brain tumour classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f233b46a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.applications import VGG16, ResNet50, InceptionV3\n",
    "from tensorflow.keras.layers import Dense, Dropout, GlobalAveragePooling2D\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.models import Model\n",
    "\n",
    "\n",
    "from tqdm import tqdm\n",
    "import os\n",
    "import random\n",
    "\n",
    "# For Data Processing\n",
    "import numpy as np\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from PIL import Image, ImageEnhance\n",
    "from tensorflow.keras.preprocessing.image import load_img\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b5ee89da",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dir = 'archive/Training/'\n",
    "test_dir = 'archive/Testing/'\n",
    "\n",
    "train_paths = []\n",
    "train_labels = []\n",
    "\n",
    "for label in os.listdir(train_dir):\n",
    "    for image in os.listdir(train_dir+\"//\" +label):\n",
    "        train_paths.append(train_dir + '//'+label+ '//'+image)\n",
    "        train_labels.append(label)\n",
    "\n",
    "train_paths, train_labels = shuffle(train_paths, train_labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d5f3cf1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_paths = []\n",
    "test_labels = []\n",
    "\n",
    "for label in os.listdir(test_dir):\n",
    "    for image in os.listdir(test_dir+ '//' + label):\n",
    "        test_paths.append(test_dir +'//'+label + '//'+image)\n",
    "        test_labels.append(label)\n",
    "\n",
    "test_paths, test_labels = shuffle(test_paths, test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4dc6b791",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5712, 5712)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = os.listdir(train_dir) #add the names of the folders inside the mentioned directory to labels\n",
    "\n",
    "train_img = [] #store images\n",
    "train_img_label = [] #store corresponding label\n",
    "\n",
    "for l in labels: #create a list of images and a list of corresponding labels.\n",
    "    for img in os.listdir(train_dir+l):\n",
    "        train_img.append(train_dir+l+'/'+img)\n",
    "        train_img_label.append(l)\n",
    "        \n",
    "train_img, train_img_label = shuffle(train_img, train_img_label) #shuffle both lists in the same order to match the right label\n",
    "len(train_img_label), len(train_img)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "49a8f642",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1311, 1311)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#repeat steps for testing set \n",
    "test_img = []\n",
    "test_img_label = []\n",
    "\n",
    "for l in labels:\n",
    "    for img in os.listdir(test_dir+l):\n",
    "        test_img.append(test_dir+l+'/'+img)\n",
    "        test_img_label.append(l)\n",
    "        \n",
    "test_img, test_img_label = shuffle(test_img, test_img_label)\n",
    "\n",
    "len(test_img),len(test_img_label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "39511676",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAGFCAYAAAAIHw/GAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAABMr0lEQVR4nO3dd3hTdd8G8Ptkr6YjnbSFUqDsKSCIPCDCg1tcKCIO3K8guHChIo97AOKegAsHKk6WgIKMMiurrNLSAt17N+v9I23aQgMdSU9ycn+uqxc24+TbCrnz24LdbreDiIioCTKxCyAiIu/FkCAiIpcYEkRE5BJDgoiIXGJIEBGRSwwJIiJyiSFBREQuMSSIiMglhgQREbnEkCAiIpcYEkRE5BJDgoiIXGJIEBGRSwwJIiJyiSFBREQuMSSIiMglhgQREbnEkCAiIpcYEkRE5BJDgoiIXGJIEBGRSwwJIiJyiSFBREQuMSSIiMglhgQREbnEkCAiIpcYEkRE5BJDgoiIXGJIEBGRSwwJIiJyiSFBREQuMSSIiMglhgQREbnEkCAiIpcYEkRE5BJDgoiIXGJIEBGRSwwJIiJyiSFBREQuMSSIiMglhgQREbnEkCAiIpcYEkRE5BJDgoiIXFKIXQCRJ5gtVlRVW1BVY6n/s8aCytr/rq79s7L2/uoaCwBALhcgl8kglwmQy2WQyQQoZDLI5LV/1t6ukAmQyWRQKmUwaFQw6Oq/9BoVZDJB5N8AkXswJMhn1ZitKCipRH5RBfKLK5BfXIm84goUl1bBarOLVpcgAHqNCpPG90VIoBY/LF0DtUaNyCgTIjuEIiIyBEqVUrT6iFqCIUFer6LK7AyBhoFQUl4tdmlNstuBssoa6DSOIPjuq9WorKivVRAEhJiMiOwQirj4aCT06IiEnnHoEBMGQWALhLwLQ4K8is1mR2Z+KdKzipGeWYzsgjJUVlvELqvFVEo5NGoFysoqGgUEANjtduTnFSM/rxj796Tg99rbDQE6dE2IRULPOGdwBAUHtH/xRA0wJEhUdrsd2QXlSM8qwvHMYpzIKUGN2Sp2WW0WoFMDAHKzC5v9nLLSCiTtPISknYect4VHhKBbj45I6NEJCT3j0DUhFmqNyu31ErnCkKB2l1tYjvSsYhzPLEJGdgmqanyvpXAuRoMjJPJymh8STcnJLkBOdgE2/Z0EAJDJZEjo0RHnj+iL80f0Q0zHiLaWSnRWDAnyuOoaCw4dz0fqqUKkZxWjososdkkeF6h3hEROdoFbr2uz2XDwQBoOHkjDko9/RXRsuCMwLuiL7r3iIJNxVju5F0OCPMJqtSHlRAH2p+bi2IlCWKw2sUtqV8bakMhtY0viXE5m5ODHb9bix2/WIig4AEOG98awEf3Q/7zuUHEGFbkBQ4Lcxm63IyO7BPuP5eDQ8TxU1/j+2EJrOUOiBWMSbVVUWIo1f2zFmj+2QqNRYcDgHjh/RF8MGdYHxkB9u9VB0uLzISEIAn766SdMmDDBbdecM2cOli9fjqSkJLddU8pyCspxIDUHB1LzUOql01LbW3u1JFypqqrB1n/2YOs/eyCTyTBkeG9cetWFGDi4B6fZUosIdrtdvFVHbpCVlYXg4GCo1Wq3XbOsrAzV1dUwmUxuu6bUlJRX48CxHOw/lou8ogqxy/E69103GIEGDe6Y+Azy84rFLscpKjoMl1xxAcZeOgwBRrYu6Nx8PiSofaVnFSNx3wkcOynOJ2RfIAjAo7eMgN1uw3XjH4ZNxNXfrqhUSowYPRCXXjUCPXp1Frsc8mJumwoxevRoTJ8+HTNnzkRwcDAiIiLw0Ucfoby8HHfccQcCAgLQpUsXrFixwvmcAwcO4LLLLoPBYEBERASmTJmCvLy8Rtd88MEHMWvWLISEhCAyMhJz5sxp9LqCIGD58uUAgLS0NAiCgB9//BEXXXQRdDod+vfvjy1btjR6zscff4zY2FjodDpcc801mDdvHoKCgpz3z5kzBwMGDHB+b7PZMHfuXMTExECtVmPAgAFYuXKl8/661/3uu+8wcuRIaLVaDBkyBIcPH8b27dsxePBgGAwGXHLJJcjNzXU+b/v27Rg3bhxCQ0MRGBiIUaNGYdeuXW34v+AZdrsdh47n4fPf/8XSVXsZEOdg0Dr2bsrLLfLKgACAmhoz1q/ehlnT5mPmPa9h1W+bUVXJrkI6k1vnyy1ZsgShoaHYtm0bpk+fjvvvvx833HADLrjgAuzatQvjx4/HlClTUFFRgczMTIwaNQoDBgzAjh07sHLlSmRnZ2PixIlnXFOv1yMxMRGvvfYa5s6dizVr1py1jqeffhqPPvookpKSkJCQgEmTJsFicczF37RpE+677z7MmDEDSUlJGDduHF588cWzXu+tt97Cm2++iTfeeAN79uzB+PHjcdVVV+HIkSONHvfcc89h9uzZ2LVrFxQKBSZNmoRZs2bhrbfewsaNG5GSkoJnn33W+fjS0lLcdttt2LhxI7Zu3Ypu3brhsssuQ2lpaUt+7R5jsdqQdDgLnyzfheV/HURmnnfU5e2MBg0AINfN01895djRE3h33je4Y+Kz+OjtZcg4niV2SeRF3NbdNHr0aFitVmzcuBEAYLVaERgYiGuvvRaff/45AMf4QVRUFLZs2YI//vgDiYmJWLVqlfMaJ06cQGxsLA4dOoSEhIQzrgkAQ4cOxZgxY/DKK684foAGA9dpaWno3LkzPvnkE9x5550AHK2V3r17Izk5GT169MBNN92EsrIy/Pbbb85r3nLLLfjtt99QVFQE4MyB6+joaDzwwAN46qmnGtUxZMgQvPvuu02+7jfffINJkyZh7dq1GDNmDADglVdeweLFi3Hw4MEmf4dWqxXBwcH4+uuvccUVV7T6/0VbVddYsPtQJnYmZ6Ksska0OnxVz85huOo/3bF+9TbMf+VLsctplUFDe+KWqZeja0JHsUshkbm1JdGvXz/nf8vlcphMJvTt29d5W0SEY3VoTk4Odu7cifXr18NgMDi/evToAQBISUlp8poAEBUVhZycnGbXERUV5XxNADh06BCGDh3a6PGnf99QSUkJTp06hREjRjS6fcSIEUhOTnb5unU/6+k/f8Pac3JycN999yEhIQGBgYEIDAxEWVkZ0tPTz/rzeUppRTXW70jFe8u24+9dxxkQrST2zCZ32LUtGQ/f9wZefu5TpKdlil0OicitU2CVysaLdwRBaHRb3dQ7m80Gm82GK6+8Eq+++uoZ16l7Y3d1TZvt7AuzXL0m4OhfP30KYHMaU0095/Tbmnrd029rWPvtt9+O3NxcLFiwAJ06dYJarcbw4cNRU9O+b84FxZXYuu8EDhzLEXWLbamQQkjU2bLxXyRu2oNRFw/GpNsuRWSHULFLonYm2jqJQYMG4YcffkBcXBwUivYro0ePHti2bVuj23bs2OHy8UajER06dMA///yD//znP87bN2/efNYWSHNs3LgR7733Hi677DIAQEZGRqOBe0+rqrHgn6R07D6YCRsnublNoMEzW3KIxWazY/2a7di4fhfGXjoMN065BKbQQLHLonYi2kYvDzzwAAoKCjBp0iRs27YNx44dw+rVqzF16lRYrZ5bqTt9+nT88ccfmDdvHo4cOYIPP/wQK1asOOsCo8ceewyvvvoqvv32Wxw6dAhPPPEEkpKSMGPGjDbV0rVrV3zxxRdITk5GYmIiJk+eDK1W26ZrNofdbkfS4Sx8/NNO7Ew+xYBws7qWRFs39/M2FosVK3/dhHunzMVn7y9HSXG52CVROxAtJDp06IBNmzbBarVi/Pjx6NOnD2bMmIHAwECPblI2YsQIfPDBB5g3bx769++PlStX4qGHHoJGo3H5nAcffBCPPPIIHnnkEfTt2xcrV67EL7/8gm7durWpls8++wyFhYUYOHAgpkyZggcffBDh4eFtuua5ZGQXY/FvSVi15ahfbLQnhvqQKBK3EA+pqTZj+ffrcPfkOfh68R+oKK8UuyTyIC6mA3D33Xfj4MGDjWZRSU1JWRXW70zDwbT2687yRyqlHA/dPBylJeWYPOFJsctpFwFGPW6ZejkuuXIEt/yQIJ/fu6k13njjDYwbNw56vR4rVqzAkiVL8N5774ldlkeYLVYk7juJxH0n/G4nVjFIbTyiOUpLyvH+gu/w99qdmPbITTzjQmL8MiS2bduG1157DaWlpYiPj8fChQtx1113iV2W2yWn5eKvHWleexa0FElpZlNLHdibghl3v4obp4zHtTeNhUIhF7skcgO/DInvvvtO7BI8KrewHGsSU5CRXSJ2KX7HOR7RjluEexOz2YIvP/sd//y1G9MfuxndunMxnq/jMVYSYrfbkbjvBJb8lsSAEIlR75gAkeOnIVEn7dgpPPbAPHz6/k+oruKiTF/GkJCIotIqfL1yL/7amcYFcSKq727ynzEJV2w2G37+fj2m3/kyknYeErscaiWGhAT8ezgLi37djRM5bD2IzWjw3zEJV7Iy8/HsY+/irVe/QmkJ11b4GoaED6uoMuOHtQewcstR1Jj996hQbyLVhXTusHZVIh644yVs3bRH7FKoBRgSPup4ZhEW/bIbR0+wW8NbyAQBBq0KZrMFhQXcVr0pRYWleOmZT7D4o59h5ZRsn8CQ8DE2mx0bdh/Ht2v2cZdWL2PQ1R42lFPYrE0j/dmP36zFs4+9i6JChqm3Y0j4kJLyaixdtRdb9mSA70Hex5/XSLTG3qQjeOje13DwQKrYpdBZMCR8RMqJAg5OezkOWrdcfl4xnnpoIX5fvkHsUsgFhoQP2JF8Cj+sO4CqaovYpdBZBNa1JPxoSw53sJit+HDhMrz50udcU+GFGBJezG6348/EFKzddozdSz6A3U1t8/efO/DotHk4deLsJ09S+2JIeKkasxU/rkvGzoM8OtJXBDhbEgyJ1jp+7BQeuf9Nr5wmGxcXhwULFji/FwQBy5cvF62e9sKQ8EJlFTX4euUeTm/1MWxJuEd5eSVefvZTLPn4l3MeVSymzMxMXHrppWKX4XF+ucGfN8stLMf3aw+glDu3+pxAg2PfJoZE29ntdvyw9E/k5RRi5hO3QC73vh1lIyMjxS6hXbAl4UVSTxbiyxV7GBA+SKNSQKWUo7ioFDXVPPHPXf5euxMvPfMJamo8/zstLS3F5MmTodfrERUVhfnz52P06NGYOXNmk48/vbtp7969GDNmDLRaLUwmE+655x6UlZU577/99tsxYcIEvPTSS4iIiEBQUBCef/55WCwWPPbYYwgJCUFMTAw+++yzRq/z+OOPIyEhATqdDvHx8XjmmWdgNrff3zGGhJdIOpyFZWsPcHsNH8WuJs/ZvnU/nn/ifVRUVHn0dR5++GFs2rQJv/zyC9asWYONGzdi165dzXpuRUUFLrnkEgQHB2P79u34/vvv8eeff2LatGmNHrdu3TqcOnUKGzZswLx58zBnzhxcccUVCA4ORmJiIu677z7cd999yMjIcD4nICAAixcvxoEDB/DWW2/h448/xvz58936s58NQ0Jkdrsdf+1MxaotR2HjFCafVbdGIieLIeEJe5OOYvYj76Ck2DMbBJaWlmLJkiV44403cPHFF6NPnz5YtGgRrNbmfWj76quvUFlZic8//xx9+vTBmDFj8M477+CLL75Adna283EhISFYuHAhunfvjqlTp6J79+6oqKjAU089hW7duuHJJ5+ESqXCpk2bnM+ZPXs2LrjgAsTFxeHKK6/EI4880q5n4jAkRGSz2fHLhkNI3HdS7FKojbhFuOcdPZSOJ2e+hfy8Yrdf+9ixYzCbzRg6dKjztsDAQHTv3r1Zz09OTkb//v2h1+udt40YMQI2mw2HDtVvk967d2/IZPVvuxEREejbt6/ze7lcDpPJhJyc+mnAy5Ytw4UXXojIyEgYDAY888wzSE9Pb9XP2RoMCZHYbHb8uvEQDqbliV0KuQF3f20fGcez8MSMBcg65d5/N3V7bQmC0OTtzXn+6c+t0/B2pVJ5xn1N3VY3q2vr1q246aabcOmll+K3337D7t278fTTT6Ompv0WHTIkRGC327Fi8xEGhIRwTKL9ZGfm44kZC3A89ZTbrtmlSxcolUps27bNeVtJSQmOHDnSrOf36tULSUlJKC+v7w7btGkTZDIZEhISWl3Xpk2b0KlTJzz99NMYPHgwunXrhuPHj7f6eq3BkBDBqq0p2JfCVaVS4hyT4EK6dlGQX4KnHlqIw8nuecMMCAjAbbfdhsceewzr16/H/v37MXXqVMhkMpcthIYmT54MjUaD2267Dfv27cP69esxffp0TJkyBREREa2uq2vXrkhPT8c333yDlJQULFy4ED/99FOrr9caDIl29ue2FPx7OEvsMsjN2JJof6UlFXjm0XewZ/dht1xv3rx5GD58OK644gqMHTsWI0aMQM+ePaHRaM75XJ1Oh1WrVqGgoABDhgzB9ddfj4svvhjvvPNOm2q6+uqr8dBDD2HatGkYMGAANm/ejGeeeaZN12wpwc6N79vNXzvTkLjvhNhlkJvJZQIeueUCmM0W3HDpozxLop2p1Eo8/+r/oXe/Lm69bnl5OaKjo/Hmm2/izjvvdOu1fQlbEu1k07/pDAiJMuhUEAQeNiSWmmozXpj9EVJT2jZLcPfu3Vi6dClSUlKwa9cuTJ48GYDj07w/Y0i0g8R9J/BPUvtNWaP2VbcdB8cjxFNeVonnn3gfWZn5bbrOG2+8gf79+2Ps2LEoLy/Hxo0bERoa6qYqfRP3bvKwncmn8NfONLHLIA/iGgnvUJBfgmcfexevLpyJ4BBji58/cOBA7Ny50wOV+Ta2JDwo6XAW/tx2TOwyyMO4Rbj3yDqVhzlPvI/yskqxS5EMhoSHHDqeh9Vbj4pdBrWDQIaEV0k9ehKvzPkUFgv3QXMHhoQH5BaW4/d/DvM0OT/B6a/e599dh/HuvG/ELkMSGBJuVlVtwY/rk2G2eO9hKeRedQvpGBLeZe3KRHz35Sqxy/B5DAk3stvt+GXDQRSVenZLY/IuRr0adrud+zZ5oa8W/YEN6zgY3RYMCTfasOs4Uk8ViV0GtSOtWgGlQo6iwlKYzRaxy6HT2O12vPXqVziwjxNIWosh4SYH0/KwlYvl/I6Rg9Zez2y24OVnP/HIFuP+gCHhBjkF5fhjk3v2jyHfwjUSvqG4qAzzXvrcuQU3NR9Doo0qq834iQPVfouD1r5jb9IRDmS3AkOiDepOlisq40C1vzLqHVtysLvJN3zz+Srs+5frl1qCIdEGG3anIY0D1X6NYxK+xWaz4c0Xl3jsrGwpYki0UnJaLs+mJo5J+KD8vGK89eqXYpfhMxgSrVBSXo2Vm9lkJY5J+KrtW/fj52XrxS7DJzAkWmHllqOoMXNfGH8nlwnQa5Sorqph94UPWvLxLzh6mFv4nwtDooX2Hs1G6kl+aiRHV5MgCGxF+CiL2YrX5i5GRTl3jD0bhkQLlFZUY+12rtwkh/pBa45H+KqsU3l4d963Ypfh1RgSLbB6Swqqa9jNRA514xE8kc63bVy/C6v/2CJ2GV6LIdFM+4/l4OgJfmKketwiXDo+ffdH5OcWiV2GV2JINEN5ZQ1PmKMzMCSko7KyGp+896PYZXglhkQzrN6agqpq7vBJjdWtts7jGglJ2PR3EnbvOCh2GV6HIXEOyam5OJyeL3YZ5IU4JiE9Hy1cxi3fT6MQuwBvVlFlxprEFLHLaLP1v32B/Ts3ICfzOJRKNTp17YNLJ96PsKiOzses+ekz7Elci6KCHMgVCsTEdcd/r7sbHbv0btZr/Lv1Tyz94Hn0Gnghbp3xsss6Vi37CCPG3YArJz/ovH3DiqXYsGIpAGDU5ZMxcvyNzvvSU/bj58/n4YHnPoJMJm/Nj+8xRr0aNpsN+XlFYpdCbnLyRA5++m4dJk7+r9ileA22JM5iTWIKKiXQzZR6MAnDxlyDB575EHc+Nh82mxWfvvEwaqrr54eHRcbiqikPYeYLS3D/0+8hKDQSn77xCMpKzv0puTAvC79/+x7iEvq7fEzGsWRs++tXRMZ2aXR7VkYK1vz0KW667zncdN+zWLXsI2SdcIz/WC0W/LTkTUy47VGvCwidRgmFXIbCglJYuLBSUr7/ajVystiFWIch4cKxk4U4mJYndhluMfXRNzF45GWIiO6MDh274vo7n0RRfjZOpB1yPmbA8HHo1nswTOEdEBHdGVdMmo7qynJknTh7S8pms+KbD+di3ISpCAmLavIx1VUV+PbDubj2jlnQ6gIa3ZeTeRyRMV3Qtdd56NprMKJiuyDn1HEAwIYVX6Nz9/6Ije/Zxt+A+3HPJumqrqrBx+/+IHYZXoMh0QS73Y6/d6aJXYbHVFU6tpDQ6Y1N3m+xmLHtr1+g0RoQFdv1rNda+/Ni6AOCMGTUFS4f8/MX89G9/3B06z34jPsiY+KRl52BovxsFOZlITcrA5ExnZGXfQI7/1mB8dfe3YKfrP0492zieIQkJW7ai+1b94tdhlfgmEQTDqTmIqdQmnvx2O12/L70HcQl9ENkTHyj+5KTNmHp+8/DXFOFgEAT7nxsHvQBQS6vlXZkD7Zv+B0z5n7m8jH/bv0TJ48fxrRnP2ry/vAOcRh/3T345PWHAACXXH8vwjvE4ZPXZuLSiffj8L5E/Ll8EWRyBa6c/CDiuw9o8c/sCdwiXPo+fnsZ+g9KgEqlFLsUUTEkTmO12rBx93Gxy/CYn7+Yj8yMFNz/9Ltn3Nel5yA8OPczVJQWY9vfv+Lr957DA89+CIMx+IzHVldW4NsPX8B1d8xyGSRF+dn49euFmProPChVapc1DRszAcPGTHB+v2PjH1BpdOjYtQ/efGIypj33EYoLc7H0/Tl4/PXvoFCqWvxzuxvXSEhfVmY+ln29BjfffpnYpYiKIXGa3YezUFxWLXYZHvHzF/ORnLQJ9z75NgJDws+4X6XWIjQiBoiIQceuvfH645OwfcNvuOiKKWc8Nj/nJArzMrFkwRPO2+x2xxGuT00djUde+QpZGSkoKynEO3Pucj7GZrMi7fC/2LL2R7zwydozBqTLS4uw7pfFuPfJd5Bx7ABCI2OdXzarBXlZGWcMfoshkPs2+YUfv1mLi8YNQVR0mNiliIYh0UC12YItezLELsPt7HY7fvlyAfbv3IB7nliIkLAOzX0iLGZzk3eFRXXEzBeWNLpt9Q8fo7qqAldOnoHAkHAYAoLPeMyyT19GWGRHjLp8cpMzln79eiFG/HciAkPCkZF6EFZr/ewyq9XqNQfZGw21x5ayJSFpNTVmfPreT5j94j1ilyIahkQD2/afREVV02+KvuznL+YhacufuHXGS1BrdCgtciwO1OgMUKrUqKmuxLpfP0evARciIMiEirJibFn3E4oLctFv6EXO63z70QsIDA7FJTfcB6VKfcaYhkZnAADn7QqF8ozHKFUa6AyBZ9wOAEf2bUd+9glMvHs2ACA2vidyM4/j0J6tKCrIgUwmb7S2Q0zsbvIf27bsw9HD6eia4B1/99obQ6JWeWUNduw/JXYZHrF13XIAwEevPNjo9uvvfBKDR14GQZAhNzMdX/4zG+VlxdAZjIjp3BP3PvUOIqI7Ox9flJ8NQRA8UqO5pho/fzkfN9//PGQyx6S7wOAwXHXLTHz/yctQKJWYeNdTZx3baC8KuQw6jRKVldUoK60QuxxqB999sRpP/e+ucz9QggS73W4XuwhvsCYxBbsOZopdBvmAEKMWd19zHtJTMzHtzqZXl5O0CIKAtz5+HHHxzeyqlRCukwBQVFqFpMNZYpdBPoJdTf7Hbrfj+69Wi12GKBgSADbsPg6bjQ0qah7nQjqGhF/Z9PdunEjPFruMduf3IZGdX4bk1FyxyyAfwpaEf7LZ/LM14fcD15slOOWVPKsuJHK8ZI3EkeM7kJmbgrKKQshlCgQHRqJXlxEw6OoXQR5KTcTJnMOoqiqDTCZHYEAYenQejuDASJfXLS3Px8HURBSX5qCyqhS9u45EfOyARo9JO7kXaSf3orKqBAAQoDehW9wQRJjinI9JSd+Fo+m7AABdO52HLrEDnfcVFmdh7+G/MHLwRAiC939m/XvtTky67VJEdggVu5R24/3/VzyopKwKRzJ4VgS1jLdtyZFfdBKdo/th5Hk3YNiAq2G327E16WdYrPXTufW6IPTtNgqjht6MEYOug05jxNZ/f0Z1TaXL61qtFug1RvSMvwBqla7Jx2jUBvTscgFGDr4RIwffCFNwDLbv/R2l5Y5/VyVleTiYmojzeo/HoF7jcfDYFpSUOe6z2azYc3g9+nW/yCcCAgBsNhuWfb1G7DLalW/8n/GQ3YeywLld1FJ1IZHnJd1Nw/pfjdiongjQmxBoCMOAHmNRWV2K4tIc52NiIrojLKQj9NpABOhN6NV1JCzWGpSUud7pOMgYgV5dL0R0RILLrdojQzsjwhQHgy4YBl0wesYPh0KuRGGxYyJIWUUhjAYTQoNjERYSC6M+FGUVjhZYSvoumIKiEWSMcONvw/PWrd7mVyvt/TYkLFYb/j3CGU3Ucka9Glar9x42ZLE4tpVRKjRN3m+zWZF+ah8UChWMBvd1m9jtNpzMPgyr1YzgQMe28QF6E8orilBRVYqKqhKUVxY5b8vIOogenYe57fXbi8VixQ/f/Cl2Ge3Gb8ckklNzJXGgELUvg1YFuVyG3JxCWK3esUVIQ3a7HfuP/oOQwCgYDaZG92XnpWLngVWwWs3QqPQY3n8C1Cptm1+zpCwP/+xaBpvNArlcicF9L0eAPgQAEKAPQY/44diatBwA0CN+OAL0IdiS9BN6drkAOQXpOJyWCEGQoU+3/8AUFN3metrDmj+24obJ42EKDRS7FI/z25bEzmRprq4mzwrw8plN+478jZLyPAzqdckZ95mCYzBq8E24cNANCDN1wo79K1Fd0/YV4wZdsPO6cR36Iil5DUrL67tj4qL7YsywKRgzbAriovsiIzMZCrkKIYFR+PfQWgzuczl6dx2JnftXwWrzjVP+zGYLflm2Xuwy2oVfhsTJnBJkF0jzvAjyLG8btG5o7+G/kZWXigsGXAOtxnDG/Qq5EnpdEIIDIzGgx8WQCQLSMw+0+XVlMjn0uiAEGSPQs8sFMBpCcexEUpOPra6pxOG0bejTbRQKS7Jg0AbBoAtCaHAM7HYryiu87/fqyrrV22Cx+EaotYVfhgS336DWCjR43xbhdrsdew//hazcFAwfcA102uZ1gdjhGJ9wf0Gur7v/6EbExw6AVmOA3W6HzV7fZWe32+FLuwQVF5Vh+5Z9YpfhcX4XEmWVNTh4XBpnV1P788aFdHsP/40T2YcwsNd4KORKVFWXo6q63LnNusVqRnLKZhQWZ6GiqgRFpTn49+BaVFWXoUN4/fG0uw+sRnLKZuf3NpsVxaW5KC7Nhc1mQ1V1GYpLc1FeUeR8THLKZuQXnURFZQlKyvKQfGwL8opOIiai+xl15hako7yiCHHR/QA4Zk+VVRQiOz8Nx0/tAwSh0doOX7B2ZaLYJXic3w1c/3s4i1twUKt5Y0gcP7UXALAl6cdGtw/oMRaxUT0hQEBZRSF27PsDNeZKKJVaBBnDMWLgdQjQ1w9uV1aXAQ12+a2qLseGHd84v0/J2I2UjN0wBUXjgoHXAgCqzZXYnbwG1dXlUCjUMBpMGNb/KoSFNN5W22q1YO/hv3Fe70ucOwlr1Qb06TYK/x5cC5kgx8AeYyGX+9Zb0s5tB1BYUILgkKbPi5cCv9oF1mqz4YNlO1BWWSN2KeSjbr9iACJMBky/6xUcP8bJDwTcfs/VuPami8Uuw2P8qrvp8PF8BgS1idELxyRIXGtXbRW7BI/yq5DYfYgD1tR6KoUcWrUS5WWVqCivErsc8hIZx7NxKDlN7DI8xm9CorSiGhnZJWKXQT4sQK8C4F3jEeQd/lwh3daE34TEkXRu5Edt442D1uQdNq7fhepqaXZl+01IHGZIUBsZDY69kDgeQaerKK/Clo3/il2GR/hFSFRWm5GRxa4mahtvXm1N4vtzhTTXTPhFSBzNKIDNf2b6koewu4nOZm/SEWRnSa/Hwi9Cgl1N5A6BXnYiHXkXu90uyS4nyYdEjdmKtFNFYpdBElC3RsJbDhsi77N7+0GxS3A7yYfEsZOFsHjhvv/kWwQBCNCpYbVaUZDP8S1q2v49KZKb5ST5kGBXE7mDXquCTCYgP7cYNhs/dFDTamrM2Jd0VOwy3ErSIWG12pBygv3H1HYcj6Dm2rU9WewS3ErSIZGWWYQas/QPBSHP48wmai6GhA/hKmtyF+fGfgwJOoeTGTmSmgor2ZCw2+04msGuAXKPupYEZzZRc+zaJp3WhGRDIq+oAuVVZrHLIIkw6h1bcnBMgppDSlNhJRsSJ3I4TZHch1tyUEvsSToMi0Ua46EMCaJmcC6ky2VI0LlVlFfh4P5jYpfhFtINCZ4dQW6iUsqhUSlQVlqByopqscshH7FLIl1OkgyJkvJqlJTzHzO5h5FrJKgVdktkKqwkQ4KtCHInjkdQa6SmnEJNje9PnpFkSAQFaDAgIRJhwToIgtjVkK/jQjpqDZvNhuOpmWKX0WYKsQvwhA5hAegQFgDAsQtsZl4pTuWV4lRuKTJzSzk1lloksG4hHbubqIVSj55At+4dxS6jTSQZEgUVgEYBaJSOQcdOUUHoFBXkvL+otAqncktxKq8Ep3JLkVNQDquNhxJR09iSoNZKTTkldgltJrmQsNiAU7VDEgIcQaFTAtraP1UKR3dUUIAGveLDHM+x2pCdX1YbHI4WBwe+qQ5DglorNeWk2CW0meRCorJBT5K99vuGtylk9YFR96dCLkN0uBHR4Ubn40orqh2hUfuVXVAGs4VbRPsjDlxTax0/xpaE16k8x3CDxQaUVju+6mgU9YGhUwEqueOAme6d1OjeKRQAYLPZkVNYXhsaJTiVV4rCkioP/iTkDWSCAINODbPZgsICzpqjlikvr0R2Vj4iIk1il9JqfhcSTamyOL4KKx3fywVHaGhrQ0OnBOQyAZEmAyJNBgzqEeV4rSqzs3vqVG4pMvNKUc2tySXFoHMcNpSTVQS7neNW1HKpKScZEt6kytL2a1jtQFmN4wvljttU8vqWhlZZ2/rQKNElJgRdYkIAOHaezS+uxMncEmdw5BdXgO8tvovjEdRWqUdPYtiIfmKX0WqSCgmbHfDUB/kaq+OrqLaHSdawteEc2xAQGqRDaJAO/btFAgCqzRZk5pU5u6ky88pQwSm4PoMhQW3l64PXkgqJaje0IprLZgfKaxxfdZTyxgPiGiWgVioQFxWEuAZTcAtLKht1U+UUlsPGKbheqW5jP27JQa2VxpDwHu0ZEk0xW4FiK1Bc29poOAVXpwS0tYPiwUYtgo1a9I4PB+CYgpuVX4aTOSXO8CirqHH9QtRunIcNcWYTtVJ2VgEqKqqg02nELqVVGBIe1HAKbt1hhg2n4NaNbyjkMsSEGxHTYApuSXmDKbh5pcjOL4PFyim47Y3dTdRWdrsdacdOoVefeLFLaRWGRDtzNQW3rqWhUwJqhePNyahXo0ecYwqu1WZDTkF5owV/RaWcgutpDAlyh8yTuQwJb+ALIdGUuim4OG0Kbl1LwzEFV4ao0ABEhQbgvNrnVVSZG7Q2HIPiNZyC61Z1YxIMCWqLIh9eYyOZkLDbHbOPpKDRFNxaanl9S6OutaHTKNE1NgRdY+un4OYVVeBkg5Xi+cUVIv0Uvk+tkkOtVKCkuAzVVRwjotYrLCgVu4RWk0xIWGyOMQCpqrYC1ZVAUW1ro24Krq7Boj+FTEBYsB5hwXoMSKidgltjOWPBX6WvNrnaGbfjIHcpKmRIiM7sZ2O6Z5uCWze+oVUAapUCnTsEo3OHYOfjCkoqG+xLVYLcwgrYuOLvDIF6x2wUTn+ltvLlLV0kExIWiXQ1tUVTU3BP315EKQdCjFqEGLXo08UxBddssSKrbhfc2q+ySnavcNCa3IUtCS/A8doz2QFUmB1f+bVDEwpZ4+m3WiWgVMgRGxGI2IhA53NLyqpwskE3VXZ+md+ducFBa3KXokK2JETnb91NrWWxASXVji/A0dpQK+pbGtq6KbgGDYwGDXrGOc7csFptyC4obzC+UYLiMmmfuRHAMQlyk7LSSlgsVigUcrFLaTHJhAS7m1rHjvopuHU973Kh8fRbrRKQy2X1x8L2dDyuvLKm0YK/rLwy1Ejof0SgnseWknvY7XYUFZYgNCz43A/2MpIJCbYk3MdqP3PBn1rReF8qtQLQa1Xo1tGEbh0d2yDbbHbkFTkW/J2sDY6C4kqRfoq245gEuVNRYSlDQkwS+gDrlaotjq/CJqbg1s2mUsgEhIcYEB5iwIDujjM3qmosjVobmbmlqKrx/im4MpkAg04Fc40ZxUVlYpdDEuCrayUkExJW/xpTFV1TU3BV8sb7UmkUgEalQHx0MOKjHZ+g7Hb7aVNwS5FbVO51Z24E6FQQBAG5uTxsiNzDV6fBSiYk/GzijVeqO3Pj9Cm4DfelUsoFmAJ1MAXq0LdrhON5Ziuy8ksbBUe5yGduGDkeQW7mq9NgJRESdjtDwhs1nIKL2im4Slnj7UU0SkCllKNjZBA6RgY5n1tcVtVoe5GcgvadgsvV1uRuJT7abSmJkGBA+A6zDTBXASWnnbnRcHxDpQACDRoEGjTo1dkxBdditSG7oPGCv5Jyz03BNdautuagNbmLxUcHThkSJKqGZ244p+DKGs+kqjtzIzrMiOiw+jM3yipqcCq3/qCmrPwymC3umebGE+nI3Ww235yCyZAgr2N1ceZGw/ENtRww6FRI6BSKhE6OMzdsNjtyCxufuVFQ0ropuM4T6diSIDexWtmSEA1DQvrqFvw1nILbcAdcx5kbAiJMBkSYDBgIxxTcympzo9DIzCtFdTP2lOcaCXI3q4+eLCmJkPDR3z21ga3hmRvljttU8sYzqTQKQKtWoktMCLrE1J+5kV9c6eimqg2PvKKKM6bgGvVqx/kcOUXt+nORdDEkiERWNwW3qHZQXCbUHg3bYF8qpVxAaJAOoUE69OvmOHOjxmxFZoPNDAtLK6FSylFYUIKaGnGn4pJ02Hw0JAS7BFYKlVUDaewVoGZQyhrvS6VROsKkKTU1Zhw7eqJ9CyTJMhr16BATLnYZLcaQIL9WNwW34WwqFdvXRE6S+OcguPgkSHQuDafg1gnTAxEBQE5lDTZm+eZWCuR9uhg1GGAyiF1Gi0kiJIg8odxiwwkPLtgj/2JS++bbrUzsAoi8TV3L1N9O4iPPcjX25e0YEkSnqfvHbPH94TryIjIf7ReXREj45q+evFXd3ycrQ4LciC0JEfloQJOXcrYk2N1EbiT46MdZSYSE3Dd/9+SlnGMSbEmQG/nq+5Q0QkISPwV5C8HZkhC3DpIWjkmIyFf7+sg7ydiSIA/QKnzz7dY3qz6NIDAoyH3q/ipxdhO5k14hF7uEVpFESAAMCXIfGddJkAcwJETGcQlyF4HrJMgD9ErffJPyzaqbwJYEuQtXXJO7yQVA66OfZH2z6ib4aEiTF6r7q8SWBLmLTiGHwNlN4lL6ZncfeSF2N5G76X10ZhPAkCA6Q/3Atbh1kHToffgNSjohIZmfhMTGlgS5m6/ObAKkFBK++/+AvIxzgz8OXJObMCS8AEOC3EEAWxLkfgYf7urw3cpP48PjQuRFGk5AYUuC3IUtCS8gCByXoLZruN6GLQlyFw5cewmVbx4hS16k4Ux2bvBH7sIpsF5Cw5CgNmrY3cStwskdlDIBWnY3eQc1Q4LaqGF3E1sS5A5hGqXYJbSJpEKCLQlqq4b7NjEiyB3CtQwJr8GWBLWVjNNfyc0itCqxS2gTSYWEXMYZTtQ2zoV0DAlykwi2JLwLWxPUFvXnWzMkqO1UMgFBPj7tUnIh4eNjRCQynm9N7hSuVfrsFuF1JBcSWt8ObRJZ/ZYc4tZB0hDu4+MRgBRDgi0JaoO6fxDckoPcwdfHIwAJhoRKwX2cqPW4uR+5U7iGLQmvJIHwJpHwfGtyF7VcQJAEZtJIMiR0vh/eJBKukyB3kUIrApBoSOjZkqBW4joJchcpjEcAEg0JrbLxbp5EzSXjOglyEynMbAIkGhKCwC4nah1OgSV3YUvCyxkYEtQKHLgmdwhSyWH08ZXWdaQbEmqxKyBfxIFrcod4o1bsEtxGsiGhVXK9BLWcc+CaLQlqg/gAjdgluI2k30bZmqCWYkuC2korl6GDhAZFJR0SAQwJaiGBG/xRG3UO0Pj8pn4NSTokOHhNLcUpsNRWXYzS6WoCJB4Schmgk8YsNGonXExHbaEQBHQ0MCR8CrucqCXqDx0Stw7yTR0Naihk0ulqAvwgJCTW8iMP46FD1BbxEnzDkXxIqBU8iIiaj1uFU2sJkNbU1zqSDwkACJTOuhbysLp/EBy4ppaK0qmgVcjFLsPt/CMkpBfu5CGcAkutJbVZTXX8IiSUct+b5fTJwpdx0yVDcH7XAIzqE44Hb5+A1KOHGj2mb5TQ5Nei9153eV2z2Yz3583FpcO64Lw4Da67uD/+Wbeyxa+9+P03MKpvBEb1jcDnH85vdN+eXYmY+N/zYLVa2/hbaH8Cp8BSK8UHSLPLwm966wO1QIVZ7Cqab8eWv3HTHQ+gz4AhsFosWPjK07j3pv9i+YYD0On0AID1/2Y2es7GdSvw3MN3Yuzl17m87tuvzsbvP3yJ5974GJ279sDmv1Zh5p3X4ItfNqNn34HNeu3DyXvx7mvP4p0vfoPdbse0W6/A8FHj0K1HH5jNZvzv8fvw3OsfQS73raZ3w0kpbElQS5jUCkmcQtcUwW73j38NFhtwMEfsKlqvIC8Xo/qGY9GPf2Pw8P80+ZgHb5+AivJSfPL9WpfXGTOgA+6e8TQm3fFAo+fp9Aa88u6XzXrtlb98hy8+nIevft8KALj5svNx2/2PYvyVN+Djt15Cfm42nnjhrTb8tOKQC0DPCMd/f3ooC2Vm32sJkTiGhxsxNDxA7DI8QprR1wSFzLFmorRa7Epap6y0GAAQGBzS5P15udnYuPZ3vPDWkrNep6amGmp1475TjVaL3dv+afZrJ/Toi7Rjh5F5Ih12ux1pxw6jW/c+SE89ip+/W4xvV+1s9s/lTRq1JNjdRM0kE4DeITqxy/AYvxiTqBPio12Gdrsdr895GIOGXohuPfo0+ZhfvlsCnSEAYy+79qzXumD0eHz+4TwcP3YENpsNm/9eg/Urf0ZuTmaTj2/qteMTemLGEy/hnpvG4d5J/8XMJ19GfEJPzH38Pjw0+zVs+msVrhndBzeMG4gdWza07YdvRw232+EUWGqu+AAN9BKc1VTHb1oSgGNXWKUc8LVehBefmobDB/Zgyc+uP+3/tPQzXH7tZKg1Z59h8cTctzDn0btx1cgeEAQBsXFdcPVNd+Dnbxa16LUn3nYfJt52n/P75d8uhl4fgP6Dh+OqC7tj6YrtyM48gVn334SVialQqb1/6bvAlgS1Qt8QvdgleJRftSQEAQj2sdbES09Px1+rf8GnP6xHZIeYJh+zc+tGpKUcwnU333XO64WEhmHh4uXYllKOVduP45eNB6HTGxDdsXOrXhsACvPz8MG8uXjyxbexd1ciOsUnoFN8NwwdcREsZjPSjh1u/g8sorruJpvdDu7KQc0RqJIjVu/9H4Dawq9CAnB0OfnCzip2ux0vPjUNa//4EZ9+vw4xTbyJ1/lx6afo1e88dO/dv9nXV2s0iIiKhsViwZ+//4CLxl/dqtcGgFefnYkp9zyEyA4xsFmtsFjqp5FZrBbYfGQqLDf3o5bqG6yX1LbgTfGr7iYAUMgd+zkVV4ldydm9+OQD+OOnr/HWop+hNwQgLycLAGAICIRGW98cKistwZpfv8ejz73Z5HWemn4rwiOjMfPplwE41jDkZJ5E9z4DkJN5Eu+/OQc2mw13PDCrxa8NAJv/XoP01CN46e3PAQB9Bg5F6tGD2Lh2BbJOZUAukyOuS3f3/WI8iGskqCXkAtArWLoD1nX8LiQAR5eTt4fEt0veBwBMvW50o9v/t2ARJtx4u/P7Fcu/gd1ux6XXTGryOpkn0yHI6huM1VVVePvV2TiRfgw6nQEjL74ML739BYyBQS1+7arKSrz89DS8/sG3kNW+RkRUNJ584W0889AdUKnUeHHhkjOCxVvVn0onbh3kGxICdZLchuN0frNO4nRH8oBqi9hVkDcxaoCOQUBRtQVLjmSLXU6T1nzyDvb8uQI5qUeh1GgQ138wrnzoKUR07uJ8zFdPP4Ttv3zf6Hmd+g3EQ1/96vK6icu/w9JnHj7j9td3HIWydsp0yo6tWLf4A2Qc2IuS3GxMXfAJ+l18SaPHr1v8AdYt+gAAMPbOBzD61rud96Xt2YVlLzyNh5f+BpmPLbRsys1dwhGm9bGtHFrBL1sSABCqA06WiF0FeRPn5n5e/LkpZccWXHjTbejYpz9sVit+X/gqPrj3ZjyxfD3Uuvqujx4jRuPmF+Y5v5crz/1mpjEE4Klf/250m7LBmprqygp0SOiFoRMmYtFD95zx/FOHk7Hi3Tdw9ztLALsdH0+7Dd2Hj0RUtx6wms34/n9P4sbnXpVEQMTo1X4REIAfh0SQFsgpA8ycxkK1fGFzv/s++KrR9zf/bx5mj+qPEwf2oMvgYc7bFSo1jKHhLbu4IJz1Ob1GjkGvkWNc3p997Cg6JPREwvkjAABRCT2Rfewoorr1wLrFH6DLeeejY58BLavJSw00SXvaa0N+GxKCAJj0QFap2JWQt/DF860ryxzNYV2DMSUAOLpjC2aP6g9tgBFdBg/D5dMfR4Ap9KzXqqkox/P/PR82qxXRPXrjsmmPIaZn04s3mxKV0AO5acdQmHkSdrsduWmpiOzWHbnpqdj283d45NsVLf75vFGwSoHOEjw3whW/DQkACNEBuWWA1XfeE8iDfKEl0ZDdbsfy1+ciftBQRHXr4by958iLMGD8FQiJikb+yQz88c7rePeuG/Hot39AoWp6Tn9E5y64+X/zEJXQE1Vlpdjw1ad469YJmLVsNcI6xTernsj4brh8xhN47x7HJIorZj6ByPhueO+um3DlQ0/j4Ka/sPL9+ZArFLj28ecbtXx8yQCT9Ke9NuTXISGrbU3klIldCXmDun/2vtKS+OHF2Th1OBkzlvzY6PZBl1zl/O+obj0Q27sf5v53GPZvWIv+Yy9r8lpx/c9DXP/znN93HjgEb0y8BBu+XoTrnvxfs2saMXEKRkyc4vw+cfl3UOv16Nz/PLx41Sg8svQ3FGVnYsmsB/Dsys0uQ8tbaeQy9PSDaa8N+d1iutOZdI03diP/VX++tbh1NMcPL83Gvr9WY9qn3yEossNZHxsYFoHgDtHIPZ7a7OvLZDJ07NO/Rc85XVlhAVZ/sADXPfk/HN+7G+GdOiOsUzy6DR0Bq8WMnLRjrb62WIaEBUAp86+3Tf/6aZsglzm6nYh8YTGd3W7Hshefxp61K/DAp9/CFNPxnM8pLypEUVYmjGERLXqdkwcPtOg5p/vp1ecwaspdCIrsAJvNCqulfs65zWKFzeZbs0aMSjn6S3yfpqb4dXdTnVAdUFABePF7A7UDmQ+MSSx78Wns/GM57nrrU6j1BpTkOQ5J0RgCoNJoUV1RjpXvzUO/sZfBGBaOglMZ+P2tV6EPCm60puHLp2YgMDwSV858EgCw8v156NRvEMI6dkZVeRk2fPUZTh7aj+uffsH5nOqKcuSmpzm/LziZgRMH90MfGITgqOhGdR7avAG56amY/JLjXJGOfQYgJ/UoDmxch6KsU5DJZQiPa95Yh7e4IMIIuR92OzAk4NiqI5RjE37POSbhxSGx6VvH9ifvTL2h0e2T/jcP50+YCEEmw6kjB7H912WoLCmBMSwcXYdcgNveeB8avcH5+MLMkxCE+o6EypISfPf84yjJy4U2IADRPfpg+qIf0Kn2tEIASN//L96dOtH5/fLXnwcADLnqBkx+sf4I25qqSix7eTZue/1950r8oIgoXPvk/7D0mUegUKlw84sLoNL4xkp8AIjQKpEQ6Dv1upPfrrg+nc0GHM5znGBH/ikm0LF+ZmduKf7J5kpLqnddXChiDL41yO4ufj8mUUcmA8IN534cSVf93k383ET14gLUfhsQAEOikWAtoPL9HQOolXxh4JralwDgwohAscsQFUOiAUEAIqR5ljk1gy8MXFP76hWsg0njH3s0ucKQOE2gBvCTfbvoNPUD16KWQV5CIQgYFm4UuwzRMSSaEMXWhF9ybsvB7iYCMCjUAIOS/c8MiSboVL53Fja1HQeuqY5WLsN5oZzJAjAkXIoMcKzGJv/BlgTVOT88ACq+AQBgSLgklzmCgvyHLxw6RJ4XrFKgrx9uv+EKQ+IsgrWAXiV2FdRefG2rcHI/AcC4mCDI/Ggr8HNhSJxDB2P9rBeSNq6ToPNCDYjS+e/CuaYwJM5BrXDs60TS50tbhZP7hWqUnPLaBIZEM4QZHGFB0tWwtciWhP+RC8D4mGC/3OX1XBgSzSATHJu/8a+PdDV8b+DAtf85P9yIUD9fWe0KQ6KZtEpHi4KkqeE4JQeu/UuUToXBXBPhEkOiBcL0gI4fNiSpYUiwu8l/KGUC/hsdDIGzmVxiSLSAIADRgTwTW4pkbEn4pQsjjAjigONZMSRaSK3gIjspqssIu93O2U1+oqNBjX4mdjOdC0OiFUJ0QACnUksKp7/6F7VcwLjoYLHL8AkMiVaKDgSU/O1JhsDN/fzK6Kgg7vDaTHybayWFDIgN4rRYqZBxcz+/0dWoQY8gndhl+AyGRBvoVByfkIr6A4cYElJmUiswlt1MLcKQaCOT3nGaHfk2bhMufVq5DFd1MkHNLcBbhL8tN4gOBDScRefTeOCQtMkF4IpOITCq+A+1pRgSbiATgI5Bjr+I5Js4cC1tY6OD0YG7u7YKQ8JNVAoOZPuyun8IVpuoZZAHDA0L4EB1G7RrSMyZMwcDBgxoz5dsVwY10CFQ7CqoNdiSkKaEQC2GhXN2SVuwJdGA3W6HxWJp0zWCtUA4F3H6HA5cS0+kVolx3JepzVoUEqNHj8aDDz6IWbNmISQkBJGRkZgzZ47z/vT0dFx99dUwGAwwGo2YOHEisrOzAQCLFy/G888/j3///ReCIEAQBCxevBhpaWkQBAFJSUnO6xQVFUEQBPz1118AgL/++guCIGDVqlUYOHAgtFotxowZg5ycHKxYsQI9e/aE0WjEpEmTUFFR4bxOdXU1HnzwQYSHh0Oj0eDCCy/E9u3bnfc3vO7gwYOhVquxcePGVvwaGws3AEHaNl+G2hEHrqUlQCnHFZ1MUHCjtTZrcUtiyZIl0Ov1SExMxGuvvYa5c+dizZo1sNvtmDBhAgoKCvD3339jzZo1SElJwY033ggAuPHGG/HII4+gd+/eyMzMRGZmpvO+5pozZw7eeecdbN68GRkZGZg4cSIWLFiAr7/+Gr///jvWrFmDt99+2/n4WbNm4YcffsCSJUuwa9cudO3aFePHj0dBQUGj686aNQsvv/wykpOT0a9fv5b+SpoUbeT52L7EuU6CLQmfp5QJuLKTCXoFV1S7Q4vng/Xr1w/PPfccAKBbt2545513sHbtWgDAnj17kJqaitjYWADAF198gd69e2P79u0YMmQIDAYDFAoFIiMjW1XsCy+8gBEjRgAA7rzzTjz55JNISUlBfHw8AOD666/H+vXr8fjjj6O8vBzvv/8+Fi9ejEsvvRQA8PHHH2PNmjX49NNP8dhjjzmvO3fuXIwbN65VNbki1M54OlYAVLetB4vagbO7iS0JnyYAuCQmGGE8QMhtWtySOP2TdlRUFHJycpCcnIzY2FhnQABAr169EBQUhOTk5LZXetprR0REQKfTOQOi7racnBwAQEpKCsxmszNUAECpVGLo0KFn1DN48GC31Hc6uQyICwa4RYz3kzEkJOHCSCPijezrdacWh4RS2TihBUGAzWaD3W5vcoDI1e3OAmQy5+PqmM3mc762IAgua2l4vdNfu6l69Hq9y/raSikHOgdzM0Bv55zdxCmwPmugyYBBoZzJ5G5ue+vq1asX0tPTkZGR4bztwIEDKC4uRs+ePQEAKpUKVqu10fPCwsIAAJmZmc7bGg5it1bXrl2hUqnwzz//OG8zm83YsWOHs572olIAcSGOTQHJO7El4dsGmgz4TxTnn3uC29aojx07Fv369cPkyZOxYMECWCwW/N///R9GjRrl7M6Ji4tDamoqkpKSEBMTg4CAAGi1WgwbNgyvvPIK4uLikJeXh9mzZ7e5Hr1ej/vvvx+PPfYYQkJC0LFjR7z22muoqKjAnXfe2ebrt5RaAXQOAVIL+GnVG3GDP9810KRnQHiQ2z7bCoKA5cuXIzg4GP/5z38wduxYxMfH49tvv3U+5rrrrsMll1yCiy66CGFhYVi6dCkA4LPPPoPZbMbgwYMxY8YMvPDCC26p6ZVXXsF1112HKVOmYNCgQTh69ChWrVqF4GBxdoFUKxxjFNy+w/s4p8BydpNPcQREkNhlSJpgt/OjU3urNANpBTwFzZt0MQFaJfDnyULsL6w49xNIdAyI9sFechFolRyj8DYCWxI+ZZDJwIBoJ3ybEolW6Rij4PRY7+Dc4I8Na683LDwAIzkG0W4YEiJSK4D4EMefJC5u8OcbRkUF4vxwo9hl+BWGhMiUckdQaLlAVFT1Z1yLWwc1TQDw3+hgDDBx98z2xpDwAnKZo+vJwL2eRMOWhPeSC8BlHUPQM5hnQoiBIeElZALQKZjnZYulblYytwr3LmqZgKs6mdDVw1ttxMXFYcGCBW2+zujRozFz5sw2X8ebcAqsF8otA7LLxK7CfwgAetfuOfnFkWwUcEdGr2BSK3BFRxOC2mHQLjc3F3q9Hjqdo7UiCAJ++uknTJgwoUXXKSgogFKpRECAY3uQuLg4zJw506eDg0OmXijM4BjMPlEM8IOt5zXcyostCe+QEKjF2OggKGXt09lRtz1QW4WEhLjlOqerqamBSiVOfzS7m7yUUeMY0OYUWc9reC4NxyTEJQMwMtKIS2ND3BoQo0ePxrRp0zBt2jQEBQXBZDJh9uzZzo1AG3Y3xcXFAQCuueYaCILg/P72228/o2Uxc+ZMjB49utHr1LUaRo8ejePHj+Ohhx5yHrQGAPn5+Zg0aRJiYmKg0+nQt29f5+4Tp9f78MMPIzQ0FOPGjcPUqVNxxRVXNHqcxWJBZGQkPvvss7b/klxgSHgxjdKxEljHmU8e1XCXFK6TEI9WLsM1caEe28l1yZIlUCgUSExMxMKFCzF//nx88sknZzyu7vTKRYsWITMzs9Fpli3x448/IiYmBnPnznUetAYAVVVVOO+88/Dbb79h3759uOeeezBlyhQkJiY2We+mTZvw4Ycf4q677sLKlSsbbYb6xx9/oKysDBMnTmxVjc3B7iYvp6id+ZRZAhRUil2NNDXsbuLmi+KI0CpxeWwIAlSee0uKjY3F/PnzIQgCunfvjr1792L+/Pm4++67Gz2uruspKCio1QekAY6uJ7lcjoCAgEbXiY6OxqOPPur8fvr06Vi5ciW+//57nH/++c7bu3btitdee63RNbt3744vvvgCs2bNAuAIshtuuAEGg+emBrMl4QMEAegQCMQENu4aIfdo+DtlS6L99Q7W4frOYR4NCAAYNmxYo7Nkhg8fjiNHjpxxfIGnWa1WvPjii+jXrx9MJhMMBgNWr16N9PT0Ro9r6jC0u+66C4sWLQIA5OTk4Pfff8fUqVM9Wi9DwocEaWs3omP7z62cR5fa7GBEtB+5AIzpEISx0cFQ+MCnH5lMhtMng7o6IO1s3nzzTcyfPx+zZs3CunXrkJSUhPHjx6OmpqbR45o6DO3WW2/FsWPHsGXLFnz55ZeIi4vDyJEjW1xDS/DtxseoFUC8CcgqBfK5WalbyLiQrt0ZFDJc3tGESF37zdjZunXrGd9369YNcvmZs0OUSmWTB6Tt27ev0W1JSUlnnJDZUFMHrW3cuBFXX301brnlFgCAzWbDkSNHmnUYmslkwoQJE7Bo0SJs2bIFd9xxxzmf01ZsSfggQQCijECnIJ5N4Q7OhXQMiXYRo1fhpq7h7RoQAJCRkYGHH34Yhw4dwtKlS/H2229jxowZTT42Li4Oa9euRVZWFgoLCwEAY8aMwY4dO/D555/jyJEjeO65584Ijaaus2HDBpw8eRJ5eXkAHGMNa9aswebNm5GcnIx7770XWVlZzf457rrrLixZsgTJycm47bbbmv281mJI+LAADdA1FNBzO4824Tbh7UMlEzCmQxCujQuFXtH+c7tvvfVWVFZWYujQoXjggQcwffp03HPPPU0+9s0338SaNWsQGxuLgQMHAgDGjx+PZ555BrNmzcKQIUNQWlqKW2+99ayvOXfuXKSlpaFLly7OAfFnnnkGgwYNwvjx4zF69GhERka2aNHe2LFjERUVhfHjx6NDhw7Nfl5rccW1RBRUOLqg+D7XcoEaIDYIKKw24/MjOWKXI0ldjBqMjgqCQaSFP6NHj8aAAQPcsvWG2CoqKtChQwd89tlnuPbaaz3+ehyTkIgQHRCgBk6VAKXVYlfjW+pbEuLWIUV6hQyjo4LQNdCzey/5A5vNhqysLLz55psIDAzEVVdd1S6vy5CQEKXcsUlgUaVjXQWPR20eHjjkGX2CdbgwMhBqOXu13SE9PR2dO3dGTEwMFi9eDIWifd6+2d0kURaro1VRwlbFOZl0jokAJ8qr8UNqntjl+LwglQIXRwchRq8WuxRyA7YkJEohBzoGO7qeskqA6vZdL+RTGq6ToNaTCcB5oQYMDTP6xLoHah6GhMQFqAFDqGNNRU4ZB7abwnUSbRehVWJsdDBCNdxoTGoYEn5AEIBQvWPFdnYpUMg9oBqp+8zLKbAtp5YJOD/ciAEmfaMtL0g6GBJ+RCEDogMdM6EyS4CKlu8oIEnO863Zkmg2lUzAQJMBA0MNHJiWOIaEH9IqHVt7lFQ5uqCq/PwgtvrzrcWtwxeoZAL6mwwYFGqAhuHgFxgSfsyocYxZlFQ5jkut8dPBbQ5cn5tSJqB/iB6DQg3QirBamsTDkPBzggAEah2BUVzbsvC3sODAtWsKQUA/kx7nhRqgYzj4JYYEAXCERZDWsUVFcRWQWw5U+0k3FAeuz6QQBPQN0eG8sABR9lki78GQoEbqwiJI61hjkV8OlNWc+3m+jAPX9eQC0DdEj8GhAdDzgHUCQ4LOIkDt+KqyOMKiqBKSPJRHYEhAK5ehV7AOA0wG0TbhI+/EkKBz0igcU2cjAhy7zRZUSGszPJkfbxXeQadC3xA9uhq1XCVNTWJIULMpZEC4AQjTO7qiCiuBsmrfb13426FDapmAHkE69A3Rw8QV0nQODAlqMUFwzIYyahwtiqJKx5evrrfwh63CBQCxejV6BOnQNVADpYxrHKh5GBLUJgqZY8uPUD1QaXa0LoqrAKsPveFKeQqsSa1AjyAdegTpONZArcKQILfRKh1fUQGOLT9KqhxblZu9fN2F1BbTBSjl6GLUoGeQDuFanm1LbcOQILcTBMe523oVEAVHC6MuMLxx7UVdx4uvtiSUMgHRehU6GTToZFAjWM1xBnIfhgR5XF0LIyLAERJl1UB5jePLG07P88UpsGEaJToZ1Oho0KCDTgU5ZyaRhzAkqF2pFY4vkx6w2x2D3XWBUV4jznkXgg9MgdUrZOhYGwodDWpukUHthiFBohGE+lZGaG1oVJobfFk83z3V8AO4N3U3GZVymDRKROtV6GjQIIxTVUkkDAnyGoIA6FSOrzo2O1DVIDQqzUCNxX1rMxp20ogxI8ugkMOkUcCkViKk9k+TRsEpquQ1GBLk1WRNBIfdDphtjrCosTr+rLbW/3dLAqThYWqeHJPQymUwaZQwqRWOP2sDgQf2kLdjSJDPEQRAJXd8NcVqcyyMc35Z6//banMMltvsjrBp+B7dnO4mAYBKLkAjl0Etl0FT++X478a31/2pV8h4BgP5LMFu96KOWCIRWWx22Ox22AHY7HbY7IANQN0/EZVcBrVM4FnO5FcYEkRE5BI7RImIyCWGBBERucSQICIilxgSRETkEkOCiIhcYkgQEZFLDAkiInKJIUFERC4xJIiIyCWGBBERucSQICIilxgSRETkEkOCiIhcYkgQEZFLDAkiInKJIUFERC4xJIiIyCWGBBERucSQICIilxgSRETkEkOCiIhcYkgQEZFLDAkiInKJIUFERC4xJIiIyCWGBBERucSQICIilxgSRETkEkOCiIhcYkgQEZFLDAkiInKJIUFERC4xJIiIyCWGBBERucSQICIilxgSRETkEkOCiIhcYkgQEZFLDAkiInKJIUFERC4xJIiIyCWGBBERucSQICIilxgSRETk0v8DneF25+Raj00AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualizing the data used for training\n",
    "\n",
    "colors=['#4d416d','#88a0c3','#d3e7ff','#90d3e4']\n",
    "plt.pie([len([l for l in train_img_label if l == labels[0]]),\n",
    "         len([l for l in train_img_label if l == labels[1]]),\n",
    "         len([l for l in train_img_label if l == labels[2]]),\n",
    "         len([l for l in train_img_label if l == labels[3]])],\n",
    "       labels=labels, autopct='%.2f%%', colors=colors, explode=(0.01,0.01,0.01,0.01))\n",
    "\n",
    "plt.rcParams.update({'font.size':14})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "59983be2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualizing training-testing ratio\n",
    "\n",
    "plt.pie([len(train_img), len(test_img)], colors=['#e5b9db', '#fffde5'],\n",
    "        labels=['training data', 'testing data'], autopct='%.2f%%', explode=(0.1,0))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4aff7b23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1200x600 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Data Augmentation\n",
    "\n",
    "from PIL import Image, ImageEnhance\n",
    "\n",
    "\n",
    "IMAGE_SIZE = 224\n",
    "\n",
    "def augment_image(image):\n",
    "    image = Image.fromarray(np.uint8(image))\n",
    "    image = ImageEnhance.Brightness(image).enhance(random.uniform(0.8,1.2))\n",
    "    image = ImageEnhance.Contrast(image).enhance(random.uniform(0.8,1.2))\n",
    "    image = ImageEnhance.Sharpness(image).enhance(random.uniform(0.8,1.2))\n",
    "    image = np.array(image)/255.0\n",
    "    return image\n",
    "\n",
    "def open_images(paths):\n",
    "    images = []\n",
    "    for path in paths:\n",
    "        image = load_img(path, target_size=(IMAGE_SIZE, IMAGE_SIZE))\n",
    "        image = np.array(image)\n",
    "        image = augment_image(image)\n",
    "        images.append(image)\n",
    "    return np.array(images)\n",
    "\n",
    "images = open_images(train_paths[50:59])\n",
    "labels = train_labels[50:59]\n",
    "fig = plt.figure(figsize=(12, 6))\n",
    "for x in range(1, 9):\n",
    "    fig.add_subplot(2, 4, x)\n",
    "    plt.axis('off')\n",
    "    plt.title(labels[x])\n",
    "    plt.imshow(images[x])\n",
    "plt.rcParams.update({'font.size': 12})\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "15ec5f1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 4571 images belonging to 4 classes.\n",
      "Found 1141 images belonging to 4 classes.\n"
     ]
    }
   ],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "# Define preprocessing parameters\n",
    "batch_size = 32\n",
    "target_size = (128, 128)\n",
    "\n",
    "# Define image data generator with preprocessing options\n",
    "datagen = ImageDataGenerator(rescale=1./255,\n",
    "                             rotation_range=30,\n",
    "                             zoom_range=0.2,\n",
    "                             horizontal_flip=True,\n",
    "                             validation_split=0.2)\n",
    "\n",
    "# Create training data generator\n",
    "train_generator = datagen.flow_from_directory(\n",
    "        'archive/Training/',\n",
    "        target_size=target_size,\n",
    "        batch_size=batch_size,\n",
    "        class_mode='categorical',\n",
    "        subset='training')\n",
    "\n",
    "# Create validation data generator\n",
    "validation_generator = datagen.flow_from_directory(\n",
    "        'archive/Training/',\n",
    "        target_size=target_size,\n",
    "        batch_size=batch_size,\n",
    "        class_mode='categorical',\n",
    "        subset='validation')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cc251da5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 5712 images belonging to 4 classes.\n",
      "Found 1311 images belonging to 4 classes.\n"
     ]
    }
   ],
   "source": [
    "# Define data generator for preprocessing and data augmentation\n",
    "train_datagen = ImageDataGenerator(\n",
    "    rescale=1./255,\n",
    "    shear_range=0.2,\n",
    "    zoom_range=0.2,\n",
    "    horizontal_flip=True)\n",
    "\n",
    "test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "# Load and preprocess training data\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "    train_dir,\n",
    "    target_size=(224, 224),\n",
    "    batch_size=32,\n",
    "    class_mode='binary')\n",
    "\n",
    "# Load and preprocess test data\n",
    "test_generator = test_datagen.flow_from_directory(\n",
    "    test_dir,\n",
    "    target_size=(224, 224),\n",
    "    batch_size=32,\n",
    "    class_mode='binary')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c1f24eaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the pre-trained VGG16 model and exclude the top layer\n",
    "base_model = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\n",
    "\n",
    "# Freeze the layers in the base model\n",
    "for layer in base_model.layers:\n",
    "    layer.trainable = False\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9a0889cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add custom layers to the top of the base model\n",
    "x = Flatten()(base_model.output)\n",
    "x = Dense(128, activation='relu')(x)\n",
    "x = Dense(1, activation='sigmoid')(x)\n",
    "\n",
    "# Define the transfer learning model\n",
    "model_vgg = Model(inputs=base_model.input, outputs=x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4fbe0e6f",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "summary() got an unexpected keyword argument 'model'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn [23], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m model_vgg\u001b[38;5;241m.\u001b[39mcompile(optimizer\u001b[38;5;241m=\u001b[39mAdam(learning_rate\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.001\u001b[39m), loss\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbinary_crossentropy\u001b[39m\u001b[38;5;124m'\u001b[39m, metrics\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maccuracy\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m      2\u001b[0m \u001b[38;5;66;03m# Display the model summary\u001b[39;00m\n\u001b[1;32m----> 3\u001b[0m \u001b[43mmodel_vgg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msummary\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mVGG\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "\u001b[1;31mTypeError\u001b[0m: summary() got an unexpected keyword argument 'model'"
     ]
    }
   ],
   "source": [
    "model_vgg.compile(optimizer=Adam(learning_rate=0.001), loss='binary_crossentropy', metrics=['accuracy'])\n",
    "# Display the model summary\n",
    "model_vgg.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f0c19fba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "179/179 [==============================] - 866s 5s/step - loss: -2999.3669 - accuracy: 0.2344 - val_loss: -8417.1797 - val_accuracy: 0.2334\n",
      "Epoch 2/10\n",
      "179/179 [==============================] - 874s 5s/step - loss: -20103.2949 - accuracy: 0.2344 - val_loss: -33653.9531 - val_accuracy: 0.2334\n",
      "Epoch 3/10\n",
      "179/179 [==============================] - 808s 5s/step - loss: -55060.9062 - accuracy: 0.2344 - val_loss: -76444.1641 - val_accuracy: 0.2334\n",
      "Epoch 4/10\n",
      "179/179 [==============================] - 795s 4s/step - loss: -107448.4141 - accuracy: 0.2344 - val_loss: -135331.7344 - val_accuracy: 0.2334\n",
      "Epoch 5/10\n",
      "179/179 [==============================] - 789s 4s/step - loss: -176002.3438 - accuracy: 0.2344 - val_loss: -209175.0156 - val_accuracy: 0.2334\n",
      "Epoch 6/10\n",
      "179/179 [==============================] - 1153s 6s/step - loss: -259178.8438 - accuracy: 0.2344 - val_loss: -297643.9375 - val_accuracy: 0.2334\n",
      "Epoch 7/10\n",
      "179/179 [==============================] - 742s 4s/step - loss: -357277.6875 - accuracy: 0.2344 - val_loss: -399354.7812 - val_accuracy: 0.2334\n",
      "Epoch 8/10\n",
      "179/179 [==============================] - 1075s 6s/step - loss: -467648.1250 - accuracy: 0.2344 - val_loss: -512600.5625 - val_accuracy: 0.2334\n",
      "Epoch 9/10\n",
      "179/179 [==============================] - 1530s 9s/step - loss: -591398.5000 - accuracy: 0.2344 - val_loss: -638900.6250 - val_accuracy: 0.2334\n",
      "Epoch 10/10\n",
      "179/179 [==============================] - 813s 5s/step - loss: -727081.3125 - accuracy: 0.2344 - val_loss: -775670.0000 - val_accuracy: 0.2334\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x28f4b01d2b0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_vgg.fit(\n",
    "    train_generator,\n",
    "    steps_per_epoch=len(train_generator),\n",
    "    epochs=10,\n",
    "    validation_data=test_generator,\n",
    "    validation_steps=len(test_generator))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "63af0be8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "  4/179 [..............................] - ETA: 21:22 - loss: -27.3717 - accuracy: 0.2500"
     ]
    },
    {
     "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 [15], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Train the model and save the history object\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m history \u001b[38;5;241m=\u001b[39m \u001b[43mmodel_vgg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m      3\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtrain_generator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m      4\u001b[0m \u001b[43m    \u001b[49m\u001b[43msteps_per_epoch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtrain_generator\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m      5\u001b[0m \u001b[43m    \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m      6\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtest_generator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m      7\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvalidation_steps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtest_generator\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     10\u001b[0m \u001b[38;5;66;03m# Extract the metrics from the history object\u001b[39;00m\n\u001b[0;32m     11\u001b[0m vgg_acc \u001b[38;5;241m=\u001b[39m history\u001b[38;5;241m.\u001b[39mhistory[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124maccuracy\u001b[39m\u001b[38;5;124m'\u001b[39m]\n",
