20_Final_Project/unet_batterry.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\pinb\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\tqdm\\auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from glob import glob\n",
    "import numpy as np\n",
    "import torch\n",
    "from torch.utils.data import Dataset\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "from torchvision import transforms, datasets\n",
    "import random\n",
    "import cv2\n",
    "\n",
    "class CustomDataset(Dataset):\n",
    "    def __init__(self, list_imgs, list_masks, transform=None):\n",
    "        self.list_imgs = list_imgs\n",
    "        self.list_masks = list_masks\n",
    "        self.transform = transform\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.list_imgs)\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        img_path = self.list_imgs[index]\n",
    "        mask_path = self.list_masks[index]\n",
    "\n",
    "        img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)\n",
    "        mask = cv2.imread(mask_path, cv2.IMREAD_GRAYSCALE)\n",
    "\n",
    "        # 이미지 크기를 512x512로 변경\n",
    "        img = cv2.resize(img, (512, 512), interpolation=cv2.INTER_LINEAR)\n",
    "        mask = cv2.resize(mask, (512, 512), interpolation=cv2.INTER_NEAREST)\n",
    "\n",
    "        img = img.astype(np.float32) / 255.0\n",
    "        mask = mask.astype(np.float32) / 255.0\n",
    "\n",
    "        if img.ndim == 2:\n",
    "            img = img[:, :, np.newaxis]\n",
    "        if mask.ndim == 2:\n",
    "            mask = mask[:, :, np.newaxis]\n",
    "\n",
    "        data = {'input': img, 'label': mask}\n",
    "\n",
    "        if self.transform:\n",
    "            data = self.transform(data)\n",
    "        \n",
    "        return data\n",
    "\n",
    "def create_datasets(img_dir, mask_dir, train_ratio=0.7, val_ratio=0.2, transform=None):\n",
    "    list_imgs = sorted(glob(os.path.join(img_dir, '**', '*.png'), recursive=True))\n",
    "    list_masks = sorted(glob(os.path.join(mask_dir, '**', '*.png'), recursive=True))\n",
    "\n",
    "    combined = list(zip(list_imgs, list_masks))\n",
    "    random.shuffle(combined)\n",
    "    list_imgs, list_masks = zip(*combined)\n",
    "\n",
    "    num_imgs = len(list_imgs)\n",
    "    num_train = int(num_imgs * train_ratio)\n",
    "    num_val = int(num_imgs * val_ratio)\n",
    "\n",
    "    train_set = CustomDataset(list_imgs[:num_train], list_masks[:num_train], transform)\n",
    "    val_set = CustomDataset(list_imgs[num_train:num_train + num_val], list_masks[num_train:num_train + num_val], transform)\n",
    "    test_set = CustomDataset(list_imgs[num_train + num_val:], list_masks[num_train + num_val:], transform)\n",
    "\n",
    "    return train_set, val_set, test_set\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test Dataset Loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 사용 예시\n",
    "dir_img = 'C:/Users/pinb/Desktop/imgs'\n",
    "dir_mask = 'C:/Users/pinb/Desktop/masks'\n",
    "train_set, val_set, test_set = create_datasets(dir_img, dir_mask)\n",
    "\n",
    "# 첫 번째 이미지 확인 (예: train set)\n",
    "data = train_set.__getitem__(7777)  # 이미지 불러오기\n",
    "\n",
    "input_img = data['input']\n",
    "label = data['label']\n",
    "\n",
    "# 이미지 시각화\n",
    "plt.subplot(121)\n",
    "plt.imshow(input_img.reshape(input_img.shape[0], input_img.shape[1]), cmap='gray')\n",
    "plt.title('Input Image')\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.imshow(label.reshape(label.shape[0], label.shape[1]), cmap='gray')\n",
    "plt.title('Label')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Argumentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 트렌스폼 구현하기\n",
    "class ToTensor(object):\n",
    "    # def __call__(self, data):\n",
    "    #     label, input = data['label'], data['input']\n",
    "\n",
    "    #     label = label.transpose((2, 0, 1)).astype(np.float32)\n",
    "    #     input = input.transpose((2, 0, 1)).astype(np.float32)\n",
    "\n",
    "    #     data = {'label': torch.from_numpy(label), 'input': torch.from_numpy(input)}\n",
    "\n",
    "    #     return data\n",
    "    def __call__(self, data):\n",
    "        label, input = data['label'], data['input']\n",
    "\n",
    "        # 이미지가 이미 그레이스케일이면 채널 차원 추가\n",
    "        if label.ndim == 2:\n",
    "            label = label[:, :, np.newaxis]\n",
    "        if input.ndim == 2:\n",
    "            input = input[:, :, np.newaxis]\n",
    "\n",
    "        # 채널을 첫 번째 차원으로 이동\n",
    "        label = label.transpose((2, 0, 1)).astype(np.float32)\n",
    "        input = input.transpose((2, 0, 1)).astype(np.float32)\n",
    "\n",
    "        data = {'label': torch.from_numpy(label), 'input': torch.from_numpy(input)}\n",
    "\n",
    "        return data\n",
    "\n",
    "class Normalization(object):\n",
    "    def __init__(self, mean=0.5, std=0.5):\n",
    "        self.mean = mean\n",
    "        self.std = std\n",
    "\n",
    "    def __call__(self, data):\n",
    "        label, input = data['label'], data['input']\n",
    "\n",
    "        input = (input - self.mean) / self.std\n",
    "\n",
    "        data = {'label': label, 'input': input}\n",
    "\n",
    "        return data\n",
    "\n",
    "class RandomFlip(object):\n",
    "    def __call__(self, data):\n",
    "        label, input = data['label'], data['input']\n",
    "\n",
    "        if np.random.rand() > 0.5:\n",
    "            label = np.fliplr(label)\n",
    "            input = np.fliplr(input)\n",
    "\n",
    "        if np.random.rand() > 0.5:\n",
    "            label = np.flipud(label)\n",
    "            input = np.flipud(input)\n",
    "\n",
    "        data = {'label': label, 'input': input}\n",
    "\n",
    "        return data\n",
    "    \n",
    "# class Resize(object):\n",
    "#     def __init__(self, output_size):\n",
    "#         assert isinstance(output_size, (int, tuple))\n",
    "#         self.output_size = output_size\n",
    "\n",
    "#     def __call__(self, data):\n",
    "#         label, input = data['label'], data['input']\n",
    "\n",
    "#         h, w = input.shape[:2]\n",
    "#         if isinstance(self.output_size, int):\n",
    "#             if h > w:\n",
    "#                 new_h, new_w = self.output_size * h / w, self.output_size\n",
    "#             else:\n",
    "#                 new_h, new_w = self.output_size, self.output_size * w / h\n",
    "#         else:\n",
    "#             new_h, new_w = self.output_size\n",
    "\n",
    "#         new_h, new_w = int(new_h), int(new_w)\n",
    "\n",
    "#         input = cv2.resize(input, (new_w, new_h))\n",
    "#         label = cv2.resize(label, (new_w, new_h))\n",
    "\n",
    "#         return {'label': label, 'input': input}\n",
    "\n",
    "class Rotate(object):\n",
    "    def __init__(self, angle_range):\n",
    "        assert isinstance(angle_range, (tuple, list)) and len(angle_range) == 2\n",
    "        self.angle_min, self.angle_max = angle_range\n",
    "\n",
    "    def __call__(self, data):\n",
    "        label, input = data['label'], data['input']\n",
    "\n",
    "        # NumPy 배열로 변환 (필요한 경우)\n",
    "        if not isinstance(input, np.ndarray):\n",
    "            input = np.array(input)\n",
    "        if not isinstance(label, np.ndarray):\n",
    "            label = np.array(label)\n",
    "\n",
    "        # (H, W, C) 형태를 (H, W)로 변경 (필요한 경우)\n",
    "        if input.ndim == 3 and input.shape[2] == 1:\n",
    "            input = input.squeeze(2)\n",
    "        if label.ndim == 3 and label.shape[2] == 1:\n",
    "            label = label.squeeze(2)\n",
    "\n",
    "        # 랜덤 각도 선택 및 회전 적용\n",
    "        angle = np.random.uniform(self.angle_min, self.angle_max)\n",
    "        h, w = input.shape[:2]\n",
    "        center = (w / 2, h / 2)\n",
    "        rot_matrix = cv2.getRotationMatrix2D(center, angle, 1.0)\n",
    "        input = cv2.warpAffine(input, rot_matrix, (w, h))\n",
    "        label = cv2.warpAffine(label, rot_matrix, (w, h))\n",
    "\n",
    "        return {'label': label, 'input': input}\n",
    "    \n",
    "# class Crop(object):\n",
    "#     def __init__(self, output_size):\n",
    "#         assert isinstance(output_size, (int, tuple))\n",
    "#         if isinstance(output_size, int):\n",
    "#             self.output_size = (output_size, output_size)\n",
    "#         else:\n",
    "#             assert len(output_size) == 2\n",
    "#             self.output_size = output_size\n",
    "\n",
    "#     def __call__(self, data):\n",
    "#         label, input = data['label'], data['input']\n",
    "\n",
    "#         h, w = input.shape[:2]\n",
    "#         new_h, new_w = self.output_size\n",
    "\n",
    "#         top = np.random.randint(0, h - new_h)\n",
    "#         left = np.random.randint(0, w - new_w)\n",
    "\n",
    "#         input = input[top: top + new_h, left: left + new_w]\n",
    "#         label = label[top: top + new_h, left: left + new_w]\n",
    "\n",
    "#         return {'label': label, 'input': input}\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test Arguemtation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 트랜스폼 잘 구현되었는지 확인\n",
    "transform = transforms.Compose([Normalization(mean=0.5, std=0.5), RandomFlip(), Rotate(angle_range=(-90, 90)), ToTensor()])\n",
    "\n",
    "dir_img = 'C:/Users/pinb/Desktop/imgs'\n",
    "dir_mask = 'C:/Users/pinb/Desktop/masks'\n",
    "train_set, val_set, test_set = create_datasets(img_dir=dir_img, mask_dir=dir_mask, transform=transform)\n",
    "\n",
    "\n",
    "data = train_set.__getitem__(12599) # 한 이미지 불러오기\n",
    "input = data['input']\n",
    "label = data['label']\n",
    "\n",
    "# 불러온 이미지 시각화\n",
    "plt.subplot(122)\n",
    "plt.hist(label.flatten(), bins=20)\n",
    "plt.title('label')\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.hist(input.flatten(), bins=20)\n",
    "plt.title('input')\n",
    "\n",
    "# 이미지 시각화\n",
    "plt.subplot(121)\n",
    "plt.imshow(input.squeeze(), cmap='gray')\n",
    "plt.title('Input Image')\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.imshow(label.squeeze(), cmap='gray')\n",
    "plt.title('Label')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Network (Origin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 라이브러리 불러오기\n",
    "import os\n",
    "import numpy as np\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import DataLoader\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "## 네트워크 구축하기\n",
    "class UNet(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(UNet, self).__init__()\n",
    "\n",
    "        # Convolution + BatchNormalization + Relu 정의하기\n",
    "        def CBR2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=True): \n",
    "            layers = []\n",
    "            layers += [nn.Conv2d(in_channels=in_channels, out_channels=out_channels,\n",
    "                                 kernel_size=kernel_size, stride=stride, padding=padding,\n",
    "                                 bias=bias)]\n",
    "            layers += [nn.BatchNorm2d(num_features=out_channels)]\n",
    "            layers += [nn.ReLU()]\n",
    "\n",
    "            cbr = nn.Sequential(*layers)\n",
    "\n",
    "            return cbr\n",
    "\n",
    "        # 수축 경로(Contracting path)\n",
    "        self.enc1_1 = CBR2d(in_channels=1, out_channels=64)\n",
    "        self.enc1_2 = CBR2d(in_channels=64, out_channels=64)\n",
    "\n",
    "        self.pool1 = nn.MaxPool2d(kernel_size=2)\n",
    "\n",
    "        self.enc2_1 = CBR2d(in_channels=64, out_channels=128)\n",
    "        self.enc2_2 = CBR2d(in_channels=128, out_channels=128)\n",
    "\n",
    "        self.pool2 = nn.MaxPool2d(kernel_size=2)\n",
    "\n",
    "        self.enc3_1 = CBR2d(in_channels=128, out_channels=256)\n",
    "        self.enc3_2 = CBR2d(in_channels=256, out_channels=256)\n",
    "\n",
    "        self.pool3 = nn.MaxPool2d(kernel_size=2)\n",
    "\n",
    "        self.enc4_1 = CBR2d(in_channels=256, out_channels=512)\n",
    "        self.enc4_2 = CBR2d(in_channels=512, out_channels=512)\n",
    "\n",
    "        self.pool4 = nn.MaxPool2d(kernel_size=2)\n",
    "\n",
    "        self.enc5_1 = CBR2d(in_channels=512, out_channels=1024)\n",
    "\n",
    "        # 확장 경로(Expansive path)\n",
    "        self.dec5_1 = CBR2d(in_channels=1024, out_channels=512)\n",
    "\n",
    "        self.unpool4 = nn.ConvTranspose2d(in_channels=512, out_channels=512,\n",
    "                                          kernel_size=2, stride=2, padding=0, bias=True)\n",
    "\n",
    "        self.dec4_2 = CBR2d(in_channels=2 * 512, out_channels=512)\n",
    "        self.dec4_1 = CBR2d(in_channels=512, out_channels=256)\n",
    "\n",
    "        self.unpool3 = nn.ConvTranspose2d(in_channels=256, out_channels=256,\n",
    "                                          kernel_size=2, stride=2, padding=0, bias=True)\n",
    "\n",
    "        self.dec3_2 = CBR2d(in_channels=2 * 256, out_channels=256)\n",
    "        self.dec3_1 = CBR2d(in_channels=256, out_channels=128)\n",
    "\n",
    "        self.unpool2 = nn.ConvTranspose2d(in_channels=128, out_channels=128,\n",
    "                                          kernel_size=2, stride=2, padding=0, bias=True)\n",
    "\n",
    "        self.dec2_2 = CBR2d(in_channels=2 * 128, out_channels=128)\n",
    "        self.dec2_1 = CBR2d(in_channels=128, out_channels=64)\n",
    "\n",
    "        self.unpool1 = nn.ConvTranspose2d(in_channels=64, out_channels=64,\n",
    "                                          kernel_size=2, stride=2, padding=0, bias=True)\n",
    "\n",
    "        self.dec1_2 = CBR2d(in_channels=2 * 64, out_channels=64)\n",
    "        self.dec1_1 = CBR2d(in_channels=64, out_channels=64)\n",
    "\n",
    "        self.fc = nn.Conv2d(in_channels=64, out_channels=1, kernel_size=1, stride=1, padding=0, bias=True)\n",
    "    \n",
    "    # forward 함수 정의하기\n",
    "    def forward(self, x):\n",
    "        enc1_1 = self.enc1_1(x)\n",
    "        enc1_2 = self.enc1_2(enc1_1)\n",
    "        pool1 = self.pool1(enc1_2)\n",
    "\n",
    "        enc2_1 = self.enc2_1(pool1)\n",
    "        enc2_2 = self.enc2_2(enc2_1)\n",
    "        pool2 = self.pool2(enc2_2)\n",
    "\n",
    "        enc3_1 = self.enc3_1(pool2)\n",
    "        enc3_2 = self.enc3_2(enc3_1)\n",
    "        pool3 = self.pool3(enc3_2)\n",
    "\n",
    "        enc4_1 = self.enc4_1(pool3)\n",
    "        enc4_2 = self.enc4_2(enc4_1)\n",
    "        pool4 = self.pool4(enc4_2)\n",
    "\n",
    "        enc5_1 = self.enc5_1(pool4)\n",
    "\n",
    "        dec5_1 = self.dec5_1(enc5_1)\n",
    "\n",
    "        unpool4 = self.unpool4(dec5_1)\n",
    "        cat4 = torch.cat((unpool4, enc4_2), dim=1)\n",
    "        dec4_2 = self.dec4_2(cat4)\n",
    "        dec4_1 = self.dec4_1(dec4_2)\n",
    "\n",
    "        unpool3 = self.unpool3(dec4_1)\n",
    "        cat3 = torch.cat((unpool3, enc3_2), dim=1)\n",
    "        dec3_2 = self.dec3_2(cat3)\n",
    "        dec3_1 = self.dec3_1(dec3_2)\n",
    "\n",
    "        unpool2 = self.unpool2(dec3_1)\n",
    "        cat2 = torch.cat((unpool2, enc2_2), dim=1)\n",
    "        dec2_2 = self.dec2_2(cat2)\n",
    "        dec2_1 = self.dec2_1(dec2_2)\n",
    "\n",
    "        unpool1 = self.unpool1(dec2_1)\n",
    "        cat1 = torch.cat((unpool1, enc1_2), dim=1)\n",
    "        dec1_2 = self.dec1_2(cat1)\n",
    "        dec1_1 = self.dec1_1(dec1_2)\n",
    "\n",
    "        x = self.fc(dec1_1)\n",
    "\n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Network (Mini)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 라이브러리 불러오기\n",
    "import os\n",
    "import numpy as np\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import DataLoader\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "## 네트워크 구축하기\n",
    "class UNet(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(UNet, self).__init__()\n",
    "\n",
    "        # Convolution + BatchNormalization + Relu 정의하기\n",
    "        def CBR2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=True): \n",
    "            layers = []\n",
    "            layers += [nn.Conv2d(in_channels=in_channels, out_channels=out_channels,\n",
    "                                 kernel_size=kernel_size, stride=stride, padding=padding,\n",
    "                                 bias=bias)]\n",
    "            layers += [nn.BatchNorm2d(num_features=out_channels)]\n",
    "            layers += [nn.ReLU()]\n",
    "\n",
    "            cbr = nn.Sequential(*layers)\n",
    "\n",
    "            return cbr\n",
    "\n",
    "        # 수축 경로(Contracting path)\n",
    "        self.enc1_1 = CBR2d(in_channels=1, out_channels=64)\n",
    "        self.pool1 = nn.MaxPool2d(kernel_size=2)\n",
    "\n",
    "        self.enc2_1 = CBR2d(in_channels=64, out_channels=128)\n",
    "        self.pool2 = nn.MaxPool2d(kernel_size=2)\n",
    "\n",
    "        self.enc3_1 = CBR2d(in_channels=128, out_channels=256)\n",
    "        self.pool3 = nn.MaxPool2d(kernel_size=2)\n",
    "\n",
    "        self.enc4_1 = CBR2d(in_channels=256, out_channels=512)\n",
    "        self.pool4 = nn.MaxPool2d(kernel_size=2)\n",
    "\n",
    "        self.enc5_1 = CBR2d(in_channels=512, out_channels=1024)\n",
    "\n",
    "        # 확장 경로(Expansive path)의 깊이 감소\n",
    "        self.dec5_1 = CBR2d(in_channels=1024, out_channels=512)\n",
    "        self.unpool4 = nn.ConvTranspose2d(in_channels=512, out_channels=512, kernel_size=2, stride=2)\n",
    "\n",
    "        self.dec4_1 = CBR2d(in_channels=512 + 512, out_channels=256)\n",
    "        self.unpool3 = nn.ConvTranspose2d(in_channels=256, out_channels=256, kernel_size=2, stride=2)\n",
    "\n",
    "        self.dec3_1 = CBR2d(in_channels=256 + 256, out_channels=128)\n",
    "        self.unpool2 = nn.ConvTranspose2d(in_channels=128, out_channels=128, kernel_size=2, stride=2)\n",
    "\n",
    "        self.dec2_1 = CBR2d(in_channels=128 + 128, out_channels=64)\n",
    "        self.unpool1 = nn.ConvTranspose2d(in_channels=64, out_channels=64, kernel_size=2, stride=2)\n",
    "\n",
    "        self.dec1_1 = CBR2d(in_channels=64 + 64, out_channels=64)\n",
    "        self.fc = nn.Conv2d(in_channels=64, out_channels=1, kernel_size=1, stride=1, padding=0, bias=True)\n",
    "    \n",
    "    # forward 함수 정의하기\n",
    "    def forward(self, x):\n",
    "        enc1_1 = self.enc1_1(x)\n",
    "        pool1 = self.pool1(enc1_1)\n",
    "\n",
    "        enc2_1 = self.enc2_1(pool1)\n",
    "        pool2 = self.pool2(enc2_1)\n",
    "\n",
    "        enc3_1 = self.enc3_1(pool2)\n",
    "        pool3 = self.pool3(enc3_1)\n",
    "\n",
    "        enc4_1 = self.enc4_1(pool3)\n",
    "        pool4 = self.pool4(enc4_1)\n",
    "\n",
    "        enc5_1 = self.enc5_1(pool4)\n",
    "\n",
    "        dec5_1 = self.dec5_1(enc5_1)\n",
    "\n",
    "        unpool4 = self.unpool4(dec5_1)\n",
    "        cat4 = torch.cat((unpool4, enc4_1), dim=1)\n",
    "        dec4_1 = self.dec4_1(cat4)\n",
    "\n",
    "        unpool3 = self.unpool3(dec4_1)\n",
    "        cat3 = torch.cat((unpool3, enc3_1), dim=1)\n",
    "        dec3_1 = self.dec3_1(cat3)\n",
    "\n",
    "        unpool2 = self.unpool2(dec3_1)\n",
    "        cat2 = torch.cat((unpool2, enc2_1), dim=1)\n",
    "        dec2_1 = self.dec2_1(cat2)\n",
    "\n",
    "        unpool1 = self.unpool1(dec2_1)\n",
    "        cat1 = torch.cat((unpool1, enc1_1), dim=1)\n",
    "        dec1_1 = self.dec1_1(cat1)\n",
    "\n",
    "        x = self.fc(dec1_1)\n",
    "\n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load, Save Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 네트워크 저장하기\n",
    "def save(ckpt_dir, net, optim, epoch):\n",
    "    if not os.path.exists(ckpt_dir):\n",
    "        os.makedirs(ckpt_dir)\n",
    "\n",
    "    torch.save({'net': net.state_dict(), 'optim': optim.state_dict()},\n",
    "               \"%s/model_epoch%d.pth\" % (ckpt_dir, epoch))\n",
    "\n",
    "## 네트워크 불러오기\n",
    "def load(ckpt_dir, net, optim):\n",
    "    if not os.path.exists(ckpt_dir):\n",
    "        epoch = 0\n",
    "        return net, optim, epoch\n",
    "\n",
    "    ckpt_lst = os.listdir(ckpt_dir)\n",
    "    ckpt_lst.sort(key=lambda f: int(''.join(filter(str.isdigit, f))))\n",
    "\n",
    "    dict_model = torch.load('%s/%s' % (ckpt_dir, ckpt_lst[-1]))\n",
    "\n",
    "    net.load_state_dict(dict_model['net'])\n",
    "    optim.load_state_dict(dict_model['optim'])\n",
    "    epoch = int(ckpt_lst[-1].split('epoch')[1].split('.pth')[0])\n",
    "\n",
    "    return net, optim, epoch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.cuda.empty_cache()\n",
    "os.environ['PYTORCH_CUDA_ALLOC_CONF'] = 'max_split_size_mb:128'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dir_img = 'C:/Users/pinb/Desktop/imgs'\n",
    "dir_mask = 'C:/Users/pinb/Desktop/masks'\n",
    "transform = transforms.Compose([Normalization(mean=0.5, std=0.5), RandomFlip(), Rotate(angle_range=(-90, 90)), ToTensor()])\n",
    "train_set, val_set, test_set = create_datasets(img_dir=dir_img, mask_dir=dir_mask, transform=transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 훈련 파라미터 설정하기\n",
    "lr = 1e-3\n",
    "batch_size = 4\n",
    "num_epoch = 10\n",
    "\n",
    "# base_dir = './2nd_Battery/unet'\n",
    "# base_dir = './2nd_Battery/unet-mini'\n",
    "# base_dir = './2nd_Battery/unet-dice-loss'\n",
    "# base_dir = './2nd_Battery/unet-focal-loss'\n",
    "# base_dir = './2nd_Battery/unet-sgd'\n",
    "# base_dir = './2nd_Battery/unet-rmsprop'\n",
    "# base_dir = './2nd_Battery/unet-l1'\n",
    "base_dir = './2nd_Battery/unet-l2'\n",
    "ckpt_dir = os.path.join(base_dir, \"checkpoint\")\n",
    "log_dir = os.path.join(base_dir, \"log\")\n",
    "\n",
    "# 네트워크 생성하기\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "net = UNet().to(device)\n",
    "\n",
    "# 손실함수 정의하기\n",
    "fn_loss = nn.BCEWithLogitsLoss().to(device)\n",
    "\n",
    "# Optimizer 설정하기\n",
    "optim = torch.optim.Adam(net.parameters(), lr=lr)\n",
    "\n",
    "# 그 밖에 부수적인 functions 설정하기\n",
    "fn_tonumpy = lambda x: x.to('cpu').detach().numpy().transpose(0, 2, 3, 1)\n",
    "fn_denorm = lambda x, mean, std: (x * std) + mean\n",
    "fn_class = lambda x: 1.0 * (x > 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test Case - Dice Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DiceLoss(nn.Module):\n",
    "    def __init__(self, smooth=1e-6):\n",
    "        super(DiceLoss, self).__init__()\n",
    "        self.smooth = smooth\n",
    "\n",
    "    def forward(self, preds, targets):\n",
    "        preds = torch.sigmoid(preds)\n",
    "        intersection = (preds * targets).sum()\n",
    "        dice = (2. * intersection + self.smooth) / (preds.sum() + targets.sum() + self.smooth)\n",
    "        return 1 - dice\n",
    "\n",
    "fn_loss = DiceLoss().to(device)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test Case - Focal Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class FocalLoss(nn.Module):\n",
    "    def __init__(self, alpha=0.8, gamma=2.0):\n",
    "        super(FocalLoss, self).__init__()\n",
    "        self.alpha = alpha\n",
    "        self.gamma = gamma\n",
    "\n",
    "    def forward(self, preds, targets):\n",
    "        BCE = nn.functional.binary_cross_entropy_with_logits(preds, targets, reduction='none')\n",
    "        BCE_exp = torch.exp(-BCE)\n",
    "        focal_loss = self.alpha * (1 - BCE_exp) ** self.gamma * BCE\n",
    "        return focal_loss.mean()\n",
    "\n",
    "fn_loss = FocalLoss().to(device)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test Case - SGD Optimizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "optim = torch.optim.SGD(net.parameters(), lr=lr, momentum=0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test Case - RMSprop Optimizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "optim = torch.optim.RMSprop(net.parameters(), lr=lr, alpha=0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test Case - L1 Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class L1Loss(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(L1Loss, self).__init__()\n",
