgyXiCBkEPwMx0jEC7TLztZL4axov69J3dF/QvuWESC3/O35F9V1V/1IGZv0cXyB9fLrw/GnXcVu5Txkc0GAc6v60dGJdm/n5pMRfJkDKQmddB1b+RmgkjrvV8l/C9WyGGQ7+S5pjfRZ+1IfC76+GHT7vlpu7Xg3PMidJzTXZM/Yu6s1yPXPZDTaoP8QYUCAQCgVEQD6BAIBAIjIJdS8E1TbOmAEopWW1zli2jpFBoBNJoNUo6WQqrlATLfFewr1Ba5/xfnFPaowVNmNjGi+VJRXoCNMKBg/vWv89n/tLumXn66OC+Lh4gsQBJqCZfav3g336tW4fX+6zspwWTIt+k7Fe2pUUOtt3e9BYntYwDxiTsAwVnAxQWKaDJtKO0Wvy/RmomoeT03AuOxf5y/CTWoSWFkveuY9olO302786HpbJVki4sJdsD19XMW1UlljjoJ1oAOZoQ1N6KcQz6OfNIE4Oz3m35r3eS5CufYBxJjb3VLj11mP51EQtoL22teD6rVfcd9HW5B83ONe1Cjwsbq4mnaZdCr7akYRNrHnxfyXFy198RxxAIBAKBXYx4AAUCgUBgFOw6Cu4br/arZffam8msXtIGK1Sk6BtiA8deOgfwdVJpkISCAzWjFESS0srPKjWDEqCqIo2Dfck51GhDhgon7TPO8OeFXky61/m6IQXHSj1PgyzlOKxOS2bMa4ENppsnTg5CS+XWTzuZpU7mQxTcokB1lFz3jImh2G8hs8Tz3G9b1f0z4s+2sZ+CI9WnfUzXdp670rgVZ+XTbZlMiCyn46mfggN7nVRz1uL6QCpvifFTg0Z31XakRCsmdHb75lirMRaHnB1Y8Wd095bxpOPd7BwUnFyDhvcD+k0puLxAFd+if5yaeTqS34OJ+4rrG3zPwerEVRbuQMElfSy/a59+4/uS32dE1u60xd8x/t//+3929dVXj92MQCAQCHyTeOCBB+yJT3xi7/pd9wBqmsb+6q/+ytq2tWuuucYeeOABOwBxLtDh1KlTdvXVV0c/7YDop/ND9NP5IfppGG3b2sMPP2xXXXVV8gav2HUUXJ7n9sQnPtFOnTplZmYHDhyIC3weiH46P0Q/nR+in84P0U/9OIjJ4udCFCEEAoFAYBTEAygQCAQCo2DXPoBms5n9u3/37xJvq4BH9NP5Ifrp/BD9dH6Ifro42HVFCIFAIBD49sCufQMKBAKBwOMb8QAKBAKBwCiIB1AgEAgERkE8gAKBQCAwCuIBFAgEAoFRsGsfQHfffbc9+clPtvl8btdff7194hOfGLtJo+HYsWP27Gc/2/bv329XXHGFvfSlL7X777/fbbO9vW1Hjx61yy+/3Pbt22c33XSTnTx5cqQW7w68+c1vtizL7Lbbblv/LfrpLP7yL//SfuRHfsQuv/xy29jYsGc84xn2qU99ar2+bVt7/etfb1deeaVtbGzYjTfeaF/4whdGbPHfPeq6tjvvvNOOHDliGxsb9p3f+Z32C7/wC4kR8bd7P31TaHch3vOe97TT6bT9z//5P7f/63/9r/Zf/+t/3V5yySXtyZMnx27aKHjRi17U3nPPPe1nP/vZ9tOf/nT7Az/wA+0111zTnj59er3NT/zET7RXX311e/z48fZTn/pU+9znPrd93vOeN2Krx8UnPvGJ9slPfnL73d/93e2rX/3q9d+jn9r2a1/7WvukJz2p/dEf/dH24x//ePvFL36x/YM/+IP2//yf/7Pe5s1vfnN78ODB9v3vf3/7mc98pv1n/+yftUeOHGm3trZGbPnfLd74xje2l19+efuBD3yg/dKXvtS+973vbfft29f+x//4H9fbRD99c9iVD6DnPOc57dGjR9fLdV23V111VXvs2LERW7V78JWvfKU1s/YjH/lI27Zt++CDD7aTyaR973vfu97mf//v/92aWXvvvfeO1czR8PDDD7dPecpT2g996EPtP/yH/3D9AIp+Oouf/dmfbV/wghf0rm+apj18+HD7H/7Df1j/7cEHH2xns1n727/9238XTdwVePGLX9z+2I/9mPvby172svbmm29u2zb66WJg11Fwy+XS7rvvPrvxxhvXf8vz3G688Ua79957R2zZ7sFDDz1kZmaXXXaZmZndd999tlqtXJ9de+21ds0113xb9tnRo0ftxS9+sesPs+inb+D3fu/37LrrrrMf+qEfsiuuuMKe+cxn2jve8Y71+i996Ut24sQJ108HDx6066+//tuqn573vOfZ8ePH7fOf/7yZmX3mM5+xj370o/b93//9Zhb9dDGw69ywv/rVr1pd13bo0CH390OHDtlf/MVfjNSq3YOmaey2226z5z//+fb0pz/dzMxOnDhh0+nULrnkErftoUOH7MSJEyO0cjy85z3vsT/90z+1T37yk8m66Kez+OIXv2hvfetb7fbbb7ef+7mfs09+8pP2qle9yqbTqd1yyy3rvjjXPfjt1E+ve93r7NSpU3bttddaURRW17W98Y1vtJtvvtnMLPrpImDXPYACwzh69Kh99rOftY9+9KNjN2XX4YEHHrBXv/rV9qEPfcjm8/nYzdm1aJrGrrvuOnvTm95kZmbPfOYz7bOf/ay97W1vs1tuuWXk1u0e/M7v/I69613vsne/+932tKc9zT796U/bbbfdZldddVX000XCrqPgnvCEJ1hRFEll0smTJ+3w4cMjtWp34NZbb7UPfOAD9od/+IcuZfDw4cO2XC7twQcfdNt/u/XZfffdZ1/5ylfse7/3e60sSyvL0j7ykY/YW97yFivL0g4dOhT9ZGZXXnmlfdd3fZf721Of+lT78pe/bGa27otv93vwZ37mZ+x1r3udvfzlL7dnPOMZ9i//5b+017zmNXbs2DEzi366GNh1D6DpdGrPetaz7Pjx4+u/NU1jx48ftxtuuGHElo2Htm3t1ltvtfe973324Q9/2I4cOeLWP+tZz7LJZOL67P7777cvf/nL31Z99sIXvtD+/M//3D796U+vf6677jq7+eab179HP5k9//nPT8r4P//5z9uTnvQkMzM7cuSIHT582PXTqVOn7OMf//i3VT9tbm4maZ5FUVjTNGYW/XRRMHYVxLnwnve8p53NZu1v/uZvtp/73OfaV77yle0ll1zSnjhxYuymjYKf/MmfbA8ePNj+0R/9UfvXf/3X65/Nzc31Nj/xEz/RXnPNNe2HP/zh9lOf+lR7ww03tDfccMOIrd4d0Cq4to1+atuzJeplWbZvfOMb2y984Qvtu971rnbPnj3tf/kv/2W9zZvf/Ob2kksuaX/3d3+3/Z//83+2L3nJS77tyotvueWW9u/9vb+3LsP+b//tv7VPeMIT2te+9rXrbaKfvjnsygdQ27btr/3ar7XXXHNNO51O2+c85zntxz72sbGbNBrM7Jw/99xzz3qbra2t9qd+6qfaSy+9tN2zZ0/7z//5P2//+q//erxG7xLwART9dBb//b//9/bpT396O5vN2muvvbZ9+9vf7tY3TdPeeeed7aFDh9rZbNa+8IUvbO+///6RWjsOTp061b761a9ur7nmmnY+n7d//+///fbf/tt/2y4Wi/U20U/fHCIPKBAIBAKjYNdpQIFAIBD49kA8gAKBQCAwCuIBFAgEAoFREA+gQCAQCIyCeAAFAoFAYBTEAygQCAQCoyAeQIFAIBAYBfEACgQCgcAoiAdQIBAIBEZBPIACgUAgMAriARQIBAKBUfD/Aw1FmxHHJdnGAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(samp[0]) # paired anchor image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# training & testing partition\n",
    "# grabbing 70% of the data to train\n",
    "train_data = data.take(round(len(data)*.7))\n",
    "# dividing the data into batches of 16\n",
    "train_data = train_data.batch(16)\n",
    "# start preprocessing the next set of data over the NN, when we train \n",
    "train_data = train_data.prefetch(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_samples = train_data.as_numpy_iterator()\n",
    "train_sample = train_samples.next()\n",
    "len(train_sample[0]) # there are 16 images in the set of each sample set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_samples = test_data.as_numpy_iterator()\n",
    "test_sample = test_samples.next()\n",
    "len(test_sample[0]) # there are 16 images in the set of each sample set"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building a Siamese NN"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ### Build an embedding layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this is like a data translator for our NN to verify up the user\n",
    "# we will be doing convolution  + RelU + max-pooling over the functoin in the image matrix( initially from the input, then over the pooled down image)\n",
    "def make_embedding():\n",
    "    inp = Input(shape=(100,100,3), name='input_image') # we used keras (tf) here\n",
    "    # FIRST LAYER \n",
    "    #convolution \n",
    "    c1 = Conv2D(64,(10,10),activation='relu')(inp)\n",
    "    #max-pooling \n",
    "    m1 = MaxPooling2D(64,(2,2), padding='same')(c1)\n",
    "    \n",
    "    #SECOND LAYER\n",
    "    c2 = Conv2D(128,(7,7),activation='relu')(m1)\n",
    "    m2 = MaxPooling2D(64,(2,2), padding='same')(c2)\n",
    "\n",
    "    #THIRD LAYER\n",
    "    c3 = Conv2D(128,(4,4),activation='relu')(m2)\n",
    "    m3 = MaxPooling2D(64,(2,2), padding='same')(c3)\n",
    "\n",
    "    #FOURTH LAYER (FINAL EMBEDDED LAYER)\n",
    "    c4 = Conv2D(256,(4,4),activation='relu')(m3)\n",
    "    # fully connected layer with a sigmoid function (flatten + dense layer)\n",
    "    f1= Flatten()(c4) # flattening the 3 dimensions, to 1 dimension\n",
    "    d1= Dense(4096,activation='sigmoid')(f1)\n",
    "    \n",
    "    return Model(inputs=[inp], outputs=[d1], name='embedding')\n",
    "    # this will return us the feture vector of 4096 units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<KerasTensor: shape=(None, 100, 100, 3) dtype=float32 (created by layer 'input_1')>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inp = Input(shape=(100,100,3))\n",
    "inp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<KerasTensor: shape=(None, 4096) dtype=float32 (created by layer 'dense')>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c1 = Conv2D(64,(10,10),activation='relu')(inp)\n",
    "#max-pooling \n",
    "m1 = MaxPooling2D(64,(2,2), padding='same')(c1)\n",
    "\n",
    "#SECOND LAYER\n",
    "c2 = Conv2D(128,(7,7),activation='relu')(m1)\n",
    "m2 = MaxPooling2D(64,(2,2), padding='same')(c2)\n",
    "#THIRD LAYER\n",
    "c3 = Conv2D(128,(4,4),activation='relu')(m2)\n",
    "m3 = MaxPooling2D(64,(2,2), padding='same')(c3)\n",
    "#FOURTH LAYER (FINAL EMBEDDED LAYER)\n",
    "c4 = Conv2D(256,(4,4),activation='relu')(m3)\n",
    "# fully connected layer with a sigmoid function (flatten + dense layer)\n",
    "f1= Flatten()(c4) # flattening the 3 dimensions, to 1 dimension\n",
    "d1= Dense(4096,activation='sigmoid')(f1)\n",
    "d1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"embedding\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (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",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 40, 40, 128)       401536    \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 20, 20, 128)      0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 17, 17, 128)       262272    \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 9, 9, 128)        0         \n",
      " 2D)                                                             \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",
      "=================================================================\n",
      "Total params: 38,960,448\n",
      "Trainable params: 38,960,448\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "mod = Model(inputs=[inp], outputs=[d1], name='embedding')\n",
    "mod.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<KerasTensor: shape=(None, 91, 91, 64) dtype=float32 (created by layer 'conv2d_4')>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c1 = Conv2D(64,(10,10),activation='relu')(inp)\n",
    "c1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"embedding\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " input_image (InputLayer)    [(None, 100, 100, 3)]     0         \n",
      "                                                                 \n",
      " conv2d_5 (Conv2D)           (None, 91, 91, 64)        19264     \n",
      "                                                                 \n",
      " max_pooling2d_3 (MaxPooling  (None, 46, 46, 64)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_6 (Conv2D)           (None, 40, 40, 128)       401536    \n",
      "                                                                 \n",
      " max_pooling2d_4 (MaxPooling  (None, 20, 20, 128)      0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_7 (Conv2D)           (None, 17, 17, 128)       262272    \n",
      "                                                                 \n",
      " max_pooling2d_5 (MaxPooling  (None, 9, 9, 128)        0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_8 (Conv2D)           (None, 6, 6, 256)         524544    \n",
      "                                                                 \n",
      " flatten_1 (Flatten)         (None, 9216)              0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 4096)              37752832  \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 38,960,448\n",
      "Trainable params: 38,960,448\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "embedding = make_embedding()\n",
    "embedding.summary()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ### create an L1 Distance layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# L1 distance layer - looks over how similar the images actually are\n",
    "# will take up the above 4096 feature vector (the embedded model) & output a value \n",
    "class L1Dist(Layer):\n",
    "    def __init__(self,**kwargs): \n",
    "        super().__init__() #inheritance\n",
    "    \n",
    "    # this is the logic here (the difference b/w the input_embedding and the validation_embedding)\n",
    "    def call(self,input_embedding, validation_embedding):\n",
    "        return tf.math.abs(input_embedding - validation_embedding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<__main__.L1Dist at 0x23b9e733fa0>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "l1 = L1Dist()\n",
    "l1"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ### compile the siamese Network/Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we make the final model over the embedding and followed by L1Dist, with activation at the end\n",
    "def make_siamese_model(): \n",
    "    # handling the inputs \n",
    "    # anchor_image input\n",
    "    input_img = Input(name='input_img', shape=(100, 100, 3))\n",
    "\n",
    "    # validation_image input\n",
    "    validation_img = Input(name='validation_img', shape=(100, 100, 3))\n",
    "\n",
    "    # combining siamese distance components \n",
    "    siamese_layer = L1Dist()\n",
    "    siamese_layer._name = 'distance'\n",
    "    distances = siamese_layer(embedding(input_img), embedding(validation_img))\n",
    "\n",
    "    # are those the same person (in the 2 images)\n",
    "    classifier = Dense(1, activation='sigmoid')(distances)\n",
    "\n",
    "    return Model(inputs=[input_img, validation_img], outputs=classifier, name='SiameseNetwork')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<KerasTensor: shape=(None, 4096) dtype=float32 (created by layer 'embedding')>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_image = Input(name='input_img',shape=(100,100,3))\n",
    "validation_image = Input(name='validation_img',shape=(100,100,3))\n",
    "\n",
    "embedding(input_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<KerasTensor: shape=(None, 4096) dtype=float32 (created by layer 'embedding')>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embedding(validation_image)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<KerasTensor: shape=(None, 4096) dtype=float32 (created by layer 'l1_dist_1')>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "siamese_layer= L1Dist()\n",
    "distances = siamese_layer(embedding(input_image), embedding(validation_image))\n",
    "distances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<KerasTensor: shape=(None, 1) dtype=float32 (created by layer 'dense_2')>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classif = Dense(1, activation='sigmoid')(distances)\n",
    "classif "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "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",
      "                                                                                                  \n",
      " validation_img (InputLayer)    [(None, 100, 100, 3  0           []                               \n",
      "                                )]                                                                \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[2][0]',              \n",
      "                                                                  'embedding[3][0]']              \n",
      "                                                                                                  \n",
      " dense_2 (Dense)                (None, 1)            4097        ['l1_dist_1[0][0]']              \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 38,964,545\n",
      "Trainable params: 38,964,545\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_fin = Model(inputs=[input_image,validation_image], outputs=[classif], name='SiameseNetwork')\n",
    "model_fin.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                  \n",
      " validation_img (InputLayer)    [(None, 100, 100, 3  0           []                               \n",
      "                                )]                                                                \n",
      "                                                                                                  \n",
      " embedding (Functional)         (None, 4096)         38960448    ['input_img[0][0]',              \n",
      "                                                                  'validation_img[0][0]']         \n",
      "                                                                                                  \n",
      " distance (L1Dist)              (None, 4096)         0           ['embedding[4][0]',              \n",
      "                                                                  'embedding[5][0]']              \n",
      "                                                                                                  \n",
      " dense_3 (Dense)                (None, 1)            4097        ['distance[0][0]']               \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 38,964,545\n",
      "Trainable params: 38,964,545\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "siamese_model = make_siamese_model()\n",
    "siamese_model.summary()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training a Siamese Neural Network"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ### setup a loss function & optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "#loss function\n",
    "binary_cross_loss = tf.losses.BinaryCrossentropy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#optimizer (adam optimizer) \n",
    "opt = tf.keras.optimizers.Adam(1e-4) #0.001 -> Learning rate\n",
    "# ref -> https://keras.io/api/optimizers/ "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ### establish Checkpoints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "#checkpoints for the model, in order if something breaks down we are able to track as to where the model broke off\n",
    "checkpoint_dir = './training_checkpointsv2'\n",
    "checkpoint_prefix = os.path.join(checkpoint_dir, 'ckpt')\n",
    "checkpoint = tf.train.Checkpoint(opt=opt, siamese_model = siamese_model)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ### build Train Step Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[[[0.36985293, 0.42867646, 0.40465686],\n",
       "          [0.37279412, 0.4372549 , 0.39656863],\n",
       "          [0.37058824, 0.43529412, 0.3852941 ],\n",
       "          ...,\n",
       "          [0.40098038, 0.4480392 , 0.4343137 ],\n",
       "          [0.38333333, 0.43039215, 0.422549  ],\n",
       "          [0.3735294 , 0.42058823, 0.4127451 ]],\n",
       " \n",
       "         [[0.37254903, 0.4345588 , 0.39754903],\n",
       "          [0.3745098 , 0.4372549 , 0.39411765],\n",
       "          [0.36544117, 0.43112746, 0.37132353],\n",
       "          ...,\n",
       "          [0.39534312, 0.44460785, 0.42671567],\n",
       "          [0.3870098 , 0.43406862, 0.4227941 ],\n",
       "          [0.37941176, 0.42647058, 0.41666666]],\n",
       " \n",
       "         [[0.37254903, 0.43529412, 0.39313725],\n",
       "          [0.37156862, 0.4343137 , 0.38921568],\n",
       "          [0.36813724, 0.4272059 , 0.36960784],\n",
       "          ...,\n",
       "          [0.39240196, 0.4490196 , 0.42230392],\n",
       "          [0.38921568, 0.4370098 , 0.41911766],\n",
       "          [0.38627452, 0.43333334, 0.41764706]],\n",
       " \n",
       "         ...,\n",
       " \n",
       "         [[0.37230393, 0.41151962, 0.36838236],\n",
       "          [0.36960784, 0.4107843 , 0.36666667],\n",
       "          [0.36764705, 0.41617647, 0.36838236],\n",
       "          ...,\n",
       "          [0.35416666, 0.3855392 , 0.34240195],\n",
       "          [0.39264706, 0.4240196 , 0.38088235],\n",
       "          [0.40490195, 0.4362745 , 0.39313725]],\n",
       " \n",
       "         [[0.3382353 , 0.38333333, 0.33137256],\n",
       "          [0.34019607, 0.38578433, 0.3394608 ],\n",
       "          [0.34044117, 0.39338234, 0.34338236],\n",
       "          ...,\n",
       "          [0.34264705, 0.3740196 , 0.33088234],\n",
       "          [0.3879902 , 0.41936275, 0.3762255 ],\n",
       "          [0.4137255 , 0.44509804, 0.4019608 ]],\n",
       " \n",
       "         [[0.36078432, 0.40784314, 0.3529412 ],\n",
       "          [0.35      , 0.3990196 , 0.34509805],\n",
       "          [0.35196078, 0.40686274, 0.35588235],\n",
       "          ...,\n",
       "          [0.35588235, 0.3872549 , 0.34411764],\n",
       "          [0.39803922, 0.42941177, 0.38627452],\n",
       "          [0.4117647 , 0.44313726, 0.4       ]]],\n",
       " \n",
       " \n",
       "        [[[0.17965686, 0.22916667, 0.20245098],\n",
       "          [0.18578431, 0.23039216, 0.20612745],\n",
       "          [0.18455882, 0.22769608, 0.21004902],\n",
       "          ...,\n",
       "          [0.26960784, 0.34509805, 0.31862745],\n",
       "          [0.2647059 , 0.34313726, 0.3019608 ],\n",
       "          [0.25784314, 0.3362745 , 0.29313725]],\n",
       " \n",
       "         [[0.18161765, 0.21151961, 0.19362745],\n",
       "          [0.18995099, 0.21985294, 0.20196079],\n",
       "          [0.19142157, 0.22377451, 0.20514706],\n",
       "          ...,\n",
       "          [0.26642156, 0.33970588, 0.3139706 ],\n",
       "          [0.27156863, 0.34705883, 0.31470588],\n",
       "          [0.2747549 , 0.34142157, 0.30857843]],\n",
       " \n",
       "         [[0.16642156, 0.19632353, 0.17843138],\n",
       "          [0.1740196 , 0.20392157, 0.1860294 ],\n",
       "          [0.1754902 , 0.20784314, 0.18921569],\n",
       "          ...,\n",
       "          [0.28431374, 0.3509804 , 0.327451  ],\n",
       "          [0.27132353, 0.3379902 , 0.31446078],\n",
       "          [0.28431374, 0.3509804 , 0.3254902 ]],\n",
       " \n",
       "         ...,\n",
       " \n",
       "         [[0.24142157, 0.2747549 , 0.23357843],\n",
       "          [0.24583334, 0.28063726, 0.23946078],\n",
       "          [0.24681373, 0.2860294 , 0.24485295],\n",
       "          ...,\n",
       "          [0.23039216, 0.27745098, 0.23186274],\n",
       "          [0.24313726, 0.29534313, 0.24754901],\n",
       "          [0.2602941 , 0.32009804, 0.28235295]],\n",
       " \n",
       "         [[0.20514706, 0.24730392, 0.21495098],\n",
       "          [0.23431373, 0.2764706 , 0.24411765],\n",
