assignment06_LSTM_sotckprice_prediction.ipynb

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  "metadata": {
    "colab": {
      "name": "assignment06_LSTM_sotckprice_prediction.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/soheekim911/Standalone_DL/blob/master/assignment06_LSTM_sotckprice_prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "fbN0oQMGlsvy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "outputId": "0b4af4ee-97d6-4f05-fc27-1b5148dbeb92"
      },
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import pandas_datareader.data as pdr\n",
        "import datetime\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "import torch.optim as optim\n",
        "import numpy as np\n",
        "import argparse\n",
        "from copy import deepcopy # Add Deepcopy for args\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "\n",
        "import seaborn as sns \n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "print(torch.__version__)\n",
        "%matplotlib inline\n",
        "%pylab inline\n",
        "pylab.rcParams['figure.figsize'] = (15, 9)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.0.1.post2\n",
            "Populating the interactive namespace from numpy and matplotlib\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/IPython/core/magics/pylab.py:161: UserWarning: pylab import has clobbered these variables: ['seed']\n",
            "`%matplotlib` prevents importing * from pylab and numpy\n",
            "  \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "fhvKGCe0rGUv",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!mkdir results"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "DvtWBpVVs2S9",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# We will look at stock prices over the past year, starting at January 1, 2016\n",
        "start = (2000, 12, 1)\n",
        "start = datetime.datetime(*start)\n",
        "end = datetime.date.today()\n",
        "\n",
        "google = pdr.DataReader('028050.KS', 'yahoo', start, end)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "fLwrgA7ys7Mz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 549
        },
        "outputId": "89421844-dcff-4f35-a658-127a3b0e0109"
      },
      "cell_type": "code",
      "source": [
        "google.Low.plot(grid=True)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f37745d84e0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IBHwAAKDAxFQu4HM6dvqnGfFVk4mbdAWYhVKuTYADveG8TOPHvnaXhsej01oXAMwVBHwA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\nvo970LNP+W82VWO2LhxqvT18bubgBTrvFauKjr/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Ntz9yemjfQCV87rBlGDSkU0aa4Np4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\nIbTOG8DGHfao85KiLC5dMjmu9ywpyeGq86eyYFpZj+248gJDhx927q9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7/aFkL2jlxoOc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\nPhERERERkWFKCZ+IiIiIiMgw9f8BKS+8JD5Oh+MAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "jRyck1bNs7xr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "6a2bb82e-5a7d-4c70-8c06-589a7ba11fbb"
      },
      "cell_type": "code",
      "source": [
        "google.tail()\n",
        "print(google.isna().sum())"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "High         0\n",
            "Low          0\n",
            "Open         0\n",
            "Close        0\n",
            "Volume       0\n",
            "Adj Close    0\n",
            "dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "qjjF89JmtHlB",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Data Preparation"
      ]
    },
    {
      "metadata": {
        "id": "KRMwIioStBUX",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "## torch.utils.data.Dataset 사용해서 dataset 새로 정의하기\n",
        "class StockDataset(Dataset):\n",
        "    \n",
        "    def __init__(self, symbol, x_frames, y_frames,start,end): \n",
        "        # x_frames랑 y_frames는 x와 y의 sequence length\n",
        "        # x_frames: 최근 n일의 데이터, y_frames: 이후 n일의 데이터\n",
        "        \n",
        "        self.symbol = symbol\n",
        "        self.x_frames = x_frames \n",
        "        self.y_frames = y_frames\n",
        "        \n",
        "        self.start = datetime.datetime(*start) \n",
        "        self.end = datetime.datetime(*end)\n",
        "        \n",
        "        # 위에서 본 pandas dataframe이 data에 저장됨\n",
        "        self.data = pdr.DataReader(self.symbol, 'yahoo', self.start, self.end)\n",
        "#         print(self.data.isna().sum) # df 안의 NaN 갯수를 알려주는 구문\n",
        "        \n",
        "    def __len__(self): # batch로 묶어주려면 전체 dataset길이를 알아야하기 때문에\n",
        "        return len(self.data) - (self.x_frames + self.y_frames) + 1\n",
        "        #만약 1,2,3,4,5일 데이터가 있고 2일 주고 2일 예측하라고 하려면\n",
        "        #1,2 - 3,4랑\n",
        "        #2,3 - 4,5 두 개의 sequence 만드는 것이 가능\n",
        "    def __getitem__(self, idx): # dataset에 i번째 요청이 왔을 때 그것을 반환하도록 인덱싱\n",
        "        idx += self.x_frames\n",
        "        data = self.data.iloc[idx-self.x_frames:idx+self.y_frames] \n",
        "        data = data[['High', 'Low', 'Open', 'Close', 'Adj Close', 'Volume']] # column 순서 고정\n",
        "        data = data.apply(lambda x: np.log(x+1) - np.log(x[self.x_frames-1]+1)) # trend는 같은데 scale만 다른 경우를 똑같이 학습하도록(나는 추세만 보면 되니까)'비율'로 normalize함\n",
        "        # normalize 방법: 데이터를 모두 마지막 날짜 가격으로 나눠줌. 굳이 log 쓰는 건 나누기 쓸 경우 0으로 나눠지는 경우가 있어서 피하려고.\n",
        "        data = data.values # pd.dataframe --> np.array\n",
        "        X = data[:self.x_frames]\n",
        "        y = data[self.x_frames:]\n",
        "        \n",
        "        return X, y"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "TRR54XXDxUXK",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "x는 벡터의 시퀀스. \n",
        "DataSet: i번째 벡터를 리턴하는 역할\n",
        "DataLoader: dataset이 리턴한 것을 배치 단위로 묶어줌"
      ]
    },
    {
      "metadata": {
        "id": "OnNBYXi_-sjj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "532a6f57-f67a-429e-dc19-5471ef93327d"
      },
      "cell_type": "code",
      "source": [
        "dataset = StockDataset('028050.KS', 10, 5, (2000,1,1), (2005, 1,1))\n",
        "dataloader = DataLoader(dataset,2)\n",
        "for X, y in dataloader:\n",
        "    print(X.shape, y.shape)\n",
        "    break"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([2, 10, 6]) torch.Size([2, 5, 6])\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "utXnDj9Jxs9J",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Model Define"
      ]
    },
    {
      "metadata": {
        "id": "wnrx7RfvxsmT",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class LSTM(nn.Module):\n",
        "    \n",
        "    def __init__(self, input_dim, hidden_dim, output_dim, num_layers, batch_size, dropout, use_bn):\n",
        "        super(LSTM, self).__init__()\n",
        "        self.input_dim = input_dim \n",
        "        self.hidden_dim = hidden_dim\n",
        "        self.output_dim = output_dim\n",
        "        self.num_layers = num_layers\n",
        "\n",
        "        self.batch_size = batch_size\n",
        "        self.dropout = dropout\n",
        "        self.use_bn = use_bn \n",
        "        \n",
        "        self.lstm = nn.LSTM(self.input_dim, self.hidden_dim, self.num_layers)\n",
        "        self.hidden = self.init_hidden()\n",
        "        self.regressor = self.make_regressor() # 여기서 out_dim\n",
        "        \n",
        "    def init_hidden(self):\n",
        "        return (torch.zeros(self.num_layers, self.batch_size, self.hidden_dim),\n",
        "                torch.zeros(self.num_layers, self.batch_size, self.hidden_dim))\n",
        "    \n",
        "    def make_regressor(self): # hid_dim to out_dim\n",
        "        layers = []\n",
        "        if self.use_bn:\n",
        "            layers.append(nn.BatchNorm1d(self.hidden_dim))\n",
        "        layers.append(nn.Dropout(self.dropout))\n",
        "        \n",
        "        layers.append(nn.Linear(self.hidden_dim, self.hidden_dim // 2))\n",
        "        layers.append(nn.ReLU())\n",
        "        layers.append(nn.Linear(self.hidden_dim // 2, self.output_dim))\n",
        "        regressor = nn.Sequential(*layers)\n",
        "        return regressor\n",
        "    \n",
        "    def forward(self, x):\n",
        "        lstm_out, self.hidden = self.lstm(x, self.hidden) #맨 마지막에 y로 보낼 ht, 각 time step의 결과값\n",
        "        y_pred = self.regressor(lstm_out[-1].view(self.batch_size, -1)) # 맨 마지막 output, batch size로 reshaping\n",
        "        return y_pred\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "5bEsRNiexz76",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Loss는 MSE를 쓰지만 평가 metric은 Mean Absolute Loss를 사용. \n",
        "# 차이의 절대값의 합. MSE가 아닌 이유는, \n",
        "\n",
        "def metric(y_pred, y_true):\n",
        "    perc_y_pred = np.exp(y_pred.cpu().detach().numpy())\n",
        "    perc_y_true = np.exp(y_true.cpu().detach().numpy())\n",
        "    mae = mean_absolute_error(perc_y_true, perc_y_pred, multioutput='raw_values')\n",
        "    return mae*100"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "--0QcI7tAMD-",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Train, Validate, Test"
      ]
    },
    {
      "metadata": {
        "id": "7Rui43eWx0cl",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def train(model, partition, optimizer, loss_fn, args):\n",
        "    trainloader = DataLoader(partition['train'],\n",
        "                            batch_size = args.batch_size,\n",
        "                            shuffle=True, drop_last=True)\n",
        "    model.train()\n",
        "    model.zero_grad()\n",
        "    optimizer.zero_grad()\n",
        "    \n",
        "    train_acc = 0.0 # 위에서 정의한 metric 값.\n",
        "    train_loss = 0.0\n",
        "    \n",
        "    for i, (X, y) in enumerate(trainloader):\n",
        "        # sequence lenth가 맨 앞으로 나오고 그 다음 batch size 순서 되도록 transpose\n",
        "        X = X.transpose(0,1).float().to(args.device)\n",
        "        y_true = y[:,:,3].float().to(args.device) # 예측은 종가(세번째에 있음)만 하려고 3번째 column 가져옴\n",
        "        #print(torch.max(X[:, :, 3]), torch.max(y_true))\n",
        "        \n",
        "        model.zero_grad()\n",
        "        optimizer.zero_grad()\n",
        "        model.hidden = [hidden.to(args.device) for hidden in model.init_hidden()]\n",
        "\n",
        "        y_pred = model(X)\n",
        "        loss = loss_fn(y_pred.view(-1), y_true.view(-1))\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "\n",
        "        train_loss += loss.item()\n",
        "        train_acc += metric(y_pred, y_true)[0]\n",
        "\n",
        "    train_loss = train_loss / len(trainloader)\n",
        "    train_acc = train_acc / len(trainloader)\n",
        "    return model, train_loss, train_acc\n",
        "        "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "l60U9FvTEldk",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def validate(model, partiiton, loss_fn, args):\n",
        "    valloader = DataLoader(partition['val'],\n",
        "                          batch_size=args.batch_size,\n",
        "                          shuffle=False, drop_last=True)\n",
        "    \n",
        "    model.eval()\n",
        "    \n",
        "    val_acc = 0.0\n",
        "    val_loss = 0.0\n",
        "    \n",
        "    with torch.no_grad():\n",
        "        for i, (X,y) in enumerate(valloader):\n",
        "            \n",
        "            X = X.transpose(0,1).float().to(args.device)\n",
        "            y_true = y[:,:,3].float().to(args.device)\n",
        "            model.hidden = [hidden.to(args.device) for hidden in model.init_hidden()]\n",
        "            \n",
        "            y_pred = model(X)\n",
        "            loss = loss_fn(y_pred.view(-1), y_true.view(-1))\n",
        "\n",
        "            val_loss += loss.item()\n",
        "            val_acc += metric(y_pred, y_true)[0]\n",
        "\n",
        "    val_loss = val_loss / len(valloader)\n",
        "    val_acc = val_acc / len(valloader)\n",
        "    return val_loss, val_acc"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "HEDkeEGVquJT",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def test(model, partition, args):\n",
        "    testloader = DataLoader(partition['test'], \n",
        "                           batch_size=args.batch_size, \n",
        "                           shuffle=False, drop_last=True)\n",
        "    model.eval()\n",
        "\n",
        "    test_acc = 0.0\n",
        "    with torch.no_grad():\n",
        "        for i, (X, y) in enumerate(testloader):\n",
        "\n",
        "            X = X.transpose(0, 1).float().to(args.device)\n",
        "            y_true = y[:, :, 3].float().to(args.device)\n",
        "            model.hidden = [hidden.to(args.device) for hidden in model.init_hidden()]\n",
        "\n",
        "            y_pred = model(X)\n",
        "            test_acc += metric(y_pred, y_true)[0]\n",
        "\n",
        "    test_acc = test_acc / len(testloader)\n",
        "    return test_acc"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kOjvBXK5qOeS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "669f19c1-2617-4b2b-e390-acdad626e441"
      },
      "cell_type": "code",
      "source": [
        "print(partition)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'train': <__main__.StockDataset object at 0x7fad2172c710>, 'val': <__main__.StockDataset object at 0x7fad21714198>, 'test': <__main__.StockDataset object at 0x7fad21899d68>}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "6YW_DcxTFG8g",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def experiment(partition, args):\n",
        "\n",
        "    model = LSTM(args.input_dim, args.hid_dim, args.y_frames, args.n_layers, args.batch_size, args.dropout, args.use_bn)\n",
        "    model.to(args.device)\n",
        "    loss_fn = torch.nn.MSELoss()\n",
        "\n",
        "    loss_fn = nn.MSELoss()\n",
        "    if args.optim == 'SGD':\n",
        "        optimizer = optim.RMSprop(model.parameters(), lr=args.lr, weight_decay=args.l2)\n",
        "    elif args.optim == 'RMSprop':\n",
        "        optimizer = optim.RMSprop(model.parameters(), lr=args.lr, weight_decay=args.l2)\n",
        "    elif args.optim == 'Adam':\n",
        "        optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.l2)\n",
        "    else:\n",
        "        raise ValueError('In-valid optimizer choice')\n",
        "    \n",
        "    # ===== List for epoch-wise data ====== #\n",
        "    train_losses = []\n",
        "    val_losses = []\n",
        "    train_accs = []\n",
        "    val_accs = []\n",
        "    # ===================================== #\n",
        "        \n",
        "    for epoch in range(args.epoch):  # loop over the dataset multiple times\n",
        "        ts = time.time()\n",
        "        model, train_loss, train_acc = train(model, partition, optimizer, loss_fn, args)\n",
        "        val_loss, val_acc = validate(model, partition, loss_fn, args)\n",
        "        te = time.time()\n",
        "        \n",
        "        # ====== Add Epoch Data ====== #\n",
        "        train_losses.append(train_loss)\n",
        "        val_losses.append(val_loss)\n",
        "        train_accs.append(train_acc)\n",
        "        val_accs.append(val_acc)\n",
        "        # ============================ #\n",
        "        \n",
        "        print('Epoch {}, Acc(train/val): {:2.2f}/{:2.2f}, Loss(train/val) {:2.5f}/{:2.5f}. Took {:2.2f} sec'.format(epoch, train_acc, val_acc, train_loss, val_loss, te-ts))\n",
        "\n",
        "#     print(type(model), type(partition))\n",
        "    test_acc = test(model, partition, args)    \n",
        "    # ======= Add Result to Dictionary ======= #\n",
        "    result = {}\n",
        "    result['train_losses'] = train_losses\n",
        "    result['val_losses'] = val_losses\n",
        "    result['train_accs'] = train_accs\n",
        "    result['val_accs'] = val_accs\n",
        "    result['train_acc'] = train_acc\n",
        "    result['val_acc'] = val_acc\n",
        "    result['test_acc'] = test_acc\n",
        "    return vars(args), result\n",
        "        "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "zABCcaxRlER7",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Manage Experiment"
      ]
    },
    {
      "metadata": {
        "id": "BeAHVqmElFfV",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import hashlib\n",
        "import json\n",
        "from os import listdir\n",
        "from os.path import isfile, join\n",
        "import pandas as pd\n",
        "\n",
        "def save_exp_result(setting, result):\n",
        "    exp_name = setting['exp_name']\n",
        "    del setting['epoch']\n",
        "\n",
        "    hash_key = hashlib.sha1(str(setting).encode()).hexdigest()[:6]\n",
        "    filename = './results/{}-{}.json'.format(exp_name, hash_key)\n",
        "    result.update(setting)\n",
        "    with open(filename, 'w') as f:\n",
        "        json.dump(result, f)\n",
        "\n",
        "    \n",
        "def load_exp_result(exp_name):\n",