    "\n",
    "    def forward(self, preds, targets):\n",
    "        return torch.mean(torch.abs(preds - targets))\n",
    "    \n",
    "fn_loss = L1Loss().to(device)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test Case - L2 Loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "class L2Loss(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(L2Loss, self).__init__()\n",
    "\n",
    "    def forward(self, preds, targets):\n",
    "        return torch.mean((preds - targets) ** 2)\n",
    "    \n",
    "fn_loss = L2Loss().to(device)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: EPOCH 0001 / 0010 | BATCH 0001 / 3410 | LOSS 0.1304\n",
      "TRAIN: EPOCH 0001 / 0010 | BATCH 0002 / 3410 | LOSS 0.1869\n",
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      "TRAIN: EPOCH 0001 / 0010 | BATCH 0006 / 3410 | LOSS 0.1222\n",
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      "TRAIN: EPOCH 0001 / 0010 | BATCH 0009 / 3410 | LOSS 0.0988\n",
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      "TRAIN: EPOCH 0001 / 0010 | BATCH 0011 / 3410 | LOSS 0.0902\n",
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      "VALID: EPOCH 0001 / 0010 | BATCH 0416 / 0974 | LOSS 0.0045\n",
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      "VALID: EPOCH 0001 / 0010 | BATCH 0428 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0429 / 0974 | LOSS 0.0045\n",
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      "VALID: EPOCH 0001 / 0010 | BATCH 0431 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0432 / 0974 | LOSS 0.0045\n",
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      "VALID: EPOCH 0001 / 0010 | BATCH 0507 / 0974 | LOSS 0.0045\n",
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      "VALID: EPOCH 0001 / 0010 | BATCH 0509 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0510 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0511 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0512 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0513 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0514 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0515 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0516 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0517 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0518 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0519 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0520 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0521 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0522 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0523 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0524 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0525 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0526 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0527 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0528 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0529 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0530 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0531 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0532 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0533 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0534 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0535 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0536 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0537 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0538 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0539 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0540 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0541 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0542 / 0974 | LOSS 0.0045\n",
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      "VALID: EPOCH 0001 / 0010 | BATCH 0544 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0545 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0546 / 0974 | LOSS 0.0045\n",
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      "VALID: EPOCH 0001 / 0010 | BATCH 0550 / 0974 | LOSS 0.0045\n",
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      "VALID: EPOCH 0001 / 0010 | BATCH 0552 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0553 / 0974 | LOSS 0.0045\n",
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      "VALID: EPOCH 0001 / 0010 | BATCH 0701 / 0974 | LOSS 0.0045\n",
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      "VALID: EPOCH 0001 / 0010 | BATCH 0710 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0711 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0712 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0713 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0714 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0715 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0716 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0717 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0718 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0719 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0720 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0721 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0722 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0723 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0724 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0725 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0726 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0727 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0728 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0729 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0730 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0731 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0732 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0733 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0734 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0735 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0736 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0737 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0738 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0739 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0740 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0741 / 0974 | LOSS 0.0045\n",
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      "VALID: EPOCH 0001 / 0010 | BATCH 0743 / 0974 | LOSS 0.0045\n",
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      "VALID: EPOCH 0001 / 0010 | BATCH 0748 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0749 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0750 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0751 / 0974 | LOSS 0.0045\n",
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      "VALID: EPOCH 0001 / 0010 | BATCH 0755 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0756 / 0974 | LOSS 0.0045\n",
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      "VALID: EPOCH 0001 / 0010 | BATCH 0758 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0759 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0760 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0761 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0762 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0763 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0764 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0765 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0766 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0767 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0768 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0769 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0770 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0771 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0772 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0773 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0774 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0775 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0776 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0777 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0778 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0779 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0780 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0781 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0782 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0783 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0784 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0785 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0786 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0787 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0788 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0789 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0790 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0791 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0792 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0793 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0794 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0795 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0796 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0797 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0798 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0799 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0800 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0801 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0802 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0803 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0804 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0805 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0806 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0807 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0808 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0809 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0810 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0811 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0812 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0813 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0814 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0815 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0816 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0817 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0818 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0819 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0820 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0821 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0822 / 0974 | LOSS 0.0045\n",
      "VALID: EPOCH 0001 / 0010 | BATCH 0823 / 0974 | LOSS 0.0045\n",
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      "VALID: EPOCH 0002 / 0010 | BATCH 0912 / 0974 | LOSS 0.0024\n",
      "VALID: EPOCH 0002 / 0010 | BATCH 0913 / 0974 | LOSS 0.0024\n",
      "VALID: EPOCH 0002 / 0010 | BATCH 0914 / 0974 | LOSS 0.0024\n",
      "VALID: EPOCH 0002 / 0010 | BATCH 0915 / 0974 | LOSS 0.0024\n",
      "VALID: EPOCH 0002 / 0010 | BATCH 0916 / 0974 | LOSS 0.0024\n",
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      "VALID: EPOCH 0002 / 0010 | BATCH 0919 / 0974 | LOSS 0.0024\n",
      "VALID: EPOCH 0002 / 0010 | BATCH 0920 / 0974 | LOSS 0.0024\n",
      "VALID: EPOCH 0002 / 0010 | BATCH 0921 / 0974 | LOSS 0.0024\n",
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      "VALID: EPOCH 0002 / 0010 | BATCH 0925 / 0974 | LOSS 0.0024\n",
      "VALID: EPOCH 0002 / 0010 | BATCH 0926 / 0974 | LOSS 0.0024\n",
      "VALID: EPOCH 0002 / 0010 | BATCH 0927 / 0974 | LOSS 0.0024\n",
      "VALID: EPOCH 0002 / 0010 | BATCH 0928 / 0974 | LOSS 0.0024\n",
      "VALID: EPOCH 0002 / 0010 | BATCH 0929 / 0974 | LOSS 0.0024\n",
      "VALID: EPOCH 0002 / 0010 | BATCH 0930 / 0974 | LOSS 0.0024\n",
      "VALID: EPOCH 0002 / 0010 | BATCH 0931 / 0974 | LOSS 0.0024\n",
      "VALID: EPOCH 0002 / 0010 | BATCH 0932 / 0974 | LOSS 0.0024\n",
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      "VALID: EPOCH 0003 / 0010 | BATCH 0680 / 0974 | LOSS 0.0158\n",
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      "VALID: EPOCH 0003 / 0010 | BATCH 0709 / 0974 | LOSS 0.0157\n",
      "VALID: EPOCH 0003 / 0010 | BATCH 0710 / 0974 | LOSS 0.0157\n",
      "VALID: EPOCH 0003 / 0010 | BATCH 0711 / 0974 | LOSS 0.0157\n",
      "VALID: EPOCH 0003 / 0010 | BATCH 0712 / 0974 | LOSS 0.0157\n",
      "VALID: EPOCH 0003 / 0010 | BATCH 0713 / 0974 | LOSS 0.0157\n",
      "VALID: EPOCH 0003 / 0010 | BATCH 0714 / 0974 | LOSS 0.0157\n",
      "VALID: EPOCH 0003 / 0010 | BATCH 0715 / 0974 | LOSS 0.0157\n",
      "VALID: EPOCH 0003 / 0010 | BATCH 0716 / 0974 | LOSS 0.0157\n",
      "VALID: EPOCH 0003 / 0010 | BATCH 0717 / 0974 | LOSS 0.0157\n",
      "VALID: EPOCH 0003 / 0010 | BATCH 0718 / 0974 | LOSS 0.0157\n",
      "VALID: EPOCH 0003 / 0010 | BATCH 0719 / 0974 | LOSS 0.0157\n",
      "VALID: EPOCH 0003 / 0010 | BATCH 0720 / 0974 | LOSS 0.0157\n",
      "VALID: EPOCH 0003 / 0010 | BATCH 0721 / 0974 | LOSS 0.0157\n",
      "VALID: EPOCH 0003 / 0010 | BATCH 0722 / 0974 | LOSS 0.0157\n",
      "VALID: EPOCH 0003 / 0010 | BATCH 0723 / 0974 | LOSS 0.0157\n",
      "VALID: EPOCH 0003 / 0010 | BATCH 0724 / 0974 | LOSS 0.0157\n",
      "VALID: EPOCH 0003 / 0010 | BATCH 0725 / 0974 | LOSS 0.0157\n",
      "VALID: EPOCH 0003 / 0010 | BATCH 0726 / 0974 | LOSS 0.0157\n",
      "VALID: EPOCH 0003 / 0010 | BATCH 0727 / 0974 | LOSS 0.0157\n",
      "VALID: EPOCH 0003 / 0010 | BATCH 0728 / 0974 | LOSS 0.0157\n",
      "VALID: EPOCH 0003 / 0010 | BATCH 0729 / 0974 | LOSS 0.0157\n",
      "VALID: EPOCH 0003 / 0010 | BATCH 0730 / 0974 | LOSS 0.0157\n",
      "VALID: EPOCH 0003 / 0010 | BATCH 0731 / 0974 | LOSS 0.0157\n",
      "VALID: EPOCH 0003 / 0010 | BATCH 0732 / 0974 | LOSS 0.0157\n",
      "VALID: EPOCH 0003 / 0010 | BATCH 0733 / 0974 | LOSS 0.0157\n",
      "VALID: EPOCH 0003 / 0010 | BATCH 0734 / 0974 | LOSS 0.0157\n",
      "VALID: EPOCH 0003 / 0010 | BATCH 0735 / 0974 | LOSS 0.0157\n",
      "VALID: EPOCH 0003 / 0010 | BATCH 0736 / 0974 | LOSS 0.0157\n",
      "VALID: EPOCH 0003 / 0010 | BATCH 0737 / 0974 | LOSS 0.0157\n",
      "VALID: EPOCH 0003 / 0010 | BATCH 0738 / 0974 | LOSS 0.0157\n",
      "VALID: EPOCH 0003 / 0010 | BATCH 0739 / 0974 | LOSS 0.0157\n",
      "VALID: EPOCH 0003 / 0010 | BATCH 0740 / 0974 | LOSS 0.0158\n",
      "VALID: EPOCH 0003 / 0010 | BATCH 0741 / 0974 | LOSS 0.0158\n",
      "VALID: EPOCH 0003 / 0010 | BATCH 0742 / 0974 | LOSS 0.0158\n",
      "VALID: EPOCH 0003 / 0010 | BATCH 0743 / 0974 | LOSS 0.0158\n",
      "VALID: EPOCH 0003 / 0010 | BATCH 0744 / 0974 | LOSS 0.0158\n",
      "VALID: EPOCH 0003 / 0010 | BATCH 0745 / 0974 | LOSS 0.0158\n",
      "VALID: EPOCH 0003 / 0010 | BATCH 0746 / 0974 | LOSS 0.0158\n",
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      "VALID: EPOCH 0003 / 0010 | BATCH 0969 / 0974 | LOSS 0.0157\n",
      "VALID: EPOCH 0003 / 0010 | BATCH 0970 / 0974 | LOSS 0.0157\n",
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      "VALID: EPOCH 0003 / 0010 | BATCH 0974 / 0974 | LOSS 0.0157\n",
      "TRAIN: EPOCH 0004 / 0010 | BATCH 0001 / 3410 | LOSS 0.0017\n",
      "TRAIN: EPOCH 0004 / 0010 | BATCH 0002 / 3410 | LOSS 0.0017\n",
      "TRAIN: EPOCH 0004 / 0010 | BATCH 0003 / 3410 | LOSS 0.0020\n",
      "TRAIN: EPOCH 0004 / 0010 | BATCH 0004 / 3410 | LOSS 0.0019\n",
      "TRAIN: EPOCH 0004 / 0010 | BATCH 0005 / 3410 | LOSS 0.0018\n",
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      "TRAIN: EPOCH 0004 / 0010 | BATCH 0012 / 3410 | LOSS 0.0017\n",
      "TRAIN: EPOCH 0004 / 0010 | BATCH 0013 / 3410 | LOSS 0.0017\n",
      "TRAIN: EPOCH 0004 / 0010 | BATCH 0014 / 3410 | LOSS 0.0017\n",
      "TRAIN: EPOCH 0004 / 0010 | BATCH 0015 / 3410 | LOSS 0.0017\n",
      "TRAIN: EPOCH 0004 / 0010 | BATCH 0016 / 3410 | LOSS 0.0017\n",
      "TRAIN: EPOCH 0004 / 0010 | BATCH 0017 / 3410 | LOSS 0.0017\n",
      "TRAIN: EPOCH 0004 / 0010 | BATCH 0018 / 3410 | LOSS 0.0016\n",
      "TRAIN: EPOCH 0004 / 0010 | BATCH 0019 / 3410 | LOSS 0.0016\n",
      "TRAIN: EPOCH 0004 / 0010 | BATCH 0020 / 3410 | LOSS 0.0016\n",
      "TRAIN: EPOCH 0004 / 0010 | BATCH 0021 / 3410 | LOSS 0.0016\n",
      "TRAIN: EPOCH 0004 / 0010 | BATCH 0022 / 3410 | LOSS 0.0016\n",
      "TRAIN: EPOCH 0004 / 0010 | BATCH 0023 / 3410 | LOSS 0.0016\n",
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      "TRAIN: EPOCH 0004 / 0010 | BATCH 0025 / 3410 | LOSS 0.0016\n",
      "TRAIN: EPOCH 0004 / 0010 | BATCH 0026 / 3410 | LOSS 0.0016\n",
      "TRAIN: EPOCH 0004 / 0010 | BATCH 0027 / 3410 | LOSS 0.0016\n",
      "TRAIN: EPOCH 0004 / 0010 | BATCH 0028 / 3410 | LOSS 0.0016\n",
      "TRAIN: EPOCH 0004 / 0010 | BATCH 0029 / 3410 | LOSS 0.0017\n",
      "TRAIN: EPOCH 0004 / 0010 | BATCH 0030 / 3410 | LOSS 0.0017\n",
      "TRAIN: EPOCH 0004 / 0010 | BATCH 0031 / 3410 | LOSS 0.0017\n",
      "TRAIN: EPOCH 0004 / 0010 | BATCH 0032 / 3410 | LOSS 0.0017\n",
      "TRAIN: EPOCH 0004 / 0010 | BATCH 0033 / 3410 | LOSS 0.0017\n",
      "TRAIN: EPOCH 0004 / 0010 | BATCH 0034 / 3410 | LOSS 0.0017\n",
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      "VALID: EPOCH 0006 / 0010 | BATCH 0526 / 0974 | LOSS 0.0013\n",
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      "VALID: EPOCH 0006 / 0010 | BATCH 0712 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0006 / 0010 | BATCH 0713 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0006 / 0010 | BATCH 0714 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0006 / 0010 | BATCH 0715 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0006 / 0010 | BATCH 0716 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0006 / 0010 | BATCH 0717 / 0974 | LOSS 0.0012\n",
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      "VALID: EPOCH 0006 / 0010 | BATCH 0720 / 0974 | LOSS 0.0013\n",
      "VALID: EPOCH 0006 / 0010 | BATCH 0721 / 0974 | LOSS 0.0013\n",
      "VALID: EPOCH 0006 / 0010 | BATCH 0722 / 0974 | LOSS 0.0012\n",
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      "VALID: EPOCH 0006 / 0010 | BATCH 0725 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0006 / 0010 | BATCH 0726 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0006 / 0010 | BATCH 0727 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0006 / 0010 | BATCH 0728 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0006 / 0010 | BATCH 0729 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0006 / 0010 | BATCH 0730 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0006 / 0010 | BATCH 0731 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0006 / 0010 | BATCH 0732 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0006 / 0010 | BATCH 0733 / 0974 | LOSS 0.0012\n",
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      "VALID: EPOCH 0006 / 0010 | BATCH 0735 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0006 / 0010 | BATCH 0736 / 0974 | LOSS 0.0012\n",
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      "VALID: EPOCH 0006 / 0010 | BATCH 0740 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0006 / 0010 | BATCH 0741 / 0974 | LOSS 0.0012\n",
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      "VALID: EPOCH 0008 / 0010 | BATCH 0412 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0413 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0414 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0415 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0416 / 0974 | LOSS 0.0012\n",
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      "VALID: EPOCH 0008 / 0010 | BATCH 0419 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0420 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0421 / 0974 | LOSS 0.0012\n",
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      "VALID: EPOCH 0008 / 0010 | BATCH 0427 / 0974 | LOSS 0.0012\n",
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      "VALID: EPOCH 0008 / 0010 | BATCH 0468 / 0974 | LOSS 0.0012\n",
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      "VALID: EPOCH 0008 / 0010 | BATCH 0475 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0476 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0477 / 0974 | LOSS 0.0012\n",
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      "VALID: EPOCH 0008 / 0010 | BATCH 0532 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0533 / 0974 | LOSS 0.0012\n",
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      "VALID: EPOCH 0008 / 0010 | BATCH 0570 / 0974 | LOSS 0.0012\n",
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      "VALID: EPOCH 0008 / 0010 | BATCH 0599 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0600 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0601 / 0974 | LOSS 0.0012\n",
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      "VALID: EPOCH 0008 / 0010 | BATCH 0608 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0609 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0610 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0611 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0612 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0613 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0614 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0615 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0616 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0617 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0618 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0619 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0620 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0621 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0622 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0623 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0624 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0625 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0626 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0627 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0628 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0629 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0630 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0631 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0632 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0633 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0634 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0635 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0636 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0637 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0638 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0639 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0640 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0641 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0642 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0643 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0644 / 0974 | LOSS 0.0012\n",
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      "VALID: EPOCH 0008 / 0010 | BATCH 0646 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0647 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0648 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0649 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0650 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0651 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0652 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0653 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0654 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0655 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0656 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0657 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0658 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0659 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0660 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0661 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0662 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0663 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0664 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0665 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0666 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0667 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0668 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0669 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0670 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0671 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0672 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0673 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0674 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0675 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0676 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0677 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0678 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0679 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0680 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0681 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0682 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0683 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0684 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0685 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0686 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0687 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0688 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0689 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0690 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0691 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0692 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0693 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0694 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0695 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0696 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0697 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0698 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0699 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0700 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0701 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0702 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0703 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0704 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0705 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0706 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0707 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0708 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0709 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0710 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0711 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0712 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0713 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0714 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0715 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0716 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0717 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0718 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0719 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0720 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0721 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0722 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0723 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0724 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0725 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0726 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0727 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0728 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0729 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0730 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0731 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0732 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0733 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0734 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0735 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0736 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0737 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0738 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0739 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0740 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0741 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0742 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0743 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0744 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0745 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0746 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0747 / 0974 | LOSS 0.0012\n",
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      "VALID: EPOCH 0008 / 0010 | BATCH 0911 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0912 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0913 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0914 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0915 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0916 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0917 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0918 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0919 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0920 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0921 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0922 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0923 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0924 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0925 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0926 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0927 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0928 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0929 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0930 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0931 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0932 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0933 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0934 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0935 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0936 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0937 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0938 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0939 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0940 / 0974 | LOSS 0.0012\n",
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      "VALID: EPOCH 0008 / 0010 | BATCH 0943 / 0974 | LOSS 0.0012\n",
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      "VALID: EPOCH 0008 / 0010 | BATCH 0948 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0949 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0950 / 0974 | LOSS 0.0012\n",
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      "VALID: EPOCH 0008 / 0010 | BATCH 0960 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0961 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0962 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0963 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0964 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0965 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0966 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0967 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0968 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0969 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0970 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0971 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0972 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0973 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0008 / 0010 | BATCH 0974 / 0974 | LOSS 0.0012\n",
      "TRAIN: EPOCH 0009 / 0010 | BATCH 0001 / 3410 | LOSS 0.0007\n",
      "TRAIN: EPOCH 0009 / 0010 | BATCH 0002 / 3410 | LOSS 0.0006\n",
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      "TRAIN: EPOCH 0009 / 0010 | BATCH 0009 / 3410 | LOSS 0.0007\n",
      "TRAIN: EPOCH 0009 / 0010 | BATCH 0010 / 3410 | LOSS 0.0007\n",
      "TRAIN: EPOCH 0009 / 0010 | BATCH 0011 / 3410 | LOSS 0.0007\n",
      "TRAIN: EPOCH 0009 / 0010 | BATCH 0012 / 3410 | LOSS 0.0008\n",
      "TRAIN: EPOCH 0009 / 0010 | BATCH 0013 / 3410 | LOSS 0.0008\n",
      "TRAIN: EPOCH 0009 / 0010 | BATCH 0014 / 3410 | LOSS 0.0008\n",
      "TRAIN: EPOCH 0009 / 0010 | BATCH 0015 / 3410 | LOSS 0.0008\n",
      "TRAIN: EPOCH 0009 / 0010 | BATCH 0016 / 3410 | LOSS 0.0008\n",
      "TRAIN: EPOCH 0009 / 0010 | BATCH 0017 / 3410 | LOSS 0.0008\n",
      "TRAIN: EPOCH 0009 / 0010 | BATCH 0018 / 3410 | LOSS 0.0008\n",
      "TRAIN: EPOCH 0009 / 0010 | BATCH 0019 / 3410 | LOSS 0.0008\n",
      "TRAIN: EPOCH 0009 / 0010 | BATCH 0020 / 3410 | LOSS 0.0008\n",
      "TRAIN: EPOCH 0009 / 0010 | BATCH 0021 / 3410 | LOSS 0.0008\n",
      "TRAIN: EPOCH 0009 / 0010 | BATCH 0022 / 3410 | LOSS 0.0008\n",
      "TRAIN: EPOCH 0009 / 0010 | BATCH 0023 / 3410 | LOSS 0.0008\n",
      "TRAIN: EPOCH 0009 / 0010 | BATCH 0024 / 3410 | LOSS 0.0008\n",
      "TRAIN: EPOCH 0009 / 0010 | BATCH 0025 / 3410 | LOSS 0.0008\n",
      "TRAIN: EPOCH 0009 / 0010 | BATCH 0026 / 3410 | LOSS 0.0008\n",
      "TRAIN: EPOCH 0009 / 0010 | BATCH 0027 / 3410 | LOSS 0.0008\n",
      "TRAIN: EPOCH 0009 / 0010 | BATCH 0028 / 3410 | LOSS 0.0008\n",
      "TRAIN: EPOCH 0009 / 0010 | BATCH 0029 / 3410 | LOSS 0.0008\n",
      "TRAIN: EPOCH 0009 / 0010 | BATCH 0030 / 3410 | LOSS 0.0008\n",
      "TRAIN: EPOCH 0009 / 0010 | BATCH 0031 / 3410 | LOSS 0.0008\n",
      "TRAIN: EPOCH 0009 / 0010 | BATCH 0032 / 3410 | LOSS 0.0008\n",
      "TRAIN: EPOCH 0009 / 0010 | BATCH 0033 / 3410 | LOSS 0.0008\n",
      "TRAIN: EPOCH 0009 / 0010 | BATCH 0034 / 3410 | LOSS 0.0008\n",
      "TRAIN: EPOCH 0009 / 0010 | BATCH 0035 / 3410 | LOSS 0.0008\n",
      "TRAIN: EPOCH 0009 / 0010 | BATCH 0036 / 3410 | LOSS 0.0008\n",
      "TRAIN: EPOCH 0009 / 0010 | BATCH 0037 / 3410 | LOSS 0.0008\n",
      "TRAIN: EPOCH 0009 / 0010 | BATCH 0038 / 3410 | LOSS 0.0008\n",
      "TRAIN: EPOCH 0009 / 0010 | BATCH 0039 / 3410 | LOSS 0.0008\n",
      "TRAIN: EPOCH 0009 / 0010 | BATCH 0040 / 3410 | LOSS 0.0008\n",
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      "TRAIN: EPOCH 0010 / 0010 | BATCH 1975 / 3410 | LOSS 0.0008\n",
      "TRAIN: EPOCH 0010 / 0010 | BATCH 1976 / 3410 | LOSS 0.0008\n",
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      "TRAIN: EPOCH 0010 / 0010 | BATCH 1978 / 3410 | LOSS 0.0008\n",
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      "TRAIN: EPOCH 0010 / 0010 | BATCH 3401 / 3410 | LOSS 0.0008\n",
      "TRAIN: EPOCH 0010 / 0010 | BATCH 3402 / 3410 | LOSS 0.0008\n",
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      "TRAIN: EPOCH 0010 / 0010 | BATCH 3410 / 3410 | LOSS 0.0008\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0001 / 0974 | LOSS 0.0010\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0002 / 0974 | LOSS 0.0008\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0003 / 0974 | LOSS 0.0008\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0004 / 0974 | LOSS 0.0012\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0005 / 0974 | LOSS 0.0011\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0006 / 0974 | LOSS 0.0010\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0007 / 0974 | LOSS 0.0010\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0008 / 0974 | LOSS 0.0010\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0009 / 0974 | LOSS 0.0010\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0010 / 0974 | LOSS 0.0009\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0011 / 0974 | LOSS 0.0009\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0012 / 0974 | LOSS 0.0009\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0013 / 0974 | LOSS 0.0010\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0014 / 0974 | LOSS 0.0010\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0015 / 0974 | LOSS 0.0010\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0016 / 0974 | LOSS 0.0010\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0017 / 0974 | LOSS 0.0010\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0018 / 0974 | LOSS 0.0010\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0019 / 0974 | LOSS 0.0010\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0020 / 0974 | LOSS 0.0010\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0021 / 0974 | LOSS 0.0010\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0022 / 0974 | LOSS 0.0010\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0023 / 0974 | LOSS 0.0013\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0024 / 0974 | LOSS 0.0013\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0025 / 0974 | LOSS 0.0014\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0026 / 0974 | LOSS 0.0014\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0027 / 0974 | LOSS 0.0014\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0028 / 0974 | LOSS 0.0014\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0029 / 0974 | LOSS 0.0014\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0030 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0031 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0032 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0033 / 0974 | LOSS 0.0016\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0034 / 0974 | LOSS 0.0016\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0035 / 0974 | LOSS 0.0016\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0036 / 0974 | LOSS 0.0016\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0037 / 0974 | LOSS 0.0016\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0038 / 0974 | LOSS 0.0015\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0039 / 0974 | LOSS 0.0015\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0040 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0041 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0042 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0043 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0044 / 0974 | LOSS 0.0019\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0045 / 0974 | LOSS 0.0019\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0046 / 0974 | LOSS 0.0019\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0047 / 0974 | LOSS 0.0019\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0048 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0049 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0050 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0051 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0052 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0053 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0054 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0055 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0056 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0057 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0058 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0059 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0060 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0061 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0062 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0063 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0064 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0065 / 0974 | LOSS 0.0019\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0066 / 0974 | LOSS 0.0019\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0067 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0068 / 0974 | LOSS 0.0019\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0069 / 0974 | LOSS 0.0019\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0070 / 0974 | LOSS 0.0019\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0071 / 0974 | LOSS 0.0019\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0072 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0073 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0074 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0075 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0076 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0077 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0078 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0079 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0080 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0081 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0082 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0083 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0084 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0085 / 0974 | LOSS 0.0019\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0086 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0087 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0088 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0089 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0090 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0091 / 0974 | LOSS 0.0019\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0092 / 0974 | LOSS 0.0019\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0093 / 0974 | LOSS 0.0019\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0094 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0095 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0096 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0097 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0098 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0099 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0100 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0101 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0102 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0103 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0104 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0105 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0106 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0107 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0108 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0109 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0110 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0111 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0112 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0113 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0114 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0115 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0116 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0117 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0118 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0119 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0120 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0121 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0122 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0123 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0124 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0125 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0126 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0127 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0128 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0129 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0130 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0131 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0132 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0133 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0134 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0135 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0136 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0137 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0138 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0139 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0140 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0141 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0142 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0143 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0144 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0145 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0146 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0147 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0148 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0149 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0150 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0151 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0152 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0153 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0154 / 0974 | LOSS 0.0017\n",
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      "VALID: EPOCH 0010 / 0010 | BATCH 0165 / 0974 | LOSS 0.0016\n",
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      "VALID: EPOCH 0010 / 0010 | BATCH 0215 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0216 / 0974 | LOSS 0.0017\n",
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      "VALID: EPOCH 0010 / 0010 | BATCH 0220 / 0974 | LOSS 0.0017\n",
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      "VALID: EPOCH 0010 / 0010 | BATCH 0236 / 0974 | LOSS 0.0017\n",
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      "VALID: EPOCH 0010 / 0010 | BATCH 0240 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0241 / 0974 | LOSS 0.0017\n",
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      "VALID: EPOCH 0010 / 0010 | BATCH 0260 / 0974 | LOSS 0.0016\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0261 / 0974 | LOSS 0.0016\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0262 / 0974 | LOSS 0.0016\n",
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      "VALID: EPOCH 0010 / 0010 | BATCH 0265 / 0974 | LOSS 0.0016\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0266 / 0974 | LOSS 0.0016\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0267 / 0974 | LOSS 0.0016\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0268 / 0974 | LOSS 0.0016\n",
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      "VALID: EPOCH 0010 / 0010 | BATCH 0270 / 0974 | LOSS 0.0016\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0271 / 0974 | LOSS 0.0016\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0272 / 0974 | LOSS 0.0016\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0273 / 0974 | LOSS 0.0017\n",
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      "VALID: EPOCH 0010 / 0010 | BATCH 0275 / 0974 | LOSS 0.0016\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0276 / 0974 | LOSS 0.0016\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0277 / 0974 | LOSS 0.0016\n",
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      "VALID: EPOCH 0010 / 0010 | BATCH 0279 / 0974 | LOSS 0.0016\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0280 / 0974 | LOSS 0.0016\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0281 / 0974 | LOSS 0.0016\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0282 / 0974 | LOSS 0.0016\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0283 / 0974 | LOSS 0.0016\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0284 / 0974 | LOSS 0.0016\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0285 / 0974 | LOSS 0.0016\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0286 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0287 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0288 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0289 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0290 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0291 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0292 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0293 / 0974 | LOSS 0.0017\n",
