Enzymes_recognition-GCNs.ipynb

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    "---\n",
    "### **<p style=\"text-align: center; text-decoration: underline;\">Introduction to GCNs</p>**\n",
    "# **<p style=\"text-align: center;\">Graph Convolutional Networks (GCNs) for ENZYMES Recognition</p>**\n",
    "---\n",
    "\n",
    "> Tutor: *Omar Ikne*.\n",
    "\n",
    "> Master 1 & 2, IMT Nord Europe.\n",
    "\n",
    "---\n",
    "\n",
    "### ■ __Overview__\n",
    "In this notebook, we will investigate the utilization of Graph Convolutional Networks (GCNs) for enzymes recognition. Specifically, we aim to classify different enzymes using graph representations. The fundamental concept involves using the capabilities of GCNs to extract insightful features from different enzymes and subsequently classify each gesture individually.\n",
    "\n",
    "The main task of this lab is to build a graph-based deep learning model to recognize the enzymes (figure bellow).\n",
    "#### **Main Task: Enzymes Recognition**\n",
    "![Alt text](https://external-content.duckduckgo.com/iu/?u=https%3A%2F%2Fvignette.wikia.nocookie.net%2Fanalytical%2Fimages%2F3%2F34%2FEnzyme.jpg%2Frevision%2Flatest%3Fcb%3D20100726084438&f=1&nofb=1&ipt=465540306a176092f491ff8a13c31f9611fcd39d7ba0e5b61e8e167b4486a4f8&ipo=images \"Enzymes\")\n",
    "\n",
    "### ■ **<a name=\"content\">Contents</a>**\n",
    "\n",
    "- [1. Preliminaries](#section1)\n",
    "\n",
    "- [1. Enzymes Dataset](#section2)\n",
    "\n",
    "- [2. GCN Model](#section3)\n",
    "\n",
    "- [3. Model Training](#section4)\n",
    "\n",
    "- [4. Model Evaluation](#section5)\n",
    "\n",
    "- [5. Demo](#section6)"
   ]
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   "source": [
    "## download the dataset\n",
    "#!pip uninstall gdown -y && pip install gdown"
   ]
  },
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   "source": [
    "\n",
    "### ■ **Libraries**"
   ]
  },
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     "text": [
      "Requirement already satisfied: torch in /home/omar/anaconda3/envs/Torch/lib/python3.10/site-packages (1.13.1)\n",
      "Requirement already satisfied: typing_extensions in /home/omar/.local/lib/python3.10/site-packages (from torch) (4.5.0)\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
      "Requirement already satisfied: torch_geometric in /home/omar/anaconda3/envs/Torch/lib/python3.10/site-packages (2.2.0)\n",
      "Requirement already satisfied: requests in /home/omar/.local/lib/python3.10/site-packages (from torch_geometric) (2.28.2)\n",
      "Requirement already satisfied: jinja2 in /home/omar/.local/lib/python3.10/site-packages (from torch_geometric) (3.1.2)\n",
      "Requirement already satisfied: tqdm in /home/omar/anaconda3/envs/Torch/lib/python3.10/site-packages (from torch_geometric) (4.64.1)\n",
      "Requirement already satisfied: pyparsing in /home/omar/.local/lib/python3.10/site-packages (from torch_geometric) (3.0.9)\n",
      "Requirement already satisfied: numpy in /home/omar/.local/lib/python3.10/site-packages (from torch_geometric) (1.23.3)\n",
      "Requirement already satisfied: scikit-learn in /home/omar/anaconda3/envs/Torch/lib/python3.10/site-packages (from torch_geometric) (1.1.3)\n",
      "Requirement already satisfied: psutil>=5.8.0 in /home/omar/.local/lib/python3.10/site-packages (from torch_geometric) (5.9.4)\n",
      "Requirement already satisfied: scipy in /home/omar/anaconda3/envs/Torch/lib/python3.10/site-packages (from torch_geometric) (1.10.0)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /home/omar/anaconda3/envs/Torch/lib/python3.10/site-packages (from jinja2->torch_geometric) (2.1.1)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /home/omar/anaconda3/envs/Torch/lib/python3.10/site-packages (from requests->torch_geometric) (2022.6.15)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /home/omar/anaconda3/envs/Torch/lib/python3.10/site-packages (from requests->torch_geometric) (3.4)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /home/omar/.local/lib/python3.10/site-packages (from requests->torch_geometric) (3.1.0)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /home/omar/anaconda3/envs/Torch/lib/python3.10/site-packages (from requests->torch_geometric) (1.26.13)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /home/omar/anaconda3/envs/Torch/lib/python3.10/site-packages (from scikit-learn->torch_geometric) (3.1.0)\n",
      "Requirement already satisfied: joblib>=1.0.0 in /home/omar/anaconda3/envs/Torch/lib/python3.10/site-packages (from scikit-learn->torch_geometric) (1.2.0)\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
      "Requirement already satisfied: networkx in /home/omar/anaconda3/envs/Torch/lib/python3.10/site-packages (2.8.8)\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
     ]
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   "source": [
    "!pip install torch\n",
    "!pip install torch_geometric\n",
    "!pip install networkx"
   ]
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   "source": [
    "## torch dependencies\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch_geometric.nn import GCNConv\n",
    "from torch_geometric.datasets import TUDataset\n",
    "from torch_geometric.loader import DataLoader\n",
    "import torch_geometric.transforms as transforms\n",
    "import torch.optim as optim\n",
    "\n",
    "## basic dependencies from math and visualization\n",
    "import networkx as nx\n",
    "import numpy as np\n",
    "import random\n",
    "import cv2\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "from tqdm import tqdm\n",
    "from copy import deepcopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0eaed71c-3986-4892-8fa3-848f2b9a53ea",
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   "source": [
    "def seed_everything(seed=51):\n",
    "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    random.seed(seed)\n",
    "    torch.cuda.manual_seed(seed)\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "    torch.backends.cudnn.benchmark = False\n",
    "    \n",
    "seed_everything()"
   ]
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    "### ■ **<a name=\"pre\">1. Preliminaries</a>** [(&#8593;)](#content)\n",
    "The ENZYMES dataset is commonly used as a benchmark dataset in the field of graph classification, particularly for evaluating the performance of graph-based machine learning models, including Graph Convolutional Networks (GCNs). \n",
    "ENZYMES is a dataset of protein tertiary structures obtained from (Borgwardt et al., 2005) consisting of 600 enzymes from the BRENDA enzyme database (Schomburg et al., 2004). In this case the task is to correctly assign each enzyme to one of the 6 EC top-level classes.\n",
    "Below is a general description of the ENZYMES dataset:\n",
    "\n",
    "#### `ENZYMES Dataset:`\n",
    "\n",
    "1. **Source:**\n",
    "   - The ENZYMES dataset is often used in the context of graph classification tasks.\n",
    "   - It is part of the TU Dortmund University Graph Database ([TUDataset](https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.datasets.TUDataset.html#torch_geometric.datasets.TUDataset)).\n",
    "\n",
    "2. **Graphs:**\n",
    "   - Each graph in the dataset represents the 3D structure of a protein.\n",
    "   - Nodes in the graph represent amino acids, and edges represent interactions between amino acids.\n",
    "\n",
    "3. **Labels:**\n",
    "   - The dataset is typically used for graph classification, where each graph is associated with a class label.\n",
    "   - Class labels represent different enzyme classes or families.\n",
    "\n",
    "4. **Tasks:**\n",
    "   - The primary task associated with the ENZYMES dataset is graph classification.\n",
    "   - The goal is to train a model to predict the enzyme class or family based on the graph representation of the protein's 3D structure.\n",
    "\n",
    "5. **Graph Properties:**\n",
    "   - The graphs are heterogeneous and vary in terms of the number of nodes and edges.\n",
    "   - Each graph captures the structural information of a different protein, making it suitable for evaluating models' ability to generalize to diverse graph structures.\n",
    "\n",
    "6. **Dataset Size:**\n",
    "   - The ENZYMES dataset typically consists of a moderate number of graphs, with each graph corresponding to a unique protein structure.\n",
    "\n",
    "7. **Use in Research:**\n",
    "   - Researchers often use the ENZYMES dataset to assess the performance of graph-based machine learning models, especially graph classification algorithms like GCNs.\n",
    "\n",
    "8. **Availability:**\n",
    "   - The ENZYMES dataset is publicly available and can be accessed through graph database [TUDataset](https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.datasets.TUDataset.html#torch_geometric.datasets.TUDataset).\n",
    "\n",
    "\n",
    "![Alt text](https://external-content.duckduckgo.com/iu/?u=https%3A%2F%2Fimage3.slideserve.com%2F6898777%2Fenzyme-and-protein-structure-l.jpg&f=1&nofb=1&ipt=81702931cf27e65b54827ac537d59f70610c91ecc48d8b8172cff0664ee9e345&ipo=images \"Enzyme Structure\")"
   ]
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   "source": [
    "#### **Dataset Overview**"
   ]
  },
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ENZYMES(600)\n",
      "------------\n",
      "Number of graphs: 600\n",
      "Number of features: 3\n",
      "Number of classes: 6\n"
     ]
    }
   ],
   "source": [
    "## load dataset\n",
    "dataset = TUDataset('./', 'ENZYMES')\n",
    "# Print information\n",
    "print(dataset)\n",
    "print('------------')\n",
    "print(f'Number of graphs: {len(dataset)}')\n",
    "print(f'Number of features: {dataset.num_features}')\n",
    "print(f'Number of classes: {dataset.num_classes}')"
   ]
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      "/home/omar/.local/lib/python3.10/site-packages/matplotlib/axes/_axes.py:4347: FutureWarning: The input object of type 'Tensor' is an array-like implementing one of the corresponding protocols (`__array__`, `__array_interface__` or `__array_struct__`); but not a sequence (or 0-D). In the future, this object will be coerced as if it was first converted using `np.array(obj)`. To retain the old behaviour, you have to either modify the type 'Tensor', or assign to an empty array created with `np.empty(correct_shape, dtype=object)`.\n",
      "  c = np.asanyarray(c, dtype=float)\n"
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",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from torch_geometric.utils import to_networkx\n",
    "\n",
    "data = dataset[15].detach()\n",
    "data.y = [data.y] * len(data.x)\n",
    "G = to_networkx(data, to_undirected=True)\n",
    "plt.figure(figsize=(12,12))\n",
    "plt.axis('off')\n",
    "nx.draw_networkx(G,\n",
    "                pos=nx.spring_layout(G, seed=0),\n",
    "                with_labels=True,\n",
    "                node_size=800,\n",
    "                node_color=data.y,\n",
    "                cmap=\"hsv\",\n",
    "                vmin=-2,\n",
    "                vmax=3,\n",
    "                width=0.8,\n",
    "                edge_color=\"grey\",\n",
    "                font_size=14\n",
    "                )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5aa30cb1-3abf-4ec9-88bb-e0a8abc26b5c",
   "metadata": {
    "id": "5aa30cb1-3abf-4ec9-88bb-e0a8abc26b5c"
   },
   "source": [
    "### ■ **<a name=\"dataset\">2. Dataset</a>** [(&#8593;)](#content)\n",
    "In this second section, our focus shifts to creating the dataset essential for training the Graph Convolutional Network (GCN). The dataset is designed to provide different enzymes, the corresponding adjacency matrices (here encoded by an edge indices map), and the associated sign labels."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d761c9ca-d13e-4da7-9161-702f3f02f74c",
   "metadata": {
    "id": "d761c9ca-d13e-4da7-9161-702f3f02f74c"
   },
   "source": [
    "#### **2.2 Define the dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a6b7b2e5-6d78-489d-a348-b6b62f8e780a",
   "metadata": {
    "id": "a6b7b2e5-6d78-489d-a348-b6b62f8e780a",
    "tags": []
   },
   "outputs": [],
   "source": [
    "class EnzymesDataModule(nn.Module):\n",
    "    def __init__(\n",
    "        self,\n",
    "        data_dir=\"/data/\",\n",
    "        batch_size=64,\n",
    "        num_workers=0,\n",
    "        splits=[0.7, 0.15, 0.15],\n",
    "        seed=51,\n",
    "    ):\n",
    "        super(EnzymesDataModule, self).__init__()\n",
    "        self.data_dir = data_dir\n",
    "        self.batch_size = batch_size\n",
    "        self.num_workers = num_workers\n",
    "        self.splits = splits\n",
    "        self.seed = seed\n",
    "        self.transform = transforms.Compose(\n",
    "            [\n",
    "                transforms.NormalizeFeatures(),\n",
    "            ]\n",
    "        )\n",
    "\n",
    "        # Number of graphs, classes and features\n",
    "        self.num_graphs = 600\n",
    "        self.num_classes = 6\n",
    "        self.num_features = 21\n",
    "\n",
    "    def prepare_data(self):\n",
    "        # Download data\n",
    "        TUDataset(\n",
    "            root=self.data_dir,\n",
    "            name=\"ENZYMES\",\n",
    "            use_node_attr=True,\n",
    "            use_edge_attr=True,\n",
    "            transform=None,\n",
    "            pre_transform=self.transform,\n",
    "        )\n",
    "\n",
    "    def setup(self, stage=None):\n",
    "        initial_seed = torch.initial_seed()\n",
    "        torch.manual_seed(self.seed)\n",
    "        dataset = TUDataset(\n",
    "            root=self.data_dir,\n",
    "            name=\"ENZYMES\",\n",
    "            use_node_attr=True,\n",
    "            use_edge_attr=True,\n",
    "            pre_transform=self.transform,\n",
    "        ).shuffle()\n",
    "\n",
    "        split_idx = np.cumsum(\n",
    "            [int(len(dataset) * prop) for prop in self.splits])\n",
    "        self.data_train = dataset[: split_idx[0]]\n",
    "        self.data_val = dataset[split_idx[0]: split_idx[1]]\n",
    "        self.data_test = dataset[split_idx[1]:]\n",
    "        torch.manual_seed(initial_seed)\n",
    "\n",
    "    def train_dataloader(self):\n",
    "        return DataLoader(\n",
    "            self.data_train,\n",
    "            batch_size=self.batch_size,\n",
    "            num_workers=self.num_workers,\n",
    "            shuffle=True,\n",
    "            pin_memory=True,\n",
    "        )\n",
    "\n",
    "    def val_dataloader(self):\n",
    "        return DataLoader(\n",
    "            self.data_val,\n",
    "            batch_size=self.batch_size,\n",
    "            num_workers=self.num_workers,\n",
    "            shuffle=False,\n",
    "            pin_memory=True,\n",
    "        )\n",
    "    \n",
    "    def eval_dataloader(self):\n",
    "        return DataLoader(\n",
    "            self.data_val,\n",
    "            batch_size=1,\n",
    "            num_workers=self.num_workers,\n",
    "            shuffle=True,\n",
    "            pin_memory=True,\n",
    "        )\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c2bbd44d-90e2-46e2-8efc-6d1e13dadabb",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c2bbd44d-90e2-46e2-8efc-6d1e13dadabb",
    "outputId": "abdf8bcf-c7db-48c7-c243-db12e775d944",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of training batches   : 15\n",
      "The number of validation batches : 4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/omar/anaconda3/envs/Torch/lib/python3.10/site-packages/torch_geometric/data/dataset.py:190: UserWarning: The `pre_transform` argument differs from the one used in the pre-processed version of this dataset. If you want to make use of another pre-processing technique, make sure to delete './ENZYMES/processed' first\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "batch_size = 32\n",
    "dm = EnzymesDataModule(data_dir=\"./\",\n",
    "                       batch_size=batch_size,\n",
    "                       splits=[0.8, 0.2])\n",
    "\n",
    "dm.prepare_data()\n",
    "dm.setup()\n",
    "\n",
    "train_loader = dm.train_dataloader()\n",
    "val_loader = dm.val_dataloader()\n",
    "\n",
    "## stats\n",
    "print('The number of training batches   :', len(train_loader))\n",
    "print('The number of validation batches :', len(val_loader))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f01269e0-1cec-4a48-9fdb-cfd3596b2082",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f01269e0-1cec-4a48-9fdb-cfd3596b2082",
    "outputId": "b1406d25-faed-42e1-d31d-6621d1c7e733",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataBatch(edge_index=[2, 3504], x=[918, 21], y=[32], batch=[918], ptr=[33])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## check that the loaders work well by extracting an element from the laoder.\n",
    "## out is a dictionary that contains a batch data from the loader.\n",
    "## out contains: Hand_pose -> shape(batch_size, 21, 3), Adjacency_matrix -> shape (batch_size, 21, 21), Label -> shape (batch_size)\n",
    "out = next(iter(train_loader))\n",
    "out"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4858951-576e-4509-aee5-19adfde5cba4",
   "metadata": {
    "id": "a4858951-576e-4509-aee5-19adfde5cba4"
   },
   "source": [
    "Here we do some data analysis by ploting the number of samples per class. This allows us to see whether the data is balanced or not !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d22368a1-2a67-47a6-a9dc-ba13f91707a8",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 410
    },
    "id": "d22368a1-2a67-47a6-a9dc-ba13f91707a8",
    "outputId": "562435bd-0265-4041-8b1c-95a7fc4a1d33",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## plot the number of samples per class\n",
    "values, counts = np.unique(dataset.data.y.numpy(), return_counts=True)\n",
    "\n",
    "plt.figure(figsize=(10, 4))\n",
    "plt.bar(values, counts)\n",
    "plt.title('Number of samples per class')\n",
    "plt.xlabel('Alphabet')\n",
    "plt.ylabel('Number of samples')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be7431a5-0574-4bf2-b8d8-6cd6b7fccc31",
   "metadata": {},
   "source": [
    "#### **Adjacency Matrix**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d9646dbc-5cb1-494f-b102-d6792f111fef",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f2543057d30>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from torch_geometric.utils import to_dense_adj\n",
    "\n",
    "adjacency_matrix = to_dense_adj(data.edge_index)[0]\n",
    "plt.matshow(adjacency_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ea44901-c4e0-4f33-92e5-bb86024ca563",
   "metadata": {},
   "source": [
