2x2.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "f20ba5d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from skimage.io import imread\n",
    "import cv2\n",
    "import os\n",
    "import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ed0580a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputfolder = 'dataset/Cyst'\n",
    "os.makedirs('resized dataset/Resized Folder 2x2/Cyst', exist_ok=True)\n",
    "\n",
    "i = 1\n",
    "\n",
    "for img in glob.glob(inputfolder + \"/*.jpg\"):\n",
    "    image = cv2.imread(img)\n",
    "    imgResized = cv2.resize(image, (2, 2))\n",
    "    cv2.imwrite(\"resized dataset/Resized Folder 2x2/Cyst/Cyst- (%i).jpg\" % i, imgResized)\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "6f68811c",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputfolder = 'dataset/Normal'\n",
    "os.makedirs('resized dataset/Resized Folder 2x2/Normal', exist_ok=True)\n",
    "\n",
    "i = 1\n",
    "\n",
    "for img in glob.glob(inputfolder + \"/*.jpg\"):\n",
    "    image = cv2.imread(img)\n",
    "    imgResized = cv2.resize(image, (2, 2))\n",
    "    cv2.imwrite(\"resized dataset/Resized Folder 2x2/Normal/Normal- (%i).jpg\" % i, imgResized)\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "108cb998",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputfolder = 'dataset/Stone'\n",
    "os.makedirs('resized dataset/Resized Folder 2x2/Stone', exist_ok=True)\n",
    "\n",
    "i = 1\n",
    "\n",
    "for img in glob.glob(inputfolder + \"/*.jpg\"):\n",
    "    image = cv2.imread(img)\n",
    "    imgResized = cv2.resize(image, (2, 2))\n",
    "    cv2.imwrite(\"resized dataset/Resized Folder 2x2/Stone/Stone- (%i).jpg\" % i, imgResized)\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "16dab6a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputfolder = 'dataset/Tumor'\n",
    "os.makedirs('resized dataset/Resized Folder 2x2/Tumor', exist_ok=True)\n",
    "\n",
    "i = 1\n",
    "\n",
    "for img in glob.glob(inputfolder + \"/*.jpg\"):\n",
    "    image = cv2.imread(img)\n",
    "    imgResized = cv2.resize(image, (2, 2))\n",
    "    cv2.imwrite(\"resized dataset/Resized Folder 2x2/Tumor/Tumor- (%i).jpg\" % i, imgResized)\n",
    "    i += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "d2ff0ab9",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = Path('resized dataset/Resized Folder 2x2')\n",
    "train_dir = data_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "6b0f8794",
   "metadata": {},
   "outputs": [],
   "source": [
    "Normal_Cases_dir = train_dir / 'Normal'\n",
    "Cyst_Cases_dir = train_dir / 'Cyst'\n",
    "Stone_Cases_dir = train_dir / 'Stone'\n",
    "Tumor_Cases_dir = train_dir / 'Tumor'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "013a9442",
   "metadata": {},
   "outputs": [],
   "source": [
    "Normal_Cases = Normal_Cases_dir.glob('*.jpg') \n",
    "Cyst_Cases = Cyst_Cases_dir.glob('*.jpg') \n",
    "Stone_Cases = Stone_Cases_dir.glob('*.jpg')\n",
    "Tumor_Cases = Tumor_Cases_dir.glob('*.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "86351d84",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "fb37ced5",
   "metadata": {},
   "outputs": [],
   "source": [
    "for img in Cyst_Cases:\n",
    "    train_data.append((img, 0))\n",
    "\n",
    "for img in Normal_Cases:\n",
    "    train_data.append((img, 1))\n",
    "\n",
    "for img in Stone_Cases:\n",
    "    train_data.append((img, 2))\n",
    "\n",
    "for img in Tumor_Cases:\n",
    "    train_data.append((img, 3))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "db428cd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>image</th>\n",
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       "      <td>resized dataset/Resized Folder 2x2/Tumor/Tumor...</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12446 rows × 2 columns</p>\n",
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      "text/plain": [
       "                                                   image  label\n",
       "0      resized dataset/Resized Folder 2x2/Cyst/Cyst- ...      0\n",
       "1      resized dataset/Resized Folder 2x2/Cyst/Cyst- ...      0\n",
       "2      resized dataset/Resized Folder 2x2/Cyst/Cyst- ...      0\n",
       "3      resized dataset/Resized Folder 2x2/Cyst/Cyst- ...      0\n",
       "4      resized dataset/Resized Folder 2x2/Cyst/Cyst- ...      0\n",
       "...                                                  ...    ...\n",
       "12441  resized dataset/Resized Folder 2x2/Tumor/Tumor...      3\n",
       "12442  resized dataset/Resized Folder 2x2/Tumor/Tumor...      3\n",
       "12443  resized dataset/Resized Folder 2x2/Tumor/Tumor...      3\n",
       "12444  resized dataset/Resized Folder 2x2/Tumor/Tumor...      3\n",
       "12445  resized dataset/Resized Folder 2x2/Tumor/Tumor...      3\n",
       "\n",
       "[12446 rows x 2 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = pd.DataFrame(train_data, columns=['image', 'label'], index=None)\n",
    "train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "1deb6b86",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image</th>\n",
       "      <th>label</th>\n",
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       "      <th>1</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>resized dataset/Resized Folder 2x2/Normal/Norm...</td>\n",
       "      <td>1</td>\n",
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      "text/plain": [
       "                                               image  label\n",
       "0  resized dataset/Resized Folder 2x2/Cyst/Cyst- ...      0\n",
       "1  resized dataset/Resized Folder 2x2/Stone/Stone...      2\n",
       "2  resized dataset/Resized Folder 2x2/Normal/Norm...      1\n",
       "3  resized dataset/Resized Folder 2x2/Cyst/Cyst- ...      0\n",
       "4  resized dataset/Resized Folder 2x2/Stone/Stone...      2"
      ]
     },
     "execution_count": 63,
     "metadata": {},
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    }
   ],
   "source": [
    "train_data = train_data.sample(frac=1.).reset_index(drop=True) \n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "1cce76be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 1, 3])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data['label'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "445572e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12446, 2)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "69bd6789",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label\n",
       "1    5077\n",
       "0    3709\n",
       "3    2283\n",
       "2    1377\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cases_count = train_data['label'].value_counts()\n",
    "cases_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "724828f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/m4/0yq10js97k90dzpb9_y3tfzc0000gn/T/ipykernel_25948/707280772.py:2: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(x = cases_count.index, y = cases_count.values, palette='bright', edgecolor='black')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8,6))\n",
    "sns.barplot(x = cases_count.index, y = cases_count.values, palette='bright', edgecolor='black')\n",
    "plt.title('Number of Cases', fontsize=14)\n",
    "plt.xlabel('Case Type', fontsize = 12)\n",
    "plt.ylabel('Count', fontsize = 12)\n",
    "plt.xticks(range(len(cases_count.index)),['Cyst(0)', 'Normal(1)','Stone(2)','Tumor(3)'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "cacdf9c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "Cyst_Samples = (train_data[train_data['label'] == 0]['image'].iloc[:5]).tolist()\n",
    "Normal_Samples = (train_data[train_data['label'] == 1]['image'].iloc[:5]).tolist()\n",
    "Stone_Samples = (train_data[train_data['label'] == 2]['image'].iloc[:5]).tolist()\n",
    "Tumor_Samples = (train_data[train_data['label'] == 3]['image'].iloc[:5]).tolist()\n",
    "\n",
    "samples = Cyst_Samples + Normal_Samples + Stone_Samples + Tumor_Samples\n",
    "\n",
    "del Cyst_Samples, Normal_Samples, Stone_Samples, Tumor_Samples\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "129898bc",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x3000 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(4, 5,figsize=(30,30)) \n",
    "\n",
    "for i in range(20):\n",
    "    img = imread(samples[i]) \n",
    "    ax[i//5, i%5].imshow(img, cmap='gray') \n",
    "    \n",
    "\n",
    "    if i<5:\n",
    "        ax[i//5, i%5].set_title(\"Cyst_samples\")\n",
    "    elif i<10:\n",
    "        ax[i//5, i%5].set_title(\"Normal_samples\")\n",
    "    elif i<15:\n",
    "        ax[i//5, i%5].set_title(\"Stone_samples\")\n",
    "    elif i<20:\n",
    "        ax[i//5, i%5].set_title(\"Tumor_samples\")\n",
    "    \n",
    " \n",
    "    ax[i//5, i%5].axis('off')\n",
    "    ax[i//5, i%5].set_aspect('auto')\n",
    "\n",
    "plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "019be1e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "Normal_Cases_dir = train_dir / 'Normal'\n",
    "Cyst_Cases_dir = train_dir / 'Cyst'\n",
    "Stone_Cases_dir = train_dir / 'Stone'\n",
    "Tumor_Cases_dir = train_dir / 'Tumor'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "f9fe6028",
   "metadata": {},
   "outputs": [],
   "source": [
    "Normal_Cases = Normal_Cases_dir.glob('*.jpg')\n",
    "Cyst_Cases = Cyst_Cases_dir.glob('*.jpg')\n",
    "Stone_Cases = Stone_Cases_dir.glob('*.jpg')\n",
    "Tumor_Cases = Tumor_Cases_dir.glob('*.jpg')\n",
    "train_data = []\n",
    "train_labels = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "7e068ab5",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "for img in Cyst_Cases:\n",
    "    img = cv2.imread(str(img))\n",
    "    if img.shape[2] == 1: \n",
    "        img = np.dstack([img, img, img])\n",
    "    img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB) \n",
    "    img = np.array(img)\n",
    "    img = img/255  \n",
    "    label = 'Cyst' \n",
    "    train_data.append(img)\n",
    "    train_labels.append(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "c7b1d7a7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for img in Normal_Cases:\n",
    "    img = cv2.imread(str(img))\n",
    "    if img.shape[2] == 1:\n",
    "        img = np.dstack([img, img, img])\n",
    "    img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)\n",
    "    img = np.array(img)\n",
    "    img = img/255\n",
    "    label = 'Normal'\n",
    "    train_data.append(img)\n",
    "    train_labels.append(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "e61237be",
   "metadata": {},
   "outputs": [],
   "source": [
    "for img in Stone_Cases:\n",
    "    img = cv2.imread(str(img))\n",
    "    if img.shape[2] == 1:\n",
    "        img = np.dstack([img,img,img])\n",
    "    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
    "    img = np.array(img)\n",
    "    img = img/255\n",
    "    label = \"Stone\"\n",
    "    train_data.append(img)\n",
    "    train_labels.append(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "100dd1dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "for img in Tumor_Cases:\n",
    "    img = cv2.imread(str(img))\n",
    "    if img.shape[2] == 1:\n",
    "        img = np.dstack([img, img, img])\n",
    "    img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)\n",
    "    img = np.array(img)\n",
    "    img = img/255\n",
    "    label = \"Tumor\"\n",
    "    train_data.append(img)\n",
    "    train_labels.append(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "4d25dfc5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of validation example:  (12446, 2, 2, 3)\n",
      "Total number of Labels :  (12446,)\n"
     ]
    }
   ],
   "source": [
    "train_data1 = np.array(train_data) \n",
    "train_labels1 = np.array(train_labels)\n",
    "print('Total number of validation example: ', train_data1.shape)\n",
    "print('Total number of Labels : ', train_labels1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "2dac3401",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0.22352941, 0.22352941, 0.22352941],\n",
       "        [0.01176471, 0.01176471, 0.01176471]],\n",
       "\n",
       "       [[0.00784314, 0.00784314, 0.00784314],\n",
       "        [0.        , 0.        , 0.        ]]])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data1[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "e6954e54",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Cyst</td>\n",
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       "      <td>Cyst</td>\n",
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       "      <th>4</th>\n",
       "      <td>Cyst</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>12441</th>\n",
       "      <td>Tumor</td>\n",
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       "    <tr>\n",
       "      <th>12442</th>\n",
       "      <td>Tumor</td>\n",
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       "    <tr>\n",
       "      <th>12443</th>\n",
       "      <td>Tumor</td>\n",
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       "    <tr>\n",
       "      <th>12444</th>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12445</th>\n",
       "      <td>Tumor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12446 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       label\n",
       "0       Cyst\n",
       "1       Cyst\n",
       "2       Cyst\n",
       "3       Cyst\n",
       "4       Cyst\n",
       "...      ...\n",
       "12441  Tumor\n",
       "12442  Tumor\n",
       "12443  Tumor\n",
       "12444  Tumor\n",
       "12445  Tumor\n",
       "\n",
       "[12446 rows x 1 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels1 = pd.DataFrame(train_labels1, columns = ['label'],index=None)\n",
    "train_labels1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "05c72a38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Cyst', 'Normal', 'Stone', 'Tumor'], dtype=object)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels1['label'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "7d2b8674",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_labels1['label'] = train_labels1['label'].map({'Cyst':0,'Normal':1,'Stone':2,'Tumor':3})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "0cb5492e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>12441</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12442</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12443</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12444</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12445</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12446 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       label\n",
       "0          0\n",
       "1          0\n",
       "2          0\n",
       "3          0\n",
       "4          0\n",
       "...      ...\n",
       "12441      3\n",
       "12442      3\n",
       "12443      3\n",
       "12444      3\n",
       "12445      3\n",
       "\n",
       "[12446 rows x 1 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "aec9aefe",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12446, 2, 2, 3)\n",
      "(12446, 1)\n"
     ]
    }
   ],
   "source": [
    "print(train_data1.shape)\n",
    "print(train_labels1.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "c7da7867",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "label    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "d70badda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original subset data shape: (1244, 12)\n",