      "File \u001b[1;32m~\\miniconda3\\lib\\site-packages\\keras\\utils\\traceback_utils.py:64\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     62\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m     63\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m---> 64\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m     65\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:  \u001b[38;5;66;03m# pylint: disable=broad-except\u001b[39;00m\n\u001b[0;32m     66\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;32m~\\miniconda3\\lib\\site-packages\\keras\\engine\\training.py:1409\u001b[0m, in \u001b[0;36mModel.fit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m   1402\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m tf\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mexperimental\u001b[38;5;241m.\u001b[39mTrace(\n\u001b[0;32m   1403\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m   1404\u001b[0m     epoch_num\u001b[38;5;241m=\u001b[39mepoch,\n\u001b[0;32m   1405\u001b[0m     step_num\u001b[38;5;241m=\u001b[39mstep,\n\u001b[0;32m   1406\u001b[0m     batch_size\u001b[38;5;241m=\u001b[39mbatch_size,\n\u001b[0;32m   1407\u001b[0m     _r\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m):\n\u001b[0;32m   1408\u001b[0m   callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[1;32m-> 1409\u001b[0m   tmp_logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1410\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m data_handler\u001b[38;5;241m.\u001b[39mshould_sync:\n\u001b[0;32m   1411\u001b[0m     context\u001b[38;5;241m.\u001b[39masync_wait()\n",
      "File \u001b[1;32m~\\miniconda3\\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 fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\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;32m~\\miniconda3\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py:915\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    912\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    914\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[1;32m--> 915\u001b[0m   result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)\n\u001b[0;32m    917\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[0;32m    918\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n",
      "File \u001b[1;32m~\\miniconda3\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py:947\u001b[0m, in \u001b[0;36mFunction._call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    944\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[0;32m    945\u001b[0m   \u001b[38;5;66;03m# In this case we have created variables on the first call, so we run the\u001b[39;00m\n\u001b[0;32m    946\u001b[0m   \u001b[38;5;66;03m# defunned version which is guaranteed to never create variables.\u001b[39;00m\n\u001b[1;32m--> 947\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stateless_fn(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwds)  \u001b[38;5;66;03m# pylint: disable=not-callable\u001b[39;00m\n\u001b[0;32m    948\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_stateful_fn \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    949\u001b[0m   \u001b[38;5;66;03m# Release the lock early so that multiple threads can perform the call\u001b[39;00m\n\u001b[0;32m    950\u001b[0m   \u001b[38;5;66;03m# in parallel.\u001b[39;00m\n\u001b[0;32m    951\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n",
      "File \u001b[1;32m~\\miniconda3\\lib\\site-packages\\tensorflow\\python\\eager\\function.py:2453\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2450\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[0;32m   2451\u001b[0m   (graph_function,\n\u001b[0;32m   2452\u001b[0m    filtered_flat_args) \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_maybe_define_function(args, kwargs)\n\u001b[1;32m-> 2453\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgraph_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_flat\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   2454\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfiltered_flat_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgraph_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcaptured_inputs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\miniconda3\\lib\\site-packages\\tensorflow\\python\\eager\\function.py:1860\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[1;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[0;32m   1856\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[0;32m   1857\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[0;32m   1858\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[0;32m   1859\u001b[0m   \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[1;32m-> 1860\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_build_call_outputs(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inference_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1861\u001b[0m \u001b[43m      \u001b[49m\u001b[43mctx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcancellation_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcancellation_manager\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m   1862\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[0;32m   1863\u001b[0m     args,\n\u001b[0;32m   1864\u001b[0m     possible_gradient_type,\n\u001b[0;32m   1865\u001b[0m     executing_eagerly)\n\u001b[0;32m   1866\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n",
      "File \u001b[1;32m~\\miniconda3\\lib\\site-packages\\tensorflow\\python\\eager\\function.py:497\u001b[0m, in \u001b[0;36m_EagerDefinedFunction.call\u001b[1;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[0;32m    495\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _InterpolateFunctionError(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m    496\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m cancellation_manager \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 497\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    498\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mstr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msignature\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    499\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_num_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    500\u001b[0m \u001b[43m        \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    501\u001b[0m \u001b[43m        \u001b[49m\u001b[43mattrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    502\u001b[0m \u001b[43m        \u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mctx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    503\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    504\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[0;32m    505\u001b[0m         \u001b[38;5;28mstr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msignature\u001b[38;5;241m.\u001b[39mname),\n\u001b[0;32m    506\u001b[0m         num_outputs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_outputs,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    509\u001b[0m         ctx\u001b[38;5;241m=\u001b[39mctx,\n\u001b[0;32m    510\u001b[0m         cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_manager)\n",
      "File \u001b[1;32m~\\miniconda3\\lib\\site-packages\\tensorflow\\python\\eager\\execute.py:54\u001b[0m, in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     52\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m     53\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[1;32m---> 54\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_Execute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     55\u001b[0m \u001b[43m                                      \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     56\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[0;32m     57\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \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[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "\n",
    "# Train the model and save the history object\n",
    "history = model_vgg.fit(\n",
    "    train_generator,\n",
    "    steps_per_epoch=len(train_generator),\n",
    "    epochs=10,\n",
    "    validation_data=test_generator,\n",
    "    validation_steps=len(test_generator))\n",
    "\n",
    "\n",
    "# Extract the metrics from the history object\n",
    "vgg_acc = history.history['accuracy']\n",
    "vgg_val_acc = history.history['val_accuracy']\n",
    "vgg_loss = history.history['loss']\n",
    "vgg_val_loss = history.history['val_loss']\n",
    "\n",
    "# Create visualizations for the metrics\n",
    "sns.set(style='darkgrid')\n",
    "plt.plot(vgg_acc)\n",
    "plt.plot(vgg_val_acc)\n",
    "plt.title('Training and Validation Accuracy for VGG-16 Model')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "plt.plot(vgg_loss)\n",
    "plt.plot(vgg_val_loss)\n",
    "plt.title('Training and Validation Loss for VGG-16 Model')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "# Compute the classification report for the test set\n",
    "y_pred = model_vgg.predict(test_generator)\n",
    "y_pred = np.argmax(y_pred, axis=1)\n",
    "y_true = test_generator.classes\n",
    "vgg_report = classification_report(y_true, y_pred, target_names=test_generator.class_indices.keys())\n",
    "\n",
    "# Print the classification report for the VGG-16 model\n",
    "print(vgg_report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "df734e14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_2 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
      "                                )]                                                                \n",
      "                                                                                                  \n",
      " conv1_pad (ZeroPadding2D)      (None, 230, 230, 3)  0           ['input_2[0][0]']                \n",
      "                                                                                                  \n",
      " conv1_conv (Conv2D)            (None, 112, 112, 64  9472        ['conv1_pad[0][0]']              \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv1_bn (BatchNormalization)  (None, 112, 112, 64  256         ['conv1_conv[0][0]']             \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv1_relu (Activation)        (None, 112, 112, 64  0           ['conv1_bn[0][0]']               \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " pool1_pad (ZeroPadding2D)      (None, 114, 114, 64  0           ['conv1_relu[0][0]']             \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " pool1_pool (MaxPooling2D)      (None, 56, 56, 64)   0           ['pool1_pad[0][0]']              \n",
      "                                                                                                  \n",
      " conv2_block1_1_conv (Conv2D)   (None, 56, 56, 64)   4160        ['pool1_pool[0][0]']             \n",
      "                                                                                                  \n",
      " conv2_block1_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block1_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block1_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block1_0_conv (Conv2D)   (None, 56, 56, 256)  16640       ['pool1_pool[0][0]']             \n",
      "                                                                                                  \n",
      " conv2_block1_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block1_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block1_0_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block1_0_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block1_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_add (Add)         (None, 56, 56, 256)  0           ['conv2_block1_0_bn[0][0]',      \n",
      "                                                                  'conv2_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv2_block1_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block1_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block2_1_conv (Conv2D)   (None, 56, 56, 64)   16448       ['conv2_block1_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block2_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block2_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block2_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block2_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block2_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block2_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block2_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block2_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block2_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block2_add (Add)         (None, 56, 56, 256)  0           ['conv2_block1_out[0][0]',       \n",
      "                                                                  'conv2_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv2_block2_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block2_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block3_1_conv (Conv2D)   (None, 56, 56, 64)   16448       ['conv2_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block3_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                  \n",
      " conv2_block3_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block3_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block3_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block3_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block3_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block3_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block3_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block3_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block3_add (Add)         (None, 56, 56, 256)  0           ['conv2_block2_out[0][0]',       \n",
      "                                                                  'conv2_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv2_block3_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block3_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block1_1_conv (Conv2D)   (None, 28, 28, 128)  32896       ['conv2_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block1_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block1_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block1_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block1_0_conv (Conv2D)   (None, 28, 28, 512)  131584      ['conv2_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block1_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block1_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block1_0_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block1_0_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block1_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_add (Add)         (None, 28, 28, 512)  0           ['conv3_block1_0_bn[0][0]',      \n",
      "                                                                  'conv3_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block1_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block1_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block2_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block1_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block2_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block2_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block2_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block2_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block2_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block2_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block2_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block2_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block2_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block2_add (Add)         (None, 28, 28, 512)  0           ['conv3_block1_out[0][0]',       \n",
      "                                                                  'conv3_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block2_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block2_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block3_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block3_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                  \n",
      " conv3_block3_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block3_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block3_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block3_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block3_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block3_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block3_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block3_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block3_add (Add)         (None, 28, 28, 512)  0           ['conv3_block2_out[0][0]',       \n",
      "                                                                  'conv3_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block3_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block3_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block4_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block4_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block4_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block4_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block4_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block4_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block4_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block4_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block4_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block4_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block4_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block4_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block4_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block4_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block4_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block4_add (Add)         (None, 28, 28, 512)  0           ['conv3_block3_out[0][0]',       \n",
      "                                                                  'conv3_block4_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block4_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block4_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block1_1_conv (Conv2D)   (None, 14, 14, 256)  131328      ['conv3_block4_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block1_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block1_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block1_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block1_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block1_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block1_0_conv (Conv2D)   (None, 14, 14, 1024  525312      ['conv3_block4_out[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block1_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_0_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block1_0_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block1_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_add (Add)         (None, 14, 14, 1024  0           ['conv4_block1_0_bn[0][0]',      \n",
      "                                )                                 'conv4_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block1_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block1_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block2_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block1_out[0][0]']       \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                  \n",
      " conv4_block2_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block2_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block2_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block2_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block2_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block2_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block2_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block2_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block2_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block2_add (Add)         (None, 14, 14, 1024  0           ['conv4_block1_out[0][0]',       \n",