       "          [0.24411765, 0.28627452, 0.25392157],\n",
       "          ...,\n",
       "          [0.23357843, 0.27622548, 0.23872548],\n",
       "          [0.24901961, 0.29460785, 0.25931373],\n",
       "          [0.2720588 , 0.31715685, 0.29264706]],\n",
       " \n",
       "         [[0.21666667, 0.26764706, 0.24019608],\n",
       "          [0.22254902, 0.2735294 , 0.24607843],\n",
       "          [0.24117647, 0.28627452, 0.25588235],\n",
       "          ...,\n",
       "          [0.21862745, 0.2627451 , 0.22941177],\n",
       "          [0.2509804 , 0.29509804, 0.2617647 ],\n",
       "          [0.25588235, 0.2990196 , 0.27892157]]],\n",
       " \n",
       " \n",
       "        [[[0.23921569, 0.28799018, 0.26715687],\n",
       "          [0.25220588, 0.29828432, 0.29436275],\n",
       "          [0.2529412 , 0.29607844, 0.3156863 ],\n",
       "          ...,\n",
       "          [0.09705883, 0.12254902, 0.09607843],\n",
       "          [0.10490196, 0.11568628, 0.09509804],\n",
       "          [0.10784314, 0.11568628, 0.09607843]],\n",
       " \n",
       "         [[0.23627451, 0.28333333, 0.26960784],\n",
       "          [0.2531863 , 0.2992647 , 0.30122548],\n",
       "          [0.25784314, 0.3009804 , 0.32254902],\n",
       "          ...,\n",
       "          [0.08578432, 0.10833333, 0.09264706],\n",
       "          [0.10686275, 0.11764706, 0.1004902 ],\n",
       "          [0.10980392, 0.1127451 , 0.09607843]],\n",
       " \n",
       "         [[0.23112746, 0.2752451 , 0.28112745],\n",
       "          [0.24289216, 0.2860294 , 0.29583332],\n",
       "          [0.2617647 , 0.3019608 , 0.33431372],\n",
       "          ...,\n",
       "          [0.09803922, 0.11985294, 0.11078431],\n",
       "          [0.11446078, 0.1245098 , 0.11838235],\n",
       "          [0.12352941, 0.12352941, 0.12156863]],\n",
       " \n",
       "         ...,\n",
       " \n",
       "         [[0.25735295, 0.28308824, 0.27450982],\n",
       "          [0.24191177, 0.24093138, 0.2389706 ],\n",
       "          [0.22034314, 0.2134804 , 0.20661765],\n",
       "          ...,\n",
       "          [0.23112746, 0.2367647 , 0.20833333],\n",
       "          [0.22254902, 0.22745098, 0.20490196],\n",
       "          [0.2245098 , 0.22941177, 0.20686275]],\n",
       " \n",
       "         [[0.2860294 , 0.29583332, 0.29191175],\n",
       "          [0.25073528, 0.24313726, 0.23848039],\n",
       "          [0.23921569, 0.21764706, 0.21666667],\n",
       "          ...,\n",
       "          [0.2134804 , 0.23088235, 0.20833333],\n",
       "          [0.19877452, 0.21544118, 0.19877452],\n",
       "          [0.1882353 , 0.20490196, 0.1882353 ]],\n",
       " \n",
       "         [[0.30318627, 0.30318627, 0.29534313],\n",
       "          [0.26911765, 0.25073528, 0.24803922],\n",
       "          [0.25      , 0.22352941, 0.2245098 ],\n",
       "          ...,\n",
       "          [0.20392157, 0.22352941, 0.20784314],\n",
       "          [0.19313726, 0.2127451 , 0.19705883],\n",
       "          [0.20392157, 0.22352941, 0.20784314]]],\n",
       " \n",
       " \n",
       "        ...,\n",
       " \n",
       " \n",
       "        [[[0.297549  , 0.34068626, 0.3107843 ],\n",
       "          [0.28112745, 0.32254902, 0.29338235],\n",
       "          [0.27034312, 0.3095588 , 0.27818626],\n",
       "          ...,\n",
       "          [0.30784315, 0.34705883, 0.35171568],\n",
       "          [0.29681373, 0.34191176, 0.33553922],\n",
       "          [0.29509804, 0.34215686, 0.33284312]],\n",
       " \n",
       "         [[0.28259805, 0.3247549 , 0.3120098 ],\n",
       "          [0.27279413, 0.3125    , 0.3       ],\n",
       "          [0.25833333, 0.29362744, 0.27745098],\n",
       "          ...,\n",
       "          [0.30514705, 0.3509804 , 0.33235294],\n",
       "          [0.30343136, 0.3529412 , 0.33431372],\n",
       "          [0.30392158, 0.35784313, 0.327451  ]],\n",
       " \n",
       "         [[0.2784314 , 0.3154412 , 0.2990196 ],\n",
       "          [0.26078433, 0.29607844, 0.2784314 ],\n",
       "          [0.2531863 , 0.2884804 , 0.27083334],\n",
       "          ...,\n",
       "          [0.3112745 , 0.3644608 , 0.3348039 ],\n",
       "          [0.2977941 , 0.35980392, 0.32132354],\n",
       "          [0.29313725, 0.35588235, 0.31666666]],\n",
       " \n",
       "         ...,\n",
       " \n",
       "         [[0.33995098, 0.36740196, 0.33995098],\n",
       "          [0.30735293, 0.32696077, 0.2995098 ],\n",
       "          [0.29411766, 0.30784315, 0.28039217],\n",
       "          ...,\n",
       "          [0.29117647, 0.327451  , 0.29705882],\n",
       "          [0.29264706, 0.32009804, 0.29264706],\n",
       "          [0.30416667, 0.33161765, 0.30416667]],\n",
       " \n",
       "         [[0.34509805, 0.37254903, 0.34509805],\n",
       "          [0.3142157 , 0.33382353, 0.30637255],\n",
       "          [0.29191175, 0.30563724, 0.27818626],\n",
       "          ...,\n",
       "          [0.28063726, 0.31691176, 0.28651962],\n",
       "          [0.28357843, 0.3110294 , 0.28357843],\n",
       "          [0.27450982, 0.3019608 , 0.27450982]],\n",
       " \n",
       "         [[0.33431372, 0.3617647 , 0.33431372],\n",
       "          [0.30784315, 0.33333334, 0.30294117],\n",
       "          [0.28431374, 0.30392158, 0.2764706 ],\n",
       "          ...,\n",
       "          [0.2872549 , 0.32352942, 0.29313725],\n",
       "          [0.27941176, 0.30686274, 0.27941176],\n",
       "          [0.28235295, 0.30980393, 0.28235295]]],\n",
       " \n",
       " \n",
       "        [[[0.13478433, 0.2524314 , 0.26027453],\n",
       "          [0.1564706 , 0.26666668, 0.26941174],\n",
       "          [0.14901961, 0.2764706 , 0.28666666],\n",
       "          ...,\n",
       "          [0.31313726, 0.35921568, 0.3392157 ],\n",
       "          [0.3054903 , 0.3254902 , 0.30980393],\n",
       "          [0.30313736, 0.34470588, 0.3396079 ]],\n",
       " \n",
       "         [[0.17254902, 0.26666668, 0.27450982],\n",
       "          [0.17356862, 0.25635293, 0.28235295],\n",
       "          [0.16392156, 0.26980394, 0.27764708],\n",
       "          ...,\n",
       "          [0.32156864, 0.34235296, 0.33980393],\n",
       "          [0.30980393, 0.33333334, 0.3254902 ],\n",
       "          [0.3188235 , 0.3541176 , 0.3423529 ]],\n",
       " \n",
       "         [[0.2072549 , 0.2817647 , 0.28764704],\n",
       "          [0.21156862, 0.28686273, 0.30607843],\n",
       "          [0.19313726, 0.28627452, 0.30686274],\n",
       "          ...,\n",
       "          [0.32254902, 0.35980392, 0.34901962],\n",
       "          [0.32392147, 0.34745088, 0.34666675],\n",
       "          [0.30745107, 0.36137253, 0.35627455]],\n",
       " \n",
       "         ...,\n",
       " \n",
       "         [[0.11431352, 0.13392137, 0.11039195],\n",
       "          [0.11039193, 0.11470588, 0.10470577],\n",
       "          [0.13333334, 0.15294118, 0.14705883],\n",
       "          ...,\n",
       "          [0.26764688, 0.33725467, 0.37352934],\n",
       "          [0.33450967, 0.4133333 , 0.4347058 ],\n",
       "          [0.32823485, 0.3713721 , 0.39490154]],\n",
       " \n",
       "         [[0.1039609 , 0.12749033, 0.11180405],\n",
       "          [0.11490197, 0.13411765, 0.11960785],\n",
       "          [0.13117658, 0.15078442, 0.1409805 ],\n",
       "          ...,\n",
       "          [0.26392165, 0.33215678, 0.35823527],\n",
       "          [0.3326668 , 0.4034119 , 0.4199999 ],\n",
       "          [0.30780402, 0.362706  , 0.37376478]],\n",
       " \n",
       "         [[0.12180393, 0.14533335, 0.12964708],\n",
       "          [0.11521571, 0.13803923, 0.13054904],\n",
       "          [0.13313724, 0.15666665, 0.13509803],\n",
       "          ...,\n",
       "          [0.27450982, 0.32686272, 0.34470585],\n",
       "          [0.35019615, 0.39254904, 0.4086275 ],\n",
       "          [0.327451  , 0.3627451 , 0.35882354]]],\n",
       " \n",
       " \n",
       "        [[[0.33088234, 0.3509804 , 0.29607844],\n",
       "          [0.33578432, 0.35784313, 0.30490196],\n",
       "          [0.32352942, 0.34705883, 0.3       ],\n",
       "          ...,\n",
       "          [0.34019607, 0.40588236, 0.34607843],\n",
       "          [0.3394608 , 0.40220588, 0.3512255 ],\n",
       "          [0.3362745 , 0.3990196 , 0.3480392 ]],\n",
       " \n",
       "         [[0.31936276, 0.3526961 , 0.30367646],\n",
       "          [0.31960785, 0.3529412 , 0.30392158],\n",
       "          [0.3156863 , 0.34901962, 0.3       ],\n",
       "          ...,\n",
       "          [0.34289217, 0.4034314 , 0.35318628],\n",
       "          [0.3392157 , 0.3990196 , 0.35539216],\n",
       "          [0.34313726, 0.40588236, 0.36372548]],\n",
       " \n",
       "         [[0.3022059 , 0.34142157, 0.30416667],\n",
       "          [0.31691176, 0.35612744, 0.31740198],\n",
       "          [0.3112745 , 0.34607843, 0.30294117],\n",
       "          ...,\n",
       "          [0.35784313, 0.40588236, 0.36593136],\n",
       "          [0.34583333, 0.39534312, 0.35980392],\n",
       "          [0.3529412 , 0.40686274, 0.37058824]],\n",
       " \n",
       "         ...,\n",
       " \n",
       "         [[0.3735294 , 0.40098038, 0.36960784],\n",
       "          [0.33700982, 0.3781863 , 0.33995098],\n",
       "          [0.31666666, 0.36078432, 0.30784315],\n",
       "          ...,\n",
       "          [0.3409314 , 0.38602942, 0.3360294 ],\n",
       "          [0.3382353 , 0.37745097, 0.34215686],\n",
       "          [0.3392157 , 0.3764706 , 0.33333334]],\n",
       " \n",
       "         [[0.38039216, 0.40784314, 0.3764706 ],\n",
       "          [0.3409314 , 0.38063726, 0.34313726],\n",
       "          [0.3115196 , 0.35416666, 0.3019608 ],\n",
       "          ...,\n",
       "          [0.31936276, 0.3622549 , 0.3122549 ],\n",
       "          [0.3139706 , 0.3465686 , 0.3134804 ],\n",
       "          [0.3156863 , 0.34705883, 0.30392158]],\n",
       " \n",
       "         [[0.37254903, 0.4117647 , 0.3764706 ],\n",
       "          [0.34117648, 0.38039216, 0.34313726],\n",
       "          [0.32156864, 0.35490197, 0.30588236],\n",
       "          ...,\n",
       "          [0.3362745 , 0.37843138, 0.32843137],\n",
       "          [0.327451  , 0.35784313, 0.3254902 ],\n",
       "          [0.33235294, 0.36372548, 0.32058823]]]], dtype=float32),\n",
       " array([[[[0.31887254, 0.3620098 , 0.34632352],\n",
       "          [0.32107842, 0.36053923, 0.35882354],\n",
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       "          ...,\n",
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       "          [0.2884804 , 0.32769608, 0.33161765],\n",
       "          [0.29117647, 0.33039215, 0.33431372]],\n",
       " \n",
       "         [[0.3247549 , 0.35710785, 0.34632352],\n",
       "          [0.33112746, 0.3625    , 0.3625    ],\n",
       "          [0.33039215, 0.35882354, 0.37058824],\n",
       "          ...,\n",
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       "          [0.28431374, 0.32352942, 0.3254902 ],\n",
       "          [0.2784314 , 0.31764707, 0.31960785]],\n",
       " \n",
       "         [[0.3382353 , 0.3617647 , 0.35392156],\n",
       "          [0.3362745 , 0.35882354, 0.3617647 ],\n",
       "          [0.33700982, 0.35661766, 0.36911765],\n",
       "          ...,\n",
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       "          [0.29705882, 0.33333334, 0.32352942],\n",
       "          [0.29117647, 0.327451  , 0.31764707]],\n",
       " \n",
       "         ...,\n",
       " \n",
       "         [[0.30514705, 0.3247549 , 0.30906862],\n",
       "          [0.27745098, 0.28480393, 0.2752451 ],\n",
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       "          ...,\n",
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       "          [0.2767157 , 0.28455883, 0.27279413],\n",
       "          [0.27941176, 0.2872549 , 0.2754902 ]],\n",
       " \n",
       "         [[0.30588236, 0.32254902, 0.3156863 ],\n",
       "          [0.27720588, 0.28308824, 0.27867648],\n",
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       "          ...,\n",
       "          [0.26838234, 0.27622548, 0.26446077],\n",
       "          [0.27083334, 0.27867648, 0.26691177],\n",
       "          [0.25882354, 0.2637255 , 0.24607843]],\n",
       " \n",
       "         [[0.3019608 , 0.32156864, 0.30588236],\n",
       "          [0.28039217, 0.28627452, 0.2754902 ],\n",
       "          [0.25      , 0.25588235, 0.24509804],\n",
       "          ...,\n",
       "          [0.26078433, 0.26862746, 0.25686276],\n",
       "          [0.2637255 , 0.26862746, 0.2509804 ],\n",
       "          [0.26960784, 0.2735294 , 0.25392157]]],\n",
       " \n",
       " \n",
       "        [[[0.20269608, 0.3262255 , 0.422549  ],\n",
       "          [0.09019608, 0.3115196 , 0.4125    ],\n",
       "          [0.05416667, 0.29191175, 0.42156863],\n",
       "          ...,\n",
       "          [0.49264705, 0.40980393, 0.39436275],\n",
       "          [0.5595588 , 0.42794117, 0.38308823],\n",
       "          [0.54583335, 0.4227941 , 0.34411764]],\n",
       " \n",
       "         [[0.09656863, 0.30759802, 0.40637255],\n",
       "          [0.02647059, 0.3007353 , 0.41421568],\n",
       "          [0.02279412, 0.28186274, 0.422549  ],\n",
       "          ...,\n",
       "          [0.41985294, 0.40514705, 0.39142156],\n",
       "          [0.51666665, 0.43480393, 0.3884804 ],\n",
       "          [0.53333336, 0.43014705, 0.34681374]],\n",
       " \n",
       "         [[0.00465686, 0.30465686, 0.42352942],\n",
       "          [0.        , 0.33406863, 0.4629902 ],\n",
       "          [0.02156863, 0.30171567, 0.45686275],\n",
       "          ...,\n",
       "          [0.29166666, 0.37843138, 0.3767157 ],\n",
       "          [0.4259804 , 0.42303923, 0.37965685],\n",
       "          [0.4872549 , 0.42843136, 0.3615196 ]],\n",
       " \n",
       "         ...,\n",
       " \n",
       "         [[0.29166666, 0.36617646, 0.2877451 ],\n",
       "          [0.29558823, 0.37009802, 0.29166666],\n",
       "          [0.28480393, 0.35931373, 0.28088236],\n",
       "          ...,\n",
       "          [0.2840686 , 0.35857844, 0.28112745],\n",
       "          [0.2492647 , 0.32377452, 0.24632353],\n",
       "          [0.20882353, 0.29411766, 0.21372549]],\n",
       " \n",
       "         [[0.27009803, 0.35049018, 0.275     ],\n",
       "          [0.24632353, 0.32671568, 0.2512255 ],\n",
       "          [0.30784315, 0.3882353 , 0.3127451 ],\n",
       "          ...,\n",
       "          [0.26936275, 0.3409314 , 0.2752451 ],\n",
       "          [0.25465685, 0.3262255 , 0.2605392 ],\n",
       "          [0.20882353, 0.28235295, 0.21666667]],\n",
       " \n",
       "         [[0.27107844, 0.3612745 , 0.29068628],\n",
       "          [0.24068627, 0.32892156, 0.25931373],\n",
       "          [0.3360294 , 0.41838235, 0.35171568],\n",
       "          ...,\n",
       "          [0.24411765, 0.3107843 , 0.24803922],\n",
       "          [0.25      , 0.31960785, 0.2627451 ],\n",
       "          [0.19901961, 0.26960784, 0.21470588]]],\n",
       " \n",
       " \n",
       "        [[[0.15269607, 0.16838235, 0.16446078],\n",
       "          [0.18946078, 0.20514706, 0.20122549],\n",
       "          [0.21495098, 0.23063725, 0.22671568],\n",
       "          ...,\n",
       "          [0.65710783, 0.5625    , 0.43970588],\n",
       "          [0.6958333 , 0.64191175, 0.5056372 ],\n",
       "          [0.84313726, 0.8117647 , 0.67058825]],\n",
       " \n",
       "         [[0.1264706 , 0.14215687, 0.1382353 ],\n",
       "          [0.15588236, 0.17156863, 0.16764706],\n",
       "          [0.2007353 , 0.21642157, 0.2125    ],\n",
       "          ...,\n",
       "          [0.65318626, 0.56985295, 0.4365196 ],\n",
       "          [0.74289215, 0.69779414, 0.57230395],\n",
       "          [0.8737745 , 0.84656864, 0.7357843 ]],\n",
       " \n",
       "         [[0.10686275, 0.12254902, 0.11862745],\n",
       "          [0.14387254, 0.15955882, 0.15563725],\n",
       "          [0.19901961, 0.21470588, 0.21078432],\n",
       "          ...,\n",
       "          [0.68039215, 0.6186274 , 0.47965688],\n",
       "          [0.80808824, 0.78039217, 0.6718137 ],\n",
       "          [0.8887255 , 0.87083334, 0.78161764]],\n",
       " \n",
       "         ...,\n",
       " \n",
       "         [[0.11985294, 0.15514706, 0.14534314],\n",
       "          [0.14705883, 0.18235295, 0.17254902],\n",
       "          [0.14313726, 0.16960785, 0.1627451 ],\n",
       "          ...,\n",
       "          [0.1115196 , 0.15514706, 0.13137256],\n",
       "          [0.11446078, 0.16544117, 0.13799019],\n",
       "          [0.11176471, 0.16470589, 0.13725491]],\n",
       " \n",
       "         [[0.12818627, 0.16348039, 0.15955882],\n",
       "          [0.13161765, 0.16691177, 0.1629902 ],\n",
       "          [0.13455883, 0.16102941, 0.16004902],\n",
       "          ...,\n",
       "          [0.1       , 0.1495098 , 0.12279411],\n",
       "          [0.10318627, 0.16004902, 0.13259804],\n",
       "          [0.09901961, 0.15784314, 0.13039216]],\n",
       " \n",
       "         [[0.12745099, 0.1627451 , 0.15882353],\n",
       "          [0.10955882, 0.14485294, 0.14093137],\n",
       "          [0.12843138, 0.15490197, 0.15392157],\n",
       "          ...,\n",
       "          [0.09607843, 0.14705883, 0.11960784],\n",
       "          [0.08897059, 0.14583333, 0.11838235],\n",
       "          [0.08627451, 0.14509805, 0.11764706]]],\n",
       " \n",
       " \n",
       "        ...,\n",
       " \n",
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       "        [[[0.        , 0.        , 0.        ],\n",
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       " \n",
       "         ...,\n",
       " \n",
       "         [[0.02352941, 0.01568628, 0.02745098],\n",
       "          [0.02720588, 0.01936275, 0.03112745],\n",
       "          [0.01666667, 0.00882353, 0.02058824],\n",
       "          ...,\n",
       "          [0.01078431, 0.00490196, 0.00784314],\n",
       "          [0.        , 0.00294118, 0.        ],\n",
       "          [0.        , 0.00392157, 0.        ]],\n",
       " \n",
       "         [[0.02426471, 0.01642157, 0.02818627],\n",
       "          [0.01740196, 0.00955882, 0.02132353],\n",
       "          [0.01568628, 0.00784314, 0.01960784],\n",
       "          ...,\n",
       "          [0.01004902, 0.00416667, 0.00710784],\n",
       "          [0.        , 0.00294118, 0.        ],\n",
       "          [0.        , 0.00392157, 0.        ]],\n",
       " \n",
       "         [[0.0254902 , 0.01764706, 0.02941176],\n",
       "          [0.01176471, 0.00392157, 0.01568628],\n",
       "          [0.01470588, 0.00686275, 0.01862745],\n",
       "          ...,\n",
       "          [0.00980392, 0.00392157, 0.00686275],\n",
       "          [0.        , 0.00294118, 0.        ],\n",
       "          [0.        , 0.00392157, 0.        ]]],\n",
       " \n",
       " \n",
       "        [[[0.3362745 , 0.4019608 , 0.38921568],\n",
       "          [0.34534314, 0.40612745, 0.3973039 ],\n",
       "          [0.34019607, 0.38921568, 0.38333333],\n",
       "          ...,\n",
       "          [0.32377452, 0.3865196 , 0.33553922],\n",
       "          [0.3245098 , 0.39019608, 0.33039215],\n",
       "          [0.33039215, 0.3970588 , 0.33431372]],\n",
       " \n",
       "         [[0.34215686, 0.3990196 , 0.37745097],\n",
       "          [0.35710785, 0.40318626, 0.3855392 ],\n",
       "          [0.3529412 , 0.39313725, 0.38431373],\n",
       "          ...,\n",
       "          [0.3254902 , 0.39117646, 0.33137256],\n",
       "          [0.3254902 , 0.39117646, 0.33137256],\n",
       "          [0.3254902 , 0.39117646, 0.33137256]],\n",
       " \n",
       "         [[0.34877452, 0.38897058, 0.35465688],\n",
       "          [0.35906863, 0.3992647 , 0.36985293],\n",
       "          [0.3497549 , 0.3884804 , 0.35980392],\n",
       "          ...,\n",
       "          [0.32328433, 0.38602942, 0.33504903],\n",
       "          [0.32647058, 0.38921568, 0.3382353 ],\n",
       "          [0.33137256, 0.39411765, 0.34313726]],\n",
       " \n",
       "         ...,\n",
       " \n",
       "         [[0.35784313, 0.40098038, 0.3382353 ],\n",
       "          [0.34019607, 0.38039216, 0.32843137],\n",
