test-model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4e3dc55c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import matplotlib.pyplot as plt\n",
    "import PIL\n",
    "import os\n",
    "\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.models import Sequential\n",
    "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0174b569",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1477 files belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "# Read and split data\n",
    "batch_size = 1\n",
    "img_height = 519\n",
    "img_width = 695\n",
    "data_dir = 'labeled_sets/labeled_blo'\n",
    "\n",
    "test_ds = keras.utils.image_dataset_from_directory(\n",
    "  data_dir, \n",
    "  batch_size=batch_size, \n",
    "  image_size=(img_height, img_width)\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f1b89733",
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = test_ds.class_names\n",
    "num_classes = len(class_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3d6ee964",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-04-13 21:27:57.197043: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_4' with dtype int32 and shape [1477]\n",
      "\t [[{{node Placeholder/_4}}]]\n",
      "2023-04-13 21:27:57.197763: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_0' with dtype string and shape [1477]\n",
      "\t [[{{node Placeholder/_0}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1477/1477 - 12s - loss: 3.5440 - accuracy: 0.5362 - 12s/epoch - 8ms/step\n",
      "Accuracy: 53.62%\n"
     ]
    }
   ],
   "source": [
    "model = tf.keras.models.load_model(\"trained_model/all40\")\n",
    "\n",
    "# Re-evaluate the model\n",
    "loss, acc = model.evaluate(test_ds, verbose=2)\n",
    "print(\"Accuracy: {:5.2f}%\".format(100 * acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7c5fea89",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = []\n",
    "predictions = []\n",
    "# count = 0\n",
    "for x, y in test_ds:\n",
    "    pred = np.argmax(model(x))\n",