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      "VALID: EPOCH 0010 / 0010 | BATCH 0295 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0296 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0297 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0298 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0299 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0300 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0301 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0302 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0303 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0304 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0305 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0306 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0307 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0308 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0309 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0310 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0311 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0312 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0313 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0314 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0315 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0316 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0317 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0318 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0319 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0320 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0321 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0322 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0323 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0324 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0325 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0326 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0327 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0328 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0329 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0330 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0331 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0332 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0333 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0334 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0335 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0336 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0337 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0338 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0339 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0340 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0341 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0342 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0343 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0344 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0345 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0346 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0347 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0348 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0349 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0350 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0351 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0352 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0353 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0354 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0355 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0356 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0357 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0358 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0359 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0360 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0361 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0362 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0363 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0364 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0365 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0366 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0367 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0368 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0369 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0370 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0371 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0372 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0373 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0374 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0375 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0376 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0377 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0378 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0379 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0380 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0381 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0382 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0383 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0384 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0385 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0386 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0387 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0388 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0389 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0390 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0391 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0392 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0393 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0394 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0395 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0396 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0397 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0398 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0399 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0400 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0401 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0402 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0403 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0404 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0405 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0406 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0407 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0408 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0409 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0410 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0411 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0412 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0413 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0414 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0415 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0416 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0417 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0418 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0419 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0420 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0421 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0422 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0423 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0424 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0425 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0426 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0427 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0428 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0429 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0430 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0431 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0432 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0433 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0434 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0435 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0436 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0437 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0438 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0439 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0440 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0441 / 0974 | LOSS 0.0017\n",
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      "VALID: EPOCH 0010 / 0010 | BATCH 0444 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0445 / 0974 | LOSS 0.0017\n",
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      "VALID: EPOCH 0010 / 0010 | BATCH 0453 / 0974 | LOSS 0.0018\n",
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      "VALID: EPOCH 0010 / 0010 | BATCH 0458 / 0974 | LOSS 0.0018\n",
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      "VALID: EPOCH 0010 / 0010 | BATCH 0465 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0466 / 0974 | LOSS 0.0018\n",
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      "VALID: EPOCH 0010 / 0010 | BATCH 0468 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0469 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0470 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0471 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0472 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0473 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0474 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0475 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0476 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0477 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0478 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0479 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0480 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0481 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0482 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0483 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0484 / 0974 | LOSS 0.0017\n",
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      "VALID: EPOCH 0010 / 0010 | BATCH 0486 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0487 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0488 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0489 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0490 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0491 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0492 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0493 / 0974 | LOSS 0.0017\n",
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      "VALID: EPOCH 0010 / 0010 | BATCH 0495 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0496 / 0974 | LOSS 0.0017\n",
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      "VALID: EPOCH 0010 / 0010 | BATCH 0499 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0500 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0501 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0502 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0503 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0504 / 0974 | LOSS 0.0017\n",
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      "VALID: EPOCH 0010 / 0010 | BATCH 0506 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0507 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0508 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0509 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0510 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0511 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0512 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0513 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0514 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0515 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0516 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0517 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0518 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0519 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0520 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0521 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0522 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0523 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0524 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0525 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0526 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0527 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0528 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0529 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0530 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0531 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0532 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0533 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0534 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0535 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0536 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0537 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0538 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0539 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0540 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0541 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0542 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0543 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0544 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0545 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0546 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0547 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0548 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0549 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0550 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0551 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0552 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0553 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0554 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0555 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0556 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0557 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0558 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0559 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0560 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0561 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0562 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0563 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0564 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0565 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0566 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0567 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0568 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0569 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0570 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0571 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0572 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0573 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0574 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0575 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0576 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0577 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0578 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0579 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0580 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0581 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0582 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0583 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0584 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0585 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0586 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0587 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0588 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0589 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0590 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0591 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0592 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0593 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0594 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0595 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0596 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0597 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0598 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0599 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0600 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0601 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0602 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0603 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0604 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0605 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0606 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0607 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0608 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0609 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0610 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0611 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0612 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0613 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0614 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0615 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0616 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0617 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0618 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0619 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0620 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0621 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0622 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0623 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0624 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0625 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0626 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0627 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0628 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0629 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0630 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0631 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0632 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0633 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0634 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0635 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0636 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0637 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0638 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0639 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0640 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0641 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0642 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0643 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0644 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0645 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0646 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0647 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0648 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0649 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0650 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0651 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0652 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0653 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0654 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0655 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0656 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0657 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0658 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0659 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0660 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0661 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0662 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0663 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0664 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0665 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0666 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0667 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0668 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0669 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0670 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0671 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0672 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0673 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0674 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0675 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0676 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0677 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0678 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0679 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0680 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0681 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0682 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0683 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0684 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0685 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0686 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0687 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0688 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0689 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0690 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0691 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0692 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0693 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0694 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0695 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0696 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0697 / 0974 | LOSS 0.0018\n",
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      "VALID: EPOCH 0010 / 0010 | BATCH 0699 / 0974 | LOSS 0.0017\n",
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      "VALID: EPOCH 0010 / 0010 | BATCH 0701 / 0974 | LOSS 0.0018\n",
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      "VALID: EPOCH 0010 / 0010 | BATCH 0711 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0712 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0713 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0714 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0715 / 0974 | LOSS 0.0017\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0716 / 0974 | LOSS 0.0017\n",
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      "VALID: EPOCH 0010 / 0010 | BATCH 0720 / 0974 | LOSS 0.0018\n",
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      "VALID: EPOCH 0010 / 0010 | BATCH 0769 / 0974 | LOSS 0.0018\n",
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      "VALID: EPOCH 0010 / 0010 | BATCH 0801 / 0974 | LOSS 0.0018\n",
