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GA Ns Py Torch

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    "# Generative Adversarial Networks (GANs)\n",
    "### What is a GAN?\n",
    "\n",
    "In 2014, [Goodfellow et al.](https://arxiv.org/abs/1406.2661) presented a method for training generative models called Generative Adversarial Networks (GANs for short). In a GAN, we build two different neural networks. Our first network is a traditional classification network, called the **discriminator**. We will train the discriminator to take images, and classify them as being real (belonging to the training set) or fake (not present in the training set). Our other network, called the **generator**, will take random noise as input and transform it using a neural network to produce images. The goal of the generator is to fool the discriminator into thinking the images it produced are real.\n",
    "\n",
    "We can think of this back and forth process of the generator ($G$) trying to fool the discriminator ($D$), and the discriminator trying to correctly classify real vs. fake as a minimax game:\n",
    "$$\\underset{G}{\\text{minimize}}\\; \\underset{D}{\\text{maximize}}\\; \\mathbb{E}_{x \\sim p_\\text{data}}\\left[\\log D(x)\\right] + \\mathbb{E}_{z \\sim p(z)}\\left[\\log \\left(1-D(G(z))\\right)\\right]$$\n",
    "where $z \\sim p(z)$ are the random noise samples, $G(z)$ are the generated images using the neural network generator $G$, and $D$ is the output of the discriminator, specifying the probability of an input being real. In [Goodfellow et al.](https://arxiv.org/abs/1406.2661), they analyze this minimax game and show how it relates to minimizing the Jensen-Shannon divergence between the training data distribution and the generated samples from $G$.\n",
    "\n",
    "To optimize this minimax game, we will aternate between taking gradient *descent* steps on the objective for $G$, and gradient *ascent* steps on the objective for $D$:\n",
    "1. update the **generator** ($G$) to minimize the probability of the __discriminator making the correct choice__. \n",
    "2. update the **discriminator** ($D$) to maximize the probability of the __discriminator making the correct choice__.\n",
    "\n",
    "While these updates are useful for analysis, they do not perform well in practice. Instead, we will use a different objective when we update the generator: maximize the probability of the **discriminator making the incorrect choice**. This small change helps to allevaiate problems with the generator gradient vanishing when the discriminator is confident. This is the standard update used in most GAN papers, and was used in the original paper from [Goodfellow et al.](https://arxiv.org/abs/1406.2661). \n",
    "\n",
    "In this assignment, we will alternate the following updates:\n",
    "1. Update the generator ($G$) to maximize the probability of the discriminator making the incorrect choice on generated data:\n",
    "$$\\underset{G}{\\text{maximize}}\\;  \\mathbb{E}_{z \\sim p(z)}\\left[\\log D(G(z))\\right]$$\n",
    "2. Update the discriminator ($D$), to maximize the probability of the discriminator making the correct choice on real and generated data:\n",
    "$$\\underset{D}{\\text{maximize}}\\; \\mathbb{E}_{x \\sim p_\\text{data}}\\left[\\log D(x)\\right] + \\mathbb{E}_{z \\sim p(z)}\\left[\\log \\left(1-D(G(z))\\right)\\right]$$\n",
    "\n",
    "### What else is there in this notebook?\n",
    "![caption](gan_outputs_pytorch.png)"
   ]
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   "source": [
    "## Setup"
   ]
  },
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   "execution_count": 2,
   "metadata": {
    "id": "CYVwNTuFdtoX"
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   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.nn import init\n",
    "import torchvision\n",
    "import torchvision.transforms as T\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader\n",
    "from torch.utils.data import sampler\n",
    "import torchvision.datasets as dset\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "def show_images(images):\n",
    "    images = np.reshape(images, [images.shape[0], -1])  # images reshape to (batch_size, D)\n",
    "    sqrtn = int(np.ceil(np.sqrt(images.shape[0])))\n",
    "    sqrtimg = int(np.ceil(np.sqrt(images.shape[1])))\n",
    "\n",
    "    fig = plt.figure(figsize=(sqrtn, sqrtn))\n",
    "    gs = gridspec.GridSpec(sqrtn, sqrtn)\n",
    "    gs.update(wspace=0.05, hspace=0.05)\n",
    "\n",
    "    for i, img in enumerate(images):\n",
    "        ax = plt.subplot(gs[i])\n",
    "        plt.axis('off')\n",
    "        ax.set_xticklabels([])\n",
    "        ax.set_yticklabels([])\n",
    "        ax.set_aspect('equal')\n",
    "        plt.imshow(img.reshape([sqrtimg,sqrtimg]))\n",
    "    return \n",
    "\n",
    "def preprocess_img(x):\n",
    "    return 2 * x - 1.0\n",
    "\n",
    "def deprocess_img(x):\n",
    "    return (x + 1.0) / 2.0\n",
    "\n",
    "def rel_error(x,y):\n",
    "    return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))\n",
    "\n",
    "def count_params(model):\n",
    "    \"\"\"Count the number of parameters in the current TensorFlow graph \"\"\"\n",
    "    param_count = np.sum([np.prod(p.size()) for p in model.parameters()])\n",
    "    return param_count\n",
    "\n",
    "answers = dict(np.load('gan-checks-tf.npz'))"
   ]
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   "source": [
    "## Dataset"
   ]
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     "text": [
      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./utils/datasets/MNIST_data/MNIST/raw/train-images-idx3-ubyte.gz\n"
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     "text": [
      "Extracting ./utils/datasets/MNIST_data/MNIST/raw/train-images-idx3-ubyte.gz to ./utils/datasets/MNIST_data/MNIST/raw\n",
      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./utils/datasets/MNIST_data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
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     "text": [
      "Extracting ./utils/datasets/MNIST_data/MNIST/raw/train-labels-idx1-ubyte.gz to ./utils/datasets/MNIST_data/MNIST/raw\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./utils/datasets/MNIST_data/MNIST/raw/t10k-images-idx3-ubyte.gz\n",
      "\n"
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     "text": [
      "Extracting ./utils/datasets/MNIST_data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./utils/datasets/MNIST_data/MNIST/raw\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./utils/datasets/MNIST_data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
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     "text": [
      "Extracting ./utils/datasets/MNIST_data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./utils/datasets/MNIST_data/MNIST/raw\n",
      "Processing...\n",
      "Done!\n"
     ]
    },
    {
     "name": "stderr",
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     "text": [
      "/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:469: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)\n",
      "  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n"
     ]
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\n",
      "text/plain": [
       "<Figure size 864x864 with 128 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "class ChunkSampler(sampler.Sampler):\n",
    "    \"\"\"Samples elements sequentially from some offset. \n",
    "    Arguments:\n",
    "        num_samples: # of desired datapoints\n",
    "        start: offset where we should start selecting from\n",
    "    \"\"\"\n",
    "    def __init__(self, num_samples, start=0):\n",
    "        self.num_samples = num_samples\n",
    "        self.start = start\n",
    "\n",
    "    def __iter__(self):\n",
    "        return iter(range(self.start, self.start + self.num_samples))\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.num_samples\n",
    "\n",
    "NUM_TRAIN = 50000\n",
    "NUM_VAL = 5000\n",
    "\n",
    "NOISE_DIM = 96\n",
    "batch_size = 128\n",
    "\n",
    "mnist_train = dset.MNIST('./utils/datasets/MNIST_data', train=True, download=True,\n",
    "                           transform=T.ToTensor())\n",
    "loader_train = DataLoader(mnist_train, batch_size=batch_size,\n",
    "                          sampler=ChunkSampler(NUM_TRAIN, 0))\n",
    "\n",
    "mnist_val = dset.MNIST('./utils/datasets/MNIST_data', train=True, download=True,\n",
    "                           transform=T.ToTensor())\n",
    "loader_val = DataLoader(mnist_val, batch_size=batch_size,\n",
    "                        sampler=ChunkSampler(NUM_VAL, NUM_TRAIN))\n",
    "\n",
    "\n",
    "imgs = loader_train.__iter__().next()[0].view(batch_size, 784).numpy().squeeze()\n",
    "show_images(imgs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oXmeqMF_dtoe"
   },
   "source": [
    "## Random Noise\n",
    "Generate uniform noise from -1 to 1 with shape `[batch_size, dim]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "mTSBJLnDdtoe"
   },
   "outputs": [],
   "source": [
    "def sample_noise(batch_size, dim):\n",
    "    \"\"\"\n",
    "    Generate a PyTorch Tensor of uniform random noise.\n",
    "\n",
    "    Input:\n",
    "    - batch_size: Integer giving the batch size of noise to generate.\n",
    "    - dim: Integer giving the dimension of noise to generate.\n",
    "    \n",
    "    Output:\n",
    "    - A PyTorch Tensor of shape (batch_size, dim) containing uniform\n",
    "      random noise in the range (-1, 1).\n",
    "    \"\"\"\n",
    "    return torch.FloatTensor(batch_size, dim).uniform_(-1, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pyZvX4kYdtoh"
   },
   "source": [
    "Check noise is the correct shape and type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "Nqw_i7Yadtoh",
    "outputId": "36d3917f-d5cd-43ef-8bb2-ac3ae6896d84"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All tests passed!\n"
     ]
    }
   ],
   "source": [
    "def test_sample_noise():\n",
    "    batch_size = 3\n",
    "    dim = 4\n",
    "    torch.manual_seed(231)\n",
    "    z = sample_noise(batch_size, dim)\n",
    "    np_z = z.cpu().numpy()\n",
    "    assert np_z.shape == (batch_size, dim)\n",
    "    assert torch.is_tensor(z)\n",
    "    assert np.all(np_z >= -1.0) and np.all(np_z <= 1.0)\n",
    "    assert np.any(np_z < 0.0) and np.any(np_z > 0.0)\n",
    "    print('All tests passed!')\n",
    "    \n",
    "test_sample_noise()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SS_F4WRHdtok"
   },
   "source": [
    "## Flatten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "AliSaexBdtok"
   },
   "outputs": [],
   "source": [
    "class Flatten(nn.Module):\n",
    "    def forward(self, x):\n",
    "        N, C, H, W = x.size() # read in N, C, H, W\n",
    "        return x.view(N, -1)  # \"flatten\" the C * H * W values into a single vector per image\n",
    "    \n",
    "class Unflatten(nn.Module):\n",
    "    \"\"\"\n",
    "    An Unflatten module receives an input of shape (N, C*H*W) and reshapes it\n",
    "    to produce an output of shape (N, C, H, W).\n",
    "    \"\"\"\n",
    "    def __init__(self, N=-1, C=128, H=7, W=7):\n",
    "        super(Unflatten, self).__init__()\n",
    "        self.N = N\n",
    "        self.C = C\n",
    "        self.H = H\n",
    "        self.W = W\n",
    "    def forward(self, x):\n",
    "        return x.view(self.N, self.C, self.H, self.W)\n",
    "\n",
    "def initialize_weights(m):\n",
    "    if isinstance(m, nn.Linear) or isinstance(m, nn.ConvTranspose2d):\n",
    "        init.xavier_uniform_(m.weight.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cjQipV5idton"
   },
   "source": [
    "## CPU / GPU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "Ss-M5fZwdton"
   },
   "outputs": [],
   "source": [
    "dtype = torch.FloatTensor\n",
    "dtype = torch.cuda.FloatTensor # COMMENT THIS LINE IF YOU'RE ON A CPU!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OcpYcDNLdtoq"
   },
   "source": [
    "# Discriminator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "gT4rloGkdtor"
   },
   "outputs": [],
   "source": [
    "def discriminator():\n",
    "    \"\"\"\n",
    "    Build and return a PyTorch model implementing the architecture.\n",
    "    \"\"\"\n",
    "    model = nn.Sequential( Flatten(),\n",
    "                           nn.Linear(784, 256),\n",
    "                           nn.LeakyReLU(inplace=True),\n",
    "                           nn.Linear(256,256),\n",
    "                           nn.LeakyReLU(inplace=True),\n",
    "                           nn.Linear(256,1)\n",
    "                         )\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0MbstME3dtot"
   },
   "source": [
    "Test to make sure the number of parameters in the discriminator is correct:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "APf0Nevndtot",
    "outputId": "01740507-7878-47be-f656-be677d6079a7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correct number of parameters in discriminator.\n"
     ]
    }
   ],
   "source": [
    "def test_discriminator(true_count=267009):\n",
    "    model = discriminator()\n",
    "    cur_count = count_params(model)\n",
    "    if cur_count != true_count:\n",
    "        print('Incorrect number of parameters in discriminator. Check your achitecture.')\n",
    "    else:\n",
    "        print('Correct number of parameters in discriminator.')     \n",
    "\n",
    "test_discriminator()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "K03pVpqqdtow"
   },
   "source": [
    "# Generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "4GdEZ7grdtow"
   },
   "outputs": [],
   "source": [
    "def generator(noise_dim=NOISE_DIM):\n",
    "    \"\"\"\n",
    "    Build and return a PyTorch model implementing the architecture.\n",
    "    \"\"\"\n",
    "    model = nn.Sequential( nn.Linear(noise_dim,1024),\n",
    "                           nn.ReLU(inplace=True),\n",
    "                           nn.Linear(1024,1024),\n",
    "                           nn.ReLU(inplace=True),\n",
    "                           nn.Linear(1024,784),\n",
    "                           nn.Tanh()\n",
    "                         )\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cBIjphBKdtoz"
   },
   "source": [
    "Test to make sure the number of parameters in the generator is correct:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "Lfc_zIWJdtoz",
    "outputId": "0c1c13cb-7307-4a91-abc2-3460b3886cc8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correct number of parameters in generator.\n"
     ]
    }
   ],
   "source": [
    "def test_generator(true_count=1858320):\n",
    "    model = generator(4)\n",
    "    cur_count = count_params(model)\n",
    "    if cur_count != true_count:\n",
    "        print('Incorrect number of parameters in generator. Check your achitecture.')\n",
    "    else:\n",
    "        print('Correct number of parameters in generator.')\n",
    "\n",
    "test_generator()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "xnMGNozNdto2"
   },
   "source": [
    "# GAN Loss\n",
    "\n",
    "Compute the generator and discriminator loss. The generator loss is:\n",
    "$$\\ell_G  =  -\\mathbb{E}_{z \\sim p(z)}\\left[\\log D(G(z))\\right]$$\n",
    "and the discriminator loss is:\n",
    "$$ \\ell_D = -\\mathbb{E}_{x \\sim p_\\text{data}}\\left[\\log D(x)\\right] - \\mathbb{E}_{z \\sim p(z)}\\left[\\log \\left(1-D(G(z))\\right)\\right]$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "9yu9yAO6dto2"
   },
   "outputs": [],
   "source": [
    "def bce_loss(input, target):\n",
    "    \"\"\"pa \n",
    "    Inputs:\n",
    "    - input: PyTorch Tensor of shape (N, ) giving scores.\n",
    "    - target: PyTorch Tensor of shape (N,) containing 0 and 1 giving targets.\n",
    "\n",
    "    Returns:\n",
    "    - A PyTorch Tensor containing the mean BCE loss over the minibatch of input data.\n",
    "    \"\"\"\n",
    "    neg_abs = - input.abs()\n",
    "    loss = input.clamp(min=0) - input * target + (1 + neg_abs.exp()).log()\n",
    "    return loss.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "AOCyZALXdto5"
   },
   "outputs": [],
   "source": [
    "def discriminator_loss(logits_real, logits_fake):\n",
    "    \"\"\"\n",
    "    Computes the discriminator loss described above.\n",
    "    \n",
    "    Inputs:\n",
    "    - logits_real: PyTorch Tensor of shape (N,) giving scores for the real data.\n",
    "    - logits_fake: PyTorch Tensor of shape (N,) giving scores for the fake data.\n",
    "    \n",
    "    Returns:\n",
    "    - loss: PyTorch Tensor containing (scalar) the loss for the discriminator.\n",
    "    \"\"\"\n",
    "    N, _ = logits_real.size() \n",
    "    loss = (bce_loss(logits_real, torch.ones(N).type(dtype)))+(bce_loss(logits_fake, torch.zeros(N).type(dtype)))\n",
    "    return loss\n",
    "\n",
    "def generator_loss(logits_fake):\n",
    "    \"\"\"\n",
    "    Computes the generator loss described above.\n",
    "\n",
    "    Inputs:\n",
    "    - logits_fake: PyTorch Tensor of shape (N,) giving scores for the fake data.\n",
    "    \n",
    "    Returns:\n",
    "    - loss: PyTorch Tensor containing the (scalar) loss for the generator.\n",
    "    \"\"\"\n",
    "    N, _ = logits_fake.size()\n",
    "    loss = (bce_loss(logits_fake, torch.ones(N).type(dtype)))\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TBsofz6Adto7"
   },
   "source": [
    "Check generator and discriminator loss. We should see errors < 1e-7."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "9qVTG21-dto7",
    "outputId": "fd6dbf37-e87d-4e0a-9781-58de0fc98eea"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum error in d_loss: 2.83811e-08\n"
     ]
    }
   ],
   "source": [
    "def test_discriminator_loss(logits_real, logits_fake, d_loss_true):\n",
    "    d_loss = discriminator_loss(torch.Tensor(logits_real).type(dtype),\n",
    "                                torch.Tensor(logits_fake).type(dtype)).cpu().numpy()\n",
    "    print(\"Maximum error in d_loss: %g\"%rel_error(d_loss_true, d_loss))\n",
    "\n",
    "test_discriminator_loss(answers['logits_real'], answers['logits_fake'],\n",
    "                        answers['d_loss_true'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "AK2fPRgNdto-",
    "outputId": "6b71b6d9-92e2-4206-a311-5cda0d1060d0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum error in g_loss: 3.4188e-08\n"
     ]
    }
   ],
   "source": [
    "def test_generator_loss(logits_fake, g_loss_true):\n",
    "    g_loss = generator_loss(torch.Tensor(logits_fake).type(dtype)).cpu().numpy()\n",
    "    print(\"Maximum error in g_loss: %g\"%rel_error(g_loss_true, g_loss))\n",
    "\n",
    "test_generator_loss(answers['logits_fake'], answers['g_loss_true'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hZ9a-AOgdtpA"
   },
   "source": [
    "# Optimizing our loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "sJeiH6ZJdtpA"
   },
   "outputs": [],
   "source": [
    "def get_optimizer(model):\n",
    "    \"\"\"\n",
    "    Construct and return an Adam optimizer for the model with learning rate 1e-3,\n",
    "    beta1=0.5, and beta2=0.999.\n",
    "    \n",
    "    Input:\n",
    "    - model: A PyTorch model that we want to optimize.\n",
    "    \n",
    "    Returns:\n",
    "    - An Adam optimizer for the model with the desired hyperparameters.\n",
    "    \"\"\"\n",
    "    optimizer = optim.Adam(model.parameters(), lr = 1e-3, betas = (0.5,0.999))\n",
    "    return optimizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5eeMguyGdtpD"
   },
   "source": [
    "# Training a GAN!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "0Qj-KOMBdtpE"
   },
   "outputs": [],
   "source": [
    "def run_a_gan(D, G, D_solver, G_solver, discriminator_loss, generator_loss, show_every=250, \n",
    "              batch_size=128, noise_size=96, num_epochs=10):\n",
    "    \"\"\"\n",
    "    Train a GAN!\n",
    "    \n",
    "    Inputs:\n",
    "    - D, G: PyTorch models for the discriminator and generator\n",
    "    - D_solver, G_solver: torch.optim Optimizers to use for training the\n",
    "      discriminator and generator.\n",
    "    - discriminator_loss, generator_loss: Functions to use for computing the generator and\n",
    "      discriminator loss, respectively.\n",
    "    - show_every: Show samples after every show_every iterations.\n",
    "    - batch_size: Batch size to use for training.\n",
    "    - noise_size: Dimension of the noise to use as input to the generator.\n",
    "    - num_epochs: Number of epochs over the training dataset to use for training.\n",
    "    \"\"\"\n",
    "    iter_count = 0\n",
    "    for epoch in range(num_epochs):\n",
    "        for x, _ in loader_train:\n",
    "            if len(x) != batch_size:\n",
    "                continue\n",
    "            D_solver.zero_grad()\n",
    "            real_data = x.type(dtype)\n",
    "            logits_real = D(2* (real_data - 0.5)).type(dtype)\n",
    "\n",
    "            g_fake_seed = sample_noise(batch_size, noise_size).type(dtype)\n",
    "            fake_images = G(g_fake_seed).detach()\n",
    "            logits_fake = D(fake_images.view(batch_size, 1, 28, 28))\n",
    "\n",
    "            d_total_error = discriminator_loss(logits_real, logits_fake)\n",
    "            d_total_error.backward()        \n",
    "            D_solver.step()\n",
    "\n",
    "            G_solver.zero_grad()\n",
    "            g_fake_seed = sample_noise(batch_size, noise_size).type(dtype)\n",
    "            fake_images = G(g_fake_seed)\n",
    "\n",
    "            gen_logits_fake = D(fake_images.view(batch_size, 1, 28, 28))\n",
    "            g_error = generator_loss(gen_logits_fake)\n",
    "            g_error.backward()\n",
    "            G_solver.step()\n",
    "\n",
    "            if (iter_count % show_every == 0):\n",
    "                print('Iter: {}, D: {:.4}, G:{:.4}'.format(iter_count,d_total_error.item(),g_error.item()))\n",
    "                imgs_numpy = fake_images.data.cpu().numpy()\n",
    "                show_images(imgs_numpy[0:16])\n",
    "                plt.show()\n",
    "                print()\n",
    "            iter_count += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "B9miV1qfdtpG",
    "outputId": "dbb0c084-b3f3-4983-ecb8-bee24e7d8dac",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter: 0, D: 1.328, G:0.7202\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 250, D: 1.43, G:0.6752\n"
     ]
    },
    {
     "data": {
