10_Deep_Neural_Network.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<torch._C.Generator at 0x7f9af15c55b0>"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# import the libraries that we need\n",
        "import torch \n",
        "import torch.nn as nn\n",
        "import torchvision.transforms as transforms\n",
        "import torchvision.datasets as dsets\n",
        "import torch.nn.functional as F\n",
        "import matplotlib.pylab as plt\n",
        "import numpy as np\n",
        "torch.manual_seed(2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "make a model with two hidden layer as a deep neural network in three different form of the activation function(Sigmoid, Tanh, RelU)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Create the model class using sigmoid as the activation function\n",
        "\n",
        "class Net(nn.Module):\n",
        "    \n",
        "    # Constructor\n",
        "    def __init__(self, D_in, H1, H2, D_out):\n",
        "        super(Net, self).__init__()\n",
        "        self.linear1 = nn.Linear(D_in, H1)\n",
        "        self.linear2 = nn.Linear(H1, H2)\n",
        "        self.linear3 = nn.Linear(H2, D_out)\n",
        "    \n",
        "    # Prediction\n",
        "    def forward(self,x):\n",
        "        x = torch.sigmoid(self.linear1(x)) \n",
        "        x = torch.sigmoid(self.linear2(x))\n",
        "        x = self.linear3(x)\n",
        "        return x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Create the model class using Tanh as a activation function\n",
        "\n",
        "class NetTanh(nn.Module):\n",
        "    \n",
        "    # Constructor\n",
        "    def __init__(self, D_in, H1, H2, D_out):\n",
        "        super(NetTanh, self).__init__()\n",
        "        self.linear1 = nn.Linear(D_in, H1)\n",
        "        self.linear2 = nn.Linear(H1, H2)\n",
        "        self.linear3 = nn.Linear(H2, D_out)\n",
        "    \n",
        "    # Prediction\n",
        "    def forward(self, x):\n",
        "        x = torch.tanh(self.linear1(x))\n",
        "        x = torch.tanh(self.linear2(x))\n",
        "        x = self.linear3(x)\n",
        "        return x"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Create the model class using Relu as a activation function\n",
        "\n",
        "class NetRelu(nn.Module):\n",
        "    \n",
        "    # Constructor\n",
        "    def __init__(self, D_in, H1, H2, D_out):\n",
        "        super(NetRelu, self).__init__()\n",
        "        self.linear1 = nn.Linear(D_in, H1)\n",
        "        self.linear2 = nn.Linear(H1, H2)\n",
        "        self.linear3 = nn.Linear(H2, D_out)\n",
        "    \n",
        "    # Prediction\n",
        "    def forward(self, x):\n",
        "        x = torch.relu(self.linear1(x))  \n",
        "        x = torch.relu(self.linear2(x))\n",
        "        x = self.linear3(x)\n",
        "        return x"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "define the train function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Train the model\n",
        "\n",
        "def train(model, criterion, train_loader, validation_loader, optimizer, epochs=100):\n",
        "    i = 0\n",
        "    useful_stuff = {'training_loss': [], 'validation_accuracy': []}  \n",
        "    \n",
        "    for epoch in range(epochs):\n",
        "        for i, (x, y) in enumerate(train_loader):\n",
        "            optimizer.zero_grad()\n",
        "            z = model(x.view(-1, 28 * 28))\n",
        "            loss = criterion(z, y)\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            useful_stuff['training_loss'].append(loss.data.item())\n",
        "        \n",
        "        correct = 0\n",
        "        for x, y in validation_loader:\n",
        "            z = model(x.view(-1, 28 * 28))\n",
        "            _, label = torch.max(z, 1)\n",
        "            correct += (label == y).sum().item()\n",
        "    \n",
        "        accuracy = 100 * (correct / len(validation_dataset))\n",