      "                                )                                 'conv4_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block2_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block2_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block3_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block3_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block3_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block3_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block3_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block3_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block3_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block3_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block3_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block3_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block3_add (Add)         (None, 14, 14, 1024  0           ['conv4_block2_out[0][0]',       \n",
      "                                )                                 'conv4_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block3_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block3_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block4_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block4_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block4_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block4_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block4_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block4_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block4_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block4_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block4_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block4_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block4_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block4_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block4_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block4_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block4_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block4_add (Add)         (None, 14, 14, 1024  0           ['conv4_block3_out[0][0]',       \n",
      "                                )                                 'conv4_block4_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block4_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block4_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " conv4_block5_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block4_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block5_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block5_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block5_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block5_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block5_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block5_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block5_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block5_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block5_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block5_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block5_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block5_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block5_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block5_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block5_add (Add)         (None, 14, 14, 1024  0           ['conv4_block4_out[0][0]',       \n",
      "                                )                                 'conv4_block5_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block5_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block5_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block6_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block5_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block6_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block6_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block6_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block6_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block6_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block6_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block6_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block6_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block6_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block6_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block6_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block6_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block6_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block6_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block6_add (Add)         (None, 14, 14, 1024  0           ['conv4_block5_out[0][0]',       \n",
      "                                )                                 'conv4_block6_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block6_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block6_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv5_block1_1_conv (Conv2D)   (None, 7, 7, 512)    524800      ['conv4_block6_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block1_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block1_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block1_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block1_0_conv (Conv2D)   (None, 7, 7, 2048)   2099200     ['conv4_block6_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block1_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block1_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block1_0_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block1_0_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block1_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block1_0_bn[0][0]',      \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                  'conv5_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv5_block1_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block1_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block2_1_conv (Conv2D)   (None, 7, 7, 512)    1049088     ['conv5_block1_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block2_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block2_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block2_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block2_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block2_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block2_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block2_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block2_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block2_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block2_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block1_out[0][0]',       \n",
      "                                                                  'conv5_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv5_block2_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block2_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block3_1_conv (Conv2D)   (None, 7, 7, 512)    1049088     ['conv5_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block3_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block3_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block3_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block3_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block3_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block3_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block3_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block3_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block3_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block3_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block2_out[0][0]',       \n",
      "                                                                  'conv5_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv5_block3_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block3_add[0][0]']       \n",
      "                                                                                                  \n",
      " global_average_pooling2d (Glob  (None, 2048)        0           ['conv5_block3_out[0][0]']       \n",
      " alAveragePooling2D)                                                                              \n",
      "                                                                                                  \n",
      " dense_2 (Dense)                (None, 1024)         2098176     ['global_average_pooling2d[0][0]'\n",
      "                                                                 ]                                \n",
      "                                                                                                  \n",
      " dense_3 (Dense)                (None, 10)           10250       ['dense_2[0][0]']                \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 25,696,138\n",
      "Trainable params: 2,108,426\n",
      "Non-trainable params: 23,587,712\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# ResNet50 model\n",
    "num_classes = 10\n",
    "base_model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\n",
    "x = base_model.output\n",
    "x = GlobalAveragePooling2D()(x)\n",
    "x = Dense(1024, activation='relu')(x)\n",
    "predictions = Dense(num_classes, activation='softmax')(x)\n",
    "model_resnet = Model(inputs=base_model.input, outputs=predictions)\n",
    "for layer in base_model.layers:\n",
    "    layer.trainable = False\n",
    "model_resnet.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])\n",
    "model_resnet.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "91b46d98",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_11284\\1083722063.py:26: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
      "  history_resnet = model_resnet.fit_generator(train_generator,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "in user code:\n\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 1051, in train_function  *\n        return step_function(self, iterator)\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 1040, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 1030, in run_step  **\n        outputs = model.train_step(data)\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 890, in train_step\n        loss = self.compute_loss(x, y, y_pred, sample_weight)\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 948, in compute_loss\n        return self.compiled_loss(\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\engine\\compile_utils.py\", line 201, in __call__\n        loss_value = loss_obj(y_t, y_p, sample_weight=sw)\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\losses.py\", line 139, in __call__\n        losses = call_fn(y_true, y_pred)\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\losses.py\", line 243, in call  **\n        return ag_fn(y_true, y_pred, **self._fn_kwargs)\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\losses.py\", line 1787, in categorical_crossentropy\n        return backend.categorical_crossentropy(\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\backend.py\", line 5119, in categorical_crossentropy\n        target.shape.assert_is_compatible_with(output.shape)\n\n    ValueError: Shapes (None, 1) and (None, 10) are incompatible\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn [17], line 26\u001b[0m\n\u001b[0;32m     20\u001b[0m early_stop_resnet \u001b[38;5;241m=\u001b[39m EarlyStopping(monitor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mval_accuracy\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m     21\u001b[0m                            patience\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m,\n\u001b[0;32m     22\u001b[0m                            mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmax\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m     23\u001b[0m                            verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m     25\u001b[0m \u001b[38;5;66;03m# Train the ResNet50 model\u001b[39;00m\n\u001b[1;32m---> 26\u001b[0m history_resnet \u001b[38;5;241m=\u001b[39m \u001b[43mmodel_resnet\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_generator\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_generator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     27\u001b[0m \u001b[43m                                             \u001b[49m\u001b[43msteps_per_epoch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtrain_generator\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     28\u001b[0m \u001b[43m                                             \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m     29\u001b[0m \u001b[43m                                             \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mcheckpoint_resnet\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mearly_stop_resnet\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     30\u001b[0m \u001b[43m                                             \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidation_generator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     31\u001b[0m \u001b[43m                                             \u001b[49m\u001b[43mvalidation_steps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mvalidation_generator\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     33\u001b[0m \u001b[38;5;66;03m# Load the best weights for the ResNet50 model\u001b[39;00m\n\u001b[0;32m     34\u001b[0m model_resnet\u001b[38;5;241m.\u001b[39mload_weights(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresnet50_brain_tumor.h5\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[1;32m~\\miniconda3\\lib\\site-packages\\keras\\engine\\training.py:2260\u001b[0m, in \u001b[0;36mModel.fit_generator\u001b[1;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[0;32m   2249\u001b[0m \u001b[38;5;124;03m\"\"\"Fits the model on data yielded batch-by-batch by a Python generator.\u001b[39;00m\n\u001b[0;32m   2250\u001b[0m \n\u001b[0;32m   2251\u001b[0m \u001b[38;5;124;03mDEPRECATED:\u001b[39;00m\n\u001b[0;32m   2252\u001b[0m \u001b[38;5;124;03m  `Model.fit` now supports generators, so there is no longer any need to use\u001b[39;00m\n\u001b[0;32m   2253\u001b[0m \u001b[38;5;124;03m  this endpoint.\u001b[39;00m\n\u001b[0;32m   2254\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   2255\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m   2256\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m`Model.fit_generator` is deprecated and \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m   2257\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwill be removed in a future version. \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m   2258\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPlease use `Model.fit`, which supports generators.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m   2259\u001b[0m     stacklevel\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m-> 2260\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[43mfit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   2261\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgenerator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2262\u001b[0m \u001b[43m    \u001b[49m\u001b[43msteps_per_epoch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msteps_per_epoch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2263\u001b[0m \u001b[43m    \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mepochs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2264\u001b[0m \u001b[43m    \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2265\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallbacks\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2266\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidation_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2267\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvalidation_steps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidation_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2268\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvalidation_freq\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidation_freq\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2269\u001b[0m \u001b[43m    \u001b[49m\u001b[43mclass_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclass_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2270\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmax_queue_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_queue_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2271\u001b[0m \u001b[43m    \u001b[49m\u001b[43mworkers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mworkers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2272\u001b[0m \u001b[43m    \u001b[49m\u001b[43muse_multiprocessing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_multiprocessing\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2273\u001b[0m \u001b[43m    \u001b[49m\u001b[43mshuffle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mshuffle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2274\u001b[0m \u001b[43m    \u001b[49m\u001b[43minitial_epoch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minitial_epoch\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\miniconda3\\lib\\site-packages\\keras\\utils\\traceback_utils.py:67\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     65\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:  \u001b[38;5;66;03m# pylint: disable=broad-except\u001b[39;00m\n\u001b[0;32m     66\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[1;32m---> 67\u001b[0m   \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28mNone\u001b[39m\n\u001b[0;32m     68\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     69\u001b[0m   \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
      "File \u001b[1;32m~\\AppData\\Local\\Temp\\__autograph_generated_file7wttv3t9.py:15\u001b[0m, in \u001b[0;36mouter_factory.<locals>.inner_factory.<locals>.tf__train_function\u001b[1;34m(iterator)\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m     14\u001b[0m     do_return \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m---> 15\u001b[0m     retval_ \u001b[38;5;241m=\u001b[39m ag__\u001b[38;5;241m.\u001b[39mconverted_call(ag__\u001b[38;5;241m.\u001b[39mld(step_function), (ag__\u001b[38;5;241m.\u001b[39mld(\u001b[38;5;28mself\u001b[39m), ag__\u001b[38;5;241m.\u001b[39mld(iterator)), \u001b[38;5;28;01mNone\u001b[39;00m, fscope)\n\u001b[0;32m     16\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[0;32m     17\u001b[0m     do_return \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
      "\u001b[1;31mValueError\u001b[0m: in user code:\n\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 1051, in train_function  *\n        return step_function(self, iterator)\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 1040, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 1030, in run_step  **\n        outputs = model.train_step(data)\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 890, in train_step\n        loss = self.compute_loss(x, y, y_pred, sample_weight)\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 948, in compute_loss\n        return self.compiled_loss(\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\engine\\compile_utils.py\", line 201, in __call__\n        loss_value = loss_obj(y_t, y_p, sample_weight=sw)\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\losses.py\", line 139, in __call__\n        losses = call_fn(y_true, y_pred)\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\losses.py\", line 243, in call  **\n        return ag_fn(y_true, y_pred, **self._fn_kwargs)\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\losses.py\", line 1787, in categorical_crossentropy\n        return backend.categorical_crossentropy(\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\backend.py\", line 5119, in categorical_crossentropy\n        target.shape.assert_is_compatible_with(output.shape)\n\n    ValueError: Shapes (None, 1) and (None, 10) are incompatible\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "from keras.applications.inception_v3 import InceptionV3\n",