      "Resampled subset data shape: (2092, 2, 2, 3)\n",
      "Resampled subset labels shape: (2092, 1)\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Assuming train_data1 is your original dataset\n",
    "# Assuming train_labels1 is your corresponding labels\n",
    "\n",
    "# Create a subset of the data\n",
    "subset_fraction = 0.1  # 10% of the data\n",
    "subset_data, _, subset_labels, _ = train_test_split(\n",
    "    train_data1, train_labels1, test_size=(1 - subset_fraction), random_state=42\n",
    ")\n",
    "\n",
    "# Reshape the subset data if needed\n",
    "subset_rows = len(subset_data)\n",
    "subset_data = subset_data.reshape(subset_rows, -1)\n",
    "\n",
    "# Apply SMOTE to the subset\n",
    "smote = SMOTE()\n",
    "subset_data_resampled, subset_labels_resampled = smote.fit_resample(subset_data, subset_labels)\n",
    "\n",
    "# Reshape the resampled data back to the original shape if needed\n",
    "subset_data_resampled = subset_data_resampled.reshape(subset_data_resampled.shape[0], 2, 2, 3)\n",
    "\n",
    "# Print the shapes to check\n",
    "print(\"Original subset data shape:\", subset_data.shape)\n",
    "print(\"Resampled subset data shape:\", subset_data_resampled.shape)\n",
    "print(\"Resampled subset labels shape:\", subset_labels_resampled.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "bb0f5ada",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/m4/0yq10js97k90dzpb9_y3tfzc0000gn/T/ipykernel_25948/1410693467.py:4: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(x=cases_count1.index, y=cases_count1.values, palette='bright', edgecolor='black')\n"
     ]
    },
    {
     "data": {
      "image/png": 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AAMCyCLsAAACwLMIuAAAALIuwCwAAAMsi7AIAAMCyCLsAAACwLMIuAAAALIuwCwAAAMsi7AIAAMCyCLsAAACwLMIuAAAALIuwCwAAAMsi7AIAAMCyCLsAAACwLMIuAAAALMupuAuwosTERJ08ebK4y0ARKV++vIKCgopl35xrtxfONRQVzjUUlaI41wi7hSwxMVGhodWVkXG+uEtBEXFzK634+F+L/AdDYmKiqlcL1fk/M4p0vyg+pd3d9Otv8cVyroVWD1XGec6124VbaTfF/1o851r10FCdz+Bcu12UdnPTr/E391wj7BaykydPKiPjvPzqjJOL113FXQ5usqz0/TqxvZ9OnjxZ5D8UTp48qfN/Zmhc+/K6q7xzke4bRW//yQvqt+BksZ1rGeczFPRKuNwqehTpvlH0Mo6cU+KY3cX3uZaRoeFlA1TZ2aVI942id/BClt46nXzTzzXC7k3i4nWXXH1rFHcZuA3cVd5ZNe5wLe4ycBtwq+ih0nd5F3cZuA1UdnZRNRe34i4DFsENagAAALAswi4AAAAsi7ALAAAAyyLsAgAAwLIIuwAAALAswi4AAAAsi7ALAAAAyyLsAgAAwLIIuwAAALAswi4AAAAsi7ALAAAAyyLsAgAAwLJKVNgdOnSoHBwc7JZq1arZ+jMyMtSnTx+VK1dOnp6e6tChg5KTk+22kZiYqKioKJUuXVr+/v4aOHCgLl68WNSHAgAAgBLAqbgLuNy9996rH3/80fbayel/Jfbr109LlizR3Llz5ePjoxdeeEGPP/64NmzYIEnKzs5WVFSUAgMDtXHjRh07dkzPPPOMnJ2dNXLkyCI/FgAAABSvEhd2nZycFBgYmKc9NTVV06dP1+zZs9W8eXNJUkxMjKpXr65NmzapQYMGWr58ufbu3asff/xRAQEBql27tkaMGKHXXntNQ4cOlYuLS1EfDgAAAIpRiZrGIEn79u1ThQoVVKVKFXXp0kWJiYmSpG3btunChQtq0aKFbWy1atUUFBSk2NhYSVJsbKzCw8MVEBBgGxMZGam0tDTt2bPnivvMzMxUWlqa3QIAAIBbX4kKu/Xr19eMGTO0bNkyTZ48WQcPHlSjRo2Unp6upKQkubi4yNfX126dgIAAJSUlSZKSkpLsgm5uf27flbz77rvy8fGxLZUqVSrcAwMAAECxKFHTGFq3bm37d82aNVW/fn0FBwfr66+/lru7+03b76BBg9S/f3/b67S0NAIvAACABZSoK7uX8/X11T333KP9+/crMDBQWVlZSklJsRuTnJxsm+MbGBiY5+kMua/zmwecy9XVVd7e3nYLAAAAbn0lOuyePXtWCQkJuuOOO1S3bl05Oztr5cqVtv74+HglJiYqIiJCkhQREaHdu3fr+PHjtjErVqyQt7e3wsLCirx+AAAAFK8SNY1hwIABatu2rYKDg3X06FENGTJEjo6O6ty5s3x8fNSjRw/1799fZcuWlbe3t/r27auIiAg1aNBAktSqVSuFhYWpa9euGjVqlJKSkjR48GD16dNHrq6uxXx0AAAAKGolKuweOXJEnTt31qlTp+Tn56eHHnpImzZtkp+fnyRp3LhxKlWqlDp06KDMzExFRkbq448/tq3v6OioxYsXq3fv3oqIiJCHh4eio6M1fPjw4jokAAAAFKMSFXbnzJlz1X43NzdNmjRJkyZNuuKY4OBgLV26tLBLAwAAwC2oRM/ZBQAAAG4EYRcAAACWRdgFAACAZRF2AQAAYFmEXQAAAFgWYRcAAACWRdgFAACAZRF2AQAAYFmEXQAAAFgWYRcAAACWRdgFAACAZRF2AQAAYFmEXQAAAFgWYRcAAACWRdgFAACAZRF2AQAAYFmEXQAAAFgWYRcAAACWRdgFAACAZRF2AQAAYFmEXQAAAFgWYRcAAACWRdgFAACAZRF2AQAAYFmEXQAAAFgWYRcAAACWRdgFAACAZRF2AQAAYFmEXQAAAFgWYRcAAACWRdgFAACAZRF2AQAAYFmEXQAAAFgWYRcAAACWRdgFAACAZRF2AQAAYFmEXQAAAFgWYRcAAACWRdgFAACAZRF2AQAAYFmEXQAAAFgWYRcAAACWRdgFAACAZRF2AQAAYFmEXQAAAFgWYRcAAACWRdgFAACAZRF2AQAAYFmEXQAAAFgWYRcAAACWRdgFAACAZRF2AQAAYFmEXQAAAFgWYRcAAACWRdgFAACAZRF2AQAAYFmEXQAAAFgWYRcAAACWRdgFAACAZRF2AQAAYFmEXQAAAFgWYRcAAACWRdgFAACAZRF2AQAAYFmEXQAAAFgWYRcAAACWRdgFAACAZRF2AQAAYFmEXQAAAFgWYRcAAACWRdgFAACAZRF2AQAAYFmEXQAAAFhWiQ277733nhwcHPTyyy/b2jIyMtSnTx+VK1dOnp6e6tChg5KTk+3WS0xMVFRUlEqXLi1/f38NHDhQFy9eLOLqAQAAUBKUyLC7ZcsWTZkyRTVr1rRr79evnxYtWqS5c+dq7dq1Onr0qB5//HFbf3Z2tqKiopSVlaWNGzdq5syZmjFjht56662iPgQAAACUACUu7J49e1ZdunTRtGnTVKZMGVt7amqqpk+frrFjx6p58+aqW7euYmJitHHjRm3atEmStHz5cu3du1eff/65ateurdatW2vEiBGaNGmSsrKyiuuQAAAAUExKXNjt06ePoqKi1KJFC7v2bdu26cKFC3bt1apVU1BQkGJjYyVJsbGxCg8PV0BAgG1MZGSk0tLStGfPnivuMzMzU2lpaXYLAAAAbn1OxV3ApebMmaPt27dry5YtefqSkpLk4uIiX19fu/aAgAAlJSXZxlwadHP7c/uu5N1339WwYcNusHoAAACUNCXmyu7hw4f10ksv6YsvvpCbm1uR7nvQoEFKTU21LYcPHy7S/QMAAODmKDFhd9u2bTp+/Ljq1KkjJycnOTk5ae3atZo4caKcnJwUEBCgrKwspaSk2K2XnJyswMBASVJgYGCepzPkvs4dkx9XV1d5e3vbLQAAALj1lZiw+/DDD2v37t3asWOHbbn//vvVpUsX27+dnZ21cuVK2zrx8fFKTExURESEJCkiIkK7d+/W8ePHbWNWrFghb29vhYWFFfkxAQAAoHiVmDm7Xl5eqlGjhl2bh4eHypUrZ2vv0aOH+vfvr7Jly8rb21t9+/ZVRESEGjRoIElq1aqVwsLC1LVrV40aNUpJSUkaPHiw+vTpI1dX1yI/JgAAABSvEhN2r8W4ceNUqlQpdejQQZmZmYqMjNTHH39s63d0dNTixYvVu3dvRUREyMPDQ9HR0Ro+fHgxVg0AAIDiUqLD7po1a+xeu7m5adKkSZo0adIV1wkODtbSpUtvcmUAAAC4FZSYObsAAABAYSPsAgAAwLIIuwAAALAswi4AAAAsi7ALAAAAyyLsAgAAwLIIuwAAALAswi4AAAAsi7ALAAAAyyLsAgAAwLIIuwAAALAswi4AAAAsi7ALAAAAyyLsAgAAwLIIuwAAALAswi4AAAAsi7ALAAAAyyLsAgAAwLIIuwAAALAswi4AAAAsi7ALAAAAyyLsAgAAwLIIuwAAALAswi4AAAAsi7ALAAAAyyLsAgAAwLIIuwAAALAswi4AAAAsi7ALAAAAyyLsAgAAwLIIuwAAALAswi4AAAAsi7ALAAAAyyLsAgAAwLIIuwAAALAswi4AAAAsi7ALAAAAyyLsAgAAwLIIuwAAALAswi4AAAAsi7ALAAAAyyLsAgAAwLIIuwAAALAswi4AAAAsi7ALAAAAyyLsAgAAwLIIuwAAALAswi4AAAAsi7ALAAAAyyLsAgAAwLIIuwAAALAswi4AAAAsi7ALAAAAyyLsAgAAwLIIuwAAALAswi4AAAAsi7ALAAAAyyLsAgAAwLIIuwAAALAswi4AAAAsi7ALAAAAyyLsAgAAwLIIuwAAALAswi4AAAAsi7ALAAAAyyLsAgAAwLIIuwAAALAswi4AAAAsi7ALAAAAyyLsAgAAwLIIuwAAALAswi4AAAAsi7ALAAAAyypRYXfy5MmqWbOmvL295e3trYiICH3//fe2/oyMDPXp00flypWTp6enOnTooOTkZLttJCYmKioqSqVLl5a/v78GDhyoixcvFvWhAAAAoAQoUWG3YsWKeu+997Rt2zZt3bpVzZs3V7t27bRnzx5JUr9+/bRo0SLNnTtXa9eu1dGjR/X444/b1s/OzlZUVJSysrK0ceNGzZw5UzNmzNBbb71VXIcEAACAYuRU3AVcqm3btnav33nnHU2ePFmbNm1SxYoVNX36dM2ePVvNmzeXJMXExKh69eratGmTGjRooOXLl2vv3r368ccfFRAQoNq1a2vEiBF67bXXNHToULm4uBTHYQEAAKCYlKgru5fKzs7WnDlzdO7cOUVERGjbtm26cOGCWrRoYRtTrVo1BQUFKTY2VpIUGxur8PBwBQQE2MZERkYqLS3NdnU4P5mZmUpLS7NbAAAAcOsrcNht3ry5Vq5cecX+1atX267AXo/du3fL09NTrq6uev755zV//nyFhYUpKSlJLi4u8vX1tRsfEBCgpKQkSVJSUpJd0M3tz+27knfffVc+Pj62pVKlStddNwAAAEqeAofdNWvW5Lk57FLHjx/X2rVrr3u7oaGh2rFjhzZv3qzevXsrOjpae/fuLWiZ12TQoEFKTU21LYcPH76p+wMAAEDRuKE5uw4ODlfs279/v7y8vK57my4uLrrrrrskSXXr1tWWLVs0YcIEPfnkk8rKylJKSord1d3k5GQFBgZKkgIDA/Xzzz/bbS83kOeOyY+rq6tcXV2vu1YAAACUbNcVdmfOnKmZM2faXr/99tuaNm1annEpKSnatWuX2rRpc8MF5uTkKDMzU3Xr1pWzs7NWrlypDh06SJLi4+OVmJioiIgISVJERITeeecdHT9+XP7+/pKkFStWyNvbW2FhYTdcCwAAAG4t1xV2z58/rxMnTthep6enq1Qp+5kQDg4O8vDw0PPPP3/dj/waNGiQWrduraCgIKWnp2v27Nlas2aNfvjhB/n4+KhHjx7q37+/ypYtK29vb/Xt21cRERFq0KCBJKlVq1YKCwtT165dNWrUKCUlJWnw4MHq06cPV24BAABuQ9cVdnv37q3evXtLkipXrqwJEyboH//4R6EVc/z4cT3zzDM6duyYfHx8VLNmTf3www9q2bKlJGncuHEqVaqUOnTooMzMTEVGRurjjz+2re/o6KjFixerd+/eioiIkIeHh6KjozV8+PBCqxEAAAC3jgLP2T148GBh1iFJmj59+lX73dzcNGnSJE2aNOmKY4KDg7V06dLCLg0AAAC3oBv+Uon09HT997//1ZkzZ2SMydPfuHHjG90FAAAAUCAFDrsnT55U37599c033yg7OztPvzFGDg4O+fYBAAAARaHAYfe5557TokWL9OKLL6pRo0YqU6ZMYdYFAAAA3LACh93ly5erX79+GjVqVGHWAwAAABSaAn+DWunSpRUSElKIpQAAAACFq8Bh9+mnn9b8+fMLsxYAAACgUBV4GkPHjh21du1aPfLII3ruuedUqVIlOTo65hlXp06dGyoQAAAAKKgCh92HHnrI9u8VK1bk6edpDAAAAChuBQ67MTExhVkHAAAAUOgKHHajo6MLsw4AAACg0BX4BjUAAACgpCvwld1nn332b8c4ODho+vTpBd0FAAAAcEMKHHZXrVolBwcHu7bs7GwdO3ZM2dnZ8vPzk4eHxw0XCAAAABRUgcPuoUOH8m2/cOGCpkyZovHjx+f7lAYAAACgqBT6nF1nZ2e98MILatWqlV544YXC3jwAAABwzW7aDWq1atXSunXrbtbmAQAAgL9108LuihUrVLp06Zu1eQAAAOBvFXjO7vDhw/NtT0lJ0bp167R9+3a9/vrrBS4MAAAAuFEFDrtDhw7Nt71MmTKqWrWqPvnkE/Xs2bOgmwcAAABuWIHDbk5OTmHWAQAAABQ6vkENAAAAllXgK7u51q5dqyVLlui///2vJCk4OFhRUVFq0qTJDRcHAAAA3IgCh92srCx17txZCxYskDFGvr6+kv66QW3MmDF67LHH9OWXX8rZ2bmwagUAAACuS4GnMQwbNkzz58/XK6+8omPHjun06dM6ffq0kpKSNGDAAH377bdXfGIDAAAAUBQKHHZnz56t6OhojRo1SgEBAbZ2f39/vf/++3rmmWf02WefFUqRAAAAQEEUOOweO3ZM9evXv2J//fr1lZSUVNDNAwAAADeswGG3YsWKWrNmzRX7165dq4oVKxZ08wAAAMANK3DYjY6O1tdff63nn39e8fHxys7OVk5OjuLj49W7d2/NnTtX3bp1K8RSAQAAgOtT4KcxvPHGG0pISNDUqVM1bdo0lSr1V27OycmRMUbR0dF64403Cq1QAAAA4HoVOOw6OjpqxowZ6t+/v5YuXWr3nN02bdqoZs2ahVYkAAAAUBDXFXYzMjL08ssv695771Xfvn0lSTVr1swTbCdOnKhPPvlEEyZM4Dm7AAAAKDbXNWd36tSpmjFjhqKioq46LioqSp9++qn+85//3FBxAAAAwI24rrD79ddfq0OHDqpSpcpVx1WtWlVPPPGEvvzyyxsqDgAAALgR1xV2d+/erYceeuiaxj744IPatWtXgYoCAAAACsN1hd2srCy5uLhc01gXFxdlZmYWqCgAAACgMFxX2K1QoYLi4uKuaWxcXJwqVKhQoKIAAACAwnBdYbdFixaaNWuWjh8/ftVxx48f16xZs9SyZcsbKg4AAAC4EdcVdl977TVlZGSoefPm2rx5c75jNm/erIcfflgZGRkaOHBgoRQJAAAAFMR1PWe3SpUq+vrrr9W5c2c9+OCDqlKlisLDw+Xl5aX09HTFxcUpISFBpUuX1pw5c1S1atWbVTcAAADwt677G9SioqK0a9cuvf/++1q8eLEWLFhg66tQoYJ69uypV1999W8fTwYAAADcbAX6uuCQkBBNnjxZkydPVnp6utLS0uTt7S0vL6/Crg8AAAAosAKF3Ut5eXkRcgEAAFAiXdcNagAAAMCthLALAAAAyyLsAgAAwLIIuwAAALAswi4AAAAsi7ALAAAAyyLsAgAAwLIIuwAAALAswi4AAAAsi7ALAAAAyyLsAgAAwLIIuwAAALAswi4AAAAsi7ALAAAAyyLsAgAAwLIIuwAAALAswi4AAAAsi7ALAAAAyyLsAgAAwLIIuwAAALAswi4AAAAsi7ALAAAAyyLsAgAAwLIIuwAAALAswi4AAAAsi7ALAAAAyyLsAgAAwLIIuwAAALAswi4AAAAsi7ALAAAAyyLsAgAAwLIIuwAAALCsEhV23333XT3wwAPy8vKSv7+/2rdvr/j4eLsxGRkZ6tOnj8qVKydPT0916NBBycnJdmMSExMVFRWl0qVLy9/fXwMHDtTFixeL8lAAAABQApSosLt27Vr16dNHmzZt0ooVK3ThwgW1atVK586ds43p16+fFi1apLlz52rt2rU6evSoHn/8cVt/dna2oqKilJWVpY0bN2rmzJmaMWOG3nrrreI4JAAAABQjp+Iu4FLLli2zez1jxgz5+/tr27Ztaty4sVJTUzV9+nTNnj1bzZs3lyTFxMSoevXq2rRpkxo0aKDly5dr7969+vHHHxUQEKDatWtrxIgReu211zR06FC5uLjk2W9mZqYyMzNtr9PS0m7ugQIAAKBIlKgru5dLTU2VJJUtW1aStG3bNl24cEEtWrSwjalWrZqCgoIUGxsrSYqNjVV4eLgCAgJsYyIjI5WWlqY9e/bku593331XPj4+tqVSpUo365AAAABQhEps2M3JydHLL7+shg0bqkaNGpKkpKQkubi4yNfX125sQECAkpKSbGMuDbq5/bl9+Rk0aJBSU1Nty+HDhwv5aAAAAFAcStQ0hkv16dNHcXFx+umnn276vlxdXeXq6nrT9wMAAICiVSKv7L7wwgtavHixVq9erYoVK9raAwMDlZWVpZSUFLvxycnJCgwMtI25/OkMua9zxwAAAOD2UKLCrjFGL7zwgubPn69Vq1apcuXKdv1169aVs7OzVq5caWuLj49XYmKiIiIiJEkRERHavXu3jh8/bhuzYsUKeXt7KywsrGgOBAAAACVCiZrG0KdPH82ePVvfffedvLy8bHNsfXx85O7uLh8fH/Xo0UP9+/dX2bJl5e3trb59+yoiIkINGjSQJLVq1UphYWHq2rWrRo0apaSkJA0ePFh9+vRhqgIAAMBtpkSF3cmTJ0uSmjZtatceExOjbt26SZLGjRunUqVKqUOHDsrMzFRkZKQ+/vhj21hHR0ctXrxYvXv3VkREhDw8PBQdHa3hw4cX1WEAAACghChRYdcY87dj3NzcNGnSJE2aNOmKY4KDg7V06dLCLA0AAAC3oBI1ZxcAAAAoTIRdAAAAWBZhFwAAAJZF2AUAAIBlEXYBAABgWYRdAAAAWBZhFwAAAJZF2AUAAIBlEXYBAABgWYRdAAAAWBZhFwAAAJZF2AUAAIBlEXYBAABgWYRdAAAAWBZhFwAAAJZF2AUAAIBlEXYBAABgWYRdAAAAWBZhFwAAAJZF2AUAAIBlEXYBAABgWYRdAAAAWBZhFwAAAJZF2AUAAIBlEXYBAABgWYRdAAAAWBZhFwAAAJZF2AUAAIBlEXYBAABgWYRdAAAAWBZhFwAAAJZF2AUAAIBlEXYBAABgWYRdAAAAWBZhFwAAAJZF2AUAAIBlEXYBAABgWYRdAAAAWBZhFwAAAJZF2AUAAIBlEXYBAABgWYRdAAAAWBZhFwAAAJZF2AUAAIBlEXYBAABgWYRdAAAAWBZhFwAAAJZF2AUAAIBlEXYBAABgWYRdAAAAWBZhFwAAAJZF2AUAAIBlEXYBAABgWYRdAAAAWBZhFwAAAJZF2AUAAIBlEXYBAABgWYRdAAAAWBZhFwAAAJZF2AUAAIBlEXYBAABgWYRdAAAAWBZhFwAAAJZF2AUAAIBlEXYBAABgWYRdAAAAWBZhFwAAAJZF2AUAAIBlEXYBAABgWYRdAAAAWBZhFwAAAJZF2AUAAIBlEXYBAABgWSUq7K5bt05t27ZVhQoV5ODgoAULFtj1G2P01ltv6Y477pC7u7tatGihffv22Y05ffq0unTpIm9vb/n6+qpHjx46e/ZsER4FAAAASooSFXbPnTunWrVqadKkSfn2jxo1ShMnTtQnn3yizZs3y8PDQ5GRkcrIyLCN6dKli/bs2aMVK1Zo8eLFWrdunZ577rmiOgQAAACUIE7FXcClWrdurdatW+fbZ4zR+PHjNXjwYLVr106SNGvWLAUEBGjBggV66qmn9Ouvv2rZsmXasmWL7r//fknShx9+qDZt2mj06NGqUKFCkR0LAAAAil+JurJ7NQcPHlRSUpJatGhha/Px8VH9+vUVGxsrSYqNjZWvr68t6EpSixYtVKpUKW3evPmK287MzFRaWprdAgAAgFvfLRN2k5KSJEkBAQF27QEBAba+pKQk+fv72/U7OTmpbNmytjH5effdd+Xj42NbKlWqVMjVAwAAoDjcMmH3Zho0aJBSU1Nty+HDh4u7JAAAABSCWybsBgYGSpKSk5Pt2pOTk219gYGBOn78uF3/xYsXdfr0aduY/Li6usrb29tuAQAAwK3vlgm7lStXVmBgoFauXGlrS0tL0+bNmxURESFJioiIUEpKirZt22Ybs2rVKuXk5Kh+/fpFXjMAAACKV4l6GsPZs2e1f/9+2+uDBw9qx44dKlu2rIKCgvTyyy/r7bff1t13363KlSvrzTffVIUKFdS+fXtJUvXq1fXII4+oZ8+e+uSTT3ThwgW98MILeuqpp3gSAwAAwG2oRIXdrVu3qlmzZrbX/fv3lyRFR0drxowZevXVV3Xu3Dk999xzSklJ0UMPPaRly5bJzc3Nts4XX3yhF154QQ8//LBKlSqlDh06aOLEiUV+LAAAACh+JSrsNm3aVMaYK/Y7ODho+PDhGj58+BXHlC1bVrNnz74Z5QEAAOAWc8vM2QUAAACuF2EXAAAAlkXYBQAAgGURdgEAAGBZhF0AAABYFmEXAAAAlkXYBQAAgGURdgEAAGBZhF0AAABYFmEXAAAAlkXYBQAAgGURdgEAAGBZhF0AAABYFmEXAAAAlkXYBQAAgGURdgEAAGBZhF0AAABYFmEXAAAAlkXYBQAAgGURdgEAAGBZhF0AAABYFmEXAAAAlkXYBQAAgGURdgEAAGBZhF0AAABYFmEXAAAAlkXYBQAAgGURdgEAAGBZhF0AAABYFmEXAAAAlkXYBQAAgGURdgEAAGBZhF0AAABYFmEXAAAAlkXYBQAAgGURdgEAAGBZhF0AAABYFmEXAAAAlkXYBQAAgGURdgEAAGBZhF0AAABYFmEXAAAAlkXYBQAAgGURdgEAAGBZhF0AAABYFmEXAAAAlkXYBQAAgGURdgEAAGBZhF0AAABYFmEXAAAAlkXYBQAAgGURdgEAAGBZhF0AAABYFmEXAAAAlkXYBQAAgGURdgEAAGBZhF0AAABYFmEXAAAAlkXYBQAAgGURdgEAAGBZhF0AAABYFmEXAAAAlkXYBQAAgGURdgEAAGBZhF0AAABYFmEXAAAAlkXYBQAAgGURdgEAAGBZhF0AAABYFmEXAAAAlkXYBQAAgGURdgEAAGBZhF0AAABYlmXD7qRJkxQSEiI3NzfVr19fP//8c3GXBAAAgCJmybD71VdfqX///hoyZIi2b9+uWrVqKTIyUsePHy/u0gAAAFCELBl2x44dq549e6p79+4KCwvTJ598otKlS+vTTz8t7tIAAABQhJyKu4DClpWVpW3btmnQoEG2tlKlSqlFixaKjY3Nd53MzExlZmbaXqempkqS0tLSrnv/Z8+elSRlpMQp5+L5614ft5asswck/fXfvSDny43IPdfijmXqfFZOke4bRe/AqQuSivdcO5+QpuyMi0W6bxS9zD/++tlVnOfar1kZ+jOHzzWrO3QxS1LBz7XcdYwxVx9oLOaPP/4wkszGjRvt2gcOHGjq1auX7zpDhgwxklhYWFhYWFhYWG6x5fDhw1fNhpa7slsQgwYNUv/+/W2vc3JydPr0aZUrV04ODg7FWNmtIy0tTZUqVdLhw4fl7e1d3OXAwjjXUFQ411BUONcKxhij9PR0VahQ4arjLBd2y5cvL0dHRyUnJ9u1JycnKzAwMN91XF1d5erqatfm6+t7s0q0NG9vb/5HRZHgXENR4VxDUeFcu34+Pj5/O8ZyN6i5uLiobt26Wrlypa0tJydHK1euVERERDFWBgAAgKJmuSu7ktS/f39FR0fr/vvvV7169TR+/HidO3dO3bt3L+7SAAAAUIQsGXaffPJJnThxQm+99ZaSkpJUu3ZtLVu2TAEBAcVdmmW5urpqyJAheaaDAIWNcw1FhXMNRYVz7eZyMObvntcAAAAA3JosN2cXAAAAyEXYBQAAgGURdgEAAGBZhF0UmenTp6tVq1bXtU6DBg30zTff3KSKAACA1RF2b0NJSUnq27evqlSpIldXV1WqVElt27a1ezZxQXXr1k3t27fP056RkaE333xTQ4YMsWufO3euqlWrJjc3N4WHh2vp0qV2/YMHD9brr7+uHL4jHQW0Zs0aOTg4KCUlxa69a9euGjly5DVvJysrSyEhIdq6dWshVwgrOXXqlPz9/XXo0KFrXueTTz5R27Ztb15RsLT4+HgFBgYqPT39mtd5/fXX1bdv35tYVclC2L3NHDp0SHXr1tWqVav0wQcfaPfu3Vq2bJmaNWumPn363LT9zps3T97e3mrYsKGtbePGjercubN69OihX375Re3bt1f79u0VFxdnG9O6dWulp6fr+++/v2m14dp169ZNDg4Oeu+99+zaFyxYcEt9tfbOnTu1dOlSvfjii7a2b7/9Vq1atbJ9TfiOHTvs1nFxcdGAAQP02muvFXG1uNSJEyfUu3dvBQUFydXVVYGBgYqMjNSGDRskSQ4ODlqwYEGx1ffOO++oXbt2CgkJkfTXuda5c2dVqlRJ7u7uql69uiZMmGC3zrPPPqvt27dr/fr1xVAxLuXg4HDVZejQocVdYh6DBg1S37595eXlJemv8NusWTMFBATIzc1NVapU0eDBg3XhwgXbOgMGDNDMmTN14MCB4iq7aBncVlq3bm3uvPNOc/bs2Tx9Z86cMd27dzdRUVF27VlZWcbPz8/85z//McYYM3fuXFOjRg3j5uZmypYtax5++GFz9uxZM2TIECPJblm9erUxxpioqCgzYMAAu+126tQpz77q169vevXqZdfWvXt38/TTT9/ooaMQREdHGzc3N+Pr62tOnz5ta58/f765kY+TzMzMwigvX6tXrzaSzJkzZ2xtPXr0yHOezZo1ywwbNsxMmzbNSDK//PJLnm2dPn3auLi4mLi4uJtWL66uUaNGpn79+mbVqlXm0KFDZvPmzWbkyJHmu+++M8YYI8nMnz+/WGo7d+6c8fb2NrGxsba26dOnmxdffNGsWbPGJCQkmM8++8y4u7ubDz/80G7dAQMGmI4dOxZ1ybjMsWPHbMv48eONt7e3XVt6enpxl2iM+d9n5n//+1/j7Oxsjhw5YutLSEgwn376qdmxY4c5dOiQ+e6774y/v78ZNGiQ3TY6duyY5+eyVRF2byOnTp0yDg4OZuTIkVccs2HDBuPo6GiOHj1qa/v222+Nh4eHSU9PN0ePHjVOTk5m7Nix5uDBg2bXrl1m0qRJJj093aSnp5tOnTqZRx55xPbBkPs/pI+Pj5kzZ47dvipVqmTGjRtn1/bWW2+ZmjVr2rVNnjzZBAcH39jBo1BER0ebRx991FSrVs0MHDjQ1n552J03b54JCwszLi4uJjg42IwePdpuO8HBwWb48OGma9euxsvLy0RHR5uYmBjj4+NjFi1aZO655x7j7u5uOnToYM6dO2dmzJhhgoODja+vr+nbt6+5ePGibVuzZs0ydevWNZ6eniYgIMB07tzZJCcn2/ovD7sXL140Pj4+ZvHixfke48GDB68Ydo0xplmzZmbw4MHX+9ahEJw5c8ZIMmvWrMm3Pzg42O6X7Us/Nz7++GNTpUoV4+zsbO655x4za9Ysu3UlmWnTppn27dsbd3d3c9ddd9kCdK7du3ebRx55xHh4eBh/f3/z9NNPmxMnTtj6586da/z8/P72OP7973+bZs2a2bWtXbvWuLi4mPPnz//t+igauZ9JuYYMGWJq1aplN2bcuHF251l0dLRp166deeedd4y/v7/x8fExw4YNMxcuXDADBgwwZcqUMXfeeaf59NNP7baza9cu06xZM9tFpJ49e9oF69ztvv322+aOO+4wISEhxhhjPvjgA3P//ff/7bH069fPPPTQQ3ZtM2fONBUrVrzGd+PWxjSG28j+/ftljFG1atWuOObBBx9UaGioPvvsM1tbTEyMnnjiCXl6eurYsWO6ePGiHn/8cYWEhCg8PFz//ve/5enpKU9PT7m7u9v+tBgYGCgXFxelpKQoNTVVFSpUsNtXUlJSnm+1CwgIUFJSkl1bhQoVdPjwYebtlhCOjo4aOXKkPvzwQx05ciRP/7Zt29SpUyc99dRT2r17t4YOHao333xTM2bMsBs3evRo1apVS7/88ovefPNNSdL58+c1ceJEzZkzR8uWLdOaNWv02GOPaenSpVq6dKk+++wzTZkyRfPmzbNt58KFCxoxYoR27typBQsW6NChQ+rWrdsV69+1a5dSU1N1//33F+j469Wrx5+bi0nu58yCBQuUmZmZp3/Lli2S/vrMOnbsmO31/Pnz9dJLL+mVV15RXFycevXqpe7du2v16tV26w8bNkydOnXSrl271KZNG3Xp0kWnT5+WJKWkpKh58+a67777tHXrVi1btkzJycnq1KmTbf3169erbt26f3scqampKlu2rF3b/fffr4sXL2rz5s3X96agxFm1apWOHj2qdevWaezYsRoyZIgeffRRlSlTRps3b9bzzz+vXr162T4/z507p8jISJUpU0ZbtmzR3Llz9eOPP+qFF16w2+7KlSsVHx+vFStWaPHixZL+Ouf+7rNs//79WrZsmZo0aWLXXq9ePR05cuS65pffsoo7baPobNq0yUgy33777VXHjR071lSrVs0YY0xSUpJxcnIy69atM8b8dVXs4YcfNl5eXqZjx45m6tSpdn/Ozv3t81JHjx41kszPP/9s1+7s7Gxmz55t1zZp0iTj7+9v17Z8+XIjiSseJcCl/30bNGhgnn32WWOM/ZXdf/7zn6Zly5Z26w0cONCEhYXZXgcHB5v27dvbjYmJiTGSzP79+21tvXr1MqVLl7a7whEZGZlnCsKltmzZYiTZ1rn8yu78+fONo6OjycnJyXf9v7uyO2HCBNtVFRS9efPmmTJlyhg3Nzfz4IMPmkGDBpmdO3fa+pXPNIYHH3zQ9OzZ067tiSeeMG3atLFb79Ir9mfPnjWSzPfff2+MMWbEiBGmVatWdts4fPiwkWTi4+ONMca0a9fO9v/ElWzYsME4OTmZH374IU9fmTJlzIwZM666PopOQa/sBgcHm+zsbFtbaGioadSoke31xYsXjYeHh/nyyy+NMcZMnTrVlClTxm564ZIlS0ypUqVMUlKSbbsBAQF5pnzVqlXLDB8+PN/6IyIijKurq5FknnvuObuajDEmNTX1qn8psRKu7N5G7r77bjk4OOi333676rhnnnlGBw4cUGxsrD7//HNVrlxZjRo1kvTXVb0VK1bo+++/V1hYmD788EOFhobq4MGDV9xe7g0/Z86csWsPDAxUcnKyXVtycrICAwPt2k6fPi0PDw+5u7tfz+HiJnv//fc1c+ZM/frrr3btv/76q92NiJLUsGFD7du3T9nZ2ba2/K5GlC5dWlWrVrW9DggIUEhIiDw9Pe3ajh8/bnu9bds2tW3bVkFBQfLy8rJdvUhMTMy37j///FOurq4FvqHO3d1d58+fL9C6uHEdOnTQ0aNHtXDhQj3yyCNas2aN6tSpk+cvB5e60jl5+blbs2ZN2789PDzk7e1tO9d27typ1atX264ue3p62v5KlpCQIOmvc8vNze2KdcTFxaldu3YaMmRIvo9h5NyyhnvvvVelSv0vXgUEBCg8PNz22tHRUeXKlbOdW7/++qtq1aolDw8P25iGDRsqJydH8fHxtrbw8HC5uLjY7etq59xXX32l7du3a/bs2VqyZIlGjx5t15/7M/V2OOcIu7eRsmXLKjIyUpMmTdK5c+fy9Oc+mqlcuXJq3769YmJiNGPGDHXv3t1unIODgxo2bKhhw4bpl19+kYuLi+bPny/przvWLw00uW1hYWHau3evXXtERESex52tWLFCERERdm1xcXG67777CnTMuHkaN26syMhIDRo0qEDrX/rBnsvZ2dnutYODQ75tuVNacv/85+3trS+++EJbtmyxnYtZWVn57rd8+fI6f/78Ffv/zunTp+Xn51egdVE43Nzc1LJlS7355pvauHGjunXrluexhgVxtXPt7Nmzatu2rXbs2GG37Nu3T40bN5b017l1+S/1ufbu3auHH35Yzz33nAYPHpzvGM6tkq1UqVIyxti1XfqEg1zX+zl2rfL7zLzaOVepUiWFhYWpc+fOeu+99zR06FC7n8+5U3Ruh3OOsHubmTRpkrKzs1WvXj1988032rdvn3799VdNnDjRLmT+61//sl21i46OtrVv3rxZI0eO1NatW5WYmKhvv/1WJ06cUPXq1SVJISEh2rVrl+Lj43Xy5EnbB0FkZKR++uknu1peeuklLVu2TGPGjNFvv/2moUOHauvWrXnmKa1fv/66v4wCReO9997TokWLFBsba2urXr267TFQuTZs2KB77rlHjo6Ohbr/3377TadOndJ7772nRo0aqVq1anZXffNTu3ZtScrzy9e14pevkicsLMz2C7yzs3OeX7ivdE6GhYVd8z7q1KmjPXv2KCQkRHfddZfdkhtC7rvvvnzPqz179qhZs2aKjo7WO++8k+/2ExISlJGRwblVgvn5+SkpKcku8F7+iMKCqF69unbu3Gl3EWrDhg0qVaqUQkNDr7rulc65y+Xk5OjChQt2ATsuLk7Ozs669957C178LYKwe5upUqWKtm/frmbNmumVV15RjRo11LJlS61cuVKTJ0+2jWvRooXuuOMORUZG2t1Y5u3trXXr1qlNmza65557NHjwYI0ZM0atW7eWJPXs2VOhoaG6//775efnZ/sB06NHDy1dulSpqam2bT344IOaPXu2pk6dqlq1amnevHlasGCBatSoYRvzxx9/aOPGjXmuLqNkCA8PV5cuXTRx4kRb2yuvvKKVK1dqxIgR+v333zVz5kx99NFHGjBgQKHvPygoSC4uLvrwww914MABLVy4UCNGjLjqOn5+fqpTp06eX75Onz6tHTt22H5wxMfHa8eOHXlumOSXr+Jz6tQpNW/eXJ9//rl27dqlgwcPau7cuRo1apTatWsn6a9fuFeuXKmkpCTbFa+BAwdqxowZmjx5svbt26exY8fq22+/va5zsk+fPjp9+rQ6d+6sLVu2KCEhQT/88IO6d+9uC9eRkZHas2eP3ZW2uLg4NWvWTK1atVL//v2VlJSkpKQknThxwm7769evV5UqVeym8aBkadq0qU6cOKFRo0YpISFBkyZNKpRnwHfp0kVubm6Kjo5WXFycVq9erb59+6pr1655buK+XGRkpGJjY+1+wfviiy/09ddf69dff9WBAwf09ddfa9CgQXryySftrjCvX79ejRo1uj2mCBb3pGGUTOnp6cbb29t88803hbbNjh07XvWxZ/l59dVX89xYguKT3w2IBw8eNC4uLvk+eszZ2dkEBQWZDz74wG6d4ODgPI+du/xmEGPyvyHk8hpmz55tQkJCjKurq4mIiDALFy60u8Esv+fsfvzxx6ZBgwZ59q/LnhMtyQwZMsQ2ZuPGjcbX15ebJYtJRkaGef31102dOnWMj4+PKV26tAkNDTWDBw+2/TdZuHChueuuu4yTk9N1P3rs8hvbfHx8TExMjO3177//bh577DHj6+tr3N3dTbVq1czLL79sd7NjvXr1zCeffGJ7nd/zx3XZY9GMMaZVq1bm3XffvbE3CIUqv8+kyZMnm0qVKhkPDw/zzDPPmHfeeSffR49dqkmTJuall16ya7v8M/BaHz12uQsXLpgKFSqYZcuW2drmzJlj6tSpYzw9PY2Hh4cJCwszI0eONH/++afduqGhobab5KzOwZjLJqDgtpaTk6OTJ09qzJgxmjNnjhISEuTk5FQo2z506JAWLVp0XV9ROGbMGD399NN/+9stcD3+/PNPhYaG6quvvsozR/xqnnzySdWqVUtvvPHGTawOt7IlS5Zo4MCBiouLs7tJ6Wr27Nmj5s2b6/fff5ePj89NrhBWM2nSJC1cuFA//PDDNa/z/fff65VXXtGuXbsK7Wd8SWb9I8R1SUxMVOXKlVWxYkXNmDGjUP8nCAkJue7v4n7llVcKbf9ALnd3d82aNUsnT5685nWysrIUHh6ufv363cTKcKuLiorSvn379Mcff6hSpUrXtM6xY8c0a9Ysgi4KpFevXkpJSVF6errtK4P/zrlz5xQTE3NbBF1J4souAAAALIsb1AAAAGBZhF0AAABYFmEXAAAAlkXYBQAAgGURdgEAAGBZhF0AAABYFmEXAApRQkKCevXqpSpVqsjNzU3e3t5q2LChJkyYoD///LO4y7M5dOiQHBwcrmk5dOhQcZcLAAV2ezxNGACKwJIlS/TEE0/I1dVVzzzzjGrUqKGsrCz99NNPGjhwoPbs2aOpU6cWd5mSJD8/P3322Wd2bWPGjNGRI0c0bty4PGMB4FbFl0oAQCE4ePCgatasqYoVK2rVqlW644477Pr379+vJUuW6KWXXiqmCv/eo48+qri4OK7kArAUpjEAQCEYNWqUzp49q+nTp+cJupJ011132QXdmJgYNW/eXP7+/nJ1dVVYWJgmT56cZ72tW7cqMjJS5cuXl7u7uypXrqxnn33WbkxOTo7Gjx+ve++9V25ubgoICFCvXr105syZGzqmJk2aqFatWvn2hYaGKjIyUtL/pkSMHj1a48aNU3BwsNzd3dWkSRPFxcXlWfe3335Tx44dVbZsWbm5uen+++/XwoULb6hWALgSruwCQCGoWLGiXF1dlZCQcE3j69Wrp3vvvVe1atWSk5OTFi1apOXLl+ujjz5Snz59JEnHjx9XtWrV5Ofnp549e8rX11eHDh3St99+q71799q21bNnT82YMUPdu3dX3bp1dfDgQX300UcKCwvThg0b5OzsfE01XX5l9z//+Y969uyp3bt3q0aNGrZxW7ZsUb169TRr1ix17dpVhw4dUuXKlRUeHq709HT17t1bGRkZmjBhghwdHbV7924FBARIkvbs2aOGDRvqzjvvVHR0tDw8PPT1119r/fr1+uabb/TYY49dU60AcM0MAOCGpKamGkmmXbt217zO+fPn87RFRkaaKlWq2F7Pnz/fSDJbtmy54nbWr19vJJkvvvjCrn3ZsmX5tl9NVFSUCQ4Otr1OSUkxbm5u5rXXXrMb9+KLLxoPDw9z9uxZY4wxBw8eNJKMu7u7OXLkiG3c5s2bjSTTr18/W9vDDz9swsPDTUZGhq0tJyfHPPjgg+buu+++5loB4FoxjQEAblBaWpokycvL65rXcXd3t/07NTVVJ0+eVJMmTXTgwAGlpqZKknx9fSVJixcv1oULF/Ldzty5c+Xj46OWLVvq5MmTtqVu3bry9PTU6tWrC3hUko+Pj9q1a6cvv/xS5v//ETA7O1tfffWV2rdvLw8PD7vx7du315133ml7Xa9ePdWvX19Lly6VJJ0+fVqrVq1Sp06dlJ6ebqv11KlTioyM1L59+/THH38UuF4AyA9hFwBukLe3tyQpPT39mtfZsGGDWrRoIQ8PD/n6+srPz09vvPGGJNnCbpMmTdShQwcNGzZM5cuXV7t27RQTE6PMzEzbdvbt26fU1FT5+/vLz8/Pbjl79qyOHz9+Q8f2zDPPKDExUevXr5ck/fjjj0pOTlbXrl3zjL377rvztN1zzz22aRH79++XMUZvvvlmnlqHDBkiSTdcLwBcjkePAcAN8vb2VoUKFfK9GSs/CQkJevjhh1WtWjWNHTtWlSpVkouLi5YuXapx48YpJydHkuTg4KB58+Zp06ZNWrRokX744Qc9++yzGjNmjDZt2iRPT0/l5OTI399fX3zxRb77utHHhkVGRiogIECff/65GjdurM8//1yBgYFq0aLFdW8r97gGDBhgu7ntcnfdddcN1QsAlyPsAkAhePTRRzV16lTFxsYqIiLiqmMXLVqkzMxMLVy4UEFBQbb2K005aNCggRo0aKB33nlHs2fPVpcuXTRnzhz961//UtWqVfXjjz+qYcOGdlMjCoujo6P++c9/asaMGXr//fe1YMEC9ezZU46OjnnG7tu3L0/b77//rpCQEElSlSpVJEnOzs4FCssAUBBMYwCAQvDqq6/Kw8ND//rXv5ScnJynPyEhQRMmTJAkW1A0lzwMJzU1VTExMXbrnDlzxm6MJNWuXVuSbFMZOnXqpOzsbI0YMSLPPi9evKiUlJQCH1Ourl276syZM+rVq5fOnj2rp59+Ot9xCxYssJtz+/PPP2vz5s1q3bq1JMnf319NmzbVlClTdOzYsTzrnzhx4oZrBYDL8egxACgkCxcu1JNPPil3d3e7b1DbuHGj5s6dq27dumnKlCmKj49XzZo1FRoaaguQ06ZNk6enp3bu3KmDBw8qJCRE48eP18cff6zHHntMVatWVXp6uqZNm6Zjx45px44dqly5siTp+eef15QpU9S6dWu1atVKzs7O2rdvn+bOnasJEyaoY8eO11T/1b5UIjw8XHFxcapevbrdY88k5fvosczMTI0fP14ODg7avXu37dnDe/fu1UMPPaRSpUqpZ8+eqlKlipKTkxUbG6sjR45o586dN/YfAQAuV5yPggAAq/n9999Nz549TUhIiHFxcTFeXl6mYcOG5sMPP7R73NbChQtNzZo1jZubmwkJCTHvv/+++fTTT40kc/DgQWOMMdu3bzedO3c2QUFBxtXV1fj7+5tHH33UbN26Nc9+p06daurWrWvc3d2Nl5eXCQ8PN6+++qo5evToNdd++aPHLjVq1CgjyYwcOTJPX+6jxz744AMzZswYU6lSJePq6moaNWpkdu7cmWd8QkKCeeaZZ0xgYKBxdnY2d955p3n00UfNvHnzrrlWALhWXNkFAPytCRMmqF+/fjp06JDdPGPpf1d2P/jgAw0YMKCYKgSA/DFnFwBwVcYYTZ8+XU2aNMkTdAGgpONpDACAfJ07d04LFy7U6tWrtXv3bn333XfFXRIAXDfCLgAgXydOnNA///lP+fr66o033tA//vGP4i4JAK4bc3YBAABgWczZBQAAgGURdgEAAGBZhF0AAABYFmEXAAAAlkXYBQAAgGURdgEAAGBZhF0AAABYFmEXAAAAlvX/AH4FRPnzgga+AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cases_count1 = subset_labels_resampled['label'].value_counts() \n",
    "\n",
    "plt.figure(figsize=(8,6)) \n",
    "sns.barplot(x=cases_count1.index, y=cases_count1.values, palette='bright', edgecolor='black')\n",
    "plt.title('Number of cases', fontsize = 14)\n",
    "plt.xlabel('Case Type',fontsize = 12)\n",
    "plt.ylabel('Count', fontsize = 12)\n",
    "plt.xticks(range(len(cases_count1.index)), ['Cyst(0)', 'Normal(1)', 'Stone(2)', 'Tumor(3)'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "50ca916b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2092, 2, 2, 3)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset_data_resampled.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "077fb245",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2092, 2, 2, 3)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data2 = subset_data_resampled.reshape(-1,2,2,3) # Converting 2D array to 4D Array\n",
    "train_data2.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "676fa9f7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "         [0.15294118, 0.15294118, 0.15294118]],\n",
       " \n",
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       " array([[[0.27058824, 0.27058824, 0.27058824],\n",
       "         [0.00392157, 0.00392157, 0.00392157]],\n",
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       "         [0.25490196, 0.25490196, 0.25490196]]]),\n",
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       "         [0.01960784, 0.01960784, 0.01960784]]]),\n",
       " array([[[0.31764706, 0.31764706, 0.31764706],\n",
       "         [0.29803922, 0.29803922, 0.29803922]],\n",
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       "         [0.30196078, 0.30196078, 0.30196078]]]),\n",
       " array([[[0.01176471, 0.01176471, 0.01176471],\n",
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       " array([[[0.38431373, 0.38431373, 0.38431373],\n",
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       "         [0.16862745, 0.16862745, 0.16862745]]]),\n",
       " array([[[0.23529412, 0.23529412, 0.23529412],\n",
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       " array([[[0.42745098, 0.42745098, 0.42745098],\n",
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       "         [0.36078431, 0.36078431, 0.36078431]]]),\n",
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       "         [0.        , 0.        , 0.        ]],\n",
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       " array([[[0.38431373, 0.38431373, 0.38431373],\n",
       "         [0.38039216, 0.38039216, 0.38039216]],\n",
       " \n",
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       "         [0.01960784, 0.01960784, 0.01960784]]]),\n",
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       " \n",
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       " \n",
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       "         [0.01568627, 0.01568627, 0.01568627]]]),\n",
       " array([[[0.03921569, 0.03921569, 0.03921569],\n",
       "         [0.00392157, 0.00392157, 0.00392157]],\n",
       " \n",
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       "         [0.01176471, 0.01176471, 0.01176471]]]),\n",
       " array([[[0.00784314, 0.00784314, 0.00784314],\n",
       "         [0.16862745, 0.16862745, 0.16862745]],\n",
       " \n",
       "        [[0.22352941, 0.22352941, 0.22352941],\n",
       "         [0.30196078, 0.30196078, 0.30196078]]]),\n",
       " array([[[0.29411765, 0.29411765, 0.29411765],\n",
       "         [0.        , 0.        , 0.        ]],\n",
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       "        [[0.27843137, 0.27843137, 0.27843137],\n",
       "         [0.2745098 , 0.2745098 , 0.2745098 ]]]),\n",
       " array([[[0., 0., 0.],\n",
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       " array([[[0.29411765, 0.29411765, 0.29411765],\n",
       "         [0.00784314, 0.00784314, 0.00784314]],\n",
       " \n",
       "        [[0.16470588, 0.16470588, 0.16470588],\n",
       "         [0.27058824, 0.27058824, 0.27058824]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
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       " \n",
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       " array([[[0.25098039, 0.25098039, 0.25098039],\n",
       "         [0.18431373, 0.18431373, 0.18431373]],\n",
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       "         [0.        , 0.        , 0.        ]]]),\n",
       " array([[[0.38823529, 0.38823529, 0.38823529],\n",
       "         [0.01176471, 0.01176471, 0.01176471]],\n",
       " \n",
       "        [[0.        , 0.        , 0.        ],\n",
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       " array([[[0.        , 0.        , 0.        ],\n",
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       " \n",
       "        [[0.99215686, 0.99215686, 0.99215686],\n",
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       " array([[[0.        , 0.        , 0.        ],\n",
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       "         [0.3254902 , 0.3254902 , 0.3254902 ]]]),\n",
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       "         [0.14117647, 0.14117647, 0.14117647]]]),\n",
       " array([[[0.03921569, 0.03921569, 0.03921569],\n",
       "         [0.3254902 , 0.3254902 , 0.3254902 ]],\n",
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       "         [0.01176471, 0.01176471, 0.01176471]]]),\n",
       " array([[[0.41568627, 0.41568627, 0.41568627],\n",
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       "         [0.01176471, 0.01176471, 0.01176471]]]),\n",
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       " array([[[0.37647059, 0.37647059, 0.37647059],\n",
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       " array([[[0.36470588, 0.36470588, 0.36470588],\n",
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       " array([[[0.        , 0.        , 0.        ],\n",
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       " \n",
       "        [[0.01568627, 0.01568627, 0.01568627],\n",
       "         [0.1254902 , 0.1254902 , 0.1254902 ]]]),\n",
       " array([[[0.34117647, 0.34117647, 0.34117647],\n",
       "         [0.41568627, 0.41568627, 0.41568627]],\n",
       " \n",
       "        [[0.96862745, 0.96862745, 0.96862745],\n",
       "         [0.1254902 , 0.1254902 , 0.1254902 ]]]),\n",
       " array([[[0.01568627, 0.01568627, 0.01568627],\n",
       "         [0.        , 0.        , 0.        ]],\n",
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       " array([[[0.30980392, 0.30980392, 0.30980392],\n",
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       "        [[0.25098039, 0.25098039, 0.25098039],\n",
       "         [0.28627451, 0.28627451, 0.28627451]]]),\n",
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       " array([[[0.42745098, 0.42745098, 0.42745098],\n",
       "         [0.48627451, 0.48627451, 0.48627451]],\n",
       " \n",
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       " array([[[0.02352941, 0.02352941, 0.02352941],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.36470588, 0.36470588, 0.36470588],\n",
       "         [0.25098039, 0.25098039, 0.25098039]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
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       " \n",
       "        [[0.11372549, 0.11372549, 0.11372549],\n",
       "         [0.33333333, 0.33333333, 0.33333333]]]),\n",
       " array([[[0.24705882, 0.24705882, 0.24705882],\n",
       "         [0.35294118, 0.35294118, 0.35294118]],\n",
       " \n",
       "        [[0.34509804, 0.34509804, 0.34509804],\n",
       "         [0.37254902, 0.37254902, 0.37254902]]]),\n",
       " array([[[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.38039216, 0.38039216, 0.38039216],\n",
       "         [0.01960784, 0.01960784, 0.01960784]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.14509804, 0.14509804, 0.14509804],\n",
       "         [0.39607843, 0.39607843, 0.39607843]]]),\n",
       " array([[[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.18823529, 0.18823529, 0.18823529],\n",
       "         [0.02745098, 0.02745098, 0.02745098]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
       "         [0.1372549 , 0.1372549 , 0.1372549 ]],\n",
       " \n",
       "        [[0.01176471, 0.01176471, 0.01176471],\n",
       "         [0.23137255, 0.23137255, 0.23137255]]]),\n",
       " array([[[0.49803922, 0.49803922, 0.49803922],\n",
       "         [0.45098039, 0.45098039, 0.45098039]],\n",
       " \n",
       "        [[0.45490196, 0.45490196, 0.45490196],\n",