        "        useful_stuff['validation_accuracy'].append(accuracy)\n",
        "    \n",
        "    return useful_stuff"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Define the dataset which we use the MNIST dataset as library"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Create the training dataset and validation dataset\n",
        "\n",
        "train_dataset = dsets.MNIST(root='./data', train=True, download=True, transform=transforms.ToTensor())\n",
        "validation_dataset = dsets.MNIST(root = './data', train=False, download=True, transform=transforms.ToTensor())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "dataset loader"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Create the training data loader and validation data loader object\n",
        "\n",
        "train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=2000, shuffle=True)\n",
        "validation_loader = torch.utils.data.DataLoader(dataset=validation_dataset, batch_size=5000, shuffle=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Here as like as previous lecture for deep neural network we use the Cross Entropy loss function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Create the criterion function\n",
        "\n",
        "criterion = nn.CrossEntropyLoss()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Set the number of iterations\n",
        "\n",
        "cust_epochs = 10"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Set the parameters for create the model\n",
        "\n",
        "input_dim = 28 * 28\n",
        "hidden_dim1 = 50\n",
        "hidden_dim2 = 50\n",
        "output_dim = 10"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Train the model with sigmoid function\n",
        "\n",
        "learning_rate = 0.01\n",
        "model = Net(input_dim, hidden_dim1, hidden_dim2, output_dim)\n",
        "optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)\n",
        "training_results = train(model, criterion, train_loader, validation_loader, optimizer, epochs=cust_epochs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Train the model with tanh function\n",
        "\n",
        "learning_rate = 0.01\n",
        "model_Tanh = NetTanh(input_dim, hidden_dim1, hidden_dim2, output_dim)\n",
        "optimizer = torch.optim.SGD(model_Tanh.parameters(), lr=learning_rate)\n",
        "training_results_tanch = train(model_Tanh, criterion, train_loader, validation_loader, optimizer, epochs=cust_epochs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Train the model with relu function\n",
        "\n",
        "learning_rate = 0.01\n",
        "modelRelu = NetRelu(input_dim, hidden_dim1, hidden_dim2, output_dim)\n",
        "optimizer = torch.optim.SGD(modelRelu.parameters(), lr=learning_rate)\n",
        "training_results_relu = train(modelRelu, criterion, train_loader, validation_loader, optimizer, epochs=cust_epochs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f9a4edbb310>"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Compare the training loss\n",
        "\n",
        "plt.plot(training_results_tanch['training_loss'], label='tanh')\n",
        "plt.plot(training_results['training_loss'], label='sigmoid')\n",
        "plt.plot(training_results_relu['training_loss'], label='relu')\n",
        "plt.ylabel('loss')\n",
        "plt.title('training loss iterations')\n",
        "plt.legend()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f9a49d2f400>"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Compare the validation loss\n",
        "\n",
        "plt.plot(training_results_tanch['validation_accuracy'], label = 'tanh')\n",
        "plt.plot(training_results['validation_accuracy'], label = 'sigmoid')\n",
        "plt.plot(training_results_relu['validation_accuracy'], label = 'relu') \n",
        "plt.ylabel('validation accuracy')\n",
        "plt.xlabel('Iteration')   \n",
