DL101_part_2_OXML.ipynb

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      "cell_type": "markdown",
      "metadata": {
        "id": "zvbu6-RoTti1",
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      "source": [
        "# Deep Learning 101 Tutorial for OxML 2020\n",
        "### Prepared by Melvin Selim Atay\n",
        "# Tutorial Objectives: \n",
        "\n",
        "*   Learn and implement convolution operation.\n",
        "*   Get intuition about deep convolutional neural networks (CNNs). \n",
        "*   Learn properties of CNNs, implement and train a CNN model\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "   For convenience we will be using well-known datasets available in PyTorch dataset library, it is possible to customize the dataset loaders for your needs.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CE62jPxmW4eF",
        "colab_type": "code",
        "cellView": "form",
        "colab": {}
      },
      "source": [
        "#@title Imports\n",
        "%matplotlib inline\n",
        "# Python 2/3 compatibility\n",
        "from __future__ import print_function, division\n",
        "\n",
        "import itertools\n",
        "import time\n",
        "\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import numpy as np\n",
        "# Import torch and create the alias \"th\"\n",
        "# instead of writing torch.name_of_a_method() , we only need to write th.name_of_a_method()\n",
        "# (similarly to numpy imported as np)\n",
        "import torch as th"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eK9O9lrUVjH1",
        "colab_type": "code",
        "cellView": "form",
        "colab": {}
      },
      "source": [
        "#@title Plotting Functions\n",
        "\n",
        "# Colors from Colorbrewer Paired_12\n",
        "colors = [[31, 120, 180], [51, 160, 44]]\n",
        "colors = [(r / 255, g / 255, b / 255) for (r, g, b) in colors]\n",
        "\n",
        "# functions to show an image\n",
        "def imshow(img):\n",
        "    \"\"\"\n",
        "    :param img: (PyTorch Tensor)\n",
        "    \"\"\"\n",
        "    # unnormalize\n",
        "    img = img / 2 + 0.5     \n",
        "    # Convert tensor to numpy array\n",
        "    npimg = img.numpy()\n",
        "    # Color channel first -> color channel last\n",
        "    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
        "\n",
        "\n",
        "\n",
        "def plot_losses(train_history, val_history):\n",
        "    x = np.arange(1, len(train_history) + 1)\n",
        "\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    plt.plot(x, train_history, color=colors[0], label=\"Training loss\", linewidth=2)\n",
        "    plt.plot(x, val_history, color=colors[1], label=\"Validation loss\", linewidth=2)\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.ylabel('Loss')\n",
        "    plt.legend(loc='upper right')\n",
        "    plt.title(\"Evolution of the training and validation loss\")\n",
        "    plt.show()\n",
        "\n",
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    from http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html\n",
        "    :param cm: (numpy matrix) confusion matrix\n",
        "    :param classes: [str]\n",
        "    :param normalize: (bool)\n",
        "    :param title: (str)\n",
        "    :param cmap: (matplotlib color map)\n",
        "    \"\"\"\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        \n",
        "    plt.figure(figsize=(8, 8))   \n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "\n",
        "    fmt = '.2f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, format(cm[i, j], fmt),\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')"
      ],
      "execution_count": 2,
      "outputs": []
    },
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      "metadata": {
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          "height": 51
        },
        "outputId": "88330fda-5c7b-4f73-8b5e-82da487d8b53"
      },
      "source": [
        "#@title Add GPU support\n",
        "if th.cuda.is_available():\n",
        "  # Create tensors\n",
        "  x = th.ones(1000, 1000)\n",
        "  y = 2 * x + 3\n",
        "  # Do the calculation on cpu (default)\n",
        "  start_time = time.time()\n",
        "  # Matrix multiplication (for benchmark purpose)\n",
        "  results = th.mm(x, y)\n",
        "  time_cpu = time.time() - start_time\n",
        "  \n",
        "  # Do the same calculation but on the gpu\n",
        "  # First move tensors to gpu\n",
        "  x = x.to(\"cuda\")\n",
        "  y = y.to(\"cuda\")\n",
        "  start_time = time.time()\n",
        "  # Matrix multiplication (for benchmark purpose)\n",
        "  results = th.mm(x, y)\n",
        "  time_gpu = time.time() - start_time\n",
        "  \n",
        "  print(\"Time on CPU: {:.5f}s \\t Time on GPU: {:.5f}s\".format(time_cpu, time_gpu))\n",
        "  print(\"Speed up: Computation was {:.0f}X faster on GPU!\".format(time_cpu / time_gpu))\n",
        "  \n",
        "else:\n",
        "  print(\"You need to enable GPU accelaration in colab (runtime->change runtime type)\")"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Time on CPU: 0.06434s \t Time on GPU: 0.00696s\n",
            "Speed up: Computation was 9X faster on GPU!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VmcHR0J0XiQD",
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      "source": [
        "\n",
        "For this tutorial, we will use the CIFAR10 dataset.\n",
        "There are 10 classes: ‘airplane’, ‘automobile’, ‘bird’, ‘cat’, ‘deer’,\n",
        "‘dog’, ‘frog’, ‘horse’, ‘ship’, ‘truck’. The images in CIFAR-10 are of\n",
        "size 3x32x32, i.e. 3-channel color images of 32x32 pixels in size.\n",
        "\n",
        "\n",
        "![CIFAR10](http://pytorch.org/tutorials/_images/cifar10.png)\n",
        "\n",
        "\n",
        "Training an image classifier\n",
        "----------------------------\n",
        "\n",
        "We will do the following steps in order:\n",
        "\n",
        "1. Load and normalize the CIFAR10 training and test datasets using\n",
        "   ``torchvision``\n",
        "2. Define a Convolution Neural Network\n",
        "3. Define a loss function\n",
        "4. Train the network on the training data\n",
        "5. Test the network on the test data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fz0iV7HIWlFW",
        "colab_type": "code",
        "cellView": "form",
        "colab": {}
      },
      "source": [
        "#@title Import Torchvision\n",
        "import torch\n",
        "import torchvision\n",
        "import torchvision.transforms as transforms"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AS2rByFMXv90",
        "colab_type": "code",
        "cellView": "form",
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      },
      "source": [
        "#@title Seed the random generator to have reproducible results:\n",
        "seed = 2020\n",
        "np.random.seed(seed)\n",
        "torch.manual_seed(seed)\n",
        "\n",
        "if th.cuda.is_available():\n",
        "  # Make CuDNN Determinist\n",
        "  th.backends.cudnn.deterministic = True\n",
        "  th.cuda.manual_seed(seed)\n",
        "\n",
        "# Define default device, we should use the GPU (cuda) if available\n",
        "device = th.device(\"cuda\" if th.cuda.is_available() else \"cpu\")\n"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
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        "colab_type": "code",
        "cellView": "form",
        "colab": {}
      },
      "source": [
        "#@title Setting random sampler\n",
        "from torch.utils.data.sampler import SubsetRandomSampler\n",
        "\n",
        "n_training_samples = 20000 # Max: 50 000 - n_val_samples\n",
        "n_val_samples = 5000\n",
        "n_test_samples = 5000\n",
        "\n",
        "train_sampler = SubsetRandomSampler(np.arange(n_training_samples, dtype=np.int64))\n",
        "val_sampler = SubsetRandomSampler(np.arange(n_training_samples, n_training_samples + n_val_samples, dtype=np.int64))\n",
        "test_sampler = SubsetRandomSampler(np.arange(n_test_samples, dtype=np.int64))\n",
        "# (In the last case, indexes do not need to account for training ones because the train=False parameter in datasets.CIFAR will select from the test set)"
      ],
      "execution_count": 6,
      "outputs": []
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      "source": [
        "#@title Load data, set train, test and validation splits and show some of the training images\n",
        "num_workers = 2\n",
        "test_batch_size = 4\n",
        "\n",
        "transform = transforms.Compose(\n",
        "    [transforms.ToTensor(),\n",
        "     transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))])\n",
        "\n",
        "train_set = torchvision.datasets.CIFAR10(root='./data', train=True,\n",
        "                                        download=True, transform=transform)\n",
        "\n",
        "train_loader = torch.utils.data.DataLoader(train_set, batch_size=test_batch_size, sampler=train_sampler,\n",
        "                                          num_workers=num_workers)\n",
        "\n",
        "test_set = torchvision.datasets.CIFAR10(root='./data', train=False,\n",
        "                                       download=True, transform=transform)\n",
        "test_loader = torch.utils.data.DataLoader(test_set, batch_size=test_batch_size, sampler=test_sampler,\n",
        "                                         num_workers=num_workers)\n",
        "\n",
        "classes = ('plane', 'car', 'bird', 'cat',\n",
        "           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')\n",
        "# get some random training images\n",
        "dataiter = iter(train_loader)\n",
        "images, labels = dataiter.next()\n",
        "\n",
        "# show images\n",
        "imshow(torchvision.utils.make_grid(images))\n",
        "# print labels\n",
        "print(' '.join('{:>10}'.format(classes[labels[j]]) for j in range(test_batch_size)))"
      ],
      "execution_count": 7,
      "outputs": [
        {
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          "text": [
            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./data/cifar-10-python.tar.gz\n"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Extracting ./data/cifar-10-python.tar.gz to ./data\n",
            "Files already downloaded and verified\n",
            "\n",
            "      bird        dog      truck       frog\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qnD9xhv9aBYh",
        "colab_type": "code",
        "cellView": "form",
        "colab": {}
      },
      "source": [
        "#@title Import torch.nn and functional api\n",
        "# Useful imports\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cVHTK_YcXvGi",
        "colab_type": "text"
      },
      "source": [
        "### PyTorch functions to build the network\n",
        "\n",
        "In PyTorch, there are built-in functions that carry out the convolution steps for you.\n",
        "\n",
        "- **nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=0):** Convolution layer. You can read the full documentation [here](http://pytorch.org/docs/master/nn.html#conv2d)\n",
        "\n",
        "- **nn.MaxPool2d(kernel_size, stride=None, padding=0):** Max pooling layer. You can read the full documentation [here](http://pytorch.org/docs/master/nn.html#maxpool2d)\n",
        "\n",
        "- **F.relu(Z1):** computes the elementwise ReLU of Z1 (which can be any shape). You can read the full documentation [here.](http://pytorch.org/docs/master/nn.html#torch.nn.ReLU)\n",
        "\n",
        "- **x.view(new_shape)**: Returns a new tensor with the same data but different size. It is the equivalent of numpy function *reshape* (Gives a new shape to an array without changing its data). You can read the full documentation [here.](http://pytorch.org/docs/master/tensors.html#torch.Tensor.view)\n",
        "\n",
        "- **nn.Linear(in_features, out_features):** Applies a linear transformation to the incoming data: $y = Ax + b$, it is also called a fully connected layer. You can read the full documentation [here.](http://pytorch.org/docs/master/nn.html#linear-layers)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fhl3GdE_aelK",
        "colab_type": "text"
      },
      "source": [
        "How convolution works in 2D: \n",
        "\n",
        "![picture](https://upload.wikimedia.org/wikipedia/commons/1/19/2D_Convolution_Animation.gif)\n",
        "\n",
        "Source: https://commons.wikimedia.org/wiki/File:2D_Convolution_Animation.gif"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IrAZZkXii4mD",
        "colab_type": "text"
      },
      "source": [
        "**Convolution Formulas**:\n",
        "\n",
        "The formulas relating the output shape $(C_2, H_2, W_2)$ of the convolution to the input shape $(C_1, H_1, W_1)$ are:\n",
        "\n",
        "\n",
        "$$ H_2 = \\lfloor \\frac{H_1 - kernel\\_size + 2 \\times padding}{stride} \\rfloor +1 $$\n",
        "\n",
        "$$ W_2 = \\lfloor \\frac{W_1 - kernel\\_size + 2 \\times padding}{stride} \\rfloor +1 $$\n",
        "\n",
        "$$ C_2 = \\text{number of filters used in the convolution}$$\n",
        "\n",
        "NOTE: $C_2 = C_1$ in the case of max pooling\n",
        "\n",
        "where:\n",
        "- $H_2$: height of the output volume  \n",
        "- $W_2$: width of the output volume  \n",
        "- $C_1$: in_channels, number of channels in the input volume\n",
        "- $C_2$: out_channels"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u1O3UJsti9Bg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_output_size(in_size, kernel_size, stride=1, padding=0):\n",
        "    \"\"\"\n",
        "    Get the output size given all the parameters of the convolution\n",
        "    :param in_size: (int) input size\n",
        "    :param kernel_size: (int)\n",
        "    :param stride: (int)\n",
        "    :param paddind: (int)\n",
        "    :return: (int)\n",
        "    \"\"\"\n",
        "    return int((in_size - kernel_size + 2 * padding) / stride) + 1"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5Zoxw725pbnf",
        "colab_type": "text"
      },
      "source": [
        "#### Example of use of helper method get_output_size() \n",
        "\n",
        " *input volume of size 3x32x32* (where 3 is the number of channels)\n",
        " 2D convolution with the following parameters:\n",
        "\n",
        "```python\n",
        "conv1 = nn.Conv2d(3, 18, kernel_size=7, stride=2, padding=1)\n",
        "```\n",
        "then, the size of the output volume is 18x?x? (because we have 18 filters) where ? is given by the convolution formulas \n",
        "\n",
        "**get_output_size()** function allows to compute that size:\n",
        "\n",
        "```\n",
        "out_size = get_output_size(in_size=32, kernel_size=7, stride=2, padding=1)\n",
        "print(out_size) # prints 14\n",