      "image/png": 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yhkcddZSkgOzwiKZz6623SpLe9a53SZrQ7vDPnH322ZKy+c9S2BfcA83Ql2fOnTvX5sXn4pdqZQQlhE2UqIJov1rEgCTYHdCMGTNyVSZIFTyOeEx97macj3v99ddLCihz6qmnSgqxSVAC6Q0q4h188MEHJU3EY6mcQIJxX5BscHCwaPEzdhu2BfMyc+ZMQztsJKqVQBxfOkbMEw/p5s2bddNNN0kKGgh2Dx5xkJT38ZDiP6ACZO/evcY3CIT2gE0/MDBgPDY2NhbisWATg0ZLliwxuw07F5SBT/hDg4A/vKXt7e3mXwBJabUCX2hraB3YqcQ5ySffu3evNfvDjkfDYu/09vZm1pBGC6ClX8NZs2aZPY7XHB7ZH2Rk+X3KGre3t+vaa6/NjIfMJvY+vL7//e/P8Mx6wWN/f7/NIbFZ1gdNw+9TKCFsokQVRGURlioIPJrYXejkw8PDZgt5SYUN4zN2eJ94486dO81+QCLdcccdkoK3DWmEdDz22GMlhfgWyCKF2CQ2ATYCkmxkZKRotQ4ePbzc2McjIyM2drQHUIT5QKL7xtfEHHt7e817CrJQxYKtBI+gHHFA4pvwWF1dbTYePPq2q8PDw7lqHbLBvM3W29trfFBlBKLyPmgDf6w/qDA0NGT8oRHceOONkqTvfOc7kvINBTx/IEuhUDCbkPswdt7fu3dv0QJ2H6nAJt+3b5/NFZodiOvb3bDWvM+c9PX12XhYQ/wx3/jGNzLXID5PRRbxbLQ/KcTV43a6Ma9DQ0P/9wL2GTNmZCr5fR/emTNnmrOBG+KCZ3NDBN+ZTNz7Z511lqm+vj8um4pSLTY3heFMAJP61FNPmRrFA+qTqX15HUX6EBPJGGbPnm3j4EH0ZWZ8lzAUm5eSuNNPP90eSN9dn/mDRxw6zAnqP/dau3atmSasBw4tKC5gX7p0aSEeIw8+G+WAAw4w5xJjYi0xUVCfTz75ZElhTn/6059Kkt75znfamvg19CGwH//4x5KCKYC6yOePPfaYjZFrAQYAx6ZNmzJrOHv27Mw+9WvY1tZmZh2C2feBhkdKInGG3XnnnZImzDFSExHQPMB+n9JVEhMKIAKk1qxZY45S5pw1JLzjO6NASSVOlKiCqCzC1tbWFiRZjx3S2VAvxsbGDCGQEEgmVK/zzjtPUkASnCdIks7OTpPOSKyrr75aUkgT+/rXvy4pFBNT2I5URPJKQS3BiQTSIlm9MY9jjTKr733ve5KyQXu0AniCR5wulKExftADVbWrq8sQlu9ccsklkoKjAh4J++DE8UkNhULBUJd0NsI7IO6+fftyTidCEfCHurtv3z7ji+QGEIM5xNnH+6h8cVsY+EC7YN3hG9UYBMPRyDVAxUKhYNoFyArvIJt3OrGGOCvp+cW1R0dHLSTjk/z5//zzz8+877tYdnR02D4lzEQyDE4y1vCDH/ygJOmaa64xnqSwF2tqakyzYA+hCkf7NSFsokSVTmURlpAHEoWnH6l/0EEHWfiG0AtojNRBR6dBFXYgkq22ttYkKU2+KB7GJsARw/dAemwt7JNVq1aZNAPRkZigUhzykEIbHHgEpbjOkiVLzM7BYYItB5IyL9hhSHYQqaqqypL+SUz/5Cc/KUl6xStekeGR0BBtb3zLmlWrVpn9BY8gPlJ7z549xmNzc3Mh/i4oBXIcddRRxt/9998vKWgMrCl2N8kG/vS+qVOnGhoTxiMEQhIGc8Ua/uxnP5MUNB/4W7lypc0968peobjCNyijtzTozpzFXR3RHrBD8amgxbHufp8yhrq6Ohs7a3jppZdKCmvI/sAZh73OesHjo48+miuWYX1w2vkUWighbKJEFUT7ZcNCPul5ypQpZpMhIZBuBN0JgeD5I4GBoPODDz5oAXlONCPFCwnPNUAhpDnhBiQw0lIK0hX7DI+yDwnAI4gDaoCara2tZs9iM+KlxROONxWvJvYYie9PPPGEaSK07gStaKMJz2gmoCY2IDxSJC0Fyc18MMdx4jg2LOuMxsE6zZw506Q8n+HBJEEA/kBnvJ4keaxatcrGB0qDrKwJ2hLlbD5BgXX64x//aHzxSvICKO0RFhsWiu1z+GIN4ZE1IunkJS95iaQQhmS/kJr48MMP2z79wx/+ICmEMEFteCaRh++j+cAjfpyYRzQw5iPWkmJKCJsoUQVR2eR/9H6QA3sUyfLss8+arg/6II0hJBu2AMiCJKuurja7B08zkonT0EE70Dm+vxQQb2xszLQAxkqQu5QmgSbAtZG83KO9vd14RIsgQZs4HzwyfpCJQH91dbVJX6Q/EpUkEDzdSHbsYWxvxlcoFMxWg2/S+9AqYiJGTUoi805Du7Vr19pcUTzAGjKXvJKIQJMxrikFdMF+Z+8wj4wNLz5rjnc5PjkC/rAr/Vm5nvzJb/yO5Iz169ebJx1thHlhvPCGhkVqJQkd4+PjNmaiBnh4OZGe8bEerG1cuM732Aesuy9gKEUJYRMlqiAqi7BIKm8HEo+qra01yeQzipDapGWRpoe+f84550iayArhzBZiYUibL37xi5KCNAYdaMcJSiG1m5qacmmCoGCp9iKME0TwzcTq6+tN6mLrkemFHUirEKQ2dihJ4HfffbelURLvZcwUrONp9JlQjAukbW5uNqkfnw4uBaSPiTXEDmTM+ANqa2tN+0GDAEFACuxs+MMbSluVn/3sZ8Yf7XLQbG655RZJQWNA64BPPNR4R2fOnGk2MloR8w3yluIR9MTHwl6rra2138Ij/4PKeOUp+2N+iF/fdddd1uaHNq/sU5rBg6hcE23N2+stLS2mMUEgPfu1FCWETZSogqjs4wyS8fQjiclrXb16tXmJ8Wpib5DRwv/kVWL/4RXt7Ow0NIHwDuN5xA5Csv/oRz+aGPzfpFHcwtO3KOF/fy4pBCKjKeDNxQP85JNP5nKbQQMyr9A4QGVQAx737NljUpf5gke0F8oUQQUaWKMpIJ1HRkZyp9szLpAoJjzH8Ie9B1o++uijuUO+4IesK+bXZyCRG7tjxw5DE2wytA7mH7ue94nDQmglAwMDts88f6UalPlT7EFa9trjjz9ua4ifgf2IRoBmxbr4Ndy+fXtun7LejJc1JF7MPmUN2TfxGrJP8ROU2qdQQthEiSqIysZhKc3yVTrYTFOnTrU8z+OOO05SkNLYClSw0KoUu4643OGHH26eWsqVICQs0pC4GlISaYhd1t3dbRId7QC7jvH4KoiZM2cW4u8XO7qD+8IjEhwpiUcRiX7uuedKClkzS5YsMcS+++67MzyCTNh88ILUhkfGt23btkzTOSmgJ/MSnw97wAEHZJrB+2bgs2fPtmuzVlyfueTsVNaJPGjQ59BDD7XxkseLvQ8qYYP79rNoFLz/9NNPW7YXWoXPT1+zZk1mDdva2jJr6O3AlpYWizSQpcQ12Re0u/GHteFbOOyww0w7wS4HFdEq0CbhhWcBzYS13bZtm+0dtAO8xukwrESJnke0Xwc6IxmIEYG0hULB9Hb0eRCNWB7SjqwfJDxNnltbWy2HmKwPEBw7jswQJD+I66muri5TESEFlIZ8LvHixYszWgQ2BeOOecQGQRoijRkXWgN2CB7y6dOnW0wZHiE85HhiQTXajvgDyaqrq2094NHHJ+MsGVrVguT+uJXx8XFbG2wvpDxxWe5DS1YQHa/p1KlTTctgLKAObVHgh7mLM7Zi/urr682DzF5hTdmrO3bsyKzhwoULM2tIzDduA8vaoLEwDrQi5oO5YA3hsa2tzdCZ6zOXNHsnwwmbm3tB7Ou6ujrTPBgjnm2+kzKdEiV6HlBZhKXSA2TlNbZ/eA8JgaREf//sZz8rKUhevLB4Cbu6ukyPx4OIbQA6ImHJ+0QKkZeLJK6pqbH7ky2F/QAqeMnV2tpakPJSk/8XLlxo78Fj3HFDkq644gpJQVryPujZ19dn8UffsJzfYI9i63JPpDbjjz3B2HSgM5I97qqxYMGCTJsfXuFv+fLltjYgFPdiDWnVg0cdGx5k2bNnj6EidhwI5o8hJR4PEf+Mj8NkDYkd/+53v5OUacSdWcNp06YV4jnjlX06d+7cXF1x3I1Ckr761a9KCnYwdjT7tLOz0z5jDX22EjyC2uxTco/jo1S4P/ch7hsdUlYUYcuGdfypaL6b/gEHHGAhAAbLBBDcJ9mbomeC4jxQ+/btyyTaS2HxfFI/57CQEsYrKXK1tbW5Hk4sGuprKR59Lx94XLx4cSbkEPNIIgmfwyOhlLgtCb9F3eSBRQXGwUICArzFJ6UzTnhjA8FjsZ62/vQ++GIzHXzwwWZyIFQIacAfRRm8+jXs6+uzeYQf1tCfYUNpJILJJxU0NTUZX6jxrA2hIU/elPK8Hnjggaaesk8ZO3uHhwoBSSgLgOnt7bW9xT5FQPIdit45F5ikFx5KHIv19fXGI+YNlPoSJ0r0PKKyKjHd6AgX4NZHJWhqajIk9WeE+LNUkXpI3DjQDpqQekiKF5IflY170dsXdEcDmDRpkqEf1/ItSHzXxJaWlgyPIDFq5tSpUy2NEFREciM5vTlAyiRjGBoasu/yHk4QtAeuiRSGR1R61P7GxsZcOR1zD0LGXRPnzJmT6dkbJ0xIEyoYIRdUtli7kAIKoK6BivAyODho74FMONlYQx/6IDEfVIKHKVOm2N5hvdk7Uc/psmuI6cT+mDx5sqVZsh/Yp+w9fz4r72OuDQ4OGpKCyiT9wyPhP9aFsBd7KU4hRfvCzIDHYmsYU0LYRIkqiMoiLJKLIC9Pf3xOCVIZZAMh+B/JhcTFTU5gffbs2Rk0k4JN5l3dOKGQRrxi08UhE2woJBlSz0suEid8j+H4PB0+A9lAJJAAuwiJS69d7j1t2jSzoUoVSYCg8AxvoHYcWmN+sKG4JjzGjeZInMDu8qhcX19vdnWpc0/RTt7whjdICskRfH/OnDlmvzMW1gokgw/PL3yCZENDQ8Yfe8SPyztkcDpxb98GJ96n8Sl5UlhTxkWIEVubMcybNy8X9kITgVd49GvIK/eIw3DsLe8XSZ3/EyV6HlBZhJ0/f36mKz42LHZKVVWVSRdekWpIkThAH18DdHrwwQdzrVe8pAJJkFCggy9JqqqqMrTF3qCMCenrU74WLVqU6RpPZ3h4lPIhEe7Pb+DRe6SR5itXrszZ6VwjbmMiBc8vn/vwT8wjNiZSGRsubrTN2ameP7SEqqoq04L8KeXcx5+3SgkadvlvfvMbuy4aFuP1aXp4u7kmyMeax78h+YakHK7hUxP9Ghbbp3zm9ylrynhKreEDDzxgaOz3KfOGhsj4mYM47Mjv0JhYQ2xsNKGOjo6EsIkSVTqVRdhEiRL9fVFC2ESJKojKZjqROE7sjLQyin1HR0fNxvIpb9iX3h4lewlbpra21uwJX1zAtbgH9p4Nvkj2jtcY+B8bxcfwlixZUpCCTUcGDrbY+Pi43ccXEvhyQ+x2bBnslOrqavsO42Fe/Hz5e/jvxTz6a0aeZuPxhBNOKEghgwpPNp75PXv25FqfspbwwXWxv7FhKY1raWmxMWDP4f3Ec8898CnAl29JWlNTk2tExv/sHW/fnXTSSQUppDnSJI5Yc09Pj9m/eLyxz7H72R94j4mT07Y1TsNlnsjqYm24B7Yz+8PPb21tbc6WZp7wC2zYsCHZsIkSVTqVRVgkBohKeVmMiEhjL+1LtaTECxo3EEPK+rIxjy6Mhzgb78c5zqUQDMnlyWsCZDWBqqOjo7mcaq5ZqiUl2gRUVVWV0xJ8AzXGjdTGI16Mn1IIi0czJvJZuS6xRK43PDyc80wj/WMNQQpzhUcTGh8fNw8q8+c9o+wHcmXJQuJejG9gYCCnWfFbcpw98T75yiTSc519+/bl4uusA0ULcVxaCnseGhkZsciDL5KA0C5oSs44fLQj1hR9/J22O6UoIWyiRBVEZb3E1dXVmcOAkUJxCZqPoyGVfc4mlRZIeO47b948k/C+6sLn6EL+sCQkcG9vr6GwP+yYcfnyuqqqqkLMG/dm3IVCwaQg40BC+nH5GCfjbGtryyGmL+cr1YbVZ9X09/fbe4zD/x8fpFRfX5/JVvNF4aOjo4aOjAH7nSwfkIMidQ6qhpfDDz/cEIzvMjdkx7EfvM0GOrLWTz/9tNnIxFPHfRYAACAASURBVJfREriWP9CZ40gYL8jMmsfoyLjwy5APwHc5moMDx5n3o48+2uxvX3xObjH39T4N4snMzZNPPmmtZ0ByfCf4CdauXZts2ESJKp32C2G915MqhNWrV+dsAp+1hGTlle/HUgophoTELkayUh3k7T6uyb3r6ursWn7MkdTLSC54xB7hWtgSa9euNUnqvadcG9TiHt4zXlNTkzsGk2v53FbPo28hUltba+iM7Y/khsbGxnIIS14wNiae1JUrV9p4KaKnUgifBfyiTbGGoEFzc7ONgTVjvNzXH5Dsj8ggatDc3GxrwLV8/vPu3buLHhlKdhLo+Y53vEOSdN999xmPoDatc6mGQltjTiHuPW3aNNNK4An7m+ogCu19Iz/mDSSeMWOG7X+ystBq+G1nZ2dC2ESJKp3+T8dN8vRjfzQ0NMStVyRl44VSOKIhrr+MrzV9+nSTxv7oSiQlEg1JRkU/Y4+7RiA5kf4gSqQJZCRXTU1Nhkdvw9bX19tvfayZ71JjiibAnMBjU1NTLj6MRMfeBU3g9b777svcK74n6A8igk6R59d4bGpqKkj5BtXwMn36dNNsQABfbULzMRDEe6OXLl2aa7kKKtP8HTvOt16J/Q/MFW1VuSaVQFzbt4ihqsx7bUHqmTNnmp2OJuOrhmhzyt7yUYWDDz7YbFi8vbzSUI5jUZmfe+65R1LYn3FeO/fD18J6g8bPPPNMUYQt+8Cy2EA/NyZBeXh4OBfy4IH0jirOx2EzoG7NmzfPXPo4Fdi0bCCKnXHbo9Yw+XFiAw9CfDpePI6hoaGip3fDByo1izE6OmqbigfUJ8PzimONh4OHsa2tzX7jT42H5+OPP15S6DLI+JmDOOTC5vPns0QCNdfTCXWODYKDbHh42MbEuElQiOZIUiiRZLNT7nbwwQfbdxg3Se2oeswN4RyEDA907LSkGJ6SxLi4/W/3yKzh0qVLC1IwDZgXQkiDg4O2hvCIs8kXkrz+9a+XFB52VOZDDjkkV2TOPmUfwiO/wSxE4LBPent7bf7hn3HwbHnHGpRU4kSJKojKIizqItITpOX/oaEhQyZfyI46Q1gFCUKv24997GOSJox51CPU19NOO02SdN5550kK3deR3qA410SN2bVrlyFkHJaRFDuOiqrEuOThEYk7Ojpq1+QaSEHS3OCReyE9P/nJT9rnd955p41Rkk499VRJ0ooVKyQF6ewD/CAPKtPevXtL8lgMYdGSGDNIC8Lv2rXL3gPZeCXkgCkCSqH6cfZOa2urqYVoBCDVpz/9aUnBkeU7T7JvfvrTn0qaSMoAKeGTPYap5BGWzpdoBv40u66uLjPZcEzh7PHnITGnmDmnnHIK82hoyN5+7WtfK0m68cYbJYXUXZ84gwZJ4X9HR0euhzL7jr20cePGhLCJElU67ZfTyTfn4v/du3fnTovD2UA7EewQpDi9er/97W9LmugIj/SlyBt0Q1pfeOGFkkLKF9dComEX9fb22thAYVAQrSDu2SvlEdYn4/f39+fOo8GWetWrXiUpOMdw5NAy8wtf+IKkCSnOCeWgAPYMWsXFF18sKZxkjgT2oYq9e/faWHn1CSZxcgi9pUFU1pCE9A0bNpjDCxRiHXD+kMzO5zfddJOkcLLBU089ZSiMhgV/pP6dfPLJksL5PdiwrCkJNdu2bTMnWFSQn7n2qlWrip6t421vtIrVq1fb+BgPPHMSPeuA8+vqq6+WFPwm69evN7vcryHX5Fqc3EgTuaOOOkpSaIfb0dFh68G18OlETQ8SwiZKVOm0X03YsCl8sv3o6KghGhIblMEOQoIgLbkW0ufGG280RMJGwL4ADWhgzb2w8yA8enEyPhLcN872iROktWFDFEu2j1uMwrcUPI1Ifl65J7bOjTfeaHY64S0kPvfh7Bm0B99gOk7E5/5oD1AU5jIely1bVpCC/c/Y4GlgYCDTTE0K84kdhy2I1sJ9QN4Pf/jDpklRtkdYhPvBNxoYCQkQWsLQ0JD5Crimb+PS3d2dWcOjjjqqIAUk5p5xiiLoyL5k7vAdgOrwyDXg8bzzzjObHo2OdEfGh5YQtXmRFHwfzOvg4KC9B+qy7mhv7e3tCWETJap0Kltehx2FPUdKIpKktrY2l7yOhMD7RpCfWBQF1EjYDRs2mAcVSY4+T0I0Nq63i0lCJ+62b98+k4y+CVyx08mlvNeS2B1JENXV1TlU4pUYIzYK6XecI0u8+IknnrDTuGkyjv0H0oIA/BYesfmRwMPDwzkvsS8NiwlUQOpzAh3XbWhoyJ0sjraEZoM3GPQkps7xGlu3brWTBkFpvNrY4HiW2UP4J2h7y1zt2rXL9hT2JPui1BqyH/keaZfsj6amJkNUbEQ0P45/ITJBDJUCdmzbbdu2GY9oJMTM8TCD1vhW0GpoHoAGsGPHDuMRLQzkL8UjlBA2UaIKorI2LN43Mjv80x+3d0FigMbYoaAAWTLYRdi0s2bNslPZQUPQBKmHxCJ2i82Ady5Oj/StN8rFKKVgp6NN+OyluL0LyOazZbBH4JHxYq9Nnz5d559/fua78IDXFAS67LLLJOWLyONC5/8vHuPkf9qcovH4LK04bRJtg3gn5+Ky7nh0ly9fnpmHBQsW6MMf/rCk/KnklOSBtMQsQTx8Gdyzs7PTUMeXaLI/fCPxo48+uiAFOxktj/lgHaR8HJoYPwRKovmwXosWLbKYOTwyXzQfByU5oR0esWW5Z3t7u2mYrAcaIVGXrq6uZMMmSlTpVNaGBcEgbyPF7UqQZujz2BNIOxDkM5/5jCQZ4qxYscLQlswgJLpviIW0I5OIXFbQsbW11TzIvo2Kj9FB3hvLPaDq6urccRNoD0hD7EN4//znPy9JOueccyRJl19+uc3HBz/4wcx48BIi6bkm18IjCTJMnjw5d04vBErFhJ3p88DjYnxfAkkBObYaiALq3H777ZJCttYFF1xg1z333HMz1wdl8EOwHuwHxgzSzp8/33j1R2WWOooR7QGUYm7jZuY+ds39vQec+OwPf/hDSUFDuOSSS8yTfMYZZ0gKGheRDwocfMN1fALYtIsWLbI9C6+MCxu/FCWETZSogmi/Cth9K1OooaHB7DXQDqlMjMrbdSAKOcUbN27UtddeKyl4hS+66CJJwdPIb8gG4l7+IOXBwUGT6L6QHUkW23cxj77NaFxA7quHkODYJByP6T3MZC099thj+spXviIpoPPZZ58tKXiW8XRiz2PjcJQhSDs4OGi8gPzephofH88VsDNmtCbmZ86cOVaSyHyyDniuX/7yl2f+x4vPWm/ZskWf+9znJAX7Fv8D48b7CjoR3+RatBMdHBy0rC7ilmhUxQ77kkLFFehI1AAeDzroIOMROxebmrXFXid+zPjwKm/ZssXsb5D0bW97m6SQhQYvFM6j3eABX7lypaSJvUjcHaRFA6HCK27zE1NC2ESJKoj2K5fYx/vitqIgEWiDJxH7AanobUNsiO7ubrMfsFmJb33iE5+QFGKJHGcPalNQjVdRyh9Yhe0WNY4rmkvs7VRe4/kBrX0rSt94DGTA9t64caP9FjuGHNUrrrhCUmjKTbYXqE0WWOxPiI/VjMce2ba5ah3f3ibWKNCgfDyR76JZQMwtaNrT02O+ClCFXPGvfe1rGb6J7VLpwtGc2LCjo6OmJfnWsMSLfcUV1Tp8jjaBB7a/v9/2GFEKEK1U5hW/BYEHBgbM3kbDI6f6tttuy/CIVsQaEnNnHsfHx83Xwn3QmuDdN7yHyj6wkyZNKkj581nZmI2NjXZDNguvqEv89kMf+pCkEM756le/KmkijAGDqA5ck0kmqfqzn/2spKBykn6IyvTss8/m0hR94r5f7Lq6uswE+FTGSZMm2eb2fYi9IwseGTcL+fGPf9ySDhAuPlxDeAeHlS/0jkvFcK74kxKKpV9yAjtr5nttzZkzxzYi64tqzebmPqh6mDvf+973JElnnnmm8cd44QuHC8Lr1ltvlRRUZBITUDMff/xxE07+5Lso2SCzhpyBCyjw4PDAL1y40PYdvPGKqca1KVJgbX/84x9LmuimSDILc8j94BET6I477pAUnGHwiBPqscceM7Wda6GKswYpNTFRoucB7ZfTifYlpHHFPXSRYoQjUI2QaBQw4/ZHjQUlOjo6TDojsb7zne9k7nfddddJCsXeJFD4UwaqqqpMpQDlkFiRBlC0LzEqGmlkMY8+9c+Xf1GKxRzQ8Z2xdHZ2mlaAJEcKo+ZfeeWVkkLR/qc+9anMuOOeTqAzc++1m1jtR4MgBEOKJLyMjo5mThmXlDutnDHBH84p5n3Lli2GJqw/ITpQCY3qfe97n6SwL/zp5OPj46ZWg3KgIXz7vlw4nXDYkeoaNyUgyYXUUPYH9+C3vI9zCs1k27ZtpgqDqGhUOIoId9HbGG2Ja6AxjI2N2XwxHq8S9/X1JYRNlKjS6f/U+R904Detra3mbCL96sQTT5QUwhRIR/R7nB/YuFKwI0ABJBTJ/oR3QADfcQ+Jh60gKVcSh61QKqwTpfVl5mDy5MmGQNgdhDlw5zMvBL3jUw2YA3hEksMrieq0D8HJgTMGHuMEA9/2xne6jxEW9PHnF3GNpUuXWhgH5GRs/M+a4SiEGFNjY2Ou6B1tiLnCJge1f/GLX0gKyMo+evTRR41ntDJeCZv4kAeONRxIPuHnxS9+sfFICNCHnSg0ILyDZsa9GxsbzY+AxokWRGdQnKPwyGl68Mi1/vznP9ue4ZrMJePxjRaghLCJElUQ7RfCRv9LyrZRKXXOJxITqYzbHC8ptsy9995rSdMEz327SEJFhDy4NuVt/I/tI+XbuWA/+OR/bFgoDlnxPzzGzeekYMuSDIH9gUTFu/3ggw8aj9isnkff25j5xOOJFhGHH/zZt5GHPGfDQj7JoqWlxRA6bswmhTAPCMK9CU+8/e1vlzTRlxcbkTVk3UEMShBZQ2xHb68+8sgjuXOHuDbr7c9HIpoBgbSsQ1tbm+0DPM5oS3inGS+88Ur/4Mcee8zGyBryG9YQLZLQFfYpNi7z+6c//SnHI9oY85EQNlGi5wGVTf5HKvm2k9hZnZ2dJinwIII2eFmR6KARUgibt1AoWII1EogUM1K/kJSgDR7db37zm5ICehYKBbPrsIPiz8rxGKUuSlLmFDxf3kXMjPe5BvdGI0CbqKqq0ute97rMb7FzaKMJb6AYiEQRNjZWfB/sLJC1GI9IeVCT+aDYYOPGjTZXrBH2Nn4AYrbYWdjqJDuMjY3ZmrDeaBS8z34gYYD7f/3rX5cU7M7h4WHTLtBk8E0w354oSmC+ecUHsn79etMo8OyT/MJc8uqbCLIu4+PjZo/DI/uU9+GR35AOiUe4HI+gsE+K8ZQQNlGiCqKyCMvT7+1A9PuqqiqTTEhfpAyoTFyTZGuymojTPfPMMxa/uuaaaySF2N1VV10lSYZO2B0++wdJX1dXV/KEc3+WK4TkReKBtCBSbMNiR/rzbuAJm4+4MqmV119/vXmBiT/CIwXrSHTsYG8ngxrV1dW5IoxyPLIOeHHhF9uypqbG1g7+QFbmABuNdD48rcQh77zzTmsETswWW4z4KyjkfRn+hLrW1lZDKMgfp+GJNUN7wRNOpKKmpsa0Nzy1XItxgIIUcGBrv/e975U0EZkgxRBPP6m0pGGiWfF8sE8pmiCTb/r06fY3aweP/vQ8TwlhEyWqICqLsNhIPP14I/Fotbe322fo/pQlfeADH5AUpDPJ7dgbNPJqb2/PZe6AUNiRJPvjfQOt/CnfhUIh11bEH8rlKT75LuaRe2/fvj33GWhAMTpxYiQuNiA8dnV1mYeTa1BWBypw4BfJ/jRJ496gSKFQsPc8j8VOccduwu5CK0Eb+Otf/5o5SVAKye2gC/YVa4gPA+/xjh07Mja2FGKmIBtN14ntYpvDAxrHyMiIaXTY1qBOKYRF84JH7HbQ8oknnrA5QmMhhk7MmfVhv8IjnuAdO3bYGjAu5oW9TwyXa9NgjnWB98HBQRsHGg/XKNZIL6aEsIkSVRDtV7WOL+rmtb6+3qQN8SyfuXHmmWdKCpKKLBG8h8ccc4xVMWDDQtzXt0kB/cgsib2l3ksMeiC5fB4qcUrvnYvL7EABPJ/EBZGSaBN4dmklSkvX5cuXG4KSdwwv/vhNeIFHNBb4GRoaymVl+dPa4/NhaaQHAvoyx5aWFhsL58Biy/JKJRFNB+AXL+iRRx5piIQ/gvlGewKN8IP4driMb/v27WZn4lEGOXnfnw/LkZp83yNxW1ub3fekk06SFBANHuGdyq9LLrnEeJMm9i97m/YxID9+Aux27sU9QG00weeee86iBNiyfBeNJB2GlSjR84DKIuzkyZMLUpDuvrFZoVCwuKsvVCd3E7uUnFkk71133SVpIsOFWCSIAYLwHbywSGVfUA3Fx1j41qRRO9ayBzr709WlgKhIcHjEw4lU9PE4cknnzp1rXmDmkHmgMgm7kXh2XJQfU1VVVS7f2a9LnOm0cOHCghSQzrdqHRsbs9g4aMcYsAH5LnnC2OE/+clPJE0g31ve8hZJ+YOxOQqU7CA0L+xh38SvurraEIt59sc3+lxiDnT27YGY4/HxcSsmB+3YS2SYMXcgLTyS89zc3GyZazwPaEM0XPD7lIof71uoq6uz3ALf9ID52L17d0LYRIkqncoiLPYd3/GvcRPquCZVClLm5ptvlhSkJPFYMly2bt1qnkzsNd/FAokLonEPECHuuOBjpsT0+I4/DIsWMZCfj0mTJtl7PtMGJLr00kslhVgnXspvfOMbkia0D3hEE2GcoAg8+uMa8V761jVSmGOuXaxZOo3S/cHB0MKFCw0VPbKhORBL5T7YqxxdsWvXLkM3PKG+iwW/xQ5mPLTKYc2lsFbYeaBxVI2TWUO6aviOGdDSpUvtM+x1tAbm+0tf+lJmnKwl9ds7d+40VKSBGjYs98N2pXUORPwWW1wKc8xeoUEbdnqpRuJlwzpePfTQPnv2bDOaeWAZFIP/9a9/LSmEZEhyiN3aMMwGRI3mf9RFEuNZOF65V3V1tS2MP42uVF9ir076bvotLS2mCvswCmomqhCF6zgX4iA4i+1Pj2e+2PA8oHzPJ+uPjY3ZOPgNYyUsE5M/PZ6NGp8Wz0MGP5gAhEvYtPBJqiDhvZ6entz5wIzbl19yLR4IHkr2xaRJk2w9GQ9rQkjQE4kk8Mb+gV7wghdYIgQPJMkM3JdmCaix8Mg4e3t7bU+xL3llvnjoCMkh2AAartnY2Gh7Kt67UmgjU4qSSpwoUQXRfnVNRF1BGuL0mTRpUk4K+rI237gNyI9PY/NlXYSACKZ7lQ2ph7obp3ehpsbnqcbjKNU10ZeYgS6TJk2ysXoe/cn0EOp47Ojx/ZFxdhBk55o4H9BA/BmwNTU1dl8/rqjBgPE4derUQswXYYvYwYSWxBz4ki+vKvM+67Vv3z5DETQEEiVQZ5l/TALCgKwx125oaDA1Fccb2gj/e7PGn8COSorm0NTUZOaWP+MXpylIh6OKEB7aUn9/v/FIggwhyjghQgrhnWOOOUZS0EzgsampyZ4l9jD3ZS1872UoIWyiRBVE++V08v1hoZqamlxZEjaCb4KGxMVWALWnT59u9o1PyyrVf9cnu/uQDGOLx1GsfYoUtAjsH59YHyf/I525Fjxwf1L1HnroocznTU1NuRPZGB/38+jtbepiZVdFkkK4hvE4Y8aMTIsY1ofr1tXVmTZEYgAIAQpgf5MIgl8CLWDWrFmWToqzCZvWp36C1oyZ95nT4eFhGxtjxjZHs/PJL/Pnzy9IQauLCyW4F5oXqaD4RfgfTYY0WNIKQe05c+aYfYsGBaKyZp4XfzZxXNgBj8wX9wG9fZE+lBA2UaIKorIIS3MrpCUSOA7qF2vDGf/vS76QytgpGzZsyHlCS53H6kNHXDtGI77jC9ORdr6juk+cQJrHjbx8OV2pc1n5H20D6bllyxZDFJ/m6T3P5VIkIebFn3kUnSFrPJK2x33xXGJr/u37mbFxL97nPt5biy/h/vvvN0+ptzuZf3wXoIw/MS/2Q/AbPLloYOyd5557LrOGy5Yty/DI79AUqqurc6mgvgyUPQjvJI2AwL/97W9t7nySkG94zhygYbEu8X4FhSmkx5cB0q5bty4hbKJElU5lETZRokR/X5QQNlGiCqKymU7HHntsQQpZSiRQ4xEcGBgwew2dHBsSrxx6PjE7UhPR2fmeFGwQdH5sNbxycVMzKZ8aVl1dXSzZX1KIQ27fvj1jG7z0pS8tSCHDhXIqvIhDQ0O5MirugY3ivYOkm8Xebz8e5sU3sfOF4PGRGvBVyl+A3d7T02M8Hn/88QUppABS1kj7lHgNo1awmTH5dioU6BND5b5SWEN/jAj2fKk1xMMdryE2n/ehdHR0FD0MC7vcnxM7Ojpashl+kYPEJCm3r6urq0v6Z7iWP2MY8u2Bi5EvCvGHttm1Sl4hUaJEf3dUFmGRVCAJGR1IjDhm6YuqfRaObwEZE55Z8jo9wiKxyNLxheuxBzZuci5lj6QoRnge+b4vKBgZGck1duMePsmcz/1REVVVVbk4tUdYJDteSdDZ5wKPjo6W9E7jHY2JOSPPmZxa+It58LHSYqVvUvE1xEOKB9VrBrziYUaD8Yg8NDRUtIxTyifVQ6B3fHBVzM/4+Hiu7JJr+iw1Pvc5B1VVVbEXPnM/H0VA4/DHosTfL7WG7MdSlBA2UaIKov3KdPJNsJA+o6OjZhsizchGwf4BOWhVyiFPSKMXvvCFcY5o5v4+ZgchFUEUpPTmzZutfAv7BXuSnFB/UK7PJY6RlXuBjqAclRbeTqMQnGJt5vbAAw80ZPU8+tPbIX5LrBNtpru72zQf/AKsARlJ8YHH9fX1BSlvj4ISIyMjFvtjDOSzglzcmwJ2yupYwyOOOMJQzfPnD5DmN17zQSPbtGmTxSbRMlhn9smmTZuKHrfi7dH4XqAjn3mUZDw0XqClDdTa2mr73vPg0Rvic5/9NTAwkMupZ32Y61InsCeETZSogqgswiKdkfLYJ7TRWLlypUlOEAwvMFkmeHh9Di0oNXny5Jz9BlJQSUHjam9Lgfxca8qUKSahfFYW0s/naJJLjB0NH7QofeSRR2zseFEZJ7acz2gBiUHVSZMm5Y4XRMKjAVAU7XkExbl3fC3GDIpF6GE80kiPChtQi+NR7rvvvpwXmCwfGsN5O581RItpbm42fkB//gcdaevqG2eTYUTT7ZaWFvst2VPMM3PS29tbFGFZB36Pzbthw4ac5uS9wPDm559r1dTUlKxEQ8OJDyqLr+HzxmtqagylfQSiWJufzDWLvZkoUaK/T9qvJmxe6iApWltbTT9HQiKRkFhUP1Cl423e+fPn56obuCaxQmonyeXkWAiQBkRuaGiw+4G61GNy7di+k4IWUaoCqKmpKVfpwmd8FzQmXo1WEbc6wVYBfUFHYtrYTiAUzctYn3heiYcz53i2I03AeCQf3Et7rjdjxozMYdFSvoE8bVs5dgUtgWsuWrTI/A0gJgjGGnK8JtoJDdxAvLgCijXkPTzbaDA+ls6xqD4iEUcMmHf2ro9lg8bkB4DW8Dht2rRcZRVriL2LJoo2g2boPdLV1dV2P7QjX2EUt6qNqewDO3fu3IIU4JoJ5MGJ27v4PkAsNgNgEXyv4WXLltkEsOj+LBI6qrNwbCzUSO7V399vajTX96Vxu3btykzE9OnTC1JwpLEolJYNDQ3lEvbZ4Dwg8M6JbL4z/KJFi+yhR+Vm08MzpXk4rFADCX9w76Ghodx5qXGpnCTt3bs3l/zPGpKYwTwNDQ0Z777PEcSDguOQOaID4ZIlS+w7XN8XGdCzGQHKGhIqhIe+vj4L/SDMiphAmTVE7fdllqx5HNbxJYm+txZJIbzP+OfMmZMrr8RZhyBnDdmnrCFmSJw8g1D3IcBSvcegpBInSlRBVBZhGxsbC1K+rQv/9/b2muQGMWitQUsOJC0Sy3ePnzZtmvV1RY1FBbv22mslBannS6NwhaMid3Z25tIHeS2FsKjEOBJAG649MDBgCAuy+sZhIB5SGqcNp7u1tLSYOoljgg70nOIHAqHmMl54RUXu7u7OhJykgCio/XHfXlRiHEeYD3ELGtYVlc6vIWgMoc5xiltzc7OdvI6K9/rXv16S9JnPfCbzG8bsT4/nvKTu7m4bTynE9AiLSoyGwO9A0ZGRkVyvYl/ux3xwL/Yppwu2trZaSJJ54eyhc889V1JAWJxxPpTEHPX39+fmwSdS+P7ZxmuxNxMlSvT3SftVwI5xjVSIT6oDbUAm0OaNb3yjpGDEIzW/9a1vSQqnl61du9YC5dhOSDlsgNNOO01SOD0AJAaVSLvr7u422wA0RNqBmBs2bChq/8ATPPL9+KwXkBZpTDd/nD7wePfdd0sKJxc89dRTxqNPUgBRkdYgE7YNDjfsuZ07d9pYselANf6Pk+Obm5uLriEaxbPPPptrtsb/JEqQCkpoiZ7StFFZvXq1aRVoWqAKWsh73vMeSSGchJMFZxS2bLyGzDvXYE1Xr15dNPkl7vQf/9/f359LcmF9aV3k013pJcx+/ctf/mLnv7KGIDptXS644AJJ4QQIbF2SQ/BHDAwM2NjQMNg7vB87DmNKCJsoUQVRWYRdvHhx5lQw385kaGjIUBHJipTGZkEaIR25BimMH/3oRw0h8fpi9yKl+RyJBvL6hIa4vQoSE3sP75sPus+aNasQX8PzODIykjtZDG0C2w4tg8/xooIMV1xxhTWZpiQNO5ExwyMSFs0AJEYCx43E8T56D2jcpGzJkiUFKSC5b7szODho4/driB3H+2gWXAMP6sUXX2zeX7+GzD8ICtLFrXKlMP9xaRqJG1yDNdm5c2dmDfG1+ObxcaI/84oWwpxht6OB+IbzrOHnPvc5az7HWrH+rAdzAPHcQHFBDHPo29hCyYZNlOh5QGXL64iDou/TVXY+uAAAIABJREFUOJnjFhoaGgxtQDAkFUHj008/XVIIoBP0R+/ftGmT7rnnHkkhqI5HlXgcdhwNxvG0YkNhw/X09Bi64Q3FDvLHN0A+kZ9YKkkQdXV1uYJkf+4n9ilF8MwTCLhhwwbzgmIf3nvvvZJCKufhhx8uKdg/2MWcgMe1+vv7DR2Ye1Cj2OndeDQZM7FU0KKhocEkP8Qa0gD7jDPOyPBHogjXLraG2IDwh6cfPkFYPK+sw65du2y+iemixcXn78QUpw/G98RmLFYUj8cZLYHGBcTByRuAxyeffNJ8EqST8hxgv/N63HHHSQq5AD/72c8kBbs9LgP15ZP+/F5PCWETJaogKmvDcu4mUsbHleLTuEA2JDneP66PVKagmt/OmjVLK1askBQkKDYU53FiD3/hC1+QFLxv2LKgaGdnp3lX4+LleOz+bFEygZD4SLj4lHnfPoSxY+MhtdEekPAg34wZM3TxxRdneAQd3/GOd0gKR3dcccUVkgJqIJW5Vm9vb+5s0SK+BePx0EMPLUjBW+8zfOIDu7gnXlo0GuaSE9fxevL+3LlzLV4Jf74kj9/cdNNNkoLtxrxjH3d1dRnacn3fksd7UPGE+2Nj4nRT3zrXnx6HTY+GA+IyF9OmTTMvsD/+5e1vf7skmRf5ox/9aGY+fYFBsew5H4dNyf+JEj0PaL9sWB9/hcbGxkwiID1AVrxtoB9ew1tvvVWS9O53v1uSdMMNN5gUO+eccyYGFbyBmWtiZyKtsfPwgM6ePduQy3sWSa73hDcWCRcjTsy7FKQfCIqHERuf9zkX9swzz5QkffrTnza78Pzzz8/MF/dHooMi2O/wGGfkgF6MDSTCPo6JMkfsXjzC8bm+/J4xceQh/BGzxSt62223SZJOOeUUSdKKFStsnclgA8lZG/wQ3AM04tpoS3GhhD9Gkvn1hMYB+fZAMcUFC1KwjxkHvH/+85+XFPbkihUrLApw0UUXSQqRD45BRQOBd/Y8OeUgbWNjY64ZnM9TL0UJYRMlqiDarxYxxY6v4H0qacjEQQpTcYPXlbIxPHcgyNNPP23SDG/rqaeeKinELPHGkvHEwbhci+LovXv32me+QBxJ79tHUsCOhPXSesqUKYYO3A9e8IiSLYNNi8TFXm9vb9f1118vKaDEWWedJSl4Xpm/973vfZICMsI7HtvBwUFDZbQIUAIUjis9yJXmO2gtUFtbm7W2YQysDQgBcsCvzzHeuHGjbrjhBkkBobDNWUM0hXe9612SQpQA9ME729/fb++hLfnDl/1RjBSw+1ziuP0oiMo1fP4AB30xftYF7/a6dev0la98RVLYUxdeeGFm7MRnQWXiyKwdczA0NGRahM+TZt/6Q9ughLCJElUQ7VcBO7aEb+QV2z9IY6Q1thh2MLYkNgRoumvXLrsukvuOO+6QJN18882SgjTmGEfiXMTwsH8KhYLZCdwPiYXn2dcZgkC+miP2UDJmbDrsL+YBr6ZvpAYyd3d3G4/E/cilppoFzYCMKGK5xDPREAqFgvHi255Etm2uWofP0CTiNWSuQEyqThgzMWBf44z3fvfu3WZ7sYb4KsjF5X3i8/BHnDlunYqm4I/RRPvxxd1U65RqyxrvccaJNuRrXCHmlP3c3t5udjq8oNldfvnlkgKi8j6REarR4KtQKOQOdvNx2fjI0JjKPrCcLRr1Q5IUFq6trc0cUbipUUdwDPFbnEx8nyD0mWeeaSEHn47H5oLxW265RVJQE9n8bJw1a9ZYsB2VA4cW5NPapkyZkuEx7kDP7/1J67wipJhs1FlMiNtvv13ShJOC1DwePBxoCDY28Je//GXjRQphj7ibX3yWqpQNr0nZ0BWpl/DH/PCQzp49O9ejiFc2NYSpwqb/0Y9+JGmizI419P2XGBtClgcZ9RDVPz4RgkQY+CTkw1715XWYbpDvNdzQ0JDrA821fPcMijAoYqHQ4corrzSTgX3HtVjDk08+WZJ01VVXGS9/G68kZU5d9yWSRU4gSCpxokSVTmURFocM7vsf//jHkoI6FffsxYhHbSCsgwscSYsxjxTfvn27OXNQga655hpJIWH8q1/9qiTpgx/8YOZz34G/UCiY9GM8ICZqpHc6UZp14oknSgqF4nHiBaEekAXUhsdLLrkkwztOCNTQzs5OS1ODR5JAcEjwP0Xhn/3sZyUF1Szu9xt3K4zvw5zG0pk1JEUUzSZ20Hh+QDQQgUJ87seY0aa2bdtmxRbwR2E+2hDmDVrIddddp5ji9EhQl/GwvqjEpZxOJD2gksYqMQgLGqJVcI+rr75aUgjrkUqLk2779u2mFYCCmG6ovOxL5gsevWZWKBRy5XReJU5Op0SJngdUFmEp7vZFtvFZLrjL0e9x5/M/v8GRgXQCjerr6+0a9DsmzY1UPxCL75FoDmpyrdWrV+eSPEAFnDq+ozoIFJ0aluFx1qxZhjQ4GyjCRgqDbDijIKR3TU2NhUpoDUNbEcIJFBvg2KK7PugCj+vXr8+deAcqYD/GThlOmPfOFfhbsmSJhZ/gx4dkQDZCeBDIXFtba/Y8mgpriP+BxmSEhkiIZ3+A6k888UTu1EJeQfG4yZwUnE7+fCeosbHRkh7QTo455hhJwVHEbwjrQCSjDA0NWfooySHME/sWJyhryP/sqbg7JfMP8kcN9CQlhE2U6HlBZREW+w5U9J3Vp06dmmtqhReSYDdphXhHkXBvectbJE0EmwlE/+EPf5AUXOkUBPtWMEhLAux49LCPpSAxsWUJmJeSzr5VaJzoj+RGQiIFuS88gnAk7GP7P/DAA4ZANOIiII/9jmTHbsfTDKrD89q1a3OJ7KAT3sjYTvdr6M9jbW5utnXlOiTI+PJGvLfMZaxpgET33XefpIDG8MOaYv/inQXh0GIefvjhnD3Hd/GY+9MbsGEhXxRRU1OT81qzh+EZjzhziCby5je/2fgi7IXGR3SCfcj/2LrMK/sBrQ8+pKDpEDVgXCn5P1Gi5wHt1/mwSAiQBcTbvHmzIRGSiiJeH0vFziPJHcSVQmof38UGoNgaW5UAPumOoA9ScWxsLGerIc1KaRIgMJoB9gbSctu2bbnzWEAef/4NKOjbXUqhIBpNBFsOCQ7iMS94PEm0Z3zj4+N2P8bF+hQj7H6PHKQbxmvI2LDV/Pk3aBiU3cVxc8bLGsIf8VfWnzUEgYk3w8PY2JjZ5iA9DQu8bQoVSeuTFNajr68vd0o6GgE8Mqf4HYrtU9aKPYPtz/+0ufFNAomuxP6DuAWRlD+PthQlhE2UqIJovzKdkKxIR3Tzuro6s4lo/YF0JA0Luw47CCl49tlnS5LuvPNOk24kU+MN/OIXvygpeIuxcV/zmtdICvYQqYu7du2yuJkvYkai+eJnMp0YN9IxTlHkb5/OhlTEpgOZQBP4ueWWW4zHSy+9VFKwcz73uc9JCt5i2uOgRWC3M/d9fX25Qm2QPsrWMh5nz56dWUO85axbbW2toSJrhCbBGvpT7UAFygd/8IMfGH+UD2KbkzAPAuMNf+UrXykp+B2Ivfb29uaypfwa9vT0FM10Yp38mbvV1dWZYvv4u6AxPJKhxz6locBnPvMZmxca3BNj/tSnPiUpfwYyWgwecvbW4OBgru2QP1s22bCJEj0PqKwNi92ENEK/R3d/6qmnTOqBUKAjGS3E8sirxP4jDtbb25sr+SKGi23gJRUNzbg3tuzIyEju5HfQsFRzK6Qxtg1SFI9gfLYoKAWP5J3CGxIX+xeU7Orqyp1ex2/wgPJdPJAUB/B9UGdsbMzQzB9JUYxH5pbv4I0FSZ544gn7HTYs6E/WFVlavM9vsUN3796dK73ku54/5gj+mFsQdnh42BCfNWRN+a4n5oHP4RUbtru7OxfpYD5ZQ2LQeK3Zp2hzO3bsMBRmfOxTogUgLJogWXM+F79QKOQKE3xhRSlKCJsoUQVRWRu2tbW1ICnX9IvXpqYm0/WpNkGqgUZve9vbJAX7DolGBswLX/hCk2ZU4yCRuC/SF5uKeyD5Qa/u7m6TfnwXNAI5/fmw2LDe7okbzeE5BlG4PxoAGS+UDPI/PB5xxBEWd8WmAz2YP1CbcaPNgMSMr6+vz3jChgYho0ZtxiN+CDyYfg2nTp1q13nta1+b4Ys5owoFzQHbFbv08MMPN280+bXMH2vj15B7oIGhzXV2dlpmE9oB/KIB+PNhyVbzXuTY9uUzNCfWkHkmewkPP/sZL/fRRx9tWiE2K7yBnL7w3vt+4moo32DO2+mlzodNCJsoUQXRfh3ojASJM0ekCf2b/Fl0f6Q89Y3YH0hvpOUvfvELSRPxW3Ix/VjIN8VT6pteGRORt9Q3NvftJH0u8bRp0wpSkIrwGNsYeEfxsGIjwSN2MjYMn5MP3NbWZgd5QUhWvkNxPhoCnm9PNTU1hvj+tPjo0GfjceHChRmE5btI8kKhYFlIoDlr6L3iHNSFvf/LX/5S0gTy0c7UV6bwHb+G2H/e011TU2OalS/QZ3/4bDXypf33Ys8rtjuozbXJNYBnDjhjDTnKdMaMGaYtskd45fAzeOJa2Lx+PPFRHZDf+ymXOFGi5wGVRdipU6dmbAN/CO2cOXNMYiP1/ZEd1Hki/YiD4endtWuXeRjxLILCSDDQAe80hEeV79fU1JgUwzbhmqCEj8MineHJdyRobW01vj2SYVPRYI3fkkdNhsuWLVuMB8aDBI9QQ1Jo8AXikE8NqlVVVdkcc39qgKMDrozHlpaWzBr6BmULFizI1dNyb+xMYsV8zhqiJe3cudPyjH3DcuaEz/EwQ8Rli60hHmY8y1GMv2hNM+QPSW5sbMzlJ/tm+NTDMg7s9W9/+9uSJtYQ+5uoBPPNfKEZ+GNTybaL/SPea83+YG1LHYZVNqzjzyOBGMiiRYvMaYBTBGMet/kDDzwgKRQV0+OJB2rfvn2mjqIOMon8D8NcC/UmbrkhTUygL3Zm0Ur1JfYqsE8cnz9/fu7sUJwxqFck9JPIQSodCzcwMGDzA0++kBr1iQeUVE/mk3vFaj+v8Ih6F5MPefhWKQcccICdOIfQYG1ICGHteLhwosRNAhgLc8Ma+r5Qf/zjHyWFh56QCGtYU1Nj12ITQzxEnoqpnDG1trZaiiF8+3I2QjGEdxAwjH90dNT2FiqvL6gAeNjjrLEvmqmqqrJxxCcuSvmWRp6SSpwoUQVRWZWYkAeqFy5x1LqmpiaTwl5yg37+ZDHUHKTR8PBwDilJW0PaoU7zOSEi1C9QsaGhwRAJiRqdC8u1MuLXn8CO5I9T+PxZL8wZKOidQHHSOZ+DWiAl4QPQDUmL2kU5HtI6Lv9DCoMCjAsUj9PaOHeGsaKSohlNnjzZ0A81Fs2KufBryHpx/+Hh4RxSUtBB6iFriPZBGiShubjQwCM8e4v7+TX0J7D7csO6ujpDbX+WDZqAN3fYD1G3TXOYob0RjkRF5pr+jCL2HteuqanJnSPFZ5HJkpxOiRJVOpVFWBwWvtwplvZIKKQi4QjfBI2QRxwKkCbQiDAN9hyEpPKnhSGNeY1PJ/eSE2mLtPYBaU7v9m1woNraWnNEIGFBbyQo4yRRggR+5q2trc0SB3xLUp8E7sMuzDXfi3kEUUADECh2WEyfPj2zhrEtzDXikwSlkOTgT2SnRJBzedEkZsyYYYjNWNC0vDOS/+GP9+PQoW+Bw17Bl1Hq9AYfBoLi5H9ffsm68xsSXHB0sfaNjY02v9i/EGvmW9R4308cyvE9lH2LohTWSZToeUBlEXbOnDmZrvHYVYQRpExrzYkLurNUffiCayC9H3nkEZPUIDhSB+Ty597409biJsygAQXKFFmDhtu2bctILtIvGS+/Q5pXVVWZBPV2jj9rFB6x/dA6nnzySeMBBPdag/ckxokE8f9VVVW2HoTOsHsjTch45PxbrosfIk7MKBXW4X1/sju2G/w99NBD9re397332Dej9yctVFVV2RyxV7CxQdotW7YU9UP45m2xl9knO3hb1nuWGS/X6ujosLH6ZBx/Pq/fJ8Ve/Zm3rA/38K1coYSwiRJVEJVF2ESJEv19UULYRIkqiMpmOi1fvrwghXYfJLtj/4yOjub0ep9J4r2ceG3jGKK3BXxxry8ns8H/zWYo5n3zNkp0jYxtcMYZZxQk6Yc//KGkcH4pyfg9PT1mnzFmbCPscOwQ4tMkydMqZNGiRWYjE5PDNvLtWCmi8HFr7l1bW5uzobA1WZ8///nPxuNrXvOaghROxaMgm/aj+/bty/kIShWOYytzH/idPHlySbsX/vCw+2YFZAnFtrtPH+R/bFhfInnCCScUpOCdZ/6JAff399tvsaF9CRz7lQiAbzvb0tKSi7P6FFrW1Le4YX7jg9a8z8fvA2+nQwlhEyWqICqLsHjKkBRkdMT5tl7aQ16CQHgAY48ZsTE+iw+ikvISFm+yj3fx/fj6vIf315MvaAZZfRF9PC5QEgJN4AONBBoZGbGkflCK7yK1mUeyvDhgifuDBL29veaF9MhI9lBM3Bc0AHViL7X33DKv3qPNKwgSryGaU5zXLSnXzpMCDvKzQWTGNzQ0lDvGAn7xTnvimv4kdGhoaMjm3cdq4d175ckwg8bHx03bIUrCmCHWkEw8YtMgsm/eF/PIb4kDl6KEsIkSVRCV9RJzjIWPBUZH4pn9xme+2RgI56UONHv2bJNyRQqxM9eGfC4vv+vr67PYJNcElRlXfNixFLK5sFORgthaY2Nj1v7El7xRHQPi0zrlO9/5jqQgxY8//niL6/pWNGQVYS9C8E6DAK61cuVKnXDCCZJCHi42JTHnhx56yHikBWjc1lQKUn90dNTmjDGRFYaGxVh8G0/W4ZBDDrH4q4/lMjc+O8iXIvK7zZs3W74z/NCUHDu+VBwWjQONhzUfHR21/GcIlGQPsS4cIUPhOnv9JS95iWkPPlZOpZUvt2PNWJ+4HRA2MuMg5sy1Nm3alGzYRIkqnfYLYZH+SCyOanjyySdNqqCXI62RrL4FZzEk9l42pC12J55TnzHi7x032wIdYhtUyhcGU82CxAM9adP6+9//3sZMky2aftN2lcwmtA0Ib/qcOXPM7sXeYpzU6f7qV7/K/Jb58i1jFixYYHMLEoHSzEssnUFYxgjSg5YPPvigzSMIBiLgs2AuvV0KGjQ1NdmagYYgFj4CfAPeHgb5QegpU6YYooOKIG3U2K0owqIJUWnFYV0PPPBArl0sje/ZW/gIWEO0Sr7f3Nxs+4z7wCPzRa2v10jR2uJDsUBb5gutBh59kT6UEDZRogqi/TpuEvI27KRJk3LHI/hcTY5ipO4TacO1WlpaTDojwePDi6Ug0UCj3/72t5KCDRV3VeAAIqQyeahRnDAjuTjKgnF57/D8+fPN00pVEWiJNKatKc2xQVGutWzZMovv+Y4TDz/8sKTQQpU4HDYUaAPCzpw505qBYacTfwStVq1aZTzSAsdLfa7b0tKSq0LyVVGgMQjCGOFv3rx5Nl/++FG80niwWUvmirVD45o0aZIdCg3qsHe49o4dOzJr2NTUVJDyWhwoNmPGDLNVqccFLdlzb3zjGyWFveVt3oMOOsjmh/livtG00NKwR2kRxPOCZtTU1GToD+ri4WZfbN26tSjCln1gJ0+enEmMZ3IZ8MjISK60iBv6yaOHLZshPtsVlZuEBIqhUZMoW+PsUSYd13vcIRHnkQ89RI6AzES85CUvyXQV5Jo4e4aHh20zskA8wL7MjYA9wosSrUMPPdTGiCBB+KDOstgIKRaXhAf42LFjh3VxZD58p/vVq1cbjxRwMCZ4gYehoSF7eNm8mDXck/AFzi4EBQLswAMPtHXHWYcqjFPFn96Aas7DyH7p7+83RxTX96plXNwgSYsWLSpI4aEnhIPgjM+ygUeEDuvC+BFOPNCMb9myZfYdBAlhJlRw35QAHhFacSkhDzV7hM94Fjo7O5NKnChRpdN+qcRIFh/kHh4ezqjHUpDOSFr+R4IglS6//HJJE6jwox/9SFJQD0499VRJ0kc+8hFJ4ZxNf04MhErY09OT6+zozy7xBeyzZs0qSMH4B9kI2XR0dBjSo977NiuErFDB+B+1p7W11aQs6jS9gG+//XZJAQ18oTcOH+aoo6MjE3KKvxt1GTQeKdBHKwJp4xI00ATUQW1FDfdhLfoVcwJha2urfve730kKa0Qf5iuvvFJScFT6UyTQWuii2dnZGZ9CnnkthbCoxKjh8Mj/vb29ua6UaABoNMw/+4U15ESApqYma0KH9kY/7WuuuUZS2Kc+4YRx/+QnP5E0sYZx04X4lXXyZaBQQthEiSqIyqYmQj6MAsLt27cv18QKaX/SSSdJCt3QkZrf+ta3JElf//rXJU0E/7EXQTD0epwP5557rqRgB2FDYcvFvXaR2L4ViEdlyLeiBGVwDvX19dm4QF8Ql0QJbBhsp9NOO01SsNeee+45Qz8kvLeVPa/Y6dhFcQE2WgpjpRkdtn9M/swW5gd+u7q67HdIfez5008/XVJAIeYSRAEx1q9fb6jrCwM48QGtCT4JIYG8rOHY2JjZ4lHTAUkBfTyVKoaP0Mq0BBAeTYA19GcbXXLJJZJCksjatWuNN/wPzDthPgpH0Kz8SezwOD4+biFLtIDYqViOEsImSlRBtF+d/33D6ji8g6TG1vJJD7jHkZpcC2/pjTfeaOdoYueBIKAKjayRnP58FJBhfHzcxoh082Em7yU+7rjjClLwWiMFscl7e3sNPbB7QAeQF6no2+HgAX7/+99vfyPJ+Q3fRRMBFUBgHzbbu3evaQ8kI4B8rMGaNWuMx8WLFxek4B/w5Y+Dg4O2NqwV80kiOn4I0ICoAZrFJz7xCUu4pxwNLyjzzhriBcWDCn/wMDQ0ZNqAb+DOevvTGw455JBCfE2/hgMDA6ZF4KtAiyDcxHr484XZFytWrDCvNRog2hLjIzTDfKElMQdomcPDwzb/XIvngufJh66ghLCJElUQlbVh0feReEgj0KCmpiYXh0VCgSikgJEggL6P9Hnqqaf0gx/8QFKQWL/+9a8lBc8dkh57CO8sLVOxcYaGhnJnyCDJfPI5RJwTyfvWt75VUihob2lpMbuGa4JEHO/w7ne/O8MzNgt2SVdXlyUdYMtyrAfeaM6WxU4CEfkd1+rs7DRpDApzhEWxE8r5Drbrsccem7luQ0NDrsk2iAsqvuc975EUbHKK4Eld3LRpk3l50axIQMCjjq2OzUp8lrWGv97eXkMsX8Dhy9kgbGxsXk6gY380NjbaXvaF4hz/gt8BDYHYP+PaunWrJbOA1vCIRoi/gfVn7rGD2fM7d+7MNY5nX5Q6ZR5KCJsoUQVRWRuWJtS+/Wh85ACE5MImAbFAZ5AVNEBqzpkzR2eddZakrF0lSaeccoqkgOzEbrEvGFfcBBwvtS+cj85SLdpeBKTFO8y4W1tbTRqCRCAsaBWXmUn5U/YWL16sK664QlLQQEARtAa0i7vuuktSsMGxE0ne37hxoyWs+2sxpxs3bjQely5dWuB3Ur51T0NDQ66ckTkg+4rPKa4HNUHtOXPm6KKLLrLrxXNFtIDfcJohdjJ2J2Pv6uqydfVHo0QHWGXW8LDDDitIITPKJ/A3NjYaD4wLNAb5IU5ZZ7yg+sKFC3XhhRdmrs8+pSQPH8dNN90kKWgI8BjbtqyvL0ONfCfJhk2UqNKprA3rW6EUswO95xgpg04OCoCSnDV6/vnnS5I+9rGPmeS57LLLJIUYKnGuV7ziFZKCZMNmINaHfTJlypRMrEsK6MA9PGGzcA1sSDSF3bt359q5kKhPQjlIg2eUk+OJOV599dXmwcUe5Fr+voz3yCOPlBS8l9hDixcvNi85EpzxEaeOid+hATHmuMjax9m5N2uI55oxcmYqvFx//fWGWMSRuRa2OHMGooDecTyYe4KC3jvsNRcI34HnMY4ieF8GtjSaIHMIj7fddpuk4J+46KKLbB7gkXljHUBr7guPrD2RkXnz5pmGAUozL2hppSghbKJEFUT7VcAeN8iKqb6+3mKUSDk8aNh52K54evEw4xVdt26dbrjhBklBgp533nmSgl3JsZPYSSAv6Mjng4ODFl/zByohteOjGKVQkQQfIBJ28vLly+3oRLykfJeqEuKxIBLXYA62bdumL3/5y5KC15w8VLyQzN+b3vQmSaEsEM8ieax9fX3mccfWoxKKljG7du3KFbCDOnglWfcZM2aYd5PifTJ58GDDP+uDrYim8/TTT9sass5kSbE2/IbGAMwRcUg80gMDA+ZB9kXeoLUvkayvry9IYf7RQOKjU7BNWUNsVDKgsM/535d2bt68Wddff72koC3CI/kD/MZnT+E/IKowODho3yW+ztyy3umojkSJnge0X9U6/rCfYm1F8fKhx6PfIxV961Kk+pYtWwwFkbr333+/JJlnFekMshAHJJYYN6f2xxv6g6z8QbnUi5K9BKpgl/b19Zntgn2B/YNtwriZF+wkbNrh4WGz1cic+dSnPiUpFKpj34AA2O1UwSB5q6ur7W/GiK+BuY49jFTrYA9iq8UN4FlXNAbWBs2K+0F8Hy2pp6fHvovGgLf7i1/8oqTgKWVtObry3nvvlRRi6ePj42YTMo+sadRUrWi1Dt9jveIG9PwNklEthUccDYc19DZlb2+vfRfkvPXWWyVJX/va1zLzxD6FR+LBzMH4+Lhpgj6TDd59VRlU9oFF1fAn0vF/Y2Nj7nxNrucr9j/0oQ9JCu70b37zm5ImnE4wCENMFpOMmojDigeYDcpD1t7ebovmEycgPxGEBLhnfPK6NOFEw/HDpKKqsWERQiR98yCTFPDmN7/ZQjE8XMwTzjDUahLq4RF1is8fffRRK+9CbUY15aGMUxMJzXkHCQJt+vTptsH5PXOH2gh/JBcwH3SHPPvss02d5sFj3rn2cccdJymsO+o7800525o1a0xt96WAjMOX1yF0edj8Oa6zZ8/O9QvzpZA4rAglsn+///36aATeAAAMmklEQVTvS5pIL4VH9in7k2sT5sMph1OUNYxPnWfv+lMkeI66urqSSpwoUaVTWYStqqoqSEE60pIkPrfGd71HoqIif/rTn5YUnB5cAyTcsmVLrk0Iqgbfxdi/4IILJElXXXWVpCBx437GSCzG49vb+JOtUadwaN1yyy2Ssueh4EgDcdAqeJ8eS6AyqZOg2NatW00VBn3f+973Sgooeccdd0iS3v72t0uSOamYE1BneHjY5guVGyQiNLJz507jkdPJSbmklxLoMzY2llszXkGZiy++ODMnpPOBaO3t7YYmzA3rjmPwK1/5iqRQ9H711VdLCppG3A0fEwPtAzTkO6WcTpTwUezP3hwZGckVOLBW/I+jk7mgsILxdXR0GI+s4cc//nFJwZF68803S5LOOeccSaF4nzVEu5GyLZKkfAufgYGBhLCJElU6/Z86//v2K83NzSYF0esJ4+Cu9x3sQTpS7QqFgoUCCMQTrCb0gWOC9L2f//znkoI9glQEraSAICAtCOXDOth4SE1QCvR45StfaWEOQiugI3YpkhMbhflCsra0tNg8EYLBGUOSAtfCHidhHInL+P70pz/lwh3xSehStuMe6APyIclZw0WLFtm8YleCVPHpb1JIzEBrAbXq6+sN7fE3oLEcf/zxmbHB3z333CMpoCeovnr1atOc4JlX7F1/AiGdIXFesh7wePDBB1tCDzz5sJPnMe64KE3Yq8wTCTErVqyQFNIZ0awogECb8dd65JFHjEc0T/Yw+8CfUAElhE2UqIJov2xYyCNtXV1dxmMsBURFmpDsT7IzIRhQ9Pe//71JZxIlCJuA0r6sD2mEGx0phQSWgnSNW738bexFEycgyq7wNC5YsMCuzyshGFLPGB8ozjhoC7pmzRqzlbD/SNVD4hPGAcVBIsINzOcf/vAHm380DNADhIwRFhuW+fAN9ZqamszbybwyV1wXLQC+0aaw3R9++GFDFfwOvuUn/1NWiMaBBxUvKTavFLQxNAg80N6+IzkEYi+CbC0tLca3bxKIh50wD3sKbe2d73ynpAkNAYQl1MYa4j1Ge6KQnTkhSSIurQRh0VbQOEka2bNnT0LYRIkqncom/4NO2D1IdmynHTt2mKTA60bw3V/DJ0wgwWpqaqzgGAkEupx88smSQgoeEpZ413e/+11JWZsF2xX0AS1KEbY1qIiHEm/tunXrck3fsLmxmUAo7k3JXHwmDCmJaCTMIfFVtBfGgfRGywAhh4aGcnFTbCekdUzxWklhLWO+vU1KbBIbHMRiLfEtxG14sFUZG/elMTdjBUF8zDI+8xc05DdxUkUxIn2Q8bDmJNisX7/eruV5xJPMPRk/MXXuLYWidn7DPsVvw/jQQPg+qbTwODw8bHPq54VnrBQlhE2UqIKoLMLGdo4UPGkgR3V1tSED2R6+4RQ2GzFMbLZPfOITkqTPf/7zuvvuuyWF9pkgBqmJSGkkKPalP5ajrq7O7utPuitF2J3YMkhBbK3a2lqzL7B/mA88yqAgUhubhqyZe++912xTbCIk9/e+9z1JAWmxrRgHqXSk/M2ePdtieL60kTWICbTBNkf6E1Osq6sz7ci3YmEsNMlDs2ANKZHs6Ogwry8xW9L3yE4D7bgm4wKl0CyamprsO/6YlVJtfkhZZJ3YA9jP1dXVhpy8+hPlsP/hETsUHu+8806LThCzxTYlxowNy9qyX/EFsG7Nzc22dyDfqrUUJYRNlKiCqCzCYtvw9CN5yc7YsmVL7hAfJCUlRr68ChRCv9+2bZtJVK5PAjz3IYkaLzIF4khe8nPHx8dzLTd8wYKnWLJLwauNpH3ooYdM0hMPJFZG9hB2LxIX+w3v8Z49ewzJGRff9e1YeJ84LEQb0YGBAUNLn+xeTDr7RupoCUj9devWZeLFUlgr1hA0RvNBW6LZ2Pbt23M50nj0GSsleuSN0+SO/YMGMTIyYoiPl9gfsOYJjQseuSc+h/gcY/jHrjzjjDMkBfQDlfEIo/ls2bLF5hI7k32AZoKnH42LxnTsPdZ2eHg41xYYnktpEVBC2ESJKoj2q1rHV+nExz8gbUAmpBtSh8OEkFQcyYhn7WUve5nFXa+99lrF9wNBkPjkkmK7+kbTg4ODubI6xor09YXBNCnD7vCtNOfOnWvogU0KGoOO2NhkRGF74xk++OCDTbJz0jq/RWpH8TdJAe2Q+Nha69atyx3HyJxjwz3++OPGY2tra0HKH0LFvDQ1NZnNx5oQb2YNibeidXAkBaj5ohe9yFCXahzQEPvSZ4VFR2Nmvrdjx47c8aOsHajkm2zPnDmzIAV/hEfi1tZWuz7nwHqblkZq7GMK7cl8Ouqoowx1yTdnnMwfCMq4mUfWkPe3b99uc4udi03N+6XOh00ImyhRBdF+HegMgvHduFqHGBieWl8MjW6OHYo9QuH2zJkzLd4KWoOS1CKS/QIqcS9P1dXVOWT1sTuf6XTEEUdkWrn6Vp+jo6MWV0VSIjnxLIMA8Mj4aQnS1NRkWU9IY+aSgm6QhvnBq8x88lpTU2NIAlrGR5X8bX6MR47T9LH0uIUomUwgBGtGK5z/19694zS2BGEA7mPwyFgWcmAJAiBzSsI6WBcSO2AfeAHEiD1YQgQOEEhIyMLBTHDnq9MuGHRvdj3qShA+9ulHdddfr67WJ2OABng4m80ihxjRkm5vb0spvdfdvLKLUV06V1+N2frznZzp5ELn2pdRyvaJJPm+2jUGdq7vXl5ellJ6Tcv55Ol0Gj4La0y/XHcqIgIlrZe8b4bDYfDMGPO6aLnEjRr9BfQtwuY8VOT/8Xgc0ixnaJCk19fXpZQeDaASW+fp6SnQjR1H2uWzknIzSSp2EbToui6esU3Yfn8qJO6yKIhAuhvX+fl5SD92IOnLZry6utp6zj6Tc/rx8RGoCEnNj3fzdJL45pO3mP3WdV2MhXbDLuZVXy6XMcbJZLJV5qe+mtNvfJZLbrK7nc1lg8n9hj6r1Sp4mM/FejfbFf/xjCZBSxkMBsFD+dds9eoK0S0estPNWa5+cnJyEs/wN3uNb25uSil9nNjc0iKen5+Dh2K2eEgr81wkRPs0Lf2v27eGxLbZxa+vr18i7LdhnbxRa1W4lH8WJmdNvjEMc3XW8SpJFzbUZrOJDZmr7uu8BPEcCvHXRum6LhiTK+dxzGQy2RZYVhnn83lU9LP5bSqhCKGK+j7YUvoN9Pb2FvODafnGeuEloQAOLJvDIYDJZBKbwxgxXzpjTcZjk+faWmdnZ7EhqGnmiulB5TMPxieV9P39/RMPqdX+tyCpjX6rLXO7v78fgkN/UE57ReY2H2vE+/l8HuFF61LoLY+RAMk3Lry8vEQ7xoSXxuLggEQTTiptSccdjUafakujVpe4UaO/iP6VSgwNSJI6BEJC5BTAHPzOqmpVAS+MdE4DSQRUj6zO6AepXDsstJvDGNSkXCLm9PT0Zym9NCThIMLBwUGoeSSr/nDKUIVyZT4JFev1OhIVICUHFVTWP44Kji7H1erkBhKbRPdM8L9WGZXAESaC2MyJ8Xi8ldr5e45KKX34DHpqh6paO+poE94l5GMeOWik5JlnWlN91w+nXo26pfTrI5s1h4eHP0vptRVqt/UzHo8D3fAIDyGo9ZiTg4xxvV7HvOOrdEvobZ7wRSiTGWQtjkaj0CyEdTyDvH+qmtgQtlGjHaL/hLBsCzQYDD7Zm6RgLoLGrc6GYTNOp9NPt3HnsqqI5M/B/+xIKaW3Z+qb7X6/48vbu6Gnvmjjx48foQFAYRKTnUkKQ0WpkxLbZ7NZ2PDeBXFI+owi+QY2kpeWUUqPKCQ/W7QuwgZ9aB60gTrEoC3OMmgEBcydBJHFYrE1ltlsFlqIdYCME+V0V3+NCy9L6dddLhyQ0SeX+cmHB4bDYfSLdsAvAknZthJ7JPqb4+Pj4zjUoZ18F7E1kx1bxkjr22w2sVb1y9xD51wGBzWEbdRoh+hbhFU1niQhBepK+1Av3xaX0VA7Oe1tuVx+um8zvwP61NK3fned2O87JBdpR1rnoPvFxcVWcgjUrI+wkYw52A2toJd+SIpnHy0Wi0h58978DpIfitBQMmLt7e3F/GtHoj20eHh4iDEeHR1tVcWH+nXBOqhnDrLdn8NsQh7au7+/j7WR73bF03xE0/OMQl3XhS1o/nJkYbVafZk4YYy8ybz4XddFP3zHGH2eU1KlWtJe7u7uoj/s8Kwd8BPgab4xr9YM/UZf8cO+eHx8bAjbqNGu07cI26hRo/8XNYRt1GiHqG3YRo12iNqGbdRoh6ht2EaNdojahm3UaIeobdhGjXaIfgFhGZhgm1TK7QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 288x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 500, D: 1.181, G:1.414\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 750, D: 1.204, G:1.556\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 1000, D: 1.174, G:1.126\n"
     ]
    },
    {
     "data": {
      "image/png": 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qQkM//OEPJXntBHoelRDrTekLETNrLGE/DOWUDRGhARJeO+KIIyS1axM4QZk/miASnrmVkbCsl633lHrrJCT0InSqt05DQ4N+9atfSfI2KgR+dg7CEuxGSJ8//elPktpJENik2LWQypHG2EykMT3++OOS5JxVFHFrbW11NkCsZI0lVTPHWB+XQYMGOefOxhtvLMnboUhJXseq5+VJE2wlSPFIaRwavCYpftiwYTUSj/MH9nuXpNeVxZgxY5xz6dhjj636zNq9+BY6U63RSp+WlpZKeE577qwuizGwtoRskJ6vv/66Oy/3neeR3wCOxXoQ60SQKv8nJPQClJKwsX4vra2t7jMIAejmvCbliJ3Khmr69u3r3PB4hW1pVEpz4KXFHmYchJAuuOCCaBpVURE2K7VCLx5SDi0CUgBSzwb06+ncZq9LeIc6xlA4ofStuOKKzk4EtptaveT/joJ5r7jiio4Qjw3IGuFlRcLWg5i3uGxt6ZCiaFMRYzYtzy++BNZ9rbXWcnNDo/n6178uyWtS9r6UmVtG4kbmHN1cS18hISFhniM3DmtLfoJwR+d/bFIKY+PhvfjiiyV5mpj9+8orr7idiqJrUBDZdfAeI2kpN0MQHskbSlfsOSQ7nt4Y7E5LvPDdd9910gI7HKpezJakRAgUxqw+pfY1NhMSifVjx8deX2mllZxGQkqgJZ5koTvKm4blU+iJa5PdKfvTEVjCfoyayJxsWaBw3csWdGOdKJbA35deeknrrLOOJG+HkxyBLduRNeY55TfG7yKGJGETEnoQcm3Yfv36VaQ4+6KpqcntEOyosD+QUMQTkQrEqpB4bW1tbvcj4RvPKRKXEpkwm7ApuUZYUJy4Jju+1Q5i9o/dgcMYJMR7PLnYRsRI77zzTkk+dkdhdMb3wQcfOOlADNXaO5SGsUwwaG5hSVfsK9Yjo71EjQ3bHRKWsc6ZM8cxtJBIrD9rUM91i6RhUSFxW/Y1yyNtizLYuCi+F1hNL7/8siu3C0uL10QJyq5xQ0NDYdf2ZMMmJPQC5NqwSNa8Ll3o3Nh1eFKRlrvsskvVd+DMsgs1NTW5OCepV0iQ66+/XpL3xuExxSPJOeC0NjQ0ODvW9ghlDhZBSY6acUntko9SH3BFl19++arvMB44rHThg2O8wgoruCJspLJRKpVUwksuuUSSl0hoIkhvOLmDBw+u0XjKsni6GmhPd999t7PnNtxwQ0ne498R1MuCQrJZ8j+vV1ppJfeMWe8sGiLH2gZnlC4dPny4S3lE6iKN65GsgOvFOM4xJAmbkNCDkGvDwrPlGJtsK/mdijgbko2Caewk8C+RxBw3ceJEJ43ZnbEfkDZ8TgoXnlSkThgXtmVTGDs24eTJkzO5xHn9Q217R2wXUsjw8CL5KHdK4bGzzz7bZXRg4xPfo2TphRdeKMm3dcDGYqcnK+q9996r6T+LHYZ9/Oqrr0bjsJwHPnZngCf78ccfdwy3K6+8UpJPqi/yzufBFioDMT8Ez5Rduzlz5kRLsSBRiWHzbGGDU9L2mWeecVlSSF0S1ykHa5GXIcTcYM/xzIDEdEpI6AXoEJc41LPZ1YhNYfttvfXWkvzuSBI0Ni/SMXdwRp9nN6LRElxjkuIl763Go2thd+fm5uYqCZt1beKdZNQgaSl3Ghb5lrwUI6d1wQUXdLmaFFcbOXKkJB87/p//+R9Jvkg35yITKKtpc5mWmtaDilcSjefjjz+u24PM8XjnV1ttNVcoD7udDCrm25Wd3mP30BaTg5O+wQYb1BThYz3DMjJfnVuSZ9khkRdZZBHX6IsMJIrRFRXTD8YtqbpogGURBlpskrAJCT0ddeXD2uoIU6dOje7KtsEvO5z1ynYEWU2XgZUkFkUlYrI4nrHrwTDCzoF5RTlUjjvqqKNcMyxYMcR2yQsGrJO128K2FXaOjI+xz5o1qzSXuKGhobAqhwU2NNrMf/7zH2cDYsviq8ALXhZhWdkYiu6hXY85c+a4+DwRBebAWmJr8xrfAhrk5MmTXQUOcq45hkZmeXP6atyS2p+tmE0d47y7c3UkgX1eI+uG8D4PcSxdroj8X9BrqOq60PBw7+Ps4DXj3HXXXXXTTTdJ8g8ENEdCQsF4JHlnCEH58HMberIbSZZKnEdGYEy2ri9g80DlxGEGlltuOf385z+X5LshQKCwHQEsvbAjG3fM6WSdVOHzwfytCopKTK9bQnaYP6SHbrrpps7JBN2S/kh2Dlw3Ro5obm6uSQW0tMrkdEpI6AXIJU7YndcG6FtbW2sKdHUlBS52Llu4Kitx2UrWWEA6JontLinV7pS27yeOC/v5//7v/0bPkZG4LMmHiKxTr1KpOOmLSmapiSFikjW8rnVohV3fJJ+YTUkeJC29Xy+44AInfZBYPDuEPKhbHBsHqFQqdT9DzDGs9C95UsSMGTOcCsw9wjFISIzKlxT6o/cPztMddtjBSXBUYNtzCcnOuBkP2hRJJLNnz3bPFfe7KMEBJAmbkNCD0CGnU9Z36qVnhTp7rC4uO6QtahZLbC6DmA3LzsbfvCT0IvqYTfeqVCo1dmYsgZm/OPZwkoTaRBGdLSuB3V6vjO3I+lubEIkCHfHxxx+vWS8cbyQF5BWmK4vgfmeSXwhVYTeHoZOw+7lUq6XFSBr4Kb744osaCZ5hd1Z9N9Z3KmscRXN058x6MyEhYf5EqQ7str9liNhOAVWR7+TZULYspbXzYtewXcrK2DxF6XVZ9nCsqzx9QaFM0ovGFn5ra2tzOzU2HsXFi6iCtixqVqX+vDl2Jr0OO5QxWBs9C9a+DkOAnUWs8r8lh2REBmrmj3TkfeimED2ytBjrwYcwwWv8DLYzgtXawnsQK2iQ+sMmJPQC5ErYhISE+QtJwiYk9CAUFWHLJcaH6WyxDtIxCU6620cffVTariqKmTY0NES7kccoXwMGDKiy8UiDI4F86NChVfEzqba9hrWLsGmgu73xxhtZ1DNJtYXGuL61f8LYN8diW+JJxqafMWOGmyM2OtfHhsZmHjx4sCs1A0h2sIWxs2LT4VjLIBYzziLG23TOmAeVOdr7wHkWWmght0Z23Yu85aFdXPY5tX4HEM65iLeQSsQkJPQC1JVelyUliniTwbmqvguyurjbGFnZ0pQtLS15pH/+ZsbwuBYSLijK7Tze7JjWW07CPWR4Yo+MpV+/fs5LjKeVOcFhffHFF6vmjARghweDBw+uaethe/DmFRLPug+cJ2RThSBKgOSlfGsoJTgHDC0+Q8uwUhxYZk9zc7OTqDlpapleYjQMW8Qg6zoxT3ssdho+p3a9yvIBYK19+eWX7lhbnDyIeCQJm5DQ01GqRAzIKqPC/3llQqsumJFhkxejDV/HdrJwDEUcYmv/LLzwwhXJSz4bQ2toaHCcWsaJ9LVNgG25Ec6x8MILu/MjrfmMWJ6NeSLFOSfvT5s2rSYFj9cwff7973+7OdIoKlb0Kzy3ZezEGGj2foXSB00CbSR2PyxvOdTQYlIO7efTTz+tuod2jlmIMcti7CSQJTWLYtp5pWGk6oKAsQZqtmmbG1/mFRMSEuZL5HqJQaycRQgrWWMNtACvl1lmGdeoOZapYNtuWOD5/Pzzz6M831iZU95n3HiA8aJ+9tlnzsMIGAfjYtxIF8tLXXrppR0bCunLZxyLnYjE5X1bhK2xsbFGIiKdKHAdwq6HzeGtVCpOGsYkh+V2Y7uFn/OdLDZc1rnt8xHeWysFeQ17ysLartaeDs+VVfQgC1laXFE52djvg+Mp6jBlyhQ3RluEvMgOThI2IaEHoVMNnUPbJSbZYtkpSIWBAwc6vin8Wr5jvXB2B+sIS8t631pbWytSLV829N7Z68YyPWzsFDQ2NjpvMJKWsSPdkKAUU6fAm7Ub+/Xr585vvboc29UNncvwkG3erv0O60t7k7Bwnr1WLL8UFJWIAVkSrzP52h0tVtcVzykopRLbAYA5c+ZE1QCIEaiWQ4cOleQTgimZ0tra6h5SKgWiVlHjlhvIuagXRH1YFmTgwIHuBxdLJLDg3DFSQFtbmzsGBxCECSohUoMXAjl1jS677DI3Po7FMURNJ8qMUJOZkBBdD370ox9J8upea2ur+/HihEH1y5qrLWuT5eCzPyo2EbtZoMbRXZCyMJIPT/Hd448/vupY+t2i+lPbCjUxr/hAnimWd1zW8TF135oXsbGE36EMDl0FAGtN4jpVHHm/X79+7hmPkYFiSCpxQkIPQl0d2O0u3adPH7ejWjqYDQgDVD+cKeuuu65z9Nx7772SfCEsW1sYhwk9bLKSksN6u+GYY8SJvn37ViQvnVDPkdQtLS1OwjJHPkPCsdPiPEO7gDQwatQoV8OXyor0GuWcaA8333yzJN9nl3QvjmtpaXGhIao22gJjoUpMzV4rQUInoS1qZ8uVcM9sJcGNNtpIknTNNde4dedecg/RLJDedIS45ZZbJEn77bcfY3ZzsWVTQIyaaFXivJTEopBLDP369dNWW20lyfc7ipmBVvOyJktjY6N7lrMKFHz1nRTWSUjo6egQcQL069evpjuc3d1sWRF2a2y2ZZZZpsZGRbKyS1Ppn16pkAwsuTqU+NYFX1RexHaN43urr7662ylxwbODMnckASUx6bYXShsKsVGMjI54dLGj492YMWMkeXuebvM45FZeeWVX6xmtAJsaJ01Wf1jrGAPLLrus6z7A2oUUuhBIfeYQ9qOhvOlVV10lqbY/LDbbww8/LMn7MvbZZx9JvrfMYost5tYbLSDjnmZK2Ji0HDRokNPoMhxYVa85B/b6euutJ0kaO3as60CINOQ5hbJJLyXuD1oV8+G5XWedddyzbJM/gnElCZuQ0NPRoSJsIIuaaCUsNhkSw7rql112WUcMX2uttST5Mpq4/tntkCRIuqxUtVgl9WDMmTZsaO9I3j4Ni8QheZCgyy23nCRf7pP+tfTJgdBxyimnuP6vdEfDW/r4449Lkk477TRJXgPBi45dzA786KOPRsutcg9mzpxZk14XkyRZJWeQGCeddJIkub452Ft4wZE+559/vrPrkCasHxoCEoU1RFrRp4bjDzvssGgqIigb1iljn3IM0hLtyab4TZkyxd0LNAu0CLQE6z1mzllrH/N4J2piQkIvQodadYQpYLb0IzuSJRnEqGn9+/d3/VWJ5V177bWSvFcYCWtT1JBSIWXNUg0zUvWqdq7+/ftnSlhsvj59+ji7FolJhzYkPmuApxObkKLUb7/9tuvezrnwkmKn03tnp512kuRjvsSosRtvvPFGZ+szR5t88O6777o52h6/1qZvbm6uoVoeccQRVWMlhs7rq6++WpLvdTt9+nQnSfFqU2ybNeO7JFLYuPezzz4rqb3rHZLLIkh6qIs4sc0227gIhP2MZ4t1YQ0Z73HHHSepXUvieUPCohUAvss4+ZxnC02yoaHB/U5YB/whsefUjTvrzYSEhPkTHaIm5hW1jjUkwi5i90TyrbLKKm7nwr6BQcM5iN1hG9qubWGs19rZliZobQMkEB49W4T6iy++qNmFd999d0lemtAcCQ8pkhWb7+23367pmkYTLBhNJL8jpbHtjjzySElyndNefvllV06VY1k31mX69Ok1XmLGOHHixKr5hWvGX/rdohVgq2622WaSPPsKqfjSSy/V9AfmNYyuM888U5K0/vrrS5Iee+wxSV6jwKZ95JFHdPjhh0uqTQkEsThsHj3WPqfE2/EeU4QAnwId6oh5r7vuuu6e0dsY/8wZZ5whSVpjjTUkeW2IOaEZIpmzxpOR9pckbEJCT0dRETZJtXZouDvbHcLatJwDD5tlRL355psuBokti41KuhjeV2AZV1kSP2ZDW7D7YXdix4UaA/YGDCY6cbM7w2hC4mKPwET66KOP9Mtf/lKSl5xIGmK3SMnttttOku/uDbsGCTVmzBhnF+LZ5Hq2nEw4b/isWZ/bcjScHy+4tdWJrVIq5osvvnBSf88996w6F/OioBvkf/rG7r///pI8H/eYY45x37Fd0WP3sCh1T6otYUSkwb6Pz8AWRB87dqyza9GWLr30UkleW3rooYck1TL0iEGH47fXLdtuJknYhIQehFwJm1H8qv1LgZS0icF2t7MSLijzIaldgiC50PFhCLHzW6Ob45sAACAASURBVHvD2t15Et8m31vYmKZtCN3c3OxsUmzpZ555RpK3xymghhRhVw7bQd5xxx2SvF2I7YpNi6SxbT5oKkXbxqWWWsppHkh6bKW8cib2/oRxWOw2bHO8mVznvPPOk+QlB3MJM1tgQbEm2KgATYHPaaSFRkFse7vttnMxXfwexHZjiD0PYSZMLBuG93m2WBeeQe7hkCFD3LPMe0cffbQkz1Yj/hp71sJxxhqPo+HFkCRsQkIPQqlmWHllX/IYNOFrPK02W+TDDz90XmGkyBVXXCHJe9vYufBwxsqQ5CEW3yKBnXkwTq7Rp08fNwdsK/JxsUufeOIJSZ5Devfdd0vy3sR9993XeZB32WUXSV5awZPGK4l9eM8990jyOb/go48+clIZycrYGd/nn39e4yWOFe4O38eeJ5MIfi+F1J9++mlJXrIgaZZZZhk3FryqaFLMc9y4cZI8m40YLvccyTJx4kTnscXvYWGZTjynGcdJyi70B6yHHc3GxmOnTJniNBA0AOLV11xzjSR/b9FAwhI1ZREUT68/gT2W2Bx+bh1TsS5w/ACg3vFQhCEPqjHYh4sHxtYSynJCWBXQusstuGFsJFmJxag8qHnDhg2T5N363EBUSZxQPLTjxo1zPwZuJhRFNiPb25RNgDAZ1M677rrLJQKEans41xAxIgtobGzU6aefLsmHNHjwID+MGDFCkncCMSbOGXYIQLUMNzzJh814iA888MCqMZMUsOeeezqHW6y2k4Wtx4UDMS85nOfEJhawPjzznPPhhx92AgMq6m233SbJE0tYP5s0kYWYQ7cokT2pxAkJPQidoiZWKpUa1SpWuxZiALS+vffe272P6sOOvcMOO0jyRGx28DKu71h6H7AB6QUWWKAq/YzdMaRfIh0hAfzgBz+Q5N36JNRDM7ROkra2Njcekr6RKJAEKBUDkESsG+rnzJkzXfiLsArqHOr8tGnTalTivI7frBmkftRzaHlIDs6f1yfWSrnddttNktcsuBaSmBASmDZtmnM2ERYLxyoVJ7BnJamXravEfUd1hxRx5ZVX6qCDDpLUTnWUfBhy2223leQTNspcq6i3T0qvS0joBajL6ZQVoI5JvVgwG5obCeyjRo1y0ga7DQcGwXRsmthuVKZIV2x3xulkid2Me/nll9euu+4qSbr88ssleSlBOIrxIUUJQ2G/DRgwwEkcJAo7LJoGjjbOjZTBkUWBtzvvvNPZZpZgEDhKSkvYAQMGuPGj0VhtKewzVARrdyKhIH6wZtx/wlzbb7+9JOniiy+uuX5YgE6qTh+UasvgWNQjYe08WOsJEya49UbTwPcCRZNyNx2pkmiRJGxCQi9Ah8j/WYnBdme1JAvI50ghSqYsvPDCTpLeeuutknx6HSECS/XiWjFyeAi7S8fqEtuyM3htp06d6uZw1FFHSfISFbuMnRcJAOEA23fgwIFOsrI74/m25ABsVq6PR5hzLrbYYu491gFbCls67A9bJoGD8ROmIqyCd7wooTw8n6XcWSoqKZRcA1ofNu0yyyxT4wPA206UYNasWaXS6zoCSyxZZZVVJEknn3yydtxxR0n+HiJRIf2ffPLJdV/PditM3esSEnoRcuOwlgRhJWxbW1s0ngT4DhLinHPOkeSlwqmnnup2UDyhxLVsvJHdx5ZE4W8Y/0Lacf0sYrwU79DG+KZNm+bsTJIQkEg2CQEJQDEupMbf/va3sHyLJNV0xEOyMh5iflyDNLRbb73VaR4UqkZKbbLJJplzDNcBYEtOnjzZxZG/+93vSvKagf2uJZeEnQfs+fEJhB38JH9f6GzA/H/yk59Iak8K4PzEbtHADj300Oj8pHxfRr1V+LlfSNgzzzzTcQfwhJNmyP3uCGyXec4dQ5KwCQk9CKW8xBZZ37HlTDkG8jq7Md5A0qzGjBlTE5ODUQRRHIli+7JmEfu5vmVFBWPPjMPaAuJhAj7eU4B0QmtASmCXYZ8Sy5s0aZKTOD/+8Y8lyRVlgx1FKhvxV8bP2oQMGI4lDosGwj3Ioiaykwe2vDsfrCukP55qUtDs+9wHXn/55ZdOKiOtKWPKPWLNGCPrzLlDDQ1vN9cHsQJltuB9nq0dfKfqmNhr7PrLLrvMRTF4HonHx/oE5V0bLS1GX0w2bEJCL0BdXmKLsKkPuxu7Dzs6Oy+lS5EoW265paR2RgweY3ZZdljOBdPJenIZl+15GqKokDgxPOwmJCt29SqrrOJI7yRnjxw5UpKXMHgH+Q6pYnBx11xzTd13331V4yHdjCZRF1xwgSS/42LPkzx+4oknSmq3pWK+BV6HxHHuoeXV8nq11VZzsVC83iTVcx3seaQCyfckB4wcOdJpRRxDiVTS50aNGiXJJxCw3niESZiANZSHokLiFn379nVaQfAdzpX5PkD7uOeeexxry3YPLMvEq8eOThI2IaEXoJQNG8t4yfPGIfWQhmSw8Pnaa68tSXrqqaecxxTuKGl1SB3OwfWQwHbXDHu52lgtiBUS5zhsTaR1a2urk0bMH3uMDBqkIR4+SsXgPRw3bpyzz5EwaBUXXXSRJF90mjWwdjs29uTJk93/2HS2NcSkSZNqJCzI8qSiufAZnGhaQhITZh3w6JIyeNttt7lrI2kpjI7kPOaYYxgb45Lk1zTUkvBDAO4/tvtbb72Va8OyPmQ+ZV0v9r5N/7z44osltZd2JfURbjXpk1yvHoQajuSLIIAkYRMSegFKSVjbXRtp8cknn0S7orNLsnPaOBMSrW/fvi4GSdzV5iiSRGxtWCt5w+/EmD02gX3QoEGV8ByMk7jhZ5995nZBpB9zwxMKw2j48OGSfLYRHtOmpiZXzgXtAC8pHGP41HiRyZSBr8rxzc3NNVoDkhb7N7TTY4XEWcu2tjaX30vWFNrQDTfcIMlLUrJ4mAte/D59+lS1NpH8epOFNHr0aEm+pSied56prHsIMphWmQnsSEA0DvJWjzjiiBrGVey5Z9yM7+CDD5bUXpxt4403luRj5GhS8KRtAcKia2Uh5mtx4yt9poSEhHmOTjXDCktkxho5WwYUkits7YGnFFsQqQK3OJCOVd/NirdllbExx1ZNAglkS32C5ubmmkwe5gjzBvsDW5wcV6Ti6NGjXVlPsnIY14UXXijJe5y5PjFey9GdPXt2DTMMic+xU6dOLbRhw3WysXPWjqwpSpgSO0eSMYcrrrjCFTzHZkUL23zzzavObVlrdlxz5syp8Whbfq/VkmJzDIFPwFbN4JyMh3vNHBn/pEmTnMaEZMW2JwJSb84t881CzIYtpRLHelhmDSJG9eNBZGFQlddYYw1HNGAskAWsSmH75thrZaVRFXW27tevX1UCO9cOz2k3G0ISNlkepwihCo476aSTdPzxx0vyNxnHFRUYbUoZJAxbLqWpqcmp7YyZv2woITmee5hVU9qCHwr3BkcSKj+UUds1fOzYsS5tknpX/GCpOW3B56FjiHHmJZl89TpTJbY0P3ve8C9jZ5PFyUjoBponIbxFF13U/VChKPJDtckpeZTZGIoEi5tr4ZkSEhLmG5TqwB6TWm1tbVFKF+B91DjbeW769OmlXe4WseJwkblIqqW1Lb744hXJS1Z2Q9TelpaWqh6hkp8/VeKhCKJ2cRxSZOrUqTXOJtINkca2mzsSlmuhIn/xxRcumG+7FQS0PzfHgQMHVqTqDn/hsa2tre4aliZHeMd2HreU0KweS0gopFKQNibJaytItvBzwiRQE8uW+bHXCFVrWwKI+8vasT6/+93vJHlzh4SPgw46yK07zjmeC5ymsSqPPPthIYCYRpqcTgkJvQh1ESesTVmm9EZRT5sZM2bUXfqlTC+V2PXtztXS0lKR/C6YJU2RelzH0i4JTQTUQEleQs2YMcOFQGySBJIXyiZrQWgFamKo1aAt2LlxTBb5n+8gdfK0Es4bc/5YB+OcOXNqnEhW6oepeFJ1aDCGHMJOptOJ8diwYN5zatfSaopoUa+88krNc4omgHQuek7LfJYkbEJCL0Kn+8Na2AC17cAdnNud0+7osfBNDPWkU9mdCy+xpQKGiQTY2+zc7Mp4Cxk3Hl9bjHrmzJlOkiJ1CQURoIeUEfarCa8dEidsX1gkeiA93RxjJXBCaREr0EbiPgXe6VJgNY5KpeI0BkrA0C0PTzOkGIssKV6vl9hSE8sUi+PcrC9ebggsed+B/ANBg3toS/fENMc8pA7sCQm9CLkSNiEhYf5CkrAJCT0IRUXYcsVvXlewItRbFKuj37GIURNjfWQHDhxYw7wqayuFJVTKjjkWW85rj5Jn4xXdw+bmZncP611XWEJ4ssugaO06Y8MCkhNgaLW1tdU8p0WtMvLQFc9hERLTKSGhF6BDzbC6C/Ni57IeRryWIf/T9rqNpU3lFVNnJ7exQlsqNYawj6lt7QkCSRmNw+Zdpx4va9lz1HtPW1tbqwrghYjxbGPRjLxxxVBPfLjedbLF+6T4fUkSNiGhF2C+krBzA3bnoghbXrKxzaSJHZvBqnLvF3GtbRZJrDB7Y2NjTbaS/W7Il+5Itk5RU+Hu1ITKsOeKbNg8dMZ2Da7HOLr8HLDnQj54iCRhExJ6EOpqNzm/ApZNyGeNjdnuzv37969I1UXXpGob1uZSxuyzmP3W0NBQaO8USW9sqy+++MJJQsZspXDIkokVIejKe7rkkku6gmQxwCRjzFbCUdjtoosuqrtEjM1Istk+9aA7tYdwXJYBVpS3DZKETUjoQfj/3oaNzTGUeEVxwSI0NjZG+aWxc3Vmp68nDtsVWGCBBWqKa1u+LegKT3TZe9gV15pX6FCJmK682TFnS9YxsZsNYs6RrIqCFkUhgazxxW68rSYJwZ9zheVmIN+jctN3h3rE9gdNTSGrag4YMMCpftaBkqUyduYe2k2D69GR/o9//KO7PuVxNtpoI0m+FA7VEq3ZQMkcak+DlpaWmuqDZYkTXaHOdoVTKhhXh8eTVOKEhF6Aea4SNzQ05BbPClFm98tyGoUoUqdssbimpqYaSqIlLlhHkZXIgwYNcmlnzz77bNU4rTTBMUE6IudkXHPmzHHJ7STFZ0jaGgkb0yAWW2yxuqiFWdcbOnSok6T0mEX7oFAZaYs4n+gPm/X8dfYedhco28O6dxTNzc3u2ai3amKSsAkJPQidkrBlgtwxsAOfcMIJzibbYIMNJLX3MZF8mZRbbrlFkqfzxWzKkKgfQ6xEZoxY0KdPn5riXrG+PbxPuRkSm88++2xXynWFFVaQJN1///2S/K597bXXSvIlMymRShgEqbb00ku7/jSxGr/12LAjRozITdoOYamQFJALJQb9VE844QRJPpmBomwk7lOEjkT3sFhfWKwsC/XasB15TrmXdGD/+9//7rr60Z2B/kjUoabXUhEWWGCBmqIDefewalzlp5CQkDCv0e02LN3avvWtb2V+Pn78eHcMEhbiNR2ubReBznjwyto/WSVsgK2eT+mUDTfcUFKtJvDQQw85wjfaBBQ0KuZbWLsytKOLitblESc6gueee06S73trceaZZ7r+MvQhogA6tjteYzQIys5YdDc10QINAE//j370I0nSddddJ0n67W9/K0nab7/9nEZ16aWXSvIlXOlQ8Y1vfEOS9zfQ5a5MhMQiSdiEhF6ATknYBRdcMLPr+VfflVRLbuc1Nuxpp53mOrWhx2MD2nPZYm2cK7Qli4LlMRs2liq3+uqruw7lXJ8dFI8nEpXdGkocEunoo4/Wt7/9bUne+4v3FFAMjHOzBuz8YboXa8eYKQCeVSy96B42NjZ2mFjAWLfaaivtv//+krzNR4E6YO1frpkVU6/3HhbZsNOnT3frC1hDWxyecVBA/KijjnLvMwc0KmxasM0220jyvYgoLE6f4Oeff75mXmFf3Lw5giRhExJ6ELrcho3tcqNGjZLkmS/scBdeeKHb3caNGydJ+v3vfy/J2w/EvfBAFrGZ8lCW6RR6jW3cESI+9iiv6UT3i1/8our9rbfe2nk+mcsll1wiye/krMGvfvUrSd6GRYox56y2GLZJWFfbsID+qEgOmGjrrbdeTSOw8ePHS/LaBn1h99prr6qxdqQ0aayQeB7sdWCS4dmFcWVbozDH0H5nHNwT2GocQ1F4q21g0+bNLZbg4OZRMM+EhIT5CPOc6RS2T0QKsTuff/75knzLws5IVhBLYLedw8MyL1bKWVsWDjH2qWXq9O/f36VR8Rce7gsvvCDJ20UUDrMsn3AHjnGpsyRQd9xDK62am5td/Jv4MWBdbYH0ruTZlpkjza2uvPJKSd7fwPovu+yykvyzt+OOO0ryz6TktYRddtlFkvcdEB147LHHJHlNcbvttouOp2j+ScImJPQCzHUJa+3BVVdd1XnPaEeBh5RMFdhBXZFUXHZ3DqWqZUFZKWzbk+A9RupsuOGGuuGGGyT5OCSlOGnluMUWW0jynvI8bcKWrOHYgJPdrRKWWOsrr7wiSRo+fLiz42KlcPDScm/r5S+H5+yMDcv40IJgoz344IOSpO23316SZ2SFGDJkiCSvBfHXNunGLkaa14PYHN086j5jQkLCPMM8t2Fnzpzp4lk0k8K7RoModjDbUKsjKLJhrW2RlWPLMezSNlsH+wgb57XXXtNhhx0mSU7SklP6xBNPSPIeRri1oe1kEbN/GE/YDKsr7iHXQxrhHUcjmjVrlm666SZJ0h577CHJryfNvy677DJJ0k477ZQ59npQrw2bFWumCDrrDT/45ptvtteSVF1+lXtFfJ7nA21pq622kuS9xcR660HMhs2t/A+6MnPfkiBuueUWFyZAhSA0wIPfkQmXBQ+WVWPDQHqsdhM30D58OKFQle+66y5H1Tv33HMlSRMnTpQkFw5hcypKMQyvbx1llkAeAmeQ7bJeBsyP+4KDhgSGSqXiyP2AeUAIOOigg+q+bllgGmASQDaJdVGQ/I8JAgvhJ6syM49w04aqyXPBurOBnXbaaZKkfffdNzpmvmupkUVIKnFCQg/CXFeJkWS33367pPZg8pZbbinJp1yRVmeTubsCZYkT5jsKj4ml4rHTowaiZr3wwgvOUTNy5EhJ7WQKyZdUufvuu6uuVVDNsur6eUH3rlSJIU6Qjsd8Z86c6aSb7Whuy+iUQVGieGfI/9aMsaE6WzAAc+yDDz5w6w7pH5ILaXaYdv/4xz+qzt0RpLBOQkIvwDxzOrETf/LJJ44GhvTFeN9tt90kSW+88UaXXdfuXHSvi0m2jpDjsV2RLrfffrtLOsdhgdYwduxYSdJVV10lqWsq0ncXcYJ5YY+/+uqrkuQ0JMmnU+6+++6SfDiHMElXFErriITt7HWPPvpoF8aCGGG1spVXXlmSX5+uTAMFScImJPQglPISdweOOOIISe32z1JLLSXJS112bJtm1x2I7bgd8Yzb3rIk7b///vsaPny4JLm5Yt/ica1nN7a2dKxkTUdhqY/Yc3/4wx8k+XAGKYPhWEgX++CDDyR5LzjoiITrolrGHf6uJI0ZM8Z5kpkrc4QwEUvKL4Oy5VWThE1I6EGY6xKW3Rvv6D777OPS6Phsv/32k+RLcXQFYl5gSy/M6kZe7w5PzBPp8stf/tIl6bMrQ1+jp0xHYMusIt3qhbXvrKTmOqQLQmzhe4899pjWW289Sd5DDnngoYceklRbPrYM7D3hHGXn0ZWYMWOGHnjgAUl+jvZvR1Bv4fIkYRMSehDmuoRlJ1lyySUltTOLYDLxGbZSV6JEYS9J2bu0layxmB2SCTYRc5wwYUINs4W4K8eWlQ4NDQ1Oglv6YqxcTxFiTC1ikEjPP//5z5K8VGB+EydO1NChQyV5Kcz8WBPKp3COguhE1XdBkXTuDskajok54g3meSVlsh6U7cVrkSRsQkIPQqckbEcKNHP8z372M0nSxRdf7Dyo3WmDxGBZS9bW7du3b5R/a72z1qa88847JUmHHHKIs1WJNROjLWvbhXZ0R/jAMYTJDcydWDFNyXifYmJ4Q5ES++yzjyu5QmIA30EbuO+++0qPqaP3vzufn9GjRzsmEwns2267bYfP11GPfpKwCQk9CPOM6UT5jMcee8xJGdg/lOKgsVJHELMRYiwZpCN/syRerBSqtWWxbQ4++GBJ0iOPPOLsPXioFORCuyiyk7NKxGAvknmSxyW28yuTFUR8kfPvs88+kjx3FnbWoYce6lLvyDpCCmP3dQQxD2oR04m1LuoKXwZrr722pPaibDvssIMknyKJx9+WUK0HZecIkoRNSOhBKCVh7S5AnJRypPUAKUQLh+HDh7tcSjJ4kELs7F2JWCFxMoPIh/3+978vSbrtttsyC7NJtYwjgLShkNeiiy7qJCk5pcwRjzgSjzXOa0dYxM7KKnNqC1Yj+V5//fVonJn3kSB4pa0mseSSSzrpQ9lWPrONxDrStsIiJmHtPPB2T5s2rcP2LYnuI0aM0EknnSRJGjZsmCQfS6egXld2ek8SNiGhF6DTNmxRSw57HPYX3lLJZzfAr2UH++lPf1piCvWhyP6x0jSMe5LTicRBKrKzEmNdeumlJXkpNmvWLFd6hN0YOwspbKV37L40NDTUVJzoTD5sQ9BQ2zaXZp7M2/oD+F6lUtGf/vQnSXKlYsiZxc7rStSbrRNK9bIS1mqC//rXv9w6UCie53TnnXeWVF7ClskA61SJmIIT5762icyo1xDHzz//fFfnhz6buM3nBmyNYeuMqVQqThXkh4JaaZO2UYVx1my66aaS2rvw4aAhIWDvvfeWVHuTLf0uyzkUU13z6G0xtbdSqWTOWfIlT/guajXrQeXHTTbZxD203OeOkjg6g7w5MnZCYiTJQ9phDUjGIJUOE+lrX/ua23T/67/+S5Kv6G+rado+QtaB2RnVOanECQk9CF1OTcxQzyT5glQUWsOYHzJkiNuZIFfHzmXRFWlXsQJmoZpo6/9SZoT3UZU222wzSb6DPP1C11xzTSd1URExDaD/4WBjp7fpalyjra2tRm23xdhCxMrZ5MGuuy2nwvs2kV2qX7J2xT0sQ/Oz4+I1WhDPHiYLJhshxvHjx7tevjasxjpQP7uePkH1OsGShE1I6EGYZ8SJvBKU3YlYWCcvvS7mULO2Siy8884770T78sS6yseceV+NOfM73dWBPSYNOpIy1xUocjrVQ6yHfmk1HwoB4l8ZM2ZMzfnOOussSV5rpBNAZ5B1D6s+7/QVEhIS5hq6XcJCSKC8Ru5gIgW7uzMgHQu65wHJyndIVM8rEcLcwmC+JC288MKSvLfS2st5RIPYenWVhOX8zI/ieHmIlWCNSemuJE50xC5ES8C7zX3JA+tBAfyisqz1IEnYhIRehFwJm5CQMH8hSdiEhB6E3DgsRbYBxabC3p7o/Oje2AK2fIm1xbI8nUWeY5sgbtHQ0BD9LGYb2O51drx9+vRxMVBrKxbZYyHBvqx9ZW1A+7qpqakm7prB7OnW/rDzGkVeYtuOY86cOTVrRML9+++/323j7Mou8yBJ2ISEHoRSXmI8aewYIfe0SCoiBWA2kaRO7K6xsdEdY2OhwMb5bAdy4p2TJk1yOynEdbijWcT4cI553liuY5k/Fnmx3FgjLZv2FkPYkzaWfN5dzbDmN8TuIYXn4UCH6Mq4f72lSUGWRsT9596hmSYJm5DQC5ArYa19l7VDsNvYBrjApoCBkC3EMdi/7IKxzJ88WxIpZ1PFYLS89957mTYs38vagW0h7aLEertOLS0tLq5qx1wU980qgB5jHHGN7mqGNb+gM+0mO+pL6GoUnT9J2ISEXoBcCTtkyJCK5MuaYIeGXmIQ2zGK8k1LDbJgVyRh/N1333VFs55//nlJXmoHtmxmu0nGnWWH5mkH4XftePn8oosu0tFHH131ni27EtrhWefcYIMNJEl//etfo+MAs2fP7nESth5ecowPznp0pIk06M5SqXn+irwiBCGShE1I6EGoi0tsq0fMmDEj6v2cGyizGxbtXLFCc3nnKCokhrRA6xg8eLCGDBkiyTc25npUNLBriy3N+/V4JP9/9RL3JsQkbO4PFlUjT50tquHE+5DceUDzHsAit7l10ePwmj17dmF936IfbFZ5kRgII6DiULeXZP1TTjlFUruaS5V4SpBQl5jaR4Dr4ySDUB6q7NaBlVfTdl49zJgp9fad6Ui9o86Q/y3KCCDMJhI5SNywuPHGGyVJe+21V93jSCpxQkIvQCkJm+dkiZWEiamNWelXNn0rtlPGJC/H9+/f353fdssuK2HtPLKogHxmk525JhoAWGihhVy5GLq34XTi3EhrtIU33ngjcw0aGxvdMThV7JrODxK2OzE3VGL73Hz961/Xs88+K8n3C7L0UTRQaI+28wDnbGxsLDRxkoRNSOgFKOV0QmKwg/Cd1tZW939INZRqdX+kEefClhs9erQOO+wwSd4GpLco3cIoERoLCYXOnU8++USSd6HzOrBJMsM6MefOIoss4ogSHGNtaObGzosEpAvcJ5984oqtEbbBlt9///0lSb/5zW8k+WQKW+aEde7Xr5/73yYlZM1xbkjYHXfcUZdeeqmk9h40kr+/jJHXwbgk1WoQgwcPdvTVGOqVsH379q1ZqxhWXXVVSdKvf/1rSb6vbVjgjnNxn8PnT/L3MGbjhkSamK2cJGxCQi9AXTas+1LgSYuVAoFkQQnI4447TpJchfgDDzxQUnsVdSQYxazYpSk5iaRlp3ryyScl+RAIu9WsWbPq6jvz1XgzQ1ehBLA2fOiVlnw3N4qD234yI0aMcN7gpZZaSpK3d1dZZRVJvszI008/XbUGIFznohI6XW3D7rnnnpL8PC3ee+8912kQOw+yDSmZc6PMTyfPKcmXN0WbQ9sbOXKke/4uvPBCSdJLL70kyZdCJRJyxBFHSJJOO+20zownSdiEhJ6OXAmLfWcLg/H6ySefdNKP3Z6C2Y8++qgkb4uh92PrXHLJJZLak7vxqtEKgR0KKUcnuaeeekqSnYduFgAAC7lJREFUNG7cOEm+i/tf/vIXSdU2LhKeBOWg72surS14X1K7h9faINiqNknBFvIOvchIUEphIoHQEkgsYJdmLkhkqJZhYfPAKyypyg6fqzbslClT3PhjyEpi6CjqlbBnnXWWTj311MzPeE6++c1vSpLWW289Sd6bz7MV0j9J38OzD5DOaFhW08rSMspoSVXHZ84iISFhvkQpL3Fsd6wETYYgsWOTsWNddtllknyskh6w2K3HHXdcVfkTybN7sFXXWGMNSZ7Wd8ABB0jy0odeq3PmzKmJo2JvsttZL7Etc2q/39jYWPOe9eAybrSNd999V5Jn+Sy00ELuGDyINPyi1y6sGOb8z3/+U5Lvcsc5w3HE6IzdJWFjnt05c+a4zyjIDVPrkEMOkeTXAnuvM+iKUrX2mX7wwQcltfsbJL+WSNiddtrJPeNIVrzZaHFoRWuuuaak2mZi9djxScImJPQC5BZhs1LHMo0aGhq02267SZKuvfZaSV6C0PeUVDwaRJ133nmSquOjF198saT2NDTJe0yxXYmJ4XHG/rPMKKmW92ybS8XmaL3dYM6cOTW2qU3FIy6Lh9eWvPn000+1xx57SPLSEL4xtt/1118vyWsN2MlIVphRn3/+eY2kI3UwqxlWVyLPA89nSFaA9OkKyVqEOiWYJL/O2KzEXWmHyrgbGxtrbFKebTSta665RlJt+lxXesiThE1I6EGoy4bNiseyQyF1kRjstDS/pXEQrQnDXQe7F8l55513SpJWW201SarJdLnnnntqzhGOKfxrpaH1Esfsn/A8RTxpzs138fQSS21qanIlT9EA8JZjoyKBmSM7u/UEh2PNsrelue8lHjVqlNOOYpiXXuIygOGEb+WWW26R5G3axRdf3NnhSFrbfJv1X+GrtpNvvvlm4XXtbyv2nLprlJpNQkLCfIFSDZ1jUrhPnz7OfsIbS0z1/PPPl+RtVxpF2R1liSWWcB7R9ddfX5Jv8Ufs9qabbpIknX766ZK8VMrKtSxqxRhDng1rwS6IZmBtFux4bP6rr77aMbtee+01Sd6GReOgoTBsKVtmhvmE2UNZ3tp5gYsuush59vE/IF1WWmmleTKmssBbD6MJTjd+Cdb4nXfecfyA0aNHS/JeYLLN8Bqvs846pa9v72FRi8xclbhv376Vr/5K8o4iSM2ffvqpe3itEU/XOiYF2d12s+vfv38YjpDkFwunEg81qvC5556bOd7m5uaaUAfXzyIVSJ4cwjyyEgxsojrBdhxBhDL4LmvA91ZbbTXnkNp4440l+cQAwgY42DAHOB6E9Es2n5xKjHNVJa5UKk4NxDFoE0a6+HqZKjGhQ9Ra7gNCJQu22if39rrrrpPkN+Wll15aN9xwgyRp1113leSpiZD+ETA77LCDpHLd/mJIYZ2EhF6AUk6nWFgjVB/ZZZCSSGN2MJuozY784osvup3wd7/7nSSvnqBOo2KwYxVVyQ/HXET5KiL/f/UdST5lD7UflZw5bb755pKktdZaS5IPyXz44YdO8nzve9+TJP385z+XJJ144omSfKID0gGpkNVtAfqn7Uc6t9LrqOAIySMLXelksugOpxOaH88pJhzvH3jggU4L2n333SV5DYt7xm8AimJev9iicjZJwiYk9AKUSq+LJdk2NTU5p4glKMScP1aCHX/88XrhhRckeVc65+SYLbbYQpI0YcKEqmvZMEs4l1g5mXp357Akiy36RggLiY/kPeaYYyR5O3qPPfbQQQcdVPVdQlg4ae67776qzzlnFoE8Nu+5ncAeu8eSNH78eEnSZptt1uXXnRslYrDB8RNMmDDBhRnxv/CMYTtz71ZccUVJnl7aESQJm5DQC9CpBPbIdyQV19HluCFDhrhUPGr38hrSP2GcLBJBiDAFijHagmWxBPaYtAqT9Ak7QYIYO3asJK8BUN2fVDjIEAMGDHDn+8lPfiJJuuOOOyR5CXT//fdL8lKcXZrUPiTuzJkza9bBpj/OrSJsZepBd9N1u03C2nGH5V/wWUCYwE+DnWv9NWXKrXJ+/CBEU5KETUjoBcglTsRS1EIpaiVTkWS1Nu7bb7/tpA39ZYh9IbksBS+Gtra2aCd4238GWHsZKRX2Z2FHfeaZZyTVplO98sorknyJG5LzoWced9xxrkQOdi70RTyMw4YNqzr322+/XXWcjb1K3kvJfbHlVbsLjCkPHS0kPq9hNUReT5482REmjjzySEneh0FBBaQkkrcMeLaQrEQAouMrfeaEhIR5jro6sNuSm5HvFB4TnrO5udntXNDyoPaxS+cxVey1iYHhybOwtkFLS0tFyqeEWa2AOCu29UYbbSTJs5ewl7/zne9IkiZOnFiVHidJRx11lCRP4SPRG6YTmgGSNrwHIdPMzK1mjt1hw9oUxra2thqvPMyx7kBX2LBln1O0vnfeeccx2vDsk1JKEYKtttpKkqeZ5sEWQbBINmxCQi9A7jaYVfJTyt8dyjYiYic+9thjXTE1bFmSh8t6GsPxxSRrDJaPa1lcIdkeKYKNgiTFo7v66qtL8nYoCc4DBgxwZUApwgZrCsbXFVdcUTUO29UuLIzOZzF7q7uBzYyEbW1t7VavcFkUNVHrCLhft912myP/o+GQwMG9JZZeBkUk/xiShE1I6EGoK4EdrycSJNzR603twu77+OOPNXz4cEle98frRiG3onNn7e62FGkQG8vM1gleS8q3121JVyQe5V2I12LrXn755W5XtqmEsLys1II9Y0usVjI6wlutYG4xnc455xxJ0sknn1yzTmgMhx9+eIfPH7Mzi2xYks5tK88ysN3bn3vuOUnSzjvvrCeeeEKSdOaZZ0pqv6+S1zg7ItlTmdOEhF6MUkynsOO65L2j77//fljSgu9IiktFJNjKK68sqb00x8iRIyX5LAiSvR955BFJXupYqWclTV5yd6wZlvWEM4/QTrc5k4D3mZPlGodd1GMca5tPDGsGTznnIq83vF4ZD2NMwtpWiR0B3x0xYoTz9FNqpTsRk7C2SRnP6XvvvVdo39qGVmhNlJ/dfPPNnUZleQGUNSUe35GypkVzdOPs8BkTEhLmOkrZsCCrobGVLha2QBi2GZ7Vt956yzW+xd6BU4yNFBQBlxSXLAsssID7LCzFGv6ttxlWyCW2sNpFrKyL5FkwdnxIbbu2GU2a3TXsde3Y53bFiXAs5MoSP7ZNvboC9cZhW1paavKKWUNi/bQB5Tji+WSQTZo0ycVbsyIJX42r1PjDwn4xxCRsqbrEqBqWwDBr1ixHU0OF43VIVpc8YR76HiGPrbfe2vU9IZmbnqlWzSa0AaxDZubMmZkJ6FJcPYlVWSyz+PaHCmyoqLW1tUqlzTp/rAtg1jhzVOHCMXcl2GDPO+88tyFRJfOMM86Yq2ORassPAbuxSbWpm9SFppAAZV64hw0NDTXVEnHC1rvunblPSSVOSOhByFWJF1xwwYrkXdy2xm8YYmDXh7aFIU4iM27+Y489VpIvkXL//fe797bbbjtJPjxy9tlnS/JOKK6BI8Y6n7788ssaUkeRStzc3FyVQpjlrIrV/7VS2zquyjgfiihy9px53wmcUd2iElPmBrIHISnCV3MLVl0cOHBgRfJaXhnENCueX1R5Qouo+JLXDvku0pi+UfVcP5WISUjoxairCJslI4S2lN0xqEfLzgT5nZSkrbfeWlJ7R3Zc6EhHJC1ucusuj1XiD8MntidnEJrKTWC3O26eg6CIjsdcp02bVmN32v47NmRUlpyeNfa5Xfl/biPmdKpnzSzsd7k/SNinn3665rwQNCi40J1d5kGSsAkJPQi5EpbUM5s8He4kMamH1xDPLqlJeaDIFfQ9+swgpesJSNvQSpGEzQusx8rHIB1t4bSs79mSNfUWrSuTxN9dYR3suryynXMTXVkihoICpDPalMUsoC3ynSw/Q0eBFjt9+vQkYRMSejpyJWxCQsL8hSRhExJ6ENIPNiGhByH9YBMSehDSDzYhoQch/WATEnoQ0g82IaEH4f8BB4HisDKeedUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 288x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 1250, D: 1.255, G:1.068\n"
     ]
    },
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\n",
      "text/plain": [
       "<Figure size 288x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 1500, D: 1.136, G:0.971\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 1750, D: 1.317, G:0.7927\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 2000, D: 1.274, G:0.9762\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 2250, D: 1.258, G:0.9521\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 2500, D: 1.202, G:0.833\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 2750, D: 1.288, G:0.8659\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 3000, D: 1.379, G:0.824\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 3250, D: 1.392, G:0.8353\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 3500, D: 1.296, G:0.8011\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 3750, D: 1.221, G:0.841\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Make the discriminator\n",
    "D = discriminator().type(dtype)\n",
    "\n",
    "# Make the generator\n",
    "G = generator().type(dtype)\n",
    "\n",
    "# Use the function you wrote earlier to get optimizers for the Discriminator and the Generator\n",
    "D_solver = get_optimizer(D)\n",
    "G_solver = get_optimizer(G)\n",
    "# Run it!\n",
    "run_a_gan(D, G, D_solver, G_solver, discriminator_loss, generator_loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vnLQHE1VdtpJ"
   },
   "source": [
    "In the iterations in the low 100s we should see black backgrounds, fuzzy shapes as you approach iteration 1000, and decent shapes, about half of which will be sharp and clearly recognizable as we pass 3000."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RAITXp5ZdtpK"
   },
   "source": [
    "# Least Squares GAN\n",
    "We'll now look at [Least Squares GAN](https://arxiv.org/abs/1611.04076), a newer, more stable alernative to the original GAN loss function. For this part, all we have to do is change the loss function and retrain the model. We'll implement equation (9) in the paper, with the generator loss:\n",
    "$$\\ell_G  =  \\frac{1}{2}\\mathbb{E}_{z \\sim p(z)}\\left[\\left(D(G(z))-1\\right)^2\\right]$$\n",
    "and the discriminator loss:\n",
    "$$ \\ell_D = \\frac{1}{2}\\mathbb{E}_{x \\sim p_\\text{data}}\\left[\\left(D(x)-1\\right)^2\\right] + \\frac{1}{2}\\mathbb{E}_{z \\sim p(z)}\\left[ \\left(D(G(z))\\right)^2\\right]$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "nuOMD1TWdtpK"
   },
   "outputs": [],
   "source": [
    "def ls_discriminator_loss(scores_real, scores_fake):\n",
    "    \"\"\"\n",
    "    Compute the Least-Squares GAN loss for the discriminator.\n",
    "    \n",
    "    Inputs:\n",
    "    - scores_real: PyTorch Tensor of shape (N,) giving scores for the real data.\n",
    "    - scores_fake: PyTorch Tensor of shape (N,) giving scores for the fake data.\n",
    "    \n",
    "    Outputs:\n",
    "    - loss: A PyTorch Tensor containing the loss.\n",
    "    \"\"\"\n",
    "    N,_ = scores_real.size()\n",
    "    loss = (0.5 * torch.mean((scores_real-torch.ones(N).type(dtype))**2)) + (0.5 * torch.mean(scores_fake**2))\n",
    "    return loss\n",
    "\n",
    "def ls_generator_loss(scores_fake):\n",
    "    \"\"\"\n",
    "    Computes the Least-Squares GAN loss for the generator.\n",
    "    \n",
    "    Inputs:\n",
    "    - scores_fake: PyTorch Tensor of shape (N,) giving scores for the fake data.\n",
    "    \n",
    "    Outputs:\n",
    "    - loss: A PyTorch Tensor containing the loss.\n",
    "    \"\"\"\n",
    "    N,_ = scores_fake.size()\n",
    "    loss = (0.5 * torch.mean((scores_fake-torch.ones(N).type(dtype))**2))\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "krGClF97dtpM"
   },
   "source": [
    "Before running a GAN with our new loss function, let's check it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "id": "Wo_nel7-dtpM",
    "outputId": "101f7a56-1b28-4236-8633-4171ae4283ee"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum error in d_loss: 1.64377e-08\n",
      "Maximum error in g_loss: 2.7837e-09\n"
     ]
    }
   ],
   "source": [
    "def test_lsgan_loss(score_real, score_fake, d_loss_true, g_loss_true):\n",
    "    score_real = torch.Tensor(score_real).type(dtype)\n",
    "    score_fake = torch.Tensor(score_fake).type(dtype)\n",
    "    d_loss = ls_discriminator_loss(score_real, score_fake).cpu().numpy()\n",
    "    g_loss = ls_generator_loss(score_fake).cpu().numpy()\n",
    "    print(\"Maximum error in d_loss: %g\"%rel_error(d_loss_true, d_loss))\n",
    "    print(\"Maximum error in g_loss: %g\"%rel_error(g_loss_true, g_loss))\n",
    "\n",
    "test_lsgan_loss(answers['logits_real'], answers['logits_fake'],\n",
    "                answers['d_loss_lsgan_true'], answers['g_loss_lsgan_true'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "q82122yedtpO"
   },
   "source": [
    "Run the following cell to train model!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "htEifHj5dtpP",
    "outputId": "3f4566d9-02c7-4948-db13-ba5934e86437",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter: 0, D: 0.5689, G:0.51\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 250, D: 0.1481, G:0.3264\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 500, D: 0.2063, G:0.4708\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 750, D: 0.1258, G:0.2649\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 1000, D: 0.152, G:0.4361\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 1250, D: 0.1842, G:0.2598\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 1500, D: 0.1986, G:0.2422\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 1750, D: 0.2018, G:0.2362\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 2000, D: 0.2339, G:0.1912\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 2250, D: 0.2559, G:0.2198\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 2500, D: 0.2503, G:0.1511\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 2750, D: 0.2112, G:0.1597\n"
     ]
    },
    {
     "data": {
      "image/png": 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N4lTyL7vssnWQPYjLvztqVw6bB7Yjz/5ooY6rpmxiXD0HMbt+tiPTVVVVBdeZlg+rtjlSeqmdTVDONS2kmYeVwk1rVE51yJAhXu2y4gvSf+t5KHahiqB8TcgLIZUWMdXV1d6HWaaugjSFejnp54EDB3rtT4Jem+O68v49Hwo9bLvXUvPmzT0Rt33taQRWdOxu3boBpvOi4C/GFmwzb2kQ/6eJUggntHGqzc/ChQu9NjhKXanoXemdQs8lF4I+S84kdnCoAKQ2W8d2uO3fSzg/e/Zsr7RJTCbTOMikSJNhZarKLI9TpF8Mgu6f1lxdXe39P2iimmPYdCBT154DGxRcSvJzKjiGdXAoI5Q8rSOxg3afZZdd1guPKw2i5mLawWy/Mg6CducgiaJ/HmsxQo0g2Luv1ijI/5GV4Z9SL4tEO7uuz9f2xDFsmcMxrINDBSAxhi1kr0dJvQSxXRIoxLC5IsFBf8sT4Qs8f1QpnHxaYcmSJYEReN9rHMOWORzDOjhUAPIyrIODw9IFx7AODmWEvAXsfwXfoNLXWOnrAyMvVXahHJFPreaHY1gHhzJCg7U5zZVbDasMydeQWQJsNcSy4Ri2/FHKZ6jmAC1atCg4CkZ6ehX22/oCPwo1LncM6+BQAUiNYaXJlU0eZQRCVETRbIZtc1rO0fO/OsOqwN5unZMLUQapxYWtU88Hx7AODhWAkimdlhak4f9onKBaiKiqQ9h4442z2ofYIzNtBPnpixYt8qpFgvBXZdjDDjsMgNtuuy30sdL83AZp4Bs1alSwRY1jWAeHCkCDRYmTRJwWMUmsUY3S7r77bp0DgOeeew6AbbfdNvC99lhODc1SvbBGGEZBqRjWr2G2n4GazKkVqBq1KZbRr18/AI499tjI5y1llNjPwLa/myY7BzFsUZ3/lzbEmYQWB2oVovk3mmZ20kknATB8+PCs9xx33HEA3HjjjQCce+65gJmds+WWWwLmixplpmjSGDp0KGD66trCBH1Q+/bt66Uw5syZA8C///1vwJj+M2fOBEyTggkTJgCZZZZgelw1NJR2UTdLf/ljQ7p/ziR2cCgjpG4SiznsRmtR+8MmhTTMKTHtSy+9BJiwvcL4fvgnu4MxGTVjJwnENYnDNsHT9IY333yTF154ATCT/L744gvAzMPt0qULYAIwYusOHTrkPVcuFPMM1UtYHR71HOzZRbJk7LJGf5nliBEjADjxxBNDX3NUuKCTg0MFoCgfVmmLJUuWZPXx3XPPPQEzI0ZSQUGv79WrFwCnnXaaN8FazNS5c2fA+D35JF4Axx9/PACjRo3yOuZr50wS6hGsti1iCXsW0EEHHQSYtd96661ZReYtW7YEDLPq71F7IKeBILazBQlKZ33yySest956AJx//vmAYdhTTjkFgGnTpgHG4rr22mszzqXJhYceemhkfz2MT7nddtsBhln12kcffRQwDCtmVXsi+dZ1dXVZPaN1H3RfRo8eDcDRRx+d8xpyzWuKsgZwDOvgUFaI3UjcnoWp3UOTzm6//XYArr/+eiDbNwDTPMwWHGgigHY7Max2S6VR5AeHme4dx4dVFPijjz7K+L0ms0eJcOpan3766Yxjq9N8HJQqreNP5cgykKhDIpJx48YBRtSg9SrqKmulTZs2AMyfP78gy9jPsKampk7X4Yef0fTZ8pWvAdEmwh9wwAGA+dzJmhg0aBBgLENF+DVjqXv37kD25MJ8cD6sg0MFIBTDqmxIvqTeM2DAAC8yKl9A2GmnnQCYPHlyYhdrR/BmzJgBmNxl+/bt+fLLL/MeY2krr9O91CwXWRFxcssNIU0Ua2iS2z777ANA7969AZPXtKeSa/36fRj/NegZyoo7+eSTM17fpk0bL94g0YPOs+aaawLBMY9bbrkFgKOOOspjZ4k9lG/v2rUrYHxaTfXbZpttMs4lls81bqXQGgXHsA4OZYSifNjHHnsMqI+CaheRpE4Mod3k1ltvBeCQQw4BcvuwNjbffHPAqGWCosMrrrgiYAqGw6ChGdaOBtr3X/dHkUcpg/wo5H+VmmE7dOjgRX/l58l/U55Zucsw0fvrrrsOMBFmG2GfoXzK++67j06dOgHGh507dy5gptVdffXVgFGe2Z/bsWPHehMY9UxkNSjfLlVUUJtev3UR5Rn64RjWwaGMUBTD+nWWUrhsvfXWgPERBg4cCMA111wDmJ1NqiDZ+UuWLPF2Jl2LmFMjMezJ3/379wfgsssuy7gu/1rCjvELw0CyFoKGT0WB7o/WImhH1y4eB6Vm2I022sgrH7Sh/KvGZyaBsM9QjOa/NuWLhfnz5wMmSq+ote1jNmnSxMuN258pvVdMq9iKshv6fK+66qpAvRXlWsQ4OPwFEIlhbdbq06cP//znPwETKZN9L5aUekm+mfJvuXwzG9qFxLT6V0OpdEzthv48bphRjLnWmBZ073Q/pKDRjq61JoG0Gfaqq64CjBLt0ksvzcof6/63bt0aMGyTBAo9Q+Xpn332WaCeaYOiz/pMFWoKEAZSvIk1xfCq5lK2pVOnTlx55ZUAXHDBBRmv9SmxHMM6OJQ7ImmJxWjaPceNG8cOO+wAGKZ4/vnnATjnnHMAU6WjHS4Ms4qFP/3004zfi1l1rGOOOQaAm266Cahn/oaqjS0EWSViVkH3I2wRfpj2Imlj7NixgKlWyaXOuvTSS4FkmTUsxKyCsg1g7q8snrZt28Y+n44V9NmWJkHZjNra2qzqrLCf21jSxDfffNOTmikELznYV199FeoC/JBJO2vWLADWWWedjL8rIW4XhqubwYIFC3j11VcBI6q3hRTFmMTFyNhsSK5mrynqMdu1axfYc1lI2yRWcYM6anz11VfeOmRi6kscpkNgVER9hhMnTmS//fbL+J2CT0pHxsEnn3wCwBprrAEY8YiCXfqi5vquKWWpNJPgTGIHhwpALIatqqryzDNbuF8MdAwFABQOF+TUB/WbbdKkSdbfCjnzxTBQkPkqE/2OO+4ATPFC//79vRJA+37bhQ9xWFxIm2G1/hNOOAGoN38//vhjAAYPHgzApEmTdC1Jnz7yM/z000+9sktJFFX6mEQ/YnuNKoRQ36qzzz474/eNGjXy2DhISOIY1sGhAlAUwyoo9Mcff3gs88orrwAmvVMMJMRW+ZoCWGKhvn37AvDaa6/lvJ7ff/89cmlWFAaS6EPnEINKDK7EurooCh07duSdd97JuFbdN5VmqVRLvrfSYmoEoHM2a9asICsUy7D5ptD7oWCTxB6PPPKIF3wsVHgf1CUyCop5hhLbDBgwIOM64sAucNl7770B0/FScs1chQ1qcHf66afnPLZjWAeHCsBS0ZdYu52YVDuSrm3MmDGA8RHjoJjdWdcn5h8/fjxgdmvtrGo4FvR+MAKJHj16APDggw8CxoddaaWVANO9vmfPngD861//AmDIkCGFLjd1H1bX6heuhE01RZkvE4Soz7Bly5ZMnDgRwGtHpM9aHB/bfq8YVMyq9rC5zqGyw7vuuivo2I5hHRzKHQ3eSDyfbEyle7vvvnspLykL8iclxROzNmvWDMhOmNs+oD+arN9Jrqa8pS2l1HtUnqaSQ3+7zbRg+652VFy5drHqDz/8EMiwioyqjK1Qg4E0UF1d7RWKKEZw2mmnAYbppkyZAuD54sIee+wB1Mdo1MDhjDPOALL9dcUn9HkRlGtVLnjcuHFFT3N0DOvgUEYo+fQ6e/REp06dPGWTDe3OhcraopS/FePD6poVCd9ggw0yzjtq1CjANBi75557ANN+deTIkV45nV2iJR9WfrDdhNy+r7W1tQXbqCTlw6oV6/fff++d2w8xysorr8wHH3wABI8WscsHY/qOkZ+hYgKHHnooYNRJUjzZbGkXlDz33HOeok73Q0yqtep+6JnpmLonslRatGgRKNnU5+TXX391PqyDQ7mjwaPEuc6vnUmtMcVYCZ0v0u5cV1eXVdYn2I217ZavUtX8+eef3s6uvKrW6G/MBXDzzTcDZs0rr7wyYCLT/ubtYdZYaH0dO3b0dM7KeytmID9LTCINszSyano2cuTIrIbyeq52Y/dCLWGXWWYZ736HWV+YNVZXV3tr0TN76KGHAGPZBDUWUE599OjRXhZAz9KGYhmKbay99tqAGROiz8lKK61UcDq8ixI7OFQASs6wmpWqap6ZM2dm2f5pRkGL8X/EfmrxIUbSdb/77ruA2VGHDRuW8e/PP//Md999l3GsINjlX77r1PUnyrB+yIIQyyhHqMiqYgRnnXUWUN8aBupHZ6r5mq3usfXWYh8xWjG+bBy1mtR0GiWiShr5pUcddRRgKpEU/VY8xQ9ZR2p3qoFfGkOiyjFZHVp7PvjGbjqGdXAod5ScYbXz9unTB6jXVGrIlFQ+yndFRVVVVapaYh176tSpgIkWq8a1UJ1qGCiaqfaaH374IWC0tyGvMzTD+u+Z/Lx9990XMAyrXV9saUdQW7ZsGXkQsyKndluVMIjzDIN8aOVjpRfXwGk1yu/cubM37Er635dffjnjGPps69+grEWuqjIbzod1cKgAlIxhL7744ox/dd558+Z5OTFVgUjdExbyLTWAKx8aupF4VKhzg3yuMCjWh1UdsnxkRaql0NEQKLGDVEAHHHAA999/f+jri4s0n6FGpqh1jHzb9u3be5p2ZS/sXLOYVX6v3TQ/bBsgCGbYWF9Yfwhes0K33357ILvIXF/GRx55BDASsFGjRnkL08Qz9SwOgno4HXfccRm/X3311Qt2li+3L2wxSEo4IVNfooilBcWk5rQJSSYY1e2qqamJPLdWZrZm3+oaFi5cmNXbK8c1O5PYwaHcEdskVtBAYXqlBuzkt0rj1MFOO9yBBx7o7UAqIVNIXaayLRsLEt3ng6+I2DFsmSNNKymf2RrFpC0WvoIRx7AODuWOxINOtvjbTvJL/iZ/tVGjRl77DhWIS0SvNIl+r6R3HDgftvwR5xl26NABMNZbWLzzzjteGi8IshqDpi1GgfNhHRwqAIkzrF2S5TsWEE6KZovqk4Rj2PLH0vIMC02giwPHsA4OFYC8DOvg4LB0wTGsg0MZIW8TNuf/lCecD1v+cD6sg0MFwH1hHRzKCO4L6+BQRnBf2BJgww039P5fXV1dsE2MQ240adIkq0naXw3uk+PgUEZwX9gEMWnSJCZNmkTr1q1p3bo1bdu2pW3btgwfPtxj1l9++YVffvmFmpoaampqaNq0qVfx5EcQE6vhV0OgqqqqYAM4rasQ1lprLdZaa62s36uFSy4sXrw4FfVbqfF///d/Rb/XfWEdHMoIDd5IvNRIModn10eqFlhjNjSqo1+/flkDsgQ1MtPf7ZEScduApvEM1dxcrWrBDMgK6vgh1tVAsYEDBwJmvUEjPnKhofKwth6+c+fOgFmToAbyGvg8bdo0IN4ahQafXldbWxtqJs7SCD04uxOfSrfUFwjMw1bvI/X6VZd9ddGXmbg0SEbt5gRCrgl0mg5gf2HVCmXHHXcETAMDfYi12c2fPx/IvGdLA/TchgwZwv777w+YTorqYazXqLGC+lVrjfqCagLAH3/84XVnbNWqFQALFiwIdT3OJHZwKCOkZhKrk6F2XE3+evvttwHDIM2bN/davUQxGYpFMeZUoel46vSufrX5MHbsWMDcH61V5lMSiGsSB5VCasbr3LlzAdMNv6amxpu6lyuABoZpzz33XADOPvtswHSFjNIUrRQmca7Zvja22247wFhWYl6ttVu3bgA888wzGcfcd999mTRpUt7zO2mig0MFILYPK/bRzvrUU08BZkqbdpWDDjoIMLuQHPMvv/ySo48+GoBnn30WgM8++wwwvqEmjGl6mA3taD/++GOkQvmwsJlVXfnVYzlXegKMX7JgwQJv7o5gT7oTWwVNMi8ldO/shnqtW7cGDMPKOvjoo48CmVXQ85YfKKbVnFQFshpiQjtkWxX5mFX9r5944gkA2rVrB5jvgpoG6v6pxZGOHUf84RjWwaGMEMuHraqq8qJcapPx888/A2aH0i4s/zRXUt3PkH58/PHHQDCDCZpXKj85H8L6P7mYWhMJ1Alfs2c233zzjPdqzf369QNg0KBBWccXs8ovVLsR7cZxUKryuo4dOwIwe/Zs3nnnHWjRO2YAABYzSURBVIDARmVt2rQBsiPp8mHFuLnulY1S+LBqtbvNNttk/U0xF0WJ58yZA+DdA9vHt33/MNMZnQ/r4FABCMWwyivZDb1btGjhzXuVfylmlZ0eJsc6btw4wMwb1ZxNG/q7GHW33XYD4LHHHit4DiGJ3dnOT9r3UBHQUaNGAcZ/ywXNs5G/o3mlYRDU2Dptho0SzRe7yL+TjyprSmtQ874wn5dSNBLX2jTTaOWVV+bTTz8FTLtdMeX5558PmBzzW2+9lXFMO17x8ccfF7QaHcM6OFQAivJh1Si5cePGnjxNk87UBFwTvqToOPbYYwEzyOqWW24B6qPHmhim3KSuyZ63qd/rX/kIUUYnJLk7y19Xrnn99de3z+U/T9bvwExLUwxAVoR8O1v2Btk7to2GbBFjxyiGDx8OGJnm4MGDATjnnHMAY4n5o+PK1fvLEv1Ik2EVBxGbaj2TJ0/28q6KmuuZ6nolVRSUm5b14P8MBElVBcewDg4VgFhR4kceecTLtz700EOAaSAu9hEUHdQ4ytdeew2oj5y1b98eMLvxyJEjAeMLaEeT7yhmLQZJ7M5iNkUQNTd10003BYwfmsvHk950ypQpAGy55ZYADBs2DDDRUpWRFZOfjcqwQUquKMOfxER9+vQBjP8uP19jV2SBnXfeeRm/DxqilgtRn6F/ynwh6F7I4tFI07vvvtt7JralJ+hzq3E0ytOG1Qn74RjWwaECEDsPe8ABBwCGOU855RQgeMxGUMTZD1V+fPjhhwBccMEFgNn1rrrqKsCUsdnXBMFKp2IYVsfUv/K1+/btC5iosKyKV199FTCM64d2cEXXn3zyScCouvSv7s93330HhGO5XKMKo1gQujYdR6wedE/1+27dunnldbfffnvGazXAW+WCOoeqc+xjhWHCUuRhdR3rrrsuAO+++673DIIsPI1NPfjgg5M4v2NYB4dyR+xqnSi+RyEoJ6k6QkVdtftOnz4dgF69egGmwiONKHG+HV/nveyyywBTgaN8nCyCXNAaunbtmnHt0k/LZ5L/LhVNjx49ADjyyCO94dhB606qWsfOSQp2ZPebb77x6lh1b9Zbb72M90odJHbS+qUoioI0GVZxkijDwmUtSM2VBIIYNtYXtkWLFp4oQEGHOOJ1BSpUDGCnCIJC4BKOz507l5VWWgkI7nxQzMN++eWXAdOLRx9Cfej0JbSvRyIBf9DDvt962KNHjwZM0GnXXXcFjEnpT6VJfB6EqF9YBf0UANL6dG4FyIYOHQqYNIWkiRMmTPBMYgXgtOEojaMvtOR5cVAKk1jP44gjjgByS2qF7t27A6Z4JQk4k9jBoQIQ2yS+7777AGOmxoGcdu1UCpMLEsjbKSMhTPje3rlqamrqIL9ZbZvHQT+r/EysKZPyhx9+8ORsNsPYxRE77LADYOSX+rvSI3/++aeXfgkTWCv0DJs1a+aZf2eddRZgRA4zZswAoGfPnoBxWRQ49EPWx6OPPgoYSaKCa/feey9gZH0SjMTtWQXpMOzs2bMBE3SCYMHKJZdcAsCIESOAdKwIwTGsg0MZIRbDVldXRwr4hIVY+/DDDwfgzDPPBGDAgAE5X7/zzjsD8Pjjjxc8dhq7s/w0XZ9EImKbXr16ZaVIBLGWgnbywdVuRkGaI488EqiXLOqeBwX6ig06KYUk8YPEMCq+t2WSCj7NmTOH9957DzAWggrV1QpF6Rz5ySqpLGYKQpoMq/udL+1oQ2IYu1ldnGYKjmEdHCoAsVrEJMWuijqeccYZAOyzzz6A2fGvuOIKwAjiVfIkhGHWOAjaKVWE3aFDBwDGjx+f8Xr5n+q9CzB16lQA+vfvDxjBidhM/rn69kreJjGJxCNpQJFcyUu1Xokh5HcqCi6RjFq0+jFx4kQgu5mZUh+6Zw0NW8ijiLniBGqwd84553ivlShIqSubWYU0WtU6hnVwKCM0WOd/7bzNmzf3JIiS/ClqqUjjqaeeCsBFF10U+7xx/B9boijWUBmVdmn5Z7mabek1YljJLOWPqomdpHzazXWsRYsWFbRsovqwvXv3BkxUWK1OlGOXn21L8vw/63OklkFiabvYXXJNsZKEIVEE8kn4sLJYdH7BFovIP128eLFX1KHP5YMPPggYizBJRnU+rINDBaDkDGs3Vps5c6YXTb388ssB025GLWAK+W1S2Ujelw9JRhhXX311wChdxowZY59L5/B8U0nfpMSS/65/1TJTEj9FXVX437lz54IT3MIwrKyE1VZbzfPXdB+VB1dkV3lYMcmll14KmDJCf/57jz32AIxKStaAzidhvPxgRdijIAkrSdertavoQr+XskyF7HV1dV6DPOWSlauVL5skHMM6OFQASs6w8gnkpy5atMjzkXQt2smkbJK/UQhNmzYNjNgJUXfnxo0be36nrWQSVLAstU