    "### **Distribution of the number of nodes in enzymes in the dataset**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8f73b865-40f5-4a95-b5c4-1cff17aa6471",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribution of the number of nodes in enzymes in the dataset')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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G33//vU15/fr1bWbXlCtXTn5+fjp48OA99xMSEqL27dtby3LmzKm+ffvq8uXLWrdunQOOJrkXXnjB5v1jjz2ms2fPKj4+XpL0zTffKDExUW3atNHff/9tfYWEhKhEiRJau3atQ/aTHt27d7f+7O7urkqVKskYo27dulnLAwICVLJkyRTP/73G1M6dO7V//349/fTTOnv2rPXYExISVK9ePa1fvz7Z+L/zOFPz3XffqUqVKja3hvn4+KhHjx6Ki4vTnj170nYSUtC2bVvlyZPH+j5pdlvSOThx4oR27typzp07KzAw0NquXLlyatCggfX4b926peXLl6tFixYqXLiwtV3p0qUVHR1ts8+0jpOkmT3Lly9P8fbCzDje1MydO1ePPfaY8uTJYxNz/fr1devWLa1fvz5D+3nttde0ZMkSzZo1y7pGl5ubmzp06KDFixfbXGPnzJmj6tWrKzw83KaPevXq2dzqVrVqVUlSq1atbMZuUnlSLPaM3YCAAG3evFnHjx+/6/lKq4iICOu5kf6d1ZPad9BeKV1Hbu937ty58vf3V4MGDWw+06ioKPn4+Nz1epX0vejUqZPNDLQGDRqkuMba7TNOz58/r4sXL+qxxx675+1yKW1/48YNnT17VsWLF1dAQIBNHwEBAdq9e7f279+fYj/nzp3TmjVr1KZNG126dMl6zGfPnlV0dLT279+vY8eOpSkmAHA2kj8AkMUuX75s84fFndq2basaNWqoe/fuyp8/v9q1a6evv/7arkTQQw89ZNfiziVKlLB5b7FYVLx48Ux/asnhw4cVGhqa7HwkTZU/fPiwTfntfyQnyZMnzz3Xmzh8+LBKlCghNzfbX3up7cdR7ow36Y/bpHj3798vY4xKlCihfPny2bz27t2r06dPO2Q/jojd399fnp6eyps3b7LylPZzrzGV9MdWp06dkh37Rx99pGvXriVbp+bOP95Tc/jwYZv1XZI44vO+17lO6ju1/SclCc6cOaOrV68mO08pbZvWcRIeHq6BAwfqo48+Ut68eRUdHa1p06bdc72fjBxvavbv369ly5Yli7d+/fqSlGxs27OfZcuWacyYMRo+fLhatWplU/fss8/q6tWr1rVk9u3bp23btumZZ56557ElJSUKFSqUYvnt31spbWN30qRJ+v3331WoUCFVqVJFo0ePzlCiJr3XwHvx9PRMdovrnf3u379fFy9eVHBwcLLjvnz58l2vV0nfi7SMd0lasmSJHn30UXl6eiowMNB661pax/LVq1c1cuRI63pTefPmVb58+XThwgWbPsaOHasLFy7o4YcfVmRkpIYMGWLzRM7Y2FgZYzRixIhkx5x0G2Far9MA4Gys+QMAWeivv/7SxYsXVbx48VTbeHl5af369Vq7dq2WLl2qZcuW6auvvlLdunW1YsWKND0Bxp51etIqtcVwb926lWVPpUltP+Yu6784073iTUxMlMVi0ffff59iWx8fH4fs526fnT19OvL8JyUz33jjDVWoUCHFNncef2aMa3s5YwzaM07eeustde7cWYsWLdKKFSvUt29fTZgwQZs2bUpxbZV7Se/xJiYmqkGDBho6dGiK9Q8//HC69nPo0CF16NBBDRo00Ouvv56sfUREhKKiovTZZ5/p2Wef1WeffaZcuXKpTZs2ydqmts+0fG+ltI3dNm3a6LHHHtOCBQu0YsUKvfHGG5o4caK++eYbNWrUKMVt7yazxl9aruGJiYkKDg7WnDlzUqxPbX00e/34449q1qyZatWqpffee08FChRQzpw5NXPmTH3++edp6qNPnz6aOXOm+vfvr2rVqsnf318Wi0Xt2rWz+YeUWrVq6cCBA9bvy0cffaS3335bM2bMUPfu3a1tBw8enGxGXpK7/T4HgOyE5A8AZKGkBUtT+5/IJG5ubqpXr57q1aunyZMna/z48XrllVe0du1a1a9f/65PJUqPO6e8G2MUGxtrsyBonjx5dOHChWTbHj58WEWLFrW+tye2IkWKaNWqVbp06ZLN7J8//vjDWu8IRYoU0a5du5SYmGgz+yej+8no51CsWDEZYxQeHp7sj2FHSppFcefnl1kznqR7j6mk2/f8/Pyss0EcpUiRItq3b1+yckePq9T2LSnV/efNm1fe3t7y9PSUl5dXireb3LmtveMkMjJSkZGRevXVV/Xzzz+rRo0amjFjRorJksxSrFgxXb582aGf7dWrV9WyZUsFBAToiy++SDaTL8mzzz6rgQMH6sSJE/r888/VuHFjm1vKMsresVugQAH17NlTPXv21OnTp/XII49o3Lhx6Ur+pJcjfmcUK1ZMq1atUo0aNexOxCZ9L9Iy3ufPny9PT08tX75cHh4e1vKZM2cm2za145o3b546depk8zTCf/75J8XfYYGBgerSpYu6dOmiy5cvq1atWho9erS6d+9u/d2WM2fOe37Wjv69DACOxm1fAJBF1qxZo9dee03h4eHq0KFDqu3OnTuXrCzpX5evXbsmSfL29paU/I/59Prkk09s1siYN2+eTpw4YfPHSbFixbRp0yZdv37dWrZkyZJkj5q2J7YnnnhCt27d0n//+1+b8rffflsWi8Vhfxw98cQTOnnypL766itr2c2bN/Xuu+/Kx8dHtWvXTle/3t7eGfoMWrZsKXd3d40ZMybZv9wbY3T27Nl09307Pz8/5c2bN9k6K++9955D+k/JvcZUVFSUihUrpjfffFOXL19Otv2ZM2fSve8nnnhCv/zyizZu3GgtS0hI0AcffKCwsLAU1xhxlAIFCqhChQqaPXu2zdj4/ffftWLFCj3xxBOS/p1pER0drYULF+rIkSPWdnv37tXy5ctt+kzrOImPj9fNmzdt6iMjI+Xm5ma9dmSVNm3aaOPGjcmORfr32nBnnGnxwgsv6M8//9SCBQvumsxp3769LBaL+vXrp4MHD6pjx4527+tu0jp2b926lew2peDgYIWGhmb555H0dKuMXK/atGmjW7du6bXXXktWd/Pmzbv2ffv34vZzsnLlymRrcLm7u8tisdjMTIyLi9PChQuT9ZvaNdjd3T3Zd+Xdd99NNtvxzmusj4+Pihcvbv18goODVadOHb3//vs6ceJEsv3cfp1y9O9lAHA0Zv4AQCb4/vvv9ccff+jmzZs6deqU1qxZo5UrV6pIkSJavHixPD09U9127NixWr9+vRo3bqwiRYro9OnTeu+991SwYEHrArbFihVTQECAZsyYIV9fX3l7e6tq1appXhPlToGBgapZs6a6dOmiU6dOacqUKSpevLjN4+i7d++uefPmqWHDhmrTpo0OHDigzz77LNnjze2JrWnTpvrPf/6jV155RXFxcSpfvrxWrFihRYsWqX///sn6Tq8ePXro/fffV+fOnbVt2zaFhYVp3rx5+umnnzRlypS7rsF0N1FRUVq1apUmT56s0NBQhYeHWxeHTYtixYrp9ddf1/DhwxUXF6cWLVrI19dXhw4d0oIFC9SjRw8NHjw4XbHdqXv37oqJiVH37t1VqVIlrV+/Xn/++adD+k7JvcaUm5ubPvroIzVq1EhlypRRly5d9NBDD+nYsWNau3at/Pz89O2336Zr38OGDdMXX3yhRo0aqW/fvgoMDNTs2bN16NAhzZ8/P9UZI47yxhtvqFGjRqpWrZq6detmfdS7v7+/Ro8ebW03ZswYLVu2TI899ph69uxpTUiWKVPGZt2RtI6TNWvWqHfv3nrqqaf08MMP6+bNm/r000/l7u6ebG2czDZkyBAtXrxYTZo0sT6KPCEhQb/99pvmzZunuLi4ZOtH3c3SpUv1ySefqFWrVtq1a5fN+fHx8VGLFi2s7/Ply6eGDRtq7ty5CggIsD4a3FHSOnYvXbqkggULqnXr1ipfvrx8fHy0atUqbdmyxWZGSlbw8vJSRESEvvrqKz388MMKDAxU2bJlVbZs2TT3Ubt2bT3//POaMGGCdu7cqccff1w5c+bU/v37NXfuXL3zzjtq3bp1qttPmDBBjRs3Vs2aNdW1a1edO3fOOt5vT6I1btxYkydPVsOGDfX000/r9OnTmjZtmooXL27zuUupX4ObNGmiTz/9VP7+/oqIiNDGjRu1atUqBQUF2WwfERGhOnXqKCoqSoGBgdq6davmzZun3r17W9tMmzZNNWvWVGRkpJ577jkVLVpUp06d0saNG/XXX3/p119/lfTvP9K4u7tr4sSJunjxojw8PFS3bl0FBwen+RwDQKbKwieLAYDLS3o8b9IrV65cJiQkxDRo0MC88847No8PTnLno95Xr15tmjdvbkJDQ02uXLlMaGioad++fbJHJi9atMhERESYHDly2DzGu3bt2qZMmTIpxpfao96/+OILM3z4cBMcHGy8vLxM48aNbR4/neStt94yDz30kPHw8DA1atQwW7duTdbn3WK783HSxvz7CO4BAwaY0NBQkzNnTlOiRAnzxhtv2DxC2ZjUH8md2iPo73Tq1CnTpUsXkzdvXpMrVy4TGRmZ4qPP7XnU+x9//GFq1aplvLy8jCRrHEmf6ZkzZ2zap/T4ZmOMmT9/vqlZs6bx9vY23t7eplSpUqZXr15m3759d92/Pfu5cuWK6datm/H39ze+vr6mTZs25vTp06k+6v3OPjt16mS8vb2TxXDneLN3TO3YscO0bNnSBAUFGQ8PD1OkSBHTpk0bs3r16nvGdDcHDhwwrVu3NgEBAcbT09NUqVLFLFmyJFm71MbVnZIeSf7GG2+k2Medj9BetWqVqVGjhvHy8jJ+fn6madOmZs+ePcm2XbdunYmKijK5cuUyRYsWNTNmzEh2TUhyr3Fy8OBB07VrV1OsWDHj6elpAgMDzX/+8x+zatWqex5fao96T+vxpuTSpUtm+PDhpnjx4iZXrlwmb968pnr16ubNN980169ft2s/d15bb3/deU0xxpivv/7aSDI9evRIMbaUPvfUYkka03PnzrUpv9fYvXbtmhkyZIgpX7688fX1Nd7e3qZ8+fLmvffeu+e5S+1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      "text/plain": [
       "<Figure size 1400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 4))\n",
    "plt.bar(*np.unique([d.num_nodes for d in dataset], return_counts=True))\n",
    "plt.xlabel('Number of nodes')\n",
    "plt.ylabel('Number of samples')\n",
    "plt.title('Distribution of the number of nodes in enzymes in the dataset')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "270fa8e4-d335-4e62-809b-6733784efcb3",
   "metadata": {
    "id": "270fa8e4-d335-4e62-809b-6733784efcb3"
   },
   "source": [
    "### ■ **<a name=\"model\">3. GCN Model</a>** [(&#8593;)](#content)\n",
    "No that the dataloaders are ready, we can build our GCN model to classify enzymes ! We start by defining the Graph Convolution Layer."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44c7e671-d32d-46a2-97e9-6f9f7c5914aa",
   "metadata": {
    "id": "44c7e671-d32d-46a2-97e9-6f9f7c5914aa"
   },
   "source": [
    "#### **GCN module**\n",
    "Now we define the GCN model by stacking a set of GCN layers to extract features, and an MLP layer to classify the extracted features into different classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5ee0acc9-cc91-435e-9318-b2a01494c51e",
   "metadata": {
    "id": "5ee0acc9-cc91-435e-9318-b2a01494c51e",
    "tags": []
   },
   "outputs": [],
   "source": [
    "from torch_geometric.nn import (GCNConv, global_add_pool, global_max_pool,\n",
    "                                global_mean_pool)\n",
    "\n",
    "\n",
    "class GCN(nn.Module):\n",
    "    def __init__(\n",
    "        self,\n",
    "        n_node_features: int,\n",
    "        n_classes: int,\n",
    "        conv_channels: int = 32,\n",
    "        fc_size: int = 32,\n",
    "        global_pooling = global_mean_pool,\n",
    "        activation = nn.LeakyReLU,\n",
    "        dropout: float = 0.15,\n",
    "    ):\n",
    "        super(GCN, self).__init__()\n",
    "        self.n_node_features = n_node_features\n",
    "        self.n_classes = n_classes\n",
    "        self.conv_channels = conv_channels\n",
    "        self.fc_size = fc_size\n",
    "        self.activation = activation()\n",
    "        self.dropout = dropout\n",
    "\n",
    "        self.gcn1 = GCNConv(\n",
    "            in_channels=self.n_node_features,\n",
    "            out_channels=self.conv_channels,\n",
    "            normalize=True,\n",
    "        )\n",
    "        self.gcn2 = GCNConv(\n",
    "            in_channels=self.conv_channels,\n",
    "            out_channels=self.conv_channels,\n",
    "            normalize=True,\n",
    "        )\n",
    "\n",
    "        \n",
    "        self.fc1 = nn.Linear(\n",
    "            in_features=self.conv_channels, out_features=self.fc_size)\n",
    "        self.fc2 = nn.Linear(\n",
    "            in_features=self.fc_size, out_features=self.n_classes)\n",
    "\n",
    "\n",
    "        self.global_pooling = global_pooling\n",
    "        self.dropout = nn.Dropout(p=self.dropout)\n",
    "\n",
    "    def forward(self, x: torch.Tensor, edge_index: torch.Tensor, batch: torch.Tensor):\n",
    "        # Check input dimension\n",
    "        if x.shape[1] != self.n_node_features:\n",
    "            raise ValueError(\n",
    "                f\"Number of node features of input x is correct. \"\n",
    "                f\"Expected {self.n_node_features} but got {x.shape[1]}.\"\n",
    "            )\n",
    "        if batch.shape[0] != x.shape[0]:\n",
    "            raise ValueError(\n",
    "                \"Number of nodes in x is incompatible with number of nodes \"\n",
    "                \"in batch.\"\n",
    "            )\n",
    "\n",
    "        # Perform message passing\n",
    "        x = self.activation(self.gcn1(x, edge_index))  # [nodes_batch, hidden]\n",
    "        x = self.gcn2(x, edge_index)  # [nodes_in_batch, hidden_size]\n",
    "        \n",
    "        # Apply global pooling layer\n",
    "        embed = self.global_pooling(x, batch)  # [batch_size, hidden_size]\n",
    "        \n",
    "        # Feed through linear layer for prediction\n",
    "        embed = self.dropout(self.activation(self.fc1(embed)))  # [batch_size, hidden_size]\n",
    "        embed = self.fc2(embed)  # [batch_size, n_classes]\n",
    "\n",
    "        return embed"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c04124f7-633b-48fb-b6ec-d3c9897ae6b9",
   "metadata": {
    "id": "c04124f7-633b-48fb-b6ec-d3c9897ae6b9"
   },
   "source": [
    "### ■ **<a name=\"train\">4. Model Training</a>** [(&#8593;)](#content)\n",
    "We are now ready to train our model. We define a `train_epoch` function to train our model for one epoch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d8189824-5865-488c-b6ce-f9969995c2dc",
   "metadata": {
    "id": "d8189824-5865-488c-b6ce-f9969995c2dc",
    "tags": []
   },
   "outputs": [],
   "source": [
    "def train_epoch(epoch, num_epochs, model, optimizer, dataloader, device, criterion, scheduler=None):\n",
    "    \"\"\"\n",
    "    to train the model for one epoch !\n",
    "    Params:\n",
    "    ======\n",
    "        - epoch: current epoch,\n",
    "        - num_epochs: total number of epochs,\n",
    "        - model: model to train,\n",
    "        - optimizer: optimizer to optimize the loss of the model,\n",
    "        - dataloader: the training data laoder.\n",
    "        - device: cpu or gpu,\n",
    "        - criterion: the loss function: crossentropy,\n",
    "        - sheduler[defualt=None]: if any to adjust the learning rate of the optimizer.\n",
    "    \"\"\"\n",
    "\n",
    "    ## make the model in train mode\n",
    "    model.train()\n",
    "\n",
    "    ## create a progression bar to display the progression of training\n",
    "    pbar = tqdm(dataloader, total=len(dataloader))\n",
    "    total_loss = 0\n",
    "\n",
    "    ## for each batch\n",
    "    for batch in pbar:\n",
    "        ## get data from the batch\n",
    "        batch = batch.to(device)\n",
    "\n",
    "        ## predict and calculate the loss on the given batch, and optimize the loss accordingly\n",
    "        optimizer.zero_grad()\n",
    "        pred = model(batch.x, batch.edge_index, batch.batch)\n",
    "        loss_train = criterion(pred, batch.y)\n",
    "        loss_train.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        total_loss += loss_train.item()\n",
    "        ## set the discription bar\n",
    "        pbar.set_description(f'[%.3g/%.3g] train loss. %.3f' % (epoch, num_epochs, total_loss/len(batch.y)))\n",
    "\n",
    "    if scheduler is not None:\n",
    "        scheduler.step()\n",
    "\n",
    "    return total_loss\n",
    "\n",
    "\n",
    "def validate_epoch(model, dataloader, criterion):\n",
    "    \"\"\"to evaluate the model !\n",
    "    Params:\n",
    "    ======\n",
    "        - model: model to evaluate,\n",
    "        - dataloader: the testing data laoder.\n",
    "        - criterion: the loss function to calculate the validation loss,\n",
    "    \"\"\"\n",
    "\n",
    "    acc = 0.0\n",
    "    n = 0\n",
    "    pred_labels, true_labels = [], []\n",
    "    total_loss = 0.0\n",
    "\n",
    "    ## turn the model into evaluation mode\n",
    "    model.eval()\n",
    "\n",
    "    ## no need to compute grads in validation stage, this makes the process run fast\n",
    "    with torch.no_grad():\n",
    "        pbar = tqdm(dataloader, total=len(dataloader))\n",
    "\n",
    "        for batch in pbar:\n",
    "            batch = batch.to(device)\n",
    "\n",
    "            pred = model(batch.x, batch.edge_index, batch.batch)\n",
    "            loss = criterion(pred, batch.y)\n",
    "\n",
    "            ## calculate accuracy\n",
    "            acc += (pred.argmax(dim=1) == batch.y.flatten()).sum().item()\n",
    "            n += len(batch.y.flatten())\n",
    "\n",
    "            total_loss += loss.item()\n",
    "\n",
    "            true_labels.extend(batch.y.tolist())\n",
    "            pred_labels.extend(pred.argmax(dim=1).tolist())\n",
    "\n",
    "            desc = '[VALID]> loss. %.2f > acc. %.2g%%' % (total_loss/len(batch.y), (acc / n)*100)\n",
    "            pbar.set_description(desc)\n",
    "\n",
    "    accuracy = acc / n\n",
    "\n",
    "    return total_loss, accuracy, true_labels, pred_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "55548773-1fe4-49d8-9450-3e56a77572a9",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "55548773-1fe4-49d8-9450-3e56a77572a9",
    "outputId": "912e295a-985d-4261-8b08-a1c0ba031cf8",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "available device: cpu\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[1/500] train loss. 0.970: 100%|████████████████| 15/15 [00:00<00:00, 78.27it/s]\n",
      "[VALID]> loss. 0.32 > acc. 23%: 100%|████████████| 4/4 [00:00<00:00, 115.48it/s]\n",
      "[2/500] train loss. 0.833: 100%|███████████████| 15/15 [00:00<00:00, 102.12it/s]\n",
      "[VALID]> loss. 0.30 > acc. 18%: 100%|████████████| 4/4 [00:00<00:00, 153.35it/s]\n",
      "[3/500] train loss. 0.832: 100%|████████████████| 15/15 [00:00<00:00, 91.18it/s]\n",
      "[VALID]> loss. 0.30 > acc. 24%: 100%|████████████| 4/4 [00:00<00:00, 152.98it/s]\n",
      "[4/500] train loss. 0.829: 100%|████████████████| 15/15 [00:00<00:00, 85.38it/s]\n",
      "[VALID]> loss. 0.28 > acc. 27%: 100%|████████████| 4/4 [00:00<00:00, 149.69it/s]\n",
      "[5/500] train loss. 0.809: 100%|████████████████| 15/15 [00:00<00:00, 95.35it/s]\n",
      "[VALID]> loss. 0.30 > acc. 24%: 100%|████████████| 4/4 [00:00<00:00, 165.48it/s]\n",
      "[6/500] train loss. 0.802: 100%|████████████████| 15/15 [00:00<00:00, 93.40it/s]\n",
      "[VALID]> loss. 0.29 > acc. 24%: 100%|████████████| 4/4 [00:00<00:00, 169.82it/s]\n",
      "[7/500] train loss. 0.803: 100%|████████████████| 15/15 [00:00<00:00, 90.82it/s]\n",
      "[VALID]> loss. 0.28 > acc. 24%: 100%|████████████| 4/4 [00:00<00:00, 155.45it/s]\n",
      "[8/500] train loss. 0.797: 100%|████████████████| 15/15 [00:00<00:00, 84.53it/s]\n",
      "[VALID]> loss. 0.31 > acc. 25%: 100%|████████████| 4/4 [00:00<00:00, 153.20it/s]\n",
      "[9/500] train loss. 0.799: 100%|████████████████| 15/15 [00:00<00:00, 85.21it/s]\n",
      "[VALID]> loss. 0.28 > acc. 25%: 100%|████████████| 4/4 [00:00<00:00, 148.83it/s]\n",
      "[10/500] train loss. 0.797: 100%|███████████████| 15/15 [00:00<00:00, 84.52it/s]\n",
      "[VALID]> loss. 0.29 > acc. 24%: 100%|████████████| 4/4 [00:00<00:00, 152.53it/s]\n",
      "[11/500] train loss. 0.793: 100%|███████████████| 15/15 [00:00<00:00, 87.58it/s]\n",
      "[VALID]> loss. 0.28 > acc. 29%: 100%|████████████| 4/4 [00:00<00:00, 154.06it/s]\n",
      "[12/500] train loss. 0.788: 100%|███████████████| 15/15 [00:00<00:00, 89.84it/s]\n",
      "[VALID]> loss. 0.29 > acc. 22%: 100%|████████████| 4/4 [00:00<00:00, 154.95it/s]\n",
      "[13/500] train loss. 0.786: 100%|███████████████| 15/15 [00:00<00:00, 91.08it/s]\n",
      "[VALID]> loss. 0.29 > acc. 24%: 100%|████████████| 4/4 [00:00<00:00, 151.04it/s]\n",
      "[14/500] train loss. 0.794: 100%|███████████████| 15/15 [00:00<00:00, 81.89it/s]\n",
      "[VALID]> loss. 0.28 > acc. 28%: 100%|████████████| 4/4 [00:00<00:00, 129.55it/s]\n",
      "[15/500] train loss. 0.779: 100%|███████████████| 15/15 [00:00<00:00, 84.34it/s]\n",
      "[VALID]> loss. 0.29 > acc. 28%: 100%|████████████| 4/4 [00:00<00:00, 139.08it/s]\n",
      "[16/500] train loss. 0.773: 100%|███████████████| 15/15 [00:00<00:00, 92.42it/s]\n",
      "[VALID]> loss. 0.28 > acc. 29%: 100%|████████████| 4/4 [00:00<00:00, 163.52it/s]\n",
      "[17/500] train loss. 0.774: 100%|██████████████| 15/15 [00:00<00:00, 100.42it/s]\n",
      "[VALID]> loss. 0.29 > acc. 26%: 100%|████████████| 4/4 [00:00<00:00, 153.59it/s]\n",
      "[18/500] train loss. 0.774: 100%|███████████████| 15/15 [00:00<00:00, 93.23it/s]\n",
      "[VALID]> loss. 0.28 > acc. 27%: 100%|████████████| 4/4 [00:00<00:00, 155.15it/s]\n",
      "[19/500] train loss. 0.771: 100%|██████████████| 15/15 [00:00<00:00, 100.81it/s]\n",
      "[VALID]> loss. 0.30 > acc. 26%: 100%|████████████| 4/4 [00:00<00:00, 160.55it/s]\n",
      "[20/500] train loss. 0.765: 100%|██████████████| 15/15 [00:00<00:00, 100.69it/s]\n",
      "[VALID]> loss. 0.28 > acc. 28%: 100%|████████████| 4/4 [00:00<00:00, 152.12it/s]\n",
      "[21/500] train loss. 0.759: 100%|██████████████| 15/15 [00:00<00:00, 102.74it/s]\n",
      "[VALID]> loss. 0.28 > acc. 34%: 100%|████████████| 4/4 [00:00<00:00, 156.05it/s]\n",
      "[22/500] train loss. 0.749: 100%|██████████████| 15/15 [00:00<00:00, 104.84it/s]\n",
      "[VALID]> loss. 0.28 > acc. 30%: 100%|████████████| 4/4 [00:00<00:00, 170.48it/s]\n",
      "[23/500] train loss. 0.750: 100%|██████████████| 15/15 [00:00<00:00, 101.84it/s]\n",
      "[VALID]> loss. 0.28 > acc. 32%: 100%|████████████| 4/4 [00:00<00:00, 155.10it/s]\n",
      "[24/500] train loss. 0.750: 100%|███████████████| 15/15 [00:00<00:00, 95.57it/s]\n",
      "[VALID]> loss. 0.27 > acc. 28%: 100%|████████████| 4/4 [00:00<00:00, 153.92it/s]\n",
      "[25/500] train loss. 0.753: 100%|██████████████| 15/15 [00:00<00:00, 102.49it/s]\n",
      "[VALID]> loss. 0.30 > acc. 27%: 100%|████████████| 4/4 [00:00<00:00, 167.90it/s]\n",
      "[26/500] train loss. 0.756: 100%|██████████████| 15/15 [00:00<00:00, 102.51it/s]\n",
      "[VALID]> loss. 0.28 > acc. 29%: 100%|████████████| 4/4 [00:00<00:00, 165.28it/s]\n",
      "[27/500] train loss. 0.746: 100%|███████████████| 15/15 [00:00<00:00, 98.79it/s]\n",
      "[VALID]> loss. 0.27 > acc. 34%: 100%|████████████| 4/4 [00:00<00:00, 182.04it/s]\n",
      "[28/500] train loss. 0.748: 100%|███████████████| 15/15 [00:00<00:00, 96.55it/s]\n",
      "[VALID]> loss. 0.29 > acc. 32%: 100%|████████████| 4/4 [00:00<00:00, 149.03it/s]\n",
      "[29/500] train loss. 0.732: 100%|███████████████| 15/15 [00:00<00:00, 85.35it/s]\n",
      "[VALID]> loss. 0.29 > acc. 32%: 100%|████████████| 4/4 [00:00<00:00, 141.17it/s]\n",
      "[30/500] train loss. 0.722: 100%|███████████████| 15/15 [00:00<00:00, 86.78it/s]\n",
      "[VALID]> loss. 0.27 > acc. 36%: 100%|████████████| 4/4 [00:00<00:00, 146.68it/s]\n",
      "[31/500] train loss. 0.721: 100%|███████████████| 15/15 [00:00<00:00, 97.86it/s]\n",
      "[VALID]> loss. 0.29 > acc. 31%: 100%|████████████| 4/4 [00:00<00:00, 156.22it/s]\n",
      "[32/500] train loss. 0.724: 100%|███████████████| 15/15 [00:00<00:00, 98.57it/s]\n",
      "[VALID]> loss. 0.28 > acc. 30%: 100%|████████████| 4/4 [00:00<00:00, 152.19it/s]\n",
      "[33/500] train loss. 0.727: 100%|███████████████| 15/15 [00:00<00:00, 96.76it/s]\n",
      "[VALID]> loss. 0.27 > acc. 31%: 100%|████████████| 4/4 [00:00<00:00, 171.31it/s]\n",
      "[34/500] train loss. 0.719: 100%|██████████████| 15/15 [00:00<00:00, 102.98it/s]\n",
      "[VALID]> loss. 0.26 > acc. 37%: 100%|████████████| 4/4 [00:00<00:00, 163.43it/s]\n",
