EVA5_Session_6_v4.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "EVA5 - Session 6_1.ipynb",
      "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/thamizhannal/eva5/blob/master/EVA5_Session_6_v4.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U7OU46ANBvfB",
        "colab_type": "text"
      },
      "source": [
        "###Target:\n",
        "Reach 99.4% accuracy consistently in last 3-4 epochs by a applying more regularization in the form of dropout and increase LR rate to understand if convergence occurs faster and use LR schedular to change LR wherever Loss gets to plateau\n",
        "\n",
        "###Results:\n",
        "1. Parameters: 7,917\n",
        "2. Best Train Accuracy: 99.08%\n",
        "3. Best Test Accuracy: 99.43% \n",
        "4. greater than 99.4% in last 4 epochs(12-15)\n",
        "\n",
        "###Analysis:\n",
        "1. Dropout added the necessary regularization required to make model robust\n",
        "2. Though increased LR was helping in fast convergence but after 5-6 epochs it would tapper off implying a need for smaller LR so used a step LR schedule at after 4 epochs and decreased it to 0.8 times the previous LR\n",
        "\n",
        "###All the changes added to the base CNN architechture to acheive the target accuracy \n",
        "1. Input image normalization\n",
        "2. Image augmentation - rotation for Train data \n",
        "3. Batch Normalization\n",
        "4. Dropouts \n",
        "5. LR scheduler \n",
        "6. GAP\n",
        "\n",
        " "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0m2JWFliFfKT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from __future__ import print_function\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torchvision import datasets, transforms"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DPzD_rXJQYFg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Reused below code from https://github.com/apple/ml-cifar-10-faster/blob/master/utils.py\n",
        "# GhostBatchNormalization\n",
        "class BatchNorm(nn.BatchNorm2d):\n",
        "    def __init__(self, num_features, eps=1e-05, momentum=0.1, weight=True, bias=True):\n",
        "        super().__init__(num_features, eps=eps, momentum=momentum)\n",
        "        self.weight.data.fill_(1.0)\n",
        "        self.bias.data.fill_(0.0)\n",
        "        self.weight.requires_grad = weight\n",
        "        self.bias.requires_grad = bias\n",
        "\n",
        "class GhostBatchNorm(BatchNorm):\n",
        "    def __init__(self, num_features, num_splits, **kw):\n",
        "        super().__init__(num_features, **kw)\n",
        "        self.num_splits = num_splits\n",
        "        self.register_buffer('running_mean', torch.zeros(num_features * self.num_splits))\n",
        "        self.register_buffer('running_var', torch.ones(num_features * self.num_splits))\n",
        "\n",
        "    def train(self, mode=True):\n",
        "        if (self.training is True) and (mode is False):  # lazily collate stats when we are going to use them\n",
        "            self.running_mean = torch.mean(self.running_mean.view(self.num_splits, self.num_features), dim=0).repeat(\n",
        "                self.num_splits)\n",
        "            self.running_var = torch.mean(self.running_var.view(self.num_splits, self.num_features), dim=0).repeat(\n",
        "                self.num_splits)\n",
        "        return super().train(mode)\n",
        "\n",
        "    def forward(self, input):\n",
        "        N, C, H, W = input.shape\n",
        "        if self.training or not self.track_running_stats:\n",
        "            return F.batch_norm(\n",
        "                input.view(-1, C * self.num_splits, H, W), self.running_mean, self.running_var,\n",
        "                self.weight.repeat(self.num_splits), self.bias.repeat(self.num_splits),\n",
        "                True, self.momentum, self.eps).view(N, C, H, W)\n",
        "        else:\n",
        "            return F.batch_norm(\n",
        "                input, self.running_mean[:self.num_features], self.running_var[:self.num_features],\n",
        "                self.weight, self.bias, False, self.momentum, self.eps)\n",
        "            "
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h_Cx9q2QFgM7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "num_splits = 2\n",
        "#Receptive field is calcualted based on actual formula\n",
        "class Net(nn.Module):\n",
        "    def __init__(self, enable_bn=True ):\n",
        "        super(Net, self).__init__()\n",
        "        self.enable_bn = enable_bn\n",
        "\n",
        "        #block1\n",
        "        self.conv1 = nn.Conv2d(1, 8, 3,padding = True) #o/p size:28; rf: 3\n",
        "        self.Batchnorm1 = nn.BatchNorm2d(8)\n",
        "        if not enable_bn:\n",
        "            self.Batchnorm1 = GhostBatchNorm(num_features=8, num_splits=num_splits)\n",
        "        \n",
        "        self.conv2 = nn.Conv2d(8, 16, 3) #o/p size: 26; rf: 5\n",
        "        self.Batchnorm2 = nn.BatchNorm2d(16)\n",
        "        if not enable_bn:\n",
        "            self.Batchnorm2 = GhostBatchNorm(num_features=16, num_splits=num_splits)\n",
        "\n",
        "\n",
        "        #transition block\n",
        "        self.pool1 = nn.MaxPool2d(2, 2) #o/p size: 13; rf: 6\n",
        "        self.pool1trns = nn.Conv2d(16, 10, 1)#o/p size: 13; rf: 6 \n",
        "        self.Batchnormtrns1 = nn.BatchNorm2d(10)\n",
        "        if not enable_bn:\n",
        "            self.Batchnormtrns1 = GhostBatchNorm(num_features=10, num_splits=num_splits)\n",
        "        \n",
        "        #block2\n",
        "        self.conv3 = nn.Conv2d(10,14,3) #o/p size: 11; rf: 10 # 14\n",
        "        self.Batchnorm3 = nn.BatchNorm2d(14)\n",
        "        if not enable_bn:\n",
        "            self.Batchnorm3 = GhostBatchNorm(num_features=14, num_splits=num_splits)\n",
        "\n",
        "        self.dp3 = nn.Dropout(p = 0.10) \n",
        "        self.conv4 = nn.Conv2d(14, 16, 3) #o/p size: 9; rf: 14\n",
        "        self.Batchnorm4 = nn.BatchNorm2d(16)\n",
        "        if not enable_bn:\n",
        "            self.Batchnorm4 = GhostBatchNorm(num_features=16, num_splits=num_splits)\n",
        "\n",
        "        self.dp4 = nn.Dropout(p = 0.10)\n",
        "        self.conv5 = nn.Conv2d(16, 20, 3) #o/p size: 7; rf: 18\n",
        "        self.Batchnorm5 = nn.BatchNorm2d(20)\n",
        "        if not enable_bn:\n",
        "            self.Batchnorm5 = GhostBatchNorm(num_features=20, num_splits=num_splits)\n",
        "\n",
        "        self.dp5 = nn.Dropout(p = 0.10)\n",
        "\n",
        "        #gap and 1X1 \n",
        "        self.conv6_avgp = nn.AvgPool2d(kernel_size=7) #o/p size: 1; rf: 30\n",
        "        self.pool2trns = nn.Conv2d(20,10,1) #6\n",
        "\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.conv1(x)\n",
        "        x = F.relu(x)\n",
        "        x = self.Batchnorm1(x)\n",
        "\n",
        "        x = self.conv2(x)\n",
        "        x = F.relu(x)\n",
        "        x = self.Batchnorm2(x)\n",
        "\n",
        "\n",
        "        x = self.pool1(x)\n",
        "        x = self.pool1trns(x)\n",
        "        x = F.relu(x)\n",
        "        x = self.Batchnormtrns1(x)\n",
        "\n",
        "        x = self.conv3(x)\n",
        "        x = F.relu(x)\n",
        "        x = self.Batchnorm3(x)\n",
        "        x = self.dp3(x)\n",
        " \n",
        "        x = self.conv4(x)\n",
        "        x = F.relu(x)\n",
        "        x = self.Batchnorm4(x)\n",
        "        x = self.dp4(x)\n",
        "\n",
        "        x = self.conv5(x)\n",
        "        x = F.relu(x)\n",
        "        x = self.Batchnorm5(x)\n",
        "        x = self.dp5(x)\n",
        "\n",
        "        x = self.conv6_avgp(x)\n",
        "\n",
        "        x = self.pool2trns(x)\n",
        "\n",
        "\n",
        "        x = x.view(-1, 10)\n",
        "        return F.log_softmax(x,dim=-1)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "xdydjYTZFyi3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 553
        },
        "outputId": "69fe64af-cd47-4b18-9e3c-64d1e64a3663"
      },
      "source": [
        "#!pip install torchsummary\n",
        "from torchsummary import summary\n",
        "use_cuda = torch.cuda.is_available()\n",
        "device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n",
        "model = Net(True).to(device)\n",
        "\n",
        "summary(model, input_size=(1, 28, 28))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Conv2d-1            [-1, 8, 28, 28]              80\n",
            "       BatchNorm2d-2            [-1, 8, 28, 28]              16\n",
            "            Conv2d-3           [-1, 16, 26, 26]           1,168\n",
            "       BatchNorm2d-4           [-1, 16, 26, 26]              32\n",
            "         MaxPool2d-5           [-1, 16, 13, 13]               0\n",
            "            Conv2d-6           [-1, 10, 13, 13]             170\n",
            "       BatchNorm2d-7           [-1, 10, 13, 13]              20\n",
            "            Conv2d-8           [-1, 14, 11, 11]           1,274\n",
            "       BatchNorm2d-9           [-1, 14, 11, 11]              28\n",
            "          Dropout-10           [-1, 14, 11, 11]               0\n",
            "           Conv2d-11             [-1, 16, 9, 9]           2,032\n",
            "      BatchNorm2d-12             [-1, 16, 9, 9]              32\n",
            "          Dropout-13             [-1, 16, 9, 9]               0\n",
            "           Conv2d-14             [-1, 20, 7, 7]           2,900\n",
            "      BatchNorm2d-15             [-1, 20, 7, 7]              40\n",
            "          Dropout-16             [-1, 20, 7, 7]               0\n",
            "        AvgPool2d-17             [-1, 20, 1, 1]               0\n",
            "           Conv2d-18             [-1, 10, 1, 1]             210\n",
            "================================================================\n",
            "Total params: 8,002\n",
            "Trainable params: 8,002\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.00\n",
            "Forward/backward pass size (MB): 0.40\n",
            "Params size (MB): 0.03\n",
            "Estimated Total Size (MB): 0.43\n",
            "----------------------------------------------------------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DqTWLaM5GHgH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "torch.manual_seed(1)\n",
        "batch_size = 64\n",
        "\n",
        "kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}\n",
        "train_loader = torch.utils.data.DataLoader(\n",
        "    datasets.MNIST('../data', train=True, download=True,\n",
        "                    transform=transforms.Compose([\n",
        "                        transforms.RandomRotation((-8.0, 8.0), fill=(1,)),\n",
        "                        transforms.ToTensor(),\n",
        "                        transforms.Normalize((0.1307,), (0.3081,))\n",
        "                    ])),\n",
        "    batch_size=batch_size, shuffle=True, **kwargs)\n",
        "\n",
        "\n",
        "test_loader = torch.utils.data.DataLoader(\n",
        "    datasets.MNIST('../data', train=False, transform=transforms.Compose([\n",
        "                        transforms.ToTensor(),\n",
        "                        transforms.Normalize((0.1307,), (0.3081,))\n",
        "                    ])),\n",
        "    batch_size=batch_size, shuffle=True, **kwargs)\n"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8fDefDhaFlwH",
        "colab_type": "code",
        "cellView": "both",
        "colab": {}
      },
      "source": [
        "train_losses, train_accuracy = [], []\n",
        "val_losses = []\n",
        "val_accuracy = []\n",
        "\n",
        "from tqdm import tqdm\n",
        "def train(model, device, train_loader, optimizer, epoch, \n",
        "          enable_l1=False, enable_l2=False, enable_gbn=False):\n",
        "    model.train()\n",
        "    #pbar = tqdm(train_loader)\n",
        "    train_loss = 0 \n",
        "    train_correct = 0\n",
        "    train_acc = 0\n",
        "\n",
        "    # For best lambda value, apply grid search\n",
        "    lambda_l1 = 0.001\n",
        "    #scheduler.step()\n",
        "    for batch_idx, (data, target) in enumerate(train_loader):\n",
        "        data, target = data.to(device), target.to(device)\n",
        "        optimizer.zero_grad()\n",
        "        output = model(data)\n",