       "         [0.48627451, 0.48627451, 0.48627451]]]),\n",
       " array([[[0., 0., 0.],\n",
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       " array([[[0.00784314, 0.00784314, 0.00784314],\n",
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       "        [[0.03529412, 0.03529412, 0.03529412],\n",
       "         [0.00392157, 0.00392157, 0.00392157]]]),\n",
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       " \n",
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       "         [0.01960784, 0.01960784, 0.01960784]]]),\n",
       " array([[[0.00784314, 0.00784314, 0.00784314],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.14901961, 0.14901961, 0.14901961],\n",
       "         [0.1254902 , 0.1254902 , 0.1254902 ]]]),\n",
       " array([[[0.23137255, 0.23137255, 0.23137255],\n",
       "         [0.98431373, 0.98431373, 0.98431373]],\n",
       " \n",
       "        [[0.03529412, 0.03529412, 0.03529412],\n",
       "         [0.36078431, 0.36078431, 0.36078431]]]),\n",
       " array([[[0., 0., 0.],\n",
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       " array([[[0.10588235, 0.10588235, 0.10588235],\n",
       "         [0.01176471, 0.01176471, 0.01176471]],\n",
       " \n",
       "        [[0.01176471, 0.01176471, 0.01176471],\n",
       "         [0.        , 0.        , 0.        ]]]),\n",
       " array([[[0.58039216, 0.58039216, 0.58039216],\n",
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       " array([[[0., 0., 0.],\n",
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       "         [0.00392157, 0.00392157, 0.00392157]]]),\n",
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       " array([[[0.01176471, 0.01176471, 0.01176471],\n",
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       " array([[[0.25098039, 0.25098039, 0.25098039],\n",
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       "         [0.        , 0.        , 0.        ]],\n",
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       "         [0.01960784, 0.01960784, 0.01960784]]]),\n",
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       "         [0.18823529, 0.18823529, 0.18823529]]]),\n",
       " array([[[0.31764706, 0.31764706, 0.31764706],\n",
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       " array([[[0.00784314, 0.00784314, 0.00784314],\n",
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       "         [0.01568627, 0.01568627, 0.01568627]]]),\n",
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       "         [0.05882353, 0.05882353, 0.05882353]]]),\n",
       " array([[[0.14509804, 0.14509804, 0.14509804],\n",
       "         [0.10588235, 0.10588235, 0.10588235]],\n",
       " \n",
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       "         [0.3254902 , 0.3254902 , 0.3254902 ]]]),\n",
       " array([[[0.01176471, 0.01176471, 0.01176471],\n",
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       " array([[[0.07843137, 0.07843137, 0.07843137],\n",
       "         [0.        , 0.        , 0.        ]],\n",
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       "         [0.22352941, 0.22352941, 0.22352941]]]),\n",
       " array([[[0.43921569, 0.43921569, 0.43921569],\n",
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       "         [0.12941176, 0.12941176, 0.12941176]]]),\n",
       " array([[[0.76470588, 0.76470588, 0.76470588],\n",
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       "         [0.14509804, 0.14509804, 0.14509804]]]),\n",
       " array([[[0.00784314, 0.00784314, 0.00784314],\n",
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       " array([[[0.47058824, 0.47058824, 0.47058824],\n",
       "         [0.4745098 , 0.4745098 , 0.4745098 ]],\n",
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       "         [0.41960784, 0.41960784, 0.41960784]]]),\n",
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       " array([[[0.44705882, 0.44705882, 0.44705882],\n",
       "         [0.00784314, 0.00784314, 0.00784314]],\n",
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       "         [0.03921569, 0.03921569, 0.03921569]]]),\n",
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       " \n",
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       "         [0.37254902, 0.37254902, 0.37254902]]]),\n",
       " array([[[0.36078431, 0.36078431, 0.36078431],\n",
       "         [0.34901961, 0.34901961, 0.34901961]],\n",
       " \n",
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       "         [0.31764706, 0.31764706, 0.31764706]]]),\n",
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       " \n",
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       "         [0.42352941, 0.42352941, 0.42352941]]]),\n",
       " array([[[0.29019608, 0.29019608, 0.29019608],\n",
       "         [0.2627451 , 0.2627451 , 0.2627451 ]],\n",
       " \n",
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       "         [0.01960784, 0.01960784, 0.01960784]]]),\n",
       " array([[[0.01568627, 0.01568627, 0.01568627],\n",
       "         [0.        , 0.        , 0.        ]],\n",
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       "        [[0.0627451 , 0.0627451 , 0.0627451 ],\n",
       "         [0.00392157, 0.00392157, 0.00392157]]]),\n",
       " array([[[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
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       " array([[[0.34117647, 0.34117647, 0.34117647],\n",
       "         [0.25882353, 0.25882353, 0.25882353]],\n",
       " \n",
       "        [[0.25098039, 0.25098039, 0.25098039],\n",
       "         [0.29803922, 0.29803922, 0.29803922]]]),\n",
       " array([[[0.31764706, 0.31764706, 0.31764706],\n",
       "         [0.        , 0.        , 0.        ]],\n",
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       "        [[0.17647059, 0.17647059, 0.17647059],\n",
       "         [0.21176471, 0.21176471, 0.21176471]]]),\n",
       " array([[[0.01176471, 0.01176471, 0.01176471],\n",
       "         [0.09411765, 0.09411765, 0.09411765]],\n",
       " \n",
       "        [[0.37647059, 0.37647059, 0.37647059],\n",
       "         [0.35686275, 0.35686275, 0.35686275]]]),\n",
       " array([[[0.32156863, 0.32156863, 0.32156863],\n",
       "         [0.27843137, 0.27843137, 0.27843137]],\n",
       " \n",
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       "         [0.        , 0.        , 0.        ]]]),\n",
       " array([[[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.37647059, 0.37647059, 0.37647059],\n",
       "         [0.01568627, 0.01568627, 0.01568627]]]),\n",
       " array([[[0.34509804, 0.34509804, 0.34509804],\n",
       "         [0.32941176, 0.32941176, 0.32941176]],\n",
       " \n",
       "        [[0.36862745, 0.36862745, 0.36862745],\n",
       "         [0.38039216, 0.38039216, 0.38039216]]]),\n",
       " array([[[0.27843137, 0.27843137, 0.27843137],\n",
       "         [0.32941176, 0.32941176, 0.32941176]],\n",
       " \n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.00392157, 0.00392157, 0.00392157]]]),\n",
       " array([[[0.40392157, 0.40392157, 0.40392157],\n",
       "         [0.24313725, 0.24313725, 0.24313725]],\n",
       " \n",
       "        [[0.01568627, 0.01568627, 0.01568627],\n",
       "         [0.        , 0.        , 0.        ]]]),\n",
       " array([[[0.24313725, 0.24313725, 0.24313725],\n",
       "         [0.38431373, 0.38431373, 0.38431373]],\n",
       " \n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.04313725, 0.04313725, 0.04313725]]]),\n",
       " array([[[0.33333333, 0.33333333, 0.33333333],\n",
       "         [0.27058824, 0.27058824, 0.27058824]],\n",
       " \n",
       "        [[0.01568627, 0.01568627, 0.01568627],\n",
       "         [0.        , 0.        , 0.        ]]]),\n",
       " array([[[0.19607843, 0.19607843, 0.19607843],\n",
       "         [0.00784314, 0.00784314, 0.00784314]],\n",
       " \n",
       "        [[0.01176471, 0.01176471, 0.01176471],\n",
       "         [0.        , 0.        , 0.        ]]]),\n",
       " array([[[0.34117647, 0.34117647, 0.34117647],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.        , 0.        , 0.        ]]]),\n",
       " array([[[0.29411765, 0.29411765, 0.29411765],\n",
       "         [0.00392157, 0.00392157, 0.00392157]],\n",
       " \n",
       "        [[0.17254902, 0.17254902, 0.17254902],\n",
       "         [0.22745098, 0.22745098, 0.22745098]]]),\n",
       " array([[[0., 0., 0.],\n",
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       " \n",
       "        [[0., 0., 0.],\n",
       "         [0., 0., 0.]]]),\n",
       " array([[[0., 0., 0.],\n",
       "         [0., 0., 0.]],\n",
       " \n",
       "        [[0., 0., 0.],\n",
       "         [0., 0., 0.]]]),\n",
       " array([[[0.3372549 , 0.3372549 , 0.3372549 ],\n",
       "         [0.39607843, 0.39607843, 0.39607843]],\n",
       " \n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.00784314, 0.00784314, 0.00784314]]]),\n",
       " array([[[0.28235294, 0.28235294, 0.28235294],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.31372549, 0.31372549, 0.31372549],\n",
       "         [0.34117647, 0.34117647, 0.34117647]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
       "         [0.00784314, 0.00784314, 0.00784314]],\n",
       " \n",
       "        [[0.30980392, 0.30980392, 0.30980392],\n",
       "         [0.01960784, 0.01960784, 0.01960784]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
       "         [0.00784314, 0.00784314, 0.00784314]],\n",
       " \n",
       "        [[0.01176471, 0.01176471, 0.01176471],\n",
       "         [0.08235294, 0.08235294, 0.08235294]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
       "         [0.00784314, 0.00784314, 0.00784314]],\n",
       " \n",
       "        [[0.00784314, 0.00784314, 0.00784314],\n",
       "         [0.21176471, 0.21176471, 0.21176471]]]),\n",
       " array([[[0., 0., 0.],\n",
       "         [0., 0., 0.]],\n",
       " \n",
       "        [[0., 0., 0.],\n",
       "         [0., 0., 0.]]]),\n",
       " array([[[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.18823529, 0.18823529, 0.18823529],\n",
       "         [0.05098039, 0.05098039, 0.05098039]]]),\n",
       " array([[[0.47058824, 0.47058824, 0.47058824],\n",
       "         [0.98431373, 0.98431373, 0.98431373]],\n",
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       " array([[[0.36078431, 0.36078431, 0.36078431],\n",
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       " array([[[0.03137255, 0.03137255, 0.03137255],\n",
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       " array([[[0.01176471, 0.01176471, 0.01176471],\n",
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       " array([[[0.00392157, 0.00392157, 0.00392157],\n",
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       " \n",
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       "         [0.00784314, 0.00784314, 0.00784314]]]),\n",
       " array([[[0.32941176, 0.32941176, 0.32941176],\n",
       "         [0.00784314, 0.00784314, 0.00784314]],\n",
       " \n",
       "        [[0.01568627, 0.01568627, 0.01568627],\n",
       "         [0.05882353, 0.05882353, 0.05882353]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
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       " \n",
       "        [[0.32156863, 0.32156863, 0.32156863],\n",
       "         [0.38431373, 0.38431373, 0.38431373]]]),\n",
       " array([[[0.40392157, 0.40392157, 0.40392157],\n",
       "         [0.39607843, 0.39607843, 0.39607843]],\n",
       " \n",
       "        [[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.        , 0.        , 0.        ]]]),\n",
       " array([[[0.09019608, 0.09019608, 0.09019608],\n",
       "         [0.01176471, 0.01176471, 0.01176471]],\n",
       " \n",
       "        [[0.27058824, 0.27058824, 0.27058824],\n",
       "         [0.32941176, 0.32941176, 0.32941176]]]),\n",
       " array([[[0.36078431, 0.36078431, 0.36078431],\n",
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       "         [0.01176471, 0.01176471, 0.01176471]]]),\n",
       " array([[[0.31764706, 0.31764706, 0.31764706],\n",
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       " \n",
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       "         [0.01568627, 0.01568627, 0.01568627]]]),\n",
       " array([[[0.00784314, 0.00784314, 0.00784314],\n",
       "         [0.        , 0.        , 0.        ]],\n",
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       "        [[0.35686275, 0.35686275, 0.35686275],\n",
       "         [0.34509804, 0.34509804, 0.34509804]]]),\n",
       " array([[[0.40392157, 0.40392157, 0.40392157],\n",
       "         [0.40392157, 0.40392157, 0.40392157]],\n",
       " \n",
       "        [[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.00392157, 0.00392157, 0.00392157]]]),\n",
       " array([[[0.00784314, 0.00784314, 0.00784314],\n",
       "         [0.        , 0.        , 0.        ]],\n",
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       "        [[0.29803922, 0.29803922, 0.29803922],\n",
       "         [0.27058824, 0.27058824, 0.27058824]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
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       " \n",
       "        [[0.38039216, 0.38039216, 0.38039216],\n",
       "         [0.39215686, 0.39215686, 0.39215686]]]),\n",
       " array([[[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.36078431, 0.36078431, 0.36078431],\n",
       "         [0.00392157, 0.00392157, 0.00392157]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
       "         [0.01960784, 0.01960784, 0.01960784]],\n",
       " \n",
       "        [[0.09411765, 0.09411765, 0.09411765],\n",
       "         [0.14117647, 0.14117647, 0.14117647]]]),\n",
       " array([[[0., 0., 0.],\n",
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       " array([[[0.        , 0.        , 0.        ],\n",
       "         [0.00392157, 0.00392157, 0.00392157]],\n",
       " \n",
       "        [[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.36470588, 0.36470588, 0.36470588]]]),\n",
       " array([[[0.36862745, 0.36862745, 0.36862745],\n",
       "         [0.83921569, 0.83921569, 0.83921569]],\n",
       " \n",
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       "         [0.22352941, 0.22352941, 0.22352941]]]),\n",
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       " \n",
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       " \n",
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       " array([[[0.01568627, 0.01568627, 0.01568627],\n",
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       " array([[[0.36470588, 0.36470588, 0.36470588],\n",
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       "         [0.2627451 , 0.2627451 , 0.2627451 ]]]),\n",
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       "         [0.40784314, 0.40784314, 0.40784314]]]),\n",
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       "         [0.25882353, 0.25882353, 0.25882353]]]),\n",
       " array([[[0.00392157, 0.00392157, 0.00392157],\n",
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       "         [0.01568627, 0.01568627, 0.01568627]]]),\n",
       " array([[[0.43529412, 0.43529412, 0.43529412],\n",
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       "         [0.36470588, 0.36470588, 0.36470588]]]),\n",