      "[35/500] train loss. 0.712: 100%|███████████████| 15/15 [00:00<00:00, 95.02it/s]\n",
      "[VALID]> loss. 0.27 > acc. 35%: 100%|████████████| 4/4 [00:00<00:00, 151.08it/s]\n",
      "[36/500] train loss. 0.715: 100%|███████████████| 15/15 [00:00<00:00, 90.92it/s]\n",
      "[VALID]> loss. 0.27 > acc. 38%: 100%|████████████| 4/4 [00:00<00:00, 149.26it/s]\n",
      "[37/500] train loss. 0.697: 100%|███████████████| 15/15 [00:00<00:00, 90.45it/s]\n",
      "[VALID]> loss. 0.27 > acc. 33%: 100%|████████████| 4/4 [00:00<00:00, 140.16it/s]\n",
      "[38/500] train loss. 0.703: 100%|███████████████| 15/15 [00:00<00:00, 90.47it/s]\n",
      "[VALID]> loss. 0.26 > acc. 36%: 100%|████████████| 4/4 [00:00<00:00, 157.57it/s]\n",
      "[39/500] train loss. 0.703: 100%|███████████████| 15/15 [00:00<00:00, 97.14it/s]\n",
      "[VALID]> loss. 0.28 > acc. 31%: 100%|████████████| 4/4 [00:00<00:00, 165.68it/s]\n",
      "[40/500] train loss. 0.695: 100%|███████████████| 15/15 [00:00<00:00, 96.33it/s]\n",
      "[VALID]> loss. 0.27 > acc. 34%: 100%|████████████| 4/4 [00:00<00:00, 159.18it/s]\n",
      "[41/500] train loss. 0.692: 100%|██████████████| 15/15 [00:00<00:00, 100.03it/s]\n",
      "[VALID]> loss. 0.27 > acc. 34%: 100%|████████████| 4/4 [00:00<00:00, 153.78it/s]\n",
      "[42/500] train loss. 0.686: 100%|██████████████| 15/15 [00:00<00:00, 103.47it/s]\n",
      "[VALID]> loss. 0.26 > acc. 36%: 100%|████████████| 4/4 [00:00<00:00, 150.24it/s]\n",
      "[43/500] train loss. 0.686: 100%|███████████████| 15/15 [00:00<00:00, 87.50it/s]\n",
      "[VALID]> loss. 0.26 > acc. 36%: 100%|████████████| 4/4 [00:00<00:00, 151.99it/s]\n",
      "[44/500] train loss. 0.679: 100%|███████████████| 15/15 [00:00<00:00, 86.59it/s]\n",
      "[VALID]> loss. 0.27 > acc. 40%: 100%|████████████| 4/4 [00:00<00:00, 156.89it/s]\n",
      "[45/500] train loss. 0.679: 100%|███████████████| 15/15 [00:00<00:00, 92.79it/s]\n",
      "[VALID]> loss. 0.26 > acc. 33%: 100%|████████████| 4/4 [00:00<00:00, 155.41it/s]\n",
      "[46/500] train loss. 0.679: 100%|███████████████| 15/15 [00:00<00:00, 83.77it/s]\n",
      "[VALID]> loss. 0.26 > acc. 38%: 100%|████████████| 4/4 [00:00<00:00, 143.65it/s]\n",
      "[47/500] train loss. 0.669: 100%|███████████████| 15/15 [00:00<00:00, 86.00it/s]\n",
      "[VALID]> loss. 0.26 > acc. 42%: 100%|████████████| 4/4 [00:00<00:00, 152.94it/s]\n",
      "[48/500] train loss. 0.664: 100%|███████████████| 15/15 [00:00<00:00, 74.36it/s]\n",
      "[VALID]> loss. 0.25 > acc. 43%: 100%|████████████| 4/4 [00:00<00:00, 136.25it/s]\n",
      "[49/500] train loss. 0.669: 100%|███████████████| 15/15 [00:00<00:00, 88.16it/s]\n",
      "[VALID]> loss. 0.26 > acc. 34%: 100%|████████████| 4/4 [00:00<00:00, 147.44it/s]\n",
      "[50/500] train loss. 0.687: 100%|███████████████| 15/15 [00:00<00:00, 85.37it/s]\n",
      "[VALID]> loss. 0.25 > acc. 42%: 100%|████████████| 4/4 [00:00<00:00, 143.37it/s]\n",
      "[51/500] train loss. 0.664: 100%|███████████████| 15/15 [00:00<00:00, 86.67it/s]\n",
      "[VALID]> loss. 0.26 > acc. 43%: 100%|████████████| 4/4 [00:00<00:00, 147.86it/s]\n",
      "[52/500] train loss. 0.662: 100%|███████████████| 15/15 [00:00<00:00, 85.77it/s]\n",
      "[VALID]> loss. 0.26 > acc. 38%: 100%|████████████| 4/4 [00:00<00:00, 133.46it/s]\n",
      "[53/500] train loss. 0.658: 100%|███████████████| 15/15 [00:00<00:00, 88.49it/s]\n",
      "[VALID]> loss. 0.25 > acc. 42%: 100%|████████████| 4/4 [00:00<00:00, 159.25it/s]\n",
      "[54/500] train loss. 0.660: 100%|███████████████| 15/15 [00:00<00:00, 86.62it/s]\n",
      "[VALID]> loss. 0.26 > acc. 42%: 100%|████████████| 4/4 [00:00<00:00, 158.51it/s]\n",
      "[55/500] train loss. 0.658: 100%|███████████████| 15/15 [00:00<00:00, 90.21it/s]\n",
      "[VALID]> loss. 0.25 > acc. 45%: 100%|████████████| 4/4 [00:00<00:00, 154.60it/s]\n",
      "[56/500] train loss. 0.645: 100%|███████████████| 15/15 [00:00<00:00, 88.18it/s]\n",
      "[VALID]> loss. 0.27 > acc. 45%: 100%|████████████| 4/4 [00:00<00:00, 154.03it/s]\n",
      "[57/500] train loss. 0.665: 100%|███████████████| 15/15 [00:00<00:00, 91.53it/s]\n",
      "[VALID]> loss. 0.27 > acc. 39%: 100%|████████████| 4/4 [00:00<00:00, 153.24it/s]\n",
      "[58/500] train loss. 0.644: 100%|███████████████| 15/15 [00:00<00:00, 91.70it/s]\n",
      "[VALID]> loss. 0.25 > acc. 48%: 100%|████████████| 4/4 [00:00<00:00, 156.99it/s]\n",
      "[59/500] train loss. 0.635: 100%|███████████████| 15/15 [00:00<00:00, 93.51it/s]\n",
      "[VALID]> loss. 0.26 > acc. 46%: 100%|████████████| 4/4 [00:00<00:00, 154.72it/s]\n",
      "[60/500] train loss. 0.636: 100%|███████████████| 15/15 [00:00<00:00, 92.71it/s]\n",
      "[VALID]> loss. 0.25 > acc. 48%: 100%|████████████| 4/4 [00:00<00:00, 128.90it/s]\n",
      "[61/500] train loss. 0.651: 100%|███████████████| 15/15 [00:00<00:00, 87.18it/s]\n",
      "[VALID]> loss. 0.25 > acc. 45%: 100%|████████████| 4/4 [00:00<00:00, 151.23it/s]\n",
      "[62/500] train loss. 0.623: 100%|███████████████| 15/15 [00:00<00:00, 86.55it/s]\n",
      "[VALID]> loss. 0.27 > acc. 43%: 100%|████████████| 4/4 [00:00<00:00, 150.93it/s]\n",
      "[63/500] train loss. 0.645: 100%|███████████████| 15/15 [00:00<00:00, 88.12it/s]\n",
      "[VALID]> loss. 0.25 > acc. 43%: 100%|████████████| 4/4 [00:00<00:00, 162.41it/s]\n",
      "[64/500] train loss. 0.616: 100%|██████████████| 15/15 [00:00<00:00, 107.69it/s]\n",
      "[VALID]> loss. 0.25 > acc. 46%: 100%|████████████| 4/4 [00:00<00:00, 154.14it/s]\n",
      "[65/500] train loss. 0.622: 100%|██████████████| 15/15 [00:00<00:00, 105.57it/s]\n",
      "[VALID]> loss. 0.27 > acc. 44%: 100%|████████████| 4/4 [00:00<00:00, 163.38it/s]\n",
      "[66/500] train loss. 0.620: 100%|██████████████| 15/15 [00:00<00:00, 106.29it/s]\n",
      "[VALID]> loss. 0.24 > acc. 45%: 100%|████████████| 4/4 [00:00<00:00, 168.58it/s]\n",
      "[67/500] train loss. 0.627: 100%|██████████████| 15/15 [00:00<00:00, 104.72it/s]\n",
      "[VALID]> loss. 0.26 > acc. 42%: 100%|████████████| 4/4 [00:00<00:00, 147.64it/s]\n",
      "[68/500] train loss. 0.615: 100%|██████████████| 15/15 [00:00<00:00, 106.06it/s]\n",
      "[VALID]> loss. 0.26 > acc. 42%: 100%|████████████| 4/4 [00:00<00:00, 153.76it/s]\n",
      "[69/500] train loss. 0.603: 100%|███████████████| 15/15 [00:00<00:00, 85.80it/s]\n",
      "[VALID]> loss. 0.26 > acc. 44%: 100%|████████████| 4/4 [00:00<00:00, 151.36it/s]\n",
      "[70/500] train loss. 0.620: 100%|███████████████| 15/15 [00:00<00:00, 89.66it/s]\n",
      "[VALID]> loss. 0.24 > acc. 53%: 100%|████████████| 4/4 [00:00<00:00, 155.15it/s]\n",
      "[71/500] train loss. 0.600: 100%|███████████████| 15/15 [00:00<00:00, 87.07it/s]\n",
      "[VALID]> loss. 0.24 > acc. 48%: 100%|████████████| 4/4 [00:00<00:00, 169.09it/s]\n",
      "[72/500] train loss. 0.595: 100%|██████████████| 15/15 [00:00<00:00, 100.41it/s]\n",
      "[VALID]> loss. 0.25 > acc. 45%: 100%|████████████| 4/4 [00:00<00:00, 174.68it/s]\n",
      "[73/500] train loss. 0.579: 100%|███████████████| 15/15 [00:00<00:00, 92.96it/s]\n",
      "[VALID]> loss. 0.25 > acc. 50%: 100%|████████████| 4/4 [00:00<00:00, 189.20it/s]\n",
      "[74/500] train loss. 0.585: 100%|███████████████| 15/15 [00:00<00:00, 92.40it/s]\n",
      "[VALID]> loss. 0.25 > acc. 47%: 100%|████████████| 4/4 [00:00<00:00, 142.14it/s]\n",
      "[75/500] train loss. 0.585: 100%|███████████████| 15/15 [00:00<00:00, 89.14it/s]\n",
      "[VALID]> loss. 0.26 > acc. 51%: 100%|████████████| 4/4 [00:00<00:00, 154.81it/s]\n",
      "[76/500] train loss. 0.599: 100%|███████████████| 15/15 [00:00<00:00, 93.84it/s]\n",
      "[VALID]> loss. 0.26 > acc. 48%: 100%|████████████| 4/4 [00:00<00:00, 157.82it/s]\n",
      "[77/500] train loss. 0.602: 100%|███████████████| 15/15 [00:00<00:00, 91.91it/s]\n",
      "[VALID]> loss. 0.24 > acc. 52%: 100%|████████████| 4/4 [00:00<00:00, 161.22it/s]\n",
      "[78/500] train loss. 0.570: 100%|███████████████| 15/15 [00:00<00:00, 92.52it/s]\n",
      "[VALID]> loss. 0.25 > acc. 48%: 100%|████████████| 4/4 [00:00<00:00, 157.70it/s]\n",
      "[79/500] train loss. 0.573: 100%|███████████████| 15/15 [00:00<00:00, 97.80it/s]\n",
      "[VALID]> loss. 0.26 > acc. 48%: 100%|████████████| 4/4 [00:00<00:00, 163.36it/s]\n",
      "[80/500] train loss. 0.562: 100%|███████████████| 15/15 [00:00<00:00, 96.66it/s]\n",
      "[VALID]> loss. 0.24 > acc. 52%: 100%|████████████| 4/4 [00:00<00:00, 156.45it/s]\n",
      "[81/500] train loss. 0.567: 100%|███████████████| 15/15 [00:00<00:00, 95.51it/s]\n",
      "[VALID]> loss. 0.25 > acc. 48%: 100%|████████████| 4/4 [00:00<00:00, 160.84it/s]\n",
      "[82/500] train loss. 0.556: 100%|██████████████| 15/15 [00:00<00:00, 101.93it/s]\n",
      "[VALID]> loss. 0.25 > acc. 44%: 100%|████████████| 4/4 [00:00<00:00, 154.58it/s]\n",
      "[83/500] train loss. 0.561: 100%|███████████████| 15/15 [00:00<00:00, 95.74it/s]\n",
      "[VALID]> loss. 0.25 > acc. 52%: 100%|████████████| 4/4 [00:00<00:00, 161.48it/s]\n",
      "[84/500] train loss. 0.557: 100%|███████████████| 15/15 [00:00<00:00, 96.68it/s]\n",
      "[VALID]> loss. 0.25 > acc. 50%: 100%|████████████| 4/4 [00:00<00:00, 151.33it/s]\n",
      "[85/500] train loss. 0.561: 100%|███████████████| 15/15 [00:00<00:00, 86.02it/s]\n",
      "[VALID]> loss. 0.25 > acc. 50%: 100%|████████████| 4/4 [00:00<00:00, 135.93it/s]\n",
      "[86/500] train loss. 0.555: 100%|███████████████| 15/15 [00:00<00:00, 85.09it/s]\n",
      "[VALID]> loss. 0.25 > acc. 53%: 100%|████████████| 4/4 [00:00<00:00, 150.97it/s]\n",
      "[87/500] train loss. 0.542: 100%|██████████████| 15/15 [00:00<00:00, 100.87it/s]\n",
      "[VALID]> loss. 0.25 > acc. 50%: 100%|████████████| 4/4 [00:00<00:00, 191.92it/s]\n",
      "[88/500] train loss. 0.545: 100%|███████████████| 15/15 [00:00<00:00, 99.56it/s]\n",
      "[VALID]> loss. 0.25 > acc. 49%: 100%|████████████| 4/4 [00:00<00:00, 145.51it/s]\n",
      "[89/500] train loss. 0.535: 100%|███████████████| 15/15 [00:00<00:00, 97.26it/s]\n",
      "[VALID]> loss. 0.25 > acc. 48%: 100%|████████████| 4/4 [00:00<00:00, 154.26it/s]\n",
      "[90/500] train loss. 0.536: 100%|██████████████| 15/15 [00:00<00:00, 103.61it/s]\n",
      "[VALID]> loss. 0.26 > acc. 48%: 100%|████████████| 4/4 [00:00<00:00, 162.05it/s]\n",
      "[91/500] train loss. 0.529: 100%|██████████████| 15/15 [00:00<00:00, 103.31it/s]\n",
      "[VALID]> loss. 0.26 > acc. 47%: 100%|████████████| 4/4 [00:00<00:00, 167.47it/s]\n",
      "[92/500] train loss. 0.548: 100%|███████████████| 15/15 [00:00<00:00, 87.67it/s]\n",
      "[VALID]> loss. 0.24 > acc. 52%: 100%|████████████| 4/4 [00:00<00:00, 152.83it/s]\n",
      "[93/500] train loss. 0.525: 100%|███████████████| 15/15 [00:00<00:00, 97.49it/s]\n",
      "[VALID]> loss. 0.24 > acc. 55%: 100%|████████████| 4/4 [00:00<00:00, 175.50it/s]\n",
      "[94/500] train loss. 0.508: 100%|██████████████| 15/15 [00:00<00:00, 108.68it/s]\n",
      "[VALID]> loss. 0.24 > acc. 55%: 100%|████████████| 4/4 [00:00<00:00, 170.07it/s]\n",
      "[95/500] train loss. 0.508: 100%|██████████████| 15/15 [00:00<00:00, 104.53it/s]\n",
      "[VALID]> loss. 0.24 > acc. 53%: 100%|████████████| 4/4 [00:00<00:00, 178.50it/s]\n",
      "[96/500] train loss. 0.512: 100%|██████████████| 15/15 [00:00<00:00, 106.00it/s]\n",
      "[VALID]> loss. 0.25 > acc. 53%: 100%|████████████| 4/4 [00:00<00:00, 155.51it/s]\n",
      "[97/500] train loss. 0.523: 100%|███████████████| 15/15 [00:00<00:00, 94.62it/s]\n",
      "[VALID]> loss. 0.26 > acc. 50%: 100%|████████████| 4/4 [00:00<00:00, 151.01it/s]\n",
      "[98/500] train loss. 0.515: 100%|███████████████| 15/15 [00:00<00:00, 88.16it/s]\n",
      "[VALID]> loss. 0.26 > acc. 48%: 100%|████████████| 4/4 [00:00<00:00, 151.33it/s]\n",
      "[99/500] train loss. 0.542: 100%|███████████████| 15/15 [00:00<00:00, 85.80it/s]\n",
      "[VALID]> loss. 0.25 > acc. 45%: 100%|████████████| 4/4 [00:00<00:00, 150.97it/s]\n",
      "[100/500] train loss. 0.532: 100%|██████████████| 15/15 [00:00<00:00, 88.22it/s]\n",
      "[VALID]> loss. 0.25 > acc. 48%: 100%|████████████| 4/4 [00:00<00:00, 153.43it/s]\n",
      "[101/500] train loss. 0.507: 100%|██████████████| 15/15 [00:00<00:00, 89.24it/s]\n",
      "[VALID]> loss. 0.25 > acc. 49%: 100%|████████████| 4/4 [00:00<00:00, 152.08it/s]\n",
      "[102/500] train loss. 0.502: 100%|█████████████| 15/15 [00:00<00:00, 102.78it/s]\n",
      "[VALID]> loss. 0.26 > acc. 50%: 100%|████████████| 4/4 [00:00<00:00, 161.87it/s]\n",
      "[103/500] train loss. 0.507: 100%|█████████████| 15/15 [00:00<00:00, 106.18it/s]\n",
      "[VALID]> loss. 0.25 > acc. 52%: 100%|████████████| 4/4 [00:00<00:00, 155.36it/s]\n",
      "[104/500] train loss. 0.497: 100%|█████████████| 15/15 [00:00<00:00, 109.83it/s]\n",
      "[VALID]> loss. 0.25 > acc. 48%: 100%|████████████| 4/4 [00:00<00:00, 148.49it/s]\n",
      "[105/500] train loss. 0.493: 100%|██████████████| 15/15 [00:00<00:00, 96.19it/s]\n",
      "[VALID]> loss. 0.26 > acc. 49%: 100%|████████████| 4/4 [00:00<00:00, 154.67it/s]\n",
      "[106/500] train loss. 0.483: 100%|█████████████| 15/15 [00:00<00:00, 101.18it/s]\n",
      "[VALID]> loss. 0.25 > acc. 54%: 100%|████████████| 4/4 [00:00<00:00, 165.60it/s]\n",
      "[107/500] train loss. 0.494: 100%|██████████████| 15/15 [00:00<00:00, 89.82it/s]\n",
      "[VALID]> loss. 0.25 > acc. 50%: 100%|████████████| 4/4 [00:00<00:00, 146.84it/s]\n",
      "[108/500] train loss. 0.498: 100%|██████████████| 15/15 [00:00<00:00, 90.47it/s]\n",
      "[VALID]> loss. 0.24 > acc. 55%: 100%|████████████| 4/4 [00:00<00:00, 147.49it/s]\n",
      "[109/500] train loss. 0.485: 100%|██████████████| 15/15 [00:00<00:00, 87.00it/s]\n",
      "[VALID]> loss. 0.25 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 159.39it/s]\n",
      "[110/500] train loss. 0.483: 100%|██████████████| 15/15 [00:00<00:00, 89.47it/s]\n",
      "[VALID]> loss. 0.25 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 153.07it/s]\n",
      "[111/500] train loss. 0.467: 100%|██████████████| 15/15 [00:00<00:00, 83.48it/s]\n",
      "[VALID]> loss. 0.24 > acc. 58%: 100%|████████████| 4/4 [00:00<00:00, 143.80it/s]\n",
      "[112/500] train loss. 0.469: 100%|██████████████| 15/15 [00:00<00:00, 99.21it/s]\n",
      "[VALID]> loss. 0.24 > acc. 52%: 100%|████████████| 4/4 [00:00<00:00, 163.36it/s]\n",
      "[113/500] train loss. 0.460: 100%|█████████████| 15/15 [00:00<00:00, 102.59it/s]\n",
      "[VALID]> loss. 0.25 > acc. 48%: 100%|████████████| 4/4 [00:00<00:00, 157.80it/s]\n",
      "[114/500] train loss. 0.468: 100%|█████████████| 15/15 [00:00<00:00, 101.53it/s]\n",
      "[VALID]> loss. 0.25 > acc. 52%: 100%|████████████| 4/4 [00:00<00:00, 145.38it/s]\n",
      "[115/500] train loss. 0.478: 100%|██████████████| 15/15 [00:00<00:00, 98.81it/s]\n",
      "[VALID]> loss. 0.24 > acc. 56%: 100%|████████████| 4/4 [00:00<00:00, 154.70it/s]\n",
      "[116/500] train loss. 0.468: 100%|██████████████| 15/15 [00:00<00:00, 87.65it/s]\n",
      "[VALID]> loss. 0.27 > acc. 49%: 100%|████████████| 4/4 [00:00<00:00, 144.20it/s]\n",
      "[117/500] train loss. 0.465: 100%|██████████████| 15/15 [00:00<00:00, 87.33it/s]\n",
      "[VALID]> loss. 0.24 > acc. 51%: 100%|████████████| 4/4 [00:00<00:00, 152.10it/s]\n",
      "[118/500] train loss. 0.455: 100%|██████████████| 15/15 [00:00<00:00, 87.64it/s]\n",
      "[VALID]> loss. 0.26 > acc. 54%: 100%|████████████| 4/4 [00:00<00:00, 129.04it/s]\n",
      "[119/500] train loss. 0.496: 100%|██████████████| 15/15 [00:00<00:00, 84.53it/s]\n",
      "[VALID]> loss. 0.25 > acc. 50%: 100%|████████████| 4/4 [00:00<00:00, 149.26it/s]\n",
      "[120/500] train loss. 0.465: 100%|██████████████| 15/15 [00:00<00:00, 91.13it/s]\n",
      "[VALID]> loss. 0.25 > acc. 50%: 100%|████████████| 4/4 [00:00<00:00, 150.33it/s]\n",
      "[121/500] train loss. 0.466: 100%|██████████████| 15/15 [00:00<00:00, 88.50it/s]\n",
      "[VALID]> loss. 0.25 > acc. 50%: 100%|████████████| 4/4 [00:00<00:00, 154.38it/s]\n",
      "[122/500] train loss. 0.459: 100%|██████████████| 15/15 [00:00<00:00, 90.75it/s]\n",
      "[VALID]> loss. 0.24 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 156.16it/s]\n",
      "[123/500] train loss. 0.449: 100%|██████████████| 15/15 [00:00<00:00, 91.19it/s]\n",
      "[VALID]> loss. 0.24 > acc. 58%: 100%|████████████| 4/4 [00:00<00:00, 157.33it/s]\n",
      "[124/500] train loss. 0.441: 100%|██████████████| 15/15 [00:00<00:00, 84.87it/s]\n",
      "[VALID]> loss. 0.24 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 156.39it/s]\n",
      "[125/500] train loss. 0.433: 100%|██████████████| 15/15 [00:00<00:00, 90.83it/s]\n",
      "[VALID]> loss. 0.25 > acc. 53%: 100%|████████████| 4/4 [00:00<00:00, 157.86it/s]\n",
      "[126/500] train loss. 0.445: 100%|██████████████| 15/15 [00:00<00:00, 91.18it/s]\n",
      "[VALID]> loss. 0.24 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 156.46it/s]\n",
      "[127/500] train loss. 0.437: 100%|██████████████| 15/15 [00:00<00:00, 89.65it/s]\n",
      "[VALID]> loss. 0.24 > acc. 56%: 100%|████████████| 4/4 [00:00<00:00, 149.61it/s]\n",
      "[128/500] train loss. 0.436: 100%|██████████████| 15/15 [00:00<00:00, 86.91it/s]\n",
      "[VALID]> loss. 0.24 > acc. 60%: 100%|████████████| 4/4 [00:00<00:00, 136.55it/s]\n",
      "[129/500] train loss. 0.436: 100%|██████████████| 15/15 [00:00<00:00, 72.78it/s]\n",
      "[VALID]> loss. 0.25 > acc. 54%: 100%|████████████| 4/4 [00:00<00:00, 137.30it/s]\n",
      "[130/500] train loss. 0.437: 100%|██████████████| 15/15 [00:00<00:00, 90.60it/s]\n",
      "[VALID]> loss. 0.25 > acc. 52%: 100%|████████████| 4/4 [00:00<00:00, 166.99it/s]\n",
      "[131/500] train loss. 0.430: 100%|██████████████| 15/15 [00:00<00:00, 99.54it/s]\n",
      "[VALID]> loss. 0.24 > acc. 56%: 100%|████████████| 4/4 [00:00<00:00, 163.20it/s]\n",
      "[132/500] train loss. 0.435: 100%|██████████████| 15/15 [00:00<00:00, 95.54it/s]\n",
      "[VALID]> loss. 0.25 > acc. 54%: 100%|████████████| 4/4 [00:00<00:00, 167.32it/s]\n",
      "[133/500] train loss. 0.435: 100%|█████████████| 15/15 [00:00<00:00, 101.32it/s]\n",
      "[VALID]> loss. 0.26 > acc. 56%: 100%|████████████| 4/4 [00:00<00:00, 155.51it/s]\n",
      "[134/500] train loss. 0.437: 100%|██████████████| 15/15 [00:00<00:00, 98.47it/s]\n",
      "[VALID]> loss. 0.24 > acc. 53%: 100%|████████████| 4/4 [00:00<00:00, 146.70it/s]\n",
      "[135/500] train loss. 0.428: 100%|█████████████| 15/15 [00:00<00:00, 108.56it/s]\n",
      "[VALID]> loss. 0.25 > acc. 56%: 100%|████████████| 4/4 [00:00<00:00, 151.52it/s]\n",
      "[136/500] train loss. 0.427: 100%|██████████████| 15/15 [00:00<00:00, 99.36it/s]\n",
      "[VALID]> loss. 0.25 > acc. 55%: 100%|████████████| 4/4 [00:00<00:00, 166.47it/s]\n",
      "[137/500] train loss. 0.437: 100%|██████████████| 15/15 [00:00<00:00, 99.43it/s]\n",
      "[VALID]> loss. 0.25 > acc. 56%: 100%|████████████| 4/4 [00:00<00:00, 153.83it/s]\n",
      "[138/500] train loss. 0.427: 100%|█████████████| 15/15 [00:00<00:00, 102.08it/s]\n",
      "[VALID]> loss. 0.25 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 152.69it/s]\n",
      "[139/500] train loss. 0.421: 100%|██████████████| 15/15 [00:00<00:00, 88.16it/s]\n",
      "[VALID]> loss. 0.24 > acc. 53%: 100%|████████████| 4/4 [00:00<00:00, 153.23it/s]\n",
      "[140/500] train loss. 0.429: 100%|██████████████| 15/15 [00:00<00:00, 95.69it/s]\n",
      "[VALID]> loss. 0.23 > acc. 58%: 100%|████████████| 4/4 [00:00<00:00, 156.50it/s]\n",
      "[141/500] train loss. 0.422: 100%|█████████████| 15/15 [00:00<00:00, 106.13it/s]\n",
      "[VALID]> loss. 0.25 > acc. 52%: 100%|████████████| 4/4 [00:00<00:00, 154.27it/s]\n",