        "        loss = F.nll_loss(output, target)\n",
        "\n",
        "        # loss function\n",
        "        # L1 loss\n",
        "        if (enable_l1 and not (enable_l2 and enable_gbn)):\n",
        "            l1_loss = 0\n",
        "            for param in model.parameters():\n",
        "                l1_loss = l1_loss + param.abs().sum()\n",
        "            loss = loss + lambda_l1 * l1_loss\n",
        "\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        #pbar.set_description(desc= f'loss={loss.item()} batch_id={batch_idx}')\n",
        "        train_loss += F.nll_loss(output, target, reduction='sum').item()  # sum up batch loss\n",
        "        pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability\n",
        "        train_correct += pred.eq(target.view_as(pred)).sum().item()\n",
        "\n",
        "    #\n",
        "    train_loss /= len(train_loader.dataset)\n",
        "    train_acc = 100. * train_correct / len(train_loader.dataset)\n",
        "    train_losses.append(train_loss)\n",
        "    train_accuracy.append(train_acc)\n",
        "\n",
        "    #train_loss /= len(test_loader.dataset)\n",
        "    \n",
        "    print('Epoch: {:.0f},LR: {}.\\nTrain set: train Average loss: {:.4f}, train_Accuracy: {}/{} ({:.4f}%)\\n'.format(\n",
        "        epoch,optimizer.param_groups[0]['lr'],train_loss, train_correct, len(train_loader.dataset),\n",
        "        100. * train_correct / len(train_loader.dataset)))\n",
        "    return train_losses,train_accuracy\n",
        "        \n",
        "\n",
        "def test(model, device, test_loader):\n",
        "    model.eval()\n",
        "    test_loss = 0\n",
        "    correct = 0\n",
        "\n",
        "    with torch.no_grad():\n",
        "        for data, target in test_loader:\n",
        "            data, target = data.to(device), target.to(device)\n",
        "            output = model(data)\n",
        "            test_loss += F.nll_loss(output, target, reduction='sum').item()  # sum up batch loss\n",
        "\n",
        "            pred_test = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability\n",
        "            correct += pred_test.eq(target.view_as(pred_test)).sum().item()\n",
        "\n",
        "    test_loss /= len(test_loader.dataset)\n",
        "    val_acc = 100. * correct / len(test_loader.dataset)\n",
        "\n",
        "    \n",
        "    print('Test set: test Average loss: {:.4f}, test Accuracy: {}/{} ({:.4f}%)\\n'.format(\n",
        "        test_loss, correct, len(test_loader.dataset),\n",
        "        100. * correct / len(test_loader.dataset)))\n",
        "    return test_loss, val_acc\n",
        "    \n"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MMWbLWO6FuHb",
        "colab_type": "code",
        "cellView": "both",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "55eb435c-a0d1-4533-9f01-2ea4a50c0311"
      },
      "source": [
        "from torch.optim.lr_scheduler import StepLR\n",
        "\n",
        "# Loop To Run Network \n",
        "# 1. with L1 + BN\n",
        "# 2. with L2 + BN\n",
        "# 3. with L1 and L2 with BN\n",
        "# 4. with GBN\n",
        "# 5. with L1 and L2 with GBN\n",
        "\n",
        "losses = ['l1', 'l2', 'l1&l2', 'gbn', 'l1l2gbn']\n",
        "\n",
        "val_losses = []\n",
        "val_accs = []\n",
        "\n",
        "for idx in range(len(losses)): #len(losses)):\n",
        "    i = idx+1\n",
        "    enable_l1 = False\n",
        "    enable_l2 = False\n",
        "    enable_gbn = False\n",
        "\n",
        "    print(\"{} {} loss\".format((i),losses[idx]))\n",
        "    ######### Model Initialization #############################################\n",
        "    # Enable BN for option 1, 2 & 3\n",
        "    if i == 1 or i==2 or i==3:\n",
        "        model = Net(enable_bn=True).to(device)\n",
        "        print(\"BN enabled for option: {}\".format(i))\n",
        "    # Enable GBN for option 4 & 5( disable BN)\n",
        "    elif i==4 or i==5:\n",
        "        model = Net(enable_bn=False).to(device)\n",
        "        print(\"GBN Enabled & BN disabled for option: {}\".format(i))\n",
        "    print (\"Model Summary:\")\n",
        "    summary(model, input_size=(1, 28, 28))\n",
        "\n",
        "    ########## Optimizer Initialization #######################################\n",
        "    optimizer = optim.SGD(model.parameters(), lr=0.03, momentum=0.9)\n",
        "    if i==2 or i==3 or i==5:\n",
        "        # decay of 0.0001 gave best val accuracy 99.38\n",
        "        decay = 0.0001\n",
        "        optimizer = optim.SGD(model.parameters(), lr=0.03, momentum=0.9, weight_decay=decay)  \n",
        "        print(\"Weight decay {} set for option: {}\".format(decay, i))\n",
        "    \n",
        "    scheduler = StepLR(optimizer, step_size=4, gamma=0.8)\n",
        "    ######### Loss Function Selection #########################################\n",
        "    if i==1:\n",
        "         enable_l1 = True\n",
        "         print(\"LOSS: l1 loss enabled\")\n",
        "    elif i==2:\n",
        "         enable_l2 = True\n",
        "         print(\"LOSS: l2 loss enabled\")\n",
        "    elif i==3:\n",
        "         enable_l1 = True\n",
        "         enable_l2=True\n",
        "         print(\"LOSS: l1l2 loss enabled\")\n",
        "    elif i==4:         \n",
        "         enable_gbn = True\n",
        "         print(\"LOSS: gbn loss enabled\")\n",
        "    elif i==5:\n",
        "         enable_l1 = True\n",
        "         enable_l2=True\n",
        "         enable_gbn = True\n",
        "         print(\"LOSS: l1l2gbn loss enabled\")\n",
        "    val_loss = []\n",
        "    val_acc = []\n",
        "    for epoch in range(1, 26):\n",
        "        train(model, device, train_loader, optimizer, epoch, \n",
        "              enable_l1=enable_l1, enable_l2=enable_l2, enable_gbn=enable_gbn)\n",
        "        scheduler.step()\n",
        "        loss, acc = test(model, device, test_loader)\n",
        "        val_loss.append(loss)\n",
        "        val_acc.append(acc)\n",
        "\n",
        "    val_losses.append(val_loss)\n",
        "    val_accs.append(val_acc)\n",
        "\n",
        "    # Save GBN Model\n",
        "    if i == 4:\n",
        "        PATH = \"gbn_model.pt\"\n",
        "        torch.save(model, PATH)\n",
        "        print(\"GBN Model saved at {}\".format(PATH))\n"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1 l1 loss\n",
            "BN enabled for option: 1\n",
            "Model Summary:\n",
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Conv2d-1            [-1, 8, 28, 28]              80\n",
            "       BatchNorm2d-2            [-1, 8, 28, 28]              16\n",
            "            Conv2d-3           [-1, 16, 26, 26]           1,168\n",
            "       BatchNorm2d-4           [-1, 16, 26, 26]              32\n",
            "         MaxPool2d-5           [-1, 16, 13, 13]               0\n",
            "            Conv2d-6           [-1, 10, 13, 13]             170\n",
            "       BatchNorm2d-7           [-1, 10, 13, 13]              20\n",
            "            Conv2d-8           [-1, 14, 11, 11]           1,274\n",
            "       BatchNorm2d-9           [-1, 14, 11, 11]              28\n",
            "          Dropout-10           [-1, 14, 11, 11]               0\n",
            "           Conv2d-11             [-1, 16, 9, 9]           2,032\n",
            "      BatchNorm2d-12             [-1, 16, 9, 9]              32\n",
            "          Dropout-13             [-1, 16, 9, 9]               0\n",
            "           Conv2d-14             [-1, 20, 7, 7]           2,900\n",
            "      BatchNorm2d-15             [-1, 20, 7, 7]              40\n",
            "          Dropout-16             [-1, 20, 7, 7]               0\n",
            "        AvgPool2d-17             [-1, 20, 1, 1]               0\n",
            "           Conv2d-18             [-1, 10, 1, 1]             210\n",
            "================================================================\n",
            "Total params: 8,002\n",
            "Trainable params: 8,002\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.00\n",
            "Forward/backward pass size (MB): 0.40\n",
            "Params size (MB): 0.03\n",
            "Estimated Total Size (MB): 0.43\n",
            "----------------------------------------------------------------\n",
            "LOSS: l1 loss enabled\n",
            "Epoch: 1,LR: 0.03.\n",
            "Train set: train Average loss: 0.2860, train_Accuracy: 55017/60000 (91.6950%)\n",
            "\n",
            "Test set: test Average loss: 0.1071, test Accuracy: 9697/10000 (96.9700%)\n",
            "\n",
            "Epoch: 2,LR: 0.03.\n",
            "Train set: train Average loss: 0.1347, train_Accuracy: 57639/60000 (96.0650%)\n",
            "\n",
            "Test set: test Average loss: 0.1010, test Accuracy: 9700/10000 (97.0000%)\n",
            "\n",
            "Epoch: 3,LR: 0.03.\n",
            "Train set: train Average loss: 0.1317, train_Accuracy: 57625/60000 (96.0417%)\n",
            "\n",
            "Test set: test Average loss: 0.1439, test Accuracy: 9575/10000 (95.7500%)\n",
            "\n",
            "Epoch: 4,LR: 0.03.\n",
            "Train set: train Average loss: 0.1264, train_Accuracy: 57737/60000 (96.2283%)\n",
            "\n",
            "Test set: test Average loss: 0.2361, test Accuracy: 9281/10000 (92.8100%)\n",
            "\n",
            "Epoch: 5,LR: 0.024.\n",
            "Train set: train Average loss: 0.1194, train_Accuracy: 57858/60000 (96.4300%)\n",
            "\n",
            "Test set: test Average loss: 0.1251, test Accuracy: 9598/10000 (95.9800%)\n",
            "\n",
            "Epoch: 6,LR: 0.024.\n",
            "Train set: train Average loss: 0.1222, train_Accuracy: 57766/60000 (96.2767%)\n",
            "\n",
            "Test set: test Average loss: 0.0970, test Accuracy: 9706/10000 (97.0600%)\n",
            "\n",
            "Epoch: 7,LR: 0.024.\n",
            "Train set: train Average loss: 0.1162, train_Accuracy: 57900/60000 (96.5000%)\n",
            "\n",
            "Test set: test Average loss: 0.1078, test Accuracy: 9656/10000 (96.5600%)\n",
            "\n",
            "Epoch: 8,LR: 0.024.\n",
            "Train set: train Average loss: 0.1186, train_Accuracy: 57843/60000 (96.4050%)\n",
            "\n",
            "Test set: test Average loss: 0.5182, test Accuracy: 8286/10000 (82.8600%)\n",
            "\n",
            "Epoch: 9,LR: 0.019200000000000002.\n",
            "Train set: train Average loss: 0.1107, train_Accuracy: 58011/60000 (96.6850%)\n",
            "\n",
            "Test set: test Average loss: 0.0891, test Accuracy: 9710/10000 (97.1000%)\n",
            "\n",
            "Epoch: 10,LR: 0.019200000000000002.\n",
            "Train set: train Average loss: 0.1114, train_Accuracy: 57993/60000 (96.6550%)\n",
            "\n",
            "Test set: test Average loss: 0.0701, test Accuracy: 9780/10000 (97.8000%)\n",
            "\n",
            "Epoch: 11,LR: 0.019200000000000002.\n",
            "Train set: train Average loss: 0.1088, train_Accuracy: 58052/60000 (96.7533%)\n",
            "\n",
            "Test set: test Average loss: 0.0785, test Accuracy: 9732/10000 (97.3200%)\n",
            "\n",
            "Epoch: 12,LR: 0.019200000000000002.\n",
            "Train set: train Average loss: 0.1120, train_Accuracy: 57987/60000 (96.6450%)\n",
            "\n",
            "Test set: test Average loss: 0.2288, test Accuracy: 9318/10000 (93.1800%)\n",
            "\n",
            "Epoch: 13,LR: 0.015360000000000002.\n",
            "Train set: train Average loss: 0.1049, train_Accuracy: 58077/60000 (96.7950%)\n",
            "\n",
            "Test set: test Average loss: 0.0956, test Accuracy: 9703/10000 (97.0300%)\n",
            "\n",
            "Epoch: 14,LR: 0.015360000000000002.\n",
            "Train set: train Average loss: 0.1077, train_Accuracy: 58061/60000 (96.7683%)\n",
            "\n",
            "Test set: test Average loss: 0.0983, test Accuracy: 9682/10000 (96.8200%)\n",
            "\n",
            "Epoch: 15,LR: 0.015360000000000002.\n",
            "Train set: train Average loss: 0.1078, train_Accuracy: 58036/60000 (96.7267%)\n",
            "\n",
            "Test set: test Average loss: 0.0704, test Accuracy: 9794/10000 (97.9400%)\n",
            "\n",
            "Epoch: 16,LR: 0.015360000000000002.\n",
            "Train set: train Average loss: 0.1091, train_Accuracy: 58024/60000 (96.7067%)\n",