        "    dir_path = './results'\n",
        "    filenames = [f for f in listdir(dir_path) if isfile(join(dir_path, f)) if '.json' in f]\n",
        "    list_result = []\n",
        "    for filename in filenames:\n",
        "        if exp_name in filename:\n",
        "            with open(join(dir_path, filename), 'r') as infile:\n",
        "                results = json.load(infile)\n",
        "                list_result.append(results)\n",
        "    df = pd.DataFrame(list_result) # .drop(columns=[])\n",
        "    return df"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "cJQuRuTulISU",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def plot_acc(var1, var2, df):\n",
        "\n",
        "    fig, ax = plt.subplots(1, 3)\n",
        "    fig.set_size_inches(15, 6)\n",
        "    sns.set_style(\"darkgrid\", {\"axes.facecolor\": \".9\"})\n",
        "\n",
        "    sns.barplot(x=var1, y='train_acc', hue=var2, data=df, ax=ax[0])\n",
        "    sns.barplot(x=var1, y='val_acc', hue=var2, data=df, ax=ax[1])\n",
        "    sns.barplot(x=var1, y='test_acc', hue=var2, data=df, ax=ax[2])\n",
        "    \n",
        "    ax[0].set_title('Train Accuracy')\n",
        "    ax[1].set_title('Validation Accuracy')\n",
        "    ax[2].set_title('Test Accuracy')\n",
        "\n",
        "    \n",
        "def plot_loss_variation(var1, var2, df, **kwargs):\n",
        "\n",
        "    list_v1 = df[var1].unique()\n",
        "    list_v2 = df[var2].unique()\n",
        "    list_data = []\n",
        "\n",
        "    for value1 in list_v1:\n",
        "        for value2 in list_v2:\n",
        "            row = df.loc[df[var1]==value1]\n",
        "            row = row.loc[df[var2]==value2]\n",
        "\n",
        "            train_losses = list(row.train_losses)[0]\n",
        "            val_losses = list(row.val_losses)[0]\n",
        "\n",
        "            for epoch, train_loss in enumerate(train_losses):\n",
        "                list_data.append({'type':'train', 'loss':train_loss, 'epoch':epoch, var1:value1, var2:value2})\n",
        "            for epoch, val_loss in enumerate(val_losses):\n",
        "                list_data.append({'type':'val', 'loss':val_loss, 'epoch':epoch, var1:value1, var2:value2})\n",
        "\n",
        "    df = pd.DataFrame(list_data)\n",
        "    g = sns.FacetGrid(df, row=var2, col=var1, hue='type', **kwargs)\n",
        "    g = g.map(plt.plot, 'epoch', 'loss', marker='.')\n",
        "    g.add_legend()\n",
        "    g.fig.suptitle('Train loss vs Val loss')\n",
        "    plt.subplots_adjust(top=0.89) # 만약 Title이 그래프랑 겹친다면 top 값을 조정해주면 됩니다! 함수 인자로 받으면 그래프마다 조절할 수 있겠죠?\n",
        "\n",
        "\n",
        "def plot_acc_variation(var1, var2, df, **kwargs):\n",
        "    list_v1 = df[var1].unique()\n",
        "    list_v2 = df[var2].unique()\n",
        "    list_data = []\n",
        "\n",
        "    for value1 in list_v1:\n",
        "        for value2 in list_v2:\n",
        "            row = df.loc[df[var1]==value1]\n",
        "            row = row.loc[df[var2]==value2]\n",
        "\n",
        "            train_accs = list(row.train_accs)[0]\n",
        "            val_accs = list(row.val_accs)[0]\n",
        "            test_acc = list(row.test_acc)[0]\n",
        "\n",
        "            for epoch, train_acc in enumerate(train_accs):\n",
        "                list_data.append({'type':'train', 'Acc':train_acc, 'test_acc':test_acc, 'epoch':epoch, var1:value1, var2:value2})\n",
        "            for epoch, val_acc in enumerate(val_accs):\n",
        "                list_data.append({'type':'val', 'Acc':val_acc, 'test_acc':test_acc, 'epoch':epoch, var1:value1, var2:value2})\n",
        "\n",
        "    df = pd.DataFrame(list_data)\n",
        "    g = sns.FacetGrid(df, row=var2, col=var1, hue='type', **kwargs)\n",
        "    g = g.map(plt.plot, 'epoch', 'Acc', marker='.')\n",
        "\n",
        "    def show_acc(x, y, metric, **kwargs):\n",
        "        plt.scatter(x, y, alpha=0.3, s=1)\n",
        "        metric = \"Test Acc: {:1.3f}\".format(list(metric.values)[0])\n",
        "        plt.text(0.05, 0.95, metric,  horizontalalignment='left', verticalalignment='center', transform=plt.gca().transAxes, bbox=dict(facecolor='yellow', alpha=0.5, boxstyle=\"round,pad=0.1\"))\n",
        "    g = g.map(show_acc, 'epoch', 'Acc', 'test_acc')\n",
        "\n",
        "    g.add_legend()\n",
        "    g.fig.suptitle('Train Accuracy vs Val Accuracy')\n",
        "    plt.subplots_adjust(top=0.89)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "WBK6UHkflK1z",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "trainset = StockDataset(args.symbol, args.x_frames, args.y_frames, (2000,1,1), (2012,1,1))\n",
        "valset = StockDataset(args.symbol, args.x_frames, args.y_frames, (2012,1,1), (2016,1,1))\n",
        "testset = StockDataset(args.symbol, args.x_frames, args.y_frames, (2016,1,1), (2019,2,1))\n",
        "partition = {'train': trainset, 'val':valset, 'test':testset}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "0y7gYjCLlRPT",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Exp1. original code"
      ]
    },
    {
      "metadata": {
        "id": "G2f0ikKTlP6p",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "outputId": "1201de48-66d9-46d9-88c2-a0391ea9986c"
      },
      "cell_type": "code",
      "source": [
        "# ====== Random Seed Initialization ====== #\n",
        "seed = 666\n",
        "np.random.seed(seed)\n",
        "torch.manual_seed(seed)\n",
        "\n",
        "parser = argparse.ArgumentParser()\n",
        "args = parser.parse_args(\"\")\n",
        "args.exp_name = \"exp1_lr\"\n",
        "args.device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "\n",
        "# ====== Data Loading ====== #\n",
        "args.symbol = '028050.KS'\n",
        "args.batch_size = 128\n",
        "args.x_frames = 5\n",
        "args.y_frames = 5\n",
        "\n",
        "# ====== Model Capacity ===== #\n",
        "args.input_dim = 6\n",
        "args.hid_dim = 50\n",
        "args.n_layers = 2\n",
        "\n",
        "# ====== Regularization ======= #\n",
        "args.l2 = 0.00001\n",
        "args.dropout = 0.0\n",
        "args.use_bn = True\n",
        "\n",
        "# ====== Optimizer & Training ====== #\n",
        "args.optim = 'RMSprop' #'RMSprop' #SGD, RMSprop, ADAM...\n",
        "args.lr = 0.0001\n",
        "args.epoch = 2\n",
        "\n",
        "\n",
        "# ====== Experiment Variable ====== #\n",
        "name_var1 = 'lr'\n",
        "name_var2 = 'n_layers'\n",
        "list_var1 = [0.001, 0.0001, 0.00001]\n",
        "list_var2 = [1,2,3]\n",
        "\n",
        "\n",
        "for var1 in list_var1:\n",
        "    for var2 in list_var2:\n",
        "        setattr(args, name_var1, var1)\n",
        "        setattr(args, name_var2, var2)\n",
        "        print(args)\n",
        "                \n",
        "        setting, result = experiment(partition, deepcopy(args))\n",
        "        save_exp_result(setting, result)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Namespace(batch_size=128, device='cuda', dropout=0.0, epoch=2, exp_name='exp1_lr', hid_dim=50, input_dim=6, l2=1e-05, lr=0.001, n_layers=1, optim='RMSprop', symbol='028050.KS', use_bn=True, x_frames=5, y_frames=5)\n",
            "Epoch 0, Acc(train/val): 5.15/5.78, Loss(train/val) 0.00663/0.00367. Took 18.46 sec\n",
            "Epoch 1, Acc(train/val): 2.65/4.73, Loss(train/val) 0.00347/0.00313. Took 18.46 sec\n",
            "Namespace(batch_size=128, device='cuda', dropout=0.0, epoch=2, exp_name='exp1_lr', hid_dim=50, input_dim=6, l2=1e-05, lr=0.001, n_layers=2, optim='RMSprop', symbol='028050.KS', use_bn=True, x_frames=5, y_frames=5)\n",
            "Epoch 0, Acc(train/val): 4.56/3.64, Loss(train/val) 0.00811/0.00275. Took 18.51 sec\n",
            "Epoch 1, Acc(train/val): 2.53/3.03, Loss(train/val) 0.00342/0.00257. Took 18.39 sec\n",
            "Namespace(batch_size=128, device='cuda', dropout=0.0, epoch=2, exp_name='exp1_lr', hid_dim=50, input_dim=6, l2=1e-05, lr=0.001, n_layers=3, optim='RMSprop', symbol='028050.KS', use_bn=True, x_frames=5, y_frames=5)\n",
            "Epoch 0, Acc(train/val): 3.50/1.98, Loss(train/val) 0.00769/0.00274. Took 18.80 sec\n",
            "Epoch 1, Acc(train/val): 2.42/2.05, Loss(train/val) 0.00332/0.00272. Took 18.81 sec\n",
            "Namespace(batch_size=128, device='cuda', dropout=0.0, epoch=2, exp_name='exp1_lr', hid_dim=50, input_dim=6, l2=1e-05, lr=0.0001, n_layers=1, optim='RMSprop', symbol='028050.KS', use_bn=True, x_frames=5, y_frames=5)\n",
            "Epoch 0, Acc(train/val): 14.72/12.78, Loss(train/val) 0.02348/0.01821. Took 18.77 sec\n",
            "Epoch 1, Acc(train/val): 12.74/12.37, Loss(train/val) 0.01799/0.01630. Took 18.68 sec\n",
            "Namespace(batch_size=128, device='cuda', dropout=0.0, epoch=2, exp_name='exp1_lr', hid_dim=50, input_dim=6, l2=1e-05, lr=0.0001, n_layers=2, optim='RMSprop', symbol='028050.KS', use_bn=True, x_frames=5, y_frames=5)\n",
            "Epoch 0, Acc(train/val): 11.70/10.94, Loss(train/val) 0.00967/0.00702. Took 18.82 sec\n",
            "Epoch 1, Acc(train/val): 8.08/9.39, Loss(train/val) 0.00583/0.00579. Took 18.74 sec\n",
            "Namespace(batch_size=128, device='cuda', dropout=0.0, epoch=2, exp_name='exp1_lr', hid_dim=50, input_dim=6, l2=1e-05, lr=0.0001, n_layers=3, optim='RMSprop', symbol='028050.KS', use_bn=True, x_frames=5, y_frames=5)\n",
            "Epoch 0, Acc(train/val): 13.34/17.66, Loss(train/val) 0.01354/0.01550. Took 18.64 sec\n",
            "Epoch 1, Acc(train/val): 8.85/15.99, Loss(train/val) 0.00825/0.01304. Took 18.49 sec\n",
            "Namespace(batch_size=128, device='cuda', dropout=0.0, epoch=2, exp_name='exp1_lr', hid_dim=50, input_dim=6, l2=1e-05, lr=1e-05, n_layers=1, optim='RMSprop', symbol='028050.KS', use_bn=True, x_frames=5, y_frames=5)\n",
            "Epoch 0, Acc(train/val): 19.76/21.85, Loss(train/val) 0.03102/0.01994. Took 18.35 sec\n",
            "Epoch 1, Acc(train/val): 19.17/21.53, Loss(train/val) 0.02867/0.02017. Took 18.47 sec\n",
            "Namespace(batch_size=128, device='cuda', dropout=0.0, epoch=2, exp_name='exp1_lr', hid_dim=50, input_dim=6, l2=1e-05, lr=1e-05, n_layers=2, optim='RMSprop', symbol='028050.KS', use_bn=True, x_frames=5, y_frames=5)\n",
            "Epoch 0, Acc(train/val): 11.79/15.31, Loss(train/val) 0.01973/0.00947. Took 18.54 sec\n",
            "Epoch 1, Acc(train/val): 11.30/14.75, Loss(train/val) 0.01602/0.00901. Took 18.47 sec\n",
            "Namespace(batch_size=128, device='cuda', dropout=0.0, epoch=2, exp_name='exp1_lr', hid_dim=50, input_dim=6, l2=1e-05, lr=1e-05, n_layers=3, optim='RMSprop', symbol='028050.KS', use_bn=True, x_frames=5, y_frames=5)\n",
            "Epoch 0, Acc(train/val): 19.57/19.81, Loss(train/val) 0.01816/0.01435. Took 18.65 sec\n",
            "Epoch 1, Acc(train/val): 18.72/19.52, Loss(train/val) 0.01564/0.01416. Took 18.68 sec\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "RJlEvDNwlpLw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1733
        },
        "outputId": "f9a050aa-aced-476a-9012-6f5994a0b9a2"
      },
      "cell_type": "code",
      "source": [
        "var1 = 'lr'\n",
        "var2 = 'n_layers'\n",
        "df = load_exp_result('exp1')\n",
        "\n",
        "plot_acc(var1, var2, df)\n",
        "plot_loss_variation(var1, var2, df, sharey=False) #sharey를 True로 하면 모둔 subplot의 y축의 스케일이 같아집니다.\n",
        "plot_acc_variation(var1, var2, df, margin_titles=True, sharey=True)"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1468: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
            "  stat_data = remove_na(group_data[hue_mask])\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1gMxcCiyNiP673hUR7wXuAd6VmffW7FsPWFCz3VuWPTTYdaZPn0xPz6TGBD0C\nM2ZMbfk1x2K8xdup/B3bz4RPkgYwmudoJKkJzgPuy8xfRsRxwInAu4Y4fsJwFS5a9GiDQhuZ3t6H\n23Ld0Rpv8XYqf8fWGCqxNuGTpH5G8xyNJDVDZta2Q5cCX+h3yEKKEb0+GwB3NjsuSeOHz/BJ0jON\n5jkaSWq4iLgkIjYtN2cBv+13yA+APcpjXwYszEyHVCQ9xRE+SepnlM/RrMBnZOoz3uLtVP6O1RAR\n2wCzgU2AJRGxB8Vsg4si4lHgEWD/8tgLgf0zc35ELIiI+cCTwOFtCV5SxzLhk6T6jOg5Gp+Rqc94\ni7dT+Tu2RrMT68xcQDGK198lAxz71prPxzUxLEnjnAmfJNWhjudoJEmSOo4JnyTVISIuAY7NzL8w\n8HM0kqQhHHvZCU2p97+bUqtUHSZ8ktTPSJ6j6TTeUEmSpFomfJLUz0ieo5EkSepkvpZBkiRJkirK\nhE+SJEmSKsqET5IkSZIqyoRPkiRJkirKRVtUl6NOubQp9Z567JuaUq8kSZKkJid8EbElMA+Yk5lz\nI2Jj4GxgJWAJsHdm3lVz/CzgW8DvyqLfZOYRzYxRkiRJkqqqaQlfREyheG/VNTXFHwO+lJnfjIjD\ngfcC7+t36vWZuUez4pIkSZKkbtHMZ/gWA7sAC2vKDuPp91j1Ams38fqSJEmS1NWaNsKXmUuBpRFR\nW/YvgIiYBBwOfGSAU18YEZcCawEnZeZVQ11n+vTJ9PRMaljc9ZoxY2rLrzkWnRpvp8YlSZIkVUHL\nF20pk73zgGsz85p+u28DTgK+CWwK/DAinp+ZTwxW36JFjzYt1qH09j7cluuOVqfG26lxqb3sCJAk\nSWqMuhO+iJgAzCg3ezNz+SiveTZwW2ae1H9HZv4TuKjc/HNE3AVsCPx1lNeSJEmSpK417DN8EfHy\niPgO8CDwW4oVNB+IiG9HxMtHcrGI2At4IjM/PNj+iDim/Lwe8CzgnyO5hiRJkiSpMOQIX0R8BJgF\nzAb2ycxHyvIpwGuB2RFx7UAJXERsU563CbAkIvYA1gUej4jrysN+n5mHRcSFwP7ApcD5EbEbsDJw\n6FDTOSVJkjpRREwE/o3iPgjgduBnmflku2KS1J2Gm9J5X2a+un9hufjKPGBeRBw10ImZuYAiWRxW\nZr61ZvON9ZwjSZLUacpE7xiKV0/9Dfh7ues5wMYR8VlgtomfpFYZMuHLzFMBIuKFFCN8x5fbZ1M0\nVr/tO0aSOlFEvBjYmRV72a/MzF+3KyZJlXY5sAB4UWbeV7sjItYG3gNcRvHqKklqunoXbTkD+FDN\n9leBudQ5gidJrRYR61O0VesnwGSSAAAey0lEQVQBV1M8fwxFL/s5EXEncFBm3tmmECVV04cy8xYo\nFrzrW+QuInrKBPCEiNi2rRFK6ir1Jnw9mfmjvo3MvLFctVOSOtXFwIcz8+qBdkbE64BvAa9qaVSS\nKq0m2dsDeDtPP6pyY0R8JjMv7jtGklqh3oTvwYg4FLiOYmXPnQFfoCapk+2SmQ8CRMRmmXlb+Xnr\nzPxFZl4VETe3N0RJFfZe4A012zsB36fojJKklhn2tQyl/YFtKF6IfgHw/LJMkjpSTbJ3MnB8za7j\nIuKTtcdIUhNMqG1jMvMhwIVaJLVcXSN8mdkbEZ/q10Pe29zQJKkhZmXmK/s2MnPPiLixnQFJ6go/\ni4iLWHF21IK2RiSpK9U1wjdUD7kkdbiVI2Llvo2IWB1YqY3xSOoORwLfA14IBPB14N1tjUhSV6r3\nGT57yCWNV2cCt0bEzyg6uV4OnNjWiCR1g8nAE5l5BEBEHFKWPdLWqCR1nXqf4bOHXNK4lJlfBXbg\n6WeQX5WZZ7c3Kkld4FyK18L0mQyc16ZYJHWxekf4anvIJwHbYg+5pHEgIl4I7JOZx5fbZ0fE7Mz8\nbZtDk1Rta2XmaX0bmfnZiHjjUCcARMSWwDxgTmbOjYiNgbMpOtqXAHtn5l01x8+ieMVM37tGf9M3\nqihJUOcIX78e8vMp3lv17SbGJUmNcgZwRc32V4G5bYpFUvdYJSK26NuIiG2AlYc4noiYApwOXFNT\n/DHgS5k5E/gOxese+rs+M2eV/5nsSVpBvVM6AVYHeoF7gc2BnzQlIklqrJ7M/FHfRmbeCExoYzyS\nusN7gHkRcXdE3EuxaMtRw5yzGNgFWFhTdhhwSfm5F1i70YFKqra6pnRGxKkULwxdD/gT8DzgM02M\nS5Ia5cGIOJQVl0Z/uK0RSaq8zPwp8IKIWBtYnpn3R8T/G+acpcDSiKgt+xdAREwCDgc+MsCpL4yI\nS4G1gJMy86oGfQ1JFVDvM3wvz8wtIuKHmblDOS3hP5sZmCQ1yP7AJyh6yZcD84H92hmQpOqLiGnA\n3sA65fYqFO3RBqOoaxLFgi/XZuY1/XbfBpxE8djNpsAPI+L5mfnEYPVNnz6Znp5JIw2j68yYMbXd\nIVSCv2P71ZvwLS7/XSUiJmTmgojo6hG+Yy87oSn1/ndTapW6V2b2AgfVlkXEkcBpA58hSQ1xEfA3\n4PXAxRQzpQ4dZV1nA7dl5kn9d2TmP8trAfw5Iu4CNgT+OlhlixY9Osowuktvr5NBGsHfsTWGSqzr\nTfgyIg4DbgCuiogE1mxAbJLUVBHxUuADlL3swCrAxpjwSWquVTPzkIi4LjOPjYhPUCzIMm8klUTE\nXhTv8/vwEPvXz8zPRMR6wLOAf441eEnVUW/CdwgwHXgAeCtFY/IJgIhYNzPvaU54kjRmn6dI7o4D\n/hd4C0UCKEnNtEq56ubEiFg7M++LiOcNdUL5yMxsYBNgSUTsAawLPB4R15WH/T4zD4uICymmiF4K\nnB8Ru1GsAnroUNM5JXWfuhK+zFwO3F9unt9v94XAaxoZlCQ10KOZeWFEHJqZl0fElRQ97Ne3OzBJ\nlXYu8A7gKxTvMu6leN5uUJm5AJhVT+WZ+daazWHf7yepe9U7wjcUlzeX1MlWLV9k/HhEzAR+T9F7\nroo46pRLG17nqce+qeF1qrtk5pl9nyPiGoqRul+W2ztl5g/aFZuk7tKIhG95A+qQpGZ5P8XKdR+i\nWOVuXeBTbY1IUlcpF1apfa7uOMCEr+Ka0RkFdkhp5BqR8ElSx8rMH9dsvqB2X0S8LzM/3eKQJMnZ\nUZJaZmK7A5CkNtq53QFI6krOjpLUMo1I+OylkjRe2X5JkqRKq3tKZ0SsAaxFzQ1SZv4FOL4JcUlS\nK9jLLkmSKq2uhC8iTqN410svTyd8y4FNM/MnTYpNkiSpMiJitcx8DGcXSGqhekf4dgBmZObjzQxG\nkiSpCiLiyszs/5zwDcC2wBvaEJKkLlVvwnebyZ6kCvpjuwOQVC0RsRfFa2CeExF/r9m1MnAXgPdU\nklqp3oTvHxFxA3AjsLSvMDM/1JSoJGmMIuI8hnhGLzP3zcxDWhiSpC6Qmd+IiAuBrwIfrtn1JLCw\nPVFJ6mb1Jnz3Adc0MxBJarCrh9jnYi2SmiYzl0XEe4H1M/N3EfF64OXAlylH+SSpVYZM+CJiQmYu\nBz7aongkqSEy82sDlUfEysA3gHNbG5GkLnMe8LmIeAL4LHAGxajff7Q1KkldZ7j38PWN6i0FltT8\n17ctSR0tIvaJiN6IWBYRy4B/AVPbHZekypucmVcBbwFOz8zPUzzHJ0ktNeQIX2a+pvz3GYlhRGw2\nXOURsSUwD5iTmXMjYmOKHq9JwJ3APpm5uN85c4BXUEy5Oiozb6nzu0jSQI4EtgIupOhZ3wt4sK0R\nSeoGUyJiBrAHsFtETACmtzkmSV1ouBE+ACJiUkTsEhH7lv+9A7hqmHOmAKez4rN/HwHOyMztgT8B\nB/Q7ZyawWWZuBxwInFb/V5GkAT2YmXcBkzLzX5n5Jfq1PZLUBN8AbgOuzcw7KFbuvK6tEUnqSvUu\n2vJ1il6pl1Cs1PkKVlx5aiCLgV2A99eUzQL6VsX7HnAM8IWa/TsC3wXIzFsjYnpETMvMh+qMU5L6\nWxYRuwJ3RMSJwO+A57Q3JElVl5mnAqfWFJ2amQ+0Kx5J3auuET5go/LloZmZbwFeRfHi0EFl5tLM\nfKxf8ZSaKZz3AOv3278e0Fuz3VuWSdJo7QP8A3g3sAGwN3BEWyOSVHkR8ZKI+FlE/KEsOiIi/r2t\nQUnqSvWO8D11fESsmpl/i4gXjfHaExpxzPTpk+npmTTGUKpvxozOXKOiU+NSpbwHOC8z7wEObncw\nkrrGXIrp432jfBcBZwOvbFtEkrpSvQnftRHxPorplgsi4nbqHx2s9UhErFaO/G3IM19AupAVR/Q2\noFjcZVCLFj06ijC6T2/vw+0OYUCdGpfaq8EdAY8AF0bEEorp6edn5t2NvIAkDWBJZv46IgDIzD9G\nxNI2xySpC9WVtGXmh4HZmfkZ4B3AV4DXj+J6VwO7l593B67st/8HFKtZEREvAxZmphmBpFHLzJMz\n88UUUznXAC6PiCvaHJak6lsaEc+lWHWciHgD9c1ukqSGqmuELyIuysw9ATJzfp3nbAPMBjYBlkTE\nHhTLoZ8TEe8E/gZ8rTz2QmD/zJwfEQsiYj7wJHD4CL+PJA3mMYp38D0KTGlzLJKq72iKV1NFRDwI\n3A7s29aIJHWleqd0/jUiDgDmA0/0FWbmXwY7ITMXUKzK2d/rBjj2rTWfj6szJkkaVkQcTzFzYGXg\nfGDfzLy9rUFJ6gZLMvPF5bv4FmfmQxHxinYHJan71Jvw7TlA2XJg0wbGIkkNExErZeYSilfKHJCZ\nvxriGElqiIhYE1gbODsi3kYxjXNqRKwPnAu8oJ3xSeo+9SZ8u2TmrbUFEbFdE+KRpEa5MiIOzcz3\nDbQzIjYHzqB4/6ckNcp2FKsDvxS4tqb8SeD7bYlIUlcbMuGr6aU6q6aXCmAliufv7KWS1KmOpFid\n8w6KBaLuKMs3BnYGNsLnaSQ13tWZ+X8RcUhmnjnQAc4ukNRKw43w2UslaVzKzN+Vi0ftRpHg7Vru\nuoPiXVjzMnN5u+KTVFl9swsGS/acXSCppYZL+OylkjRulQndd8v/JKkVnF0gqaMMl/DZSyVpXIuI\n/wHeB6xFzTuwMvPZbQtKUmU5u0BSpxku4bOXStJ4dxJwEMW7PyWp6cYyuyAitqR4f9+czJwbERsD\n5wGTgDuBfTJzcb9z5gCvoFhB/ajMvGWMX0FShQyZ8NlLJakCbsvMG9odhKTuMprZBRExBTgduKam\n+CPAGZn5rYj4OHAA8IWac2YCm2XmdhGxBXAWxRoMkgTU8VoGn4GRNM7NL2+SrgOW9hVm5rWDniFJ\nYzea2QWLgV2A99eUzQIOKT9/DziGmoSP4rGa7wJk5q0RMT0ipmXmQ6OMW1LF1PUePp+BkTSOvbb8\nt7bHezkrrjz8DKOZViVJNUY8uyAzlwJLI6K2eEpNW3MPsH6/09YDFtRs95ZlJnySgPpfvO4zMJLG\