      "[142/500] train loss. 0.411: 100%|█████████████| 15/15 [00:00<00:00, 105.08it/s]\n",
      "[VALID]> loss. 0.25 > acc. 55%: 100%|████████████| 4/4 [00:00<00:00, 191.52it/s]\n",
      "[143/500] train loss. 0.435: 100%|██████████████| 15/15 [00:00<00:00, 97.32it/s]\n",
      "[VALID]> loss. 0.24 > acc. 56%: 100%|████████████| 4/4 [00:00<00:00, 187.49it/s]\n",
      "[144/500] train loss. 0.415: 100%|█████████████| 15/15 [00:00<00:00, 113.86it/s]\n",
      "[VALID]> loss. 0.27 > acc. 52%: 100%|████████████| 4/4 [00:00<00:00, 191.44it/s]\n",
      "[145/500] train loss. 0.429: 100%|██████████████| 15/15 [00:00<00:00, 93.87it/s]\n",
      "[VALID]> loss. 0.24 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 150.88it/s]\n",
      "[146/500] train loss. 0.414: 100%|██████████████| 15/15 [00:00<00:00, 89.14it/s]\n",
      "[VALID]> loss. 0.24 > acc. 54%: 100%|████████████| 4/4 [00:00<00:00, 151.97it/s]\n",
      "[147/500] train loss. 0.407: 100%|██████████████| 15/15 [00:00<00:00, 88.14it/s]\n",
      "[VALID]> loss. 0.25 > acc. 53%: 100%|████████████| 4/4 [00:00<00:00, 153.14it/s]\n",
      "[148/500] train loss. 0.406: 100%|██████████████| 15/15 [00:00<00:00, 86.54it/s]\n",
      "[VALID]> loss. 0.24 > acc. 50%: 100%|████████████| 4/4 [00:00<00:00, 139.54it/s]\n",
      "[149/500] train loss. 0.409: 100%|██████████████| 15/15 [00:00<00:00, 87.96it/s]\n",
      "[VALID]> loss. 0.25 > acc. 54%: 100%|████████████| 4/4 [00:00<00:00, 146.26it/s]\n",
      "[150/500] train loss. 0.412: 100%|██████████████| 15/15 [00:00<00:00, 87.59it/s]\n",
      "[VALID]> loss. 0.25 > acc. 54%: 100%|████████████| 4/4 [00:00<00:00, 133.24it/s]\n",
      "[151/500] train loss. 0.412: 100%|██████████████| 15/15 [00:00<00:00, 85.20it/s]\n",
      "[VALID]> loss. 0.23 > acc. 62%: 100%|████████████| 4/4 [00:00<00:00, 148.90it/s]\n",
      "[152/500] train loss. 0.421: 100%|██████████████| 15/15 [00:00<00:00, 90.75it/s]\n",
      "[VALID]> loss. 0.25 > acc. 55%: 100%|████████████| 4/4 [00:00<00:00, 166.53it/s]\n",
      "[153/500] train loss. 0.418: 100%|█████████████| 15/15 [00:00<00:00, 102.47it/s]\n",
      "[VALID]> loss. 0.24 > acc. 53%: 100%|████████████| 4/4 [00:00<00:00, 170.14it/s]\n",
      "[154/500] train loss. 0.409: 100%|██████████████| 15/15 [00:00<00:00, 95.89it/s]\n",
      "[VALID]> loss. 0.25 > acc. 52%: 100%|████████████| 4/4 [00:00<00:00, 152.25it/s]\n",
      "[155/500] train loss. 0.421: 100%|██████████████| 15/15 [00:00<00:00, 88.65it/s]\n",
      "[VALID]> loss. 0.26 > acc. 53%: 100%|████████████| 4/4 [00:00<00:00, 135.37it/s]\n",
      "[156/500] train loss. 0.413: 100%|██████████████| 15/15 [00:00<00:00, 88.35it/s]\n",
      "[VALID]> loss. 0.24 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 155.58it/s]\n",
      "[157/500] train loss. 0.425: 100%|██████████████| 15/15 [00:00<00:00, 88.40it/s]\n",
      "[VALID]> loss. 0.25 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 132.49it/s]\n",
      "[158/500] train loss. 0.424: 100%|██████████████| 15/15 [00:00<00:00, 86.42it/s]\n",
      "[VALID]> loss. 0.24 > acc. 56%: 100%|████████████| 4/4 [00:00<00:00, 133.86it/s]\n",
      "[159/500] train loss. 0.416: 100%|██████████████| 15/15 [00:00<00:00, 90.71it/s]\n",
      "[VALID]> loss. 0.25 > acc. 55%: 100%|████████████| 4/4 [00:00<00:00, 157.87it/s]\n",
      "[160/500] train loss. 0.414: 100%|██████████████| 15/15 [00:00<00:00, 89.00it/s]\n",
      "[VALID]> loss. 0.25 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 152.53it/s]\n",
      "[161/500] train loss. 0.393: 100%|██████████████| 15/15 [00:00<00:00, 72.97it/s]\n",
      "[VALID]> loss. 0.24 > acc. 58%: 100%|████████████| 4/4 [00:00<00:00, 148.55it/s]\n",
      "[162/500] train loss. 0.388: 100%|██████████████| 15/15 [00:00<00:00, 89.38it/s]\n",
      "[VALID]> loss. 0.26 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 154.07it/s]\n",
      "[163/500] train loss. 0.396: 100%|██████████████| 15/15 [00:00<00:00, 85.28it/s]\n",
      "[VALID]> loss. 0.24 > acc. 52%: 100%|████████████| 4/4 [00:00<00:00, 128.63it/s]\n",
      "[164/500] train loss. 0.379: 100%|██████████████| 15/15 [00:00<00:00, 89.00it/s]\n",
      "[VALID]> loss. 0.25 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 161.31it/s]\n",
      "[165/500] train loss. 0.386: 100%|██████████████| 15/15 [00:00<00:00, 95.68it/s]\n",
      "[VALID]> loss. 0.25 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 152.61it/s]\n",
      "[166/500] train loss. 0.390: 100%|█████████████| 15/15 [00:00<00:00, 101.82it/s]\n",
      "[VALID]> loss. 0.25 > acc. 55%: 100%|████████████| 4/4 [00:00<00:00, 177.95it/s]\n",
      "[167/500] train loss. 0.389: 100%|██████████████| 15/15 [00:00<00:00, 99.51it/s]\n",
      "[VALID]> loss. 0.23 > acc. 56%: 100%|████████████| 4/4 [00:00<00:00, 167.56it/s]\n",
      "[168/500] train loss. 0.385: 100%|██████████████| 15/15 [00:00<00:00, 94.41it/s]\n",
      "[VALID]> loss. 0.24 > acc. 59%: 100%|████████████| 4/4 [00:00<00:00, 142.99it/s]\n",
      "[169/500] train loss. 0.380: 100%|██████████████| 15/15 [00:00<00:00, 93.82it/s]\n",
      "[VALID]> loss. 0.25 > acc. 56%: 100%|████████████| 4/4 [00:00<00:00, 141.73it/s]\n",
      "[170/500] train loss. 0.373: 100%|██████████████| 15/15 [00:00<00:00, 97.55it/s]\n",
      "[VALID]> loss. 0.24 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 168.24it/s]\n",
      "[171/500] train loss. 0.377: 100%|█████████████| 15/15 [00:00<00:00, 105.42it/s]\n",
      "[VALID]> loss. 0.25 > acc. 53%: 100%|████████████| 4/4 [00:00<00:00, 152.66it/s]\n",
      "[172/500] train loss. 0.376: 100%|█████████████| 15/15 [00:00<00:00, 105.76it/s]\n",
      "[VALID]> loss. 0.24 > acc. 58%: 100%|████████████| 4/4 [00:00<00:00, 164.39it/s]\n",
      "[173/500] train loss. 0.371: 100%|█████████████| 15/15 [00:00<00:00, 105.07it/s]\n",
      "[VALID]> loss. 0.24 > acc. 55%: 100%|████████████| 4/4 [00:00<00:00, 157.68it/s]\n",
      "[174/500] train loss. 0.368: 100%|██████████████| 15/15 [00:00<00:00, 98.51it/s]\n",
      "[VALID]> loss. 0.25 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 174.09it/s]\n",
      "[175/500] train loss. 0.375: 100%|██████████████| 15/15 [00:00<00:00, 91.45it/s]\n",
      "[VALID]> loss. 0.24 > acc. 55%: 100%|████████████| 4/4 [00:00<00:00, 151.17it/s]\n",
      "[176/500] train loss. 0.366: 100%|██████████████| 15/15 [00:00<00:00, 85.53it/s]\n",
      "[VALID]> loss. 0.24 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 149.65it/s]\n",
      "[177/500] train loss. 0.388: 100%|██████████████| 15/15 [00:00<00:00, 85.40it/s]\n",
      "[VALID]> loss. 0.24 > acc. 61%: 100%|████████████| 4/4 [00:00<00:00, 149.45it/s]\n",
      "[178/500] train loss. 0.371: 100%|█████████████| 15/15 [00:00<00:00, 101.89it/s]\n",
      "[VALID]> loss. 0.25 > acc. 51%: 100%|████████████| 4/4 [00:00<00:00, 155.99it/s]\n",
      "[179/500] train loss. 0.380: 100%|█████████████| 15/15 [00:00<00:00, 106.57it/s]\n",
      "[VALID]> loss. 0.24 > acc. 58%: 100%|████████████| 4/4 [00:00<00:00, 151.59it/s]\n",
      "[180/500] train loss. 0.371: 100%|█████████████| 15/15 [00:00<00:00, 104.48it/s]\n",
      "[VALID]> loss. 0.25 > acc. 56%: 100%|████████████| 4/4 [00:00<00:00, 189.71it/s]\n",
      "[181/500] train loss. 0.366: 100%|██████████████| 15/15 [00:00<00:00, 98.21it/s]\n",
      "[VALID]> loss. 0.26 > acc. 47%: 100%|████████████| 4/4 [00:00<00:00, 159.60it/s]\n",
      "[182/500] train loss. 0.386: 100%|█████████████| 15/15 [00:00<00:00, 103.45it/s]\n",
      "[VALID]> loss. 0.26 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 161.49it/s]\n",
      "[183/500] train loss. 0.372: 100%|██████████████| 15/15 [00:00<00:00, 96.71it/s]\n",
      "[VALID]> loss. 0.25 > acc. 56%: 100%|████████████| 4/4 [00:00<00:00, 154.19it/s]\n",
      "[184/500] train loss. 0.365: 100%|██████████████| 15/15 [00:00<00:00, 97.46it/s]\n",
      "[VALID]> loss. 0.26 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 164.47it/s]\n",
      "[185/500] train loss. 0.375: 100%|█████████████| 15/15 [00:00<00:00, 104.63it/s]\n",
      "[VALID]> loss. 0.24 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 154.45it/s]\n",
      "[186/500] train loss. 0.381: 100%|██████████████| 15/15 [00:00<00:00, 95.37it/s]\n",
      "[VALID]> loss. 0.23 > acc. 60%: 100%|████████████| 4/4 [00:00<00:00, 175.11it/s]\n",
      "[187/500] train loss. 0.384: 100%|██████████████| 15/15 [00:00<00:00, 94.03it/s]\n",
      "[VALID]> loss. 0.26 > acc. 52%: 100%|████████████| 4/4 [00:00<00:00, 149.08it/s]\n",
      "[188/500] train loss. 0.378: 100%|█████████████| 15/15 [00:00<00:00, 100.44it/s]\n",
      "[VALID]> loss. 0.25 > acc. 55%: 100%|████████████| 4/4 [00:00<00:00, 154.23it/s]\n",
      "[189/500] train loss. 0.372: 100%|██████████████| 15/15 [00:00<00:00, 95.65it/s]\n",
      "[VALID]> loss. 0.24 > acc. 58%: 100%|████████████| 4/4 [00:00<00:00, 163.07it/s]\n",
      "[190/500] train loss. 0.375: 100%|█████████████| 15/15 [00:00<00:00, 100.97it/s]\n",
      "[VALID]> loss. 0.24 > acc. 58%: 100%|████████████| 4/4 [00:00<00:00, 145.31it/s]\n",
      "[191/500] train loss. 0.353: 100%|█████████████| 15/15 [00:00<00:00, 102.96it/s]\n",
      "[VALID]> loss. 0.25 > acc. 58%: 100%|████████████| 4/4 [00:00<00:00, 156.64it/s]\n",
      "[192/500] train loss. 0.357: 100%|█████████████| 15/15 [00:00<00:00, 102.53it/s]\n",
      "[VALID]> loss. 0.26 > acc. 52%: 100%|████████████| 4/4 [00:00<00:00, 178.56it/s]\n",
      "[193/500] train loss. 0.364: 100%|█████████████| 15/15 [00:00<00:00, 103.59it/s]\n",
      "[VALID]> loss. 0.25 > acc. 56%: 100%|████████████| 4/4 [00:00<00:00, 145.23it/s]\n",
      "[194/500] train loss. 0.371: 100%|██████████████| 15/15 [00:00<00:00, 84.95it/s]\n",
      "[VALID]> loss. 0.25 > acc. 53%: 100%|████████████| 4/4 [00:00<00:00, 142.51it/s]\n",
      "[195/500] train loss. 0.354: 100%|█████████████| 15/15 [00:00<00:00, 103.77it/s]\n",
      "[VALID]> loss. 0.26 > acc. 54%: 100%|████████████| 4/4 [00:00<00:00, 162.83it/s]\n",
      "[196/500] train loss. 0.372: 100%|██████████████| 15/15 [00:00<00:00, 87.23it/s]\n",
      "[VALID]> loss. 0.25 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 166.23it/s]\n",
      "[197/500] train loss. 0.376: 100%|██████████████| 15/15 [00:00<00:00, 98.40it/s]\n",
      "[VALID]> loss. 0.25 > acc. 56%: 100%|████████████| 4/4 [00:00<00:00, 157.66it/s]\n",
      "[198/500] train loss. 0.360: 100%|█████████████| 15/15 [00:00<00:00, 103.74it/s]\n",
      "[VALID]> loss. 0.25 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 148.14it/s]\n",
      "[199/500] train loss. 0.350: 100%|█████████████| 15/15 [00:00<00:00, 103.02it/s]\n",
      "[VALID]> loss. 0.24 > acc. 59%: 100%|████████████| 4/4 [00:00<00:00, 179.04it/s]\n",
      "[200/500] train loss. 0.363: 100%|██████████████| 15/15 [00:00<00:00, 88.73it/s]\n",
      "[VALID]> loss. 0.24 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 153.16it/s]\n",
      "[201/500] train loss. 0.367: 100%|██████████████| 15/15 [00:00<00:00, 87.48it/s]\n",
      "[VALID]> loss. 0.26 > acc. 55%: 100%|████████████| 4/4 [00:00<00:00, 142.01it/s]\n",
      "[202/500] train loss. 0.356: 100%|██████████████| 15/15 [00:00<00:00, 92.08it/s]\n",
      "[VALID]> loss. 0.26 > acc. 54%: 100%|████████████| 4/4 [00:00<00:00, 169.25it/s]\n",
      "[203/500] train loss. 0.347: 100%|█████████████| 15/15 [00:00<00:00, 104.58it/s]\n",
      "[VALID]> loss. 0.25 > acc. 58%: 100%|████████████| 4/4 [00:00<00:00, 165.41it/s]\n",
      "[204/500] train loss. 0.333: 100%|█████████████| 15/15 [00:00<00:00, 101.86it/s]\n",
      "[VALID]> loss. 0.25 > acc. 56%: 100%|████████████| 4/4 [00:00<00:00, 163.01it/s]\n",
      "[205/500] train loss. 0.333: 100%|█████████████| 15/15 [00:00<00:00, 101.03it/s]\n",
      "[VALID]> loss. 0.26 > acc. 54%: 100%|████████████| 4/4 [00:00<00:00, 163.11it/s]\n",
      "[206/500] train loss. 0.343: 100%|█████████████| 15/15 [00:00<00:00, 106.31it/s]\n",
      "[VALID]> loss. 0.24 > acc. 59%: 100%|████████████| 4/4 [00:00<00:00, 186.76it/s]\n",
      "[207/500] train loss. 0.344: 100%|█████████████| 15/15 [00:00<00:00, 109.19it/s]\n",
      "[VALID]> loss. 0.24 > acc. 59%: 100%|████████████| 4/4 [00:00<00:00, 151.25it/s]\n",
      "[208/500] train loss. 0.360: 100%|██████████████| 15/15 [00:00<00:00, 88.39it/s]\n",
      "[VALID]> loss. 0.26 > acc. 58%: 100%|████████████| 4/4 [00:00<00:00, 153.10it/s]\n",
      "[209/500] train loss. 0.356: 100%|██████████████| 15/15 [00:00<00:00, 87.62it/s]\n",
      "[VALID]> loss. 0.26 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 156.57it/s]\n",
      "[210/500] train loss. 0.353: 100%|██████████████| 15/15 [00:00<00:00, 89.10it/s]\n",
      "[VALID]> loss. 0.24 > acc. 59%: 100%|████████████| 4/4 [00:00<00:00, 146.90it/s]\n",
      "[211/500] train loss. 0.351: 100%|██████████████| 15/15 [00:00<00:00, 86.40it/s]\n",
      "[VALID]> loss. 0.25 > acc. 55%: 100%|████████████| 4/4 [00:00<00:00, 150.27it/s]\n",
      "[212/500] train loss. 0.347: 100%|██████████████| 15/15 [00:00<00:00, 95.63it/s]\n",
      "[VALID]> loss. 0.25 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 162.40it/s]\n",
      "[213/500] train loss. 0.340: 100%|██████████████| 15/15 [00:00<00:00, 91.98it/s]\n",
      "[VALID]> loss. 0.25 > acc. 59%: 100%|████████████| 4/4 [00:00<00:00, 141.10it/s]\n",
      "[214/500] train loss. 0.332: 100%|██████████████| 15/15 [00:00<00:00, 88.00it/s]\n",
      "[VALID]> loss. 0.25 > acc. 55%: 100%|████████████| 4/4 [00:00<00:00, 151.77it/s]\n",
      "[215/500] train loss. 0.341: 100%|██████████████| 15/15 [00:00<00:00, 81.44it/s]\n",
      "[VALID]> loss. 0.24 > acc. 61%: 100%|████████████| 4/4 [00:00<00:00, 170.83it/s]\n",
      "[216/500] train loss. 0.356: 100%|██████████████| 15/15 [00:00<00:00, 97.49it/s]\n",
      "[VALID]> loss. 0.26 > acc. 53%: 100%|████████████| 4/4 [00:00<00:00, 135.43it/s]\n",
      "[217/500] train loss. 0.359: 100%|██████████████| 15/15 [00:00<00:00, 85.23it/s]\n",
      "[VALID]> loss. 0.26 > acc. 53%: 100%|████████████| 4/4 [00:00<00:00, 153.20it/s]\n",
      "[218/500] train loss. 0.335: 100%|██████████████| 15/15 [00:00<00:00, 86.66it/s]\n",
      "[VALID]> loss. 0.26 > acc. 58%: 100%|████████████| 4/4 [00:00<00:00, 150.20it/s]\n",
      "[219/500] train loss. 0.336: 100%|██████████████| 15/15 [00:00<00:00, 88.27it/s]\n",
      "[VALID]> loss. 0.26 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 151.98it/s]\n",
      "[220/500] train loss. 0.332: 100%|██████████████| 15/15 [00:00<00:00, 87.52it/s]\n",
      "[VALID]> loss. 0.26 > acc. 53%: 100%|████████████| 4/4 [00:00<00:00, 156.14it/s]\n",
      "[221/500] train loss. 0.333: 100%|██████████████| 15/15 [00:00<00:00, 91.68it/s]\n",
      "[VALID]> loss. 0.26 > acc. 55%: 100%|████████████| 4/4 [00:00<00:00, 155.61it/s]\n",
      "[222/500] train loss. 0.325: 100%|██████████████| 15/15 [00:00<00:00, 85.51it/s]\n",
      "[VALID]> loss. 0.27 > acc. 55%: 100%|████████████| 4/4 [00:00<00:00, 144.44it/s]\n",
      "[223/500] train loss. 0.334: 100%|█████████████| 15/15 [00:00<00:00, 103.63it/s]\n",
      "[VALID]> loss. 0.25 > acc. 56%: 100%|████████████| 4/4 [00:00<00:00, 191.89it/s]\n",
      "[224/500] train loss. 0.327: 100%|█████████████| 15/15 [00:00<00:00, 106.80it/s]\n",
      "[VALID]> loss. 0.24 > acc. 62%: 100%|████████████| 4/4 [00:00<00:00, 178.72it/s]\n",
      "[225/500] train loss. 0.325: 100%|██████████████| 15/15 [00:00<00:00, 86.88it/s]\n",
      "[VALID]> loss. 0.26 > acc. 56%: 100%|████████████| 4/4 [00:00<00:00, 153.07it/s]\n",
      "[226/500] train loss. 0.328: 100%|██████████████| 15/15 [00:00<00:00, 87.99it/s]\n",
      "[VALID]> loss. 0.25 > acc. 56%: 100%|████████████| 4/4 [00:00<00:00, 145.69it/s]\n",
      "[227/500] train loss. 0.327: 100%|██████████████| 15/15 [00:00<00:00, 87.20it/s]\n",
      "[VALID]> loss. 0.24 > acc. 56%: 100%|████████████| 4/4 [00:00<00:00, 149.06it/s]\n",
      "[228/500] train loss. 0.329: 100%|██████████████| 15/15 [00:00<00:00, 87.50it/s]\n",
      "[VALID]> loss. 0.24 > acc. 54%: 100%|████████████| 4/4 [00:00<00:00, 152.76it/s]\n",
      "[229/500] train loss. 0.323: 100%|██████████████| 15/15 [00:00<00:00, 87.76it/s]\n",
      "[VALID]> loss. 0.26 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 153.98it/s]\n",
      "[230/500] train loss. 0.330: 100%|██████████████| 15/15 [00:00<00:00, 92.98it/s]\n",
      "[VALID]> loss. 0.26 > acc. 54%: 100%|████████████| 4/4 [00:00<00:00, 175.64it/s]\n",
      "[231/500] train loss. 0.322: 100%|██████████████| 15/15 [00:00<00:00, 88.11it/s]\n",
      "[VALID]> loss. 0.24 > acc. 60%: 100%|████████████| 4/4 [00:00<00:00, 154.73it/s]\n",
      "[232/500] train loss. 0.323: 100%|██████████████| 15/15 [00:00<00:00, 83.49it/s]\n",
      "[VALID]> loss. 0.25 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 153.52it/s]\n",
      "[233/500] train loss. 0.320: 100%|██████████████| 15/15 [00:00<00:00, 97.81it/s]\n",
      "[VALID]> loss. 0.25 > acc. 61%: 100%|████████████| 4/4 [00:00<00:00, 160.77it/s]\n",
      "[234/500] train loss. 0.334: 100%|██████████████| 15/15 [00:00<00:00, 94.84it/s]\n",
      "[VALID]> loss. 0.25 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 153.67it/s]\n",
      "[235/500] train loss. 0.338: 100%|██████████████| 15/15 [00:00<00:00, 86.99it/s]\n",
      "[VALID]> loss. 0.26 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 155.76it/s]\n",
      "[236/500] train loss. 0.335: 100%|██████████████| 15/15 [00:00<00:00, 89.18it/s]\n",
      "[VALID]> loss. 0.26 > acc. 56%: 100%|████████████| 4/4 [00:00<00:00, 137.35it/s]\n",
      "[237/500] train loss. 0.343: 100%|██████████████| 15/15 [00:00<00:00, 88.91it/s]\n",
      "[VALID]> loss. 0.25 > acc. 56%: 100%|████████████| 4/4 [00:00<00:00, 153.50it/s]\n",
      "[238/500] train loss. 0.344: 100%|██████████████| 15/15 [00:00<00:00, 85.86it/s]\n",
      "[VALID]> loss. 0.28 > acc. 56%: 100%|████████████| 4/4 [00:00<00:00, 133.49it/s]\n",
      "[239/500] train loss. 0.330: 100%|██████████████| 15/15 [00:00<00:00, 87.24it/s]\n",
      "[VALID]> loss. 0.27 > acc. 55%: 100%|████████████| 4/4 [00:00<00:00, 136.25it/s]\n",
      "[240/500] train loss. 0.326: 100%|██████████████| 15/15 [00:00<00:00, 92.33it/s]\n",
      "[VALID]> loss. 0.25 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 170.42it/s]\n",
      "[241/500] train loss. 0.322: 100%|██████████████| 15/15 [00:00<00:00, 99.46it/s]\n",
      "[VALID]> loss. 0.25 > acc. 58%: 100%|████████████| 4/4 [00:00<00:00, 161.80it/s]\n",
      "[242/500] train loss. 0.319: 100%|█████████████| 15/15 [00:00<00:00, 104.25it/s]\n",
      "[VALID]> loss. 0.25 > acc. 53%: 100%|████████████| 4/4 [00:00<00:00, 152.75it/s]\n",
      "[243/500] train loss. 0.346: 100%|█████████████| 15/15 [00:00<00:00, 100.21it/s]\n",
      "[VALID]> loss. 0.26 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 141.93it/s]\n",
      "[244/500] train loss. 0.331: 100%|█████████████| 15/15 [00:00<00:00, 101.63it/s]\n",
      "[VALID]> loss. 0.26 > acc. 55%: 100%|████████████| 4/4 [00:00<00:00, 158.59it/s]\n",
      "[245/500] train loss. 0.331: 100%|█████████████| 15/15 [00:00<00:00, 105.96it/s]\n",
      "[VALID]> loss. 0.25 > acc. 54%: 100%|████████████| 4/4 [00:00<00:00, 175.75it/s]\n",
      "[246/500] train loss. 0.316: 100%|█████████████| 15/15 [00:00<00:00, 105.90it/s]\n",
      "[VALID]> loss. 0.25 > acc. 55%: 100%|████████████| 4/4 [00:00<00:00, 161.11it/s]\n",
      "[247/500] train loss. 0.311: 100%|██████████████| 15/15 [00:00<00:00, 96.98it/s]\n",
      "[VALID]> loss. 0.24 > acc. 61%: 100%|████████████| 4/4 [00:00<00:00, 156.13it/s]\n",
      "[248/500] train loss. 0.318: 100%|█████████████| 15/15 [00:00<00:00, 104.60it/s]\n",