       " array([[[0.00392157, 0.00392157, 0.00392157],\n",
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       " \n",
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       " array([[[0.00392157, 0.00392157, 0.00392157],\n",
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       " array([[[0.        , 0.        , 0.        ],\n",
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       " array([[[0.01960784, 0.01960784, 0.01960784],\n",
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       "        [[0.00784314, 0.00784314, 0.00784314],\n",
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       " \n",
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       " array([[[0.01176471, 0.01176471, 0.01176471],\n",
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       " array([[[0.01176471, 0.01176471, 0.01176471],\n",
       "         [0.        , 0.        , 0.        ]],\n",
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       "         [0.2627451 , 0.2627451 , 0.2627451 ]]]),\n",
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       " array([[[0.26666667, 0.26666667, 0.26666667],\n",
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       "         [0.40392157, 0.40392157, 0.40392157]]]),\n",
       " array([[[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.        , 0.        , 0.        ]],\n",
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       "         [0.24705882, 0.24705882, 0.24705882]]]),\n",
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       "         [0.35294118, 0.35294118, 0.35294118]],\n",
       " \n",
       "        [[0.34117647, 0.34117647, 0.34117647],\n",
       "         [0.30980392, 0.30980392, 0.30980392]]]),\n",
       " array([[[0., 0., 0.],\n",
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       " array([[[0.3372549 , 0.3372549 , 0.3372549 ],\n",
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       " \n",
       "        [[0.99607843, 0.99607843, 0.99607843],\n",
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       " array([[[0.        , 0.        , 0.        ],\n",
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       " array([[[0.3372549 , 0.3372549 , 0.3372549 ],\n",
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       "         [0.01568627, 0.01568627, 0.01568627]]]),\n",
       " array([[[0.01176471, 0.01176471, 0.01176471],\n",
       "         [0.05490196, 0.05490196, 0.05490196]],\n",
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       " array([[[0.3254902 , 0.3254902 , 0.3254902 ],\n",
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       " \n",
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       "         [0.28627451, 0.28627451, 0.28627451]]]),\n",
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       "         [0.30196078, 0.30196078, 0.30196078]]]),\n",
       " array([[[0.38039216, 0.38039216, 0.38039216],\n",
       "         [0.43921569, 0.43921569, 0.43921569]],\n",
       " \n",
       "        [[0.32941176, 0.32941176, 0.32941176],\n",
       "         [0.23137255, 0.23137255, 0.23137255]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
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       " \n",
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       "         [0.19607843, 0.19607843, 0.19607843]]]),\n",
       " array([[[0.21568627, 0.21568627, 0.21568627],\n",
       "         [0.02745098, 0.02745098, 0.02745098]],\n",
       " \n",
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       "         [0.        , 0.        , 0.        ]]]),\n",
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       "         [0.25490196, 0.25490196, 0.25490196]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
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       " \n",
       "        [[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.20784314, 0.20784314, 0.20784314]]]),\n",
       " array([[[0.00784314, 0.00784314, 0.00784314],\n",
       "         [0.        , 0.        , 0.        ]],\n",
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       "         [0.01960784, 0.01960784, 0.01960784]]]),\n",
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       " array([[[0.        , 0.        , 0.        ],\n",
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       " \n",
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       "         [0.35294118, 0.35294118, 0.35294118]]]),\n",
       " array([[[0.25882353, 0.25882353, 0.25882353],\n",
       "         [0.        , 0.        , 0.        ]],\n",
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       "         [0.        , 0.        , 0.        ]]]),\n",
       " array([[[0.03137255, 0.03137255, 0.03137255],\n",
       "         [0.01176471, 0.01176471, 0.01176471]],\n",
       " \n",
       "        [[0.01176471, 0.01176471, 0.01176471],\n",
       "         [0.        , 0.        , 0.        ]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
       "         [0.01176471, 0.01176471, 0.01176471]],\n",
       " \n",
       "        [[0.01568627, 0.01568627, 0.01568627],\n",
       "         [0.19215686, 0.19215686, 0.19215686]]]),\n",
       " array([[[0.39607843, 0.39607843, 0.39607843],\n",
       "         [0.52941176, 0.52941176, 0.52941176]],\n",
       " \n",
       "        [[0.2       , 0.2       , 0.2       ],\n",
       "         [0.17647059, 0.17647059, 0.17647059]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
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       " \n",
       "        [[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.36862745, 0.36862745, 0.36862745]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
       "         [0.09803922, 0.09803922, 0.09803922]],\n",
       " \n",
       "        [[0.00784314, 0.00784314, 0.00784314],\n",
       "         [0.24313725, 0.24313725, 0.24313725]]]),\n",
       " array([[[0.29411765, 0.29411765, 0.29411765],\n",
       "         [0.2745098 , 0.2745098 , 0.2745098 ]],\n",
       " \n",
       "        [[0.01176471, 0.01176471, 0.01176471],\n",
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       "         [0.00392157, 0.00392157, 0.00392157]]]),\n",
       " array([[[0.24313725, 0.24313725, 0.24313725],\n",
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       " array([[[0.00392157, 0.00392157, 0.00392157],\n",
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       "         [0.33333333, 0.33333333, 0.33333333]],\n",
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       " array([[[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.        , 0.        , 0.        ]],\n",
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       "         [0.01960784, 0.01960784, 0.01960784]]]),\n",
       " array([[[1.        , 1.        , 1.        ],\n",
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       " \n",
       "        [[0.38431373, 0.38431373, 0.38431373],\n",
       "         [0.32156863, 0.32156863, 0.32156863]]]),\n",
       " array([[[0.32156863, 0.32156863, 0.32156863],\n",
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       " array([[[0.00784314, 0.00784314, 0.00784314],\n",
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       "         [0.0745098 , 0.0745098 , 0.0745098 ]]]),\n",
       " array([[[0.81176471, 0.81176471, 0.81176471],\n",
       "         [0.49803922, 0.49803922, 0.49803922]],\n",
       " \n",
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       "         [0.        , 0.        , 0.        ]]]),\n",
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       "        [[0.29019608, 0.29019608, 0.29019608],\n",
       "         [0.05098039, 0.05098039, 0.05098039]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
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       " \n",
       "        [[0.98039216, 0.98039216, 0.98039216],\n",
       "         [0.31372549, 0.31372549, 0.31372549]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
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       "         [0.4       , 0.4       , 0.4       ]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
       "         [0.00392157, 0.00392157, 0.00392157]],\n",
       " \n",
       "        [[0.28235294, 0.28235294, 0.28235294],\n",
       "         [0.29411765, 0.29411765, 0.29411765]]]),\n",
       " array([[[0.00784314, 0.00784314, 0.00784314],\n",
       "         [0.21960784, 0.21960784, 0.21960784]],\n",
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       " array([[[0.        , 0.        , 0.        ],\n",
       "         [0.01568627, 0.01568627, 0.01568627]],\n",
       " \n",
       "        [[0.35294118, 0.35294118, 0.35294118],\n",
       "         [0.39607843, 0.39607843, 0.39607843]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
       "         [0.01176471, 0.01176471, 0.01176471]],\n",
       " \n",
       "        [[0.01176471, 0.01176471, 0.01176471],\n",
       "         [0.07843137, 0.07843137, 0.07843137]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.32941176, 0.32941176, 0.32941176],\n",
       "         [0.3254902 , 0.3254902 , 0.3254902 ]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.25490196, 0.25490196, 0.25490196],\n",
       "         [0.09411765, 0.09411765, 0.09411765]]]),\n",
       " array([[[0.20392157, 0.20392157, 0.20392157],\n",
       "         [0.93333333, 0.93333333, 0.93333333]],\n",
       " \n",
       "        [[0.01568627, 0.01568627, 0.01568627],\n",
       "         [0.39607843, 0.39607843, 0.39607843]]]),\n",
       " array([[[0.03529412, 0.03529412, 0.03529412],\n",
       "         [0.00784314, 0.00784314, 0.00784314]],\n",
       " \n",
       "        [[0.37647059, 0.37647059, 0.37647059],\n",
       "         [0.        , 0.        , 0.        ]]]),\n",
       " array([[[0., 0., 0.],\n",
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       " \n",
       "        [[0., 0., 0.],\n",
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       " array([[[0.12941176, 0.12941176, 0.12941176],\n",
       "         [0.01568627, 0.01568627, 0.01568627]],\n",
       " \n",
       "        [[0.01568627, 0.01568627, 0.01568627],\n",
       "         [0.        , 0.        , 0.        ]]]),\n",
       " array([[[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.38823529, 0.38823529, 0.38823529],\n",
       "         [0.01568627, 0.01568627, 0.01568627]]]),\n",
       " array([[[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.37254902, 0.37254902, 0.37254902],\n",
       "         [0.01176471, 0.01176471, 0.01176471]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
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       " \n",
       "        [[0.2627451 , 0.2627451 , 0.2627451 ],\n",
       "         [0.01176471, 0.01176471, 0.01176471]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
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       " \n",
       "        [[0.08627451, 0.08627451, 0.08627451],\n",
       "         [0.01176471, 0.01176471, 0.01176471]]]),\n",
       " array([[[0., 0., 0.],\n",
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       " array([[[0.00784314, 0.00784314, 0.00784314],\n",
       "         [0.01176471, 0.01176471, 0.01176471]],\n",
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       " array([[[0., 0., 0.],\n",
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       " array([[[0.99607843, 0.99607843, 0.99607843],\n",
       "         [0.00784314, 0.00784314, 0.00784314]],\n",
       " \n",
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       "         [0.        , 0.        , 0.        ]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
       "         [0.01176471, 0.01176471, 0.01176471]],\n",
       " \n",
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       "         [0.23529412, 0.23529412, 0.23529412]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
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       " \n",
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       "         [0.23529412, 0.23529412, 0.23529412]]]),\n",
       " array([[[0.03529412, 0.03529412, 0.03529412],\n",
       "         [0.01960784, 0.01960784, 0.01960784]],\n",
       " \n",
       "        [[0.28627451, 0.28627451, 0.28627451],\n",
       "         [0.30196078, 0.30196078, 0.30196078]]]),\n",
       " array([[[0.3372549 , 0.3372549 , 0.3372549 ],\n",
       "         [0.        , 0.        , 0.        ]],\n",
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       "         [0.01960784, 0.01960784, 0.01960784]]]),\n",
       " array([[[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.        , 0.        , 0.        ]],\n",
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       "         [0.33333333, 0.33333333, 0.33333333]]]),\n",
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       " \n",
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       "         [0.23921569, 0.23921569, 0.23921569]]]),\n",
       " array([[[0.47058824, 0.47058824, 0.47058824],\n",
       "         [0.46666667, 0.46666667, 0.46666667]],\n",
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       " array([[[0.07843137, 0.07843137, 0.07843137],\n",
       "         [0.29803922, 0.29803922, 0.29803922]],\n",
       " \n",
       "        [[0.97647059, 0.97647059, 0.97647059],\n",
       "         [0.12941176, 0.12941176, 0.12941176]]]),\n",
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       " array([[[0.34901961, 0.34901961, 0.34901961],\n",
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       " array([[[0.        , 0.        , 0.        ],\n",
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       " \n",
       "        [[0.01176471, 0.01176471, 0.01176471],\n",
       "         [0.06666667, 0.06666667, 0.06666667]]]),\n",
       " array([[[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.        , 0.        , 0.        ]],\n",
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       "        [[0.29019608, 0.29019608, 0.29019608],\n",
       "         [0.15686275, 0.15686275, 0.15686275]]]),\n",
       " array([[[0.30196078, 0.30196078, 0.30196078],\n",
       "         [0.30588235, 0.30588235, 0.30588235]],\n",
       " \n",
       "        [[0.00784314, 0.00784314, 0.00784314],\n",
       "         [0.2745098 , 0.2745098 , 0.2745098 ]]]),\n",
       " array([[[0.00784314, 0.00784314, 0.00784314],\n",
       "         [0.00392157, 0.00392157, 0.00392157]],\n",
       " \n",
       "        [[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.00392157, 0.00392157, 0.00392157]]]),\n",
       " array([[[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.05882353, 0.05882353, 0.05882353]],\n",
       " \n",
       "        [[0.27058824, 0.27058824, 0.27058824],\n",
       "         [0.32156863, 0.32156863, 0.32156863]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
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       " \n",
       "        [[0.19607843, 0.19607843, 0.19607843],\n",
       "         [0.37254902, 0.37254902, 0.37254902]]]),\n",
       " array([[[0.0627451 , 0.0627451 , 0.0627451 ],\n",
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       "         [0.05098039, 0.05098039, 0.05098039]]]),\n",
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       " array([[[0.2627451 , 0.2627451 , 0.2627451 ],\n",
       "         [0.08627451, 0.08627451, 0.08627451]],\n",
       " \n",