        "```\n",
        "\n",
        " *the output volume is 18x14x14*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rlToUpEmptbA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "3af9e276-ad0d-42d8-f780-461de7ed20ca"
      },
      "source": [
        "out_size = get_output_size(in_size=32, kernel_size=3, stride=1, padding=1)\n",
        "print(out_size)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "32\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4eReC_TLae71",
        "colab_type": "text"
      },
      "source": [
        "#### Simple Convolutional Neural Network\n",
        "\n",
        "ConvNet with one convolution layer followed by a max pooling operation,\n",
        "one fully connected layer and an output layer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8JpV5zEHpwbM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class SimpleConvolutionalNetwork(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(SimpleConvolutionalNetwork, self).__init__()\n",
        "        \n",
        "        self.conv1 = nn.Conv2d(3, 18, kernel_size=3, stride=1, padding=1)\n",
        "        self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)\n",
        "        \n",
        "        # cf comments in forward() to have step by step comments\n",
        "        # on the shape (how we pass from a 3x32x32 input image to a 18x16x16 volume)\n",
        "        self.fc1 = nn.Linear(18 * 16 * 16, 64) \n",
        "        self.fc2 = nn.Linear(64, 10)\n",
        "\n",
        "    def forward(self, x):\n",
        "        \"\"\"\n",
        "        Forward pass,\n",
        "        x shape is (batch_size, 3, 32, 32)\n",
        "        (color channel first)\n",
        "        in the comments, we omit the batch_size in the shape\n",
        "        \"\"\"\n",
        "        # shape : 3x32x32 -> 18x32x32\n",
        "        x = F.relu(self.conv1(x))\n",
        "        # 18x32x32 -> 18x16x16\n",
        "        x = self.pool(x)\n",
        "        # 18x16x16 -> 4608\n",
        "        x = x.view(-1, 18 * 16 * 16)\n",
        "        # 4608 -> 64\n",
        "        x = F.relu(self.fc1(x))\n",
        "        # 64 -> 10\n",
        "        # The softmax non-linearity is applied later (cf createLossAndOptimizer() fn)\n",
        "        x = self.fc2(x)\n",
        "        return x"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7a0L4C0KXvRc",
        "colab_type": "text"
      },
      "source": [
        "### Define a loss function and optimizer\n",
        "\n",
        "Let's use a Classification Cross-Entropy loss and ADAM (optionally, SGD with momentum). You can read more about  [optimization methods](https://pytorch.org/docs/stable/optim.html).\n",
        "\n",
        "![picture](https://gombru.github.io/assets/cross_entropy_loss/intro.png)\n",
        "\n",
        "![picture](https://gombru.github.io/assets/cross_entropy_loss/multiclass_multilabel.png)\n",
        "\n",
        "Image sources: https://gombru.github.io\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IoH14mh8bbtv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch.optim as optim\n",
        "\n",
        "def createLossAndOptimizer(net, learning_rate=0.001):\n",
        "    # it combines softmax with negative log likelihood loss\n",
        "    criterion = nn.CrossEntropyLoss()  \n",
        "    #optimizer = optim.SGD(net.parameters(), lr=learning_rate, momentum=0.9)\n",
        "    optimizer = optim.Adam(net.parameters(), lr=learning_rate)\n",
        "    return criterion, optimizer"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OgStImwVcdS0",
        "colab_type": "text"
      },
      "source": [
        "###  Train the network\n",
        "\n",
        "\n",
        "We have to loop over our data iterator, feed the inputs to the network, and optimize.\n",
        "\n",
        "#### Training loop\n",
        "The training script: it takes ~10s per epoch with batch_size = 32\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ps6I493_cVWV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_train_loader(batch_size):\n",
        "    return torch.utils.data.DataLoader(train_set, batch_size=batch_size, sampler=train_sampler,\n",
        "                                              num_workers=num_workers)\n",
        "\n",
        "# Use larger batch size for validation to speed up computation\n",
        "val_loader = torch.utils.data.DataLoader(train_set, batch_size=128, sampler=val_sampler,\n",
        "                                          num_workers=num_workers)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NCrADNiacVy1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train(net, batch_size, n_epochs, learning_rate):\n",
        "    \"\"\"\n",
        "    Train a neural network and print statistics of the training\n",
        "    \n",
        "    :param net: (PyTorch Neural Network)\n",
        "    :param batch_size: (int)\n",
        "    :param n_epochs: (int)  Number of iterations on the training set\n",
        "    :param learning_rate: (float) learning rate used by the optimizer\n",
        "    \"\"\"\n",
        "    print(\"===== HYPERPARAMETERS =====\")\n",
        "    print(\"batch_size=\", batch_size)\n",
        "    print(\"n_epochs=\", n_epochs)\n",
        "    print(\"learning_rate=\", learning_rate)\n",
        "    print(\"=\" * 30)\n",
        "    \n",
        "    train_loader = get_train_loader(batch_size)\n",
        "    n_minibatches = len(train_loader)\n",
        "\n",
        "    criterion, optimizer = createLossAndOptimizer(net, learning_rate)\n",
        "    # Init variables used for plotting the loss\n",
        "    train_history = []\n",
        "    val_history = []\n",
        "\n",
        "    training_start_time = time.time()\n",
        "    best_error = np.inf\n",
        "    best_model_path = \"best_model.pth\"\n",
        "    \n",
        "    # Move model to gpu if possible\n",
        "    net = net.to(device)\n",
        "\n",
        "    for epoch in range(n_epochs):  # loop over the dataset multiple times\n",
        "\n",
        "        running_loss = 0.0\n",
        "        print_every = n_minibatches // 10\n",
        "        start_time = time.time()\n",
        "        total_train_loss = 0\n",
        "        \n",
        "        for i, (inputs, labels) in enumerate(train_loader):\n",
        "\n",
        "            # Move tensors to correct device\n",
        "            inputs, labels = inputs.to(device), labels.to(device)\n",
        "\n",
        "            # zero the parameter gradients\n",
        "            optimizer.zero_grad()\n",
        "\n",
        "            # forward + backward + optimize\n",
        "            outputs = net(inputs)\n",
        "            loss = criterion(outputs, labels)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "\n",
        "            # print statistics\n",
        "            running_loss += loss.item()\n",
        "            total_train_loss += loss.item()\n",
        "\n",
        "            # print every 10th of epoch\n",
        "            if (i + 1) % (print_every + 1) == 0:    \n",
        "                print(\"Epoch {}, {:d}% \\t train_loss: {:.2f} took: {:.2f}s\".format(\n",
        "                      epoch + 1, int(100 * (i + 1) / n_minibatches), running_loss / print_every,\n",
        "                      time.time() - start_time))\n",
        "                running_loss = 0.0\n",
        "                start_time = time.time()\n",
        "\n",
        "        train_history.append(total_train_loss / len(train_loader))\n",
        "\n",
        "        total_val_loss = 0\n",
        "        # Do a pass on the validation set\n",
        "        # We don't need to compute gradient,\n",
        "        # we save memory and computation using th.no_grad()\n",
        "        with th.no_grad():\n",
        "          for inputs, labels in val_loader:\n",
        "              # Move tensors to correct device\n",
        "              inputs, labels = inputs.to(device), labels.to(device)\n",
        "              # Forward pass\n",
        "              predictions = net(inputs)\n",
        "              val_loss = criterion(predictions, labels)\n",
        "              total_val_loss += val_loss.item()\n",
        "            \n",
        "        val_history.append(total_val_loss / len(val_loader))\n",
        "        # Save model that performs best on validation set\n",
        "        if total_val_loss < best_error:\n",
        "            best_error = total_val_loss\n",
        "            th.save(net.state_dict(), best_model_path)\n",
        "\n",
        "        print(\"Validation loss = {:.2f}\".format(total_val_loss / len(val_loader)))\n",
        "\n",
        "    print(\"Training Finished, took {:.2f}s\".format(time.time() - training_start_time))\n",
        "    \n",
        "    # Load best model\n",
        "    net.load_state_dict(th.load(best_model_path))\n",
        "    \n",
        "    return train_history, val_history"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "itpAKe2fcV71",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0bd57cdd-c683-43d0-9a4e-d47d9953717e"
      },
      "source": [
        "net = SimpleConvolutionalNetwork()\n",
        "\n",
        "train_history, val_history = train(net, batch_size=32, n_epochs=10, learning_rate=0.001)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "===== HYPERPARAMETERS =====\n",
            "batch_size= 32\n",
            "n_epochs= 10\n",
            "learning_rate= 0.001\n",
            "==============================\n",
            "Epoch 1, 10% \t train_loss: 2.08 took: 0.74s\n",
            "Epoch 1, 20% \t train_loss: 1.89 took: 0.56s\n",
            "Epoch 1, 30% \t train_loss: 1.74 took: 0.55s\n",
            "Epoch 1, 40% \t train_loss: 1.63 took: 0.56s\n",
            "Epoch 1, 50% \t train_loss: 1.56 took: 0.58s\n",
            "Epoch 1, 60% \t train_loss: 1.51 took: 0.56s\n",
            "Epoch 1, 70% \t train_loss: 1.50 took: 0.54s\n",
            "Epoch 1, 80% \t train_loss: 1.48 took: 0.55s\n",
            "Epoch 1, 90% \t train_loss: 1.44 took: 0.55s\n",
            "Validation loss = 1.34\n",
            "Epoch 2, 10% \t train_loss: 1.32 took: 0.62s\n",
            "Epoch 2, 20% \t train_loss: 1.34 took: 0.54s\n",
            "Epoch 2, 30% \t train_loss: 1.34 took: 0.56s\n",
            "Epoch 2, 40% \t train_loss: 1.34 took: 0.56s\n",
            "Epoch 2, 50% \t train_loss: 1.31 took: 0.56s\n",
            "Epoch 2, 60% \t train_loss: 1.34 took: 0.56s\n",
            "Epoch 2, 70% \t train_loss: 1.31 took: 0.55s\n",
            "Epoch 2, 80% \t train_loss: 1.30 took: 0.56s\n",
            "Epoch 2, 90% \t train_loss: 1.25 took: 0.55s\n",
            "Validation loss = 1.26\n",
            "Epoch 3, 10% \t train_loss: 1.18 took: 0.62s\n",
            "Epoch 3, 20% \t train_loss: 1.17 took: 0.53s\n",
            "Epoch 3, 30% \t train_loss: 1.19 took: 0.54s\n",
            "Epoch 3, 40% \t train_loss: 1.18 took: 0.55s\n",
            "Epoch 3, 50% \t train_loss: 1.19 took: 0.55s\n",
            "Epoch 3, 60% \t train_loss: 1.17 took: 0.54s\n",
            "Epoch 3, 70% \t train_loss: 1.20 took: 0.55s\n",
            "Epoch 3, 80% \t train_loss: 1.16 took: 0.58s\n",
            "Epoch 3, 90% \t train_loss: 1.17 took: 0.56s\n",
            "Validation loss = 1.22\n",
            "Epoch 4, 10% \t train_loss: 1.03 took: 0.63s\n",
            "Epoch 4, 20% \t train_loss: 1.07 took: 0.54s\n",
            "Epoch 4, 30% \t train_loss: 1.05 took: 0.55s\n",
            "Epoch 4, 40% \t train_loss: 1.06 took: 0.54s\n",
            "Epoch 4, 50% \t train_loss: 1.10 took: 0.55s\n",
            "Epoch 4, 60% \t train_loss: 1.06 took: 0.56s\n",
            "Epoch 4, 70% \t train_loss: 1.09 took: 0.54s\n",
            "Epoch 4, 80% \t train_loss: 1.09 took: 0.55s\n",
            "Epoch 4, 90% \t train_loss: 1.11 took: 0.57s\n",
            "Validation loss = 1.18\n",
            "Epoch 5, 10% \t train_loss: 0.99 took: 0.61s\n",
            "Epoch 5, 20% \t train_loss: 0.99 took: 0.55s\n",
            "Epoch 5, 30% \t train_loss: 0.97 took: 0.54s\n",
            "Epoch 5, 40% \t train_loss: 0.99 took: 0.55s\n",
            "Epoch 5, 50% \t train_loss: 0.99 took: 0.56s\n",
            "Epoch 5, 60% \t train_loss: 1.00 took: 0.54s\n",
            "Epoch 5, 70% \t train_loss: 1.00 took: 0.57s\n",
            "Epoch 5, 80% \t train_loss: 0.99 took: 0.55s\n",
            "Epoch 5, 90% \t train_loss: 0.99 took: 0.56s\n",
            "Validation loss = 1.17\n",
            "Epoch 6, 10% \t train_loss: 0.87 took: 0.63s\n",
            "Epoch 6, 20% \t train_loss: 0.95 took: 0.56s\n",
            "Epoch 6, 30% \t train_loss: 0.90 took: 0.57s\n",
            "Epoch 6, 40% \t train_loss: 0.89 took: 0.57s\n",
            "Epoch 6, 50% \t train_loss: 0.91 took: 0.57s\n",
            "Epoch 6, 60% \t train_loss: 0.88 took: 0.58s\n",
            "Epoch 6, 70% \t train_loss: 0.95 took: 0.56s\n",
            "Epoch 6, 80% \t train_loss: 0.86 took: 0.58s\n",
            "Epoch 6, 90% \t train_loss: 0.92 took: 0.58s\n",
            "Validation loss = 1.13\n",
            "Epoch 7, 10% \t train_loss: 0.75 took: 0.63s\n",
            "Epoch 7, 20% \t train_loss: 0.83 took: 0.56s\n",
            "Epoch 7, 30% \t train_loss: 0.83 took: 0.54s\n",
            "Epoch 7, 40% \t train_loss: 0.84 took: 0.53s\n",
            "Epoch 7, 50% \t train_loss: 0.85 took: 0.56s\n",
            "Epoch 7, 60% \t train_loss: 0.79 took: 0.53s\n",
            "Epoch 7, 70% \t train_loss: 0.86 took: 0.55s\n",
            "Epoch 7, 80% \t train_loss: 0.88 took: 0.53s\n",
            "Epoch 7, 90% \t train_loss: 0.82 took: 0.55s\n",
            "Validation loss = 1.14\n",
            "Epoch 8, 10% \t train_loss: 0.72 took: 0.61s\n",
            "Epoch 8, 20% \t train_loss: 0.74 took: 0.55s\n",
            "Epoch 8, 30% \t train_loss: 0.77 took: 0.56s\n",
            "Epoch 8, 40% \t train_loss: 0.79 took: 0.54s\n",
            "Epoch 8, 50% \t train_loss: 0.79 took: 0.54s\n",
            "Epoch 8, 60% \t train_loss: 0.78 took: 0.55s\n",
            "Epoch 8, 70% \t train_loss: 0.77 took: 0.53s\n",
            "Epoch 8, 80% \t train_loss: 0.80 took: 0.57s\n",
            "Epoch 8, 90% \t train_loss: 0.78 took: 0.56s\n",
            "Validation loss = 1.14\n",
            "Epoch 9, 10% \t train_loss: 0.64 took: 0.59s\n",
            "Epoch 9, 20% \t train_loss: 0.68 took: 0.56s\n",
            "Epoch 9, 30% \t train_loss: 0.71 took: 0.53s\n",
            "Epoch 9, 40% \t train_loss: 0.68 took: 0.54s\n",
            "Epoch 9, 50% \t train_loss: 0.73 took: 0.52s\n",
            "Epoch 9, 60% \t train_loss: 0.68 took: 0.55s\n",
            "Epoch 9, 70% \t train_loss: 0.74 took: 0.53s\n",
            "Epoch 9, 80% \t train_loss: 0.72 took: 0.56s\n",
            "Epoch 9, 90% \t train_loss: 0.72 took: 0.55s\n",
            "Validation loss = 1.19\n",
            "Epoch 10, 10% \t train_loss: 0.59 took: 0.61s\n",
            "Epoch 10, 20% \t train_loss: 0.60 took: 0.54s\n",