npczcoX9ZROw+1DmjmVYlSf00Y3bBhOEPGf6Y6dMn09MzaQxhdIcZM6a2O4QBdWpcgxlv8VZRvQmf\nz8BIGpci4tnAu4B1yqJVgNcAlwxx2mimVUlSrVHNLhjAIxGxWmY+BmwILOy3fyHFiF6fDShmIQxq\n0aJHRxhCd+rtfbjdIQyoU+MazHiLd7waKrGuN+HzGRhJ49V5wP8BbwTmUixCtc9QJ4xyWpUkPWU0\nswsGcTWwO/D18t8r++3/AcVMrC9GxMuAhZnpHbakp9Sb8DWql0pqiVuOPrIp9W47+7Sm1KumWpqZ\nn4yInTPzjIj4KnABxU3UaDllqkE6dapPp8Y1mPEWbzcYzeyCcmX02cAmwJKI2APYCzgnIt5J8WjN\n18pjLwT2z8z5EbEgIuYDTwKHN+cbSRqv6kr4GthLJUmttlpEbAQ8GRGbUtwwbTKKeoabVrUCp0zV\np1On+nRqXIMZb/GOVyNMrEczu2ABxfTx/l43wLFvrfl83EgCk9Rd6l2lczTPwEhSJ/g0xSyFU4Bf\nAsuA80dRz3DTqiSpVjNmF0gce9kJTan3v5tSqzpBvVM6R9xLJUmdIDOfeodoRKwFTM3MRUOdM5Jp\nVZI0iEbNLpCkMak34bOXStK4FBHPoUje1s7MHSJi94i4PjNvG+yckUyrkqRBNGp2gSSNSb0Jn71U\nksarL1PMTDi63P4j8CXgGc8mS1KjjGZ2gSQ1w8Q6j+vfS3UvML9ZQUlSA62UmZdSrF6H7xSV1AoR\n8ZyIuDgifli+6mX3iNis3XFJ6j71rtJpL5WkcSsi1qR4lQwR8SJgtfZGJKkLOLtAUkeod5XOET8D\nI0kd4iPAT4D1I+LXFKsN793ekNTpXAVPDbBSZl4aEe+BYnZBRLQ7JkldqN4pnV8Gzq05vq+XSpI6\nXVKsqDkb+BNFW/aqtkYkqSs4u0BSJ6g34fMZGEnj1f8BmwErAb8DlpSfJamZ+mYXbFPOLrgK+EB7\nQ5LUjepdpdNeKknj1X2ZeUC7g5DUdfpmF6wEvBS4gmJ2wbXtDEpS96k34fMZGEnj1XciYi/gJmBp\nX2Fm/r19IUnqAv8HLAD+STG7AJxdIKkN6k347KVSU7gwglrgxcBewH01ZcuBZ7cnHEldwtkFkjpC\nvQmfvVSSxqtXANMzc3G7A5HUVZxdIKkj1Jvw2Uslaby6BVgVMOGT1ErOLpDUEepN+OylkjRebQTc\nHhG3smL79er2hSSpCzi7QFJHqDfhs5dK0nh1crsDkNSVnF0gqSPUm/DZSyVpXMrM69sdg6Su5OwC\nSR2h3oSvIb1UEXEgsE9N0b9l5uo1+5cAP67Zv2NmLhvLNSVJktrA2QWSOkK9CV9Deqky86vAVwEi\nYibPXD3/wcycNZI6JUmSOo2zCyR1inoTvmb0Un2I4rlASZIkSVIT1JXwNbqXKiK2Be7IzLv67Vo1\nIs4HngNckpmfHa6u6dMn09MzqZHhVdKMGVPbHUIl+DtKkiRpPKl3hK/RDgLOGaD8GODrFCuA3hAR\nN2Tmz4aqaNGiRxsfXQX19j7c7hAqwd+xNUysJUmSGqNdCd8s4Ij+hZl5Zt/niLgG2AoYMuGTJEmS\nJA2s5QlfRGwAPJKZT/QrD+DDFM/1TQJeCVzc6vgkSZIkqSraMcK3PnBP30ZEHAdcn5k3RcQdwM3A\nk8ClmXlzG+KTJEmSpEpoecKXmQuAN9Rsf7Lm8/tbHY8kSZIkVdXEdgcgSZIkSWoOEz5JkiRJqqh2\nrdIpSZKkYUTEgcA+NUX/lpmr1+xfAvy4Zv+OmbmsVfFJ6nwmfJIkSR0qM78KfBUgImYC/93vkAcz\nc1ar45I0fpjwSZIkjQ8fonh9lSTVzYRPkiSpw0XEtsAdmXlXv12rRsT5wHOASzLzs8PVNX36ZHp6\nJjUjzEqZMWNqu0OoBH/H9jPhkyRJ6nwHAecMUH4M8HVgOXBDRNyQmT8bqqJFix5tfHQV1Nv7cLtD\nqAR/x9YYKrE24ZMkSep8s4Aj+hdm5pl9nyPiGmArYMiET1J3MeGTJEnqYBGxAfBIZj7RrzyAD1M8\n1zcJeCVwcesjlNTJTPgkSZI62/rAPX0bEXEccH1m3hQRdwA3A08Cl2bmzW2KUVKHMuGTJEnqYJm5\nAHhDzfYnaz6/vy1BSRo3JrY7AEmSJElSc5jwSZIkSVJFmfBJkiRJUkWZ8EmSJElSRZnwSZIkSVJF\nmfBJkiRJUkWZ8EmSJElSRZnwSZIkSVJFmfBJkiRJUkWZ8EmSJElSRZnwSZIkSVJFmfBJkiRJUkWZ\n8EmSJElSRZnwSZIkSVJFmfBJkiRJUkWZ8EmSJElSRZnwSZIkSVJFmfBJkiRJUkWZ8EmSJElSRZnw\nSZIkSVJF9bTyYhExC/gW8Luy6DeZeUTN/tcCHweWAVdk5kdbGZ8kSZIkVUlLE77S9Zm5xyD7TgNe\nD/wTuD4iLsnM37cuNEmSJEmqjo6Z0hkRmwL3Z+YdmfkkcAWwY5vDkiRJkqRxqx0jfC+MiEuBtYCT\nMvOqsnw9oLfmuHuA57U6OEmSJEmqilYnfLcBJwHfBDYFfhgRz8/MJwY4dkI9FU6fPpmenkkNDLGa\nZsyY2u4QKsHfUZIkSeNJSxO+zPwncFG5+eeIuAvYEPgrsJBilK/PhmXZkBYterTRYVZSb+/D7Q6h\nEvwdW8PEWpIkqTFa+gxfROwVEceUn9cDnkWxQAuZeTswLSI2iYgeYFfgB62MT5IkSZKqpNWLtlwK\nzIyIHwHzgEOBt0XEf5b7DwUuAH4EXJSZf2xxfJIkSZJUGa2e0vkw8MYh9t8AbNe6iCRJkjqX7zCW\nNFbtWKVTksad4W66JKmJfIexpFEz4ZOk+g110yVJLVX7DuNyu+8dxiZ8kp5iwidJktTZGvoOY19p\nVR9XjG4Mf8f2M+GTpPoNdtMlSc3S8HcY+0qr+vgqpsbwd2yNoRJrEz5Jqs9IbrrsQa+TPb+N4e9Y\nXc14h7Gk7mLCJ0l1GOam6xnsQa+PPb+N4e/YGu1IrCNiL2D9zPzMQO8wjohpEbEJ8A+Kdxjv1fIg\nJXW0Vr+HT5LGpYjYKyKOKT+vcNMlSU3kO4wljYkjfJJUn0uB8yNiN2Bl4NDBpnNKUqP4DmNJY2XC\nJ0l1GO6mSxrvjjrl0obXeeqxb2p4nZK6SzPaJuiu9skpnZIkSZJUUSZ8kiRJklRRJnySJEmSVFEm\nfJIkSZJUUSZ8kiRJklRRJnySJEmSVFEmfJIkSZJUUSZ8kiRJklRRJnySJEmSVFEmfJIkSZJUUSZ8\nkiRJklRRJnySJEmSVFEmfJIkSZJUUSZ8kiRJklRRJnySJEmSVFEmfJIkSZJUUSZ8kiRJklRRJnyS\nJEmSVFEmfJIkSZJUUSZ8kiRJklRRJnySJEmSVFEmfJIkSZJUUSZ8kiRJklRRPa2+YER8Gti+vPYn\nMvPbNftuB+4AlpVFe2XmP1sdoyRJGrtjLzuhKfWesuvHmlKvpO7RTe1TSxO+iNgB2DIzt4uItYFf\nAN/ud9gbMvORVsYltcpRp1zalHpPPfZNTalXkiRJ41urp3TeALyl/PwAMCUiJrU4BkmSJEnqCi0d\n4cvMZcC/ys0DgSvKslpnRsQmwI3A8Zm5fKg6p0+fTE+POeNwZsyY2u4QKqFTf8dmTUs4Z/9Tm1Kv\nJEmSWqPlz/ABRMRuFAnfTv12fQi4Ergf+C6wO3DxUHUtWvRoM0KsnN7eh9sdQiV02+/Yru/bqYm1\nJLWD6x9IGot2LNryeuB/gZ0z88HafZl5bs1xVwBbMUzCJ6l5bjn6yKbUu+3s05pSryRVjesfSBqr\nlj7DFxFrAKcAu2bm/f33RcT3I2Llsmgm8NtWxidJktRhXP9A0pi0eoRvT2Ad4JsR0Vd2LfCbzPxO\nOar3k4h4jKIHy9E9SZLUtVz/oH18vKAxuu137MTv2+pFW74EfGmI/acCrhIhSZJUw/UPWq/bnttv\nlm77HTtx/YO2LNoiSZKk+rj+gaSxaPV7+CRJklQn1z+QNFaO8EmSJHUu1z+QNCYmfJIkSR3K9Q+k\n8aUTX2nllE5JkiRJqihH+CRJ0rjSiT3oktSpHOGTJEmSpIoy4ZMkSZKkijLhkyRJkqSKMuGTJEmS\npIoy4ZMkSZKkijLhkyRJkqSKMuGTJEmSpIoy4ZMkSZKkijLhkyRJkqSKMuGTJEmSpIoy4ZMkSZKk\nijLhkyRJkqSKMuGTJEmSpIoy4ZMkSZKkijLhkyRJkqSKMuGTJEmSpIoy4ZMkSZKkijLhkyRJkqSK\nMuGTJEmSpIoy4ZMkSZKkijLhkyRJkqSKMuGTJEmSpIoy4ZMkSZKkijLhkyRJkqSKMuGTJEmSpIrq\nafUFI2IO8ApgOXBUZt5Ss++1wMeBZcAVmfnRVscnSYMZqv2SpGbx3knSWLR0hC8iZgKbZeZ2wIHA\naf0OOQ3YHXglsFNEvLCV8UnSYOpovySp4bx3kjRWrZ7SuSPwXYDMvBWYHhHTACJiU+D+zLwjM58E\nriiPl6ROMGj7JUlN5L2TpDFpdcK3HtBbs91blg207x5g/RbFJUnDGar9kqRm8d5J0pi0/Bm+fiaM\nct9TZsyYOuRx5396rxEFVL8m1bt/c6odK3/HxvB3rJQh2x7bptZpzm/p79gY3fc7toD3Tv116P8e\n/B0bw99x7Fo9wreQFXvENwDuHGTfhmWZJHWCodovSWoW750kjUmrE74fAHsARMTLgIWZ+TBAZt4O\nTIuITSKiB9i1PF6SOsGg7ZckNZH3TpLGZMLy5ctbesGI+CTwauBJ4HBga+DBzPxORLwa+FR56CWZ\n+ZmWBidJQ+jffmXmr9ockqQu4L2TpLFoecInSZIkSWqNVk/plCRJkiS1iAmfJEmSJFVUu1/LMG5F\nxJbAPGBO5v9v725C4yqjOIw/wYKVQmlWWkUoghwQXKggFrGNTRURd3YhioIf4KKgIOImuxa1BUVo\nFQruVOpKWioUEVOktHYRF3ZR5IALRdqFhYIQLGls4+LesUOchDh5M3Nz5/lByGTufcP7noQ/nDv3\nIz9e4Zi7gc+BW6jusPViZs5FxDxwtmvXycy8XnrOTRERHwGPAAvAm5k507VtN/AecB04mZn7lxqz\nTD3HgS+B2czcM8ClDU2pmtbvvwF8CIxn5uxAF6JVM5v6ZzaVZzapw2xaHfOpvFHKJz/h60NEbAIO\nA9P/c+g+4JPMfAz4BXilfv/PzJzo+mptaEXETuDezNwOvAocWrTLIeBZ4FHgyYi4b5kxS9XzCHBm\nbVfSHCVrGhEvAbfjbb3XJbOpf2ZTeWaTOsym1TGfyhu1fLLh688c8DRdf9j6H+FURExHxPGI2NJj\n3ARwon79NbB7zWfaPJPAcYDM/BkYj4jNABFxD3AlM3/PzBvAyXr/pcZM0LuerzFCoUXZmh7LzCmq\nI1daf8ym/plN5ZlN6jCbVsd8Km+k8smGrw+Z+XdmXl309mHg9cycpHoGzt4eQzdl5lz9+g9ga/16\nY0QcjYizEfHW2sy6Me4ALnf9fJmbD41dvK1To6XG9KznCD4brVhNR7B2rWI2rYrZVJ7ZJMBsKsB8\nKm+k8slr+Mp5GPg0IgBuBWaW352xrtdvA19QHRk4HRGnM/PHNZll84z1sa3X+8v9nlFTqqZqB7Op\nP2ZTeWaTuplN/TOfymt1PtnwlfMX8Hhm/vtxbkRsB96vf3wBmI2I2+qjXHdRn9qQmUe6xkwD9wNt\nDa5L3DyCAnAn1UXDvbZ1anRtiTE96zmCStZU7WM2rYzZVJ7ZpOWYTStnPpU3UvnkKZ3lnAeeAoiI\n5yJiMjPPdV1QfBH4juoCUOrv30TlaESMRcQGqotDLwxlBYPxLbAHICIeBC51PgrPzF+BzRGxra7F\nM/X+S435Tz0HuI4mKVlTtY/ZtDJmU3lmk5ZjNq2c+VTeSOXT2MJCY68vbKyIeIjq1qvbgHngIjAF\nHABuAFeB5zPzyqJxW4HPgI3Ab8DLmTkfEQeBXfXYE5n57oCWMhQRcQDYQbXevcADVHfcOhYRO4CD\n9a5fZeYHvcZk5vle9ay3TwNbqI7IXAD2ZeapQa1vGArWdAp4guqWwzPAucx8Z7CrUb/MptUxm8oz\nmwRmUwnmU3mjlE82fJIkSZLUUp7SKUmSJEktZcMnSZIkSS1lwydJkiRJLWXDJ0mSJEktZcMnSZIk\nSS1lw6fGi4iJiDgz7HlIUjezSVITmU1azIZPkiRJklpqw7AnIK1URHwP/ET1YMxdmXl9uDOSJLNJ\nUjOZTeqw4dN6M5uZO4c9CUlaxGyS1ERmkzylU+vOD8OegCT1YDZJaiKzSTZ8WneuDXsCktSD2SSp\nicwm2fBJkiRJUlvZ8EmSJElSS40tLCwMew6SJEmSpDXgJ3ySJEmS1FI2fJIkSZLUUjZ8kiRJktRS\nNnySJEmS1FI2fJIkSZLUUjZ8kiRJktRSNnySJEmS1FI2fJIkSZLUUv8A2LqNOeRqsIAAAAAASUVO\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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r17Jp0ybgwfSUm5sbaWlp2qmytLQ0XF1dtbfhwRvA888/z+nTpyWRfQbkfCnZ05wvD3/f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\nCCrnajcVSeIpmaHFI0rHTK3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fno8//lg7y/HoY1944QVdHpL4S/3qNowI9WX2utOs/OEC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\nV1dXrK2tAfDy8qKqqorjx49z+fJlkpOT6dWr1y0f69tvv0Wj0TBp0qRWq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DUAAB/lSURB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33ccHH3xAeno6AKdOneL5559HKUV3d3fQTzny008/sWrVKo4fP47BYMBqtZKfn09mZmbE3kPh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\nQlYIIYQQQkSl/wcnBKo3RS12MgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 703.375x648 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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uxIQJE/DNN98gPDzc9yF+Wu/evbFp0yZ0794dx44dw7Fjx3DNNdegtLQUy5cvx7Bhw1Bc\nXIyMjAxMmTIFeXl5eOyxx/DAAw8AAD766CP8/e9/D8rzJKpLKH8XiEKFIbaFWrFiBTZu3IjffvsN\n+/btw4YNGzBnzhyMHj0ahw4dwvDhw6EoCm666SZYrdZq28+ZMwezZs3C+++/D1mWsWDBAgDA2LFj\n8dRTT8FkMsHpdGLBggWIjIxs7KdH5GfSpEmYOXOmb/qfhQsXAgB++uknrF69GnPnzsWoUaPw+OOP\nY8SIEVBVFQsWLIDJZEK/fv3w9ddf+5ZPnjwZrVq1QqtWrTB48GCMHj3aN5SmY8eOcLlcGDduHJxO\nJwoKCpCWlob27dtXO4mSKBSC8buQmZmJmTNnoqSkBCdOnEBaWhp69eqF++67L5RPlVoAQattsAsR\nERERURPEix0QERERke4wxBIRERGR7jDEEhEREZHuMMQSERERke406dkJsrKy6lwnPj7ed6WoUGEN\noT++XmqoPI9uXfTQ/kN9fNbQdGqoz/Ebsv2H+vmyhqZxfL3UcDZtn+pP9z2xgSZ9Zg0t7/gttYZQ\nP+dQH581NJ0aWlrbZw1N4/isoWVr0j2xdfn558/x00+bUF5uD3EdYbDbHbqowem8DN26PdQIFVEw\nqaqKL79chLi4/JC2PT21fYdDwqWXTkViYptGqIqC6ejRgzhwYDbcbltI62iK7d/hkHHllTMQE1P3\nFRqJ9E7XIbasbAMGDjwOp9MZ0jpOT+yvhxq++OIoVPVBiKLuO+FbtMLCQlx44S78+c+RIW17emr7\nALBp0wdITHw4yBVRsGVlfYChQ4/oqu01Vg2apmHjxnW4/vr7Q1gVUePQdYg1GDRMmpSFQ4cUlJQA\nLpeA+HgNERHAe+/V/1voL784kZPjwQ03hFd7rKxMRa9ehRg9WsQjj8Q0SN0ZGXakp9sgikBEBPDG\nG3GIiBBx6JATEyeW4OqrRTz7bFy17TweDY89lo8vvgD27o33LU9PL8CePQoAYPx4M4YOjcSsWfnY\nu1dFdLR3nQEDjPjrX60QRbVBngOFlqIokCTosv3X1I6feaYAu3Yp0DRgwAAJDz/sf7y9e+34299s\nEATgoosEvPRSPFwuFePHn8SpUxo8HuD2240YN84Kp1PF+PH5yMkBwsKAN9+MQVycBIDtvzkQRe81\neqZNy8Phw5qu2v9pL7xQiHXrPNi1y7/Wl18uxPvvK4iL8z7HLl0MmDs3FsuWFWPFChcMBiAuDnjt\ntTi4XBrS0k6iuNjb/u+/34ShQ6PAdk4tha5DLAC89FIynE4nXn+9CL/84sGiRWf/J5Tt2+3weBDw\nTWzNmlJERanYsUPFI480RMVAeroNs2eHo0cPC6ZOzcPSpcWYNCkGM2aU4KqrhFq2K0CbNgYAim/Z\ngQPl2LNHwebNrXHypB1paUUYNCgCADB0qITJkxv2jZeaFn22/+rteP/+cuzYoWDjxjh4PMAttxTg\n7rsVREcbfOs8/bQNjz/u/b1JS8vFhg2lsNs1KAqwYUMCSksV3HJLIcaO1fDSS8WIjxfw9tvxWLy4\nEJ9+asftt0c1zBOgJmPRIu+XID21fwA4ftyF3bs9NT7eo4eAZ5+N91v26qtubNkSC6vVgPHj87Bq\nVSlcLuCqqyRMn27FTz95O0GGDm24OomaOl2HWEVTYXc7ICJw8Js+PR8//6xCVYE775Rx113RWLu2\nBG+84YTRCMTEAPPnR+P99xVIkoY2bUqqfdBt2eLCuHEyXnnFjcOHXbj0UtnXy5OfDxiNwCuvJCA2\n1n/ZkiXRUBTg+edL8PLL/m9Gy5bFIjbW++EcEyOiqMj7jXvp0li8914Jjh4N/C166lQrLBYBK1YU\n+Jbt3OlAly4izGYRbdoYYbUC+/eH9s9b1DQ0VvtfsiQarVpJ9W7/gdpxu3YmrFplhCyLkGVAkjQU\nF58JsXa7iqIiAT16WAAAPXsasXu3CykpJpSVaVAUDcXFKsxmDYIgYM8eDx591BtKpkzhF7mWqKm2\nfwCYN68YDz9swbx59T+fIyxMQ36+guhoETabhrg4Eampkb7hBL//7kIMmzq1MLodGOlUXPg25wcc\nKTyOI8XHYXM74FY9yLLn4pQ9FyvWn0JegQf/WiHin28JeOstF44X5OPd9xx4cKqGpSsl3NhbhQNl\n6HGDglsHa+g7EChylqDYVYpiVymOZBbjyBEBNw8U0eVaFe8vL0aZ246XXy1AXIKGlesikHqriFVr\nCvDyqwWITwDWrI9G6q0GrF1XhjZtBSx+yQqP6oFHU6BoChRNhTVGgKppKCr24NNPFQwZ4v1gtloN\ntT7nQI/n5amIjT3zvzEqCsjMdAMAduzwYNiwXAwfnotDhxhsW5ItW0pRWKhiw4YELF8eh7fecsHh\nUPHf/zoxY4YFGzYk4OabZciygBtvFDB0qFTtAzw314MjRwQMGxaJrl2BlSvLAACvvVaMxEQBGzcm\nYPBgM9avt/ktGzjQiPXrbbjoIjngB3igdmwwCL7AumZNCaxW4IILZN/jmZkeWCya735iogEFBSr6\n9YuA1SrgppvyMWxYMSZMMAEA8vOBbdscGDQoF/fck4v8/Jp7vaj5acrtf+PGUoSHC+jRI6zG+vfv\nV3HHHbkYOjQXGRneoPvII2aMGFGCnj3zYTAAqamRAAC3W8Wtt+bi73934vHHIxrqJSTSBd32xGbZ\ncuBSXAAAFRo8mgJVE2Fze8Pat19L+PWwjLHDvR9eqiri8NEyXNfTgBcWytjzhQ23DFEgx5bDrUpw\nKRryyv3PMl213IAr2htQKtjQM1XA6/8wY+SkXPzwo4xrb3Aj0+ZAn7941316unfZ8TK7b9nR0tIa\n6y8r0TB3Yhhuvd2FsAvsOFwMCABKXQY4FRFHSo4DAAQI8P7n7W32uAFNM+F4mXcOxXIFsLuB3wuP\nQ9M0eFQNJU4buvUButwIXH+ThFXLPJgztwgvLZWQ51Dx3aHNkAwSREGEQRAhVvwzCCIEQfBbVnUd\nEVWXCxAFEXliMYqKCv22C7S9QRAhIPAxhIr90R/31VduHDoEDB6cCwDQNODECTf69jXib3+z4/PP\nnRgxIhznnVfztDArVpSiY0cNFouIYcPC8Le/2TF7NnDwoIJevbwB869/jYfT6cSECXm+ZePGWc+5\n7k8+KcOrrzrx5pvRta6nVvyxYsuWUhQXa9i5Mx5Hj7oxblwxhgzxPnjZZRKefDIac+fm49lni7Fw\nYfVx5tQ8NdX273CoeOONcrz1VmyN6/ToEYakJBduvz0K27eXYe5cGzZvDsPixeV4990oXHqpjLvu\nysOaNd6eY6NRxKZNCdizx46ZM8uwfXvN4ZioudFtiE0OT0SYFAagGLJoRJQxDCaDgoujkqABiDIX\nIuUWYOasGHj7bzRosOL6qzTcMcSD9Wvt+NujGp57wQxZLEeYJKB1eLh3XU2DBuDLXWUoLBQw4Q7v\ny5SXJyLv10gYJTdMYhjizN43LdEgwmiwwyiYYDV5l2namaOevn96idOl4YmHNPRJUTH8HtnvmKKg\neoOkaAC0Stt770Ct2K9b9QCahpg4ETmnBNjc3kBfVGiGtVU5Lm3vfZ3K3E5cn6ph5X/DUeIqQ6lb\nxedFXzfZ2QkECL5gXC1A+0JwoOUCzD+a4XF7ag7gle9DqD1on0OYB4BiyQ6zIsFkkOt4psFlNAIp\nKQbMnu3/YXnZZSYMHOjGBx+U4YEHSrBkSWSN+9i+3YPCQuDmm71BIC9PxP795RBFbyioLNCys7Vn\njx1PP+3Aa69F46KL/F+/886TYLOd+YKTleVBXJyIr75y49prjZAkAZdcIsNsBo4d8yAyEujb1/sX\njj59zHj99dBOw0eNq6m2/z177CgpAcaM8Q6lKSkRce+9uVi69MxY3muuMeOaa8wAgJtvjsDcueU4\nftwNUQT+9CfvXxq6dJHw/fduJCbacNFFGtq08Q61cbttyMlx110IUTOh2xBrMsjolHAFLooxQlQF\nCEIxAEAUvOHs2mtN+Oc/HRA0Axx2DXPmFGDJknjMm5ePyZOtmDrZgoK8fBw+qEEyiIAqwiKd+QZ7\n5IgLeXkidu2KgyR5Pzwffzwfm9Zq6NRRxr6vFNw7JhrLl5fgyBEFna6S8X//UzAuLQbLl5fg1189\nePzxwN+25z6dj2u7CJg6sfrj4cZCyKKKCyIC9xq5XCoEoQAXR50PABg6wIlJk0pwSVRbHDlig62s\nBDd3a4PJDxdgyFAzevexYNXWUpzX2oW2kechM8KDmVc8AEEAVE2FoqlQK/5Vvq1CC7y84r6maX7L\nLRHhKC4prrS9ClXTajyGVtuxNRWKpkFD4GOrmqfifpUay1Tfl4eQ+QFoFRaPaZ3vC2mQ7dJFxj//\n6YCiaLAHaP8PPxyD3Nx8/PijC6IIKIr/9t72L1Rr/ytX2tChgwF797owciTw3nsF+Plnp9+yutp/\nIC6XivnzbVi8OBKXXlr9dQsLExEXp+HTT2246aZwfPqpB/fea0ZOjopPP/V+aBcUKCgtFXDeeRK6\ndDFg40Yb7r/fin37XGjThj38LUlTbf99+0agb1/vn/xdLhU335zvF2AB74mPF15oQFpaNDIy7IiM\n1NC6tYSyMgGnTrmRlGTEDz94cP31MnbvdmL9eieefz4Wx4654PEISEjQ7cc60VnTdWs3CCIsxrCA\n8/TdcksEPv7YiYEDvZeBGzbM+2ejpCQRI0cWwGIBIiOBmTMjYDTa8OyzTsTGFuOee7x/xlyxogzX\nXy/43sAAYMyYCEycWIJNm2Lw4IP5GDQoFwaD98Quq1X1W7ZkSTSOHHEFHNi/fbuG+HjV96euzp0N\nGD06AtOmFaO4GPB4BAwenIv09EgcOuRCZqaCSZNiMHFiHo4f1+BwiBg8OBc33GDA9Omx6NfPgFtv\nzYIgADNnmmEQDUhLC8dTT9nw4hJvz8HTT0fDKEowikDr8MQG74lNTk6u12VSgyk5ORknMzN9AVmD\nVkMIrv7vj4b5fEchdmf9DwCQ7chDli0HF0aFblL9xmr/kgQsXhyNhASp3u0/UDu++GIJRUUCHn+8\nFIB3GM7MmeHIyvL42v+cORGYP78Mzz1nx5VXCkhNjYTHo+HLLwtw2225UFXggQdkhIWJmDYtGg88\nUID163NhNgMvvXTuQxxIf5py+w/E49Fw9915eO+9BIwc6f0sWLXK26bnzbPAZBLx0EMyxo4tgiQB\nSUkCRo+OhNMJPPSQt/273cDUqSYIHJJFLYighbzrqmZ1haLvvpuO1NQTTW6y6aZcw969Hlx22bpm\nG2JDVYNTcWHRt/9CtiOv1p7Yhrp2fE5ODoCx6NSJFzs4u4sd9ET37lMavIaW3v7re/yGav9fffV3\nDB58QFdtrzFr+PDDFFx//V8b5fihbnd6qeFs2j7Vn657Yl2upjmusylTFL5mwWAyyJjW+T64w1QY\nHWLQhxIYjUaUlQX1EM2OpmlQ1dpnACF98HhEqCon9A9EUTQANZ+wRtSc6DrEJiam4ZNPNsJuD+2n\neXh4OGy20F7Du741CEL7JntSl96ZDDIujGucHoGYmBgcOjQMO3Zkh7Tt6antl5cb0bHjmEaoiILt\n8svvxfbtb8PlqnkGmMbQFNu/w2HCn/88IoQVETUeXYfYCy/sgOTkm5v8nxFaSg3UuLp2HRvy/++h\nPn5TqYEaV3x8Ejp2XBzy/+9Noe01hRqIQoVdckRERESkOwyxRERERKQ7DLFEREREpDtBHRP77LPP\n4ptvvoHH48Ff//pXdOzYETNmzICiKEhISMBzzz0HWQ7tlY2IiIiISH+CFmK//PJLHDp0CCtWrEBh\nYSGGDh2K7t27Y9SoUUhNTcU//vEPrF69GqNGjQpWCURERETUTAVtOMG1116LJUuWAACioqLgcDiQ\nkZGBvn37AgB69+6NvXv3BuvwRERERNSMBS3EGgwGWCwWAMDq1avRs2dPOBwO3/CBuLg45Obm/qFj\nlLsU/Hy0AOUupe6ViYiIiKjZCPplZz/++GO8/vrr+M9//oNbbrnF1/t69OhRzJw5E8uXL69xW7fb\nDaMx8JVHHE4PHl60E6fy7bBGmjDqlj8hPMwIoyTCKBlgNIiQJBGyseK+JMJoECseF2E0etcRRV5n\nmpqm2to/UXPH9k9EdQnqiV27d+/Ga6+9hn/961+IjIyExWJBeXk5zGYzsrOzkZiYWOv2eXl5NT72\nW1YZTuXbAQBFpU68sub7c6qrQ2WJAAAgAElEQVRRFAUYDQIkgwCpIuSevi0ZBN99Y8V9qcp9oyTC\nGh2Fcoet0j4qflZa33e/0uO+40hn9icI5xaqQz3hdaiPr5cazub62bW1//oeL9hCfXzW0HRqqM/x\nG7L9h/r5soamcXy91HA2bZ/qL2ghtrS0FM8++yzeeustWK1WAECPHj2wbds2DB48GB999BFuvPHG\nc95/67gwJMaYkFPoRHS4Ebd2aw1BEOBRVLgVzfvTo8JTcdujaPB4vI+5FRUeT6Xlp7epeNzpVs5s\n59FQd1d1w/3ySJUCddVgbawUer2h+EwAjorMgcvpqBbEjVWDd6UAbawS1KsGd4N47qGaiIiIKJiC\nFmI3b96MwsJCTJkyxbfsmWeewZw5c7BixQq0bt0aQ4YMOef9m2UD5qR1gAsWyLDDLBsaouxqNE2D\nomrwKFq1UHz6fpQ1Btk5eb7Q664Smv3Csm8f/mHa/7bmt365S6m0j5oi9R8bXxyIAPiF3tp6kyPC\nj0HxuKr1Pp8Jz2e29fZoVw/idfWAGzj0g4iIiCoELcQOHz4cw4cPr7Z86dKlDXYMs2zARcmxyMpy\nNtg+qxKE0z2jqDEoJyfHI97iDloNlWla9QDsUTRYY+OQdSqnenD2VO6Z9g/UlYN45eBd+X7V4O7y\nqLCVeyrW0aCqp0N1cdCfuyDArzfZWCX0WsIOQ1U81XqXjQF6n6sFcalyQD+9TtXg7j8ERKzSS336\nREMZStC+VBEREZFXUMfEUsMTBAFGyRvSYDqzPDkpCrJma/R61Ipe6riERJw4mVmlN7l6EHZXDcqV\ngnVNwdldw/oeRYXDpfiCuqLYoAb1NEV/vvHUkghJBMocChRVQ1KsGbPHtGeQJSIiCiKGWPpDRFGA\nLAqICDMiOjy0V19LTk6uCNL+odddtfdZUf17rD3+wzf8eqbrCNKn7zucHigVCfpUQTky8x24ODki\npK8HERFRc8YQS82KQRRgkA2VO6kbRblLwYJ3f8CpgnIkxZrROi6skSsgIiJqWRhiiRqAWTZg9pj2\nQT/RkIiIiLyCdsUuopbGLBvwp7axDLBERESNgCGWiIiIiHSHIZaIiIiIdIchloiIiIh0hyGWiIiI\niHSHIZaIiIiIdIchloiIiIh0hyGWiIiIiHSHIZaIiIiIdIchloiIiIh0hyGWiIiIiHSHIZaIiIiI\ndIchloiIiIh0hyGWiIiIiHSHIZaIiIiIdIchloiIiIh0hyGWiIiIiHSHIZaIiIiIdIchloiIiIh0\nhyGWiIiIKEhkWQ51Cc0WQywRERER6Q5DLBERERHpDkMsEREREekOQywRERER6Q5DLBERERHpjhTq\nAoiIiIhaqq+++irg8q5duzZyJfoT1BD7yy+/4MEHH8Q999yDMWPG4LHHHsMPP/wAq9UKABg3bhxu\nuummYJZARERE1GQtWbLEd1tRFPz666/o1KkTQ2w9BC3E2u12pKeno3v37n7LH3nkEfTu3TtYhyUi\nIiLSjffee8/vfmFhIRYtWhSiavQlaGNiZVnGm2++icTExGAdgoiIiKhZiYmJweHDh0Ndhi4ErSdW\nkiRIUvXdv/vuu1i6dCni4uIwd+5cxMbGBqsEIiIioiZt1qxZfvdPnjwJo9EYomr0RdA0TQvmAV56\n6SXExMRgzJgx2Lt3L6xWK6688kq88cYbOHXqFJ544okat3W73fwfSS1WXe0/O9+GI1klaHdRHKLC\neVlDal74/k/NndPphMlkwurVq33LBEFAdHQ0brzxRphMphBWpw+NOjtB5fGxffr0wfz582tdPy8v\nr859JicnIysr64+W9oewhtAfXy81JCcn13tftbX/4jIXpr/2ne++xSQiOsKE6HAjosKNiLYYER3h\nvX36flS4EeFhEkRBqHcNddHDa84ams7xG6r91/d4wcYaQn98PdQgyzLi4uKqLb/vvvvwzjvv4I47\n7gh2ec1Wo4bYSZMmYcaMGTj//PORkZGByy67rDEPT9Rs5Jc4/e6HmYwosbmQle+odTtRFBBlkc6E\n3XDZd9t7/0zoNcmGYD4FIqIWTWjADoWWKmgh9sCBA1i4cCFOnjwJSZKwbds2jBkzBlOmTEFYWBgs\nFguefvrpYB2eqFlrHW9BUqwZpwrKkRRrxuwx7WGWDXB7VJTa3SixuVFk8/4ssblRbHOjxF7x0+ZG\nZr4DR7PttR7DZBSrBdzocNkv7MoWBxRVg0HkmzERETWuoIXYDh064J133qm2PCUlJViHJGoxzLIB\ns8e0hwsWyLDDXNFrapRExEaZEBtV+1gqTdNQ7lJRYnNVD7tVQm9uZhlqHjn/AwQAEWFSrWH39E+L\nycDeByIiahC8YheRTpllAy5KjkVWlrPulasQBAFhJgPCTGFoFRtW67qqqqHM4UGxzeUfcm1uuFQJ\n2XklKLa7UVDqwsm82oczSAYBURZjtYBbNfRGWYyQjbwqNhE1X0E+r75FYIglolqJouDrZT2/ymNV\nT2ZwudWKHlxX9Z7dSj9P5Nrx+6na38DDTAa/k9KiIyrdDjciqmI8b6LKDwIi0h/+VeqPY4glogYj\nG0XER5sQH133cAa7U0FxmbvW0FticyO7oLzWfYnCPkRYKoJtxawMlU9Qqxx6zbLIDw4iahLGjx8f\n6hJ0jyGWiBqdIAgIN0sIN0tojdqHM3gU1TucoazqCWre4Q3lbgF5hTbkFJbjeE7tJ6sZJbHKMIbq\nszKc7nWWDBzOQETBc8MNNwAArrzySmiaFvALtqZpOHjwYGOXphsMsUTUpEkGEdYIGdaIwBd0qDyk\nodylBDxBzXv7TG/v76dsUOsYhhBuNviGLFQNu1Xn3iUiOlc//PBDqEvQLb77ElGzYZYNMMsGJMaY\na11P1TTYHJ6AwxeK7ZWX1W/u3ZhIEyLMYvUT1Dj3LhHVYe/evXj33XcRHx+PKVOmICcnB4mJiQEv\nkED+GGKJqMURBQGRFiMiLUYgofZ13R61Um9u4Ll3beXqWc+9W+OFJsK9dXHuXaKWIT09HZMnT8bh\nw4exYMECDBw4EM8//zz+/e9/h7q0Jo8hloioFkZJRFyUCXG1zL2bnJyMzMxMOKoMZzi3uXfhN/fu\nmVkZOPcuUXN03nnnITU1FQAwatQo9O7dG2+//XaIq9IHhlgiogYgCAIsJgkWk4Skesy9W+oIPP1Y\ncaVhDWcz9258zC8Ik3FmloYAodco8WQ1oqbm9ttvx4oVK3DbbbfBYDAgNzcXpaWloS5LFxhiiYga\nmSgKFcMJ5Gpz71bldCsotXlQbK997t3fTpbAo6i17ss3926AGRkqz70bESZB5HAGokaxZs0afPnl\nl5g/fz4A4LbbbsOsWbNCW5ROMMQS6ZTdU44fc36ByWNEmMHEPyk3UyajASarAfHW2ufeTUpKwq9H\nTlQatlBz6K1r7l1BACIttU9DVnnuXadbxc9HCyBD8V0CmYjqJzw8HN999x2MRqNvGa/mVT/1CrGV\n5y/zeDyQJGZfolAqcZXhiS9fgIYzb3QGQYRBMMAgiBBFg999g2CAKFa+X3FbFCEKVdat2Na3XKyy\nn0rrxNpiUFZSVv/9V1pHrHK8M8t5QYJzIQgCwsMkhIfVb+7dUrun2ljdkiqXFs4uqHvuXckgQNMA\nRdWQFGvG7DHtGWSJ6iE9PR1z587Fiy++6FuWnZ2NNWvWYPXq1dixY0cIq9OHOtPo1q1bsW7dOrz2\n2msAvIOOx44di/79+we9OCIKLN9R5Bdgz7O0gtEgQdHUin8KFFWFqilwqx6Ua07vclWBonmXN+Xv\n+WLlIFwRdMUqodwbiEWYfwyD4vbUKySLAUJ5TaHfP4DXEMIr7ks2GcXO0oD7aoqBXDKIiImUERMZ\neO7dyuqaeze3qBynCpwAgFMF5cjMd+Di5IhgPwUi3fv+++/x/fffo2PHjti9ezdWrlyJw4cPY+DA\ngXjnnXdCXZ4u1Bli33rrLbz55pu++//5z38wbtw4hliiEGodkYhWYfHIduShVVg8Jl9zL0yGugNJ\nZerpwFsRbBVN8S6rdN+3XPW/791ORZQ1EvkFBb7QfPox1beO/zZnu3+18jJVhUf1wFl1/2Vqk/7T\nmwihWggXaw3FtfWgVw31Z5ZZC62wl9lq7/k+6/17t4mJlhBvlSEK1U8MK3cp+Pt7/4ec8jwkmuPR\nOq72XmAi8nruuefw+OOP4+jRo7jgggvwyCOPoEuXLqEuS1fqDLGapiEyMtJ3PyIiokn2LBC1JCaD\njGmd74M7TIXRIZ51gAW8vZ2iIMIonvvwoOTkZGQZs855+4aQnJyMk5mZNQbngCFZrRTaK4XmOkN4\ntX171zGZTSizl9Vr/2qlddwed7UvEGoT7iMXgGo93CJElF1ihxkqTOY4CIYOADicgKguF154Id57\n7z18//33WLVqFZ5++mn07NkTqampuPzyy0Ndni7U+enVoUMHTJkyBV27doWmadi9ezc6dOjQGLUR\nUS1MBhkXxp255GpLJgoCREHyvqGFID9VvvTtH6VpWh3BWQnYSx0dY0Vufu6ZEB6gd7vmfVUO21WC\nfI297N5l5YoLKryzIuSW5yPLloMLo9o0yGtB1BJcddVVuOqqq+BwOLB582bMmzcPJSUl2LRpU6hL\na/LqDLFz5szBhg0b8P3330MQBAwaNIhDCYiIgkQQBEiCAdJZpvHk5GRkofG/0DgVFxZ9+y/f0Jbk\n8MRGr4GoOVAUBb1798btt9+OX3/9NdTl6EKdIdbhcMBoNGLu3LkAgPfffx8OhwPh4eFBL46IiJq2\nhhjaQtSSHTx4EDNmzMDJkyfhdDpx0UUX4fnnnw91WbpQ5+VbZs6ciby8PN/98vJyzJgxI6hFERGR\nfpgMMi6Lu4gBlugcpKenY9q0afjmm2/QuXNnvPLKK1i4cGGoy9KFOkNsUVER7rrrLt/9e++9FyUl\nJUEtioiIiKglEAQBvXr1AuAdE3/++efD4/GEuCp9qDPEut1uv7EZ+/fvh9vtDmpRRERERC2Bx+PB\nkSNHfPcPHjzIEFtPdY6JnTVrFh588EGUlpZCVVXExMTg2WefbYzaiIiIiJq1WbNmYfPmzZg4cSJy\nc3Mxe/ZsLFiwINRl6UKdIbZTp07Ytm0bsrKykJGRgbVr12LChAn4/PPPG6M+IiIiomarU6dO6NSp\nEwBgy5YtnIv/LNQZYr/77jt88MEH2Lx5M1RVRXp6Om655ZbGqI2IiIioWbvrrrsCXnWQl56tW40h\n9s0338TatWvhcDgwePBgrFmzBpMnT8att97amPURERERNVsTJkzw3Xa73di3bx9sNlsIK9KPGkPs\n4sWLcemll+KJJ55At27dAIBd3EREREQNqHv37n73e/bsibS0tBBVoy81hthPP/0Ua9euxbx586Cq\nKoYOHcpZCYiIiIgaUElJCY4dO4YOHTrA5XLh119/xb///e9Ql6ULNU6xlZCQgPHjx2Pbtm1YsGAB\njh07hpMnT+KBBx7Arl27GrNGIiIiomZpxowZOHz4MABg7NixePTRR/HEE0+EuCp9qHOeWAC49tpr\n8cwzz2D37t246aab8PLLLwe7LiIiIqJmr7i4GEOGDMFPP/2E8PBwbNq0Cb///nuoy9KFeoXY0yIi\nIjBixAisXLkyWPUQERERtRiy7L1c844dO3DTTTcB4DlI9XVWIfZs/fLLL+jXrx/effddAEBWVhbS\n0tIwatQoTJ48GS6XK5iHJyIiImrS2rZti8mTJ2Pt2rVITU3FqlWr0K5du1CXpQtBC7F2ux3p6el+\nZ929+OKLGDVqFP773/+ibdu2WL16dbAOT0RERNTkzZ8/H2lpaVi+fDmsVitSU1Mxd+7cUJelC0EL\nsbIs480330RiYqJvWUZGBvr27QsA6N27N/bu3RuswxMRERE1eaIookuXLoiPjwfgHbpJ9VPnFbvO\neceSBEny373D4fCN/YiLi0Nubm6t+4iPj4fRaKzzWMnJyedeaANhDaE/fnOrQS/tP9THZw1Np4aG\nPH592n+ony9raBrHZw0tV9BCbF0CXWKtqry8vDrXSU5ORlZWVkOUdM5YQ+iPr5cazuZNTg/tP9TH\nZw1Np4b6HL8h23+ony9raBrH10MNsiwjLi6ukStqGYJ6YldVFosF5eXlAIDs7Gy/oQZERERELdVz\nzz0X8DbVrFFDbI8ePbBt2zYAwEcffYQbb7yxMQ9PRERE1CR9//33AW9TzYI2nODAgQNYuHAhTp48\nCUmSsG3bNjz//PN47LHHsGLFCrRu3RpDhgwJ1uGJiIiIdKPy3LCcJ7Z+ghZiO3TogHfeeafa8qVL\nlwbrkERERES6VPlcofqcN0SNPJyAiIiIiKpjT+zZY4glIiIiCrHK5wnxnKH6YYglIiIiCrH7778/\n4G2qGUMsEREREekOQywRERER6Q5DLBEREVGIuN3uUJegWwyxRERERCEyduzYastsNhsmT54Mm80W\ngor0gyGWiIiIKETcbjdOnDiBkydP+v4VFRXhuuuuw9y5c0NdXpMWtIsdEBEREVHtfvzxR4wePTrg\n3LC86EHtGGKJiIiIQuTqq6/GsmXLQl2GLnE4AREREVGItG/fPtQl6BZDLBEREVGIzJw5M9Ql6BZD\nLBERERHpDkMsEREREekOQywRERFRkLhcrlCX0GwxxBIRERGR7jDEEhEREZHuMMQSERERke4wxBIR\nERGR7jDEEhEREZHuMMQSERERke4wxBIRERGR7jDEEhEREZHuMMQSERERke4wxBIRERGR7jDEEhGR\nrjgVFw7lH4FT4eU8iVoyKdQFEBER1ZdTcWHhN68jv7wQ4ZIFfdp0Q7gxHGZJhskgw2QwwWw4c9tk\nkGEUJQiCEOrSiaiBMcQSEZFuZNlykF9eCACweez48PcddW4jQoBJ8gZac0WwrXzbLJ1ZVjUImw0m\nmKTKy0yQREOwnyYR1QNDLBER6UZyeCISw+KQ48iH1RSF2y7qC1VVUa444VRcvp9n/jlRrrjg9Hhv\nl7jK4FScUDT1nGswCAaYDTIsJgskGCrC7pmeX7+g7AvIVYLx6cAsmWAQOLKP6FwwxBIRkW6YDDIe\n7Xw/3GEqjA4RJoN8TvvxqJ6KcOsNua5KAbi8Ivz6QrHHVSUge7fxaAqKXDY4PU6o0M75ORlFKeBQ\nCLNUORifuV05CNtkJ8rspb5lskGGyKET1EI0aojNyMjA5MmTcdlllwEALr/8csydO7cxSyAiIp3T\nFAPUskhosAPn+Jd9SZQQIUqIMFrOuY7k5GRkZWVB0zS4VY9/z2+VHuAag7LHPzTnuW1wKa76R+L9\n1RfJorFaD7BcrbfY2wtcbVnF8InTIVkWjRxPTE1Wo/fEdu3aFS+++GJjH5aIiJqBcpeC9LcPILfY\niUiLhOG92yIp1ozYKBnh5tCcwCUIAmSDEbLBiEiE/+H9qZoGt+pGuccJp+ofhP2HSDghmY3ILyms\nEpTPhOJiZylcqvvcnxtQEYCrDIuo6CWOPWmF4vT4PW6qeNxs8B9LzJPsqKFxOAEREelGZr4DucVO\nAECp3YN/bfrV95jJKCI2UkZslAmxUTLiKv+MlGGNlGEQm36AEgXBN2ygLqd7g2ujaipcitt/3HC1\nIRIBgrKn8rAKF+weBwqdxXCrnjM7zznL51bDSXZ+96UAoTngSXYyJJExpiUTNE0794E8ZykjIwNP\nPvkkLrjgAhQXF+Ohhx7C9ddfX+P6brcbRqOxscojalLY/qklq6n9O5wePLJ4F07klCE22owB3S9E\nUakTuUUO5BTakVvoQJkjcM+jKACx0WFIjAlDgtWCxNgwJFjDkBBjQUJMGBJjLAgzMRTVRVEVlHuc\ncHjKUe72/nS4y73L3OXe5ZVvu52V1i+Ho8ptT+VQfJYMogFhkhlhkglmo9l722iCWfLeNhtNFcvM\nMEsmv2VmyYQwo/96Bs48oSuNGmKzs7PxzTffIDU1FcePH8ddd92Fjz76CLIc+NtmXd8ugfp9Cw02\n1hD64+ulhuTk5HrvSw/tP9THZw1Np4b6HL+h2n+5S4ELFsiwwyxXDx3lLgUFJU7kl7hQUOJCfokT\nBaUVP0tcKCpzoaZPPovZ4Ou5Pd2Te6ZnV0akxeg7cSrUr3lTqKEhju9RFb+xxGd6iaucYFfRS1zu\n8V+mCCpsTrtvH+ofmHmippPsqs42UXmIhCSIaJt0AcLd5hp7z8+m7VP9NepXzlatWmHAgAEAgAsu\nuADx8fHIzs7G+eef35hlEBGRjpllAy5KjkVWlrPGx1vHW9A6PvBJWx5FRXGZu1K4daGgIuAWlDiR\nXVCO4zn2gNtKBsE3ZKFNq1MIMyrewBvtDb4xkTKMEqfMOhuSaIAkWhB+jifZVQ7SmqbBoym+WSf8\nwrHi8hsiUWNQ9nhv57vtcCouaPU5ze4g0CosHtM633fOM2bQ2WvUELthwwbk5uZi3LhxyM3NRX5+\nPlq1atWYJRARUQsnGUTERZsQF20K+LimabCVe3w9uacDbuUe3YPHSnDwWEnA7aPCjYiLkhEbafL+\nrOjNjav4aTEZeHJTkAiCAKMgwShLiGiAk+w0TYNLdfuPG64UhE/asvHx8S8AANmOPGTZcnBhVJs/\nfFyqn0YNsX369MGjjz6KTz75BG63G/Pnz69xKAEREVEoCIKAiDAjIsKMaNsqcBByuVVIYdE4+OsJ\nX9DNLzkzZOFYth1HsmwBtzUZRf+Tzk4H3YrQGx2hjxPQWgKhjpPs2iuXY3/ez8h25KFVWDySwxMb\nucKWrVFDbEREBF577bXGPCQREVGDk40ikhMiIHqiAz6uahpKbG5fqPUbslDqDbyZ+Y6A24oCEFNp\nloXK43NPj9c1BRgLTI3PZJAxrfN9f/jiG3RueBomERFRAxMFAdYIGdYIGZe0DryO3emp6MWtCLiV\nTj7LL3Hi8InSGkdjhodJiIuS0TrhGMJl7cyQhYrwG2nhfKyNxWSQcWFc6E/ya4kYYomIiELAYpJg\nSZDQJqHmE9CKSt3IL3X6DVk43ZOblV+OY9mBT0AzSgJiI/17byuPzY2JlCEZeAIa6RtDLBERURMk\nGUTEW02It9Z8Alp4VBx+OnS82jRip4cuZBeWB9xWABAdYaz14hAWMyMCNW1soURERDokCAKiI0xo\nmxSOtkmBT0BzuhUUVplGrPKQhd+z7fithhPQzLLBNz/umZkWzvyMDufFWCi0GGKJiIiaKZPRgKTY\nMCTFhgV8XFU1FNvclS4O4T9koaDEicy8Gk5AEwXEW8NgtRh8wbbqkAWTkSegUfAwxBIREbVQoigg\npuIiDZecF3gde7mn2klnp4csFNkUHDphxy817D8yTKpy1TP/q6FFhPEENDp3DLFERERUI4tZgsUc\n+AS05ORkHDt+EkVlVacROxN2M/MdOFrjCWhiwCELsVEy4iJNsEYaeQIa1YghloiIiM6ZURKRYDUj\nwWoO+LimaSi1e7xDFUorDVmodBW07ILaT0CretJZ5Z5darkYYomIiChoBEFAVLgRUeFGXJgceJ1y\n1+kT0Pwv8Xt62MKRrDL8mhl423Dz/yEm0uibUuzMyWfekBsVboTIIQvNEkMsERERhZRZNiA5LgzJ\ncYFPQFNUDcWnhyz45s31ht0Sh4rsfBtO5AY+Ac1QMe636uwK3iEL3ttGiUMW9IghloiIiJo0gyhU\nBE8TgEi/x5KTk5GZmQm7U/GbZcF3ud+KmRZ+Pl4KoDTg/iMtUrWTzs4MWZARbuYJaE0RQywRERHp\nmiAICDdLCDdLOD8x8Jy5bo+KwtKKk86Kq18c4kSuHb+fCjxnrsko+o3F9fXoRsqIsEgodhbAJCgw\ny5xSrDExxBIREVGzZ5REJMaYkRgT+AQ0VdNQanf7TjorqDI+N7/EiawaTkADgKRYM2aPac8g24gY\nYomIiKjFEwUB0eEyosNlXFzLCWiVZ1f4LasUe3/IBwCcKihHZr4DFydHNGLVLRtDLBEREVE9mGUD\nWsdb0DreO2fude3icCTLhlMF5UiKNaN1DSemUXAwxBIRERGdA7NswOwx7eGCBTLsHErQyDinBBER\nEdE5MssG/KltLANsCDDEEhEREZHuMMQSERERke4wxBIRERGR7jDEEhEREZHuMMQSERERke4wxBIR\nERGR7jDEEhEREZHuMMQSERERke4wxBIRERGR7giapmmhLoKIiIiI6GywJ5aIiIiIdIchloiIiIh0\nhyGWiIiIiHSHIZaIiIiIdIchloiIiIh0hyGWiIiIiHSHIZaIiIiIdIchloiIiIh0hyGWiIiIiHSH\nIZaIiIiIdEcKdQG1ycrKqnOd+Ph45OXlNUI1rKEpH18vNSQnJ9d7X3po/6E+PmtoOjXU5/gN2f5D\n/XxZQ9M4vl5qOJu2T/Wn+55Yo9EY6hJYQxM4fkutIdTPOdTHZw1Np4aW1vZZQ9M4Pmto2Zp0T2xd\nvv12OSIjt8LtdoS0jsOHTXC5nDqoQUBu7gXo1evZRqmJgsfj8eDzz6eiTZsSuFzlIaujabZ9AXl5\nl6Fnz/SQ1UTB9dNPO3Dw4LtQVVtI62iK7d/lkhAWNh5/+lOvEFZF1Dh0HWKBb9CzpxNOpzukVZhM\nom5q2Lv3Z6iqClHUfSd8i1ZQUIArr/wNV18dFdK211Tb/uefHwxRNdQYSkv3YvBgR5Nse6GvwY2t\nW/cCYIil5k/XIVYUgUmTsnDokIKSEsDlEhAfryEiAnjvvYR67+eXX5zIyfHghhvCqz1WVqaiV69C\njB4t4pFHYhqkbo9Hw2OP5eOLL4C9e+P9HlMUDbfdloeuXQ2YPz/W77GBA3OhKIAse+/Pnh2O666z\nID29AHv2KACA8ePNGDo0EgAweXIeDh7UYDQCL7wQhcsuMwHQGuQ5UOgJgqDL9r99exleeMEBSQI6\ndBCxYEFctXXS0wvw2WcKJAmYOzccPXpYfI+98EIh1q3zYNcu73OcNi0L+/YpMBiAadMs6N07HGzn\nLcO0aXk4fFjTVfvPyK7pyrAAACAASURBVLAjPd0GUQQiIoA33ohDRMSZToWiIgUTJxagtNT7Xr9k\niRXnnWcM+P4fGWnAXXfl47zzVACA1Qq8/Xb9nzuR3uk6xALASy8lw+l04vXXi/DLLx4sWnT2v8Db\nt9vh8SDgm9iaNaWIilKxY4eKRx5piIq9H9Bt2hgAKNUee+mlIqhqzds+80wkOnUy++4fOFCOPXsU\nbN7cGidP2pGWVoRBgyKwZk0p8vI0bNuWgOXLS/DRR/aKEEvNiR7b/zPPOLB4cSQ6djTjvvty8dln\nNvTseebY//ufA3v2KNiyJR579tixbZvDF2KPH3dh926Pb93du23Yv1/Bxo3xyM314L77iipCLLUE\nixZ5OwH01P7T022YPdv7xWzq1DwsXVqMSZPOBORXXinGBReIePrpOGzZUooFC4rx8sve51n1/f/H\nH51ITFSxfj2DK7VMug+xtZk+PR8//6xCVYE775Rx113RWLu2BG+84YTRCMTEAPPnR+P99xVIkoY2\nbUpw++1RfvvYssWFceNkvPKKG4cPu3DppTKcThXjx+cjPx8wGoFXXklAbKz/siVLoqEowPPPl/je\ngE6bOtUKi0XAihUFfstPnnRj1y4PBgyQUFBQv56knTsd6NJFhNksok0bI6xWYP9+J3bscOHWW72h\ndcSIqDr2Qs1RY7X/JUui0aqVVO/2b7MJ6NjR+0Hco4eMHTucfiF282YHevUyQJIE9OwZ7vfYvHnF\nePhhC+bNswMADh3y4LLLDDAYBCQlGWEyAUeOuACYQS1bU23/y5bFIjbWAACIiRFRVOT/Xn/8uIpe\nvbzdrampkXjuudCNeSdq6prtwMgtW0pRWKhiw4YELF8eh7fecsHhUPHf/zoxY4YFGzYk4OabZciy\ngBtvFDB0qFTtDSw314MjRwQMGxaJrl2BlSvLAACvvVaMxEQBGzcmYOBAIz74oKTasvXrbbjoIrna\nGxgAWK3/z96dh0lR3esDf2vtdXq2nqXZDWBcQAwK7lFEUVxA9CYoIhg14kY0ElBwAS/RKEYjGr1e\ncctP8UqCGgyiLHq90XsBd3EXkbDMDLOvvXdV/f7omZ7umZ6V6e6p4f08D89UV1dXfXvmUPPO6XOq\npKQ1L11ah9/+1g4p+dMAgBUrGjFtWiVuvLEKPp+OqiodeXmtP0aXCygtDaOiwsBnn4UxY0YlLr20\nErt2hXrxXSSzSlf7nz7dinXrvD1s/wbeeacJmmZg69YQamoSP3ooK9NRVqbjkksqMWNGJT76KDpx\nc/36RjgcAk4+2RbbduxYFV9+qaGpScfevSGUlgooK4uADm39uf23BNj6eg3vvqvhoovsCc+PGiXh\nf/4ner5ev74RDQ1C7Lm25//ofgRcfnklzj+/Es8/X99330QiExiwIfaDD8LYuROYPr0Sl19eDcMA\n9u8PY/JkBb//vQ/Ll9fghBOsGDy448tirFnTiLFjDdjtIi6+2IZ3341+/P/ttxqOOy76l/LVV+fg\n1lsL2q2L/3ioOzZsaITFIiT0OrU1a5YFd9/twrp1bmga8Oc/13W4rWEAubkCXnutAJMmKfj3f+fJ\n7VCSrvY/b54b8+fn9qj933OPA48+6scvf1mF7GwBRpsPHQwj+u+VVwpw7bVWLF3aBL9fx1NPBXDP\nPYn7nTDBhnPOUTBzZjXuuacegwYZ0HWOhz3U9ef2D0QD7Jw5NbjsMiX2qUSLG27IBmDgwgsr8X//\nF4K9OeMmO/8XF8u48koVzz2Xj6efzsEzz4Sxf39mJ5oRpdOAHU6gKMA550hYsiRxctTo0RZccEEY\nr77ahOuua8DKlVkd7mPz5ghqa4Gzz64EAFRVifjiiwBEEe1+8SZb1xObNgWxc6eB886rRFMToGkC\nhg2rx1VXZce2mTWrtafgzDNVbNkSwrhxEkpKWnuyamqAwYMV5OQEcOqp0eEEU6fa8be/McQeSvpz\n+z/hBDv+/vfob+Y//akWgUDiC/PyBBx1VPTUNHVqFpYtC+L//s+HhgZg9uzoEJyGBhG/+lUlnnuu\nAEuWFGHBguglhs49txIjR6rYtat7tdDA1J/bfzCoY+7cGpx/voJf/zqn3fM2m4j/+I/oGNeaGg0f\nfhht88nO/3l5Em66qQDBYBAej4hhwwx8801mL/lFlE4Dtif2+ONVbNumQdMMNDbquPnm6J00li6t\nht0u4je/ycXJJ4v4+usQRBHQ2syx2r07hKoqAe+848bmzQXYvLkA550H/PWvXowZI2Hr1ujHPS+/\n3IBly8rbrbv33sTxrl155BE3Nm0qwIYNBZg5U8bZZ4sJAVbTDMyYUYmammihW7eGMGqUiLPOsuOj\nj3QEAjp27w6hsRE45hgLTjlFwZYt0bFUH3wQAG8WcmhJV/tfvboG995b06P2f/XVlfjkEz8iEQNb\ntkQwdWrix6mTJ1vw3nvR3qSPPvIjN9fA5MlOvPtu9P/H3/+eD5dLx3PPFWDfvhBmzNgPIDohTJIA\nj4cXHT/U9ef2//vf1+K44yRce237AAsAf/tbA+66qxoA8PzzDRg/Xuzw/L9+fSN+85vonc3q6zXs\n3w8ccYTa+28ckckM2J7YKVOc2LIliAsuiJ68Lr44+outuFjEZZfVwG4HsrKA225zQlG8WLEiiLy8\nelx5ZTQ4rlnThFNOESDLreORZs924sYbG/DGG7m44YZqTJtWCUmKTuzKydET1q1cmY3du0NJB/bf\neGMV9u0z4PeLmD69EqeeKmHhwsQegxavvNKA0lIN8+fnYto0FZddVgNVBdxuYPnyHDidIs46S8L5\n55dCEIDbbrNCEARceWU2brqpGhdcEK3nD3/g5K5DSbravywDjzySjYICudvtf+ZMG26/vQlAE846\nS8Kxx1oRiRiYO7cKq1cX4KyznHj77SDOPz/aA7ZkSWLIjTd0qIrhwyWce24lBAFYvtzZl99GMqn+\n3P43bzbgduuYPj3avsePl3DHHbmx9n/uuQ6sWVOD886rbL4EVx4kSUh6/lcU4LXXanD++ZXQdeDy\nyxUMHariq6/S8V0myjzBMA7mQ/DU6ure2Z99thBTp+5HMJjZj08sFotpati6NYLRo//e5zc78Hg8\nXf68Us0MNfTVveMrKioAXIVx47Iy2vb6a9t//30RRx21Nm01mKHt9Yfj91X7/+CDezF9+pf9su31\nhxreemsiJk68PS3Hz3S7M0sNPWn71H2mHk7g81mgd3ZRVWrH5+NHTQOB1WpFbW0nl7E4xPl8vCby\nQBYK2REK8dyfTCikIxLp+NMLooHE1MMJxoz5HTZv3oimpo5n6aeDy5WFhobGfl+DYQBFRcfxlrMD\ngMvlQknJAmzeXIKGhoYM1tH/2r5hAIMGnZjBiijVJk68Ee++OwyNjVUZraM/tn9dVzFx4uUZrIgo\nfUwdYl2uHJxzzi39/mOEQ6UGSq8jj/x5xn/umT5+f6mB0ktVVUyZclPGf+79oe31hxqIMoVdckRE\nRERkOgyxRERERGQ6DLFEREREZDopHRO7YsUKfPzxx4hEIpg3bx7Gjh2LRYsWQdM0FBQU4MEHH4Sq\ncrY8EREREfVMykLstm3bsHPnTqxZswa1tbWYMWMGTjrpJMyaNQtTp07Fww8/jLVr12LWrFmpKoGI\niIiIBqiUDSeYMGECVq5cCSB6OSC/34/t27dj8uTJAIBJkyZh69atqTo8EREREQ1gablj15o1a/DR\nRx/h/fffjwXXvXv3YtGiRXj55Zc7fF04HIai8D7odGhi+6dDGds/EXUl5deJ3bJlC9auXYtnn30W\nU6ZMia3vTnauqur6Qtb94Rp5rCHzxzdLDT259aAZ2n+mj88a+k8NfX3b2a7af6bfL2voH8c3Sw28\n7WxqpPTqBO+99x6efPJJrFq1CllZWbDb7QgEAgCA8vJyFBYWpvLwRERERDRApSzENjY2YsWKFfjP\n//xP5OTkAABOPvlkbNy4EQCwadMmnHbaaak6PBERERENYCkbTrBhwwbU1tbilltuia27//77ceed\nd2LNmjUYNGgQLrroolQdnoiIiIgGsJSF2JkzZ2LmzJnt1j/33HOpOiQRERERHSJ4xy4iIiIiMh2G\nWCIiIiIyHYZYIiIiIjIdhlgiIiIiMh2GWCIiIiIyHYZYIiIiIjIdhlgiIiIiMh2GWCIiIiIyHYZY\nIiIiIjIdhlgiIiIiMh2GWCIiIiIyHYZYIiIiIjIdhlgiIiIiMh2GWCIiIiIyHYZYIiIiIjIdhlgi\nIiIiMh2GWCIiIiIyHYZYIiIiIjIdhlgiIiIiMh2GWCIiIiIyHYZYIiIiIjIdhlgiIiIiMh2GWCIi\nIiIyHYZYIiIiohRRVTXTJQxYDLFEREREZDoMsURERERkOgyxRERERJSguroad911V6bL6JSc6QKI\niIiIqH959NFHsW7dOvz973/HF198AQCYM2cOHnjgAcyaNQsXXHABmpqaoCgKlixZgm3btuH5559H\nXl4ecnNzsXDhwpTXyBBLRERERAnOO+88RCIR7N27F6WlpZAkCYqiwOPxoKqqCtdffz3sdjvmzp2L\nAwcO4IEHHsBf/vIXuFwuLF68GDt27MAxxxyT0hpNHWKDWgg7q3dD0URYJM7+IyIiInP54IMPkq6f\nOHFimitJ7uKLL8arr74Kh8OBGTNmAADy8/Nht9sBAG63GxUVFSgrK8N9990HIDoUoaqqKuW1pTTE\nfv/997jhhhtw5ZVXYvbs2bj99tvx1VdfIScnBwBw9dVX44wzzujVvoNaCPd88Ci8YR8AwCqqkCUF\nsihBFuToV1GCLEiQRTlhvSTKzeubn2u33Lq91Ml+Wra3+GxoCvti20iCCEEQ+urbSERERAPUypUr\nY8uapmHXrl0YN25cxkOsIAjQdR3nnnsu5syZA7vdjqeeegpANKQ2NTXB6XSitLQURUVFGDZsGO6+\n+27Y7Xbs3bsXxcXFKa8xZSHW5/Nh+fLlOOmkkxLW33rrrZg0adJB77/MWxELsADgVJ0QBQERXUNI\nD8EX0RDRI9AMDZqhH/TxeqrjMBwfsBPDsNSjUJ24nyY1gLqmusTAHfdaSZAYrImIiPqZ1atXJzyu\nra3FQw89lKFqWg0bNgyffPIJNmzYgCOPPBKCIMBisQAAcnNz8fjjj6OiogJjx45FUVERbr31Vixc\nuBB2ux2apuHee+9NeY0pC7GqqmLVqlVYtWpVSvbvcRSiyOZGub8KRTY3Foy/psMhBbphQDOioTai\nN3+Ne5zwnNGyTfxypHmb+Ne2LssWBU3eptg+taT7iSAQCcReqxlaSr4vnZGS9T63CcOxIN1BGE4I\nxnH7cQf3oamhsX04bw7Q7fYjypAECSKDNRERUUxubi5++OGHTJeB4uJibNy4EQDw4YcfYu7cubHn\nLBYLbrvttoTtTzzxRJx44olprTFlIVaWZchy+92/+OKLeO6555Cfn4+77roLeXl5He7D7XZDUZQO\nn3+w6A7sayjDUJcHVsXaJ3Wni2EYiOgRhPUIIlr0a8tyy/pwwnI4uty8baT5X/zj2LIWQVgPI6Jr\nCfuPaOGE44T1CPx6AOFI6+N0k0QJiihDFuXoVyn6NWFZkiGL0aEireuV9tvUte4n+pro1+j+leZ1\ncceTlKTHFIXeXXkuEA5gZ/VuDHX3TXvsqv238Hg8B32sg5Hp47OG/lNDXx6/O+0/0++XNfSP45u9\nhsWLFyc8Likp6da5Px0CgQAWLFiAkSNH4sgjj8x0Oe0IhmEYqTzAY489htzcXMyePRtbt25FTk4O\njjzySDz11FM4cOAA7r777g5fW1ZW1uX+PR5Pt7ZLpYFSg2EY0Ay9mz3ViT3STpcT1bU17V7bdlnr\ndD+t67W4/aS0gSYhQkjoMY6NdY7vZY7v1RYlCIaAb2p3wa8FOv1koCcnOTO0/0wfnzX0nxq6c/y+\nbP+Zfr+soX8c3ww1qKqK/Pz8duuDwSAsFgvWrl0bWycIArKzs3HaaafFPrqnjqX16gTx42PPPPNM\nLFu2LJ2Hpy4IghALZz2VqpOIYRjQDb2TYNwahl052aiormwznKNtqG4JyR3vJzbkI269PzYUJPrY\n6CRal/urUOatwAjXkD7/fhAR0cBwzTXX4IUXXsC//du/ZboU00priJ0/fz4WLVqEoUOHYvv27Rg9\nenQ6D08mJAgCJEGCBKnLy6h5PB6UCen5a7ylx7olDPvCPqz6ag2qArUosrnhcRSmpQ4iIjInTrY+\neCkLsV9++SUeeOABlJSUQJZlbNy4EbNnz8Ytt9wCm80Gu92OP/zhD6k6PFFKSYIISVKB5k5rl+rE\nouPmIWzTofh53WIiIqJUS1mIHTNmDF544YV2688555xUHZIooyySihH5mR+bRUREdCjo3TRsIiIi\nIuq1FM+rz5iWy3J15d5778W+ffsO6lgMsURERERpNhDHxO7fvx9vvPFGt7a94447MHTo0IM6Xlon\ndhERERERcO2112a6BACAPxjB3gMNGFbsgs1ycLHw3//937Fjxw4cccQRmDZtGvbv34/nn38eixcv\nRnl5OXw+H+bPn49JkybhiiuuwF133YWNGzeisbERu3fvxt69e7FkyRKcfvrp3ToeQywRERFRmp16\n6qkAgCOPPBKGYSTtmTUMA99++22v9v/sP77C/35e0uk2umGgtiEITTcgiQJyXZZO76R5yrjBuOrC\nozt8/uqrr8bq1asxevRo/Pjjj3jppZdQXV2NU089FTNmzMC+fftw8803Y9KkSQmvO3DgAFatWoV/\n/vOfePnllxliiYiIiPq7r776KmPH1jQDmh4dm6vpBjTNgCj3zTCHY445BgDgcrnwxRdfYM2aNRBF\nEXV1de22HT9+PIDorW4bGxu7fQyGWCIiIqIM2bp1K1588UW43W7ccsstqKioQGFhYdK7fPXEVRce\n3WmvKRAdSnDrI/+D/RVNGFLoxMO3nH7QQwpatNw6d/369aivr8dLL72Eurq6pDd3kOXeHZMTu4iI\niIgyZPny5Zg2bRoKCwtx33334cCBA1i0aFFajm2zyHj4ltPxx9+c1icBVhRFRCKRhHW1tbUYMmQI\nRFHE5s2bEQqFDuoYCcfrsz0RERERUY8MHjwYU6dOxfz581FWVoZJkyZB07S0Hd9mkfHT4Xl90gM7\ncuRIfP311wlDAqZMmYJ33nkHc+fOhc1mQ3FxMf785z8f9LEADicgIiIiyphLLrkEa9aswYUXXghJ\nklBZWdmjcaH9SV5eHt59992EdUOGDME//vGP2ONp06YBAG666SYAwOGHHx577vDDD096o6yOMMQS\nERERZcgrr7yCbdu2YdmyZQCACy+8EIsXL85sUSbBEEtERESUIQ6HA5999llsIhQwcO/m1de6NSY2\n/pvZdsAuEREREfXM8uXLAQCPPvpoLMCWl5fjiSeewOTJkzNZmml0GWLfeustXH/99bHHs2bNwltv\nvZXSooiIiIgGsh07dmDHjh0wDAP//Oc/cdNNN2Hu3LnQdb1H40IPZV0OJ3j++eexatWq2ONnn30W\nV199Nc4999yUFkZEREQ0UD344IO44447sGfPHgwbNgy33norjj/++EyXZSpd9sQahoGsrKzYY6fT\nmfTWaERERETUPSNGjMDq1avxxBNPYOTIkfjDH/6AlStX4vvvv890aabRZU/smDFjcMstt2DixIkw\nDAPvvfcexowZk47aiIiIiAa0Y445Bscccwz8fj82bNiApUuXoqGhAW+88UamS0uZM888E//4xz/g\ncDgOaj9dhtg777wTr7/+Onbs2AFBEDBt2jQOJSAiIiLqQ5qmYdKkSbjkkkuwa9euTJdjCl0OJ/D7\n/VAUBXfddRfuvPNO1NfXw+/3p6O2LgVCGr7bU4NAKH13tiAiIiLqK99++y2mTZuG008/HT//+c9x\n4YUXpvVKUIFwADurdyMQDhz0vmbMmIHS0lIAQElJCS666CLMmzcPV1xxBX7xi19gx44dB32MeF32\nxN52222YMGFC7HEgEMCiRYvw+OOP92khPRUIabjnL1+iuj4Ih1XGGccWwKLKkCUBsihAkgTIkhj9\nKgqQJQGSJLYui82PJSFum9btpeZtOP6X+quWP+JUaLCqUqbLISKiXli+fDkWLFiA008/HXPmzMG9\n996LpUuX4tlnnz2o/b7w2SvYtu+TTrcxDAO1gXpohg5JEJFrze4095w4dDyuOPaSDp8/66yz8N//\n/d+4/PLL8fbbb+Oss87CEUccgbPOOgtbt27FqlWr8Nhjj/X6PbXVZYitq6vDnDlzYo9/9atf4Z13\n3umzAnqrtNqP6vogAMAbiOCNbWUpOU5L4G0XgiUxtt5m/QG6Fk4emuNeL8e9Jv71rds0vz4WpMV2\ny20Dd8s+eGHkQ0ujL4zFT32OUESH3SLh/BMHYXixA4PddjhsvIcJEZFZCIKA008/HUA0VA4dOjRt\nPbERXYNm6AAAzdAR0TUoUu9/h0yZMgX3339/LMQuXrwYzzzzDJ555hmEQiHY7fa+Kh1AN0JsOBzG\nrl27MHLkSADAF198gXA43KdF9MagfBuKcq0orw0gL0vFpWcNhyQIiGgGNE1HRDealw1EdD36VTMQ\n0XRobZ5rWW5Zn7hNm31pOkIRHZGgFl2n64hoXvSHDNkakAXIYvJA3FGvc/ttErePBeokgVsSBVQ0\nyaiva4zbJu71bephD/fBO1DjRygSPfH4ghr+9j/7Ys9lOxQMctswxG3HILcNg902ePJtsLC3loio\n34lEIti9ezcOO+wwANHhBX0RYq849pJOe02B6FCCxZsfQEnjAQzOKsYfzr4NVsXa62OOHj0aFRUV\nKCsrQ2NjI7Zs2YKioiI8+OCD+OKLL7BixYpe7zuZLkPs4sWLccMNN6CxsRG6riM3N7fPi+gNqyrh\njiuORgh2qPBl9ONUj8eDkpLS5lCbJARrBiJ6awhuXW4fkrWWUB0fqDt5fcuxREmB3x9ofa75azii\nI9D8uOV4ekoCd88uCZIscCf2THccgpMFbkkSkJPdAL+vKeH1SUN9u17w1h7ztttIkgCxm4E7nR/v\nDy10oDjPigM1AeS7VEw9YRAq64IoqfKhpMqPb/Y04Js9DbHtBQD52RYMdtswuKA53ObbUJRnhSx1\n68Z9RESUAosXL8aGDRtw4403orKyEkuWLMF9992XlmNbFSv+cPZt2NdQhqEuz0EF2BZnnHEG/vSn\nP+HMM89EbW0tfvrTnwIAtmzZ0uedoF2G2HHjxmHjxo0oKyvD9u3b8dprr+H666/H+++/36eF9IZV\nlXCYJw9lZcFMlwJRFKCKLWEn/YHa4/GgrKx7Qyp0vZNe55YAHR+a40J1Yihvfc5mc6C2viEhQLcE\n+kjb5Tavjz9WMBQXuHUDeo8Sd2nvvnldEMX4ISLJQ7AoACXVfoTCOorzrFgy++iUBlmrKmHJ7I7/\niPMFIyir8qOkyo/SKn8s3H6+qw6f76pLeG/FudZoqC2wY7DbhkFuG9zZlm6HdyIi6r1x48Zh3Lhx\nAIA333wz7Z9UWhUrRucf1mf7O/vss3HppZfi9ddfh8/nw2233Ya33noLl19+OdavX49XXnmlz47V\nZYj97LPP8Oqrr2LDhg3QdR3Lly/HlClT+qwASj9RFCCKAhQZ6KvA3ZMQ3RO6kSQQdxCCs7NzUV5Z\n1Rq4E4aRxPVyx3qmW5/vMHDH95o3v75lfTCsxZ4La3psSMmBmgBKq/34icfZ59+PeJ39EWe3yBg5\nOAsjB7feqMQwDDT6Iiip8jUHWz9Km8NtabUfH31XE9tWlUV48m2xUNsScLMdCoeCEBH1oTlz5iSd\n12LWW88ec8wx+Prrr2OP33zzzdjy5MmTAQCXXNL5MIfu6jDErlq1Cq+99hr8fj+mT5+OV155BTff\nfDPOP//8PjkwUXeIggBRFqAA6CpwezwFKMtK32VJ4gVCGu578SscqAmgOM+KQfm2lB8zqIWws3o3\nFE2ERVK73F4QBLgcClyObBw5PDu2XjcM1DSEWgNtc8AtqfJhT7k3YR92q4TBbjsGNQfcY/wqLIhw\nMhkRUS9df/31seVwOIxPP/0UXq+3k1dQiw5/8zzyyCMYNWoU7r77bpx44okAwB4Yog509fF+X2sM\nebF0+yPQDA2SIGF41iA4VQccsh0OxQaHbINdaV12KPbmdTaIQuIYWFEQ4M62wJ1twTEjc2PrNd1A\nRW0gsce2yo8fShqxc38jAOClt/cAAHKcSvM4W04mIyLqiZNOOinh8c9//nNcccUVGarGXDoMse++\n+y5ee+01LF26FLquY8aMGf3iqgRE/VU6x2hX+mugGdGbfGiGhh8b9nXxilY22docbG2wt4TeuJAb\nC8KKDXaHHWNznDju8NzYH7GhsI4DNdHe2nq/iO/3VKKkyo+v/9WAr/+VOJnMnWNpDrWcTEZElExD\nQwP27t2LMWPGIBQKYdeuXXjmmWcyXZYpdBhiCwoKcO211+Laa6/Fhx9+iFdeeQUlJSW47rrrcNll\nl8WuaUZE6TfYWYQimxvl/ioU2dy45WdXQdc1eCN+eMO+5q9++OKWvREffOHW5dqmhlgQ7oosSHAo\n9uaQ2xx6FRsKCwswpljHCbINMrLg94qoqxdQU6ujvEpHaVUAn/9Qh89/aJ1MJokCivKszeNtOZmM\niA5tixYtwrnnnosxY8bgqquuQm1tLcaOHYv7778/06X1e90ayDZhwgRMmDABd955J9avX4/HH3+c\nIZYogyySigXjr0HYpkPxt46JdaqObu/DMAyE9HA09MaH3JYg3LwcH4TrAvUo0ypad3Kgg50rgOAB\nbEOtyJFskA0LoCmIBBUE/CKqG0WU18j4pEKBEVFhRBQohgWeHBcG52clBFxOJiOigay+vh4XXXQR\nvvnmGzgcDrz44ou49NJLM12WKfRoNobT6cSll17Kby5RP2CRVIzI7/1VIQRBgEVSYZFU5Flzuv06\nzdCbw64PNpcdZtVrOwAAIABJREFUew/shzfiS9rb6w374Yv4UR+O3tYQMoAsQMwCkk1FqwBQrov4\npE6BURUNt5KuwqHYkW11IM/uRJErC4NzspHncMIh2+EMZkE39HZjfYmIzEBVo2fDd955B2eccQYA\nzkHqrpROKf7+++9xww034Morr8Ts2bNRVlaGRYsWQdM0FBQU4MEHH4z98IjIHCRBRJbqQJbqgKfA\ng+xI172/hmEgqIVae3abw23bXl9vyIf6oBeNIR8CWgARRCeQeZv/lQaBLysBVMbt/KPoF6tohUO1\nwRmb0NZ++IM9bpKbQ7FDlZS+/vYQEfXI8OHDcfPNN+Orr77C2rVr8be//Q1HHXVUpssyhZSFWJ/P\nh+XLlyfMunv00Ucxa9YsTJ06FQ8//DDWrl2LWbNmpaoEIuonBEGAVbbAKluQ35NeX12DLxJAnd+L\nfTW1KKlrQHlDA6q9TagPeBHUAxDkMCCH4JPD8Ms+VCt1gNC9m2Qootwm2DZf1aF5ORaE4ya82WQb\nx+4SUZ9ZtmwZPvnkE4wYMQI5OTmYOnUqfvGLX2S6LFNIWYhVVRWrVq3CqlWrYuu2b9+Oe+65BwAw\nadIkPPvsswyxRNQhSZRivb5DswvbPe8LRBCEHTu+2x+7DNj+Kh98wSAEOQTIYQhyGJIaRpbLQJbT\ngNWuQbZEACmMMALwRfyoCdShVCvvVk0CAFss5EZDr3tvHsQwopPf2oTilnXs9SWiZERRxPHHHx97\n7HSm9kY5A0nKQqwsy5DlxN37/f7Y8IH8/HxUVlYme2mM2+2GonR94vd4PL0vtI+whswff6DVYJb2\nn+njA8BRh+XHlg3DQF1jEHsONGDPgUbsKWvA3gON2LO/ATWhxKsxWFQJw4qycESxC0OL7Sh0K8jJ\nFSEqYTSFvGgKedEY9KIx5EVTsCn6NW5dTVNZdKxvTduK2lMlBVmqE05LNJS3fM2yOOBUnXHLDmRZ\noo/tavvr+nYm0z+Lvjx+d9p/pt8va+gfx2cNh66M3WYn2S3W2qqqqupym1Td7rQnWEPmj2+WGnpy\nkjND+8/08TuroSgLKMqyY+JoO4Di6J3J6kMoqU687e6PJfXYua8u4bUOqxR3+a9CDHPbMajABoc1\n8ZTZMtbXkevE7tI9scubRSe3xV3qrGXiW9iPA40V2KOFuvXeBAiwy9Z2lzezy4lDHByKDcM9w+Cr\n88Kh2KGI6T+1d6ct9GX7789t71CqIdPHN0MNqqoiPz8/6XN0cNJ6prPb7QgEArBarSgvL0dhYfuP\nB4mIUkEUBLhzLHDnWDAu7s5kEU1HRW2w9a5k1dFb7v6wv/XOZC1ynErrjRuar2/rybeh0OmGltX9\nm8FEdC06sS3ppcziJr7FBeEqfw10dPLH/47WRVVU2t+4Qk68g5u9zd3crLKVY32JMujBBx/EwoUL\n2y1Tx9IaYk8++WRs3LgR06dPx6ZNm3Daaael8/BERO3IkohBzYH0+Lj1wbCGAzUBlFTG9dxW+/HV\nv+rx1b/qY9sJAIrd36IoR40F28FuGwpzO74zmSxKcKlOuNTuj30zDAMBLdjhpcygCqisr0oIxVWB\nGpR4e9DrGxvn23mvb/yd3hRRRlALYWf1biha6zWLiahnduzYkXSZOpayEPvll1/igQceQElJCWRZ\nxsaNG/HHP/4Rt99+O9asWYNBgwbhoosuStXhiYgOikWRMLzIgeFFiZcQ8wUi0d7aymiPbWmVH2U1\nAZRVefHZD7Wx7SRRQHHcnclawm1+L+9MJggCbLIVNtkKN3LbPd/Rx5kRPZL0xhXesK+5tzfumr7N\ny132+sZRBBkadOiGjkJbPn43/tcMskS9EH9tWF4ntntSFmLHjBmDF154od365557LlWHJCJKObtV\nxqjBWRg1OCu2rri4GN/t2ouSqmi4bRmaUFYd7cGNn/llUUR48m3tbrubqjuTyaKMbEsWsi1ZXW/c\nTG/u9fXF3c0tIQjH3dmtNliPSn/0/VX4q1HmrcAI15A+fx9EA138XKHuzBuiDE7sIiIaKARBQLZD\nRbZDxVHDs2Pr200mq4yG230VPvzrgDdhH/GTyVrG3Q5yt59Mlg6iEJ1QZpetcNs63zaohfDQJ0+j\n3F+FIpsbHgfnOhD1Bntie44hlogoRbo7mazlSgmdTSZrHW9rR3G+FRZFSvfbScoiqVgw/hqEbToU\nP8fEEvVW/DwhzhnqHoZYIqI063QyWXUgNtY2GnB9SSeTFeRY4npubRgnOCBoeoeTyVLJIqkYkZ/5\nyxwRmdmvf/3rpMvUMYZYIqJ+wqJIGF7swPDixMlk3kAEpVX+5mDri3397Ifa1slk63e1m0w2uMCG\nwfl25GWrvHwWEQ04DLFERP2cwypj9JAsjB7SOjnLMAzUe8OxHts6H7Bzb3XscVeTyQYX2OCyp2Yy\nGRFROjDEEhGZkCAIyHGqyHGqOGpEduwSW7phoLo+2DzOtvVKCUknk9lkDM5vHWvbchkwewYmkxEd\nqt566y1Mnjy53W2Wd+7cidGjR2eoKnPgmYqIaAARBQEFOVYU5Fhx7Ki2k8kCsXDbMpls5/5GfN9m\nMllulopB+baEyWSefBtUJf3jbYkGurvvvhtPPPEEHn74YYwaNQoA4PP5MH/+fMyePRuzZ8/OcIX9\nF0MsEdEhIDqZzI5BbnvC+mBYQ1l1fLDtZDJZriXaYxs3NKEw14KIZuC7PTVQocGq9o+rJhCZxRFH\nHIHbb78dN954I5544gmMHDkSdrsd69evx6WXXsoQ2wmGWCKiQ5hFkTCi2IkRxYm3wG2ZTNb2Sgmf\n7qzFpzvj70wWHdoQ0QwU51mxZPbRDLJEPWAYBo466ig88MADuOGGG/D0009j6NChkGUZssyY1hl+\nd4iIqJ2uJ5NFx9ruLm1CWU0AAHCgJoDSaj9+4nF2tFsiaqNlcuWxxx6Lu+++G3PmzMHPf/5z1NbW\nYujQoRmurn9jiCUiom5pO5kMAAIhDfe9+BUO1ARQnGfFoPwubvFFRAlOOeWUhOWXX34Zb775Jn72\ns5/hggsuyGBl/R9DLBER9ZpVlbBk9tEIwQ4VPg4lIOqhefPmJTwuKirClVdemZliTIZTTYmI6KBY\nVQk/HZ7HAEtEacUQS0RERESmwxBLRERElCKhUCjTJQxYDLFEREREZDoMsURERERkOgyxRERERGQ6\nDLFEREREZDoMsURERERkOgyxRERERGQ6DLFEREREZDoMsURERERkOgyxRERERGQ6DLFEREREZDoM\nsURERERkOgyxRERERGQ6DLFEREREZDoMsURERERkOgyxRERERGQ6cjoPtn37dtx8880YPXo0AODw\nww/HXXfdlc4SiIiIiGgASGuIBYCJEyfi0UcfTfdhiYiIiGgA4XACIiIiIjIdwTAMI10H2759O+65\n5x4MGzYM9fX1uOmmm3DKKad0uH04HIaiKOkqj6hfYfunQxnbPxF1Ja0htry8HB9//DGmTp2Kffv2\nYc6cOdi0aRNUVU26fVlZWZf79Hg83doulVhD5o9vlho8Hk+392WG9p/p47OG/lNDd47fl+0/0++X\nNfSP45ulhp60feq+tA4nKCoqwnnnnQdBEDBs2DC43W6Ul5enswQiIjI5byCCdz/ZhwM1fkQ0PdPl\nEFGGpHVi1+uvv47KykpcffXVqKysRHV1NYqKitJZAhERmVggpOHuZ3ag0R+JrXPaZGQ7FGQ7FWQ7\n1NZlu4JsZ/NjhwKLKmWwciLqa2kNsWeeeSZ+97vf4e2330Y4HMayZcs6HEpARETUVmm1PyHADi2w\nIxTRUd0QQkmVv9PXWlURLkeboOtoDbkt6xxWGYIgpPqtENFBSmuIdTqdePLJJ9N5SCIiGkAG5dtQ\nnGfFgZoAivOsWHjZkbA297AGQxrqfWHUN4VR7w2jwRtCvbf1cb03hPqmMCpqGzs9hiwJcNmjgdbl\nUJCTEHqVWBAuLORQBqJMSvt1YomIiHrLqkpYMvtohGCHCl8swAKARZVQqEoozLF2uo+IpqPRF4mG\nWm986A2jrjno1nvD2Fvug6Z3PPdZED5rHsqgJvTkZrf09saFXovCoQxEfY0hloiITMWqSjjMk4ey\nsmCvXi9LInKzVORmdT6cTTcM+AKRuJ7cMOqbQrFlf1hAZW0TKusC2F/p67LmllCbE9eb2zb02i0S\nhzIQdRNDLBERURKiIMBpU+C0KRhc0P75+MsqBUIaGpIE3fqmUMIQh/KaQKfHlCUhrle3tYe3beh1\n2RWIIsMuHdoYYomIyFSCWghfln8HJSTCqdghCpm/+aRVlWBVJRTmdj2UIRZ2k4Teluf+Ve6DXubt\ncD+CAGTZFLhzvoPdgoTQ62ru7W0JvYqc+e8PUSowxBIRkWkEtRCWf/AYGsOtAU+EAFmUIYsyFFGG\nLErRx4IMpWU5/jkh2bZS8+Oun5NFCYogw+K3wRv2x7btTpiWJRF5LgvyXJZOt9MNA15/pH3PbmwM\nb3RdSVUTgiGt033ZLVJrb66z7fjd1sc2DmUgk2GIJSIi0yjzViQE2KEOD2RJQljXENEjzf80BDRf\n7LFmpOcqAqIgJg+8zUG4NRzLScJx3PNtt3XKyHJJyBNatrNCFp1QRBmDijwoK6uEz6ejyW+gyaej\nyaclBN2W5bIuhjIoshALta4kQxpymq/WkGXjUAbqHxhiiYjINDyOQhTZ3Cj3V6HI5sb8Y+fCInU9\nQSuiRxAxokE3HAu70cAb1iOIGG0e6xoiRkfbRvcjWxQ0ehuj2+oRhI1IbLnlOAEtmJEwrcgy5BwJ\ncl40CBeKMiRBgmCIgCHC0EXomggtIkDTBIRDAsJhIBAC6oOA0SQCjdHtYIiALsWWBUOEXVXhtKrI\nd2XBKglw2SzIsVuQ47Qi12FDvtOGbKfKoQyUUgyxRERkGhZJxYLx1yBs06H4xS4DLBCdoKVKClQo\nfVpL/MSu7oiF6ZYgbSQG3lgANiKJPcuGlhCm47dVrCoavI2JQbtdII8gEAnE9qO3DdMSAFvzP3Qv\nGEQA1DX/AwAYALzN/1pW6QIEQ4QACSIkSILUPMRDhiLJsMgyLLICq6LAIiutPc+xHuo2Q0ESeqhb\nh4x41SDqmmqTbiuLMkQOkRiwGGKJiMhULJKKEfk9C5D9QSxMS30XpnsapAFAN/TW3uPOeqiT9Cy3\n3VZUFFTV1cEfCiMQif4LRSIIaZFYINcMDWFoiIhhBIUgIOqApkOIdHwN3r4kCWJrwBXahuPWMdKx\noR9Jw3Dcc0L8YwkwgFqpCXZN7dYfVdR3GGKJiIgOIaIgQpXEPgnT3Q3R4Ygeu2taQ/M43bqmIOp8\nQdR6/WjwB9HoD6EpGIQh6ICgQxB1QNQAwYAgatF1kgGbBbBaAYsqQLUADrsCAyFIsgFJMiBI0ddq\nRrLe7OhXXyQQe05HH4Tpr4EimxsLxl/DIJtGDLFERESUUooswp1tgTu7i6sy6AYa/eHEG0y0vUJD\nfQi1pWFEtM7Dp8Mmxyal5cdfZ9elJFyLV1GEWO9zrNc5rhc6HNc73TpOOvpcha8a75V9CAAo91eh\nzFuBEa4hffZ9o84xxBIREVG/IIotV0jovDfTMAz4gxrqvWFIlizs3nsgaeitbQyhtMrf6b4sitju\nOrutIdcWW3bY2o+vDWohfF+3OzbR0OMoPOjvAXUfQywRERGZiiAIsFtl2K0yPJ4CFDojHW4bCrfc\nYCKEurhLjjW0ufZu5f7GTgcWSKLQJuBG75x2nOVC5I+SMDo3l0MJ0owhloiIiAYsVRHhzrHAndP5\nUAZNN9Doa39DiZahDQ3Nj/dX+vCvA+3jbnFeFZbMPhpWVUrVW6E2GGKJiIjokCeJAnKcKnKcKlDU\n8XaGYcAX0FDvDeHbvQ14+Z29AIADNQGUVvvxE48zTRUTr0JMRERE1E2CIMBhkzHIbcfJYwpQnGcF\nABTnWTEo35bh6g4t7IklIiIi6gWrKmHJ7KMRgh0qfBxKkGbsiSUiIiLqJasq4afD8xhgM4AhloiI\niIhMhyGWiIiIiEyHIZaIiIiITIchloiIiIhMhyGWiIiIiEyHIZaIiIiITIchloiIiIhMhyGWiIiI\niExHMAzDyHQRREREREQ9wZ5YIiIiIjIdhlgiIiIiMh2GWCIiIiIyHYZYIiIiIjIdhlgiIiIiMh2G\nWCIiIiIyHYZYIiIiIjIdhlgiIiIiMh2GWCIiIiIyHTnTBXSmrKysy23cbjeqqqrSUA1r6M/HN0sN\nHo+n2/syQ/vP9PFZQ/+poTvH78v2n+n3yxr6x/HNUkNP2j51n+l7YhVFyXQJrKEfHP9QrSHT7znT\nx2cN/aeGQ63ts4b+cXzWcGjr1z2xXdF1Hfv370dpaWlG6wiFQigvLzdFDQUFBbBarWmoiFLN5/Nh\nz549GW17Zmr7NpsNbrc7DRVROhw4cAD79u3LaA39sf1bLBYUFhZmsCKi9DF1iN227VGMGfM/cLlC\nGa1D0xS4XOF+X4NhCNi+PR+nn/6XNFVFqRIIBPDpp7/C+PGhjLZ/s7R9AKirE1BScj3GjTs/DVVR\nKn388X9h+PC/weUKZrSO/tj+GxsFfPLJHIwf/8sMVkWUHqYOsVZrCX76UyuCQSGjdVgsFgSDmR2Z\n0d0aysvroOs6RNH0I0kOaQ0NDRg1qgk/+Ykro+3fTG0fAN5444cUV0PpoGk/YuxYC4KZzbD9tv2/\n9daPGaqGKL1MHWJFEZg/vww7d2poaABCIQFutwGnE1i9uqDb+/n++yAqKiI49VRHu+eamnScfnot\nLr9cxK235vZJ3ZGIgdtvr8b//i+wdWvrx5uLF1fjs890SBKwYIEdkyYl1rN9uw/Ll3shioDTCTz1\nVD6czujJS9N0nHdeJSZOlLBsWR6qqyO47rpa+P2AzQY8+WQu8vNlAEafvAfKPEEQBkz7//rrIObM\nacLgwToAICcH+MtfWt9DMKjjl7+sjj2uqwOmTJFwxx15mD+/DN98o0HXgblzLbj0Uhd27gxi0aIG\n6Dogy8DKlTkYMkQB2//AsmBBFX74wTBV++/sPN5iw4ZG3HNPELffbsGMGVkAgAsuqISmAaoa3WbJ\nEgdOOMGOBQvK8OmnWoe/N4gGMtN3xz32mAfr1hXg0ksVnHgisG5dQY9OYACwebMPH3+c/CPZV15p\nhMul4513In1RLgBg+fIaDBkiJax77z0vvvxSx/r1bjz9dA4eesiX5HVeLFniwOuvF6CoSMBzz9XH\nnvvTn6qg663bPvRQPSZMkLB+fQFOOUXGH/9Y325/ZH4Dpf0DQGGhjnXrCrBuXUFCgAUAi0WMPbdu\nXQHcbmD2bCdef70R+/freOONAjz7bA4efzz6Pu67rwG/+IUF69YV4MQTJfz5zw19Vj/1Hw895DZh\n++/4PA4AO3YE8OKLARx2WPs/uO6/Pyv2f+CEE+x47z0vvvhC6/T3BtFAZuqe2K4sXFiN777ToevA\nL3+pYs6cbLz2WgOeeioIRQFyc4Fly7LxX/+lQZYNDBnSgEsucSXs4803Q7j6ahVPPBHGDz+EMGqU\nimBQx7XXVqO6GlAU4IknCpCXl7hu5cpsaBrwxz824PHHEyeT/Pa3ObDbBaxZUxNbt3NnBKNGCZAk\nAcXFCiwWYPfuEA47TI1t8//+Xx7y8qK//HNzRdTVRU9yJSVhvPNOCOedJ6OmJrru8891/OlP0fdy\n4YUO3HQTQ+yhJl3tf+XKbBQVyQfV/nvitdcaMWKEgOHDVQwerOCcc3IBRJCfLyEYBAzDwKOP5sFm\ni/6Nnp8v4ccfMztuntKvv7b/js7jLQ47TMELL7hxzTVdXzJq584IRo+W2v3eIDpUmL4ntiNvvtmI\n2lodr79egJdfzsfzz4fg9+t46aUgFi2y4/XXC3D22SpUVcBppwmYMUNudwKrrIxg924BF1+chYkT\ngb/+tQkA8OST9SgsFLB+fQEuuEDBq682tFu3bp0Xhx2mtjuBAUBOTvteqLFjVXz1lYGmJh1794ZQ\nWiqgrCzxr/+WE199vYZ339Vw0UV2AMDSpXVYuNAFKW63jY0CPJ7o3yiDBslobMzsuGFKr3S1/+nT\nrVi3znvQ7R8A6usFXH55Jc4/vxLPP9/xH12rVwdw3XXRj1hlWYDLFd3fE0/UY9w4A4IgICtLgiwL\nCIV0vPpqCNOm8Yoch5L+3P47Oo+3yMqKhtJkVqxoxLRplbjxxir4fDrGjlXx5Zdap783iAayAdsT\n+8EHYezcCUyfXgkAMAxg//4wJk9W8Pvf+/D++0FceqkDgwd3fG23NWsaMXasAbtdxMUX2/D73/uw\nZAnw7bcaTj892kN69dU5sFgsuOqqkoR1PTVhgg1nn+3HzJnVKC4GBg0CdL39x0n19RrmzKnBZZcp\nGDvWig0bGmGxCJg0yYVPP03+UZLBYYCHnHS1/3nz3AgGg7j++qqDav/FxTLmzJHxq1+5UF2t4Ze/\nrMdZZ4Wbx7G2+uGHEHQdGD3akrD+pZcasGlTBC++mBdbFwrpuOaaakyYIGHq1Kwe10Tm1d/bf9vz\neHfMmmXBccdZcPjhKq6/vhp//nMdFi3KwznnBLv8vUE0UA3YEKsowDnnSFiyJC9h/ejRFlxwQRiv\nvtqE665rwMqVHf9y27w5gtpa4OyzoyfCqioRX3wRgCi2D4bJ1vXUwoV5WLgwunzuuZUYOVJNeD4Y\n1DF3bg3OP1/Br38dPVFu2hTEzp0GJk/ej6YmQNMEDBtWD5fLwP79ERx5pIQ9e8JwuXhiO5SYrf3n\n5UmYNy/apj0eEcOGGfjmm2C7ELt+vRc/+1liT+5f/1qLF18M4i9/yY31cgHAvHnVOOwwEUuXJn4P\naODrz+0/2Xm8O2bNau0pPvNMFVu2RIcNLFlShAULopdpaPm98fnn3d4tkakN2OEExx+vYts2DZpm\noLFRx803R8cXLV1aDbtdxG9+k4uTTxbx9dchiCKgaYmv3707hKoqAe+848bmzQXYvLkA550H/PWv\nXowZI2Hr1ugJ5OWXG7BsWXm7dffe27Pxfvv2hTBzZvRk+eGHfkgS4PEk/gL//e9rcdxxEq69tvXE\n98gjbmzaVIC33x6CmTNlnH22iKuuysZxx4lYu9YLAHj1VS+OO27A/qgpiXS1/9Wra3DvvTUH3f7X\nr2/E734XrbG+XsP+/cARR6jttvv6aw1jxrT+7V1ZGcFjj3nx7LM5KChoXf/UU3Ww2YB77snvUR00\nMPTn9p/sPN4VTTMwY0YlamqihW7dGsKoUSL27Qthxoz9ADr+vUE0kA3YntgpU5zYsiWICy6Inrwu\nvjj6H7u4WMRll9XAbgeysoDbbnNCUbxYsSKIvLx6XHllNgBgzZomnHKKAFluHZs0e7YTN97YgDfe\nyMUNN1Rj2rRKSFJ0YldOjp6wbuXKbOzeHUo6sP/GG6uwb58Bv1/E9OmVOPVUCQsX5mHYMBHnnlsJ\nQQCWL3cCAF55pQGlpRrmz8/F5s0G3G499hHZ+PFSh71Mv/1tDq67rgYXXlgJhwN48kn2Rh1K0tX+\nZRl45JFsFBTIB9X+f/ObHLz6agDnn18JXQcuv1zB0KFqQvsHgJoaA0VFraet1asb4fMJmDevLrbu\nkUey8dprYeh668fJw4cLePRR3q3rUNGf23+y8/gdd+Ri7twqrF5dgL/+tQGrVwdRXi5g374AVq8O\nYO3aAkybpuKyy2qgqoDbDSxfngOnU8Tw4VK73xtEhwrBMPrviMmysrJOn//ss4WYOnU/ghm+4nX0\nYtPmqGHr1ghGj/57n9/swOPxdPnzSjUz1ODxeLq9r872U1FRAeAqjBuXldG2Z6a2DwBvvPFznHTS\nLX1egxnaXn84fl+1/w8+uBfTp39pqraXzhreemsiJk68PS3Hz3S7M0sNPWn71H2m7on1+QqxZ8/3\nADJ72z+LxegHJ7Lu1VBZmYvRo9NQEKWUy+XC559bUVDgRzicuUvqmKnt19cLsFgOS0NFlHpD8MMP\nH0JRMns5qf7Y/r1eQBSHZrAiovQxdYg98cQFaGi4CgcO7MtoHQUFBaisrDRBDSKOOWYEbzk7AFit\nVhx++CrU1ARQWVmesTrM0/YBmy0b48czxA4EEyfORVPTdPz4478yWkd/bP8WixPHHz8qgxURpY+p\nQ6woijjyyDHIycns5A0zfJRBA09ubm7Gf+6ZPn5/qYHSb/ToI+F09vxybn2pP7S9/lADUaawS46I\niIiITIchloiIiIhMhyGWiIiIiEwnpWNiV6xYgY8//hiRSATz5s3D2LFjsWjRImiahoKCAjz44INQ\n1fYXNCciIiIi6kzKQuy2bduwc+dOrFmzBrW1tZgxYwZOOukkzJo1C1OnTsXDDz+MtWvXYtasWakq\ngYiIiIgGqJQNJ5gwYQJWrlwJIHpNS7/fj+3bt2Py5MkAgEmTJmHr1q2pOjwRERERDWBpuWPXmjVr\n8NFHH+H999+PBde9e/di0aJFePnllzt8XTgchqJ0fB/oKl8Ntu39BAXOPNhkG0RBgChIkEQRoiBC\nEqJfRUGEJEqJ68TWZUmQILZ5jSAIHR6XKB26av9EAxnbPxF1JeXXid2yZQvWrl2LZ599FlOmTImt\n7052rqqq6vC5oBbC3dv+hICWmrulCBCaA7AQC8ItIVeAkBCQVUWFpmmt65D4mnb7QeI6SRAhxO27\ndV3icTrbd15OHhoaGmL7jn99wn6QpJ5O9p+4n+jzyQJ+f7hWoRlq6MmtBztr/909Xqpl+visof/U\n0Ne3ne2q/Wf6/bKG/nF8s9TA286mRkpD7HvvvYcnn3wSTz/9NLKysmC32xEIBGC1WlFeXo7CwsJe\n73tP3YGEADvKejRyLE6IIiCIgCAYEEQj+lUADMGAbuht/jWvQ5J1zf80w4ABHVrzY8MwoBk6DENH\nWI9AN3T49SAiWqTdvg2kvJM7I6JBNr6nW4AsyYCBLkJyc0BHNBh3GJaRuO92+0D7fYqCiJz6HDQ1\nNcWCeMLV+UCwAAAgAElEQVQfB+2CfJL9dvOPjHZ/WDRvG9LC2Fm9G4omwiJxwiIREVEqpSzENjY2\nYsWKFXj++eeRkxO9q8rJJ5+MjRs3Yvr06di0aRNOO+20Xu9fDzih+x0QbV7ofge++MgD6B2/HVEU\nYFFEqLIY/dq8rCoSVCW6ziqLzctS83Ot21mat1MVEZbYc9Hthg8dhOrqCohteih1w4Bh6NDRNhgn\nhmENyQN0u3VJt4suZ2VnobauNmHfCftBV/s24mpLfF4z9IQg3/q61teLsohQOBRbF9a1Do+vGXqv\nf+5mUGRzY8H4axhkiYiIUihlIXbDhg2ora3FLbfcElt3//33484778SaNWswaNAgXHTRRb3e/4iC\nbLj2n4GqUDWcQg6mnjwYug6EIjpCYR3BsIZQREcwHH0cXa81P6ej0R9BKKxD0/uit/RTAIAiC7Fg\na+kgAKttgnJioJZjgVqVRVjbBGpZSv5RPpD5j1N6evyWgB8NyElCd7d7yptDO3Tk5GSjqqamucc8\nSejuZD/tg3zcHxnd+EPAF/Zjnzf6/sv9VSjzVmCEa0iqvt1ERESHvJSF2JkzZ2LmzJnt1j/33HN9\nsn+rKuGuK45FCHao8MGqSr3aT0TTY8G3JewG48Ju63PJ1wUjOkRJRUOjLy40a/AGIqhtjG7XV4MK\nBAEdhmKXcw8MLdwmFEd7iy0J4bm19zlhnRxdJ4npmdAmCgIgSJDQu59bMh6PB2ViZoJ8UAvhoU+e\nRrm/CkU2NzyO3g+VISIioq6lfGJXKllVCYd58lBW1vvJXbIkQpZE2C29r6OzXkjDMBCOGAhFOgjG\ncb3FwTY9xok9yVqbXmUd9U0hBMM6IpoBoLH3byCOLAlthlJIrcE4NpRCiutljq4vyA/A72tK6Enu\naGjGQLzyg0VSsWD8NQjbdCh+joklIiJKNVOHWDMQBAGqIkBVRMCWmmPouoE8dyH27itNGErRVTCO\n365tL3QooiMQ0tHgjSAU1tD1qIt93a43sQc4cXxxYmCO63HuxtjkrJwwIpoOSex42EUqWSQVI/Iz\nP0uWiIjoUMAQOwCIogCbRYbLkbprKka0tqG4dThFKKzD7nChoqqmNQR3Z3hGpHlsckN0/cH7DAAg\nCkiYsNdZr7AlyXbdGdcsJhl2EQhp+G5PDVRovR7eQkRERN3DEEvdEht2YU3+fHRIRe+Dm2EY7XqF\ne9KrHAprECQVDU2+duHZG4hO4osOu+gbsiQkjC+WRQGVdUGEIjqK86xYMvtoBlkiIqIUYoilfkEQ\nBFgUCRal98GvqyskaLoRG1/cnYl7nQ23iN/OH9AQCEUQikRD8oGaAEqr/fiJx9nr90JERESdY4il\nQ4YkCrBZJNgsfd9DGghpuO/Fr3CgJoDiPCsG5adoADQREREBYIgl6hNWVcKS2Ucf9CXfiIiIqHvE\nTBdANFBYVQk/HZ7HAEtERJQGDLFEREREZDoMsURERERkOgyxRCYV1ELYWb0bQS2U6VKIiIjSjhO7\niEwoqIWw/IPH0Bj2IktxYPpPpiDX4oJTscOh2OFQbBAF/o1KREQDF0MskQntaShBY9gLAGgMe/Hi\nd68lPC8AsMm2WKjt8KvcumyR1IzcrpeIiKg3GGKJTGi4azDc1lxUBWrhUp04ffAJCEaCaIr44Q37\n0BT2whv2oynsQ6W/Bga6vluZLEhJg27LsjN+XXP4JSIiyhSGWCITskgqFh03D2GbDsUvwiKpHW6r\nGwb8kUBzsPU1h1svmsI+eCM+NIWiX6Ph14eqQC1KvOXdqsOu2GCXrEkDb2IgdsCh2GGTLOztJSKi\nPsEQS2RSFknFiPzOb7ULAKIgwKHY4FC6fxexiB6JhtzmYNvytWW5JfwGEUadvx77msqgGXqX+xUF\nMRps5ei43ZZw64xbblnfEoAVkacpIiJqj78diKgdWZSRY3Ehx+LqdDuPJxqiDcNAQAu2C7vtvjb3\n+NaFGlDmq+hWLRZJjQ5fUFuHMcT38g7VBiPUFIz1ANtkG0T29hIRDXgMsUR00ARBgE22wiZb4bbl\ndes1mq7BG/G3C7odBeHSpnJEDK39jna2qQUtPc/RiWsOtflrm2EO8UMdVEnpg+8CERGlE0MsEWWE\nJEpwqU64VGe3tjcMAyE9HBduo5PXRJuE0uoDsbG+sXG/IS8qfFXdmNIGKKIcC7ROxdbc8+uAQ7Yl\nrm9etis2SLyEGRFRRjHEEpEpCIIAi6TCIqnIt+bE1rcMaUhGN3T44ia1NYX98LZMakvS21vhq8J+\nPdx1LYi/hJkN+bvyIWti63jedmN+eQkzIqK+xhBLRANWy0SynlwOLKSFEyavNUU6H+5Q5a/B7ob9\nXe5XEqTEy5QlG97QZtiDLEoH8/aJiAY0hlgiojiqpECVspGL7G5trxsGst3Z2LVvd2zyWtvLlsX3\n+FYH6rp9CTOrZEl6o4pkQTgr5IJhGOztJaJDBkMsEdFBEAXh/7d359FRlff/wN939skkZJ9kwpag\nwlcS5CsFKkVBEKxp3VqtLAbqSl0rWpoClcXDIUhBrQu/Y+Un1gZaN6DHY1E4R0uxLUYrQiSULRCW\nMNlDtplktuf7xySTmWwTMDM3d+b9+sOTuTN3ns/Iw/j2yec+F7E6E8wxyQCS+3WOy+Pu3s7gsqHZ\n0eK72K2j57fFacP5/mxh9pV35bmzj7cj4Ab283rbHDr37uUWZkSkVPz2IiIKM41KjXh9HOL1cf16\nvRACbW6HX7j1u2mFy/uzS+VGbXMdWpx2NDqaUGGr7td761TagN7djj7fzn17A1d/Y3rYwqzN7cCJ\n2tPQuvu+8QZRNNLp+HciVBhiiYgGOUmSYNDoYdDokWJM7PE1XS9wcwsPbH47NvS0bVmLq/N4ha0K\nTo8reC2QEKM1IrZ9z16DxoBTDWdgd7chzZiCX014iEGWiMKCIZaIKAKpJRXidCbE6Uz9PqfN7ehz\nr17/m1Y0O22osgduYVZpr4G1pQqZQ4YN/AciIuqCIZaIiADAt4VZkt8WZn3xCA8utjXiteI/obb1\nItKMKbCYzCGukojIiyGWiIgui0pSIcmQgN987xE4jR5o7eyJJaLw4S1niIjoO9GrdbgqOYsBlojC\niiuxRERERDL58ssvezw+efLkMFeiPCENscePH8djjz2G++67D3l5eVi6dClKSkqQkODtt3rwwQdx\n4403hrIEIiIiokHr5Zdf9v3sdrtRWlqK8ePHM8T2Q8hCrM1mw5o1azBlypSA48888wxmzJgRqmGJ\niIiIFGPbtm0Bj+vr6/HCCy/IVI2yhKwnVqfTYfPmzTCbeaUqERERUX8kJibi5MmTcpehCJIQQgR/\n2eV79dVXkZiY6GsnqK6uhtPpRHJyMlasWIGkpKRez3U6ndBqtaEsj2jQ4vynaMb5T9Fi2bJlAY/L\ny8shhEBhYaFMFSlHWC/suuOOO5CQkICrr74ab7zxBl577TWsXLmy19fX1NQEfc+ud6mRA2uQf3yl\n1GCxWPr9XkqY/3KPzxoGTw39GX8g57/cn5c1DI7xlVCDTqdDcnJyt+NtbW3Q6/X43ve+5zsmSRJu\nuukm3HDDDSGrNZKENcT698fOnDkTq1evDufwRERERIPCQw89hMLCQtx9991yl6JYYd0n9sknn8S5\nc+cAAEVFRbjqqqvCOTwRERHRoCBJktwlKF7IVmIPHz6M9evXo7y8HBqNBrt370ZeXh4WL14Mo9GI\nmJgYrFu3LlTDExEREVEEC1mIzcnJ6bEp+Yc//GGohiQiIiKiKMHbzhIRERGFWYg3h4oKDLFERERE\nYcae2O+OIZaIiIgozBYtWiR3CYoX1i22iIiIiAi4/vrrAQBXX301hBA9rswKIXD06NFwl6YYDLFE\nREREMikpKZG7BMViiCVSqFaHG8fO1EEHNww6tdzlEBHRZdi/fz+2bt2KlJQULF68GFVVVTCbzT3e\n5YsCsSeWSIGabE4s+X/fYMkrn2PZGwdRdKQG9U0OucsiIqJLtGbNGtx+++0wm80oKChARUUF8vPz\n5S5LEbgSS6RAFXV2OFweAEBLqxtv7joFAEiI1SLLEousdBOyLLEYmW7iKi0R0SA2dOhQ5ObmAgDm\nz5+PGTNm4O2335a5KmVgiCVSoOFmE9KTDKioa0VSnA5Tx6XgbJUNp60t+OZEPb45UQ8AkCQgI9no\nDbYWb7DNSDZCpeLWLkREg8Fdd92Fd999F7fddhvUajWqq6vR1NQkd1mKwBBLpEAGnRrL87LhQAx0\nsPlWW4UQqGty4LS1BWXWZpyuaEFZRQvKa+z457fVAAC9VoWR6SaMssQis33FNjFOJ+fHISKKWtu3\nb8cXX3yB1atXAwBuu+02LFu2TN6iFIIhlkihDDo1sixJsFrbfMckSULyED2Sh+gxcUwSAMDtEbhQ\nY8MpawtOW5tx2tqCE+eacPxc5//p+9oQ2ldsR6axDYGIKBxMJhMOHjwIrVbrO8a7efVPv0Ks//5l\nLpcLGg2zL5FSqFUShptNGG42Yfp4MwDA3uZGWYU30HYE265tCENTjJ3BNt0EC9sQiIgGzJo1a7Bi\nxQq88sorvmOVlZXYvn07PvjgA3z22WcyVqcMQdPoJ598gr/+9a94/fXXAXibjh944AHccsstIS+O\niELDqFfj6pHxuHpkPIDANgRvqG3GmUobzlfb8Xlx9zaEjv5ai5wfgohIwYqLi1FcXIxx48bh888/\nx3vvvYeTJ0/i1ltvRWFhodzlKULQEPvHP/4Rmzdv9j3esmULHnzwQYZYogjSUxuCy+3BhRp7QLDt\n2oaQEn8UI8zGgDYEPdsQiIiC2rBhA37729/izJkzGDFiBJ555hlMnDhR7rIUJWiIFUIgLi7O9zg2\nNrbHW6MRUWTRqFUYkWbCiDQTpv+vtw3B1uZCWUULytqD7ZkqOw6cqMeB9jYElQRkpMT4VmrZhkBE\n1LPMzExs27YNxcXFeP/997Fu3TpMmzYNubm5GD16tNzlKULQEJuTk4PFixdj8uTJEELg888/R05O\nTjhqI6JBJkavwdiR8Rjb3oaQnp6OkmNnfH21pyqacbayBeerbb42BINOhZFppoALxxJiuRsCEREA\nXHPNNbjmmmtgt9uxa9curFq1Co2Njfjb3/4md2mDXtAQ++yzz+LDDz9EcXExJEnC7bffzlYCIgLQ\n3oYQr0dyvB4T/8d7i0SX24PyGrsv2J62NuPYuSYc82tDSIzTIcvS0V8bixHmGLYhEFFUc7vdmDFj\nBu666y6UlpbKXY4iBA2xdrsdWq0WK1asAAD85S9/gd1uh8lkCnlxRKQ8GrV35XVkmgk3/q/3WEcb\ngn9/7YHj9ThwvOc2hFEWE9KTjVCxdYmIItzRo0eRn5+P8vJytLW1ISsrCxs3bpS7LEUIGmJ/85vf\nYNKkSb7Hra2tyM/Px6ZNm0JaGBFFjq5tCEII1DY6fIH2tLUFZ6u6tyFkpnfuhJBlMSHexDYEIoos\na9aswa9+9StMnz4dCxcuxNq1a7Fq1Sps2bJF7tIGvaAh9uLFi1i4cKHv8f3338+9y4joO5EkCSnx\neqTE6zHJvw2hur0NocK7Ynv0bCOOnm30nZcUp0OmfxtCWoxcH4GIaEBIkoTp06cD8P4P/vDhw+Fy\nuWSuShmChlin04nS0lJcccUVAIBvv/0WTqcz5IURUXTRqL370I5MN+HG9mO21o42hM7+2q5tCJmW\nkxiWqkNWujfYpicb2IZARIrhcrlw+vRpZGVlAfC2FzDE9k/QELts2TI89thjaGpqgsfjQWJiIn73\nu9+FozYiinIxBg3GZsZjbKZfG0KDA6cqOtsQzlU14dQFD/YdYhsCESnPsmXLsGvXLjz++OOorq7G\n8uXLUVBQIHdZihA0xI4fPx67d++G1WpFUVERdu7ciUcffRT//Oc/w1EfEZGPJElISdAjJUGPye1t\nCCmpafj621O+/tpTFS09tiFk+d1pbERaDPRa7oZARPIbP348xo8fDwD4+OOPuRf/JQgaYg8ePIgd\nO3Zg165d8Hg8WLNmDW6++eZw1EZEFJRW49eGcG0aAKAloA3Bu2L79fE6fH28DoC3DWFoaozvFrqZ\n6WxDICJ5LFy4EEKIbsd569ngeg2xmzdvxs6dO2G323HHHXdg+/bteOqpp/DjH/84nPUREV0yk0GD\n7Mx4ZPu1IdQ0tHVu8VXRgrOVLThXZcM/DnnPMejUyEw3+W3zFYshJq2Mn4KIosGjjz7q+9npdOKb\nb75BS0uLjBUpR68h9ve//z2uvPJKrFy5Etdddx0AcImbiBRJkiSkJhiQmmDA5Ks7d0M4X23z27u2\nextC8pAubQhmE3RalVwfg4gi0JQpUwIeT5s2DQsWLJCpGmXpNcTu3bsXO3fuxKpVq+DxePCTn/yE\nuxIQUcTQqL0XgGWmx2KGfxtCxy1021ds/3OsDv851t6GoJIwLNXYvhOCN9imJRnk/BhEpHCNjY04\ne/YscnJy4HA4UFpaijfffFPushSh1xCbmpqKRYsWYdGiRfjqq6+wfft2lJeX45FHHsG8efN8e5oR\nEUUKk0GD7KwEZGclAPC2IVQ3tKHM705jZ6tsOFvZ2YZg1KsxZkQZMpK13mCbzjYEIuq//Px83HLL\nLcjJycEDDzyA+vp6jBs3Ds8//7zcpQ16QS/sAoBJkyZh0qRJePbZZ/HRRx9h06ZNDLFEFPEkSYI5\nwQBz1zaEKhtOWVtwun2rr4MnqnHwROd5bEMgov5qaGjAnXfeif/+978wmUzYunUr5s6dK3dZitCv\nENshNjYWc+fO5b9cIopaGrUKmZZYZFpiAXjbEGLjk/HFwdKAC8f604bA3RCISKfz7mP92Wef4cYb\nbwTAa5D665JC7KU6fvw4HnvsMdx3333Iy8uD1WpFfn4+3G43UlNTsWHDBt8fHhGRUsXF6JCTlYCc\nLm0I/ncaO9dDG0Jmuilgmy+2IRBFn5EjR+Kpp55CSUkJPvjgA7z//vsYO3as3GUpQshCrM1mw5o1\nawKuunvllVcwf/585Obm4sUXX8QHH3yA+fPnh6oEIiJZ+LchfP/qFACA09WxG0JnsP3vmUb890xP\nuyHEYpTFhOFsQyCKeKtXr8aBAweQmZmJhIQE5Obm4mc/+5ncZSlCyEKsTqfD5s2bsXnzZt+xoqIi\nPPfccwCAGTNmYMuWLQyxRBQVtBqVL6B2aLY722/K0HnhWNc2hOGpRt95WRYTzIlsQyCKJCqVChMn\nTvQ9jo2N7ePV5E8SPd0mYgC9+uqrSExMRF5eHqZMmYL9+/cDAM6ePYv8/Hy88847vZ7rdDqh1fLX\naxSdOP+jjxAC1toWHD9Tj2Nn63H8bD1OlTfC5fb4XmMyanHV8ASMGZGI0SMTMWZEIuJj9TJWHRqc\n/0QUTEh7YvvSn+xcU1MT9DUWiwVWq3UgSrpsrEH+8ZVSg8Vi6fd7KWH+yz1+JNYgARiTocGYjFTg\nulQ4XR6c82tDKLM24+Dxahw8Xu07JyVej7FZKUhPVCPLYsIIswlaTXjbEPrz72Ag53+k/bkrtQa5\nx1dCDTqdDsnJyWGuKDqENcTGxMSgtbUVBoMBlZWVMJvN4RyeiEhxtBoVRrXfBrdDs90ZcKexsopm\n7DtY7nterZIwLDXGtxMC2xCIBr8NGzbg17/+dbefqXdhDbE/+MEPsHv3btxxxx3Ys2cPbrjhhnAO\nT0QUEWKNWowblYBxozp3Q4B2CIqKT/uC7bkqG85UtmDvwSoAQIxejcz2mzF0BNu4GP66nmiwKC4u\n7vFn6l3IQuzhw4exfv16lJeXQ6PRYPfu3di4cSOWLl2Kd999FxkZGbjzzjtDNTwRUdSQJAmW1Fhc\nNzYF143t3A3hXJWt/YYM3lXbI2WNOFLWuRtCSrweo3yrtbEYbo4JexsCEXn57w3LfWL7J2QhNicn\nB4WFhd2Ov/XWW6EakoiI2mk1KozKiMWojM42hCZbx24I7cG2ohlfHq3Dl0e9uyGoVRKGm2OQmW7y\nbfNlTjTwP6hEYeB/rVCIr7mPGLJd2EVEROEVF9O9DaGqvtV7C12/NoSyCr82BIM64E5jmelsQyAK\nBa7EXjqGWCKiKCVJEtKSjEhLMmJKdpc2hPZ9a09ZW1BS1oCSsgbfeakJemSld7YhpCbo0XimDjq4\nYdCp5fo4RIrmf50QrxnqH4ZYIiLy6a0NwbcbQkUzyqwtAW0IHdKTDFiel80gS3QZHn744R5/pt4x\nxBIRUZ/iYrS45ooEXHOFtw3B096GcNragkMn63HgRD0AoKKuFRdq7QHbgRERhQpDLBERXRKVJCE9\nyYj0JCOuvSoRF2pLUFHXivQkAzKSjXKXR0RRgiGWiIgum0GnxvK8bDgQAx1sbCUgugz79u3D8OHD\nkZWV5TtWUlKC7OxsGasa/LghIBERfScGnRpjRiYxwBJdhk2bNuHVV1+F3W4POP7GG2/gpZdekqkq\nZWCIJSIiIpLJP/7xD/z5z3/G2LFjAQAvvvgiAODll1/G/v375Sxt0GOIJSIiIpKJVquFVuvde9nl\ncmHnzp1wOp2+56h37IklIiIikklSUhI2bNiACRMm4OOPP0Zubi4eeeQRpKSkICUlRe7yBjWuxBIR\nERHJpKCgAJIk4f3330d2djaWL1+OefPmITs7G+vWrZO7vEGNK7FEREREMomLi8OSJUsCjs2aNUum\napSFK7FEREREpDgMsURERESkOAyxRERERCHicDjkLiFiMcQSERERkeIwxBIRERGR4jDEEhEREZHi\nMMQSERERkeIwxBIRERGR4jDEEhEREZHiMMQSERERkeIwxBIRERGR4jDEEhEREZHiMMQSERERkeIw\nxBIRERGR4jDEEhEREZHiMMQSERERkeIwxBIRERGR4jDEEhEREZHiaMI5WFFREZ566ilcddVVAIDR\no0djxYoV4SyBiIiIiCJAWEMsAEyePBmvvPJKuIclIiIiogjCdgIiIiIiUhxJCCHCNVhRURGee+45\njBgxAg0NDXjiiScwderUXl/vdDqh1WrDVR7RoML5T9GM85+IgglriK2srMTXX3+N3NxcnDt3DgsX\nLsSePXug0+l6fL3Vag36nhaLpV+vCyXWIP/4SqnBYrH0+72UMP/lHp81DJ4a+jP+QM5/uT8vaxgc\n4yulhkuZ+9R/YW0nSEtLw49+9CNIkoQRI0YgJSUFlZWV4SyBiIiIiCJAWEPshx9+iDfffBMAUF1d\njdraWqSlpYWzBCIiIiKKAGHdnWDmzJlYsmQJPv30UzidTqxevbrXVgIiIiIiot6ENcTGxsbi9ddf\nD+eQRERERBSBuMUWERERESkOQywRERERKQ5DLBEREREpDkMsERERESlOWC/sIiIi+q7sbS7869AF\nxGhaMcSkhUGrhkolyV0WEYUZQywRESlGq8ONZ98sRpPNFXBcr1VBr1PDoFPBoFPDoFXDoFd7f9ap\nvI91auh1ahj1ahh8r/d7TfvPWg1/SUmkBAyxRESkGBdq7QEB9sqMWEgqCa0ON9ocbrS2eXCxyQmH\ny3PZY6hVki/U6nVqGHUdAVjlF3rVMCc3w9HWEnDMoFX5hWc1dFoVVBJXiYlCgSGWiIgUIyPZiPQk\nAyrqWpGeZMAv7x4Dg07d7XVuj/CGWocbrQ4PWp1uX9C1t3mPtTk7nm9/jaP744tNDlQ43PCInqq5\n0K+a9dr28KtvXyHuEob1OpVfUO6+Muz/WKPmKjFRB4ZYIiJSDINOjeV52XAgBjrYegywgHc1Ncag\nQYzhu/9nTggBh8uDti5BNyY2HhcqagKOtfkFZnubG23OznNsrS7UNTrg/A6rxBq15Lc6rEKc6SRU\ncHcGXV9Q7gy//qvJHY8NOjX0WhUkrhKTgjHEEhGRohh0amRZkmC1toVlPEmSoNeqodeqMcSk9R23\nWNJhTexxibZPbo/wC72dq7523+PAleE2hxv2Lo9bnR7UNjpwocbeyypxPz4XENhH3MPKcOfqsKrH\nPmO13oYWuwt6nYqrxBR2DLFERERhpFZJMBk0MA3AKnF6ejrOnC1vX/3t2hLR3jbRQ5tE18ctdhdq\nGtrgcl9qIj7s+0mrkWDQBm+J6HrM/wI7Y3uI1mm4SkzBMcQSEREplCR52wv0OjXiTd/9/VzuLm0T\nTjda2/zCr7MzGNvb3JA0OtQ3NHdrtahpcKLN4cFlLhJDktDrxXL+O0sY9WqYU+xoszf3GZi5BVtk\nYoglIiIiAIBGrYLGqILJ2L94YLFYYLVae3zOIwQc7T3BHW0T9h7aKHpaGW51dr6m0e5CdZ+rxOeD\n1qnVqPpeGfa1SnTuStFxEZ5R7/9YDa1GClglbnW4cexMHXTtvckUPgyxRERENOBUkuQLiQPB5fb0\n2BJhNA2BtbLW1zZh77jArms4bl9Vbmxxos15+RfXqfxWiXVaFWob2+B0CaQnGbA8L5tBNowYYomI\niGjQ06hViDWqEGsMPO5dDb60i8r8V4lbHe0tE86e+4W77T7h9/hiswNOl3eFuKKuFRdq7RhliR2o\nj0xBMMQSERFRVBmoVeJWhxtrtx1CVWsNzIYUZCQbg59EA4YhloiIFKXR0Ywvj+4BWgW0Km8IEQIA\nBITvnwBE589dj3Uc7Xqu/7Fu58K7Z2zHsSFNQ9DQ2AiIHl7ZwzH/c3t6v77O7TgWcC4ETFWxaG5u\nCjgW+Hn6HjvgM3cZu+OffdVtOGOA3W7v9Vz41eN7P+8fQrfPEvjn1fO5Hcf8z9Ud1aGtra1/79de\nZ9djQgSc6Te232va39P/VW7hQcuVF2EQbugNyZDUOQDYThAuDLFERKQYbW4H1n/9OpqdNrlLIQWQ\nuv5Tkrod67hIq+MZ9HCst3PdwgO3cAMAqltrYW2pQuaQYSH6NNQVQywRESmGtaUqIMDOGDYFyYYE\nAP6BQ0LnxeN+R7sckyB15JGAc/2P9XYuICEpKRH19RfbH3We6xeFuoSf3se53LpTklNQW1t7SXV/\np7ETlmIAAAkmSURBVH8PvlO9P6eZ01BVVdV+qO8w2PXcnt6vryDZcazrUYvFgoqKioBj4dpjts3t\nwAsH/j8q7TVIM6bAYjKHZVzyYoglIiLFsJjMSDOm+EJD7sjp0Kt18tRiscCq7nl7qbDVkGKB1Slf\nH2aKKQlOQ3junNYbtUoNlSTP3cL0ah1+NeEhOI0eaO0q2eZitGKIJSIixWBooMFGr9YhM7n3/XIp\ndHijYyIiUhS9WoerkrMYYImiHEMsERERESkOQywRERERKQ5DLBEREREpDkMsERERESmOJETnPSmI\niIiIiJSAK7FEREREpDgMsURERESkOAyxRERERKQ4DLFEREREpDgMsURERESkOAyxRERERKQ4igqx\nBQUFmDNnDubOnYvi4uKA5/7973/j7rvvxpw5c7Bp06awj//FF1/gnnvuwdy5c7Fs2TJ4PJ6w19Dh\nhRdewIIFC0IyfrAarFYr5s2bh7vvvhsrV64M+/jbtm3DnDlzMG/ePKxduzYk4wPA8ePHMWvWLGzd\nurXbc6GYi3LP/WA1RMv8l3vuB6uB85/f/ZH83R+shkid/9QHoRBFRUVi0aJFQgghTp48Ke65556A\n53Nzc8WFCxeE2+0W8+bNEydOnAjr+LNnzxZWq1UIIcSTTz4p9u7dO6Dj96cGIYQ4ceKEmDNnjsjL\nyxvw8ftTwy9/+UuxZ88eIYQQq1evFuXl5WEbv6mpScyYMUM4nU4hhBD333+/+OabbwZ0fCGEaGlp\nEXl5eeLZZ58VhYWF3Z4f6Lko99zvTw3RMP/lnvvBauD853d/JH/3B6shUuc/9U0xK7H79+/HrFmz\nAABXXHEFGhoa0NzcDAA4d+4c4uPjYbFYoFKpMH36dOzfvz9s4wPAjh07kJ6eDgBISkpCfX39gI7f\nnxoA4Pnnn8fTTz894GP3pwaPx4Ovv/4aM2fOBACsWrUKGRkZYRtfq9VCq9XCZrPB5XLBbrcjPj5+\nQMcHAJ1Oh82bN8NsNnd7LhRzUe65H6wGIDrmv9xzP1gNnP/87o/k7/5gNUTq/Ke+KSbE1tTUIDEx\n0fc4KSkJ1dXVAIDq6mokJSX1+Fw4xgeA2NhYAEBVVRX+9a9/Yfr06QM6fn9q2LFjByZPnoyhQ4cO\n+Nj9qaGurg4mkwnr1q3DvHnz8MILL4R1fL1ej8cffxyzZs3CjBkzMH78eGRlZQ14DRqNBgaDocfn\nQjEX5Z77wWoAomP+yz33g9XA+c/v/kj+7g9WQ6TOf+qbYkJsV0Lmu+X2NH5tbS0eeeQRrFq1KuAv\nWjhquHjxInbs2IH7778/5OP2VoMQApWVlVi4cCG2bt2KI0eOYO/evWEbv7m5GX/4wx/wySef4NNP\nP8WhQ4dw9OjRkI4vB7nnfm81RNv8l3vud62B81++8aNt7netgfOf5KKYEGs2m1FTU+N7XFVVhdTU\n1B6fq6ys7HGpP1TjA96/QA8//DAWL16M66+/fkDH7k8NX3zxBerq6nDvvffiiSeeQElJCQoKCsJa\nQ2JiIjIyMjBixAio1WpMmTIFJ06cCNv4paWlGD58OJKSkqDT6TBx4kQcPnx4QMe/1PoGYi7KPfeD\n1QBEx/yXe+4Hq4Hzn9/9kfzdH6yGSJ3/1DfFhNipU6di9+7dAICSkhKYzWbfr3GGDRuG5uZmnD9/\nHi6XC3//+98xderUsI0PePuRfv7zn2PatGkDOm5/a7jllluwa9cuvPfee3jttdeQnZ2N5cuXh7UG\njUaD4cOHo6yszPf8QP86p6/xhw4ditLSUrS2tgIADh8+jMzMzAEdP5hQzEW5536wGoDomP9yz/1g\nNXD+87s/kr/7g9UQqfOf+iYJuX83cwk2btyI//znP5AkCatWrcKRI0cQFxeH2bNn46uvvsLGjRsB\nADfffDMefPDBsI1//fXXY9KkSbj22mt9r7311lsxZ86csNUwe/Zs32vOnz+PZcuWobCwcMDHD1bD\nmTNnsHTpUgghMHr0aKxevRoq1cD+v1Jf47/zzjvYsWMH1Go1rr32WuTn5w/o2ID3y3H9+vUoLy+H\nRqNBWloaZs6ciWHDhoVsLso99/uqIZrmv9xzP1gNnP/87o/k7/5gNUTq/KfeKSrEEhEREREBCmon\nICIiIiLqwBBLRERERIrDEEtEREREisMQS0RERESKwxBLRERERIrDEEv9smPHDixZskTuMohkwflP\n0YzznwYrhlgiIiIiUhyN3AXQwCosLMTHH38Mt9uNUaNG4aGHHsIvfvELTJs2zXcf6ZdeeglpaWnY\nu3cvNm3aBIPBAKPRiDVr1iAtLQ2HDh1CQUEBtFot4uPjsX79egDe2ysuWbIEpaWlyMjIwGuvvQZJ\nkuT8uEQBOP8pmnH+U9QRFDEOHTokFixYIDwejxBCiLVr14o//elPYvTo0eLbb78VQgjx0ksviYKC\nAmGz2cTUqVOF1WoVQghRWFgoli5dKoQQYvbs2eLYsWNCCCHeeust8dFHH4nt27eLm266SdhsNuHx\neMTs2bN970k0GHD+UzTj/KdoxJXYCFJUVISzZ89i4cKFAACbzYbKykokJCQgJycHADBhwgS8/fbb\nKCsrQ3JyMtLT0wEAkydPxjvvvIO6ujo0NjZi9OjRAID77rsPgLcnaty4cTAajQCAtLQ0NDU1hfkT\nEvWO85+iGec/RSOG2Aii0+kwc+ZMrFy50nfs/Pnz+OlPf+p7LISAJEndfg3kf1z0ciditVrd7Ryi\nwYLzn6IZ5z9FI17YFUEmTJiAffv2oaWlBQCwbds2VFdXo6GhAUeOHAEAHDhwAGPGjEFmZiZqa2tx\n4cIFAMD+/fsxfvx4JCYmIiEhAcXFxQCALVu2YNu2bfJ8IKJLwPlP0Yzzn6IRV2IjyLhx43Dvvfdi\nwYIF0Ov1MJvN+P73v4+0tDTs2LEDzz//PIQQePHFF2EwGLB27Vo8/fTT0Ol0iImJwdq1awEAGzZs\nQEFBATQaDeLi4rBhwwbs2bNH5k9H1DfOf4pmnP8UjSTB3wlEtPPnz2P+/PnYt2+f3KUQhR3nP0Uz\nzn+KdGwnICIiIiLF4UosERERESkOV2KJiIiISHEYYomIiIhIcRhiiYiIiEhxGGKJiIiISHEYYomI\niIhIcRhiiYiIiEhx/g9Iy+CC7bEdGwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 703.375x648 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "mVTxRajr97Od",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "learning rate 은 0.0000일 때 가장 높았으므로 고정하고, 그 떄 layer 수가 1,3개일 때가 2개일 때보다 높으므로 n_layer = 1 또는 3으로 놓고 optimizer를 바꿔가며 실험해보자"
      ]
    },
    {
      "metadata": {
        "id": "SQ4MJjY0tZCB",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Exp2. Optimizer & number of layers"
      ]
    },
    {
      "metadata": {
        "id": "Jqn9t1PVl4Yc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 649
        },
        "outputId": "94e7e796-60e9-4947-c44a-3583409319dd"
      },
      "cell_type": "code",
      "source": [
        "# ====== Random Seed Initialization ====== #\n",
        "seed = 666\n",
        "np.random.seed(seed)\n",
        "torch.manual_seed(seed)\n",
        "\n",
        "parser = argparse.ArgumentParser()\n",
        "args = parser.parse_args(\"\")\n",
        "args.exp_name = \"exp2_optim\"\n",
        "args.device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "\n",
        "# ====== Data Loading ====== #\n",
        "args.symbol = '028050.KS'\n",
        "args.batch_size = 512\n",
        "args.x_frames = 5\n",
        "args.y_frames = 5\n",
        "\n",
        "# ====== Model Capacity ===== #\n",
        "args.input_dim = 6\n",
        "args.hid_dim = 50\n",
        "args.n_layers = 2\n",
        "\n",
        "# ====== Regularization ======= #\n",
        "args.l2 = 0.00001\n",
        "args.dropout = 0.0\n",
        "args.use_bn = True\n",
        "\n",
        "# ====== Optimizer & Training ====== #\n",
        "args.optim = 'RMSprop' #'RMSprop' #SGD, RMSprop, ADAM...\n",
        "args.lr = 0.00005\n",
        "args.epoch = 5\n",
        "\n",
        "\n",
        "# ====== Experiment Variable ====== #\n",
        "name_var1 = 'optim'\n",
        "name_var2 = 'n_layers'\n",
        "list_var1 = ['RMSprop','SGD','Adam']\n",
        "list_var2 = [1,3]\n",
        "\n",
        "\n",
        "for var1 in list_var1:\n",
        "    for var2 in list_var2:\n",
        "        setattr(args, name_var1, var1)\n",
        "        setattr(args, name_var2, var2)\n",
        "        print(args)\n",
        "                \n",
        "        setting, result = experiment(partition, deepcopy(args))\n",
        "        save_exp_result(setting, result)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Namespace(batch_size=512, device='cuda', dropout=0.0, epoch=5, exp_name='exp2_optim', hid_dim=50, input_dim=6, l2=1e-05, lr=5e-05, n_layers=1, optim='RMSprop', symbol='028050.KS', use_bn=True, x_frames=5, y_frames=5)\n",
            "Epoch 0, Acc(train/val): 18.14/17.96, Loss(train/val) 0.02294/0.01551. Took 14.81 sec\n",
            "Epoch 1, Acc(train/val): 17.19/17.69, Loss(train/val) 0.01936/0.01496. Took 14.76 sec\n",
            "Epoch 2, Acc(train/val): 16.53/17.47, Loss(train/val) 0.01748/0.01454. Took 14.80 sec\n",
            "Epoch 3, Acc(train/val): 16.03/17.29, Loss(train/val) 0.01632/0.01418. Took 14.63 sec\n",
            "Epoch 4, Acc(train/val): 15.61/17.14, Loss(train/val) 0.01528/0.01386. Took 14.62 sec\n",
            "Namespace(batch_size=512, device='cuda', dropout=0.0, epoch=5, exp_name='exp2_optim', hid_dim=50, input_dim=6, l2=1e-05, lr=5e-05, n_layers=3, optim='RMSprop', symbol='028050.KS', use_bn=True, x_frames=5, y_frames=5)\n",
            "Epoch 0, Acc(train/val): 15.33/12.44, Loss(train/val) 0.02801/0.01832. Took 14.59 sec\n",
            "Epoch 1, Acc(train/val): 13.56/12.17, Loss(train/val) 0.01876/0.01759. Took 14.73 sec\n",
            "Epoch 2, Acc(train/val): 12.94/11.95, Loss(train/val) 0.01688/0.01702. Took 14.63 sec\n",
            "Epoch 3, Acc(train/val): 12.50/11.77, Loss(train/val) 0.01590/0.01654. Took 14.70 sec\n",
            "Epoch 4, Acc(train/val): 12.08/11.61, Loss(train/val) 0.01508/0.01615. Took 14.67 sec\n",
            "Namespace(batch_size=512, device='cuda', dropout=0.0, epoch=5, exp_name='exp2_optim', hid_dim=50, input_dim=6, l2=1e-05, lr=5e-05, n_layers=1, optim='SGD', symbol='028050.KS', use_bn=True, x_frames=5, y_frames=5)\n",
            "Epoch 0, Acc(train/val): 7.34/6.76, Loss(train/val) 0.01743/0.00847. Took 14.70 sec\n",
            "Epoch 1, Acc(train/val): 6.53/6.53, Loss(train/val) 0.01282/0.00825. Took 14.61 sec\n",
            "Epoch 2, Acc(train/val): 5.98/6.34, Loss(train/val) 0.01073/0.00806. Took 14.63 sec\n",
            "Epoch 3, Acc(train/val): 5.60/6.17, Loss(train/val) 0.00936/0.00790. Took 14.65 sec\n",
            "Epoch 4, Acc(train/val): 5.34/6.01, Loss(train/val) 0.00827/0.00773. Took 14.61 sec\n",
            "Namespace(batch_size=512, device='cuda', dropout=0.0, epoch=5, exp_name='exp2_optim', hid_dim=50, input_dim=6, l2=1e-05, lr=5e-05, n_layers=3, optim='SGD', symbol='028050.KS', use_bn=True, x_frames=5, y_frames=5)\n",
            "Epoch 0, Acc(train/val): 9.94/7.76, Loss(train/val) 0.02583/0.02085. Took 14.64 sec\n",
            "Epoch 1, Acc(train/val): 8.67/7.59, Loss(train/val) 0.01939/0.01977. Took 14.63 sec\n",
            "Epoch 2, Acc(train/val): 7.68/7.44, Loss(train/val) 0.01680/0.01897. Took 14.84 sec\n",
            "Epoch 3, Acc(train/val): 6.94/7.31, Loss(train/val) 0.01500/0.01834. Took 14.79 sec\n",
            "Epoch 4, Acc(train/val): 6.36/7.20, Loss(train/val) 0.01413/0.01782. Took 14.65 sec\n",
            "Namespace(batch_size=512, device='cuda', dropout=0.0, epoch=5, exp_name='exp2_optim', hid_dim=50, input_dim=6, l2=1e-05, lr=5e-05, n_layers=1, optim='Adam', symbol='028050.KS', use_bn=True, x_frames=5, y_frames=5)\n",
            "Epoch 0, Acc(train/val): 4.66/2.02, Loss(train/val) 0.01518/0.00333. Took 14.92 sec\n",
            "Epoch 1, Acc(train/val): 4.49/1.94, Loss(train/val) 0.01323/0.00331. Took 14.66 sec\n",
            "Epoch 2, Acc(train/val): 4.50/1.90, Loss(train/val) 0.01175/0.00329. Took 14.71 sec\n",
            "Epoch 3, Acc(train/val): 4.37/1.87, Loss(train/val) 0.01000/0.00326. Took 14.63 sec\n",
            "Epoch 4, Acc(train/val): 4.30/1.86, Loss(train/val) 0.00909/0.00323. Took 14.81 sec\n",
            "Namespace(batch_size=512, device='cuda', dropout=0.0, epoch=5, exp_name='exp2_optim', hid_dim=50, input_dim=6, l2=1e-05, lr=5e-05, n_layers=3, optim='Adam', symbol='028050.KS', use_bn=True, x_frames=5, y_frames=5)\n",
            "Epoch 0, Acc(train/val): 7.49/8.56, Loss(train/val) 0.01933/0.01680. Took 14.66 sec\n",
            "Epoch 1, Acc(train/val): 7.29/8.23, Loss(train/val) 0.01806/0.01649. Took 14.82 sec\n",
            "Epoch 2, Acc(train/val): 7.09/8.02, Loss(train/val) 0.01718/0.01626. Took 14.74 sec\n",
            "Epoch 3, Acc(train/val): 6.87/7.86, Loss(train/val) 0.01625/0.01610. Took 14.85 sec\n",
            "Epoch 4, Acc(train/val): 6.74/7.72, Loss(train/val) 0.01581/0.01593. Took 14.81 sec\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "lMHVOW1rt-7a",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 774
        },
        "outputId": "030006a8-93b2-49f1-d15a-b0412406bd8c"
      },
      "cell_type": "code",
      "source": [
        "var1 = 'optim'\n",
        "var2 = 'n_layers'\n",
        "df = load_exp_result('exp2')\n",
        "\n",
        "plot_acc(var1, var2, df)\n",
        "plot_loss_variation(var1, var2, df, sharey=False) #sharey를 True로 하면 모둔 subplot의 y축의 스케일이 같아집니다.\n",
        "plot_acc_variation(var1, var2, df, margin_titles=True, sharey=True)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1468: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
            "  stat_data = remove_na(group_data[hue_mask])\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "error",