      "[VALID]> loss. 0.25 > acc. 59%: 100%|████████████| 4/4 [00:00<00:00, 158.12it/s]\n",
      "[249/500] train loss. 0.318: 100%|██████████████| 15/15 [00:00<00:00, 94.54it/s]\n",
      "[VALID]> loss. 0.25 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 152.90it/s]\n",
      "[250/500] train loss. 0.322: 100%|█████████████| 15/15 [00:00<00:00, 100.33it/s]\n",
      "[VALID]> loss. 0.25 > acc. 56%: 100%|████████████| 4/4 [00:00<00:00, 185.46it/s]\n",
      "[251/500] train loss. 0.322: 100%|██████████████| 15/15 [00:00<00:00, 86.19it/s]\n",
      "[VALID]> loss. 0.25 > acc. 60%: 100%|████████████| 4/4 [00:00<00:00, 128.10it/s]\n",
      "[252/500] train loss. 0.308: 100%|██████████████| 15/15 [00:00<00:00, 93.58it/s]\n",
      "[VALID]> loss. 0.26 > acc. 56%: 100%|████████████| 4/4 [00:00<00:00, 157.08it/s]\n",
      "[253/500] train loss. 0.312: 100%|█████████████| 15/15 [00:00<00:00, 100.61it/s]\n",
      "[VALID]> loss. 0.26 > acc. 56%: 100%|████████████| 4/4 [00:00<00:00, 170.54it/s]\n",
      "[254/500] train loss. 0.326: 100%|█████████████| 15/15 [00:00<00:00, 110.32it/s]\n",
      "[VALID]> loss. 0.29 > acc. 54%: 100%|████████████| 4/4 [00:00<00:00, 171.25it/s]\n",
      "[255/500] train loss. 0.354: 100%|█████████████| 15/15 [00:00<00:00, 104.29it/s]\n",
      "[VALID]> loss. 0.28 > acc. 52%: 100%|████████████| 4/4 [00:00<00:00, 190.22it/s]\n",
      "[256/500] train loss. 0.334: 100%|██████████████| 15/15 [00:00<00:00, 95.01it/s]\n",
      "[VALID]> loss. 0.26 > acc. 52%: 100%|████████████| 4/4 [00:00<00:00, 155.48it/s]\n",
      "[257/500] train loss. 0.325: 100%|█████████████| 15/15 [00:00<00:00, 102.13it/s]\n",
      "[VALID]> loss. 0.26 > acc. 54%: 100%|████████████| 4/4 [00:00<00:00, 177.07it/s]\n",
      "[258/500] train loss. 0.321: 100%|█████████████| 15/15 [00:00<00:00, 113.89it/s]\n",
      "[VALID]> loss. 0.25 > acc. 59%: 100%|████████████| 4/4 [00:00<00:00, 191.90it/s]\n",
      "[259/500] train loss. 0.327: 100%|█████████████| 15/15 [00:00<00:00, 110.43it/s]\n",
      "[VALID]> loss. 0.26 > acc. 58%: 100%|████████████| 4/4 [00:00<00:00, 156.12it/s]\n",
      "[260/500] train loss. 0.313: 100%|██████████████| 15/15 [00:00<00:00, 78.61it/s]\n",
      "[VALID]> loss. 0.26 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 152.87it/s]\n",
      "[261/500] train loss. 0.308: 100%|█████████████| 15/15 [00:00<00:00, 100.42it/s]\n",
      "[VALID]> loss. 0.26 > acc. 60%: 100%|████████████| 4/4 [00:00<00:00, 166.91it/s]\n",
      "[262/500] train loss. 0.322: 100%|██████████████| 15/15 [00:00<00:00, 98.62it/s]\n",
      "[VALID]> loss. 0.26 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 173.14it/s]\n",
      "[263/500] train loss. 0.331: 100%|█████████████| 15/15 [00:00<00:00, 105.33it/s]\n",
      "[VALID]> loss. 0.25 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 183.60it/s]\n",
      "[264/500] train loss. 0.316: 100%|██████████████| 15/15 [00:00<00:00, 97.15it/s]\n",
      "[VALID]> loss. 0.25 > acc. 60%: 100%|████████████| 4/4 [00:00<00:00, 171.84it/s]\n",
      "[265/500] train loss. 0.310: 100%|██████████████| 15/15 [00:00<00:00, 96.65it/s]\n",
      "[VALID]> loss. 0.25 > acc. 56%: 100%|████████████| 4/4 [00:00<00:00, 171.57it/s]\n",
      "[266/500] train loss. 0.306: 100%|██████████████| 15/15 [00:00<00:00, 84.36it/s]\n",
      "[VALID]> loss. 0.26 > acc. 58%: 100%|████████████| 4/4 [00:00<00:00, 154.34it/s]\n",
      "[267/500] train loss. 0.301: 100%|██████████████| 15/15 [00:00<00:00, 92.24it/s]\n",
      "[VALID]> loss. 0.26 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 156.00it/s]\n",
      "[268/500] train loss. 0.306: 100%|██████████████| 15/15 [00:00<00:00, 90.47it/s]\n",
      "[VALID]> loss. 0.26 > acc. 62%: 100%|████████████| 4/4 [00:00<00:00, 154.50it/s]\n",
      "[269/500] train loss. 0.306: 100%|██████████████| 15/15 [00:00<00:00, 93.77it/s]\n",
      "[VALID]> loss. 0.26 > acc. 60%: 100%|████████████| 4/4 [00:00<00:00, 162.34it/s]\n",
      "[270/500] train loss. 0.296: 100%|█████████████| 15/15 [00:00<00:00, 104.35it/s]\n",
      "[VALID]> loss. 0.26 > acc. 59%: 100%|████████████| 4/4 [00:00<00:00, 174.76it/s]\n",
      "[271/500] train loss. 0.298: 100%|█████████████| 15/15 [00:00<00:00, 102.69it/s]\n",
      "[VALID]> loss. 0.26 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 188.87it/s]\n",
      "[272/500] train loss. 0.312: 100%|█████████████| 15/15 [00:00<00:00, 103.40it/s]\n",
      "[VALID]> loss. 0.25 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 170.62it/s]\n",
      "[273/500] train loss. 0.310: 100%|█████████████| 15/15 [00:00<00:00, 103.74it/s]\n",
      "[VALID]> loss. 0.26 > acc. 58%: 100%|████████████| 4/4 [00:00<00:00, 169.72it/s]\n",
      "[274/500] train loss. 0.297: 100%|██████████████| 15/15 [00:00<00:00, 99.67it/s]\n",
      "[VALID]> loss. 0.25 > acc. 56%: 100%|████████████| 4/4 [00:00<00:00, 168.50it/s]\n",
      "[275/500] train loss. 0.300: 100%|█████████████| 15/15 [00:00<00:00, 102.36it/s]\n",
      "[VALID]> loss. 0.26 > acc. 58%: 100%|████████████| 4/4 [00:00<00:00, 183.14it/s]\n",
      "[276/500] train loss. 0.302: 100%|██████████████| 15/15 [00:00<00:00, 97.99it/s]\n",
      "[VALID]> loss. 0.27 > acc. 56%: 100%|████████████| 4/4 [00:00<00:00, 158.97it/s]\n",
      "[277/500] train loss. 0.293: 100%|██████████████| 15/15 [00:00<00:00, 98.58it/s]\n",
      "[VALID]> loss. 0.25 > acc. 58%: 100%|████████████| 4/4 [00:00<00:00, 158.50it/s]\n",
      "[278/500] train loss. 0.315: 100%|██████████████| 15/15 [00:00<00:00, 89.11it/s]\n",
      "[VALID]> loss. 0.25 > acc. 61%: 100%|████████████| 4/4 [00:00<00:00, 171.90it/s]\n",
      "[279/500] train loss. 0.299: 100%|██████████████| 15/15 [00:00<00:00, 93.87it/s]\n",
      "[VALID]> loss. 0.26 > acc. 56%: 100%|████████████| 4/4 [00:00<00:00, 154.75it/s]\n",
      "[280/500] train loss. 0.303: 100%|██████████████| 15/15 [00:00<00:00, 88.54it/s]\n",
      "[VALID]> loss. 0.27 > acc. 56%: 100%|████████████| 4/4 [00:00<00:00, 154.36it/s]\n",
      "[281/500] train loss. 0.300: 100%|██████████████| 15/15 [00:00<00:00, 87.05it/s]\n",
      "[VALID]> loss. 0.26 > acc. 54%: 100%|████████████| 4/4 [00:00<00:00, 165.78it/s]\n",
      "[282/500] train loss. 0.309: 100%|██████████████| 15/15 [00:00<00:00, 84.88it/s]\n",
      "[VALID]> loss. 0.26 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 137.55it/s]\n",
      "[283/500] train loss. 0.301: 100%|██████████████| 15/15 [00:00<00:00, 89.01it/s]\n",
      "[VALID]> loss. 0.25 > acc. 54%: 100%|████████████| 4/4 [00:00<00:00, 163.56it/s]\n",
      "[284/500] train loss. 0.305: 100%|█████████████| 15/15 [00:00<00:00, 102.28it/s]\n",
      "[VALID]> loss. 0.26 > acc. 55%: 100%|████████████| 4/4 [00:00<00:00, 163.74it/s]\n",
      "[285/500] train loss. 0.317: 100%|██████████████| 15/15 [00:00<00:00, 99.96it/s]\n",
      "[VALID]> loss. 0.27 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 163.28it/s]\n",
      "[286/500] train loss. 0.301: 100%|██████████████| 15/15 [00:00<00:00, 86.96it/s]\n",
      "[VALID]> loss. 0.26 > acc. 56%: 100%|████████████| 4/4 [00:00<00:00, 157.02it/s]\n",
      "[287/500] train loss. 0.298: 100%|██████████████| 15/15 [00:00<00:00, 87.46it/s]\n",
      "[VALID]> loss. 0.25 > acc. 58%: 100%|████████████| 4/4 [00:00<00:00, 131.46it/s]\n",
      "[288/500] train loss. 0.286: 100%|██████████████| 15/15 [00:00<00:00, 86.11it/s]\n",
      "[VALID]> loss. 0.25 > acc. 59%: 100%|████████████| 4/4 [00:00<00:00, 157.12it/s]\n",
      "[289/500] train loss. 0.294: 100%|██████████████| 15/15 [00:00<00:00, 90.59it/s]\n",
      "[VALID]> loss. 0.26 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 143.55it/s]\n",
      "[290/500] train loss. 0.292: 100%|██████████████| 15/15 [00:00<00:00, 98.94it/s]\n",
      "[VALID]> loss. 0.26 > acc. 62%: 100%|████████████| 4/4 [00:00<00:00, 169.75it/s]\n",
      "[291/500] train loss. 0.302: 100%|██████████████| 15/15 [00:00<00:00, 90.26it/s]\n",
      "[VALID]> loss. 0.27 > acc. 61%: 100%|████████████| 4/4 [00:00<00:00, 154.91it/s]\n",
      "[292/500] train loss. 0.309: 100%|█████████████| 15/15 [00:00<00:00, 105.42it/s]\n",
      "[VALID]> loss. 0.27 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 156.59it/s]\n",
      "[293/500] train loss. 0.319: 100%|██████████████| 15/15 [00:00<00:00, 96.74it/s]\n",
      "[VALID]> loss. 0.25 > acc. 59%: 100%|████████████| 4/4 [00:00<00:00, 146.87it/s]\n",
      "[294/500] train loss. 0.304: 100%|█████████████| 15/15 [00:00<00:00, 101.29it/s]\n",
      "[VALID]> loss. 0.27 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 160.78it/s]\n",
      "[295/500] train loss. 0.314: 100%|██████████████| 15/15 [00:00<00:00, 95.49it/s]\n",
      "[VALID]> loss. 0.26 > acc. 58%: 100%|████████████| 4/4 [00:00<00:00, 144.92it/s]\n",
      "[296/500] train loss. 0.298: 100%|██████████████| 15/15 [00:00<00:00, 87.69it/s]\n",
      "[VALID]> loss. 0.26 > acc. 60%: 100%|████████████| 4/4 [00:00<00:00, 155.19it/s]\n",
      "[297/500] train loss. 0.312: 100%|██████████████| 15/15 [00:00<00:00, 88.65it/s]\n",
      "[VALID]> loss. 0.25 > acc. 61%: 100%|████████████| 4/4 [00:00<00:00, 137.98it/s]\n",
      "[298/500] train loss. 0.303: 100%|██████████████| 15/15 [00:00<00:00, 84.41it/s]\n",
      "[VALID]> loss. 0.25 > acc. 58%: 100%|████████████| 4/4 [00:00<00:00, 153.45it/s]\n",
      "[299/500] train loss. 0.292: 100%|██████████████| 15/15 [00:00<00:00, 87.33it/s]\n",
      "[VALID]> loss. 0.26 > acc. 62%: 100%|████████████| 4/4 [00:00<00:00, 155.14it/s]\n",
      "[300/500] train loss. 0.291: 100%|██████████████| 15/15 [00:00<00:00, 83.17it/s]\n",
      "[VALID]> loss. 0.26 > acc. 58%: 100%|█████████████| 4/4 [00:00<00:00, 95.96it/s]\n",
      "[301/500] train loss. 0.302: 100%|██████████████| 15/15 [00:00<00:00, 93.99it/s]\n",
      "[VALID]> loss. 0.26 > acc. 58%: 100%|████████████| 4/4 [00:00<00:00, 148.92it/s]\n",
      "[302/500] train loss. 0.297: 100%|██████████████| 15/15 [00:00<00:00, 90.63it/s]\n",
      "[VALID]> loss. 0.26 > acc. 61%: 100%|████████████| 4/4 [00:00<00:00, 148.52it/s]\n",
      "[303/500] train loss. 0.290: 100%|██████████████| 15/15 [00:00<00:00, 93.97it/s]\n",
      "[VALID]> loss. 0.27 > acc. 60%: 100%|████████████| 4/4 [00:00<00:00, 150.83it/s]\n",
      "[304/500] train loss. 0.289: 100%|██████████████| 15/15 [00:00<00:00, 90.55it/s]\n",
      "[VALID]> loss. 0.26 > acc. 61%: 100%|████████████| 4/4 [00:00<00:00, 147.86it/s]\n",
      "[305/500] train loss. 0.285: 100%|██████████████| 15/15 [00:00<00:00, 90.61it/s]\n",
      "[VALID]> loss. 0.25 > acc. 62%: 100%|████████████| 4/4 [00:00<00:00, 135.02it/s]\n",
      "[306/500] train loss. 0.289: 100%|██████████████| 15/15 [00:00<00:00, 87.68it/s]\n",
      "[VALID]> loss. 0.26 > acc. 62%: 100%|████████████| 4/4 [00:00<00:00, 151.11it/s]\n",
      "[307/500] train loss. 0.291: 100%|██████████████| 15/15 [00:00<00:00, 86.43it/s]\n",
      "[VALID]> loss. 0.26 > acc. 61%: 100%|████████████| 4/4 [00:00<00:00, 143.84it/s]\n",
      "[308/500] train loss. 0.293: 100%|██████████████| 15/15 [00:00<00:00, 86.60it/s]\n",
      "[VALID]> loss. 0.25 > acc. 59%: 100%|████████████| 4/4 [00:00<00:00, 134.38it/s]\n",
      "[309/500] train loss. 0.290: 100%|██████████████| 15/15 [00:00<00:00, 88.28it/s]\n",
      "[VALID]> loss. 0.25 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 147.55it/s]\n",
      "[310/500] train loss. 0.286: 100%|██████████████| 15/15 [00:00<00:00, 86.03it/s]\n",
      "[VALID]> loss. 0.28 > acc. 60%: 100%|████████████| 4/4 [00:00<00:00, 165.94it/s]\n",
      "[311/500] train loss. 0.293: 100%|██████████████| 15/15 [00:00<00:00, 99.41it/s]\n",
      "[VALID]> loss. 0.25 > acc. 60%: 100%|████████████| 4/4 [00:00<00:00, 179.51it/s]\n",
      "[312/500] train loss. 0.303: 100%|█████████████| 15/15 [00:00<00:00, 108.22it/s]\n",
      "[VALID]> loss. 0.26 > acc. 62%: 100%|████████████| 4/4 [00:00<00:00, 192.32it/s]\n",
      "[313/500] train loss. 0.291: 100%|██████████████| 15/15 [00:00<00:00, 92.22it/s]\n",
      "[VALID]> loss. 0.27 > acc. 59%: 100%|████████████| 4/4 [00:00<00:00, 190.23it/s]\n",
      "[314/500] train loss. 0.286: 100%|█████████████| 15/15 [00:00<00:00, 108.37it/s]\n",
      "[VALID]> loss. 0.25 > acc. 59%: 100%|████████████| 4/4 [00:00<00:00, 158.05it/s]\n",
      "[315/500] train loss. 0.290: 100%|█████████████| 15/15 [00:00<00:00, 103.59it/s]\n",
      "[VALID]> loss. 0.24 > acc. 63%: 100%|████████████| 4/4 [00:00<00:00, 160.26it/s]\n",
      "[316/500] train loss. 0.312: 100%|██████████████| 15/15 [00:00<00:00, 98.47it/s]\n",
      "[VALID]> loss. 0.27 > acc. 61%: 100%|████████████| 4/4 [00:00<00:00, 191.00it/s]\n",
      "[317/500] train loss. 0.294: 100%|█████████████| 15/15 [00:00<00:00, 108.61it/s]\n",
      "[VALID]> loss. 0.27 > acc. 56%: 100%|████████████| 4/4 [00:00<00:00, 157.74it/s]\n",
      "[318/500] train loss. 0.292: 100%|██████████████| 15/15 [00:00<00:00, 91.72it/s]\n",
      "[VALID]> loss. 0.24 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 165.46it/s]\n",
      "[319/500] train loss. 0.286: 100%|█████████████| 15/15 [00:00<00:00, 101.78it/s]\n",
      "[VALID]> loss. 0.24 > acc. 62%: 100%|████████████| 4/4 [00:00<00:00, 160.37it/s]\n",
      "[320/500] train loss. 0.295: 100%|██████████████| 15/15 [00:00<00:00, 93.63it/s]\n",
      "[VALID]> loss. 0.26 > acc. 59%: 100%|████████████| 4/4 [00:00<00:00, 153.51it/s]\n",
      "[321/500] train loss. 0.295: 100%|█████████████| 15/15 [00:00<00:00, 101.58it/s]\n",
      "[VALID]> loss. 0.25 > acc. 63%: 100%|████████████| 4/4 [00:00<00:00, 156.07it/s]\n",
      "[322/500] train loss. 0.285: 100%|██████████████| 15/15 [00:00<00:00, 76.96it/s]\n",
      "[VALID]> loss. 0.26 > acc. 62%: 100%|████████████| 4/4 [00:00<00:00, 149.39it/s]\n",
      "[323/500] train loss. 0.289: 100%|██████████████| 15/15 [00:00<00:00, 85.24it/s]\n",
      "[VALID]> loss. 0.27 > acc. 58%: 100%|████████████| 4/4 [00:00<00:00, 146.68it/s]\n",
      "[324/500] train loss. 0.294: 100%|██████████████| 15/15 [00:00<00:00, 83.77it/s]\n",
      "[VALID]> loss. 0.25 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 141.69it/s]\n",
      "[325/500] train loss. 0.305: 100%|██████████████| 15/15 [00:00<00:00, 87.66it/s]\n",
      "[VALID]> loss. 0.24 > acc. 61%: 100%|████████████| 4/4 [00:00<00:00, 152.05it/s]\n",
      "[326/500] train loss. 0.298: 100%|██████████████| 15/15 [00:00<00:00, 86.99it/s]\n",
      "[VALID]> loss. 0.27 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 155.35it/s]\n",
      "[327/500] train loss. 0.287: 100%|██████████████| 15/15 [00:00<00:00, 88.60it/s]\n",
      "[VALID]> loss. 0.24 > acc. 62%: 100%|████████████| 4/4 [00:00<00:00, 156.34it/s]\n",
      "[328/500] train loss. 0.281: 100%|██████████████| 15/15 [00:00<00:00, 88.22it/s]\n",
      "[VALID]> loss. 0.25 > acc. 58%: 100%|████████████| 4/4 [00:00<00:00, 143.65it/s]\n",
      "[329/500] train loss. 0.285: 100%|██████████████| 15/15 [00:00<00:00, 87.17it/s]\n",
      "[VALID]> loss. 0.26 > acc. 58%: 100%|████████████| 4/4 [00:00<00:00, 151.86it/s]\n",
      "[330/500] train loss. 0.292: 100%|██████████████| 15/15 [00:00<00:00, 94.80it/s]\n",
      "[VALID]> loss. 0.24 > acc. 62%: 100%|████████████| 4/4 [00:00<00:00, 161.59it/s]\n",
      "[331/500] train loss. 0.291: 100%|█████████████| 15/15 [00:00<00:00, 103.33it/s]\n",
      "[VALID]> loss. 0.27 > acc. 64%: 100%|████████████| 4/4 [00:00<00:00, 157.15it/s]\n",
      "[332/500] train loss. 0.303: 100%|█████████████| 15/15 [00:00<00:00, 100.36it/s]\n",
      "[VALID]> loss. 0.27 > acc. 58%: 100%|████████████| 4/4 [00:00<00:00, 163.96it/s]\n",
      "[333/500] train loss. 0.295: 100%|██████████████| 15/15 [00:00<00:00, 91.87it/s]\n",
      "[VALID]> loss. 0.25 > acc. 58%: 100%|████████████| 4/4 [00:00<00:00, 151.38it/s]\n",
      "[334/500] train loss. 0.282: 100%|██████████████| 15/15 [00:00<00:00, 89.28it/s]\n",
      "[VALID]> loss. 0.26 > acc. 55%: 100%|████████████| 4/4 [00:00<00:00, 151.43it/s]\n",
      "[335/500] train loss. 0.277: 100%|██████████████| 15/15 [00:00<00:00, 88.29it/s]\n",
      "[VALID]> loss. 0.27 > acc. 60%: 100%|████████████| 4/4 [00:00<00:00, 154.04it/s]\n",
      "[336/500] train loss. 0.288: 100%|██████████████| 15/15 [00:00<00:00, 88.04it/s]\n",
      "[VALID]> loss. 0.28 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 150.63it/s]\n",
      "[337/500] train loss. 0.277: 100%|██████████████| 15/15 [00:00<00:00, 85.19it/s]\n",
      "[VALID]> loss. 0.25 > acc. 61%: 100%|████████████| 4/4 [00:00<00:00, 142.20it/s]\n",
      "[338/500] train loss. 0.284: 100%|██████████████| 15/15 [00:00<00:00, 86.49it/s]\n",
      "[VALID]> loss. 0.26 > acc. 61%: 100%|████████████| 4/4 [00:00<00:00, 153.41it/s]\n",
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      "[VALID]> loss. 0.26 > acc. 60%: 100%|████████████| 4/4 [00:00<00:00, 158.81it/s]\n",
      "[340/500] train loss. 0.280: 100%|█████████████| 15/15 [00:00<00:00, 100.57it/s]\n",
      "[VALID]> loss. 0.26 > acc. 62%: 100%|████████████| 4/4 [00:00<00:00, 154.73it/s]\n",
      "[341/500] train loss. 0.281: 100%|██████████████| 15/15 [00:00<00:00, 81.66it/s]\n",
      "[VALID]> loss. 0.25 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 123.07it/s]\n",
      "[342/500] train loss. 0.287: 100%|██████████████| 15/15 [00:00<00:00, 91.20it/s]\n",
      "[VALID]> loss. 0.29 > acc. 56%: 100%|████████████| 4/4 [00:00<00:00, 138.62it/s]\n",
      "[343/500] train loss. 0.290: 100%|██████████████| 15/15 [00:00<00:00, 94.85it/s]\n",
      "[VALID]> loss. 0.26 > acc. 60%: 100%|████████████| 4/4 [00:00<00:00, 158.85it/s]\n",
      "[344/500] train loss. 0.294: 100%|██████████████| 15/15 [00:00<00:00, 92.01it/s]\n",
      "[VALID]> loss. 0.26 > acc. 61%: 100%|████████████| 4/4 [00:00<00:00, 143.26it/s]\n",
      "[345/500] train loss. 0.278: 100%|██████████████| 15/15 [00:00<00:00, 91.77it/s]\n",
      "[VALID]> loss. 0.26 > acc. 62%: 100%|████████████| 4/4 [00:00<00:00, 143.90it/s]\n",
      "[346/500] train loss. 0.268: 100%|██████████████| 15/15 [00:00<00:00, 93.24it/s]\n",
      "[VALID]> loss. 0.25 > acc. 59%: 100%|████████████| 4/4 [00:00<00:00, 146.86it/s]\n",
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      "[VALID]> loss. 0.27 > acc. 62%: 100%|████████████| 4/4 [00:00<00:00, 149.72it/s]\n",
      "[348/500] train loss. 0.279: 100%|██████████████| 15/15 [00:00<00:00, 93.33it/s]\n",
      "[VALID]> loss. 0.25 > acc. 62%: 100%|████████████| 4/4 [00:00<00:00, 158.12it/s]\n",
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      "[VALID]> loss. 0.26 > acc. 62%: 100%|████████████| 4/4 [00:00<00:00, 141.48it/s]\n",
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      "[VALID]> loss. 0.27 > acc. 58%: 100%|████████████| 4/4 [00:00<00:00, 151.96it/s]\n",
      "[351/500] train loss. 0.279: 100%|██████████████| 15/15 [00:00<00:00, 85.73it/s]\n",
      "[VALID]> loss. 0.25 > acc. 63%: 100%|████████████| 4/4 [00:00<00:00, 139.85it/s]\n",
      "[352/500] train loss. 0.277: 100%|██████████████| 15/15 [00:00<00:00, 88.10it/s]\n",
      "[VALID]> loss. 0.25 > acc. 59%: 100%|████████████| 4/4 [00:00<00:00, 193.00it/s]\n",
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      "[VALID]> loss. 0.26 > acc. 59%: 100%|████████████| 4/4 [00:00<00:00, 142.90it/s]\n",
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      "[VALID]> loss. 0.28 > acc. 60%: 100%|████████████| 4/4 [00:00<00:00, 149.31it/s]\n",
      "[355/500] train loss. 0.290: 100%|██████████████| 15/15 [00:00<00:00, 84.58it/s]\n",
      "[VALID]> loss. 0.27 > acc. 62%: 100%|████████████| 4/4 [00:00<00:00, 151.66it/s]\n",
      "[356/500] train loss. 0.271: 100%|██████████████| 15/15 [00:00<00:00, 87.63it/s]\n",