    "from keras.layers import Dense, GlobalAveragePooling2D\n",
    "from keras.models import Model\n",
    "from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
    "\n",
    "\n",
    "# Checkpoint to save the best model weights\n",
    "checkpoint_resnet = ModelCheckpoint('resnet50_brain_tumor.h5',\n",
    "                             monitor='val_accuracy',\n",
    "                             save_best_only=True,\n",
    "                             mode='max',\n",
    "                             verbose=1)\n",
    "\n",
    "# Early stopping to stop training if the validation accuracy doesn't improve\n",
    "early_stop_resnet = EarlyStopping(monitor='val_accuracy',\n",
    "                           patience=5,\n",
    "                           mode='max',\n",
    "                           verbose=1)\n",
    "\n",
    "# Train the ResNet50 model\n",
    "history_resnet = model_resnet.fit_generator(train_generator,\n",
    "                                             steps_per_epoch=len(train_generator),\n",
    "                                             epochs=10,\n",
    "                                             callbacks=[checkpoint_resnet, early_stop_resnet],\n",
    "                                             validation_data=validation_generator,\n",
    "                                             validation_steps=len(validation_generator))\n",
    "\n",
    "# Load the best weights for the ResNet50 model\n",
    "model_resnet.load_weights('resnet50_brain_tumor.h5')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "42b96aaa",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_11284\\2333767499.py:2: UserWarning: `Model.evaluate_generator` is deprecated and will be removed in a future version. Please use `Model.evaluate`, which supports generators.\n",
      "  scores_resnet = model_resnet.evaluate_generator(test_generator, steps=len(test_generator), verbose=1)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "in user code:\n\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 1557, in test_function  *\n        return step_function(self, iterator)\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 1546, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 1535, in run_step  **\n        outputs = model.test_step(data)\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 1501, in test_step\n        self.compute_loss(x, y, y_pred, sample_weight)\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 948, in compute_loss\n        return self.compiled_loss(\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\engine\\compile_utils.py\", line 201, in __call__\n        loss_value = loss_obj(y_t, y_p, sample_weight=sw)\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\losses.py\", line 139, in __call__\n        losses = call_fn(y_true, y_pred)\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\losses.py\", line 243, in call  **\n        return ag_fn(y_true, y_pred, **self._fn_kwargs)\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\losses.py\", line 1787, in categorical_crossentropy\n        return backend.categorical_crossentropy(\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\backend.py\", line 5119, in categorical_crossentropy\n        target.shape.assert_is_compatible_with(output.shape)\n\n    ValueError: Shapes (None, 1) and (None, 10) are incompatible\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn [18], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Evaluate the model on the test set\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m scores_resnet \u001b[38;5;241m=\u001b[39m \u001b[43mmodel_resnet\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mevaluate_generator\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtest_generator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msteps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtest_generator\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTest loss:\u001b[39m\u001b[38;5;124m\"\u001b[39m, scores_resnet[\u001b[38;5;241m0\u001b[39m])\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTest accuracy:\u001b[39m\u001b[38;5;124m\"\u001b[39m, scores_resnet[\u001b[38;5;241m1\u001b[39m])\n",
      "File \u001b[1;32m~\\miniconda3\\lib\\site-packages\\keras\\engine\\training.py:2298\u001b[0m, in \u001b[0;36mModel.evaluate_generator\u001b[1;34m(self, generator, steps, callbacks, max_queue_size, workers, use_multiprocessing, verbose)\u001b[0m\n\u001b[0;32m   2291\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m   2292\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m`Model.evaluate_generator` is deprecated and \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m   2293\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwill be removed in a future version. \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m   2294\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPlease use `Model.evaluate`, which supports generators.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m   2295\u001b[0m     stacklevel\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[0;32m   2296\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_call_args(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mevaluate_generator\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m-> 2298\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[43mevaluate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   2299\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgenerator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2300\u001b[0m \u001b[43m    \u001b[49m\u001b[43msteps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msteps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2301\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmax_queue_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_queue_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2302\u001b[0m \u001b[43m    \u001b[49m\u001b[43mworkers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mworkers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2303\u001b[0m \u001b[43m    \u001b[49m\u001b[43muse_multiprocessing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_multiprocessing\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2304\u001b[0m \u001b[43m    \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2305\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallbacks\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\miniconda3\\lib\\site-packages\\keras\\utils\\traceback_utils.py:67\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     65\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:  \u001b[38;5;66;03m# pylint: disable=broad-except\u001b[39;00m\n\u001b[0;32m     66\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[1;32m---> 67\u001b[0m   \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28mNone\u001b[39m\n\u001b[0;32m     68\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     69\u001b[0m   \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
      "File \u001b[1;32m~\\AppData\\Local\\Temp\\__autograph_generated_filed8l5s5na.py:15\u001b[0m, in \u001b[0;36mouter_factory.<locals>.inner_factory.<locals>.tf__test_function\u001b[1;34m(iterator)\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m     14\u001b[0m     do_return \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m---> 15\u001b[0m     retval_ \u001b[38;5;241m=\u001b[39m ag__\u001b[38;5;241m.\u001b[39mconverted_call(ag__\u001b[38;5;241m.\u001b[39mld(step_function), (ag__\u001b[38;5;241m.\u001b[39mld(\u001b[38;5;28mself\u001b[39m), ag__\u001b[38;5;241m.\u001b[39mld(iterator)), \u001b[38;5;28;01mNone\u001b[39;00m, fscope)\n\u001b[0;32m     16\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[0;32m     17\u001b[0m     do_return \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
      "\u001b[1;31mValueError\u001b[0m: in user code:\n\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 1557, in test_function  *\n        return step_function(self, iterator)\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 1546, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 1535, in run_step  **\n        outputs = model.test_step(data)\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 1501, in test_step\n        self.compute_loss(x, y, y_pred, sample_weight)\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 948, in compute_loss\n        return self.compiled_loss(\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\engine\\compile_utils.py\", line 201, in __call__\n        loss_value = loss_obj(y_t, y_p, sample_weight=sw)\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\losses.py\", line 139, in __call__\n        losses = call_fn(y_true, y_pred)\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\losses.py\", line 243, in call  **\n        return ag_fn(y_true, y_pred, **self._fn_kwargs)\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\losses.py\", line 1787, in categorical_crossentropy\n        return backend.categorical_crossentropy(\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\backend.py\", line 5119, in categorical_crossentropy\n        target.shape.assert_is_compatible_with(output.shape)\n\n    ValueError: Shapes (None, 1) and (None, 10) are incompatible\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model on the test set\n",
    "scores_resnet = model_resnet.evaluate_generator(test_generator, steps=len(test_generator), verbose=1)\n",
    "\n",
    "print(\"Test loss:\", scores_resnet[0])\n",
    "print(\"Test accuracy:\", scores_resnet[1])\n",
    "\n",
    "# Get the predictions on the test set\n",
    "y_pred_resnet = model.predict_generator(test_generator, steps=len(test_generator), verbose=1)\n",
    "y_true_resnet = test_generator.classes\n",
    "\n",
    "# Compute the confusion matrix\n",
    "conf_mat_resnet = confusion_matrix(y_true_resnet, np.argmax(y_pred_resnet, axis=1))\n",
    "\n",
    "# Plot the confusion matrix using seaborn\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(conf_mat_resnet, cmap=\"Blues\", annot=True, fmt='g', xticklabels=train_generator.class_indices, yticklabels=train_generator.class_indices)\n",
    "plt.title(\"ResNet50 Confusion Matrix\")\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.show()\n",
    "\n",
    "# Print the classification report\n",
    "target_names = list(train_generator.class_indices.keys())\n",
    "print(classification_report(y_true_resnet, np.argmax(y_pred_resnet, axis=1), target_names=target_names))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c9ac7c49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_2\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_3 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
      "                                )]                                                                \n",
      "                                                                                                  \n",
      " conv2d (Conv2D)                (None, 111, 111, 32  864         ['input_3[0][0]']                \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization (BatchNorm  (None, 111, 111, 32  96         ['conv2d[0][0]']                 \n",
      " alization)                     )                                                                 \n",
      "                                                                                                  \n",
      " activation (Activation)        (None, 111, 111, 32  0           ['batch_normalization[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_1 (Conv2D)              (None, 109, 109, 32  9216        ['activation[0][0]']             \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_1 (BatchNo  (None, 109, 109, 32  96         ['conv2d_1[0][0]']               \n",
      " rmalization)                   )                                                                 \n",
      "                                                                                                  \n",
      " activation_1 (Activation)      (None, 109, 109, 32  0           ['batch_normalization_1[0][0]']  \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv2d_2 (Conv2D)              (None, 109, 109, 64  18432       ['activation_1[0][0]']           \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " batch_normalization_2 (BatchNo  (None, 109, 109, 64  192        ['conv2d_2[0][0]']               \n",
      " rmalization)                   )                                                                 \n",
      "                                                                                                  \n",
      " activation_2 (Activation)      (None, 109, 109, 64  0           ['batch_normalization_2[0][0]']  \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " max_pooling2d (MaxPooling2D)   (None, 54, 54, 64)   0           ['activation_2[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_3 (Conv2D)              (None, 54, 54, 80)   5120        ['max_pooling2d[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_3 (BatchNo  (None, 54, 54, 80)  240         ['conv2d_3[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_3 (Activation)      (None, 54, 54, 80)   0           ['batch_normalization_3[0][0]']  \n",
      "                                                                                                  \n",
      " conv2d_4 (Conv2D)              (None, 52, 52, 192)  138240      ['activation_3[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_4 (BatchNo  (None, 52, 52, 192)  576        ['conv2d_4[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_4 (Activation)      (None, 52, 52, 192)  0           ['batch_normalization_4[0][0]']  \n",
      "                                                                                                  \n",
      " max_pooling2d_1 (MaxPooling2D)  (None, 25, 25, 192)  0          ['activation_4[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_8 (Conv2D)              (None, 25, 25, 64)   12288       ['max_pooling2d_1[0][0]']        \n",
      "                                                                                                  \n",
      " batch_normalization_8 (BatchNo  (None, 25, 25, 64)  192         ['conv2d_8[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_8 (Activation)      (None, 25, 25, 64)   0           ['batch_normalization_8[0][0]']  \n",
      "                                                                                                  \n",
      " conv2d_6 (Conv2D)              (None, 25, 25, 48)   9216        ['max_pooling2d_1[0][0]']        \n",
      "                                                                                                  \n",
      " conv2d_9 (Conv2D)              (None, 25, 25, 96)   55296       ['activation_8[0][0]']           \n",
      "                                                                                                  \n",
      " batch_normalization_6 (BatchNo  (None, 25, 25, 48)  144         ['conv2d_6[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " batch_normalization_9 (BatchNo  (None, 25, 25, 96)  288         ['conv2d_9[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " activation_6 (Activation)      (None, 25, 25, 48)   0           ['batch_normalization_6[0][0]']  \n",
      "                                                                                                  \n",
      " activation_9 (Activation)      (None, 25, 25, 96)   0           ['batch_normalization_9[0][0]']  \n",
      "                                                                                                  \n",
      " average_pooling2d (AveragePool  (None, 25, 25, 192)  0          ['max_pooling2d_1[0][0]']        \n",
      " ing2D)                                                                                           \n",
      "                                                                                                  \n",
      " conv2d_5 (Conv2D)              (None, 25, 25, 64)   12288       ['max_pooling2d_1[0][0]']        \n",
      "                                                                                                  \n",
      " conv2d_7 (Conv2D)              (None, 25, 25, 64)   76800       ['activation_6[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_10 (Conv2D)             (None, 25, 25, 96)   82944       ['activation_9[0][0]']           \n",
      "                                                                                                  \n",
      " conv2d_11 (Conv2D)             (None, 25, 25, 32)   6144        ['average_pooling2d[0][0]']      \n",
      "                                                                                                  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " batch_normalization_5 (BatchNo  (None, 25, 25, 64)  192         ['conv2d_5[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " batch_normalization_7 (BatchNo  (None, 25, 25, 64)  192         ['conv2d_7[0][0]']               \n",
      " rmalization)                                                                                     \n",
      "                                                                                                  \n",
      " batch_normalization_10 (BatchN  (None, 25, 25, 96)  288         ['conv2d_10[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_11 (BatchN  (None, 25, 25, 32)  96          ['conv2d_11[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_5 (Activation)      (None, 25, 25, 64)   0           ['batch_normalization_5[0][0]']  \n",
      "                                                                                                  \n",
      " activation_7 (Activation)      (None, 25, 25, 64)   0           ['batch_normalization_7[0][0]']  \n",
      "                                                                                                  \n",
      " activation_10 (Activation)     (None, 25, 25, 96)   0           ['batch_normalization_10[0][0]'] \n",
      "                                                                                                  \n",
      " activation_11 (Activation)     (None, 25, 25, 32)   0           ['batch_normalization_11[0][0]'] \n",
      "                                                                                                  \n",
      " mixed0 (Concatenate)           (None, 25, 25, 256)  0           ['activation_5[0][0]',           \n",
      "                                                                  'activation_7[0][0]',           \n",
      "                                                                  'activation_10[0][0]',          \n",
      "                                                                  'activation_11[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_15 (Conv2D)             (None, 25, 25, 64)   16384       ['mixed0[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_15 (BatchN  (None, 25, 25, 64)  192         ['conv2d_15[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_15 (Activation)     (None, 25, 25, 64)   0           ['batch_normalization_15[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_13 (Conv2D)             (None, 25, 25, 48)   12288       ['mixed0[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_16 (Conv2D)             (None, 25, 25, 96)   55296       ['activation_15[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_13 (BatchN  (None, 25, 25, 48)  144         ['conv2d_13[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_16 (BatchN  (None, 25, 25, 96)  288         ['conv2d_16[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_13 (Activation)     (None, 25, 25, 48)   0           ['batch_normalization_13[0][0]'] \n",