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      "VALID: EPOCH 0010 / 0010 | BATCH 0809 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0810 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0811 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0812 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0813 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0814 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0815 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0816 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0817 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0818 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0819 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0820 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0821 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0822 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0823 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0824 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0825 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0826 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0827 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0828 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0829 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0830 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0831 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0832 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0833 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0834 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0835 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0836 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0837 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0838 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0839 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0840 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0841 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0842 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0843 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0844 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0845 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0846 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0847 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0848 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0849 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0850 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0851 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0852 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0853 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0854 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0855 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0856 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0857 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0858 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0859 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0860 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0861 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0862 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0863 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0864 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0865 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0866 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0867 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0868 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0869 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0870 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0871 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0872 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0873 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0874 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0875 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0876 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0877 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0878 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0879 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0880 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0881 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0882 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0883 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0884 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0885 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0886 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0887 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0888 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0889 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0890 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0891 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0892 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0893 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0894 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0895 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0896 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0897 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0898 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0899 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0900 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0901 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0902 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0903 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0904 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0905 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0906 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0907 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0908 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0909 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0910 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0911 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0912 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0913 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0914 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0915 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0916 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0917 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0918 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0919 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0920 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0921 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0922 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0923 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0924 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0925 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0926 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0927 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0928 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0929 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0930 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0931 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0932 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0933 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0934 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0935 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0936 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0937 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0938 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0939 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0940 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0941 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0942 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0943 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0944 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0945 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0946 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0947 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0948 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0949 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0950 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0951 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0952 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0953 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0954 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0955 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0956 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0957 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0958 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0959 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0960 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0961 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0962 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0963 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0964 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0965 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0966 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0967 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0968 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0969 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0970 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0971 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0972 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0973 / 0974 | LOSS 0.0018\n",
      "VALID: EPOCH 0010 / 0010 | BATCH 0974 / 0974 | LOSS 0.0018\n"
     ]
    }
   ],
   "source": [
    "# 훈련을 위한 Transform과 DataLoader\n",
    "loader_train = DataLoader(train_set, batch_size=batch_size, shuffle=True, num_workers=0)\n",
    "loader_val = DataLoader(val_set, batch_size=batch_size, shuffle=False, num_workers=0)\n",
    "\n",
    "# 그밖에 부수적인 variables 설정하기\n",
    "num_data_train = len(train_set)\n",
    "num_data_val = len(val_set)\n",
    "\n",
    "num_batch_train = np.ceil(num_data_train / batch_size)\n",
    "num_batch_val = np.ceil(num_data_val / batch_size)\n",
    "\n",
    "\n",
    "# Tensorboard 를 사용하기 위한 SummaryWriter 설정\n",
    "writer_train = SummaryWriter(log_dir=os.path.join(log_dir, 'train'))\n",
    "writer_val = SummaryWriter(log_dir=os.path.join(log_dir, 'val'))\n",
    "\n",
    "# 네트워크 학습시키기\n",
    "st_epoch = 0\n",
    "# 학습한 모델이 있을 경우 모델 로드하기\n",
    "net, optim, st_epoch = load(ckpt_dir=ckpt_dir, net=net, optim=optim) \n",
    "\n",
    "for epoch in range(st_epoch + 1, num_epoch + 1):\n",
    "        net.train()\n",
    "        loss_arr = []\n",
    "\n",
    "        for batch, data in enumerate(loader_train, 1):\n",
    "            # forward pass\n",
    "            label = data['label'].to(device)\n",
    "            input = data['input'].to(device)\n",
    "\n",
    "            output = net(input)\n",
    "\n",
    "            # backward pass\n",
    "            optim.zero_grad()\n",
    "\n",
    "            loss = fn_loss(output, label)\n",
    "            loss.backward()\n",
    "\n",
    "            optim.step()\n",
    "\n",
    "            # 손실함수 계산\n",
    "            loss_arr += [loss.item()]\n",
    "\n",
    "            print(\"TRAIN: EPOCH %04d / %04d | BATCH %04d / %04d | LOSS %.4f\" %\n",
    "                  (epoch, num_epoch, batch, num_batch_train, np.mean(loss_arr)))\n",
    "\n",
    "            # Tensorboard 저장하기\n",
    "            label = fn_tonumpy(label)\n",
    "            input = fn_tonumpy(fn_denorm(input, mean=0.5, std=0.5))\n",
    "            output = fn_tonumpy(fn_class(output))\n",
    "\n",
    "            writer_train.add_image('label', label, num_batch_train * (epoch - 1) + batch, dataformats='NHWC')\n",
    "            writer_train.add_image('input', input, num_batch_train * (epoch - 1) + batch, dataformats='NHWC')\n",
    "            writer_train.add_image('output', output, num_batch_train * (epoch - 1) + batch, dataformats='NHWC')\n",
    "\n",
    "        writer_train.add_scalar('loss', np.mean(loss_arr), epoch)\n",
    "\n",
    "        with torch.no_grad():\n",
    "            net.eval()\n",
    "            loss_arr = []\n",
    "\n",
    "            for batch, data in enumerate(loader_val, 1):\n",
    "                # forward pass\n",
    "                label = data['label'].to(device)\n",
    "                input = data['input'].to(device)\n",
    "\n",
    "                output = net(input)\n",
    "\n",
    "                # 손실함수 계산하기\n",
    "                loss = fn_loss(output, label)\n",
    "\n",
    "                loss_arr += [loss.item()]\n",
    "\n",
    "                print(\"VALID: EPOCH %04d / %04d | BATCH %04d / %04d | LOSS %.4f\" %\n",
    "                      (epoch, num_epoch, batch, num_batch_val, np.mean(loss_arr)))\n",
    "\n",
    "                # Tensorboard 저장하기\n",
    "                label = fn_tonumpy(label)\n",
    "                input = fn_tonumpy(fn_denorm(input, mean=0.5, std=0.5))\n",
    "                output = fn_tonumpy(fn_class(output))\n",
    "\n",
    "                writer_val.add_image('label', label, num_batch_val * (epoch - 1) + batch, dataformats='NHWC')\n",
    "                writer_val.add_image('input', input, num_batch_val * (epoch - 1) + batch, dataformats='NHWC')\n",
    "                writer_val.add_image('output', output, num_batch_val * (epoch - 1) + batch, dataformats='NHWC')\n",
    "\n",
    "        writer_val.add_scalar('loss', np.mean(loss_arr), epoch)\n",
    "\n",
    "        # epoch 5마다 모델 저장하기\n",
    "        if epoch % 1 == 0:\n",
    "            save(ckpt_dir=ckpt_dir, net=net, optim=optim, epoch=epoch)\n",
    "\n",
    "        writer_train.close()\n",
    "        writer_val.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print('train set: ' + str(len(train_set)))\n",
    "print('val set: ' + str(len(val_set)))\n",
    "print('test set: ' + str(len(test_set)))\n",
    "print('total: ' + str(len(train_set)+ len(val_set)+ len(test_set)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "loader_test = DataLoader(test_set, batch_size=batch_size, shuffle=False, num_workers=0)\n",
    "\n",
    "# 그밖에 부수적인 variables 설정하기\n",
    "num_data_test = len(test_set)\n",
    "num_batch_test = np.ceil(num_data_test / batch_size)\n",
    "\n",
    "# 결과 디렉토리 생성하기\n",
    "result_dir = os.path.join(base_dir, 'result')\n",
    "if not os.path.exists(result_dir):\n",
    "    os.makedirs(os.path.join(result_dir, 'png'))\n",
    "    os.makedirs(os.path.join(result_dir, 'numpy'))\n",
    "\n",
    "net, optim, st_epoch = load(ckpt_dir=ckpt_dir, net=net, optim=optim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "AVERAGE TEST: BATCH 0488 / 0488 | LOSS 0.0017\n"
     ]
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "      net.eval()\n",
    "      loss_arr = []\n",
    "\n",
    "      for batch, data in enumerate(loader_test, 1):\n",
    "          # forward pass\n",
    "          label = data['label'].to(device)\n",
    "          input = data['input'].to(device)\n",
    "\n",
    "          output = net(input)\n",
    "\n",
    "          # 손실함수 계산하기\n",
    "          loss = fn_loss(output, label)\n",
    "\n",
    "          loss_arr += [loss.item()]\n",
    "\n",
    "          print(\"TEST: BATCH %04d / %04d | LOSS %.4f\" %\n",
    "                (batch, num_batch_test, np.mean(loss_arr)))\n",
    "\n",
    "          # Tensorboard 저장하기\n",
    "          label = fn_tonumpy(label)\n",
    "          input = fn_tonumpy(fn_denorm(input, mean=0.5, std=0.5))\n",
    "          output = fn_tonumpy(fn_class(output))\n",
    "\n",
    "          # 테스트 결과 저장하기\n",
    "          for j in range(label.shape[0]):\n",
    "              id = num_batch_test * (batch - 1) + j\n",
    "\n",
    "              plt.imsave(os.path.join(result_dir, 'png', 'label_%04d.png' % id), label[j].squeeze(), cmap='gray')\n",
    "              plt.imsave(os.path.join(result_dir, 'png', 'input_%04d.png' % id), input[j].squeeze(), cmap='gray')\n",
    "              plt.imsave(os.path.join(result_dir, 'png', 'output_%04d.png' % id), output[j].squeeze(), cmap='gray')\n",
    "\n",
    "              np.save(os.path.join(result_dir, 'numpy', 'label_%04d.npy' % id), label[j].squeeze())\n",
    "              np.save(os.path.join(result_dir, 'numpy', 'input_%04d.npy' % id), input[j].squeeze())\n",
    "              np.save(os.path.join(result_dir, 'numpy', 'output_%04d.npy' % id), output[j].squeeze())\n",
    "\n",
    "print(\"AVERAGE TEST: BATCH %04d / %04d | LOSS %.4f\" %\n",
    "        (batch, num_batch_test, np.mean(loss_arr)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "##\n",
    "lst_data = os.listdir(os.path.join(result_dir, 'numpy'))\n",
    "\n",
    "lst_label = [f for f in lst_data if f.startswith('label')]\n",
    "lst_input = [f for f in lst_data if f.startswith('input')]\n",
    "lst_output = [f for f in lst_data if f.startswith('output')]\n",
    "\n",
    "lst_label.sort()\n",
    "lst_input.sort()\n",
    "lst_output.sort()\n",
    "\n",
    "##\n",
    "id = 0\n",
    "\n",
    "label = np.load(os.path.join(result_dir,\"numpy\", lst_label[id]))\n",
    "input = np.load(os.path.join(result_dir,\"numpy\", lst_input[id]))\n",
    "output = np.load(os.path.join(result_dir,\"numpy\", lst_output[id]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## 플롯 그리기\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.subplot(131)\n",
    "plt.imshow(input, cmap='gray')\n",
    "plt.title('input')\n",
    "\n",
    "plt.subplot(132)\n",
    "plt.imshow(label, cmap='gray')\n",
    "plt.title('label')\n",
    "\n",
    "plt.subplot(133)\n",
    "plt.imshow(output, cmap='gray')\n",
    "plt.title('output')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, precision_recall_curve\n",
    "import itertools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data types:  int32 int32\n",
      "Shapes:  (262144,) (262144,)\n",
      "Label sample:  [0 0 0 ... 0 0 0]\n",
      "Output sample:  [0 0 0 ... 0 0 0]\n",
      "Accuracy: 0.998199462890625\n",
      "Precision: 0.9588396707173658\n",
      "Recall: 0.9947132980886539\n",
      "F1 Score: 0.9764471057884232\n"
     ]
    },
    {
     "data": {
      "image/png": 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2bFb7FyxYoG+++Ua+vr569dVXNXToUGXKlEmSFBoaqlKlSlm1GAQHB6tbt246cuSIXnjhBYWGhiooKMhqzODgYPXu3VuSdPfuXYWFhem9996zHHdwcFBQUJBCQ0MlSWFhYbp3757VOEWLFlWePHlIegEAADKi6Ohoq9eurq5ydXV97DkJCQnq3bu3KleurJIlS1r2t23bVgEBAfLz89Ovv/6qgQMH6vjx41qxYoUkKSIiIklPbeLriIiIx8ZER0fr9u3bun79uuLj4x8ac+zYMcsYLi4u8vb2ThKT+D7JQdILAABgNJMkW1ae/38of39/q93Dhw/XiBEjHntq9+7ddfjwYf38889W+7t27Wr591KlSil37tyqVauWTp06pQIFCthk2kYi6QUAALAT58+ft2qlelKVt0ePHlq7dq127Nih559//rGxlSpVkiSdPHlSBQoUkK+vr/bu3WsVc+nSJUmSr6+v5f8m7nswxtPTU+7u7nJ0dJSjo+NDYx4c4+7du4qMjLSq9j4Ykxxpv24HAABARmNysP2m+/cOPLg9Kuk1m83q0aOHVq5cqa1btypfvnxPnHJ4eLgkKXfu3JKkwMBAHTp0SJcvX7bEbNq0SZ6enipevLglZsuWLVbjbNq0SYGBgZLuL7NWvnx5q5iEhARt2bLFElO+fHk5OztbxRw/flznzp2zxCQHlV4AAACjmUw2bm9I2Vjdu3fXwoUL9d1338nDw8PSG+vl5SV3d3edOnVKCxcuVP369ZU9e3b9+uuv6tOnj6pVq6bSpUtLkurUqaPixYvrjTfe0Lhx4xQREaEhQ4aoe/fulmT7rbfe0rRp0zRgwAB17NhRW7du1dKlS7Vu3TrLXPr27auQkBBVqFBBL774oj755BPdvHlTHTp0sMypU6dO6tu3r7JlyyZPT0/17NlTgYGByb6JTSLpBQAAyHBmzpwp6f6yZA/6+uuv1b59e7m4uGjz5s2WBNTf31/NmzfXkCFDLLGOjo5au3atunXrpsDAQGXOnFkhISEaNWqUJSZfvnxat26d+vTpoylTpuj555/X7NmzFRwcbIlp1aqVrly5omHDhikiIkJly5bVhg0brG5umzx5shwcHNS8eXOrh1OkBOv0ZhCWdQFLdWHJMiCNXN83La2nAGRI0dHR8snu9dRr2Np6Ll5eXnIt10Mmx8f326aEOT5WsQempYtrTK/o6QUAAIDdo70BAADAaGnc05sRUekFAACA3aPSCwAAYLh/lhmz2Xh4LJJeAAAAo9HeYDh+LQAAAIDdo9ILAABgNJON2xts2iphn/iEAAAAYPeo9AIAABiNnl7DUekFAACA3aPSCwAAYDR6eg1H0gsAAGA02hsMx68FAAAAsHtUegEAAIxGe4Ph+IQAAABg96j0AgAAGM1ksnGll57eJ6HSCwAAALtHpRcAAMBoDqb7my3Hw2OR9AIAABiNG9kMxycEAAAAu0elFwAAwGg8nMJwVHoBAABg96j0AgAAGI2eXsPxCQEAAMDuUekFAAAwGj29hiPpBQAAMBrtDYbjEwIAAIDdo9ILAABgNNobDEelFwAAAHaPSi8AAIDR6Ok1HJ8QAAAA7B6VXgAAAKPR02s4kl4AAADD2bi9gT/ePxGfEAAAAOwelV4AAACj0d5gOCq9AAAAsHtUegEAAIxmMtl4yTIqvU9CpRcAAAB2j0ovAACA0Xg4heH4hAAAAGD3qPQCAAAYjdUbDEfSCwAAYDTaGwzHJwQAAAC7R6UXAADAaLQ3GI5KLwAAAOwelV4AAACj0dNrOD4hAAAA2D0qvQAAAEajp9dwJL0AAAAGM5lMMpH0Gor2BgAAANg9Kr0AAAAGo9JrPCq9AAAAsHtUegEAAIxm+v/NluPhsaj0AgAAwO5R6QUAADAYPb3GI+kFAAAwGEmv8WhvAAAAgN2j0gsAAGAwKr3Go9ILAAAAu0elFwAAwGBUeo1HpRcAAAB2j0ovAACA0Xg4heFIegEAAAxGe4PxaG8AAACA3aPSCwAAYDCTSTau9NpuKHtFpRcAAAB2j0ovAACAwUyycU8vpd4notILAAAAu0elFwAAwGCs3mA8kl4AAACjsU6v4WhvAAAAgN2j0gsAAGA0G7c3mGlveCIqvQAAALB7VHoBAAAMZusb2Wy7/Jl9otILAAAAu0elFwAAwGBUeo1H0gsAAGA0liwzHO0NAAAAsHskvQAAAAZLbG+w5ZYSY8eOVcWKFeXh4aFcuXKpSZMmOn78uFXMnTt31L17d2XPnl1ZsmRR8+bNdenSJauYc+fOqUGDBsqUKZNy5cql/v37Ky4uzipm27ZtKleunFxdXVWwYEHNmTMnyXymT5+uvHnzys3NTZUqVdLevXtTPJcnIekFAADIYLZv367u3btr9+7d2rRpk+7du6c6dero5s2blpg+ffpozZo1WrZsmbZv364LFy6oWbNmluPx8fFq0KCB7t69q127dmnu3LmaM2eOhg0bZok5c+aMGjRooJo1ayo8PFy9e/dW586d9cMPP1hilixZor59+2r48OE6cOCAypQpo+DgYF2+fDnZc0kOk9lsNj/Nh4VnS3R0tLy8vORaqotMji5pPR0gQ7q+b1paTwHIkKKjo+WT3UtRUVHy9PRM87l4eXkp55tz5eCSyWbjJty9pSvzQp76Gq9cuaJcuXJp+/btqlatmqKiopQzZ04tXLhQLVq0kCQdO3ZMxYoVU2hoqF566SV9//33atiwoS5cuCAfHx9J0qxZszRw4EBduXJFLi4uGjhwoNatW6fDhw9b3qt169aKjIzUhg0bJEmVKlVSxYoVNW3a/f9GJiQkyN/fXz179tSgQYOSNZfkoNILAACQwUVFRUmSsmXLJkkKCwvTvXv3FBQUZIkpWrSo8uTJo9DQUElSaGioSpUqZUl4JSk4OFjR0dE6cuSIJebBMRJjEse4e/euwsLCrGIcHBwUFBRkiUnOXJKD1RsAAAAMllpLlkVHR1vtd3V1laur62PPTUhIUO/evVW5cmWVLFlSkhQRESEXFxd5e3tbxfr4+CgiIsIS82DCm3g88djjYqKjo3X79m1dv35d8fHxD405duxYsueSHFR6AQAADJZaN7L5+/vLy8vLso0dO/aJc+nevbsOHz6sxYsXp/ZlpykqvQAAAHbi/PnzVj29T6ry9ujRQ2vXrtWOHTv0/PPPW/b7+vrq7t27ioyMtKqwXrp0Sb6+vpaYf6+ykLiiwoMx/15l4dKlS/L09JS7u7scHR3l6Oj40JgHx3jSXJKDSi8AAIDRTKmwSfL09LTaHpX0ms1m9ejRQytXrtTWrVuVL18+q+Ply5eXs7OztmzZYtl3/PhxnTt3ToGBgZKkwMBAHTp0yGqVhU2bNsnT01PFixe3xDw4RmJM4hguLi4qX768VUxCQoK2bNliiUnOXJKDSi8AAEAG0717dy1cuFDfffedPDw8LL2xXl5ecnd3l5eXlzp16qS+ffsqW7Zs8vT0VM+ePRUYGGhZLaFOnToqXry43njjDY0bN04REREaMmSIunfvbkm233rrLU2bNk0DBgxQx44dtXXrVi1dulTr1q2zzKVv374KCQlRhQoV9OKLL+qTTz7RzZs31aFDB8ucnjSX5CDpBQAAMFhq3ciWXDNnzpQk1ahRw2r/119/rfbt20uSJk+eLAcHBzVv3lyxsbEKDg7WjBkzLLGOjo5au3atunXrpsDAQGXOnFkhISEaNWqUJSZfvnxat26d+vTpoylTpuj555/X7NmzFRwcbIlp1aqVrly5omHDhikiIkJly5bVhg0brG5ue9JckoN1ejMI1ukF0h7r9AJpIz2u0+vb8Rubr9Mb8dXr6eIa0ysqvQAAAAZL60pvRkTSCwAAYDCSXuOxegMAAADsHpVeAAAAoz2wzJjNxsNjUekF/qN+Hevo52/66/LPE/THlrFaOqmLCgXksor54Yteun1wmtU2dXBrq5iJA1po54IBitwzWbsXD3roewUFFtP2ue/q8s8TdG7rWC2a0Fl5cmezimldr4L2LBmkq7sm6fTGMZo1vJ2yeWW2iunRtoZ+WTlU10In6cT3ozXu3WZydeF3YGRc48d9JHdnk/r17S1Junbtmvr06qnSJYooq4e7CuXPo76931FUVJTVeefOnVPTRg2UzTOT8vjl0nsD+ysuLi4NrgDAk/AtB/xHVcsV1KwlOxR25A85OTlqZI9XtXZmD73Q7APdunPXEvfl8p0aPXOt5fWtO/eSjDXvu92qWCpAJQs9l+RYgF92LZvcVVO/2ar2g+fKK4ubxvVrrsUTu+jlth9LkgLL5Nfs0W9qwMTlWrf9sJ7L5aWpg1trxtA2at1vtiSpVd0KGv1OY701YoFCfzmtQgG59MWoN2SWNHDiCht/OkD6t3/fPn35xWcqVaq0Zd/FCxd08eIFjf14gooVK65z5/5Qz+5v6eLFC1q05FtJUnx8vJo1aiAfX1/9uGOXIiIuqnOHN+Xs7KxRH3yYVpeDZwQ9vcYj6QX+o8Y9rNcJ7Dr8G53f+pFeKO6vnQdOWfbfvnNXl67eeOQ47467/0WaI2v9hya95Yr7y9HBQSOmr1XiSoOfzNuiZZO7ysnJQXFxCapUOp/+uHBVMxZtlyT9ceGqvly+U++2D7KM81KZfAoNP60lG/ZLks5dvKalG/arYsm8T/cBAM+wmJgYdQhppxmzvtBHH35g2V+iZEktXrrc8jp/gQIaMWqMOoa8rri4ODk5OWnzpo06evQ3rfths3x8fFRGZTVsxGgNeX+ghgwbIRcXlocE0hPaGwAb88ziJkm6HnXLan+r+hV0futH2r/sfY3q2Ujubs4pGvfAb+eVYE7Qm41fkoODSZ5Z3NS2wYvauue44uISJEl7fj2j532zKrjK/cc/5srmoaZBZbXh598s4+z+5YxeKO6vCiUCJEl5n8uu4MoltOHnI099zcCzqnfP7qpbr4FeqRX0xNjo/1//1Mnpfr1oz+5QlSxZymoB/dp1ghUdHa3fjvDzhMdLrPTacsPj2X2lN2/evOrdu7d69+6d1lOxmTlz5qh3796KjIxM66ngX0wmk8b3a6FdB0/pt1MXLfuXfL9f5y5e08UrUSpVyE8f9GqswgG5LC0HyfHHhatq+PZ0ffNxR00b3FpOTo7a/ctpNekx0xIT+stpdXh/ruZ/1FFuLs5ydnbU2u2H1PujJf/MZcN+Zc+aWVu+7iOTTHJ2dtTny37S+K822uZDAJ4RS5csVvjBA/p5974nxv79998a++Fodezc1bLvUkSEcj2Q8EqyvL50KcK2k4XdMcnG7Q3cyfZEaVrpbd++vdVvKNmzZ1fdunX166+/puW0gKf2yXstVaJgbr056Gur/V+t2KnNoUd15OQFLf5+vzoNna/Gtcoq3/M5kj22T3YPzRjaVgvW7FGV18crqNNk3b0Xr4UTOlliiub31YQBLTT28+/1cruP9erb0xWQO5s+feCmuarlC6l/x2D1GrtEgW0/Vqu+n6telRIa1KXuf/8AgGfE+fPn1b9vL309b4Hc3NweGxsdHa2mjRqoWLHiGjJshDETBGBzad7eULduXV28eFEXL17Uli1b5OTkpIYNG6b1tB7r7t27Tw5ChjN54GuqX7WkgrtM1V+XIx8bu+/QWUlSAf+cyR7/f62qKTrmtgZP+U6/HP9TOw+cUsfBc/VKpaJ6sVReSVL/DnUUGn5Kk+dt0eETF7Q59Kh6j12i9k1elm+O+4+lHP52Ay1at1dzVobqyMkLWv3jrxo2bY36d6jDn8eQYRw8EKbLly8r8MVyyuLmpCxuTvppx3bNmDZVWdycFB8fL0m6ceOGGjWoKw8PDy35dqWcnf9pS/Lx9dXlS5esxk187ePja9zF4JlEe4Px0jzpdXV1la+vr3x9fVW2bFkNGjRI58+f15UrVyRJAwcOVOHChZUpUyblz59fQ4cO1b171ne9r1mzRhUrVpSbm5ty5Mihpk2bPvL9Zs+eLW9vb23ZskXS/f+gtWvXTpkzZ1bu3Lk1efJk1ahRw6odIm/evBo9erTefPNNeXp6qmvX+3/eWr58uUqUKCFXV1flzZtXEydOtHovk8mkVatWWe3z9vbWnDlzJElnz56VyWTSihUrVLNmTWXKlEllypRRaGio1Tlz5sxRnjx5lClTJjVt2lRXr15N9ucLY0we+JoavVJGdf83VX9cePL/f8oUeV6SFPF31BMi/5HJzUUJCWarffEJ93t5HRzu/8cuk/vDYu6/TvwPovtDxkn4/3H4byYyipqv1NL+g4e0Z3+4ZStXvoJat2mnPfvD5ejoqOjoaDWsV0cuLi76duXqJBXhSi8F6vDhQ7p8+bJl35bNm+Tp6alixYsbfUkAniDNk94HxcTE6JtvvlHBggWVPXt2SZKHh4fmzJmj3377TVOmTNEXX3yhyZMnW85Zt26dmjZtqvr16+vgwYPasmWLXnzxxYeOP27cOA0aNEgbN25UrVq1JEl9+/bVzp07tXr1am3atEk//fSTDhw4kOTcCRMmqEyZMjp48KCGDh2qsLAwtWzZUq1bt9ahQ4c0YsQIDR061JLQpsTgwYPVr18/hYeHq3DhwmrTpo1lncc9e/aoU6dO6tGjh8LDw1WzZk198MEHTxgRRvrkvZZq3aCiQt6fo5ibd+ST3UM+2T3k5nq/IpTv+Rwa1KWuXijmrzy5s6lB9VKaPfoN/RR2QodPXLCMk98/h0oXfk4+OTzl7uqs0oWfU+nCz8nZyVGS9P1PR1S+RB6917WuCuTJqbJFn9dnI17XHxeuKvzYn5KkddsPqfErZdXltSrK+1x2BZbJr4kDWmjfobO6eOV+gr1+x2F1ea2KXgsurwC/7HqlUlEN69ZQ63ccSpIMA/bKw8NDJUqWtNoyZ86sbNmzq0TJkpaE99bNm5r1+ZeKjo5WRESEIiIiLFXgoNp1VKxYcXVq/4Z+/eUXbdr4g0YOH6L/desuV1fXNL5CpHumVNjwWGl+I9vatWuVJUsWSdLNmzeVO3durV27Vg4O9/PxIUOGWGLz5s2rfv36afHixRowYIAkacyYMWrdurVGjhxpiStTpkyS9xk4