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       "          ...,\n",
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       "          [0.3382353 , 0.3617647 , 0.32058823],\n",
       "          [0.34607843, 0.36568627, 0.3382353 ]],\n",
       " \n",
       "         [[0.35784313, 0.40098038, 0.3382353 ],\n",
       "          [0.3362745 , 0.3764706 , 0.3245098 ],\n",
       "          [0.3156863 , 0.34117648, 0.3009804 ],\n",
       "          ...,\n",
       "          [0.33112746, 0.35465688, 0.30759802],\n",
       "          [0.31470588, 0.3382353 , 0.29705882],\n",
       "          [0.31176472, 0.33137256, 0.30392158]],\n",
       " \n",
       "         [[0.35392156, 0.3970588 , 0.33431372],\n",
       "          [0.34019607, 0.38039216, 0.32843137],\n",
       "          [0.33039215, 0.35588235, 0.3156863 ],\n",
       "          ...,\n",
       "          [0.3362745 , 0.35980392, 0.3127451 ],\n",
       "          [0.33235294, 0.35588235, 0.31470588],\n",
       "          [0.33333334, 0.3529412 , 0.3254902 ]]],\n",
       " \n",
       " \n",
       "        [[[0.        , 0.        , 0.        ],\n",
       "          [0.        , 0.        , 0.        ],\n",
       "          [0.        , 0.        , 0.        ],\n",
       "          ...,\n",
       "          [0.        , 0.        , 0.        ],\n",
       "          [0.        , 0.        , 0.        ],\n",
       "          [0.        , 0.        , 0.        ]],\n",
       " \n",
       "         [[0.        , 0.        , 0.        ],\n",
       "          [0.        , 0.        , 0.        ],\n",
       "          [0.        , 0.        , 0.        ],\n",
       "          ...,\n",
       "          [0.        , 0.        , 0.        ],\n",
       "          [0.        , 0.        , 0.        ],\n",
       "          [0.        , 0.        , 0.        ]],\n",
       " \n",
       "         [[0.        , 0.        , 0.        ],\n",
       "          [0.        , 0.        , 0.        ],\n",
       "          [0.        , 0.        , 0.        ],\n",
       "          ...,\n",
       "          [0.        , 0.        , 0.        ],\n",
       "          [0.        , 0.        , 0.        ],\n",
       "          [0.        , 0.        , 0.        ]],\n",
       " \n",
       "         ...,\n",
       " \n",
       "         [[0.19901961, 0.1629902 , 0.10441177],\n",
       "          [0.43357843, 0.40416667, 0.35416666],\n",
       "          [0.34558824, 0.32794118, 0.28088236],\n",
       "          ...,\n",
       "          [0.7921569 , 0.76862746, 0.63529414],\n",
       "          [0.8       , 0.7764706 , 0.6431373 ],\n",
       "          [0.7997549 , 0.7762255 , 0.6428922 ]],\n",
       " \n",
       "         [[0.20269608, 0.15416667, 0.1       ],\n",
       "          [0.24044117, 0.20318627, 0.15710784],\n",
       "          [0.41789216, 0.4002451 , 0.35539216],\n",
       "          ...,\n",
       "          [0.7870098 , 0.76348037, 0.63014704],\n",
       "          [0.7997549 , 0.7762255 , 0.6428922 ],\n",
       "          [0.8       , 0.7764706 , 0.6431373 ]],\n",
       " \n",
       "         [[0.15955882, 0.09485294, 0.04485294],\n",
       "          [0.18406862, 0.13897058, 0.09387255],\n",
       "          [0.37769607, 0.360049  , 0.31593138],\n",
       "          ...,\n",
       "          [0.8002451 , 0.7767157 , 0.6433824 ],\n",
       "          [0.80490196, 0.78137255, 0.6480392 ],\n",
       "          [0.7911765 , 0.7676471 , 0.6343137 ]]]], dtype=float32),\n",
       " array([1., 0., 0., 0., 0., 0., 1., 1., 0., 0., 0., 1., 0., 0., 1., 0.],\n",
       "       dtype=float32))"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_batch = train_data.as_numpy_iterator()\n",
    "test_batch.next()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_1 = test_batch.next()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = batch_1[:2]\n",
    "Y = batch_1[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 16, 100, 100, 3)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(X).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 0., 1., 1., 0., 0., 1., 0., 0., 0., 1., 0., 1., 1., 0.],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "#the actual training step for a single loop over the siamese, looped over by the next aspect\n",
    "# one batch of data (inp_image(100,100,3), & validation_img & the validator -> res value (embed) each-> 4064 to classifier to 1)\n",
    "# over the abv process we calculate our loss & apply backpropgation over the NN, and get the best possible model\n",
    "# @tf.function # tf.funnction decorator (compiles a fuinction into a callable TF graph) - we are compiling whatever is happening under this\n",
    "@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": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "keras.losses.BinaryCrossentropy"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.losses.BinaryCrossentropy"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ### create a training Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.metrics import Precision, Recall"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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",
    "        #creating a metric object \n",
    "        r = Recall()\n",
    "        p = Precision()\n",
    "\n",
    "        # Loop through each batch\n",
    "        for idx, batch in enumerate(data):\n",
    "            # Run train step here\n",
    "            loss = train_step(batch)\n",
    "            yhat = siamese_model.predict(batch[:2])\n",
    "            r.update_state(batch[2], yhat)\n",
    "            p.update_state(batch[2], yhat)\n",
    "            progbar.update(idx+1)\n",
    "        print(loss.numpy(),r.result().numpy(),p.result().numpy())\n",
    "        \n",
    "        # Save checkpoints\n",
    "        if epoch % 10 == 0: \n",
    "            checkpoint.save(file_prefix=checkpoint_prefix)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ### train the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "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",
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      "1/1 [==============================] - 1s 1s/step:\n",
      "1/1 [==============================] - 1s 924ms/step\n",
      "1/1 [==============================] - 1s 1s/step:\n",
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      "1/1 [==============================] - 1s 750ms/step\n",
      "1/1 [==============================] - 1s 787ms/step\n",
      "1/1 [==============================] - 1s 1s/step:\n",
      "1/1 [==============================] - 1s 652ms/step\n",
      "1/1 [==============================] - 1s 620ms/step\n",
      "1/1 [==============================] - 1s 590ms/step\n",
      "1/1 [==============================] - 1s 608ms/step\n",
      "1/1 [==============================] - 1s 638ms/step\n",
      "1/1 [==============================] - 1s 782ms/step\n",
      "1/1 [==============================] - 1s 618ms/step\n",
      "1/1 [==============================] - 1s 784ms/step\n",
      "1/1 [==============================] - 1s 885ms/step\n",
      "1/1 [==============================] - 1s 549ms/step\n",
      "1/1 [==============================] - 1s 583ms/step\n",
      "1/1 [==============================] - 1s 573ms/step\n",
      "1/1 [==============================] - 1s 616ms/step\n",
      "1/1 [==============================] - 1s 599ms/step\n",
      "1/1 [==============================] - 1s 639ms/step\n",
      "1/1 [==============================] - 1s 603ms/step\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[71], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m EPOCHS \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m50\u001b[39m\n\u001b[1;32m----> 2\u001b[0m \u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mEPOCHS\u001b[49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[1;32mIn[70], line 15\u001b[0m, in \u001b[0;36mtrain\u001b[1;34m(data, EPOCHS)\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m idx, batch \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(data):\n\u001b[0;32m     13\u001b[0m     \u001b[38;5;66;03m# Run train step here\u001b[39;00m\n\u001b[0;32m     14\u001b[0m     loss \u001b[38;5;241m=\u001b[39m train_step(batch)\n\u001b[1;32m---> 15\u001b[0m     yhat \u001b[38;5;241m=\u001b[39m \u001b[43msiamese_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     16\u001b[0m     r\u001b[38;5;241m.\u001b[39mupdate_state(batch[\u001b[38;5;241m2\u001b[39m], yhat)\n\u001b[0;32m     17\u001b[0m     p\u001b[38;5;241m.\u001b[39mupdate_state(batch[\u001b[38;5;241m2\u001b[39m], yhat)\n",
      "File \u001b[1;32md:\\ANACONDA\\envs\\tf\\lib\\site-packages\\keras\\utils\\traceback_utils.py:65\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     63\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m     64\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m---> 65\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     66\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m     67\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[1;32md:\\ANACONDA\\envs\\tf\\lib\\site-packages\\keras\\engine\\training.py:2297\u001b[0m, in \u001b[0;36mModel.predict\u001b[1;34m(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m   2294\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m original_pss_strategy \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   2295\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_distribution_strategy \u001b[38;5;241m=\u001b[39m original_pss_strategy\n\u001b[1;32m-> 2297\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtf_utils\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msync_to_numpy_or_python_type\u001b[49m\u001b[43m(\u001b[49m\u001b[43mall_outputs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32md:\\ANACONDA\\envs\\tf\\lib\\site-packages\\keras\\utils\\tf_utils.py:635\u001b[0m, in \u001b[0;36msync_to_numpy_or_python_type\u001b[1;34m(tensors)\u001b[0m\n\u001b[0;32m    632\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m t\n\u001b[0;32m    633\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m t\u001b[38;5;241m.\u001b[39mitem() \u001b[38;5;28;01mif\u001b[39;00m np\u001b[38;5;241m.\u001b[39mndim(t) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m t\n\u001b[1;32m--> 635\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnest\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap_structure\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_to_single_numpy_or_python_type\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32md:\\ANACONDA\\envs\\tf\\lib\\site-packages\\tensorflow\\python\\util\\nest.py:917\u001b[0m, in \u001b[0;36mmap_structure\u001b[1;34m(func, *structure, **kwargs)\u001b[0m\n\u001b[0;32m    913\u001b[0m flat_structure \u001b[38;5;241m=\u001b[39m (flatten(s, expand_composites) \u001b[38;5;28;01mfor\u001b[39;00m s \u001b[38;5;129;01min\u001b[39;00m structure)\n\u001b[0;32m    914\u001b[0m entries \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mzip\u001b[39m(\u001b[38;5;241m*\u001b[39mflat_structure)\n\u001b[0;32m    916\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m pack_sequence_as(\n\u001b[1;32m--> 917\u001b[0m     structure[\u001b[38;5;241m0\u001b[39m], [func(\u001b[38;5;241m*\u001b[39mx) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m entries],\n\u001b[0;32m    918\u001b[0m     expand_composites\u001b[38;5;241m=\u001b[39mexpand_composites)\n",
      "File \u001b[1;32md:\\ANACONDA\\envs\\tf\\lib\\site-packages\\tensorflow\\python\\util\\nest.py:917\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    913\u001b[0m flat_structure \u001b[38;5;241m=\u001b[39m (flatten(s, expand_composites) \u001b[38;5;28;01mfor\u001b[39;00m s \u001b[38;5;129;01min\u001b[39;00m structure)\n\u001b[0;32m    914\u001b[0m entries \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mzip\u001b[39m(\u001b[38;5;241m*\u001b[39mflat_structure)\n\u001b[0;32m    916\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m pack_sequence_as(\n\u001b[1;32m--> 917\u001b[0m     structure[\u001b[38;5;241m0\u001b[39m], [\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m entries],\n\u001b[0;32m    918\u001b[0m     expand_composites\u001b[38;5;241m=\u001b[39mexpand_composites)\n",
      "File \u001b[1;32md:\\ANACONDA\\envs\\tf\\lib\\site-packages\\keras\\utils\\tf_utils.py:628\u001b[0m, in \u001b[0;36msync_to_numpy_or_python_type.<locals>._to_single_numpy_or_python_type\u001b[1;34m(t)\u001b[0m\n\u001b[0;32m    625\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_to_single_numpy_or_python_type\u001b[39m(t):\n\u001b[0;32m    626\u001b[0m     \u001b[38;5;66;03m# Don't turn ragged or sparse tensors to NumPy.\u001b[39;00m\n\u001b[0;32m    627\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(t, tf\u001b[38;5;241m.\u001b[39mTensor):\n\u001b[1;32m--> 628\u001b[0m         t \u001b[38;5;241m=\u001b[39m \u001b[43mt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnumpy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    629\u001b[0m     \u001b[38;5;66;03m# Strings, ragged and sparse tensors don't have .item(). Return them\u001b[39;00m\n\u001b[0;32m    630\u001b[0m     \u001b[38;5;66;03m# as-is.\u001b[39;00m\n\u001b[0;32m    631\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(t, (np\u001b[38;5;241m.\u001b[39mndarray, np\u001b[38;5;241m.\u001b[39mgeneric)):\n",
      "File \u001b[1;32md:\\ANACONDA\\envs\\tf\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py:1157\u001b[0m, in \u001b[0;36m_EagerTensorBase.numpy\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1134\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Copy of the contents of this Tensor into a NumPy array or scalar.\u001b[39;00m\n\u001b[0;32m   1135\u001b[0m \n\u001b[0;32m   1136\u001b[0m \u001b[38;5;124;03mUnlike NumPy arrays, Tensors are immutable, so this method has to copy\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1154\u001b[0m \u001b[38;5;124;03m    NumPy dtype.\u001b[39;00m\n\u001b[0;32m   1155\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   1156\u001b[0m \u001b[38;5;66;03m# TODO(slebedev): Consider avoiding a copy for non-CPU or remote tensors.\u001b[39;00m\n\u001b[1;32m-> 1157\u001b[0m maybe_arr \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_numpy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# pylint: disable=protected-access\u001b[39;00m\n\u001b[0;32m   1158\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m maybe_arr\u001b[38;5;241m.\u001b[39mcopy() \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(maybe_arr, np\u001b[38;5;241m.\u001b[39mndarray) \u001b[38;5;28;01melse\u001b[39;00m maybe_arr\n",
      "File \u001b[1;32md:\\ANACONDA\\envs\\tf\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py:1123\u001b[0m, in \u001b[0;36m_EagerTensorBase._numpy\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1121\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_numpy\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m   1122\u001b[0m   \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 1123\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_numpy_internal\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1124\u001b[0m   \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:  \u001b[38;5;66;03m# pylint: disable=protected-access\u001b[39;00m\n\u001b[0;32m   1125\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_status_to_exception(e) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "EPOCHS = 50\n",
    "train(train_data, EPOCHS)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation of the Model - Making predictions"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ### Import Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import metric calculations\n",
    "from tensorflow.keras.metrics import Precision, Recall"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ### Make Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# getting a batch from our test data\n",
    "test_input, test_val, y_true = test_data.as_numpy_iterator().next()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 26ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[9.9999964e-01],\n",
       "       [9.7117966e-01],\n",
       "       [8.0830383e-01],\n",
       "       [9.9988019e-01],\n",
       "       [4.3957232e-05],\n",
       "       [9.9999976e-01],\n",
       "       [2.7097086e-04],\n",
       "       [9.9998391e-01],\n",
       "       [9.9901128e-01],\n",
       "       [9.9999595e-01],\n",
       "       [9.9934202e-01],\n",
       "       [1.0392140e-03],\n",
       "       [7.4591291e-01],\n",
       "       [1.0000000e+00],\n",
       "       [7.9741228e-01],\n",
       "       [9.9999869e-01]], dtype=float32)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make predictions over the model\n",
    "y_hat = siamese_model.predict([test_input, test_val])\n",
    "y_hat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#post processing the results\n",
    "[1 if prediction > 0.5 else 0 for prediction in y_hat]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1., 0., 1., 0., 1., 1., 1., 1., 0., 1., 1., 1., 1.],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_true"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ### Calculating Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using the metric to test out the accuracy of the model\n",
    "m = Recall() # metric object\n",
    "\n",
    "#calc the recall value\n",
    "m.update_state(y_true, y_hat)\n",
    "#return the recall result\n",
    "m.result().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using the metric to test out the accuracy of the model\n",
    "m = Precision() # metric object\n",
    "\n",
    "#calc the Precision value\n",
    "m.update_state(y_true, y_hat)\n",
    "#return the Precision result\n",
    "m.result().numpy()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ### Visualize Our result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x24b09b8bca0>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "plt.subplot(1,2,1)\n",
    "plt.imshow(test_input[2])\n",
    "plt.subplot(1,2,2)\n",
    "plt.imshow(test_val[2])\n",
    "# example of a 1 predicted outcome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x24c2157bbb0>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "plt.subplot(1,2,1)\n",
    "plt.imshow(test_input[4])\n",
    "plt.subplot(1,2,2)\n",
    "plt.imshow(test_val[4])\n",
    "# example of a 0 predicted outcome"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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": [
    "# save weights\n",
    "siamese_model.save('siamesemodelv2.h5')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:No training configuration found in the save file, so the model was *not* compiled. Compile it manually.\n"
     ]
    }
   ],
   "source": [
    "# reload the model \n",
    "model = tf.keras.models.load_model('siamesemodelv2.h5', custom_objects={'L1Dist':L1Dist, 'BinaryCrossentropy':tf.losses.BinaryCrossentropy})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 93ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[9.9999964e-01],\n",
       "       [9.7117966e-01],\n",
       "       [8.0830383e-01],\n",
       "       [9.9988019e-01],\n",
       "       [4.3957232e-05],\n",
       "       [9.9999976e-01],\n",
       "       [2.7097086e-04],\n",
       "       [9.9998391e-01],\n",
       "       [9.9901128e-01],\n",
       "       [9.9999595e-01],\n",
       "       [9.9934202e-01],\n",
       "       [1.0392140e-03],\n",
       "       [7.4591291e-01],\n",
       "       [1.0000000e+00],\n",
       "       [7.9741228e-01],\n",
       "       [9.9999869e-01]], dtype=float32)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod_pred_import = model.predict([test_input, test_val])\n",
    "mod_pred_import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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",
      "                                                                                                  \n",
      " validation_img (InputLayer)    [(None, 100, 100, 3  0           []                               \n",
      "                                )]                                                                \n",
      "                                                                                                  \n",
      " embedding (Functional)         (None, 4096)         38960448    ['input_img[0][0]',              \n",
      "                                                                  'validation_img[0][0]']         \n",
      "                                                                                                  \n",