            "\n",
            "Test set: test Average loss: 0.0733, test Accuracy: 9782/10000 (97.8200%)\n",
            "\n",
            "Epoch: 17,LR: 0.012288000000000002.\n",
            "Train set: train Average loss: 0.1022, train_Accuracy: 58138/60000 (96.8967%)\n",
            "\n",
            "Test set: test Average loss: 0.1064, test Accuracy: 9679/10000 (96.7900%)\n",
            "\n",
            "Epoch: 18,LR: 0.012288000000000002.\n",
            "Train set: train Average loss: 0.1036, train_Accuracy: 58108/60000 (96.8467%)\n",
            "\n",
            "Test set: test Average loss: 0.0997, test Accuracy: 9679/10000 (96.7900%)\n",
            "\n",
            "Epoch: 19,LR: 0.012288000000000002.\n",
            "Train set: train Average loss: 0.0991, train_Accuracy: 58213/60000 (97.0217%)\n",
            "\n",
            "Test set: test Average loss: 0.0953, test Accuracy: 9707/10000 (97.0700%)\n",
            "\n",
            "Epoch: 20,LR: 0.012288000000000002.\n",
            "Train set: train Average loss: 0.1023, train_Accuracy: 58156/60000 (96.9267%)\n",
            "\n",
            "Test set: test Average loss: 0.2698, test Accuracy: 9099/10000 (90.9900%)\n",
            "\n",
            "Epoch: 21,LR: 0.009830400000000003.\n",
            "Train set: train Average loss: 0.0957, train_Accuracy: 58232/60000 (97.0533%)\n",
            "\n",
            "Test set: test Average loss: 0.0867, test Accuracy: 9742/10000 (97.4200%)\n",
            "\n",
            "Epoch: 22,LR: 0.009830400000000003.\n",
            "Train set: train Average loss: 0.0977, train_Accuracy: 58211/60000 (97.0183%)\n",
            "\n",
            "Test set: test Average loss: 0.0649, test Accuracy: 9812/10000 (98.1200%)\n",
            "\n",
            "Epoch: 23,LR: 0.009830400000000003.\n",
            "Train set: train Average loss: 0.0983, train_Accuracy: 58235/60000 (97.0583%)\n",
            "\n",
            "Test set: test Average loss: 0.1148, test Accuracy: 9612/10000 (96.1200%)\n",
            "\n",
            "Epoch: 24,LR: 0.009830400000000003.\n",
            "Train set: train Average loss: 0.0989, train_Accuracy: 58168/60000 (96.9467%)\n",
            "\n",
            "Test set: test Average loss: 0.1275, test Accuracy: 9618/10000 (96.1800%)\n",
            "\n",
            "Epoch: 25,LR: 0.007864320000000003.\n",
            "Train set: train Average loss: 0.0907, train_Accuracy: 58403/60000 (97.3383%)\n",
            "\n",
            "Test set: test Average loss: 0.1129, test Accuracy: 9655/10000 (96.5500%)\n",
            "\n",
            "2 l2 loss\n",
            "BN enabled for option: 2\n",
            "Model Summary:\n",
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Conv2d-1            [-1, 8, 28, 28]              80\n",
            "       BatchNorm2d-2            [-1, 8, 28, 28]              16\n",
            "            Conv2d-3           [-1, 16, 26, 26]           1,168\n",
            "       BatchNorm2d-4           [-1, 16, 26, 26]              32\n",
            "         MaxPool2d-5           [-1, 16, 13, 13]               0\n",
            "            Conv2d-6           [-1, 10, 13, 13]             170\n",
            "       BatchNorm2d-7           [-1, 10, 13, 13]              20\n",
            "            Conv2d-8           [-1, 14, 11, 11]           1,274\n",
            "       BatchNorm2d-9           [-1, 14, 11, 11]              28\n",
            "          Dropout-10           [-1, 14, 11, 11]               0\n",
            "           Conv2d-11             [-1, 16, 9, 9]           2,032\n",
            "      BatchNorm2d-12             [-1, 16, 9, 9]              32\n",
            "          Dropout-13             [-1, 16, 9, 9]               0\n",
            "           Conv2d-14             [-1, 20, 7, 7]           2,900\n",
            "      BatchNorm2d-15             [-1, 20, 7, 7]              40\n",
            "          Dropout-16             [-1, 20, 7, 7]               0\n",
            "        AvgPool2d-17             [-1, 20, 1, 1]               0\n",
            "           Conv2d-18             [-1, 10, 1, 1]             210\n",
            "================================================================\n",
            "Total params: 8,002\n",
            "Trainable params: 8,002\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.00\n",
            "Forward/backward pass size (MB): 0.40\n",
            "Params size (MB): 0.03\n",
            "Estimated Total Size (MB): 0.43\n",
            "----------------------------------------------------------------\n",
            "Weight decay 0.0001 set for option: 2\n",
            "LOSS: l2 loss enabled\n",
            "Epoch: 1,LR: 0.03.\n",
            "Train set: train Average loss: 0.2503, train_Accuracy: 55608/60000 (92.6800%)\n",
            "\n",
            "Test set: test Average loss: 0.0617, test Accuracy: 9818/10000 (98.1800%)\n",
            "\n",
            "Epoch: 2,LR: 0.03.\n",
            "Train set: train Average loss: 0.0789, train_Accuracy: 58564/60000 (97.6067%)\n",
            "\n",
            "Test set: test Average loss: 0.0396, test Accuracy: 9877/10000 (98.7700%)\n",
            "\n",
            "Epoch: 3,LR: 0.03.\n",
            "Train set: train Average loss: 0.0615, train_Accuracy: 58884/60000 (98.1400%)\n",
            "\n",
            "Test set: test Average loss: 0.0354, test Accuracy: 9888/10000 (98.8800%)\n",
            "\n",
            "Epoch: 4,LR: 0.03.\n",
            "Train set: train Average loss: 0.0554, train_Accuracy: 58946/60000 (98.2433%)\n",
            "\n",
            "Test set: test Average loss: 0.0321, test Accuracy: 9899/10000 (98.9900%)\n",
            "\n",
            "Epoch: 5,LR: 0.024.\n",
            "Train set: train Average loss: 0.0480, train_Accuracy: 59105/60000 (98.5083%)\n",
            "\n",
            "Test set: test Average loss: 0.0246, test Accuracy: 9925/10000 (99.2500%)\n",
            "\n",
            "Epoch: 6,LR: 0.024.\n",
            "Train set: train Average loss: 0.0457, train_Accuracy: 59177/60000 (98.6283%)\n",
            "\n",
            "Test set: test Average loss: 0.0221, test Accuracy: 9927/10000 (99.2700%)\n",
            "\n",
            "Epoch: 7,LR: 0.024.\n",
            "Train set: train Average loss: 0.0434, train_Accuracy: 59203/60000 (98.6717%)\n",
            "\n",
            "Test set: test Average loss: 0.0229, test Accuracy: 9936/10000 (99.3600%)\n",
            "\n",
            "Epoch: 8,LR: 0.024.\n",
            "Train set: train Average loss: 0.0405, train_Accuracy: 59252/60000 (98.7533%)\n",
            "\n",
            "Test set: test Average loss: 0.0220, test Accuracy: 9928/10000 (99.2800%)\n",
            "\n",
            "Epoch: 9,LR: 0.019200000000000002.\n",
            "Train set: train Average loss: 0.0382, train_Accuracy: 59278/60000 (98.7967%)\n",
            "\n",
            "Test set: test Average loss: 0.0216, test Accuracy: 9939/10000 (99.3900%)\n",
            "\n",
            "Epoch: 10,LR: 0.019200000000000002.\n",
            "Train set: train Average loss: 0.0379, train_Accuracy: 59296/60000 (98.8267%)\n",
            "\n",
            "Test set: test Average loss: 0.0175, test Accuracy: 9946/10000 (99.4600%)\n",
            "\n",
            "Epoch: 11,LR: 0.019200000000000002.\n",
            "Train set: train Average loss: 0.0374, train_Accuracy: 59297/60000 (98.8283%)\n",
            "\n",
            "Test set: test Average loss: 0.0227, test Accuracy: 9924/10000 (99.2400%)\n",
            "\n",
            "Epoch: 12,LR: 0.019200000000000002.\n",
            "Train set: train Average loss: 0.0372, train_Accuracy: 59291/60000 (98.8183%)\n",
            "\n",
            "Test set: test Average loss: 0.0176, test Accuracy: 9944/10000 (99.4400%)\n",
            "\n",
            "Epoch: 13,LR: 0.015360000000000002.\n",
            "Train set: train Average loss: 0.0326, train_Accuracy: 59403/60000 (99.0050%)\n",
            "\n",
            "Test set: test Average loss: 0.0184, test Accuracy: 9944/10000 (99.4400%)\n",
            "\n",
            "Epoch: 14,LR: 0.015360000000000002.\n",
            "Train set: train Average loss: 0.0326, train_Accuracy: 59409/60000 (99.0150%)\n",
            "\n",
            "Test set: test Average loss: 0.0197, test Accuracy: 9943/10000 (99.4300%)\n",
            "\n",
            "Epoch: 15,LR: 0.015360000000000002.\n",
            "Train set: train Average loss: 0.0332, train_Accuracy: 59392/60000 (98.9867%)\n",
            "\n",
            "Test set: test Average loss: 0.0194, test Accuracy: 9939/10000 (99.3900%)\n",
            "\n",
            "Epoch: 16,LR: 0.015360000000000002.\n",
            "Train set: train Average loss: 0.0328, train_Accuracy: 59399/60000 (98.9983%)\n",
            "\n",
            "Test set: test Average loss: 0.0189, test Accuracy: 9941/10000 (99.4100%)\n",
            "\n",
            "Epoch: 17,LR: 0.012288000000000002.\n",
            "Train set: train Average loss: 0.0292, train_Accuracy: 59471/60000 (99.1183%)\n",
            "\n",
            "Test set: test Average loss: 0.0173, test Accuracy: 9948/10000 (99.4800%)\n",
            "\n",
            "Epoch: 18,LR: 0.012288000000000002.\n",
            "Train set: train Average loss: 0.0296, train_Accuracy: 59454/60000 (99.0900%)\n",
            "\n",
            "Test set: test Average loss: 0.0178, test Accuracy: 9946/10000 (99.4600%)\n",
            "\n",
            "Epoch: 19,LR: 0.012288000000000002.\n",
            "Train set: train Average loss: 0.0298, train_Accuracy: 59441/60000 (99.0683%)\n",
            "\n",
            "Test set: test Average loss: 0.0210, test Accuracy: 9933/10000 (99.3300%)\n",
            "\n",
            "Epoch: 20,LR: 0.012288000000000002.\n",
            "Train set: train Average loss: 0.0294, train_Accuracy: 59482/60000 (99.1367%)\n",
            "\n",
            "Test set: test Average loss: 0.0180, test Accuracy: 9948/10000 (99.4800%)\n",
            "\n",
            "Epoch: 21,LR: 0.009830400000000003.\n",
            "Train set: train Average loss: 0.0274, train_Accuracy: 59500/60000 (99.1667%)\n",
            "\n",
            "Test set: test Average loss: 0.0169, test Accuracy: 9950/10000 (99.5000%)\n",
            "\n",
            "Epoch: 22,LR: 0.009830400000000003.\n",
            "Train set: train Average loss: 0.0280, train_Accuracy: 59478/60000 (99.1300%)\n",
            "\n",
            "Test set: test Average loss: 0.0158, test Accuracy: 9950/10000 (99.5000%)\n",
            "\n",
            "Epoch: 23,LR: 0.009830400000000003.\n",
            "Train set: train Average loss: 0.0260, train_Accuracy: 59505/60000 (99.1750%)\n",
            "\n",
            "Test set: test Average loss: 0.0149, test Accuracy: 9959/10000 (99.5900%)\n",
            "\n",
            "Epoch: 24,LR: 0.009830400000000003.\n",
            "Train set: train Average loss: 0.0265, train_Accuracy: 59513/60000 (99.1883%)\n",
            "\n",
            "Test set: test Average loss: 0.0159, test Accuracy: 9956/10000 (99.5600%)\n",
            "\n",
            "Epoch: 25,LR: 0.007864320000000003.\n",
            "Train set: train Average loss: 0.0265, train_Accuracy: 59531/60000 (99.2183%)\n",
            "\n",
            "Test set: test Average loss: 0.0163, test Accuracy: 9955/10000 (99.5500%)\n",
            "\n",
            "3 l1&l2 loss\n",
            "BN enabled for option: 3\n",
            "Model Summary:\n",
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Conv2d-1            [-1, 8, 28, 28]              80\n",
            "       BatchNorm2d-2            [-1, 8, 28, 28]              16\n",
            "            Conv2d-3           [-1, 16, 26, 26]           1,168\n",
            "       BatchNorm2d-4           [-1, 16, 26, 26]              32\n",
            "         MaxPool2d-5           [-1, 16, 13, 13]               0\n",
            "            Conv2d-6           [-1, 10, 13, 13]             170\n",
            "       BatchNorm2d-7           [-1, 10, 13, 13]              20\n",
            "            Conv2d-8           [-1, 14, 11, 11]           1,274\n",
            "       BatchNorm2d-9           [-1, 14, 11, 11]              28\n",
            "          Dropout-10           [-1, 14, 11, 11]               0\n",
            "           Conv2d-11             [-1, 16, 9, 9]           2,032\n",
            "      BatchNorm2d-12             [-1, 16, 9, 9]              32\n",
            "          Dropout-13             [-1, 16, 9, 9]               0\n",
            "           Conv2d-14             [-1, 20, 7, 7]           2,900\n",
            "      BatchNorm2d-15             [-1, 20, 7, 7]              40\n",
            "          Dropout-16             [-1, 20, 7, 7]               0\n",