+YNpY25MNq177yyisB09hLedyffvrJu4dBiOLDfvjhh9651R5FKqtbb70VMCyoonMV2xczxEx5Zv/kOwjOAORC1GfoV3PZmmA9M2mHg5qj9e7dmwkTJmT8Ts9ZegHdr0Lo3r27N74jCI5hHRwqACVnWLGV/KSDDz7Y8xPEqMWoX8IiSR9WPt2LL74IhNOQSi1kN9yWukiNtK+77jrA+PFbbbUVUK/NLVSSFoVh/daAor577rknYHKpSZaPidFUBaMmAIoqh0HUEsnq6urAlqzKrR544IGAyZnrfsviady4seezqyJJI1vSGN7mGNbBoQJQMobV7q2Ir1j0t99+48ILLwTqh+ZCcgqqXEgywmg3SFOkUYog/b13797eTi2Gtf0/O8erQVvyH/2R8iBfOtcao6xP61LbGumX5bNKHxwFdh5WrK01KKocRcEV9RlWV1d741TUHEGaZkWDpd1WK1MxrCyBYcOGeeos+fy5WhT5oeejcxTbSC9jLaGP4ODg0OBInWHFGGIUMYyaRNfV1XlDhVTZkW+0RVykWelhR4d1b9u2beuxbilQLMOK9eRP2uMT4yDJMaDFPENZO2qvK3WdGFeKM7GgPoOKJQwYMMBrrrfLLrsARuueBoIYNpb435/ykEmjwJGgB6V0i0TuH3zwAQA77rijN1snbDokqFdvy5YtIwUvwiLshy3o76X8ssbBG2+8ARiJaJwvqn3P0hDCR4E+K/qyqX2P0jgydyWRFMFoEzv11FMDSyRLCWcSOziUERI3ibVTSXqnAJJm7SjoIBH4iy++6PU21t9kikkgYM+NjdKR3kYp2os0NBpytk4p8Fd7hn44hnVwKCMkzrCFGpbZk9gXLVpE8+bNM44hYbjE/PKLkyje/qvtzpW+PqiMNebw+R3DOjiUO0omnIgytTtNVOLubMMxbPnDMayDQwUgL8M6ODgsXXAM6+BQRsirdPor+AZprDGXEitoynkacD5sfMTJ9ScB58M6OFQAYmmJKwlqXaIi5Tg7a64yqlIwq0NyyPX8gzTspYRjWAeHMkKDNRJvKDREDq+mpsbzibQ72z6SflYTcjUnF/w+sJrWBZUhOh926YRfzRTkI/uGcTkf1sGh3LFU+bBBA3OXVogNdd3SOttDk5ZffnmvJYnar2h48mWXXQaYhm52ZZIGVKmmtqqqqsHVYpWEIL9UDd/ztc2NWpSf63W2Tr6Qf1we3wwHBwcgJMOG2W0Knuh/PtiOO+4ImFEO66yzjjfqomvXroAZ/KRmVxpboVESDZUbs3djmw0FrVX/fv31116DNg2aUgMvjaxUlFoVSjqX3Witrq7OG/thoyE7IZQLbFYMYrQwn3W1OVJMQaNEdQ517tAz9Z/btsqEZZZZJu85Q31hw1y8+jHtu+++gOk/q2ls+hKqcF1mY64bpp69WpQWLlMwSZM5illjtwixp9gJ9kPYbLPNePnllwEz8UBfXHXrs2fQ2PfF/2UMutZykJkGbf5qVtCqVStvKoDat2gSRBDCFJYEBXnCPv+qqipvpo42TLkxmiFrn8vuU7311lsD9V0U77nnHgCvtFTHFEkFriPvXx0cHJYqpJ7W0W4j4UBQgXvA+YFsWZ+OKeZVO5rffvvNY+4glDIloJ2/traWhQsXAmYHPfPMMwEzj6UYdgxillKldfQc/GkKrUPMocCbphWo36+m8mlyoSywr776ynMP8lgSkZ9hIavIhiwB3dva2lpvQoWsH01iHDhwIABPP/00YD6nHTt2BEyHRh1rm2228eYxhXmGfjiGdXAoI8RmWE3vvvPOOzN+rx11m222AUxbSe0+mq2aC6+//joAO++8M5Bsm9BSMqz89w033NCbTC7oZzFvIfgnhge1zPFND0+VYQ866CAA7rjjDp3DW8f6668PmOl0EoAomCJrScyiNSiAt8IKK/jFAznPn8YzDPJl/YKXILGLAopak4KjmgLou24AzjjjDK+1r+6bzfiOYR0cKgCJ+7Dt27cHjIg+jG8WtLttueWWAF6ENQmUkmEV5bz66qu9KXS2P677VaiLvO5R48aNPR8qyA9Ly4cVo4g9NTPGj1NOOQWA3XffHTA+q2YFab3y4TW/55ZbbgFMOisfSvkMFel95plnvAl3vusAzOwhRfxlAa277roAvP3224DxfSdNmuTNKZLvbkfNHcM6OFQAEmdYW5iuaWDvvfceYEQQQnV1tReRU45SuOuuuwDjJzfUXJaoEEtcfvnlQP3OK4ZU1LRly5aA2XXDru2YY47xpoWHiaImsb5CPuW8efO8SKnYRmysfOPee+8NGD9PUVLFJ5SH7Natm5fvDEIpnqH8UkVvf/zxx6yMxnHHHQeYmbJiSbGyLBDdC/29S5cu3vchKHfsGNbBoQKQuPhfuSpFhzVp/Ysvvsj5+pYtW3o+kZhWihBFI7VDyQ/Sv7kYRrmxQrt0mlBRgAT+/p35pptuAowPG9VqGD16NJdccgmAN1c3LYwcORIweW5B16wIp/xSP7bffnvAPEsxiqLIih7LemrIovBc0PVoruyvv/6aNTpGw8Ns/3P55ZfP+Fks6tcIPPzwwwBebCMsHMM6OJQREvdh7RxV0PHbtGkDZEZHFSlUBFG5WxUFaIq5fAPbHw6DUvg/Gmn44IMPApmRz759+wJmHmkaiOvDyiLQdStn/v7772e8Tv5ormi1fifrST6hflbxg8ayRCkZjPoMq6qqIlsyugey+n766acsDbuUdkHWYxw4H9bBoQIQm2GDqiCkYNHftVPZ2GKLLZg+fTpgIqjyFewKFvkKynPZ+uQwSJNhTzjhBMBESpVzXbJkiXetys0qT51GMXpchlXk1i6el1XkO3bWex944AEA9tprr4zfq4Bf/t0mm2wCGFVbFCTxDAtV6TzxxBOA8TEXLFjgZUBeeuklwMRp0miw5xjWwaECEDtKHKS26datG2AiaUGYPn26l9+T6kPHVHG3vQt26dIFWPoKtqX3lYa0Xbt2QL22VJUcYhrtzu+++y6QXTtZDJK6H7ruIPaxI9xLlizxfFEN7lYkVdFWO3IaZHGVCvbabMZVXfeYMWOAen2B/Tfl0pPUuhdCbJNYH0A9ZEGCiWeeeSbv+3OVZgl62Ap+yCSWuS0TTWmUoA4Q1vlSM4mvv/56wJjGRxxxBGAS62CuWZPq9YUVgrpJ5EO+2aLFrM+WB2qzOfnkkwGzCX/++eeACb7kgtycJL+gpRS/yK3xPxe5MRKL9O/fP/b57DI7ZxI7OFQAUi9gDxNOt1uByOTSLhd0DLv/TZgJ7WnszjIHZREsWLAAgIceegiATp06ee1EJCA47bTTgPrCADDmlSyWOEhKmqgidElGxTZKxcmyyQe9VwGbJIJspRT/+8X5ctXWXnttwKxNAcQk4RjWwaECkFpf4kLMusoqqwBGHOF/z4QJEzJeGxSCnzx5MmAaviWJKM3ZtAaJAySpPOSQQ4D63sNiK7GvivNlRah1jJhYLVROPPHEwPMm0c0yHyRyEPuLYew2PHV1dYEBrySZtZRQ6vCGG27wfqdYi+SvSTQDVLG7xBeFWtc4hnVwKCOk5sPaRdZiElsckffi/rdr243cbKiRVxghdRr+j0QA++23H2BE+bvuuisAU6ZM8UT/KoZQW1gxkKwEtdaZM2dOznPlk9n5GoylUsCugooePXoAJmrcr18/r0WQnuuIESOA/BZCsSilD7vOOusA9WWCG220EWDSOCp6SNJ6yNXmJ+PviZ3JwcEhdaTmw4ox1N7lpJNOAkwbDUUcV1hhhQw/1g8VPV955ZUZv9cxVfY1YMCABK+8HnaDZwiew6I2lv369QMMy6gIYMSIEV5DNkWFlcuUVaCu8UHMKlRXV+crei64rjiQ7yYpqTrfDx06NEtOOn78+FSvJQyizr7xQ89aZYBiVzDR8TT8cufDOjhUEFLzYaV+mTVrFmAimlLHqLyqdevWBY8VZoxCWCTh/9gFDyrKVvmfdl6pmQYPHsygQYMAmDFjhq4DMKNLVCitEsM4bJl2I3G1vpGCa7nllvMi9vLJJdMsRY4yDfG/oGc7c+ZM//kBo05Kw7JxeVgHhwpAagw7ceJEAHr16gUE56xyseMLL7wABOdXdc2HH344APfddx+QqUYJQtjdOcwgpqDSQlkXe+yxB1Dvi2vAl3xUMZAijWF36V122cUr/Sp1m1NBzKJ87Lx587x1KaqaZmFG2GeoURnvv/9+ZBbUs1WhybRp07z1Ku8dJtORC1988YWnQwiCY1gHhwpAagxrF5+HgcrsCg20EorZxaP6P8svv3zo0jeN31BkUZHwb7/91tOkqjWMIuBi3rCorq7Ourf2M0ybYe0hT6ussorXfK8UJY9Rn2FtbW3oInP/ADMgY46rIsfy03XMqEqzMNfjGNbBoQKQGMOqHjao2iROJE11hxdddBEAb731FlBfBfO/6wx9LHvnqqmpqYPs5nFhprwHMZwsBCmeZs2a5fmq999/P2B0usW099TxwwyLSoNhxazyW4cPH84FF1wAmNEcYp0w9zEqiokS2zXX+lc66e+//z7j9YphKPJfU1PjrUnvUTYg7WeYsY7IZ3JwcGgwpNaEzUYYhlXOTmykJmCqv1S7TbuxdRSUUofavXt3AF588cWsJtRpqpKiMGyYFqDSDk+ZMgUwrKM8c+PGjb3n37NnT8CM2kwDcZ7hxhtvDJg2PrKSVGklhZui3f6OKdLBq/m5rKVCyJfzjTrQOfUCdht6yP50iRY0bdo0wPSDCoLEGEpqR0Ghhx1mAwq6yUkiimluo1iTWEGz+fPnA6bkTyafCvLV+sZ3Pm688UbAyBXV8T8NxPnC6tlpTSpGl+xS3R4l9NEUPvWqguwJhElCpvLixYudSezgUO4oOcM2NOzdubq6uu5/v2+YCwpAHOF63KCTAkcSvGvGjtIZYmClqurq6kp6/+xn2KRJkzowjGdbJbnMfjt1aE8R1NpkBpcaLujk4FAB+MszbBpzWeLATlHZQnO/3xwmYJHGMyyUkkgb9jNs1qxZHRTXKsdukiB2TuKZ288nl9UUNejkGNbBoYzgGLbAGv1SwHJB2gzb0GgIKylpS6tQFsAxrINDBSAvwzo4OCxdcAzr4FBGcF9YB4cygvvCOjiUEdwX1sGhjOC+sA4OZQT3hXVwKCP8Pzm4zJ1gJ1U5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 288x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 3000, D: 0.2393, G:0.1796\n"
     ]
    },
    {
     "data": {
      "image/png": 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dHIoIWTFs2AortlSRNiV7220uatSo4TGDysyobIgSpLWyNmrUCDAMnMo4kiGTfEzN4d133834upkirJVF0PF+JNoJBTG0XQJHzGqfO9H993nlY14/6aSTALMrkedXLA6mRI1YOJMk91KFY1gHhyJCWl7iRF5C/V/2jeyN1atXx5xLr4t5d9llF6CqoPYdd9wBwJVXXgmYQtyzZs0CTAUElWJRkykVMdf79erV894LQjZx2EyrJCSq6uC7PhBsn6lYtWz9zTffPGnzr0ReYo3dbrxdWVnpMZrGohI3Nmy2lje5vLzc8yjre2C3/WjWrBlg2quoGbR2S9qBNWvWjM8//9wbW6Lrr1+/PmdeYl1jv/32A0zj6W7dunkF7+fPn5/wsx06dABMgy8VYNcuMx3krUSM/aM+8cQTAfMDfvPNNwG8PiWzZ8/2qsNfccUVAAwcOBCAV199FTCVFhX8D6o9lMrW2L4RQXV7/fNJd8utjgCal/8Hqx+FfiS6H+3bt0/5/MkWjrAfbNhiYy8euq/2Yqzj/F9EzUcVGFX5v3///gAMGTIEMGaNFmd1yBs7dixQVWJG19e//g4Df74e+Q9W5XBUn0o1yWrVquWNQd9hOcgef/xxwNSr0nPXNv+mm24C4Nprr017PC6s4+BQAsh5ep1WRf2byIGgbZ+9jdZnFALSCrZ06dKEx/ths4MvFBG6OqfC1jZbiXn0WW1f/dvC7bffHjCFy9S3RpXug7a59i7Cz5BBY01FOJGJ/FDQPfV/9oYbbgAM+zzyyCMAvP/++wBMmzYNMEXZvvrqKwCv966YuayszGNwMZd2IUHy0ii/p9oZaFvrh5yh2h2qX7C6FqgTnzrTq0NfJnAM6+BQAshbAnsy50oqEBOLZWQrKBj/888/JxVVRLE6y67RddWJb9ddd405Ts6Tjz/+2GP4Jk2aAOY+qCOeVmOxs+YqB1CmdrrmFyavzCSZAcx9uOqqqzwbXOyiXZB2EhMnTgTgvvvuAwzDaueh+7Nq1SqPbWVP6nq+e5LxM+zVqxcADz/8MBC/W9KcwpIlbNg+lU8//RSI/z6kA8ewDg4lgJwzbDpyQvs1e/Wz35cnz+9mb9euHWBswygY1rYjtdLLbX/RRRcBRizfu3dvwLDpsmXLvHHJprvssssAw86yzxWqUshE3lelmM2fPz/Q5hf8c9xkk00qwdx/u7t9OvCnwIHx+I4aNcoLeYhZ//nPfwImStCtWzcAZs6cCcR3AFT/nuXLl7PtttsC8R0FcmnDZoKDDz4YgAkTJgDm+Z9yyimAsb0zgWNYB4cSQEbSxHTsqSAvpD678cYbe7agVn/ZM3Z6lz4j1lH7hyeeeAKoYjYxq5g92TiCkIjFbM9zly5dvDmA6Rujz6r36bx581iwYAGA10fIFhiIVfS67oWE+BIYlJeXe2NKxS9gywmzgcb22muvAUYMIaYF02m9adOmgOkDfOeddwLwzDPPAHDyyScDpuOfjt944409G7C6pS0Kuu/aDQlKBtE9yAUcwzo4FBHSYthMGlrZaXS2HbTVVlt5iiC9J9F3EJPrXD/88AMAZ511VtxxmQrGxZZisbKyMm+sYm0x/3/+8x/AeBY/+OCDmPFJ3jZjxgyPMcWGuodarWWH6x77r+//t2bNmh5r6lx2QkUi2OcRwpRPsiFlkx1++OEAHHrooTHXBWOL695Mnz4dwNtZyIbVv5KmSlY6aNAgoKqxlhRC1Q12TN/GokWLcj4Gx7AODkWEtBg2HWbVSq5VWmwoKB7Xt2/fOAWTVv0gdhZ76l8VEVu5cmXW8V579aysrIzTtmqltdlRelmlBYpxEzGf5ijlk5jpk08+AYxNO3fu3MDxpTNXP0P7z+OPw9qCfdmX8vS2bds25jh9btKkSZ5d+9RTTwHxKp/ddtsNMOJ/zVf+B73fq1cv7/5VtxI8yfwB8+bNy/kYHMM6OBQRct4MS/bpIYccApiYVcuWLYEqNYjsHLvpkZ01Ii2nP9kZjCpl00039Wy/VBsqpbKK28foXGICNTiWLnbMmDFAarblpZdeCpi4rJhJMV4xj5jXD90fuwxoItge+ETzFmNrpyDPrVRJBxxwAGDmreMPO+ww737rPUH3QJ7f0047DYA999wz5hzyAzRo0MDL8NFntaPRs8wntt56a8Ck/yWC7k8+dgKOYR0cighZF2ELKnlprzZS+Dz55JOAsd1Wr17trbJSv8hTqyRnxbWUnK5r6Tixxtq1az0W1rHJVr1UCl/bx9glTMT4b731FhDOrLIhpZJp3rw5YLyqBx54YMw4EjGrjTBmTaYWA2Oziu0FKcmk99XY5cvYZ599vGuoCME555wTcw7b7zBnzhwA3nnnHQCef/55AG699VagKsarIgdffvklEB81yCfk/T711FO9qICwfPlywBRWyAeyrumkrYxdBU/QQ9YXp0ePHoCR7S1cuNDb2snJoR+iHFUSG5xxxhkA3H777YDZmuqae++9t/fFz2ZOEL5VtiWBmqMt/xNq1KjhvaexS86o+6ZtvRYpmQlaeDJ1otljSbQgyUy5/vrrAdhjjz0A4/iSg0g/NhUW8NfWsn+ogp6l5q8fprbEcmT50y+/+OKLmLEKWtjzCclMH3jggbj3bOlkPuC2xA4ORYSUxP9hVfW0ndI2yWYbm7G0SvqFDVqFxaDaEp1wwgmAYSWxs7bT/sqLOqe9HQ1L7v5zDqElYsJg35egvysrK72SKBII6D5oOy9GVbqd0urCSrsEVeL31zxKlqDfoUMHb9v32GOPASaJW7j//vsBuOWWWwCTYPHKK6945wqauxxGmpeqJSq8o+22TINVq1Yl7bieD/G/no++W2vWrIlj/Fxu0Z3438GhBJCSDRtkB61fv95jVr2mEINdKc52PmgFW79+vSc8kC310EMPAdCvXz/A2HdarVWUTWJ7f8kSu2xLMoliMmatqKiIsx+DHDn2fdLfxx13nCfBsysTyuZ+6aWXYuZqnyMR0qnEL9hs/NFHH3lhKQnzFb5RUTw5hmTLJup0YFcI1HUk11OxNSWyK51OVQnlcFy+fHnSWsr5gHwL2jGCmWNQVcl8wDGsg0MRISMvsVa8evXqeWlSw4YNA4hjXB1rM59YqmvXrl7hLkkM9Z5smRYtWgDQsGFDwJT3EBTWqFu3rrfCi1kzSVjwjz+Rd9ZmPX+qIBjGE5tefvnlXs1aQffh73//O2B2F3boJAz2dVLp42uz8urVq70Sn357DYyYQ95QeZEFXbe8vNyzQfWMJO7Xs7n66qsBEwno3r07QGAN4kTXyUdvHVvC6YeYVXOwRSL5gGNYB4ciQkYMqxVv2bJlnjBcggixj5KaZcMoKK+kXwWh/QXKVMBL72mFV1lJrXq6hopcSfZmJ7xD5qtyWOmaINjicDHTTjvt5I1ZMUZ5XiUg0Pvp7gQgvTlqjNrFbL311h7Lyevbpk0bwIgGBNmhSkbX/JYuXeolsyulUAwqRte/2jV99tlngLm32hmF9Q6ye/1ECbGldnk23n77bW8HcsQRRwDG46+5B8EuLJ8NHMM6OBQR0irCZntHN9poIy+pWYqce+65BzAxVNlu8izqs3fffTdQlcCsGJ2g1VerXd++fQF4/fXXNa6Yc6WDZDE8raI6dybXEJTw8MQTT3i7BxWfVv+gTJCM8f1ztHsH2Z9t2rQpkydPBkzChErcvPHGG4CJy0pxJv9A69atgSrBvtRPKjzWqVMnwJS40fdESqfOnTsnHHuNGjXiWnSEze/POWXtRrbj9rpPo0ePBqpSDO0CBnb7lSjh4rAODiWAUIaVCkiwV5/69et7tooKNNveYr2vOJu8htKSDh482NPVKr6qEpmKe2kFs1VS6ah/gkpkZqN0sq+pHYH01VIODRw40BOyZ9O+IVWkU0h8xIgRXosJeXrFcLq/ipUed9xxgCkupwT9xYsXezsIW2mmmLoaQkkw72tspXF6n7MT6e0CBlF2r5NuwC7JY6OsrMz77soWVfTC1pbb0YJM4BjWwaEEkHUhcXkO5VlUepi8glqF1QRJqhqtsHfffbcXgxw/fjxg7J5sbNUg5ML+0Tj9pToBbrzxRqBqXlJzKVVQ9ycsNS5T+OdYv379SjA+Bt13fwxVqiOlDWqHYEOJ7fIeT5o0CYCRI0fGxdBl5+meyC4O2mGk4on37bAyLgZvn18KNDW/UiTCLlpXUVHhMb1fpQfmuy1ve6Z9hP1wDOvgUALIKh9255135tFHHwVMrPT4448H4KSTTgKMh1e2glY4rWSyXxOhuhTfCoIUL9oRqJB0z549AZOJMmLEiLiCbEHMmknhsbDP2En/dpmbGjVqeNlPKqSmBlZSK4kpFEPX/JRhtGbNGu95HnnkkYBplWIXRM9mntkonYKuoyL2eh7a+dif+/333z2b1WZOuyN7lDtCG45hHRyKCJE1w1KMTvZNXgpSZWArpGrDyk4pKyuLiwuKHcQ0iq2KccWmqshQs2ZNL9Ysna5seeWFJtPLhjFSmCfcnp+drwzGZlVJmFNPPRWAWbNmAaZw2vDhwwFTccKfj5xJW8xECCs7JOQyHzaVgnb5QJANm/UPNptO3rlAOqKCP4/P2GGh1yUKkAStQYMGMcfVqVPHc2rI6ZRMHJDNFz/sB5sKgq4dhTMl3WtC/HesunSvyyWc08nBoQSQltMpkSihujCrkIutuM4ph4Qkh2KcadOmAabHz6hRowDDRHPmzIlLXEhWrTHZ+4leS6dkSSKhgj1fwWZWey5RpL2FPbfq9h0rJBzDOjgUEdJi2DBnQD76oCSTHebymmDklmPHjgWMVE8MIOGBHBZioo022igusd9GuvWTMz3GPtbv5LF7GQm2zarjoqjEH4WMb0OCY1gHhyJCZGGdBJ8FomG/ZF7aJHOI+TuZcDys8n+y6wV5Uf1jyGQO6Rz35zEpe4kTJYzbJWltG9UOfVRUVHi7jHwUUHNeYgcHh6JAKMM6ODhULziGdXAoIoR6iVu0aFEJJm2ouiDIm5kItl25odk/pT4/2DDmKDiGdXAoIuTMS5z0wiGeU0HKIsU3/X1gE6G8vDypznVDW51LfX6wYcxRcAzr4FBEKBjDRolUUqJyUcCruqLUGFZtLlXIzTGsg4NDUSAlhs0kqVclStU+McocykxUVNKsrl27doNanbOZn9qr3HvvvUBuS58Izg9RBcewDg4lgMhsWK3CKu2p4l+C3aJCzDt27Fjvs2+99RYQw4YaBxCNLnVDW50zmZ8aOwe1E1F7UBVHr127tqclVnUIPStl9Hz88ccAtG3bFjDFz5TFpO9Dw4YN+eGHH0LHt6E9Qz8idzrZPy49EIkd7KryS5cu5cUXXwTwqvcdddRRgOnSrXNIjD548GDAVGRMBxvaw45ifqo7te222wYeY/9gbagulDoTXnfddQDcfPPNaY9nQ3uGfrgtsYNDESFrhg3armp7e9BBBwF49Yv79OkDmH6grVu3Zt68eYBh3/vuuw+A888/HzDyQtWFnTt3bszrYU6K6lbAS/1gDzjgAI1H44g5LpOyL77PpsWwdhqd7qu2vpKm6jiVyJkyZYr3ut27VaysnkoqTDdy5EgAunTpApjuhipSlwoK/QxVFdPuuhglHMM6OJQAUupel4qzR4XI1IFOq7JWafUN1cq72267AVXdA+weOhMnTgSgXbt2gKk4r27vqp979NFHA/D4448DhrXDUIjVecmSJR6TaP5yuqiOc9euXQE8ez4bB1umNqy62C1atAjAc/7I/3D22WcDxhmoUjmLFy/2Oq+LKfUs5UBUoobOqQ5wLVu2BAwT2x0Cks0v3TmmijPOOAOAJ598EqjqITVw4EDA3A89o3HjxgHG9xIFHMM6OJQAIrdhZec8/fTTgOkHqhVLggp/2RF18p45cyZgCnJ/+OGHMecUWwtjxowB4Igjjkg2TA/5ZFh/n9NkqYC6f+pNpI71YedNpVh6FJ7+ZGVfKioqkpY6le0uP4T9LNNBLp+hbcf7oWQU7TReeOEFAOrVqweYXWUmfgcbjmEdHEoAORP/77PPPoBhWnWpGz16dNyxtpdSvV5kk6pLmnqOqnu7oNf32msvz0YMQrqrcypSORvqIK8VOMwDuvvuu7Fy3dMAABO/SURBVAPw0Ucfxbwu208x6HRQncT/YhvZqprnoYceCph7k47sNZcMq/HaneHbtGnDgAEDABNLlg9FvYjEytk8O8ExrINDCSBnSietUNrXq9eojfLycho3bgwYW0Axu/bt2wOm56heV6c8MbAUOGvXrs1JM6xk2GGHHQATe5YNLs/nn9eJ+UwQswqy32Xfp4MwhrW7heca11xzDQCDBg0CqpRtAFtvvTWQ/Q4ConmGelZ2pMEvj1USjGL78kvouzZ79mzA7B4010zgGNbBoQSQEsOmIr6XqNsWbiumF9aKQZ5jqZEkGLdZSQyrmK7YYsaMGUBVHEzlZH788ceE14pidVZvV3Xe1rgUQ7Q9pom8ht27dwdMnM/GZZddBhh7OB0U0oaVHad/xVCvvPJKzOvyhmeCXDCsFHl6Hk2aNAHgscceA0yjMz/0fZUPRhADZ9kx3jGsg0OxI6VmWDazJmriXKdOHcDoS1XW4/rrr096/iCFkn1dsea+++4LGIaVN3a33XajX79+gIn7ivUSxdUyhTzRdmZKp06dgPAdid5L5s2WnS77XJ7y6gDdS9nqu+yyCxMmTABMrPLOO+8EYPXq1YCx2fVvdYMabas595w5c5J+xn6+06dPB8x3b9iwYVEOEXAM6+BQVEir3aSQqMHuJ598EvPeXXfdBZgVN8wbuMceewDGy5bq9aXpFGtNnjyZiy++GIBVq1YB0TKrDXuF1d+TJ0+OGZcfYmU7wV/2jl6/4ooroh1sBLCzo7R7mTBhgjcvZVqdfvrpgHlG0lIrHiu/Q3WBdjSpMKvytO0d1qeffgrkhlmFrMM6X331FWB+IK1atQLg8ssvB+D2229POohMakaBEaufcMIJAAwYMIBJkyYBxolgIwqHhYT7EmwEORf8W2OFbyTN02c09/feew8wzrtskK7TKZU0xTDUrFnTC3Eo9eyll14C8MQGWrAVqtP9yCTJIZdhHX2PwyC57eabbx7zehSSRME5nRwcSgBZMewjjzziscyxxx4LGBGBxN5BTJcNtJIpRU8OmR9++IHOnTsDJkXPRhSrs6STSi9TGqANbW+32GILL/1MaYi671HI2GxEHdYJcqIlMnfkfJRT7cILLwRMuETlfWS6ZIJcShMVopPZtWLFirhjFDqUQ+2GG24ATD2zKOAY1sGhBJCR00no2bOnZ4MpnPPss88CJsSRCtKtiqjjtPqJ5VesWOGNI5dQhb+hQ4cC8QyrEIeYp0ePHnz//feAGbvsRQlKorR/soVYX2PTmHv16gVU7awg8a5AJYAkPPj2228BY+/p3kmO+s0330Q/gSygXZFtz/uTQMSso0aNAkx4MR9wDOvgUETIimErKiq8NDqlHMlWkWxPyeVyl2v1lmu8UaNGaXsnJWWUYEKe6iFDhniCax2TS6gur8JRSvvTfOR5tL2JEB9uslmtEJDEzmZ9MawK6SXaCYlRdQ/ExltttRUA9evXjzm37kl1Y1g9Oz0fzXXHHXf0iv8JKgmTj++a4BjWwaGIkBXDrl+/nvPOOw+ASy65BDDeP9mXqviuOJ1WbXlxw9hVq5y8copdduzYETCpZ+oq0KRJk7jVLp1u7elCAgk7Bc5mT9k8fqjYmli4kMyqtEAVck/mU7DZp7KykuHDhwNw5plnAqZ8j2LW+j5IWKHvhUrZ6vXqAvt7OXfuXM+DLEhzkErsNio4hnVwKCJkFYctLy/3GOz1118HTGkYMZ1WY7GjXTpzyy239MqXyv7p3bs3YJKcBcX4ZBfZsd7dd9/ds6WDUKgi1HbCumRsiiVHiXTjsBLxq7i5yra0adMGML127GcqFiorKwvcKcmXIXtPfytFUuVd/QkgyZRXhXqGQUkw2aTRhVzLxWEdHIodWTFs7dq142KSstfUW9SOL/paZgBV5S6lO9axWo179uwJGE+zml/Jph0/fjxg2j5IgRSGQq3OSg2Ut1RzzUX8NR2GPfnkkz3vsFIhp06dChhd86xZs2I+M2TIEADPbm3RooVXdEBxV5UxlY2uvxWzVpRA2uN0NMWFeoaNGjUCzNifeOIJAM4555zIr+UY1sGhBJAWw4oN/HapbDApnmTvnHzyyYBZQWWPyC5VZs4tt9zi6U3tGJgN2X9KYH/qqacAE/OzU9YSoRCrc2VlpZcgbWur1drk4YcfjvJ6admw8qBL/6sSOHoOel/aYe0S5EsYPHiw57nXDkpqKCWyb7bZZoCJx2sHds8998RcI935Qf6eoRLTxay+6+fieo5hHRyKHWnFYcXG/iLLSlxX6z3leyphWcm8itdqpZXt27Rp0zj7RTFJ2X1qnKQVvmHDhoBpjlW3bt10plEQ2MyqBG/ZgYXCzTff7Nn+UjLJ3tR9lQpLHn4d52/+tGTJEsCUs9XfdsxW/ggxb3XF888/DxgvNsQzq3wp+YRjWAeHIkJk7SazgUpePvDAA4Bh0uuuuw4w7STFzmJeeTcTlawJQj7tn7D7pkwXsVfE180oH1ZxRWUbqXGzdlTyV8jeVo7vgQce6FX6yAcK9Qz1f30Pg8rxRnTdhCfNKqxTVlaW9Y/Zfw5tn+zuAcmQqMRMUMe1Qv9gFfaS+D0Xksnq0FsnUWXNqFDoZygpYi5F/87p5OBQAshK/B/FVtl/jmRpdkGStUTF23K9jQ+DxqeawgsXLvQcORIj5IJZqxNywaz5hAQeklI2b97cM9UK+ewcwzo4FBEi616XbanMMKRTQiZZIng+7Z90SmdGiepgw+YS+XyGShM84ogj8rprczasg0MJILLudcWCQgnH8wnHsMUPx7AODiWAUIZ1cHCoXnAM6+BQRAiNw24ItkGpz7GioqIScuO9jxJ+ZVqyXd+G9gz9cAzr4FBEyErp5FD9kQmzJouph72faolUWy+eJFqR9JgNBY5hHRyKCI5hs4BW/Hw0ssqnakrMqWwUaWeVEhjGdHpPqY86l90EzIaftW1GtUvlbshwDOvgUERwDJsFgorF5QL51iODybhJlA2VDEFs+O9//xswpUGVNxvGwLlk1mKzjx3DOjgUESLL1ikWbGgxvHTK/Khx1fLly3M1NM+21e5khx12AODzzz8Hqor3qVVIEDa0Z+hHtf7BNmvWDIAvvvgisnPm42EXeptVCPG/PWf1QVLfHj1Dvf/YY48BZmusbW/NmjW9/xeit46S1LWgyBxo166d1x9Wr6napCpiqtJiFHDCCQeHEkC1Ylg7qG6HBKJALlZnO/xQaAdGvhm2rKyMevXqAfDVV18BphKm+iap/5GYVZ0hXn75ZQBOOeUU73xiZxUhsIsRRPkMtUVX50R1j1iwYEHMWNasWUPLli0BmDdvHmBqaycLWWUCx7AODiWAvId1Etl3Z511FmAcD/feey9gCmEVMmDeuHFjvvnmGwBeeOEFAE444QTA2Dt21zzNTV3GW7VqFde1T0IIrdLqnKBeRMWE5s2bM2rUKMDU7H3uuecA+OmnnwDzDMVOmr++D+oysG7dOl566SUgtrNAVND11Hlg2rRpALz66qsATJw4EYBbb70VMM/lpptu8r6fqpOtOs3qiZyP0JtjWAeHIkLOGTZIZnb44YcDVSuaepbIVpGrX/ZFLmyEVLF27VpvXLLPBEn1gqCu67/99lvc2LUa6/Wrr74agBdffBEwXf7E4lEUbI/iPH6oi+CYMWNiurEDXHbZZQDcdtttgGGj+++/H4D77rsPML14Dj74YO+86o4XZTlRux+vGPTAAw8EDLN++eWXAJ5H+NRTTwWqBB6NGzcG4Oijjwbwui4OHjwYMN0Ux44dG9m4bTiGdXAoIuTcSxy0sqvI9oIFC7wi22eeeSYAd911F2BW8Pfeey/bYXgI8jCqdcaKFSvs4wseVwXDxNp1hCEVL3E2ZWmVFPD9998DVaVlNS71mJXNKgb77LPPALMr6dmzJ2Ce+aJFi4AqJrvggguAYIaNwksskYiuESTD1Lz++OMPatasCRi/g3waCxcuBGDAgAExc8wGzkvs4FACyLkNK1Zq1KgRAEuXLgVMnAtMN7pOnToBeCuZ7Ix8IBGzJvp/GD799FMAdt1116THyh5WJ3LZcvaco268ZMe4EyGokZU8u/J+yyPsP1bx12effTbmGPkl1GP2mGOOAeCVV14B4MknnwSqmD8fvoog+aXtN/FHKDTH7777DjDd40877TQgPy08HMM6OBQR8mbD2iog/yqqVVhtNtTZWquw+sZGgVTtH7H8unXrYuwYP+y/lTqmlVjx5VatWjFnzhxdT+MATAxXMUfFL33jS3FmBrlSOuk5yYYT5s2bx5tvvgkYz6lYWgooeV31rxh3q622AkioHw6ys3OhVkulPaaelcY8fPhwwLQ73WuvvbIdhgdnwzo4lADyZsOGqZVGjhwJGA/jueeeC+Sn9EoQZI+sXbvW8yjadq7ihUE2V9++feNes3c0YivFpYPGIcavXbt2nKImX6Vqhg0bBpjslLfffhuAjh07enbcjjvuCJjsHNnqK1euBKBNmzYAXmTg2GOPBUymS61atbx7Ysd2c+mlF7Pa11I0Y9GiRXTt2hXAU3V1794dMBlJ+YBjWAeHIkK1yNaRbfDzzz8DJnaXLJE5E6Rr/yxbtsxT4Zx00kmAyUDxe0kjGFfM3/IKi238Rc2uv/56AG644Yagc2Vkw0rPq+cgtGrVCoDx48cDZicku69jx4507NgRMB5Uxc61G9D8pBoTEyv+mg4KkcBeq1YtL0bbv39/wGjfZ82aBRj7PKJG5wm3SwWv6VSnTh3vCyKRfT63QskwYsQIZs6cCcAtt9wC5PaHqqC77onCIPrhLly40FvQlCjw2muvZTUG3WcJ9bX91usTJkwAzCKiEFS/fv281yXY32mnnYB4c0BbTjkWJbZXKE9b5kKnJgZh55139r4HV111FWDCePoB6webS7gtsYNDEaHgDHv++ed7QWwlC8+YMQOoHqtt3759vXDNhx9+GNl5gxxVEphLTCLnjLaj7du3T0memA7sxAyxoMIUErdfdNFFgBFQDBkyBKjaruvZ9e7dG4A+ffoAsN9++wGGcfUZhULE2mL36oq5c+d6OwqFHZUooN1QPuonO4Z1cCgiFNzp1LBhQ8/xsGbNGiA+ITxKZOOw0Lhs4UAmkE1ns2VQaEbJCWvWrIkrmWIjHfH/n8fH/HvTTTcBMHDgQMCIHEaMGAGYpG6do2bNmt4uSfav7D05rJQuKCfU6tWrQ+cABApWCuF06tOnjyev1X3R/LUr+vbbb4Fo0kCdcMLBoQRQcIZds2aNZ8906NABgMmTJ+fseoWuaSu2sLsGzJ49G4AlS5YAcOihhwaeI5n3PIxhbdYqLy/3bFaFLSQIEZO3b98eqEpUB5NULw/voEGDaNq0KWBCQxLDSGwyevRowIgOVOwsFdhjLsQznDRpEgcccEDMa5q//AsaZ5i8MVU4hnVwKAEU3Eu8ySabeAKJXDJroWEXqLbxj3/8AzAFvsKQjffctq/Wr18fZyPKHpU3VEyrOWhHpISGhg0beswqdrnxxhsB2G677YDsimwXsgif5vrJJ594Oxv5MubPnw+Y5xEFsyaDY1gHhyJCwRhWLRoA9t9//0INI6dQmdapU6cGenanT58OBDNrKmlf2UIJBm3btgWM6kjpjbJpVaJU3lDZbr/++qvH3FOmTAFM2RTNLx1kU74maujedOzY0VNxKQXSX4QhX3AM6+BQRMg7w8qmefjhhwHYZpttPMF4qWHq1KkAnHjiiYEpcBLyByFqZrXt37KyMi/OunjxYsDYsPIaS3mmMi49evQAzLOcOHEiDRs2BKBXr15AdkW1qwOz6jmphM8+++zjFQW0izLkc7yOYR0cigh5j8NqdVLx8A4dOnDIIYcAJm0rlyhEDG/lypWBRdSU/CztcBRIN71O8cMGDRoAxiur8i6Kw9pFyKR4GjRokDd+2cH6rBRPUSKfz1BNvO655x5vR/H1118Dph2q7leU2WUuDuvgUALImw2r/b70wsr7bNSokfdaqUBsqiyOROwqGy9KZs0U22yzDWDGJK20snPeffddAJo0aQKYlpHyFq9Zs8ZjaeU0i4WKFdJuS3F25plneg20hg4dChjbNZ+2rGNYB4ciQs4YVivTRx99BBiPo93GoF+/fnGeUtlBagVYbBCzhnl4paUtdDPosrIyj+Wl6tEY3nnnHaBK5QPmeaj58WGHHQZUFQ+3G0BVl+bWyWDbnfpbBdGVfXTttdd6pYFUBeTBBx8ETNHAfDBtzpxOmrjKqSgJXNuszp07A6YSX76QrsOirKws4y/d+eefD5ikbT90X1q3bg3EyzKDUstat27thVmCkIrTKcxBIrGGvnj6V06piy++GIC7774bqNpKK4FdIaFc/lDz6XTyyzOVXienmzryaUGLEs7p5OBQAoicYSVjU1qVtgn6VxK9IAbJNXK5OqsYmphV0rW3337bq+VrwxZS6G8lkasjWjrItGqizbraDtridkn0FIb7/fff81o4L58Mm2g++dj6OoZ1cCgBRO50Unc6rc5ahewVt5ApU1Fj5513Bky6lS0KP+igg3jooYeAeAeF7EU5ftRlIBNmzRb2M5ITSk4027YV/H+ny6zVoZRtKvD7MgopnXQM6+BQRCh4iZh8Iwr7R+U/cyG7iwK56l4nNrR7zIZ50nPBoIUu85MPOBvWwaEEEMqwDg4O1QuOYR0cigjuB+vgUERwP1gHhyKC+8E6OBQR3A/WwaGI4H6wDg5FhP8HI5PgIAharPYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 288x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 3250, D: 0.2336, G:0.1621\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 3500, D: 0.2206, G:0.1707\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 3750, D: 0.2488, G:0.1253\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "D_LS = discriminator().type(dtype)\n",
    "G_LS = generator().type(dtype)\n",
    "\n",
    "D_LS_solver = get_optimizer(D_LS)\n",
    "G_LS_solver = get_optimizer(G_LS)\n",
    "\n",
    "run_a_gan(D_LS, G_LS, D_LS_solver, G_LS_solver, ls_discriminator_loss, ls_generator_loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UlXAGStmdtpR"
   },
   "source": [
    "# Deeply Convolutional GANs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "E-IK3TnQdtpR",
    "outputId": "c8406a18-d75e-4920-bd63-f74816d11610"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([128, 1])\n"
     ]
    }
   ],
   "source": [
    "def build_dc_classifier():\n",
    "    \"\"\"\n",
    "    Build and return a PyTorch model for the DCGAN discriminator implementing\n",
    "    the architecture above.\n",
    "    \"\"\"\n",
    "    return nn.Sequential( Unflatten(batch_size, 1, 28, 28),\n",
    "                          nn.Conv2d(1, 32, kernel_size = 5, stride = 1),\n",
    "                          nn.LeakyReLU(inplace=True),\n",
    "                          nn.MaxPool2d(2,2),\n",
    "                          nn.Conv2d(32, 64,kernel_size = 5, stride = 1),\n",
    "                          nn.LeakyReLU(inplace=True),\n",
    "                          nn.MaxPool2d(2,2),\n",
    "                          Flatten(),\n",
    "                          nn.Linear(1024, 1024),\n",
    "                          nn.LeakyReLU(inplace=True),\n",
    "                          nn.Linear(1024,1)\n",
    "                        )\n",
    "\n",
    "data = next(enumerate(loader_train))[-1][0].type(dtype)\n",
    "b = build_dc_classifier().type(dtype)\n",
    "out = b(data)\n",
    "print(out.size())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nZfy0cr7dtpT"
   },
   "source": [
    "Check the number of parameters in your classifier as a sanity check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "6umx5BopdtpU",
    "outputId": "6bd3fdbd-fdf7-497f-f878-5d33d564fb42"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correct number of parameters in generator.\n"
     ]
    }
   ],
   "source": [
    "def test_dc_classifer(true_count=1102721):\n",
    "    model = build_dc_classifier()\n",
    "    cur_count = count_params(model)\n",
    "    if cur_count != true_count:\n",
    "        print('Incorrect number of parameters in generator. Check your achitecture.')\n",
    "    else:\n",
    "        print('Correct number of parameters in generator.')\n",
    "\n",
    "test_dc_classifer()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "O6sREEKzdtpW"
   },
   "source": [
    "#### Generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "6yl-onTwdtpW",
    "outputId": "4e84088f-9fdb-4a3e-d2ce-a7e5a97d4dde"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([128, 784])"
      ]
     },
     "execution_count": 25,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def build_dc_generator(noise_dim=NOISE_DIM):\n",
    "    \"\"\"\n",
    "    Build and return a PyTorch model implementing the DCGAN generator using\n",
    "    the architecture described above.\n",
    "    \"\"\"\n",
    "    return nn.Sequential( nn.Linear(noise_dim,1024),\n",
    "                          nn.ReLU(inplace=True),\n",
    "                          nn.BatchNorm1d(1024),\n",
    "                          nn.Linear(1024,6272),\n",
    "                          nn.ReLU(inplace=True),\n",
    "                          nn.BatchNorm1d(6272),\n",
    "                          Unflatten(batch_size, 128, 7, 7),\n",
    "                          nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1),\n",
    "                          nn.ReLU(inplace=True),\n",
    "                          nn.BatchNorm2d(64),\n",
    "                          nn.ConvTranspose2d(64, 1, kernel_size=4, stride=2, padding=1),\n",
    "                          nn.Tanh(),\n",
    "                          Flatten()\n",
    "                        )\n",
    "\n",
    "test_g_gan = build_dc_generator().type(dtype)\n",
    "test_g_gan.apply(initialize_weights)\n",
    "\n",
    "fake_seed = torch.randn(batch_size, NOISE_DIM).type(dtype)\n",
    "fake_images = test_g_gan.forward(fake_seed)\n",