      " l1_dist_3 (L1Dist)             (None, 4096)         0           ['embedding[0][0]',              \n",
      "                                                                  'embedding[1][0]']              \n",
      "                                                                                                  \n",
      " dense_3 (Dense)                (None, 1)            4097        ['l1_dist_3[0][0]']              \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 38,964,545\n",
      "Trainable params: 38,964,545\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[1 if prediction_import > 0.5 else 0 for prediction_import in mod_pred_import]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1., 0., 1., 0., 1., 1., 1., 1., 0., 1., 1., 1., 1.],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_true"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Real Time Test"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ### Build a verify Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# detection_threshold: metric above which a prediction is considered +ve\n",
    "# verfication_threshold: proportion of +ve predictions/ total +ve samples \n",
    "def verify(model, detection_threshold, verification_threshold):\n",
    "    # buidl results array \n",
    "    results = []\n",
    "    for image in os.listdir(os.path.join('application_data','verification_images')):\n",
    "        # looping through each images in verification folder\n",
    "        input_img = preprocess(os.path.join('application_data','input_image','input_image.jpg'))\n",
    "        validation_img = preprocess(os.path.join('application_data','verification_images',image))\n",
    "\n",
    "        #make predictions\n",
    "        result = model.predict(list(np.expand_dims([input_img, validation_img],axis=1)))\n",
    "        results.append(result)\n",
    "    \n",
    "    # detection_threshold\n",
    "    detection = np.sum(np.array(results)> detection_threshold)\n",
    "    # verfication_threshold\n",
    "    verification = detection / len(os.listdir(os.path.join('application_data','verification_images')))\n",
    "    verified = verification > verification_threshold\n",
    "\n",
    "    return results, verified\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor(\n",
      "[[[0.25710785 0.31593138 0.33553922]\n",
      "  [0.26764706 0.3245098  0.35392156]\n",
      "  [0.27058825 0.31862745 0.3627451 ]\n",
      "  ...\n",
      "  [0.2252451  0.2615196  0.25955883]\n",
      "  [0.23137255 0.27058825 0.27450982]\n",
      "  [0.23137255 0.27058825 0.27107844]]\n",
      "\n",
      " [[0.25196078 0.30294117 0.32647058]\n",
      "  [0.2617647  0.3127451  0.34338236]\n",
      "  [0.2647059  0.3134804  0.35514706]\n",
      "  ...\n",
      "  [0.21078432 0.24387255 0.2531863 ]\n",
      "  [0.22156863 0.26127452 0.27132353]\n",
      "  [0.22205882 0.26593137 0.2754902 ]]\n",
      "\n",
      " [[0.25857842 0.30367646 0.3272059 ]\n",
      "  [0.26936275 0.3137255  0.34583333]\n",
      "  [0.26789215 0.31887254 0.35318628]\n",
      "  ...\n",
      "  [0.2009804  0.23406863 0.24338235]\n",
      "  [0.21176471 0.2529412  0.26960784]\n",
      "  [0.20784314 0.25882354 0.2882353 ]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.2634804  0.27132353 0.25955883]\n",
      "  [0.22818628 0.23529412 0.23357843]\n",
      "  [0.20808823 0.2122549  0.21936275]\n",
      "  ...\n",
      "  [0.24166666 0.24166666 0.23970588]\n",
      "  [0.24313726 0.24313726 0.24117647]\n",
      "  [0.24387255 0.24387255 0.24191177]]\n",
      "\n",
      " [[0.26789215 0.2757353  0.26397058]\n",
      "  [0.23627451 0.24411765 0.2357843 ]\n",
      "  [0.20294118 0.21004902 0.20833333]\n",
      "  ...\n",
      "  [0.23039216 0.23039216 0.22254902]\n",
      "  [0.22965686 0.22965686 0.22181372]\n",
      "  [0.22843137 0.22843137 0.22058824]]\n",
      "\n",
      " [[0.27058825 0.2784314  0.26666668]\n",
      "  [0.24093138 0.24877451 0.23700981]\n",
      "  [0.20612745 0.21397059 0.20808823]\n",
      "  ...\n",
      "  [0.22965686 0.22965686 0.22181372]\n",
      "  [0.22745098 0.22745098 0.21960784]\n",
      "  [0.22254902 0.22254902 0.21470588]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.2742647  0.3134804  0.32132354]\n",
      "  [0.28431374 0.30980393 0.33333334]\n",
      "  [0.29191175 0.30857843 0.3409314 ]\n",
      "  ...\n",
      "  [0.22671568 0.2737745  0.2737745 ]\n",
      "  [0.22132353 0.26544118 0.27132353]\n",
      "  [0.22352941 0.26666668 0.27450982]]\n",
      "\n",
      " [[0.2735294  0.31176472 0.32254902]\n",
      "  [0.27867648 0.3120098  0.33259803]\n",
      "  [0.28970587 0.31053922 0.34215686]\n",
      "  ...\n",
      "  [0.2252451  0.26936275 0.2735294 ]\n",
      "  [0.22671568 0.26642156 0.2764706 ]\n",
      "  [0.21960784 0.2627451  0.27254903]]\n",
      "\n",
      " [[0.2637255  0.30490196 0.32156864]\n",
      "  [0.2747549  0.3095588  0.32941177]\n",
      "  [0.27941176 0.3127451  0.3348039 ]\n",
      "  ...\n",
      "  [0.22034314 0.26740196 0.28921568]\n",
      "  [0.2245098  0.26764706 0.28921568]\n",
      "  [0.21666667 0.26568627 0.2872549 ]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.24705882 0.25490198 0.2509804 ]\n",
      "  [0.21960784 0.2254902  0.22254902]\n",
      "  [0.19779412 0.19779412 0.19779412]\n",
      "  ...\n",
      "  [0.22745098 0.22941177 0.22647059]\n",
      "  [0.21960784 0.22156863 0.21862745]\n",
      "  [0.22843137 0.22843137 0.22058824]]\n",
      "\n",
      " [[0.26078433 0.26862746 0.2647059 ]\n",
      "  [0.21887255 0.2262255  0.22254902]\n",
      "  [0.19632353 0.19779412 0.19705883]\n",
      "  ...\n",
      "  [0.20637254 0.21421568 0.20245098]\n",
      "  [0.20392157 0.21176471 0.2       ]\n",
      "  [0.20588236 0.20588236 0.19803922]]\n",
      "\n",
      " [[0.25857842 0.26642156 0.2625    ]\n",
      "  [0.22941177 0.2372549  0.23333333]\n",
      "  [0.2007353  0.20269608 0.2017157 ]\n",
      "  ...\n",
      "  [0.20882353 0.21666667 0.20490196]\n",
      "  [0.20882353 0.21666667 0.20490196]\n",
      "  [0.21176471 0.21176471 0.20392157]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.27769607 0.31691176 0.32083333]\n",
      "  [0.27916667 0.31838235 0.3262255 ]\n",
      "  [0.28137255 0.31764707 0.33431372]\n",
      "  ...\n",
      "  [0.21862745 0.27745098 0.2637255 ]\n",
      "  [0.21691176 0.26691177 0.2502451 ]\n",
      "  [0.22352941 0.27058825 0.25490198]]\n",
      "\n",
      " [[0.28137255 0.3127451  0.32156864]\n",
      "  [0.29264706 0.3252451  0.33700982]\n",
      "  [0.28357843 0.31911764 0.3367647 ]\n",
      "  ...\n",
      "  [0.21544118 0.2735294  0.26397058]\n",
      "  [0.21764706 0.26764706 0.25441176]\n",
      "  [0.21862745 0.2647059  0.24901961]]\n",
      "\n",
      " [[0.28357843 0.30612746 0.32083333]\n",
      "  [0.28357843 0.3139706  0.32867646]\n",
      "  [0.28039217 0.3137255  0.33872548]\n",
      "  ...\n",
      "  [0.2125     0.27181372 0.26960784]\n",
      "  [0.21372549 0.2629902  0.26078433]\n",
      "  [0.22352941 0.2629902  0.25833333]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.2509804  0.2509804  0.2509804 ]\n",
      "  [0.2245098  0.2245098  0.2245098 ]\n",
      "  [0.21495098 0.21495098 0.21495098]\n",
      "  ...\n",
      "  [0.23431373 0.24215686 0.2382353 ]\n",
      "  [0.22745098 0.23529412 0.23137255]\n",
      "  [0.2254902  0.23333333 0.22941177]]\n",
      "\n",
      " [[0.2529412  0.2529412  0.2529412 ]\n",
      "  [0.22965686 0.22965686 0.22965686]\n",
      "  [0.21789216 0.21789216 0.21789216]\n",
      "  ...\n",
      "  [0.22352941 0.23137255 0.22745098]\n",
      "  [0.21593137 0.22377451 0.21985294]\n",
      "  [0.20490196 0.2127451  0.20882353]]\n",
      "\n",
      " [[0.25784314 0.25784314 0.25784314]\n",
      "  [0.23627451 0.23627451 0.23627451]\n",
      "  [0.20588236 0.20588236 0.20588236]\n",
      "  ...\n",
      "  [0.21985294 0.22769608 0.22377451]\n",
      "  [0.21691176 0.2247549  0.22083333]\n",
      "  [0.20392157 0.21176471 0.20784314]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.26593137 0.30906862 0.32818627]\n",
      "  [0.27132353 0.3129902  0.33088234]\n",
      "  [0.2757353  0.31446078 0.33235294]\n",
      "  ...\n",
      "  [0.20980392 0.2647059  0.26862746]\n",
      "  [0.20392157 0.25882354 0.2627451 ]\n",
      "  [0.20882353 0.2637255  0.2637255 ]]\n",
      "\n",
      " [[0.26838234 0.30563724 0.3134804 ]\n",
      "  [0.28039217 0.30882353 0.32058823]\n",
      "  [0.29117647 0.30980393 0.3245098 ]\n",
      "  ...\n",
      "  [0.21862745 0.2617647  0.26960784]\n",
      "  [0.20882353 0.25196078 0.25980392]\n",
      "  [0.20980392 0.2627451  0.2627451 ]]\n",
      "\n",
      " [[0.28039217 0.31176472 0.31960785]\n",
      "  [0.2904412  0.30906862 0.32083333]\n",
      "  [0.3        0.30686274 0.32156864]\n",
      "  ...\n",
      "  [0.22083333 0.26397058 0.27181372]\n",
      "  [0.21176471 0.25710785 0.2605392 ]\n",
      "  [0.21862745 0.26568627 0.26568627]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.26666668 0.26960784 0.2529412 ]\n",
      "  [0.23848039 0.24142157 0.2247549 ]\n",
      "  [0.22058824 0.22352941 0.20686275]\n",
      "  ...\n",
      "  [0.22573529 0.22573529 0.22377451]\n",
      "  [0.22745098 0.22745098 0.21960784]\n",
      "  [0.23235294 0.23235294 0.2245098 ]]\n",
      "\n",
      " [[0.25759804 0.25759804 0.2497549 ]\n",
      "  [0.23112746 0.23112746 0.22328432]\n",
      "  [0.21838236 0.21838236 0.21053922]\n",
      "  ...\n",
      "  [0.22352941 0.22352941 0.22156863]\n",
      "  [0.22058824 0.22058824 0.2127451 ]\n",
      "  [0.22647059 0.22647059 0.21862745]]\n",
      "\n",
      " [[0.2531863  0.2531863  0.24534313]\n",
      "  [0.22941177 0.22941177 0.22156863]\n",
      "  [0.20514706 0.20514706 0.19730392]\n",
      "  ...\n",
      "  [0.21960784 0.21960784 0.21960784]\n",
      "  [0.22352941 0.22352941 0.21764706]\n",
      "  [0.22352941 0.22352941 0.21568628]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.30245098 0.31691176 0.30294117]\n",
      "  [0.30122548 0.31691176 0.3129902 ]\n",
      "  [0.3022059  0.3252451  0.32352942]\n",
      "  ...\n",
      "  [0.12058824 0.12058824 0.12058824]\n",
      "  [0.12156863 0.12156863 0.12156863]\n",
      "  [0.12058824 0.12058824 0.12058824]]\n",
      "\n",
      " [[0.28946078 0.3127451  0.3156863 ]\n",
      "  [0.29509804 0.32058823 0.32254902]\n",
      "  [0.2901961  0.32156864 0.33039215]\n",
      "  ...\n",
      "  [0.11740196 0.11740196 0.11740196]\n",
      "  [0.11666667 0.11666667 0.11666667]\n",
      "  [0.11764706 0.11764706 0.11764706]]\n",
      "\n",
      " [[0.2764706  0.3137255  0.32156864]\n",
      "  [0.28014705 0.31446078 0.33210784]\n",
      "  [0.28014705 0.32230392 0.3360294 ]\n",
      "  ...\n",
      "  [0.1257353  0.1257353  0.1257353 ]\n",
      "  [0.11593137 0.11593137 0.11593137]\n",
      "  [0.11348039 0.11348039 0.11348039]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.25710785 0.264951   0.26102942]\n",
      "  [0.22745098 0.23529412 0.23137255]\n",
      "  [0.19632353 0.20416667 0.2002451 ]\n",
      "  ...\n",
      "  [0.20784314 0.21372549 0.20637254]\n",
      "  [0.22377451 0.22377451 0.22377451]\n",
      "  [0.22843137 0.22843137 0.22843137]]\n",
      "\n",
      " [[0.25490198 0.2627451  0.25882354]\n",
      "  [0.21691176 0.2247549  0.22083333]\n",
      "  [0.18333334 0.19117647 0.1872549 ]\n",
      "  ...\n",
      "  [0.20710784 0.20857844 0.20784314]\n",
      "  [0.21568628 0.21568628 0.21568628]\n",
      "  [0.20808823 0.20808823 0.20808823]]\n",
      "\n",
      " [[0.25686276 0.25686276 0.25686276]\n",
      "  [0.2129902  0.22083333 0.21691176]\n",
      "  [0.1872549  0.19509804 0.19117647]\n",
      "  ...\n",
      "  [0.21495098 0.22279412 0.21887255]\n",
      "  [0.20882353 0.21666667 0.2127451 ]\n",
      "  [0.2        0.20784314 0.20392157]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.25490198 0.31764707 0.31764707]\n",
      "  [0.26397058 0.31740198 0.32156864]\n",
      "  [0.27745098 0.31764707 0.3254902 ]\n",
      "  ...\n",
      "  [0.12083333 0.12671569 0.11789215]\n",
      "  [0.1375     0.1375     0.1375    ]\n",
      "  [0.1382353  0.1382353  0.1382353 ]]\n",
      "\n",
      " [[0.2647059  0.31666666 0.32941177]\n",
      "  [0.27132353 0.32107842 0.3345588 ]\n",
      "  [0.28308824 0.32034314 0.3389706 ]\n",
      "  ...\n",
      "  [0.12794118 0.13431373 0.1252451 ]\n",
      "  [0.12916666 0.13112745 0.13014705]\n",
      "  [0.13529412 0.13725491 0.13627452]]\n",
      "\n",
      " [[0.27254903 0.30980393 0.33431372]\n",
      "  [0.28995097 0.3272059  0.35171568]\n",
      "  [0.30269608 0.33848038 0.36372548]\n",
      "  ...\n",
      "  [0.15955882 0.1615196  0.15465686]\n",
      "  [0.14166667 0.14803922 0.14485294]\n",
      "  [0.12843138 0.13627452 0.13235295]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.25490198 0.2627451  0.25882354]\n",
      "  [0.22843137 0.23627451 0.23235294]\n",
      "  [0.20490196 0.20686275 0.20588236]\n",
      "  ...\n",
      "  [0.23112746 0.23308824 0.22573529]\n",
      "  [0.22083333 0.22132353 0.22058824]\n",
      "  [0.23137255 0.23137255 0.23137255]]\n",
      "\n",
      " [[0.25490198 0.2627451  0.25882354]\n",
      "  [0.21936275 0.22720589 0.22328432]\n",
      "  [0.20416667 0.20612745 0.20514706]\n",
      "  ...\n",
      "  [0.22426471 0.23210785 0.22034314]\n",
      "  [0.20465687 0.21102941 0.20588236]\n",
      "  [0.19803922 0.19803922 0.19803922]]\n",
      "\n",
      " [[0.24803922 0.25588235 0.25196078]\n",
      "  [0.2127451  0.22058824 0.21666667]\n",
      "  [0.19975491 0.2017157  0.2007353 ]\n",
      "  ...\n",
      "  [0.21764706 0.2254902  0.20784314]\n",
      "  [0.20490196 0.2127451  0.2009804 ]\n",
      "  [0.20784314 0.20784314 0.20784314]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.264951   0.3002451  0.31985295]\n",
      "  [0.2627451  0.30588236 0.32352942]\n",
      "  [0.26568627 0.30588236 0.3382353 ]\n",
      "  ...\n",
      "  [0.22426471 0.25955883 0.29485294]\n",
      "  [0.22647059 0.2617647  0.29705882]\n",
      "  [0.21960784 0.2627451  0.28627452]]\n",
      "\n",
      " [[0.2627451  0.30588236 0.32352942]\n",
      "  [0.2634804  0.30563724 0.33210784]\n",
      "  [0.26568627 0.30490196 0.34019607]\n",
      "  ...\n",
      "  [0.23014706 0.26568627 0.3009804 ]\n",
      "  [0.22254902 0.25882354 0.29411766]\n",
      "  [0.21960784 0.25882354 0.29411766]]\n",
      "\n",
      " [[0.26862746 0.31176472 0.33529413]\n",
      "  [0.26887256 0.31176472 0.3360294 ]\n",
      "  [0.26666668 0.30661765 0.33970588]\n",
      "  ...\n",
      "  [0.23308824 0.2737745  0.30245098]\n",
      "  [0.21960784 0.25882354 0.29411766]\n",
      "  [0.21960784 0.25882354 0.29411766]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.2509804  0.2509804  0.24509804]\n",
      "  [0.22965686 0.22965686 0.22377451]\n",
      "  [0.20294118 0.20294118 0.19705883]\n",
      "  ...\n",
      "  [0.22132353 0.22328432 0.22230393]\n",
      "  [0.22573529 0.22769608 0.22671568]\n",
      "  [0.24019608 0.24215686 0.24117647]]\n",
      "\n",
      " [[0.25686276 0.25686276 0.2509804 ]\n",
      "  [0.23039216 0.23039216 0.22598039]\n",
      "  [0.20514706 0.20514706 0.20514706]\n",
      "  ...\n",
      "  [0.20784314 0.21568628 0.21176471]\n",
      "  [0.2009804  0.20882353 0.20490196]\n",
      "  [0.20490196 0.2127451  0.20882353]]\n",
      "\n",
      " [[0.25588235 0.25588235 0.24803922]\n",
      "  [0.23039216 0.23039216 0.2245098 ]\n",
      "  [0.20294118 0.20294118 0.20294118]\n",
      "  ...\n",
      "  [0.20980392 0.21764706 0.21372549]\n",
      "  [0.2        0.20784314 0.20392157]\n",
      "  [0.20392157 0.21176471 0.20784314]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.2620098  0.30514705 0.32083333]\n",
      "  [0.27156863 0.31470588 0.33235294]\n",
      "  [0.27769607 0.32083333 0.34436274]\n",
      "  ...\n",
      "  [0.2245098  0.26764706 0.29117647]\n",
      "  [0.22818628 0.27132353 0.2889706 ]\n",
      "  [0.22843137 0.2637255  0.28333333]]\n",
      "\n",
      " [[0.25588235 0.3019608  0.30882353]\n",
      "  [0.25882354 0.3137255  0.31911764]\n",
      "  [0.26691177 0.31519607 0.32867646]\n",
      "  ...\n",
      "  [0.22843137 0.2617647  0.29215688]\n",
      "  [0.22843137 0.2617647  0.29215688]\n",
      "  [0.23137255 0.25882354 0.2901961 ]]\n",
      "\n",
      " [[0.25220588 0.30710784 0.31691176]\n",
      "  [0.26102942 0.31593138 0.3257353 ]\n",
      "  [0.26593137 0.3142157  0.32769608]\n",
      "  ...\n",
      "  [0.23137255 0.25882354 0.29215688]\n",
      "  [0.23137255 0.25882354 0.29215688]\n",
      "  [0.22843137 0.2617647  0.28431374]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.25392157 0.25392157 0.24607843]\n",
      "  [0.23161764 0.23308824 0.2245098 ]\n",
      "  [0.20514706 0.2125     0.19509804]\n",
      "  ...\n",
      "  [0.2254902  0.2254902  0.22352941]\n",
      "  [0.2247549  0.2247549  0.22426471]\n",
      "  [0.22745098 0.22745098 0.22745098]]\n",
      "\n",
      " [[0.24632353 0.25220588 0.23553921]\n",
      "  [0.2254902  0.22843137 0.21176471]\n",
      "  [0.20122549 0.20563726 0.1867647 ]\n",
      "  ...\n",
      "  [0.21789216 0.21789216 0.21004902]\n",
      "  [0.2127451  0.2127451  0.20637254]\n",
      "  [0.20857844 0.20857844 0.20857844]]\n",
      "\n",
      " [[0.24411765 0.25196078 0.23235294]\n",
      "  [0.22058824 0.2245098  0.20392157]\n",
      "  [0.19705883 0.2009804  0.18039216]\n",
      "  ...\n",
      "  [0.21568628 0.21568628 0.20784314]\n",
      "  [0.2127451  0.2127451  0.20490196]\n",
      "  [0.20392157 0.20392157 0.19607843]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.2627451  0.31764707 0.31764707]\n",
      "  [0.26985294 0.3227941  0.3227941 ]\n",
      "  [0.28333333 0.32671568 0.33406863]\n",
      "  ...\n",
      "  [0.22058824 0.2637255  0.27941176]\n",
      "  [0.22058824 0.2637255  0.2735294 ]\n",
      "  [0.2245098  0.2637255  0.27156863]]\n",
      "\n",
      " [[0.25808823 0.3129902  0.31887254]\n",
      "  [0.26397058 0.3137255  0.32328433]\n",
      "  [0.2737745  0.31691176 0.3345588 ]\n",
      "  ...\n",
      "  [0.21960784 0.2627451  0.28480393]\n",
      "  [0.21960784 0.2627451  0.27892157]\n",
      "  [0.22352941 0.2617647  0.27254903]]\n",
      "\n",
      " [[0.26397058 0.31004903 0.3247549 ]\n",
      "  [0.26544118 0.30710784 0.3254902 ]\n",
      "  [0.27132353 0.30857843 0.33504903]\n",
      "  ...\n",
      "  [0.22254902 0.2634804  0.29362744]\n",
      "  [0.22352941 0.26666668 0.2887255 ]\n",
      "  [0.22745098 0.2627451  0.2882353 ]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.25588235 0.25588235 0.25392157]\n",
      "  [0.21960784 0.21960784 0.21764706]\n",
      "  [0.19411765 0.19411765 0.19215687]\n",
      "  ...\n",
      "  [0.23112746 0.23112746 0.23112746]\n",
      "  [0.22352941 0.22352941 0.22352941]\n",
      "  [0.21372549 0.21372549 0.21372549]]\n",
      "\n",
      " [[0.24509804 0.24509804 0.2372549 ]\n",
      "  [0.2127451  0.2127451  0.20490196]\n",
      "  [0.19313726 0.19313726 0.18529412]\n",
      "  ...\n",
      "  [0.22058824 0.22058824 0.22058824]\n",
      "  [0.21372549 0.21372549 0.21372549]\n",
      "  [0.20612745 0.20612745 0.20612745]]\n",
      "\n",
      " [[0.25       0.25       0.24215686]\n",
      "  [0.2245098  0.2245098  0.21666667]\n",
      "  [0.19803922 0.19803922 0.19019608]\n",
      "  ...\n",
      "  [0.22181372 0.22181372 0.22181372]\n",
      "  [0.21642157 0.21642157 0.21642157]\n",
      "  [0.21176471 0.21176471 0.21176471]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.27279413 0.30808824 0.32769608]\n",
      "  [0.27941176 0.31470588 0.3362745 ]\n",
      "  [0.28235295 0.31764707 0.34509805]\n",
      "  ...\n",
      "  [0.22916667 0.2625     0.2875    ]\n",
      "  [0.23137255 0.2627451  0.2737745 ]\n",
      "  [0.2382353  0.27132353 0.2740196 ]]\n",
      "\n",
      " [[0.27867648 0.3120098  0.3345588 ]\n",
      "  [0.28235295 0.31764707 0.34509805]\n",
      "  [0.28235295 0.31764707 0.3465686 ]\n",
      "  ...\n",
      "  [0.22671568 0.2634804  0.2887255 ]\n",
      "  [0.22745098 0.2627451  0.28235295]\n",
      "  [0.24117647 0.28039217 0.2875    ]]\n",
      "\n",
      " [[0.28921568 0.31666666 0.3480392 ]\n",
      "  [0.28455883 0.3154412  0.34509805]\n",
      "  [0.28235295 0.31764707 0.3465686 ]\n",
      "  ...\n",
      "  [0.22377451 0.26642156 0.28431374]\n",
      "  [0.22156863 0.2627451  0.27990195]\n",