       "        [[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.23137255, 0.23137255, 0.23137255]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
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       " \n",
       "        [[0.19607843, 0.19607843, 0.19607843],\n",
       "         [0.37254902, 0.37254902, 0.37254902]]]),\n",
       " array([[[0.11764706, 0.11764706, 0.11764706],\n",
       "         [0.        , 0.        , 0.        ]],\n",
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       "         [0.        , 0.        , 0.        ]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
       "         [0.00392157, 0.00392157, 0.00392157]],\n",
       " \n",
       "        [[0.34117647, 0.34117647, 0.34117647],\n",
       "         [0.00392157, 0.00392157, 0.00392157]]]),\n",
       " array([[[0.09411765, 0.09411765, 0.09411765],\n",
       "         [0.01960784, 0.01960784, 0.01960784]],\n",
       " \n",
       "        [[0.04313725, 0.04313725, 0.04313725],\n",
       "         [0.        , 0.        , 0.        ]]]),\n",
       " array([[[0.00784314, 0.00784314, 0.00784314],\n",
       "         [0.        , 0.        , 0.        ]],\n",
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       "        [[0.25882353, 0.25882353, 0.25882353],\n",
       "         [0.16078431, 0.16078431, 0.16078431]]]),\n",
       " array([[[0.00784314, 0.00784314, 0.00784314],\n",
       "         [0.        , 0.        , 0.        ]],\n",
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       "        [[0.25490196, 0.25490196, 0.25490196],\n",
       "         [0.23529412, 0.23529412, 0.23529412]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
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       " \n",
       "        [[0.01176471, 0.01176471, 0.01176471],\n",
       "         [0.18823529, 0.18823529, 0.18823529]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
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       " \n",
       "        [[0.00784314, 0.00784314, 0.00784314],\n",
       "         [0.24705882, 0.24705882, 0.24705882]]]),\n",
       " array([[[0.33333333, 0.33333333, 0.33333333],\n",
       "         [0.03529412, 0.03529412, 0.03529412]],\n",
       " \n",
       "        [[0.31372549, 0.31372549, 0.31372549],\n",
       "         [0.25882353, 0.25882353, 0.25882353]]]),\n",
       " array([[[0., 0., 0.],\n",
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       " array([[[0.        , 0.        , 0.        ],\n",
       "         [0.00784314, 0.00784314, 0.00784314]],\n",
       " \n",
       "        [[0.01176471, 0.01176471, 0.01176471],\n",
       "         [0.18823529, 0.18823529, 0.18823529]]]),\n",
       " array([[[0.00784314, 0.00784314, 0.00784314],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
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       "         [0.00784314, 0.00784314, 0.00784314]]]),\n",
       " array([[[0.32156863, 0.32156863, 0.32156863],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.        , 0.        , 0.        ]]]),\n",
       " array([[[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.24313725, 0.24313725, 0.24313725]],\n",
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       " array([[[0.        , 0.        , 0.        ],\n",
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       " \n",
       "        [[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.00784314, 0.00784314, 0.00784314]]]),\n",
       " array([[[0., 0., 0.],\n",
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       " array([[[0.00784314, 0.00784314, 0.00784314],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.43137255, 0.43137255, 0.43137255],\n",
       "         [0.36078431, 0.36078431, 0.36078431]]]),\n",
       " array([[[0.00784314, 0.00784314, 0.00784314],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.32941176, 0.32941176, 0.32941176],\n",
       "         [0.2627451 , 0.2627451 , 0.2627451 ]]]),\n",
       " array([[[0.15294118, 0.15294118, 0.15294118],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.34509804, 0.34509804, 0.34509804],\n",
       "         [0.03137255, 0.03137255, 0.03137255]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
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       " \n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.39607843, 0.39607843, 0.39607843]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
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       " \n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.00392157, 0.00392157, 0.00392157]]]),\n",
       " array([[[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.        , 0.        , 0.        ]]]),\n",
       " array([[[0., 0., 0.],\n",
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       " \n",
       "        [[0., 0., 0.],\n",
       "         [0., 0., 0.]]]),\n",
       " array([[[0., 0., 0.],\n",
       "         [0., 0., 0.]],\n",
       " \n",
       "        [[0., 0., 0.],\n",
       "         [0., 0., 0.]]]),\n",
       " array([[[0.36078431, 0.36078431, 0.36078431],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.41176471, 0.41176471, 0.41176471],\n",
       "         [0.00784314, 0.00784314, 0.00784314]]]),\n",
       " array([[[0.26666667, 0.26666667, 0.26666667],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.29019608, 0.29019608, 0.29019608],\n",
       "         [0.33333333, 0.33333333, 0.33333333]]]),\n",
       " array([[[0.45882353, 0.45882353, 0.45882353],\n",
       "         [0.47058824, 0.47058824, 0.47058824]],\n",
       " \n",
       "        [[0.02745098, 0.02745098, 0.02745098],\n",
       "         [0.35294118, 0.35294118, 0.35294118]]]),\n",
       " array([[[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.43137255, 0.43137255, 0.43137255],\n",
       "         [0.07843137, 0.07843137, 0.07843137]]]),\n",
       " array([[[0.30196078, 0.30196078, 0.30196078],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.        , 0.        , 0.        ]]]),\n",
       " array([[[0.99607843, 0.99607843, 0.99607843],\n",
       "         [0.00392157, 0.00392157, 0.00392157]],\n",
       " \n",
       "        [[0.38431373, 0.38431373, 0.38431373],\n",
       "         [0.        , 0.        , 0.        ]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
       "         [0.30196078, 0.30196078, 0.30196078]],\n",
       " \n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.00392157, 0.00392157, 0.00392157]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
       "         [0.01568627, 0.01568627, 0.01568627]],\n",
       " \n",
       "        [[0.01176471, 0.01176471, 0.01176471],\n",
       "         [0.0745098 , 0.0745098 , 0.0745098 ]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.10196078, 0.10196078, 0.10196078],\n",
       "         [0.10196078, 0.10196078, 0.10196078]]]),\n",
       " array([[[0., 0., 0.],\n",
       "         [0., 0., 0.]],\n",
       " \n",
       "        [[0., 0., 0.],\n",
       "         [0., 0., 0.]]]),\n",
       " array([[[0.34509804, 0.34509804, 0.34509804],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.32156863, 0.32156863, 0.32156863],\n",
       "         [0.29411765, 0.29411765, 0.29411765]]]),\n",
       " array([[[0.29803922, 0.29803922, 0.29803922],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.19607843, 0.19607843, 0.19607843],\n",
       "         [0.24313725, 0.24313725, 0.24313725]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
       "         [0.01176471, 0.01176471, 0.01176471]],\n",
       " \n",
       "        [[0.22745098, 0.22745098, 0.22745098],\n",
       "         [0.40392157, 0.40392157, 0.40392157]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.3254902 , 0.3254902 , 0.3254902 ]]]),\n",
       " array([[[0., 0., 0.],\n",
       "         [0., 0., 0.]],\n",
       " \n",
       "        [[0., 0., 0.],\n",
       "         [0., 0., 0.]]]),\n",
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       "         [0.38823529, 0.38823529, 0.38823529]]]),\n",
       " array([[[0.34509804, 0.34509804, 0.34509804],\n",
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       " array([[[0.28235294, 0.28235294, 0.28235294],\n",
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       " \n",
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       "         [0.17254902, 0.17254902, 0.17254902]]]),\n",
       " array([[[0.00784314, 0.00784314, 0.00784314],\n",
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       " array([[[0.28235294, 0.28235294, 0.28235294],\n",
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       " \n",
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       "         [0.01176471, 0.01176471, 0.01176471]]]),\n",
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       "         [0.1254902 , 0.1254902 , 0.1254902 ]]]),\n",
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       "         [0.03921569, 0.03921569, 0.03921569]]]),\n",
       " array([[[0.2745098 , 0.2745098 , 0.2745098 ],\n",
       "         [0.00392157, 0.00392157, 0.00392157]],\n",
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       " array([[[0.00392157, 0.00392157, 0.00392157],\n",
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       "         [0.28627451, 0.28627451, 0.28627451]]]),\n",
       " array([[[0.34509804, 0.34509804, 0.34509804],\n",
       "         [0.00784314, 0.00784314, 0.00784314]],\n",
       " \n",
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       " array([[[0.00784314, 0.00784314, 0.00784314],\n",
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       " array([[[0.18431373, 0.18431373, 0.18431373],\n",
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       "         [0.00784314, 0.00784314, 0.00784314]]]),\n",
       " array([[[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.38039216, 0.38039216, 0.38039216]],\n",
       " \n",
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       "         [0.31372549, 0.31372549, 0.31372549]]]),\n",
       " array([[[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.        , 0.        , 0.        ]],\n",
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       "         [0.31372549, 0.31372549, 0.31372549]]]),\n",
       " array([[[0.0745098 , 0.0745098 , 0.0745098 ],\n",
       "         [0.02352941, 0.02352941, 0.02352941]],\n",
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       "        [[0.01568627, 0.01568627, 0.01568627],\n",
       "         [0.        , 0.        , 0.        ]]]),\n",
       " array([[[0.20784314, 0.20784314, 0.20784314],\n",
       "         [0.01568627, 0.01568627, 0.01568627]],\n",
       " \n",
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       "         [0.        , 0.        , 0.        ]]]),\n",
       " array([[[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.        , 0.        , 0.        ]],\n",
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       "        [[0.00784314, 0.00784314, 0.00784314],\n",
       "         [0.2627451 , 0.2627451 , 0.2627451 ]]]),\n",
       " array([[[0.00784314, 0.00784314, 0.00784314],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.39607843, 0.39607843, 0.39607843],\n",
       "         [0.01176471, 0.01176471, 0.01176471]]]),\n",
       " array([[[0., 0., 0.],\n",
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       "         [0., 0., 0.]]]),\n",
       " array([[[0.18823529, 0.18823529, 0.18823529],\n",
       "         [0.        , 0.        , 0.        ]],\n",
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       "        [[0.3372549 , 0.3372549 , 0.3372549 ],\n",
       "         [0.01568627, 0.01568627, 0.01568627]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.41568627, 0.41568627, 0.41568627],\n",
       "         [0.14117647, 0.14117647, 0.14117647]]]),\n",
       " array([[[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.34117647, 0.34117647, 0.34117647],\n",
       "         [0.09411765, 0.09411765, 0.09411765]]]),\n",
       " array([[[0.32156863, 0.32156863, 0.32156863],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.36470588, 0.36470588, 0.36470588],\n",
       "         [0.02745098, 0.02745098, 0.02745098]]]),\n",
       " array([[[0.48627451, 0.48627451, 0.48627451],\n",
       "         [0.98431373, 0.98431373, 0.98431373]],\n",
       " \n",
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       "         [0.44705882, 0.44705882, 0.44705882]]]),\n",
       " array([[[0.01176471, 0.01176471, 0.01176471],\n",
       "         [0.38431373, 0.38431373, 0.38431373]],\n",
       " \n",
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       "         [0.33333333, 0.33333333, 0.33333333]]]),\n",
       " array([[[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.        , 0.        , 0.        ]]]),\n",
       " array([[[0.00784314, 0.00784314, 0.00784314],\n",
       "         [0.        , 0.        , 0.        ]],\n",
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       "        [[0.01176471, 0.01176471, 0.01176471],\n",
       "         [0.        , 0.        , 0.        ]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
       "         [0.00784314, 0.00784314, 0.00784314]],\n",
       " \n",
       "        [[0.00784314, 0.00784314, 0.00784314],\n",
       "         [0.38823529, 0.38823529, 0.38823529]]]),\n",
       " array([[[0.44313725, 0.44313725, 0.44313725],\n",
       "         [0.26666667, 0.26666667, 0.26666667]],\n",
       " \n",
       "        [[0.38431373, 0.38431373, 0.38431373],\n",
       "         [0.01176471, 0.01176471, 0.01176471]]]),\n",
       " array([[[0.56862745, 0.56862745, 0.56862745],\n",
       "         [0.11764706, 0.11764706, 0.11764706]],\n",
       " \n",
       "        [[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.        , 0.        , 0.        ]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
       "         [0.00784314, 0.00784314, 0.00784314]],\n",
       " \n",
       "        [[0.00784314, 0.00784314, 0.00784314],\n",
       "         [0.03921569, 0.03921569, 0.03921569]]]),\n",
       " array([[[0.41568627, 0.41568627, 0.41568627],\n",
       "         [0.00784314, 0.00784314, 0.00784314]],\n",
       " \n",
       "        [[0.4       , 0.4       , 0.4       ],\n",
       "         [0.        , 0.        , 0.        ]]]),\n",
       " array([[[0.3254902 , 0.3254902 , 0.3254902 ],\n",
       "         [0.32156863, 0.32156863, 0.32156863]],\n",
       " \n",
       "        [[0.34117647, 0.34117647, 0.34117647],\n",
       "         [0.34509804, 0.34509804, 0.34509804]]]),\n",
       " array([[[0.2627451 , 0.2627451 , 0.2627451 ],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.1372549 , 0.1372549 , 0.1372549 ],\n",
       "         [0.21568627, 0.21568627, 0.21568627]]]),\n",
       " array([[[0., 0., 0.],\n",
       "         [0., 0., 0.]],\n",
       " \n",
       "        [[0., 0., 0.],\n",
       "         [0., 0., 0.]]]),\n",
       " array([[[0., 0., 0.],\n",
       "         [0., 0., 0.]],\n",
       " \n",
       "        [[0., 0., 0.],\n",
       "         [0., 0., 0.]]]),\n",
       " array([[[0.29019608, 0.29019608, 0.29019608],\n",
       "         [0.27058824, 0.27058824, 0.27058824]],\n",
       " \n",
       "        [[0.01176471, 0.01176471, 0.01176471],\n",
       "         [0.        , 0.        , 0.        ]]]),\n",
       " array([[[0.39215686, 0.39215686, 0.39215686],\n",
       "         [0.40392157, 0.40392157, 0.40392157]],\n",