      "                                                                                                  \n",
      " activation_16 (Activation)     (None, 25, 25, 96)   0           ['batch_normalization_16[0][0]'] \n",
      "                                                                                                  \n",
      " average_pooling2d_1 (AveragePo  (None, 25, 25, 256)  0          ['mixed0[0][0]']                 \n",
      " oling2D)                                                                                         \n",
      "                                                                                                  \n",
      " conv2d_12 (Conv2D)             (None, 25, 25, 64)   16384       ['mixed0[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_14 (Conv2D)             (None, 25, 25, 64)   76800       ['activation_13[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_17 (Conv2D)             (None, 25, 25, 96)   82944       ['activation_16[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_18 (Conv2D)             (None, 25, 25, 64)   16384       ['average_pooling2d_1[0][0]']    \n",
      "                                                                                                  \n",
      " batch_normalization_12 (BatchN  (None, 25, 25, 64)  192         ['conv2d_12[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_14 (BatchN  (None, 25, 25, 64)  192         ['conv2d_14[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_17 (BatchN  (None, 25, 25, 96)  288         ['conv2d_17[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_18 (BatchN  (None, 25, 25, 64)  192         ['conv2d_18[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_12 (Activation)     (None, 25, 25, 64)   0           ['batch_normalization_12[0][0]'] \n",
      "                                                                                                  \n",
      " activation_14 (Activation)     (None, 25, 25, 64)   0           ['batch_normalization_14[0][0]'] \n",
      "                                                                                                  \n",
      " activation_17 (Activation)     (None, 25, 25, 96)   0           ['batch_normalization_17[0][0]'] \n",
      "                                                                                                  \n",
      " activation_18 (Activation)     (None, 25, 25, 64)   0           ['batch_normalization_18[0][0]'] \n",
      "                                                                                                  \n",
      " mixed1 (Concatenate)           (None, 25, 25, 288)  0           ['activation_12[0][0]',          \n",
      "                                                                  'activation_14[0][0]',          \n",
      "                                                                  'activation_17[0][0]',          \n",
      "                                                                  'activation_18[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_22 (Conv2D)             (None, 25, 25, 64)   18432       ['mixed1[0][0]']                 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                  \n",
      " batch_normalization_22 (BatchN  (None, 25, 25, 64)  192         ['conv2d_22[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_22 (Activation)     (None, 25, 25, 64)   0           ['batch_normalization_22[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_20 (Conv2D)             (None, 25, 25, 48)   13824       ['mixed1[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_23 (Conv2D)             (None, 25, 25, 96)   55296       ['activation_22[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_20 (BatchN  (None, 25, 25, 48)  144         ['conv2d_20[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_23 (BatchN  (None, 25, 25, 96)  288         ['conv2d_23[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_20 (Activation)     (None, 25, 25, 48)   0           ['batch_normalization_20[0][0]'] \n",
      "                                                                                                  \n",
      " activation_23 (Activation)     (None, 25, 25, 96)   0           ['batch_normalization_23[0][0]'] \n",
      "                                                                                                  \n",
      " average_pooling2d_2 (AveragePo  (None, 25, 25, 288)  0          ['mixed1[0][0]']                 \n",
      " oling2D)                                                                                         \n",
      "                                                                                                  \n",
      " conv2d_19 (Conv2D)             (None, 25, 25, 64)   18432       ['mixed1[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_21 (Conv2D)             (None, 25, 25, 64)   76800       ['activation_20[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_24 (Conv2D)             (None, 25, 25, 96)   82944       ['activation_23[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_25 (Conv2D)             (None, 25, 25, 64)   18432       ['average_pooling2d_2[0][0]']    \n",
      "                                                                                                  \n",
      " batch_normalization_19 (BatchN  (None, 25, 25, 64)  192         ['conv2d_19[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_21 (BatchN  (None, 25, 25, 64)  192         ['conv2d_21[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_24 (BatchN  (None, 25, 25, 96)  288         ['conv2d_24[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_25 (BatchN  (None, 25, 25, 64)  192         ['conv2d_25[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_19 (Activation)     (None, 25, 25, 64)   0           ['batch_normalization_19[0][0]'] \n",
      "                                                                                                  \n",
      " activation_21 (Activation)     (None, 25, 25, 64)   0           ['batch_normalization_21[0][0]'] \n",
      "                                                                                                  \n",
      " activation_24 (Activation)     (None, 25, 25, 96)   0           ['batch_normalization_24[0][0]'] \n",
      "                                                                                                  \n",
      " activation_25 (Activation)     (None, 25, 25, 64)   0           ['batch_normalization_25[0][0]'] \n",
      "                                                                                                  \n",
      " mixed2 (Concatenate)           (None, 25, 25, 288)  0           ['activation_19[0][0]',          \n",
      "                                                                  'activation_21[0][0]',          \n",
      "                                                                  'activation_24[0][0]',          \n",
      "                                                                  'activation_25[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_27 (Conv2D)             (None, 25, 25, 64)   18432       ['mixed2[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_27 (BatchN  (None, 25, 25, 64)  192         ['conv2d_27[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_27 (Activation)     (None, 25, 25, 64)   0           ['batch_normalization_27[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_28 (Conv2D)             (None, 25, 25, 96)   55296       ['activation_27[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_28 (BatchN  (None, 25, 25, 96)  288         ['conv2d_28[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_28 (Activation)     (None, 25, 25, 96)   0           ['batch_normalization_28[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_26 (Conv2D)             (None, 12, 12, 384)  995328      ['mixed2[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_29 (Conv2D)             (None, 12, 12, 96)   82944       ['activation_28[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_26 (BatchN  (None, 12, 12, 384)  1152       ['conv2d_26[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_29 (BatchN  (None, 12, 12, 96)  288         ['conv2d_29[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_26 (Activation)     (None, 12, 12, 384)  0           ['batch_normalization_26[0][0]'] \n",
      "                                                                                                  \n",
      " activation_29 (Activation)     (None, 12, 12, 96)   0           ['batch_normalization_29[0][0]'] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                  \n",
      " max_pooling2d_2 (MaxPooling2D)  (None, 12, 12, 288)  0          ['mixed2[0][0]']                 \n",
      "                                                                                                  \n",
      " mixed3 (Concatenate)           (None, 12, 12, 768)  0           ['activation_26[0][0]',          \n",
      "                                                                  'activation_29[0][0]',          \n",
      "                                                                  'max_pooling2d_2[0][0]']        \n",
      "                                                                                                  \n",
      " conv2d_34 (Conv2D)             (None, 12, 12, 128)  98304       ['mixed3[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_34 (BatchN  (None, 12, 12, 128)  384        ['conv2d_34[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_34 (Activation)     (None, 12, 12, 128)  0           ['batch_normalization_34[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_35 (Conv2D)             (None, 12, 12, 128)  114688      ['activation_34[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_35 (BatchN  (None, 12, 12, 128)  384        ['conv2d_35[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_35 (Activation)     (None, 12, 12, 128)  0           ['batch_normalization_35[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_31 (Conv2D)             (None, 12, 12, 128)  98304       ['mixed3[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_36 (Conv2D)             (None, 12, 12, 128)  114688      ['activation_35[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_31 (BatchN  (None, 12, 12, 128)  384        ['conv2d_31[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_36 (BatchN  (None, 12, 12, 128)  384        ['conv2d_36[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_31 (Activation)     (None, 12, 12, 128)  0           ['batch_normalization_31[0][0]'] \n",
      "                                                                                                  \n",
      " activation_36 (Activation)     (None, 12, 12, 128)  0           ['batch_normalization_36[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_32 (Conv2D)             (None, 12, 12, 128)  114688      ['activation_31[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_37 (Conv2D)             (None, 12, 12, 128)  114688      ['activation_36[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_32 (BatchN  (None, 12, 12, 128)  384        ['conv2d_32[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_37 (BatchN  (None, 12, 12, 128)  384        ['conv2d_37[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_32 (Activation)     (None, 12, 12, 128)  0           ['batch_normalization_32[0][0]'] \n",
      "                                                                                                  \n",
      " activation_37 (Activation)     (None, 12, 12, 128)  0           ['batch_normalization_37[0][0]'] \n",
      "                                                                                                  \n",
      " average_pooling2d_3 (AveragePo  (None, 12, 12, 768)  0          ['mixed3[0][0]']                 \n",
      " oling2D)                                                                                         \n",
      "                                                                                                  \n",
      " conv2d_30 (Conv2D)             (None, 12, 12, 192)  147456      ['mixed3[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_33 (Conv2D)             (None, 12, 12, 192)  172032      ['activation_32[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_38 (Conv2D)             (None, 12, 12, 192)  172032      ['activation_37[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_39 (Conv2D)             (None, 12, 12, 192)  147456      ['average_pooling2d_3[0][0]']    \n",
      "                                                                                                  \n",
      " batch_normalization_30 (BatchN  (None, 12, 12, 192)  576        ['conv2d_30[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_33 (BatchN  (None, 12, 12, 192)  576        ['conv2d_33[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_38 (BatchN  (None, 12, 12, 192)  576        ['conv2d_38[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_39 (BatchN  (None, 12, 12, 192)  576        ['conv2d_39[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_30 (Activation)     (None, 12, 12, 192)  0           ['batch_normalization_30[0][0]'] \n",
      "                                                                                                  \n",
      " activation_33 (Activation)     (None, 12, 12, 192)  0           ['batch_normalization_33[0][0]'] \n",
      "                                                                                                  \n",
      " activation_38 (Activation)     (None, 12, 12, 192)  0           ['batch_normalization_38[0][0]'] \n",
      "                                                                                                  \n",
      " activation_39 (Activation)     (None, 12, 12, 192)  0           ['batch_normalization_39[0][0]'] \n",
      "                                                                                                  \n",
      " mixed4 (Concatenate)           (None, 12, 12, 768)  0           ['activation_30[0][0]',          \n",
      "                                                                  'activation_33[0][0]',          \n",
      "                                                                  'activation_38[0][0]',          \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                  'activation_39[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_44 (Conv2D)             (None, 12, 12, 160)  122880      ['mixed4[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_44 (BatchN  (None, 12, 12, 160)  480        ['conv2d_44[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_44 (Activation)     (None, 12, 12, 160)  0           ['batch_normalization_44[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_45 (Conv2D)             (None, 12, 12, 160)  179200      ['activation_44[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_45 (BatchN  (None, 12, 12, 160)  480        ['conv2d_45[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_45 (Activation)     (None, 12, 12, 160)  0           ['batch_normalization_45[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_41 (Conv2D)             (None, 12, 12, 160)  122880      ['mixed4[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_46 (Conv2D)             (None, 12, 12, 160)  179200      ['activation_45[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_41 (BatchN  (None, 12, 12, 160)  480        ['conv2d_41[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_46 (BatchN  (None, 12, 12, 160)  480        ['conv2d_46[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_41 (Activation)     (None, 12, 12, 160)  0           ['batch_normalization_41[0][0]'] \n",
      "                                                                                                  \n",
      " activation_46 (Activation)     (None, 12, 12, 160)  0           ['batch_normalization_46[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_42 (Conv2D)             (None, 12, 12, 160)  179200      ['activation_41[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_47 (Conv2D)             (None, 12, 12, 160)  179200      ['activation_46[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_42 (BatchN  (None, 12, 12, 160)  480        ['conv2d_42[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_47 (BatchN  (None, 12, 12, 160)  480        ['conv2d_47[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_42 (Activation)     (None, 12, 12, 160)  0           ['batch_normalization_42[0][0]'] \n",
      "                                                                                                  \n",
      " activation_47 (Activation)     (None, 12, 12, 160)  0           ['batch_normalization_47[0][0]'] \n",
      "                                                                                                  \n",
      " average_pooling2d_4 (AveragePo  (None, 12, 12, 768)  0          ['mixed4[0][0]']                 \n",
      " oling2D)                                                                                         \n",
      "                                                                                                  \n",
      " conv2d_40 (Conv2D)             (None, 12, 12, 192)  147456      ['mixed4[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_43 (Conv2D)             (None, 12, 12, 192)  215040      ['activation_42[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_48 (Conv2D)             (None, 12, 12, 192)  215040      ['activation_47[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_49 (Conv2D)             (None, 12, 12, 192)  147456      ['average_pooling2d_4[0][0]']    \n",
      "                                                                                                  \n",
      " batch_normalization_40 (BatchN  (None, 12, 12, 192)  576        ['conv2d_40[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_43 (BatchN  (None, 12, 12, 192)  576        ['conv2d_43[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_48 (BatchN  (None, 12, 12, 192)  576        ['conv2d_48[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_49 (BatchN  (None, 12, 12, 192)  576        ['conv2d_49[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_40 (Activation)     (None, 12, 12, 192)  0           ['batch_normalization_40[0][0]'] \n",
      "                                                                                                  \n",
      " activation_43 (Activation)     (None, 12, 12, 192)  0           ['batch_normalization_43[0][0]'] \n",
      "                                                                                                  \n",
      " activation_48 (Activation)     (None, 12, 12, 192)  0           ['batch_normalization_48[0][0]'] \n",
      "                                                                                                  \n",
      " activation_49 (Activation)     (None, 12, 12, 192)  0           ['batch_normalization_49[0][0]'] \n",
      "                                                                                                  \n",
      " mixed5 (Concatenate)           (None, 12, 12, 768)  0           ['activation_40[0][0]',          \n",
      "                                                                  'activation_43[0][0]',          \n",
      "                                                                  'activation_48[0][0]',          \n",