cKDmz5+v7du3q0SJEpLuV3nnzp2rhQsXWpLgr7/+Wn5+fknOf+WVV/Tuu+9aXrdr1061atXS0KFDJUmFCxfWb7/9pvHjx6t9+/Yp+gz69eunBg0aSJJGjhypEiVK6OTJkypatKimTJmiunXrWq63cOHC2rVrlzZs2PDYMWNjYxUbG2t5HR0dnaI5Ifn+17KaJGnT7N5W+7sMm69v1uzRvXtxeqVSEfVoW1OZ3V3056XrWrUlXB/N/sEqfuawdqpWoZDl9Z4l70mSitQfpnMXr2n7vt/V/v256hMSpL4htXXrzl3t+fWMGnWfoTux9//68c2aPfLI7Ka3WlXXR32aKSrmtrbtPa4hU76zjPvR7A0ym80a/nZD+eXy0t/XY7Rux2GNmLYmNT4e4JkUfvCA9u3dI0kqUbSg1bFjJ84oIG9eOTo6avl3a9WrRzfVqBqozJkzq90bIRo2YlRaTBnAE6R50luzZk3NnHn/7vPr169rxowZqlevnvbu3auAgAAtWbJEU6dO1alTpxQTE6O4uDh5enpazg8PD1eXLl0e+x4TJ07UzZs3tX//fuXPn9+y//Tp07p3755VZdjLy0tFihRJMkaFChWsXh89elSNGze22le5cmV98sknio+Pl6OjY7I/g9Kl/1kQPXfu3JKky5cvq2jRojp69GiSdo3AwMAnJr1jx461+kUAqcf9hR6PPf7npUjV6TzlieMEd3lyzLIfwrTsh7DHxsxcvF0zF29/5PH4+AR9+Pn3+vDz75/4fkBGsnHLNsu/V6teQ7fvPfkvHwEBAVq1Zn0qzgr2iodTGC/N2xsyZ86sggULqmDBgqpYsaJmz56tmzdv6osvvlBoaKjatWun+vXra+3atTp48KAGDx5sdSOZu7v7E9+jatWqio+P19KlS//TPFPKZDJZHiKQ6N/9yJKsboxI/B9tYo/l03rvvfcUFRVl2c6fP/+fxgMAALbDjWzGS/Ok999MJpMcHBx0+/Zt7dq1SwEBARo8eLAqVKigQoUK6Y8//rCKL126tOWmtEd58cUX9f333+vDDz/UhAkTLPvz588vZ2dn7dv3zxqNUVFR+v333584z2LFimnnzp1W+3bu3KnChQtbqrw5c+bUxYv/rNV64sQJ3bpl/cCC5LzPnj17rPbt3r37iee5urrK09PTagMAAMio0ry9ITY2VhER9xfxvn79uqZNm6aYmBi9+uqrio6O1rlz57R48WJVrFhR69at08qVK63OHz58uGrVqqUCBQqodevWiouL0/r16zVw4ECruJdfflnr169XvXr15OTkpN69e8vDw0MhISHq37+/smXLply5cmn48OFycHB44m9M7777ripWrKjRo0erVatWCg0N1bRp0zRjxj+PpH3llVc0bdo0BQYGKj4+XgMHDrSq6ibHO++8o8qVK2vChAlq3Lixfvjhhye2NgAAgPTNZLLtijkUep8szSu9GzZsUO7cuZU7d25VqlRJ+/bt07Jly1SjRg01atRIffr0UY8ePVS2bFnt2rXLcuNYoho1amjZsmVavXq1ypYtq1deeUV79+596HtVqVJF69at05AhQ/Tpp59KkiZNmqTAwEA1bNhQQUFBqly5sooVK/bExcrLlSunpUuXavHixSpZsqSGDRumUaNGWd3ENnHiRPn7+6tq1apq27at+vXrp0yZMqXo83nppZf0xRdfaMqUKSpTpow2btxodXMfAAAAnsxk/nfTaQZ38+ZNPffcc5o4caI6der05BOeEdHR0fLy8pJrqS4yObqk9XSADOn6vmlpPQUgQ4qOjpZPdi9FRUWlebtf4vdx/p7fysE15fcLPUpC7E2d/rRFurjG9CrN2xvS2sGDB3Xs2DG9+OKLioqK0qhR95ea+ffKDAAAAHh2ZfikV7r/4Injx4/LxcVF5cuX108//aQcOXKk9bQAAIC9snFPLw+neLIMn/S+8MILCgt7/LqnAAAAeLZl+KQXAADAaDycwngkvQAAAAZjyTLjpfmSZQAAAEBqo9ILAABgMAcHkxwcbFeeNdtwLHtFpRcAAAB2j0ovAACAwejpNR6VXgAAANg9Kr0AAAAGY8ky45H0AgAAGIz2BuPR3gAAAAC7R6UXAADAYLQ3GI9KLwAAAOwelV4AAACDUek1HpVeAAAA2D0qvQAAAAZj9QbjkfQCAAAYzCQbtzeIrPdJaG8AAACA3aPSCwAAYDDaG4xHpRcAAAB2j0ovAACAwViyzHhUegEAAGD3qPQCAAAYjJ5e45H0AgAAGIz2BuPR3gAAAAC7R6UXAADAYLQ3GI9KLwAAAOwelV4AAACD0dNrPCq9AAAAsHtUegEAAIxm455eUeh9IpJeAAAAg9HeYDzaGwAAAGD3qPQCAAAYjCXLjEelFwAAAHaPSi8AAIDB6Ok1HpVeAAAA2D0qvQAAAAajp9d4JL0AAAAGo73BeLQ3AAAAwO5R6QUAADAYlV7jUekFAACA3aPSCwAAYDBuZDMelV4AAADYPSq9AAAABqOn13gkvQAAAAajvcF4tDcAAADA7lHpBQAAMBjtDcaj0gsAAAC7R6UXAADAYCbZuKfXdkPZLSq9AAAAsHtUegEAAAzmYDLJwYalXluOZa9IegEAAAzGkmXGo70BAAAAdo9KLwAAgMFYssx4VHoBAABg96j0AgAAGMzBdH+z5Xh4PCq9AAAAsHskvQAAAEYz/dPXa4stpU+n2LFjh1599VX5+fnJZDJp1apVVsfbt2+f5D3q1q1rFXPt2jW1a9dOnp6e8vb2VqdOnRQTE2MV8+uvv6pq1apyc3OTv7+/xo0bl2Quy5YtU9GiReXm5qZSpUpp/fr1VsfNZrOGDRum3Llzy93dXUFBQTpx4kTKLlgkvQAAAIZLXLLMlltK3Lx5U2XKlNH06dMfGVO3bl1dvHjRsi1atMjqeLt27XTkyBFt2rRJa9eu1Y4dO9S1a1fL8ejoaNWpU0cBAQEKCwvT+PHjNWLECH3++eeWmF27dqlNmzbq1KmTDh48qCZNmqhJkyY6fPiwJWbcuHGaOnWqZs2apT179ihz5swKDg7WnTt3UnTN9PQCAABkMPXq1VO9evUeG+Pq6ipfX9+HHjt69Kg2bNigffv2qUKFCpKkTz/9VPXr19eECRPk5+enBQsW6O7du/rqq6/k4uKiEiVKKDw8XJMmTbIkx1OmTFHdunXVv39/SdLo0aO1adMmTZs2TbNmzZLZbNYnn3yiIUOGqHHjxpKkefPmycfHR6tWrVLr1q2Tfc1UegEAAAxmSoV/pPvV1Qe32NjYp57jtm3blCtXLhUpUkTdunXT1atXLcdCQ0Pl7e1tSXglKSgoSA4ODtqzZ48lplq1anJxcbHEBAcH6/jx47p+/bolJigoyOp9g4ODFRoaKkk6c+aMIiIirGK8vLxUqVIlS0xykfQCAADYCX9/f3l5eVm2sWPHPtU4devW1bx587RlyxZ9/PHH2r59u+rVq6f4+HhJUkREhHLlymV1jpOTk7Jly6aIiAhLjI+Pj1VM4usnxTx4/MHzHhaTXLQ3AAAAGCy1liw7f/68PD09LftdXV2farwH2wZKlSql0qVLq0CBAtq2bZtq1ar1n+aaVqj0AgAA2AlPT0+r7WmT3n/Lnz+/cuTIoZMnT0qSfH19dfnyZauYuLg4Xbt2zdIH7Ovrq0uXLlnFJL5+UsyDxx8872ExyUXSCwAAYDBbLldm60caP8yff/6pq1evKnfu3JKkwMBARUZGKiwszBKzdetWJSQkqFKlSpaYHTt26N69e5aYTZs2qUiRIsqaNaslZsuWLVbvtWnTJgUGBkqS8uXLJ19fX6uY6Oho7dmzxxKTXCS9AAAAGUxMTIzCw8MVHh4u6f4NY+Hh4Tp37pxiYmLUv39/7d69W2fPntWWLVvUuHFjFSxYUMHBwZKkYsWKqW7duurSpYv27t2rnTt3qkePHmrdurX8/PwkSW3btpWLi4s6deqkI0eOaMmSJZoyZYr69u1rmUevXr20YcMGTZw4UceOHdOIESO0f/9+9ejRQ9L9Xw569+6tDz74QKtXr9ahQ4f05ptvys/PT02aNEnRNdPTCwAAYLCnWVv3SeOlxP79+1WzZk3L68RENCQkRDNnztSvv/6quXPnKjIyUn5+fqpTp45Gjx5t1S6xYMEC9ejRQ7Vq1ZKDg4OaN2+uqVOnWo57eXlp48aN6t69u8qXL68cOXJo2LBhVmv5vvzyy1q4cKGGDBmi999/X4UKFdKqVatUsmRJS8yAAQN08+ZNde3aVZGRkapSpYo2bNggNze3lH1GZrPZnLKPCc+i6OhoeXl5ybVUF5kcXZ58AgCbu75vWlpPAciQoqOj5ZPdS1FRUVY3eaXVXLy8vNTw021yds9is3Hv3Y7R2p410sU1ple0NwAAAMDu0d4AAABgsLRub8iIqPQCAADA7lHpBQAAMJitlxlL7SXL7EGykt7Vq1cne8BGjRo99WQAAACA1JCspDe566CZTCbLM5kBAADwcPT0Gi9ZSW9CQkJqzwMAACDDcDCZ5GDDTNWWY9mr/3Qj2507d2w1DwAAACDVpDjpjY+P1+jRo/Xcc88pS5YsOn36tCRp6NCh+vLLL20+QQAAAHtjSoUNj5fipHfMmDGaM2eOxo0bJxeXf57sVbJkSc2ePdumkwMAAABsIcVJ77x58/T555+rXbt2cnR0tOwvU6aMjh07ZtPJAQAA2KPEJctsueHxUpz0/vXXXypYsGCS/QkJCbp3755NJgUAAADYUoqT3uLFi+unn35Ksv/bb7/VCy+8YJNJAQAA2DMHk+03PF6Kn8g2bNgwhYSE6K+//lJCQoJWrFih48ePa968eVq7dm1qzBEAAMCu8EQ246W40tu4cWOtWbNGmzdvVubMmTVs2DAdPXpUa9asUe3atVNjjgAAAMB/kuJKryRVrVpVmzZtsvVcAAAAMgyKs8Z6qqRXkvbv36+jR49Kut/nW758eZtNCgAAALClFCe9f/75p9q0aaOdO3fK29tbkhQZGamXX35Zixcv1vPPP2/rOQIAANgVenqNl+Ke3s6dO+vevXs6evSorl27pmvXruno0aNKSEhQ586dU2OOAAAAwH+S4krv9u3btWvXLhUpUsSyr0iRIvr0009VtWpVm04OAADAHtl6mTGWLHuyFCe9/v7+D30IRXx8vPz8/GwyKQAAAHtGe4PxUtzeMH78ePXs2VP79++37Nu/f7969eqlCRMm2HRyAAAAgC0kq9KbNWtWq98gbt68qUqVKsnJ6f7pcXFxcnJyUseOHdWkSZNUmSgAAIC9MP3/Zsvx8HjJSno/+eSTVJ4GAAAAkHqSlfSGhISk9jwAAAAyDAeTSQ427MO15Vj26qkfTiFJd+7c0d27d632eXp6/qcJAQAAALaW4hvZbt68qR49eihXrlzKnDmzsmbNarUBAADg8Uwm2294vBQnvQMGDNDWrVs1c+ZMubq6avbs2Ro5cqT8/Pw0b9681JgjAACAXUlcssyWGx4vxe0Na9as0bx581SjRg116NBBVatWVcGCBRUQEKAFCxaoXbt2qTFPAAAA4KmluNJ77do15c+fX9L9/t1r165JkqpUqaIdO3bYdnYAAAB2iPYG46U46c2fP7/OnDkjSSpatKiWLl0q6X4F2Nvb26aTAwAAAGwhxe0NHTp00C+//KLq1atr0KBBevXVVzVt2jTdu3dPkyZNSo05AgAA2BWWLDNeipPePn36WP49KChIx44dU1hYmAoWLKjSpUvbdHIAAACALfyndXolKSAgQAEBAbaYCwAAQIZg6z5cCr1Plqykd+rUqcke8J133nnqyQAAAGQEtl5mjCXLnixZSe/kyZOTNZjJZCLpBQAAQLqTrKQ3cbUGPPvObZvAo6KBNHLnXnxaTwHIkNLjz56DnmIJrSeMh8fjMwIAAIDd+883sgEAACBl6Ok1HpVeAAAA2D0qvQAAAAYzmSQHliwzFEkvAACAwRxsnPTacix79VTtDT/99JNef/11BQYG6q+//pIkzZ8/Xz///LNNJwcAAADYQoqT3uXLlys4OFju7u46ePCgYmNjJUlRUVH68MMPbT5BAAAAe5N4I5stNzxeipPeDz74QLNmzdIXX3whZ2dny/7KlSvrwIEDNp0cAAAAYAsp7uk9fvy4qlWrlmS/l5eXIiMjbTEnAAAAu0ZPr/FSXOn19fXVyZMnk+z/+eeflT9/fptMCgAAALClFCe9Xbp0Ua9evbRnzx6ZTCZduHBBCxYsUL9+/dStW7fUmCMAAIBdMZlsv+HxUtzeMGjQICUkJKhWrVq6deuWqlWrJldXV/Xr1089e/ZMjTkCAADYFQeTSQ42zFRtOZa9SnHSazKZNHjwYPXv318nT55UTEyMihcvrixZsqTG/AAAAID/7KkfTuHi4qLixYvbci4AAAAZgoOe8mEJjxkPj5fipLdmzZqPXQtu69at/2lCAAAAgK2lOOktW7as1et79+4pPDxchw8fVkhIiK3mBQAAYLdsffMZLb1PluKkd/LkyQ/dP2LECMXExPznCQEAAAC2ZrMWkNdff11fffWVrYYDAACwWw4yWVZwsMkmSr1P8tQ3sv1baGio3NzcbDUcAACA3aK9wXgpTnqbNWtm9dpsNuvixYvav3+/hg4darOJAQAAALaS4qTXy8vL6rWDg4OKFCmiUaNGqU6dOjabGAAAgL1yMN3fbDkeHi9FSW98fLw6dOigUqVKKWvWrKk1JwAAAMCmUnQjm6Ojo+rUqaPIyMhUmg4AAID9M5lk0xvZ6Ol9shSv3lCyZEmdPn06NeYCAAAApIoUJ70ffPCB+vXrp7Vr1+rixYuKjo622gAAAPB4ias32HLD4yW7p3fUqFF69913Vb9+fUlSo0aNrB5HbDabZTKZFB8fb/tZAgAA2BFuZDNespPekSNH6q233tKPP/6YmvMBAAAAbC7ZSa/ZbJYkVa9ePdUmAwAAkBGY/v8fW46Hx0tRT6+JhhEAAAA8g1K0Tm/hwoWfmPheu3btP00IAADA3tHTa7wUJb0jR45M8kQ2AAAAIL1LUdLbunVr5cqVK7XmAgAAkCFQ6TVesnt66ecFAADAsyrFqzcAAADgvzGZTDYtKFKcfLJkJ70JCQmpOQ8AAIAMg/YG46X4McQAAADAsyZFN7IBAADgvzOZ7m+2HA+PR6UXAAAAdo9KLwAAgMEcTCY52LA8a8ux7BWVXgAAANg9Kr0AAAAGY/UG41HpBQAAMJrpn5vZbLEphUnvjh079Oqrr8rPz08mk0mrVq2yOm42mzVs2DDlzp1b7u7uCgoK0okTJ6xirl27pnbt2snT01Pe3t7q1KmTYmJirGJ+/fVXVa1aVW5ubvL399e4ceOSzGXZsmUqWrSo3NzcVKpUKa1fvz7Fc0kOkl4AAIAM5ubNmypTpoymT5/+0OPjxo3T1KlTNWvWLO3Zs0eZM2dWcHCw7ty5Y4lp166djhw5ok2bNmnt2rXasWOHunbtajkeHR2tOnXqKCAgQGFhYRo/frxGjBihzz//3BKza9cutWnTRp06ddLBgwfVpEkTNWnSRIcPH07RXJLDZOZRaxlCdHS0vLy8dOlqlDw9PdN6OkCGdOdefFpPAciQoqOjFeCbTVFRaf8dmPh9PP6HX+We2cNm496+eUP9g0s/1TWaTCatXLlSTZo0kXS/surn56d3331X/fr1kyRFRUXJx8dHc+bMUevWrXX06FEVL15c+/btU4UKFSRJGzZsUP369fXnn3/Kz89PM2fO1ODBgxURESEXFxdJ0qBBg7Rq1SodO3ZMktSqVSvdvHlTa9eutcznpZdeUtmyZTVr1qxkzSW5qPQCAADA4syZM4qIiFBQUJBln5eXlypVqqTQ0FBJUmhoqLy9vS0JryQFBQXJwcFBe/bsscRUq1bNkvBKUnBwsI4fP67r169bYh58n8SYxPdJzlySixvZAAAADJZaD6eIjo622u/q6ipXV9cUjRURESFJ8vHxsdrv4+NjORYREaFcuXJZHXdyclK2bNmsYvLly5dkjMRjWbNmVURExBPf50lzSS4qvQAAAHbC399fXl5elm3s2LFpPaV0g0ovAACAwVJrybLz589b9fSmtMorSb6+vpKkS5cuKXfu3Jb9ly5dUtmyZS0xly9ftjovLi5O165ds5zv6+urS5cuWcUkvn5SzIPHnzSX5KLSCwAAYLDEJ7LZcpMkT09Pq+1pkt58+fLJ19dXW7ZsseyLjo7Wnj17FBgYKEkKDAxUZGSkwsLCLDFbt25VQkKCKlWqZInZsWOH7t27Z4nZtGmTihQpoqxZs1piHnyfxJjE90nOXJKLpBcAACCDiYmJUXh4uMLDwyXdv2EsPDxc586dk8lkUu/evfXBBx9o9erVOnTokN588035+flZVngoVqyY6tatqy5dumjv3r3auXOnevToodatW8vPz0+S1LZtW7m4uKhTp046cuSIlixZoilTpqhv376WefTq1UsbNmzQxIkTdezYMY0YMUL79+9Xjx49JClZc0ku2hsAAAAMllo3siXX/v37VbNmTcvrxEQ0JCREc+bM0YABA3Tz5k117dpVkZGRqlKlijZs2CA3NzfLOQsWLFCPHj1Uq1YtOTg4qHnz5po6darluJeXlzZu3Kju3burfPnyypEjh4YNG2a1lu/LL7+shQsXasiQIXr//fdVqFAhrVq1SiVLlrTEJGcuyfqMWKc3Y2CdXiDtsU4vkDbS4zq9U7Ycsvk6vb1qlUoX15heUekFAAAwmIP+6cO11Xh4PHp6AQAAYPeo9AIAABgsrXt6MyKSXgAAAIM5yLZ/budP90/GZwQAAAC7R6UXAADAYCaTSSYb9iTYcix7RaUXAAAAdo9KLwAAgMFM/7/Zcjw8HpVeAAAA2D0qvQAAAAZzMNn44RT09D4RSS8AAEAaIE01Fu0NAAAAsHtUegEAAAzGE9mMR6UXAAAAdo9KLwAAgMF4OIXxqPQCAADA7lHpBQAAMJiDbFt5pIr5ZCS9AAAABqO9wXj8YgAAAAC7R6UXAADAYCbZ9uEU1HmfjEovAAAA7B6VXgAAAIPR02s8Kr0AAACwe1R6AQAADMaSZcYj6QUAADAY7Q3G4xcDAAAA2D0qvQAAAAZjyTLjUekFAACA3aPSCwAAYDCT6f5my/HweFR6AQAAYPeo9AIAABjMQSY52LAT15Zj2SuSXgAAAIPR3mA82hsAAABg96j0AgAAGMz0///Ycjw8HpVeAAAA2D0qvQAAAAajp9d4VHoBAABg96j0AgAAGMxk4yXL6Ol9MpJeAAAAg9HeYDzaGwAAAGD3qPQCAAAYjEqv8aj0AgAAwO5R6QUAADAYD6cwHpVeAAAA2D0qvQAAAAZzMN3fbDkeHo+kFwAAwGC0NxiP9gYAAADYPSq9AAAABmPJMuNR6QUAAIDdo9ILAABgMJNs24dLoffJqPQC6cQHo0bI3dlktZUpWVSSdO3aNfXp1VOlSxRRVg93FcqfR317v6OoqKi0nTTwDLlx44be699XpYrkV+5sWVSnZhUd2L/PcjxrJqeHblMnT7DEnDzxu9q+1lQF/H2Uxyer6taqpp+2//jQ97t29apKFAxQ1kxOioqMTO3LA/AEVHqBdKR4iRJat2Gz5bWT0/0f0YsXLujixQsa+/EEFStWXOfO/aGe3d/SxYsXtGjJt2k1XeCZ0uvtrjr62xHN+nKOcuf209JFC9SkYbB2hx2S33PP6djpP63iN2/coJ7duqhRk2aWfa2bN1b+AgX13fpNcnd318xpU9W6eWMdOPy7fHx9rc7v2a2LipcspQsX/jLk+vBsYcky45H0AumIk6OTfP/1xSlJJUqW1OKlyy2v8xcooBGjxqhjyOuKi4uzJMcAHu727dtavWqFFixdocpVqkmSBg0Zrg3r1+mrL2ZpyIjRSZLW9WtXq2r1GsqbL78k6erff+vUyROaOvNzlSxVWpI0fPSH+vLzmTr622Gr87/8fJaioiI14L0h2rxxg0FXCeBxaG8A0pGTJ08oXx4/FSucX+3faKdz5849MjY6Kkqenp4kvEAyxMXFKT4+Xm5ublb73dzdtDt0Z5L4y5cuaeOG9Xo9pKNlX7bs2VWocBEtWfCNbt68qbi4OM358nPlzJVLZV8ob4k7dvQ3jR/7gWbOniMHB75m8XCmVPgHj8dP43+QN29effLJJ/85BpCkii9W0udfztHqtRs0ddpMnT17RkE1q+rGjRtJYv/++2+N/XC0OnbumgYzBZ49Hh4eqljpJY3/aIwuXrig+Ph4LVm0QPv27NaliIgk8YsWzFMWDw+92ripZZ/JZNLKtT/o11/C5Z/LW75ZM2vG1E/07ap18s6aVZIUGxurzu1f18gPP5a/fx7Drg/PnsQly2y54fFIeh/h/Pnz6tixo/z8/OTi4qKAgAD16tVLV69eTdE4+/btU9eutktMSKLtV3Ddemre4jWVKl1atesEa9Wa9YqKjNTyZUut4qKjo9W0UQMVK1ZcQ4aNSJvJAs+gz76cK7PZrOIF88jHO5M+n/Gpmrds/dBq7IJ5c/Raq7ZWlWGz2az+fXoqR86cWr95m7bsCFX9VxurTYsmirh4UZI0atj7KlykqFq1aWfYdQFIHpLehzh9+rQqVKigEydOaNGiRTp58qRmzZqlLVu2KDAwUNeuXUv2WDlz5lSmTJlScbawV97e3ipYqLBOnTpp2Xfjxg01alBXHh4eWvLtSjk7O6fhDIFnS778BbRu44/680qUDv9+Vlt+2q24e/cUkDefVdyunT/pxO/H9Ub7jlb7d2zbqh++X6cv5y3US4GVVeaFcpo4ZZrc3N21aMG8/4/Zpu9WfKscHq7K4eGqxvXrSJIK+Pto7OgRhlwnng2mVNjweCS9D9G9e3e5uLho48aNql69uvLkyaN69epp8+bN+uuvvzR48GBL7I0bN9SmTRtlzpxZzz33nKZPn2411r8rs5GRkercubNy5swpT09PvfLKK/rll1+szlmzZo0qVqwoNzc35ciRQ02b3v/zWo0aNfTHH3+oT58+MplMMvG3DLsWExOjM6dPydc3t6T7Fd6G9erIxcVF365cnaQ3EUDyZM6cWb65cyvy+nVt2bxR9Rs2sjr+zdyvVfaF8ipVuozV/lu3bklSksqwg4ODEhISJEnzFi3VT3sOaMfuMO3YHaapMz6XJK3fvE2d//d2al0SgGQg6f2Xa9eu6YcfftDbb78td3d3q2O+vr5q166dlixZIrPZLEkaP368ypQpo4MHD2rQoEHq1auXNm3a9MjxX3vtNV2+fFnff/+9wsLCVK5cOdWqVctSPV63bp2aNm2q+vXr6+DBg9qyZYtefPFFSdKKFSv0/PPPa9SoUbp48aIu/v+f02AfBg3op592bNcfZ88qdNcutWrRVI6OjmrZuo0l4b1186Zmff6loqOjFRERoYiICMXHx6f11IFnwpZNP2jzxg364+wZ/bhlk16tG6TChYuo3ZvtLTHR0dH6bsW3Saq8kvRipUB5Z82qt7t00KFff9HJE79r6PsD9MfZM6pTt76k+9Xk4iVKWraAvHklSUWKFFPOXLmMuEw8IxxkkoPJhhu13ifitu9/OXHihMxms4oVK/bQ48WKFdP169d15coVSVLlypU1aNAgSVLhwoW1c+dOTZ48WbVr105y7s8//6y9e/fq8uXLcnV1lSRNmDBBq1at0rfffquuXbtqzJgxat26tUaOHGk5r0yZ+9WGbNmyydHRUR4eHg9d1upBsbGxio2NtbyOjo5OwaeAtPDXX3/qzdfb6NrVq8qRM6derlxF23/erZw5c2rH9m3at3ePJKlE0YJW5x07ccbyxQrg0aKjozVq2GBd+OtPZc2aTa82aaYhI0ZbtQmtWHa/qNG8Zesk52fPkUPfrlqnD0YOVeP6tRV3756KFiuuBUtXJKkKA0h/SHofIbGS+ySBgYFJXj/qRrNffvlFMTExyp49u9X+27dv69SpU5Kk8PBwdenSJeUT/pexY8daJc5I/+YvWPzIY9Wq19Dte8n73ySAh2va/DU1bf7aY2Pad+qi9p0e/d/gF8pX0PLV3yf7PatUq6Hrt+KSHY+Mw9Z9uNR5n4yk918KFiwok8mko0ePWnppH3T06FFlzZpVOXPmTPHYMTExyp07t7Zt25bkmLe3tyQlaal4Wu+995769u1reR0dHS1/f3+bjA0AAP4jsl7D0dP7L9mzZ1ft2rU1Y8YM3b592+pYRESEFixYoFatWlluItu9e7dVzO7dux/ZGlGuXDlFRETIyclJBQsWtNpy5MghSSpdurS2bNnyyPm5uLgkq4fT1dVVnp6eVhsAAEBGRdL7ENOmTVNsbKyCg4O1Y8cOnT9/Xhs2bFDt2rX13HPPacyYMZbYnTt3aty4cfr99981ffp0LVu2TL169XrouEFBQQoMDFSTJk20ceNGnT17Vrt27dLgwYO1f/9+SdLw4cO1aNEiDR8+XEePHtWhQ4f08ccfW8bImzevduzYob/++kt///136n4QAAAgVfBENuOR9D5EoUKFtH//fuXPn18tW7ZUgQIF1LVrV9WsWVOhoaHKli2bJfbdd9/V/v379cILL+iDDz7QpEmTFBwc/NBxTSaT1q9fr2rVqqlDhw4qXLiwWrdurT/++EM+Pj6S7i9LtmzZMq1evVply5bVK6+8or1791rGGDVqlM6ePasCBQo8VYsFAABARmQyJ/eOLTyV3Llza/To0ercuXOaziM6OlpeXl66dDWKVgcgjdy5x/JyQFqIjo5WgG82RUWl/Xdg4vfxlvBzyuJhu7nE3IhWrbJ50sU1plfcyJZKbt26pZ07d+rSpUsqUaJEWk8HAAAgQ6O9IZV8/vnnat26tXr37p1kWTMAAJCx8Rhi41HpTSW9e/dW796903oaAAAgPWLJMsNR6QUAAIDdo9ILAABgMFsvM8aSZU9GpRcAAAB2j0ovAACAwUym+5stx8PjUekFAACA3aPSCwAAYDAWbzAeSS8AAIDRyHoNR3sDAAAA7B6VXgAAAIOxZJnxqPQCAABkMCNGjJDJZLLaihYtajl+584dde/eXdmzZ1eWLFnUvHlzXbp0yWqMc+fOqUGDBsqUKZNy5cql/v37Ky4uzipm27ZtKleunFxdXVWwYEHNmTMnyVymT5+uvHnzys3NTZUqVdLevXtT5ZpJegEAAAyWuGSZLbeUKlGihC5evGjZfv75Z8uxPn36aM2aNVq2bJm2b9+uCxcuqFmzZpbj8fHxatCgge7evatdu3Zp7ty5mjNnjoYNG2aJOXPmjBo0aKCaNWsqPDxcvXv3VufOnfXDDz9YYpYsWaK+fftq+PDhOnDggMqUKaPg4GBdvnz56T7YxzCZzWazzUdFuhMdHS0vLy9duholT0/PtJ4OkCHduRef1lMAMqTo6GgF+GZTVFTafwcmfh//dPhPZfGw3VxibkSrasnnk32NI0aM0KpVqxQeHp7kWFRUlHLmzKmFCxeqRYsWkqRjx46pWLFiCg0N1UsvvaTvv/9eDRs21IULF+Tj4yNJmjVrlgYOHKgrV67IxcVFAwcO1Lp163T48GHL2K1bt1ZkZKQ2bNggSapUqZIqVqyoadOmSZISEhLk7++vnj17atCgQf/1Y7FCpRcAAMBgplTYUurEiRPy8/NT/vz51a5dO507d06SFBYWpnv37ikoKMgSW7RoUeXJk0ehoaGSpNDQUJUqVcqS8EpScHCwoqOjdeTIEUvMg2MkxiSOcffuXYWFhVnFODg4KCgoyBJjS9zIBgAAYLRUWrIsOjraarerq6tcXV2ThFeqVElz5sxRkSJFdPHiRY0cOVJVq1bV4cOHFRERIRcXF3l7e1ud4+Pjo4iICElSRESEVcKbeDzx2ONioqOjdfv2bV2/fl3x8fEPjTl27FjKrj8ZSHoBAADshL+/v9Xr4cOHa8SIEUni6tWrZ/n30qVLq1KlSgoICNDSpUvl7u6e2tNMEyS9AAAABkutJcvOnz9v1dP7sCrvw3h7e6tw4cI6efKkateurbt37yoyMtKq2nvp0iX5+vpKknx9fZOsspC4usODMf9e8eHSpUvy9PSUu7u7HB0d5ejo+NCYxDFsiZ5eAAAAO+Hp6Wm1JTfpjYmJ0alTp5Q7d26VL19ezs7O2rJli+X48ePHde7cOQUGBkqSAgMDdejQIatVFjZt2iRPT08VL17cEvPgGIkxiWO4uLiofPnyVjEJCQnasmWLJcaWqPQCAAAY7GmXGXvceCnRr18/vfrqqwoICNCFCxc0fPhwOTo6qk2bNvLy8lKnTp3Ut29fZcuWTZ6enurZs6cCAwP10ksvSZLq1Kmj4sWL64033tC4ceMUERGhIUOGqHv37pZE+6233tK0adM0YMAAdezYUVu3btXSpUu1bt06yzz69u2rkJAQVahQQS+++KI++eQT3bx5Ux06dLDZZ5OIpBcAACCD+fPPP9WmTRtdvXpVOXPmVJUqVbR7927lzJlTkjR58mQ5ODioefPmio2NVXBwsGbMmGE539HRUWvXrlW3bt0UGBiozJkzKyQkRKNGjbLE5MuXT+vWrVOfPn00ZcoUPf/885o9e7aCg4MtMa1atdKVK1c0bNgwRUREqGzZstqwYUOSm9tsgXV6MwjW6QXSHuv0AmkjPa7TG/rbXzZfpzew+HPp4hrTKyq9AAAARkulJcvwaNzIBgAAALtHpRcAAMBgqbVkGR6NSi8AAADsHpVeAAAAg6X1kmUZEZVeAAAA2D0qvQAAAAZj8QbjkfQCAAAYjazXcLQ3AAAAwO5R6QUAADAYS5YZj0ovAAAA7B6VXgAAAIOxZJnxqPQCAADA7lHpBQAAMBiLNxiPpBcAAMBoZL2Go70BAAAAdo9KLwAAgMFYssx4VHoBAABg96j0AgAAGM3GS5ZR6H0yKr0AAACwe1R6AQAADMbiDcYj6QUAADAaWa/haG8AAACA3aPSCwAAYDCWLDMelV4AAADYPSq9AAAABjPZeMkymy5/Zqeo9AIAAMDuUekFAAAwGIs3GI9KLwAAAOwelV4AAACjUeo1HEkvAACAwViyzHi0NwAAAMDuUekFAAAwmEk2XrLMdkPZLSq9AAAAsHtUegEAAAzGfWzGo9ILAAAAu0elFwAAwGA8hth4JL0AAACGo8HBaLQ3AAAAwO5R6QUAADAY7Q3Go9ILAAAAu0elFwAAwGB09BqPSi8AAADsHpVeAAAAg9HTazySXgAAAIOZ/v8fW46Hx6O9AQAAAHaPSi8AAIDRuJPNcFR6AQAAYPeo9AIAABiMQq/xqPQCAADA7lHpBQAAMBhLlhmPpBcAAMBgLFlmPNobAAAAYPeo9AIAABiNO9kMR6UXAAAAdo9KLwAAgMEo9BqPSi8AAADsHpVeAAAAg7FkmfFIegEAAAxn2yXLaHB4MtobAAAAYPeo9AIAABiM9gbjUekFAACA3SPpBQAAgN0j6QUAAIDdo6cXAADAYPT0Go+kFwAAwGAmGy9ZZtvlz+wT7Q0AAACwe1R6AQAADEZ7g/Go9AIAAMDuUekFAAAwmEm2fXAwhd4no9ILAAAAu0elFwAAwGiUeg1H0gsAAGAwliwzHu0NAAAAsHtUegEAAAzGkmXGo9ILAAAAu0elFwAAwGDcx2Y8Kr0AAACwe1R6AQAAjEap13AkvQAAAAZjyTLj0d4AAAAAu0elFwAAwGAsWWY8kt4Mwmw2S5JuREen8UyAjOvOvfi0ngKQId24cf+7L/G7MD2ItvH3sa3Hs0ckvRnEjRs3JEkF8/mn8UwAAEgbN27ckJeXV5rOwcXFRb6+viqUCt/Hvr6+cnFxsfm49sJkTk+/9iDVJCQk6MKFC/Lw8JCJv4E8c6Kjo+Xv76/z58/L09MzracDZDj8DD7bzGazbty4IT8/Pzk4pP3tTHfu3NHdu3dtPq6Li4vc3NxsPq69oNKbQTg4OOj5559P62ngP/L09OQLF0hD/Aw+u9K6wvsgNzc3ktM0kPa/7gAAAACpjKQXAAAAdo+kF3gGuLq6avjw4XJ1dU3rqQAZEj+DwLOPG9kAAABg96j0AgAAwO6R9AIAAMDukfQCAADA7pH0ApCUvh7PCQCArZH0ApAkmUwmEl8AgN0i6QUyuE6dOqlp06aSSHyB/+rfPz8JCQlpNBMA/0bSC2RgsbGxevHFF7V371517txZEokv8LT++usvmUwmSdJXX30l6f4j4AGkD/w0AhmYq6ur3nzzTX300UfavHmzOnXqJInEF0iprVu3qnHjxvr555/Vp08fde7cWWfOnEnraQF4gFNaTwBA2oiPj5ejo6Pc3d3l4+Oj1q1ba9y4cfLw8NAnn3xiSXwTK1cAHq18+fJycXFRmzZtdOPGDYWFhSlfvnxKSEig2gukE/wkAhmUo6OjJKlfv37q37+/rl69qtKlS+uzzz6j1QFIJrPZrPj4eHl5eal+/fq6dOmS8ubNq8jISMXFxcnBwYGfISCd4DHEQAa2efNmtWzZUqtXr1aVKlV0/fp1LVy4UKNGjVLjxo31+eefSxLVKuAhHvy5iImJ0blz5xQTE6MBAwbo7t27Gjx4sOrWrWv5BfNh5wEwDj91QAZ28eJFeXp6qnz58pKkrFmzqm3bturZs6dmz56t/v37S+JmHODfHkxcP/jgAzVt2lQmk0kvvviili9fLkdHR40ZM0abNm2ynDNmzBhJ/DwBaYWfPCCDeNgfdYoVK6b4+Hjt2LHDsi9r1qxq1KiRsmbNqokTJ2r06NFGThN4JiQmru+9956mT5+uN998U05O92+TyZ49u1avXi1nZ2eNHDlSI0eOVMOGDTV+/HjFx8en5bSBDI2kF8gAEhISLDekmc1mSwLs4+OjggULat68edq3b58lPlOmTAoKCtK6dev0/vvvp8mcgfRu3759WrZsmebPn6833nhDhQoVkiTFxcUpa9as+u6775QvXz7t2rVLknTlyhU5Ojqydi+QRujpBezcgyswTJw4UYcOHVJMTIw++OADFS1aVNu2bVOPHj1UoEAB1ahRQ+XKldOYMWPk7u6uVatWyWQyWVZ6APCP7777Tu+8844OHDig7NmzS/rn5y02Nlaurq6KjY3V7du35eXlJZPJpLi4OEtFGICxqPQCduzBCu/o0aP14YcfysnJSWfPntVLL72k7777TjVq1NBnn32m7Nmz66OPPtLbb7+tW7du6dtvv5XJZFJCQgIJL/CAxFpRlixZlJCQoFOnTlkdM5vNWrhwocLCwuTq6ipvb2/LSigkvEDa4acPsGOJfYcRERG6dOmS1q5dq8DAQElS586d1a5dO82fP19NmzbVSy+9pMjISEVHRytv3rxUpYD/9+/VFhKrufnz55eDg4O+/PJL+fr6Kk+ePHJwcFBcXJy++eYbVahQwXKTqCTWvAbSGO0NgJ1btGiR2rVrpyJFimjBggUqV66c5Vjnzp21ZMkSffPNN2rQoIFVgsuySoD1z8H06dN19OhRHT58WD179lSDBg30008/qVmzZmrcuLEqV66s3Llz69NPP9Xff/+tsLAwfmkE0hG+0QA78++bZJo2baqWLVvq+PHjunDhgqR//jw7e/ZstW3bVk2bNlVoaKjVeSS8wD8/BwMHDtTo0aOVLVs2lStXTp06dVKfPn1Uu3ZtLVu2TFFRURo5cqTGjBmjLFmyaP/+/XJycmK1BiAdodIL2Knvv/9eBQoUUOHChXXnzh21aNFCYWFhWrNmjSpUqGAV+9FHH6lfv35UpYCH+PHHH9WpUyd9++23KleunPbt26dKlSpp/vz5ateunSTp3r17io6OVnx8vHLmzEl7EJAOUcoB7IzZbNbJkyfVoEEDjR8/XqdPn5abm5tWrFihsmXLqnHjxtq/f78lVpIGDRokJycnxcXFpeXUgTQ3atQoHT9+3GrfnTt35O/vr3LlymnRokWqVauWpk+frnbt2unGjRvau3evzGazsmfPrly5cnHTGpBOkfQCduDBP9iYTCYVLFhQ3333nVauXKlx48bp1KlTcnFx0XfffacyZcqoWbNm2rVrV5Iba/iSRkYWFham0NBQFShQwGr/9evXde3aNa1fv17dunXTRx99pG7dukm6/yjvWbNm6erVq1bncNMakP7Q3gDYkcS1QROtXbtWr7/+ulq1aqVBgwYpX758unv3rqpUqaLcuXPru+++S8PZAulP4soM3333nfz8/FSxYkXFxsaqRo0a2rNnj6ZOnaoePXpIuv/z1qJFC3l5eWn+/PkkukA6R9IL2IkPP/xQV69e1eDBg5UtWzbL/nXr1qlp06bq0KGD+vXrp0KFCikuLk4ODg7crAb8i9ls1rlz51SqVCk1aNBAAwcOVNmyZbV69WqNGDFCHh4eGj16tP7880/Nnz9ff/75pw4ePCgnJyerB8EASH/4xgPshLe3tyZPnqypU6fq+vXrku5/gTdo0EDvv/++5s+fb/mydnJykoODA49DBZS0PSggIEArV67U/v37NX78eB09elSvvvqqPvzwQzk4OKh58+aaOnWqvL29deDAAcsqDSS8QPpGpRd4Bj1qDd3Zs2era9euGjJkiHr37m2p+E6cOFEbN26UyWTS+vXrqfAC/+/evXtydnaW9E97UGLFdtOmTeratasCAwM1ZMgQFS9eXJJ0+vRp+fj4KFOmTKzSADxDSHqBZ8yDCe++ffsUHR2tbNmyqWTJknJ2dtbnn3+ut956S0OGDNFrr72mokWLqlWrVurSpYvq1auXZAwgIzpw4IBKly5tSVYnTZqk3bt3Kz4+XlWrVtUbb7yh7NmzWxLfl19+WX379rV6wpokWhqAZwhJL/CMGjhwoJYvX65r167J19dX2bNn1/r16+Xh4aHZs2dryJAhypw5s0wmk9zd3ek7BP7f4MGD9e233+qTTz5RvXr1NG7cOI0ePVrdu3fXL7/8or///lvOzs5atWqVcuXKpc2bN6tLly6qXLmy+vXrp7Jly6b1JQB4CiS9wDNo+vTpGjZsmFatWiUfHx8dP35co0ePVlRUlA4cOKDMmTPr559/1unTpxUTE6OuXbta+g4dHR3TevpAmrpy5YqaNWsms9msvn37auHCheratavq1KkjSdqwYYM++ugjOTk5acWKFfL09NTWrVvVuXNnvfLKK/r444+VPXv2NL4KAClF0gs8A/7djtCxY0dlzZpVEydOlHT/T6xHjhzRm2++qdKlS+urr75K0r5AwouM7sG/cvz9999q3LixYmNjdfPmTS1ZskSlS5eWJMXFxWnlypX64IMPNH36dFWpUkWStGXLFtWuXVuLFy9Wy5Yt0+w6ADwdmvqAdM5sNlsS2O+//1537tzRjRs3FB4ebokxmUwqWbKkGjVqpOPHj+vOnTtJxiHhRUZ3+fJlnT9/XmFhYcqWLZs2b96s5557TsePH9fmzZsVHx8v6f5DWurWrauLFy/q4MGDku7/HNaqVUuVK1fW77//npaXAeApkfQC6diDlamRI0eqT58++uOPP1SnTh1FR0dr1apVVsuOFSxYULGxsQ9NeoGMbOHChXrttddUqVIlVaxYUXnz5tW4ceM0f/58BQUFaf78+Vq9erUlPiEhQT4+PsqSJYuk+79Y/vrrr4qPj1fz5s3T6jIA/AessQKkY4kJ75EjRxQeHq6ZM2eqSJEi8vb21uLFizVr1izFxMSoRYsWunHjhubNm6d8+fIpa9asaTxzIP34+uuv9fbbb2vixIkqWrSonJ2d9dVXX2n06NE6c+aM5s2bpzfeeEP9+vXTxo0bVapUKW3cuFFxcXF64403LOMUKlRIa9eutXr4C4BnBz29QDo3a9YsffXVV5Kk5cuXy9/fX5J07tw5de/eXadPn9aFCxeUN29emc1m7du3T87OzqzSAEg6ePCgXnvtNX344YdWfbhXr17V0qVL1atXL/Xo0UPjxo3Tq6++qh9++EENGjRQxYoV9f7771tuAHVwcODnCXjGUekF0pl/37RWsWJFTZkyRWfOnNHu3bstSW+ePHk0f/58nT9/Xrt27ZKvr68aNmwoR0dHFssH/t/58+eVJUsWVatWzXIzp9lsVvbs2dWmTRtduHBBkyZNUqdOnbRo0SK9/PLLKlasmIYNGyaJG0ABe0KlF0induzYocKFC8vX11dHjx5VixYt9Nxzz2n48OGqXLnyI8/jSxr4x8iRIzVz5kxFRERISvowid9//10lSpTQF198ofbt2ysqKkpZsmSxJMdUdwH7wY1sQDrx4A1pmzZt0ltvvaUZM2bo6tWrKlasmBYuXKjz58/r448/1q5duyyx//69lYQX+EexYsV048YNbdy4UZKSJLH58+eXr6+v5eZPLy8vOTo6Kj4+noQXsDMkvUA68OCyZJ999pm2bNmiS5cuaebMmZoxY4auXLmiMmXKaPHixTp58qTGjx+vH3/8UVLSL3EA/6hQoYLl8dznzp2z7E9cnuzcuXPKkSOHChcubHUevzwC9oekF0gHHlyWbODAgXrhhRc0b9481axZU4sWLdL06dP1999/q0yZMlq0aJG2b9+uDRs2pPGsgfQvf/78mjlzptauXav33ntPBw4ckHQ/qb1165beeecdeXp6qkaNGmk7UQCpjp5eIB0wm836+++/Vbt2bXXp0kXdu3e3HOvVq5eWL1+u//3vf+rWrZty5MihEydOKH/+/FSjgGSIi4vTnDlz1L17d+XMmVNlypSRt7e3zp07pxs3blhWPKEfHrBvVHqBNPJgD6/JZJKnp6ecnJx08+ZNSfe/qCVpypQpyps3r2bPnq2ZM2fq2rVrKlSokKXvEMDjOTk5qXPnztqzZ48aN26s27dvy9nZWQ0bNtT+/fvl7OysuLg4El7AzlHpBdLAg3eFL1++XFWrVlXOnDnVqFEjXbt2TT/++KNcXFwslae33npLYWFhMplMGjRokJo1a8ad5YCNUOEFMgYqvYCBTpw4Iel+ZddsNmvv3r1666235OTkJJPJpJkzZ+rMmTNq3bq1oqOjLZXc69eva8yYMcqWLZsmT55sGQNAyjyszkPCC2QMJL2AQbp3766uXbtq7969ku4nrbdu3ZKnp6eyZMmi+Ph4Pf/881q+fLn27t2rl156SfXr11f58uUVFhamOnXqqF69erp9+7bu3buXxlcDPJv4ZRHIuEh6AYO0a9dOFy5c0Pjx4xUaGipJcnBwUI4cOeTi4mJZDD8wMFC//fabWrRooZIlS6pu3bo6duyYJGnfvn3KkyfPQ6tVAADg0ejpBQyQ+Gjh/fv3q02bNipVqpSGDx+ukydPasqUKdqxY8djz//rr780YcIEzZ8/X9u3b1eJEiUMmjkAAPaBpBdIZYkJb6Ldu3frjTfeULVq1eTt7a2lS5eqT58+unXrlry9veXq6qoTJ07otddeU8WKFRUREaGlS5dq9uzZmjdvnsqWLZt2FwMAwDOKpBdIRQ8mvOvWrVP58uXl6+urPXv26I033tD169fl6empF154QX/99ZcyZcokJycn3b17V5s3b7bcYPP333/LZDIpe/bsaXk5AAA8s0h6gVTy4JJi7733nhYtWqROnTqpf//+cnNzU3h4uFq2bKlSpUppyJAheuGFF5KMERcXJycnJ6OnDgCA3SHpBVLZxx9/rPHjx2vDhg3Knz+/smXLZtXj265dO5UuXVo9e/ZUtWrVLOexDi8AALbD6g1AKrp165Z+/vlnjRgxQhUqVJC3t7ekf9YKrVChgr755htt3LhRP/zwg9W5JLwAANgOfzcFUtGdO3e0Z88e1ahRQ5Is/b2Ojo66deuWYmNjVbFiRYWGhqpIkSJpOFMAAOwblV7ARhKrtw92DLm6uqpSpUr6/fffdfXqVav4ffv2qU+fPrp+/bqKFy8uR0dHyxPYAACAbZH0AjaQkJBgaUeIi4uz7M+cObNeeeUVzZ8/X998840uXbokSYqMjNTkyZN1+fJleXl5WeJ5HCoAAKmDG9mA/+jBZclmzpypbdu2ycHBQWXKlNGgQYMkScOGDdMXX3yhQoUKyd3dXZGRkbp9+7bCwsLk7OycZC1fAABgWyS9gI0MGjRIc+fOVceOHXX37l0tX75cNWrU0FdffSVJWrFihU6ePKkTJ06oSJEi6t27t5ycnFiWDAAAA5D0AjawaNEijRgxQvPmzVOlSpX07bffKiQkRI6OjgoKCtKKFSseel58fDwtDQAAGIC/pwJP4e7du7p165bldVRUlFq3bq1KlSppzZo16tq1qz788ENNmjRJq1evVufOnR86DgkvAADGoNILpNDy5cu1cOFCnTlzRk2bNtXQoUMlSWfOnJGnp6fq1Kmjli1bauDAgTp58qRq1KihCxcuaMCAAfroo4/SePYAAGRMVHqBFPjss8/UsWNHBQQEqHr16ho5cqRmzJghScqXL59OnTqlv//+W82aNbOcU716dW3cuFFjxoxJq2kDAJDhcfcMkEyzZ89Wz549tXTpUjVp0kSSdOnSJcXHx+vSpUvy8fFRjhw55OzsrE8//VTdunVTnz59lClTJtWqVUsmk4keXgAA0gjtDUAybNu2Ta+88opGjBihYcOGWfaXLVtWZrNZZ86cUenSpdWqVSvdu3dPkydPlqOjo3x9ffXTTz/J2dlZZrOZRwsDAJBGaG8AkuG5555TlSpVFBYWpv3790uSmjdvrps3b2rw4MFaunSpIiMjNW/ePAUFBWnPnj1avHixdu3aJWdnZ8XFxZHwAgCQhqj0Asl04sQJvfPOO3J0dLQ8XGL58uXKmzevJOnAgQOqUKGCVq1apUaNGlnO48ETAACkPb6JgWQqVKiQpk6dqtjYWB0+fFiDBg1S3rx5lZCQoMTfHYsVK6bs2bNbnUfCCwBA2qPSC6TQqVOn1L17dzk4OOi9995T1apVJUmvvvqqYmJitGXLFhJdAADSGZJe4CkktjokJr6TJ0/W4cOHdfjwYTk7O9PSAABAOsO3MvAUElsdTCaTXnnlFR05csSS8MbFxZHwAgCQzlDpBf6DY8eOacaMGZo0aZKcnJwUFxcnJyeWvwYAIL0h6QVshIQXAID0i6QXAAAAdo/GQwAAANg9kl4AAADYPZJeAAAA2D2SXgAAANg9kl4AAADYPZJeAAAA2D2SXgD4f+3bt1eTJk0sr2vUqKHevXsbPo9t27bJZDIpMjLykTEmk0mrVq1K9pgjRoxQ2bJl/9O8zp49K5PJpPDw8P80DgCkBZJeAOla+/btZTKZZDKZ5OLiooIFC2rUqFGKi4tL9fdesWKFRo8enazY5CSqAIC0w+OjAKR7devW1ddff63Y2FitX79e3bt3l7Ozs957770ksXfv3pWLi4tN3jdbtmw2GQcAkPao9AJI91xdXeXr66uAgAB169ZNQUFBWr16taR/WhLGjBkjPz8/FSlSRJJ0/vx5tWzZUt7e3sqWLZsaN26ss2fPWsaMj49X37595e3trezZs2vAgAH69wMq/93eEBsbq4EDB8rf31+urq4qWLCgvvzyS509e1Y1a9aUJGXNmlUmk0nt27eXJCUkJGjs2LHKly+f3N3dVaZMGX377bdW77N+/XoVLlxY7u7uqlmzptU8k2vgwIEqXLiwMmXKpPz582vo0KG6d+9ekrjPPvtM/v7+ypQpk1q2bKmoqCir47Nnz1axYsXk5uamokWLasaMGSmeCwCkRyS9AJ457u7uunv3ruX1li1bdPz4cW3atElr167VvXv3FBwcLA8PD/3000/auXOnsmTJorp161rOmzhxoubMmaOvvvpKP//8s65du6aVK1c+9n3ffPNNLVq0SFOnTtXRo0f12WefKUuWLPL399fy5cslScePH9fFixc1ZcoUSdLYsWM1b948zZo1S0eOHFGfPn30+uuva/v27ZLuJ+fNmjXTq6++qvDwcHXu3FmDBg1K8Wfi4eGhOXPm6LffftOUKVP0xRdfaPLkyVYxJ0+e1NKlS7VmzRpt2LBBBw8e1Ntvv205vmDBAg0bNkxjxozR0aNH9eGHH2ro0KGaO3duiucDAOmOGQDSsZCQEHPjxo3NZrPZnJCQYN60aZPZ1dXV3K9fP8txHx8fc2xsrOWc+fPnm4sUKWJOSEiw7IuNjTW7u7ubf/jhB7PZbDbnzp3bPG7cOMvxe/fumZ9//nnLe5nNZnP16tXNvXr1MpvNZvPx48fNksybNm166Dx//PFHsyTz9evXLfvu3LljzpQpk3nXrl1WsZ06dTK3adPGbDabze+99565ePHiVscHDhyYZKx/k2ReuXLlI4+PHz/eXL58ecvr4cOHmx0dHc1//vmnZd/3339vdnBwMF+8eNFsNpvNBQoUMC9cuNBqnNGjR5sDAwPNZrPZfObMGbMk88GDBx/5vgCQXtHTCyDdW7t2rbJkyaJ79+4pISFBbdu21YgRIyzHS5UqZdXH+8svv+jkyZPy8PCwGufOnTs6deqUoqKidPHiRVWqVMlyzMnJSRUqVEjS4pAoPDxcjo6Oql69erLnffLkSd26dUu1a9e22n/37l298MILkqSjR49azUOSAgMDk/0eiZYsWaKpU6fq1KlTiomJUVxcnDw9Pa1i8uTJo+eee87qfRISEnT8+HF5eHjo1KlT6tSpk7p06WKJiYuLk5eXV4rnAwDpDUkvgHSvZs2amjlzplxcXOTn5ycnJ+v/dGXOnNnqdUxMjMqXL68FCxYkGStnzpxPNQd3d/cUnxMTEyNJWrdunVWyKd3vU7aV0NBQtWvXTiNHjlRwcLC8vLy0ePFiTZw4McVz/eKLL5Ik4Y6OjjabKwCkFZJeAOle5syZVbBgwWTHlytXTkuWLFGuXLmSVDsT5c6dW3v27FG1atUk3a9ohoWFqVy5cg+NL1WqlBISErR9+3YFBQUlOZ5YaY6Pj7fsK168uFxdXXXu3LlHVoiLFStmuSkv0e7du598kQ/YtWuXAgICNHjwYMu+P/74I0ncuXPndOHCBfn5+Vnex8HBQUWKFJGPj4/8/Px0+vRptWvXLkXvDwDPAm5kA2B32rVrpxw5cqhx48b66aefdObMGW3btk3vvPOO/vzzT0lSr1699NFHH2nVqlU6duyY3n777ceusZs3b16FhISoY8eOWrVqlWXMpUuXSpICAgJkMpm0du1aXblyRTExMfLw8FC/fv3Up08fzZ07V6dOndKBAwf06aefWm4Oe+utt3TixAn1799fx48f18KFCzVnzpwUXW+hQoV07tw5LV68WKdOndLUqVMfelOem5ubQkJC9Msvv+inn37SO++8o5YtW8rX11eSNHLkSI0dO1ZTp07V77//rkOHDunrr7/WpEmTUjQfAEiPSHoB2J1MmTJpx44dypMnj5o1a6ZixYqpU6dOunPnjqXy++677+qNN95QSEiIAgMD5eHhoaZNmz523JkzZ6pFixZ6++23VbRoUXXp0kU3b96UJD333HMaOXKkBg0aJB8fH/Xo0UOSNHr0aA0dOlRjx45VsWLFVLduXa1bt0758uWTdL/Pdvny5Vq1apXKlCmjWbNm6cMPP0zR9TZq1Eh9+vRRjx49VLZsWe3atUtDhw5NElewYEE1a9ZM9evXV506dVS6dGmrJck6d+6s2bNn6+uvv1apUqVUvXp1zZkzxzJXAHiWmcyPumsDAAAAsBNUegEAAGD3SHoBAABg90h6AQAAYPdIegEAAGD3SHoBAABg90h6AQAAYPdIegEAAGD3SHoBAABg90h6AQAAYPdIegEAAGD3SHoBAABg90h6AQAAYPf+DzSaseg+fGdDAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x600 with 2 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"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "threshold = 0.5  # 임계값 설정\n",