            "Epoch 10, 30% \t train_loss: 0.63 took: 0.54s\n",
            "Epoch 10, 40% \t train_loss: 0.66 took: 0.56s\n",
            "Epoch 10, 50% \t train_loss: 0.65 took: 0.56s\n",
            "Epoch 10, 60% \t train_loss: 0.66 took: 0.51s\n",
            "Epoch 10, 70% \t train_loss: 0.64 took: 0.56s\n",
            "Epoch 10, 80% \t train_loss: 0.67 took: 0.52s\n",
            "Epoch 10, 90% \t train_loss: 0.69 took: 0.56s\n",
            "Validation loss = 1.21\n",
            "Training Finished, took 66.31s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K1lDIbTRc-ZU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "outputId": "301fbe7d-1097-4fca-bec7-dfea5968f855"
      },
      "source": [
        "plot_losses(train_history, val_history)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gl1ZcV5CdGv1",
        "colab_type": "text"
      },
      "source": [
        "###  Test the network on the test data\n",
        "\n",
        "\n",
        "We have trained the network for 2 passes over the training dataset.\n",
        "We need to check if network learnt.\n",
        "We will check this by predicting the class label that the neural network\n",
        "outputs, and checking it against the ground-truth. If the prediction is\n",
        "correct, we add the sample to the list of correct predictions.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n2ozM0cpdDbQ",
        "colab_type": "code",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 189
        },
        "outputId": "aa2c0af4-707f-44a6-fbdf-0f3b74047ec6"
      },
      "source": [
        "#@title Ground truth images\n",
        "try:\n",
        "  images, labels = next(iter(test_loader))\n",
        "except EOFError:\n",
        "  pass\n",
        "\n",
        "# print images\n",
        "imshow(torchvision.utils.make_grid(images))\n",
        "print(\"Ground truth:\\n\")\n",
        "\n",
        "print(' '.join('{:>10}'.format(classes[labels[j]]) for j in range(test_batch_size)))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Ground truth:\n",
            "\n",
            "      bird        car        dog       bird\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UsXHRkIGdSId",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "9fcd2ed1-0b7c-4458-9593-e4f4c37106da"
      },
      "source": [
        "outputs = net(images.to(device))\n",
        "print(outputs.size())\n",
        "\n",
        "_, predicted = torch.max(outputs, 1)\n",
        "\n",
        "print(\"Predicted:\\n\")\n",
        "imshow(torchvision.utils.make_grid(images))\n",
        "\n",
        "print(' '.join('{:>10}'.format(classes[predicted[j]]) for j in range(test_batch_size)))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "torch.Size([4, 10])\n",
            "Predicted:\n",
            "\n",
            "       cat        car        cat        cat\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tS-K-bo2dZKr",
        "colab_type": "text"
      },
      "source": [
        "How network performs over the test set:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YSfYJZf0dSR5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        },
        "outputId": "5a811bf6-9802-4a19-d1ed-31fee65af90c"
      },
      "source": [
        "def dataset_accuracy(net, data_loader, name=\"\"):\n",
        "    net = net.to(device)\n",
        "    correct = 0\n",
        "    total = 0\n",
        "    for images, labels in data_loader:\n",
        "        images, labels = images.to(device), labels.to(device)\n",
        "        outputs = net(images)\n",
        "        _, predicted = torch.max(outputs, 1)\n",
        "        total += labels.size(0)\n",
        "        correct += (predicted == labels).sum()\n",
        "    accuracy = 100 * float(correct) / total\n",
        "    print('Accuracy of the network on the {} {} images: {:.2f} %'.format(total, name, accuracy))\n",
        "\n",
        "def train_set_accuracy(net):\n",
        "    dataset_accuracy(net, train_loader, \"train\")\n",
        "\n",
        "def val_set_accuracy(net):\n",
        "    dataset_accuracy(net, val_loader, \"validation\")  \n",
        "    \n",
        "def test_set_accuracy(net):\n",
        "    dataset_accuracy(net, test_loader, \"test\")\n",
        "\n",
        "def compute_accuracy(net):\n",
        "    train_set_accuracy(net)\n",
        "    val_set_accuracy(net)\n",
        "    test_set_accuracy(net)\n",
        "    \n",
        "print(\"Computing accuracy...\")\n",
        "compute_accuracy(net)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Computing accuracy...\n",
            "Accuracy of the network on the 20000 train images: 72.33 %\n",
            "Accuracy of the network on the 5000 validation images: 60.68 %\n",
            "Accuracy of the network on the 5000 test images: 60.72 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k0RHsQsCq-M6",
        "colab_type": "text"
      },
      "source": [
        "To determine the accuracy further we compute accuracy per class and confusion matrix."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iRmLQVjbrM2w",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "2596f3c8-d0d2-4e8f-c484-d658fcc42bc9"
      },
      "source": [
        "def accuracy_per_class(net):\n",
        "    net = net.to(device)\n",
        "    n_classes = 10\n",
        "    # (real, predicted)\n",
        "    confusion_matrix = np.zeros((n_classes, n_classes), dtype=np.int64)\n",
        "\n",
        "    for images, labels in test_loader:\n",
        "        images, labels = images, labels = images.to(device), labels.to(device)\n",
        "        outputs = net(images)\n",
        "        _, predicted = torch.max(outputs.data, 1)\n",
        "        for i in range(test_batch_size):\n",
        "            confusion_matrix[labels[i], predicted[i]] += 1\n",
        "            label = labels[i]\n",
        "\n",
        "    print(\"{:<10} {:^10}\".format(\"Class\", \"Accuracy (%)\"))\n",
        "    for i in range(n_classes):\n",
        "        class_total = confusion_matrix[i, :].sum()\n",
        "        class_correct = confusion_matrix[i, i]\n",
        "        percentage_correct = 100.0 * float(class_correct) / class_total\n",
        "        \n",
        "        print('{:<10} {:^10.2f}'.format(classes[i], percentage_correct))\n",
        "    return confusion_matrix\n",
        "\n",
        "confusion_matrix = accuracy_per_class(net)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Class      Accuracy (%)\n",
            "plane        60.86   \n",
            "car          74.46   \n",
            "bird         26.56   \n",
            "cat          50.91   \n",
            "deer         53.25   \n",
            "dog          46.93   \n",
            "frog         70.67   \n",
            "horse        75.96   \n",
            "ship         81.35   \n",
            "truck        66.67   \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_5oxWaLDdlbR",
        "colab_type": "code",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "outputId": "0c2f7f09-48ae-49fd-8fe7-9967efd5d2d5"
      },
      "source": [
        "#@title Normalized Confusion Matrix Plot\n",
        "plot_confusion_matrix(confusion_matrix, classes, normalize=True,\n",
        "                      title='Normalized confusion matrix')"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ue28nnV7dQlS4hdsrlHQoU7VscR7pUIO+b35N56lf8uKyjMHNvL/pOI9/utftOdPS0njisTEsWraC7bu+Y9HC+Rw6eMChzMdzPsTX14/d+w7z6OhxTHzmKQCCQ+uyedt2tm6PZ/GyWB4b8wipqaluy/mvx8ewcOkKvon/jsXZ5Zz7IaV9/dj13WEeGTWOic/acobUZdPW7Xz5TTyLlsXy2Gj35ExLS+P/Hh/D/CUxbIv7liUL53HYKeOncz/E19eXnd8e4uGRY5n07L8dlj874V906NjZ5dmcc3pDXT4+dhRLlq8kbu9+Fs6fx0GnjHNn/w9fX1++PXiEkWPG8ezT1m80Fi1alGeff4HJU15zea7sco4fO5ol0bHs3LOPRQvmZanLj2zHz94D3zNy9FieeyYz5zPPT2LylKyNWpdnHDeaRdGx7Ni9j8ULb5DRz489+7/n0dFjed5Wl2XKlGX+omi+jtvLu+/P5qHhQ92a89XnxvPmnEUsXreD1csXc/zIIYcyAZYgJk57h849+zrMb9S8NfNWbWXeqq389/PlFC1WjKat27st55OPj2HB0hV8fYPj55O51n0e73T8pHt6whN0uNO9x/mtwhiTCowC1gAHgQXGmP0i8oKI9LArOgCYZ3L5K6TaWLEJq+zLyXO/c/rnK/yRZlixO5mOdSs4lOnftBIfbzvJpSvWk/35y9cyln115Dy/pbjng99efNwOqteoQdVq1SlcuDC9+/Rj5QrHRuvK2OXcM2gwAD3v7s0XmzdijKF48eL4+FiHKV1NuerWa5zxcTuoXj0zZ69scq5asZx77r15zhQ35twVt4Nqdhnv7tOfVbExjhljYxhgy9jj7t58acsIsDImmspVq1I7OMQt+dJ5Q13G7dxB9Rq3U626NWOffv2JjYl2KBMbs5x7B1s/PO/u1YfNmzZgjKFEiRI0b9GSokWLuiVb1pw1MnL27tufFTGOdRkbE83AQUMAuKtXHzZv2uiQs0gR9+aMT8+Yvr/79ifW+RhfEc3AezMzpu/vsAYNCbBYAAgOCeXK1SukpKS4Jee+PfEEValOUOVqFCpcmE5Rvdi8NtahjKVSFWoF16WA3PijZv3KaFq07UixYsXdkjPe6Tjv1acfq7LU5/KM47zn3b3ZYnecx8ZEU6VKVeq4+Tj/u/JRzwrGmJXGmFrGmBrGmMm2ec8ZY5bblZlojMlyD5Yb0caKTcXSRUm+cDVjOvnCFSqULuJQplq5ElQrV4IFo5uyeGwzWtcpm9cxSU5KIjAw83KgJTCI5KSkG5bx8fGhVKnS/Hz+PABxO7bTNKI+LRo14PVZ/8n4IHNLziCnnMmOOZPsymTJuXM7zSLr06JxA15/wz05k5OSsAQF2WUMJDkpMUsZh4ylrRkvX77MGzNe419PPevyXNnlzO91mZSUSFClzLoMDAwiKTExaxm7jKVLlea8LWNeSU5KdKjLwGz2eVJSkkdzJmWXMTGH96Xd/k4XvXQxYQ3CKVLE8TzmKj+dTaKiJfNLHuUDAvnxbPKfXs+amMV06tHHldEc5Ob4uVF9Xr58mVmvT+XJfz/ntnwqd/K8sSIim0XE/QM73MCnQAGqlivBwLe3M/bjPbzctx63FfWuL1RFNm7CN/HfsvHLb5gxbQpXr17N+UkeENmoCV/HfcuGLfkz59SXX+DhkWMpWbJkzoU9LL/XpXK9gwf28/wzTzHzrXc8HeWmfvrxDEcPH6BZ6w6ejpKtVydP4pFR47ziOM9PPSvuoD0rNmcuXiXAN7N7N8C3GGcvpmQps2Hfj6ReNyT8fIUTP/1GtXIl8jRngMVCYmLmV9iTEhMyun2zK5OamsqlSxfxL1PGoUztOsGUKFmSg/v3uS9nglPOAMecFrsyN81ZoiQHD7g+Z4DFQlJCgl3GRAIsgVnKOGS8aM24a+cOJj37FA1Dbue//3mDmdOm8MG7b7s8o3MGa878V5cWSyAJpzPrMjExAUtgYNYydhkvXrpIGaeM7hZgCXSoy8Rs9rnFYvFoTkt2GQNzeF/a7e/EhATu7d+b/34wh+rVa7gtZ7kKFs7Y9Ur9mJxI+QoBf2od61YspV2n7hQqVMjV8TLk5vi5UX3Gx+1g4jMTCAuuwbtvv8GMaVN4303Hubo5tzVWRKSqiBwSkU9F5KCILBKR4k5l3rHdVGa/iEyym39CRCaJyC4R+c42ahgRKSEiH4rIDhHZLSI3uyven/Lt6YtULVeCIP9iFCoodG8YwPp9Zx3KrN13hia3+wPgV6IQVcuV4NT5310VIVfCIxpx7OhRTpz4gWvXrrF40QK6dItyKNOlaxSff/IxYO0Kbt2mHSLCiRM/ZAyuPHXqJEcOH6Zylaruy3nsKCdtOZdkk7Nztyg+/zRrzpPOOb8/TOXKrs/ZMKIRx+0yLl00n85duztm7NqdebaMy5cuppUt44p1m9l94Ci7DxzloUfHMO6JCTzw8EiXZwTvqMuIyEYcO3qEEz9YMy5aMJ+u3Xs4lOnaPYpPP54LwNIli2jTtn2e/6VmzXk0I+fihfPp1t2xLrt278Fnn3wEwLIli2jTtl2e5gyPdDzGlyycT1en/d21Ww8++zQzY/r+vnDhAv16RTHxxZdp2ryFW3OGhoVz+sQxEk+f4I9r11gTs4Q2Hbv+qXWsXr6IzlHuuwQE1uPnuNPx09n5nNktKuM4j7Y7zleu+4K9B4+x9+AxHh45hseemMCDbjrO1c25+xpGbeB+Y8w2EfkQeNRp+dPGmJ/F+lsCG0SkvjHmW9uyc8aYcBF5FHgCeAB4GthojBkuIr7ADhFZb4z57e8GTbtumLhkP3NHNKZAAVi4I4EjZy8zrnNNvjt9kQ37f2TLoXO0qlWONU+24rqBKTGHuPD7HwDMH9WU6uVLUKKID9uea8eE+d/x5eFzfzdWFj4+Prz2+ix69+hKWloag4YMIzgklMkvPE/D8Ei6do9i8LDhPHT/UBrWrY2fnx8ffvQZAN98tY2Z06fi41OIAgUKMG3mW5Qp655xNz4+PkydPovePa0577XlfPnF52kQHknXblEMHjqchx8YSng9a87/zbXm/Pqrbcx63f05fXx8mDJ9Fn3v6sb1tDQGDh5GnZBQXnlxIg3CI+jSLYp7hw7n0QeG0ah+HXz9/Hh/zqcuz5GbnN5Ql9Nnvsld3TuTlpbG4GH3ERISyouTniM8PJJuUT0Yet/9PHDfEOoH18TP3585H2feXiGkVjV+vXSJa9eusSImmujYNQS7YUCjj48P02a+wV1RXbielsbgofcRHBLKS5Oep2FEBN2692DIsOE8OHwIYSG18PP3Z7bt+AEIrVWdX3+1y7litcsHXvr4+DBtxhv0iupiPcZtGa3HeARdu/dg8LDhjBg+hAahtfDz8+fDj60Z33/3bY4fO8rUV15i6isvAbA0ZjXlypd3acb0nP/3wjRGDunF9bQ0evQbRI1awbzz+mRC6jWkTceu7N8bz/iHBnHp4gW2bFjFuzNeYdG67QAknT7J2eREIpq2dHk255xTp8+iTzbHT8PwSLp0i2KQ7fiJsB0/H8z9LOcV5ye5vxW+15Jcfmvoz69YpCqwxRhT2TbdHhgD+AJPGGPiRORhYATWRlMAMNoYM09ETgAtjDGJItIEmGyMuUNE4oCiQPrXbvyBTsaYg07bHmFbLwVvKxdR+YE5bnmNrrR7cidPR8iRm94qLpfmBUELevDa759RyMc7rhS76zzmStfzf0QAjp297OkIOaqax5ff/yr/Ej7xLrzb6w0VLFPNlOz0grs3A8Clz4fkyWty5u6eFefDM2NaRKph7TFpZIz5RUTmYG2IpEsfMJJGZk4BehtjDt90o8a8B7wHUKRCTS85RSillFJ/nuTt7fY9wt1/NlUWkWa2/w8EttotKwX8BlwUkQpAl1ysbw0wWmx7RURy/FlppZRSSnk3dzdWDgMjReQg1tvqZnyPzhizF9gNHAI+A7blYn0vAoWAb0Vkv21aKaWU+ke71b+67O7LQKnGmEFO89qm/8cYMyy7Jxljqtr9Py79OcaYK8BDLs6olFJKqXzMu+5oppRSSqksbvUxK25rrBhjTgB13bV+pZRSSv0zaM+KUkop5eVu9Z4V77iJglJKKaX+sbRnRSmllPJm/4A72GrPilJKKaXyNe1ZUUoppbycjllRSimllPIgbawopZRSKl/Ty0BKKaWUF9MfMlRKKaWU8jDtWVFKKaW8nPasKKWUUkp5kPasKKWUUt7u1u5Y0Z4VpZRSSuVvt3zPSkhgaTa+1MnTMXJUsecMT0fI0c+x4z0dIVfSrhtPR8iRt1xf9o6UgBfUZ8H8HxGAIP9ino6Qo19+u+bpCPmLeM855a/SnhWllFJK5Wu3fM+KUkopdavTnhWllFJKKQ/SnhWllFLKy2nPilJKKaWUB2nPilJKKeXF9LeBlFJKKaU8TBsrSimllMrX9DKQUkop5e1u7atA2rOilFJKqfxNe1aUUkopb6a321dKKaWU8iztWVFKKaW8nPas/IOsX7uaxg1CiKhXm5nTXs2yPCUlheFD7iGiXm3uaNOMUydPOCxPOH2KSuVL8+bM6W7N2TGyKnv/N5x9s+/nif6Nsyyf+nBbvnlnCN+8M4RvPxxO8pJRDstvK16Yo58+xIyRHdyac+2a1YSF1qFucE2mTZ2SZXlKSgqDBw6gbnBNWrdoyskTJzKWvfbqK9QNrklYaB3WrV3jtozr1q6mYb1gwkJqMf217Pf50EEDCAupRbtWzTIybly/jlbNGtEkIoxWzRrxxaaNbssIsG7NahrWrUP94JpMfy37uhxy7wDqB9ekbcvMujx//jxd7mxPBf/beHzsqCzPc6W1a1bToG4d6gXXZNpNMtYLrkmbbDKWz4OM4B116Q0ZATauW0Oz8FAahwXzxutTs8354LCBNA4LpnO7FhnnzFMnT1C5fCnatYikXYtInhg30m0Zv9i4ljuahdGucV3efWNaluU7vt5Kjw7NqBVwG6tiljosWzzvE9o3qUf7JvVYPO8Tt2VUOdPGik1aWhpPPj6GBUtX8HX8dyxeOJ9DBw84lPlk7of4+voR/91hHhk1jonPPuWw/OkJT9Dhzs5uzVmggDBz1B30fHoxDR+cTd+2dahTuYxDmSff3UzTRz6i6SMf8U70bqK3HnFY/vzQFmz9LsGtOdPS0nhs7CiWxaxk1979LJw/j4MHHOtzzuz/4evny76DRxg9ZhzP/HsCAAcPHGDRgvnE79lH9IpVjBszkrS0NLdkHD92NEuiY9m5Zx+LFszLss8/mmPd53sPfM/I0WN57hlrxjJly7JgcTTb4/fy3w9m8+D9Q12ezz7n42NHsWT5SuLS69Ip59zZ/8PX15dvDx5h5JhxPPu0NWfRokV59vkXmDzlNbfls8+4dPlK4nPI+N3BI4zKJuPLbs5on9Mb6jI/Z0zP+X/jx/L54hi27tzLkkXzOXzIMeenH82mtK8fO/Ye5KGRY3jx+X9nLKtarTqbtsWxaVsc02a+7baME//vMT78fBlrtu4iZslCjhw+6FDGEliJqW+8R1Sv/g7zL/zyM29Oe5klq79g6ZotvDntZS5e+MUtOV1BRPLk4SnaWLGJj9tBteo1qFqtOoULF6ZXn36sWrHcoczKFcsZcO9gAHre3ZstmzdijAEgNiaaKlWqUic4xK05G9WuyLGkXzhx5iJ/pF5n4ReH6N68xg3L92tbhwWbD2VMN6xZgfJ+JVgff8KtOeN27qBGjdupVt1an3369WdFTLRDmdiY5QwabP2Qv7t3HzZv2oAxhhUx0fTp158iRYpQtVo1atS4nbidO9ySsXqNGhkZe/ftz4oYx30eGxPNwEFDALirVx82b7Lu87AGDQmwWAAIDgnl6pUrpKSkuDxjZk7HuozNpi7vTa/LXpl1WaJECZq3aEnRokXdku1mGZ3394ocMhZxc8Yb5fSGusxvGQF2xe10OGfe3bsfq2NjHMqsjo2h/z3Wc2bUXb35cvOmjHNmXti7K44q1WpQuWo1ChcuTPe7+7B+9QqHMkGVq1AntB4FCjh+HG7ZtJ4Wbdrj6+dPaV8/WrRpzxcb1+VZduVIGys2yUlJBAZVypi2BAaRnJx0wzI+Pj6UKtpPfpkAACAASURBVFWan8+f5/Lly8x6fSpP/vs5t+e0lL2NhJ9+zZhO/OkygWVuy7Zs5fKlqFKxNJv3nAJABKaMaMtT7212e86kxEQCg4IypgMDg0hKSsymjF19li7N+fPnSUpKJMhhXwSSlOj4XFdITkp02OeBgYEkO2dMSsrI4uPjQ+lS1oz2opcuJqxBOEWKFHF5RmuGRIIqOdVlonPOxBxzulN2GZNzyFgqjzPeKKc31GV+ywhwJtnxGA+wBJKclHTDMj4+PtxWqjQ//2zNeerkCdq3bETPLh345qutbsl49kwSAYGBGdMVAwI563Rev+Fzk5MICMx8fRUtuX+uR0gePTxEB9i6wKuTJ/HIqHGULFnS01Ec9G1bh2Vffs/169a/ZB6KasiaHcdJPHfZw8luHQcP7Oe5p59i2YrVno6ilNeoUDGAXfuP4V+mDHt372LowD58uX0Pt5Uq5eloKp/y+saKiPgYY1L/7noCLBYSE05nTCclJhAQYMm2TGBgEKmpqVy6dBH/MmWIj9vB8mVLmPjMBC5evECBAgUoWrQoDz7s+kFjSed+JahcZk9KYLmSJJ7/NduyfdrW5rG3NmRMNwkJoEXdIEZENaBEsUIU9inI5SvXePbDL12e0xIYSGJC5riYxMQELJbAbMqcJijIVp8XL1KmTBkslkASHPZFIpZAx+e6QoAl0GGfJyYmEuCc0WIhIeE0gbaMFy9ZMwIkJiRwT7/e/Pd/c6he48aX4v4uiyWQhNNOdRnonDPwhjnzQnYZA3LIeCmPM94opzfUZX7LCNZeCvtjPDkpMePSqHMZi+2c+euli/j7l0FEMnoiwxqGU7VadY4dPUKD8AiXZqxQ0eLQw3cmOZEKTuf1Gz43wML2bVsyn5uUSJMWrV2az5X020B5SESGiMi3IrJXRD4WkSgR2S4iu0VkvYhUsJWbaFu+DfjYFdsOj2jE8WNHOXniB65du8aSRQvo3C3KoUyXblHM+9S6ueili2nVph0iwsp1X7D34DH2HjzGwyPH8NgTE9zSUAGIO3yG2wP9qFKxNIV8CtC3TR1ivz6WpVytSv74lSzKNwcyuy3vm7KSWoPeo86Q93nqvS/4bP0BtzRUACIiG3H06BFO/GCtz0UL5tOtew+HMl27R/HJx3MBWLp4EW3atkdE6Na9B4sWzCclJYUTP/zA0aNHiGyU9VtPrsh47OjRjIyLF86nW3fHfd61ew8+++QjAJYtWUSbttZ9fuHCBfrcHcWkl16mWfMWLs+WNadjXXbNpi4/Ta/LJZl1mVeyy+i8v7t5OOONcnpDXea3jAANIyI5fjzznLl08QI6de3uUKZT1+7M/9x6zoxZtpiWbdoiIpw791PGoPkTPxzn+LGjVKlazeUZ6zeM4MTxo5w+eYJr166xYukiOnTqlqvntm53B1u/2MDFC79w8cIvbP1iA63b3eHyjCp38k3PioiEAs8AzY0x50TEHzBAU2OMEZEHgCeB8banhAAtjTFXslnXCGAEQFClyrnavo+PD1Onz6JPz66kpaVx75BhBIeE8vKLz9MwPJIu3aIYNHQ4Dz8wlIh6tfHz8+ODuZ/9/Rf+J6VdNzz21gZiXu5NwQIFmLvmOw6ePM+zQ1qw6/szxH5jbbj0bVuHhXYDa/Oaj48Pr898kx7dOpN2PY0hQ+8jJDSUFyY+R3hEJN2jejDsvvu5f9gQ6gbXxM/Pn48++RyAkNBQevXpS3hYKD4FfZgx6y0KFizolozTZr7BXVFduJ6WxuCh9xEcEspLk56nYUQE3br3YMiw4Tw4fAhhIbXw8/dn9kfWff7eO29z/NhRXn35JV59+SUAolesplz58m7JOX3mm9zVvTNpaWkMHnYfISGhvDjpOcLDI+kW1YOh993PA/cNoX5wTfz8/Znz8ecZzw+pVY1fL12ynqxjoomOXUOwiweCp2fsacs45CYZ69kyzrXLGGyXMSYmmuVuyGif0xvqMj9nTM855bWZ9L+7G2lp1xk4eCh1gkOZ8tJEGoRH0LlrFPcOuY+RI4bROCwYPz8//jvb+vXfr7d9ydTJk/ApVIgCBQrw2sy38PP3d0vG56e8zrD+PbielkafgUOoVSeEGVNeoF6DcO7o3J1vd8fxyLABXLx4gY1rVzJr6kus/jIeXz9/Rj0+gbvubAXA6PFP4evn+oyu4Olv6uQFycuR2TcjIqOBisaYp+3m1QOmAwFAYeAHY0xnEZkIGGPMpJzW2zA80mzcut1NqV3HctcMT0fI0c+x43MulA+kXc8f7+mb8ZYTi3ektP5Vo1zj95S/fVXd7X69mv8zAtQoXzzeGBPp7u0UqVDTVOz/urs3A8CpN3vkyWtylq8uA2XjTeAtY0w94CHA/vt4v3kmklJKKaXyUn5qrGwE+opIGQDbZaDSQProKPfddUsppZTyYrf6TeHyzZgVY8x+EZkMfCEiacBuYCKwUER+wdqYcf0ILKWUUkrla/mmsQJgjJkLzHWaHZ1NuYl5EkgppZTyAt4yDu6vyk+XgZRSSimlsshXPStKKaWU+gtu7Y4V7VlRSimlVP6mPStKKaWUl9MxK0oppZRSHqQ9K0oppZQ3E+1ZUUoppZTKNRHpLCKHReSoiEy4QZl+InJARPaLSI4/tKc9K0oppZQXEyC/dKyISEHgbaAjkADsFJHlxpgDdmVqAk8BLYwxv4hIjr8Aqz0rSimllHKVxsBRY8xxY8w1YB7Q06nMg8DbxphfAIwxP+a0Uu1ZUUoppbxanv5uT1kRibObfs8Y857ddCBw2m46AWjitI5aACKyDSgITDTGrL7ZRrWxopRSSqncOmeMifyb6/ABagJtgSBgi4jUM8ZcuNET9DKQUkoppVwlEahkNx1km2cvAVhujPnDGPMD8D3WxssNaWNFKaWU8nIiefPIhZ1ATRGpJiKFgQHAcqcyy7D2qiAiZbFeFjp+s5VqY0UppZRSLmGMSQVGAWuAg8ACY8x+EXlBRHrYiq0BzovIAWAT8C9jzPmbrfeWH7OSet1w4fc/PB0jR+dWjPd0hBytOnDG0xFyJbRCaU9HyNGVa2mejpArvsULeTpCrhQskE++t3kTadeNpyPkSqli+f9joXypIp6OkO/kp5vCGWNWAiud5j1n938DPG575Ir2rCillFIqX8v/TWillFJK3Vjux5N4Le1ZUUoppVS+pj0rSimllBcToIAXjNv6O7RnRSmllFL5mvasKKWUUl5Ox6wopZRSSnmQ9qwopZRSXi4/3WfFHbRnRSmllFL5mvasKKWUUt5M77OilFJKKeVZ2lhRSimlVL6ml4GUUkopLyboANt/lC82rKVD0/q0axTKO7Ney7J8x1dbiWrfjJoVS7Jy+RKHZYvnfUK7xnVp17gui+d94tac69aspmHdOtQPrsn016ZkWZ6SksKQewdQP7gmbVs25eSJEwCcP3+eLne2p4L/bTw+dpRbM+7etonRPVsyMqo5Sz58M8vy5R//l7G92vBY3w5MHNGPH5MSAPhu5zbG97sj4zGgcTW2b1zltpxfblxL55YNuLNZPd57c1qW5Tu/3kqvjs0JDSrF6hVLM+Ynnj5Fr47NueuOpnRvE8m8uR+4LSPAts3ruKtdOD1ah/Hhf17Psjx++zbu6dqKyOp+rItd5rAsOfE0jwzqSa/2kfTq0Iik0yfdkvGLDWtp37Q+bW9w/Gz/aivd2zfjdg8fP5s3rKVt43q0igzh7ZlZc6akpPDo/YNoFRlCj46tOH3qBAB//PEHjz16Px1bRtC+aRhvzZjqtozeUpcb1q2hccNQIuvXYeb0rPWRkpLC/UMGElm/Dh3bNufUyRMOyxNOn6JyBV/empX1Pe0q69aupmG9YMJCajH9tVezzTh00ADCQmrRrlUzh/Nl1zs7ULFMKcaPG+22fCp3tLFik5aWxvMTxjF7XjRrtu0mZulCjhw+6FDGElSJqW++R4/e/R3mX/jlZ96YNpmla7awbO2XvDFtMhcv/OK2nI+PHcWS5SuJ27ufhfPncfDgAYcyc2f/D19fX749eISRY8bx7NMTAChatCjPPv8Ck6dkPfm5OuP7r/ybp9/+lJlLNrN1dTSnj33vUKZanbpM/XQVMxZuoOkd3fh45osA1GvUgukL1jN9wXomvr+QIkWL0aBZG7flfOHfj/P+p0tZ8UU8scsWctRpnwcEVeKVWf+l+939HOaXq1CReSs2sWz9N8xfuZn33prO2TPJbss55dnxvDV3MYvX72T18kUc+/6QY05LEJOmv0Pnnn2zPP/Zxx9i6ENjWbIxjk+Wb8KvbDm3ZHxuwjjmzItm7bbdLM/m+AkMqsRrNzh+ZtkdP7PcfPw88+RY5i6IZsNXe1i+ZAHfH3LMOf+TOZT29eXLuAM88MhoXpn0DACx0Yu5du0a67bGE7vxaz6b+0FGQ8bVGb2lLp98fAwLlsTwVdy3LFk4j0NO56JP5n6Ir68vcd8e4pGRY5n07L8dlj8z4V906NjZLfnSM44fO5ol0bHs3LOPRQuyZvxozof4+vqx98D3jBw9lueeyTxfPvP8JCZPcV+j1HUEkbx5eIo2Vmz27tpJlao1qFy1GoULF6b7XX1Zt2qFQ5mgylUIDq1HAXGsti2b1tGyTQd8/fwp7etHyzYd+GLjWrfkjNu5g+o1bqda9eoULlyYPv36ExsT7VAmNmY59w4eCsDdvfqwedMGjDGUKFGC5i1aUrRoUbdkS3d0324qVqpKxaAqFCpUmJaderJz8xqHMvUataBIseIA1KofzvmzWT/ov14XS8MW7TLKudq3u+OoXLU6lapY93nXnn3YsMZpn1eqQu2QekgBx31euHBhChcpAsC1lBTM9etuyQiwb08clapWJ6hyNQoVLkynqN5sXhfrUMZSqQq1gutSwCnnse8PkZaaStNW7QEoXqIkxdxQn87HT1Q+PX727NpJ1Wo1qFLVevxE3d2XtatiHMqsXRVDnwGDAOjaoxfbtmzCGIOI8Pvvv5GamsrVq1coVLgwt91WyuUZvaUud8XtoFr1GlStZq3Lu/v0Z1WsY12uio1hwL2DAehxd2+2bN6IMQaA2JhoqlStSp3gELfkg/TzZY2M82Xvvv1ZEbPcoUxsTDQDBw0B4K5efdi8aaPD+bJIEfeeL1XuaGPF5kxyEgGBQRnTAZZAziYn5uq5Z5OTCLBkPreiJZCzyUkuzwiQlJRIUKXMbQUGBpGUmJi1TFAlAHx8fChdqjTnz593S57s/PzjGcpWtGRM+1cI4PyPN+512LD0c8Jbts8yf9uaaFp2ucstGQHOnnHc5xUDAv9U70hyYgI92jemXURtHhj1OBUqBrgjJj+eSaZCQGbOCgEWfjqTu/fXqR+Oclup0owfcS8DurRkxuRnSEtLc3lG5+OnoiWQM7k8fs5kc/yccdPxcyY5CUuW4zwpaxlbHh8fH24rVYpffj5P1x69KF68BJEhVWkaVpMRI8fh6+fvlozeUJfJSUkEBmVuyxIYSHJSYpYyFrtzUanSpfn5/HkuX77MGzNe419PPeuWbJnbTyTQtn2AwGwyJiUlefR86SoiefPwFI81VkSkqojsy2b+ByKSY1NbRIaJyFvuSafyyhexizl24Ft6Dn3EYf4vP53l1NGDNGjW1jPBciEgMIjlG3ew5uvvWLbgU879dNbTkbJITU1l986veeyZl/gkZjMJp06wfOGnno7llfbs2knBggXYuf8Htu06xPtvz+LkieOejuWVpr78Ao+MHEvJkiU9HUV5iXzXs2KMecAYc8B5vogUdOd2KwZYSE5MyJhOTkqkQkBgrp5bIcBCclLmc88kJVIhwHKTZ/x1FksgCaczt5WYmIAlMDBrmYTTgPXD6uKli5QpU8YtebLjX74i5+z+8v/5bDJlymftddj7zRYWfzCLp2bNoVDhIg7Ltq2NoXG7LvgUKuS2nBUqOu7zM8mJf6l3pELFAGrWCSFu+1eujJehfMUAziZn5jybnES5irl7f1UIsFArpB5Blavh4+NDu07dOLRvj8szOh8/Z5ISqZjL46diNsdPRTcdPxUDLCRlOc4tWcvY8qSmpvLrpUv4+ZchetF82rS/k0KFClG2XHkimzTj2z273JLRG+oywGIhMSFzW0mJiQRYArOUSbI7F126eBH/MmWI37mDic8+RYOQ23n3P28wY9oU3n/3bTdkDCTRtn2AxGwyWiwWj54vXUXHrLiXj4h8KiIHRWSRiBQXkc0iEgkgIpdFZLqI7AWaich9IvK9iOwAWrgySP2GkZz44SinT57g2rVrrFi2kDs6d8vVc1u368iXm9dz8cIvXLzwC19uXk/rdh1dGS9DRGQjjh09wokffuDatWssWjCfrt17OJTp2j2KTz+eC8DSJYto07Z9nr7Jbg9tQPKpHzibeIo//rjG1jXRRLa506HM8UPf8d+X/o8JM+dQ2r9slnVsXb3MrZeAAOo1iODkD8dIOGXd5yujF9G+U+72+ZmkRK5euQLAxQu/EL/ja6rVqOmWnKFhEZz64TiJp07wx7VrrIlZTNuOXXP93F8vXeTn8+cA2PnVFqrXrOPyjM7HT0w+PX7CGkbyw/GjnDppPX5ili6kY5fuDmU6du7OItu3aFYuX0LzVm0RESxBlfjqy80A/P7bb+yK28HtNWu7PKO31GXDiEYcP3aUkyesdbl00Xy6dHWsy85duzPv048BWL50Ma3atENEiF23mT0HjrLnwFEefnQMjz0xgQcfHunyjNbz5dGM8+XihfPp1j3KoUzX7j347JOPAFi2ZBFt2ra75b8G7I08fZ+V2sD9xphtIvIh8KjT8hLAdmPMeBEJAD4DIoCLwCZgt6uC+Pj4MPGVGQztF8X162n0vWcoteqEMGPKC9RrEM4dnbuzd3ccjwztz8WLF9iwdiWzpr7Emq278PXzZ9TjT3FXx5YAjB7/b7dcy07POX3mm9zVvTNpaWkMHnYfISGhvDjpOcLDI+kW1YOh993PA/cNoX5wTfz8/Znz8ecZzw+pVY1fL12yNshioomOXUOwiwe4FfTx4YEJk3nxkYFcv55G+54DqHx7bT7/z1RuDwmjUdtOfDTjRa7+/hvT/zUCgLIBgTw1y9rA+jHxNOfPJBEa0cyluZz5+Pjw7MvTuf+enlxPS6P3gCHUrB3CG1NfpG5YOO07deO7PfGMGj6ASxcusGndKt56bTIrvojj2JFDvDrpKUQEYwzDHx5L7eC6bsv5fy+8xqND7uZ6Who9+w2mRq1g/jP9JULqh9O2Y1f2743n8RH3cuniBbasX8W7M15m8fodFCxYkMeffomHB0ZhjCG4XgN63TPMLRknvTKDIU7Hz+u246ej7fh52O74mTn1Jdbajp/Rjz9FT9vxM8bNx8+Lr85kcN8o0tLS6D9wKLXrhDD9lUnUaxDBnV2603/QMMY9MpxWkSH4+vrz1gfWD7Kh9z/M+NEj6NC8IcYY+g0cQnBoPbdk9Ja6fHX6LPre1Y20tDQGDh5GnZBQXnlxIg3CI+jSLYpBQ4fzyAPDiKxfB18/Pz6Yk7eXIH18fJg28w3uiurC9bQ0Bg+9j+CQUF6a9DwNIyLo1r0HQ4YN58HhQwgLqYWfvz+zP/os4/mhtarz669258sVq906IPgv+wfcbl/SR2bn+YZFqgJbjDGVbdPtgTGAL/CEMSZORFKBIsaYNBG5C+hljBliKz8GqGWMyXLDEBEZAYwAsARViti6+3vnIvlO+VJFci7kYWsOnvF0hFwJrVDa0xFydOWa6we5uoNvcfddhnOlggXy/5k67bpnzrV/Vqlinv4bNmeFfTx9USB3bitaMN4YE+nu7RQPrG3qPPSOuzcDwO7nO+TJa3Lm6T3ufPQ6T181xvzps7ox5j1jTKQxJtK/jOvvKaGUUkrlF+l3sNUxK+5TWUTS+/oHAltvUnY70EZEyohIISDr3a+UUkopdcvxdGPlMDBSRA4CfsAN+7GMMcnAROBrYBtw8EZllVJKqX+SW/0+Kx67OGmMOQFk97WEtnZlHL6Eb4yZDcx2azCllFJK5Sue7llRSimllLqp/D/sWymllFI3davfG0Z7VpRSSimVr2nPilJKKeXlbvGOFe1ZUUoppVT+pj0rSimllDcTHbOilFJKKeVR2rOilFJKeTHr7fY9ncK9tGdFKaWUUvma9qwopZRSXs2zPzKYF7RnRSmllFL5mvasKKWUUl7uFu9Y0Z4VpZRSSuVv2rOilFJKeTkds6KUUkop5UG3fM9KoYJC2ZKFPR0jR97QJm5fq7ynI+RKr/e3ezpCjj4aHOHpCLny06UUT0fIFYtfMU9HyFHadePpCLlSqGD+/xvWW+pSuc4t31hRSimlbmmiA2yVUkoppTxKe1aUUkopL2a93f6t3bWiPStKKaWUyte0Z0UppZTyctqzopRSSinlQdqzopRSSnm5W7xjRXtWlFJKKZW/ac+KUkop5eV0zIpSSimllAdpz4pSSinlzfQOtkoppZRSnqU9K0oppZQXE0THrPyTrFu7mvD6wYSF1uL1117NsjwlJYVhgwYQFlqLdq2acfLkCQA2blhH6+aNaBoZRuvmjfhi80a35ly7ZjUN6tahXnBNpr02JducQ+4dQL3gmrRp2ZSTJ6w5z58/T5c721Pe/zYeHzvKrRkB1q9dTaOwEMLr1mbGtOzrc/jgewivW5s7WjfjlK0+43fuoFWTCFo1iaBlk3BWRC9zW8ZGVXyZO7ghHw9pyD0RgVmWdwoux5IHG/HePWG8d08YXUMzf3l6Ss9glj/UmMlRddyWL92m9Wtp07geLSNCeHvma1mWp6Sk8MjwQbSMCCHqjlacPnUCgD/++IPHHr2fO1pE0K5JGG/NmOq2jNs2r+fu9hH0aNOA2f95Pcvy+O3bGNitFY1q+LN+ZeY+3fnVFgZ0aZnxaFqrPJvWrHBbzo3r19AiIpSmDYJ58/Ws9ZGSksKIYQNp2iCYLu1bZLwvAQ7s+5Zud7SidZMw2jZryNWrV92ScdP6NbRqVJcW4cG8NSP7/f3w8HtpER5M9ztaZuzvJQs+p2OrRhmPIP+i7Ptur1sygvecM9evXU1kWAgNb3Iuum/wPTSsW5sOrTNzxu/cQcsmEbRsEkGLJuHEuPFcpG5OGys2aWlpjB83msXRsezcvY9FC+dx6OABhzIfzfkQXz8/9u7/npGjx/L80xMAKFOmLPMXRfNN3F7efX82I4YPdWvOx8eOYunylcTv3c/C+fM46JRz7uz/4evry3cHjzBqzDieteUsWrQozz7/Ai9PyXryc0fOfz02hoXLVvDNru9YvHB+lvr8eM6HlPb1Y9e+wzwyehwTn3kKgODQumzatp0vt8ezaFksj415hNTUVJdnLCAwtm11JkQf4L5P9tC+Vlmq+BfLUm7z9+cY8fleRny+l5X7f8yYPz8+iVfWHnF5LmdpaWk88+RYPloQzcav9xC9eAHfHzroUGbeJ3Pw9fVla/wBHnhkNC9PfAaAFdGLSUm5xvpt8azc9DWfzvkg44PN1RlffW48b85ZxOJ1O1i9fDHHjxxyKBNgCWLitHfo3LOvw/xGzVszb9VW5q3ayn8/X07RYsVo2rq9yzOm53xq/Fg+WxTDlh17Wbp4PocPOb4vP/toNr6+fnyz5yAPPTqGl57/NwCpqamMHDGMqTPeYsv2vSyJXU+hQoXckvHpf43lk4XL2fTNXpYtnp9lf3/+8WxKl/Zl266DPPjIGCZPfBqAXv3uYd2XO1n35U7eeHc2latUpW69MJdnTM/pLefMJx4bw6JlK9i+6zsW3eBc5Ovrx+59h3nU6Vy0edt2tm6PZ7Ebz0UqZ9pYsYnbuYPqNWpQrVp1ChcuTO++/YldsdyhTOyKaO65dwgAd/Xqw+bNGzHGENagIQEWCwDBIaFcuXqFlJQUN+a8nWrVrTn79OvPiphohzIrYpZz72DrwX93rz5s3rQBYwwlSpSgeYuWFCla1C3Z7MXHWeuzqq0+e/Xpx0qn+lwVu5x7Bg0GoOfdvfnCVp/FixfHx8d6hTIl5arbujfrVChJ4oUrJF9KIfW6YeORczSv7p/r5+9OuMjv19Lcks3envidVK1WgypVrXXZo1df1q6KcSizdmUMfQYMAqBbz15s27IJYwwiwpXffyM1NZWrV69QqHBhSt5WyuUZ9+2JJ6hKdYIqV6NQ4cJ0iurF5rWxDmUslapQK7guBeTGp531K6Np0bYjxYoVd3lGgN3xO6lWvQZVbO/Lu3r1Y02sY12uWRlDv4HW92X3u3qz9QtrXW7euI6Q0HqE2j78/f3LULBgQbdkrFo9c3/37NWPNSud9veqGPreY83YrWevjIz2li2eT49e/VyeL523nDOdz0W9szkXrczFueiqG89FriCSNw9P0caKTXJSIkFBlTKmLYGBJCUmOpVJyijj4+NDqVKl+fn8eYcy0UsX06BBOEWKFHFLzqSkRIIqBWVMBwYGkeyUM8nutaTnPO+U092Sk5IIDLSvzyCSk5KccmaWca7PuB3baRZRnxaNGvD6rP9knDBcqWzJIvx4+VrG9LnL1yhXonCWcq1uL8P7A8N4vmttypXMutzdziQnYQnM3OcBlkDOJCfdsIyPjw+3lSrFLz+fp1uPXhQrXoKI4Ko0qV+Th0aOw88v9w2y3PrpbBIVLZmX0coHBPLj2eQ/vZ41MYvp1KOPK6M5SE5KdKzLwECSneoyOTnRqS5L8/PP5zl+9AgiwoC7u9GxVWPemjnNLRmt+zLz2LHub8dj/EyS4/4uZdvf9mKWLuSu3v3dkhG855yZm3NRcg7noqZuPhepnHllrYtIW+CaMeYrT2exd/DAfp575imWrVjt6SheL7JxE76O/5bDhw7y6IP3cUenzhTNgx4hZ1//8Asbvz/HH2mG7nUrMKFjTcYv3Z/nOf6qPfE7KViwAHEHfuDihV/o3a0DLdu2p0rV6p6OlsVPDRdzNwAAIABJREFUP57h6OEDNGvdwdNRspWamsr2r79i9eavKFasOH17dCKsQTit2rrnktXfsStuB8WKFadOSKino9yUN5wzIxs34RvbueiRB++jo4fORTkpkI96fUSkMzALKAh8YIyZ4rR8GPAakN66fcsY88HN1umtPSttgeauXGGAJZCEhNMZ00mJiVgCA53KWDLKpKamcunSRfzLlAEgMSGBgf17894Hc6hevYYrozmwWAJJOJ2QMZ2YmECAU06L3WtJz1nGljOvBFgsJCba12dCRrdvZs7MMs71ma52nWBKlCzJwf37XJ7x3OUUytv1lJQtWZiffrvmUObS1VT+SLN2r6/cf5aa5Uu4PEdOKgZYSErM3OfJSYlUDLDcsExqaiq/XrqEn38Zli2eT9sOd1KoUCHKlitPZONmfLt7l8szlqtg4UxS5l/VPyYnUr5CwJ9ax7oVS2nXqbtbxoGkC7AEOtZlYiIBTnUZEBDoVJcX8fcvg+X/2bvzuKiq/4/jr4OIpqksbszgBqKyKAiYu6WlKYsa7vvWnlvL9/v7Vn6tLC2XTLO+33a3SlFcENzLJS0X0NRcKnFnwErNpVKI4fz+GLYBFLIZBvx+no/HPGpmzp37nnPvPRzPPfeOwUib9h3w8KhJlSpVuL9bdw4d/NbmGS3bMu/YsWxv62O8rsF6e1/N3t454lYuo5cdR1Wg/LSZJWmLPB3cFt1JlFIVgHeBHoA/MEgp5V9E0RitdXD245YdFShjnRWl1HCl1CGl1EGl1GKlVJRSao9S6lul1BdKqTpKqYbA48DTSqkDSqmOtlh3aFgrTiYnc/r0KTIyMlixPIbwiCirMuERPVny2SIAVq+M5d57O6OU4vLly/SLjuKVV6fRpl17W8S5Zc4Tycc5fcqSM3ZZDBGRPa3KRERG8dnihQCsWhnLvfd1KfVzrSGhrTiRnMyZ7PpcGbuMHgXqs3t4FEs+XQxYhoI7ZdfnmdOnciexnT17huM//ED9Bg1tnvH7n37D6HoXdatXwtlJ0cW3JrtOXrIq414l7w9nu0bunP31us1zFCcoJIzTJ5M5e8ZSl2tWLqdr90irMl17RBK79FMA1satpH3H+1BKYfSqx9dfbQPgj99/59ukvTRu0tTmGQOCQjh3+gSmc6f5MyODjfErubdr+F/6jA1rYukeZb9TQADBIWGcPJG3X65euYxu4dZ12S08kmWfW/bLhNUraN/JUpf33d+N748c5o8//iAzM5NdO3fQpJmfXTKeOpG3veNWLqNbjwIZu0eyfIkl49q4lbkZAbKyskhYvYJeffoV+mxbKi9tZk5blJuziLaox03aotOl1BbZQhmas3IPkKy1Pqm1zgCWAr3+7vcrM6eBlFIBwCSgndb6glLKHdBAG621Vko9DPxTa/2sUuo94DetdZEnjZVSjwKPAtSrV79E63d2dmbmW2/zUFQPzGYzw0aMws8/gNemvERISCjhkT0ZPnI0j44eTlBAE9zc3Jm/+HMAPnjvXU6eSGb6668x/fXXAFgdv4FatWvfapW3xdnZmTfnzKNXZHfMZjPDR47C3z+AV1+ZTEhIGBFRPRkxagwPjxpOcz9f3NzdWbh4Se7yfk0ace3qVTIyMoiPj2PN2o34+RXV6f37OWfMnkufnuGYzWaGDB+Jn38A06a8RHBIGOGRUQwbOZrHx4wgJLApbm5ufLzIUp+7vvmauW/OwNm5Ik5OTsya8w4eNWvaPGOWhnnbTjK9lz8VnBTrj/zE6UvXGdm6Hj/+/BvfnPqV6GBP2jVyx5yluZqeyfTNybnLz+kTSH33u7irohMxo0OZ+cUJks5etnlOZ2dnXp0xh6F9ozCbzQwYMoKmfv7MmvYKLVqG0q1HJAOHjmTi46PpEOqPq5s7735k+QMxYszjPDv2Ue5v2xKtNf0HD8cvoLldMv7flFk8NTyaLLOZnv2H4tPEj//Onop/85bc2zWcIwf38exjQ7l65TJffbme9956ndjNewBIPXeGn9JMhLbpYPNsBXNOmzWHQdERmM1ZDBo6gmZ+AUyf+jLBLUN5MDyKwcNGMfbRkbQJ9sPVzY33P7F0Al3d3Hhs7AS6d26LUor7u3an64N/rUNW0oyvzZjD4D6RZJnNDBgykqZ+/syc9gpBwSF0C49i4LBRjH98FO1D/HB1c+c/Hy/OXX73NzvwNHrZ/VRfeWozZ+Zri4Zmt0VTp7xEy3xt0WNjRtAyuy36JLst2v3N18wphbaonKmplErK9/wDrfUH+Z4bgXP5nqcArYv4nD5KqU7Aj8DTWutzRZTJpQrOIHcUpdQ4oK7W+sV8rzUH3gQ8ARfglNa6u1LqZW7RWckvJDRMb/96r51S204Fp7JzvvFmMsxZjo5QItEf7nF0hGItGhbq6Agl8stV+1yhYWsGt8KXnJc15qyy0dYWp1rlMvNv2JvKKiN/t4rjWsV5n9Y6zN7rqdHAT7f5vwX2Xg0Am55qc8vvpJTqC3TXWj+c/XwY0FprPTZfGQ8sf8PTlVKPAQO01rec/FWmTgMVYR6WiTfNgceAsjerSQghhBA5TEC9fM+9yJtIC4DW+qLWOudfQh8Bxf7rrSx1VrYA/bJ7XGSfBqpB3pfMf9ega0C10o0nhBBClE1OqnQeJZAI+CqlGimlXICBgNWNbZRS+Wff9wSs73pY1PcreVXYl9b6CDAV2K6UOgjMBl4Gliul9gEX8hWPBx6y5QRbIYQQQvw9WutMYCywEUsnZJnW+ohSaopSKudqkPFKqSPZf+vHAyOL+9wydXJSa70QWFjg5bgiyv0ItCiVUEIIIUQZV5burqu1XgesK/Da5Hz//zzw/F/5zDIzsiKEEEIIUZQyNbIihBBCiL+uDA2s2IWMrAghhBCiTJPOihBCCCHKNDkNJIQQQpRjClDc2eeBZGRFCCGEEGWajKwIIYQQ5Vw5+MWWv0VGVoQQQghRpsnIihBCCFGeKVWmbgpnDzKyIoQQQogyTUZWhBBCiHLuDh9YkZEVIYQQQpRtMrIihBBClGMKcLrDh1ZkZEUIIYQQZdodP7Kisx/if8d/+gU5OkKxBny0x9ERSmTNE+0cHaFEbmRmOTpCsX67kenoCCVyd+Wy/2fBxVn+nV3QHT6wIiMrQgghhCjbyn4XWgghhBC3JPdZEUIIIYRwIOmsCCGEEKJMk9NAQgghRDmmlEywFUIIIYRwKBlZEUIIIco5uSmcEEIIIYQDyciKEEIIUc7d2eMqMrIihBBCiDJORlaEEEKIck5uCieEEEII4UAysiKEEEKUYwpwurMHVmRkRQghhBBlm3RW8vli0wZCW/gRHNCE2TOnF3o/PT2dkUMHEhzQhC4d23LmzGkAtny5mU7tWtE2LIhO7VqxfdsWu+bctHEDwYHNaO7ny6yZbxSZc/iQgTT38+XeDm04c9qS8+LFi/To1oXa7tV4ZsJYu2YES322CvInJLApb80quj5HDxtESGBTHujUlrPZ9bkvcS8dW4fSsXUoHVqHkBC32m4Zd2zdTI8OLXmwXQs+nPdmofcTd+8kult7AuvVYGPCqtzXTSlnie7WnoceaEvkfWEsXfSR3TICtG7oxpIxYcQ83Iqh99Qr9H54QB0SnmrDghEhLBgRQlTzugDUqV6JT4a3ZMGIED4dFUrvIE+7Zfxy80ZatwygVVAz5r45o9D76enpjBkxmFZBzejWuV3u9j575jRetapxX7tQ7msXyrMTnrRbRoCtX2ykU6tA2of48c5bM4vM+cToIbQP8SPygQ6cO2vJuXLZErp1bJX7qOdemSPfHbRLxh1bNtG9QzDd2jbng3mzCr2fuGsn0V3bEeBVnQ3598tzZ4nu2o7eD7Qh8t4wli60735ZHtrMTRs3EBTQjEA/X2bNKLq9HDZ4IIF+vnRqb91edu/ahVpu1Xi6FNrLv0UpVCk9HEVOA2Uzm808O3Ecq9duxGj0onOH1oRHRtHMzz+3zKIFn+Dq5saBIz8Su2wpL734LxZ8uhQPj5rExMbhaTBw9MhhoqN68P3Jc3bL+cyEscSv24TRy4uO7e4hIrInfvlyLpz/Ma6urnx37DjLly3l3y/+i0WfLaVy5cr8+6UpHD1ymKNHDtslX/6c/3h6PKsSNmAwetGlYxt6RFjX5+IFn1DD1Y39h39gxfIYXp70PJ8sXoJfQCBbv96Ds7Mz59PS6NgmhO4RkTg723Z3NZvNvPrCM3y8dA11PI30D+9E5wfDadzEL7eMwViP1+e8zyfvzbVatlbtuiyN34JLpUr8/vtv9Ox8D126RVC7ru07A04Knu3amInLvuPna+l8NKwlO09c5PTFP6zKbfn+F2Z/ecLqtYu/ZfDYZwf406y5q6ITi0eFsTP5Ihd+z7BpRrPZzP89O57YuPUYjF50vbcN3SMiadosb3t/tugTXF1dSTz4PStjY3hl8gt8vPBzABo28mHbN/tsmulmOSf9YwKfr1qHp8GLiC7t6NYjkibN8rb50sXzqVHDla/3HyNuxTKmvfwi//3kM6L7DyK6/yAAjh05zMND+xLQPMguGae88AyfxMRTx9NIvx4d6dItgsZN8zJ6etXj9bnv88l/C+yXdeqyNGFr7n4ZdV8rOj8YQR077Jfloc00m808PWEsCTntZdvs9tI/L+OC+R/j6ubK4WPHWR6zlEkv/IvFn1vay8kvT+FIKbSXongyspJtX+JevH18aNTIGxcXF6L7DWBtwhqrMusS4hg8ZDgAvaP7sn3bFrTWBAW3xNNgAMDPP4DrN66Tnp5ul5xJiXvx9mlMI29Lzr79B5AQH2dVJiF+DUOGjQDgoei+bNv6JVprqlatSrv2HahUubJdsuW3L8lSnw1z6rNvf9YVqM/1a9cwaOgwAHo91Ce3PqtUqZLbMUlPv2G33vyhb5Oo39Cbeg0a4eLiQnivvmzZuNaqjLFeA5r6B+LkZH2ouLi44FKpEgAZ6enorCy7ZATw86xGyq/XSb1yg8wszZff/0LHxh4lWjYzS/OnWQNQsYKT3X4/ZH/SXhp5523vh/oMYH1CvFWZ9WvjGTjYsr179u7DjuztXZoO7EukobcPDRpacvaK7s+mddY5N62Pp98gS86IXtHs3L61UM64FTH0jO5vl4xF7ZdfbkywKuNVrwFN/ZujHLhfloc2MylxLz7FtJdr49cwNKe97FO4vaxcCu2lLeT8PpC9H44inZVsqakmjF55w+tGo5E0k8mqTFpqam4ZZ2dnqlevwaWLF63KxK1aQVBwCJWyGwx75PSq55Uvp1ehnKmpJrwK5LxYIKe9paWmYjTm1afB6EVaamqBnHllCtZn0t49tA1tQftWwcye+x+bj6oA/Hw+lbqGvLqs42nkp7TUWyxhLc2UQq/7W9MlrBljnnraLqMqALXursTP1/Ia8p+vpVPrbpdC5e5tUpOFI0N4racftavl7X+1q1Vi4cgQVj3ems/2nrP5qApAWloqBmNeXRqMRtLSijl+auRt77NnTtG5fRhR3buw6+udNs+XP6dnvv2yrqFwzvOpqXhmfxfLflmdXy9ZHz/xq5bTq88Au2T86Xze+gHqehr56XxaiZdPM6XQs8s9dA5tysNjn7HLqAqUjzYz1WTC6GXdXqammoooY71flnZ7KYrn0M6KUuplpdRzjsxgS8eOHuGlSc8z553/OjpKuRd2T2t27TvElzt289asN7hx44ajIxXiafQi7ss9bPzmEHHLP+fCLz85LMvOExfp+8FeRizYT+KZy0zq0TT3vZ+vpTNiwX4GfJhIj4A6uFWp6LCcRalT15MDR0+y9eskXn19Jo+NGca1q1cdHeum9iftpfJdVWjmH+DoKEXyNHqxZsteNu76jtXLPnPoflkcaTNFSZX7kRWllE3+yW0wGDGl5J0zNZlMeBqNVmU8DYbcMpmZmVy9egV3D8twvCklhSED+vD+Rwvw9vaxRaSb5kw5l5IvZ0qhnAaDkZQCOT08SnbawFY8DQZMprz6TDWl5A775