       " \n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]]]),\n",
       " array([[[0.99215686, 0.99215686, 0.99215686],\n",
       "         [0.38039216, 0.38039216, 0.38039216]],\n",
       " \n",
       "        [[0.25882353, 0.25882353, 0.25882353],\n",
       "         [0.24313725, 0.24313725, 0.24313725]]]),\n",
       " array([[[0.01176471, 0.01176471, 0.01176471],\n",
       "         [0.00392157, 0.00392157, 0.00392157]],\n",
       " \n",
       "        [[0.36078431, 0.36078431, 0.36078431],\n",
       "         [0.03529412, 0.03529412, 0.03529412]]]),\n",
       " array([[[0., 0., 0.],\n",
       "         [0., 0., 0.]],\n",
       " \n",
       "        [[0., 0., 0.],\n",
       "         [0., 0., 0.]]]),\n",
       " array([[[0.08235294, 0.08235294, 0.08235294],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.35294118, 0.35294118, 0.35294118],\n",
       "         [0.21176471, 0.21176471, 0.21176471]]]),\n",
       " array([[[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.33333333, 0.33333333, 0.33333333]]]),\n",
       " array([[[0.00784314, 0.00784314, 0.00784314],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.3372549 , 0.3372549 , 0.3372549 ],\n",
       "         [0.28235294, 0.28235294, 0.28235294]]]),\n",
       " array([[[0.21176471, 0.21176471, 0.21176471],\n",
       "         [0.59215686, 0.59215686, 0.59215686]],\n",
       " \n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.01568627, 0.01568627, 0.01568627]]]),\n",
       " array([[[0.27843137, 0.27843137, 0.27843137],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.01176471, 0.01176471, 0.01176471],\n",
       "         [0.        , 0.        , 0.        ]]]),\n",
       " array([[[0.3372549 , 0.3372549 , 0.3372549 ],\n",
       "         [0.07058824, 0.07058824, 0.07058824]],\n",
       " \n",
       "        [[0.01176471, 0.01176471, 0.01176471],\n",
       "         [0.        , 0.        , 0.        ]]]),\n",
       " array([[[0., 0., 0.],\n",
       "         [0., 0., 0.]],\n",
       " \n",
       "        [[0., 0., 0.],\n",
       "         [0., 0., 0.]]]),\n",
       " array([[[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.16862745, 0.16862745, 0.16862745]],\n",
       " \n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.02352941, 0.02352941, 0.02352941]]]),\n",
       " array([[[0., 0., 0.],\n",
       "         [0., 0., 0.]],\n",
       " \n",
       "        [[0., 0., 0.],\n",
       "         [0., 0., 0.]]]),\n",
       " array([[[0.35686275, 0.35686275, 0.35686275],\n",
       "         [0.43137255, 0.43137255, 0.43137255]],\n",
       " \n",
       "        [[0.43921569, 0.43921569, 0.43921569],\n",
       "         [0.38431373, 0.38431373, 0.38431373]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
       "         [0.00784314, 0.00784314, 0.00784314]],\n",
       " \n",
       "        [[0.23137255, 0.23137255, 0.23137255],\n",
       "         [0.44313725, 0.44313725, 0.44313725]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
       "         [0.01568627, 0.01568627, 0.01568627]],\n",
       " \n",
       "        [[0.01568627, 0.01568627, 0.01568627],\n",
       "         [0.11764706, 0.11764706, 0.11764706]]]),\n",
       " array([[[0.51764706, 0.51764706, 0.51764706],\n",
       "         [0.52941176, 0.52941176, 0.52941176]],\n",
       " \n",
       "        [[0.27843137, 0.27843137, 0.27843137],\n",
       "         [0.20784314, 0.20784314, 0.20784314]]]),\n",
       " array([[[0., 0., 0.],\n",
       "         [0., 0., 0.]],\n",
       " \n",
       "        [[0., 0., 0.],\n",
       "         [0., 0., 0.]]]),\n",
       " array([[[0.48235294, 0.48235294, 0.48235294],\n",
       "         [0.43137255, 0.43137255, 0.43137255]],\n",
       " \n",
       "        [[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.30588235, 0.30588235, 0.30588235]]]),\n",
       " array([[[0., 0., 0.],\n",
       "         [0., 0., 0.]],\n",
       " \n",
       "        [[0., 0., 0.],\n",
       "         [0., 0., 0.]]]),\n",
       " array([[[0.00784314, 0.00784314, 0.00784314],\n",
       "         [0.41176471, 0.41176471, 0.41176471]],\n",
       " \n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.00784314, 0.00784314, 0.00784314]]]),\n",
       " array([[[0.01176471, 0.01176471, 0.01176471],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.18431373, 0.18431373, 0.18431373],\n",
       "         [0.12156863, 0.12156863, 0.12156863]]]),\n",
       " array([[[0., 0., 0.],\n",
       "         [0., 0., 0.]],\n",
       " \n",
       "        [[0., 0., 0.],\n",
       "         [0., 0., 0.]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
       "         [0.00392157, 0.00392157, 0.00392157]],\n",
       " \n",
       "        [[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.01568627, 0.01568627, 0.01568627]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
       "         [0.52156863, 0.52156863, 0.52156863]],\n",
       " \n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]]]),\n",
       " array([[[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.03921569, 0.03921569, 0.03921569]],\n",
       " \n",
       "        [[0.38431373, 0.38431373, 0.38431373],\n",
       "         [0.33333333, 0.33333333, 0.33333333]]]),\n",
       " array([[[0.08235294, 0.08235294, 0.08235294],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.35294118, 0.35294118, 0.35294118],\n",
       "         [0.21176471, 0.21176471, 0.21176471]]]),\n",
       " array([[[0.01176471, 0.01176471, 0.01176471],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.0745098 , 0.0745098 , 0.0745098 ],\n",
       "         [0.03137255, 0.03137255, 0.03137255]]]),\n",
       " array([[[0.24705882, 0.24705882, 0.24705882],\n",
       "         [0.3372549 , 0.3372549 , 0.3372549 ]],\n",
       " \n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.01568627, 0.01568627, 0.01568627]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
       "         [0.00392157, 0.00392157, 0.00392157]],\n",
       " \n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.01176471, 0.01176471, 0.01176471]]]),\n",
       " array([[[0.00784314, 0.00784314, 0.00784314],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.07843137, 0.07843137, 0.07843137],\n",
       "         [0.01176471, 0.01176471, 0.01176471]]]),\n",
       " array([[[0.27843137, 0.27843137, 0.27843137],\n",
       "         [0.27843137, 0.27843137, 0.27843137]],\n",
       " \n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]]]),\n",
       " array([[[0., 0., 0.],\n",
       "         [0., 0., 0.]],\n",
       " \n",
       "        [[0., 0., 0.],\n",
       "         [0., 0., 0.]]]),\n",
       " array([[[0.2627451 , 0.2627451 , 0.2627451 ],\n",
       "         [0.99215686, 0.99215686, 0.99215686]],\n",
       " \n",
       "        [[0.12941176, 0.12941176, 0.12941176],\n",
       "         [0.45490196, 0.45490196, 0.45490196]]]),\n",
       " array([[[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.03137255, 0.03137255, 0.03137255],\n",
       "         [0.03137255, 0.03137255, 0.03137255]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
       "         [0.00784314, 0.00784314, 0.00784314]],\n",
       " \n",
       "        [[0.2627451 , 0.2627451 , 0.2627451 ],\n",
       "         [0.39607843, 0.39607843, 0.39607843]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
       "         [0.01568627, 0.01568627, 0.01568627]],\n",
       " \n",
       "        [[0.01176471, 0.01176471, 0.01176471],\n",
       "         [0.17254902, 0.17254902, 0.17254902]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
       "         [0.56078431, 0.56078431, 0.56078431]],\n",
       " \n",
       "        [[0.00392157, 0.00392157, 0.00392157],\n",
       "         [0.00784314, 0.00784314, 0.00784314]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
       "         [0.01176471, 0.01176471, 0.01176471]],\n",
       " \n",
       "        [[0.04705882, 0.04705882, 0.04705882],\n",
       "         [0.11372549, 0.11372549, 0.11372549]]]),\n",
       " array([[[0.38039216, 0.38039216, 0.38039216],\n",
       "         [0.27843137, 0.27843137, 0.27843137]],\n",
       " \n",
       "        [[0.00784314, 0.00784314, 0.00784314],\n",
       "         [0.        , 0.        , 0.        ]]]),\n",
       " array([[[0.2627451 , 0.2627451 , 0.2627451 ],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.01176471, 0.01176471, 0.01176471],\n",
       "         [0.        , 0.        , 0.        ]]]),\n",
       " array([[[0.43137255, 0.43137255, 0.43137255],\n",
       "         [0.45098039, 0.45098039, 0.45098039]],\n",
       " \n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]]]),\n",
       " array([[[0., 0., 0.],\n",
       "         [0., 0., 0.]],\n",
       " \n",
       "        [[0., 0., 0.],\n",
       "         [0., 0., 0.]]]),\n",
       " array([[[0.        , 0.        , 0.        ],\n",
       "         [0.01568627, 0.01568627, 0.01568627]],\n",
       " \n",
       "        [[0.20392157, 0.20392157, 0.20392157],\n",
       "         [0.26666667, 0.26666667, 0.26666667]]]),\n",
       " array([[[0.38431373, 0.38431373, 0.38431373],\n",
       "         [0.05098039, 0.05098039, 0.05098039]],\n",
       " \n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.        , 0.        , 0.        ]]]),\n",
       " array([[[0.00784314, 0.00784314, 0.00784314],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.36470588, 0.36470588, 0.36470588],\n",
       "         [0.29803922, 0.29803922, 0.29803922]]]),\n",
       " array([[[0.0745098 , 0.0745098 , 0.0745098 ],\n",
       "         [0.17254902, 0.17254902, 0.17254902]],\n",
       " \n",
       "        [[0.        , 0.        , 0.        ],\n",
       "         [0.00784314, 0.00784314, 0.00784314]]]),\n",
       " array([[[0., 0., 0.],\n",
       "         [0., 0., 0.]],\n",
       " \n",
       "        [[0., 0., 0.],\n",
       "         [0., 0., 0.]]]),\n",
       " array([[[0.3372549 , 0.3372549 , 0.3372549 ],\n",
       "         [0.29411765, 0.29411765, 0.29411765]],\n",
       " \n",
       "        [[0.38431373, 0.38431373, 0.38431373],\n",
       "         [0.34901961, 0.34901961, 0.34901961]]]),\n",
       " array([[[0.38431373, 0.38431373, 0.38431373],\n",
       "         [0.99607843, 0.99607843, 0.99607843]],\n",
       " \n",
       "        [[0.36078431, 0.36078431, 0.36078431],\n",
       "         [0.00392157, 0.00392157, 0.00392157]]]),\n",
       " array([[[0.00784314, 0.00784314, 0.00784314],\n",
       "         [0.        , 0.        , 0.        ]],\n",
       " \n",
       "        [[0.14117647, 0.14117647, 0.14117647],\n",
       "         [0.11764706, 0.11764706, 0.11764706]]]),\n",
       " ...]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "93137916",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "subset_size = 1000  # Adjust the size based on your available memory\n",
    "X_train, X_test, y_train, y_test = train_test_split(train_data2[:subset_size], subset_labels_resampled[:subset_size], test_size=0.20, random_state=42)\n",
    "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.10, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "be2dbb3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Data X :  (720, 2, 2, 3)\n",
      "Testing Data X :  (200, 2, 2, 3)\n",
      "Training Data y :  (720, 1)\n",
      "Testing Data y :  (200, 1)\n",
      "Validating Data X :  (80, 2, 2, 3)\n",
      "Validating Data y (80, 1)\n",
      "Image Size :  (2, 2, 3)\n"
     ]
    }
   ],
   "source": [
    "print(\"Training Data X : \",X_train.shape)\n",
    "print(\"Testing Data X : \",X_test.shape)\n",
    "print(\"Training Data y : \",y_train.shape)\n",
    "print(\"Testing Data y : \",y_test.shape)\n",
    "print(\"Validating Data X : \",X_val.shape)\n",
    "print(\"Validating Data y\",y_val.shape)\n",
    "print(\"Image Size : \",X_train[0].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "5a11474f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "data_augmentation = tf.keras.preprocessing.image.ImageDataGenerator(\n",
    "                    rotation_range=30, \n",
    "                    horizontal_flip=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "97e657c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_2 (Conv2D)           (None, 2, 2, 64)          832       \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPoolin  (None, 2, 2, 64)          0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_3 (Conv2D)           (None, 2, 2, 64)          16448     \n",
      "                                                                 \n",
      " flatten_1 (Flatten)         (None, 256)               0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 32)                8224      \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 4)                 132       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 25636 (100.14 KB)\n",
      "Trainable params: 25636 (100.14 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense\n",
    "\n",
    "model = Sequential([\n",
    "    Conv2D(64, (2, 2), activation='relu', input_shape=(2, 2, 3), padding='same'),  # Added padding layer\n",
    "    MaxPooling2D((1, 1)),\n",
    "    Conv2D(64, (2, 2), activation='relu', padding='same'),  # Added padding layer\n",
    "    Flatten(),\n",
    "    Dense(32, activation='relu'),\n",
    "    Dense(4, activation='softmax')\n",
    "])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "3ea74248",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "23/23 [==============================] - 0s 5ms/step - loss: 1.3232 - accuracy: 0.4014 - val_loss: 1.2040 - val_accuracy: 0.4750\n",
      "Epoch 2/10\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 1.2439 - accuracy: 0.4097 - val_loss: 1.1588 - val_accuracy: 0.4750\n",
      "Epoch 3/10\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 1.2282 - accuracy: 0.4625 - val_loss: 1.1472 - val_accuracy: 0.5125\n",
      "Epoch 4/10\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 1.2031 - accuracy: 0.4833 - val_loss: 1.1370 - val_accuracy: 0.5125\n",
      "Epoch 5/10\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 1.1958 - accuracy: 0.4958 - val_loss: 1.1505 - val_accuracy: 0.5625\n",
      "Epoch 6/10\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 1.1795 - accuracy: 0.5319 - val_loss: 1.1425 - val_accuracy: 0.5625\n",
      "Epoch 7/10\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 1.1734 - accuracy: 0.5153 - val_loss: 1.1385 - val_accuracy: 0.5625\n",
      "Epoch 8/10\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 1.1654 - accuracy: 0.5389 - val_loss: 1.1465 - val_accuracy: 0.5375\n",
      "Epoch 9/10\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 1.1600 - accuracy: 0.5361 - val_loss: 1.1343 - val_accuracy: 0.5750\n",
      "Epoch 10/10\n",
      "23/23 [==============================] - 0s 2ms/step - loss: 1.1566 - accuracy: 0.5306 - val_loss: 1.1323 - val_accuracy: 0.5500\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "history = model.fit(np.array(X_train), np.array(y_train), epochs=10, validation_data=(np.array(X_val), np.array(y_val)))\n",
    "\n",
    "plt.plot(history.history['loss'])\n",
    "plt.ylabel(\"loss\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.title(\"Training Loss Curve\")\n",
    "plt.show()\n",
    "\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.ylabel(\"accuracy\")\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.title(\"Training accuracy Curve\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "6a7caf4a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7/7 [==============================] - 0s 747us/step\n",