      "  [0.22156863 0.2637255  0.27156863]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.2627451  0.2627451  0.2627451 ]\n",
      "  [0.22941177 0.22941177 0.22941177]\n",
      "  [0.20980392 0.20980392 0.20980392]\n",
      "  ...\n",
      "  [0.2254902  0.23137255 0.22254902]\n",
      "  [0.2254902  0.23137255 0.22843137]\n",
      "  [0.22647059 0.23235294 0.22941177]]\n",
      "\n",
      " [[0.2767157  0.2767157  0.2767157 ]\n",
      "  [0.23529412 0.23529412 0.23529412]\n",
      "  [0.20661765 0.20661765 0.20661765]\n",
      "  ...\n",
      "  [0.2134804  0.21789216 0.20980392]\n",
      "  [0.21053922 0.21053922 0.21053922]\n",
      "  [0.20122549 0.20122549 0.20122549]]\n",
      "\n",
      " [[0.26666668 0.26666668 0.26666668]\n",
      "  [0.23014706 0.23014706 0.23014706]\n",
      "  [0.21446079 0.21446079 0.21446079]\n",
      "  ...\n",
      "  [0.20857844 0.21446079 0.20563726]\n",
      "  [0.19705883 0.19705883 0.19705883]\n",
      "  [0.2        0.2        0.2       ]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.27279413 0.30808824 0.32769608]\n",
      "  [0.27941176 0.31470588 0.3362745 ]\n",
      "  [0.28235295 0.31764707 0.34509805]\n",
      "  ...\n",
      "  [0.22916667 0.2625     0.2875    ]\n",
      "  [0.23137255 0.2627451  0.2737745 ]\n",
      "  [0.2382353  0.27132353 0.2740196 ]]\n",
      "\n",
      " [[0.27867648 0.3120098  0.3345588 ]\n",
      "  [0.28235295 0.31764707 0.34509805]\n",
      "  [0.28235295 0.31764707 0.3465686 ]\n",
      "  ...\n",
      "  [0.22671568 0.2634804  0.2887255 ]\n",
      "  [0.22745098 0.2627451  0.28235295]\n",
      "  [0.24117647 0.28039217 0.2875    ]]\n",
      "\n",
      " [[0.28921568 0.31666666 0.3480392 ]\n",
      "  [0.28455883 0.3154412  0.34509805]\n",
      "  [0.28235295 0.31764707 0.3465686 ]\n",
      "  ...\n",
      "  [0.22377451 0.26642156 0.28431374]\n",
      "  [0.22156863 0.2627451  0.27990195]\n",
      "  [0.22156863 0.2637255  0.27156863]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.2627451  0.2627451  0.2627451 ]\n",
      "  [0.22941177 0.22941177 0.22941177]\n",
      "  [0.20980392 0.20980392 0.20980392]\n",
      "  ...\n",
      "  [0.2254902  0.23137255 0.22254902]\n",
      "  [0.2254902  0.23137255 0.22843137]\n",
      "  [0.22647059 0.23235294 0.22941177]]\n",
      "\n",
      " [[0.2767157  0.2767157  0.2767157 ]\n",
      "  [0.23529412 0.23529412 0.23529412]\n",
      "  [0.20661765 0.20661765 0.20661765]\n",
      "  ...\n",
      "  [0.2134804  0.21789216 0.20980392]\n",
      "  [0.21053922 0.21053922 0.21053922]\n",
      "  [0.20122549 0.20122549 0.20122549]]\n",
      "\n",
      " [[0.26666668 0.26666668 0.26666668]\n",
      "  [0.23014706 0.23014706 0.23014706]\n",
      "  [0.21446079 0.21446079 0.21446079]\n",
      "  ...\n",
      "  [0.20857844 0.21446079 0.20563726]\n",
      "  [0.19705883 0.19705883 0.19705883]\n",
      "  [0.2        0.2        0.2       ]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.28235295 0.30588236 0.30588236]\n",
      "  [0.2840686  0.3122549  0.32696077]\n",
      "  [0.28651962 0.3139706  0.34338236]\n",
      "  ...\n",
      "  [0.22647059 0.26960784 0.27892157]\n",
      "  [0.22034314 0.2634804  0.27867648]\n",
      "  [0.22254902 0.26568627 0.28137255]]\n",
      "\n",
      " [[0.26666668 0.30441177 0.31004903]\n",
      "  [0.27279413 0.31004903 0.33063725]\n",
      "  [0.27181372 0.31470588 0.34632352]\n",
      "  ...\n",
      "  [0.22745098 0.26960784 0.29607844]\n",
      "  [0.23137255 0.27450982 0.29803923]\n",
      "  [0.22745098 0.27058825 0.2882353 ]]\n",
      "\n",
      " [[0.25980392 0.2990196  0.30686274]\n",
      "  [0.25906864 0.30416667 0.32181373]\n",
      "  [0.2637255  0.31470588 0.34411764]\n",
      "  ...\n",
      "  [0.22058824 0.25980392 0.29509804]\n",
      "  [0.23039216 0.27034312 0.30343136]\n",
      "  [0.23039216 0.26764706 0.29411766]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.2627451  0.2627451  0.26862746]\n",
      "  [0.22769608 0.22916667 0.22843137]\n",
      "  [0.19730392 0.20465687 0.1995098 ]\n",
      "  ...\n",
      "  [0.23529412 0.2382353  0.22156863]\n",
      "  [0.21642157 0.21715686 0.20710784]\n",
      "  [0.22156863 0.22156863 0.21372549]]\n",
      "\n",
      " [[0.26004902 0.26593137 0.2629902 ]\n",
      "  [0.22156863 0.22156863 0.22009803]\n",
      "  [0.19558823 0.19705883 0.1889706 ]\n",
      "  ...\n",
      "  [0.21642157 0.21642157 0.21446079]\n",
      "  [0.21495098 0.21495098 0.2129902 ]\n",
      "  [0.20710784 0.20710784 0.1992647 ]]\n",
      "\n",
      " [[0.25269607 0.2605392  0.24877451]\n",
      "  [0.22230393 0.22230393 0.21446079]\n",
      "  [0.19901961 0.19901961 0.19117647]\n",
      "  ...\n",
      "  [0.22867647 0.22867647 0.22867647]\n",
      "  [0.20857844 0.20857844 0.20857844]\n",
      "  [0.2127451  0.2127451  0.2127451 ]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.28235295 0.31764707 0.34509805]\n",
      "  [0.28946078 0.31691176 0.3482843 ]\n",
      "  [0.2901961  0.31764707 0.34901962]\n",
      "  ...\n",
      "  [0.23921569 0.28235295 0.30392158]\n",
      "  [0.23406863 0.2742647  0.28357843]\n",
      "  [0.23235294 0.27156863 0.2754902 ]]\n",
      "\n",
      " [[0.28627452 0.32156864 0.34901962]\n",
      "  [0.28627452 0.3137255  0.34509805]\n",
      "  [0.2901961  0.31764707 0.34901962]\n",
      "  ...\n",
      "  [0.24705882 0.2901961  0.31176472]\n",
      "  [0.24411765 0.2872549  0.29852942]\n",
      "  [0.2372549  0.2764706  0.28357843]]\n",
      "\n",
      " [[0.28627452 0.32156864 0.34313726]\n",
      "  [0.28799018 0.31985295 0.34901962]\n",
      "  [0.29313725 0.32058823 0.35196078]\n",
      "  ...\n",
      "  [0.23333333 0.2764706  0.29362744]\n",
      "  [0.22941177 0.27254903 0.2882353 ]\n",
      "  [0.24019608 0.2764706  0.29313725]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.27156863 0.27156863 0.27156863]\n",
      "  [0.23700981 0.23700981 0.23700981]\n",
      "  [0.20906863 0.20906863 0.20906863]\n",
      "  ...\n",
      "  [0.23308824 0.23308824 0.23308824]\n",
      "  [0.22843137 0.22843137 0.22843137]\n",
      "  [0.24117647 0.24117647 0.24117647]]\n",
      "\n",
      " [[0.28161764 0.28161764 0.28161764]\n",
      "  [0.24240196 0.24240196 0.24240196]\n",
      "  [0.20612745 0.20612745 0.20612745]\n",
      "  ...\n",
      "  [0.21666667 0.21666667 0.21666667]\n",
      "  [0.21740197 0.21740197 0.21740197]\n",
      "  [0.22058824 0.22058824 0.22058824]]\n",
      "\n",
      " [[0.25588235 0.25588235 0.25588235]\n",
      "  [0.23210785 0.23210785 0.23210785]\n",
      "  [0.20196079 0.20196079 0.20196079]\n",
      "  ...\n",
      "  [0.21078432 0.21078432 0.21078432]\n",
      "  [0.21102941 0.21102941 0.21102941]\n",
      "  [0.21568628 0.21568628 0.21568628]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.28627452 0.32941177 0.34509805]\n",
      "  [0.28333333 0.32647058 0.34411764]\n",
      "  [0.2875     0.3247549  0.3512255 ]\n",
      "  ...\n",
      "  [0.23848039 0.28161764 0.30514705]\n",
      "  [0.23995098 0.28455883 0.30808824]\n",
      "  [0.2367647  0.28504902 0.2990196 ]]\n",
      "\n",
      " [[0.2882353  0.31960785 0.3409314 ]\n",
      "  [0.28970587 0.32083333 0.34583333]\n",
      "  [0.30318627 0.32769608 0.360049  ]\n",
      "  ...\n",
      "  [0.25392157 0.28161764 0.29583332]\n",
      "  [0.25196078 0.28627452 0.30392158]\n",
      "  [0.25       0.28333333 0.30392158]]\n",
      "\n",
      " [[0.29803923 0.32352942 0.3529412 ]\n",
      "  [0.30686274 0.33137256 0.36372548]\n",
      "  [0.3107843  0.33529413 0.36764705]\n",
      "  ...\n",
      "  [0.26789215 0.28529412 0.29803923]\n",
      "  [0.2620098  0.28382352 0.30318627]\n",
      "  [0.25882354 0.28627452 0.30980393]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.2882353  0.29607844 0.29215688]\n",
      "  [0.24485295 0.25073528 0.24779412]\n",
      "  [0.22720589 0.22720589 0.22720589]\n",
      "  ...\n",
      "  [0.2531863  0.2531863  0.2512255 ]\n",
      "  [0.25       0.25       0.24215686]\n",
      "  [0.26078433 0.26078433 0.2529412 ]]\n",
      "\n",
      " [[0.29068628 0.29852942 0.29460785]\n",
      "  [0.24681373 0.25269607 0.2497549 ]\n",
      "  [0.21740197 0.21740197 0.21740197]\n",
      "  ...\n",
      "  [0.24485295 0.24485295 0.23848039]\n",
      "  [0.24191177 0.24191177 0.23406863]\n",
      "  [0.24411765 0.24411765 0.24215686]]\n",
      "\n",
      " [[0.28039217 0.2882353  0.28431374]\n",
      "  [0.25686276 0.2627451  0.25980392]\n",
      "  [0.23014706 0.23014706 0.23014706]\n",
      "  ...\n",
      "  [0.23921569 0.23921569 0.23137255]\n",
      "  [0.23529412 0.23529412 0.23333333]\n",
      "  [0.23529412 0.23529412 0.23529412]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.27254903 0.3257353  0.34264705]\n",
      "  [0.28627452 0.32843137 0.34215686]\n",
      "  [0.2990196  0.33235294 0.34019607]\n",
      "  ...\n",
      "  [0.24117647 0.2872549  0.30784315]\n",
      "  [0.23406863 0.2889706  0.29485294]\n",
      "  [0.24411765 0.29117647 0.29117647]]\n",
      "\n",
      " [[0.29509804 0.3372549  0.3509804 ]\n",
      "  [0.3009804  0.34191176 0.3497549 ]\n",
      "  [0.2997549  0.33039215 0.3382353 ]\n",
      "  ...\n",
      "  [0.2517157  0.29705882 0.3120098 ]\n",
      "  [0.24313726 0.29730392 0.30343136]\n",
      "  [0.24607843 0.29509804 0.29607844]]\n",
      "\n",
      " [[0.30490196 0.3382353  0.34607843]\n",
      "  [0.30759802 0.3382353  0.34607843]\n",
      "  [0.3095588  0.32941177 0.33137256]\n",
      "  ...\n",
      "  [0.26078433 0.30392158 0.32107842]\n",
      "  [0.2605392  0.31029412 0.33112746]\n",
      "  [0.25       0.30269608 0.32058823]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.28529412 0.29313725 0.28921568]\n",
      "  [0.23946078 0.24730392 0.24338235]\n",
      "  [0.21764706 0.2254902  0.22156863]\n",
      "  ...\n",
      "  [0.25588235 0.2637255  0.25980392]\n",
      "  [0.25269607 0.2605392  0.25661764]\n",
      "  [0.25196078 0.25980392 0.24803922]]\n",
      "\n",
      " [[0.26985294 0.27181372 0.27083334]\n",
      "  [0.2379902  0.24583334 0.24191177]\n",
      "  [0.22156863 0.22941177 0.2254902 ]\n",
      "  ...\n",
      "  [0.23921569 0.24705882 0.24313726]\n",
      "  [0.23529412 0.24313726 0.23921569]\n",
      "  [0.23431373 0.24215686 0.23039216]]\n",
      "\n",
      " [[0.26960784 0.26960784 0.26960784]\n",
      "  [0.23921569 0.24117647 0.24019608]\n",
      "  [0.2127451  0.22058824 0.21666667]\n",
      "  ...\n",
      "  [0.23946078 0.24730392 0.24338235]\n",
      "  [0.23039216 0.2382353  0.23431373]\n",
      "  [0.23137255 0.23921569 0.22745098]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.3129902  0.3247549  0.34436274]\n",
      "  [0.30539215 0.31813726 0.32892156]\n",
      "  [0.2875     0.29142156 0.2992647 ]\n",
      "  ...\n",
      "  [0.26642156 0.29485294 0.30367646]\n",
      "  [0.27009803 0.29264706 0.29558823]\n",
      "  [0.25588235 0.2872549  0.29509804]]\n",
      "\n",
      " [[0.31715685 0.32107842 0.3348039 ]\n",
      "  [0.30539215 0.30882353 0.32328433]\n",
      "  [0.27009803 0.2735294  0.2901961 ]\n",
      "  ...\n",
      "  [0.26862746 0.2992647  0.30784315]\n",
      "  [0.26936275 0.29411766 0.29828432]\n",
      "  [0.25392157 0.29142156 0.28921568]]\n",
      "\n",
      " [[0.31960785 0.32058823 0.32254902]\n",
      "  [0.29656863 0.29632354 0.30710784]\n",
      "  [0.2620098  0.26397058 0.28357843]\n",
      "  ...\n",
      "  [0.25882354 0.29460785 0.3115196 ]\n",
      "  [0.2605392  0.29558823 0.3107843 ]\n",
      "  [0.24803922 0.29215688 0.29803923]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.27622548 0.27622548 0.2840686 ]\n",
      "  [0.2497549  0.2497549  0.2497549 ]\n",
      "  [0.23921569 0.23921569 0.23921569]\n",
      "  ...\n",
      "  [0.2512255  0.2512255  0.2492647 ]\n",
      "  [0.24436274 0.24436274 0.2365196 ]\n",
      "  [0.25490198 0.25490198 0.24705882]]\n",
      "\n",
      " [[0.2742647  0.2742647  0.28210783]\n",
      "  [0.25196078 0.25196078 0.25196078]\n",
      "  [0.23014706 0.23014706 0.23014706]\n",
      "  ...\n",
      "  [0.24289216 0.24289216 0.2379902 ]\n",
      "  [0.23602942 0.23602942 0.23406863]\n",
      "  [0.23529412 0.23529412 0.23333333]]\n",
      "\n",
      " [[0.27745098 0.27745098 0.28529412]\n",
      "  [0.2529412  0.2529412  0.2529412 ]\n",
      "  [0.22647059 0.22647059 0.22647059]\n",
      "  ...\n",
      "  [0.24313726 0.24313726 0.2372549 ]\n",
      "  [0.24289216 0.24289216 0.24289216]\n",
      "  [0.24313726 0.24313726 0.24313726]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.2901961  0.3254902  0.32156864]\n",
      "  [0.2875     0.30906862 0.31004903]\n",
      "  [0.25416666 0.26102942 0.2737745 ]\n",
      "  ...\n",
      "  [0.25       0.28921568 0.29411766]\n",
      "  [0.2509804  0.29313725 0.30686274]\n",
      "  [0.25588235 0.2990196  0.32254902]]\n",
      "\n",
      " [[0.2872549  0.32156864 0.32058823]\n",
      "  [0.2747549  0.29558823 0.3007353 ]\n",
      "  [0.24509804 0.25196078 0.26666668]\n",
      "  ...\n",
      "  [0.25416666 0.29338235 0.2990196 ]\n",
      "  [0.24460784 0.2867647  0.3004902 ]\n",
      "  [0.24705882 0.2901961  0.31176472]]\n",
      "\n",
      " [[0.28357843 0.30612746 0.31691176]\n",
      "  [0.2625     0.2767157  0.28921568]\n",
      "  [0.23872548 0.24338235 0.25882354]\n",
      "  ...\n",
      "  [0.25955883 0.2987745  0.30661765]\n",
      "  [0.25441176 0.29215688 0.30808824]\n",
      "  [0.24901961 0.28627452 0.30490196]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.26960784 0.28921568 0.2735294 ]\n",
      "  [0.24901961 0.26568627 0.2509804 ]\n",
      "  [0.22573529 0.23357843 0.22181372]\n",
      "  ...\n",
      "  [0.25514707 0.25514707 0.24730392]\n",
      "  [0.25196078 0.25196078 0.24558823]\n",
      "  [0.2529412  0.2529412  0.2529412 ]]\n",
      "\n",
      " [[0.2647059  0.28431374 0.26862746]\n",
      "  [0.24460784 0.25465685 0.24215686]\n",
      "  [0.22647059 0.23431373 0.22254902]\n",
      "  ...\n",
      "  [0.24387255 0.24387255 0.23602942]\n",
      "  [0.24632353 0.24632353 0.24436274]\n",
      "  [0.24019608 0.24019608 0.24019608]]\n",
      "\n",
      " [[0.2637255  0.27156863 0.26764706]\n",
      "  [0.24215686 0.25       0.24607843]\n",
      "  [0.2254902  0.22745098 0.22647059]\n",
      "  ...\n",
      "  [0.23848039 0.23848039 0.23259804]\n",
      "  [0.23921569 0.23921569 0.23921569]\n",
      "  [0.23921569 0.23921569 0.23921569]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.2127451  0.22843137 0.2245098 ]\n",
      "  [0.21470588 0.22254902 0.21862745]\n",
      "  [0.19803922 0.20588236 0.20196079]\n",
      "  ...\n",
      "  [0.2254902  0.27058825 0.26568627]\n",
      "  [0.24215686 0.28137255 0.2882353 ]\n",
      "  [0.22941177 0.27254903 0.28039217]]\n",
      "\n",
      " [[0.23161764 0.23946078 0.23553921]\n",
      "  [0.21764706 0.2254902  0.22156863]\n",
      "  [0.2009804  0.20882353 0.20490196]\n",
      "  ...\n",
      "  [0.23504902 0.27818626 0.27279413]\n",
      "  [0.2492647  0.28799018 0.29068628]\n",
      "  [0.23529412 0.2784314  0.28627452]]\n",
      "\n",
      " [[0.2254902  0.23333333 0.22941177]\n",
      "  [0.21078432 0.21862745 0.21470588]\n",
      "  [0.19632353 0.20416667 0.2002451 ]\n",
      "  ...\n",
      "  [0.23774509 0.27058825 0.28063726]\n",
      "  [0.23848039 0.27132353 0.28137255]\n",
      "  [0.23137255 0.27450982 0.2882353 ]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.27941176 0.2872549  0.2754902 ]\n",
      "  [0.25784314 0.26568627 0.25392157]\n",
      "  [0.24730392 0.25514707 0.24338235]\n",
      "  ...\n",
      "  [0.2754902  0.28333333 0.27941176]\n",
      "  [0.27328432 0.28112745 0.27720588]\n",
      "  [0.26960784 0.27745098 0.2735294 ]]\n",
      "\n",
      " [[0.27867648 0.28651962 0.28063726]\n",
      "  [0.25784314 0.26568627 0.25980392]\n",
      "  [0.24730392 0.25514707 0.2492647 ]\n",
      "  ...\n",
      "  [0.2617647  0.26960784 0.26568627]\n",
      "  [0.2605392  0.26838234 0.26446077]\n",
      "  [0.2531863  0.26102942 0.25710785]]\n",
      "\n",
      " [[0.28921568 0.29705882 0.29313725]\n",
      "  [0.2784314  0.28627452 0.28235295]\n",
      "  [0.2617647  0.26960784 0.26568627]\n",
      "  ...\n",
      "  [0.2627451  0.27058825 0.26078433]\n",
      "  [0.25196078 0.25980392 0.25588235]\n",
      "  [0.25       0.25784314 0.25392157]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.28651962 0.29436275 0.28259805]\n",
      "  [0.31446078 0.30563724 0.2997549 ]\n",
      "  [0.35955882 0.32205883 0.31789216]\n",
      "  ...\n",
      "  [0.22647059 0.26568627 0.2735294 ]\n",
      "  [0.21960784 0.25882354 0.2637255 ]\n",
      "  [0.22745098 0.26666668 0.27058825]]\n",
      "\n",
      " [[0.31495097 0.3139706  0.30416667]\n",
      "  [0.34289217 0.3262255  0.31446078]\n",
      "  [0.38137254 0.34411764 0.33529413]\n",
      "  ...\n",
      "  [0.21764706 0.2637255  0.2720588 ]\n",
      "  [0.22083333 0.26911765 0.27083334]\n",
      "  [0.22156863 0.27156863 0.2627451 ]]\n",
      "\n",
      " [[0.35882354 0.34019607 0.3254902 ]\n",
      "  [0.37696078 0.3507353  0.32941177]\n",
      "  [0.39803922 0.35882354 0.34117648]\n",
      "  ...\n",
      "  [0.22156863 0.26764706 0.28186274]\n",
      "  [0.2245098  0.27058825 0.27745098]\n",
      "  [0.2245098  0.2737745  0.27303922]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.3392157  0.33529413 0.31960785]\n",
      "  [0.35882354 0.34313726 0.32254902]\n",
      "  [0.39607844 0.3627451  0.34215686]\n",
      "  ...\n",
      "  [0.2517157  0.2517157  0.24387255]\n",
      "  [0.25906864 0.25906864 0.2512255 ]\n",
      "  [0.2637255  0.2637255  0.25588235]]\n",
      "\n",
      " [[0.35245097 0.35       0.33357844]\n",
      "  [0.38333333 0.36960784 0.34901962]\n",
      "  [0.41740197 0.38406864 0.35906863]\n",
      "  ...\n",
      "  [0.24730392 0.24730392 0.23946078]\n",
      "  [0.24044117 0.24044117 0.23259804]\n",
      "  [0.24191177 0.24191177 0.23406863]]\n",
      "\n",
      " [[0.38455883 0.3884804  0.36887255]\n",
      "  [0.40269607 0.3884804  0.36642158]\n",
      "  [0.42647058 0.39313725 0.3647059 ]\n",
      "  ...\n",
      "  [0.2502451  0.2502451  0.24240196]\n",
      "  [0.24338235 0.24338235 0.23553921]\n",
      "  [0.24705882 0.24705882 0.23921569]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.50661767 0.43210784 0.40857843]\n",
      "  [0.50416666 0.42965686 0.40294117]\n",
      "  [0.5019608  0.42745098 0.4       ]\n",
      "  ...\n",
      "  [0.24632353 0.27769607 0.28651962]\n",
      "  [0.24681373 0.26936275 0.2840686 ]\n",
      "  [0.24044117 0.26004902 0.2757353 ]]\n",
      "\n",
      " [[0.51642156 0.44313726 0.40563726]\n",
      "  [0.5139706  0.44142157 0.40122548]\n",
      "  [0.5085784  0.4360294  0.39583334]\n",
      "  ...\n",
      "  [0.24044117 0.27279413 0.28651962]\n",
      "  [0.2357843  0.26666668 0.28112745]\n",
      "  [0.24019608 0.2617647  0.27941176]]\n",
      "\n",
      " [[0.52156866 0.44607842 0.39411765]\n",
      "  [0.5213235  0.44583333 0.39387256]\n",
      "  [0.5176471  0.44215685 0.39019608]\n",
      "  ...\n",
      "  [0.22990195 0.26960784 0.29289216]\n",
      "  [0.22990195 0.2637255  0.28995097]\n",
      "  [0.23137255 0.2647059  0.29117647]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.30784315 0.31470588 0.29411766]\n",
      "  [0.31004903 0.3127451  0.29485294]\n",
      "  [0.31470588 0.3137255  0.30980393]\n",
      "  ...\n",
      "  [0.27254903 0.27254903 0.2647059 ]\n",
      "  [0.2647059  0.2647059  0.25686276]\n",
      "  [0.26666668 0.26666668 0.25882354]]\n",
      "\n",
      " [[0.34019607 0.34117648 0.31176472]\n",
      "  [0.33553922 0.33529413 0.30857843]\n",
      "  [0.33039215 0.32647058 0.3115196 ]\n",
      "  ...\n",
      "  [0.2617647  0.2617647  0.25392157]\n",
      "  [0.2512255  0.2512255  0.24338235]\n",
      "  [0.25       0.25       0.24215686]]\n",
      "\n",
      " [[0.38627452 0.3745098  0.3392157 ]\n",
      "  [0.38455883 0.37279412 0.34583333]\n",
      "  [0.37916666 0.3644608  0.3507353 ]\n",
      "  ...\n",
      "  [0.26568627 0.26568627 0.26568627]\n",
      "  [0.25367647 0.25367647 0.25367647]\n",
      "  [0.25882354 0.25882354 0.25882354]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.5254902  0.44705883 0.4117647 ]\n",
      "  [0.5205882  0.44411764 0.40784314]\n",
      "  [0.5213235  0.4507353  0.40563726]\n",
      "  ...\n",
      "  [0.2372549  0.28039217 0.30392158]\n",