    "    predictions.append(pred)\n",
    "    labels.append(y.numpy()[0])\n",
    "    # if (y.numpy()[0] != pred):\n",
    "    #     if pred == 1:\n",
    "    #         tf.keras.preprocessing.image.save_img('./wrong/clearPredCloudy/' + str(count) + '.png', x[0])\n",
    "    #     else:\n",
    "    #         tf.keras.preprocessing.image.save_img('./wrong/cloudyPredClear/' + str(count) + '.png', x[0])\n",
    "    #     count = count + 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0fcf06af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.54      1.00      0.70       747\n",
      "           1       0.98      0.12      0.21       730\n",
      "\n",
      "    accuracy                           0.56      1477\n",
      "   macro avg       0.76      0.56      0.46      1477\n",
      "weighted avg       0.75      0.56      0.46      1477\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(labels, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a208fcb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f7f70e46c10>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVMAAAEGCAYAAADYCHYwAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAAsTAAALEwEAmpwYAAAffElEQVR4nO3de5xXVb3/8dd7ABEVQUAIBYUURVRAJSUtQrBSy7BSsyz5qedQZnY7HrWOncyTXU4Xyy4aaQaapuQxKU0z1NSOkiBC3jqQlwBR5CJyVcHP74+9Br4OM/P9DrNnvvs7vJ899mP2XnvtvT/DPPy01tp7r62IwMzMWqeu2gGYmXUETqZmZjlwMjUzy4GTqZlZDpxMzcxy0LnaAVSDOncL7dC92mFYCxxywF7VDsFa6JFHZi+LiN239fhOu+4dsXF9RXVj/Ut3RsSx23qtPGyfyXSH7nTd/5Rqh2Et8JeZP652CNZC3broudYcHxs30HXoqRXV3TDnR31ac608bJfJ1MxqgACp2lFUzMnUzIpLtXNbx8nUzIrLLVMzs9YS1HWqdhAVczI1s2IS7uabmbWe3M03M8uFW6ZmZjmooZZp7aR9M9vOKGuZVrKUO5O0v6RHS5ZXJH1eUi9Jd0man37ulupL0uWSFkiaJ+nQctdwMjWzYhLZ3fxKljIi4u8RMTIiRgKHAeuAW4ALgRkRMQSYkbYBjgOGpGUScEW5aziZmllB5dcybWA88I+IeA6YAExJ5VOAE9P6BGBqZB4Cekrq39xJPWZqZsVV1yZjpqcCN6T1fhGxJK2/APRL63sCC0uOWZTKltAEt0zNrJjqnzOtrGXaR9KskmVSo6eUdgA+AExruC+yD+Jt80fx3DI1s+Kq/G7+sogYVUG944BHIuLFtP2ipP4RsSR145em8sXAwJLjBqSyJrllamYFpdxuQJX4KFu6+ADTgYlpfSJwa0n56emu/mhgVclwQKPcMjWz4srxoX1JOwPvBj5ZUvwt4CZJZwHPAfUTHd8OHA8sILvzf0a58zuZmlkxKd/XSSNiLdC7Qdlysrv7DesGcE5Lzu9kambF5ddJzcxyUEOvkzqZmllByS1TM7NWq3+dtEY4mZpZQbllamaWD4+ZmpnlwC1TM7McuGVqZtZK8pipmVkuVOdkambWKgLkbr6ZWSspLTXCydTMCkpumZqZ5cHJ1MwsB3W+AWVm1koeMzUzaz15zNTMLB9OpmZmOXAyNTPLgZOpmVlrCVRXO8m0dp47MLPtSv0NqEqWis4n9ZT0G0lPSXpS0tsl9ZJ0l6T56eduqa4kXS5pgaR5kg4td34nUzMrrDyTKfBD4I6IGAqMAJ4ELgRmRMQQYEbaBjgOGJKWScAV5U7uZGpmxaUKl3KnkXoAY4CrASLitYh4GZgATEnVpgAnpvUJwNTIPAT0lNS/uWs4mZpZMalFLdM+kmaVLJManG0w8BJwjaQ5kq6StDPQLyKWpDovAP3S+p7AwpLjF6WyJvkGlJkVVgu68MsiYlQz+zsDhwLnRsRMST9kS5cegIgISbFtkbplamYFJURdXV1FSwUWAYsiYmba/g1Zcn2xvvuefi5N+xcDA0uOH5DKmuRkambFldOYaUS8ACyUtH8qGg88AUwHJqayicCtaX06cHq6qz8aWFUyHNAod/PNrJiU+0P75wK/krQD8DRwBlmD8iZJZwHPAaekurcDxwMLgHWpbrOcTM2ssPJMphHxKNDYuOr4RuoGcE5Lzu9kamaF5ddJzcxyUEuvkzqZ1pB99+7LL75x5ubtvffozTcn38aVN9wLwDmnjePrn/8Q+xxzAStWreWoQ4dw/fcm8dzzywH43T2P8p2r7qhG6NbAohdWcvbFU3lpxWoETPzgUXzqo0dXO6xCaeHbTVVXqGQq6WJgTUR8t9qxFNGC55Yy5rRvAVBXJ564/VJuu2cuAHv268nRRxzAwiUr3nTMg3P+walfvLLdY7Xmde5cx9c//yFGDB3I6rUbOPr0bzP2iKEMfWuzL9lsd2opmXbIR6MkFer/JNrCu962P88ueomFL6wE4NIvfJiLf/RbsnFzK7q39OnBiKHZY4zdd96R/Qa9hSUvvVzdoAoo53fz21RVk6mk09OMLHMlXdtg3z6S7pA0W9L9koam8hMkzUyvhP1JUr9UfrGkayX9Bbi2kct1KB96z2HcfOdsAI4bczBLXnqZx+Zv/Uzx2w4ezP2/upBpPzyboW99S3uHaRX45/PLmff3RRx24KBqh1I8OT1n2h6q1oKTdCBwEXBkRCyT1Av4bEmVycCnImK+pCOAnwLjgAeA0enVr38Bzgf+LR0zDHhHRKxv5HqTyGZ/gS67tNFv1T66dO7EcWMO5pKfTKdb1y588Yz38uHP/HirevP+vpDhH/gKa9e/xruPHMZ135nEqA9fUoWIrSlr1r3K6RdcxTe/+GF23aVbtcMpnKK0OitRze7wOGBaRCwDiIgV9f9wknYBjgSmlfxjdk0/BwA3ple/dgCeKTnn9MYSaTr/ZLIETd1OfWu6L3zMkcOY+9RCXlqxmmH77MHee/Tm/uu/BMAefXvy5+suYPz/+w5Ll6/efMxd//sE372gE7167MyKVWurFbqVeH3jJiZe8HNOPnYUJ4wbWe1wCkfK7g3UiqKOLdYBL0fEyEb2/Qj4fkRMlzQWuLhk33aRJU567yhu/mPWxX/iH8+z33u/tHnf3Fu/xtGn/zcrVq2lb+/umxPqocP2pq5OTqQFERGc+1+/Yr9Bb+Gc07Z6ZtwA/HXSit0N3CLp+xGxPHXzAYiIVyQ9I+nkiJim7F90eETMBXqwZcKBiY2ct0PbaccdGHv4UL7wjRvK1p0w7hDOOOmdbNq4ifWvvs5Z/3FNO0RolXho7tPcePtfGbbvHrzzY98E4CvnfID3HHVglSMrlhrKpdVLphHxuKRLgT9L2gTMAZ4tqXIacIWki4AuwK+BuWQt0WmSVpIl5MHtGXe1rdvwGvu8+4Im94+Y8NXN6z+fdh8/n3Zfe4RlLfT2kfuw8uGtx7ntzdwyrVBETGHLLNcN9z0DHNtI+a1smdmltPzivOMzsyqSW6ZmZq0mfAPKzCwXTqZmZq3lbr6ZWesJ34AyM8uBnzM1M8tFDeVSJ1MzKyi/Tmpm1nq1NmbaIeczNbOOQapsqexcelbS3yQ9KmlWKusl6S5J89PP3VK5JF0uaUGaJvTQcud3MjWzwmqDyaGPjoiREVH/ldILgRkRMQSYkbYBjgOGpGUScEW5EzuZmllh5dkybcIEtrzSPgU4saR8amQeAnqmaT+b5GRqZsWkFrVM+0iaVbJMauSMAfwxfb2jfn+/iFiS1l8A+qX1PYGFJccuSmVN8g0oMyskoZbczV9W0nVvyjsiYrGkvsBdkp4q3Zm+3rHNE8e7ZWpmhZVnNz8iFqefS4FbgMOBF+u77+nn0lR9MTCw5PABbJlHuVFOpmZWWHndgJK0s6Tu9evAe4DHgOlsmWR+Ilum95wOnJ7u6o8GVpUMBzTK3XwzK6Z8JzrpR/ZlD8jy3vURcYekh4GbJJ0FPAeckurfDhwPLADWAWeUu4CTqZkVUp4P7UfE08CIRsqXA1t9hCsiAjinJddwMjWzwqqlN6CcTM2ssPxuvplZa3lyaDOz1pPnMzUzy0cN5VInUzMrrroayqZOpmZWSPLk0GZm+aihXOpkambF1SFuQEn6EdmUVY2KiM+2SURmZkkN5dJmW6az2i0KM7MGRPZ4VK1oMplGxJTSbUk7RcS6tg/JzCxTS2OmZafgk/R2SU8AT6XtEZJ+2uaRmdn2Tdnk0JUsRVDJfKY/AN4LLAeIiLnAmDaMycwMkT1nWslSBBXdzY+IhQ3uqm1qm3DMzLYoSJ6sSCXJdKGkI4GQ1AX4HPBk24ZlZlZbj0ZV0s3/FNkkqXsCzwMjaeGkqWZmLVXp95+Kkm/LtkwjYhlwWjvEYmb2Jp2KkikrUMnd/LdK+p2klyQtlXSrpLe2R3Bmtn3L64N67aGSbv71wE1Af2APYBpwQ1sGZWaW3c2vbCmCSpLpThFxbURsTMt1wI5tHZiZbecqbJW2pGUqqZOkOZJ+n7YHS5opaYGkGyXtkMq7pu0Faf+gcuduMplK6iWpF/AHSRdKGiRpb0nnk30G1cysTbXBDaiGTyN9G7gsIvYFVgJnpfKzgJWp/LJUr1nNtUxnk72ffwrwSeAe4F7gbOAjLQrfzGwb5NkylTQAeB9wVdoWMA74TaoyBTgxrU9I26T941XmQs29mz+4ogjNzNqAgE6VD4j2kVQ6OdPkiJjcoM4PgPOB7mm7N/ByRGxM24vIHgEl/VwIEBEbJa1K9Zc1FUBFb0BJOggYRslYaURMreRYM7Nt1YIe/LKIGNXkeaT3A0sjYraksa0OrBFlk6mkrwJjyZLp7cBxwAOAk6mZtRkp129AHQV8QNLxZI3CXYEfAj0ldU6t0wHA4lR/MTAQWCSpM9CDND9JUyq5m38SMB54ISLOAEakE5uZtam8bkBFxJciYkBEDAJOBe6OiNPI7gWdlKpNBG5N69PTNmn/3RHR5GT5UFkyXR8RbwAbJe0KLCXL2GZmbaodHtq/APiipAVkY6JXp/Krgd6p/IvAheVOVMmY6SxJPYGfk93hXwM8uA1Bm5m1SFu83BQR95I9mUREPA0c3kidDcDJLTlvJe/mfzqtXinpDmDXiJjXkouYmbWUpJbcza+65j6od2hz+yLikbYJycwsU5T37ivRXMv0e83sC7KHXWuSdtyJun0Pq3YY1gIvrtpQ7RCsCiq5qVMUzT20f3R7BmJmVkp0nJapmVlV1dCQqZOpmRWT1KLXSavOydTMCquGcmlFM+1L0scl/Wfa3kvSVs9lmZnlrZa+AVXJzbKfAm8HPpq2VwM/abOIzMyon2lfFS1FUEk3/4iIOFTSHICIWFk/G7WZWVvqEI9GlXhdUieyZ0uRtDvwRptGZWZGcbrwlagkmV4O3AL0lXQp2QwqF7VpVGa23eswr5PWi4hfSZpNNg2fgBMj4skyh5mZtVoN5dKKJofeC1gH/K60LCL+2ZaBmdn2rf4GVK2opJt/G9l4qchmqB4M/B04sA3jMjPrWGOmEXFw6XaaTerTTVQ3M8uHOlg3v6GIeETSEW0RjJlZKbXkk3pVVsmY6RdLNuuAQ4Hn2ywiMzOyccXONfSgaSUt0+4l6xvJxlBvbptwzMy26DBT8KWH9btHxHntFI+ZGVB/N7/aUVSuyUZ0+pb0JrLvTZuZta8KJzmppPEqaUdJf5U0V9Ljkr6WygdLmilpgaQb61+Vl9Q1bS9I+weVu0ZzIxJ/TT8flTRd0ickfah+KR++mVnr5DjRyavAuIgYAYwEjpU0Gvg2cFlE7AusBM5K9c8CVqbyy1K95mOtIIgdgeVk33x6P3BC+mlm1mYEdKqrbCknMmvSZpe01H/L7jepfApwYlqfkLZJ+8erzABuc2OmfdOd/MfY8tD+5tjKh29m1hqiLsdHo9I9oNnAvmTTiP4DeDkiNqYqi4A90/qewEKAiNgoaRXQG1jW1PmbS6adgF2g0d/GydTM2lT2Qb2Kq/eRNKtke3JETC6tkO4BjZTUk2zypqE5hLlZc8l0SURckufFzMwq1rI3oJZFxKhKKkbEy5LuIZv0vme62b4RGAAsTtUWAwOBRZI6Az3Ihjub1NxoQw09lGBmHVFeN6Ak7Z5apEjqBrwbeBK4h2xaUYCJwK1pfXraJu2/OyKa7ZE31zIdXzZCM7M20sJufjn9gSlp3LQOuCkifi/pCeDXkr4OzAGuTvWvBq6VtABYAZxa7gJNJtOIWNHa6M3MWiOvyaEjYh5wSCPlTwNbfSA0IjYAJ7fkGv7Us5kVkuh434AyM2t/6kDv5puZVVPtpFInUzMrqI742RIzs6qonVTqZGpmhSXqamgOPidTMysk3803M8uJ7+abmeWgdlKpk6mZFZWfMzUzaz0BnZxMzcxar3ZSqZOpmRVYDTVMnUzNrJiyR6NqJ5s6mZpZYbllambWakJumZqZtY7v5puZ5UHu5puZ5cLJ1MwsBx4zNTNrpWxy6GpHUblamuHKzLYzdVJFSzmSBkq6R9ITkh6X9LlU3kvSXZLmp5+7pXJJulzSAknzJB1aNtZW/7ZmZm1EFf6vAhuBf4uIYcBo4BxJw4ALgRkRMQSYkbYBjgOGpGUScEW5C7ibX2N23WkHfjjpKA4Y0JMAzv3ZA8ya/xIAn37fgfzXxw9nyKTrWbH6VY47bC++dMohvPFGsOmN4MtTZzLz70ur+wts5375m/v4zR9mIsGQwf355r9/hDPPn8za9a8CsPzlNQzffyA/ueSMKkdafXl28yNiCbAkra+W9CSwJzABGJuqTQHuBS5I5VMjIoCHJPWU1D+dp1HtmkwlXQysiYjv5nCue4HzImJWa89VS7458QhmzF3EGT+4hy6d6ujWNfsT7tFrZ44+eE8WvrRmc937HnueP8z+JwDD9tqNX3x2LKPPu6UqcRu8uGwV1/72fm67+nx27NqFz18yldvueZRf/eCczXXOvXgK4488sIpRFkmLHtrvI6k0F0yOiMmNnlUaBBwCzAT6lSTIF4B+aX1PYGHJYYtSWZPJ1N38GtK9WxfePrQf190zH4DXN73BK+teA+DS0w/n4usfJojN9de+unHz+k5dO5fssWrZtOkNNrz6Ohs3bWL9q6/Tt/eum/etWbuBmY8u4JijDqpihAWSnjOtZAGWRcSokqWpRLoLcDPw+Yh4pXRfaoVu838mbdoylXQ6cB5ZgPOAf5TsGwlcCeyUys+MiJWlLU5JfYBZETFIUjfgGmAE8BTQLZ3nTGB4RHw+bf8rMCwivtCWv1s17N23O8tf2cCPP/UODty7F3OfXs6Xp87kXQftwZIV63j8nyu3OuZ9o/biK6ceRp8e3Tj1v++qQtRWr1+fHpx58ljGfezrdO3ahaMO2493jNp/8/4//eUxRh+yL7vsvGMVoyyWPG/mS+pClkh/FRH/k4pfrO++S+oP1I+DLQYGlhw+IJU1qc1appIOBC4CxkXECOBzDapMBS6IiOHA34Cvljnl2cC6iDgg1T0sld8EnJD+oQDOAH7RSDyTJM2SNCteXdNwd03o3EkMH9yba+56iqO/NJ11r27k/A+P5AsnDueb0x5p9JjbZv2T0efdwie+N4Mvn1z2hqS1oVWr1zHjfx/jT9d9mftu/E/Wb3iN6X+avXn/bffM4X1HH1LFCIul/nXSSpay58qm7L8aeDIivl+yazowMa1PBG4tKT893dUfDaxqbrwU2rabPw6YFhHLACJiRf0OST2AnhHx51Q0BRhT5nxjgOvSueaRtXSJiDXA3cD7JQ0FukTE3xoeHBGT67sA6rpL636zKnl++TqeX7GW2f9YBsD0mc8yYnBv9tp9F+779gTmXH4Se/TamXu+8QH69uj2pmMffOpF9u7bnV7du1YjdAMefGQ+A97Sm149d6FL5068+x0HM+fxZwFYuWot855ayNjRB1Q3yKJRhUt5RwGfAMZJejQtxwPfAt4taT5wTNoGuB14GlgA/Bz4dLkLFPFu/ka2JPlK+ztXAV8m6/5f0xZBFcHSVetZvHwt+/bflQVLXmHMQf2Z+8xyPnjpnZvrzLn8JMb/x+9YsfpVBvfrzjMvrgZg+KDedO1Sx4rVr1Yr/O1e/749mfvkc6zf8Bo7du3Cg3Pmc9B+WU/yzvvmMnb0AXTdoUuZs2xf8noDKiIeoOm0O76R+gGc00jdJrVlMr0buEXS9yNiuaRe9TsiYpWklZLeGRH3k/0/Rn0r9VmyLvxfgZNKzncf8DHgbkkHAcNLzjdT0kDg0NLyjujCX87kZ595F1061/Hci6v5zM8eaLLuCYcP4iNj9uH1jW+w4bVNnHX5ve0XqG1lxAF7854xw/nQ2ZfRuVMdB+y7Jx9532gAbrvnUSadOq7KERZPLb2brywBt9HJpYnAvwObgDlkiXJNRHy3wQ2op4Ez0g2ooWTjoJuA24CPN3IDqv4ZsXPqH42SdCEwMiJOLRdXp16DYsfx5YZorUieuPKj1Q7BWmhQn26zI2LUth5/wMGHxNRb762o7uH79GzVtfLQpt38iJhCNh7a2L5Hyd5EaFj+FG9uXV6UytcDzSXKdwCXbWusZlZANdQyrfnnTNObCf8HrI+IGdWOx8zyIeX3bn57KOINqBaJiJeB/aodh5nlrxhpsjI1n0zNrAOroWzqZGpmBeUP6pmZ5aIgw6EVcTI1s0ISTqZmZrlwN9/MLAdumZqZ5aCGcqmTqZkVVOUzQhWCk6mZFZbHTM3MWinPD+q1BydTMysuJ1Mzs9ZzN9/MLAd+NMrMLAc1lEudTM2swGoom9b85NBm1jHlOTm0pF9IWirpsZKyXpLukjQ//dwtlUvS5ZIWSJonqaJvpDuZmllh5felZ34JHNug7EJgRkQMAWakbYDjgCFpmQRcUckFnEzNrLhyyqYRcR+wokHxBLZ8o24KcGJJ+dTIPAT0lNS/3DU8ZmpmBdWiyaH7SJpVsj05IiaXOaZfRCxJ6y8A/dL6nsDCknqLUtkSmuFkamaF1YJHo5a15lPPERGSWvXde3fzzayQ6ieHrmTZRi/Wd9/Tz6WpfDEwsKTegFTWLCdTMyssVfi/bTQdmJjWJwK3lpSfnu7qjwZWlQwHNMndfDMrrLzegJJ0AzCWbGx1EfBV4FvATZLOAp4DTknVbweOBxYA64AzKrmGk6mZFVZez+xHxEeb2DW+kboBnNPSaziZmlkxtW48tN05mZpZgdVONnUyNbNC8uTQZmY5cTffzCwHnhzazCwPtZNLnUzNrLhqKJc6mZpZMbXyVdF252RqZoWlGsqmTqZmVli1k0qdTM2swGqoYepkamZF1aoZodqdk6mZFVL9fKa1wsnUzArLydTMLAfu5puZtZafMzUza70Kv+JcGE6mZlZcNZRNnUzNrLA8ZmpmlgNPDm1mlgcnUzOz1nM338yslWrtDShln4jevkh6CXiu2nG0kT7AsmoHYRXryH+vvSNi9209WNIdZP8+lVgWEcdu67XysF0m045M0qyIGFXtOKwy/nt1HHXVDsDMrCNwMjUzy4GTacczudoBWIv479VBeMzUzCwHbpmameXAydTMLAdOpjVI0sWSzqt2HNa4PP8+ku6V5EenaoCT6XZMkt+AM8uJk2kNkHS6pHmS5kq6tsG+fSTdIWm2pPslDU3lJ0iaKWmOpD9J6pfKL5Z0raS/ANc2cjlroTJ/n5GSHkr7b5G0Wyrf3OKU1EfSs2m9m6RfS3pS0i1At1R+pqQflJz3XyVd1k6/olXAybTgJB0IXASMi4gRwOcaVJkMnBsRhwHnAT9N5Q8AoyPiEODXwPklxwwDjomIj7Zp8NuBCv4+U4ELImI48Dfgq2VOeTawLiIOSHUPS+U3ASdI6pK2zwB+kcOvYDlxN6/4xgHTImIZQESsUJr9QdIuwJHANG2ZEaJr+jkAuFFSf2AH4JmSc06PiPXtEPv2oLm/Tw+gZ0T8OdWdAkwrc74xwOXpXPMkzUvrayTdDbxf0pNAl4j4W+6/jW0zJ9PaVge8HBEjG9n3I+D7ETFd0ljg4pJ9a9s8MitnI1t6hjtWeMxVwJeBp4Br2iIo23bu5hff3cDJknoDSOpVvyMiXgGekXRy2idJI9LuHsDitD6xHePd3jT391kFrJT0zlT0CaC+lfosW7rwJ5Wc7z7gY+lcBwHDS843ExiY9t+Q9y9ireOWacFFxOOSLgX+LGkTMIfsP8R6pwFXSLoI6EI2PjqXrCU6TdJKsv/gB7dn3NuLCv4+E4ErJe0EPE021gnwXeAmSZOA20rqXwFck7ryTwKzG1zyJmBkRKzM/ZexVvHrpGY1RNLvgcsiYka1Y7E3czffrAZI6inp/4D1TqTF5JapmVkO3DI1M8uBk6mZWQ6cTM3McuBkao2StEnSo5IekzQtPdqzref6paST0vpVkoY1U3espCO34RrPStrqS5ZNlTeos6aF1/KsXbYVJ1NryvqIGBkRBwGvAZ8q3bmtM05FxL9ExBPNVBlL9oqsWU1xMrVK3A/sm1qN90uaDjwhqZOk70h6OM2K9EnY/CbWjyX9XdKfgL71J2owW9Kxkh5Jsy3NkDSILGl/IbWK3ylpd0k3p2s8LOmodGxvSX+U9LikqwBRhqTfptm1Hk8Py5fuuyyVz5C0eyprdEYus8b4DShrVmqBHgfckYoOBQ6KiGdSQloVEW+T1BX4i6Q/AocA+5PNTtUPeIIGMxylhPVzYEw6V680SciVwJqI+G6qdz3ZQ+oPSNoLuBOon1HpgYi4RNL7gLMq+HXOTNfoBjws6eaIWA7sDMyKiC9I+s907s+Qzcj1qYiYL+kIshm5xm3DP6NtB5xMrSndJD2a1u8Hribrfv81IupnoHoPMLx+PJRsPoAhZDMf3RARm4Dn02xHDY0G7qs/V0SsaCKOY4BhJbNi7ZpmyxoDfCgde1t6bbacz0r6YFofmGJdDrwB3JjKrwP+p8yMXGZbcTK1pqxvOBtVSiqlM06JbC7VOxvUOz7HOOrI5mXd0EgsFUszZx0DvD0i1km6l6Znawqan5HLbCseM7XWuBM4W2nCYkn7SdqZbOajj6Qx1f7A0Y0c+xAwRtLgdGz9bEurge4l9f4InFu/IWlkWi2dXek4YLcysfYAVqZEOpSsZVyvji0zN32MbPiguRm5zLbiZGqtcRXZeOgjkh4DfkbW27kFmJ/2TQUebHhgRLwETCLrUs9lSzf7d8AH629AAZ8FRqUbXE+w5amCr5El48fJuvv/LBPrHUDnNBvTt8iSeb21wOHpdxgHXJLKTwPOSvE9Dkyo4N/EtlN+N9/MLAdumZqZ5cDJ1MwsB06mZmY5cDI1M8uBk6mZWQ6cTM3McuBkamaWg/8PyMuWEKwXgt0AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ConfusionMatrixDisplay.from_predictions(labels, predictions, cmap=plt.cm.Blues, display_labels=class_names )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fe133201",
   "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": "3.9.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}