    "fake_images.size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "csR22YyGdtpY"
   },
   "source": [
    "Check the number of parameters in your generator as a sanity check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "id": "p8V4huVWdtpZ",
    "outputId": "6873c9bf-1d92-4047-bf23-c0ffed1d09ee"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correct number of parameters in generator.\n"
     ]
    }
   ],
   "source": [
    "def test_dc_generator(true_count=6580801):\n",
    "    model = build_dc_generator(4)\n",
    "    cur_count = count_params(model)\n",
    "    if cur_count != true_count:\n",
    "        print('Incorrect number of parameters in generator. Check your achitecture.')\n",
    "    else:\n",
    "        print('Correct number of parameters in generator.')\n",
    "\n",
    "test_dc_generator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "exVyeW2mdtpa",
    "outputId": "1e95b795-b5d4-4a43-8c9f-27a8b707ce23",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter: 0, D: 1.448, G:1.464\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 250, D: 1.198, G:0.7545\n"
     ]
    },
    {
     "data": {
      "image/png": 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pVBKPH9knFTNpPYGKcDX1+BkXSYEjzkAyqdL5aC+V3mafffYBXHD93LlzufDCC4HGbJIrIrQ2b775Jk8//TTgUrTKSlGUE21GpFSksBERbYSWJWGTTKYSkt/5zncAl7BM/sFz5sxho402AuC2224DnPZX2mEl11Z6TUX6y0Mq7eSq99RLy96gVJiiKDYdax5mzpyZJNbOi3etJ/Im9H+Re8BRUHE6SrIdal8J36XFnz9/Ppdddlnp8Rcdax5lamQNQ9CaKBPFgQcemFBUW3amrLdSGqylJITM+rCdnZ2VtAEVgWWz1IYUN36KDrGSNuWL2GctnmAdtrMmqX7Taqe+00fFH6+9r1KpJA4TCif79Kc/XXWNDiWx6mrr+9//PjCgYJM7n9CIySzPCSSrPqy9tlKpJGujeeqA1LwU5K29lIJJrPHrr7+emHPs3tSDvAfe1ocNzdFHnpOLoGdus802A5zi88ADD0xcUpWySAEsZcfvXxM6lGJ92IiIlQCFKKzQCKtRTwhUqI1Qm5VKJTeMzp5c3d3dFShmVrFV3KTsEuWRw4Q4AjnHL126tGbe1sxT1KCfxfar376+vuQiW5087V7r+GGvVXV1mWzE1t96663JvPVdXgWIIshjhctS2CxlpA0o0VzFCn/gAx8A4KyzzkraULJA1etphCW2z0GksBERKxFKUdg0NGogb0Q+LpPe0qv4VojCplHtlJO+qm2LtDC8UFtlXebS2kjjIoYOHVo1vzQOx47fXqvfJePqfzmQdHd3JwqZelLcFIWnEEzVQxQxYdlr7BpqbjZY/5577knWV2mOJLeHuLMsPUUZPYSPSGEjItoImWYdnaiS1Sy/v2zZsppkydaBXyeVpTY2KNxHqOKadbrQ6eef7qFTzYb5CXJ7tJQhbVzW+UOnsb6XhtSuSWdnZ3KNTZka+rTulj5F1N82GbnMMj7ksK81SqM+IW5HfWvMakvzk3mnUqkke+FXJfTbDKX1EdKojx1PyDFFz6l9xnwOwY5D47NrGPp90qRJiY5C45Duwq5taPxpc7TXKkgkhEhhIyLaCJkybERExIqFSGEjItoImTKsNKhWvkuzBQp5IU5lPHyKpnXxIXlGNVwlZyiB97333lvVaG9vbwVqvayy5pin4bZtdHV11ci/NmVrnqbR125KvlVbknskj8+cOTMZ8IgRIyrg5GtrB/b7DhVvytuHnp6emjZCa2NlxjSbq/VO0/+a7+LFi6sGNHz48Epa3/79eXumcVm9hNoYNmxY0r51+pf8rvdE/1vtu5/SSOOwehjt5fz586OWOCKi3dEyX+LlDXtSitK+8cYbmTa8VpSuTKPWzajbaqmAtJeLFi2q8XRqRZ3YelDE/h7iwkJ2WOvpVMTzqOg6aG17e3sTzbG4Mau1DllTBJ9Ttc+b5Qqip1NExEqAQSuGNdiwdtSQj2szfJzz0NnZWdgbRsgaT9EwM1hxKKvQyDjyIm5aMUdxar29vUlKWivvWputZG3ZbWW/njZtGpBdDiRvDpHCRkS0ERqmsEUTZC1v5JXXKCJTNeI3bSNhRPmkFZQG0nIGaX2GPJJCWt52RSv81IvupWRJJStYf/31k8B1xTYrabqNJ5YHmAp6S9ZVAvr+/v66S6VGChsR0UZoWprTFYGSjho1qiazg5DHCaSlhmkWenp6kvhKpbWRTD127FgAnnrqKcCd2kVgbbvW1xqao4UWhVDKWiWQk237jjvu4PzzzwdcEetGEomXHXOR6/NsyYKo49133w042/YLL7yQFGlTuqNx48YBbk+1HkrWt+222wLwy1/+EnCUedmyZaU02j4KscTWgO1jRXhRxYqMHz+eG264IfWatIc5DWlzLTtH65w/evTo5IVVJkK9BNtssw3g8jR/61vfAorVGtW4dG3aHBth53WPqscrNY7tZ/fdd0/y9urAUx3cO++8s+r7VsALnawat29W03qHzGrK7nnBBRcALpWRghmef/55Hn30UQBefPFFoDahgZx11Jbufe6554DsGlBC3gsbWeKIiDZCoayJg2H6qAcPPvgg4Gru9PX1JSFmVnWel1E9dF1/f3/h+VpHCTlr9Pb2Jkm9rLJJKv8TTzwRgKOPPhqA9dZbD6jNDJkG/dZspZPmodq148ePBxx3MHv2bGCgUoOyQlr2WVUcZsyYUTXGZj47tjatxZAhQ1hnnXUA+NjHPgY4TufVV18FHDurtC9K7/PTn/4UGJhrKERReOyxx6r+13OplDK/+tWvkt/qnX+ksBERbYRBS8JWD0KUTcK+1OS+3CnKJXnBBr/7Ccqg1q0tlBTLbzM0TusOudpqqwGw9dZbs/nmmwMklEhzUJKvUJoZyUHiHArKtsmgm+FeqrXzEoSpn+QayW/iCASlkRHHoDpKDTpQpAZwKMDBS0YHDOypnBhUiXDfffetavOEE04A4I9//CNQn8OJnh2lP1VaXM1Ze97X1xdsN/ScJn0UHk1ERMRyx3JzTRTFWLhwYZJBXtREavC99toLcCFIOkFDztV+u74KPQtWvW5D4/z0LqFEZr42GBxl/fd//3dgIDG3koyLA9D4VLFPMq7Gb43x5513HgBf+9rXBp3TKULVQ+Y0jV+UthlmJguNT59pTgn6W/Kk9ur6668H4K677qq6tx5IfhclFcR9SF9hKweUQaSwERFthJbVhw1BhvWdd9655rcrr7wScCUvrFwn/l6Vr8844wzA1S+94IILkpo9lvraGrOCV8IDqKW4WfZYUY9f/OIXgLM9SnYSNZ03b17CHTz++OMA3HTTTQC8/PLLgNO4qhbtIYccAjiK++EPfxjIToqdRoHy9jCrvXoghxCbbE6O840gNM487XhHR0dCfVXbVeOSjdm/Fmo1z52dnQkFFaVUmypdIjk5lP42bZyh5AghRAobEdFGyNQS5wUGl4EomU5aUSdwRaNOOeUUwJ1+OtFUcV31OeuRAeQuNnv27FQtcRlPHFF6ydg6pTVeUXklmj7nnHN48sknAVcPVjKd7VfrJNc4FabyU4uGwrPSkqXnaYmbTWFfeOEFADbccEPAUVx/vxuF1RLbORaxuVu9hPbOunmqzTXWWKOmfIfmdOmllwJur374wx8CTk6Wu+mHPvQhYMA2HUqZpH7ffvvtqCWOiGh3tIzC2pNDXj/yTpJdcsaMGay77rrqr6ofnX69vb1Vv+vULlN4SfLkkiVLMk/nrLnoU+UGJYdKKyiq+bnPfQ6AJ554AhgorVnWl/bnP/85AAcddBDgTulx48YF5UFPm57MsQgH0UxdhfZE66027d42AkthOzo6Kub/tHtS27JpdrwULVW/d3V11QSu61r9f9hhhwGOsqq+sfwFjj/+eGCgJnJasj+/rWiHjYhYCdAyO6w0aZMnTwacHLrBBhtUXbfaaqslIU36lLeMqIpC0CQDKNWGoiaKhKTleSllnfyiFtICStOoOU6ZMgWAM888E6gtklQPZyIfXGkgZaPu6ekJUtg0b6l6+i4a3C0sW7Ys0QrbUhNq80c/+hEAX/rSlwqPpyjKFEWz0LW2VIr9Pa0sjSAOcKuttgLgb3/7G+DC8GTVUHheFvLk70hhIyLaCIVk2DLyl047yXXyIJGcamMIwVHU1157DYCpU6cC8Jvf/AZwfp/Szv36178G4L//+7+BYlrjUIpMJUvPsuUpkkPcgmyi3/zmNwG4+uqrgbCt0V/jUCFnC8lBWkdp0h966CFOPfXUqray/KWb4UtsE2ZL4679qlQqiTfPQw89BJBExwgaozgsyXV1xumW1kOE0IyypwpUv/HGGwHnF6A4WGn1pYnu7OxM9B1WP6I9fOutt1Jl2KbnJbZV2vbee2/A1dSUKcQPgraB2LYanB6CmTNnAs5Re+7cuUCxAyX0wvb09FQ5jqdBzg06dNSvWHXLRtmHwHdvtFX7Qm5/9iW57777AHj00Uc55phjqtoX0sw6ViFTBjoQZK4455xzACcSyIXUz7Orh/LjH/84AP/5n/8JDARA+GOUA40O46z1t2jmC5vG3heFnod77rkHcGYefS8xRtAhtttuuyWHvK04kZZbumq8hUcXERGx3NE0ChtyqdIJJtdAUUedQhtvvHFiZNcpK+d5sRInnXQS4By1Z82aBZRz1A5RWGXGD7XV2dlZ89v9998POCpikWbC0HdSVInSysnCrrEo66RJkwA4++yzgQHxYY011si8xzfr2BBJi6y9lVvkI488UvX9pptuWnXv0KFDE5FCY9Ca6X/tsRxIRH3kjKAA9yyE9rARTlDmRo03L0gd3N799a9/BVxmRbmdKoTSpu6R489qq63GwQcfDMDvf/97zamqrTlz5kQKGxHR7iiUIqYIQtfq5FD4lRQmUv+/733v4yc/+QngTibdo5Ay/W4rvpVBnlN1CGkudaL0IaQFEGjMMtOEKpxJ9j/ggAMAOO200wB3Ss+dO7dG7irikldPeh+NUboDUdy0vMjq+7vf/W7VPKQQlOlLKVisorEI6t3DNIiyKqxR45kzZw7g9smH1kNBKuIE9VzK7VTBK2pjhx12AKpNmgroF7S/MhGFEClsREQboZAM28qcvR0dHdx2220AbLfddoCjLr/73e+A5rizeWF0pZz/V1lllUQFL+TJsEUogSi3ZFqlO5WxXbKdOBGN78gjj0zcFi1F1bW+hrERLbFOfVFYpf6UA4DvUioqI+d/maUEmeo0ZukynnnmGaCYljhNC/5Om4VlWK3R6aefDrhnTXoBmeaOOOIIAKZPnw4MOPRrv/NqG0k+v/XWWwGXvE6mr7fffpu1114bqE2yp2v+/ve/Rxk2IqLdUchxopk1TS1GjRqVBG/L1VAuXs3sJ6+2aIjC9vT01AQZSC5Tkq2Qa6RvKLeOGXKIOPLII4Ha1CWSsUTdFXp4ww03BMcqudGvUN4IhRW+8Y1vAHDuuedWfS+Zbb311kvGKwcPObnIQUI2a8lxWjNxC6J4b731VnDfQ1xSGQorefK3v/0t4ChayKElTS9g08rafqWvUfCHXBOV+vW2225LZH3bXyhIJZlr7gwjIiJWGGRSWLnt6URPO9nrpYKiBrNmzUqoijSIIdmwGbBeMvXIsLLVbbHFFoCTc9SG5NPPf/7zAOy0006JbCf5b5dddgGc7CT3R7lyyntGIXxFQgnT7LDNoLCCOCG5Jmq+48ePTzyxNAbZIEUxJAfbVD2i0pLpFixYEHym0uYHtXuYlvheeyKqt/7661eNU3KnZGlpetVWX19fIu/KLi3bsdX0W889wS8XYpMQ6J6Y5jQiYiVCph22mUHHFvfeey8wEFKn9kVdWoF6bXhpdjHJLvLplW+tiiXdcsstgPPB1ckK7gQXNZCD+F/+8hfAyYnyoimjmS+SjrQRiBuQ7VTpaadMmZIERKj0hSCKIRtuKLGBkKNTKTVeP8D85JNPBhwnI4oqWVt7qk/5j2v8PhdhUdTjTlQ1bR42+VsIkcJGRLQRMimsLXdh4Z+WRamw5FPZHSuVSmKLslrIRlA0faRNZ2mTb6XNS1T32GOPBVwBK2mPJeOl9an+dIIrnYzkQ2lP6+FqylKgeqH5iTtYunQpO+20E+AS0mk9pf3Oq8+qeftJ4Wz4YIiDsJpc+//w4cOT502/idPRpyiuPJDkzSStt5ImNANp71Ph5AFNG0VERETLkUlhQ3bXrITVoe+V3kVxkMLixYuT5FRlqo83CzaO01Lc3t7emkgUwabEtFE0Or3/8Y9/cO211wLOD1neUmmFpRqdS6shzksROGPGjEk8hKy2U14+Idg97+joqAnqtp8WNsbY7mF/f3+NTkDXyF9dWmrJmUqoJjtyM1GpVGrmUnTvIoWNiGgjFKKwRb63p6Ao1qc+9SnAaVKtnPXss88mPpf1IkuWzju5rKxqS0yMHj06kV+U3jQUFSNqqdNcUUaXXnppYm+0a9eM5Ow2lehgQ+UUfWieijY66qijgFqtu9L8FJHrQtpYO3/toe+J9F//9V+AswerLe2p4lCl4VcitVYhFD2VZxXIfGGt0J8G+6LKQL377rsDroK1fZgUuH3KKacUyiaX1XfWd3nslM1LK8O+r+aXSWCfffYBXE4nXSunCD2cgqp4L1iwoCVunUVElSJ72KcnbnkAAB3/SURBVErI6UShaHL2l5lHzjI+8hSEedeLrdX3/f39yaGr/m2OYbko6mAtk7ImD0XY37znVIgscUREG6FQ9bqQytk/DfS3XNEUNiYWxAb1Ks3I9ttvn5y2Ot3EruSdTEUoTOhaC/Up04xfbVyuh4899hhQ61AiRwK1YZUgy5YtC461GdkMBeuE0Gj7zYD6F9WT87+t6i50dnYGTWohUSQtYYCPZcuWBWsYWVY0VLeoEYTq6KQhUtiIiJUImc7/Q4cOrUpQlsV7C7ZGibL5S/5T0qlXXnkFGFBO2VCykKmjGdQolJfYXuefvFbOlULNUo9Q1vhmV4izY9WnZOrXX389mWM9uaUHE1bGTlMgWkWmDT0bPnx41XNq9QX+HoaUPaG6ToO1h4JMh6G8xJHCRkS0ETJlWMmjWXy9ZBHvZADcySSZ8PLLLwfcSab7uru7Ew2i3MNsPRl72mWddCEZwNZ8EWRmsFTdp5Z2jpK5BYW+WQ1jEaphv8+bh08pQxp6H6JKNtjad8G062u1sUXqEhUdv/pVn9oX9bHqqqsmnJb2X+uuFDUW0vDaZ097unTp0qD1QOPRnDWO0LNXZI55Mqv/veXWlNgghEhhIyLaCJkybERExIqFSGEjItoImTLsyJEjK+D4+TR7V0g2yEtc7Qfslk3yFpIR0uzCfjkJqC0ytO6661bAyc1WU53WVkh7HZqjXwzLVu/L0sDnzdlqWNPmuMoqq1QgXJ3P38PQvKysnFatvGigSJ6d0f/dam4l7y1YsKCqkWHDhlU0jrTxp43dXmMDBmyV9Y6OjkQ3YfcslCImtKdpAQ6CZOpYDCsiYiVApgybVyiqKQOoIwi+SFshWDusuAidnpYSNcM5v6urq6aUpm0/T3tcxkvGTwPajNqpeWPJotL1IDRXj6KnJmErYrcvO05dP3To0GCV9nrbzIJ9ToVIYSMi2gh1hdc1E5VKpcYG1uz2Id9mllVIqtG+007ePHk4K3qjzIneDO+wvDF1dHT4ZTSa1r9Flkzof2Y9t/WOJ0s/kucnXmS/inJSkcJGRLQRmlZuMoQip1+9lLXIyZV3Ysk7xmpvm5myxac6eaexjSKxlN+fTxkPsDLI0xoL0kqPGDEi0a6HKG0ZFPUGE5qRFEDrrDnJa0zzWbRoUcMcYNazWESuhUhhIyLaCoXiYZtxcrdCHk6jAEVid9PaCNkpmw3rSx2KDrHj8/8v4qPaCPL2W/0ccsghwEBCdf2thOitGEfe/Bp5TpUUXcnhldJGz8UOO+xQOCFbI9rqvDlmvrDNQGgRxYLstddeSU7ef/mXfwFcnp9Zs2YB1NS2qae/ote32lVTL6bmrwfljDPOAODmm28GXG1RsZb+gVdmjM08dG3/fhXzG264AYCtt94aoO60P8sLOkB18Cj3snD99dfzyU9+EnAVHVStXckZ7rjjDiA/22hDpsK674yIiBh0ZDpOdDSx8pmgDPG33347EA57A5e5TvVP0rLzFUUogF2OBcJgBUNoPBMmTADg6quvBlwt0d122w1wbqFl4M+xEceJPCg74ZtvvplQKFXb+8xnPtP0/vL2sJE5KqWRartad8PXXnsteR4322yzqmtEUW+88UYADjzwQMA545Qxv4XmKEQKGxHRRmi50kmymmQCVb7OoqyCMul/5StfAeCkk06qexwhtELGKwL1p2rzMiNonWwwdpoyoqzCotmwYwRX4Xww0Up3SM1x//33TyjqlClTACfD63txFeIiVY2xSLqZmOY0ImIlRMu0xFYLetlllwEuvYqM0DvvvHNiCjjrrLMAOOKIIwB3gik1ZhmUpTaDTWHFYXzrW98C3Ckt2T6U0C0NRU/norChaHlaz3nz5iV1V6XRX16cS7045ZRTgFrZVVrjO++8M/lOz7TVPyj8T3s6depUoFaW9cPryq5PpLAREW2ETC1xPdo3e9rr3nXWWQdwNUyefPJJoJqS6B4lotLJ9fnPfx5wNWuK9B+S/az2bTBCCNMgrkJ2S53C4irqGU+ahrGMpt8PBwRHMUQ1Q8/B5MmTOeCAAwAn84kK1VPyouweNmLNUNuTJk0C4Ac/+AHgEhooTW9aMLzVy6hCo57Tj370o4BLNF9GHxG1xBERKwGaJsOG5B59r1NaNUWzQuok36mSWD3217xTuuh9zYY036KsggpuNULpGx2zvT9NC+xDa7X66qvXpLmVTFu2vmpa+pSye1gGIU832WXlxfTPf/4z8ToT5yE3xk022aTqXnno2XcibW/zEhhYRAobEdFGqIvCpp0CeRpF/T927FjAlR+cPXt2IrfpVFOV8ksuuQSor0BRWWqTl0yuUag9K4drbt///veb0k+o3zwq6aMolVebu+yyS/Kd9rIohbX9+wnritqZ864vsofrrrtuzTjAWS4uvvhiDj74YAC23XZbAD72sY9V3XPfffcBcNxxxwFOdm1mGGSksBERbYS6KGwav23D5yz/Lp/Y3/zmN0C1bUpy3dFHHw04H9Vrrrmm7rHVi1bJrnvssQdQa+eT99aKZK8sKk/pd/kR+7BynOYt/YQoscqcSD7s6+urO/SsEa5INtNDDz0UICkAfdFFFwEDcqo8mWTx0LO9YMECwBV8U7HyViBS2IiINkIhO6yQdvKGTkGdoGuuuSYAL7/8MlAbKN7T08NVV10FwJ577gnAww8/XPV/mXjYEJZntE5PT0/CYWgcKpwlO18zA/wrlUrhaJ00v1b/t6x7RWEWLVqUUE4VP9too40AF/0izaqKj+lTcbOisIsXLw6uhah0X19foT0sk0JX1gtF4qgsqp7XMWPGJMXJpRHXmEVZFcvcDPh76KMhljjrN22gXlSb40eL+frrrycssCA2pBkvqh1XHlph1vnxj39c8921114LLP+6rWkZLIoqR/Tg6mUEWLhwITCwr+AUWP4LCbWmuiIvV2it8sZZZC9feuklwD2vNkj/scceS+ar3/71X/8VaO6LmofIEkdEtBEKUdgy1EZskliiZ599FnCCuU5cnVZdXV01LMyFF14IOPfFF198sXD/IbQiH1BRlnH//fev6UdulysC6uUmJk6cCFSvrUwbZalhmmN8o+Mrc5/NrSXI3VCinX+twucGE5HCRkS0EQpR2DJynSinKKmqY8tRWvLqj370I2BA+TJy5EjAyUJy/n/++ecBF9xdjwNFK5DncqbfNa80KBVMO0PJyMDtjZwGGtEBlHHyaDYks8p5R4nXOjs7axwgdK0CBQYDkcJGRLQRmpb532b2l+FZDhIKt1IA+3XXXQcMUKHVV18dgGOPPTa1zf322w+AK664ovA4NHaZAorm8q1H7glBmtFFixYlXMKK5CBRL8RZSL5btmwZX/va1wBnxpEzRdH6t0Wo52Csnfbpwx/+cFWfCxYsSNKaynFC85cJaDDGFylsREQboelpTvMqd6XJwzqNRY3taavviyRuC6VL0f+2tmgjjhNFHevvvvtudt55Z8BxIHI2nzFjRuH+iqKM40Q92HzzzQEXhN/f38/o0aOTv6Fqve3Yqv5PC6XLW89mBrBbKADlhRdeAJyDy+GHH55wid/73veAgST44HQtTU7WHgPYIyLaHQ0HsIdkwLxaNj50Cm+66aYAPPLII4CTd6V5lhuf3N/SxmId0Yt6EtVzOpaRyzRH2aWLpLspisFOePad73wHcPsyZ86cZJ1lBZB1QBpUea1pHaRbyAruthgMLbGCEfSpZ/Hggw9ONPvybFLoYCPrXnZOkcJGRLQRGqaw9TqOp0Fyg0KdJCMIxx9/PACnnnpq6v1pwc+tTC8SgqiGOIKxY8cmlOWhhx6qGk8zPawGa47SoArjxo1LQiMl862//voAXHnllYCzsSv0TBRW5S/SkpzVm4SgEYq31lprAS41jPyFgcSacf/991f110i/9vmMKWIiIlYitLxURz12zZ/+9KeAKz+pcey9996Ak6HkXeN7Hlm7X71pOYrMPe8aeTpNnz490XD/5Cc/KTWOImMU0sbRCvlWkS3ySAO3J5JlRYW+8IUvAI46aX+UyODrX/864PQS9UQvNZOzmDt3LgDPPfcc4BKsjRgxIuGYFJGkudj0sFrrrJrDIStGHiKFjYhoIzSUhC3r1G4kzvOee+4B3EmmYGcVjlLqGMkXGkd/f/+g+qHmRenohJ08eXJS0CuUSscia43zbJmthi24vfHGGyeB6FtuuSUAW2yxBeDiYuVLLi2+rpPdU7Jv1nNTxvLgX1+GuxDVtIWuwM3piSeeAKjxXtt1110BF1Mri4Cegyx9StExRgobEdFGKCXD2pO8o6OjqRkTLGV68MEHARcxYb2EQjGMZdAKGU/jEXWZOnVqIvcpxtcmWtentKnSKofSxvrIOrlt+tlGIGooyiHfbr9tqzMQpVKxKSVQlwwr3+NW7mFWGhwLjeOpp54CXIaMkSNHJml3FcusGG+tsdKaivLaVDJCWpaPosh8YTWQrAeiGQ+8+rETFOslwV99nXbaaVX/+59llUwh1Xw989E8xPb5zv9SquSxs3pg8vI7F4UNd2zEjKSDUi+Z5pfl9KCHWpUN3v/+9wNO3Mk6UIo+W3nKt5y8ZVWfyqcsFv9Pf/oTAL/+9a8Ts6PGrjFr/iIwch6R80URFDU/RpY4IqKNkOn8393dXYHsE7QRiiTksW06sZS/WG59aYHjodNVygPr/N/T01OBbBV80blZtb4+e3t7k5NTrnqhNtWGXYsiY0hzjh81alTVHorFK8OCyiSlyuJKAyMKUmbvxfJb8ccPGghRGy9IpMN8XxXgUMQUFxJFpAzT93I/nDdvXmEuRXMTyrwjoQCHpO3cFiIiIlYYZFLYoUOHFqawjUByVojK6YTX7yHqkJUq03Myrzq5LBdRRLlTFBrPiBEjknvzaq02ElCfdjqPHDmyan6WohVpX2unYAzJcFlcSQjiIKycnyaz27UI5SUWhRWsQi+rTcuB+Slb3+krabOIAtBHWTOUf0+ksBERKwEytcRSyUvuEfyTK6TVzNV2edo5nbb25JHDhE45tamAdpv+xZd/LBVWWxahBG9pAdV5c7R9Wu23f6+tvarPvPDALJNAWoC/5m0pmdawv78/obZ2flYWk8yaNsaQ9tvK9TINSS+hcaltfx/svQogt1CbWUn6QiZKzd32pf/9vdP6hBwhssyf/u9p0G95SRoihY2IaCNkyrARERErFiKFjYhoI2TKsL29vRVw8o7gyzwh/l3yj+Xjrc01yx5oZaiQfcuXvUJyg2SRRYsWVQmAI0aMqJpjmlwSmqMdV2hOnZ2diUwkmdlqa63GNSQf2TXwf5OG05/jmDFjKu98hz/PRjyfsmD33XoSCdbebDW7/t8aq2Tn0B6GPK8qlUqN157/W9b/dh7gniW7z6HQzlDffvv6TbL9ggULopY4IqLdkSnDyr5V1v7UKGyVciHkMdKIB0lXV1fVHIu0kYc0baHVaIsaFD3hi2jfPYqUXCQ7c6i/VkPURZTDVl4Xxc8an5273cM8T6c0zXqIgxks5IVVWn8BIVLYiIg2QqYMO9iU1coL9sRsht+yRZm2itrdhLTv89a02UHorZJVi0L9jh07FnBlLuSjqyRsfghavR5l9azp8liXRvY4UtiIiDZC04phlUWa368+Q1ShkfE0w3c3LxVLlgwVoqxWe2rjYhuluMvbzq7+Vd7jzDPPBOCCCy4A4MYbbwScTOsnRSg69hCn499flDvLotaN+HmH2in7jEcKGxHRRmg4kXhZKFHz4YcfDgyUPVAJBKspbCZ1KEupysg9oXH6J3+ICsh2KqoSKh6VpSUu6r+9PCAOQuUoN9hgA8CV8rCpQrMS6eUhtMZpvs5l0dPTk2SjUEkZpS669957AXj44YcBJ5fnjdP+7Y85hKZXr8uDsvYrX+2YMWMSp4EpU6YALsN/mRQbecgz62Q5r1s2NRRuGGKZent7k3v23HNPwB1YCgpXUP7ll18OOJYx7RALsW1pIYStqF5XBnLYf+aZZwCSKnfKeKlKCMp/1dHRURMYIYRCJDVHq6z0khbks5rv3CvHFo1Tzvjnn38+G2+8MeAyQK622mpV9wr/8R//AcDPfvazzD7TEMPrIiJWIgw6hVW4lxJ6XXjhhYwfPx5wFEyVrcUqNwNlKazQ2dlZ44pm2Ti5zH3gAx8AHKu0//77AwMZ8nVy26ptFqKoZ5xxBgDnnnsuUCxYPC0NTiv2sAz++Mc/AiT1cUU9d999d8DVx1Vit+7u7qT+TihcMURhQ2a1IpXxtMeq5/TLX/4ScBR38eLFyRg/+9nPAo4K/+EPf6ganzhD3WuRlcUxUtiIiJUIg650UtrL//3f/wUG0l8+8MADgKMQqnCtU7gZyFMMhf73T2eNT59KmSKZRpXa1lxzTaA23Uhau9YNU9T629/+NjCQXhMG6vNYhIIQVgQoqFwclU1CpzQzNinc4sWLSwV+p6EepaX2Y5999gFqM//39/dz1llnAfDoo49W3Su3S7lZah+0BpprM7Di7HBEREQuBp3CWixevDg5XXWaqbboYDpmZ/Wh8dmULxrvRhttBDiNqE03snTp0kQLKg7j0ksvBdxcJRdJ86g2jjvuuKrPFR3as8022wxwlMrWzBXltfJqFpeQ9xw0wxw4e/ZswFF+VbHbfvvta8JMBZsyRlzSLrvsAsCtt95aM656n+1IYSMi2gjLncL6mkD9Lcfw5e1WZ2FPcMkmqkhuk8RJTt9nn32YN28eUBWgXHWP2po0aRLgKM0222xT9X/WKV0kQLpVEMXccccdgQG7JVCTYO/pp58GnBxoNcFpSRGEVs5B6ztt2jTAzUO1kIq4rOoa1di5++67U+9NC/crPM667oqIiFguaHkF9ry2zznnnOR0kzzTzCrlZceTVjbEejoJulYFkz7ykY8ALh2LPF3mzZuXUF0r70hW3Xbbbav6EiT7po21THB7q/HNb34TcF5qoqwav4pLHXvssUB2StLlyVndfvvtAMyZM6fwWCSr6nkQZ6XnoJmIFDYioo2w3CjsyJEjAdhjjz1qkk7Lw6kZ/ddLddKSk+vTUkvJOSeeeCLg5FR5c62yyio1bUh7+sUvfhGopbDq47vf/W7VfZ2dnYUCAgYTw4YN4xvf+AZQy6FcdNFFgPONDvkJNxP1hMHpmpCXVRqku5A3l6Dat1njq3f+kcJGRLQRClHYZkI2TEVBVCqVmtNGGkfZN1dddVXAUR2dftLo+XJhXnKrEDQGGzieFpplfYnlY6pPyW8q4tzd3Z20J85CdtVjjjmmqi31ddNNNwHw2GOPVX1fJEnZYOOiiy6qsZ/KM+vss88GivnzNhtlqJgtYZJ1r57hyy67rOpaafqvuuqqpo3LIlLYiIg2QqEkbM2AijErGdfzzz8PwM9//nMOOuggwGnXJPttscUWAGyyySaAoyDyDrr66qsBd6K9+eabwSJTflGjNNRDnXxPJv9/UXxRma6ursQLSnbWI444Aqj1N545cyYAhx12GODstX6fZbiIwdC4KqbXh2JCV3TKamHT0UrX8N73vpcJEyYAcN555wHu/RBlVZTW/Pnz6+4/Dy13nNACSBBX2NLLL78MDAQwH3DAAYAzqitcSfl+tGjaCLnv6RDQQ/+e97wnUcfbzA1FasqmfZ/2ghRxWPCve9e73pUoKBS+ZZ3iNT69qHKST0PRQIZWQ6z/hhtuWPPbI488Mqhj8VGP0kksvUIkv/zlLwPwuc99Dhg48G1VA0HP3OOPP164vxBiTqeIiJUILaewY8aMARwbq/w3YnN7e3v5/e9/Dzgh3oYjWWojNnHWrFkArLfeesAAxRU7KvcwnYa2pqmQVyfH/9262VkqbtsU5Zk4cSJf/epXARf0bCmrWOEnnngidVz+yRuqMZQ1llZQ36OOOgpIr9TQzPQ+gwFxadtvvz0Au+66a9X3/f39SUCAwua0tqKsoeehmYgUNiKijVCXWafIaa17JWdK7pTyRRngZ82axdFHHw04s4jal+knpGrXiaaTb+zYsYmiR8oO65QRgm07zTXRl5X9/qUk0zhOO+00AD7xiU8AA/KPVXqpfTnBS46XC58NlvfXwHei8MfeTCVhFmSa+spXvlLzWyj7YytRDxdh3Tu1P6Ke4uoefPBBAE4//XReeeUVwOlaxHEpW2IzkKf8jBQ2IqKNUCjzfz3yj06KO+64A3DU6c9//jPgnAqWLFmSaOIk8+27777AgNui3780y0ceeSTgHCt0qvf29iYByHYOIdg5plUd0FzWXnttAH784x8DTqMoubSIA4PGqpSuculTMjJRVK2XTSTmp+wssz/NlGVFWSR3S4cArXF4bxS+K6DWV3P44Ac/CDjXUOlAZGrUvsydOze5Z+7cuYDjHidPntyUMRZBpLAREW2EltXWESVR4mxrl1Pm9yuuuCIJwZLWUTZLq9lVIuebb74ZcNo5BQvceuutNXVZynIJWddNnDgRcMHNeXVxBN+90cqsVtazFdmtnJpWCS9r7I24LWr9bZV0yXcnnHACAPfff3/SvrTzKxJ8W7o4FwVqnHTSSYBbb+2Lni1/jXWNAgSU7qYZcy66T5HCRkS0EVpuhxVFkduWZFo//Eqa3WuuuQaAu+66C4D99tsPcOFqTz31FODcGmW/FfV+4403gq5wRSlsmqwnKifKH0oUpr7lmiYt9+zZs7nzzjsB+OEPfwjka1GtTG3HlzbWtNO5HtlV81MdHGmFbdij5vD2228nc1VyuRUV8pqT7kDaYX1KB5KmcZd1QAnmJMM3ohEvuz+RwkZEtBEGLYBdQb46uSQfXXLJJXz6058G4OKLLwYcVTn55JOr2tDJb72R/MpnIYhKh5DlDyy5JysxOLiUmKJI6nPUqFHccsstgNN01wtfHrMUNc3WXI9fraiN9A+S0VTQSuuw3XbbAdW6BnE/yxOW4/DXwBa5snj11VeD7V5xxRWAW2ebULwMYprTiIj/D1DK06kZlFYFolRWsaOjI4lckfbRhqsJ1kZqtbJZ48sbeygip6OjIylqJPubxmfTueh3azudPHlyIru30sc2S0tcZu80P/lqS2MqjkH2R7X5s5/9LJn79ddfX8/QG4KdW5osL6q49dZbV91TtJRod3c3u+22W9U9lgMsiixdQx4ihY2IaCMMeiJxxcXK0yct3jQ3JtDIsmmnYlkZIURhwVEWyW6yx9nUqJtvvjng/FFFeWfMmJGZ1rPIeAR/PvXamvOgdiSPfv3rXwec/XncuHEAiW/tIYccEuSKlifS1lCFsxXxJe5OkB5FFePlH/673/0uodJKuiedRVmk6SGiHTYiYiVEJoUNUbBmaI/TKEVRWM+bIlEqeRkn7AnnR8SIUk6dOhVwsbz6tOUFRYklrz7wwAOlKb318iqSaiVtjnnZMNKu1ae0wioHqmwYgvxsp02bllCiFQlp/uCSy8XpKb2NoskUD6v4Zd235ZZbJm1Je14vGpFh62KJlzfbU48TQF5Op6y2xc4q55RMNAqz2mqrrQBXF1bGeZkI6qkPalmmtBe2iLIl73D1HeNtnzaHljWnXXLJJcDAwVSv6JF2X+iaIul8QtCYFb6ofMlyc11rrbUAV9dJe62Dc8KECYk7rVwTlwciSxwR0UboyMm/WoFas4l/4i0PalvklLY1WmWaWbBgQdXN3d3dFQiznB0dHUlbyvgo47vYKI1H7JVOYrnpvfTSS4VZRssSi6qlKXXsvDXOvr6+ZI5Dhgyp2kPbT1dXV007NtOkXDIlEogKKbyxo6OjJQHrIXFl2bJlVXuoOYbm0dHRUROobhMEiAuyz7jW/4477kgUVHIYaSQjZIgrCM1RiBQ2IqKNkCnD6jQKmSSWlyxbpF9LUWSKsQip1/0+1JYcw5XKRkH4OqWlnJGSqh5Th03wZg37WcqJepKx+VTCtq0+5Thx6KGHAi6Rnr/GaVX/GkWIYuZdH3LdBLeuofUNpQq66aabEs6pGRUW6k1VGylsREQbIZPCKkBX1EnGZp1OaXVnQkHdNlFYlhyc5Sbof4b68tvXp05HC8klNrWJn8RN8qQ+5dyve0VxQ2YmX4bKM5Rb2UlIS2Jt20wLcJDGWlTf3uu3rfnZdsVhyRRiqVNnZ2fCZWjd0lLthPov+lso+EKO/DbxXtoc7bNjZVg7bu3xM888k6yhfWby5pqG0BxD6XiTeRTuISIiYrkjU0scERGxYiFS2IiINkJ8YSMi2gjxhY2IaCPEFzYioo0QX9iIiDZCfGEjItoI/w+yHThFi0kJzwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 288x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 500, D: 1.233, G:1.017\n"
     ]
    },
    {
     "data": {
      "image/png": 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fDXS2GwFj8ZfNQ1FVHR0dodlhOte3337rGNbBoejI1AxL0urRRx/1ksvTFM+Gauuorf/qf0k9XU/dyVVGUj7MrBZUMNLZttaKZVZZZRXuv/9+wMSbKsJJhdQURaPx1QPN9U9/+hNgyoxIb+/o6AiND/Ylzntz1A5C9gFZfqVTz5gxw9NrpSer6J1YWYyq78hnqe/5xyI97ZhjjgGMLmvrhPXENMfpsIJ/Tf2F+vyox0986aWXAp3PCMAll1wCmF2U7DbK35bFf86cOTU7Eu1ygtbQD8ewDg4FQiaGlbRaZJFFvLhe7esF+Vmlh+o6sk7K0tzS0uIxqaSM9vGSfmKZQw45BDC6lC0dS6VSjc8rrhmwpHOY5G9ra/N0ax2jqJ5GFvmWdFZDJWUE+f2Bul/2fZs1a5Y3xzj9rlKpeKwr/V3F8GQxl747ePBgwDCt2GDixIleNsxNN90EGH3eLpeTxacvxPlhw+aY9rOk0POoooDSUbUOauWiXYe8Gt9++23N86ZnKc5KXHd/WG0d1X1NgRFhTmTBb4yya/DqMxlktNW0k3zDQhiDPgtz6+gHG/XjS1K7OG/oQZfBQknSUJswENBz1ZujLZBs+LeLNmzhp/Orppbe//rrrwOF53+vD2T7wYYlpttrGOZ+9Nc8rkdQhI1LayN3l50EIAEoo11QYoR9j32uTbcldnAoOjIxrF9xt6VvPUaFONgOcbv4lr9UiQ1fhfXIsLY0rN1IhDGUf0tsw2ecC2XYRqyT/x7Z4Zp2qF+aXUoYw4ftksKMlx0dHYnnmcR4aifKhyVWRIUkhrl1pJ741Ro/HMM6OBQImRLY/YYcm/WagbBrRknRZuqfeSJIN7QLx2neQeF/SUrg5AE7pU3/S2/Lct2037ENOFnOkzD4Boh/prLM2SWwOzjMQ0jVW8feu7e3t9dIG/s79vuC/3P7s7B0Nbtkqm1d9ls87aJmYaVYZJWzC8AFSc8wyZpGKgu2Hm7P2dY1pbe2t7d7eo6drC+HfdB7UawT5gKL0+v962GXVtH1pLsmTaEMOr+gtbJh91OKYkDbgmx3zwsbV5DLMCmCzmUXihPC7BOCY1gHhwIh0krs4ODQveAY1sGhQIjcMCcpwpa0fKhg6yX+4P84to/7PGosYWlLPXr0qEB0B+yw8yYptqbPbR9hmA4Vpk/6dcCweypdbsaMGTWhiVGFxOPGH3ffW1tb+clPfgKYQnXqCqfk7rj5+f+3G6jZevDs2bNT+dL9sO97mH5u65Jz5swJPTau0KDGLXuC/1xJI/K8z4On5eDg0B1Rdyxx0WAnP9dTaC4Ofikex9JJzxV1jqAomXK5HJh6ljdsK3we7TLDvBR28H+ez2ka334jYT+ngmNYB4cCIVOkUxJpXxTYUjxLa4uwcwb9n/X+BOnL9rmCxtys9bB94oLup+3Dt32mQWjG2O1xKWlfLWW6GxzDOjgUCHXFEucBvwU1ayRJXuPI69qNHH9LS0toa8qujJeW/jxw4EDANKpWmw+VSUnCrM2EmFUFGFTStruMz4ZjWAeHAqHhDZ3j0NbWVsMYzZRutnW4u0lWu8B4vQW5g+wPecxZBfFU2saOJT744IMBuOuuu6o+T4NGWPBV7kalbFU9Ik/It6rWMh9++GGozh+HLvvB6oYNGTKEp556CjBbOtV7LQrCXAFBbp24h07dvFXzVwHv119/PQBTp04N/dFGBUU0QiApEGDrrbf2us5L+KqkjdZWNXrVa3fixIlAY+tiRUH3RRUiVSdadZnygM4pYabOAf379w+sWZ0Ebkvs4FAgdBnDKvTrkEMOYfvttwdg9OjRAFxxxRVNG0eeJVJsNlP1wUql4oXZiR1VYO6aa64B8O5BWGrhYYcdBnRKZ1XhS4JGbPG1TZdhqV+/ft68VFFfW0v7+ipcptePPvqo7vFkMRj6e8eCWSvVWs7DOPab3/wGMJVCZZiLK1AYBcewDg4FQpcx7IgRIwDYaKONPCmmfjKqet8MN0WeDKvwPBkw1Blg3XXX9XrbSqLL2BGUdA61wfLSh15++WWvP07YOILeyzJP+3xyfajj+Iorruh99uSTTwKmC1zY9dTVULr65ZdfDtS31mnmpjlph6P7Kp1bJVy1Y5BO7i+VGocdd9wRMH2F9L1TTz0VqC08EDS+MDiGdXAoEFKViMkD6sVz6KGHeu+JTdQV+7TTTgPgD3/4AxAtkboSuj/qNTNkyBDAdNuTntbS0lJTIsW+t5qjdEA58M877zzASOeoEiL1Mqyti+taq666KtDZjR5MCp3O+eKLL3LEEUcAtSGKdnrgtGnTgOCi2s2Aff81HlmL9SxOnz4dMD2B3nvvvdiEBrHzqFGjAHP/hg0bBsBvf/vb2PGF2TC8z2PP4ODg0G3QNB1WVrnzzz8fMIwybNgwz1J4wQUXAHD00UcDxn+lXjqNQB46nsLxzjnnHMDop0HnlsR+5ZVXALz+umHWUoX0CWl1vTRtRuxAeO0QdthhB8Cwv477+uuvgc55h3UXF8vouwpVVBf3LOPLA7JiSx/XLumss84CTJsNdS4cPnx46LnUc/b9998Haou/J9HT7cIGocdFfurg4NCt0DSGVSc0WUmPP/54AG644QYvmufCCy8EjHRrRopTloRlfUfW2osvvhioZVaxzeTJkz2dXbuGuAgf+ezUs1WYM2cO22yzDWAsszpXkP6TZj5aB3Xr23XXXQHYfffdAcO8ak6m7uVjx44NDbWTlVVraQfXp9kxNMKnrB2PdNi+ffsChoHPPPNMIDgkVGuk/rm2feGFF14AkiXz261UwuAY1sGhQEjEsHlYi3WOt99+GzAxpn379qV///6AYSjpAnnGdcYhDRNpl6D4XvlYBennkyZNAjr7q6pre9y5Bw0aBMDIkSMBE4EjCfzJJ5940Tjy+8r3GcTaYQXe/bALxGk9dtttt6r5qpWorJ2PPfYY0BkVFNa4ShFe0l2ls8uHq/63eXSvzwLFNstnLpY88MADgWBm1U7j3HPPBWoLnMsCvtNOO6UeT9yOwzGsg0OBEMmwYayThXGlK0jvOuGEEwDYf//9PWYQY8ha3FWZHGFQJJaYRdJZEldW05NPPhmA++67D4CPP/449Jz6rjqXKzpG74utlcF0ySWX8MQTTwC10j+qRGuSUrVil/333x8wOwcxxh//+EcArrrqKsCsaUdHh8f6sihvttlmgLGGK6Krd+/eQK0tQ2zVrPRG3V+NVwx/7LHHAsZ6HATFUu+1115A7e9BJV+1I0mDuPk7hnVwKBAyRTplkYLvvfceAFdffTVgopnK5XKNLqYokzwjrcL8W0l8e4pgefzxxwET6SOI6WQtvvPOOwHDQP4yOIKktBgo7JyKmpE/dty4cd55kyBNoy7F98r6LT1UuayXXXYZYHYSwhprrMFxxx0HGIuyGFWWVFmUlQWjNd9nn30A458PK38TBTuKLMmcNUfZTRTjrJjiKGy55ZZV/8tCrmf8tddeSzLsTGh68L8WTCF3L7/8MrfddhtgFllunTyCGuyqfVmg1Df9qOzO7woCuPXWWwEjeITvf//7XrC5tlFDhw4FYMkll6w6Vvfn7rvvBjq3wGBcA0keKD/SGNNWWWUVwPzIVLV/4403BswWz+4Fu9pqq3lC7dFHHwVMIIJ+5DLAKSjG343P/3+WH2xYemOQGqBn7IEHHgDM/T/99NNjr3HMMccAcPjhhwPGICiV4cEHHwwcR9SYhaTPuNsSOzgUCF2WXid2euihh7wtsFKdJk+eDKQLW7MR159WSMK82u7Z4WO6xoQJEwDDFmIof/C8VIABAwZUHaPtpbbAMujcfPPNgGFWbeGi3ENBiQFJpL3GLfbTbuDEE08EausciVnlznj//fe90L233nor8Br6jh3eqLC+uL6oUQjr8RoEXW+dddYBzJqeccYZgNm6K9RSapAMjlFQAoS2zP/5z38A+OCDDwDjyrz33ns9VrafbRea6OAwDyGuex0Q3fmsXnR0dHjXkSFCUi6LPmOj3o54AOPHjwdqpbKMP2KowYMHA0Zf07l32GEHL3DA7p4uI5wMVZLs0oOlG0pap622l0afEvvo2JdffjnwuxqD1ueDDz6IDSPV2oqVxSwK36uHYXWuJGupdDqtnfRQMazOIVec3FWTJk3y1l16r9Zd33nuuecAOOqoowCzxtpV6X61t7dn7j3kGNbBoUBI5NZpNCTFbObKA3Gm/iRS+eGHHwZM6Q/peApkl2tAuosswdJ7evTo4c1N35GlUUEYkrg2I8r9kyR0L01vWz90DQUziDnkcglzU/jZSGtoQ/NWgT0xqeZrlweaOXNm6oCZNDYOPVvyRMjyrzDCsWPHAsbyK538m2++8XZH2kEtscQSALz66quAeT5k6RfuuecewOwuyuVyTQqenegfBsewDg4FQiTDNqtXi6Se9vi2hOpqiFEldWWxlXRUgWhJUCV++wus6V6qxKUshrIO293z9L98n0lYJyqwPwrSk8Ugwn777QcYH2XY87DuuuuywQYbACZpQdZgJYDrf51DOq/sFfIUNCscVeNQapxKkkZBz6e8GRqrkiTCnlsdp7VubW311jmtncYxrINDgZAp+D9vSPJkbV9QD2KDrctlL6lZ1kExkXRX6YBiVDu6qqOjw5ujkh/ExjpWLC69SGFusjRmZZ4kuyTdA+0gpMNqvrIWq1TP1ltvDZjSJ8OHD/fuyUUXXQQYq7CtQ4tRFOwvXdYfRdWVXfiioKB+rbN2CUkLu2sNOzo6Qm0LLvjfwWEeQpd3ryuVSt7eX/pPo66T5fiFF17Y86GKecSK9rG2f1LHf/HFF56lWVZHpeYp6FxpZ4ryUkSTGLaRux3p1fIf3njjjYCxWKojnV4FtVRJcm/FmioSL8uprOZi9+7WPdAPRX4JStxoZic+x7AODgVCIh220VJPOmDWnplRiJP+dqaHWEV6Snt7uxcrvMkmmwAmE0XfkZX7nXfeAUz0jph4woQJnr6j8ivSD2WhlTVVDCzmaWYSv+KXpasdeeSRkccnYVbpqEoIV5aMso66M6P60dra6hXB17y1K0iaVeaPRQ/LKHIlYhwc5iF0Cx1WkkexmPWeLws0Blk7xbCzZs3yrKGylqpg19prrw2YuFO9Sv+UnjZnzhyPlW02FpPKSmr78rI0eqoX0mWVOaRWl8pbVgE1xR6vssoqnH322UBnwTkwuaHaZSgW2o5wKgqGDRvmRUcJaSPykuxYnZXYwWEeQinqF10ulyvQWGnY2trKXXfdBZjC1Vn9cAmtlVUHaY52hQq/NLRzamX9Faso8km5rdopKJrpiy++8KJjxOSKDRbj+H10/mulsSP44lO9Oba0tFT85+3uaGlpidXbK5VK1RqWSqWGPaBar3vuuccr2Ka2Kmp6pjI+WXZD9nd8vvvAh7nLt8Rz5871Qvv0Q0ibepRnkoIWSK6Zjo6Omh+Ntq3a9qlEjLa3gn8x4jqNxy121nI5RfmhJqnu2BVQgMu4ceM8F5yqYsqAlhblcjm0OIML/ndwmIfQ9P6wQfjLX/4CmHC2rMm9lUoltLhVPSVigq4DtcyaxJiQ9HphW6VSqZRpuxx0Hug+jJZHV4lGPKc69zXXXOPVhlZd6KwGtEqlErm+UXAM6+BQIEQanZplsJCeENTHJG/YBgvNMawzendzPwRJYDs1b+7cud5BMqrZ3+8uzJoFYWvYiDn5w1D1dyPTP30ldAOp1jGsg0OBEKnDKnhAIYNpqsgn1StKpZIX4mUXfYuDfQ2/fmePxw7YF+QKCbum/3x2hfm4cSbRV+N0bvu1paWlxsKoV9kA/FDanoq6JWEhnV8ho3ZJTrson38Ocfe7nlDLsPup59Rv2Q8bX9JzBnW70Ng1t7Dib3Hr7j/ePjauGJ1jWAeHAiFSh3VwcOhecAzr4FAgRG6YW1tbq6xvzWJjO9UtqrlRUgRZUAHa2toqYHSrJE2KbEtrEl213nuXxE/na/9RE5pYT5nX7gbbSqzn1O7+HvW8NMILELZGQd3pw/RereGXX37prMQODkVHlwf/Nxu2dA6bY7OivPJEkA/vf2ENkwT/Z23v2GjY4/LFsjuGdXAoOnIrc9oIRlLS989+9jMA/vznP1BshqAAABlQSURBVAMmGbqrkMbP1kxE6dvdhVG6Ct11B5U2ss4xrINDgVB3Pqxtyc1DcumchxxyCAC777470JkxkTfs8aeJ5ioCijTWZqK73Bfbku0Y1sFhHkKklThN6Y08mFW+UjU5VtGvl156CUjWcjEOcdk6AcfXfc1mIai8iLMSFxP2HIXILXESB7TvAlnGBZiK8qoyqOp06uUybty4zOdOi3npwYbu487QONRFQRUo7QQGh2i4LbGDQ4HQ5UXYevXqVVMpX4nsb7/9NmBSw2y2UNE2pf9lkdLdNVE9L3R1wroMheo1q/Sx559/HjC1jVVuZ8iQIU3pJ9Rd4UrEODjMQ+gyhl1ggQUAGD9+vMeUgqTMFltsAZg+LKq0L93WLtbm71mSFHlI8TipuOCCCzJkyBAAtt12W8DUMN5rr70As1tQsriCRvR/VjSbWcWc6623HlBb51n3SmWBNM+tttoK6Fxr9RtK2508CXr27AmY7oG6v5MmTQLM7i7oudDuYOmllwZM179m7mIcwzo4FAiJirDVo+fZFfXVvU26zUorreRJrunTpwPw7rvvAibVSFL4kUceAeDUU08FDMOmkWxhwf++zxOfS5BVe//99wfg0EMPBYwUb29vT11OVXr9xhtvDCQrrRIU/N8sl8d1110HmDBSQfdTa/Xpp58CZrf017/+FYB1110X6FzLsPIyvnNmdutofOptK6a3y97YKaX+NRTzq/O61uqAAw6o+rwehLl1HMM6OBQIiXTYevQ8fVc6zb333gvAoosu6n3+z3/+E4ChQ4cCRkL9/ve/B0wvVYUoyl/79NNPZx5XHpDeqZYd8jGqKJjgZ8dp06YBRpfSZzqXWFKvcQW//MizZUka3HHHHeyxxx6AGZ/W8Le//S1gLP6vv/46YKzDeg6ERs/h2WefrbqOXsOKn/l3l9Jv1cO3b9++gOkbrDVVgE8jevs6hnVwKBASMWw9YYdiiEsvvRSAxRZbrOrzMWPGsM022wC1Vl/1UBXTbrTRRgDceOONQKf+C/kUds6SSij9cq211gIMK0oX33fffQEYPXq0x5A6ZvXVVwdghRVWAIz+s8EGGwCGpdUZT71IZ86cGZpoEVciMy/06tULMP1wZWsAY8GXjWKZZZYBDKOq45v0e/93oXOe2m0kXdc0EXmTJ08G8Lqpr7LKKoDZvema6hj/5JNPAjBx4sSaMqfSgxWpt8YaawBGp3UM6+DwP45EJWKELAwrVpQ1UIWtxRgLL7xwrFVNUlh9V9dZZx0ALr74YgBOOeWUxOPJI3BcEl0SVVbr0aNHA4ZdohLK+/TpA8DOO+8MwEknnQQYRpJ/cMSIEQBceeWVQGd3d7vguiK99L6/P2yewf9aO+lo/taI2lXI3iC2Vw/dY489FjC6eliM80YbbeQxVERCRmARtkYwWhS0/nfeeScARxxxBGD05Hqsxc5K7OAwDyCSYVUCNIukECt+9tlnQG3c77LLLgvA1KlTE59TupPOqXPpWgmTz3NLzbKb7ybRocSCO+20EwB//OMfAaOrag6Kp91ll10Ao1NVKpUaS7LWJ6iAVx5+WEUh3X333YCxhmqsX3zxBf369QMMw4ptDjzwwKp5C/qumiL/6le/AkwqZRS6C8NqruPHjwfMLujcc88F0ln0bbtEWAd2x7AODgVC7mZFSYiJEycCJpJELCB9Lw2zCjNmzAAMs4pRtt9+ewAeeuihrMPOhDQRVrov0lFPP/10wFjNNacxY8YAcPzxxwMmi8UvrcUktm+23iJsttSXnr333nsDcN999wF4sb4qNDB8+HDPynrJJZcAhlFtZpVuLj1funu9MdPNhO7TwIEDAeNLtpu62TtTv+83y+4MYn6wWYKZF198ccCY8YVRo0YBxkVTD+TuWX755QE45phjgGw/2GZVz5NKMHz4cABWXHFFwLiyFGBw/vnnA+YHHIW8xqx7MGDAAACuuuoqwLic9BzIZaNAeW3nl156aW9LbD+s/m0zwDnnnAPAtddeCxTrhypIJRg2bBhQWxdMKppcc3L76Z68/fbb3t/2Nt7+IdtwW2IHhwIh9y2xgvvtlKPDDz+87nMrwGK11VYDjDT64IMP6j53I1Eul720ukGDBgHGQS/XlNw3SZg1b2y44YYAPPXUU0C4lN9ss82A2tS0d999lyeeeAKAww47rOocCuO74IILABPGqTC/PNCsFELNabvttgOgd+/egFlL7UiOPvpowCSFfP7554AJqZ09e3ZoD9u4uTiGdXAoEHJnWLkAJDFU3kWvWaAwPqXX2WlOZ511VuZzNxKSyGuttZZnoJEuq5IpmlOjEKfnlkolTjjhBO/voO+K9eW+2GGHHQDDHC0tLV4Y5lFHHVV1LqVKSv9V4n4RIWObXHJiViU0qLCcUgi1i9A9UFhkkPspqT3CMayDQ4GQW28dQQHxghgly7nEUK+99hpQywAq0fHRRx+lPnczsOCCCwKdOwC5bySNFaqZJ7IWGFBYoSzWepUlW9ZOHWf3xV1++eU9HdzWxaTbqh9SI6zxzSrWpuAWBUyIQceOHQuYEEUFvdhMmkdgh2NYB4cCIXcdNqyznApXqaxGFOTLe/XVV4HatLEJEyYARretB42UzgqWX3/99T3WUqJCdymc3dHR4e1U9txzT8Cwop3uKMjP+NOf/hSAkSNH1qS4KUBGARLdZb71QPq3QmT1+u9//xswumsjnynHsA4OBULuDDty5EjA+Bulw8pSpjIw119/PdBZXkP+K0mqgw46CDDJxcL7778PGD9sd4cKjfXq1csbe1eXtbFRKpW4//77ARNBFpbsIf30lltuAUzSt7+8rGwYKnc6L0GeDoUi6n7IOlwPs9ola8LgGNbBoUBIlMCeRnJI/5Q0skuARMEuoyKrmoq0ibHqiZJpRuczzVmxzZtuuqmXPqbXRiAoNSvJ/GQjsEt9CquuuipgfMaK4fbjpptuAkw6XSPRVd3r5IdVYst8880HwHLLLQek665oM6nNsP4UST8cwzo4FAi567CS0kof+/Wvfw1Q044jCHbWg/xbip7KUmytWdk4fohhVcqmUqlw8803N+36fiSZv3RWMa2s23YJFPkhbYwdO7YpzNrVUMSXUkbFsHGFz6OQtqyrY1gHhwKhYUXYxJZiG+VaypeleNRll13WKyWpBHX5AVWoLY8ypmGlN/LUf3QNMauKxr3zzjteMTrNqZHw63j1FJlTtolKsKpQgMp6Kmsnje6WB7pKhxWzKuJLz7bajChuIA6lUimUWZ0O6+AwDyH3WGJBFl9Fyzz22GNVn6uoWLPQDB1WMbeK7pG0vO2227qsskKYJI+6H3as8MMPPwyYeclK3+yiZ10NzVfRfMqH1Wsae0nW57HhpeL/l7poqxKkAjtkzBkxYkRDep3aiDJgpFkHbfVUtubyyy8HmrOdz4JmGRal1ukHKiGsEFkFi+g+ZQnHdIETDg7zECIZNu7XHlWlz65Ob8MOjugq5CmdVXhOJWvOOOMMoDp5X66TZjBuViywwAKAKTam9MbuimYxrBj1lVdeAeDCCy8EjLont0+WLXFS945jWAeHAiHSraOK6rZjWKzoZ0ebWfVqd7S2GbXZOq7tElCX+TzSv2T21+5BLqyunGOW8NK4UMWuhr2GefYPioJdjCHPgnl2aOJ3333n3DoODkVHpA4rx7kkrsz5kjBfffWV97fCtHSsyqPoc7GN9Dm7vKMfYWU2bQkapP/a3426DoT3fBH8Tm67l419bkncsMBu//XE6PUwu30du/N70DFR0PejOgnkhSi9My6owIbGba9LkuvG6b/6vK2trSZ0Nux6ceP3f25fP2gN/XAM6+BQIETqsA4ODt0LjmEdHAqESB22R48eVX03o3SbrEwdtJ+3/7fbGoSdI0pfk24wa9asqoM0R+kjQTpl0vC+sPG3trZ6ur3Obyc0JJ2r/1j7erJifvPNN94HPXv2rPivZ3e+i7uWH2H2AP/3bX1f87bnF/ZMBZ1L0BrOnj276oP29vaqNWzErrFcLnteAPmnVXRNtos8PA2uP6yDwzyESB1WPspmWA3zgL8YWNBnUCu5muXDawaC5ij2SbJLauSY7FebjdL0r21kimSS6wuNvIe2r1lwDOvgUCAkSq8rCvtkGWeWzuT1XC8PNCONKy/Yeq+t4xbpGesOY3QM6+BQIDQ8H7YRyBLrGnZMXFZRqVSqiYvualZIc13b6tzVY5al1bbohrUFCTpH0RD3jKWBY1gHhwKhYSViGgHpP2oDorKbTz31FABTpkxJ7QuLi5+N8u12RQnVKASNtauZVVhrrbUAU5VBzaAHDx4MJGt3ERej2+g56jpq1aEC6++99x5Q65dtxL3PbUvcjJumhAL1dFlppZUAmDRpEhDdGS8uCD7MZF+pVHLZVmpbpIqDCmRQvWJVj1d/myOOOAIwyeNFMDBFQZUkVX5GwlZdBPSD7c5QQYLhw4cDZosbVnNbyTKLLLIIkGzbHwe3JXZwKBASlYjpKumu62+44YYAXHTRRYDZXqk+7CeffAJkU+pt40fUljhzpbvWVnbddVfAjF1d48Uw6jX6k5/8BDCMI2ZKWvPWRndh3X79+gFmPGKfrPNqNtZee22v5612R+pMsd566wGm946eGRnY1E/p5JNPBupbE8ewDg4FQrcyOsldo54uF1xwAQDbbLMNUFt2Rl3E1Hs1C8KMVHkwq4qZDR48mC233BKAqVOnAsboMm3aNMAElCugQNeXVN93332BTiNZHp0Q8oYdCKG1ErPutddeQG1Hwjy6ljfyOV1sscWAzr6+qkf8pz/9CTB9csWwm2++OQBDhgwBzJoOHDgQgN///vdApx6v+dtwZU4dHOYhdHngREtLC1tssQVgykaq87p0U5WXkY4gKX3DDTcAyXrvxHUYyyMoQiyjcUp32Weffbxjbr/9dgD23HNPwFgO33rrLcB0oZfeLh1Wkr7ZfWyi4A831Ny1qzjyyCMBY+1eYoklAHNfpaNrbe1z+v/Wd8JKBzUCcvc9+OCDQKc+Kov+iBEjAGMd/vDDDwH45S9/CcDQoUMBOOaYYwDTwVE9pGTPyALHsA4OBULTGVZMJ6voKaecwtFHHw0YtvnHP/4BwPXXXw8Y/W3TTTcFjIXx2WefTXzdPJKLwyAm0Jykwxx++OFAJzNozGJS9al59913AVOkWvdHwSDSj/R+dyhArrGIYfx9g8Qq8lmqOJ+g3ZDWLmhdbD3e7vXTSOjaZ511FgAbbLAB0Mny0rc1X41r/PjxgedaaKGFANNvV8/HzJkzs48v8zcdHByajoYzrKSiIlwUVigJvOaaa3pSTX7WW2+9FYAdd9wRML5L6a733nsvYJi4HvbMoqvaqWLSd7QDOPvss6venzt3Ls888wwAV111VeSYxaAff/wxYBhq2223Bajq5G7rdDab5Q0xhF5l6Z49e7YXhSartj0WzWvkyJFAZ0c/qC3GXalUavzpSUoA1Qu7t6/0Ub3/6aefemMPKqQfhD322KPqf+1IZKcIQljUlOAY1sGhQGgYw0oySWJIKsv6ufbaawOd+pD8W/JTycr685//vOp/RQedfvrpQDpmzdNXF2ZJPuqoowAzV+HFF19kt912A5KPWT5m4fjjjwfgrrvuqrGsCnkzkG353XrrrQHj91akWblcZs011wSML13QPXr99dcBs8Pobr5k7RDkW9WOULj22msTt9vUOsgHLahpVpQdIo61HcM6OBQIDWNYWcjWX399wPgRpeeJab766ivPb6XYS1mFl1xySQAmTJgAGJ1W7RwbwZpZ8MMf/hAwEVqSktoRbL/99qk7sIthNS4x2KBBg7jvvvuAWkmdRzaIH9odaV4777wzYPzlmlOvXr047LDDgNq2GX//+98B43uUhbS7xDjb9hPFdtvjHzZsWOJzaqeo3Ybm+re//a3q/yDEeQEcwzo4FAi5M6yk8nbbbQeYeEpZFCVhFbHz6aefen8rMqh///6dg/uvhPrDH/4AmIiS7iKdNT7prsp7FMPK0jhjxozU55ZVWJBetNRSS4XqOUHvh2VcJcnEEoPKd6xdkZLO//rXvwKdzLviiisCZp0nT54MGEup3q8HUWV8INtzsfLKKwPmfusZky573nnnRV7bD+0eFVcgSF+/4447Uo/PRmRd4jT1XvXwKmhAPz4ZJrS1GDBgAAArrLAC0Bn8ri2vPtPNkdvmgAMOAIyxI0sana/je241bbV9kuNcDnKlXcmFlWarKoOV3DoSgNoqLbzwwlUd3YNQb3/YMEgA6YHUmKZOneoZlZRwf9111wFm7vVcP653apY5au0eeeQRwFSRuPTSSwFDEnH32j+uKVOmALD00ksDRsBddtllAJx44omJx+fqEjs4zAOoe0ss6SKJpWBvlQDRa58+fQBjWJJjvVevXp6zWgwlZ7pM7TIIyBDz0EMPAUaiacsxderUUPaN6wiQRjorNO+cc84BDAtqN3HSSScB6ZhVOxSViJFbQVJ69OjRQDKJ70ee1eovv/xywAQXaAex3HLLeQkbuge2kU07HM1La5wk1DKux69v9xR7Lq2VAlC0JdZ41RNZz2eS+y1j3FJLLVX1vkIZta3OA45hHRwKhLpLxEi6yfQv44MtDe0O7ZKGPXr08KSsXsWgjz76qHcMmJQ0GQjErDKKDB061DPwJGWSLAwrN44YRlJbOrb0uCTQ/ZMe3Lt376rxKEheLq0oBDFQngyrHYMCWmRsO/HEE721C+udo3naXfSSIM/QxNVXXx0whk2dU0axzz77DKjtuue/b9phKQT1tNNOqzpW90LFAnXOPOAY1sGhQKhbh5XkEctJz1x88cUBI1mln8qxLik1bdo0TxdVfVeF4UkaS4+44oorqq6pMqfSHcrlcip9Jiukw8mia+ufSVhM41RAuRIcBAWIyy2SJPAiTU/ZepIeNG+l/s2ZM8dbV+2wbNSzHnmW0FXRA41X47rrrrsA+POf/1x1Tf+1tc5KiVx22WWB2uIICux54YUX6h6vDcewDg4FQt1F2KSrKAxPzKDiYtI/JXll+VUAxeuvv+4VZn7llVeAcGlsvy9Jp9dSqRRbHd5GGqmtc8hXJ91VOwE526MC/HU/5Ofbb7/9qsah+yeru51+lmR8fmTpw2qfT8yiMi/nn38+YEJHZ82a5SUkqKRKHrCZtR4dVt+1dwC6Pyr4p0QUWYuVeDJ48GBuvPFGoJZRpbOOGTMG6AxFhWS7orRwDOvgUCDkFpooJpDvSVZj+WGl94glVWDtoosu8iyk9eoolUol9TnSHC8fsvRzQeFsUWVPxEaygNuJAoceeihgdKy8dfAs91ahlptssglgyqXIWvzSSy8BnXq2Us+efvrpuseaFmlsBjY76jmUlVjnkjVZCflbbbVVzXd1H84991wALrnkEqCxZXwcwzo4FAi5Mawkk+KB5U+Uzio9T6/SdcaNG9dtgvnjoKgYu2i20gHDkrJ79+7txRcvs8wyVZ9dffXVgIm9rauNQ05lQHUeJazvsssugElz1FwUf1sul73C2HmUYQ0rwlbPvdE51BpELVHk3RB7ysagnY6alPnvrfR1eQvUjC0PZnWFxB0c5iHkrsMq+kPxk5JMijEdNWoUAGeeeSbQvLKdebC4LLgas6ynsvRKWiuqS9bCESNGeDsNSVBZyZUBk8f48tqp6DzyVco//uabbwLwr3/9C4CDDjoI6NxFXXvttUB9JTyh2tKfB7MK9m5IPtKNNtoIgCeeeAIwVmFZ6f2MJ7vCxRdfDJh2I430+dtwDOvgUCDklg8rSCqr/YbiTlXmUwwrxm0WfFI7cz6smFN5jcrWsaNitNvQ7qJcLtf4Eu0k8DwKnQfl/GZZQ41R8bay8Ms3KZ1WazxlyhSv2VeWZP2w64c9m3auaEtLS+CBQfdUvnO12zj44IOBZPq/IphUBF67yDw7rYc9p4JjWAeHAiF3hhUU57vaaqsB8MYbbwAmh7WRrTOiYEvnsDlGSXl9pvaCDz/8MGAY2Ja4n3zyCTfddBMAV155JWD0wDwt5HkxrKDdUt++fQETySUfssa+2267ef7lZlj87TVUxQnBXjv/mOzyu6qUIQ9AmJX2kUce8Rp8qWVo2tzkJIhj2Nx/sHpoFMwuk3ceW6V6kHRLLPO+v5dNWuGia+kc3333XeyDnEfopIxgc+bMyeUHK8jVoY4MW221FWCCZPr165d7xcYghK1hmh+sfYxqjgm2a05r34gwwyCElTLyPm/KKBwcHHJB7gwrKa8ga6XbddUWWIiTzvpc9ZTFGN9++21DzfZJE8yTpJj5dgUNKcJmJwP4jWzN2AqnZdiufuayIGgN/XAM6+BQIEQybBrpnIcO1gyEuQRs9pBrJktCQRKEJZSHpZKlCXD36z+aXxHZxkYcw3a3Zy0LnFvHwWEeQmRoot/KCcHV4+0eqUl7ZzYaNkPZqVGCOrMJGrc/6ME+l0ITbVYMq67v/1v3yX9+MIyu62u8QYyv74g19apz+2EXvbOZ1j/GrAEA/qADe2wRwQ+h5wsruhbWO1X3Ksn47RJC9ZTK0XVtnTnsHvt3T2E7qKA1rBp/6tE6ODh0GSJ1WAcHh+4Fx7AODgWC+8E6OBQI7gfr4FAguB+sg0OB4H6wDg4FgvvBOjgUCP8PMBhzr0CUuYgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 288x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 750, D: 1.185, G:1.144\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 1000, D: 1.198, G:1.03\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 1250, D: 1.216, G:0.9504\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 1500, D: 1.115, G:1.105\n"
     ]
    },
    {
     "data": {
      "image/png": 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SycGhDiDvCeyZQPqmLyoEqDph9f+oJkwHjXyUjk0Htc3LTt9LliK5McExrINDhJBSpFOhns7ST/2lQOtS1s3GjlSfu7Cit8K6djZwDOvgECEE1gwrnydSoZ2CNurVq+dZPOVbbN68OQA77bQTAG+99RaQWomYjRX5aOFit+vMNwIzOuXjpQmyqmJW1dj/2FS9fHaH8p122smrnq+NV1kcja8axm3atAGC6afqv75QKIebncBQKC+EIPfg9OnTgaoOfYcddli13+UDTiR2cIgQCjpwQsw1ZMgQoOqUA7j88ssBU6wtHdgO6SCc7gp7VGG1U045BaieKiXDmJhWne4kKiscT31tXnvttYzn41+jQhPDTK+rDeoe8OijjwLQu3dvwDCpOhuoH5HWnwpykcCucjCqPd2gQQOv1nL79u01j6CH9eACJxwc6gAKMnBi6623BkyJSbtK/urVqwG48MILgfzrZXLwjx49GjDlLvv27QtUJfqLab744gvA9A9Sb1lBum1QyBWzqrjB008/DcDBBx8MGAnC7u5+xBFHVPu7dBjWRhiGIT1ja9euBarWIYlJ7CtpKRuka0NxDOvgECHkXYf1lxdp2LAhUFVJHmoyq07QfffdF4CPPvoorXH+uEbOAsdrO/m1RnUvV4lMMYy6wP3yyy8Zj5ur4H+x5V133eX1hdWatZ6LL74YgGnTpgHw+uuvA0bvV5cE9SdKBbkswvbEE08ARgcHOOeccwDT6SEMOB3WwaEOIO+9dcAwkNLk1AXO/vyNN94AqnrKRgG16VTSf8RIYpp58+YB8ds9FBI222wzwFiAVWwbjGQgK7BYSGGk6hcrHVYSR6Fi6NChACxcuNDbu6+++iqw6zsd1sGhDiNnDJtKxI2/TIj/OytWrADg3HPPBcJJnQs7tFJre/HFFwHDLCojc8ABB4Q6fhCQ/1Ed9/x9UgcOHAjgdcKTdKEOePfccw+A14tWEoW6vKeCfKTXyVo8btw4z7KvnrfdunXL+vr2fidbo2NYB4cIIWcMmygJWexz7733AjWLmI0dOxaABQsWZD1+PlCvXj2vK/vee+9dbT6Kigoi6D9oBtL1dt11V8DorLbf+aGHHuKnn34CjN+1R48egLGGK9lBmD9/PpB2hFP6iwgI48aN47LLLgOMJT8fcAzr4BAh5N0PCybLRXqNcMsttwBw4403AkaXzQbxYol9n2c9hiCGGjlypKf/6Hfff/89ALvssgsQXHYOBOeHVcdxpf7Joj1gwAAA5s6dC0B5ebmXwaIoNTGt3T5EOqEkDXUxTweZ+GGDsFFojyQBtmjRAjCZVkHANcNycKhDyHsscXFxMffffz9gLIsvv/wyYCyLQcRsJkMY+tHmm28OGEYCs8bly5cD5rQOkmGzhU754cOHA9C6dWsAZs2aBRj27NOnD1C1T5tuuilQs+i37qticmWPWLhwYahrsBHE/krf1p7Jah4kwyabZ95f2LKyMvbbbz8AVq5cCRhDxeLFi4Fggrrz4RJQsPicOXM8N47EzCZNmgDGNaIXuBDcOqqKcfzxxwPmBZXh6JlnngHM3MF0H9ceduzYETDrGTVqFGDUnGyC/W3kKklfKoHGUwBJLuFEYgeHCCFvDKvTauLEiR77TJw4ETChh4VWNiRdKMH+kksu8aQISQ8Kc+vcuTNgAggKgWEVRihXTXl5OQDbbbcdYMRdGZC6d+/uJXePGTMGMEYlJWjo92GoN7m6Z5I8dH/yUX/LMayDQ4SQN7dOq1atgKq0KjGplPivv/4aCMfFko/eOv4UQoUmHnjggYBJaDjuuOMAY5zJBkG5daRna18kFQhTp04FqiQhpdrZrNOpUyfASE1B7GkYZX5Swbp166r9rFDZ8ePHBz6WS69zcKgDyLkOK4ujwtr8bp1FixYBhaHHBYlYLObpPY8//jhgguD32msvwFiPg2DYoCB9U4ETidC/f3/AuDxeffVVwARX1IU91R5Klw3S0p0qHMM6OEQIOWfYLbbYAsCzmq5bt4777rsPiL5VOBUoQV06tRhJZUGDRi46MpSVlXkBEQoAGTFiBBBsKmQqnc3DQlFRUY3xVao2l3AM6+AQIeSMYSX3b7XVVgBeKNuXX37p6Tl2t/S6BK3/yiuvrPZ7pRAqQihKkC/9iSee8PbzscceA+DDDz8EwrH05wPFxcWeBCg7QxjlfFwCu4NDHULOGFZxtcOGDasa+A9r8WOPPZYXa1suUVRU5MXd/vnPf672mRK5w4qaCVO/U1JAp06dPOnokUceAWr6LOsC9Jyq+LmKzylCLxdwDOvgECHkjGGluyrCR+UwJ02aFFnd1bb0SsexU8vatGnjWVElaei7M2fOBMIpLOefY5BMqzUo/bFhw4ZedJpih4MYz557Pn25lZWVnr1B+6tovVzCMayDQ4SQM4aV31VsdNFFFwHBJv9CzUbBiYq/ZQtlr6jR70EHHQSYPElFL7Vr185jJfkpVRpF/soo+aDV9lNRWr/++ivHHHMMYPJ6s4G9V/m0DguxWMyzM8gifv755wd2/VTX6BjWwSFCCJ1hZVFTNJOKdT311FOhjBePUcPQf2Q1lF7er18/wPhcNYcNGzZ4fkk1o1a5lShZU2XZVzla+WFHjx5do4BeNoi3h/FYKBfRXKWlpaxZswYwcfAffPBB4OMkY9qEL2wQN+LWW28FjBihlKSwkQtDhRK8JRop/UzJ20rqvuuuu5gxYwZgOrHnyoASZDifetmqyqWML1OmTMmrQSgXY69fv55JkyYBVfsZNFxvHQeHOojQGFbikyoHqkref//737SvlQy1zTOXJ76MEeq4Xteg+ytDofY27HtcSCl5lZWVjBw5Mt/TcAzr4BAlJCwRU79+/Rhk5nLQqazTWIEB+T41c9m9O1/wrzGI8ikyLinIQ/aIwYMHA/Dee+/lNLihLu+hq/zv4FCHkJBhS0pKYpA47U0nglwZCt9K9cStX7++Z220w/Ns8368a6Wia8c7uSRFpMIMYbJHMjdUojXqMxVCq6io8C5m72Ft143nRpF0tOOOOwImUERM60/aSHf+mQRDaD7r169P2B/J93ug6r7o2QojQCVdS3xtyfCC1rhu3TrHsA4OUUdChnVwcCgsOIZ1cIgQEvphg7AwpqKrpHr9VPQgfSZdQG1ApN+tWLEisCLUuqbdA9WeS7169TzdSf+GYVXVfDZs2OCtsbi4OKkdIhlS0dH0Hd0Le6+07ni6eCr2B9lJbP0u3h4mevbsa8oS7rsmYOwqGzZsSKoHp2pzqW0c/atQ3pUrVzod1sEh6iiIDuxhwGY9XyD+RuXDy/X6wky68BXwDmwP4z0ntiQUFuz7JaZfu3atY1gHh6gj7w2d/Uim/6QDW0cshCTosJFPi7+tw9p7Fq+wQCpzDnLv4o2fr3tnj5vsWXcM6+AQIYSeD5sO0rEcpnutjRW53sN4Sec286bDrPY1skGQz1gYcAzr4FCHkJBhc336hKlPhLkWnfxXXHEFAFtvvTUA++yzDwAdOnTw2ju8+eabAJx44olA+C0Lc82ssuTaftdMrK62TcP2ldpj2yg09qwN6er0BeXWiRcYEeSND6N799ChQwFTtyqR6KYH9+WXXwbg6KOPBoKtSxxUB/ZUsdlmm3HeeecBcMQRRwCmUMHkyZMB+PjjjwH45ptvgMSin15M7b8SSvT73377LWHwfxgvqgJx/NcPMhjFfuZdep2DQx1A3tw6ChksKyvzipYpjeurr74CYN999wXMqTNhwgTA1DIulJ485eXlgJmXeuAKq1ev9gq06aRWneY999wTMAxUyF0Q7E4HRx11FAD/+Mc/vFrMki60PvWfEaP26NEDgE8++QQw6xV7NmrUyKtOaFeUjHdvgkjOV0igeiBpTzXv0tJSTwpSp0HVllat6XTmEe+7ya7hGNbBIUIIXYe1DQY77LADAA8++CBQxTBiWxs60VavXl3tZ51sxx9/PAA//PBDyvMJo7xIkyZNAMMuYgbNt7i4mO7duwOmu5s6rr/22msADBkyBEhvLfEQlA6rZAKxTc+ePQFo2bIlACeccAJQJSWJibT2JUuWVLuWJAjVoxZLq8eSSqdOnjzZ228bur+///572nuo51A6tgyEO++8M2DKz2peYk1f0rz3nIrptVd33303AE8++WS1z7OB/ZwKjmEdHCKE0HRYmff/9re/AaaAuHQEv9UtHvQdsbNYSe6SZ555BjCnZlg9VpNh1apVCT+vqKjwqsTrVJakIfZS39gff/wRyK9LQiyjbvHnnHMOYAqli32E2bNnezqprMKSgrQn9t5IZ1QHvA4dOgDwxhtvxGXYTO9JeXk5jz76KGD6HyVLUpBlWlJGLBbz/i9pQpKG7BDqAKH7FcYeOoZ1cIgQAmdY6RnyL6o7WzxGjcVi3kkk/UcnmYINpBPoZJfO2K5dO8B0U3v11VcDXElwKC4u9jq9vffee97vwARZ9OnTB6i9wFmuIKlIEosKh0uy0T7IGq5+QaNHj+b7778HYNmyZUBydtFetm/fHjAF3rbaaivvWkEx1Lx58zzpIB7sAI9vv/0WgG222QaoeiY1H/URkuQn/fv0008HjJR0zTXXBLoOcAzr4BApBMawOqHUaf3OO++sGsBq67B48WLA+OG++eYbHnvsMcCc3NLnhg0bBhgGFfP6ynkC5hT0l+xMd95h6ozNmjXz1iKrpHQ4nfz56OYtSCoqKysD4OKLLwZMp3VJPpdeeimAt1/S3TORBrRn2mtdSwxdGzLd29q8ELqWmF4d5E855RTAsKhiA0pLSz37g+ao68rSL9+zLNAPPPAAEGx7GsewDg4RQuAMO3DgQAC23HLLap/Lz3XaaacBphVjRUWFp6vaJ7VtMbX9YDqVly5dClQvGJ1PiLGk+x122GHeGqT3qN2FWEw6nCKG1Moyl+jatStgIsw057feeguAl156CTCslA6z6p40b94cgC+++AKoGTfst2nYSDeRXdcZOHCgZyXWPD799FMAbrjhBsDEdtsW6i+//DLu9fVdJXK8//77gJFUZAO44IIL0pp3IjiGdXCIEAKLdJJe+c477wCmqbGgLI0BAwYA5pTu3Lmz16bxhRdeAKBFixYAHHzwwYDxd4mNpBfrhFu0aBFQ5et9++23gfglL+0siCCydQSxpthSUkbLli095heDqhO7GEfWSfkvO3bsCODF1aaDdCOdxDoPP/wwACeddBJgpKLZs2cDhhW1T7LKx/ObAjRt2hQw7H3HHXcAZk8FSUt9+/b1UhDF4LYuumbNmrQinYqKirzncpdddgFMhtUbb7xRbfxMngPdP1m85QmQ7i99PZW9dNk6Dg51CIExrFhPFjRZP3V9+dZmzJgBwG677QZU+bCkx8ivJzaSLqDTWDqhbdnVSXbJJZd4OamyIAu+wtBpMWw6lmf5hw844ADA6OCNGzf2JIyff/652tqmTp0KGJ+y1jZr1iwAunXrpnmnNIc/1pIWw2qvJJ20adOm2pi6v9on3UvZHkaMGMHYsWMBs0fSEWVv0LVkDZfPV79XrHGvXr28Z0h/K+lDz5i/2VeqaxTDKqJKkWdiw1tvvRUwFt1MmFb3R54QNTMfM2YMAJdddlnSaydj2MBeWLkrJNJpYN10mcntSutNmjTxRK3hw4cDxuUhsUUhXxIn7Rf2888/B2DQoEHMnTsXMOKUvhOvanw2PXBtaF4S3XVoNGrUyAvNs++31qoHXPdR961Lly6AeeBSQbovrMIjn332WcCIjXaPX3vv/Pug+62wRnuPJF5LrNX9lkukf//+ACxfvjzuPLMJ/r/xxhsBEzbo65JQ7fObbrqp2vwywSWXXALA9ddfDxj1r3Xr1oAxktaGeGv0Ps94Vg4ODjlHYG4dGVxsBpFBQmKOTmCJfLNmzWL69OmASQyWuKiTX2KhbdbXqT5lypRqY/nnkU1tY8Eu+ZKst4ptXLDFcz/03T322AMw6WZiIhne0mHYdKH7roAJud7EtAqflAisvZYxpUGDBt6+CmIusYukDt2jadOmAXDyyScDiZk1CFx11VWAYTexn56pIENBldBw3XXXAUaKUpJANiG0jmEdHCKEwBhWYYXSxVTm5ZZbbgFMIIBOWIV5/WOCRIYAABDnSURBVP77794pp9IqCqJWQLxtwNLfKoFdBov169enHTiRDvOGGb4o9pKBRwyrIPRMkUropcbWfVSghFwyMsy88sor1f7u0EMPBaB79+6eC2vFihUAvPvuuwAcc8wxgNF/FYhw5JFHArm7/5JylFwhm4KeVxUWCMKWYRcwkPTRu3dvIDHDuhIxDg51CIExrE7Ws846CzCWW53e8XTJhg0beknFt912G2CSu+3EAVmg5YSXiySVkzfTsh1FRUV+y11G10h2fYC2bdsChlmlU8n9kO31E90j7Yn0zYceegiASZMmVbuG5qTvi0Xnz5/vFSZ47rnnABPyJ8u+2KZXr17VrpEOguhTPH/+fMCsUc9tEMwq6Fq2ZTxRYkOqcAzr4BAhJPTDphO2Z/vo7DIitVwbgMGDB3sWPIXp2Sf6hRdeCMD9998PJLa6JkMmhcRlFZWeppMynm81FcjCqnSuq6++GjCns4qVyWKbDrv71xikn9mGmKNjx46eFVhhjfpX+pxCVSUlZYNsCunpuVNQg4JcZHNRTEA2bC6pQiGzGlNSSKJ3w4UmOjjUIaSkw6aiB+mzZP4sXUv+xVGjRnkhffpMzKUE4PHjxwPBtrNIB4qKEQvKryY9bc6cOYBhQZ2o0sEbN25Ms2bNADjkkEMA43/cddddAcOAKv/697//vdo1M0UYlm3tk5ikY8eO9OvXDzC6uL4jPVgMlm/oPsvfLVuL9kVW+UwK+knKVNlT++dkUmcqcAzr4BAhpMSw6eiw+q7YUD+LbWQllN+rcePG3qmnaBf57gqlfYWkBvmJFdusCCAxr9Z43HHHAcba3aZNG+/+KDhePyvSSQHispTLup4twmBY6avXXnstUFVwT3q+mFV+d62n0DrJaT4LFy4EjC6reHZZkxXj/N1339VYgyQvJRQodVA/67lWLEIqSJak7xjWwSFCCMwPa+uudhlPtaoYNWoUYE7pyspKL0pKmRSKM86kp2i6SOXaYrunn34aMMWolRKn2FHF5CqyRbHQTZs29U5jWU3lQ7ajtfItTSSC1qCGVrIEN2zYsEZbFfljlVZZqNC8JS0oAkuF/6R7z5o1y7PcS3dX6Re/rQJMMrx0V6VZ+iE7hx3z7iKdHBzqEBIybDolQG1m0N+qXIqsiDqd9P05c+Z4JWLktyo0ltH6FUurE1OWX5W0EWsqCVrfq1+/vrc25UoqSiiTEjD5gmK6pddJkli7di2vv/46YDrM33vvvYCJ9il0SGdV8r6yiVRooWvXrnTq1AkwrKznVNlmkirUQiZR2Zl4uqrTYR0c6hBCb+is6A4VqNJpowyQHj16ePGc+UA6UoR0bUXtqDSmflakliymYs/HH3/cKy6dSfPfVJFuGdBUIX3r7LPPBkyTZo33+eefexlW0uNz2WokyGLwehYVAyALb/369WvonR999BFgCocriiuVqLJ4uqude20jsNBEGwpbGzduHGBcNbq5Uu5VRyhXsMPaFLqXqtLvh9Yi0VgPsurTLliwAKgSC8MIDbRRW3mRIPrfKhhCaWHq7qCHu2fPnqEm2NuIF17q+zzja2tPlVqoqoq77767JwKr961cQCqtE88Vp2v6/7VLKNlwoYkODnUAoYnEMi7J9SFzv4wTcmPkG3bXsnSgk1wnrsIKcw2tIZk4lel1FToqg5L64YhZFDCfL2jdtmiciVtQ35X0oF7E3bp184yLkjRkVLSfHXsflBDjVxPsgCIxrXPrODjUIQRW5tSGThk5k5USZ5cfzTVs/ae0tDQGNWvgRhEKd1y/fn2gOqytgwn5ulf2Hm6yySYxMGxlJ+QH8az5156UBS3GF/z6qi0V6TOXXufgUIeQUIfV2x/vJPWfILb+YPffzHfwt8LqbNimevvn4uJib/12QkOQsE/jVFwV9imtcM+wkIklPUiIQW1ob7VPdppjbc9vvN5L8dYWi8WSus00D0k6mm9tLi5bd9Vcpe/Gg2NYB4cIIaEO6+DgUFhwDOvgECEkC/6vlX6T6bbpwB/1kQsdKZsCXkI8v5/dPMpfTE73ytY77e/Kmp5OAoSupX/93d2Ki4urWcGFdML5bCux/LIK/q+oqPB0L/kv7ZKo6SBe6pmvgVVoPX7TgeajqD5FOmWTvOKaYTk41CGE5octVATBsEEiyMD12nx4QbKPLUH4fddBriMZCmUPtWbFGiiVMAjJ016j4BjWwSFCCCyB3SE1hJUCl+q42eyl/ra2Qu4b4zOie6qi5CqdGmZRAsewDg4RQp1lWHvuQWeypIOmTZt6hdPVLEwlYtTk98UXXwRMxk86CeDxYn39CFKHDbM5WJSgRuPXXHMNYFrJqIRvGPfHMayDQ4QQWsWJQkEtPtHQLYzySR522GEATJgwwcsPTsb08+bNA0wxsESIx6hhWYnLysoA0zpFTcrWrFnjSQQ77bQTULN9SSaIJ+Hl20rcuXNnwFSj0DOmkq4qSq5ifJkgnpU4pQT2IAMlwoac2DKM5LIfj+6TRKUJEyYApto/1AwG0AOtTVcNrHSQSs+jIKAqgEKfPn2AqprTrVq1Akwt5rFjxwJw8803A6YeczooNKLQszV58mTAHMwqzjB16lQg3FrMTiR2cIgQQq+amAqCMm41aNCA++67DzDGG9WXDRNiVvV8PfTQQwGzntWrV/Phhx8CpvO3yq2IYT/77DPA9O+RWDVz5sxqYxUVFflD9IJfTAJorhIFVS1y2rRp3pxUh/rYY48FjPhc6B0AkqG4uJgRI0YANbvKay/VcTCb/sXJ4BjWwSFCSMiwuUpYjsew0uuGDRsGmDqws2fPBoxOpc7ogwYN8twnChPLBcNKt1dPUdWnFRM1atSIM844A4AffvgBqJkwoKRn4bzzzgNM/Wad2rFYLG99coW77roLgJYtWwLVK/8///zzgCm+d/TRRwOmE0DUoP1p3bq11wdJe6G9lFtn6dKloc/HMayDQ4SQdx22pKTE04WeeOIJwHS8E0vKyrps2TLAFB9XVXZ1Qt9888354IMPABOgkEt07doVMNZDVcpft26dZ0m0YVuL9bNcAoUcnKDeqmBKfqqjuYqr51sayBRiVtklRo0a5RVQlwtL9ghJfDkpFh/6CA4ODoEh7ww7Z84cdtlll2q/ExvZep3aJ/Tu3RswrTHEwOvWreOtt94C8uPD09ivvfYakJq1UGtUUrggq3Gh+SLjQfOUv1WWcwVSRAViVlmCL730UgAOPPBAT9qRVfjhhx8GcttHyDGsg0OEkDeGVdTIkiVLOPXUUwHDoB06dACMX8tODJY1Tj1XTznlFACWL1/udU+zEa/MaZCQlTgdDBgwAKhZElMW5qgwrJhpr732AozuqmZoUYF64F5++eUAXle+Bg0aeA2/ZB+RlTiXcAzr4BAh5Jxh5bsbPXo0UKXjdOnSBTCdqxWLK7+ropb0uZgskyZHhQJJE7J0C7KA5+P0zgYqyCYLvyKbsgmAzyUkIaiP8UknnQSY8i+rVq3y9NmffvopDzOsgmNYB4cIIXCGtXVFMZtOLkXEqEt5aWkpo0aNAmDIkCGAiRyRDhsEOxZaplH//v0Bw7TSXRURVMj+19qgGGJZvSUdBSnZhFleRxLCySefDBg/sp6bM88804sH15q0Vvln5d2ws5qChGNYB4cIIXCGtfM7lX1iZzLoNCorK/NOTiVtK4LkueeeA4yVuC5AJ/edd94JGNb49NNPAZgxY0ag4+WqzI/8rdrfp556KvAxwmxCJl/+McccAxg/siSFTz75xPPNysYyfPhwwEgXuta1114LwG233Rb4vEMzOunFlXggUUNihIwTjRo18l5IdWWXaX358uWAeRi+++67sKabM3z99ddAzer2OpyCTkPL1Qs7ePBgwIRlbr/99qGOFxS0D5qvqmrofn377bdAVZBE27ZtAfNy21UktfaRI0cCJtHdH8KZ9XwDu5KDg0PoSMiwMiAlMoAkO8HVb8T+ubYgA4Xn6cQSG19xxRWASbOLGurXr++5phQwIihZQady0MiVO2vu3LmAeR5Uz+rGG2/MyfiZQs+4CgbYfZMUBuuvxSVD1M8//wzgBeuoTI4MWPrb22+/PbD5OoZ1cIgQEjJsKq4QOZZl2l68eDGQXVqVlPmrr74aMKd3GAhCx9thhx0AuPjiiwE48sgjAWNgatiwYY1qiQrZCzsNMFcMO2XKFACvjEpUgv51f6SX6pkX8/o7EMrOcPfddwNVhiiAHj16ACYZQH+jdNAg4RjWwSFCSKnyv2Cf1kVFRZ7bpmPHjoBh2GygMi/SnVUuNAykw0DSqWUNVIC4XFaJHPuSOBSqd/3116c/2T9gn/6FkCTeokULwNzPL774Ip/TSRm6h5KGbMiDsf/++3u1lrVG2SNuuukmoGY5YNkngoRjWAeHCCElhrX7geqE6dSpE2PGjAGMH0upcWIfGzqFxBKbbLKJV9RLLK2wRYXp/frrr+mtKmBozvKrHXTQQUCVDxnMfVLytu6Xv6+P1qvkB/ljVVJFp7fumxhLBeYUFjdhwgSv8vzQoUMBU1onn53xVDBckC+90PszyQYjqUnPuKQpdWKYN29ejcJ5CvBR8XRBHpAlS5YEPl/HsA4OEULC3jrFxcUxMDK5/d2SkhLmz58PGEawIdbRiWVHhYA5sVTIa//99wdMgnqQp3MmfVn69u0LGCYT48qn/NVXXwFmrUp00BorKysD655XWVnp9R9VWqLYWf6/NWvWeGsMs++M1tSlSxdeeuklwEgdCunbb7/9vHkHhSB764hh1UVQ1vvddtsNMGmOF1xwgcec6il0yCGHAOY+yJag4u89e/YEMpMQXQd2B4c6gKwKiVdUVHg6q2IudWIJOvUFMa3SyZYtW+ZZTOXfKiR9p7i42Eu2lyQgKUFFsmUtvPLKKwGjn/br1w8wZTCh6qQG0ywqVeh+tWvXzru+HYEWdA9c6d2K+1Zst5hEUlW7du081pdUId95oaU12tCeSppTaxFFaGnt9957r/dsy0ah+629kWX82WefBcJJWnEM6+AQISTUYbPRDXQ6y1ooppU8r39zzaa2bpCsf2rTpk0ZOHAgYNjx6aefBmD69OmAiSlVNpEybhKtbcGCBYCxAivSxraqip3VYEtj1QZfk6xAdFjNRRKQGESW7tatWwNV/VKbN2+usQGj90u3DRLp7mEi2KV09bN02V69egHVLfCSJjTexIkTAbxCDCtWrACy8487HdbBoQ4gNIYtVNgnV0lJSQyMPmgzXHFxsaeHKYFZOb5BthWUb1csrrhkMXEqJWOkW1VUVARqJZZ/XK1HlMAtna6srMyzoMoOoewjsVGQCJJh40HPgWIC2rdv70Xx2aV0tfYgy/o4hnVwqANwDPsHw9qwo7rChk70IJo1+9cYxB7KGqp2i7KgSuJYvny51whMfsswi8gF6YctVMRj2I3+hW3QoEG1Nfr7sEYVQb+wNiR6+102uXTfbMwvrBOJHRwihJSC/6PMNslgB0NEYa12Glcq300WBJMOCiGdb2OFY1gHhwgh7/1h8w270FyQTBQW0tEX7ZQ7O3wxFosVfPigg4FjWAeHCCGlMqeCHeycLyQrXVPbd+MFxqu0qjoRSJfVv0VFRXHTC1Odp3++8djMthfEsx8UFRV5n9nXqm2Ntr6rPVWoaElJiRcmqjWnyrj+8fzJ+v6f492zVO6lff/sMEL7e4UsFUHiedrFIuLBMayDQ4SQ0A/r4OBQWHAM6+AQIbgX1sEhQnAvrINDhOBeWAeHCMG9sA4OEYJ7YR0cIoT/ByPRldqe95PrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 288x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 1750, D: 1.069, G:0.8992\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 288x288 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "D_DC = build_dc_classifier().type(dtype) \n",
    "D_DC.apply(initialize_weights)\n",
    "G_DC = build_dc_generator().type(dtype)\n",
    "G_DC.apply(initialize_weights)\n",
    "\n",
    "D_DC_solver = get_optimizer(D_DC)\n",
    "G_DC_solver = get_optimizer(G_DC)\n",
    "\n",
    "run_a_gan(D_DC, G_DC, D_DC_solver, G_DC_solver, discriminator_loss, generator_loss, num_epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "A8a3b73Ofb3m"
   },
   "outputs": [],
   "source": []
  }
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关于这个算法