      "  [0.2252451  0.26838234 0.29191175]\n",
      "  [0.22352941 0.26666668 0.2901961 ]]\n",
      "\n",
      " [[0.5264706  0.4480392  0.4127451 ]\n",
      "  [0.52843136 0.45196077 0.4137255 ]\n",
      "  [0.5232843  0.45269608 0.40710783]\n",
      "  ...\n",
      "  [0.2372549  0.27254903 0.3       ]\n",
      "  [0.22696078 0.26666668 0.29338235]\n",
      "  [0.22156863 0.25980392 0.29509804]]\n",
      "\n",
      " [[0.5296569  0.45710784 0.41299018]\n",
      "  [0.5262255  0.45416668 0.40784314]\n",
      "  [0.52843136 0.45784312 0.40637255]\n",
      "  ...\n",
      "  [0.24117647 0.2764706  0.29803923]\n",
      "  [0.23504902 0.27034312 0.2977941 ]\n",
      "  [0.22745098 0.2627451  0.29803923]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.33014706 0.3360294  0.33112746]\n",
      "  [0.3154412  0.32132354 0.31642157]\n",
      "  [0.31323528 0.31911764 0.3142157 ]\n",
      "  ...\n",
      "  [0.26446077 0.26446077 0.26446077]\n",
      "  [0.2637255  0.2637255  0.2637255 ]\n",
      "  [0.27058825 0.27058825 0.27058825]]\n",
      "\n",
      " [[0.36053923 0.35514706 0.34215686]\n",
      "  [0.34191176 0.3365196  0.325     ]\n",
      "  [0.3389706  0.32867646 0.3230392 ]\n",
      "  ...\n",
      "  [0.26740196 0.26740196 0.26740196]\n",
      "  [0.25759804 0.25759804 0.25759804]\n",
      "  [0.25490198 0.25490198 0.25490198]]\n",
      "\n",
      " [[0.4007353  0.36740196 0.35661766]\n",
      "  [0.39044118 0.35710785 0.34730393]\n",
      "  [0.39411765 0.35931373 0.36053923]\n",
      "  ...\n",
      "  [0.27156863 0.27156863 0.27156863]\n",
      "  [0.26764706 0.26764706 0.26764706]\n",
      "  [0.25882354 0.25882354 0.25882354]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.23995098 0.24779412 0.24387255]\n",
      "  [0.2382353  0.24411765 0.23921569]\n",
      "  [0.24215686 0.24215686 0.23431373]\n",
      "  ...\n",
      "  [0.25833333 0.27598038 0.29264706]\n",
      "  [0.2531863  0.2723039  0.2875    ]\n",
      "  [0.24509804 0.2735294  0.28455883]]\n",
      "\n",
      " [[0.24289216 0.25073528 0.24681373]\n",
      "  [0.24828431 0.25416666 0.2492647 ]\n",
      "  [0.25196078 0.25196078 0.24411765]\n",
      "  ...\n",
      "  [0.24411765 0.27965686 0.30318627]\n",
      "  [0.23210785 0.27303922 0.29803923]\n",
      "  [0.22843137 0.2735294  0.29705882]]\n",
      "\n",
      " [[0.23848039 0.24632353 0.24240196]\n",
      "  [0.24583334 0.2517157  0.24681373]\n",
      "  [0.2509804  0.2509804  0.24313726]\n",
      "  ...\n",
      "  [0.24509804 0.26568627 0.30686274]\n",
      "  [0.23921569 0.26862746 0.30686274]\n",
      "  [0.22867647 0.2605392  0.29803923]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.46813726 0.452451   0.44656864]\n",
      "  [0.47671568 0.45416668 0.44338235]\n",
      "  [0.4757353  0.4384804  0.43063724]\n",
      "  ...\n",
      "  [0.47549018 0.47549018 0.4735294 ]\n",
      "  [0.5029412  0.5029412  0.5009804 ]\n",
      "  [0.54509807 0.54509807 0.54313725]]\n",
      "\n",
      " [[0.4644608  0.44877452 0.4370098 ]\n",
      "  [0.4715686  0.4490196  0.4375    ]\n",
      "  [0.48406863 0.4512255  0.4404412 ]\n",
      "  ...\n",
      "  [0.57843137 0.57843137 0.57058823]\n",
      "  [0.5865196  0.5865196  0.57867646]\n",
      "  [0.59166664 0.59166664 0.5838235 ]]\n",
      "\n",
      " [[0.4862745  0.46666667 0.45490196]\n",
      "  [0.4852941  0.46568626 0.45294118]\n",
      "  [0.49117646 0.4627451  0.45      ]\n",
      "  ...\n",
      "  [0.5872549  0.5872549  0.57941175]\n",
      "  [0.58431375 0.58431375 0.5764706 ]\n",
      "  [0.5803922  0.5803922  0.57254905]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.22941177 0.2372549  0.23333333]\n",
      "  [0.22916667 0.22916667 0.22916667]\n",
      "  [0.22843137 0.22843137 0.22843137]\n",
      "  ...\n",
      "  [0.23921569 0.27254903 0.29313725]\n",
      "  [0.24044117 0.2620098  0.2855392 ]\n",
      "  [0.23774509 0.25882354 0.28235295]]\n",
      "\n",
      " [[0.23210785 0.23995098 0.23602942]\n",
      "  [0.23014706 0.2365196  0.23333333]\n",
      "  [0.23995098 0.24044117 0.24019608]\n",
      "  ...\n",
      "  [0.24411765 0.27794117 0.29828432]\n",
      "  [0.2365196  0.27181372 0.29142156]\n",
      "  [0.23627451 0.2735294  0.29215688]]\n",
      "\n",
      " [[0.24117647 0.24901961 0.24509804]\n",
      "  [0.24632353 0.25416666 0.2502451 ]\n",
      "  [0.25073528 0.25710785 0.25392157]\n",
      "  ...\n",
      "  [0.2531863  0.2855392  0.2992647 ]\n",
      "  [0.24044117 0.28235295 0.29240197]\n",
      "  [0.22916667 0.28333333 0.28921568]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.564951   0.564951   0.564951  ]\n",
      "  [0.5002451  0.49240196 0.47671568]\n",
      "  [0.4872549  0.45294118 0.44313726]\n",
      "  ...\n",
      "  [0.5022059  0.5022059  0.5022059 ]\n",
      "  [0.49460784 0.49460784 0.49460784]\n",
      "  [0.5262255  0.5262255  0.5262255 ]]\n",
      "\n",
      " [[0.5561274  0.55759805 0.5495098 ]\n",
      "  [0.4990196  0.49117646 0.47549018]\n",
      "  [0.49117646 0.45686275 0.44705883]\n",
      "  ...\n",
      "  [0.5686275  0.5686275  0.5686275 ]\n",
      "  [0.57034314 0.57034314 0.57034314]\n",
      "  [0.57843137 0.57843137 0.57843137]]\n",
      "\n",
      " [[0.5301471  0.5301471  0.52230394]\n",
      "  [0.49264705 0.48480392 0.46911764]\n",
      "  [0.48897058 0.46053922 0.4477941 ]\n",
      "  ...\n",
      "  [0.59117645 0.59117645 0.59117645]\n",
      "  [0.5803922  0.5803922  0.5803922 ]\n",
      "  [0.5833333  0.5833333  0.5833333 ]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.29142156 0.2992647  0.2875    ]\n",
      "  [0.28137255 0.28921568 0.27745098]\n",
      "  [0.26397058 0.27181372 0.26004902]\n",
      "  ...\n",
      "  [0.23039216 0.2637255  0.29215688]\n",
      "  [0.23921569 0.26666668 0.29803923]\n",
      "  [0.23921569 0.26666668 0.29803923]]\n",
      "\n",
      " [[0.28627452 0.29313725 0.29215688]\n",
      "  [0.26666668 0.27450982 0.27058825]\n",
      "  [0.24779412 0.25563726 0.2517157 ]\n",
      "  ...\n",
      "  [0.2382353  0.2637255  0.28529412]\n",
      "  [0.24509804 0.27058825 0.29215688]\n",
      "  [0.24901961 0.26666668 0.28921568]]\n",
      "\n",
      " [[0.27328432 0.28014705 0.27916667]\n",
      "  [0.25490198 0.2627451  0.25882354]\n",
      "  [0.2509804  0.25882354 0.25490198]\n",
      "  ...\n",
      "  [0.24705882 0.2637255  0.27132353]\n",
      "  [0.25       0.26666668 0.27941176]\n",
      "  [0.24803922 0.2627451  0.2764706 ]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.6039216  0.6117647  0.5921569 ]\n",
      "  [0.5990196  0.60490197 0.59411764]\n",
      "  [0.58235294 0.57941175 0.5656863 ]\n",
      "  ...\n",
      "  [0.57254905 0.5745098  0.56764704]\n",
      "  [0.5529412  0.56078434 0.5509804 ]\n",
      "  [0.5294118  0.5372549  0.527451  ]]\n",
      "\n",
      " [[0.6093137  0.61715686 0.597549  ]\n",
      "  [0.60269606 0.60857844 0.5977941 ]\n",
      "  [0.5803922  0.577451   0.5637255 ]\n",
      "  ...\n",
      "  [0.5892157  0.59117645 0.5901961 ]\n",
      "  [0.5747549  0.58259803 0.57867646]\n",
      "  [0.5742647  0.58210784 0.5781863 ]]\n",
      "\n",
      " [[0.61960787 0.627451   0.60784316]\n",
      "  [0.60588235 0.6117647  0.6009804 ]\n",
      "  [0.56960785 0.56764704 0.5509804 ]\n",
      "  ...\n",
      "  [0.59411764 0.59411764 0.59411764]\n",
      "  [0.57843137 0.58431375 0.58137256]\n",
      "  [0.5715686  0.57941175 0.5754902 ]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.2784314  0.32156864 0.32941177]\n",
      "  [0.28235295 0.32058823 0.33137256]\n",
      "  [0.28235295 0.31764707 0.3372549 ]\n",
      "  ...\n",
      "  [0.5009804  0.4117647  0.3509804 ]\n",
      "  [0.48063725 0.40318626 0.3482843 ]\n",
      "  [0.4715686  0.39313725 0.35      ]]\n",
      "\n",
      " [[0.28235295 0.32058823 0.33137256]\n",
      "  [0.28235295 0.31764707 0.3392157 ]\n",
      "  [0.28161764 0.31764707 0.34509805]\n",
      "  ...\n",
      "  [0.5002451  0.4107843  0.35      ]\n",
      "  [0.4865196  0.4002451  0.34754902]\n",
      "  [0.47254902 0.39509803 0.34117648]]\n",
      "\n",
      " [[0.28235295 0.31764707 0.3509804 ]\n",
      "  [0.28235295 0.31764707 0.3509804 ]\n",
      "  [0.27941176 0.31764707 0.35245097]\n",
      "  ...\n",
      "  [0.49803922 0.40784314 0.34117648]\n",
      "  [0.49338236 0.40392157 0.34313726]\n",
      "  [0.4852941  0.4019608  0.3382353 ]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.6127451  0.6127451  0.6127451 ]\n",
      "  [0.6164216  0.6164216  0.6164216 ]\n",
      "  [0.6110294  0.6110294  0.6110294 ]\n",
      "  ...\n",
      "  [0.55196077 0.5598039  0.55588233]\n",
      "  [0.5497549  0.55759805 0.5536765 ]\n",
      "  [0.5411765  0.54901963 0.54509807]]\n",
      "\n",
      " [[0.622549   0.622549   0.622549  ]\n",
      "  [0.6218137  0.6218137  0.6218137 ]\n",
      "  [0.6156863  0.6156863  0.6156863 ]\n",
      "  ...\n",
      "  [0.55196077 0.5598039  0.55588233]\n",
      "  [0.55196077 0.5598039  0.55588233]\n",
      "  [0.5509804  0.5588235  0.55490196]]\n",
      "\n",
      " [[0.62352943 0.62352943 0.62352943]\n",
      "  [0.62352943 0.62352943 0.62352943]\n",
      "  [0.62352943 0.62352943 0.62352943]\n",
      "  ...\n",
      "  [0.5634804  0.5713235  0.56740195]\n",
      "  [0.5656863  0.5735294  0.56960785]\n",
      "  [0.5598039  0.56764704 0.5637255 ]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.2784314  0.32156864 0.32941177]\n",
      "  [0.28235295 0.32058823 0.33137256]\n",
      "  [0.28235295 0.31764707 0.3372549 ]\n",
      "  ...\n",
      "  [0.5009804  0.4117647  0.3509804 ]\n",
      "  [0.48063725 0.40318626 0.3482843 ]\n",
      "  [0.4715686  0.39313725 0.35      ]]\n",
      "\n",
      " [[0.28235295 0.32058823 0.33137256]\n",
      "  [0.28235295 0.31764707 0.3392157 ]\n",
      "  [0.28161764 0.31764707 0.34509805]\n",
      "  ...\n",
      "  [0.5002451  0.4107843  0.35      ]\n",
      "  [0.4865196  0.4002451  0.34754902]\n",
      "  [0.47254902 0.39509803 0.34117648]]\n",
      "\n",
      " [[0.28235295 0.31764707 0.3509804 ]\n",
      "  [0.28235295 0.31764707 0.3509804 ]\n",
      "  [0.27941176 0.31764707 0.35245097]\n",
      "  ...\n",
      "  [0.49803922 0.40784314 0.34117648]\n",
      "  [0.49338236 0.40392157 0.34313726]\n",
      "  [0.4852941  0.4019608  0.3382353 ]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.6127451  0.6127451  0.6127451 ]\n",
      "  [0.6164216  0.6164216  0.6164216 ]\n",
      "  [0.6110294  0.6110294  0.6110294 ]\n",
      "  ...\n",
      "  [0.55196077 0.5598039  0.55588233]\n",
      "  [0.5497549  0.55759805 0.5536765 ]\n",
      "  [0.5411765  0.54901963 0.54509807]]\n",
      "\n",
      " [[0.622549   0.622549   0.622549  ]\n",
      "  [0.6218137  0.6218137  0.6218137 ]\n",
      "  [0.6156863  0.6156863  0.6156863 ]\n",
      "  ...\n",
      "  [0.55196077 0.5598039  0.55588233]\n",
      "  [0.55196077 0.5598039  0.55588233]\n",
      "  [0.5509804  0.5588235  0.55490196]]\n",
      "\n",
      " [[0.62352943 0.62352943 0.62352943]\n",
      "  [0.62352943 0.62352943 0.62352943]\n",
      "  [0.62352943 0.62352943 0.62352943]\n",
      "  ...\n",
      "  [0.5634804  0.5713235  0.56740195]\n",
      "  [0.5656863  0.5735294  0.56960785]\n",
      "  [0.5598039  0.56764704 0.5637255 ]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.25490198 0.30980393 0.3137255 ]\n",
      "  [0.25661764 0.30857843 0.3154412 ]\n",
      "  [0.26789215 0.3110294  0.33259803]\n",
      "  ...\n",
      "  [0.5        0.41960785 0.3745098 ]\n",
      "  [0.50661767 0.41446078 0.36642158]\n",
      "  [0.50392157 0.40980393 0.3627451 ]]\n",
      "\n",
      " [[0.25661764 0.3115196  0.31740198]\n",
      "  [0.2637255  0.31862745 0.33039215]\n",
      "  [0.26887256 0.32377452 0.33553922]\n",
      "  ...\n",
      "  [0.48921567 0.41691175 0.37009802]\n",
      "  [0.49068627 0.40882352 0.35833332]\n",
      "  [0.49019608 0.40392157 0.3529412 ]]\n",
      "\n",
      " [[0.26862746 0.31470588 0.33529413]\n",
      "  [0.26740196 0.32230392 0.33406863]\n",
      "  [0.26960784 0.3245098  0.3362745 ]\n",
      "  ...\n",
      "  [0.48308823 0.4137255  0.35367647]\n",
      "  [0.48014706 0.40563726 0.34779412]\n",
      "  [0.47745097 0.4        0.34142157]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.6156863  0.6156863  0.6156863 ]\n",
      "  [0.6139706  0.6139706  0.6139706 ]\n",
      "  [0.6117647  0.6117647  0.6117647 ]\n",
      "  ...\n",
      "  [0.5443627  0.54632354 0.5375    ]\n",
      "  [0.5421569  0.55       0.5382353 ]\n",
      "  [0.5382353  0.54607844 0.5421569 ]]\n",
      "\n",
      " [[0.6215686  0.6215686  0.6215686 ]\n",
      "  [0.6186274  0.6186274  0.6186274 ]\n",
      "  [0.6186274  0.6186274  0.6186274 ]\n",
      "  ...\n",
      "  [0.5509804  0.5529412  0.5441176 ]\n",
      "  [0.55196077 0.5598039  0.5480392 ]\n",
      "  [0.55196077 0.5598039  0.55588233]]\n",
      "\n",
      " [[0.63529414 0.63529414 0.63529414]\n",
      "  [0.6281863  0.6281863  0.6281863 ]\n",
      "  [0.627451   0.627451   0.627451  ]\n",
      "  ...\n",
      "  [0.560049   0.5620098  0.5531863 ]\n",
      "  [0.56078434 0.5686275  0.5568628 ]\n",
      "  [0.56078434 0.5686275  0.5647059 ]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.2754902  0.30686274 0.31862745]\n",
      "  [0.2784314  0.30882353 0.32352942]\n",
      "  [0.27745098 0.30490196 0.33431372]\n",
      "  ...\n",
      "  [0.39117646 0.34411764 0.28333333]\n",
      "  [0.37156862 0.3245098  0.26960784]\n",
      "  [0.35784313 0.3107843  0.2637255 ]]\n",
      "\n",
      " [[0.28210783 0.3007353  0.31053922]\n",
      "  [0.2754902  0.29509804 0.30784315]\n",
      "  [0.27328432 0.3002451  0.32769608]\n",
      "  ...\n",
      "  [0.38455883 0.3394608  0.2884804 ]\n",
      "  [0.36936274 0.3242647  0.27916667]\n",
      "  [0.3509804  0.30294117 0.26666668]]\n",
      "\n",
      " [[0.3009804  0.3107843  0.30882353]\n",
      "  [0.2990196  0.31495097 0.32083333]\n",
      "  [0.28995097 0.3095588  0.33112746]\n",
      "  ...\n",
      "  [0.37941176 0.34019607 0.29362744]\n",
      "  [0.36887255 0.32965687 0.2889706 ]\n",
      "  [0.35171568 0.3125     0.27328432]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.6166667  0.6166667  0.6166667 ]\n",
      "  [0.62083334 0.62083334 0.62083334]\n",
      "  [0.6245098  0.6245098  0.6245098 ]\n",
      "  ...\n",
      "  [0.5627451  0.5627451  0.55490196]\n",
      "  [0.5590686  0.5590686  0.5512255 ]\n",
      "  [0.5561274  0.5561274  0.5482843 ]]\n",
      "\n",
      " [[0.63014704 0.63014704 0.63014704]\n",
      "  [0.63039213 0.63039213 0.63039213]\n",
      "  [0.6365196  0.6365196  0.6365196 ]\n",
      "  ...\n",
      "  [0.5757353  0.5757353  0.56789213]\n",
      "  [0.5754902  0.5754902  0.56764704]\n",
      "  [0.57254905 0.57254905 0.5647059 ]]\n",
      "\n",
      " [[0.63529414 0.63529414 0.63529414]\n",
      "  [0.6362745  0.6362745  0.6362745 ]\n",
      "  [0.6460784  0.6460784  0.6460784 ]\n",
      "  ...\n",
      "  [0.57965684 0.57965684 0.5718137 ]\n",
      "  [0.5772059  0.5772059  0.56936276]\n",
      "  [0.5803922  0.5803922  0.57254905]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.22745098 0.23529412 0.23137255]\n",
      "  [0.22328432 0.23014706 0.22916667]\n",
      "  [0.22867647 0.23259804 0.24044117]\n",
      "  ...\n",
      "  [0.2247549  0.26544118 0.26862746]\n",
      "  [0.22254902 0.2617647  0.26862746]\n",
      "  [0.22745098 0.25882354 0.27058825]]\n",
      "\n",
      " [[0.23406863 0.23995098 0.23700981]\n",
      "  [0.2375     0.24387255 0.24068627]\n",
      "  [0.2497549  0.25539216 0.25808823]\n",
      "  ...\n",
      "  [0.2379902  0.26642156 0.27622548]\n",
      "  [0.2357843  0.26642156 0.2754902 ]\n",
      "  [0.23627451 0.26764706 0.2784314 ]]\n",
      "\n",
      " [[0.23357843 0.23357843 0.22769608]\n",
      "  [0.24779412 0.2497549  0.24436274]\n",
      "  [0.275      0.2784314  0.2767157 ]\n",
      "  ...\n",
      "  [0.24166666 0.2634804  0.2784314 ]\n",
      "  [0.2357843  0.26642156 0.2784314 ]\n",
      "  [0.2382353  0.26960784 0.28039217]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.5117647  0.46078432 0.42941177]\n",
      "  [0.5012255  0.452451   0.42009804]\n",
      "  [0.48480392 0.44485295 0.40661764]\n",
      "  ...\n",
      "  [0.5757353  0.5757353  0.5757353 ]\n",
      "  [0.5735294  0.5735294  0.5735294 ]\n",
      "  [0.56397057 0.56397057 0.56397057]]\n",
      "\n",
      " [[0.5245098  0.4735294  0.44215685]\n",
      "  [0.5110294  0.46004903 0.42769608]\n",
      "  [0.49362746 0.44485295 0.40882352]\n",
      "  ...\n",
      "  [0.58259803 0.58259803 0.58259803]\n",
      "  [0.5833333  0.5833333  0.5833333 ]\n",
      "  [0.5593137  0.5593137  0.5593137 ]]\n",
      "\n",
      " [[0.53455883 0.48357844 0.4522059 ]\n",
      "  [0.5205882  0.46862745 0.4362745 ]\n",
      "  [0.50710785 0.4522059  0.41691175]\n",
      "  ...\n",
      "  [0.6068627  0.6068627  0.6068627 ]\n",
      "  [0.5948529  0.5948529  0.5948529 ]\n",
      "  [0.59117645 0.59117645 0.59117645]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.22156863 0.22156863 0.22156863]\n",
      "  [0.22745098 0.22941177 0.22843137]\n",
      "  [0.22965686 0.2375     0.23357843]\n",
      "  ...\n",
      "  [0.22426471 0.25955883 0.27916667]\n",
      "  [0.22647059 0.2617647  0.2872549 ]\n",
      "  [0.22671568 0.2620098  0.28946078]]\n",
      "\n",
      " [[0.23137255 0.23333333 0.23235294]\n",
      "  [0.22254902 0.22598039 0.22426471]\n",
      "  [0.21936275 0.22720589 0.22328432]\n",
      "  ...\n",
      "  [0.23406863 0.2617647  0.2767157 ]\n",
      "  [0.23259804 0.25808823 0.27965686]\n",
      "  [0.22941177 0.25490198 0.28431374]]\n",
      "\n",
      " [[0.26397058 0.27181372 0.26789215]\n",
      "  [0.24534313 0.2531863  0.2492647 ]\n",
      "  [0.23039216 0.2382353  0.23431373]\n",
      "  ...\n",
      "  [0.24583334 0.2617647  0.27598038]\n",
      "  [0.24117647 0.25490198 0.2735294 ]\n",
      "  [0.2382353  0.25196078 0.27254903]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.5095588  0.45857844 0.42328432]\n",
      "  [0.49093136 0.43995097 0.40563726]\n",
      "  [0.47745097 0.42647058 0.39583334]\n",
      "  ...\n",
      "  [0.57965684 0.57965684 0.57965684]\n",
      "  [0.58259803 0.58259803 0.58259803]\n",
      "  [0.5686275  0.5686275  0.5686275 ]]\n",
      "\n",
      " [[0.5254902  0.4745098  0.44215685]\n",
      "  [0.50686276 0.45588234 0.4259804 ]\n",
      "  [0.4879902  0.4370098  0.41299018]\n",
      "  ...\n",
      "  [0.5894608  0.5894608  0.5894608 ]\n",
      "  [0.58431375 0.58431375 0.58431375]\n",
      "  [0.5803922  0.5803922  0.5803922 ]]\n",
      "\n",
      " [[0.53137255 0.48039216 0.4490196 ]\n",
      "  [0.50784314 0.45686275 0.42745098]\n",
      "  [0.48921567 0.43529412 0.42058823]\n",
      "  ...\n",
      "  [0.6041667  0.6041667  0.6041667 ]\n",
      "  [0.5990196  0.5990196  0.5990196 ]\n",
      "  [0.5889706  0.5889706  0.5889706 ]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.24215686 0.29313725 0.3245098 ]\n",
      "  [0.2517157  0.29191175 0.3242647 ]\n",
      "  [0.26078433 0.29803923 0.3230392 ]\n",
      "  ...\n",
      "  [0.20588236 0.24117647 0.2627451 ]\n",
      "  [0.21568628 0.2509804  0.2784314 ]\n",
      "  [0.21764706 0.2529412  0.28039217]]\n",
      "\n",
      " [[0.2531863  0.29338235 0.3257353 ]\n",
      "  [0.25980392 0.2977941  0.33357844]\n",
      "  [0.26568627 0.29411766 0.33431372]\n",
      "  ...\n",
      "  [0.1985294  0.24803922 0.2735294 ]\n",
      "  [0.20416667 0.24705882 0.27916667]\n",
      "  [0.20490196 0.24705882 0.28137255]]\n",
      "\n",
      " [[0.24215686 0.29117647 0.31470588]\n",
      "  [0.24877451 0.29411766 0.32843137]\n",
      "  [0.26862746 0.29411766 0.34460783]\n",
      "  ...\n",
      "  [0.2127451  0.25784314 0.28333333]\n",
      "  [0.20882353 0.25392157 0.27941176]\n",
      "  [0.20367648 0.24877451 0.2742647 ]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.37745097 0.37745097 0.37745097]\n",
      "  [0.40294117 0.40294117 0.40294117]\n",
      "  [0.43357843 0.43357843 0.43357843]\n",
      "  ...\n",
      "  [0.24117647 0.24117647 0.24117647]\n",
      "  [0.22573529 0.22573529 0.22573529]\n",
      "  [0.21372549 0.21372549 0.21372549]]\n",
      "\n",
      " [[0.50808823 0.50808823 0.50808823]\n",
      "  [0.5115196  0.5115196  0.5115196 ]\n",
      "  [0.51642156 0.51642156 0.51642156]\n",
      "  ...\n",