            "        AvgPool2d-17             [-1, 20, 1, 1]               0\n",
            "           Conv2d-18             [-1, 10, 1, 1]             210\n",
            "================================================================\n",
            "Total params: 8,002\n",
            "Trainable params: 8,002\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.00\n",
            "Forward/backward pass size (MB): 0.40\n",
            "Params size (MB): 0.03\n",
            "Estimated Total Size (MB): 0.43\n",
            "----------------------------------------------------------------\n",
            "Weight decay 0.0001 set for option: 3\n",
            "LOSS: l1l2 loss enabled\n",
            "Epoch: 1,LR: 0.03.\n",
            "Train set: train Average loss: 0.2831, train_Accuracy: 55114/60000 (91.8567%)\n",
            "\n",
            "Test set: test Average loss: 0.1379, test Accuracy: 9598/10000 (95.9800%)\n",
            "\n",
            "Epoch: 2,LR: 0.03.\n",
            "Train set: train Average loss: 0.1387, train_Accuracy: 57586/60000 (95.9767%)\n",
            "\n",
            "Test set: test Average loss: 0.1297, test Accuracy: 9636/10000 (96.3600%)\n",
            "\n",
            "Epoch: 3,LR: 0.03.\n",
            "Train set: train Average loss: 0.1388, train_Accuracy: 57535/60000 (95.8917%)\n",
            "\n",
            "Test set: test Average loss: 0.1719, test Accuracy: 9449/10000 (94.4900%)\n",
            "\n",
            "Epoch: 4,LR: 0.03.\n",
            "Train set: train Average loss: 0.1378, train_Accuracy: 57564/60000 (95.9400%)\n",
            "\n",
            "Test set: test Average loss: 0.1096, test Accuracy: 9685/10000 (96.8500%)\n",
            "\n",
            "Epoch: 5,LR: 0.024.\n",
            "Train set: train Average loss: 0.1233, train_Accuracy: 57800/60000 (96.3333%)\n",
            "\n",
            "Test set: test Average loss: 0.1207, test Accuracy: 9653/10000 (96.5300%)\n",
            "\n",
            "Epoch: 6,LR: 0.024.\n",
            "Train set: train Average loss: 0.1232, train_Accuracy: 57799/60000 (96.3317%)\n",
            "\n",
            "Test set: test Average loss: 0.0640, test Accuracy: 9811/10000 (98.1100%)\n",
            "\n",
            "Epoch: 7,LR: 0.024.\n",
            "Train set: train Average loss: 0.1213, train_Accuracy: 57845/60000 (96.4083%)\n",
            "\n",
            "Test set: test Average loss: 0.1358, test Accuracy: 9552/10000 (95.5200%)\n",
            "\n",
            "Epoch: 8,LR: 0.024.\n",
            "Train set: train Average loss: 0.1235, train_Accuracy: 57825/60000 (96.3750%)\n",
            "\n",
            "Test set: test Average loss: 0.1708, test Accuracy: 9485/10000 (94.8500%)\n",
            "\n",
            "Epoch: 9,LR: 0.019200000000000002.\n",
            "Train set: train Average loss: 0.1100, train_Accuracy: 58032/60000 (96.7200%)\n",
            "\n",
            "Test set: test Average loss: 0.1131, test Accuracy: 9646/10000 (96.4600%)\n",
            "\n",
            "Epoch: 10,LR: 0.019200000000000002.\n",
            "Train set: train Average loss: 0.1162, train_Accuracy: 57972/60000 (96.6200%)\n",
            "\n",
            "Test set: test Average loss: 0.0955, test Accuracy: 9723/10000 (97.2300%)\n",
            "\n",
            "Epoch: 11,LR: 0.019200000000000002.\n",
            "Train set: train Average loss: 0.1132, train_Accuracy: 58007/60000 (96.6783%)\n",
            "\n",
            "Test set: test Average loss: 0.0843, test Accuracy: 9742/10000 (97.4200%)\n",
            "\n",
            "Epoch: 12,LR: 0.019200000000000002.\n",
            "Train set: train Average loss: 0.1136, train_Accuracy: 57957/60000 (96.5950%)\n",
            "\n",
            "Test set: test Average loss: 0.1542, test Accuracy: 9506/10000 (95.0600%)\n",
            "\n",
            "Epoch: 13,LR: 0.015360000000000002.\n",
            "Train set: train Average loss: 0.1072, train_Accuracy: 58059/60000 (96.7650%)\n",
            "\n",
            "Test set: test Average loss: 0.0883, test Accuracy: 9734/10000 (97.3400%)\n",
            "\n",
            "Epoch: 14,LR: 0.015360000000000002.\n",
            "Train set: train Average loss: 0.1107, train_Accuracy: 57979/60000 (96.6317%)\n",
            "\n",
            "Test set: test Average loss: 0.0793, test Accuracy: 9749/10000 (97.4900%)\n",
            "\n",
            "Epoch: 15,LR: 0.015360000000000002.\n",
            "Train set: train Average loss: 0.1125, train_Accuracy: 58008/60000 (96.6800%)\n",
            "\n",
            "Test set: test Average loss: 0.1553, test Accuracy: 9510/10000 (95.1000%)\n",
            "\n",
            "Epoch: 16,LR: 0.015360000000000002.\n",
            "Train set: train Average loss: 0.1113, train_Accuracy: 57984/60000 (96.6400%)\n",
            "\n",
            "Test set: test Average loss: 0.0896, test Accuracy: 9714/10000 (97.1400%)\n",
            "\n",
            "Epoch: 17,LR: 0.012288000000000002.\n",
            "Train set: train Average loss: 0.1014, train_Accuracy: 58213/60000 (97.0217%)\n",
            "\n",
            "Test set: test Average loss: 0.0700, test Accuracy: 9771/10000 (97.7100%)\n",
            "\n",
            "Epoch: 18,LR: 0.012288000000000002.\n",
            "Train set: train Average loss: 0.1046, train_Accuracy: 58182/60000 (96.9700%)\n",
            "\n",
            "Test set: test Average loss: 0.0860, test Accuracy: 9752/10000 (97.5200%)\n",
            "\n",
            "Epoch: 19,LR: 0.012288000000000002.\n",
            "Train set: train Average loss: 0.1044, train_Accuracy: 58143/60000 (96.9050%)\n",
            "\n",
            "Test set: test Average loss: 0.2158, test Accuracy: 9316/10000 (93.1600%)\n",
            "\n",
            "Epoch: 20,LR: 0.012288000000000002.\n",
            "Train set: train Average loss: 0.1047, train_Accuracy: 58156/60000 (96.9267%)\n",
            "\n",
            "Test set: test Average loss: 0.0718, test Accuracy: 9785/10000 (97.8500%)\n",
            "\n",
            "Epoch: 21,LR: 0.009830400000000003.\n",
            "Train set: train Average loss: 0.0984, train_Accuracy: 58253/60000 (97.0883%)\n",
            "\n",
            "Test set: test Average loss: 0.0604, test Accuracy: 9828/10000 (98.2800%)\n",
            "\n",
            "Epoch: 22,LR: 0.009830400000000003.\n",
            "Train set: train Average loss: 0.0985, train_Accuracy: 58228/60000 (97.0467%)\n",
            "\n",
            "Test set: test Average loss: 0.0755, test Accuracy: 9776/10000 (97.7600%)\n",
            "\n",
            "Epoch: 23,LR: 0.009830400000000003.\n",
            "Train set: train Average loss: 0.0993, train_Accuracy: 58186/60000 (96.9767%)\n",
            "\n",
            "Test set: test Average loss: 0.0868, test Accuracy: 9712/10000 (97.1200%)\n",
            "\n",
            "Epoch: 24,LR: 0.009830400000000003.\n",
            "Train set: train Average loss: 0.0983, train_Accuracy: 58270/60000 (97.1167%)\n",
            "\n",
            "Test set: test Average loss: 0.0811, test Accuracy: 9740/10000 (97.4000%)\n",
            "\n",
            "Epoch: 25,LR: 0.007864320000000003.\n",
            "Train set: train Average loss: 0.0920, train_Accuracy: 58336/60000 (97.2267%)\n",
            "\n",
            "Test set: test Average loss: 0.1069, test Accuracy: 9668/10000 (96.6800%)\n",
            "\n",
            "4 gbn loss\n",
            "GBN Enabled & BN disabled for option: 4\n",
            "Model Summary:\n",
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Conv2d-1            [-1, 8, 28, 28]              80\n",
            "    GhostBatchNorm-2            [-1, 8, 28, 28]              16\n",
            "            Conv2d-3           [-1, 16, 26, 26]           1,168\n",
            "    GhostBatchNorm-4           [-1, 16, 26, 26]              32\n",
            "         MaxPool2d-5           [-1, 16, 13, 13]               0\n",
            "            Conv2d-6           [-1, 10, 13, 13]             170\n",
            "    GhostBatchNorm-7           [-1, 10, 13, 13]              20\n",
            "            Conv2d-8           [-1, 14, 11, 11]           1,274\n",
            "    GhostBatchNorm-9           [-1, 14, 11, 11]              28\n",
            "          Dropout-10           [-1, 14, 11, 11]               0\n",
            "           Conv2d-11             [-1, 16, 9, 9]           2,032\n",
            "   GhostBatchNorm-12             [-1, 16, 9, 9]              32\n",
            "          Dropout-13             [-1, 16, 9, 9]               0\n",
            "           Conv2d-14             [-1, 20, 7, 7]           2,900\n",
            "   GhostBatchNorm-15             [-1, 20, 7, 7]              40\n",
            "          Dropout-16             [-1, 20, 7, 7]               0\n",
            "        AvgPool2d-17             [-1, 20, 1, 1]               0\n",
            "           Conv2d-18             [-1, 10, 1, 1]             210\n",
            "================================================================\n",
            "Total params: 8,002\n",
            "Trainable params: 8,002\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.00\n",
            "Forward/backward pass size (MB): 0.40\n",
            "Params size (MB): 0.03\n",
            "Estimated Total Size (MB): 0.43\n",
            "----------------------------------------------------------------\n",
            "LOSS: gbn loss enabled\n",
            "Epoch: 1,LR: 0.03.\n",
            "Train set: train Average loss: 0.2460, train_Accuracy: 55745/60000 (92.9083%)\n",
            "\n",
            "Test set: test Average loss: 0.0632, test Accuracy: 9801/10000 (98.0100%)\n",
            "\n",
            "Epoch: 2,LR: 0.03.\n",
            "Train set: train Average loss: 0.0800, train_Accuracy: 58529/60000 (97.5483%)\n",
            "\n",
            "Test set: test Average loss: 0.0450, test Accuracy: 9864/10000 (98.6400%)\n",
            "\n",
            "Epoch: 3,LR: 0.03.\n",
            "Train set: train Average loss: 0.0655, train_Accuracy: 58792/60000 (97.9867%)\n",
            "\n",
            "Test set: test Average loss: 0.0362, test Accuracy: 9886/10000 (98.8600%)\n",
            "\n",
            "Epoch: 4,LR: 0.03.\n",
            "Train set: train Average loss: 0.0572, train_Accuracy: 58950/60000 (98.2500%)\n",
            "\n",
            "Test set: test Average loss: 0.0355, test Accuracy: 9884/10000 (98.8400%)\n",
            "\n",
            "Epoch: 5,LR: 0.024.\n",
            "Train set: train Average loss: 0.0497, train_Accuracy: 59100/60000 (98.5000%)\n",
            "\n",
            "Test set: test Average loss: 0.0244, test Accuracy: 9924/10000 (99.2400%)\n",
            "\n",
            "Epoch: 6,LR: 0.024.\n",
            "Train set: train Average loss: 0.0464, train_Accuracy: 59119/60000 (98.5317%)\n",
            "\n",
            "Test set: test Average loss: 0.0287, test Accuracy: 9899/10000 (98.9900%)\n",
            "\n",
            "Epoch: 7,LR: 0.024.\n",
            "Train set: train Average loss: 0.0431, train_Accuracy: 59207/60000 (98.6783%)\n",
            "\n",
            "Test set: test Average loss: 0.0220, test Accuracy: 9928/10000 (99.2800%)\n",
            "\n",
            "Epoch: 8,LR: 0.024.\n",
            "Train set: train Average loss: 0.0430, train_Accuracy: 59203/60000 (98.6717%)\n",
            "\n",
            "Test set: test Average loss: 0.0260, test Accuracy: 9916/10000 (99.1600%)\n",
            "\n",
            "Epoch: 9,LR: 0.019200000000000002.\n",
            "Train set: train Average loss: 0.0402, train_Accuracy: 59240/60000 (98.7333%)\n",
            "\n",
            "Test set: test Average loss: 0.0243, test Accuracy: 9924/10000 (99.2400%)\n",
            "\n",
            "Epoch: 10,LR: 0.019200000000000002.\n",
            "Train set: train Average loss: 0.0375, train_Accuracy: 59281/60000 (98.8017%)\n",
            "\n",
            "Test set: test Average loss: 0.0206, test Accuracy: 9937/10000 (99.3700%)\n",
            "\n",
            "Epoch: 11,LR: 0.019200000000000002.\n",
            "Train set: train Average loss: 0.0379, train_Accuracy: 59269/60000 (98.7817%)\n",
            "\n",
            "Test set: test Average loss: 0.0203, test Accuracy: 9939/10000 (99.3900%)\n",
            "\n",
            "Epoch: 12,LR: 0.019200000000000002.\n",
            "Train set: train Average loss: 0.0360, train_Accuracy: 59310/60000 (98.8500%)\n",
            "\n",
            "Test set: test Average loss: 0.0188, test Accuracy: 9935/10000 (99.3500%)\n",
            "\n",
            "Epoch: 13,LR: 0.015360000000000002.\n",
            "Train set: train Average loss: 0.0333, train_Accuracy: 59370/60000 (98.9500%)\n",