      "                                                                  'activation_49[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_54 (Conv2D)             (None, 12, 12, 160)  122880      ['mixed5[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_54 (BatchN  (None, 12, 12, 160)  480        ['conv2d_54[0][0]']              \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_54 (Activation)     (None, 12, 12, 160)  0           ['batch_normalization_54[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_55 (Conv2D)             (None, 12, 12, 160)  179200      ['activation_54[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_55 (BatchN  (None, 12, 12, 160)  480        ['conv2d_55[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_55 (Activation)     (None, 12, 12, 160)  0           ['batch_normalization_55[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_51 (Conv2D)             (None, 12, 12, 160)  122880      ['mixed5[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_56 (Conv2D)             (None, 12, 12, 160)  179200      ['activation_55[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_51 (BatchN  (None, 12, 12, 160)  480        ['conv2d_51[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_56 (BatchN  (None, 12, 12, 160)  480        ['conv2d_56[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_51 (Activation)     (None, 12, 12, 160)  0           ['batch_normalization_51[0][0]'] \n",
      "                                                                                                  \n",
      " activation_56 (Activation)     (None, 12, 12, 160)  0           ['batch_normalization_56[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_52 (Conv2D)             (None, 12, 12, 160)  179200      ['activation_51[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_57 (Conv2D)             (None, 12, 12, 160)  179200      ['activation_56[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_52 (BatchN  (None, 12, 12, 160)  480        ['conv2d_52[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_57 (BatchN  (None, 12, 12, 160)  480        ['conv2d_57[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_52 (Activation)     (None, 12, 12, 160)  0           ['batch_normalization_52[0][0]'] \n",
      "                                                                                                  \n",
      " activation_57 (Activation)     (None, 12, 12, 160)  0           ['batch_normalization_57[0][0]'] \n",
      "                                                                                                  \n",
      " average_pooling2d_5 (AveragePo  (None, 12, 12, 768)  0          ['mixed5[0][0]']                 \n",
      " oling2D)                                                                                         \n",
      "                                                                                                  \n",
      " conv2d_50 (Conv2D)             (None, 12, 12, 192)  147456      ['mixed5[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_53 (Conv2D)             (None, 12, 12, 192)  215040      ['activation_52[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_58 (Conv2D)             (None, 12, 12, 192)  215040      ['activation_57[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_59 (Conv2D)             (None, 12, 12, 192)  147456      ['average_pooling2d_5[0][0]']    \n",
      "                                                                                                  \n",
      " batch_normalization_50 (BatchN  (None, 12, 12, 192)  576        ['conv2d_50[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_53 (BatchN  (None, 12, 12, 192)  576        ['conv2d_53[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_58 (BatchN  (None, 12, 12, 192)  576        ['conv2d_58[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_59 (BatchN  (None, 12, 12, 192)  576        ['conv2d_59[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_50 (Activation)     (None, 12, 12, 192)  0           ['batch_normalization_50[0][0]'] \n",
      "                                                                                                  \n",
      " activation_53 (Activation)     (None, 12, 12, 192)  0           ['batch_normalization_53[0][0]'] \n",
      "                                                                                                  \n",
      " activation_58 (Activation)     (None, 12, 12, 192)  0           ['batch_normalization_58[0][0]'] \n",
      "                                                                                                  \n",
      " activation_59 (Activation)     (None, 12, 12, 192)  0           ['batch_normalization_59[0][0]'] \n",
      "                                                                                                  \n",
      " mixed6 (Concatenate)           (None, 12, 12, 768)  0           ['activation_50[0][0]',          \n",
      "                                                                  'activation_53[0][0]',          \n",
      "                                                                  'activation_58[0][0]',          \n",
      "                                                                  'activation_59[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_64 (Conv2D)             (None, 12, 12, 192)  147456      ['mixed6[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_64 (BatchN  (None, 12, 12, 192)  576        ['conv2d_64[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_64 (Activation)     (None, 12, 12, 192)  0           ['batch_normalization_64[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_65 (Conv2D)             (None, 12, 12, 192)  258048      ['activation_64[0][0]']          \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                  \n",
      " batch_normalization_65 (BatchN  (None, 12, 12, 192)  576        ['conv2d_65[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_65 (Activation)     (None, 12, 12, 192)  0           ['batch_normalization_65[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_61 (Conv2D)             (None, 12, 12, 192)  147456      ['mixed6[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_66 (Conv2D)             (None, 12, 12, 192)  258048      ['activation_65[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_61 (BatchN  (None, 12, 12, 192)  576        ['conv2d_61[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_66 (BatchN  (None, 12, 12, 192)  576        ['conv2d_66[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_61 (Activation)     (None, 12, 12, 192)  0           ['batch_normalization_61[0][0]'] \n",
      "                                                                                                  \n",
      " activation_66 (Activation)     (None, 12, 12, 192)  0           ['batch_normalization_66[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_62 (Conv2D)             (None, 12, 12, 192)  258048      ['activation_61[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_67 (Conv2D)             (None, 12, 12, 192)  258048      ['activation_66[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_62 (BatchN  (None, 12, 12, 192)  576        ['conv2d_62[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_67 (BatchN  (None, 12, 12, 192)  576        ['conv2d_67[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_62 (Activation)     (None, 12, 12, 192)  0           ['batch_normalization_62[0][0]'] \n",
      "                                                                                                  \n",
      " activation_67 (Activation)     (None, 12, 12, 192)  0           ['batch_normalization_67[0][0]'] \n",
      "                                                                                                  \n",
      " average_pooling2d_6 (AveragePo  (None, 12, 12, 768)  0          ['mixed6[0][0]']                 \n",
      " oling2D)                                                                                         \n",
      "                                                                                                  \n",
      " conv2d_60 (Conv2D)             (None, 12, 12, 192)  147456      ['mixed6[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_63 (Conv2D)             (None, 12, 12, 192)  258048      ['activation_62[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_68 (Conv2D)             (None, 12, 12, 192)  258048      ['activation_67[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_69 (Conv2D)             (None, 12, 12, 192)  147456      ['average_pooling2d_6[0][0]']    \n",
      "                                                                                                  \n",
      " batch_normalization_60 (BatchN  (None, 12, 12, 192)  576        ['conv2d_60[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_63 (BatchN  (None, 12, 12, 192)  576        ['conv2d_63[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_68 (BatchN  (None, 12, 12, 192)  576        ['conv2d_68[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_69 (BatchN  (None, 12, 12, 192)  576        ['conv2d_69[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_60 (Activation)     (None, 12, 12, 192)  0           ['batch_normalization_60[0][0]'] \n",
      "                                                                                                  \n",
      " activation_63 (Activation)     (None, 12, 12, 192)  0           ['batch_normalization_63[0][0]'] \n",
      "                                                                                                  \n",
      " activation_68 (Activation)     (None, 12, 12, 192)  0           ['batch_normalization_68[0][0]'] \n",
      "                                                                                                  \n",
      " activation_69 (Activation)     (None, 12, 12, 192)  0           ['batch_normalization_69[0][0]'] \n",
      "                                                                                                  \n",
      " mixed7 (Concatenate)           (None, 12, 12, 768)  0           ['activation_60[0][0]',          \n",
      "                                                                  'activation_63[0][0]',          \n",
      "                                                                  'activation_68[0][0]',          \n",
      "                                                                  'activation_69[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_72 (Conv2D)             (None, 12, 12, 192)  147456      ['mixed7[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_72 (BatchN  (None, 12, 12, 192)  576        ['conv2d_72[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_72 (Activation)     (None, 12, 12, 192)  0           ['batch_normalization_72[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_73 (Conv2D)             (None, 12, 12, 192)  258048      ['activation_72[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_73 (BatchN  (None, 12, 12, 192)  576        ['conv2d_73[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_73 (Activation)     (None, 12, 12, 192)  0           ['batch_normalization_73[0][0]'] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                  \n",
      " conv2d_70 (Conv2D)             (None, 12, 12, 192)  147456      ['mixed7[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_74 (Conv2D)             (None, 12, 12, 192)  258048      ['activation_73[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_70 (BatchN  (None, 12, 12, 192)  576        ['conv2d_70[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_74 (BatchN  (None, 12, 12, 192)  576        ['conv2d_74[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_70 (Activation)     (None, 12, 12, 192)  0           ['batch_normalization_70[0][0]'] \n",
      "                                                                                                  \n",
      " activation_74 (Activation)     (None, 12, 12, 192)  0           ['batch_normalization_74[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_71 (Conv2D)             (None, 5, 5, 320)    552960      ['activation_70[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_75 (Conv2D)             (None, 5, 5, 192)    331776      ['activation_74[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_71 (BatchN  (None, 5, 5, 320)   960         ['conv2d_71[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_75 (BatchN  (None, 5, 5, 192)   576         ['conv2d_75[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_71 (Activation)     (None, 5, 5, 320)    0           ['batch_normalization_71[0][0]'] \n",
      "                                                                                                  \n",
      " activation_75 (Activation)     (None, 5, 5, 192)    0           ['batch_normalization_75[0][0]'] \n",
      "                                                                                                  \n",
      " max_pooling2d_3 (MaxPooling2D)  (None, 5, 5, 768)   0           ['mixed7[0][0]']                 \n",
      "                                                                                                  \n",
      " mixed8 (Concatenate)           (None, 5, 5, 1280)   0           ['activation_71[0][0]',          \n",
      "                                                                  'activation_75[0][0]',          \n",
      "                                                                  'max_pooling2d_3[0][0]']        \n",
      "                                                                                                  \n",
      " conv2d_80 (Conv2D)             (None, 5, 5, 448)    573440      ['mixed8[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_80 (BatchN  (None, 5, 5, 448)   1344        ['conv2d_80[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_80 (Activation)     (None, 5, 5, 448)    0           ['batch_normalization_80[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_77 (Conv2D)             (None, 5, 5, 384)    491520      ['mixed8[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_81 (Conv2D)             (None, 5, 5, 384)    1548288     ['activation_80[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_77 (BatchN  (None, 5, 5, 384)   1152        ['conv2d_77[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_81 (BatchN  (None, 5, 5, 384)   1152        ['conv2d_81[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_77 (Activation)     (None, 5, 5, 384)    0           ['batch_normalization_77[0][0]'] \n",
      "                                                                                                  \n",
      " activation_81 (Activation)     (None, 5, 5, 384)    0           ['batch_normalization_81[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_78 (Conv2D)             (None, 5, 5, 384)    442368      ['activation_77[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_79 (Conv2D)             (None, 5, 5, 384)    442368      ['activation_77[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_82 (Conv2D)             (None, 5, 5, 384)    442368      ['activation_81[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_83 (Conv2D)             (None, 5, 5, 384)    442368      ['activation_81[0][0]']          \n",
      "                                                                                                  \n",
      " average_pooling2d_7 (AveragePo  (None, 5, 5, 1280)  0           ['mixed8[0][0]']                 \n",
      " oling2D)                                                                                         \n",
      "                                                                                                  \n",
      " conv2d_76 (Conv2D)             (None, 5, 5, 320)    409600      ['mixed8[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_78 (BatchN  (None, 5, 5, 384)   1152        ['conv2d_78[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_79 (BatchN  (None, 5, 5, 384)   1152        ['conv2d_79[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_82 (BatchN  (None, 5, 5, 384)   1152        ['conv2d_82[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_83 (BatchN  (None, 5, 5, 384)   1152        ['conv2d_83[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " conv2d_84 (Conv2D)             (None, 5, 5, 192)    245760      ['average_pooling2d_7[0][0]']    \n",
      "                                                                                                  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " batch_normalization_76 (BatchN  (None, 5, 5, 320)   960         ['conv2d_76[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_78 (Activation)     (None, 5, 5, 384)    0           ['batch_normalization_78[0][0]'] \n",
      "                                                                                                  \n",
      " activation_79 (Activation)     (None, 5, 5, 384)    0           ['batch_normalization_79[0][0]'] \n",
      "                                                                                                  \n",
      " activation_82 (Activation)     (None, 5, 5, 384)    0           ['batch_normalization_82[0][0]'] \n",
      "                                                                                                  \n",
      " activation_83 (Activation)     (None, 5, 5, 384)    0           ['batch_normalization_83[0][0]'] \n",
      "                                                                                                  \n",
      " batch_normalization_84 (BatchN  (None, 5, 5, 192)   576         ['conv2d_84[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_76 (Activation)     (None, 5, 5, 320)    0           ['batch_normalization_76[0][0]'] \n",
      "                                                                                                  \n",
      " mixed9_0 (Concatenate)         (None, 5, 5, 768)    0           ['activation_78[0][0]',          \n",
      "                                                                  'activation_79[0][0]']          \n",
      "                                                                                                  \n",
      " concatenate (Concatenate)      (None, 5, 5, 768)    0           ['activation_82[0][0]',          \n",
      "                                                                  'activation_83[0][0]']          \n",
      "                                                                                                  \n",
      " activation_84 (Activation)     (None, 5, 5, 192)    0           ['batch_normalization_84[0][0]'] \n",
      "                                                                                                  \n",
      " mixed9 (Concatenate)           (None, 5, 5, 2048)   0           ['activation_76[0][0]',          \n",
      "                                                                  'mixed9_0[0][0]',               \n",
      "                                                                  'concatenate[0][0]',            \n",
      "                                                                  'activation_84[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_89 (Conv2D)             (None, 5, 5, 448)    917504      ['mixed9[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_89 (BatchN  (None, 5, 5, 448)   1344        ['conv2d_89[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_89 (Activation)     (None, 5, 5, 448)    0           ['batch_normalization_89[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_86 (Conv2D)             (None, 5, 5, 384)    786432      ['mixed9[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_90 (Conv2D)             (None, 5, 5, 384)    1548288     ['activation_89[0][0]']          \n",
      "                                                                                                  \n",
      " batch_normalization_86 (BatchN  (None, 5, 5, 384)   1152        ['conv2d_86[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_90 (BatchN  (None, 5, 5, 384)   1152        ['conv2d_90[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_86 (Activation)     (None, 5, 5, 384)    0           ['batch_normalization_86[0][0]'] \n",