      "  [0.23039216 0.23039216 0.23039216]\n",
      "  [0.20686275 0.20686275 0.20686275]\n",
      "  [0.20196079 0.20196079 0.20196079]]\n",
      "\n",
      " [[0.4990196  0.4990196  0.4990196 ]\n",
      "  [0.50490195 0.50490195 0.50490195]\n",
      "  [0.5176471  0.5176471  0.5176471 ]\n",
      "  ...\n",
      "  [0.22647059 0.22647059 0.22647059]\n",
      "  [0.2134804  0.2134804  0.2134804 ]\n",
      "  [0.21176471 0.21176471 0.21176471]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.25563726 0.2987745  0.31446078]\n",
      "  [0.25784314 0.3009804  0.31862745]\n",
      "  [0.26078433 0.29803923 0.3245098 ]\n",
      "  ...\n",
      "  [0.2        0.2509804  0.28235295]\n",
      "  [0.19632353 0.24730392 0.27867648]\n",
      "  [0.2007353  0.2517157  0.28308824]]\n",
      "\n",
      " [[0.2509804  0.29411766 0.31176472]\n",
      "  [0.25686276 0.29803923 0.31617647]\n",
      "  [0.26666668 0.29411766 0.3227941 ]\n",
      "  ...\n",
      "  [0.19411765 0.24779412 0.26813725]\n",
      "  [0.19215687 0.2509804  0.275     ]\n",
      "  [0.19215687 0.2509804  0.2764706 ]]\n",
      "\n",
      " [[0.25245097 0.29705882 0.32058823]\n",
      "  [0.26691177 0.3002451  0.32083333]\n",
      "  [0.28039217 0.29558823 0.31691176]\n",
      "  ...\n",
      "  [0.19803922 0.24387255 0.25931373]\n",
      "  [0.19509804 0.24877451 0.26911765]\n",
      "  [0.19509804 0.24803922 0.27058825]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.32328433 0.3154412  0.31936276]\n",
      "  [0.35416666 0.3497549  0.35196078]\n",
      "  [0.38112745 0.3870098  0.38406864]\n",
      "  ...\n",
      "  [0.2382353  0.2382353  0.23235294]\n",
      "  [0.23431373 0.23431373 0.23431373]\n",
      "  [0.24803922 0.24803922 0.24803922]]\n",
      "\n",
      " [[0.5034314  0.50147057 0.502451  ]\n",
      "  [0.502451   0.502451   0.502451  ]\n",
      "  [0.50661767 0.50808823 0.5029412 ]\n",
      "  ...\n",
      "  [0.24191177 0.24191177 0.23602942]\n",
      "  [0.23137255 0.23137255 0.23137255]\n",
      "  [0.23357843 0.23357843 0.23357843]]\n",
      "\n",
      " [[0.5019608  0.5019608  0.5019608 ]\n",
      "  [0.5007353  0.5007353  0.5007353 ]\n",
      "  [0.5120098  0.5120098  0.5061275 ]\n",
      "  ...\n",
      "  [0.23431373 0.23431373 0.22843137]\n",
      "  [0.23137255 0.23137255 0.23137255]\n",
      "  [0.23137255 0.23137255 0.23137255]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.23921569 0.2901961  0.3254902 ]\n",
      "  [0.24901961 0.29705882 0.33529413]\n",
      "  [0.26642156 0.30563724 0.34681374]\n",
      "  ...\n",
      "  [0.22352941 0.24607843 0.26078433]\n",
      "  [0.21078432 0.24215686 0.2509804 ]\n",
      "  [0.21764706 0.24901961 0.25686276]]\n",
      "\n",
      " [[0.2382353  0.28921568 0.31862745]\n",
      "  [0.25196078 0.30294117 0.33235294]\n",
      "  [0.26078433 0.30294117 0.3389706 ]\n",
      "  ...\n",
      "  [0.22843137 0.2509804  0.25980392]\n",
      "  [0.21887255 0.2495098  0.25759804]\n",
      "  [0.21666667 0.24803922 0.25686276]]\n",
      "\n",
      " [[0.24607843 0.3        0.31470588]\n",
      "  [0.25269607 0.30441177 0.31985295]\n",
      "  [0.25857842 0.3022059  0.31985295]\n",
      "  ...\n",
      "  [0.23627451 0.25955883 0.25588235]\n",
      "  [0.23627451 0.26544118 0.2720588 ]\n",
      "  [0.23039216 0.2617647  0.2735294 ]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.30906862 0.31691176 0.3129902 ]\n",
      "  [0.34534314 0.3512255  0.3482843 ]\n",
      "  [0.38382354 0.38382354 0.38382354]\n",
      "  ...\n",
      "  [0.24509804 0.24509804 0.24509804]\n",
      "  [0.2372549  0.2372549  0.2372549 ]\n",
      "  [0.23921569 0.23921569 0.23921569]]\n",
      "\n",
      " [[0.49362746 0.50147057 0.49754903]\n",
      "  [0.50514704 0.5110294  0.50808823]\n",
      "  [0.51593137 0.51593137 0.51593137]\n",
      "  ...\n",
      "  [0.22843137 0.22843137 0.22843137]\n",
      "  [0.21568628 0.21568628 0.21568628]\n",
      "  [0.21666667 0.21666667 0.21666667]]\n",
      "\n",
      " [[0.5022059  0.51004905 0.5061275 ]\n",
      "  [0.5120098  0.5178922  0.514951  ]\n",
      "  [0.5154412  0.5154412  0.5154412 ]\n",
      "  ...\n",
      "  [0.22818628 0.22818628 0.22818628]\n",
      "  [0.22769608 0.22769608 0.22769608]\n",
      "  [0.22745098 0.22745098 0.22745098]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.3009804  0.30882353 0.30490196]\n",
      "  [0.28210783 0.2987745  0.29191175]\n",
      "  [0.26691177 0.28651962 0.27083334]\n",
      "  ...\n",
      "  [0.22818628 0.2634804  0.29289216]\n",
      "  [0.22818628 0.2634804  0.2987745 ]\n",
      "  [0.22745098 0.2627451  0.29803923]]\n",
      "\n",
      " [[0.2997549  0.30759802 0.30367646]\n",
      "  [0.2870098  0.29485294 0.29093137]\n",
      "  [0.24901961 0.26127452 0.25735295]\n",
      "  ...\n",
      "  [0.24019608 0.25784314 0.28039217]\n",
      "  [0.2365196  0.2620098  0.29142156]\n",
      "  [0.23627451 0.2637255  0.3009804 ]]\n",
      "\n",
      " [[0.3019608  0.30686274 0.31176472]\n",
      "  [0.2784314  0.28333333 0.2882353 ]\n",
      "  [0.21960784 0.2245098  0.22941177]\n",
      "  ...\n",
      "  [0.25392157 0.26960784 0.2769608 ]\n",
      "  [0.2509804  0.26936275 0.2860294 ]\n",
      "  [0.24607843 0.2735294  0.2990196 ]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.30686274 0.31470588 0.3107843 ]\n",
      "  [0.2634804  0.27132353 0.25906864]\n",
      "  [0.23872548 0.24656862 0.23431373]\n",
      "  ...\n",
      "  [0.25563726 0.25563726 0.2497549 ]\n",
      "  [0.24877451 0.24877451 0.24877451]\n",
      "  [0.25686276 0.25686276 0.25686276]]\n",
      "\n",
      " [[0.3137255  0.32156864 0.31176472]\n",
      "  [0.30637255 0.3142157  0.3004902 ]\n",
      "  [0.32352942 0.33137256 0.31764707]\n",
      "  ...\n",
      "  [0.24019608 0.24019608 0.23872548]\n",
      "  [0.23627451 0.23627451 0.23627451]\n",
      "  [0.24411765 0.24411765 0.24411765]]\n",
      "\n",
      " [[0.44607842 0.44607842 0.43823528]\n",
      "  [0.46642157 0.47034314 0.4507353 ]\n",
      "  [0.4752451  0.47916666 0.4595588 ]\n",
      "  ...\n",
      "  [0.2382353  0.2382353  0.2382353 ]\n",
      "  [0.2375     0.2375     0.2375    ]\n",
      "  [0.23431373 0.23431373 0.23431373]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.2002451  0.2002451  0.20808823]\n",
      "  [0.18431373 0.18431373 0.18431373]\n",
      "  [0.19779412 0.19779412 0.19779412]\n",
      "  ...\n",
      "  [0.22941177 0.26862746 0.2735294 ]\n",
      "  [0.23161764 0.264951   0.2757353 ]\n",
      "  [0.23235294 0.2637255  0.2754902 ]]\n",
      "\n",
      " [[0.19901961 0.19901961 0.19901961]\n",
      "  [0.19313726 0.19313726 0.19313726]\n",
      "  [0.19901961 0.19901961 0.19901961]\n",
      "  ...\n",
      "  [0.21887255 0.25612745 0.2620098 ]\n",
      "  [0.2254902  0.25759804 0.27083334]\n",
      "  [0.22647059 0.25588235 0.28039217]]\n",
      "\n",
      " [[0.1882353  0.1882353  0.1882353 ]\n",
      "  [0.18921569 0.18921569 0.18333334]\n",
      "  [0.20122549 0.20122549 0.19534314]\n",
      "  ...\n",
      "  [0.22941177 0.26078433 0.26960784]\n",
      "  [0.22720589 0.25563726 0.27622548]\n",
      "  [0.22352941 0.25882354 0.2877451 ]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.2754902  0.2754902  0.2754902 ]\n",
      "  [0.25514707 0.25661764 0.25588235]\n",
      "  [0.22867647 0.23602942 0.23235294]\n",
      "  ...\n",
      "  [0.23700981 0.23700981 0.22916667]\n",
      "  [0.24142157 0.24142157 0.23357843]\n",
      "  [0.2497549  0.2497549  0.24191177]]\n",
      "\n",
      " [[0.27254903 0.2784314  0.2754902 ]\n",
      "  [0.2509804  0.2509804  0.2509804 ]\n",
      "  [0.22303921 0.2245098  0.22377451]\n",
      "  ...\n",
      "  [0.22647059 0.22647059 0.21862745]\n",
      "  [0.22328432 0.22328432 0.21544118]\n",
      "  [0.22647059 0.22647059 0.21862745]]\n",
      "\n",
      " [[0.28039217 0.2882353  0.28431374]\n",
      "  [0.25686276 0.25686276 0.25686276]\n",
      "  [0.23259804 0.23259804 0.23259804]\n",
      "  ...\n",
      "  [0.22352941 0.22352941 0.21568628]\n",
      "  [0.22058824 0.22058824 0.2127451 ]\n",
      "  [0.22156863 0.22156863 0.21372549]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.2007353  0.2007353  0.2007353 ]\n",
      "  [0.20882353 0.20882353 0.20882353]\n",
      "  [0.20784314 0.20784314 0.20784314]\n",
      "  ...\n",
      "  [0.24093138 0.26642156 0.26838234]\n",
      "  [0.22769608 0.2620098  0.27941176]\n",
      "  [0.22352941 0.26666668 0.2901961 ]]\n",
      "\n",
      " [[0.19509804 0.19705883 0.19607843]\n",
      "  [0.19975491 0.2017157  0.2007353 ]\n",
      "  [0.19975491 0.20465687 0.20220588]\n",
      "  ...\n",
      "  [0.23529412 0.26568627 0.28039217]\n",
      "  [0.2254902  0.26740196 0.28186274]\n",
      "  [0.22156863 0.26764706 0.28235295]]\n",
      "\n",
      " [[0.19240196 0.2002451  0.19632353]\n",
      "  [0.19215687 0.2        0.19607843]\n",
      "  [0.19289216 0.1992647  0.19607843]\n",
      "  ...\n",
      "  [0.23357843 0.27181372 0.28259805]\n",
      "  [0.2240196  0.26666668 0.28112745]\n",
      "  [0.21568628 0.27058825 0.28235295]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.28235295 0.28431374 0.26568627]\n",
      "  [0.28431374 0.28651962 0.2720588 ]\n",
      "  [0.28504902 0.28504902 0.27720588]\n",
      "  ...\n",
      "  [0.23235294 0.23235294 0.23235294]\n",
      "  [0.22941177 0.22941177 0.22941177]\n",
      "  [0.23602942 0.23602942 0.23602942]]\n",
      "\n",
      " [[0.27009803 0.26911765 0.25931373]\n",
      "  [0.26764706 0.26764706 0.26127452]\n",
      "  [0.27450982 0.27450982 0.27254903]\n",
      "  ...\n",
      "  [0.21887255 0.21887255 0.21887255]\n",
      "  [0.21446079 0.21446079 0.21446079]\n",
      "  [0.21593137 0.21593137 0.21593137]]\n",
      "\n",
      " [[0.27058825 0.27058825 0.2627451 ]\n",
      "  [0.26666668 0.26666668 0.26078433]\n",
      "  [0.27058825 0.27058825 0.27058825]\n",
      "  ...\n",
      "  [0.21764706 0.21764706 0.21764706]\n",
      "  [0.20490196 0.20490196 0.20490196]\n",
      "  [0.20784314 0.20784314 0.20784314]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.20392157 0.20392157 0.20392157]\n",
      "  [0.20294118 0.20490196 0.20392157]\n",
      "  [0.19534314 0.20318627 0.1992647 ]\n",
      "  ...\n",
      "  [0.23431373 0.27745098 0.3009804 ]\n",
      "  [0.2365196  0.27965686 0.29730392]\n",
      "  [0.24313726 0.28627452 0.29852942]]\n",
      "\n",
      " [[0.2134804  0.2134804  0.2134804 ]\n",
      "  [0.20784314 0.20980392 0.20882353]\n",
      "  [0.20882353 0.21666667 0.2127451 ]\n",
      "  ...\n",
      "  [0.23848039 0.28799018 0.31789216]\n",
      "  [0.24583334 0.2887255  0.31495097]\n",
      "  [0.24681373 0.28921568 0.3134804 ]]\n",
      "\n",
      " [[0.21495098 0.21495098 0.21495098]\n",
      "  [0.21151961 0.2134804  0.2125    ]\n",
      "  [0.22156863 0.22941177 0.2254902 ]\n",
      "  ...\n",
      "  [0.2379902  0.29338235 0.31666666]\n",
      "  [0.23921569 0.28357843 0.30980393]\n",
      "  [0.23700981 0.27622548 0.3115196 ]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.35       0.33529413 0.3019608 ]\n",
      "  [0.3767157  0.35       0.31715685]\n",
      "  [0.3865196  0.3495098  0.3139706 ]\n",
      "  ...\n",
      "  [0.24803922 0.24803922 0.24019608]\n",
      "  [0.24191177 0.24191177 0.23406863]\n",
      "  [0.24117647 0.24117647 0.23529412]]\n",
      "\n",
      " [[0.32352942 0.31985295 0.29166666]\n",
      "  [0.33848038 0.3242647  0.29362744]\n",
      "  [0.34607843 0.327451   0.2872549 ]\n",
      "  ...\n",
      "  [0.23210785 0.23210785 0.23014706]\n",
      "  [0.23063725 0.23063725 0.22867647]\n",
      "  [0.22377451 0.22377451 0.22377451]]\n",
      "\n",
      " [[0.31176472 0.30784315 0.2882353 ]\n",
      "  [0.31960785 0.31666666 0.28627452]\n",
      "  [0.32083333 0.30906862 0.2742647 ]\n",
      "  ...\n",
      "  [0.22352941 0.22352941 0.22352941]\n",
      "  [0.22352941 0.22352941 0.22352941]\n",
      "  [0.22352941 0.22352941 0.22352941]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.23357843 0.22965686 0.2492647 ]\n",
      "  [0.22426471 0.22230393 0.24093138]\n",
      "  [0.21691176 0.22083333 0.23063725]\n",
      "  ...\n",
      "  [0.26102942 0.30416667 0.3257353 ]\n",
      "  [0.25490198 0.29803923 0.3137255 ]\n",
      "  [0.2617647  0.30490196 0.32058823]]\n",
      "\n",
      " [[0.22818628 0.22818628 0.23406863]\n",
      "  [0.22647059 0.22769608 0.23333333]\n",
      "  [0.21985294 0.2247549  0.22965686]\n",
      "  ...\n",
      "  [0.2637255  0.30588236 0.31960785]\n",
      "  [0.2622549  0.30514705 0.31887254]\n",
      "  [0.25955883 0.30269608 0.31053922]]\n",
      "\n",
      " [[0.23357843 0.23357843 0.23357843]\n",
      "  [0.23774509 0.23970588 0.23872548]\n",
      "  [0.23039216 0.2382353  0.23431373]\n",
      "  ...\n",
      "  [0.26666668 0.30588236 0.3137255 ]\n",
      "  [0.26593137 0.30588236 0.3137255 ]\n",
      "  [0.26568627 0.30588236 0.3137255 ]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.3747549  0.3767157  0.33063725]\n",
      "  [0.37965685 0.38112745 0.33333334]\n",
      "  [0.3872549  0.39019608 0.3372549 ]\n",
      "  ...\n",
      "  [0.27156863 0.27156863 0.26960784]\n",
      "  [0.2617647  0.2617647  0.25686276]\n",
      "  [0.26764706 0.26764706 0.26764706]]\n",
      "\n",
      " [[0.35220587 0.35416666 0.3139706 ]\n",
      "  [0.35490197 0.35637254 0.31593138]\n",
      "  [0.36078432 0.36078432 0.31960785]\n",
      "  ...\n",
      "  [0.2509804  0.2509804  0.24313726]\n",
      "  [0.24583334 0.24583334 0.23946078]\n",
      "  [0.23946078 0.23946078 0.23946078]]\n",
      "\n",
      " [[0.3482843  0.3365196  0.30906862]\n",
      "  [0.34607843 0.33382353 0.30661765]\n",
      "  [0.35514706 0.33700982 0.31862745]\n",
      "  ...\n",
      "  [0.24705882 0.2529412  0.24215686]\n",
      "  [0.24240196 0.24240196 0.23455882]\n",
      "  [0.24215686 0.24215686 0.24215686]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.2754902  0.2754902  0.2754902 ]\n",
      "  [0.27058825 0.27745098 0.2764706 ]\n",
      "  [0.26862746 0.28137255 0.28627452]\n",
      "  ...\n",
      "  [0.29705882 0.32843137 0.34019607]\n",
      "  [0.2882353  0.3254902  0.33431372]\n",
      "  [0.2754902  0.31470588 0.32254902]]\n",
      "\n",
      " [[0.26568627 0.26568627 0.26568627]\n",
      "  [0.26838234 0.2747549  0.27156863]\n",
      "  [0.27745098 0.28529412 0.28137255]\n",
      "  ...\n",
      "  [0.29142156 0.3262255  0.33529413]\n",
      "  [0.28235295 0.32156864 0.32843137]\n",
      "  [0.28333333 0.32254902 0.32647058]]\n",
      "\n",
      " [[0.2627451  0.2627451  0.25686276]\n",
      "  [0.26764706 0.26960784 0.26421568]\n",
      "  [0.27156863 0.27941176 0.2754902 ]\n",
      "  ...\n",
      "  [0.29215688 0.33137256 0.33529413]\n",
      "  [0.28333333 0.32254902 0.32647058]\n",
      "  [0.28333333 0.32254902 0.32647058]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.35759804 0.36053923 0.34387255]\n",
      "  [0.33063725 0.3345588  0.31715685]\n",
      "  [0.31176472 0.31862745 0.2990196 ]\n",
      "  ...\n",
      "  [0.31960785 0.31960785 0.3137255 ]\n",
      "  [0.31960785 0.31960785 0.3137255 ]\n",
      "  [0.31862745 0.31862745 0.3127451 ]]\n",
      "\n",
      " [[0.34705883 0.3529412  0.34215686]\n",
      "  [0.33235294 0.33259803 0.3240196 ]\n",
      "  [0.3139706  0.31495097 0.30416667]\n",
      "  ...\n",
      "  [0.30686274 0.30686274 0.30245098]\n",
      "  [0.30686274 0.30686274 0.30686274]\n",
      "  [0.30294117 0.30294117 0.30294117]]\n",
      "\n",
      " [[0.3372549  0.34509805 0.34117648]\n",
      "  [0.3254902  0.3254902  0.3254902 ]\n",
      "  [0.3154412  0.3154412  0.3095588 ]\n",
      "  ...\n",
      "  [0.30980393 0.30980393 0.30392158]\n",
      "  [0.3107843  0.3107843  0.3107843 ]\n",
      "  [0.30490196 0.30490196 0.30490196]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.27745098 0.27745098 0.27745098]\n",
      "  [0.27083334 0.27769607 0.2767157 ]\n",
      "  [0.25686276 0.26960784 0.27450982]\n",
      "  ...\n",
      "  [0.29534313 0.33848038 0.34632352]\n",
      "  [0.2884804  0.33161765 0.3394608 ]\n",
      "  [0.28014705 0.32328433 0.3389706 ]]\n",
      "\n",
      " [[0.26887256 0.26887256 0.26887256]\n",
      "  [0.26102942 0.26813725 0.26642156]\n",
      "  [0.2629902  0.2737745  0.27867648]\n",
      "  ...\n",
      "  [0.29093137 0.33308825 0.3409314 ]\n",
      "  [0.2840686  0.3262255  0.33406863]\n",
      "  [0.27941176 0.32254902 0.3382353 ]]\n",
      "\n",
      " [[0.25906864 0.264951   0.25612745]\n",
      "  [0.25416666 0.2620098  0.25808823]\n",
      "  [0.25955883 0.26446077 0.26936275]\n",
      "  ...\n",
      "  [0.30122548 0.3360294  0.34607843]\n",
      "  [0.28627452 0.3254902  0.33333334]\n",
      "  [0.2855392  0.3227941  0.34142157]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.3512255  0.35318628 0.35220587]\n",
      "  [0.33431372 0.3362745  0.33186275]\n",
      "  [0.31470588 0.31666666 0.30784315]\n",
      "  ...\n",
      "  [0.3156863  0.3156863  0.3156863 ]\n",
      "  [0.30392158 0.30392158 0.30392158]\n",
      "  [0.31862745 0.31862745 0.31862745]]\n",
      "\n",
      " [[0.34534314 0.35318628 0.34338236]\n",
      "  [0.33137256 0.3392157  0.327451  ]\n",
      "  [0.3139706  0.31740198 0.30784315]\n",
      "  ...\n",
      "  [0.31176472 0.31176472 0.31176472]\n",
      "  [0.29803923 0.29803923 0.29803923]\n",
      "  [0.29803923 0.29803923 0.29803923]]\n",
      "\n",
      " [[0.3485294  0.35637254 0.34460783]\n",
      "  [0.34264705 0.35049018 0.33872548]\n",
      "  [0.32818627 0.33014706 0.32132354]\n",
      "  ...\n",
      "  [0.30980393 0.30980393 0.30980393]\n",
      "  [0.30686274 0.30686274 0.30686274]\n",
      "  [0.3019608  0.3019608  0.3019608 ]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.29705882 0.29705882 0.28921568]\n",
      "  [0.29411766 0.29411766 0.29607844]\n",
      "  [0.2872549  0.2872549  0.29509804]\n",
      "  ...\n",
      "  [0.29583332 0.3379902  0.34583333]\n",
      "  [0.2872549  0.32647058 0.33431372]\n",
      "  [0.2901961  0.32941177 0.3372549 ]]\n",
      "\n",
      " [[0.29215688 0.29215688 0.28431374]\n",
      "  [0.28504902 0.28504902 0.27916667]\n",
      "  [0.28137255 0.28137255 0.2872549 ]\n",
      "  ...\n",
      "  [0.29534313 0.34191176 0.34215686]\n",
      "  [0.29534313 0.3360294  0.3392157 ]\n",
      "  [0.2901961  0.32941177 0.33333334]]\n",
      "\n",
      " [[0.2784314  0.2784314  0.27058825]\n",
      "  [0.28137255 0.28137255 0.2754902 ]\n",
      "  [0.27745098 0.27745098 0.27892157]\n",
      "  ...\n",
      "  [0.30661765 0.33995098 0.34681374]\n",
      "  [0.30882353 0.34436274 0.34460783]\n",
      "  [0.29215688 0.32843137 0.32647058]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.34779412 0.34779412 0.33995098]\n",
      "  [0.32818627 0.33014706 0.32132354]\n",
      "  [0.31176472 0.31960785 0.30784315]\n",
      "  ...\n",
      "  [0.3137255  0.31960785 0.30882353]\n",
      "  [0.3127451  0.3127451  0.30490196]\n",
      "  [0.31764707 0.31764707 0.30980393]]\n",
      "\n",
      " [[0.35       0.35       0.34215686]\n",
      "  [0.33259803 0.3345588  0.3257353 ]\n",
      "  [0.31470588 0.32254902 0.3107843 ]\n",
      "  ...\n",
      "  [0.30392158 0.30980393 0.2990196 ]\n",
      "  [0.3007353  0.3007353  0.29289216]\n",
      "  [0.3019608  0.3019608  0.29411766]]\n",
      "\n",
      " [[0.35465688 0.35465688 0.34681374]\n",
      "  [0.33382353 0.33382353 0.3259804 ]\n",
      "  [0.3154412  0.32132354 0.31053922]\n",
      "  ...\n",
      "  [0.30686274 0.3127451  0.3019608 ]\n",
      "  [0.30980393 0.30980393 0.3019608 ]\n",
      "  [0.30588236 0.30588236 0.29803923]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.29705882 0.29705882 0.28921568]\n",
      "  [0.29411766 0.29411766 0.29607844]\n",
      "  [0.2872549  0.2872549  0.29509804]\n",
      "  ...\n",
      "  [0.29583332 0.3379902  0.34583333]\n",
      "  [0.2872549  0.32647058 0.33431372]\n",
      "  [0.2901961  0.32941177 0.3372549 ]]\n",
      "\n",
      " [[0.29215688 0.29215688 0.28431374]\n",
      "  [0.28504902 0.28504902 0.27916667]\n",
      "  [0.28137255 0.28137255 0.2872549 ]\n",
      "  ...\n",
      "  [0.29534313 0.34191176 0.34215686]\n",
      "  [0.29534313 0.3360294  0.3392157 ]\n",
      "  [0.2901961  0.32941177 0.33333334]]\n",
      "\n",
      " [[0.2784314  0.2784314  0.27058825]\n",
      "  [0.28137255 0.28137255 0.2754902 ]\n",
      "  [0.27745098 0.27745098 0.27892157]\n",
      "  ...\n",
      "  [0.30661765 0.33995098 0.34681374]\n",
      "  [0.30882353 0.34436274 0.34460783]\n",
      "  [0.29215688 0.32843137 0.32647058]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.34779412 0.34779412 0.33995098]\n",
      "  [0.32818627 0.33014706 0.32132354]\n",
      "  [0.31176472 0.31960785 0.30784315]\n",
      "  ...\n",
      "  [0.3137255  0.31960785 0.30882353]\n",
      "  [0.3127451  0.3127451  0.30490196]\n",
      "  [0.31764707 0.31764707 0.30980393]]\n",
      "\n",
      " [[0.35       0.35       0.34215686]\n",
      "  [0.33259803 0.3345588  0.3257353 ]\n",
      "  [0.31470588 0.32254902 0.3107843 ]\n",
      "  ...\n",
      "  [0.30392158 0.30980393 0.2990196 ]\n",
      "  [0.3007353  0.3007353  0.29289216]\n",
      "  [0.3019608  0.3019608  0.29411766]]\n",
      "\n",
      " [[0.35465688 0.35465688 0.34681374]\n",
      "  [0.33382353 0.33382353 0.3259804 ]\n",
      "  [0.3154412  0.32132354 0.31053922]\n",
      "  ...\n",
      "  [0.30686274 0.3127451  0.3019608 ]\n",
      "  [0.30980393 0.30980393 0.3019608 ]\n",
      "  [0.30588236 0.30588236 0.29803923]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.26666668 0.29411766 0.3254902 ]\n",
      "  [0.26838234 0.3022059  0.33039215]\n",
      "  [0.26985294 0.3125     0.3362745 ]\n",
      "  ...\n",
      "  [0.13946079 0.13946079 0.14730392]\n",
      "  [0.1370098  0.1370098  0.14485294]\n",
      "  [0.12965687 0.12965687 0.12965687]]\n",
      "\n",
      " [[0.26764706 0.30392158 0.32058823]\n",
      "  [0.26985294 0.30857843 0.33431372]\n",
      "  [0.2647059  0.3134804  0.34436274]\n",
      "  ...\n",
      "  [0.15416667 0.15416667 0.16004902]\n",
      "  [0.15269607 0.15269607 0.15416667]\n",
      "  [0.12745099 0.12745099 0.12745099]]\n",
      "\n",
      " [[0.27450982 0.3137255  0.32156864]\n",
      "  [0.2735294  0.3112745  0.33406863]\n",
      "  [0.27303922 0.31446078 0.35196078]\n",
      "  ...\n",
      "  [0.13627452 0.13627452 0.13627452]\n",
      "  [0.16127451 0.16127451 0.16127451]\n",
      "  [0.14509805 0.14509805 0.1392157 ]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.2764706  0.27941176 0.2627451 ]\n",
      "  [0.25686276 0.25686276 0.2509804 ]\n",
      "  [0.23210785 0.23210785 0.2379902 ]\n",
      "  ...\n",
      "  [0.37279412 0.35367647 0.33333334]\n",
      "  [0.34509805 0.33112746 0.3137255 ]\n",
      "  [0.31960785 0.30686274 0.29607844]]\n",
      "\n",
      " [[0.2742647  0.2742647  0.26642156]\n",
      "  [0.24705882 0.24705882 0.24117647]\n",
      "  [0.22671568 0.22671568 0.22818628]\n",
      "  ...\n",
      "  [0.36397058 0.3514706  0.33333334]\n",
      "  [0.33553922 0.32892156 0.31813726]\n",
      "  [0.30808824 0.30490196 0.29705882]]\n",
      "\n",
      " [[0.27450982 0.27450982 0.26666668]\n",
      "  [0.24411765 0.24411765 0.2382353 ]\n",
      "  [0.22058824 0.22058824 0.22058824]\n",
      "  ...\n",
      "  [0.35563725 0.34583333 0.3272059 ]\n",
      "  [0.30686274 0.30588236 0.29607844]\n",
      "  [0.28651962 0.28995097 0.2882353 ]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.2870098  0.31053922 0.30269608]\n",
      "  [0.29142156 0.3139706  0.31691176]\n",
      "  [0.2884804  0.31691176 0.32867646]\n",
      "  ...\n",
      "  [0.12745099 0.12745099 0.12156863]\n",
      "  [0.11078431 0.11078431 0.11078431]\n",
      "  [0.12352941 0.12352941 0.12352941]]\n",
      "\n",
      " [[0.27745098 0.30661765 0.31617647]\n",
      "  [0.28137255 0.31446078 0.32377452]\n",
      "  [0.28063726 0.3129902  0.32671568]\n",
      "  ...\n",
      "  [0.14460784 0.14460784 0.1382353 ]\n",
      "  [0.1242647  0.1242647  0.12377451]\n",
      "  [0.11642157 0.11642157 0.11642157]]\n",
      "\n",
      " [[0.2509804  0.28529412 0.30294117]\n",
      "  [0.27254903 0.30857843 0.327451  ]\n",
      "  [0.27083334 0.30612746 0.33308825]\n",
      "  ...\n",
      "  [0.14558823 0.14558823 0.1377451 ]\n",
      "  [0.15318628 0.15318628 0.14681372]\n",
      "  [0.14607844 0.14607844 0.14607844]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.27818626 0.27818626 0.27818626]\n",
      "  [0.24142157 0.24142157 0.24142157]\n",
      "  [0.2254902  0.2254902  0.2254902 ]\n",
      "  ...\n",
      "  [0.3867647  0.35759804 0.33112746]\n",
      "  [0.3625     0.33039215 0.31715685]\n",
      "  [0.3242647  0.30637255 0.29093137]]\n",
      "\n",
      " [[0.26666668 0.26666668 0.26666668]\n",
      "  [0.24117647 0.24117647 0.24117647]\n",
      "  [0.22867647 0.22867647 0.22867647]\n",
      "  ...\n",
      "  [0.37941176 0.34877452 0.33553922]\n",
      "  [0.3492647  0.3245098  0.3134804 ]\n",
      "  [0.31176472 0.29607844 0.28431374]]\n",
      "\n",
      " [[0.2735294  0.2735294  0.2735294 ]\n",
      "  [0.24803922 0.24803922 0.24803922]\n",
      "  [0.22156863 0.22156863 0.22156863]\n",
      "  ...\n",
      "  [0.36544117 0.33406863 0.32328433]\n",
      "  [0.32254902 0.3        0.28921568]\n",
      "  [0.28235295 0.26960784 0.25686276]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.26985294 0.30906862 0.3110294 ]\n",
      "  [0.2647059  0.30759802 0.3259804 ]\n",
      "  [0.26421568 0.3122549  0.34754902]\n",
      "  ...\n",
      "  [0.11544117 0.11544117 0.11544117]\n",
      "  [0.11176471 0.11176471 0.11176471]\n",
      "  [0.11372549 0.11372549 0.10588235]]\n",
      "\n",
      " [[0.27107844 0.3093137  0.30490196]\n",
      "  [0.26887256 0.30710784 0.31789216]\n",
      "  [0.27083334 0.3112745  0.34117648]\n",
      "  ...\n",
      "  [0.11568628 0.11568628 0.10784314]\n",
      "  [0.11666667 0.11666667 0.10882353]\n",
      "  [0.10980392 0.10980392 0.10196079]]\n",
      "\n",
      " [[0.27916667 0.30563724 0.30416667]\n",
      "  [0.27034312 0.3022059  0.3107843 ]\n",
      "  [0.27745098 0.3134804  0.33235294]\n",
      "  ...\n",
      "  [0.11372549 0.11666667 0.1       ]\n",
      "  [0.11372549 0.11446078 0.10441177]\n",
      "  [0.10980392 0.10980392 0.10196079]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.2764706  0.2764706  0.27450982]\n",
      "  [0.24289216 0.24289216 0.24093138]\n",
      "  [0.2127451  0.2127451  0.21078432]\n",
      "  ...\n",
      "  [0.23431373 0.23431373 0.22843137]\n",
      "  [0.2365196  0.2365196  0.2365196 ]\n",
      "  [0.24313726 0.24313726 0.24313726]]\n",
      "\n",
      " [[0.29093137 0.29093137 0.28308824]\n",
      "  [0.25269607 0.25269607 0.24485295]\n",
      "  [0.21887255 0.21887255 0.21102941]\n",
      "  ...\n",
      "  [0.22916667 0.22916667 0.22328432]\n",
      "  [0.23137255 0.23137255 0.23137255]\n",
      "  [0.23137255 0.23137255 0.23137255]]\n",
      "\n",
      " [[0.2747549  0.2747549  0.26691177]\n",
      "  [0.2389706  0.2389706  0.23112746]\n",
      "  [0.21078432 0.21078432 0.20294118]\n",
      "  ...\n",
      "  [0.22965686 0.22965686 0.22377451]\n",
      "  [0.22058824 0.22058824 0.22058824]\n",
      "  [0.21960784 0.21960784 0.21960784]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.28504902 0.29558823 0.30906862]\n",
      "  [0.2901961  0.30490196 0.31862745]\n",
      "  [0.29338235 0.3125     0.33431372]\n",
      "  ...\n",
      "  [0.2127451  0.25196078 0.25661764]\n",
      "  [0.21176471 0.2509804  0.25857842]\n",
      "  [0.20710784 0.24803922 0.25588235]]\n",
      "\n",
      " [[0.26887256 0.28995097 0.3019608 ]\n",
      "  [0.28921568 0.3107843  0.32990196]\n",
      "  [0.2877451  0.3137255  0.3372549 ]\n",
      "  ...\n",
      "  [0.21544118 0.24730392 0.27058825]\n",
      "  [0.22132353 0.24656862 0.27279413]\n",
      "  [0.21568628 0.24019608 0.26666668]]\n",
      "\n",
      " [[0.2627451  0.30294117 0.31764707]\n",
      "  [0.2764706  0.30980393 0.33039215]\n",
      "  [0.28259805 0.31593138 0.3365196 ]\n",
      "  ...\n",
      "  [0.2245098  0.25637254 0.2855392 ]\n",
      "  [0.225      0.2502451  0.28235295]\n",
      "  [0.22843137 0.24411765 0.28137255]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.25416666 0.26789215 0.26397058]\n",
      "  [0.23137255 0.23921569 0.23529412]\n",
      "  [0.2125     0.21446079 0.2134804 ]\n",
      "  ...\n",
      "  [0.21544118 0.22132353 0.21642157]\n",
      "  [0.21764706 0.22352941 0.21421568]\n",
      "  [0.22843137 0.23431373 0.22352941]]\n",
      "\n",
      " [[0.25710785 0.264951   0.26102942]\n",
      "  [0.22254902 0.23039216 0.22647059]\n",
      "  [0.21470588 0.21666667 0.21568628]\n",
      "  ...\n",
      "  [0.21470588 0.21691176 0.20245098]\n",
      "  [0.21544118 0.21544118 0.20759805]\n",
      "  [0.20784314 0.20784314 0.2       ]]\n",
      "\n",
      " [[0.2509804  0.25882354 0.25490198]\n",
      "  [0.22745098 0.23333333 0.23039216]\n",
      "  [0.2127451  0.2127451  0.2127451 ]\n",
      "  ...\n",
      "  [0.22745098 0.23137255 0.21176471]\n",
      "  [0.2245098  0.2254902  0.21470588]\n",
      "  [0.20686275 0.20686275 0.20686275]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.25833333 0.3093137  0.33284312]\n",
      "  [0.25563726 0.30686274 0.32965687]\n",
      "  [0.25882354 0.3137255  0.3254902 ]\n",
      "  ...\n",
      "  [0.22794117 0.25490198 0.2784314 ]\n",
      "  [0.22990195 0.2509804  0.26862746]\n",
      "  [0.21960784 0.23921569 0.2531863 ]]\n",
      "\n",
      " [[0.2627451  0.3        0.3372549 ]\n",
      "  [0.27132353 0.3095588  0.34485295]\n",
      "  [0.26764706 0.30882353 0.33529413]\n",
      "  ...\n",
      "  [0.21838236 0.25563726 0.28063726]\n",
      "  [0.21470588 0.25196078 0.27107844]\n",
      "  [0.21764706 0.25       0.2637255 ]]\n",
      "\n",
      " [[0.26568627 0.29705882 0.34019607]\n",
      "  [0.26593137 0.29828432 0.3394608 ]\n",
      "  [0.26666668 0.3019608  0.33284312]\n",
      "  ...\n",
      "  [0.20808823 0.25931373 0.28063726]\n",
      "  [0.21323529 0.25857842 0.2740196 ]\n",
      "  [0.21078432 0.24607843 0.26568627]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.26446077 0.26446077 0.26446077]\n",
      "  [0.2372549  0.2372549  0.2372549 ]\n",
      "  [0.21372549 0.21372549 0.21372549]\n",
      "  ...\n",
      "  [0.22941177 0.22941177 0.22156863]\n",
      "  [0.21862745 0.22083333 0.20637254]\n",
      "  [0.22647059 0.23039216 0.21078432]]\n",
      "\n",
      " [[0.2752451  0.2752451  0.2752451 ]\n",
      "  [0.24338235 0.24338235 0.24338235]\n",
      "  [0.21617647 0.21617647 0.21617647]\n",
      "  ...\n",
      "  [0.21960784 0.21960784 0.21176471]\n",
      "  [0.20882353 0.20882353 0.2009804 ]\n",
      "  [0.20588236 0.20686275 0.19607843]]\n",
      "\n",
      " [[0.2617647  0.2617647  0.2617647 ]\n",
      "  [0.23235294 0.23235294 0.23235294]\n",
      "  [0.2127451  0.2127451  0.2127451 ]\n",
      "  ...\n",
      "  [0.21176471 0.21176471 0.20392157]\n",
      "  [0.20980392 0.20980392 0.20196079]\n",
      "  [0.20392157 0.20392157 0.19607843]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.27058825 0.30588236 0.33333334]\n",
      "  [0.27058825 0.3137255  0.3372549 ]\n",
      "  [0.2742647  0.31740198 0.3409314 ]\n",
      "  ...\n",
      "  [0.20955883 0.24877451 0.25661764]\n",
      "  [0.20367648 0.24142157 0.25      ]\n",
      "  [0.20220588 0.23504902 0.24215686]]\n",
      "\n",
      " [[0.27279413 0.30906862 0.3257353 ]\n",
      "  [0.2784314  0.31617647 0.33210784]\n",
      "  [0.27867648 0.32156864 0.3367647 ]\n",
      "  ...\n",
      "  [0.1985294  0.2512255  0.25588235]\n",
      "  [0.20220588 0.24828431 0.25514707]\n",
      "  [0.20122549 0.24730392 0.25612745]]\n",
      "\n",
      " [[0.27279413 0.30612746 0.31691176]\n",
      "  [0.2784314  0.3125     0.3247549 ]\n",
      "  [0.2747549  0.31176472 0.32990196]\n",
      "  ...\n",
      "  [0.2127451  0.26102942 0.2634804 ]\n",
      "  [0.20955883 0.25563726 0.2625    ]\n",
      "  [0.20882353 0.25490198 0.2637255 ]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.2752451  0.28308824 0.27132353]\n",
      "  [0.23602942 0.24387255 0.23406863]\n",
      "  [0.20588236 0.21372549 0.20980392]\n",
      "  ...\n",
      "  [0.23014706 0.23014706 0.22230393]\n",
      "  [0.2254902  0.2254902  0.21764706]\n",
      "  [0.22941177 0.22941177 0.22156863]]\n",
      "\n",
      " [[0.26127452 0.26911765 0.25735295]\n",
      "  [0.22598039 0.23235294 0.22328432]\n",
      "  [0.2017157  0.20367648 0.20269608]\n",
      "  ...\n",
      "  [0.22647059 0.22647059 0.21862745]\n",
      "  [0.21960784 0.21960784 0.21176471]\n",
      "  [0.21642157 0.21642157 0.20857844]]\n",
      "\n",
      " [[0.25392157 0.2617647  0.25      ]\n",
      "  [0.23137255 0.2372549  0.22843137]\n",
      "  [0.21568628 0.21568628 0.21568628]\n",
      "  ...\n",
      "  [0.23039216 0.23039216 0.22254902]\n",
      "  [0.21764706 0.21764706 0.20980392]\n",
      "  [0.21568628 0.21568628 0.20784314]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.27058825 0.30588236 0.33333334]\n",
      "  [0.27058825 0.3137255  0.3372549 ]\n",
      "  [0.2742647  0.31740198 0.3409314 ]\n",
      "  ...\n",
      "  [0.20955883 0.24877451 0.25661764]\n",
      "  [0.20367648 0.24142157 0.25      ]\n",
      "  [0.20220588 0.23504902 0.24215686]]\n",
      "\n",
      " [[0.27279413 0.30906862 0.3257353 ]\n",
      "  [0.2784314  0.31617647 0.33210784]\n",
      "  [0.27867648 0.32156864 0.3367647 ]\n",
      "  ...\n",
      "  [0.1985294  0.2512255  0.25588235]\n",
      "  [0.20220588 0.24828431 0.25514707]\n",
      "  [0.20122549 0.24730392 0.25612745]]\n",
      "\n",
      " [[0.27279413 0.30612746 0.31691176]\n",
      "  [0.2784314  0.3125     0.3247549 ]\n",
      "  [0.2747549  0.31176472 0.32990196]\n",
      "  ...\n",
      "  [0.2127451  0.26102942 0.2634804 ]\n",
      "  [0.20955883 0.25563726 0.2625    ]\n",
      "  [0.20882353 0.25490198 0.2637255 ]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.2752451  0.28308824 0.27132353]\n",
      "  [0.23602942 0.24387255 0.23406863]\n",
      "  [0.20588236 0.21372549 0.20980392]\n",
      "  ...\n",
      "  [0.23014706 0.23014706 0.22230393]\n",
      "  [0.2254902  0.2254902  0.21764706]\n",
      "  [0.22941177 0.22941177 0.22156863]]\n",
      "\n",
      " [[0.26127452 0.26911765 0.25735295]\n",
      "  [0.22598039 0.23235294 0.22328432]\n",
      "  [0.2017157  0.20367648 0.20269608]\n",
      "  ...\n",
      "  [0.22647059 0.22647059 0.21862745]\n",
      "  [0.21960784 0.21960784 0.21176471]\n",
      "  [0.21642157 0.21642157 0.20857844]]\n",
      "\n",
      " [[0.25392157 0.2617647  0.25      ]\n",
      "  [0.23137255 0.2372549  0.22843137]\n",
      "  [0.21568628 0.21568628 0.21568628]\n",
      "  ...\n",
      "  [0.23039216 0.23039216 0.22254902]\n",
      "  [0.21764706 0.21764706 0.20980392]\n",
      "  [0.21568628 0.21568628 0.20784314]]], shape=(100, 100, 3), dtype=float32)\n",
      "tf.Tensor(\n",
      "[[[0.2602941  0.26813725 0.26421568]\n",
      "  [0.2872549  0.28333333 0.28431374]\n",
      "  [0.30735293 0.2857843  0.2867647 ]\n",
      "  ...\n",
      "  [0.24019608 0.25588235 0.25392157]\n",
      "  [0.24019608 0.25588235 0.25980392]\n",
      "  [0.24019608 0.25588235 0.25980392]]\n",
      "\n",
      " [[0.30588236 0.3137255  0.3019608 ]\n",
      "  [0.32034314 0.31862745 0.31740198]\n",
      "  [0.32794118 0.30637255 0.30686274]\n",
      "  ...\n",
      "  [0.22916667 0.2509804  0.25612745]\n",
      "  [0.22916667 0.24877451 0.26127452]\n",
      "  [0.22843137 0.24803922 0.26078433]]\n",
      "\n",
      " [[0.35343137 0.3612745  0.35539216]\n",
      "  [0.35490197 0.35392156 0.34607843]\n",
      "  [0.3502451  0.3375     0.33259803]\n",
      "  ...\n",
      "  [0.22941177 0.25784314 0.26666668]\n",
      "  [0.22867647 0.25710785 0.26887256]\n",
      "  [0.22843137 0.25686276 0.26960784]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.50416666 0.46691176 0.4404412 ]\n",
      "  [0.51053923 0.46348038 0.43823528]\n",
      "  [0.51642156 0.45759803 0.42867646]\n",
      "  ...\n",
      "  [0.31715685 0.31715685 0.3112745 ]\n",
      "  [0.30980393 0.30980393 0.30392158]\n",
      "  [0.3137255  0.3137255  0.30784315]]\n",
      "\n",
      " [[0.5264706  0.47745097 0.45392156]\n",
      "  [0.5323529  0.4735294  0.44534314]\n",
      "  [0.5379902  0.46740195 0.4384804 ]\n",
      "  ...\n",
      "  [0.3720588  0.3720588  0.36764705]\n",
      "  [0.32352942 0.32352942 0.32352942]\n",
      "  [0.3137255  0.3137255  0.3137255 ]]\n",
      "\n",
      " [[0.5382353  0.4872549  0.45588234]\n",
      "  [0.5392157  0.47843137 0.45      ]\n",
      "  [0.5482843  0.47867647 0.44142157]\n",
      "  ...\n",
      "  [0.53431374 0.53431374 0.52843136]\n",
      "  [0.53210783 0.53210783 0.53210783]\n",
      "  [0.48161766 0.48161766 0.48161766]]], shape=(100, 100, 3), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "for image in os.listdir(os.path.join('application_data','verification_images')):\n",
    "    validation_img = preprocess(os.path.join('application_data','verification_images',image))\n",
    "    print(validation_img)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- ### OpenCV real time verification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "True\n",
      "1/1 [==============================] - 0s 17ms/step\n",
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      "1/1 [==============================] - 0s 18ms/step\n",
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      "1/1 [==============================] - 0s 17ms/step\n",
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      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
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      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 17ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 17ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 16ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 17ms/step\n",
      "1/1 [==============================] - 0s 17ms/step\n",
      "True\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
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      "1/1 [==============================] - 0s 18ms/step\n",
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      "1/1 [==============================] - 0s 20ms/step\n",
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      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
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      "1/1 [==============================] - 0s 18ms/step\n",
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      "1/1 [==============================] - 0s 18ms/step\n",
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      "1/1 [==============================] - 0s 20ms/step\n",
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      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 17ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 17ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 17ms/step\n",
      "1/1 [==============================] - 0s 17ms/step\n",
      "True\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
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      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 17ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
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      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 17ms/step\n",
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      "1/1 [==============================] - 0s 21ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 20ms/step\n",
      "1/1 [==============================] - 0s 17ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "1/1 [==============================] - 0s 18ms/step\n",
      "1/1 [==============================] - 0s 17ms/step\n",
      "1/1 [==============================] - 0s 19ms/step\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "# accessing of the web-cam\n",
    "cap = cv2.VideoCapture(0)\n",
    "\n",
    "while cap.isOpened():\n",
    "    ret, frame = cap.read() \n",
    "    frame = frame[120:120+250,200:200+250 ,:]\n",
    "    cv2.imshow('verificaion', frame) \n",
    "    \n",
    "    # verfication trigger\n",
    "    if cv2.waitKey(10) & 0XFF == ord('v'):\n",
    "        imgname = os.path.join('application_data', 'input_image','input_image.jpg')\n",
    "        cv2.imwrite(imgname, frame)\n",
    "        #run verficatoin \n",
    "        results, verified = verify(model, 0.9, 0.7)\n",
    "        print(verified)\n",
    "        \n",
    "    if cv2.waitKey(10) & 0XFF == ord('q'): \n",
    "        break\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.squeeze(results) > 0.8"
   ]
  }
 ],
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