      "[[0.4262239  0.19678284 0.09088643 0.2861069 ]\n",
      " [0.23739292 0.4945755  0.1509439  0.1170877 ]\n",
      " [0.21310788 0.5633102  0.13823761 0.08534429]\n",
      " [0.4524758  0.20845447 0.08984945 0.24922025]\n",
      " [0.21303296 0.45850036 0.10826388 0.22020277]\n",
      " [0.43079844 0.34139782 0.09500952 0.13279425]\n",
      " [0.23845458 0.45056257 0.13389371 0.17708907]\n",
      " [0.24351265 0.47933102 0.15262932 0.12452699]\n",
      " [0.16751482 0.64088887 0.11756267 0.07403363]\n",
      " [0.4354978  0.23966175 0.07624077 0.24859971]\n",
      " [0.3322296  0.1917205  0.07266191 0.403388  ]\n",
      " [0.35077822 0.52308387 0.08687223 0.03926563]\n",
      " [0.28555527 0.45359695 0.15319654 0.10765128]\n",
      " [0.33950928 0.20879094 0.07921857 0.37248126]\n",
      " [0.20568174 0.59849447 0.13071975 0.06510405]\n",
      " [0.32846063 0.21503451 0.07909376 0.3774111 ]\n",
      " [0.21921054 0.5930557  0.10478628 0.08294743]\n",
      " [0.19411846 0.5822872  0.1318921  0.09170226]\n",
      " [0.40299752 0.36814642 0.1454155  0.08344057]\n",
      " [0.60153186 0.1481608  0.08735257 0.16295473]\n",
      " [0.5284978  0.24350987 0.11206282 0.11592948]\n",
      " [0.3322296  0.1917205  0.07266191 0.403388  ]\n",
      " [0.3322296  0.1917205  0.07266191 0.403388  ]\n",
      " [0.35844588 0.22711061 0.07931421 0.33512926]\n",
      " [0.3322296  0.1917205  0.07266191 0.403388  ]\n",
      " [0.15433021 0.52584565 0.06942643 0.25039768]\n",
      " [0.40564278 0.38908377 0.14175111 0.06352233]\n",
      " [0.19347674 0.6344768  0.11642269 0.05562381]\n",
      " [0.38641006 0.39129254 0.09981267 0.12248466]\n",
      " [0.21250361 0.6040045  0.12893341 0.05455846]\n",
      " [0.3322296  0.1917205  0.07266191 0.403388  ]\n",
      " [0.19702539 0.45797905 0.09282736 0.25216827]\n",
      " [0.14607167 0.71401864 0.10012797 0.03978172]\n",
      " [0.20554616 0.44793338 0.0975498  0.24897061]\n",
      " [0.14818211 0.7071567  0.10199293 0.0426682 ]\n",
      " [0.168367   0.6652278  0.11349428 0.05291093]\n",
      " [0.3322296  0.1917205  0.07266191 0.403388  ]\n",
      " [0.41802126 0.35348523 0.11505357 0.11344004]\n",
      " [0.24360323 0.56198394 0.12582994 0.06858295]\n",
      " [0.46190578 0.20896338 0.10155565 0.22757511]\n",
      " [0.2594243  0.4267853  0.11593365 0.19785681]\n",
      " [0.19251554 0.62423015 0.12448575 0.05876861]\n",
      " [0.19431613 0.6574633  0.11344035 0.03478012]\n",
      " [0.3322296  0.1917205  0.07266191 0.403388  ]\n",
      " [0.17748657 0.629341   0.09517867 0.09799371]\n",
      " [0.6503915  0.15401526 0.08844212 0.10715114]\n",
      " [0.5260871  0.19296516 0.10072329 0.18022454]\n",
      " [0.19837558 0.5498699  0.07770663 0.17404795]\n",
      " [0.16493131 0.7710144  0.05080698 0.01324724]\n",
      " [0.24556175 0.56830776 0.13017695 0.05595357]\n",
      " [0.3322296  0.1917205  0.07266191 0.403388  ]\n",
      " [0.2247984  0.58298844 0.11252606 0.07968709]\n",
      " [0.21296096 0.43898904 0.10259037 0.24545965]\n",
      " [0.4651486  0.23891118 0.0710536  0.22488663]\n",
      " [0.3322296  0.1917205  0.07266191 0.403388  ]\n",
      " [0.19608465 0.64733386 0.10656834 0.05001315]\n",
      " [0.3322296  0.1917205  0.07266191 0.403388  ]\n",
      " [0.21625239 0.41818392 0.10225294 0.26331076]\n",
      " [0.18881841 0.47432047 0.08952561 0.24733548]\n",
      " [0.22258289 0.42252463 0.10772208 0.2471704 ]\n",
      " [0.20756431 0.5621281  0.13763455 0.09267305]\n",
      " [0.20554616 0.44793338 0.0975498  0.24897061]\n",
      " [0.1787209  0.65038353 0.11773667 0.05315885]\n",
      " [0.32376882 0.35539135 0.12485441 0.19598542]\n",
      " [0.2662277  0.56480813 0.11510163 0.05386264]\n",
      " [0.3322296  0.1917205  0.07266191 0.403388  ]\n",
      " [0.23087396 0.41609192 0.11375201 0.23928213]\n",
      " [0.3322296  0.1917205  0.07266191 0.403388  ]\n",
      " [0.2588277  0.4390712  0.13560115 0.16649997]\n",
      " [0.15157871 0.78574765 0.05072094 0.01195265]\n",
      " [0.21889444 0.42775032 0.10570315 0.24765216]\n",
      " [0.24165757 0.5673914  0.12882759 0.06212344]\n",
      " [0.07496197 0.8279192  0.05879299 0.03832586]\n",
      " [0.58249867 0.2222375  0.05161304 0.14365079]\n",
      " [0.19801559 0.4883225  0.08400469 0.22965725]\n",
      " [0.3322296  0.1917205  0.07266191 0.403388  ]\n",
      " [0.21121629 0.6227367  0.10958591 0.05646113]\n",
      " [0.26103306 0.3769893  0.12537521 0.23660243]\n",
      " [0.3322296  0.1917205  0.07266191 0.403388  ]\n",
      " [0.47166434 0.2383066  0.0697399  0.22028914]\n",
      " [0.3322296  0.1917205  0.07266191 0.403388  ]\n",
      " [0.17972551 0.48388785 0.0835806  0.25280604]\n",
      " [0.3322296  0.1917205  0.07266191 0.403388  ]\n",
      " [0.1990993  0.48396066 0.10167271 0.21526735]\n",
      " [0.37758443 0.3813683  0.14619121 0.09485599]\n",
      " [0.39052212 0.20458591 0.08641857 0.3184734 ]\n",
      " [0.23941198 0.5095837  0.14942794 0.10157643]\n",
      " [0.3322296  0.1917205  0.07266191 0.403388  ]\n",
      " [0.44745877 0.24324687 0.1111901  0.19810437]\n",
      " [0.20894639 0.5175544  0.1277407  0.14575845]\n",
      " [0.3322296  0.1917205  0.07266191 0.403388  ]\n",
      " [0.4073889  0.2184379  0.06665972 0.30751362]\n",
      " [0.14308637 0.691829   0.1036394  0.06144521]\n",
      " [0.5700258  0.17211454 0.09525149 0.16260816]\n",
      " [0.6917354  0.12065802 0.0795885  0.1080181 ]\n",
      " [0.22886588 0.42374206 0.11415143 0.23324066]\n",
      " [0.4345848  0.20020972 0.05492567 0.31027973]\n",
      " [0.3322296  0.1917205  0.07266191 0.403388  ]\n",
      " [0.15318532 0.70058066 0.09122232 0.05501171]\n",
      " [0.15039812 0.5309882  0.06732014 0.25129342]\n",
      " [0.5785149  0.19014135 0.10310687 0.12823692]\n",
      " [0.1656859  0.62537616 0.11454623 0.09439164]\n",
      " [0.450013   0.24755558 0.07769839 0.224733  ]\n",
      " [0.15691267 0.65717065 0.11201263 0.07390407]\n",
      " [0.14485925 0.6097149  0.07871682 0.16670904]\n",
      " [0.2046844  0.44468236 0.09674323 0.25389004]\n",
      " [0.22297129 0.5736869  0.13639835 0.06694338]\n",
      " [0.2130484  0.5967625  0.10642044 0.08376872]\n",
      " [0.36809298 0.44376722 0.10881047 0.07932936]\n",
      " [0.2116033  0.44447017 0.10309031 0.24083628]\n",
      " [0.12246084 0.76483977 0.08506881 0.02763054]\n",
      " [0.31505013 0.5298963  0.09299174 0.06206185]\n",
      " [0.30904332 0.48052618 0.07213234 0.13829817]\n",
      " [0.5994883  0.14885107 0.08755215 0.16410848]\n",
      " [0.6278106  0.19347933 0.07607801 0.10263205]\n",
      " [0.5204344  0.1953949  0.10122336 0.18294734]\n",
      " [0.16082318 0.6574448  0.11362687 0.06810516]\n",
      " [0.30349895 0.42232344 0.14640103 0.12777658]\n",
      " [0.26700184 0.46720544 0.1059006  0.1598922 ]\n",
      " [0.17326531 0.49463192 0.08130279 0.25079998]\n",
      " [0.4716601  0.24254951 0.0751413  0.21064909]\n",
      " [0.4311735  0.19851856 0.05381963 0.31648827]\n",
      " [0.59319603 0.16625668 0.09193321 0.14861412]\n",
      " [0.35194376 0.19854578 0.07582035 0.3736901 ]\n",
      " [0.11341796 0.77434856 0.08199282 0.0302406 ]\n",
      " [0.3542507  0.19394332 0.07690179 0.3749043 ]\n",
      " [0.3322296  0.1917205  0.07266191 0.403388  ]\n",
      " [0.3322296  0.1917205  0.07266191 0.403388  ]\n",
      " [0.40005195 0.19754651 0.06042532 0.3419762 ]\n",
      " [0.26010874 0.46661055 0.15503284 0.11824787]\n",
      " [0.3322296  0.1917205  0.07266191 0.403388  ]\n",
      " [0.17460807 0.6559865  0.1161221  0.05328333]\n",
      " [0.5770272  0.17432526 0.09660868 0.15203886]\n",
      " [0.38118863 0.2134542  0.06840156 0.33695552]\n",
      " [0.26139098 0.49652347 0.13977987 0.10230576]\n",
      " [0.27671224 0.4713414  0.1523356  0.09961083]\n",
      " [0.18778229 0.6382839  0.1209556  0.05297816]\n",
      " [0.11823355 0.75860006 0.08643059 0.03673577]\n",
      " [0.11313803 0.7849359  0.07874144 0.02318462]\n",
      " [0.21962842 0.52673596 0.14395441 0.10968121]\n",
      " [0.16699551 0.6373147  0.11761051 0.07807931]\n",
      " [0.6786878  0.13875148 0.08231407 0.10024659]\n",
      " [0.18453169 0.48084173 0.0872382  0.24738838]\n",
      " [0.52350175 0.21720937 0.05322926 0.20605953]\n",
      " [0.48013034 0.2737486  0.12568122 0.12043981]\n",
      " [0.3322296  0.1917205  0.07266191 0.403388  ]\n",
      " [0.13600321 0.6924221  0.09869001 0.07288472]\n",
      " [0.23647729 0.4984488  0.1504849  0.11458898]\n",
      " [0.17990433 0.6391762  0.12023725 0.06068222]\n",
      " [0.2046844  0.44468236 0.09674323 0.25389004]\n",
      " [0.53987277 0.23184672 0.11659775 0.11168277]\n",
      " [0.3322296  0.1917205  0.07266191 0.403388  ]\n",
      " [0.19382648 0.6577245  0.10431643 0.04413259]\n",
      " [0.3322296  0.1917205  0.07266191 0.403388  ]\n",
      " [0.6587966  0.15394175 0.0891684  0.09809322]\n",
      " [0.60758543 0.14977112 0.0886708  0.15397267]\n",
      " [0.3322296  0.1917205  0.07266191 0.403388  ]\n",
      " [0.5488585  0.22838935 0.0556272  0.16712494]\n",
      " [0.3322296  0.1917205  0.07266191 0.403388  ]\n",
      " [0.21088664 0.5697489  0.13687979 0.08248468]\n",
      " [0.3322296  0.1917205  0.07266191 0.403388  ]\n",
      " [0.3199226  0.43030965 0.15022054 0.09954716]\n",
      " [0.17508885 0.60309845 0.11917417 0.10263855]\n",
      " [0.20867352 0.59830284 0.13076429 0.06225938]\n",
      " [0.3322296  0.1917205  0.07266191 0.403388  ]\n",
      " [0.3322296  0.1917205  0.07266191 0.403388  ]\n",
      " [0.32366687 0.27235946 0.09630653 0.30766705]\n",
      " [0.21840754 0.44322464 0.109737   0.2286308 ]\n",
      " [0.11134206 0.7820057  0.07978223 0.02687003]\n",
      " [0.23399772 0.51508445 0.14811014 0.10280763]\n",
      " [0.1842568  0.6349414  0.12155975 0.059242  ]\n",
      " [0.3322296  0.1917205  0.07266191 0.403388  ]\n",
      " [0.3706563  0.40667546 0.14675999 0.07590823]\n",
      " [0.2137485  0.5692086  0.13721588 0.07982694]\n",
      " [0.3322296  0.1917205  0.07266191 0.403388  ]\n",
      " [0.13218503 0.70981395 0.09739516 0.06060582]\n",
      " [0.3322296  0.1917205  0.07266191 0.403388  ]\n",
      " [0.20648178 0.5883576  0.13297911 0.07218143]\n",
      " [0.4834868  0.21385936 0.05445909 0.24819475]\n",
      " [0.1640379  0.7338443  0.07807449 0.02404328]\n",
      " [0.32274637 0.44731733 0.14900975 0.08092657]\n",
      " [0.22081338 0.42562658 0.10675257 0.24680744]\n",
      " [0.33240035 0.42255265 0.11830882 0.1267382 ]\n",
      " [0.3152273  0.4761919  0.12469895 0.08388182]\n",
      " [0.45704886 0.32050726 0.13857107 0.0838728 ]\n",
      " [0.6085437  0.1449908  0.08642256 0.160043  ]\n",
      " [0.20086655 0.6147526  0.1267769  0.05760396]\n",
      " [0.290771   0.5433182  0.10787355 0.05803725]\n",
      " [0.2243387  0.5831787  0.12735711 0.06512548]\n",
      " [0.2437366  0.47449657 0.15108192 0.13068497]\n",
      " [0.17005092 0.65287966 0.11629127 0.06077818]\n",
      " [0.3322296  0.1917205  0.07266191 0.403388  ]\n",
      " [0.3322296  0.1917205  0.07266191 0.403388  ]\n",
      " [0.21889444 0.42775032 0.10570315 0.24765216]\n",
      " [0.20805235 0.60133994 0.1296812  0.06092653]\n",
      " [0.3322296  0.1917205  0.07266191 0.403388  ]\n",
      " [0.12241823 0.7101682  0.08792823 0.07948534]\n",
      " [0.21882942 0.55459493 0.14036135 0.0862143 ]\n",
      " [0.40330493 0.20505689 0.08914966 0.3024885 ]\n",
      " [0.45793834 0.21992105 0.07951045 0.24263017]]\n"
     ]
    }
   ],
   "source": [
    "a = model.predict(X_test)\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "efcfb265",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 3, 1, 1, 3, 1, 3, 1, 1, 0, 0, 0, 3, 3, 0, 3, 1, 0, 1, 1, 1, 3, 1, 1, 1, 1, 1, 3, 0, 1, 0, 1, 1, 1, 3, 1, 0, 0, 1, 1, 1, 3, 1, 1, 0, 3, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 3, 1, 1, 1, 1, 1, 0, 1, 3, 1, 1, 3, 0, 3, 1, 3, 1, 1, 0, 1, 3, 0, 1, 3, 0, 1, 0, 0, 1, 0, 3, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 3, 1, 3, 3, 3, 0, 1, 3, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 3, 1, 1, 1, 1, 0, 3, 1, 3, 0, 0, 3, 0, 3, 1, 3, 1, 1, 1, 3, 3, 0, 1, 1, 1, 1, 3, 1, 1, 3, 1, 3, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 3, 3, 1, 1, 3, 1, 1, 0, 0]\n"
     ]
    }
   ],
   "source": [
    "b = []\n",
    "for i in a:\n",
    "    if i[0] > i[1] and i[0] > i[2] and i[0] > i[3]:\n",
    "        b.append(0)\n",
    "    elif i[1] > i[0] and i[1] > i[2] and i[1] > i[3]:\n",
    "        b.append(1)\n",
    "    elif i[2] > i[1] and i[2] > i[0] and i[2] > i[3]:\n",
    "        b.append(2)\n",
    "    else:\n",
    "        b.append(3)\n",
    "\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "b9dcfe10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score :  0.445\n",
      "\n",
      "\n",
      "Confusion Matrix : \n",
      "\n",
      " [[22 30  0  8]\n",
      " [ 9 56  0 18]\n",
      " [ 6 10  0  3]\n",
      " [ 9 18  0 11]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix,f1_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "print(\"Accuracy Score : \",accuracy_score(y_test,b))\n",
    "cm = confusion_matrix(y_test, b)\n",
    "print(\"\\n\\nConfusion Matrix : \\n\\n\",cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "89819408",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# Assuming cm is your confusion matrix\n",
    "ax = plt.subplot()\n",
    "sns.heatmap(cm, annot=True, fmt='g', ax=ax)\n",
    "\n",
    "# Fixing the issue with tick labels\n",
    "classes = ['Cyst', 'Normal', 'Stone', 'Tumor']\n",
    "tick_positions = [0.5, 1.5, 2.5, 3.5]\n",
    "\n",
    "ax.set_xlabel('Predicted labels')\n",
    "ax.set_ylabel('True labels')\n",
    "ax.set_title('Confusion Matrix') \n",
    "ax.set_xticks(tick_positions)\n",
    "ax.set_yticks(tick_positions)\n",
    "ax.set_xticklabels(classes)\n",
    "ax.set_yticklabels(classes)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "0965ef35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 Score is : 0.445\n"
     ]
    }
   ],
   "source": [
    "print(\"F1 Score is :\",f1_score(y_test,b,average='micro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "9563eb68",
   "metadata": {},
   "outputs": [],
   "source": [
    "pic = []\n",
    "image='resized dataset/Resized Folder 2x2/Cyst/Cyst- (1052).jpg'\n",
    "img = cv2.imread(str(image))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "192b7288",
   "metadata": {},
   "outputs": [],
   "source": [
    "pic.append(img)\n",
    "pic1 = np.array(pic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "69a9730a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 36ms/step\n"
     ]
    }
   ],
   "source": [
    "a = model.predict(pic1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "db281d97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The detected disease is \"Tumor\"\n"
     ]
    }
   ],
   "source": [
    "if a.argmax() == 0 :\n",
    "    print(\"The detected disease is \\\"Cyst\\\"\")\n",
    "elif a.argmax() == 1 :\n",
    "    print(\"Kidney is \\\"Normal\\\" \")\n",
    "elif a.argmax() == 2:\n",
    "    print(\"The detected disease is \\\"Stone\\\"\")\n",
    "else:\n",
    "    print(\"The detected disease is \\\"Tumor\\\"\")"
   ]
  }
 ],
 "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}