    "\n",
    "# 모델 출력을 이진 형태로 변환 (np.int 대신 np.int32 사용)\n",
    "output_binary = (output > threshold).astype(np.int32)\n",
    "label_flat = label.flatten().astype(np.int32)\n",
    "output_flat = output_binary.flatten().astype(np.int32)\n",
    "\n",
    "# 데이터 유형과 형태 확인\n",
    "print(\"Data types: \", label_flat.dtype, output_flat.dtype)\n",
    "print(\"Shapes: \", label_flat.shape, output_flat.shape)\n",
    "\n",
    "# 데이터 샘플 출력\n",
    "print(\"Label sample: \", label_flat[:len(test_set)])\n",
    "print(\"Output sample: \", output_flat[:len(test_set)])\n",
    "\n",
    "# 이진화된 출력으로 지표 계산\n",
    "try:\n",
    "    accuracy = accuracy_score(label_flat, output_flat)\n",
    "    precision = precision_score(label_flat, output_flat, average='binary')\n",
    "    recall = recall_score(label_flat, output_flat, average='binary')\n",
    "    f1 = f1_score(label_flat, output_flat, average='binary')\n",
    "\n",
    "    print(f\"Accuracy: {accuracy}\")\n",
    "    print(f\"Precision: {precision}\")\n",
    "    print(f\"Recall: {recall}\")\n",
    "    print(f\"F1 Score: {f1}\")\n",
    "except ValueError as e:\n",
    "    print(\"Error in metric calculation: \", e)\n",
    "\n",
    "# Confusion Matrix 계산 및 시각화\n",
    "cm = confusion_matrix(label_flat, output_flat)\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)\n",
    "plt.title('Confusion Matrix')\n",
    "plt.colorbar()\n",
    "classes = ['Background', 'Object']  # 클래스 이름\n",
    "tick_marks = np.arange(len(classes))\n",
    "plt.xticks(tick_marks, classes, rotation=45)\n",
    "plt.yticks(tick_marks, classes)\n",
    "\n",
    "# 텍스트 추가\n",
    "thresh = cm.max() / 2.\n",
    "for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "    plt.text(j, i, format(cm[i, j], 'd'),\n",
    "             horizontalalignment=\"center\",\n",
    "             color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "plt.ylabel('True label')\n",
    "plt.xlabel('Predicted label')\n",
    "plt.tight_layout()\n",
    "\n",
    "# PR Curve 계산 및 시각화\n",
    "precision_array, recall_array, _ = precision_recall_curve(label_flat, output_flat)\n",
    "plt.figure()\n",
    "plt.plot(recall_array, precision_array, label='PR Curve')\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.title('Precision-Recall Curve')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# 여러 임계값에 대한 F1 점수 계산\n",
    "thresholds = np.linspace(0, 1, num=100)\n",
    "f1_scores = []\n",
    "\n",
    "for t in thresholds:\n",
    "    output_binary_t = (output > t).astype(np.int32).flatten()\n",
    "    f1_t = f1_score(label_flat, output_binary_t, average='binary')\n",
    "    f1_scores.append(f1_t)\n",
    "\n",
    "# F1 Score Curve 시각화\n",
    "plt.figure(figsize=(10, 4))\n",
    "plt.plot(thresholds, f1_scores, label='F1 Score Curve')\n",
    "plt.xlabel('Threshold')\n",
    "plt.ylabel('F1 Score')\n",
    "plt.title('F1 Score per Threshold')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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h4+MDOzs7rfvKmg0UG+rJzc1FbGwswsLCCoqxsEBYWBhiYmKKfUxMTIzW9QAQHh5e4vXFSUtLw+HDh1G3bl20b98erq6u6NKlC/bv31/q43JycpCZmal1IyIiIiLjwVlMpE+6+PlS7Cf05s2byM/Ph6urq9Z5V1dXpKSkFPuYlJSUcl1fnIsXLwIAZs+ejbFjx2Lbtm1o1aoVunfvjvPnz5f4uDlz5sDFxUVz8/T0LPNrEhERERERVbk/D6jVagDAK6+8glGjRqFly5b46quv0KRJE3z//fclPm7atGnIyMjQ3K5cuWKokomIiIiIyAwoFr5q164NS0tLpKamap1PTU2Fm5tbsY9xc3Mr1/XFcXd3BwA0a9ZM63zTpk2RlJRU4uNsbW3h7OysdSMiIiIiMjbe3t6YN2+e0mVQMRQLXzY2NmjdujV27typOadWq7Fz506EhoYW+5jQ0FCt6wEgOjq6xOuL4+3tDQ8PDyQkJGidP3fuHLy8vMrxDoiIiIiIKk6lUpV6mz17doWe9+jRo3j55ZcrVVvXrl0xefLkSj0HFaVoq/kpU6ZgxIgRaNOmDYKDgzFv3jxkZ2dj1KhRAIDhw4ejXr16mDNnDgBg0qRJ6NKlC+bOnYvevXtjzZo1OHbsGJYtW6Z5ztu3byMpKQnJyckAoAlZbm5ucHNzg0qlwltvvYVZs2YhMDAQQUFB+OGHH3D27Fls2LDBwJ8AEREREVVV169f1xyvXbsWM2fO1BogqFatmuZYkiTk5+fDyurxv77XqVNHt4WSzii65mvgwIH44osvMHPmTAQFBSE+Ph7btm3TNNVISkrS+qFs3749oqKisGzZMgQGBmLDhg3YvHmzZo8vANiyZQtatmyJ3r17AwAGDRqEli1barWinzx5MqZNm4Y33ngDgYGB2LlzJ6Kjo9GoUSMDvXMiHfrgA2DCBOC/9YxEREQESBKQna3MrawbOcmDA25ubnBxcYFKpdJ8f/bsWTg5OeGPP/5A69atYWtri/379+Pff/9F37594erqimrVqqFt27bYsWOH1vM+Ou1QpVLh22+/xXPPPQcHBwf4+vpiy5Ytlfp8f/75Z/j7+8PW1hbe3t6YO3eu1v1LliyBr68v7Ozs4Orqiueff15z34YNGxAQEAB7e3vUqlULYWFhyM7OrlQ9JkOiCsnIyJAASBkZGUqXQlVZcrIkif/GS9LRo0pXQ0REpIj79+9L//zzj3T//n3Nuaysgv9FGvqWlVX+97B8+XLJxcVF8/3u3bslAFKLFi2kP//8U7pw4YJ069YtKT4+XoqMjJROnTolnTt3Tpo+fbpkZ2cnXb58WfNYLy8v6auvvtJ8D0CqX7++FBUVJZ0/f16aOHGiVK1aNenWrVsl1tOlSxdp0qRJxd537NgxycLCQvrggw+khIQEafny5ZK9vb20fPlySZIk6ejRo5KlpaUUFRUlXbp0SYqLi5Pmz58vSZIkJScnS1ZWVtKXX34pJSYmSidPnpQWL14s3b17t/wfmoEV93MmK2s2UHTaIRFV0u7dBcd79gBt2ihXCxEREencBx98gKeeekrzfc2aNREYGKj5/sMPP8SmTZuwZcsWjB8/vsTnGTlyJAYPHgwA+Pjjj7FgwQIcOXIEPXr0KHdNX375Jbp3744ZM2YAABo3box//vkHn3/+OUaOHImkpCQ4OjrimWeegZOTE7y8vNCyZUsAYqplXl4e+vXrp+m3EBAQUO4aTFWVazVPZFYKN6DZs0e5OoiIiIyMgwOQlaXMzcFBd++jzSN/WM3KysLUqVPRtGlTVK9eHdWqVcOZM2dK7doNAC1atNAcOzo6wtnZGWlpaRWq6cyZM+jQoYPWuQ4dOuD8+fPIz8/HU089BS8vLzRs2BDDhg3DTz/9hHv37gEAAgMD0b17dwQEBOCFF17AN998gzt37lSoDlPE8EVkynbtKjjet4/rvoiIiP6jUgGOjsrcVCrdvQ9HR0et76dOnYpNmzbh448/xr59+xAfH4+AgADk5uaW+jzW1taPfD4qzf63uubk5IS4uDisXr0a7u7umDlzJgIDA5Geng5LS0tER0fjjz/+QLNmzbBw4UI0adIEiYmJeqnF2DB8EZmqixeBS5cAKyvxX/r0dODUKaWrIiIiIj06cOAARo4cieeeew4BAQFwc3PDpUuXDFpD06ZNceDAgSJ1NW7cGJaWlgAAKysrhIWF4bPPPsPJkydx6dIl7Prvj8YqlQodOnTA+++/j+PHj8PGxgabNm0y6HtQCtd8EZkqecphu3YifG3fLqYeFpoHTkRERObF19cXGzduRJ8+faBSqTBjxgy9jWDduHED8fHxWufc3d3x5ptvom3btvjwww8xcOBAxMTEYNGiRViyZAkAYOvWrbh48SI6d+6MGjVq4Pfff4darUaTJk1w+PBh7Ny5E08//TTq1q2Lw4cP48aNG2jatKle3oOx4cgXkamSpxw++STQubM45rovIiIis/bll1+iRo0aaN++Pfr06YPw8HC0atVKL68VFRWFli1bat2++eYbtGrVCuvWrcOaNWvQvHlzzJw5Ex988AFGjhwJAKhevTo2btyIJ598Ek2bNkVkZCRWr14Nf39/ODs7Y+/evejVqxcaN26M6dOnY+7cuejZs6de3oOxUUlSWXcioMIyMzPh4uKCjIwMODs7K10OVTWSBLi5AWlpInBZWgIdOwK1a4tzupxsTkREZOQePHiAxMRE+Pj4wM7OTulyyEyV9nNW1mzAaYdEpujvv0XIsrcHQkJE2LK3B27eBP75B/D3V7pCIiIiInoEpx0SmSJ5vVenToCtLWBjA4SGinN79ypXFxERERGViOGLyBTJ6726dy8416WL+Mp1X0RERERGieGLyNTk5QF//SWOn3yy4Hzh8MWlnERERERGh+GLyNTExgKZmUD16kDLlgXng4PF9MOUFOD8ecXKIyIiIqLiMXwRmRp5ymHXrqLLoUxuvgFw6iERERGREWL4IjI1crONwuu9ZPLUQzbdICIiIjI6DF9EpuTBA+DAAXFceL2XjOu+iIiIiIwWwxeRKYmJEQHM3R1o2rTo/aGhgJUVcOUKcOmSwcsjIiIiopIxfBGZEnm915NPio2VH+XoCLRpI4657ouIiKhK6Nq1KyZPnqz53tvbG/PmzSv1MSqVCps3b670a+vqeaoKhi8iUyKv9ypuyqGM+30RERGZhD59+qBHjx7F3rdv3z6oVCqcPHmy3M979OhRvPzyy5UtT8vs2bMRFBRU5Pz169fRs2dPnb7Wo1asWIHq1avr9TUMheGLyFRkZgJHjojj4pptyNh0g4iIyCSMGTMG0dHRuHr1apH7li9fjjZt2qBFixblft46derAwcFBFyU+lpubG2xtbQ3yWuaA4YvIVOzbB+TnAw0bAl5eJV/XoQNgYQFcvAgU8x9zIiKiKkGSgOxsZW5lbHr1zDPPoE6dOlixYoXW+aysLKxfvx5jxozBrVu3MHjwYNSrVw8ODg4ICAjA6tWrS33eR6cdnj9/Hp07d4adnR2aNWuG6OjoIo95++230bhxYzg4OKBhw4aYMWMGHj58CECMPL3//vs4ceIEVCoVVCqVpuZHpx2eOnUKTz75JOzt7VGrVi28/PLLyMrK0tw/cuRIRERE4IsvvoC7uztq1aqFcePGaV6rIpKSktC3b19Uq1YNzs7OGDBgAFJTUzX3nzhxAt26dYOTkxOcnZ3RunVrHDt2DABw+fJl9OnTBzVq1ICjoyP8/f3x+++/V7iWx7HS2zMTkW6V1mK+MGdnoFUr4NgxMfVw6FD910ZERGRs7t0DqlVT5rWzssQ67MewsrLC8OHDsWLFCrz33ntQ/beee/369cjPz8fgwYORlZWF1q1b4+2334azszN+++03DBs2DI0aNUJwcPBjX0OtVqNfv35wdXXF4cOHkZGRobU+TObk5IQVK1bAw8MDp06dwtixY+Hk5IT/+7//w8CBA3H69Gls27YNO3bsAAC4uLgUeY7s7GyEh4cjNDQUR48eRVpaGl566SWMHz9eK2Du3r0b7u7u2L17Ny5cuICBAwciKCgIY8eOfez7Ke79ycFrz549yMvLw7hx4zBw4ED89ddfAIChQ4eiZcuWWLp0KSwtLREfHw9ra2sAwLhx45Cbm4u9e/fC0dER//zzD6rp8eeG4YvIVBRutvE4nTszfBEREZmA0aNH4/PPP8eePXvQtWtXAGLKYf/+/eHi4gIXFxdMnTpVc/2ECROwfft2rFu3rkzha8eOHTh79iy2b98ODw8PAMDHH39cZJ3W9OnTNcfe3t6YOnUq1qxZg//7v/+Dvb09qlWrBisrK7i5uZX4WlFRUXjw4AF+/PFHOP4XPhctWoQ+ffrg008/haurKwCgRo0aWLRoESwtLeHn54fevXtj586dFQpfO3fuxKlTp5CYmAhPT08AwI8//gh/f38cPXoUbdu2RVJSEt566y34+fkBAHx9fTWPT0pKQv/+/REQEAAAaNiwYblrKA+GLyJTcOMGcOKEOC5L+OrSBfjySzbdICKiqsvBQYxAKfXaZeTn54f27dvj+++/R9euXXHhwgXs27cPH3zwAQAgPz8fH3/8MdatW4dr164hNzcXOTk5ZV7TdebMGXh6emqCFwCEhoYWuW7t2rVYsGAB/v33X2RlZSEvLw/Ozs5lfh/yawUGBmqCFwB06NABarUaCQkJmvDl7+8PS0tLzTXu7u44depUuV6r8Gt6enpqghcANGvWDNWrV8eZM2fQtm1bTJkyBS+99BJWrlyJsLAwvPDCC2jUqBEAYOLEiXjttdfw559/IiwsDP3796/QOruy4povIlOwe7f4GhAA1K37+Os7dRKt6M+dA65f129tRERExkilElP/lLgVtx1MKcaMGYOff/4Zd+/exfLly9GoUSN0+a+B1ueff4758+fj7bffxu7duxEfH4/w8HDk5ubq7KOKiYnB0KFD0atXL2zduhXHjx/He++9p9PXKEye8idTqVRQq9V6eS1AdGr8+++/0bt3b+zatQvNmjXDpk2bAAAvvfQSLl68iGHDhuHUqVNo06YNFi5cqLdaGL6ITEF5phwCQI0agPxXm3379FMTERER6cSAAQNgYWGBqKgo/Pjjjxg9erRm/deBAwfQt29fvPjiiwgMDETDhg1x7ty5Mj9306ZNceXKFVwv9MfYQ4cOaV1z8OBBeHl54b333kObNm3g6+uLy5cva11jY2OD/Pz8x77WiRMnkJ2drTl34MABWFhYoEmTJmWuuTzk93flyhXNuX/++Qfp6elo1qyZ5lzjxo3xxhtv4M8//0S/fv2wfPlyzX2enp549dVXsXHjRrz55pv45ptv9FIrwPBFZBrK2myjMO73RUREZBKqVauGgQMHYtq0abh+/TpGjhypuc/X1xfR0dE4ePAgzpw5g1deeUWrk9/jhIWFoXHjxhgxYgROnDiBffv24b333tO6xtfXF0lJSVizZg3+/fdfLFiwQDMyJPP29kZiYiLi4+Nx8+ZN5OTkFHmtoUOHws7ODiNGjMDp06exe/duTJgwAcOGDdNMOayo/Px8xMfHa93OnDmDsLAwBAQEYOjQoYiLi8ORI0cwfPhwdOnSBW3atMH9+/cxfvx4/PXXX7h8+TIOHDiAo0ePomnTpgCAyZMnY/v27UhMTERcXBx2796tuU8fGL6IjF1SEnDhgmgf37lz2R/H8EVERGQyxowZgzt37iA8PFxrfdb06dPRqlUrhIeHo2vXrnBzc0NERESZn9fCwgKbNm3C/fv3ERwcjJdeegn/+9//tK559tln8cYbb2D8+PEICgrCwYMHMWPGDK1r+vfvjx49eqBbt26oU6dOse3uHRwcsH37dty+fRtt27bF888/j+7du2PRokXl+zCKkZWVhZYtW2rd+vTpA5VKhV9++QU1atRA586dERYWhoYNG2Lt2rUAAEtLS9y6dQvDhw9H48aNMWDAAPTs2RPvv/8+ABHqxo0bh6ZNm6JHjx5o3LgxlixZUul6S6KSpDJuREBaMjMz4eLigoyMjHIvRiQqlxUrgFGjgJAQ4JFpAqW6caNgfdiNG0Dt2nopj4iISGkPHjxAYmIifHx8YGdnp3Q5ZKZK+zkrazbgyBeRsSvvei9ZnTqAPNd5717d1kRERERE5cbwRWTMJKli671k8tRDhi8iIiIixTF8ERmzhAQgORmwtQXaty//47nui4iIiMhoMHwRGTN5ymH79oC9ffkfLzfoOHECuHNHd3URERERUbkxfBEZs8pMOQQAd3fA11dMX9y/X3d1ERERGSH2kSN90sXPF8MXkbFSq4Hdu8VxeZttFMaph0REZOasra0BAPfu3VO4EjJn8s+X/PNWEVa6KoaIdCw+XkwVdHIC2rat+PN06QJ8+y2bbhARkdmytLRE9erVkZaWBkDsN6VSqRSuisyFJEm4d+8e0tLSUL16dVhaWlb4uRi+iIyVvN6rSxfAqhL/qsojX3FxwN27IswRERGZGTc3NwDQBDAiXatevbrm56yijCJ8LV68GJ9//jlSUlIQGBiIhQsXIjg4uMTr169fjxkzZuDSpUvw9fXFp59+il69emnu37hxIyIjIxEbG4vbt2/j+PHjCAoKKva5JElCr169sG3bNmzatKlcO4YT6ZW83qsyUw4BwNMT8PEBEhOBAweAHj0qXxsREZGRUalUcHd3R926dfHw4UOlyyEzY21tXakRL5ni4Wvt2rWYMmUKIiMjERISgnnz5iE8PBwJCQmoW7dukesPHjyIwYMHY86cOXjmmWcQFRWFiIgIxMXFoXnz5gCA7OxsdOzYEQMGDMDYsWNLff158+ZxWJqMT25uwTTBijbbKKxLFxG+9uxh+CIiIrNmaWmpk1+SifRBJSncFiYkJARt27bFokWLAABqtRqenp6YMGEC3nnnnSLXDxw4ENnZ2di6davmXLt27RAUFITIyEitay9dugQfH58SR77i4+PxzDPP4NixY3B3dy915CsnJwc5OTma7zMzM+Hp6YmMjAw4OztX4J0TlWL/fqBTJ6B2bSA1FbCoZG+c5cuB0aOB0FDg4EHd1EhEREREAEQ2cHFxeWw2ULTbYW5uLmJjYxEWFqY5Z2FhgbCwMMTExBT7mJiYGK3rASA8PLzE60ty7949DBkyBIsXLy7T3M05c+bAxcVFc/P09CzX6xGVS+Eph5UNXkDBuq+jRwF2giIiIiJShKLh6+bNm8jPz4erq6vWeVdXV6SkpBT7mJSUlHJdX5I33ngD7du3R9++fct0/bRp05CRkaG5XblypVyvR1QucrONyq73kvn4APXrA3l5QDn/UEFEREREuqH4mi8lbNmyBbt27cLx48fL/BhbW1vY2trqsSqi/2RnFwQkXaz3AgCVSox+/fSTWPelq+clIiIiojJTdOSrdu3asLS0RGpqqtb51NTUEqcCurm5lev64uzatQv//vsvqlevDisrK1j918a7f//+6Nq1a/neBJGuHTgAPHwINGgANGqku+flZstEREREilI0fNnY2KB169bYKa9vgWi4sXPnToSGhhb7mNDQUK3rASA6OrrE64vzzjvv4OTJk4iPj9fcAOCrr77C8uXLy/9GiHSp8HovXXbi7NxZfD18GHjwQHfPS0RERERlovi0wylTpmDEiBFo06YNgoODMW/ePGRnZ2PUqFEAgOHDh6NevXqYM2cOAGDSpEno0qUL5s6di969e2PNmjU4duwYli1bpnnO27dvIykpCcnJyQCAhIQEAGLUrPDtUQ0aNICPj4++3zJR6eTwpeupgY0bA66uonvi4cMFI2FEREREZBCKjnwBonX8F198gZkzZyIoKAjx8fHYtm2bpqlGUlISrl+/rrm+ffv2iIqKwrJlyxAYGIgNGzZg8+bNmj2+ALGmq2XLlujduzcAYNCgQWjZsmWRVvRERufOHSAuThzrqtmGTF73BRTsIUZEREREBqP4Pl+mqqy9/InKZdMmoF8/wM8POHNG98+/ZAkwbpwYVduxQ/fPT0RERFQFmcQ+X0T0CF23mH+UPPJ18CCQm6uf1yAiIiKiYjF8ERkTfa33kjVtCtSqBdy/Dxw7pp/XICIiIqJiMXwRGYvkZDHVUKUC9LXlgYVFQddDtpwnIiIiMiiGLyJjsXu3+NqyJVCzpv5eh003iIiIiBTB8EVkLPQ95VAmh6/9+4G8PP2+FhERERFpMHwRGQNJ0t5cWZ8CAoDq1YGsLOD4cf2+FhERERFpMHwRGYOLF4GkJMDaGujUSb+vZWlZ8Bpc90VERERkMAxfRMZAbjHfrh3g6Kj/12PTDSIiIiKDY/giMgaGmnIok9d97dsH5Ocb5jWJiIiIqjiGLyKlqdUFI1/6brYha9kScHICMjKAU6cM85pEREREVRzDF5HS/v4buHEDcHAAQkIM85pWVkCHDuKYUw+JiIiIDILhi0hp8pTDTp0AGxvDva489ZDhi4iIiMggGL6IlCZPOTTUei+Z3HRj714x9ZGIiIiI9Irhi0hJeXkFI0+GWu8la9MGsLcHbt0C/vnHsK9NREREVAUxfBEpKTYWyMwEatQAgoIM+9o2NkD79uJ4717DvjYRERFRFcTwRaQkeb1X165i82ND47ovIiIiIoNh+CJSkhy+DD3lUFY4fEmSMjUQERERVREMX0RKefAAOHBAHBu62YYsOBiwtQVSU4Fz55SpgYiIiKiKYPgiUsrBg0BODuDuDvj5KVODnV3B3mKcekhERESkVwxfREop3GJepVKuDnnqIZtuEBEREekVwxeRUpRe7yXjui8iIiIig2D4IlJCZiZw9Kg4Vmq9lyw0FLC2Bq5eBRITla2FiIiIyIwxfBEpYe9eID8faNQI8PJSthYHB6BtW3HMdV9EREREesPwRaQEY5lyKOvcWXxl+CIiIiLSG4YvIiUUbrZhDNh0g4iIiEjvGL6IDC0tDTh5UhwbS/jq0AGwtBRrvq5cUboaIiIiIrPE8EVkaH/9Jb62aAHUqaNoKRpOTkCrVuKYUw+JiIiI9ILhi8jQ5PVexjLqJSvccp6IiIiIdI7hi8jQjK3ZhoxNN4iIiIj0iuGLyJAuXwb+/Vesr5LDjrHo1AlQqYDz54Hr15WuhoiIiMjsMHwRGZLc5bBtW8DZWdlaHlW9OhAYKI7Z9ZCIiIhI5xi+iAzJ2FrMP4rrvoiIiIj0huGLyFAkyXjXe8kYvoiIiIj0huGLyFASEsRaKltboH17paspXqdO4us//wA3bihbCxEREZGZYfgiMhR51KtDB8DOTtlaSlK7NuDvL4657ouIiIhIp4wifC1evBje3t6ws7NDSEgIjhw5Uur169evh5+fH+zs7BAQEIDff/9d6/6NGzfi6aefRq1ataBSqRAfH691/+3btzFhwgQ0adIE9vb2aNCgASZOnIiMjAxdvzWiAsY+5VAmTz1k+CIiIiLSKcXD19q1azFlyhTMmjULcXFxCAwMRHh4ONLS0oq9/uDBgxg8eDDGjBmD48ePIyIiAhERETh9+rTmmuzsbHTs2BGffvppsc+RnJyM5ORkfPHFFzh9+jRWrFiBbdu2YcyYMXp5j0TIzwf++kscG2uzDRnXfRERERHphUqSJEnJAkJCQtC2bVssWrQIAKBWq+Hp6YkJEybgnXfeKXL9wIEDkZ2dja1bt2rOtWvXDkFBQYiMjNS69tKlS/Dx8cHx48cRFBRUah3r16/Hiy++iOzsbFhZWT227szMTLi4uCAjIwPOxtYynIxPbCzQpg3g5ATcvg2U4WdMMSkpgLu72PPr1i2gRg2lKyIiIiIyamXNBoqOfOXm5iI2NhZhYWGacxYWFggLC0NMTEyxj4mJidG6HgDCw8NLvL6s5A+qpOCVk5ODzMxMrRtRmckt5rt0Me7gBQBubkCTJqI74759SldDREREZDYUDV83b95Efn4+XF1dtc67uroiJSWl2MekpKSU6/qy1vHhhx/i5ZdfLvGaOXPmwMXFRXPz9PSs8OtRFWQq671knTuLr5x6SERERKQziq/5UlpmZiZ69+6NZs2aYfbs2SVeN23aNGRkZGhuV65cMVyRZNpycwtGkEwlfLHpBhEREZHOKTr/qXbt2rC0tERqaqrW+dTUVLi5uRX7GDc3t3JdX5q7d++iR48ecHJywqZNm2BtbV3itba2trC1tS33axDh8GHg3j2gTp2CNu7GTg5fcXFAZibAdY1ERERElaboyJeNjQ1at26NnfKULIiGGzt37kRoaGixjwkNDdW6HgCio6NLvL4kmZmZePrpp2FjY4MtW7bAzlj3XSLTJ/+8PvkkYGEig8316wMNGwJqNXDggNLVEBEREZkFxVf+T5kyBSNGjECbNm0QHByMefPmITs7G6NGjQIADB8+HPXq1cOcOXMAAJMmTUKXLl0wd+5c9O7dG2vWrMGxY8ewbNkyzXPevn0bSUlJSE5OBgAkJCQAEKNmbm5umuB17949rFq1SquBRp06dWBpaWnIj4DMndxsw9hbzD+qSxfg4kWx7qtnT6WrISIiIjJ5ioevgQMH4saNG5g5cyZSUlIQFBSEbdu2aZpqJCUlwaLQaEH79u0RFRWF6dOn491334Wvry82b96M5s2ba67ZsmWLJrwBwKBBgwAAs2bNwuzZsxEXF4fDhw8DAJ544gmtehITE+Ht7a2vt0tVTXY2cOiQODaV9V6yLl2A5cvZdIOIiIhIRxTf58tUcZ8vKpPt24EePQAvLyAxUeydZSoSE8XUQysrID0dcHRUuiIiIiIio2QS+3wRmb3C671MKXgBgLc34OkJ5OUBldxHj4iIiIgYvoj0S17vZWpTDgERFuWuh5x6SERERFRpDF9E+nL7tmjVDgDduilbS0UxfBERERHpDMMXkb789RcgSUDTpoCHh9LVVIwcvg4fBu7fV7YWIiIiIhPH8EWkL6baYr6wJ54A3NyA3FwRwIiIiIiowhi+iPRFbrZhiuu9ZIXXfe3dq2wtRERERCaO4YtIH5KTgbNnRXjp2lXpaiqH676IiIiIdILhi0gf5CmHrVoBNWooW0tlyeErJkZMPyQiIiKiCmH4ItIHc5hyKGvaFKhdWzTcOHpU6WqIiIiITBbDF5GuSZJ5NNuQqVRA587imFMPiYiIiCqM4YtI1/79F0hKAqytgY4dla5GN9h0g4iIiKjSGL6IdE0e9WrXDnB0VLYWXZHD14EDQF6esrUQERERmSiGLyJdM6f1XrKAANE4JCsLiItTuhoiIiIik8TwRaRLajWwe7c4NqfwZWEBdOokjrnui4iIiKhCGL6IdOn0aeDGDcDBAQgOVroa3eJ+X0RERESVwvBFpEvylMPOnQEbG2Vr0TW54+G+fUB+vrK1EBEREZkghq9KOnNG6QrIqJhTi/lHBQUBTk5AZiZw8qTS1RARERGZHIavSoqMVLoCMhp5eQVT8sxpvZfMyqqgdT6nHhIRERGVG8NXJa1eDdy6pXQVZBSOHQPu3hVdAQMDla5GP7jui4iIiKjCGL4qKScH+OYbpasgoyCv9+rWDbC0VLYWfSm82bJarWwtRERERCaG4UsHFi8GHj5UugpSnLzeyxynHMpatxadHG/fBv7+W+lqiIiIiEwKw1cl1akDXL0KbNyodCWkqPv3gQMHxLE5NtuQWVsD7duL4717la2FiIiIyMQwfFXSmDHi6/z5ytZBCjt4UMxB9fAAmjRRuhr94rovIiIiogph+Kqk0aPFdk4xMcCRI0pXQ4op3GJepVK2Fn0rHL4kSdlaiIiIiEwIw1cluboCgwaJY45+VWFysw1zXu8lCw4G7OyAtDQgIUHpaoiIiIhMBsOXDkyaJL6uWwckJytbCykgIwM4elQcm/N6L5mtLdCunTjm1EMiIiKiMmP40oFWrYBOncQeu0uWKF0NGZzcdv2JJ4AGDZSuxjA6dxZf2XSDiIiIqMwYvnREHv36+mvR+I6qkKrQYv5RXPdFREREVG4MXzrSty/g5QXcvAlERSldDRmUvN6rKkw5lLVrJ9rOX7sGXLyodDVEREREJoHhS0esrIDx48Xx/PkcDKgy0tKAU6fEcbduytZiSA4OovEGwHVfRERERGXE8KVDY8YAjo7id/Hdu5WuhgxC/gfdooXYcbsq4X5fREREROXC8KVDNWoAI0aIY7adryKqUov5R7HpBhEREVG5MHzp2MSJ4uuvvwL//qtsLWQAVbHZhqx9e8DSErh0CUhKUroaIiIiIqPH8KVjTZoAPXuKNV8LFypdDenV5csiYVtair0GqhonJ6B1a3HMqYdEREREj8XwpQdy2/nvvwcyM5WthfRIHvUKDgacnZWtRSlc90VERERUZgxfevD000DTpsDdu8Dy5UpXQ3pTFVvMP4rhi4iIiKjMjCJ8LV68GN7e3rCzs0NISAiOHDlS6vXr16+Hn58f7OzsEBAQgN9//13r/o0bN+Lpp59GrVq1oFKpEB8fX+Q5Hjx4gHHjxqFWrVqoVq0a+vfvj9TUVJ28H5WqYO3XwoVAfr5OnpaMiSRV7WYbsg4dxA/8hQtAcrLS1RAREREZNcXD19q1azFlyhTMmjULcXFxCAwMRHh4ONLS0oq9/uDBgxg8eDDGjBmD48ePIyIiAhERETh9+rTmmuzsbHTs2BGffvppia/7xhtv4Ndff8X69euxZ88eJCcno1+/fjp7X8OGie6H//4LPJINyRycPQukpAB2dkBoqNLVKKd6dSAoSByz6yERERFRqVSSpOx2wCEhIWjbti0WLVoEAFCr1fD09MSECRPwzjvvFLl+4MCByM7OxtatWzXn2rVrh6CgIERGRmpde+nSJfj4+OD48eMIkn9BBJCRkYE6deogKioKzz//PADg7NmzaNq0KWJiYtCuXbvH1p2ZmQkXFxdkZGTAuYT1Pm+/DXz2mZiVJg+SkJlYtAiYMEGMeu3YoXQ1ynrjDWDePODVV4GlS5WuhoiIiMjgypINAIVHvnJzcxEbG4uwsDDNOQsLC4SFhSEmJqbYx8TExGhdDwDh4eElXl+c2NhYPHz4UOt5/Pz80KBBgxKfJycnB5mZmVq3xxk3TjTC27VLbLxMZqQqt5h/FNd9EREREZWJouHr5s2byM/Ph6urq9Z5V1dXpKSkFPuYlJSUcl1f0nPY2NigevXqZX6eOXPmwMXFRXPz9PR87Os0aADIMxm56bIZyc8Hdu8Wx1W52YZMbrN/5gxQwnRhIiIiIjKCNV+mYtq0acjIyNDcrly5UqbHyW3nf/oJuHlTjwWS4Rw/DqSni/by8j5XVVmtWkBAgDjmui8iIiKiEikavmrXrg1LS8siXQZTU1Ph5uZW7GPc3NzKdX1Jz5Gbm4v09PQyP4+trS2cnZ21bmXRvj3Qpg3w4AGwbFmZSyRjJk857NIFsLJSthZj0bmz+MrwRURERFQiRcOXjY0NWrdujZ2FulGo1Wrs3LkToSV0kAsNDdW6HgCio6NLvL44rVu3hrW1tdbzJCQkICkpqVzPUxYqVcHo1+LFwMOHOn16UgJbzBfFdV9EREREj6X4n+2nTJmCESNGoE2bNggODsa8efOQnZ2NUaNGAQCGDx+OevXqYc6cOQCASZMmoUuXLpg7dy569+6NNWvW4NixY1hWaFjp9u3bSEpKQvJ/+w4lJCQAECNebm5ucHFxwZgxYzBlyhTUrFkTzs7OmDBhAkJDQ8vU6bC8BgwA3npLbIO0YQMweLDOX4IMJTcX2LdPHDN8FZBHvk6dAm7fBmrWVLYeIiIiIiOk+JqvgQMH4osvvsDMmTMRFBSE+Ph4bNu2TdNUIykpCdevX9dc3759e0RFRWHZsmUIDAzEhg0bsHnzZjRv3lxzzZYtW9CyZUv07t0bADBo0CC0bNlSqxX9V199hWeeeQb9+/dH586d4ebmho0bN+rlPdrYAK+/Lo7nzdPLS5ChHDoE3L8P1K0L+PsrXY3xcHUF/PzE5tNyOCUiIiIiLYrv82WqytrLX5aWBnh6ioGTmBhADwNsZAizZwPvvw8MGgSsXq10Ncbl1VeBr78W+359+aXS1RAREREZjEns81WV1K0LDB0qjjn6ZcLk9V5sMV8Um24QERERlYrhy4DkxhsbNgBXrypbC1VAVpaYdghwvVdx5KYbx48DGRnK1kJERERkhBi+DCgwUPx+mp8PLFmidDVUbvv3A3l5gJcX4OOjdDXGp149oFEjQK0GDhxQuhoiIiIio8PwZWCTJ4uvX38N3LunaClUXoVbzKtUytZirNhynoiIiKhEDF8G1qePGDS5fRv46Selq6FykTdX5pTDkjF8EREREZWI4cvALC2BCRPE8fz5ojM3mYDbt8VaJgDo1k3ZWoyZHL6OHRNr5IiIiIhIg+FLAaNHA9WqAX//XTCTjYzcX3+JpNysGeDurnQ1xsvLC2jQQCxsjIlRuhoiIiIio8LwpQAXF2DUKHE8f76ytVAZscV82XHqIREREVGxGL4UMmGC6NmwdStw/rzS1dBjFW62QaVj+CIiIiIqFsOXQnx9gV69xPHChcrWQo9x7RqQkABYWBQECyqZ/BkdOQLcv69sLURERERGhOFLQXLb+eXLuSetUZO7HLZqBdSooWwtpqBRI8DDA8jNLdiUmoiIiIgYvpTUvTvg7y+awn3/vdLVUInYYr58VCqgc2dxvHevsrUQERERGRGGLwWpVMCkSeJ44ULRII6MjCSx2UZFcN0XERERUREMXwobOhSoWRNITAR+/VXpaqiIf/8FrlwBrK2Bjh2VrsZ0yOErJgbIyVG2FiIiIiIjwfClMAcH4OWXxTHbzhshedQrNFT8w6Ky8fMD6tYFHjwAjh5VuhoiIiIio8DwZQTGjQMsLcU+vvHxSldDWthivmIKr/vi1EMiIiIiAAxfRqF+feD558XxggXK1kKFqNXA7t3imOGr/OSph2y6QURERASA4ctoyG3nf/oJSEtTtBSSnToF3LwJODoCbdsqXY3pkUe+DhwAHj5UthYiIiIiI8DwZSTatQOCg8XWSF9/rXQ1BKCgxXznzoCNjbK1mKLmzUU3mexsIC5O6WqIiIiIFMfwZUTktvNLlogQRgpji/nKsbAAOnUSx1z3RURERMTwZUyefx7w8ABSUoB165Supop7+LAgMHC9V8Vxvy8iIiIiDYYvI2JjA7z+ujieP1/s70sKOXYMyMoS0+YCA5WuxnTJ4Wv/fu4iTkRERFUew5eRefllwM5O/O5/8KDS1VRh8pTDbt3E9DmqmMBAwNkZyMwETpxQuhoiIiIiRVXot8orV67g6tWrmu+PHDmCyZMnY9myZTorrKqqUwcYOlQcc9NlBcnNNjjlsHIsLYGOHcUxpx4SERFRFVeh8DVkyBDs/m//o5SUFDz11FM4cuQI3nvvPXzwwQc6LbAqkhtvbNwIJCUpW0uVdP9+wbAjm21UHtd9EREREQGoYPg6ffo0goODAQDr1q1D8+bNcfDgQfz0009YsWKFLuurkgICxO/8+fnA4sVKV1MFHTwI5OQA9eoBjRsrXY3pk8PXvn1i42oiIiKiKqpC4evhw4ewtbUFAOzYsQPPPvssAMDPzw/Xr1/XXXVVmDz69c03YpskMqDCLeZVKmVrMQetWomNqm/fBk6fVroaIiIiIsVUKHz5+/sjMjIS+/btQ3R0NHr06AEASE5ORq1atXRaYFXVuzfQqBFw5w6wcqXS1VQxcvjiei/dsLYG2rcXx3v3KlsLERERkYIqFL4+/fRTfP311+jatSsGDx6MwP9acW/ZskUzHZEqx9ISmDBBHC9YwLbzBpORIVpNAlzvpUtc90VEREQElSRV7Nf6/Px8ZGZmokaNGppzly5dgoODA+rWrauzAo1VZmYmXFxckJGRAWdnZz29BlC/PnD3LrB9O/D003p5GSpsyxagb1/A1xc4d07paszH/v1Ap05A3bpiF3FO5yQiIiIzUtZsUKGRr/v37yMnJ0cTvC5fvox58+YhISGhSgQvQ3F2BkaPFsfz5ilaStXBFvP60bat2MAuLQ04e1bpaoiIiIgUUaHw1bdvX/z4448AgPT0dISEhGDu3LmIiIjA0qVLdVpgVTdhghgk+OMPICFB6WqqgMLNNkh3bG2B0FBxzKmHREREVEVVKHzFxcWhU6dOAIANGzbA1dUVly9fxo8//ogFCxbotMCqrlEjoE8fccyPVs9SUwu68XXrpmwt5khe98WmG0RERFRFVSh83bt3D05OTgCAP//8E/369YOFhQXatWuHy5cv67RAKmg7v2KF6H5IevLfxuEIDARq11a2FnPUubP4umcPO8gQERFRlVSh8PXEE09g8+bNuHLlCrZv346n/+sEkZaWprfmE1VZt25i4+V794DvvlO6GjPGFvP61a4dYGMDJCcD//6rdDVEREREBleh8DVz5kxMnToV3t7eCA4ORuh/azn+/PNPtGzZUqcFkljzJY9+LVoE5OUpW4/ZYrMN/bK3B+StKLjui4iIiKqgCoWv559/HklJSTh27Bi2b9+uOd+9e3d89dVX5X6+xYsXw9vbG3Z2dggJCcGRI0dKvX79+vXw8/ODnZ0dAgIC8Pvvv2vdL0kSZs6cCXd3d9jb2yMsLAznz5/XuubcuXPo27cvateuDWdnZ3Ts2BG75WlnRmjIEKBWLeDyZdENnXTs0iXg4kXAykq0RCf94H5fREREVIVVKHwBgJubG1q2bInk5GRcvXoVABAcHAw/P79yPc/atWsxZcoUzJo1C3FxcQgMDER4eDjS0tKKvf7gwYMYPHgwxowZg+PHjyMiIgIRERE4LTdKAPDZZ59hwYIFiIyMxOHDh+Ho6Ijw8HA8ePBAc80zzzyDvLw87Nq1C7GxsQgMDMQzzzyDlJSUCnwa+mdvD7z6qjhm23k9kEe9goOB/9Yzkh4wfBEREVFVJlVAfn6+9P7770vOzs6ShYWFZGFhIbm4uEgffPCBlJ+fX67nCg4OlsaNG6f13B4eHtKcOXOKvX7AgAFS7969tc6FhIRIr7zyiiRJkqRWqyU3Nzfp888/19yfnp4u2draSqtXr5YkSZJu3LghAZD27t2ruSYzM1MCIEVHR5ep7oyMDAmAlJGRUbY3qgPXrkmSlZUkAZIUG2uwl60ahgwRH+z06UpXYt7u3pUkS0vxWV+6pHQ1RERERDpR1mxQoZGv9957D4sWLcInn3yC48eP4/jx4/j444+xcOFCzJgxo8zPk5ubi9jYWISFhWnOWVhYICwsDDExMcU+JiYmRut6AAgPD9dcn5iYiJSUFK1rXFxcEBISormmVq1aaNKkCX788UdkZ2cjLy8PX3/9NerWrYvWrVsX+7o5OTnIzMzUuhmahwcwYIA4nj/f4C9vviSJ670MpVo1oE0bcczRLyIiIqpiKhS+fvjhB3z77bd47bXX0KJFC7Ro0QKvv/46vvnmG6xYsaLMz3Pz5k3k5+fD1dVV67yrq2uJ0/9SUlJKvV7+Wto1KpUKO3bswPHjx+Hk5AQ7Ozt8+eWX2LZtG2rUqFHs686ZMwcuLi6am6enZ5nfpy7JjTfWrBHbUpEOnDkDpKQAdnaiIx/pF6ceEhERURVVofB1+/btYtd2+fn54fbt25UuSt8kScK4ceNQt25d7Nu3D0eOHEFERAT69OmD69evF/uYadOmISMjQ3O7cuWKgasWgoOB0FAgNxeIjFSkBPMjt5jv2FEEMNIvhi8iIiKqoioUvgIDA7Fo0aIi5xctWoQWLVqU+Xlq164NS0tLpD4yhJOamgo3N7diH+Pm5lbq9fLX0q7ZtWsXtm7dijVr1qBDhw5o1aoVlixZAnt7e/zwww/Fvq6trS2cnZ21bkqRR7+WLAFychQrw3xwyqFhdewIWFiIvb6uXVO6GiIiIiKDqVD4+uyzz/D999+jWbNmGDNmDMaMGYNmzZphxYoV+OKLL8r8PDY2NmjdujV2yiMPANRqNXbu3KnZO+xRoaGhWtcDQHR0tOZ6Hx8fuLm5aV2TmZmJw4cPa665d+8eALG+rDALCwuo1eoy16+Ufv2AevWAtDRg7VqlqzFx+fnAX3+J4yefVLSUKsPZGZD3A9y7V9laiIiIiAyoQuGrS5cuOHfuHJ577jmkp6cjPT0d/fr1w99//42VK1eW67mmTJmCb775Bj/88APOnDmD1157DdnZ2Rg1ahQAYPjw4Zg2bZrm+kmTJmHbtm2YO3cuzp49i9mzZ+PYsWMYP348ALGea/Lkyfjoo4+wZcsWnDp1CsOHD4eHhwciIiIAiABXo0YNjBgxAidOnMC5c+fw1ltvITExEb17967IR2JQ1tbAf28X8+aJfhFUQcePA+npgIsL0KqV0tVUHZ07i6+cekhERERViS5bLMbHx0sWFhblftzChQulBg0aSDY2NlJwcLB06NAhzX1dunSRRowYoXX9unXrpMaNG0s2NjaSv7+/9Ntvv2ndr1arpRkzZkiurq6Sra2t1L17dykhIUHrmqNHj0pPP/20VLNmTcnJyUlq166d9Pvvv5e5ZiVazRd286Yk2duLjt2FOuZTeX3yifgQn31W6Uqqls2bxefu56d0JURERESVVtZsoJIk3Y2bnDhxAq1atUJ+fr6untJoZWZmwsXFBRkZGYqt/3rlFWDZMqB/f2DDBkVKMH1PPw1ER4ve/RMnKl1N1XH7NlC7thi2TUkBHulOSkRERGRKypoNKjTtkIyDnBU2bQIuXVK0FNOUkwPs3y+Oud7LsGrWBAICxDHXfREREVEVwfBlwvz9gbAwQK0GFi9WuhoTdOgQcP++GHXx91e6mqpHbjnP8EVERERVhFV5Lu7Xr1+p96enp1emFqqAyZOBHTuAb74BZs0CqlVTuiITIreYf/JJQKVStpaqqHNnYOFCNt0gIiKiKqNc4cvFxeWx9w8fPrxSBVH59OwJ+PoC588DP/4IvP660hWZEHk7Ak45VIbc8fDUKeDWLaBWLWXrISIiItIznTbcqEqMoeGGbNEiYMIEoHFj4MwZsX8tPUZWFlCjBpCXB1y8CPj4KF1R1dSsmfih3bQJ+G8rCCIiIiJTw4YbVciIEWLf2nPngO3bla7GROzbJ4KXtzeDl5LkdV+cekhERERVAMOXGXByAsaMEcfz5ytbi8mQpxx2765sHVUdwxcRERFVIQxfZmLCBDHdcPt2MYuLHqNwsw1SjrzuKz4eyMhQtBQiIiIifWP4MhM+PsCzz4rjBQuUrcXo3bolftkHGL6U5uEBPPGE2GxZ3nONiIiIyEwxfJmRyZPF1x9+AG7fVrQU4/bXX+KXfX9/wM1N6WqIUw+JiIioimD4MiOdOwOBgWLf4G+/VboaI8YW88aF4YuIiIiqCIYvM6JSAZMmieNFi0QzPyqGvN6LzTaMgxy+YmOBu3eVrYWIiIhIjxi+zMzgwUCdOsCVK2LrJHrEtWtAQoLoTiL/0k/KatBAtPzPzwdiYpSuhoiIiEhvGL7MjJ0d8Oqr4pht54shTzls3RqoXl3RUqgQueshpx4SERGRGWP4MkOvvQZYWwMHDgDHjildjZHhlEPjxHVfREREVAUwfJkhd3dg4EBxzNGvQiSJzTaMlRy+jhwB7t1TthYiIiIiPWH4MlNy2/m1a4Hr1xUtxXhcuABcvQrY2AAdOihdDRXWsCFQrx7w8CFw6JDS1RARERHpBcOXmWrdWuSLhw+BpUuVrsZIyKNeoaGAg4OytZA2lapg9GvvXmVrISIiItIThi8zJredj4wEHjxQthajwPVexo1NN4iIiMjMMXyZseeeAzw9gRs3gNWrla5GYWp1Qfjiei/jJI98HToE5OQoWwsRERGRHjB8mTErK2D8eHE8f77oN1FlnTwJ3LoFVKsGBAcrXQ0Vp0kTwNVVDNMeOaJ0