      "                                                                                                  \n",
      " activation_90 (Activation)     (None, 5, 5, 384)    0           ['batch_normalization_90[0][0]'] \n",
      "                                                                                                  \n",
      " conv2d_87 (Conv2D)             (None, 5, 5, 384)    442368      ['activation_86[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_88 (Conv2D)             (None, 5, 5, 384)    442368      ['activation_86[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_91 (Conv2D)             (None, 5, 5, 384)    442368      ['activation_90[0][0]']          \n",
      "                                                                                                  \n",
      " conv2d_92 (Conv2D)             (None, 5, 5, 384)    442368      ['activation_90[0][0]']          \n",
      "                                                                                                  \n",
      " average_pooling2d_8 (AveragePo  (None, 5, 5, 2048)  0           ['mixed9[0][0]']                 \n",
      " oling2D)                                                                                         \n",
      "                                                                                                  \n",
      " conv2d_85 (Conv2D)             (None, 5, 5, 320)    655360      ['mixed9[0][0]']                 \n",
      "                                                                                                  \n",
      " batch_normalization_87 (BatchN  (None, 5, 5, 384)   1152        ['conv2d_87[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_88 (BatchN  (None, 5, 5, 384)   1152        ['conv2d_88[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_91 (BatchN  (None, 5, 5, 384)   1152        ['conv2d_91[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " batch_normalization_92 (BatchN  (None, 5, 5, 384)   1152        ['conv2d_92[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " conv2d_93 (Conv2D)             (None, 5, 5, 192)    393216      ['average_pooling2d_8[0][0]']    \n",
      "                                                                                                  \n",
      " batch_normalization_85 (BatchN  (None, 5, 5, 320)   960         ['conv2d_85[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_87 (Activation)     (None, 5, 5, 384)    0           ['batch_normalization_87[0][0]'] \n",
      "                                                                                                  \n",
      " activation_88 (Activation)     (None, 5, 5, 384)    0           ['batch_normalization_88[0][0]'] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                  \n",
      " activation_91 (Activation)     (None, 5, 5, 384)    0           ['batch_normalization_91[0][0]'] \n",
      "                                                                                                  \n",
      " activation_92 (Activation)     (None, 5, 5, 384)    0           ['batch_normalization_92[0][0]'] \n",
      "                                                                                                  \n",
      " batch_normalization_93 (BatchN  (None, 5, 5, 192)   576         ['conv2d_93[0][0]']              \n",
      " ormalization)                                                                                    \n",
      "                                                                                                  \n",
      " activation_85 (Activation)     (None, 5, 5, 320)    0           ['batch_normalization_85[0][0]'] \n",
      "                                                                                                  \n",
      " mixed9_1 (Concatenate)         (None, 5, 5, 768)    0           ['activation_87[0][0]',          \n",
      "                                                                  'activation_88[0][0]']          \n",
      "                                                                                                  \n",
      " concatenate_1 (Concatenate)    (None, 5, 5, 768)    0           ['activation_91[0][0]',          \n",
      "                                                                  'activation_92[0][0]']          \n",
      "                                                                                                  \n",
      " activation_93 (Activation)     (None, 5, 5, 192)    0           ['batch_normalization_93[0][0]'] \n",
      "                                                                                                  \n",
      " mixed10 (Concatenate)          (None, 5, 5, 2048)   0           ['activation_85[0][0]',          \n",
      "                                                                  'mixed9_1[0][0]',               \n",
      "                                                                  'concatenate_1[0][0]',          \n",
      "                                                                  'activation_93[0][0]']          \n",
      "                                                                                                  \n",
      " global_average_pooling2d_1 (Gl  (None, 2048)        0           ['mixed10[0][0]']                \n",
      " obalAveragePooling2D)                                                                            \n",
      "                                                                                                  \n",
      " dense_4 (Dense)                (None, 1024)         2098176     ['global_average_pooling2d_1[0][0\n",
      "                                                                 ]']                              \n",
      "                                                                                                  \n",
      " dense_5 (Dense)                (None, 10)           10250       ['dense_4[0][0]']                \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 23,911,210\n",
      "Trainable params: 2,108,426\n",
      "Non-trainable params: 21,802,784\n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_11284\\1319682932.py:39: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
      "  history_inception = model_inception.fit_generator(train_generator,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "in user code:\n\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 1051, in train_function  *\n        return step_function(self, iterator)\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 1040, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 1030, in run_step  **\n        outputs = model.train_step(data)\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 890, in train_step\n        loss = self.compute_loss(x, y, y_pred, sample_weight)\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 948, in compute_loss\n        return self.compiled_loss(\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\engine\\compile_utils.py\", line 201, in __call__\n        loss_value = loss_obj(y_t, y_p, sample_weight=sw)\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\losses.py\", line 139, in __call__\n        losses = call_fn(y_true, y_pred)\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\losses.py\", line 243, in call  **\n        return ag_fn(y_true, y_pred, **self._fn_kwargs)\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\losses.py\", line 1787, in categorical_crossentropy\n        return backend.categorical_crossentropy(\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\backend.py\", line 5119, in categorical_crossentropy\n        target.shape.assert_is_compatible_with(output.shape)\n\n    ValueError: Shapes (None, 1) and (None, 10) are incompatible\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn [22], line 39\u001b[0m\n\u001b[0;32m     33\u001b[0m early_stop \u001b[38;5;241m=\u001b[39m EarlyStopping(monitor\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mval_accuracy\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m     34\u001b[0m                            patience\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m,\n\u001b[0;32m     35\u001b[0m                            mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmax\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m     36\u001b[0m                            verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m     38\u001b[0m \u001b[38;5;66;03m# Train the model\u001b[39;00m\n\u001b[1;32m---> 39\u001b[0m history_inception \u001b[38;5;241m=\u001b[39m \u001b[43mmodel_inception\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_generator\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_generator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     40\u001b[0m \u001b[43m                                        \u001b[49m\u001b[43msteps_per_epoch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtrain_generator\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     41\u001b[0m \u001b[43m                                        \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m     42\u001b[0m \u001b[43m                                        \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidation_generator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     43\u001b[0m \u001b[43m                                        \u001b[49m\u001b[43mvalidation_steps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mvalidation_generator\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     44\u001b[0m \u001b[43m                                        \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mcheckpoint\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mearly_stop\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\miniconda3\\lib\\site-packages\\keras\\engine\\training.py:2260\u001b[0m, in \u001b[0;36mModel.fit_generator\u001b[1;34m(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)\u001b[0m\n\u001b[0;32m   2249\u001b[0m \u001b[38;5;124;03m\"\"\"Fits the model on data yielded batch-by-batch by a Python generator.\u001b[39;00m\n\u001b[0;32m   2250\u001b[0m \n\u001b[0;32m   2251\u001b[0m \u001b[38;5;124;03mDEPRECATED:\u001b[39;00m\n\u001b[0;32m   2252\u001b[0m \u001b[38;5;124;03m  `Model.fit` now supports generators, so there is no longer any need to use\u001b[39;00m\n\u001b[0;32m   2253\u001b[0m \u001b[38;5;124;03m  this endpoint.\u001b[39;00m\n\u001b[0;32m   2254\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m   2255\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m   2256\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m`Model.fit_generator` is deprecated and \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m   2257\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwill be removed in a future version. \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m   2258\u001b[0m     \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPlease use `Model.fit`, which supports generators.\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m   2259\u001b[0m     stacklevel\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[1;32m-> 2260\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[43mfit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   2261\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgenerator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2262\u001b[0m \u001b[43m    \u001b[49m\u001b[43msteps_per_epoch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msteps_per_epoch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2263\u001b[0m \u001b[43m    \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mepochs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2264\u001b[0m \u001b[43m    \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverbose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2265\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallbacks\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2266\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidation_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2267\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvalidation_steps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidation_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2268\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvalidation_freq\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mvalidation_freq\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2269\u001b[0m \u001b[43m    \u001b[49m\u001b[43mclass_weight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclass_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2270\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmax_queue_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_queue_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2271\u001b[0m \u001b[43m    \u001b[49m\u001b[43mworkers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mworkers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2272\u001b[0m \u001b[43m    \u001b[49m\u001b[43muse_multiprocessing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_multiprocessing\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2273\u001b[0m \u001b[43m    \u001b[49m\u001b[43mshuffle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mshuffle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2274\u001b[0m \u001b[43m    \u001b[49m\u001b[43minitial_epoch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minitial_epoch\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\miniconda3\\lib\\site-packages\\keras\\utils\\traceback_utils.py:67\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     65\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:  \u001b[38;5;66;03m# pylint: disable=broad-except\u001b[39;00m\n\u001b[0;32m     66\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[1;32m---> 67\u001b[0m   \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28mNone\u001b[39m\n\u001b[0;32m     68\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     69\u001b[0m   \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
      "File \u001b[1;32m~\\AppData\\Local\\Temp\\__autograph_generated_file7wttv3t9.py:15\u001b[0m, in \u001b[0;36mouter_factory.<locals>.inner_factory.<locals>.tf__train_function\u001b[1;34m(iterator)\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m     14\u001b[0m     do_return \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m---> 15\u001b[0m     retval_ \u001b[38;5;241m=\u001b[39m ag__\u001b[38;5;241m.\u001b[39mconverted_call(ag__\u001b[38;5;241m.\u001b[39mld(step_function), (ag__\u001b[38;5;241m.\u001b[39mld(\u001b[38;5;28mself\u001b[39m), ag__\u001b[38;5;241m.\u001b[39mld(iterator)), \u001b[38;5;28;01mNone\u001b[39;00m, fscope)\n\u001b[0;32m     16\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[0;32m     17\u001b[0m     do_return \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
      "\u001b[1;31mValueError\u001b[0m: in user code:\n\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 1051, in train_function  *\n        return step_function(self, iterator)\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 1040, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 1030, in run_step  **\n        outputs = model.train_step(data)\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 890, in train_step\n        loss = self.compute_loss(x, y, y_pred, sample_weight)\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 948, in compute_loss\n        return self.compiled_loss(\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\engine\\compile_utils.py\", line 201, in __call__\n        loss_value = loss_obj(y_t, y_p, sample_weight=sw)\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\losses.py\", line 139, in __call__\n        losses = call_fn(y_true, y_pred)\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\losses.py\", line 243, in call  **\n        return ag_fn(y_true, y_pred, **self._fn_kwargs)\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\losses.py\", line 1787, in categorical_crossentropy\n        return backend.categorical_crossentropy(\n    File \"C:\\Users\\Admin\\miniconda3\\lib\\site-packages\\keras\\backend.py\", line 5119, in categorical_crossentropy\n        target.shape.assert_is_compatible_with(output.shape)\n\n    ValueError: Shapes (None, 1) and (None, 10) are incompatible\n"
     ]
    }
   ],
   "source": [
    "# InceptionV3 model\n",
    "# Define the image size\n",
    "img_size = 224\n",
    "\n",
    "# Load the InceptionV3 model\n",
    "base_model = InceptionV3(weights='imagenet', include_top=False, input_shape=(img_size, img_size, 3))\n",
    "\n",
    "\n",
    "\n",
    "# Add layers to the base model\n",
    "x = base_model.output\n",
    "x = GlobalAveragePooling2D()(x)\n",
    "x = Dense(1024, activation='relu')(x)\n",
    "predictions = Dense(num_classes, activation='softmax')(x)\n",
    "\n",
    "# Define the model\n",
    "model_inception = Model(inputs=base_model.input, outputs=predictions)\n",
    "\n",
    "# Freeze all the layers in the base model\n",
    "for layer in base_model.layers:\n",
    "    layer.trainable = False\n",
    "\n",
    "# Compile the model\n",
    "model_inception.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy', 'precision', 'recall', 'f1_score'])\n",
    "model_inception.summary()\n",
    "# Define callbacks\n",
    "checkpoint = ModelCheckpoint('inceptionv3_brain_tumor.h5',\n",
    "                             monitor='val_accuracy',\n",
    "                             save_best_only=True,\n",
    "                             mode='max',\n",
    "                             verbose=1)\n",
    "\n",
    "early_stop = EarlyStopping(monitor='val_accuracy',\n",
    "                           patience=5,\n",
    "                           mode='max',\n",
    "                           verbose=1)\n",
    "\n",
    "# Train the model\n",
    "history_inception = model_inception.fit_generator(train_generator,\n",
    "                                        steps_per_epoch=len(train_generator),\n",
    "                                        epochs=10,\n",
    "                                        validation_data=validation_generator,\n",
    "                                        validation_steps=len(validation_generator),\n",
    "                                        callbacks=[checkpoint, early_stop])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0732c1ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the best weights\n",
    "model_inception.load_weights('inceptionv3_brain_tumor.h5')\n",
    "\n",
    "# Evaluate the model on the test set\n",
    "scores_inception = model_inception.evaluate_generator(test_generator, steps=len(test_generator), verbose=1)\n",
    "\n",
    "print(\"Test loss:\", scores_inception[0])\n",
    "print(\"Test accuracy:\", scores_inception[1])\n",
    "\n",
    "# Get the predictions on the test set\n",
    "y_pred_inception = model.predict_generator(test_generator, steps=len(test_generator), verbose=1)\n",
    "y_true_inception = test_generator.classes\n",
    "\n",
    "# Compute the confusion matrix\n",
    "conf_mat_inception = confusion_matrix(y_true_inception, np.argmax(y_pred_inception, axis=1))\n",
    "\n",
    "# Plot the confusion matrix using seaborn\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(conf_mat_inception, cmap=\"Blues\", annot=True, fmt='g', xticklabels=train_generator.class_indices, yticklabels=train_generator.class_indices)\n",
    "plt.title(\"InceptionV3 Confusion Matrix\")\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.show()\n",
    "\n",
    "# Print the classification report\n",
    "target_names = list(train_generator.class_indices.keys())\n",
    "print(classification_report(y_true_inception, np.argmax(y_pred_inception, axis=1), target_names=target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7bc995c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8306f4db",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}