uXMK1OwPnM0beZH1bvv5pgdzhnXrmvgfGpeXf6UZqKOp+EWS9zsczzxberPvj3f2DJerl9+Sy80UvLLb9ajI1dvZOae7ok/lEbTuncX+pwLv2dw8sIfBHnVsHlGT08Dqaa8ukw1mfD0LOb4uWLZ3pUqVcrd7sEtQ2nYyJvk5B9tnjEnZ1q+/fJ8auGcdQ0G0rK/i2W/vIqbe95+uWblMnrbaVQFoE7dvPUDnE8z3dboSJ26nvg28yfJTvtleWgzDUYjphTr9tJgMBZRxnq/LO320hbu9Am2N+2sKKXmKaXevtnjdleolHpRKfWjUmon0DT7NR+l1Aal1D6l1A6lVLPs12sppVYopRKzH+2zX39ZKbVYKfU1sPh2s+QXEtaKE8nJnD59ioyMDFYujyE8IsqqTHhETz7/bBEAq1fG0uneziiluHz5Mv2jo3j51Wm0adfeFnFuKjSsFSeSj3P6lCVn7LIYIiJ7WpWJiIzis8ULAVi1MpZ77+tS6jtZSKilPs/k1GfsMnoUqM/u4VEs+dSy+eJWrcitzzOnT5GZmQnA2bNnOP7DD9Rv0NDmGZsHh3Lm1AlSzp4mIyODdXGxdO4WXqJlz6eauHH9OgBXLv/KvsRdNPLxtXlGgO/TruHldheeNSrj7KS4v1ktdiZbD1N7VM07LdShsQdnsiff1rrbBRdny2FerZIzLYzVOXvJemKuLbQMbcXJE3nbe9WKGLpHRFqV6R4eydLPLdt7zeoVdMze3hd++QWz2QzA6VMnOXkimYYNvW2eESAoJIxTJ5I5e8aSM27lMrr2sM7ZtXsky5dYcq6NW0n7TvflHj9ZWVnEr15Bzz797JIPit4vuzwYUaJlC+2Xe+23X5aHNjM0rBXJxbSX4ZFRfJrTXq5wTHspinerUYkkW69MKRUKDASCs9e9H9gHfAA8rrU+rpRqDfwH6ALMBd7SWu9UStUHNgI5U+L9gQ5a6+tFrOdR4FGAevXqlyibs7Mzs956m+ioHpjNZoaOGIWffwBTp7xEy5BQwiN7MmzkaB4dPZzggCa4ubnzyWLLlQwfvvcuJ08kM+P115jx+msArIrfQK3atW+rnorL+eacefSK7I7ZbGb4yFH4+wfw6iuTCQkJIyKqJyNGjeHhUcNp7ueLm7s7CxcvyV3er0kjrl29SkZGBvHxcaxZu9HqSiJb5pwxey59eoZjNpsZMnwkfv4BTJvyEsEhYYRHRjFs5GgeHzOCkMCmuLm58fEiS33u+uZr5r45A2fnijg5OTFrzjt41Kxpl4yTpr7Jw4N7k2U2Ez1wGL5N/Xl7xqsEBoXQ5cEIvjuwj3FjBnH18mW2bl7PvFlTSdiWxInjPzBjyvMopdBaM/rx8TTxC7R5RgCzhre+SGZ230AqOCkSvjvPqYt/8HD7Bnx//ho7T1yiX4iBDo09yMzSXLuRyWvrfwCgoUcVxnb2RmvL5LgliSmcvGD7zoqzszNvzJpLv94RZGWZGTxsJM38Anj9tZcJbhlKj4gohgwfzZOPjKRVUDNc3dz4cP5nAOz6ZgdvvPYKFSs6o5ycmDXnXdzc3W2eMSfnqzPmMKRPJFlmMwOGjKSpnz8zp71CUHAI3cKjGDhsFBMeH0X7ED9c3dz5z8d5/x7a/c0ODEYvGtipM5WT8d/T3mTMoF5kmc30GTi8yP1y7OiBufvlOzOnkrA9iRPHv2f6K/n3ywk0tdN+WR7aTGdnZ2bPmUfPiO6Ys8wMHzEK/4AAprw8mZDQMCKjejJy1BjGjBxOoJ8vbm7uLPo0r71s5puvvVwTR/zajVZXEpUV/ws3hVMlnY2vlKqitf5brZxSaiLgrrWenP18NnAJeBH4IV/RSlprP6XUz0D+GY+1sIzGPAdorfUrxa2zZWiY3v713r8Tu1Q4l4M9LcNsvysLbOn85bI3v6WgMZ/a/zJdW1jzRDtHRyiRG5llf9/87UamoyOUiKdr2b/6pWKFst9eAlRxcdqntQ6z93pqegfoyKlLii9oAwsHB5XKdyqo2PkeSqm2wMfA3UB9pVQQ8JjW2lZ3bnICLmutg2/yXhuttdVfn+whut9ttH4hhBCiXLvTT12VZILtHOBB4CKA1vog0Ok21/cV0FspdZdSqhoQBfwBnFJK9QNQFjl3WtoEjMtZWClVVIdGCCGEEHewEl0NpLUueGtB8+2sTGu9H4gBDgLrgcTst4YAY5RSB4EjQK/s18cDYUqpQ0qpo8Djt7NeIYQQ4k6mSunhKCW57PecUqodoJVSFYEJwLHbXaHWeiowtYi3uhdR9gJQ6BpBrfXLt7t+IYQQQpQvJemsPI7lqhwjlsmuG4Gn7BlKCCGEECWjFDjd4XNWiu2sZI9uDCmFLEIIIYQQhRQ7Z0Up5a2UildK/aKU+lkpFaeUst9NBoQQQgjxl8gPGcLnwDLAEzAAy4HSuaBbCCGEEP/zStJZqaK1Xqy1zsx+fAqU/bsGCSGEEP8j7vTfBrrpnBWlVM79rtcrpf4FLAU0lqtz1pVCNiGEEEKIW06w3Yelc5LTlXos33saeN5eoYQQQgghcty0s6K1blSaQYQQQghxe+7wK5dLdJ8VlFKBWH7lOHeuitZ6kb1CCSGEEELkKMkPGb4E3Iels7IO6AHsBKSzIoQQQjiYQt3xN4UrydVAfYH7gfNa61FAEFDDrqmEEEIIIbKV5DTQda11llIqUylVHfgZqGfnXEIIIYQoCQffsK00lKSzkqSUcgU+xHKF0G/ALrumEkIIIYTIVpLfBnoy+3/fU0ptAKprrQ/ZN5YQQgghSsqRN2wrDbe6KVzIrd7TWu+3TyTbUkAFp7K/EcvDflYOIgJw/MJvjo5QrNhH2zg6Qok8EnPQ0RFKZMGQlo6OUKw/0jMdHaFEykFzKf4H3Wpk5c1bvKeBLjbOIoQQQojbUJKrZcqzW90UrnNpBhFCCCFE+aeU6g7MBSoAH2mt37hJuT5ALNBKa510q88s0U3hhBBCCFE2KcrOnBWlVAXgXaArkAIkKqXWaK2PFihXDZgA7CnJ597pI0dCCCGEKD33AMla65Na6wwsP4Lcq4hyrwLTgRsl+VDprAghhBDlnJMqnQdQUymVlO/xaIEoRuBcvucp2a/lyr6Ap57Wem1Jv19JbrevgCGAt9Z6ilKqPlBXa723pCsRQgghxB3hgtY67HYXVko5AbOBkX9luZKMrPwHaAsMyn5+Dcv5KCGEEEKI/ExY3+XeK/u1HNWAQGCbUuo00AZYo5S6ZQeoJBNsW2utQ5RS3wJorX9VSrn8leRCCCGEsJ8ydH+cRMBXKdUISydlIDA4502t9RWgZs5zpdQ24LnirgYqycjKn9mze3X2B9cCsv5qeiGEEELc2bTWmcBYYCNwDFimtT6ilJqilOp5u59bkpGVt4FVQG2l1FQsv8I86XZXKIQQQgjbUarsXLoMoLVeB6wr8Nrkm5S9rySfWZLfBvpMKbUPuB/L5dy9tdbHSvLhQgghhBB/V0muBqoP/AHE539Na33WnsGEEEIIUTJlaM6KXZTkNNBaLPNVFFAZaAT8AATYMZcQQgghBFCCCbZa6+Za6xbZ//XFcne6XfaPVvo2b9xAy8BmtPDz5c2ZhX/KID09neFDBtLCz5f7OrThzOnTAFy8eJEe3bpQx70az0wYa/ecmzZuICigGYF+vsyaUXTOYYMHEujnS6f21jm7d+1CLbdqPF0KOb/YtIGwIH9aBjblrVnTi8w5atggWgY25f5ObTlzxpJzX+JeOrQOpUPrUNq3DiE+brXdMu7buYUnotrzaEQbYj+eV+j91Yve46neHRnXpzOTHu7Lz6l59zrqHWxgQr/7mdDvfl4bN9xuGQG2frGRDmGBtGvpx7y3ZhZ6Pz09ncdGDaFdSz8i7u/Auey6XLlsCQ90aJX7MLpV5vAh+/yScktjdeb1CeDdfgE81KLOTcu1aejKyjGh+NSsAkAnH3fe7O2X+4gdHUJD97vskhEs+2VoCz+CA5owe2bR++XIoQMJDmhCl455++WWLzfTqV0r2oYF0aldK7Zv22K3jF9t2cSD7YN5oE1z3p83q9D7ibt20rtrO/yM1dkQvyr39aOHD9I/ojPhncKI6nwPa1fH2i0jwOZNG2jZ3I8g/ya8eZO6HDF0IEH+TejcsW1uW7Tli810bNuK1qFBdGzbiu1b7VeXcPttJsDM6a8T6OdLUEAzNm/aaNecf4dl3or9H47yl+9gq7XeD7S2QxaHMpvNPDNhLCvXrCPp4BGWxyzl2DGrnzJg4fyPcXV15dCx4zw1fiL/fvFfAFSuXJl/vzSFqW8U/iNij5xPTxjL6vh17M/JedQ654L5H+Pq5srhY8cZN34ik17Iyzn55SlMm146OZ97ejyxqxPYs/87YpfH8H2B+ly84BNcXd349vAPPDluIi9Peh4Av4BAtn29h5179rFi9VqeHv8EmZmZdsn4/rTneem/n/Pu6q/4av0qzp74waqMd7NAZi/ZyLwVW2nXNZIFb72a+55LpcrMXf4lc5d/yaR5i2yeL3/OF56bwGexa9i25yBxsTH8+L31tLEli+fj6urKN98e45Enx/Payy8CEN1/EF/sTOSLnYnMe38+9Rs0JLBFkM0zOil4pF19Xtt0nAkrjtLR2x0v18qFylWu6EREQG1+/Pm33Ne+OnGJZ1cf49nVx5i7/RQ/X8vg9KXrNs8Ilrp8duI4YuPWsvfbw6xYvrTQfrlowSe4urlx4MiPPDluAi9lH+ceHjWJiY1jV9JB3vtwPo+NHmG3jK88/wwffr6KdV/tI2HVcpJ/sN7ensZ6vDH3fSIf6m/1+l13VWHGvA9Z91USHy2JY9rkf3L1ymW75Xx2wjhWxq0l8cBhYpfdpC5d3Th49EeeGjeByZOy67JmTZatiGPPvoO8/9F8Hhljn7rMyXm7beaxo0eJXRbDvgOHiUtYz8TxT2E2m+2WVdyljZmDAAAgAElEQVRcsZ0VpdQz+R7PKaU+B1JLIVupSkrci7dPYxp5e+Pi4kLf/gNYGx9nVWZt/BqGDLMcVA9F92Xb1i/RWlO1alXate9A5cqFG2d75PQpkDOhiJxDc3L2cUzOfUl78fbxoWEjS84+ffuzLmGNVZl1a9cwaOgwAHo91Ift27agtaZKlSo4O1vOUN5Iv2G3We7HD3+LZ/1G1PVqQMWKLnTs3ps9W63/5dTing5UussyAtC0RSgXfkqzS5Zb+XZfIg29fWjQ0FKXvfr0Z+O6eKsyG9fF02+QpS4je0Wzc/tWtNZWZVaviKFXH+s/brbSuFZV0q7e4KdrGWRmaXae/JV76rsWKjc4xMDqQ+fJMOsiPgU6eruz8+Qlu2QEy6idt48PjbL3y+h+A1hbcL9MiGPwEMtIWe/ovrn7ZVBwSzwNBgD8/AO4fuM66enpNs946NskGjTypn6DRri4uBDRuy9fbEywKuNVvwHN/Jvj5GTdhDfy8aWhd2MA6tT1xL1mLS5dvGDzjJDTZvrktkV9+g0gId66LtfGxzF4aF5dbttadF3euG6fuszJebttZkJ8HH37D6BSpUo0bNQIH5/GJCWWvZu3K8BJqVJ5OEpJRlaq5XtUwjKHpagfJSrXUlNNeNXzyn1uNHqRajIVLuNluTGfs7MzNarX4OLFi6Wb02TC6FUgZ6qpiDJ5OavXKP2caampGI15NzE0GL1IS029aRlnZ2eqV6/BpeycSXv30Ca0Be1bBTN77n9yOy+2dPGnNGrWMeQ+r1nHk4s/37wzsnnV54R26JL7PCMjnWcGduO5IeHs3rLe5vlynE9LxZCvLj0NRtLSTEWUsewXlrqszqVL1tt8zcrl9O4zwC4ZPapU5OLvf+Y+v/hHBu5VK1qV8fa4C4+qLuw7d/Wmn9Pezp2V1NS8YwPAaDSSVuA4T0tNtT5+8u2XOeJWrSAoOIRKlSrZPONPaanUNeQd43U9jfyU9tc7yQf3J/Hnn39Sv6G3LePlSiuqLgu2RampxbaZ9qxL+HttZv42H8BgNBb6uyBKxy3/AmTfDK6a1vq50gijlHoZ+E1rXfgkrfifEnZPa3bvO8QP3x/jiUdG0fXB7qUyInQzWxNiST5ykNfn580P+HhDEh51PDmfcoZJD/ehga8fnvUaOizjrexP2stdVarQzN8x8+IVMLJ1PeZ9dfqmZXxrVSE9M4uzv5boR1gd5tjRI7w06XlWJWxwdJSb+vmnNP457mGmv/1BodGXsuTY0SNMfvF5Vpfhuiwvyu5Wto2bfj+llLPW2gy0L8U8DmMwGEk5l5L73GRKwWA0Fi6TYplgmZmZyZWrV/Dw8CjdnEYjppQCOQ3GIsrk5bx6pfRzehoMmEx5k1FTTSm5w75FlcnMzOTq1Su4F8jZtJkfVe++m2NHDts8o0cdTy78lDfac+GnNDxqexYqd2D3Vyz/cC6T3l5IRZdKVssD1PVqQGBYO04e+87mGQHqehpIzVeXaakmPD2NRZSx7BeWuryKu3teXcatWGa3URWAi3/8iUe+kRSPKi5cyjfScldFJ+q73cWr4U14r38gTWpV5fkHfHIn2QJ0sPOoCliO4ZxjA8BkMuFZ4Dj3NBisj598+6UpJYUhA/rw/kcL8Pb2sUvGOp4GzqfmHePn00zU8Sy8X97Mb9eu8ujQPjz9r5cIDr3HHhEBywhfobos2BYZDDdtM00pKQzq34f3P16At4996hL+XpuZv80HywhMwb8LonTcqjOWc2LugFJqjVJqmFIqOudhqwBKqReVUj8qpXYCTbNfC1ZK7VZKHVJKrVJKuWW/3ir7tQNKqZlKKZv9BQsNa8WJ5OOcPnWKjIwMYpfFEB5pfWfg8MgoPlu8EIBVK2O5974upX7XwNCwViQXyBlRRM5Pc3KucEzOkNBWnEhO5vRpS84VscvoERFlVaZHeBRLPl0MWIaCO93bGaUUp0+fyp1Qe/bsGY7/8AP1GzS0eUbfgGBSz5zkfMoZ/vwzgx0bVtP6vm5WZU4c+47/TPkHk95eiKtHrdzXf7t6mT8zLOfYr/56kWMHEqnn08TmGQGCQ8I4dSKZs9l1GbdiGd16RFqV6dYjkuVLLHWZELeSDp3uy93mWVlZxK9eQa8+/eySDyD5l9/xrF6Z2ne74Oyk6ODtRuLZvImdf/yZxcjPDvL4ssM8vuwwP/7yO69/cYITF/4ALCMv7Rq52b2zEhJmvV+uXB5DeIH9MjyiJ59/ZpkwvXplbO5+efnyZfpHR/Hyq9No085+/4ZrHhzK6ZMnOHfmNBkZGaxdHcv93SJKtGxGRgZPjhpI736D6R71kN0yQk6bmZzbFq1YHkNEZIG6jOzJ55/m1eW99+XVZd+HonjltWm0tWNd5uS83TYzIrInsctiSE9P5/SpUyQnHyeslf06gH/HnX41UEkmAlQGLgJdyLvfigZW/t2VK6VCsfzIUXB2lv3APmARME5rvV0pNQV4CZgIzAce0VrvUkoVvv4s73MfBR4FqFe/fomyODs78+acefSO7I7ZbGbYyFH4+wfw6iuTCQkJIyKqJyNGjeHhUcNp4eeLm7s7CxYvyV3ev0kjrl29SkZGBgnxccSt3Yifn//tVEuxOWfPmUfPiO6Ys8wMHzEK/4AAprw8mZDQMCKjejJy1BjGjBxOoJ8vbm7uLPo0L2cz37yc8WviiF+7ET9/++ScOXsufXqGYzabGTp8JH7+AUyd8hItQ8IIj4xi2MjRPDZmBC0Dm+Lm5sYniz4HYPc3XzPnzRk4O1fEycmJWXPewaNmzWLW+NdVcHbmsRem8fITg8gym3mg9yDqN27GZ+9Op7F/MK07P8iC2VO4/sfvTH/uEQBq1TUyad4izp08zn+m/APl5ITOyqLP6HHU92lq84xgqcupM+cwuE8kZrOZgUNH0tTPnxlTXyGoZQgPhkcxaNgoxj82inYt/XB1c+e/nyzOXX731zswGL1oYKe5CwBZGj7adZbJ3X1xUoovf7zAucs3GBjiyYkLf5B49sotl/evezcXf8/gp2sZdssIlrqc9dbbREf1sOyXI0bl2y9DCY/sybCRo3l09HCCA5rg5ubOJ4st++WH773LyRPJzHj9NWa8/hoAq+I3UKt2bZtnnDztTcYM6oXZbKbvoOH4NvNn7vRXCQwO4f4HIzj07T6eGj2Qq5cvs3Xzet6eOZV1XyWxfs0KknZ/zeVfL7Ey5lMA3pj7Pv6Btr8CzNnZmVlz3qZ3VA+yzGaGZdfla6+8RMvQUCIiezJ85GgeGT2cIP8muLm7Mz/7GP/gv5a6nD7tNaZPs9RlXILt6zIn5+22mf4BAUT37UdIUADOFZx5a+47VKhQweYZRfFUwSsGct9QKgWYTV7nJH+fSmutZ//tlSs1EXDP+c0ApdRs4AowRmtdP/s1H2A5ls7SQa11g+zXWwCfa60Db7WOkNAwvWNX4t+Nanfl4e6DGZnl4/crv0q2z9UPthRW383REUrkqVj7nNqytQVDWjo6QrF+ulK25+Lk8CzikvOypkJ5aDCBKi5O+7TWYfZej6dvoB799t8ePyiRaeFNS+U7FXSrkZUKwN1Yd1JyFN3DEUIIIUSpUg6+rLg03Kqzkqa1nmLn9X8FLFBKvZ6dJQp4H/hVKdVRa70DGAZs11pfVkpdU0q11lrvwXL6SAghhBB3uFt1VuzeTdNa71dKxQAHgZ+BnPM1I4D3lFJVgJPAqOzXxwAfKqWygO1YThkJIYQQ/9Pu8IGVW3ZW7i+NAFrrqcDUIt5qU8RrR7TWLQCUUv8CkuyZTQghhBCOd9POitbavtcQ3p4IpdTzWHKfAUY6No4QQgjheOVkzvFts/09zO1Iax0DxDg6hxBCCCFKT7nqrAghhBDCWs4PGd7J7vSfExBCCCFEOScjK0IIIUQ5d4cPrMjIihBCCCHKNhlZEUIIIcozdedfDSQjK0IIIYQo02RkRQghhCjnlP1vOu9QMrIihBBCiDJNOitCCCGEKNPkNJAQQghRjlluCufoFPYlIytCCCGEKNNkZEUIIYQo52RkRQghhBDCgWRkpYxQ5eBeyRXKSdfdt+bdjo5QrEyzdnSEElk0tKWjI5RIrcHzHR2hWL98PsrREUqkPLRForA7fbvJyIoQQgghyjQZWRFCCCHKMbkaSAghhBDCwWRkRQghhCjPFNzhU1ZkZEUIIYQQZZuMrAghhBDlnNMdPrQiIytCCCGEKNNkZEUIIYQox+RqICGEEEIIB5POihBCCCHKNDkNJIQQQpRzd/j8WhlZEUIIIUTZJiMrQgghRLmmcOLOHlqRkZV8Nm/cQMvAZrTw8+XNmW8Uej89PZ3hQwbSws+X+zq04czp0wBcvHiRHt26UMe9Gs9MGGv3nJs2bqBFQFMCmjVm5oyicw4dPICAZo3p2K51bk6AmdNfJ6BZY1oENGXzpo12zbl50wZaNvcjyL8Jb86cXmTOEUMHEuTfhM4d2+bm3PLFZjq2bUXr0CA6tm3F9q1b7JZxx9bN9OjQkgfbteDDeW8Wej9x906iu7UnsF4NNiasyn3dlHKW6G7teeiBtkTeF8bSRR/ZLSPAti83cd89zekY5s+7c2YWej89PZ0nxwylY5g/Pbt25NzZ0wD8+eefPP3kGLp2CKVLmyDeeWuG3TKWh+0N0DXYyMG3+3D4nX4891CLQu/PGNma3bN6s3tWbw7N60vaoqG578VNepC0RUNZ8XxXu2YsL3VZntrMoIBmBPr5MusmbeawwQMJ9POlU/s2hdrMQD9fggKa2b3NFDcnnZVsZrOZZyaMZeWadSQdPMLymKUcO3bUqszC+R/j6urKoWPHeWr8RP794r8AqFy5Mv9+aQpT3yj8R8QeOSeOf4q4+PV8e+goy5cu4dhR65wLPvkYN1c3jnyfzLgJT/PiC/8HwLGjR1kes5T9B4+wJmEDE8Y9idlstlvOZyeMY2XcWhIPHCZ22VK+L1CfixZ8gqurGweP/shT4yYweZKlPj1q1mTZijj27DvI+x/N55ExI+yW8dUXnuGDz1YSvy2JtXHLSf7xmFUZg7Eer895n4iH+lu9Xqt2XZbGb2HVF7uIWbuND9+Zzc/n0+yWc9I/J7BwWRxffnOANSuX8eP31jljPl1ADVdXdiQd5eEnxvH6K5MAWBu3goyMDDbv3MfaLbv4fOFHuR0ZW2cs69sbwMlJMeeRdvSauomWE1fQr4M3zbxcrcr8c8Ee2jy3mjbPrea/644St/tM7ntvxR1izNvb7ZYPyk9dlqc28+kJY1kdv479OTkLtpnzP8bVzZXDx44zbvxEJr1gyXns6FFil8Ww78Bh4hLWM3H8U3ZrM/8OhWXOSmk8HEU6K9mSEvfi7dOYRt7euLi40Lf/ANbGx1mVWRu/hiHDLAf/Q9F92bb1S7TWVK1alXbtO1C5cmW750zcuxeffDn7DRhIQoGcCfFxuTmj+/Rl2xZLzoT4OPoNGEilSpVo2KgRPj6NSdy71y45LfXpk5uzT78BJMSvsSqzNj6OwUOHA9A7ui/btm5Ba01QcEs8DQYA/PwDuHH9Ounp6TbPeOjbJOo39KZeg0a4uLgQ3qsvWzautSpjrNeApv6BODlZHyouLi64VKoEQEZ6Ojory+b5chzYn0jDRj40aGipy6iH+rFpfbxVmU3r4+k70DICEN4zmq+/2orWGqUUf/zxO5mZmdy4cZ2KLi5Uq1bd5hnLw/YGaNW4FifOX+X0T9f4MzOL5TtPEtmq/k3L9+/gzbKdJ3Kfb/sujWvX/7RLthzlpS7LS5uZlGjdZvbtP6BQm7k2fg1Dc3L2ycuZEB9H3/4DrNrMpET7tJni1qSzki011YRXPa/c50ajF6kmU+EyXvUAcHZ2pkb1Gly8eLH0c2ZnyMlpKipnvbyc1WtYcppMhZdNTbVe1lbSUk0YrdZlJC21YM7UYuszbtUKgoJDqJTdMbCln8+nUteQt83reBr5KS21xMunmVLodX9ruoQ1Y8xTT1O7rqfNMwKcT0vFYMzL6WkonPN8WiqG7O/i7OxMterV+fXSRcJ7RlOlSlXC/BvSJsiXR5+aiKubu80zloftDWBwr0LKhd9zn5su/YHRo2qRZevXupsGdaqx7bB9RsxuprzUZblpM00mjF4FchasT1NenedvMwu2twajsdB3LBOU5aZwpfFwFIdNsFVKjQeeAPZrrYc4Kocou44dPcLkF59ndcIGR0cpkqfRi7gv9/Dz+TTGjh7Ig5G9qVmrjqNjWTmwP5EKFZxIPHKKK5d/pW/E/XS4twsNGno7OlohZW1792vvzepdp8jK0o6O8peVtboU4u9y5MjKk0DX/B0VpZTDOk8Gg5GUcym5z02mFAxGY+EyKecAyMzM5MrVK3h4eJR+zuwMOTmNReU8l5fz6hVLTqOx8LIGg/WytuJpMGKyWpcJT0PBnIab1qcpJYVB/fvw/scL8PbxsUvG2nUNnE/N2+Y/pZmo42m4jc/xxLepP/v2fGPLeLnqehpINeXlTEstnLOup4HU7O+SmZnJtatXcXP3IC42hnu7dKNixYrUrFWbsNZtOXRgv80zloftDZB66Q+8auaNpBjdq2C6+HuRZfu292bZzpN2y3Iz5aUuy02baTRiSimQs2B9GvPqPH+bWbC9TTWZCn3HssJJqVJ5OOz7OWKlSqn3AG9gvVLqilJqsVLqa2CxUqqhUmqLUuqQUupLpVT97GV8lFK7lVLfKaVeU0r9ZstMoWGtOJF8nNOnTpGRkUHsshjCI3talQmPjOKzxQsBWLUylnvv64Iq5Y0X1qoVyflyLo9ZSkSBnBGRPXNzrlwRy72dLTkjInuyPGYp6enpnD51iuTk47S65x675LTUZ3JuzhXLY4iIjLIqEx7Zk88/XQTA6pWx3HtfZ5RSXL58mb4PRfHKa9No2669XfIBNA8O5cypE6ScPU1GRgbr4mLp3C28RMueTzVx4/p1AK5c/pV9ibto5ONrl5xBLcM4dTKZs2csdRm/ajlde0RalenaPZLYpZ8CsG7NStp1vA+lFAavenyzYxsAf/z+O/uT9tLYt6nNM5aH7Q2QlPwLjT2r06D23VR0dqJfB2/WJp0tVK6JsQZud7uw+4ef7ZqnKOWlLstLmxkaZt1mxi6LKdRmhkdG8WlOzhV5OSMiexK7LMaqzQxrZZ82U9yaQ0YytNaPK6W6A52BsUAU0EFrfV0pFQ8s1FovVEqNBt4GegNzgbla6yVKqcdv9flKqUeBRwHq1b/55Ln8nJ2deXPOPHpHdsdsNjNs5Cj8/QN49ZXJhISEERHVkxGjxvDwqOG08PPFzd2dBYuX5C7v36QR165eJSMjg4T4OOLWbsTPz/+vV04Jcr419x2iIh7EbDYzYuRo/AMCmPLyZEJCw4iM6snI0WMYPXIYAc0a4+bmzuLPlloyBgTQp19/Wrbwx9nZmTlvv0uFChVsnjEn56w5b9M7qgdZZjPDRozCzz+A1155iZahoURE9mT4yNE8Mno4Qf5NcHN3Z/6izwH44L/vcvJEMtOnvcb0aa8BEJewgVq1a9s846Spb/Lw4N5kmc1EDxyGb1N/3p7xKoFBIXR5MILvDuxj3JhBXL18ma2b1zNv1lQStiVx4vgPzJjyPEoptNaMfnw8TfwCbZovf85Xp89hWL8ozGYzAwaPoGkzf958/RWaB4fSrUckA4aOZOITo+kY5o+rqzvvfGT5QzZizOM8O+5R7m/XEq01/QcPxy+guV0ylvXtDWDO0jz90S7i/92dCk6KhVt+5Ni5y/x7YAj7ky/kdlz6tfdm+deFR1W+eDWCJsYa3F25IskfDOTx/+zgiwO2ncNQXuqyPLWZs+fMo2dEd8xZZoaPGFW4zRw1hjEjhxPo54ubmzuLPrXk9A8IILpvP0KCAnCuYGl77dVm/h05VwPdyZTWjjkfq5Q6DYRh6axorfUr2a9fADy11n8qpSoCaVrrmkqpi0AdrXWmUqo6kKq1vru49YSEhukduxLt90VspEI5+MnMTLP9rnixpZRL1x0doVhVK5WP+zG6Va3o6AglUmvwfEdHKNYvn49ydIQSKe2Rj9tRDppLAKq4OO3TWofZez0N/VroFxfEF1/QBh5t07BUvlNBZaXFLPqksRBCCCGK5cj5JKWhLF66/A0wMPv/hwA7sv9/N9An+/8HFlxICCGEEHemsthZGQeMUkodAoYBE7Jfnwg8k/16Y+CKg/IJIYQQohQ57DSQ1rph9v++XOD1M0CXIhYxAW201lopNRCw/SUNQgghRDl0h58FKpMjKzcTChzIHll5EnjWwXmEEEIIUYBSqrtS6gelVLJS6l9FvP949m1IDiildiqlir0MrKxMsC2W1noHEOToHEIIIURZoig7Iw9KqQrAu0BXIAVIVEqt0Vrn//XIz7XW72WX7wnMBrrf6nPLyvcTQgghRPl3D5CstT6ptc4AlgK98hfQWl/N97QqUOw9VMrNyIoQQgghiqDK1P1xjMC5fM9TgNYFCymlngKeAVwoep6qFRlZEUIIIURJ1VRKJeV7PHo7H6K1fldr7QP8HzCpuPIysiKEEEKUc6U4rnKhmDvYmoB6+Z57Zb92M0uB/xa3UhlZEUIIIYStJAK+SqlGSikXLDdxXZO/gFIq/6++RgDHi/tQGVkRQgghyjFF2bndfvbv940FNgIVgE+01keUUlOAJK31GmCsUuoB4E/gV2BEcZ8rnRUhhBBC2IzWeh2wrsBrk/P9/4RCCxVDOitCCCFEOVc2xlXsR+asCCGEEKJMk5EVIYQQopwrI1NW7EZGVoQQQghRpklnRQghhBBl2v/EaaCsrGJ/dsDhKjjd4WN4pci1SkVHRyhW5YoVHB2hRK5ez3R0hBK5sGS0oyMUq+aDUx0doUR+2fCCoyMU704/5/GXqbJ0u327kJEVIYQQQpRp/xMjK0IIIcSdSnHnjzzc6d9PCCGEEOWcjKwIIYQQ5ZzMWRFCCCGEcCAZWRFCCCHKuTt7XEVGVoQQQghRxsnIihBCCFGeKZmzIoQQQgjhUDKyIoQQQpRjcp8VIYQQQggHk5EVIYQQopyTOStCCCGEEA4knZV8Nm/aQEgLP4ICmjB75vRC76enpzNy6ECCAprQuWNbzpw5DcCWLzfTqV0r2oQF0aldK7Zv22LXnJs2bqBFQFMCmjVm5ow3isw5dPAAApo1pmO71pw5fTr3vZnTXyegWWNaBDRl86aNds25edMGWjb3I8i/CW/epD5HDB1IkH92fWbnvHjxIuHd7qeuR3WenTjOrhm3fLGRDmGBtG3px7y3ZhaZ8bFRQ2jb0o/w+ztwLnubAxw9/B2RXTtxb5tgOrcL4caNG3bL+cWmDbQK9iekeVPemlV0XY4ePoiQ5k154N62nM3OuS9pLx3bhNKxTSgdWoeQsGa13TJu/WIjHVsF0j7Ej3duUpePjx5C+xA/Ih/owLmzlowrly2ha8dWuQ8v98oc/u6g3XJu3riBloHNaOHny5sziz5+hg8ZSAs/X+7r0MZqv+zRrQt13KvxzISxdssH0LWVNwcXPsHhT5/kuUHtCr0/48mu7P7wYXZ/+DCHFj1BWvxzue/Vq12d+BmD+XbB4+yf/xj169SwW87bPca3fLGZjm1b0To0iI5tW7F9q/3azE0bNxAU0IxAP19m3aS9HDZ4IIF+vnRqb729u3ftQi23ajxt5+1tC6qUHo4inZVsZrOZZyeOY0XcWhK/PUzs8qV8f+yoVZlFCz7B1c2Ng0d+5KlxE3jpxX8B4OFRk5jYOHYnHeS9D+fz6OgRds05cfxTxMWv59tDR1m+dAnHjlrnXPDJx7i5unHk+2TGTXiaF1/4PwCOHT3K8pil7D94hDUJG5gw7knMZrPdcj47YRwr49aSeOAwsctuUp+ubhw8aqnPyZMs9Vm5cmUmvfQKU9+YYZds+TO+8NwEPotdw/Y9B1kdG8MP3x+zKrNk8XxquLqy69tjPPrkeF57+UUAMjMzGfvoSKbPfoftuw+wImEzFStWtFvOfzwznuWrEti97ztWLI8pVJeLF35CDVc39n/3A0+MncjL/34eAD//QLbu3MOO3fuIXb2Wp8c9QWZmpl0yvviPCXy6fA1bdx9k9YoYfiyqLmu48vX+YzzyxHimZtdldP9BbN6RyOYdibz93nzqN2hIYPMgm2fMyfnMhLGsXLOOpINHWB6zlGMF6nLh/I9xdXXl0LHjPDV+Iv9+MW+//PdLU5j6RuGOmC05OSnmTOhBr38toeXI9+h3fwDNGtS0KvPP/2ymzSMf0eaRj/jvqkTidnyf+95Hz/firZhdtBz5Hh2f+IRfLv9ul5x/5xj3qFmTZSvi2LPvIO9/NJ9HxtinzTSbzTw9YSyr49exP2d7F2wv53+Mq5srh48dZ9z4iUx6IW97T355CtOm23d7i5KRzkq2pMS9ePv40KiRNy4uLvTpN4C1CWusyqxNiGPQkOEA9I7uy7ZtW9BaExTcEk+DAQA//wCu37hOenq6XXIm7t2Lj09jGnlbcvYbMJCE+DirMgnxcQwZZjn4o/v0ZduWL9FakxAfR78BA6lUqRINGzXCx6cxiXv32iVnbn1659VnQnyB+oyPY/DQfPW51VKfVatWpV37DlSqVNku2XJ8uy+Rht4+NGhoydirT382rou3KrNhXTz9Bw0DILJXNDu2b0VrzfYtm/ELbE5A8xYAuLt7UKFCBbvk3Je0F29vHxpm75vRffuzrsC+uT5hDYOGWHL2eqgP27P3zSpVquDsbJmalp5+w27ntQvVZXThuty0Pp5+2XUZ0Suandl1md/qFTH0jO5vl4yQs1/mHT99+w9gbYHjZ238mtzj56Hovmzb+qXVflm5sn33y1bNDJxIvcTptMv8mZnF8i1HiGzf5Kbl+3cJYNmXRwBo1qAmzhWc2LLvFAC/3/iT6+m275zC3zvGC7aZN67bp81MSveEQDMAACAASURBVLRuL/v2H1CovVwbv4ahOdu7T+lvb1Ey0lnJlpZqwsurXu5zg9FIqslUoExqbhlnZ2eqV6/BpYsXrcrErVpBcHAIlSpVskvO1AI5jUYvTAVypqaa8KqXL2eNGly8eBGTqfCyqanWy9pKWqoJo9W6jKSlFsxpXZ81qltylpbzaakYjXkZPQ1GzqeZCpUxGL1yM1avXp1Lly5yIvk4CsXA6Ai6dmrNu3Nn2S1nWmqqVV0ajF6kpaValUnNV6bgvpmUuIe2YS1of08ws9/+T27nxZYs9VRMXaYWrstfL1lv7/hVy+ndZ4DN8+WwHBteuc+NRq9Cx3n+Y8wR+6WhZjVSfr6a+9z0yzWMNasVWbZ+nRo08HRl27enAfD1cufybzdY+kpfdn3wMNMeux8nJ/t0UG11jMetWkGQndrMVJMJo1eB7V0wo8lkfezUKN3tbStKlc7DUezWWVFKNVRKHbbX55dFx44eYfKk55nzzn8dHUXYmdmcyd7dX/PuhwuJ27CV9Qlr2LHdvnOVbldYq9bsSjrEl1/t5q1Zb9h1bs3fsT9pL3fdVYVm/gGOjlJu9Ovsz+rt35OVZRmhcq7gRPvm9fjXe1/Q4fGPaWRwZVh3+5xSs4VjR48w+cXnmSttpihGmRxZUUqV+iXVngYjKSnncp+nmkwYjMYCZQy5ZTIzM7l69QruHh4AmFJSGDygDx98tABvbx+75TQUyGkypWAskNNgMJJyLl/OK1fw8PDAaCy8rMFgvayteBqMmKzWZcLTUDCndX1euWrJWVrqehowmfIypqWaqOtpLFQm1ZSSm/Hq1au4u3vgafCiTbuOeHjUpEqVKnTp2p3vDn5rl5yeBoNVXaaaUvD0NFiVMeQrU3DfzNG0mR9Vq97NsaO2/zeEpZ6KqUtD4bp0c8/LGLdyGb3sOKoCOcdGSu5zkyml0HGe/xhzxH6ZeuEaXrWr5z431qqG6cK1Isv27RLAsi1Hcp+bfrnGoRM/cTrtMuYszZqdPxLsW9cuOf/uMW5KSWFQ/z68//ECvH3s02YajEZMKQW2d8GMRqP1sXOldLe3LVhuCqdK5eEo9u6sVFBKfaiUOqKU2qSUuksp9f/t3Xd8VFX6x/HPA6EICklAMQkgRZQk1CSAiEpRaQlNQESlrx1BXfdnW+sqKqCAbdVdKxZ6BwEbiiDSFEUQCVUSdBUpVjDD8/vj3IRJCCRqpsXnzSsvMjN3Zr459869Z84599xmIrJCRD4VkZkiEgMgIktEZLyIrAZGikhfEVkvIutE5H1vmbIiMkZEVnnPv6qkgqamtWBrZibbt2/j0KFDTJ86ma7p3fIt0zW9O6+/+jIAs2ZMo23b9ogI+/bto+9F3bj3X6M46+w2JRWpUGktWpCZuZnt21zOqZMnkZ7RPd8y6RndeXXiSwDMmD6Ntu07ICKkZ3Rn6uRJHDx4kO3btpGZuZkWLVsGJGdqWgu2ZGbm5Zw+dTLpGQXKM6M7r73iV57t2gd1roBmKWls25LJTm+dz54+hU5dMvIt06lLBlNenwjAvNkzOOe8dogI7c6/kI0b1vPzzz+Tk5PDimXvc8aZiQHJmZLagi1bMtnh5ZwxbQpdCmybndO78fqrLufsmdM5z9s2d2zfljegdufOHWz+chO1a9cp8Yx5ZbnDK8sZU+hYoCw7ds5gqleW82fPoI1XlgCHDx9m3qzp9Ojdt8Sz+XPb5ZHPz7Qpk+la4PPTNaNb3udn5oxptG3XIajb5eovsjk9IZbTTo2mXFQZ+nZIZv7yL49a7oxa1Yg5qSIrPj9yMF69KZuqJ1aketVKALRrXocvdnwbkJx/5jO+b98++vTqxr33j6J1APeZqWn595fTpkw+an/ZNaMbr+Su7+nBX9+meALdgtEA6K+qV4jIFKA38H/A9ar6nojcB9wN3OAtX15V0wBE5DOgk6pmiUi09/gwYL+qthCRCsAyEVmsqtv+bNCoqCjGjHuMXt264PP5GDBoCIlJydx/392kpKTSNaM7AwcP5cqhA2mafAYxMbG8MPE1AJ59+km2bsnk4Qfv5+EH7wdg1tyFnHzKKX82VqE5x014gm7pnfD5fAwaPJSk5GTuu+cuUlLTyOjWncFDhzF08ACSG55OTEwsE1+dBEBScjK9+15M8yZJREVFMf6xJwM2KDQqKoqx4x+jZ7cuHPYvz3vvpnlqKuleeV4xdCBNk84gJjaWF15+Le/5yWfU44cfDnDo0CHmzZ3N7HkLaZiYVOIZR40ZT//eGfh8Pi65fDBnJiYx+oF7ado8hU5du9F/wBCuv2oIrZsnEh0Ty9PPu4NtdHQMV103ki4dzkZEOP/CzlzQqWuJ5vPPOfqRCfTu0RWfz8dlAweTmJTMqH/dTbOUNLqmd2PAoKFc/bdBpDQ+k5iYGJ57yZXlh8uXMeHR0URFlaNMmTKMHf8E1apXL+Id/1jG+0eP59LeGRz2+eh3mSvLMaPupWmzFDp27cYlA4Yw4uohtElxZfnUcxPznr9i+VLiEmpyWp16JZ6tYM5Hxj9Oz4zO7nM+eAhJScn86967SElJI71bdwYNGcbfhgykSWIDYmJjeXHi63nPTzqjLj8c8Nsu5y8isYS3S99h5cbHFjJ3dH/KlinDS298wsbt33HnkLas3ZTN/OWbAejbIZmpfq0qAIcPK7f9+y0WPHIZIsLHX+7m+XmBafH7M5/xZ//t7TNH3c/Do9w+c/a8kt9nRkVF8ej4x+me3hnfYR8DBw05en85ZBjDBg+kUWIDYmJiefmVI+u7YYMj63vunNnMnb+IxKSSXd8lpbTXr6TgaPwSe2GROsCbqtrAu30LUBEYpqq1vfvqA1NVNUVElgB3q+p73mNPA/WBKcAMVd0jItOAJsDP3ttUBa5S1cUF3vtK4EqAWrVqp37+5Z+uywRcuaiw7JHLJ8d3ONQRiuXHXwNz9kNJqlguMJXEkvbzocCc2l7SqlYKzGnjJal6pwdCHaFYvl14e6gjFKlsgAYNl7RK5cusyf0CHkgNkpvquMmLi16wBHRrfGpQ/qaCAt2y4n8umg+IPtaCnrwJAVT1ahFpBaQDa0QkFdc1d72qHnc2M1V9FngWICU1LTC1MWOMMSYsCBLSKdsCL9hf5/cDe0XkXO/2AOC9whYUkfqq+pGq3gV8C9QCFgHXiEg5b5kzRKRyEHIbY4wxJkRCcSHDQcDTIlIJ2AoMOcZyY0SkAa415W1gHfApUAdYK24E1LdAz4AnNsYYY8JYaR+zErDKiqpuBxr53fafNeusQpZvV+D2RYW9LHC792OMMcaYv4BQtKwYY4wxpoTkzrNSmoX/KSjGGGOM+UuzlhVjjDEmkoX4uj3BYC0rxhhjjAlrVlkxxhhjTFizbiBjjDEmwlk3kDHGGGNMCFnLijHGGBPhbLp9Y4wxxpgQspYVY4wxJoIJECEXov7DrGXFGGOMMWHNWlaMMcaYCGdjVowxxhhjQshaVowxxpgIZ/OsGGOMMcaEUKlvWfEdVn4+5At1jCJF5RwOdYQiVapQNtQRiuXEiuG/WUuEfA2KKRcZ32dUQ52gaN8tuiPUEYqleqvrQx2hSF8vnxDqCGHHxqwYY4wxxoRQ+H8FNcYYY8wx2TwrxhhjjDEhZpUVY4wxxoQ16wYyxhhjIprYAFtjjDHGmFCylhVjjDEmkolNCmeMMcYYE1LWsmKMMcZEuFLesGItK8YYY4wJb9ayYowxxkQwNylc6W5bsZYVY4wxxpQYEeksIptEJFNEbi3k8ZtEZIOIfCoib4vIaUW9plVWjDHGmAgnQfopModIWeBJoAuQBPQXkaQCi30MpKlqE2AaMLqo17XKip933lpEm9RkzmqWyOOPHl12Bw8e5MrBl3JWs0S6dGjDzh3b8x7bsP5T0i84l/NaNaVd6+b8+uuvAcv59puLaNU8mRZNGzLhkcJzDht0KS2aNqRj+7Pzcu7csZ2aJ59Eu7NTaXd2Kn8feW3AMgIsXrSQpskNaZTYgLGjHyo054BLL6FRYgPOa3MWO7a7nHv27KHzhR04OeYkbhw5PKAZ31y8kOaNE2madAaPjHm40IyDLr+Epkln0P7c1nkZ33nrTc5t3YJWqU05t3UL3nv3ncDmXLSQ5o0a0iSxAY+MKbwsB152CU0SG9DunPxl2aVjB2rEnsRNAS7LP7q+AcY8/CCNEhvQNLkhby5eFPCczRo1pHFiA8YepywbJzagbSFleUoQyjIS1jfAhWcnsm7mnayffTc3D7nwqMdrnRrDwmdH8OHrt7By8m10Oscds2KrVmbhsyP4dtkjjLulb8BzvrV4IWlNk2je6EzGjS38cz5kQH+aNzqT889rzQ5vn7lm1UrOaZXKOa1SadMqhbmzZwU8aynQEshU1a2qegiYBPTwX0BV31XVn72bK4CaRb2oVVY8Pp+P2/4+ktemzeX9leuYOX0ym77YkG+Z115+gejoGFZ8spGrrh3B/XffDkBOTg7XXTmY0eOe4P2P1jFj/luUK1cuYDlv+fsIJs+Yy7JVnzJj2qSjcr768vNER0ezat0XXH3dSO696/a8x+rUrc+S5WtYsnwNj0x4KiAZc3PeOHI4s+YuYO26z5k6eRIbN+TP+eILzxEdE836jZu5fsQN/PN211pYsWJF7rrnPkY9PCZg+XIz/n3k9cyYPZ9Vn6xn2pRJfLExf8aXX3ye6OgY1m34kuuuH8ld/3QZq1WvzpTps/lozTqe+e8LXDFsUEBz3jRyODPmLGB1blkWyPnSC88RHR3Npxs3c92IG7jzjiNleefd9/HAQ4Evyz+6vjdu2MC0KZNZ88l6Zs97gxtGXIfP5wtYzptGDmfmnAWsKaIsP9u4meGFlOWoIJRluK9vgDJlhPG3XkyP4U/RvPf99O2cSsN6p+Zb5pa/dWb6m2tp3f9hBt72AhNu6wfArwd/476n5nHbuJkBz+nz+bj5xhFMmzWPj9Z+xrSpk4/6nE/0Pucfr9/EtdffwD3/vA2AxORGLFn2ER98tIbps+Zz44hryMnJCXjmPyR4TSvVRWS138+VBZIkAF/53d7l3Xcsw4A3ivrzrLLi+XjNKurWq89pdetRvnx5el50MYvmz823zKIFc7n40gEAZPTszQfvvYuqsuSdN0lKbkxy46YAxMZWo2zZsgHJuXb1SurWq08dL2ev3v14Y17+nG/Mn8slXs7uPXuzdMk7qGpA8hzL6lUrqV//dOrWczn7XNyPeXNn51tm/tw5XD7AHeR79e7DknffRlWpXLkyZ7c5h4oVKwY8Y7369fMy9u7bj3lz5xTIOJtLLx8IQM+L+rDkXVeWTZs1Jy4+HoDEpGR+/eUXDh48GMCc+ctyfiFleVluWV4UmrL8o+t73tzZ9Lm4HxUqVKBO3brUr386q1etDFjOgmVZMOe8IsqyQlC2y/Be3wAtGtVhy1ffsT1rD7/l+Ji6aC0Z7ZrkW0ZVqVLZZal64gns/nY/AD//eojln2zl14O/BTznmtXuc567z+zd52IWzMv/OV8wfw79L3f7zB69evOet8+sVKkSUVHuPJRfD/6KlPJBrMX0naqm+f08+0dfSEQuB9KAImvXVlnx7M7OIj7hSEtUXEICu3dn519m95FloqKiOKlKVb7/fg9bMzcjIlzSK50Lz23JE+PHBi7n7ux8OeMTEti9O6vA35JNQs1aeTmrVK3K93v2ALBzxzbat0mjW+cOfLjsg4DlzM7KIqHmkZwJCTXJzs4qZJn8Ofd4OYNhd/aR93cZE9hdMGN2NjX9MlatcnTG2TOn07RZChUqVAhIzuzsLGrWKlCWWQVzZhWZM5D+zPr2zw5umy7495VYzkLKcncRZVkl2GUZAesbIP6Uquz6Zm/e7axv9pJwctV8yzzwzAIu6dqSzIX/Yubj13DTw1ODmhG8/WGC//ZVk93Z2cdcJned5+4zV6/8iLNSm9CmRTMenfBUXuUl3EiQ/hVDFlDL73ZN7778eUUuAO4Auqtqkd/0wqbURWQ7bsDNdwXu7w4kqerRHbdhIicnh48+XM7CJcs54YRK9O3eiabNUji3XYdQR8unxqlxfLJhK7HVqvHJx2sY2L8Py1au46QqVUIdLWJt3PA5d91xG7PmLQx1FGPCzsWd03hl7gomTHyHVk3q8tz9A0ntMyroLb1/RlrLVqxY8ymbvtjINVcM4cJOnYPSchXBVgENRKQurpJyCXCp/wIi0hx4Buisqv8rzouGfcuKqs4JRkUlLj6B7Kxdebd3Z2URFxeff5m4I8vk5OTww4H9xMZWIz4+gbPanEO1atWpVKkS53fszKfrPg5Mzrj4fDmzs7KIi8vfHRgXH0/Wrq/ych7Yv5/YatWoUKECsdWqAdCseSp16tYjM/PLgOSMT0gga9eRnFlZu4iPTyhkmfw5q3n5giEu/sj7u4xZxBXMGB/PLr+M+w8cyZi1axf9L+7NM8+9SL369QOWMz4+gV1fFSjLhII5E46ZMxj+zPr2zw5umy7495VYzkLKMq6IsjwQ7LKMgPUNkP2//dSsEZN3O6FGDFleN0+uQT1bM33xWgA++nQbFcuXo3p05aDmjIuPJyvLf/valdeFW9gyues8tkB5ntkwkconnsjGz9cHPnQEU9UcYDiwCNgITFHVz0XkPq/xAVy3z4nAVBH5RETmHOPl8oSksiIilUVkvoisE5H1ItLPe+h6EVkrIp+JSENv2cEi8oT3+4si8rQ3qOdLEckoqUzNUtLYuiWTHdu3cejQIWbNmELHrvlfvmPXDKa8NhGAebOm0+a8dogI7c7vyBefr+fnn38mJyeHDz9YyhkNE0sqWj7NU1vkyzlz+mQ6p+fP2blrBpO8nHNmTefctu0REb779tu8gYvbt21l65ZM6tSpF5CcqWktyMzczPZtLue0KZNJz+ieb5muGd14ZeJLAMycPo227ToEtU84Na0FWzIz8zJOnzqZ9IxuBTJ257VXXgZg1oxptG3nynLfvn306dWNe+8fReuz2wQhZ/6y7FpIWb6aW5YzQlOWf3R9p2d0Z9qUyRw8eJDt27aRmbmZtBYtA5azYFkWzJkeBmUZ7usbYPXnOzi99smcFl+NclFl6dsphflLPs23zFdff0+7lmcCcGbdGlSsUI5v9/4Y1Jwpqd7n3NtnTp82hS7p+T/nXbp24/VX3D5z9szpnOftM7dv35Y3oHbnzh1s3rSJ2qfVCWr+4hIJzk9xqOoCVT1DVeur6gPefXep6hzv9wtUtYaqNvN+uh//FUPXDdQZyFbVdAARqQo8jBu4kyIi1wI3A38r5Ll1cKdG1QfeFZHTVTXfecLe6OQrAWrWql2sQFFRUYwaO57+F6Xj8x2m/+WDaJiYzMMP3EOz5ql06tqNSwcMYfiVgzmrWSLRMTE88/wrAETHxHDV8JF0bt8aEeH8CztzYaeuf6BYipfzobET6NszncOHfVw6YDANE5N58H6Xs0t6Ny4bOJRrrxhMi6YNiY6J4T8vvArAh8uX8tD991KuXBRSpgxjxz9JTGxswHI+Ov5xuqd3xnfYx8BBQ0hKTua+e+4iJTWNjG7dGTxkGMMGD6RRYgNiYmJ5+ZXX857fsEFdfjhwgEOHDjF3zmzmzl9EYlLBU/X/fMax4x+jZ7cuHPb5GDBoCIlJydx/7900T00lPaM7AwcP5YqhA2madAYxsbG88PJrADz77yfZuiWTh0fdz8Oj7gdg9ryFnHzKKSWaMTfnI+Mfp2dGZ3w+HwMGDyEpKZl/3XsXKSlppHfrzqAhw/jbkIE0SWxATGwsL048UpZJZxwpy3lzZzN7/iISE0u+LP/o+k5KTuaiPn1JaZpMVNkoxk14ImAD1HPLsodXlgOPU5aNvbJ8ya8sE/3Kcu7c2cwJUFmG+/oG8PkOc+PDU5j71HWULSO8NHsFG7d+zZ3XpLN2w07mv/cZtz46k6fu7M/1l7dHFa64a2Le87+Yfy8nVa5I+XJRdGvfhIxrn+SLrV+XeM6oqCjGPDqB3t274vP5uHzgYBKTknngvrtpnpJG14xuDBg8lKuGDaJ5ozOJiYnhee9zvmL5MsY/MpqoqHKUKVOGseOfoFr16iWe0RRNQtF3KCJnAIuBycA8VV3qjVlpo6pZItIKeEBVLxCRwbixLMNF5EXgfVV93nud94ERqvrJsd6rafNUXfzeigD/RX9eVJnwH2VeqUJgDiAlzXc4/PvDI+WsggjYLAGIhCEQERARgOqtrg91hCJ9vXxCqCMUS3SlqDWqmhbo90ls3Fxfnr0k0G8DQMv60UH5mwoKScuKqn4pIilAV+B+EXnbeyh3RLDvONkKfuYjZR9gjDHGmD8gVGNW4oGfVfUV3ECblN/x9L4iUkZE6gP1gE2ByGiMMcZEjHCZbz9AQjVmpTEwRkQOA78B1+CuD1AcO4GVQBXg6oLjVYwxxhhTuoSqG2gR7rQmf3X8Hl8NtPN+fxF40W+5t1T16oAGNMYYYyKEa/SIkAFmf1DYz7NijDHGmL+2sJnBtjhUdXCoMxhjjDFh5XfMgRKprGXFGGOMMWEtolpWjDHGGHO0Ut6wYi0rxhhjjAlv1rJijDHGRLpS3rRiLSvGGGOMCWtWWTHGGGNMWLNuIGOMMSaiiU0KZ4wxxhgTStayYowxxkQ4mxTOGGOMMSaErGXFGGOMiWBCqT9z2VpWjDHGGBPeSn3LShkRKpQL/zrZ3p9+C3WEIp1QvmyoIxTLoZzDoY5QpIrlIqMsD2uoE5QePx/MCXWEYtny7qOhjlCk+tdMDXWE8FPKm1bC/yhujDHGmL+0Ut+yYowxxpR2Ns+KMcYYY0wIWcuKMcYYE+FsnhVjjDHGmBCylhVjjDEmwpXyhhVrWTHGGGNMeLPKijHGGGPCmnUDGWOMMZHsLzDfvrWsGGOMMSasWcuKMcYYE+FsUjhjjDHGmBCylhVjjDEmggk2KdxfyluLF9KiaRIpjc5k3NiHj3r84MGDDB3Qn5RGZ3LBea3ZuWM7AGtWreTcVqmc2yqVc1qlMG/2rIDmfO/txZx/VhPat0jm3xPGHPX4yuUf0K1DaxqceiIL5szI99j0Sa/QvmUj2rdsxPRJrwQ05+JFC2nWqCGNExswdsxDRz1+8OBBBl52CY0TG9D2nLPYsX07AHv27KFLxw6cEnsSN40cHtCMb7+5iJbNk0lr0pDxj4wuNOOwgZeS1qQhF7Y7O2+d59r11U5q14jmiQmBvVJtJJTlm4sW0rxRQ5okNuCR42RsktiAdoVkrBGEjJGS8503F9E6JZmWTRN57NHCt8srBl9Ky6aJdG7fJm+73LljO7VPqUL7Nmm0b5PGzTdcF9Cc7761iHNbNKJNSiJPjDt6X3Tw4EGuHnoZbVISybjgHL7a6XLOmPI6F57bIu+nZmxF1n+2LiAZOzQ+lRUPdmXlw+mMSE8sdJkeLWqx7IEufPBAF565qjUA5zQ8hXfv65T3s+s/femSkhCQjKZoVlnx+Hw+/nHjCKbOmseKtZ8xfepkvti4Id8yE198nqrRMaxdv4lrrr+Be/55GwCJyY14d9lHLP1oDdNmzefGEdeQkxOYy8H7fD7uvvUGXpg0m0XLPmbuzKls3rQx3zLxNWsx+vFn6d67X7779+39nsfGPsDMRe8za/FSHhv7APv37Q1YzptGDmfmnAWsWfc5UydPYmOB8nzpheeIjo7ms42bGT7iBu6841YAKlasyJ1338eoh47e+ZV0xv+7aQRTZsxl+epPmTF10lHr/JWXnic6OprVn37BNdeN5N47b8/3+D9v/QfnX9g54DkjoSxvGjmcGXMWsLqIjJ9u3Mx1hWR8IMAZIyWnz+fjlr+P5PXpc/lg1TpmTJvMpi/yZ3z15ReoGh3DynUbueq6Efzr7iPbZZ269Xh32WreXbaaseOfDGjOO/4xklemzuHdFeuYNX0yX36Rf1/0+sQXqFo1mmVrN3LFNSN44J47ALjo4v68uXQVby5dxWNPv0Dt0+rQqHHTEs9YRoSHB6TR79H3aHP7G1zUqjZnxFfJt0y9GicyMiOJrg+8xTl3vMEdr60F4IMv/kf7uxbR/q5F9Hr4XX45mMOS9V+XeMaSIkH6CRWrrHjWrF5Jvfr1qVO3HuXLl+eiPhezYN6cfMu8MX8O/S8fAECPXr15b8k7qCqVKlUiKsr1qB08+CsSwPa4dWtXcVqd+tSuU5fy5cuT0bMvb74xL98yNWufRmJyY8pI/tX7/rtvck7b84mOiaVqdAzntD2f995ZHJCcq1etpF7906lbz5Vnn4v7MW/u7HzLzJs7h8sGDAKg10V9WPLu26gqlStX5uw251ChYsWAZMu1dvVK6tY7ss579enHG/Pn5lvmjflzueQyt8679+rN+946B5g/dzan1alDw8SkgOaMhLIsLOP8AhnnF5GxYoAzRkrOtatX5d8ue1/MwgLb5cL5c+nX322X3Xr2ZumSd/O2y2D5eM0q6tSrz2l1XM4eF13MogX5cy5+Yy59vZzpPS7ig/eOzjlr+mS6X3RxQDKm1Itl2zc/sOPbn/jNd5iZH+2kS/P8rSMD2tbn+bc3s//n3wD47oeDR71O97RavP3Zbn455AtITlM0q6x4dmdnk5BQK+92fEJNdmdn51sm22+ZqKgoqlSpyvd79gCweuVHtE5tQpsWzXh0wlN5lZeS9vXubOISaubdjotP4JvdWcV67je7s4mLP/LcU+MT+GZ39nGe8cdlZ2dRs9aR90pIqMnurKyjl6mZvzz3eOUZDLuzs0moeSRjfEICu7Ozjlom3j9jVbfOf/zxRx4bN4Z/3HZnwHNGQlkWljG7iIxVg5wxUnJ+vTsr33YZF59w1L7If5moqChOqlKV7793GXfu2E6Hc1rQo8v5rFj+QQBzZhPvt8+Mi0/g6wL7oq+zs4lPOJKzSpUq7P0+f1nOnTmVngVagUtKXMwJZH//c97t7L2/EBdzQr5lAaWN1gAAH+FJREFU6p96EvVPPYn5d5zPwjsvoEPjU496nV6tajNjxc6AZCwxpbxpJeCVFRGJFpFrS+i12onIvKKXDL60lq34cM2nvL10BePGPsSvv/4a6kgmgEaPuo9rrhvJiSeeGOooxuSpcWocaz/fwjsfrOK+UWO4ethAfjhwINSxjmnt6pWccEIlGiYlhyxDVBmhXo2T6PHQO1z57w8ZN7glVSqVy3u8RtWKJNasyjvrd4csowlOy0o0cFRlRUTC6kykuPh4srK+yrudnbWLuPj4fMvE+y2Tk5PDgQP7ia1WLd8yZzZMpPKJJ7Lx8/UByXlqXDy7s3bl3d6dnUWNuOIN+qoRF8/u7CPP/To7ixpx8cd5xh8XH5/Arq+OvFdW1i7iEhKOXmZX/vKsVqA8AykuPp6sXUcyZmdlERefcNQy2f4Z97t1vmbVSu658zaaJZ3O0089xrixD/GfpwMzPiASyrKwjPFFZNwf5IyRkvPUuIR82+Xu7Kyj9kX+y+Tk5PDDgf3ExlajQoUKefukps1TqFO3HlsyNwcoZzzZfvvM3dlZnFpgX3RqfDzZWUdyHjhwgJjYI2U5e8YUegSoVQVg995fiI+tlHc7PuYEdu/9Jd8y2Xt/YeHHWeT4lJ3f/cSWb36gfo2T8h7v0bI2C9a6x8OZBOlfqASjsvIQUF9EPhGRVSKyVETmABtEpI6I5B3VReRmEbnH+/10EXlLRNaJyFoRqe//oiLSQkQ+Lnj/H5WS2oItmZns2L6NQ4cOMWPaFLqkd8u3TOeu3Xj9lYkAzJ45nfPatkdE2LF9W96A2p07d7B50yZqn1anJGIdpUnzNLZvy+SrHds5dOgQ82ZN5YLO6cV67nntL2TpkrfYv28v+/ftZemStziv/YUByZma1oItmZvZvs2V57Qpk0nP6J5vmfSMbrw68SUAZs6YRtt2HQI63qeg5qkt2LrlyDqfOW0yXbpm5Fumc9cMJr3q1vmcmdM511vn899cwicbMvlkQyZXXzuCG2++lSuuDsyZF5FQloVl7FogY9cQZ4yUnM1T09i61W+7nD6FTgW2y05dM5j8utsu586azjlt2yEifPfdt/h8blzF9m1b2bolk9Pq1A1IzmYpaWzbksnOHS7n7BlT6Nglf86OnTOY6uWcP3sGbc5rl1eWhw8fZt6s6fTo3Tcg+QA+3vY99WqcRO3qlSlXtgy9WtVm4cf5u6oWrN1Fm4anABB7Ynnq1ziJ7f/7Me/xi86qzYwVOwKW0RRPMFo3bgUaqWozEWkHzPdubxOROsd53qvAQ6o6U0Qq4ipWtQBE5GzgcaCHqpZIR2JUVBSjH51A7+5d8fl8XDZwMIlJyYy6726apaTRNaMbAwYP5ephg0hpdCYxMTE89/JrAHy4fBkTHhlNVFQ5ypQpw9jxT1CtevWSiFVoznseHMegi7tx+LCPvv0HcUbDJMY9dB+Nm6VwQecM1n28mmsG9WP//n28vXgBE0bfz6IP1hIdE8vwm26j54XnAHD9328nOiY2YDkfGf84PTI64/P5GDh4CElJyfzr3rtISUkjvVt3Bg0Zxt+GDKRxYgNiYmN5aeLrec9PPKMuPxw4wKFDh5g7dzZz5i8isYQHskZFRfHwIxPo2zMdn8/HpQMG0zApmQf/dQ/NUlLpkt6NywcN5Zq/DSatSUOiY2L474uvlmiG4uaMhLJ8ZPzj9PQyDjhOxiZexhf9Mib5ZZw3dzazA5AxUnJGRUXx0Jjx9OuVjs93mEsHDKJhYjIP3e+2y85du3HZwCFcd+VgWjZNJCYmhmdecNMQfLhsKaMfuJeocm5fNGb8E8TEBu4zfv/o8VzaO4PDPh/9LhvMmYlJjBl1L02bpdCxazcuGTCEEVcPoU1KItExsTz13MS8569YvpS4hJqcVqdeQPIB+A4rt76yhqk3t6VMmTK8tnQrm7IPcGuvRnyy7XsWfpLNO599TfvkU1n2QBd8h5V7pnzC3p8OAVCremUSYiuxbNP/ApaxpJT2eVYk0CPIvQrJPFVt5FVW7lbV9gUf827fDJwIPAJsVNWaBV6rHfAc8AvQUVULHR0qIlcCVwLUrFU79bNNW0v6zypxe3/6LdQRilSjSoVQRyiWX38L/xH7FcuVDXWEYgnvhu/I8vPBwExnUNJ+C/PuDoAmN8woeqEwsOel/mtUNS3Q79OoaYpOXxS4wdT+GsZVDsrfVFAozgb6ye/3nAIZinNO4G7gV6D5sRZQ1WdVNU1V06pXP/mPpTTGGGNMWAhGZeUH4KRjPPYNcIqIVBORCkAGgKr+AOwSkZ4AIlJBRHJHSe0D0oEHvZYWY4wx5i+tlJ+5HPjKiqruAZZ5A2nHFHjsN+A+YCXwJvCF38MDgBEi8imwHDjV73nf4Co2T4pIq8D+BcYYY4wJpaCcPqyqlx7nsceAxwq5fzPQocDdW4El3uM7gdCdnG+MMcaEi1I+wNZmsDXGGGNMWAuridmMMcYY8/u48SSlu2nFWlaMMcYYE9asZcUYY4yJZFL6J4WzlhVjjDHGhDVrWTHGGGMiXClvWLGWFWOMMcaEN2tZMcYYYyJdKW9asZYVY4wxxoQ1a1kxxhhjIprYPCvGGGOMMaFklRVjjDHGlBgR6Swim0QkU0RuLeTx80RkrYjkiEif4rymVVaMMcaYCCcSnJ+ic0hZ4EmgC5AE9BeRpAKL7QQGA68V9++zMSvGGGOMKSktgUxV3QogIpOAHsCG3AVUdbv32OHivqhVVowxxpgIJgT1zOXqIrLa7/azqvqs3+0E4Cu/27uAVn/2TUt9ZeWTj9d8F1MpakcJv2x14LsSfs2SFgkZwXKWpEjICJGRMxIyQmTkjISMEJicp5Xw64WD71Q1LdhvWuorK6p6ckm/poisDsXK+j0iISNYzpIUCRkhMnJGQkaIjJyRkBEiJ+cxhc+Zy1lALb/bNb37/hQbYGuMMcaYkrIKaCAidUWkPHAJMOfPvqhVVowxxpgIJ0H6VxRVzQGGA4uAjcAUVf1cRO4Tke4AItJCRHYBfYFnROTzol631HcDBcizRS8ScpGQESxnSYqEjBAZOSMhI0RGzkjICJGTM+yp6gJgQYH77vL7fRWue6jYRFVLJp0xxhhjgq5Js1Sd987yoLzXadUqrgnF2B7rBjLGGGNMWLNuIGOMMSbChc/JQIFhLSvGGGOMCWvWsvIXISKiYTxASUTKqGqxp142xxfu6zuSWFmasFfM6/ZEMmtZKeVE5CyAcN7ZikhD4G4ROTHUWQrjXZgr9/eTQpmlOPwPriLSLNR5iiISnrtZv1xR3u2yx1nclAIFt0URsWNkmLAV8SflbtwiUkVEqoY6jz8RGQzcKyKxoc5yLN7OoCZwMnCjiFQKcaR8vAPUBSLSTkRGAINEJKxbJP0qKsOAUeG2XeYSkbrg8oZbhSW3wiciLYGNIlJDVX3hXGHxL8NwO8j67SerikhMqPMcR94XJm//eUvoohh/YbVBRyJvh9YDeB2YJCIjvVn7QkpE2gAXAzer6vfhuJPN7fpR1beAdUAyMDzMKiwCVAHGACOABaqaE24Hg4K89d8HGKGq+8Nl/fsdtBoAC0TkDgi/CouXpyPQzbtriYic6lVYwm7dF2hNGwjcKiL9w+WLileePXEThS0SkTvDJVsuETkNeF1EWnh3lQO2hDDS7yRB+gmNsPvQRRoRaQX8AxgIfA5cSgjHAolIGe+bfxpwCtBbRMp7O9mwORgA5I5REZGbgH64T8J5wP+JSOVQZsvlzca4EjgELAcaisgJ4Ta+psC36grAubgWqx7egSws1r9f5f5BXLleLCL3+D0W8owAInIm8Bzu4NoamAes9FpYDodbhcWvonIFcBWubP/LkcpWSHnleR1wLTAYaOH9Hk5+AZYCt4tIE0CBsO/2/asIqw9chKoKPANcCLQBLlHVn71aeiicrKo5qjoBmIC7kuhFIlI2XA4GBQ6sNYCuQDdV7Qc8hqtkXRsOLSzewWkH0AF4A8gAenqPJYnIqaHM5+Xw/1ZdGzhBVR8CnsRdUKw7hEdlQESigX/its0huEr+hSJyG4TV2KqfgDdV9QPcVWb/AXwCvCsi1b0KS8g/S7m8LymxuEpAPyAOV7l+JUR5aojIYHFqAg/hDv4bVXUDrpVykIj0C0U+f7kVT1X9H66C+h5wL26f3lBE0sRND9/F21+FHcENsA3GT6iEdd97OPI/MHgOAkNxZdlPVXeKu/7BtSJyCbA/WDtgEbkO9016HbBeVV8SkXK4b4YVRWSiqvqCkeU4GfPO+hGRLsA3QDxwDu5b7DtAO1wLFSIyNlQHMBEZjivPT4BPVXWiiJwAnO21DiQCHUORzZ9fReUGIB3wichG4C7gSuBcEamgqlPCoDLgA74DtnoH/PW4A+rfReQnVX0sFKH8xqiUV9VDwH4gVUSGqurz3mKTgKuBiSLSS1V/DUXWXP77Iu8z9b2IZAL/wR2/Onp/0/8Bq1T13SDGOx1XWYpV1V0iMhO4HOgsIu+p6nYR+Q9wQhAzHcUrw9z9UX9gAzAFOIyrULXCbQvNgWrAgBBF/cuzysrv5H34LwSSgJ2qOlNEPgZOAxJEpDnwAHCrqu4LVi5xg8EuBS4DRgMdvVaB0V4lJgmoDBwIVqbC+O0Y2uK+YZ+DGw/SQ0R+VNVlIvIlrvn15RBWVAYD/clfnnFeeZ6Nq1D9S1W/CUW+grzxAF2BTri8qar6g3dAGA40F5EFqvpjEDMJ5H1m4oE9XqYVwHQRaa+qv4jIV7hWqwtF5C3vm3dQeRk7AVeLyDe4b9fdcV0/ccC3uIrfP4DehEGrtF8l9XogQVVvBX7EHVRHeH9TH9w2PCvI2ZaJO3NujIjsUNUHvS9OPYHW3jYwHNe6FjJ+ZXi1l6e7qmaLyOu4L6JdgddU9f4QxiyWsGnmCxCrrBST3zevpsBTwEzch66Vqt4kInfimrRjgX+o6huFtMIEKlsa8AOui+Iy3IDQEcDDInJYVceKSFVVDVlFRUTOAL7xBnt2A14CrvLKdBWucvKCiCzBNb92CVVFoIjyjFLVUbhvjeFkH/AI8H9AE460+NTCVQZPCmZFBfIdCDoDdwObxQ30vR3XJbBWRJ7Dle0AXFmHpBIgIq2BccAduC6gZ3GVvhTcQSwed2A9BWgJVAR+DkVWf+LGe/XGjVMB+DdQA1fpuge37V6uql8GIUte5dS76xdgDpAhIjeo6ngR+RU3dqUqcKWqvi0hnGPJyxyP+6J3kapu9fbb34rIXFzF7wERGQocDLexan8lVlkpJu+g2ha3UV+vqgvFndFwj4g85H2rQUQqq+pPuc8JdC4RuQZ3YPoHbn1egNs5fSci2UA7EXleVb8PdJZj5BOgPG4HdQ+Aqs4Vkc3AjcBUVV3vdVssxfW1P6iq20KUt6jyPEvcmIXvQpHPy5ivEpx7kMD1t69V1Y7e/X8DugADg7n+ReRkXIVzFhCDG4c0DNfl1xN4DegMfIk746ILrrKaRhBb/sSN4/KJO5VWgLmqOtN7rAXwLrBMVW/z7jsXeBroFcrPk18lsBquu6UXcKJ3QD0PVzndC0Tjxtv8Lwi5KqjqQe/39rjB3btUdYGI/AJcIiIjVPUxcScAnAuU8d9fhojgKvo/AtnefeVwA+rBjUdUVf0lBNl+l/AZQRUYIW/KjDAn4wavNfdub8F9YzxdRP7r3Re0fmxxY2OuAW5Q1UzcwbUKcIbXjXEYGByqHaunjLcTuxloICJPiEg5VW0FlBeR2QCq6lPVj1V1QQgrKsUpz6GhrKhAvhaL4SLyCK4V4DPcoNX6ItJJ3IDVEcBdwTwYeBWnjsD5uFOnY4G3VXUpkKmqo3EDVbur6iRVnQhUAsYDQ1R1ZxAy1hGROl5FpSMwEXfafLvcZVT1W+AtXNdp7nw7PwGdVHV9oDMWpkBFZSjQF0gAXsWV3ym4LwY3qGq2qm4IUkUlBjfwOFFEkoEXcQN9R4rIbd5YmddxY4D+rqovAVtxldSQHWJF5Bzgau/zsQ+XG1U95H3enwB+CvH+03isZeU4/Lp+Tgb2qeo0rxnzMRH52Gtd2QLchtvhosEdwBoPTFLVHV4FYLeIzAeuB2oD14XBgTW3PKoDe4DGwE0i8qiqponI+yLyjqp2CF3KPGFdnt64j33qzja7DtdKcSWuS/KfqnqjiBzGfWuNBS5W1S+CmdE7mL4q7iyps3DN6D1EZKWqvuAttgfwP4vqf0BPVf060PlEpA6wEOgjIknA34A7VfVjEekgIh/hBszH4VrVXvP+Lh+wNtD5jsevotIa6KuqXYCnxQ32XqGq33iVr2tE5MRgdfup6l7vczIZV7ZDVPUdEUnBzUx9q6o+5FX4vvWeM0pEYoLZNem3Py+DqyQlAU3FnZE0DHhZRN4HPsKd/TdEQzyI+veQUj5qxSorx+C3YffAHRD2ishUVZ3tbeyPicjNqjoH2ByimDuAniIyXVU3efdtwh0MJoey6VLcINTaqjpJ3ADAK4A3cU2tFwEqIuNU9TwRWSwiNVV1V6jyesK5PBOAW4H1IvI87iyK/rhxUruA27yDwWOq+luIxwF0wg1OLYvripgC3CduoOoX3mM35C6v7tTwYOQSXFfTAtyYmeXA90BT4GNV7S8iE4CbgDrALaq6MhjZisPL3xjXkrY+twtFVWd7j4/EVbQuD1YlQNwYrhzcafIn47bJ3Aryp7iW5zFe5f9f3nPKei2pe4ORMZdf12lNdWdtvoIbRNsGKKuqfUWkF27beMZrXTVhwiorBciRWVXV63u9CzfQcjxufEqcqj4tbpbaJ0XkQ6/JOBSWAWcDg0VkGe7AMBLoHwZ9rDHAg16zcH1cv3p94EzcTq0zEOc1C4f89F9POJdnNrAGaIQbjNoMN+HXbqCHull1h+NOW34Gt8MNOhE5BbgTN3hyg9cCVAN4wcteCdcKtCTY2bzP9Fu45v2LcANm6wONRKStqr6nqiPFnbVSUd2ZSyG9iKH/+3v/fyoio3EDalNEZJm6U8DL4lovL1HVjcHK5213PTmyn/wSuFlEPlLVz0XkM9yU9eL3nKBOn1Cg+yweWCoiV6s7CWIaUIEjl9F4zat8RZ7S3bBiY1b8ed9eb5EjE/+cjptXIRX3Teu/wFARuVFVp+FODw1VRQV1Z/c8hWsRuBY3x8awcPhGoKrzcS1SvdxN3YI7HfQt3DeuJ3CVluohC1lAuJan3862DK7pui9uvFQy8L53wBiMy/xWbmU7RHF/w30Jyl2vz+K6fFrj5tC5U1Xnea0EoXAA+BjX6lMGN5biF6CTiHQAUNXfVPUH7/eQzkvjd5C9TETuEZFrgdm4loy7cHP+lPNaKu4MZkXFy9UMN3D+UnXjZJ7Cle8zItLEy7VWVdcEM5dfPv+Kyv/htsPbcdfM6ui1TD2LG+vTBL9rA5nwYi0r+R3GnVd/goiMVtX/iJtF9e+4b9dbxc2x0kpEaqnqVyFNC6jqbly/9fPe7UNFPCVoVPVNcdd+eVFE+qnqZNxF4U4H5qnq5SGOeJRwLE+vReAy3NiZIbhxFj7gZeAGEWmM29H2UdVQdUkCeeMXpuDOQvte3Zle03EDlz8o0EoQinyHgS7iZph+Gzcn0pO4rp8u4saiBbV7oihe69QAXMXqTNzkiem4CtdY3Fl1H4Yo3kHcgOm2InIxbrzUbrzuPxFpkVvxCwW/ikpX3ADqSV4XkAKPisgtuHL8BXhEgzg3Vkkr5Q0rVlnJ5fW97vb6LP+LGwT6b9xsm+WB60XkVdyH8P/CoaLiLxwOqoXxxvgMwI3xScTt2OoCAT/r488Iw/I8E9dE/Ym4uTWuxXWrPIObsyYnjHa0U3Atko+Km0OnD25wclAH+x6PukHUfXEDaMvh5lg5KRwqKrld0X6tAo1xk7yt9B6/HRitqn8Td0XtrBDG/QpYDQzCVZym4SZ6fBF39ldIKiped+TJXlfUYNx4r0z1zjZT1ddE5BBuYspfcGdQ7Q5FVlM81g3k8ZrS2+LOYJiOmydiIK5ycjdu3oB/A+PCadBdJFDVebhvrnfiWgZ6hLprJQKtBdqISLKqHlLV8bhKXw3gUBhVVFA3UPph3MFrD278yluhTXU0Vf0YNwX8bbgBlgGfOK049MjA6Abe+Jma+J1WjbuoYu71bJ7UIJzufSyq+qOqPgG0U9UZuFO9bwAklLlwk86NE5GXcBWpB3Bj5EbkLuB15XfGXZfs09DENMX1l29Z8Tvr52zchE9rcM2YPuBiXNfQRHUjxeO81peQDrqLRKo6yxsTsENVt4c6TwRagpu74lIReQd3NtB+YLyGcGbiY/G+US/2fsKWqq7xxlaErKsilxx9Bt1I3Gnp64ARIvKduusUNQbqiLsoZNCuPVYEn4ik4sai3aaqb4cyjKpuFpFPcePmblF3Xa/vgKu83ffj3nL7Q5mzpIT6IoPB8JevrHgVlZa4mvcQVV3hjanYhTvj4iJcjfyW3GbCMNk5RBxVfS/UGSKVqu4TkSdx2+MdQA5wk6pmH/+ZphiCehmC48g9g64hrjWlE26CvSq4gen3i7v2WHvcRVPDqTXNJyJf4M5G2hYmX+iexlX0bvLGT00Wkf8BT3kVv9dDnM/8Dn/5yoqnKm6a6g7ACtzZINtwZzH8E6gRBh888xfnVUyeEJEXcM3s4XKQjWjh8tlW1fneOIpHcZO8bfEGevf2Fvkad3bVPaq6J1Q5j0XdTLDbvN9DXqZeV3OmiOzDXd9nH+6aTodw+/lSpbRPCmdjVnBnreC+sQ4Vkf6q+huuib0TbrrlkM5caYw/73RLq6iUQt6+6J+4WX8vUXepikm4mV8PA9+HY0UlnKnqXNwFPsfixiddrSG6pIf546xlxeOdtXIYN1V4b9yO4Z5wamo1xpR+3r4oB9clhDeG5UWgcjiMrYlE6i6Nstb9Grq5sQKqdDesWGXFn7qrAV8O3Ae8qqpzcievCodmTWPMX4PXJXQYeFZEcrwzV6yi8idoEC7qaALHKisFeBWUX4HnRWSLdzqeMcYElbrp4IfiZis25rhKecOKVVYKo6qLRWQItpMwxoSQN4bFmL88q6wcg+0kjDHGRIrSPs+KnQ1kjDHGmLBmLSvGGGNMRBObZ8UYY4wxJpSssmJMGBERn4h8IiLrRWSqiFT6E6/1ooj08X7/r4gkHWfZdt61aX7ve2wXkerFvb/AMr9rYjsRuUdEbv69GY0p7YQj1wcK9E+oWGXFmPDyi6o2U9VGuGnBr/Z/UET+UNetqv5NVTccZ5F2wO+urBhjTDBYZcWY8LUUON1r9VgqInOADSJSVkTGiMgqEflURK4CdwVxEXlCRDaJyFvAKbkvJCJLRCTN+72ziKwVkXUi8raI1MFVim70WnXOFZGTRWS69x6rRKSN99xqIrJYRD4Xkf9SjOkdRGSWiKzxnnNlgcfGefe/LSIne/fVF5GF3nOWehf2M8b8hdkAW2PCkNeC0gVY6N2VAjTyrmh7JbBfVVuISAVgmYgsBpoDZwJJQA1gA/B8gdc9GfgPcJ73WrGq+r2IPA38qKpjveVeA8ap6gciUhtYBCQCdwMfqOp9IpIODCvGnzPUe48TgFUiMt27vk1lYLWq3igid3mvPRx3sb6rVXWziLQCnsJdZNQY8xdllRVjwssJIvKJ9/tS4Dlc98xKv4uvdQSa5I5HwV01vAHuyuGvq6oPyBaRdwp5/bOA93NfS1W/P0aOC4AkOdJJXUVETvTe4yLvufNFZG8x/qYRItLL+72Wl3UP7vpbk737XwFmeO9xNjDV770rFOM9jDGlmFVWjAkvv6hqM/87vIP2T/53Ader6qICy3UtwRxlgLNU9ddCshSbiLTDVXxaq+rPIrIEqHiMxdV7330Fy8AYc3w2KZwxJtwsAq4RkXIAInKGiFQG3gf6eWNa4oD2hTx3BXCeiNT1nhvr3f8DcJLfcouB63NviEhu5eF94FLvvi5ATBFZqwJ7vYpKQ1zLTq4yQG7r0KW47qUDwDYR6eu9h4hI0yLewxhTylllxZjI81/ceJS1IrIeeAbXSjoT2Ow99jLwYcEnquq3wJW4Lpd1HOmGmQv0yh1gC4wA0rwBvBs4clbSvbjKzue47qCdRWRdCESJyEbgIVxlKddPQEvvb+iAu9o5wGXAMC/f50CPYpSJMX9pEqR/Ifv7VDVkb26MMcaYP6d5SpouWbYyKO8VXansGlVNC8qb+bExK8YYY0wkC/GEbcFg3UDGGGOMCWvWsmKMMcZEMKEYszNGOGtZMcYYY0xYs5YVY4wxJtKV8qYVa1kxxhhjTFizlhVjjDEmwoVyDpRgsJYVY4wxxoQ1q6wYY4wxJqxZN5AxxhgT4WxSOGOMMcaYELKWFWOMMSbClfKGFWtZMcYYY0x4s5YVY4wxJtKV8qYVa1kxxhhjTFizlhVjjDEmwtmkcMYYY4wxxSQinUVkk4hkisithTxeQUQme49/JCJ1inpNq6wYY4wxEUxw86wE46fILCJlgSeBLkAS0F9EkgosNgzYq6qnA+OAh4t6XausGGOMMaaktAQyVXWrqh4CJgE9CizTA3jJ+30acL7I8atCNmbFGGOMiWBr165ZdEI5qR6kt6soIqv9bj+rqs/63U4AvvK7vQtoVeA18pZR1RwR2Q9UA7471ptaZcUYY4yJYKraOdQZAs26gYwxxhhTUrKAWn63a3r3FbqMiEQBVYE9x3tRq6wYY4wxpqSsAhqISF0RKQ9cAswpsMwcYJD3ex/gHVXV472odQMZY4wxpkR4Y1CGA4uAssDzqvq5iNwHrFbVOcBzwEQRyQS+x1VojkuKqMwYY4wxxoSUdQMZY4wxJqxZZcUYY4wxYc0qK8YYY4wJa1ZZMcYYY0xYs8qKMcYYY8KaVVaMMcYYE9assmKMMcaYsPb/xUwIiG5UYUsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YKu9rxuTrrBy",
        "colab_type": "text"
      },
      "source": [
        "Play with your own custom convolutional neural network and compare!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8FUBp6tMdlrH",
        "colab_type": "code",
        "colab": {},
        "cellView": "both"
      },
      "source": [
        "#@title 2 Layer CNN\n",
        "class MyConvolutionalNetwork(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(MyConvolutionalNetwork, self).__init__()\n",
        "        \n",
        "        self.conv1 = nn.Conv2d(3, 18, kernel_size=3, stride=1, padding=1)\n",
        "        self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)\n",
        "\n",
        "        #### START CODE: ADD NEW LAYERS ####\n",
        "        # (do not forget to update `flattened_size`:\n",
        "        # the input size of the first fully connected layer self.fc1)\n",
        "        self.conv2 = nn.Conv2d(18, 18, kernel_size=3, stride=1, padding=1)\n",
        "        self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)\n",
        "        \n",
        "        # Size of the output of the last convolution:\n",
        "        self.flattened_size = 18 * 8 * 8\n",
        "        ### END CODE ###\n",
        "        \n",
        "        self.fc1 = nn.Linear(self.flattened_size, 64)\n",
        "        self.fc2 = nn.Linear(64, 10)\n",
        "\n",
        "    def forward(self, x):\n",
        "        \"\"\"\n",
        "        Forward pass,\n",
        "        x shape is (batch_size, 3, 32, 32)\n",
        "        (color channel first)\n",
        "        in the comments, we omit the batch_size in the shape\n",
        "        \"\"\"\n",
        "        # shape : 3x32x32 -> 18x32x32\n",
        "        x = F.relu(self.conv1(x))\n",
        "        # 18x32x32 -> 18x16x16\n",
        "        x = self.pool(x)\n",
        "        \n",
        "        #### START CODE: USE YOUR NEW LAYERS HERE ####\n",
        "        x =  F.relu(self.conv2(x))\n",
        "        # 18x16x16 -> 18x8x8\n",
        "        x = self.pool(x)\n",
        "        \n",
        "        #### END CODE ####\n",
        "        \n",
        "        # Check the output size\n",
        "        output_size = np.prod(x.size()[1:])\n",
        "        assert output_size == self.flattened_size,\\\n",
        "                \"self.flattened_size is invalid {} != {}\".format(output_size, self.flattened_size)\n",
        "        \n",
        "        # 18x16x16 -> 4608\n",
        "        x = x.view(-1, self.flattened_size)\n",
        "        # 4608 -> 64\n",
        "        x = F.relu(self.fc1(x))\n",
        "        # 64 -> 10\n",
        "        x = self.fc2(x)\n",
        "        return x"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q3BWTWrsdl0O",
        "colab_type": "code",
        "cellView": "both",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "7d2074a0-75f0-4df1-d14a-0b90868f79fe"
      },
      "source": [
        "#@title Train 2 layer CNN\n",
        "net = MyConvolutionalNetwork()\n",
        "train_history, val_history = train(net, batch_size=32, n_epochs=10, learning_rate=0.001)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "===== HYPERPARAMETERS =====\n",
            "batch_size= 32\n",
            "n_epochs= 10\n",
            "learning_rate= 0.001\n",
            "==============================\n",
            "Epoch 1, 10% \t train_loss: 2.15 took: 0.70s\n",
            "Epoch 1, 20% \t train_loss: 1.89 took: 0.61s\n",
            "Epoch 1, 30% \t train_loss: 1.79 took: 0.61s\n",
            "Epoch 1, 40% \t train_loss: 1.66 took: 0.62s\n",
            "Epoch 1, 50% \t train_loss: 1.59 took: 0.62s\n",
            "Epoch 1, 60% \t train_loss: 1.56 took: 0.65s\n",
            "Epoch 1, 70% \t train_loss: 1.56 took: 0.60s\n",
            "Epoch 1, 80% \t train_loss: 1.53 took: 0.62s\n",
            "Epoch 1, 90% \t train_loss: 1.48 took: 0.60s\n",
            "Validation loss = 1.37\n",
            "Epoch 2, 10% \t train_loss: 1.39 took: 0.69s\n",
            "Epoch 2, 20% \t train_loss: 1.39 took: 0.60s\n",
            "Epoch 2, 30% \t train_loss: 1.40 took: 0.62s\n",
            "Epoch 2, 40% \t train_loss: 1.36 took: 0.64s\n",
            "Epoch 2, 50% \t train_loss: 1.35 took: 0.62s\n",
            "Epoch 2, 60% \t train_loss: 1.36 took: 0.61s\n",
            "Epoch 2, 70% \t train_loss: 1.29 took: 0.62s\n",
            "Epoch 2, 80% \t train_loss: 1.30 took: 0.60s\n",
            "Epoch 2, 90% \t train_loss: 1.30 took: 0.62s\n",
            "Validation loss = 1.21\n",
            "Epoch 3, 10% \t train_loss: 1.22 took: 0.69s\n",
            "Epoch 3, 20% \t train_loss: 1.17 took: 0.61s\n",
            "Epoch 3, 30% \t train_loss: 1.18 took: 0.61s\n",
            "Epoch 3, 40% \t train_loss: 1.23 took: 0.61s\n",
            "Epoch 3, 50% \t train_loss: 1.21 took: 0.63s\n",
            "Epoch 3, 60% \t train_loss: 1.16 took: 0.61s\n",
            "Epoch 3, 70% \t train_loss: 1.19 took: 0.65s\n",
            "Epoch 3, 80% \t train_loss: 1.20 took: 0.62s\n",
            "Epoch 3, 90% \t train_loss: 1.22 took: 0.63s\n",
            "Validation loss = 1.15\n",
            "Epoch 4, 10% \t train_loss: 1.09 took: 0.70s\n",
            "Epoch 4, 20% \t train_loss: 1.07 took: 0.61s\n",
            "Epoch 4, 30% \t train_loss: 1.07 took: 0.61s\n",
            "Epoch 4, 40% \t train_loss: 1.08 took: 0.60s\n",
            "Epoch 4, 50% \t train_loss: 1.09 took: 0.64s\n",
            "Epoch 4, 60% \t train_loss: 1.09 took: 0.60s\n",
            "Epoch 4, 70% \t train_loss: 1.10 took: 0.62s\n",
            "Epoch 4, 80% \t train_loss: 1.09 took: 0.62s\n",
            "Epoch 4, 90% \t train_loss: 1.09 took: 0.62s\n",
            "Validation loss = 1.08\n",
            "Epoch 5, 10% \t train_loss: 1.02 took: 0.70s\n",
            "Epoch 5, 20% \t train_loss: 0.99 took: 0.59s\n",
            "Epoch 5, 30% \t train_loss: 0.98 took: 0.59s\n",
            "Epoch 5, 40% \t train_loss: 1.01 took: 0.61s\n",
            "Epoch 5, 50% \t train_loss: 1.00 took: 0.62s\n",
            "Epoch 5, 60% \t train_loss: 1.02 took: 0.62s\n",
            "Epoch 5, 70% \t train_loss: 1.01 took: 0.61s\n",
            "Epoch 5, 80% \t train_loss: 1.00 took: 0.61s\n",
            "Epoch 5, 90% \t train_loss: 0.99 took: 0.61s\n",
            "Validation loss = 1.08\n",
            "Epoch 6, 10% \t train_loss: 0.87 took: 0.70s\n",
            "Epoch 6, 20% \t train_loss: 0.96 took: 0.60s\n",
            "Epoch 6, 30% \t train_loss: 0.96 took: 0.62s\n",
            "Epoch 6, 40% \t train_loss: 0.92 took: 0.59s\n",
            "Epoch 6, 50% \t train_loss: 0.98 took: 0.63s\n",
            "Epoch 6, 60% \t train_loss: 0.93 took: 0.60s\n",
            "Epoch 6, 70% \t train_loss: 0.93 took: 0.62s\n",
            "Epoch 6, 80% \t train_loss: 0.94 took: 0.62s\n",
            "Epoch 6, 90% \t train_loss: 0.96 took: 0.59s\n",
            "Validation loss = 1.07\n",
            "Epoch 7, 10% \t train_loss: 0.84 took: 0.70s\n",
            "Epoch 7, 20% \t train_loss: 0.89 took: 0.61s\n",
            "Epoch 7, 30% \t train_loss: 0.87 took: 0.64s\n",
            "Epoch 7, 40% \t train_loss: 0.87 took: 0.61s\n",
            "Epoch 7, 50% \t train_loss: 0.90 took: 0.61s\n",
            "Epoch 7, 60% \t train_loss: 0.85 took: 0.63s\n",
            "Epoch 7, 70% \t train_loss: 0.89 took: 0.63s\n",
            "Epoch 7, 80% \t train_loss: 0.91 took: 0.63s\n",
            "Epoch 7, 90% \t train_loss: 0.89 took: 0.61s\n",
            "Validation loss = 1.06\n",
            "Epoch 8, 10% \t train_loss: 0.84 took: 0.71s\n",
            "Epoch 8, 20% \t train_loss: 0.81 took: 0.61s\n",
            "Epoch 8, 30% \t train_loss: 0.78 took: 0.61s\n",
            "Epoch 8, 40% \t train_loss: 0.83 took: 0.63s\n",
            "Epoch 8, 50% \t train_loss: 0.84 took: 0.60s\n",
            "Epoch 8, 60% \t train_loss: 0.86 took: 0.63s\n",