NUREREQ6x/Bl5l56SSxvOnGiis/mkke9OncWffjJ+KhUnHpIREREZo3hy8zVrAkMHy6Oq3TbebaYNw1sukFERERmjOGrCpg4UXz95Rfg4kVla1HEw4cFv8yz2YZxk8PXwYPinxsRERGRGWH4qgKaNgXCw8War0WLlK5GAUePAllZQK1aQIsWSldDpWnWTAzXZmcDsbFKV0NERESkUwxfVYTcdv6774C7d5WtxeDkKYfdugEW/JE3ahYWXPdFREREZou/iVYR4eGimVxmJrBihdLVGBhbzJsWeeohwxcRERGZGYavKsLComDt18KFYturKuHePbF+COB6L1Mhh6/9+4G8PGVrISIiItIhhq8qZPhwwMUFOH8e+OMPpasxkIMHgdxcoH59wNdX6WqoLFq0ED+od++KPRKIiIiIzATDVxVSrRowdqw4njdP0VIMp3CLeZVK2VqobCwtgY4dxTGnHhIREZEZYfiqYsaPF1MQd+wA/v5b6WoMQF7vxSmHpoXrvoiIiMgMMXxVMV5ewHPPiWOz33Q5PR04dkwcs9mGaZHD1759VWiBIhEREZk7hq8qSG47v3IlcOuWsrXo1Z494hf3xo3Fmi8yHa1aiXmyd+4Ap04pXQ0RERGRTjB8VUEdO4rfbR88AJYtU7oaPeKUQ9NlZQV06CCO9+5VthYiIiIiHWH4qoJUqoLRr8WLgYcPla1Hbwo32yDTw82WiYiIyMwwfFVRAwcCrq7AtWvAxo1KV6MHqakFHUW6dVO2FqoYed3X3r2AJClbCxEREZEOGEX4Wrx4Mby9vWFnZ4eQkBAcOXKk1OvXr18PPz8/2NnZISAgAL///rvW/ZIkYebMmXB3d4e9vT3CwsJw/vz5Is/z22+/ISQkBPb29qhRowYiIiJ0+baMmq0t8Npr4tgs287LUw6DgoBatRQthSqobVvA3h64cQM4c0bpaoiIiIgqTfHwtXbtWkyZMgWzZs1CXFwcAgMDER4ejrS0tGKvP3jwIAYPHowxY8bg+PHjiIiIQEREBE6fPq255rPPPsOCBQsQGRmJw4cPw9HREeHh4Xjw4IHmmp9//hnDhg3DqFGjcOLECRw4cABDhgzR+/s1Jq++CtjYAIcOAYcPK12NjnG9l+mzsQFCQ8Uxpx4SERGRGVBJkrLzeUJCQtC2bVssWrQIAKBWq+Hp6YkJEybgnXfeKXL9wIEDkZ2dja1bt2rOtWvXDkFBQYiMjIQkSfDw8MCbb76JqVOnAgAyMjLg6uqKFStWYNCgQcjLy4O3tzfef/99jBkzpkJ1Z2ZmwsXFBRkZGXB2dq7QcxiDkSOBH34ABg8GoqKUrkaHGjYEEhOB334DevVSuhqqqA8+AGbNAgYNAlavVroaIiIiomKVNRsoOvKVm5uL2NhYhIWFac5ZWFggLCwMMTExxT4mJiZG63oACA8P11yfmJiIlJQUrWtcXFwQEhKiuSYuLg7Xrl2DhYUFWrZsCXd3d/Ts2VNr9OxROTk5yMzM1LqZA7nxxvr1Yv2XWUhMFDcrq4KmDWSaCm+2zHVfREREZOIUDV83b95Efn4+XF1dtc67uroiJSWl2MekpKSUer38tbRrLl68CACYPXs2pk+fjq1bt6JGjRro2rUrbt++XezrzpkzBy4uLpqbp6dnOd+tcWrZEujUCcjLA5YuVboaHZGnHIaEiL2iyHQFB4vph9evAxcuKF0NERERUaUovuZLCWq1GgDw3nvvoX///mjdujWWL18OlUqF9evXF/uYadOmISMjQ3O7cuWKIUvWq8mTxdfISOD+fUVL0Q22mDcf9vYiRANc90VEREQmT9HwVbt2bVhaWiI1NVXrfGpqKtzc3Ip9jJubW6nXy19Lu8bd3R0A0KxZM839tra2aNiwIZKSkop9XVtbWzg7O2vdzEXfvoCXF3Drlhms+5IkNtswN4WnHhIRERGZMEXDl42NDVq3bo2d8kgFxKjUzp07ESp3OXtEaGio1vUAEB0drbnex8cHbm5uWtdkZmbi8OHDmmtat24NW1tbJCQkaK55+PAhLl26BC8vL529P1NhaQlMmCCO580z8aU1//wj9viytwfatVO6GtIFrvsiIiIiM6H4tMMpU6bgm2++wQ8//IAzZ87gtddeQ3Z2NkaNGgUAGD58OKZNm6a5ftKkSdi2bRvmzp2Ls2fPYvbs2Th27BjGjx8PAFCpVJg8eTI++ugjbNmyBadOncLw4cPh4eGh2cfL2dkZr776KmbNmoU///wTCQkJeO2/Ta9eeOEFw34ARmLMGMDRETh9Gti9W+lqKkEe9erYUWxmRqYvNFQ0T7lyBbh8WelqiIiIiCrMSukCBg4ciBs3bmDmzJlISUlBUFAQtm3bpmmYkZSUBAuLgozYvn17REVFYfr06Xj33Xfh6+uLzZs3o3nz5ppr/u///g/Z2dl4+eWXkZ6ejo4dO2Lbtm2ws7PTXPP555/DysoKw4YNw/379xESEoJdu3ahRo0ahnvzRqR6dWDECGDJEmD+fBNeLsX1XubH0RFo00ZsSLdnD+DtrXRFRERERBWi+D5fpspc9vkqLCEB8PMDVCrg/HmgUSOlKyqnvDygdm0gIwM4cgRo21bpikhX3nkH+PRTYNQo4Pvvla6GiIiISItJ7PNFxqVJE6BnT7GsZuFCpaupgOPHRfBycQFatVK6GtIlNt0gIiIiM8DwRVrktvPffw+Y3D7S8pTDrl1FFxEyHx06ABYWwMWLwNWrSldDREREVCEMX6TlqaeApk2Bu3eB5cuVrqac2GLefDk7F4xm7t2rbC1EREREFcTwRVpUKmDSJHG8YAGQn69sPWWWkwPs3y+O2WzDPHHqIREREZk4hi8qYtgwoEYNMcPrt9+UrqaMYmKA+/cBNzeg0ObZZEY6dxZfGb6IiIjIRDF8UREODsDYseJ4/nxlaykzecrhk0+K4TsyP506iX+2CQlASorS1RARERGVG8MXFWvcONGzYtcu4ORJpaspA+7vZf5q1ABatBDHXPdFREREJojhi4rVoAHQr584XrBA2Voe6+5dsa8XwGYb5k5e98XwRURERCaI4YtKJLedX7UKuHFD0VJKt2+f2GDZxwfw9la6GtInNt0gIiIiE8bwRSUKDQXatBGNBJctU7qaUrDFfNXRqZP4evo0cPOmsrUQERERlRPDF5WocNv5JUuA3Fxl6ykR13tVHXXqFHSz3LdP2VqIiIiIyonhi0o1YIDo3p6cDGzYoHQ1xbh5E4iPF8cMX1UDpx4SERGRiWL4olLZ2ACvvy6O580DJEnRcor66y/xtXlzwNVV0VLIQBi+iIiIyEQxfNFjvfIKYGsLHD0KHDqkdDWP4JTDqkfebPnECSA9XdFSiIiIiMqD4Yseq25dYMgQcWx0my6z2UbV4+4O+PqKYdj9+5WuhoiIiKjMGL6oTOTGGxs2AFevKluLxtWrwLlzgIVFwWgIVQ2cekhEREQmiOGLyiQwEOjaFcjPBxYvVrqa/8ijXm3aANWrK1oKGRjDFxEREZkghi8qM3n0a9ky4N49ZWsBwPVeVZkcvuLigLt3la2FiIiIqIwYvqjM+vQBfHyA27eBVasULkaSCsIX13tVPZ6e4ocxPx84eFDpaoiIiIjKhOGLyszSEpgwQRzPn69w2/nz54Fr10Qv/A4dFCyEFMOph0RERGRiGL6oXEaPBqpVA/75B9ixQ8FC5FGv9u0Be3sFCyHFyE1WGL6IiIjIRDB8Ubm4uACjRoljRdvOs8U8ySNfR48aySJEIiIiotIxfFG5TZgAqFTAb7+J2X8Gp1YDu3eLYzbbqLp8fID69YGHD4GYGKWrISIiInoshi8qN19foHdvcbxggQIFnDwJ3Lol5j+2batAAWQUVKqC0a+9e5WthYiIiKgMGL6oQuS288uXA+npBn5xeb1Xly6AtbWBX5yMCptuEBERkQlh+KIK6d4d8PcHsrOB77838Itzfy+SyU03Dh0CHjxQthYiIiKix2D4ogpRqQpGvxYuFNstGcTDhwVTzNhsgxo3BlxdgZwc4MgRpashIiIiKhXDV2UputmVsoYOBWrWBC5dAn791UAveuSIGG6rXRsICDDQi5LRKrzui1MPiYiIyMgxfFVWv37AhQtKV6EIBwfglVfE8bx5BnpRucV8t26ABX98CQxfREREZDL422tl7doFNG8OfPihmPpUxbz+OmBpKX7vjY83wAtyvRc9Sg5fBw8CubnK1kJERERUCoavyurWTYSumTOBwMCCkZkqon594IUXxLHeN12+d69gPyeu9yJZs2ZiGur9+0BsrNLVEBEREZWI4auyNm0CVq8Wi/4TEkQoGDYMSEtTujKDkRtvREXp+W0fOCBGNjw9gSee0OMLkUlRqYBOncQxpx4SERGREWP4qiyVChg0CDh7VszBU6mAVauAJk2AZcsAtVrpCvWuXTsgOFjkoq+/1uMLFZ5yqFLp8YXI5HDdFxEREZkAhi9dqV4dWLxY7DfUsqXYefiVV4AOHYATJ5SuTu8mTxZflyzR47IbeUonpxzSo+TwtX8/kJenbC1EREREJWD40rXgYNEOfd48oFo1EcZatwamTgWyspSuTm+efx7w8ABSUoB16/TwAunpBet52GyDHhUQIP4AkpVloM4vREREROXH8KUPVlZiIdTZsyKV5OcDc+eKxgC//KJ0dXphbQ2MGyeO583Tw/Zne/aIKZxNmgD16un4ycnkWVpy3RcREREZPaMIX4sXL4a3tzfs7OwQEhKCI0eOlHr9+vXr4efnBzs7OwQEBOD333/Xul+SJMycORPu7u6wt7dHWFgYzp8/X+xz5eTkICgoCCqVCvG6/ot5vXrA+vXAb78B3t7AlStARATQty9w+bJuX8sIvPwyYGcnBqgOHtTxk7PFPD1O587iK8MXERERGSnFw9fatWsxZcoUzJo1C3FxcQgMDER4eDjSSmibd/DgQQwePBhjxozB8ePHERERgYiICJw+fVpzzWeffYYFCxYgMjIShw8fhqOjI8LDw/HgwYMiz/d///d/8PDw0Nv7AwD06gX8/Tfw7rtiiGjLFjEK9vnnwMOH+n1tA6pdG3jxRXGs802X5fDF9V5UEnnd1759YrSZiIiIyMioJEnnE8TKJSQkBG3btsWiRYsAAGq1Gp6enpgwYQLeeeedItcPHDgQ2dnZ2Lp1q+Zcu3btEBQUhMjISEiSBA8PD7z55puYOnUqACAjIwOurq5YsWIFBg0apHncH3/8gSlTpuDnn3+Gv78/jh8/jqCgoDLVnZmZCRcXF2RkZMDZ2bnsb/iff4BXXxW/IAJig+bISNGYwwycOgW0aCFmgV28CDRooIMnTUkB3N1Fh8MbN4BatXTwpGR28vKAmjWBu3eB48eBMv67TERERFRZZc0Gio585ebmIjY2FmFhYZpzFhYWCAsLQ4y8me4jYmJitK4HgPDwcM31iYmJSElJ0brGxcUFISEhWs+ZmpqKsWPHYuXKlXBwcHhsrTk5OcjMzNS6VUizZmJa1PLlIkScPg107AiMHQvculWx5zQiAQFiZmB+vmj+qBNyl8OgIAYvKpmVVcEfMfbuVbYWIiIiomIoGr5u3ryJ/Px8uLq6ap13dXVFSkpKsY9JSUkp9Xr5a2nXSJKEkSNH4tVXX0WbNm3KVOucOXPg4uKiuXl6epbpccVSqYCRI8WmzGPGiHPffgv4+QE//KCHbhWGJbedX7YMyM7WwROyxTyVFff7IiIiIiOm+JovJSxcuBB3797FtGnTyvyYadOmISMjQ3O7cuVK5QupVUuErn37AH9/4OZNEcq6dQPOnKn88yukd2+gUSPRHX7lSh08IZttUFnJTTf27jX5P2IQERGR+VE0fNWuXRuWlpZITU3VOp+amgo3N7diH+Pm5lbq9fLX0q7ZtWsXYmJiYGtrCysrKzzxxBMAgDZt2mDEiBHFvq6trS2cnZ21bjrTsSMQFwd88glgby/+ah8YCEyfDty/r7vXMRALC2DiRHE8f77oEF9hiYnApUtiSpncSpyoJG3aiH+Hbt4U6yuJiIiIjIii4cvGxgatW7fGTnlkA6Lhxs6dOxEaGlrsY0JDQ7WuB4Do6GjN9T4+PnBzc9O6JjMzE4cPH9Zcs2DBApw4cQLx8fGIj4/XtKpfu3Yt/ve//+n0PZaZjQ3w9tviF8bevUUXxP/9TzTk2LZNmZoqYeRIwMlJbHUWHV2JJ5L/OYaEiE2riUpjYwO0by+OOfWQiIiIjIzi0w6nTJmCb775Bj/88APOnDmD1157DdnZ2Rg1ahQAYPjw4VrTAydNmoRt27Zh7ty5OHv2LGbPno1jx45h/PjxAACVSoXJkyfjo48+wpYtW3Dq1CkMHz4cHh4eiIiIAAA0aNAAzZs319waN24MAGjUqBHq169v2A/gUd7ewK+/Ahs3AvXri5aBPXsCAwYAycnK1lYOzs7A6NHieP78SjwRW8xTeXHdFxERERkpxcPXwIED8cUXX2DmzJkICgpCfHw8tm3bpmmYkZSUhOvXr2uub9++PaKiorBs2TIEBgZiw4YN2Lx5M5o3b6655v/+7/8wYcIEvPzyy2jbti2ysrKwbds22NnZGfz9VYhKBTz3nBgFmzJF9G1fv1405Fi40GT2MJowQbyVP/4QI2DlJklstkHlJ4cvrvsiIiIiI6P4Pl+mqsL7fFVEfLzYG+zwYfF969Zib7AydmpUUt++Yk/p11+vQOv506dF73p7e+DOHcDWVi81kpl58ACoXh3IyREdRf8b2SYiIiLSF5PY54vKKCgIOHgQWLoUcHEBYmOB4GAxtJSRoXR1pZLbzq9YIfJTucijXp06MXhR2dnZiTWCAKceEhERkVFh+DIVFhZi9CshARg6VEynWrRITEVcu9Zop1d17Qq0aAHcuwd89105H8wW81RRXPdFRERERojhy9S4ugKrVgE7dgC+vkBKCjBoENCjB3DhgtLVFaFSFbSdX7QIyMsr4wPz8oC//hLHXO9F5VU4fBnpHyaIiIio6mH4MlXduwMnTwLvvy+m5P35p2hL/+GHYq2LERkyBKhdG7h8GfjllzI+KC4OyMwUa3dattRneWSOQkMBa2vg6lWxTxwRERGREWD4MmV2dsDMmcCpU0BYmAhdM2eKDZp371a6Og17e+CVV8RxmdvOy1MOu3YV3R6JysPBAWjbVhxz6iEREREZCYYvc+DrK0a+oqLEtMSEBLFOavhwIC1N6eoAiG6HVlbAvn1iUOux2GKeKqtzZ/GV4YuIiIiMBMOXuVCpgMGDxYZar78uvl+5EmjSBFi2DFCrFS3Pw0PsEw2UYfTrwQNg/35xzGYbVFFsukFERERGhuHL3FSvLjbUOnRItKhPTxdz/jp2BE6cULS0SZPE19WrRZ+QEh06JAKYmxvQtKlBaiMz1KGDmLKamAhcuaJ0NUREREQMX2YrOBg4ehSYNw+oVg2IiRGbM0+dCmRlKVZSaCjw8KHYI7pEhVvMq1QGqY3MkJMT0KqVON67V9laiIiIiMDwZd6srMRw09mzQP/+QH4+MHcu0KxZOdoO6pY8+rV0aSlNGeXwxfVeVFmcekhERERGhOGrKqhXD9iwAfjtN8DbW0zBiogA+vYV/d8NqF8/oH590QdkzZpiLrh7FzhyRBwzfFFlMXwRERGREWH4qkp69QL+/huYNk2Mim3ZIkbBPv9czAU0AGtrYNw4cTx/fjH73+7dK0boGjYEvLwMUhOZsY4dxdTVc+eA69eVroaIiIiqOIavqsbBAfj4YyA+HujUCbh3D/i//xNrYw4cMEgJY8eKvb+OHxet57WwxTzpUvXqYt87gOu+iIiISHEMX1WVv7+YirV8OVCrFnD6tBglGDsWuH1bry9dqxYwbJg4LtJ2vnCzDSJdkKceMnwRERGRwhi+qjKVChg5UmzKPHq0OPftt2JvsB9/LGZOoO5MnCi+bt4MnDr138mbNwva4XfrprfXpiqG676IiIjISDB8kRiK+u47MTLg7y9C0IgRIgCdOaOXl/T3B556Suz93KIF0LIlsPa13QAAqXlzwNVVL69LVVCnTuLr33+Ln20iIiIihTB8UYFOnYC4OOCTT8SirD17xHqZ6dOB+/d1/nJLlgBduwIWFmIJ2p0NYsrhd5e6Y9w4YPv2UtrRE5VV7doi7QOcekhERESKYvgibTY2wNtvA//8A/TuLbog/u9/QPPmwLZtOn2pJ54Adu8GUlOBH34Anq0mmm38ktUdS5YAPXoAdeoAAwYAP/2k96VoZM449ZCIiIiMAMMXFc/bG/j1V2DjRrFP2MWLQM+eIgklJ+v0pWrXBoZ3uwKPrPOQLCwwYV1nvPIK4O4utv1avx548UWgbl0xE3LePFEOUZkxfBEREZERUEmSHrsqmLHMzEy4uLggIyMDzs7OSpejX3fvArNni9aE+fmAk5MYDXv9dcDSUjev8cMPovlHSAhw6BAAsR4sNhb45RexJZmmMcd/mjcHnn1W7BXdpo2YvkhUrJQUkeZVKuDWLaBGDaUrIiIiIjNS1mzAX1fp8ZycgLlzgWPHgOBgEcYmThRB6dgx3bxGMS3mLSyAtm2Bjz4CTp4E/v1XjHp16yYy3+nTYsuykBCgfn3glVeA334DHjzQTUlkRtzcRBdPSQL271e6GiIiIqqiGL6o7IKCgIMHgaVLARcXMSwVHAxMmABkZFT8eSWpTJsrN2wITJokLr1xQ6wDGzBAZMPr14Fly4BnnhHNG/v1E4NpbG5HGp07i6+cekhEREQK4bTDCqpS0w6Lk5ICTJ0qEhAgRhbmzRNpSKUq33MlJAB+foCtLXDnjui0WA65ucBff4mpib/8Aly9WnCfhQXQvr2Ymvjss0DjxuUrjczITz+JxYNt2gBHjypdDREREZmRsmYDhq8KqvLhS7ZzJ/Daa8D58+L78HBg8WKgUaOyP8eSJcC4cWI+oTwCVkGSJNrWy+vEjh/Xvt/Pr2CdWEiI7paskQm4ehXw9BSJ/M4doCr/e0tEREQ6xTVfZBjdu4sFWbNnizb127eLPZU++qjsm3SVYcphWalUYsPm2bPFlmWXLwOLFokNna2tgbNngc8+Azp0ADw8gDFjRFC7d6/SL03Grn59MXdVrRbTZ4mIiIgMjOGLKs/ODpg1S3TACAsToWvGDLFB8+7dpT9WrS64plCzDV1p0EAMqv35p1gntmYNMGSIWLKWlgZ8/z0QESHWiT37LPDdd2LfMTJTbDlPRERECmL4It3x9RUpJyoKcHUVa7mefBIYPlwkneKcOCF2T3ZyEq0N9cjFBRg4UCz9uXFDzJicOFFsafbggdjW7KWXREfy9u2BTz4Re01zYq4ZYfgiIiIiBTF8kW6pVMDgwWJ+3+uvi+9XrhSLrZYtEyNdhckt5jt3BqysDFamtbXIhfPniw2bT54EPvxQ5D9JAmJigGnTxAzKxo2BN98E9u4F8vIMViLpg9zx8OhRIDtb2VqIiIioymH4Iv2oXl003jh0SLSov3NHbMTVsaNIOjIdrveqKJUKCAgApk8HjhwRfRkiI4GePcUytgsXgC+/FIMmbm7AiBHAzz8DWVmKlUwV5e0tmm7k5YmETUTm6e5d8f8a/sWMiIwMwxfpV3CwGGX46iugWjXxC2+rVsBbb4lAtnevuE7B8PWoevVETvz9d7FP2IYNYuZkzZrArVvAjz8Czz8v1on16gV8/TWQnKx01VQmKlXB1EP5Z4+IzENOjuigNHAgULeuWHfs6QlMmSJa33IOOREZAbaaryC2mq+Aq1eByZPFsBEA1KghAljt2qLLhYVx/y0gL080yfvlF3H791/t+9u2FU07nn1WjKSVd7szMpBvvwXGjhVTELn2i8i05eeLP6RERYm/lKWnF9xnYyM2gpT5+4u9/oYOFaGMiEiHuM+XnjF8VcJvvwHjxwOXLonvBwwA1q5VtKTykiTgzJmCjZ0PH9b+o6q3d8F+Yp06iTVmZCTOnxcL+WxtxS9qdnZKV0RE5SFJYi+RqCjRwrbw1AMPD2DQINHWNiAA2LYNWLVK/Mda3v5EHgEfNkxMY+D/w4lIBxi+9Izhq5Lu3RMdLtasESMRRjTtsCJSUoCtW8X/36OjRfdEWfXqYnris8+KdWT8cVGYJIlf0FJSgL/+KpiGSETG7fx5EbiiooBz5wrOV68uQtSQIWJE29Ky6GPT08XI2KpV2iPednbir2QvvgiEh/MvZURUYQxfesbwRSXJzgZ27BAjYlu3irb2MmtroGtX8f/6Pn3EPmSkgEGDxGjr++8DM2cqXQ0RlSQ5Wfy7GhUFHDtWcN7OTvxFa8gQoEcPMZJdVpcviz1HVq4UnXlldeqI/za8+KKYR86540RUDgxfesbwRWWRny8aPsrTExMStO9v2bJgemJQEP9fbzBLl4qtEJ58smC7AyIyDunpYm1wVBSwe3fBnG5LS+Cpp0TgiogQ+0NWhjx9cdUq8VqF96Ns3FiEsBdfBHx8Kvc6RFQlMHzpGcMXVURCgghiW7aI5h2Ftz2rX78giHXtKtaKk578849YfG9vL37R44dNpKz798VUgago0Wq2cKOM9u1F4HrhBdHFUB/y8sSc8VWrgE2bRD2yjh1FCBswQDSKIiIqRlmzgVG0l1u8eDG8vb1hZ2eHkJAQHDlypNTr169fDz8/P9jZ2SEgIAC///671v2SJGHmzJlwd3eHvb09wsLCcP78ec39ly5dwpgxY+Dj4wN7e3s0atQIs2bNQm7h/9gT6UGTJqLL/r59YsnR8uXAc88BDg6iGeSSJWLZQe3aolvyTz+JhpCkY02biilG9+9rT2UiIsPJywP+/FNsnujqKsLN5s0iePn7Ax9/DFy8CBw4AIwbp7/gBQBWVmJR7k8/ie67K1YAYWFiOsL+/cCrr4qNHvv3F+FMbt5BRFROioevtWvXYsqUKZg1axbi4uIQGBiI8PBwpBUe/i/k4MGDGDx4MMaMGYPjx48jIiICEREROH36tOaazz77DAsWLEBkZCQOHz4MR0dHhIeH48F/XRDOnj0LtVqNr7/+Gn///Te++uorREZG4t133zXIeyYCxO/+I0cCGzeK/cS2bhUd0N3cxP6g69aJP7bWqSNmx82fDyQmKl21mVCpxMJ8gO3miQxJksRc7IkTxaaK4eFi88S7d8Ui2HfeEZsjnz4NTJumzJQ/JycRCKOjgStXgM8+A1q0EKFw40agXz/A3R147TURDDmBiIjKQfFphyEhIWjbti0WLVoEAFCr1fD09MSECRPwzjvvFLl+4MCByM7OxtatWzXn2rVrh6CgIERGRkKSJHh4eODNN9/E1KlTAQAZGRlwdXXFihUrMGjQoGLr+Pzzz7F06VJcvHixTHVz2iHpi1ot9qWW14n9/bf2/QEBBdMTW7c2+u3RjNeCBcCkSeKXv23blK6GyLz9/beYUrh6tfZfkWrXFiNeQ4YAoaHG/R+0kydFk46oKO329g0bFqwP8/VVrj4iUpRJTDvMzc1FbGwswsLCNOcsLCwQFhaGmJiYYh8TExOjdT0AhIeHa65PTExESkqK1jUuLi4ICQkp8TkBEdBq1qxZ4v05OTnIzMzUuhHpg4UFEBIC/O9/4o+/Fy4AX34p1oFZWgKnTon7goPFPqGvvip+n9m9WyxlunVLey0ZlUBuMX/ggJj+RBWXmSl+Mf31V9HO+9IljgaQ6Cr46adAYCDQvLmYRpiYCDg6iqDy++8ixCxeDHToYNzBCxCjX59/DiQliVGx4cPFe7l4EfjgA9Gko1078X5u3lS6WiIyUlZKvvjNmzeRn58PV1dXrfOurq44W7j9ayEpKSnFXp+SkqK5Xz5X0jWPunDhAhYuXIgvvviixFrnzJmD999/v/Q3RKQHjRoBb7whbrdvi99XfvlFDNYkJwNffy1uhVlZiemKrq7iVrduycd16lTRrW0CAsTi+Tt3gOPHRWtpKkqSxGd06ZL4Zbq4r+npRR/n6ip+EQ0JEV/btgWqVTNo6aSAmzeB9evF6ND+/QXnra3FmqohQ8Q+Gw4OytVYWZaWYj1YWJhYqPvLL6JRx59/AocPi9vkyeL9DhsGPPOMaO5DRASFw5cxuHbtGnr06IEXXngBY8eOLfG6adOmYcqUKZrvMzMz4enpaYgSiTRq1iyY3ZKTI/YI3rJFzOhJTRWdkm/fFgM516+LW1nUqlV6SCv8vdn8DmFhAXTqJD7APXuqbviSJLEZXWnhKivr8c9Tqxbg5SXW0504IX4gf/lF3ADxeTdvLoKYfGvSxPhHO+jxsrLEP+eoKBFA5JFklUqMMA8ZIhpVlDK7xGQ5Oor3N2SI+JlfvVoEsdhYMRL866+As7Po1DhsmPhvDn/miao0RcNX7dq1YWlpidTUVK3zqampcHNzK/Yxbm5upV4vf01NTYW7u7vWNUFBQVqPS05ORrdu3dC+fXssW7as1FptbW1hW55NHIn0zNZWLFcKD9c+n5srfpeWw1hqatFj+fsbN8ReZLduids//zz+dZ2cSg5mjx47Oxv53mVduhSEr//WiJodtVq01iwpXF2+rN1WuySuriJceXsX/7XwqNb9+2I08dChgtuVK2Jq4smTgPzfWxcXMX9WDmMhISLEkfHLzRVBKypKBK979wrua9VKhJGBA8UeGlWFq6sY8Zo8WfzHdNUq0T0xKQn47jtxa9AAGDpUBLGmTZWumIgUYBQNN4KDg7Fw4UIAouFGgwYNMH78+BIbbty7dw+//vqr5lz79u3RokULrYYbU6dOxZtvvglAjFLVrVtXq+HGtWvX0K1bN7Ru3RqrVq2CpaVluepmww0yB2q1CF3FBbPijsvbXdnWtvQpj4UDXM2aYjaPQR07Jka8XFzEB2HwAnQgLw+4dq1ooJKPk5K090wqjkoFeHiUHK4aNKj8kGdyspiOJYexo0eLD32+vtqjYwEBVXRerBFSq8VUwqgoMbXw9u2C+554QgSuwYMBPz/lajQ2arXYW2TlSvGZFV4v3qqVCGGDBok2t0Rk0kxmk+W1a9dixIgR+PrrrxEcHIx58+Zh3bp1OHv2LFxdXTF8+HDUq1cPc+bMASBazXfp0gWffPIJevfujTVr1uDjjz9GXFwcmjdvDgD49NNP8cknn+CHH36Aj48PZsyYgZMnT+Kff/6BnZ0drl27hq5du8LLyws//PCDVvAqacTtUQxfVNVIkvi94XGjafLx3bvle34Li4J1ao+b/li3ro72Rc7LE6nv7l0gLg5o2VIHT6pjubli1KikKYFXr4rhy9JYWooRiJLClaen4TeafvhQdJSRw9jhw2IX8kfZ24u2noUDWb16hq21KpMkMY1U7lR49WrBfW5uIjgMGQK0aWPkw9xG4MEDMQ1x5Urgjz8KpmdaWgJPPSWCWN++YiojEZkckwlfALBo0SJ8/vnnSElJQVBQEBYsWICQkBAAQNeuXeHt7Y0VK1Zorl+/fj2mT5+OS5cuwdfXF5999hl69eqluV+SJMyaNQvLli1Deno6OnbsiCVLlqBx48YAgBUrVmDUqFHF1lLWj4Phi6h09++XHNQeDW23bpX/+WvUKPv0x1J/l+nVS/wiNG+eaD1vaPfvi9GpksJVcvLjOwdaW4vRqZLCVb16oguLsbt9GzhyRDuQFdfMo3597TDWqpUZLUY0Ev/+K8JWVBRw5kzBeRcXsX5ryJCCFqxUfjdvAmvXiiB2+HDB+WrVxD5iw4YB3brx8yVSq0WX1Ph40e55xgyj/ffCpMKXKWL4ItKdvLzi16mV9P3jBnoe5eBQckjrHPMpWkS9g7thzyF//Ua4uOj4D/hZWUWnAhb++sga1mLZ2YkgVVK4cnMz2v8ZVYpaDZw7px3GTp4supeClZVoZ144kDVqxJGY8kpJEbu7R0VpBwJbW9GhcMgQ0cHPzk65Gs3R+fNifdiqVaJtvczDQ3zmw4aJNvdE5i47WwSsEycKbidPajd9SkgQ2zoYIYYvPWP4IlKGWi06nz8uqMnHj+slEYJDOIRQ3EQt1EUabGwt4OYGuLuLTFP4Vvicq6v4nRQZGaV3CizLsJ6jY8mNLLy9RVpkkBCyskQnOTmQxcQUH2Br1dIOY/LaPtKWkQFs2iQC186dBcHWwkK0Uh8yBIiI4GdnCJIkfp5XrhSjYnfuFNwXECBC2JAhnHZLpk+SxBTmwiHrxAnxh4jiYomtLeDvL/7INm2a0W5mzvClZwxfRMZPksTv6qVNe7yV8hC/HawOB+kemuMU/kbzws+AmrgNb1yCFy4X+eqjugQXKePxdbi4QFVauKpZk+GqoiRJTNssPDoWG1u0yYhKJbrLFQ5kzZqZ54jh4zx4IDYMjIoCtm7V7qTTrp34BX/AAPEXBlJGTo6YDr1ypfhnJP88q1TAk0+KINavn2g/S2TMcnJE989Hg1bhhj2FubqKkCXfgoLESJcJNF5i+NIzhi8iM/LUU8COHciP6I/saq7Iv3gJllcuwz7tEqxzsh/78JuohUvwxmV4Ffl6GV7ItqoOV9fHj6a5uZn23rNGIydH/M+9cKv7xMSi11WrVrTVfd26hq/XEPLzgd27ReD6+WftrntNm4r254MGiemaZFzu3BGdEleu1N642t5ejEoOGyb+G2YK6zrJvKWlFQ1ZZ84UNJcpzNJSdEYtHLICA036jz4MX3rG8EVkRj76SCziLYmrq9ZIldTAC1m1vZFm74Wrll64llEN16+LJTOFb9evl7+ZiJNT8SHt0aBWp07VHLSpsNRUMSomt7s/cqT4zaN9fLRHx4KCDN8JUlckSbT0j4oS09hSUgru8/QUbeGHDBHriTjyahoSE8XeYStXivWQsrp1xT/PYcNEAxr+8yR9yssTP3+PBq3r14u/vnr1oqNZzZqZ3fpRhi89Y/giMiM3bgATJog08+i0wErucZWbK/4YWDiQPRrS5PNl2etYJrfmL8tompMTfxcrIj9fTIUpPDpW3C7jtrbil9nCgczT07g/0DNnCjoV/vtvwfmaNcV0wiFDgA4dxA8RmSZJEvsUrlwp/lnfvFlwX9OmwIsvitFMLy/laiTzkJFRNGSdPi2mLxfniSeKjmYZ+38zdYThS88YvohIlyRJbDf2aCArLqSlpT2++3xhDg6PH0mTm4iYwLR6/UlPFyNFhTeDLm7o0s1NO4y1aaP83kxXrwJr1ojAdfx4wXkHBzE1bcgQMTXNVEfxqGQPHwJ//imC2C+/aP9S3KWLCGLPPy9GH4hKIrd0fzRoXbpU/PWOjqIRTOGQFRAgpnNXUQxfesbwRURKycsTf+guS1Ar72bXtWs/PqS5uYl91sz+D5mSJEaOCo+OnThRdP2CpaX4paNwIPP11f/I0u3bwIYNInDt3VuQyK2sgB49ROB69lnlgyEZTkYGsHGjCGJ//VXwMyFvFzBsmPjZYAiv2u7dK76le0n/w/D0LDqa1agRR88fwfClZwxfRGQKsrPFcqfHTXtMSSl+TXRJbGyKH02rU0fM0rSzEzf5uLRzNjYmFOTu3wfi4rQD2dWrRa+rXl008JDDWHCwmPZXWdnZwK+/isC1bZsY9ZB17iwC1/PPi1b7VLVduSJ+TlauBP7+u+B8rVrAwIEiiIWEmNC/fFRukgRcu1Z0NOvcueKnT9jYFLR0l0NWixa6+W9XFcDwpWcMX0RkTtRqMZBSltG0wtsP6YJKVRDMyhrYdHVOJ01Lrl7Vnqp47Fjx6yGaNNHurBgQULYOdQ8fAtHR4hfpzZtFAJMFBYnANXCgWJ9I9ChJEr9wr1wpfoYKN1554gkxLfHFF9np0tTl5hZt6R4fX3JL97p1i45mNWlSxeeeVw7Dl54xfBFRVZWToz2aVjio3bolcseDB2KQqPDXR88Zw/99rK11H/DsrR6iVvIp1L5wCNXPHkK104dgc/l80Rd3cBDrxQpPV3R3F/ep1cDBg+KX5XXrtNeeNWwoAtfgwaJjGFFZ5eUBu3aJILZxo5h+JmvfXoSwAQM4cmrsbtwoGrJKa+nepElBwJJvbm6GrtrsMXzpGcMXEVHFSZIY0CkpmOnjnPy18Ew9Q6mFmwjGEbTDIbTDIQTjCKqj6Abd120a4Hy1VmhyLw6uD5I057Md6+LftoOQ3HUI8loFo3oNFVxcABcXMcOxWjUuv6ByysoSI6krVwI7dojAD4i/SPTuLYLYM8+I9WKkjPz8oi3d4+NLbunu4qI9khUYKKYRmllLd2PF8KVnDF9ERKYpP79oQNNX0Hv0nEwFNZogQRPG2uEQmuM0LKHWXJMJJ/yM/ojCEOxGN+Sj5CmKKhXg7FwQxgoHs+KOi7vP3p7Lf6qs69dFy/qVK8Uv9zJ57aKdnQhhj34t6fhx9z96rbU1f/gyMkTTi8Ihq7SW7o0aFR3NatCAn6OCGL70jOGLiIjKQ5LEsoySQlrurbuwPXUMDmfjcMvJG6cb9MLt+/bIyBBd8DMyCm7y9+npuhvJs7J6fGh7XJhjEz0zcPo0sGqV2My5uGYy+qKrIFfZUGhlpd8AI0lFW7rHx5fc0t3BQawPLRy0AgLEBo5kVBi+9Izhi4iIlCZJIrg9GsqK+760+9Tq0l+nrOzsyjbKVlKAc3HRURMUqrz8fODAAREUcnLED1rhr2U5Lu1+Jeb/loWFhe5DnySJNVnx8aW3dK9fv+hoVqNG/JfCRDB86RnDFxERmQNJEst/yhvgCh+Xdz+50lSrVv4pk4+uf+PMKxOgVheEscoGufJe++i5/HzDvncbG9Esp3DQatGCjU5MXFmzQRl63BIREZG5UqnEDCYnJ/GH94rIzwcyMysX4OQ1cVlZ4nbtWsVqsbAQ69+cnMQMMkvLgpuFhfb3xZ0ryzXGdE6fz6/XEGth8V97UHs9vkgZ5efrN/Tl5YmN1+Wg5efHlu5VGMMXERERVYqlJVCjhrhVVG5uyYGtrAHu4UMxoJKeLm5UOSqVGKSRZ9CVdnvcNRW9vyxb4VWapaVYW+XgYIAXo6qO4YuIiIgUZ2MD1KkjbhUhr3+Tw9jdu2JAo/BNrS79e2M6Z4jXLMtnKg/eZGZW7J9LZVla6i7gVeQ5bG05jZV0i+GLiIiITJ5KVTCLTd6rmkonSY8PaQ8fFnTklG/yrLqSbo+7/3HXFO7FkZ8PZGeLm1KKC2TlDXjW1gU3Gxvt70s6V9Zr2Y/DtDB8EREREVVBKpWY1meQqX3lIC/B0nfIK+3+wu3o5Foyiu6LbhTk6aEVCW7lvVbX5+Tzel9jaESM7F83IiIiIqrKlF6CJUnaI366CIEPHxbccnO1vy/vueLqlQOiKStLeNu4EWjYUOlKK4fhi4iIiIjoP/JIko2N6JxpTOSpopUJbxU9p8vnLG5vQfm+0uTl6edzNSSGLyIiIiIiE1B4qqgxdOmvKLW6YiGvotthGBOGLyIiIiIiMhgLC9GYxNZW6UoMz0LpAoiIiIiIiKoChi8iIiIiIiIDYPgiIiIiIiIyAIYvIiIiIiIiA2D4IiIiIiIiMgCGLyIiIiIiIgNg+CIiIiIiIjIAhi8iIiIiIiIDYPgiIiIiIiIyAIYvIiIiIiIiA2D4IiIiIiIiMgCGLyIiIiIiIgNg+CIiIiIiIjIAhi8iIiIiIiIDsFK6AFMlSRIAIDMzU+FKiIiIiIhISXImkDNCSRi+KujWrVsAAE9PT4UrISIiIiIiY3D37l24uLiUeD/DVwXVrFkTAJCUlFTqB0wly8zMhKenJ65cuQJnZ2elyzFZ/Bx1g59j5fEz1A1+jpXHz1A3+DnqBj/HyjOFz1CSJNy9exceHh6lXsfwVUEWFmK5nIuLi9H+EJgKZ2dnfoY6wM9RN/g5Vh4/Q93g51h5/Ax1g5+jbvBzrDxj/wzLMiDDhhtEREREREQGwPBFRERERERkAAxfFWRra4tZs2bB1tZW6VJMFj9D3eDnqBv8HCuPn6Fu8HOsPH6GusHPUTf4OVaeOX2GKulx/RCJiIiIiIio0jjyRUREREREZAAMX0RERERERAbA8EVERERERGQADF9EREREREQGwPBVTnv37kWfPn3g4eEBlUqFzZs3K12SyZkzZw7atm0LJycn1K1bFxEREUhISFC6LJOzdOlStGjRQrPhYGhoKP744w+lyzJpn3zyCVQqFSZPnqx0KSZl9uzZUKlUWjc/Pz+lyzI5165dw4svvohatWrB3t4eAQEBOHbsmNJlmRRvb+8iP4sqlQrjxo1TujSTkp+fjxkzZsDHxwf29vZo1KgRPvzwQ7BHW/ncvXsXkydPhpeXF+zt7dG+fXscPXpU6bKM2uN+z5YkCTNnzoS7uzvs7e0RFhaG8+fPK1NsBTF8lVN2djYCAwOxePFipUsxWXv27MG4ceNw6NAhREdH4+HDh3j66aeRnZ2tdGkmpX79+vjkk08QGxuLY8eO4cknn0Tfvn3x999/K12aSTp69Ci+/vprtGjRQulSTJK/vz+uX7+uue3fv1/pkkzKnTt30KFDB1hbW+OPP/7AP//8g7lz56JGjRpKl2ZSjh49qvVzGB0dDQB44YUXFK7MtHz66adYunQpFi1ahDNnzuDTTz/FZ599hoULFypdmkl56aWXEB0djZUrV+LUqVN4+umnERYWhmvXrildmtF63O/Zn332GRYsWIDIyEgcPnwYjo6OCA8Px4MHDwxcaSVIVGEApE2bNildhslLS0uTAEh79uxRuhSTV6NGDenbb79VugyTc/fuXcnX11eKjo6WunTpIk2aNEnpkkzKrFmzpMDAQKXLMGlvv/221LFjR6XLMDuTJk2SGjVqJKnVaqVLMSm9e/eWRo8erXWuX79+0tChQxWqyPTcu3dPsrS0lLZu3ap1vlWrVtJ7772nUFWm5dHfs9VqteTm5iZ9/vnnmnPp6emSra2ttHr1agUqrBiOfJHiMjIyAAA1a9ZUuBLTlZ+fjzVr1iA7OxuhoaFKl2Nyxo0bh969eyMsLEzpUkzW+fPn4eHhgYYNG2Lo0KFISkpSuiSTsmXLFrRp0wYvvPAC6tati5YtW+Kbb75RuiyTlpubi1WrVmH06NFQqVRKl2NS2rdvj507d+LcuXMAgBMnTmD//v3o2bOnwpWZjry8POTn58POzk7rvL29PWcGVFBiYiJSUlK0/l/t4uKCkJAQxMTEKFhZ+VgpXQBVbWq1GpMnT0aHDh3QvHlzpcsxOadOnUJoaCgePHiAatWqYdOmTWjWrJnSZZmUNWvWIC4ujvPwKyEkJAQrVqxAkyZNcP36dbz//vvo1KkTTp8+DScnJ6XLMwkXL17E0qVLMWXKFLz77rs4evQoJk6cCBsbG4wYMULp8kzS5s2bkZ6ejpEjRypdisl55513kJmZCT8/P1haWiI/Px//+9//MHToUKVLMxlOTk4IDQ3Fhx9+iKZNm8LV1RWrV69GTEwMnnjiCaXLM0kpKSkAAFdXV63zrq6umvtMAcMXKWrcuHE4ffo0/wpUQU2aNEF8fDwyMjKwYcMGjBgxAnv27GEAK6MrV65g0qRJiI6OLvLXSSq7wn8Nb9GiBUJCQuDl5YV169ZhzJgxClZmOtRqNdq0aYOPP/4YANCyZUucPn0akZGRDF8V9N1336Fnz57w8PBQuhSTs27dOvz000+IioqCv78/4uPjMXnyZHh4ePDnsRxWrlyJ0aNHo169erC0tESrVq0wePBgxMbGKl0aKYjTDkkx48ePx9atW7F7927Ur19f6XJMko2NDZ544gm0bt0ac+bMQWBgIObPn690WSYjNjYWaWlpaNWqFaysrGBlZYU9e/ZgwYIFsLKyQn5+vtIlmqTq1aujcePGuHDhgtKlmAx3d/cifzRp2rQpp29W0OXLl7Fjxw689NJLSpdikt566y288847GDRoEAICAjBs2DC88cYbmDNnjtKlmZRGjRphz549yMrKwpUrV3DkyBE8fPgQDRs2VLo0k+Tm5gYASE1N1Tqfmpqquc8UMHyRwUmShPHjx2PTpk3YtWsXfHx8lC7JbKjVauTk5Chdhsno3r07Tp06hfj4eM2tTZs2GDp0KOLj42Fpaal0iSYpKysL//77L9zd3ZUuxWR06NChyJYb586dg5eXl0IVmbbly5ejbt266N27t9KlmKR79+7BwkL7V0RLS0uo1WqFKjJtjo6OcHd3x507d7B9+3b07dtX6ZJMko+PD9zc3LBz507NuczMTBw+fNik1rtz2mE5ZWVlaf01NzExEfHx8ahZsyYaNGigYGWmY9y4cYiKisIvv/wCJycnzTxdFxcX2NvbK1yd6Zg2bRp69uyJBg0a4O7du4iKisJff/2F7du3K12ayXByciqy1tDR0RG1atXiGsRymDp1Kvr06QMvLy8kJydj1qxZsLS0xODBg5UuzWS88cYbaN++PT7++GMMGDAAR44cwbJly7Bs2TKlSzM5arUay5cvx4gRI2BlxV9zKqJPnz743//+hwYNGsDf3x/Hjx/Hl19+idGjRytdmknZvn07JElCkyZNcOHCBbz11lvw8/PDqFGjlC7NaD3u9+zJkyfjo48+gq+vL3x8fDBjxgx4eHggIiJCuaLLS+l2i6Zm9+7dEoAitxEjRihdmsko7vMDIC1fvlzp0kzK6NGjJS8vL8nGxkaqU6eO1L17d+nPP/9UuiyTx1bz5Tdw4EDJ3d1dsrGxkerVqycNHDhQunDhgtJlmZxff/1Vat68uWRrayv5+flJy5YtU7okk7R9+3YJgJSQkKB0KSYrMzNTmjRpktSgQQPJzs5OatiwofTee+9JOTk5SpdmUtauXSs1bNhQsrGxkdzc3KRx48ZJ6enpSpdl1B73e7ZarZZmzJghubq6Sra2tlL37t1N7t91lSRxu3IiIiIiIiJ945ovIiIiIiIiA2D4IiIiIiIiMgCGLyIiIiIiIgNg+CIiIiIiIjIAhi8iIiIiIiIDYPgiIiIiIiIyAIYvIiIiIiIiA2D4IiIiIiIiMgCGLyIiIgWoVCps3rxZ6TKIiMiAGL6IiKjKGTlyJFQqVZFbjx49lC6NiIjMmJXSBRARESmhR48eWL58udY5W1tbhaohIqKqgCNfRERUJdna2sLNzU3rVqNGDQBiSuDSpUvRs2dP2Nvbo2HDhtiwYYPW40+dOoUnn3wS9vb2qFWrFl5++WVkZWVpXfP999/D398ftra2cHd3x/jx47Xuv3nzJp577jk4ODjA19cXW7Zs0e+bJiIiRTF8ERERFWPGjBno378/Tpw4gaFDh2LQoEE4c+YMACA7Oxvh4eGoUaMGjh49ivXr12PHjh1a4Wrp0qUYN24cXn75ZZw6dQpbtmzBE088ofUa77//PgYMGICTJ0+iV69eGDp0KG7fvm3Q90lERIajkiRJUroIIiIiQxo5ciRWrVoFOzs7rfPvvvsu3n33XahUKrz66qtYunSp5r527dqhVatWWLJkCb755hu8/fbbuHLlChwdHQEAv//+O/r06YPk5GS4urqiXr16GDVqFD766KNia1CpVJg+fTo+/PBDACLQVatWDX/88QfXnhERmSmu+SIioiqpW7duWuEKAGrWrKk5Dg0N1bovNDQU8fHxAIAzZ84gMDBQE7wAoEOHDlCr1UhISIBKpUJycjK6d+9eag0tWrTQHDs6OsLZ2RlpaWkVfUtERGTkGL6IiKhKcnR0LDINUFfs7e3LdJ21tbXW9yqVCmq1Wh8lERGREeCaLyIiomIcOnSoyPdNmzYFADRt2hQnTpxAdna25v4DBw7AwsICTZo0gZOTE7y9vbFz506D1kxERMaNI19ERFQl5eTkICUlReuclZUVateuDQBYv3492rRpg44dO+Knn37CkSNH8N133wEAhg4dilmzZmHEiBGYPXs2bty4gQkTJmDYsGFwdXUFAMyePRuvvvoq6tati549e+Lu3bs4cOAAJkyYYNg3SkRERoPhi4iIqqRt27bB3d1d61yTJk1w9uxZAKIT4Zo1a/D666/D3d0dq1evRrNmzQAADg4O2L59OyZNmoS2bdvCwcEB/fv3x5dffql5rhEjRuDBgwf46quvMHXqVNSuXRvPP/+84d4gEREZHXY7JCIieoRKpcKmTZsQERGhdClERGRGuOaLiIiIiIjIABi+iIiIiIiIDIBrvoiIiB7BGflERKQPHPkiIiIiIiIyAIYvIiIiIiIiA2D4IiIiIiIiMgCGLyIiIiIiIgNg+CIiIiIiIjIAhi8iIiIiIiIDYPgiIiIiIiIyAIYvIiIiIiIiA/h/KZRYA25wiKAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from collections import defaultdict\n",
    "\n",
    "log_dir = './loss-log/'\n",
    "log_files = ['unet',\n",
    "'mini',\n",
    "'dice',\n",
    "'focal',\n",
    "'sgd',\n",
    "'rmsprop',\n",
    "'l1',\n",
    "'l2',\n",
    "'l1+l2',\n",
    "]\n",
    "log_ext = '.log'\n",
    "\n",
    "for log_file in log_files:\n",
    "    with open(log_dir + log_file + log_ext, 'r') as log_data:\n",
    "        train_loss_per_epoch = defaultdict(list)\n",
    "        val_loss_per_epoch = defaultdict(list)\n",
    "\n",
    "        for line in log_data:\n",
    "            if \"TRAIN\" in line:\n",
    "                epoch = int(line.split('|')[0].split()[2])\n",
    "                loss = float(line.split('|')[2].split()[1])\n",
    "                train_loss_per_epoch[epoch].append(loss)\n",
    "\n",
    "            elif \"VALID\" in line:\n",
    "                epoch = int(line.split('|')[0].split()[2])\n",
    "                loss = float(line.split('|')[2].split()[1])\n",
    "                val_loss_per_epoch[epoch].append(loss)\n",
    "\n",
    "        train_loss_avg = [np.mean(train_loss_per_epoch[e]) for e in sorted(train_loss_per_epoch)]\n",
    "        val_loss_avg = [np.mean(val_loss_per_epoch[e]) for e in sorted(val_loss_per_epoch)]\n",
    "        # train_loss_avg = [np.mean(train_loss_per_epoch[e]) for e in train_loss_per_epoch]\n",
    "        # val_loss_avg = [np.mean(val_loss_per_epoch[e]) for e in val_loss_per_epoch]\n",
    "        epochs = range(1, len(train_loss_avg) + 1)\n",
    "\n",
    "        max_y = np.maximum(train_loss_avg, val_loss_avg)\n",
    "\n",
    "        plt.figure(figsize=(10, 5))\n",
    "        plt.plot(epochs, train_loss_avg, label='Train Loss', color='blue')\n",
    "        plt.plot(epochs, val_loss_avg, label='Validation Loss', color='red')\n",
    "\n",
    "        plt.xlabel('Epoch')\n",
    "        plt.ylabel('Loss')\n",
    "        plt.title(log_file + ' - Loss per Epoch')\n",
    "        plt.legend()\n",
    "        plt.xticks(epochs)\n",
    "        plt.xlim([1.0, 10.5])\n",
    "        plt.show()\n"
   ]
  }
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