          "ename": "IndexError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-51-0e3bc3713638>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplot_acc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvar2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mplot_loss_variation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvar2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msharey\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#sharey를 True로 하면 모둔 subplot의 y축의 스케일이 같아집니다.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0mplot_acc_variation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvar2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmargin_titles\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msharey\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-45-c895bf21b11e>\u001b[0m in \u001b[0;36mplot_loss_variation\u001b[0;34m(var1, var2, df, **kwargs)\u001b[0m\n\u001b[1;32m     25\u001b[0m             \u001b[0mrow\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mvar2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0mvalue2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m             \u001b[0mtrain_losses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_losses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     28\u001b[0m             \u001b[0mval_losses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mval_losses\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mIndexError\u001b[0m: list index out of range"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6/r+5++RqKpnvd94fEnhXcQEAAAC4Pbslc35+foqJiTEfBwYGksgBAAAAwB2y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\nYII=\n",
            "text/plain": [
              "<Figure size 1080x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "_ZT9eTnZ-QvW",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "RMSprop이 전반적으로 가장 높은 acc보임. RMSprop으로 고정하고 hid_dim과 dropout 비율을 조정해보자"
      ]
    },
    {
      "metadata": {
        "id": "NGTsZ-F50QlQ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Exp3. hid_dim & dropout"
      ]
    },
    {
      "metadata": {
        "id": "ZKGtWCaZ0BC9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 955
        },
        "outputId": "d7cf6311-5f0a-4e85-a553-1897604bc024"
      },
      "cell_type": "code",
      "source": [
        "# ====== Random Seed Initialization ====== #\n",
        "seed = 666\n",
        "np.random.seed(seed)\n",
        "torch.manual_seed(seed)\n",
        "\n",
        "parser = argparse.ArgumentParser()\n",
        "args = parser.parse_args(\"\")\n",
        "args.exp_name = \"exp3_hiddim_dropout\"\n",
        "args.device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "\n",
        "# ====== Data Loading ====== #\n",
        "args.symbol = '028050.KS'\n",
        "args.batch_size = 512\n",
        "args.x_frames = 5\n",
        "args.y_frames = 5\n",
        "\n",
        "# ====== Model Capacity ===== #\n",
        "args.input_dim = 6\n",
        "args.hid_dim = 50\n",
        "args.n_layers = 1\n",
        "\n",
        "# ====== Regularization ======= #\n",
        "args.l2 = 0.00001\n",
        "args.dropout = 0.1\n",
        "args.use_bn = True\n",
        "\n",
        "# ====== Optimizer & Training ====== #\n",
        "args.optim = 'RMSprop' #'RMSprop' #SGD, RMSprop, ADAM...\n",
        "args.lr = 0.00005\n",
        "args.epoch = 5\n",
        "\n",
        "\n",
        "# ====== Experiment Variable ====== #\n",
        "name_var1 = 'hid_dim'\n",
        "name_var2 = 'dropout'\n",
        "list_var1 = [30,50,100]\n",
        "list_var2 = [0.1,0.3,0.5]\n",
        "\n",
        "\n",
        "for var1 in list_var1:\n",
        "    for var2 in list_var2:\n",
        "        setattr(args, name_var1, var1)\n",
        "        setattr(args, name_var2, var2)\n",
        "        print(args)\n",
        "                \n",
        "        setting, result = experiment(partition, deepcopy(args))\n",
        "        save_exp_result(setting, result)"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Namespace(batch_size=512, device='cuda', dropout=0.1, epoch=5, exp_name='exp3_hiddim_dropout', hid_dim=30, input_dim=6, l2=1e-05, lr=5e-05, n_layers=1, optim='RMSprop', symbol='028050.KS', use_bn=True, x_frames=5, y_frames=5)\n",
            "Epoch 0, Acc(train/val): 11.15/8.44, Loss(train/val) 0.03107/0.01912. Took 14.85 sec\n",
            "Epoch 1, Acc(train/val): 10.84/8.16, Loss(train/val) 0.02810/0.01859. Took 14.64 sec\n",
            "Epoch 2, Acc(train/val): 10.46/7.95, Loss(train/val) 0.02637/0.01818. Took 14.51 sec\n",
            "Epoch 3, Acc(train/val): 10.26/7.78, Loss(train/val) 0.02506/0.01784. Took 14.56 sec\n",
            "Epoch 4, Acc(train/val): 10.07/7.66, Loss(train/val) 0.02467/0.01754. Took 14.65 sec\n",
            "Namespace(batch_size=512, device='cuda', dropout=0.3, epoch=5, exp_name='exp3_hiddim_dropout', hid_dim=30, input_dim=6, l2=1e-05, lr=5e-05, n_layers=1, optim='RMSprop', symbol='028050.KS', use_bn=True, x_frames=5, y_frames=5)\n",
            "Epoch 0, Acc(train/val): 19.34/15.03, Loss(train/val) 0.04730/0.02259. Took 14.67 sec\n",
            "Epoch 1, Acc(train/val): 18.47/14.76, Loss(train/val) 0.04394/0.02216. Took 14.54 sec\n",
            "Epoch 2, Acc(train/val): 18.04/14.55, Loss(train/val) 0.04186/0.02183. Took 14.60 sec\n",
            "Epoch 3, Acc(train/val): 17.88/14.39, Loss(train/val) 0.03885/0.02155. Took 14.61 sec\n",
            "Epoch 4, Acc(train/val): 17.00/14.26, Loss(train/val) 0.03790/0.02131. Took 14.67 sec\n",
            "Namespace(batch_size=512, device='cuda', dropout=0.5, epoch=5, exp_name='exp3_hiddim_dropout', hid_dim=30, input_dim=6, l2=1e-05, lr=5e-05, n_layers=1, optim='RMSprop', symbol='028050.KS', use_bn=True, x_frames=5, y_frames=5)\n",
            "Epoch 0, Acc(train/val): 23.22/21.22, Loss(train/val) 0.05231/0.01778. Took 14.63 sec\n",
            "Epoch 1, Acc(train/val): 22.76/20.84, Loss(train/val) 0.04335/0.01715. Took 14.65 sec\n",
            "Epoch 2, Acc(train/val): 22.45/20.51, Loss(train/val) 0.03940/0.01665. Took 14.64 sec\n",
            "Epoch 3, Acc(train/val): 22.30/20.23, Loss(train/val) 0.03728/0.01622. Took 14.60 sec\n",
            "Epoch 4, Acc(train/val): 22.13/19.99, Loss(train/val) 0.03785/0.01586. Took 14.59 sec\n",
            "Namespace(batch_size=512, device='cuda', dropout=0.1, epoch=5, exp_name='exp3_hiddim_dropout', hid_dim=50, input_dim=6, l2=1e-05, lr=5e-05, n_layers=1, optim='RMSprop', symbol='028050.KS', use_bn=True, x_frames=5, y_frames=5)\n",
            "Epoch 0, Acc(train/val): 5.29/3.79, Loss(train/val) 0.02055/0.00567. Took 14.63 sec\n",
            "Epoch 1, Acc(train/val): 5.01/3.71, Loss(train/val) 0.01466/0.00552. Took 14.68 sec\n",
            "Epoch 2, Acc(train/val): 4.78/3.66, Loss(train/val) 0.01232/0.00542. Took 14.75 sec\n",
            "Epoch 3, Acc(train/val): 4.66/3.61, Loss(train/val) 0.01106/0.00533. Took 14.64 sec\n",
            "Epoch 4, Acc(train/val): 4.46/3.55, Loss(train/val) 0.01074/0.00524. Took 14.64 sec\n",
            "Namespace(batch_size=512, device='cuda', dropout=0.3, epoch=5, exp_name='exp3_hiddim_dropout', hid_dim=50, input_dim=6, l2=1e-05, lr=5e-05, n_layers=1, optim='RMSprop', symbol='028050.KS', use_bn=True, x_frames=5, y_frames=5)\n",
            "Epoch 0, Acc(train/val): 12.85/3.50, Loss(train/val) 0.04457/0.01452. Took 14.72 sec\n",
            "Epoch 1, Acc(train/val): 10.85/3.41, Loss(train/val) 0.03511/0.01422. Took 14.70 sec\n",
            "Epoch 2, Acc(train/val): 9.61/3.34, Loss(train/val) 0.03111/0.01398. Took 14.69 sec\n",
            "Epoch 3, Acc(train/val): 9.44/3.29, Loss(train/val) 0.02992/0.01375. Took 14.68 sec\n",
            "Epoch 4, Acc(train/val): 9.02/3.25, Loss(train/val) 0.02849/0.01346. Took 14.65 sec\n",
            "Namespace(batch_size=512, device='cuda', dropout=0.5, epoch=5, exp_name='exp3_hiddim_dropout', hid_dim=50, input_dim=6, l2=1e-05, lr=5e-05, n_layers=1, optim='RMSprop', symbol='028050.KS', use_bn=True, x_frames=5, y_frames=5)\n",
            "Epoch 0, Acc(train/val): 14.20/12.80, Loss(train/val) 0.04979/0.02468. Took 14.75 sec\n",
            "Epoch 1, Acc(train/val): 14.25/12.42, Loss(train/val) 0.04004/0.02379. Took 14.48 sec\n",
            "Epoch 2, Acc(train/val): 13.94/12.15, Loss(train/val) 0.04028/0.02312. Took 14.63 sec\n",
            "Epoch 3, Acc(train/val): 13.62/11.92, Loss(train/val) 0.03940/0.02256. Took 14.65 sec\n",
            "Epoch 4, Acc(train/val): 13.26/11.73, Loss(train/val) 0.03682/0.02213. Took 14.70 sec\n",
            "Namespace(batch_size=512, device='cuda', dropout=0.1, epoch=5, exp_name='exp3_hiddim_dropout', hid_dim=100, input_dim=6, l2=1e-05, lr=5e-05, n_layers=1, optim='RMSprop', symbol='028050.KS', use_bn=True, x_frames=5, y_frames=5)\n",
            "Epoch 0, Acc(train/val): 4.27/2.69, Loss(train/val) 0.01375/0.00846. Took 14.67 sec\n",
            "Epoch 1, Acc(train/val): 3.45/2.55, Loss(train/val) 0.00867/0.00794. Took 14.68 sec\n",
            "Epoch 2, Acc(train/val): 3.37/2.48, Loss(train/val) 0.00773/0.00755. Took 14.66 sec\n",
            "Epoch 3, Acc(train/val): 3.20/2.44, Loss(train/val) 0.00700/0.00722. Took 14.74 sec\n",
            "Epoch 4, Acc(train/val): 3.34/2.41, Loss(train/val) 0.00657/0.00693. Took 14.62 sec\n",
            "Namespace(batch_size=512, device='cuda', dropout=0.3, epoch=5, exp_name='exp3_hiddim_dropout', hid_dim=100, input_dim=6, l2=1e-05, lr=5e-05, n_layers=1, optim='RMSprop', symbol='028050.KS', use_bn=True, x_frames=5, y_frames=5)\n",
            "Epoch 0, Acc(train/val): 18.87/14.22, Loss(train/val) 0.03053/0.00849. Took 14.66 sec\n",
            "Epoch 1, Acc(train/val): 16.96/13.94, Loss(train/val) 0.01970/0.00818. Took 14.70 sec\n",
            "Epoch 2, Acc(train/val): 15.68/13.72, Loss(train/val) 0.01673/0.00794. Took 14.62 sec\n",
            "Epoch 3, Acc(train/val): 14.92/13.51, Loss(train/val) 0.01381/0.00772. Took 14.64 sec\n",
            "Epoch 4, Acc(train/val): 14.46/13.33, Loss(train/val) 0.01315/0.00752. Took 14.66 sec\n",
            "Namespace(batch_size=512, device='cuda', dropout=0.5, epoch=5, exp_name='exp3_hiddim_dropout', hid_dim=100, input_dim=6, l2=1e-05, lr=5e-05, n_layers=1, optim='RMSprop', symbol='028050.KS', use_bn=True, x_frames=5, y_frames=5)\n",
            "Epoch 0, Acc(train/val): 16.50/13.70, Loss(train/val) 0.02767/0.00879. Took 14.77 sec\n",
            "Epoch 1, Acc(train/val): 15.60/13.38, Loss(train/val) 0.02271/0.00843. Took 14.73 sec\n",
            "Epoch 2, Acc(train/val): 14.70/13.16, Loss(train/val) 0.01890/0.00816. Took 14.74 sec\n",
            "Epoch 3, Acc(train/val): 14.54/12.93, Loss(train/val) 0.01824/0.00793. Took 14.75 sec\n",
            "Epoch 4, Acc(train/val): 13.96/12.71, Loss(train/val) 0.01840/0.00770. Took 14.73 sec\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "O9Ut5EYM0-Ju",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1733
        },
        "outputId": "81ae1be0-8849-41c3-f344-090fd44caf89"
      },
      "cell_type": "code",
      "source": [
        "var1 = 'hid_dim'\n",
        "var2 = 'dropout'\n",
        "df = load_exp_result('exp3')\n",
        "\n",
        "plot_acc(var1, var2, df)\n",
        "plot_loss_variation(var1, var2, df, sharey=False) #sharey를 True로 하면 모둔 subplot의 y축의 스케일이 같아집니다.\n",
        "plot_acc_variation(var1, var2, df, margin_titles=True, sharey=True)"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1468: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
            "  stat_data = remove_na(group_data[hue_mask])\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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NevHFFxUZGalTp05JevDwlo8++ijT9mLFisnR0dF0L+D/PkzlYdHR0fq///s/FS5cWOHh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\nhjkAAIACMswBAAAUkGEOAACggP4fJCWbtQBlqXkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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j2QzsIV8QhajLwB6OHEjOIf5IliS+QjQCWbmtEXVtZ8tzoT1wtNWwJTad1dvO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5YsWYJOp+PXX38lICDgmscoz/6Ew3N3dWLSrUHcPsTI90ez+HJ/Ol//lMHXP2UA4KJRcdvN/gT6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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 703.375x648 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VPf+TkpIQExOD/fv347bbbmO/oCvGoBuCnnjiCTz33HPYvHkzbrnllmrfdQ8ePBg7duzA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\nSAy6RERERBRIDLpEREREFEj/H0Pia8BjHaDSAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 703.375x648 with 9 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Q20_remY-u29",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "hid_dim이 적을 때는 dropout 비율이 높을 때 정확도 높지만 hid_dim이 100일 때는 그렇지 않다."
      ]
    },
    {
      "metadata": {
        "id": "Ilgxh8HE9SPd",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Exp4\n"
      ]
    },
    {
      "metadata": {
        "id": "XPfEB5eh3vpU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "outputId": "7f28b133-5825-4036-866c-ff853a9111fd"
      },
      "cell_type": "code",
      "source": [
        "# ====== Random Seed Initialization ====== #\n",
        "seed = 666\n",
        "np.random.seed(seed)\n",
        "torch.manual_seed(seed)\n",
        "\n",
        "parser = argparse.ArgumentParser()\n",
        "args = parser.parse_args(\"\")\n",
        "args.exp_name = \"exp4_hiddim_dropout\"\n",
        "args.device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
        "\n",
        "# ====== Data Loading ====== #\n",
        "args.symbol = '028050.KS'\n",
        "args.batch_size = 512\n",
        "args.x_frames = 5\n",
        "args.y_frames = 5\n",
        "\n",
        "# ====== Model Capacity ===== #\n",
        "args.input_dim = 6\n",
        "args.hid_dim = 30\n",
        "args.n_layers = 1\n",
        "\n",
        "# ====== Regularization ======= #\n",
        "args.l2 = 0.00001\n",
        "args.dropout = 0.1\n",
        "args.use_bn = True\n",
        "\n",
        "# ====== Optimizer & Training ====== #\n",
        "args.optim = 'RMSprop' #'RMSprop' #SGD, RMSprop, ADAM...\n",
        "args.lr = 0.00005\n",
        "args.epoch = 5\n",
        "\n",
        "\n",
        "# ====== Experiment Variable ====== #\n",
        "name_var1 = 'use_bn'\n",
        "name_var2 = 'dropout'\n",
        "list_var1 = [True, False]\n",
        "list_var2 = [0.3,0.5]\n",
        "\n",
        "\n",
        "for var1 in list_var1:\n",
        "    for var2 in list_var2:\n",
        "        setattr(args, name_var1, var1)\n",
        "        setattr(args, name_var2, var2)\n",
        "        print(args)\n",
        "                \n",
        "        setting, result = experiment(partition, deepcopy(args))\n",
        "        save_exp_result(setting, result)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Namespace(batch_size=512, device='cuda', dropout=0.3, epoch=5, exp_name='exp4_hiddim_dropout', hid_dim=30, input_dim=6, l2=1e-05, lr=5e-05, n_layers=1, optim='RMSprop', symbol='028050.KS', use_bn=True, x_frames=5, y_frames=5)\n",
            "Epoch 0, Acc(train/val): 11.49/8.43, Loss(train/val) 0.03472/0.01914. Took 15.03 sec\n",
            "Epoch 1, Acc(train/val): 11.23/8.16, Loss(train/val) 0.03242/0.01862. Took 15.06 sec\n",
            "Epoch 2, Acc(train/val): 10.86/7.96, Loss(train/val) 0.03002/0.01822. Took 15.02 sec\n",
            "Epoch 3, Acc(train/val): 10.72/7.80, Loss(train/val) 0.02855/0.01790. Took 14.84 sec\n",
            "Epoch 4, Acc(train/val): 10.51/7.68, Loss(train/val) 0.02755/0.01761. Took 15.01 sec\n",
            "Namespace(batch_size=512, device='cuda', dropout=0.5, epoch=5, exp_name='exp4_hiddim_dropout', hid_dim=30, input_dim=6, l2=1e-05, lr=5e-05, n_layers=1, optim='RMSprop', symbol='028050.KS', use_bn=True, x_frames=5, y_frames=5)\n",
            "Epoch 0, Acc(train/val): 20.94/15.03, Loss(train/val) 0.05752/0.02260. Took 15.04 sec\n",
            "Epoch 1, Acc(train/val): 19.85/14.75, Loss(train/val) 0.05158/0.02218. Took 15.08 sec\n",
            "Epoch 2, Acc(train/val): 19.48/14.55, Loss(train/val) 0.05161/0.02186. Took 15.00 sec\n",
            "Epoch 3, Acc(train/val): 19.17/14.39, Loss(train/val) 0.04612/0.02158. Took 15.02 sec\n",
            "Epoch 4, Acc(train/val): 18.50/14.25, Loss(train/val) 0.04598/0.02135. Took 14.88 sec\n",
            "Namespace(batch_size=512, device='cuda', dropout=0.3, epoch=5, exp_name='exp4_hiddim_dropout', hid_dim=30, input_dim=6, l2=1e-05, lr=5e-05, n_layers=1, optim='RMSprop', symbol='028050.KS', use_bn=False, x_frames=5, y_frames=5)\n",
            "Epoch 0, Acc(train/val): 8.97/8.38, Loss(train/val) 0.03488/0.03409. Took 14.67 sec\n",
            "Epoch 1, Acc(train/val): 8.57/8.09, Loss(train/val) 0.03353/0.03314. Took 14.42 sec\n",
            "Epoch 2, Acc(train/val): 8.30/7.86, Loss(train/val) 0.03272/0.03241. Took 14.54 sec\n",
            "Epoch 3, Acc(train/val): 8.07/7.67, Loss(train/val) 0.03207/0.03179. Took 14.63 sec\n",
            "Epoch 4, Acc(train/val): 7.97/7.50, Loss(train/val) 0.03146/0.03124. Took 14.79 sec\n",
            "Namespace(batch_size=512, device='cuda', dropout=0.5, epoch=5, exp_name='exp4_hiddim_dropout', hid_dim=30, input_dim=6, l2=1e-05, lr=5e-05, n_layers=1, optim='RMSprop', symbol='028050.KS', use_bn=False, x_frames=5, y_frames=5)\n",
            "Epoch 0, Acc(train/val): 2.94/2.45, Loss(train/val) 0.01204/0.01149. Took 14.82 sec\n",
            "Epoch 1, Acc(train/val): 2.91/2.43, Loss(train/val) 0.01162/0.01122. Took 14.91 sec\n",
            "Epoch 2, Acc(train/val): 2.82/2.42, Loss(train/val) 0.01143/0.01102. Took 14.89 sec\n",
            "Epoch 3, Acc(train/val): 2.84/2.40, Loss(train/val) 0.01131/0.01085. Took 14.82 sec\n",