            "\n",
            "Test set: test Average loss: 0.0185, test Accuracy: 9946/10000 (99.4600%)\n",
            "\n",
            "Epoch: 14,LR: 0.015360000000000002.\n",
            "Train set: train Average loss: 0.0340, train_Accuracy: 59329/60000 (98.8817%)\n",
            "\n",
            "Test set: test Average loss: 0.0207, test Accuracy: 9931/10000 (99.3100%)\n",
            "\n",
            "Epoch: 15,LR: 0.015360000000000002.\n",
            "Train set: train Average loss: 0.0321, train_Accuracy: 59375/60000 (98.9583%)\n",
            "\n",
            "Test set: test Average loss: 0.0195, test Accuracy: 9935/10000 (99.3500%)\n",
            "\n",
            "Epoch: 16,LR: 0.015360000000000002.\n",
            "Train set: train Average loss: 0.0337, train_Accuracy: 59352/60000 (98.9200%)\n",
            "\n",
            "Test set: test Average loss: 0.0194, test Accuracy: 9943/10000 (99.4300%)\n",
            "\n",
            "Epoch: 17,LR: 0.012288000000000002.\n",
            "Train set: train Average loss: 0.0312, train_Accuracy: 59373/60000 (98.9550%)\n",
            "\n",
            "Test set: test Average loss: 0.0175, test Accuracy: 9946/10000 (99.4600%)\n",
            "\n",
            "Epoch: 18,LR: 0.012288000000000002.\n",
            "Train set: train Average loss: 0.0308, train_Accuracy: 59418/60000 (99.0300%)\n",
            "\n",
            "Test set: test Average loss: 0.0169, test Accuracy: 9951/10000 (99.5100%)\n",
            "\n",
            "Epoch: 19,LR: 0.012288000000000002.\n",
            "Train set: train Average loss: 0.0299, train_Accuracy: 59427/60000 (99.0450%)\n",
            "\n",
            "Test set: test Average loss: 0.0171, test Accuracy: 9951/10000 (99.5100%)\n",
            "\n",
            "Epoch: 20,LR: 0.012288000000000002.\n",
            "Train set: train Average loss: 0.0312, train_Accuracy: 59415/60000 (99.0250%)\n",
            "\n",
            "Test set: test Average loss: 0.0196, test Accuracy: 9941/10000 (99.4100%)\n",
            "\n",
            "Epoch: 21,LR: 0.009830400000000003.\n",
            "Train set: train Average loss: 0.0292, train_Accuracy: 59460/60000 (99.1000%)\n",
            "\n",
            "Test set: test Average loss: 0.0166, test Accuracy: 9948/10000 (99.4800%)\n",
            "\n",
            "Epoch: 22,LR: 0.009830400000000003.\n",
            "Train set: train Average loss: 0.0288, train_Accuracy: 59438/60000 (99.0633%)\n",
            "\n",
            "Test set: test Average loss: 0.0164, test Accuracy: 9946/10000 (99.4600%)\n",
            "\n",
            "Epoch: 23,LR: 0.009830400000000003.\n",
            "Train set: train Average loss: 0.0283, train_Accuracy: 59434/60000 (99.0567%)\n",
            "\n",
            "Test set: test Average loss: 0.0184, test Accuracy: 9946/10000 (99.4600%)\n",
            "\n",
            "Epoch: 24,LR: 0.009830400000000003.\n",
            "Train set: train Average loss: 0.0271, train_Accuracy: 59481/60000 (99.1350%)\n",
            "\n",
            "Test set: test Average loss: 0.0174, test Accuracy: 9948/10000 (99.4800%)\n",
            "\n",
            "Epoch: 25,LR: 0.007864320000000003.\n",
            "Train set: train Average loss: 0.0275, train_Accuracy: 59457/60000 (99.0950%)\n",
            "\n",
            "Test set: test Average loss: 0.0163, test Accuracy: 9947/10000 (99.4700%)\n",
            "\n",
            "GBN Model saved at gbn_model.pt\n",
            "5 l1l2gbn loss\n",
            "GBN Enabled & BN disabled for option: 5\n",
            "Model Summary:\n",
            "----------------------------------------------------------------\n",
            "        Layer (type)               Output Shape         Param #\n",
            "================================================================\n",
            "            Conv2d-1            [-1, 8, 28, 28]              80\n",
            "    GhostBatchNorm-2            [-1, 8, 28, 28]              16\n",
            "            Conv2d-3           [-1, 16, 26, 26]           1,168\n",
            "    GhostBatchNorm-4           [-1, 16, 26, 26]              32\n",
            "         MaxPool2d-5           [-1, 16, 13, 13]               0\n",
            "            Conv2d-6           [-1, 10, 13, 13]             170\n",
            "    GhostBatchNorm-7           [-1, 10, 13, 13]              20\n",
            "            Conv2d-8           [-1, 14, 11, 11]           1,274\n",
            "    GhostBatchNorm-9           [-1, 14, 11, 11]              28\n",
            "          Dropout-10           [-1, 14, 11, 11]               0\n",
            "           Conv2d-11             [-1, 16, 9, 9]           2,032\n",
            "   GhostBatchNorm-12             [-1, 16, 9, 9]              32\n",
            "          Dropout-13             [-1, 16, 9, 9]               0\n",
            "           Conv2d-14             [-1, 20, 7, 7]           2,900\n",
            "   GhostBatchNorm-15             [-1, 20, 7, 7]              40\n",
            "          Dropout-16             [-1, 20, 7, 7]               0\n",
            "        AvgPool2d-17             [-1, 20, 1, 1]               0\n",
            "           Conv2d-18             [-1, 10, 1, 1]             210\n",
            "================================================================\n",
            "Total params: 8,002\n",
            "Trainable params: 8,002\n",
            "Non-trainable params: 0\n",
            "----------------------------------------------------------------\n",
            "Input size (MB): 0.00\n",
            "Forward/backward pass size (MB): 0.40\n",
            "Params size (MB): 0.03\n",
            "Estimated Total Size (MB): 0.43\n",
            "----------------------------------------------------------------\n",
            "Weight decay 0.0001 set for option: 5\n",
            "LOSS: l1l2gbn loss enabled\n",
            "Epoch: 1,LR: 0.03.\n",
            "Train set: train Average loss: 0.2758, train_Accuracy: 55101/60000 (91.8350%)\n",
            "\n",
            "Test set: test Average loss: 0.0788, test Accuracy: 9762/10000 (97.6200%)\n",
            "\n",
            "Epoch: 2,LR: 0.03.\n",
            "Train set: train Average loss: 0.0865, train_Accuracy: 58436/60000 (97.3933%)\n",
            "\n",
            "Test set: test Average loss: 0.0380, test Accuracy: 9884/10000 (98.8400%)\n",
            "\n",
            "Epoch: 3,LR: 0.03.\n",
            "Train set: train Average loss: 0.0708, train_Accuracy: 58658/60000 (97.7633%)\n",
            "\n",
            "Test set: test Average loss: 0.0354, test Accuracy: 9897/10000 (98.9700%)\n",
            "\n",
            "Epoch: 4,LR: 0.03.\n",
            "Train set: train Average loss: 0.0622, train_Accuracy: 58856/60000 (98.0933%)\n",
            "\n",
            "Test set: test Average loss: 0.0357, test Accuracy: 9887/10000 (98.8700%)\n",
            "\n",
            "Epoch: 5,LR: 0.024.\n",
            "Train set: train Average loss: 0.0548, train_Accuracy: 58980/60000 (98.3000%)\n",
            "\n",
            "Test set: test Average loss: 0.0286, test Accuracy: 9914/10000 (99.1400%)\n",
            "\n",
            "Epoch: 6,LR: 0.024.\n",
            "Train set: train Average loss: 0.0505, train_Accuracy: 59118/60000 (98.5300%)\n",
            "\n",
            "Test set: test Average loss: 0.0245, test Accuracy: 9922/10000 (99.2200%)\n",
            "\n",
            "Epoch: 7,LR: 0.024.\n",
            "Train set: train Average loss: 0.0484, train_Accuracy: 59118/60000 (98.5300%)\n",
            "\n",
            "Test set: test Average loss: 0.0263, test Accuracy: 9910/10000 (99.1000%)\n",
            "\n",
            "Epoch: 8,LR: 0.024.\n",
            "Train set: train Average loss: 0.0464, train_Accuracy: 59101/60000 (98.5017%)\n",
            "\n",
            "Test set: test Average loss: 0.0244, test Accuracy: 9924/10000 (99.2400%)\n",
            "\n",
            "Epoch: 9,LR: 0.019200000000000002.\n",
            "Train set: train Average loss: 0.0434, train_Accuracy: 59168/60000 (98.6133%)\n",
            "\n",
            "Test set: test Average loss: 0.0278, test Accuracy: 9910/10000 (99.1000%)\n",
            "\n",
            "Epoch: 10,LR: 0.019200000000000002.\n",
            "Train set: train Average loss: 0.0410, train_Accuracy: 59250/60000 (98.7500%)\n",
            "\n",
            "Test set: test Average loss: 0.0232, test Accuracy: 9925/10000 (99.2500%)\n",
            "\n",
            "Epoch: 11,LR: 0.019200000000000002.\n",
            "Train set: train Average loss: 0.0409, train_Accuracy: 59212/60000 (98.6867%)\n",
            "\n",
            "Test set: test Average loss: 0.0195, test Accuracy: 9937/10000 (99.3700%)\n",
            "\n",
            "Epoch: 12,LR: 0.019200000000000002.\n",
            "Train set: train Average loss: 0.0402, train_Accuracy: 59229/60000 (98.7150%)\n",
            "\n",
            "Test set: test Average loss: 0.0197, test Accuracy: 9934/10000 (99.3400%)\n",
            "\n",
            "Epoch: 13,LR: 0.015360000000000002.\n",
            "Train set: train Average loss: 0.0381, train_Accuracy: 59292/60000 (98.8200%)\n",
            "\n",
            "Test set: test Average loss: 0.0203, test Accuracy: 9940/10000 (99.4000%)\n",
            "\n",
            "Epoch: 14,LR: 0.015360000000000002.\n",
            "Train set: train Average loss: 0.0383, train_Accuracy: 59302/60000 (98.8367%)\n",
            "\n",
            "Test set: test Average loss: 0.0221, test Accuracy: 9929/10000 (99.2900%)\n",
            "\n",
            "Epoch: 15,LR: 0.015360000000000002.\n",
            "Train set: train Average loss: 0.0364, train_Accuracy: 59333/60000 (98.8883%)\n",
            "\n",
            "Test set: test Average loss: 0.0179, test Accuracy: 9946/10000 (99.4600%)\n",
            "\n",
            "Epoch: 16,LR: 0.015360000000000002.\n",
            "Train set: train Average loss: 0.0345, train_Accuracy: 59367/60000 (98.9450%)\n",
            "\n",
            "Test set: test Average loss: 0.0167, test Accuracy: 9946/10000 (99.4600%)\n",
            "\n",
            "Epoch: 17,LR: 0.012288000000000002.\n",
            "Train set: train Average loss: 0.0347, train_Accuracy: 59367/60000 (98.9450%)\n",
            "\n",
            "Test set: test Average loss: 0.0163, test Accuracy: 9950/10000 (99.5000%)\n",
            "\n",
            "Epoch: 18,LR: 0.012288000000000002.\n",
            "Train set: train Average loss: 0.0346, train_Accuracy: 59357/60000 (98.9283%)\n",
            "\n",
            "Test set: test Average loss: 0.0178, test Accuracy: 9947/10000 (99.4700%)\n",
            "\n",
            "Epoch: 19,LR: 0.012288000000000002.\n",
            "Train set: train Average loss: 0.0328, train_Accuracy: 59400/60000 (99.0000%)\n",
            "\n",
            "Test set: test Average loss: 0.0179, test Accuracy: 9939/10000 (99.3900%)\n",
            "\n",
            "Epoch: 20,LR: 0.012288000000000002.\n",
            "Train set: train Average loss: 0.0342, train_Accuracy: 59366/60000 (98.9433%)\n",
            "\n",
            "Test set: test Average loss: 0.0192, test Accuracy: 9943/10000 (99.4300%)\n",
            "\n",
            "Epoch: 21,LR: 0.009830400000000003.\n",
            "Train set: train Average loss: 0.0327, train_Accuracy: 59409/60000 (99.0150%)\n",
            "\n",
            "Test set: test Average loss: 0.0166, test Accuracy: 9944/10000 (99.4400%)\n",
            "\n",
            "Epoch: 22,LR: 0.009830400000000003.\n",
            "Train set: train Average loss: 0.0309, train_Accuracy: 59406/60000 (99.0100%)\n",
            "\n",
            "Test set: test Average loss: 0.0172, test Accuracy: 9950/10000 (99.5000%)\n",
            "\n",
            "Epoch: 23,LR: 0.009830400000000003.\n",
            "Train set: train Average loss: 0.0310, train_Accuracy: 59430/60000 (99.0500%)\n",
            "\n",
            "Test set: test Average loss: 0.0168, test Accuracy: 9948/10000 (99.4800%)\n",
            "\n",
            "Epoch: 24,LR: 0.009830400000000003.\n",
            "Train set: train Average loss: 0.0309, train_Accuracy: 59439/60000 (99.0650%)\n",
            "\n",
            "Test set: test Average loss: 0.0168, test Accuracy: 9942/10000 (99.4200%)\n",
            "\n",
            "Epoch: 25,LR: 0.007864320000000003.\n",
            "Train set: train Average loss: 0.0290, train_Accuracy: 59462/60000 (99.1033%)\n",
            "\n",
            "Test set: test Average loss: 0.0164, test Accuracy: 9942/10000 (99.4200%)\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q7ySNCiv4FX5",
        "colab_type": "text"
      },
      "source": [