      "[VALID]> loss. 0.26 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 154.12it/s]\n",
      "[357/500] train loss. 0.283: 100%|██████████████| 15/15 [00:00<00:00, 87.88it/s]\n",
      "[VALID]> loss. 0.27 > acc. 62%: 100%|████████████| 4/4 [00:00<00:00, 154.33it/s]\n",
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      "[VALID]> loss. 0.25 > acc. 62%: 100%|████████████| 4/4 [00:00<00:00, 153.18it/s]\n",
      "[359/500] train loss. 0.274: 100%|██████████████| 15/15 [00:00<00:00, 88.94it/s]\n",
      "[VALID]> loss. 0.25 > acc. 62%: 100%|████████████| 4/4 [00:00<00:00, 153.99it/s]\n",
      "[360/500] train loss. 0.279: 100%|██████████████| 15/15 [00:00<00:00, 98.19it/s]\n",
      "[VALID]> loss. 0.27 > acc. 61%: 100%|████████████| 4/4 [00:00<00:00, 179.03it/s]\n",
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      "[VALID]> loss. 0.26 > acc. 59%: 100%|████████████| 4/4 [00:00<00:00, 185.65it/s]\n",
      "[362/500] train loss. 0.275: 100%|██████████████| 15/15 [00:00<00:00, 97.37it/s]\n",
      "[VALID]> loss. 0.25 > acc. 64%: 100%|████████████| 4/4 [00:00<00:00, 165.96it/s]\n",
      "[363/500] train loss. 0.284: 100%|██████████████| 15/15 [00:00<00:00, 96.88it/s]\n",
      "[VALID]> loss. 0.26 > acc. 60%: 100%|████████████| 4/4 [00:00<00:00, 153.77it/s]\n",
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      "[VALID]> loss. 0.26 > acc. 62%: 100%|████████████| 4/4 [00:00<00:00, 132.89it/s]\n",
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      "[VALID]> loss. 0.27 > acc. 60%: 100%|████████████| 4/4 [00:00<00:00, 130.92it/s]\n",
      "[366/500] train loss. 0.294: 100%|██████████████| 15/15 [00:00<00:00, 90.17it/s]\n",
      "[VALID]> loss. 0.26 > acc. 62%: 100%|████████████| 4/4 [00:00<00:00, 157.36it/s]\n",
      "[367/500] train loss. 0.281: 100%|█████████████| 15/15 [00:00<00:00, 104.60it/s]\n",
      "[VALID]> loss. 0.29 > acc. 60%: 100%|████████████| 4/4 [00:00<00:00, 154.11it/s]\n",
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      "[VALID]> loss. 0.26 > acc. 64%: 100%|████████████| 4/4 [00:00<00:00, 147.49it/s]\n",
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      "[VALID]> loss. 0.26 > acc. 62%: 100%|████████████| 4/4 [00:00<00:00, 159.72it/s]\n",
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      "[VALID]> loss. 0.27 > acc. 59%: 100%|████████████| 4/4 [00:00<00:00, 158.63it/s]\n",
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      "[VALID]> loss. 0.26 > acc. 63%: 100%|████████████| 4/4 [00:00<00:00, 177.04it/s]\n",
      "[372/500] train loss. 0.270: 100%|██████████████| 15/15 [00:00<00:00, 76.83it/s]\n",
      "[VALID]> loss. 0.25 > acc. 63%: 100%|████████████| 4/4 [00:00<00:00, 152.53it/s]\n",
      "[373/500] train loss. 0.273: 100%|██████████████| 15/15 [00:00<00:00, 86.85it/s]\n",
      "[VALID]> loss. 0.26 > acc. 62%: 100%|████████████| 4/4 [00:00<00:00, 154.75it/s]\n",
      "[374/500] train loss. 0.274: 100%|██████████████| 15/15 [00:00<00:00, 87.16it/s]\n",
      "[VALID]> loss. 0.25 > acc. 61%: 100%|████████████| 4/4 [00:00<00:00, 150.42it/s]\n",
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      "[VALID]> loss. 0.27 > acc. 62%: 100%|████████████| 4/4 [00:00<00:00, 131.37it/s]\n",
      "[379/500] train loss. 0.268: 100%|██████████████| 15/15 [00:00<00:00, 86.79it/s]\n",
      "[VALID]> loss. 0.25 > acc. 64%: 100%|████████████| 4/4 [00:00<00:00, 142.33it/s]\n",
      "[380/500] train loss. 0.261: 100%|██████████████| 15/15 [00:00<00:00, 89.60it/s]\n",
      "[VALID]> loss. 0.25 > acc. 63%: 100%|████████████| 4/4 [00:00<00:00, 157.05it/s]\n",
      "[381/500] train loss. 0.269: 100%|██████████████| 15/15 [00:00<00:00, 86.71it/s]\n",
      "[VALID]> loss. 0.26 > acc. 61%: 100%|████████████| 4/4 [00:00<00:00, 157.05it/s]\n",
      "[382/500] train loss. 0.277: 100%|██████████████| 15/15 [00:00<00:00, 86.18it/s]\n",
      "[VALID]> loss. 0.26 > acc. 61%: 100%|████████████| 4/4 [00:00<00:00, 155.23it/s]\n",
      "[383/500] train loss. 0.272: 100%|██████████████| 15/15 [00:00<00:00, 88.21it/s]\n",
      "[VALID]> loss. 0.26 > acc. 62%: 100%|████████████| 4/4 [00:00<00:00, 154.41it/s]\n",
      "[384/500] train loss. 0.267: 100%|██████████████| 15/15 [00:00<00:00, 87.04it/s]\n",
      "[VALID]> loss. 0.25 > acc. 61%: 100%|████████████| 4/4 [00:00<00:00, 153.90it/s]\n",
      "[385/500] train loss. 0.270: 100%|██████████████| 15/15 [00:00<00:00, 88.29it/s]\n",
      "[VALID]> loss. 0.27 > acc. 60%: 100%|████████████| 4/4 [00:00<00:00, 156.84it/s]\n",
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      "[VALID]> loss. 0.27 > acc. 62%: 100%|████████████| 4/4 [00:00<00:00, 150.64it/s]\n",
      "[387/500] train loss. 0.278: 100%|██████████████| 15/15 [00:00<00:00, 84.00it/s]\n",
      "[VALID]> loss. 0.28 > acc. 62%: 100%|████████████| 4/4 [00:00<00:00, 146.09it/s]\n",
      "[388/500] train loss. 0.278: 100%|██████████████| 15/15 [00:00<00:00, 86.63it/s]\n",
      "[VALID]> loss. 0.24 > acc. 70%: 100%|████████████| 4/4 [00:00<00:00, 144.04it/s]\n",
      "[389/500] train loss. 0.269: 100%|██████████████| 15/15 [00:00<00:00, 78.53it/s]\n",
      "[VALID]> loss. 0.25 > acc. 66%: 100%|████████████| 4/4 [00:00<00:00, 160.02it/s]\n",
      "[390/500] train loss. 0.264: 100%|██████████████| 15/15 [00:00<00:00, 87.65it/s]\n",
      "[VALID]> loss. 0.26 > acc. 62%: 100%|████████████| 4/4 [00:00<00:00, 147.02it/s]\n",
      "[391/500] train loss. 0.270: 100%|██████████████| 15/15 [00:00<00:00, 88.10it/s]\n",
      "[VALID]> loss. 0.25 > acc. 68%: 100%|████████████| 4/4 [00:00<00:00, 151.19it/s]\n",
      "[392/500] train loss. 0.267: 100%|██████████████| 15/15 [00:00<00:00, 85.74it/s]\n",
      "[VALID]> loss. 0.26 > acc. 63%: 100%|████████████| 4/4 [00:00<00:00, 154.58it/s]\n",
      "[393/500] train loss. 0.270: 100%|██████████████| 15/15 [00:00<00:00, 85.26it/s]\n",
      "[VALID]> loss. 0.27 > acc. 63%: 100%|████████████| 4/4 [00:00<00:00, 141.12it/s]\n",
      "[394/500] train loss. 0.263: 100%|██████████████| 15/15 [00:00<00:00, 84.21it/s]\n",
      "[VALID]> loss. 0.26 > acc. 64%: 100%|████████████| 4/4 [00:00<00:00, 139.87it/s]\n",
      "[395/500] train loss. 0.269: 100%|██████████████| 15/15 [00:00<00:00, 89.10it/s]\n",
      "[VALID]> loss. 0.25 > acc. 65%: 100%|████████████| 4/4 [00:00<00:00, 148.39it/s]\n",
      "[396/500] train loss. 0.270: 100%|██████████████| 15/15 [00:00<00:00, 86.05it/s]\n",
      "[VALID]> loss. 0.27 > acc. 62%: 100%|████████████| 4/4 [00:00<00:00, 138.88it/s]\n",
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      "[VALID]> loss. 0.26 > acc. 64%: 100%|████████████| 4/4 [00:00<00:00, 131.42it/s]\n",
      "[398/500] train loss. 0.266: 100%|██████████████| 15/15 [00:00<00:00, 88.15it/s]\n",
      "[VALID]> loss. 0.27 > acc. 66%: 100%|████████████| 4/4 [00:00<00:00, 156.10it/s]\n",
      "[399/500] train loss. 0.274: 100%|██████████████| 15/15 [00:00<00:00, 89.10it/s]\n",
      "[VALID]> loss. 0.27 > acc. 65%: 100%|████████████| 4/4 [00:00<00:00, 149.43it/s]\n",
      "[400/500] train loss. 0.289: 100%|██████████████| 15/15 [00:00<00:00, 89.49it/s]\n",
      "[VALID]> loss. 0.25 > acc. 68%: 100%|████████████| 4/4 [00:00<00:00, 150.53it/s]\n",
      "[401/500] train loss. 0.287: 100%|██████████████| 15/15 [00:00<00:00, 86.15it/s]\n",
      "[VALID]> loss. 0.24 > acc. 64%: 100%|████████████| 4/4 [00:00<00:00, 153.02it/s]\n",
      "[402/500] train loss. 0.296: 100%|██████████████| 15/15 [00:00<00:00, 92.06it/s]\n",
      "[VALID]> loss. 0.27 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 192.00it/s]\n",
      "[403/500] train loss. 0.284: 100%|██████████████| 15/15 [00:00<00:00, 96.97it/s]\n",
      "[VALID]> loss. 0.26 > acc. 63%: 100%|████████████| 4/4 [00:00<00:00, 183.92it/s]\n",
      "[404/500] train loss. 0.270: 100%|█████████████| 15/15 [00:00<00:00, 101.24it/s]\n",
      "[VALID]> loss. 0.27 > acc. 61%: 100%|████████████| 4/4 [00:00<00:00, 158.50it/s]\n",
      "[405/500] train loss. 0.264: 100%|█████████████| 15/15 [00:00<00:00, 100.39it/s]\n",
      "[VALID]> loss. 0.25 > acc. 63%: 100%|████████████| 4/4 [00:00<00:00, 147.84it/s]\n",
      "[406/500] train loss. 0.262: 100%|██████████████| 15/15 [00:00<00:00, 99.38it/s]\n",
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      "[407/500] train loss. 0.271: 100%|██████████████| 15/15 [00:00<00:00, 97.75it/s]\n",
      "[VALID]> loss. 0.26 > acc. 63%: 100%|████████████| 4/4 [00:00<00:00, 155.07it/s]\n",
      "[408/500] train loss. 0.268: 100%|██████████████| 15/15 [00:00<00:00, 89.64it/s]\n",
      "[VALID]> loss. 0.25 > acc. 64%: 100%|████████████| 4/4 [00:00<00:00, 150.92it/s]\n",
      "[409/500] train loss. 0.260: 100%|██████████████| 15/15 [00:00<00:00, 86.69it/s]\n",
      "[VALID]> loss. 0.26 > acc. 62%: 100%|████████████| 4/4 [00:00<00:00, 160.91it/s]\n",
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      "[VALID]> loss. 0.25 > acc. 64%: 100%|████████████| 4/4 [00:00<00:00, 193.72it/s]\n",
      "[411/500] train loss. 0.268: 100%|█████████████| 15/15 [00:00<00:00, 106.93it/s]\n",
      "[VALID]> loss. 0.27 > acc. 62%: 100%|████████████| 4/4 [00:00<00:00, 154.26it/s]\n",
      "[412/500] train loss. 0.261: 100%|██████████████| 15/15 [00:00<00:00, 93.23it/s]\n",
      "[VALID]> loss. 0.26 > acc. 63%: 100%|████████████| 4/4 [00:00<00:00, 149.05it/s]\n",
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      "[VALID]> loss. 0.26 > acc. 62%: 100%|████████████| 4/4 [00:00<00:00, 191.65it/s]\n",
      "[414/500] train loss. 0.262: 100%|██████████████| 15/15 [00:00<00:00, 99.25it/s]\n",
      "[VALID]> loss. 0.28 > acc. 62%: 100%|████████████| 4/4 [00:00<00:00, 151.37it/s]\n",
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      "[VALID]> loss. 0.26 > acc. 60%: 100%|████████████| 4/4 [00:00<00:00, 168.58it/s]\n",
      "[416/500] train loss. 0.272: 100%|██████████████| 15/15 [00:00<00:00, 99.37it/s]\n",
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      "[VALID]> loss. 0.25 > acc. 62%: 100%|████████████| 4/4 [00:00<00:00, 153.90it/s]\n",
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      "[VALID]> loss. 0.25 > acc. 66%: 100%|████████████| 4/4 [00:00<00:00, 138.84it/s]\n",
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      "[VALID]> loss. 0.27 > acc. 59%: 100%|████████████| 4/4 [00:00<00:00, 139.25it/s]\n",
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      "[VALID]> loss. 0.26 > acc. 61%: 100%|████████████| 4/4 [00:00<00:00, 141.92it/s]\n",
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      "[VALID]> loss. 0.29 > acc. 59%: 100%|████████████| 4/4 [00:00<00:00, 148.75it/s]\n",
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      "[VALID]> loss. 0.26 > acc. 63%: 100%|████████████| 4/4 [00:00<00:00, 153.85it/s]\n",
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      "[VALID]> loss. 0.25 > acc. 62%: 100%|████████████| 4/4 [00:00<00:00, 151.57it/s]\n",
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      "[VALID]> loss. 0.27 > acc. 61%: 100%|████████████| 4/4 [00:00<00:00, 129.89it/s]\n",
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      "[VALID]> loss. 0.27 > acc. 57%: 100%|████████████| 4/4 [00:00<00:00, 123.85it/s]\n",
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      "[VALID]> loss. 0.25 > acc. 64%: 100%|████████████| 4/4 [00:00<00:00, 137.15it/s]\n",
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      "[VALID]> loss. 0.24 > acc. 65%: 100%|████████████| 4/4 [00:00<00:00, 121.57it/s]\n",
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      "[VALID]> loss. 0.26 > acc. 60%: 100%|████████████| 4/4 [00:00<00:00, 144.19it/s]\n",
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      "[VALID]> loss. 0.25 > acc. 62%: 100%|████████████| 4/4 [00:00<00:00, 146.69it/s]\n",
      "[497/500] train loss. 0.249: 100%|██████████████| 15/15 [00:00<00:00, 67.95it/s]\n",
      "[VALID]> loss. 0.24 > acc. 64%: 100%|████████████| 4/4 [00:00<00:00, 128.03it/s]\n",
      "[498/500] train loss. 0.248: 100%|██████████████| 15/15 [00:00<00:00, 70.77it/s]\n",
      "[VALID]> loss. 0.25 > acc. 64%: 100%|████████████| 4/4 [00:00<00:00, 132.90it/s]\n",
      "[499/500] train loss. 0.252: 100%|██████████████| 15/15 [00:00<00:00, 72.55it/s]\n",
      "[VALID]> loss. 0.28 > acc. 61%: 100%|████████████| 4/4 [00:00<00:00, 125.52it/s]\n",
      "[500/500] train loss. 0.245: 100%|██████████████| 15/15 [00:00<00:00, 76.65it/s]\n",
      "[VALID]> loss. 0.27 > acc. 64%: 100%|████████████| 4/4 [00:00<00:00, 136.44it/s]\n"
     ]
    }
   ],
   "source": [
    "## model parameters\n",
    "n_node_features = 21\n",
    "conv_channels = 128\n",
    "n_classes = 6\n",
    "fc_size = 256\n",
    "dropout = 0.15\n",
    "\n",
    "activation = nn.GELU # in [nn.ReLU, nn.LeakyReLU, nn.GELU, etc]\n",
    "global_pooling = global_mean_pool # in [global_add_pool, global_max_pool, global_mean_pool]\n",
    "\n",
    "## device: cpu / gpu\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "print('available device:', device)\n",
    "\n",
    "## build model\n",
    "model = GCN(n_node_features=n_node_features,\n",
    "            n_classes=n_classes,\n",
    "            conv_channels=conv_channels,\n",
    "            fc_size=fc_size,\n",
    "            global_pooling=global_pooling,\n",
    "            activation=activation,\n",
    "            dropout=dropout)\n",
    "\n",
    "\n",
    "## trainig parameters\n",
    "lr = 1e-3\n",
    "weight_decay = 5e-2\n",
    "num_epochs = 500\n",
    "\n",
    "## create optimizer, schedular and loss function\n",
    "optimizer = optim.AdamW(model.parameters(), lr=lr, weight_decay=weight_decay)\n",
    "scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=20, eta_min=0, last_epoch=-1, verbose=False)\n",
    "criterion = nn.CrossEntropyLoss(label_smoothing=0.1)\n",
    "\n",
    "## put model into device\n",
    "model = nn.DataParallel(model).to(device)\n",
    "\n",
    "# Training loop\n",
    "valid_accuracies = []\n",
    "valid_losses = []\n",
    "\n",
    "best_preds = None\n",
    "best_acc = 0.0\n",
    "best_model = model\n",
    "\n",
    "for epoch in range(1, num_epochs+1):\n",
    "    train_loss = train_epoch(epoch, num_epochs, model, optimizer, train_loader, device, criterion)\n",
    "    valid_loss, val_acc, true_labels, pred_labels = validate_epoch(model, val_loader, criterion)\n",
    "\n",
    "    valid_accuracies.append(val_acc * 100)\n",
    "    valid_losses.append(valid_loss)\n",
    "    \n",
    "    if best_acc < val_acc:\n",
    "        best_preds = pred_labels\n",
    "        best_acc = val_acc\n",
    "        best_model = deepcopy(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37fa4918-cc02-4e79-ac35-562650c0dcd0",
   "metadata": {
    "id": "37fa4918-cc02-4e79-ac35-562650c0dcd0"
   },
   "source": [
    "### ■ **<a name=\"eval\">4. Model Evaluation</a>** [(&#8593;)](#content)\n",
    "Now we evaluate our trained model. We plot both the validation loss and accuracy. We also plot the confusion matrix for different classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7e9e57f3-4ed2-494c-ba90-72533a94144e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 581
    },
    "id": "7e9e57f3-4ed2-494c-ba90-72533a94144e",
    "outputId": "5a34e559-1a06-4916-ff58-0c9e651a312b",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best validation accuracy: 70.0%\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f'The best validation accuracy: {round(best_acc * 100, 2)}%')\n",
    "\n",
    "## display validation loss and accuracy\n",
    "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(18, 6))\n",
    "\n",
    "## loss\n",
    "ax1.plot(range(1, num_epochs+1), valid_losses)\n",
    "ax1.set_xlabel('epoch')\n",
    "ax1.set_ylabel('loss')\n",
    "ax1.set_title('Validation Loss')\n",
    "\n",
    "## accuracy\n",
    "ax2.plot(range(1, num_epochs+1), valid_accuracies)\n",
    "ax2.set_xlabel('epoch')\n",
    "ax2.set_ylabel('accuracy (%)')\n",
    "ax2.set_title('Validation Accuracy')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e9c51837-1d48-4272-92b5-c4a19526c5c3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "e9c51837-1d48-4272-92b5-c4a19526c5c3",
    "outputId": "247a47eb-85fe-4c4d-8646-089401dfd44b",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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73vcyaNCgjBo1aoHXGTZsWE477bQma99faaX8YOWVF6WcqlV+//3Mff31tFphhTQ88kjq2rRJXYcOTbpcWiy//DxnviT/6mxp0blzkz0tll8+H7/wwuItnqVey1Ytc+alp6Rnn545aq8hC+w4mJf3Z36QiS+9nj4r915MFVItvNZYHN566518/PHH6d6ja5P17t27ZfKUafM8Z/LkaenRvWk3S48eXee7H/5T6/o22fcHB+S8756Vx+765CPdE597NSuv0y9fO3z3+QYue31/v9x3wz2565o7kySvjX819cu0zeHDjswNF12XcrncbM+BJdfUKdMyYfyLTdYmPP9Sdt51h888d9c9dso5F56aIw75fh64d/TiKhGYj0VqKXniiScyePDgT4UtySefCxw8eHAef/zxz7zO0KFDM2PGjCa3o/v2XZRSqlpdu3Zp2bt35r79dj5+/vmU58xJm89/vvF4yxVXTMuePec7MHfupEmZ+/bbTc6pW2aZtF5nncyezzmQ/OsX4D79+uTovb+fme/Of/r9/LRbpm36rNQ7b031FxTmz2uNxWXOnDl59NEns/12WzWu1dXVZfvttsro0WPnec7oh8Zm++23arK2w5e2me9++E+tWrdMqzatUy41DUhKc0upW0AHd327+nmekySZx8/b1KZHHno8q662cpO1VVZbOa+/PmmB5319j53zs4t+kqO+c0LuuuP+Be4FFo9F6nDp2bNnHn744ay11lrzPP7www+nR48en3md+vr61NfXN1n7qEY/TpQkHY44Ig0PPpi5U6akZZcuaX/IIUmplFkjR6b8wQf56JZbsuyRR6Y0c2bKH36YZY8+OrOffrpJ4NLlyivz/q9+lYYHHkiSfHj99Wn/rW9l7uuvf/K10IcdlrlvvdV4nNrUbpm26dNvhcb7vfv2yurrrpqZ776Xt6a+nZ/+6rSsuf7qOe6gH6ZFyxbp3G35JMnM6e/l4zkfJ0ku+uPPcu+t9+f6K25Mkgw6+Xt54I5RmfT65HTr2TXf/v7BmVsq5Y4bRzb782PJ4bVGJZ1/4WUZcfn5Gfvokxkz5rEcPeg7ad++Xa747R+TJCN+c2HefHNSfnTSWUmSiy66PHeNvD6Dj/1ubrn1zuy9127ZeOMN8r0jj6/k02AJU79M2/RcuVfj/e4rds9K6/TL+9Pfy9tvvpVxo57OAT88KLNnzc60N6Zmnc3WyzYDvpgr/+3rnQf+/Ji8M/nt/OGc3ydJxt45Jrt8++t5ZdxLmfD48+m5Uq/s/f39MvbOMSmXzFvgE7++5MrccNvvMnDwt/PXG2/PRp9fP/sdOCAnDj69cc8JJx+Tnr26Z/CRP0ryyceIfn7xGTl16Nl5fOyT6da9S5Jk1kcNee+9BQ+oB4qzSIHLcccdl8MPPzxjx47Nl770pcZwZcqUKRk5cmQuu+yynHfeeYul0GrWslu3dDr55LTo2DGlGTMy56mn8s6RR6Y845Mp4u8NH56USlnu9NNT17p1GsaMyXsXXNDkGq369k1dhw6N9z/8wx9S17Ztlj3uuLTo0CGzn3oq048/Ppk9uzmfGkuYtTZcMxdff0Hj/WNOHZgk+du1t+XXP7si2+y4ZZLkd3f8usl5R37z2Dw26okkyQor9U6nzp0aj3Xr1S2nDT8pnZbvmOnvzMgTDz+V7+w6MNPfMQW/lnmtUUnXXXdTunXtnFN/fFx69uyWJ54Yl12+dkCmTv1kkG7fFXun9G+/zI4a/UgOOPConH7a8TnjJydkwgsvZ8A3D8u4ceMr9RRYAq26wWo55Y9nNN4/6MeHJUnuue6uXHLcL3LhoPOy3/HfyqALB6fDch0y7fVpuebcq3LH729rPKdL724p/VtHy58vujYpl7P3cfunc8/Omfn2zIwdOSbXnHtV8z0xlnhPPjYuh3/r2Jzw42NzzA++l9cmvpHTfnRObrz+b417uvfolt59/hUI7nfQN9O6deuced5JOfO8kxrXr7v6L/n+USeFGlSaW+kKalJdeRE/HPrHP/4x559/fsaOHZu5cz/5H61ly5bZeOONM2TIkOy1117/VSFTvvjF/+o8WFS7T9CiC1SXMdOer3QJ1IgBvTatdAnUiFHvvfjZm6AAE995qtIlNItZY/5U6RKaRdtNF+0r0xe3Rf5a6L333jt777135syZk7fe+uQvRV27dk3r1q0LLw4AAABgabTIgcs/tW7dOr169frsjQAAAAA15r8OXAAAAIClQNkg7kqo3a8GAgAAAFhMBC4AAAAABRO4AAAAABRM4AIAAABQMENzAQAAoJqVDM2tBB0uAAAAAAUTuAAAAAAUTOACAAAAUDAzXAAAAKCalc1wqQQdLgAAAAAFE7gAAAAAFEzgAgAAAFAwgQsAAABAwQzNBQAAgGpWMjS3EnS4AAAAABRM4AIAAABQMIELAAAAQMHMcAEAAIBqZoZLRehwAQAAACiYwAUAAACgYAIXAAAAgIIJXAAAAAAKZmguAAAAVLFyeW6lS6hJOlwAAAAACiZwAQAAACiYwAUAAACgYGa4AAAAQDUrlSpdQU3S4QIAAABQMIELAAAAQMEELgAAAAAFE7gAAAAAFMzQXAAAAKhmZUNzK0GHCwAAAEDBBC4AAAAABRO4AAAAABTMDBcAAACoZiUzXCpBhwsAAABAwQQuAAAAAAUTuAAAAAAUTOACAAAAUDBDcwEAAKCalQ3NrQQdLgAAAAAFE7gAAAAAFEzgAgAAAFAwM1wAAACgmpXMcKkEHS4AAAAABRO4AAAAABRM4AIAAABQMIELAAAAQMEMzQUAAIBqVjY0txJ0uAAAAAAUTOACAAAAUDCBCwAAAEDBzHABAACAalYyw6USdLgAAAAAFEzgAgAAAFAwgQsAAABAwQQuAAAAAAUzNBcAAACqmaG5FaHDBQAAAKBgAhcAAACAgglcAAAAAApmhgsAAABUs7IZLpWgwwUAAACgYAIXAAAAgIIJXAAAAAAKJnABAAAAKJihuQAAAFDNSobmVoIOFwAAAICCCVwAAAAACiZwAQAAACiYGS4AAABQzcpmuFSCDhcAAACAgglcAAAAAAomcAEAAAAomMAFAAAAoGACFwAAAKhmpVJt3BbSyiuvnLq6uk/dBg4cmCSZNWtWBg4cmC5duqRDhw4ZMGBApkyZssj/2gUuAAAAQM0YM2ZMJk2a1Hi74447kiR77rlnkmTw4MG5+eabc9111+Xee+/Nm2++mT322GORH8fXQgMAAAA1o1u3bk3un3XWWVl11VWz7bbbZsaMGbn88stz9dVXZ/vtt0+SjBgxImuvvXZGjx6dzTfffKEfR4cLAAAAsNRraGjIzJkzm9waGhoWeM7s2bPz+9//Poceemjq6uoyduzYzJkzJzvssEPjnrXWWit9+/bNqFGjFqkegQsAAABUs3KpJm7Dhg1Lp06dmtyGDRu2wH81N954Y6ZPn56DDz44STJ58uS0adMmyy23XJN9PXr0yOTJkxfpX7uPFAEAAABLvaFDh2bIkCFN1urr6xd4zuWXX56dd945vXv3LrwegQsAAACw1Kuvr//MgOXfvfrqq7nzzjvz5z//uXGtZ8+emT17dqZPn96ky2XKlCnp2bPnItXjI0UAAABAzRkxYkS6d++eXXbZpXFt4403TuvWrTNy5MjGtfHjx2fixInp37//Il1fhwsAAABQU0qlUkaMGJGDDjoorVr9Kxrp1KlTDjvssAwZMiSdO3dOx44dM2jQoPTv33+RvqEoEbgAAABAdSuVKl3BEufOO+/MxIkTc+ihh37q2Pnnn58WLVpkwIABaWhoyI477piLL754kR9D4AIAAADUlK985Sspl8vzPNa2bdsMHz48w4cP/58ewwwXAAAAgIItMR0uKzw4odIlUCM+evP+SpdAjWjXe+tKl0CN6NF+uUqXQI3406QxlS6BGrFRl1UqXQLA/2yJCVwAAACAxcAMl4rwkSIAAACAgglcAAAAAAomcAEAAAAomMAFAAAAoGCG5gIAAEA1K5crXUFN0uECAAAAUDCBCwAAAEDBBC4AAAAABTPDBQAAAKpZqVTpCmqSDhcAAACAgglcAAAAAAomcAEAAAAomMAFAAAAoGCG5gIAAEA1MzS3InS4AAAAABRM4AIAAABQMIELAAAAQMHMcAEAAIBqVjbDpRJ0uAAAAAAUTOACAAAAUDCBCwAAAEDBBC4AAAAABTM0FwAAAKpZydDcStDhAgAAAFAwgQsAAABAwQQuAAAAAAUzwwUAAACqWblc6Qpqkg4XAAAAgIIJXAAAAAAKJnABAAAAKJjABQAAAKBghuYCAABANSuVKl1BTdLhAgAAAFAwgQsAAABAwQQuAAAAAAUzwwUAAACqmRkuFaHDBQAAAKBgAhcAAACAgglcAAAAAAomcAEAAAAomKG5AAAAUM3KhuZWgg4XAAAAgIIJXAAAAAAKJnABAAAAKJgZLgAAAFDFyqVypUuoSTpcAAAAAAomcAEAAAAomMAFAAAAoGACFwAAAICCGZoLAAAA1axUqnQFNUmHCwAAAEDBBC4AAAAABRO4AAAAABTMDBcAAACoZmUzXCpBhwsAAABAwQQuAAAAAAUTuAAAAAAUTOACAAAAUDBDcwEAAKCalcqVrqAm6XABAAAAKJjABQAAAKBgAhcAAACAgpnhAgAAANWsVKp0BTVJhwsAAABAwQQuAAAAAAUTuAAAAAAUTOACAAAAUDBDcwEAAKCaGZpbETpcAAAAAAomcAEAAAAomMAFAAAAoGBmuAAAAEA1K5crXUFN0uECAAAAUDCBCwAAAEDBBC4AAAAABRO4AAAAABTM0FwAAACoZqVSpSuoSTpcAAAAAAomcAEAAAAomMAFAAAAoGBmuAAAAEA1K5UrXUFN0uECAAAAUDCBCwAAAEDBBC4AAAAABRO4AAAAABTM0FwAAACoZuVSpSuoSTpcAAAAAAomcAEAAAAomMAFAAAAoGAClyXYEd87KC88Pzrvz3wxDz5wczbdZKMF7h8w4Gt5+ql78/7MF/PYo3dm5522b55CWWrMnTs3F/3qyuz4zYOz8Xa7Zac9D8mlI65OuVxu3DP88t9n132/k02/tHu22GnPfPuYoXly3HMLvO41N/w13zjwiGz25T2y2Zf3yP6HD879o8Ys7qfDUsj7GovbkBOOzBvvjmtyu/ehmxd4TseOy+bMc0/Ko8/ek5cmP5b7x/wt239562aqmKWd9zWK9rnNN8zPf3tWbn3shjwy6f5su1PT96PtvrpNfnnNz3LnuL/mkUn3Z411V/vMa66yxso559c/yU0PX5tHJt2ffb+z5+IqH/g3Apcl1J57fj3nnXtKfnLGz7PpZjvliSefyS1/uyrdunWZ5/7+m2+Sq343PCNG/CGbfGHH3HTT7fnT9Zdn3XXXbObKWZJd/vvr8scb/5YfDjkyN139qww58tD85qrrc9X1NzXuWXnFFfLDIUfmz1dekisvPi+9e/bI4YN/lHfenT7f6/bs1jWDv3dIrv3NRfnj5b/IFzbeMINOPD0vvPRqMzwrlhbe12guzz07IRutuW3jbfedvzXfva1bt84fbvh1VuzbO4cfPDjbbLpLfnDMKZk8aWozVszSyvsai0O7ZdpmwjMv5Owf/nw+x9vl8YeeykVnXrrQ12zbrm1ef3VSfnnm/+WtKW8XVSpLk1K5Nm5LmLryv/9pu4JatVmh0iUsUR584OaMeeSJHHPsSUmSurq6vPLSmAy/eETOOXf4p/ZffdUlab/MMtntGwc1rv3j/pvz+BPjMvCoE5ut7qXBR2/eX+kSKubIH5ySLp2Xy0+GDm5cO/aHZ6S+vk3OPuX4eZ7z/gcfZPOvfDO/vvCn2XyTzy30Y22x0575/sBvZ8CuO/7PdS+t2vX2F/J/531t8enRfrlKl7DEGHLCkdlply/lK9sMWKj93zpkr3xv0CHZ9gu75uOPP17M1S39pnwwvdIlLFG8ry0+G3VZpdIlLBEemXR/vn/ID3PvbZ/++bVXn565ecx12W+HQ/L8uBcW+po3PXxt/nDZdfnDZdcVWepS65FJtfG7wYdnH1LpEprFMieMqHQJTehwWQK1bt06n//8Bhl517/+4y+Xyxl51wPZfPON53nO5ptt3GR/kvz9jnvmu5/atNF6a+ehRx7PKxNfT5I8N+GlPPrkuGy9+Sbz3D9nzpxc95dbs2yH9llztYX7wWfu3Lm55c578tGsWdlovbUKq52lm/c1mlO/Vfpm7DN358HHbstFvzo7vfv0mu/eL++8XcaOeSJnnntSHh9/b0Y+eGMGDflOWrTwIxIL5n0NgM/SqugLvvbaaznllFPym9/8Zr57Ghoa0tDQ0GStXC6nrq6u6HKWSl27dk6rVq0ydcpbTdanTp2WtdZcdZ7n9OzZLVOmTmuyNmXKW+nZo9tiq5Olz7e/tVc++PDD7Lrf4WnZokXmlko5+vCD8rUdm35+/J5/PJQfnHJWZs1qSLcunfOrC87M8st1WuC1n3/x5ez/3SGZPXt2lmnXLhf+9OSs2m+lxfl0WIp4X6O5PDb2yQwe+KO8+MIr6d6jW4accERuuOXKbL/Fbvng/Q8/tX+llfpky603yw3X/TXf2uuI9Fulb3563slp1apVzj/nkgo8A5YW3tcA+CyFBy7vvPNOfvvb3y4wcBk2bFhOO+20Jmt1LTqkrmXHossB/s1td92Xv/797px96vFZrd9KeW7CSzn7wv9L966ds9tXv9y47wuf3zB/umJ43p0+I9fffFuOO3lYrr7sgnRZfrn5Xrtf3z750xXD8977H+Tvdz+QH535s1zxy3OELkCzuvvOBxr/+dlxz+exR57MQ0/dkV133ynX/P7Pn9rfokWLvP3WOzn+2FNTKpXy1BPPpGevHvneoEMELgBUjXKpVOkSatIiBy433XTTAo+/9NJLn3mNoUOHZsiQIU3Wlu/iowf/9NZb7+Tjjz9O9x5dm6x3794tk6dMm+c5kydPS4/uTf860qNH1/nupzb9bPjl+fYBe+WrO3wxSbLGqv0yafLU/Pp31zYJXJZp1zZ9+/RO3z69s+F6a+erex+WP998e75z4N7zvXbr1q3Tt0/vJMm6a62ecc89n99f95eccvzRi/U5sXTwvkalzJz5Xl564dWsvErfeR6fMmVaPp7zcUr/9oPohOdfTI+e3dK6devMmTOnuUplKeN9DYDPssgfUN59993zjW98I7vvvvs8b/8ZpMxLfX19Onbs2OTm40T/MmfOnDz66JPZfrutGtfq6uqy/XZbZfTosfM8Z/RDY7P99ls1WdvhS9vMdz+1adashtS1aPrfWosWLVL6jNnZpVIpsxfxl45SqZzZs/2iwie8r1Epy7RfJiv1WzFTJ8/7F9pHHnosK6/St8nPIausunImT5oqbGGBvK8B8FkWOXDp1atX/vznP6dUKs3z9uijjy6OOmvO+Rdelm8ftl++9a09s9Zaq2X4L89K+/btcsVv/5gkGfGbC3PmGf+aZn/RRZdnx698MYOP/W7WXHPV/PjkIdl44w1y8SVL1pRmKuuLW26Wy357Te598OG8MWlK7rz3H7nyj3/Ol7bpnyT58KNZueDSK/LE08/mzclTMu65CTnppz/P1Lfezo7b/esbdw47+sRc/W9fJX3+JSPyyONP5Y1JU/L8iy/n/EtGZMxjT2aXr2zX7M+RJZf3NZrDyacfl8232CR9VuydTb6wUS7/3YUpzZ2bG/90S5Lkwkt+mhN/fGzj/it/88cst1ynnH7W0Kyy6kr50le2yaAh38lvL/9DhZ4BSxPvaywO7ZZplzXWXS1rrLtakmSFvr2yxrqrpccK3ZMkHZdbNmusu1pWWWPlJMlKq/bNGuuuli7dOjde47Rf/CgDf/jdxvutWrdqvGbr1q3TrWe3rLHuaumzsm+KhcVpkT9StPHGG2fs2LHZbbfd5nm8rq4uS8g3TS/VrrvupnTr2jmn/vi49OzZLU88MS67fO2ATJ36yWC2viv2btL+PGr0IzngwKNy+mnH54yfnJAJL7ycAd88LOPGja/UU2AJ9MPBR+Siy67MGecNzzvvTk+3rp2z525fzRGH7JckadmiRV5+9bXcdOudeXfGjCzXsWPWW3uN/Pbic7PaKv+axfLaG5Py7oyZjfffmT49P/zJeZn29jtZtn37rLFav/zfz8/IFl/4fLM/R5Zc3tdoDr1W6JHhvz43y3deLu+89U4efujR7Prl/fLO2+8mSXr36ZVS6V8/p7z5xuTs/83Dc+qZJ+SOB27I5ElTcvn//T7DL7i8Uk+BpYj3NRaHdTZcM//354sa7w85bVCS5OY/3prTjv1ptvnKVjn1wh82Hh/2f5/MxvzVeb/Jr372SXjXc4UeTd7ruvXomqvv/Fewd+CR++bAI/fN2Acfy3cH+Pg3LC515UVMR+6///588MEH2WmnneZ5/IMPPsgjjzySbbfddpEKadVGukrz+OjN+z97ExSgXe+tP3sTFKBH++UqXQI1YsoH0ytdAjVioy6rVLoEasQjk2rjd4MPzjyw0iU0i/Y/urLSJTSxyB0uW2+94F8g2rdvv8hhCwAAAEA1WeQZLgAAAAAsmMAFAAAAoGCL/JEiAAAAYClSLn32HgqnwwUAAACgYAIXAAAAgIIJXAAAAAAKJnABAAAAKJihuQAAAFDNSuVKV1CTdLgAAAAAFEzgAgAAAFAwgQsAAABAwcxwAQAAgGpWKlW6gpqkwwUAAACgYAIXAAAAgIIJXAAAAAAKJnABAAAAKJihuQAAAFDNSuVKV1CTdLgAAAAAFEzgAgAAAFAwgQsAAABAwcxwAQAAgGpWLlW6gpqkwwUAAACgYAIXAAAAgIIJXAAAAAAKJnABAAAAasobb7yRAw44IF26dEm7du2y/vrr55FHHmk8Xi6X8+Mf/zi9evVKu3btssMOO2TChAmL9BgCFwAAAKhmpXJt3BbSu+++my233DKtW7fOrbfemmeeeSY/+9nPsvzyyzfuOeecc/KLX/wil156aR566KG0b98+O+64Y2bNmrXQj+NbigAAAICacfbZZ2fFFVfMiBEjGtf69evX+M/lcjkXXHBBTjrppOy2225JkiuvvDI9evTIjTfemH322WehHkeHCwAAALDUa2hoyMyZM5vcGhoaPrXvpptuyiabbJI999wz3bt3z+c+97lcdtlljcdffvnlTJ48OTvssEPjWqdOnbLZZptl1KhRC12PwAUAAABY6g0bNiydOnVqchs2bNin9r300ku55JJLsvrqq+f222/PEUcckaOPPjq//e1vkySTJ09OkvTo0aPJeT169Gg8tjB8pAgAAACqWLlUqnQJzWLo0KEZMmRIk7X6+vpP7SuVStlkk03y05/+NEnyuc99Lk8//XQuvfTSHHTQQYXVo8MFAAAAWOrV19enY8eOTW7zClx69eqVddZZp8na2muvnYkTJyZJevbsmSSZMmVKkz1TpkxpPLYwBC4AAABAzdhyyy0zfvz4JmvPP/98VlpppSSfDNDt2bNnRo4c2Xh85syZeeihh9K/f/+FfhwfKQIAAABqxuDBg7PFFlvkpz/9afbaa688/PDD+dWvfpVf/epXSZK6uroce+yxOeOMM7L66qunX79+Ofnkk9O7d+/svvvuC/04AhcAAACgZmy66aa54YYbMnTo0Jx++unp169fLrjgguy///6Ne44//vh88MEHOfzwwzN9+vRstdVWue2229K2bduFfpy6crlcXhxPYFG1arNCpUugRnz05v2VLoEa0a731pUugRrRo/1ylS6BGjHlg+mVLoEasVGXVSpdAjXikUm18bvB+yfsUekSmkWHs/9c6RKaMMMFAAAAoGACFwAAAICCCVwAAAAACmZoLgAAAFSz0hIxurXm6HABAAAAKJjABQAAAKBgAhcAAACAgglcAAAAAApmaC4AAABUs3Kp0hXUJB0uAAAAAAUTuAAAAAAUTOACAAAAUDAzXAAAAKCalcqVrqAm6XABAAAAKJjABQAAAKBgAhcAAACAgglcAAAAAApmaC4AAABUsbKhuRWhwwUAAACgYAIXAAAAgIIJXAAAAAAKZoYLAAAAVDMzXCpChwsAAABAwQQuAAAAAAUTuAAAAAAUTOACAAAAUDBDcwEAAKCalUqVrqAm6XABAAAAKJjABQAAAKBgAhcAAACAgpnhAgAAANWsVK50BTVJhwsAAABAwQQuAAAAAAUTuAAAAAAUTOACAAAAUDBDcwEAAKCaGZpbETpcAAAAAAomcAEAAAAomMAFAAAAoGBmuAAAAEAVK5fNcKkEHS4AAAAABRO4AAAAABRM4AIAAABQMIELAAAAQMEMzQUAAIBqVjI0txJ0uAAAAAAUTOACAAAAUDCBCwAAAEDBzHABAACAamaGS0XocAEAAAAomMAFAAAAoGACFwAAAICCCVwAAAAACmZoLgAAAFSxsqG5FbHEBC6bdluj0iVQI9r13rrSJVAj3r/3vEqXQI3osO1xlS4BoFCtWywxv6YA/Nd8pAgAAACgYAIXAAAAgILp1QMAAIBqZoZLRehwAQAAACiYwAUAAACgYAIXAAAAgIIJXAAAAAAKZmguAAAAVLNSpQuoTTpcAAAAAAomcAEAAAAomMAFAAAAoGBmuAAAAEAVK5fKlS6hJulwAQAAACiYwAUAAACgYAIXAAAAgIIJXAAAAAAKZmguAAAAVDNDcytChwsAAABAwQQuAAAAAAUTuAAAAAAUzAwXAAAAqGalShdQm3S4AAAAABRM4AIAAABQMIELAAAAQMEELgAAAAAFMzQXAAAAqli5VK50CTVJhwsAAABAwQQuAAAAAAUTuAAAAAAUzAwXAAAAqGalShdQm3S4AAAAABRM4AIAAABQMIELAAAAQMEELgAAAAAFMzQXAAAAqli5VK50CTVJhwsAAABAwQQuAAAAAAUTuAAAAAAUzAwXAAAAqGalShdQm3S4AAAAABRM4AIAAABQMIELAAAAQMEELgAAAAAFMzQXAAAAqljZ0NyK0OECAAAAUDCBCwAAAEDBBC4AAAAABTPDBQAAAKqZGS4VocMFAAAAoGACFwAAAICCCVwAAAAACiZwAQAAACiYobkAAABQxcqG5laEDhcAAACAgglcAAAAAAomcAEAAAAomBkuAAAAUM3McKkIHS4AAAAABRO4AAAAABRM4AIAAABQMIELAAAAQMEMzQUAAIAqVjY0tyJ0uAAAAAAUTOACAAAAUDCBCwAAAEDBzHABAACAKmaGS2XocAEAAAAomMAFAAAAoGACFwAAAICCCVwAAAAACmZoLgAAAFQxQ3MrQ4cLAAAAQMEELgAAAAAFE7gAAAAANePUU09NXV1dk9taa63VeHzWrFkZOHBgunTpkg4dOmTAgAGZMmXKIj+OGS4AAABQzcp1la5gibPuuuvmzjvvbLzfqtW/4pHBgwfnb3/7W6677rp06tQpRx11VPbYY4/84x//WKTHELgAAAAAS72GhoY0NDQ0Wauvr099ff2n9rZq1So9e/b81PqMGTNy+eWX5+qrr87222+fJBkxYkTWXnvtjB49OptvvvlC1+MjRQAAAMBSb9iwYenUqVOT27Bhw+a5d8KECendu3dWWWWV7L///pk4cWKSZOzYsZkzZ0522GGHxr1rrbVW+vbtm1GjRi1SPTpcAAAAgKXe0KFDM2TIkCZr8+pu2WyzzXLFFVdkzTXXzKRJk3Laaadl6623ztNPP53JkyenTZs2WW655Zqc06NHj0yePHmR6hG4AAAAAEu9+X186D/tvPPOjf+8wQYbZLPNNstKK62Ua6+9Nu3atSusHh8pAgAAgCpWLtXG7b+13HLLZY011sgLL7yQnj17Zvbs2Zk+fXqTPVOmTJnnzJcFEbgAAAAANev999/Piy++mF69emXjjTdO69atM3LkyMbj48ePz8SJE9O/f/9Fuq6PFAEAAAA147jjjsuuu+6alVZaKW+++WZOOeWUtGzZMvvuu286deqUww47LEOGDEnnzp3TsWPHDBo0KP3791+kbyhKBC4AAABADXn99dez77775u233063bt2y1VZbZfTo0enWrVuS5Pzzz0+LFi0yYMCANDQ0ZMcdd8zFF1+8yI8jcAEAAIAqVi7VVbqEJco111yzwONt27bN8OHDM3z48P/pccxwAQAAACiYwAUAAACgYAIXAAAAgIIJXAAAAAAKZmguAAAAVLFyqdIV1CYdLgAAAAAFE7gsATbabIOce8WZuWnsdRn1xt3ZZsctG4+1bNUyR/7w8Pz+zstz14RbctPY6/LjC4ema48uC7zmYUMOyqg37m5yu+be3y7up8JS6ojvHZQXnh+d92e+mAcfuDmbbrLRAvcPGPC1PP3UvXl/5ot57NE7s/NO2zdPoSw1dv7++dnw4FM/dfvplX9Lkhw2bMSnjv3kipsXeM1yuZzhf74rXzrmvHzhO2fk8HN+m1cnv90cT4elkPc1movXGkXzuwFUDx8pWgK0XaZtJjzzYv56za056/KfND3Wrm3WXH/1jLjwd5nwzItZtlOHDD5tUM4ZcWYO/er3FnjdF597OUfv8/3G+3M/nrtY6mfptueeX895556SIweemIfHPJajB307t/ztqqyz3jaZNu3Tv8z233yTXPW74fnRScPyt1vuzL77fCN/uv7ybLrZThk3bnwFngFLoqtOOTyl0r96V194Y2q+e+7v8uVN12lcG7Dt53PkN7ZrvN+2vvUCrzniln/kD3c8lJ985xtZodtyGf7nu3PEz36XG84cmPo2Cz6X2uJ9jebitcbi4HcDqB515XK5XOkikqT/Ctt99qYaMOqNu3PCoSflvtv/Md89a2+4Zn5zy6XZfdO9M+XNqfPcc9iQg7LNTlvloK98Z3GVutQaM+35SpewRHnwgZsz5pEncsyxJyVJ6urq8spLYzL84hE559zhn9p/9VWXpP0yy2S3bxzUuPaP+2/O40+My8CjTmy2upcG7997XqVLWGKcc9Wtue+J53Pz2Uenrq4uhw0bkTX79szx+++8UOeXy+XscOzPcuBO/XPQzp/8pe+9D2dl+6PPzenf3j07b77+4ix/iddh2+MqXcISxfsazcVrbfHZtNsalS5hieB3g8Vv1Bt3V7qEZvHmFrXx+3bvB5es/z19pGgp1KFj+5RKpbw38/0F7lux3wq5aex1uf7Bq3LqRT9Kj97dm6lClhatW7fO5z+/QUbedX/jWrlczsi7Hsjmm288z3M232zjJvuT5O933DPf/TDn44/zt1FPZvetP5e6urrG9VtGP5Vtjzo7e/xoeC687s581DB7vtd4Y9q7eWvG+9lsnVUa15Zdpm3WX7VPnnzx9cVaP0sX72s0F681lhR+N2BhlMt1NXFb0vhI0VKmTX3rHPnD7+aOG+/Kh+9/ON994x57NmcMPjuvvvhaunbvksOGHJhLbrgwB2x/aD784KNmrJglWdeundOqVatMnfJWk/WpU6dlrTVXnec5PXt2y5Sp05qsTZnyVnr26LbY6mTpdtejz+W9D2fl61tt1Li2c//106vLcum+3LJ5/rUpueC6O/LK5Ldy/qB95nmNt2Z88kNkl04dmqx36di+8Rgk3tdoPl5rLAn8bgBLtkUOXD766KOMHTs2nTt3zjrrrNPk2KxZs3LttdfmwAMPXOA1Ghoa0tDQ0GStVC6lRZ2GmwVp2aplzrj0lNTVJecMPX+Be0ff/XDjP7/47EsZ99gzueGha/KlXbfLzdfcsrhLBWh0w32PZcv1V0/35Ts2rn3zi5s0/vPqK/ZI1+U65PBzrsxrU9/Jit07V6JMAFiq+N0AlnyLlHA8//zzWXvttbPNNttk/fXXz7bbbptJkyY1Hp8xY0YOOeSQz7zOsGHD0qlTpya3N957ddGrryEtW7XMmZeekp59eubofX+wwAR7Xt6f+UEmvvR6+qzcezFVyNLorbfeyccff5zuPbo2We/evVsmT5k2z3MmT56WHt2b/iWuR4+u891PbXvzrel5aNxL2WPbzy9w3/qr9kmSTJzyzjyPd/3/nS1v/0c3y9szP2g8Bon3NZqP1xqV5HcDWDosUuBywgknZL311svUqVMzfvz4LLvsstlyyy0zceLERXrQoUOHZsaMGU1uKyy70iJdo5b88w21T78+OXrv72fmuzMX+RrtlmmbPiv1zltT5/3LDLVpzpw5efTRJ7P9dls1rtXV1WX77bbK6NFj53nO6IfGZvvtt2qytsOXtpnvfmrbX+5/LJ07ts/WG66+wH3jJ05OknRbbt7hyQrdlk/XTh3y0DMvN669/9GsPPXi69ng/4c1kHhfo/l4rVEpfjfgv1Eu1cZtSbNIHyl68MEHc+edd6Zr167p2rVrbr755hx55JHZeuutc/fdd6d9+/YLdZ36+vrU19c3WavljxO1W6Zt+vRbofF+7769svq6q2bmu+/lralv56e/Oi1rrr96jjvoh2nRskU6d1s+STJz+nv5eM7HSZKL/viz3Hvr/bn+ihuTJINO/l4euGNUJr0+Od16ds23v39w5pZKuePGkc3+/FiynX/hZRlx+fkZ++iTGTPmsRw96Dtp375drvjtH5MkI35zYd58c1J+dNJZSZKLLro8d428PoOP/W5uufXO7L3Xbtl44w3yvSOPr+TTYAlUKpXylwcez65bbphWLVs2rr829Z3cMuqpbL3h6unUvl0mvD4l5159ezZec6WssWLPxn27nXhRjt5zh3xp47VTV1eX/b+yeS67+b6s1LNzVui6fIb/+a50W37ZbP/5tSrx9FiCeV+juXitsTj43QCqxyIFLh999FFatfrXKXV1dbnkkkty1FFHZdttt83VV19deIG1YK0N18zF11/QeP+YUwcmSf527W359c+uyDY7fvIVqL+749dNzjvym8fmsVFPJElWWKl3OnXu1HisW69uOW34Sem0fMdMf2dGnnj4qXxn14GZ/s6MxfxsWNpcd91N6da1c0798XHp2bNbnnhiXHb52gGZOvWTIYB9V+ydUulfcfGo0Y/kgAOPyumnHZ8zfnJCJrzwcgZ887CMGze+Uk+BJdToZ17KpLdnZPdtPtdkvXXLlnnomZdy1d9H56OG2enZpVN22GTtfOfr2zTZ98rkt/P+h7Ma7x/y1S3zUcPsnD7i5rz34ax8bo2+ufj7B6S+TetmeT4sPbyv0Vy81lgc/G4A1aOuXC6XF3bzF77whQwaNCjf+ta3PnXsqKOOylVXXZWZM2dm7ty5i1xI/xVq43vBqbwx056vdAnUiPfvPa/SJVAjOmx7XKVLACjUpt3WqHQJ1IhRb9xd6RKaxeubbV/pEppFn4fuqnQJTSzS53i+8Y1v5A9/+MM8j/3yl7/Mvvvum0XIbwAAAACq0iIFLkOHDs0tt8z/a8MuvvjiJm2TAAAAQGWVS3U1cVvS1O6kWgAAAIDFROACAAAAUDCBCwAAAEDBFulroQEAAICli++2qQwdLgAAAAAFE7gAAAAAFEzgAgAAAFAwgQsAAABAwQzNBQAAgCpWLtVVuoSapMMFAAAAoGACFwAAAICCCVwAAAAACmaGCwAAAFQxM1wqQ4cLAAAAQMEELgAAAAAFE7gAAAAAFEzgAgAAAFAwQ3MBAACgipXLla6gNulwAQAAACiYwAUAAACgYAIXAAAAgIKZ4QIAAABVrFyqq3QJNUmHCwAAAEDBBC4AAAAABRO4AAAAABRM4AIAAABQMENzAQAAoIqVy4bmVoIOFwAAAICCCVwAAAAACiZwAQAAACiYGS4AAABQxcqlSldQm3S4AAAAABRM4AIAAABQMIELAAAAQMEELgAAAAAFMzQXAAAAqlipXFfpEmqSDhcAAACAgglcAAAAAAomcAEAAAAomBkuAAAAUMXKZrhUhA4XAAAAgIIJXAAAAAAKJnABAAAAKJjABQAAAKBghuYCAABAFSuXDM2tBB0uAAAAAAUTuAAAAAAUTOACAAAAUDAzXAAAAKCKlcuVrqA26XABAAAAKJjABQAAAKBgAhcAAACAgglcAAAAAApmaC4AAABUsXKprtIl1CQdLgAAAAAFE7gAAAAAFEzgAgAAAFAwM1wAAACgipXKZrhUgg4XAAAAgIIJXAAAAAAKJnABAAAAKJjABQAAAKBghuYCAABAFSsbmlsROlwAAAAACiZwAQAAACiYwAUAAACgYGa4AAAAQBUrlytdQW3S4QIAAABQMIELAAAAQMEELgAAAAAFE7gAAAAAFMzQXAAAAKhipXJdpUuoSTpcAAAAAAomcAEAAAAomMAFAAAAoGBmuAAAAEAVK5vhUhE6XAAAAAAKJnABAAAAKJjABQAAAKBgAhcAAACAghmaCwAAAFWsXK50BbVJhwsAAABAwQQuAAAAAAUTuAAAAAAUzAwXAAAAqGKlcl2lS6hJOlwAAAAACiZwAQAAACiYwAUAAACgYAIXAAAAgIItMUNz35z1dqVLoEassGyXSpdAjej15ZMrXQI14r3bT6t0CdSI3rueVekSqBF+N4BilQ3NrQgdLgAAAAAFE7gAAAAAFEzgAgAAAFCwJWaGCwAAAFC8khkuFaHDBQAAAKBgAhcAAACAgglcAAAAAAomcAEAAAAomKG5AAAAUMXKlS6gRulwAQAAACiYwAUAAACgYAIXAAAAgIKZ4QIAAABVrFSuq3QJNUmHCwAAAEDBBC4AAAAABRO4AAAAABRM4AIAAABQMENzAQAAoIqVDc2tCB0uAAAAQM0666yzUldXl2OPPbZxbdasWRk4cGC6dOmSDh06ZMCAAZkyZcoiXVfgAgAAANSkMWPG5P/+7/+ywQYbNFkfPHhwbr755lx33XW599578+abb2aPPfZYpGsLXAAAAICa8/7772f//ffPZZddluWXX75xfcaMGbn88svz85//PNtvv3023njjjBgxIg8++GBGjx690NcXuAAAAEAVK9XIraGhITNnzmxya2homO+/l4EDB2aXXXbJDjvs0GR97NixmTNnTpP1tdZaK3379s2oUaM+49/2vwhcAAAAgKXesGHD0qlTpya3YcOGzXPvNddck0cffXSexydPnpw2bdpkueWWa7Leo0ePTJ48eaHr8S1FAAAAwFJv6NChGTJkSJO1+vr6T+177bXXcswxx+SOO+5I27ZtF1s9AhcAAABgqVdfXz/PgOU/jR07NlOnTs3nP//5xrW5c+fmvvvuyy9/+cvcfvvtmT17dqZPn96ky2XKlCnp2bPnQtcjcAEAAABqxpe+9KU89dRTTdYOOeSQrLXWWjnhhBOy4oorpnXr1hk5cmQGDBiQJBk/fnwmTpyY/v37L/TjCFwAAACgipVTV+kSlijLLrts1ltvvSZr7du3T5cuXRrXDzvssAwZMiSdO3dOx44dM2jQoPTv3z+bb775Qj+OwAUAAADg35x//vlp0aJFBgwYkIaGhuy44465+OKLF+kaAhcAAACgpt1zzz1N7rdt2zbDhw/P8OHD/+tr+lpoAAAAgILpcAEAAIAqVipXuoLapMMFAAAAoGACFwAAAICCCVwAAAAACiZwAQAAACiYobkAAABQxUqpq3QJNUmHCwAAAEDBBC4AAAAABRO4AAAAABTMDBcAAACoYmUzXCpChwsAAABAwQQuAAAAAAUTuAAAAAAUTOACAAAAUDBDcwEAAKCKlSpdQI3S4QIAAABQMIELAAAAQMEELgAAAAAFM8MFAAAAqlg5dZUuoSbpcAEAAAAomMAFAAAAoGACFwAAAICCCVwAAAAACmZoLgAAAFSxUqULqFE6XAAAAAAKJnABAAAAKJjABQAAAKBgZrgAAABAFTPDpTJ0uAAAAAAUTOACAAAAUDCBCwAAAEDBBC4AAAAABTM0FwAAAKpYOXWVLqEm6XABAAAAKJjABQAAAKBgAhcAAACAgpnhAgAAAFWsZIRLRehwAQAAACiYwAUAAACgYAIXAAAAgIIJXAAAAAAKZmguAAAAVLFSTM2tBB0uAAAAAAUTuAAAAAAUTOACAAAAUDAzXAAAAKCKlStdQI3S4QIAAABQMIELAAAAQMEELgAAAAAFE7gAAAAAFMzQXAAAAKhipUoXUKN0uAAAAAAUTOACAAAAUDCBCwAAAEDBBC4AAAAABTM0FwAAAKpYqa6u0iXUJB0uAAAAAAUTuAAAAAAUTOACAAAAUDAzXAAAAKCKlStdQI0SuCyhevTqnqGnHJsvfmmrtGvXNq+8/FqOG3Rynnr8mXnuP++XP8me++72qfXnn3shX95yj8VdLksxrzWaw6Hf3i+Hfnu/rNi3T5LkuWcn5NyzLsqdd9w3z/1f+/pXMuS4I7LKKiulVetWeenFVzL8F7/JH6+5sRmrZmmw8w//L5Pemfmp9b223Sg/3PfL+clVt+ehZ1/NtBkfZJn61tlwlRVyzB7bpF/PLvO95iU3/yO3P/JcJr/7Xlq3apF1+vbIUbttnfX79V6cT4WljPc1mpOf12DpVFcul5eIsGulLhtUuoQlRsdOy+aWe67NqAfG5Pcjrs07b72blVfpm1dfeS0TX3l9nucsu2yHtG1X33i/ZatWue3e63LFZX/IBedc0lyls5TxWlu8ZjZ8WOkSlhg77bx95s6dmxdffCV1dXXZd/89MuiYb2fbLXfLc89O+NT+LbfeLMst1zETnn8ps2fPyY47b5czfjo0ew/4Tu4aeX8FnsGS7c2bT6x0CRXzznsfplQqNd5/4c238r0Lr8tlg/fOpmv2zfX3P5F+PTun5/IdM/PDWbn0r//I+Nem5m9nHp6WLeb9yepbHn4mnZddJn26LpdZcz7OVSMfyR1jx+emn3wnnZddprme2hKp965nVbqEJYb3tcWrY31t/7f27/y8tni9+vaTlS6hWVzXa/9Kl9As9px0VaVLaEKHyxLoiGMOzaQ3puQHg37cuPbaxDcWeM57772f9957v/H+V766XTot1zHXXX3j4iqTKuC1RnO57da7mtw/47Sf59DD9ssmm240z19M/nH/Q03u/9/Fv82+++2RzbfY2C8mNPGfAchvbn84K3ZbLpussWKS5Jtbb9h4bIV0ysCvb5W9zvht3nx7Rlbstvw8r/nVL6zT5P73v7ldbvjHU5nwxrRsttZKBT8Dllbe12gufl6DpZehuUugL+/0xTz5+Lhc/JvzMva5e3LL3X/MPt8asEjX2Hv/PfLAvaPzxuuTFlOVVAOvNSqhRYsW2eObu2SZ9stkzMOPLdQ523yxf1ZbvV8efGDMYq6Opdmcj+fmloeeyW5brJ+6urpPHf+oYXb+8uDTWaFrp/RcvuNCX/NP9z+RDu3qs0afbkWXTJXwvsbi5Oc1WHotcofLs88+m9GjR6d///5Za6218txzz+XCCy9MQ0NDDjjggGy//fafeY2GhoY0NDQ0WSuXS6mrk/8kyYor9ckBh+yVX1/yuww//9fZ4HPr5rRhJ2TOnDn50zU3feb53Xt2yxd32DJHH167LeYsHK81mtM6666R20del7Zt6/PB+x/mW/sekfHPvTDf/R07dsi45/+R+vo2mTu3lOMGn5J77v5HM1bM0uauxyfkvY9m5ev912uy/sd7HssFN9ybjxrmZOUenXPpMXumdauWC7zWfU++mBMuvzmzZs9J144dcukxe2b5Dj7iQFPe12gOfl6jCKXP3sJisEiBy2233ZbddtstHTp0yIcffpgbbrghBx54YDbccMOUSqV85Stfyd///vfPDF2GDRuW0047rclax7bds9wyPRb9GVShFi1a5KnHx+XcM36RJBn31HNZc+3VcsDBey7Um+o39/l6Zs54L3+/5a7P3Ett81qjOU14/uVss8XX07Fjh+y2+865+Ffn5ms77TffX07ee++DbLPF19O+/TLZ9otb5MxhP8wrr7z2qbZ8+KcbH3wqW667Srov16HJ+lc3Wyebr71y3pr5fq68Y0yOv+zmXPGD/VLfev4/Bm265or5448OyvT3P8qfH3gyx192c35/wv7p3LH94n4aLEW8r9Ec/LwGS69Faik5/fTT84Mf/CBvv/12RowYkf322y/f+c53cscdd2TkyJH5wQ9+kLPO+uxhakOHDs2MGTOa3Dq106b7T1OnTMuE8S81WXvh+ZfTu0/PhTp/r/12z5+v/WvmzPl4cZRHFfFaoznNmTMnL7/0ap54fFxOP/W8PP3Us/nekQfNd3+5XM7LL72ap596NsMvujx/ufG2DP7+95qxYpYmb749Iw89+2q+seX6nzq2bLv6rNRj+Wy8+oo57/Dd8vLkd3LX45+esfHv2tW3Sd/uy2eDVXrn1AN3SssWdbnhwacWV/kspbyv0Rz8vAZLr0UKXMaNG5eDDz44SbLXXnvlvffeyze/+c3G4/vvv3+efPKzpzzX19enY8eOTW4+TvQvYx96PKustnKTtX6rrpQ3Xvvsz1xuvuUm6bfqSvnj729YTNVRTbzWqKQWLVqkTZs2i7C/LvX1C7+f2vKXB59O52WXydbrr7rAfeVyOSmXM3vO3EW6fvm/OIfa432NxcHPa7D0WuSU459D6Fq0aJG2bdumU6dOjceWXXbZzJgxo7jqatSvL/1dPrfJ+hk4+NtZqd+K2W3AV7Pfgd/MlZdf07jn+JOPzs8vPvNT5+59wDfy6CNP5vkFfH4Y/slrjeby41OPyxZbbpoV+66QddZdIz8+9bhstfVmue6Pn7RCX/Krc/PjU49r3D/4+9/LF7fbMiutvGLWWHPVDBx0WPbed/dce81fKvUUWIKVSuXcNOrp7Np/3bRq+a8fbV6fNj2X3zY6z7w6OZPemZnHX3wjP7jsptS3aZWt1+vXuG/3Uy7PXY89n+STwbq/uPG+PPnSm3nz7Rl55tXJOeXKWzN1+vv58sZrNvtzY8nlfY3m4uc1ilCqq43bkmaRZrisvPLKmTBhQlZd9ZO/Ho0aNSp9+/ZtPD5x4sT06tWr2Apr0JOPjcvhBw7OCScfk6OP+25en/hGTvvRObnx+lsa93Tv0S29V2jaRrjssh2y89d2yKk/PKe5S2Yp5bVGc+narUsu+dW56dGze2bOfC/jnn4uA3Y7pHFYZJ8Ve6dU+tc4t2Xat8t555+W3iv0zKyPZmXC8y/lu9/+fm740y3zewhq2OjnXsmkd2Zm9y2afpyoTetWeXTC67lq5NjM/HBWunRsn8+v1ie//UHTWSyvTHkn7300O8knf1B6ZfI7+f6ov2T6Bx9lufZts+5KvfKb4/bNar27NuvzYsnmfY3m4uc1WHrVlcvl8sJuvvTSS7Piiitml112mefxH/7wh5k6dWp+/etfL3IhK3XZYJHPAViSzWz4sNIlUCPevNk3T9A8eu/62bP6oAgd630rGM3j1bc/eyRGNfhD7/0rXUKz2PfNqypdQhOL1OHyve8teKjXT3/60/+pGAAAAIBqYFItAAAAQMEWqcMFAAAAWLqUsgROlK0BOlwAAAAACiZwAQAAACiYwAUAAACgYGa4AAAAQBUrV7qAGqXDBQAAAKBgAhcAAACAgglcAAAAAAomcAEAAAAomKG5AAAAUMVKdZWuoDbpcAEAAAAomMAFAAAAoGACFwAAAICCmeECAAAAVaxU6QJqlA4XAAAAgIIJXAAAAAAKJnABAAAAKJjABQAAAKBghuYCAABAFStXuoAapcMFAAAAoGACFwAAAICCCVwAAAAACmaGCwAAAFSxUl2lK6hNOlwAAAAACiZwAQAAACiYwAUAAACgYAIXAAAAgIIZmgsAAABVrFTpAmqUDhcAAACAgglcAAAAAAomcAEAAAAomBkuAAAAUMXMcKkMHS4AAAAABRO4AAAAABRM4AIAAABQMIELAAAAQMEMzQUAAIAqVq6rdAW1SYcLAAAAQMEELgAAAAAFE7gAAAAAFMwMFwAAAKhipUoXUKN0uAAAAAAUTOACAAAAUDCBCwAAAEDBBC4AAAAABTM0FwAAAKqYobmVocMFAAAAoGACFwAAAICCCVwAAAAACmaGCwAAAFSxcqULqFE6XAAAAAAKJnABAAAAKJjABQAAAKBgAhcAAACAgglcAAAAoIqV6mrjtrAuueSSbLDBBunYsWM6duyY/v3759Zbb208PmvWrAwcODBdunRJhw4dMmDAgEyZMmWR/70LXAAAAICa0adPn5x11lkZO3ZsHnnkkWy//fbZbbfdMm7cuCTJ4MGDc/PNN+e6667LvffemzfffDN77LHHIj+Or4UGAAAAlnoNDQ1paGhoslZfX5/6+voma7vuumuT+2eeeWYuueSSjB49On369Mnll1+eq6++Ottvv32SZMSIEVl77bUzevTobL755gtdjw4XAAAAYKk3bNiwdOrUqclt2LBhCzxn7ty5ueaaa/LBBx+kf//+GTt2bObMmZMddtihcc9aa62Vvn37ZtSoUYtUjw4XAAAAqGKlShfQTIYOHZohQ4Y0WfvP7pZ/euqpp9K/f//MmjUrHTp0yA033JB11lknjz/+eNq0aZPllluuyf4ePXpk8uTJi1SPwAUAAABY6s3r40Pzs+aaa+bxxx/PjBkzcv311+eggw7KvffeW2g9AhcAAACgprRp0yarrbZakmTjjTfOmDFjcuGFF2bvvffO7NmzM3369CZdLlOmTEnPnj0X6THMcAEAAABqWqlUSkNDQzbeeOO0bt06I0eObDw2fvz4TJw4Mf3791+ka+pwAQAAAGrG0KFDs/POO6dv37557733cvXVV+eee+7J7bffnk6dOuWwww7LkCFD0rlz53Ts2DGDBg1K//79F+kbihKBCwAAAFS1Whmau7CmTp2aAw88MJMmTUqnTp2ywQYb5Pbbb8+Xv/zlJMn555+fFi1aZMCAAWloaMiOO+6Yiy++eJEfR+ACAAAA1IzLL798gcfbtm2b4cOHZ/jw4f/T45jhAgAAAFAwgQsAAABAwXykCAAAAKpYudIF1CgdLgAAAAAFE7gAAAAAFEzgAgAAAFAwgQsAAABAwQzNBQAAgCpWqqt0BbVJhwsAAABAwQQuAAAAAAUTuAAAAAAUzAwXAAAAqGKlShdQo3S4AAAAABRM4AIAAABQMIELAAAAQMEELgAAAAAFMzQXAAAAqli50gXUKB0uAAAAAAUTuAAAAAAUTOACAAAAUDAzXAAAAKCKlUxxqQgdLgAAAAAFE7gAAAAAFGyJ+UjRMi3bVroEgEK9MfvtSpdAjdjxgD9UugRqxBunb1fpEqgRK512X6VLAPif6XABAAAAKNgS0+ECAAAAFK9U6QJqlA4XAAAAgIIJXAAAAAAKJnABAAAAKJgZLgAAAFDFypUuoEbpcAEAAAAomMAFAAAAoGACFwAAAICCCVwAAAAACmZoLgAAAFSxUqULqFE6XAAAAAAKJnABAAAAKJjABQAAAKBgZrgAAABAFSvVVbqC2qTDBQAAAKBgAhcAAACAgglcAAAAAAomcAEAAAAomKG5AAAAUMVKKVe6hJqkwwUAAACgYAIXAAAAgIIJXAAAAAAKZoYLAAAAVDETXCpDhwsAAABAwQQuAAAAAAUTuAAAAAAUTOACAAAAUDBDcwEAAKCKlSpdQI3S4QIAAABQMIELAAAAQMEELgAAAAAFM8MFAAAAqlgp5UqXUJN0uAAAAAAUTOACAAAAUDCBCwAAAEDBBC4AAAAABTM0FwAAAKqYkbmVocMFAAAAoGACFwAAAICCCVwAAAAACmaGCwAAAFSxUqULqFE6XAAAAAAKJnABAAAAKJjABQAAAKBgAhcAAACAghmaCwAAAFWslHKlS6hJOlwAAAAACiZwAQAAACiYwAUAAACgYGa4AAAAQBUzwaUydLgAAAAAFEzgAgAAAFAwgQsAAABAwQQuAAAAAAUzNBcAAACqWKnSBdQoHS4AAAAABRO4AAAAABRM4AIAAABQMDNcAAAAoIqVU650CTVJhwsAAABAwQQuAAAAAAUTuAAAAAAUTOACAAAAUDBDcwEAAKCKlSpdQI3S4QIAAABQMIELAAAAQMEELgAAAAAFM8MFAAAAqlgp5UqXUJN0uAAAAAAUTOACAAAAUDCBCwAAAEDBBC4AAAAABTM0FwAAAKqYkbmVocMFAAAAoGACFwAAAICCCVwAAAAACmaGCwAAAFSxkikuFaHDBQAAAKBgAhcAAACAgglcAAAAAAomcAEAAAAomKG5AAAAUMVKlS6gRulwAQAAACiYwAUAAACgYAIXAAAAgIIJXAAAAAAKZmguAAAAVLFyypUuoSbpcAEAAAAomMAFAAAAoGAClyXUMu2XydCfDM7IsX/JY6/el6v/9uust9Ha893frXuXnHvJT3LrqOszbvLoDP3J4GaslqWZ1xrN6YjvHZQXnh+d92e+mAcfuDmbbrLRAvcPGPC1PP3UvXl/5ot57NE7s/NO2zdPoSw1Ntxs/Qy74oz8eewfc98bI7PVjls2OX7IkAPzu3tH5PYJf83fxt2Yn19zTtb+3Fqfed2uPbvmpF8Mzc1P35A7XrglV9x5WdbcYI3F9TRYStR1WC5tdj4s7Y44P+2OHp62B56SFj1Wajzeuv+uaXvw6Wk36Jdpd+QFqR8wOC169vufrwv/7ujB38m0GeNzxrAfznfPPvt9I9NmjG9ye23Kk81YJZCY4bLEOuP8H2X1tVbNCQNPzdQp07LrN3fOb64fnq9ttXemTp72qf2t69vknbffzaXn/yYHfnffClTM0sprjeay555fz3nnnpIjB56Yh8c8lqMHfTu3/O2qrLPeNpk27e1P7e+/+Sa56nfD86OThuVvt9yZfff5Rv50/eXZdLOdMm7c+Ao8A5ZEbZdplxefeTG3XHNrzrz89E8df+2l13PBSRflzVcnpb5tm+z1nW/mZ1efnX23PDAz3pkxz2t26NQhw2+8MI89+HiOP+DETH97Rvr0WyHvzXhvcT8dlmT1y6R+7xNSem18Gm64MOUP30/d8t1TnvVh45bSu1My964/pDxjWtKqTVp/fofUDzg2H/3mR8lH7//X14V/2ujz6+fAQ/bJ008995l7Z854L/032anxfrlshkctK1W6gBqlw2UJVN+2Pl/+2nY57/SL8sjoxzLx5dcz/NzLMvHl17LvwQPmec6br03KsJN+nr9ce0venzmf/0OH/+C1RnMafMx38uvLr85vr7w2zz47IUcOPDEffvhRDjl4n3nuHzTosNx++z352c8vzXPPvZBTTj03jz32dI484pBmrpwl2UN3P5xfnzMi99/2j3kev/PGuzL2/kczaeKkvPL8q/nlaZekQ8cOWXWdVeZ7zf2P3CdT35yWs4acm2cfH59Jr03OmPvG5s1XJy2up8FSoPWmO6X83ruZ/fcrUpr8Ssoz30rp1Wc+CVf+v7nPPZzSxGdTnvFWym+/mdn3Xpu6+mXSomuf/+m6kCTt2y+TSy87N0OOPikzps87MP535XI5U6e+1Xib1x83gMWrkMBFWlqsli1bplWrVmlomN1kfdashnx+sw0rVBXVyGuN5tK6det8/vMbZORd9zeulcvljLzrgWy++cbzPGfzzTZusj9J/n7HPfPdD5+lVetW+fr+u+S9Ge/nxXEvznffll/ZIuOfHJ/T/u/H+csT1+fXt1+ar+331WaslCVRy1U3TGnKK2nzte+m3fd+lrYHnJyW6289/xNatEyr9bdJedaHKU17vbjrUrPOPu/HueP2e3PfPaMWan/7Dsvk0afuyuPj7smVV1+cNddabTFXCPynQj5SVF9fnyeeeCJrrz3/uQ8svA8/+DCPjXkyRww5NC8+/3LenvZOdtnjK9lok/Uz8eX5/x82LCqvNZpL166d06pVq0yd8laT9alTp2WtNVed5zk9e3bLlKlN/8I7Zcpb6dmj22Krk+rUf4fNc8rFJ6Vtu/q8PeWdfH/f4zPj3Znz3d+rb6/s9q2v59rLrs/vf3F11tpozRxz+lH5eM7Hue26vzdj5SxJ6jp1S6sNv5iPx96RWQ/dkhY9V06b7fbJ7LkfZ+4z//oFuEW/DVK/y3eS1m1S/mBGGv50fjJr/h2hC3tdatvuA76a9TdcJ1/Z7psLtf+FCS/nmIE/zDPjxqdjx2Vz5KBDc8vfr8lWm++SSW9OWczVAv+0SIHLkCFD5rk+d+7cnHXWWenSpUuS5Oc///kCr9PQ0JCGhoYma6VyKS3qfMLpn04YeErOvODk3PfULfn444/zzJPj87cb/p51N/jsQX+wKLzWgGr32D8ez2FfOTydOnfKrvvtktMuPTnf/dpRmf729Hnub9GiLuOffD6XnXV5kmTCuBfSb82V8/Vv7SpwqWV1dSlNeSVz/nFDkmTutNfycdcV0mqDbZsEI6XXnsus35+eunbLptX6W6fN176bWVf/NPloPjOAFvK61K7eK/TMmWf9KHvufuinupLn55Exj+eRMY833n/4ocfy4JhbctAh++SsMy9cTJUC/2mRApcLLrggG264YZZbbrkm6+VyOc8++2zat2+furq6z7zOsGHDctpppzVZ67JM73Rrv8KilFPVXnvljRy4+/fSbpm26dChfaZNfTs//9WZef3VNypdGlXGa43m8NZb7+Tjjz9O9x5dm6x3794tk6fMe07B5MnT0qN7026WHj26znc/zM+sj2bljVfezBuvvJlnHn02Vz/w2+yy78656pd/mOf+t6e+k1eef7XJ2qsvTMy2X92mOcplCVX+YEbKbzed41N+e1LqVv98040fz055+rSUp0/L7Ekvpe0hZ6TVelvl4zG3/m/XpWZtuNG66d69a0be9+fGtVatWqX/lpvmsMP3zwrd1k+ptOCRqB9//HGeevLZ9Ful7+IulyVUOcaAVMIitZT89Kc/zYwZM3LyySfn7rvvbry1bNkyV1xxRe6+++7cddddn3mdoUOHZsaMGU1uXZbp9V8/iWr20YezMm3q2+nYadlsud3mGXnbfZUuiSrltcbiNGfOnDz66JPZfrutGtfq6uqy/XZbZfTosfM8Z/RDY7P99ls1WdvhS9vMdz8srLq6FmnTpvV8jz815umsuOqKTdZWXKVPpryhDb+Wld58IXXL92yyVrd8j5RnfsYg0rq61LWa/984/+vrUjPuu3d0tt78a9luq90bb489+lSuv/bmbLfV7p8ZtiRJixYtsvY6a2TKPL6BElh8FqnD5cQTT8yXvvSlHHDAAdl1110zbNiwtG49/x9Y5qe+vj719fVN1nycqKktt9s8dUlefnFiVurXJ8edcnRenvBKbvjDzUmSwT86Mj16dc+JR53aeM5a662eJFmm/TJZvuvyWWu91TNn9sd58fmXK/AMWFp4rdFczr/wsoy4/PyMffTJjBnzWI4e9J20b98uV/z2j0mSEb+5MG++OSk/OumsJMlFF12eu0Zen8HHfje33Hpn9t5rt2y88Qb53pHHV/JpsIRpt0zbrNDvXx2yvfr2zGrrrpqZ776Xme/OzLeO2T//+PuDeXvK2+nUuVO+cfBu6dqza+7+672N55z/x3Nz/60P5M9X/CVJct1lf8rFf/lFDhi0X+6++Z6svdFa2XX/XXLe8ec399NjCfLx2DtTv88JafWFr2bu82PSome/tNpgm8y+43efbGjVJq032yVzX3oi5fenJ+06pPVG26Wuw/L5+Pl/BcX13xySuS88lo8fv3vhrkvN++D9D/LcsxOarH34wYd5953pjeu/vPTsTJ40JWec9sloh+8fPzBjH3k8L7/0ajp16piBRx+WPiv2zu+vvK7Z64datshDczfddNOMHTs2AwcOzCabbJKrrrpqoT5GxKJZdtkOGXzSkenZq3tmTJ+Zv//1rlzw00vy8cdzkyTdenRNrxV6NDnnhruuavzn9TZaO7sO2ClvTHwzO2yye3OWzlLGa43mct11N6Vb18459cfHpWfPbnniiXHZ5WsHZOrUTwbp9l2xd5O/0o0a/UgOOPConH7a8TnjJydkwgsvZ8A3D8u4ceMr9RRYAq254Zr5xfX/mh036NQjkyS3Xnt7fnbi+Vlp1RWz069OTafOHTPz3Zl57onxGbTHsU0+MtR7pd7p1LlT4/3nnhifH337lHz3xMNy0LHfyuTXJuWiUy7OHTeMbL4nxhKnNOWVNNx0Sdps/Y203vxrKc94K7Pv+WPmPvfQJxvKpdR17pk26/ZPXdsOKc/6IKXJr6Thj+ek/Pabjdep69Qtde06LPx1YSH06dMr5X/7/9DlluuYn1/4k3Tv0S0zps/IE4+Pyy5f2SfPj5//N7QBxasr/w/f6XzNNdfk2GOPzbRp0/LUU09lnXXW+a8LWbv7F/7rcwGWRBOmm4ND89iimyHXNI/bvr9KpUugRqx0mo820zymzaiNP+QctPKASpfQLH77yp8qXUIT/9PXQu+zzz7ZaqutMnbs2Ky00kpF1QQAAACwVPufApck6dOnT/r06VNELQAAAABVwaRaAAAAoGYMGzYsm266aZZddtl07949u+++e8aPb/rxslmzZmXgwIHp0qVLOnTokAEDBmTKlEX7xkKBCwAAAFAz7r333gwcODCjR4/OHXfckTlz5uQrX/lKPvjgg8Y9gwcPzs0335zrrrsu9957b958883ssccei/Q4//NHigAAAIAlV+m//66cqnTbbbc1uX/FFVeke/fuGTt2bLbZZpvMmDEjl19+ea6++upsv/32SZIRI0Zk7bXXzujRo7P55psv1OPocAEAAACWeg0NDZk5c2aTW0NDw2eeN2PGjCRJ586dkyRjx47NnDlzssMOOzTuWWuttdK3b9+MGjVqoesRuAAAAABLvWHDhqVTp05NbsOGDVvgOaVSKccee2y23HLLrLfeekmSyZMnp02bNlluueWa7O3Ro0cmT5680PX4SBEAAACw1Bs6dGiGDBnSZK2+vn6B5wwcODBPP/10HnjggcLrEbgAAABAFauVCS719fWfGbD8u6OOOip//etfc99996VPnz6N6z179szs2bMzffr0Jl0uU6ZMSc+ePRf6+j5SBAAAANSMcrmco446KjfccEPuuuuu9OvXr8nxjTfeOK1bt87IkSMb18aPH5+JEyemf//+C/04OlwAAACAmjFw4MBcffXV+ctf/pJll122cS5Lp06d0q5du3Tq1CmHHXZYhgwZks6dO6djx44ZNGhQ+vfvv9DfUJQIXAAAAIAacskllyRJvvjFLzZZHzFiRA4++OAkyfnnn58WLVpkwIABaWhoyI477piLL754kR5H4AIAAADUjHL5s6fatG3bNsOHD8/w4cP/68cRuAAAAEAVK9XM2Nwli6G5AAAAAAUTuAAAAAAUTOACAAAAUDAzXAAAAKCKlc1wqQgdLgAAAAAFE7gAAAAAFEzgAgAAAFAwgQsAAABAwQzNBQAAgCpWqnQBNUqHCwAAAEDBBC4AAAAABRO4AAAAABTMDBcAAACoYqWUK11CTdLhAgAAAFAwgQsAAABAwQQuAAAAAAUTuAAAAAAUzNBcAAAAqGJlQ3MrQocLAAAAQMEELgAAAAAFE7gAAAAAFMwMFwAAAKhipUoXUKN0uAAAAAAUTOACAAAAUDCBCwAAAEDBBC4AAAAABTM0FwAAAKpYuVyudAk1SYcLAAAAQMEELgAAAAAFE7gAAAAAFMwMFwAAAKhipZjhUgk6XAAAAAAKJnABAAAAKJjABQAAAKBgAhcAAACAghmaCwAAAFWsVOkCapQOFwAAAICCCVwAAAAACiZwAQAAACiYGS4AAABQxcopV7qEmqTDBQAAAKBgAhcAAACAgglcAAAAAAomcAEAAAAomKG5AAAAUMVKhuZWhA4XAAAAgIIJXAAAAAAKJnABAAAAKJgZLgAAAFDFymUzXCpBhwsAAABAwQQuAAAAAAUTuAAAAAAUTOACAAAAUDBDcwEAAKCKlSpdQI3S4QIAAABQMIELAAAAQMEELgAAAAAFM8MFAAAAqlg55UqXUJN0uAAAAAAUTOACAAAAUDCBCwAAAEDBBC4AAAAABTM0FwAAAKpYydDcitDhAgAAAFAwgQsAAABAwQQuAAAAAAUzwwUAAACqWLlshksl6HABAAAAKJjABQAAAKBgAhcAAACAgglcAAAAAApmaC4AAABUsVIMza0EHS4AAAAABRO4AAAAABRM4AIAwP9r796jrKrP+wF/RkBABQwCw01uIWjVBVRQoDHWC0poQiVN0khNJS5jmog0hCZG6gVQE6wkKRqJmsZLTGvU2oK5ag1WCD8BuYjxXjGWaIQBL2FkKsNlzu8Pk0mnoELczGEOz7PWWYuz9z77vHvxXWsPH979DgBQMDNcAAAAoIKVzHApCx0uAAAAAAXbZzpcarfVlbsEgEINPWxAuUtgP/HQxqfLXQL7ifddsb7cJbCf+NVdk8tdAsC7psMFAAAAoGACFwAAAICC7TOPFAEAAADFaygZmlsOOlwAAAAACiZwAQAAACiYwAUAAACgYGa4AAAAQAUzwaU8dLgAAAAAFEzgAgAAAFAwgQsAAABAwQQuAAAAAAUzNBcAAAAqWIOxuWWhwwUAAACgYAIXAAAAgIIJXAAAAAAKZoYLAAAAVDAzXMpDhwsAAABAwQQuAAAAAAUTuAAAAAAUTOACAAAAUDBDcwEAAKCClUqG5paDDhcAAACAgglcAAAAAAomcAEAAAAomBkuAAAAUMEaYoZLOehwAQAAACiYwAUAAACgYAIXAAAAgIIJXAAAAAAKZmguAAAAVLCSobllocMFAAAAoGACFwAAAICCCVwAAAAACmaGCwAAAFSwUskMl3LQ4QIAAABQMIELAAAAQMEELgAAAAAFE7gAAAAAFMzQXAAAAKhgDTE0txx0uAAAAAAUTOACAAAAUDCBCwAAAEDBzHABAACAClYqmeFSDjpcAAAAAAomcAEAAAAomMAFAAAAoGACFwAAAICCGZoLAAAAFawhhuaWgw4XAAAAgIIJXAAAAID9yqJFizJu3Lj07NkzVVVVmT9/fpP9pVIpl112WXr06JH27dtn9OjRefbZZ/foOwQuAAAAwH6lrq4uQ4YMydy5c3e5/+qrr861116bG264IcuWLcvBBx+cMWPGZMuWLbv9HWa4AAAAQAUrmeGyk7Fjx2bs2LG73FcqlTJnzpxccsklOeOMM5Ikt912W6qrqzN//vyceeaZu/UdOlwAAACAFq++vj61tbVNXvX19Xt8nueffz7r16/P6NGjG7d16tQpI0aMyJIlS3b7PAIXAAAAoMWbNWtWOnXq1OQ1a9asPT7P+vXrkyTV1dVNtldXVzfu2x0eKQIAAABavGnTpmXq1KlNtrVt27ZM1QhcAAAAgArQtm3bQgKW7t27J0lqamrSo0ePxu01NTUZOnTobp/HI0UAAABQwRpKpf3iVZT+/fune/fuWbBgQeO22traLFu2LKNGjdrt8+hwAQAAAPYrmzdvzpo1axrfP//881m9enU6d+6cPn36ZMqUKbnyyivzvve9L/3798+ll16anj17Zvz48bv9HQIXAAAAYL+yYsWKnHzyyY3vfzf7ZeLEibn11ltz4YUXpq6uLp/5zGfym9/8JieccELuvffetGvXbre/Q+ACAAAA7FdOOumklN7mMaSqqqpcfvnlufzyy//g7xC4AAAAQAUrpbj5Juw+Q3MBAAAACiZwAQAAACiYwAUAAACgYAIXAAAAgIIZmgsAAAAVrOFtfhsPe48OFwAAAICCCVwAAAAACiZwAQAAACiYGS4AAABQwUoxw6UcdLgAAAAAFEzgAgAAAFAwgQsAAABAwQQuAAAAAAUzNBcAAAAqWEPJ0Nxy0OECAAAAUDCBCwAAAEDBBC4AAAAABTPDBQAAACpYKWa4lIMOFwAAAICCCVwAAAAACiZwAQAAACiYwAUAAACgYIbmAgAAQAVrKBmaWw46XAAAAAAKJnABAAAAKJjABQAAAKBgAhcAAACAghmaCwAAABWsFENzy0GHCwAAAEDBBC77oKlfPj+/fu2JJq+Fy374tp/p2LFDvjL7kqx66sH8cv0j+fnyH+eU0z7QTBXTUllr7C1/PHJIvvHdq/LTR+Zlxbqf508/2HSNnPxnJ+a6O76enz3xo6xY9/MMOnrgO55zwKB+ufo7V+QHD9+VFet+ngnnfXxvlU8F+NxnJ2bNfy3N5trn8tDiH+a44UPf9viPfvTDefyxhdlc+1weWfWzjP3gKc1TKC2Weyh7y9gZt2bo335zp9dX73owm+q25Kq7F+aMK7+XEX/3rXxw+i35h7sX5vU36t/yfNt27Mice/5fPjbr9oz84vU57ZKbc8n3/iMbNm1uxquC/ZNHivZRTz/1bM4c/+nG99u3b3/LY9u0aZPvz/tOXnn5lXzmU1/I+pdq0vvwnqmtfb05SqWFs9bYG9of1C7PPrkmP7jjx/nazV/dxf72Wb3ssdz/g//MpV//8m6ds137dnlx7br87IcPZurMyQVXTCX5+Mf/PF+bPT3nT7ooDy9/JH87+dP5yY//JUcdc2I2bnxlp+NHjRyef/ne3Fx8yaz8+Cc/y4QzP5J/u/umHDfig3niiWfKcAW0FO6h7A3/8nefSEOpofH9mnWv5LNz78lpfzwwGzfVZeOmukw944QM6N45616rzZV3PpiNm+rytXP/bJfn27J1e556cWPOG3NcjujVJbX/U5+r/31Rpnz7x7n9S59orsuC/ZLAZR+1Y/uObNzw8m4de+YnP5JD39MxZ4w5q/FG/+ILL+3N8qgg1hp7w0MPLMtDDyx7y/0/ufu+JEmP3t13+5xPPvp0nnz06STJBRf/zbsrkIr2hc+fl+/cdHu+e9tdSZLzJ12UPxt7as751Jm5evbcnY6fPPnc3Hffg/n6N25IkkyfMTujTz0x53/unEy64KJmrZ2WxT2UvaFzh/ZN3t98/8oc3qVThg/slaqqqnz9fwUrh3ftlAs+PDIX3/Yf2b6jIa1b7fwAQ4f2bXPjpPFNtl30sT/NJ79+V9a9+np6dO6wV66DfUvpf4V4NB+PFO2j+g/ok5VP/mceeuTefPPb/5CevXu85bGnjT05K5c/mq/MviSrn1mYBQ/Nz+Sp5+WAA/z18s6sNaCStGnTJsceOzgLHvh547ZSqZQFDyzOyJHDdvmZkSOGNTk+Sf7j/gff8nj4HfdQ9rZt23fkJyueyRkj/yhVVVW7PGbzG1tzSLsDdxm2vJXNW+pTVfVmGAPsPe+qw6Wuri533XVX1qxZkx49emTChAk57LDD3vFz9fX1qa9v+pxhqdSQqio3nCR5ZOUv8oVJF+e5Nf+dbtVdM/XLn8u8n9yWU/7kjNRt/p+dju/bt3fe/4ERmfevP8pf/+Xn0n9An3z1a5emdevW+cerry/DFdBSWGtApenSpXNat26dDTVNuw42bNiYI4947y4/071719Rs2NhkW03Ny+le3XWv1UnL5x5Kc3jgF7/M62/U589H/NEu97+2+Y38033L8xfvP2a3z1m/bXuuueehfPDYQTmk/YFFlQrswh4FLkcddVQWL16czp0754UXXsiJJ56Y1157LYMGDcpzzz2XK664IkuXLk3//v3f9jyzZs3KzJkzm2w7pG2XdGzfbc+voAL9588WN/75qSf+K4+s+EWWPXZ/xo3/YO7453/f6fgDDjggr7z8ai6cMiMNDQ157NEn071HdT47+Rw3cN6WtQYAfxj3UJrD/KVP5v1/1DfdOh2y077Nb2zN5Bt/mAHd35PPjj1+t863bceOXHjLvSklufgvTy64WuD/2qOWkqeffrrxmdNp06alZ8+eWbt2bR5++OGsXbs2gwcPzsUXX/yO55k2bVo2bdrU5NWhXZc/7Ar2A7W1r+eXa9am34A+u9xfU7Mxv1zz32lo+P1zec/+13Op7t41bdq0aa4yqQDWGtDSvfzyq9m+fXu6VTf9uaJbt65ZX7Nxl59Zv35jqrs17Wapru7ylsfDrriHUrSXXq3NsmdeyEdGHb3TvrotW3P+9ffk4LZt8o1PfyhtWrV6x/P9LmxZ92ptbph0hu4WaAZ/8DM8S5YsyYwZM9KpU6ckySGHHJKZM2dm8eLF7/DJpG3btunYsWOTl8eJ3tpBBx+Uvv0Pz4b1u/7Bb8WyR9JvQJ8mz3UOeG+/rF+3Idu2bWuuMqkA1hrQ0m3bti2rVv0ip5x8QuO2qqqqnHLyCVm6dOUuP7N02cqccsoJTbaNPvXEtzwedsU9lKLds/SpdO7QPh84ul+T7Zvf2JrPfeuetGndKnM+8+G0bfPODy38Lmz51cbf5IZJH8mhB7d/x89QWRpS2i9e+5o9Tjl+d5PYsmVLevRoOhisV69e2bjR/wa9W5de/sWM/JPh6X14zww/fmhu+t41adixI/P/7SdJkmuu/2ouumxK4/G33XxnDj20Uy6/aloGvLdvTj39xEyeel6+e9P3y3QFtBTWGntL+4PaZ9DRAzPo6IFJkl59emTQ0QNT3evNR0c7Htohg44emAGD+iVJ+r63TwYdPTCHde3ceI6Z116cSX//+99G1LpN68ZztmnTJl27d82gowemd79ezXdhtAj/eM0/5dPn/lX++q8/niOPHJi5112Vgw9un1u/e2eS5Jabr8lXrvz9bx/65jdvypjTT8oXpvxNjjjivbns0qkZNmxwvnX9LeW6BFoA91D2poaGUn6w7KmMO/7IJsNw3wxb5ueNrdsyY8KpqduyNS/X1uXl2rrs+F/dU+Ov/F4eePS5JG+GLV+66ad58lcb8tWzT09DqaHxM9u272j2a4P9yR4PzT311FPTunXr1NbW5plnnskxx/x+QNPatWt3a2gub69Hr+rM/c7svKfzoXn15Vfz8LJVGXfaX+XVV15LkvTs3SMNDb9P71769fqc9bHPZMZXvpz7F8/L+nU1uenGf87cOTeV6xJoIaw19pajhhyRG//9m43vp86cnCT54Z0/zcwpX82Jp5+QGdf8feP+WTe+Odfr21+7Od/++pv/yO3eq7rJ+uta3SW3/+z3/wA++/wJOfv8CVn50CP5m4/+7V69HlqWf/3XH6Rrl86ZcdkX07171zz66BP50Ic/mQ2//fW9fQ7v2eSxjiVLV+STZ1+Qy2demCuv+HKeXfN8Pvqxc/PEE8+U6xJoAdxD2ZuWPvNC1r32esaPPKrJ9qde3JDH1tYkScZdcVuTfT+ePjG9DuuYJPnvDb/J61u2Jkk2/KYuDz7+fJLkE/9wR5PP/NPkj+S49/XeK9cAJFWlUmm3+27+76DbkSNHZsyYMY3vv/SlL+XFF1/M97+/50l9r/fs/GwiQEvWo13ndz4ICrD6lV+WuwT2E9UHH1ruEthPrLnjc+Uugf1E+zEXlLuEZtH3sMHlLqFZrH3lF+UuoYk96nCZPn362+6fPXv2uyoGAAAAKNYe9FlQIJNqAQAAAAomcAEAAAAomMAFAAAAoGACFwAAAICC7fGvhQYAAABajoYYmlsOOlwAAAAACiZwAQAAACiYwAUAAACgYGa4AAAAQAUrlcxwKQcdLgAAAAAFE7gAAAAAFEzgAgAAAFAwgQsAAABAwQzNBQAAgArWYGhuWehwAQAAACiYwAUAAACgYAIXAAAAgIKZ4QIAAAAVrBQzXMpBhwsAAABAwQQuAAAAAAUTuAAAAAAUTOACAAAAUDBDcwEAAKCClUqG5paDDhcAAACAgglcAAAAAAomcAEAAAAomBkuAAAAUMEaYoZLOehwAQAAACiYwAUAAACgYAIXAAAAgIIJXAAAAAAKZmguAAAAVLBSydDcctDhAgAAAFAwgQsAAABAwQQuAAAAAAUzwwUAAAAqWIMZLmWhwwUAAACgYAIXAAAAgIIJXAAAAAAKJnABAAAAKJihuQAAAFDBSobmloUOFwAAAICCCVwAAAAACiZwAQAAACiYGS4AAABQwRpihks56HABAAAAKJjABQAAAKBgAhcAAACAgglcAAAAAApmaC4AAABUsFLJ0Nxy0OECAAAAUDCBCwAAAEDBBC4AAAAABTPDBQAAACpYgxkuZaHDBQAAAKBgAhcAAACAgglcAAAAAAomcAEAAAAomKG5AAAAUMFKMTS3HHS4AAAAABRM4AIAAABQMIELAAAAQMHMcAEAAIAK1lAyw6UcdLgAAAAAFEzgAgAAAFAwgQsAAABAwQQuAAAAAAUzNBcAAAAqWMnQ3LLQ4QIAAABQMIELAAAAQMEELgAAAAAFM8MFAAAAKlgpZriUgw4XAAAAgIIJXAAAAAAKJnABAAAAKJjABQAAAKBghuYCAABABSuVDM0tBx0uAAAAAAUTuAAAAAAUTOACAAAAUDAzXAAAAKCCmeFSHjpcAAAAAAomcAEAAAAomMAFAAAAoGACFwAAAICCGZoLAAAAFczI3PLQ4QIAAADsd+bOnZt+/fqlXbt2GTFiRB5++OFCzy9wAQAAAPYrd955Z6ZOnZrp06dn1apVGTJkSMaMGZMNGzYU9h0CFwAAAKDFq6+vT21tbZNXfX39Lo/9xje+kfPOOy/nnHNOjjrqqNxwww056KCDcvPNNxdWzz4zw+XXrz1R7hJalPr6+syaNSvTpk1L27Zty10OFcxao7lYazQXa43mYq3RXKw13sn2rb8udwnNYsaMGZk5c2aTbdOnT8+MGTOabNu6dWtWrlyZadOmNW474IADMnr06CxZsqSweqpKpZL5OS1QbW1tOnXqlE2bNqVjx47lLocKZq3RXKw1mou1RnOx1mgu1hq8qb6+fqeOlrZt2+4URL700kvp1atXHnrooYwaNapx+4UXXpiFCxdm2bJlhdSzz3S4AAAAAPyhdhWulJMZLgAAAMB+o0uXLmnVqlVqamqabK+pqUn37t0L+x6BCwAAALDfOPDAAzNs2LAsWLCgcVtDQ0MWLFjQ5BGjd8sjRS1U27ZtM3369H2qXYrKZK3RXKw1mou1RnOx1mgu1hrsualTp2bixIkZPnx4jj/++MyZMyd1dXU555xzCvsOQ3MBAACA/c51112X2bNnZ/369Rk6dGiuvfbajBgxorDzC1wAAAAACmaGCwAAAEDBBC4AAAAABRO4AAAAABRM4AIAAABQMIFLCzV37tz069cv7dq1y4gRI/Lwww+XuyQqzKJFizJu3Lj07NkzVVVVmT9/frlLokLNmjUrxx13XDp06JBu3bpl/PjxeeaZZ8pdFhXo+uuvz+DBg9OxY8d07Ngxo0aNyk9/+tNyl8V+4KqrrkpVVVWmTJlS7lKoMDNmzEhVVVWT15FHHlnusoDfEri0QHfeeWemTp2a6dOnZ9WqVRkyZEjGjBmTDRs2lLs0KkhdXV2GDBmSuXPnlrsUKtzChQszadKkLF26NPfff3+2bduW008/PXV1deUujQrTu3fvXHXVVVm5cmVWrFiRU045JWeccUaeeOKJcpdGBVu+fHluvPHGDB48uNylUKGOPvrorFu3rvG1ePHicpcE/JZfC90CjRgxIscdd1yuu+66JElDQ0MOP/zwTJ48ORdddFGZq6MSVVVVZd68eRk/fny5S2E/sHHjxnTr1i0LFy7MiSeeWO5yqHCdO3fO7Nmzc+6555a7FCrQ5s2bc+yxx+Zb3/pWrrzyygwdOjRz5swpd1lUkBkzZmT+/PlZvXp1uUsBdkGHSwuzdevWrFy5MqNHj27cdsABB2T06NFZsmRJGSsDKMamTZuSvPkPYdhbduzYkTvuuCN1dXUZNWpUucuhQk2aNCkf+tCHmvzcBkV79tln07NnzwwYMCBnnXVWfvWrX5W7JOC3Wpe7APbMyy+/nB07dqS6urrJ9urq6jz99NNlqgqgGA0NDZkyZUre//7355hjjil3OVSgxx57LKNGjcqWLVtyyCGHZN68eTnqqKPKXRYV6I477siqVauyfPnycpdCBRsxYkRuvfXWHHHEEVm3bl1mzpyZD3zgA3n88cfToUOHcpcH+z2BCwD7jEmTJuXxxx/3/Dl7zRFHHJHVq1dn06ZNufvuuzNx4sQsXLhQ6EKhXnjhhXz+85/P/fffn3bt2pW7HCrY2LFjG/88ePDgjBgxIn379s1dd93lUUnYBwhcWpguXbqkVatWqampabK9pqYm3bt3L1NVAO/eBRdckB/96EdZtGhRevfuXe5yqFAHHnhgBg4cmCQZNmxYli9fnmuuuSY33nhjmSujkqxcuTIbNmzIscce27htx44dWbRoUa677rrU19enVatWZayQSnXooYdm0KBBWbNmTblLAWKGS4tz4IEHZtiwYVmwYEHjtoaGhixYsMAz6ECLVCqVcsEFF2TevHl54IEH0r9//3KXxH6koaEh9fX15S6DCnPqqafmsccey+rVqxtfw4cPz1lnnZXVq1cLW9hrNm/enOeeey49evQodylAdLi0SFOnTs3EiRMzfPjwHH/88ZkzZ07q6upyzjnnlLs0KsjmzZub/O/I888/n9WrV6dz587p06dPGSuj0kyaNCm333577rnnnnTo0CHr169PknTq1Cnt27cvc3VUkmnTpmXs2LHp06dPXn/99dx+++158MEHc99995W7NCpMhw4ddppDdfDBB+ewww4zn4pCffGLX8y4cePSt2/fvPTSS5k+fXpatWqVCRMmlLs0IAKXFukTn/hENm7cmMsuuyzr16/P0KFDc++99+40SBfejRUrVuTkk09ufD916tQkycSJE3PrrbeWqSoq0fXXX58kOemkk5psv+WWW/KpT32q+QuiYm3YsCFnn3121q1bl06dOmXw4MG57777ctppp5W7NIA/yIsvvpgJEybklVdeSdeuXXPCCSdk6dKl6dq1a7lLA5JUlUqlUrmLAAAAAKgkZrgAAAAAFEzgAgAAAFAwgQsAAABAwQQuAAAAAAUTuAAAAAAUTOACAAAAUDCBCwAAAEDBBC4AAAAABRO4AAAAABRM4AIAAABQMIELAAAAQMH+P6kSY4lJbH9QAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1500x1500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 15))\n",
    "sns.heatmap(confusion_matrix(true_labels, best_preds, normalize='true')*100, ax=ax, annot=True, fmt='.1f')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8f3c20d-5183-4110-b5de-9dcd8393d500",
   "metadata": {
    "id": "f8f3c20d-5183-4110-b5de-9dcd8393d500"
   },
   "source": [
    "### ■ **<a name=\"demo\">4. Demo</a>** [(&#8593;)](#content)\n",
    "You now test the model on any given  image. You can also try your own image. An interesting perspective is to build a real time Alphabet recognition to communicate with the computer !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e290ddac-ff9e-4ab1-8b7d-a388569bf942",
   "metadata": {
    "id": "e290ddac-ff9e-4ab1-8b7d-a388569bf942",
    "tags": []
   },
   "outputs": [],
   "source": [
    "def plot_prediction_probability(probas):\n",
    "    \"\"\"plots the prediction confidence across classes !\"\"\"\n",
    "    fig, ax = plt.subplots(figsize=(10, 4))\n",
    "    x = np.arange(n_classes)\n",
    "\n",
    "    ax.bar(x, probas, width=0.2, color='b', align='center', alpha=0.8)\n",
    "    ax.set_xlabel('Class')\n",
    "    ax.set_ylabel('Probability')\n",
    "    ax.set_title('Prediction Probability Distribution')\n",
    "    plt.show()\n",
    "\n",
    "def enzym_recognition_demo(model, enzym_data):\n",
    "    \"\"\"Predicts the enzym class given an enzym data, and a trained model !\"\"\"\n",
    "\n",
    "    ## display enzym\n",
    "    G = to_networkx(enzym_data, to_undirected=True)\n",
    "    plt.figure(figsize=(12,12))\n",
    "    plt.axis('off')\n",
    "    nx.draw_networkx(G,\n",
    "                    pos=nx.spring_layout(G, seed=0),\n",
    "                    with_labels=True,\n",
    "                    node_size=800,\n",
    "                    node_color=[enzym_data.y] * len(enzym_data.x),\n",
    "                    cmap=\"hsv\",\n",
    "                    vmin=-2,\n",
    "                    vmax=3,\n",
    "                    width=0.8,\n",
    "                    edge_color=\"grey\",\n",
    "                    font_size=14\n",
    "                    )\n",
    "    plt.show()\n",
    "\n",
    "    ## put model in mode evaluation\n",
    "    model.eval()\n",
    "\n",
    "    ## predict\n",
    "    enzym_data = enzym_data.to(device)\n",
    "    pred = model(enzym_data.x, enzym_data.edge_index, enzym_data.batch)\n",
    "    probas = F.softmax(pred, dim=1)\n",
    "    pred_labels = probas.argmax(dim=1)\n",
    "    \n",
    "    ## print results\n",
    "    print('\\t> True label     :', enzym_data.y.item())\n",
    "    print('\\t> Predicted label:', pred_labels.item(), '| Probability:', round(probas[0].max().item(), 2))\n",
    "\n",
    "    ## plot prediction probability\n",
    "    plot_prediction_probability(probas[0].detach().numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "55ec786d-2547-4616-89bc-8f61448f0ef9",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 903
    },
    "id": "55ec786d-2547-4616-89bc-8f61448f0ef9",
    "outputId": "fa29d580-2373-4cd1-8357-b7b1a2be3fdb",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\t> True label     : 5\n",
      "\t> Predicted label: 5 | Probability: 0.6\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## get evaluation loader\n",
    "eval_loader = dm.eval_dataloader()\n",
    "\n",
    "## pick an enzym\n",
    "enzym_data = next(iter(eval_loader))\n",
    "\n",
    "## get the prediction\n",
    "enzym_recognition_demo(model, enzym_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55137de7-dc52-41f2-b80c-5eb0377493ee",
   "metadata": {
    "id": "55137de7-dc52-41f2-b80c-5eb0377493ee"
   },
   "source": [
    "---\n",
    "<p style=\"text-align: center;\">Copyright © 2023 Omar Ikne</p>"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}