            "Epoch 4, Acc(train/val): 2.83/2.39, Loss(train/val) 0.01108/0.01070. Took 14.93 sec\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "T5UmQ6vlAA9B",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1309
        },
        "outputId": "7f66bc07-36d3-4717-e927-5bbb1b56e04c"
      },
      "cell_type": "code",
      "source": [
        "var1 = 'use_bn'\n",
        "var2 = 'dropout'\n",
        "df = load_exp_result('exp4')\n",
        "\n",
        "plot_acc(var1, var2, df)\n",
        "plot_loss_variation(var1, var2, df, sharey=False) #sharey를 True로 하면 모둔 subplot의 y축의 스케일이 같아집니다.\n",
        "plot_acc_variation(var1, var2, df, margin_titles=True, sharey=True)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/categorical.py:1468: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
            "  stat_data = remove_na(group_data[hue_mask])\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EXAisARyZmT/uVIySJEmSVGddX7SlLPZOBy7PzMvG7L4ROBI4B9gY+ElEPDsz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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1080x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ailqtrnRMRio1E9q8xohWwahQ8t2pzVy4/nBzFSsVCv4T5I6hgZJ1vyVzI+/W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\nQmUSxkIIIYTKJIyFEEIIlf0/698QIlIyyEcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 487.375x432 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HDMNg27ZttG7duiZqE0IIUQc8/fTTLFmyhA8//BDDMGjTpg0PPfRQsMsKurMG\n8IwZM1i7di0//PADiqIwcOBAOf4rhBCiwux2O/fee2+wywg5Zw1gp9OJxWLhiSeeAOCDDz7A6XTK\nKeRCCCEqZOHChSxatIicnBwAFEXBMAx++eWXIFcWXGcN4Mcee4wOHToEhl0uF1OmTGH+/PnVWpgQ\noki+y8fWbw9jU1xERVixW03YLKqcjyFqhZUrV7J69Wri4+ODXUpIOWsAZ2VlMXbs2MDw3XffzZYt\nW6q1KCFEEZdH48m3f+BUfsnv4CuAzWoizGbCblGx20zYrf6fsIJHe7FxYVaTf/pi4/xtKlazhLmo\nPpdccglNmzYNdhkh56wB7PV6+e2337jssssA2LdvH16vt9oLE0L4HUt3lgjfq5rXw2xScHl0nG4N\nl0cjx+kjNduNTzPOMKfyqQoFYW0KBHkgqK1qYFxYsZC3F4a/VUVRICX7JPVtPhx2ucCeKKlVq1ZM\nmjSJTp06YbFYAu0X+uUoz/qfMnXqVP7yl7+Qk5ODrus0aNCAuXPn1kRtQgigSUwYjaPtHM9w0Tja\nzgODWmK3msqc1uvTcXm0gh//c6dbw+XVcLm1wDinR8NdLMAL21xujaxcDy63hn5+WY7DphLhsBAR\nZiHCbiY8zExEwU948eFiz80muTFbXZaSkoLJZGL37t2BNsMwJIDPNkG7du3YtGkTKSkpJCcns2rV\nKh544AG2b99eE/UJccGzW01MG30NHhxYyS83fAEsZhWLWSXSYSl3moowDAOPT/eHdGGguwuDuyis\nXV6dExku9vyaEXhtmM2M062RluWucIjbrSrh9sKgtviDuWA4vOCn+HCE3YxVjoHXGmV12pKSkoJQ\nSWg5awB/9913fPzxx2zYsAFd15k9ezY333xzTdQmhChgt5q4ND6alBR3jSxPURRsFhM2i4l64WcO\nc5dH42hafqCHPm30NditJgzDwOnWyHX5yHP6yC34yStjuPDxWLoTry+/QjVazEogtP2PlqKADrQV\n9LDNCseyTxBu8hAeZsakKhLeNWjhwoV89dVX6LoeaPvll1/497//zdChQ7nuuuuCWF3wlBvAixYt\nYtWqVTidTgYNGsTKlSuZOHEit9xyS03WJ4QIceX10BVFwWE3+48JR1V8fm6vRl5BIOc4/WGd5/SR\nWxjUpwV3WraHI6nOc67bbFIwm1Qs5oLHgmFzwbDZpGApGLYUDJfbblbLn1/B+MJhS2AZReO9Pp3/\n/pGBFe2Mezhqq2XLljFr1izM5qLImTNnDv379ycuLi6IlQVXuQH8yiuvcPnll/Pkk0/SuXNnAPnE\nKIQoU1X20At73tH1bBV+jU8mJT6vAAAgAElEQVTTyXdp5Dq9JcI51+njaGo+u34p2kV+SWMHFrMJ\nr0/Hpxn4NP+j26uT5/Lh0wy8moF+vgfBK0FVICrCgt3q38VutajYLCpWs/9rZ0VtBcPmouEzTR/s\ns9ynTZtG9+7dS7RNnTqVTp06Bami0FBuAG/dupVVq1Yxc+ZMdF1nyJAhcvazECIkmU0q9cLVMneX\nuzwah04W7SJ/9I6rKtTL1HWjIIx1f0j7/GHtLQhtr68ovAPtvqLxvmLjvQXDXq1k6Ht9Ojn5Xv44\n4d/trhvg1QzceR7cXv28z2o/nQIlwvj0wLZZTKXC3FZselVVuOSUiSj7+fXQ+/TpU6rtQg9fOEMA\nx8XFce+993LvvffyzTffsHLlSo4ePcr999/PyJEj6dGjR03WKYQQ5+VcTmIrTlUVrKqC1VK9Z2i7\nPBrP/uunUsfQwf8hwOPTcXs1PF4dt1fH49UDbYHhEs913L7Thr1awWv8w9nnFfC/l6pPVE6FvrDX\noUMHOnTowIwZM1i/fj3z58+XABZC1Bo1fRLbuTjTBwRVVQLfua4Omu7vhbtPD/BigX00NZ+NySkA\nHM9wcSzdSYv4iGqp50JzTh/tIiIiGDFiBB9++GGFpv/111/p06cP//rXvwD/d8HGjBnDqFGjmDhx\nIh6P59wrFkKIOsZuNXFl8+ga71maCgK+friVhlF2msU5aNEkgqua16fdZQ3o2CqGxE5NaBxtB6Bx\ntJ0mMed/H9/x48eX+fxCVW2XrMnPz2f27NnceOONgbbXXnuNUaNGkZiYyEsvvcSKFSsYNWpUdZUg\nhBCiks53F35Zip9HJOcUnWMP+FxYrVYWLVpEw4YNA23Jycn07t0bgJ49e7Jjx47qWrwQQogqEqwe\nel1XbT1gs9lc4jtf4L+1odVqBSAmJobU1NQzziM2NrbEdUPLE+p32JD6KieU66vK2iqyvofyewFS\nX2XV9foMwyjz+YUqaFdNr8ibn5aWdtZp4uPjSUlJqYqSqoXUVzmhXF9FajuXDdbZ1vdQfi9A6qus\n2lyf1WolJibmrPMo/l1kua5ENe6CLovD4cDlcgFw4sSJErunhRBC1G333ntvmc8vVDUawF26dGHT\npk0AfPbZZ6WujCKEEKLu6tatW5nPL1TVtgv6xx9/ZM6cORw9ehSz2cymTZt44YUXePzxx1m+fDlN\nmjRh8ODB1bV4IYQQIqRVWwC3bt2a9957r1T74sWLq2uRQgghRK0RtJOwqoLLo9XpO4gIIURdkZSU\nxBdffMHx48cBaNiwId27dycxMfGCPSGr1gawy6MxbdH35Dp92KwqHa6Ipn6ElQiHmciC+4JGOoru\nEWox1+jhbiGEEAXmzJnD/v376d+/f+Dk27S0NNauXcvu3bt58skng1xhcNTaAD6W7iTX6QPA7dHZ\n/uOZv8Jht6qBMPaHc0FIh5mJKOO5w2a6YD+VCSFEVfr2229ZtmxZqW3q4MGDGTFiRJCqCr5aG8BN\nYsJoHG3neIaLhlE2xvdvgcdnkOP0+e8Jmu+/F2hOwf1Bc/L97UdS8yt0BxBVVYiwm4lwFAR2iV51\n8dC2EOkwE24v3cuWXeRCCOG/7kNWVhYNGjQo0Z6bm4uu60GqKvhqbQCf7/VJDcN/4+2c/JLBXHjz\n7kB7QZBn5Xg4luascE2FIR1mM3HwWB5Oj0ZEmJm+7RsR4bAQZjMTZlWx20yEWf3T2a0mbFYVVXrc\nohxuzcP+9INYNBWbyRrscoQ4J2PHjuW2226jY8eONGzYEEVRSEtLY+fOndx3333BLi9oam0AA6D6\nICwTxWUGKhbAilJ0e6+4qIotxqfp5Ll85OYXBrOP3EBQFw9yf2gfOpGPphf1snOdPlZtP3rmugCb\n1RQI5DCrSpjN7H9uMxFmNWEvHFdsOMxarM3mv5G2BHnd4tY8zNuzkFRXBpGWcG65tBeRlnDsJht2\ns63Eo1mVPS0i9AwYMIBOnTqxfft2jh8/jmEYXHvttTz00EM0btw42OUFTa0NYLfm4cnkl3FrHhSg\nniWSMIudMLMNu8mO3WwjrGDDFGa2YzcVPAba7YFHu9mGSSn/JC2zSaV+uJX64RXreRiGQXaul3nL\n/kNqtpvoSCvDelyETzNwujVcHv+P063h9Gi4Ch8L2k7lezmRqaHr536tVAWKgrpYSIcVawuz+dtN\nqoL5p1M4LD4iw8zYrCZsFhWbxd8jt1n804jgSsk7SaorA4Acbx7Lfl1X7rQW1YzNVDyUrUUhXUZg\nl9lmtmFW5BwIUbXi4uIYMmRIsMsIKbU2gFPyTuLW/PcTNgADg1OeHE7kp2Fw7sFlVS0F4VwQ0gVB\nHma2lWw/bXzxQC/sfSiKQlSklSmjr+BIfg7NHJHUDzu3e2gahoHXZ/hDuVhIFwZ48fAunMbp9uHy\n6IFpsvM8HM/QOI8cDzCbFH8wm9ViAe1/breoWAvC2m7x975tFhN2a0G7RcVu9bfbC15TOE1Fgl2O\nofvFhzekYVgMJ53p1LdG0qtZFzRDw6W5cfnc/seC527NjcvnwaW5yXHmBv5HzpVJUUsFtc1cMtgL\nP9jaTTZUVcWc8wthXisRVgdWkxWrasFqsmA1Wc/4AVfUfWPHjmXJkiXBLiPkKEYI35LiTBcmd2se\nXvz2LU4402gUFsuk6/+MzWTFMAw8uhenz4XL58apuQIbKafPjcvnwlmwsXL6XAXtLlyap9g4F5px\n7icGWFRzoKdtNVk4mZ+OR/diN9m4NvZqHBY7NpMVm8mKVbUWPTdZij0vaq+KXkhhkDs9vmI9bZ3/\npeTy8bYjgem6XBOLw27C7dX9Px4Nt1fH5dHweHXcXg2XV8fj1fD6Kr/KWMwKVos/xG0F4W0vFvBm\nk8p3v2WS79IIt5vpcW0cdosJk0nBpKqYTApmVSnxaFJVzCbF37MveDSZ1NOGC8cXTaueRy/f5dEq\ndP7BudyM4WzruzdMx+I8t2PAuqHj1jxFQX1aYJds85Q53l3wWJm/uklRS4ayai14LArpwuc21YKl\noK3wue0M01ck4M/3/atJdflmDIZhyB6VMtTaAIbq+6cyDAOv7isK57MFecE4p8+Nq+B5vs+FZmiV\nqkNFKRHK/kdLqRAvHuAlgly1lAr1wo2Vy6PxzPvfc9KVRkN7LNPvbFfhXqamG7i9BcHsKQxmf1i7\nC0La5dED07i8Gm5PQXuxcC8Kev9wRc5Orw6KwhlDu3i7WVVQFPjf8TzcXp2GUTZmjG1d7ntXVQFc\nOK9gbaB1w8CjecoIbw9Hco+z+fC2wLTXxl6N3WzDo3nwaF48uheP5sGte/EWDLsLxp3P3qqylB3w\nZqyqFZNq4vfsQ7g0N2FmOx0atiXMbMOsmrGo5rIflWLPTf7h4uPNqrnKz7WoywGsaRoffvgh7dq1\n4+qrrw60b9myhe7du1fotrN1Ua3dBQ1gM1m5JKbqV1pFUQo+WVuoZ404r3m4NQ8vfLuIk850Yu0N\nGNdqGAYGbs2DW/cUbIA8uLWCjVPxH92/cSqaxoPT5yLLfQqP7q3072dWTFhVC67LPNjRyVdMLPj5\nO2wmG9bC3kfBhsyimos9twR6IYFpzBYsNgvhqrlgvB2rasF0HicD+TS9oLetcyrfw4I1B0g/5SE6\n0sqIPs0xKQqabqBpBj5dR9MMNN3Ad9qjpumnDRv4ymjzD5ff7vXpuDQDX2CZRqnj8iez3BxLd9Ii\n/vzWk9pCVRT/rmezrdS4q6Iv44e0/wT2Ro26cmCFPhAbhoFmaLg1L95ioezRiwe3t8w2t+bBW/hc\n9+DVvLiLjTvlyS0z4J0+F18e21Ul74lJMZUI7qLnJiyq5QwBbyoV6ABqhorVZ/GfTKeYMKkmzKop\n8NykmPzhXziuWHt1n3hZ2bPwn332WU6ePEnnzp1LtH/99dds3bqVv//971VVaq1SqwM4lNlMVv52\n/f+r8h66bhjFNlZFoV24ISoaLnxeLOCLBX+OJ598zX9rSJ+hcfDUkbMs+dyoiloyuFULVpO5ZIgH\ngtxcRvBbGDhAIcujEB8WRlRYXsH0BdMWBL5FNdfori3DMMhz+Ziz9D+cyHTRONpOk5hzO75f19hM\nViZd/+dzXtcVRcGsFAZQ1b+HhQF/ypPH/B+WkObKJNoWxagrBqIoCj7dh9fw+R81H97iw/rZH09v\nc/pcRcOV3Pt1rlRFPS2oTSWC2lzQbirRbj5tuPR0ZtWEYRhsObKTHG9uicN95+Knn35i2bJlgeGp\nU6fy3HPPMWPGDLkQh6ge1dFDVxUlsDu5Mk4/hv7odeMxqSY8Bb2Rwt5G4S7DEs9Pn0b3lTO9LzBd\nts+Ft2DaqmYJ9ECKBbOpqC0Q2CZL6WkLPgBYAh8IzMWGS05XOBwRZuFvo1oGTrC7kE8QK1Rde6Mq\nozDgo+31mdL+vho9BqwXhH/Zwe0t9lwjJe8kG//YGnht9/gO1LNF4NM1NEMr91HTNXyGr1i7XvDo\nCyzbpbkLpvO/rrK7/E8400jJO8kl9Zqd9zycTifbtm0jLy+P8PDwStVT20kAX6DK67VYqqk3Ukg3\ndP+Gp5xg9xSMS8k/SdLhrwKvax/XGoclrCD8fYEwDzxqRc/zfc5AD6U6mFDRMTAwiLFH8Vj7+0P2\nxB7hV9MfEFRFQS04jnw2rRq04NuTPwY+DN/aone1rU+6oZ8W4GUFvK/EsNPnYd3BJLI9OTQKiyU+\nvOE5L/eyyy7jkUce4brrriMpKYl77rmHO++8k8jISK688spq+E1rBwngC1gwei2qolaoB+/WPOxL\n+29gozT8igHnvFHSDaOgp1HQSy/WKy8vvL2BHn5Rb8VTOG3BdLnefI7npwKQ7sqqdI9AXNjOdxf+\n+VAVFatJBc7tpKe2sVdWqr5Zs2bx0UcfceDAAUaPHs3NN99Mp06dOH78OL169Trn+dUVEsAiJFXF\nRkktdjJdVTp99/359AiEKC4Ud+EXV9n6zGYzI0eOLNF2zTXXcM0111RFebWWBLAIWaG6UarJHosQ\nou6Sy9MIcR5sJistYy6V8BUC8HjO74prFzoJYCGEECIIJICFEEKIIJAAFkIIIYJAAlgIIYQIAglg\nIYQQIggkgIUQQoggkAAWQgghgkACWAghhAgCCWAhhBAiCCSAhRBCiCCQABZCCCGCQAJYCCGECAIJ\nYCGEECIIavR2hMnJyUycOJGWLVsCcMUVV/DEE0/UZAlCCCFESKjx+wF37NiR1157raYXK4QQQoQU\n2QUthBBCBIFiGIZRUwtLTk5m1qxZXHzxxWRnZ/PQQw/RtWvXcqf3er1YLJaaKk+IoJL1XYgLS40G\n8IkTJ9izZw+JiYkcPnyYsWPH8tlnn2G1WsucPiUl5azzjI+Pr9B0wSL1VU4o11eR2uLj4ys8v4rM\nK1TfC5D6Kqu213cu67rwq9Fd0I0aNaJ///4oisLFF19MbGwsJ06cqMkShBBCiJBQowG8du1a/u//\n/g+A1NRU0tPTadSoUU2WIIQQQoSEGj0LulevXvztb3/j888/x+v18tRTT5W7+1kIIYSoy2o0gCMi\nIliwYEFNLlIIIYQISfI1JCGEECIIJICFEEKIIJAAFkIIIYJAAlgIIYQIAglgIYQQIggkgIUQQogg\nkAAWQgghgkACWAghRJ2Tnp4e8vebr/H7AQshhBDV7bXXXmPNmjWsXr2affv2ATB27FjmzJnDqFGj\nGDBgALm5uVgsFqZNm8bOnTt55513iI6OpkGDBkyePLnaa5QAFkIIUef0798fn8/HoUOHOHbsGCaT\nCYvFQnx8PGlpaTzwwAM4HA7GjRvH8ePHmTNnDu+++y716tVj6tSp/PDDD7Rt27Zaa5QAFkIIUWcN\nHTqUjz/+mPDwcIYMGQJATEwMDocDgNjYWE6ePElKSgrPPvss4N99nZaWVu21SQALIYSocxRFQdd1\n+vXrx9ixY3E4HCxcuBDwB2xubi4REREcO3aMRo0acfHFF/Pkk0/icDg4dOgQjRs3rvYaJYCFEELU\nORdffDHffvstGzZs4KqrrkJRFGw2GwANGjRg/vz5nDx5kjZt2tCoUSMeffRRJk+ejMPhQNM0nnnm\nmWqvUQJYCCFEndO4cWM2bdoEwDfffMO4ceMC42w2G4899liJ6Tt37kznzp1rtEb5GpIQQog6yeVy\n8eCDD9KwYUOuuuqqYJdTivSAhRBC1El2u5358+eXat+8eXMQqilNesBCCCFEEEgACyGEEEEgASyE\nEEIEgQSwEEIIEQQSwEIIIUSBwq8unc0zzzzD4cOHK7UsCWAhhBACOHLkCJ988kmFpp0+fToXXXRR\npZYnX0MSQghRazndPg4dP8XFjesRZqtcpP3973/nhx9+oFWrVgwcOJAjR47wzjvvMHXqVE6cOEF+\nfj4TJkygZ8+ejBkzhieeeIJNmzaRk5PDwYMHOXToENOmTaNHjx4VWl5IB3B8fHyVThcsUl/lhHJ9\nVVlbReYVyu8FSH2VJfUVeXvdT3z1/dEzTqMbBpmn3Gi6gUlVaFDPhqoo5U7ftV1T7rn1mnLHjx8/\nnvfff5+WLVvy+++/s3TpUtLT0+nWrRtDhgzh8OHDTJw4kZ49e5Z43fHjx1m0aBFffvkly5YtqxsB\nLIQQQpRH0ww03fA/1w00zUA1lx/A56LwVoT16tVj3759LF++HFVVycrKKjXt9ddfD/gvf5mTk1Ph\nZUgACyGECDn33HrNGXur4N/9/Ogr/+bIyVyaNYzgpYd7VHo3dCGLxQLA+vXryc7OZunSpWRlZXHb\nbbeVmtZsPr9lSgALIYSolcJsZl56uEeVHQNWVRWfz1eiLTMzk2bNmqGqKps3b8bj8VRqGSWWV2Vz\nEkIIIWpYmM3Mlc2jq6Tne9lll/Hzzz+X2I188803s2XLFsaNG0dYWBiNGzfmH//4R6WXBaAYhmFU\nyZyEEEIIUWHSAxZCCCGCQAJYCCGECAIJYCGEECIIJICFEEKIIJAAFkIIIYIgpL8HnJKSctZpYmNj\nSUtLq4Fqzo/UVzmhXF9FajuXS/edbX0P5fcCpL7Kqu31hfplNENRSAfw2Zw8eYR9+z4hKysz2KWU\nKyqqPllZ2TW+3IYNr+DKKzuedbrCq72EqlCuryZr03Wdr75aycmTlbv9WXUK1rpeFsOA5s070Lz5\nlYG2UF6XQOqrTXr16sW6desIDw+v1HxqdQD/9ttTDB6cg9frDXYp5bLZbLjd7hpf7p49Po4fX0jj\nxo1rfNmi6u3c+Sb9+n2B2ew7+8RBEqx1vSyGYZCUtJaLLlqGqsqRNhGaavWaWa+eS/65yhEf7yMn\nJ3T3DIhzYzZnEh5uCnYZtYaiKNSv7yp1WUFR97i8LvanH8TldVV6XkOGDOHYsWMAHD16lMGDB3Pf\nffcxZswYbr/9dn744YdKL6O4Wt0DBpgwIYX9+zVOnQKPRyE21iAiAt5/P67C8/j1VzcnT/ro1q30\n7oTcXJ0ePTK5806VRx9tUCU1JyfnM3t2HqoKERGwcGEMERElP0jMnp3Bl19qmM3wxBPhdOniYMCA\nVDQNrFb/NNOmhdOpkwPw3xXk1lvT6NjRxFNPRVdJnSL0TJqUxoEDRq1a330+g8cfT+err2DHjthA\n24QJaRw+DJoGgwdbuO++qBKv27w5l5dfdmI2Q+vWKs8+G4PHo/Pgg+mcOAE+HwwbZmH8+CiSknJ5\n5RUnJhNYLPCPf0QBVXNXHBEc7323kp2Hvz3jNIZhkOnKRjN0TIpKA3t9lDPcjrDzRdcz5tph5Y7v\n06cPX3zxBXfeeSeff/45ffr0oVWrVvTp04cdO3awaNEiXn/99fP+nU5X67uPr78ez5o1cYwYYaFz\nZ1izJu6cNkYAmzfns2dP2RfYXrkyh3r1dLZsqbpP0rNn5zFtWjhr18bRqJHC4sUlj5t9842Tr7/W\n2LgxlqlTHWza5AyMe/75SNasiWPNmrhA+AK8/noWul5lJYoQ9eKLsbVwfc+gWbOSvffPP8/DbldY\nvz6ODz6IZvFiH6dfFff5553MmRPJ2rVxnDyp8+WXeXz8cS6aBmvXxrF8eTRvveV/3eLFTl55pT5r\n1sRxxRUq//d/Fb8lnKi9fLqGZvg3fJqh49O1Ss2v8LrPQCCAN23axMiRI3nhhRfKvBVhZdT6HvCZ\nTJ6czn//q6PrcMcdVsaOrc+qVadYuNCNxQINGsBTT9Xngw80zGaDZs1OMWxYvRLz2LjRw/jxVt54\nw8uBAx4uv9yK261z773ppKf7P22/+mp9GjUyl2rTNHjppXRefz2mxDyXLIkmOtq/QWrQQCUrq+SG\nZ8MGJz16mDCbFW66KZybbjrzgf6jR738+98++vc3k5Ehl/aui9wejfRsJzYzWMxlf24O9vpuMhk8\n+2w68+fHlpjnI49E4XAoLF+eEWhLSIggISECgGPHfDgcRqmeS16eQps2dgC6dLGyZYubbt2s5Ob6\n9/hkZ+vY7f7XFX4IMQyDtDSDyy+v05u2C8KYa4edsbcK/t3PUzfP4WjOcZpGNua5vo9ht9jPe5kt\nW7bk5MmTpKSkkJOTQ1JSEo0aNWLevHns27ePuXPnnve8y1Lre8Dl2bgxh8xMnbVr41i2LIZ33vHg\ndOosXepmyhQHa9fG0bevFatVoXt3hSFDzKU2RqmpPg4eVBg6NJKOHeHDD3MBWLAgm4YN/Z/eBwyw\nsGZNXpltl15qZeHCJqVqKwzf7GyNrVs1Bg92lBifkqKTkqIzbFgqQ4aksnt3UQ947twcBg5M5cEH\n08jP93/ymzkzi0cecWCSQ4R1ksujsW1fKsdS8ziYksfvKbnku3xomoHLo2EQGut7ixb2UuELEBVV\n/oo5alQq48fnMHly6Y1mVJTBli25aJrBjh0eMjJ0+vSJICpK4U9/Smfo0GweeMAWmH7lylP86U9p\n5OUZ3H13vVLzE3WP3WLnub6P8UyfKZUO30J/+tOfePnll+nVqxeZmZlcfPHFACQlJVX5Cb91NoB3\n7fKyfz8MGpTKnXemYxhw5IiX3r0tPP10PrNnZ9Cpk52mTcs/tX758hzatDFwOFSGDg1j61b/7o1f\nftFo395/IHb8+CgmTGhQZtuZZGdrjB2bwciRlsCn/EKG4f9ZuTKOe++1M3Omf0M4apSNJ5+sx5o1\nsWga/OMfWWzYkIPNppy1lyxqr2PpTjzeoj0bug5eTcer6Rw6kc+BIzl8ud3J/v0wcGAqo0JwfS/P\n0qVxvPtuJHPnusjJKbn7cNascF57zckdd6RRv76CYfg/aGRnG3zxRQwffFCPN95w4/H4P4gOG1aP\nrVtjadxY4dln5QTEC4XdYqdlzKVVEr4Affv2Zf369fTr149BgwaxePFi7rnnHtq2bUtqaiorV66s\nkuVAHd4FbbFAQoKJadNKnpDUsqWNAQO8fPxxLvfff4pXX40sdx6bN/vIzIS+fVMBSEtT2bfPhar6\nA7K4strK43brjBuXwS23WPh//y+q1PjoaIWrr/b/aRITI3nqKf9XO0aNKvpU36uXlaQkD8eOudm/\n36B//9SCXXMKF1+cTb9+tlLzFbVTk5gwIsL864PVonJxIwcRYV4sZh9RERacHg3VBF17GNz1/8IA\nMJsVwqw6w+900DdB55N1zqCt72X57jv/fNu2tXPFFTaio2HfPjdduhTtDerUycHq1f7hl1/OxOUy\n2LXLyw03+A/PXHaZFbsdDh3ysXu3ixEj6qEoCrfcYmfhQid9+55/feLC1bZtW37++efA8MaNGwPP\ne/fuDcCwYWfeNV5RdbYHfMMNVnbu1NA0g5wcnYkT/VdwmTkzHYdD5a9/bUCXLio//+xBVf1nYhZ3\n8KCHtDSFLVti2bw5js2b4+jfHz78MI/WrU3s2OE/iWXZslM880xGmW3lefrpTNq3N3HvvaXDF6B3\nbxvbtvl3deze7aRBAwNNMxgyJJWMDH+hO3Z4uPxylVdeieWzz+LYsCGO4cPN9O2rcs899Sv13onQ\nYrea6HJNLJc1q8/FjcJRFQWT6v9p2MBO80bh3NTVwb69Cg0iLSi6yryn88nJ9/HM05lk5rlIHGrm\n+hvgmz35GBh4fSXTszrX97Ls2+dm3jz/iVI5ORqpqQqXX16ydz5+fCrffuvE5zNISvKRmOjgkktM\n/PST/38gI0MjJ0ehaVMzixa5A4dqdu/20KyZnAEtQl+d7QHffHMESUluBgzwB+/Qof5/7saNVUaO\nzMDhgMhIeOyxCCyWPObOdRMdnc1dd/nDa/nyXLp2VTCbi/6RR4+O4MEHT/HJJw34y1/SGTgwFZPJ\nfwJKXJy5VNvBg54yT8LavNkgNlZn0CB/T+P6601Mn96AcePSeP/9OPr0ieDzz93ccot//LRpDkwm\nhYEDrYwcmYHVCrGxMHt22QEu6h6zWcFht5R7oYuEBP86M3akP9SGDrVyaZMILm7mZdqjXsIcHsLD\nDUbfY0UzdP7vTR2LPY3bR4QTZjWxdGlOpdf33393lXkS1oMPpnH4sIHTqTJoUCrdupl49NEGJCen\nc8st/q/WjRploWFDCytXnuLYMY0JExowfHgYjz+eC+TSp4+Ja6+107q1jR070rn11lR0He6/30pY\nmMqTTzqYOTMXkykXqxVefz2Kw6F70TAhAFCM08/9r0Jz585lz549+Hw+7rvvPtq0acOUKVPQNI24\nuDjmzZuHtfBLrWU427Vxf/ppNH36aCFz9Z2yBOvqQMeOuTh8+GVatrzyjNPFx8dX6JrbwRLK9VWk\ntqq6FvSuXc8zaND3lVqXDANcXg2XW8Pp8T/6tGL//grYLSp2m4kwqwm71VTuGddlCaUrYQEkJ2s0\nb/5RYBsTyusS1P765FrQ567aesA7d+5k//79LF++nMzMTIYMGcKNN97IqFGjSExM5KWXXmLFihWM\nGjXqvJeRnn4JX331K2536H4B1mbTglLfyZOxtG/ftMaXK6qHw3EdW7d+j17pL3sr+P/t/f/6Pk0v\nFso6Hq8Pwyj6DrBJVdlbT7IAACAASURBVLBbVew2M2EWFZvVVO6FDoK1rpdN4fjxprRsWf4HfCGC\nrdoCuEOHDrRt2xaAevXq4XQ6SU5OZtasWQD07NmTt99+u1IBfNNNT9f6T43V5aqranyRohq1bp1A\nfPxd1b4ueX06h1Pz+f1YbuAnI6fooh2KAs3iHLSIj6BFkwhaxIfTsIEdt1fHgwMr+ditofF9OPkf\nEKGuWndBF1q+fDm7d+9m+/bt7NixA4BDhw4xZcoUli1bVu7rvF6v3IFDXDBCdX1Pz3by3z8y+e8f\nmfzyRwYHDmfh8RX1dCPCzPgKvpMcFWFjTGIrYqLCiHRYqRfu/wmzmc94iUAhLkTVfhJWUlISK1as\n4O233+bmm28OtFck9ytyb0zpAVeO1Hf+qvoYcEXuLRys9+LSOIVL46Lpd0M0Pk3nSGo+vx/zXxTk\nv4dOkev077bOynXz+kffl3q9SVUIt5uJCDMTHuZ/jAgzB9oC7fai8Q6bGVWtutAO5XUJan99cgz4\n3FVrAG/bto0FCxbw1ltvERkZicPhwOVyYbfbOXHiBA0bNqzOxQshqoHZpHJJ4wguaRxBLxrh8mg8\nveRHTma5iYqwkNAhHo9PJzffR67LR57TR67TS67LR3aeh5R0JxXZ7aYADruJiDBLqXAuHt6njzOb\nSp845vJo/PePDKxoIbOLXIhqC+CcnBzmzp3LO++8Q1SU/+syXbp0YdOmTQwaNIjPPvuM7t27V9fi\nhRA1xG41MWNs6wofA9Z1gzyXjzyXj1yn/yfPedrwaeNSs93oesWOltmtakE4W4gIM2O3mfjP/7LJ\nd2tEOswM6tqMeuEWHDYTDrs58GizqLKbXNSoagvgDRs2kJmZycMPPxxoe/7555kxYwbLly+nSZMm\nDB48uLoWL4SoQXariUvjo0lJOfvXkFRVIdJhIdJR8ePdhmHg9GgFvemikD59uPjzY+n5pS44kpPv\n41+b/1d2XQqEFQZyQSiH2Uw4bGYc9oLH4u32kuPO5StbQkA1BvDw4cMZPnx4qfbFixdX1yKFEHWU\noigFAWgm7hyuP+P2amSccvPayl9JP+UhKsJCYqcmeH06+W6NfJcPp1sj3+0j//+3d+/BUZX3/8Df\nZ89eQyAJuZFQFBFQIYKlEiRURzBSpa2C0iFFyK+tCNrSgVaKgII6FDDg1xt1ZEiLWi4CAyEDrRK+\ntKDQwTAI5aZcjF+5hJAbJBCy931+f+xms5tNSEKy++xu3q+ZuOc8ey6fXY773ufZs2ctTtRbnTBb\nHbhUZ4fd0b6vVOm0CkwG/wCPMajutiZhbTL6L6MoCofIu6CovRIWEZFBpyItMQav/uredn9Nyu5w\nNYaz1QmzxYEbVifMFneb2erEjYAAd/fKK2raPmTuS6cqGNI/Ab0SjEiONyA53n0b103H4fEoxAAm\noqjXniHyBjqtBjqtBj26tf+rYUII2OyNveyGnrXfvMWJG1YHqmqtOHPBfQlRu1Pgq9OB19XWaTVI\njjN4QrkxmJPjjUjsoW/2xDMKfwxgIqJOpigKDHoVBr2KhO43vxqXxebE0nUncfmKBb16GjHjif64\ndsOOyhqr58+Cylr39KVqc8D6igL07K5vDOW4xoBOijcgxsCX+XDFfxkiIomMehULpgz2GyLvnQTc\nc7v/ckII1HnOCK+ssXgDusozf+r8NZw6H7j9biYtkuMMSGnSc06KMyAuVgcNh7alYQATEUnWliFy\nRWk8e7xfWmzA/Va7E9W1VlT49JyrPD3nCxX1+P7yjYB1dFoFSXHGxqFtn+nEHgbvmd38HnVwMICJ\niKKAQaciPSkG6UkxAfe5XAJXr9v8es8VPgFd1tzQNoCE7nok9tDjYpUZZqsTvXoasWDKYIZwJ2EA\nExFFOY1GQWKcAYlxBtx9Ww+/+4RwXxjF7zPnGqs3rM+W1nmXvXzFgkvV5mZ74NR+DGAioi5MURTP\nVcN0uKOZYL1+w45lG75GVa0VvXoakZ5oklBldGIAExFRi7p302HR/2v7pUap7fjlMSIiuimjXsVd\nt/dk+HYyBjAREZEEDGAiIiIJGMBEREQSMICJiIgkYAATERFJwAAmIiKSgAFMREQkAQOYiIhIAgYw\nERGRBAxgIiIiCRjAREREEjCAiYiIJGAAExERScAAJiIikoABTEREJAEDmIiIOkSv18suISIxgImI\niCRgABMREUnAACYiIpKAAUxERCQBA5iIiEgCrewCiIgourlcLuzbtw8lJSVQFAUDBw5EVlYWFEWR\nXZpUQe0BnzlzBtnZ2Vi3bh0AYN68efj5z3+OqVO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            "text/plain": [
              "<Figure size 487.375x432 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "oBH06QfY__w3",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "가장 좋은 결과는 고정하고 나머지 하이퍼파라미터를 수정하면서 테스트를 해봤는데,  결국 batch_size=128, device='cuda', dropout=0.0, epoch=2, exp_name='exp1_lr', hid_dim=50, input_dim=6, l2=1e-05, lr=1e-05, n_layers=1, optim='RMSprop', symbol='028050.KS', use_bn=True, x_frames=5, y_frames=5) 조건의 val_acc 21.85퍼센트를 넘지 못했습니다. \n",
        "\n",
        "그나마 가장 높은 건 Namespace(batch_size=512, device='cuda', dropout=0.5, epoch=5, exp_name='exp3_hiddim_dropout', hid_dim=30, input_dim=6, l2=1e-05, lr=5e-05, n_layers=1, optim='RMSprop', symbol='028050.KS', use_bn=True, x_frames=5, y_frames=5)에서  val_acc = 21.2가 나온 것이네요.\n",
        "\n",
        "하나의 조건에서 최적이었더라도 조건이 바뀌면서 다른 결과가 난 것 같은데, 말씀하신대로 좀 더 촘촘하게 파라미터 튜닝을 해야할 것 같습니다.\n",
        "\n",
        "궁금한 것은, 모든 실험에서 epoch이 증가할수록 Loss는 감소하지만 정확도는 변하지 않거나 떨어지기만 하는데, 왜 두 지표가 같이 움직이지 않는 걸까요?"
      ]
    },
    {
      "metadata": {
        "id": "QZJIpRuUChob",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}