Generative Adversarial Networks (GANs)

What is a GAN?

In 2014, Goodfellow et al. presented a method for training generative models called Generative Adversarial Networks (GANs for short). In a GAN, we build two different neural networks. Our first network is a traditional classification network, called the discriminator. We will train the discriminator to take images, and classify them as being real (belonging to the training set) or fake (not present in the training set). Our other network, called the generator, will take random noise as input and transform it using a neural network to produce images. The goal of the generator is to fool the discriminator into thinking the images it produced are real.

We can think of this back and forth process of the generator ($G$) trying to fool the discriminator ($D$), and the discriminator trying to correctly classify real vs. fake as a minimax game: $$\underset{G}{\text{minimize}}; \underset{D}{\text{maximize}}; \mathbb{E}{x \sim p_\text{data}}\left[\log D(x)\right] + \mathbb{E}{z \sim p(z)}\left[\log \left(1-D(G(z))\right)\right]$$ where $z \sim p(z)$ are the random noise samples, $G(z)$ are the generated images using the neural network generator $G$, and $D$ is the output of the discriminator, specifying the probability of an input being real. In Goodfellow et al., they analyze this minimax game and show how it relates to minimizing the Jensen-Shannon divergence between the training data distribution and the generated samples from $G$.

To optimize this minimax game, we will aternate between taking gradient descent steps on the objective for $G$, and gradient ascent steps on the objective for $D$:

  1. update the generator ($G$) to minimize the probability of the discriminator making the correct choice.
  2. update the discriminator ($D$) to maximize the probability of the discriminator making the correct choice.

While these updates are useful for analysis, they do not perform well in practice. Instead, we will use a different objective when we update the generator: maximize the probability of the discriminator making the incorrect choice. This small change helps to allevaiate problems with the generator gradient vanishing when the discriminator is confident. This is the standard update used in most GAN papers, and was used in the original paper from Goodfellow et al..

In this assignment, we will alternate the following updates:

  1. Update the generator ($G$) to maximize the probability of the discriminator making the incorrect choice on generated data: $$\underset{G}{\text{maximize}}; \mathbb{E}_{z \sim p(z)}\left[\log D(G(z))\right]$$
  2. Update the discriminator ($D$), to maximize the probability of the discriminator making the correct choice on real and generated data: $$\underset{D}{\text{maximize}}; \mathbb{E}{x \sim p_\text{data}}\left[\log D(x)\right] + \mathbb{E}{z \sim p(z)}\left[\log \left(1-D(G(z))\right)\right]$$

What else is there in this notebook?

caption

Setup

import torch
import torch.nn as nn
from torch.nn import init
import torchvision
import torchvision.transforms as T
import torch.optim as optim
from torch.utils.data import DataLoader
from torch.utils.data import sampler
import torchvision.datasets as dset

import numpy as np

import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec

%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'

def show_images(images):
    images = np.reshape(images, [images.shape[0], -1])  # images reshape to (batch_size, D)
    sqrtn = int(np.ceil(np.sqrt(images.shape[0])))
    sqrtimg = int(np.ceil(np.sqrt(images.shape[1])))

    fig = plt.figure(figsize=(sqrtn, sqrtn))
    gs = gridspec.GridSpec(sqrtn, sqrtn)
    gs.update(wspace=0.05, hspace=0.05)

    for i, img in enumerate(images):
        ax = plt.subplot(gs[i])
        plt.axis('off')
        ax.set_xticklabels([])
        ax.set_yticklabels([])
        ax.set_aspect('equal')
        plt.imshow(img.reshape([sqrtimg,sqrtimg]))
    return 

def preprocess_img(x):
    return 2 * x - 1.0

def deprocess_img(x):
    return (x + 1.0) / 2.0

def rel_error(x,y):
    return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))

def count_params(model):
    """Count the number of parameters in the current TensorFlow graph """
    param_count = np.sum([np.prod(p.size()) for p in model.parameters()])
    return param_count

answers = dict(np.load('gan-checks-tf.npz'))

Dataset

class ChunkSampler(sampler.Sampler):
    """Samples elements sequentially from some offset. 
    Arguments:
        num_samples: # of desired datapoints
        start: offset where we should start selecting from
    """
    def __init__(self, num_samples, start=0):
        self.num_samples = num_samples
        self.start = start

    def __iter__(self):
        return iter(range(self.start, self.start + self.num_samples))

    def __len__(self):
        return self.num_samples

NUM_TRAIN = 50000
NUM_VAL = 5000

NOISE_DIM = 96
batch_size = 128

mnist_train = dset.MNIST('./utils/datasets/MNIST_data', train=True, download=True,
                           transform=T.ToTensor())
loader_train = DataLoader(mnist_train, batch_size=batch_size,
                          sampler=ChunkSampler(NUM_TRAIN, 0))

mnist_val = dset.MNIST('./utils/datasets/MNIST_data', train=True, download=True,
                           transform=T.ToTensor())
loader_val = DataLoader(mnist_val, batch_size=batch_size,
                        sampler=ChunkSampler(NUM_VAL, NUM_TRAIN))


imgs = loader_train.__iter__().next()[0].view(batch_size, 784).numpy().squeeze()
show_images(imgs)
Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./utils/datasets/MNIST_data/MNIST/raw/train-images-idx3-ubyte.gz
HBox(children=(FloatProgress(value=1.0, bar_style=&#x27;info&#x27;, max=1.0), HTML(value=&#x27;&#x27;)))
Extracting ./utils/datasets/MNIST_data/MNIST/raw/train-images-idx3-ubyte.gz to ./utils/datasets/MNIST_data/MNIST/raw
Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./utils/datasets/MNIST_data/MNIST/raw/train-labels-idx1-ubyte.gz
HBox(children=(FloatProgress(value=1.0, bar_style=&#x27;info&#x27;, max=1.0), HTML(value=&#x27;&#x27;)))
Extracting ./utils/datasets/MNIST_data/MNIST/raw/train-labels-idx1-ubyte.gz to ./utils/datasets/MNIST_data/MNIST/raw
Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./utils/datasets/MNIST_data/MNIST/raw/t10k-images-idx3-ubyte.gz

HBox(children=(FloatProgress(value=1.0, bar_style=&#x27;info&#x27;, max=1.0), HTML(value=&#x27;&#x27;)))
Extracting ./utils/datasets/MNIST_data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./utils/datasets/MNIST_data/MNIST/raw
Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./utils/datasets/MNIST_data/MNIST/raw/t10k-labels-idx1-ubyte.gz
HBox(children=(FloatProgress(value=1.0, bar_style=&#x27;info&#x27;, max=1.0), HTML(value=&#x27;&#x27;)))
Extracting ./utils/datasets/MNIST_data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./utils/datasets/MNIST_data/MNIST/raw
Processing...
Done!
/usr/local/lib/python3.6/dist-packages/torchvision/datasets/mnist.py:469: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)
  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)


Random Noise

Generate uniform noise from -1 to 1 with shape [batch_size, dim].

def sample_noise(batch_size, dim):
    """
    Generate a PyTorch Tensor of uniform random noise.

    Input:
    - batch_size: Integer giving the batch size of noise to generate.
    - dim: Integer giving the dimension of noise to generate.
    
    Output:
    - A PyTorch Tensor of shape (batch_size, dim) containing uniform
      random noise in the range (-1, 1).
    """
    return torch.FloatTensor(batch_size, dim).uniform_(-1, 1)

Check noise is the correct shape and type:

def test_sample_noise():
    batch_size = 3
    dim = 4
    torch.manual_seed(231)
    z = sample_noise(batch_size, dim)
    np_z = z.cpu().numpy()
    assert np_z.shape == (batch_size, dim)
    assert torch.is_tensor(z)
    assert np.all(np_z &gt;= -1.0) and np.all(np_z &lt;= 1.0)
    assert np.any(np_z &lt; 0.0) and np.any(np_z &gt; 0.0)
    print('All tests passed!')
    
test_sample_noise()
All tests passed!

Flatten

class Flatten(nn.Module):
    def forward(self, x):
        N, C, H, W = x.size() # read in N, C, H, W
        return x.view(N, -1)  # "flatten" the C * H * W values into a single vector per image
    
class Unflatten(nn.Module):
    """
    An Unflatten module receives an input of shape (N, C*H*W) and reshapes it
    to produce an output of shape (N, C, H, W).
    """
    def __init__(self, N=-1, C=128, H=7, W=7):
        super(Unflatten, self).__init__()
        self.N = N
        self.C = C
        self.H = H
        self.W = W
    def forward(self, x):
        return x.view(self.N, self.C, self.H, self.W)

def initialize_weights(m):
    if isinstance(m, nn.Linear) or isinstance(m, nn.ConvTranspose2d):
        init.xavier_uniform_(m.weight.data)

CPU / GPU

dtype = torch.FloatTensor
dtype = torch.cuda.FloatTensor # COMMENT THIS LINE IF YOU'RE ON A CPU!

Discriminator

def discriminator():
    """
    Build and return a PyTorch model implementing the architecture.
    """
    model = nn.Sequential( Flatten(),
                           nn.Linear(784, 256),
                           nn.LeakyReLU(inplace=True),
                           nn.Linear(256,256),
                           nn.LeakyReLU(inplace=True),
                           nn.Linear(256,1)
                         )
    return model

Test to make sure the number of parameters in the discriminator is correct:

def test_discriminator(true_count=267009):
    model = discriminator()
    cur_count = count_params(model)
    if cur_count != true_count:
        print('Incorrect number of parameters in discriminator. Check your achitecture.')
    else:
        print('Correct number of parameters in discriminator.')     

test_discriminator()
Correct number of parameters in discriminator.

Generator

def generator(noise_dim=NOISE_DIM):
    """
    Build and return a PyTorch model implementing the architecture.
    """
    model = nn.Sequential( nn.Linear(noise_dim,1024),
                           nn.ReLU(inplace=True),
                           nn.Linear(1024,1024),
                           nn.ReLU(inplace=True),
                           nn.Linear(1024,784),
                           nn.Tanh()
                         )
    return model

Test to make sure the number of parameters in the generator is correct:

def test_generator(true_count=1858320):
    model = generator(4)
    cur_count = count_params(model)
    if cur_count != true_count:
        print('Incorrect number of parameters in generator. Check your achitecture.')
    else:
        print('Correct number of parameters in generator.')

test_generator()
Correct number of parameters in generator.

GAN Loss

Compute the generator and discriminator loss. The generator loss is: $$\ell_G = -\mathbb{E}{z \sim p(z)}\left[\log D(G(z))\right]$$ and the discriminator loss is: $$ \ell_D = -\mathbb{E}{x \sim p_\text{data}}\left[\log D(x)\right] - \mathbb{E}_{z \sim p(z)}\left[\log \left(1-D(G(z))\right)\right]$$

def bce_loss(input, target):
    """pa 
    Inputs:
    - input: PyTorch Tensor of shape (N, ) giving scores.
    - target: PyTorch Tensor of shape (N,) containing 0 and 1 giving targets.

    Returns:
    - A PyTorch Tensor containing the mean BCE loss over the minibatch of input data.
    """
    neg_abs = - input.abs()
    loss = input.clamp(min=0) - input * target + (1 + neg_abs.exp()).log()
    return loss.mean()
def discriminator_loss(logits_real, logits_fake):
    """
    Computes the discriminator loss described above.
    
    Inputs:
    - logits_real: PyTorch Tensor of shape (N,) giving scores for the real data.
    - logits_fake: PyTorch Tensor of shape (N,) giving scores for the fake data.
    
    Returns:
    - loss: PyTorch Tensor containing (scalar) the loss for the discriminator.
    """
    N, _ = logits_real.size() 
    loss = (bce_loss(logits_real, torch.ones(N).type(dtype)))+(bce_loss(logits_fake, torch.zeros(N).type(dtype)))
    return loss

def generator_loss(logits_fake):
    """
    Computes the generator loss described above.

    Inputs:
    - logits_fake: PyTorch Tensor of shape (N,) giving scores for the fake data.
    
    Returns:
    - loss: PyTorch Tensor containing the (scalar) loss for the generator.
    """
    N, _ = logits_fake.size()
    loss = (bce_loss(logits_fake, torch.ones(N).type(dtype)))
    return loss

Check generator and discriminator loss. We should see errors < 1e-7.

def test_discriminator_loss(logits_real, logits_fake, d_loss_true):
    d_loss = discriminator_loss(torch.Tensor(logits_real).type(dtype),
                                torch.Tensor(logits_fake).type(dtype)).cpu().numpy()
    print("Maximum error in d_loss: %g"%rel_error(d_loss_true, d_loss))

test_discriminator_loss(answers['logits_real'], answers['logits_fake'],
                        answers['d_loss_true'])
Maximum error in d_loss: 2.83811e-08
def test_generator_loss(logits_fake, g_loss_true):
    g_loss = generator_loss(torch.Tensor(logits_fake).type(dtype)).cpu().numpy()
    print("Maximum error in g_loss: %g"%rel_error(g_loss_true, g_loss))

test_generator_loss(answers['logits_fake'], answers['g_loss_true'])
Maximum error in g_loss: 3.4188e-08

Optimizing our loss

def get_optimizer(model):
    """
    Construct and return an Adam optimizer for the model with learning rate 1e-3,
    beta1=0.5, and beta2=0.999.
    
    Input:
    - model: A PyTorch model that we want to optimize.
    
    Returns:
    - An Adam optimizer for the model with the desired hyperparameters.
    """
    optimizer = optim.Adam(model.parameters(), lr = 1e-3, betas = (0.5,0.999))
    return optimizer

Training a GAN!

def run_a_gan(D, G, D_solver, G_solver, discriminator_loss, generator_loss, show_every=250, 
              batch_size=128, noise_size=96, num_epochs=10):
    """
    Train a GAN!
    
    Inputs:
    - D, G: PyTorch models for the discriminator and generator
    - D_solver, G_solver: torch.optim Optimizers to use for training the
      discriminator and generator.
    - discriminator_loss, generator_loss: Functions to use for computing the generator and
      discriminator loss, respectively.
    - show_every: Show samples after every show_every iterations.
    - batch_size: Batch size to use for training.
    - noise_size: Dimension of the noise to use as input to the generator.
    - num_epochs: Number of epochs over the training dataset to use for training.
    """
    iter_count = 0
    for epoch in range(num_epochs):
        for x, _ in loader_train:
            if len(x) != batch_size:
                continue
            D_solver.zero_grad()
            real_data = x.type(dtype)
            logits_real = D(2* (real_data - 0.5)).type(dtype)

            g_fake_seed = sample_noise(batch_size, noise_size).type(dtype)
            fake_images = G(g_fake_seed).detach()
            logits_fake = D(fake_images.view(batch_size, 1, 28, 28))

            d_total_error = discriminator_loss(logits_real, logits_fake)
            d_total_error.backward()        
            D_solver.step()

            G_solver.zero_grad()
            g_fake_seed = sample_noise(batch_size, noise_size).type(dtype)
            fake_images = G(g_fake_seed)

            gen_logits_fake = D(fake_images.view(batch_size, 1, 28, 28))
            g_error = generator_loss(gen_logits_fake)
            g_error.backward()
            G_solver.step()

            if (iter_count % show_every == 0):
                print('Iter: {}, D: {:.4}, G:{:.4}'.format(iter_count,d_total_error.item(),g_error.item()))
                imgs_numpy = fake_images.data.cpu().numpy()
                show_images(imgs_numpy[0:16])
                plt.show()
                print()
            iter_count += 1
# Make the discriminator
D = discriminator().type(dtype)

# Make the generator
G = generator().type(dtype)

# Use the function you wrote earlier to get optimizers for the Discriminator and the Generator
D_solver = get_optimizer(D)
G_solver = get_optimizer(G)
# Run it!
run_a_gan(D, G, D_solver, G_solver, discriminator_loss, generator_loss)
Iter: 0, D: 1.328, G:0.7202
Iter: 250, D: 1.43, G:0.6752
Iter: 500, D: 1.181, G:1.414
Iter: 750, D: 1.204, G:1.556
Iter: 1000, D: 1.174, G:1.126
Iter: 1250, D: 1.255, G:1.068
Iter: 1500, D: 1.136, G:0.971
Iter: 1750, D: 1.317, G:0.7927
Iter: 2000, D: 1.274, G:0.9762
Iter: 2250, D: 1.258, G:0.9521
Iter: 2500, D: 1.202, G:0.833
Iter: 2750, D: 1.288, G:0.8659
Iter: 3000, D: 1.379, G:0.824
Iter: 3250, D: 1.392, G:0.8353
Iter: 3500, D: 1.296, G:0.8011
Iter: 3750, D: 1.221, G:0.841

In the iterations in the low 100s we should see black backgrounds, fuzzy shapes as you approach iteration 1000, and decent shapes, about half of which will be sharp and clearly recognizable as we pass 3000.

Least Squares GAN

We'll now look at Least Squares GAN, a newer, more stable alernative to the original GAN loss function. For this part, all we have to do is change the loss function and retrain the model. We'll implement equation (9) in the paper, with the generator loss: $$\ell_G = \frac{1}{2}\mathbb{E}{z \sim p(z)}\left[\left(D(G(z))-1\right)^2\right]$$ and the discriminator loss: $$ \ell_D = \frac{1}{2}\mathbb{E}{x \sim p_\text{data}}\left[\left(D(x)-1\right)^2\right] + \frac{1}{2}\mathbb{E}_{z \sim p(z)}\left[ \left(D(G(z))\right)^2\right]$$

def ls_discriminator_loss(scores_real, scores_fake):
    """
    Compute the Least-Squares GAN loss for the discriminator.
    
    Inputs:
    - scores_real: PyTorch Tensor of shape (N,) giving scores for the real data.
    - scores_fake: PyTorch Tensor of shape (N,) giving scores for the fake data.
    
    Outputs:
    - loss: A PyTorch Tensor containing the loss.
    """
    N,_ = scores_real.size()
    loss = (0.5 * torch.mean((scores_real-torch.ones(N).type(dtype))**2)) + (0.5 * torch.mean(scores_fake**2))
    return loss

def ls_generator_loss(scores_fake):
    """
    Computes the Least-Squares GAN loss for the generator.
    
    Inputs:
    - scores_fake: PyTorch Tensor of shape (N,) giving scores for the fake data.
    
    Outputs:
    - loss: A PyTorch Tensor containing the loss.
    """
    N,_ = scores_fake.size()
    loss = (0.5 * torch.mean((scores_fake-torch.ones(N).type(dtype))**2))
    return loss

Before running a GAN with our new loss function, let's check it:

def test_lsgan_loss(score_real, score_fake, d_loss_true, g_loss_true):
    score_real = torch.Tensor(score_real).type(dtype)
    score_fake = torch.Tensor(score_fake).type(dtype)
    d_loss = ls_discriminator_loss(score_real, score_fake).cpu().numpy()
    g_loss = ls_generator_loss(score_fake).cpu().numpy()
    print("Maximum error in d_loss: %g"%rel_error(d_loss_true, d_loss))
    print("Maximum error in g_loss: %g"%rel_error(g_loss_true, g_loss))

test_lsgan_loss(answers['logits_real'], answers['logits_fake'],
                answers['d_loss_lsgan_true'], answers['g_loss_lsgan_true'])
Maximum error in d_loss: 1.64377e-08
Maximum error in g_loss: 2.7837e-09

Run the following cell to train model!

D_LS = discriminator().type(dtype)
G_LS = generator().type(dtype)

D_LS_solver = get_optimizer(D_LS)
G_LS_solver = get_optimizer(G_LS)

run_a_gan(D_LS, G_LS, D_LS_solver, G_LS_solver, ls_discriminator_loss, ls_generator_loss)
Iter: 0, D: 0.5689, G:0.51
Iter: 250, D: 0.1481, G:0.3264
Iter: 500, D: 0.2063, G:0.4708
Iter: 750, D: 0.1258, G:0.2649
Iter: 1000, D: 0.152, G:0.4361
Iter: 1250, D: 0.1842, G:0.2598
Iter: 1500, D: 0.1986, G:0.2422
Iter: 1750, D: 0.2018, G:0.2362
Iter: 2000, D: 0.2339, G:0.1912
Iter: 2250, D: 0.2559, G:0.2198
Iter: 2500, D: 0.2503, G:0.1511
Iter: 2750, D: 0.2112, G:0.1597
Iter: 3000, D: 0.2393, G:0.1796
Iter: 3250, D: 0.2336, G:0.1621
Iter: 3500, D: 0.2206, G:0.1707
Iter: 3750, D: 0.2488, G:0.1253

Deeply Convolutional GANs

def build_dc_classifier():
    """
    Build and return a PyTorch model for the DCGAN discriminator implementing
    the architecture above.
    """
    return nn.Sequential( Unflatten(batch_size, 1, 28, 28),
                          nn.Conv2d(1, 32, kernel_size = 5, stride = 1),
                          nn.LeakyReLU(inplace=True),
                          nn.MaxPool2d(2,2),
                          nn.Conv2d(32, 64,kernel_size = 5, stride = 1),
                          nn.LeakyReLU(inplace=True),
                          nn.MaxPool2d(2,2),
                          Flatten(),
                          nn.Linear(1024, 1024),
                          nn.LeakyReLU(inplace=True),
                          nn.Linear(1024,1)
                        )

data = next(enumerate(loader_train))[-1][0].type(dtype)
b = build_dc_classifier().type(dtype)
out = b(data)
print(out.size())
torch.Size([128, 1])

Check the number of parameters in your classifier as a sanity check:

def test_dc_classifer(true_count=1102721):
    model = build_dc_classifier()
    cur_count = count_params(model)
    if cur_count != true_count:
        print('Incorrect number of parameters in generator. Check your achitecture.')
    else:
        print('Correct number of parameters in generator.')

test_dc_classifer()
Correct number of parameters in generator.

Generator

def build_dc_generator(noise_dim=NOISE_DIM):
    """
    Build and return a PyTorch model implementing the DCGAN generator using
    the architecture described above.
    """
    return nn.Sequential( nn.Linear(noise_dim,1024),
                          nn.ReLU(inplace=True),
                          nn.BatchNorm1d(1024),
                          nn.Linear(1024,6272),
                          nn.ReLU(inplace=True),
                          nn.BatchNorm1d(6272),
                          Unflatten(batch_size, 128, 7, 7),
                          nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1),
                          nn.ReLU(inplace=True),
                          nn.BatchNorm2d(64),
                          nn.ConvTranspose2d(64, 1, kernel_size=4, stride=2, padding=1),
                          nn.Tanh(),
                          Flatten()
                        )

test_g_gan = build_dc_generator().type(dtype)
test_g_gan.apply(initialize_weights)

fake_seed = torch.randn(batch_size, NOISE_DIM).type(dtype)
fake_images = test_g_gan.forward(fake_seed)
fake_images.size()
torch.Size([128, 784])

Check the number of parameters in your generator as a sanity check:

def test_dc_generator(true_count=6580801):
    model = build_dc_generator(4)
    cur_count = count_params(model)
    if cur_count != true_count:
        print('Incorrect number of parameters in generator. Check your achitecture.')
    else:
        print('Correct number of parameters in generator.')

test_dc_generator()
Correct number of parameters in generator.
D_DC = build_dc_classifier().type(dtype) 
D_DC.apply(initialize_weights)
G_DC = build_dc_generator().type(dtype)
G_DC.apply(initialize_weights)

D_DC_solver = get_optimizer(D_DC)
G_DC_solver = get_optimizer(G_DC)

run_a_gan(D_DC, G_DC, D_DC_solver, G_DC_solver, discriminator_loss, generator_loss, num_epochs=5)
Iter: 0, D: 1.448, G:1.464
Iter: 250, D: 1.198, G:0.7545
Iter: 500, D: 1.233, G:1.017
Iter: 750, D: 1.185, G:1.144
Iter: 1000, D: 1.198, G:1.03
Iter: 1250, D: 1.216, G:0.9504
Iter: 1500, D: 1.115, G:1.105
Iter: 1750, D: 1.069, G:0.8992