            "Epoch 8, 70% \t train_loss: 0.81 took: 0.63s\n",
            "Epoch 8, 80% \t train_loss: 0.85 took: 0.61s\n",
            "Epoch 8, 90% \t train_loss: 0.82 took: 0.65s\n",
            "Validation loss = 1.02\n",
            "Epoch 9, 10% \t train_loss: 0.77 took: 0.67s\n",
            "Epoch 9, 20% \t train_loss: 0.75 took: 0.59s\n",
            "Epoch 9, 30% \t train_loss: 0.78 took: 0.61s\n",
            "Epoch 9, 40% \t train_loss: 0.77 took: 0.62s\n",
            "Epoch 9, 50% \t train_loss: 0.76 took: 0.59s\n",
            "Epoch 9, 60% \t train_loss: 0.79 took: 0.60s\n",
            "Epoch 9, 70% \t train_loss: 0.81 took: 0.60s\n",
            "Epoch 9, 80% \t train_loss: 0.76 took: 0.59s\n",
            "Epoch 9, 90% \t train_loss: 0.81 took: 0.63s\n",
            "Validation loss = 1.05\n",
            "Epoch 10, 10% \t train_loss: 0.72 took: 0.70s\n",
            "Epoch 10, 20% \t train_loss: 0.72 took: 0.62s\n",
            "Epoch 10, 30% \t train_loss: 0.71 took: 0.62s\n",
            "Epoch 10, 40% \t train_loss: 0.74 took: 0.61s\n",
            "Epoch 10, 50% \t train_loss: 0.71 took: 0.60s\n",
            "Epoch 10, 60% \t train_loss: 0.74 took: 0.61s\n",
            "Epoch 10, 70% \t train_loss: 0.72 took: 0.62s\n",
            "Epoch 10, 80% \t train_loss: 0.76 took: 0.60s\n",
            "Epoch 10, 90% \t train_loss: 0.75 took: 0.59s\n",
            "Validation loss = 1.06\n",
            "Training Finished, took 72.83s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2VK5tvXPr7jW",
        "colab_type": "code",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "outputId": "cf5b43c3-6e9e-4643-8bec-2021120d2ed2"
      },
      "source": [
        "#@title Plot Losses\n",
        "plot_losses(train_history, val_history)"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ae21wbvfr8Mj",
        "colab_type": "code",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "c50be020-8002-4623-fed9-84c4029e60ed"
      },
      "source": [
        "#@title Compute Accuracy\n",
        "compute_accuracy(net)"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy of the network on the 20000 train images: 75.15 %\n",
            "Accuracy of the network on the 5000 validation images: 64.04 %\n",
            "Accuracy of the network on the 5000 test images: 64.96 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3U5MVMYjr_Dn",
        "colab_type": "code",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "db673e28-cb89-4278-e278-bf406bad74c6"
      },
      "source": [
        "#@title Compute Accuracy Per Class\n",
        "confusion_matrix = accuracy_per_class(net)"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Class      Accuracy (%)\n",
            "plane        75.20   \n",
            "car          80.79   \n",
            "bird         50.00   \n",
            "cat          48.69   \n",
            "deer         57.40   \n",
            "dog          46.52   \n",
            "frog         79.84   \n",
            "horse        69.29   \n",
            "ship         67.86   \n",
            "truck        74.07   \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "srbZ0YZNsDcv",
        "colab_type": "code",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "outputId": "404cbb13-931b-4a9e-b1fe-e4c51f158e7c"
      },
      "source": [
        "#@title Plot Confusion Matrix \n",
        "plot_confusion_matrix(confusion_matrix, classes, normalize=True,\n",
        "                      title='Normalized confusion matrix')"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oGbdaxzE025f",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5df675ae-c26e-4431-e45d-4b15c8f07eb1"
      },
      "source": [
        "out_size = get_output_size(in_size=18, kernel_size=3, stride=1, padding=1)\n",
        "print(out_size)"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "18\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2W9_XpZSwT0N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class CustomConvolutionalNetwork(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(CustomConvolutionalNetwork, self).__init__()\n",
        "        \n",
        "        self.conv1 = nn.Conv2d(3, 18, kernel_size=3, stride=1, padding=1)\n",
        "        self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)\n",
        "\n",
        "        #### START CODE: ADD NEW LAYERS ####\n",
        "        # (do not forget to update `flattened_size`:\n",
        "        # the input size of the first fully connected layer self.fc1)\n",
        "        # self.conv2 = ...\n",
        "        \n",
        "        # Size of the output of the last convolution:\n",
        "        self.flattened_size = 18 * 16 * 16\n",
        "        ### END CODE ###\n",
        "        \n",
        "        self.fc1 = nn.Linear(self.flattened_size, 64)\n",
        "        self.fc2 = nn.Linear(64, 10)\n",
        "\n",
        "    def forward(self, x):\n",
        "        \"\"\"\n",
        "        Forward pass,\n",
        "        x shape is (batch_size, 3, 32, 32)\n",
        "        (color channel first)\n",
        "        in the comments, we omit the batch_size in the shape\n",
        "        \"\"\"\n",
        "        # shape : 3x32x32 -> 18x32x32\n",
        "        x = F.relu(self.conv1(x))\n",
        "        # 18x32x32 -> 18x16x16\n",
        "        x = self.pool(x)\n",
        "        \n",
        "        #### START CODE: USE YOUR NEW LAYERS HERE ####\n",
        "        # x = ...\n",
        "        \n",
        "        #### END CODE ####\n",
        "        \n",
        "        # Check the output size\n",
        "        output_size = np.prod(x.size()[1:])\n",
        "        assert output_size == self.flattened_size,\\\n",
        "                \"self.flattened_size is invalid {} != {}\".format(output_size, self.flattened_size)\n",
        "        \n",
        "        # 18x16x16 -> 4608\n",
        "        x = x.view(-1, self.flattened_size)\n",
        "        # 4608 -> 64\n",
        "        x = F.relu(self.fc1(x))\n",
        "        # 64 -> 10\n",
        "        x = self.fc2(x)\n",
        "        return x"
      ],
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "cellView": "both",
        "id": "sn0srlfP1nML",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "0b3274ad-cefd-4fb6-bb6b-d42ec2862f4a"
      },
      "source": [
        "#@title Train custom CNN\n",
        "net = CustomConvolutionalNetwork()\n",
        "train_history, val_history = train(net, batch_size=32, n_epochs=10, learning_rate=0.001)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "===== HYPERPARAMETERS =====\n",
            "batch_size= 32\n",
            "n_epochs= 10\n",
            "learning_rate= 0.001\n",
            "==============================\n",
            "Epoch 1, 10% \t train_loss: 2.14 took: 0.66s\n",
            "Epoch 1, 20% \t train_loss: 1.79 took: 0.58s\n",
            "Epoch 1, 30% \t train_loss: 1.64 took: 0.57s\n",
            "Epoch 1, 40% \t train_loss: 1.54 took: 0.57s\n",
            "Epoch 1, 50% \t train_loss: 1.51 took: 0.58s\n",
            "Epoch 1, 60% \t train_loss: 1.50 took: 0.56s\n",
            "Epoch 1, 70% \t train_loss: 1.46 took: 0.58s\n",
            "Epoch 1, 80% \t train_loss: 1.41 took: 0.58s\n",
            "Epoch 1, 90% \t train_loss: 1.37 took: 0.60s\n",
            "Validation loss = 1.33\n",
            "Epoch 2, 10% \t train_loss: 1.34 took: 0.65s\n",
            "Epoch 2, 20% \t train_loss: 1.30 took: 0.56s\n",
            "Epoch 2, 30% \t train_loss: 1.28 took: 0.55s\n",
            "Epoch 2, 40% \t train_loss: 1.25 took: 0.57s\n",
            "Epoch 2, 50% \t train_loss: 1.27 took: 0.56s\n",
            "Epoch 2, 60% \t train_loss: 1.35 took: 0.57s\n",
            "Epoch 2, 70% \t train_loss: 1.32 took: 0.58s\n",
            "Epoch 2, 80% \t train_loss: 1.27 took: 0.57s\n",
            "Epoch 2, 90% \t train_loss: 1.24 took: 0.57s\n",
            "Validation loss = 1.25\n",
            "Epoch 3, 10% \t train_loss: 1.15 took: 0.66s\n",
            "Epoch 3, 20% \t train_loss: 1.16 took: 0.55s\n",
            "Epoch 3, 30% \t train_loss: 1.16 took: 0.56s\n",
            "Epoch 3, 40% \t train_loss: 1.13 took: 0.61s\n",
            "Epoch 3, 50% \t train_loss: 1.16 took: 0.57s\n",
            "Epoch 3, 60% \t train_loss: 1.17 took: 0.58s\n",
            "Epoch 3, 70% \t train_loss: 1.18 took: 0.55s\n",
            "Epoch 3, 80% \t train_loss: 1.17 took: 0.58s\n",
            "Epoch 3, 90% \t train_loss: 1.20 took: 0.58s\n",
            "Validation loss = 1.21\n",
            "Epoch 4, 10% \t train_loss: 1.08 took: 0.65s\n",
            "Epoch 4, 20% \t train_loss: 1.04 took: 0.54s\n",
            "Epoch 4, 30% \t train_loss: 1.06 took: 0.57s\n",
            "Epoch 4, 40% \t train_loss: 1.06 took: 0.56s\n",
            "Epoch 4, 50% \t train_loss: 1.07 took: 0.60s\n",
            "Epoch 4, 60% \t train_loss: 1.05 took: 0.57s\n",
            "Epoch 4, 70% \t train_loss: 1.05 took: 0.58s\n",
            "Epoch 4, 80% \t train_loss: 1.06 took: 0.57s\n",
            "Epoch 4, 90% \t train_loss: 1.07 took: 0.56s\n",
            "Validation loss = 1.17\n",
            "Epoch 5, 10% \t train_loss: 0.98 took: 0.65s\n",
            "Epoch 5, 20% \t train_loss: 0.98 took: 0.56s\n",
            "Epoch 5, 30% \t train_loss: 0.95 took: 0.57s\n",
            "Epoch 5, 40% \t train_loss: 0.99 took: 0.58s\n",
            "Epoch 5, 50% \t train_loss: 0.98 took: 0.57s\n",
            "Epoch 5, 60% \t train_loss: 0.98 took: 0.55s\n",
            "Epoch 5, 70% \t train_loss: 1.04 took: 0.58s\n",
            "Epoch 5, 80% \t train_loss: 0.93 took: 0.57s\n",
            "Epoch 5, 90% \t train_loss: 1.00 took: 0.57s\n",
            "Validation loss = 1.13\n",
            "Epoch 6, 10% \t train_loss: 0.87 took: 0.65s\n",
            "Epoch 6, 20% \t train_loss: 0.85 took: 0.54s\n",
            "Epoch 6, 30% \t train_loss: 0.91 took: 0.57s\n",
            "Epoch 6, 40% \t train_loss: 0.91 took: 0.60s\n",
            "Epoch 6, 50% \t train_loss: 0.90 took: 0.56s\n",
            "Epoch 6, 60% \t train_loss: 0.93 took: 0.57s\n",
            "Epoch 6, 70% \t train_loss: 0.91 took: 0.59s\n",
            "Epoch 6, 80% \t train_loss: 0.94 took: 0.61s\n",
            "Epoch 6, 90% \t train_loss: 0.95 took: 0.57s\n",
            "Validation loss = 1.16\n",
            "Epoch 7, 10% \t train_loss: 0.81 took: 0.66s\n",
            "Epoch 7, 20% \t train_loss: 0.82 took: 0.56s\n",
            "Epoch 7, 30% \t train_loss: 0.79 took: 0.57s\n",
            "Epoch 7, 40% \t train_loss: 0.84 took: 0.56s\n",
            "Epoch 7, 50% \t train_loss: 0.83 took: 0.58s\n",
            "Epoch 7, 60% \t train_loss: 0.87 took: 0.57s\n",
            "Epoch 7, 70% \t train_loss: 0.88 took: 0.59s\n",
            "Epoch 7, 80% \t train_loss: 0.83 took: 0.56s\n",
            "Epoch 7, 90% \t train_loss: 0.84 took: 0.57s\n",
            "Validation loss = 1.26\n",
            "Epoch 8, 10% \t train_loss: 0.76 took: 0.65s\n",
            "Epoch 8, 20% \t train_loss: 0.77 took: 0.56s\n",
            "Epoch 8, 30% \t train_loss: 0.75 took: 0.58s\n",
            "Epoch 8, 40% \t train_loss: 0.73 took: 0.56s\n",
            "Epoch 8, 50% \t train_loss: 0.82 took: 0.58s\n",
            "Epoch 8, 60% \t train_loss: 0.76 took: 0.57s\n",
            "Epoch 8, 70% \t train_loss: 0.77 took: 0.59s\n",
            "Epoch 8, 80% \t train_loss: 0.81 took: 0.57s\n",
            "Epoch 8, 90% \t train_loss: 0.80 took: 0.58s\n",
            "Validation loss = 1.18\n",
            "Epoch 9, 10% \t train_loss: 0.70 took: 0.65s\n",
            "Epoch 9, 20% \t train_loss: 0.70 took: 0.58s\n",
            "Epoch 9, 30% \t train_loss: 0.66 took: 0.56s\n",
            "Epoch 9, 40% \t train_loss: 0.72 took: 0.58s\n",
            "Epoch 9, 50% \t train_loss: 0.69 took: 0.59s\n",
            "Epoch 9, 60% \t train_loss: 0.72 took: 0.57s\n",
            "Epoch 9, 70% \t train_loss: 0.73 took: 0.58s\n",
            "Epoch 9, 80% \t train_loss: 0.73 took: 0.58s\n",
            "Epoch 9, 90% \t train_loss: 0.74 took: 0.57s\n",
            "Validation loss = 1.20\n",
            "Epoch 10, 10% \t train_loss: 0.61 took: 0.66s\n",
            "Epoch 10, 20% \t train_loss: 0.60 took: 0.60s\n",
            "Epoch 10, 30% \t train_loss: 0.64 took: 0.58s\n",
            "Epoch 10, 40% \t train_loss: 0.63 took: 0.58s\n",
            "Epoch 10, 50% \t train_loss: 0.64 took: 0.56s\n",
            "Epoch 10, 60% \t train_loss: 0.65 took: 0.57s\n",
            "Epoch 10, 70% \t train_loss: 0.67 took: 0.59s\n",
            "Epoch 10, 80% \t train_loss: 0.67 took: 0.56s\n",
            "Epoch 10, 90% \t train_loss: 0.74 took: 0.57s\n",
            "Validation loss = 1.24\n",
            "Training Finished, took 68.71s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "cellView": "both",
        "id": "CBB_2Ssn1GnS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "outputId": "ed66cbdb-e652-4d38-f4d2-0f0aab8300a8"
      },
      "source": [
        "#@title Plot Losses\n",
        "plot_losses(train_history, val_history)"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "cellView": "form",
        "id": "J18VSa-B1LEd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "c50be020-8002-4623-fed9-84c4029e60ed"
      },
      "source": [
        "#@title Compute Accuracy\n",
        "compute_accuracy(net)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy of the network on the 20000 train images: 75.15 %\n",
            "Accuracy of the network on the 5000 validation images: 64.04 %\n",
            "Accuracy of the network on the 5000 test images: 64.96 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "cellView": "form",
        "id": "BxIQ5Zep1WCp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "db673e28-cb89-4278-e278-bf406bad74c6"
      },
      "source": [
        "#@title Compute Accuracy Per Class\n",
        "confusion_matrix = accuracy_per_class(net)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Class      Accuracy (%)\n",
            "plane        75.20   \n",
            "car          80.79   \n",
            "bird         50.00   \n",
            "cat          48.69   \n",
            "deer         57.40   \n",
            "dog          46.52   \n",
            "frog         79.84   \n",
            "horse        69.29   \n",
            "ship         67.86   \n",
            "truck        74.07   \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "cellView": "form",
        "id": "GzuSlxyY1c5H",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "outputId": "404cbb13-931b-4a9e-b1fe-e4c51f158e7c"
      },
      "source": [
        "#@title Plot Confusion Matrix \n",
        "plot_confusion_matrix(confusion_matrix, classes, normalize=True,\n",
        "                      title='Normalized confusion matrix')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W3VA5fIoVjXl",
        "colab_type": "text"
      },
      "source": [
        "This tutorial is edited version of official [pytorch](https://colab.research.google.com/drive/1B5KQvPySqYEa6XicRHdOwgv8fN1BrCgQ) tutorials."
      ]
    }
  ]
}