        "### Validation Accuracy & Loss Plot\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IvGPL-vSbvZG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 716
        },
        "outputId": "32dc32d7-a748-4d1b-d040-7f8906050453"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "epoch = 25\n",
        "plt.figure(figsize=(12,12))\n",
        "plt.subplot(2,1,1)\n",
        "ax = plt.gca()\n",
        "ax.set_xlim([0, epoch+2])\n",
        "plt.ylabel('Loss')\n",
        "plt.title('Validation Loss Plot')\n",
        "plt.plot(range(1, epoch+1), val_losses[0], 'r', label='l1 val Loss')\n",
        "plt.plot(range(1, epoch+1), val_losses[1], 'b', label='l2 Val Loss')\n",
        "plt.plot(range(1, epoch+1), val_losses[2], 'c', label='l1l2 Val Loss')\n",
        "plt.plot(range(1, epoch+1), val_losses[3], 'g', label='gbn Val Loss')\n",
        "plt.plot(range(1, epoch+1), val_losses[4], 'y', label='l1l2gbn Val Loss')\n",
        "\n",
        "\n",
        "ax.grid(linestyle='-.')\n",
        "plt.legend()\n",
        "plt.subplot(2,1,2)\n",
        "\n",
        "ax = plt.gca()\n",
        "ax.set_xlim([0, epoch+2])\n",
        "plt.ylabel('Accuracy')\n",
        "plt.title('Validation Accuracy plot')\n",
        "plt.plot(range(1, epoch+1), val_accs[0], 'r', label='l1 Accuracy')\n",
        "plt.plot(range(1, epoch+1), val_accs[1], 'b', label='l2 Accuracy')\n",
        "plt.plot(range(1, epoch+1), val_accs[2], 'c', label='l1l2 Accuracy')\n",
        "plt.plot(range(1, epoch+1), val_accs[3], 'g', label='gbn Accuracy')\n",
        "plt.plot(range(1, epoch+1), val_accs[4], 'y', label='l1l2gbn Accuracy')\n",
        "\n",
        "ax.grid(linestyle='-.')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x864 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y19wRyfYxJKU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Misclassified Image Plotting\n",
        "\n",
        "#PATH = \"gbn_model.pt\"\n",
        "#torch.save(model, PATH)\n",
        "\n",
        "# Save Model\n",
        "model = torch.load(PATH)\n",
        "model.eval()\n",
        "\n",
        "tot = 0\n",
        "data_ = []\n",
        "target_ = []\n",
        "pred_ = []\n",
        "correct = 0\n",
        "tot_correct = 0\n",
        "indx = []\n",
        "\n",
        "for data, target in test_loader:\n",
        "  data, target = data.to(device), target.to(device)\n",
        "  output = model(data)\n",
        "  pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability\n",
        "  correct += pred.eq(target.view_as(pred)).sum().item()\n",
        "  equals = pred.eq(target.view_as(pred)).tolist()\n",
        "    \n",
        "  for idx in range(0,len(equals)):\n",
        "    if equals[idx] == [False]:\n",
        "      indx.append(idx)\n",
        "      data_.append(data[idx])\n",
        "      target_.append(target[idx])\n",
        "      pred_.append(pred[idx])\n",
        "\n",
        "  if len(indx) >= 25:\n",
        "    break"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AE73nvjwyqyO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "# plots images with labels within jupyter notebook\n",
        "def plots(ims, figsize=(12,12), rows=5, interp=False,titles = None):\n",
        "    f = plt.figure(figsize=figsize)\n",
        "    cols = len(ims)//rows if len(ims) % 2 == 0 else len(ims)//rows + 1\n",
        "    for i in range(len(ims)):\n",
        "        sp = f.add_subplot(rows, cols, i+1)\n",
        "        if titles is not None:\n",
        "            sp.set_title(\"Actual:{}\".format(titles[i].tolist(), fontsize=16))\n",
        "        sp.axis('Off')\n",
        "        plt.imshow((torch.squeeze(ims[i]).tolist()))"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w3OW8LuIzf2o",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 682
        },
        "outputId": "70f786e3-11e1-4b59-a532-c739734c4bf6"
      },
      "source": [
        "plots(data_,titles = pred_)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XrXo2+b27qqs6HlrHf1fjmo8vUbn2U74v0dfOukIvNXbUh+e5+OsD39SNn/zShad9fbyLczfopdJKG1dYAQAAEGhMWAEAABBoFaYkYP2RzWPm/jbtTHXcbsfM0u4O8iy8srKLsypVVrmblxzn4hpvUAaQLNIy/Xvx1AtiL+sSrdOXfgekdoN+UTm759fid6wC23XPVnW8LeRvzEfvaBZ5y/5fwy9w8Tl3jIx5/p6zBqjj2sOrx2iJfJXXR+w5VcmXtD2b1Saq5eyy6ZCItBq9Qh1PatXJxbmZX6lcyJoy6VOiyT6pu4tf6fCoyu2xfumqurPK9npizju+3GNpF72cZ8s0/7O52YfZLl51pl5GrbR3xOIKKwAAAAKNCSsAAAACjQkrAAAAAq3C1LAeeZOugVye45fcaP+o3v6PZVWAkpXWppWLm7/ht3q8t2HsevFhG/VSSx3u8Euo5LBlZ7nJPfogF7926yMRGb115APr/Xa5tU5Zqk8SYvGxvdnawS+3eHC7JS4uzyWiFl+hvwvS6p09/uDkMu5MgtqW6addjVL1lshrcne6uMYKvVV5aas3aoqLTz/pKpWb2etFF49s7muVe/W7Tp/jOWpYAQAAUIExYQUAAECgJXVJgOm+v4tDdq7KHT/lGhe3mlF2y4JAqzelkosr9U1VOWN8cQZlGolt6Xl++ZP/NnsnZrs/Q37JlK/6d1a5nN+WRTdHKWmRVtXFty+Zo3Kt0vxSZG0rxV6e6t3hx7q4XmhKzHYoWKUdIRcvWN/QxU1lfXl0p0DrrtoZMzdzYUsXZ8m6suhOQmh54eKYuR+z/ThXnjg9ZrvSVuWTGvqBXuXTj2hcYQUAAECgMWEFAABAoCV1SYCd7m9lXVZP35L6esShZd0d7MUeq785bNkpJWHZww5Ux9dfML7AdpElACIi3d+82cVtl7K7WVn5fW4jdfx5W//t5ROq7olqXXAZwH5TL1DHLcYtcDFrAuy79An+lnDTCeXYkULs3FE5Zq7pJ0k9vSiyWUszXZzSVl8z7F9th4vvu0Lfh6/3XNmV1dR/Vr/WZ7f7EoG+VXwfe175o2q36LnS7RdXWAEAABBoTFgBAAAQaExYAQAAEGhJV2SSWq+ui1dc5nfKOfP7A1S7Vi+xzEoQqLqce3Tu/1p/4OJbB13t4voTl6h2uWvWlkbXKpSUGr5GyTRpWEjL2Fb38c97728PqVyj1CrRzUVEZLsNqeMa7Te7eO34jtHNi63y2Dourv0qnwH52t2s64UfeeNcF9/bsqrKVb5ytYu/6OJrk5s8rusZczdsFBTd6hsOc/HWzr6OOOuqsl3uKK15MxfvqaEXGKw+07+v3+jeQOVqzd3kYmqYvX6d/RKbIdGff0v2+GXC6szfVWZ92ptc669thiIWmQxJ2X7PhCusAAAACDQmrAAAAAi0pCsJ2HlIGxfPuWmki0/qd75qpy/EIwh6/zRQHX954BgXf/3AcBcPvup41e7bBQe7uPOdq1VuzUktXFyWy4IEXfbJ3dVx6k1rXDyx01sl8AoFlwBEiy4VmHbI6CK9WuSSaH/a6GWYvCNn3ebi2kV6pQpiml8SsPo0nXrnUV8+sDbX/7unZHPjtyTtidhsqO0b5fdve+RHC138Xt33Ve63HL/E0XWnXq5yobnzS7djSahFmv88/O2UDJVr83VZ9yZ4uMIKAACAQGPCCgAAgEBjwgoAAIBAS/ga1lDvbnG1M3+sL+WeoLhq9VusjvuLr7NMy2zu4l/ubazaHd3ZbwH57PeTY7/APfqwz7V+qawq/40q1EtC2f38v+crTz2qck1iLDsVVFmfXKmOa/zstxJt8sh3MZ/XUmLnULDNF+ktItPNDBf3nnGJixtOnV1WXaoQqqyNWD4o1S8flFrKr5tz7MHq+Jyaj0cc6SXO+r3qa8JbzeY7AvH4+R6/xOaykZNUrnWar1v9x+lvqNyrzxzl4pxffyudzgUcV1gBAAAQaExYAQAAEGgJXxKwpa1e+mF3DX/r5KWtETv27I691A2CL2fFShdnXbZS5dY08Dus9G96ocrtalLdxVc9PlblUq/1SznlbPO3wdI+nyHJaNwz/tZe9ZTSLQF4ZWszdfzQW2e6uM3w4i93k7Vpln4gxJJKpWVdD70IYCXjb0rnflU3ujmKKLVLB3VcabsvCaj0Wel+JtleB7p4eV+9Y9n4P/dz8Ygfj1G5dn+nDGBfpU/wO5X1++Y6lVt8zIsuPqu6LmPcOsGXrf3rm5NVrtNf/e6PRd1hLqWqL/dYfkNXlTupqv//98nOai7+dUjbqLPMldLEFVYAAAAEGhNWAAAABBoTVgAAAARawtewRvvk9odcPGDoTS6usin5ly2qqHLXrfMHkbGIVP7Jxy9edIrKXfjypy5u/MxmF99/5yWqXc0xUyUZPL9lfxe3rKzro4Yt9tvdpr1Qz8UmqjR00hMjJR5vXt5XHbf81i8nRbVp8KXW9/8HZp7xWFTW1z83f89vhcy4Fk/u3AXquFbplgPKlgt7unjMPx928Q6rF84a/MtFLu5w/W8qx5gXT9sLZqrj9q9f4uKfj3pO5QbVXObjfvpz+KFD/Wf726OOdXGdhbG/u7PqKD3963qE34L3x9aPq1xkFfstoy9zccsZZbtMIFdYAQAAEGhMWAEAABBoCVkSYA7u4uJu1+jlbR5Zf7iLq4yfLoAzbY46fKuT3zHrnHn+8UF3vq/ajRvTQJLB5P7+tlGoul4OrtZsv9RUav1NLl7xXKOY53vnz/rq+I6Pz3Fxhxk/qZxeGAmJpFbUEmidv/NLx7X4bWF0cySI42791sWNUv1SVjN362nBzk/88pC5m/RuhChZ7S7376cjzr1e5bpf7csHHm36tcr9pZ6vH7ntdv1zLpaUqOuVoUI+pfvNO8vFbV9e5eKcuF6p5HCFFQAAAIHGhBUAAACBlhAlAcvvOUwdV97i4ycbfapyT2/o7Q+sFSCWUO9uLu6S/ryL52Y3K6h5wsv59be42q0/OcvFP/Z4Ima7O8efq47b3+53vaEEIHnt3JbuYrtndzn2JLmkHNBRP7D0dxeGtm0r9vkjd7MSEbm/4csufmC9Lxf68gBdAtJYyvab4BVZaMcOF9d9Qe8i9tvYmi7ufO9QlRt4tB+juxvG3hXtrrXdXfzW9O46GTFdavNW1O52X/oSr5ycsi4E8LjCCgAAgEBjwgoAAIBAY8IKAACAQEuIGtb6s/V+Gl8/8YyLt4T0rhzfPXioi6tLcuxQlExSa9dSx5v6dXLxxtN2qFzd8VWL/Xo7Gvjfya4f8q7KRdatdkv3NTuzdun/UxVNl6t/dnG21TulHDj5ahe3eX9XmfUJwfHw4W+7+Im+A11ceXO2ape6YIWLczdtEhRu/pCa6rhjxGZGqRHfx4jeESu1SwcXZzeqrnKrjvT1xt8Nfljl5kWUH396+5EuriwsBxlEuVu3urjdTXpuMyPi2mN/iapNjSFrH8Y5KN8G4gorAAAAAo0JKwAAAAItIUoCqr77vTo+8d2uMdtSBhBwzRqrw0F/97tK7Z+xQuUOOUKXgsQSuWNH9G4dW0L+vte727JULnL5qtsX9XKxeULvbJUh0+LqRzJ698/m6rjdhTNjtEQysX9ud/Hx805VuU87+ffsWS885+I/Q7pEpN91fqeeKv+tuO+heGVdo/+NNl7sP5P+fbcvg5u+s7VqF7I/uriS0Z+ZN9TxO1ON2NxZ5V58rp+LG09k6SoEH1dYAQAAEGhMWAEAABBoTFgBAAAQaAlRw4rkEb0ky7jOvl70nd4nqNzvR+ktAvMd1O8Xdfxiy89jvl7P/97s4vZDv4/ZroosjThaGrNdRbCqp98GcrQ0L6QlklVol69H3f1YE5Xrc0N/F3/e+T0Xd3/uZtWuxX+piyyO7NrGxb0z/HaYR1dZotpN3eXrVkeuOUblTrrCb1WeNn+5yjXexPggsXCFFQAAAIHGhBUAAACBRkkAAiPla71kUubXBbdbd78+PkUOjnnO9hK7DADA3mW8H7UklV/VSk4Uv8RgC+EWc0lqNNz/e/YbflCcz9qqjoz85OL4FgkEgosrrAAAAAg0JqwAAAAINCasAAAACDQmrAAAAAg0JqwAAAAINCasAAAACDRjrS3vPgAAAAAxcYUVAAAAgcaEFQAAAIHGhBUAAACBxoQVAAAAgcaEFQAAAIHGhBUAAACBxoQVAAAAgcaEFQAAAIHGhBUAAACBxoQVAAAAgcaEFQAAAIHGhBUAAACBxoQVAAAAgcaEFQAAAIHGhBUAAACBxoQVAAAAgcaEFQAAAIHGhBUAAACBxoQVAAAAgcaEFQAAAIHGhBUAAACBxoQVAAAAgcaEFQAAAIHGhBUAAACBVuEmrMaYe4wxr8XIHW2MCRlj/jTG9I3zfJOMMbuMMd+UbE+xN4xlcmAck0cpjOVLxpidxpiVJdtT7A1jmTxKYSzvNcZsN8ZYY0xayfY2tnKZsBpjJhtjNhlj0uNoe0kZ/+BZZa2tbq2dGNGH840xy/IG6L/GmLr5OWvtsSIypAz7FyiJNpYRfXkh783WLv+xijyWiTSOUR+w+X8G5TeuyOMoknBjebIx5htjzGZjzGpjzPPGmBr5ja21l4jISWXYv0BJsLE8xhgzJ28sNxhjxhljmuU3ZiwTZyzz+jDUGLPUGLPVGPODMeaI/Jy19m4R6VKG/RORcpiwGmNaiUhvEbEi0r+sX39fGWO6iMgzInKRiDQSkR0iMrJcOxUQiTaW+fLeeG3Lux9BkaDjmP8Bm//n5fLuUBAk4FjWEpH7RaSpiHQSkWYi8lC59iggEnAsfxGRE621tSU8notE5Kny7VIwJNpYGmMOFZEHRWSAhN+jo0RknDEmtTz7VR5XWC8Wkaki8pKIuKsixphMY8y7xph1eb+dPWGM6SQiT4tIr7yrKJvz2k42xlwe8Vz124gx5nFjzIq83wxmGGN6F6O/F4jI+9bar6y1f4rInSJyZuRVgAos0cZS8m5fjBCRocU5T5JJuHFETAk1ltba1621E621O6y1m0TkORE5vKjnSzKJNpZrrLWrIh7KFZF2sdpXMAk1liLSSkTmWmtnWGutiLwiIvVFpGExzlls5TVhHZ3350RjTKO8WfsHIrJMwv9QzUTkDWvtPAnf2puSdxWldpyvMV1EuopIXRF5XUTeNsZkFNTQGDPbGHN+IefqIiI/5R9Ya5eIyG4RyYqzL8ks0cZSROQmEfnKWjs7ztevCBJxHBsaY9aY8C2rR40x1eLsR7JLxLGMdKSIzN2H9sks4cbSGNMib4K1U0RuFZH/xNmPZJdoY/mRiKQaYw7N6+dlIjJLRFbH2ZdSUWbFsiLuVmxLEXnLWrveGLNERM6X8G8eTUXkNmttTl7zItdvWGsji4sfMcb8XUQ6SMTEM6LtAXs5XXUR2RL12BYRqdBXWBNxLI0xmSJylYgcXNT+JJtEHEcRmS/hD+b5eX1/WUSGSXhsK6wEHcvI/h8v4atPhxa1b8kiUcfSWrtcRGqb8Pc8rpDwe7RCS9Cx3CYi7+T1x4jIZhE5Ke9qa7kp6yusg0TkE2vt+rzj1/MeyxSRZRGDVizGmFuNMfOMMVvyfturJeHL2UXxp4jUjHqspoQHtCJLxLF8TETus9ZG/wJSkSXcOFprV1trf7HWhqy1S0XkdhE5qyT6meASbiwjztlTwv0dYK1dWBL9THAJO5YiItbajRL+RXK8KcNvkQdUIo7lYBG5VMJ3mCuLyIUi8oExpmlJ9LWoyuw/kjGmioicI+HLzPmXldNFpLaIrBGRFsaYtAIGr6AZ/XYRqRpx3DjidXpL+AdYHwnXYISMMZsk/FtCUcwVkQMjzt8mr98V9kM1gceyj4gcYYyJvE01xRhzg7X29SKeM2El8DhGs1IBl+iLlMhjaYzpJiLvichl1trPi3qeZJHIYxklTcI1jzVFZGMJnTOhJPBYdhWRDyJ+eZxojPlDRA4TkbFFPGexleWH/OkSLsLuLOF/jK4S/lbo13m5P0TkQWNMNWNMhjEmv/B+jYg0N8ZUjjjXLAl/8amqCS9LNDgiV0NEckRknYikGWPukv+9QrovRovIqcaY3nl1cveJyLvW2op8hTVRxzJLwlkB/bUAACAASURBVL985PdZRORUERlXjHMmsoQcRxNePqelCcuU8LdZxxf1fEkiUcdyPxGZKCJDrbXvF/U8SSZRx/JMY0wHY0yKMaaBhMt0ZuZdba2oEnIsJVwPe7Ixpk3e5+zxEv75+XMxzllsZTlhHSQiL1prl+fd0lttrV0tIk+IyHkSnji0E5HlIrJSRAbmPW+ShK9yrjbG5F9Sf1TCX3xaI+HbDqMjXudjCX8ALpRwMfMuEVkRq1PGmLnGmAti5a21cyVcAD1aRNZK+D/GNfvw905GiTqWa6P6KyKy3lq7cx/+7skkIcdRRLqJyHcSvuLwnYjMEZHr4/1LJ6lEHctbRKSBiIwyfk3div6lq0Qdy2Z559sm4fdkSETOiPcvnaQSdSxfEZE3RGSyiGwVkeEicpW1tlxrkk0519AGijHmSAkPfLaIDLTWfhzHcz4VkZ4iMs1a26eUu4g4MZbJgXFMHkUcy1EicraIrLXWskRSQDCWyaOIY3m3iNws4fKGatba3NLtZd7rMmEFAABAkFXoLyoAAAAg+JiwAgAAINAKXdbq+JSzqRcoR5+G3i6p5UUYy3LGWCYPxjJ5lNRYMo7li/dk8ihsLLnCCgAAgEBjwgoAAIBAY8IKAACAQGPCCgAAgEBjwgoAAIBAY8IKAACAQGPCCgAAgEBjwgoAAIBAY8IKAACAQGPCCgAAgEBjwgoAAIBAY8IKAACAQGPCCgAAgEBLK+8OAAAS04e//+jiI+cMUDkTEa9a2EDl2l//fWl2C0AS4gorAAAAAo0JKwAAAAItIUsClt91mItb3PddOfYEpanDD5VcPLzpdBd3fP5q1a7lXVPKrE8AvJBYF0/a/02VS4m4HhLaP6Ryh/90vYvrjeL9W94WP9bTxQvPHqlyqcaP41e79PP+ee5F/mDanFLpG/5XqHc3dfz70VVcnF3Pv9fmn/2kanfliqNdvObUDJXLXbeuBHtYOrjCCgAAgEBjwgoAAIBAY8IKAACAQAtsDWtq7Vounv94W5XLylzmYntfmXUJpSyyZlVE161Gmn/5U+q432i/nE7ugsUl3zEABeo2fGjM3AtDHvftKutrIydc962LZ4ziuklxhI7o6uJl/aqo3BPnPO/ix1Ycr3Kj2411cdUU/1kbirqOFbK5Lu6Vrl/7xBf8OH68X8196DX2Zuv5PdXx5Xf+18Vd0p9XuW7pukY8X/Sjz2ZOdvGBTw1SucwB1LACAAAAxcKEFQAAAIEW2JKAnM6tXLzguOdUru/FV7o4TX4vqy6hFCx8rruLP26qx3nCDr/sxpMnn+LiD78Yq9ptfNTHtfqVcAcRGGnNmrrYVq8as92uTF9O9PtRlVUuY6Pff6nJyBkqZ7Ozi9vFCqfZv2MvK3j+gVe4eO5Rz8Vsh+JZdp2/8Tu39xMx2x2T9X7UI+kFttsX6Sl7in0OeJFlAA/fr5cXOyTdl2akRJdtFOG1Dmq6Uh1vjCjDzN28pQhnLH1cYQUAAECgMWEFAABAoAW2JKDuQytcPGxjR5VL3ZFT1t1BKXnimFdj5oa3ixx3/+3/61d1V+2mdvUlAv06DFA5Vg0Inl2n9HDxhv30R1ClXhtdPKjdVJXrV91/I7ltmv42dFF0bHStOm7zV3ZcKi3RtzDHzPD/B7Lkh7LuTkL745bD1PGsIx6LOEot076MnHeki5vL3DJ97YRlfFnSwlEHq9T4Y/1YdqhUumM5quWn6viYvn7FjxpvTI1uHghcYQUAAECgMWEFAABAoDFhBQAAQKAFpob1z3P0rg5NU309zKT9q6mckZ+K/XopB3Zy8e56eomctEl+uZsdZx6qcn/0im+OX2+Oj2u/Qm1cLCdX3eXi1hOuULksKXinq2lrW+oHInbE+vWCBirV8i5qWEtLSjX9vtx20n4uXtVfL3fz0hEvuLhV2jcuHrG+t2o3dpqvT35rTF+V+/T7A12cs3SZFEXu0Qe5eNHrese0E//aNbo5iuG1nqNcHIpaeKfFeK6VFFWtpbnqeGNot4sbpRa/tjvajIjV3s6fcI3KdbjjFxcXZWmliiiybnX+iU9FZYtft3r8z+e4eMXKevq1+z4T83kX3+WXPRv3RoOY7coTnxoAAAAINCasAAAACLTAlASsOk7f5mgdKtql8dR2rV28/9tLY7Zrm/Gli+ul/qlyn2/p7OKu1fXuIJfWXCHxiLyNMu4mvXTFrG5xnSIpLbuvV9Qjs1zU6eFNKpMrBYtcxipa7UPWFbFniEdqlw4u3vKIvu3/4X5+SZaH1vdQuaHD/a3E5mP97fyclXqnuiyZFvO1S2IxuyXn+o+8/ov6RmVXl8ArIN+FUwe7eN5Ro1Qu4/3Y44zCVX33e3V85VB/C3j8/+xmVXxL9jR0cccR61Uud9u2En+9RBX52bjpwDoql3HpHy4e3z5yN7KizXPGba+rjv8yaaCLO90+38VZnWvoJ0Z/5EUYUGOhi5++/jSVazQ89o52ZYkrrAAAAAg0JqwAAAAINCasAAAACLRyrWGNrDft3vlXldswpLGLc/q0VbmMxWt9bpmuKa08aoeL22b4dqlRi25cXFPXzkW6fYJf3mZi6oEqd+lZI2M+L9LB6T6+e2PzqOzKuM6RjAqrMS1sG1Vd+zorZrseDfVyRwvi7hnypdbz9VHz726vct+e/oiLj/pWb2068LTLXWxn6G0aG4uvgSrLjZVX36S3sby4l69d/+GU1tHNsY/SMv1n2y93N1a5MT39EjpPbtaf4Sg5Kya08gdZJX/+c6r7n6Nz31ykcrPO9S9Y0bfB/v04v4TU57c+pHI1UipHHMVXt/rZTl1/+n+PX+biO4aOVrlOHSLmFOOru/DSpvHXNH+f7fvf9LOoWuW4z1K6uMIKAACAQGPCCgAAgEAr15KA2i9vdvHLrT5TuQtGnuDiE+tNVrnXrzvZxWlRJQHrdvrdd8Ze7s9hjX7tF5pmxOxX1gezIw5aqdyo41q4eGANf3ukekq6anfGolN8Hy/R5y/LW6JBE33LPl4n9PshrnbDm+rdsU4Udi+KR2p9fzuo0Qd+55zr67+k2p3+99tc3DpqBzdbOl3bZ7tO9UtqVTl+rcp938PfLrPZFbc0Z1+kdo66z7zSL/+15HL/ebjwpBGq2ZrcnS6eeIne0UxkjqBktBjty+k+H6J3bexTZUd082K5t+FMdXz3Gz6eNVCXD+UuXFKirx10jR/3ZU/jh+gSmAvjXA4zUrPULer4jMsn+7jaRpU7LWt8nGeNfY0y8vX+bF9b5ar8Et26fHCFFQAAAIHGhBUAAACBxoQVAAAAgVbmNay7TzzExf9s7rdyvHed3rJzw12tXPzEDQ1UrvG67S7Wi1WJVO/7qxTERLcrpI/qnLN08cZ7vf32a08+f5SLf+zxqmq3aI3vc6sVswVh78/UNaWRNafZJ3VXufSPphfYLlrPWQNcXNi2rfDSWrdUx+3H+mXefv2zvotH9D5Gtav9h65bDaJqU3ztXPVZVVQuJzs7ujn24q4Pxqjjq4cNdfFLF/m61VDUp/ExY3y9c5vpwf9/k6hy/vA1xTfPOlvlJnR/2sXN0/R74YZVh7v4tz/9Unb/aPVf1e6AyrGXYYqsae14tV5Crt1NFauGNdJ/Zp+gji8+4sUC2z2wfn91/NIM/2/47yPeVrk76vu670pGj8meOL9AEPm86Od0qeyvX+6+StfIion4XsB/y29bZa6wAgAAINCYsAIAACDQyrwkYPXl/pZc5C2KseP1sictJ/klIhpO0ueILgMoSzbT7+YSXQaAvev08Cb9gF+hTG4bof8973ro0ogjv7tV6wlXqHZLT34uZi5LYpcSVGQr+zdTx30z/L/vwn7+9mDuGr0sVGFSG/gyGNuknsqFZs/f1y4WWe76DWX2Wslqw2BfotU9/UeVu+zqCRE5X2w1cnM71a7NXygDKGuZA35Wx1ccfb2Lc6rq28hVp/idqXI3rXLx3w7Tn6G/Xuuva73W63mVi9zRccqAR1Tu6I2+JCTzH99JRdL2it/Ucf/WFxbYLmXDVnWctdIv3/jSF4erXOTSVdG386PLcWKJfF5hz/nyQF0GtPgxvxjnbQsudnHuPL3zWWnjCisAAAACjQkrAAAAAq3USwKWPNJTHc/q5VcGGLLiOBe3ul/vZBSUXXMWP6r7/33EbY+sj25wcccb5ql2rXMWuDgof5cgyF2wWB0X9g3/k+9+qsBzNGq2qcDHC7L+Kn9rs9Jp61SuVr/F0c0rjCan6R3HHvv0JBe3WzO1SOfc84bfPe7pdvrW4TWLz3XxHxP87kjNX9W3lHI3RHw7NZRbpH4gTj38N5R/HaDXTfn83IdcHBL97fJHP/P/V97o4EtGJu3/pmo3/D/+dnSb2ykPKA+pk305R/R3/WO9u8x3P6njthF3828551qVe/TffpWIbpX17pHDBo3y7f7Rae+dTSK5W/Wtfvlpa4HtCruRv2xjnSK99mc7a7h4xW5dmvXyP051sYmamFS+zK828Vh7/V7uUrmyi23l8tsglSusAAAACDQmrAAAAAg0JqwAAAAItFIvRlhw7kh1HJJKLt5j/XzZ7tld2l2J26InD3XxuYfp5TgOHXuLi1t+4quAQtu3C/ZdZB1px/uuVrn5lxdcw1rYblaRS1yJiFzfze+etfgCvbtTRa6QPKjOCnV8yNHLXTz9f6rd4pPSx59zaIdLVG7lmQ1d3O9CX8/44C0zVLvjfjnDxVWuq6Ry0fXP2DepnbPU8db7/GfW/P1fVrnLV/g61dPqzVS5rNf88/bU8HV2nU6/TrVbdMGTLj5kuc41fKJiLXOULKq/pevbrxt0vou/7fpGWXcnqbW8Rn/notsV/jszh5ysly/7YcJ+Lm717noX5/6yULWrKYV8PyFi+M54QdcqLzzxGRcvObe2i1vrcudSxxVWAAAABBoTVgAAAARaqZcEpBo9Jw5ZfyO2RRW/PNGG1m1Uu5yletmdkpbT52AXr7xyj8rNPvxxF0/aWVflZr7Y2cVluXtPRdDyrqilby6P73mRS2PVvUnn9G1kbinnm3F1V3X8zthnXXzga740o+P/rVftcpbpUoJYom/fN/+XP57zsF8i5bDz9K2nmoNWunjEJy+p3MW3+nKc6m9/H1c/4DV5YZU6Hpf5hYunZ+vP6RV/a+/i5xbVVDm7co6L02v6XEe9MqGcOM6/gav+ZbXKbdjpl5urN4olrxJFapcO6vj+Du+WU08qIL+pnKzppZfJyhRfYlMSpW71puhyrEp9fZnY3IufcPGRs6M+v8cUbUnEeHGFFQAAAIHGhBUAAACBxoQVAAAAgVbqNay5NvbmY3c3mOXigS831smB/jjnD13/FK/QEb5Ob8k56So380y/Rexpv5ynct1G+0LIZl/lqFz67OlF6gv2LrVDu6hHZhXYLlrk0lgVeamqfTJ1tjo89MmbXfzeVX774dwvjWp31ndDXNz09coql/HBtLheOnIJu9qvRNUvvuLD457RBckvPuiXLHvoh1NVrrRr3hNVWmZzFz+b+Z7KXb7iWBev6rlN5VLFb+mpPwG1/9mCMvIcX/hzVP9C5x5d6rd+vOPXq2I+D+UvpYbf6nPerXoL32Oq7Crr7lQYoaYN1PHMIf67NV1qDVW5treWbO1ovef05/KwG/x3jK6t47ed39RRX/PU1e4ljyusAAAACDQmrAAAAAi0Ui8JGL2toTruWcXfumudluHiN9tOVO3Oe+tEF28f2lnldv5nh4v7N9W3NiO1S3/Lxbe9MUjlzj7b39qs9tsalWvzB8uslId5t9bZeyMRuX5V96hH9hTYDvFr/i+/LMrN//JLDv32j16qXfsj/Pv36ZFvqVz9p32JwPlL9C37eB1Sx5//w3rPqFyHyYNd3Hap3n0JBVt2fgsXh8Sq3NSP9ndxCynbnadu/Ju/pbljP33dpNEX0a1RllKqVVPH84f7HdIWnvBsdPOY5uxqvvdGKJIH+o9RxyM/P8fFVaf43a1yN28p0vlX/u0wdZxiPiyw3Y1nj1fH4+5uUGC7ksIVVgAAAAQaE1YAAAAEGhNWAAAABFrp17B21HUs/3r7JBdnb/c1b2cfOEO1G9PmY38wIf7X22H9kjldP7jBxe0+08tvmCk/ubiwZVtQdk7tFt8yVrPv1NuKpgtLjZWWVnfqeu7IZcOuzjxX5XZlNXLx2m56GblIe3r4JZQqTauhckukrYu/+u5QlWs7NXa9OgqWsd7Xrc7crZcYvOYc/8H68sp+KlfS26VuGKxroX/4x1Mu3mP1YnSnjDhYEL9dp/Zw8eoLo37OLfD1qG1e1d/ViLS9Q30XZ9ykt/Bd2DG+utVsq79LMOaJE1zcQPheSEk6o9pGdXza8yNdvP9Xfkvk1uf9JPGKXALvnHMnq9y1tZe4OPZCpaWPK6wAAAAINCasAAAACLRSLwmI1vLBiAvKs+f4sFNH1e7JMZtd3LvqQonXxc/d4uKsB8p2qRbsu8jdrYY3HRuzXc9ZA1xc6yNKAIIgZ8VKdZwWcdz087LuDQoSeWt/UOYNKnfV2X6pmnfvfkjl+mTd5uI2fyna7dxf/+PLAD4/V59/j63i4g5jr1W59lKyu/YkOzN0rYt/7vKOTh4REQ+WUrX/x9ep46ynKQMojtT1ekmqhzb4Zej+Xn++yu2JWLFu7pEv+IPf9TkrmdSI50TvCzlDYtHPi3y8bPeW5AorAAAAAo0JKwAAAAKtzEsC7Iy5BT8+W1/i/qhLbR9Lj+jmMTUv4x1bUDzx7m5Vq9/iUu4JkNxa3KM/Gz/6sLeLW41er3JZI5a7ON5VVP74byd1fHbLb108fVdTlfvXvy9wcfsSXpGgormjzT4so1NM0beRx21v4uIOT2ernN5XDfsquuRqSj+/gsqwibtV7to6C+I6Z+Tt/NA+fN8/1vNeXq5X/6giS+M+Z1FwhRUAAACBxoQVAAAAgcaEFQAAAIFW5jWsQLzUUlZCDStQoqb5ZQUfvfl8laoevR5OLD38UjsfHPSUSvUZ45fG+mlEpsrVW0ndaknZZStFHpX4+SNrFru8F7V01TXTIo7mCEpPzkr/nvzsnENUbsFLfpfB2xt96uLmabF3HCyqsX82drF5okFUlhpWAAAAVGBMWAEAABBolASgXNX9IeK/4Mk6lzEiviWvABRPxvvT1HG8S1lFlhVc0eIIlWoj/rZ/3OfDPhtx+UAX33JyRsx2VbI2q+Oseutc3L/BLBf/472z9fPWGP+cR1g2Mghyf9G7f/4WsfLn0C/OcfG4rPEl8noHPjXUxa2eWeTijHXTCmpearjCCgAAgEBjwgoAAIBAY8IKAACAQKOGFeWq/jO+zu3EZ7qqXLpML+vuAEBCSflypovbfBn/87ZFxKOluT+HsORYIss9ZpWL+0v3EjlnZsSW97mFtCttXGEFAABAoDFhBQAAQKAxYQUAAECgMWEFAABAoDFhBQAAQKAxYQUAAECgMWEFAABAoDFhBQAAQKAxYQUAAECgGWttefcBAAAAiIkrrAAAAAg0JqwAAAAINCasAAAACDQmrAAAAAg0JqwAAAAINCasAAAACDQmrAAAAAg0JqwAAAAINCasAAAACDQmrAAAAAg0JqwAAAAINCasAAAACDQmrAAAAAg0JqwAAAAINCasAAAACDQmrAAAAAg0JqwAAAAINCasAAAACDQmrAAAAAg0JqwAAAAINCasAAAACDQmrAAAAAg0JqwAAAAINCasAAAACLQKN2E1xtxjjHktRu5oY0zIGPOnMaZvnOd7yRiz0xizsmR7isIwjsmjFMZykjFmlzHmm5LtKfaG92XyYCyTR7KMZblMWI0xk40xm4wx6XG0vaSMf/CsstZWt9ZOzHt9Y4z5P2PMcmPMVmPMG8aYmvmNrbWXiMhJZdi/wEiwcTzGGDPHGLPZGLPBGDPOGNMsv3FFHkeRhBvLyA/Y/D+D8htba48VkSFl2L9ASbCxvCNqHHfmjW19Ed6XCTaWfMYWIsHGsokx5j1jzCpjjDXGtIpsXF5jWeYT1ry/eG8RsSLSv6xfvwguFpGLRORwEWkqIlVEZES59igAEnAcfxGRE621tSU8jotE5Kny7VIwJOBYivgP2Pw/L5d3h4Ig0cbSWvtA5DiKyL9FZLK1dn159628JdpYCp+xMSXgWIZEZKKInFXeHYlUHldYLxaRqSLykoi4qyLGmExjzLvGmHV5v509YYzpJCJPi0ivvN++N+e1nWyMuTziueq3EWPM48aYFXlXRGcYY3oXo7+nisgoa+0Ka+2fEv5AHWiMqVqMcyaDhBpHa+0aa+2qiIdyRaRdUc+XZBJqLFGohB1LY4zJ6z+/fIQl1FjyGVuoRBzLkSIyvajnKA3lNWEdnffnRGNMI2NMqoh8ICLLRKSViDQTkTestfMkfGtvSt5v4LXjfI3pItJVROqKyOsi8rYxJqOghsaY2caY8/dyPhMVp4tI+zj7kqwSbhyNMS3y3vw7ReRWEflPnP1Idgk3liLS0Bizxhiz1BjzqDGmWpz9SHaJOJb5eotIQxF5J872yS7hxpLP2JgSbiyDqEwnrMaYI0SkpYi8Za2dISJLROR8Eekh4VsIt1lrt1trd1lri1y/Ya19zVq7wVqbY619RMITzA4x2h5grX29kNNNFJHLjTGtjDG1ROQveY9X2CusCTqOYq1dnvfmry8ifxeR+UXtW7JI0LGcL+EP5iYicqyIHCwiw4rat2SRoGMZaZCIjM27k1WhJepY8hn7vxJ1LIOorK+wDhKRTyLqk17PeyxTRJZZa3NK4kWMMbcaY+YZY7bk/bZXS8JvoKJ4QUTGiMhkEZkrIl/kPV6Rv+mYiOPoWGs3Svi243hjTFpxz5fgEm4srbWrrbW/WGtD1tqlInK7BKzWqpwk3FhGnLOqiJwtlAPkS9ixFOEzNkpCj2WQlNl/JGNMFRE5R0RSjTGr8x5OF5HaIrJGRFoYY9IKGDxbwOm2i77C2TjidXpL+AdYHxGZa60NGWM2ib6tHzdrbUhE7s77I8aYE0Tk97w/FU6ijmMB0iR8+7GmiGwsoXMmlCQaSysVcIm+SEkwlmdI+H04uZjnSXhJMJb5+IxNnrEMhLL8kD9dwkXYnSV8O6+riHQSka/zcn+IyIPGmGrGmAxjzOF5z1sjIs2NMZUjzjVLRM40xlQ1xrQTkcERuRoikiMi60QkzRhzl4TfMEVijKlrjGlrwjpL+NbjfXkT2YooUcfxTGNMB2NMijGmgYTHcWbelYCKKlHH8hhjTMu892SmiDwoIuOLer4kkZBjGWGQiLxirS3oB3VFk5BjyWdsgRJyLEVETLj+NX8JrnQTox62LJXlhHWQiLyYV+OyOv+PiDwhIudJ+Nv47URkuYRvtw/Me94kCd+KX22Myb+k/qiI7JbwoL4s4ULmfB9LuO50oYSLmXeJyIpYnTLGzDXGXFBIv+uLyIcS/u3mIxF5wVr7bNx/6+STqOPYLO9820RkjoSX7Tgj3r90kkrUsewmIt9J+D35nYTH8/p4/9JJKlHHUkx4rc5jReSV+P+6SS1Rx5LP2P+VqGMpEv7iXH49+fy843Jl+IXWM8YcKeGBzxaRgdbaj+N4zigJ116ttdayhEcAMI7Jo4hj+amI9BSRadbaPqXcRcSJ92XyYCyTRyKNJRNWAAAABFqF/qICAAAAgo8JKwAAAAKt0GWtjk85m3qBcvRp6O0SW5KCsSxfjGXyYCyTR0mNJeNYvnhPJo/CxpIrrAAAAAg0JqwAAAAINCasAAAACDQmrAAAAAg0JqwAAAAINCasAAAACDQmrAAAAAg0JqwAAAAINCasAAAACDQmrAAAAAg0JqwAAAAINCasAAAACDQmrAAAAAi0tPLuACq4lFQXrr7hUJW66+rXXJwrxsV/nXiualdzkT9Hk1GzVC60c6c/sLZYXQUAFGzZvYep41Clgj9v9zTIUcfV6u1wcbMz55Z8x5A0uMIKAACAQGPCCgAAgEBLyJKA1Hp1Xbzotg4x2/U6yt9eGNb8I5XrMf5mF3f61wqVy/l9VXG7iBhMmv4vt+Sf3V08/8In4jrHWWc9FTv5V33Y6atLXdz+b5tULue35XG9HgAkm3VDerk4p5qJ2a7xVH/LPvfejSr3SNu3Xdw1XZdjxeuUhSe5eE+RzoCKgiusAAAACDQmrAAAAAg0JqwAAAAItISoYTWH7KeOR7zztIubp6XHfF5KxHw8JJVVbt4Zvl5y1anZKnfys7e7OPOf3+1bZ1Eoe1AndfzDBcMijmKPZVHNO/JFF3d99EKVa3qWXw5LQrkl/tpAeUtt1NDFyy9tp3LNHiz+Z1tqnTouvufHT1TuisducPH2FiGVa3/nbBeHduwQlL4Tf96qjofUftzFVVMqRzd3pmX7ytIe6ZWisv4zu/+iviqTnVPw9GLj6Ex13GDCkpivjSLosb8LR0fMlUREHlp/uItnHZyqconwM5ArrAAAAAg0JqwAAAAItMCWBKQ1b+biWo//rnKFlQEURdOo831+1X9cfPGx56lcSh+9BBb2Tdqazer4/rV+d5QHG81QufvW+1sbW3KquPjU2jNVu0PTt7u4iol9a2tWj9fUcd/DB7s45Vt/izIRbo0A8chds9bFLd6toXIbL+jp4jrvRO0Qt2tXzHOmtW7p4vnXN3HxsFV6aaTDL/zRxXc0+kzlrnzBv/dk7oKYr4WSs3hnQ3V8Z3ZtF3+yvKOLdyyrqdotOUffVo6039QLXNz8nKhxzMmRgtSVleqYT9viS2vTysVHjprq4i0hvdvY5yP9Umb1QlNKvV8ljSusAAAACDQmrAAAAAi0wJQEpLXU3xysNtp/c/TFVp9ENy9VdVN9iUCnWqtVjptXxZOzTJdU/NLfl350PbeXymWO9//2uYt+dfE82V+1M5P8Od7MGqty1U3s8pGDhvvSgtlH+tuloW3bYj4HSCQpVau6t+aLNQAAIABJREFUuOqoLSr3fhu/S9GhVa5VuXrP+9uFqQ0aqNzSC/z77bYTx7v4kff7q3bt/+M/LS/tPFTlFt/qr5VkXSooA0u6xy7zyKzpb9O3+Tw7ZrtoOXNqudjGKAFAydt5eg91XPsmv2vjrXX9++7Qu29T7SLf14mIK6wAAAAINCasAAAACDQmrAAAAAi0wNSwLjtP17DOaP14jJbajtAeddzzpVtc3OzL3S5eepbe1WF+/yf3tYsoBTkr/ZJlTR/Wy5fFu9xJylm+3nnclJYqd1GN1dHNnQca+mV3+lfv52JqWJEsTGZTF49s9bLKdZh8nYvbvjAt5jl+v7C9Om53vK8nf/n+U13c5g19jtyI5eG2N8tSuR+P8zvcnXThzSpX67WpgtK354RDXJz6t1UufqLZV6pdrvW7lM3aretUW97NTpDFEbkTnYhehk6MXiZu+V3+Ox5jL31E5fqPu8nF7zTxu8/taKLPUa/IPQ0GrrACAAAg0JiwAgAAINACUxJw4tlFuw30/Ba9xFGrOwtetqHKoYcV+DjKV0qNiN132jRXudBP81ycWsff5sjp1EK123KXv4VfWAlAtDm7fTmJzWG/FXipnfRtcElJ0N/t12904YDrblKptuNjlwFEqro6pI5/nu3Lbtq/Hvtze90Qfwtzyp3DVS5NMlxcd/BylcvVG9KhhGT3666OzU3+9vOHHT6M+bzPd/qlAR9pd1DJd6yCidzF87fHa6tc7Tdau7jjLXNV7vr6L7h48N/1e7ldRBnN54d3LpF+BlGCfgoDAACgomDCCgAAgEBjwgoAAIBAC0wN6754arOvL5t0bveo7PwCn3PcGdOL9FpzNzdRx2myPEZLxJJSrZqLN/fXNcdth/rxur3piyp33YLzXLxf3T9cPCKqXVGd+fXVLm6/7sdCWqIgJk1/fIR6dHHxH4dXU7nsg7a7+KAWfnvecxvqOspbp53t4g5/XadyOStWSiyRWztvOMLXiG1vqn8nj9WP6L5kmHkqd8OMgTFfO8hyN/ga1iqF1KymtdJ14fNu8Z97dWfppXE6Pr3Jnz/i8ej/DwOumeTPL3pZwee2+PHa9Kx+7ZqySlDyxj7zmDqun1otRkvt9sevcHEjYRmr4pp/s/+//0vPESqX3quSi/vOP1nlRpx9lotrzYxdO/7ptANcnBGzVWLiCisAAAACjQkrAAAAAi0hSwLGrzrQxZV/LrgEQERk88V+WZW7Gj0cla0c12tlP0VJwL5KqVpVHS94sqOLFx0/spBn6jH5Yr93it2XGbv9TcvzvrlS5do9zVJW+2rXqT1cfOXDenzOre5vOb+0tanK/Wf2CS6e/mvLAmMRkUd6vu3iz97tonITZvjXPrjLryr3WMsxLl6wp5aLr/vxfNUud5f/yJv/ZkeVe/TXdi6u+oVeUqbV9tn+4GxJOvNv0ON13TGfuPjzf+olvnJWrynwHIsfPEQdn5o2IebrfbelrYtrvzdH5ULRjRG3naf1UMd3Dxvl4nhLAKKNvsXvqrT6xuoqd8NP57q4+XmLVc5mZxfp9ZJdm3d3ufiA7derXOv3/vQHM3VZks3RO0HGwybZJckk++sAAAAg2TBhBQAAQKAxYQUAAECglWsNa6h3NxefWvvluJ+3bFU9F7eXZTHbre9mXVwjJb6aVRGRZzf7Wraac9arHFWPe2daZ6rjRcc/V6qvd9D0C1xcfXRNlau20tcLtZsys1T7URFk3OiXHBq/rqvKje7vt88NLflN5Vrl+BrQref1dHG9q/T795gqfimrn3ZsU7mMejtdvGhslspd+ellLrbLfK1Xi+26PjJeFa2OMrXJDnX841a/1NTyi9qqXOYzfhxWXLmfP8cuq9oNqeXHtt1EXT++fzu/RFloe/zbKUMktVFDdfz7M/7n4atd9Ra4B1Qu/sJGXSpX8XHUT8Cfe4528bAZbVTuszP9z/fchUuK3Y9kkfLNLBe3+kbnrBRf/en+OuSB1/ykcsvvi1iizpbEq5UtrrACAAAg0JiwAgAAINDKtSRgc3t/u6JXRvxLYFSr6W/zRu6iJCKy4N/+FtWCM5508b7c4vtig7/dyK2MfbeqT729NyrA/ev3U8ej5/llchqPiX1rq9lXi1ycu2lezHYovsU/NXfxFwP0UnEnn3q7i7PrNVC5rF6/ufjtNv55faZco9qddc+1Lk79UpdwtLCxb+9TqlM8dSboz9E1l9Rw8YNXvaByT447ycUt3vHlFzZDl129OqCxi5866lWV+9uwwS5uKJQE7IulTzRSx/N6RP7bxv6cnJa9Rx1/sm3/GC21F748ysVvn6J3Zjo43Y/5zXX1UnOfZugltlA26k9Y6OIr7pmscvc09Ltn5a5ZW1ZdKjFcYQUAAECgMWEFAABAoJVrSUDdF6a4+O/X6dsHDzT6Iebzfoy8BbIwOvuViyqZVBfv2YcvxG2+z39DtpKsK6QlClJtdfwFGEty/DeOv7yll8q1/mxGXOeI93Zwaie9Y8/Gg33pQp2x/puboV27BAXLutPflv+wbweVm3Oz38Us1+r/Awc8eZ2Lrxzpb0u12jxbUP5qvzpFPxDxETuy9Ykqlbt0aYHnWHbvYer4ohr+Vv+wTfq91/iliPfbvnS0oup5gAsf7zYmZrN5u/VqD6d/P8TFzZ+ppHJpk+L7fM2q5EtzLmw8WOXmHf5qdHOUs9z1G1y8MVfvTLbuZL/iR90XKAkAAAAAShQTVgAAAAQaE1YAAAAEWrnWsEYKWaOPS6CyKbJutSTOh9jSMv1yR0/++/GobCWJZeCsiOVt4qxZLaouoxer4wcbveni/p/3c3HoD5bZyWfS09Vx+y93u3iX1eO6/2N+iaqHrhylchm9/I5xdgTvxUSSszT2boI7T/ffPfjmsodULtX4pbI+uvFolau0o3Tf60lnqq/1vv71y1Uq1N7XrbZ6Qv8cbfXtLCmulKzWLqZmNbFcPfkidXzxDf47PlNfiloCLRT8xQG5wgoAAIBAY8IKAACAQAtMScBXTx6qH/jH9+XTERTJ5p7NXLx/5dglAK9ua6yOmwz2y4aVxg2J0BFdXXxZ3ZFR2di7wiBs0ajO6vivdV528YMDzlO5pjO/c/FDP1yocmNf9GUi54y5zMV1T9lWIv1E+VjR18d1Uqqo3IxsXz6S/r1ef5CikKJredeUvTcqhjVD9fJkF1z1cVzPu3tdF3WcsnaTixnvkpV79EHqeOnpfscxW9vvaNas8UbV7q76flnCTxfq9+s/b7/ExVXfDeb8iyusAAAACDQmrAAAAAg0JqwAAAAItMDUsDZ49xd13Ger38rRXh57e9QrWn2jjs+r8Xux+7Jp6HYXN/ys2KdLWilVq7o488ZFcT3n8QXHquOm6VtdvPqGw6Kbx6X5W7+6OCdqSarNWb5OJ9umqtxzWzJdbHdlF+m1k91TPV9Tx4Pfv9LF7WZOjfm86G0fz/rJL192YZvpLv5Yaha3iyhjqY0auvjDvo9FZHRN+NlfXu3i9ttYxirI1l/pt8UecvV4lRtSO/bP1Mi61enHNVW53HVrSqh3ENG1xd/95TGVSzcFT+UG/nqCOt5pfV15rwz9M6/Gz37pwaAucMUVVgAAAAQaE1YAAAAEWmBKAnI3b1HH1cZGLKswNvbzxvQ4UR2fN+6FYveldZ0NLt5eSLuKbuvJ+7v4g1ZPxfWc5w94RR1v+8rfRuydkVOkfnx9vf9v/PAK/f8hbedaF18252KVa/wXv9hK7qb4ShoquhuP/8jFjz7ZV+VMTX+7qXED/X6e2MW/L/sMv83FTeU7QWJZf1JbF2dV8u/fybv0cnad/u8PFxftnY2SZLrpZacWDq7u4pmnD3Nxrajlydbm+p+Cp8y+ROUaDPYlXZQAlK4mo/yuZb2zb4jZrtH4JS7OXavLKQ8cdqOLvx/wiMqFfo29o11QcIUVAAAAgcaEFQAAAIHGhBUAAACBFpga1qJK/XVViZ/z/zInuPgvRw/Rrzf5xxJ/vUS1qq9f/CLVxP7dJ9f6WtGulfV/uVQTUUdqi9aPyNrX3u0nxGzXbuKV6rjBvB+K9oIVyOPH6Jrg+f9q4OKHjh+jcptz/TJnj87ro3IDrrvJxU3HU7eayDZ18vGRcwa42ES1q9KmlotTfi/5z+lks+Z6vazf8Zf4LVjnXpTl4ty5C2Kewx7eVR0fOOInF/euMU7l+lfbEXHk61Yf2thWtXv1Bf8Z0GSYfu9Sm1x2Qjv8eNV/Nvb2vIUtSdXuJr8UYdWzdc35xgu7u7jOS6W7/W9RcYUVAAAAgcaEFQAAAIGW8CUBpWG/yv7m1q56+rJ5tbLuTIKIvO1fFs8rikpV95TZayWLnBUr1XG7C/3xs9Im5vOaydxS6xPKVmq9uuo4t5p/zw7v8IaLd1n94+SfC/2yZ0HdOSdIdjTWNVEPNZ7p4ktH+SWo1uzUO0pFGtL8LXWsb/trS/b86eLLF17g4qqX6hv9TVZSwpOMDvxKl8jdcvt7Lh73UoPo5oHAFVYAAAAEGhNWAAAABBolAShxpy06WR3PnZfp4srrU1Wu7bD5Mc+z6aQOLq7zkf9m7NKhnVS7Bof7HXW+2O+d2P3qMFv3q04dF+du2hTzeUBFZjIy1HH91htd/M7mQ1z83ptHqHbN1nAreV+k7dTrLCzP8bfsX2zxdVzniC6xmpHtb