        "plt.legend()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "till now we understand how we can make two hidden layer for making a type of the neural network and if we want to extend these hidden layed we can do it manually in the NEt function but its clear that it takes time and its not a wisely  method and instead of that we can use nn.ModuleList() which quite help full in such situation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<torch._C.Generator at 0x7f9af15c55b0>"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Import the libraries we need\n",
        "\n",
        "import matplotlib.pyplot as plt \n",
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from matplotlib.colors import ListedColormap\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "\n",
        "torch.manual_seed(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<torch._C.Generator at 0x7f9af15c55b0>"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Import the libraries we need for this lab\n",
        "\n",
        "import matplotlib.pyplot as plt \n",
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "from matplotlib.colors import ListedColormap\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "\n",
        "torch.manual_seed(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Create Data Class\n",
        "\n",
        "class Data(Dataset):\n",
        "    \n",
        "    #  modified from: http://cs231n.github.io/neural-networks-case-study/\n",
        "    # Constructor\n",
        "    def __init__(self, K=3, N=500):\n",
        "        D = 2\n",
        "        X = np.zeros((N * K, D)) # data matrix (each row = single example)\n",
        "        y = np.zeros(N * K, dtype='uint8') # class labels\n",
        "        for j in range(K):\n",
        "          ix = range(N * j, N * (j + 1))\n",
        "          r = np.linspace(0.0, 1, N) # radius\n",
        "          t = np.linspace(j * 4, (j + 1) * 4, N) + np.random.randn(N) * 0.2 # theta\n",
        "          X[ix] = np.c_[r * np.sin(t), r*np.cos(t)]\n",
        "          y[ix] = j\n",
        "        self.y = torch.from_numpy(y).type(torch.LongTensor)\n",
        "        self.x = torch.from_numpy(X).type(torch.FloatTensor)\n",
        "        self.len = y.shape[0]\n",
        "    \n",
        "    # Getter\n",
        "    def __getitem__(self, index):    \n",
        "        return self.x[index], self.y[index]\n",
        "    \n",
        "    # Get Length\n",
        "    def __len__(self):\n",
        "        return self.len\n",
        "    \n",
        "    # Plot the diagram\n",
        "    def plot_stuff(self):\n",
        "        plt.plot(self.x[self.y[:] == 0, 0].numpy(), self.x[self.y[:] == 0, 1].numpy(), 'o', label=\"y = 0\")\n",
        "        plt.plot(self.x[self.y[:] == 1, 0].numpy(), self.x[self.y[:] == 1, 1].numpy(), 'ro', label=\"y = 1\")\n",
        "        plt.plot(self.x[self.y[:] == 2, 0].numpy(), self.x[self.y[:] == 2, 1].numpy(), 'go', label=\"y = 2\")\n",
        "        plt.legend()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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jasYhEvJmdVUoV1PZUmqSAeqKtqlJUf0UGYFg0DjPXeviUXcKXmF1bfVxIzeRfrUeCikuK3XXYZVmSqTUQTz+ePZuZcsWxRAcOQKsX5+7m7G6rhm1tebnq0ZPqxi7cKFSCyJdR54YgqkA3tbcPpq+T8+VRHSQiJ4nokscngsiaieiLiLqOn78uAfDNkbvBjLKDjqROIFFly4CQbzNrQnU2Hq+2mAtzgyfkd3GVEQBv9ZW96mNXqVfBoPZk9qJE+LjUqnSXnEauagWL86eoJ94QnFZqYZl3Tpj95I64a9fb98g2bmuFRMnArfc4vwzoo9DVCleuIZuBnANM38tfbsNwOXM/D80x3wEQIqZzxBRK4B1zDzDzrki/HQNidxAquqnnqY6ZUUlMhQidVEtQQoixSlMDk/G+x++n6NCWrUBYlGKpJnrxSn5yDvr0StblrMUcr4uKr9cW27cVm7e23J4rzzAT9fQUQAXaG5PA3BMewAzv8/MZ9J/7wIQIqIGO+cWGpEbiME5q341pdMojVSkLqpFLUQ79cEp08Y1VYdI92d42BsjoM/asWr8YrS6VIvWVMpZ+CxfF5Vfri01mJ6Pm2hoKP9dX5UXpnlhCF4GMIOIphNRLYDbAOzUHkBEHyNSvlVEdHn6eQfsnFtojCZ2Bgsre9349hlsWJlcdYJyWhlov2htHZvAmIELLjA+Vs3AEeWuv/9+titBCp95h1mrUDvkK9hXajGoAuPaEDDzCIB7AOwG8BqAp5j5EBEtJqLF6cO+DOBVIjoI4FEAt7GC8Fy3Y3KD2cSuSk2fGT6Tua+zpTMnYBwJRRANuwh8WYyj4nDTWtEJu3Zl3zZ7PjWgOXFi7mPJZG5Kop/B32rCTqtQM5qalFRTdWcQDALnnGN+Trns3nxESkzoiHfH0ba9zVJRNBQI4Yn5TyA2M4Z4dxwdezrQO9iLxrpGdLZ0Ym/vXmzo2pDXGNQ2llUTI/B7J6Ci9+2bBRbV74XsgFVY3KQJq+1I9WmpoZDyfmlTWVWqLI1Utqp0gN12k2b6P0bZRkEK2hKq45Xl977kTaFqBPQBQTuGoJwDweWIk0VBNApMmJAdsHYSaK6tVVJcq8QIAFJryBFqNpAVZn58o5TTUR61rD2w+/wVQyH8s0T5bf/LORBcjtitfK6tVdJNte44wNnOUpWlkEhDIELk9xfBYMO8f1HPYfX+Tddvykz2RtlIVYGbAHGNvRqNDKLJxUgWQnu/DAQXFtHrvWRJdhZRNJq7klfjTE7xU0q8jJCuIQNUv78dqWlR3r+Ze0nr9hHFFyo+NhCPKyuxfL+ES5YogV+nBkQv9xCPA7ffnu3rDwSAH/1ITvTlhps4UxnOgfkiXUMOic2M4ci9R8ArGVsXbDXNAtLn/ce744bVxnq3j/o8VdOE3m16IKD0/83nS68XIIvFlElfu/qURqA8ybcOwKheIR5X5CeIlB8zKYoKkL6WOwKHGPUo1orGGQWKqy4bSEQhMoRUgTEjiLwXepMUl3w+V6HQmOS3tlI6EhHLlIuCy6JK+BLupSx3BB5hlN+vvd+sKK1te1v16gnF4/4bgWjUWndfatVXHk7ltVUNJdUI3HnnWB8Do14Vw8O59SOiuocylL6WhsAmqhBdz2CPZYDXrBiMwegZ7EH7s+3VYwzUbfbChf4/18CA8nx2JoYy/MJKDLDbvS0aHRMLVFfsy5aJawxE6F1QRi6pMpOskIbABqoQneru0bqGRI3k7WQdVY2ekBcxAafccQewdy8QDlsfW2ZfWIkJ2upuo2K/gYFcP76Tz6Y+1dko9bnMJCukIbCBSIgOUHz+oiyf2MxYJkXUKGgMVImekFvJgHxIJoGNG7O/4EbFY2X2hZXYxOx9zdctWFubWz9SIXUm0hDo0PciiHfHTX3+Rqt6bTaQUYFYRekJGWVOFGvFrU+CYM41BmX4hZXYxMo1qLoFnRgDURWy1iUFKC6pfK5dZKQh0KB1AWl9+ZPDkw3PsbOqNxKmq5jCMa1onBqIbWsDli4trRW32iZRFoZVPuoEbdW1bNEie9cLBs0/K2fSQpTaDmhllIwg00c1GKV9RsNRnEicEKaNRsNR9N/fb3ntii4cM0vdGz/eOAuj0Eh9oOrDy3Rlda7UpppOngycPm0cbC6xz5wUnbOBUY0AAIwPjTdsVB8NR7Fu3rrKmdidUkqN5cePV6qF9QqUJZzbLfERrz6b6oQuqhswo8RUamUdgQ2MfPYEMjQCADCQGKiudFA9xXL/tLRku3q2blW26KLG6NIIVCdefTbVWJLT5Afm7JhZiVYhyx2BBif9ikWYyVJXNPG4EhMo9GdJrvIlVthdwQeDY/59PdEo0J92/+a7wzDqlVDgz7DcEdhAn/bZVNdk2wgAivS0k12BKEOpLInFlLaOZvr+fiALwiRW6AvNotHc9qORiDJBb92a+5gqd62S7w5jaEgZR4lWIXtiCIjoWiI6TERvEtEKweMxInol/fM7IrpU89gRIuomogNEVBwBIQ16ETinvQHsuoiMMpTK1hisXw9s2ZL9hQuF/H9eWRAmsUJbaNbfr6SBGrkNRWnHWjo781/wGO04SuAz7No1RERBAH8C8CUAR6E0pP8KM/9Rc8xfA3iNmU8S0TwAq5j5c+nHjgCYzczWqTdp/HQN6TN7ANhqXanFjovIKEOpotxLS5cqSqF+UmJZGZIyxm43unwNgZH7SRuIVrORfBJF9NM1dDmAN5n5L8w8DOBJADdqD2Dm3zHzyfTNlwBM8+B5PUF1z9BqwsLtC3NW6ACwePZi0wphPfraAidFamVbbSwKgumbxXuNLAiTeIld3SAzQUMz2ttzi9xCISXBgUiJs2lrcQpYh+CFIZgK4G3N7aPp+4y4C8DzmtsM4JdEtI+IDFsMEVE7EXURUdfx48ddDVhFryGkR9UDWn/demxZsMWw65gebfaR0yK1sqw2FhWUqWqOXqLPEirjQPGO/X2Ys+ZFTF/xHOaseRE79vcVe0gSM90g7UJHLR5zQjSam80WjSq/VSkUvXemgPEDh/3+hIiWykI/ChHNhWII/kZz9xxmPkZE5wL4FRG9zsy/zbkg8yYAmwDFNeR+2MYaQlp6B3szLiM7Tef1FcOi5xhKDiFcE0YkFMl6rGyrjUUpdXbVHO2yZInyRaoAduzvwwPbu5FIKp+nvlMJPLC9GwAwf5bZGkriK52d4t4Cra3Z9w8MKAbBbn2AGisLBLJdPs3N1oJ3BYofeLEjOArgAs3taQCO6Q8iok8BeAzAjcyc+e+Z+Vj693sAnobiaioIdtwwk8OTTXcNgNKHWM0y0iuRGj3HicSJnAwl/bllQyE+rHPm+P8cBWLt7sMZI6CSSI5i7e7DRRqRBIBxf+pdu3IXOqmUsqIX9b0OhcZW+9pVv1Z+hcjejnmysbyNl3hhCF4GMIOIphNRLYDbAOzUHkBEjQC2A2hj5j9p7h9PRBPVvwFcDeBVD8ZkCzMNIZUTiROmu4ZIKILNN21GamUKnS2dWPb8MtBqAq0mNDzcYOoCKrs2lUbFMIUoKLvjjpIpvnHLsVMJR/dLCog2w0jtWWC00DlxQnETbd2abTyeeELJTkqlgAkTcnfHJVi75do1xMwjRHQPgN0AggAeZ+ZDRLQ4/fhGAA8BiAJYT0rEfSQduf4ogKfT99UA+DEz/8LtmOwQ747j9PBp02MCCCAF4+2fVloi3h3Hnc/cieHRsTd9IDGAIAURCoSQTCUz9xMIrTNa3f8ThURfmKMGswBlq/u3fwuMjPj3/Mmk0kDE56yKQnB+fRh9gkn//Powduzvw9rdh3HsVALn14ex/JoLhe6ifI+be9EU/Pr145bnSTQ0NopX7+oCKBYz/hy63S2fOOHufJtUbWWxUfomoKRwnhk+g4GEuf8uEopk3Dlm15tQOwFnh89mpaCq5wIoDzE6s9S61lb/00RFlEhlsdmkrH2sLhwCEXByKAlCdiCNAPz1xyfjD72DOW4jlUmREFZefwkAZMUYACAcCuLbC2ZmPe+qnYdwKpEUXsvoPIkAo77EixYBTz015uePRpXiM+3nsaHBXVOmYFApdvPoMy5F53RYNaE3E6DT0lTXhM6WTizc7rwNYzQcRWIkkRMwLslYgVFpPZHymFGxDJHi53z/fWVV7zVFriPQB36BsckVyJ2w/WRqfRh7V1wlHJOd8yQm6HP8W1uBxx7L/UzrG9y7NQSApwseaQh0WBV0ma3w9eizf9xSkkVlZjsCs6DX1q3KF6inx1mmhV2KrO44Z82LQjdPkAijRfpuBQhIOXhqAvDWmut8G09FYiZvrV2ceK1+6hKpNaTDqllM64xWW0VkQQp6agSAEi0qM2vJZ9QoPBAYqy8A/Jmwi9z4xijAWywjADgzAoASm5A4xMz3r33Mq8+nz5l5VWsI9AJz0XAU4Zow2ra3oeHhBjz2h8dsuYbs1BaIiIQiiIbF3ZNKsqjMKLUuFhsLGusJh/3tV1wClcXlPomGQ0Esv+bCYg+j/DCb4LWPtXqUFOLzgqdqDQEwJjC3ZcEWJEYSGEgMgMEYSAxkZfmYYVd6IkCBnJqBdfPWlVcLS1FqHaAUei1ZMrYzCAaV2343rS9SoFhbFXz2wxGEggVWXfWISZGQDBTnS2enWFRR3+DeC5kV/TV9oGpjBFqcxAPyZXxoPIaSQzmZQVXbwtItRQgSG2XihAJKPMCpS6bYBImQYvY8jdRuamvZE48rKc36rCFgLC5mRSC9Fjdzm2r7IbhEBotNsJsh5BUlmxnkNU7b+tmlCGmjTjNxyg2naaRGk71ZFlVFGgM9Tj7z48crWUdWciweJkRIQ2BCIXYEekoyM8gP9KsmLyiC7pBRdlAlYTeN1MgoTooorpKTQ7lu1apJUXWyCzbriqbFw92vkSHwQnSu7Ols6cxpUek3JZkZVC74LW8toBrkH/pOJTB9xXOZwrdTQ8mc1f7a3YcNDaLIAKhUw+sHwFl2jx0joIreNTf7WlFf1cFiFX0GkV25aTeUZGaQl8TjSjHNwoXe7gYAZcVV4Cbg5Z4dZBcGcCqRxMmhJBiKcVj+04O4+MHnce+2A3nviqri9YvHx3z+bggGxzLzrrwS2LjR9z4F0hCk0QrApdjfAqWSzgzyAtVP6rUB0KLvf+BzE4+5F03x7dqlTjLFGErm/52oihRV9TNvZ5VvRX290va1sxN48cWC9CmQMQIBfsYMCAQGZ6QpKjJg7Ge2kBk+ZBJZuUMkudSHQxh/Tk3lZw1p8fozH4kodThGi6k8A8gyRuAAUcxAncCdop4XDUfx/ofvZ+oTtK0wK84YFKsZd0/PmAa8XvwrD0o1U0gVn1P99nbE5QpFOBTEqhsuqfyJX4/Xn/mhIfPMI48LzKraNSTqJQzkxgya6pryTi9VV/8TaifkFKmprTArDqsPKZFSJOMXAwOKMqRLV5GogYxfhAJkWZgWDgXxyK2XYf9DV2cm2vmzpuLAyqtRHxYUNxWYIFH1pInqKaTUCZHnBWZVawiMeglrjYG2aUxTXZ4Nq6FkCFVcs3ozRLpEWpi9b2WpZ3RUSVt1gd+ZLkFS6tKn1oex9uZLsfbLl2JqfThz38IrGrNum02ygyWwI0gxV6cRAIy1uFpavH0eImDxYs+zhqrWNWTUS7hjT4fQVeMmxVTNEBLFHSoye0j9kC50Ls3tKS6D1UYNZLzAqMgq34nUz7E6GUPVon7mValqtcXkiy96o7qryrkDShbRrl2eppFW7Y7A6Qpd5C6yQygQQmdLp6XaacURiynB2zJm+TUX2lSSymV8bTCzkn/k1svwyK2X2V7d5zvWcCg77TkcCmLhFYVZaFRFZpAVqhbXli1AIjHWp9jMCIRC9tykixdnX9PjTDlPsoaI6FoA66C0qnyMmdfoHqf0460AhgD8LTP/wc65IgrRj8AMVR/ITmZRNBxF//39WedVpK6QiHhcadRdzMy08eOBs2eVv/VBZH2zEcEKq3nFc5m/J77fgdemHMQoAUEGPnn8Upz+SLYhn1rELBmjtpR+7xSK+T+XJFYZROoOIRoFTp60t1sgEn+PHGbK+SYxQURBAH8C8CUAR6E0s/8KM/9Rc0wrgP8BxRB8DsA6Zv6cnXNFeGEI1BiB0+5govOsIFB1TPwiqASVOaNR4JZblBaA+vaDOg2jVbf+I/7+6e/h/i9+gA2XA1lbBP1XJ/2Ytpd1sShUxlN9OIQDK6/29TnKDqtmNE1NyqLDCx0uh2mkfhqCKwGsYuZr0rcfAABm/rbmmO8D+F/M/JP07cMAvgCg2epcEV7VEeSzQndTY1A1YnNajFZHdnVWikV6J8FQ5nd6CHk5UgmExbMXY/11lauNpO4IAFSH6qgVVjsCImUH6kXdgUc7Ai9iBFMBvK25fTR9n51j7JwLACCidiLqIqKu48ePux40kJsZpJWGFqWVxrvjrgrNzNJFjZ6z7DHKpti8WWljaZZdVEzS7qTMBiDPjQ2DsaFrA774oy8CKNz7XEhtH1WGYvnPDqLvVCIjTfHA9m7s2N9XsHGUDFZZc42N+dUd6HfXHjZm8iJrSPQVMdg05xxj51zlTuZNADYByo7AyQCdEO+O485n7sTwqJLe2DPYgzufuRN7e/di88HNrq8vCkbr3U0VVWymz6YQ+eK1j5054680RZHY89Ye1HyjJqujXc9gD+7YcQcA79/nQmcRJQXNGBLJUXz9qYMA8s+GKkvUz7ZIdVedvO32K1AJBoFx44zjXS7xYkdwFMAFmtvTAByzeYydcwvKsueXZYyAyvDoML6/7/ueqJOK0kXNUlkrAqPOZqLH1q3zt9gsT8iDpYeorWkylcSy593VO4gwyiKa8/HJeWdC5cMoc3XuDGIxpZnM1q257V0BZcGjJxRSJngRo6NjRgBQMog8xAtD8DKAGUQ0nYhqAdwGYKfumJ0AbieFKwAMMvM7Ns8tKAMJ8WrUCyE6o3RRI3dTRRabWRGLARMnFnsUOSx+GQZ7VfcMJAZMXUX5uJPmz5qKby+YmZOyGv+/r8T3NKms9eGQ7602E8lRrN192NfnKFn0Cx1ALMgYjQJPPKEYDztp1x4Lz3mVPtoK4BEoKaCPM3MnES0GAGbemE4f/f8AXAslffQOZu4yOtfq+fwUnaPV+X8pouFojiERicxpg9STw5MNjU/VNK/RY5V1USSWzgM2fBZjDk0f5k/95wVAXtltTiiUXhEB1R1EBswDyWo2kd2U6zyE52SHMps0PNwgnJjHh8YjmUrmuI1U1EnbKhNp6XNLsbFro6V2EYGwZcGW8o8ROEHN6y+GcqkTJkxA/Hcbcfezd+Ns8qz18XkSCUUQrgkLP49eLhIKLa5XVa0r9VgtcqxUR7XkobbrZ9ZQRbFu3jqEAtkCXqFACN+//vuYWCt2WRAos3ozykQClC2+HSMAKNkmVWcE1B4DRoRC3mu35MPGjYjNjOHMP54Br2Qsmb3El6cZSg4Z7ha9dBsWUlwPqHJXkZU4nd26Ao+F56Qh0BGbGcMT85/IkpJ4Yv4TiM2M4UTihPAcdWK38uN27OmwrWLqRuSuLOnoMP8SRKPA174G/O//Xbgx6SFS+iXrMjV2vVH41plealQVo41k1bSu1NPZqSxozBgYsO50xuyp8FzVis6ZEZsZE67GG+sahYHdaDhqK/3T7iquojWIjDDKq9b6QZub3Vdi2qWpSekVu2uXZa9YP4P60XAUiZFETozAy8+HUappkAhf+dwF+PXrx3HsVAIBIox65EquaoE6O9X2Vr5/j3W85I7AAUbCcQBspX+areKi4WhmB1J11ceA8ZZZe7+dIhyrL5mdnrLMiu91/XrjtFftEH1SkI2EIlg3b12O2KHXnw9RqikBuOKvJmWMwPn1Yc+MQFUL1HV0uJdg97CQTEUaAhuo6Xtt29sQrgnnTNpGLiP9SrGzpTMn/gAAS2YvQf/9/cK4gt2xlX1FslEFsvYDb+VfbWpSlB+ZxVXLkQhw993mVZ95rLRECwS3EAiLLl2U2Z0axZ3csGN/H+aseRH3bTsAfW4sA9j75xNZlcJu0PZeqNpAMeCuk5m2FsHjfgTSEFigb2AzkBhAYiSBLQu24Mi9R7C3d6+h35/BqPlGDWg1ofmRZuzt3QsSrFg3dG3IaxK3aq5TVsRiygdcX3yj/cAbGYutW8dW8erxRtdbv175LSrcyXOlpZcoj4YNioIcwGBfYw9qppA60SdcNKe3wyizTB0F8u9k1tRkuTN1g0wftcBMrrp1Ris2dG2wfS2rvsdO88PdSGmXLTako4tyLQ1uhAn1LJm9BLve2OW5dHkhRem0hENB/LfPTM1yOVWVcVCz45zEuoiUna4Hn01ZR5AngdUB4eRNIAQoIJQNcEOQgkhxytaX3mxsqZX+rvAkxhi9L17gVTHZ9BXP+VUo7RiC4oqaqumhUNFGQrsAmTwZOH3aOG6gtqZc7416rawjyBOjQGBjXaPnRgBQ9GjsunnMxiYpHn6+/l5pUJVS1o5qkPpOJbD1pd7KVzDVyk709wOPPz7mwoxGlR/Vnblli2dGwAxpCCwwazEZpKDBWd6gfunj3XE0PNwAWk2g1YSGhxsQ745XX/vLEkcN3HvlFjLCi3RVUaZQKECYFAllgroLr2jE1CIbjKooPtMbhv5+X+MBIqQhsEDUq1jdmrd/pl14TsDDl7VnsAeLnl6UVWE6kBjIyBf7nVoosUe8O447dtzhuxEAvNlxiETp1t58KfY/dDXeWnMd9q64Ct+aP1NoMApNxRafxeNKbUwgoPzW9x+2etxDZIzAJUufW4pN+zZhlEcRpCDaP9OOOY1zbPc0dkNFB4XLDCONKq8pdJc7u0HlcCgAgHyRqphaH8beFVd5ft2iEo8Dd9wBJDVCf6GQokAaixkHlV32IZDB4iLhZ+BQBoVLBzeqtZbXTsucFqPvtd2gciQUwP9c8Ck8sP0VT1NRK1agbsKE7P4CKtGo4hoyUykV9Na2iwwWFwk/A4cBCpR/IZnEklAwlKlbKbTbry5soYuTZiiZwr3bDjg2AqpbSh+XqOjis3hcbASAMZ0hM/FFj3sRAFJryHc6Wzpxx447kEyZa71PqJ2AaDjqyJ2kZi2Ztba0ksW2e4zEnPGh8b5JUg+PDqNjT0fB35Md+/vw/gf+9iiIXdGIb82f6etzlBxWk7gdL42bCmUBckdQAETVxHrODCut61qmt2RcAU4YSg5h4faFWbsDO5XHFVWdXETG1Yzz9fqF7la3Y38f7tt2AIJWxJ4QJMLCajQCgDeTeL4VygbIGIHPFCKdUE8kFMGiSxdlgth6tEHmqqxO9gE/YwSAIkrYf3+/r8+h4nejmiARvnPLpRmXz479fVi7+3BlF5EB3jVe8iFGIF1DPlOMvsNDySHTBjjaMRmNryr7JedJvDtuKR/iltPDpxHvjhfEPeR3oxq1ob2K1uioRWQAKssY5CMtIcJl1pARrlxDRDSZiH5FRG+kf08SHHMBEf2aiF4jokNEtEzz2Coi6iOiA+mfVjfjKUWMgsV+F6OZTUraIPPk8GThMbI62T5OGg7lixonKASFyNtXC8VERqcii8jMGi81NSkNj6xQM4p8KDJzGyNYAWAPM88AsCd9W88IgK8z8ycBXAHgvxPRxZrHv8fMl6V/Ct/qyWdE1b+1wVrUj6svzoCQLWNxevh0jjR2NVYnu5HzLpTrr1C7tELJT/SdShganYorIjNrvKT2vhAp4mo5IZa79wK3huBGAJvTf28GMF9/ADO/w8x/SP99GsBrACpoz2eOSKKYmQtSfGSH4dFhfOScj1R1dbLTgLnWaEz8triPtR8UapdmVE0c8DgMQgDqI+L01FLSQvIEO42X1q0z75XhcYBYi1tD8FFmfgdQJnwA55odTETNAGYB+L3m7nuI6BUielzkWtKc205EXUTUdfz4cZfDLizaxiITaidYppJ6gZNGKScSJ3xpfFIudOzpsNVhDsg1Gmq2l9cUc5eml5+oD4cwKRJCipFHPpsxDOBUIve7UJEdzOw0XlJ7aHjYK8MuloaAiF4golcFPzc6eSIimgDg5wDuZeb303dvAPBxAJcBeAfAd4zOZ+ZNzDybmWdPmTLFyVOXFIXY3qurenWVb0W1F6YZvSc9gz05r4vIaPgBERW1fen8WVOxd8VV+N6tl+HDkRRODikTNsNjY6ALrdSHQ5VZRGan8ZJ6XH+/0mzJ6lgPsTQEzPxFZv4/BT/PAHiXiM4DgPTv90TXIKIQFCMQZ+btmmu/y8yjzJwC8K8ALvfinyplCrG97x/qR9v2NgDAlgVb0FRn3n7RifR1pRHvjiNAxl8D9XVp296Gpc8tLZiffnh0GBNqJxR9lyYK5voZFh9/Tk3lGQEVVWV0yxbldltbrpicKjTXpnx/sWVLQVRI3bqGdgJYlP57EYBn9AeQUk31AwCvMfN3dY+dp7l5E4BXXY6n5PGjv62es8mzmQnszmfuRP+Q/fxzr/TugdLvp7z0uaVo295mq68Eg7Gxa6NhlpUflEIKb6GDthUXJNajppH29CjboZ4e5XY8bv6Yz7g1BGsAfImI3gDwpfRtENH5RKRmAM0B0AbgKkGa6MNE1E1ErwCYC+A+l+MpebTB40IwPDrsWPrAiwmo1CuW491x01oLEeqxfhtylVJI4S100LbigsR6RGmkqnaQ2WM+IyuLi4gbZVI/C5i8qCou9YplNxXf0XDU96wvAmHLgi1FD9z7XWWspWKVRrUEAmItIVWGxuixlDeKrlJ9tARxs+JjcF6aRFZYZafYdfcUomLZ7li0xzU83ICGhxtc5f4Xwggsnr246EYAyM0gCtrQzTJikkGqKFDBSqN6zNJI7aSY+oSUmCginS2daH+23TQLpTZYi4m1E4WTD4MRDUeRGEl4kskSpCA2Xb8JwNiKOUhBjPIoouEoPhj5IMvNZKZ62ljXKJxsvXJ3qK4n9f82Gov+uFKp3zCiqa6pJNRfjfR/pq94Lu9rqplHegiovMYzRnR25kpNaFNDzR7zEbkjKCKiNphLZi/Juv34jY+j//5+w9X/icQJbLp+kyeSFZtvUmoDVd8+MCZ1PZAYEMYajILLrTPEaiH9Q/2exAns5v4XKt3TK0rFCDywvVvYRH5cyPspo+LjAlrM0kjtppj6gIwRlAlWPnf9yjcfeCXn5TsXdUozu466yzmROJF3/wOj+Ip+LH52iPODUoihGLWnnFofxjuDibylqQnAuFAwK95QFXGBEkLGCMocUdqp1p+v313kSz4+fJG7x+w6w6PDGEgMZLKJFm5fCFpNmPjtiWh4uMFWuqmRi0l/fyHTPb3Az5TRHfv7MGfNi5i+4jnMWfMiduzvEx5npv/jpj8BA1nxhqqJC5QB0hCUCSI3kr7aVCtlYZSealY8VfONGsepkUbB5XxiAWeGz2QZCG26qT4w3Dqj1dQwquec+uCU43E4JRqOmr6uTvArZdTM3aPHyFVzfnoCz5ep9eFMxfJba67LxAXsGCeJv0hDUEZoJ3qralOjwjUzV+AojzqqORgfGo9wTRht29tyOqN5ocGj+vxFNQmbD27GoksXIRqOZh3ftr0NtJrQ/Egz7n72blvFYm45+cFJpNg6vU/dqamxICtD5iVO5J5FonOq/k+kNv9Y1NyLsqVhnBgnib9IQ1ChqDsI7UQJmPcpsEuQgmiZ3gIG56zglz63FO3PtnuWndM72GsYGN7yypac51H/v57BHt96COuxYwSi4Si2LNgCXsk4cu8RrL9uveUOz0ucyD3rU0a1Lpyh4fwN669fzxaLrJpeBGWADBZXOH61yjQqaFPTTSUKAQrg7s/cjfXXrS/qOMwCwE5SN42uYwcC8Naa6zK3p694Trgs0R8n8Y6qDxbbDZRVGn4FH412FtIIZJPiFDZ2bcy4q4olsWHm7nF7HbucXx/O+h4GDIrTqiqdtESoCkNQbb5IbWDVqyCmJH+07qpi6S2ZuXvcXmfOx60zs8KhIOZeNCXrezgq8EZUZC+CMqAqXENebYvLAS/qCcoBv5vF+0kp1Ap4zT/t6Eb8pd7MO1IbJIw/pwanhpKZyuS1uw8Lv4dBIqSYsyqYJf5g5BqqComJqumLCuNK2krz3ZerEQBKQ17aCCNpCaNj6sIhEAGnhpJZf0+ZOC7n3Pu2HRA+Z4pZxgSKTFUYgvPrw8KVSCX6Io0mmRSnwCvZ8x1DU10Tegd7y3piLjSlIC8tYsf+Piz/6UEk01VjfacS+H+2HcDqZw9lVvZzL5qCn+/ry2T7aFtNav9W3a8AMsagmr6H5UZVOJC9CpSVA1YVt/rCtGg4mmmJ6JQAAuhs6SzZic0LrF4XUS2AqhclOr+QvYedsmrnoYwRUElBEYtTY2vxl3ptS1LrU0Gr6XtYblSFIfAqUFYOWElRANmFaf3396P//n7TamQjQbtJ4UmIzYxZdl0jEJbMXoKtC7aWTPDarkhfY12j4eui7w2t3l5/3XocufcIeCVnWoUWq/ewHdRMHlEjeT1O9319pxKZTD1ASkyUKlURLK424t1xdOzpQO9gryNRN5HbKBKKGLqRtAJv6nNqpatFksqlEswWyWrriYQiGVlu0etSipO6HfQ+/rPDI0iO+j8PSIG54mMULHZlCIhoMoBtAJoBHAFwCzOfFBx3BMBpAKMARtSB2D1fj9eGwE6ArFoQGRF1gteTb/ZLvDuOZc8v86T6WG3isqFrg+tradEbsXyNazEw+zwXsuOYiErM1Csn/DIEDwM4wcxriGgFgEnM/A+C444AmM3M/fmcr8dLQyD6YsiVSzZGOwW3K2LtLiJfouEo1s1b55lhAco7vdPq8+ymMtgLZNVwcfGrsvhGAJvTf28GML/A57tG6p1YY0f5NN/rHrn3iKEP3g4DiQG0P9uOWy65BbXBWlfjAUo7mGsHq8+z3ynTj9x6GY6suQ5TTRRMJaWHW0PwUWZ+BwDSv881OI4B/JKI9hFRex7ng4jaiaiLiLqOHz9udJhjqqnGwA1OlE+d4javfig5hF1v7MLjNz4uzIZqqmvKEd9TiYajJR/MdYLV59nPiTgSCmR20TJDqLywrCMgohcAfEzwUG5/QmPmMPMxIjoXwK+I6HVm/q2D88HMmwBsAhTXkJNzzZC5zcXHqL+xE3oHexGbGTOcxI3cW+vmrSvriV+P1ed57kVTsPUlfwrakinGjv19mD9rasYgyNhbeWBpCJj5i0aPEdG7RHQeM79DROcBeM/gGsfSv98joqcBXA7gtwBsne8ny6+5UOhTlSuXwtHZ0uk6k8iqlkGd7Msl4JsvZp9nVQbCL5KjjHu3HcDa3Yczk36xJ/5kMomjR4/igw8+KOo4Cs24ceMwbdo0hEIhW8e7rSzeCWARgDXp38/oDyCi8QACzHw6/ffVAL5h93y/yXflIjONvEM7SZvtDAiEyeHJeP/D95FMjeW82/Xrm+0YyhGzz6D+fgBZWkB+IqoqLhZHjx7FxIkT0dzcDDJQO600mBkDAwM4evQopk+fbusct1lDUQBPAWgE0AvgZmY+QUTnA3iMmVuJ6K8APJ0+pQbAj5m50+x8q+ctdh2BzDTyD6P+CQTClgVbEJsZK6tUTr8QfQYJQOyKRnxr/syc44uRLVQKqaKvvfYaLrrooqoxAirMjNdffx2f/OQns+73RXSOmQcAtAjuPwagNf33XwBc6uT8UscsM0MaAnd0tnSibXtbjnYRg9GxpyOzqq+2iV+P6DPIUFb9s5sm53wOvUp+UCd3O/UIpZJwUW1GAHD+P5dGvX+ZITON/CM2M2YoYFfKqp2FxuizxoAw9bkubM9XbIY2dqaVbTFCJlyUD1WhPuo1VpkZTuIHMtaQS1Ndk9A9VO7idvr3eu5FU/Dr1487fu937O9DgEjY2AXINRI79vfh7PBI3uMmQDg+NRhs5Cotx4SLcvg+fvjhh7j99tuxb98+RKNRbNu2Dc3Nza6uKXcEeWCWI+2kG1q1dU6zix3hvHJD9F5vfanX8XuvXsfICAC5K/G1uw/nrSVUHw7h/Powjp1KYO3uw8LxVYqoY7l8H3/wgx9g0qRJePPNN3HffffhH/7BUozBEmkI8sDsg28UP/j6Uwdz+iXLqmYxflUyFxPRe63Hzntv5zpzL5oCYExV1G6QOKBzK4cChLPDI7YmxvmzpmLviqvw1prrsHfFVWVnBAB/vo8PPvgg1q1bl7nd0dGBRx99NO/rAcAzzzyDRYsWAQC+/OUvY8+ePXArHipdQ3kiypHesb/P8EunruC0qXUy1mBMpQWE7b6nVsfZuc6vXz+el7hcMED4SG0NBhNKE5qh4RGcHMqWpq7kpAg/vo933XUXFixYgGXLliGVSuHJJ5/Ef/zHf+Qc9/nPfx6nT5/Ouf+f//mf8cUvZpdy9fX14YILLgAA1NTUoK6uDgMDA2hoaMh7nNIQeIT6xbOD+mWSVc3Vg9F7LTrO7XVUN45ThdHkKGP8OTVYdcMlWLv7cI4R0F6/EvHj+9jc3IxoNIr9+/fj3XffxaxZsxCN5sqd/Pu//7vta4pW/24zo6RryCOcfvGOnUpIPZYqQvRe67Hz3i+/5kKE9D4cHapPPx/UHauZsanUhYpf38evfe1r+OEPf4gnnngCd955p/CYz3/+87jssstyfl544YWcY6dNm4a3334bADAyMoLBwUFMnjzZ1RjljsAjnH7xzq8PSz2WKkL0XmuzhtTG7/fpJBpE1/nH7a/ktJRUCQUIy6+5EGt3H86rgCxIZLqgqeSFil/fx5tuugkPPfQQkskkfvzjHwuPcbIjuOGGG7B582ZceeWV+NnPfoarrrrK9Y5AGgKPsLv1V1EDeqWgxyIpDEZxpdXPHspyw/SdSuC+bQdw77YDmCqYjIaSKeMnSc8HIs0hK0IBMjQwAIRjqTT8+D7W1tZi7ty5qK+vRzBor0WqGXfddRfa2trwiU98ApMnT8aTTz7p+prSEHiE6ItHMO7x+vN9fcIKUEn1YBbQVT83TnV7kqOMtbsPZ6QdVu08ZKsXMQCAlHRR0fGlIBdRrqRSKbz00kv46U9/6sn1xo0b59m1VGSMwCNEKaVmCV1GKaWlhJp+WMpjLAeMXke7cSV9CuOkiHmVsOqmnD9rKg6svBoLr2iE3nEgciQkRxlEkHErD/njH/+IT3ziE2hpacGMGTOKPRxDZPN6H/n4A7tMC3+0hIKE8ZrUvWIroEphPQW3r7GROFw+37qp6SBwfSSEwUQSRl6c+nAIB1Zebfp/GLkxCcD3br2sYuJWr732Wo7wWrUg+t996VlcLMrFEDSveM7V+fXhEFbdcInwS+j3RG1UiFRNLgIvXmO/VD9DQQKYIQoXhIKEtV++1HSMRuMKEiHFXPYGQEUaAnuGQLqGfGLH/j7h9tsJpxJJw0pOv6uSy6XYzY77Kl8Xl93X2Oz6fr1eyVHGiEHMWI0TmGGUzjrKXNLyChJ/kMFin1i7+7AnTUCMKjn9nqjLodhNv2IXBVbtHGOE1WtslPGz/KcHsfrZQzg1lDQVh3OL2VWtPgf6VEnROCu5iliSjdwR+ISXK0HRtYwmZK8malHhkpqjXirYWbG72TkZvZYMYNY3fonlPz0orL5Nphgnh5JgIC8jECTKJBzki53PgVYfKGVTyVRSmUhD4BNerpxF1ypIVbKdVBML/Mw8srMrMjrGjt/erBr45FDSNOfeDR8Jj23UwyHnX9F8Pgd+LyzKhngcaG4GAgHldzxe7BHl8Nvf/haf/vSnUVNTg5/97GeeXNOVISCiyUT0KyJ6I/17kuCYC4nogObnfSK6N/3YKiLq0zzW6mY8pYQdSQE7GH2praR/nU7A+uNX7TyUI10s8j2bPY/fsr52Ji+jYyg9PjPsNF/xklBACfSqu4m+UwkkzIrHBNSHQ3klDEi5EyiTfns70NMDMCu/29tLzhg0Njbihz/8Ib761a96dk23MYIVAPYw8xoiWpG+nSWOzcyHAVwGAEQUBNCHsR7GAPA9Zv5nl+MoOUTl6mc/HLFf3JNGP7nr0/pEGTxO/eKi443QrrBF51n5x730O4uK+PST1/JrLsR92w7k+NPVTl7acRilis6fNRXTVzznKuYTIBimewJjbiC3GUb6tFG7SLkTAB0dwNBQ9n1DQ8r9sfyUcB988EE0NDRg2bJl6afowEc/+lH83d/9Xd7DVJvQBALeOXTcGoIbAXwh/fdmAP8LOkOgowXAn5k5t/1UBaIvV3cqDRzU6IcIJ92fHcSqnYdyag+M/OKrnz0k/KI7EczTrrBF56n+ccDYP953KoE5a14UTjRO8vbtTF7zZ03FvdsOCM+3Mmpa41lnUHGrJxwKYCTFWbupcCiI//aZqdj2H28bupO88MW73blUvdxJr0ErVKP7beCHDLUfuDUEH2XmdwCAmd8honMtjr8NwE90991DRLcD6ALwdWY+KTqRiNoBtAPK1qgc0U9ckdogzg4bT8CjzJnJSDjpjnJmcuo7lcC9aX0aI04OJTOTtHq8PuvFDP1q283kZZThs/ynBzOTpbrD0B6jRzt5qUbkvm0HUB8JgRkYTCQRNMjcsTJq2t3L8Ig9Qxkgwq2fnSZsQfncK+8YvtbjQgEMj3DeGUZV58bxg8ZGxR0kuj9P/JCh9gNLQ0BELwD4mOChDidPRES1AG4A8IDm7g0Avgllp/5NAN8BINRpZeZNADYBSkGZk+cuJfSrrn/a0Y0f/77X0G2QSI6aTu5uOTmUNK10VR9TBccA74qk9G6iBwSqmskUY9XOQ7aqrLUreu2EK5pc7Rq1Y6cS2LG/z1zoTcPZ4VFsfUlZQepF2k6ZGFynsQBgLHZflW4cP+jsVGICWvdQJKLc7wJVhvo///M/TWWoS3pHwMyGoyCid4novPRu4DwA75lcah6APzDzu5prZ/4mon8F8G/2hl05fGv+THxr/kwAcO2HzheGseyBagT2rrgqr65XVmhz8o0mQ71LRuQ+suPeMqqaNWsGXxcOYdXOQ/n8azm7HqcKtVr07081yn34jhoH6OhQ3EGNjYoRyDM+oOK1DLUfuHUN7QSwCMCa9O9nTI79CnRuIdWIpG/eBOBVl+Mpa9xMFG6xKk7asb8PX3/qoOfFUXVhRUDNKq9fnfz7TiWyJkV1srVjnFLMeGvNdVn3/dOObsRf6jX8/9//wFjTxw6J5GhmRzP3oimZ3YJTYlc0Ct1NEo+JxVxP/Hq8lqF++eWXcdNNN+HkyZN49tlnsXLlShw6lN9iRcWtIVgD4CkiugtAL4CbAYCIzgfwGDO3pm9HAHwJwN268x8mosugfK+PCB6vKvLRkPcSs12BKPPGC04lkvinHd2m8QYCsmIH+nEkkqOGcQAtqtFR2bG/z9QIAOaZPnZR/8ef7xOnq1qJ0E2KhDK7Rkn54bUM9Wc/+1kcPXrUk2upuMo/YuYBZm5h5hnp3yfS9x9TjUD69hAzR5l5UHd+GzPPZOZPMfMNmt1BVWInb31qfRiP3HqZJzUKeswmIz9dVlarZAYsi7fs7FTODo9k1Q64lQGZce5428f+5PdvCw18kAixKxoN389wKIiV11+S9xglxaVcZKhlZXGJoZb9iyZ7NcCpNxhBl23qSoFCxEb0BXFuUzaPnvwAC69oRH3YvD8AYGyoUsz41vyZwvdTXyQoKT8uvvhi/OUvf8F3vvOdYg/FFCk6V6JY5cjrs4+cZvLkq4lf7qg1DEZCa3pCAQIIOVXWgOKS+vXrx7OKuGZ945fCFFGrFNaqz+GXFBVpCEoYJ5OD1epWlAZazHhEsSCMVe9aGQECMGFcjWmdhf51X3n9JcLXVakTSGW5uGTuv6RUkIagQrDKODrfoKGMmokjotJ2DUb/Dyk9XnJgwLLYTq9lpBpufa/gs8OjCAUJ9eGQoy50EkkhkDGCCmH5NRcqXasMEO0YrOIRsSsaM6J2buMQAUEvXCPCoSDG14qPdTKKc2rGPt714ZCxUWPgkVsvy/jonTzHkC4ADSiv6/hzctdYyVHG+HNq8Naa67B3xVXSCEhKBmkIKoT5s6Zi7ZcvRcBgFjOTEzZSMv3W/JkZvfrv3HKpq0ylFCsCemZon3vIQHrDrAJaSzBA+FDTwuvDkZRhUPf8+nDGKE6tDzvaBZ0cEneRK5cOb5Js4t1xND/SjMDqAJofaUa8u7SURwHgu9/9Li6++GJ86lOfQktLC3pEshgOka6hCmJMqsFckdPoXLMVqjZ4rS/qssPU9GRr5IqqD4cw/pwaHDuVwNrdh22LvAFAJBTAH785L3NbFDhPJEcxLhRAOBQ0fW2sJmpR0FekqFoOHd4k2cS742h/th1DSUViomewB+3PtgMAYjO9LTJzw6xZs9DV1YVIJIINGzbg/vvvx7Zt21xdU+4IKgyrPgVur713xVU4suY6fC/tSrHjNtJOtiLd+1CAcHZ4JKtvwdnhkZwOaUYMJVO2+gSfGkpavjZmE3U4FDQMMOufU+r7lx8dezoyRkBlKDmEjj2OZNWyePDBB7Fu3bqx5+jowKOPPpr39QBg7ty5iEQiAIArrrjCk+IyuSOoQAqRiqhX/dTvQvRZStq0VyA7LXZoeCQnKJscZUyKhBCprTE9TkW7IjdbjVu9NkbV3fXhEFbdcInhjsYoaFzV+v5lRu+guLDR6H47+C1D/YMf/ADz5s0zfNwu0hBIXON00tNPxtNXPCc87tRQEvsfGsvR37G/z1CJte9UAtNXPIfz68OYe9EU/Hxfn2P3mN3/xa7rTdYGlBeNdY3oGcz1tzfWlaYM9datW9HV1YXf/OY3eY9PRRoCiSe4mfSM4gGiVbZZ/wTVraTqB6n+fP2uxAqz/0Wu9CuXzpbOrBgBAERCEXS2lJ4M9QsvvIDOzk785je/wTnnnONqfIA0BJIis2N/H84Oj+TcHwqQcJVtVLClRfXijzJnyXJ4hVzpVyZqQLhjTwd6B3vRWNeIzpZO14Fir2Wo9+/fj7vvvhu/+MUvcO65Vr3A7CENgaSorN19WCjfMGFcjXCy1a/IrTKXvOyRLKl8YjNjnmcIeS1DvXz5cpw5cwY333wzAKVj486dO11dUxoCSVExy/AxQrsit6OxJHP3JcXEaxnqF154wZPraJHpo5KiYpSuaTffXpSmme+1JBKvKRcZarkjkBQVUbqmk3x7q0I3mbsvKSaqDHWpIw2BpKh4kYWjr2mQGT0SLcwMqoCeHU5ghy1lyekJpcDs2bO5q6ur2MOQSCQlzltvvYWJEyciGo1WjTFgZgwMDOD06dOYPn161mNEtI+ZZ+vPcbUjIKKbAawC8EkAlzOzcHYmomsBrAMQhNLLeE36/skAtgFohtKz+BZmPulmTBKJRKIybdo0HD16FMePHy/2UArKuHHjMG3aNNvHu3UNvQpgAYDvGx1AREEA/wKlef1RAC8T0U5m/iOAFQD2MPMaIlqRvv0PLsckkUgkAIBQKJSzKpbk4rZ5/WvMfNjisMsBvMnMf2HmYQBPArgx/diNADan/94MYL6b8UgkEonEOYVIH50K4G3N7aPp+wDgo8z8DgCkfxuWyRFROxF1EVFXtW3zJBKJxE8sXUNE9AKAjwke6mDmZ2w8hyhC4zhCzcybAGwClGCx0/MlEolEIsbSEDCzsQaqPY4CuEBzexqAY+m/3yWi85j5HSI6D8B7di64b9++fiJy35bHHxoA9Bd7ECbI8bmn1Mcox+eOUh8fkP8Ym0R3FqKO4GUAM4hoOoA+ALcB+Gr6sZ0AFgFYk/5tZ4cBZp7iwzg9gYi6ROlZpYIcn3tKfYxyfO4o9fEB3o/RVYyAiG4ioqMArgTwHBHtTt9/PhHtAgBmHgFwD4DdAF4D8BQzH0pfYg2ALxHRG1Cyita4GY9EIpFInONqR8DMTwN4WnD/MQCtmtu7AOwSHDcAoMXNGCQSiUTiDik65z2bij0AC+T43FPqY5Tjc0epjw/weIxlKTEhkUgkEu+QOwKJRCKpcqQhkEgkkipHGgKHENFkIvoVEb2R/j1JcMyFRHRA8/M+Ed2bfmwVEfVpHmvNeZICjDF93BEi6k6Po8vp+X6Oj4guIKJfE9FrRHSIiJZpHvPlNSSia4noMBG9mda+0j9ORPRo+vFXiOjTds8t0Phi6XG9QkS/I6JLNY8J3+sCj+8LRDSoed8esntuAce4XDO+V4loNC2O6ftrSESPE9F7RPSqweP+ff6YWf44+AHwMIAV6b9XAPh/LY4PAvhPAE3p26sA/H0pjBGK4muD2//Rj/EBOA/Ap9N/TwTwJwAX+/Uapt+nPwP4KwC1AA6qz6c5phXA81Cq5a8A8Hu75xZofH8NYFL673nq+Mze6wKP7wsA/i2fcws1Rt3x1wN4sYCv4X8B8GkArxo87tvnT+4InONUKK8FwJ+ZuZCV0G7F/PwWA7S8PjO/w8x/SP99GkoNip8dZszEEVVuBPAjVngJQD0pFfF2zvV9fMz8Ox6TcX8JShV/oXDzGhTi9cvneb4C4Cc+jEMIM/8WwAmTQ3z7/ElD4BzbQnlpbkPuh+me9Nbuca/dLg7HyAB+SUT7iKg9j/P9Hh8AgIiaAcwC8HvN3V6/hmbiiFbH2Dm3EOPTcheU1aOK0Xtd6PFdSUQHieh5IrrE4bmFGiOIKALgWgA/19zt92tohW+fP9mqUgCZCO05vE4tgBsAPKC5ewOAb0L5UH0TwHcA3FmkMc5h5mNEdC6AXxHR6+lViWs8fA0nQPky3svM76fv9uQ11D+V4D59brXRMZ4IK1pg+zmIaC4UQ/A3mrt9e68djO8PUFykZ9JxnR0AZtg81wucPM/1APYys3aF7vdraIVvnz9pCASwidAeETkRypsH4A/M/K7m2pm/iehfAfxbscbISgU4mPk9Inoayhbzt8hTDNDr8RFRCIoRiDPzds21PXkNdZiJI1odU2vj3EKMD0T0KQCPAZjHSuU+ANP3umDj0xhyMPMuIlpPRA12zi3UGDXk7OQL8Bpa4dvnT7qGnKMK5QHWQnk5Psb0xKdyE5Qub15jOUYiGk9EE9W/AVytGYuT/9Gv8RGAHwB4jZm/q3vMj9cwI46Y3sndlh6nfty3p7M3rgAwmHZt2TnX9/ERUSOA7QDamPlPmvvN3utCju9j6fcVRHQ5lPlnwM65hRpjemx1AP4rNJ/LAr2GVvj3+fMrAl6pPwCiAPYAeCP9e3L6/vMB7NIcF4HyIa/Tnb8FQDeAV9Jv1nnFGCOUDIOD6Z9DUPpLmJ5f4PH9DZTt7SsADqR/Wv18DaFkZfwJSgZGR/q+xQAWp/8mKG1X/5x+/tlm5/rwvlqN7zEAJzWvV5fVe13g8d2Tfv6DUILZf13I18/OGNO3/xbAk7rzfH8NoSwa3wGQhLL6v6tQnz8pMSGRSCRVjnQNSSQSSZUjDYFEIpFUOdIQSCQSSZUjDYFEIpFUOdIQSCQSSZUjDYFEIpFUOdIQSCQSSZXz/wP2ipV8ahoq8gAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Create a Dataset object\n",
        "\n",
        "data_set = Data()\n",
        "data_set.plot_stuff()\n",
        "data_set.y = data_set.y.view(-1)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.12"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}