+9f8uSNKldnkc9VGT/NxXzzv2LI+j/9M++UL/3P2HcPO07lzHc/SRBwhRUAAACBxoQVAAAAgcaEFQAAAIGW+DWsVi8FsiW028V1UjKiW8fl2c3tXFxzznqVY3kWr8NVs1x8SprfpSW0e61qlxX6Q2Ip7N+z5utTC2zX4j5dG/f7X/1rH2kHqNy3B7zr4p8v7ahyoc2x62cBhOVE7VJVY9jBLh5zVk8Xdxw+S7UruwXrkkPmP/Tn2uWTrnPx/a895+Ie6Xqpxdm7d7l44Is3q1yLe/05mwo1xfByV/5/e3ceH1V1NnD8eZKQsIuy78geFISi1g2lpVbqglZFK2hRwUqtaF3R1q2tb0u1bhUttlp3XGjFqlgqLigilkURX0ARlU32XQQCyZz3jwnn3jNvJg6TZObcye/7+fD5PCfnzp1Dbmby5NxnznF/L49bF+xO2HCc+5r/5viMDOlbMcMKAAAAr5GwAgAAwGuRLwko27TZaZ895iobv/HAX1I6x72beznt5x4MlnRovmRWFUaX20xpaYVxprUdF7rVNc7tO0n6hlqLMzIeIJd8NfYYp/3CZXfa+Nw7r7NxbGfyXZWw/3RmUGJx88FHpPSYDtz2R4rM3j1O+8uhHWx86bTXnb4JPU62cdmnS2t2YJVghhUAAABeI2EFAACA10hYAQAA4LXI17AmqvdisMXcqS/2r+TI5JoLdasAao/YCf2c9sbe9Wxc0tRdOnDIY0Hdasfx1EwCuaB02Qob/3LWT5y+4u2rEw/PCmZYAQAA4DUSVgAAAHgt50oCAAD7J+/tD512i7dDcYbHAiC7uv30A6edvUUrXcywAgAAwGskrAAAAPAaCSsAAAC8RsIKAAAAr5GwAgAAwGskrAAAAPCaGmO+/SgAAAAgS5hhBQAAgNdIWAEAAOA1ElYAAAB4jYQVAAAAXiNhBQAAgNdIWAEAAOA1ElYAAAB4jYQVAAAAXiNhBQAAgNdIWAEAAOA1ElYAAAB4jYQVAAAAXiNhBQAAgNdIWAEAAOA1ElYAAAB4jYQVAAAAXiNhBQAAgNdIWAEAAOA1ElYAAAB4jYQVAAAAXiNhBQAAgNdIWAEAAOA1ElYAAAB4jYQVAAAAXqt1Cauq3qaqTyXpG6iqMVXdoaqDUzzfY6q6S1VXVe9I8W1q4Fq+qaq7VfXd6h0pKsNrMndwLXMH76+5I1del1lJWFV1uqpuUdWiFI69MMM/4KuNMQ2NMVNDY2iuqhNVdVv5uJ/e12eMuVBEfpTB8XklatcyNJa/q6pR1a77vmaM+b6IjM7g+LwRpeuoqt9T1Y9VdauqblLVyaradt/BvCYjdS1PUdV3y6/lWlV9WFUb7TuYaxmda5kwFt5fE0TpWqpqa1V9SVVXl1/HTuGDs/W6zHjCWv4fHyAiRkSGZPr50/SCiKwVkQ4i0kJE/pTd4fghotdSVPU4EemS7XH4IoLXcZGInGSMaSIibUTkMxH5S3aH5IcIXssDROR2iV/HYhFpKyJ3ZnVEnojgtRQR3l8rEsFrGRORqSJyVrYHEpaNGdafisj7IvKYiIzY90VVba+qL6jqhvJZk/GqWiwiE0Tk6PLp6q3lx05X1VGhxzp/jajqfaq6UlW3q+o8VR2Q7mBV9Yci0l5ErjPGbDPG7DXGfJju+XJMpK5l+fkKROR+ERlTlfPkmEhdR2PMOmPM6tCXykSka7Lja5moXcuJxpipxpidxpgtIvI3ETk23fPlmEhdy/Lz8f5asUhdy/L32AdFZE6656gJ2UpYny7/d5KqtlTVfBF5RUSWi0gnif+V/awxZrHEbyHMKp+ubpLic8wRkb4icpCITBSRSapat6IDVXWBqg6r5FxHicinIvJ4+Q/UHFU9IcVx5LqoXUsRkatE5B1jzIIUn782iNx1VNUO5W/ku0TkWhG5I8Vx5LrIXcsEx4vIwv04PpdF8Vry/lqxKF5L7xRk8sk0fqugo4g8b4zZqKqfi8gwif/l0Ubis5il5YenXb9hjAkXF9+lqjeJSA8R+aiCY/t8y+naicgPRWSUiFwk8Snyf6lqV2PMxnTHGHVRvJaq2l5ELhWR/umOJ9dE8TqWH7NCRJqo6kEicomIfJLu2HJFVK9laPwnSnz26bvpji1XRPFa8v5asSheS19leoZ1hIi8Fkr0JpZ/rb2ILA9dtCpR1WtVdbHGPyS1VeJ1Us3SPN0uEVlmjHmkvBzgWRFZKdy2iuK1vFdEfmuM2VYdY8sRUbyOljFms4g8LvE/IjP6B7iHInstVfUoiY/3bGPMkuoYZ8RF8Vry/lqxKF5LL2XsDV5V64nIOSKSr6pry79cJCJNRGSdiHRQ1YIKLp6p4HTfiEj9ULtV6HkGiMj1IjJIRBYaY2KqukVENM2hLxCR01IYU60R4Ws5SESOU9Xw7eNZqnqlMWZimueMrAhfx0QFEv8wZGMR2VxN54yUKF9LVe0nIi+JyMXGmDfSPU+uiPC15P01QYSvpZcyOcN6hsQ/HNFL4nUWfSX+qdAZ5X1rRGScqjZQ1bqqum8Gc52ItFPVwtC55ovImapaX+PLZowM9TUSkVIR2SAiBap6i8R/kaVrsogcqKojVDVfVc+WeJnAzCqcM+qiei27i8hhoTGLxP8YmVyFc0ZZJK+jqp6pqj1UNU9Vm4vI3SLyYflsa20V1Wt5qMQ/jTzGGPNyuufJMZG8lsL7a0Wiei1F4/Wv+5bgKtIk9bCZlMmEdYSIPGqMWWGMWbvvn4iMF5HzJP6D3VVEVojIKhE5t/xxb0q8CH+tqu6bUr9HRPZI/KI+LvFC5n3+I/E3wCUSL2beLfFb+BVS1YWqOjxZf/kvwSES/2DHNhG5QUROr831qxLda7k+YbwiIhuNMbv24/+eSyJ5HSX+4YSpIvK1iHws8SVYfpzqfzpHRfVaXiMizUXkEY1/InqHqtb2D11F8lry/lqhSF7LcrtEZEd5/El5O6vUmFp9d9uhqsdL/MKXiMi5xpj/pPCYR0RkqIisN8awtI4n0ryW0yS+KsRsY8ygGh4iUsBrMndwLXMH76+5I0qvSxJWAAAAeC0rW7MCAAAAqSJhBQAAgNcqXdbqxLyh1Atk0bTYpGpbkoJrmV1cy9zBtcwd1XUtuY7ZxWsyd1R2LZlhBQAAgNdIWAEAAOA1ElYAAAB4jYQVAAAAXiNhBQAAgNdIWAEAAOC1Spe1AgAgFfmNGzvtvZMPsPHS5S2dvu4Xz83ImADkDmZYAQAA4DUSVgAAAHiNkoBvkXiba+eAHjZePSD49uXvdjdnaDlnr43rL9nk9JV99kV1DhEAsi7Wrb3TntrzSRuv77bT6RvV6iwbl65dV7MDA5ATmGEFAACA10hYAQAA4DUSVgAAAHiNGtYK5NWvb+NPbi92+j4968HgOAnqVmNi3JNcEoSzS9z61lu+OMPGBT9YUZWhRkJBpw42/uYQd3mb3Qfm27j3mI+dvs9+26vC8xVNmVONowNQ097b3cZpm927szQSAFHFDCsAAAC8RsIKAAAAr1ESICLbhh/ltI+6OtiF5aVWDyYebg1aeKaNY8a97b/xndY2brgqoVwg5EDJvZKAgrbu7b9NDxbaeMZhE1I/0V/fqfDLR4wb47Rb3v9e6udEtflq7DFO+6Mrxts4sUTm1Lb9MzIm+On62Wc57a5bP8zSSHJcXr7T3DziSBvP+Z+/OH2XrDzWxl+dUtfGZRvdZRgBXzDDCgAAAK+RsAIAAMBrJKwAAADwWq2tYY0N6GfjB27/s9PXpzCoA/rVusOdvgWjD7Vx0Wx3Gaaw9vJlVYcYWTsfq+O0Z/R6rlrP/8Q1dzvtC/KvtnGre6lnzZZw3WpMYlkcCbLh08vqOe3tsWDpqm737HH6klf1oyrMUYc67fduD+rK9yZ80x9sF3xG4Ijzgs8F8JkA+IoZVgAAAHiNhBUAAABeqzUlAdr/EKe94vIyG7fJd29XFT97rY27/iph+ZWS5GUAtU14+apwGcCU4kkJR+ZLdWqeV+q0dzflBmM25B29xW2Hdn6bV8LfwrVBfnE3G78y6H6nb2VZ8DNg5i3M2Jhqsy9Pr5+07+hbL3faW3sG75vdn1ps4zJBlK29Mlhu8KOx7rKcT2xvZuNnj+3j9JVt2lyzA6sG/FYBAACA10hYAQAA4LWcLgnIb9zYxltu3+30fXrYP218/McXOH1drnnfxtxsDhR06uC0nR2snJUAqrcEINHxE69z2p1vnlXlc+Y1aGDjVT8/zOlrPWunjXXm/Co/V5QVtG9n41t7TXH6wqsElIm78xty06qTm9u4Z50ip6/4iV/Y+GCp+msUFctv1tTGjw19wOmbsvMAG7d8Y7XT1/Th5TamDCBawrmNiMjiP/a08Qen/snGZaauc9zwRutt/GSvjk5f3gxKAgAAAIAqIWEFAACA10hYAQAA4LWcrmFd93QrG7/f5xmnr3jmhTbufLW7PI+7aBL2+eaQlk57xmETqnzOQ54Mllqpuz61usfOd1f/Tiyf3NvLxktOdpfnueG8I2y8aGQvpy82f1G1j8Vnu7sHPwNDGiRf1urIIrf6+5upnW3c+JyNNi7bvr26h4gaVtCxvY0vH/WijWcmLGXW7aGvbMx7as1Zf3p3Gx9Z9JrTVzwx+HxGly+pI44yrRN8ZmTv5AOcvqU9g9/FW2K5+8kbZlgBAADgNRJWAAAAeC3yJQF59d2dPTadEyxJ9EyfP4V63OUdDno+eFzpSnavqk47jbtz2OI9wa2Mi54Y4/R1GfeBjWO73aXHMmnssa8m7RvXao6NT2zd3+krqmWrXG3qFSxdFJNYQm9e0r63ege7nw18fqiNG97iLq0is3kt+m7Vj4OSgJGN/2XjYz86xznugGVLbZzXqJHTp4XBznhR2GEnqmItSrI9BKQp8TWzZ/KBNn6t54tO3xu7gvfle08O3l9ffitx18loY4YVAAAAXiNhBQAAgNdIWAEAAOC1SNawhrcl++qiQ52+udeHlyRy61bD/nxHcFzZHe5ySqM/Pt/Gra526zHLln65P0OtlZ7Y1tNpv3JIUHvTQdwlqRKrIDOpbOB3bNyp8OksjiQ6dhwdbFObl/D3bngbyJv/d4jTN//Ip2w8PVTP+uFz7k/AiMevtHHnR1Y4faUrV6UxYlRVfhN3CZ3ioZ9UeFyDu9zjwsvwhOvvRET6Hhhcy09ObeX0la5Zm9Y4a6sWszbZePHevU7fNUdMs/GUA7s6fWVb3GXpkH1l3wt+JzW93c01nuwU1K12e32U09fzd6FruX5DzQzOA8ywAgAAwGskrAAAAPBaJEsCtjzX3MZz+9xfyZHJ9S0M/usxcXeG+G//iTb+/T97O32zTw9uq5Quc29Z5rptP/s620Ookr0/PNxpn3TX2zYeVG9n4uGoQMeH821cbNzbUj3Grrdxm1XuDmDHTz3bxm/2fs7G/Qrdv5nnX3KfjV8Z1tTpu2HycBt3HsuuPZmy5Nfu7m6fdnrAxhcsG2TjgjfmOcdtP/coG88ofjDp+Qd3Hem08ygJ2C9li5bY+I7Vg52+Rzu+YeNJx7h9RVPmSFVtveBoG7cc5d7CLr2kQTDGJZ9X+bmiLFwek9+imY0/+4W7rN8z5wXvf3XULZfqPumXNu72y/edvrJQHC6ZzDXMsAIAAMBrJKwAAADwGgkrAAAAvOZtDWu4DmPVKHfpqgWHBfVQZcZdkirsmrVH2vjVaUc4fQffENTAxY7r6/Qd98BsG9/U7H+dvuJLTwjOcWPtqmGde/hEp53NJanSsaFPodO++qCKl+dBcgVvBnWKXd50+0oreVzDwV/Y+NiRV9h4z2lbneNu7TXFxmc0cPuGnD8+6Bt/uvvcLHlVrcwxwRbX9/340aTHLX6m2MZt2i13+kbe9mLi4db03cHWrIUrNzl9lf0coXJbzmngfuG/Qbj5kh1OV+t/B/XoEiuTVGi/Q5z2FTc9b+M66l65R1e5x9Zmy24OtvReOPKBpMed/lnwvhY7w/1cRdet7yceXqHYzuBxo1cNcPomtJuR0jl8xQwrAAAAvEbCCgAAAK95UxKQ38xdwuaTW4Lloz49y126alss2H3q0uWnOH0r7+1m40aTg9uXB5cmXwanzoIvnPa0NcFOTb9q9rHTV9qmJOl5ct19W9ydUq48cGmFxyXeGsqoo/o4zfX9G9p4/lXjnb58DW6JlZkUCxySV6AgRU0fCb0WH3H7/t4uuIV198NFTl94OaxFt7q7I3UfRUnA/tKi4Pu75dzvOH2/u+VhGw+ql/xkm4WuAAASx0lEQVQ9r/UTQcnU+rPdW8AXNn456eN2m6AkwNRxfw3l1a9v4/DtTXw7s2NH0r55RzzltHvfdrmNO96S/Pfj7tOC0ror7nrW6RvSINhh6UfDL3H68nd+UPlga5EmS4KlM69cHSwF9up7/Zzjety4wMbp/uyHXz/3tX09odeblC8tzLACAADAaySsAAAA8FrG54cL2rax8ae/DHZ5eOjMvzrHHV/3taTnGDjvIhu3ON39pHfD0Mci3f2rktPGjZz2W70nJT22w3P5Sfty3UtjBzntMX/9rMLjetdd6bQfH3aajRtPTO2Tjuna87ttTnt2r+ATzok3/WMmtU/GOlL9oUJ6NKi5GNbB3YknL/T39dIfue8XPZ4OdkvqMvzDGhpctOU3b+60D/rXXhtP6Zj8k8uVeXnx9FBrepKj/r/BoZ3lBk//h9M3ZWdQxvPHX//Uxg2fr9n3jlwQ2/GN0z5i3Bgbz7nBLa2be/E9Nr71lGOSnvM3Lf9s4yKt4/R1f/nnNu7xrlsCwFtl4ICngp/dz0KVGd3E/Zmu7pV3itRN8WaWBO+hhas2O31RWJ2DGVYAAAB4jYQVAAAAXiNhBQAAgNcyXsP6ybiWQfz98ZUcGXhkWwen3WpYUCNZHTUfn17VPmnfr9Yd7rTrvhEsO0GNTsWOLHK/M9feFuyQdffe85y+hpP+K/stYemqcN3qTZ1f2f/zfYveMy+0cZf5bn1uFOp+omT5sOC1/rMD/uX0xUKv9nkl7t/anR5mvbGKhOtWl97f2ulb3PGxpI/7qiyoMf3eP651+up8HXzvTx/yno1/36J6ljH6OlbPxhv6Bde14fMVHY0wU+q+I7X6S7BrY/finzt9Tw2eYOMLDgqWtTqkMDEtCOpWi6ePcnp6XB5c88Tnhn/e+rqXjUu/XF7JkX5ihhUAAABeI2EFAACA1zK/rFVhxUsJ9Xjd3SVjVL+ZNr6u6SKn796xQ2xc2Q4dldk0KthtYvrQOxN6g1tSC0Yf6naVfCy1VcFO99rNLglu1yWWAYSFd0P5zp/ucvo231G43+Nokveu0+5QUC/JkdWjZHNw/sRlY1A168a4y+k8fGmw9E5ewrZi4TKAWzv3d/ryhV119gnvYBUuA1g84LGUz/HDx66zcdeE99i8Bg1sPHxEeFke97UcCxVN/XGTuwvWI+8HO5q1+7c7b9JoRrCD3sEb03t/R1z4Nn33y2Y7fb+VYHez8HKTQ9+Y6xx3eoNlNq77sfteSxmAf5b/IshZ8vUdp++ZFwfauKO8J1HDDCsAAAC8RsIKAAAAr5GwAgAAwGsZr2ENC9eoNXuzyOl7Z/yRNh774mKn75Hzg20Ef//oj52+8FIN+Y0b23jLc+62hHMP+4uNP9/rdMnAX15m44az01h2KUflv+XWCV57U/B9Ci9dFa5ZTdQuod60XRrjyJP6TjtWwwuMLTkt+Fk58V+jnb6if89JPBzf5sjeNpxwlbtdZL+iYOmqWMLf01ff+AsbNxK26UzG9Oth41TrVvvOPt9pd/pt8HOd+OpafclhNu5TGHzWoMy4iwyeviTYkrnse6udvu6S/HWTxobJqKq84LVWV91fiGNWnmzjtuOiV/dYmyW+JqOOGVYAAAB4jYQVAAAAXst4SUDvNsGtofCt3E193RtPzd8Odi86bcmpTt8NHV+18Ybj2zh9pYOD9kWXB8eNbvKWc9yUncHSLPdccrHT1/AtygBS0XhicFt2/EXfs/GQXi/U6PPmq/t3VsykdhPxZysHOu280M/fhPZvV3lc2H9/W3+C0w5fh7OWnuL0NXqOMoBUrLoutdfDpB1Nbdz+1+7yRGWVLFe0u1nwugnfcjzsvxc4x7U9+5OUxoHs29mrlY3ParjR6bvppWIbdxGWGUP2MMMKAAAAr5GwAgAAwGskrAAAAPBaxmtYv74m2Crw/r93s/En5zzgHNdTgiVsDslb5vQdG1r6Zubvx6f0vOd+Pthpb7rjYBvXfWt24uHYT/Uuy7fxMy+3dPq6F66zcceCXU5fs/z931Y1camONWXBOU+993qnr02oFjp/09dO36JbWti4pN3rNi7SOvs9JuyH2cH2xpe0mOd0xSS4ttv+1MHpqytra3ZcERXeVlNE5A99Jld43PbYbqf90BVn2bhw0dzEw5Pa0zpY9mhLLHjtNfxnI/fAGAtURcWKwVld4RJICTOsAAAA8BoJKwAAALyW+fsAoduBjz4e3Kbvcam7E0piiYBLk/b8e2dwW+rqF0fYuNuT7u5LdRdQBlCdyj77wsZP90zcvypor7z5GKen2XFrbNy36Sqnr33dzTZ++as+Ns5Tdwm0LVOCW6Kt73N3YgkfmbhQT/eLV9j4qUVdbDzygBWCmvPFHUfb+Ki6852+i1YMtHHdl3mNpqTQLWFpkR8ufQneK0+76irnuAb/SW/5vl7/Eyx79JMnx9j4gOksOxZV9TttT9oX/t2ZW/smIWqYYQUAAIDXSFgBAADgtax+NLDNncHt2wefGuj0XfuzzjYuK96R9BylJe5/ofiGoLSgy5pgVw5uZfih/e/eS9r3Wd9eTvt/mwUrCNR7fV7i4VYrWVblcaFmbRoZlAG88ZM7bXzRih85x628MVg5JF8+qPmB5YDSL5c77Vs796/wuAZSPTv4lX6xzMb5oRjR9c3XdZP2LR1+oI07L8jEaFAVscJsj6DmMMMKAAAAr5GwAgAAwGskrAAAAPCaN9tblK5xd7Hp8Jv0drVJXLoI0RGbv8hps99U7tg6KNgRqXVod7NNJQ2c4/Lfom4VyLTu9we7ly09vsTpO3/w2zaePaGb05dYP43syG/e3MY3nvd80uPaT9uZieHUGGZYAQAA4DUSVgAAAHjNm5IAIJsm9wpuqUyW5kmPK5I5mRhOTntga7CrWNnw/CyOBICIiJkT7EB5xpPXOH3f/cFCG5d0aur05VMS4IWyDRtsPHfHwTYe3mi9c1zh50GpZRTLJ5lhBQAAgNdIWAEAAOA1ElYAAAB4jRpWADWuy7D5Nv63NAn1fJX5wQBIqtPNs5z2upuDmO2S/Tf1tcNtfM+I6tmO2RfMsAIAAMBrJKwAAADwGiUBAAAAOaDrU5tsPHrQAKfP7C5JPDxSmGEFAACA10hYAQAA4DUSVgAAAHiNGlYAAIAcULZoiY1XfDex95uMjqW6McMKAAAAr5GwAgAAwGtqjMn2GAAAAICkmGEFAACA10hYAQAA4DUSVgAAAHiNhBUAAABeI2EFAACA10hYAQAA4DUSVgAAAHiNhBUAAABeI2EFAACA10hYAQAA4DUSVgAAAHiNhBUAAABeI2EFAACA10hYAQAA4DUSVgAAAHiNhBUAAABeI2EFAACA10hYAQAA4DUSVgAAAHiNhBUAAABeI2EFAACA10hYAQAA4DUSVgAAAHiNhBUAAABeq3UJq6repqpPJekbqKoxVd2hqoNTPN+bqrpbVd+t3pECAABAJEsJq6pOV9UtqlqUwrEXZjgZXG2MaWiMmRoawzBVXa6q36jqi6p60L4+Y8z3RWR0BscHAABQq2Q8YVXVTiIyQESMiAzJ9PPvL1U9REQeEpELRKSliOwUkQezOigAAIBaJBszrD8VkfdF5DERGbHvi6raXlVfUNUNqrpJVcerarGITBCRo8tv028tP3a6qo4KPdaZhVXV+1R1papuV9V5qjqgCuMdLiIvG2PeMcbsEJGbReRMVW1UhXMCAAAgRdlKWJ8u/3eSqrZU1XwReUVElotIJxFpKyLPGmMWS/x2+6zy2/RNUnyOOSLSV0QOEpGJIjJJVetWdKCqLlDVYZWc6xAR+WhfwxjzuYjsEZHuKY4FAAAAVVCQySdT1eNEpKOIPG+M2aiqn4vIMInPuLYRkeuMMaXlh6ddt2qMCX+o6i5VvUlEekgo8Qwd2+dbTtdQRLYlfG2biDDDCgAAkAGZnmEdISKvGWM2lrcnln+tvYgsDyWrVaKq16rqYlXdVl5GcICINEvzdDtEpHHC1xqLyNdVGSMAAABSk7EZVlWtJyLniEi+qq4t/3KRiDQRkXUi0kFVCypIWk0Fp/tGROqH2q1CzzNARK4XkUEistAYE1PVLSKiaQ59oYgcFjp/5/JxL0nzfAAAANgPmZxhPUNEykSkl8TrS/uKSLGIzCjvWyMi41S1garWVdVjyx+3TkTaqWph6FzzJf7Bp/qq2lVERob6GolIqYhsEJECVb1F/v8M6f54WkROU9UBqtpARH4rIi8YY5hhBQAAyIBMJqwjRORRY8wKY8zaff9EZLyInCcip4lIVxFZISKrROTc8se9KfFZzrWquq+U4B6Jf/BpnYg8LvGkcp//iMhUic+ALheR3SKyMtmgVHWhqg5P1m+MWSjxD349LSLrJZ4QX7Yf/28AAABUgRpT0R332klVj5d4wlsiIucaY/6TwmOmichRIjLbGDOohocIAABQ65CwAgAAwGtZ2ZoVAAAASBUJKwAAALxW6bJWJ+YNpV4gi6bFJqW7FBcAAEDOYIYVAAAAXiNhBQAAgNdIWAEAAOA1ElYAAAB4jYQVAAAAXiNhBQAAgNdIWAEAAOA1ElYAAAB4jYQVAAAAXiNhBQAAgNdIWAEAAOA1ElYAAAB4jYQVAAAAXivI9gDSoUVFNl7z8/5OX+yErTb++LsTq/25+/3+Mhu3fGiujc3ePdX+XAAAAGCGFQAAAJ4jYQUAAIDX/C0JyMu34dorvut0XTX6Hza+oNGspKcoM9U/rLk3jrdxtyNH2bjnVcvc5968JWiYGhgIAABALcEMKwAAALxGwgoAAACvkbACAADAa97WsGq/njb+4LrxlRyZPZ8NejhoLHD7Tut7ko3LNmzI0IgAAAByDzOsAAAA8BoJKwAAALzmTUmA1il02l8MbZylkVSPxX/oaOPuoygJAAAASBczrAAAAPAaCSsAAAC85k1JwIqxhzvtxRdUfWWAmSVBPn7x5NFO36kD59r4Ny1nOH0NtajKz730R3+18cnynSqfDwAAoLZihhUAAABeI2EFAACA10hYAQAA4LWs1rDmt2xh4xvOfz6tc2yK7XLaxzx3jY17jF9t4y7L3neOWxyKf/jTq52+88e+auPLmnyZ1rgAAABQPZhhBQAAgNdIWAEAAOC1rJYE7O3e1sbDG01N6xy3rxvotLtcG9z6L03xHE2emOW0Hz7wFBtfdn3Vl9cCAABA+phhBQAAgNdIWAEAAOA1ElYAAAB4Las1rF9cms1nBwAAQBQwwwoAAACvkbACAADAa1ktCcj7qm6VzzFnQwen3Vg+r/I5AQAA4A9mWAEAAOA1ElYAAAB4jYQVAAAAXstqDWv3h9YEjfPTO0fjwhKnveuMIys8ruG7bm1r2cZN6T1hivrNGW7j1rK4Rp8LAAAglzHDCgAAAK+RsAIAAMBrGS8JiJ3Qz8bd7v64yud7tedL7hceqPi42zce6rRX7DrIxh882cfp215cWuVxtb01iGNVPhsAAEDtxQwrAAAAvEbCCgAAAK+pMSZp54l5Q5N3pig2oJ/T/vVjj9t4QN2q33r3Rd/Z7jIHbc76NGjEytI657TYJK3KmAAAAHIBM6wAAADwGgkrAAAAvEbCCgAAAK/V+LJWS4fXcdq5VLcadmD9XdkeAgAAQE5ihhUAAABeI2EFAACA12qkJGD95cfY+B8n3ZuJp8y6tw79p9MePGCkjfPe/jDTwwEAAMgZzLACAADAaySsAAAA8BoJKwAAALxWIwWlH/7qQRuXmdysWf02n59ZZONub2dxIAAAABHHDCsAAAC8RsIKAAAAr9XO+/UZ0PQjzfYQAAAAcgIzrAAAAPAaCSsAAAC8RsIKAAAAr1HDup8OfW+EjffsDr59jf9bzzmuxaOzMjYmAACAXMYMKwAAALxGwgoAAACv1UhJQP/bfm7j2bc+UBNPsd8m7WjqtG+ccZaNe0zY7fTlf7Ux6Xk6rFsUNGJl1TM4AAAAJMUMKwAAALxGwgoAAACv1UhJQLO/vW/jo/f8wun7wZUzbfy7FvOr/Fxv7Cpy2pdOu6jC43pO+Nppd/9oro1NwrGlVR4VAAAAqgszrAAAAPAaCSsAAAC8RsIKAAAAr6kxiRWcgRPzhibvTPcJC4Ky2fzmzWy8+IZOznFFW4JcuuMf5iU/Ycwdotm7p2oD9Mi02CTN9hgAAACyjRlWAAAAeI2EFQAAAF6rkWWtKmNKg0WjStestXG3K9dWdHj8MTU6IgAAAPiMGVYAAAB4jYQVAAAAXiNhBQAAgNdIWAEAAOA1ElYAAAB4jYQVAAAAXiNhBQAAgNdIWAEAAOA1ElYAAAB4jYQVAAAAXiNhBQAAgNdIWAEAAOA1ElYAAAB4TY0x2R4DAAAAkBQzrAAAAPAaCSsAAAC8RsIKAAAAr5GwAgAAwGskrAAAAPAaCSsAAAC89n+ZLOMiC7eEvwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x864 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6Po-OIgK1XeX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}